diff --git a/.circleci/config.yml b/.circleci/config.yml
new file mode 100644
index 0000000..5e233f8
--- /dev/null
+++ b/.circleci/config.yml
@@ -0,0 +1,34 @@
+version: 2.1
+
+# this allows you to use CircleCI's dynamic configuration feature
+setup: true
+
+# the path-filtering orb is required to continue a pipeline based on
+# the path of an updated fileset
+orbs:
+  path-filtering: circleci/path-filtering@0.1.2
+
+workflows:
+  # the always-run workflow is always triggered, regardless of the pipeline parameters.
+  always-run:
+    jobs:
+      # the path-filtering/filter job determines which pipeline
+      # parameters to update.
+      - path-filtering/filter:
+          name: check-updated-files
+          # 3-column, whitespace-delimited mapping. One mapping per
+          # line:
+          # <regex path-to-test> <parameter-to-set> <value-of-pipeline-parameter>
+          mapping: |
+            mmdet/.* lint_only false
+            requirements/.* lint_only false
+            tests/.* lint_only false
+            tools/.* lint_only false
+            configs/.* lint_only false
+            .circleci/.* lint_only false
+          base-revision: master
+          # this is the path of the configuration we should trigger once
+          # path filtering and pipeline parameter value updates are
+          # complete. In this case, we are using the parent dynamic
+          # configuration itself.
+          config-path: .circleci/test.yml
diff --git a/.circleci/docker/Dockerfile b/.circleci/docker/Dockerfile
new file mode 100644
index 0000000..5154130
--- /dev/null
+++ b/.circleci/docker/Dockerfile
@@ -0,0 +1,11 @@
+ARG PYTORCH="1.8.1"
+ARG CUDA="10.2"
+ARG CUDNN="7"
+
+FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
+
+# To fix GPG key error when running apt-get update
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+
+RUN apt-get update && apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx git
diff --git a/.circleci/scripts/get_mmcv_var.sh b/.circleci/scripts/get_mmcv_var.sh
new file mode 100644
index 0000000..552ff87
--- /dev/null
+++ b/.circleci/scripts/get_mmcv_var.sh
@@ -0,0 +1,19 @@
+#!/bin/bash
+
+TORCH=$1
+CUDA=$2
+
+# 10.2 -> cu102
+MMCV_CUDA="cu`echo ${CUDA} | tr -d '.'`"
+
+# MMCV only provides pre-compiled packages for torch 1.x.0
+# which works for any subversions of torch 1.x.
+# We force the torch version to be 1.x.0 to ease package searching
+# and avoid unnecessary rebuild during MMCV's installation.
+TORCH_VER_ARR=(${TORCH//./ })
+TORCH_VER_ARR[2]=0
+printf -v MMCV_TORCH "%s." "${TORCH_VER_ARR[@]}"
+MMCV_TORCH=${MMCV_TORCH%?}  # Remove the last dot
+
+echo "export MMCV_CUDA=${MMCV_CUDA}" >> $BASH_ENV
+echo "export MMCV_TORCH=${MMCV_TORCH}" >> $BASH_ENV
diff --git a/.circleci/test.yml b/.circleci/test.yml
new file mode 100644
index 0000000..d413d49
--- /dev/null
+++ b/.circleci/test.yml
@@ -0,0 +1,189 @@
+
+
+version: 2.1
+
+# the default pipeline parameters, which will be updated according to
+# the results of the path-filtering orb
+parameters:
+  lint_only:
+    type: boolean
+    default: true
+
+jobs:
+  lint:
+    docker:
+      - image: cimg/python:3.7.4
+    steps:
+      - checkout
+      - run:
+          name: Install pre-commit hook
+          command: |
+            pip install pre-commit
+            pre-commit install
+      - run:
+          name: Linting
+          command: pre-commit run --all-files
+      - run:
+          name: Check docstring coverage
+          command: |
+            pip install interrogate
+            interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 50 mmdet
+  build_cpu:
+    parameters:
+      # The python version must match available image tags in
+      # https://circleci.com/developer/images/image/cimg/python
+      python:
+        type: string
+      torch:
+        type: string
+      torchvision:
+        type: string
+    docker:
+      - image: cimg/python:<< parameters.python >>
+    resource_class: large
+    steps:
+      - checkout
+      - run:
+          name: Get MMCV_TORCH as environment variables
+          command: |
+            . .circleci/scripts/get_mmcv_var.sh << parameters.torch >>
+            source $BASH_ENV
+      - run:
+          name: Install Libraries
+          command: |
+            sudo apt-get update
+            sudo apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx libjpeg-dev zlib1g-dev libtinfo-dev libncurses5 libgeos-dev cmake git
+      - run:
+          name: Configure Python & pip
+          command: |
+            python -m pip install --upgrade pip
+            python -m pip install wheel
+      - run:
+          name: Install PyTorch
+          command: |
+            python -V
+            python -m pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - run:
+          name: Install mmdet dependencies
+          command: |
+            python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch${MMCV_TORCH}/index.html
+            python -m pip install -r requirements.txt
+            python -m pip install albumentations --no-binary qudida,albumentations
+            python -m pip install git+https://github.com/cocodataset/panopticapi.git
+      - run:
+          name: Build and install
+          command: |
+            python -m pip install -e .
+      - run:
+          name: Run unittests
+          command: |
+            python -m coverage run --branch --source mmdet -m pytest tests/
+            python -m coverage xml
+            python -m coverage report -m
+  build_cuda:
+    parameters:
+      torch:
+        type: string
+      cuda:
+        type: enum
+        enum: ["10.1", "10.2", "11.1"]
+      cudnn:
+        type: integer
+        default: 7
+    machine:
+      image: ubuntu-2004-cuda-11.4:202110-01
+      docker_layer_caching: true
+    resource_class: gpu.nvidia.small
+    steps:
+      - checkout
+      - run:
+          name: Get MMCV_TORCH and MMCV_CUDA as environment variables
+          command: |
+            . .circleci/scripts/get_mmcv_var.sh << parameters.torch >> << parameters.cuda >>
+            source $BASH_ENV
+      - run:
+          name: Build Docker image
+          command: |
+            docker build .circleci/docker -t mmdet:gpu --build-arg PYTORCH=<< parameters.torch >> --build-arg CUDA=<< parameters.cuda >> --build-arg CUDNN=<< parameters.cudnn >>
+            docker run --gpus all -t -d -v /home/circleci/project:/mmdet -w /mmdet --name mmdet mmdet:gpu
+      - run:
+          name: Install mmdet dependencies
+          command: |
+            docker exec mmdet pip install --upgrade pip
+            docker exec mmdet pip install wheel
+            docker exec mmdet pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/${MMCV_CUDA}/torch${MMCV_TORCH}/index.html
+            docker exec mmdet pip install -r requirements.txt
+            docker exec mmdet pip install typing-extensions -U
+            docker exec mmdet pip install albumentations --use-pep517 qudida albumentations
+            docker exec mmdet python -c 'import albumentations; print(albumentations.__version__)'
+            docker exec mmdet pip install git+https://github.com/cocodataset/panopticapi.git
+      - run:
+          name: Build and install
+          command: |
+            docker exec mmdet pip install -e .
+      - run:
+          name: Run unittests
+          command: |
+            docker exec mmdet python -m pytest tests/
+workflows:
+  pr_stage_lint:
+    when: << pipeline.parameters.lint_only >>
+    jobs:
+      - lint:
+          name: lint
+          filters:
+            branches:
+              ignore:
+                - master
+  pr_stage_test:
+    when:
+      not:
+        << pipeline.parameters.lint_only >>
+    jobs:
+      - lint:
+          name: lint
+          filters:
+            branches:
+              ignore:
+                - master
+      - build_cpu:
+          name: minimum_version_cpu
+          torch: 1.6.0
+          torchvision: 0.7.0
+          python: 3.7.7
+          requires:
+            - lint
+      - build_cpu:
+          name: maximum_version_cpu
+          torch: 1.9.0
+          torchvision: 0.10.0
+          python: 3.8.0
+          requires:
+            - minimum_version_cpu
+      - hold:
+          type: approval
+          requires:
+            - maximum_version_cpu
+      - build_cuda:
+          name: mainstream_version_gpu
+          torch: 1.8.1
+          # Use double quotation mark to explicitly specify its type
+          # as string instead of number
+          cuda: "10.2"
+          requires:
+            - hold
+  merge_stage_test:
+    when:
+      not:
+        << pipeline.parameters.lint_only >>
+    jobs:
+      - build_cuda:
+          name: minimum_version_gpu
+          torch: 1.6.0
+          # Use double quotation mark to explicitly specify its type
+          # as string instead of number
+          cuda: "10.1"
+          filters:
+            branches:
+              only:
+                - master
diff --git a/.dev_scripts/batch_test_list.py b/.dev_scripts/batch_test_list.py
new file mode 100644
index 0000000..1e74ce2
--- /dev/null
+++ b/.dev_scripts/batch_test_list.py
@@ -0,0 +1,359 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# yapf: disable
+atss = dict(
+    config='configs/atss/atss_r50_fpn_1x_coco.py',
+    checkpoint='atss_r50_fpn_1x_coco_20200209-985f7bd0.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=39.4),
+)
+autoassign = dict(
+    config='configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py',
+    checkpoint='auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=40.4),
+)
+carafe = dict(
+    config='configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py',
+    checkpoint='faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth',  # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=38.6),
+)
+cascade_rcnn = [
+    dict(
+        config='configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py',
+        checkpoint='cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth',
+        eval='bbox',
+        metric=dict(bbox_mAP=40.3),
+    ),
+    dict(
+        config='configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py',
+        checkpoint='cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth',
+        eval=['bbox', 'segm'],
+        metric=dict(bbox_mAP=41.2, segm_mAP=35.9),
+    ),
+]
+cascade_rpn = dict(
+    config='configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py',
+    checkpoint='crpn_faster_rcnn_r50_caffe_fpn_1x_coco-c8283cca.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=40.4),
+)
+centripetalnet = dict(
+    config='configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py',  # noqa
+    checkpoint='centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth',  # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=44.7),
+)
+cornernet = dict(
+    config='configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py',
+    checkpoint='cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth',  # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=41.2),
+)
+dcn = dict(
+    config='configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py',
+    checkpoint='faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=41.3),
+)
+deformable_detr = dict(
+    config='configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py',
+    checkpoint='deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth',  # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=44.5),
+)
+detectors = dict(
+    config='configs/detectors/detectors_htc_r50_1x_coco.py',
+    checkpoint='detectors_htc_r50_1x_coco-329b1453.pth',
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=49.1, segm_mAP=42.6),
+)
+detr = dict(
+    config='configs/detr/detr_r50_8x2_150e_coco.py',
+    checkpoint='detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=40.1),
+)
+double_heads = dict(
+    config='configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py',
+    checkpoint='dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=40.0),
+)
+dynamic_rcnn = dict(
+    config='configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py',
+    checkpoint='dynamic_rcnn_r50_fpn_1x-62a3f276.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=38.9),
+)
+empirical_attention = dict(
+    config='configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py',  # noqa
+    checkpoint='faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth',  # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=40.0),
+)
+faster_rcnn = dict(
+    config='configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py',
+    checkpoint='faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=37.4),
+)
+fcos = dict(
+    config='configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py',  # noqa
+    checkpoint='fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth',  # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=38.7),
+)
+foveabox = dict(
+    config='configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py',
+    checkpoint='fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=37.9),
+)
+free_anchor = dict(
+    config='configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py',
+    checkpoint='retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=38.7),
+)
+fsaf = dict(
+    config='configs/fsaf/fsaf_r50_fpn_1x_coco.py',
+    checkpoint='fsaf_r50_fpn_1x_coco-94ccc51f.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=37.4),
+)
+gcnet = dict(
+    config='configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py',  # noqa
+    checkpoint='mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth',  # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=40.4, segm_mAP=36.2),
+)
+gfl = dict(
+    config='configs/gfl/gfl_r50_fpn_1x_coco.py',
+    checkpoint='gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=40.2),
+)
+gn = dict(
+    config='configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py',
+    checkpoint='mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth',
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=40.1, segm_mAP=36.4),
+)
+gn_ws = dict(
+    config='configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py',
+    checkpoint='faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=39.7),
+)
+grid_rcnn = dict(
+    config='configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py',
+    checkpoint='grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=40.4),
+)
+groie = dict(
+    config='configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py',
+    checkpoint='faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth',  # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=38.3),
+)
+guided_anchoring = [
+    dict(
+        config='configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py',  # noqa
+        checkpoint='ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth',
+        eval='bbox',
+        metric=dict(bbox_mAP=36.9),
+    ),
+    dict(
+        config='configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py',
+        checkpoint='ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth',  # noqa
+        eval='bbox',
+        metric=dict(bbox_mAP=39.6),
+    ),
+]
+hrnet = dict(
+    config='configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py',
+    checkpoint='faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=36.9),
+)
+htc = dict(
+    config='configs/htc/htc_r50_fpn_1x_coco.py',
+    checkpoint='htc_r50_fpn_1x_coco_20200317-7332cf16.pth',
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=42.3, segm_mAP=37.4),
+)
+libra_rcnn = dict(
+    config='configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py',
+    checkpoint='libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=38.3),
+)
+mask_rcnn = dict(
+    config='configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py',
+    checkpoint='mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth',
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=38.2, segm_mAP=34.7),
+)
+ms_rcnn = dict(
+    config='configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py',
+    checkpoint='ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth',
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=38.2, segm_mAP=36.0),
+)
+nas_fcos = dict(
+    config='configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py',  # noqa
+    checkpoint='nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth',  # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=39.4),
+)
+nas_fpn = dict(
+    config='configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py',
+    checkpoint='retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=40.5),
+)
+paa = dict(
+    config='configs/paa/paa_r50_fpn_1x_coco.py',
+    checkpoint='paa_r50_fpn_1x_coco_20200821-936edec3.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=40.4),
+)
+pafpn = dict(
+    config='configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py',
+    checkpoint='faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth',  # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=37.5),
+)
+pisa = dict(
+    config='configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py',
+    checkpoint='pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=38.4),
+)
+point_rend = dict(
+    config='configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py',
+    checkpoint='point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth',
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=38.4, segm_mAP=36.3),
+)
+regnet = dict(
+    config='configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py',
+    checkpoint='mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth',  # noqa
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=40.4, segm_mAP=36.7),
+)
+reppoints = dict(
+    config='configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py',
+    checkpoint='reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=37.0),
+)
+res2net = dict(
+    config='configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py',
+    checkpoint='faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=43.0),
+)
+resnest = dict(
+    config='configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py',  # noqa
+    checkpoint='faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco_20200926_125502-20289c16.pth',  # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=42.0),
+)
+retinanet = dict(
+    config='configs/retinanet/retinanet_r50_fpn_1x_coco.py',
+    checkpoint='retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=36.5),
+)
+rpn = dict(
+    config='configs/rpn/rpn_r50_fpn_1x_coco.py',
+    checkpoint='rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth',
+    eval='proposal_fast',
+    metric=dict(AR_1000=58.2),
+)
+sabl = [
+    dict(
+        config='configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py',
+        checkpoint='sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth',
+        eval='bbox',
+        metric=dict(bbox_mAP=37.7),
+    ),
+    dict(
+        config='configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py',
+        checkpoint='sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth',
+        eval='bbox',
+        metric=dict(bbox_mAP=39.9),
+    ),
+]
+scnet = dict(
+    config='configs/scnet/scnet_r50_fpn_1x_coco.py',
+    checkpoint='scnet_r50_fpn_1x_coco-c3f09857.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=43.5),
+)
+sparse_rcnn = dict(
+    config='configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py',
+    checkpoint='sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=37.9),
+)
+ssd = [
+    dict(
+        config='configs/ssd/ssd300_coco.py',
+        checkpoint='ssd300_coco_20210803_015428-d231a06e.pth',
+        eval='bbox',
+        metric=dict(bbox_mAP=25.5),
+    ),
+    dict(
+        config='configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py',
+        checkpoint='ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth',# noqa
+        eval='bbox',
+        metric=dict(bbox_mAP=21.3),
+    ),
+]
+tridentnet = dict(
+    config='configs/tridentnet/tridentnet_r50_caffe_1x_coco.py',
+    checkpoint='tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=37.6),
+)
+vfnet = dict(
+    config='configs/vfnet/vfnet_r50_fpn_1x_coco.py',
+    checkpoint='vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=41.6),
+)
+yolact = dict(
+    config='configs/yolact/yolact_r50_1x8_coco.py',
+    checkpoint='yolact_r50_1x8_coco_20200908-f38d58df.pth',
+    eval=['bbox', 'segm'],
+    metric=dict(bbox_mAP=31.2, segm_mAP=29.0),
+)
+yolo = dict(
+    config='configs/yolo/yolov3_d53_320_273e_coco.py',
+    checkpoint='yolov3_d53_320_273e_coco-421362b6.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=27.9),
+)
+yolof = dict(
+    config='configs/yolof/yolof_r50_c5_8x8_1x_coco.py',
+    checkpoint='yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth',
+    eval='bbox',
+    metric=dict(bbox_mAP=37.5),
+)
+centernet = dict(
+    config='configs/centernet/centernet_resnet18_dcnv2_140e_coco.py',
+    checkpoint='centernet_resnet18_dcnv2_140e_coco_20210702_155131-c8cd631f.pth',  # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=29.5),
+)
+yolox = dict(
+    config='configs/yolox/yolox_tiny_8x8_300e_coco.py',
+    checkpoint='yolox_tiny_8x8_300e_coco_20210806_234250-4ff3b67e.pth',  # noqa
+    eval='bbox',
+    metric=dict(bbox_mAP=31.5),
+)
+# yapf: enable
diff --git a/.dev_scripts/batch_train_list.txt b/.dev_scripts/batch_train_list.txt
new file mode 100644
index 0000000..a7004d7
--- /dev/null
+++ b/.dev_scripts/batch_train_list.txt
@@ -0,0 +1,66 @@
+configs/atss/atss_r50_fpn_1x_coco.py
+configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py
+configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py
+configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py
+configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py
+configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py
+configs/detectors/detectors_htc_r50_1x_coco.py
+configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py
+configs/detr/detr_r50_8x2_150e_coco.py
+configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py
+configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py
+configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py
+configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py
+configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py
+configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py
+configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py
+configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py
+configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py
+configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py
+configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py
+configs/fsaf/fsaf_r50_fpn_1x_coco.py
+configs/gfl/gfl_r50_fpn_1x_coco.py
+configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py
+configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py
+configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py
+configs/htc/htc_r50_fpn_1x_coco.py
+configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py
+configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py
+configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py
+configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py
+configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py
+configs/paa/paa_r50_fpn_1x_coco.py
+configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py
+configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py
+configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py
+configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py
+configs/rpn/rpn_r50_fpn_1x_coco.py
+configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py
+configs/ssd/ssd300_coco.py
+configs/tridentnet/tridentnet_r50_caffe_1x_coco.py
+configs/vfnet/vfnet_r50_fpn_1x_coco.py
+configs/yolact/yolact_r50_8x8_coco.py
+configs/yolo/yolov3_d53_320_273e_coco.py
+configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py
+configs/scnet/scnet_r50_fpn_1x_coco.py
+configs/yolof/yolof_r50_c5_8x8_1x_coco.py
+configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py
+configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py
+configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
+configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py
+configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
+configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py
+configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py
+configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py
+configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py
+configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py
+configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py
+configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py
+configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py
+configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
+configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py
+configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py
+configs/centernet/centernet_resnet18_dcnv2_140e_coco.py
+configs/yolox/yolox_tiny_8x8_300e_coco.py
+configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py
+configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py
diff --git a/.dev_scripts/benchmark_filter.py b/.dev_scripts/benchmark_filter.py
new file mode 100644
index 0000000..178cd9c
--- /dev/null
+++ b/.dev_scripts/benchmark_filter.py
@@ -0,0 +1,167 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Filter configs to train')
+    parser.add_argument(
+        '--basic-arch',
+        action='store_true',
+        help='to train models in basic arch')
+    parser.add_argument(
+        '--datasets', action='store_true', help='to train models in dataset')
+    parser.add_argument(
+        '--data-pipeline',
+        action='store_true',
+        help='to train models related to data pipeline, e.g. augmentations')
+    parser.add_argument(
+        '--nn-module',
+        action='store_true',
+        help='to train models related to neural network modules')
+    parser.add_argument(
+        '--model-options',
+        nargs='+',
+        help='custom options to special model benchmark')
+    parser.add_argument(
+        '--out',
+        type=str,
+        default='batch_train_list.txt',
+        help='output path of gathered metrics to be stored')
+    args = parser.parse_args()
+    return args
+
+
+basic_arch_root = [
+    'atss', 'autoassign', 'cascade_rcnn', 'cascade_rpn', 'centripetalnet',
+    'cornernet', 'detectors', 'deformable_detr', 'detr', 'double_heads',
+    'dynamic_rcnn', 'faster_rcnn', 'fcos', 'foveabox', 'fp16', 'free_anchor',
+    'fsaf', 'gfl', 'ghm', 'grid_rcnn', 'guided_anchoring', 'htc', 'ld',
+    'libra_rcnn', 'mask_rcnn', 'ms_rcnn', 'nas_fcos', 'paa', 'pisa',
+    'point_rend', 'reppoints', 'retinanet', 'rpn', 'sabl', 'ssd', 'tridentnet',
+    'vfnet', 'yolact', 'yolo', 'sparse_rcnn', 'scnet', 'yolof', 'centernet'
+]
+
+datasets_root = [
+    'wider_face', 'pascal_voc', 'cityscapes', 'lvis', 'deepfashion'
+]
+
+data_pipeline_root = ['albu_example', 'instaboost']
+
+nn_module_root = [
+    'carafe', 'dcn', 'empirical_attention', 'gcnet', 'gn', 'gn+ws', 'hrnet',
+    'pafpn', 'nas_fpn', 'regnet', 'resnest', 'res2net', 'groie'
+]
+
+benchmark_pool = [
+    'configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py',
+    'configs/atss/atss_r50_fpn_1x_coco.py',
+    'configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py',
+    'configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py',
+    'configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py',
+    'configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py',
+    'configs/centernet/centernet_resnet18_dcnv2_140e_coco.py',
+    'configs/centripetalnet/'
+    'centripetalnet_hourglass104_mstest_16x6_210e_coco.py',
+    'configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py',
+    'configs/cornernet/'
+    'cornernet_hourglass104_mstest_8x6_210e_coco.py',
+    'configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py',
+    'configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py',
+    'configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py',
+    'configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py',
+    'configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py',
+    'configs/detectors/detectors_htc_r50_1x_coco.py',
+    'configs/detr/detr_r50_8x2_150e_coco.py',
+    'configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py',
+    'configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py',
+    'configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py',  # noqa
+    'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py',
+    'configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py',
+    'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py',
+    'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py',
+    'configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py',
+    'configs/fcos/fcos_center_r50_caffe_fpn_gn-head_4x4_1x_coco.py',
+    'configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py',
+    'configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py',
+    'configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py',
+    'configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py',
+    'configs/fsaf/fsaf_r50_fpn_1x_coco.py',
+    'configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py',
+    'configs/gfl/gfl_r50_fpn_1x_coco.py',
+    'configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py',
+    'configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py',
+    'configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py',
+    'configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py',
+    'configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py',
+    'configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py',
+    'configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py',
+    'configs/htc/htc_r50_fpn_1x_coco.py',
+    'configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py',
+    'configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py',
+    'configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py',
+    'configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py',
+    'configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py',
+    'configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py',
+    'configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py',
+    'configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py',
+    'configs/paa/paa_r50_fpn_1x_coco.py',
+    'configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py',
+    'configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py',
+    'configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py',
+    'configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py',
+    'configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py',
+    'configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py',
+    'configs/resnest/'
+    'mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py',
+    'configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py',
+    'configs/rpn/rpn_r50_fpn_1x_coco.py',
+    'configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py',
+    'configs/ssd/ssd300_coco.py',
+    'configs/tridentnet/tridentnet_r50_caffe_1x_coco.py',
+    'configs/vfnet/vfnet_r50_fpn_1x_coco.py',
+    'configs/yolact/yolact_r50_1x8_coco.py',
+    'configs/yolo/yolov3_d53_320_273e_coco.py',
+    'configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py',
+    'configs/scnet/scnet_r50_fpn_1x_coco.py',
+    'configs/yolof/yolof_r50_c5_8x8_1x_coco.py',
+]
+
+
+def main():
+    args = parse_args()
+
+    benchmark_type = []
+    if args.basic_arch:
+        benchmark_type += basic_arch_root
+    if args.datasets:
+        benchmark_type += datasets_root
+    if args.data_pipeline:
+        benchmark_type += data_pipeline_root
+    if args.nn_module:
+        benchmark_type += nn_module_root
+
+    special_model = args.model_options
+    if special_model is not None:
+        benchmark_type += special_model
+
+    config_dpath = 'configs/'
+    benchmark_configs = []
+    for cfg_root in benchmark_type:
+        cfg_dir = osp.join(config_dpath, cfg_root)
+        configs = os.scandir(cfg_dir)
+        for cfg in configs:
+            config_path = osp.join(cfg_dir, cfg.name)
+            if (config_path in benchmark_pool
+                    and config_path not in benchmark_configs):
+                benchmark_configs.append(config_path)
+
+    print(f'Totally found {len(benchmark_configs)} configs to benchmark')
+    with open(args.out, 'w') as f:
+        for config in benchmark_configs:
+            f.write(config + '\n')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/.dev_scripts/benchmark_inference_fps.py b/.dev_scripts/benchmark_inference_fps.py
new file mode 100644
index 0000000..81dcd6b
--- /dev/null
+++ b/.dev_scripts/benchmark_inference_fps.py
@@ -0,0 +1,170 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+import mmcv
+from mmcv import Config, DictAction
+from mmcv.runner import init_dist
+from terminaltables import GithubFlavoredMarkdownTable
+
+from tools.analysis_tools.benchmark import repeat_measure_inference_speed
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='MMDet benchmark a model of FPS')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint_root', help='Checkpoint file root path')
+    parser.add_argument(
+        '--round-num',
+        type=int,
+        default=1,
+        help='round a number to a given precision in decimal digits')
+    parser.add_argument(
+        '--repeat-num',
+        type=int,
+        default=1,
+        help='number of repeat times of measurement for averaging the results')
+    parser.add_argument(
+        '--out', type=str, help='output path of gathered fps to be stored')
+    parser.add_argument(
+        '--max-iter', type=int, default=2000, help='num of max iter')
+    parser.add_argument(
+        '--log-interval', type=int, default=50, help='interval of logging')
+    parser.add_argument(
+        '--fuse-conv-bn',
+        action='store_true',
+        help='Whether to fuse conv and bn, this will slightly increase'
+        'the inference speed')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--launcher',
+        choices=['none', 'pytorch', 'slurm', 'mpi'],
+        default='none',
+        help='job launcher')
+    parser.add_argument('--local_rank', type=int, default=0)
+    args = parser.parse_args()
+    if 'LOCAL_RANK' not in os.environ:
+        os.environ['LOCAL_RANK'] = str(args.local_rank)
+    return args
+
+
+def results2markdown(result_dict):
+    table_data = []
+    is_multiple_results = False
+    for cfg_name, value in result_dict.items():
+        name = cfg_name.replace('configs/', '')
+        fps = value['fps']
+        ms_times_pre_image = value['ms_times_pre_image']
+        if isinstance(fps, list):
+            is_multiple_results = True
+            mean_fps = value['mean_fps']
+            mean_times_pre_image = value['mean_times_pre_image']
+            fps_str = ','.join([str(s) for s in fps])
+            ms_times_pre_image_str = ','.join(
+                [str(s) for s in ms_times_pre_image])
+            table_data.append([
+                name, fps_str, mean_fps, ms_times_pre_image_str,
+                mean_times_pre_image
+            ])
+        else:
+            table_data.append([name, fps, ms_times_pre_image])
+
+    if is_multiple_results:
+        table_data.insert(0, [
+            'model', 'fps', 'mean_fps', 'times_pre_image(ms)',
+            'mean_times_pre_image(ms)'
+        ])
+
+    else:
+        table_data.insert(0, ['model', 'fps', 'times_pre_image(ms)'])
+    table = GithubFlavoredMarkdownTable(table_data)
+    print(table.table, flush=True)
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    assert args.round_num >= 0
+    assert args.repeat_num >= 1
+
+    config = Config.fromfile(args.config)
+
+    if args.launcher == 'none':
+        raise NotImplementedError('Only supports distributed mode')
+    else:
+        init_dist(args.launcher)
+
+    result_dict = {}
+    for model_key in config:
+        model_infos = config[model_key]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            record_metrics = model_info['metric']
+            cfg_path = model_info['config'].strip()
+            cfg = Config.fromfile(cfg_path)
+            checkpoint = osp.join(args.checkpoint_root,
+                                  model_info['checkpoint'].strip())
+            try:
+                fps = repeat_measure_inference_speed(cfg, checkpoint,
+                                                     args.max_iter,
+                                                     args.log_interval,
+                                                     args.fuse_conv_bn,
+                                                     args.repeat_num)
+                if args.repeat_num > 1:
+                    fps_list = [round(fps_, args.round_num) for fps_ in fps]
+                    times_pre_image_list = [
+                        round(1000 / fps_, args.round_num) for fps_ in fps
+                    ]
+                    mean_fps = round(
+                        sum(fps_list) / len(fps_list), args.round_num)
+                    mean_times_pre_image = round(
+                        sum(times_pre_image_list) / len(times_pre_image_list),
+                        args.round_num)
+                    print(
+                        f'{cfg_path} '
+                        f'Overall fps: {fps_list}[{mean_fps}] img / s, '
+                        f'times per image: '
+                        f'{times_pre_image_list}[{mean_times_pre_image}] '
+                        f'ms / img',
+                        flush=True)
+                    result_dict[cfg_path] = dict(
+                        fps=fps_list,
+                        mean_fps=mean_fps,
+                        ms_times_pre_image=times_pre_image_list,
+                        mean_times_pre_image=mean_times_pre_image)
+                else:
+                    print(
+                        f'{cfg_path} fps : {fps:.{args.round_num}f} img / s, '
+                        f'times per image: {1000 / fps:.{args.round_num}f} '
+                        f'ms / img',
+                        flush=True)
+                    result_dict[cfg_path] = dict(
+                        fps=round(fps, args.round_num),
+                        ms_times_pre_image=round(1000 / fps, args.round_num))
+            except Exception as e:
+                print(f'{cfg_path} error: {repr(e)}')
+                if args.repeat_num > 1:
+                    result_dict[cfg_path] = dict(
+                        fps=[0],
+                        mean_fps=0,
+                        ms_times_pre_image=[0],
+                        mean_times_pre_image=0)
+                else:
+                    result_dict[cfg_path] = dict(fps=0, ms_times_pre_image=0)
+
+    if args.out:
+        mmcv.mkdir_or_exist(args.out)
+        mmcv.dump(result_dict, osp.join(args.out, 'batch_inference_fps.json'))
+
+    results2markdown(result_dict)
diff --git a/.dev_scripts/benchmark_test_image.py b/.dev_scripts/benchmark_test_image.py
new file mode 100644
index 0000000..75f7576
--- /dev/null
+++ b/.dev_scripts/benchmark_test_image.py
@@ -0,0 +1,102 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import logging
+import os.path as osp
+from argparse import ArgumentParser
+
+from mmcv import Config
+
+from mmdet.apis import inference_detector, init_detector, show_result_pyplot
+from mmdet.utils import get_root_logger
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint_root', help='Checkpoint file root path')
+    parser.add_argument('--img', default='demo/demo.jpg', help='Image file')
+    parser.add_argument('--aug', action='store_true', help='aug test')
+    parser.add_argument('--model-name', help='model name to inference')
+    parser.add_argument('--show', action='store_true', help='show results')
+    parser.add_argument(
+        '--wait-time',
+        type=float,
+        default=1,
+        help='the interval of show (s), 0 is block')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--score-thr', type=float, default=0.3, help='bbox score threshold')
+    args = parser.parse_args()
+    return args
+
+
+def inference_model(config_name, checkpoint, args, logger=None):
+    cfg = Config.fromfile(config_name)
+    if args.aug:
+        if 'flip' in cfg.data.test.pipeline[1]:
+            cfg.data.test.pipeline[1].flip = True
+        else:
+            if logger is not None:
+                logger.error(f'{config_name}: unable to start aug test')
+            else:
+                print(f'{config_name}: unable to start aug test', flush=True)
+
+    model = init_detector(cfg, checkpoint, device=args.device)
+    # test a single image
+    result = inference_detector(model, args.img)
+
+    # show the results
+    if args.show:
+        show_result_pyplot(
+            model,
+            args.img,
+            result,
+            score_thr=args.score_thr,
+            wait_time=args.wait_time)
+    return result
+
+
+# Sample test whether the inference code is correct
+def main(args):
+    config = Config.fromfile(args.config)
+
+    # test single model
+    if args.model_name:
+        if args.model_name in config:
+            model_infos = config[args.model_name]
+            if not isinstance(model_infos, list):
+                model_infos = [model_infos]
+            model_info = model_infos[0]
+            config_name = model_info['config'].strip()
+            print(f'processing: {config_name}', flush=True)
+            checkpoint = osp.join(args.checkpoint_root,
+                                  model_info['checkpoint'].strip())
+            # build the model from a config file and a checkpoint file
+            inference_model(config_name, checkpoint, args)
+            return
+        else:
+            raise RuntimeError('model name input error.')
+
+    # test all model
+    logger = get_root_logger(
+        log_file='benchmark_test_image.log', log_level=logging.ERROR)
+
+    for model_key in config:
+        model_infos = config[model_key]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            print('processing: ', model_info['config'], flush=True)
+            config_name = model_info['config'].strip()
+            checkpoint = osp.join(args.checkpoint_root,
+                                  model_info['checkpoint'].strip())
+            try:
+                # build the model from a config file and a checkpoint file
+                inference_model(config_name, checkpoint, args, logger)
+            except Exception as e:
+                logger.error(f'{config_name} " : {repr(e)}')
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)
diff --git a/.dev_scripts/check_links.py b/.dev_scripts/check_links.py
new file mode 100755
index 0000000..b195d2a
--- /dev/null
+++ b/.dev_scripts/check_links.py
@@ -0,0 +1,157 @@
+# Modified from:
+# https://github.com/allenai/allennlp/blob/main/scripts/check_links.py
+
+import argparse
+import logging
+import os
+import pathlib
+import re
+import sys
+from multiprocessing.dummy import Pool
+from typing import NamedTuple, Optional, Tuple
+
+import requests
+from mmcv.utils import get_logger
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Goes through all the inline-links '
+        'in markdown files and reports the breakages')
+    parser.add_argument(
+        '--num-threads',
+        type=int,
+        default=100,
+        help='Number of processes to confirm the link')
+    parser.add_argument('--https-proxy', type=str, help='https proxy')
+    parser.add_argument(
+        '--out',
+        type=str,
+        default='link_reports.txt',
+        help='output path of reports')
+    args = parser.parse_args()
+    return args
+
+
+OK_STATUS_CODES = (
+    200,
+    401,  # the resource exists but may require some sort of login.
+    403,  # ^ same
+    405,  # HEAD method not allowed.
+    # the resource exists, but our default 'Accept-' header may not
+    # match what the server can provide.
+    406,
+)
+
+
+class MatchTuple(NamedTuple):
+    source: str
+    name: str
+    link: str
+
+
+def check_link(
+        match_tuple: MatchTuple,
+        http_session: requests.Session,
+        logger: logging = None) -> Tuple[MatchTuple, bool, Optional[str]]:
+    reason: Optional[str] = None
+    if match_tuple.link.startswith('http'):
+        result_ok, reason = check_url(match_tuple, http_session)
+    else:
+        result_ok = check_path(match_tuple)
+    if logger is None:
+        print(f"  {'✓' if result_ok else '✗'} {match_tuple.link}")
+    else:
+        logger.info(f"  {'✓' if result_ok else '✗'} {match_tuple.link}")
+    return match_tuple, result_ok, reason
+
+
+def check_url(match_tuple: MatchTuple,
+              http_session: requests.Session) -> Tuple[bool, str]:
+    """Check if a URL is reachable."""
+    try:
+        result = http_session.head(
+            match_tuple.link, timeout=5, allow_redirects=True)
+        return (
+            result.ok or result.status_code in OK_STATUS_CODES,
+            f'status code = {result.status_code}',
+        )
+    except (requests.ConnectionError, requests.Timeout):
+        return False, 'connection error'
+
+
+def check_path(match_tuple: MatchTuple) -> bool:
+    """Check if a file in this repository exists."""
+    relative_path = match_tuple.link.split('#')[0]
+    full_path = os.path.join(
+        os.path.dirname(str(match_tuple.source)), relative_path)
+    return os.path.exists(full_path)
+
+
+def main():
+    args = parse_args()
+
+    # setup logger
+    logger = get_logger(name='mmdet', log_file=args.out)
+
+    # setup https_proxy
+    if args.https_proxy:
+        os.environ['https_proxy'] = args.https_proxy
+
+    # setup http_session
+    http_session = requests.Session()
+    for resource_prefix in ('http://', 'https://'):
+        http_session.mount(
+            resource_prefix,
+            requests.adapters.HTTPAdapter(
+                max_retries=5,
+                pool_connections=20,
+                pool_maxsize=args.num_threads),
+        )
+
+    logger.info('Finding all markdown files in the current directory...')
+
+    project_root = (pathlib.Path(__file__).parent / '..').resolve()
+    markdown_files = project_root.glob('**/*.md')
+
+    all_matches = set()
+    url_regex = re.compile(r'\[([^!][^\]]+)\]\(([^)(]+)\)')
+    for markdown_file in markdown_files:
+        with open(markdown_file) as handle:
+            for line in handle.readlines():
+                matches = url_regex.findall(line)
+                for name, link in matches:
+                    if 'localhost' not in link:
+                        all_matches.add(
+                            MatchTuple(
+                                source=str(markdown_file),
+                                name=name,
+                                link=link))
+
+    logger.info(f'  {len(all_matches)} markdown files found')
+    logger.info('Checking to make sure we can retrieve each link...')
+
+    with Pool(processes=args.num_threads) as pool:
+        results = pool.starmap(check_link, [(match, http_session, logger)
+                                            for match in list(all_matches)])
+
+    # collect unreachable results
+    unreachable_results = [(match_tuple, reason)
+                           for match_tuple, success, reason in results
+                           if not success]
+
+    if unreachable_results:
+        logger.info('================================================')
+        logger.info(f'Unreachable links ({len(unreachable_results)}):')
+        for match_tuple, reason in unreachable_results:
+            logger.info('  > Source: ' + match_tuple.source)
+            logger.info('    Name: ' + match_tuple.name)
+            logger.info('    Link: ' + match_tuple.link)
+            if reason is not None:
+                logger.info('    Reason: ' + reason)
+        sys.exit(1)
+    logger.info('No Unreachable link found.')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/.dev_scripts/convert_test_benchmark_script.py b/.dev_scripts/convert_test_benchmark_script.py
new file mode 100644
index 0000000..c31cad4
--- /dev/null
+++ b/.dev_scripts/convert_test_benchmark_script.py
@@ -0,0 +1,119 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+from mmcv import Config
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert benchmark model list to script')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('--port', type=int, default=29666, help='dist port')
+    parser.add_argument(
+        '--work-dir',
+        default='tools/batch_test',
+        help='the dir to save metric')
+    parser.add_argument(
+        '--run', action='store_true', help='run script directly')
+    parser.add_argument(
+        '--out', type=str, help='path to save model benchmark script')
+
+    args = parser.parse_args()
+    return args
+
+
+def process_model_info(model_info, work_dir):
+    config = model_info['config'].strip()
+    fname, _ = osp.splitext(osp.basename(config))
+    job_name = fname
+    work_dir = osp.join(work_dir, fname)
+    checkpoint = model_info['checkpoint'].strip()
+    if not isinstance(model_info['eval'], list):
+        evals = [model_info['eval']]
+    else:
+        evals = model_info['eval']
+    eval = ' '.join(evals)
+    return dict(
+        config=config,
+        job_name=job_name,
+        work_dir=work_dir,
+        checkpoint=checkpoint,
+        eval=eval)
+
+
+def create_test_bash_info(commands, model_test_dict, port, script_name,
+                          partition):
+    config = model_test_dict['config']
+    job_name = model_test_dict['job_name']
+    checkpoint = model_test_dict['checkpoint']
+    work_dir = model_test_dict['work_dir']
+    eval = model_test_dict['eval']
+
+    echo_info = f' \necho \'{config}\' &'
+    commands.append(echo_info)
+    commands.append('\n')
+
+    command_info = f'GPUS=8  GPUS_PER_NODE=8  ' \
+                   f'CPUS_PER_TASK=2 {script_name} '
+
+    command_info += f'{partition} '
+    command_info += f'{job_name} '
+    command_info += f'{config} '
+    command_info += f'$CHECKPOINT_DIR/{checkpoint} '
+    command_info += f'--work-dir {work_dir} '
+
+    command_info += f'--eval {eval} '
+    command_info += f'--cfg-option dist_params.port={port} '
+    command_info += ' &'
+
+    commands.append(command_info)
+
+
+def main():
+    args = parse_args()
+    if args.out:
+        out_suffix = args.out.split('.')[-1]
+        assert args.out.endswith('.sh'), \
+            f'Expected out file path suffix is .sh, but get .{out_suffix}'
+    assert args.out or args.run, \
+        ('Please specify at least one operation (save/run/ the '
+         'script) with the argument "--out" or "--run"')
+
+    commands = []
+    partition_name = 'PARTITION=$1 '
+    commands.append(partition_name)
+    commands.append('\n')
+
+    checkpoint_root = 'CHECKPOINT_DIR=$2 '
+    commands.append(checkpoint_root)
+    commands.append('\n')
+
+    script_name = osp.join('tools', 'slurm_test.sh')
+    port = args.port
+    work_dir = args.work_dir
+
+    cfg = Config.fromfile(args.config)
+
+    for model_key in cfg:
+        model_infos = cfg[model_key]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            print('processing: ', model_info['config'])
+            model_test_dict = process_model_info(model_info, work_dir)
+            create_test_bash_info(commands, model_test_dict, port, script_name,
+                                  '$PARTITION')
+            port += 1
+
+    command_str = ''.join(commands)
+    if args.out:
+        with open(args.out, 'w') as f:
+            f.write(command_str)
+    if args.run:
+        os.system(command_str)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/.dev_scripts/convert_train_benchmark_script.py b/.dev_scripts/convert_train_benchmark_script.py
new file mode 100644
index 0000000..1ccd8e9
--- /dev/null
+++ b/.dev_scripts/convert_train_benchmark_script.py
@@ -0,0 +1,99 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert benchmark model json to script')
+    parser.add_argument(
+        'txt_path', type=str, help='txt path output by benchmark_filter')
+    parser.add_argument(
+        '--partition',
+        type=str,
+        default='openmmlab',
+        help='slurm partition name')
+    parser.add_argument(
+        '--max-keep-ckpts',
+        type=int,
+        default=1,
+        help='The maximum checkpoints to keep')
+    parser.add_argument(
+        '--run', action='store_true', help='run script directly')
+    parser.add_argument(
+        '--out', type=str, help='path to save model benchmark script')
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    if args.out:
+        out_suffix = args.out.split('.')[-1]
+        assert args.out.endswith('.sh'), \
+            f'Expected out file path suffix is .sh, but get .{out_suffix}'
+    assert args.out or args.run, \
+        ('Please specify at least one operation (save/run/ the '
+         'script) with the argument "--out" or "--run"')
+
+    partition = args.partition  # cluster name
+
+    root_name = './tools'
+    train_script_name = osp.join(root_name, 'slurm_train.sh')
+    # stdout is no output
+    stdout_cfg = '>/dev/null'
+
+    max_keep_ckpts = args.max_keep_ckpts
+
+    commands = []
+    with open(args.txt_path, 'r') as f:
+        model_cfgs = f.readlines()
+        for i, cfg in enumerate(model_cfgs):
+            cfg = cfg.strip()
+            if len(cfg) == 0:
+                continue
+            # print cfg name
+            echo_info = f'echo \'{cfg}\' &'
+            commands.append(echo_info)
+            commands.append('\n')
+
+            fname, _ = osp.splitext(osp.basename(cfg))
+            out_fname = osp.join(root_name, 'work_dir', fname)
+            # default setting
+            if cfg.find('16x') >= 0:
+                command_info = f'GPUS=16  GPUS_PER_NODE=8  ' \
+                               f'CPUS_PER_TASK=2 {train_script_name} '
+            elif cfg.find('gn-head_4x4_1x_coco.py') >= 0 or \
+                    cfg.find('gn-head_4x4_2x_coco.py') >= 0:
+                command_info = f'GPUS=4  GPUS_PER_NODE=4  ' \
+                               f'CPUS_PER_TASK=2 {train_script_name} '
+            else:
+                command_info = f'GPUS=8  GPUS_PER_NODE=8  ' \
+                               f'CPUS_PER_TASK=2 {train_script_name} '
+            command_info += f'{partition} '
+            command_info += f'{fname} '
+            command_info += f'{cfg} '
+            command_info += f'{out_fname} '
+            if max_keep_ckpts:
+                command_info += f'--cfg-options ' \
+                                f'checkpoint_config.max_keep_ckpts=' \
+                                f'{max_keep_ckpts}' + ' '
+            command_info += f'{stdout_cfg} &'
+
+            commands.append(command_info)
+
+            if i < len(model_cfgs):
+                commands.append('\n')
+
+        command_str = ''.join(commands)
+        if args.out:
+            with open(args.out, 'w') as f:
+                f.write(command_str)
+        if args.run:
+            os.system(command_str)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/.dev_scripts/gather_models.py b/.dev_scripts/gather_models.py
new file mode 100644
index 0000000..dbfef88
--- /dev/null
+++ b/.dev_scripts/gather_models.py
@@ -0,0 +1,342 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import glob
+import json
+import os.path as osp
+import shutil
+import subprocess
+from collections import OrderedDict
+
+import mmcv
+import torch
+import yaml
+
+
+def ordered_yaml_dump(data, stream=None, Dumper=yaml.SafeDumper, **kwds):
+
+    class OrderedDumper(Dumper):
+        pass
+
+    def _dict_representer(dumper, data):
+        return dumper.represent_mapping(
+            yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG, data.items())
+
+    OrderedDumper.add_representer(OrderedDict, _dict_representer)
+    return yaml.dump(data, stream, OrderedDumper, **kwds)
+
+
+def process_checkpoint(in_file, out_file):
+    checkpoint = torch.load(in_file, map_location='cpu')
+    # remove optimizer for smaller file size
+    if 'optimizer' in checkpoint:
+        del checkpoint['optimizer']
+
+    # remove ema state_dict
+    for key in list(checkpoint['state_dict']):
+        if key.startswith('ema_'):
+            checkpoint['state_dict'].pop(key)
+
+    # if it is necessary to remove some sensitive data in checkpoint['meta'],
+    # add the code here.
+    if torch.__version__ >= '1.6':
+        torch.save(checkpoint, out_file, _use_new_zipfile_serialization=False)
+    else:
+        torch.save(checkpoint, out_file)
+    sha = subprocess.check_output(['sha256sum', out_file]).decode()
+    final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8])
+    subprocess.Popen(['mv', out_file, final_file])
+    return final_file
+
+
+def is_by_epoch(config):
+    cfg = mmcv.Config.fromfile('./configs/' + config)
+    return cfg.runner.type == 'EpochBasedRunner'
+
+
+def get_final_epoch_or_iter(config):
+    cfg = mmcv.Config.fromfile('./configs/' + config)
+    if cfg.runner.type == 'EpochBasedRunner':
+        return cfg.runner.max_epochs
+    else:
+        return cfg.runner.max_iters
+
+
+def get_best_epoch_or_iter(exp_dir):
+    best_epoch_iter_full_path = list(
+        sorted(glob.glob(osp.join(exp_dir, 'best_*.pth'))))[-1]
+    best_epoch_or_iter_model_path = best_epoch_iter_full_path.split('/')[-1]
+    best_epoch_or_iter = best_epoch_or_iter_model_path.\
+        split('_')[-1].split('.')[0]
+    return best_epoch_or_iter_model_path, int(best_epoch_or_iter)
+
+
+def get_real_epoch_or_iter(config):
+    cfg = mmcv.Config.fromfile('./configs/' + config)
+    if cfg.runner.type == 'EpochBasedRunner':
+        epoch = cfg.runner.max_epochs
+        if cfg.data.train.type == 'RepeatDataset':
+            epoch *= cfg.data.train.times
+        return epoch
+    else:
+        return cfg.runner.max_iters
+
+
+def get_final_results(log_json_path,
+                      epoch_or_iter,
+                      results_lut,
+                      by_epoch=True):
+    result_dict = dict()
+    last_val_line = None
+    last_train_line = None
+    last_val_line_idx = -1
+    last_train_line_idx = -1
+    with open(log_json_path, 'r') as f:
+        for i, line in enumerate(f.readlines()):
+            log_line = json.loads(line)
+            if 'mode' not in log_line.keys():
+                continue
+
+            if by_epoch:
+                if (log_line['mode'] == 'train'
+                        and log_line['epoch'] == epoch_or_iter):
+                    result_dict['memory'] = log_line['memory']
+
+                if (log_line['mode'] == 'val'
+                        and log_line['epoch'] == epoch_or_iter):
+                    result_dict.update({
+                        key: log_line[key]
+                        for key in results_lut if key in log_line
+                    })
+                    return result_dict
+            else:
+                if log_line['mode'] == 'train':
+                    last_train_line_idx = i
+                    last_train_line = log_line
+
+                if log_line and log_line['mode'] == 'val':
+                    last_val_line_idx = i
+                    last_val_line = log_line
+
+    # bug: max_iters = 768, last_train_line['iter'] = 750
+    assert last_val_line_idx == last_train_line_idx + 1, \
+        'Log file is incomplete'
+    result_dict['memory'] = last_train_line['memory']
+    result_dict.update({
+        key: last_val_line[key]
+        for key in results_lut if key in last_val_line
+    })
+
+    return result_dict
+
+
+def get_dataset_name(config):
+    # If there are more dataset, add here.
+    name_map = dict(
+        CityscapesDataset='Cityscapes',
+        CocoDataset='COCO',
+        CocoPanopticDataset='COCO',
+        DeepFashionDataset='Deep Fashion',
+        LVISV05Dataset='LVIS v0.5',
+        LVISV1Dataset='LVIS v1',
+        VOCDataset='Pascal VOC',
+        WIDERFaceDataset='WIDER Face',
+        OpenImagesDataset='OpenImagesDataset',
+        OpenImagesChallengeDataset='OpenImagesChallengeDataset',
+        Objects365V1Dataset='Objects365 v1',
+        Objects365V2Dataset='Objects365 v2')
+    cfg = mmcv.Config.fromfile('./configs/' + config)
+    return name_map[cfg.dataset_type]
+
+
+def convert_model_info_to_pwc(model_infos):
+    pwc_files = {}
+    for model in model_infos:
+        cfg_folder_name = osp.split(model['config'])[-2]
+        pwc_model_info = OrderedDict()
+        pwc_model_info['Name'] = osp.split(model['config'])[-1].split('.')[0]
+        pwc_model_info['In Collection'] = 'Please fill in Collection name'
+        pwc_model_info['Config'] = osp.join('configs', model['config'])
+
+        # get metadata
+        memory = round(model['results']['memory'] / 1024, 1)
+        meta_data = OrderedDict()
+        meta_data['Training Memory (GB)'] = memory
+        if 'epochs' in model:
+            meta_data['Epochs'] = get_real_epoch_or_iter(model['config'])
+        else:
+            meta_data['Iterations'] = get_real_epoch_or_iter(model['config'])
+        pwc_model_info['Metadata'] = meta_data
+
+        # get dataset name
+        dataset_name = get_dataset_name(model['config'])
+
+        # get results
+        results = []
+        # if there are more metrics, add here.
+        if 'bbox_mAP' in model['results']:
+            metric = round(model['results']['bbox_mAP'] * 100, 1)
+            results.append(
+                OrderedDict(
+                    Task='Object Detection',
+                    Dataset=dataset_name,
+                    Metrics={'box AP': metric}))
+        if 'segm_mAP' in model['results']:
+            metric = round(model['results']['segm_mAP'] * 100, 1)
+            results.append(
+                OrderedDict(
+                    Task='Instance Segmentation',
+                    Dataset=dataset_name,
+                    Metrics={'mask AP': metric}))
+        if 'PQ' in model['results']:
+            metric = round(model['results']['PQ'], 1)
+            results.append(
+                OrderedDict(
+                    Task='Panoptic Segmentation',
+                    Dataset=dataset_name,
+                    Metrics={'PQ': metric}))
+        pwc_model_info['Results'] = results
+
+        link_string = 'https://download.openmmlab.com/mmdetection/v2.0/'
+        link_string += '{}/{}'.format(model['config'].rstrip('.py'),
+                                      osp.split(model['model_path'])[-1])
+        pwc_model_info['Weights'] = link_string
+        if cfg_folder_name in pwc_files:
+            pwc_files[cfg_folder_name].append(pwc_model_info)
+        else:
+            pwc_files[cfg_folder_name] = [pwc_model_info]
+    return pwc_files
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Gather benchmarked models')
+    parser.add_argument(
+        'root',
+        type=str,
+        help='root path of benchmarked models to be gathered')
+    parser.add_argument(
+        'out', type=str, help='output path of gathered models to be stored')
+    parser.add_argument(
+        '--best',
+        action='store_true',
+        help='whether to gather the best model.')
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    models_root = args.root
+    models_out = args.out
+    mmcv.mkdir_or_exist(models_out)
+
+    # find all models in the root directory to be gathered
+    raw_configs = list(mmcv.scandir('./configs', '.py', recursive=True))
+
+    # filter configs that is not trained in the experiments dir
+    used_configs = []
+    for raw_config in raw_configs:
+        if osp.exists(osp.join(models_root, raw_config)):
+            used_configs.append(raw_config)
+    print(f'Find {len(used_configs)} models to be gathered')
+
+    # find final_ckpt and log file for trained each config
+    # and parse the best performance
+    model_infos = []
+    for used_config in used_configs:
+        exp_dir = osp.join(models_root, used_config)
+        by_epoch = is_by_epoch(used_config)
+        # check whether the exps is finished
+        if args.best is True:
+            final_model, final_epoch_or_iter = get_best_epoch_or_iter(exp_dir)
+        else:
+            final_epoch_or_iter = get_final_epoch_or_iter(used_config)
+            final_model = '{}_{}.pth'.format('epoch' if by_epoch else 'iter',
+                                             final_epoch_or_iter)
+
+        model_path = osp.join(exp_dir, final_model)
+        # skip if the model is still training
+        if not osp.exists(model_path):
+            continue
+
+        # get the latest logs
+        log_json_path = list(
+            sorted(glob.glob(osp.join(exp_dir, '*.log.json'))))[-1]
+        log_txt_path = list(sorted(glob.glob(osp.join(exp_dir, '*.log'))))[-1]
+        cfg = mmcv.Config.fromfile('./configs/' + used_config)
+        results_lut = cfg.evaluation.metric
+        if not isinstance(results_lut, list):
+            results_lut = [results_lut]
+        # case when using VOC, the evaluation key is only 'mAP'
+        # when using Panoptic Dataset, the evaluation key is 'PQ'.
+        for i, key in enumerate(results_lut):
+            if 'mAP' not in key and 'PQ' not in key:
+                results_lut[i] = key + '_mAP'
+        model_performance = get_final_results(log_json_path,
+                                              final_epoch_or_iter, results_lut,
+                                              by_epoch)
+
+        if model_performance is None:
+            continue
+
+        model_time = osp.split(log_txt_path)[-1].split('.')[0]
+        model_info = dict(
+            config=used_config,
+            results=model_performance,
+            model_time=model_time,
+            final_model=final_model,
+            log_json_path=osp.split(log_json_path)[-1])
+        model_info['epochs' if by_epoch else 'iterations'] =\
+            final_epoch_or_iter
+        model_infos.append(model_info)
+
+    # publish model for each checkpoint
+    publish_model_infos = []
+    for model in model_infos:
+        model_publish_dir = osp.join(models_out, model['config'].rstrip('.py'))
+        mmcv.mkdir_or_exist(model_publish_dir)
+
+        model_name = osp.split(model['config'])[-1].split('.')[0]
+
+        model_name += '_' + model['model_time']
+        publish_model_path = osp.join(model_publish_dir, model_name)
+        trained_model_path = osp.join(models_root, model['config'],
+                                      model['final_model'])
+
+        # convert model
+        final_model_path = process_checkpoint(trained_model_path,
+                                              publish_model_path)
+
+        # copy log
+        shutil.copy(
+            osp.join(models_root, model['config'], model['log_json_path']),
+            osp.join(model_publish_dir, f'{model_name}.log.json'))
+        shutil.copy(
+            osp.join(models_root, model['config'],
+                     model['log_json_path'].rstrip('.json')),
+            osp.join(model_publish_dir, f'{model_name}.log'))
+
+        # copy config to guarantee reproducibility
+        config_path = model['config']
+        config_path = osp.join(
+            'configs',
+            config_path) if 'configs' not in config_path else config_path
+        target_config_path = osp.split(config_path)[-1]
+        shutil.copy(config_path, osp.join(model_publish_dir,
+                                          target_config_path))
+
+        model['model_path'] = final_model_path
+        publish_model_infos.append(model)
+
+    models = dict(models=publish_model_infos)
+    print(f'Totally gathered {len(publish_model_infos)} models')
+    mmcv.dump(models, osp.join(models_out, 'model_info.json'))
+
+    pwc_files = convert_model_info_to_pwc(publish_model_infos)
+    for name in pwc_files:
+        with open(osp.join(models_out, name + '_metafile.yml'), 'w') as f:
+            ordered_yaml_dump(pwc_files[name], f, encoding='utf-8')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/.dev_scripts/gather_test_benchmark_metric.py b/.dev_scripts/gather_test_benchmark_metric.py
new file mode 100644
index 0000000..07c6bf4
--- /dev/null
+++ b/.dev_scripts/gather_test_benchmark_metric.py
@@ -0,0 +1,96 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import glob
+import os.path as osp
+
+import mmcv
+from mmcv import Config
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Gather benchmarked models metric')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument(
+        'root',
+        type=str,
+        help='root path of benchmarked models to be gathered')
+    parser.add_argument(
+        '--out', type=str, help='output path of gathered metrics to be stored')
+    parser.add_argument(
+        '--not-show', action='store_true', help='not show metrics')
+    parser.add_argument(
+        '--show-all', action='store_true', help='show all model metrics')
+
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    root_path = args.root
+    metrics_out = args.out
+    result_dict = {}
+
+    cfg = Config.fromfile(args.config)
+
+    for model_key in cfg:
+        model_infos = cfg[model_key]
+        if not isinstance(model_infos, list):
+            model_infos = [model_infos]
+        for model_info in model_infos:
+            record_metrics = model_info['metric']
+            config = model_info['config'].strip()
+            fname, _ = osp.splitext(osp.basename(config))
+            metric_json_dir = osp.join(root_path, fname)
+            if osp.exists(metric_json_dir):
+                json_list = glob.glob(osp.join(metric_json_dir, '*.json'))
+                if len(json_list) > 0:
+                    log_json_path = list(sorted(json_list))[-1]
+
+                    metric = mmcv.load(log_json_path)
+                    if config in metric.get('config', {}):
+
+                        new_metrics = dict()
+                        for record_metric_key in record_metrics:
+                            record_metric_key_bk = record_metric_key
+                            old_metric = record_metrics[record_metric_key]
+                            if record_metric_key == 'AR_1000':
+                                record_metric_key = 'AR@1000'
+                            if record_metric_key not in metric['metric']:
+                                raise KeyError(
+                                    'record_metric_key not exist, please '
+                                    'check your config')
+                            new_metric = round(
+                                metric['metric'][record_metric_key] * 100, 1)
+                            new_metrics[record_metric_key_bk] = new_metric
+
+                        if args.show_all:
+                            result_dict[config] = dict(
+                                before=record_metrics, after=new_metrics)
+                        else:
+                            for record_metric_key in record_metrics:
+                                old_metric = record_metrics[record_metric_key]
+                                new_metric = new_metrics[record_metric_key]
+                                if old_metric != new_metric:
+                                    result_dict[config] = dict(
+                                        before=record_metrics,
+                                        after=new_metrics)
+                                    break
+                    else:
+                        print(f'{config} not included in: {log_json_path}')
+                else:
+                    print(f'{config} not exist file: {metric_json_dir}')
+            else:
+                print(f'{config} not exist dir: {metric_json_dir}')
+
+    if metrics_out:
+        mmcv.mkdir_or_exist(metrics_out)
+        mmcv.dump(result_dict,
+                  osp.join(metrics_out, 'batch_test_metric_info.json'))
+    if not args.not_show:
+        print('===================================')
+        for config_name, metrics in result_dict.items():
+            print(config_name, metrics)
+        print('===================================')
diff --git a/.dev_scripts/gather_train_benchmark_metric.py b/.dev_scripts/gather_train_benchmark_metric.py
new file mode 100644
index 0000000..f9c6c80
--- /dev/null
+++ b/.dev_scripts/gather_train_benchmark_metric.py
@@ -0,0 +1,150 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import glob
+import os.path as osp
+
+import mmcv
+from gather_models import get_final_results
+
+try:
+    import xlrd
+except ImportError:
+    xlrd = None
+try:
+    import xlutils
+    from xlutils.copy import copy
+except ImportError:
+    xlutils = None
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Gather benchmarked models metric')
+    parser.add_argument(
+        'root',
+        type=str,
+        help='root path of benchmarked models to be gathered')
+    parser.add_argument(
+        'txt_path', type=str, help='txt path output by benchmark_filter')
+    parser.add_argument(
+        '--out', type=str, help='output path of gathered metrics to be stored')
+    parser.add_argument(
+        '--not-show', action='store_true', help='not show metrics')
+    parser.add_argument(
+        '--excel', type=str, help='input path of excel to be recorded')
+    parser.add_argument(
+        '--ncol', type=int, help='Number of column to be modified or appended')
+
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    if args.excel:
+        assert args.ncol, 'Please specify "--excel" and "--ncol" ' \
+                          'at the same time'
+        if xlrd is None:
+            raise RuntimeError(
+                'xlrd is not installed,'
+                'Please use “pip install xlrd==1.2.0” to install')
+        if xlutils is None:
+            raise RuntimeError(
+                'xlutils is not installed,'
+                'Please use “pip install xlutils==2.0.0” to install')
+        readbook = xlrd.open_workbook(args.excel)
+        sheet = readbook.sheet_by_name('Sheet1')
+        sheet_info = {}
+        total_nrows = sheet.nrows
+        for i in range(3, sheet.nrows):
+            sheet_info[sheet.row_values(i)[0]] = i
+        xlrw = copy(readbook)
+        table = xlrw.get_sheet(0)
+
+    root_path = args.root
+    metrics_out = args.out
+
+    result_dict = {}
+    with open(args.txt_path, 'r') as f:
+        model_cfgs = f.readlines()
+        for i, config in enumerate(model_cfgs):
+            config = config.strip()
+            if len(config) == 0:
+                continue
+
+            config_name = osp.split(config)[-1]
+            config_name = osp.splitext(config_name)[0]
+            result_path = osp.join(root_path, config_name)
+            if osp.exists(result_path):
+                # 1 read config
+                cfg = mmcv.Config.fromfile(config)
+                total_epochs = cfg.runner.max_epochs
+                final_results = cfg.evaluation.metric
+                if not isinstance(final_results, list):
+                    final_results = [final_results]
+                final_results_out = []
+                for key in final_results:
+                    if 'proposal_fast' in key:
+                        final_results_out.append('AR@1000')  # RPN
+                    elif 'mAP' not in key:
+                        final_results_out.append(key + '_mAP')
+
+                # 2 determine whether total_epochs ckpt exists
+                ckpt_path = f'epoch_{total_epochs}.pth'
+                if osp.exists(osp.join(result_path, ckpt_path)):
+                    log_json_path = list(
+                        sorted(glob.glob(osp.join(result_path,
+                                                  '*.log.json'))))[-1]
+
+                    # 3 read metric
+                    model_performance = get_final_results(
+                        log_json_path, total_epochs, final_results_out)
+                    if model_performance is None:
+                        print(f'log file error: {log_json_path}')
+                        continue
+                    for performance in model_performance:
+                        if performance in ['AR@1000', 'bbox_mAP', 'segm_mAP']:
+                            metric = round(
+                                model_performance[performance] * 100, 1)
+                            model_performance[performance] = metric
+                    result_dict[config] = model_performance
+
+                    # update and append excel content
+                    if args.excel:
+                        if 'AR@1000' in model_performance:
+                            metrics = f'{model_performance["AR@1000"]}' \
+                                      f'(AR@1000)'
+                        elif 'segm_mAP' in model_performance:
+                            metrics = f'{model_performance["bbox_mAP"]}/' \
+                                      f'{model_performance["segm_mAP"]}'
+                        else:
+                            metrics = f'{model_performance["bbox_mAP"]}'
+
+                        row_num = sheet_info.get(config, None)
+                        if row_num:
+                            table.write(row_num, args.ncol, metrics)
+                        else:
+                            table.write(total_nrows, 0, config)
+                            table.write(total_nrows, args.ncol, metrics)
+                            total_nrows += 1
+
+                else:
+                    print(f'{config} not exist: {ckpt_path}')
+            else:
+                print(f'not exist: {config}')
+
+        # 4 save or print results
+        if metrics_out:
+            mmcv.mkdir_or_exist(metrics_out)
+            mmcv.dump(result_dict,
+                      osp.join(metrics_out, 'model_metric_info.json'))
+        if not args.not_show:
+            print('===================================')
+            for config_name, metrics in result_dict.items():
+                print(config_name, metrics)
+            print('===================================')
+        if args.excel:
+            filename, sufflx = osp.splitext(args.excel)
+            xlrw.save(f'{filename}_o{sufflx}')
+            print(f'>>> Output {filename}_o{sufflx}')
diff --git a/.dev_scripts/linter.sh b/.dev_scripts/linter.sh
new file mode 100644
index 0000000..b0fe0ac
--- /dev/null
+++ b/.dev_scripts/linter.sh
@@ -0,0 +1,3 @@
+yapf -r -i mmdet/ configs/ tests/ tools/
+isort -rc mmdet/ configs/ tests/ tools/
+flake8 .
diff --git a/.dev_scripts/test_benchmark.sh b/.dev_scripts/test_benchmark.sh
new file mode 100644
index 0000000..cb79950
--- /dev/null
+++ b/.dev_scripts/test_benchmark.sh
@@ -0,0 +1,119 @@
+PARTITION=$1
+CHECKPOINT_DIR=$2
+
+echo 'configs/atss/atss_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION atss_r50_fpn_1x_coco configs/atss/atss_r50_fpn_1x_coco.py $CHECKPOINT_DIR/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth --work-dir tools/batch_test/atss_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29666  &
+echo 'configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION autoassign_r50_fpn_8x2_1x_coco configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py $CHECKPOINT_DIR/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth --work-dir tools/batch_test/autoassign_r50_fpn_8x2_1x_coco --eval bbox --cfg-option dist_params.port=29667  &
+echo 'configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r50_fpn_carafe_1x_coco configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_carafe_1x_coco --eval bbox --cfg-option dist_params.port=29668  &
+echo 'configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION cascade_rcnn_r50_fpn_1x_coco configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth --work-dir tools/batch_test/cascade_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29669  &
+echo 'configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION cascade_mask_rcnn_r50_fpn_1x_coco configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth --work-dir tools/batch_test/cascade_mask_rcnn_r50_fpn_1x_coco --eval bbox segm --cfg-option dist_params.port=29670  &
+echo 'configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION crpn_faster_rcnn_r50_caffe_fpn_1x_coco configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py $CHECKPOINT_DIR/crpn_faster_rcnn_r50_caffe_fpn_1x_coco-c8283cca.pth --work-dir tools/batch_test/crpn_faster_rcnn_r50_caffe_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29671  &
+echo 'configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION centripetalnet_hourglass104_mstest_16x6_210e_coco configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py $CHECKPOINT_DIR/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth --work-dir tools/batch_test/centripetalnet_hourglass104_mstest_16x6_210e_coco --eval bbox --cfg-option dist_params.port=29672  &
+echo 'configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION cornernet_hourglass104_mstest_8x6_210e_coco configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py $CHECKPOINT_DIR/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth --work-dir tools/batch_test/cornernet_hourglass104_mstest_8x6_210e_coco --eval bbox --cfg-option dist_params.port=29673  &
+echo 'configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco --eval bbox --cfg-option dist_params.port=29674  &
+echo 'configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION deformable_detr_r50_16x2_50e_coco configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py $CHECKPOINT_DIR/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth --work-dir tools/batch_test/deformable_detr_r50_16x2_50e_coco --eval bbox --cfg-option dist_params.port=29675  &
+echo 'configs/detectors/detectors_htc_r50_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION detectors_htc_r50_1x_coco configs/detectors/detectors_htc_r50_1x_coco.py $CHECKPOINT_DIR/detectors_htc_r50_1x_coco-329b1453.pth --work-dir tools/batch_test/detectors_htc_r50_1x_coco --eval bbox segm --cfg-option dist_params.port=29676  &
+echo 'configs/detr/detr_r50_8x2_150e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION detr_r50_8x2_150e_coco configs/detr/detr_r50_8x2_150e_coco.py $CHECKPOINT_DIR/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth --work-dir tools/batch_test/detr_r50_8x2_150e_coco --eval bbox --cfg-option dist_params.port=29677  &
+echo 'configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION dh_faster_rcnn_r50_fpn_1x_coco configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth --work-dir tools/batch_test/dh_faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29678  &
+echo 'configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION dynamic_rcnn_r50_fpn_1x_coco configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/dynamic_rcnn_r50_fpn_1x-62a3f276.pth --work-dir tools/batch_test/dynamic_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29679  &
+echo 'configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r50_fpn_attention_1111_1x_coco configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_attention_1111_1x_coco --eval bbox --cfg-option dist_params.port=29680  &
+echo 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r50_fpn_1x_coco configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29681  &
+echo 'configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py $CHECKPOINT_DIR/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth --work-dir tools/batch_test/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco --eval bbox --cfg-option dist_params.port=29682  &
+echo 'configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION fovea_align_r50_fpn_gn-head_4x4_2x_coco configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py $CHECKPOINT_DIR/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth --work-dir tools/batch_test/fovea_align_r50_fpn_gn-head_4x4_2x_coco --eval bbox --cfg-option dist_params.port=29683  &
+echo 'configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION retinanet_free_anchor_r50_fpn_1x_coco configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py $CHECKPOINT_DIR/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth --work-dir tools/batch_test/retinanet_free_anchor_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29684  &
+echo 'configs/fsaf/fsaf_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION fsaf_r50_fpn_1x_coco configs/fsaf/fsaf_r50_fpn_1x_coco.py $CHECKPOINT_DIR/fsaf_r50_fpn_1x_coco-94ccc51f.pth --work-dir tools/batch_test/fsaf_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29685  &
+echo 'configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth --work-dir tools/batch_test/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco --eval bbox segm --cfg-option dist_params.port=29686  &
+echo 'configs/gfl/gfl_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION gfl_r50_fpn_1x_coco configs/gfl/gfl_r50_fpn_1x_coco.py $CHECKPOINT_DIR/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth --work-dir tools/batch_test/gfl_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29687  &
+echo 'configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION mask_rcnn_r50_fpn_gn-all_2x_coco configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth --work-dir tools/batch_test/mask_rcnn_r50_fpn_gn-all_2x_coco --eval bbox segm --cfg-option dist_params.port=29688  &
+echo 'configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r50_fpn_gn_ws-all_1x_coco configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_gn_ws-all_1x_coco --eval bbox --cfg-option dist_params.port=29689  &
+echo 'configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION grid_rcnn_r50_fpn_gn-head_2x_coco configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py $CHECKPOINT_DIR/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth --work-dir tools/batch_test/grid_rcnn_r50_fpn_gn-head_2x_coco --eval bbox --cfg-option dist_params.port=29690  &
+echo 'configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r50_fpn_groie_1x_coco configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth --work-dir tools/batch_test/faster_rcnn_r50_fpn_groie_1x_coco --eval bbox --cfg-option dist_params.port=29691  &
+echo 'configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION ga_retinanet_r50_caffe_fpn_1x_coco configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py $CHECKPOINT_DIR/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth --work-dir tools/batch_test/ga_retinanet_r50_caffe_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29692  &
+echo 'configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION ga_faster_r50_caffe_fpn_1x_coco configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py $CHECKPOINT_DIR/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth --work-dir tools/batch_test/ga_faster_r50_caffe_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29693  &
+echo 'configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_hrnetv2p_w18_1x_coco configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth --work-dir tools/batch_test/faster_rcnn_hrnetv2p_w18_1x_coco --eval bbox --cfg-option dist_params.port=29694  &
+echo 'configs/htc/htc_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION htc_r50_fpn_1x_coco configs/htc/htc_r50_fpn_1x_coco.py $CHECKPOINT_DIR/htc_r50_fpn_1x_coco_20200317-7332cf16.pth --work-dir tools/batch_test/htc_r50_fpn_1x_coco --eval bbox segm --cfg-option dist_params.port=29695  &
+echo 'configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION libra_faster_rcnn_r50_fpn_1x_coco configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth --work-dir tools/batch_test/libra_faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29696  &
+echo 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION mask_rcnn_r50_fpn_1x_coco configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth --work-dir tools/batch_test/mask_rcnn_r50_fpn_1x_coco --eval bbox segm --cfg-option dist_params.port=29697  &
+echo 'configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION ms_rcnn_r50_caffe_fpn_1x_coco configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py $CHECKPOINT_DIR/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth --work-dir tools/batch_test/ms_rcnn_r50_caffe_fpn_1x_coco --eval bbox segm --cfg-option dist_params.port=29698  &
+echo 'configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py $CHECKPOINT_DIR/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth --work-dir tools/batch_test/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco --eval bbox --cfg-option dist_params.port=29699  &
+echo 'configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION retinanet_r50_nasfpn_crop640_50e_coco configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py $CHECKPOINT_DIR/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth --work-dir tools/batch_test/retinanet_r50_nasfpn_crop640_50e_coco --eval bbox --cfg-option dist_params.port=29700  &
+echo 'configs/paa/paa_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION paa_r50_fpn_1x_coco configs/paa/paa_r50_fpn_1x_coco.py $CHECKPOINT_DIR/paa_r50_fpn_1x_coco_20200821-936edec3.pth --work-dir tools/batch_test/paa_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29701  &
+echo 'configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r50_pafpn_1x_coco configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth --work-dir tools/batch_test/faster_rcnn_r50_pafpn_1x_coco --eval bbox --cfg-option dist_params.port=29702  &
+echo 'configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION pisa_faster_rcnn_r50_fpn_1x_coco configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth --work-dir tools/batch_test/pisa_faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29703  &
+echo 'configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION point_rend_r50_caffe_fpn_mstrain_1x_coco configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py $CHECKPOINT_DIR/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth --work-dir tools/batch_test/point_rend_r50_caffe_fpn_mstrain_1x_coco --eval bbox segm --cfg-option dist_params.port=29704  &
+echo 'configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION mask_rcnn_regnetx-3.2GF_fpn_1x_coco configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py $CHECKPOINT_DIR/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth --work-dir tools/batch_test/mask_rcnn_regnetx-3.2GF_fpn_1x_coco --eval bbox segm --cfg-option dist_params.port=29705  &
+echo 'configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION reppoints_moment_r50_fpn_1x_coco configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py $CHECKPOINT_DIR/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth --work-dir tools/batch_test/reppoints_moment_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29706  &
+echo 'configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_r2_101_fpn_2x_coco configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py $CHECKPOINT_DIR/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth --work-dir tools/batch_test/faster_rcnn_r2_101_fpn_2x_coco --eval bbox --cfg-option dist_params.port=29707  &
+echo 'configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py $CHECKPOINT_DIR/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco_20200926_125502-20289c16.pth --work-dir tools/batch_test/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco --eval bbox --cfg-option dist_params.port=29708  &
+echo 'configs/retinanet/retinanet_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION retinanet_r50_fpn_1x_coco configs/retinanet/retinanet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth --work-dir tools/batch_test/retinanet_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29709  &
+echo 'configs/rpn/rpn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION rpn_r50_fpn_1x_coco configs/rpn/rpn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth --work-dir tools/batch_test/rpn_r50_fpn_1x_coco --eval proposal_fast --cfg-option dist_params.port=29710  &
+echo 'configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION sabl_retinanet_r50_fpn_1x_coco configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth --work-dir tools/batch_test/sabl_retinanet_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29711  &
+echo 'configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION sabl_faster_rcnn_r50_fpn_1x_coco configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth --work-dir tools/batch_test/sabl_faster_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29712  &
+echo 'configs/scnet/scnet_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION scnet_r50_fpn_1x_coco configs/scnet/scnet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/scnet_r50_fpn_1x_coco-c3f09857.pth --work-dir tools/batch_test/scnet_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29713  &
+echo 'configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION sparse_rcnn_r50_fpn_1x_coco configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py $CHECKPOINT_DIR/sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth --work-dir tools/batch_test/sparse_rcnn_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29714  &
+echo 'configs/ssd/ssd300_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION ssd300_coco configs/ssd/ssd300_coco.py $CHECKPOINT_DIR/ssd300_coco_20210803_015428-d231a06e.pth --work-dir tools/batch_test/ssd300_coco --eval bbox --cfg-option dist_params.port=29715  &
+echo 'configs/tridentnet/tridentnet_r50_caffe_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION tridentnet_r50_caffe_1x_coco configs/tridentnet/tridentnet_r50_caffe_1x_coco.py $CHECKPOINT_DIR/tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth --work-dir tools/batch_test/tridentnet_r50_caffe_1x_coco --eval bbox --cfg-option dist_params.port=29716  &
+echo 'configs/vfnet/vfnet_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION vfnet_r50_fpn_1x_coco configs/vfnet/vfnet_r50_fpn_1x_coco.py $CHECKPOINT_DIR/vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth --work-dir tools/batch_test/vfnet_r50_fpn_1x_coco --eval bbox --cfg-option dist_params.port=29717  &
+echo 'configs/yolact/yolact_r50_1x8_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION yolact_r50_1x8_coco configs/yolact/yolact_r50_1x8_coco.py $CHECKPOINT_DIR/yolact_r50_1x8_coco_20200908-f38d58df.pth --work-dir tools/batch_test/yolact_r50_1x8_coco --eval bbox segm --cfg-option dist_params.port=29718  &
+echo 'configs/yolo/yolov3_d53_320_273e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION yolov3_d53_320_273e_coco configs/yolo/yolov3_d53_320_273e_coco.py $CHECKPOINT_DIR/yolov3_d53_320_273e_coco-421362b6.pth --work-dir tools/batch_test/yolov3_d53_320_273e_coco --eval bbox --cfg-option dist_params.port=29719  &
+echo 'configs/yolof/yolof_r50_c5_8x8_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION yolof_r50_c5_8x8_1x_coco configs/yolof/yolof_r50_c5_8x8_1x_coco.py $CHECKPOINT_DIR/yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth --work-dir tools/batch_test/yolof_r50_c5_8x8_1x_coco --eval bbox --cfg-option dist_params.port=29720  &
+echo 'configs/centernet/centernet_resnet18_dcnv2_140e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION centernet_resnet18_dcnv2_140e_coco configs/centernet/centernet_resnet18_dcnv2_140e_coco.py $CHECKPOINT_DIR/centernet_resnet18_dcnv2_140e_coco_20210702_155131-c8cd631f.pth --work-dir tools/batch_test/centernet_resnet18_dcnv2_140e_coco --eval bbox --cfg-option dist_params.port=29721  &
+echo 'configs/yolox/yolox_tiny_8x8_300e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION yolox_tiny_8x8_300e_coco configs/yolox/yolox_tiny_8x8_300e_coco.py $CHECKPOINT_DIR/yolox_tiny_8x8_300e_coco_20210806_234250-4ff3b67e.pth --work-dir tools/batch_test/yolox_tiny_8x8_300e_coco --eval bbox --cfg-option dist_params.port=29722  &
+echo 'configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 tools/slurm_test.sh $PARTITION ssdlite_mobilenetv2_scratch_600e_coco configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py $CHECKPOINT_DIR/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth --work-dir tools/batch_test/ssdlite_mobilenetv2_scratch_600e_coco --eval bbox --cfg-option dist_params.port=29723  &
diff --git a/.dev_scripts/test_init_backbone.py b/.dev_scripts/test_init_backbone.py
new file mode 100644
index 0000000..862f4af
--- /dev/null
+++ b/.dev_scripts/test_init_backbone.py
@@ -0,0 +1,181 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Check out backbone whether successfully load pretrained checkpoint."""
+import copy
+import os
+from os.path import dirname, exists, join
+
+import pytest
+from mmcv import Config, ProgressBar
+from mmcv.runner import _load_checkpoint
+
+from mmdet.models import build_detector
+
+
+def _get_config_directory():
+    """Find the predefined detector config directory."""
+    try:
+        # Assume we are running in the source mmdetection repo
+        repo_dpath = dirname(dirname(__file__))
+    except NameError:
+        # For IPython development when this __file__ is not defined
+        import mmdet
+        repo_dpath = dirname(dirname(mmdet.__file__))
+    config_dpath = join(repo_dpath, 'configs')
+    if not exists(config_dpath):
+        raise Exception('Cannot find config path')
+    return config_dpath
+
+
+def _get_config_module(fname):
+    """Load a configuration as a python module."""
+    from mmcv import Config
+    config_dpath = _get_config_directory()
+    config_fpath = join(config_dpath, fname)
+    config_mod = Config.fromfile(config_fpath)
+    return config_mod
+
+
+def _get_detector_cfg(fname):
+    """Grab configs necessary to create a detector.
+
+    These are deep copied to allow for safe modification of parameters without
+    influencing other tests.
+    """
+    config = _get_config_module(fname)
+    model = copy.deepcopy(config.model)
+    return model
+
+
+def _traversed_config_file():
+    """We traversed all potential config files under the `config` file. If you
+    need to print details or debug code, you can use this function.
+
+    If the `backbone.init_cfg` is None (do not use `Pretrained` init way), you
+    need add the folder name in `ignores_folder` (if the config files in this
+    folder all set backbone.init_cfg is None) or add config name in
+    `ignores_file` (if the config file set backbone.init_cfg is None)
+    """
+    config_path = _get_config_directory()
+    check_cfg_names = []
+
+    # `base`, `legacy_1.x` and `common` ignored by default.
+    ignores_folder = ['_base_', 'legacy_1.x', 'common']
+    # 'ld' need load teacher model, if want to check 'ld',
+    # please check teacher_config path first.
+    ignores_folder += ['ld']
+    # `selfsup_pretrain` need convert model, if want to check this model,
+    # need to convert the model first.
+    ignores_folder += ['selfsup_pretrain']
+
+    # the `init_cfg` in 'centripetalnet', 'cornernet', 'cityscapes',
+    # 'scratch' is None.
+    # the `init_cfg` in ssdlite(`ssdlite_mobilenetv2_scratch_600e_coco.py`)
+    # is None
+    # Please confirm `bockbone.init_cfg` is None first.
+    ignores_folder += ['centripetalnet', 'cornernet', 'cityscapes', 'scratch']
+    ignores_file = ['ssdlite_mobilenetv2_scratch_600e_coco.py']
+
+    for config_file_name in os.listdir(config_path):
+        if config_file_name not in ignores_folder:
+            config_file = join(config_path, config_file_name)
+            if os.path.isdir(config_file):
+                for config_sub_file in os.listdir(config_file):
+                    if config_sub_file.endswith('py') and \
+                            config_sub_file not in ignores_file:
+                        name = join(config_file, config_sub_file)
+                        check_cfg_names.append(name)
+    return check_cfg_names
+
+
+def _check_backbone(config, print_cfg=True):
+    """Check out backbone whether successfully load pretrained model, by using
+    `backbone.init_cfg`.
+
+    First, using `mmcv._load_checkpoint` to load the checkpoint without
+        loading models.
+    Then, using `build_detector` to build models, and using
+        `model.init_weights()` to initialize the parameters.
+    Finally, assert weights and bias of each layer loaded from pretrained
+        checkpoint are equal to the weights and bias of original checkpoint.
+        For the convenience of comparison, we sum up weights and bias of
+        each loaded layer separately.
+
+    Args:
+        config (str): Config file path.
+        print_cfg (bool): Whether print logger and return the result.
+
+    Returns:
+        results (str or None): If backbone successfully load pretrained
+            checkpoint, return None; else, return config file path.
+    """
+    if print_cfg:
+        print('-' * 15 + 'loading ', config)
+    cfg = Config.fromfile(config)
+    init_cfg = None
+    try:
+        init_cfg = cfg.model.backbone.init_cfg
+        init_flag = True
+    except AttributeError:
+        init_flag = False
+    if init_cfg is None or init_cfg.get('type') != 'Pretrained':
+        init_flag = False
+    if init_flag:
+        checkpoint = _load_checkpoint(init_cfg.checkpoint)
+        if 'state_dict' in checkpoint:
+            state_dict = checkpoint['state_dict']
+        else:
+            state_dict = checkpoint
+
+        model = build_detector(
+            cfg.model,
+            train_cfg=cfg.get('train_cfg'),
+            test_cfg=cfg.get('test_cfg'))
+        model.init_weights()
+
+        checkpoint_layers = state_dict.keys()
+        for name, value in model.backbone.state_dict().items():
+            if name in checkpoint_layers:
+                assert value.equal(state_dict[name])
+
+        if print_cfg:
+            print('-' * 10 + 'Successfully load checkpoint' + '-' * 10 +
+                  '\n', )
+            return None
+    else:
+        if print_cfg:
+            print(config + '\n' + '-' * 10 +
+                  'config file do not have init_cfg' + '-' * 10 + '\n')
+            return config
+
+
+@pytest.mark.parametrize('config', _traversed_config_file())
+def test_load_pretrained(config):
+    """Check out backbone whether successfully load pretrained model by using
+    `backbone.init_cfg`.
+
+    Details please refer to `_check_backbone`
+    """
+    _check_backbone(config, print_cfg=False)
+
+
+def _test_load_pretrained():
+    """We traversed all potential config files under the `config` file. If you
+    need to print details or debug code, you can use this function.
+
+    Returns:
+        check_cfg_names (list[str]): Config files that backbone initialized
+        from pretrained checkpoint might be problematic. Need to recheck
+        the config file. The output including the config files that the
+        backbone.init_cfg is None
+    """
+    check_cfg_names = _traversed_config_file()
+    need_check_cfg = []
+
+    prog_bar = ProgressBar(len(check_cfg_names))
+    for config in check_cfg_names:
+        init_cfg_name = _check_backbone(config)
+        if init_cfg_name is not None:
+            need_check_cfg.append(init_cfg_name)
+        prog_bar.update()
+    print('These config files need to be checked again')
+    print(need_check_cfg)
diff --git a/.dev_scripts/train_benchmark.sh b/.dev_scripts/train_benchmark.sh
new file mode 100644
index 0000000..dc30be9
--- /dev/null
+++ b/.dev_scripts/train_benchmark.sh
@@ -0,0 +1,134 @@
+echo 'configs/atss/atss_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab atss_r50_fpn_1x_coco configs/atss/atss_r50_fpn_1x_coco.py ./tools/work_dir/atss_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab autoassign_r50_fpn_8x2_1x_coco configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py ./tools/work_dir/autoassign_r50_fpn_8x2_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab cascade_mask_rcnn_r50_fpn_1x_coco configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/cascade_mask_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab crpn_faster_rcnn_r50_caffe_fpn_1x_coco configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py ./tools/work_dir/crpn_faster_rcnn_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/centernet/centernet_resnet18_dcnv2_140e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab centernet_resnet18_dcnv2_140e_coco configs/centernet/centernet_resnet18_dcnv2_140e_coco.py ./tools/work_dir/centernet_resnet18_dcnv2_140e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py' &
+GPUS=16  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab centripetalnet_hourglass104_mstest_16x6_210e_coco configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py ./tools/work_dir/centripetalnet_hourglass104_mstest_16x6_210e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab cornernet_hourglass104_mstest_8x6_210e_coco configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py ./tools/work_dir/cornernet_hourglass104_mstest_8x6_210e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/detectors/detectors_htc_r50_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab detectors_htc_r50_1x_coco configs/detectors/detectors_htc_r50_1x_coco.py ./tools/work_dir/detectors_htc_r50_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py' &
+GPUS=16  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab deformable_detr_r50_16x2_50e_coco configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py ./tools/work_dir/deformable_detr_r50_16x2_50e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/detr/detr_r50_8x2_150e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab detr_r50_8x2_150e_coco configs/detr/detr_r50_8x2_150e_coco.py ./tools/work_dir/detr_r50_8x2_150e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab dh_faster_rcnn_r50_fpn_1x_coco configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/dh_faster_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab dynamic_rcnn_r50_fpn_1x_coco configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/dynamic_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_1x_coco configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_caffe_dc5_mstrain_1x_coco configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py ./tools/work_dir/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_caffe_fpn_mstrain_1x_coco configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py ./tools/work_dir/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_caffe_fpn_1x_coco configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py ./tools/work_dir/faster_rcnn_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_ohem_1x_coco configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_ohem_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab fovea_align_r50_fpn_gn-head_4x4_2x_coco configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py ./tools/work_dir/fovea_align_r50_fpn_gn-head_4x4_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_fp16_1x_coco configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_fp16_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_r50_fpn_fp16_1x_coco configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py ./tools/work_dir/retinanet_r50_fpn_fp16_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_free_anchor_r50_fpn_1x_coco configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py ./tools/work_dir/retinanet_free_anchor_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/fsaf/fsaf_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab fsaf_r50_fpn_1x_coco configs/fsaf/fsaf_r50_fpn_1x_coco.py ./tools/work_dir/fsaf_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/gfl/gfl_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab gfl_r50_fpn_1x_coco configs/gfl/gfl_r50_fpn_1x_coco.py ./tools/work_dir/gfl_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_ghm_r50_fpn_1x_coco configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py ./tools/work_dir/retinanet_ghm_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab grid_rcnn_r50_fpn_gn-head_2x_coco configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py ./tools/work_dir/grid_rcnn_r50_fpn_gn-head_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab ga_faster_r50_caffe_fpn_1x_coco configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py ./tools/work_dir/ga_faster_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/htc/htc_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab htc_r50_fpn_1x_coco configs/htc/htc_r50_fpn_1x_coco.py ./tools/work_dir/htc_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab ld_r18_gflv1_r101_fpn_coco_1x configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py ./tools/work_dir/ld_r18_gflv1_r101_fpn_coco_1x --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab libra_faster_rcnn_r50_fpn_1x_coco configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/libra_faster_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py ./tools/work_dir/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab ms_rcnn_r50_caffe_fpn_1x_coco configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py ./tools/work_dir/ms_rcnn_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py' &
+GPUS=4  GPUS_PER_NODE=4  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py ./tools/work_dir/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/paa/paa_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab paa_r50_fpn_1x_coco configs/paa/paa_r50_fpn_1x_coco.py ./tools/work_dir/paa_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab pisa_mask_rcnn_r50_fpn_1x_coco configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/pisa_mask_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab point_rend_r50_caffe_fpn_mstrain_1x_coco configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py ./tools/work_dir/point_rend_r50_caffe_fpn_mstrain_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab reppoints_moment_r50_fpn_gn-neck+head_1x_coco configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py ./tools/work_dir/reppoints_moment_r50_fpn_gn-neck+head_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_r50_caffe_fpn_1x_coco configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py ./tools/work_dir/retinanet_r50_caffe_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/rpn/rpn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab rpn_r50_fpn_1x_coco configs/rpn/rpn_r50_fpn_1x_coco.py ./tools/work_dir/rpn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab sabl_retinanet_r50_fpn_1x_coco configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py ./tools/work_dir/sabl_retinanet_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/ssd/ssd300_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab ssd300_coco configs/ssd/ssd300_coco.py ./tools/work_dir/ssd300_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/tridentnet/tridentnet_r50_caffe_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab tridentnet_r50_caffe_1x_coco configs/tridentnet/tridentnet_r50_caffe_1x_coco.py ./tools/work_dir/tridentnet_r50_caffe_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/vfnet/vfnet_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab vfnet_r50_fpn_1x_coco configs/vfnet/vfnet_r50_fpn_1x_coco.py ./tools/work_dir/vfnet_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/yolact/yolact_r50_8x8_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab yolact_r50_8x8_coco configs/yolact/yolact_r50_8x8_coco.py ./tools/work_dir/yolact_r50_8x8_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/yolo/yolov3_d53_320_273e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab yolov3_d53_320_273e_coco configs/yolo/yolov3_d53_320_273e_coco.py ./tools/work_dir/yolov3_d53_320_273e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab sparse_rcnn_r50_fpn_1x_coco configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py ./tools/work_dir/sparse_rcnn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/scnet/scnet_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab scnet_r50_fpn_1x_coco configs/scnet/scnet_r50_fpn_1x_coco.py ./tools/work_dir/scnet_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/yolof/yolof_r50_c5_8x8_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab yolof_r50_c5_8x8_1x_coco configs/yolof/yolof_r50_c5_8x8_1x_coco.py ./tools/work_dir/yolof_r50_c5_8x8_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_carafe_1x_coco configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_carafe_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_mdpool_1x_coco configs/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_mdpool_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_dpool_1x_coco configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_dpool_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco configs/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_gn-all_2x_coco configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_gn-all_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_gn_ws-all_2x_coco configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py ./tools/work_dir/mask_rcnn_r50_fpn_gn_ws-all_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_hrnetv2p_w18_1x_coco configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py ./tools/work_dir/mask_rcnn_hrnetv2p_w18_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_pafpn_1x_coco configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py ./tools/work_dir/faster_rcnn_r50_pafpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab retinanet_r50_nasfpn_crop640_50e_coco configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py ./tools/work_dir/retinanet_r50_nasfpn_crop640_50e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_regnetx-3.2GF_fpn_1x_coco configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py ./tools/work_dir/mask_rcnn_regnetx-3.2GF_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py ./tools/work_dir/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r2_101_fpn_2x_coco configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py ./tools/work_dir/faster_rcnn_r2_101_fpn_2x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab faster_rcnn_r50_fpn_groie_1x_coco configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py ./tools/work_dir/faster_rcnn_r50_fpn_groie_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab mask_rcnn_r50_fpn_1x_cityscapes configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py ./tools/work_dir/mask_rcnn_r50_fpn_1x_cityscapes --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab panoptic_fpn_r50_fpn_1x_coco configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py ./tools/work_dir/panoptic_fpn_r50_fpn_1x_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/yolox/yolox_tiny_8x8_300e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab yolox_tiny_8x8_300e_coco configs/yolox/yolox_tiny_8x8_300e_coco.py ./tools/work_dir/yolox_tiny_8x8_300e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
+echo 'configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py' &
+GPUS=8  GPUS_PER_NODE=8  CPUS_PER_TASK=2 ./tools/slurm_train.sh openmmlab ssdlite_mobilenetv2_scratch_600e_coco configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py ./tools/work_dir/ssdlite_mobilenetv2_scratch_600e_coco --cfg-options checkpoint_config.max_keep_ckpts=1 >/dev/null &
diff --git a/.github/CODE_OF_CONDUCT.md b/.github/CODE_OF_CONDUCT.md
new file mode 100644
index 0000000..92afad1
--- /dev/null
+++ b/.github/CODE_OF_CONDUCT.md
@@ -0,0 +1,76 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to making participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+- Using welcoming and inclusive language
+- Being respectful of differing viewpoints and experiences
+- Gracefully accepting constructive criticism
+- Focusing on what is best for the community
+- Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+- The use of sexualized language or imagery and unwelcome sexual attention or
+  advances
+- Trolling, insulting/derogatory comments, and personal or political attacks
+- Public or private harassment
+- Publishing others' private information, such as a physical or electronic
+  address, without explicit permission
+- Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies both within project spaces and in public spaces
+when an individual is representing the project or its community. Examples of
+representing a project or community include using an official project e-mail
+address, posting via an official social media account, or acting as an appointed
+representative at an online or offline event. Representation of a project may be
+further defined and clarified by project maintainers.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at chenkaidev@gmail.com. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq
+
+[homepage]: https://www.contributor-covenant.org
diff --git a/.github/CONTRIBUTING.md b/.github/CONTRIBUTING.md
new file mode 100644
index 0000000..c669626
--- /dev/null
+++ b/.github/CONTRIBUTING.md
@@ -0,0 +1 @@
+We appreciate all contributions to improve MMDetection. Please refer to [CONTRIBUTING.md](https://github.com/open-mmlab/mmcv/blob/master/CONTRIBUTING.md) in MMCV for more details about the contributing guideline.
diff --git a/.github/ISSUE_TEMPLATE/1-bug-report.yml b/.github/ISSUE_TEMPLATE/1-bug-report.yml
new file mode 100644
index 0000000..4199391
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/1-bug-report.yml
@@ -0,0 +1,105 @@
+name: "🐞 Bug report"
+description: "Create a report to help us reproduce and fix the bug"
+labels: "kind/bug,status/unconfirmed"
+title: "[Bug] "
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        If you have already identified the reason, we strongly appreciate you creating a new PR to fix it [here](https://github.com/open-mmlab/mmdetection/pulls)!
+        If this issue is about installing MMCV, please file an issue at [MMCV](https://github.com/open-mmlab/mmcv/issues/new/choose).
+        If you need our help, please fill in as much of the following form as you're able to.
+
+        **The less clear the description, the longer it will take to solve it.**
+
+  - type: checkboxes
+    attributes:
+      label: Prerequisite
+      description: Please check the following items before creating a new issue.
+      options:
+      - label: I have searched [Issues](https://github.com/open-mmlab/mmdetection/issues) and [Discussions](https://github.com/open-mmlab/mmdetection/discussions) but cannot get the expected help.
+        required: true
+      - label: I have read the [FAQ documentation](https://mmdetection.readthedocs.io/en/latest/faq.html) but cannot get the expected help.
+        required: true
+      - label: The bug has not been fixed in the [latest version (master)](https://github.com/open-mmlab/mmdetection) or [latest version (3.x)](https://github.com/open-mmlab/mmdetection/tree/dev-3.x).
+        required: true
+
+  - type: dropdown
+    id: task
+    attributes:
+      label: Task
+      description: The problem arises when
+      options:
+        - I'm using the official example scripts/configs for the officially supported tasks/models/datasets.
+        - I have modified the scripts/configs, or I'm working on my own tasks/models/datasets.
+    validations:
+      required: true
+
+  - type: dropdown
+    id: branch
+    attributes:
+      label: Branch
+      description: The problem arises when I'm working on
+      options:
+        - master branch https://github.com/open-mmlab/mmdetection
+        - 3.x branch https://github.com/open-mmlab/mmdetection/tree/3.x
+    validations:
+      required: true
+
+
+  - type: textarea
+    attributes:
+      label: Environment
+      description: |
+        Please run `python mmdet/utils/collect_env.py` to collect necessary environment information and copy-paste it here.
+        You may add additional information that may be helpful for locating the problem, such as
+          - How you installed PyTorch \[e.g., pip, conda, source\]
+          - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Reproduces the problem - code sample
+      description: |
+        Please provide a code sample that reproduces the problem you ran into. It can be a Colab link or just a code snippet.
+      placeholder: |
+        ```python
+        # Sample code to reproduce the problem
+        ```
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Reproduces the problem - command or script
+      description: |
+        What command or script did you run?
+      placeholder: |
+        ```shell
+        The command or script you run.
+        ```
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Reproduces the problem - error message
+      description: |
+        Please provide the error message or logs you got, with the full traceback.
+      placeholder: |
+        ```
+        The error message or logs you got, with the full traceback.
+        ```
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Additional information
+      description: Tell us anything else you think we should know.
+      placeholder: |
+        1. What's your expected result?
+        2. What dataset did you use?
+        3. What do you think might be the reason?
diff --git a/.github/ISSUE_TEMPLATE/2-feature-request.yml b/.github/ISSUE_TEMPLATE/2-feature-request.yml
new file mode 100644
index 0000000..317f720
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/2-feature-request.yml
@@ -0,0 +1,31 @@
+name: 🚀 Feature request
+description: Suggest an idea for this project
+labels: "kind/enhancement,status/unconfirmed"
+title: "[Feature] "
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        We strongly appreciate you creating a PR to implement this feature [here](https://github.com/open-mmlab/mmdetection/pulls)!
+        If you need our help, please fill in as much of the following form as you're able to.
+
+        **The less clear the description, the longer it will take to solve it.**
+
+  - type: textarea
+    attributes:
+      label: What's the feature?
+      description: |
+        Tell us more about the feature and how this feature can help.
+      placeholder: |
+        E.g., It is inconvenient when \[....\].
+        This feature can \[....\].
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Any other context?
+      description: |
+        Have you considered any alternative solutions or features? If so, what are they?
+        Also, feel free to add any other context or screenshots about the feature request here.
diff --git a/.github/ISSUE_TEMPLATE/3-new-model.yml b/.github/ISSUE_TEMPLATE/3-new-model.yml
new file mode 100644
index 0000000..2346685
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/3-new-model.yml
@@ -0,0 +1,32 @@
+name: "\U0001F31F New model/dataset/scheduler addition"
+description: Submit a proposal/request to implement a new model / dataset / scheduler
+labels: "kind/feature,status/unconfirmed"
+title: "[New Models] "
+
+
+body:
+  - type: textarea
+    id: description-request
+    validations:
+      required: true
+    attributes:
+      label: Model/Dataset/Scheduler description
+      description: |
+        Put any and all important information relative to the model/dataset/scheduler
+
+  - type: checkboxes
+    attributes:
+      label: Open source status
+      description: |
+          Please provide the open-source status, which would be very helpful
+      options:
+        - label: "The model implementation is available"
+        - label: "The model weights are available."
+
+  - type: textarea
+    id: additional-info
+    attributes:
+      label: Provide useful links for the implementation
+      description: |
+        Please provide information regarding the implementation, the weights, and the authors.
+        Please mention the authors by @gh-username if you're aware of their usernames.
diff --git a/.github/ISSUE_TEMPLATE/4-documentation.yml b/.github/ISSUE_TEMPLATE/4-documentation.yml
new file mode 100644
index 0000000..32a62e2
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/4-documentation.yml
@@ -0,0 +1,34 @@
+name: 📚 Documentation
+description: Report an issue related to the documentation.
+labels: "kind/doc,status/unconfirmed"
+title: "[Docs] "
+
+body:
+- type: dropdown
+  id: branch
+  attributes:
+    label: Branch
+    description: This issue is related to the
+    options:
+      - master branch  https://mmdetection.readthedocs.io/en/latest/
+      - 3.x branch https://mmdetection.readthedocs.io/en/3.x/
+  validations:
+    required: true
+
+- type: textarea
+  attributes:
+    label: 📚 The doc issue
+    description: >
+      A clear and concise description the issue.
+  validations:
+    required: true
+
+- type: textarea
+  attributes:
+    label: Suggest a potential alternative/fix
+    description: >
+      Tell us how we could improve the documentation in this regard.
+- type: markdown
+  attributes:
+    value: >
+      Thanks for contributing 🎉!
diff --git a/.github/ISSUE_TEMPLATE/5-reimplementation.yml b/.github/ISSUE_TEMPLATE/5-reimplementation.yml
new file mode 100644
index 0000000..ea41531
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/5-reimplementation.yml
@@ -0,0 +1,89 @@
+name: "💥 Reimplementation Questions"
+description: "Ask about questions during model reimplementation"
+labels: "kind/enhancement,status/unconfirmed"
+title: "[Reimplementation] "
+
+body:
+  - type: markdown
+    attributes:
+      value: |
+        We strongly appreciate you creating a PR to implement this feature [here](https://github.com/open-mmlab/mmdetection/pulls)!
+        If you need our help, please fill in as much of the following form as you're able to.
+
+        **The less clear the description, the longer it will take to solve it.**
+
+  - type: checkboxes
+    attributes:
+      label: Prerequisite
+      description: Please check the following items before creating a new issue.
+      options:
+      - label: I have searched [Issues](https://github.com/open-mmlab/mmdetection/issues) and [Discussions](https://github.com/open-mmlab/mmdetection/discussions) but cannot get the expected help.
+        required: true
+      - label: I have read the [FAQ documentation](https://mmdetection.readthedocs.io/en/latest/faq.html) but cannot get the expected help.
+        required: true
+      - label: The bug has not been fixed in the [latest version (master)](https://github.com/open-mmlab/mmdetection) or [latest version (3.x)](https://github.com/open-mmlab/mmdetection/tree/dev-3.x).
+        required: true
+
+  - type: textarea
+    attributes:
+      label: 💬 Describe the reimplementation questions
+      description: |
+        A clear and concise description of what the problem you meet and what have you done.
+        There are several common situations in the reimplementation issues as below
+
+        1. Reimplement a model in the model zoo using the provided configs
+        2. Reimplement a model in the model zoo on other dataset (e.g., custom datasets)
+        3. Reimplement a custom model but all the components are implemented in MMDetection
+        4. Reimplement a custom model with new modules implemented by yourself
+
+        There are several things to do for different cases as below.
+
+        - For case 1 & 3, please follow the steps in the following sections thus we could help to quick identify the issue.
+        - For case 2 & 4, please understand that we are not able to do much help here because we usually do not know the full code and the users should be responsible to the code they write.
+        - One suggestion for case 2 & 4 is that the users should first check whether the bug lies in the self-implemented code or the original code. For example, users can first make sure that the same model runs well on supported datasets. If you still need help, please describe what you have done and what you obtain in the issue, and follow the steps in the following sections and try as clear as possible so that we can better help you.
+      placeholder: |
+        A clear and concise description of what the bug is.
+        What config dir you run?
+
+        ```none
+        A placeholder for the config.
+        ```
+
+        ```shell
+        The command or script you run.
+        ```
+
+        ```
+        The error message or logs you got, with the full traceback.
+        ```
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Environment
+      description: |
+         Please run `python mmdet/utils/collect_env.py` to collect necessary environment information and paste it here.
+         You may add addition that may be helpful for locating the problem, such as
+            - How you installed PyTorch \[e.g., pip, conda, source\]
+            - Other environment variables that may be related (such as `$PATH`, `$LD_LIBRARY_PATH`, `$PYTHONPATH`, etc.)
+    validations:
+      required: true
+
+  - type: textarea
+    attributes:
+      label: Expected results
+      description: If applicable, paste the related results here, e.g., what you expect and what you get.
+      placeholder: |
+         ```none
+         A placeholder for results comparison
+         ```
+
+  - type: textarea
+    attributes:
+      label: Additional information
+      description: Tell us anything else you think we should know.
+      placeholder: |
+        1. Did you make any modifications on the code or config? Did you understand what you have modified?
+        2. What dataset did you use?
+        3. What do you think might be the reason?
diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml
new file mode 100644
index 0000000..7211b31
--- /dev/null
+++ b/.github/ISSUE_TEMPLATE/config.yml
@@ -0,0 +1,9 @@
+blank_issues_enabled: true
+
+contact_links:
+  - name: 💬 Forum
+    url: https://github.com/open-mmlab/mmdetection/discussions
+    about: Ask general usage questions and discuss with other MMDetection community members
+  - name: 🌐 Explore OpenMMLab
+    url: https://openmmlab.com/
+    about: Get know more about OpenMMLab
diff --git a/.github/pull_request_template.md b/.github/pull_request_template.md
new file mode 100644
index 0000000..8f8e289
--- /dev/null
+++ b/.github/pull_request_template.md
@@ -0,0 +1,25 @@
+Thanks for your contribution and we appreciate it a lot. The following instructions would make your pull request more healthy and more easily get feedback. If you do not understand some items, don't worry, just make the pull request and seek help from maintainers.
+
+## Motivation
+
+Please describe the motivation of this PR and the goal you want to achieve through this PR.
+
+## Modification
+
+Please briefly describe what modification is made in this PR.
+
+## BC-breaking (Optional)
+
+Does the modification introduce changes that break the backward-compatibility of the downstream repos?
+If so, please describe how it breaks the compatibility and how the downstream projects should modify their code to keep compatibility with this PR.
+
+## Use cases (Optional)
+
+If this PR introduces a new feature, it is better to list some use cases here, and update the documentation.
+
+## Checklist
+
+1. Pre-commit or other linting tools are used to fix the potential lint issues.
+2. The modification is covered by complete unit tests. If not, please add more unit test to ensure the correctness.
+3. If the modification has potential influence on downstream projects, this PR should be tested with downstream projects, like MMDet or MMCls.
+4. The documentation has been modified accordingly, like docstring or example tutorials.
diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml
new file mode 100644
index 0000000..30ba5f8
--- /dev/null
+++ b/.github/workflows/build.yml
@@ -0,0 +1,286 @@
+name: build
+
+on:
+  push:
+    paths-ignore:
+      - ".dev_scripts/**"
+      - ".github/**.md"
+      - "demo/**"
+      - "docker/**"
+      - "tools/**"
+      - "README.md"
+      - "README_zh-CN.md"
+
+  pull_request:
+    paths-ignore:
+      - ".dev_scripts/**"
+      - ".github/**.md"
+      - "demo/**"
+      - "docker/**"
+      - "docs/**"
+      - "docs_zh-CN/**"
+      - "tools/**"
+      - "README.md"
+      - "README_zh-CN.md"
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+
+jobs:
+  build_cpu:
+    runs-on: ubuntu-18.04
+    strategy:
+      matrix:
+        python-version: [3.7]
+        torch: [1.5.1, 1.6.0, 1.7.0, 1.8.0, 1.9.0, 1.10.1]
+        include:
+          - torch: 1.5.1
+            torchvision: 0.6.1
+            mmcv: 1.5
+          - torch: 1.6.0
+            torchvision: 0.7.0
+            mmcv: 1.6
+          - torch: 1.7.0
+            torchvision: 0.8.1
+            mmcv: 1.7
+          - torch: 1.8.0
+            torchvision: 0.9.0
+            mmcv: 1.8
+          - torch: 1.9.0
+            torchvision: 0.10.0
+            mmcv: 1.9
+          - torch: 1.10.1
+            torchvision: 0.11.2
+            mmcv: "1.10"
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install PyTorch
+        run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install MMCV
+        run: |
+          pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch${{matrix.mmcv}}/index.html
+          python -c 'import mmcv; print(mmcv.__version__)'
+      - name: Install unittest dependencies
+        run: |
+          pip install -r requirements/tests.txt -r requirements/optional.txt
+          pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
+          pip install git+https://github.com/cocodataset/panopticapi.git
+      - name: Build and install
+        run: rm -rf .eggs && pip install -e .
+      - name: Run unittests and generate coverage report
+        run: |
+          coverage run --branch --source mmdet -m pytest tests/
+          coverage xml
+          coverage report -m
+
+  build_cuda101:
+    runs-on: ubuntu-18.04
+    container:
+      image: pytorch/pytorch:1.6.0-cuda10.1-cudnn7-devel
+
+    strategy:
+      matrix:
+        python-version: [3.7]
+        torch: [1.5.1+cu101, 1.6.0+cu101, 1.7.0+cu101, 1.8.0+cu101]
+        include:
+          - torch: 1.5.1+cu101
+            torch_version: torch1.5.1
+            torchvision: 0.6.1+cu101
+            mmcv: 1.5
+          - torch: 1.6.0+cu101
+            torch_version: torch1.6.0
+            torchvision: 0.7.0+cu101
+            mmcv: 1.6
+          - torch: 1.7.0+cu101
+            torch_version: torch1.7.0
+            torchvision: 0.8.1+cu101
+            mmcv: 1.7
+          - torch: 1.8.0+cu101
+            torch_version: torch1.8.0
+            torchvision: 0.9.0+cu101
+            mmcv: 1.8
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Fetch GPG keys
+        run: |
+          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+      - name: Install system dependencies
+        run: |
+          apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 python${{matrix.python-version}}-dev
+          apt-get clean
+          rm -rf /var/lib/apt/lists/*
+      - name: Install PyTorch
+        run: python -m pip install torch==${{matrix.torch}} torchvision==${{matrix.torchvision}} -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install dependencies for compiling onnx when python=3.9
+        run: python -m pip install "protobuf <= 3.20.1" && apt-get install libprotobuf-dev protobuf-compiler
+        if: ${{matrix.python-version == '3.9'}}
+      - name: Install mmdet dependencies
+        run: |
+          python -V
+          export CFLAGS=`python -c 'import sysconfig;print("-I"+sysconfig.get_paths()["include"])'`
+          export CXXFLAGS="${CFLAGS}"
+          python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu101/torch${{matrix.mmcv}}/index.html
+          python -m pip install pycocotools
+          python -m pip install -r requirements/tests.txt -r requirements/optional.txt
+          python -m pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
+          python -m pip install git+https://github.com/cocodataset/panopticapi.git
+          python -c 'import mmcv; print(mmcv.__version__)'
+      - name: Build and install
+        run: |
+          rm -rf .eggs
+          python setup.py check -m -s
+          TORCH_CUDA_ARCH_LIST=7.0 pip install .
+      - name: Run unittests and generate coverage report
+        run: |
+          coverage run --branch --source mmdet -m pytest tests/
+          coverage xml
+          coverage report -m
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v1.0.10
+        with:
+          file: ./coverage.xml
+          flags: unittests
+          env_vars: OS,PYTHON
+          name: codecov-umbrella
+          fail_ci_if_error: false
+
+  build_cuda102:
+    runs-on: ubuntu-18.04
+    container:
+      image: pytorch/pytorch:1.9.0-cuda10.2-cudnn7-devel
+
+    strategy:
+      matrix:
+        python-version: [3.7, 3.8, 3.9]
+        torch: [1.9.0+cu102, 1.10.1+cu102]
+        include:
+          - torch: 1.9.0+cu102
+            torch_version: torch1.9.0
+            torchvision: 0.10.0+cu102
+            mmcv: 1.9
+          - torch: 1.10.1+cu102
+            torch_version: torch1.10.1
+            torchvision: 0.11.2+cu102
+            mmcv: "1.10"
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Fetch GPG keys
+        run: |
+          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+      # Add ppa source repo for python3.9.
+      - name: Add python3.9 source
+        run: |
+          apt-get update && apt-get install -y software-properties-common
+          add-apt-repository -y ppa:deadsnakes/ppa
+        if: ${{matrix.python-version == '3.9'}}
+      # Install python-dev for some packages which require libpython3.Xm.
+      # Github's setup-python cannot install python3.9-dev, so we have to use apt install.
+      # Set DEBIAN_FRONTEND=noninteractive to avoid some interactions.
+      - name: Install python-dev
+        run: apt-get update && DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends python${{matrix.python-version}}-dev
+      - name: Install system dependencies
+        run: |
+          apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6
+          apt-get clean
+          rm -rf /var/lib/apt/lists/*
+      - name: Install PyTorch
+        run: python -m pip install torch==${{matrix.torch}} torchvision==${{matrix.torchvision}} -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install dependencies for compiling onnx when python=3.9
+        run: python -m pip install "protobuf <= 3.20.1" && apt-get update && apt-get -y install libprotobuf-dev protobuf-compiler cmake
+        if: ${{matrix.python-version == '3.9'}}
+      - name: Install mmdet dependencies
+        run: |
+          python -V
+          export CFLAGS=`python -c 'import sysconfig;print("-I"+sysconfig.get_paths()["include"])'`
+          export CXXFLAGS="${CFLAGS}"
+          python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu102/torch${{matrix.mmcv}}/index.html
+          python -m pip install pycocotools
+          python -m pip install -r requirements/tests.txt -r requirements/optional.txt
+          python -m pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
+          python -m pip install git+https://github.com/cocodataset/panopticapi.git
+          python -c 'import mmcv; print(mmcv.__version__)'
+      - name: Build and install
+        run: |
+          rm -rf .eggs
+          python setup.py check -m -s
+          TORCH_CUDA_ARCH_LIST=7.0 pip install .
+      - name: Run unittests and generate coverage report
+        run: |
+          coverage run --branch --source mmdet -m pytest tests/
+          coverage xml
+          coverage report -m
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v2
+        with:
+          files: ./coverage.xml
+          flags: unittests
+          env_vars: OS,PYTHON
+          name: codecov-umbrella
+          fail_ci_if_error: false
+
+  build_windows:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [windows-2022]
+        python: [3.8]
+        platform: [cpu, cu111]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python }}
+      - name: Upgrade pip
+        run: python -m pip install pip --upgrade --user
+      - name: Install PyTorch
+        # As a complement to Linux CI, we test on PyTorch LTS version
+        run: pip install torch==1.8.2+${{ matrix.platform }} torchvision==0.9.2+${{ matrix.platform }} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
+      - name: Install MMCV
+        run: pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch1.8/index.html --only-binary mmcv-full
+      - name: Install unittest dependencies
+        run: |
+          python -V
+          python -m pip install pycocotools
+          python -m pip install -r requirements/tests.txt -r requirements/optional.txt
+          python -m pip install albumentations>=0.3.2 --no-binary imgaug,albumentations
+          python -m pip install git+https://github.com/cocodataset/panopticapi.git
+          python -c 'import mmcv; print(mmcv.__version__)'
+      - name: Show pip list
+        run: pip list
+      - name: Build and install
+        run: pip install -e .
+      - name: Run unittests
+        run: coverage run --branch --source mmdet -m pytest tests
+      - name: Generate coverage report
+        run: |
+          coverage xml
+          coverage report -m
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v2
+        with:
+          file: ./coverage.xml
+          flags: unittests
+          env_vars: OS,PYTHON
+          name: codecov-umbrella
+          fail_ci_if_error: false
diff --git a/.github/workflows/build_pat.yml b/.github/workflows/build_pat.yml
new file mode 100644
index 0000000..82eaafc
--- /dev/null
+++ b/.github/workflows/build_pat.yml
@@ -0,0 +1,31 @@
+name: build_pat
+
+on: push
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+
+jobs:
+  build_parrots:
+    runs-on: ubuntu-latest
+    container:
+      image: ghcr.io/zhouzaida/parrots-mmcv:1.3.4
+      credentials:
+        username: zhouzaida
+        password: ${{ secrets.CR_PAT }}
+
+    steps:
+      - uses: actions/checkout@v2
+      - name: Install mmdet dependencies
+        run: |
+          git clone https://github.com/open-mmlab/mmcv.git && cd mmcv
+          MMCV_WITH_OPS=1 python setup.py install
+          cd .. && rm -rf mmcv
+          python -c 'import mmcv; print(mmcv.__version__)'
+          pip install -r requirements.txt
+      - name: Build and install
+        run: rm -rf .eggs && pip install -e .
diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
new file mode 100644
index 0000000..c7fed28
--- /dev/null
+++ b/.github/workflows/deploy.yml
@@ -0,0 +1,31 @@
+name: deploy
+
+on: push
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+
+jobs:
+  build-n-publish:
+    runs-on: ubuntu-latest
+    if: startsWith(github.event.ref, 'refs/tags')
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.7
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.7
+      - name: Install torch
+        run: pip install torch
+      - name: Install wheel
+        run: pip install wheel
+      - name: Build MMDetection
+        run: python setup.py sdist bdist_wheel
+      - name: Publish distribution to PyPI
+        run: |
+          pip install twine
+          twine upload dist/* -u __token__ -p ${{ secrets.pypi_password }}
diff --git a/.github/workflows/lint.yml b/.github/workflows/lint.yml
new file mode 100644
index 0000000..a1e5aa2
--- /dev/null
+++ b/.github/workflows/lint.yml
@@ -0,0 +1,30 @@
+name: lint
+
+on: [push, pull_request]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.7
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.7
+      - name: Install pre-commit hook
+        run: |
+          pip install pre-commit
+          pre-commit install
+      - name: Linting
+        run: pre-commit run --all-files
+      - name: Check docstring coverage
+        run: |
+          pip install interrogate
+          interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 80 mmdet
diff --git a/.github/workflows/stale.yml b/.github/workflows/stale.yml
new file mode 100644
index 0000000..1ceee3d
--- /dev/null
+++ b/.github/workflows/stale.yml
@@ -0,0 +1,31 @@
+name: 'Close stale issues and PRs'
+
+on:
+  schedule:
+    # check issue and pull request once every day
+    - cron: '25 11 * * *'
+
+permissions:
+  contents: read
+
+jobs:
+  invalid-stale-close:
+    permissions:
+      issues: write
+      pull-requests: write
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/stale@v4
+        with:
+          stale-issue-message: 'This issue is marked as stale because it has been marked as invalid or awaiting response for 7 days without any further response. It will be closed in 5 days if the stale label is not removed or if there is no further response.'
+          stale-pr-message: 'This PR is marked as stale because there has been no activity in the past 45 days. It will be closed in 10 days if the stale label is not removed or if there is no further updates.'
+          close-issue-message: 'This issue is closed because it has been stale for 5 days. Please open a new issue if you have similar issues or you have any new updates now.'
+          close-pr-message: 'This PR is closed because it has been stale for 10 days. Please reopen this PR if you have any updates and want to keep contributing the code.'
+          # only issues/PRS with any of invalid and awaiting response labels are checked
+          any-of-labels: 'invalid, awaiting response'
+          days-before-issue-stale: 7
+          days-before-pr-stale: 45
+          days-before-issue-close: 5
+          days-before-pr-close: 10
+          # automatically remove the stale label when the issues or the pull reqquests are updated or commented
+          remove-stale-when-updated: true
diff --git a/.github/workflows/test_mim.yml b/.github/workflows/test_mim.yml
new file mode 100644
index 0000000..1a8e8ef
--- /dev/null
+++ b/.github/workflows/test_mim.yml
@@ -0,0 +1,50 @@
+name: test-mim
+
+on:
+  push:
+    paths:
+      - 'model-index.yml'
+      - 'configs/**'
+
+  pull_request:
+    paths:
+      - 'model-index.yml'
+      - 'configs/**'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+permissions:
+  contents: read
+
+jobs:
+  build_cpu:
+    runs-on: ubuntu-18.04
+    strategy:
+      matrix:
+        python-version: [3.7]
+        torch: [1.8.0]
+        include:
+          - torch: 1.8.0
+            torch_version: torch1.8
+            torchvision: 0.9.0
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Upgrade pip
+        run: pip install pip --upgrade
+      - name: Install Pillow
+        run: pip install Pillow==6.2.2
+        if: ${{matrix.torchvision == '0.4.2'}}
+      - name: Install PyTorch
+        run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install openmim
+        run: pip install openmim
+      - name: Build and install
+        run: rm -rf .eggs && mim install -e .
+      - name: test commands of mim
+        run: mim search mmdet
diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..892731d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,124 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/en/_build/
+docs/zh_cn/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+data/
+data
+.vscode
+.idea
+.DS_Store
+
+# custom
+*.pkl
+*.pkl.json
+*.log.json
+docs/modelzoo_statistics.md
+mmdet/.mim
+work_dirs/
+
+# Pytorch
+*.pth
+*.py~
+*.sh~
diff --git a/.owners.yml b/.owners.yml
new file mode 100644
index 0000000..0d782a7
--- /dev/null
+++ b/.owners.yml
@@ -0,0 +1,13 @@
+assign:
+  strategy:
+    # random
+    daily-shift-based
+  scedule: "*/1 * * * *"
+  assignees:
+    - Czm369
+    - hhaAndroid
+    - zwhus
+    - RangiLyu
+    - BIGWangYuDong
+    - ZwwWayne
+    - ZwwWayne
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
new file mode 100644
index 0000000..6ea250c
--- /dev/null
+++ b/.pre-commit-config.yaml
@@ -0,0 +1,50 @@
+repos:
+  - repo: https://github.com/PyCQA/flake8
+    rev: 5.0.4
+    hooks:
+      - id: flake8
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.11.5
+    hooks:
+      - id: isort
+  - repo: https://github.com/pre-commit/mirrors-yapf
+    rev: v0.32.0
+    hooks:
+      - id: yapf
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: requirements-txt-fixer
+      - id: double-quote-string-fixer
+      - id: check-merge-conflict
+      - id: fix-encoding-pragma
+        args: ["--remove"]
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.1
+    hooks:
+      - id: codespell
+  - repo: https://github.com/executablebooks/mdformat
+    rev: 0.7.9
+    hooks:
+      - id: mdformat
+        args: ["--number"]
+        additional_dependencies:
+          - mdformat-openmmlab
+          - mdformat_frontmatter
+          - linkify-it-py
+  - repo: https://github.com/myint/docformatter
+    rev: v1.3.1
+    hooks:
+      - id: docformatter
+        args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: https://github.com/open-mmlab/pre-commit-hooks
+    rev: v0.2.0  # Use the ref you want to point at
+    hooks:
+      - id: check-algo-readme
+      - id: check-copyright
+        args: ["mmdet"]  # replace the dir_to_check with your expected directory to check
diff --git a/.readthedocs.yml b/.readthedocs.yml
new file mode 100644
index 0000000..82a1543
--- /dev/null
+++ b/.readthedocs.yml
@@ -0,0 +1,9 @@
+version: 2
+
+formats: all
+
+python:
+  version: 3.8
+  install:
+    - requirements: requirements/docs.txt
+    - requirements: requirements/readthedocs.txt
diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 0000000..aac9313
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,8 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+  - name: "MMDetection Contributors"
+title: "OpenMMLab Detection Toolbox and Benchmark"
+date-released: 2018-08-22
+url: "https://github.com/open-mmlab/mmdetection"
+license: Apache-2.0
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000..1bfc23e
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,203 @@
+Copyright 2018-2023 OpenMMLab. All rights reserved.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
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+   APPENDIX: How to apply the Apache License to your work.
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+      To apply the Apache License to your work, attach the following
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+   Copyright 2018-2023 OpenMMLab.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
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+       http://www.apache.org/licenses/LICENSE-2.0
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+   Unless required by applicable law or agreed to in writing, software
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+   See the License for the specific language governing permissions and
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diff --git a/MANIFEST.in b/MANIFEST.in
new file mode 100644
index 0000000..6300b22
--- /dev/null
+++ b/MANIFEST.in
@@ -0,0 +1,6 @@
+include requirements/*.txt
+include mmdet/VERSION
+include mmdet/.mim/model-index.yml
+include mmdet/.mim/demo/*/*
+recursive-include mmdet/.mim/configs *.py *.yml
+recursive-include mmdet/.mim/tools *.sh *.py
diff --git a/configs/_base_/datasets/cityscapes_detection.py b/configs/_base_/datasets/cityscapes_detection.py
new file mode 100644
index 0000000..e341b59
--- /dev/null
+++ b/configs/_base_/datasets/cityscapes_detection.py
@@ -0,0 +1,56 @@
+# dataset settings
+dataset_type = 'CityscapesDataset'
+data_root = 'data/cityscapes/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2048, 1024),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=2,
+    train=dict(
+        type='RepeatDataset',
+        times=8,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root +
+            'annotations/instancesonly_filtered_gtFine_train.json',
+            img_prefix=data_root + 'leftImg8bit/train/',
+            pipeline=train_pipeline)),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root +
+        'annotations/instancesonly_filtered_gtFine_val.json',
+        img_prefix=data_root + 'leftImg8bit/val/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root +
+        'annotations/instancesonly_filtered_gtFine_test.json',
+        img_prefix=data_root + 'leftImg8bit/test/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')
diff --git a/configs/_base_/datasets/cityscapes_instance.py b/configs/_base_/datasets/cityscapes_instance.py
new file mode 100644
index 0000000..4e3c34e
--- /dev/null
+++ b/configs/_base_/datasets/cityscapes_instance.py
@@ -0,0 +1,56 @@
+# dataset settings
+dataset_type = 'CityscapesDataset'
+data_root = 'data/cityscapes/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2048, 1024),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=2,
+    train=dict(
+        type='RepeatDataset',
+        times=8,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root +
+            'annotations/instancesonly_filtered_gtFine_train.json',
+            img_prefix=data_root + 'leftImg8bit/train/',
+            pipeline=train_pipeline)),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root +
+        'annotations/instancesonly_filtered_gtFine_val.json',
+        img_prefix=data_root + 'leftImg8bit/val/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root +
+        'annotations/instancesonly_filtered_gtFine_test.json',
+        img_prefix=data_root + 'leftImg8bit/test/',
+        pipeline=test_pipeline))
+evaluation = dict(metric=['bbox', 'segm'])
diff --git a/configs/_base_/datasets/coco_detection.py b/configs/_base_/datasets/coco_detection.py
new file mode 100644
index 0000000..4f08b38
--- /dev/null
+++ b/configs/_base_/datasets/coco_detection.py
@@ -0,0 +1,49 @@
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = '/workspace/whole_world/rdata/share/datasets/COCO/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')
diff --git a/configs/_base_/datasets/coco_instance.py b/configs/_base_/datasets/coco_instance.py
new file mode 100644
index 0000000..9901a85
--- /dev/null
+++ b/configs/_base_/datasets/coco_instance.py
@@ -0,0 +1,49 @@
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+evaluation = dict(metric=['bbox', 'segm'])
diff --git a/configs/_base_/datasets/coco_instance_semantic.py b/configs/_base_/datasets/coco_instance_semantic.py
new file mode 100644
index 0000000..6c8bf07
--- /dev/null
+++ b/configs/_base_/datasets/coco_instance_semantic.py
@@ -0,0 +1,54 @@
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='SegRescale', scale_factor=1 / 8),
+    dict(type='DefaultFormatBundle'),
+    dict(
+        type='Collect',
+        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        seg_prefix=data_root + 'stuffthingmaps/train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+evaluation = dict(metric=['bbox', 'segm'])
diff --git a/configs/_base_/datasets/coco_panoptic.py b/configs/_base_/datasets/coco_panoptic.py
new file mode 100644
index 0000000..dbade7c
--- /dev/null
+++ b/configs/_base_/datasets/coco_panoptic.py
@@ -0,0 +1,59 @@
+# dataset settings
+dataset_type = 'CocoPanopticDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadPanopticAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        with_seg=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='SegRescale', scale_factor=1 / 4),
+    dict(type='DefaultFormatBundle'),
+    dict(
+        type='Collect',
+        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/panoptic_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        seg_prefix=data_root + 'annotations/panoptic_train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/panoptic_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        seg_prefix=data_root + 'annotations/panoptic_val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/panoptic_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        seg_prefix=data_root + 'annotations/panoptic_val2017/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric=['PQ'])
diff --git a/configs/_base_/datasets/deepfashion.py b/configs/_base_/datasets/deepfashion.py
new file mode 100644
index 0000000..308b4b2
--- /dev/null
+++ b/configs/_base_/datasets/deepfashion.py
@@ -0,0 +1,53 @@
+# dataset settings
+dataset_type = 'DeepFashionDataset'
+data_root = 'data/DeepFashion/In-shop/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(750, 1101), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(750, 1101),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    imgs_per_gpu=2,
+    workers_per_gpu=1,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json',
+        img_prefix=data_root + 'Img/',
+        pipeline=train_pipeline,
+        data_root=data_root),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/DeepFashion_segmentation_query.json',
+        img_prefix=data_root + 'Img/',
+        pipeline=test_pipeline,
+        data_root=data_root),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root +
+        'annotations/DeepFashion_segmentation_gallery.json',
+        img_prefix=data_root + 'Img/',
+        pipeline=test_pipeline,
+        data_root=data_root))
+evaluation = dict(interval=5, metric=['bbox', 'segm'])
diff --git a/configs/_base_/datasets/lvis_v0.5_instance.py b/configs/_base_/datasets/lvis_v0.5_instance.py
new file mode 100644
index 0000000..207e005
--- /dev/null
+++ b/configs/_base_/datasets/lvis_v0.5_instance.py
@@ -0,0 +1,24 @@
+# dataset settings
+_base_ = 'coco_instance.py'
+dataset_type = 'LVISV05Dataset'
+data_root = 'data/lvis_v0.5/'
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        _delete_=True,
+        type='ClassBalancedDataset',
+        oversample_thr=1e-3,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/lvis_v0.5_train.json',
+            img_prefix=data_root + 'train2017/')),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/lvis_v0.5_val.json',
+        img_prefix=data_root + 'val2017/'),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/lvis_v0.5_val.json',
+        img_prefix=data_root + 'val2017/'))
+evaluation = dict(metric=['bbox', 'segm'])
diff --git a/configs/_base_/datasets/lvis_v1_instance.py b/configs/_base_/datasets/lvis_v1_instance.py
new file mode 100644
index 0000000..be791ed
--- /dev/null
+++ b/configs/_base_/datasets/lvis_v1_instance.py
@@ -0,0 +1,24 @@
+# dataset settings
+_base_ = 'coco_instance.py'
+dataset_type = 'LVISV1Dataset'
+data_root = 'data/lvis_v1/'
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        _delete_=True,
+        type='ClassBalancedDataset',
+        oversample_thr=1e-3,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/lvis_v1_train.json',
+            img_prefix=data_root)),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/lvis_v1_val.json',
+        img_prefix=data_root),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/lvis_v1_val.json',
+        img_prefix=data_root))
+evaluation = dict(metric=['bbox', 'segm'])
diff --git a/configs/_base_/datasets/objects365v1_detection.py b/configs/_base_/datasets/objects365v1_detection.py
new file mode 100644
index 0000000..8989b6f
--- /dev/null
+++ b/configs/_base_/datasets/objects365v1_detection.py
@@ -0,0 +1,49 @@
+# dataset settings
+dataset_type = 'Objects365V1Dataset'
+data_root = 'data/Objects365/Obj365_v1/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/objects365_train.json',
+        img_prefix=data_root + 'train/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/objects365_val.json',
+        img_prefix=data_root + 'val/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/objects365_val.json',
+        img_prefix=data_root + 'val/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')
diff --git a/configs/_base_/datasets/objects365v2_detection.py b/configs/_base_/datasets/objects365v2_detection.py
new file mode 100644
index 0000000..99942c1
--- /dev/null
+++ b/configs/_base_/datasets/objects365v2_detection.py
@@ -0,0 +1,49 @@
+# dataset settings
+dataset_type = 'Objects365V2Dataset'
+data_root = 'data/Objects365/Obj365_v2/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/zhiyuan_objv2_train.json',
+        img_prefix=data_root + 'train/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/zhiyuan_objv2_val.json',
+        img_prefix=data_root + 'val/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/zhiyuan_objv2_val.json',
+        img_prefix=data_root + 'val/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')
diff --git a/configs/_base_/datasets/openimages_detection.py b/configs/_base_/datasets/openimages_detection.py
new file mode 100644
index 0000000..a65d306
--- /dev/null
+++ b/configs/_base_/datasets/openimages_detection.py
@@ -0,0 +1,65 @@
+# dataset settings
+dataset_type = 'OpenImagesDataset'
+data_root = 'data/OpenImages/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, denorm_bbox=True),
+    dict(type='Resize', img_scale=(1024, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1024, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ],
+    ),
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=0,  # workers_per_gpu > 0 may occur out of memory
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/oidv6-train-annotations-bbox.csv',
+        img_prefix=data_root + 'OpenImages/train/',
+        label_file=data_root + 'annotations/class-descriptions-boxable.csv',
+        hierarchy_file=data_root +
+        'annotations/bbox_labels_600_hierarchy.json',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/validation-annotations-bbox.csv',
+        img_prefix=data_root + 'OpenImages/validation/',
+        label_file=data_root + 'annotations/class-descriptions-boxable.csv',
+        hierarchy_file=data_root +
+        'annotations/bbox_labels_600_hierarchy.json',
+        meta_file=data_root + 'annotations/validation-image-metas.pkl',
+        image_level_ann_file=data_root +
+        'annotations/validation-annotations-human-imagelabels-boxable.csv',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/validation-annotations-bbox.csv',
+        img_prefix=data_root + 'OpenImages/validation/',
+        label_file=data_root + 'annotations/class-descriptions-boxable.csv',
+        hierarchy_file=data_root +
+        'annotations/bbox_labels_600_hierarchy.json',
+        meta_file=data_root + 'annotations/validation-image-metas.pkl',
+        image_level_ann_file=data_root +
+        'annotations/validation-annotations-human-imagelabels-boxable.csv',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='mAP')
diff --git a/configs/_base_/datasets/voc0712.py b/configs/_base_/datasets/voc0712.py
new file mode 100644
index 0000000..ae4fd90
--- /dev/null
+++ b/configs/_base_/datasets/voc0712.py
@@ -0,0 +1,64 @@
+# dataset settings
+dataset_type = 'VOCDataset'
+data_root = '/workspace/package_for_transfer/VOC/VOCdevkit/'
+classes = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
+               'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
+               'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
+               'tvmonitor')
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1000, 600),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='RepeatDataset',
+        times=3,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=[
+                data_root + 'VOC2007/ImageSets/Main/trainval.txt',
+                data_root + 'VOC2012/ImageSets/Main/trainval.txt'
+            ],
+            img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
+            pipeline=train_pipeline,
+            classes=classes)),
+    val=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',  # 注意这里走的是COCO格式
+        img_prefix=data_root,
+        pipeline=test_pipeline,
+        classes=classes),
+    test=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',
+        img_prefix=data_root,
+        pipeline=test_pipeline,
+        classes=classes))
+
+
+evaluation = dict(interval=1, metric='mAP')
diff --git a/configs/_base_/datasets/wider_face.py b/configs/_base_/datasets/wider_face.py
new file mode 100644
index 0000000..d1d649b
--- /dev/null
+++ b/configs/_base_/datasets/wider_face.py
@@ -0,0 +1,63 @@
+# dataset settings
+dataset_type = 'WIDERFaceDataset'
+data_root = 'data/WIDERFace/'
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(300, 300),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=60,
+    workers_per_gpu=2,
+    train=dict(
+        type='RepeatDataset',
+        times=2,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'train.txt',
+            img_prefix=data_root + 'WIDER_train/',
+            min_size=17,
+            pipeline=train_pipeline)),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'val.txt',
+        img_prefix=data_root + 'WIDER_val/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'val.txt',
+        img_prefix=data_root + 'WIDER_val/',
+        pipeline=test_pipeline))
diff --git a/configs/_base_/default_runtime.py b/configs/_base_/default_runtime.py
new file mode 100644
index 0000000..4916ad1
--- /dev/null
+++ b/configs/_base_/default_runtime.py
@@ -0,0 +1,27 @@
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+    interval=200,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook')
+    ])
+# yapf:enable
+custom_hooks = [dict(type='NumClassCheckHook')]
+
+dist_params = dict(backend='nccl')
+log_level = 'INFO'
+load_from = None
+resume_from = None
+workflow = [('train', 1)]
+
+# disable opencv multithreading to avoid system being overloaded
+opencv_num_threads = 0
+# set multi-process start method as `fork` to speed up the training
+mp_start_method = 'fork'
+
+# Default setting for scaling LR automatically
+#   - `enable` means enable scaling LR automatically
+#       or not by default.
+#   - `base_batch_size` = (8 GPUs) x (2 samples per GPU).
+auto_scale_lr = dict(enable=False, base_batch_size=16)
diff --git a/configs/_base_/models/ascend_retinanet_r50_fpn.py b/configs/_base_/models/ascend_retinanet_r50_fpn.py
new file mode 100644
index 0000000..9a18fd7
--- /dev/null
+++ b/configs/_base_/models/ascend_retinanet_r50_fpn.py
@@ -0,0 +1,60 @@
+# model settings
+model = dict(
+    type='RetinaNet',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_input',
+        num_outs=5),
+    bbox_head=dict(
+        type='AscendRetinaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    # model training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='AscendMaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),
+        max_per_img=100))
diff --git a/configs/_base_/models/ascend_ssd300.py b/configs/_base_/models/ascend_ssd300.py
new file mode 100644
index 0000000..cf47fca
--- /dev/null
+++ b/configs/_base_/models/ascend_ssd300.py
@@ -0,0 +1,56 @@
+# model settings
+input_size = 300
+model = dict(
+    type='SingleStageDetector',
+    backbone=dict(
+        type='SSDVGG',
+        depth=16,
+        with_last_pool=False,
+        ceil_mode=True,
+        out_indices=(3, 4),
+        out_feature_indices=(22, 34),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://vgg16_caffe')),
+    neck=dict(
+        type='SSDNeck',
+        in_channels=(512, 1024),
+        out_channels=(512, 1024, 512, 256, 256, 256),
+        level_strides=(2, 2, 1, 1),
+        level_paddings=(1, 1, 0, 0),
+        l2_norm_scale=20),
+    bbox_head=dict(
+        type='AscendSSDHead',
+        in_channels=(512, 1024, 512, 256, 256, 256),
+        num_classes=80,
+        anchor_generator=dict(
+            type='SSDAnchorGenerator',
+            scale_major=False,
+            input_size=input_size,
+            basesize_ratio_range=(0.15, 0.9),
+            strides=[8, 16, 32, 64, 100, 300],
+            ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2])),
+    # model training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='AscendMaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.5,
+            min_pos_iou=0.,
+            ignore_iof_thr=-1,
+            gt_max_assign_all=False),
+        smoothl1_beta=1.,
+        allowed_border=-1,
+        pos_weight=-1,
+        neg_pos_ratio=3,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        nms=dict(type='nms', iou_threshold=0.45),
+        min_bbox_size=0,
+        score_thr=0.02,
+        max_per_img=200))
+# cudnn_benchmark = True
diff --git a/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py b/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py
new file mode 100644
index 0000000..2902cca
--- /dev/null
+++ b/configs/_base_/models/cascade_mask_rcnn_r50_fpn.py
@@ -0,0 +1,196 @@
+# model settings
+model = dict(
+    type='CascadeRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+    roi_head=dict(
+        type='CascadeRoIHead',
+        num_stages=3,
+        stage_loss_weights=[1, 0.5, 0.25],
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=[
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ],
+        mask_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        mask_head=dict(
+            type='FCNMaskHead',
+            num_convs=4,
+            in_channels=256,
+            conv_out_channels=256,
+            num_classes=80,
+            loss_mask=dict(
+                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=[
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.5,
+                    min_pos_iou=0.5,
+                    match_low_quality=False,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.6,
+                    neg_iou_thr=0.6,
+                    min_pos_iou=0.6,
+                    match_low_quality=False,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.7,
+                    neg_iou_thr=0.7,
+                    min_pos_iou=0.7,
+                    match_low_quality=False,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False)
+        ]),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100,
+            mask_thr_binary=0.5)))
diff --git a/configs/_base_/models/cascade_rcnn_r50_fpn.py b/configs/_base_/models/cascade_rcnn_r50_fpn.py
new file mode 100644
index 0000000..42f74ae
--- /dev/null
+++ b/configs/_base_/models/cascade_rcnn_r50_fpn.py
@@ -0,0 +1,179 @@
+# model settings
+model = dict(
+    type='CascadeRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+    roi_head=dict(
+        type='CascadeRoIHead',
+        num_stages=3,
+        stage_loss_weights=[1, 0.5, 0.25],
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=[
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ]),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=[
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.5,
+                    min_pos_iou=0.5,
+                    match_low_quality=False,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.6,
+                    neg_iou_thr=0.6,
+                    min_pos_iou=0.6,
+                    match_low_quality=False,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.7,
+                    neg_iou_thr=0.7,
+                    min_pos_iou=0.7,
+                    match_low_quality=False,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                pos_weight=-1,
+                debug=False)
+        ]),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100)))
diff --git a/configs/_base_/models/fast_rcnn_r50_fpn.py b/configs/_base_/models/fast_rcnn_r50_fpn.py
new file mode 100644
index 0000000..9982fe0
--- /dev/null
+++ b/configs/_base_/models/fast_rcnn_r50_fpn.py
@@ -0,0 +1,62 @@
+# model settings
+model = dict(
+    type='FastRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    roi_head=dict(
+        type='StandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=False,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100)))
diff --git a/configs/_base_/models/faster_rcnn_r50_caffe_c4.py b/configs/_base_/models/faster_rcnn_r50_caffe_c4.py
new file mode 100644
index 0000000..dbf965a
--- /dev/null
+++ b/configs/_base_/models/faster_rcnn_r50_caffe_c4.py
@@ -0,0 +1,117 @@
+# model settings
+norm_cfg = dict(type='BN', requires_grad=False)
+model = dict(
+    type='FasterRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=3,
+        strides=(1, 2, 2),
+        dilations=(1, 1, 1),
+        out_indices=(2, ),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=1024,
+        feat_channels=1024,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[2, 4, 8, 16, 32],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[16]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    roi_head=dict(
+        type='StandardRoIHead',
+        shared_head=dict(
+            type='ResLayer',
+            depth=50,
+            stage=3,
+            stride=2,
+            dilation=1,
+            style='caffe',
+            norm_cfg=norm_cfg,
+            norm_eval=True,
+            init_cfg=dict(
+                type='Pretrained',
+                checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=1024,
+            featmap_strides=[16]),
+        bbox_head=dict(
+            type='BBoxHead',
+            with_avg_pool=True,
+            roi_feat_size=7,
+            in_channels=2048,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=12000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=False,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=6000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100)))
diff --git a/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py b/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py
new file mode 100644
index 0000000..a377a6f
--- /dev/null
+++ b/configs/_base_/models/faster_rcnn_r50_caffe_dc5.py
@@ -0,0 +1,105 @@
+# model settings
+norm_cfg = dict(type='BN', requires_grad=False)
+model = dict(
+    type='FasterRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        strides=(1, 2, 2, 1),
+        dilations=(1, 1, 1, 2),
+        out_indices=(3, ),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=2048,
+        feat_channels=2048,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[2, 4, 8, 16, 32],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[16]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    roi_head=dict(
+        type='StandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=2048,
+            featmap_strides=[16]),
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            in_channels=2048,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=12000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=False,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms=dict(type='nms', iou_threshold=0.7),
+            nms_pre=6000,
+            max_per_img=1000,
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100)))
diff --git a/configs/_base_/models/faster_rcnn_r50_fpn.py b/configs/_base_/models/faster_rcnn_r50_fpn.py
new file mode 100644
index 0000000..9bb42cf
--- /dev/null
+++ b/configs/_base_/models/faster_rcnn_r50_fpn.py
@@ -0,0 +1,108 @@
+# model settings
+model = dict(
+    type='FasterRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet50-19c8e357.pth')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    roi_head=dict(
+        type='StandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=False,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100)
+        # soft-nms is also supported for rcnn testing
+        # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
+    ))
diff --git a/configs/_base_/models/faster_rcnn_r50_fpn_no_freeze.py b/configs/_base_/models/faster_rcnn_r50_fpn_no_freeze.py
new file mode 100644
index 0000000..2066e37
--- /dev/null
+++ b/configs/_base_/models/faster_rcnn_r50_fpn_no_freeze.py
@@ -0,0 +1,108 @@
+# model settings
+model = dict(
+    type='FasterRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        # frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet50-19c8e357.pth')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    roi_head=dict(
+        type='StandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=False,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100)
+        # soft-nms is also supported for rcnn testing
+        # e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05)
+    ))
diff --git a/configs/_base_/models/mask_rcnn_r50_caffe_c4.py b/configs/_base_/models/mask_rcnn_r50_caffe_c4.py
new file mode 100644
index 0000000..122202e
--- /dev/null
+++ b/configs/_base_/models/mask_rcnn_r50_caffe_c4.py
@@ -0,0 +1,125 @@
+# model settings
+norm_cfg = dict(type='BN', requires_grad=False)
+model = dict(
+    type='MaskRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=3,
+        strides=(1, 2, 2),
+        dilations=(1, 1, 1),
+        out_indices=(2, ),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=1024,
+        feat_channels=1024,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[2, 4, 8, 16, 32],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[16]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    roi_head=dict(
+        type='StandardRoIHead',
+        shared_head=dict(
+            type='ResLayer',
+            depth=50,
+            stage=3,
+            stride=2,
+            dilation=1,
+            style='caffe',
+            norm_cfg=norm_cfg,
+            norm_eval=True),
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=1024,
+            featmap_strides=[16]),
+        bbox_head=dict(
+            type='BBoxHead',
+            with_avg_pool=True,
+            roi_feat_size=7,
+            in_channels=2048,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+        mask_roi_extractor=None,
+        mask_head=dict(
+            type='FCNMaskHead',
+            num_convs=0,
+            in_channels=2048,
+            conv_out_channels=256,
+            num_classes=80,
+            loss_mask=dict(
+                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=12000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=False,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            mask_size=14,
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=6000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            max_per_img=1000,
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100,
+            mask_thr_binary=0.5)))
diff --git a/configs/_base_/models/mask_rcnn_r50_fpn.py b/configs/_base_/models/mask_rcnn_r50_fpn.py
new file mode 100644
index 0000000..d903e55
--- /dev/null
+++ b/configs/_base_/models/mask_rcnn_r50_fpn.py
@@ -0,0 +1,120 @@
+# model settings
+model = dict(
+    type='MaskRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    roi_head=dict(
+        type='StandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+        mask_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        mask_head=dict(
+            type='FCNMaskHead',
+            num_convs=4,
+            in_channels=256,
+            conv_out_channels=256,
+            num_classes=80,
+            loss_mask=dict(
+                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            mask_size=28,
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100,
+            mask_thr_binary=0.5)))
diff --git a/configs/_base_/models/retinanet_r50_fpn.py b/configs/_base_/models/retinanet_r50_fpn.py
new file mode 100644
index 0000000..4150732
--- /dev/null
+++ b/configs/_base_/models/retinanet_r50_fpn.py
@@ -0,0 +1,60 @@
+# model settings
+model = dict(
+    type='RetinaNet',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet50-19c8e357.pth')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_input',
+        num_outs=5),
+    bbox_head=dict(
+        type='RetinaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    # model training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),
+        max_per_img=100))
diff --git a/configs/_base_/models/retinanet_r50_fpn_no_freeze.py b/configs/_base_/models/retinanet_r50_fpn_no_freeze.py
new file mode 100644
index 0000000..478a90d
--- /dev/null
+++ b/configs/_base_/models/retinanet_r50_fpn_no_freeze.py
@@ -0,0 +1,60 @@
+# model settings
+model = dict(
+    type='RetinaNet',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        # frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=False,  # 这就是eval,不能让他为True
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet50-19c8e357.pth')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_input',
+        num_outs=5),
+    bbox_head=dict(
+        type='RetinaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    # model training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),
+        max_per_img=100))
diff --git a/configs/_base_/models/rpn_r50_caffe_c4.py b/configs/_base_/models/rpn_r50_caffe_c4.py
new file mode 100644
index 0000000..8b32ca9
--- /dev/null
+++ b/configs/_base_/models/rpn_r50_caffe_c4.py
@@ -0,0 +1,58 @@
+# model settings
+model = dict(
+    type='RPN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=3,
+        strides=(1, 2, 2),
+        dilations=(1, 1, 1),
+        out_indices=(2, ),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    neck=None,
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=1024,
+        feat_channels=1024,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[2, 4, 8, 16, 32],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[16]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=12000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0)))
diff --git a/configs/_base_/models/rpn_r50_fpn.py b/configs/_base_/models/rpn_r50_fpn.py
new file mode 100644
index 0000000..edaf4d4
--- /dev/null
+++ b/configs/_base_/models/rpn_r50_fpn.py
@@ -0,0 +1,58 @@
+# model settings
+model = dict(
+    type='RPN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0)))
diff --git a/configs/_base_/models/ssd300.py b/configs/_base_/models/ssd300.py
new file mode 100644
index 0000000..9fdf7bc
--- /dev/null
+++ b/configs/_base_/models/ssd300.py
@@ -0,0 +1,56 @@
+# model settings
+input_size = 300
+model = dict(
+    type='SingleStageDetector',
+    backbone=dict(
+        type='SSDVGG',
+        depth=16,
+        with_last_pool=False,
+        ceil_mode=True,
+        out_indices=(3, 4),
+        out_feature_indices=(22, 34),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://vgg16_caffe')),
+    neck=dict(
+        type='SSDNeck',
+        in_channels=(512, 1024),
+        out_channels=(512, 1024, 512, 256, 256, 256),
+        level_strides=(2, 2, 1, 1),
+        level_paddings=(1, 1, 0, 0),
+        l2_norm_scale=20),
+    bbox_head=dict(
+        type='SSDHead',
+        in_channels=(512, 1024, 512, 256, 256, 256),
+        num_classes=80,
+        anchor_generator=dict(
+            type='SSDAnchorGenerator',
+            scale_major=False,
+            input_size=input_size,
+            basesize_ratio_range=(0.15, 0.9),
+            strides=[8, 16, 32, 64, 100, 300],
+            ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2])),
+    # model training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.5,
+            min_pos_iou=0.,
+            ignore_iof_thr=-1,
+            gt_max_assign_all=False),
+        smoothl1_beta=1.,
+        allowed_border=-1,
+        pos_weight=-1,
+        neg_pos_ratio=3,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        nms=dict(type='nms', iou_threshold=0.45),
+        min_bbox_size=0,
+        score_thr=0.02,
+        max_per_img=200))
+# cudnn_benchmark = True
diff --git a/configs/_base_/models/ssd300_20.py b/configs/_base_/models/ssd300_20.py
new file mode 100644
index 0000000..16a2b43
--- /dev/null
+++ b/configs/_base_/models/ssd300_20.py
@@ -0,0 +1,56 @@
+# model settings
+input_size = 300
+model = dict(
+    type='SingleStageDetector',
+    backbone=dict(
+        type='SSDVGG',
+        depth=16,
+        with_last_pool=False,
+        ceil_mode=True,
+        out_indices=(3, 4),
+        out_feature_indices=(22, 34),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://vgg16_caffe')),
+    neck=dict(
+        type='SSDNeck',
+        in_channels=(512, 1024),
+        out_channels=(512, 1024, 512, 256, 256, 256),
+        level_strides=(2, 2, 1, 1),
+        level_paddings=(1, 1, 0, 0),
+        l2_norm_scale=20),
+    bbox_head=dict(
+        type='SSDHead',
+        in_channels=(512, 1024, 512, 256, 256, 256),
+        num_classes=20,
+        anchor_generator=dict(
+            type='SSDAnchorGenerator',
+            scale_major=False,
+            input_size=input_size,
+            basesize_ratio_range=(0.15, 0.9),
+            strides=[8, 16, 32, 64, 100, 300],
+            ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2])),
+    # model training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.5,
+            min_pos_iou=0.,
+            ignore_iof_thr=-1,
+            gt_max_assign_all=False),
+        smoothl1_beta=1.,
+        allowed_border=-1,
+        pos_weight=-1,
+        neg_pos_ratio=3,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        nms=dict(type='nms', iou_threshold=0.45),
+        min_bbox_size=0,
+        score_thr=0.02,
+        max_per_img=200))
+# cudnn_benchmark = True
diff --git a/configs/_base_/schedules/schedule_17biglr_qat_w4a4.py b/configs/_base_/schedules/schedule_17biglr_qat_w4a4.py
new file mode 100644
index 0000000..f8706b2
--- /dev/null
+++ b/configs/_base_/schedules/schedule_17biglr_qat_w4a4.py
@@ -0,0 +1,11 @@
+# optimizer
+optimizer = dict(type='SGD', lr=0.024, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[2, 12, 15])
+runner = dict(type='EpochBasedRunner', max_epochs=17)
diff --git a/configs/_base_/schedules/schedule_17lr_qat_w4a4.py b/configs/_base_/schedules/schedule_17lr_qat_w4a4.py
new file mode 100644
index 0000000..4d810af
--- /dev/null
+++ b/configs/_base_/schedules/schedule_17lr_qat_w4a4.py
@@ -0,0 +1,11 @@
+# optimizer
+optimizer = dict(type='SGD', lr=0.024, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[3, 14, 18])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/_base_/schedules/schedule_1x.py b/configs/_base_/schedules/schedule_1x.py
new file mode 100644
index 0000000..13b3783
--- /dev/null
+++ b/configs/_base_/schedules/schedule_1x.py
@@ -0,0 +1,11 @@
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[8, 11])
+runner = dict(type='EpochBasedRunner', max_epochs=12)
diff --git a/configs/_base_/schedules/schedule_1x_voc_general.py b/configs/_base_/schedules/schedule_1x_voc_general.py
new file mode 100644
index 0000000..532daad
--- /dev/null
+++ b/configs/_base_/schedules/schedule_1x_voc_general.py
@@ -0,0 +1,11 @@
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[5, 8])
+runner = dict(type='EpochBasedRunner', max_epochs=10)
diff --git a/configs/_base_/schedules/schedule_20e.py b/configs/_base_/schedules/schedule_20e.py
new file mode 100644
index 0000000..00e8590
--- /dev/null
+++ b/configs/_base_/schedules/schedule_20e.py
@@ -0,0 +1,11 @@
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[16, 19])
+runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/configs/_base_/schedules/schedule_2x.py b/configs/_base_/schedules/schedule_2x.py
new file mode 100644
index 0000000..69dc9ee
--- /dev/null
+++ b/configs/_base_/schedules/schedule_2x.py
@@ -0,0 +1,11 @@
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/_base_/schedules/schedule_fine_tune_general.py b/configs/_base_/schedules/schedule_fine_tune_general.py
new file mode 100644
index 0000000..c3d8848
--- /dev/null
+++ b/configs/_base_/schedules/schedule_fine_tune_general.py
@@ -0,0 +1,12 @@
+# optimizer
+optimizer = dict(type='SGD', lr=0.0001, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='exp',
+    warmup_by_epoch=True,
+    warmup_iters=1,
+    warmup_ratio=0.1,
+    step=[5])
+runner = dict(type='EpochBasedRunner', max_epochs=8)
diff --git a/configs/_base_/schedules/schedule_qat_w2a2.py b/configs/_base_/schedules/schedule_qat_w2a2.py
new file mode 100644
index 0000000..2f52a60
--- /dev/null
+++ b/configs/_base_/schedules/schedule_qat_w2a2.py
@@ -0,0 +1,13 @@
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='exp',
+    warmup_by_epoch=True,
+    warmup_iters=1,
+    warmup_ratio=0.0001,
+    step=[9, 14, 17])
+    # step=[8, 13, 17])
+runner = dict(type='EpochBasedRunner', max_epochs=18)
diff --git a/configs/_base_/schedules/schedule_qat_w2a2_big.py b/configs/_base_/schedules/schedule_qat_w2a2_big.py
new file mode 100644
index 0000000..6b565fd
--- /dev/null
+++ b/configs/_base_/schedules/schedule_qat_w2a2_big.py
@@ -0,0 +1,13 @@
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='exp',
+    warmup_by_epoch=True,
+    warmup_iters=1,
+    warmup_ratio=0.004,
+    gamma=0.5,
+    step=[8, 13, 14, 15, 16, 17])  # 0.01 0.005 0.0025 0.00125 0.000625 
+runner = dict(type='EpochBasedRunner', max_epochs=18)
diff --git a/configs/_base_/schedules/schedule_qat_w4a4.py b/configs/_base_/schedules/schedule_qat_w4a4.py
new file mode 100644
index 0000000..c5f5ce6
--- /dev/null
+++ b/configs/_base_/schedules/schedule_qat_w4a4.py
@@ -0,0 +1,14 @@
+# optimizer
+optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='exp',
+    warmup_by_epoch=True,
+    warmup_iters=1,
+    warmup_ratio=0.001,
+    # step=[9, 14])
+    # step=[7, 13, 15])  # 这是属于voc ret18的
+    step=[8, 14])  # 这是属于voc ret18的
+runner = dict(type='EpochBasedRunner', max_epochs=16)
diff --git a/configs/_base_/schedules/schedule_qat_w4a4_big.py b/configs/_base_/schedules/schedule_qat_w4a4_big.py
new file mode 100644
index 0000000..2fa930b
--- /dev/null
+++ b/configs/_base_/schedules/schedule_qat_w4a4_big.py
@@ -0,0 +1,13 @@
+# 用来分析到底是训练参数的问题吗
+# optimizer
+optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='exp',
+    warmup_by_epoch=True,
+    warmup_iters=1,
+    warmup_ratio=0.01,
+    step=[8, 14])
+runner = dict(type='EpochBasedRunner', max_epochs=16)
diff --git a/configs/_base_/schedules/schedule_smal_qat_w4a4.py b/configs/_base_/schedules/schedule_smal_qat_w4a4.py
new file mode 100644
index 0000000..e7ce65e
--- /dev/null
+++ b/configs/_base_/schedules/schedule_smal_qat_w4a4.py
@@ -0,0 +1,11 @@
+# optimizer
+optimizer = dict(type='SGD', lr=0.0001, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=200,
+    warmup_ratio=0.001,
+    step=[9, 13])
+runner = dict(type='EpochBasedRunner', max_epochs=16)
diff --git a/configs/albu_example/README.md b/configs/albu_example/README.md
new file mode 100644
index 0000000..9a180f0
--- /dev/null
+++ b/configs/albu_example/README.md
@@ -0,0 +1,31 @@
+# Albu Example
+
+> [Albumentations: fast and flexible image augmentations](https://arxiv.org/abs/1809.06839)
+
+<!-- [OTHERS] -->
+
+## Abstract
+
+Data augmentation is a commonly used technique for increasing both the size and the diversity of labeled training sets by leveraging input transformations that preserve output labels. In computer vision domain, image augmentations have become a common implicit regularization technique to combat overfitting in deep convolutional neural networks and are ubiquitously used to improve performance. While most deep learning frameworks implement basic image transformations, the list is typically limited to some variations and combinations of flipping, rotating, scaling, and cropping. Moreover, the image processing speed varies in existing tools for image augmentation. We present Albumentations, a fast and flexible library for image augmentations with many various image transform operations available, that is also an easy-to-use wrapper around other augmentation libraries. We provide examples of image augmentations for different computer vision tasks and show that Albumentations is faster than other commonly used image augmentation tools on the most of commonly used image transformations.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143870703-74f3ea3f-ae23-4035-9856-746bc3f88464.png" height="400" />
+</div>
+
+## Results and Models
+
+| Backbone |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                         Config                                                         |                                                                                                                                                        Download                                                                                                                                                         |
+| :------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | pytorch |   1x    |   4.4    |      16.6      |  38.0  |  34.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208-ab203bcd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/albu_example/mask_rcnn_r50_fpn_albu_1x_coco/mask_rcnn_r50_fpn_albu_1x_coco_20200208_225520.log.json) |
+
+## Citation
+
+```latex
+@article{2018arXiv180906839B,
+  author = {A. Buslaev, A. Parinov, E. Khvedchenya, V.~I. Iglovikov and A.~A. Kalinin},
+  title = "{Albumentations: fast and flexible image augmentations}",
+  journal = {ArXiv e-prints},
+  eprint = {1809.06839},
+  year = 2018
+}
+```
diff --git a/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py b/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py
new file mode 100644
index 0000000..b3f879a
--- /dev/null
+++ b/configs/albu_example/mask_rcnn_r50_fpn_albu_1x_coco.py
@@ -0,0 +1,73 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+albu_train_transforms = [
+    dict(
+        type='ShiftScaleRotate',
+        shift_limit=0.0625,
+        scale_limit=0.0,
+        rotate_limit=0,
+        interpolation=1,
+        p=0.5),
+    dict(
+        type='RandomBrightnessContrast',
+        brightness_limit=[0.1, 0.3],
+        contrast_limit=[0.1, 0.3],
+        p=0.2),
+    dict(
+        type='OneOf',
+        transforms=[
+            dict(
+                type='RGBShift',
+                r_shift_limit=10,
+                g_shift_limit=10,
+                b_shift_limit=10,
+                p=1.0),
+            dict(
+                type='HueSaturationValue',
+                hue_shift_limit=20,
+                sat_shift_limit=30,
+                val_shift_limit=20,
+                p=1.0)
+        ],
+        p=0.1),
+    dict(type='JpegCompression', quality_lower=85, quality_upper=95, p=0.2),
+    dict(type='ChannelShuffle', p=0.1),
+    dict(
+        type='OneOf',
+        transforms=[
+            dict(type='Blur', blur_limit=3, p=1.0),
+            dict(type='MedianBlur', blur_limit=3, p=1.0)
+        ],
+        p=0.1),
+]
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='Pad', size_divisor=32),
+    dict(
+        type='Albu',
+        transforms=albu_train_transforms,
+        bbox_params=dict(
+            type='BboxParams',
+            format='pascal_voc',
+            label_fields=['gt_labels'],
+            min_visibility=0.0,
+            filter_lost_elements=True),
+        keymap={
+            'img': 'image',
+            'gt_masks': 'masks',
+            'gt_bboxes': 'bboxes'
+        },
+        update_pad_shape=False,
+        skip_img_without_anno=True),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(
+        type='Collect',
+        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'],
+        meta_keys=('filename', 'ori_shape', 'img_shape', 'img_norm_cfg',
+                   'pad_shape', 'scale_factor'))
+]
+data = dict(train=dict(pipeline=train_pipeline))
diff --git a/configs/atss/README.md b/configs/atss/README.md
new file mode 100644
index 0000000..055ed05
--- /dev/null
+++ b/configs/atss/README.md
@@ -0,0 +1,31 @@
+# ATSS
+
+> [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://arxiv.org/abs/1912.02424)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Object detection has been dominated by anchor-based detectors for several years. Recently, anchor-free detectors have become popular due to the proposal of FPN and Focal Loss. In this paper, we first point out that the essential difference between anchor-based and anchor-free detection is actually how to define positive and negative training samples, which leads to the performance gap between them. If they adopt the same definition of positive and negative samples during training, there is no obvious difference in the final performance, no matter regressing from a box or a point. This shows that how to select positive and negative training samples is important for current object detectors. Then, we propose an Adaptive Training Sample Selection (ATSS) to automatically select positive and negative samples according to statistical characteristics of object. It significantly improves the performance of anchor-based and anchor-free detectors and bridges the gap between them. Finally, we discuss the necessity of tiling multiple anchors per location on the image to detect objects. Extensive experiments conducted on MS COCO support our aforementioned analysis and conclusions. With the newly introduced ATSS, we improve state-of-the-art detectors by a large margin to 50.7% AP without introducing any overhead.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143870776-c81168f5-e8b2-44ee-978b-509e4372c5c9.png"/>
+</div>
+
+## Results and Models
+
+| Backbone |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                Config                                                 |                                                                                                                            Download                                                                                                                             |
+| :------: | :-----: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | pytorch |   1x    |   3.7    |      19.7      |  39.4  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r50_fpn_1x_coco.py)  | [model](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209_102539.log.json) |
+|  R-101   | pytorch |   1x    |   5.6    |      12.3      |  41.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/atss/atss_r101_fpn_1x_coco.py) |   [model](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.log.json)   |
+
+## Citation
+
+```latex
+@article{zhang2019bridging,
+  title   =  {Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection},
+  author  =  {Zhang, Shifeng and Chi, Cheng and Yao, Yongqiang and Lei, Zhen and Li, Stan Z.},
+  journal =  {arXiv preprint arXiv:1912.02424},
+  year    =  {2019}
+}
+```
diff --git a/configs/atss/atss_quant_general.py b/configs/atss/atss_quant_general.py
new file mode 100644
index 0000000..6dc2598
--- /dev/null
+++ b/configs/atss/atss_quant_general.py
@@ -0,0 +1,39 @@
+trace_config = dict(
+    backbone_detail = dict(
+        input_concrete_args = dict(),
+        preserve_attr = ['arch_settings', 'avg_down', 'base_channels', 'conv_cfg', 'dcn', 'deep_stem', 'depth', 'dump_patches', 'feat_dim'
+        , 'frozen_stages', 'init_cfg', 'inplanes', 'is_init', 'norm_cfg', 'norm_eval', 'out_indices'
+        , 'plugins', 'res_layers', 'stage_block', 'stage_with_dcn', 'stem_channels', 'zero_init_residual', 'strides', 'with_cp', 'make_res_layer', 'make_stage_plugins'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = [],  
+        specified_general_quantizers = [],
+        last_8bit_module = []
+    )),
+    neck_detail = dict(
+        input_concrete_args = dict(in_num = 4),
+        preserve_attr = ['in_channels', 'init_cfg', 'is_init', 'l2_norm', 'no_norm_on_lateral', 'num_ins', 'num_outs', 'out_channels', 'relu_before_extra_convs', 'start_level', 'upsample_cfg'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(  # 'getitem_post_act_fake_quantizer'
+        exclude_prefixes = [],  
+        removed_quantizer_names = ['getitem_1_post_act_fake_quantizer','getitem_2_post_act_fake_quantizer'],  
+        specified_general_quantizers = ['getitem_1_post_act_fake_quantizer', 'getitem_2_post_act_fake_quantizer', 'getitem_3_post_act_fake_quantizer'],
+        last_8bit_module = []
+    )),
+    bbox_head_detail = dict(
+        input_concrete_args = dict(in_num = 5),
+        preserve_attr = ['anchor_generator', 'assigner', 'bbox_coder', 'cls_out_channels', 'conv_cfg', 'dump_patches', 'feat_channels', 'fp16_enabled', 
+        'in_channels', 'init_cfg', 'is_init', 'norm_cfg', 'loss_bbox', 'loss_cls', 'loss_centerness', 'num_anchors'
+        , 'num_base_priors', 'num_classes', 'prior_generator', 'reg_decoded_bbox', 'sampler', 'sampling', 'stacked_convs', 'test_cfg', 'train_cfg', 'use_sigmoid_cls'
+        
+        , 'simple_test', 'async_simple_test_rpn', 'aug_test', 'aug_test_bboxes', 'aug_test_rpn', 'forward_single', 'forward_train', 'get_anchors', 'get_bboxes', 'get_targets', 'loss', 'loss_single', 'merge_aug_bboxes', 
+        'simple_test_bboxes', 'simple_test_rpn', '_get_bboxes_single', '_get_target_single', 'get_num_level_anchors_inside', 'centerness_target', '_bbox_post_process', 'get_anchors', '_get_targets_single'],
+        not_duplicated_prefixes = ['cls_convs', 'reg_convs', 'atss_cls', 'atss_reg', 'atss_centerness'],
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = ['getitem_2_post_act_fake_quantizer','getitem_3_post_act_fake_quantizer'],  
+        qloss_flag = True, 
+        specified_general_quantizers = [],
+        last_8bit_module = ['atss_cls', 'atss_reg', 'atss_centerness']
+    )))
\ No newline at end of file
diff --git a/configs/atss/atss_r101_fpn_1x_coco.py b/configs/atss/atss_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..5225d2a
--- /dev/null
+++ b/configs/atss/atss_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './atss_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/atss/atss_r50_fpn_1x_coco.py b/configs/atss/atss_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..3b84df8
--- /dev/null
+++ b/configs/atss/atss_r50_fpn_1x_coco.py
@@ -0,0 +1,62 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='ATSS',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/bdata1/long.huang/temp/pretrained/backbones/resnet50-19c8e357.pth')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='ATSSHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(type='ATSSAssigner', topk=9),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/atss/atss_r50_fpn_1x_coco_quant_w2a2.py b/configs/atss/atss_r50_fpn_1x_coco_quant_w2a2.py
new file mode 100644
index 0000000..949b961
--- /dev/null
+++ b/configs/atss/atss_r50_fpn_1x_coco_quant_w2a2.py
@@ -0,0 +1,76 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_qat_w2a2.py', '../_base_/default_runtime.py'
+    , 'atss_quant_general.py', 
+]
+model = dict(
+    type='ATSS',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        # frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet50-19c8e357.pth')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='ATSSHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(type='ATSSAssigner', topk=9),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),
+        max_per_img=100))
+# optimizer
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+data = dict(
+    samples_per_gpu=8,  # 就是16
+    workers_per_gpu=8,
+)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = '../long_used_pretrained/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth'
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=10, norm_type=2))
diff --git a/configs/atss/atss_r50_fpn_1x_coco_quant_w4a4.py b/configs/atss/atss_r50_fpn_1x_coco_quant_w4a4.py
new file mode 100644
index 0000000..e09073b
--- /dev/null
+++ b/configs/atss/atss_r50_fpn_1x_coco_quant_w4a4.py
@@ -0,0 +1,76 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_qat_w4a4.py', '../_base_/default_runtime.py'
+    , 'atss_quant_general.py', 
+]
+model = dict(
+    type='ATSS',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        # frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet50-19c8e357.pth')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='ATSSHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(type='ATSSAssigner', topk=9),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),
+        max_per_img=100))
+# optimizer
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+data = dict(
+    samples_per_gpu=16,  # 就是16
+    workers_per_gpu=8,
+)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = '../long_used_pretrained/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth'
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=10, norm_type=2))
diff --git a/configs/atss/atss_r50_fpn_1x_voc.py b/configs/atss/atss_r50_fpn_1x_voc.py
new file mode 100644
index 0000000..24f97a1
--- /dev/null
+++ b/configs/atss/atss_r50_fpn_1x_voc.py
@@ -0,0 +1,69 @@
+_base_ = [
+    '../_base_/datasets/voc0712.py',
+    '../_base_/schedules/schedule_1x_voc_general.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='ATSS',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/bdata1/long.huang/temp/pretrained/backbones/resnet50-19c8e357.pth')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='ATSSHead',
+        num_classes=20,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(type='ATSSAssigner', topk=9),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),  # 奇怪，为甚恶魔atss这里是0.6？？
+        max_per_img=100))
+# dataset settings
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4)
+
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+
+# optimizer
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/atss/atss_r50_fpn_1x_voc_quant_w2a2.py b/configs/atss/atss_r50_fpn_1x_voc_quant_w2a2.py
new file mode 100644
index 0000000..5fc072e
--- /dev/null
+++ b/configs/atss/atss_r50_fpn_1x_voc_quant_w2a2.py
@@ -0,0 +1,70 @@
+_base_ = [
+    '../_base_/datasets/voc0712.py',
+    '../_base_/schedules/schedule_qat_w2a2.py', '../_base_/default_runtime.py', 'atss_quant_general.py'
+]
+model = dict(
+    type='ATSS',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        # frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/bdata1/long.huang/temp/pretrained/backbones/resnet50-19c8e357.pth')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='ATSSHead',
+        num_classes=20,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(type='ATSSAssigner', topk=9),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),  # 奇怪，为甚恶魔atss这里是0.6？？
+        max_per_img=100))
+# dataset settings
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4)
+
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+# optimizer
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+load_from = 'work_dirs/atss_r50_fpn_1x_voc/best_bbox_mAP_epoch_6.pth'
diff --git a/configs/atss/metafile.yml b/configs/atss/metafile.yml
new file mode 100644
index 0000000..f4c567e
--- /dev/null
+++ b/configs/atss/metafile.yml
@@ -0,0 +1,60 @@
+Collections:
+  - Name: ATSS
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ATSS
+        - FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1912.02424
+      Title: 'Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection'
+    README: configs/atss/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/atss.py#L6
+      Version: v2.0.0
+
+Models:
+  - Name: atss_r50_fpn_1x_coco
+    In Collection: ATSS
+    Config: configs/atss/atss_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.7
+      inference time (ms/im):
+        - value: 50.76
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r50_fpn_1x_coco/atss_r50_fpn_1x_coco_20200209-985f7bd0.pth
+
+  - Name: atss_r101_fpn_1x_coco
+    In Collection: ATSS
+    Config: configs/atss/atss_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.6
+      inference time (ms/im):
+        - value: 81.3
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/atss/atss_r101_fpn_1x_coco/atss_r101_fpn_1x_20200825-dfcadd6f.pth
diff --git a/configs/autoassign/README.md b/configs/autoassign/README.md
new file mode 100644
index 0000000..1297206
--- /dev/null
+++ b/configs/autoassign/README.md
@@ -0,0 +1,35 @@
+# AutoAssign
+
+> [AutoAssign: Differentiable Label Assignment for Dense Object Detection](https://arxiv.org/abs/2007.03496)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Determining positive/negative samples for object detection is known as label assignment. Here we present an anchor-free detector named AutoAssign. It requires little human knowledge and achieves appearance-aware through a fully differentiable weighting mechanism. During training, to both satisfy the prior distribution of data and adapt to category characteristics, we present Center Weighting to adjust the category-specific prior distributions. To adapt to object appearances, Confidence Weighting is proposed to adjust the specific assign strategy of each instance. The two weighting modules are then combined to generate positive and negative weights to adjust each location's confidence. Extensive experiments on the MS COCO show that our method steadily surpasses other best sampling strategies by large margins with various backbones. Moreover, our best model achieves 52.1% AP, outperforming all existing one-stage detectors. Besides, experiments on other datasets, e.g., PASCAL VOC, Objects365, and WiderFace, demonstrate the broad applicability of AutoAssign.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143870875-33567e44-0584-4470-9a90-0df0fb6c1fe2.png"/>
+</div>
+
+## Results and Models
+
+| Backbone | Style | Lr schd | Mem (GB) | box AP |                                                        Config                                                        |                                                                                                                                                        Download                                                                                                                                                         |
+| :------: | :---: | :-----: | :------: | :----: | :------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | caffe |   1x    |   4.08   |  40.4  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.log.json) |
+
+**Note**:
+
+1. We find that the performance is unstable with 1x setting and may fluctuate by about 0.3 mAP. mAP 40.3 ~ 40.6 is acceptable. Such fluctuation can also be found in the original implementation.
+2. You can get a more stable results ~ mAP 40.6 with a schedule total 13 epoch, and learning rate is divided by 10 at 10th and 13th epoch.
+
+## Citation
+
+```latex
+@article{zhu2020autoassign,
+  title={AutoAssign: Differentiable Label Assignment for Dense Object Detection},
+  author={Zhu, Benjin and Wang, Jianfeng and Jiang, Zhengkai and Zong, Fuhang and Liu, Songtao and Li, Zeming and Sun, Jian},
+  journal={arXiv preprint arXiv:2007.03496},
+  year={2020}
+}
+```
diff --git a/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py b/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py
new file mode 100644
index 0000000..db548dc
--- /dev/null
+++ b/configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py
@@ -0,0 +1,85 @@
+# We follow the original implementation which
+# adopts the Caffe pre-trained backbone.
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='AutoAssign',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs=True,
+        num_outs=5,
+        relu_before_extra_convs=True,
+        init_cfg=dict(type='Caffe2Xavier', layer='Conv2d')),
+    bbox_head=dict(
+        type='AutoAssignHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        strides=[8, 16, 32, 64, 128],
+        loss_bbox=dict(type='GIoULoss', loss_weight=5.0)),
+    train_cfg=None,
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+img_norm_cfg = dict(
+    mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(lr=0.01, paramwise_cfg=dict(norm_decay_mult=0.))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=1.0 / 1000,
+    step=[8, 11])
+total_epochs = 12
diff --git a/configs/autoassign/metafile.yml b/configs/autoassign/metafile.yml
new file mode 100644
index 0000000..f1e9051
--- /dev/null
+++ b/configs/autoassign/metafile.yml
@@ -0,0 +1,33 @@
+Collections:
+  - Name: AutoAssign
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - AutoAssign
+        - FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/2007.03496
+      Title: 'AutoAssign: Differentiable Label Assignment for Dense Object Detection'
+    README: configs/autoassign/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.12.0/mmdet/models/detectors/autoassign.py#L6
+      Version: v2.12.0
+
+Models:
+  - Name: autoassign_r50_fpn_8x2_1x_coco
+    In Collection: AutoAssign
+    Config: configs/autoassign/autoassign_r50_fpn_8x2_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.08
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/autoassign/auto_assign_r50_fpn_1x_coco/auto_assign_r50_fpn_1x_coco_20210413_115540-5e17991f.pth
diff --git a/configs/carafe/README.md b/configs/carafe/README.md
new file mode 100644
index 0000000..803abe0
--- /dev/null
+++ b/configs/carafe/README.md
@@ -0,0 +1,42 @@
+# CARAFE
+
+> [CARAFE: Content-Aware ReAssembly of FEatures](https://arxiv.org/abs/1905.02188)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Feature upsampling is a key operation in a number of modern convolutional network architectures, e.g. feature pyramids. Its design is critical for dense prediction tasks such as object detection and semantic/instance segmentation. In this work, we propose Content-Aware ReAssembly of FEatures (CARAFE), a universal, lightweight and highly effective operator to fulfill this goal. CARAFE has several appealing properties: (1) Large field of view. Unlike previous works (e.g. bilinear interpolation) that only exploit sub-pixel neighborhood, CARAFE can aggregate contextual information within a large receptive field. (2) Content-aware handling. Instead of using a fixed kernel for all samples (e.g. deconvolution), CARAFE enables instance-specific content-aware handling, which generates adaptive kernels on-the-fly. (3) Lightweight and fast to compute. CARAFE introduces little computational overhead and can be readily integrated into modern network architectures. We conduct comprehensive evaluations on standard benchmarks in object detection, instance/semantic segmentation and inpainting. CARAFE shows consistent and substantial gains across all the tasks (1.2%, 1.3%, 1.8%, 1.1db respectively) with negligible computational overhead. It has great potential to serve as a strong building block for future research. It has great potential to serve as a strong building block for future research.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143872016-48225685-0e59-49cf-bd65-a50ee04ca8a2.png"/>
+</div>
+
+## Results and Models
+
+The results on COCO 2017 val is shown in the below table.
+
+|         Method         | Backbone |  Style  | Lr schd | Test Proposal Num | Inf time (fps) | Box AP | Mask AP |                                                        Config                                                        |                                                                                                                                                                         Download                                                                                                                                                                          |
+| :--------------------: | :------: | :-----: | :-----: | :---------------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Faster R-CNN w/ CARAFE | R-50-FPN | pytorch |   1x    |       1000        |      16.5      |  38.6  |  38.6   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py) |     [model](https://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_20200504_175733.log.json)     |
+|           -            |    -     |    -    |    -    |       2000        |                |        |         |                                                                                                                      |                                                                                                                                                                                                                                                                                                                                                           |
+|  Mask R-CNN w/ CARAFE  | R-50-FPN | pytorch |   1x    |       1000        |      14.0      |  39.3  |  35.8   |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py)  | [model](https://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.393__segm_mAP-0.358_20200503_135957-8687f195.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_20200503_135957.log.json) |
+|           -            |    -     |    -    |    -    |       2000        |                |        |         |                                                                                                                      |                                                                                                                                                                                                                                                                                                                                                           |
+
+## Implementation
+
+The CUDA implementation of CARAFE can be find at https://github.com/myownskyW7/CARAFE.
+
+## Citation
+
+We provide config files to reproduce the object detection & instance segmentation results in the ICCV 2019 Oral paper for [CARAFE: Content-Aware ReAssembly of FEatures](https://arxiv.org/abs/1905.02188).
+
+```latex
+@inproceedings{Wang_2019_ICCV,
+    title = {CARAFE: Content-Aware ReAssembly of FEatures},
+    author = {Wang, Jiaqi and Chen, Kai and Xu, Rui and Liu, Ziwei and Loy, Chen Change and Lin, Dahua},
+    booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
+    month = {October},
+    year = {2019}
+}
+```
diff --git a/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py b/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py
new file mode 100644
index 0000000..dedac3f
--- /dev/null
+++ b/configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py
@@ -0,0 +1,50 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    neck=dict(
+        type='FPN_CARAFE',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5,
+        start_level=0,
+        end_level=-1,
+        norm_cfg=None,
+        act_cfg=None,
+        order=('conv', 'norm', 'act'),
+        upsample_cfg=dict(
+            type='carafe',
+            up_kernel=5,
+            up_group=1,
+            encoder_kernel=3,
+            encoder_dilation=1,
+            compressed_channels=64)))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=64),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=64),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py b/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py
new file mode 100644
index 0000000..668c023
--- /dev/null
+++ b/configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py
@@ -0,0 +1,60 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    neck=dict(
+        type='FPN_CARAFE',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5,
+        start_level=0,
+        end_level=-1,
+        norm_cfg=None,
+        act_cfg=None,
+        order=('conv', 'norm', 'act'),
+        upsample_cfg=dict(
+            type='carafe',
+            up_kernel=5,
+            up_group=1,
+            encoder_kernel=3,
+            encoder_dilation=1,
+            compressed_channels=64)),
+    roi_head=dict(
+        mask_head=dict(
+            upsample_cfg=dict(
+                type='carafe',
+                scale_factor=2,
+                up_kernel=5,
+                up_group=1,
+                encoder_kernel=3,
+                encoder_dilation=1,
+                compressed_channels=64))))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=64),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=64),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/carafe/metafile.yml b/configs/carafe/metafile.yml
new file mode 100644
index 0000000..b58a3f6
--- /dev/null
+++ b/configs/carafe/metafile.yml
@@ -0,0 +1,55 @@
+Collections:
+  - Name: CARAFE
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RPN
+        - FPN_CARAFE
+        - ResNet
+        - RoIPool
+    Paper:
+      URL: https://arxiv.org/abs/1905.02188
+      Title: 'CARAFE: Content-Aware ReAssembly of FEatures'
+    README: configs/carafe/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.12.0/mmdet/models/necks/fpn_carafe.py#L11
+      Version: v2.12.0
+
+Models:
+  - Name: faster_rcnn_r50_fpn_carafe_1x_coco
+    In Collection: CARAFE
+    Config: configs/carafe/faster_rcnn_r50_fpn_carafe_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.26
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/carafe/faster_rcnn_r50_fpn_carafe_1x_coco/faster_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.386_20200504_175733-385a75b7.pth
+
+  - Name: mask_rcnn_r50_fpn_carafe_1x_coco
+    In Collection: CARAFE
+    Config: configs/carafe/mask_rcnn_r50_fpn_carafe_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.31
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 35.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/carafe/mask_rcnn_r50_fpn_carafe_1x_coco/mask_rcnn_r50_fpn_carafe_1x_coco_bbox_mAP-0.393__segm_mAP-0.358_20200503_135957-8687f195.pth
diff --git a/configs/cascade_rcnn/README.md b/configs/cascade_rcnn/README.md
new file mode 100644
index 0000000..5a9e817
--- /dev/null
+++ b/configs/cascade_rcnn/README.md
@@ -0,0 +1,79 @@
+# Cascade R-CNN
+
+> [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://arxiv.org/abs/1906.09756)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+In object detection, the intersection over union (IoU) threshold is frequently used to define positives/negatives. The threshold used to train a detector defines its quality. While the commonly used threshold of 0.5 leads to noisy (low-quality) detections, detection performance frequently degrades for larger thresholds. This paradox of high-quality detection has two causes: 1) overfitting, due to vanishing positive samples for large thresholds, and 2) inference-time quality mismatch between detector and test hypotheses. A multi-stage object detection architecture, the Cascade R-CNN, composed of a sequence of detectors trained with increasing IoU thresholds, is proposed to address these problems. The detectors are trained sequentially, using the output of a detector as training set for the next. This resampling progressively improves hypotheses quality, guaranteeing a positive training set of equivalent size for all detectors and minimizing overfitting. The same cascade is applied at inference, to eliminate quality mismatches between hypotheses and detectors. An implementation of the Cascade R-CNN without bells or whistles achieves state-of-the-art performance on the COCO dataset, and significantly improves high-quality detection on generic and specific object detection datasets, including VOC, KITTI, CityPerson, and WiderFace. Finally, the Cascade R-CNN is generalized to instance segmentation, with nontrivial improvements over the Mask R-CNN.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143872197-d99b90e4-4f05-4329-80a4-327ac862a051.png"/>
+</div>
+
+## Results and Models
+
+### Cascade R-CNN
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                            Config                                                            |                                                                                                                                                                             Download                                                                                                                                                                              |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :--------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50-FPN     |  caffe  |   1x    |   4.2    |                |  40.4  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.404_20200504_174853-b857be87.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_20200504_174853.log.json)   |
+|    R-50-FPN     | pytorch |   1x    |   4.4    |      16.1      |  40.3  |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py)     |                          [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316_214748.log.json)                          |
+|    R-50-FPN     | pytorch |   20e   |    -     |       -        |  41.0  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py)     |             [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_bbox_mAP-0.41_20200504_175131-e9872a90.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_20200504_175131.log.json)              |
+|    R-101-FPN    |  caffe  |   1x    |   6.2    |                |  42.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py)  | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.423_20200504_175649-cab8dbd5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_20200504_175649.log.json) |
+|    R-101-FPN    | pytorch |   1x    |   6.4    |      13.5      |  42.0  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py)     |                        [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317_101744.log.json)                        |
+|    R-101-FPN    | pytorch |   20e   |    -     |       -        |  42.5  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py)    |           [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_bbox_mAP-0.425_20200504_231812-5057dcc5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_20200504_231812.log.json)           |
+| X-101-32x4d-FPN | pytorch |   1x    |   7.6    |      10.9      |  43.7  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py)  |            [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316_055608.log.json)            |
+| X-101-32x4d-FPN | pytorch |   20e   |   7.6    |                |  43.7  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py) |      [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608-9ae0a720.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608.log.json)       |
+| X-101-64x4d-FPN | pytorch |   1x    |   10.7   |                |  44.7  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py)  |        [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702-43ce6a30.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702.log.json)         |
+| X-101-64x4d-FPN | pytorch |   20e   |   10.7   |                |  44.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py) |      [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357.log.json)       |
+
+### Cascade Mask R-CNN
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                              Config                                                               |                                                                                                                                                                                               Download                                                                                                                                                                                                |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :-------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50-FPN     |  caffe  |   1x    |   5.9    |                |  41.2  |  36.0   |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.412__segm_mAP-0.36_20200504_174659-5004b251.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_20200504_174659.log.json)    |
+|    R-50-FPN     | pytorch |   1x    |   6.0    |      11.2      |  41.2  |  35.9   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py)     |                                  [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203_170449.log.json)                                  |
+|    R-50-FPN     | pytorch |   20e   |    -     |       -        |  41.9  |  36.5   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py)     |             [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_20200504_174711.log.json)             |
+|    R-101-FPN    |  caffe  |   1x    |   7.8    |                |  43.2  |  37.6   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py)  | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.432__segm_mAP-0.376_20200504_174813-5c1e9599.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_20200504_174813.log.json) |
+|    R-101-FPN    | pytorch |   1x    |   7.9    |      9.8       |  42.9  |  37.3   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py)     |                                [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203_092521.log.json)                                |
+|    R-101-FPN    | pytorch |   20e   |    -     |       -        |  43.4  |  37.8   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py)    |           [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_20200504_174836.log.json)           |
+| X-101-32x4d-FPN | pytorch |   1x    |   9.2    |      8.6       |  44.3  |  38.3   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py)  |                    [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201_052416.log.json)                    |
+| X-101-32x4d-FPN | pytorch |   20e   |   9.2    |       -        |  45.0  |  39.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py) |              [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917-ed1f4751.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917.log.json)               |
+| X-101-64x4d-FPN | pytorch |   1x    |   12.2   |      6.7       |  45.3  |  39.2   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py)  |                    [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203_044059.log.json)                    |
+| X-101-64x4d-FPN | pytorch |   20e   |   12.2   |                |  45.6  |  39.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py) |              [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033.log.json)               |
+
+**Notes:**
+
+- The `20e` schedule in Cascade (Mask) R-CNN indicates decreasing the lr at 16 and 19 epochs, with a total of 20 epochs.
+
+## Pre-trained Models
+
+We also train some models with longer schedules and multi-scale training for Cascade Mask R-CNN. The users could finetune them for downstream tasks.
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                                  Config                                                                  |                                                                                                                                                                                                Download                                                                                                                                                                                                |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50-FPN     |  caffe  |   3x    |   5.7    |                |  44.0  |  38.1   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210707_002651-6e29b3a6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210707_002651.log.json)   |
+|    R-50-FPN     | pytorch |   3x    |   5.9    |                |  44.3  |  38.5   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco_20210628_164719-5bdc3824.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco_20210628_164719.log.json)               |
+|    R-101-FPN    |  caffe  |   3x    |   7.7    |                |  45.4  |  39.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210707_002620-a5bd2389.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210707_002620.log.json) |
+|    R-101-FPN    | pytorch |   3x    |   7.8    |                |  45.5  |  39.6   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco_20210628_165236-51a2d363.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco_20210628_165236.log.json)             |
+| X-101-32x4d-FPN | pytorch |   3x    |   9.0    |                |  46.3  |  40.1   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210706_225234-40773067.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210706_225234.log.json) |
+| X-101-32x8d-FPN | pytorch |   3x    |   12.1   |                |  46.1  |  39.9   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210719_180640-9ff7e76f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210719_180640.log.json) |
+| X-101-64x4d-FPN | pytorch |   3x    |   12.0   |                |  46.6  |  40.3   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311-d3e64ba0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311.log.json) |
+
+## Citation
+
+```latex
+@article{Cai_2019,
+   title={Cascade R-CNN: High Quality Object Detection and Instance Segmentation},
+   ISSN={1939-3539},
+   url={http://dx.doi.org/10.1109/tpami.2019.2956516},
+   DOI={10.1109/tpami.2019.2956516},
+   journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
+   author={Cai, Zhaowei and Vasconcelos, Nuno},
+   year={2019},
+   pages={1–1}
+}
+```
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..5ee6231
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..1df87fc
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..f59c155
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py
new file mode 100644
index 0000000..45ab7ed
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py
@@ -0,0 +1,6 @@
+_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..1b20f16
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..12d37ef
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,41 @@
+_base_ = ['./cascade_mask_rcnn_r50_fpn_1x_coco.py']
+
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..9fb817e
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py
@@ -0,0 +1,49 @@
+_base_ = ['./cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py']
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))
+
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
+# multiscale_mode='range'
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+data = dict(
+    train=dict(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..49ab539
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = [
+    '../_base_/models/cascade_mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py
new file mode 100644
index 0000000..1296dc4
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py
@@ -0,0 +1,5 @@
+_base_ = [
+    '../_base_/models/cascade_mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_20e.py', '../_base_/default_runtime.py'
+]
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..ed0c6d1
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py
@@ -0,0 +1,4 @@
+_base_ = [
+    '../common/mstrain_3x_coco_instance.py',
+    '../_base_/models/cascade_mask_rcnn_r50_fpn.py'
+]
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..06cbbe7
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py
new file mode 100644
index 0000000..4e35236
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py
@@ -0,0 +1,14 @@
+_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..7d37d17
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..eeec1aa
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py
@@ -0,0 +1,60 @@
+_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
+
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=8,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnext101_32x8d')))
+
+# ResNeXt-101-32x8d model trained with Caffe2 at FB,
+# so the mean and std need to be changed.
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675],
+    std=[57.375, 57.120, 58.395],
+    to_rgb=False)
+
+# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
+# multiscale_mode='range'
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+data = dict(
+    train=dict(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..7dbef5f
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py
new file mode 100644
index 0000000..579b1ac
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py
@@ -0,0 +1,14 @@
+_base_ = './cascade_mask_rcnn_r50_fpn_20e_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..ed6cf4b
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..1e90f4b
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './cascade_rcnn_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))
diff --git a/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..5c07776
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py
new file mode 100644
index 0000000..b1719c2
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py
@@ -0,0 +1,6 @@
+_base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..696bcfb
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,42 @@
+_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
+
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))
+
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..87e21fb
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = [
+    '../_base_/models/cascade_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
diff --git a/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py
new file mode 100644
index 0000000..6f886e1
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py
@@ -0,0 +1,4 @@
+_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 19])
+runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..5ac02c1
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py
new file mode 100644
index 0000000..486e45e
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py
@@ -0,0 +1,14 @@
+_base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..78229f0
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py
@@ -0,0 +1,15 @@
+_base_ = './cascade_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    type='CascadeRCNN',
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py
new file mode 100644
index 0000000..58812de
--- /dev/null
+++ b/configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py
@@ -0,0 +1,15 @@
+_base_ = './cascade_rcnn_r50_fpn_20e_coco.py'
+model = dict(
+    type='CascadeRCNN',
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/cascade_rcnn/metafile.yml b/configs/cascade_rcnn/metafile.yml
new file mode 100644
index 0000000..6586325
--- /dev/null
+++ b/configs/cascade_rcnn/metafile.yml
@@ -0,0 +1,545 @@
+Collections:
+  - Name: Cascade R-CNN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Cascade R-CNN
+        - FPN
+        - RPN
+        - ResNet
+        - RoIAlign
+    Paper:
+      URL: http://dx.doi.org/10.1109/tpami.2019.2956516
+      Title: 'Cascade R-CNN: Delving into High Quality Object Detection'
+    README: configs/cascade_rcnn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/cascade_rcnn.py#L6
+      Version: v2.0.0
+  - Name: Cascade Mask R-CNN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Cascade R-CNN
+        - FPN
+        - RPN
+        - ResNet
+        - RoIAlign
+    Paper:
+      URL: http://dx.doi.org/10.1109/tpami.2019.2956516
+      Title: 'Cascade R-CNN: Delving into High Quality Object Detection'
+    README: configs/cascade_rcnn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/cascade_rcnn.py#L6
+      Version: v2.0.0
+
+Models:
+  - Name: cascade_rcnn_r50_caffe_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.2
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco/cascade_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.404_20200504_174853-b857be87.pth
+
+  - Name: cascade_rcnn_r50_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.4
+      inference time (ms/im):
+        - value: 62.11
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco/cascade_rcnn_r50_fpn_1x_coco_20200316-3dc56deb.pth
+
+  - Name: cascade_rcnn_r50_fpn_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 4.4
+      inference time (ms/im):
+        - value: 62.11
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco/cascade_rcnn_r50_fpn_20e_coco_bbox_mAP-0.41_20200504_175131-e9872a90.pth
+
+  - Name: cascade_rcnn_r101_caffe_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.2
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_caffe_fpn_1x_coco/cascade_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.423_20200504_175649-cab8dbd5.pth
+
+  - Name: cascade_rcnn_r101_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.4
+      inference time (ms/im):
+        - value: 74.07
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco/cascade_rcnn_r101_fpn_1x_coco_20200317-0b6a2fbf.pth
+
+  - Name: cascade_rcnn_r101_fpn_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 6.4
+      inference time (ms/im):
+        - value: 74.07
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_r101_fpn_20e_coco/cascade_rcnn_r101_fpn_20e_coco_bbox_mAP-0.425_20200504_231812-5057dcc5.pth
+
+  - Name: cascade_rcnn_x101_32x4d_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.6
+      inference time (ms/im):
+        - value: 91.74
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_1x_coco/cascade_rcnn_x101_32x4d_fpn_1x_coco_20200316-95c2deb6.pth
+
+  - Name: cascade_rcnn_x101_32x4d_fpn_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 7.6
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_32x4d_fpn_20e_coco/cascade_rcnn_x101_32x4d_fpn_20e_coco_20200906_134608-9ae0a720.pth
+
+  - Name: cascade_rcnn_x101_64x4d_fpn_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.7
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_1x_coco/cascade_rcnn_x101_64x4d_fpn_1x_coco_20200515_075702-43ce6a30.pth
+
+  - Name: cascade_rcnn_x101_64x4d_fpn_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 10.7
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_rcnn_x101_64x4d_fpn_20e_coco/cascade_rcnn_x101_64x4d_fpn_20e_coco_20200509_224357-051557b1.pth
+
+  - Name: cascade_mask_rcnn_r50_caffe_fpn_1x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.9
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  36.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_1x_coco/cascade_mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.412__segm_mAP-0.36_20200504_174659-5004b251.pth
+
+  - Name: cascade_mask_rcnn_r50_fpn_1x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.0
+      inference time (ms/im):
+        - value: 89.29
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  35.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth
+
+  - Name: cascade_mask_rcnn_r50_fpn_20e_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 6.0
+      inference time (ms/im):
+        - value: 89.29
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  36.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco/cascade_mask_rcnn_r50_fpn_20e_coco_bbox_mAP-0.419__segm_mAP-0.365_20200504_174711-4af8e66e.pth
+
+  - Name: cascade_mask_rcnn_r101_caffe_fpn_1x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.8
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_1x_coco/cascade_mask_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.432__segm_mAP-0.376_20200504_174813-5c1e9599.pth
+
+  - Name: cascade_mask_rcnn_r101_fpn_1x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.9
+      inference time (ms/im):
+        - value: 102.04
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco/cascade_mask_rcnn_r101_fpn_1x_coco_20200203-befdf6ee.pth
+
+  - Name: cascade_mask_rcnn_r101_fpn_20e_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 7.9
+      inference time (ms/im):
+        - value: 102.04
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_20e_coco/cascade_mask_rcnn_r101_fpn_20e_coco_bbox_mAP-0.434__segm_mAP-0.378_20200504_174836-005947da.pth
+
+  - Name: cascade_mask_rcnn_x101_32x4d_fpn_1x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 9.2
+      inference time (ms/im):
+        - value: 116.28
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  38.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco_20200201-0f411b1f.pth
+
+  - Name: cascade_mask_rcnn_x101_32x4d_fpn_20e_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 9.2
+      inference time (ms/im):
+        - value: 116.28
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  39.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco/cascade_mask_rcnn_x101_32x4d_fpn_20e_coco_20200528_083917-ed1f4751.pth
+
+  - Name: cascade_mask_rcnn_x101_64x4d_fpn_1x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 12.2
+      inference time (ms/im):
+        - value: 149.25
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  39.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco/cascade_mask_rcnn_x101_64x4d_fpn_1x_coco_20200203-9a2db89d.pth
+
+  - Name: cascade_mask_rcnn_x101_64x4d_fpn_20e_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 12.2
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco/cascade_mask_rcnn_x101_64x4d_fpn_20e_coco_20200512_161033-bdb5126a.pth
+
+  - Name: cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 5.7
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210707_002651-6e29b3a6.pth
+
+  - Name: cascade_mask_rcnn_r50_fpn_mstrain_3x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 5.9
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco/cascade_mask_rcnn_r50_fpn_mstrain_3x_coco_20210628_164719-5bdc3824.pth
+
+  - Name: cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 7.7
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210707_002620-a5bd2389.pth
+
+  - Name: cascade_mask_rcnn_r101_fpn_mstrain_3x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 7.8
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco/cascade_mask_rcnn_r101_fpn_mstrain_3x_coco_20210628_165236-51a2d363.pth
+
+  - Name: cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 9.0
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 40.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210706_225234-40773067.pth
+
+  - Name: cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 12.1
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210719_180640-9ff7e76f.pth
+
+  - Name: cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 12.0
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 40.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco/cascade_mask_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210719_210311-d3e64ba0.pth
diff --git a/configs/cascade_rpn/README.md b/configs/cascade_rpn/README.md
new file mode 100644
index 0000000..fb2b482
--- /dev/null
+++ b/configs/cascade_rpn/README.md
@@ -0,0 +1,41 @@
+# Cascade RPN
+
+> [Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution](https://arxiv.org/abs/1909.06720)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+This paper considers an architecture referred to as Cascade Region Proposal Network (Cascade RPN) for improving the region-proposal quality and detection performance by systematically addressing the limitation of the conventional RPN that heuristically defines the anchors and aligns the features to the anchors. First, instead of using multiple anchors with predefined scales and aspect ratios, Cascade RPN relies on a single anchor per location and performs multi-stage refinement. Each stage is progressively more stringent in defining positive samples by starting out with an anchor-free metric followed by anchor-based metrics in the ensuing stages. Second, to attain alignment between the features and the anchors throughout the stages, adaptive convolution is proposed that takes the anchors in addition to the image features as its input and learns the sampled features guided by the anchors. A simple implementation of a two-stage Cascade RPN achieves AR 13.4 points higher than that of the conventional RPN, surpassing any existing region proposal methods. When adopting to Fast R-CNN and Faster R-CNN, Cascade RPN can improve the detection mAP by 3.1 and 3.5 points, respectively.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143872368-1580193a-d19c-4723-a579-c7ed2d5da4d1.png"/>
+</div>
+
+## Results and Models
+
+### Region proposal performance
+
+| Method | Backbone | Style | Mem (GB) | Train time (s/iter) | Inf time (fps) | AR 1000 |                                                      Config                                                       |                                                                    Download                                                                    |
+| :----: | :------: | :---: | :------: | :-----------------: | :------------: | :-----: | :---------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------: |
+|  CRPN  | R-50-FPN | caffe |    -     |          -          |       -        |  72.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rpn/crpn_r50_caffe_fpn_1x_coco/cascade_rpn_r50_caffe_fpn_1x_coco-7aa93cef.pth) |
+
+### Detection performance
+
+|    Method    |  Proposal   | Backbone | Style | Schedule | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP |                                                            Config                                                             |                                                                            Download                                                                             |
+| :----------: | :---------: | :------: | :---: | :------: | :------: | :-----------------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  Fast R-CNN  | Cascade RPN | R-50-FPN | caffe |    1x    |    -     |          -          |       -        |  39.9  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco/crpn_fast_rcnn_r50_caffe_fpn_1x_coco-cb486e66.pth)   |
+| Faster R-CNN | Cascade RPN | R-50-FPN | caffe |    1x    |    -     |          -          |       -        |  40.4  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco/crpn_faster_rcnn_r50_caffe_fpn_1x_coco-c8283cca.pth) |
+
+## Citation
+
+We provide the code for reproducing experiment results of [Cascade RPN](https://arxiv.org/abs/1909.06720).
+
+```latex
+@inproceedings{vu2019cascade,
+  title={Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution},
+  author={Vu, Thang and Jang, Hyunjun and Pham, Trung X and Yoo, Chang D},
+  booktitle={Conference on Neural Information Processing Systems (NeurIPS)},
+  year={2019}
+}
+```
diff --git a/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..29f5d07
--- /dev/null
+++ b/configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,77 @@
+_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    roi_head=dict(
+        bbox_head=dict(
+            bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rcnn=dict(
+            assigner=dict(
+                pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65),
+            sampler=dict(num=256))),
+    test_cfg=dict(rcnn=dict(score_thr=1e-3)))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadProposals', num_max_proposals=300),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadProposals', num_max_proposals=300),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='ToTensor', keys=['proposals']),
+            dict(
+                type='ToDataContainer',
+                fields=[dict(key='proposals', stack=False)]),
+            dict(type='Collect', keys=['img', 'proposals']),
+        ])
+]
+data = dict(
+    train=dict(
+        proposal_file=data_root +
+        'proposals/crpn_r50_caffe_fpn_1x_train2017.pkl',
+        pipeline=train_pipeline),
+    val=dict(
+        proposal_file=data_root +
+        'proposals/crpn_r50_caffe_fpn_1x_val2017.pkl',
+        pipeline=test_pipeline),
+    test=dict(
+        proposal_file=data_root +
+        'proposals/crpn_r50_caffe_fpn_1x_val2017.pkl',
+        pipeline=test_pipeline))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..bad86e6
--- /dev/null
+++ b/configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,92 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py'
+rpn_weight = 0.7
+model = dict(
+    rpn_head=dict(
+        _delete_=True,
+        type='CascadeRPNHead',
+        num_stages=2,
+        stages=[
+            dict(
+                type='StageCascadeRPNHead',
+                in_channels=256,
+                feat_channels=256,
+                anchor_generator=dict(
+                    type='AnchorGenerator',
+                    scales=[8],
+                    ratios=[1.0],
+                    strides=[4, 8, 16, 32, 64]),
+                adapt_cfg=dict(type='dilation', dilation=3),
+                bridged_feature=True,
+                sampling=False,
+                with_cls=False,
+                reg_decoded_bbox=True,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=(.0, .0, .0, .0),
+                    target_stds=(0.1, 0.1, 0.5, 0.5)),
+                loss_bbox=dict(
+                    type='IoULoss', linear=True,
+                    loss_weight=10.0 * rpn_weight)),
+            dict(
+                type='StageCascadeRPNHead',
+                in_channels=256,
+                feat_channels=256,
+                adapt_cfg=dict(type='offset'),
+                bridged_feature=False,
+                sampling=True,
+                with_cls=True,
+                reg_decoded_bbox=True,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=(.0, .0, .0, .0),
+                    target_stds=(0.05, 0.05, 0.1, 0.1)),
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=True,
+                    loss_weight=1.0 * rpn_weight),
+                loss_bbox=dict(
+                    type='IoULoss', linear=True,
+                    loss_weight=10.0 * rpn_weight))
+        ]),
+    roi_head=dict(
+        bbox_head=dict(
+            bbox_coder=dict(target_stds=[0.04, 0.04, 0.08, 0.08]),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.5),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=[
+            dict(
+                assigner=dict(
+                    type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5),
+                allowed_border=-1,
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.7,
+                    neg_iou_thr=0.7,
+                    min_pos_iou=0.3,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=256,
+                    pos_fraction=0.5,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=False),
+                allowed_border=-1,
+                pos_weight=-1,
+                debug=False)
+        ],
+        rpn_proposal=dict(max_per_img=300, nms=dict(iou_threshold=0.8)),
+        rcnn=dict(
+            assigner=dict(
+                pos_iou_thr=0.65, neg_iou_thr=0.65, min_pos_iou=0.65),
+            sampler=dict(type='RandomSampler', num=256))),
+    test_cfg=dict(
+        rpn=dict(max_per_img=300, nms=dict(iou_threshold=0.8)),
+        rcnn=dict(score_thr=1e-3)))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py b/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..5562e69
--- /dev/null
+++ b/configs/cascade_rpn/crpn_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,77 @@
+_base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    rpn_head=dict(
+        _delete_=True,
+        type='CascadeRPNHead',
+        num_stages=2,
+        stages=[
+            dict(
+                type='StageCascadeRPNHead',
+                in_channels=256,
+                feat_channels=256,
+                anchor_generator=dict(
+                    type='AnchorGenerator',
+                    scales=[8],
+                    ratios=[1.0],
+                    strides=[4, 8, 16, 32, 64]),
+                adapt_cfg=dict(type='dilation', dilation=3),
+                bridged_feature=True,
+                sampling=False,
+                with_cls=False,
+                reg_decoded_bbox=True,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=(.0, .0, .0, .0),
+                    target_stds=(0.1, 0.1, 0.5, 0.5)),
+                loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0)),
+            dict(
+                type='StageCascadeRPNHead',
+                in_channels=256,
+                feat_channels=256,
+                adapt_cfg=dict(type='offset'),
+                bridged_feature=False,
+                sampling=True,
+                with_cls=True,
+                reg_decoded_bbox=True,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=(.0, .0, .0, .0),
+                    target_stds=(0.05, 0.05, 0.1, 0.1)),
+                loss_cls=dict(
+                    type='CrossEntropyLoss', use_sigmoid=True,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='IoULoss', linear=True, loss_weight=10.0))
+        ]),
+    train_cfg=dict(rpn=[
+        dict(
+            assigner=dict(
+                type='RegionAssigner', center_ratio=0.2, ignore_ratio=0.5),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.7,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1,
+                iou_calculator=dict(type='BboxOverlaps2D')),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False)
+    ]),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.8),
+            min_bbox_size=0)))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/cascade_rpn/metafile.yml b/configs/cascade_rpn/metafile.yml
new file mode 100644
index 0000000..335b2bc
--- /dev/null
+++ b/configs/cascade_rpn/metafile.yml
@@ -0,0 +1,44 @@
+Collections:
+  - Name: Cascade RPN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Cascade RPN
+        - FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1909.06720
+      Title: 'Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution'
+    README: configs/cascade_rpn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.8.0/mmdet/models/dense_heads/cascade_rpn_head.py#L538
+      Version: v2.8.0
+
+Models:
+  - Name: crpn_fast_rcnn_r50_caffe_fpn_1x_coco
+    In Collection: Cascade RPN
+    Config: configs/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rpn/crpn_fast_rcnn_r50_caffe_fpn_1x_coco/crpn_fast_rcnn_r50_caffe_fpn_1x_coco-cb486e66.pth
+
+  - Name: crpn_faster_rcnn_r50_caffe_fpn_1x_coco
+    In Collection: Cascade RPN
+    Config: configs/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cascade_rpn/crpn_faster_rcnn_r50_caffe_fpn_1x_coco/crpn_faster_rcnn_r50_caffe_fpn_1x_coco-c8283cca.pth
diff --git a/configs/centernet/README.md b/configs/centernet/README.md
new file mode 100644
index 0000000..0f951a0
--- /dev/null
+++ b/configs/centernet/README.md
@@ -0,0 +1,40 @@
+# CenterNet
+
+> [Objects as Points](https://arxiv.org/abs/1904.07850)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Detection identifies objects as axis-aligned boxes in an image. Most successful object detectors enumerate a nearly exhaustive list of potential object locations and classify each. This is wasteful, inefficient, and requires additional post-processing. In this paper, we take a different approach. We model an object as a single point --- the center point of its bounding box. Our detector uses keypoint estimation to find center points and regresses to all other object properties, such as size, 3D location, orientation, and even pose. Our center point based approach, CenterNet, is end-to-end differentiable, simpler, faster, and more accurate than corresponding bounding box based detectors. CenterNet achieves the best speed-accuracy trade-off on the MS COCO dataset, with 28.1% AP at 142 FPS, 37.4% AP at 52 FPS, and 45.1% AP with multi-scale testing at 1.4 FPS. We use the same approach to estimate 3D bounding box in the KITTI benchmark and human pose on the COCO keypoint dataset. Our method performs competitively with sophisticated multi-stage methods and runs in real-time.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143873810-85ffa6e7-915b-46a4-9b8f-709e5d7700bb.png"/>
+</div>
+
+## Results and Models
+
+| Backbone  | DCN | Mem (GB) | Box AP | Flip box AP |                                                         Config                                                          |                                                                                                                                                                 Download                                                                                                                                                                 |
+| :-------: | :-: | :------: | :----: | :---------: | :---------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| ResNet-18 |  N  |   3.45   |  25.9  |    27.3     |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centernet/centernet_resnet18_140e_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210705_093630-bb5b3bf7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210705_093630.log.json)             |
+| ResNet-18 |  Y  |   3.47   |  29.5  |    30.9     | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210702_155131-c8cd631f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210702_155131.log.json) |
+
+Note:
+
+- Flip box AP setting is single-scale and `flip=True`.
+- Due to complex data enhancement, we find that the performance is unstable and may fluctuate by about 0.4 mAP. mAP 29.4 ~ 29.8 is acceptable in ResNet-18-DCNv2.
+- Compared to the source code, we refer to [CenterNet-Better](https://github.com/FateScript/CenterNet-better), and make the following changes
+  - fix wrong image mean and variance in image normalization to be compatible with the pre-trained backbone.
+  - Use SGD rather than ADAM optimizer and add warmup and grad clip.
+  - Use DistributedDataParallel as other models in MMDetection rather than using DataParallel.
+
+## Citation
+
+```latex
+@article{zhou2019objects,
+  title={Objects as Points},
+  author={Zhou, Xingyi and Wang, Dequan and Kr{\"a}henb{\"u}hl, Philipp},
+  booktitle={arXiv preprint arXiv:1904.07850},
+  year={2019}
+}
+```
diff --git a/configs/centernet/centernet_resnet18_140e_coco.py b/configs/centernet/centernet_resnet18_140e_coco.py
new file mode 100644
index 0000000..52c86a5
--- /dev/null
+++ b/configs/centernet/centernet_resnet18_140e_coco.py
@@ -0,0 +1,3 @@
+_base_ = './centernet_resnet18_dcnv2_140e_coco.py'
+
+model = dict(neck=dict(use_dcn=False))
diff --git a/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py b/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py
new file mode 100644
index 0000000..b8a0bb1
--- /dev/null
+++ b/configs/centernet/centernet_resnet18_dcnv2_140e_coco.py
@@ -0,0 +1,127 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    type='CenterNet',
+    backbone=dict(
+        type='ResNet',
+        depth=18,
+        norm_eval=False,
+        norm_cfg=dict(type='BN'),
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
+    neck=dict(
+        type='CTResNetNeck',
+        in_channel=512,
+        num_deconv_filters=(256, 128, 64),
+        num_deconv_kernels=(4, 4, 4),
+        use_dcn=True),
+    bbox_head=dict(
+        type='CenterNetHead',
+        num_classes=80,
+        in_channel=64,
+        feat_channel=64,
+        loss_center_heatmap=dict(type='GaussianFocalLoss', loss_weight=1.0),
+        loss_wh=dict(type='L1Loss', loss_weight=0.1),
+        loss_offset=dict(type='L1Loss', loss_weight=1.0)),
+    train_cfg=None,
+    test_cfg=dict(topk=100, local_maximum_kernel=3, max_per_img=100))
+
+# We fixed the incorrect img_norm_cfg problem in the source code.
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True, color_type='color'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(
+        type='RandomCenterCropPad',
+        crop_size=(512, 512),
+        ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
+        mean=[0, 0, 0],
+        std=[1, 1, 1],
+        to_rgb=True,
+        test_pad_mode=None),
+    dict(type='Resize', img_scale=(512, 512), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(
+        type='MultiScaleFlipAug',
+        scale_factor=1.0,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(
+                type='RandomCenterCropPad',
+                ratios=None,
+                border=None,
+                mean=[0, 0, 0],
+                std=[1, 1, 1],
+                to_rgb=True,
+                test_mode=True,
+                test_pad_mode=['logical_or', 31],
+                test_pad_add_pix=1),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='DefaultFormatBundle'),
+            dict(
+                type='Collect',
+                meta_keys=('filename', 'ori_filename', 'ori_shape',
+                           'img_shape', 'pad_shape', 'scale_factor', 'flip',
+                           'flip_direction', 'img_norm_cfg', 'border'),
+                keys=['img'])
+        ])
+]
+
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+
+# Use RepeatDataset to speed up training
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=4,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+# optimizer
+# Based on the default settings of modern detectors, the SGD effect is better
+# than the Adam in the source code, so we use SGD default settings and
+# if you use adam+lr5e-4, the map is 29.1.
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+
+# learning policy
+# Based on the default settings of modern detectors, we added warmup settings.
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=1.0 / 1000,
+    step=[18, 24])  # the real step is [18*5, 24*5]
+runner = dict(max_epochs=28)  # the real epoch is 28*5=140
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (16 samples per GPU)
+auto_scale_lr = dict(base_batch_size=128)
diff --git a/configs/centernet/metafile.yml b/configs/centernet/metafile.yml
new file mode 100644
index 0000000..e86e57b
--- /dev/null
+++ b/configs/centernet/metafile.yml
@@ -0,0 +1,46 @@
+Collections:
+  - Name: CenterNet
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x TITANXP GPUs
+      Architecture:
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1904.07850
+      Title: 'Objects as Points'
+    README: configs/centernet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.13.0/mmdet/models/detectors/centernet.py#L10
+      Version: v2.13.0
+
+Models:
+  - Name: centernet_resnet18_dcnv2_140e_coco
+    In Collection: CenterNet
+    Config: configs/centernet/centernet_resnet18_dcnv2_140e_coco.py
+    Metadata:
+      Batch Size: 128
+      Training Memory (GB): 3.47
+      Epochs: 140
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 29.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_dcnv2_140e_coco/centernet_resnet18_dcnv2_140e_coco_20210702_155131-c8cd631f.pth
+
+  - Name: centernet_resnet18_140e_coco
+    In Collection: CenterNet
+    Config: configs/centernet/centernet_resnet18_140e_coco.py
+    Metadata:
+      Batch Size: 128
+      Training Memory (GB): 3.45
+      Epochs: 140
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 25.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/centernet/centernet_resnet18_140e_coco/centernet_resnet18_140e_coco_20210705_093630-bb5b3bf7.pth
diff --git a/configs/centripetalnet/README.md b/configs/centripetalnet/README.md
new file mode 100644
index 0000000..b01b00a
--- /dev/null
+++ b/configs/centripetalnet/README.md
@@ -0,0 +1,36 @@
+# CentripetalNet
+
+> [CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection](https://arxiv.org/abs/2003.09119)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Keypoint-based detectors have achieved pretty-well performance. However, incorrect keypoint matching is still widespread and greatly affects the performance of the detector. In this paper, we propose CentripetalNet which uses centripetal shift to pair corner keypoints from the same instance. CentripetalNet predicts the position and the centripetal shift of the corner points and matches corners whose shifted results are aligned. Combining position information, our approach matches corner points more accurately than the conventional embedding approaches do. Corner pooling extracts information inside the bounding boxes onto the border. To make this information more aware at the corners, we design a cross-star deformable convolution network to conduct feature adaption. Furthermore, we explore instance segmentation on anchor-free detectors by equipping our CentripetalNet with a mask prediction module. On MS-COCO test-dev, our CentripetalNet not only outperforms all existing anchor-free detectors with an AP of 48.0% but also achieves comparable performance to the state-of-the-art instance segmentation approaches with a 40.2% MaskAP.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143873955-42804e0e-3638-4c5b-8bf4-ac8133bbcdc8.png"/>
+</div>
+
+## Results and Models
+
+|     Backbone     |                            Batch Size                            | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP |                                                                   Config                                                                    |                                                                                                                                                                                                    Download                                                                                                                                                                                                    |
+| :--------------: | :--------------------------------------------------------------: | :---------------: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| HourglassNet-104 | [16 x 6](./centripetalnet_hourglass104_mstest_16x6_210e_coco.py) |      190/210      |   16.7   |      3.7       |  44.8  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804.log.json) |
+
+Note:
+
+- TTA setting is single-scale and `flip=True`.
+- The model we released is the best checkpoint rather than the latest checkpoint (box AP 44.8 vs 44.6 in our experiment).
+
+## Citation
+
+```latex
+@InProceedings{Dong_2020_CVPR,
+author = {Dong, Zhiwei and Li, Guoxuan and Liao, Yue and Wang, Fei and Ren, Pengju and Qian, Chen},
+title = {CentripetalNet: Pursuing High-Quality Keypoint Pairs for Object Detection},
+booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+month = {June},
+year = {2020}
+}
+```
diff --git a/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py b/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py
new file mode 100644
index 0000000..5281c5b
--- /dev/null
+++ b/configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py
@@ -0,0 +1,110 @@
+_base_ = [
+    '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
+]
+
+# model settings
+model = dict(
+    type='CornerNet',
+    backbone=dict(
+        type='HourglassNet',
+        downsample_times=5,
+        num_stacks=2,
+        stage_channels=[256, 256, 384, 384, 384, 512],
+        stage_blocks=[2, 2, 2, 2, 2, 4],
+        norm_cfg=dict(type='BN', requires_grad=True)),
+    neck=None,
+    bbox_head=dict(
+        type='CentripetalHead',
+        num_classes=80,
+        in_channels=256,
+        num_feat_levels=2,
+        corner_emb_channels=0,
+        loss_heatmap=dict(
+            type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1),
+        loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1),
+        loss_guiding_shift=dict(
+            type='SmoothL1Loss', beta=1.0, loss_weight=0.05),
+        loss_centripetal_shift=dict(
+            type='SmoothL1Loss', beta=1.0, loss_weight=1)),
+    # training and testing settings
+    train_cfg=None,
+    test_cfg=dict(
+        corner_topk=100,
+        local_maximum_kernel=3,
+        distance_threshold=0.5,
+        score_thr=0.05,
+        max_per_img=100,
+        nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian')))
+# data settings
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(
+        type='RandomCenterCropPad',
+        crop_size=(511, 511),
+        ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
+        test_mode=False,
+        test_pad_mode=None,
+        **img_norm_cfg),
+    dict(type='Resize', img_scale=(511, 511), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(
+        type='MultiScaleFlipAug',
+        scale_factor=1.0,
+        flip=True,
+        transforms=[
+            dict(type='Resize'),
+            dict(
+                type='RandomCenterCropPad',
+                crop_size=None,
+                ratios=None,
+                border=None,
+                test_mode=True,
+                test_pad_mode=['logical_or', 127],
+                **img_norm_cfg),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(
+                type='Collect',
+                keys=['img'],
+                meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape',
+                           'scale_factor', 'flip', 'img_norm_cfg', 'border')),
+        ])
+]
+data = dict(
+    samples_per_gpu=6,
+    workers_per_gpu=3,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='Adam', lr=0.0005)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    step=[190])
+runner = dict(type='EpochBasedRunner', max_epochs=210)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (16 GPUs) x (6 samples per GPU)
+auto_scale_lr = dict(base_batch_size=96)
diff --git a/configs/centripetalnet/metafile.yml b/configs/centripetalnet/metafile.yml
new file mode 100644
index 0000000..61aed3e
--- /dev/null
+++ b/configs/centripetalnet/metafile.yml
@@ -0,0 +1,39 @@
+Collections:
+  - Name: CentripetalNet
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - Adam
+      Training Resources: 16x V100 GPUs
+      Architecture:
+        - Corner Pooling
+        - Stacked Hourglass Network
+    Paper:
+      URL: https://arxiv.org/abs/2003.09119
+      Title: 'CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection'
+    README: configs/centripetalnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.5.0/mmdet/models/detectors/cornernet.py#L9
+      Version: v2.5.0
+
+Models:
+  - Name: centripetalnet_hourglass104_mstest_16x6_210e_coco
+    In Collection: CentripetalNet
+    Config: configs/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco.py
+    Metadata:
+      Batch Size: 96
+      Training Memory (GB): 16.7
+      inference time (ms/im):
+        - value: 270.27
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 210
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/centripetalnet/centripetalnet_hourglass104_mstest_16x6_210e_coco/centripetalnet_hourglass104_mstest_16x6_210e_coco_20200915_204804-3ccc61e5.pth
diff --git a/configs/cityscapes/README.md b/configs/cityscapes/README.md
new file mode 100644
index 0000000..c52a79f
--- /dev/null
+++ b/configs/cityscapes/README.md
@@ -0,0 +1,46 @@
+# Cityscapes
+
+> [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://arxiv.org/abs/1604.01685)
+
+<!-- [DATASET] -->
+
+## Abstract
+
+Visual understanding of complex urban street scenes is an enabling factor for a wide range of applications. Object detection has benefited enormously from large-scale datasets, especially in the context of deep learning. For semantic urban scene understanding, however, no current dataset adequately captures the complexity of real-world urban scenes.
+To address this, we introduce Cityscapes, a benchmark suite and large-scale dataset to train and test approaches for pixel-level and instance-level semantic labeling. Cityscapes is comprised of a large, diverse set of stereo video sequences recorded in streets from 50 different cities. 5000 of these images have high quality pixel-level annotations; 20000 additional images have coarse annotations to enable methods that leverage large volumes of weakly-labeled data. Crucially, our effort exceeds previous attempts in terms of dataset size, annotation richness, scene variability, and complexity. Our accompanying empirical study provides an in-depth analysis of the dataset characteristics, as well as a performance evaluation of several state-of-the-art approaches based on our benchmark.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143874154-db4484a5-9211-41f6-852a-b7f0a8c9ec26.png"/>
+</div>
+
+## Common settings
+
+- All baselines were trained using 8 GPU with a batch size of 8 (1 images per GPU) using the [linear scaling rule](https://arxiv.org/abs/1706.02677) to scale the learning rate.
+- All models were trained on `cityscapes_train`, and tested on `cityscapes_val`.
+- 1x training schedule indicates 64 epochs which corresponds to slightly less than the 24k iterations reported in the original schedule from the [Mask R-CNN paper](https://arxiv.org/abs/1703.06870)
+- COCO pre-trained weights are used to initialize.
+- A conversion [script](../../tools/dataset_converters/cityscapes.py) is provided to convert Cityscapes into COCO format. Please refer to [install.md](../../docs/1_exist_data_model.md#prepare-datasets) for details.
+- `CityscapesDataset` implemented three evaluation methods. `bbox` and `segm` are standard COCO bbox/mask AP. `cityscapes` is the cityscapes dataset official evaluation, which may be slightly higher than COCO.
+
+### Faster R-CNN
+
+| Backbone |  Style  | Lr schd |  Scale   | Mem (GB) | Inf time (fps) | box AP |                                                         Config                                                          |                                                                                                                          Download                                                                                                                           |
+| :------: | :-----: | :-----: | :------: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R-50-FPN | pytorch |   1x    | 800-1024 |   5.2    |       -        |  40.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200502-829424c0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes_20200502_114915.log.json) |
+
+### Mask R-CNN
+
+| Backbone |  Style  | Lr schd |  Scale   | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                        Config                                                         |                                                                                                                                                            Download                                                                                                                                                            |
+| :------: | :-----: | :-----: | :------: | :------: | :------------: | :----: | :-----: | :-------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R-50-FPN | pytorch |   1x    | 800-1024 |   5.3    |       -        |  40.9  |  36.4   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes/mask_rcnn_r50_fpn_1x_cityscapes_20201211_133733-d2858245.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes/mask_rcnn_r50_fpn_1x_cityscapes_20201211_133733.log.json) |
+
+## Citation
+
+```latex
+@inproceedings{Cordts2016Cityscapes,
+   title={The Cityscapes Dataset for Semantic Urban Scene Understanding},
+   author={Cordts, Marius and Omran, Mohamed and Ramos, Sebastian and Rehfeld, Timo and Enzweiler, Markus and Benenson, Rodrigo and Franke, Uwe and Roth, Stefan and Schiele, Bernt},
+   booktitle={Proc. of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+   year={2016}
+}
+```
diff --git a/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py b/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py
new file mode 100644
index 0000000..ca636bd
--- /dev/null
+++ b/configs/cityscapes/faster_rcnn_r50_fpn_1x_cityscapes.py
@@ -0,0 +1,44 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/cityscapes_detection.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(
+    backbone=dict(init_cfg=None),
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=8,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))))
+# optimizer
+# lr is set for a batch size of 8
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    # [7] yields higher performance than [6]
+    step=[7])
+runner = dict(
+    type='EpochBasedRunner', max_epochs=8)  # actual epoch = 8 * 8 = 64
+log_config = dict(interval=100)
+# For better, more stable performance initialize from COCO
+load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'  # noqa
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (1 samples per GPU)
+auto_scale_lr = dict(base_batch_size=8)
diff --git a/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py b/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py
new file mode 100644
index 0000000..83ea058
--- /dev/null
+++ b/configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py
@@ -0,0 +1,51 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    backbone=dict(init_cfg=None),
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=8,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
+        mask_head=dict(
+            type='FCNMaskHead',
+            num_convs=4,
+            in_channels=256,
+            conv_out_channels=256,
+            num_classes=8,
+            loss_mask=dict(
+                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))))
+# optimizer
+# lr is set for a batch size of 8
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    # [7] yields higher performance than [6]
+    step=[7])
+runner = dict(
+    type='EpochBasedRunner', max_epochs=8)  # actual epoch = 8 * 8 = 64
+log_config = dict(interval=100)
+# For better, more stable performance initialize from COCO
+load_from = 'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth'  # noqa
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (1 samples per GPU)
+auto_scale_lr = dict(base_batch_size=8)
diff --git a/configs/common/lsj_100e_coco_instance.py b/configs/common/lsj_100e_coco_instance.py
new file mode 100644
index 0000000..cacf23d
--- /dev/null
+++ b/configs/common/lsj_100e_coco_instance.py
@@ -0,0 +1,90 @@
+_base_ = '../_base_/default_runtime.py'
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+image_size = (1024, 1024)
+
+file_client_args = dict(backend='disk')
+# comment out the code below to use different file client
+# file_client_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=image_size,
+        ratio_range=(0.1, 2.0),
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(
+        type='RandomCrop',
+        crop_type='absolute_range',
+        crop_size=image_size,
+        recompute_bbox=True,
+        allow_negative_crop=True),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=image_size),  # padding to image_size leads 0.5+ mAP
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+# Use RepeatDataset to speed up training
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='RepeatDataset',
+        times=4,  # simply change this from 2 to 16 for 50e - 400e training.
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=5, metric=['bbox', 'segm'])
+
+# optimizer assumes bs=64
+optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.00004)
+optimizer_config = dict(grad_clip=None)
+
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.067,
+    step=[22, 24])
+runner = dict(type='EpochBasedRunner', max_epochs=25)
diff --git a/configs/common/mstrain-poly_3x_coco_instance.py b/configs/common/mstrain-poly_3x_coco_instance.py
new file mode 100644
index 0000000..c22ed94
--- /dev/null
+++ b/configs/common/mstrain-poly_3x_coco_instance.py
@@ -0,0 +1,80 @@
+_base_ = '../_base_/default_runtime.py'
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
+# multiscale_mode='range'
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        poly2mask=False),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+# Use RepeatDataset to speed up training
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='RepeatDataset',
+        times=3,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric=['bbox', 'segm'])
+
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+
+# learning policy
+# Experiments show that using step=[9, 11] has higher performance
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[9, 11])
+runner = dict(type='EpochBasedRunner', max_epochs=12)
diff --git a/configs/common/mstrain_3x_coco.py b/configs/common/mstrain_3x_coco.py
new file mode 100644
index 0000000..80ec8b8
--- /dev/null
+++ b/configs/common/mstrain_3x_coco.py
@@ -0,0 +1,76 @@
+_base_ = '../_base_/default_runtime.py'
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
+# multiscale_mode='range'
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+# Use RepeatDataset to speed up training
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='RepeatDataset',
+        times=3,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')
+
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+
+# learning policy
+# Experiments show that using step=[9, 11] has higher performance
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[9, 11])
+runner = dict(type='EpochBasedRunner', max_epochs=12)
diff --git a/configs/common/mstrain_3x_coco_instance.py b/configs/common/mstrain_3x_coco_instance.py
new file mode 100644
index 0000000..50f39be
--- /dev/null
+++ b/configs/common/mstrain_3x_coco_instance.py
@@ -0,0 +1,76 @@
+_base_ = '../_base_/default_runtime.py'
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
+# multiscale_mode='range'
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+# Use RepeatDataset to speed up training
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='RepeatDataset',
+        times=3,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric=['bbox', 'segm'])
+
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+
+# learning policy
+# Experiments show that using step=[9, 11] has higher performance
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[9, 11])
+runner = dict(type='EpochBasedRunner', max_epochs=12)
diff --git a/configs/common/ssj_270k_coco_instance.py b/configs/common/ssj_270k_coco_instance.py
new file mode 100644
index 0000000..851098f
--- /dev/null
+++ b/configs/common/ssj_270k_coco_instance.py
@@ -0,0 +1,91 @@
+_base_ = '../_base_/default_runtime.py'
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+image_size = (1024, 1024)
+
+file_client_args = dict(backend='disk')
+
+# Standard Scale Jittering (SSJ) resizes and crops an image
+# with a resize range of 0.8 to 1.25 of the original image size.
+train_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=image_size,
+        ratio_range=(0.8, 1.25),
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(
+        type='RandomCrop',
+        crop_type='absolute_range',
+        crop_size=image_size,
+        recompute_bbox=True,
+        allow_negative_crop=True),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=image_size),  # padding to image_size leads 0.5+ mAP
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+
+evaluation = dict(interval=6000, metric=['bbox', 'segm'])
+
+# optimizer assumes batch_size = (32 GPUs) x (2 samples per GPU)
+optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.00004)
+optimizer_config = dict(grad_clip=None)
+
+# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=0.001,
+    step=[243000, 256500, 263250])
+checkpoint_config = dict(interval=6000)
+# The model is trained by 270k iterations with batch_size 64,
+# which is roughly equivalent to 144 epochs.
+runner = dict(type='IterBasedRunner', max_iters=270000)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (32 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/common/ssj_scp_270k_coco_instance.py b/configs/common/ssj_scp_270k_coco_instance.py
new file mode 100644
index 0000000..540839f
--- /dev/null
+++ b/configs/common/ssj_scp_270k_coco_instance.py
@@ -0,0 +1,97 @@
+_base_ = '../_base_/default_runtime.py'
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+image_size = (1024, 1024)
+
+file_client_args = dict(backend='disk')
+
+# Standard Scale Jittering (SSJ) resizes and crops an image
+# with a resize range of 0.8 to 1.25 of the original image size.
+load_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=image_size,
+        ratio_range=(0.8, 1.25),
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(
+        type='RandomCrop',
+        crop_type='absolute_range',
+        crop_size=image_size,
+        recompute_bbox=True,
+        allow_negative_crop=True),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Pad', size=image_size),
+]
+train_pipeline = [
+    dict(type='CopyPaste', max_num_pasted=100),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='MultiImageMixDataset',
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=load_pipeline),
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+
+evaluation = dict(interval=6000, metric=['bbox', 'segm'])
+
+# optimizer assumes batch_size = (32 GPUs) x (2 samples per GPU)
+optimizer = dict(type='SGD', lr=0.1, momentum=0.9, weight_decay=0.00004)
+optimizer_config = dict(grad_clip=None)
+
+# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=0.001,
+    step=[243000, 256500, 263250])
+checkpoint_config = dict(interval=6000)
+# The model is trained by 270k iterations with batch_size 64,
+# which is roughly equivalent to 144 epochs.
+runner = dict(type='IterBasedRunner', max_iters=270000)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (32 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/convnext/README.md b/configs/convnext/README.md
new file mode 100644
index 0000000..edf72e8
--- /dev/null
+++ b/configs/convnext/README.md
@@ -0,0 +1,40 @@
+# ConvNeXt
+
+> [A ConvNet for the 2020s](https://arxiv.org/abs/2201.03545)
+
+## Abstract
+
+The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model. A vanilla ViT, on the other hand, faces difficulties when applied to general computer vision tasks such as object detection and semantic segmentation. It is the hierarchical Transformers (e.g., Swin Transformers) that reintroduced several ConvNet priors, making Transformers practically viable as a generic vision backbone and demonstrating remarkable performance on a wide variety of vision tasks. However, the effectiveness of such hybrid approaches is still largely credited to the intrinsic superiority of Transformers, rather than the inherent inductive biases of convolutions. In this work, we reexamine the design spaces and test the limits of what a pure ConvNet can achieve. We gradually "modernize" a standard ResNet toward the design of a vision Transformer, and discover several key components that contribute to the performance difference along the way. The outcome of this exploration is a family of pure ConvNet models dubbed ConvNeXt. Constructed entirely from standard ConvNet modules, ConvNeXts compete favorably with Transformers in terms of accuracy and scalability, achieving 87.8% ImageNet top-1 accuracy and outperforming Swin Transformers on COCO detection and ADE20K segmentation, while maintaining the simplicity and efficiency of standard ConvNets.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/8370623/148624004-e9581042-ea4d-4e10-b3bd-42c92b02053b.png" width="90%"/>
+</div>
+
+## Results and models
+
+|       Method       |  Backbone  |  Pretrain   | Lr schd | Multi-scale crop | FP16 | Mem (GB) | box AP | mask AP |                                         Config                                          |                                                                                                                                                                                                                                            Download                                                                                                                                                                                                                                             |
+| :----------------: | :--------: | :---------: | :-----: | :--------------: | :--: | :------: | :----: | :-----: | :-------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|     Mask R-CNN     | ConvNeXt-T | ImageNet-1K |   3x    |       yes        | yes  |   7.3    |  46.2  |  41.7   |           [config](./mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco.py)            |                                           [model](https://download.openmmlab.com/mmdetection/v2.0/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco_20220426_154953-050731f4.pth)  \| [log](https://download.openmmlab.com/mmdetection/v2.0/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco_20220426_154953.log.json)                                           |
+| Cascade Mask R-CNN | ConvNeXt-T | ImageNet-1K |   3x    |       yes        | yes  |   9.0    |  50.3  |  43.6   | [config](./cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220509_204200-8f07c40b.pth)  \| [log](https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220509_204200.log.json) |
+| Cascade Mask R-CNN | ConvNeXt-S | ImageNet-1K |   3x    |       yes        | yes  |   12.3   |  51.8  |  44.8   | [config](./cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220510_201004-3d24f5a4.pth)  \| [log](https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220510_201004.log.json) |
+
+**Note**:
+
+- ConvNeXt backbone needs to install [MMClassification](https://github.com/open-mmlab/mmclassification) first, which has abundant backbones for downstream tasks.
+
+```shell
+pip install mmcls>=0.22.0
+```
+
+- The performance is unstable. `Cascade Mask R-CNN` may fluctuate about 0.2 mAP.
+
+## Citation
+
+```bibtex
+@article{liu2022convnet,
+  title={A ConvNet for the 2020s},
+  author={Liu, Zhuang and Mao, Hanzi and Wu, Chao-Yuan and Feichtenhofer, Christoph and Darrell, Trevor and Xie, Saining},
+  journal={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year={2022}
+}
+```
diff --git a/configs/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py b/configs/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
new file mode 100644
index 0000000..0ccc31d
--- /dev/null
+++ b/configs/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
@@ -0,0 +1,32 @@
+_base_ = './cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py'  # noqa
+
+# please install mmcls>=0.22.0
+# import mmcls.models to trigger register_module in mmcls
+custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
+checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-small_3rdparty_32xb128-noema_in1k_20220301-303e75e3.pth'  # noqa
+
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='mmcls.ConvNeXt',
+        arch='small',
+        out_indices=[0, 1, 2, 3],
+        drop_path_rate=0.6,
+        layer_scale_init_value=1.0,
+        gap_before_final_norm=False,
+        init_cfg=dict(
+            type='Pretrained', checkpoint=checkpoint_file,
+            prefix='backbone.')))
+
+optimizer = dict(
+    _delete_=True,
+    constructor='LearningRateDecayOptimizerConstructor',
+    type='AdamW',
+    lr=0.0002,
+    betas=(0.9, 0.999),
+    weight_decay=0.05,
+    paramwise_cfg={
+        'decay_rate': 0.7,
+        'decay_type': 'layer_wise',
+        'num_layers': 12
+    })
diff --git a/configs/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py b/configs/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
new file mode 100644
index 0000000..93304c0
--- /dev/null
+++ b/configs/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
@@ -0,0 +1,149 @@
+_base_ = [
+    '../_base_/models/cascade_mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# please install mmcls>=0.22.0
+# import mmcls.models to trigger register_module in mmcls
+custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
+checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth'  # noqa
+
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='mmcls.ConvNeXt',
+        arch='tiny',
+        out_indices=[0, 1, 2, 3],
+        drop_path_rate=0.4,
+        layer_scale_init_value=1.0,
+        gap_before_final_norm=False,
+        init_cfg=dict(
+            type='Pretrained', checkpoint=checkpoint_file,
+            prefix='backbone.')),
+    neck=dict(in_channels=[96, 192, 384, 768]),
+    roi_head=dict(bbox_head=[
+        dict(
+            type='ConvFCBBoxHead',
+            num_shared_convs=4,
+            num_shared_fcs=1,
+            in_channels=256,
+            conv_out_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            reg_decoded_bbox=True,
+            norm_cfg=dict(type='SyncBN', requires_grad=True),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
+        dict(
+            type='ConvFCBBoxHead',
+            num_shared_convs=4,
+            num_shared_fcs=1,
+            in_channels=256,
+            conv_out_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.05, 0.05, 0.1, 0.1]),
+            reg_class_agnostic=False,
+            reg_decoded_bbox=True,
+            norm_cfg=dict(type='SyncBN', requires_grad=True),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='GIoULoss', loss_weight=10.0)),
+        dict(
+            type='ConvFCBBoxHead',
+            num_shared_convs=4,
+            num_shared_fcs=1,
+            in_channels=256,
+            conv_out_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.033, 0.033, 0.067, 0.067]),
+            reg_class_agnostic=False,
+            reg_decoded_bbox=True,
+            norm_cfg=dict(type='SyncBN', requires_grad=True),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='GIoULoss', loss_weight=10.0))
+    ]))
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# augmentation strategy originates from DETR / Sparse RCNN
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='AutoAugment',
+        policies=[[
+            dict(
+                type='Resize',
+                img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                           (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                           (736, 1333), (768, 1333), (800, 1333)],
+                multiscale_mode='value',
+                keep_ratio=True)
+        ],
+                  [
+                      dict(
+                          type='Resize',
+                          img_scale=[(400, 1333), (500, 1333), (600, 1333)],
+                          multiscale_mode='value',
+                          keep_ratio=True),
+                      dict(
+                          type='RandomCrop',
+                          crop_type='absolute_range',
+                          crop_size=(384, 600),
+                          allow_negative_crop=True),
+                      dict(
+                          type='Resize',
+                          img_scale=[(480, 1333), (512, 1333), (544, 1333),
+                                     (576, 1333), (608, 1333), (640, 1333),
+                                     (672, 1333), (704, 1333), (736, 1333),
+                                     (768, 1333), (800, 1333)],
+                          multiscale_mode='value',
+                          override=True,
+                          keep_ratio=True)
+                  ]]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+data = dict(train=dict(pipeline=train_pipeline), persistent_workers=True)
+
+optimizer = dict(
+    _delete_=True,
+    constructor='LearningRateDecayOptimizerConstructor',
+    type='AdamW',
+    lr=0.0002,
+    betas=(0.9, 0.999),
+    weight_decay=0.05,
+    paramwise_cfg={
+        'decay_rate': 0.7,
+        'decay_type': 'layer_wise',
+        'num_layers': 6
+    })
+
+lr_config = dict(warmup_iters=1000, step=[27, 33])
+runner = dict(max_epochs=36)
+
+# you need to set mode='dynamic' if you are using pytorch<=1.5.0
+fp16 = dict(loss_scale=dict(init_scale=512))
diff --git a/configs/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco.py b/configs/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco.py
new file mode 100644
index 0000000..e8a283f
--- /dev/null
+++ b/configs/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco.py
@@ -0,0 +1,90 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# please install mmcls>=0.22.0
+# import mmcls.models to trigger register_module in mmcls
+custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
+checkpoint_file = 'https://download.openmmlab.com/mmclassification/v0/convnext/downstream/convnext-tiny_3rdparty_32xb128-noema_in1k_20220301-795e9634.pth'  # noqa
+
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='mmcls.ConvNeXt',
+        arch='tiny',
+        out_indices=[0, 1, 2, 3],
+        drop_path_rate=0.4,
+        layer_scale_init_value=1.0,
+        gap_before_final_norm=False,
+        init_cfg=dict(
+            type='Pretrained', checkpoint=checkpoint_file,
+            prefix='backbone.')),
+    neck=dict(in_channels=[96, 192, 384, 768]))
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# augmentation strategy originates from DETR / Sparse RCNN
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='AutoAugment',
+        policies=[[
+            dict(
+                type='Resize',
+                img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                           (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                           (736, 1333), (768, 1333), (800, 1333)],
+                multiscale_mode='value',
+                keep_ratio=True)
+        ],
+                  [
+                      dict(
+                          type='Resize',
+                          img_scale=[(400, 1333), (500, 1333), (600, 1333)],
+                          multiscale_mode='value',
+                          keep_ratio=True),
+                      dict(
+                          type='RandomCrop',
+                          crop_type='absolute_range',
+                          crop_size=(384, 600),
+                          allow_negative_crop=True),
+                      dict(
+                          type='Resize',
+                          img_scale=[(480, 1333), (512, 1333), (544, 1333),
+                                     (576, 1333), (608, 1333), (640, 1333),
+                                     (672, 1333), (704, 1333), (736, 1333),
+                                     (768, 1333), (800, 1333)],
+                          multiscale_mode='value',
+                          override=True,
+                          keep_ratio=True)
+                  ]]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+data = dict(train=dict(pipeline=train_pipeline), persistent_workers=True)
+
+optimizer = dict(
+    _delete_=True,
+    constructor='LearningRateDecayOptimizerConstructor',
+    type='AdamW',
+    lr=0.0001,
+    betas=(0.9, 0.999),
+    weight_decay=0.05,
+    paramwise_cfg={
+        'decay_rate': 0.95,
+        'decay_type': 'layer_wise',
+        'num_layers': 6
+    })
+
+lr_config = dict(warmup_iters=1000, step=[27, 33])
+runner = dict(max_epochs=36)
+
+# you need to set mode='dynamic' if you are using pytorch<=1.5.0
+fp16 = dict(loss_scale=dict(init_scale=512))
diff --git a/configs/convnext/metafile.yml b/configs/convnext/metafile.yml
new file mode 100644
index 0000000..84e50e8
--- /dev/null
+++ b/configs/convnext/metafile.yml
@@ -0,0 +1,93 @@
+Models:
+  - Name: mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco.py
+    Metadata:
+      Training Memory (GB): 7.3
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - AdamW
+        - Mixed Precision Training
+      Training Resources: 8x A100 GPUs
+      Architecture:
+        - ConvNeXt
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco_20220426_154953-050731f4.pth
+    Paper:
+      URL: https://arxiv.org/abs/2201.03545
+      Title: 'A ConvNet for the 2020s'
+    README: configs/convnext/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/backbones/swin.py#L465
+      Version: v2.16.0
+
+  - Name: cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
+    Metadata:
+      Training Memory (GB): 9.0
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - AdamW
+        - Mixed Precision Training
+      Training Resources: 8x A100 GPUs
+      Architecture:
+        - ConvNeXt
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 50.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 43.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220509_204200-8f07c40b.pth
+    Paper:
+      URL: https://arxiv.org/abs/2201.03545
+      Title: 'A ConvNet for the 2020s'
+    README: configs/convnext/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/backbones/swin.py#L465
+      Version: v2.25.0
+
+  - Name: cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco
+    In Collection: Cascade Mask R-CNN
+    Config: configs/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
+    Metadata:
+      Training Memory (GB): 12.3
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - AdamW
+        - Mixed Precision Training
+      Training Resources: 8x A100 GPUs
+      Architecture:
+        - ConvNeXt
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 51.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 44.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/convnext/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco/cascade_mask_rcnn_convnext-s_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco_20220510_201004-3d24f5a4.pth
+    Paper:
+      URL: https://arxiv.org/abs/2201.03545
+      Title: 'A ConvNet for the 2020s'
+    README: configs/convnext/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/backbones/swin.py#L465
+      Version: v2.25.0
diff --git a/configs/cornernet/README.md b/configs/cornernet/README.md
new file mode 100644
index 0000000..d0b9e98
--- /dev/null
+++ b/configs/cornernet/README.md
@@ -0,0 +1,43 @@
+# CornerNet
+
+> [Cornernet: Detecting objects as paired keypoints](https://arxiv.org/abs/1808.01244)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We propose CornerNet, a new approach to object detection where we detect an object bounding box as a pair of keypoints, the top-left corner and the bottom-right corner, using a single convolution neural network. By detecting objects as paired keypoints, we eliminate the need for designing a set of anchor boxes commonly used in prior single-stage detectors. In addition to our novel formulation, we introduce corner pooling, a new type of pooling layer that helps the network better localize corners. Experiments show that CornerNet achieves a 42.2% AP on MS COCO, outperforming all existing one-stage detectors.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143876061-4de20768-c812-4b97-b089-944d8db91ca2.png"/>
+</div>
+
+## Results and Models
+
+|     Backbone     |                         Batch Size                          | Step/Total Epochs | Mem (GB) | Inf time (fps) | box AP |                                                              Config                                                               |                                                                                                                                                                                     Download                                                                                                                                                                                     |
+| :--------------: | :---------------------------------------------------------: | :---------------: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| HourglassNet-104 | [10 x 5](./cornernet_hourglass104_mstest_10x5_210e_coco.py) |      180/210      |   13.9   |      4.2       |  41.2  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720.log.json) |
+| HourglassNet-104 |  [8 x 6](./cornernet_hourglass104_mstest_8x6_210e_coco.py)  |      180/210      |   15.9   |      4.2       |  41.2  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618.log.json)   |
+| HourglassNet-104 | [32 x 3](./cornernet_hourglass104_mstest_32x3_210e_coco.py) |      180/210      |   9.5    |      3.9       |  40.4  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110-1efaea91.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110.log.json) |
+
+Note:
+
+- TTA setting is single-scale and `flip=True`.
+- Experiments with `images_per_gpu=6` are conducted on Tesla V100-SXM2-32GB, `images_per_gpu=3` are conducted on GeForce GTX 1080 Ti.
+- Here are the descriptions of each experiment setting:
+  - 10 x 5: 10 GPUs with 5 images per gpu. This is the same setting as that reported in the original paper.
+  - 8 x 6: 8 GPUs with 6 images per gpu. The total batchsize is similar to paper and only need 1 node to train.
+  - 32 x 3: 32 GPUs with 3 images per gpu. The default setting for 1080TI and need 4 nodes to train.
+
+## Citation
+
+```latex
+@inproceedings{law2018cornernet,
+  title={Cornernet: Detecting objects as paired keypoints},
+  author={Law, Hei and Deng, Jia},
+  booktitle={15th European Conference on Computer Vision, ECCV 2018},
+  pages={765--781},
+  year={2018},
+  organization={Springer Verlag}
+}
+```
diff --git a/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py b/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py
new file mode 100644
index 0000000..6cb05a7
--- /dev/null
+++ b/configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py
@@ -0,0 +1,110 @@
+_base_ = [
+    '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
+]
+
+# model settings
+model = dict(
+    type='CornerNet',
+    backbone=dict(
+        type='HourglassNet',
+        downsample_times=5,
+        num_stacks=2,
+        stage_channels=[256, 256, 384, 384, 384, 512],
+        stage_blocks=[2, 2, 2, 2, 2, 4],
+        norm_cfg=dict(type='BN', requires_grad=True)),
+    neck=None,
+    bbox_head=dict(
+        type='CornerHead',
+        num_classes=80,
+        in_channels=256,
+        num_feat_levels=2,
+        corner_emb_channels=1,
+        loss_heatmap=dict(
+            type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1),
+        loss_embedding=dict(
+            type='AssociativeEmbeddingLoss',
+            pull_weight=0.10,
+            push_weight=0.10),
+        loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)),
+    # training and testing settings
+    train_cfg=None,
+    test_cfg=dict(
+        corner_topk=100,
+        local_maximum_kernel=3,
+        distance_threshold=0.5,
+        score_thr=0.05,
+        max_per_img=100,
+        nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian')))
+# data settings
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(
+        type='RandomCenterCropPad',
+        crop_size=(511, 511),
+        ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
+        test_mode=False,
+        test_pad_mode=None,
+        **img_norm_cfg),
+    dict(type='Resize', img_scale=(511, 511), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(
+        type='MultiScaleFlipAug',
+        scale_factor=1.0,
+        flip=True,
+        transforms=[
+            dict(type='Resize'),
+            dict(
+                type='RandomCenterCropPad',
+                crop_size=None,
+                ratios=None,
+                border=None,
+                test_mode=True,
+                test_pad_mode=['logical_or', 127],
+                **img_norm_cfg),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(
+                type='Collect',
+                keys=['img'],
+                meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape',
+                           'scale_factor', 'flip', 'img_norm_cfg', 'border')),
+        ])
+]
+data = dict(
+    samples_per_gpu=5,
+    workers_per_gpu=3,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='Adam', lr=0.0005)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    step=[180])
+runner = dict(type='EpochBasedRunner', max_epochs=210)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (10 GPUs) x (5 samples per GPU)
+auto_scale_lr = dict(base_batch_size=50)
diff --git a/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py b/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py
new file mode 100644
index 0000000..f539cdb
--- /dev/null
+++ b/configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py
@@ -0,0 +1,110 @@
+_base_ = [
+    '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
+]
+
+# model settings
+model = dict(
+    type='CornerNet',
+    backbone=dict(
+        type='HourglassNet',
+        downsample_times=5,
+        num_stacks=2,
+        stage_channels=[256, 256, 384, 384, 384, 512],
+        stage_blocks=[2, 2, 2, 2, 2, 4],
+        norm_cfg=dict(type='BN', requires_grad=True)),
+    neck=None,
+    bbox_head=dict(
+        type='CornerHead',
+        num_classes=80,
+        in_channels=256,
+        num_feat_levels=2,
+        corner_emb_channels=1,
+        loss_heatmap=dict(
+            type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1),
+        loss_embedding=dict(
+            type='AssociativeEmbeddingLoss',
+            pull_weight=0.10,
+            push_weight=0.10),
+        loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)),
+    # training and testing settings
+    train_cfg=None,
+    test_cfg=dict(
+        corner_topk=100,
+        local_maximum_kernel=3,
+        distance_threshold=0.5,
+        score_thr=0.05,
+        max_per_img=100,
+        nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian')))
+# data settings
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(
+        type='RandomCenterCropPad',
+        crop_size=(511, 511),
+        ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
+        test_mode=False,
+        test_pad_mode=None,
+        **img_norm_cfg),
+    dict(type='Resize', img_scale=(511, 511), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(
+        type='MultiScaleFlipAug',
+        scale_factor=1.0,
+        flip=True,
+        transforms=[
+            dict(type='Resize'),
+            dict(
+                type='RandomCenterCropPad',
+                crop_size=None,
+                ratios=None,
+                border=None,
+                test_mode=True,
+                test_pad_mode=['logical_or', 127],
+                **img_norm_cfg),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(
+                type='Collect',
+                keys=['img'],
+                meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape',
+                           'scale_factor', 'flip', 'img_norm_cfg', 'border')),
+        ])
+]
+data = dict(
+    samples_per_gpu=3,
+    workers_per_gpu=3,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='Adam', lr=0.0005)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    step=[180])
+runner = dict(type='EpochBasedRunner', max_epochs=210)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (32 GPUs) x (3 samples per GPU)
+auto_scale_lr = dict(base_batch_size=96)
diff --git a/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py b/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py
new file mode 100644
index 0000000..9b115d7
--- /dev/null
+++ b/configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py
@@ -0,0 +1,110 @@
+_base_ = [
+    '../_base_/default_runtime.py', '../_base_/datasets/coco_detection.py'
+]
+
+# model settings
+model = dict(
+    type='CornerNet',
+    backbone=dict(
+        type='HourglassNet',
+        downsample_times=5,
+        num_stacks=2,
+        stage_channels=[256, 256, 384, 384, 384, 512],
+        stage_blocks=[2, 2, 2, 2, 2, 4],
+        norm_cfg=dict(type='BN', requires_grad=True)),
+    neck=None,
+    bbox_head=dict(
+        type='CornerHead',
+        num_classes=80,
+        in_channels=256,
+        num_feat_levels=2,
+        corner_emb_channels=1,
+        loss_heatmap=dict(
+            type='GaussianFocalLoss', alpha=2.0, gamma=4.0, loss_weight=1),
+        loss_embedding=dict(
+            type='AssociativeEmbeddingLoss',
+            pull_weight=0.10,
+            push_weight=0.10),
+        loss_offset=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1)),
+    # training and testing settings
+    train_cfg=None,
+    test_cfg=dict(
+        corner_topk=100,
+        local_maximum_kernel=3,
+        distance_threshold=0.5,
+        score_thr=0.05,
+        max_per_img=100,
+        nms=dict(type='soft_nms', iou_threshold=0.5, method='gaussian')))
+# data settings
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(
+        type='RandomCenterCropPad',
+        crop_size=(511, 511),
+        ratios=(0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 1.2, 1.3),
+        test_mode=False,
+        test_pad_mode=None,
+        **img_norm_cfg),
+    dict(type='Resize', img_scale=(511, 511), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(
+        type='MultiScaleFlipAug',
+        scale_factor=1.0,
+        flip=True,
+        transforms=[
+            dict(type='Resize'),
+            dict(
+                type='RandomCenterCropPad',
+                crop_size=None,
+                ratios=None,
+                border=None,
+                test_mode=True,
+                test_pad_mode=['logical_or', 127],
+                **img_norm_cfg),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(
+                type='Collect',
+                keys=['img'],
+                meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape',
+                           'scale_factor', 'flip', 'img_norm_cfg', 'border')),
+        ])
+]
+data = dict(
+    samples_per_gpu=6,
+    workers_per_gpu=3,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='Adam', lr=0.0005)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    step=[180])
+runner = dict(type='EpochBasedRunner', max_epochs=210)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (6 samples per GPU)
+auto_scale_lr = dict(base_batch_size=48)
diff --git a/configs/cornernet/metafile.yml b/configs/cornernet/metafile.yml
new file mode 100644
index 0000000..c2f6143
--- /dev/null
+++ b/configs/cornernet/metafile.yml
@@ -0,0 +1,83 @@
+Collections:
+  - Name: CornerNet
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - Adam
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Corner Pooling
+        - Stacked Hourglass Network
+    Paper:
+      URL: https://arxiv.org/abs/1808.01244
+      Title: 'CornerNet: Detecting Objects as Paired Keypoints'
+    README: configs/cornernet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.3.0/mmdet/models/detectors/cornernet.py#L9
+      Version: v2.3.0
+
+Models:
+  - Name: cornernet_hourglass104_mstest_10x5_210e_coco
+    In Collection: CornerNet
+    Config: configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py
+    Metadata:
+      Training Resources: 10x V100 GPUs
+      Batch Size: 50
+      Training Memory (GB): 13.9
+      inference time (ms/im):
+        - value: 238.1
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 210
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco/cornernet_hourglass104_mstest_10x5_210e_coco_20200824_185720-5fefbf1c.pth
+
+  - Name: cornernet_hourglass104_mstest_8x6_210e_coco
+    In Collection: CornerNet
+    Config: configs/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco.py
+    Metadata:
+      Batch Size: 48
+      Training Memory (GB): 15.9
+      inference time (ms/im):
+        - value: 238.1
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 210
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_8x6_210e_coco/cornernet_hourglass104_mstest_8x6_210e_coco_20200825_150618-79b44c30.pth
+
+  - Name: cornernet_hourglass104_mstest_32x3_210e_coco
+    In Collection: CornerNet
+    Config: configs/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco.py
+    Metadata:
+      Training Resources: 32x V100 GPUs
+      Batch Size: 96
+      Training Memory (GB): 9.5
+      inference time (ms/im):
+        - value: 256.41
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 210
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/cornernet/cornernet_hourglass104_mstest_32x3_210e_coco/cornernet_hourglass104_mstest_32x3_210e_coco_20200819_203110-1efaea91.pth
diff --git a/configs/dcn/README.md b/configs/dcn/README.md
new file mode 100644
index 0000000..745b01c
--- /dev/null
+++ b/configs/dcn/README.md
@@ -0,0 +1,48 @@
+# DCN
+
+> [Deformable Convolutional Networks](https://arxiv.org/abs/1703.06211)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Convolutional neural networks (CNNs) are inherently limited to model geometric transformations due to the fixed geometric structures in its building modules. In this work, we introduce two new modules to enhance the transformation modeling capacity of CNNs, namely, deformable convolution and deformable RoI pooling. Both are based on the idea of augmenting the spatial sampling locations in the modules with additional offsets and learning the offsets from target tasks, without additional supervision. The new modules can readily replace their plain counterparts in existing CNNs and can be easily trained end-to-end by standard back-propagation, giving rise to deformable convolutional networks. Extensive experiments validate the effectiveness of our approach on sophisticated vision tasks of object detection and semantic segmentation.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143876246-c4985e25-e286-4511-9b7c-97af2857461e.png"/>
+</div>
+
+## Results and Models
+
+|    Backbone     |    Model     |  Style  |     Conv     | Pool  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                               Config                                                                |                                                                                                                                                                                       Download                                                                                                                                                                                       |
+| :-------------: | :----------: | :-----: | :----------: | :---: | :-----: | :------: | :------------: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50-FPN     |    Faster    | pytorch | dconv(c3-c5) |   -   |   1x    |   4.0    |      17.8      |  41.3  |         |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py)        |                      [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130_212941.log.json)                       |
+|    R-50-FPN     |    Faster    | pytorch |      -       | dpool |   1x    |   5.0    |      17.2      |  38.9  |         |          [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py)           |                                  [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307-90d3c01d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307_203250.log.json)                                   |
+|    R-101-FPN    |    Faster    | pytorch | dconv(c3-c5) |   -   |   1x    |   6.0    |      12.5      |  42.7  |         |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py)       |                    [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-1377f13d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203_230019.log.json)                     |
+| X-101-32x4d-FPN |    Faster    | pytorch | dconv(c3-c5) |   -   |   1x    |   7.3    |      10.0      |  44.5  |         |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py)    |        [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203-4f85c69c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203_001325.log.json)         |
+|    R-50-FPN     |     Mask     | pytorch | dconv(c3-c5) |   -   |   1x    |   4.5    |      15.4      |  41.8  |  37.4   |        [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py)         |                          [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203-4d9ad43b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203_061339.log.json)                           |
+|    R-101-FPN    |     Mask     | pytorch | dconv(c3-c5) |   -   |   1x    |   6.5    |      11.7      |  43.5  |  38.9   |        [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py)        |                        [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216-a71f5bce.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216_191601.log.json)                         |
+|    R-50-FPN     |   Cascade    | pytorch | dconv(c3-c5) |   -   |   1x    |   4.5    |      14.6      |  43.8  |         |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py)       |                    [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-2f1fca44.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130_220843.log.json)                     |
+|    R-101-FPN    |   Cascade    | pytorch | dconv(c3-c5) |   -   |   1x    |   6.4    |      11.0      |  45.0  |         |      [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py)       |                  [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-3b2f0594.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203_224829.log.json)                   |
+|    R-50-FPN     | Cascade Mask | pytorch | dconv(c3-c5) |   -   |   1x    |   6.0    |      10.0      |  44.4  |  38.6   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py)     |          [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202-42e767a2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202_010309.log.json)           |
+|    R-101-FPN    | Cascade Mask | pytorch | dconv(c3-c5) |   -   |   1x    |   8.0    |      8.6       |  45.8  |  39.7   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py)    |        [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204-df0c5f10.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204_134006.log.json)         |
+| X-101-32x4d-FPN | Cascade Mask | pytorch | dconv(c3-c5) |   -   |   1x    |   9.2    |                |  47.3  |  41.1   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-e75f90c8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-20200606_183737.log.json) |
+| R-50-FPN (FP16) |     Mask     | pytorch | dconv(c3-c5) |   -   |   1x    |   3.0    |                |  41.9  |  37.5   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py)      |            [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco_20210520_180247-c06429d2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco_20210520_180247.log.json)            |
+
+**Notes:**
+
+- `dconv` denotes deformable convolution, `c3-c5` means adding dconv in resnet stage 3 to 5. `dpool` denotes deformable roi pooling.
+- The dcn ops are modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch, which should be more memory efficient and slightly faster.
+- (\*) For R-50-FPN (dg=4), dg is short for deformable_group. This model is trained and tested on Amazon EC2 p3dn.24xlarge instance.
+- **Memory, Train/Inf time is outdated.**
+
+## Citation
+
+```latex
+@inproceedings{dai2017deformable,
+  title={Deformable Convolutional Networks},
+  author={Dai, Jifeng and Qi, Haozhi and Xiong, Yuwen and Li, Yi and Zhang, Guodong and Hu, Han and Wei, Yichen},
+  booktitle={Proceedings of the IEEE international conference on computer vision},
+  year={2017}
+}
+```
diff --git a/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..081b998
--- /dev/null
+++ b/configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = '../cascade_rcnn/cascade_mask_rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
diff --git a/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..3b3683a
--- /dev/null
+++ b/configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
diff --git a/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..daaa472
--- /dev/null
+++ b/configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
diff --git a/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..a01df33
--- /dev/null
+++ b/configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = '../cascade_rcnn/cascade_rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
diff --git a/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..aa664bd
--- /dev/null
+++ b/configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
diff --git a/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..f5fee7e
--- /dev/null
+++ b/configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = '../faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
diff --git a/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..8787088
--- /dev/null
+++ b/configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
diff --git a/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py b/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py
new file mode 100644
index 0000000..1b695f0
--- /dev/null
+++ b/configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py
@@ -0,0 +1,12 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(
+                _delete_=True,
+                type='DeformRoIPoolPack',
+                output_size=7,
+                output_channels=256),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32])))
diff --git a/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..e3bea19
--- /dev/null
+++ b/configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
@@ -0,0 +1,16 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..cb34002
--- /dev/null
+++ b/configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
diff --git a/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py b/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..ababe58
--- /dev/null
+++ b/configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
diff --git a/configs/dcn/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py b/configs/dcn/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..ee5cca7
--- /dev/null
+++ b/configs/dcn/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
+
+fp16 = dict(loss_scale=512.)
diff --git a/configs/dcn/metafile.yml b/configs/dcn/metafile.yml
new file mode 100644
index 0000000..36f3887
--- /dev/null
+++ b/configs/dcn/metafile.yml
@@ -0,0 +1,272 @@
+Collections:
+  - Name: Deformable Convolutional Networks
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Deformable Convolution
+    Paper:
+      URL: https://arxiv.org/abs/1703.06211
+      Title: "Deformable Convolutional Networks"
+    README: configs/dcn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/dcn/deform_conv.py#L15
+      Version: v2.0.0
+
+Models:
+  - Name: faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco
+    In Collection: Deformable Convolutional Networks
+    Config: configs/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.0
+      inference time (ms/im):
+        - value: 56.18
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-d68aed1e.pth
+
+  - Name: faster_rcnn_r50_fpn_dpool_1x_coco
+    In Collection: Deformable Convolutional Networks
+    Config: configs/dcn/faster_rcnn_r50_fpn_dpool_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.0
+      inference time (ms/im):
+        - value: 58.14
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_dpool_1x_coco/faster_rcnn_r50_fpn_dpool_1x_coco_20200307-90d3c01d.pth
+
+  - Name: faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco
+    In Collection: Deformable Convolutional Networks
+    Config: configs/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.0
+      inference time (ms/im):
+        - value: 80
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco/faster_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-1377f13d.pth
+
+  - Name: faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco
+    In Collection: Deformable Convolutional Networks
+    Config: configs/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.3
+      inference time (ms/im):
+        - value: 100
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/faster_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco_20200203-4f85c69c.pth
+
+  - Name: mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco
+    In Collection: Deformable Convolutional Networks
+    Config: configs/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.5
+      inference time (ms/im):
+        - value: 64.94
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200203-4d9ad43b.pth
+
+  - Name: mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco
+    In Collection: Deformable Convolutional Networks
+    Config: configs/dcn/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+        - Mixed Precision Training
+      Training Memory (GB): 3.0
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_dconv_c3-c5_1x_coco_20210520_180247-c06429d2.pth
+
+  - Name: mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco
+    In Collection: Deformable Convolutional Networks
+    Config: configs/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.5
+      inference time (ms/im):
+        - value: 85.47
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200216-a71f5bce.pth
+
+  - Name: cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco
+    In Collection: Deformable Convolutional Networks
+    Config: configs/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.5
+      inference time (ms/im):
+        - value: 68.49
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200130-2f1fca44.pth
+
+  - Name: cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco
+    In Collection: Deformable Convolutional Networks
+    Config: configs/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.4
+      inference time (ms/im):
+        - value: 90.91
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200203-3b2f0594.pth
+
+  - Name: cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco
+    In Collection: Deformable Convolutional Networks
+    Config: configs/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.0
+      inference time (ms/im):
+        - value: 100
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r50_fpn_dconv_c3-c5_1x_coco_20200202-42e767a2.pth
+
+  - Name: cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco
+    In Collection: Deformable Convolutional Networks
+    Config: configs/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.0
+      inference time (ms/im):
+        - value: 116.28
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_r101_fpn_dconv_c3-c5_1x_coco_20200204-df0c5f10.pth
+
+  - Name: cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco
+    In Collection: Deformable Convolutional Networks
+    Config: configs/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 9.2
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco-e75f90c8.pth
diff --git a/configs/dcnv2/README.md b/configs/dcnv2/README.md
new file mode 100644
index 0000000..d230f20
--- /dev/null
+++ b/configs/dcnv2/README.md
@@ -0,0 +1,37 @@
+# DCNv2
+
+> [Deformable ConvNets v2: More Deformable, Better Results](https://arxiv.org/abs/1811.11168)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+The superior performance of Deformable Convolutional Networks arises from its ability to adapt to the geometric variations of objects. Through an examination of its adaptive behavior, we observe that while the spatial support for its neural features conforms more closely than regular ConvNets to object structure, this support may nevertheless extend well beyond the region of interest, causing features to be influenced by irrelevant image content. To address this problem, we present a reformulation of Deformable ConvNets that improves its ability to focus on pertinent image regions, through increased modeling power and stronger training. The modeling power is enhanced through a more comprehensive integration of deformable convolution within the network, and by introducing a modulation mechanism that expands the scope of deformation modeling. To effectively harness this enriched modeling capability, we guide network training via a proposed feature mimicking scheme that helps the network to learn features that reflect the object focus and classification power of RCNN features. With the proposed contributions, this new version of Deformable ConvNets yields significant performance gains over the original model and produces leading results on the COCO benchmark for object detection and instance segmentation.
+
+## Results and Models
+
+|     Backbone      | Model  |  Style  |     Conv      |  Pool  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                              Config                                                              |                                                                                                                                                                                 Download                                                                                                                                                                                  |
+| :---------------: | :----: | :-----: | :-----------: | :----: | :-----: | :------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|     R-50-FPN      | Faster | pytorch | mdconv(c3-c5) |   -    |   1x    |   4.1    |      17.6      |  41.4  |         |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130-d099253b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130_222144.log.json)               |
+| \*R-50-FPN (dg=4) | Faster | pytorch | mdconv(c3-c5) |   -    |   1x    |   4.2    |      17.4      |  41.5  |         | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130-01262257.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130_222058.log.json) |
+|     R-50-FPN      | Faster | pytorch |       -       | mdpool |   1x    |   5.8    |      16.6      |  38.7  |         |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcnv2/faster_rcnn_r50_fpn_mdpool_1x_coco.py)        |                           [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307_203304.log.json)                           |
+|     R-50-FPN      |  Mask  | pytorch | mdconv(c3-c5) |   -    |   1x    |   4.5    |      15.1      |  41.5  |  37.1   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dcnv2/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py)      |                   [model](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203-ad97591f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203_063443.log.json)                   |
+|  R-50-FPN (FP16)  |  Mask  | pytorch | mdconv(c3-c5) |   -    |   1x    |   3.1    |                |  42.0  |  37.6   |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py)    |    [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco_20210520_180434-cf8fefa5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco_20210520_180434.log.json)     |
+
+**Notes:**
+
+- `mdconv` denotes modulated deformable convolution, `c3-c5` means adding dconv in resnet stage 3 to 5. `mdpool` denotes modulated deformable roi pooling.
+- The dcn ops are modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch, which should be more memory efficient and slightly faster.
+- (\*) For R-50-FPN (dg=4), dg is short for deformable_group. This model is trained and tested on Amazon EC2 p3dn.24xlarge instance.
+- **Memory, Train/Inf time is outdated.**
+
+## Citation
+
+```latex
+@article{zhu2018deformable,
+  title={Deformable ConvNets v2: More Deformable, Better Results},
+  author={Zhu, Xizhou and Hu, Han and Lin, Stephen and Dai, Jifeng},
+  journal={arXiv preprint arXiv:1811.11168},
+  year={2018}
+}
+```
diff --git a/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py b/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..d1bcf3c
--- /dev/null
+++ b/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
diff --git a/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py b/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py
new file mode 100644
index 0000000..d0ab89c
--- /dev/null
+++ b/configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCNv2', deform_groups=4, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
diff --git a/configs/dcnv2/faster_rcnn_r50_fpn_mdpool_1x_coco.py b/configs/dcnv2/faster_rcnn_r50_fpn_mdpool_1x_coco.py
new file mode 100644
index 0000000..ad7b034
--- /dev/null
+++ b/configs/dcnv2/faster_rcnn_r50_fpn_mdpool_1x_coco.py
@@ -0,0 +1,12 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(
+                _delete_=True,
+                type='ModulatedDeformRoIPoolPack',
+                output_size=7,
+                output_channels=256),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32])))
diff --git a/configs/dcnv2/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py b/configs/dcnv2/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..7e21454
--- /dev/null
+++ b/configs/dcnv2/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
+
+fp16 = dict(loss_scale=512.)
diff --git a/configs/dcnv2/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py b/configs/dcnv2/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..5ca2a67
--- /dev/null
+++ b/configs/dcnv2/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
diff --git a/configs/dcnv2/metafile.yml b/configs/dcnv2/metafile.yml
new file mode 100644
index 0000000..f6d5381
--- /dev/null
+++ b/configs/dcnv2/metafile.yml
@@ -0,0 +1,123 @@
+Collections:
+  - Name: Deformable Convolutional Networks v2
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Deformable Convolution
+    Paper:
+      URL: https://arxiv.org/abs/1811.11168
+      Title: "Deformable ConvNets v2: More Deformable, Better Results"
+    README: configs/dcnv2/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/dcn/deform_conv.py#L15
+      Version: v2.0.0
+
+Models:
+  - Name: faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco
+    In Collection: Deformable Convolutional Networks v2
+    Config: configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.1
+      inference time (ms/im):
+        - value: 56.82
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200130-d099253b.pth
+
+  - Name: faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco
+    In Collection: Deformable Convolutional Networks v2
+    Config: configs/dcnv2/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.2
+      inference time (ms/im):
+        - value: 57.47
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco/faster_rcnn_r50_fpn_mdconv_c3-c5_group4_1x_coco_20200130-01262257.pth
+
+  - Name: faster_rcnn_r50_fpn_mdpool_1x_coco
+    In Collection: Deformable Convolutional Networks v2
+    Config: configs/dcnv2/faster_rcnn_r50_fpn_mdpool_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.8
+      inference time (ms/im):
+        - value: 60.24
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/faster_rcnn_r50_fpn_mdpool_1x_coco/faster_rcnn_r50_fpn_mdpool_1x_coco_20200307-c0df27ff.pth
+
+  - Name: mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco
+    In Collection: Deformable Convolutional Networks v2
+    Config: configs/dcnv2/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.5
+      inference time (ms/im):
+        - value: 66.23
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dcn/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_mdconv_c3-c5_1x_coco_20200203-ad97591f.pth
+
+  - Name: mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco
+    In Collection: Deformable Convolutional Networks v2
+    Config: configs/dcnv2/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.1
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+        - Mixed Precision Training
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco/mask_rcnn_r50_fpn_fp16_mdconv_c3-c5_1x_coco_20210520_180434-cf8fefa5.pth
diff --git a/configs/ddod/README.md b/configs/ddod/README.md
new file mode 100644
index 0000000..9ab1f48
--- /dev/null
+++ b/configs/ddod/README.md
@@ -0,0 +1,31 @@
+# DDOD
+
+> [Disentangle Your Dense Object Detector](https://arxiv.org/pdf/2107.02963.pdf)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Deep learning-based dense object detectors have achieved great success in the past few years and have been applied to numerous multimedia applications such as video understanding. However, the current training pipeline for dense detectors is compromised to lots of conjunctions that may not hold. In this paper, we investigate three such important conjunctions: 1) only samples assigned as positive in classification head are used to train the regression head; 2) classification and regression share the same input feature and computational fields defined by the parallel head architecture; and 3) samples distributed in different feature pyramid layers are treated equally when computing the loss. We first carry out a series of pilot experiments to show disentangling such conjunctions can lead to persistent performance improvement. Then, based on these findings, we propose Disentangled Dense Object Detector(DDOD), in which simple and effective disentanglement mechanisms are designed and integrated into the current state-of-the-art dense object detectors. Extensive experiments on MS COCO benchmark show that our approach can lead to 2.0 mAP, 2.4 mAP and 2.2 mAP absolute improvements on RetinaNet, FCOS, and ATSS baselines with negligible extra overhead. Notably, our best model reaches 55.0 mAP on the COCO test-dev set and 93.5 AP on the hard subset of WIDER FACE, achieving new state-of-the-art performance on these two competitive benchmarks. Code is available at https://github.com/zehuichen123/DDOD.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/17425982/159212920-2e99d433-82c9-46cf-8f3a-32fdf3c566f5.png"/>
+</div>
+
+## Results and Models
+
+|   Model   | Backbone |  Style  | Lr schd | Mem (GB) | box AP |                                                Config                                                |                                                                                                                                Download                                                                                                                                |
+| :-------: | :------: | :-----: | :-----: | :------: | :----: | :--------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| DDOD-ATSS |   R-50   | pytorch |   1x    |   3.4    |  41.7  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ddod/ddod_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ddod/ddod_r50_fpn_1x_coco/ddod_r50_fpn_1x_coco_20220523_223737-29b2fc67.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ddod/ddod_r50_fpn_1x_coco/ddod_r50_fpn_1x_coco_20220523_223737.log.json) |
+
+## Citation
+
+```latex
+@inproceedings{chen2021disentangle,
+title={Disentangle Your Dense Object Detector},
+author={Chen, Zehui and Yang, Chenhongyi and Li, Qiaofei and Zhao, Feng and Zha, Zheng-Jun and Wu, Feng},
+booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
+pages={4939--4948},
+year={2021}
+}
+```
diff --git a/configs/ddod/ddod_r50_fpn_1x_coco.py b/configs/ddod/ddod_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..02dd2fe
--- /dev/null
+++ b/configs/ddod/ddod_r50_fpn_1x_coco.py
@@ -0,0 +1,67 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    type='DDOD',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='DDODHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+        loss_iou=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    train_cfg=dict(
+        # assigner is mean cls_assigner
+        assigner=dict(type='ATSSAssigner', topk=9, alpha=0.8),
+        reg_assigner=dict(type='ATSSAssigner', topk=9, alpha=0.5),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+
+# This `persistent_workers` is only valid when PyTorch>=1.7.0
+data = dict(persistent_workers=True)
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/ddod/metafile.yml b/configs/ddod/metafile.yml
new file mode 100644
index 0000000..c223950
--- /dev/null
+++ b/configs/ddod/metafile.yml
@@ -0,0 +1,33 @@
+Collections:
+  - Name: DDOD
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - DDOD
+        - FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/pdf/2107.02963.pdf
+      Title: 'Disentangle Your Dense Object Detector'
+    README: configs/ddod/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.25.0/mmdet/models/detectors/ddod.py#L6
+      Version: v2.25.0
+
+Models:
+  - Name: ddod_r50_fpn_1x_coco
+    In Collection: DDOD
+    Config: configs/ddod/ddod_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.4
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ddod/ddod_r50_fpn_1x_coco/ddod_r50_fpn_1x_coco_20220523_223737-29b2fc67.pth
diff --git a/configs/deepfashion/README.md b/configs/deepfashion/README.md
new file mode 100644
index 0000000..45daec0
--- /dev/null
+++ b/configs/deepfashion/README.md
@@ -0,0 +1,70 @@
+# DeepFashion
+
+> [DeepFashion: Powering Robust Clothes Recognition and Retrieval With Rich Annotations](https://openaccess.thecvf.com/content_cvpr_2016/html/Liu_DeepFashion_Powering_Robust_CVPR_2016_paper.html)
+
+<!-- [DATASET] -->
+
+## Abstract
+
+Recent advances in clothes recognition have been driven by the construction of clothes datasets. Existing datasets are limited in the amount of annotations and are difficult to cope with the various challenges in real-world applications. In this work, we introduce DeepFashion, a large-scale clothes dataset with comprehensive annotations. It contains over 800,000 images, which are richly annotated with massive attributes, clothing landmarks, and correspondence of images taken under different scenarios including store, street snapshot, and consumer. Such rich annotations enable the development of powerful algorithms in clothes recognition and facilitating future researches. To demonstrate the advantages of DeepFashion, we propose a new deep model, namely FashionNet, which learns clothing features by jointly predicting clothing attributes and landmarks. The estimated landmarks are then employed to pool or gate the learned features. It is optimized in an iterative manner. Extensive experiments demonstrate the effectiveness of FashionNet and the usefulness of DeepFashion.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143876310-08470a6a-ea3a-4ec1-a6f2-8ec5df36a8a0.png"/>
+</div>
+
+## Introduction
+
+[MMFashion](https://github.com/open-mmlab/mmfashion) develops "fashion parsing and segmentation" module
+based on the dataset
+[DeepFashion-Inshop](https://drive.google.com/drive/folders/0B7EVK8r0v71pVDZFQXRsMDZCX1E?usp=sharing).
+Its annotation follows COCO style.
+To use it, you need to first download the data. Note that we only use "img_highres" in this task.
+The file tree should be like this:
+
+```sh
+mmdetection
+├── mmdet
+├── tools
+├── configs
+├── data
+│   ├── DeepFashion
+│   │   ├── In-shop
+│   │   ├── Anno
+│   │   │   ├── segmentation
+│   │   │   |   ├── DeepFashion_segmentation_train.json
+│   │   │   |   ├── DeepFashion_segmentation_query.json
+│   │   │   |   ├── DeepFashion_segmentation_gallery.json
+│   │   │   ├── list_bbox_inshop.txt
+│   │   │   ├── list_description_inshop.json
+│   │   │   ├── list_item_inshop.txt
+│   │   │   └── list_landmarks_inshop.txt
+│   │   ├── Eval
+│   │   │   └── list_eval_partition.txt
+│   │   ├── Img
+│   │   │   ├── img
+│   │   │   │   ├──XXX.jpg
+│   │   │   ├── img_highres
+│   │   │   └── ├──XXX.jpg
+
+```
+
+After that you can train the Mask RCNN r50 on DeepFashion-In-shop dataset by launching training with the `mask_rcnn_r50_fpn_1x.py` config
+or creating your own config file.
+
+## Results and Models
+
+| Backbone | Model type |       Dataset       | bbox detection Average Precision | segmentation Average Precision |                                                          Config                                                          |                                                                                                                                       Download (Google)                                                                                                                                       |
+| :------: | :--------: | :-----------------: | :------------------------------: | :----------------------------: | :----------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| ResNet50 | Mask RCNN  | DeepFashion-In-shop |              0.599               |             0.584              | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion/mask_rcnn_r50_fpn_15e_deepfashion_20200329_192752.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion/20200329_192752.log.json) |
+
+## Citation
+
+```latex
+@inproceedings{liuLQWTcvpr16DeepFashion,
+   author = {Liu, Ziwei and Luo, Ping and Qiu, Shi and Wang, Xiaogang and Tang, Xiaoou},
+   title = {DeepFashion: Powering Robust Clothes Recognition and Retrieval with Rich Annotations},
+   booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+   month = {June},
+   year = {2016}
+}
+```
diff --git a/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py b/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py
new file mode 100644
index 0000000..c4e8638
--- /dev/null
+++ b/configs/deepfashion/mask_rcnn_r50_fpn_15e_deepfashion.py
@@ -0,0 +1,10 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/deepfashion.py', '../_base_/schedules/schedule_1x.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(num_classes=15), mask_head=dict(num_classes=15)))
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=15)
diff --git a/configs/deformable_detr/README.md b/configs/deformable_detr/README.md
new file mode 100644
index 0000000..378e1f2
--- /dev/null
+++ b/configs/deformable_detr/README.md
@@ -0,0 +1,41 @@
+# Deformable DETR
+
+> [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance. However, it suffers from slow convergence and limited feature spatial resolution, due to the limitation of Transformer attention modules in processing image feature maps. To mitigate these issues, we proposed Deformable DETR, whose attention modules only attend to a small set of key sampling points around a reference. Deformable DETR can achieve better performance than DETR (especially on small objects) with 10 times less training epochs. Extensive experiments on the COCO benchmark demonstrate the effectiveness of our approach.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143877617-ad9b24fd-77ce-46aa-9689-1a44b5594132.png"/>
+</div>
+
+## Results and Models
+
+| Backbone |                Model                | Lr schd | box AP |                                                                    Config                                                                    |                                                                                                                                                                                                         Download                                                                                                                                                                                                          |
+| :------: | :---------------------------------: | :-----: | :----: | :------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   |           Deformable DETR           |   50e   |  44.5  |         [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py)         |                                 [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.log.json)                                 |
+|   R-50   | + iterative bounding box refinement |   50e   |  46.1  |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py)      |                   [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.log.json)                   |
+|   R-50   |    ++ two-stage Deformable DETR     |   50e   |  46.8  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.log.json) |
+
+# NOTE
+
+1. All models are trained with batch size 32.
+2. The performance is unstable. `Deformable DETR` and `iterative bounding box refinement` may fluctuate about 0.3 mAP. `two-stage Deformable DETR` may fluctuate about 0.2 mAP.
+
+## Citation
+
+We provide the config files for Deformable DETR: [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159).
+
+```latex
+@inproceedings{
+zhu2021deformable,
+title={Deformable DETR: Deformable Transformers for End-to-End Object Detection},
+author={Xizhou Zhu and Weijie Su and Lewei Lu and Bin Li and Xiaogang Wang and Jifeng Dai},
+booktitle={International Conference on Learning Representations},
+year={2021},
+url={https://openreview.net/forum?id=gZ9hCDWe6ke}
+}
+```
diff --git a/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py b/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py
new file mode 100644
index 0000000..c64d09f
--- /dev/null
+++ b/configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py
@@ -0,0 +1,177 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='DeformableDETR',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='ChannelMapper',
+        in_channels=[512, 1024, 2048],
+        kernel_size=1,
+        out_channels=256,
+        act_cfg=None,
+        norm_cfg=dict(type='GN', num_groups=32),
+        num_outs=4),
+    bbox_head=dict(
+        type='DeformableDETRHead',
+        num_query=300,
+        num_classes=80,
+        in_channels=2048,
+        sync_cls_avg_factor=True,
+        as_two_stage=False,
+        transformer=dict(
+            type='DeformableDetrTransformer',
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=dict(
+                        type='MultiScaleDeformableAttention', embed_dims=256),
+                    feedforward_channels=1024,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
+            decoder=dict(
+                type='DeformableDetrTransformerDecoder',
+                num_layers=6,
+                return_intermediate=True,
+                transformerlayers=dict(
+                    type='DetrTransformerDecoderLayer',
+                    attn_cfgs=[
+                        dict(
+                            type='MultiheadAttention',
+                            embed_dims=256,
+                            num_heads=8,
+                            dropout=0.1),
+                        dict(
+                            type='MultiScaleDeformableAttention',
+                            embed_dims=256)
+                    ],
+                    feedforward_channels=1024,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
+                                     'ffn', 'norm')))),
+        positional_encoding=dict(
+            type='SinePositionalEncoding',
+            num_feats=128,
+            normalize=True,
+            offset=-0.5),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=2.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
+        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='HungarianAssigner',
+            cls_cost=dict(type='FocalLossCost', weight=2.0),
+            reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
+            iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
+    test_cfg=dict(max_per_img=100))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
+# from the default setting in mmdet.
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='AutoAugment',
+        policies=[
+            [
+                dict(
+                    type='Resize',
+                    img_scale=[(480, 1333), (512, 1333), (544, 1333),
+                               (576, 1333), (608, 1333), (640, 1333),
+                               (672, 1333), (704, 1333), (736, 1333),
+                               (768, 1333), (800, 1333)],
+                    multiscale_mode='value',
+                    keep_ratio=True)
+            ],
+            [
+                dict(
+                    type='Resize',
+                    # The radio of all image in train dataset < 7
+                    # follow the original impl
+                    img_scale=[(400, 4200), (500, 4200), (600, 4200)],
+                    multiscale_mode='value',
+                    keep_ratio=True),
+                dict(
+                    type='RandomCrop',
+                    crop_type='absolute_range',
+                    crop_size=(384, 600),
+                    allow_negative_crop=True),
+                dict(
+                    type='Resize',
+                    img_scale=[(480, 1333), (512, 1333), (544, 1333),
+                               (576, 1333), (608, 1333), (640, 1333),
+                               (672, 1333), (704, 1333), (736, 1333),
+                               (768, 1333), (800, 1333)],
+                    multiscale_mode='value',
+                    override=True,
+                    keep_ratio=True)
+            ]
+        ]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=1),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+# test_pipeline, NOTE the Pad's size_divisor is different from the default
+# setting (size_divisor=32). While there is little effect on the performance
+# whether we use the default setting or use size_divisor=1.
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=1),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(filter_empty_gt=False, pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    type='AdamW',
+    lr=2e-4,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(
+        custom_keys={
+            'backbone': dict(lr_mult=0.1),
+            'sampling_offsets': dict(lr_mult=0.1),
+            'reference_points': dict(lr_mult=0.1)
+        }))
+optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
+# learning policy
+lr_config = dict(policy='step', step=[40])
+runner = dict(type='EpochBasedRunner', max_epochs=50)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (16 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=32)
diff --git a/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py b/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py
new file mode 100644
index 0000000..01f13df
--- /dev/null
+++ b/configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py
@@ -0,0 +1,2 @@
+_base_ = 'deformable_detr_r50_16x2_50e_coco.py'
+model = dict(bbox_head=dict(with_box_refine=True))
diff --git a/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py b/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py
new file mode 100644
index 0000000..2aa840d
--- /dev/null
+++ b/configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py
@@ -0,0 +1,2 @@
+_base_ = 'deformable_detr_refine_r50_16x2_50e_coco.py'
+model = dict(bbox_head=dict(as_two_stage=True))
diff --git a/configs/deformable_detr/metafile.yml b/configs/deformable_detr/metafile.yml
new file mode 100644
index 0000000..873292d
--- /dev/null
+++ b/configs/deformable_detr/metafile.yml
@@ -0,0 +1,56 @@
+Collections:
+  - Name: Deformable DETR
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - AdamW
+        - Multi Scale Train
+        - Gradient Clip
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNet
+        - Transformer
+    Paper:
+      URL: https://openreview.net/forum?id=gZ9hCDWe6ke
+      Title: 'Deformable DETR: Deformable Transformers for End-to-End Object Detection'
+    README: configs/deformable_detr/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.12.0/mmdet/models/detectors/deformable_detr.py#L6
+      Version: v2.12.0
+
+Models:
+  - Name: deformable_detr_r50_16x2_50e_coco
+    In Collection: Deformable DETR
+    Config: configs/deformable_detr/deformable_detr_r50_16x2_50e_coco.py
+    Metadata:
+      Epochs: 50
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_r50_16x2_50e_coco/deformable_detr_r50_16x2_50e_coco_20210419_220030-a12b9512.pth
+
+  - Name: deformable_detr_refine_r50_16x2_50e_coco
+    In Collection: Deformable DETR
+    Config: configs/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco.py
+    Metadata:
+      Epochs: 50
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_refine_r50_16x2_50e_coco/deformable_detr_refine_r50_16x2_50e_coco_20210419_220503-5f5dff21.pth
+
+  - Name: deformable_detr_twostage_refine_r50_16x2_50e_coco
+    In Collection: Deformable DETR
+    Config: configs/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco.py
+    Metadata:
+      Epochs: 50
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/deformable_detr/deformable_detr_twostage_refine_r50_16x2_50e_coco/deformable_detr_twostage_refine_r50_16x2_50e_coco_20210419_220613-9d28ab72.pth
diff --git a/configs/detectors/README.md b/configs/detectors/README.md
new file mode 100644
index 0000000..baa245f
--- /dev/null
+++ b/configs/detectors/README.md
@@ -0,0 +1,69 @@
+# DetectoRS
+
+> [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/abs/2006.02334)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Many modern object detectors demonstrate outstanding performances by using the mechanism of looking and thinking twice. In this paper, we explore this mechanism in the backbone design for object detection. At the macro level, we propose Recursive Feature Pyramid, which incorporates extra feedback connections from Feature Pyramid Networks into the bottom-up backbone layers. At the micro level, we propose Switchable Atrous Convolution, which convolves the features with different atrous rates and gathers the results using switch functions. Combining them results in DetectoRS, which significantly improves the performances of object detection. On COCO test-dev, DetectoRS achieves state-of-the-art 55.7% box AP for object detection, 48.5% mask AP for instance segmentation, and 50.0% PQ for panoptic segmentation.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143877901-24451581-2c50-4a54-b000-c4cb111e29ad.png"/>
+</div>
+
+## Introduction
+
+DetectoRS requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path.
+The directory should be like this.
+
+```none
+mmdetection
+├── mmdet
+├── tools
+├── configs
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+|   |   ├── stuffthingmaps
+```
+
+## Results and Models
+
+DetectoRS includes two major components:
+
+- Recursive Feature Pyramid (RFP).
+- Switchable Atrous Convolution (SAC).
+
+They can be used independently.
+Combining them together results in DetectoRS.
+The results on COCO 2017 val are shown in the below table.
+
+|  Method   |      Detector       | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                         Config                                                          |                                                                                                                                                         Download                                                                                                                                                         |
+| :-------: | :-----------------: | :-----: | :------: | :------------: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    RFP    | Cascade + ResNet-50 |   1x    |   7.5    |       -        |  44.8  |         |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco-8cf51bfd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco_20200624_104126.log.json)             |
+|    SAC    | Cascade + ResNet-50 |   1x    |   5.6    |       -        |  45.0  |         |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco-24bfda62.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco_20200624_104402.log.json)             |
+| DetectoRS | Cascade + ResNet-50 |   1x    |   9.9    |       -        |  47.4  |         | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco-32a10ba0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco_20200706_001203.log.json) |
+|    RFP    |   HTC + ResNet-50   |   1x    |   11.2   |       -        |  46.6  |  40.9   |        [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/htc_r50_rfp_1x_coco.py)         |                               [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco-8ff87c51.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco_20200624_103053.log.json)                               |
+|    SAC    |   HTC + ResNet-50   |   1x    |   9.3    |       -        |  46.4  |  40.9   |        [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/htc_r50_sac_1x_coco.py)         |                               [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco-bfa60c54.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco_20200624_103111.log.json)                               |
+| DetectoRS |   HTC + ResNet-50   |   1x    |   13.6   |       -        |  49.1  |  42.6   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r50_1x_coco.py)      |                   [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco-329b1453.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco_20200624_103659.log.json)                   |
+| DetectoRS |  HTC + ResNet-101   |   20e   |   19.6   |                |  50.5  |  43.9   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detectors/detectors_htc_r101_20e_coco.py)     |       [model](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r101_20e_coco/detectors_htc_r101_20e_coco_20210419_203638-348d533b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r101_20e_coco/detectors_htc_r101_20e_coco_20210419_203638.log.json)       |
+
+*Note*: This is a re-implementation based on MMDetection-V2.
+The original implementation is based on MMDetection-V1.
+
+## Citation
+
+We provide the config files for [DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution](https://arxiv.org/pdf/2006.02334.pdf).
+
+```latex
+@article{qiao2020detectors,
+  title={DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution},
+  author={Qiao, Siyuan and Chen, Liang-Chieh and Yuille, Alan},
+  journal={arXiv preprint arXiv:2006.02334},
+  year={2020}
+}
+```
diff --git a/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py b/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py
new file mode 100644
index 0000000..4430d8a
--- /dev/null
+++ b/configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py
@@ -0,0 +1,28 @@
+_base_ = [
+    '../_base_/models/cascade_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    backbone=dict(
+        type='DetectoRS_ResNet',
+        conv_cfg=dict(type='ConvAWS'),
+        output_img=True),
+    neck=dict(
+        type='RFP',
+        rfp_steps=2,
+        aspp_out_channels=64,
+        aspp_dilations=(1, 3, 6, 1),
+        rfp_backbone=dict(
+            rfp_inplanes=256,
+            type='DetectoRS_ResNet',
+            depth=50,
+            num_stages=4,
+            out_indices=(0, 1, 2, 3),
+            frozen_stages=1,
+            norm_cfg=dict(type='BN', requires_grad=True),
+            norm_eval=True,
+            conv_cfg=dict(type='ConvAWS'),
+            pretrained='torchvision://resnet50',
+            style='pytorch')))
diff --git a/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py b/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py
new file mode 100644
index 0000000..ccd9319
--- /dev/null
+++ b/configs/detectors/cascade_rcnn_r50_sac_1x_coco.py
@@ -0,0 +1,12 @@
+_base_ = [
+    '../_base_/models/cascade_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    backbone=dict(
+        type='DetectoRS_ResNet',
+        conv_cfg=dict(type='ConvAWS'),
+        sac=dict(type='SAC', use_deform=True),
+        stage_with_sac=(False, True, True, True)))
diff --git a/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py b/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py
new file mode 100644
index 0000000..f760404
--- /dev/null
+++ b/configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py
@@ -0,0 +1,32 @@
+_base_ = [
+    '../_base_/models/cascade_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    backbone=dict(
+        type='DetectoRS_ResNet',
+        conv_cfg=dict(type='ConvAWS'),
+        sac=dict(type='SAC', use_deform=True),
+        stage_with_sac=(False, True, True, True),
+        output_img=True),
+    neck=dict(
+        type='RFP',
+        rfp_steps=2,
+        aspp_out_channels=64,
+        aspp_dilations=(1, 3, 6, 1),
+        rfp_backbone=dict(
+            rfp_inplanes=256,
+            type='DetectoRS_ResNet',
+            depth=50,
+            num_stages=4,
+            out_indices=(0, 1, 2, 3),
+            frozen_stages=1,
+            norm_cfg=dict(type='BN', requires_grad=True),
+            norm_eval=True,
+            conv_cfg=dict(type='ConvAWS'),
+            sac=dict(type='SAC', use_deform=True),
+            stage_with_sac=(False, True, True, True),
+            pretrained='torchvision://resnet50',
+            style='pytorch')))
diff --git a/configs/detectors/detectors_htc_r101_20e_coco.py b/configs/detectors/detectors_htc_r101_20e_coco.py
new file mode 100644
index 0000000..93d7d2b
--- /dev/null
+++ b/configs/detectors/detectors_htc_r101_20e_coco.py
@@ -0,0 +1,28 @@
+_base_ = '../htc/htc_r101_fpn_20e_coco.py'
+
+model = dict(
+    backbone=dict(
+        type='DetectoRS_ResNet',
+        conv_cfg=dict(type='ConvAWS'),
+        sac=dict(type='SAC', use_deform=True),
+        stage_with_sac=(False, True, True, True),
+        output_img=True),
+    neck=dict(
+        type='RFP',
+        rfp_steps=2,
+        aspp_out_channels=64,
+        aspp_dilations=(1, 3, 6, 1),
+        rfp_backbone=dict(
+            rfp_inplanes=256,
+            type='DetectoRS_ResNet',
+            depth=101,
+            num_stages=4,
+            out_indices=(0, 1, 2, 3),
+            frozen_stages=1,
+            norm_cfg=dict(type='BN', requires_grad=True),
+            norm_eval=True,
+            conv_cfg=dict(type='ConvAWS'),
+            sac=dict(type='SAC', use_deform=True),
+            stage_with_sac=(False, True, True, True),
+            pretrained='torchvision://resnet101',
+            style='pytorch')))
diff --git a/configs/detectors/detectors_htc_r50_1x_coco.py b/configs/detectors/detectors_htc_r50_1x_coco.py
new file mode 100644
index 0000000..0d2fc4f
--- /dev/null
+++ b/configs/detectors/detectors_htc_r50_1x_coco.py
@@ -0,0 +1,28 @@
+_base_ = '../htc/htc_r50_fpn_1x_coco.py'
+
+model = dict(
+    backbone=dict(
+        type='DetectoRS_ResNet',
+        conv_cfg=dict(type='ConvAWS'),
+        sac=dict(type='SAC', use_deform=True),
+        stage_with_sac=(False, True, True, True),
+        output_img=True),
+    neck=dict(
+        type='RFP',
+        rfp_steps=2,
+        aspp_out_channels=64,
+        aspp_dilations=(1, 3, 6, 1),
+        rfp_backbone=dict(
+            rfp_inplanes=256,
+            type='DetectoRS_ResNet',
+            depth=50,
+            num_stages=4,
+            out_indices=(0, 1, 2, 3),
+            frozen_stages=1,
+            norm_cfg=dict(type='BN', requires_grad=True),
+            norm_eval=True,
+            conv_cfg=dict(type='ConvAWS'),
+            sac=dict(type='SAC', use_deform=True),
+            stage_with_sac=(False, True, True, True),
+            pretrained='torchvision://resnet50',
+            style='pytorch')))
diff --git a/configs/detectors/htc_r50_rfp_1x_coco.py b/configs/detectors/htc_r50_rfp_1x_coco.py
new file mode 100644
index 0000000..496104e
--- /dev/null
+++ b/configs/detectors/htc_r50_rfp_1x_coco.py
@@ -0,0 +1,24 @@
+_base_ = '../htc/htc_r50_fpn_1x_coco.py'
+
+model = dict(
+    backbone=dict(
+        type='DetectoRS_ResNet',
+        conv_cfg=dict(type='ConvAWS'),
+        output_img=True),
+    neck=dict(
+        type='RFP',
+        rfp_steps=2,
+        aspp_out_channels=64,
+        aspp_dilations=(1, 3, 6, 1),
+        rfp_backbone=dict(
+            rfp_inplanes=256,
+            type='DetectoRS_ResNet',
+            depth=50,
+            num_stages=4,
+            out_indices=(0, 1, 2, 3),
+            frozen_stages=1,
+            norm_cfg=dict(type='BN', requires_grad=True),
+            norm_eval=True,
+            conv_cfg=dict(type='ConvAWS'),
+            pretrained='torchvision://resnet50',
+            style='pytorch')))
diff --git a/configs/detectors/htc_r50_sac_1x_coco.py b/configs/detectors/htc_r50_sac_1x_coco.py
new file mode 100644
index 0000000..72d4db9
--- /dev/null
+++ b/configs/detectors/htc_r50_sac_1x_coco.py
@@ -0,0 +1,8 @@
+_base_ = '../htc/htc_r50_fpn_1x_coco.py'
+
+model = dict(
+    backbone=dict(
+        type='DetectoRS_ResNet',
+        conv_cfg=dict(type='ConvAWS'),
+        sac=dict(type='SAC', use_deform=True),
+        stage_with_sac=(False, True, True, True)))
diff --git a/configs/detectors/metafile.yml b/configs/detectors/metafile.yml
new file mode 100644
index 0000000..4bed569
--- /dev/null
+++ b/configs/detectors/metafile.yml
@@ -0,0 +1,114 @@
+Collections:
+  - Name: DetectoRS
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ASPP
+        - FPN
+        - RFP
+        - RPN
+        - ResNet
+        - RoIAlign
+        - SAC
+    Paper:
+      URL: https://arxiv.org/abs/2006.02334
+      Title: 'DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution'
+    README: configs/detectors/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/backbones/detectors_resnet.py#L205
+      Version: v2.2.0
+
+Models:
+  - Name: cascade_rcnn_r50_rfp_1x_coco
+    In Collection: DetectoRS
+    Config: configs/detectors/cascade_rcnn_r50_rfp_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.5
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_rfp_1x_coco/cascade_rcnn_r50_rfp_1x_coco-8cf51bfd.pth
+
+  - Name: cascade_rcnn_r50_sac_1x_coco
+    In Collection: DetectoRS
+    Config: configs/detectors/cascade_rcnn_r50_sac_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.6
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/cascade_rcnn_r50_sac_1x_coco/cascade_rcnn_r50_sac_1x_coco-24bfda62.pth
+
+  - Name: detectors_cascade_rcnn_r50_1x_coco
+    In Collection: DetectoRS
+    Config: configs/detectors/detectors_cascade_rcnn_r50_1x_coco.py
+    Metadata:
+      Training Memory (GB): 9.9
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_cascade_rcnn_r50_1x_coco/detectors_cascade_rcnn_r50_1x_coco-32a10ba0.pth
+
+  - Name: htc_r50_rfp_1x_coco
+    In Collection: DetectoRS
+    Config: configs/detectors/htc_r50_rfp_1x_coco.py
+    Metadata:
+      Training Memory (GB): 11.2
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  40.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_rfp_1x_coco/htc_r50_rfp_1x_coco-8ff87c51.pth
+
+  - Name: htc_r50_sac_1x_coco
+    In Collection: DetectoRS
+    Config: configs/detectors/htc_r50_sac_1x_coco.py
+    Metadata:
+      Training Memory (GB): 9.3
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  40.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/htc_r50_sac_1x_coco/htc_r50_sac_1x_coco-bfa60c54.pth
+
+  - Name: detectors_htc_r50_1x_coco
+    In Collection: DetectoRS
+    Config: configs/detectors/detectors_htc_r50_1x_coco.py
+    Metadata:
+      Training Memory (GB): 13.6
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 49.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  42.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/detectors/detectors_htc_r50_1x_coco/detectors_htc_r50_1x_coco-329b1453.pth
diff --git a/configs/detr/README.md b/configs/detr/README.md
new file mode 100644
index 0000000..9f2485d
--- /dev/null
+++ b/configs/detr/README.md
@@ -0,0 +1,37 @@
+# DETR
+
+> [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We present a new method that views object detection as a direct set prediction problem. Our approach streamlines the detection pipeline, effectively removing the need for many hand-designed components like a non-maximum suppression procedure or anchor generation that explicitly encode our prior knowledge about the task. The main ingredients of the new framework, called DEtection TRansformer or DETR, are a set-based global loss that forces unique predictions via bipartite matching, and a transformer encoder-decoder architecture. Given a fixed small set of learned object queries, DETR reasons about the relations of the objects and the global image context to directly output the final set of predictions in parallel. The new model is conceptually simple and does not require a specialized library, unlike many other modern detectors. DETR demonstrates accuracy and run-time performance on par with the well-established and highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive baselines.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143878072-0a7434e4-416b-4315-aeea-a8297f4d6453.png"/>
+</div>
+
+## Results and Models
+
+| Backbone | Model | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                 Config                                                 |                                                                                                                                    Download                                                                                                                                    |
+| :------: | :---: | :-----: | :------: | :------------: | :----: | :----------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | DETR  |  150e   |   7.9    |                |  40.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/detr/detr_r50_8x2_150e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835.log.json) |
+
+## Citation
+
+We provide the config files for DETR: [End-to-End Object Detection with Transformers](https://arxiv.org/abs/2005.12872).
+
+```latex
+@inproceedings{detr,
+  author    = {Nicolas Carion and
+               Francisco Massa and
+               Gabriel Synnaeve and
+               Nicolas Usunier and
+               Alexander Kirillov and
+               Sergey Zagoruyko},
+  title     = {End-to-End Object Detection with Transformers},
+  booktitle = {ECCV},
+  year      = {2020}
+}
+```
diff --git a/configs/detr/detr_r50_8x2_150e_coco.py b/configs/detr/detr_r50_8x2_150e_coco.py
new file mode 100644
index 0000000..892447d
--- /dev/null
+++ b/configs/detr/detr_r50_8x2_150e_coco.py
@@ -0,0 +1,150 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='DETR',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    bbox_head=dict(
+        type='DETRHead',
+        num_classes=80,
+        in_channels=2048,
+        transformer=dict(
+            type='Transformer',
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=[
+                        dict(
+                            type='MultiheadAttention',
+                            embed_dims=256,
+                            num_heads=8,
+                            dropout=0.1)
+                    ],
+                    feedforward_channels=2048,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
+            decoder=dict(
+                type='DetrTransformerDecoder',
+                return_intermediate=True,
+                num_layers=6,
+                transformerlayers=dict(
+                    type='DetrTransformerDecoderLayer',
+                    attn_cfgs=dict(
+                        type='MultiheadAttention',
+                        embed_dims=256,
+                        num_heads=8,
+                        dropout=0.1),
+                    feedforward_channels=2048,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
+                                     'ffn', 'norm')),
+            )),
+        positional_encoding=dict(
+            type='SinePositionalEncoding', num_feats=128, normalize=True),
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            bg_cls_weight=0.1,
+            use_sigmoid=False,
+            loss_weight=1.0,
+            class_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
+        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='HungarianAssigner',
+            cls_cost=dict(type='ClassificationCost', weight=1.),
+            reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
+            iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
+    test_cfg=dict(max_per_img=100))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
+# from the default setting in mmdet.
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='AutoAugment',
+        policies=[[
+            dict(
+                type='Resize',
+                img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                           (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                           (736, 1333), (768, 1333), (800, 1333)],
+                multiscale_mode='value',
+                keep_ratio=True)
+        ],
+                  [
+                      dict(
+                          type='Resize',
+                          img_scale=[(400, 1333), (500, 1333), (600, 1333)],
+                          multiscale_mode='value',
+                          keep_ratio=True),
+                      dict(
+                          type='RandomCrop',
+                          crop_type='absolute_range',
+                          crop_size=(384, 600),
+                          allow_negative_crop=True),
+                      dict(
+                          type='Resize',
+                          img_scale=[(480, 1333), (512, 1333), (544, 1333),
+                                     (576, 1333), (608, 1333), (640, 1333),
+                                     (672, 1333), (704, 1333), (736, 1333),
+                                     (768, 1333), (800, 1333)],
+                          multiscale_mode='value',
+                          override=True,
+                          keep_ratio=True)
+                  ]]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=1),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+# test_pipeline, NOTE the Pad's size_divisor is different from the default
+# setting (size_divisor=32). While there is little effect on the performance
+# whether we use the default setting or use size_divisor=1.
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=1),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(
+        custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
+optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
+# learning policy
+lr_config = dict(policy='step', step=[100])
+runner = dict(type='EpochBasedRunner', max_epochs=150)
diff --git a/configs/detr/metafile.yml b/configs/detr/metafile.yml
new file mode 100644
index 0000000..45622cf
--- /dev/null
+++ b/configs/detr/metafile.yml
@@ -0,0 +1,33 @@
+Collections:
+  - Name: DETR
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - AdamW
+        - Multi Scale Train
+        - Gradient Clip
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNet
+        - Transformer
+    Paper:
+      URL: https://arxiv.org/abs/2005.12872
+      Title: 'End-to-End Object Detection with Transformers'
+    README: configs/detr/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/detectors/detr.py#L7
+      Version: v2.7.0
+
+Models:
+  - Name: detr_r50_8x2_150e_coco
+    In Collection: DETR
+    Config: configs/detr/detr_r50_8x2_150e_coco.py
+    Metadata:
+      Training Memory (GB): 7.9
+      Epochs: 150
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth
diff --git a/configs/double_heads/README.md b/configs/double_heads/README.md
new file mode 100644
index 0000000..4a149b5
--- /dev/null
+++ b/configs/double_heads/README.md
@@ -0,0 +1,32 @@
+# Double Heads
+
+> [Rethinking Classification and Localization for Object Detection](https://arxiv.org/abs/1904.06493)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Two head structures (i.e. fully connected head and convolution head) have been widely used in R-CNN based detectors for classification and localization tasks. However, there is a lack of understanding of how does these two head structures work for these two tasks. To address this issue, we perform a thorough analysis and find an interesting fact that the two head structures have opposite preferences towards the two tasks. Specifically, the fully connected head (fc-head) is more suitable for the classification task, while the convolution head (conv-head) is more suitable for the localization task. Furthermore, we examine the output feature maps of both heads and find that fc-head has more spatial sensitivity than conv-head. Thus, fc-head has more capability to distinguish a complete object from part of an object, but is not robust to regress the whole object. Based upon these findings, we propose a Double-Head method, which has a fully connected head focusing on classification and a convolution head for bounding box regression. Without bells and whistles, our method gains +3.5 and +2.8 AP on MS COCO dataset from Feature Pyramid Network (FPN) baselines with ResNet-50 and ResNet-101 backbones, respectively.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143879010-e30f654b-f93e-44b2-a186-c251fdca5bda.png"/>
+</div>
+
+## Results and Models
+
+| Backbone |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                         Config                                                         |                                                                                                                                                        Download                                                                                                                                                         |
+| :------: | :-----: | :-----: | :------: | :------------: | :----: | :--------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R-50-FPN | pytorch |   1x    |   6.8    |      9.5       |  40.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130_220238.log.json) |
+
+## Citation
+
+```latex
+@article{wu2019rethinking,
+    title={Rethinking Classification and Localization for Object Detection},
+    author={Yue Wu and Yinpeng Chen and Lu Yuan and Zicheng Liu and Lijuan Wang and Hongzhi Li and Yun Fu},
+    year={2019},
+    eprint={1904.06493},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
diff --git a/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py b/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..9b8118b
--- /dev/null
+++ b/configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,23 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        type='DoubleHeadRoIHead',
+        reg_roi_scale_factor=1.3,
+        bbox_head=dict(
+            _delete_=True,
+            type='DoubleConvFCBBoxHead',
+            num_convs=4,
+            num_fcs=2,
+            in_channels=256,
+            conv_out_channels=1024,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=2.0),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=2.0))))
diff --git a/configs/double_heads/metafile.yml b/configs/double_heads/metafile.yml
new file mode 100644
index 0000000..6fe9b7a
--- /dev/null
+++ b/configs/double_heads/metafile.yml
@@ -0,0 +1,41 @@
+Collections:
+  - Name: Rethinking Classification and Localization for Object Detection
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - RPN
+        - ResNet
+        - RoIAlign
+    Paper:
+      URL: https://arxiv.org/pdf/1904.06493
+      Title: 'Rethinking Classification and Localization for Object Detection'
+    README: configs/double_heads/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/roi_heads/double_roi_head.py#L6
+      Version: v2.0.0
+
+Models:
+  - Name: dh_faster_rcnn_r50_fpn_1x_coco
+    In Collection: Rethinking Classification and Localization for Object Detection
+    Config: configs/double_heads/dh_faster_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.8
+      inference time (ms/im):
+        - value: 105.26
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/double_heads/dh_faster_rcnn_r50_fpn_1x_coco/dh_faster_rcnn_r50_fpn_1x_coco_20200130-586b67df.pth
diff --git a/configs/dyhead/README.md b/configs/dyhead/README.md
new file mode 100644
index 0000000..8e6aed3
--- /dev/null
+++ b/configs/dyhead/README.md
@@ -0,0 +1,52 @@
+# DyHead
+
+> [Dynamic Head: Unifying Object Detection Heads with Attentions](https://arxiv.org/abs/2106.08322)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+The complex nature of combining localization and classification in object detection has resulted in the flourished development of methods. Previous works tried to improve the performance in various object detection heads but failed to present a unified view. In this paper, we present a novel dynamic head framework to unify object detection heads with attentions. By coherently combining multiple self-attention mechanisms between feature levels for scale-awareness, among spatial locations for spatial-awareness, and within output channels for task-awareness, the proposed approach significantly improves the representation ability of object detection heads without any computational overhead. Further experiments demonstrate that the effectiveness and efficiency of the proposed dynamic head on the COCO benchmark. With a standard ResNeXt-101-DCN backbone, we largely improve the performance over popular object detectors and achieve a new state-of-the-art at 54.0 AP. Furthermore, with latest transformer backbone and extra data, we can push current best COCO result to a new record at 60.6 AP.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/42844407/149169448-fcafb6d0-b866-41cc-9422-94de9f1e1761.png" height="300"/>
+</div>
+
+## Results and Models
+
+| Method | Backbone |  Style  |   Setting    | Lr schd | Mem (GB) | Inf time (fps) | box AP |                      Config                      |                                                                                                                                                                                      Download                                                                                                                                                                                      |
+| :----: | :------: | :-----: | :----------: | :-----: | :------: | :------------: | :----: | :----------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  ATSS  |   R-50   |  caffe  | reproduction |   1x    |   5.4    |      13.2      |  42.5  | [config](./atss_r50_caffe_fpn_dyhead_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_for_reproduction_1x_coco/atss_r50_fpn_dyhead_for_reproduction_4x4_1x_coco_20220107_213939-162888e6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_for_reproduction_1x_coco/atss_r50_fpn_dyhead_for_reproduction_4x4_1x_coco_20220107_213939.log.json) |
+|  ATSS  |   R-50   | pytorch |    simple    |   1x    |   4.9    |      13.7      |  43.3  |    [config](./atss_r50_fpn_dyhead_1x_coco.py)    |                               [model](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_4x4_1x_coco/atss_r50_fpn_dyhead_4x4_1x_coco_20211219_023314-eaa620c6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_4x4_1x_coco/atss_r50_fpn_dyhead_4x4_1x_coco_20211219_023314.log.json)                               |
+
+- We trained the above models with 4 GPUs and 4 `samples_per_gpu`.
+- The `reproduction` setting aims to reproduce the official implementation based on Detectron2.
+- The `simple` setting serves as a minimum example to use DyHead in MMDetection. Specifically,
+  - it adds `DyHead` to `neck` after `FPN`
+  - it sets `stacked_convs=0` to `bbox_head`
+- The `simple` setting achieves higher AP than the original implementation.
+  We have not conduct ablation study between the two settings.
+  `dict(type='Pad', size_divisor=128)` may further improve AP by prefer spatial alignment across pyramid levels, although large padding reduces efficiency.
+
+We also trained the model with Swin-L backbone. Results are as below.
+
+| Method | Backbone | Style |   Setting    | Lr schd | mstrain  | box AP |                            Config                            |                                                                                                                                                                                    Download                                                                                                                                                                                    |
+| :----: | :------: | :---: | :----------: | :-----: | :------: | :----: | :----------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  ATSS  |  Swin-L  | caffe | reproduction |   2x    | 480~1200 |  56.2  | [config](./atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco_20220509_100315-bc5b6516.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco_20220509_100315.log.json) |
+
+## Relation to Other Methods
+
+- DyHead can be regarded as an improved [SEPC](https://arxiv.org/abs/2005.03101) with [DyReLU modules](https://arxiv.org/abs/2003.10027) and simplified [SE blocks](https://arxiv.org/abs/1709.01507).
+- Xiyang Dai et al., the author team of DyHead, adopt it for [Dynamic DETR](https://openaccess.thecvf.com/content/ICCV2021/html/Dai_Dynamic_DETR_End-to-End_Object_Detection_With_Dynamic_Attention_ICCV_2021_paper.html).
+  The description of Dynamic Encoder in Sec. 3.2 will help you understand DyHead.
+
+## Citation
+
+```latex
+@inproceedings{DyHead_CVPR2021,
+  author    = {Dai, Xiyang and Chen, Yinpeng and Xiao, Bin and Chen, Dongdong and Liu, Mengchen and Yuan, Lu and Zhang, Lei},
+  title     = {Dynamic Head: Unifying Object Detection Heads With Attentions},
+  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year      = {2021}
+}
+```
diff --git a/configs/dyhead/atss_r50_caffe_fpn_dyhead_1x_coco.py b/configs/dyhead/atss_r50_caffe_fpn_dyhead_1x_coco.py
new file mode 100644
index 0000000..223b653
--- /dev/null
+++ b/configs/dyhead/atss_r50_caffe_fpn_dyhead_1x_coco.py
@@ -0,0 +1,112 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='ATSS',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    neck=[
+        dict(
+            type='FPN',
+            in_channels=[256, 512, 1024, 2048],
+            out_channels=256,
+            start_level=1,
+            add_extra_convs='on_output',
+            num_outs=5),
+        dict(
+            type='DyHead',
+            in_channels=256,
+            out_channels=256,
+            num_blocks=6,
+            # disable zero_init_offset to follow official implementation
+            zero_init_offset=False)
+    ],
+    bbox_head=dict(
+        type='ATSSHead',
+        num_classes=80,
+        in_channels=256,
+        pred_kernel_size=1,  # follow DyHead official implementation
+        stacked_convs=0,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128],
+            center_offset=0.5),  # follow DyHead official implementation
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(type='ATSSAssigner', topk=9),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+
+# use caffe img_norm, size_divisor=128, pillow resize
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=(1333, 800),
+        keep_ratio=True,
+        backend='pillow'),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=128),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True, backend='pillow'),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=128),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py b/configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py
new file mode 100644
index 0000000..8c5109d
--- /dev/null
+++ b/configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py
@@ -0,0 +1,65 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='ATSS',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=[
+        dict(
+            type='FPN',
+            in_channels=[256, 512, 1024, 2048],
+            out_channels=256,
+            start_level=1,
+            add_extra_convs='on_output',
+            num_outs=5),
+        dict(type='DyHead', in_channels=256, out_channels=256, num_blocks=6)
+    ],
+    bbox_head=dict(
+        type='ATSSHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=0,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(type='ATSSAssigner', topk=9),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco.py b/configs/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco.py
new file mode 100644
index 0000000..dc9c328
--- /dev/null
+++ b/configs/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco.py
@@ -0,0 +1,164 @@
+_base_ = '../_base_/default_runtime.py'
+
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth'  # noqa
+model = dict(
+    type='ATSS',
+    backbone=dict(
+        type='SwinTransformer',
+        pretrain_img_size=384,
+        embed_dims=192,
+        depths=[2, 2, 18, 2],
+        num_heads=[6, 12, 24, 48],
+        window_size=12,
+        mlp_ratio=4,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.,
+        attn_drop_rate=0.,
+        drop_path_rate=0.2,
+        patch_norm=True,
+        out_indices=(1, 2, 3),
+        # Please only add indices that would be used
+        # in FPN, otherwise some parameter will not be used
+        with_cp=False,
+        convert_weights=True,
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    neck=[
+        dict(
+            type='FPN',
+            in_channels=[384, 768, 1536],
+            out_channels=256,
+            start_level=0,
+            add_extra_convs='on_output',
+            num_outs=5),
+        dict(
+            type='DyHead',
+            in_channels=256,
+            out_channels=256,
+            num_blocks=6,
+            # disable zero_init_offset to follow official implementation
+            zero_init_offset=False)
+    ],
+    bbox_head=dict(
+        type='ATSSHead',
+        num_classes=80,
+        in_channels=256,
+        pred_kernel_size=1,  # follow DyHead official implementation
+        stacked_convs=0,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128],
+            center_offset=0.5),  # follow DyHead official implementation
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(type='ATSSAssigner', topk=9),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(2000, 480), (2000, 1200)],
+        multiscale_mode='range',
+        keep_ratio=True,
+        backend='pillow'),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=128),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(2000, 1200),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True, backend='pillow'),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=128),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+# Use RepeatDataset to speed up training
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='RepeatDataset',
+        times=2,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')
+
+# optimizer
+optimizer_config = dict(grad_clip=None)
+optimizer = dict(
+    type='AdamW',
+    lr=0.00005,
+    betas=(0.9, 0.999),
+    weight_decay=0.05,
+    paramwise_cfg=dict(
+        custom_keys={
+            'absolute_pos_embed': dict(decay_mult=0.),
+            'relative_position_bias_table': dict(decay_mult=0.),
+            'norm': dict(decay_mult=0.)
+        }))
+
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[8, 11])
+runner = dict(type='EpochBasedRunner', max_epochs=12)
diff --git a/configs/dyhead/metafile.yml b/configs/dyhead/metafile.yml
new file mode 100644
index 0000000..3fb7370
--- /dev/null
+++ b/configs/dyhead/metafile.yml
@@ -0,0 +1,76 @@
+Collections:
+  - Name: DyHead
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 4x T4 GPUs
+      Architecture:
+        - ATSS
+        - DyHead
+        - FPN
+        - ResNet
+        - Deformable Convolution
+        - Pyramid Convolution
+    Paper:
+      URL: https://arxiv.org/abs/2106.08322
+      Title: 'Dynamic Head: Unifying Object Detection Heads with Attentions'
+    README: configs/dyhead/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.22.0/mmdet/models/necks/dyhead.py#L130
+      Version: v2.22.0
+
+Models:
+  - Name: atss_r50_caffe_fpn_dyhead_1x_coco
+    In Collection: DyHead
+    Config: configs/dyhead/atss_r50_caffe_fpn_dyhead_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.4
+      inference time (ms/im):
+        - value: 75.7
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_for_reproduction_1x_coco/atss_r50_fpn_dyhead_for_reproduction_4x4_1x_coco_20220107_213939-162888e6.pth
+
+  - Name: atss_r50_fpn_dyhead_1x_coco
+    In Collection: DyHead
+    Config: configs/dyhead/atss_r50_fpn_dyhead_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.9
+      inference time (ms/im):
+        - value: 73.1
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_r50_fpn_dyhead_4x4_1x_coco/atss_r50_fpn_dyhead_4x4_1x_coco_20211219_023314-eaa620c6.pth
+
+  - Name: atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco
+    In Collection: DyHead
+    Config: configs/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco.py
+    Metadata:
+      Training Memory (GB): 58.4
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 56.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dyhead/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco/atss_swin-l-p4-w12_fpn_dyhead_mstrain_2x_coco_20220509_100315-bc5b6516.pth
diff --git a/configs/dynamic_rcnn/README.md b/configs/dynamic_rcnn/README.md
new file mode 100644
index 0000000..0045df7
--- /dev/null
+++ b/configs/dynamic_rcnn/README.md
@@ -0,0 +1,30 @@
+# Dynamic R-CNN
+
+> [Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training](https://arxiv.org/abs/2004.06002)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Although two-stage object detectors have continuously advanced the state-of-the-art performance in recent years, the training process itself is far from crystal. In this work, we first point out the inconsistency problem between the fixed network settings and the dynamic training procedure, which greatly affects the performance. For example, the fixed label assignment strategy and regression loss function cannot fit the distribution change of proposals and thus are harmful to training high quality detectors. Consequently, we propose Dynamic R-CNN to adjust the label assignment criteria (IoU threshold) and the shape of regression loss function (parameters of SmoothL1 Loss) automatically based on the statistics of proposals during training. This dynamic design makes better use of the training samples and pushes the detector to fit more high quality samples. Specifically, our method improves upon ResNet-50-FPN baseline with 1.9% AP and 5.5% AP90 on the MS COCO dataset with no extra overhead.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143879518-842f5bec-9f65-4454-93a1-9b3b0c42ec3c.png"/>
+</div>
+
+## Results and Models
+
+| Backbone |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                        Config                                                        |                                                                                                                                      Download                                                                                                                                      |
+| :------: | :-----: | :-----: | :------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | pytorch |   1x    |   3.8    |                |  38.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x-62a3f276.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x_20200618_095048.log.json) |
+
+## Citation
+
+```latex
+@article{DynamicRCNN,
+    author = {Hongkai Zhang and Hong Chang and Bingpeng Ma and Naiyan Wang and Xilin Chen},
+    title = {Dynamic {R-CNN}: Towards High Quality Object Detection via Dynamic Training},
+    journal = {arXiv preprint arXiv:2004.06002},
+    year = {2020}
+}
+```
diff --git a/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py b/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..f2deb99
--- /dev/null
+++ b/configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,28 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        type='DynamicRoIHead',
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    train_cfg=dict(
+        rpn_proposal=dict(nms=dict(iou_threshold=0.85)),
+        rcnn=dict(
+            dynamic_rcnn=dict(
+                iou_topk=75,
+                beta_topk=10,
+                update_iter_interval=100,
+                initial_iou=0.4,
+                initial_beta=1.0))),
+    test_cfg=dict(rpn=dict(nms=dict(iou_threshold=0.85))))
diff --git a/configs/dynamic_rcnn/metafile.yml b/configs/dynamic_rcnn/metafile.yml
new file mode 100644
index 0000000..fec43db
--- /dev/null
+++ b/configs/dynamic_rcnn/metafile.yml
@@ -0,0 +1,35 @@
+Collections:
+  - Name: Dynamic R-CNN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Dynamic R-CNN
+        - FPN
+        - RPN
+        - ResNet
+        - RoIAlign
+    Paper:
+      URL: https://arxiv.org/pdf/2004.06002
+      Title: 'Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training'
+    README: configs/dynamic_rcnn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/roi_heads/dynamic_roi_head.py#L11
+      Version: v2.2.0
+
+Models:
+  - Name: dynamic_rcnn_r50_fpn_1x_coco
+    In Collection: Dynamic R-CNN
+    Config: configs/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.8
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/dynamic_rcnn/dynamic_rcnn_r50_fpn_1x/dynamic_rcnn_r50_fpn_1x-62a3f276.pth
diff --git a/configs/efficientnet/README.md b/configs/efficientnet/README.md
new file mode 100644
index 0000000..99b0572
--- /dev/null
+++ b/configs/efficientnet/README.md
@@ -0,0 +1,30 @@
+# EfficientNet
+
+> [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946v5)
+
+<!-- [BACKBONE] -->
+
+## Introduction
+
+Convolutional Neural Networks (ConvNets) are commonly developed at a fixed resource budget, and then scaled up for better accuracy if more resources are available. In this paper, we systematically study model scaling and identify that carefully balancing network depth, width, and resolution can lead to better performance. Based on this observation, we propose a new scaling method that uniformly scales all dimensions of depth/width/resolution using a simple yet highly effective compound coefficient. We demonstrate the effectiveness of this method on scaling up MobileNets and ResNet.
+
+To go even further, we use neural architecture search to design a new baseline network and scale it up to obtain a family of models, called EfficientNets, which achieve much better accuracy and efficiency than previous ConvNets. In particular, our EfficientNet-B7 achieves state-of-the-art 84.3% top-1 accuracy on ImageNet, while being 8.4x smaller and 6.1x faster on inference than the best existing ConvNet. Our EfficientNets also transfer well and achieve state-of-the-art accuracy on CIFAR-100 (91.7%), Flowers (98.8%), and 3 other transfer learning datasets, with an order of magnitude fewer parameters.
+
+## Results and Models
+
+### RetinaNet
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                             Config                                                              |                                                                                                                                                                              Download                                                                                                                                                                              |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Efficientnet-b3 | pytorch |   1x    |    -     |       -        |  40.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco/retinanet_effb3_fpn_crop896_8x4_1x_coco_20220322_234806-615a0dda.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco/retinanet_effb3_fpn_crop896_8x4_1x_coco_20220322_234806.log.json) |
+
+## Citation
+
+```latex
+@article{tan2019efficientnet,
+  title={Efficientnet: Rethinking model scaling for convolutional neural networks},
+  author={Tan, Mingxing and Le, Quoc V},
+  journal={arXiv preprint arXiv:1905.11946},
+  year={2019}
+}
+```
diff --git a/configs/efficientnet/metafile.yml b/configs/efficientnet/metafile.yml
new file mode 100644
index 0000000..de40b95
--- /dev/null
+++ b/configs/efficientnet/metafile.yml
@@ -0,0 +1,19 @@
+Models:
+  - Name: retinanet_effb3_fpn_crop896_8x4_1x_coco
+    In Collection: RetinaNet
+    Config: configs/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco/retinanet_effb3_fpn_crop896_8x4_1x_coco_20220322_234806-615a0dda.pth
+    Paper:
+      URL: https://arxiv.org/abs/1905.11946v5
+      Title: 'EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks'
+    README: configs/efficientnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.23.0/mmdet/models/backbones/efficientnet.py#L159
+      Version: v2.23.0
diff --git a/configs/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco.py b/configs/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco.py
new file mode 100644
index 0000000..39ccb37
--- /dev/null
+++ b/configs/efficientnet/retinanet_effb3_fpn_crop896_8x4_1x_coco.py
@@ -0,0 +1,94 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
+]
+
+# cudnn_benchmark = True
+norm_cfg = dict(type='BN', requires_grad=True)
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/efficientnet/efficientnet-b3_3rdparty_8xb32-aa_in1k_20220119-5b4887a0.pth'  # noqa
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='EfficientNet',
+        arch='b3',
+        drop_path_rate=0.2,
+        out_indices=(3, 4, 5),
+        frozen_stages=0,
+        norm_cfg=dict(
+            type='SyncBN', requires_grad=True, eps=1e-3, momentum=0.01),
+        norm_eval=False,
+        init_cfg=dict(
+            type='Pretrained', prefix='backbone', checkpoint=checkpoint)),
+    neck=dict(
+        in_channels=[48, 136, 384],
+        start_level=0,
+        out_channels=256,
+        relu_before_extra_convs=True,
+        no_norm_on_lateral=True,
+        norm_cfg=norm_cfg),
+    bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg),
+    # training and testing settings
+    train_cfg=dict(assigner=dict(neg_iou_thr=0.5)))
+
+# dataset settings
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+img_size = (896, 896)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=img_size,
+        ratio_range=(0.8, 1.2),
+        keep_ratio=True),
+    dict(type='RandomCrop', crop_size=img_size),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=img_size),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=img_size,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size=img_size),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer_config = dict(grad_clip=None)
+optimizer = dict(
+    type='SGD',
+    lr=0.04,
+    momentum=0.9,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=0.1,
+    step=[8, 11])
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=12)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (4 samples per GPU)
+auto_scale_lr = dict(base_batch_size=32)
diff --git a/configs/empirical_attention/README.md b/configs/empirical_attention/README.md
new file mode 100644
index 0000000..fc2620a
--- /dev/null
+++ b/configs/empirical_attention/README.md
@@ -0,0 +1,33 @@
+# Empirical Attention
+
+> [An Empirical Study of Spatial Attention Mechanisms in Deep Networks](https://arxiv.org/abs/1904.05873)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Attention mechanisms have become a popular component in deep neural networks, yet there has been little examination of how different influencing factors and methods for computing attention from these factors affect performance. Toward a better general understanding of attention mechanisms, we present an empirical study that ablates various spatial attention elements within a generalized attention formulation, encompassing the dominant Transformer attention as well as the prevalent deformable convolution and dynamic convolution modules. Conducted on a variety of applications, the study yields significant findings about spatial attention in deep networks, some of which run counter to conventional understanding. For example, we find that the query and key content comparison in Transformer attention is negligible for self-attention, but vital for encoder-decoder attention. A proper combination of deformable convolution with key content only saliency achieves the best accuracy-efficiency tradeoff in self-attention. Our results suggest that there exists much room for improvement in the design of attention mechanisms.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143879619-f1817da9-1573-45c9-891d-cfe55ad54911.png"/>
+</div>
+
+## Results and Models
+
+| Backbone | Attention Component | DCN | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                                    Config                                                                     |                                                                                                                                                                                               Download                                                                                                                                                                                                |
+| :------: | :-----------------: | :-: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   |        1111         |  N  |   1x    |   8.0    |      13.8      |  40.0  |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py)   |         [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130_210344.log.json)         |
+|   R-50   |        0010         |  N  |   1x    |   4.2    |      18.4      |  39.1  |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py)   |         [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130-7cb0c14d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130_210125.log.json)         |
+|   R-50   |        1111         |  Y  |   1x    |   8.0    |      12.7      |  42.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130-8b2523a6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130_204442.log.json) |
+|   R-50   |        0010         |  Y  |   1x    |   4.2    |      17.1      |  42.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130-1a2e831d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130_210410.log.json) |
+
+## Citation
+
+```latex
+@article{zhu2019empirical,
+  title={An Empirical Study of Spatial Attention Mechanisms in Deep Networks},
+  author={Zhu, Xizhou and Cheng, Dazhi and Zhang, Zheng and Lin, Stephen and Dai, Jifeng},
+  journal={arXiv preprint arXiv:1904.05873},
+  year={2019}
+}
+```
diff --git a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py
new file mode 100644
index 0000000..a544e3a
--- /dev/null
+++ b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py
@@ -0,0 +1,13 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(plugins=[
+        dict(
+            cfg=dict(
+                type='GeneralizedAttention',
+                spatial_range=-1,
+                num_heads=8,
+                attention_type='0010',
+                kv_stride=2),
+            stages=(False, False, True, True),
+            position='after_conv2')
+    ]))
diff --git a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py
new file mode 100644
index 0000000..bbefd27
--- /dev/null
+++ b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py
@@ -0,0 +1,16 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        plugins=[
+            dict(
+                cfg=dict(
+                    type='GeneralizedAttention',
+                    spatial_range=-1,
+                    num_heads=8,
+                    attention_type='0010',
+                    kv_stride=2),
+                stages=(False, False, True, True),
+                position='after_conv2')
+        ],
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
diff --git a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py
new file mode 100644
index 0000000..13a4645
--- /dev/null
+++ b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py
@@ -0,0 +1,13 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(plugins=[
+        dict(
+            cfg=dict(
+                type='GeneralizedAttention',
+                spatial_range=-1,
+                num_heads=8,
+                attention_type='1111',
+                kv_stride=2),
+            stages=(False, False, True, True),
+            position='after_conv2')
+    ]))
diff --git a/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py
new file mode 100644
index 0000000..b1f26c0
--- /dev/null
+++ b/configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py
@@ -0,0 +1,16 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        plugins=[
+            dict(
+                cfg=dict(
+                    type='GeneralizedAttention',
+                    spatial_range=-1,
+                    num_heads=8,
+                    attention_type='1111',
+                    kv_stride=2),
+                stages=(False, False, True, True),
+                position='after_conv2')
+        ],
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)))
diff --git a/configs/empirical_attention/metafile.yml b/configs/empirical_attention/metafile.yml
new file mode 100644
index 0000000..923bcb2
--- /dev/null
+++ b/configs/empirical_attention/metafile.yml
@@ -0,0 +1,103 @@
+Collections:
+  - Name: Empirical Attention
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Deformable Convolution
+        - FPN
+        - RPN
+        - ResNet
+        - RoIAlign
+        - Spatial Attention
+    Paper:
+      URL: https://arxiv.org/pdf/1904.05873
+      Title: 'An Empirical Study of Spatial Attention Mechanisms in Deep Networks'
+    README: configs/empirical_attention/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/generalized_attention.py#L10
+      Version: v2.0.0
+
+Models:
+  - Name: faster_rcnn_r50_fpn_attention_1111_1x_coco
+    In Collection: Empirical Attention
+    Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.0
+      inference time (ms/im):
+        - value: 72.46
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_1x_coco/faster_rcnn_r50_fpn_attention_1111_1x_coco_20200130-403cccba.pth
+
+  - Name: faster_rcnn_r50_fpn_attention_0010_1x_coco
+    In Collection: Empirical Attention
+    Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.2
+      inference time (ms/im):
+        - value: 54.35
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_1x_coco/faster_rcnn_r50_fpn_attention_0010_1x_coco_20200130-7cb0c14d.pth
+
+  - Name: faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco
+    In Collection: Empirical Attention
+    Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.0
+      inference time (ms/im):
+        - value: 78.74
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco/faster_rcnn_r50_fpn_attention_1111_dcn_1x_coco_20200130-8b2523a6.pth
+
+  - Name: faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco
+    In Collection: Empirical Attention
+    Config: configs/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.2
+      inference time (ms/im):
+        - value: 58.48
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/empirical_attention/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco/faster_rcnn_r50_fpn_attention_0010_dcn_1x_coco_20200130-1a2e831d.pth
diff --git a/configs/fast_rcnn/README.md b/configs/fast_rcnn/README.md
new file mode 100644
index 0000000..767f76c
--- /dev/null
+++ b/configs/fast_rcnn/README.md
@@ -0,0 +1,73 @@
+# Fast R-CNN
+
+> [Fast R-CNN](https://arxiv.org/abs/1504.08083)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+This paper proposes a Fast Region-based Convolutional Network method (Fast R-CNN) for object detection. Fast R-CNN builds on previous work to efficiently classify object proposals using deep convolutional networks. Compared to previous work, Fast R-CNN employs several innovations to improve training and testing speed while also increasing detection accuracy. Fast R-CNN trains the very deep VGG16 network 9x faster than R-CNN, is 213x faster at test-time, and achieves a higher mAP on PASCAL VOC 2012. Compared to SPPnet, Fast R-CNN trains VGG16 3x faster, tests 10x faster, and is more accurate.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143882189-6258c05c-f2a1-4320-9282-7e2f2d502eb2.png"/>
+</div>
+
+## Introduction
+
+Before training the Fast R-CNN, users should first train an [RPN](../rpn/README.md), and use the RPN to extract the region proposals.
+
+- Firstly, extract the region proposals of the val set by this command as below:
+
+```bash
+./tools/dist_test.sh \
+    configs/rpn_r50_fpn_1x_coco.py \
+    checkpoints/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth \
+    8 \
+    --out proposals/rpn_r50_fpn_1x_val2017.pkl
+```
+
+- Then, change the `ann_file` and `img_prefix` of `data.test` in the RPN config to train set as below:
+
+```python
+data = dict(
+    test=dict(
+        ann_file='data/coco/annotations/instances_train2017.json',
+        img_prefix='data/coco/train2017/'))
+```
+
+- Extract the region proposals of the train set by this command as below:
+
+```bash
+./tools/dist_test.sh \
+    configs/rpn_r50_fpn_1x_coco.py \
+    checkpoints/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth \
+    8 \
+    --out proposals/rpn_r50_fpn_1x_train2017.pkl
+```
+
+- Modify the path of `proposal_file` in Fast R-CNN config as below:
+
+```python
+data = dict(
+    train=dict(
+        proposal_file='proposals/rpn_r50_fpn_1x_train2017.pkl'),
+    val=dict(
+        proposal_file='proposals/rpn_r50_fpn_1x_val2017.pkl'),
+    test=dict(
+        proposal_file='proposals/rpn_r50_fpn_1x_val2017.pkl'))
+```
+
+Finally, users can start training the Fast R-CNN.
+
+## Results and Models
+
+## Citation
+
+```latex
+@inproceedings{girshick2015fast,
+  title={Fast r-cnn},
+  author={Girshick, Ross},
+  booktitle={Proceedings of the IEEE international conference on computer vision},
+  year={2015}
+}
+```
diff --git a/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..3ab8e98
--- /dev/null
+++ b/configs/fast_rcnn/fast_rcnn_r101_caffe_fpn_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './fast_rcnn_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))
diff --git a/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..83852b2
--- /dev/null
+++ b/configs/fast_rcnn/fast_rcnn_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './fast_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py b/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py
new file mode 100644
index 0000000..c220885
--- /dev/null
+++ b/configs/fast_rcnn/fast_rcnn_r101_fpn_2x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './fast_rcnn_r50_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..f1b29ef
--- /dev/null
+++ b/configs/fast_rcnn/fast_rcnn_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,48 @@
+_base_ = './fast_rcnn_r50_fpn_1x_coco.py'
+
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='BN', requires_grad=False),
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))
+
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadProposals', num_max_proposals=2000),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadProposals', num_max_proposals=None),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='ToTensor', keys=['proposals']),
+            dict(
+                type='ToDataContainer',
+                fields=[dict(key='proposals', stack=False)]),
+            dict(type='Collect', keys=['img', 'proposals']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py b/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..d2f080e
--- /dev/null
+++ b/configs/fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,52 @@
+_base_ = [
+    '../_base_/models/fast_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadProposals', num_max_proposals=2000),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadProposals', num_max_proposals=None),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='ToTensor', keys=['proposals']),
+            dict(
+                type='ToDataContainer',
+                fields=[dict(key='proposals', stack=False)]),
+            dict(type='Collect', keys=['img', 'proposals']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_train2017.pkl',
+        pipeline=train_pipeline),
+    val=dict(
+        proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl',
+        pipeline=test_pipeline),
+    test=dict(
+        proposal_file=data_root + 'proposals/rpn_r50_fpn_1x_val2017.pkl',
+        pipeline=test_pipeline))
diff --git a/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py b/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py
new file mode 100644
index 0000000..228e856
--- /dev/null
+++ b/configs/fast_rcnn/fast_rcnn_r50_fpn_2x_coco.py
@@ -0,0 +1,5 @@
+_base_ = './fast_rcnn_r50_fpn_1x_coco.py'
+
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/faster_rcnn/README.md b/configs/faster_rcnn/README.md
new file mode 100644
index 0000000..47c8ec7
--- /dev/null
+++ b/configs/faster_rcnn/README.md
@@ -0,0 +1,88 @@
+# Faster R-CNN
+
+> [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143881188-ab87720f-5059-4b4e-a928-b540fb8fb84d.png" height="300"/>
+</div>
+
+## Results and Models
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                          Config                                                           |                                                                                                                                                                          Download                                                                                                                                                                           |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|     R-50-C4     |  caffe  |   1x    |    -     |       -        |  35.6  |  [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py)  |            [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco/faster_rcnn_r50_caffe_c4_1x_coco_20220316_150152-3f885b85.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco/faster_rcnn_r50_caffe_c4_1x_coco_20220316_150152.log.json)             |
+|    R-50-DC5     |  caffe  |   1x    |    -     |       -        |  37.2  | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py)  |          [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909-531f0f43.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909.log.json)           |
+|    R-50-FPN     |  caffe  |   1x    |   3.8    |                |  37.8  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_20200504_180032.log.json)   |
+|    R-50-FPN     | pytorch |   1x    |   4.0    |      21.4      |  37.4  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py)     |                          [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json)                          |
+| R-50-FPN (FP16) | pytorch |   1x    |   3.4    |      28.8      |  37.5  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_fp16_1x_coco.py)  |                       [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204_143530.log.json)                       |
+|    R-50-FPN     | pytorch |   2x    |    -     |       -        |  38.4  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_20200504_210434.log.json)               |
+|    R-101-FPN    |  caffe  |   1x    |   5.7    |                |  39.8  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.398_20200504_180057-b269e9dd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_20200504_180057.log.json) |
+|    R-101-FPN    | pytorch |   1x    |   6.0    |      15.6      |  39.4  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py)    |                        [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130_204655.log.json)                        |
+|    R-101-FPN    | pytorch |   2x    |    -     |       -        |  39.8  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_20200504_210455.log.json)             |
+| X-101-32x4d-FPN | pytorch |   1x    |   7.2    |      13.8      |  41.2  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py) |            [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203-cff10310.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203_000520.log.json)            |
+| X-101-32x4d-FPN | pytorch |   2x    |    -     |       -        |  41.2  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.412_20200506_041400-64a12c0b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_20200506_041400.log.json) |
+| X-101-64x4d-FPN | pytorch |   1x    |   10.3   |      9.4       |  42.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py) |            [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204_134340.log.json)            |
+| X-101-64x4d-FPN | pytorch |   2x    |    -     |       -        |  41.6  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py) |        [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033-5961fa95.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033.log.json)         |
+
+## Different regression loss
+
+We trained with R-50-FPN pytorch style backbone for 1x schedule.
+
+| Backbone |   Loss type    | Mem (GB) | Inf time (fps) | box AP |                                                             Config                                                             |                                                                                                                                                             Download                                                                                                                                                             |
+| :------: | :------------: | :------: | :------------: | :----: | :----------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R-50-FPN |     L1Loss     |   4.0    |      21.4      |  37.4  |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py)       |            [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json)             |
+| R-50-FPN |    IoULoss     |          |                |  37.9  |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py)     | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco_20200506_095954-938e81f0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco_20200506_095954.log.json) |
+| R-50-FPN |    GIoULoss    |          |                |  37.6  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py)     |            [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco-0eada910.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco_20200505_161120.log.json)            |
+| R-50-FPN | BoundedIoULoss |          |                |  37.4  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py) |     [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco_20200505_160738.log.json)     |
+
+## Pre-trained Models
+
+We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks.
+
+|                              Backbone                              |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                              Config                                                               |                                                                                                                                                                                        Download                                                                                                                                                                                         |
+| :----------------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|      [R-50-C4](./faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py)      |  caffe  |   1x    |    -     |                |  35.9  |  [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py)  |          [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco/faster_rcnn_r50_caffe_c4_mstrain_1x_coco_20220316_150527-db276fed.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco/faster_rcnn_r50_caffe_c4_mstrain_1x_coco_20220316_150527.log.json)           |
+|     [R-50-DC5](./faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py)     |  caffe  |   1x    |    -     |                |  37.4  | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py)  |        [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851-b33d21b9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851.log.json)         |
+|     [R-50-DC5](./faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py)     |  caffe  |   3x    |    -     |                |  38.7  | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py)  |        [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107-34a53b2c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107.log.json)         |
+|     [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py)     |  caffe  |   2x    |   3.7    |                |  39.7  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py)  | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_20200504_231813.log.json) |
+|     [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py)     |  caffe  |   3x    |   3.7    |                |  39.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py)  |        [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054.log.json)         |
+|        [R-50-FPN](./faster_rcnn_r50_fpn_mstrain_3x_coco.py)        | pytorch |   3x    |   3.9    |                |  40.3  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py)     |                    [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822-e10bd31c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822.log.json)                     |
+|    [R-101-FPN](./faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py)    |  caffe  |   3x    |   5.6    |                |  42.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py) |      [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742-a7ae426d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742.log.json)       |
+|       [R-101-FPN](./faster_rcnn_r101_fpn_mstrain_3x_coco.py)       | pytorch |   3x    |   5.8    |                |  41.8  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py)    |                  [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822-4d4d2ca8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822.log.json)                   |
+| [X-101-32x4d-FPN](./faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py) | pytorch |   3x    |   7.0    |                |  42.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py) |      [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151-16b9b260.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151.log.json)       |
+| [X-101-32x8d-FPN](./faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py) | pytorch |   3x    |   10.1   |                |  42.4  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py) |      [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954-002e082a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954.log.json)       |
+| [X-101-64x4d-FPN](./faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py) | pytorch |   3x    |   10.0   |                |  43.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py) |      [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528-26c63de6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528.log.json)       |
+
+We further finetune some pre-trained models on the COCO subsets, which only contain only a few of the 80 categories.
+
+| Backbone                                                                      | Style | Class name         | Pre-traind model                                                    | Mem (GB) | box AP | Config                                                                      | Download                                                                                                                                                                                                                                                                                                                                                                                     |
+| ----------------------------------------------------------------------------- | ----- | ------------------ | ------------------------------------------------------------------- | -------- | ------ | --------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py)             | caffe | person             | [R-50-FPN-Caffe-3x](./faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | 3.7      | 55.8   | [config](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py)             | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person/faster_rcnn_r50_fpn_1x_coco-person_20201216_175929-d022e227.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person/faster_rcnn_r50_fpn_1x_coco-person_20201216_175929.log.json)                                                 |
+| [R-50-FPN](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py) | caffe | person-bicycle-car | [R-50-FPN-Caffe-3x](./faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py) | 3.7      | 44.1   | [config](./faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car_20201216_173117-6eda6d92.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car/faster_rcnn_r50_fpn_1x_coco-person-bicycle-car_20201216_173117.log.json) |
+
+## Torchvision New Receipe (TNR)
+
+Torchvision released its high-precision ResNet models. The training details can be found on the [Pytorch website](https://pytorch.org/blog/how-to-train-state-of-the-art-models-using-torchvision-latest-primitives/). Here, we have done grid searches on learning rate and weight decay and found the optimal hyper-parameter on the detection task.
+
+|                         Backbone                          |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                             Config                                                              |                                                                                                                                                                               Download                                                                                                                                                                               |
+| :-------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [R-50-TNR](./faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py) | pytorch |   1x    |    -     |                |  40.2  | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco_20220320_085147-efedfda4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco_20220320_085147.log.json) |
+
+## Citation
+
+```latex
+@article{Ren_2017,
+   title={Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks},
+   journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
+   author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
+   year={2017},
+   month={Jun},
+}
+```
diff --git a/configs/faster_rcnn/faster_rcnn_quant_general.py b/configs/faster_rcnn/faster_rcnn_quant_general.py
new file mode 100644
index 0000000..b2b3af6
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_quant_general.py
@@ -0,0 +1,56 @@
+trace_config = dict(
+    model_general_architecture = 'FasterRCNN', 
+    backbone_detail = dict(
+        input_concrete_args = dict(),
+        preserve_attr = ['arch_settings', 'avg_down', 'base_channels', 'conv_cfg', 'dcn', 'deep_stem', 'depth', 'dump_patches', 'feat_dim'
+        , 'frozen_stages', 'init_cfg', 'inplanes', 'is_init', 'norm_cfg', 'norm_eval', 'out_indices'
+        , 'plugins', 'res_layers', 'stage_block', 'stage_with_dcn', 'stem_channels', 'zero_init_residual', 'strides', 'with_cp', 'make_res_layer', 'make_stage_plugins'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = [],  
+        specified_general_quantizers = [],
+        last_8bit_module = []
+    )),
+    neck_detail = dict(
+        input_concrete_args = dict(in_num = 4),
+        preserve_attr = ['add_extra_convs', 'backbone_end_level', 'dump_patches', 'end_level', 'fp16_enabled'
+                         , 'in_channels', 'init_cfg', 'is_init', 'l2_norm', 'no_norm_on_lateral', 'num_ins', 'num_outs', 'out_channels'
+                         , 'relu_before_extra_convs', 'start_level', 'upsample_cfg'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = [],  
+        specified_general_quantizers = ['getitem_post_act_fake_quantizer', 'getitem_1_post_act_fake_quantizer', 'getitem_2_post_act_fake_quantizer', 'getitem_3_post_act_fake_quantizer'],
+        last_8bit_module = []
+    )),
+    rpn_head_detail = dict(
+        input_concrete_args = dict(in_num = 5),
+        preserve_attr = ['anchor_generator', 'assigner', 'bbox_coder', 'cls_out_channels', 'conv_cfg', 'dump_patches', 'feat_channels', 'fp16_enabled', 
+        'in_channels', 'init_cfg', 'is_init', 'norm_cfg', 'loss_bbox', 'loss_cls', 'num_anchors'
+        , 'num_base_priors', 'num_classes', 'num_convs', 'prior_generator', 'reg_decoded_bbox', 'sampler', 'sampling', 'stacked_convs', 'test_cfg', 'train_cfg', 'use_sigmoid_cls'
+        
+        , 'simple_test', 'async_simple_test_rpn', 'aug_test', 'aug_test_bboxes', 'aug_test_rpn', 'forward_single', 'forward_train', 'get_anchors', 'get_bboxes', 'get_targets', 'loss', 'loss_single', 'merge_aug_bboxes', 
+        'simple_test_bboxes', 'simple_test_rpn', '_get_bboxes_single', '_bbox_post_process', 'get_anchors', '_get_targets_single'],
+        not_duplicated_prefixes = ['rpn_conv', 'rpn_cls', 'rpn_reg'],  # 避免共享头被复制、独立化
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = [],  
+        specified_general_quantizers = [],
+        last_8bit_module = []
+    )), 
+    roi_head_bbox_head_detail = dict(
+        input_concrete_args = dict(dim_setting = 2),
+        preserve_attr = ['bbox_coder', 'cls_last_dim', 'cls_predictor_cfg', 'conv_cfg', 'conv_out_channels', 'custom_accuracy', 'custom_activation', 'custom_cls_channels'
+                         , 'debug_imgs', 'dump_patches', 'fc_out_channels', 'fp16_enabled', 'in_channels', 'init_cfg', 'is_init', 'loss_bbox', 'loss_cls', 'norm_cfg', 'num_classes', 'num_cls_convs', 'num_cls_fcs', 'num_reg_convs', 'num_reg_fcs'
+                         , 'num_shared_convs', 'num_shared_fcs', 'reg_class_agnostic', 'reg_decoded_bbox', 'reg_last_dim', 'reg_predictor_cfg', 'roi_feat_size', 'share_out_channels', 'with_avg_pool', 'with_cls', 'with_reg'
+                         , 'get_bboxes', 'get_targets', 'loss', 'refine_bboxes', 'regress_by_class', '_get_bboxes_single', '_bbox_post_process', 'get_anchors', '_get_targets_single'],
+        not_duplicated_prefixes = ['cls_convs', 'reg_convs', 'retina_cls', 'retina_reg'],  # 避免共享头被复制、独立化
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = [],  
+        qloss_flag = True, 
+        specified_general_quantizers = [],
+        last_8bit_module = [] 哦草，这里没写
+    ))
+    )
\ No newline at end of file
diff --git a/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..c6f078c
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './faster_rcnn_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))
diff --git a/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..6a13fe9
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py
@@ -0,0 +1,49 @@
+_base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py'
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        norm_cfg=dict(requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))
+
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+data = dict(
+    train=dict(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..1de53a6
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py
new file mode 100644
index 0000000..0d41599
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './faster_rcnn_r50_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..0b498bb
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py
@@ -0,0 +1,7 @@
+_base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py'
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/faster_rcnn/faster_rcnn_r18_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r18_fpn_1x_coco.py
new file mode 100644
index 0000000..b19bbe2
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r18_fpn_1x_coco.py
@@ -0,0 +1,18 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='../long_used_pretrained/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]))
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+data = dict(
+    samples_per_gpu=16,  # 就是8
+    workers_per_gpu=8,  # 就是8
+)
diff --git a/configs/faster_rcnn/faster_rcnn_r18_fpn_1x_coco_quant_w4a4.py b/configs/faster_rcnn/faster_rcnn_r18_fpn_1x_coco_quant_w4a4.py
new file mode 100644
index 0000000..00e1fe9
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r18_fpn_1x_coco_quant_w4a4.py
@@ -0,0 +1,19 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn_no_freeze.py',
+    '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_qat_w4a4.py', 
+    'faster_rcnn_quant_general.py', '../_base_/default_runtime.py'
+]
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]))
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+data = dict(
+    samples_per_gpu=4,  # 就是8
+    workers_per_gpu=4,  # 就是8
+)
+# 确实没有预训练模型，得自己训
diff --git a/configs/faster_rcnn/faster_rcnn_r18_fpn_1x_voc.py b/configs/faster_rcnn/faster_rcnn_r18_fpn_1x_voc.py
new file mode 100644
index 0000000..cac5d8c
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r18_fpn_1x_voc.py
@@ -0,0 +1,23 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_1x_voc_general.py',
+    '../_base_/default_runtime.py'
+]
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='../long_used_pretrained/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]),
+    roi_head=dict(
+        bbox_head=dict(
+            num_classes=20)))
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+
+
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8)
+
diff --git a/configs/faster_rcnn/faster_rcnn_r18_fpn_1x_voc_quant_w4a4.py b/configs/faster_rcnn/faster_rcnn_r18_fpn_1x_voc_quant_w4a4.py
new file mode 100644
index 0000000..3cc6a69
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r18_fpn_1x_voc_quant_w4a4.py
@@ -0,0 +1,24 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn_no_freeze.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_qat_w4a4.py', 
+    'faster_rcnn_quant_general.py', '../_base_/default_runtime.py'
+]
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='../long_used_pretrained/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]),
+    roi_head=dict(
+        bbox_head=dict(
+            num_classes=20)))
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+
+# dataset settings
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8)
+
+# load_from = '../long_used_pretrained/retinanet_r18_fpn_1x_coco_20220407_171055-614fd399.pth'
diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py
new file mode 100644
index 0000000..b071962
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py
@@ -0,0 +1,39 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_caffe_c4.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py
new file mode 100644
index 0000000..f4d83e6
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py
@@ -0,0 +1,38 @@
+_base_ = './faster_rcnn_r50_caffe_c4_1x_coco.py'
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py
new file mode 100644
index 0000000..ee2010c
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py
@@ -0,0 +1,37 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_caffe_dc5.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py
new file mode 100644
index 0000000..14eaef2
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py
@@ -0,0 +1,42 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_caffe_dc5.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py
new file mode 100644
index 0000000..403747f
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py'
+# learning policy
+lr_config = dict(step=[28, 34])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..56c01bd
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,41 @@
+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_90k_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_90k_coco.py
new file mode 100644
index 0000000..b5aea6a
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_90k_coco.py
@@ -0,0 +1,15 @@
+_base_ = 'faster_rcnn_r50_caffe_fpn_1x_coco.py'
+
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[60000, 80000])
+
+# Runner type
+runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
+
+checkpoint_config = dict(interval=10000)
+evaluation = dict(interval=10000, metric='bbox')
diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py
new file mode 100644
index 0000000..4f1f376
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person-bicycle-car.py
@@ -0,0 +1,9 @@
+_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
+model = dict(roi_head=dict(bbox_head=dict(num_classes=3)))
+classes = ('person', 'bicycle', 'car')
+data = dict(
+    train=dict(classes=classes),
+    val=dict(classes=classes),
+    test=dict(classes=classes))
+
+load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth'  # noqa
diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py
new file mode 100644
index 0000000..b5dfb4f
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-person.py
@@ -0,0 +1,9 @@
+_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
+model = dict(roi_head=dict(bbox_head=dict(num_classes=1)))
+classes = ('person', )
+data = dict(
+    train=dict(classes=classes),
+    val=dict(classes=classes),
+    test=dict(classes=classes))
+
+load_from = 'https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_bbox_mAP-0.398_20200504_163323-30042637.pth'  # noqa
diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py
new file mode 100644
index 0000000..f807a19
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py
@@ -0,0 +1,46 @@
+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py
new file mode 100644
index 0000000..df58973
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 23])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..9eeaace
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py
@@ -0,0 +1,47 @@
+_base_ = 'faster_rcnn_r50_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))
+
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+data = dict(
+    train=dict(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py
new file mode 100644
index 0000000..74dca24
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_90k_coco.py
@@ -0,0 +1,15 @@
+_base_ = 'faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
+
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[60000, 80000])
+
+# Runner type
+runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
+
+checkpoint_config = dict(interval=10000)
+evaluation = dict(interval=10000, metric='bbox')
diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..009bd93
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco_quant_w4a4.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco_quant_w4a4.py
new file mode 100644
index 0000000..75d5bcc
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco_quant_w4a4.py
@@ -0,0 +1,18 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn_no_freeze.py',
+    '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_qat_w4a4.py',
+    'faster_rcnn_quant_general.py', '../_base_/default_runtime.py'
+]
+
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+data = dict(
+    samples_per_gpu=16,  # 就是8
+    workers_per_gpu=8,  # 就是8
+)
+
+load_from = '../long_used_pretrained/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
+
+
+
diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_voc.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_voc.py
new file mode 100644
index 0000000..9a9ee6d
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_voc.py
@@ -0,0 +1,16 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_1x_voc_general.py',
+    '../_base_/default_runtime.py'
+]
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            num_classes=20)))
+# dataset settings
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8)
diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_voc_quant_w4a4.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_voc_quant_w4a4.py
new file mode 100644
index 0000000..5bf2a3c
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_voc_quant_w4a4.py
@@ -0,0 +1,18 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn_no_freeze.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_qat_w4a4.py', 
+    'faster_rcnn_quant_general.py', '../_base_/default_runtime.py'
+]
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            num_classes=20)))
+
+# dataset settings
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4)
+# load_from = '../long_used_pretrained/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth'
diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py
new file mode 100644
index 0000000..e77a7fa
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py
@@ -0,0 +1,5 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py
new file mode 100644
index 0000000..648081f
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            reg_decoded_bbox=True,
+            loss_bbox=dict(type='BoundedIoULoss', loss_weight=10.0))))
diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_ciou_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_ciou_1x_coco.py
new file mode 100644
index 0000000..886d566
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_ciou_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            reg_decoded_bbox=True,
+            loss_bbox=dict(type='CIoULoss', loss_weight=12.0))))
diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_fp16_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_fp16_1x_coco.py
new file mode 100644
index 0000000..acd4040
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_fp16_1x_coco.py
@@ -0,0 +1,3 @@
+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)
diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py
new file mode 100644
index 0000000..5556c49
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            reg_decoded_bbox=True,
+            loss_bbox=dict(type='GIoULoss', loss_weight=10.0))))
diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py
new file mode 100644
index 0000000..ddf663e
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            reg_decoded_bbox=True,
+            loss_bbox=dict(type='IoULoss', loss_weight=10.0))))
diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..faf8f92
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py
@@ -0,0 +1,3 @@
+_base_ = [
+    '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
+]
diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py
new file mode 100644
index 0000000..f897e7c
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py
@@ -0,0 +1,2 @@
+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(train_cfg=dict(rcnn=dict(sampler=dict(type='OHEMSampler'))))
diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py
new file mode 100644
index 0000000..759ae3a
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_soft_nms_1x_coco.py
@@ -0,0 +1,12 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    test_cfg=dict(
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='soft_nms', iou_threshold=0.5),
+            max_per_img=100)))
diff --git a/configs/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py b/configs/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py
new file mode 100644
index 0000000..ecbfb92
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py
@@ -0,0 +1,17 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+checkpoint = 'https://download.pytorch.org/models/resnet50-11ad3fa6.pth'
+model = dict(
+    backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=checkpoint)))
+
+# `lr` and `weight_decay` have been searched to be optimal.
+optimizer = dict(
+    _delete_=True,
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.1,
+    paramwise_cfg=dict(norm_decay_mult=0., bypass_duplicate=True))
diff --git a/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..3808c9f
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py
new file mode 100644
index 0000000..e93f5d8
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './faster_rcnn_r50_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..f55985d
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py
@@ -0,0 +1,16 @@
+_base_ = [
+    '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
+]
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..a5d5aeb
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py
@@ -0,0 +1,62 @@
+_base_ = [
+    '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
+]
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=8,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnext101_32x8d')))
+
+# ResNeXt-101-32x8d model trained with Caffe2 at FB,
+# so the mean and std need to be changed.
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675],
+    std=[57.375, 57.120, 58.395],
+    to_rgb=False)
+
+# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
+# multiscale_mode='range'
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+# Use RepeatDataset to speed up training
+data = dict(
+    train=dict(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..8bf2b65
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py
new file mode 100644
index 0000000..7ea9b2d
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './faster_rcnn_r50_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..80397f4
--- /dev/null
+++ b/configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py
@@ -0,0 +1,16 @@
+_base_ = [
+    '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
+]
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/faster_rcnn/metafile.yml b/configs/faster_rcnn/metafile.yml
new file mode 100644
index 0000000..3011b15
--- /dev/null
+++ b/configs/faster_rcnn/metafile.yml
@@ -0,0 +1,452 @@
+Collections:
+  - Name: Faster R-CNN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - RPN
+        - ResNet
+        - RoIPool
+    Paper:
+      URL: https://arxiv.org/abs/1506.01497
+      Title: "Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks"
+    README: configs/faster_rcnn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/faster_rcnn.py#L6
+      Version: v2.0.0
+
+Models:
+  - Name: faster_rcnn_r50_caffe_c4_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 35.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_1x_coco/faster_rcnn_r50_caffe_c4_1x_coco_20220316_150152-3f885b85.pth
+
+  - Name: faster_rcnn_r50_caffe_c4_mstrain_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 35.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_c4_mstrain_1x_coco/faster_rcnn_r50_caffe_c4_mstrain_1x_coco_20220316_150527-db276fed.pth
+
+  - Name: faster_rcnn_r50_caffe_dc5_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_1x_coco/faster_rcnn_r50_caffe_dc5_1x_coco_20201030_151909-531f0f43.pth
+
+  - Name: faster_rcnn_r50_caffe_fpn_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.8
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco/faster_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.378_20200504_180032-c5925ee5.pth
+
+  - Name: faster_rcnn_r50_fpn_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.0
+      inference time (ms/im):
+        - value: 46.73
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
+
+  - Name: faster_rcnn_r50_fpn_fp16_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_fpn_fp16_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.4
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+        - Mixed Precision Training
+      inference time (ms/im):
+        - value: 34.72
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP16
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/faster_rcnn_r50_fpn_fp16_1x_coco/faster_rcnn_r50_fpn_fp16_1x_coco_20200204-d4dc1471.pth
+
+  - Name: faster_rcnn_r50_fpn_2x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 4.0
+      inference time (ms/im):
+        - value: 46.73
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_2x_coco/faster_rcnn_r50_fpn_2x_coco_bbox_mAP-0.384_20200504_210434-a5d8aa15.pth
+
+  - Name: faster_rcnn_r101_caffe_fpn_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.7
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_1x_coco/faster_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.398_20200504_180057-b269e9dd.pth
+
+  - Name: faster_rcnn_r101_fpn_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.0
+      inference time (ms/im):
+        - value: 64.1
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_1x_coco/faster_rcnn_r101_fpn_1x_coco_20200130-f513f705.pth
+
+  - Name: faster_rcnn_r101_fpn_2x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r101_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 6.0
+      inference time (ms/im):
+        - value: 64.1
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_2x_coco/faster_rcnn_r101_fpn_2x_coco_bbox_mAP-0.398_20200504_210455-1d2dac9c.pth
+
+  - Name: faster_rcnn_x101_32x4d_fpn_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.2
+      inference time (ms/im):
+        - value: 72.46
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco/faster_rcnn_x101_32x4d_fpn_1x_coco_20200203-cff10310.pth
+
+  - Name: faster_rcnn_x101_32x4d_fpn_2x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 7.2
+      inference time (ms/im):
+        - value: 72.46
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_2x_coco/faster_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.412_20200506_041400-64a12c0b.pth
+
+  - Name: faster_rcnn_x101_64x4d_fpn_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.3
+      inference time (ms/im):
+        - value: 106.38
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_1x_coco/faster_rcnn_x101_64x4d_fpn_1x_coco_20200204-833ee192.pth
+
+  - Name: faster_rcnn_x101_64x4d_fpn_2x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 10.3
+      inference time (ms/im):
+        - value: 106.38
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_2x_coco/faster_rcnn_x101_64x4d_fpn_2x_coco_20200512_161033-5961fa95.pth
+
+  - Name: faster_rcnn_r50_fpn_iou_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.9
+    # re-release
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_iou_1x_coco/faster_rcnn_r50_fpn_iou_1x_coco_20200506_095954-938e81f0.pth
+
+  - Name: faster_rcnn_r50_fpn_giou_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_fpn_giou_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_giou_1x_coco-0eada910.pth
+
+  - Name: faster_rcnn_r50_fpn_bounded_iou_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_fpn_bounded_iou_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_bounded_iou_1x_coco-98ad993b.pth
+
+  - Name: faster_rcnn_r50_caffe_dc5_mstrain_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco/faster_rcnn_r50_caffe_dc5_mstrain_1x_coco_20201028_233851-b33d21b9.pth
+
+  - Name: faster_rcnn_r50_caffe_dc5_mstrain_3x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco/faster_rcnn_r50_caffe_dc5_mstrain_3x_coco_20201028_002107-34a53b2c.pth
+
+  - Name: faster_rcnn_r50_caffe_fpn_mstrain_2x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco.py
+    Metadata:
+      Training Memory (GB): 4.3
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco/faster_rcnn_r50_caffe_fpn_mstrain_2x_coco_bbox_mAP-0.397_20200504_231813-10b2de58.pth
+
+  - Name: faster_rcnn_r50_caffe_fpn_mstrain_3x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 3.7
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth
+
+  - Name: faster_rcnn_r50_fpn_mstrain_3x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 3.9
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_mstrain_3x_coco/faster_rcnn_r50_fpn_mstrain_3x_coco_20210524_110822-e10bd31c.pth
+
+  - Name: faster_rcnn_r101_caffe_fpn_mstrain_3x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 5.6
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco/faster_rcnn_r101_caffe_fpn_mstrain_3x_coco_20210526_095742-a7ae426d.pth
+
+  - Name: faster_rcnn_r101_fpn_mstrain_3x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 5.8
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r101_fpn_mstrain_3x_coco/faster_rcnn_r101_fpn_mstrain_3x_coco_20210524_110822-4d4d2ca8.pth
+
+  - Name: faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 7.0
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x4d_fpn_mstrain_3x_coco_20210524_124151-16b9b260.pth
+
+  - Name: faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 10.1
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco/faster_rcnn_x101_32x8d_fpn_mstrain_3x_coco_20210604_182954-002e082a.pth
+
+  - Name: faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 10.0
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco/faster_rcnn_x101_64x4d_fpn_mstrain_3x_coco_20210524_124528-26c63de6.pth
+
+  - Name: faster_rcnn_r50_fpn_tnr-pretrain_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.0
+      inference time (ms/im):
+        - value: 46.73
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco/faster_rcnn_r50_fpn_tnr-pretrain_1x_coco_20220320_085147-efedfda4.pth
diff --git a/configs/fcos/README.md b/configs/fcos/README.md
new file mode 100644
index 0000000..76be365
--- /dev/null
+++ b/configs/fcos/README.md
@@ -0,0 +1,45 @@
+# FCOS
+
+> [FCOS: Fully Convolutional One-Stage Object Detection](https://arxiv.org/abs/1904.01355)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We propose a fully convolutional one-stage object detector (FCOS) to solve object detection in a per-pixel prediction fashion, analogue to semantic segmentation. Almost all state-of-the-art object detectors such as RetinaNet, SSD, YOLOv3, and Faster R-CNN rely on pre-defined anchor boxes. In contrast, our proposed detector FCOS is anchor box free, as well as proposal free. By eliminating the predefined set of anchor boxes, FCOS completely avoids the complicated computation related to anchor boxes such as calculating overlapping during training. More importantly, we also avoid all hyper-parameters related to anchor boxes, which are often very sensitive to the final detection performance. With the only post-processing non-maximum suppression (NMS), FCOS with ResNeXt-64x4d-101 achieves 44.7% in AP with single-model and single-scale testing, surpassing previous one-stage detectors with the advantage of being much simpler. For the first time, we demonstrate a much simpler and flexible detection framework achieving improved detection accuracy. We hope that the proposed FCOS framework can serve as a simple and strong alternative for many other instance-level tasks.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143882011-45b234bc-d04b-4bbe-a822-94bec057ac86.png"/>
+</div>
+
+## Results and Models
+
+| Backbone | Style | GN  | MS train | Tricks | DCN | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                                         Config                                                                          |                                                                                                                                                                                          Download                                                                                                                                                                                          |
+| :------: | :---: | :-: | :------: | :----: | :-: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | caffe |  Y  |    N     |   N    |  N  |   1x    |   3.6    |      22.7      |  36.6  |                   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py)                    |                                                        [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/fcos_r50_caffe_fpn_gn-head_1x_coco-821213aa.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/20201227_180009.log.json)                                                         |
+|   R-50   | caffe |  Y  |    N     |   Y    |  N  |   1x    |   3.7    |       -        |  38.7  |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py)   |       [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/20210105_135818.log.json)       |
+|   R-50   | caffe |  Y  |    N     |   Y    |  Y  |   1x    |   3.8    |       -        |  42.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco-ae4d8b3d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/20210105_224556.log.json) |
+|  R-101   | caffe |  Y  |    N     |   N    |  N  |   1x    |   5.5    |      17.3      |  39.1  |                   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py)                   |                                                       [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/fcos_r101_caffe_fpn_gn-head_1x_coco-0e37b982.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/20210103_155046.log.json)                                                       |
+
+| Backbone |  Style  | GN  | MS train | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                               Config                                                                |                                                                                                                                                            Download                                                                                                                                                            |
+| :------: | :-----: | :-: | :------: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   |  caffe  |  Y  |    Y     |   2x    |   2.6    |      22.9      |  38.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py)  |  [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco-d92ceeea.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/20201227_161900.log.json)   |
+|  R-101   |  caffe  |  Y  |    Y     |   2x    |   5.5    |      17.3      |  40.8  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco-511424d6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/20210103_155046.log.json) |
+|  X-101   | pytorch |  Y  |    Y     |   2x    |   10.0   |      9.7       |  42.6  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco-ede514a8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/20210114_133041.log.json) |
+
+**Notes:**
+
+- The X-101 backbone is X-101-64x4d.
+- Tricks means setting `norm_on_bbox`, `centerness_on_reg`, `center_sampling` as `True`.
+- DCN means using `DCNv2` in both backbone and head.
+
+## Citation
+
+```latex
+@article{tian2019fcos,
+  title={FCOS: Fully Convolutional One-Stage Object Detection},
+  author={Tian, Zhi and Shen, Chunhua and Chen, Hao and He, Tong},
+  journal={arXiv preprint arXiv:1904.01355},
+  year={2019}
+}
+```
diff --git a/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py
new file mode 100644
index 0000000..2699bdb
--- /dev/null
+++ b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py
@@ -0,0 +1,54 @@
+_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
+
+model = dict(
+    backbone=dict(
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    bbox_head=dict(
+        norm_on_bbox=True,
+        centerness_on_reg=True,
+        dcn_on_last_conv=False,
+        center_sampling=True,
+        conv_bias=True,
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.0)),
+    # training and testing settings
+    test_cfg=dict(nms=dict(type='nms', iou_threshold=0.6)))
+
+# dataset settings
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+optimizer_config = dict(_delete_=True, grad_clip=None)
+
+lr_config = dict(warmup='linear')
diff --git a/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py
new file mode 100644
index 0000000..cf93c91
--- /dev/null
+++ b/configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py
@@ -0,0 +1,56 @@
+_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
+
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True),
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    bbox_head=dict(
+        norm_on_bbox=True,
+        centerness_on_reg=True,
+        dcn_on_last_conv=True,
+        center_sampling=True,
+        conv_bias=True,
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.0)),
+    # training and testing settings
+    test_cfg=dict(nms=dict(type='nms', iou_threshold=0.6)))
+
+# dataset settings
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+optimizer_config = dict(_delete_=True, grad_clip=None)
+
+lr_config = dict(warmup='linear')
diff --git a/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py
new file mode 100644
index 0000000..9f502e7
--- /dev/null
+++ b/configs/fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py
@@ -0,0 +1,2 @@
+_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
+model = dict(bbox_head=dict(center_sampling=True, center_sample_radius=1.5))
diff --git a/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py
new file mode 100644
index 0000000..45bea48
--- /dev/null
+++ b/configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron/resnet101_caffe')))
diff --git a/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py b/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
new file mode 100644
index 0000000..f4d36f1
--- /dev/null
+++ b/configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
@@ -0,0 +1,47 @@
+_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron/resnet101_caffe')))
+img_norm_cfg = dict(
+    mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py b/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py
new file mode 100644
index 0000000..955787b
--- /dev/null
+++ b/configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py
@@ -0,0 +1,106 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+    type='FCOS',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron/resnet50_caffe')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',  # use P5
+        num_outs=5,
+        relu_before_extra_convs=True),
+    bbox_head=dict(
+        type='FCOSHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        strides=[8, 16, 32, 64, 128],
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='IoULoss', loss_weight=1.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),
+        max_per_img=100))
+img_norm_cfg = dict(
+    mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='constant',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    step=[8, 11])
+runner = dict(type='EpochBasedRunner', max_epochs=12)
diff --git a/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py
new file mode 100644
index 0000000..2816b16
--- /dev/null
+++ b/configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py
@@ -0,0 +1,4 @@
+# TODO: Remove this config after benchmarking all related configs
+_base_ = 'fcos_r50_caffe_fpn_gn-head_1x_coco.py'
+
+data = dict(samples_per_gpu=4, workers_per_gpu=4)
diff --git a/configs/fcos/fcos_r50_caffe_fpn_gn-head_fp16_1x_bs8x8_coco.py b/configs/fcos/fcos_r50_caffe_fpn_gn-head_fp16_1x_bs8x8_coco.py
new file mode 100644
index 0000000..f7c973c
--- /dev/null
+++ b/configs/fcos/fcos_r50_caffe_fpn_gn-head_fp16_1x_bs8x8_coco.py
@@ -0,0 +1,13 @@
+_base_ = ['./fcos_r50_caffe_fpn_gn-head_1x_coco.py']
+
+data = dict(samples_per_gpu=8, workers_per_gpu=8)
+
+# optimizer
+optimizer = dict(lr=0.04)
+fp16 = dict(loss_scale='dynamic')
+
+# learning policy
+# In order to avoid non-convergence in the early stage of
+# mixed-precision training, the warmup in the lr_config is set to linear,
+# warmup_iters increases and warmup_ratio decreases.
+lr_config = dict(warmup='linear', warmup_iters=1000, warmup_ratio=1.0 / 10)
diff --git a/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py b/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
new file mode 100644
index 0000000..497d03f
--- /dev/null
+++ b/configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
@@ -0,0 +1,39 @@
+_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
+img_norm_cfg = dict(
+    mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py b/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py
new file mode 100644
index 0000000..e70e465
--- /dev/null
+++ b/configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py
@@ -0,0 +1,60 @@
+_base_ = './fcos_r50_caffe_fpn_gn-head_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/fcos/metafile.yml b/configs/fcos/metafile.yml
new file mode 100644
index 0000000..ae922eb
--- /dev/null
+++ b/configs/fcos/metafile.yml
@@ -0,0 +1,146 @@
+Collections:
+  - Name: FCOS
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - Group Normalization
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1904.01355
+      Title: 'FCOS: Fully Convolutional One-Stage Object Detection'
+    README: configs/fcos/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/fcos.py#L6
+      Version: v2.0.0
+
+Models:
+  - Name: fcos_r50_caffe_fpn_gn-head_1x_coco
+    In Collection: FCOS
+    Config: configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.6
+      inference time (ms/im):
+        - value: 44.05
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco/fcos_r50_caffe_fpn_gn-head_1x_coco-821213aa.pth
+
+  - Name: fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco
+    In Collection: FCOS
+    Config: configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.7
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_1x_coco-0a0d75a8.pth
+
+  - Name: fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco
+    In Collection: FCOS
+    Config: configs/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.8
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco/fcos_center-normbbox-centeronreg-giou_r50_caffe_fpn_gn-head_dcn_1x_coco-ae4d8b3d.pth
+
+  - Name: fcos_r101_caffe_fpn_gn-head_1x_coco
+    In Collection: FCOS
+    Config: configs/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.5
+      inference time (ms/im):
+        - value: 57.8
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_1x_coco/fcos_r101_caffe_fpn_gn-head_1x_coco-0e37b982.pth
+
+  - Name: fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco
+    In Collection: FCOS
+    Config: configs/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
+    Metadata:
+      Training Memory (GB): 2.6
+      inference time (ms/im):
+        - value: 43.67
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r50_caffe_fpn_gn-head_mstrain_640-800_2x_coco-d92ceeea.pth
+
+  - Name: fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco
+    In Collection: FCOS
+    Config: configs/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco.py
+    Metadata:
+      Training Memory (GB): 5.5
+      inference time (ms/im):
+        - value: 57.8
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco/fcos_r101_caffe_fpn_gn-head_mstrain_640-800_2x_coco-511424d6.pth
+
+  - Name: fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco
+    In Collection: FCOS
+    Config: configs/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco.py
+    Metadata:
+      Training Memory (GB): 10.0
+      inference time (ms/im):
+        - value: 103.09
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fcos/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco/fcos_x101_64x4d_fpn_gn-head_mstrain_640-800_2x_coco-ede514a8.pth
diff --git a/configs/foveabox/README.md b/configs/foveabox/README.md
new file mode 100644
index 0000000..7fcd094
--- /dev/null
+++ b/configs/foveabox/README.md
@@ -0,0 +1,53 @@
+# FoveaBox
+
+> [FoveaBox: Beyond Anchor-based Object Detector](https://arxiv.org/abs/1904.03797)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We present FoveaBox, an accurate, flexible, and completely anchor-free framework for object detection. While almost all state-of-the-art object detectors utilize predefined anchors to enumerate possible locations, scales and aspect ratios for the search of the objects, their performance and generalization ability are also limited to the design of anchors. Instead, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object. The scales of target boxes are naturally associated with feature pyramid representations. In FoveaBox, an instance is assigned to adjacent feature levels to make the model more accurate.We demonstrate its effectiveness on standard benchmarks and report extensive experimental analysis. Without bells and whistles, FoveaBox achieves state-of-the-art single model performance on the standard COCO and Pascal VOC object detection benchmark. More importantly, FoveaBox avoids all computation and hyper-parameters related to anchor boxes, which are often sensitive to the final detection performance. We believe the simple and effective approach will serve as a solid baseline and help ease future research for object detection.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143885497-332d38a7-b492-4f51-b9d2-ef9d4ad4412a.png"/>
+</div>
+
+## Introduction
+
+FoveaBox is an accurate, flexible and completely anchor-free object detection system for object detection framework, as presented in our paper [https://arxiv.org/abs/1904.03797](https://arxiv.org/abs/1904.03797):
+Different from previous anchor-based methods, FoveaBox directly learns the object existing possibility and the bounding box coordinates without anchor reference. This is achieved by: (a) predicting category-sensitive semantic maps for the object existing possibility, and (b) producing category-agnostic bounding box for each position that potentially contains an object.
+
+## Results and Models
+
+### Results on R50/101-FPN
+
+| Backbone |  Style  | align | ms-train | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                                    Config                                                                    |                                                                                                                                                                                                        Download                                                                                                                                                                                                         |
+| :------: | :-----: | :---: | :------: | :-----: | :------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | pytorch |   N   |    N     |   1x    |   5.6    |      24.1      |  36.5  |                [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py)                 |                                                               [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219_223025.log.json)                                                               |
+|   R-50   | pytorch |   N   |    N     |   2x    |   5.6    |       -        |  37.2  |                [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py)                 |                                                               [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203-2df792b1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203_112043.log.json)                                                               |
+|   R-50   | pytorch |   Y   |    N     |   2x    |   8.1    |      19.4      |  37.9  |         [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py)          |                                   [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203_134252.log.json)                                   |
+|   R-50   | pytorch |   Y   |    Y     |   2x    |   8.1    |      18.3      |  40.4  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205-85ce26cb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205_112557.log.json)   |
+|  R-101   | pytorch |   N   |    N     |   1x    |   9.2    |      17.4      |  38.6  |                [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py)                |                                                             [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219-05e38f1c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219_011740.log.json)                                                             |
+|  R-101   | pytorch |   N   |    N     |   2x    |   11.7   |       -        |  40.0  |                [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py)                |                                                             [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208-02320ea4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208_202059.log.json)                                                             |
+|  R-101   | pytorch |   Y   |    N     |   2x    |   11.7   |      14.7      |  40.0  |         [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py)         |                                 [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208-c39a027a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208_203337.log.json)                                 |
+|  R-101   | pytorch |   Y   |    Y     |   2x    |   11.7   |      14.7      |  42.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208-649c5eb6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208_202124.log.json) |
+
+\[1\] *1x and 2x mean the model is trained for 12 and 24 epochs, respectively.* \
+\[2\] *Align means utilizing deformable convolution to align the cls branch.* \
+\[3\] *All results are obtained with a single model and without any test time data augmentation.*\
+\[4\] *We use 4 GPUs for training.*
+
+Any pull requests or issues are welcome.
+
+## Citation
+
+Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows.
+
+```latex
+@article{kong2019foveabox,
+  title={FoveaBox: Beyond Anchor-based Object Detector},
+  author={Kong, Tao and Sun, Fuchun and Liu, Huaping and Jiang, Yuning and Shi, Jianbo},
+  journal={arXiv preprint arXiv:1904.03797},
+  year={2019}
+}
+```
diff --git a/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py b/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py
new file mode 100644
index 0000000..c5d1784
--- /dev/null
+++ b/configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py
@@ -0,0 +1,12 @@
+_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')),
+    bbox_head=dict(
+        with_deform=True,
+        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
new file mode 100644
index 0000000..cc5affe
--- /dev/null
+++ b/configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
@@ -0,0 +1,29 @@
+_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')),
+    bbox_head=dict(
+        with_deform=True,
+        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py b/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py
new file mode 100644
index 0000000..e7265bc
--- /dev/null
+++ b/configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py
@@ -0,0 +1,10 @@
+_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
+model = dict(
+    bbox_head=dict(
+        with_deform=True,
+        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
new file mode 100644
index 0000000..8fc39be
--- /dev/null
+++ b/configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
@@ -0,0 +1,25 @@
+_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
+model = dict(
+    bbox_head=dict(
+        with_deform=True,
+        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py b/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py
new file mode 100644
index 0000000..9201af1
--- /dev/null
+++ b/configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py b/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py
new file mode 100644
index 0000000..1ef5243
--- /dev/null
+++ b/configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './fovea_r50_fpn_4x4_2x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py b/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py
new file mode 100644
index 0000000..7e986eb
--- /dev/null
+++ b/configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py
@@ -0,0 +1,52 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+    type='FOVEA',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        num_outs=5,
+        add_extra_convs='on_input'),
+    bbox_head=dict(
+        type='FoveaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        strides=[8, 16, 32, 64, 128],
+        base_edge_list=[16, 32, 64, 128, 256],
+        scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)),
+        sigma=0.4,
+        with_deform=False,
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=1.50,
+            alpha=0.4,
+            loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(),
+    test_cfg=dict(
+        nms_pre=1000,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),
+        max_per_img=100))
+data = dict(samples_per_gpu=4, workers_per_gpu=4)
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py b/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py
new file mode 100644
index 0000000..68ce4d2
--- /dev/null
+++ b/configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './fovea_r50_fpn_4x4_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/foveabox/metafile.yml b/configs/foveabox/metafile.yml
new file mode 100644
index 0000000..fe9a283
--- /dev/null
+++ b/configs/foveabox/metafile.yml
@@ -0,0 +1,172 @@
+Collections:
+  - Name: FoveaBox
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 4x V100 GPUs
+      Architecture:
+        - FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1904.03797
+      Title: 'FoveaBox: Beyond Anchor-based Object Detector'
+    README: configs/foveabox/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/fovea.py#L6
+      Version: v2.0.0
+
+Models:
+  - Name: fovea_r50_fpn_4x4_1x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_r50_fpn_4x4_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.6
+      inference time (ms/im):
+        - value: 41.49
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_1x_coco/fovea_r50_fpn_4x4_1x_coco_20200219-ee4d5303.pth
+
+  - Name: fovea_r50_fpn_4x4_2x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_r50_fpn_4x4_2x_coco.py
+    Metadata:
+      Training Memory (GB): 5.6
+      inference time (ms/im):
+        - value: 41.49
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r50_fpn_4x4_2x_coco/fovea_r50_fpn_4x4_2x_coco_20200203-2df792b1.pth
+
+  - Name: fovea_align_r50_fpn_gn-head_4x4_2x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py
+    Metadata:
+      Training Memory (GB): 8.1
+      inference time (ms/im):
+        - value: 51.55
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco/fovea_align_r50_fpn_gn-head_4x4_2x_coco_20200203-8987880d.pth
+
+  - Name: fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
+    Metadata:
+      Training Memory (GB): 8.1
+      inference time (ms/im):
+        - value: 54.64
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200205-85ce26cb.pth
+
+  - Name: fovea_r101_fpn_4x4_1x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_r101_fpn_4x4_1x_coco.py
+    Metadata:
+      Training Memory (GB): 9.2
+      inference time (ms/im):
+        - value: 57.47
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_1x_coco/fovea_r101_fpn_4x4_1x_coco_20200219-05e38f1c.pth
+
+  - Name: fovea_r101_fpn_4x4_2x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_r101_fpn_4x4_2x_coco.py
+    Metadata:
+      Training Memory (GB): 11.7
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_r101_fpn_4x4_2x_coco/fovea_r101_fpn_4x4_2x_coco_20200208-02320ea4.pth
+
+  - Name: fovea_align_r101_fpn_gn-head_4x4_2x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco.py
+    Metadata:
+      Training Memory (GB): 11.7
+      inference time (ms/im):
+        - value: 68.03
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_4x4_2x_coco/fovea_align_r101_fpn_gn-head_4x4_2x_coco_20200208-c39a027a.pth
+
+  - Name: fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco
+    In Collection: FoveaBox
+    Config: configs/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py
+    Metadata:
+      Training Memory (GB): 11.7
+      inference time (ms/im):
+        - value: 68.03
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/foveabox/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco/fovea_align_r101_fpn_gn-head_mstrain_640-800_4x4_2x_coco_20200208-649c5eb6.pth
diff --git a/configs/fpg/README.md b/configs/fpg/README.md
new file mode 100644
index 0000000..0ffd2e7
--- /dev/null
+++ b/configs/fpg/README.md
@@ -0,0 +1,43 @@
+# FPG
+
+> [Feature Pyramid Grids](https://arxiv.org/abs/2004.03580)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Feature pyramid networks have been widely adopted in the object detection literature to improve feature representations for better handling of variations in scale. In this paper, we present Feature Pyramid Grids (FPG), a deep multi-pathway feature pyramid, that represents the feature scale-space as a regular grid of parallel bottom-up pathways which are fused by multi-directional lateral connections. FPG can improve single-pathway feature pyramid networks by significantly increasing its performance at similar computation cost, highlighting importance of deep pyramid representations. In addition to its general and uniform structure, over complicated structures that have been found with neural architecture search, it also compares favorably against such approaches without relying on search. We hope that FPG with its uniform and effective nature can serve as a strong component for future work in object recognition.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143885611-85902399-2885-4a85-9126-9b9b7464ad08.png"/>
+</div>
+
+## Results and Models
+
+We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN.
+All backbones are Resnet-50 in pytorch style.
+
+|    Method    |    Neck    | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                           Config                                                           |                                                                                                                                                                             Download                                                                                                                                                                             |
+| :----------: | :--------: | :-----: | :------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Faster R-CNN |    FPG     |   50e   |   20.0   |       -        |  42.3  |    -    |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py)     |        [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/faster_rcnn_r50_fpg_crop640_50e_coco_20220311_011856-74109f42.pth) \|               [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/faster_rcnn_r50_fpg_crop640_50e_coco_20220311_011856.log.json)        |
+| Faster R-CNN | FPG-chn128 |   50e   |   11.9   |       -        |  41.2  |    -    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/faster_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011857-9376aa9d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/faster_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011857.log.json) |
+| Faster R-CNN |    FPN     |   50e   |   20.0   |       -        |  38.9  |    -    |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py)     |        [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpn_crop640_50e_coco/faster_rcnn_r50_fpn_crop640_50e_coco_20220311_011857-be7c9f42.pth) \|               [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpn_crop640_50e_coco/faster_rcnn_r50_fpn_crop640_50e_coco_20220311_011857.log.json)        |
+|  Mask R-CNN  |    FPG     |   50e   |   23.2   |       -        |  43.0  |  38.1   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py)      |          [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco_20220311_011857-233b8334.pth) \|                   [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco_20220311_011857.log.json)          |
+|  Mask R-CNN  | FPG-chn128 |   50e   |   15.3   |       -        |  41.7  |  37.1   |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/mask_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011859-043c9b4e.pth) \|     [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/mask_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011859.log.json)   |
+|  Mask R-CNN  |    FPN     |   50e   |   23.2   |       -        |  39.6  |  35.6   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py)      |          [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpn_crop640_50e_coco/mask_rcnn_r50_fpn_crop640_50e_coco_20220311_011855-a756664a.pth) \|                   [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpn_crop640_50e_coco/mask_rcnn_r50_fpn_crop640_50e_coco_20220311_011855.log.json)          |
+|  RetinaNet   |    FPG     |   50e   |   20.8   |       -        |  40.5  |    -    |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py)      |          [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/retinanet_r50_fpg_crop640_50e_coco_20220311_110809-b0bcf5f4.pth) \|                   [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/retinanet_r50_fpg_crop640_50e_coco_20220311_110809.log.json)          |
+|  RetinaNet   | FPG-chn128 |   50e   |   19.9   |       -        |  39.9  |    -    |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/retinanet_r50_fpg-chn128_crop640_50e_coco_20220313_104829-ee99a686.pth) \|     [log](https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/retinanet_r50_fpg-chn128_crop640_50e_coco_20220313_104829.log.json)   |
+
+**Note**: Chn128 means to decrease the number of channels of features and convs from 256 (default) to 128 in
+Neck and BBox Head, which can greatly decrease memory consumption without sacrificing much precision.
+
+## Citation
+
+```latex
+@article{chen2020feature,
+  title={Feature pyramid grids},
+  author={Chen, Kai and Cao, Yuhang and Loy, Chen Change and Lin, Dahua and Feichtenhofer, Christoph},
+  journal={arXiv preprint arXiv:2004.03580},
+  year={2020}
+}
+```
diff --git a/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py b/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py
new file mode 100644
index 0000000..4535034
--- /dev/null
+++ b/configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py
@@ -0,0 +1,9 @@
+_base_ = 'faster_rcnn_r50_fpg_crop640_50e_coco.py'
+
+norm_cfg = dict(type='BN', requires_grad=True)
+model = dict(
+    neck=dict(out_channels=128, inter_channels=128),
+    rpn_head=dict(in_channels=128),
+    roi_head=dict(
+        bbox_roi_extractor=dict(out_channels=128),
+        bbox_head=dict(in_channels=128)))
diff --git a/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py b/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py
new file mode 100644
index 0000000..3ab2a2c
--- /dev/null
+++ b/configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py
@@ -0,0 +1,48 @@
+_base_ = 'faster_rcnn_r50_fpn_crop640_50e_coco.py'
+
+norm_cfg = dict(type='BN', requires_grad=True)
+model = dict(
+    neck=dict(
+        type='FPG',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        inter_channels=256,
+        num_outs=5,
+        stack_times=9,
+        paths=['bu'] * 9,
+        same_down_trans=None,
+        same_up_trans=dict(
+            type='conv',
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        across_lateral_trans=dict(
+            type='conv',
+            kernel_size=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        across_down_trans=dict(
+            type='interpolation_conv',
+            mode='nearest',
+            kernel_size=3,
+            norm_cfg=norm_cfg,
+            order=('act', 'conv', 'norm'),
+            inplace=False),
+        across_up_trans=None,
+        across_skip_trans=dict(
+            type='conv',
+            kernel_size=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        output_trans=dict(
+            type='last_conv',
+            kernel_size=3,
+            order=('act', 'conv', 'norm'),
+            inplace=False),
+        norm_cfg=norm_cfg,
+        skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()]))
diff --git a/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py b/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py
new file mode 100644
index 0000000..e4ec940
--- /dev/null
+++ b/configs/fpg/faster_rcnn_r50_fpn_crop640_50e_coco.py
@@ -0,0 +1,73 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+norm_cfg = dict(type='BN', requires_grad=True)
+model = dict(
+    backbone=dict(norm_cfg=norm_cfg, norm_eval=False),
+    neck=dict(norm_cfg=norm_cfg),
+    roi_head=dict(bbox_head=dict(norm_cfg=norm_cfg)))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=(640, 640),
+        ratio_range=(0.8, 1.2),
+        keep_ratio=True),
+    dict(type='RandomCrop', crop_size=(640, 640)),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=(640, 640)),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(640, 640),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=64),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# learning policy
+optimizer = dict(
+    type='SGD',
+    lr=0.08,
+    momentum=0.9,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True))
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=0.1,
+    step=[30, 40])
+# runtime settings
+runner = dict(max_epochs=50)
+evaluation = dict(interval=2)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py b/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py
new file mode 100644
index 0000000..baa4a5a
--- /dev/null
+++ b/configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py
@@ -0,0 +1,10 @@
+_base_ = 'mask_rcnn_r50_fpg_crop640_50e_coco.py'
+
+model = dict(
+    neck=dict(out_channels=128, inter_channels=128),
+    rpn_head=dict(in_channels=128),
+    roi_head=dict(
+        bbox_roi_extractor=dict(out_channels=128),
+        bbox_head=dict(in_channels=128),
+        mask_roi_extractor=dict(out_channels=128),
+        mask_head=dict(in_channels=128)))
diff --git a/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py b/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py
new file mode 100644
index 0000000..3c9ea27
--- /dev/null
+++ b/configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py
@@ -0,0 +1,48 @@
+_base_ = 'mask_rcnn_r50_fpn_crop640_50e_coco.py'
+
+norm_cfg = dict(type='BN', requires_grad=True)
+model = dict(
+    neck=dict(
+        type='FPG',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        inter_channels=256,
+        num_outs=5,
+        stack_times=9,
+        paths=['bu'] * 9,
+        same_down_trans=None,
+        same_up_trans=dict(
+            type='conv',
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        across_lateral_trans=dict(
+            type='conv',
+            kernel_size=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        across_down_trans=dict(
+            type='interpolation_conv',
+            mode='nearest',
+            kernel_size=3,
+            norm_cfg=norm_cfg,
+            order=('act', 'conv', 'norm'),
+            inplace=False),
+        across_up_trans=None,
+        across_skip_trans=dict(
+            type='conv',
+            kernel_size=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        output_trans=dict(
+            type='last_conv',
+            kernel_size=3,
+            order=('act', 'conv', 'norm'),
+            inplace=False),
+        norm_cfg=norm_cfg,
+        skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()]))
diff --git a/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py b/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py
new file mode 100644
index 0000000..c6bcc24
--- /dev/null
+++ b/configs/fpg/mask_rcnn_r50_fpn_crop640_50e_coco.py
@@ -0,0 +1,79 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+norm_cfg = dict(type='BN', requires_grad=True)
+model = dict(
+    backbone=dict(norm_cfg=norm_cfg, norm_eval=False),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        norm_cfg=norm_cfg,
+        num_outs=5),
+    roi_head=dict(
+        bbox_head=dict(norm_cfg=norm_cfg), mask_head=dict(norm_cfg=norm_cfg)))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=(640, 640),
+        ratio_range=(0.8, 1.2),
+        keep_ratio=True),
+    dict(type='RandomCrop', crop_size=(640, 640)),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=(640, 640)),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(640, 640),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=64),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# learning policy
+optimizer = dict(
+    type='SGD',
+    lr=0.08,
+    momentum=0.9,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True))
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=0.1,
+    step=[30, 40])
+# runtime settings
+runner = dict(max_epochs=50)
+evaluation = dict(interval=2)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/fpg/metafile.yml b/configs/fpg/metafile.yml
new file mode 100644
index 0000000..6b0a6a7
--- /dev/null
+++ b/configs/fpg/metafile.yml
@@ -0,0 +1,104 @@
+Collections:
+  - Name: Feature Pyramid Grids
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Feature Pyramid Grids
+    Paper:
+      URL: https://arxiv.org/abs/2004.03580
+      Title: 'Feature Pyramid Grids'
+    README: configs/fpg/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.10.0/mmdet/models/necks/fpg.py#L101
+      Version: v2.10.0
+
+Models:
+  - Name: faster_rcnn_r50_fpg_crop640_50e_coco
+    In Collection: Feature Pyramid Grids
+    Config: configs/fpg/faster_rcnn_r50_fpg_crop640_50e_coco.py
+    Metadata:
+      Training Memory (GB): 20.0
+      Epochs: 50
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg_crop640_50e_coco/faster_rcnn_r50_fpg_crop640_50e_coco_20220311_011856-74109f42.pth
+
+  - Name: faster_rcnn_r50_fpg-chn128_crop640_50e_coco
+    In Collection: Feature Pyramid Grids
+    Config: configs/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco.py
+    Metadata:
+      Training Memory (GB): 11.9
+      Epochs: 50
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/faster_rcnn_r50_fpg-chn128_crop640_50e_coco/faster_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011857-9376aa9d.pth
+
+  - Name: mask_rcnn_r50_fpg_crop640_50e_coco
+    In Collection: Feature Pyramid Grids
+    Config: configs/fpg/mask_rcnn_r50_fpg_crop640_50e_coco.py
+    Metadata:
+      Training Memory (GB): 23.2
+      Epochs: 50
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  38.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg_crop640_50e_coco/mask_rcnn_r50_fpg_crop640_50e_coco_20220311_011857-233b8334.pth
+
+  - Name: mask_rcnn_r50_fpg-chn128_crop640_50e_coco
+    In Collection: Feature Pyramid Grids
+    Config: configs/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco.py
+    Metadata:
+      Training Memory (GB): 15.3
+      Epochs: 50
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/mask_rcnn_r50_fpg-chn128_crop640_50e_coco/mask_rcnn_r50_fpg-chn128_crop640_50e_coco_20220311_011859-043c9b4e.pth
+
+  - Name: retinanet_r50_fpg_crop640_50e_coco
+    In Collection: Feature Pyramid Grids
+    Config: configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py
+    Metadata:
+      Training Memory (GB): 20.8
+      Epochs: 50
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg_crop640_50e_coco/retinanet_r50_fpg_crop640_50e_coco_20220311_110809-b0bcf5f4.pth
+
+  - Name: retinanet_r50_fpg-chn128_crop640_50e_coco
+    In Collection: Feature Pyramid Grids
+    Config: configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py
+    Metadata:
+      Training Memory (GB): 19.9
+      Epochs: 50
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.9
+    Weights:  https://download.openmmlab.com/mmdetection/v2.0/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco/retinanet_r50_fpg-chn128_crop640_50e_coco_20220313_104829-ee99a686.pth
diff --git a/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py b/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py
new file mode 100644
index 0000000..9a6cf7e
--- /dev/null
+++ b/configs/fpg/retinanet_r50_fpg-chn128_crop640_50e_coco.py
@@ -0,0 +1,5 @@
+_base_ = 'retinanet_r50_fpg_crop640_50e_coco.py'
+
+model = dict(
+    neck=dict(out_channels=128, inter_channels=128),
+    bbox_head=dict(in_channels=128))
diff --git a/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py b/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py
new file mode 100644
index 0000000..504ed5e
--- /dev/null
+++ b/configs/fpg/retinanet_r50_fpg_crop640_50e_coco.py
@@ -0,0 +1,53 @@
+_base_ = '../nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py'
+
+norm_cfg = dict(type='BN', requires_grad=True)
+model = dict(
+    neck=dict(
+        _delete_=True,
+        type='FPG',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        inter_channels=256,
+        num_outs=5,
+        add_extra_convs=True,
+        start_level=1,
+        stack_times=9,
+        paths=['bu'] * 9,
+        same_down_trans=None,
+        same_up_trans=dict(
+            type='conv',
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        across_lateral_trans=dict(
+            type='conv',
+            kernel_size=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        across_down_trans=dict(
+            type='interpolation_conv',
+            mode='nearest',
+            kernel_size=3,
+            norm_cfg=norm_cfg,
+            order=('act', 'conv', 'norm'),
+            inplace=False),
+        across_up_trans=None,
+        across_skip_trans=dict(
+            type='conv',
+            kernel_size=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        output_trans=dict(
+            type='last_conv',
+            kernel_size=3,
+            order=('act', 'conv', 'norm'),
+            inplace=False),
+        norm_cfg=norm_cfg,
+        skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()]))
+
+evaluation = dict(interval=2)
diff --git a/configs/free_anchor/README.md b/configs/free_anchor/README.md
new file mode 100644
index 0000000..d24c340
--- /dev/null
+++ b/configs/free_anchor/README.md
@@ -0,0 +1,37 @@
+# FreeAnchor
+
+> [FreeAnchor: Learning to Match Anchors for Visual Object Detection](https://arxiv.org/abs/1909.02466)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Modern CNN-based object detectors assign anchors for ground-truth objects under the restriction of object-anchor Intersection-over-Unit (IoU). In this study, we propose a learning-to-match approach to break IoU restriction, allowing objects to match anchors in a flexible manner. Our approach, referred to as FreeAnchor, updates hand-crafted anchor assignment to "free" anchor matching by formulating detector training as a maximum likelihood estimation (MLE) procedure. FreeAnchor targets at learning features which best explain a class of objects in terms of both classification and localization. FreeAnchor is implemented by optimizing detection customized likelihood and can be fused with CNN-based detectors in a plug-and-play manner. Experiments on COCO demonstrate that FreeAnchor consistently outperforms their counterparts with significant margins.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143886006-8374bf9b-28af-442e-9abf-eb16562bb7d3.png"/>
+</div>
+
+## Results and Models
+
+|  Backbone   |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                               Config                                                                |                                                                                                                                                                                   Download                                                                                                                                                                                    |
+| :---------: | :-----: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50     | pytorch |   1x    |   4.9    |      18.4      |  38.7  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130_095625.log.json)               |
+|    R-101    | pytorch |   1x    |   6.8    |      14.9      |  40.3  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130-358324e6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130_100723.log.json)             |
+| X-101-32x4d | pytorch |   1x    |   8.1    |      11.1      |  41.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130-d4846968.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130_095627.log.json) |
+
+**Notes:**
+
+- We use 8 GPUs with 2 images/GPU.
+- For more settings and models, please refer to the [official repo](https://github.com/zhangxiaosong18/FreeAnchor).
+
+## Citation
+
+```latex
+@inproceedings{zhang2019freeanchor,
+  title   =  {{FreeAnchor}: Learning to Match Anchors for Visual Object Detection},
+  author  =  {Zhang, Xiaosong and Wan, Fang and Liu, Chang and Ji, Rongrong and Ye, Qixiang},
+  booktitle =  {Neural Information Processing Systems},
+  year    =  {2019}
+}
+```
diff --git a/configs/free_anchor/metafile.yml b/configs/free_anchor/metafile.yml
new file mode 100644
index 0000000..170fb5c
--- /dev/null
+++ b/configs/free_anchor/metafile.yml
@@ -0,0 +1,79 @@
+Collections:
+  - Name: FreeAnchor
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FreeAnchor
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1909.02466
+      Title: 'FreeAnchor: Learning to Match Anchors for Visual Object Detection'
+    README: configs/free_anchor/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/dense_heads/free_anchor_retina_head.py#L10
+      Version: v2.0.0
+
+Models:
+  - Name: retinanet_free_anchor_r50_fpn_1x_coco
+    In Collection: FreeAnchor
+    Config: configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.9
+      inference time (ms/im):
+        - value: 54.35
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco/retinanet_free_anchor_r50_fpn_1x_coco_20200130-0f67375f.pth
+
+  - Name: retinanet_free_anchor_r101_fpn_1x_coco
+    In Collection: FreeAnchor
+    Config: configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.8
+      inference time (ms/im):
+        - value: 67.11
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco/retinanet_free_anchor_r101_fpn_1x_coco_20200130-358324e6.pth
+
+  - Name: retinanet_free_anchor_x101_32x4d_fpn_1x_coco
+    In Collection: FreeAnchor
+    Config: configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.1
+      inference time (ms/im):
+        - value: 90.09
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco/retinanet_free_anchor_x101_32x4d_fpn_1x_coco_20200130-d4846968.pth
diff --git a/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py b/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..f4aea53
--- /dev/null
+++ b/configs/free_anchor/retinanet_free_anchor_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py b/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..28f983c
--- /dev/null
+++ b/configs/free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py
@@ -0,0 +1,22 @@
+_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
+model = dict(
+    bbox_head=dict(
+        _delete_=True,
+        type='FreeAnchorRetinaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.75)))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py b/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..65f8a9e
--- /dev/null
+++ b/configs/free_anchor/retinanet_free_anchor_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,13 @@
+_base_ = './retinanet_free_anchor_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/fsaf/README.md b/configs/fsaf/README.md
new file mode 100644
index 0000000..4392a6e
--- /dev/null
+++ b/configs/fsaf/README.md
@@ -0,0 +1,57 @@
+# FSAF
+
+> [Feature Selective Anchor-Free Module for Single-Shot Object Detection](https://arxiv.org/abs/1903.00621)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We motivate and present feature selective anchor-free (FSAF) module, a simple and effective building block for single-shot object detectors. It can be plugged into single-shot detectors with feature pyramid structure. The FSAF module addresses two limitations brought up by the conventional anchor-based detection: 1) heuristic-guided feature selection; 2) overlap-based anchor sampling. The general concept of the FSAF module is online feature selection applied to the training of multi-level anchor-free branches. Specifically, an anchor-free branch is attached to each level of the feature pyramid, allowing box encoding and decoding in the anchor-free manner at an arbitrary level. During training, we dynamically assign each instance to the most suitable feature level. At the time of inference, the FSAF module can work jointly with anchor-based branches by outputting predictions in parallel. We instantiate this concept with simple implementations of anchor-free branches and online feature selection strategy. Experimental results on the COCO detection track show that our FSAF module performs better than anchor-based counterparts while being faster. When working jointly with anchor-based branches, the FSAF module robustly improves the baseline RetinaNet by a large margin under various settings, while introducing nearly free inference overhead. And the resulting best model can achieve a state-of-the-art 44.6% mAP, outperforming all existing single-shot detectors on COCO.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143887228-c790b542-c0f7-4113-8597-12250c50fe8f.png"/>
+</div>
+
+## Introduction
+
+FSAF is an anchor-free method published in CVPR2019 ([https://arxiv.org/pdf/1903.00621.pdf](https://arxiv.org/pdf/1903.00621.pdf)).
+Actually it is equivalent to the anchor-based method with only one anchor at each feature map position in each FPN level.
+And this is how we implemented it.
+Only the anchor-free branch is released for its better compatibility with the current framework and less computational budget.
+
+In the original paper, feature maps within the central 0.2-0.5 area of a gt box are tagged as ignored. However,
+it is empirically found that a hard threshold (0.2-0.2) gives a further gain on the performance. (see the table below)
+
+## Results and Models
+
+### Results on R50/R101/X101-FPN
+
+| Backbone | ignore range | ms-train | Lr schd | Train Mem (GB) | Train time (s/iter) | Inf time (fps) |   box AP    |                                                   Config                                                    |                                                                                                                                                                    Download                                                                                                                                                                     |
+| :------: | :----------: | :------: | :-----: | :------------: | :-----------------: | :------------: | :---------: | :---------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   |   0.2-0.5    |    N     |   1x    |      3.15      |        0.43         |      12.3      | 36.0 (35.9) |                                                                                                             | [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715-b555b0e0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco/fsaf_pscale0.2_nscale0.5_r50_fpn_1x_coco_20200715_094657.log.json) |
+|   R-50   |   0.2-0.2    |    N     |   1x    |      3.15      |        0.43         |      13.0      |    37.4     |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r50_fpn_1x_coco.py)     |                                             [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco_20200428_072327.log.json)                                              |
+|  R-101   |   0.2-0.2    |    N     |   1x    |      5.08      |        0.58         |      10.8      | 39.3 (37.9) |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_r101_fpn_1x_coco.py)    |                                           [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco-9e71098f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco_20200428_160348.log.json)                                            |
+|  X-101   |   0.2-0.2    |    N     |   1x    |      9.38      |        1.23         |      5.6       | 42.4 (41.0) | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py) |                               [model](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco-e3f6e6fd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco_20200428_160424.log.json)                                |
+
+**Notes:**
+
+- *1x means the model is trained for 12 epochs.*
+- *AP values in the brackets represent those reported in the original paper.*
+- *All results are obtained with a single model and single-scale test.*
+- *X-101 backbone represents ResNext-101-64x4d.*
+- *All pretrained backbones use pytorch style.*
+- *All models are trained on 8 Titan-XP gpus and tested on a single gpu.*
+
+## Citation
+
+BibTeX reference is as follows.
+
+```latex
+@inproceedings{zhu2019feature,
+  title={Feature Selective Anchor-Free Module for Single-Shot Object Detection},
+  author={Zhu, Chenchen and He, Yihui and Savvides, Marios},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  pages={840--849},
+  year={2019}
+}
+```
diff --git a/configs/fsaf/fsaf_r101_fpn_1x_coco.py b/configs/fsaf/fsaf_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..12b49fe
--- /dev/null
+++ b/configs/fsaf/fsaf_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './fsaf_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/fsaf/fsaf_r50_fpn_1x_coco.py b/configs/fsaf/fsaf_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..67f3ec1
--- /dev/null
+++ b/configs/fsaf/fsaf_r50_fpn_1x_coco.py
@@ -0,0 +1,48 @@
+_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
+# model settings
+model = dict(
+    type='FSAF',
+    bbox_head=dict(
+        type='FSAFHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        reg_decoded_bbox=True,
+        # Only anchor-free branch is implemented. The anchor generator only
+        #  generates 1 anchor at each feature point, as a substitute of the
+        #  grid of features.
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=1,
+            scales_per_octave=1,
+            ratios=[1.0],
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(_delete_=True, type='TBLRBBoxCoder', normalizer=4.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0,
+            reduction='none'),
+        loss_bbox=dict(
+            _delete_=True,
+            type='IoULoss',
+            eps=1e-6,
+            loss_weight=1.0,
+            reduction='none')),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            _delete_=True,
+            type='CenterRegionAssigner',
+            pos_scale=0.2,
+            neg_scale=0.2,
+            min_pos_iof=0.01),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False))
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=10, norm_type=2))
diff --git a/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py b/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..89c0c63
--- /dev/null
+++ b/configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './fsaf_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/fsaf/metafile.yml b/configs/fsaf/metafile.yml
new file mode 100644
index 0000000..5434e9a
--- /dev/null
+++ b/configs/fsaf/metafile.yml
@@ -0,0 +1,80 @@
+Collections:
+  - Name: FSAF
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x Titan-XP GPUs
+      Architecture:
+        - FPN
+        - FSAF
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1903.00621
+      Title: 'Feature Selective Anchor-Free Module for Single-Shot Object Detection'
+    README: configs/fsaf/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/detectors/fsaf.py#L6
+      Version: v2.1.0
+
+Models:
+  - Name: fsaf_r50_fpn_1x_coco
+    In Collection: FSAF
+    Config: configs/fsaf/fsaf_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.15
+      inference time (ms/im):
+        - value: 76.92
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r50_fpn_1x_coco/fsaf_r50_fpn_1x_coco-94ccc51f.pth
+
+  - Name: fsaf_r101_fpn_1x_coco
+    In Collection: FSAF
+    Config: configs/fsaf/fsaf_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.08
+      inference time (ms/im):
+        - value: 92.59
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.3 (37.9)
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_r101_fpn_1x_coco/fsaf_r101_fpn_1x_coco-9e71098f.pth
+
+  - Name: fsaf_x101_64x4d_fpn_1x_coco
+    In Collection: FSAF
+    Config: configs/fsaf/fsaf_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 9.38
+      inference time (ms/im):
+        - value: 178.57
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.4 (41.0)
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fsaf/fsaf_x101_64x4d_fpn_1x_coco/fsaf_x101_64x4d_fpn_1x_coco-e3f6e6fd.pth
diff --git a/configs/gcnet/README.md b/configs/gcnet/README.md
new file mode 100644
index 0000000..403e086
--- /dev/null
+++ b/configs/gcnet/README.md
@@ -0,0 +1,69 @@
+# GCNet
+
+> [GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond](https://arxiv.org/abs/1904.11492)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+The Non-Local Network (NLNet) presents a pioneering approach for capturing long-range dependencies, via aggregating query-specific global context to each query position. However, through a rigorous empirical analysis, we have found that the global contexts modeled by non-local network are almost the same for different query positions within an image. In this paper, we take advantage of this finding to create a simplified network based on a query-independent formulation, which maintains the accuracy of NLNet but with significantly less computation. We further observe that this simplified design shares similar structure with Squeeze-Excitation Network (SENet). Hence we unify them into a three-step general framework for global context modeling. Within the general framework, we design a better instantiation, called the global context (GC) block, which is lightweight and can effectively model the global context. The lightweight property allows us to apply it for multiple layers in a backbone network to construct a global context network (GCNet), which generally outperforms both simplified NLNet and SENet on major benchmarks for various recognition tasks.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143887418-c5d2c906-d6c9-415d-bce7-c5355c20a10a.png" height="300"/>
+</div>
+
+## Introduction
+
+By [Yue Cao](http://yue-cao.me), [Jiarui Xu](http://jerryxu.net), [Stephen Lin](https://scholar.google.com/citations?user=c3PYmxUAAAAJ&hl=en), Fangyun Wei, [Han Hu](https://sites.google.com/site/hanhushomepage/).
+
+We provide config files to reproduce the results in the paper for
+["GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond"](https://arxiv.org/abs/1904.11492) on COCO object detection.
+
+**GCNet** is initially described in [arxiv](https://arxiv.org/abs/1904.11492). Via absorbing advantages of Non-Local Networks (NLNet) and Squeeze-Excitation Networks (SENet),  GCNet provides a simple, fast and effective approach for global context modeling, which generally outperforms both NLNet and SENet on major benchmarks for various recognition tasks.
+
+## Results and Models
+
+The results on COCO 2017val are shown in the below table.
+
+| Backbone  | Model |    Context     | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                          Config                                                           |                                                                                                                                                                       Download                                                                                                                                                                       |
+| :-------: | :---: | :------------: | :-----: | :------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R-50-FPN  | Mask  | GC(c3-c5, r16) |   1x    |   5.0    |                |  39.7  |  35.9   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py)  | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915-187da160.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915.log.json) |
+| R-50-FPN  | Mask  | GC(c3-c5, r4)  |   1x    |   5.1    |      15.0      |  39.9  |  36.0   |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py)  |      [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204-17235656.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204_024626.log.json)       |
+| R-101-FPN | Mask  | GC(c3-c5, r16) |   1x    |   7.6    |      11.4      |  41.3  |  37.2   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py) |  [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205-e58ae947.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205_192835.log.json)   |
+| R-101-FPN | Mask  | GC(c3-c5, r4)  |   1x    |   7.8    |      11.6      |  42.2  |  37.8   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py)  |    [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206-af22dc9d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206_112128.log.json)     |
+
+| Backbone  |      Model       |    Context     | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                                               Config                                                                                |                                                                                                                                                                                                                                                             Download                                                                                                                                                                                                                                                             |
+| :-------: | :--------------: | :------------: | :-----: | :------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R-50-FPN  |       Mask       |       -        |   1x    |   4.4    |      16.6      |  38.4  |  34.6   |                     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py)                      |                                                                                      [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202-bb3eb55c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202_214122.log.json)                                                                                       |
+| R-50-FPN  |       Mask       | GC(c3-c5, r16) |   1x    |   5.0    |      15.5      |  40.4  |  36.2   |              [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py)               |                                                          [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202_174907.log.json)                                                           |
+| R-50-FPN  |       Mask       | GC(c3-c5, r4)  |   1x    |   5.1    |      15.1      |  40.7  |  36.5   |               [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py)               |                                                            [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json)                                                             |
+| R-101-FPN |       Mask       |       -        |   1x    |   6.4    |      13.3      |  40.5  |  36.3   |                     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py)                     |                                                                                    [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210-81658c8a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210_220422.log.json)                                                                                     |
+| R-101-FPN |       Mask       | GC(c3-c5, r16) |   1x    |   7.6    |      12.0      |  42.2  |  37.8   |              [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py)              |                                                        [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207-945e77ca.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207_015330.log.json)                                                         |
+| R-101-FPN |       Mask       | GC(c3-c5, r4)  |   1x    |   7.8    |      11.8      |  42.2  |  37.8   |              [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py)               |                                                          [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json)                                                           |
+| X-101-FPN |       Mask       |       -        |   1x    |   7.6    |      11.3      |  42.4  |  37.7   |                  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py)                  |                                                                        [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211-7584841c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211_054326.log.json)                                                                         |
+| X-101-FPN |       Mask       | GC(c3-c5, r16) |   1x    |   8.8    |      9.8       |  43.5  |  38.6   |           [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py)           |                                            [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-cbed3d2c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_164715.log.json)                                             |
+| X-101-FPN |       Mask       | GC(c3-c5, r4)  |   1x    |   9.0    |      9.7       |  43.9  |  39.0   |           [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py)            |                                              [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212-68164964.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212_070942.log.json)                                               |
+| X-101-FPN |   Cascade Mask   |       -        |   1x    |   9.2    |      8.4       |  44.7  |  38.6   |              [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py)              |                                                        [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310-d5ad2a5e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310_115217.log.json)                                                         |
+| X-101-FPN |   Cascade Mask   | GC(c3-c5, r16) |   1x    |   10.3   |      7.7       |  46.2  |  39.7   |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py)       |                            [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-10bf2463.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211_184154.log.json)                             |
+| X-101-FPN |   Cascade Mask   | GC(c3-c5, r4)  |   1x    |   10.6   |                |  46.4  |  40.1   |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py)        |                           [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653-ed035291.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653.log.json)                           |
+| X-101-FPN | DCN Cascade Mask |       -        |   1x    |          |                |  47.5  |  40.9   |        [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py)        |                             [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20210615_211019-abbc39ea.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20210615_211019.log.json)                             |
+| X-101-FPN | DCN Cascade Mask | GC(c3-c5, r16) |   1x    |          |                |  48.0  |  41.3   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20210615_215648-44aa598a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20210615_215648.log.json) |
+| X-101-FPN | DCN Cascade Mask | GC(c3-c5, r4)  |   1x    |          |                |  47.9  |  41.1   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20210615_161851-720338ec.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20210615_161851.log.json)   |
+
+**Notes:**
+
+- The `SyncBN` is added in the backbone for all models in **Table 2**.
+- `GC` denotes Global Context (GC) block is inserted after 1x1 conv of backbone.
+- `DCN` denotes replace 3x3 conv with 3x3 Deformable Convolution in `c3-c5` stages of backbone.
+- `r4` and `r16` denote ratio 4 and ratio 16 in GC block respectively.
+
+## Citation
+
+```latex
+@article{cao2019GCNet,
+  title={GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond},
+  author={Cao, Yue and Xu, Jiarui and Lin, Stephen and Wei, Fangyun and Hu, Han},
+  journal={arXiv preprint arXiv:1904.11492},
+  year={2019}
+}
+```
diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
new file mode 100644
index 0000000..5118895
--- /dev/null
+++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
@@ -0,0 +1,4 @@
+_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..413499d
--- /dev/null
+++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py
@@ -0,0 +1,4 @@
+_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py
new file mode 100644
index 0000000..50689aa
--- /dev/null
+++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        plugins=[
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 16),
+                stages=(False, True, True, True),
+                position='after_conv3')
+        ]))
diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py
new file mode 100644
index 0000000..1367231
--- /dev/null
+++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = '../dcn/cascade_mask_rcnn_x101_32x4d_fpn_dconv_c3-c5_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        plugins=[
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 4),
+                stages=(False, True, True, True),
+                position='after_conv3')
+        ]))
diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
new file mode 100644
index 0000000..50883ff
--- /dev/null
+++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        plugins=[
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 16),
+                stages=(False, True, True, True),
+                position='after_conv3')
+        ]))
diff --git a/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
new file mode 100644
index 0000000..31fdd07
--- /dev/null
+++ b/configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = '../cascade_rcnn/cascade_mask_rcnn_x101_32x4d_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        plugins=[
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 4),
+                stages=(False, True, True, True),
+                position='after_conv3')
+        ]))
diff --git a/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py
new file mode 100644
index 0000000..ad6ad47
--- /dev/null
+++ b/configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py
@@ -0,0 +1,8 @@
+_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(plugins=[
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16),
+            stages=(False, True, True, True),
+            position='after_conv3')
+    ]))
diff --git a/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py
new file mode 100644
index 0000000..29f9167
--- /dev/null
+++ b/configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py
@@ -0,0 +1,8 @@
+_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(plugins=[
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 4),
+            stages=(False, True, True, True),
+            position='after_conv3')
+    ]))
diff --git a/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py
new file mode 100644
index 0000000..6e1c5d0
--- /dev/null
+++ b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py
@@ -0,0 +1,4 @@
+_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
diff --git a/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
new file mode 100644
index 0000000..781dba7
--- /dev/null
+++ b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        plugins=[
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 16),
+                stages=(False, True, True, True),
+                position='after_conv3')
+        ]))
diff --git a/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
new file mode 100644
index 0000000..32972de
--- /dev/null
+++ b/configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = '../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        plugins=[
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 4),
+                stages=(False, True, True, True),
+                position='after_conv3')
+        ]))
diff --git a/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py
new file mode 100644
index 0000000..d299b69
--- /dev/null
+++ b/configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py
@@ -0,0 +1,8 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(plugins=[
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16),
+            stages=(False, True, True, True),
+            position='after_conv3')
+    ]))
diff --git a/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py
new file mode 100644
index 0000000..5ac908e
--- /dev/null
+++ b/configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py
@@ -0,0 +1,8 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(plugins=[
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 4),
+            stages=(False, True, True, True),
+            position='after_conv3')
+    ]))
diff --git a/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py
new file mode 100644
index 0000000..0308a56
--- /dev/null
+++ b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py
@@ -0,0 +1,4 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
diff --git a/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
new file mode 100644
index 0000000..e04780c
--- /dev/null
+++ b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        plugins=[
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 16),
+                stages=(False, True, True, True),
+                position='after_conv3')
+        ]))
diff --git a/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
new file mode 100644
index 0000000..980f819
--- /dev/null
+++ b/configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        plugins=[
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 4),
+                stages=(False, True, True, True),
+                position='after_conv3')
+        ]))
diff --git a/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
new file mode 100644
index 0000000..f0c96e5
--- /dev/null
+++ b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
@@ -0,0 +1,4 @@
+_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True), norm_eval=False))
diff --git a/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
new file mode 100644
index 0000000..7fb8e82
--- /dev/null
+++ b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        plugins=[
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 16),
+                stages=(False, True, True, True),
+                position='after_conv3')
+        ]))
diff --git a/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
new file mode 100644
index 0000000..b1ddbee
--- /dev/null
+++ b/configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        plugins=[
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 4),
+                stages=(False, True, True, True),
+                position='after_conv3')
+        ]))
diff --git a/configs/gcnet/metafile.yml b/configs/gcnet/metafile.yml
new file mode 100644
index 0000000..1281122
--- /dev/null
+++ b/configs/gcnet/metafile.yml
@@ -0,0 +1,440 @@
+Collections:
+  - Name: GCNet
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Global Context Block
+        - FPN
+        - RPN
+        - ResNet
+        - ResNeXt
+    Paper:
+      URL: https://arxiv.org/abs/1904.11492
+      Title: 'GCNet: Non-local Networks Meet Squeeze-Excitation Networks and Beyond'
+    README: configs/gcnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/ops/context_block.py#L13
+      Version: v2.0.0
+
+Models:
+  - Name: mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.0
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  35.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r16_gcb_c3-c5_1x_coco_20200515_211915-187da160.pth
+
+  - Name: mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.1
+      inference time (ms/im):
+        - value: 66.67
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  36.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_r4_gcb_c3-c5_1x_coco_20200204-17235656.pth
+
+  - Name: mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.6
+      inference time (ms/im):
+        - value: 87.72
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r16_gcb_c3-c5_1x_coco_20200205-e58ae947.pth
+
+  - Name: mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.8
+      inference time (ms/im):
+        - value: 86.21
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_r4_gcb_c3-c5_1x_coco_20200206-af22dc9d.pth
+
+  - Name: mask_rcnn_r50_fpn_syncbn-backbone_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.4
+      inference time (ms/im):
+        - value: 60.24
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  34.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_1x_coco_20200202-bb3eb55c.pth
+
+  - Name: mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.0
+      inference time (ms/im):
+        - value: 64.52
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  36.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200202-587b99aa.pth
+
+  - Name: mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.1
+      inference time (ms/im):
+        - value: 66.23
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  36.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth
+
+  - Name: mask_rcnn_r101_fpn_syncbn-backbone_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.4
+      inference time (ms/im):
+        - value: 75.19
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  36.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_1x_coco_20200210-81658c8a.pth
+
+  - Name: mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.6
+      inference time (ms/im):
+        - value: 83.33
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200207-945e77ca.pth
+
+  - Name: mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.8
+      inference time (ms/im):
+        - value: 84.75
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth
+
+  - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.6
+      inference time (ms/im):
+        - value: 88.5
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200211-7584841c.pth
+
+  - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.8
+      inference time (ms/im):
+        - value: 102.04
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  38.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-cbed3d2c.pth
+
+  - Name: mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 9.0
+      inference time (ms/im):
+        - value: 103.09
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  39.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200212-68164964.pth
+
+  - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco.py
+    Metadata:
+      Training Memory (GB): 9.2
+      inference time (ms/im):
+        - value: 119.05
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  38.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_1x_coco_20200310-d5ad2a5e.pth
+
+  - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.3
+      inference time (ms/im):
+        - value: 129.87
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  39.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r16_gcb_c3-c5_1x_coco_20200211-10bf2463.pth
+
+  - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.6
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:    40.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200703_180653-ed035291.pth
+
+  - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  40.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_1x_coco_20210615_211019-abbc39ea.pth
+
+  - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 48.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r16_gcb_c3-c5_1x_coco_20210615_215648-44aa598a.pth
+
+  - Name: cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco
+    In Collection: GCNet
+    Config: configs/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:   41.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gcnet/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco/cascade_mask_rcnn_x101_32x4d_fpn_syncbn-backbone_dconv_c3-c5_r4_gcb_c3-c5_1x_coco_20210615_161851-720338ec.pth
diff --git a/configs/gfl/README.md b/configs/gfl/README.md
new file mode 100644
index 0000000..703936b
--- /dev/null
+++ b/configs/gfl/README.md
@@ -0,0 +1,42 @@
+# GFL
+
+> [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://arxiv.org/abs/2006.04388)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+One-stage detector basically formulates object detection as dense classification and localization. The classification is usually optimized by Focal Loss and the box location is commonly learned under Dirac delta distribution. A recent trend for one-stage detectors is to introduce an individual prediction branch to estimate the quality of localization, where the predicted quality facilitates the classification to improve detection performance. This paper delves into the representations of the above three fundamental elements: quality estimation, classification and localization. Two problems are discovered in existing practices, including (1) the inconsistent usage of the quality estimation and classification between training and inference and (2) the inflexible Dirac delta distribution for localization when there is ambiguity and uncertainty in complex scenes. To address the problems, we design new representations for these elements. Specifically, we merge the quality estimation into the class prediction vector to form a joint representation of localization quality and classification, and use a vector to represent arbitrary distribution of box locations. The improved representations eliminate the inconsistency risk and accurately depict the flexible distribution in real data, but contain continuous labels, which is beyond the scope of Focal Loss. We then propose Generalized Focal Loss (GFL) that generalizes Focal Loss from its discrete form to the continuous version for successful optimization. On COCO test-dev, GFL achieves 45.0% AP using ResNet-101 backbone, surpassing state-of-the-art SAPD (43.5%) and ATSS (43.6%) with higher or comparable inference speed, under the same backbone and training settings. Notably, our best model can achieve a single-model single-scale AP of 48.2%, at 10 FPS on a single 2080Ti GPU.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143887865-44dc384d-ba0d-44e8-b3d7-d5fa837838cf.png"/>
+</div>
+
+## Results and Models
+
+|     Backbone      |  Style  | Lr schd | Multi-scale Training | Inf time (fps) | box AP |                                                            Config                                                             |                                                                                                                                                                                   Download                                                                                                                                                                                   |
+| :---------------: | :-----: | :-----: | :------------------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|       R-50        | pytorch |   1x    |          No          |      19.5      |  40.2  |              [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_1x_coco.py)               |                                                       [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244.log.json)                                                       |
+|       R-50        | pytorch |   2x    |         Yes          |      19.5      |  42.9  |          [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py)           |                                       [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802-37bb1edc.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802.log.json)                                       |
+|       R-101       | pytorch |   2x    |         Yes          |      14.7      |  44.7  |          [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py)          |                                     [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126.log.json)                                     |
+|    R-101-dcnv2    | pytorch |   2x    |         Yes          |      12.9      |  47.1  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002.log.json)             |
+|    X-101-32x4d    | pytorch |   2x    |         Yes          |      12.1      |  45.9  |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py)       |                         [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002-50c1ffdb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002.log.json)                         |
+| X-101-32x4d-dcnv2 | pytorch |   2x    |         Yes          |      10.7      |  48.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002-14a2bf25.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002.log.json) |
+
+\[1\] *1x and 2x mean the model is trained for 90K and 180K iterations, respectively.* \
+\[2\] *All results are obtained with a single model and without any test time data augmentation such as multi-scale, flipping and etc..* \
+\[3\] *`dcnv2` denotes deformable convolutional networks v2.* \
+\[4\] *FPS is tested with a single GeForce RTX 2080Ti GPU, using a batch size of 1.*
+
+## Citation
+
+We provide config files to reproduce the object detection results in the paper [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://arxiv.org/abs/2006.04388)
+
+```latex
+@article{li2020generalized,
+  title={Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection},
+  author={Li, Xiang and Wang, Wenhai and Wu, Lijun and Chen, Shuo and Hu, Xiaolin and Li, Jun and Tang, Jinhui and Yang, Jian},
+  journal={arXiv preprint arXiv:2006.04388},
+  year={2020}
+}
+```
diff --git a/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py b/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
new file mode 100644
index 0000000..b72c2b6
--- /dev/null
+++ b/configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
@@ -0,0 +1,15 @@
+_base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNet',
+        depth=101,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py b/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py
new file mode 100644
index 0000000..e33b5c0
--- /dev/null
+++ b/configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py
@@ -0,0 +1,13 @@
+_base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNet',
+        depth=101,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/gfl/gfl_r50_fpn_1x_coco.py b/configs/gfl/gfl_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..cfd4b02
--- /dev/null
+++ b/configs/gfl/gfl_r50_fpn_1x_coco.py
@@ -0,0 +1,57 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='GFL',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='GFLHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        loss_cls=dict(
+            type='QualityFocalLoss',
+            use_sigmoid=True,
+            beta=2.0,
+            loss_weight=1.0),
+        loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
+        reg_max=16,
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(type='ATSSAssigner', topk=9),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py b/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py
new file mode 100644
index 0000000..b8be601
--- /dev/null
+++ b/configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py
@@ -0,0 +1,22 @@
+_base_ = './gfl_r50_fpn_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
+# multi-scale training
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 480), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
diff --git a/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py b/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py
new file mode 100644
index 0000000..2539807
--- /dev/null
+++ b/configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py
@@ -0,0 +1,18 @@
+_base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
+model = dict(
+    type='GFL',
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, False, True, True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py b/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py
new file mode 100644
index 0000000..effda19
--- /dev/null
+++ b/configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py
@@ -0,0 +1,16 @@
+_base_ = './gfl_r50_fpn_mstrain_2x_coco.py'
+model = dict(
+    type='GFL',
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/gfl/metafile.yml b/configs/gfl/metafile.yml
new file mode 100644
index 0000000..8f049c6
--- /dev/null
+++ b/configs/gfl/metafile.yml
@@ -0,0 +1,134 @@
+Collections:
+  - Name: Generalized Focal Loss
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Generalized Focal Loss
+        - FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/2006.04388
+      Title: 'Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection'
+    README: configs/gfl/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/detectors/gfl.py#L6
+      Version: v2.2.0
+
+Models:
+  - Name: gfl_r50_fpn_1x_coco
+    In Collection: Generalized Focal Loss
+    Config: configs/gfl/gfl_r50_fpn_1x_coco.py
+    Metadata:
+      inference time (ms/im):
+        - value: 51.28
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_1x_coco/gfl_r50_fpn_1x_coco_20200629_121244-25944287.pth
+
+  - Name: gfl_r50_fpn_mstrain_2x_coco
+    In Collection: Generalized Focal Loss
+    Config: configs/gfl/gfl_r50_fpn_mstrain_2x_coco.py
+    Metadata:
+      inference time (ms/im):
+        - value: 51.28
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r50_fpn_mstrain_2x_coco/gfl_r50_fpn_mstrain_2x_coco_20200629_213802-37bb1edc.pth
+
+  - Name: gfl_r101_fpn_mstrain_2x_coco
+    In Collection: Generalized Focal Loss
+    Config: configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py
+    Metadata:
+      inference time (ms/im):
+        - value: 68.03
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth
+
+  - Name: gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco
+    In Collection: Generalized Focal Loss
+    Config: configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
+    Metadata:
+      inference time (ms/im):
+        - value: 77.52
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth
+
+  - Name: gfl_x101_32x4d_fpn_mstrain_2x_coco
+    In Collection: Generalized Focal Loss
+    Config: configs/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco.py
+    Metadata:
+      inference time (ms/im):
+        - value: 82.64
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_mstrain_2x_coco/gfl_x101_32x4d_fpn_mstrain_2x_coco_20200630_102002-50c1ffdb.pth
+
+  - Name: gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco
+    In Collection: Generalized Focal Loss
+    Config: configs/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py
+    Metadata:
+      inference time (ms/im):
+        - value: 93.46
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 48.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco/gfl_x101_32x4d_fpn_dconv_c4-c5_mstrain_2x_coco_20200630_102002-14a2bf25.pth
diff --git a/configs/ghm/README.md b/configs/ghm/README.md
new file mode 100644
index 0000000..cf9fb73
--- /dev/null
+++ b/configs/ghm/README.md
@@ -0,0 +1,33 @@
+# GHM
+
+> [Gradient Harmonized Single-stage Detector](https://arxiv.org/abs/1811.05181)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Despite the great success of two-stage detectors, single-stage detector is still a more elegant and efficient way, yet suffers from the two well-known disharmonies during training, i.e. the huge difference in quantity between positive and negative examples as well as between easy and hard examples. In this work, we first point out that the essential effect of the two disharmonies can be summarized in term of the gradient. Further, we propose a novel gradient harmonizing mechanism (GHM) to be a hedging for the disharmonies. The philosophy behind GHM can be easily embedded into both classification loss function like cross-entropy (CE) and regression loss function like smooth-L1 (SL1) loss. To this end, two novel loss functions called GHM-C and GHM-R are designed to balancing the gradient flow for anchor classification and bounding box refinement, respectively. Ablation study on MS COCO demonstrates that without laborious hyper-parameter tuning, both GHM-C and GHM-R can bring substantial improvement for single-stage detector. Without any whistles and bells, our model achieves 41.6 mAP on COCO test-dev set which surpasses the state-of-the-art method, Focal Loss (FL) + SL1, by 0.8.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143889057-0341f32b-1291-4b9a-8444-52ad266ae709.png"/>
+</div>
+
+## Results and Models
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                       Config                                                        |                                                                                                                                                           Download                                                                                                                                                            |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50-FPN     | pytorch |   1x    |   4.0    |      3.3       |  37.0  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130_004213.log.json)               |
+|    R-101-FPN    | pytorch |   1x    |   6.0    |      4.4       |  39.1  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130-c148ee8f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130_145259.log.json)             |
+| X-101-32x4d-FPN | pytorch |   1x    |   7.2    |      5.1       |  40.7  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131-e4333bd0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131_113653.log.json) |
+| X-101-64x4d-FPN | pytorch |   1x    |   10.3   |      5.2       |  41.4  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131-dd381cef.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131_113723.log.json) |
+
+## Citation
+
+```latex
+@inproceedings{li2019gradient,
+  title={Gradient Harmonized Single-stage Detector},
+  author={Li, Buyu and Liu, Yu and Wang, Xiaogang},
+  booktitle={AAAI Conference on Artificial Intelligence},
+  year={2019}
+}
+```
diff --git a/configs/ghm/metafile.yml b/configs/ghm/metafile.yml
new file mode 100644
index 0000000..b4f488c
--- /dev/null
+++ b/configs/ghm/metafile.yml
@@ -0,0 +1,101 @@
+Collections:
+  - Name: GHM
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - GHM-C
+        - GHM-R
+        - FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1811.05181
+      Title: 'Gradient Harmonized Single-stage Detector'
+    README: configs/ghm/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/losses/ghm_loss.py#L21
+      Version: v2.0.0
+
+Models:
+  - Name: retinanet_ghm_r50_fpn_1x_coco
+    In Collection: GHM
+    Config: configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.0
+      inference time (ms/im):
+        - value: 303.03
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r50_fpn_1x_coco/retinanet_ghm_r50_fpn_1x_coco_20200130-a437fda3.pth
+
+  - Name: retinanet_ghm_r101_fpn_1x_coco
+    In Collection: GHM
+    Config: configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.0
+      inference time (ms/im):
+        - value: 227.27
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_r101_fpn_1x_coco/retinanet_ghm_r101_fpn_1x_coco_20200130-c148ee8f.pth
+
+  - Name: retinanet_ghm_x101_32x4d_fpn_1x_coco
+    In Collection: GHM
+    Config: configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.2
+      inference time (ms/im):
+        - value: 196.08
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco/retinanet_ghm_x101_32x4d_fpn_1x_coco_20200131-e4333bd0.pth
+
+  - Name: retinanet_ghm_x101_64x4d_fpn_1x_coco
+    In Collection: GHM
+    Config: configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.3
+      inference time (ms/im):
+        - value: 192.31
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco/retinanet_ghm_x101_64x4d_fpn_1x_coco_20200131-dd381cef.pth
diff --git a/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..aaf6fc2
--- /dev/null
+++ b/configs/ghm/retinanet_ghm_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..61b9751
--- /dev/null
+++ b/configs/ghm/retinanet_ghm_r50_fpn_1x_coco.py
@@ -0,0 +1,19 @@
+_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
+model = dict(
+    bbox_head=dict(
+        loss_cls=dict(
+            _delete_=True,
+            type='GHMC',
+            bins=30,
+            momentum=0.75,
+            use_sigmoid=True,
+            loss_weight=1.0),
+        loss_bbox=dict(
+            _delete_=True,
+            type='GHMR',
+            mu=0.02,
+            bins=10,
+            momentum=0.7,
+            loss_weight=10.0)))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..cd2e4cc
--- /dev/null
+++ b/configs/ghm/retinanet_ghm_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py b/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..b6107d8
--- /dev/null
+++ b/configs/ghm/retinanet_ghm_x101_64x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './retinanet_ghm_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/gn+ws/README.md b/configs/gn+ws/README.md
new file mode 100644
index 0000000..184bed3
--- /dev/null
+++ b/configs/gn+ws/README.md
@@ -0,0 +1,54 @@
+# GN + WS
+
+> [Weight Standardization](https://arxiv.org/abs/1903.10520)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Batch Normalization (BN) has become an out-of-box technique to improve deep network training. However, its effectiveness is limited for micro-batch training, i.e., each GPU typically has only 1-2 images for training, which is inevitable for many computer vision tasks, e.g., object detection and semantic segmentation, constrained by memory consumption. To address this issue, we propose Weight Standardization (WS) and Batch-Channel Normalization (BCN) to bring two success factors of BN into micro-batch training: 1) the smoothing effects on the loss landscape and 2) the ability to avoid harmful elimination singularities along the training trajectory. WS standardizes the weights in convolutional layers to smooth the loss landscape by reducing the Lipschitz constants of the loss and the gradients; BCN combines batch and channel normalizations and leverages estimated statistics of the activations in convolutional layers to keep networks away from elimination singularities. We validate WS and BCN on comprehensive computer vision tasks, including image classification, object detection, instance segmentation, video recognition and semantic segmentation. All experimental results consistently show that WS and BCN improve micro-batch training significantly. Moreover, using WS and BCN with micro-batch training is even able to match or outperform the performances of BN with large-batch training.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143889309-b1f3487d-abfe-4615-befc-79384bc8b65c.png"/ height="200">
+</div>
+
+## Results and Models
+
+Faster R-CNN
+
+|    Backbone     |  Style  | Normalization | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                             Config                                                              |                                                                                                                                                                               Download                                                                                                                                                                                |
+| :-------------: | :-----: | :-----------: | :-----: | :------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50-FPN     | pytorch |     GN+WS     |   1x    |   5.9    |      11.7      |  39.7  |    -    |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130_210936.log.json)               |
+|    R-101-FPN    | pytorch |     GN+WS     |   1x    |   8.9    |      9.0       |  41.7  |    -    |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205-a93b0d75.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205_232146.log.json)             |
+| X-50-32x4d-FPN  | pytorch |     GN+WS     |   1x    |   7.0    |      10.3      |  40.7  |    -    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203-839c5d9d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203_220113.log.json)   |
+| X-101-32x4d-FPN | pytorch |     GN+WS     |   1x    |   10.8   |      7.6       |  42.1  |    -    | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212-27da1bc2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212_195302.log.json) |
+
+Mask R-CNN
+
+|    Backbone     |  Style  | Normalization |  Lr schd  | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                                Config                                                                |                                                                                                                                                                                         Download                                                                                                                                                                                          |
+| :-------------: | :-----: | :-----------: | :-------: | :------: | :------------: | :----: | :-----: | :----------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50-FPN     | pytorch |     GN+WS     |    2x     |   7.3    |      10.5      |  40.6  |  36.6   |        [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py)        |                             [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226-16acb762.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226_062128.log.json)                             |
+|    R-101-FPN    | pytorch |     GN+WS     |    2x     |   10.3   |      8.6       |  42.0  |  37.7   |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py)        |                           [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212-ea357cd9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212_213627.log.json)                           |
+| X-50-32x4d-FPN  | pytorch |     GN+WS     |    2x     |   8.4    |      9.3       |  41.1  |  37.0   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py)     |                 [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216-649fdb6f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216_201500.log.json)                 |
+| X-101-32x4d-FPN | pytorch |     GN+WS     |    2x     |   12.2   |      7.1       |  42.1  |  37.9   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319-33fb95b5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319_104101.log.json)               |
+|    R-50-FPN     | pytorch |     GN+WS     | 20-23-24e |   7.3    |       -        |  41.1  |  37.1   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213-487d1283.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213_035123.log.json)               |
+|    R-101-FPN    | pytorch |     GN+WS     | 20-23-24e |   10.3   |       -        |  43.1  |  38.6   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213-57b5a50f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213_130142.log.json)             |
+| X-50-32x4d-FPN  | pytorch |     GN+WS     | 20-23-24e |   8.4    |       -        |  42.1  |  38.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226-969bcb2c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226_093732.log.json)   |
+| X-101-32x4d-FPN | pytorch |     GN+WS     | 20-23-24e |   12.2   |       -        |  42.7  |  38.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316-e6cd35ef.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316_013741.log.json) |
+
+Note:
+
+- GN+WS requires about 5% more memory than GN, and it is only 5% slower than GN.
+- In the paper, a 20-23-24e lr schedule is used instead of 2x.
+- The X-50-GN and X-101-GN pretrained models are also shared by the authors.
+
+## Citation
+
+```latex
+@article{weightstandardization,
+  author    = {Siyuan Qiao and Huiyu Wang and Chenxi Liu and Wei Shen and Alan Yuille},
+  title     = {Weight Standardization},
+  journal   = {arXiv preprint arXiv:1903.10520},
+  year      = {2019},
+}
+```
diff --git a/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py
new file mode 100644
index 0000000..cd2cb2b
--- /dev/null
+++ b/configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws')))
diff --git a/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py
new file mode 100644
index 0000000..1b326b8
--- /dev/null
+++ b/configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py
@@ -0,0 +1,16 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+conv_cfg = dict(type='ConvWS')
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    backbone=dict(
+        conv_cfg=conv_cfg,
+        norm_cfg=norm_cfg,
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://jhu/resnet50_gn_ws')),
+    neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg),
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            conv_out_channels=256,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg)))
diff --git a/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py
new file mode 100644
index 0000000..f64ae89
--- /dev/null
+++ b/configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py
@@ -0,0 +1,18 @@
+_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py'
+conv_cfg = dict(type='ConvWS')
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        style='pytorch',
+        conv_cfg=conv_cfg,
+        norm_cfg=norm_cfg,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://jhu/resnext101_32x4d_gn_ws')))
diff --git a/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py b/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py
new file mode 100644
index 0000000..246851b
--- /dev/null
+++ b/configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py
@@ -0,0 +1,18 @@
+_base_ = './faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py'
+conv_cfg = dict(type='ConvWS')
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=50,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        style='pytorch',
+        conv_cfg=conv_cfg,
+        norm_cfg=norm_cfg,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://jhu/resnext50_32x4d_gn_ws')))
diff --git a/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py b/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py
new file mode 100644
index 0000000..a790d93
--- /dev/null
+++ b/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py
@@ -0,0 +1,4 @@
+_base_ = './mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py'
+# learning policy
+lr_config = dict(step=[20, 23])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py
new file mode 100644
index 0000000..a9fa6a2
--- /dev/null
+++ b/configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://jhu/resnet101_gn_ws')))
diff --git a/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py
new file mode 100644
index 0000000..5516808
--- /dev/null
+++ b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py
@@ -0,0 +1,4 @@
+_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py'
+# learning policy
+lr_config = dict(step=[20, 23])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py
new file mode 100644
index 0000000..63be60f
--- /dev/null
+++ b/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py
@@ -0,0 +1,20 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+conv_cfg = dict(type='ConvWS')
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    backbone=dict(
+        conv_cfg=conv_cfg,
+        norm_cfg=norm_cfg,
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://jhu/resnet50_gn_ws')),
+    neck=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg),
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            conv_out_channels=256,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg),
+        mask_head=dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg)))
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py
new file mode 100644
index 0000000..cfa14c9
--- /dev/null
+++ b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py
@@ -0,0 +1,4 @@
+_base_ = './mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py'
+# learning policy
+lr_config = dict(step=[20, 23])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py
new file mode 100644
index 0000000..6498b03
--- /dev/null
+++ b/configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py
@@ -0,0 +1,19 @@
+_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py'
+# model settings
+conv_cfg = dict(type='ConvWS')
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        style='pytorch',
+        conv_cfg=conv_cfg,
+        norm_cfg=norm_cfg,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://jhu/resnext101_32x4d_gn_ws')))
diff --git a/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py b/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py
new file mode 100644
index 0000000..79ce0ad
--- /dev/null
+++ b/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py
@@ -0,0 +1,4 @@
+_base_ = './mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py'
+# learning policy
+lr_config = dict(step=[20, 23])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py b/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py
new file mode 100644
index 0000000..7fac317
--- /dev/null
+++ b/configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py
@@ -0,0 +1,19 @@
+_base_ = './mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py'
+# model settings
+conv_cfg = dict(type='ConvWS')
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=50,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        style='pytorch',
+        conv_cfg=conv_cfg,
+        norm_cfg=norm_cfg,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://jhu/resnext50_32x4d_gn_ws')))
diff --git a/configs/gn+ws/metafile.yml b/configs/gn+ws/metafile.yml
new file mode 100644
index 0000000..6cfcb07
--- /dev/null
+++ b/configs/gn+ws/metafile.yml
@@ -0,0 +1,263 @@
+Collections:
+  - Name: Weight Standardization
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Group Normalization
+        - Weight Standardization
+    Paper:
+      URL: https://arxiv.org/abs/1903.10520
+      Title: 'Weight Standardization'
+    README: configs/gn+ws/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py
+      Version: v2.0.0
+
+Models:
+  - Name: faster_rcnn_r50_fpn_gn_ws-all_1x_coco
+    In Collection: Weight Standardization
+    Config: configs/gn+ws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.9
+      inference time (ms/im):
+        - value: 85.47
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r50_fpn_gn_ws-all_1x_coco/faster_rcnn_r50_fpn_gn_ws-all_1x_coco_20200130-613d9fe2.pth
+
+  - Name: faster_rcnn_r101_fpn_gn_ws-all_1x_coco
+    In Collection: Weight Standardization
+    Config: configs/gn+ws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.9
+      inference time (ms/im):
+        - value: 111.11
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_r101_fpn_gn_ws-all_1x_coco/faster_rcnn_r101_fpn_gn_ws-all_1x_coco_20200205-a93b0d75.pth
+
+  - Name: faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco
+    In Collection: Weight Standardization
+    Config: configs/gn+ws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.0
+      inference time (ms/im):
+        - value: 97.09
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x50_32x4d_fpn_gn_ws-all_1x_coco_20200203-839c5d9d.pth
+
+  - Name: faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco
+    In Collection: Weight Standardization
+    Config: configs/gn+ws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.8
+      inference time (ms/im):
+        - value: 131.58
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco/faster_rcnn_x101_32x4d_fpn_gn_ws-all_1x_coco_20200212-27da1bc2.pth
+
+  - Name: mask_rcnn_r50_fpn_gn_ws-all_2x_coco
+    In Collection: Weight Standardization
+    Config: configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco.py
+    Metadata:
+      Training Memory (GB): 7.3
+      inference time (ms/im):
+        - value: 95.24
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  36.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_2x_coco/mask_rcnn_r50_fpn_gn_ws-all_2x_coco_20200226-16acb762.pth
+
+  - Name: mask_rcnn_r101_fpn_gn_ws-all_2x_coco
+    In Collection: Weight Standardization
+    Config: configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco.py
+    Metadata:
+      Training Memory (GB): 10.3
+      inference time (ms/im):
+        - value: 116.28
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_2x_coco/mask_rcnn_r101_fpn_gn_ws-all_2x_coco_20200212-ea357cd9.pth
+
+  - Name: mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco
+    In Collection: Weight Standardization
+    Config: configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco.py
+    Metadata:
+      Training Memory (GB): 8.4
+      inference time (ms/im):
+        - value: 107.53
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_2x_coco_20200216-649fdb6f.pth
+
+  - Name: mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco
+    In Collection: Weight Standardization
+    Config: configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco.py
+    Metadata:
+      Training Memory (GB): 12.2
+      inference time (ms/im):
+        - value: 140.85
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_2x_coco_20200319-33fb95b5.pth
+
+  - Name: mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco
+    In Collection: Weight Standardization
+    Config: configs/gn+ws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco.py
+    Metadata:
+      Training Memory (GB): 7.3
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r50_fpn_gn_ws-all_20_23_24e_coco_20200213-487d1283.pth
+
+  - Name: mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco
+    In Collection: Weight Standardization
+    Config: configs/gn+ws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco.py
+    Metadata:
+      Training Memory (GB): 10.3
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  38.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_r101_fpn_gn_ws-all_20_23_24e_coco_20200213-57b5a50f.pth
+
+  - Name: mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco
+    In Collection: Weight Standardization
+    Config: configs/gn+ws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco.py
+    Metadata:
+      Training Memory (GB): 8.4
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  38.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x50_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200226-969bcb2c.pth
+
+  - Name: mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco
+    In Collection: Weight Standardization
+    Config: configs/gn+ws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco.py
+    Metadata:
+      Training Memory (GB): 12.2
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  38.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn%2Bws/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco/mask_rcnn_x101_32x4d_fpn_gn_ws-all_20_23_24e_coco_20200316-e6cd35ef.pth
diff --git a/configs/gn/README.md b/configs/gn/README.md
new file mode 100644
index 0000000..9bb2888
--- /dev/null
+++ b/configs/gn/README.md
@@ -0,0 +1,41 @@
+# GN
+
+> [Group Normalization](https://arxiv.org/abs/1803.08494)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Batch Normalization (BN) is a milestone technique in the development of deep learning, enabling various networks to train. However, normalizing along the batch dimension introduces problems --- BN's error increases rapidly when the batch size becomes smaller, caused by inaccurate batch statistics estimation. This limits BN's usage for training larger models and transferring features to computer vision tasks including detection, segmentation, and video, which require small batches constrained by memory consumption. In this paper, we present Group Normalization (GN) as a simple alternative to BN. GN divides the channels into groups and computes within each group the mean and variance for normalization. GN's computation is independent of batch sizes, and its accuracy is stable in a wide range of batch sizes. On ResNet-50 trained in ImageNet, GN has 10.6% lower error than its BN counterpart when using a batch size of 2; when using typical batch sizes, GN is comparably good with BN and outperforms other normalization variants. Moreover, GN can be naturally transferred from pre-training to fine-tuning. GN can outperform its BN-based counterparts for object detection and segmentation in COCO, and for video classification in Kinetics, showing that GN can effectively replace the powerful BN in a variety of tasks. GN can be easily implemented by a few lines of code in modern libraries.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143889171-d4660307-3a4f-4ac2-a0ad-7d17c2f045b1.png" height="200"/>
+</div>
+
+## Results and Models
+
+|   Backbone    |   model    | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                         Config                                                         |                                                                                                                                                                  Download                                                                                                                                                                   |
+| :-----------: | :--------: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R-50-FPN (d)  | Mask R-CNN |   2x    |   7.1    |      11.0      |  40.2  |  36.4   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py)     |                 [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206_050355.log.json)                 |
+| R-50-FPN (d)  | Mask R-CNN |   3x    |   7.1    |       -        |  40.5  |  36.7   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py)     |                 [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214-8b23b1e5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214_063512.log.json)                 |
+| R-101-FPN (d) | Mask R-CNN |   2x    |   9.9    |      9.0       |  41.9  |  37.6   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205-d96b1b50.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205_234402.log.json)               |
+| R-101-FPN (d) | Mask R-CNN |   3x    |   9.9    |                |  42.1  |  38.0   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py)     |           [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609-0df864f4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609.log.json)            |
+| R-50-FPN (c)  | Mask R-CNN |   2x    |   7.1    |      10.9      |  40.0  |  36.1   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207-20d3e849.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207_225832.log.json) |
+| R-50-FPN (c)  | Mask R-CNN |   3x    |   7.1    |       -        |  40.1  |  36.2   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225-542aefbc.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225_235135.log.json) |
+
+**Notes:**
+
+- (d) means pretrained model converted from Detectron, and (c) means the contributed model pretrained by [@thangvubk](https://github.com/thangvubk).
+- The `3x` schedule is epoch \[28, 34, 36\].
+- **Memory, Train/Inf time is outdated.**
+
+## Citation
+
+```latex
+@inproceedings{wu2018group,
+  title={Group Normalization},
+  author={Wu, Yuxin and He, Kaiming},
+  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
+  year={2018}
+}
+```
diff --git a/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py b/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py
new file mode 100644
index 0000000..a505ba0
--- /dev/null
+++ b/configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron/resnet101_gn')))
diff --git a/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py b/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py
new file mode 100644
index 0000000..12a9d17
--- /dev/null
+++ b/configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py
@@ -0,0 +1,5 @@
+_base_ = './mask_rcnn_r101_fpn_gn-all_2x_coco.py'
+
+# learning policy
+lr_config = dict(step=[28, 34])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py b/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py
new file mode 100644
index 0000000..1de7d98
--- /dev/null
+++ b/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py
@@ -0,0 +1,49 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    backbone=dict(
+        norm_cfg=norm_cfg,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron/resnet50_gn')),
+    neck=dict(norm_cfg=norm_cfg),
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            conv_out_channels=256,
+            norm_cfg=norm_cfg),
+        mask_head=dict(norm_cfg=norm_cfg)))
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py b/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py
new file mode 100644
index 0000000..f917719
--- /dev/null
+++ b/configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py
@@ -0,0 +1,5 @@
+_base_ = './mask_rcnn_r50_fpn_gn-all_2x_coco.py'
+
+# learning policy
+lr_config = dict(step=[28, 34])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py b/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py
new file mode 100644
index 0000000..2f430fd
--- /dev/null
+++ b/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py
@@ -0,0 +1,17 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    backbone=dict(
+        norm_cfg=norm_cfg,
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://contrib/resnet50_gn')),
+    neck=dict(norm_cfg=norm_cfg),
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            conv_out_channels=256,
+            norm_cfg=norm_cfg),
+        mask_head=dict(norm_cfg=norm_cfg)))
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py b/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py
new file mode 100644
index 0000000..66834f0
--- /dev/null
+++ b/configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py
@@ -0,0 +1,5 @@
+_base_ = './mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py'
+
+# learning policy
+lr_config = dict(step=[28, 34])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/configs/gn/metafile.yml b/configs/gn/metafile.yml
new file mode 100644
index 0000000..4a1ecae
--- /dev/null
+++ b/configs/gn/metafile.yml
@@ -0,0 +1,162 @@
+Collections:
+  - Name: Group Normalization
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Group Normalization
+    Paper:
+      URL: https://arxiv.org/abs/1803.08494
+      Title: 'Group Normalization'
+    README: configs/gn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py
+      Version: v2.0.0
+
+Models:
+  - Name: mask_rcnn_r50_fpn_gn-all_2x_coco
+    In Collection: Group Normalization
+    Config: configs/gn/mask_rcnn_r50_fpn_gn-all_2x_coco.py
+    Metadata:
+      Training Memory (GB): 7.1
+      inference time (ms/im):
+        - value: 90.91
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  36.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_2x_coco/mask_rcnn_r50_fpn_gn-all_2x_coco_20200206-8eee02a6.pth
+
+  - Name: mask_rcnn_r50_fpn_gn-all_3x_coco
+    In Collection: Group Normalization
+    Config: configs/gn/mask_rcnn_r50_fpn_gn-all_3x_coco.py
+    Metadata:
+      Training Memory (GB): 7.1
+      inference time (ms/im):
+        - value: 90.91
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  36.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_3x_coco/mask_rcnn_r50_fpn_gn-all_3x_coco_20200214-8b23b1e5.pth
+
+  - Name: mask_rcnn_r101_fpn_gn-all_2x_coco
+    In Collection: Group Normalization
+    Config: configs/gn/mask_rcnn_r101_fpn_gn-all_2x_coco.py
+    Metadata:
+      Training Memory (GB): 9.9
+      inference time (ms/im):
+        - value: 111.11
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  37.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_2x_coco/mask_rcnn_r101_fpn_gn-all_2x_coco_20200205-d96b1b50.pth
+
+  - Name: mask_rcnn_r101_fpn_gn-all_3x_coco
+    In Collection: Group Normalization
+    Config: configs/gn/mask_rcnn_r101_fpn_gn-all_3x_coco.py
+    Metadata:
+      Training Memory (GB): 9.9
+      inference time (ms/im):
+        - value: 111.11
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  38.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r101_fpn_gn-all_3x_coco/mask_rcnn_r101_fpn_gn-all_3x_coco_20200513_181609-0df864f4.pth
+
+  - Name: mask_rcnn_r50_fpn_gn-all_contrib_2x_coco
+    In Collection: Group Normalization
+    Config: configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco.py
+    Metadata:
+      Training Memory (GB): 7.1
+      inference time (ms/im):
+        - value: 91.74
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  36.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco/mask_rcnn_r50_fpn_gn-all_contrib_2x_coco_20200207-20d3e849.pth
+
+  - Name: mask_rcnn_r50_fpn_gn-all_contrib_3x_coco
+    In Collection: Group Normalization
+    Config: configs/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco.py
+    Metadata:
+      Training Memory (GB): 7.1
+      inference time (ms/im):
+        - value: 91.74
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:  36.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/gn/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco/mask_rcnn_r50_fpn_gn-all_contrib_3x_coco_20200225-542aefbc.pth
diff --git a/configs/grid_rcnn/README.md b/configs/grid_rcnn/README.md
new file mode 100644
index 0000000..e844021
--- /dev/null
+++ b/configs/grid_rcnn/README.md
@@ -0,0 +1,47 @@
+# Grid R-CNN
+
+> [Grid R-CNN](https://arxiv.org/abs/1811.12030)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+This paper proposes a novel object detection framework named Grid R-CNN, which adopts a grid guided localization mechanism for accurate object detection. Different from the traditional regression based methods, the Grid R-CNN captures the spatial information explicitly and enjoys the position sensitive property of fully convolutional architecture. Instead of using only two independent points, we design a multi-point supervision formulation to encode more clues in order to reduce the impact of inaccurate prediction of specific points. To take the full advantage of the correlation of points in a grid, we propose a two-stage information fusion strategy to fuse feature maps of neighbor grid points. The grid guided localization approach is easy to be extended to different state-of-the-art detection frameworks. Grid R-CNN leads to high quality object localization, and experiments demonstrate that it achieves a 4.1% AP gain at IoU=0.8 and a 10.0% AP gain at IoU=0.9 on COCO benchmark compared to Faster R-CNN with Res50 backbone and FPN architecture.
+
+Grid R-CNN is a well-performed objection detection framework. It transforms the traditional box offset regression problem into a grid point estimation problem. With the guidance of the grid points, it can obtain high-quality localization results. However, the speed of Grid R-CNN is not so satisfactory. In this technical report we present Grid R-CNN Plus, a better and faster version of Grid R-CNN. We have made several updates that significantly speed up the framework and simultaneously improve the accuracy. On COCO dataset, the Res50-FPN based Grid R-CNN Plus detector achieves an mAP of 40.4%, outperforming the baseline on the same model by 3.0 points with similar inference time.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143890379-5d9e6233-0533-48b4-88b9-bc33abbd9f82.png"/>
+</div>
+
+## Results and Models
+
+|  Backbone   | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                            Config                                                             |                                                                                                                                                                         Download                                                                                                                                                                          |
+| :---------: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50     |   2x    |   5.1    |      15.0      |  40.4  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130_221140.log.json)               |
+|    R-101    |   2x    |   7.0    |      12.6      |  41.5  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309-d6eca030.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309_164224.log.json)             |
+| X-101-32x4d |   2x    |   8.3    |      10.8      |  42.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130-d8f0e3ff.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130_215413.log.json) |
+| X-101-64x4d |   2x    |   11.3   |      7.7       |  43.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204-ec76a754.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204_080641.log.json) |
+
+**Notes:**
+
+- All models are trained with 8 GPUs instead of 32 GPUs in the original paper.
+- The warming up lasts for 1 epoch and `2x` here indicates 25 epochs.
+
+## Citation
+
+```latex
+@inproceedings{lu2019grid,
+  title={Grid r-cnn},
+  author={Lu, Xin and Li, Buyu and Yue, Yuxin and Li, Quanquan and Yan, Junjie},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  year={2019}
+}
+
+@article{lu2019grid,
+  title={Grid R-CNN Plus: Faster and Better},
+  author={Lu, Xin and Li, Buyu and Yue, Yuxin and Li, Quanquan and Yan, Junjie},
+  journal={arXiv preprint arXiv:1906.05688},
+  year={2019}
+}
+```
diff --git a/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py
new file mode 100644
index 0000000..1bb5889
--- /dev/null
+++ b/configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py'
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py
new file mode 100644
index 0000000..4aa00ec
--- /dev/null
+++ b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = ['grid_rcnn_r50_fpn_gn-head_2x_coco.py']
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[8, 11])
+checkpoint_config = dict(interval=1)
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=12)
diff --git a/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py
new file mode 100644
index 0000000..df63cd5
--- /dev/null
+++ b/configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py
@@ -0,0 +1,131 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+    type='GridRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+    roi_head=dict(
+        type='GridRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            with_reg=False,
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False),
+        grid_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        grid_head=dict(
+            type='GridHead',
+            grid_points=9,
+            num_convs=8,
+            in_channels=256,
+            point_feat_channels=64,
+            norm_cfg=dict(type='GN', num_groups=36),
+            loss_grid=dict(
+                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=15))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.5,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True),
+            pos_radius=1,
+            pos_weight=-1,
+            max_num_grid=192,
+            debug=False)),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.03,
+            nms=dict(type='nms', iou_threshold=0.3),
+            max_per_img=100)))
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=3665,
+    warmup_ratio=1.0 / 80,
+    step=[17, 23])
+runner = dict(type='EpochBasedRunner', max_epochs=25)
diff --git a/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py
new file mode 100644
index 0000000..3bc8516
--- /dev/null
+++ b/configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py
@@ -0,0 +1,24 @@
+_base_ = './grid_rcnn_r50_fpn_gn-head_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=3665,
+    warmup_ratio=1.0 / 80,
+    step=[17, 23])
+runner = dict(type='EpochBasedRunner', max_epochs=25)
diff --git a/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py b/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py
new file mode 100644
index 0000000..c78f8f6
--- /dev/null
+++ b/configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py
@@ -0,0 +1,13 @@
+_base_ = './grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/grid_rcnn/metafile.yml b/configs/grid_rcnn/metafile.yml
new file mode 100644
index 0000000..d1aa851
--- /dev/null
+++ b/configs/grid_rcnn/metafile.yml
@@ -0,0 +1,101 @@
+Collections:
+  - Name: Grid R-CNN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RPN
+        - Dilated Convolution
+        - ResNet
+        - RoIAlign
+    Paper:
+      URL: https://arxiv.org/abs/1906.05688
+      Title: 'Grid R-CNN'
+    README: configs/grid_rcnn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/grid_rcnn.py#L6
+      Version: v2.0.0
+
+Models:
+  - Name: grid_rcnn_r50_fpn_gn-head_2x_coco
+    In Collection: Grid R-CNN
+    Config: configs/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py
+    Metadata:
+      Training Memory (GB): 5.1
+      inference time (ms/im):
+        - value: 66.67
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco/grid_rcnn_r50_fpn_gn-head_2x_coco_20200130-6cca8223.pth
+
+  - Name: grid_rcnn_r101_fpn_gn-head_2x_coco
+    In Collection: Grid R-CNN
+    Config: configs/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco.py
+    Metadata:
+      Training Memory (GB): 7.0
+      inference time (ms/im):
+        - value: 79.37
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_r101_fpn_gn-head_2x_coco/grid_rcnn_r101_fpn_gn-head_2x_coco_20200309-d6eca030.pth
+
+  - Name: grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco
+    In Collection: Grid R-CNN
+    Config: configs/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco.py
+    Metadata:
+      Training Memory (GB): 8.3
+      inference time (ms/im):
+        - value: 92.59
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_32x4d_fpn_gn-head_2x_coco_20200130-d8f0e3ff.pth
+
+  - Name: grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco
+    In Collection: Grid R-CNN
+    Config: configs/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco.py
+    Metadata:
+      Training Memory (GB): 11.3
+      inference time (ms/im):
+        - value: 129.87
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/grid_rcnn/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco/grid_rcnn_x101_64x4d_fpn_gn-head_2x_coco_20200204-ec76a754.pth
diff --git a/configs/groie/README.md b/configs/groie/README.md
new file mode 100644
index 0000000..126773f
--- /dev/null
+++ b/configs/groie/README.md
@@ -0,0 +1,72 @@
+# GRoIE
+
+> [A novel Region of Interest Extraction Layer for Instance Segmentation](https://arxiv.org/abs/2004.13665)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Given the wide diffusion of deep neural network architectures for computer vision tasks, several new applications are nowadays more and more feasible. Among them, a particular attention has been recently given to instance segmentation, by exploiting the results achievable by two-stage networks (such as Mask R-CNN or Faster R-CNN), derived from R-CNN. In these complex architectures, a crucial role is played by the Region of Interest (RoI) extraction layer, devoted to extracting a coherent subset of features from a single Feature Pyramid Network (FPN) layer attached on top of a backbone.
+This paper is motivated by the need to overcome the limitations of existing RoI extractors which select only one (the best) layer from FPN. Our intuition is that all the layers of FPN retain useful information. Therefore, the proposed layer (called Generic RoI Extractor - GRoIE) introduces non-local building blocks and attention mechanisms to boost the performance.
+A comprehensive ablation study at component level is conducted to find the best set of algorithms and parameters for the GRoIE layer. Moreover, GRoIE can be integrated seamlessly with every two-stage architecture for both object detection and instance segmentation tasks. Therefore, the improvements brought about by the use of GRoIE in different state-of-the-art architectures are also evaluated. The proposed layer leads up to gain a 1.1% AP improvement on bounding box detection and 1.7% AP improvement on instance segmentation.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143891453-afdcdaf4-a868-4a28-ad20-dc710a517a76.png"/>
+</div>
+
+## Introduction
+
+By Leonardo Rossi, Akbar Karimi and Andrea Prati from
+[IMPLab](http://implab.ce.unipr.it/).
+
+We provide configs to reproduce the results in the paper for
+"*A novel Region of Interest Extraction Layer for Instance Segmentation*"
+on COCO object detection.
+
+This paper is motivated by the need to overcome to the limitations of existing
+RoI extractors which select only one (the best) layer from FPN.
+
+Our intuition is that all the layers of FPN retain useful information.
+
+Therefore, the proposed layer (called Generic RoI Extractor - **GRoIE**)
+introduces non-local building blocks and attention mechanisms to boost the
+performance.
+
+## Results and Models
+
+The results on COCO 2017 minival (5k images) are shown in the below table.
+
+### Application of GRoIE to different architectures
+
+| Backbone  |     Method      | Lr schd | box AP | mask AP |                                    Config                                     |                                                                                                                                                                                                                   Download                                                                                                                                                                                                                   |
+| :-------: | :-------------: | :-----: | :----: | :-----: | :---------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R-50-FPN  | Faster Original |   1x    |  37.4  |         |            [config](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py)            |                                                                  [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json)                                                                   |
+| R-50-FPN  |     + GRoIE     |   1x    |  38.3  |         |               [config](./faster_rcnn_r50_fpn_groie_1x_coco.py)                |                                                         [model](https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715.log.json)                                                         |
+| R-50-FPN  |   Grid R-CNN    |   1x    |  39.1  |         |               [config](./grid_rcnn_r50_fpn_gn-head_1x_coco.py)                |                                             [model](https://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco/grid_rcnn_r50_fpn_gn-head_groie_1x_coco_20200605_202059-4b75d86f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco/grid_rcnn_r50_fpn_gn-head_groie_1x_coco_20200605_202059.log.json)                                             |
+| R-50-FPN  |     + GRoIE     |   1x    |        |         |            [config](./grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py)             |                                                                                                                                                                                                                                                                                                                                                                                                                                              |
+| R-50-FPN  |   Mask R-CNN    |   1x    |  38.2  |  34.7   |              [config](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)              |                                                                        [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json)                                                                         |
+| R-50-FPN  |     + GRoIE     |   1x    |  39.0  |  36.0   |                [config](./mask_rcnn_r50_fpn_groie_1x_coco.py)                 |                                                             [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715-50d90c74.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715.log.json)                                                             |
+| R-50-FPN  |     GC-Net      |   1x    |  40.7  |  36.5   | [config](../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py)  |                  [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202-50b90e5c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200202_085547.log.json)                   |
+| R-50-FPN  |     + GRoIE     |   1x    |  41.0  |  37.8   |  [config](./mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py)  | [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth) |
+| R-101-FPN |     GC-Net      |   1x    |  42.2  |  37.8   | [config](../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py) |                [model](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206-8407a3f0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco_20200206_142508.log.json)                 |
+| R-101-FPN |     + GRoIE     |   1x    |  42.6  |  38.7   | [config](./mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py)  | [model](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507.log.json) |
+
+## Citation
+
+If you use this work or benchmark in your research, please cite this project.
+
+```latex
+@inproceedings{rossi2021novel,
+  title={A novel region of interest extraction layer for instance segmentation},
+  author={Rossi, Leonardo and Karimi, Akbar and Prati, Andrea},
+  booktitle={2020 25th International Conference on Pattern Recognition (ICPR)},
+  pages={2203--2209},
+  year={2021},
+  organization={IEEE}
+}
+```
+
+## Contact
+
+The implementation of GRoIE is currently maintained by
+[Leonardo Rossi](https://github.com/hachreak/).
diff --git a/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py b/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py
new file mode 100644
index 0000000..0fc528b
--- /dev/null
+++ b/configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py
@@ -0,0 +1,25 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+# model settings
+model = dict(
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            aggregation='sum',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2))))
diff --git a/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py b/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py
new file mode 100644
index 0000000..8e4b4ab
--- /dev/null
+++ b/configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py
@@ -0,0 +1,45 @@
+_base_ = '../grid_rcnn/grid_rcnn_r50_fpn_gn-head_1x_coco.py'
+# model settings
+model = dict(
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            aggregation='sum',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2)),
+        grid_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2))))
diff --git a/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py b/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
new file mode 100644
index 0000000..8b83722
--- /dev/null
+++ b/configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
@@ -0,0 +1,45 @@
+_base_ = '../gcnet/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py'
+# model settings
+model = dict(
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            aggregation='sum',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2)),
+        mask_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2))))
diff --git a/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py b/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py
new file mode 100644
index 0000000..81dfb48
--- /dev/null
+++ b/configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py
@@ -0,0 +1,45 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+# model settings
+model = dict(
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            aggregation='sum',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2)),
+        mask_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2))))
diff --git a/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py b/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
new file mode 100644
index 0000000..852c5ca
--- /dev/null
+++ b/configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
@@ -0,0 +1,45 @@
+_base_ = '../gcnet/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_1x_coco.py'
+# model settings
+model = dict(
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            aggregation='sum',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2)),
+        mask_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32],
+            pre_cfg=dict(
+                type='ConvModule',
+                in_channels=256,
+                out_channels=256,
+                kernel_size=5,
+                padding=2,
+                inplace=False,
+            ),
+            post_cfg=dict(
+                type='GeneralizedAttention',
+                in_channels=256,
+                spatial_range=-1,
+                num_heads=6,
+                attention_type='0100',
+                kv_stride=2))))
diff --git a/configs/groie/metafile.yml b/configs/groie/metafile.yml
new file mode 100644
index 0000000..badf53a
--- /dev/null
+++ b/configs/groie/metafile.yml
@@ -0,0 +1,94 @@
+Collections:
+  - Name: GRoIE
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Generic RoI Extractor
+        - FPN
+        - RPN
+        - ResNet
+        - RoIAlign
+    Paper:
+      URL: https://arxiv.org/abs/2004.13665
+      Title: 'A novel Region of Interest Extraction Layer for Instance Segmentation'
+    README: configs/groie/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/roi_heads/roi_extractors/groie.py#L15
+      Version: v2.1.0
+
+Models:
+  - Name: faster_rcnn_r50_fpn_groie_1x_coco
+    In Collection: GRoIE
+    Config: configs/groie/faster_rcnn_r50_fpn_groie_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/faster_rcnn_r50_fpn_groie_1x_coco/faster_rcnn_r50_fpn_groie_1x_coco_20200604_211715-66ee9516.pth
+
+  - Name: grid_rcnn_r50_fpn_gn-head_groie_1x_coco
+    In Collection: GRoIE
+    Config: configs/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/grid_rcnn_r50_fpn_gn-head_groie_1x_coco/grid_rcnn_r50_fpn_gn-head_groie_1x_coco_20200605_202059-4b75d86f.pth
+
+  - Name: mask_rcnn_r50_fpn_groie_1x_coco
+    In Collection: GRoIE
+    Config: configs/groie/mask_rcnn_r50_fpn_groie_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_groie_1x_coco/mask_rcnn_r50_fpn_groie_1x_coco_20200604_211715-50d90c74.pth
+
+  - Name: mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco
+    In Collection: GRoIE
+    Config: configs/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP:   37.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r50_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200604_211715-42eb79e1.pth
+
+  - Name: mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco
+    In Collection: GRoIE
+    Config: configs/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/groie/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco/mask_rcnn_r101_fpn_syncbn-backbone_r4_gcb_c3-c5_groie_1x_coco_20200607_224507-8daae01c.pth
diff --git a/configs/guided_anchoring/README.md b/configs/guided_anchoring/README.md
new file mode 100644
index 0000000..563e43f
--- /dev/null
+++ b/configs/guided_anchoring/README.md
@@ -0,0 +1,59 @@
+# Guided Anchoring
+
+> [Region Proposal by Guided Anchoring](https://arxiv.org/abs/1901.03278)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Region anchors are the cornerstone of modern object detection techniques. State-of-the-art detectors mostly rely on a dense anchoring scheme, where anchors are sampled uniformly over the spatial domain with a predefined set of scales and aspect ratios. In this paper, we revisit this foundational stage. Our study shows that it can be done much more effectively and efficiently. Specifically, we present an alternative scheme, named Guided Anchoring, which leverages semantic features to guide the anchoring. The proposed method jointly predicts the locations where the center of objects of interest are likely to exist as well as the scales and aspect ratios at different locations. On top of predicted anchor shapes, we mitigate the feature inconsistency with a feature adaption module. We also study the use of high-quality proposals to improve detection performance. The anchoring scheme can be seamlessly integrated into proposal methods and detectors. With Guided Anchoring, we achieve 9.1% higher recall on MS COCO with 90% fewer anchors than the RPN baseline. We also adopt Guided Anchoring in Fast R-CNN, Faster R-CNN and RetinaNet, respectively improving the detection mAP by 2.2%, 2.7% and 1.2%.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143891529-4c178948-c3fd-4543-ae6e-bb2aa3c8147e.png"/>
+</div>
+
+## Results and Models
+
+The results on COCO 2017 val is shown in the below table. (results on test-dev are usually slightly higher than val).
+
+| Method |    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | AR 1000 |                                                          Config                                                           |                                                                                                                                                          Download                                                                                                                                                           |
+| :----: | :-------------: | :-----: | :-----: | :------: | :------------: | :-----: | :-----------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| GA-RPN |    R-50-FPN     |  caffe  |   1x    |   5.3    |      15.8      |  68.4   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531-899008a6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531_011819.log.json)   |
+| GA-RPN |    R-101-FPN    |  caffe  |   1x    |   7.3    |      13.0      |  69.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531-ca9ba8fb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531_011812.log.json) |
+| GA-RPN | X-101-32x4d-FPN | pytorch |   1x    |   8.5    |      10.0      |  70.6   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220-c28d1b18.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220_221326.log.json) |
+| GA-RPN | X-101-64x4d-FPN | pytorch |   1x    |   7.1    |      7.5       |  71.2   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225-3c6e1aa2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225_152704.log.json) |
+
+|     Method     |    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                             Config                                                              |                                                                                                                                                                           Download                                                                                                                                                                            |
+| :------------: | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| GA-Faster RCNN |    R-50-FPN     |  caffe  |   1x    |   5.5    |                |  39.6  |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py)   |          [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718.log.json)           |
+| GA-Faster RCNN |    R-101-FPN    |  caffe  |   1x    |   7.5    |                |  41.5  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py)   | [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_bbox_mAP-0.415_20200505_115528-fb82e499.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_20200505_115528.log.json) |
+| GA-Faster RCNN | X-101-32x4d-FPN | pytorch |   1x    |   8.7    |      9.7       |  43.0  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py)   |            [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215-1ded9da3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215_184547.log.json)            |
+| GA-Faster RCNN | X-101-64x4d-FPN | pytorch |   1x    |   11.8   |      7.3       |  43.9  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py)   |            [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215-0fa7bde7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215_104455.log.json)            |
+|  GA-RetinaNet  |    R-50-FPN     |  caffe  |   1x    |   3.5    |      16.8      |  36.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py)  |        [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020_225450.log.json)        |
+|  GA-RetinaNet  |    R-101-FPN    |  caffe  |   1x    |   5.5    |      12.9      |  39.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py) |      [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531-6266453c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531_012847.log.json)      |
+|  GA-RetinaNet  | X-101-32x4d-FPN | pytorch |   1x    |   6.9    |      10.6      |  40.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py) |      [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219-40c56caa.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219_223025.log.json)      |
+|  GA-RetinaNet  | X-101-64x4d-FPN | pytorch |   1x    |   9.9    |      7.7       |  41.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py) |      [model](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226-ef9f7f1f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226_221123.log.json)      |
+
+- In the Guided Anchoring paper, `score_thr` is set to 0.001 in Fast/Faster RCNN and 0.05 in RetinaNet for both baselines and Guided Anchoring.
+
+- Performance on COCO test-dev benchmark are shown as follows.
+
+|     Method     | Backbone  | Style | Lr schd | Aug Train | Score thr | AP  | AP_50 | AP_75 | AP_small | AP_medium | AP_large | Download |
+| :------------: | :-------: | :---: | :-----: | :-------: | :-------: | :-: | :---: | :---: | :------: | :-------: | :------: | :------: |
+| GA-Faster RCNN | R-101-FPN | caffe |   1x    |     F     |   0.05    |     |       |       |          |           |          |          |
+| GA-Faster RCNN | R-101-FPN | caffe |   1x    |     F     |   0.001   |     |       |       |          |           |          |          |
+|  GA-RetinaNet  | R-101-FPN | caffe |   1x    |     F     |   0.05    |     |       |       |          |           |          |          |
+|  GA-RetinaNet  | R-101-FPN | caffe |   2x    |     T     |   0.05    |     |       |       |          |           |          |          |
+
+## Citation
+
+We provide config files to reproduce the results in the CVPR 2019 paper for [Region Proposal by Guided Anchoring](https://arxiv.org/abs/1901.03278).
+
+```latex
+@inproceedings{wang2019region,
+    title={Region Proposal by Guided Anchoring},
+    author={Jiaqi Wang and Kai Chen and Shuo Yang and Chen Change Loy and Dahua Lin},
+    booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+    year={2019}
+}
+```
diff --git a/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..8fc203c
--- /dev/null
+++ b/configs/guided_anchoring/ga_fast_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,65 @@
+_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    roi_head=dict(
+        bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))),
+    # model training and testing settings
+    train_cfg=dict(
+        rcnn=dict(
+            assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6),
+            sampler=dict(num=256))),
+    test_cfg=dict(rcnn=dict(score_thr=1e-3)))
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadProposals', num_max_proposals=300),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'proposals', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadProposals', num_max_proposals=None),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img', 'proposals']),
+        ])
+]
+data = dict(
+    train=dict(
+        proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_train2017.pkl',
+        pipeline=train_pipeline),
+    val=dict(
+        proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl',
+        pipeline=test_pipeline),
+    test=dict(
+        proposal_file=data_root + 'proposals/ga_rpn_r50_fpn_1x_val2017.pkl',
+        pipeline=test_pipeline))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..a40e7c6
--- /dev/null
+++ b/configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './ga_faster_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))
diff --git a/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..b0add92
--- /dev/null
+++ b/configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,65 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    rpn_head=dict(
+        _delete_=True,
+        type='GARPNHead',
+        in_channels=256,
+        feat_channels=256,
+        approx_anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=8,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        square_anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[8],
+            strides=[4, 8, 16, 32, 64]),
+        anchor_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.07, 0.07, 0.14, 0.14]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.07, 0.07, 0.11, 0.11]),
+        loc_filter_thr=0.01,
+        loss_loc=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
+    roi_head=dict(
+        bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            ga_assigner=dict(
+                type='ApproxMaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1),
+            ga_sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            center_ratio=0.2,
+            ignore_ratio=0.5),
+        rpn_proposal=dict(nms_post=1000, max_per_img=300),
+        rcnn=dict(
+            assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6),
+            sampler=dict(type='RandomSampler', num=256))),
+    test_cfg=dict(
+        rpn=dict(nms_post=1000, max_per_img=300), rcnn=dict(score_thr=1e-3)))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..e3d8238
--- /dev/null
+++ b/configs/guided_anchoring/ga_faster_r50_fpn_1x_coco.py
@@ -0,0 +1,65 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    rpn_head=dict(
+        _delete_=True,
+        type='GARPNHead',
+        in_channels=256,
+        feat_channels=256,
+        approx_anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=8,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        square_anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[8],
+            strides=[4, 8, 16, 32, 64]),
+        anchor_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.07, 0.07, 0.14, 0.14]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.07, 0.07, 0.11, 0.11]),
+        loc_filter_thr=0.01,
+        loss_loc=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
+    roi_head=dict(
+        bbox_head=dict(bbox_coder=dict(target_stds=[0.05, 0.05, 0.1, 0.1]))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            ga_assigner=dict(
+                type='ApproxMaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1),
+            ga_sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            center_ratio=0.2,
+            ignore_ratio=0.5),
+        rpn_proposal=dict(nms_post=1000, max_per_img=300),
+        rcnn=dict(
+            assigner=dict(pos_iou_thr=0.6, neg_iou_thr=0.6, min_pos_iou=0.6),
+            sampler=dict(type='RandomSampler', num=256))),
+    test_cfg=dict(
+        rpn=dict(nms_post=1000, max_per_img=300), rcnn=dict(score_thr=1e-3)))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..f1dda94
--- /dev/null
+++ b/configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './ga_faster_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..fb9e2af
--- /dev/null
+++ b/configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './ga_faster_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..1b1cccd
--- /dev/null
+++ b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './ga_retinanet_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))
diff --git a/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py
new file mode 100644
index 0000000..260895b
--- /dev/null
+++ b/configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_mstrain_2x.py
@@ -0,0 +1,169 @@
+_base_ = '../_base_/default_runtime.py'
+
+# model settings
+model = dict(
+    type='RetinaNet',
+    backbone=dict(
+        type='ResNet',
+        depth=101,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs=True,
+        num_outs=5),
+    bbox_head=dict(
+        type='GARetinaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        approx_anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        square_anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[4],
+            strides=[8, 16, 32, 64, 128]),
+        anchor_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loc_filter_thr=0.01,
+        loss_loc=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)))
+# training and testing settings
+train_cfg = dict(
+    ga_assigner=dict(
+        type='ApproxMaxIoUAssigner',
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.4,
+        min_pos_iou=0.4,
+        ignore_iof_thr=-1),
+    ga_sampler=dict(
+        type='RandomSampler',
+        num=256,
+        pos_fraction=0.5,
+        neg_pos_ub=-1,
+        add_gt_as_proposals=False),
+    assigner=dict(
+        type='MaxIoUAssigner',
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+        min_pos_iou=0.0,
+        ignore_iof_thr=-1),
+    allowed_border=-1,
+    pos_weight=-1,
+    center_ratio=0.2,
+    ignore_ratio=0.5,
+    debug=False)
+test_cfg = dict(
+    nms_pre=1000,
+    min_bbox_size=0,
+    score_thr=0.05,
+    nms=dict(type='nms', iou_threshold=0.5),
+    max_per_img=100)
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 480), (1333, 960)],
+        keep_ratio=True,
+        multiscale_mode='range'),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='bbox')
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    step=[16, 22])
+checkpoint_config = dict(interval=1)
+# yapf:disable
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        # dict(type='TensorboardLoggerHook')
+    ])
+# yapf:enable
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..3351201
--- /dev/null
+++ b/configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,62 @@
+_base_ = '../retinanet/retinanet_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    bbox_head=dict(
+        _delete_=True,
+        type='GARetinaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        approx_anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        square_anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[4],
+            strides=[8, 16, 32, 64, 128]),
+        anchor_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loc_filter_thr=0.01,
+        loss_loc=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(
+        ga_assigner=dict(
+            type='ApproxMaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0.4,
+            ignore_iof_thr=-1),
+        ga_sampler=dict(
+            type='RandomSampler',
+            num=256,
+            pos_fraction=0.5,
+            neg_pos_ub=-1,
+            add_gt_as_proposals=False),
+        assigner=dict(neg_iou_thr=0.5, min_pos_iou=0.0),
+        center_ratio=0.2,
+        ignore_ratio=0.5))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..7694723
--- /dev/null
+++ b/configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py
@@ -0,0 +1,62 @@
+_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
+model = dict(
+    bbox_head=dict(
+        _delete_=True,
+        type='GARetinaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        approx_anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        square_anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[4],
+            strides=[8, 16, 32, 64, 128]),
+        anchor_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loc_filter_thr=0.01,
+        loss_loc=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=0.04, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(
+        ga_assigner=dict(
+            type='ApproxMaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0.4,
+            ignore_iof_thr=-1),
+        ga_sampler=dict(
+            type='RandomSampler',
+            num=256,
+            pos_fraction=0.5,
+            neg_pos_ub=-1,
+            add_gt_as_proposals=False),
+        assigner=dict(neg_iou_thr=0.5, min_pos_iou=0.0),
+        center_ratio=0.2,
+        ignore_ratio=0.5))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..c5eb34f
--- /dev/null
+++ b/configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './ga_retinanet_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..5c69a6f
--- /dev/null
+++ b/configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './ga_retinanet_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..039703e
--- /dev/null
+++ b/configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py
@@ -0,0 +1,8 @@
+_base_ = './ga_rpn_r50_caffe_fpn_1x_coco.py'
+# model settings
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))
diff --git a/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..7830894
--- /dev/null
+++ b/configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,58 @@
+_base_ = '../rpn/rpn_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    rpn_head=dict(
+        _delete_=True,
+        type='GARPNHead',
+        in_channels=256,
+        feat_channels=256,
+        approx_anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=8,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        square_anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[8],
+            strides=[4, 8, 16, 32, 64]),
+        anchor_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.07, 0.07, 0.14, 0.14]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.07, 0.07, 0.11, 0.11]),
+        loc_filter_thr=0.01,
+        loss_loc=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            ga_assigner=dict(
+                type='ApproxMaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1),
+            ga_sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            center_ratio=0.2,
+            ignore_ratio=0.5)),
+    test_cfg=dict(rpn=dict(nms_post=1000)))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..27ab3e7
--- /dev/null
+++ b/configs/guided_anchoring/ga_rpn_r50_fpn_1x_coco.py
@@ -0,0 +1,58 @@
+_base_ = '../rpn/rpn_r50_fpn_1x_coco.py'
+model = dict(
+    rpn_head=dict(
+        _delete_=True,
+        type='GARPNHead',
+        in_channels=256,
+        feat_channels=256,
+        approx_anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=8,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        square_anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[8],
+            strides=[4, 8, 16, 32, 64]),
+        anchor_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.07, 0.07, 0.14, 0.14]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.07, 0.07, 0.11, 0.11]),
+        loc_filter_thr=0.01,
+        loss_loc=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            ga_assigner=dict(
+                type='ApproxMaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1),
+            ga_sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            center_ratio=0.2,
+            ignore_ratio=0.5)),
+    test_cfg=dict(rpn=dict(nms_post=1000)))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..cccc985
--- /dev/null
+++ b/configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './ga_rpn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py b/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..4e134d2
--- /dev/null
+++ b/configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './ga_rpn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/guided_anchoring/metafile.yml b/configs/guided_anchoring/metafile.yml
new file mode 100644
index 0000000..f39d183
--- /dev/null
+++ b/configs/guided_anchoring/metafile.yml
@@ -0,0 +1,246 @@
+Collections:
+  - Name: Guided Anchoring
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - Guided Anchoring
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1901.03278
+      Title: 'Region Proposal by Guided Anchoring'
+    README: configs/guided_anchoring/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/dense_heads/ga_retina_head.py#L10
+      Version: v2.0.0
+
+Models:
+  - Name: ga_rpn_r50_caffe_fpn_1x_coco
+    In Collection: Guided Anchoring
+    Config: configs/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.3
+      inference time (ms/im):
+        - value: 63.29
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Region Proposal
+        Dataset: COCO
+        Metrics:
+          AR@1000: 68.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r50_caffe_fpn_1x_coco/ga_rpn_r50_caffe_fpn_1x_coco_20200531-899008a6.pth
+
+  - Name: ga_rpn_r101_caffe_fpn_1x_coco
+    In Collection: Guided Anchoring
+    Config: configs/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.3
+      inference time (ms/im):
+        - value: 76.92
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Region Proposal
+        Dataset: COCO
+        Metrics:
+          AR@1000: 69.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_r101_caffe_fpn_1x_coco/ga_rpn_r101_caffe_fpn_1x_coco_20200531-ca9ba8fb.pth
+
+  - Name: ga_rpn_x101_32x4d_fpn_1x_coco
+    In Collection: Guided Anchoring
+    Config: configs/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.5
+      inference time (ms/im):
+        - value: 100
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Region Proposal
+        Dataset: COCO
+        Metrics:
+          AR@1000: 70.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_32x4d_fpn_1x_coco/ga_rpn_x101_32x4d_fpn_1x_coco_20200220-c28d1b18.pth
+
+  - Name: ga_rpn_x101_64x4d_fpn_1x_coco
+    In Collection: Guided Anchoring
+    Config: configs/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.1
+      inference time (ms/im):
+        - value: 133.33
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Region Proposal
+        Dataset: COCO
+        Metrics:
+          AR@1000: 70.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_rpn_x101_64x4d_fpn_1x_coco/ga_rpn_x101_64x4d_fpn_1x_coco_20200225-3c6e1aa2.pth
+
+  - Name: ga_faster_r50_caffe_fpn_1x_coco
+    In Collection: Guided Anchoring
+    Config: configs/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.5
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r50_caffe_fpn_1x_coco/ga_faster_r50_caffe_fpn_1x_coco_20200702_000718-a11ccfe6.pth
+
+  - Name: ga_faster_r101_caffe_fpn_1x_coco
+    In Collection: Guided Anchoring
+    Config: configs/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.5
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_r101_caffe_fpn_1x_coco/ga_faster_r101_caffe_fpn_1x_coco_bbox_mAP-0.415_20200505_115528-fb82e499.pth
+
+  - Name: ga_faster_x101_32x4d_fpn_1x_coco
+    In Collection: Guided Anchoring
+    Config: configs/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.7
+      inference time (ms/im):
+        - value: 103.09
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_32x4d_fpn_1x_coco/ga_faster_x101_32x4d_fpn_1x_coco_20200215-1ded9da3.pth
+
+  - Name: ga_faster_x101_64x4d_fpn_1x_coco
+    In Collection: Guided Anchoring
+    Config: configs/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 11.8
+      inference time (ms/im):
+        - value: 136.99
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_faster_x101_64x4d_fpn_1x_coco/ga_faster_x101_64x4d_fpn_1x_coco_20200215-0fa7bde7.pth
+
+  - Name: ga_retinanet_r50_caffe_fpn_1x_coco
+    In Collection: Guided Anchoring
+    Config: configs/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.5
+      inference time (ms/im):
+        - value: 59.52
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r50_caffe_fpn_1x_coco/ga_retinanet_r50_caffe_fpn_1x_coco_20201020-39581c6f.pth
+
+  - Name: ga_retinanet_r101_caffe_fpn_1x_coco
+    In Collection: Guided Anchoring
+    Config: configs/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.5
+      inference time (ms/im):
+        - value: 77.52
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_r101_caffe_fpn_1x_coco/ga_retinanet_r101_caffe_fpn_1x_coco_20200531-6266453c.pth
+
+  - Name: ga_retinanet_x101_32x4d_fpn_1x_coco
+    In Collection: Guided Anchoring
+    Config: configs/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.9
+      inference time (ms/im):
+        - value: 94.34
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_32x4d_fpn_1x_coco/ga_retinanet_x101_32x4d_fpn_1x_coco_20200219-40c56caa.pth
+
+  - Name: ga_retinanet_x101_64x4d_fpn_1x_coco
+    In Collection: Guided Anchoring
+    Config: configs/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 9.9
+      inference time (ms/im):
+        - value: 129.87
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/guided_anchoring/ga_retinanet_x101_64x4d_fpn_1x_coco/ga_retinanet_x101_64x4d_fpn_1x_coco_20200226-ef9f7f1f.pth
diff --git a/configs/hrnet/README.md b/configs/hrnet/README.md
new file mode 100644
index 0000000..e340c78
--- /dev/null
+++ b/configs/hrnet/README.md
@@ -0,0 +1,101 @@
+# HRNet
+
+> [Deep High-Resolution Representation Learning for Human Pose Estimation](https://arxiv.org/abs/1902.09212)
+
+<!-- [BACKBONE] -->
+
+## Abstract
+
+This is an official pytorch implementation of Deep High-Resolution Representation Learning for Human Pose Estimation. In this work, we are interested in the human pose estimation problem with a focus on learning reliable high-resolution representations. Most existing methods recover high-resolution representations from low-resolution representations produced by a high-to-low resolution network. Instead, our proposed network maintains high-resolution representations through the whole process. We start from a high-resolution subnetwork as the first stage, gradually add high-to-low resolution subnetworks one by one to form more stages, and connect the mutli-resolution subnetworks in parallel. We conduct repeated multi-scale fusions such that each of the high-to-low resolution representations receives information from other parallel representations over and over, leading to rich high-resolution representations. As a result, the predicted keypoint heatmap is potentially more accurate and spatially more precise. We empirically demonstrate the effectiveness of our network through the superior pose estimation results over two benchmark datasets: the COCO keypoint detection dataset and the MPII Human Pose dataset.
+
+High-resolution representation learning plays an essential role in many vision problems, e.g., pose estimation and semantic segmentation. The high-resolution network (HRNet), recently developed for human pose estimation, maintains high-resolution representations through the whole process by connecting high-to-low resolution convolutions in parallel and produces strong high-resolution representations by repeatedly conducting fusions across parallel convolutions.
+In this paper, we conduct a further study on high-resolution representations by introducing a simple yet effective modification and apply it to a wide range of vision tasks. We augment the high-resolution representation by aggregating the (upsampled) representations from all the parallel convolutions rather than only the representation from the high-resolution convolution as done in HRNet. This simple modification leads to stronger representations, evidenced by superior results. We show top results in semantic segmentation on Cityscapes, LIP, and PASCAL Context, and facial landmark detection on AFLW, COFW, 300W, and WFLW. In addition, we build a multi-level representation from the high-resolution representation and apply it to the Faster R-CNN object detection framework and the extended frameworks. The proposed approach achieves superior results to existing single-model networks on COCO object detection.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143892740-a4e9743e-a323-4ace-8025-50e251ef43ff.png"/>
+</div>
+
+## Results and Models
+
+### Faster R-CNN
+
+|   Backbone   |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                      Config                                                       |                                                                                                                                                         Download                                                                                                                                                         |
+| :----------: | :-----: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| HRNetV2p-W18 | pytorch |   1x    |   6.6    |      13.4      |  36.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py) |    [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130_211246.log.json)     |
+| HRNetV2p-W18 | pytorch |   2x    |   6.6    |       -        |  38.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731-a4ec0611.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731.log.json) |
+| HRNetV2p-W32 | pytorch |   1x    |   9.0    |      12.4      |  40.2  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py) |    [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130-6e286425.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130_204442.log.json)     |
+| HRNetV2p-W32 | pytorch |   2x    |   9.0    |       -        |  41.4  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927-976a9c15.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927.log.json) |
+| HRNetV2p-W40 | pytorch |   1x    |   10.4   |      10.5      |  41.2  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py) |    [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210-95c1f5ce.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210_125315.log.json)     |
+| HRNetV2p-W40 | pytorch |   2x    |   10.4   |       -        |  42.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033-0f236ef4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033.log.json) |
+
+### Mask R-CNN
+
+|   Backbone   |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                     Config                                                      |                                                                                                                                                     Download                                                                                                                                                     |
+| :----------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :-------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| HRNetV2p-W18 | pytorch |   1x    |   7.0    |      11.7      |  37.7  |  34.2   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py) |    [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205-1c3d78ed.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205_232523.log.json)     |
+| HRNetV2p-W18 | pytorch |   2x    |   7.0    |       -        |  39.8  |  36.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py) |    [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212-b3c825b1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212_134222.log.json)     |
+| HRNetV2p-W32 | pytorch |   1x    |   9.4    |      11.3      |  41.2  |  37.1   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py) |    [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207-b29f616e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207_055017.log.json)     |
+| HRNetV2p-W32 | pytorch |   2x    |   9.4    |       -        |  42.5  |  37.8   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py) |    [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213-45b75b4d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213_150518.log.json)     |
+| HRNetV2p-W40 | pytorch |   1x    |   10.9   |                |  42.1  |  37.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646-66738b35.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646.log.json) |
+| HRNetV2p-W40 | pytorch |   2x    |   10.9   |                |  42.8  |  38.2   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732-aed5e4ab.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732.log.json) |
+
+### Cascade R-CNN
+
+|   Backbone   |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                       Config                                                        |                                                                                                                                                             Download                                                                                                                                                             |
+| :----------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| HRNetV2p-W18 | pytorch |   20e   |   7.0    |      11.0      |  41.2  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py) |    [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210-434be9d7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210_105632.log.json)     |
+| HRNetV2p-W32 | pytorch |   20e   |   9.4    |      11.0      |  43.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py) |    [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208-928455a4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208_160511.log.json)     |
+| HRNetV2p-W40 | pytorch |   20e   |   10.8   |                |  43.8  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112-75e47b04.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112.log.json) |
+
+### Cascade Mask R-CNN
+
+|   Backbone   |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                          Config                                                          |                                                                                                                                                                       Download                                                                                                                                                                       |
+| :----------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :----------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| HRNetV2p-W18 | pytorch |   20e   |   8.5    |      8.5       |  41.6  |  36.4   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py) |    [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210-b543cd2b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210_093149.log.json)     |
+| HRNetV2p-W32 | pytorch |   20e   |          |      8.3       |  44.3  |  38.6   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043-39d9cf7b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043.log.json) |
+| HRNetV2p-W40 | pytorch |   20e   |   12.5   |                |  45.1  |  39.3   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922-969c4610.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922.log.json) |
+
+### Hybrid Task Cascade (HTC)
+
+|   Backbone   |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                   Config                                                   |                                                                                                                                           Download                                                                                                                                           |
+| :----------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| HRNetV2p-W18 | pytorch |   20e   |   10.8   |      4.7       |  42.8  |  37.9   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py) |    [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210-b266988c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210_182735.log.json)     |
+| HRNetV2p-W32 | pytorch |   20e   |   13.1   |      4.9       |  45.4  |  39.9   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py) |    [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207-7639fa12.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207_193153.log.json)     |
+| HRNetV2p-W40 | pytorch |   20e   |   14.6   |                |  46.4  |  40.8   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411-417c4d5b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411.log.json) |
+
+### FCOS
+
+|   Backbone   |  Style  | GN  | MS train | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                                 Config                                                                 |                                                                                                                                                                                                   Download                                                                                                                                                                                                   |
+| :----------: | :-----: | :-: | :------: | :-----: | :------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| HRNetV2p-W18 | pytorch |  Y  |    N     |   1x    |   13.0   |      12.9      |  35.3  |         [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py)         |                                 [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710-4ad151de.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710.log.json)                                 |
+| HRNetV2p-W18 | pytorch |  Y  |    N     |   2x    |   13.0   |       -        |  38.2  |         [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py)         |                                 [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110-5c575fa5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110.log.json)                                 |
+| HRNetV2p-W32 | pytorch |  Y  |    N     |   1x    |   17.5   |      12.9      |  39.5  |         [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py)         |                                 [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730-cb8055c0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730.log.json)                                 |
+| HRNetV2p-W32 | pytorch |  Y  |    N     |   2x    |   17.5   |       -        |  40.8  |         [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py)         |                                 [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133-77b6b9bb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133.log.json)                                 |
+| HRNetV2p-W18 | pytorch |  Y  |    Y     |   2x    |   13.0   |      12.9      |  38.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651-441e9d9f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651.log.json) |
+| HRNetV2p-W32 | pytorch |  Y  |    Y     |   2x    |   17.5   |      12.4      |  41.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846-b6f2b49f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846.log.json) |
+| HRNetV2p-W48 | pytorch |  Y  |    Y     |   2x    |   20.3   |      10.8      |  42.7  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752-f22d2ce5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752.log.json) |
+
+**Note:**
+
+- The `28e` schedule in HTC indicates decreasing the lr at 24 and 27 epochs, with a total of 28 epochs.
+- HRNetV2 ImageNet pretrained models are in [HRNets for Image Classification](https://github.com/HRNet/HRNet-Image-Classification).
+
+## Citation
+
+```latex
+@inproceedings{SunXLW19,
+  title={Deep High-Resolution Representation Learning for Human Pose Estimation},
+  author={Ke Sun and Bin Xiao and Dong Liu and Jingdong Wang},
+  booktitle={CVPR},
+  year={2019}
+}
+
+@article{SunZJCXLMWLW19,
+  title={High-Resolution Representations for Labeling Pixels and Regions},
+  author={Ke Sun and Yang Zhao and Borui Jiang and Tianheng Cheng and Bin Xiao
+  and Dong Liu and Yadong Mu and Xinggang Wang and Wenyu Liu and Jingdong Wang},
+  journal   = {CoRR},
+  volume    = {abs/1904.04514},
+  year={2019}
+}
+```
diff --git a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
new file mode 100644
index 0000000..839cf3e
--- /dev/null
+++ b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
@@ -0,0 +1,11 @@
+_base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
+# model settings
+model = dict(
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(18, 36)),
+            stage3=dict(num_channels=(18, 36, 72)),
+            stage4=dict(num_channels=(18, 36, 72, 144))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
+    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
diff --git a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py
new file mode 100644
index 0000000..9942602
--- /dev/null
+++ b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py
@@ -0,0 +1,40 @@
+_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='HRNet',
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(32, 64)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(32, 64, 128)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(32, 64, 128, 256))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
+    neck=dict(
+        _delete_=True,
+        type='HRFPN',
+        in_channels=[32, 64, 128, 256],
+        out_channels=256))
+# learning policy
+lr_config = dict(step=[16, 19])
+runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py
new file mode 100644
index 0000000..10d5e83
--- /dev/null
+++ b/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py
@@ -0,0 +1,12 @@
+_base_ = './cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
+# model settings
+model = dict(
+    backbone=dict(
+        type='HRNet',
+        extra=dict(
+            stage2=dict(num_channels=(40, 80)),
+            stage3=dict(num_channels=(40, 80, 160)),
+            stage4=dict(num_channels=(40, 80, 160, 320))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
+    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
diff --git a/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py b/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py
new file mode 100644
index 0000000..ebd5e20
--- /dev/null
+++ b/configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py
@@ -0,0 +1,11 @@
+_base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py'
+# model settings
+model = dict(
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(18, 36)),
+            stage3=dict(num_channels=(18, 36, 72)),
+            stage4=dict(num_channels=(18, 36, 72, 144))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
+    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
diff --git a/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py b/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py
new file mode 100644
index 0000000..e7f89a9
--- /dev/null
+++ b/configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py
@@ -0,0 +1,40 @@
+_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='HRNet',
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(32, 64)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(32, 64, 128)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(32, 64, 128, 256))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
+    neck=dict(
+        _delete_=True,
+        type='HRFPN',
+        in_channels=[32, 64, 128, 256],
+        out_channels=256))
+# learning policy
+lr_config = dict(step=[16, 19])
+runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py b/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py
new file mode 100644
index 0000000..265e8d6
--- /dev/null
+++ b/configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py
@@ -0,0 +1,12 @@
+_base_ = './cascade_rcnn_hrnetv2p_w32_20e_coco.py'
+# model settings
+model = dict(
+    backbone=dict(
+        type='HRNet',
+        extra=dict(
+            stage2=dict(num_channels=(40, 80)),
+            stage3=dict(num_channels=(40, 80, 160)),
+            stage4=dict(num_channels=(40, 80, 160, 320))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
+    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py
new file mode 100644
index 0000000..1df2c3d
--- /dev/null
+++ b/configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
+# model settings
+model = dict(
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(18, 36)),
+            stage3=dict(num_channels=(18, 36, 72)),
+            stage4=dict(num_channels=(18, 36, 72, 144))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
+    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py
new file mode 100644
index 0000000..a4b987a
--- /dev/null
+++ b/configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py
@@ -0,0 +1,5 @@
+_base_ = './faster_rcnn_hrnetv2p_w18_1x_coco.py'
+
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py
new file mode 100644
index 0000000..be05809
--- /dev/null
+++ b/configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py
@@ -0,0 +1,37 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='HRNet',
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(32, 64)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(32, 64, 128)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(32, 64, 128, 256))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
+    neck=dict(
+        _delete_=True,
+        type='HRFPN',
+        in_channels=[32, 64, 128, 256],
+        out_channels=256))
diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py
new file mode 100644
index 0000000..63c8717
--- /dev/null
+++ b/configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py
new file mode 100644
index 0000000..886a7c9
--- /dev/null
+++ b/configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = './faster_rcnn_hrnetv2p_w32_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='HRNet',
+        extra=dict(
+            stage2=dict(num_channels=(40, 80)),
+            stage3=dict(num_channels=(40, 80, 160)),
+            stage4=dict(num_channels=(40, 80, 160, 320))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
+    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
diff --git a/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py b/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py
new file mode 100644
index 0000000..585cc2c
--- /dev/null
+++ b/configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './faster_rcnn_hrnetv2p_w40_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py
new file mode 100644
index 0000000..fd662bd
--- /dev/null
+++ b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py
@@ -0,0 +1,10 @@
+_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
+model = dict(
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(18, 36)),
+            stage3=dict(num_channels=(18, 36, 72)),
+            stage4=dict(num_channels=(18, 36, 72, 144))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
+    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
diff --git a/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py
new file mode 100644
index 0000000..3497595
--- /dev/null
+++ b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py
new file mode 100644
index 0000000..37bfdae
--- /dev/null
+++ b/configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py
@@ -0,0 +1,10 @@
+_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py'
+model = dict(
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(18, 36)),
+            stage3=dict(num_channels=(18, 36, 72)),
+            stage4=dict(num_channels=(18, 36, 72, 144))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
+    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
diff --git a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py
new file mode 100644
index 0000000..10617f2
--- /dev/null
+++ b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py
@@ -0,0 +1,70 @@
+_base_ = '../fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py'
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='HRNet',
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(32, 64)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(32, 64, 128)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(32, 64, 128, 256))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
+    neck=dict(
+        _delete_=True,
+        type='HRFPN',
+        in_channels=[32, 64, 128, 256],
+        out_channels=256,
+        stride=2,
+        num_outs=5))
+img_norm_cfg = dict(
+    mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py
new file mode 100644
index 0000000..7b38130
--- /dev/null
+++ b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py
new file mode 100644
index 0000000..482f887
--- /dev/null
+++ b/configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py
@@ -0,0 +1,39 @@
+_base_ = './fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py'
+img_norm_cfg = dict(
+    mean=[103.53, 116.28, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py b/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py
new file mode 100644
index 0000000..0ae9dbe
--- /dev/null
+++ b/configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py
@@ -0,0 +1,11 @@
+_base_ = './fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='HRNet',
+        extra=dict(
+            stage2=dict(num_channels=(40, 80)),
+            stage3=dict(num_channels=(40, 80, 160)),
+            stage4=dict(num_channels=(40, 80, 160, 320))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
+    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
diff --git a/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py b/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py
new file mode 100644
index 0000000..3c2eb1d
--- /dev/null
+++ b/configs/hrnet/htc_hrnetv2p_w18_20e_coco.py
@@ -0,0 +1,10 @@
+_base_ = './htc_hrnetv2p_w32_20e_coco.py'
+model = dict(
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(18, 36)),
+            stage3=dict(num_channels=(18, 36, 72)),
+            stage4=dict(num_channels=(18, 36, 72, 144))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
+    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
diff --git a/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py b/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py
new file mode 100644
index 0000000..545cb83
--- /dev/null
+++ b/configs/hrnet/htc_hrnetv2p_w32_20e_coco.py
@@ -0,0 +1,37 @@
+_base_ = '../htc/htc_r50_fpn_20e_coco.py'
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='HRNet',
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(32, 64)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(32, 64, 128)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(32, 64, 128, 256))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
+    neck=dict(
+        _delete_=True,
+        type='HRFPN',
+        in_channels=[32, 64, 128, 256],
+        out_channels=256))
diff --git a/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py b/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py
new file mode 100644
index 0000000..94bff1b
--- /dev/null
+++ b/configs/hrnet/htc_hrnetv2p_w40_20e_coco.py
@@ -0,0 +1,11 @@
+_base_ = './htc_hrnetv2p_w32_20e_coco.py'
+model = dict(
+    backbone=dict(
+        type='HRNet',
+        extra=dict(
+            stage2=dict(num_channels=(40, 80)),
+            stage3=dict(num_channels=(40, 80, 160)),
+            stage4=dict(num_channels=(40, 80, 160, 320))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
+    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
diff --git a/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py b/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py
new file mode 100644
index 0000000..7067e8b
--- /dev/null
+++ b/configs/hrnet/htc_hrnetv2p_w40_28e_coco.py
@@ -0,0 +1,4 @@
+_base_ = './htc_hrnetv2p_w40_20e_coco.py'
+# learning policy
+lr_config = dict(step=[24, 27])
+runner = dict(type='EpochBasedRunner', max_epochs=28)
diff --git a/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py b/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py
new file mode 100644
index 0000000..815f285
--- /dev/null
+++ b/configs/hrnet/htc_x101_64x4d_fpn_16x1_28e_coco.py
@@ -0,0 +1,4 @@
+_base_ = '../htc/htc_x101_64x4d_fpn_16x1_20e_coco.py'
+# learning policy
+lr_config = dict(step=[24, 27])
+runner = dict(type='EpochBasedRunner', max_epochs=28)
diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py
new file mode 100644
index 0000000..cb12200
--- /dev/null
+++ b/configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py
@@ -0,0 +1,10 @@
+_base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py'
+model = dict(
+    backbone=dict(
+        extra=dict(
+            stage2=dict(num_channels=(18, 36)),
+            stage3=dict(num_channels=(18, 36, 72)),
+            stage4=dict(num_channels=(18, 36, 72, 144))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w18')),
+    neck=dict(type='HRFPN', in_channels=[18, 36, 72, 144], out_channels=256))
diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py
new file mode 100644
index 0000000..ca62682
--- /dev/null
+++ b/configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py
new file mode 100644
index 0000000..d5f0eb5
--- /dev/null
+++ b/configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py
@@ -0,0 +1,37 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='HRNet',
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(32, 64)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(32, 64, 128)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(32, 64, 128, 256))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w32')),
+    neck=dict(
+        _delete_=True,
+        type='HRFPN',
+        in_channels=[32, 64, 128, 256],
+        out_channels=256))
diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py
new file mode 100644
index 0000000..63d5d13
--- /dev/null
+++ b/configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './mask_rcnn_hrnetv2p_w32_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py
new file mode 100644
index 0000000..5a76f4b
--- /dev/null
+++ b/configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py
@@ -0,0 +1,11 @@
+_base_ = './mask_rcnn_hrnetv2p_w18_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='HRNet',
+        extra=dict(
+            stage2=dict(num_channels=(40, 80)),
+            stage3=dict(num_channels=(40, 80, 160)),
+            stage4=dict(num_channels=(40, 80, 160, 320))),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://msra/hrnetv2_w40')),
+    neck=dict(type='HRFPN', in_channels=[40, 80, 160, 320], out_channels=256))
diff --git a/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py b/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py
new file mode 100644
index 0000000..3a2a510
--- /dev/null
+++ b/configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './mask_rcnn_hrnetv2p_w40_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/hrnet/metafile.yml b/configs/hrnet/metafile.yml
new file mode 100644
index 0000000..ac36efa
--- /dev/null
+++ b/configs/hrnet/metafile.yml
@@ -0,0 +1,971 @@
+Models:
+  - Name: faster_rcnn_hrnetv2p_w18_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.6
+      inference time (ms/im):
+        - value: 74.63
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_1x_coco/faster_rcnn_hrnetv2p_w18_1x_coco_20200130-56651a6d.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: faster_rcnn_hrnetv2p_w18_2x_coco
+    In Collection: Faster R-CNN
+    Config: configs/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco.py
+    Metadata:
+      Training Memory (GB): 6.6
+      inference time (ms/im):
+        - value: 74.63
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w18_2x_coco/faster_rcnn_hrnetv2p_w18_2x_coco_20200702_085731-a4ec0611.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: faster_rcnn_hrnetv2p_w32_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco.py
+    Metadata:
+      Training Memory (GB): 9.0
+      inference time (ms/im):
+        - value: 80.65
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_1x_coco/faster_rcnn_hrnetv2p_w32_1x_coco_20200130-6e286425.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: faster_rcnn_hrnetv2p_w32_2x_coco
+    In Collection: Faster R-CNN
+    Config: configs/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco.py
+    Metadata:
+      Training Memory (GB): 9.0
+      inference time (ms/im):
+        - value: 80.65
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w32_2x_coco/faster_rcnn_hrnetv2p_w32_2x_coco_20200529_015927-976a9c15.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: faster_rcnn_hrnetv2p_w40_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.4
+      inference time (ms/im):
+        - value: 95.24
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_1x_coco/faster_rcnn_hrnetv2p_w40_1x_coco_20200210-95c1f5ce.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: faster_rcnn_hrnetv2p_w40_2x_coco
+    In Collection: Faster R-CNN
+    Config: configs/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco.py
+    Metadata:
+      Training Memory (GB): 10.4
+      inference time (ms/im):
+        - value: 95.24
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/faster_rcnn_hrnetv2p_w40_2x_coco/faster_rcnn_hrnetv2p_w40_2x_coco_20200512_161033-0f236ef4.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: mask_rcnn_hrnetv2p_w18_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.0
+      inference time (ms/im):
+        - value: 85.47
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 34.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_1x_coco/mask_rcnn_hrnetv2p_w18_1x_coco_20200205-1c3d78ed.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: mask_rcnn_hrnetv2p_w18_2x_coco
+    In Collection: Mask R-CNN
+    Config: configs/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco.py
+    Metadata:
+      Training Memory (GB): 7.0
+      inference time (ms/im):
+        - value: 85.47
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w18_2x_coco/mask_rcnn_hrnetv2p_w18_2x_coco_20200212-b3c825b1.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: mask_rcnn_hrnetv2p_w32_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco.py
+    Metadata:
+      Training Memory (GB): 9.4
+      inference time (ms/im):
+        - value: 88.5
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_1x_coco/mask_rcnn_hrnetv2p_w32_1x_coco_20200207-b29f616e.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: mask_rcnn_hrnetv2p_w32_2x_coco
+    In Collection: Mask R-CNN
+    Config: configs/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco.py
+    Metadata:
+      Training Memory (GB): 9.4
+      inference time (ms/im):
+        - value: 88.5
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w32_2x_coco/mask_rcnn_hrnetv2p_w32_2x_coco_20200213-45b75b4d.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: mask_rcnn_hrnetv2p_w40_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.9
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_1x_coco/mask_rcnn_hrnetv2p_w40_1x_coco_20200511_015646-66738b35.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: mask_rcnn_hrnetv2p_w40_2x_coco
+    In Collection: Mask R-CNN
+    Config: configs/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco.py
+    Metadata:
+      Training Memory (GB): 10.9
+      Epochs: 24
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/mask_rcnn_hrnetv2p_w40_2x_coco/mask_rcnn_hrnetv2p_w40_2x_coco_20200512_163732-aed5e4ab.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: cascade_rcnn_hrnetv2p_w18_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco.py
+    Metadata:
+      Training Memory (GB): 7.0
+      inference time (ms/im):
+        - value: 90.91
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w18_20e_coco/cascade_rcnn_hrnetv2p_w18_20e_coco_20200210-434be9d7.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: cascade_rcnn_hrnetv2p_w32_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco.py
+    Metadata:
+      Training Memory (GB): 9.4
+      inference time (ms/im):
+        - value: 90.91
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w32_20e_coco/cascade_rcnn_hrnetv2p_w32_20e_coco_20200208-928455a4.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: cascade_rcnn_hrnetv2p_w40_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco.py
+    Metadata:
+      Training Memory (GB): 10.8
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_rcnn_hrnetv2p_w40_20e_coco/cascade_rcnn_hrnetv2p_w40_20e_coco_20200512_161112-75e47b04.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: cascade_mask_rcnn_hrnetv2p_w18_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
+    Metadata:
+      Training Memory (GB): 8.5
+      inference time (ms/im):
+        - value: 117.65
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210-b543cd2b.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: cascade_mask_rcnn_hrnetv2p_w32_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py
+    Metadata:
+      inference time (ms/im):
+        - value: 120.48
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco/cascade_mask_rcnn_hrnetv2p_w32_20e_coco_20200512_154043-39d9cf7b.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: cascade_mask_rcnn_hrnetv2p_w40_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco.py
+    Metadata:
+      Training Memory (GB): 12.5
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w40_20e_coco/cascade_mask_rcnn_hrnetv2p_w40_20e_coco_20200527_204922-969c4610.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: htc_hrnetv2p_w18_20e_coco
+    In Collection: HTC
+    Config: configs/hrnet/htc_hrnetv2p_w18_20e_coco.py
+    Metadata:
+      Training Memory (GB): 10.8
+      inference time (ms/im):
+        - value: 212.77
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w18_20e_coco/htc_hrnetv2p_w18_20e_coco_20200210-b266988c.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: htc_hrnetv2p_w32_20e_coco
+    In Collection: HTC
+    Config: configs/hrnet/htc_hrnetv2p_w32_20e_coco.py
+    Metadata:
+      Training Memory (GB): 13.1
+      inference time (ms/im):
+        - value: 204.08
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w32_20e_coco/htc_hrnetv2p_w32_20e_coco_20200207-7639fa12.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: htc_hrnetv2p_w40_20e_coco
+    In Collection: HTC
+    Config: configs/hrnet/htc_hrnetv2p_w40_20e_coco.py
+    Metadata:
+      Training Memory (GB): 14.6
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 40.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/htc_hrnetv2p_w40_20e_coco/htc_hrnetv2p_w40_20e_coco_20200529_183411-417c4d5b.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: fcos_hrnetv2p_w18_gn-head_4x4_1x_coco
+    In Collection: FCOS
+    Config: configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco.py
+    Metadata:
+      Training Resources: 4x V100 GPUs
+      Batch Size: 16
+      Training Memory (GB): 13.0
+      inference time (ms/im):
+        - value: 77.52
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 35.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco/fcos_hrnetv2p_w18_gn-head_4x4_1x_coco_20201212_100710-4ad151de.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: fcos_hrnetv2p_w18_gn-head_4x4_2x_coco
+    In Collection: FCOS
+    Config: configs/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco.py
+    Metadata:
+      Training Resources: 4x V100 GPUs
+      Batch Size: 16
+      Training Memory (GB): 13.0
+      inference time (ms/im):
+        - value: 77.52
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_4x4_2x_coco_20201212_101110-5c575fa5.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: fcos_hrnetv2p_w32_gn-head_4x4_1x_coco
+    In Collection: FCOS
+    Config: configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco.py
+    Metadata:
+      Training Resources: 4x V100 GPUs
+      Batch Size: 16
+      Training Memory (GB): 17.5
+      inference time (ms/im):
+        - value: 77.52
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco/fcos_hrnetv2p_w32_gn-head_4x4_1x_coco_20201211_134730-cb8055c0.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: fcos_hrnetv2p_w32_gn-head_4x4_2x_coco
+    In Collection: FCOS
+    Config: configs/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco.py
+    Metadata:
+      Training Resources: 4x V100 GPUs
+      Batch Size: 16
+      Training Memory (GB): 17.5
+      inference time (ms/im):
+        - value: 77.52
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_4x4_2x_coco_20201212_112133-77b6b9bb.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco
+    In Collection: FCOS
+    Config: configs/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco.py
+    Metadata:
+      Training Resources: 4x V100 GPUs
+      Batch Size: 16
+      Training Memory (GB): 13.0
+      inference time (ms/im):
+        - value: 77.52
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w18_gn-head_mstrain_640-800_4x4_2x_coco_20201212_111651-441e9d9f.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco
+    In Collection: FCOS
+    Config: configs/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco.py
+    Metadata:
+      Training Resources: 4x V100 GPUs
+      Batch Size: 16
+      Training Memory (GB): 17.5
+      inference time (ms/im):
+        - value: 80.65
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w32_gn-head_mstrain_640-800_4x4_2x_coco_20201212_090846-b6f2b49f.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
+
+  - Name: fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco
+    In Collection: FCOS
+    Config: configs/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco.py
+    Metadata:
+      Training Resources: 4x V100 GPUs
+      Batch Size: 16
+      Training Memory (GB): 20.3
+      inference time (ms/im):
+        - value: 92.59
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Architecture:
+        - HRNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/hrnet/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco/fcos_hrnetv2p_w40_gn-head_mstrain_640-800_4x4_2x_coco_20201212_124752-f22d2ce5.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.04514
+      Title: 'Deep High-Resolution Representation Learning for Visual Recognition'
+    README: configs/hrnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/backbones/hrnet.py#L195
+      Version: v2.0.0
diff --git a/configs/htc/README.md b/configs/htc/README.md
new file mode 100644
index 0000000..747f8f6
--- /dev/null
+++ b/configs/htc/README.md
@@ -0,0 +1,67 @@
+# HTC
+
+> [Hybrid Task Cascade for Instance Segmentation](https://arxiv.org/abs/1901.07518)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Cascade is a classic yet powerful architecture that has boosted performance on various tasks. However, how to introduce cascade to instance segmentation remains an open question. A simple combination of Cascade R-CNN and Mask R-CNN only brings limited gain. In exploring a more effective approach, we find that the key to a successful instance segmentation cascade is to fully leverage the reciprocal relationship between detection and segmentation. In this work, we propose a new framework, Hybrid Task Cascade (HTC), which differs in two important aspects: (1) instead of performing cascaded refinement on these two tasks separately, it interweaves them for a joint multi-stage processing; (2) it adopts a fully convolutional branch to provide spatial context, which can help distinguishing hard foreground from cluttered background. Overall, this framework can learn more discriminative features progressively while integrating complementary features together in each stage. Without bells and whistles, a single HTC obtains 38.4 and 1.5 improvement over a strong Cascade Mask R-CNN baseline on MSCOCO dataset. Moreover, our overall system achieves 48.6 mask AP on the test-challenge split, ranking 1st in the COCO 2018 Challenge Object Detection Task.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143893906-e05acaa6-d46f-4c11-84e8-bb9940a95b44.png"/>
+</div>
+
+## Introduction
+
+HTC requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path.
+The directory should be like this.
+
+```none
+mmdetection
+├── mmdet
+├── tools
+├── configs
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+|   |   ├── stuffthingmaps
+```
+
+## Results and Models
+
+The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val)
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                     Config                                                      |                                                                                                                                                   Download                                                                                                                                                    |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :-------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50-FPN     | pytorch |   1x    |   8.2    |      5.8       |  42.3  |  37.4   |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r50_fpn_1x_coco.py)        |                           [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317-7332cf16.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317_070435.log.json)                           |
+|    R-50-FPN     | pytorch |   20e   |   8.2    |       -        |  43.3  |  38.3   |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r50_fpn_20e_coco.py)       |                         [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319-fe28c577.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319_070313.log.json)                         |
+|    R-101-FPN    | pytorch |   20e   |   10.2   |      5.5       |  44.8  |  39.6   |      [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_r101_fpn_20e_coco.py)       |                       [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317-9b41b48f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317_153107.log.json)                       |
+| X-101-32x4d-FPN | pytorch |   20e   |   11.4   |      5.0       |  46.1  |  40.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318-de97ae01.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318_034519.log.json) |
+| X-101-64x4d-FPN | pytorch |   20e   |   14.5   |      4.4       |  47.0  |  41.4   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318-b181fd7a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318_081711.log.json) |
+
+- In the HTC paper and COCO 2018 Challenge, `score_thr` is set to 0.001 for both baselines and HTC.
+- We use 8 GPUs with 2 images/GPU for R-50 and R-101 models, and 16 GPUs with 1 image/GPU for X-101 models.
+  If you would like to train X-101 HTC with 8 GPUs, you need to change the lr from 0.02 to 0.01.
+
+We also provide a powerful HTC with DCN and multi-scale training model. No testing augmentation is used.
+
+|    Backbone     |  Style  |  DCN  | training scales | Lr schd | box AP | mask AP |                                                                    Config                                                                    |                                                                                                                                                                                                             Download                                                                                                                                                                                                              |
+| :-------------: | :-----: | :---: | :-------------: | :-----: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| X-101-64x4d-FPN | pytorch | c3-c5 |    400~1400     |   20e   |  50.4  |  43.8   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312-946fd751.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312_203410.log.json) |
+
+## Citation
+
+We provide config files to reproduce the results in the CVPR 2019 paper for [Hybrid Task Cascade](https://arxiv.org/abs/1901.07518).
+
+```latex
+@inproceedings{chen2019hybrid,
+  title={Hybrid task cascade for instance segmentation},
+  author={Chen, Kai and Pang, Jiangmiao and Wang, Jiaqi and Xiong, Yu and Li, Xiaoxiao and Sun, Shuyang and Feng, Wansen and Liu, Ziwei and Shi, Jianping and Ouyang, Wanli and Chen Change Loy and Dahua Lin},
+  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+  year={2019}
+}
+```
diff --git a/configs/htc/htc_r101_fpn_20e_coco.py b/configs/htc/htc_r101_fpn_20e_coco.py
new file mode 100644
index 0000000..b42297b
--- /dev/null
+++ b/configs/htc/htc_r101_fpn_20e_coco.py
@@ -0,0 +1,9 @@
+_base_ = './htc_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
+# learning policy
+lr_config = dict(step=[16, 19])
+runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/configs/htc/htc_r50_fpn_1x_coco.py b/configs/htc/htc_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..1e8e18a
--- /dev/null
+++ b/configs/htc/htc_r50_fpn_1x_coco.py
@@ -0,0 +1,56 @@
+_base_ = './htc_without_semantic_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        semantic_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[8]),
+        semantic_head=dict(
+            type='FusedSemanticHead',
+            num_ins=5,
+            fusion_level=1,
+            num_convs=4,
+            in_channels=256,
+            conv_out_channels=256,
+            num_classes=183,
+            loss_seg=dict(
+                type='CrossEntropyLoss', ignore_index=255, loss_weight=0.2))))
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='SegRescale', scale_factor=1 / 8),
+    dict(type='DefaultFormatBundle'),
+    dict(
+        type='Collect',
+        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(
+        seg_prefix=data_root + 'stuffthingmaps/train2017/',
+        pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/htc/htc_r50_fpn_20e_coco.py b/configs/htc/htc_r50_fpn_20e_coco.py
new file mode 100644
index 0000000..7d2e011
--- /dev/null
+++ b/configs/htc/htc_r50_fpn_20e_coco.py
@@ -0,0 +1,4 @@
+_base_ = './htc_r50_fpn_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 19])
+runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py b/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..565104f
--- /dev/null
+++ b/configs/htc/htc_without_semantic_r50_fpn_1x_coco.py
@@ -0,0 +1,236 @@
+_base_ = [
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+    type='HybridTaskCascade',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+    roi_head=dict(
+        type='HybridTaskCascadeRoIHead',
+        interleaved=True,
+        mask_info_flow=True,
+        num_stages=3,
+        stage_loss_weights=[1, 0.5, 0.25],
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=[
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ],
+        mask_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        mask_head=[
+            dict(
+                type='HTCMaskHead',
+                with_conv_res=False,
+                num_convs=4,
+                in_channels=256,
+                conv_out_channels=256,
+                num_classes=80,
+                loss_mask=dict(
+                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
+            dict(
+                type='HTCMaskHead',
+                num_convs=4,
+                in_channels=256,
+                conv_out_channels=256,
+                num_classes=80,
+                loss_mask=dict(
+                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
+            dict(
+                type='HTCMaskHead',
+                num_convs=4,
+                in_channels=256,
+                conv_out_channels=256,
+                num_classes=80,
+                loss_mask=dict(
+                    type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))
+        ]),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=[
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.5,
+                    min_pos_iou=0.5,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.6,
+                    neg_iou_thr=0.6,
+                    min_pos_iou=0.6,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.7,
+                    neg_iou_thr=0.7,
+                    min_pos_iou=0.7,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False)
+        ]),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.001,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100,
+            mask_thr_binary=0.5)))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    val=dict(pipeline=test_pipeline), test=dict(pipeline=test_pipeline))
diff --git a/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py b/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py
new file mode 100644
index 0000000..0c834f2
--- /dev/null
+++ b/configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py
@@ -0,0 +1,19 @@
+_base_ = './htc_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
+data = dict(samples_per_gpu=1, workers_per_gpu=1)
+# learning policy
+lr_config = dict(step=[16, 19])
+runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py b/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py
new file mode 100644
index 0000000..8b0d962
--- /dev/null
+++ b/configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py
@@ -0,0 +1,19 @@
+_base_ = './htc_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
+data = dict(samples_per_gpu=1, workers_per_gpu=1)
+# learning policy
+lr_config = dict(step=[16, 19])
+runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py b/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py
new file mode 100644
index 0000000..c8d8703
--- /dev/null
+++ b/configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py
@@ -0,0 +1,43 @@
+_base_ = './htc_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
+# dataset settings
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations', with_bbox=True, with_mask=True, with_seg=True),
+    dict(
+        type='Resize',
+        img_scale=[(1600, 400), (1600, 1400)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='SegRescale', scale_factor=1 / 8),
+    dict(type='DefaultFormatBundle'),
+    dict(
+        type='Collect',
+        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
+]
+data = dict(
+    samples_per_gpu=1, workers_per_gpu=1, train=dict(pipeline=train_pipeline))
+# learning policy
+lr_config = dict(step=[16, 19])
+runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/configs/htc/metafile.yml b/configs/htc/metafile.yml
new file mode 100644
index 0000000..acd038c
--- /dev/null
+++ b/configs/htc/metafile.yml
@@ -0,0 +1,165 @@
+Collections:
+  - Name: HTC
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - HTC
+        - RPN
+        - ResNet
+        - ResNeXt
+        - RoIAlign
+    Paper:
+      URL: https://arxiv.org/abs/1901.07518
+      Title: 'Hybrid Task Cascade for Instance Segmentation'
+    README: configs/htc/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/htc.py#L6
+      Version: v2.0.0
+
+Models:
+  - Name: htc_r50_fpn_1x_coco
+    In Collection: HTC
+    Config: configs/htc/htc_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.2
+      inference time (ms/im):
+        - value: 172.41
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_1x_coco/htc_r50_fpn_1x_coco_20200317-7332cf16.pth
+
+  - Name: htc_r50_fpn_20e_coco
+    In Collection: HTC
+    Config: configs/htc/htc_r50_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 8.2
+      inference time (ms/im):
+        - value: 172.41
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r50_fpn_20e_coco/htc_r50_fpn_20e_coco_20200319-fe28c577.pth
+
+  - Name: htc_r101_fpn_20e_coco
+    In Collection: HTC
+    Config: configs/htc/htc_r101_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 10.2
+      inference time (ms/im):
+        - value: 181.82
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_r101_fpn_20e_coco/htc_r101_fpn_20e_coco_20200317-9b41b48f.pth
+
+  - Name: htc_x101_32x4d_fpn_16x1_20e_coco
+    In Collection: HTC
+    Config: configs/htc/htc_x101_32x4d_fpn_16x1_20e_coco.py
+    Metadata:
+      Training Resources: 16x V100 GPUs
+      Batch Size: 16
+      Training Memory (GB): 11.4
+      inference time (ms/im):
+        - value: 200
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 40.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_32x4d_fpn_16x1_20e_coco/htc_x101_32x4d_fpn_16x1_20e_coco_20200318-de97ae01.pth
+
+  - Name: htc_x101_64x4d_fpn_16x1_20e_coco
+    In Collection: HTC
+    Config: configs/htc/htc_x101_64x4d_fpn_16x1_20e_coco.py
+    Metadata:
+      Training Resources: 16x V100 GPUs
+      Batch Size: 16
+      Training Memory (GB): 14.5
+      inference time (ms/im):
+        - value: 227.27
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_16x1_20e_coco/htc_x101_64x4d_fpn_16x1_20e_coco_20200318-b181fd7a.pth
+
+  - Name: htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco
+    In Collection: HTC
+    Config: configs/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco.py
+    Metadata:
+      Training Resources: 16x V100 GPUs
+      Batch Size: 16
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 50.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 43.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/htc/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco/htc_x101_64x4d_fpn_dconv_c3-c5_mstrain_400_1400_16x1_20e_coco_20200312-946fd751.pth
diff --git a/configs/instaboost/README.md b/configs/instaboost/README.md
new file mode 100644
index 0000000..82ed334
--- /dev/null
+++ b/configs/instaboost/README.md
@@ -0,0 +1,58 @@
+# Instaboost
+
+> [Instaboost: Boosting instance segmentation via probability map guided copy-pasting](https://arxiv.org/abs/1908.07801)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Instance segmentation requires a large number of training samples to achieve satisfactory performance and benefits from proper data augmentation. To enlarge the training set and increase the diversity, previous methods have investigated using data annotation from other domain (e.g. bbox, point) in a weakly supervised mechanism. In this paper, we present a simple, efficient and effective method to augment the training set using the existing instance mask annotations. Exploiting the pixel redundancy of the background, we are able to improve the performance of Mask R-CNN for 1.7 mAP on COCO dataset and 3.3 mAP on Pascal VOC dataset by simply introducing random jittering to objects. Furthermore, we propose a location probability map based approach to explore the feasible locations that objects can be placed based on local appearance similarity. With the guidance of such map, we boost the performance of R101-Mask R-CNN on instance segmentation from 35.7 mAP to 37.9 mAP without modifying the backbone or network structure. Our method is simple to implement and does not increase the computational complexity. It can be integrated into the training pipeline of any instance segmentation model without affecting the training and inference efficiency.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143894053-ecfe8406-1a05-461b-953c-efeef22d7a60.png" height="300"/>
+</div>
+
+## Introduction
+
+Configs in this directory is the implementation for ICCV2019 paper "InstaBoost: Boosting Instance Segmentation Via Probability Map Guided Copy-Pasting" and provided by the authors of the paper. InstaBoost is a data augmentation method for object detection and instance segmentation. The paper has been released on [`arXiv`](https://arxiv.org/abs/1908.07801).
+
+## Usage
+
+### Requirements
+
+You need to install `instaboostfast` before using it.
+
+```shell
+pip install instaboostfast
+```
+
+The code and more details can be found [here](https://github.com/GothicAi/Instaboost).
+
+### Integration with MMDetection
+
+InstaBoost have been already integrated in the data pipeline, thus all you need is to add or change **InstaBoost** configurations after **LoadImageFromFile**. We have provided examples like [this](mask_rcnn_r50_fpn_instaboost_4x#L121). You can refer to [`InstaBoostConfig`](https://github.com/GothicAi/InstaBoost-pypi#instaboostconfig) for more details.
+
+## Results and Models
+
+- All models were trained on `coco_2017_train` and tested on `coco_2017_val` for convenience of evaluation and comparison. In the paper, the results are obtained from `test-dev`.
+- To balance accuracy and training time when using InstaBoost, models released in this page are all trained for 48 Epochs. Other training and testing configs strictly follow the original framework.
+- For results and models in MMDetection V1.x, please refer to [Instaboost](https://github.com/GothicAi/Instaboost).
+
+|    Network    |    Backbone     | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                               Config                                                               |                                                                                                                                                                                    Download                                                                                                                                                                                    |
+| :-----------: | :-------------: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  Mask R-CNN   |    R-50-FPN     |   4x    |   4.4    |      17.5      |  40.6  |  36.6   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py)     |                  [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223635.log.json)                   |
+|  Mask R-CNN   |    R-101-FPN    |   4x    |   6.4    |                |  42.5  |  38.0   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py)     |             [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738-f23f3a5f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738.log.json)             |
+|  Mask R-CNN   | X-101-64x4d-FPN |   4x    |   10.7   |                |  44.7  |  39.7   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py)  | [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947-8ed58c1b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947.log.json) |
+| Cascade R-CNN |    R-101-FPN    |   4x    |   6.0    |      12.0      |  43.7  |  38.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py) |  [model](https://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-c19d98d9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307_223646.log.json)   |
+
+## Citation
+
+```latex
+@inproceedings{fang2019instaboost,
+  title={Instaboost: Boosting instance segmentation via probability map guided copy-pasting},
+  author={Fang, Hao-Shu and Sun, Jianhua and Wang, Runzhong and Gou, Minghao and Li, Yong-Lu and Lu, Cewu},
+  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
+  pages={682--691},
+  year={2019}
+}
+```
diff --git a/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py b/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py
new file mode 100644
index 0000000..9d0515d
--- /dev/null
+++ b/configs/instaboost/cascade_mask_rcnn_r101_fpn_instaboost_4x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py'
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py b/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py
new file mode 100644
index 0000000..a89a81f
--- /dev/null
+++ b/configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py
@@ -0,0 +1,28 @@
+_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='InstaBoost',
+        action_candidate=('normal', 'horizontal', 'skip'),
+        action_prob=(1, 0, 0),
+        scale=(0.8, 1.2),
+        dx=15,
+        dy=15,
+        theta=(-1, 1),
+        color_prob=0.5,
+        hflag=False,
+        aug_ratio=0.5),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+# learning policy
+lr_config = dict(step=[32, 44])
+runner = dict(type='EpochBasedRunner', max_epochs=48)
diff --git a/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py b/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
new file mode 100644
index 0000000..d67b799
--- /dev/null
+++ b/configs/instaboost/cascade_mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py b/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py
new file mode 100644
index 0000000..ebbb43e
--- /dev/null
+++ b/configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py b/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py
new file mode 100644
index 0000000..55ca62b
--- /dev/null
+++ b/configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py
@@ -0,0 +1,28 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='InstaBoost',
+        action_candidate=('normal', 'horizontal', 'skip'),
+        action_prob=(1, 0, 0),
+        scale=(0.8, 1.2),
+        dx=15,
+        dy=15,
+        theta=(-1, 1),
+        color_prob=0.5,
+        hflag=False,
+        aug_ratio=0.5),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+# learning policy
+lr_config = dict(step=[32, 44])
+runner = dict(type='EpochBasedRunner', max_epochs=48)
diff --git a/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py b/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
new file mode 100644
index 0000000..2010f44
--- /dev/null
+++ b/configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './mask_rcnn_r50_fpn_instaboost_4x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/instaboost/metafile.yml b/configs/instaboost/metafile.yml
new file mode 100644
index 0000000..325283d
--- /dev/null
+++ b/configs/instaboost/metafile.yml
@@ -0,0 +1,99 @@
+Collections:
+  - Name: InstaBoost
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - InstaBoost
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+    Paper:
+      URL: https://arxiv.org/abs/1908.07801
+      Title: 'Instaboost: Boosting instance segmentation via probability map guided copy-pasting'
+    README: configs/instaboost/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/datasets/pipelines/instaboost.py#L7
+      Version: v2.0.0
+
+Models:
+  - Name: mask_rcnn_r50_fpn_instaboost_4x_coco
+    In Collection: InstaBoost
+    Config: configs/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco.py
+    Metadata:
+      Training Memory (GB): 4.4
+      inference time (ms/im):
+        - value: 57.14
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 48
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r50_fpn_instaboost_4x_coco/mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-d025f83a.pth
+
+  - Name: mask_rcnn_r101_fpn_instaboost_4x_coco
+    In Collection: InstaBoost
+    Config: configs/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco.py
+    Metadata:
+      Training Memory (GB): 6.4
+      Epochs: 48
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_r101_fpn_instaboost_4x_coco/mask_rcnn_r101_fpn_instaboost_4x_coco_20200703_235738-f23f3a5f.pth
+
+  - Name: mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco
+    In Collection: InstaBoost
+    Config: configs/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco.py
+    Metadata:
+      Training Memory (GB): 10.7
+      Epochs: 48
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco/mask_rcnn_x101_64x4d_fpn_instaboost_4x_coco_20200515_080947-8ed58c1b.pth
+
+  - Name: cascade_mask_rcnn_r50_fpn_instaboost_4x_coco
+    In Collection: InstaBoost
+    Config: configs/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco.py
+    Metadata:
+      Training Memory (GB): 6.0
+      inference time (ms/im):
+        - value: 83.33
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 48
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/instaboost/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco/cascade_mask_rcnn_r50_fpn_instaboost_4x_coco_20200307-c19d98d9.pth
diff --git a/configs/lad/README.md b/configs/lad/README.md
new file mode 100644
index 0000000..f2b7c20
--- /dev/null
+++ b/configs/lad/README.md
@@ -0,0 +1,44 @@
+# LAD
+
+> [Improving Object Detection by Label Assignment Distillation](https://arxiv.org/abs/2108.10520)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Label assignment in object detection aims to assign targets, foreground or background, to sampled regions in an image. Unlike labeling for image classification, this problem is not well defined due to the object's bounding box. In this paper, we investigate the problem from a perspective of distillation, hence we call Label Assignment Distillation (LAD). Our initial motivation is very simple, we use a teacher network to generate labels for the student. This can be achieved in two ways: either using the teacher's prediction as the direct targets (soft label), or through the hard labels dynamically assigned by the teacher (LAD). Our experiments reveal that: (i) LAD is more effective than soft-label, but they are complementary. (ii) Using LAD, a smaller teacher can also improve a larger student significantly, while soft-label can't. We then introduce Co-learning LAD, in which two networks simultaneously learn from scratch and the role of teacher and student are dynamically interchanged. Using PAA-ResNet50 as a teacher, our LAD techniques can improve detectors PAA-ResNet101 and PAA-ResNeXt101 to 46AP and 47.5AP on the COCO test-dev set. With a stronger teacher PAA-SwinB, we improve the students PAA-ResNet50 to 43.7AP by only 1x schedule training and standard setting, and PAA-ResNet101 to 47.9AP, significantly surpassing the current methods.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143894499-c2a3a243-988f-4604-915b-17918732bf03.png"/>
+</div>
+
+## Results and Models
+
+We provide config files to reproduce the object detection results in the
+WACV 2022 paper for Improving Object Detection by Label Assignment
+Distillation.
+
+### PAA with LAD
+
+| Teacher | Student | Training schedule | AP (val) |                                                   Config                                                    |                                                                                                                                               Download                                                                                                                                               |
+| :-----: | :-----: | :---------------: | :------: | :---------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   --    |  R-50   |        1x         |   40.4   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_1x_coco.py)      |                     [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.log.json)                      |
+|   --    |  R-101  |        1x         |   42.6   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_1x_coco.py)     |                   [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.log.json)                    |
+|  R-101  |  R-50   |        1x         |   41.4   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lad/lad_r50_paa_r101_fpn_coco_1x.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lad/lad_r50_paa_r101_fpn_coco_1x/lad_r50_paa_r101_fpn_coco_1x_20220708_124246-74c76ff0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lad/lad_r50_paa_r101_fpn_coco_1x/lad_r50_paa_r101_fpn_coco_1x_20220708_124246.log.json) |
+|  R-50   |  R-101  |        1x         |   43.2   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lad/lad_r101_paa_r50_fpn_coco_1x.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lad/lad_r101_paa_r50_fpn_coco_1x/lad_r101_paa_r50_fpn_coco_1x_20220708_124357-9407ac54.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lad/lad_r101_paa_r50_fpn_coco_1x/lad_r101_paa_r50_fpn_coco_1x_20220708_124357.log.json) |
+
+## Note
+
+- Meaning of Config name: lad_r50(student model)\_paa(based on paa)\_r101(teacher model)\_fpn(neck)\_coco(dataset)\_1x(12 epoch).py
+- Results may fluctuate by about 0.2 mAP.
+
+## Citation
+
+```latex
+@inproceedings{nguyen2021improving,
+  title={Improving Object Detection by Label Assignment Distillation},
+  author={Chuong H. Nguyen and Thuy C. Nguyen and Tuan N. Tang and Nam L. H. Phan},
+  booktitle = {WACV},
+  year={2022}
+}
+```
diff --git a/configs/lad/lad_r101_paa_r50_fpn_coco_1x.py b/configs/lad/lad_r101_paa_r50_fpn_coco_1x.py
new file mode 100644
index 0000000..4877d95
--- /dev/null
+++ b/configs/lad/lad_r101_paa_r50_fpn_coco_1x.py
@@ -0,0 +1,126 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth'  # noqa
+model = dict(
+    type='LAD',
+    # student
+    backbone=dict(
+        type='ResNet',
+        depth=101,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='LADHead',
+        reg_decoded_bbox=True,
+        score_voting=True,
+        topk=9,
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
+    # teacher
+    teacher_ckpt=teacher_ckpt,
+    teacher_backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch'),
+    teacher_neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    teacher_bbox_head=dict(
+        type='LADHead',
+        reg_decoded_bbox=True,
+        score_voting=True,
+        topk=9,
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.1,
+            neg_iou_thr=0.1,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        score_voting=True,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+data = dict(samples_per_gpu=8, workers_per_gpu=4)
+optimizer = dict(lr=0.01)
+fp16 = dict(loss_scale=512.)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/lad/lad_r50_paa_r101_fpn_coco_1x.py b/configs/lad/lad_r50_paa_r101_fpn_coco_1x.py
new file mode 100644
index 0000000..29bbe69
--- /dev/null
+++ b/configs/lad/lad_r50_paa_r101_fpn_coco_1x.py
@@ -0,0 +1,125 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+teacher_ckpt = 'http://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth'  # noqa
+model = dict(
+    type='LAD',
+    # student
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='LADHead',
+        reg_decoded_bbox=True,
+        score_voting=True,
+        topk=9,
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
+    # teacher
+    teacher_ckpt=teacher_ckpt,
+    teacher_backbone=dict(
+        type='ResNet',
+        depth=101,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch'),
+    teacher_neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    teacher_bbox_head=dict(
+        type='LADHead',
+        reg_decoded_bbox=True,
+        score_voting=True,
+        topk=9,
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.1,
+            neg_iou_thr=0.1,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        score_voting=True,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+data = dict(samples_per_gpu=8, workers_per_gpu=4)
+optimizer = dict(lr=0.01)
+fp16 = dict(loss_scale=512.)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/lad/metafile.yml b/configs/lad/metafile.yml
new file mode 100644
index 0000000..11a9fa9
--- /dev/null
+++ b/configs/lad/metafile.yml
@@ -0,0 +1,45 @@
+Collections:
+  - Name: Label Assignment Distillation
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - Label Assignment Distillation
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 2x V100 GPUs
+      Architecture:
+        - FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/2108.10520
+      Title: 'Improving Object Detection by Label Assignment Distillation'
+    README: configs/lad/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.19.0/mmdet/models/detectors/lad.py#L10
+      Version: v2.19.0
+
+Models:
+  - Name: lad_r101_paa_r50_fpn_coco_1x
+    In Collection: Label Assignment Distillation
+    Config: configs/lad/lad_r101_paa_r50_fpn_coco_1x.py
+    Metadata:
+      Training Memory (GB): 12.4
+      Epochs: 12
+    Results:
+    - Task: Object Detection
+      Dataset: COCO
+      Metrics:
+        box AP: 43.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/lad/lad_r101_paa_r50_fpn_coco_1x/lad_r101_paa_r50_fpn_coco_1x_20220708_124357-9407ac54.pth
+  - Name: lad_r50_paa_r101_fpn_coco_1x
+    In Collection: Label Assignment Distillation
+    Config: configs/lad/lad_r50_paa_r101_fpn_coco_1x.py
+    Metadata:
+      Training Memory (GB): 8.9
+      Epochs: 12
+    Results:
+    - Task: Object Detection
+      Dataset: COCO
+      Metrics:
+        box AP: 41.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/lad/lad_r50_paa_r101_fpn_coco_1x/lad_r50_paa_r101_fpn_coco_1x_20220708_124246-74c76ff0.pth
diff --git a/configs/ld/README.md b/configs/ld/README.md
new file mode 100644
index 0000000..0109729
--- /dev/null
+++ b/configs/ld/README.md
@@ -0,0 +1,43 @@
+# LD
+
+> [Localization Distillation for Dense Object Detection](https://arxiv.org/abs/2102.12252)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Knowledge distillation (KD) has witnessed its powerful capability in learning compact models in object detection. Previous KD methods for object detection mostly focus on imitating deep features within the imitation regions instead of mimicking classification logits due to its inefficiency in distilling localization information. In this paper, by reformulating the knowledge distillation process on localization, we present a novel localization distillation (LD) method which can efficiently transfer the localization knowledge from the teacher to the student. Moreover, we also heuristically introduce the concept of valuable localization region that can aid to selectively distill the semantic and localization knowledge for a certain region. Combining these two new components, for the first time, we show that logit mimicking can outperform feature imitation and localization knowledge distillation is more important and efficient than semantic knowledge for distilling object detectors. Our distillation scheme is simple as well as effective and can be easily applied to different dense object detectors. Experiments show that our LD can boost the AP score of GFocal-ResNet-50 with a single-scale 1× training schedule from 40.1 to 42.1 on the COCO benchmark without any sacrifice on the inference speed.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143966265-48a03668-8585-4525-8a86-afa2209d1602.png"/>
+</div>
+
+## Results and Models
+
+### GFocalV1 with LD
+
+|  Teacher  | Student | Training schedule | Mini-batch size | AP (val) |                                                     Config                                                      |                                                                                                                                                        Download                                                                                                                                                        |
+| :-------: | :-----: | :---------------: | :-------------: | :------: | :-------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    --     |  R-18   |        1x         |        6        |   35.8   |                                                                                                                 |                                                                                                                                                                                                                                                                                                                        |
+|   R-101   |  R-18   |        1x         |        6        |   36.5   |   [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py)   |         [model](https://download.openmmlab.com/mmdetection/v2.0/ld/ld_r18_gflv1_r101_fpn_coco_1x/ld_r18_gflv1_r101_fpn_coco_1x_20220702_062206-330e6332.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ld/ld_r18_gflv1_r101_fpn_coco_1x/ld_r18_gflv1_r101_fpn_coco_1x_20220702_062206.log.json)         |
+|    --     |  R-34   |        1x         |        6        |   38.9   |                                                                                                                 |                                                                                                                                                                                                                                                                                                                        |
+|   R-101   |  R-34   |        1x         |        6        |   39.9   |   [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py)   |         [model](https://download.openmmlab.com/mmdetection/v2.0/ld/ld_r34_gflv1_r101_fpn_coco_1x/ld_r34_gflv1_r101_fpn_coco_1x_20220630_134007-9bc69413.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ld/ld_r34_gflv1_r101_fpn_coco_1x/ld_r34_gflv1_r101_fpn_coco_1x_20220630_134007.log.json)         |
+|    --     |  R-50   |        1x         |        6        |   40.1   |                                                                                                                 |                                                                                                                                                                                                                                                                                                                        |
+|   R-101   |  R-50   |        1x         |        6        |   41.0   |   [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py)   |         [model](https://download.openmmlab.com/mmdetection/v2.0/ld/ld_r50_gflv1_r101_fpn_coco_1x/ld_r50_gflv1_r101_fpn_coco_1x_20220629_145355-8dc5bad8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ld/ld_r50_gflv1_r101_fpn_coco_1x/ld_r50_gflv1_r101_fpn_coco_1x_20220629_145355.log.json)         |
+|    --     |  R-101  |        2x         |        6        |   44.6   |                                                                                                                 |                                                                                                                                                                                                                                                                                                                        |
+| R-101-DCN |  R-101  |        2x         |        6        |   45.5   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x/ld_r101_gflv1_r101dcn_fpn_coco_2x_20220629_185920-9e658426.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x/ld_r101_gflv1_r101dcn_fpn_coco_2x_20220629_185920.log.json) |
+
+## Note
+
+- Meaning of Config name: ld_r18(student model)\_gflv1(based on gflv1)\_r101(teacher model)\_fpn(neck)\_coco(dataset)\_1x(12 epoch).py
+
+## Citation
+
+```latex
+@Inproceedings{zheng2022LD,
+  title={Localization Distillation for Dense Object Detection},
+  author= {Zheng, Zhaohui and Ye, Rongguang and Wang, Ping and Ren, Dongwei and Zuo, Wangmeng and Hou, Qibin and Cheng, Mingming},
+  booktitle={CVPR},
+  year={2022}
+}
+```
diff --git a/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py b/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py
new file mode 100644
index 0000000..1cbdb4c
--- /dev/null
+++ b/configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py
@@ -0,0 +1,44 @@
+_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py']
+teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20200630_102002-134b07df.pth'  # noqa
+model = dict(
+    teacher_config='configs/gfl/gfl_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py',
+    teacher_ckpt=teacher_ckpt,
+    backbone=dict(
+        type='ResNet',
+        depth=101,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5))
+
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
+# multi-scale training
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 480), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
diff --git a/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py b/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py
new file mode 100644
index 0000000..18dce81
--- /dev/null
+++ b/configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py
@@ -0,0 +1,62 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+teacher_ckpt = 'https://download.openmmlab.com/mmdetection/v2.0/gfl/gfl_r101_fpn_mstrain_2x_coco/gfl_r101_fpn_mstrain_2x_coco_20200629_200126-dd12f847.pth'  # noqa
+model = dict(
+    type='KnowledgeDistillationSingleStageDetector',
+    teacher_config='configs/gfl/gfl_r101_fpn_mstrain_2x_coco.py',
+    teacher_ckpt=teacher_ckpt,
+    backbone=dict(
+        type='ResNet',
+        depth=18,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
+    neck=dict(
+        type='FPN',
+        in_channels=[64, 128, 256, 512],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='LDHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        loss_cls=dict(
+            type='QualityFocalLoss',
+            use_sigmoid=True,
+            beta=2.0,
+            loss_weight=1.0),
+        loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
+        loss_ld=dict(
+            type='KnowledgeDistillationKLDivLoss', loss_weight=0.25, T=10),
+        reg_max=16,
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(type='ATSSAssigner', topk=9),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py b/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py
new file mode 100644
index 0000000..3b6996d
--- /dev/null
+++ b/configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py
@@ -0,0 +1,19 @@
+_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py']
+model = dict(
+    backbone=dict(
+        type='ResNet',
+        depth=34,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet34')),
+    neck=dict(
+        type='FPN',
+        in_channels=[64, 128, 256, 512],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5))
diff --git a/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py b/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py
new file mode 100644
index 0000000..2b18785
--- /dev/null
+++ b/configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py
@@ -0,0 +1,19 @@
+_base_ = ['./ld_r18_gflv1_r101_fpn_coco_1x.py']
+model = dict(
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5))
diff --git a/configs/ld/metafile.yml b/configs/ld/metafile.yml
new file mode 100644
index 0000000..2055e32
--- /dev/null
+++ b/configs/ld/metafile.yml
@@ -0,0 +1,69 @@
+Collections:
+  - Name: Localization Distillation
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - Localization Distillation
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/2102.12252
+      Title: 'Localization Distillation for Dense Object Detection'
+    README: configs/ld/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.11.0/mmdet/models/dense_heads/ld_head.py#L11
+      Version: v2.11.0
+
+Models:
+  - Name: ld_r18_gflv1_r101_fpn_coco_1x
+    In Collection: Localization Distillation
+    Config: configs/ld/ld_r18_gflv1_r101_fpn_coco_1x.py
+    Metadata:
+      Training Memory (GB): 1.8
+      Epochs: 12
+    Results:
+    - Task: Object Detection
+      Dataset: COCO
+      Metrics:
+        box AP: 36.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ld/ld_r18_gflv1_r101_fpn_coco_1x/ld_r18_gflv1_r101_fpn_coco_1x_20220702_062206-330e6332.pth
+  - Name: ld_r34_gflv1_r101_fpn_coco_1x
+    In Collection: Localization Distillation
+    Config: configs/ld/ld_r34_gflv1_r101_fpn_coco_1x.py
+    Metadata:
+      Training Memory (GB): 2.2
+      Epochs: 12
+    Results:
+    - Task: Object Detection
+      Dataset: COCO
+      Metrics:
+        box AP: 39.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ld/ld_r34_gflv1_r101_fpn_coco_1x/ld_r34_gflv1_r101_fpn_coco_1x_20220630_134007-9bc69413.pth
+  - Name: ld_r50_gflv1_r101_fpn_coco_1x
+    In Collection: Localization Distillation
+    Config: configs/ld/ld_r50_gflv1_r101_fpn_coco_1x.py
+    Metadata:
+      Training Memory (GB): 3.6
+      Epochs: 12
+    Results:
+    - Task: Object Detection
+      Dataset: COCO
+      Metrics:
+        box AP: 41.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ld/ld_r50_gflv1_r101_fpn_coco_1x/ld_r50_gflv1_r101_fpn_coco_1x_20220629_145355-8dc5bad8.pth
+  - Name: ld_r101_gflv1_r101dcn_fpn_coco_2x
+    In Collection: Localization Distillation
+    Config: configs/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x.py
+    Metadata:
+      Training Memory (GB): 5.5
+      Epochs: 24
+    Results:
+    - Task: Object Detection
+      Dataset: COCO
+      Metrics:
+        box AP: 45.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ld/ld_r101_gflv1_r101dcn_fpn_coco_2x/ld_r101_gflv1_r101dcn_fpn_coco_2x_20220629_185920-9e658426.pth
diff --git a/configs/legacy_1.x/README.md b/configs/legacy_1.x/README.md
new file mode 100644
index 0000000..c48477f
--- /dev/null
+++ b/configs/legacy_1.x/README.md
@@ -0,0 +1,54 @@
+# Legacy Configs in MMDetection V1.x
+
+<!-- [OTHERS] -->
+
+Configs in this directory implement the legacy configs used by MMDetection V1.x and its model zoos.
+
+To help users convert their models from V1.x to MMDetection V2.0, we provide v1.x configs to inference the converted v1.x models.
+Due to the BC-breaking changes in MMDetection V2.0 from MMDetection V1.x, running inference with the same model weights in these two version will produce different results. The difference will cause within 1% AP absolute difference as can be found in the following table.
+
+## Usage
+
+To upgrade the model version, the users need to do the following steps.
+
+### 1. Convert model weights
+
+There are three main difference in the model weights between V1.x and V2.0 codebases.
+
+1. Since the class order in all the detector's classification branch is reordered, all the legacy model weights need to go through the conversion process.
+2. The regression and segmentation head no longer contain the background channel. Weights in these background channels should be removed to fix in the current codebase.
+3. For two-stage detectors, their wegihts need to be upgraded since MMDetection V2.0 refactors all the two-stage detectors with `RoIHead`.
+
+The users can do the same modification as mentioned above for the self-implemented
+detectors. We provide a scripts `tools/model_converters/upgrade_model_version.py` to convert the model weights in the V1.x model zoo.
+
+```bash
+python tools/model_converters/upgrade_model_version.py ${OLD_MODEL_PATH} ${NEW_MODEL_PATH} --num-classes ${NUM_CLASSES}
+
+```
+
+- OLD_MODEL_PATH: the path to load the model weights in 1.x version.
+- NEW_MODEL_PATH: the path to save the converted model weights in 2.0 version.
+- NUM_CLASSES: number of classes of the original model weights. Usually it is 81 for COCO dataset, 21 for VOC dataset.
+  The number of classes in V2.0 models should be equal to that in V1.x models - 1.
+
+### 2. Use configs with legacy settings
+
+After converting the model weights, checkout to the v1.2 release to find the corresponding config file that uses the legacy settings.
+The V1.x models usually need these three legacy modules: `LegacyAnchorGenerator`, `LegacyDeltaXYWHBBoxCoder`, and `RoIAlign(align=False)`.
+For models using ResNet Caffe backbones, they also need to change the pretrain name and the corresponding `img_norm_cfg`.
+An example is in [`retinanet_r50_caffe_fpn_1x_coco_v1.py`](retinanet_r50_caffe_fpn_1x_coco_v1.py)
+Then use the config to test the model weights. For most models, the obtained results should be close to that in V1.x.
+We provide configs of some common structures in this directory.
+
+## Performance
+
+The performance change after converting the models in this directory are listed as the following.
+
+|           Method            |  Style  | Lr schd | V1.x box AP | V1.x mask AP | V2.0 box AP | V2.0 mask AP |                                                           Config                                                           |                                                             Download                                                              |
+| :-------------------------: | :-----: | :-----: | :---------: | :----------: | :---------: | :----------: | :------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------: |
+|     Mask R-CNN R-50-FPN     | pytorch |   1x    |    37.3     |     34.2     |    36.8     |     33.9     |     [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py)     |     [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth)     |
+|     RetinaNet R-50-FPN      |  caffe  |   1x    |    35.8     |      -       |    35.4     |      -       |    [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/retinanet_r50_caffe_1x_coco_v1.py)    |                                                                                                                                   |
+|     RetinaNet R-50-FPN      | pytorch |   1x    |    35.6     |      -       |    35.2     |      -       |     [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py)     |     [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_1x_20181125-7b0c2548.pth)     |
+| Cascade Mask R-CNN R-50-FPN | pytorch |   1x    |    41.2     |     35.7     |    40.8     |     35.6     | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py) | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_1x_20181123-88b170c9.pth) |
+|        SSD300-VGG16         |  caffe  |  120e   |    25.7     |      -       |    25.4     |      -       |            [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/legacy_1.x/ssd300_coco_v1.py)            | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/ssd300_coco_vgg16_caffe_120e_20181221-84d7110b.pth) |
diff --git a/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py b/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py
new file mode 100644
index 0000000..fc9d004
--- /dev/null
+++ b/configs/legacy_1.x/cascade_mask_rcnn_r50_fpn_1x_coco_v1.py
@@ -0,0 +1,79 @@
+_base_ = [
+    '../_base_/models/cascade_mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='CascadeRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5),
+        bbox_coder=dict(
+            type='LegacyDeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0])),
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(
+                type='RoIAlign',
+                output_size=7,
+                sampling_ratio=2,
+                aligned=False)),
+        bbox_head=[
+            dict(
+                type='Shared2FCBBoxHead',
+                reg_class_agnostic=True,
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='LegacyDeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2])),
+            dict(
+                type='Shared2FCBBoxHead',
+                reg_class_agnostic=True,
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='LegacyDeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1])),
+            dict(
+                type='Shared2FCBBoxHead',
+                reg_class_agnostic=True,
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='LegacyDeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067])),
+        ],
+        mask_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(
+                type='RoIAlign',
+                output_size=14,
+                sampling_ratio=2,
+                aligned=False))))
+dist_params = dict(backend='nccl', port=29515)
diff --git a/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py b/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py
new file mode 100644
index 0000000..8c573be
--- /dev/null
+++ b/configs/legacy_1.x/faster_rcnn_r50_fpn_1x_coco_v1.py
@@ -0,0 +1,38 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    type='FasterRCNN',
+    backbone=dict(
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    rpn_head=dict(
+        type='RPNHead',
+        anchor_generator=dict(
+            type='LegacyAnchorGenerator',
+            center_offset=0.5,
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+    roi_head=dict(
+        type='StandardRoIHead',
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(
+                type='RoIAlign',
+                output_size=7,
+                sampling_ratio=2,
+                aligned=False),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=dict(
+            bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn_proposal=dict(max_per_img=2000),
+        rcnn=dict(assigner=dict(match_low_quality=True))))
diff --git a/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py b/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py
new file mode 100644
index 0000000..04581bb
--- /dev/null
+++ b/configs/legacy_1.x/mask_rcnn_r50_fpn_1x_coco_v1.py
@@ -0,0 +1,34 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    rpn_head=dict(
+        anchor_generator=dict(type='LegacyAnchorGenerator', center_offset=0.5),
+        bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(
+                type='RoIAlign',
+                output_size=7,
+                sampling_ratio=2,
+                aligned=False)),
+        mask_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(
+                type='RoIAlign',
+                output_size=14,
+                sampling_ratio=2,
+                aligned=False)),
+        bbox_head=dict(
+            bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+
+    # model training and testing settings
+    train_cfg=dict(
+        rpn_proposal=dict(max_per_img=2000),
+        rcnn=dict(assigner=dict(match_low_quality=True))))
diff --git a/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py b/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py
new file mode 100644
index 0000000..a63d248
--- /dev/null
+++ b/configs/legacy_1.x/retinanet_r50_caffe_fpn_1x_coco_v1.py
@@ -0,0 +1,41 @@
+_base_ = './retinanet_r50_fpn_1x_coco_v1.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron/resnet50_caffe')))
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[102.9801, 115.9465, 122.7717], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py b/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py
new file mode 100644
index 0000000..6198b97
--- /dev/null
+++ b/configs/legacy_1.x/retinanet_r50_fpn_1x_coco_v1.py
@@ -0,0 +1,17 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    bbox_head=dict(
+        type='RetinaHead',
+        anchor_generator=dict(
+            type='LegacyAnchorGenerator',
+            center_offset=0.5,
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(type='LegacyDeltaXYWHBBoxCoder'),
+        loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)))
diff --git a/configs/legacy_1.x/ssd300_coco_v1.py b/configs/legacy_1.x/ssd300_coco_v1.py
new file mode 100644
index 0000000..65ccc1e
--- /dev/null
+++ b/configs/legacy_1.x/ssd300_coco_v1.py
@@ -0,0 +1,84 @@
+_base_ = [
+    '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+# model settings
+input_size = 300
+model = dict(
+    bbox_head=dict(
+        type='SSDHead',
+        anchor_generator=dict(
+            type='LegacySSDAnchorGenerator',
+            scale_major=False,
+            input_size=input_size,
+            basesize_ratio_range=(0.15, 0.9),
+            strides=[8, 16, 32, 64, 100, 300],
+            ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
+        bbox_coder=dict(
+            type='LegacyDeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2])))
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(300, 300),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=3,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict(_delete_=True)
+dist_params = dict(backend='nccl', port=29555)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/libra_rcnn/README.md b/configs/libra_rcnn/README.md
new file mode 100644
index 0000000..87a128a
--- /dev/null
+++ b/configs/libra_rcnn/README.md
@@ -0,0 +1,53 @@
+# Libra R-CNN
+
+> [Libra R-CNN: Towards Balanced Learning for Object Detection](https://arxiv.org/abs/1904.02701)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Compared with model architectures, the training process, which is also crucial to the success of detectors, has received relatively less attention in object detection. In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level. To mitigate the adverse effects caused thereby, we propose Libra R-CNN, a simple but effective framework towards balanced learning for object detection. It integrates three novel components: IoU-balanced sampling, balanced feature pyramid, and balanced L1 loss, respectively for reducing the imbalance at sample, feature, and objective level. Benefitted from the overall balanced design, Libra R-CNN significantly improves the detection performance. Without bells and whistles, it achieves 2.5 points and 2.0 points higher Average Precision (AP) than FPN Faster R-CNN and RetinaNet respectively on MSCOCO.
+
+Instance recognition is rapidly advanced along with the developments of various deep convolutional neural networks. Compared to the architectures of networks, the training process, which is also crucial to the success of detectors, has received relatively less attention. In this work, we carefully revisit the standard training practice of detectors, and find that the detection performance is often limited by the imbalance during the training process, which generally consists in three levels - sample level, feature level, and objective level. To mitigate the adverse effects caused thereby, we propose Libra R-CNN, a simple yet effective framework towards balanced learning for instance recognition. It integrates IoU-balanced sampling, balanced feature pyramid, and objective re-weighting, respectively for reducing the imbalance at sample, feature, and objective level. Extensive experiments conducted on MS COCO, LVIS and Pascal VOC datasets prove the effectiveness of the overall balanced design.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143966392-2633684c-a67a-4269-b71b-afe945c67bcd.png"/>
+</div>
+
+## Results and Models
+
+The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val)
+
+| Architecture |    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                             Config                                                             |                                                                                                                                                                          Download                                                                                                                                                                           |
+| :----------: | :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :----------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Faster R-CNN |    R-50-FPN     | pytorch |   1x    |   4.6    |      19.0      |  38.3  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json)               |
+|  Fast R-CNN  |    R-50-FPN     | pytorch |   1x    |          |                |        |                                                                                                                                |                                                                                                                                                                                                                                                                                                                                                             |
+| Faster R-CNN |    R-101-FPN    | pytorch |   1x    |   6.5    |      14.4      |  40.1  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203-8dba6a5a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203_001405.log.json)             |
+| Faster R-CNN | X-101-64x4d-FPN | pytorch |   1x    |   10.8   |      8.5       |  42.7  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315-3a7d0488.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315_231625.log.json) |
+|  RetinaNet   |    R-50-FPN     | pytorch |   1x    |   4.2    |      17.7      |  37.6  |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py)      |                   [model](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205-804d94ce.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205_112757.log.json)                   |
+
+## Citation
+
+We provide config files to reproduce the results in the CVPR 2019 paper [Libra R-CNN](https://arxiv.org/pdf/1904.02701.pdf).
+
+The extended version of [Libra R-CNN](https://arxiv.org/pdf/2108.10175.pdf) is accpeted by IJCV.
+
+```latex
+@inproceedings{pang2019libra,
+  title={Libra R-CNN: Towards Balanced Learning for Object Detection},
+  author={Pang, Jiangmiao and Chen, Kai and Shi, Jianping and Feng, Huajun and Ouyang, Wanli and Dahua Lin},
+  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+  year={2019}
+}
+
+@article{pang2021towards,
+  title={Towards Balanced Learning for Instance Recognition},
+  author={Pang, Jiangmiao and Chen, Kai and Li, Qi and Xu, Zhihai and Feng, Huajun and Shi, Jianping and Ouyang, Wanli and Lin, Dahua},
+  journal={International Journal of Computer Vision},
+  volume={129},
+  number={5},
+  pages={1376--1393},
+  year={2021},
+  publisher={Springer}
+}
+```
diff --git a/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py b/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..efbedc8
--- /dev/null
+++ b/configs/libra_rcnn/libra_fast_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,50 @@
+_base_ = '../fast_rcnn/fast_rcnn_r50_fpn_1x_coco.py'
+# model settings
+model = dict(
+    neck=[
+        dict(
+            type='FPN',
+            in_channels=[256, 512, 1024, 2048],
+            out_channels=256,
+            num_outs=5),
+        dict(
+            type='BFP',
+            in_channels=256,
+            num_levels=5,
+            refine_level=2,
+            refine_type='non_local')
+    ],
+    roi_head=dict(
+        bbox_head=dict(
+            loss_bbox=dict(
+                _delete_=True,
+                type='BalancedL1Loss',
+                alpha=0.5,
+                gamma=1.5,
+                beta=1.0,
+                loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rcnn=dict(
+            sampler=dict(
+                _delete_=True,
+                type='CombinedSampler',
+                num=512,
+                pos_fraction=0.25,
+                add_gt_as_proposals=True,
+                pos_sampler=dict(type='InstanceBalancedPosSampler'),
+                neg_sampler=dict(
+                    type='IoUBalancedNegSampler',
+                    floor_thr=-1,
+                    floor_fraction=0,
+                    num_bins=3)))))
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+data = dict(
+    train=dict(proposal_file=data_root +
+               'libra_proposals/rpn_r50_fpn_1x_train2017.pkl'),
+    val=dict(proposal_file=data_root +
+             'libra_proposals/rpn_r50_fpn_1x_val2017.pkl'),
+    test=dict(proposal_file=data_root +
+              'libra_proposals/rpn_r50_fpn_1x_val2017.pkl'))
diff --git a/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py b/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..e899706
--- /dev/null
+++ b/configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py b/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..89a0d7b
--- /dev/null
+++ b/configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,41 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+# model settings
+model = dict(
+    neck=[
+        dict(
+            type='FPN',
+            in_channels=[256, 512, 1024, 2048],
+            out_channels=256,
+            num_outs=5),
+        dict(
+            type='BFP',
+            in_channels=256,
+            num_levels=5,
+            refine_level=2,
+            refine_type='non_local')
+    ],
+    roi_head=dict(
+        bbox_head=dict(
+            loss_bbox=dict(
+                _delete_=True,
+                type='BalancedL1Loss',
+                alpha=0.5,
+                gamma=1.5,
+                beta=1.0,
+                loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(sampler=dict(neg_pos_ub=5), allowed_border=-1),
+        rcnn=dict(
+            sampler=dict(
+                _delete_=True,
+                type='CombinedSampler',
+                num=512,
+                pos_fraction=0.25,
+                add_gt_as_proposals=True,
+                pos_sampler=dict(type='InstanceBalancedPosSampler'),
+                neg_sampler=dict(
+                    type='IoUBalancedNegSampler',
+                    floor_thr=-1,
+                    floor_fraction=0,
+                    num_bins=3)))))
diff --git a/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..06740a7
--- /dev/null
+++ b/configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './libra_faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py b/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..be27420
--- /dev/null
+++ b/configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py
@@ -0,0 +1,26 @@
+_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
+# model settings
+model = dict(
+    neck=[
+        dict(
+            type='FPN',
+            in_channels=[256, 512, 1024, 2048],
+            out_channels=256,
+            start_level=1,
+            add_extra_convs='on_input',
+            num_outs=5),
+        dict(
+            type='BFP',
+            in_channels=256,
+            num_levels=5,
+            refine_level=1,
+            refine_type='non_local')
+    ],
+    bbox_head=dict(
+        loss_bbox=dict(
+            _delete_=True,
+            type='BalancedL1Loss',
+            alpha=0.5,
+            gamma=1.5,
+            beta=0.11,
+            loss_weight=1.0)))
diff --git a/configs/libra_rcnn/metafile.yml b/configs/libra_rcnn/metafile.yml
new file mode 100644
index 0000000..8c32795
--- /dev/null
+++ b/configs/libra_rcnn/metafile.yml
@@ -0,0 +1,99 @@
+Collections:
+  - Name: Libra R-CNN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - IoU-Balanced Sampling
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Balanced Feature Pyramid
+    Paper:
+      URL: https://arxiv.org/abs/1904.02701
+      Title: 'Libra R-CNN: Towards Balanced Learning for Object Detection'
+    README: configs/libra_rcnn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/necks/bfp.py#L10
+      Version: v2.0.0
+
+Models:
+  - Name: libra_faster_rcnn_r50_fpn_1x_coco
+    In Collection: Libra R-CNN
+    Config: configs/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.6
+      inference time (ms/im):
+        - value: 52.63
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r50_fpn_1x_coco/libra_faster_rcnn_r50_fpn_1x_coco_20200130-3afee3a9.pth
+
+  - Name: libra_faster_rcnn_r101_fpn_1x_coco
+    In Collection: Libra R-CNN
+    Config: configs/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.5
+      inference time (ms/im):
+        - value: 69.44
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_r101_fpn_1x_coco/libra_faster_rcnn_r101_fpn_1x_coco_20200203-8dba6a5a.pth
+
+  - Name: libra_faster_rcnn_x101_64x4d_fpn_1x_coco
+    In Collection: Libra R-CNN
+    Config: configs/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.8
+      inference time (ms/im):
+        - value: 117.65
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_faster_rcnn_x101_64x4d_fpn_1x_coco/libra_faster_rcnn_x101_64x4d_fpn_1x_coco_20200315-3a7d0488.pth
+
+  - Name: libra_retinanet_r50_fpn_1x_coco
+    In Collection: Libra R-CNN
+    Config: configs/libra_rcnn/libra_retinanet_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.2
+      inference time (ms/im):
+        - value: 56.5
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/libra_rcnn/libra_retinanet_r50_fpn_1x_coco/libra_retinanet_r50_fpn_1x_coco_20200205-804d94ce.pth
diff --git a/configs/lvis/README.md b/configs/lvis/README.md
new file mode 100644
index 0000000..0c2760e
--- /dev/null
+++ b/configs/lvis/README.md
@@ -0,0 +1,56 @@
+# LVIS
+
+> [LVIS: A Dataset for Large Vocabulary Instance Segmentation](https://arxiv.org/abs/1908.03195)
+
+<!-- [DATASET] -->
+
+## Abstract
+
+Progress on object detection is enabled by datasets that focus the research community's attention on open challenges. This process led us from simple images to complex scenes and from bounding boxes to segmentation masks. In this work, we introduce LVIS (pronounced \`el-vis'): a new dataset for Large Vocabulary Instance Segmentation. We plan to collect ~2 million high-quality instance segmentation masks for over 1000 entry-level object categories in 164k images. Due to the Zipfian distribution of categories in natural images, LVIS naturally has a long tail of categories with few training samples. Given that state-of-the-art deep learning methods for object detection perform poorly in the low-sample regime, we believe that our dataset poses an important and exciting new scientific challenge.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143967423-85b9b705-05ea-4bbc-9a41-eccc14240c7a.png" height="300"/>
+</div>
+
+## Common Setting
+
+- Please follow [install guide](../../docs/get_started.md#install-mmdetection) to install open-mmlab forked cocoapi first.
+
+- Run following scripts to install our forked lvis-api.
+
+  ```shell
+  pip install git+https://github.com/lvis-dataset/lvis-api.git
+  ```
+
+- All experiments use oversample strategy [here](../../docs/tutorials/customize_dataset.md#class-balanced-dataset) with oversample threshold `1e-3`.
+
+- The size of LVIS v0.5 is half of COCO, so schedule `2x` in LVIS is roughly the same iterations as `1x` in COCO.
+
+## Results and models of LVIS v0.5
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                                  Config                                                                  |                                                                                                                                                                                      Download                                                                                                                                                                                      |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50-FPN     | pytorch |   2x    |    -     |       -        |  26.1  |  25.9   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis-dbd06831.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_20200531_160435.log.json)               |
+|    R-101-FPN    | pytorch |   2x    |    -     |       -        |  27.1  |  27.0   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis-54582ee2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_20200601_134748.log.json)             |
+| X-101-32x4d-FPN | pytorch |   2x    |    -     |       -        |  26.7  |  26.9   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis-3cf55ea2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_20200531_221749.log.json) |
+| X-101-64x4d-FPN | pytorch |   2x    |    -     |       -        |  26.4  |  26.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis-1c99a5ad.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_20200601_194651.log.json) |
+
+## Results and models of LVIS v1
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                                 Config                                                                 |                                                                                                                                                                                            Download                                                                                                                                                                                            |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50-FPN     | pytorch |   1x    |   9.1    |       -        |  22.5  |  21.7   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1-aa78ac3d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_061305.log.json)               |
+|    R-101-FPN    | pytorch |   1x    |   10.8   |       -        |  24.6  |  23.6   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1-ec55ce32.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_070959.log.json)             |
+| X-101-32x4d-FPN | pytorch |   1x    |   11.8   |       -        |  26.7  |  25.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-ebbc5c81.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-20200829_071317.log.json) |
+| X-101-64x4d-FPN | pytorch |   1x    |   14.6   |       -        |  27.2  |  25.8   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-43d9edfe.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1-20200830_060206.log.json) |
+
+## Citation
+
+```latex
+@inproceedings{gupta2019lvis,
+  title={{LVIS}: A Dataset for Large Vocabulary Instance Segmentation},
+  author={Gupta, Agrim and Dollar, Piotr and Girshick, Ross},
+  booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
+  year={2019}
+}
+```
diff --git a/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py
new file mode 100644
index 0000000..0f017f5
--- /dev/null
+++ b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_1x_lvis_v1.py
@@ -0,0 +1,6 @@
+_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
new file mode 100644
index 0000000..637f4a6
--- /dev/null
+++ b/configs/lvis/mask_rcnn_r101_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
@@ -0,0 +1,6 @@
+_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py
new file mode 100644
index 0000000..92ddb52
--- /dev/null
+++ b/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py
@@ -0,0 +1,31 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/lvis_v1_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(num_classes=1203), mask_head=dict(num_classes=1203)),
+    test_cfg=dict(
+        rcnn=dict(
+            score_thr=0.0001,
+            # LVIS allows up to 300
+            max_per_img=300)))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+data = dict(train=dict(dataset=dict(pipeline=train_pipeline)))
diff --git a/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
new file mode 100644
index 0000000..d53c5dc
--- /dev/null
+++ b/configs/lvis/mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
@@ -0,0 +1,31 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/lvis_v0.5_instance.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(num_classes=1230), mask_head=dict(num_classes=1230)),
+    test_cfg=dict(
+        rcnn=dict(
+            score_thr=0.0001,
+            # LVIS allows up to 300
+            max_per_img=300)))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+data = dict(train=dict(dataset=dict(pipeline=train_pipeline)))
diff --git a/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py
new file mode 100644
index 0000000..a6115c1
--- /dev/null
+++ b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py
@@ -0,0 +1,14 @@
+_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
new file mode 100644
index 0000000..96b6252
--- /dev/null
+++ b/configs/lvis/mask_rcnn_x101_32x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
@@ -0,0 +1,14 @@
+_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py
new file mode 100644
index 0000000..0f95a73
--- /dev/null
+++ b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_1x_lvis_v1.py
@@ -0,0 +1,14 @@
+_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_1x_lvis_v1.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
new file mode 100644
index 0000000..986acda
--- /dev/null
+++ b/configs/lvis/mask_rcnn_x101_64x4d_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py
@@ -0,0 +1,14 @@
+_base_ = './mask_rcnn_r50_fpn_sample1e-3_mstrain_2x_lvis_v0.5.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/mask2former/README.md b/configs/mask2former/README.md
new file mode 100644
index 0000000..ebce50d
--- /dev/null
+++ b/configs/mask2former/README.md
@@ -0,0 +1,73 @@
+# Mask2Former
+
+> [Masked-attention Mask Transformer for Universal Image Segmentation](http://arxiv.org/abs/2112.01527)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Image segmentation is about grouping pixels with different semantics, e.g., category or instance membership, where each choice of semantics defines a task. While only the semantics of each task differ, current research focuses on designing specialized architectures for each task. We present Masked-attention Mask Transformer (Mask2Former), a new architecture capable of addressing any image segmentation task (panoptic, instance or semantic). Its key components include masked attention, which extracts localized features by constraining cross-attention within predicted mask regions. In addition to reducing the research effort by at least three times, it outperforms the best specialized architectures by a significant margin on four popular datasets. Most notably, Mask2Former sets a new state-of-the-art for panoptic segmentation (57.8 PQ on COCO), instance segmentation (50.1 AP on COCO) and semantic segmentation (57.7 mIoU on ADE20K).
+
+<div align=center>
+<img src="https://camo.githubusercontent.com/455d3116845b1d580b1f8a8542334b9752fdf39364deee2951cdd231524c7725/68747470733a2f2f626f77656e63303232312e6769746875622e696f2f696d616765732f6d61736b666f726d657276325f7465617365722e706e67" height="300"/>
+</div>
+
+## Introduction
+
+Mask2Former requires COCO and [COCO-panoptic](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip) dataset for training and evaluation. You need to download and extract it in the COCO dataset path.
+The directory should be like this.
+
+```none
+mmdetection
+├── mmdet
+├── tools
+├── configs
+├── data
+│   ├── coco
+│   │   ├── annotations
+|   |   |   ├── instances_train2017.json
+|   |   |   ├── instances_val2017.json
+│   │   │   ├── panoptic_train2017.json
+│   │   │   ├── panoptic_train2017
+│   │   │   ├── panoptic_val2017.json
+│   │   │   ├── panoptic_val2017
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+```
+
+## Results and Models
+
+### Panoptic segmentation
+
+| Backbone |  style  |   Pretrain   | Lr schd | Mem (GB) | Inf time (fps) |  PQ  | box mAP | mask mAP |                                                                         Config                                                                         |                                                                                                                                                                                                                             Download                                                                                                                                                                                                                             |
+| :------: | :-----: | :----------: | :-----: | :------: | :------------: | :--: | :-----: | :------: | :----------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | pytorch | ImageNet-1K  |   50e   |   13.9   |       -        | 51.9 |  44.8   |   41.9   |            [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py)            |                                             [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic/mask2former_r50_lsj_8x2_50e_coco-panoptic_20220326_224516-11a44721.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic/mask2former_r50_lsj_8x2_50e_coco-panoptic_20220326_224516.log.json)                                             |
+|  R-101   | pytorch | ImageNet-1K  |   50e   |   16.1   |       -        | 52.4 |  45.3   |   42.4   |           [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic.py)            |                                           [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic/mask2former_r101_lsj_8x2_50e_coco-panoptic_20220329_225104-c54e64c9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic/mask2former_r101_lsj_8x2_50e_coco-panoptic_20220329_225104.log.json)                                           |
+|  Swin-T  |    -    | ImageNet-1K  |   50e   |   15.9   |       -        | 53.4 |  46.3   |   43.4   |     [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py)      |                   [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220326_224553-fc567107.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220326_224553.log.json)                   |
+|  Swin-S  |    -    | ImageNet-1K  |   50e   |   19.1   |       -        | 54.5 |  47.8   |   44.5   |     [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic.py)      |                   [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220329_225200-c7b94355.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220329_225200.log.json)                   |
+|  Swin-B  |    -    | ImageNet-1K  |   50e   |   26.0   |       -        | 55.1 |  48.2   |   44.9   |     [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py)     |                 [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic_20220331_002244-c149a9e9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic_20220331_002244.log.json)                 |
+|  Swin-B  |    -    | ImageNet-21K |   50e   |   25.8   |       -        | 56.3 |  50.0   |   46.3   |  [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic.py)  |     [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic_20220329_230021-3bb8b482.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic_20220329_230021.log.json)     |
+|  Swin-L  |    -    | ImageNet-21K |  100e   |   21.1   |       -        | 57.6 |  52.2   |   48.5   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic_20220407_104949-d4919c44.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic_20220407_104949.log.json) |
+
+### Instance segmentation
+
+| Backbone | style   | Pretrain    | Lr schd | Mem (GB) | Inf time (fps) | box mAP | mask mAP | Config                                                                                                                               | Download                                                                                                                                                                                                                                                                                                                                                                                 |
+| -------- | ------- | ----------- | ------- | -------- | -------------- | ------- | -------- | ------------------------------------------------------------------------------------------------------------------------------------ | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| R-50     | pytorch | ImageNet-1K | 50e     | 13.7     | -              | 45.7    | 42.9     | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py)              | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco/mask2former_r50_lsj_8x2_50e_coco_20220506_191028-8e96e88b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco/mask2former_r50_lsj_8x2_50e_coco_20220506_191028.log.json)                                                     |
+| R-101    | pytorch | ImageNet-1K | 50e     | 15.5     | -              | 46.7    | 44.0     | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco.py)             | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco/mask2former_r101_lsj_8x2_50e_coco_20220426_100250-c50b6fa6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco/mask2former_r101_lsj_8x2_50e_coco_20220426_100250.log.json)                                                 |
+| Swin-T   | -       | ImageNet-1K | 50e     | 15.3     | -              | 47.7    | 44.7     | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco_20220508_091649-4a943037.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco_20220508_091649.log.json) |
+| Swin-S   | -       | ImageNet-1K | 50e     | 18.8     | -              | 49.3    | 46.1     | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco_20220504_001756-743b7d99.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco_20220504_001756.log.json) |
+
+Note: We have trained the instance segmentation models many times (see more details in [PR 7571](https://github.com/open-mmlab/mmdetection/pull/7571)). The results of the trained models are relatively stable (+- 0.2), and have a certain gap (about 0.2 AP) in comparison with the results in the [paper](http://arxiv.org/abs/2112.01527). However, the performance of the model trained with the official code is unstable and may also be slightly lower than the reported results as mentioned in the [issue](https://github.com/facebookresearch/Mask2Former/issues/46).
+
+## Citation
+
+```latex
+@article{cheng2021mask2former,
+  title={Masked-attention Mask Transformer for Universal Image Segmentation},
+  author={Bowen Cheng and Ishan Misra and Alexander G. Schwing and Alexander Kirillov and Rohit Girdhar},
+  journal={arXiv},
+  year={2021}
+}
+```
diff --git a/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic.py b/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic.py
new file mode 100644
index 0000000..33fdde6
--- /dev/null
+++ b/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic.py
@@ -0,0 +1,7 @@
+_base_ = './mask2former_r50_lsj_8x2_50e_coco-panoptic.py'
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco.py b/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco.py
new file mode 100644
index 0000000..5543fb0
--- /dev/null
+++ b/configs/mask2former/mask2former_r101_lsj_8x2_50e_coco.py
@@ -0,0 +1,7 @@
+_base_ = ['./mask2former_r50_lsj_8x2_50e_coco.py']
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py b/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py
new file mode 100644
index 0000000..2c23625
--- /dev/null
+++ b/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py
@@ -0,0 +1,253 @@
+_base_ = [
+    '../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
+]
+num_things_classes = 80
+num_stuff_classes = 53
+num_classes = num_things_classes + num_stuff_classes
+model = dict(
+    type='Mask2Former',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=-1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    panoptic_head=dict(
+        type='Mask2FormerHead',
+        in_channels=[256, 512, 1024, 2048],  # pass to pixel_decoder inside
+        strides=[4, 8, 16, 32],
+        feat_channels=256,
+        out_channels=256,
+        num_things_classes=num_things_classes,
+        num_stuff_classes=num_stuff_classes,
+        num_queries=100,
+        num_transformer_feat_level=3,
+        pixel_decoder=dict(
+            type='MSDeformAttnPixelDecoder',
+            num_outs=3,
+            norm_cfg=dict(type='GN', num_groups=32),
+            act_cfg=dict(type='ReLU'),
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=dict(
+                        type='MultiScaleDeformableAttention',
+                        embed_dims=256,
+                        num_heads=8,
+                        num_levels=3,
+                        num_points=4,
+                        im2col_step=64,
+                        dropout=0.0,
+                        batch_first=False,
+                        norm_cfg=None,
+                        init_cfg=None),
+                    ffn_cfgs=dict(
+                        type='FFN',
+                        embed_dims=256,
+                        feedforward_channels=1024,
+                        num_fcs=2,
+                        ffn_drop=0.0,
+                        act_cfg=dict(type='ReLU', inplace=True)),
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm')),
+                init_cfg=None),
+            positional_encoding=dict(
+                type='SinePositionalEncoding', num_feats=128, normalize=True),
+            init_cfg=None),
+        enforce_decoder_input_project=False,
+        positional_encoding=dict(
+            type='SinePositionalEncoding', num_feats=128, normalize=True),
+        transformer_decoder=dict(
+            type='DetrTransformerDecoder',
+            return_intermediate=True,
+            num_layers=9,
+            transformerlayers=dict(
+                type='DetrTransformerDecoderLayer',
+                attn_cfgs=dict(
+                    type='MultiheadAttention',
+                    embed_dims=256,
+                    num_heads=8,
+                    attn_drop=0.0,
+                    proj_drop=0.0,
+                    dropout_layer=None,
+                    batch_first=False),
+                ffn_cfgs=dict(
+                    embed_dims=256,
+                    feedforward_channels=2048,
+                    num_fcs=2,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    ffn_drop=0.0,
+                    dropout_layer=None,
+                    add_identity=True),
+                feedforward_channels=2048,
+                operation_order=('cross_attn', 'norm', 'self_attn', 'norm',
+                                 'ffn', 'norm')),
+            init_cfg=None),
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=False,
+            loss_weight=2.0,
+            reduction='mean',
+            class_weight=[1.0] * num_classes + [0.1]),
+        loss_mask=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=True,
+            reduction='mean',
+            loss_weight=5.0),
+        loss_dice=dict(
+            type='DiceLoss',
+            use_sigmoid=True,
+            activate=True,
+            reduction='mean',
+            naive_dice=True,
+            eps=1.0,
+            loss_weight=5.0)),
+    panoptic_fusion_head=dict(
+        type='MaskFormerFusionHead',
+        num_things_classes=num_things_classes,
+        num_stuff_classes=num_stuff_classes,
+        loss_panoptic=None,
+        init_cfg=None),
+    train_cfg=dict(
+        num_points=12544,
+        oversample_ratio=3.0,
+        importance_sample_ratio=0.75,
+        assigner=dict(
+            type='MaskHungarianAssigner',
+            cls_cost=dict(type='ClassificationCost', weight=2.0),
+            mask_cost=dict(
+                type='CrossEntropyLossCost', weight=5.0, use_sigmoid=True),
+            dice_cost=dict(
+                type='DiceCost', weight=5.0, pred_act=True, eps=1.0)),
+        sampler=dict(type='MaskPseudoSampler')),
+    test_cfg=dict(
+        panoptic_on=True,
+        # For now, the dataset does not support
+        # evaluating semantic segmentation metric.
+        semantic_on=False,
+        instance_on=True,
+        # max_per_image is for instance segmentation.
+        max_per_image=100,
+        iou_thr=0.8,
+        # In Mask2Former's panoptic postprocessing,
+        # it will filter mask area where score is less than 0.5 .
+        filter_low_score=True),
+    init_cfg=None)
+
+# dataset settings
+image_size = (1024, 1024)
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(
+        type='LoadPanopticAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        with_seg=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    # large scale jittering
+    dict(
+        type='Resize',
+        img_scale=image_size,
+        ratio_range=(0.1, 2.0),
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(
+        type='RandomCrop',
+        crop_size=image_size,
+        crop_type='absolute',
+        recompute_bbox=True,
+        allow_negative_crop=True),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=image_size),
+    dict(type='DefaultFormatBundle', img_to_float=True),
+    dict(
+        type='Collect',
+        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data_root = 'data/coco/'
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(pipeline=train_pipeline),
+    val=dict(
+        pipeline=test_pipeline,
+        ins_ann_file=data_root + 'annotations/instances_val2017.json',
+    ),
+    test=dict(
+        pipeline=test_pipeline,
+        ins_ann_file=data_root + 'annotations/instances_val2017.json',
+    ))
+
+embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
+# optimizer
+optimizer = dict(
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.05,
+    eps=1e-8,
+    betas=(0.9, 0.999),
+    paramwise_cfg=dict(
+        custom_keys={
+            'backbone': dict(lr_mult=0.1, decay_mult=1.0),
+            'query_embed': embed_multi,
+            'query_feat': embed_multi,
+            'level_embed': embed_multi,
+        },
+        norm_decay_mult=0.0))
+optimizer_config = dict(grad_clip=dict(max_norm=0.01, norm_type=2))
+
+# learning policy
+lr_config = dict(
+    policy='step',
+    gamma=0.1,
+    by_epoch=False,
+    step=[327778, 355092],
+    warmup='linear',
+    warmup_by_epoch=False,
+    warmup_ratio=1.0,  # no warmup
+    warmup_iters=10)
+
+max_iters = 368750
+runner = dict(type='IterBasedRunner', max_iters=max_iters)
+
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook', by_epoch=False),
+        dict(type='TensorboardLoggerHook', by_epoch=False)
+    ])
+interval = 5000
+workflow = [('train', interval)]
+checkpoint_config = dict(
+    by_epoch=False, interval=interval, save_last=True, max_keep_ckpts=3)
+
+# Before 365001th iteration, we do evaluation every 5000 iterations.
+# After 365000th iteration, we do evaluation every 368750 iterations,
+# which means that we do evaluation at the end of training.
+dynamic_intervals = [(max_iters // interval * interval + 1, max_iters)]
+evaluation = dict(
+    interval=interval,
+    dynamic_intervals=dynamic_intervals,
+    metric=['PQ', 'bbox', 'segm'])
diff --git a/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py b/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py
new file mode 100644
index 0000000..eca6135
--- /dev/null
+++ b/configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py
@@ -0,0 +1,79 @@
+_base_ = ['./mask2former_r50_lsj_8x2_50e_coco-panoptic.py']
+num_things_classes = 80
+num_stuff_classes = 0
+num_classes = num_things_classes + num_stuff_classes
+model = dict(
+    panoptic_head=dict(
+        num_things_classes=num_things_classes,
+        num_stuff_classes=num_stuff_classes,
+        loss_cls=dict(class_weight=[1.0] * num_classes + [0.1])),
+    panoptic_fusion_head=dict(
+        num_things_classes=num_things_classes,
+        num_stuff_classes=num_stuff_classes),
+    test_cfg=dict(panoptic_on=False))
+
+# dataset settings
+image_size = (1024, 1024)
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+pad_cfg = dict(img=(128, 128, 128), masks=0, seg=255)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    # large scale jittering
+    dict(
+        type='Resize',
+        img_scale=image_size,
+        ratio_range=(0.1, 2.0),
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(
+        type='RandomCrop',
+        crop_size=image_size,
+        crop_type='absolute',
+        recompute_bbox=True,
+        allow_negative_crop=True),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-5, 1e-5), by_mask=True),
+    dict(type='Pad', size=image_size, pad_val=pad_cfg),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle', img_to_float=True),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Pad', size_divisor=32, pad_val=pad_cfg),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+data = dict(
+    _delete_=True,
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+evaluation = dict(metric=['bbox', 'segm'])
diff --git a/configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic.py b/configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic.py
new file mode 100644
index 0000000..f13f5e1
--- /dev/null
+++ b/configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic.py
@@ -0,0 +1,5 @@
+_base_ = ['./mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py']
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384_22k.pth'  # noqa
+
+model = dict(
+    backbone=dict(init_cfg=dict(type='Pretrained', checkpoint=pretrained)))
diff --git a/configs/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py b/configs/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py
new file mode 100644
index 0000000..33a805c
--- /dev/null
+++ b/configs/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py
@@ -0,0 +1,42 @@
+_base_ = ['./mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py']
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_base_patch4_window12_384.pth'  # noqa
+
+depths = [2, 2, 18, 2]
+model = dict(
+    backbone=dict(
+        pretrain_img_size=384,
+        embed_dims=128,
+        depths=depths,
+        num_heads=[4, 8, 16, 32],
+        window_size=12,
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    panoptic_head=dict(in_channels=[128, 256, 512, 1024]))
+
+# set all layers in backbone to lr_mult=0.1
+# set all norm layers, position_embeding,
+# query_embeding, level_embeding to decay_multi=0.0
+backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
+backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0)
+embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
+custom_keys = {
+    'backbone': dict(lr_mult=0.1, decay_mult=1.0),
+    'backbone.patch_embed.norm': backbone_norm_multi,
+    'backbone.norm': backbone_norm_multi,
+    'absolute_pos_embed': backbone_embed_multi,
+    'relative_position_bias_table': backbone_embed_multi,
+    'query_embed': embed_multi,
+    'query_feat': embed_multi,
+    'level_embed': embed_multi
+}
+custom_keys.update({
+    f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi
+    for stage_id, num_blocks in enumerate(depths)
+    for block_id in range(num_blocks)
+})
+custom_keys.update({
+    f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi
+    for stage_id in range(len(depths) - 1)
+})
+# optimizer
+optimizer = dict(
+    paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))
diff --git a/configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic.py b/configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic.py
new file mode 100644
index 0000000..91a180d
--- /dev/null
+++ b/configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic.py
@@ -0,0 +1,26 @@
+_base_ = ['./mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py']
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth'  # noqa
+
+model = dict(
+    backbone=dict(
+        embed_dims=192,
+        num_heads=[6, 12, 24, 48],
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    panoptic_head=dict(num_queries=200, in_channels=[192, 384, 768, 1536]))
+
+data = dict(samples_per_gpu=1, workers_per_gpu=1)
+
+lr_config = dict(step=[655556, 710184])
+
+max_iters = 737500
+runner = dict(type='IterBasedRunner', max_iters=max_iters)
+
+# Before 735001th iteration, we do evaluation every 5000 iterations.
+# After 735000th iteration, we do evaluation every 737500 iterations,
+# which means that we do evaluation at the end of training.'
+interval = 5000
+dynamic_intervals = [(max_iters // interval * interval + 1, max_iters)]
+evaluation = dict(
+    interval=interval,
+    dynamic_intervals=dynamic_intervals,
+    metric=['PQ', 'bbox', 'segm'])
diff --git a/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic.py b/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic.py
new file mode 100644
index 0000000..b2b621c
--- /dev/null
+++ b/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic.py
@@ -0,0 +1,37 @@
+_base_ = ['./mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py']
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth'  # noqa
+
+depths = [2, 2, 18, 2]
+model = dict(
+    backbone=dict(
+        depths=depths, init_cfg=dict(type='Pretrained',
+                                     checkpoint=pretrained)))
+
+# set all layers in backbone to lr_mult=0.1
+# set all norm layers, position_embeding,
+# query_embeding, level_embeding to decay_multi=0.0
+backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
+backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0)
+embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
+custom_keys = {
+    'backbone': dict(lr_mult=0.1, decay_mult=1.0),
+    'backbone.patch_embed.norm': backbone_norm_multi,
+    'backbone.norm': backbone_norm_multi,
+    'absolute_pos_embed': backbone_embed_multi,
+    'relative_position_bias_table': backbone_embed_multi,
+    'query_embed': embed_multi,
+    'query_feat': embed_multi,
+    'level_embed': embed_multi
+}
+custom_keys.update({
+    f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi
+    for stage_id, num_blocks in enumerate(depths)
+    for block_id in range(num_blocks)
+})
+custom_keys.update({
+    f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi
+    for stage_id in range(len(depths) - 1)
+})
+# optimizer
+optimizer = dict(
+    paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))
diff --git a/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py b/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py
new file mode 100644
index 0000000..7b1b05a
--- /dev/null
+++ b/configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py
@@ -0,0 +1,37 @@
+_base_ = ['./mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py']
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth'  # noqa
+
+depths = [2, 2, 18, 2]
+model = dict(
+    backbone=dict(
+        depths=depths, init_cfg=dict(type='Pretrained',
+                                     checkpoint=pretrained)))
+
+# set all layers in backbone to lr_mult=0.1
+# set all norm layers, position_embeding,
+# query_embeding, level_embeding to decay_multi=0.0
+backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
+backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0)
+embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
+custom_keys = {
+    'backbone': dict(lr_mult=0.1, decay_mult=1.0),
+    'backbone.patch_embed.norm': backbone_norm_multi,
+    'backbone.norm': backbone_norm_multi,
+    'absolute_pos_embed': backbone_embed_multi,
+    'relative_position_bias_table': backbone_embed_multi,
+    'query_embed': embed_multi,
+    'query_feat': embed_multi,
+    'level_embed': embed_multi
+}
+custom_keys.update({
+    f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi
+    for stage_id, num_blocks in enumerate(depths)
+    for block_id in range(num_blocks)
+})
+custom_keys.update({
+    f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi
+    for stage_id in range(len(depths) - 1)
+})
+# optimizer
+optimizer = dict(
+    paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))
diff --git a/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py b/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py
new file mode 100644
index 0000000..04b2f10
--- /dev/null
+++ b/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py
@@ -0,0 +1,62 @@
+_base_ = ['./mask2former_r50_lsj_8x2_50e_coco-panoptic.py']
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'  # noqa
+
+depths = [2, 2, 6, 2]
+model = dict(
+    type='Mask2Former',
+    backbone=dict(
+        _delete_=True,
+        type='SwinTransformer',
+        embed_dims=96,
+        depths=depths,
+        num_heads=[3, 6, 12, 24],
+        window_size=7,
+        mlp_ratio=4,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.,
+        attn_drop_rate=0.,
+        drop_path_rate=0.3,
+        patch_norm=True,
+        out_indices=(0, 1, 2, 3),
+        with_cp=False,
+        convert_weights=True,
+        frozen_stages=-1,
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    panoptic_head=dict(
+        type='Mask2FormerHead', in_channels=[96, 192, 384, 768]),
+    init_cfg=None)
+
+# set all layers in backbone to lr_mult=0.1
+# set all norm layers, position_embeding,
+# query_embeding, level_embeding to decay_multi=0.0
+backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
+backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0)
+embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
+custom_keys = {
+    'backbone': dict(lr_mult=0.1, decay_mult=1.0),
+    'backbone.patch_embed.norm': backbone_norm_multi,
+    'backbone.norm': backbone_norm_multi,
+    'absolute_pos_embed': backbone_embed_multi,
+    'relative_position_bias_table': backbone_embed_multi,
+    'query_embed': embed_multi,
+    'query_feat': embed_multi,
+    'level_embed': embed_multi
+}
+custom_keys.update({
+    f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi
+    for stage_id, num_blocks in enumerate(depths)
+    for block_id in range(num_blocks)
+})
+custom_keys.update({
+    f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi
+    for stage_id in range(len(depths) - 1)
+})
+# optimizer
+optimizer = dict(
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.05,
+    eps=1e-8,
+    betas=(0.9, 0.999),
+    paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))
diff --git a/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py b/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py
new file mode 100644
index 0000000..0ccbe91
--- /dev/null
+++ b/configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py
@@ -0,0 +1,61 @@
+_base_ = ['./mask2former_r50_lsj_8x2_50e_coco.py']
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'  # noqa
+depths = [2, 2, 6, 2]
+model = dict(
+    type='Mask2Former',
+    backbone=dict(
+        _delete_=True,
+        type='SwinTransformer',
+        embed_dims=96,
+        depths=depths,
+        num_heads=[3, 6, 12, 24],
+        window_size=7,
+        mlp_ratio=4,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.,
+        attn_drop_rate=0.,
+        drop_path_rate=0.3,
+        patch_norm=True,
+        out_indices=(0, 1, 2, 3),
+        with_cp=False,
+        convert_weights=True,
+        frozen_stages=-1,
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    panoptic_head=dict(
+        type='Mask2FormerHead', in_channels=[96, 192, 384, 768]),
+    init_cfg=None)
+
+# set all layers in backbone to lr_mult=0.1
+# set all norm layers, position_embeding,
+# query_embeding, level_embeding to decay_multi=0.0
+backbone_norm_multi = dict(lr_mult=0.1, decay_mult=0.0)
+backbone_embed_multi = dict(lr_mult=0.1, decay_mult=0.0)
+embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
+custom_keys = {
+    'backbone': dict(lr_mult=0.1, decay_mult=1.0),
+    'backbone.patch_embed.norm': backbone_norm_multi,
+    'backbone.norm': backbone_norm_multi,
+    'absolute_pos_embed': backbone_embed_multi,
+    'relative_position_bias_table': backbone_embed_multi,
+    'query_embed': embed_multi,
+    'query_feat': embed_multi,
+    'level_embed': embed_multi
+}
+custom_keys.update({
+    f'backbone.stages.{stage_id}.blocks.{block_id}.norm': backbone_norm_multi
+    for stage_id, num_blocks in enumerate(depths)
+    for block_id in range(num_blocks)
+})
+custom_keys.update({
+    f'backbone.stages.{stage_id}.downsample.norm': backbone_norm_multi
+    for stage_id in range(len(depths) - 1)
+})
+# optimizer
+optimizer = dict(
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.05,
+    eps=1e-8,
+    betas=(0.9, 0.999),
+    paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))
diff --git a/configs/mask2former/metafile.yml b/configs/mask2former/metafile.yml
new file mode 100644
index 0000000..d9f4692
--- /dev/null
+++ b/configs/mask2former/metafile.yml
@@ -0,0 +1,223 @@
+Collections:
+  - Name: Mask2Former
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - AdamW
+        - Weight Decay
+      Training Resources: 8x A100 GPUs
+      Architecture:
+        - Mask2Former
+    Paper:
+      URL: https://arxiv.org/pdf/2112.01527
+      Title: 'Masked-attention Mask Transformer for Universal Image Segmentation'
+    README: configs/mask2former/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.23.0/mmdet/models/detectors/mask2former.py#L7
+      Version: v2.23.0
+
+Models:
+- Name: mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic
+  In Collection: Mask2Former
+  Config: configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic.py
+  Metadata:
+    Training Memory (GB): 19.1
+    Iterations: 368750
+  Results:
+  - Task: Object Detection
+    Dataset: COCO
+    Metrics:
+      box AP: 47.8
+  - Task: Instance Segmentation
+    Dataset: COCO
+    Metrics:
+      mask AP: 44.5
+  - Task: Panoptic Segmentation
+    Dataset: COCO
+    Metrics:
+      PQ: 54.5
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220329_225200-c7b94355.pth
+- Name: mask2former_r101_lsj_8x2_50e_coco
+  In Collection: Mask2Former
+  Config: configs/mask2former/mask2former_r101_lsj_8x2_50e_coco.py
+  Metadata:
+    Training Memory (GB): 15.5
+    Iterations: 368750
+  Results:
+  - Task: Object Detection
+    Dataset: COCO
+    Metrics:
+      box AP: 46.7
+  - Task: Instance Segmentation
+    Dataset: COCO
+    Metrics:
+      mask AP: 44.0
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco/mask2former_r101_lsj_8x2_50e_coco_20220426_100250-c50b6fa6.pth
+- Name: mask2former_r101_lsj_8x2_50e_coco-panoptic
+  In Collection: Mask2Former
+  Config: configs/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic.py
+  Metadata:
+    Training Memory (GB): 16.1
+    Iterations: 368750
+  Results:
+  - Task: Object Detection
+    Dataset: COCO
+    Metrics:
+      box AP: 45.3
+  - Task: Instance Segmentation
+    Dataset: COCO
+    Metrics:
+      mask AP: 42.4
+  - Task: Panoptic Segmentation
+    Dataset: COCO
+    Metrics:
+      PQ: 52.4
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r101_lsj_8x2_50e_coco-panoptic/mask2former_r101_lsj_8x2_50e_coco-panoptic_20220329_225104-c54e64c9.pth
+- Name: mask2former_r50_lsj_8x2_50e_coco-panoptic
+  In Collection: Mask2Former
+  Config: configs/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py
+  Metadata:
+    Training Memory (GB): 13.9
+    Iterations: 368750
+  Results:
+  - Task: Object Detection
+    Dataset: COCO
+    Metrics:
+      box AP: 44.8
+  - Task: Instance Segmentation
+    Dataset: COCO
+    Metrics:
+      mask AP: 41.9
+  - Task: Panoptic Segmentation
+    Dataset: COCO
+    Metrics:
+      PQ: 51.9
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic/mask2former_r50_lsj_8x2_50e_coco-panoptic_20220326_224516-11a44721.pth
+- Name: mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic
+  In Collection: Mask2Former
+  Config: configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic.py
+  Metadata:
+    Training Memory (GB): 15.9
+    Iterations: 368750
+  Results:
+  - Task: Object Detection
+    Dataset: COCO
+    Metrics:
+      box AP: 46.3
+  - Task: Instance Segmentation
+    Dataset: COCO
+    Metrics:
+      mask AP: 43.4
+  - Task: Panoptic Segmentation
+    Dataset: COCO
+    Metrics:
+      PQ: 53.4
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco-panoptic_20220326_224553-fc567107.pth
+- Name: mask2former_r50_lsj_8x2_50e_coco
+  In Collection: Mask2Former
+  Config: configs/mask2former/mask2former_r50_lsj_8x2_50e_coco.py
+  Metadata:
+    Training Memory (GB): 13.7
+    Iterations: 368750
+  Results:
+  - Task: Object Detection
+    Dataset: COCO
+    Metrics:
+      box AP: 45.7
+  - Task: Instance Segmentation
+    Dataset: COCO
+    Metrics:
+      mask AP: 42.9
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_r50_lsj_8x2_50e_coco/mask2former_r50_lsj_8x2_50e_coco_20220506_191028-8e96e88b.pth
+- Name: mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic
+  In Collection: Mask2Former
+  Config: configs/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic.py
+  Metadata:
+    Training Memory (GB): 21.1
+    Iterations: 737500
+  Results:
+  - Task: Object Detection
+    Dataset: COCO
+    Metrics:
+      box AP: 52.2
+  - Task: Instance Segmentation
+    Dataset: COCO
+    Metrics:
+      mask AP: 48.5
+  - Task: Panoptic Segmentation
+    Dataset: COCO
+    Metrics:
+      PQ: 57.6
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic/mask2former_swin-l-p4-w12-384-in21k_lsj_16x1_100e_coco-panoptic_20220407_104949-d4919c44.pth
+- Name: mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic
+  In Collection: Mask2Former
+  Config: configs/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic.py
+  Metadata:
+    Training Memory (GB): 25.8
+    Iterations: 368750
+  Results:
+  - Task: Object Detection
+    Dataset: COCO
+    Metrics:
+      box AP: 50.0
+  - Task: Instance Segmentation
+    Dataset: COCO
+    Metrics:
+      mask AP: 46.3
+  - Task: Panoptic Segmentation
+    Dataset: COCO
+    Metrics:
+      PQ: 56.3
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384-in21k_lsj_8x2_50e_coco-panoptic_20220329_230021-3bb8b482.pth
+- Name: mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic
+  In Collection: Mask2Former
+  Config: configs/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic.py
+  Metadata:
+    Training Memory (GB): 26.0
+    Iterations: 368750
+  Results:
+  - Task: Object Detection
+    Dataset: COCO
+    Metrics:
+      box AP: 48.2
+  - Task: Instance Segmentation
+    Dataset: COCO
+    Metrics:
+      mask AP: 44.9
+  - Task: Panoptic Segmentation
+    Dataset: COCO
+    Metrics:
+      PQ: 55.1
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic/mask2former_swin-b-p4-w12-384_lsj_8x2_50e_coco-panoptic_20220331_002244-c149a9e9.pth
+- Name: mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco
+  In Collection: Mask2Former
+  Config: configs/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco.py
+  Metadata:
+    Training Memory (GB): 15.3
+    Iterations: 368750
+  Results:
+  - Task: Object Detection
+    Dataset: COCO
+    Metrics:
+      box AP: 47.7
+  - Task: Instance Segmentation
+    Dataset: COCO
+    Metrics:
+      mask AP: 44.7
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-t-p4-w7-224_lsj_8x2_50e_coco_20220508_091649-4a943037.pth
+- Name: mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco
+  In Collection: Mask2Former
+  Config: configs/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco.py
+  Metadata:
+    Training Memory (GB): 18.8
+    Iterations: 368750
+  Results:
+  - Task: Object Detection
+    Dataset: COCO
+    Metrics:
+      box AP: 49.3
+  - Task: Instance Segmentation
+    Dataset: COCO
+    Metrics:
+      mask AP: 46.1
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/mask2former/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco/mask2former_swin-s-p4-w7-224_lsj_8x2_50e_coco_20220504_001756-743b7d99.pth
diff --git a/configs/mask_rcnn/README.md b/configs/mask_rcnn/README.md
new file mode 100644
index 0000000..11a39b0
--- /dev/null
+++ b/configs/mask_rcnn/README.md
@@ -0,0 +1,59 @@
+# Mask R-CNN
+
+> [Mask R-CNN](https://arxiv.org/abs/1703.06870)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps. Moreover, Mask R-CNN is easy to generalize to other tasks, e.g., allowing us to estimate human poses in the same framework. We show top results in all three tracks of the COCO suite of challenges, including instance segmentation, bounding-box object detection, and person keypoint detection. Without bells and whistles, Mask R-CNN outperforms all existing, single-model entries on every task, including the COCO 2016 challenge winners. We hope our simple and effective approach will serve as a solid baseline and help ease future research in instance-level recognition.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143967081-c2552bed-9af2-46c4-ae44-5b3b74e5679f.png"/>
+</div>
+
+## Results and Models
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                        Config                                                         |                                                                                                                                                                            Download                                                                                                                                                                             |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :-------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50-FPN     |  caffe  |   1x    |   4.3    |                |  38.0  |  34.4   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.38__segm_mAP-0.344_20200504_231812-0ebd1859.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_20200504_231812.log.json)    |
+|    R-50-FPN     | pytorch |   1x    |   4.4    |      16.1      |  38.2  |  34.7   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)     |                                  [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json)                                  |
+| R-50-FPN (FP16) | pytorch |   1x    |   3.6    |      24.1      |  38.1  |  34.7   |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py)  |                             [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205-59faf7e4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205_130539.log.json)                             |
+|    R-50-FPN     | pytorch |   2x    |    -     |       -        |  39.2  |  35.4   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_20200505_003907-3e542a40.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_20200505_003907.log.json)               |
+|    R-101-FPN    |  caffe  |   1x    |          |                |  40.4  |  36.4   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py) |                [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758-805e06c1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758.log.json)                 |
+|    R-101-FPN    | pytorch |   1x    |   6.4    |      13.5      |  40.0  |  36.1   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py)    |                                [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json)                                |
+|    R-101-FPN    | pytorch |   2x    |    -     |       -        |  40.8  |  36.6   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_bbox_mAP-0.408__segm_mAP-0.366_20200505_071027-14b391c7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_20200505_071027.log.json)             |
+| X-101-32x4d-FPN | pytorch |   1x    |   7.6    |      11.3      |  41.9  |  37.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py) |                    [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json)                    |
+| X-101-32x4d-FPN | pytorch |   2x    |    -     |       -        |  42.2  |  37.8   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.422__segm_mAP-0.378_20200506_004702-faef898c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_20200506_004702.log.json) |
+| X-101-64x4d-FPN | pytorch |   1x    |   10.7   |      8.0       |  42.8  |  38.4   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py) |                    [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201-9352eb0d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201_124310.log.json)                    |
+| X-101-64x4d-FPN | pytorch |   2x    |    -     |       -        |  42.7  |  38.1   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py) |                [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208-39d6f70c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208.log.json)                 |
+| X-101-32x8d-FPN | pytorch |   1x    |   10.6   |       -        |  42.8  |  38.3   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py) |                [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco/mask_rcnn_x101_32x8d_fpn_1x_coco_20220630_173841-0aaf329e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco/mask_rcnn_x101_32x8d_fpn_1x_coco_20220630_173841.log.json)                 |
+
+## Pre-trained Models
+
+We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks.
+
+|                               Backbone                                |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                               Config                                                               |                                                                                                                                                                                                    Download                                                                                                                                                                                                     |
+| :-------------------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|     [R-50-FPN](./mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py)     |  caffe  |   2x    |   4.3    |                |  40.3  |  36.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py)  | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_bbox_mAP-0.403__segm_mAP-0.365_20200504_231822-a75c98ce.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_20200504_231822.log.json) |
+|     [R-50-FPN](./mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py)     |  caffe  |   3x    |   4.3    |                |  40.8  |  37.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py)  | [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_20200504_163245.log.json)  |
+|        [R-50-FPN](./mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py)        | pytorch |   3x    |   4.1    |                |  40.9  |  37.1   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py)     |                            [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154.log.json)                             |
+|    [R-101-FPN](./mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py)    |  caffe  |   3x    |   5.9    |                |  42.9  |  38.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py) |                   [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco_20210526_132339-3c33ce02.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco_20210526_132339.log.json)                    |
+|       [R-101-FPN](./mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py)       | pytorch |   3x    |   6.1    |                |  42.7  |  38.5   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py)    |                          [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_fpn_mstrain-poly_3x_coco_20210524_200244-5675c317.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_fpn_mstrain-poly_3x_coco_20210524_200244.log.json)                           |
+| [x101-32x4d-FPN](./mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py)  | pytorch |   3x    |   7.3    |                |  43.6  |  39.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py) |              [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco_20210524_201410-abcd7859.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco_20210524_201410.log.json)               |
+| [X-101-32x8d-FPN](./mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) | pytorch |   1x    |   10.4   |                |  43.4  |  39.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py) |              [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco_20220630_170346-b4637974.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco_20220630_170346.log.json)               |
+| [X-101-32x8d-FPN](./mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) | pytorch |   3x    |   10.3   |                |  44.3  |  39.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py) |              [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco_20210607_161042-8bd2c639.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco_20210607_161042.log.json)               |
+| [X-101-64x4d-FPN](./mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py) | pytorch |   3x    |   10.4   |                |  44.5  |  39.7   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py) |              [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447-c376f129.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447.log.json)               |
+
+## Citation
+
+```latex
+@article{He_2017,
+   title={Mask R-CNN},
+   journal={2017 IEEE International Conference on Computer Vision (ICCV)},
+   publisher={IEEE},
+   author={He, Kaiming and Gkioxari, Georgia and Dollar, Piotr and Girshick, Ross},
+   year={2017},
+   month={Oct}
+}
+```
diff --git a/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..95b324f
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './mask_rcnn_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))
diff --git a/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py
new file mode 100644
index 0000000..e39781d
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py
@@ -0,0 +1,55 @@
+_base_ = [
+    '../common/mstrain-poly_3x_coco_instance.py',
+    '../_base_/models/mask_rcnn_r50_fpn.py'
+]
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        norm_cfg=dict(requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        poly2mask=False),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+data = dict(
+    train=dict(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..b7986e8
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py
new file mode 100644
index 0000000..c9059d5
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './mask_rcnn_r50_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py
new file mode 100644
index 0000000..0696cbe
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py
@@ -0,0 +1,10 @@
+_base_ = [
+    '../common/mstrain-poly_3x_coco_instance.py',
+    '../_base_/models/mask_rcnn_r50_fpn.py'
+]
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py
new file mode 100644
index 0000000..a44c018
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_c4_1x_coco.py
@@ -0,0 +1,39 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_caffe_c4.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..5a23f8c
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,40 @@
+_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py
new file mode 100644
index 0000000..6308e40
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py
@@ -0,0 +1,49 @@
+_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        poly2mask=False),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py
new file mode 100644
index 0000000..4f7150c
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 23])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py
new file mode 100644
index 0000000..1b48a21
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
+# learning policy
+lr_config = dict(step=[28, 34])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py
new file mode 100644
index 0000000..bebbaaa
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py
@@ -0,0 +1,45 @@
+_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py
new file mode 100644
index 0000000..3f8079d
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py
@@ -0,0 +1,61 @@
+_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    rpn_head=dict(
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+    roi_head=dict(
+        bbox_roi_extractor=dict(
+            roi_layer=dict(
+                type='RoIAlign',
+                output_size=7,
+                sampling_ratio=2,
+                aligned=False)),
+        bbox_head=dict(
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
+        mask_roi_extractor=dict(
+            roi_layer=dict(
+                type='RoIAlign',
+                output_size=14,
+                sampling_ratio=2,
+                aligned=False))))
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        poly2mask=False),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..6a6c924
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,5 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
diff --git a/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_wandb_coco.py b/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_wandb_coco.py
new file mode 100644
index 0000000..88c8576
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_wandb_coco.py
@@ -0,0 +1,26 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# Set evaluation interval
+evaluation = dict(interval=2)
+# Set checkpoint interval
+checkpoint_config = dict(interval=4)
+
+# yapf:disable
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        dict(type='MMDetWandbHook',
+             init_kwargs={
+                'project': 'mmdetection',
+                'group': 'maskrcnn-r50-fpn-1x-coco'
+             },
+             interval=50,
+             log_checkpoint=True,
+             log_checkpoint_metadata=True,
+             num_eval_images=100)
+        ])
diff --git a/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py
new file mode 100644
index 0000000..932b1f9
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py
@@ -0,0 +1,5 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
diff --git a/configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py
new file mode 100644
index 0000000..fb8289b
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py
@@ -0,0 +1,3 @@
+_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)
diff --git a/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py
new file mode 100644
index 0000000..b3d9242
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py
@@ -0,0 +1,4 @@
+_base_ = [
+    '../common/mstrain-poly_3x_coco_instance.py',
+    '../_base_/models/mask_rcnn_r50_fpn.py'
+]
diff --git a/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_1x_coco.py b/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_1x_coco.py
new file mode 100644
index 0000000..9eb6d57
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_r50_fpn_poly_1x_coco.py
@@ -0,0 +1,23 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        poly2mask=False),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..a8b3799
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './mask_rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py
new file mode 100644
index 0000000..2cd3cee
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './mask_rcnn_r101_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py
new file mode 100644
index 0000000..b698a7d
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py
@@ -0,0 +1,18 @@
+_base_ = [
+    '../common/mstrain-poly_3x_coco_instance.py',
+    '../_base_/models/mask_rcnn_r50_fpn.py'
+]
+
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py
new file mode 100644
index 0000000..108ea4e
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py
@@ -0,0 +1,65 @@
+_base_ = './mask_rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=8,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnext101_32x8d')))
+
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675],
+    std=[57.375, 57.120, 58.395],
+    to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py
new file mode 100644
index 0000000..6b912f6
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py
@@ -0,0 +1,60 @@
+_base_ = './mask_rcnn_r101_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=8,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnext101_32x8d')))
+
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675],
+    std=[57.375, 57.120, 58.395],
+    to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        poly2mask=False),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py
new file mode 100644
index 0000000..8ba0e9c
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py
@@ -0,0 +1,85 @@
+_base_ = [
+    '../common/mstrain-poly_3x_coco_instance.py',
+    '../_base_/models/mask_rcnn_r50_fpn.py'
+]
+
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=8,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnext101_32x8d')))
+
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675],
+    std=[57.375, 57.120, 58.395],
+    to_rgb=False)
+
+# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
+# multiscale_mode='range'
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        poly2mask=False),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+# Use RepeatDataset to speed up training
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='RepeatDataset',
+        times=3,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
diff --git a/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..2333b03
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './mask_rcnn_x101_32x4d_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py
new file mode 100644
index 0000000..6074cca
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './mask_rcnn_x101_32x4d_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py
new file mode 100644
index 0000000..9f9cb1c
--- /dev/null
+++ b/configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py
@@ -0,0 +1,18 @@
+_base_ = [
+    '../common/mstrain-poly_3x_coco_instance.py',
+    '../_base_/models/mask_rcnn_r50_fpn.py'
+]
+
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/mask_rcnn/metafile.yml b/configs/mask_rcnn/metafile.yml
new file mode 100644
index 0000000..30938ea
--- /dev/null
+++ b/configs/mask_rcnn/metafile.yml
@@ -0,0 +1,443 @@
+Collections:
+  - Name: Mask R-CNN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Softmax
+        - RPN
+        - Convolution
+        - Dense Connections
+        - FPN
+        - ResNet
+        - RoIAlign
+    Paper:
+      URL: https://arxiv.org/abs/1703.06870v3
+      Title: "Mask R-CNN"
+    README: configs/mask_rcnn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/mask_rcnn.py#L6
+      Version: v2.0.0
+
+Models:
+  - Name: mask_rcnn_r50_caffe_fpn_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.3
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 34.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco/mask_rcnn_r50_caffe_fpn_1x_coco_bbox_mAP-0.38__segm_mAP-0.344_20200504_231812-0ebd1859.pth
+
+  - Name: mask_rcnn_r50_fpn_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.4
+      inference time (ms/im):
+        - value: 62.11
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 34.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth
+
+  - Name: mask_rcnn_r50_fpn_fp16_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.6
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+        - Mixed Precision Training
+      inference time (ms/im):
+        - value: 41.49
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP16
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 34.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/mask_rcnn_r50_fpn_fp16_1x_coco/mask_rcnn_r50_fpn_fp16_1x_coco_20200205-59faf7e4.pth
+
+  - Name: mask_rcnn_r50_fpn_2x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 4.4
+      inference time (ms/im):
+        - value: 62.11
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 35.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_2x_coco/mask_rcnn_r50_fpn_2x_coco_bbox_mAP-0.392__segm_mAP-0.354_20200505_003907-3e542a40.pth
+
+  - Name: mask_rcnn_r101_caffe_fpn_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_1x_coco/mask_rcnn_r101_caffe_fpn_1x_coco_20200601_095758-805e06c1.pth
+
+  - Name: mask_rcnn_r101_fpn_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.4
+      inference time (ms/im):
+        - value: 74.07
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth
+
+  - Name: mask_rcnn_r101_fpn_2x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_r101_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 6.4
+      inference time (ms/im):
+        - value: 74.07
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_2x_coco/mask_rcnn_r101_fpn_2x_coco_bbox_mAP-0.408__segm_mAP-0.366_20200505_071027-14b391c7.pth
+
+  - Name: mask_rcnn_x101_32x4d_fpn_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.6
+      inference time (ms/im):
+        - value: 88.5
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth
+
+  - Name: mask_rcnn_x101_32x4d_fpn_2x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 7.6
+      inference time (ms/im):
+        - value: 88.5
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_2x_coco/mask_rcnn_x101_32x4d_fpn_2x_coco_bbox_mAP-0.422__segm_mAP-0.378_20200506_004702-faef898c.pth
+
+  - Name: mask_rcnn_x101_64x4d_fpn_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.7
+      inference time (ms/im):
+        - value: 125
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_1x_coco/mask_rcnn_x101_64x4d_fpn_1x_coco_20200201-9352eb0d.pth
+
+  - Name: mask_rcnn_x101_64x4d_fpn_2x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 10.7
+      inference time (ms/im):
+        - value: 125
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_2x_coco/mask_rcnn_x101_64x4d_fpn_2x_coco_20200509_224208-39d6f70c.pth
+
+  - Name: mask_rcnn_x101_32x8d_fpn_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.6
+      Epochs: 12
+    Results:
+    - Task: Object Detection
+      Dataset: COCO
+      Metrics:
+        box AP: 42.8
+    - Task: Instance Segmentation
+      Dataset: COCO
+      Metrics:
+        mask AP: 38.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_1x_coco/mask_rcnn_x101_32x8d_fpn_1x_coco_20220630_173841-0aaf329e.pth
+
+  - Name: mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco.py
+    Metadata:
+      Training Memory (GB): 4.3
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_2x_coco_bbox_mAP-0.403__segm_mAP-0.365_20200504_231822-a75c98ce.pth
+
+  - Name: mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py
+    Metadata:
+      Training Memory (GB): 4.3
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth
+
+  - Name: mask_rcnn_r50_fpn_mstrain-poly_3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco.py
+    Metadata:
+      Training Memory (GB): 4.1
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_fpn_mstrain-poly_3x_coco_20210524_201154-21b550bb.pth
+
+  - Name: mask_rcnn_r101_fpn_mstrain-poly_3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco.py
+    Metadata:
+      Training Memory (GB): 6.1
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_fpn_mstrain-poly_3x_coco_20210524_200244-5675c317.pth
+
+  - Name: mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco.py
+    Metadata:
+      Training Memory (GB): 5.9
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r101_caffe_fpn_mstrain-poly_3x_coco_20210526_132339-3c33ce02.pth
+
+  - Name: mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco.py
+    Metadata:
+      Training Memory (GB): 7.3
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x4d_fpn_mstrain-poly_3x_coco_20210524_201410-abcd7859.pth
+
+  - Name: mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.4
+      Epochs: 12
+    Results:
+    - Task: Object Detection
+      Dataset: COCO
+      Metrics:
+        box AP: 43.4
+    - Task: Instance Segmentation
+      Dataset: COCO
+      Metrics:
+        mask AP: 39.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_1x_coco_20220630_170346-b4637974.pth
+
+  - Name: mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco.py
+    Metadata:
+      Training Memory (GB): 10.3
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_32x8d_fpn_mstrain-poly_3x_coco_20210607_161042-8bd2c639.pth
+
+  - Name: mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py
+    Metadata:
+      Epochs: 36
+      Training Memory (GB): 10.4
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco/mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco_20210526_120447-c376f129.pth
diff --git a/configs/maskformer/README.md b/configs/maskformer/README.md
new file mode 100644
index 0000000..5d8daa2
--- /dev/null
+++ b/configs/maskformer/README.md
@@ -0,0 +1,53 @@
+# MaskFormer
+
+> [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Modern approaches typically formulate semantic segmentation as a per-pixel classification task, while instance-level segmentation is handled with an alternative mask classification. Our key insight: mask classification is sufficiently general to solve both semantic- and instance-level segmentation tasks in a unified manner using the exact same model, loss, and training procedure. Following this observation, we propose MaskFormer, a simple mask classification model which predicts a set of binary masks, each associated with a single global class label prediction. Overall, the proposed mask classification-based method simplifies the landscape of effective approaches to semantic and panoptic  segmentation tasks and shows excellent empirical results. In particular, we observe that MaskFormer outperforms per-pixel classification baselines when the number of classes is large. Our mask classification-based method outperforms both current state-of-the-art semantic (55.6 mIoU on ADE20K) and panoptic segmentation (52.7 PQ on COCO) models.
+
+<div align=center>
+<img src="https://camo.githubusercontent.com/29fb22298d506ce176caad3006a7b05ef2603ca12cece6c788b7e73c046e8bc9/68747470733a2f2f626f77656e63303232312e6769746875622e696f2f696d616765732f6d61736b666f726d65722e706e67" height="300"/>
+</div>
+
+## Introduction
+
+MaskFormer requires COCO and [COCO-panoptic](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip) dataset for training and evaluation. You need to download and extract it in the COCO dataset path.
+The directory should be like this.
+
+```none
+mmdetection
+├── mmdet
+├── tools
+├── configs
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   │   ├── panoptic_train2017.json
+│   │   │   ├── panoptic_train2017
+│   │   │   ├── panoptic_val2017.json
+│   │   │   ├── panoptic_val2017
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+```
+
+## Results and Models
+
+| Backbone |  style  | Lr schd | Mem (GB) | Inf time (fps) |   PQ   |   SQ   |   RQ   | PQ_th  | SQ_th  | RQ_th  | PQ_st  | SQ_st  | RQ_st  |                                                                Config                                                                 |                                                                                                                                                                                            Download                                                                                                                                                                                            |                                                                         detail                                                                          |
+| :------: | :-----: | :-----: | :------: | :------------: | :----: | :----: | :----: | :----: | :----: | :----: | :----: | :----: | :----: | :-----------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | pytorch |   75e   |   16.2   |       -        | 46.854 | 80.617 | 57.085 | 51.089 | 81.511 | 61.853 | 40.463 | 79.269 | 49.888 |      [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/maskformer/maskformer_r50_mstrain_16x1_75e_coco.py)       |                       [model](https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_r50_mstrain_16x1_75e_coco/maskformer_r50_mstrain_16x1_75e_coco_20220221_141956-bc2699cb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_r50_mstrain_16x1_75e_coco/maskformer_r50_mstrain_16x1_75e_coco_20220221_141956.log.json)                       | This version was mentioned in Table XI, in paper [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527) |
+|  Swin-L  | pytorch |  300e   |   27.2   |       -        | 53.249 | 81.704 | 64.231 | 58.798 | 82.923 | 70.282 | 44.874 | 79.863 | 55.097 | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco_20220326_221612-061b4eb8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco_20220326_221612.log.json) |                                                                            -                                                                            |
+
+## Citation
+
+```latex
+@inproceedings{cheng2021maskformer,
+  title={Per-Pixel Classification is Not All You Need for Semantic Segmentation},
+  author={Bowen Cheng and Alexander G. Schwing and Alexander Kirillov},
+  journal={NeurIPS},
+  year={2021}
+}
+```
diff --git a/configs/maskformer/maskformer_r50_mstrain_16x1_75e_coco.py b/configs/maskformer/maskformer_r50_mstrain_16x1_75e_coco.py
new file mode 100644
index 0000000..46b3c13
--- /dev/null
+++ b/configs/maskformer/maskformer_r50_mstrain_16x1_75e_coco.py
@@ -0,0 +1,238 @@
+_base_ = [
+    '../_base_/datasets/coco_panoptic.py', '../_base_/default_runtime.py'
+]
+num_things_classes = 80
+num_stuff_classes = 53
+num_classes = num_things_classes + num_stuff_classes
+model = dict(
+    type='MaskFormer',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=-1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    panoptic_head=dict(
+        type='MaskFormerHead',
+        in_channels=[256, 512, 1024, 2048],  # pass to pixel_decoder inside
+        feat_channels=256,
+        out_channels=256,
+        num_things_classes=num_things_classes,
+        num_stuff_classes=num_stuff_classes,
+        num_queries=100,
+        pixel_decoder=dict(
+            type='TransformerEncoderPixelDecoder',
+            norm_cfg=dict(type='GN', num_groups=32),
+            act_cfg=dict(type='ReLU'),
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=dict(
+                        type='MultiheadAttention',
+                        embed_dims=256,
+                        num_heads=8,
+                        attn_drop=0.1,
+                        proj_drop=0.1,
+                        dropout_layer=None,
+                        batch_first=False),
+                    ffn_cfgs=dict(
+                        embed_dims=256,
+                        feedforward_channels=2048,
+                        num_fcs=2,
+                        act_cfg=dict(type='ReLU', inplace=True),
+                        ffn_drop=0.1,
+                        dropout_layer=None,
+                        add_identity=True),
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm'),
+                    norm_cfg=dict(type='LN'),
+                    init_cfg=None,
+                    batch_first=False),
+                init_cfg=None),
+            positional_encoding=dict(
+                type='SinePositionalEncoding', num_feats=128, normalize=True)),
+        enforce_decoder_input_project=False,
+        positional_encoding=dict(
+            type='SinePositionalEncoding', num_feats=128, normalize=True),
+        transformer_decoder=dict(
+            type='DetrTransformerDecoder',
+            return_intermediate=True,
+            num_layers=6,
+            transformerlayers=dict(
+                type='DetrTransformerDecoderLayer',
+                attn_cfgs=dict(
+                    type='MultiheadAttention',
+                    embed_dims=256,
+                    num_heads=8,
+                    attn_drop=0.1,
+                    proj_drop=0.1,
+                    dropout_layer=None,
+                    batch_first=False),
+                ffn_cfgs=dict(
+                    embed_dims=256,
+                    feedforward_channels=2048,
+                    num_fcs=2,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    ffn_drop=0.1,
+                    dropout_layer=None,
+                    add_identity=True),
+                # the following parameter was not used,
+                # just make current api happy
+                feedforward_channels=2048,
+                operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
+                                 'ffn', 'norm')),
+            init_cfg=None),
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=False,
+            loss_weight=1.0,
+            reduction='mean',
+            class_weight=[1.0] * num_classes + [0.1]),
+        loss_mask=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            reduction='mean',
+            loss_weight=20.0),
+        loss_dice=dict(
+            type='DiceLoss',
+            use_sigmoid=True,
+            activate=True,
+            reduction='mean',
+            naive_dice=True,
+            eps=1.0,
+            loss_weight=1.0)),
+    panoptic_fusion_head=dict(
+        type='MaskFormerFusionHead',
+        num_things_classes=num_things_classes,
+        num_stuff_classes=num_stuff_classes,
+        loss_panoptic=None,
+        init_cfg=None),
+    train_cfg=dict(
+        assigner=dict(
+            type='MaskHungarianAssigner',
+            cls_cost=dict(type='ClassificationCost', weight=1.0),
+            mask_cost=dict(
+                type='FocalLossCost', weight=20.0, binary_input=True),
+            dice_cost=dict(
+                type='DiceCost', weight=1.0, pred_act=True, eps=1.0)),
+        sampler=dict(type='MaskPseudoSampler')),
+    test_cfg=dict(
+        panoptic_on=True,
+        # For now, the dataset does not support
+        # evaluating semantic segmentation metric.
+        semantic_on=False,
+        instance_on=False,
+        # max_per_image is for instance segmentation.
+        max_per_image=100,
+        object_mask_thr=0.8,
+        iou_thr=0.8,
+        # In MaskFormer's panoptic postprocessing,
+        # it will not filter masks whose score is smaller than 0.5 .
+        filter_low_score=False),
+    init_cfg=None)
+
+# dataset settings
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadPanopticAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        with_seg=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='AutoAugment',
+        policies=[[
+            dict(
+                type='Resize',
+                img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                           (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                           (736, 1333), (768, 1333), (800, 1333)],
+                multiscale_mode='value',
+                keep_ratio=True)
+        ],
+                  [
+                      dict(
+                          type='Resize',
+                          img_scale=[(400, 1333), (500, 1333), (600, 1333)],
+                          multiscale_mode='value',
+                          keep_ratio=True),
+                      dict(
+                          type='RandomCrop',
+                          crop_type='absolute_range',
+                          crop_size=(384, 600),
+                          allow_negative_crop=True),
+                      dict(
+                          type='Resize',
+                          img_scale=[(480, 1333), (512, 1333), (544, 1333),
+                                     (576, 1333), (608, 1333), (640, 1333),
+                                     (672, 1333), (704, 1333), (736, 1333),
+                                     (768, 1333), (800, 1333)],
+                          multiscale_mode='value',
+                          override=True,
+                          keep_ratio=True)
+                  ]]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=1),
+    dict(type='DefaultFormatBundle'),
+    dict(
+        type='Collect',
+        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=1),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=1,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+# optimizer
+optimizer = dict(
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.0001,
+    eps=1e-8,
+    betas=(0.9, 0.999),
+    paramwise_cfg=dict(
+        custom_keys={
+            'backbone': dict(lr_mult=0.1, decay_mult=1.0),
+            'query_embed': dict(lr_mult=1.0, decay_mult=0.0)
+        },
+        norm_decay_mult=0.0))
+optimizer_config = dict(grad_clip=dict(max_norm=0.01, norm_type=2))
+
+# learning policy
+lr_config = dict(
+    policy='step',
+    gamma=0.1,
+    by_epoch=True,
+    step=[50],
+    warmup='linear',
+    warmup_by_epoch=False,
+    warmup_ratio=1.0,  # no warmup
+    warmup_iters=10)
+runner = dict(type='EpochBasedRunner', max_epochs=75)
diff --git a/configs/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py b/configs/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py
new file mode 100644
index 0000000..bc23c54
--- /dev/null
+++ b/configs/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py
@@ -0,0 +1,67 @@
+_base_ = './maskformer_r50_mstrain_16x1_75e_coco.py'
+
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth'  # noqa
+depths = [2, 2, 18, 2]
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='SwinTransformer',
+        pretrain_img_size=384,
+        embed_dims=192,
+        patch_size=4,
+        window_size=12,
+        mlp_ratio=4,
+        depths=depths,
+        num_heads=[6, 12, 24, 48],
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.,
+        attn_drop_rate=0.,
+        drop_path_rate=0.3,
+        patch_norm=True,
+        out_indices=(0, 1, 2, 3),
+        with_cp=False,
+        convert_weights=True,
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    panoptic_head=dict(
+        in_channels=[192, 384, 768, 1536],  # pass to pixel_decoder inside
+        pixel_decoder=dict(
+            _delete_=True,
+            type='PixelDecoder',
+            norm_cfg=dict(type='GN', num_groups=32),
+            act_cfg=dict(type='ReLU')),
+        enforce_decoder_input_project=True))
+
+# weight_decay = 0.01
+# norm_weight_decay = 0.0
+# embed_weight_decay = 0.0
+embed_multi = dict(lr_mult=1.0, decay_mult=0.0)
+norm_multi = dict(lr_mult=1.0, decay_mult=0.0)
+custom_keys = {
+    'norm': norm_multi,
+    'absolute_pos_embed': embed_multi,
+    'relative_position_bias_table': embed_multi,
+    'query_embed': embed_multi
+}
+
+# optimizer
+optimizer = dict(
+    type='AdamW',
+    lr=6e-5,
+    weight_decay=0.01,
+    eps=1e-8,
+    betas=(0.9, 0.999),
+    paramwise_cfg=dict(custom_keys=custom_keys, norm_decay_mult=0.0))
+optimizer_config = dict(grad_clip=dict(max_norm=0.01, norm_type=2))
+
+# learning policy
+lr_config = dict(
+    policy='step',
+    gamma=0.1,
+    by_epoch=True,
+    step=[250],
+    warmup='linear',
+    warmup_by_epoch=False,
+    warmup_ratio=1e-6,
+    warmup_iters=1500)
+runner = dict(type='EpochBasedRunner', max_epochs=300)
diff --git a/configs/maskformer/metafile.yml b/configs/maskformer/metafile.yml
new file mode 100644
index 0000000..6530fa1
--- /dev/null
+++ b/configs/maskformer/metafile.yml
@@ -0,0 +1,43 @@
+Collections:
+  - Name: MaskFormer
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - AdamW
+        - Weight Decay
+      Training Resources: 16x V100 GPUs
+      Architecture:
+        - MaskFormer
+    Paper:
+      URL: https://arxiv.org/pdf/2107.06278
+      Title: 'Per-Pixel Classification is Not All You Need for Semantic Segmentation'
+    README: configs/maskformer/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.22.0/mmdet/models/detectors/maskformer.py#L7
+      Version: v2.22.0
+
+Models:
+  - Name: maskformer_r50_mstrain_16x1_75e_coco
+    In Collection: MaskFormer
+    Config: configs/maskformer/maskformer_r50_mstrain_16x1_75e_coco.py
+    Metadata:
+      Training Memory (GB): 16.2
+      Epochs: 75
+    Results:
+    - Task: Panoptic Segmentation
+      Dataset: COCO
+      Metrics:
+        PQ: 46.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_r50_mstrain_16x1_75e_coco/maskformer_r50_mstrain_16x1_75e_coco_20220221_141956-bc2699cb.pth
+  - Name: maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco
+    In Collection: MaskFormer
+    Config: configs/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco.py
+    Metadata:
+      Training Memory (GB): 27.2
+      Epochs: 300
+    Results:
+    - Task: Panoptic Segmentation
+      Dataset: COCO
+      Metrics:
+        PQ: 53.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/maskformer/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco/maskformer_swin-l-p4-w12_mstrain_64x1_300e_coco_20220326_221612-061b4eb8.pth
diff --git a/configs/ms_rcnn/README.md b/configs/ms_rcnn/README.md
new file mode 100644
index 0000000..97bca05
--- /dev/null
+++ b/configs/ms_rcnn/README.md
@@ -0,0 +1,36 @@
+# MS R-CNN
+
+> [Mask Scoring R-CNN](https://arxiv.org/abs/1903.00241)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Letting a deep network be aware of the quality of its own predictions is an interesting yet important problem. In the task of instance segmentation, the confidence of instance classification is used as mask quality score in most instance segmentation frameworks. However, the mask quality, quantified as the IoU between the instance mask and its ground truth, is usually not well correlated with classification score. In this paper, we study this problem and propose Mask Scoring R-CNN which contains a network block to learn the quality of the predicted instance masks. The proposed network block takes the instance feature and the corresponding predicted mask together to regress the mask IoU. The mask scoring strategy calibrates the misalignment between mask quality and mask score, and improves instance segmentation performance by prioritizing more accurate mask predictions during COCO AP evaluation. By extensive evaluations on the COCO dataset, Mask Scoring R-CNN brings consistent and noticeable gain with different models, and outperforms the state-of-the-art Mask R-CNN. We hope our simple and effective approach will provide a new direction for improving instance segmentation.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143967239-3a95ae92-6443-4181-9cbc-dfe16e81b969.png"/>
+</div>
+
+## Results and Models
+
+|   Backbone   |  style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                      Config                                                       |                                                                                                                                                                      Download                                                                                                                                                                       |
+| :----------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50-FPN   |  caffe  |   1x    |   4.5    |                |  38.2  |  36.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py)  |                  [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848.log.json)                   |
+|   R-50-FPN   |  caffe  |   2x    |    -     |       -        |  38.8  |  36.3   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_bbox_mAP-0.388__segm_mAP-0.363_20200506_004738-ee87b137.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_20200506_004738.log.json)   |
+|  R-101-FPN   |  caffe  |   1x    |   6.5    |                |  40.4  |  37.6   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.404__segm_mAP-0.376_20200506_004755-b9b12a37.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_20200506_004755.log.json) |
+|  R-101-FPN   |  caffe  |   2x    |    -     |       -        |  41.1  |  38.1   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_bbox_mAP-0.411__segm_mAP-0.381_20200506_011134-5f3cc74f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_20200506_011134.log.json) |
+| R-X101-32x4d | pytorch |   2x    |   7.9    |      11.0      |  41.8  |  38.7   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py) |                    [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206-81fd1740.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206_100113.log.json)                    |
+| R-X101-64x4d | pytorch |   1x    |   11.0   |      8.0       |  43.0  |  39.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py) |                    [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206-86ba88d2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206_091744.log.json)                    |
+| R-X101-64x4d | pytorch |   2x    |   11.0   |      8.0       |  42.6  |  39.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py) |                    [model](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308-02a445e2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308_012247.log.json)                    |
+
+## Citation
+
+```latex
+@inproceedings{huang2019msrcnn,
+    title={Mask Scoring R-CNN},
+    author={Zhaojin Huang and Lichao Huang and Yongchao Gong and Chang Huang and Xinggang Wang},
+    booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+    year={2019},
+}
+```
diff --git a/configs/ms_rcnn/metafile.yml b/configs/ms_rcnn/metafile.yml
new file mode 100644
index 0000000..a6c7dc5
--- /dev/null
+++ b/configs/ms_rcnn/metafile.yml
@@ -0,0 +1,159 @@
+Collections:
+  - Name: Mask Scoring R-CNN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RPN
+        - FPN
+        - ResNet
+        - RoIAlign
+    Paper:
+      URL: https://arxiv.org/abs/1903.00241
+      Title: 'Mask Scoring R-CNN'
+    README: configs/ms_rcnn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/mask_scoring_rcnn.py#L6
+      Version: v2.0.0
+
+Models:
+  - Name: ms_rcnn_r50_caffe_fpn_1x_coco
+    In Collection: Mask Scoring R-CNN
+    Config: configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.5
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco/ms_rcnn_r50_caffe_fpn_1x_coco_20200702_180848-61c9355e.pth
+
+  - Name: ms_rcnn_r50_caffe_fpn_2x_coco
+    In Collection: Mask Scoring R-CNN
+    Config: configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco/ms_rcnn_r50_caffe_fpn_2x_coco_bbox_mAP-0.388__segm_mAP-0.363_20200506_004738-ee87b137.pth
+
+  - Name: ms_rcnn_r101_caffe_fpn_1x_coco
+    In Collection: Mask Scoring R-CNN
+    Config: configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.5
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco/ms_rcnn_r101_caffe_fpn_1x_coco_bbox_mAP-0.404__segm_mAP-0.376_20200506_004755-b9b12a37.pth
+
+  - Name: ms_rcnn_r101_caffe_fpn_2x_coco
+    In Collection: Mask Scoring R-CNN
+    Config: configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco/ms_rcnn_r101_caffe_fpn_2x_coco_bbox_mAP-0.411__segm_mAP-0.381_20200506_011134-5f3cc74f.pth
+
+  - Name: ms_rcnn_x101_32x4d_fpn_1x_coco
+    In Collection: Mask Scoring R-CNN
+    Config: configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.9
+      inference time (ms/im):
+        - value: 90.91
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco/ms_rcnn_x101_32x4d_fpn_1x_coco_20200206-81fd1740.pth
+
+  - Name: ms_rcnn_x101_64x4d_fpn_1x_coco
+    In Collection: Mask Scoring R-CNN
+    Config: configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 11.0
+      inference time (ms/im):
+        - value: 125
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco/ms_rcnn_x101_64x4d_fpn_1x_coco_20200206-86ba88d2.pth
+
+  - Name: ms_rcnn_x101_64x4d_fpn_2x_coco
+    In Collection: Mask Scoring R-CNN
+    Config: configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 11.0
+      inference time (ms/im):
+        - value: 125
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco/ms_rcnn_x101_64x4d_fpn_2x_coco_20200308-02a445e2.pth
diff --git a/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..9b7dcbb
--- /dev/null
+++ b/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))
diff --git a/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py b/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py
new file mode 100644
index 0000000..202bcce
--- /dev/null
+++ b/configs/ms_rcnn/ms_rcnn_r101_caffe_fpn_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './ms_rcnn_r101_caffe_fpn_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..5845125
--- /dev/null
+++ b/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,16 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    type='MaskScoringRCNN',
+    roi_head=dict(
+        type='MaskScoringRoIHead',
+        mask_iou_head=dict(
+            type='MaskIoUHead',
+            num_convs=4,
+            num_fcs=2,
+            roi_feat_size=14,
+            in_channels=256,
+            conv_out_channels=256,
+            fc_out_channels=1024,
+            num_classes=80)),
+    # model training and testing settings
+    train_cfg=dict(rcnn=dict(mask_thr_binary=0.5)))
diff --git a/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py b/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py
new file mode 100644
index 0000000..008a70a
--- /dev/null
+++ b/configs/ms_rcnn/ms_rcnn_r50_caffe_fpn_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './ms_rcnn_r50_caffe_fpn_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..0a163ce
--- /dev/null
+++ b/configs/ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,16 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    type='MaskScoringRCNN',
+    roi_head=dict(
+        type='MaskScoringRoIHead',
+        mask_iou_head=dict(
+            type='MaskIoUHead',
+            num_convs=4,
+            num_fcs=2,
+            roi_feat_size=14,
+            in_channels=256,
+            conv_out_channels=256,
+            fc_out_channels=1024,
+            num_classes=80)),
+    # model training and testing settings
+    train_cfg=dict(rcnn=dict(mask_thr_binary=0.5)))
diff --git a/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..20479bb
--- /dev/null
+++ b/configs/ms_rcnn/ms_rcnn_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './ms_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py b/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..ee5b734
--- /dev/null
+++ b/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './ms_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py b/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py
new file mode 100644
index 0000000..54c605b
--- /dev/null
+++ b/configs/ms_rcnn/ms_rcnn_x101_64x4d_fpn_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './ms_rcnn_x101_64x4d_fpn_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/nas_fcos/README.md b/configs/nas_fcos/README.md
new file mode 100644
index 0000000..def8831
--- /dev/null
+++ b/configs/nas_fcos/README.md
@@ -0,0 +1,35 @@
+# NAS-FCOS
+
+> [NAS-FCOS: Fast Neural Architecture Search for Object Detection](https://arxiv.org/abs/1906.04423)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+The success of deep neural networks relies on significant architecture engineering. Recently neural architecture search (NAS) has emerged as a promise to greatly reduce manual effort in network design by automatically searching for optimal architectures, although typically such algorithms need an excessive amount of computational resources, e.g., a few thousand GPU-days. To date, on challenging vision tasks such as object detection, NAS, especially fast versions of NAS, is less studied. Here we propose to search for the decoder structure of object detectors with search efficiency being taken into consideration. To be more specific, we aim to efficiently search for the feature pyramid network (FPN) as well as the prediction head of a simple anchor-free object detector, namely FCOS, using a tailored reinforcement learning paradigm. With carefully designed search space, search algorithms and strategies for evaluating network quality, we are able to efficiently search a top-performing detection architecture within 4 days using 8 V100 GPUs. The discovered architecture surpasses state-of-the-art object detection models (such as Faster R-CNN, RetinaNet and FCOS) by 1.5 to 3.5 points in AP on the COCO dataset, with comparable computation complexity and memory footprint, demonstrating the efficacy of the proposed NAS for object detection.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143967900-1c8a65b9-c58d-4b03-8900-96af8f9768e8.png"/>
+</div>
+
+## Results and Models
+
+|     Head     | Backbone | Style | GN-head | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                                 Config                                                                  |                                                                                                                                                                                           Download                                                                                                                                                                                           |
+| :----------: | :------: | :---: | :-----: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| NAS-FCOSHead |   R-50   | caffe |    Y    |   1x    |          |                |  39.4  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520.log.json)   |
+|   FCOSHead   |   R-50   | caffe |    Y    |   1x    |          |                |  38.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521-7fdcbce0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521.log.json) |
+
+**Notes:**
+
+- To be consistent with the author's implementation, we use 4 GPUs with 4 images/GPU.
+
+## Citation
+
+```latex
+@article{wang2019fcos,
+  title={Nas-fcos: Fast neural architecture search for object detection},
+  author={Wang, Ning and Gao, Yang and Chen, Hao and Wang, Peng and Tian, Zhi and Shen, Chunhua},
+  journal={arXiv preprint arXiv:1906.04423},
+  year={2019}
+}
+```
diff --git a/configs/nas_fcos/metafile.yml b/configs/nas_fcos/metafile.yml
new file mode 100644
index 0000000..1ea28cf
--- /dev/null
+++ b/configs/nas_fcos/metafile.yml
@@ -0,0 +1,44 @@
+Collections:
+  - Name: NAS-FCOS
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 4x V100 GPUs
+      Architecture:
+        - FPN
+        - NAS-FCOS
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1906.04423
+      Title: 'NAS-FCOS: Fast Neural Architecture Search for Object Detection'
+    README: configs/nas_fcos/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/detectors/nasfcos.py#L6
+      Version: v2.1.0
+
+Models:
+  - Name: nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco
+    In Collection: NAS-FCOS
+    Config: configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200520-1bdba3ce.pth
+
+  - Name: nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco
+    In Collection: NAS-FCOS
+    Config: configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco_20200521-7fdcbce0.pth
diff --git a/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py
new file mode 100644
index 0000000..a455c92
--- /dev/null
+++ b/configs/nas_fcos/nas_fcos_fcoshead_r50_caffe_fpn_gn-head_4x4_1x_coco.py
@@ -0,0 +1,100 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    type='NASFCOS',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False, eps=0),
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    neck=dict(
+        type='NASFCOS_FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs=True,
+        num_outs=5,
+        norm_cfg=dict(type='BN'),
+        conv_cfg=dict(type='DCNv2', deform_groups=2)),
+    bbox_head=dict(
+        type='FCOSHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        strides=[8, 16, 32, 64, 128],
+        norm_cfg=dict(type='GN', num_groups=32),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='IoULoss', loss_weight=1.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=2,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+optimizer = dict(
+    lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
diff --git a/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py b/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py
new file mode 100644
index 0000000..b779492
--- /dev/null
+++ b/configs/nas_fcos/nas_fcos_nashead_r50_caffe_fpn_gn-head_4x4_1x_coco.py
@@ -0,0 +1,99 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    type='NASFCOS',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False, eps=0),
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    neck=dict(
+        type='NASFCOS_FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs=True,
+        num_outs=5,
+        norm_cfg=dict(type='BN'),
+        conv_cfg=dict(type='DCNv2', deform_groups=2)),
+    bbox_head=dict(
+        type='NASFCOSHead',
+        num_classes=80,
+        in_channels=256,
+        feat_channels=256,
+        strides=[8, 16, 32, 64, 128],
+        norm_cfg=dict(type='GN', num_groups=32),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='IoULoss', loss_weight=1.0),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=2,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+optimizer = dict(
+    lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
diff --git a/configs/nas_fpn/README.md b/configs/nas_fpn/README.md
new file mode 100644
index 0000000..c5acf40
--- /dev/null
+++ b/configs/nas_fpn/README.md
@@ -0,0 +1,36 @@
+# NAS-FPN
+
+> [NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection](https://arxiv.org/abs/1904.07392)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Current state-of-the-art convolutional architectures for object detection are manually designed. Here we aim to learn a better architecture of feature pyramid network for object detection. We adopt Neural Architecture Search and discover a new feature pyramid architecture in a novel scalable search space covering all cross-scale connections. The discovered architecture, named NAS-FPN, consists of a combination of top-down and bottom-up connections to fuse features across scales. NAS-FPN, combined with various backbone models in the RetinaNet framework, achieves better accuracy and latency tradeoff compared to state-of-the-art object detection models. NAS-FPN improves mobile detection accuracy by 2 AP compared to state-of-the-art SSDLite with MobileNetV2 model in \[32\] and achieves 48.3 AP which surpasses Mask R-CNN \[10\] detection accuracy with less computation time.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143968037-cedd76e9-1ae7-4869-bd34-c9d8611d630c.png"/>
+</div>
+
+## Results and Models
+
+We benchmark the new training schedule (crop training, large batch, unfrozen BN, 50 epochs) introduced in NAS-FPN. RetinaNet is used in the paper.
+
+|  Backbone   | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                          Config                                                          |                                                                                                                                                             Download                                                                                                                                                             |
+| :---------: | :-----: | :------: | :------------: | :----: | :----------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  R-50-FPN   |   50e   |   12.9   |      22.9      |  37.9  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py)   |       [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco_20200529_095329.log.json)       |
+| R-50-NASFPN |   50e   |   13.2   |      23.0      |  40.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco_20200528_230008.log.json) |
+
+**Note**: We find that it is unstable to train NAS-FPN and there is a small chance that results can be 3% mAP lower.
+
+## Citation
+
+```latex
+@inproceedings{ghiasi2019fpn,
+  title={Nas-fpn: Learning scalable feature pyramid architecture for object detection},
+  author={Ghiasi, Golnaz and Lin, Tsung-Yi and Le, Quoc V},
+  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
+  pages={7036--7045},
+  year={2019}
+}
+```
diff --git a/configs/nas_fpn/metafile.yml b/configs/nas_fpn/metafile.yml
new file mode 100644
index 0000000..ab8d649
--- /dev/null
+++ b/configs/nas_fpn/metafile.yml
@@ -0,0 +1,59 @@
+Collections:
+  - Name: NAS-FPN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - NAS-FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1904.07392
+      Title: 'NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection'
+    README: configs/nas_fpn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/necks/nas_fpn.py#L67
+      Version: v2.0.0
+
+Models:
+  - Name: retinanet_r50_fpn_crop640_50e_coco
+    In Collection: NAS-FPN
+    Config: configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py
+    Metadata:
+      Training Memory (GB): 12.9
+      inference time (ms/im):
+        - value: 43.67
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 50
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_fpn_crop640_50e_coco/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth
+
+  - Name: retinanet_r50_nasfpn_crop640_50e_coco
+    In Collection: NAS-FPN
+    Config: configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py
+    Metadata:
+      Training Memory (GB): 13.2
+      inference time (ms/im):
+        - value: 43.48
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 50
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco/retinanet_r50_nasfpn_crop640_50e_coco-0ad1f644.pth
diff --git a/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py b/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py
new file mode 100644
index 0000000..ae7981f
--- /dev/null
+++ b/configs/nas_fpn/retinanet_r50_fpn_crop640_50e_coco.py
@@ -0,0 +1,85 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
+]
+# cudnn_benchmark = True
+norm_cfg = dict(type='BN', requires_grad=True)
+model = dict(
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        relu_before_extra_convs=True,
+        no_norm_on_lateral=True,
+        norm_cfg=norm_cfg),
+    bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg),
+    # training and testing settings
+    train_cfg=dict(assigner=dict(neg_iou_thr=0.5)))
+# dataset settings
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=(640, 640),
+        ratio_range=(0.8, 1.2),
+        keep_ratio=True),
+    dict(type='RandomCrop', crop_size=(640, 640)),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=(640, 640)),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(640, 640),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=64),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    type='SGD',
+    lr=0.08,
+    momentum=0.9,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True))
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=0.1,
+    step=[30, 40])
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=50)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py b/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py
new file mode 100644
index 0000000..e3d4e8a
--- /dev/null
+++ b/configs/nas_fpn/retinanet_r50_nasfpn_crop640_50e_coco.py
@@ -0,0 +1,84 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
+]
+# cudnn_benchmark = True
+# model settings
+norm_cfg = dict(type='BN', requires_grad=True)
+model = dict(
+    type='RetinaNet',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(type='NASFPN', stack_times=7, norm_cfg=norm_cfg),
+    bbox_head=dict(type='RetinaSepBNHead', num_ins=5, norm_cfg=norm_cfg),
+    # training and testing settings
+    train_cfg=dict(assigner=dict(neg_iou_thr=0.5)))
+# dataset settings
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=(640, 640),
+        ratio_range=(0.8, 1.2),
+        keep_ratio=True),
+    dict(type='RandomCrop', crop_size=(640, 640)),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=(640, 640)),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(640, 640),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=128),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(
+    type='SGD',
+    lr=0.08,
+    momentum=0.9,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True))
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=0.1,
+    step=[30, 40])
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=50)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/objects365/README.md b/configs/objects365/README.md
new file mode 100644
index 0000000..b685f9b
--- /dev/null
+++ b/configs/objects365/README.md
@@ -0,0 +1,102 @@
+# Objects365 Dataset
+
+> [Objects365 Dataset](https://openaccess.thecvf.com/content_ICCV_2019/papers/Shao_Objects365_A_Large-Scale_High-Quality_Dataset_for_Object_Detection_ICCV_2019_paper.pdf)
+
+<!-- [DATASET] -->
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+#### Objects365 Dataset V1
+
+[Objects365 Dataset V1](http://www.objects365.org/overview.html) is a brand new dataset,
+designed to spur object detection research with a focus on diverse objects in the Wild.
+It has 365 object categories over 600K training images. More than 10 million, high-quality bounding boxes are manually labeled through a three-step, carefully designed annotation pipeline. It is the largest object detection dataset (with full annotation) so far and establishes a more challenging benchmark for the community. Objects365 can serve as a better feature learning dataset for localization-sensitive tasks like object detection
+and semantic segmentation.
+
+<!-- [IMAGE] -->
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/48282753/208368046-b7573022-06c9-4a99-af17-a6ac7407e3d8.png" height="400"/>
+</div>
+
+#### Objects365 Dataset V2
+
+[Objects365 Dataset V2](http://www.objects365.org/overview.html) is based on the V1 release of the Objects365 dataset.
+Objects 365 annotated 365 object classes on more than 1800k images, with more than 29 million bounding boxes in the training set, surpassing PASCAL VOC, ImageNet, and COCO datasets.
+Objects 365 includes 11 categories of people, clothing, living room, bathroom, kitchen, office/medical, electrical appliances, transportation, food, animals, sports/musical instruments, and each category has dozens of subcategories.
+
+## Citation
+
+```
+@inproceedings{shao2019objects365,
+  title={Objects365: A large-scale, high-quality dataset for object detection},
+  author={Shao, Shuai and Li, Zeming and Zhang, Tianyuan and Peng, Chao and Yu, Gang and Zhang, Xiangyu and Li, Jing and Sun, Jian},
+  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
+  pages={8430--8439},
+  year={2019}
+}
+```
+
+## Prepare Dataset
+
+1. You need to download and extract Objects365 dataset. Users can download  Objects365 V2 by using `tools/misc/download_dataset.py`.
+
+   **Usage**
+
+   ```shell
+   python tools/misc/download_dataset.py --dataset-name objects365v2 \
+   --save-dir ${SAVING PATH} \
+   --unzip \
+   --delete  # Optional, delete the download zip file
+   ```
+
+   **Note:** There is no download link for Objects365 V1 right now. If you would like to download Objects365-V1, please visit [official website](http://www.objects365.org/) to concat the author.
+
+2. The directory should be like this:
+
+   ```none
+   mmdetection
+   ├── mmdet
+   ├── tools
+   ├── configs
+   ├── data
+   │   ├── Objects365
+   │   │   ├── Obj365_v1
+   │   │   │   ├── annotations
+   │   │   │   │   ├── objects365_train.json
+   │   │   │   │   ├── objects365_val.json
+   │   │   │   ├── train        # training images
+   │   │   │   ├── val          # validation images
+   │   │   ├── Obj365_v2
+   │   │   │   ├── annotations
+   │   │   │   │   ├── zhiyuan_objv2_train.json
+   │   │   │   │   ├── zhiyuan_objv2_val.json
+   │   │   │   ├── train        # training images
+   │   │   │   │   ├── patch0
+   │   │   │   │   ├── patch1
+   │   │   │   │   ├── ...
+   │   │   │   ├── val          # validation images
+   │   │   │   │   ├── patch0
+   │   │   │   │   ├── patch1
+   │   │   │   │   ├── ...
+   ```
+
+## Results and Models
+
+### Objects365 V1
+
+| Architecture | Backbone |  Style  | Lr schd | Mem (GB) | box AP |                                                             Config                                                              |                                                                                                                                                                                Download                                                                                                                                                                                |
+| :----------: | :------: | :-----: | :-----: | :------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Faster R-CNN |   R-50   | pytorch |   1x    |    -     |  19.6  |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v1.py)    |           [model](https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v1/faster_rcnn_r50_fpn_16x4_1x_obj365v1_20221219_181226-9ff10f95.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v1/faster_rcnn_r50_fpn_16x4_1x_obj365v1_20221219_181226.log.json)           |
+| Faster R-CNN |   R-50   | pytorch |  1350K  |    -     |  22.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/objects365/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1_20220510_142457-337d8965.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1_20220510_142457.log.json) |
+|  Retinanet   |   R-50   | pytorch |   1x    |    -     |  14.8  |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/objects365/retinanet_r50_fpn_1x_obj365v1.py)       |                         [model](https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_1x_obj365v1/retinanet_r50_fpn_1x_obj365v1_20221219_181859-ba3e3dd5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_1x_obj365v1/retinanet_r50_fpn_1x_obj365v1_20221219_181859.log.json)                         |
+|  Retinanet   |   R-50   | pytorch |  1350K  |    -     |  18.0  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/objects365/retinanet_r50_fpn_syncbn_1350k_obj365v1.py)  |     [model](https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_syncbn_1350k_obj365v1/retinanet_r50_fpn_syncbn_1350k_obj365v1_20220513_111237-7517c576.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_syncbn_1350k_obj365v1/retinanet_r50_fpn_syncbn_1350k_obj365v1_20220513_111237.log.json)     |
+
+### Objects365 V2
+
+| Architecture | Backbone |  Style  | Lr schd | Mem (GB) | box AP |                                                           Config                                                           |                                                                                                                                                                      Download                                                                                                                                                                      |
+| :----------: | :------: | :-----: | :-----: | :------: | :----: | :------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Faster R-CNN |   R-50   | pytorch |   1x    |    -     |  19.8  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v2.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v2/faster_rcnn_r50_fpn_16x4_1x_obj365v2_20221220_175040-5910b015.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v2/faster_rcnn_r50_fpn_16x4_1x_obj365v2_20221220_175040.log.json) |
+|  Retinanet   |   R-50   | pytorch |   1x    |    -     |  16.7  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/objects365/retinanet_r50_fpn_1x_obj365v2.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_1x_obj365v2/retinanet_r50_fpn_1x_obj365v2_20221223_122105-d9b191f1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_1x_obj365v2/retinanet_r50_fpn_1x_obj365v2_20221223_122105.log.json)               |
diff --git a/configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v1.py b/configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v1.py
new file mode 100644
index 0000000..36bfa27
--- /dev/null
+++ b/configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v1.py
@@ -0,0 +1,25 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/objects365v1_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(roi_head=dict(bbox_head=dict(num_classes=365)))
+
+data = dict(samples_per_gpu=4)
+
+# Using 32 GPUS while training
+optimizer = dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=1.0 / 1000,
+    step=[8, 11])
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (16 GPUs) x (4 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v2.py b/configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v2.py
new file mode 100644
index 0000000..13bbeb8
--- /dev/null
+++ b/configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v2.py
@@ -0,0 +1,25 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/objects365v2_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(roi_head=dict(bbox_head=dict(num_classes=365)))
+
+data = dict(samples_per_gpu=4)
+
+# Using 32 GPUS while training
+optimizer = dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=1.0 / 1000,
+    step=[8, 11])
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (16 GPUs) x (4 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/objects365/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1.py b/configs/objects365/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1.py
new file mode 100644
index 0000000..4e6f341
--- /dev/null
+++ b/configs/objects365/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1.py
@@ -0,0 +1,31 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/objects365v1_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    backbone=dict(norm_cfg=norm_cfg),
+    roi_head=dict(bbox_head=dict(num_classes=365)))
+
+# Using 8 GPUS while training
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+
+runner = dict(
+    _delete_=True, type='IterBasedRunner', max_iters=1350000)  # 36 epochs
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=1.0 / 1000,
+    step=[900000, 1200000])
+
+checkpoint_config = dict(interval=150000)
+evaluation = dict(interval=150000, metric='bbox')
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
diff --git a/configs/objects365/metafile.yml b/configs/objects365/metafile.yml
new file mode 100644
index 0000000..5e71ad7
--- /dev/null
+++ b/configs/objects365/metafile.yml
@@ -0,0 +1,101 @@
+- Name: retinanet_r50_fpn_1x_obj365v1
+  In Collection: RetinaNet
+  Config: configs/objects365/retinanet_r50_fpn_1x_obj365v1.py
+  Metadata:
+    Training Memory (GB): 7.4
+    Epochs: 12
+    Training Data: Objects365 v1
+    Training Techniques:
+      - SGD with Momentum
+      - Weight Decay
+  Results:
+  - Task: Object Detection
+    Dataset: Objects365 v1
+    Metrics:
+      box AP: 14.8
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_1x_obj365v1/retinanet_r50_fpn_1x_obj365v1_20221219_181859-ba3e3dd5.pth
+
+- Name: retinanet_r50_fpn_syncbn_1350k_obj365v1
+  In Collection: RetinaNet
+  Config: configs/objects365/retinanet_r50_fpn_syncbn_1350k_obj365v1.py
+  Metadata:
+    Training Memory (GB): 7.6
+    Iterations: 1350000
+    Training Data: Objects365 v1
+    Training Techniques:
+      - SGD with Momentum
+      - Weight Decay
+  Results:
+  - Task: Object Detection
+    Dataset: Objects365 v1
+    Metrics:
+      box AP: 18.0
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_syncbn_1350k_obj365v1/retinanet_r50_fpn_syncbn_1350k_obj365v1_20220513_111237-7517c576.pth
+
+- Name: retinanet_r50_fpn_1x_obj365v2
+  In Collection: RetinaNet
+  Config: configs/objects365/retinanet_r50_fpn_1x_obj365v2.py
+  Metadata:
+    Training Memory (GB): 7.2
+    Epochs: 12
+    Training Data: Objects365 v2
+    Training Techniques:
+      - SGD with Momentum
+      - Weight Decay
+  Results:
+  - Task: Object Detection
+    Dataset: Objects365 v2
+    Metrics:
+      box AP: 16.7
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/objects365/retinanet_r50_fpn_1x_obj365v2/retinanet_r50_fpn_1x_obj365v2_20221223_122105-d9b191f1.pth
+
+- Name: faster_rcnn_r50_fpn_16x4_1x_obj365v1
+  In Collection: Faster R-CNN
+  Config: configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v1.py
+  Metadata:
+    Training Memory (GB): 11.4
+    Epochs: 12
+    Training Data: Objects365 v1
+    Training Techniques:
+      - SGD with Momentum
+      - Weight Decay
+  Results:
+  - Task: Object Detection
+    Dataset: Objects365 v1
+    Metrics:
+      box AP: 19.6
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v1/faster_rcnn_r50_fpn_16x4_1x_obj365v1_20221219_181226-9ff10f95.pth
+
+- Name: faster_rcnn_r50_fpn_syncbn_1350k_obj365v1
+  In Collection: Faster R-CNN
+  Config: configs/objects365/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1.py
+  Metadata:
+    Training Memory (GB): 8.6
+    Iterations: 1350000
+    Training Data: Objects365 v1
+    Training Techniques:
+      - SGD with Momentum
+      - Weight Decay
+  Results:
+  - Task: Object Detection
+    Dataset: Objects365 v1
+    Metrics:
+      box AP: 22.3
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1/faster_rcnn_r50_fpn_syncbn_1350k_obj365v1_20220510_142457-337d8965.pth
+
+- Name: faster_rcnn_r50_fpn_16x4_1x_obj365v2
+  In Collection: Faster R-CNN
+  Config: configs/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v2.py
+  Metadata:
+    Training Memory (GB): 10.8
+    Epochs: 12
+    Training Data: Objects365 v1
+    Training Techniques:
+      - SGD with Momentum
+      - Weight Decay
+  Results:
+  - Task: Object Detection
+    Dataset: Objects365 v2
+    Metrics:
+      box AP: 19.8
+  Weights: https://download.openmmlab.com/mmdetection/v2.0/objects365/faster_rcnn_r50_fpn_16x4_1x_obj365v2/faster_rcnn_r50_fpn_16x4_1x_obj365v2_20221220_175040-5910b015.pth
diff --git a/configs/objects365/retinanet_r50_fpn_1x_obj365v1.py b/configs/objects365/retinanet_r50_fpn_1x_obj365v1.py
new file mode 100644
index 0000000..080c02b
--- /dev/null
+++ b/configs/objects365/retinanet_r50_fpn_1x_obj365v1.py
@@ -0,0 +1,23 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/objects365v1_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(bbox_head=dict(num_classes=365))
+
+# Using 8 GPUS while training
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=10000,
+    warmup_ratio=1.0 / 1000,
+    step=[8, 11])
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
diff --git a/configs/objects365/retinanet_r50_fpn_1x_obj365v2.py b/configs/objects365/retinanet_r50_fpn_1x_obj365v2.py
new file mode 100644
index 0000000..9f0db00
--- /dev/null
+++ b/configs/objects365/retinanet_r50_fpn_1x_obj365v2.py
@@ -0,0 +1,23 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/objects365v2_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(bbox_head=dict(num_classes=365))
+
+# Using 8 GPUS while training
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=10000,
+    warmup_ratio=1.0 / 1000,
+    step=[8, 11])
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
diff --git a/configs/objects365/retinanet_r50_fpn_syncbn_1350k_obj365v1.py b/configs/objects365/retinanet_r50_fpn_syncbn_1350k_obj365v1.py
new file mode 100644
index 0000000..6dd9277
--- /dev/null
+++ b/configs/objects365/retinanet_r50_fpn_syncbn_1350k_obj365v1.py
@@ -0,0 +1,29 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/objects365v1_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(backbone=dict(norm_cfg=norm_cfg), bbox_head=dict(num_classes=365))
+
+# Using 8 GPUS while training
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+
+runner = dict(
+    _delete_=True, type='IterBasedRunner', max_iters=1350000)  # 36 epochs
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=10000,
+    warmup_ratio=1.0 / 1000,
+    step=[900000, 1200000])
+
+checkpoint_config = dict(interval=150000)
+evaluation = dict(interval=150000, metric='bbox')
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
diff --git a/configs/openimages/README.md b/configs/openimages/README.md
new file mode 100644
index 0000000..e5c1c27
--- /dev/null
+++ b/configs/openimages/README.md
@@ -0,0 +1,148 @@
+# Open Images Dataset
+
+> [Open Images Dataset](https://arxiv.org/abs/1811.00982)
+
+<!-- [DATASET] -->
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+#### Open Images v6
+
+[Open Images](https://storage.googleapis.com/openimages/web/index.html) is a dataset of ~9M images annotated with image-level labels,
+object bounding boxes, object segmentation masks, visual relationships,
+and localized narratives:
+
+- It contains a total of 16M bounding boxes for 600 object classes on
+  1.9M images, making it the largest existing dataset with object location
+  annotations. The boxes have been largely manually drawn by professional
+  annotators to ensure accuracy and consistency. The images are very diverse
+  and often contain complex scenes with several objects (8.3 per image on
+  average).
+
+- Open Images also offers visual relationship annotations, indicating pairs
+  of objects in particular relations (e.g. "woman playing guitar", "beer on
+  table"), object properties (e.g. "table is wooden"), and human actions (e.g.
+  "woman is jumping"). In total it has 3.3M annotations from 1,466 distinct
+  relationship triplets.
+
+- In V5 we added segmentation masks for 2.8M object instances in 350 classes.
+  Segmentation masks mark the outline of objects, which characterizes their
+  spatial extent to a much higher level of detail.
+
+- In V6 we added 675k localized narratives: multimodal descriptions of images
+  consisting of synchronized voice, text, and mouse traces over the objects being
+  described. (Note we originally launched localized narratives only on train in V6,
+  but since July 2020 we also have validation and test covered.)
+
+- Finally, the dataset is annotated with 59.9M image-level labels spanning 19,957
+  classes.
+
+We believe that having a single dataset with unified annotations for image
+classification, object detection, visual relationship detection, instance
+segmentation, and multimodal image descriptions will enable to study these
+tasks jointly and stimulate progress towards genuine scene understanding.
+
+<!-- [IMAGE] -->
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/48282753/147199750-23e17230-c0cf-49a0-a13c-0d014d49107e.png" height="400"/>
+</div>
+
+#### Open Images Challenge 2019
+
+[Open Images Challenges 2019](https://storage.googleapis.com/openimages/web/challenge2019.html) is based on the V5 release of the Open
+Images dataset. The images of the dataset are very varied and
+often contain complex scenes with several objects (explore the dataset).
+
+## Citation
+
+```
+@article{OpenImages,
+  author = {Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and Stefan Popov and Matteo Malloci and Alexander Kolesnikov and Tom Duerig and Vittorio Ferrari},
+  title = {The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale},
+  year = {2020},
+  journal = {IJCV}
+}
+```
+
+## Prepare Dataset
+
+1. You need to download and extract Open Images dataset.
+
+2. The Open Images dataset does not have image metas (width and height of the image),
+   which will be used during evaluation. We suggest to get test image metas before
+   training/testing by using `tools/misc/get_image_metas.py`.
+
+   **Usage**
+
+   ```shell
+   python tools/misc/get_image_metas.py ${CONFIG} \
+   --out ${OUTPUT FILE NAME}
+   ```
+
+3. The directory should be like this:
+
+   ```none
+   mmdetection
+   ├── mmdet
+   ├── tools
+   ├── configs
+   ├── data
+   │   ├── OpenImages
+   │   │   ├── annotations
+   │   │   │   ├── bbox_labels_600_hierarchy.json
+   │   │   │   ├── class-descriptions-boxable.csv
+   │   │   │   ├── oidv6-train-annotations-bbox.scv
+   │   │   │   ├── validation-annotations-bbox.csv
+   │   │   │   ├── validation-annotations-human-imagelabels-boxable.csv
+   │   │   │   ├── validation-image-metas.pkl      # get from script
+   │   │   ├── challenge2019
+   │   │   │   ├── challenge-2019-train-detection-bbox.txt
+   │   │   │   ├── challenge-2019-validation-detection-bbox.txt
+   │   │   │   ├── class_label_tree.np
+   │   │   │   ├── class_sample_train.pkl
+   │   │   │   ├── challenge-2019-validation-detection-human-imagelabels.csv       # download from official website
+   │   │   │   ├── challenge-2019-validation-metas.pkl     # get from script
+   │   │   ├── OpenImages
+   │   │   │   ├── train           # training images
+   │   │   │   ├── test            # testing images
+   │   │   │   ├── validation      # validation images
+   ```
+
+**Note**:
+
+1. The training and validation images of Open Images Challenge dataset are based on
+   Open Images v6, but the test images are different.
+2. The Open Images Challenges annotations are obtained from [TSD](https://github.com/Sense-X/TSD).
+   You can also download the annotations from [official website](https://storage.googleapis.com/openimages/web/challenge2019_downloads.html),
+   and set data.train.type=OpenImagesDataset, data.val.type=OpenImagesDataset, and data.test.type=OpenImagesDataset in the config
+3. If users do not want to use `validation-annotations-human-imagelabels-boxable.csv` and `challenge-2019-validation-detection-human-imagelabels.csv`
+   users can set `data.val.load_image_level_labels=False` and `data.test.load_image_level_labels=False` in the config.
+   Please note that loading image-levels label is the default of Open Images evaluation metric.
+   More details please refer to the [official website](https://storage.googleapis.com/openimages/web/evaluation.html)
+
+## Results and Models
+
+|         Architecture          | Backbone |  Style  | Lr schd |       Sampler       | Mem (GB) | Inf time (fps) | box AP |                                                                   Config                                                                   |                                                                                                                                                                                                      Download                                                                                                                                                                                                      |
+| :---------------------------: | :------: | :-----: | :-----: | :-----------------: | :------: | :------------: | :----: | :----------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|         Faster R-CNN          |   R-50   | pytorch |   1x    |    Group Sampler    |   7.7    |       -        |  51.6  |        [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages.py)        |                             [model](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_20211130_231159-e87ab7ce.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_20211130_231159.log.json)                             |
+|         Faster R-CNN          |   R-50   | pytorch |   1x    | Class Aware Sampler |   7.7    |       -        |  60.0  |      [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages.py)      |                       [model](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_20220306_202424-98c630e5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_20220306_202424.log.json)                       |
+| Faster R-CNN (Challenge 2019) |   R-50   | pytorch |   1x    |    Group Sampler    |   7.7    |       -        |  54.9  |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py)   |         [model](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge_20220114_045100-0e79e5df.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge_20220114_045100.log.json)         |
+| Faster R-CNN (Challenge 2019) |   R-50   | pytorch |   1x    | Class Aware Sampler |   7.1    |       -        |  65.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge_20220221_192021-34c402d9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge_20220221_192021.log.json) |
+|           Retinanet           |   R-50   | pytorch |   1x    |    Group Sampler    |   6.6    |       -        |  61.5  |         [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/openimages/retinanet_r50_fpn_32x2_1x_openimages.py)         |                                 [model](https://download.openmmlab.com/mmdetection/v2.0/openimages/retinanet_r50_fpn_32x2_1x_openimages/retinanet_r50_fpn_32x2_1x_openimages_20211223_071954-d2ae5462.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/openimages/retinanet_r50_fpn_32x2_1x_openimages/retinanet_r50_fpn_32x2_1x_openimages_20211223_071954.log.json)                                 |
+|              SSD              |  VGG16   | pytorch |   36e   |    Group Sampler    |   10.8   |       -        |  35.4  |              [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/openimages/ssd300_32x8_36e_openimages.py)              |                                                     [model](https://download.openmmlab.com/mmdetection/v2.0/openimages/ssd300_32x8_36e_openimages/ssd300_32x8_36e_openimages_20211224_000232-dce93846.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/openimages/ssd300_32x8_36e_openimages/ssd300_32x8_36e_openimages_20211224_000232.log.json)                                                     |
+
+**Notes:**
+
+- 'cas' is short for 'Class Aware Sampler'
+
+### Results of consider image level labels
+
+|           Architecture            |       Sampler       | Consider Image Level Labels | box AP |
+| :-------------------------------: | :-----------------: | :-------------------------: | :----: |
+| Faster R-CNN r50 (Challenge 2019) |    Group Sampler    |             w/o             | 62.19  |
+| Faster R-CNN r50 (Challenge 2019) |    Group Sampler    |             w/              | 54.87  |
+| Faster R-CNN r50 (Challenge 2019) | Class Aware Sampler |             w/o             | 71.77  |
+| Faster R-CNN r50 (Challenge 2019) | Class Aware Sampler |             w/              | 64.98  |
diff --git a/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages.py b/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages.py
new file mode 100644
index 0000000..3dfc341
--- /dev/null
+++ b/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages.py
@@ -0,0 +1,23 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/openimages_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(roi_head=dict(bbox_head=dict(num_classes=601)))
+
+# Using 32 GPUS while training
+optimizer = dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=26000,
+    warmup_ratio=1.0 / 64,
+    step=[8, 11])
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (32 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py b/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py
new file mode 100644
index 0000000..c8900ad
--- /dev/null
+++ b/configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py
@@ -0,0 +1,47 @@
+_base_ = ['faster_rcnn_r50_fpn_32x2_1x_openimages.py']
+
+model = dict(
+    roi_head=dict(bbox_head=dict(num_classes=500)),
+    test_cfg=dict(rcnn=dict(score_thr=0.01)))
+
+# dataset settings
+dataset_type = 'OpenImagesChallengeDataset'
+data_root = 'data/OpenImages/'
+data = dict(
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root +
+        'challenge2019/challenge-2019-train-detection-bbox.txt',
+        img_prefix=data_root + 'OpenImages/',
+        label_file=data_root + 'challenge2019/cls-label-description.csv',
+        hierarchy_file=data_root + 'challenge2019/class_label_tree.np'),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root +
+        'challenge2019/challenge-2019-validation-detection-bbox.txt',
+        img_prefix=data_root + 'OpenImages/',
+        label_file=data_root + 'challenge2019/cls-label-description.csv',
+        hierarchy_file=data_root + 'challenge2019/class_label_tree.np',
+        meta_file=data_root +
+        'challenge2019/challenge-2019-validation-metas.pkl',
+        image_level_ann_file=data_root +
+        'challenge2019/challenge-2019-validation-detection-'
+        'human-imagelabels.csv'),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root +
+        'challenge2019/challenge-2019-validation-detection-bbox.txt',
+        img_prefix=data_root + 'OpenImages/',
+        label_file=data_root + 'challenge2019/cls-label-description.csv',
+        hierarchy_file=data_root + 'challenge2019/class_label_tree.np',
+        meta_file=data_root +
+        'challenge2019/challenge-2019-validation-metas.pkl',
+        image_level_ann_file=data_root +
+        'challenge2019/challenge-2019-validation-detection-'
+        'human-imagelabels.csv'))
+evaluation = dict(interval=1, metric='mAP')
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (32 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages.py b/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages.py
new file mode 100644
index 0000000..88d029d
--- /dev/null
+++ b/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages.py
@@ -0,0 +1,5 @@
+_base_ = ['faster_rcnn_r50_fpn_32x2_1x_openimages.py']
+
+# Use ClassAwareSampler
+data = dict(
+    train_dataloader=dict(class_aware_sampler=dict(num_sample_class=1)))
diff --git a/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge.py b/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge.py
new file mode 100644
index 0000000..26bd64e
--- /dev/null
+++ b/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge.py
@@ -0,0 +1,5 @@
+_base_ = ['faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py']
+
+# Use ClassAwareSampler
+data = dict(
+    train_dataloader=dict(class_aware_sampler=dict(num_sample_class=1)))
diff --git a/configs/openimages/metafile.yml b/configs/openimages/metafile.yml
new file mode 100644
index 0000000..d9f924e
--- /dev/null
+++ b/configs/openimages/metafile.yml
@@ -0,0 +1,102 @@
+Models:
+  - Name: faster_rcnn_r50_fpn_32x2_1x_openimages
+    In Collection: Faster R-CNN
+    Config: configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages.py
+    Metadata:
+      Training Memory (GB): 7.7
+      Epochs: 12
+      Training Data: Open Images v6
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+    Results:
+      - Task: Object Detection
+        Dataset: Open Images v6
+        Metrics:
+          box AP: 51.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_20211130_231159-e87ab7ce.pth
+
+  - Name: retinanet_r50_fpn_32x2_1x_openimages
+    In Collection: RetinaNet
+    Config: configs/openimages/retinanet_r50_fpn_32x2_1x_openimages.py
+    Metadata:
+      Training Memory (GB): 6.6
+      Epochs: 12
+      Training Data: Open Images v6
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+    Results:
+      - Task: Object Detection
+        Dataset: Open Images v6
+        Metrics:
+          box AP: 61.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/openimages/retinanet_r50_fpn_32x2_1x_openimages/retinanet_r50_fpn_32x2_1x_openimages_20211223_071954-d2ae5462.pth
+
+  - Name: ssd300_32x8_36e_openimages
+    In Collection: SSD
+    Config: configs/openimages/ssd300_32x8_36e_openimages.py
+    Metadata:
+      Training Memory (GB): 10.8
+      Epochs: 36
+      Training Data: Open Images v6
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+    Results:
+      - Task: Object Detection
+        Dataset: Open Images v6
+        Metrics:
+          box AP: 35.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/openimages/ssd300_32x8_36e_openimages/ssd300_32x8_36e_openimages_20211224_000232-dce93846.pth
+
+  - Name: faster_rcnn_r50_fpn_32x2_1x_openimages_challenge
+    In Collection: Faster R-CNN
+    Config: configs/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge.py
+    Metadata:
+      Training Memory (GB): 7.7
+      Epochs: 12
+      Training Data: Open Images Challenge 2019
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+    Results:
+      - Task: Object Detection
+        Dataset: Open Images Challenge 2019
+        Metrics:
+          box AP: 54.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge/faster_rcnn_r50_fpn_32x2_1x_openimages_challenge_20220114_045100-0e79e5df.pth
+
+  - Name: faster_rcnn_r50_fpn_32x2_cas_1x_openimages
+    In Collection: Faster R-CNN
+    Config: configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages.py
+    Metadata:
+      Training Memory (GB): 7.7
+      Epochs: 12
+      Training Data: Open Images Challenge 2019
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+    Results:
+      - Task: Object Detection
+        Dataset: Open Images Challenge 2019
+        Metrics:
+          box AP: 60.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_20220306_202424-98c630e5.pth
+
+  - Name: faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge
+    In Collection: Faster R-CNN
+    Config: configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge.py
+    Metadata:
+      Training Memory (GB): 7.1
+      Epochs: 12
+      Training Data: Open Images Challenge 2019
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+    Results:
+      - Task: Object Detection
+        Dataset: Open Images Challenge 2019
+        Metrics:
+          box AP: 65.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge/faster_rcnn_r50_fpn_32x2_cas_1x_openimages_challenge_20220221_192021-34c402d9.pth
diff --git a/configs/openimages/retinanet_r50_fpn_32x2_1x_openimages.py b/configs/openimages/retinanet_r50_fpn_32x2_1x_openimages.py
new file mode 100644
index 0000000..0191aa1
--- /dev/null
+++ b/configs/openimages/retinanet_r50_fpn_32x2_1x_openimages.py
@@ -0,0 +1,22 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/openimages_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(bbox_head=dict(num_classes=601))
+
+optimizer = dict(type='SGD', lr=0.08, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=26000,
+    warmup_ratio=1.0 / 64,
+    step=[8, 11])
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (32 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/openimages/ssd300_32x8_36e_openimages.py b/configs/openimages/ssd300_32x8_36e_openimages.py
new file mode 100644
index 0000000..e2565b9
--- /dev/null
+++ b/configs/openimages/ssd300_32x8_36e_openimages.py
@@ -0,0 +1,83 @@
+_base_ = [
+    '../_base_/models/ssd300.py', '../_base_/datasets/openimages_detection.py',
+    '../_base_/default_runtime.py', '../_base_/schedules/schedule_1x.py'
+]
+model = dict(
+    bbox_head=dict(
+        num_classes=601,
+        anchor_generator=dict(basesize_ratio_range=(0.2, 0.9))))
+# dataset settings
+dataset_type = 'OpenImagesDataset'
+data_root = 'data/OpenImages/'
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True, normed_bbox=True),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(300, 300),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,  # using 32 GPUS while training.
+    workers_per_gpu=0,  # workers_per_gpu > 0 may occur out of memory
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=3,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root +
+            'annotations/oidv6-train-annotations-bbox.csv',
+            img_prefix=data_root + 'OpenImages/train/',
+            label_file=data_root +
+            'annotations/class-descriptions-boxable.csv',
+            hierarchy_file=data_root +
+            'annotations/bbox_labels_600_hierarchy.json',
+            pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=0.04, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict()
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=20000,
+    warmup_ratio=0.001,
+    step=[8, 11])
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (32 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=256)
diff --git a/configs/paa/README.md b/configs/paa/README.md
new file mode 100644
index 0000000..c8861ec
--- /dev/null
+++ b/configs/paa/README.md
@@ -0,0 +1,47 @@
+# PAA
+
+> [Probabilistic Anchor Assignment with IoU Prediction for Object Detection](https://arxiv.org/abs/2007.08103)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+In object detection, determining which anchors to assign as positive or negative samples, known as anchor assignment, has been revealed as a core procedure that can significantly affect a model's performance. In this paper we propose a novel anchor assignment strategy that adaptively separates anchors into positive and negative samples for a ground truth bounding box according to the model's learning status such that it is able to reason about the separation in a probabilistic manner. To do so we first calculate the scores of anchors conditioned on the model and fit a probability distribution to these scores. The model is then trained with anchors separated into positive and negative samples according to their probabilities. Moreover, we investigate the gap between the training and testing objectives and propose to predict the Intersection-over-Unions of detected boxes as a measure of localization quality to reduce the discrepancy. The combined score of classification and localization qualities serving as a box selection metric in non-maximum suppression well aligns with the proposed anchor assignment strategy and leads significant performance improvements. The proposed methods only add a single convolutional layer to RetinaNet baseline and does not require multiple anchors per location, so are efficient. Experimental results verify the effectiveness of the proposed methods. Especially, our models set new records for single-stage detectors on MS COCO test-dev dataset with various backbones.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143968195-519a116a-de29-437e-b4c8-30aef43dcb15.png"/>
+</div>
+
+## Results and Models
+
+We provide config files to reproduce the object detection results in the
+ECCV 2020 paper for Probabilistic Anchor Assignment with IoU
+Prediction for Object Detection.
+
+| Backbone  | Lr schd | Mem (GB) | Score voting | box AP |                                                   Config                                                    |                                                                                                                                               Download                                                                                                                                               |
+| :-------: | :-----: | :------: | :----------: | :----: | :---------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R-50-FPN  |   12e   |   3.7    |     True     |  40.4  |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_1x_coco.py)      |                     [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.log.json)                      |
+| R-50-FPN  |   12e   |   3.7    |    False     |  40.2  |                                                      -                                                      |                                                                                                                                                                                                                                                                                                      |
+| R-50-FPN  |   18e   |   3.7    |     True     |  41.4  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_1.5x_coco.py)     |                 [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.log.json)                  |
+| R-50-FPN  |   18e   |   3.7    |    False     |  41.2  |                                                      -                                                      |                                                                                                                                                                                                                                                                                                      |
+| R-50-FPN  |   24e   |   3.7    |     True     |  41.6  |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_2x_coco.py)      |                     [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.log.json)                      |
+| R-50-FPN  |   36e   |   3.7    |     True     |  43.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r50_fpn_mstrain_3x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722-06a6880b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722.log.json)   |
+| R-101-FPN |   12e   |   6.2    |     True     |  42.6  |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_1x_coco.py)     |                   [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.log.json)                    |
+| R-101-FPN |   12e   |   6.2    |    False     |  42.4  |                                                      -                                                      |                                                                                                                                                                                                                                                                                                      |
+| R-101-FPN |   24e   |   6.2    |     True     |  43.5  |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_2x_coco.py)     |                   [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.log.json)                    |
+| R-101-FPN |   36e   |   6.2    |     True     |  45.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/paa/paa_r101_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202-83250d22.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202.log.json) |
+
+**Note**:
+
+1. We find that the performance is unstable with 1x setting and may fluctuate by about 0.2 mAP. We report the best results.
+
+## Citation
+
+```latex
+@inproceedings{paa-eccv2020,
+  title={Probabilistic Anchor Assignment with IoU Prediction for Object Detection},
+  author={Kim, Kang and Lee, Hee Seok},
+  booktitle = {ECCV},
+  year={2020}
+}
+```
diff --git a/configs/paa/metafile.yml b/configs/paa/metafile.yml
new file mode 100644
index 0000000..e08b663
--- /dev/null
+++ b/configs/paa/metafile.yml
@@ -0,0 +1,104 @@
+Collections:
+  - Name: PAA
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - Probabilistic Anchor Assignment
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/2007.08103
+      Title: 'Probabilistic Anchor Assignment with IoU Prediction for Object Detection'
+    README: configs/paa/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.4.0/mmdet/models/detectors/paa.py#L6
+      Version: v2.4.0
+
+Models:
+  - Name: paa_r50_fpn_1x_coco
+    In Collection: PAA
+    Config: configs/paa/paa_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.7
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1x_coco/paa_r50_fpn_1x_coco_20200821-936edec3.pth
+
+  - Name: paa_r50_fpn_1.5x_coco
+    In Collection: PAA
+    Config: configs/paa/paa_r50_fpn_1.5x_coco.py
+    Metadata:
+      Training Memory (GB): 3.7
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_1.5x_coco/paa_r50_fpn_1.5x_coco_20200823-805d6078.pth
+
+  - Name: paa_r50_fpn_2x_coco
+    In Collection: PAA
+    Config: configs/paa/paa_r50_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 3.7
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_2x_coco/paa_r50_fpn_2x_coco_20200821-c98bfc4e.pth
+
+  - Name: paa_r50_fpn_mstrain_3x_coco
+    In Collection: PAA
+    Config: configs/paa/paa_r50_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 3.7
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r50_fpn_mstrain_3x_coco/paa_r50_fpn_mstrain_3x_coco_20210121_145722-06a6880b.pth
+
+  - Name: paa_r101_fpn_1x_coco
+    In Collection: PAA
+    Config: configs/paa/paa_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.2
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_1x_coco/paa_r101_fpn_1x_coco_20200821-0a1825a4.pth
+
+  - Name: paa_r101_fpn_2x_coco
+    In Collection: PAA
+    Config: configs/paa/paa_r101_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 6.2
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_2x_coco/paa_r101_fpn_2x_coco_20200821-6829f96b.pth
+
+  - Name: paa_r101_fpn_mstrain_3x_coco
+    In Collection: PAA
+    Config: configs/paa/paa_r101_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 6.2
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/paa/paa_r101_fpn_mstrain_3x_coco/paa_r101_fpn_mstrain_3x_coco_20210122_084202-83250d22.pth
diff --git a/configs/paa/paa_r101_fpn_1x_coco.py b/configs/paa/paa_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..94f1c27
--- /dev/null
+++ b/configs/paa/paa_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './paa_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/paa/paa_r101_fpn_2x_coco.py b/configs/paa/paa_r101_fpn_2x_coco.py
new file mode 100644
index 0000000..641ef76
--- /dev/null
+++ b/configs/paa/paa_r101_fpn_2x_coco.py
@@ -0,0 +1,3 @@
+_base_ = './paa_r101_fpn_1x_coco.py'
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/paa/paa_r101_fpn_mstrain_3x_coco.py b/configs/paa/paa_r101_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..71858ed
--- /dev/null
+++ b/configs/paa/paa_r101_fpn_mstrain_3x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './paa_r50_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/paa/paa_r50_fpn_1.5x_coco.py b/configs/paa/paa_r50_fpn_1.5x_coco.py
new file mode 100644
index 0000000..aabce4a
--- /dev/null
+++ b/configs/paa/paa_r50_fpn_1.5x_coco.py
@@ -0,0 +1,3 @@
+_base_ = './paa_r50_fpn_1x_coco.py'
+lr_config = dict(step=[12, 16])
+runner = dict(type='EpochBasedRunner', max_epochs=18)
diff --git a/configs/paa/paa_r50_fpn_1x_coco.py b/configs/paa/paa_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..4c9c4aa
--- /dev/null
+++ b/configs/paa/paa_r50_fpn_1x_coco.py
@@ -0,0 +1,70 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='PAA',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='PAAHead',
+        reg_decoded_bbox=True,
+        score_voting=True,
+        topk=9,
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.1,
+            neg_iou_thr=0.1,
+            min_pos_iou=0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/paa/paa_r50_fpn_2x_coco.py b/configs/paa/paa_r50_fpn_2x_coco.py
new file mode 100644
index 0000000..663d2c0
--- /dev/null
+++ b/configs/paa/paa_r50_fpn_2x_coco.py
@@ -0,0 +1,3 @@
+_base_ = './paa_r50_fpn_1x_coco.py'
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/paa/paa_r50_fpn_mstrain_3x_coco.py b/configs/paa/paa_r50_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..91fa28c
--- /dev/null
+++ b/configs/paa/paa_r50_fpn_mstrain_3x_coco.py
@@ -0,0 +1,20 @@
+_base_ = './paa_r50_fpn_1x_coco.py'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+lr_config = dict(step=[28, 34])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/configs/pafpn/README.md b/configs/pafpn/README.md
new file mode 100644
index 0000000..ae1e3a3
--- /dev/null
+++ b/configs/pafpn/README.md
@@ -0,0 +1,34 @@
+# PAFPN
+
+> [Path Aggregation Network for Instance Segmentation](https://arxiv.org/abs/1803.01534)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+The way that information propagates in neural networks is of great importance. In this paper, we propose Path Aggregation Network (PANet) aiming at boosting information flow in proposal-based instance segmentation framework. Specifically, we enhance the entire feature hierarchy with accurate localization signals in lower layers by bottom-up path augmentation, which shortens the information path between lower layers and topmost feature. We present adaptive feature pooling, which links feature grid and all feature levels to make useful information in each feature level propagate directly to following proposal subnetworks. A complementary branch capturing different views for each proposal is created to further improve mask prediction. These improvements are simple to implement, with subtle extra computational overhead. Our PANet reaches the 1st place in the COCO 2017 Challenge Instance Segmentation task and the 2nd place in Object Detection task without large-batch training. It is also state-of-the-art on MVD and Cityscapes.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143968947-5f2d7e8a-a236-4d59-8f2d-7fbb12764845.png"/>
+</div>
+
+## Results and Models
+
+| Backbone |  style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                     Config                                                     |                                                                                                                                                          Download                                                                                                                                                           |
+| :------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R-50-FPN | pytorch |   1x    |   4.0    |      17.2      |  37.5  |         | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_20200503_105836.log.json) |
+
+## Citation
+
+```latex
+@inproceedings{liu2018path,
+  author = {Shu Liu and
+            Lu Qi and
+            Haifang Qin and
+            Jianping Shi and
+            Jiaya Jia},
+  title = {Path Aggregation Network for Instance Segmentation},
+  booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year = {2018}
+}
+```
diff --git a/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py b/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py
new file mode 100644
index 0000000..b2fdef9
--- /dev/null
+++ b/configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py
@@ -0,0 +1,8 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+
+model = dict(
+    neck=dict(
+        type='PAFPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/pafpn/metafile.yml b/configs/pafpn/metafile.yml
new file mode 100644
index 0000000..f9cf97c
--- /dev/null
+++ b/configs/pafpn/metafile.yml
@@ -0,0 +1,38 @@
+Collections:
+  - Name: PAFPN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - PAFPN
+    Paper:
+      URL: https://arxiv.org/abs/1803.01534
+      Title: 'Path Aggregation Network for Instance Segmentation'
+    README: configs/pafpn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/necks/pafpn.py#L11
+      Version: v2.0.0
+
+Models:
+  - Name: faster_rcnn_r50_pafpn_1x_coco
+    In Collection: PAFPN
+    Config: configs/pafpn/faster_rcnn_r50_pafpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.0
+      inference time (ms/im):
+        - value: 58.14
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pafpn/faster_rcnn_r50_pafpn_1x_coco/faster_rcnn_r50_pafpn_1x_coco_bbox_mAP-0.375_20200503_105836-b7b4b9bd.pth
diff --git a/configs/panoptic_fpn/README.md b/configs/panoptic_fpn/README.md
new file mode 100644
index 0000000..b31c9c0
--- /dev/null
+++ b/configs/panoptic_fpn/README.md
@@ -0,0 +1,63 @@
+# Panoptic FPN
+
+> [Panoptic feature pyramid networks](https://arxiv.org/abs/1901.02446)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+The recently introduced panoptic segmentation task has renewed our community's interest in unifying the tasks of instance segmentation (for thing classes) and semantic segmentation (for stuff classes). However, current state-of-the-art methods for this joint task use separate and dissimilar networks for instance and semantic segmentation, without performing any shared computation. In this work, we aim to unify these methods at the architectural level, designing a single network for both tasks. Our approach is to endow Mask R-CNN, a popular instance segmentation method, with a semantic segmentation branch using a shared Feature Pyramid Network (FPN) backbone. Surprisingly, this simple baseline not only remains effective for instance segmentation, but also yields a lightweight, top-performing method for semantic segmentation. In this work, we perform a detailed study of this minimally extended version of Mask R-CNN with FPN, which we refer to as Panoptic FPN, and show it is a robust and accurate baseline for both tasks. Given its effectiveness and conceptual simplicity, we hope our method can serve as a strong baseline and aid future research in panoptic segmentation.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143968979-a1593758-c9d7-44a6-a3b8-d9686ef19ce8.png" height="300"/>
+</div>
+
+## Dataset
+
+PanopticFPN requires COCO and [COCO-panoptic](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip) dataset for training and evaluation. You need to download and extract it in the COCO dataset path.
+The directory should be like this.
+
+```none
+mmdetection
+├── mmdet
+├── tools
+├── configs
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   │   ├── panoptic_train2017.json
+│   │   │   ├── panoptic_train2017
+│   │   │   ├── panoptic_val2017.json
+│   │   │   ├── panoptic_val2017
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+```
+
+## Results and Models
+
+| Backbone  |  style  | Lr schd | Mem (GB) | Inf time (fps) |  PQ  |  SQ  |  RQ  | PQ_th | SQ_th | RQ_th | PQ_st | SQ_st | RQ_st |                                                            Config                                                             |                                                                                                                                                                          Download                                                                                                                                                                          |
+| :-------: | :-----: | :-----: | :------: | :------------: | :--: | :--: | :--: | :---: | :---: | :---: | :---: | :---: | :---: | :---------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R-50-FPN  | pytorch |   1x    |   4.7    |                | 40.2 | 77.8 | 49.3 | 47.8  | 80.9  | 57.5  | 28.9  | 73.1  | 37.0  |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py)      |                   [model](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco/panoptic_fpn_r50_fpn_1x_coco_20210821_101153-9668fd13.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco/panoptic_fpn_r50_fpn_1x_coco_20210821_101153.log.json)                   |
+| R-50-FPN  | pytorch |   3x    |    -     |       -        | 42.5 | 78.1 | 51.7 | 50.3  | 81.5  | 60.3  | 30.7  | 73.0  | 38.8  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco/panoptic_fpn_r50_fpn_mstrain_3x_coco_20210824_171155-5650f98b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco/panoptic_fpn_r50_fpn_mstrain_3x_coco_20210824_171155.log.json)   |
+| R-101-FPN | pytorch |   1x    |   6.7    |                | 42.2 | 78.3 | 51.4 | 50.1  | 81.4  | 59.9  | 30.3  | 73.6  | 38.5  |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py)     |                 [model](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco/panoptic_fpn_r101_fpn_1x_coco_20210820_193950-ab9157a2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco/panoptic_fpn_r101_fpn_1x_coco_20210820_193950.log.json)                 |
+| R-101-FPN | pytorch |   3x    |    -     |       -        | 44.1 | 78.9 | 53.6 | 52.1  | 81.7  | 62.3  | 32.0  | 74.6  | 40.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco/panoptic_fpn_r101_fpn_mstrain_3x_coco_20210823_114712-9c99acc4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco/panoptic_fpn_r101_fpn_mstrain_3x_coco_20210823_114712.log.json) |
+| R2-50-FPN | pytorch |   1x    |    -     |       -        | 42.5 | 78.0 | 51.8 | 50.0  | 81.4  | 60.0  | 31.1  | 72.8  | 39.4  |   [config](https://github.com/open-mmlab/mmdetection/tree/dev/configs/panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py)    |                   [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/panoptic_fpn_r2_50_fpn_fp16_1x_coco/panoptic_fpn_r2_50_fpn_fp16_1x_coco-fa6c51f0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/panoptic_fpn_r2_50_fpn_fp16_1x_coco/panoptic_fpn_r2_50_fpn_fp16_1x_coco_20221114_224729.log.json)                   |
+
+## Citation
+
+The base method for panoptic segmentation task.
+
+```latex
+@inproceedings{kirillov2018panopticfpn,
+  author = {
+    Alexander Kirillov,
+    Ross Girshick,
+    Kaiming He,
+    Piotr Dollar,
+  },
+  title = {Panoptic Feature Pyramid Networks},
+  booktitle = {Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+  year = {2019}
+}
+```
diff --git a/configs/panoptic_fpn/metafile.yml b/configs/panoptic_fpn/metafile.yml
new file mode 100644
index 0000000..c258c8e
--- /dev/null
+++ b/configs/panoptic_fpn/metafile.yml
@@ -0,0 +1,83 @@
+Collections:
+  - Name: PanopticFPN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - PanopticFPN
+    Paper:
+      URL: https://arxiv.org/pdf/1901.02446
+      Title: 'Panoptic feature pyramid networks'
+    README: configs/panoptic_fpn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/detectors/panoptic_fpn.py#L7
+      Version: v2.16.0
+
+Models:
+  - Name: panoptic_fpn_r50_fpn_1x_coco
+    In Collection: PanopticFPN
+    Config: configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.6
+      Epochs: 12
+    Results:
+    - Task: Panoptic Segmentation
+      Dataset: COCO
+      Metrics:
+        PQ: 40.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco/panoptic_fpn_r50_fpn_1x_coco_20210821_101153-9668fd13.pth
+
+  - Name: panoptic_fpn_r50_fpn_mstrain_3x_coco
+    In Collection: PanopticFPN
+    Config: configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 4.6
+      Epochs: 36
+    Results:
+    - Task: Panoptic Segmentation
+      Dataset: COCO
+      Metrics:
+        PQ: 42.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco/panoptic_fpn_r50_fpn_mstrain_3x_coco_20210824_171155-5650f98b.pth
+
+  - Name: panoptic_fpn_r101_fpn_1x_coco
+    In Collection: PanopticFPN
+    Config: configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.5
+      Epochs: 12
+    Results:
+    - Task: Panoptic Segmentation
+      Dataset: COCO
+      Metrics:
+        PQ: 42.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco/panoptic_fpn_r101_fpn_1x_coco_20210820_193950-ab9157a2.pth
+
+  - Name: panoptic_fpn_r101_fpn_mstrain_3x_coco
+    In Collection: PanopticFPN
+    Config: configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 6.5
+      Epochs: 36
+    Results:
+    - Task: Panoptic Segmentation
+      Dataset: COCO
+      Metrics:
+        PQ: 44.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco/panoptic_fpn_r101_fpn_mstrain_3x_coco_20210823_114712-9c99acc4.pth
+
+  - Name: panoptic_fpn_r2_50_fpn_fp16_1x_coco
+    In Collection: PanopticFPN
+    Config: configs/panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.5
+      Epochs: 12
+    Results:
+    - Task: Panoptic Segmentation
+      Dataset: COCO
+      Metrics:
+        PQ: 42.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/panoptic_fpn_r2_50_fpn_fp16_1x_coco/panoptic_fpn_r2_50_fpn_fp16_1x_coco-fa6c51f0.pth
diff --git a/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py b/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..78b8079
--- /dev/null
+++ b/configs/panoptic_fpn/panoptic_fpn_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './panoptic_fpn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py b/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..057e481
--- /dev/null
+++ b/configs/panoptic_fpn/panoptic_fpn_r101_fpn_mstrain_3x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './panoptic_fpn_r50_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py b/configs/panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py
new file mode 100644
index 0000000..6c75f01
--- /dev/null
+++ b/configs/panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py
@@ -0,0 +1,12 @@
+_base_ = './panoptic_fpn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='Res2Net',
+        depth=50,
+        scales=4,
+        base_width=26,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='res2net50_v1b_26w_4s-3cf99910.pth')))
+
+fp16 = dict(loss_scale='dynamic')
diff --git a/configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py b/configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..2995524
--- /dev/null
+++ b/configs/panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py
@@ -0,0 +1,33 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_panoptic.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='PanopticFPN',
+    semantic_head=dict(
+        type='PanopticFPNHead',
+        num_things_classes=80,
+        num_stuff_classes=53,
+        in_channels=256,
+        inner_channels=128,
+        start_level=0,
+        end_level=4,
+        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
+        conv_cfg=None,
+        loss_seg=dict(
+            type='CrossEntropyLoss', ignore_index=255, loss_weight=0.5)),
+    panoptic_fusion_head=dict(
+        type='HeuristicFusionHead',
+        num_things_classes=80,
+        num_stuff_classes=53),
+    test_cfg=dict(
+        panoptic=dict(
+            score_thr=0.6,
+            max_per_img=100,
+            mask_thr_binary=0.5,
+            mask_overlap=0.5,
+            nms=dict(type='nms', iou_threshold=0.5, class_agnostic=True),
+            stuff_area_limit=4096)))
+
+custom_hooks = []
diff --git a/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py b/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..b510935
--- /dev/null
+++ b/configs/panoptic_fpn/panoptic_fpn_r50_fpn_mstrain_3x_coco.py
@@ -0,0 +1,61 @@
+_base_ = './panoptic_fpn_r50_fpn_1x_coco.py'
+
+# dataset settings
+dataset_type = 'CocoPanopticDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# In mstrain 3x config, img_scale=[(1333, 640), (1333, 800)],
+# multiscale_mode='range'
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadPanopticAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        with_seg=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='SegRescale', scale_factor=1 / 4),
+    dict(type='DefaultFormatBundle'),
+    dict(
+        type='Collect',
+        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks', 'gt_semantic_seg']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+# Use RepeatDataset to speed up training
+data = dict(
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=3,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/panoptic_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            seg_prefix=data_root + 'annotations/panoptic_train2017/',
+            pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/pascal_voc/README.md b/configs/pascal_voc/README.md
new file mode 100644
index 0000000..3c09813
--- /dev/null
+++ b/configs/pascal_voc/README.md
@@ -0,0 +1,40 @@
+# Pascal VOC
+
+> [The Pascal Visual Object Classes (VOC) Challenge](https://link.springer.com/article/10.1007/s11263-009-0275-4)
+
+<!-- [DATASET] -->
+
+## Abstract
+
+The Pascal Visual Object Classes (VOC) challenge is a benchmark in visual object category recognition and detection, providing the vision and machine learning communities with a standard dataset of images and annotation, and standard evaluation procedures. Organised annually from 2005 to present, the challenge and its associated dataset has become accepted as the benchmark for object detection.
+
+This paper describes the dataset and evaluation procedure. We review the state-of-the-art in evaluated methods for both classification and detection, analyse whether the methods are statistically different, what they are learning from the images (e.g. the object or its context), and what the methods find easy or confuse. The paper concludes with lessons learnt in the three year history of the challenge, and proposes directions for future improvement and extension.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143969235-6bb4d665-0470-4bae-825c-492eb4582127.png" height="600"/>
+</div>
+
+## Results and Models
+
+|  Architecture   | Backbone |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                               Config                                                               |                                                                                                                                                                                                                                                       Download                                                                                                                                                                                                                                                       |
+| :-------------: | :------: | :-----: | :-----: | :------: | :------------: | :----: | :--------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Faster R-CNN C4 |   R-50   |  caffe  |   18k   |          |       -        |  80.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712//home/dong/code_sensetime/2022Q1/mmdetection/work_dirs/prepare_voc/gather/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712_20220314_234327-847a14d2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712_20220314_234327.log.json) |
+|  Faster R-CNN   |   R-50   | pytorch |   1x    |   2.6    |       -        |  80.4  |        [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py)        |                                                                                              [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712/faster_rcnn_r50_fpn_1x_voc0712_20220320_192712-54bef0f3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712/faster_rcnn_r50_fpn_1x_voc0712_20220320_192712.log.json)                                                                                              |
+|    Retinanet    |   R-50   | pytorch |   1x    |   2.1    |       -        |  77.3  |         [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py)         |                                                                                                     [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/retinanet_r50_fpn_1x_voc0712/retinanet_r50_fpn_1x_voc0712_20200617-47cbdd0e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/retinanet_r50_fpn_1x_voc0712/retinanet_r50_fpn_1x_voc0712_20200616_014642.log.json)                                                                                                      |
+|     SSD300      |  VGG16   |    -    |  120e   |    -     |       -        |  76.5  |                [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/ssd300_voc0712.py)                |                                                                                                                              [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/ssd300_voc0712/ssd300_voc0712_20220320_194658-17edda1b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/ssd300_voc0712/ssd300_voc0712_20220320_194658.log.json)                                                                                                                              |
+|     SSD512      |  VGG16   |    -    |  120e   |    -     |       -        |  79.5  |                [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc/ssd512_voc0712.py)                |                                                                                                                              [model](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/ssd512_voc0712/ssd512_voc0712_20220320_194717-03cefefe.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pascal_voc/ssd512_voc0712/ssd512_voc0712_20220320_194717.log.json)                                                                                                                              |
+
+## Citation
+
+```latex
+@Article{Everingham10,
+   author = "Everingham, M. and Van~Gool, L. and Williams, C. K. I. and Winn, J. and Zisserman, A.",
+   title = "The Pascal Visual Object Classes (VOC) Challenge",
+   journal = "International Journal of Computer Vision",
+   volume = "88",
+   year = "2010",
+   number = "2",
+   month = jun,
+   pages = "303--338",
+}
+```
diff --git a/configs/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712.py b/configs/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712.py
new file mode 100644
index 0000000..7bb1d73
--- /dev/null
+++ b/configs/pascal_voc/faster_rcnn_r50_caffe_c4_mstrain_18k_voc0712.py
@@ -0,0 +1,81 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_caffe_c4.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
+
+# dataset settings
+dataset_type = 'VOCDataset'
+data_root = 'data/VOCdevkit/'
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 480), (1333, 512), (1333, 544), (1333, 576),
+                   (1333, 608), (1333, 640), (1333, 672), (1333, 704),
+                   (1333, 736), (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=[
+            data_root + 'VOC2007/ImageSets/Main/trainval.txt',
+            data_root + 'VOC2012/ImageSets/Main/trainval.txt'
+        ],
+        img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt',
+        img_prefix=data_root + 'VOC2007/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'VOC2007/ImageSets/Main/test.txt',
+        img_prefix=data_root + 'VOC2007/',
+        pipeline=test_pipeline))
+
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=100,
+    warmup_ratio=0.001,
+    step=[12000, 16000])
+
+# Runner type
+runner = dict(type='IterBasedRunner', max_iters=18000)
+
+checkpoint_config = dict(interval=3000)
+evaluation = dict(interval=3000, metric='mAP')
diff --git a/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py b/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py
new file mode 100644
index 0000000..7866ace
--- /dev/null
+++ b/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712.py
@@ -0,0 +1,14 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+# actual epoch = 3 * 3 = 9
+lr_config = dict(policy='step', step=[3])
+# runtime settings
+runner = dict(
+    type='EpochBasedRunner', max_epochs=4)  # actual epoch = 4 * 3 = 12
diff --git a/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py b/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py
new file mode 100644
index 0000000..12eee2c
--- /dev/null
+++ b/configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py
@@ -0,0 +1,75 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py', '../_base_/datasets/voc0712.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(roi_head=dict(bbox_head=dict(num_classes=20)))
+
+CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
+           'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
+           'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
+
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/VOCdevkit/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1000, 600),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type='RepeatDataset',
+        times=3,
+        dataset=dict(
+            type=dataset_type,
+            ann_file='data/voc0712_trainval.json',
+            img_prefix='data/VOCdevkit',
+            pipeline=train_pipeline,
+            classes=CLASSES)),
+    val=dict(
+        type=dataset_type,
+        ann_file='data/voc07_test.json',
+        img_prefix='data/VOCdevkit',
+        pipeline=test_pipeline,
+        classes=CLASSES),
+    test=dict(
+        type=dataset_type,
+        ann_file='data/voc07_test.json',
+        img_prefix='data/VOCdevkit',
+        pipeline=test_pipeline,
+        classes=CLASSES))
+evaluation = dict(interval=1, metric='bbox')
+
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+# actual epoch = 3 * 3 = 9
+lr_config = dict(policy='step', step=[3])
+# runtime settings
+runner = dict(
+    type='EpochBasedRunner', max_epochs=4)  # actual epoch = 4 * 3 = 12
diff --git a/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py b/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py
new file mode 100644
index 0000000..b4b050d
--- /dev/null
+++ b/configs/pascal_voc/retinanet_r50_fpn_1x_voc0712.py
@@ -0,0 +1,14 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py', '../_base_/datasets/voc0712.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(bbox_head=dict(num_classes=20))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+# actual epoch = 3 * 3 = 9
+lr_config = dict(policy='step', step=[3])
+# runtime settings
+runner = dict(
+    type='EpochBasedRunner', max_epochs=4)  # actual epoch = 4 * 3 = 12
diff --git a/configs/pascal_voc/ssd300_voc0712.py b/configs/pascal_voc/ssd300_voc0712.py
new file mode 100644
index 0000000..e7008ae
--- /dev/null
+++ b/configs/pascal_voc/ssd300_voc0712.py
@@ -0,0 +1,74 @@
+_base_ = [
+    '../_base_/models/ssd300.py', '../_base_/datasets/voc0712.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(
+    bbox_head=dict(
+        num_classes=20, anchor_generator=dict(basesize_ratio_range=(0.2,
+                                                                    0.9))))
+# dataset settings
+dataset_type = 'VOCDataset'
+data_root = 'data/VOCdevkit/'
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(300, 300),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=3,
+    train=dict(
+        type='RepeatDataset', times=10, dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict()
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[16, 20])
+checkpoint_config = dict(interval=1)
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=24)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/pascal_voc/ssd512_voc0712.py b/configs/pascal_voc/ssd512_voc0712.py
new file mode 100644
index 0000000..f4627c2
--- /dev/null
+++ b/configs/pascal_voc/ssd512_voc0712.py
@@ -0,0 +1,57 @@
+_base_ = 'ssd300_voc0712.py'
+input_size = 512
+model = dict(
+    neck=dict(
+        out_channels=(512, 1024, 512, 256, 256, 256, 256),
+        level_strides=(2, 2, 2, 2, 1),
+        level_paddings=(1, 1, 1, 1, 1),
+        last_kernel_size=4),
+    bbox_head=dict(
+        in_channels=(512, 1024, 512, 256, 256, 256, 256),
+        anchor_generator=dict(
+            input_size=input_size,
+            strides=[8, 16, 32, 64, 128, 256, 512],
+            basesize_ratio_range=(0.15, 0.9),
+            ratios=([2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]))))
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(512, 512), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(512, 512),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/pisa/README.md b/configs/pisa/README.md
new file mode 100644
index 0000000..c847c85
--- /dev/null
+++ b/configs/pisa/README.md
@@ -0,0 +1,50 @@
+# PISA
+
+> [Prime Sample Attention in Object Detection](https://arxiv.org/abs/1904.04821)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+It is a common paradigm in object detection frameworks to treat all samples equally and target at maximizing the performance on average. In this work, we revisit this paradigm through a careful study on how different samples contribute to the overall performance measured in terms of mAP. Our study suggests that the samples in each mini-batch are neither independent nor equally important, and therefore a better classifier on average does not necessarily mean higher mAP. Motivated by this study, we propose the notion of Prime Samples, those that play a key role in driving the detection performance. We further develop a simple yet effective sampling and learning strategy called PrIme Sample Attention (PISA) that directs the focus of the training process towards such samples. Our experiments demonstrate that it is often more effective to focus on prime samples than hard samples when training a detector. Particularly, On the MSCOCO dataset, PISA outperforms the random sampling baseline and hard mining schemes, e.g., OHEM and Focal Loss, consistently by around 2% on both single-stage and two-stage detectors, even with a strong backbone ResNeXt-101.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143970710-5cfd5960-fcf9-4e32-860a-acd46ce5d274.png"/>
+</div>
+
+## Results and Models
+
+| PISA |   Network    |    Backbone    | Lr schd | box AP | mask AP |                                                         Config                                                          |                                                                                                                                                              Download                                                                                                                                                              |
+| :--: | :----------: | :------------: | :-----: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  ×   | Faster R-CNN |    R-50-FPN    |   1x    |  36.4  |         |                                                            -                                                            |                                                                                                                                                                                                                                                                                                                                    |
+|  √   | Faster R-CNN |    R-50-FPN    |   1x    |  38.4  |         |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco_20200506_185619.log.json)               |
+|  ×   | Faster R-CNN | X101-32x4d-FPN |   1x    |  40.1  |         |                                                            -                                                            |                                                                                                                                                                                                                                                                                                                                    |
+|  √   | Faster R-CNN | X101-32x4d-FPN |   1x    |  41.9  |         | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco-e4accec4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco_20200505_181503.log.json) |
+|  ×   | Mask   R-CNN |    R-50-FPN    |   1x    |  37.3  |  34.2   |                                                            -                                                            |                                                                                                                                                                                                                                                                                                                                    |
+|  √   | Mask   R-CNN |    R-50-FPN    |   1x    |  39.1  |  35.2   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py)      |                   [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco-dfcedba6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco_20200508_150500.log.json)                   |
+|  ×   | Mask   R-CNN | X101-32x4d-FPN |   1x    |  41.1  |  37.1   |                                                            -                                                            |                                                                                                                                                                                                                                                                                                                                    |
+|  √   | Mask   R-CNN | X101-32x4d-FPN |   1x    |        |         |                                                                                                                         |                                                                                                                                                                                                                                                                                                                                    |
+|  ×   |  RetinaNet   |    R-50-FPN    |   1x    |  35.6  |         |                                                            -                                                            |                                                                                                                                                                                                                                                                                                                                    |
+|  √   |  RetinaNet   |    R-50-FPN    |   1x    |  36.9  |         |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py)      |                   [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco-76409952.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco_20200504_014311.log.json)                   |
+|  ×   |  RetinaNet   | X101-32x4d-FPN |   1x    |  39.0  |         |                                                            -                                                            |                                                                                                                                                                                                                                                                                                                                    |
+|  √   |  RetinaNet   | X101-32x4d-FPN |   1x    |  40.7  |         |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py)  |     [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco-a0c13c73.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco_20200505_001404.log.json)     |
+|  ×   |    SSD300    |     VGG16      |   1x    |  25.6  |         |                                                            -                                                            |                                                                                                                                                                                                                                                                                                                                    |
+|  √   |    SSD300    |     VGG16      |   1x    |  27.6  |         |            [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_ssd300_coco.py)             |                                               [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco-710e3ac9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco_20200504_144325.log.json)                                               |
+|  ×   |    SSD512    |     VGG16      |   1x    |  29.3  |         |                                                            -                                                            |                                                                                                                                                                                                                                                                                                                                    |
+|  √   |    SSD512    |     VGG16      |   1x    |  31.8  |         |            [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pisa/pisa_ssd512_coco.py)             |                                               [model](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco-247addee.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco_20200508_131030.log.json)                                               |
+
+**Notes:**
+
+- In the original paper, all models are trained and tested on mmdet v1.x, thus results may not be exactly the same with this release on v2.0.
+- It is noted PISA only modifies the training pipeline so the inference time remains the same with the baseline.
+
+## Citation
+
+```latex
+@inproceedings{cao2019prime,
+  title={Prime sample attention in object detection},
+  author={Cao, Yuhang and Chen, Kai and Loy, Chen Change and Lin, Dahua},
+  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+  year={2020}
+}
+```
diff --git a/configs/pisa/metafile.yml b/configs/pisa/metafile.yml
new file mode 100644
index 0000000..cd43afb
--- /dev/null
+++ b/configs/pisa/metafile.yml
@@ -0,0 +1,110 @@
+Collections:
+  - Name: PISA
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - PISA
+        - RPN
+        - ResNet
+        - RoIPool
+    Paper:
+      URL: https://arxiv.org/abs/1904.04821
+      Title: 'Prime Sample Attention in Object Detection'
+    README: configs/pisa/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/roi_heads/pisa_roi_head.py#L8
+      Version: v2.1.0
+
+Models:
+  - Name: pisa_faster_rcnn_r50_fpn_1x_coco
+    In Collection: PISA
+    Config: configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_r50_fpn_1x_coco/pisa_faster_rcnn_r50_fpn_1x_coco-dea93523.pth
+
+  - Name: pisa_faster_rcnn_x101_32x4d_fpn_1x_coco
+    In Collection: PISA
+    Config: configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco-e4accec4.pth
+
+  - Name: pisa_mask_rcnn_r50_fpn_1x_coco
+    In Collection: PISA
+    Config: configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 35.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_mask_rcnn_r50_fpn_1x_coco/pisa_mask_rcnn_r50_fpn_1x_coco-dfcedba6.pth
+
+  - Name: pisa_retinanet_r50_fpn_1x_coco
+    In Collection: PISA
+    Config: configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_r50_fpn_1x_coco/pisa_retinanet_r50_fpn_1x_coco-76409952.pth
+
+  - Name: pisa_retinanet_x101_32x4d_fpn_1x_coco
+    In Collection: PISA
+    Config: configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco/pisa_retinanet_x101_32x4d_fpn_1x_coco-a0c13c73.pth
+
+  - Name: pisa_ssd300_coco
+    In Collection: PISA
+    Config: configs/pisa/pisa_ssd300_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 27.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd300_coco/pisa_ssd300_coco-710e3ac9.pth
+
+  - Name: pisa_ssd512_coco
+    In Collection: PISA
+    Config: configs/pisa/pisa_ssd512_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 31.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pisa/pisa_ssd512_coco/pisa_ssd512_coco-247addee.pth
diff --git a/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py b/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..71e65b0
--- /dev/null
+++ b/configs/pisa/pisa_faster_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,30 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+
+model = dict(
+    roi_head=dict(
+        type='PISARoIHead',
+        bbox_head=dict(
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    train_cfg=dict(
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            sampler=dict(
+                type='ScoreHLRSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True,
+                k=0.5,
+                bias=0.),
+            isr=dict(k=2, bias=0),
+            carl=dict(k=1, bias=0.2))),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0)))
diff --git a/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..16edd99
--- /dev/null
+++ b/configs/pisa/pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,30 @@
+_base_ = '../faster_rcnn/faster_rcnn_x101_32x4d_fpn_1x_coco.py'
+
+model = dict(
+    roi_head=dict(
+        type='PISARoIHead',
+        bbox_head=dict(
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    train_cfg=dict(
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            sampler=dict(
+                type='ScoreHLRSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True,
+                k=0.5,
+                bias=0.),
+            isr=dict(k=2, bias=0),
+            carl=dict(k=1, bias=0.2))),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0)))
diff --git a/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py b/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..047a293
--- /dev/null
+++ b/configs/pisa/pisa_mask_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,30 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+
+model = dict(
+    roi_head=dict(
+        type='PISARoIHead',
+        bbox_head=dict(
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    train_cfg=dict(
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            sampler=dict(
+                type='ScoreHLRSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True,
+                k=0.5,
+                bias=0.),
+            isr=dict(k=2, bias=0),
+            carl=dict(k=1, bias=0.2))),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0)))
diff --git a/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py b/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..2186a8f
--- /dev/null
+++ b/configs/pisa/pisa_mask_rcnn_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,30 @@
+_base_ = '../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py'
+
+model = dict(
+    roi_head=dict(
+        type='PISARoIHead',
+        bbox_head=dict(
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))),
+    train_cfg=dict(
+        rpn_proposal=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            sampler=dict(
+                type='ScoreHLRSampler',
+                num=512,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True,
+                k=0.5,
+                bias=0.),
+            isr=dict(k=2, bias=0),
+            carl=dict(k=1, bias=0.2))),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0)))
diff --git a/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py b/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..70f89e2
--- /dev/null
+++ b/configs/pisa/pisa_retinanet_r50_fpn_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = '../retinanet/retinanet_r50_fpn_1x_coco.py'
+
+model = dict(
+    bbox_head=dict(
+        type='PISARetinaHead',
+        loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
+    train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
diff --git a/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py b/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..b97b672
--- /dev/null
+++ b/configs/pisa/pisa_retinanet_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = '../retinanet/retinanet_x101_32x4d_fpn_1x_coco.py'
+
+model = dict(
+    bbox_head=dict(
+        type='PISARetinaHead',
+        loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0)),
+    train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
diff --git a/configs/pisa/pisa_ssd300_coco.py b/configs/pisa/pisa_ssd300_coco.py
new file mode 100644
index 0000000..b5cc006
--- /dev/null
+++ b/configs/pisa/pisa_ssd300_coco.py
@@ -0,0 +1,8 @@
+_base_ = '../ssd/ssd300_coco.py'
+
+model = dict(
+    bbox_head=dict(type='PISASSDHead'),
+    train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
+
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/pisa/pisa_ssd512_coco.py b/configs/pisa/pisa_ssd512_coco.py
new file mode 100644
index 0000000..3219d6d
--- /dev/null
+++ b/configs/pisa/pisa_ssd512_coco.py
@@ -0,0 +1,8 @@
+_base_ = '../ssd/ssd512_coco.py'
+
+model = dict(
+    bbox_head=dict(type='PISASSDHead'),
+    train_cfg=dict(isr=dict(k=2., bias=0.), carl=dict(k=1., bias=0.2)))
+
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/point_rend/README.md b/configs/point_rend/README.md
new file mode 100644
index 0000000..183e83d
--- /dev/null
+++ b/configs/point_rend/README.md
@@ -0,0 +1,33 @@
+# PointRend
+
+> [PointRend: Image Segmentation as Rendering](https://arxiv.org/abs/1912.08193)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We present a new method for efficient high-quality image segmentation of objects and scenes. By analogizing classical computer graphics methods for efficient rendering with over- and undersampling challenges faced in pixel labeling tasks, we develop a unique perspective of image segmentation as a rendering problem. From this vantage, we present the PointRend (Point-based Rendering) neural network module: a module that performs point-based segmentation predictions at adaptively selected locations based on an iterative subdivision algorithm. PointRend can be flexibly applied to both instance and semantic segmentation tasks by building on top of existing state-of-the-art models. While many concrete implementations of the general idea are possible, we show that a simple design already achieves excellent results. Qualitatively, PointRend outputs crisp object boundaries in regions that are over-smoothed by previous methods. Quantitatively, PointRend yields significant gains on COCO and Cityscapes, for both instance and semantic segmentation. PointRend's efficiency enables output resolutions that are otherwise impractical in terms of memory or computation compared to existing approaches.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143970097-d38b6801-d3c8-468f-b8b0-639be3689907.png"/>
+</div>
+
+## Results and Models
+
+| Backbone | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                             Config                                                             |                                                                                                                                                                      Download                                                                                                                                                                      |
+| :------: | :---: | :-----: | :------: | :------------: | :----: | :-----: | :----------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R-50-FPN | caffe |   1x    |   4.6    |                |  38.4  |  36.3   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco_20200612_161407.log.json) |
+| R-50-FPN | caffe |   3x    |   4.6    |                |  41.0  |  38.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco-e0ebb6b7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco_20200614_002632.log.json) |
+
+Note: All models are trained with multi-scale, the input image shorter side is randomly scaled to one of (640, 672, 704, 736, 768, 800).
+
+## Citation
+
+```latex
+@InProceedings{kirillov2019pointrend,
+  title={{PointRend}: Image Segmentation as Rendering},
+  author={Alexander Kirillov and Yuxin Wu and Kaiming He and Ross Girshick},
+  journal={ArXiv:1912.08193},
+  year={2019}
+}
+```
diff --git a/configs/point_rend/metafile.yml b/configs/point_rend/metafile.yml
new file mode 100644
index 0000000..82aea05
--- /dev/null
+++ b/configs/point_rend/metafile.yml
@@ -0,0 +1,54 @@
+Collections:
+  - Name: PointRend
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - PointRend
+        - FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1912.08193
+      Title: 'PointRend: Image Segmentation as Rendering'
+    README: configs/point_rend/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.2.0/mmdet/models/detectors/point_rend.py#L6
+      Version: v2.2.0
+
+Models:
+  - Name: point_rend_r50_caffe_fpn_mstrain_1x_coco
+    In Collection: PointRend
+    Config: configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.6
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco/point_rend_r50_caffe_fpn_mstrain_1x_coco-1bcb5fb4.pth
+
+  - Name: point_rend_r50_caffe_fpn_mstrain_3x_coco
+    In Collection: PointRend
+    Config: configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 4.6
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco/point_rend_r50_caffe_fpn_mstrain_3x_coco-e0ebb6b7.pth
diff --git a/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py b/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py
new file mode 100644
index 0000000..0c0e563
--- /dev/null
+++ b/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py
@@ -0,0 +1,44 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py'
+# model settings
+model = dict(
+    type='PointRend',
+    roi_head=dict(
+        type='PointRendRoIHead',
+        mask_roi_extractor=dict(
+            type='GenericRoIExtractor',
+            aggregation='concat',
+            roi_layer=dict(
+                _delete_=True, type='SimpleRoIAlign', output_size=14),
+            out_channels=256,
+            featmap_strides=[4]),
+        mask_head=dict(
+            _delete_=True,
+            type='CoarseMaskHead',
+            num_fcs=2,
+            in_channels=256,
+            conv_out_channels=256,
+            fc_out_channels=1024,
+            num_classes=80,
+            loss_mask=dict(
+                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
+        point_head=dict(
+            type='MaskPointHead',
+            num_fcs=3,
+            in_channels=256,
+            fc_channels=256,
+            num_classes=80,
+            coarse_pred_each_layer=True,
+            loss_point=dict(
+                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rcnn=dict(
+            mask_size=7,
+            num_points=14 * 14,
+            oversample_ratio=3,
+            importance_sample_ratio=0.75)),
+    test_cfg=dict(
+        rcnn=dict(
+            subdivision_steps=5,
+            subdivision_num_points=28 * 28,
+            scale_factor=2)))
diff --git a/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py b/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..169278e
--- /dev/null
+++ b/configs/point_rend/point_rend_r50_caffe_fpn_mstrain_3x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './point_rend_r50_caffe_fpn_mstrain_1x_coco.py'
+# learning policy
+lr_config = dict(step=[28, 34])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/configs/pvt/README.md b/configs/pvt/README.md
new file mode 100644
index 0000000..1fd090b
--- /dev/null
+++ b/configs/pvt/README.md
@@ -0,0 +1,57 @@
+# PVT
+
+> [Pyramid vision transformer: A versatile backbone for dense prediction without convolutions](https://arxiv.org/abs/2102.12122)
+
+<!-- [BACKBONE] -->
+
+## Abstract
+
+Although using convolutional neural networks (CNNs) as backbones achieves great successes in computer vision, this work investigates a simple backbone network useful for many dense prediction tasks without convolutions. Unlike the recently-proposed Transformer model (e.g., ViT) that is specially designed for image classification, we propose Pyramid Vision Transformer~(PVT), which overcomes the difficulties of porting Transformer to various dense prediction tasks. PVT has several merits compared to prior arts. (1) Different from ViT that typically has low-resolution outputs and high computational and memory cost, PVT can be not only trained on dense partitions of the image to achieve high output resolution, which is important for dense predictions but also using a progressive shrinking pyramid to reduce computations of large feature maps. (2) PVT inherits the advantages from both CNN and Transformer, making it a unified backbone in various vision tasks without convolutions by simply replacing CNN backbones. (3) We validate PVT by conducting extensive experiments, showing that it boosts the performance of many downstream tasks, e.g., object detection, semantic, and instance segmentation. For example, with a comparable number of parameters, RetinaNet+PVT achieves 40.4 AP on the COCO dataset, surpassing RetinNet+ResNet50 (36.3 AP) by 4.1 absolute AP. We hope PVT could serve as an alternative and useful backbone for pixel-level predictions and facilitate future researches.
+
+Transformer recently has shown encouraging progresses in computer vision. In this work, we present new baselines by improving the original Pyramid Vision Transformer (abbreviated as PVTv1) by adding three designs, including (1) overlapping patch embedding, (2) convolutional feed-forward networks, and (3) linear complexity attention layers.
+With these modifications, our PVTv2 significantly improves PVTv1 on three tasks e.g., classification, detection, and segmentation. Moreover, PVTv2 achieves comparable or better performances than recent works such as Swin Transformer. We hope this work will facilitate state-of-the-art Transformer researches in computer vision.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143969989-6f94e695-23b1-4f8f-b406-d589fdc3cfb2.png"/>
+</div>
+
+## Results and Models
+
+### RetinaNet (PVTv1)
+
+|  Backbone  | Lr schd | Mem (GB) | box AP |                                                   Config                                                   |                                                                                                                                             Download                                                                                                                                             |
+| :--------: | :-----: | :------: | :----: | :--------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  PVT-Tiny  |   12e   |   8.5    |  36.6  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_t_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-t_fpn_1x_coco/retinanet_pvt-t_fpn_1x_coco_20210831_103110-17b566bd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-t_fpn_1x_coco/retinanet_pvt-t_fpn_1x_coco_20210831_103110.log.json) |
+| PVT-Small  |   12e   |   14.5   |  40.4  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_s_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-s_fpn_1x_coco/retinanet_pvt-s_fpn_1x_coco_20210906_142921-b6c94a5b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-s_fpn_1x_coco/retinanet_pvt-s_fpn_1x_coco_20210906_142921.log.json) |
+| PVT-Medium |   12e   |   20.9   |  41.7  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_m_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-m_fpn_1x_coco/retinanet_pvt-m_fpn_1x_coco_20210831_103243-55effa1b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-m_fpn_1x_coco/retinanet_pvt-m_fpn_1x_coco_20210831_103243.log.json) |
+
+### RetinaNet (PVTv2)
+
+| Backbone | Lr schd | Mem (GB) | box AP |                                                     Config                                                     |                                                                                                                                                   Download                                                                                                                                                   |
+| :------: | :-----: | :------: | :----: | :------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| PVTv2-B0 |   12e   |   7.4    |  37.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_v2_b0_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b0_fpn_1x_coco/retinanet_pvtv2-b0_fpn_1x_coco_20210831_103157-13e9aabe.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b0_fpn_1x_coco/retinanet_pvtv2-b0_fpn_1x_coco_20210831_103157.log.json) |
+| PVTv2-B1 |   12e   |   9.5    |  41.2  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_v2_b1_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b1_fpn_1x_coco/retinanet_pvtv2-b1_fpn_1x_coco_20210831_103318-7e169a7d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b1_fpn_1x_coco/retinanet_pvtv2-b1_fpn_1x_coco_20210831_103318.log.json) |
+| PVTv2-B2 |   12e   |   16.2   |  44.6  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_v2_b2_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b2_fpn_1x_coco/retinanet_pvtv2-b2_fpn_1x_coco_20210901_174843-529f0b9a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b2_fpn_1x_coco/retinanet_pvtv2-b2_fpn_1x_coco_20210901_174843.log.json) |
+| PVTv2-B3 |   12e   |   23.0   |  46.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_v2_b3_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b3_fpn_1x_coco/retinanet_pvtv2-b3_fpn_1x_coco_20210903_151512-8357deff.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b3_fpn_1x_coco/retinanet_pvtv2-b3_fpn_1x_coco_20210903_151512.log.json) |
+| PVTv2-B4 |   12e   |   17.0   |  46.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_v2_b4_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b4_fpn_1x_coco/retinanet_pvtv2-b4_fpn_1x_coco_20210901_170151-83795c86.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b4_fpn_1x_coco/retinanet_pvtv2-b4_fpn_1x_coco_20210901_170151.log.json) |
+| PVTv2-B5 |   12e   |   18.7   |  46.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/pvt/retinanet_pvt_v2_b5_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b5_fpn_1x_coco/retinanet_pvtv2-b5_fpn_1x_coco_20210902_201800-3420eb57.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b5_fpn_1x_coco/retinanet_pvtv2-b5_fpn_1x_coco_20210902_201800.log.json) |
+
+## Citation
+
+```latex
+@article{wang2021pyramid,
+  title={Pyramid vision transformer: A versatile backbone for dense prediction without convolutions},
+  author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
+  journal={arXiv preprint arXiv:2102.12122},
+  year={2021}
+}
+```
+
+```latex
+@article{wang2021pvtv2,
+  title={PVTv2: Improved Baselines with Pyramid Vision Transformer},
+  author={Wang, Wenhai and Xie, Enze and Li, Xiang and Fan, Deng-Ping and Song, Kaitao and Liang, Ding and Lu, Tong and Luo, Ping and Shao, Ling},
+  journal={arXiv preprint arXiv:2106.13797},
+  year={2021}
+}
+```
diff --git a/configs/pvt/metafile.yml b/configs/pvt/metafile.yml
new file mode 100644
index 0000000..5884378
--- /dev/null
+++ b/configs/pvt/metafile.yml
@@ -0,0 +1,243 @@
+Models:
+  - Name: retinanet_pvt-t_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/pvt/retinanet_pvt-t_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.5
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - PyramidVisionTransformer
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-t_fpn_1x_coco/retinanet_pvt-t_fpn_1x_coco_20210831_103110-17b566bd.pth
+    Paper:
+      URL: https://arxiv.org/abs/2102.12122
+      Title: "Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions"
+    README: configs/pvt/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L315
+      Version: 2.17.0
+
+  - Name: retinanet_pvt-s_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/pvt/retinanet_pvt-s_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 14.5
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - PyramidVisionTransformer
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-s_fpn_1x_coco/retinanet_pvt-s_fpn_1x_coco_20210906_142921-b6c94a5b.pth
+    Paper:
+      URL: https://arxiv.org/abs/2102.12122
+      Title: "Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions"
+    README: configs/pvt/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L315
+      Version: 2.17.0
+
+  - Name: retinanet_pvt-m_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/pvt/retinanet_pvt-m_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 20.9
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - PyramidVisionTransformer
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvt-m_fpn_1x_coco/retinanet_pvt-m_fpn_1x_coco_20210831_103243-55effa1b.pth
+    Paper:
+      URL: https://arxiv.org/abs/2102.12122
+      Title: "Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions"
+    README: configs/pvt/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L315
+      Version: 2.17.0
+
+  - Name: retinanet_pvtv2-b0_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/pvt/retinanet_pvtv2-b0_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.4
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - PyramidVisionTransformerV2
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b0_fpn_1x_coco/retinanet_pvtv2-b0_fpn_1x_coco_20210831_103157-13e9aabe.pth
+    Paper:
+      URL: https://arxiv.org/abs/2106.13797
+      Title: "PVTv2: Improved Baselines with Pyramid Vision Transformer"
+    README: configs/pvt/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L543
+      Version: 2.17.0
+
+  - Name: retinanet_pvtv2-b1_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/pvt/retinanet_pvtv2-b1_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 9.5
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - PyramidVisionTransformerV2
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b1_fpn_1x_coco/retinanet_pvtv2-b1_fpn_1x_coco_20210831_103318-7e169a7d.pth
+    Paper:
+      URL: https://arxiv.org/abs/2106.13797
+      Title: "PVTv2: Improved Baselines with Pyramid Vision Transformer"
+    README: configs/pvt/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L543
+      Version: 2.17.0
+
+  - Name: retinanet_pvtv2-b2_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/pvt/retinanet_pvtv2-b2_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 16.2
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - PyramidVisionTransformerV2
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b2_fpn_1x_coco/retinanet_pvtv2-b2_fpn_1x_coco_20210901_174843-529f0b9a.pth
+    Paper:
+      URL: https://arxiv.org/abs/2106.13797
+      Title: "PVTv2: Improved Baselines with Pyramid Vision Transformer"
+    README: configs/pvt/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L543
+      Version: 2.17.0
+
+  - Name: retinanet_pvtv2-b3_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/pvt/retinanet_pvtv2-b3_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 23.0
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - PyramidVisionTransformerV2
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b3_fpn_1x_coco/retinanet_pvtv2-b3_fpn_1x_coco_20210903_151512-8357deff.pth
+    Paper:
+      URL: https://arxiv.org/abs/2106.13797
+      Title: "PVTv2: Improved Baselines with Pyramid Vision Transformer"
+    README: configs/pvt/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L543
+      Version: 2.17.0
+
+  - Name: retinanet_pvtv2-b4_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/pvt/retinanet_pvtv2-b4_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 17.0
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - PyramidVisionTransformerV2
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b4_fpn_1x_coco/retinanet_pvtv2-b4_fpn_1x_coco_20210901_170151-83795c86.pth
+    Paper:
+      URL: https://arxiv.org/abs/2106.13797
+      Title: "PVTv2: Improved Baselines with Pyramid Vision Transformer"
+    README: configs/pvt/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L543
+      Version: 2.17.0
+
+  - Name: retinanet_pvtv2-b5_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/pvt/retinanet_pvtv2-b5_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 18.7
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x NVIDIA V100 GPUs
+      Architecture:
+        - PyramidVisionTransformerV2
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/pvt/retinanet_pvtv2-b5_fpn_1x_coco/retinanet_pvtv2-b5_fpn_1x_coco_20210902_201800-3420eb57.pth
+    Paper:
+      URL: https://arxiv.org/abs/2106.13797
+      Title: "PVTv2: Improved Baselines with Pyramid Vision Transformer"
+    README: configs/pvt/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.17.0/mmdet/models/backbones/pvt.py#L543
+      Version: 2.17.0
diff --git a/configs/pvt/retinanet_pvt-l_fpn_1x_coco.py b/configs/pvt/retinanet_pvt-l_fpn_1x_coco.py
new file mode 100644
index 0000000..e299f2a
--- /dev/null
+++ b/configs/pvt/retinanet_pvt-l_fpn_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = 'retinanet_pvt-t_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        num_layers=[3, 8, 27, 3],
+        init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
+                      'releases/download/v2/pvt_large.pth')))
+fp16 = dict(loss_scale=dict(init_scale=512))
diff --git a/configs/pvt/retinanet_pvt-m_fpn_1x_coco.py b/configs/pvt/retinanet_pvt-m_fpn_1x_coco.py
new file mode 100644
index 0000000..b888f78
--- /dev/null
+++ b/configs/pvt/retinanet_pvt-m_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = 'retinanet_pvt-t_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        num_layers=[3, 4, 18, 3],
+        init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
+                      'releases/download/v2/pvt_medium.pth')))
diff --git a/configs/pvt/retinanet_pvt-s_fpn_1x_coco.py b/configs/pvt/retinanet_pvt-s_fpn_1x_coco.py
new file mode 100644
index 0000000..4660348
--- /dev/null
+++ b/configs/pvt/retinanet_pvt-s_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = 'retinanet_pvt-t_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        num_layers=[3, 4, 6, 3],
+        init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
+                      'releases/download/v2/pvt_small.pth')))
diff --git a/configs/pvt/retinanet_pvt-t_fpn_1x_coco.py b/configs/pvt/retinanet_pvt-t_fpn_1x_coco.py
new file mode 100644
index 0000000..a6cff7d
--- /dev/null
+++ b/configs/pvt/retinanet_pvt-t_fpn_1x_coco.py
@@ -0,0 +1,16 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='RetinaNet',
+    backbone=dict(
+        _delete_=True,
+        type='PyramidVisionTransformer',
+        num_layers=[2, 2, 2, 2],
+        init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
+                      'releases/download/v2/pvt_tiny.pth')),
+    neck=dict(in_channels=[64, 128, 320, 512]))
+# optimizer
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0001)
diff --git a/configs/pvt/retinanet_pvtv2-b0_fpn_1x_coco.py b/configs/pvt/retinanet_pvtv2-b0_fpn_1x_coco.py
new file mode 100644
index 0000000..cbe2295
--- /dev/null
+++ b/configs/pvt/retinanet_pvtv2-b0_fpn_1x_coco.py
@@ -0,0 +1,17 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='RetinaNet',
+    backbone=dict(
+        _delete_=True,
+        type='PyramidVisionTransformerV2',
+        embed_dims=32,
+        num_layers=[2, 2, 2, 2],
+        init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
+                      'releases/download/v2/pvt_v2_b0.pth')),
+    neck=dict(in_channels=[32, 64, 160, 256]))
+# optimizer
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0001, weight_decay=0.0001)
diff --git a/configs/pvt/retinanet_pvtv2-b1_fpn_1x_coco.py b/configs/pvt/retinanet_pvtv2-b1_fpn_1x_coco.py
new file mode 100644
index 0000000..5374c50
--- /dev/null
+++ b/configs/pvt/retinanet_pvtv2-b1_fpn_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        embed_dims=64,
+        init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
+                      'releases/download/v2/pvt_v2_b1.pth')),
+    neck=dict(in_channels=[64, 128, 320, 512]))
diff --git a/configs/pvt/retinanet_pvtv2-b2_fpn_1x_coco.py b/configs/pvt/retinanet_pvtv2-b2_fpn_1x_coco.py
new file mode 100644
index 0000000..cf9a18d
--- /dev/null
+++ b/configs/pvt/retinanet_pvtv2-b2_fpn_1x_coco.py
@@ -0,0 +1,8 @@
+_base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        embed_dims=64,
+        num_layers=[3, 4, 6, 3],
+        init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
+                      'releases/download/v2/pvt_v2_b2.pth')),
+    neck=dict(in_channels=[64, 128, 320, 512]))
diff --git a/configs/pvt/retinanet_pvtv2-b3_fpn_1x_coco.py b/configs/pvt/retinanet_pvtv2-b3_fpn_1x_coco.py
new file mode 100644
index 0000000..7a47f82
--- /dev/null
+++ b/configs/pvt/retinanet_pvtv2-b3_fpn_1x_coco.py
@@ -0,0 +1,8 @@
+_base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        embed_dims=64,
+        num_layers=[3, 4, 18, 3],
+        init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
+                      'releases/download/v2/pvt_v2_b3.pth')),
+    neck=dict(in_channels=[64, 128, 320, 512]))
diff --git a/configs/pvt/retinanet_pvtv2-b4_fpn_1x_coco.py b/configs/pvt/retinanet_pvtv2-b4_fpn_1x_coco.py
new file mode 100644
index 0000000..9891d7b
--- /dev/null
+++ b/configs/pvt/retinanet_pvtv2-b4_fpn_1x_coco.py
@@ -0,0 +1,18 @@
+_base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        embed_dims=64,
+        num_layers=[3, 8, 27, 3],
+        init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
+                      'releases/download/v2/pvt_v2_b4.pth')),
+    neck=dict(in_channels=[64, 128, 320, 512]))
+# optimizer
+optimizer = dict(
+    _delete_=True, type='AdamW', lr=0.0001 / 1.4, weight_decay=0.0001)
+# dataset settings
+data = dict(samples_per_gpu=1, workers_per_gpu=1)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (1 samples per GPU)
+auto_scale_lr = dict(base_batch_size=8)
diff --git a/configs/pvt/retinanet_pvtv2-b5_fpn_1x_coco.py b/configs/pvt/retinanet_pvtv2-b5_fpn_1x_coco.py
new file mode 100644
index 0000000..a9fea2e
--- /dev/null
+++ b/configs/pvt/retinanet_pvtv2-b5_fpn_1x_coco.py
@@ -0,0 +1,19 @@
+_base_ = 'retinanet_pvtv2-b0_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        embed_dims=64,
+        num_layers=[3, 6, 40, 3],
+        mlp_ratios=(4, 4, 4, 4),
+        init_cfg=dict(checkpoint='https://github.com/whai362/PVT/'
+                      'releases/download/v2/pvt_v2_b5.pth')),
+    neck=dict(in_channels=[64, 128, 320, 512]))
+# optimizer
+optimizer = dict(
+    _delete_=True, type='AdamW', lr=0.0001 / 1.4, weight_decay=0.0001)
+# dataset settings
+data = dict(samples_per_gpu=1, workers_per_gpu=1)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (1 samples per GPU)
+auto_scale_lr = dict(base_batch_size=8)
diff --git a/configs/queryinst/README.md b/configs/queryinst/README.md
new file mode 100644
index 0000000..ad6e0b3
--- /dev/null
+++ b/configs/queryinst/README.md
@@ -0,0 +1,36 @@
+# QueryInst
+
+> [Instances as Queries](https://openaccess.thecvf.com/content/ICCV2021/html/Fang_Instances_As_Queries_ICCV_2021_paper.html)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We present QueryInst, a new perspective for instance segmentation. QueryInst is a multi-stage end-to-end system that treats instances of interest as learnable queries, enabling query based object detectors, e.g., Sparse R-CNN, to have strong instance segmentation performance. The attributes of instances such as categories, bounding boxes, instance masks, and instance association embeddings are represented by queries in a unified manner. In QueryInst, a query is shared by both detection and segmentation via dynamic convolutions and driven by parallelly-supervised multi-stage learning. We conduct extensive experiments on three challenging benchmarks, i.e., COCO, CityScapes, and YouTube-VIS to evaluate the effectiveness of QueryInst in object detection, instance segmentation, and video instance segmentation tasks. For the first time, we demonstrate that a simple end-to-end query based framework can achieve the state-of-the-art performance in various instance-level recognition tasks.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143971527-c1b7ff78-e95f-4edb-9d5e-3d6d7d902999.png"/>
+</div>
+
+## Results and Models
+
+|   Model   | Backbone  |  Style  | Lr schd | Number of Proposals | Multi-Scale | RandomCrop | box AP | mask AP |                                                                       Config                                                                       |                                                                                                                                                                                                                       Download                                                                                                                                                                                                                       |
+| :-------: | :-------: | :-----: | :-----: | :-----------------: | :---------: | :--------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| QueryInst | R-50-FPN  | pytorch |   1x    |         100         |    False    |   False    |  42.0  |  37.5   |                   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_1x_coco.py)                   |                                                                         [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_1x_coco/queryinst_r50_fpn_1x_coco_20210907_084916-5a8f1998.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_1x_coco/queryinst_r50_fpn_1x_coco_20210907_084916.log.json)                                                                         |
+| QueryInst | R-50-FPN  | pytorch |   3x    |         100         |    True     |   False    |  44.8  |  39.8   |           [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py)           |                                         [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco/queryinst_r50_fpn_mstrain_480-800_3x_coco_20210901_103643-7837af86.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco/queryinst_r50_fpn_mstrain_480-800_3x_coco_20210901_103643.log.json)                                         |
+| QueryInst | R-50-FPN  | pytorch |   3x    |         300         |    True     |    True    |  47.5  |  41.7   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_101802-85cffbd8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_101802.log.json)   |
+| QueryInst | R-101-FPN | pytorch |   3x    |         100         |    True     |   False    |  46.4  |  41.0   |          [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py)           |                                       [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco/queryinst_r101_fpn_mstrain_480-800_3x_coco_20210904_104048-91f9995b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco/queryinst_r101_fpn_mstrain_480-800_3x_coco_20210904_104048.log.json)                                       |
+| QueryInst | R-101-FPN | pytorch |   3x    |         300         |    True     |    True    |  49.0  |  42.9   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_153621-76cce59f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_153621.log.json) |
+
+## Citation
+
+```latex
+@InProceedings{Fang_2021_ICCV,
+    author    = {Fang, Yuxin and Yang, Shusheng and Wang, Xinggang and Li, Yu and Fang, Chen and Shan, Ying and Feng, Bin and Liu, Wenyu},
+    title     = {Instances As Queries},
+    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
+    month     = {October},
+    year      = {2021},
+    pages     = {6910-6919}
+}
+```
diff --git a/configs/queryinst/metafile.yml b/configs/queryinst/metafile.yml
new file mode 100644
index 0000000..da7f0a7
--- /dev/null
+++ b/configs/queryinst/metafile.yml
@@ -0,0 +1,100 @@
+Collections:
+  - Name: QueryInst
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - AdamW
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - ResNet
+        - QueryInst
+    Paper:
+      URL: https://openaccess.thecvf.com/content/ICCV2021/papers/Fang_Instances_As_Queries_ICCV_2021_paper.pdf
+      Title: 'Instances as Queries'
+    README: configs/queryinst/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/detectors/queryinst.py
+      Version: v2.18.0
+
+Models:
+  - Name: queryinst_r50_fpn_1x_coco
+    In Collection: QueryInst
+    Config: configs/queryinst/queryinst_r50_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_1x_coco/queryinst_r50_fpn_1x_coco_20210907_084916-5a8f1998.pth
+
+  - Name: queryinst_r50_fpn_mstrain_480-800_3x_coco
+    In Collection: QueryInst
+    Config: configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco/queryinst_r50_fpn_mstrain_480-800_3x_coco_20210901_103643-7837af86.pth
+
+  - Name: queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco
+    In Collection: QueryInst
+    Config: configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_101802-85cffbd8.pth
+
+  - Name: queryinst_r101_fpn_mstrain_480-800_3x_coco
+    In Collection: QueryInst
+    Config: configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco/queryinst_r101_fpn_mstrain_480-800_3x_coco_20210904_104048-91f9995b.pth
+
+  - Name: queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco
+    In Collection: QueryInst
+    Config: configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 49.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 42.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20210904_153621-76cce59f.pth
diff --git a/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
new file mode 100644
index 0000000..fd138f5
--- /dev/null
+++ b/configs/queryinst/queryinst_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py'
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py b/configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py
new file mode 100644
index 0000000..07cae19
--- /dev/null
+++ b/configs/queryinst/queryinst_r101_fpn_mstrain_480-800_3x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './queryinst_r50_fpn_mstrain_480-800_3x_coco.py'
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/queryinst/queryinst_r50_fpn_1x_coco.py b/configs/queryinst/queryinst_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..48f5773
--- /dev/null
+++ b/configs/queryinst/queryinst_r50_fpn_1x_coco.py
@@ -0,0 +1,138 @@
+_base_ = [
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+num_stages = 6
+num_proposals = 100
+model = dict(
+    type='QueryInst',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=0,
+        add_extra_convs='on_input',
+        num_outs=4),
+    rpn_head=dict(
+        type='EmbeddingRPNHead',
+        num_proposals=num_proposals,
+        proposal_feature_channel=256),
+    roi_head=dict(
+        type='SparseRoIHead',
+        num_stages=num_stages,
+        stage_loss_weights=[1] * num_stages,
+        proposal_feature_channel=256,
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        mask_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=[
+            dict(
+                type='DIIHead',
+                num_classes=80,
+                num_ffn_fcs=2,
+                num_heads=8,
+                num_cls_fcs=1,
+                num_reg_fcs=3,
+                feedforward_channels=2048,
+                in_channels=256,
+                dropout=0.0,
+                ffn_act_cfg=dict(type='ReLU', inplace=True),
+                dynamic_conv_cfg=dict(
+                    type='DynamicConv',
+                    in_channels=256,
+                    feat_channels=64,
+                    out_channels=256,
+                    input_feat_shape=7,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    norm_cfg=dict(type='LN')),
+                loss_bbox=dict(type='L1Loss', loss_weight=5.0),
+                loss_iou=dict(type='GIoULoss', loss_weight=2.0),
+                loss_cls=dict(
+                    type='FocalLoss',
+                    use_sigmoid=True,
+                    gamma=2.0,
+                    alpha=0.25,
+                    loss_weight=2.0),
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    clip_border=False,
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.5, 0.5, 1., 1.])) for _ in range(num_stages)
+        ],
+        mask_head=[
+            dict(
+                type='DynamicMaskHead',
+                dynamic_conv_cfg=dict(
+                    type='DynamicConv',
+                    in_channels=256,
+                    feat_channels=64,
+                    out_channels=256,
+                    input_feat_shape=14,
+                    with_proj=False,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    norm_cfg=dict(type='LN')),
+                num_convs=4,
+                num_classes=80,
+                roi_feat_size=14,
+                in_channels=256,
+                conv_kernel_size=3,
+                conv_out_channels=256,
+                class_agnostic=False,
+                norm_cfg=dict(type='BN'),
+                upsample_cfg=dict(type='deconv', scale_factor=2),
+                loss_mask=dict(
+                    type='DiceLoss',
+                    loss_weight=8.0,
+                    use_sigmoid=True,
+                    activate=False,
+                    eps=1e-5)) for _ in range(num_stages)
+        ]),
+    # training and testing settings
+    train_cfg=dict(
+        rpn=None,
+        rcnn=[
+            dict(
+                assigner=dict(
+                    type='HungarianAssigner',
+                    cls_cost=dict(type='FocalLossCost', weight=2.0),
+                    reg_cost=dict(type='BBoxL1Cost', weight=5.0),
+                    iou_cost=dict(type='IoUCost', iou_mode='giou',
+                                  weight=2.0)),
+                sampler=dict(type='PseudoSampler'),
+                pos_weight=1,
+                mask_size=28,
+            ) for _ in range(num_stages)
+        ]),
+    test_cfg=dict(
+        rpn=None, rcnn=dict(max_per_img=num_proposals, mask_thr_binary=0.5)))
+
+# optimizer
+optimizer = dict(
+    _delete_=True,
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(
+        custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)}))
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=0.1, norm_type=2))
+# learning policy
+lr_config = dict(policy='step', step=[8, 11], warmup_iters=1000)
+runner = dict(type='EpochBasedRunner', max_epochs=12)
diff --git a/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
new file mode 100644
index 0000000..3089b3c
--- /dev/null
+++ b/configs/queryinst/queryinst_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
@@ -0,0 +1,54 @@
+_base_ = './queryinst_r50_fpn_mstrain_480-800_3x_coco.py'
+num_proposals = 300
+model = dict(
+    rpn_head=dict(num_proposals=num_proposals),
+    test_cfg=dict(
+        _delete_=True,
+        rpn=None,
+        rcnn=dict(max_per_img=num_proposals, mask_thr_binary=0.5)))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# augmentation strategy originates from DETR.
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='AutoAugment',
+        policies=[[
+            dict(
+                type='Resize',
+                img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                           (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                           (736, 1333), (768, 1333), (800, 1333)],
+                multiscale_mode='value',
+                keep_ratio=True)
+        ],
+                  [
+                      dict(
+                          type='Resize',
+                          img_scale=[(400, 1333), (500, 1333), (600, 1333)],
+                          multiscale_mode='value',
+                          keep_ratio=True),
+                      dict(
+                          type='RandomCrop',
+                          crop_type='absolute_range',
+                          crop_size=(384, 600),
+                          allow_negative_crop=True),
+                      dict(
+                          type='Resize',
+                          img_scale=[(480, 1333), (512, 1333), (544, 1333),
+                                     (576, 1333), (608, 1333), (640, 1333),
+                                     (672, 1333), (704, 1333), (736, 1333),
+                                     (768, 1333), (800, 1333)],
+                          multiscale_mode='value',
+                          override=True,
+                          keep_ratio=True)
+                  ]]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+]
+data = dict(train=dict(pipeline=train_pipeline))
diff --git a/configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py b/configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py
new file mode 100644
index 0000000..89e2cd1
--- /dev/null
+++ b/configs/queryinst/queryinst_r50_fpn_mstrain_480-800_3x_coco.py
@@ -0,0 +1,23 @@
+_base_ = './queryinst_r50_fpn_1x_coco.py'
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+min_values = (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, value) for value in min_values],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+]
+
+data = dict(train=dict(pipeline=train_pipeline))
+lr_config = dict(policy='step', step=[27, 33])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/configs/regnet/README.md b/configs/regnet/README.md
new file mode 100644
index 0000000..61dba42
--- /dev/null
+++ b/configs/regnet/README.md
@@ -0,0 +1,121 @@
+# RegNet
+
+> [Designing Network Design Spaces](https://arxiv.org/abs/2003.13678)
+
+<!-- [BACKBONE] -->
+
+## Abstract
+
+In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143971942-da50f719-61e9-43bd-9468-0dbfbe80284e.png"/>
+</div>
+
+## Introduction
+
+We implement RegNetX and RegNetY models in detection systems and provide their first results on Mask R-CNN, Faster R-CNN and RetinaNet.
+
+The pre-trained models are converted from [model zoo of pycls](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md).
+
+## Usage
+
+To use a regnet model, there are two steps to do:
+
+1. Convert the model to ResNet-style supported by MMDetection
+2. Modify backbone and neck in config accordingly
+
+### Convert model
+
+We already prepare models of FLOPs from 400M to 12G in our model zoo.
+
+For more general usage, we also provide script `regnet2mmdet.py` in the tools directory to convert the key of models pretrained by [pycls](https://github.com/facebookresearch/pycls/) to
+ResNet-style checkpoints used in MMDetection.
+
+```bash
+python -u tools/model_converters/regnet2mmdet.py ${PRETRAIN_PATH} ${STORE_PATH}
+```
+
+This script convert model from `PRETRAIN_PATH` and store the converted model in `STORE_PATH`.
+
+### Modify config
+
+The users can modify the config's `depth` of backbone and corresponding keys in `arch` according to the configs in the [pycls model zoo](https://github.com/facebookresearch/pycls/blob/master/MODEL_ZOO.md).
+The parameter `in_channels` in FPN can be found in the Figure 15 & 16 of the paper (`wi` in the legend).
+This directory already provides some configs with their performance, using RegNetX from 800MF to 12GF level.
+For other pre-trained models or self-implemented regnet models, the users are responsible to check these parameters by themselves.
+
+**Note**: Although Fig. 15 & 16 also provide `w0`, `wa`, `wm`, `group_w`, and `bot_mul` for `arch`, they are quantized thus inaccurate, using them sometimes produces different backbone that does not match the key in the pre-trained model.
+
+## Results and Models
+
+### Mask R-CNN
+
+|                                       Backbone                                       |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                               Config                                                               |                                                                                                                                                                                          Download                                                                                                                                                                                          |
+| :----------------------------------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|                [R-50-FPN](../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)                 | pytorch |   1x    |   4.4    |      12.0      |  38.2  |  34.7   |           [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py)           |                                               [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205_050542.log.json)                                                |
+|            [RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py)             | pytorch |   1x    |   5.0    |                |  40.3  |  36.6   |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py)        |                           [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json)                           |
+|             [RegNetX-4.0GF-FPN](./mask_rcnn_regnetx-4GF_fpn_1x_coco.py)              | pytorch |   1x    |   5.5    |                |  41.5  |  37.4   |        [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py)         |                               [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217-32e9c92d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217.log.json)                               |
+|               [R-101-FPN](../mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py)                | pytorch |   1x    |   6.4    |      10.3      |  40.0  |  36.1   |          [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.py)           |                                             [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204-1efe0ed5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r101_fpn_1x_coco/mask_rcnn_r101_fpn_1x_coco_20200204_144809.log.json)                                              |
+|            [RegNetX-6.4GF-FPN](./mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py)             | pytorch |   1x    |   6.1    |                |  41.0  |  37.1   |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py)        |                           [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439-3a7aae83.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439.log.json)                           |
+|         [X-101-32x4d-FPN](../mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py)          | pytorch |   1x    |   7.6    |      9.4       |  41.9  |  37.5   |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco.py)        |                                 [model](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205-478d0b67.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_x101_32x4d_fpn_1x_coco/mask_rcnn_x101_32x4d_fpn_1x_coco_20200205_034906.log.json)                                  |
+|             [RegNetX-8.0GF-FPN](./mask_rcnn_regnetx-8GF_fpn_1x_coco.py)              | pytorch |   1x    |   6.4    |                |  41.7  |  37.5   |        [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py)         |                               [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515-09daa87e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515.log.json)                               |
+|             [RegNetX-12GF-FPN](./mask_rcnn_regnetx-12GF_fpn_1x_coco.py)              | pytorch |   1x    |   7.4    |                |  42.2  |   38    |        [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py)        |                             [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552-b538bd8b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552.log.json)                             |
+| [RegNetX-3.2GF-FPN-DCN-C3-C5](./mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py) | pytorch |   1x    |   5.0    |                |  40.3  |  36.6   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726-75f40794.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726.log.json) |
+
+### Faster R-CNN
+
+|                            Backbone                             |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                         Config                                                          |                                                                                                                                                                    Download                                                                                                                                                                    |
+| :-------------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    [R-50-FPN](../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py)    | pytorch |   1x    |   4.0    |      18.2      |  37.4  |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py)    |                   [model](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130_204655.log.json)                    |
+| [RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | pytorch |   1x    |   4.5    |                |  39.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927-126fd9bf.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927.log.json) |
+| [RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py) | pytorch |   2x    |   4.5    |                |  41.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955-e2081918.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955.log.json) |
+
+### RetinaNet
+
+|                           Backbone                            |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                        Config                                                         |                                                                                                                                                                Download                                                                                                                                                                |
+| :-----------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|     [R-50-FPN](../retinanet/retinanet_r50_fpn_1x_coco.py)     | pytorch |   1x    |   3.8    |      16.6      |  36.5  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py)     |                     [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json)                      |
+| [RegNetX-800MF-FPN](./retinanet_regnetx-800MF_fpn_1x_coco.py) | pytorch |   1x    |   2.5    |                |  35.6  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403-f6f91d10.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403.log.json) |
+| [RegNetX-1.6GF-FPN](./retinanet_regnetx-1.6GF_fpn_1x_coco.py) | pytorch |   1x    |   3.3    |                |  37.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403-37009a9d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403.log.json) |
+| [RegNetX-3.2GF-FPN](./retinanet_regnetx-3.2GF_fpn_1x_coco.py) | pytorch |   1x    |   4.2    |                |  39.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141-cb1509e8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141.log.json) |
+
+### Pre-trained models
+
+We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks.
+
+|      Method       |                                   Backbone                                    |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                                Config                                                                 |                                                                                                                                                                                                Download                                                                                                                                                                                                |
+| :---------------: | :---------------------------------------------------------------------------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    Faster RCNN    |    [RegNetX-400MF-FPN](./faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py)    | pytorch |   3x    |   2.3    |                |  37.1  |    -    |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210526_095112-e1967c37.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210526_095112.log.json)             |
+|    Faster RCNN    |    [RegNetX-800MF-FPN](./faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py)    | pytorch |   3x    |   2.8    |                |  38.8  |    -    |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210526_095118-a2c70b20.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210526_095118.log.json)             |
+|    Faster RCNN    |    [RegNetX-1.6GF-FPN](./faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py)    | pytorch |   3x    |   3.4    |                |  40.5  |    -    |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py)    |                                     [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-1_20210526_095325-94aa46cc.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-1_20210526_095325.log.json)                                     |
+|    Faster RCNN    |    [RegNetX-3.2GF-FPN](./faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)    | pytorch |   3x    |   4.4    |                |  42.3  |    -    |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)    |                                     [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3_20210526_095152-e16a5227.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3_20210526_095152.log.json)                                     |
+|    Faster RCNN    |      [RegNetX-4GF-FPN](./faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py)      | pytorch |   3x    |   4.9    |                |  42.8  |    -    |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py)     |                 [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210526_095201-65eaf841.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210526_095201.log.json)                 |
+|     Mask RCNN     |  [RegNetX-400MF-FPN](./mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py)   | pytorch |   3x    |   2.5    |                |  37.6  |  34.4   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py)     |       [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco_20210601_235443-8aac57a4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco_20210601_235443.log.json)       |
+|     Mask RCNN     |  [RegNetX-800MF-FPN](./mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py)   | pytorch |   3x    |   2.9    |                |  39.5  |  36.1   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py)     |       [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco_20210602_210641-715d51f5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco_20210602_210641.log.json)       |
+|     Mask RCNN     |  [RegNetX-1.6GF-FPN](./mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py)   | pytorch |   3x    |   3.6    |                |  40.9  |  37.5   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py)     |                                    [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641-6764cff5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641.log.json)                                    |
+|     Mask RCNN     |     [RegNetX-3.2GF-FPN](./mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)     | pytorch |   3x    |   5.0    |                |  43.1  |  38.7   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py)     |                 [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221.log.json)                 |
+|     Mask RCNN     |    [RegNetX-4GF-FPN](./mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py)     | pytorch |   3x    |   5.1    |                |  43.4  |  39.2   |      [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py)      |           [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco_20210602_032621-00f0331c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco_20210602_032621.log.json)           |
+| Cascade Mask RCNN | [RegNetX-400MF-FPN](./cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py) | pytorch |   3x    |   4.3    |                |  41.6  |  36.4   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210715_211619-5142f449.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210715_211619.log.json) |
+| Cascade Mask RCNN | [RegNetX-800MF-FPN](./cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py) | pytorch |   3x    |   4.8    |                |  42.8  |  37.6   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210715_211616-dcbd13f4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210715_211616.log.json) |
+| Cascade Mask RCNN | [RegNetX-1.6GF-FPN](./cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py) | pytorch |   3x    |   5.4    |                |  44.5  |  39.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py) |                         [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-1_20210715_211616-75f29a61.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-1_20210715_211616.log.json)                         |
+| Cascade Mask RCNN | [RegNetX-3.2GF-FPN](./cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) | pytorch |   3x    |   6.4    |                |  45.8  |  40.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py) |                         [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-3_20210715_211616-b9c2c58b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-3_20210715_211616.log.json)                         |
+| Cascade Mask RCNN |   [RegNetX-4GF-FPN](./cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py)   | pytorch |   3x    |   6.9    |                |  45.8  |  40.0   |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py)  |     [model](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210715_212034-cbb1be4c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210715_212034.log.json)     |
+
+### Notice
+
+1. The models are trained using a different weight decay, i.e., `weight_decay=5e-5` according to the setting in ImageNet training. This brings improvement of at least 0.7 AP absolute but does not improve the model using ResNet-50.
+2. RetinaNets using RegNets are trained with learning rate 0.02 with gradient clip. We find that using learning rate 0.02 could improve the results by at least 0.7 AP absolute and gradient clip is necessary to stabilize the training. However, this does not improve the performance of ResNet-50-FPN RetinaNet.
+
+## Citation
+
+```latex
+@article{radosavovic2020designing,
+    title={Designing Network Design Spaces},
+    author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár},
+    year={2020},
+    eprint={2003.13678},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
diff --git a/configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py b/configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..358d85a
--- /dev/null
+++ b/configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py
@@ -0,0 +1,17 @@
+_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        type='RegNet',
+        arch='regnetx_1.6gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[72, 168, 408, 912],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py b/configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..8464571
--- /dev/null
+++ b/configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
@@ -0,0 +1,63 @@
+_base_ = [
+    '../common/mstrain_3x_coco_instance.py',
+    '../_base_/models/cascade_mask_rcnn_r50_fpn.py'
+]
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='RegNet',
+        arch='regnetx_3.2gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[96, 192, 432, 1008],
+        out_channels=256,
+        num_outs=5))
+img_norm_cfg = dict(
+    # The mean and std are used in PyCls when training RegNets
+    mean=[103.53, 116.28, 123.675],
+    std=[57.375, 57.12, 58.395],
+    to_rgb=False)
+train_pipeline = [
+    # Images are converted to float32 directly after loading in PyCls
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+data = dict(
+    train=dict(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+optimizer = dict(weight_decay=0.00005)
diff --git a/configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py b/configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..2a8990a
--- /dev/null
+++ b/configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py
@@ -0,0 +1,17 @@
+_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        type='RegNet',
+        arch='regnetx_400mf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[32, 64, 160, 384],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py b/configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..3157863
--- /dev/null
+++ b/configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py
@@ -0,0 +1,17 @@
+_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        type='RegNet',
+        arch='regnetx_4.0gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[80, 240, 560, 1360],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py b/configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..41376ad
--- /dev/null
+++ b/configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py
@@ -0,0 +1,17 @@
+_base_ = 'cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        type='RegNet',
+        arch='regnetx_800mf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[64, 128, 288, 672],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..385b5ca
--- /dev/null
+++ b/configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py
@@ -0,0 +1,17 @@
+_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        type='RegNet',
+        arch='regnetx_1.6gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[72, 168, 408, 912],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py
new file mode 100644
index 0000000..88d270e
--- /dev/null
+++ b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py
@@ -0,0 +1,57 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='RegNet',
+        arch='regnetx_3.2gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[96, 192, 432, 1008],
+        out_channels=256,
+        num_outs=5))
+img_norm_cfg = dict(
+    # The mean and std are used in PyCls when training RegNets
+    mean=[103.53, 116.28, 123.675],
+    std=[57.375, 57.12, 58.395],
+    to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
diff --git a/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py
new file mode 100644
index 0000000..612490b
--- /dev/null
+++ b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py
@@ -0,0 +1,3 @@
+_base_ = './faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..b7e6e1a
--- /dev/null
+++ b/configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
@@ -0,0 +1,61 @@
+_base_ = [
+    '../common/mstrain_3x_coco.py', '../_base_/models/faster_rcnn_r50_fpn.py'
+]
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='RegNet',
+        arch='regnetx_3.2gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[96, 192, 432, 1008],
+        out_channels=256,
+        num_outs=5))
+img_norm_cfg = dict(
+    # The mean and std are used in PyCls when training RegNets
+    mean=[103.53, 116.28, 123.675],
+    std=[57.375, 57.12, 58.395],
+    to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+data = dict(
+    train=dict(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+optimizer = dict(weight_decay=0.00005)
diff --git a/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..0a05f6e
--- /dev/null
+++ b/configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py
@@ -0,0 +1,17 @@
+_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        type='RegNet',
+        arch='regnetx_400mf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[32, 64, 160, 384],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..98b3fc2
--- /dev/null
+++ b/configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py
@@ -0,0 +1,17 @@
+_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        type='RegNet',
+        arch='regnetx_4.0gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[80, 240, 560, 1360],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py b/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..67f448b
--- /dev/null
+++ b/configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py
@@ -0,0 +1,17 @@
+_base_ = 'faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py'
+model = dict(
+    backbone=dict(
+        type='RegNet',
+        arch='regnetx_800mf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[64, 128, 288, 672],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py
new file mode 100644
index 0000000..7970c3c
--- /dev/null
+++ b/configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py
@@ -0,0 +1,26 @@
+_base_ = [
+    '../common/mstrain-poly_3x_coco_instance.py',
+    '../_base_/models/mask_rcnn_r50_fpn.py'
+]
+
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='RegNet',
+        arch='regnetx_1.6gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[72, 168, 408, 912],
+        out_channels=256,
+        num_outs=5))
+
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py
new file mode 100644
index 0000000..ce3661c
--- /dev/null
+++ b/configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py
@@ -0,0 +1,17 @@
+_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='RegNet',
+        arch='regnetx_12gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_12gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[224, 448, 896, 2240],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py
new file mode 100644
index 0000000..44bf0d1
--- /dev/null
+++ b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py
@@ -0,0 +1,58 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='RegNet',
+        arch='regnetx_3.2gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[96, 192, 432, 1008],
+        out_channels=256,
+        num_outs=5))
+img_norm_cfg = dict(
+    # The mean and std are used in PyCls when training RegNets
+    mean=[103.53, 116.28, 123.675],
+    std=[57.375, 57.12, 58.395],
+    to_rgb=False)
+train_pipeline = [
+    # Images are converted to float32 directly after loading in PyCls
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
diff --git a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py
new file mode 100644
index 0000000..5b53428
--- /dev/null
+++ b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = 'mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')))
diff --git a/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..aca64d3
--- /dev/null
+++ b/configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
@@ -0,0 +1,66 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='RegNet',
+        arch='regnetx_3.2gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[96, 192, 432, 1008],
+        out_channels=256,
+        num_outs=5))
+img_norm_cfg = dict(
+    # The mean and std are used in PyCls when training RegNets
+    mean=[103.53, 116.28, 123.675],
+    std=[57.375, 57.12, 58.395],
+    to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
+lr_config = dict(step=[28, 34])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py
new file mode 100644
index 0000000..c38dfa6
--- /dev/null
+++ b/configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py
@@ -0,0 +1,26 @@
+_base_ = [
+    '../common/mstrain-poly_3x_coco_instance.py',
+    '../_base_/models/mask_rcnn_r50_fpn.py'
+]
+
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='RegNet',
+        arch='regnetx_400mf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_400mf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[32, 64, 160, 384],
+        out_channels=256,
+        num_outs=5))
+
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py
new file mode 100644
index 0000000..874d485
--- /dev/null
+++ b/configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py
@@ -0,0 +1,17 @@
+_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='RegNet',
+        arch='regnetx_4.0gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[80, 240, 560, 1360],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py
new file mode 100644
index 0000000..f0b65ea
--- /dev/null
+++ b/configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py
@@ -0,0 +1,26 @@
+_base_ = [
+    '../common/mstrain-poly_3x_coco_instance.py',
+    '../_base_/models/mask_rcnn_r50_fpn.py'
+]
+
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='RegNet',
+        arch='regnetx_4.0gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_4.0gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[80, 240, 560, 1360],
+        out_channels=256,
+        num_outs=5))
+
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py
new file mode 100644
index 0000000..99387d8
--- /dev/null
+++ b/configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py
@@ -0,0 +1,17 @@
+_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='RegNet',
+        arch='regnetx_6.4gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_6.4gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[168, 392, 784, 1624],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py b/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py
new file mode 100644
index 0000000..335ebab
--- /dev/null
+++ b/configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py
@@ -0,0 +1,26 @@
+_base_ = [
+    '../common/mstrain-poly_3x_coco_instance.py',
+    '../_base_/models/mask_rcnn_r50_fpn.py'
+]
+
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='RegNet',
+        arch='regnetx_800mf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[64, 128, 288, 672],
+        out_channels=256,
+        num_outs=5))
+
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py b/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py
new file mode 100644
index 0000000..1e7832f
--- /dev/null
+++ b/configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py
@@ -0,0 +1,17 @@
+_base_ = './mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='RegNet',
+        arch='regnetx_8.0gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_8.0gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[80, 240, 720, 1920],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/regnet/metafile.yml b/configs/regnet/metafile.yml
new file mode 100644
index 0000000..28bd82f
--- /dev/null
+++ b/configs/regnet/metafile.yml
@@ -0,0 +1,797 @@
+Models:
+  - Name: mask_rcnn_regnetx-3.2GF_fpn_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.0
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_1x_coco_20200520_163141-2a9d1814.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: mask_rcnn_regnetx-4GF_fpn_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.5
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_1x_coco/mask_rcnn_regnetx-4GF_fpn_1x_coco_20200517_180217-32e9c92d.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: mask_rcnn_regnetx-6.4GF_fpn_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.1
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-6.4GF_fpn_1x_coco/mask_rcnn_regnetx-6.4GF_fpn_1x_coco_20200517_180439-3a7aae83.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: mask_rcnn_regnetx-8GF_fpn_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.4
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-8GF_fpn_1x_coco/mask_rcnn_regnetx-8GF_fpn_1x_coco_20200517_180515-09daa87e.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: mask_rcnn_regnetx-12GF_fpn_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.4
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-12GF_fpn_1x_coco/mask_rcnn_regnetx-12GF_fpn_1x_coco_20200517_180552-b538bd8b.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.0
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco/mask_rcnn_regnetx-3.2GF_fpn_mdconv_c3-c5_1x_coco_20200520_172726-75f40794.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: faster_rcnn_regnetx-3.2GF_fpn_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.5
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_1x_coco/faster_rcnn_regnetx-3.2GF_fpn_1x_coco_20200517_175927-126fd9bf.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: faster_rcnn_regnetx-3.2GF_fpn_2x_coco
+    In Collection: Faster R-CNN
+    Config: configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 4.5
+      Epochs: 24
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_2x_coco/faster_rcnn_regnetx-3.2GF_fpn_2x_coco_20200520_223955-e2081918.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: retinanet_regnetx-800MF_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 2.5
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 35.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-800MF_fpn_1x_coco/retinanet_regnetx-800MF_fpn_1x_coco_20200517_191403-f6f91d10.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: retinanet_regnetx-1.6GF_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.3
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco/retinanet_regnetx-1.6GF_fpn_1x_coco_20200517_191403-37009a9d.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: retinanet_regnetx-3.2GF_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.2
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco/retinanet_regnetx-3.2GF_fpn_1x_coco_20200520_163141-cb1509e8.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco
+    In Collection: Faster R-CNN
+    Config: configs/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 2.3
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210526_095112-e1967c37.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco
+    In Collection: Faster R-CNN
+    Config: configs/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 2.8
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210526_095118-a2c70b20.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco
+    In Collection: Faster R-CNN
+    Config: configs/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 3.4
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-1_20210526_095325-94aa46cc.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco
+    In Collection: Faster R-CNN
+    Config: configs/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 4.4
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-3_20210526_095152-e16a5227.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco
+    In Collection: Faster R-CNN
+    Config: configs/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 4.9
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/faster_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210526_095201-65eaf841.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 5.0
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco.py
+    Metadata:
+      Training Memory (GB): 2.5
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 34.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-400MF_fpn_mstrain-poly_3x_coco_20210601_235443-8aac57a4.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco.py
+    Metadata:
+      Training Memory (GB): 2.9
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-800MF_fpn_mstrain-poly_3x_coco_20210602_210641-715d51f5.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco.py
+    Metadata:
+      Training Memory (GB): 3.6
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-1.6GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-1_20210602_210641-6764cff5.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 5.0
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco_20200521_202221-99879813.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco.py
+    Metadata:
+      Training Memory (GB): 5.1
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco/mask_rcnn_regnetx-4GF_fpn_mstrain-poly_3x_coco_20210602_032621-00f0331c.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 4.3
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-400MF_fpn_mstrain_3x_coco_20210715_211619-5142f449.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 4.8
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-800MF_fpn_mstrain_3x_coco_20210715_211616-dcbd13f4.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 5.4
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-1.6GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-1_20210715_211616-75f29a61.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 6.4
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 40.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-3.2GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-3_20210715_211616-b9c2c58b.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
+
+  - Name: cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco.py
+    Metadata:
+      Training Memory (GB): 6.9
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RegNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 40.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/regnet/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco/cascade_mask_rcnn_regnetx-4GF_fpn_mstrain_3x_coco_20210715_212034-cbb1be4c.pth
+    Paper:
+      URL: https://arxiv.org/abs/2003.13678
+      Title: 'Designing Network Design Spaces'
+    README: configs/regnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/regnet.py#L11
+      Version: v2.1.0
diff --git a/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py b/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py
new file mode 100644
index 0000000..7395c1b
--- /dev/null
+++ b/configs/regnet/retinanet_regnetx-1.6GF_fpn_1x_coco.py
@@ -0,0 +1,17 @@
+_base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='RegNet',
+        arch='regnetx_1.6gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_1.6gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[72, 168, 408, 912],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py b/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py
new file mode 100644
index 0000000..f05307c
--- /dev/null
+++ b/configs/regnet/retinanet_regnetx-3.2GF_fpn_1x_coco.py
@@ -0,0 +1,59 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='RegNet',
+        arch='regnetx_3.2gf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_3.2gf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[96, 192, 432, 1008],
+        out_channels=256,
+        num_outs=5))
+img_norm_cfg = dict(
+    # The mean and std are used in PyCls when training RegNets
+    mean=[103.53, 116.28, 123.675],
+    std=[57.375, 57.12, 58.395],
+    to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.00005)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py b/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py
new file mode 100644
index 0000000..f6f8989
--- /dev/null
+++ b/configs/regnet/retinanet_regnetx-800MF_fpn_1x_coco.py
@@ -0,0 +1,17 @@
+_base_ = './retinanet_regnetx-3.2GF_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='RegNet',
+        arch='regnetx_800mf',
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://regnetx_800mf')),
+    neck=dict(
+        type='FPN',
+        in_channels=[64, 128, 288, 672],
+        out_channels=256,
+        num_outs=5))
diff --git a/configs/reppoints/README.md b/configs/reppoints/README.md
new file mode 100644
index 0000000..5e71ae5
--- /dev/null
+++ b/configs/reppoints/README.md
@@ -0,0 +1,59 @@
+# RepPoints
+
+> [RepPoints: Point Set Representation for Object Detection](https://arxiv.org/abs/1904.11490)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Modern object detectors rely heavily on rectangular bounding boxes, such as anchors, proposals and the final predictions, to represent objects at various recognition stages. The bounding box is convenient to use but provides only a coarse localization of objects and leads to a correspondingly coarse extraction of object features. In this paper, we present RepPoints(representative points), a new finer representation of objects as a set of sample points useful for both localization and recognition. Given ground truth localization and recognition targets for training, RepPoints learn to automatically arrange themselves in a manner that bounds the spatial extent of an object and indicates semantically significant local areas. They furthermore do not require the use of anchors to sample a space of bounding boxes. We show that an anchor-free object detector based on RepPoints can be as effective as the state-of-the-art anchor-based detection methods, with 46.5 AP and 67.4 AP50 on the COCO test-dev detection benchmark, using ResNet-101 model.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143972514-93247220-4dad-4eb3-a51b-a1115dc7d449.png"/>
+</div>
+
+## Introdution
+
+By [Ze Yang](https://yangze.tech/), [Shaohui Liu](http://b1ueber2y.me/), and [Han Hu](https://ancientmooner.github.io/).
+
+We provide code support and configuration files to reproduce the results in the paper for
+["RepPoints: Point Set Representation for Object Detection"](https://arxiv.org/abs/1904.11490) on COCO object detection.
+
+**RepPoints**, initially described in [arXiv](https://arxiv.org/abs/1904.11490), is a new representation method for visual objects, on which visual understanding tasks are typically centered. Visual object representation, aiming at both geometric description and appearance feature extraction, is conventionally achieved by `bounding box + RoIPool (RoIAlign)`. The bounding box representation is convenient to use; however, it provides only a rectangular localization of objects that lacks geometric precision and may consequently degrade feature quality. Our new representation, RepPoints, models objects by a `point set` instead of a `bounding box`, which learns to adaptively position themselves over an object in a manner that circumscribes the object’s `spatial extent` and enables `semantically aligned feature extraction`. This richer and more flexible representation maintains the convenience of bounding boxes while facilitating various visual understanding applications. This repo demonstrated the effectiveness of RepPoints for COCO object detection.
+
+Another feature of this repo is the demonstration of an `anchor-free detector`, which can be as effective as state-of-the-art anchor-based detection methods. The anchor-free detector can utilize either `bounding box` or `RepPoints` as the basic object representation.
+
+## Results and Models
+
+The results on COCO 2017val are shown in the table below.
+
+|  Method   |   Backbone    | GN  | Anchor | convert func | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                                     Config                                                                      |                                                                                                                                                                                                                 Download                                                                                                                                                                                                                  |
+| :-------: | :-----------: | :-: | :----: | :----------: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   BBox    |   R-50-FPN    |  Y  | single |      -       |   1x    |   3.9    |      15.9      |  36.4  |           [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py)           |                                     [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329_145916-0eedf8d1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329_145916.log.json)                                      |
+|   BBox    |   R-50-FPN    |  Y  |  none  |      -       |   1x    |   3.9    |      15.4      |  37.4  |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py)        |                                     [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329_145916-0eedf8d1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329_145916.log.json)                                      |
+| RepPoints |   R-50-FPN    |  N  |  none  |    moment    |   1x    |   3.3    |      18.5      |  37.0  |              [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py)              |                                                         [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330_233609.log.json)                                                         |
+| RepPoints |   R-50-FPN    |  Y  |  none  |    moment    |   1x    |   3.9    |      17.5      |  38.1  |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py)        |                       [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329_145952-3e51b550.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329_145952.log.json)                        |
+| RepPoints |   R-50-FPN    |  Y  |  none  |    moment    |   2x    |   3.9    |       -        |  38.6  |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py)        |                           [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329-91babaa2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329_150020.log.json)                           |
+| RepPoints |   R-101-FPN   |  Y  |  none  |    moment    |   2x    |   5.8    |      13.7      |  40.5  |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py)       |                         [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329-4fbc7310.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329_132205.log.json)                         |
+| RepPoints | R-101-FPN-DCN |  Y  |  none  |    moment    |   2x    |   5.9    |      12.1      |  42.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-3309fbf2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329_132134.log.json) |
+| RepPoints | X-101-FPN-DCN |  Y  |  none  |    moment    |   2x    |   7.1    |      9.3       |  44.2  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-f87da1ea.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329_132201.log.json) |
+
+**Notes:**
+
+- `R-xx`, `X-xx` denote the ResNet and ResNeXt architectures, respectively.
+- `DCN` denotes replacing 3x3 conv with the 3x3 deformable convolution in `c3-c5` stages of backbone.
+- `none` in the `anchor` column means 2-d `center point` (x,y) is used to represent the initial object hypothesis. `single` denotes one 4-d anchor box (x,y,w,h) with IoU based label assign criterion is adopted.
+- `moment`, `partial MinMax`, `MinMax` in the `convert func` column are three functions to convert a point set to a pseudo box.
+- Note the results here are slightly different from those reported in the paper, due to framework change. While the original paper uses an [MXNet](https://mxnet.apache.org/) implementation, we re-implement the method in [PyTorch](https://pytorch.org/) based on mmdetection.
+
+## Citation
+
+```latex
+@inproceedings{yang2019reppoints,
+  title={RepPoints: Point Set Representation for Object Detection},
+  author={Yang, Ze and Liu, Shaohui and Hu, Han and Wang, Liwei and Lin, Stephen},
+  booktitle={The IEEE International Conference on Computer Vision (ICCV)},
+  month={Oct},
+  year={2019}
+}
+```
diff --git a/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py b/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py
new file mode 100644
index 0000000..b24c8db
--- /dev/null
+++ b/configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py
@@ -0,0 +1,2 @@
+_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
+model = dict(bbox_head=dict(transform_method='minmax', use_grid_points=True))
diff --git a/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py b/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py
new file mode 100644
index 0000000..8d5013d
--- /dev/null
+++ b/configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py
@@ -0,0 +1,13 @@
+_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
+model = dict(
+    bbox_head=dict(transform_method='minmax', use_grid_points=True),
+    # training and testing settings
+    train_cfg=dict(
+        init=dict(
+            assigner=dict(
+                _delete_=True,
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.4,
+                min_pos_iou=0,
+                ignore_iof_thr=-1))))
diff --git a/configs/reppoints/metafile.yml b/configs/reppoints/metafile.yml
new file mode 100644
index 0000000..d94137e
--- /dev/null
+++ b/configs/reppoints/metafile.yml
@@ -0,0 +1,181 @@
+Collections:
+  - Name: RepPoints
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Group Normalization
+        - FPN
+        - RepPoints
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1904.11490
+      Title: 'RepPoints: Point Set Representation for Object Detection'
+    README: configs/reppoints/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/reppoints_detector.py#L9
+      Version: v2.0.0
+
+Models:
+  - Name: bbox_r50_grid_fpn_gn-neck+head_1x_coco
+    In Collection: RepPoints
+    Config: configs/reppoints/bbox_r50_grid_fpn_gn-neck+head_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.9
+      inference time (ms/im):
+        - value: 62.89
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329_145916-0eedf8d1.pth
+
+  - Name: bbox_r50_grid_center_fpn_gn-neck+head_1x_coco
+    In Collection: RepPoints
+    Config: configs/reppoints/bbox_r50_grid_center_fpn_gn-neck+head_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.9
+      inference time (ms/im):
+        - value: 64.94
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco/bbox_r50_grid_fpn_gn-neck%2Bhead_1x_coco_20200329_145916-0eedf8d1.pth
+
+  - Name: reppoints_moment_r50_fpn_1x_coco
+    In Collection: RepPoints
+    Config: configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.3
+      inference time (ms/im):
+        - value: 54.05
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_1x_coco/reppoints_moment_r50_fpn_1x_coco_20200330-b73db8d1.pth
+
+  - Name: reppoints_moment_r50_fpn_gn-neck+head_1x_coco
+    In Collection: RepPoints
+    Config: configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.9
+      inference time (ms/im):
+        - value: 57.14
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_1x_coco_20200329_145952-3e51b550.pth
+
+  - Name: reppoints_moment_r50_fpn_gn-neck+head_2x_coco
+    In Collection: RepPoints
+    Config: configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py
+    Metadata:
+      Training Memory (GB): 3.9
+      inference time (ms/im):
+        - value: 57.14
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r50_fpn_gn-neck%2Bhead_2x_coco_20200329-91babaa2.pth
+
+  - Name: reppoints_moment_r101_fpn_gn-neck+head_2x_coco
+    In Collection: RepPoints
+    Config: configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py
+    Metadata:
+      Training Memory (GB): 5.8
+      inference time (ms/im):
+        - value: 72.99
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_gn-neck%2Bhead_2x_coco_20200329-4fbc7310.pth
+
+  - Name: reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco
+    In Collection: RepPoints
+    Config: configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py
+    Metadata:
+      Training Memory (GB): 5.9
+      inference time (ms/im):
+        - value: 82.64
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-3309fbf2.pth
+
+  - Name: reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco
+    In Collection: RepPoints
+    Config: configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py
+    Metadata:
+      Training Memory (GB): 7.1
+      inference time (ms/im):
+        - value: 107.53
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck%2Bhead_2x_coco_20200329-f87da1ea.pth
diff --git a/configs/reppoints/reppoints.png b/configs/reppoints/reppoints.png
new file mode 100644
index 0000000..a9306d9
Binary files /dev/null and b/configs/reppoints/reppoints.png differ
diff --git a/configs/reppoints/reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py b/configs/reppoints/reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py
new file mode 100644
index 0000000..0f56a46
--- /dev/null
+++ b/configs/reppoints/reppoints_minmax_r50_fpn_gn-neck+head_1x_coco.py
@@ -0,0 +1,2 @@
+_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
+model = dict(bbox_head=dict(transform_method='minmax'))
diff --git a/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py
new file mode 100644
index 0000000..e223d80
--- /dev/null
+++ b/configs/reppoints/reppoints_moment_r101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py
@@ -0,0 +1,8 @@
+_base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True),
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py
new file mode 100644
index 0000000..1185470
--- /dev/null
+++ b/configs/reppoints/reppoints_moment_r101_fpn_gn-neck+head_2x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py b/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..158a906
--- /dev/null
+++ b/configs/reppoints/reppoints_moment_r50_fpn_1x_coco.py
@@ -0,0 +1,67 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='RepPointsDetector',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_input',
+        num_outs=5),
+    bbox_head=dict(
+        type='RepPointsHead',
+        num_classes=80,
+        in_channels=256,
+        feat_channels=256,
+        point_feat_channels=256,
+        stacked_convs=3,
+        num_points=9,
+        gradient_mul=0.1,
+        point_strides=[8, 16, 32, 64, 128],
+        point_base_scale=4,
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox_init=dict(type='SmoothL1Loss', beta=0.11, loss_weight=0.5),
+        loss_bbox_refine=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0),
+        transform_method='moment'),
+    # training and testing settings
+    train_cfg=dict(
+        init=dict(
+            assigner=dict(type='PointAssigner', scale=4, pos_num=1),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False),
+        refine=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.4,
+                min_pos_iou=0,
+                ignore_iof_thr=-1),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False)),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.5),
+        max_per_img=100))
+optimizer = dict(lr=0.01)
diff --git a/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py b/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py
new file mode 100644
index 0000000..337f167
--- /dev/null
+++ b/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './reppoints_moment_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(neck=dict(norm_cfg=norm_cfg), bbox_head=dict(norm_cfg=norm_cfg))
+optimizer = dict(lr=0.01)
diff --git a/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py
new file mode 100644
index 0000000..feca44a
--- /dev/null
+++ b/configs/reppoints/reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py
@@ -0,0 +1,3 @@
+_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py b/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py
new file mode 100644
index 0000000..c0a12d0
--- /dev/null
+++ b/configs/reppoints/reppoints_moment_x101_fpn_dconv_c3-c5_gn-neck+head_2x_coco.py
@@ -0,0 +1,16 @@
+_base_ = './reppoints_moment_r50_fpn_gn-neck+head_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        dcn=dict(type='DCN', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/reppoints/reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py b/configs/reppoints/reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py
new file mode 100644
index 0000000..9a63bd0
--- /dev/null
+++ b/configs/reppoints/reppoints_partial_minmax_r50_fpn_gn-neck+head_1x_coco.py
@@ -0,0 +1,2 @@
+_base_ = './reppoints_moment_r50_fpn_gn-neck+head_1x_coco.py'
+model = dict(bbox_head=dict(transform_method='partial_minmax'))
diff --git a/configs/res2net/README.md b/configs/res2net/README.md
new file mode 100644
index 0000000..1285870
--- /dev/null
+++ b/configs/res2net/README.md
@@ -0,0 +1,77 @@
+# Res2Net
+
+> [Res2Net: A New Multi-scale Backbone Architecture](https://arxiv.org/abs/1904.01169)
+
+<!-- [BACKBONE] -->
+
+## Abstract
+
+Representing features at multiple scales is of great importance for numerous vision tasks. Recent advances in backbone convolutional neural networks (CNNs) continually demonstrate stronger multi-scale representation ability, leading to consistent performance gains on a wide range of applications. However, most existing methods represent the multi-scale features in a layer-wise manner. In this paper, we propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g., ResNet, ResNeXt, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models on widely-used datasets, e.g., CIFAR-100 and ImageNet. Further ablation studies and experimental results on representative computer vision tasks, i.e., object detection, class activation mapping, and salient object detection, further verify the superiority of the Res2Net over the state-of-the-art baseline methods.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143972411-8d08113f-9fce-4d24-a138-4fadf2c54f9a.png" height="300"/>
+</div>
+
+## Introduction
+
+We propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.
+
+|     Backbone      | Params. | GFLOPs | top-1 err. | top-5 err. |
+| :---------------: | :-----: | :----: | :--------: | :--------: |
+|    ResNet-101     | 44.6 M  |  7.8   |   22.63    |    6.44    |
+| ResNeXt-101-64x4d |  83.5M  |  15.5  |   20.40    |     -      |
+|   HRNetV2p-W48    |  77.5M  |  16.1  |   20.70    |    5.50    |
+|    Res2Net-101    |  45.2M  |  8.3   |   18.77    |    4.64    |
+
+Compared with other backbone networks, Res2Net requires fewer parameters and FLOPs.
+
+**Note:**
+
+- GFLOPs for classification are calculated with image size (224x224).
+
+## Results and Models
+
+### Faster R-CNN
+
+|  Backbone  |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                      Config                                                       |                                                                                                                                               Download                                                                                                                                               |
+| :--------: | :-----: | :-----: | :------: | :------------: | :----: | :---------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R2-101-FPN | pytorch |   2x    |   7.4    |       -        |  43.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco_20200514_231734.log.json) |
+
+### Mask R-CNN
+
+|  Backbone  |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                     Config                                                      |                                                                                                                                           Download                                                                                                                                           |
+| :--------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :-------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R2-101-FPN | pytorch |   2x    |   7.9    |       -        |  43.6  |  38.7   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco-17f061e8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco_20200515_002413.log.json) |
+
+### Cascade R-CNN
+
+|  Backbone  |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                       Config                                                        |                                                                                                                                                   Download                                                                                                                                                   |
+| :--------: | :-----: | :-----: | :------: | :------------: | :----: | :-----------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R2-101-FPN | pytorch |   20e   |   7.8    |       -        |  45.7  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco-f4b7b7db.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco_20200515_091644.log.json) |
+
+### Cascade Mask R-CNN
+
+|  Backbone  |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                          Config                                                          |                                                                                                                                                             Download                                                                                                                                                             |
+| :--------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :----------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R2-101-FPN | pytorch |   20e   |   9.5    |       -        |  46.4  |  40.0   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco_20200515_091645.log.json) |
+
+### Hybrid Task Cascade (HTC)
+
+|  Backbone  |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                   Config                                                   |                                                                                                                                 Download                                                                                                                                 |
+| :--------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R2-101-FPN | pytorch |   20e   |    -     |       -        |  47.5  |  41.6   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/htc_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco-3a8d2112.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco_20200515_150029.log.json) |
+
+- Res2Net ImageNet pretrained models are in [Res2Net-PretrainedModels](https://github.com/Res2Net/Res2Net-PretrainedModels).
+- More applications of Res2Net are in [Res2Net-Github](https://github.com/Res2Net/).
+
+## Citation
+
+```latex
+@article{gao2019res2net,
+  title={Res2Net: A New Multi-scale Backbone Architecture},
+  author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
+  journal={IEEE TPAMI},
+  year={2020},
+  doi={10.1109/TPAMI.2019.2938758},
+}
+```
diff --git a/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py b/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py
new file mode 100644
index 0000000..6b6c001
--- /dev/null
+++ b/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py
@@ -0,0 +1,10 @@
+_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_20e_coco.py'
+model = dict(
+    backbone=dict(
+        type='Res2Net',
+        depth=101,
+        scales=4,
+        base_width=26,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
diff --git a/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py b/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py
new file mode 100644
index 0000000..10dddbb
--- /dev/null
+++ b/configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py
@@ -0,0 +1,10 @@
+_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_20e_coco.py'
+model = dict(
+    backbone=dict(
+        type='Res2Net',
+        depth=101,
+        scales=4,
+        base_width=26,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
diff --git a/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py b/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py
new file mode 100644
index 0000000..fc2221c
--- /dev/null
+++ b/configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py
@@ -0,0 +1,10 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='Res2Net',
+        depth=101,
+        scales=4,
+        base_width=26,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
diff --git a/configs/res2net/htc_r2_101_fpn_20e_coco.py b/configs/res2net/htc_r2_101_fpn_20e_coco.py
new file mode 100644
index 0000000..22d0c5d
--- /dev/null
+++ b/configs/res2net/htc_r2_101_fpn_20e_coco.py
@@ -0,0 +1,13 @@
+_base_ = '../htc/htc_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='Res2Net',
+        depth=101,
+        scales=4,
+        base_width=26,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
+# learning policy
+lr_config = dict(step=[16, 19])
+runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py b/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py
new file mode 100644
index 0000000..33aef1a
--- /dev/null
+++ b/configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py
@@ -0,0 +1,10 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='Res2Net',
+        depth=101,
+        scales=4,
+        base_width=26,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
diff --git a/configs/res2net/metafile.yml b/configs/res2net/metafile.yml
new file mode 100644
index 0000000..27bac8c
--- /dev/null
+++ b/configs/res2net/metafile.yml
@@ -0,0 +1,146 @@
+Models:
+  - Name: faster_rcnn_r2_101_fpn_2x_coco
+    In Collection: Faster R-CNN
+    Config: configs/res2net/faster_rcnn_r2_101_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 7.4
+      Epochs: 24
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Res2Net
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/faster_rcnn_r2_101_fpn_2x_coco/faster_rcnn_r2_101_fpn_2x_coco-175f1da6.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.01169
+      Title: 'Res2Net for object detection and instance segmentation'
+    README: configs/res2net/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
+      Version: v2.1.0
+
+  - Name: mask_rcnn_r2_101_fpn_2x_coco
+    In Collection: Mask R-CNN
+    Config: configs/res2net/mask_rcnn_r2_101_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 7.9
+      Epochs: 24
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Res2Net
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/mask_rcnn_r2_101_fpn_2x_coco/mask_rcnn_r2_101_fpn_2x_coco-17f061e8.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.01169
+      Title: 'Res2Net for object detection and instance segmentation'
+    README: configs/res2net/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
+      Version: v2.1.0
+
+  - Name: cascade_rcnn_r2_101_fpn_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/res2net/cascade_rcnn_r2_101_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 7.8
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Res2Net
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_rcnn_r2_101_fpn_20e_coco/cascade_rcnn_r2_101_fpn_20e_coco-f4b7b7db.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.01169
+      Title: 'Res2Net for object detection and instance segmentation'
+    README: configs/res2net/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
+      Version: v2.1.0
+
+  - Name: cascade_mask_rcnn_r2_101_fpn_20e_coco
+    In Collection: Cascade R-CNN
+    Config: configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 9.5
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Res2Net
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 40.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.01169
+      Title: 'Res2Net for object detection and instance segmentation'
+    README: configs/res2net/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
+      Version: v2.1.0
+
+  - Name: htc_r2_101_fpn_20e_coco
+    In Collection: HTC
+    Config: configs/res2net/htc_r2_101_fpn_20e_coco.py
+    Metadata:
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Res2Net
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/res2net/htc_r2_101_fpn_20e_coco/htc_r2_101_fpn_20e_coco-3a8d2112.pth
+    Paper:
+      URL: https://arxiv.org/abs/1904.01169
+      Title: 'Res2Net for object detection and instance segmentation'
+    README: configs/res2net/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.1.0/mmdet/models/backbones/res2net.py#L239
+      Version: v2.1.0
diff --git a/configs/resnest/README.md b/configs/resnest/README.md
new file mode 100644
index 0000000..3676e56
--- /dev/null
+++ b/configs/resnest/README.md
@@ -0,0 +1,54 @@
+# ResNeSt
+
+> [ResNeSt: Split-Attention Networks](https://arxiv.org/abs/2004.08955)
+
+<!-- [BACKBONE] -->
+
+## Abstract
+
+It is well known that featuremap attention and multi-path representation are important for visual recognition. In this paper, we present a modularized architecture, which applies the channel-wise attention on different network branches to leverage their success in capturing cross-feature interactions and learning diverse representations. Our design results in a simple and unified computation block, which can be parameterized using only a few variables. Our model, named ResNeSt, outperforms EfficientNet in accuracy and latency trade-off on image classification. In addition, ResNeSt has achieved superior transfer learning results on several public benchmarks serving as the backbone, and has been adopted by the winning entries of COCO-LVIS challenge.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143973475-b5b33b15-ed04-4fc6-890a-521f1a62bc52.png"/>
+</div>
+
+## Results and Models
+
+### Faster R-CNN
+
+| Backbone  |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                                       Config                                                                       |                                                                                                                                                                                                                             Download                                                                                                                                                                                                                             |
+| :-------: | :-----: | :-----: | :------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| S-50-FPN  | pytorch |   1x    |   4.8    |       -        |  42.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20200926_125502-20289c16.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20200926_125502.log.json)   |
+| S-101-FPN | pytorch |   1x    |   7.1    |       -        |  44.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201006_021058-421517f1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201006_021058.log.json) |
+
+### Mask R-CNN
+
+| Backbone  |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                                   Config                                                                   |                                                                                                                                                                                                             Download                                                                                                                                                                                                             |
+| :-------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :----------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| S-50-FPN  | pytorch |   1x    |   5.5    |       -        |  42.6  |  38.1   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20200926_125503-8a2c3d47.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20200926_125503.log.json)   |
+| S-101-FPN | pytorch |   1x    |   7.8    |       -        |  45.2  |  40.2   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_215831-af60cdf9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201005_215831.log.json) |
+
+### Cascade R-CNN
+
+| Backbone  |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                                       Config                                                                        |                                                                                                                                                                                                                              Download                                                                                                                                                                                                                              |
+| :-------: | :-----: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| S-50-FPN  | pytorch |   1x    |    -     |       -        |  44.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py)  | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201122_213640-763cc7b5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201005_113242.log.json) |
+| S-101-FPN | pytorch |   1x    |   8.4    |       -        |  46.8  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201005_113242-b9459f8f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco-20201122_213640.log.json) |
+
+### Cascade Mask R-CNN
+
+| Backbone  |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                                       Config                                                                       |                                                                                                                                                                                                                             Download                                                                                                                                                                                                                             |
+| :-------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| S-50-FPN  | pytorch |   1x    |    -     |       -        |  45.4  |  39.5   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201122_104428-99eca4c7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201122_104428.log.json)   |
+| S-101-FPN | pytorch |   1x    |   10.5   |       -        |  47.7  |  41.4   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_113243-42607475.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco-20201005_113243.log.json) |
+
+## Citation
+
+```latex
+@article{zhang2020resnest,
+title={ResNeSt: Split-Attention Networks},
+author={Zhang, Hang and Wu, Chongruo and Zhang, Zhongyue and Zhu, Yi and Zhang, Zhi and Lin, Haibin and Sun, Yue and He, Tong and Muller, Jonas and Manmatha, R. and Li, Mu and Smola, Alexander},
+journal={arXiv preprint arXiv:2004.08955},
+year={2020}
+}
+```
diff --git a/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
new file mode 100644
index 0000000..406f39d
--- /dev/null
+++ b/configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py'
+model = dict(
+    backbone=dict(
+        stem_channels=128,
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='open-mmlab://resnest101')))
diff --git a/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
new file mode 100644
index 0000000..83d7537
--- /dev/null
+++ b/configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
@@ -0,0 +1,118 @@
+_base_ = '../cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    backbone=dict(
+        type='ResNeSt',
+        stem_channels=64,
+        depth=50,
+        radix=2,
+        reduction_factor=4,
+        avg_down_stride=True,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
+    roi_head=dict(
+        bbox_head=[
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                norm_cfg=norm_cfg,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                norm_cfg=norm_cfg,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                norm_cfg=norm_cfg,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ],
+        mask_head=dict(norm_cfg=norm_cfg)))
+# # use ResNeSt img_norm
+img_norm_cfg = dict(
+    mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        poly2mask=False),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
new file mode 100644
index 0000000..0a7476a
--- /dev/null
+++ b/configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
+model = dict(
+    backbone=dict(
+        stem_channels=128,
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='open-mmlab://resnest101')))
diff --git a/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
new file mode 100644
index 0000000..6ed7730
--- /dev/null
+++ b/configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
@@ -0,0 +1,116 @@
+_base_ = '../cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    backbone=dict(
+        type='ResNeSt',
+        stem_channels=64,
+        depth=50,
+        radix=2,
+        reduction_factor=4,
+        avg_down_stride=True,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
+    roi_head=dict(
+        bbox_head=[
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                norm_cfg=norm_cfg,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                norm_cfg=norm_cfg,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared4Conv1FCBBoxHead',
+                in_channels=256,
+                conv_out_channels=256,
+                fc_out_channels=1024,
+                norm_cfg=norm_cfg,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ], ))
+# # use ResNeSt img_norm
+img_norm_cfg = dict(
+    mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=False,
+        poly2mask=False),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
new file mode 100644
index 0000000..40a2f1f
--- /dev/null
+++ b/configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py'
+model = dict(
+    backbone=dict(
+        stem_channels=128,
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='open-mmlab://resnest101')))
diff --git a/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py b/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
new file mode 100644
index 0000000..eb1ecd2
--- /dev/null
+++ b/configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
@@ -0,0 +1,62 @@
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    backbone=dict(
+        type='ResNeSt',
+        stem_channels=64,
+        depth=50,
+        radix=2,
+        reduction_factor=4,
+        avg_down_stride=True,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            conv_out_channels=256,
+            norm_cfg=norm_cfg)))
+# # use ResNeSt img_norm
+img_norm_cfg = dict(
+    mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=False,
+        poly2mask=False),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
new file mode 100644
index 0000000..c882ba1
--- /dev/null
+++ b/configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py'
+model = dict(
+    backbone=dict(
+        stem_channels=128,
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='open-mmlab://resnest101')))
diff --git a/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py b/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
new file mode 100644
index 0000000..4e50dea
--- /dev/null
+++ b/configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
@@ -0,0 +1,64 @@
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+model = dict(
+    backbone=dict(
+        type='ResNeSt',
+        stem_channels=64,
+        depth=50,
+        radix=2,
+        reduction_factor=4,
+        avg_down_stride=True,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=norm_cfg,
+        norm_eval=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://resnest50')),
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            conv_out_channels=256,
+            norm_cfg=norm_cfg),
+        mask_head=dict(norm_cfg=norm_cfg)))
+# # use ResNeSt img_norm
+img_norm_cfg = dict(
+    mean=[123.68, 116.779, 103.939], std=[58.393, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='LoadAnnotations',
+        with_bbox=True,
+        with_mask=True,
+        poly2mask=False),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/resnest/metafile.yml b/configs/resnest/metafile.yml
new file mode 100644
index 0000000..cfeec71
--- /dev/null
+++ b/configs/resnest/metafile.yml
@@ -0,0 +1,230 @@
+Models:
+  - Name: faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/resnest/faster_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.8
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNeSt
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20200926_125502-20289c16.pth
+    Paper:
+      URL: https://arxiv.org/abs/2004.08955
+      Title: 'ResNeSt: Split-Attention Networks'
+    README: configs/resnest/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
+      Version: v2.7.0
+
+  - Name: faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/resnest/faster_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.1
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNeSt
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/faster_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201006_021058-421517f1.pth
+    Paper:
+      URL: https://arxiv.org/abs/2004.08955
+      Title: 'ResNeSt: Split-Attention Networks'
+    README: configs/resnest/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
+      Version: v2.7.0
+
+  - Name: mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/resnest/mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.5
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNeSt
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20200926_125503-8a2c3d47.pth
+    Paper:
+      URL: https://arxiv.org/abs/2004.08955
+      Title: 'ResNeSt: Split-Attention Networks'
+    README: configs/resnest/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
+      Version: v2.7.0
+
+  - Name: mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/resnest/mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.8
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNeSt
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 40.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_215831-af60cdf9.pth
+    Paper:
+      URL: https://arxiv.org/abs/2004.08955
+      Title: 'ResNeSt: Split-Attention Networks'
+    README: configs/resnest/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
+      Version: v2.7.0
+
+  - Name: cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/resnest/cascade_rcnn_s50_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
+    Metadata:
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNeSt
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201122_213640-763cc7b5.pth
+    Paper:
+      URL: https://arxiv.org/abs/2004.08955
+      Title: 'ResNeSt: Split-Attention Networks'
+    README: configs/resnest/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
+      Version: v2.7.0
+
+  - Name: cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/resnest/cascade_rcnn_s101_fpn_syncbn-backbone+head_mstrain-range_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.4
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNeSt
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco/cascade_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain-range_1x_coco_20201005_113242-b9459f8f.pth
+    Paper:
+      URL: https://arxiv.org/abs/2004.08955
+      Title: 'ResNeSt: Split-Attention Networks'
+    README: configs/resnest/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
+      Version: v2.7.0
+
+  - Name: cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone+head_mstrain_1x_coco.py
+    Metadata:
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNeSt
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.4
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s50_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201122_104428-99eca4c7.pth
+    Paper:
+      URL: https://arxiv.org/abs/2004.08955
+      Title: 'ResNeSt: Split-Attention Networks'
+    README: configs/resnest/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
+      Version: v2.7.0
+
+  - Name: cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone+head_mstrain_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.5
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNeSt
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnest/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco/cascade_mask_rcnn_s101_fpn_syncbn-backbone%2Bhead_mstrain_1x_coco_20201005_113243-42607475.pth
+    Paper:
+      URL: https://arxiv.org/abs/2004.08955
+      Title: 'ResNeSt: Split-Attention Networks'
+    README: configs/resnest/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.7.0/mmdet/models/backbones/resnest.py#L273
+      Version: v2.7.0
diff --git a/configs/resnet_strikes_back/README.md b/configs/resnet_strikes_back/README.md
new file mode 100644
index 0000000..dd00b20
--- /dev/null
+++ b/configs/resnet_strikes_back/README.md
@@ -0,0 +1,40 @@
+# ResNet strikes back
+
+> [ResNet strikes back: An improved training procedure in timm](https://arxiv.org/abs/2110.00476)
+
+<!-- [OTHERS] -->
+
+## Abstract
+
+The influential Residual Networks designed by He et al. remain the gold-standard architecture in numerous scientific publications. They typically serve as the default architecture in studies, or as baselines when new architectures are proposed. Yet there has been significant progress on best practices for training neural networks since the inception of the ResNet architecture in 2015. Novel optimization & dataaugmentation have increased the effectiveness of the training recipes.
+
+In this paper, we re-evaluate the performance of the vanilla ResNet-50 when trained with a procedure that integrates such advances. We share competitive training settings and pre-trained models in the timm open-source library, with the hope that they will serve as better baselines for future work. For instance, with our more demanding training setting, a vanilla ResNet-50 reaches 80.4% top-1 accuracy at resolution 224×224 on ImageNet-val without extra data or distillation. We also report the performance achieved with popular models with our training procedure.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/12907710/149324625-4546a5a7-704f-406c-982f-0376a20d03d8.png"/>
+</div>
+
+## Results and Models
+
+|       Method       | Backbone | Lr schd | Mem (GB) | Inf time (fps) |   box AP    |   mask AP   |                            Config                             |                                                                                                                                                                                                   Download                                                                                                                                                                                                   |
+| :----------------: | :------: | :-----: | :------: | :------------: | :---------: | :---------: | :-----------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    Faster R-CNN    | R-50 rsb |   1x    |   3.9    |       -        | 40.8 (+3.4) |      -      |    [Config](./faster_rcnn_r50_fpn_rsb-pretrain_1x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_162229-32ae82a9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_162229.log.json)             |
+|     Mask R-CNN     | R-50 rsb |   1x    |   4.5    |       -        | 41.2 (+3.0) | 38.2 (+3.0) |     [Config](./mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py)     |                 [model](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_174054-06ce8ba0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_174054.log.json)                 |
+| Cascade Mask R-CNN | R-50 rsb |   1x    |   6.2    |       -        | 44.8 (+3.6) | 39.9 (+3.6) | [Config](./cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_193636-8b9ad50f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_193636.log.json) |
+|     RetinaNet      | R-50 rsb |   1x    |   3.8    |       -        | 39.0 (+2.5) |      -      |     [Config](./retinanet_r50_fpn_rsb-pretrain_1x_coco.py)     |                 [model](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/retinanet_r50_fpn_rsb-pretrain_1x_coco/retinanet_r50_fpn_rsb-pretrain_1x_coco_20220113_175432-bd24aae9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/retinanet_r50_fpn_rsb-pretrain_1x_coco/retinanet_r50_fpn_rsb-pretrain_1x_coco_20220113_175432.log.json)                 |
+
+**Notes:**
+
+- 'rsb' is short for 'resnet strikes back'
+- We have done some grid searches on learning rate and weight decay and get these optimal hyper-parameters.
+
+## Citation
+
+```latex
+@article{wightman2021resnet,
+title={Resnet strikes back: An improved training procedure in timm},
+author={Ross Wightman, Hugo Touvron, Hervé Jégou},
+journal={arXiv preprint arXiv:2110.00476},
+year={2021}
+}
+```
diff --git a/configs/resnet_strikes_back/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py b/configs/resnet_strikes_back/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
new file mode 100644
index 0000000..8b601f0
--- /dev/null
+++ b/configs/resnet_strikes_back/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
@@ -0,0 +1,18 @@
+_base_ = [
+    '../_base_/models/cascade_mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth'  # noqa
+model = dict(
+    backbone=dict(
+        init_cfg=dict(
+            type='Pretrained', prefix='backbone.', checkpoint=checkpoint)))
+
+optimizer = dict(
+    _delete_=True,
+    type='AdamW',
+    lr=0.0002,
+    weight_decay=0.05,
+    paramwise_cfg=dict(norm_decay_mult=0., bypass_duplicate=True))
diff --git a/configs/resnet_strikes_back/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco.py b/configs/resnet_strikes_back/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
new file mode 100644
index 0000000..fe86684
--- /dev/null
+++ b/configs/resnet_strikes_back/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
@@ -0,0 +1,18 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth'  # noqa
+model = dict(
+    backbone=dict(
+        init_cfg=dict(
+            type='Pretrained', prefix='backbone.', checkpoint=checkpoint)))
+
+optimizer = dict(
+    _delete_=True,
+    type='AdamW',
+    lr=0.0002,
+    weight_decay=0.05,
+    paramwise_cfg=dict(norm_decay_mult=0., bypass_duplicate=True))
diff --git a/configs/resnet_strikes_back/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py b/configs/resnet_strikes_back/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
new file mode 100644
index 0000000..321d98e
--- /dev/null
+++ b/configs/resnet_strikes_back/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
@@ -0,0 +1,18 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth'  # noqa
+model = dict(
+    backbone=dict(
+        init_cfg=dict(
+            type='Pretrained', prefix='backbone.', checkpoint=checkpoint)))
+
+optimizer = dict(
+    _delete_=True,
+    type='AdamW',
+    lr=0.0002,
+    weight_decay=0.05,
+    paramwise_cfg=dict(norm_decay_mult=0., bypass_duplicate=True))
diff --git a/configs/resnet_strikes_back/metafile.yml b/configs/resnet_strikes_back/metafile.yml
new file mode 100644
index 0000000..4c85a16
--- /dev/null
+++ b/configs/resnet_strikes_back/metafile.yml
@@ -0,0 +1,116 @@
+Models:
+  - Name: faster_rcnn_r50_fpn_rsb-pretrain_1x_coco
+    In Collection: Faster R-CNN
+    Config: configs/resnet_strikes_back/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.9
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco/faster_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_162229-32ae82a9.pth
+    Paper:
+      URL: https://arxiv.org/abs/2110.00476
+      Title: 'ResNet strikes back: An improved training procedure in timm'
+    README: configs/resnet_strikes_back/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.22.0/configs/resnet_strikes_back/README.md
+      Version: v2.22.0
+
+  - Name: cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco
+    In Collection: Cascade R-CNN
+    Config: configs/resnet_strikes_back/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
+    Metadata:
+      Training Memory (GB): 6.2
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco/cascade_mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_193636-8b9ad50f.pth
+    Paper:
+      URL: https://arxiv.org/abs/2110.00476
+      Title: 'ResNet strikes back: An improved training procedure in timm'
+    README: configs/resnet_strikes_back/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.22.0/configs/resnet_strikes_back/README.md
+      Version: v2.22.0
+
+  - Name: retinanet_r50_fpn_rsb-pretrain_1x_coco
+    In Collection: RetinaNet
+    Config: configs/resnet_strikes_back/retinanet_r50_fpn_rsb-pretrain_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.8
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/retinanet_r50_fpn_rsb-pretrain_1x_coco/retinanet_r50_fpn_rsb-pretrain_1x_coco_20220113_175432-bd24aae9.pth
+    Paper:
+      URL: https://arxiv.org/abs/2110.00476
+      Title: 'ResNet strikes back: An improved training procedure in timm'
+    README: configs/resnet_strikes_back/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.22.0/configs/resnet_strikes_back/README.md
+      Version: v2.22.0
+
+  - Name: mask_rcnn_r50_fpn_rsb-pretrain_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/resnet_strikes_back/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.5
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNet
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/resnet_strikes_back/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco/mask_rcnn_r50_fpn_rsb-pretrain_1x_coco_20220113_174054-06ce8ba0.pth
+    Paper:
+      URL: https://arxiv.org/abs/2110.00476
+      Title: 'ResNet strikes back: An improved training procedure in timm'
+    README: configs/resnet_strikes_back/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.22.0/configs/resnet_strikes_back/README.md
+      Version: v2.22.0
diff --git a/configs/resnet_strikes_back/retinanet_r50_fpn_rsb-pretrain_1x_coco.py b/configs/resnet_strikes_back/retinanet_r50_fpn_rsb-pretrain_1x_coco.py
new file mode 100644
index 0000000..480697a
--- /dev/null
+++ b/configs/resnet_strikes_back/retinanet_r50_fpn_rsb-pretrain_1x_coco.py
@@ -0,0 +1,18 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+checkpoint = 'https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_8xb256-rsb-a1-600e_in1k_20211228-20e21305.pth'  # noqa
+model = dict(
+    backbone=dict(
+        init_cfg=dict(
+            type='Pretrained', prefix='backbone.', checkpoint=checkpoint)))
+
+optimizer = dict(
+    _delete_=True,
+    type='AdamW',
+    lr=0.0001,
+    weight_decay=0.05,
+    paramwise_cfg=dict(norm_decay_mult=0., bypass_duplicate=True))
diff --git a/configs/retinanet/README.md b/configs/retinanet/README.md
new file mode 100644
index 0000000..b9e0a2a
--- /dev/null
+++ b/configs/retinanet/README.md
@@ -0,0 +1,53 @@
+# RetinaNet
+
+> [Focal Loss for Dense Object Detection](https://arxiv.org/abs/1708.02002)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+The highest accuracy object detectors to date are based on a two-stage approach popularized by R-CNN, where a classifier is applied to a sparse set of candidate object locations. In contrast, one-stage detectors that are applied over a regular, dense sampling of possible object locations have the potential to be faster and simpler, but have trailed the accuracy of two-stage detectors thus far. In this paper, we investigate why this is the case. We discover that the extreme foreground-background class imbalance encountered during training of dense detectors is the central cause. We propose to address this class imbalance by reshaping the standard cross entropy loss such that it down-weights the loss assigned to well-classified examples. Our novel Focal Loss focuses training on a sparse set of hard examples and prevents the vast number of easy negatives from overwhelming the detector during training. To evaluate the effectiveness of our loss, we design and train a simple dense detector we call RetinaNet. Our results show that when trained with the focal loss, RetinaNet is able to match the speed of previous one-stage detectors while surpassing the accuracy of all existing state-of-the-art two-stage detectors.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143973551-2b8e766a-1677-4f6d-953d-2e6d2a3c67b5.png" height="300"/>
+</div>
+
+## Results and Models
+
+|    Backbone     |  Style  |   Lr schd    | Mem (GB) | Inf time (fps) | box AP |                                                        Config                                                         |                                                                                                                                                         Download                                                                                                                                                          |
+| :-------------: | :-----: | :----------: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-18-FPN     | pytorch |      1x      |   1.7    |                |  31.7  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r18_fpn_1x_coco.py)     |           [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r18_fpn_1x_coco/retinanet_r18_fpn_1x_coco_20220407_171055-614fd399.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r18_fpn_1x_coco/retinanet_r18_fpn_1x_coco_20220407_171055.log.json)            |
+|    R-18-FPN     | pytorch | 1x(1 x 8 BS) |   5.0    |                |  31.7  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r18_fpn_1x8_1x_coco.py)   |   [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r18_fpn_1x8_1x_coco/retinanet_r18_fpn_1x8_1x_coco_20220407_171255-4ea310d7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r18_fpn_1x8_1x_coco/retinanet_r18_fpn_1x8_1x_coco_20220407_171255.log.json)    |
+|    R-50-FPN     |  caffe  |      1x      |   3.5    |      18.6      |  36.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531_012518.log.json)   |
+|    R-50-FPN     | pytorch |      1x      |   3.8    |      19.0      |  36.5  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_1x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130_002941.log.json)               |
+| R-50-FPN (FP16) | pytorch |      1x      |   2.8    |      31.6      |  36.4  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py)  |          [model](https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702-0dbfb212.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702_020127.log.json)          |
+|    R-50-FPN     | pytorch |      2x      |    -     |       -        |  37.4  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_2x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131_114738.log.json)               |
+|    R-101-FPN    |  caffe  |      1x      |   5.5    |      14.7      |  38.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531_012536.log.json) |
+|    R-101-FPN    | pytorch |      1x      |   5.7    |      15.0      |  38.5  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_1x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130_003055.log.json)             |
+|    R-101-FPN    | pytorch |      2x      |    -     |       -        |  38.9  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_2x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131_114859.log.json)             |
+| X-101-32x4d-FPN | pytorch |      1x      |   7.0    |      12.1      |  39.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130_003004.log.json) |
+| X-101-32x4d-FPN | pytorch |      2x      |    -     |       -        |  40.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131_114812.log.json) |
+| X-101-64x4d-FPN | pytorch |      1x      |   10.0   |      8.7       |  41.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130_003008.log.json) |
+| X-101-64x4d-FPN | pytorch |      2x      |    -     |       -        |  40.8  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131_114833.log.json) |
+
+## Pre-trained Models
+
+We also train some models with longer schedules and multi-scale training. The users could finetune them for downstream tasks.
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | box AP |                                                                Config                                                                 |                                                                                                                                                                                 Download                                                                                                                                                                                  |
+| :-------------: | :-----: | :-----: | :------: | :----: | :-----------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50-FPN     | pytorch |   3x    |   3.5    |  39.5  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_mstrain_3x_coco/retinanet_r50_fpn_mstrain_3x_coco_20210718_220633-88476508.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_mstrain_3x_coco/retinanet_r50_fpn_mstrain_3x_coco_20210718_220633-88476508.log.json)               |
+|    R-101-FPN    |  caffe  |   3x    |   5.4    |  40.7  |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco.py)     | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco/retinanet_r101_caffe_fpn_mstrain_3x_coco_20210721_063439-88a8a944.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco/retinanet_r101_caffe_fpn_mstrain_3x_coco_20210721_063439-88a8a944.log.json) |
+|    R-101-FPN    | pytorch |   3x    |   5.4    |   41   |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_r101_fpn_mstrain_640-800_3x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_mstrain_3x_coco/retinanet_r101_fpn_mstrain_3x_coco_20210720_214650-7ee888e0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_mstrain_3x_coco/retinanet_r101_fpn_mstrain_3x_coco_20210720_214650-7ee888e0.log.json)             |
+| X-101-64x4d-FPN | pytorch |   3x    |   9.8    |  41.6  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_mstrain_3x_coco/retinanet_x101_64x4d_fpn_mstrain_3x_coco_20210719_051838-022c2187.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_mstrain_3x_coco/retinanet_x101_64x4d_fpn_mstrain_3x_coco_20210719_051838-022c2187.log.json) |
+
+## Citation
+
+```latex
+@inproceedings{lin2017focal,
+  title={Focal loss for dense object detection},
+  author={Lin, Tsung-Yi and Goyal, Priya and Girshick, Ross and He, Kaiming and Doll{\'a}r, Piotr},
+  booktitle={Proceedings of the IEEE international conference on computer vision},
+  year={2017}
+}
+```
diff --git a/configs/retinanet/ascend_retinanet_r18_fpn_1x8_1x_coco.py b/configs/retinanet/ascend_retinanet_r18_fpn_1x8_1x_coco.py
new file mode 100644
index 0000000..8643c60
--- /dev/null
+++ b/configs/retinanet/ascend_retinanet_r18_fpn_1x8_1x_coco.py
@@ -0,0 +1,23 @@
+_base_ = [
+    '../_base_/models/ascend_retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# data
+data = dict(samples_per_gpu=8)
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
+    neck=dict(in_channels=[64, 128, 256, 512]))
+
+# Note: If the learning rate is set to 0.0025, the mAP will be 32.4.
+optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (1 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=8)
diff --git a/configs/retinanet/metafile.yml b/configs/retinanet/metafile.yml
new file mode 100644
index 0000000..8751cbb
--- /dev/null
+++ b/configs/retinanet/metafile.yml
@@ -0,0 +1,312 @@
+Collections:
+  - Name: RetinaNet
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Focal Loss
+        - FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1708.02002
+      Title: "Focal Loss for Dense Object Detection"
+    README: configs/retinanet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/mmdet/models/detectors/retinanet.py#L6
+      Version: v2.0.0
+
+Models:
+  - Name: retinanet_r18_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r18_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 1.7
+      Training Resources: 8x V100 GPUs
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 31.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r18_fpn_1x_coco/retinanet_r18_fpn_1x_coco_20220407_171055-614fd399.pth
+
+  - Name: retinanet_r18_fpn_1x8_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r18_fpn_1x8_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.0
+      Training Resources:  1x V100 GPUs
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 31.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r18_fpn_1x8_1x_coco/retinanet_r18_fpn_1x8_1x_coco_20220407_171255-4ea310d7.pth
+
+  - Name: retinanet_r50_caffe_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.5
+      inference time (ms/im):
+        - value: 53.76
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_caffe_fpn_1x_coco/retinanet_r50_caffe_fpn_1x_coco_20200531-f11027c5.pth
+
+  - Name: retinanet_r50_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.8
+      inference time (ms/im):
+        - value: 52.63
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_1x_coco/retinanet_r50_fpn_1x_coco_20200130-c2398f9e.pth
+
+  - Name: retinanet_r50_fpn_fp16_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py
+    Metadata:
+      Training Memory (GB): 2.8
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+        - Mixed Precision Training
+      inference time (ms/im):
+        - value: 31.65
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP16
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 36.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/fp16/retinanet_r50_fpn_fp16_1x_coco/retinanet_r50_fpn_fp16_1x_coco_20200702-0dbfb212.pth
+
+  - Name: retinanet_r50_fpn_2x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r50_fpn_2x_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_2x_coco/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth
+
+  - Name: retinanet_r50_fpn_mstrain_640-800_3x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r50_fpn_mstrain_3x_coco/retinanet_r50_fpn_mstrain_3x_coco_20210718_220633-88476508.pth
+
+  - Name: retinanet_r101_caffe_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.5
+      inference time (ms/im):
+        - value: 68.03
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_1x_coco/retinanet_r101_caffe_fpn_1x_coco_20200531-b428fa0f.pth
+
+  - Name: retinanet_r101_caffe_fpn_mstrain_3x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco/retinanet_r101_caffe_fpn_mstrain_3x_coco_20210721_063439-88a8a944.pth
+
+  - Name: retinanet_r101_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r101_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.7
+      inference time (ms/im):
+        - value: 66.67
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_1x_coco/retinanet_r101_fpn_1x_coco_20200130-7a93545f.pth
+
+  - Name: retinanet_r101_fpn_2x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r101_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 5.7
+      inference time (ms/im):
+        - value: 66.67
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_2x_coco/retinanet_r101_fpn_2x_coco_20200131-5560aee8.pth
+
+  - Name: retinanet_r101_fpn_mstrain_640-800_3x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_r101_fpn_mstrain_640-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_r101_fpn_mstrain_3x_coco/retinanet_r101_fpn_mstrain_3x_coco_20210720_214650-7ee888e0.pth
+
+  - Name: retinanet_x101_32x4d_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.0
+      inference time (ms/im):
+        - value: 82.64
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_1x_coco/retinanet_x101_32x4d_fpn_1x_coco_20200130-5c8b7ec4.pth
+
+  - Name: retinanet_x101_32x4d_fpn_2x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 7.0
+      inference time (ms/im):
+        - value: 82.64
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_32x4d_fpn_2x_coco/retinanet_x101_32x4d_fpn_2x_coco_20200131-237fc5e1.pth
+
+  - Name: retinanet_x101_64x4d_fpn_1x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.0
+      inference time (ms/im):
+        - value: 114.94
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_1x_coco/retinanet_x101_64x4d_fpn_1x_coco_20200130-366f5af1.pth
+
+  - Name: retinanet_x101_64x4d_fpn_2x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py
+    Metadata:
+      Training Memory (GB): 10.0
+      inference time (ms/im):
+        - value: 114.94
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_2x_coco/retinanet_x101_64x4d_fpn_2x_coco_20200131-bca068ab.pth
+
+  - Name: retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco
+    In Collection: RetinaNet
+    Config: configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/retinanet/retinanet_x101_64x4d_fpn_mstrain_3x_coco/retinanet_x101_64x4d_fpn_mstrain_3x_coco_20210719_051838-022c2187.pth
diff --git a/configs/retinanet/retinanet_fpn_quant_general.py b/configs/retinanet/retinanet_fpn_quant_general.py
new file mode 100644
index 0000000..f573014
--- /dev/null
+++ b/configs/retinanet/retinanet_fpn_quant_general.py
@@ -0,0 +1,39 @@
+trace_config = dict(
+    backbone_detail = dict(
+        input_concrete_args = dict(),
+        preserve_attr = ['arch_settings', 'avg_down', 'base_channels', 'conv_cfg', 'dcn', 'deep_stem', 'depth', 'dump_patches', 'feat_dim'
+        , 'frozen_stages', 'init_cfg', 'inplanes', 'is_init', 'norm_cfg', 'norm_eval', 'out_indices'
+        , 'plugins', 'res_layers', 'stage_block', 'stage_with_dcn', 'stem_channels', 'zero_init_residual', 'strides', 'with_cp', 'make_res_layer', 'make_stage_plugins'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = [],  
+        specified_general_quantizers = [],
+        last_8bit_module = []
+    )),
+    neck_detail = dict(
+        input_concrete_args = dict(in_num = 4),
+        preserve_attr = ['in_channels', 'init_cfg', 'is_init', 'l2_norm', 'no_norm_on_lateral', 'num_ins', 'num_outs', 'out_channels', 'relu_before_extra_convs', 'start_level', 'upsample_cfg'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = ['getitem_1_post_act_fake_quantizer','getitem_2_post_act_fake_quantizer'],  # 因为这些输入已经被做过量化了
+        specified_general_quantizers = ['getitem_1_post_act_fake_quantizer', 'getitem_2_post_act_fake_quantizer', 'getitem_3_post_act_fake_quantizer'],
+        last_8bit_module = []
+    )),
+    bbox_head_detail = dict(
+        input_concrete_args = dict(in_num = 5),
+        preserve_attr = ['anchor_generator', 'assigner', 'bbox_coder', 'cls_out_channels', 'conv_cfg', 'dump_patches', 'feat_channels', 'fp16_enabled', 
+        'in_channels', 'init_cfg', 'is_init', 'norm_cfg', 'loss_bbox', 'loss_cls', 'num_anchors'
+        , 'num_base_priors', 'num_classes', 'prior_generator', 'reg_decoded_bbox', 'sampler', 'sampling', 'stacked_convs', 'test_cfg', 'train_cfg', 'use_sigmoid_cls'
+        
+        , 'simple_test', 'async_simple_test_rpn', 'aug_test', 'aug_test_bboxes', 'aug_test_rpn', 'forward_single', 'forward_train', 'get_anchors', 'get_bboxes', 'get_targets', 'loss', 'loss_single', 'merge_aug_bboxes', 
+        'simple_test_bboxes', 'simple_test_rpn', '_get_bboxes_single', '_bbox_post_process', 'get_anchors', '_get_targets_single'],
+        not_duplicated_prefixes = ['cls_convs', 'reg_convs', 'retina_cls', 'retina_reg'],
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = ['getitem_3_post_act_fake_quantizer'],  
+        qloss_flag = True, 
+        specified_general_quantizers = [],
+        last_8bit_module = ['retina_cls', 'retina_reg']
+    )))
\ No newline at end of file
diff --git a/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py b/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..56eaae2
--- /dev/null
+++ b/configs/retinanet/retinanet_r101_caffe_fpn_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './retinanet_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))
diff --git a/configs/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco.py b/configs/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..b87295e
--- /dev/null
+++ b/configs/retinanet/retinanet_r101_caffe_fpn_mstrain_3x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
+# learning policy
+model = dict(
+    pretrained='open-mmlab://detectron2/resnet101_caffe',
+    backbone=dict(depth=101))
+lr_config = dict(step=[28, 34])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/configs/retinanet/retinanet_r101_fpn_1x_coco.py b/configs/retinanet/retinanet_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..a7f0600
--- /dev/null
+++ b/configs/retinanet/retinanet_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './retinanet_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/retinanet/retinanet_r101_fpn_2x_coco.py b/configs/retinanet/retinanet_r101_fpn_2x_coco.py
new file mode 100644
index 0000000..721112a
--- /dev/null
+++ b/configs/retinanet/retinanet_r101_fpn_2x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './retinanet_r50_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/retinanet/retinanet_r101_fpn_mstrain_640-800_3x_coco.py b/configs/retinanet/retinanet_r101_fpn_mstrain_640-800_3x_coco.py
new file mode 100644
index 0000000..6bbcac4
--- /dev/null
+++ b/configs/retinanet/retinanet_r101_fpn_mstrain_640-800_3x_coco.py
@@ -0,0 +1,6 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
+]
+# optimizer
+model = dict(pretrained='torchvision://resnet101', backbone=dict(depth=101))
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/retinanet/retinanet_r18_fpn_1x8_1x_coco.py b/configs/retinanet/retinanet_r18_fpn_1x8_1x_coco.py
new file mode 100644
index 0000000..01a35f2
--- /dev/null
+++ b/configs/retinanet/retinanet_r18_fpn_1x8_1x_coco.py
@@ -0,0 +1,23 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# data
+data = dict(samples_per_gpu=8)
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet18')),
+    neck=dict(in_channels=[64, 128, 256, 512]))
+
+# Note: If the learning rate is set to 0.0025, the mAP will be 32.4.
+optimizer = dict(type='SGD', lr=0.005, momentum=0.9, weight_decay=0.0001)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (1 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=8)
diff --git a/configs/retinanet/retinanet_r18_fpn_1x_coco.py b/configs/retinanet/retinanet_r18_fpn_1x_coco.py
new file mode 100644
index 0000000..95797a2
--- /dev/null
+++ b/configs/retinanet/retinanet_r18_fpn_1x_coco.py
@@ -0,0 +1,24 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]))
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+data = dict(
+    samples_per_gpu=2,  # 就是16
+    workers_per_gpu=2,
+)
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=8, norm_type=2))
diff --git a/configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w2a2.py b/configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w2a2.py
new file mode 100644
index 0000000..e761e5f
--- /dev/null
+++ b/configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w2a2.py
@@ -0,0 +1,26 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_qat_w2a2.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]))
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+data = dict(
+    samples_per_gpu=16,  # 就是16
+    workers_per_gpu=8,
+)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = '../long_used_pretrained/retinanet_r18_fpn_1x_coco_20220407_171055-614fd399.pth'
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=6, norm_type=2))
diff --git a/configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w2a2_aqd.py b/configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w2a2_aqd.py
new file mode 100644
index 0000000..2ae83b4
--- /dev/null
+++ b/configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w2a2_aqd.py
@@ -0,0 +1,26 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_qat_w2a2.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]))
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+data = dict(
+    samples_per_gpu=8,  # 就是16
+    workers_per_gpu=8,
+)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = '/work_dirs/retinanet_r18_fpn_1x_coco_AQD/epoch_12.pth'
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=8, norm_type=2))
diff --git a/configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w4a4.py b/configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w4a4.py
new file mode 100644
index 0000000..5511953
--- /dev/null
+++ b/configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w4a4.py
@@ -0,0 +1,26 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_qat_w4a4.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]))
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+data = dict(
+    samples_per_gpu=16,  # 就是16
+    workers_per_gpu=8,
+)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = '/workspace/whole_world/rdata/share/pretrained/long_used/retinanet_r18_fpn_1x_coco_20220407_171055-614fd399.pth'
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=6, norm_type=2))
diff --git a/configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w4a4_aqd.py b/configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w4a4_aqd.py
new file mode 100644
index 0000000..f2830ca
--- /dev/null
+++ b/configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w4a4_aqd.py
@@ -0,0 +1,26 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_qat_w4a4.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]))
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+data = dict(
+    samples_per_gpu=8,  # 就是16
+    workers_per_gpu=8,
+)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = '/work_dirs/retinanet_r18_fpn_1x_coco_AQD/epoch_12.pth'
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=8, norm_type=2))
diff --git a/configs/retinanet/retinanet_r18_fpn_1x_voc.py b/configs/retinanet/retinanet_r18_fpn_1x_voc.py
new file mode 100644
index 0000000..c6f05de
--- /dev/null
+++ b/configs/retinanet/retinanet_r18_fpn_1x_voc.py
@@ -0,0 +1,32 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_1x_voc_general.py'
+    , '../_base_/default_runtime.py'
+]
+
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/bdata1/long.huang/temp/pretrained/backbones/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]),
+    bbox_head=dict(num_classes=20))
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=4, dynamic_intervals=[(5, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+
+
+
+# dataset settings
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8)
+
+
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+# load_from = '/workspace/whole_world/bdata1/long.huang/temp/pretrained/long_used/retinanet_r18_fpn_1x_coco_20220407_171055-614fd399.pth'
diff --git a/configs/retinanet/retinanet_r18_fpn_1x_voc_harmony.py b/configs/retinanet/retinanet_r18_fpn_1x_voc_harmony.py
new file mode 100644
index 0000000..dba37a7
--- /dev/null
+++ b/configs/retinanet/retinanet_r18_fpn_1x_voc_harmony.py
@@ -0,0 +1,32 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_fp32_fine_tune_general.py'
+    , '../_base_/default_runtime.py'
+]
+
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]),
+    bbox_head=dict(num_classes=20))
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+
+
+
+# dataset settings
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8)
+
+
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = '/workspace/share/long_dir/retinanet_r18_fpn_1x_voc/best_bbox_mAP_epoch_6.pth'
diff --git a/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w2a2.py b/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w2a2.py
new file mode 100644
index 0000000..6a03dde
--- /dev/null
+++ b/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w2a2.py
@@ -0,0 +1,33 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_qat_w2a2.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+# schedule_qat_w2a2  schedule_fine_tune_general
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]),
+    bbox_head=dict(num_classes=20))
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+
+
+
+# dataset settings
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8)
+
+
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = '/workspace/share/long_dir/retinanet_r18_fpn_1x_voc/best_bbox_mAP_epoch_6.pth'
+# tune_from = 'work_dirs/retinanet_r18_fpn_voc_w2a2_LSQ/best_bbox_mAP_epoch_18.pth'
\ No newline at end of file
diff --git a/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w2a2_AQD.py b/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w2a2_AQD.py
new file mode 100644
index 0000000..11e081f
--- /dev/null
+++ b/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w2a2_AQD.py
@@ -0,0 +1,33 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_qat_w2a2.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+# schedule_qat_w2a2  schedule_fine_tune_general
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/bdata1/long.huang/temp/pretrained/backbones/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]),
+    bbox_head=dict(num_classes=20))
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+
+
+
+# dataset settings
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4)
+
+
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = 'work_dirs/retinanet_r18_fpn_1x_voc/best_bbox_mAP_epoch_6.pth'
+# tune_from = 'work_dirs/retinanet_r18_fpn_voc_w2a2_LSQ/best_bbox_mAP_epoch_18.pth'
\ No newline at end of file
diff --git a/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w2a2_tune.py b/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w2a2_tune.py
new file mode 100644
index 0000000..4477d2d
--- /dev/null
+++ b/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w2a2_tune.py
@@ -0,0 +1,35 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_fine_tune_general.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+# schedule_qat_w2a2  schedule_fine_tune_general
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/share/pretrained/checkpoints/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]),
+    bbox_head=dict(num_classes=20))
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+
+
+
+# dataset settings
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8)
+
+
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+# load_from = '/workspace/share/long_dir/retinanet_r18_fpn_1x_voc/best_bbox_mAP_epoch_6.pth'
+tune_from = 'work_dirs/retinanet_r18_fpn_voc_w2a2_LSQ/best_bbox_mAP_epoch_18.pth'
+# tune_from = 'work_dirs/retinanet_r18_fpn_voc_w2a2_LSQ_HQOD_ok/best_bbox_mAP_epoch_14.pth'
+# tune_from = 'work_dirs/retinanet_r18_fpn_voc_w2a2_LSQ_HQOD/best_bbox_mAP_epoch_16.pth'
\ No newline at end of file
diff --git a/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w4a4.py b/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w4a4.py
new file mode 100644
index 0000000..4995c5c
--- /dev/null
+++ b/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w4a4.py
@@ -0,0 +1,32 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_qat_w4a4.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/rdata/share/pretrained/checkpoints/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]),
+    bbox_head=dict(num_classes=20))
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+
+
+
+# dataset settings
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8)
+
+
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = '/workspace/share/long_dir/retinanet_r18_fpn_1x_voc/best_bbox_mAP_epoch_6.pth'
diff --git a/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w4a4_debug.py b/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w4a4_debug.py
new file mode 100644
index 0000000..529d2c1
--- /dev/null
+++ b/configs/retinanet/retinanet_r18_fpn_1x_voc_quant_w4a4_debug.py
@@ -0,0 +1,89 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/schedules/schedule_qat_w4a4.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+
+# dataset settings
+dataset_type = 'VOCDataset'
+data_root = '/workspace/whole_world/bdata1/long.huang/temp/VOC/VOCdevkit/'
+classes = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
+               'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
+               'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
+               'tvmonitor')
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1000, 600), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1000, 600),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    train=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',  # 注意这里走的是COCO格式
+        img_prefix=data_root,
+        pipeline=train_pipeline,
+        classes=classes),
+    val=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',  # 注意这里走的是COCO格式
+        img_prefix=data_root,
+        pipeline=test_pipeline,
+        classes=classes),
+    test=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',
+        img_prefix=data_root,
+        pipeline=test_pipeline,
+        classes=classes))
+
+
+
+# optimizer
+model = dict(
+    backbone=dict(
+        depth=18,
+        init_cfg=dict(type='Pretrained', checkpoint='/workspace/whole_world/bdata1/long.huang/temp/pretrained/backbones/resnet18-5c106cde.pth')),
+    neck=dict(in_channels=[64, 128, 256, 512]),
+    bbox_head=dict(num_classes=20))
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+
+
+
+# # dataset settings
+# data = dict(
+#     samples_per_gpu=8,
+#     workers_per_gpu=4)
+
+
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = 'work_dirs/retinanet_r18_fpn_1x_voc/best_bbox_mAP_epoch_6.pth'
diff --git a/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py b/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..04c9af5
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,41 @@
+_base_ = './retinanet_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py
new file mode 100644
index 0000000..4d7b8f2
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py
@@ -0,0 +1,46 @@
+_base_ = './retinanet_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py
new file mode 100644
index 0000000..eea9690
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 23])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py
new file mode 100644
index 0000000..8057650
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_3x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './retinanet_r50_caffe_fpn_mstrain_1x_coco.py'
+# learning policy
+lr_config = dict(step=[28, 34])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/configs/retinanet/retinanet_r50_fpn_1x_coco.py b/configs/retinanet/retinanet_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..dbd4813
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_fpn_1x_coco.py
@@ -0,0 +1,15 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+
+data = dict(
+    samples_per_gpu=4,  # 就是16
+    workers_per_gpu=4,
+)
+
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=10, norm_type=2))
diff --git a/configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w2a2.py b/configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w2a2.py
new file mode 100644
index 0000000..5b31690
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w2a2.py
@@ -0,0 +1,21 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_qat_w2a2.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+# optimizer
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+data = dict(
+    samples_per_gpu=4,  # 就是16
+    workers_per_gpu=4,
+)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = '../long_used_pretrained/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth'
+# resume_from = 'work_dirs/retinanet_r50_fpn_1x_coco_quant_w4a4_HQOD/best_bbox_mAP_epoch_21_can.pth'
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=10, norm_type=2))
diff --git a/configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w2a2_aqd.py b/configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w2a2_aqd.py
new file mode 100644
index 0000000..5b31690
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w2a2_aqd.py
@@ -0,0 +1,21 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_qat_w2a2.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+# optimizer
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+data = dict(
+    samples_per_gpu=4,  # 就是16
+    workers_per_gpu=4,
+)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = '../long_used_pretrained/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth'
+# resume_from = 'work_dirs/retinanet_r50_fpn_1x_coco_quant_w4a4_HQOD/best_bbox_mAP_epoch_21_can.pth'
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=10, norm_type=2))
diff --git a/configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w4a4.py b/configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w4a4.py
new file mode 100644
index 0000000..23e7709
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w4a4.py
@@ -0,0 +1,21 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_qat_w4a4.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+# optimizer
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+data = dict(
+    samples_per_gpu=16,  # 就是16
+    workers_per_gpu=8,
+)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = '../long_used_pretrained/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth'
+# resume_from = 'work_dirs/retinanet_r50_fpn_1x_coco_quant_w4a4_HQOD/best_bbox_mAP_epoch_21_can.pth'
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=10, norm_type=2))
\ No newline at end of file
diff --git a/configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w4a4_aqd.py b/configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w4a4_aqd.py
new file mode 100644
index 0000000..3a53846
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w4a4_aqd.py
@@ -0,0 +1,21 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/coco_detection.py', '../_base_/schedules/schedule_qat_w4a4.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+# optimizer
+# optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+data = dict(
+    samples_per_gpu=4,  # 就是16
+    workers_per_gpu=4,
+)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+load_from = '../long_used_pretrained/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth'
+# resume_from = 'work_dirs/retinanet_r50_fpn_1x_coco_quant_w4a4_HQOD/best_bbox_mAP_epoch_21_can.pth'
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=10, norm_type=2))
\ No newline at end of file
diff --git a/configs/retinanet/retinanet_r50_fpn_1x_voc.py b/configs/retinanet/retinanet_r50_fpn_1x_voc.py
new file mode 100644
index 0000000..d6c3d4b
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_fpn_1x_voc.py
@@ -0,0 +1,17 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_1x_voc_general.py', 
+    '../_base_/default_runtime.py'
+]
+model = dict(
+    bbox_head=dict(num_classes=20))
+
+optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+# dataset settings
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4)
+
+
diff --git a/configs/retinanet/retinanet_r50_fpn_1x_voc_harmony.py b/configs/retinanet/retinanet_r50_fpn_1x_voc_harmony.py
new file mode 100644
index 0000000..c89aa07
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_fpn_1x_voc_harmony.py
@@ -0,0 +1,18 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_fp32_fine_tune_general.py', 
+    '../_base_/default_runtime.py'
+]
+model = dict(
+    bbox_head=dict(num_classes=20))
+
+# optimizer = dict(type='SGD', lr=0.004, momentum=0.9, weight_decay=0.0001)
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+# dataset settings
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8)
+load_from = '/workspace/share/long_dir/retinanet_r50_fpn_1x_voc/best_bbox_mAP_epoch_6.pth'
+
+
diff --git a/configs/retinanet/retinanet_r50_fpn_1x_voc_quant_w2a2.py b/configs/retinanet/retinanet_r50_fpn_1x_voc_quant_w2a2.py
new file mode 100644
index 0000000..de35f58
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_fpn_1x_voc_quant_w2a2.py
@@ -0,0 +1,21 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_qat_w4a4.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    bbox_head=dict(num_classes=20))
+
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+# dataset settings
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+# load_from = '../long_used_pretrained/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth'
\ No newline at end of file
diff --git a/configs/retinanet/retinanet_r50_fpn_1x_voc_quant_w2a2_resume.py b/configs/retinanet/retinanet_r50_fpn_1x_voc_quant_w2a2_resume.py
new file mode 100644
index 0000000..e7e64d1
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_fpn_1x_voc_quant_w2a2_resume.py
@@ -0,0 +1,37 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_qat_w2a2_big.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    bbox_head=dict(num_classes=20))
+
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+# dataset settings
+data = dict(
+    samples_per_gpu=4,
+    workers_per_gpu=4)
+
+
+# optimizer
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='exp',
+    warmup_by_epoch=True,
+    warmup_iters=1,
+    warmup_ratio=0.004,
+    gamma=0.5,
+    step=[8, 13, 14, 15, 16, 17, 19, 20, 21])  # 0.01 0.005 0.0025 0.00125 0.000625 
+runner = dict(type='EpochBasedRunner', max_epochs=21)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+# load_from = 'work_dirs/retinanet_r50_fpn_1x_voc/best_bbox_mAP_epoch_6.pth'
+resume_from = 'work_dirs/retinanet_r50_fpn_voc_w2a2_LSQ_HQOD/best_bbox_mAP_epoch_18.pth'
\ No newline at end of file
diff --git a/configs/retinanet/retinanet_r50_fpn_1x_voc_quant_w4a4.py b/configs/retinanet/retinanet_r50_fpn_1x_voc_quant_w4a4.py
new file mode 100644
index 0000000..dd800d1
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_fpn_1x_voc_quant_w4a4.py
@@ -0,0 +1,21 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn_no_freeze.py',
+    '../_base_/datasets/voc0712.py', '../_base_/schedules/schedule_qat_w4a4.py'
+    , 'retinanet_fpn_quant_general.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    bbox_head=dict(num_classes=20))
+
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)],metric='bbox')
+checkpoint_config = dict(interval=10)
+# dataset settings
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (2 samples per GPU)
+auto_scale_lr = dict(base_batch_size=16)
+# load_from = '../long_used_pretrained/retinanet_r50_fpn_2x_coco_20200131-fdb43119.pth'
\ No newline at end of file
diff --git a/configs/retinanet/retinanet_r50_fpn_2x_coco.py b/configs/retinanet/retinanet_r50_fpn_2x_coco.py
new file mode 100644
index 0000000..927915f
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_fpn_2x_coco.py
@@ -0,0 +1,4 @@
+_base_ = './retinanet_r50_fpn_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/retinanet/retinanet_r50_fpn_90k_coco.py b/configs/retinanet/retinanet_r50_fpn_90k_coco.py
new file mode 100644
index 0000000..ceda327
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_fpn_90k_coco.py
@@ -0,0 +1,15 @@
+_base_ = 'retinanet_r50_fpn_1x_coco.py'
+
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[60000, 80000])
+
+# Runner type
+runner = dict(_delete_=True, type='IterBasedRunner', max_iters=90000)
+
+checkpoint_config = dict(interval=10000)
+evaluation = dict(interval=10000, metric='bbox')
diff --git a/configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py b/configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py
new file mode 100644
index 0000000..0c067e3
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './retinanet_r50_fpn_1x_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale=512.)
+
+# set grad_norm for stability during mixed-precision training
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=10, norm_type=2))
diff --git a/configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py b/configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py
new file mode 100644
index 0000000..02a2c29
--- /dev/null
+++ b/configs/retinanet/retinanet_r50_fpn_mstrain_640-800_3x_coco.py
@@ -0,0 +1,5 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
+]
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py b/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..765a4c2
--- /dev/null
+++ b/configs/retinanet/retinanet_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './retinanet_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py b/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py
new file mode 100644
index 0000000..14de96f
--- /dev/null
+++ b/configs/retinanet/retinanet_x101_32x4d_fpn_2x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './retinanet_r50_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py b/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..948cd18
--- /dev/null
+++ b/configs/retinanet/retinanet_x101_64x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './retinanet_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py b/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py
new file mode 100644
index 0000000..ad04b6e
--- /dev/null
+++ b/configs/retinanet/retinanet_x101_64x4d_fpn_2x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './retinanet_r50_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py b/configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py
new file mode 100644
index 0000000..f6ab512
--- /dev/null
+++ b/configs/retinanet/retinanet_x101_64x4d_fpn_mstrain_640-800_3x_coco.py
@@ -0,0 +1,8 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py', '../common/mstrain_3x_coco.py'
+]
+# optimizer
+model = dict(
+    pretrained='open-mmlab://resnext101_64x4d',
+    backbone=dict(type='ResNeXt', depth=101, groups=64, base_width=4))
+optimizer = dict(type='SGD', lr=0.01)
diff --git a/configs/rfnext/README.md b/configs/rfnext/README.md
new file mode 100644
index 0000000..13f3991
--- /dev/null
+++ b/configs/rfnext/README.md
@@ -0,0 +1,131 @@
+# RF-Next: Efficient Receptive Field Search for CNN
+
+> [RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks](http://mftp.mmcheng.net/Papers/22TPAMI-ActionSeg.pdf)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Temporal/spatial receptive fields of models play an important role in sequential/spatial tasks. Large receptive fields facilitate long-term relations, while small receptive fields help to capture the local details. Existing methods construct models with hand-designed receptive fields in layers. Can we effectively search for receptive field combinations to replace hand-designed patterns? To answer this question, we propose to find better receptive field combinations through a global-to-local search scheme. Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combinations further. The global search finds possible coarse combinations other than human-designed patterns. On top of the global search, we propose an expectation-guided iterative local search scheme to refine combinations effectively. Our RF-Next models, plugging receptive field search to various models, boost the performance on many tasks, e.g., temporal action segmentation, object detection, instance segmentation, and speech synthesis.
+The source code is publicly available on [http://mmcheng.net/rfnext](http://mmcheng.net/rfnext).
+
+## Results and Models
+
+### ConvNext on COCO
+
+|   Backbone    |       Method       |     RFNext      | Lr Schd | box mAP | mask mAP |                                                                                                                                                                             Config                                                                                                                                                                              |                                                                                                                                                                                                                                                                                                                                    Download                                                                                                                                                                                                                                                                                                                                    |
+| :-----------: | :----------------: | :-------------: | :-----: | :-----: | :------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  ConvNeXt-T   | Cascade Mask R-CNN |       NO        |   3x    |  50.3   |   43.6   |                                                                                                             [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco.py)                                                                                                             |                                                                                                                                   [model](https://download.openmmlab.com/mmdetection/v2.0/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco_20220426_154953-050731f4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/convnext/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco/mask_rcnn_convnext-t_p4_w7_fpn_fp16_ms-crop_3x_coco_20220426_154953.log.json)                                                                                                                                   |
+| RF-ConvNeXt-T | Cascade Mask R-CNN |  Single-Branch  |   3x    |  50.6   |   44.0   | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k-71aeb991.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k_20220131_091748.log.json) |
+| RF-ConvNeXt-T | Cascade Mask R-CNN | Multiple-Branch |   3x    |  50.9   |   44.3   | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py)  |                           [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k/rfnext_search_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k-f47db42b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k/rfnext_search_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k_20220128_200900.log.json)                           |
+
+### PVTv2 on COCO
+
+|  Backbone   |   Method   |     RFNext      | Lr Schd | box mAP | mask mAP |                                                                                                                                   Config                                                                                                                                    |                                                                                                                                                                                                    Download                                                                                                                                                                                                    |
+| :---------: | :--------: | :-------------: | :-----: | :-----: | :------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  PVTv2-b0   | Mask R-CNN |       NO        |   1x    |  38.2   |   36.2   |                                                                                                                                      -                                                                                                                                      |                                                                                                                                                                                                       -                                                                                                                                                                                                        |
+| RF-PVTv2-b0 | Mask R-CNN |  Single-Branch  |   1x    |  38.9   |   36.8   | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco-7b25d72e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco_20221120_213845.log.json) |
+| RF-PVTv2-b0 | Mask R-CNN | Multiple-Branch |   1x    |  39.3   |   37.1   | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_multi_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py)  |                           [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco-dc8fd5de.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco_20221119_204703.log.json)                           |
+
+The results of PVTv2-b0 are from [PVT](https://github.com/whai362/PVT/tree/v2/detection).
+
+### Res2Net on COCO
+
+|    Backbone    |       Method       |     RFNext      | Lr Schd | box mAP | mask mAP |                                                                                                                                           Config                                                                                                                                           |                                                                                                                                                                                                                  Download                                                                                                                                                                                                                  |
+| :------------: | :----------------: | :-------------: | :-----: | :-----: | :------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  Res2Net-101   | Cascade Mask R-CNN |       NO        |   20e   |  46.4   |   40.0   |                                                                                  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py)                                                                                  |                                                      [model](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco-8a7b41e1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco/cascade_mask_rcnn_r2_101_fpn_20e_coco_20200515_091645.log.json)                                                      |
+| RF-Res2Net-101 | Cascade Mask R-CNN |  Single-Branch  |   20e   |  46.9   |   40.7   | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco.py)  [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco-e22d5257.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco_20220402_141321.log.json) |
+| RF-Res2Net-101 | Cascade Mask R-CNN | Multiple-Branch |   20e   |  47.9   |   41.5   | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco.py)  [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py)  |                           [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco-e17510a0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco_20220327_221419.log.json)                           |
+
+### HRNet on COCO
+
+|    Backbone     |       Method       |     RFNext      | Lr Schd | box mAP | mask mAP |                                                                                                                                                Config                                                                                                                                                 |                                                                                                                                                                                                                      Download                                                                                                                                                                                                                      |
+| :-------------: | :----------------: | :-------------: | :-----: | :-----: | :------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  HRNetV2p-W18   | Cascade Mask R-CNN |       NO        |   20e   |  41.6   |   36.4   |                                                                                       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py)                                                                                        |                                                   [model](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210-b543cd2b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/hrnet/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20200210_093149.log.json)                                                    |
+| RF-HRNetV2p-W18 | Cascade Mask R-CNN |  Single-Branch  |   20e   |  43.0   |   37.6   | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfsearched_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfsearched_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco-682f121d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20221118_141400.log.json) |
+| RF-HRNetV2p-W18 | Cascade Mask R-CNN | Multiple-Branch |   20e   |  43.7   |   38.2   | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfsearched_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfsearched_fixed_multi_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py)  |                           [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco-7b9c7885.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco_20221115_230113.log.json)                           |
+
+Note: the performance of multi-branch models listed above are evaluated during searching to save computional cost, retraining would achieve similar or better performance.
+
+### Res2Net on COCO panoptic
+
+|   Backbone    |    Method    |     RFNext      | Lr schd |  PQ  |  SQ  |  RQ  |                                                                                                                                        Config                                                                                                                                         |                                                                                                                                                                                                              Download                                                                                                                                                                                                              |
+| :-----------: | :----------: | :-------------: | :-----: | :--: | :--: | :--: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  Res2Net-50   | Panoptic FPN |       NO        |   1x    | 42.5 | 78.0 | 51.8 |                                                                              [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py)                                                                              |                                                       [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/panoptic_fpn_r2_50_fpn_fp16_1x_coco/panoptic_fpn_r2_50_fpn_fp16_1x_coco-fa6c51f0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/panoptic_fpn_r2_50_fpn_fp16_1x_coco/panoptic_fpn_r2_50_fpn_fp16_1x_coco_20221114_224729.log.json)                                                       |
+| RF-Res2Net-50 | Panoptic FPN |  Single-Branch  |   1x    | 44.0 | 78.7 | 53.6 | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco-52181d5b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco_20221115_152436.log.json) |
+| RF-Res2Net-50 | Panoptic FPN | Multiple-Branch |   1x    | 44.3 | 79.0 | 53.9 | [search](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py) [retrain](https://github.com/open-mmlab/mmdetection/tree/master/configs/rfnext/rfnext_fixed_multi_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py)  |                           [model](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco-34a893a0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco_20221114_224722.log.json)                           |
+
+## Configs
+
+If you want to search receptive fields on an existing model, you need to define a `RFSearchHook` in the `custom_hooks` of config file.
+
+```python
+custom_hooks = [
+    dict(
+        type='RFSearchHook',
+        mode='search',
+        rfstructure_file=None,
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=11,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=[]))
+        ),
+]
+```
+
+Arguments:
+
+- `max_step`: The maximum number of steps to update the structures.
+- `search_interval`: The interval (epoch) between two updates.
+- `exp_rate`:  The controller of the sparsity of search space. For a conv with an initial dilation rate of `D`, dilation rates will be sampled with an interval of `exp_rate * D`.
+- `num_branches`: The controller of the size of search space (the number of branches). If you set `S=3`, the dilations are `[D - exp_rate * D, D, D + exp_rate * D]` for three branches. If you set `num_branches=2`, the dilations are `[D - exp_rate * D, D + exp_rate * D]`. With `num_branches=2`, you can achieve similar performance with less MEMORY and FLOPS.
+- `skip_layer`: The modules in skip_layer will be ignored during the receptive field search.
+
+## Training
+
+### 1. Searching Jobs
+
+You can launch searching jobs by using config files with prefix `rfnext_search`. The json files of searched structures will be saved to `work_dir`.
+
+If you want to further search receptive fields upon a searched structure, please set `rfsearch_cfg.rfstructure_file` in config file to the corresponding json file.
+
+### 2. Training Jobs
+
+Setting `rfsearch_cfg.rfstructure_file` to the searched structure file (.json) and setting `rfsearch_cfg.mode` to `fixed_single_branch` or `fixed_multi_branch`, you can retrain a model with the searched structure.
+You can launch fixed_single_branch/fixed_multi_branch training jobs by using config files with prefix `rfnext_fixed_single_branch` or `rfnext_fixed_multi_branch`.
+
+Note that the models after the searching stage is ready a `fixed_multi_branch` version, which achieves better performance than `fixed_single_branch`, without any retraining.
+
+## Inference
+
+`rfsearch_cfg.rfstructure_file` and `rfsearch_cfg.mode` should be set for inferencing stage.
+
+**Note:For the models trained with modes of `fixed_single_branch` or `fixed_multi_branch`, you can just use the training config for inferencing.**
+**But If you want to inference the models trained with the mode of `search`, please use the config with prefix of `rfnext_fixed_multi_branch` to inference the models. (Otherwise, you should set `rfsearch_cfg.mode` to `fixed_multi_branch` and set the searched rfstructure_file.)**
+
+## Citation
+
+```
+@article{gao2022rfnext,
+title={RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks},
+author={Gao, Shanghua and Li, Zhong-Yu and Han, Qi and Cheng, Ming-Ming and Wang, Liang},
+journal=TPAMI,
+year={2022}
+}
+
+@inproceedings{gao2021global2local,
+  title     = {Global2Local: Efficient Structure Search for Video Action Segmentation},
+  author    = {Gao, Shanghua and Han, Qi and Li, Zhong-Yu and Peng, Pai and Wang, Liang and Cheng, Ming-Ming},
+  booktitle = CVPR,
+  year      = {2021}
+}
+```
diff --git a/configs/rfnext/metafile.yml b/configs/rfnext/metafile.yml
new file mode 100644
index 0000000..59469d7
--- /dev/null
+++ b/configs/rfnext/metafile.yml
@@ -0,0 +1,249 @@
+Collections:
+  - Name: RF-Next
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - RF-Next
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - RF-Next
+    Paper:
+      URL: http://mftp.mmcheng.net/Papers/22TPAMI-ActionSeg.pdf
+      Title: "RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks"
+    README: configs/rfnext/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/mmdet/utils/rfnext.py
+      Version: v2.27.0
+
+Models:
+  - Name: rfnext_search_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k
+    In Collection: RF-Next
+    Config: configs/rfnext/rfnext_search_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
+    Metadata:
+      Training Memory (GB): 11.9
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        RF-Next (search)
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 50.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 44.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k/rfnext_search_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k-f47db42b.pth
+    Paper:
+      URL: https://arxiv.org/pdf/2206.06637.pdf
+      Title: 'RF-Next: Efficient Receptive Field Search for CNN'
+    README: configs/rfnext/README.md
+
+  - Name: rfnext_fixed_single_branch_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k
+    In Collection: RF-Next
+    Config: configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
+    Metadata:
+      Training Memory (GB): 9.4
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        RF-Next (fixed_single_branch)
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 50.6
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 44.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext_tiny_patch4_window7_mstrain_480-800_giou_4conv1f_adamw_3x_coco_in1k-71aeb991.pth
+    Paper:
+      URL: https://arxiv.org/pdf/2206.06637.pdf
+      Title: 'RF-Next: Efficient Receptive Field Search for CNN'
+    README: configs/rfnext/README.md
+
+  - Name: rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco
+    In Collection: RF-Next
+    Config: configs/rfnext/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
+    Metadata:
+      Training Memory (GB): 12.9
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        RF-Next (search)
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 38.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco-7b9c7885.pth
+    Paper:
+      URL: https://arxiv.org/pdf/2206.06637.pdf
+      Title: 'RF-Next: Efficient Receptive Field Search for CNN'
+    README: configs/rfnext/README.md
+
+  - Name: rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco
+    In Collection: RF-Next
+    Config: configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
+    Metadata:
+      Training Memory (GB): 8.4
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        RF-Next (fixed_single_branch)
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco-682f121d.pth
+    Paper:
+      URL: https://arxiv.org/pdf/2206.06637.pdf
+      Title: 'RF-Next: Efficient Receptive Field Search for CNN'
+    README: configs/rfnext/README.md
+
+  - Name: rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco
+    In Collection: RF-Next
+    Config: configs/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 11.9
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        RF-Next (search)
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco-e17510a0.pth
+    Paper:
+      URL: https://arxiv.org/pdf/2206.06637.pdf
+      Title: 'RF-Next: Efficient Receptive Field Search for CNN'
+    README: configs/rfnext/README.md
+
+  - Name: rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco
+    In Collection: RF-Next
+    Config: configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 9.3
+      Epochs: 20
+      Training Data: COCO
+      Training Techniques:
+        RF-Next (fixed_single_branch)
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 40.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco-e22d5257.pth
+    Paper:
+      URL: https://arxiv.org/pdf/2206.06637.pdf
+      Title: 'RF-Next: Efficient Receptive Field Search for CNN'
+    README: configs/rfnext/README.md
+
+  - Name: rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco
+    In Collection: RF-Next
+    Config: configs/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 10.3
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        RF-Next (search)
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco-dc8fd5de.pth
+    Paper:
+      URL: https://arxiv.org/pdf/2206.06637.pdf
+      Title: 'RF-Next: Efficient Receptive Field Search for CNN'
+    README: configs/rfnext/README.md
+
+  - Name: rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco
+    In Collection: RF-Next
+    Config: configs/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.3
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        RF-Next (fixed_single_branch)
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.9
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco-7b25d72e.pth
+    Paper:
+      URL: https://arxiv.org/pdf/2206.06637.pdf
+      Title: 'RF-Next: Efficient Receptive Field Search for CNN'
+    README: configs/rfnext/README.md
+
+  - Name: rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco
+    In Collection: RF-Next
+    Config: configs/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.3
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        RF-Next (search)
+    Results:
+      - Task: Panoptic Segmentation
+        Dataset: COCO
+        Metrics:
+          box AP: 44.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco-34a893a0.pth
+    Paper:
+      URL: https://arxiv.org/pdf/2206.06637.pdf
+      Title: 'RF-Next: Efficient Receptive Field Search for CNN'
+    README: configs/rfnext/README.md
+
+  - Name: rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco
+    In Collection: RF-Next
+    Config: configs/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py
+    Metadata:
+      Training Memory (GB): 3.5
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        RF-Next (fixed_single_branch)
+    Results:
+      - Task: Panoptic Segmentation
+        Dataset: COCO
+        Metrics:
+          box AP: 44.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco-52181d5b.pth
+    Paper:
+      URL: https://arxiv.org/pdf/2206.06637.pdf
+      Title: 'RF-Next: Efficient Receptive Field Search for CNN'
+    README: configs/rfnext/README.md
diff --git a/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py b/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
new file mode 100644
index 0000000..76b499c
--- /dev/null
+++ b/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
@@ -0,0 +1,23 @@
+_base_ = '../convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py'  # noqa
+
+custom_hooks = [
+    dict(
+        type='RFSearchHook',
+        mode='fixed_multi_branch',
+        rfstructure_file=  # noqa
+        './configs/rfnext/search_log/convnext_cascade_maskrcnn/local_search_config_step11.json',  # noqa
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=12,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                normlize='absavg',
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=[])))
+]
diff --git a/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py b/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
new file mode 100644
index 0000000..1751427
--- /dev/null
+++ b/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
@@ -0,0 +1,21 @@
+_base_ = '../hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
+
+custom_hooks = [
+    dict(
+        mode='fixed_multi_branch',
+        rfstructure_file=  # noqa
+        './configs/rfnext/search_log/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/local_search_config_step11.json',  # noqa
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=12,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=[])))
+]
diff --git a/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py b/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py
new file mode 100644
index 0000000..d8bc9e2
--- /dev/null
+++ b/configs/rfnext/rfnext_fixed_multi_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py
@@ -0,0 +1,23 @@
+_base_ = '../res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py'
+
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='RFSearchHook',
+        mode='fixed_multi_branch',
+        rfstructure_file=  # noqa
+        './configs/rfnext/search_log/cascade_mask_rcnn_r2_101_fpn_20e_coco/local_search_config_step11.json',  # noqa
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=12,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=['stem', 'layer1'])))
+]
diff --git a/configs/rfnext/rfnext_fixed_multi_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py b/configs/rfnext/rfnext_fixed_multi_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py
new file mode 100644
index 0000000..b98a8f9
--- /dev/null
+++ b/configs/rfnext/rfnext_fixed_multi_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py
@@ -0,0 +1,46 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model setting
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='PyramidVisionTransformerV2',
+        embed_dims=32,
+        num_layers=[2, 2, 2, 2],
+        init_cfg=dict(
+            checkpoint=  # noqa
+            'https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b0.pth'  # noqa
+        )),
+    neck=dict(
+        type='FPN',
+        in_channels=[32, 64, 160, 256],
+        out_channels=256,
+        num_outs=5))
+
+# optimizer
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0002, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+
+custom_hooks = [
+    dict(
+        type='RFSearchHook',
+        mode='fixed_multi_branch',
+        rfstructure_file=  # noqa
+        './configs/rfnext/search_log/mask_rcnn_pvtv2-b0_fpn_1x_coco/local_search_config_step10.json',  # noqa
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=11,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=[])))
+]
diff --git a/configs/rfnext/rfnext_fixed_multi_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py b/configs/rfnext/rfnext_fixed_multi_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py
new file mode 100644
index 0000000..1471f0a
--- /dev/null
+++ b/configs/rfnext/rfnext_fixed_multi_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py
@@ -0,0 +1,22 @@
+_base_ = '../panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py'
+
+custom_hooks = [
+    dict(
+        type='RFSearchHook',
+        mode='fixed_multi_branch',
+        rfstructure_file=  # noqa
+        './configs/rfnext/search_log/panoptic_fpn_r2_50_fpn_fp16_1x_coco/local_search_config_step10.json',  # noqa
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=11,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=['stem', 'layer1'])))
+]
diff --git a/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py b/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
new file mode 100644
index 0000000..19db83b
--- /dev/null
+++ b/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
@@ -0,0 +1,22 @@
+_base_ = '../convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py'  # noqa
+
+custom_hooks = [
+    dict(
+        type='RFSearchHook',
+        mode='fixed_single_branch',
+        rfstructure_file=  # noqa
+        './configs/rfnext/search_log/convnext_cascade_maskrcnn/local_search_config_step11.json',  # noqa
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=12,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=[])))
+]
diff --git a/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py b/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
new file mode 100644
index 0000000..071f510
--- /dev/null
+++ b/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
@@ -0,0 +1,22 @@
+_base_ = '../hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
+
+custom_hooks = [
+    dict(
+        type='RFSearchHook',
+        mode='fixed_single_branch',
+        rfstructure_file=  # noqa
+        './configs/rfnext/search_log/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/local_search_config_step11.json',  # noqa
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=12,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=[])))
+]
diff --git a/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py b/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py
new file mode 100644
index 0000000..3813b38
--- /dev/null
+++ b/configs/rfnext/rfnext_fixed_single_branch_cascade_mask_rcnn_r2_101_fpn_20e_coco.py
@@ -0,0 +1,23 @@
+_base_ = '../res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py'
+
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='RFSearchHook',
+        mode='fixed_single_branch',
+        rfstructure_file=  # noqa
+        './configs/rfnext/search_log/cascade_mask_rcnn_r2_101_fpn_20e_coco/local_search_config_step11.json',  # noqa
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=12,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=['stem', 'layer1'])))
+]
diff --git a/configs/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py b/configs/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py
new file mode 100644
index 0000000..dc12384
--- /dev/null
+++ b/configs/rfnext/rfnext_fixed_single_branch_mask_rcnn_pvtv2-b0_fpn_1x_coco.py
@@ -0,0 +1,46 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model setting
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='PyramidVisionTransformerV2',
+        embed_dims=32,
+        num_layers=[2, 2, 2, 2],
+        init_cfg=dict(
+            checkpoint=  # noqa
+            'https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b0.pth'  # noqa
+        )),
+    neck=dict(
+        type='FPN',
+        in_channels=[32, 64, 160, 256],
+        out_channels=256,
+        num_outs=5))
+
+# optimizer
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0002, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+
+custom_hooks = [
+    dict(
+        type='RFSearchHook',
+        mode='fixed_single_branch',
+        rfstructure_file=  # noqa
+        './configs/rfnext/search_log/mask_rcnn_pvtv2-b0_fpn_1x_coco/local_search_config_step10.json',  # noqa
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=11,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=[])))
+]
diff --git a/configs/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py b/configs/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py
new file mode 100644
index 0000000..da45eba
--- /dev/null
+++ b/configs/rfnext/rfnext_fixed_single_branch_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py
@@ -0,0 +1,22 @@
+_base_ = '../panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py'
+
+custom_hooks = [
+    dict(
+        type='RFSearchHook',
+        mode='fixed_single_branch',
+        rfstructure_file=  # noqa
+        './configs/rfnext/search_log/panoptic_fpn_r2_50_fpn_fp16_1x_coco/local_search_config_step10.json',  # noqa
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=11,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=['stem', 'layer1'])))
+]
diff --git a/configs/rfnext/rfnext_search_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py b/configs/rfnext/rfnext_search_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
new file mode 100644
index 0000000..3a99123
--- /dev/null
+++ b/configs/rfnext/rfnext_search_cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py
@@ -0,0 +1,21 @@
+_base_ = '../convnext/cascade_mask_rcnn_convnext-t_p4_w7_fpn_giou_4conv1f_fp16_ms-crop_3x_coco.py'  # noqa
+
+custom_hooks = [
+    dict(
+        type='RFSearchHook',
+        mode='search',
+        rfstructure_file=None,
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=12,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=[])))
+]
diff --git a/configs/rfnext/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py b/configs/rfnext/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
new file mode 100644
index 0000000..3a76939
--- /dev/null
+++ b/configs/rfnext/rfnext_search_cascade_mask_rcnn_hrnetv2p_w18_20e_coco.py
@@ -0,0 +1,21 @@
+_base_ = '../hrnet/cascade_mask_rcnn_hrnetv2p_w32_20e_coco.py'
+
+custom_hooks = [
+    dict(
+        type='RFSearchHook',
+        mode='search',
+        rfstructure_file=None,
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=12,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=[])))
+]
diff --git a/configs/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco.py b/configs/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco.py
new file mode 100644
index 0000000..364ce28
--- /dev/null
+++ b/configs/rfnext/rfnext_search_cascade_mask_rcnn_r2_101_fpn_20e_coco.py
@@ -0,0 +1,22 @@
+_base_ = '../res2net/cascade_mask_rcnn_r2_101_fpn_20e_coco.py'
+
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(
+        type='RFSearchHook',
+        mode='search',
+        rfstructure_file=None,
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=12,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=['stem', 'layer1'])))
+]
diff --git a/configs/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco.py b/configs/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco.py
new file mode 100644
index 0000000..ee2e288
--- /dev/null
+++ b/configs/rfnext/rfnext_search_mask_rcnn_pvtv2-b0_fpn_1x_coco.py
@@ -0,0 +1,45 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model setting
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='PyramidVisionTransformerV2',
+        embed_dims=32,
+        num_layers=[2, 2, 2, 2],
+        init_cfg=dict(
+            checkpoint=  # noqa
+            'https://github.com/whai362/PVT/releases/download/v2/pvt_v2_b0.pth'  # noqa
+        )),
+    neck=dict(
+        type='FPN',
+        in_channels=[32, 64, 160, 256],
+        out_channels=256,
+        num_outs=5))
+
+# optimizer
+optimizer = dict(_delete_=True, type='AdamW', lr=0.0002, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+
+custom_hooks = [
+    dict(
+        type='RFSearchHook',
+        mode='search',
+        rfstructure_file=None,
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=11,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=[])))
+]
diff --git a/configs/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py b/configs/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py
new file mode 100644
index 0000000..4c9816d
--- /dev/null
+++ b/configs/rfnext/rfnext_search_panoptic_fpn_r2_50_fpn_fp16_1x_coco.py
@@ -0,0 +1,21 @@
+_base_ = '../panoptic_fpn/panoptic_fpn_r2_50_fpn_fp16_1x_coco.py'
+
+custom_hooks = [
+    dict(
+        type='RFSearchHook',
+        mode='search',
+        rfstructure_file=None,
+        verbose=True,
+        by_epoch=True,
+        config=dict(
+            search=dict(
+                step=0,
+                max_step=11,
+                search_interval=1,
+                exp_rate=0.5,
+                init_alphas=0.01,
+                mmin=1,
+                mmax=24,
+                num_branches=2,
+                skip_layer=['stem', 'layer1'])))
+]
diff --git a/configs/rfnext/search_log/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/local_search_config_step11.json b/configs/rfnext/search_log/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/local_search_config_step11.json
new file mode 100644
index 0000000..e5dfef7
--- /dev/null
+++ b/configs/rfnext/search_log/cascade_mask_rcnn_hrnetv2p_w18_20e_coco/local_search_config_step11.json
@@ -0,0 +1,1133 @@
+{
+    "search": {
+        "step": 11,
+        "max_step": 12,
+        "search_interval": 1,
+        "exp_rate": 0.5,
+        "init_alphas": 0.01,
+        "mmin": 1,
+        "mmax": 24,
+        "num_branches": 2,
+        "skip_layer": [
+            "layer1"
+        ]
+    },
+    "structure": {
+        "module.backbone.conv1": [
+            1,
+            1
+        ],
+        "module.backbone.conv2": [
+            1,
+            1
+        ],
+        "module.backbone.transition1.0.0": [
+            1,
+            1
+        ],
+        "module.backbone.transition1.1.0.0": [
+            1,
+            1
+        ],
+        "module.backbone.stage2.0.branches.0.0.conv1": [
+            2,
+            2
+        ],
+        "module.backbone.stage2.0.branches.0.0.conv2": [
+            1,
+            1
+        ],
+        "module.backbone.stage2.0.branches.0.1.conv1": [
+            1,
+            1
+        ],
+        "module.backbone.stage2.0.branches.0.1.conv2": [
+            2,
+            2
+        ],
+        "module.backbone.stage2.0.branches.0.2.conv1": [
+            1,
+            1
+        ],
+        "module.backbone.stage2.0.branches.0.2.conv2": [
+            2,
+            2
+        ],
+        "module.backbone.stage2.0.branches.0.3.conv1": [
+            1,
+            1
+        ],
+        "module.backbone.stage2.0.branches.0.3.conv2": [
+            1,
+            1
+        ],
+        "module.backbone.stage2.0.branches.1.0.conv1": [
+            3,
+            3
+        ],
+        "module.backbone.stage2.0.branches.1.0.conv2": [
+            4,
+            4
+        ],
+        "module.backbone.stage2.0.branches.1.1.conv1": [
+            1,
+            1
+        ],
+        "module.backbone.stage2.0.branches.1.1.conv2": [
+            2,
+            2
+        ],
+        "module.backbone.stage2.0.branches.1.2.conv1": [
+            1,
+            1
+        ],
+        "module.backbone.stage2.0.branches.1.2.conv2": [
+            1,
+            1
+        ],
+        "module.backbone.stage2.0.branches.1.3.conv1": [
+            1,
+            1
+        ],
+        "module.backbone.stage2.0.branches.1.3.conv2": [
+            1,
+            1
+        ],
+        "module.backbone.stage2.0.fuse_layers.1.0.0.0": [
+            1,
+            1
+        ],
+        "module.backbone.transition2.2.0.0": [
+            1,
+            1
+        ],
+        "module.backbone.stage3.0.branches.0.0.conv1": [
+            1,
+            1
+        ],
+        "module.backbone.stage3.0.branches.0.0.conv2": [
+            1,
+            1
+        ],
+        "module.backbone.stage3.0.branches.0.1.conv1": [
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+            1
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+        "module.backbone.stage3.0.branches.0.1.conv2": [
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+        "module.backbone.stage3.0.branches.0.2.conv1": [
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+        "module.backbone.stage3.0.branches.0.2.conv2": [
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+        "module.backbone.stage3.0.branches.0.3.conv1": [
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diff --git a/configs/rfnext/search_log/cascade_mask_rcnn_r2_101_fpn_20e_coco/local_search_config_step11.json b/configs/rfnext/search_log/cascade_mask_rcnn_r2_101_fpn_20e_coco/local_search_config_step11.json
new file mode 100644
index 0000000..64757c6
--- /dev/null
+++ b/configs/rfnext/search_log/cascade_mask_rcnn_r2_101_fpn_20e_coco/local_search_config_step11.json
@@ -0,0 +1,124 @@
+{
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+        "search_interval": 1,
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+        "num_branches": 2,
+        "skip_layer": [
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+        "module.backbone.layer3.3.convs.0": 2,
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+        "module.backbone.layer4.0.convs.2": 3,
+        "module.backbone.layer4.1.convs.0": 1,
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+        "module.backbone.layer4.2.convs.1": 3,
+        "module.backbone.layer4.2.convs.2": 8,
+        "module.neck.fpn_convs.0.conv": 1,
+        "module.neck.fpn_convs.1.conv": 1,
+        "module.neck.fpn_convs.2.conv": 1,
+        "module.neck.fpn_convs.3.conv": 1,
+        "module.rpn_head.rpn_conv": 3,
+        "module.roi_head.mask_head.0.convs.0.conv": 1,
+        "module.roi_head.mask_head.0.convs.1.conv": 1,
+        "module.roi_head.mask_head.0.convs.2.conv": 3,
+        "module.roi_head.mask_head.0.convs.3.conv": 1,
+        "module.roi_head.mask_head.1.convs.0.conv": 1,
+        "module.roi_head.mask_head.1.convs.1.conv": 1,
+        "module.roi_head.mask_head.1.convs.2.conv": 2,
+        "module.roi_head.mask_head.1.convs.3.conv": 1,
+        "module.roi_head.mask_head.2.convs.0.conv": 1,
+        "module.roi_head.mask_head.2.convs.1.conv": 1,
+        "module.roi_head.mask_head.2.convs.2.conv": 2,
+        "module.roi_head.mask_head.2.convs.3.conv": 1
+    }
+}
diff --git a/configs/rfnext/search_log/convnext_cascade_maskrcnn/local_search_config_step11.json b/configs/rfnext/search_log/convnext_cascade_maskrcnn/local_search_config_step11.json
new file mode 100644
index 0000000..fd115c2
--- /dev/null
+++ b/configs/rfnext/search_log/convnext_cascade_maskrcnn/local_search_config_step11.json
@@ -0,0 +1,62 @@
+{
+    "search": {
+        "step": 11,
+        "max_step": 12,
+        "search_interval": 1,
+        "exp_rate": 0.5,
+        "init_alphas": 0.01,
+        "mmin": 1,
+        "mmax": 24,
+        "num_branches": 2,
+        "skip_layer": []
+    },
+    "structure": {
+        "module.backbone.stages.0.0.depthwise_conv": 1,
+        "module.backbone.stages.0.1.depthwise_conv": 1,
+        "module.backbone.stages.0.2.depthwise_conv": 1,
+        "module.backbone.stages.1.0.depthwise_conv": 1,
+        "module.backbone.stages.1.1.depthwise_conv": 1,
+        "module.backbone.stages.1.2.depthwise_conv": 1,
+        "module.backbone.stages.2.0.depthwise_conv": 1,
+        "module.backbone.stages.2.1.depthwise_conv": 1,
+        "module.backbone.stages.2.2.depthwise_conv": 1,
+        "module.backbone.stages.2.3.depthwise_conv": 1,
+        "module.backbone.stages.2.4.depthwise_conv": 1,
+        "module.backbone.stages.2.5.depthwise_conv": 1,
+        "module.backbone.stages.2.6.depthwise_conv": 2,
+        "module.backbone.stages.2.7.depthwise_conv": 1,
+        "module.backbone.stages.2.8.depthwise_conv": 1,
+        "module.backbone.stages.3.0.depthwise_conv": 2,
+        "module.backbone.stages.3.1.depthwise_conv": 2,
+        "module.backbone.stages.3.2.depthwise_conv": 2,
+        "module.neck.fpn_convs.0.conv": 1,
+        "module.neck.fpn_convs.1.conv": 1,
+        "module.neck.fpn_convs.2.conv": 1,
+        "module.neck.fpn_convs.3.conv": 1,
+        "module.rpn_head.rpn_conv": 3,
+        "module.roi_head.bbox_head.0.shared_convs.0.conv": 1,
+        "module.roi_head.bbox_head.0.shared_convs.1.conv": 1,
+        "module.roi_head.bbox_head.0.shared_convs.2.conv": 1,
+        "module.roi_head.bbox_head.0.shared_convs.3.conv": 2,
+        "module.roi_head.bbox_head.1.shared_convs.0.conv": 1,
+        "module.roi_head.bbox_head.1.shared_convs.1.conv": 2,
+        "module.roi_head.bbox_head.1.shared_convs.2.conv": 1,
+        "module.roi_head.bbox_head.1.shared_convs.3.conv": 1,
+        "module.roi_head.bbox_head.2.shared_convs.0.conv": 1,
+        "module.roi_head.bbox_head.2.shared_convs.1.conv": 2,
+        "module.roi_head.bbox_head.2.shared_convs.2.conv": 2,
+        "module.roi_head.bbox_head.2.shared_convs.3.conv": 1,
+        "module.roi_head.mask_head.0.convs.0.conv": 1,
+        "module.roi_head.mask_head.0.convs.1.conv": 3,
+        "module.roi_head.mask_head.0.convs.2.conv": 3,
+        "module.roi_head.mask_head.0.convs.3.conv": 2,
+        "module.roi_head.mask_head.1.convs.0.conv": 1,
+        "module.roi_head.mask_head.1.convs.1.conv": 3,
+        "module.roi_head.mask_head.1.convs.2.conv": 2,
+        "module.roi_head.mask_head.1.convs.3.conv": 1,
+        "module.roi_head.mask_head.2.convs.0.conv": 1,
+        "module.roi_head.mask_head.2.convs.1.conv": 2,
+        "module.roi_head.mask_head.2.convs.2.conv": 2,
+        "module.roi_head.mask_head.2.convs.3.conv": 1
+    }
+}
diff --git a/configs/rfnext/search_log/mask_rcnn_pvtv2-b0_fpn_1x_coco/local_search_config_step10.json b/configs/rfnext/search_log/mask_rcnn_pvtv2-b0_fpn_1x_coco/local_search_config_step10.json
new file mode 100644
index 0000000..f7ad2e8
--- /dev/null
+++ b/configs/rfnext/search_log/mask_rcnn_pvtv2-b0_fpn_1x_coco/local_search_config_step10.json
@@ -0,0 +1,99 @@
+{
+    "search": {
+        "step": 10,
+        "max_step": 11,
+        "search_interval": 1,
+        "exp_rate": 0.5,
+        "init_alphas": 0.01,
+        "mmin": 1,
+        "mmax": 24,
+        "num_branches": 2,
+        "skip_layer": []
+    },
+    "structure": {
+        "module.backbone.layers.0.0.projection": [
+            1,
+            1
+        ],
+        "module.backbone.layers.0.1.0.ffn.layers.1": [
+            1,
+            1
+        ],
+        "module.backbone.layers.0.1.1.ffn.layers.1": [
+            1,
+            1
+        ],
+        "module.backbone.layers.1.0.projection": [
+            1,
+            1
+        ],
+        "module.backbone.layers.1.1.0.ffn.layers.1": [
+            1,
+            1
+        ],
+        "module.backbone.layers.1.1.1.ffn.layers.1": [
+            1,
+            1
+        ],
+        "module.backbone.layers.2.0.projection": [
+            1,
+            1
+        ],
+        "module.backbone.layers.2.1.0.ffn.layers.1": [
+            1,
+            1
+        ],
+        "module.backbone.layers.2.1.1.ffn.layers.1": [
+            1,
+            1
+        ],
+        "module.backbone.layers.3.0.projection": [
+            1,
+            1
+        ],
+        "module.backbone.layers.3.1.0.ffn.layers.1": [
+            2,
+            2
+        ],
+        "module.backbone.layers.3.1.1.ffn.layers.1": [
+            1,
+            1
+        ],
+        "module.neck.fpn_convs.0.conv": [
+            1,
+            1
+        ],
+        "module.neck.fpn_convs.1.conv": [
+            2,
+            2
+        ],
+        "module.neck.fpn_convs.2.conv": [
+            2,
+            2
+        ],
+        "module.neck.fpn_convs.3.conv": [
+            2,
+            2
+        ],
+        "module.rpn_head.rpn_conv": [
+            3,
+            3
+        ],
+        "module.roi_head.mask_head.convs.0.conv": [
+            1,
+            1
+        ],
+        "module.roi_head.mask_head.convs.1.conv": [
+            2,
+            2
+        ],
+        "module.roi_head.mask_head.convs.2.conv": [
+            2,
+            2
+        ],
+        "module.roi_head.mask_head.convs.3.conv": [
+            2,
+            2
+        ]
+    }
+}
diff --git a/configs/rfnext/search_log/panoptic_fpn_r2_50_fpn_fp16_1x_coco/local_search_config_step10.json b/configs/rfnext/search_log/panoptic_fpn_r2_50_fpn_fp16_1x_coco/local_search_config_step10.json
new file mode 100644
index 0000000..dce0275
--- /dev/null
+++ b/configs/rfnext/search_log/panoptic_fpn_r2_50_fpn_fp16_1x_coco/local_search_config_step10.json
@@ -0,0 +1,238 @@
+{
+    "search": {
+        "step": 10,
+        "max_step": 11,
+        "search_interval": 1,
+        "exp_rate": 0.5,
+        "init_alphas": 0.01,
+        "mmin": 1,
+        "mmax": 24,
+        "num_branches": 2,
+        "skip_layer": [
+            "stem",
+            "layer1"
+        ]
+    },
+    "structure": {
+        "module.backbone.layer2.0.convs.0": [
+            1,
+            1
+        ],
+        "module.backbone.layer2.0.convs.1": [
+            1,
+            1
+        ],
+        "module.backbone.layer2.0.convs.2": [
+            1,
+            1
+        ],
+        "module.backbone.layer2.1.convs.0": [
+            2,
+            2
+        ],
+        "module.backbone.layer2.1.convs.1": [
+            2,
+            2
+        ],
+        "module.backbone.layer2.1.convs.2": [
+            2,
+            2
+        ],
+        "module.backbone.layer2.2.convs.0": [
+            1,
+            1
+        ],
+        "module.backbone.layer2.2.convs.1": [
+            1,
+            1
+        ],
+        "module.backbone.layer2.2.convs.2": [
+            2,
+            2
+        ],
+        "module.backbone.layer2.3.convs.0": [
+            1,
+            1
+        ],
+        "module.backbone.layer2.3.convs.1": [
+            1,
+            1
+        ],
+        "module.backbone.layer2.3.convs.2": [
+            1,
+            1
+        ],
+        "module.backbone.layer3.0.convs.0": [
+            2,
+            2
+        ],
+        "module.backbone.layer3.0.convs.1": [
+            2,
+            2
+        ],
+        "module.backbone.layer3.0.convs.2": [
+            2,
+            2
+        ],
+        "module.backbone.layer3.1.convs.0": [
+            2,
+            2
+        ],
+        "module.backbone.layer3.1.convs.1": [
+            2,
+            2
+        ],
+        "module.backbone.layer3.1.convs.2": [
+            3,
+            3
+        ],
+        "module.backbone.layer3.2.convs.0": [
+            1,
+            1
+        ],
+        "module.backbone.layer3.2.convs.1": [
+            2,
+            2
+        ],
+        "module.backbone.layer3.2.convs.2": [
+            2,
+            2
+        ],
+        "module.backbone.layer3.3.convs.0": [
+            3,
+            3
+        ],
+        "module.backbone.layer3.3.convs.1": [
+            3,
+            3
+        ],
+        "module.backbone.layer3.3.convs.2": [
+            3,
+            3
+        ],
+        "module.backbone.layer3.4.convs.0": [
+            2,
+            2
+        ],
+        "module.backbone.layer3.4.convs.1": [
+            1,
+            1
+        ],
+        "module.backbone.layer3.4.convs.2": [
+            2,
+            2
+        ],
+        "module.backbone.layer3.5.convs.0": [
+            2,
+            2
+        ],
+        "module.backbone.layer3.5.convs.1": [
+            2,
+            2
+        ],
+        "module.backbone.layer3.5.convs.2": [
+            3,
+            3
+        ],
+        "module.backbone.layer4.0.convs.0": [
+            3,
+            3
+        ],
+        "module.backbone.layer4.0.convs.1": [
+            3,
+            3
+        ],
+        "module.backbone.layer4.0.convs.2": [
+            3,
+            3
+        ],
+        "module.backbone.layer4.1.convs.0": [
+            3,
+            3
+        ],
+        "module.backbone.layer4.1.convs.1": [
+            8,
+            8
+        ],
+        "module.backbone.layer4.1.convs.2": [
+            10,
+            10
+        ],
+        "module.backbone.layer4.2.convs.0": [
+            3,
+            3
+        ],
+        "module.backbone.layer4.2.convs.1": [
+            2,
+            2
+        ],
+        "module.backbone.layer4.2.convs.2": [
+            5,
+            5
+        ],
+        "module.neck.fpn_convs.0.conv": [
+            1,
+            1
+        ],
+        "module.neck.fpn_convs.1.conv": [
+            2,
+            2
+        ],
+        "module.neck.fpn_convs.2.conv": [
+            1,
+            1
+        ],
+        "module.neck.fpn_convs.3.conv": [
+            2,
+            2
+        ],
+        "module.rpn_head.rpn_conv": [
+            3,
+            3
+        ],
+        "module.roi_head.mask_head.convs.0.conv": [
+            1,
+            1
+        ],
+        "module.roi_head.mask_head.convs.1.conv": [
+            2,
+            2
+        ],
+        "module.roi_head.mask_head.convs.2.conv": [
+            2,
+            2
+        ],
+        "module.roi_head.mask_head.convs.3.conv": [
+            1,
+            1
+        ],
+        "module.semantic_head.conv_upsample_layers.0.conv.0.conv": [
+            2,
+            2
+        ],
+        "module.semantic_head.conv_upsample_layers.1.conv.0.conv": [
+            6,
+            6
+        ],
+        "module.semantic_head.conv_upsample_layers.2.conv.0.conv": [
+            2,
+            2
+        ],
+        "module.semantic_head.conv_upsample_layers.2.conv.1.conv": [
+            1,
+            1
+        ],
+        "module.semantic_head.conv_upsample_layers.3.conv.0.conv": [
+            5,
+            5
+        ],
+        "module.semantic_head.conv_upsample_layers.3.conv.1.conv": [
+            3,
+            3
+        ],
+        "module.semantic_head.conv_upsample_layers.3.conv.2.conv": [
+            1,
+            1
+        ]
+    }
+}
diff --git a/configs/rpn/README.md b/configs/rpn/README.md
new file mode 100644
index 0000000..99addc0
--- /dev/null
+++ b/configs/rpn/README.md
@@ -0,0 +1,39 @@
+# RPN
+
+> [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://arxiv.org/abs/1506.01497)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+State-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations. Advances like SPPnet and Fast R-CNN have reduced the running time of these detection networks, exposing region proposal computation as a bottleneck. In this work, we introduce a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals. An RPN is a fully convolutional network that simultaneously predicts object bounds and objectness scores at each position. The RPN is trained end-to-end to generate high-quality region proposals, which are used by Fast R-CNN for detection. We further merge RPN and Fast R-CNN into a single network by sharing their convolutional features---using the recently popular terminology of neural networks with 'attention' mechanisms, the RPN component tells the unified network where to look. For the very deep VGG-16 model, our detection system has a frame rate of 5fps (including all steps) on a GPU, while achieving state-of-the-art object detection accuracy on PASCAL VOC 2007, 2012, and MS COCO datasets with only 300 proposals per image. In ILSVRC and COCO 2015 competitions, Faster R-CNN and RPN are the foundations of the 1st-place winning entries in several tracks.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143973617-387c7561-82f4-40b2-b78e-4776394b1b8b.png" height="300"/>
+</div>
+
+## Results and Models
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | AR1000 |                                                  Config                                                   |                                                                                                                                       Download                                                                                                                                        |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50-FPN     |  caffe  |   1x    |   3.5    |      22.6      |  58.7  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531-5b903a37.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_caffe_fpn_1x_coco/rpn_r50_caffe_fpn_1x_coco_20200531_012334.log.json)   |
+|    R-50-FPN     | pytorch |   1x    |   3.8    |      22.3      |  58.2  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_fpn_1x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218-5525fa2e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_1x_coco/rpn_r50_fpn_1x_coco_20200218_151240.log.json)               |
+|    R-50-FPN     | pytorch |   2x    |    -     |       -        |  58.6  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r50_fpn_2x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131-0728c9b3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r50_fpn_2x_coco/rpn_r50_fpn_2x_coco_20200131_190631.log.json)               |
+|    R-101-FPN    |  caffe  |   1x    |   5.4    |      17.3      |  60.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531-0629a2e2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_caffe_fpn_1x_coco/rpn_r101_caffe_fpn_1x_coco_20200531_012345.log.json) |
+|    R-101-FPN    | pytorch |   1x    |   5.8    |      16.5      |  59.7  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_fpn_1x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_1x_coco/rpn_r101_fpn_1x_coco_20200131-2ace2249.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_1x_coco/rpn_r101_fpn_1x_coco_20200131_191000.log.json)             |
+|    R-101-FPN    | pytorch |   2x    |    -     |       -        |  60.2  |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_r101_fpn_2x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_2x_coco/rpn_r101_fpn_2x_coco_20200131-24e3db1a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_r101_fpn_2x_coco/rpn_r101_fpn_2x_coco_20200131_191106.log.json)             |
+| X-101-32x4d-FPN | pytorch |   1x    |   7.0    |      13.0      |  60.6  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219-b02646c6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_1x_coco/rpn_x101_32x4d_fpn_1x_coco_20200219_012037.log.json) |
+| X-101-32x4d-FPN | pytorch |   2x    |    -     |       -        |  61.1  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208-d22bd0bb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_32x4d_fpn_2x_coco/rpn_x101_32x4d_fpn_2x_coco_20200208_200752.log.json) |
+| X-101-64x4d-FPN | pytorch |   1x    |   10.1   |      9.1       |  61.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208-cde6f7dd.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_1x_coco/rpn_x101_64x4d_fpn_1x_coco_20200208_200752.log.json) |
+| X-101-64x4d-FPN | pytorch |   2x    |    -     |       -        |  61.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208-c65f524f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/rpn/rpn_x101_64x4d_fpn_2x_coco/rpn_x101_64x4d_fpn_2x_coco_20200208_200752.log.json) |
+
+## Citation
+
+```latex
+@inproceedings{ren2015faster,
+  title={Faster r-cnn: Towards real-time object detection with region proposal networks},
+  author={Ren, Shaoqing and He, Kaiming and Girshick, Ross and Sun, Jian},
+  booktitle={Advances in neural information processing systems},
+  year={2015}
+}
+```
diff --git a/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py b/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..27be946
--- /dev/null
+++ b/configs/rpn/rpn_r101_caffe_fpn_1x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './rpn_r50_caffe_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet101_caffe')))
diff --git a/configs/rpn/rpn_r101_fpn_1x_coco.py b/configs/rpn/rpn_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..962728f
--- /dev/null
+++ b/configs/rpn/rpn_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './rpn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/rpn/rpn_r101_fpn_2x_coco.py b/configs/rpn/rpn_r101_fpn_2x_coco.py
new file mode 100644
index 0000000..ac7671c
--- /dev/null
+++ b/configs/rpn/rpn_r101_fpn_2x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './rpn_r50_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/rpn/rpn_r50_caffe_c4_1x_coco.py b/configs/rpn/rpn_r50_caffe_c4_1x_coco.py
new file mode 100644
index 0000000..6da0ee9
--- /dev/null
+++ b/configs/rpn/rpn_r50_caffe_c4_1x_coco.py
@@ -0,0 +1,38 @@
+_base_ = [
+    '../_base_/models/rpn_r50_caffe_c4.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# dataset settings
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_label=False),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+evaluation = dict(interval=1, metric='proposal_fast')
diff --git a/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py b/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py
new file mode 100644
index 0000000..68c36fa
--- /dev/null
+++ b/configs/rpn/rpn_r50_caffe_fpn_1x_coco.py
@@ -0,0 +1,41 @@
+_base_ = './rpn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        norm_cfg=dict(requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')))
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_label=False),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/rpn/rpn_r50_fpn_1x_coco.py b/configs/rpn/rpn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..26f95a3
--- /dev/null
+++ b/configs/rpn/rpn_r50_fpn_1x_coco.py
@@ -0,0 +1,18 @@
+_base_ = [
+    '../_base_/models/rpn_r50_fpn.py', '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_label=False),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+evaluation = dict(interval=1, metric='proposal_fast')
diff --git a/configs/rpn/rpn_r50_fpn_2x_coco.py b/configs/rpn/rpn_r50_fpn_2x_coco.py
new file mode 100644
index 0000000..2f264bf
--- /dev/null
+++ b/configs/rpn/rpn_r50_fpn_2x_coco.py
@@ -0,0 +1,5 @@
+_base_ = './rpn_r50_fpn_1x_coco.py'
+
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py b/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..d0c7394
--- /dev/null
+++ b/configs/rpn/rpn_x101_32x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './rpn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py b/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py
new file mode 100644
index 0000000..c6880b7
--- /dev/null
+++ b/configs/rpn/rpn_x101_32x4d_fpn_2x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './rpn_r50_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py b/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py
new file mode 100644
index 0000000..96e691a
--- /dev/null
+++ b/configs/rpn/rpn_x101_64x4d_fpn_1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './rpn_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py b/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py
new file mode 100644
index 0000000..4182a39
--- /dev/null
+++ b/configs/rpn/rpn_x101_64x4d_fpn_2x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './rpn_r50_fpn_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/sabl/README.md b/configs/sabl/README.md
new file mode 100644
index 0000000..03992be
--- /dev/null
+++ b/configs/sabl/README.md
@@ -0,0 +1,47 @@
+# SABL
+
+> [Side-Aware Boundary Localization for More Precise Object Detection](https://arxiv.org/abs/1912.04260)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Current object detection frameworks mainly rely on bounding box regression to localize objects. Despite the remarkable progress in recent years, the precision of bounding box regression remains unsatisfactory, hence limiting performance in object detection. We observe that precise localization requires careful placement of each side of the bounding box. However, the mainstream approach, which focuses on predicting centers and sizes, is not the most effective way to accomplish this task, especially when there exists displacements with large variance between the anchors and the targets. In this paper, we propose an alternative approach, named as Side-Aware Boundary Localization (SABL), where each side of the bounding box is respectively localized with a dedicated network branch. To tackle the difficulty of precise localization in the presence of displacements with large variance, we further propose a two-step localization scheme, which first predicts a range of movement through bucket prediction and then pinpoints the precise position within the predicted bucket. We test the proposed method on both two-stage and single-stage detection frameworks. Replacing the standard bounding box regression branch with the proposed design leads to significant improvements on Faster R-CNN, RetinaNet, and Cascade R-CNN, by 3.0%, 1.7%, and 0.9%, respectively.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143973698-3dfaea91-4415-4818-9781-5017183e7489.png"/>
+</div>
+
+## Results and Models
+
+The results on COCO 2017 val is shown in the below table. (results on test-dev are usually slightly higher than val).
+Single-scale testing (1333x800) is adopted in all results.
+
+|       Method       | Backbone  | Lr schd | ms-train | box AP |                                                       Config                                                       |                                                                                                                                  Download                                                                                                                                   |
+| :----------------: | :-------: | :-----: | :------: | :----: | :----------------------------------------------------------------------------------------------------------------: | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| SABL Faster R-CNN  | R-50-FPN  |   1x    |    N     |  39.9  |  [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py)  |    [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/20200830_130324.log.json)    |
+| SABL Faster R-CNN  | R-101-FPN |   1x    |    N     |  41.7  | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py)  |  [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/sabl_faster_rcnn_r101_fpn_1x_coco-f804c6c1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/20200830_183949.log.json)   |
+| SABL Cascade R-CNN | R-50-FPN  |   1x    |    N     |  41.6  | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py)  |  [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/sabl_cascade_rcnn_r50_fpn_1x_coco-e1748e5e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/20200831_033726.log.json)   |
+| SABL Cascade R-CNN | R-101-FPN |   1x    |    N     |  43.0  | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/sabl_cascade_rcnn_r101_fpn_1x_coco-2b83e87c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/20200831_141745.log.json) |
+
+|     Method     | Backbone  | GN  | Lr schd |  ms-train   | box AP |                                                            Config                                                             |                                                                                                                                                   Download                                                                                                                                                   |
+| :------------: | :-------: | :-: | :-----: | :---------: | :----: | :---------------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| SABL RetinaNet | R-50-FPN  |  N  |   1x    |      N      |  37.7  |        [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py)         |                       [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/20200830_053451.log.json)                        |
+| SABL RetinaNet | R-50-FPN  |  Y  |   1x    |      N      |  38.8  |       [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py)       |                   [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/sabl_retinanet_r50_fpn_gn_1x_coco-e16dfcf1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/20200831_141955.log.json)                   |
+| SABL RetinaNet | R-101-FPN |  N  |   1x    |      N      |  39.7  |        [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py)        |                      [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/sabl_retinanet_r101_fpn_1x_coco-42026904.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/20200831_034256.log.json)                      |
+| SABL RetinaNet | R-101-FPN |  Y  |   1x    |      N      |  40.5  |      [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py)       |                 [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/sabl_retinanet_r101_fpn_gn_1x_coco-40a893e8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/20200830_201422.log.json)                  |
+| SABL RetinaNet | R-101-FPN |  Y  |   2x    | Y (640~800) |  42.9  | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco-1e63382c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/20200830_144807.log.json) |
+| SABL RetinaNet | R-101-FPN |  Y  |   2x    | Y (480~960) |  43.6  | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco-5342f857.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/20200830_164537.log.json) |
+
+## Citation
+
+We provide config files to reproduce the object detection results in the ECCV 2020 Spotlight paper for [Side-Aware Boundary Localization for More Precise Object Detection](https://arxiv.org/abs/1912.04260).
+
+```latex
+@inproceedings{Wang_2020_ECCV,
+    title = {Side-Aware Boundary Localization for More Precise Object Detection},
+    author = {Jiaqi Wang and Wenwei Zhang and Yuhang Cao and Kai Chen and Jiangmiao Pang and Tao Gong and Jianping Shi and Chen Change Loy and Dahua Lin},
+    booktitle = {ECCV},
+    year = {2020}
+}
+```
diff --git a/configs/sabl/metafile.yml b/configs/sabl/metafile.yml
new file mode 100644
index 0000000..23c51cf
--- /dev/null
+++ b/configs/sabl/metafile.yml
@@ -0,0 +1,140 @@
+Collections:
+  - Name: SABL
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - ResNet
+        - SABL
+    Paper:
+      URL: https://arxiv.org/abs/1912.04260
+      Title: 'Side-Aware Boundary Localization for More Precise Object Detection'
+    README: configs/sabl/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.4.0/mmdet/models/roi_heads/bbox_heads/sabl_head.py#L14
+      Version: v2.4.0
+
+Models:
+  - Name: sabl_faster_rcnn_r50_fpn_1x_coco
+    In Collection: SABL
+    Config: configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r50_fpn_1x_coco/sabl_faster_rcnn_r50_fpn_1x_coco-e867595b.pth
+
+  - Name: sabl_faster_rcnn_r101_fpn_1x_coco
+    In Collection: SABL
+    Config: configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_faster_rcnn_r101_fpn_1x_coco/sabl_faster_rcnn_r101_fpn_1x_coco-f804c6c1.pth
+
+  - Name: sabl_cascade_rcnn_r50_fpn_1x_coco
+    In Collection: SABL
+    Config: configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco/sabl_cascade_rcnn_r50_fpn_1x_coco-e1748e5e.pth
+
+  - Name: sabl_cascade_rcnn_r101_fpn_1x_coco
+    In Collection: SABL
+    Config: configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco/sabl_cascade_rcnn_r101_fpn_1x_coco-2b83e87c.pth
+
+  - Name: sabl_retinanet_r50_fpn_1x_coco
+    In Collection: SABL
+    Config: configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_1x_coco/sabl_retinanet_r50_fpn_1x_coco-6c54fd4f.pth
+
+  - Name: sabl_retinanet_r50_fpn_gn_1x_coco
+    In Collection: SABL
+    Config: configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 38.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r50_fpn_gn_1x_coco/sabl_retinanet_r50_fpn_gn_1x_coco-e16dfcf1.pth
+
+  - Name: sabl_retinanet_r101_fpn_1x_coco
+    In Collection: SABL
+    Config: configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 39.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_1x_coco/sabl_retinanet_r101_fpn_1x_coco-42026904.pth
+
+  - Name: sabl_retinanet_r101_fpn_gn_1x_coco
+    In Collection: SABL
+    Config: configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_1x_coco/sabl_retinanet_r101_fpn_gn_1x_coco-40a893e8.pth
+
+  - Name: sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco
+    In Collection: SABL
+    Config: configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco-1e63382c.pth
+
+  - Name: sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco
+    In Collection: SABL
+    Config: configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco-5342f857.pth
diff --git a/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py b/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..64fe230
--- /dev/null
+++ b/configs/sabl/sabl_cascade_rcnn_r101_fpn_1x_coco.py
@@ -0,0 +1,90 @@
+_base_ = [
+    '../_base_/models/cascade_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')),
+    roi_head=dict(bbox_head=[
+        dict(
+            type='SABLHead',
+            num_classes=80,
+            cls_in_channels=256,
+            reg_in_channels=256,
+            roi_feat_size=7,
+            reg_feat_up_ratio=2,
+            reg_pre_kernel=3,
+            reg_post_kernel=3,
+            reg_pre_num=2,
+            reg_post_num=1,
+            cls_out_channels=1024,
+            reg_offset_out_channels=256,
+            reg_cls_out_channels=256,
+            num_cls_fcs=1,
+            num_reg_fcs=0,
+            reg_class_agnostic=True,
+            norm_cfg=None,
+            bbox_coder=dict(
+                type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+            loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
+                               loss_weight=1.0)),
+        dict(
+            type='SABLHead',
+            num_classes=80,
+            cls_in_channels=256,
+            reg_in_channels=256,
+            roi_feat_size=7,
+            reg_feat_up_ratio=2,
+            reg_pre_kernel=3,
+            reg_post_kernel=3,
+            reg_pre_num=2,
+            reg_post_num=1,
+            cls_out_channels=1024,
+            reg_offset_out_channels=256,
+            reg_cls_out_channels=256,
+            num_cls_fcs=1,
+            num_reg_fcs=0,
+            reg_class_agnostic=True,
+            norm_cfg=None,
+            bbox_coder=dict(
+                type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.5),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+            loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
+                               loss_weight=1.0)),
+        dict(
+            type='SABLHead',
+            num_classes=80,
+            cls_in_channels=256,
+            reg_in_channels=256,
+            roi_feat_size=7,
+            reg_feat_up_ratio=2,
+            reg_pre_kernel=3,
+            reg_post_kernel=3,
+            reg_pre_num=2,
+            reg_post_num=1,
+            cls_out_channels=1024,
+            reg_offset_out_channels=256,
+            reg_cls_out_channels=256,
+            num_cls_fcs=1,
+            num_reg_fcs=0,
+            reg_class_agnostic=True,
+            norm_cfg=None,
+            bbox_coder=dict(
+                type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.3),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+            loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, loss_weight=1.0))
+    ]))
diff --git a/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py b/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..4b28a59
--- /dev/null
+++ b/configs/sabl/sabl_cascade_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,86 @@
+_base_ = [
+    '../_base_/models/cascade_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+    roi_head=dict(bbox_head=[
+        dict(
+            type='SABLHead',
+            num_classes=80,
+            cls_in_channels=256,
+            reg_in_channels=256,
+            roi_feat_size=7,
+            reg_feat_up_ratio=2,
+            reg_pre_kernel=3,
+            reg_post_kernel=3,
+            reg_pre_num=2,
+            reg_post_num=1,
+            cls_out_channels=1024,
+            reg_offset_out_channels=256,
+            reg_cls_out_channels=256,
+            num_cls_fcs=1,
+            num_reg_fcs=0,
+            reg_class_agnostic=True,
+            norm_cfg=None,
+            bbox_coder=dict(
+                type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+            loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
+                               loss_weight=1.0)),
+        dict(
+            type='SABLHead',
+            num_classes=80,
+            cls_in_channels=256,
+            reg_in_channels=256,
+            roi_feat_size=7,
+            reg_feat_up_ratio=2,
+            reg_pre_kernel=3,
+            reg_post_kernel=3,
+            reg_pre_num=2,
+            reg_post_num=1,
+            cls_out_channels=1024,
+            reg_offset_out_channels=256,
+            reg_cls_out_channels=256,
+            num_cls_fcs=1,
+            num_reg_fcs=0,
+            reg_class_agnostic=True,
+            norm_cfg=None,
+            bbox_coder=dict(
+                type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.5),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+            loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
+                               loss_weight=1.0)),
+        dict(
+            type='SABLHead',
+            num_classes=80,
+            cls_in_channels=256,
+            reg_in_channels=256,
+            roi_feat_size=7,
+            reg_feat_up_ratio=2,
+            reg_pre_kernel=3,
+            reg_post_kernel=3,
+            reg_pre_num=2,
+            reg_post_num=1,
+            cls_out_channels=1024,
+            reg_offset_out_channels=256,
+            reg_cls_out_channels=256,
+            num_cls_fcs=1,
+            num_reg_fcs=0,
+            reg_class_agnostic=True,
+            norm_cfg=None,
+            bbox_coder=dict(
+                type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.3),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+            loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1, loss_weight=1.0))
+    ]))
diff --git a/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py b/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..e48d425
--- /dev/null
+++ b/configs/sabl/sabl_faster_rcnn_r101_fpn_1x_coco.py
@@ -0,0 +1,38 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')),
+    roi_head=dict(
+        bbox_head=dict(
+            _delete_=True,
+            type='SABLHead',
+            num_classes=80,
+            cls_in_channels=256,
+            reg_in_channels=256,
+            roi_feat_size=7,
+            reg_feat_up_ratio=2,
+            reg_pre_kernel=3,
+            reg_post_kernel=3,
+            reg_pre_num=2,
+            reg_post_num=1,
+            cls_out_channels=1024,
+            reg_offset_out_channels=256,
+            reg_cls_out_channels=256,
+            num_cls_fcs=1,
+            num_reg_fcs=0,
+            reg_class_agnostic=True,
+            norm_cfg=None,
+            bbox_coder=dict(
+                type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+            loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
+                               loss_weight=1.0))))
diff --git a/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py b/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..732c7ba
--- /dev/null
+++ b/configs/sabl/sabl_faster_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,34 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            _delete_=True,
+            type='SABLHead',
+            num_classes=80,
+            cls_in_channels=256,
+            reg_in_channels=256,
+            roi_feat_size=7,
+            reg_feat_up_ratio=2,
+            reg_pre_kernel=3,
+            reg_post_kernel=3,
+            reg_pre_num=2,
+            reg_post_num=1,
+            cls_out_channels=1024,
+            reg_offset_out_channels=256,
+            reg_cls_out_channels=256,
+            num_cls_fcs=1,
+            num_reg_fcs=0,
+            reg_class_agnostic=True,
+            norm_cfg=None,
+            bbox_coder=dict(
+                type='BucketingBBoxCoder', num_buckets=14, scale_factor=1.7),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+            loss_bbox_reg=dict(type='SmoothL1Loss', beta=0.1,
+                               loss_weight=1.0))))
diff --git a/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..b08e916
--- /dev/null
+++ b/configs/sabl/sabl_retinanet_r101_fpn_1x_coco.py
@@ -0,0 +1,54 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')),
+    bbox_head=dict(
+        _delete_=True,
+        type='SABLRetinaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        approx_anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        square_anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[4],
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
+        loss_bbox_reg=dict(
+            type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='ApproxMaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0.0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py
new file mode 100644
index 0000000..fc30d63
--- /dev/null
+++ b/configs/sabl/sabl_retinanet_r101_fpn_gn_1x_coco.py
@@ -0,0 +1,56 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model settings
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')),
+    bbox_head=dict(
+        _delete_=True,
+        type='SABLRetinaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        approx_anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        square_anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[4],
+            strides=[8, 16, 32, 64, 128]),
+        norm_cfg=norm_cfg,
+        bbox_coder=dict(
+            type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
+        loss_bbox_reg=dict(
+            type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='ApproxMaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0.0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py
new file mode 100644
index 0000000..e8fe166
--- /dev/null
+++ b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_480_960_coco.py
@@ -0,0 +1,73 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+# model settings
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')),
+    bbox_head=dict(
+        _delete_=True,
+        type='SABLRetinaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        approx_anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        square_anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[4],
+            strides=[8, 16, 32, 64, 128]),
+        norm_cfg=norm_cfg,
+        bbox_coder=dict(
+            type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
+        loss_bbox_reg=dict(
+            type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='ApproxMaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0.0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 480), (1333, 960)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py
new file mode 100644
index 0000000..30c4339
--- /dev/null
+++ b/configs/sabl/sabl_retinanet_r101_fpn_gn_2x_ms_640_800_coco.py
@@ -0,0 +1,73 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+# model settings
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')),
+    bbox_head=dict(
+        _delete_=True,
+        type='SABLRetinaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        approx_anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        square_anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[4],
+            strides=[8, 16, 32, 64, 128]),
+        norm_cfg=norm_cfg,
+        bbox_coder=dict(
+            type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
+        loss_bbox_reg=dict(
+            type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='ApproxMaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0.0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py b/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..6fe6bd6
--- /dev/null
+++ b/configs/sabl/sabl_retinanet_r50_fpn_1x_coco.py
@@ -0,0 +1,50 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+    bbox_head=dict(
+        _delete_=True,
+        type='SABLRetinaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        approx_anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        square_anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[4],
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
+        loss_bbox_reg=dict(
+            type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='ApproxMaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0.0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py b/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py
new file mode 100644
index 0000000..6acf080
--- /dev/null
+++ b/configs/sabl/sabl_retinanet_r50_fpn_gn_1x_coco.py
@@ -0,0 +1,52 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model settings
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    bbox_head=dict(
+        _delete_=True,
+        type='SABLRetinaHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        approx_anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        square_anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[4],
+            strides=[8, 16, 32, 64, 128]),
+        norm_cfg=norm_cfg,
+        bbox_coder=dict(
+            type='BucketingBBoxCoder', num_buckets=14, scale_factor=3.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.5),
+        loss_bbox_reg=dict(
+            type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='ApproxMaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0.0,
+            ignore_iof_thr=-1),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/scnet/README.md b/configs/scnet/README.md
new file mode 100644
index 0000000..773874a
--- /dev/null
+++ b/configs/scnet/README.md
@@ -0,0 +1,63 @@
+# SCNet
+
+> [SCNet: Training Inference Sample Consistency for Instance Segmentation](https://arxiv.org/abs/2012.10150)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+<!-- [ABSTRACT] -->
+
+Cascaded architectures have brought significant performance improvement in object detection and instance segmentation. However, there are lingering issues regarding the disparity in the Intersection-over-Union (IoU) distribution of the samples between training and inference. This disparity can potentially exacerbate detection accuracy. This paper proposes an architecture referred to as Sample Consistency Network (SCNet) to ensure that the IoU distribution of the samples at training time is close to that at inference time. Furthermore, SCNet incorporates feature relay and utilizes global contextual information to further reinforce the reciprocal relationships among classifying, detecting, and segmenting sub-tasks. Extensive experiments on the standard COCO dataset reveal the effectiveness of the proposed method over multiple evaluation metrics, including box AP, mask AP, and inference speed. In particular, while running 38% faster, the proposed SCNet improves the AP of the box and mask predictions by respectively 1.3 and 2.3 points compared to the strong Cascade Mask R-CNN baseline.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143974840-8fed75f3-661e-4e2a-a210-acf4ab5f42a3.png"/>
+</div>
+
+## Dataset
+
+SCNet requires COCO and [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) dataset for training. You need to download and extract it in the COCO dataset path.
+The directory should be like this.
+
+```none
+mmdetection
+├── mmdet
+├── tools
+├── configs
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+|   |   ├── stuffthingmaps
+```
+
+## Results and Models
+
+The results on COCO 2017val are shown in the below table. (results on test-dev are usually slightly higher than val)
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf speed (fps) | box AP | mask AP | TTA box AP | TTA mask AP |                                                     Config                                                     |                                                                                                                                           Download                                                                                                                                           |
+| :-------------: | :-----: | :-----: | :------: | :-------------: | :----: | :-----: | :--------: | :---------: | :------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50-FPN     | pytorch |   1x    |   7.0    |       6.2       |  43.5  |  39.2   |    44.8    |    40.9     |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_1x_coco.py)     |                 [model](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_1x_coco/scnet_r50_fpn_1x_coco-c3f09857.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_1x_coco/scnet_r50_fpn_1x_coco_20210117_192725.log.json)                 |
+|    R-50-FPN     | pytorch |   20e   |   7.0    |       6.2       |  44.5  |  40.0   |    45.8    |    41.5     |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_20e_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_20e_coco/scnet_r50_fpn_20e_coco-a569f645.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_20e_coco/scnet_r50_fpn_20e_coco_20210116_060148.log.json)               |
+|    R-101-FPN    | pytorch |   20e   |   8.9    |       5.8       |  45.8  |  40.9   |    47.3    |    42.7     |    [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r101_fpn_20e_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r101_fpn_20e_coco/scnet_r101_fpn_20e_coco-294e312c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r101_fpn_20e_coco/scnet_r101_fpn_20e_coco_20210118_175824.log.json)             |
+| X-101-64x4d-FPN | pytorch |   20e   |   13.2   |       4.9       |  47.5  |  42.3   |    48.9    |    44.0     | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_x101_64x4d_fpn_20e_coco/scnet_x101_64x4d_fpn_20e_coco-fb09dec9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_x101_64x4d_fpn_20e_coco/scnet_x101_64x4d_fpn_20e_coco_20210120_045959.log.json) |
+
+### Notes
+
+- Training hyper-parameters are identical to those of [HTC](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc).
+- TTA means Test Time Augmentation, which applies horizontal flip and multi-scale testing. Refer to [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scnet/scnet_r50_fpn_1x_coco.py).
+
+## Citation
+
+We provide the code for reproducing experiment results of [SCNet](https://arxiv.org/abs/2012.10150).
+
+```latex
+@inproceedings{vu2019cascade,
+  title={SCNet: Training Inference Sample Consistency for Instance Segmentation},
+  author={Vu, Thang and Haeyong, Kang and Yoo, Chang D},
+  booktitle={AAAI},
+  year={2021}
+}
+```
diff --git a/configs/scnet/metafile.yml b/configs/scnet/metafile.yml
new file mode 100644
index 0000000..15eaebf
--- /dev/null
+++ b/configs/scnet/metafile.yml
@@ -0,0 +1,116 @@
+Collections:
+  - Name: SCNet
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - ResNet
+        - SCNet
+    Paper:
+      URL: https://arxiv.org/abs/2012.10150
+      Title: 'SCNet: Training Inference Sample Consistency for Instance Segmentation'
+    README: configs/scnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.9.0/mmdet/models/detectors/scnet.py#L6
+      Version: v2.9.0
+
+Models:
+  - Name: scnet_r50_fpn_1x_coco
+    In Collection: SCNet
+    Config: configs/scnet/scnet_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.0
+      inference time (ms/im):
+        - value: 161.29
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_1x_coco/scnet_r50_fpn_1x_coco-c3f09857.pth
+
+  - Name: scnet_r50_fpn_20e_coco
+    In Collection: SCNet
+    Config: configs/scnet/scnet_r50_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 7.0
+      inference time (ms/im):
+        - value: 161.29
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 40.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r50_fpn_20e_coco/scnet_r50_fpn_20e_coco-a569f645.pth
+
+  - Name: scnet_r101_fpn_20e_coco
+    In Collection: SCNet
+    Config: configs/scnet/scnet_r101_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 8.9
+      inference time (ms/im):
+        - value: 172.41
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 40.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_r101_fpn_20e_coco/scnet_r101_fpn_20e_coco-294e312c.pth
+
+  - Name: scnet_x101_64x4d_fpn_20e_coco
+    In Collection: SCNet
+    Config: configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py
+    Metadata:
+      Training Memory (GB): 13.2
+      inference time (ms/im):
+        - value: 204.08
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (800, 1333)
+      Epochs: 20
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 42.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/scnet/scnet_x101_64x4d_fpn_20e_coco/scnet_x101_64x4d_fpn_20e_coco-fb09dec9.pth
diff --git a/configs/scnet/scnet_r101_fpn_20e_coco.py b/configs/scnet/scnet_r101_fpn_20e_coco.py
new file mode 100644
index 0000000..ebba529
--- /dev/null
+++ b/configs/scnet/scnet_r101_fpn_20e_coco.py
@@ -0,0 +1,6 @@
+_base_ = './scnet_r50_fpn_20e_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/scnet/scnet_r50_fpn_1x_coco.py b/configs/scnet/scnet_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..fe03b0d
--- /dev/null
+++ b/configs/scnet/scnet_r50_fpn_1x_coco.py
@@ -0,0 +1,136 @@
+_base_ = '../htc/htc_r50_fpn_1x_coco.py'
+# model settings
+model = dict(
+    type='SCNet',
+    roi_head=dict(
+        _delete_=True,
+        type='SCNetRoIHead',
+        num_stages=3,
+        stage_loss_weights=[1, 0.5, 0.25],
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=[
+            dict(
+                type='SCNetBBoxHead',
+                num_shared_fcs=2,
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='SCNetBBoxHead',
+                num_shared_fcs=2,
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='SCNetBBoxHead',
+                num_shared_fcs=2,
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ],
+        mask_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        mask_head=dict(
+            type='SCNetMaskHead',
+            num_convs=12,
+            in_channels=256,
+            conv_out_channels=256,
+            num_classes=80,
+            conv_to_res=True,
+            loss_mask=dict(
+                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0)),
+        semantic_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[8]),
+        semantic_head=dict(
+            type='SCNetSemanticHead',
+            num_ins=5,
+            fusion_level=1,
+            num_convs=4,
+            in_channels=256,
+            conv_out_channels=256,
+            num_classes=183,
+            loss_seg=dict(
+                type='CrossEntropyLoss', ignore_index=255, loss_weight=0.2),
+            conv_to_res=True),
+        glbctx_head=dict(
+            type='GlobalContextHead',
+            num_convs=4,
+            in_channels=256,
+            conv_out_channels=256,
+            num_classes=80,
+            loss_weight=3.0,
+            conv_to_res=True),
+        feat_relay_head=dict(
+            type='FeatureRelayHead',
+            in_channels=1024,
+            out_conv_channels=256,
+            roi_feat_size=7,
+            scale_factor=2)))
+
+# uncomment below code to enable test time augmentations
+# img_norm_cfg = dict(
+#     mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+# test_pipeline = [
+#     dict(type='LoadImageFromFile'),
+#     dict(
+#         type='MultiScaleFlipAug',
+#         img_scale=[(600, 900), (800, 1200), (1000, 1500), (1200, 1800),
+#                    (1400, 2100)],
+#         flip=True,
+#         transforms=[
+#             dict(type='Resize', keep_ratio=True),
+#             dict(type='RandomFlip', flip_ratio=0.5),
+#             dict(type='Normalize', **img_norm_cfg),
+#             dict(type='Pad', size_divisor=32),
+#             dict(type='ImageToTensor', keys=['img']),
+#             dict(type='Collect', keys=['img']),
+#         ])
+# ]
+# data = dict(
+#     val=dict(pipeline=test_pipeline),
+#     test=dict(pipeline=test_pipeline))
diff --git a/configs/scnet/scnet_r50_fpn_20e_coco.py b/configs/scnet/scnet_r50_fpn_20e_coco.py
new file mode 100644
index 0000000..3b121a6
--- /dev/null
+++ b/configs/scnet/scnet_r50_fpn_20e_coco.py
@@ -0,0 +1,4 @@
+_base_ = './scnet_r50_fpn_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 19])
+runner = dict(type='EpochBasedRunner', max_epochs=20)
diff --git a/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py b/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py
new file mode 100644
index 0000000..1e54b03
--- /dev/null
+++ b/configs/scnet/scnet_x101_64x4d_fpn_20e_coco.py
@@ -0,0 +1,15 @@
+_base_ = './scnet_r50_fpn_20e_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py b/configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py
new file mode 100644
index 0000000..be8ddc5
--- /dev/null
+++ b/configs/scnet/scnet_x101_64x4d_fpn_8x1_20e_coco.py
@@ -0,0 +1,8 @@
+_base_ = './scnet_x101_64x4d_fpn_20e_coco.py'
+data = dict(samples_per_gpu=1, workers_per_gpu=1)
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (1 samples per GPU)
+auto_scale_lr = dict(base_batch_size=8)
diff --git a/configs/scratch/README.md b/configs/scratch/README.md
new file mode 100644
index 0000000..189f181
--- /dev/null
+++ b/configs/scratch/README.md
@@ -0,0 +1,35 @@
+# Scratch
+
+> [Rethinking ImageNet Pre-training](https://arxiv.org/abs/1811.08883)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We report competitive results on object detection and instance segmentation on the COCO dataset using standard models trained from random initialization. The results are no worse than their ImageNet pre-training counterparts even when using the hyper-parameters of the baseline system (Mask R-CNN) that were optimized for fine-tuning pre-trained models, with the sole exception of increasing the number of training iterations so the randomly initialized models may converge. Training from random initialization is surprisingly robust; our results hold even when: (i) using only 10% of the training data, (ii) for deeper and wider models, and (iii) for multiple tasks and metrics. Experiments show that ImageNet pre-training speeds up convergence early in training, but does not necessarily provide regularization or improve final target task accuracy. To push the envelope we demonstrate 50.9 AP on COCO object detection without using any external data---a result on par with the top COCO 2017 competition results that used ImageNet pre-training. These observations challenge the conventional wisdom of ImageNet pre-training for dependent tasks and we expect these discoveries will encourage people to rethink the current de facto paradigm of \`pre-training and fine-tuning' in computer vision.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143974572-69c4f57d-0d6d-4f56-ba91-23f8a65a2a77.png" height="300"/>
+</div>
+
+## Results and Models
+
+|    Model     | Backbone |  Style  | Lr schd | box AP | mask AP |                                                            Config                                                             |                                                                                                                                                                                 Download                                                                                                                                                                                  |
+| :----------: | :------: | :-----: | :-----: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Faster R-CNN | R-50-FPN | pytorch |   6x    |  40.7  |         | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py) |     [model](https://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_bbox_mAP-0.407_20200201_193013-90813d01.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_20200201_193013.log.json)     |
+|  Mask R-CNN  | R-50-FPN | pytorch |   6x    |  41.2  |  37.4   |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py)  | [model](https://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_bbox_mAP-0.412__segm_mAP-0.374_20200201_193051-1e190a40.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_20200201_193051.log.json) |
+
+Note:
+
+- The above models are trained with 16 GPUs.
+
+## Citation
+
+```latex
+@article{he2018rethinking,
+  title={Rethinking imagenet pre-training},
+  author={He, Kaiming and Girshick, Ross and Doll{\'a}r, Piotr},
+  journal={arXiv preprint arXiv:1811.08883},
+  year={2018}
+}
+```
diff --git a/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py b/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
new file mode 100644
index 0000000..55aa3a6
--- /dev/null
+++ b/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
@@ -0,0 +1,24 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    backbone=dict(
+        frozen_stages=-1,
+        zero_init_residual=False,
+        norm_cfg=norm_cfg,
+        init_cfg=None),
+    neck=dict(norm_cfg=norm_cfg),
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            conv_out_channels=256,
+            norm_cfg=norm_cfg)))
+# optimizer
+optimizer = dict(paramwise_cfg=dict(norm_decay_mult=0))
+optimizer_config = dict(_delete_=True, grad_clip=None)
+# learning policy
+lr_config = dict(warmup_ratio=0.1, step=[65, 71])
+runner = dict(type='EpochBasedRunner', max_epochs=73)
diff --git a/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py b/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
new file mode 100644
index 0000000..cc52cb8
--- /dev/null
+++ b/configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
@@ -0,0 +1,25 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+norm_cfg = dict(type='GN', num_groups=32, requires_grad=True)
+model = dict(
+    backbone=dict(
+        frozen_stages=-1,
+        zero_init_residual=False,
+        norm_cfg=norm_cfg,
+        init_cfg=None),
+    neck=dict(norm_cfg=norm_cfg),
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            conv_out_channels=256,
+            norm_cfg=norm_cfg),
+        mask_head=dict(norm_cfg=norm_cfg)))
+# optimizer
+optimizer = dict(paramwise_cfg=dict(norm_decay_mult=0))
+optimizer_config = dict(_delete_=True, grad_clip=None)
+# learning policy
+lr_config = dict(warmup_ratio=0.1, step=[65, 71])
+runner = dict(type='EpochBasedRunner', max_epochs=73)
diff --git a/configs/scratch/metafile.yml b/configs/scratch/metafile.yml
new file mode 100644
index 0000000..65025fa
--- /dev/null
+++ b/configs/scratch/metafile.yml
@@ -0,0 +1,48 @@
+Collections:
+  - Name: Rethinking ImageNet Pre-training
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - RPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1811.08883
+      Title: 'Rethinking ImageNet Pre-training'
+    README: configs/scratch/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.0.0/configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
+      Version: v2.0.0
+
+Models:
+  - Name: faster_rcnn_r50_fpn_gn-all_scratch_6x_coco
+    In Collection: Rethinking ImageNet Pre-training
+    Config: configs/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
+    Metadata:
+      Epochs: 72
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/scratch/faster_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_faster_rcnn_r50_fpn_gn_6x_bbox_mAP-0.407_20200201_193013-90813d01.pth
+
+  - Name: mask_rcnn_r50_fpn_gn-all_scratch_6x_coco
+    In Collection: Rethinking ImageNet Pre-training
+    Config: configs/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco.py
+    Metadata:
+      Epochs: 72
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/scratch/mask_rcnn_r50_fpn_gn-all_scratch_6x_coco/scratch_mask_rcnn_r50_fpn_gn_6x_bbox_mAP-0.412__segm_mAP-0.374_20200201_193051-1e190a40.pth
diff --git a/configs/seesaw_loss/README.md b/configs/seesaw_loss/README.md
new file mode 100644
index 0000000..696b008
--- /dev/null
+++ b/configs/seesaw_loss/README.md
@@ -0,0 +1,47 @@
+# Seesaw Loss
+
+> [Seesaw Loss for Long-Tailed Instance Segmentation](https://arxiv.org/abs/2008.10032)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Instance segmentation has witnessed a remarkable progress on class-balanced benchmarks. However, they fail to perform as accurately in real-world scenarios, where the category distribution of objects naturally comes with a long tail. Instances of head classes dominate a long-tailed dataset and they serve as negative samples of tail categories. The overwhelming gradients of negative samples on tail classes lead to a biased learning process for classifiers. Consequently, objects of tail categories are more likely to be misclassified as backgrounds or head categories. To tackle this problem, we propose Seesaw Loss to dynamically re-balance gradients of positive and negative samples for each category, with two complementary factors, i.e., mitigation factor and compensation factor. The mitigation factor reduces punishments to tail categories w.r.t. the ratio of cumulative training instances between different categories. Meanwhile, the compensation factor increases the penalty of misclassified instances to avoid false positives of tail categories. We conduct extensive experiments on Seesaw Loss with mainstream frameworks and different data sampling strategies. With a simple end-to-end training pipeline, Seesaw Loss obtains significant gains over Cross-Entropy Loss, and achieves state-of-the-art performance on LVIS dataset without bells and whistles.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143974715-d181abe5-d0a2-40d3-a2bd-17d8c60b89b8.png"/>
+</div>
+
+- Please setup [LVIS dataset](../lvis/README.md) for MMDetection.
+
+- RFS indicates to use oversample strategy [here](../../docs/tutorials/customize_dataset.md#class-balanced-dataset) with oversample threshold `1e-3`.
+
+## Results and models of Seasaw Loss on LVIS v1 dataset
+
+|       Method       | Backbone  |  Style  | Lr schd | Data Sampler | Norm Mask | box AP | mask AP |                                                                                 Config                                                                                  |                                                                                                                                                              Download                                                                                                                                                              |
+| :----------------: | :-------: | :-----: | :-----: | :----------: | :-------: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|     Mask R-CNN     | R-50-FPN  | pytorch |   2x    |    random    |     N     |  25.6  |  25.0   |             [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py)              |                          [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-a698dd3d.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.log.json)                          |
+|     Mask R-CNN     | R-50-FPN  | pytorch |   2x    |    random    |     Y     |  25.6  |  25.4   |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py)        |              [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-a1c11314.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json)              |
+|     Mask R-CNN     | R-101-FPN | pytorch |   2x    |    random    |     N     |  27.4  |  26.7   |                   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py)                   |                         [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-8e6e6dd5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.log.json)                         |
+|     Mask R-CNN     | R-101-FPN | pytorch |   2x    |    random    |     Y     |  27.2  |  27.3   |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py)       |             [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-a0b59c42.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json)             |
+|     Mask R-CNN     | R-50-FPN  | pytorch |   2x    |     RFS      |     N     |  27.6  |  26.4   |           [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py)            |                      [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-392a804b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.log.json)                      |
+|     Mask R-CNN     | R-50-FPN  | pytorch |   2x    |     RFS      |     Y     |  27.6  |  26.8   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py)      |          [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-cd0f6a12.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json)          |
+|     Mask R-CNN     | R-101-FPN | pytorch |   2x    |     RFS      |     N     |  28.9  |  27.6   |           [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py)           |                     [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-e68eb464.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.log.json)                     |
+|     Mask R-CNN     | R-101-FPN | pytorch |   2x    |     RFS      |     Y     |  28.9  |  28.2   |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py)     |         [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-1d817139.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json)         |
+| Cascade Mask R-CNN | R-101-FPN | pytorch |   2x    |    random    |     N     |  33.1  |  29.2   |         [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py)         |                 [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-71e2215e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.log.json)                 |
+| Cascade Mask R-CNN | R-101-FPN | pytorch |   2x    |    random    |     Y     |  33.0  |  30.0   |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py)   |     [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-8b5a6745.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json)     |
+| Cascade Mask R-CNN | R-101-FPN | pytorch |   2x    |     RFS      |     N     |  30.0  |  29.3   |       [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py)       |             [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-5d8ca2a4.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.log.json)             |
+| Cascade Mask R-CNN | R-101-FPN | pytorch |   2x    |     RFS      |     Y     |  32.8  |  30.1   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-c8551505.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.log.json) |
+
+## Citation
+
+We provide config files to reproduce the instance segmentation performance in the CVPR 2021 paper for [Seesaw Loss for Long-Tailed Instance Segmentation](https://arxiv.org/abs/2008.10032).
+
+```latex
+@inproceedings{wang2021seesaw,
+  title={Seesaw Loss for Long-Tailed Instance Segmentation},
+  author={Jiaqi Wang and Wenwei Zhang and Yuhang Zang and Yuhang Cao and Jiangmiao Pang and Tao Gong and Kai Chen and Ziwei Liu and Chen Change Loy and Dahua Lin},
+  booktitle={Proceedings of the {IEEE} Conference on Computer Vision and Pattern Recognition},
+  year={2021}
+}
+```
diff --git a/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
new file mode 100644
index 0000000..beeb0d1
--- /dev/null
+++ b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
@@ -0,0 +1,132 @@
+_base_ = [
+    '../_base_/models/cascade_mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')),
+    roi_head=dict(
+        bbox_head=[
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=1203,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
+                loss_cls=dict(
+                    type='SeesawLoss',
+                    p=0.8,
+                    q=2.0,
+                    num_classes=1203,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=1203,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
+                loss_cls=dict(
+                    type='SeesawLoss',
+                    p=0.8,
+                    q=2.0,
+                    num_classes=1203,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=1203,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
+                loss_cls=dict(
+                    type='SeesawLoss',
+                    p=0.8,
+                    q=2.0,
+                    num_classes=1203,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ],
+        mask_head=dict(num_classes=1203)),
+    test_cfg=dict(
+        rcnn=dict(
+            score_thr=0.0001,
+            # LVIS allows up to 300
+            max_per_img=300)))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+dataset_type = 'LVISV1Dataset'
+data_root = 'data/lvis_v1/'
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/lvis_v1_train.json',
+        img_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/lvis_v1_val.json',
+        img_prefix=data_root,
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/lvis_v1_val.json',
+        img_prefix=data_root,
+        pipeline=test_pipeline))
+evaluation = dict(interval=24, metric=['bbox', 'segm'])
diff --git a/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
new file mode 100644
index 0000000..0f29948
--- /dev/null
+++ b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
@@ -0,0 +1,5 @@
+_base_ = './cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py'  # noqa: E501
+model = dict(
+    roi_head=dict(
+        mask_head=dict(
+            predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
diff --git a/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
new file mode 100644
index 0000000..bb88750
--- /dev/null
+++ b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
@@ -0,0 +1,98 @@
+_base_ = [
+    '../_base_/models/cascade_mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/lvis_v1_instance.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')),
+    roi_head=dict(
+        bbox_head=[
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=1203,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
+                loss_cls=dict(
+                    type='SeesawLoss',
+                    p=0.8,
+                    q=2.0,
+                    num_classes=1203,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=1203,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
+                loss_cls=dict(
+                    type='SeesawLoss',
+                    p=0.8,
+                    q=2.0,
+                    num_classes=1203,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=1203,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
+                loss_cls=dict(
+                    type='SeesawLoss',
+                    p=0.8,
+                    q=2.0,
+                    num_classes=1203,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ],
+        mask_head=dict(num_classes=1203)),
+    test_cfg=dict(
+        rcnn=dict(
+            score_thr=0.0001,
+            # LVIS allows up to 300
+            max_per_img=300)))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+data = dict(train=dict(dataset=dict(pipeline=train_pipeline)))
+evaluation = dict(interval=24, metric=['bbox', 'segm'])
diff --git a/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
new file mode 100644
index 0000000..262e76b
--- /dev/null
+++ b/configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
@@ -0,0 +1,5 @@
+_base_ = './cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py'  # noqa: E501
+model = dict(
+    roi_head=dict(
+        mask_head=dict(
+            predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
diff --git a/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
new file mode 100644
index 0000000..57deab1
--- /dev/null
+++ b/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
@@ -0,0 +1,6 @@
+_base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
new file mode 100644
index 0000000..a539929
--- /dev/null
+++ b/configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
@@ -0,0 +1,6 @@
+_base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py'  # noqa: E501
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
new file mode 100644
index 0000000..1f5065e
--- /dev/null
+++ b/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
@@ -0,0 +1,6 @@
+_base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
new file mode 100644
index 0000000..13d0b5f
--- /dev/null
+++ b/configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
@@ -0,0 +1,6 @@
+_base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py'  # noqa: E501
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
new file mode 100644
index 0000000..743f5f2
--- /dev/null
+++ b/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
@@ -0,0 +1,75 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            num_classes=1203,
+            cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
+            loss_cls=dict(
+                type='SeesawLoss',
+                p=0.8,
+                q=2.0,
+                num_classes=1203,
+                loss_weight=1.0)),
+        mask_head=dict(num_classes=1203)),
+    test_cfg=dict(
+        rcnn=dict(
+            score_thr=0.0001,
+            # LVIS allows up to 300
+            max_per_img=300)))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+dataset_type = 'LVISV1Dataset'
+data_root = 'data/lvis_v1/'
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/lvis_v1_train.json',
+        img_prefix=data_root,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/lvis_v1_val.json',
+        img_prefix=data_root,
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/lvis_v1_val.json',
+        img_prefix=data_root,
+        pipeline=test_pipeline))
+evaluation = dict(interval=24, metric=['bbox', 'segm'])
diff --git a/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
new file mode 100644
index 0000000..0af8921
--- /dev/null
+++ b/configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
@@ -0,0 +1,5 @@
+_base_ = './mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py'
+model = dict(
+    roi_head=dict(
+        mask_head=dict(
+            predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
diff --git a/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
new file mode 100644
index 0000000..4fc1504
--- /dev/null
+++ b/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
@@ -0,0 +1,41 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/lvis_v1_instance.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(
+            num_classes=1203,
+            cls_predictor_cfg=dict(type='NormedLinear', tempearture=20),
+            loss_cls=dict(
+                type='SeesawLoss',
+                p=0.8,
+                q=2.0,
+                num_classes=1203,
+                loss_weight=1.0)),
+        mask_head=dict(num_classes=1203)),
+    test_cfg=dict(
+        rcnn=dict(
+            score_thr=0.0001,
+            # LVIS allows up to 300
+            max_per_img=300)))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+data = dict(train=dict(dataset=dict(pipeline=train_pipeline)))
+evaluation = dict(interval=12, metric=['bbox', 'segm'])
diff --git a/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py b/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
new file mode 100644
index 0000000..0ef6bd2
--- /dev/null
+++ b/configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
@@ -0,0 +1,5 @@
+_base_ = './mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py'
+model = dict(
+    roi_head=dict(
+        mask_head=dict(
+            predictor_cfg=dict(type='NormedConv2d', tempearture=20))))
diff --git a/configs/seesaw_loss/metafile.yml b/configs/seesaw_loss/metafile.yml
new file mode 100644
index 0000000..fb90aa5
--- /dev/null
+++ b/configs/seesaw_loss/metafile.yml
@@ -0,0 +1,203 @@
+Collections:
+  - Name: Seesaw Loss
+    Metadata:
+      Training Data: LVIS
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Softmax
+        - RPN
+        - Convolution
+        - Dense Connections
+        - FPN
+        - ResNet
+        - RoIAlign
+        - Seesaw Loss
+    Paper:
+      URL: https://arxiv.org/abs/2008.10032
+      Title: 'Seesaw Loss for Long-Tailed Instance Segmentation'
+    README: configs/seesaw_loss/README.md
+
+Models:
+  - Name: mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1
+    In Collection: Seesaw Loss
+    Config: configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 25.6
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 25.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-a698dd3d.pth
+  - Name: mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1
+    In Collection: Seesaw Loss
+    Config: configs/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 25.6
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 25.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-a1c11314.pth
+  - Name: mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1
+    In Collection: Seesaw Loss
+    Config: configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 27.4
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 26.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-8e6e6dd5.pth
+  - Name: mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1
+    In Collection: Seesaw Loss
+    Config: configs/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 27.2
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 27.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-a0b59c42.pth
+  - Name: mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1
+    In Collection: Seesaw Loss
+    Config: configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 27.6
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 26.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-392a804b.pth
+  - Name: mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1
+    In Collection: Seesaw Loss
+    Config: configs/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 27.6
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 26.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r50_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-cd0f6a12.pth
+  - Name: mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1
+    In Collection: Seesaw Loss
+    Config: configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 28.9
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 27.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-e68eb464.pth
+  - Name: mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1
+    In Collection: Seesaw Loss
+    Config: configs/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 28.9
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 28.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-1d817139.pth
+  - Name: cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1
+    In Collection: Seesaw Loss
+    Config: configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 33.1
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 29.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_mstrain_2x_lvis_v1-71e2215e.pth
+  - Name: cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1
+    In Collection: Seesaw Loss
+    Config: configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 33.0
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 30.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-8b5a6745.pth
+  - Name: cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1
+    In Collection: Seesaw Loss
+    Config: configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 30.0
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 29.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_mstrain_2x_lvis_v1-5d8ca2a4.pth
+  - Name: cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1
+    In Collection: Seesaw Loss
+    Config: configs/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: LVIS v1
+        Metrics:
+          box AP: 32.8
+      - Task: Instance Segmentation
+        Dataset: LVIS v1
+        Metrics:
+          mask AP: 30.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/seesaw_loss/cascade_mask_rcnn_r101_fpn_sample1e-3_seesaw_loss_normed_mask_mstrain_2x_lvis_v1-c8551505.pth
diff --git a/configs/selfsup_pretrain/README.md b/configs/selfsup_pretrain/README.md
new file mode 100644
index 0000000..9bd92cb
--- /dev/null
+++ b/configs/selfsup_pretrain/README.md
@@ -0,0 +1,109 @@
+# Backbones Trained by Self-Supervise Algorithms
+
+<!-- [OTHERS] -->
+
+## Abstract
+
+Unsupervised image representations have significantly reduced the gap with supervised pretraining, notably with the recent achievements of contrastive learning methods. These contrastive methods typically work online and rely on a large number of explicit pairwise feature comparisons, which is computationally challenging. In this paper, we propose an online algorithm, SwAV, that takes advantage of contrastive methods without requiring to compute pairwise comparisons. Specifically, our method simultaneously clusters the data while enforcing consistency between cluster assignments produced for different augmentations (or views) of the same image, instead of comparing features directly as in contrastive learning. Simply put, we use a swapped prediction mechanism where we predict the cluster assignment of a view from the representation of another view. Our method can be trained with large and small batches and can scale to unlimited amounts of data. Compared to previous contrastive methods, our method is more memory efficient since it does not require a large memory bank or a special momentum network. In addition, we also propose a new data augmentation strategy, multi-crop, that uses a mix of views with different resolutions in place of two full-resolution views, without increasing the memory or compute requirements much. We validate our findings by achieving 75.3% top-1 accuracy on ImageNet with ResNet-50, as well as surpassing supervised pretraining on all the considered transfer tasks.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143997246-ac40fd8a-9b48-4ff5-a0d9-ba10e1e333d2.png"/>
+</div>
+
+We present Momentum Contrast (MoCo) for unsupervised visual representation learning. From a perspective on contrastive learning as dictionary look-up, we build a dynamic dictionary with a queue and a moving-averaged encoder. This enables building a large and consistent dictionary on-the-fly that facilitates contrastive unsupervised learning. MoCo provides competitive results under the common linear protocol on ImageNet classification. More importantly, the representations learned by MoCo transfer well to downstream tasks. MoCo can outperform its supervised pre-training counterpart in 7 detection/segmentation tasks on PASCAL VOC, COCO, and other datasets, sometimes surpassing it by large margins. This suggests that the gap between unsupervised and supervised representation learning has been largely closed in many vision tasks.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143997315-5ff824d4-1007-4b59-8952-bc5a2c0bfd78.png" height="300"/>
+</div>
+
+## Usage
+
+To use a self-supervisely pretrained backbone, there are two steps to do:
+
+1. Download and convert the model to PyTorch-style supported by MMDetection
+2. Modify the config and change the training setting accordingly
+
+### Convert model
+
+For more general usage, we also provide script `selfsup2mmdet.py` in the tools directory to convert the key of models pretrained by different self-supervised methods to PyTorch-style checkpoints used in MMDetection.
+
+```bash
+python -u tools/model_converters/selfsup2mmdet.py ${PRETRAIN_PATH} ${STORE_PATH} --selfsup ${method}
+```
+
+This script convert model from `PRETRAIN_PATH` and store the converted model in `STORE_PATH`.
+
+For example, to use a ResNet-50 backbone released by MoCo, you can download it from [here](https://dl.fbaipublicfiles.com/moco/moco_checkpoints/moco_v2_800ep/moco_v2_800ep_pretrain.pth.tar) and use the following command
+
+```bash
+python -u tools/model_converters/selfsup2mmdet.py ./moco_v2_800ep_pretrain.pth.tar mocov2_r50_800ep_pretrain.pth --selfsup moco
+```
+
+To use the ResNet-50 backbone released by SwAV, you can download it from [here](https://dl.fbaipublicfiles.com/deepcluster/swav_800ep_pretrain.pth.tar)
+
+### Modify config
+
+The backbone requires SyncBN and the `frozen_stages` need to be changed. A config that use the moco backbone is as below
+
+```python
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    pretrained='./mocov2_r50_800ep_pretrain.pth',
+    backbone=dict(
+        frozen_stages=0,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False))
+
+```
+
+## Results and Models
+
+|  Method   |                              Backbone                               |  Style  |    Lr schd     | Mem (GB) | Inf time (fps) | box AP | mask AP |                                                                  Config                                                                  |                                                                                                                                                                                            Download                                                                                                                                                                                            |
+| :-------: | :-----------------------------------------------------------------: | :-----: | :------------: | :------: | :------------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Mask RCNN |  [R50 by MoCo v2](./mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py)   | pytorch |       1x       |          |                |  38.0  |  34.3   |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py)   |       [model](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco_20210604_114614-a8b63483.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco_20210604_114614.log.json)       |
+| Mask RCNN | [R50 by MoCo v2](./mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py) | pytorch | multi-scale 2x |          |                |  40.8  |  36.8   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco_20210605_163717-d95df20a.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco_20210605_163717.log.json) |
+| Mask RCNN |     [R50 by SwAV](./mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py)     | pytorch |       1x       |          |                |  39.1  |  35.7   |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py)    |           [model](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco/mask_rcnn_r50_fpn_swav-pretrain_1x_coco_20210604_114640-7b9baf28.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco/mask_rcnn_r50_fpn_swav-pretrain_1x_coco_20210604_114640.log.json)           |
+| Mask RCNN |   [R50 by SwAV](./mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py)    | pytorch | multi-scale 2x |          |                |  41.3  |  37.3   |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py)  |     [model](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco_20210605_163717-08e26fca.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco_20210605_163717.log.json)     |
+
+### Notice
+
+1. We only provide single-scale 1x and multi-scale 2x configs as examples to show how to use backbones trained by self-supervised algorithms. We will try to reproduce the results in their corresponding paper using the released backbone in the future. Please stay tuned.
+
+## Citation
+
+We support to apply the backbone models pre-trained by different self-supervised methods in detection systems and provide their results on Mask R-CNN.
+
+The pre-trained models are converted from [MoCo](https://github.com/facebookresearch/moco) and downloaded from [SwAV](https://github.com/facebookresearch/swav).
+
+For SwAV, please cite
+
+```latex
+@article{caron2020unsupervised,
+  title={Unsupervised Learning of Visual Features by Contrasting Cluster Assignments},
+  author={Caron, Mathilde and Misra, Ishan and Mairal, Julien and Goyal, Priya and Bojanowski, Piotr and Joulin, Armand},
+  booktitle={Proceedings of Advances in Neural Information Processing Systems (NeurIPS)},
+  year={2020}
+}
+```
+
+For MoCo, please cite
+
+```latex
+@Article{he2019moco,
+  author  = {Kaiming He and Haoqi Fan and Yuxin Wu and Saining Xie and Ross Girshick},
+  title   = {Momentum Contrast for Unsupervised Visual Representation Learning},
+  journal = {arXiv preprint arXiv:1911.05722},
+  year    = {2019},
+}
+@Article{chen2020mocov2,
+  author  = {Xinlei Chen and Haoqi Fan and Ross Girshick and Kaiming He},
+  title   = {Improved Baselines with Momentum Contrastive Learning},
+  journal = {arXiv preprint arXiv:2003.04297},
+  year    = {2020},
+}
+```
diff --git a/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py
new file mode 100644
index 0000000..f1e0615
--- /dev/null
+++ b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_1x_coco.py
@@ -0,0 +1,13 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    backbone=dict(
+        frozen_stages=0,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        init_cfg=dict(
+            type='Pretrained', checkpoint='./mocov2_r50_800ep_pretrain.pth')))
diff --git a/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py
new file mode 100644
index 0000000..09aa156
--- /dev/null
+++ b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_mocov2-pretrain_ms-2x_coco.py
@@ -0,0 +1,32 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    backbone=dict(
+        frozen_stages=0,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        init_cfg=dict(
+            type='Pretrained', checkpoint='./mocov2_r50_800ep_pretrain.pth')))
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+]
+
+data = dict(train=dict(pipeline=train_pipeline))
diff --git a/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py
new file mode 100644
index 0000000..f92a345
--- /dev/null
+++ b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_1x_coco.py
@@ -0,0 +1,13 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    backbone=dict(
+        frozen_stages=0,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        init_cfg=dict(
+            type='Pretrained', checkpoint='./swav_800ep_pretrain.pth.tar')))
diff --git a/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py
new file mode 100644
index 0000000..fe47361
--- /dev/null
+++ b/configs/selfsup_pretrain/mask_rcnn_r50_fpn_swav-pretrain_ms-2x_coco.py
@@ -0,0 +1,32 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    backbone=dict(
+        frozen_stages=0,
+        norm_cfg=dict(type='SyncBN', requires_grad=True),
+        norm_eval=False,
+        init_cfg=dict(
+            type='Pretrained', checkpoint='./swav_800ep_pretrain.pth.tar')))
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+]
+
+data = dict(train=dict(pipeline=train_pipeline))
diff --git a/configs/simple_copy_paste/README.md b/configs/simple_copy_paste/README.md
new file mode 100644
index 0000000..46162aa
--- /dev/null
+++ b/configs/simple_copy_paste/README.md
@@ -0,0 +1,38 @@
+# SimpleCopyPaste
+
+> [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://arxiv.org/abs/2012.07177)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Building instance segmentation models that are data-efficient and can handle rare object categories is an important challenge in computer vision. Leveraging data augmentations is a promising direction towards addressing this challenge. Here, we perform a systematic study of the Copy-Paste augmentation (\[13, 12\]) for instance segmentation where we randomly paste objects onto an image. Prior studies on Copy-Paste relied on modeling the surrounding visual context for pasting the objects. However, we find that the simple mechanism of pasting objects randomly is good enough and can provide solid gains on top of strong baselines. Furthermore, we show Copy-Paste is additive with semi-supervised methods that leverage extra data through pseudo labeling (e.g. self-training). On COCO instance segmentation, we achieve 49.1 mask AP and 57.3 box AP, an improvement of +0.6 mask AP and +1.5 box AP over the previous state-of-the-art. We further demonstrate that Copy-Paste can lead to significant improvements on the LVIS benchmark. Our baseline model outperforms the LVIS 2020 Challenge winning entry by +3.6 mask AP on rare categories.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/161843866-c5b769da-58b2-4c1f-8078-db4a4ded3881.png"/>
+</div>
+
+## Results and Models
+
+### Mask R-CNN with Standard Scale Jittering (SSJ) and Simple Copy-Paste(SCP)
+
+Standard Scale Jittering(SSJ) resizes and crops an image with a resize range of 0.8 to 1.25 of the original image size, and Simple Copy-Paste(SCP) selects a random subset of objects from one of the images and pastes them onto the other image.
+
+| Backbone | Training schedule | Augmentation | batch size | box AP | mask AP |                                                                           Config                                                                           |                                                                                                                                                                                                                               Download                                                                                                                                                                                                                               |
+| :------: | :---------------: | :----------: | :--------: | :----: | :-----: | :--------------------------------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   |        90k        |     SSJ      |     64     |  43.3  |  39.0   |   [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco.py)    |           [model](https://download.openmmlab.com/mmdetection/v2.0/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco_20220316_181409-f79c84c5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco_20220316_181409.log.json)           |
+|   R-50   |        90k        |   SSJ+SCP    |     64     |  43.8  |  39.2   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco_20220316_181307-6bc5726f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco_20220316_181307.log.json)   |
+|   R-50   |       270k        |     SSJ      |     64     |  43.5  |  39.1   |   [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco.py)   |         [model](https://download.openmmlab.com/mmdetection/v2.0/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco_20220324_182940-33a100c5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco_20220324_182940.log.json)         |
+|   R-50   |       270k        |   SSJ+SCP    |     64     |  45.1  |  40.3   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco_20220324_201229-80ee90b7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco_20220324_201229.log.json) |
+
+## Citation
+
+```latex
+@inproceedings{ghiasi2021simple,
+  title={Simple copy-paste is a strong data augmentation method for instance segmentation},
+  author={Ghiasi, Golnaz and Cui, Yin and Srinivas, Aravind and Qian, Rui and Lin, Tsung-Yi and Cubuk, Ekin D and Le, Quoc V and Zoph, Barret},
+  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+  pages={2918--2928},
+  year={2021}
+}
+```
diff --git a/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco.py b/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco.py
new file mode 100644
index 0000000..d0ce917
--- /dev/null
+++ b/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco.py
@@ -0,0 +1,20 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    # 270k iterations with batch_size 64 is roughly equivalent to 144 epochs
+    '../common/ssj_270k_coco_instance.py',
+]
+
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+# Use MMSyncBN that handles empty tensor in head. It can be changed to
+# SyncBN after https://github.com/pytorch/pytorch/issues/36530 is fixed.
+head_norm_cfg = dict(type='MMSyncBN', requires_grad=True)
+model = dict(
+    backbone=dict(frozen_stages=-1, norm_eval=False, norm_cfg=norm_cfg),
+    neck=dict(norm_cfg=norm_cfg),
+    rpn_head=dict(num_convs=2),  # leads to 0.1+ mAP
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            conv_out_channels=256,
+            norm_cfg=head_norm_cfg),
+        mask_head=dict(norm_cfg=head_norm_cfg)))
diff --git a/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco.py b/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco.py
new file mode 100644
index 0000000..1eee95f
--- /dev/null
+++ b/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco.py
@@ -0,0 +1,7 @@
+_base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco.py'
+
+# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
+lr_config = dict(
+    warmup_iters=500, warmup_ratio=0.067, step=[81000, 85500, 87750])
+# 90k iterations with batch_size 64 is roughly equivalent to 48 epochs
+runner = dict(type='IterBasedRunner', max_iters=90000)
diff --git a/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco.py b/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco.py
new file mode 100644
index 0000000..bd28ddd
--- /dev/null
+++ b/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco.py
@@ -0,0 +1,20 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    # 270k iterations with batch_size 64 is roughly equivalent to 144 epochs
+    '../common/ssj_scp_270k_coco_instance.py'
+]
+
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+# Use MMSyncBN that handles empty tensor in head. It can be changed to
+# SyncBN after https://github.com/pytorch/pytorch/issues/36530 is fixed.
+head_norm_cfg = dict(type='MMSyncBN', requires_grad=True)
+model = dict(
+    backbone=dict(frozen_stages=-1, norm_eval=False, norm_cfg=norm_cfg),
+    neck=dict(norm_cfg=norm_cfg),
+    rpn_head=dict(num_convs=2),  # leads to 0.1+ mAP
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            conv_out_channels=256,
+            norm_cfg=head_norm_cfg),
+        mask_head=dict(norm_cfg=head_norm_cfg)))
diff --git a/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco.py b/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco.py
new file mode 100644
index 0000000..b632c13
--- /dev/null
+++ b/configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco.py
@@ -0,0 +1,7 @@
+_base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco.py'
+
+# lr steps at [0.9, 0.95, 0.975] of the maximum iterations
+lr_config = dict(
+    warmup_iters=500, warmup_ratio=0.067, step=[81000, 85500, 87750])
+# 90k iterations with batch_size 64 is roughly equivalent to 48 epochs
+runner = dict(type='IterBasedRunner', max_iters=90000)
diff --git a/configs/simple_copy_paste/metafile.yml b/configs/simple_copy_paste/metafile.yml
new file mode 100644
index 0000000..bb6106c
--- /dev/null
+++ b/configs/simple_copy_paste/metafile.yml
@@ -0,0 +1,92 @@
+Collections:
+  - Name: SimpleCopyPaste
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 32x A100 GPUs
+      Architecture:
+        - Softmax
+        - RPN
+        - Convolution
+        - Dense Connections
+        - FPN
+        - ResNet
+        - RoIAlign
+    Paper:
+      URL: https://arxiv.org/abs/2012.07177
+      Title: "Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation"
+    README: configs/simple_copy_paste/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.25.0/mmdet/datasets/pipelines/transforms.py#L2762
+      Version: v2.25.0
+
+Models:
+  - Name: mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco
+    In Collection: SimpleCopyPaste
+    Config: configs/simplecopypaste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco.py
+    Metadata:
+      Training Memory (GB): 7.2
+      Iterations: 270000
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.5
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_270k_coco_20220324_182940-33a100c5.pth
+
+  - Name: mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco
+    In Collection: SimpleCopyPaste
+    Config: configs/simplecopypaste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco.py
+    Metadata:
+      Training Memory (GB): 7.2
+      Iterations: 90000
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.3
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_32x2_90k_coco_20220316_181409-f79c84c5.pth
+
+  - Name: mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco
+    In Collection: SimpleCopyPaste
+    Config: configs/simplecopypaste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco.py
+    Metadata:
+      Training Memory (GB): 7.2
+      Iterations: 270000
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.1
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 40.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco_20220324_201229-80ee90b7.pth
+
+  - Name: mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco
+    In Collection: SimpleCopyPaste
+    Config: configs/simplecopypaste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco.py
+    Metadata:
+      Training Memory (GB): 7.2
+      Iterations: 90000
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.8
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_90k_coco_20220316_181307-6bc5726f.pth
diff --git a/configs/solo/README.md b/configs/solo/README.md
new file mode 100644
index 0000000..4a36676
--- /dev/null
+++ b/configs/solo/README.md
@@ -0,0 +1,54 @@
+# SOLO
+
+> [SOLO: Segmenting Objects by Locations](https://arxiv.org/abs/1912.04488)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We present a new, embarrassingly simple approach to instance segmentation in images. Compared to many other dense prediction tasks, e.g., semantic segmentation, it is the arbitrary number of instances that have made instance segmentation much more challenging. In order to predict a mask for each instance, mainstream approaches either follow the 'detect-thensegment' strategy as used by Mask R-CNN, or predict category masks first then use clustering techniques to group pixels into individual instances. We view the task of instance segmentation from a completely new perspective by introducing the notion of "instance categories", which assigns categories to each pixel within an instance according to the instance's location and size, thus nicely converting instance mask segmentation into a classification-solvable problem. Now instance segmentation is decomposed into two classification tasks. We demonstrate a much simpler and flexible instance segmentation framework with strong performance, achieving on par accuracy with Mask R-CNN and outperforming recent singleshot instance segmenters in accuracy. We hope that this very simple and strong framework can serve as a baseline for many instance-level recognition tasks besides instance segmentation.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143998371-10e6f14b-4506-481d-91a7-5f8f58213307.png"/>
+</div>
+
+## Results and Models
+
+### SOLO
+
+| Backbone |  Style  | MS train | Lr schd | Mem (GB) | Inf time (fps) | mask AP |                                                                                                                                Download                                                                                                                                |
+| :------: | :-----: | :------: | :-----: | :------: | :------------: | :-----: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | pytorch |    N     |   1x    |   8.0    |      14.0      |  33.1   | [model](https://download.openmmlab.com/mmdetection/v2.0/solo/solo_r50_fpn_1x_coco/solo_r50_fpn_1x_coco_20210821_035055-2290a6b8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/solo/solo_r50_fpn_1x_coco/solo_r50_fpn_1x_coco_20210821_035055.log.json) |
+|   R-50   | pytorch |    Y     |   3x    |   7.4    |      14.0      |  35.9   | [model](https://download.openmmlab.com/mmdetection/v2.0/solo/solo_r50_fpn_3x_coco/solo_r50_fpn_3x_coco_20210901_012353-11d224d7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/solo/solo_r50_fpn_3x_coco/solo_r50_fpn_3x_coco_20210901_012353.log.json) |
+
+### Decoupled SOLO
+
+| Backbone |  Style  | MS train | Lr schd | Mem (GB) | Inf time (fps) | mask AP |                                                                                                                                                    Download                                                                                                                                                    |
+| :------: | :-----: | :------: | :-----: | :------: | :------------: | :-----: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | pytorch |    N     |   1x    |   7.8    |      12.5      |  33.9   | [model](https://download.openmmlab.com/mmdetection/v2.0/solo/decoupled_solo_r50_fpn_1x_coco/decoupled_solo_r50_fpn_1x_coco_20210820_233348-6337c589.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/solo/decoupled_solo_r50_fpn_1x_coco/decoupled_solo_r50_fpn_1x_coco_20210820_233348.log.json) |
+|   R-50   | pytorch |    Y     |   3x    |   7.9    |      12.5      |  36.7   | [model](https://download.openmmlab.com/mmdetection/v2.0/solo/decoupled_solo_r50_fpn_3x_coco/decoupled_solo_r50_fpn_3x_coco_20210821_042504-7b3301ec.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/solo/decoupled_solo_r50_fpn_3x_coco/decoupled_solo_r50_fpn_3x_coco_20210821_042504.log.json) |
+
+- Decoupled SOLO has a decoupled head which is different from SOLO head.
+  Decoupled SOLO serves as an efficient and equivalent variant in accuracy
+  of SOLO. Please refer to the corresponding config files for details.
+
+### Decoupled Light SOLO
+
+| Backbone |  Style  | MS train | Lr schd | Mem (GB) | Inf time (fps) | mask AP |                                                                                                                                                                Download                                                                                                                                                                |
+| :------: | :-----: | :------: | :-----: | :------: | :------------: | :-----: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | pytorch |    Y     |   3x    |   2.2    |      31.2      |  32.9   | [model](https://download.openmmlab.com/mmdetection/v2.0/solo/decoupled_solo_light_r50_fpn_3x_coco/decoupled_solo_light_r50_fpn_3x_coco_20210906_142703-e70e226f.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/solo/decoupled_solo_light_r50_fpn_3x_coco/decoupled_solo_light_r50_fpn_3x_coco_20210906_142703.log.json) |
+
+- Decoupled Light SOLO using decoupled structure similar to Decoupled
+  SOLO head, with light-weight head and smaller input size, Please refer
+  to the corresponding config files for details.
+
+## Citation
+
+```latex
+@inproceedings{wang2020solo,
+  title     =  {{SOLO}: Segmenting Objects by Locations},
+  author    =  {Wang, Xinlong and Kong, Tao and Shen, Chunhua and Jiang, Yuning and Li, Lei},
+  booktitle =  {Proc. Eur. Conf. Computer Vision (ECCV)},
+  year      =  {2020}
+}
+```
diff --git a/configs/solo/decoupled_solo_light_r50_fpn_3x_coco.py b/configs/solo/decoupled_solo_light_r50_fpn_3x_coco.py
new file mode 100644
index 0000000..101f8f1
--- /dev/null
+++ b/configs/solo/decoupled_solo_light_r50_fpn_3x_coco.py
@@ -0,0 +1,63 @@
+_base_ = './decoupled_solo_r50_fpn_3x_coco.py'
+
+# model settings
+model = dict(
+    mask_head=dict(
+        type='DecoupledSOLOLightHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=4,
+        feat_channels=256,
+        strides=[8, 8, 16, 32, 32],
+        scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)),
+        pos_scale=0.2,
+        num_grids=[40, 36, 24, 16, 12],
+        cls_down_index=0,
+        loss_mask=dict(
+            type='DiceLoss', use_sigmoid=True, activate=False,
+            loss_weight=3.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(852, 512), (852, 480), (852, 448), (852, 416), (852, 384),
+                   (852, 352)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(852, 512),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/solo/decoupled_solo_r50_fpn_1x_coco.py b/configs/solo/decoupled_solo_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..b611cdf
--- /dev/null
+++ b/configs/solo/decoupled_solo_r50_fpn_1x_coco.py
@@ -0,0 +1,28 @@
+_base_ = [
+    './solo_r50_fpn_1x_coco.py',
+]
+# model settings
+model = dict(
+    mask_head=dict(
+        type='DecoupledSOLOHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=7,
+        feat_channels=256,
+        strides=[8, 8, 16, 32, 32],
+        scale_ranges=((1, 96), (48, 192), (96, 384), (192, 768), (384, 2048)),
+        pos_scale=0.2,
+        num_grids=[40, 36, 24, 16, 12],
+        cls_down_index=0,
+        loss_mask=dict(
+            type='DiceLoss', use_sigmoid=True, activate=False,
+            loss_weight=3.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
+
+optimizer = dict(type='SGD', lr=0.01)
diff --git a/configs/solo/decoupled_solo_r50_fpn_3x_coco.py b/configs/solo/decoupled_solo_r50_fpn_3x_coco.py
new file mode 100644
index 0000000..4a8c19d
--- /dev/null
+++ b/configs/solo/decoupled_solo_r50_fpn_3x_coco.py
@@ -0,0 +1,25 @@
+_base_ = './solo_r50_fpn_3x_coco.py'
+
+# model settings
+model = dict(
+    mask_head=dict(
+        type='DecoupledSOLOHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=7,
+        feat_channels=256,
+        strides=[8, 8, 16, 32, 32],
+        scale_ranges=((1, 96), (48, 192), (96, 384), (192, 768), (384, 2048)),
+        pos_scale=0.2,
+        num_grids=[40, 36, 24, 16, 12],
+        cls_down_index=0,
+        loss_mask=dict(
+            type='DiceLoss', use_sigmoid=True, activate=False,
+            loss_weight=3.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)))
diff --git a/configs/solo/metafile.yml b/configs/solo/metafile.yml
new file mode 100644
index 0000000..b6244e8
--- /dev/null
+++ b/configs/solo/metafile.yml
@@ -0,0 +1,115 @@
+Collections:
+  - Name: SOLO
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - Convolution
+        - ResNet
+    Paper: https://arxiv.org/abs/1912.04488
+    README: configs/solo/README.md
+
+Models:
+  - Name: decoupled_solo_r50_fpn_1x_coco
+    In Collection: SOLO
+    Config: configs/solo/decoupled_solo_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.8
+      Epochs: 12
+    inference time (ms/im):
+      - value: 116.4
+        hardware: V100
+        backend: PyTorch
+        batch size: 1
+        mode: FP32
+        resolution: (1333, 800)
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 33.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/solo/decoupled_solo_r50_fpn_1x_coco/decoupled_solo_r50_fpn_1x_coco_20210820_233348-6337c589.pth
+
+  - Name: decoupled_solo_r50_fpn_3x_coco
+    In Collection: SOLO
+    Config: configs/solo/decoupled_solo_r50_fpn_3x_coco.py
+    Metadata:
+      Training Memory (GB): 7.9
+      Epochs: 36
+    inference time (ms/im):
+      - value: 117.2
+        hardware: V100
+        backend: PyTorch
+        batch size: 1
+        mode: FP32
+        resolution: (1333, 800)
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 36.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/solo/decoupled_solo_r50_fpn_3x_coco/decoupled_solo_r50_fpn_3x_coco_20210821_042504-7b3301ec.pth
+
+  - Name: decoupled_solo_light_r50_fpn_3x_coco
+    In Collection: SOLO
+    Config: configs/solo/decoupled_solo_light_r50_fpn_3x_coco.py
+    Metadata:
+      Training Memory (GB): 2.2
+      Epochs: 36
+    inference time (ms/im):
+      - value: 35.0
+        hardware: V100
+        backend: PyTorch
+        batch size: 1
+        mode: FP32
+        resolution: (852, 512)
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 32.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/solo/decoupled_solo_light_r50_fpn_3x_coco/decoupled_solo_light_r50_fpn_3x_coco_20210906_142703-e70e226f.pth
+
+  - Name: solo_r50_fpn_3x_coco
+    In Collection: SOLO
+    Config: configs/solo/solo_r50_fpn_3x_coco.py
+    Metadata:
+      Training Memory (GB): 7.4
+      Epochs: 36
+    inference time (ms/im):
+      - value: 94.2
+        hardware: V100
+        backend: PyTorch
+        batch size: 1
+        mode: FP32
+        resolution: (1333, 800)
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 35.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/solo/solo_r50_fpn_3x_coco/solo_r50_fpn_3x_coco_20210901_012353-11d224d7.pth
+
+  - Name: solo_r50_fpn_1x_coco
+    In Collection: SOLO
+    Config: configs/solo/solo_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.0
+      Epochs: 12
+    inference time (ms/im):
+      - value: 95.1
+        hardware: V100
+        backend: PyTorch
+        batch size: 1
+        mode: FP32
+        resolution: (1333, 800)
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 33.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/solo/solo_r50_fpn_1x_coco/solo_r50_fpn_1x_coco_20210821_035055-2290a6b8.pth
diff --git a/configs/solo/solo_r50_fpn_1x_coco.py b/configs/solo/solo_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..9093a50
--- /dev/null
+++ b/configs/solo/solo_r50_fpn_1x_coco.py
@@ -0,0 +1,53 @@
+_base_ = [
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    type='SOLO',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
+        style='pytorch'),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=0,
+        num_outs=5),
+    mask_head=dict(
+        type='SOLOHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=7,
+        feat_channels=256,
+        strides=[8, 8, 16, 32, 32],
+        scale_ranges=((1, 96), (48, 192), (96, 384), (192, 768), (384, 2048)),
+        pos_scale=0.2,
+        num_grids=[40, 36, 24, 16, 12],
+        cls_down_index=0,
+        loss_mask=dict(type='DiceLoss', use_sigmoid=True, loss_weight=3.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)),
+    # model training and testing settings
+    test_cfg=dict(
+        nms_pre=500,
+        score_thr=0.1,
+        mask_thr=0.5,
+        filter_thr=0.05,
+        kernel='gaussian',  # gaussian/linear
+        sigma=2.0,
+        max_per_img=100))
+
+# optimizer
+optimizer = dict(type='SGD', lr=0.01)
diff --git a/configs/solo/solo_r50_fpn_3x_coco.py b/configs/solo/solo_r50_fpn_3x_coco.py
new file mode 100644
index 0000000..52302cd
--- /dev/null
+++ b/configs/solo/solo_r50_fpn_3x_coco.py
@@ -0,0 +1,28 @@
+_base_ = './solo_r50_fpn_1x_coco.py'
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 800), (1333, 768), (1333, 736), (1333, 704),
+                   (1333, 672), (1333, 640)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    step=[27, 33])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/configs/solov2/README.md b/configs/solov2/README.md
new file mode 100644
index 0000000..2ffe70f
--- /dev/null
+++ b/configs/solov2/README.md
@@ -0,0 +1,59 @@
+# SOLOv2
+
+> [SOLOv2: Dynamic and Fast Instance Segmentation](https://arxiv.org/abs/2003.10152)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+In this work, we aim at building a simple, direct, and fast instance segmentation
+framework with strong performance. We follow the principle of the SOLO method of
+Wang et al. "SOLO: segmenting objects by locations". Importantly, we take one
+step further by dynamically learning the mask head of the object segmenter such
+that the mask head is conditioned on the location. Specifically, the mask branch
+is decoupled into a mask kernel branch and mask feature branch, which are
+responsible for learning the convolution kernel and the convolved features
+respectively. Moreover, we propose Matrix NMS (non maximum suppression) to
+significantly reduce the inference time overhead due to NMS of masks. Our
+Matrix NMS performs NMS with parallel matrix operations in one shot, and
+yields better results. We demonstrate a simple direct instance segmentation
+system, outperforming a few state-of-the-art methods in both speed and accuracy.
+A light-weight version of SOLOv2 executes at 31.3 FPS and yields 37.1% AP.
+Moreover, our state-of-the-art results in object detection (from our mask byproduct)
+and panoptic segmentation show the potential to serve as a new strong baseline
+for many instance-level recognition tasks besides instance segmentation.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/48282753/167235090-f20dab74-43a5-44ed-9f11-4e5f08866f45.png"/>
+</div>
+
+## Results and Models
+
+### SOLOv2
+
+|  Backbone  |  Style  | MS train | Lr schd | Mem (GB) | mask AP |                                                    Config                                                     |                                                                                                                                                Download                                                                                                                                                |
+| :--------: | :-----: | :------: | :-----: | :------: | :-----: | :-----------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50    | pytorch |    N     |   1x    |   5.1    |  34.8   |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/solov2/solov2_r50_fpn_1x_coco.py)    |      [model](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r50_fpn_1x_coco/solov2_r50_fpn_1x_coco_20220512_125858-a357fa23.pth)           \| [log](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r50_fpn_1x_coco/solov2_r50_fpn_1x_coco_20220512_125858.log.json)      |
+|    R-50    | pytorch |    Y     |   3x    |   5.1    |  37.5   |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/solov2/solov2_r50_fpn_3x_coco.py)    |      [model](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r50_fpn_3x_coco/solov2_r50_fpn_3x_coco_20220512_125856-fed092d4.pth)           \| [log](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r50_fpn_3x_coco/solov2_r50_fpn_3x_coco_20220512_125856.log.json)      |
+|   R-101    | pytorch |    Y     |   3x    |   6.9    |  39.1   |   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/solov2/solov2_r101_fpn_3x_coco.py)   |     [model](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r101_fpn_3x_coco/solov2_r101_fpn_3x_coco_20220511_095119-c559a076.pth)         \| [log](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r101_fpn_3x_coco/solov2_r101_fpn_3x_coco_20220511_095119.log.json)     |
+| R-101(DCN) | pytorch |    Y     |   3x    |   7.1    |  41.2   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/solov2/solov2_r101_dcn_fpn_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r101_dcn_fpn_3x_coco/solov2_r101_dcn_fpn_3x_coco_20220513_214734-16c966cb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r101_dcn_fpn_3x_coco/solov2_r101_dcn_fpn_3x_coco_20220513_214734.log.json) |
+| X-101(DCN) | pytorch |    Y     |   3x    |   11.3   |  42.4   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/solov2/solov2_x101_dcn_fpn_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_x101_dcn_fpn_3x_coco/solov2_x101_dcn_fpn_3x_coco_20220513_214337-aef41095.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_x101_dcn_fpn_3x_coco/solov2_x101_dcn_fpn_3x_coco_20220513_214337.log.json) |
+
+### Light SOLOv2
+
+| Backbone |  Style  | MS train | Lr schd | Mem (GB) | mask AP |                                                     Config                                                     |                                                                                                                                                  Download                                                                                                                                                  |
+| :------: | :-----: | :------: | :-----: | :------: | :-----: | :------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-18   | pytorch |    Y     |   3x    |   9.1    |  29.7   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/solov2/solov2_light_r18_fpn_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_light_r18_fpn_3x_coco/solov2_light_r18_fpn_3x_coco_20220511_083717-75fa355b.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_light_r18_fpn_3x_coco/solov2_light_r18_fpn_3x_coco_20220511_083717.log.json) |
+|   R-34   | pytorch |    Y     |   3x    |   9.3    |  31.9   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/solov2/solov2_light_r34_fpn_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_light_r34_fpn_3x_coco/solov2_light_r34_fpn_3x_coco_20220511_091839-e51659d3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_light_r34_fpn_3x_coco/solov2_light_r34_fpn_3x_coco_20220511_091839.log.json) |
+|   R-50   | pytorch |    Y     |   3x    |   9.9    |  33.7   | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/solov2/solov2_light_r50_fpn_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_light_r50_fpn_3x_coco/solov2_light_r50_fpn_3x_coco_20220512_165256-c93a6074.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_light_r50_fpn_3x_coco/solov2_light_r50_fpn_3x_coco_20220512_165256.log.json) |
+
+## Citation
+
+```latex
+@article{wang2020solov2,
+  title={SOLOv2: Dynamic and Fast Instance Segmentation},
+  author={Wang, Xinlong and Zhang, Rufeng and  Kong, Tao and Li, Lei and Shen, Chunhua},
+  journal={Proc. Advances in Neural Information Processing Systems (NeurIPS)},
+  year={2020}
+}
+```
diff --git a/configs/solov2/metafile.yml b/configs/solov2/metafile.yml
new file mode 100644
index 0000000..656f66f
--- /dev/null
+++ b/configs/solov2/metafile.yml
@@ -0,0 +1,119 @@
+Collections:
+  - Name: SOLOv2
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x A100 GPUs
+      Architecture:
+        - FPN
+        - Convolution
+        - ResNet
+    Paper: https://arxiv.org/abs/2003.10152
+    README: configs/solov2/README.md
+
+Models:
+  - Name: solov2_r50_fpn_1x_coco
+    In Collection: SOLOv2
+    Config: configs/solov2/solov2_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 5.1
+      Epochs: 12
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 34.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r50_fpn_1x_coco/solov2_r50_fpn_1x_coco_20220512_125858-a357fa23.pth
+
+  - Name: solov2_r50_fpn_3x_coco
+    In Collection: SOLOv2
+    Config: configs/solov2/solov2_r50_fpn_3x_coco.py
+    Metadata:
+      Training Memory (GB): 5.1
+      Epochs: 36
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 37.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r50_fpn_3x_coco/solov2_r50_fpn_3x_coco_20220512_125856-fed092d4.pth
+
+  - Name: solov2_r101_fpn_3x_coco
+    In Collection: SOLOv2
+    Config: configs/solov2/solov2_r101_fpn_3x_coco.py
+    Metadata:
+      Training Memory (GB): 6.9
+      Epochs: 36
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r101_fpn_3x_coco/solov2_r101_fpn_3x_coco_20220511_095119-c559a076.pth
+
+  - Name: solov2_r101_dcn_fpn_3x_coco
+    In Collection: SOLOv2
+    Config: configs/solov2/solov2_r101_dcn_fpn_3x_coco.py
+    Metadata:
+      Training Memory (GB): 7.1
+      Epochs: 36
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_r101_dcn_fpn_3x_coco/solov2_r101_dcn_fpn_3x_coco_20220513_214734-16c966cb.pth
+
+  - Name: solov2_x101_dcn_fpn_3x_coco
+    In Collection: SOLOv2
+    Config: configs/solov2/solov2_x101_dcn_fpn_3x_coco.py
+    Metadata:
+      Training Memory (GB): 11.3
+      Epochs: 36
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 42.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_x101_dcn_fpn_3x_coco/solov2_x101_dcn_fpn_3x_coco_20220513_214337-aef41095.pth
+
+  - Name: solov2_light_r18_fpn_3x_coco
+    In Collection: SOLOv2
+    Config: configs/solov2/solov2_light_r18_fpn_3x_coco.py
+    Metadata:
+      Training Memory (GB): 9.1
+      Epochs: 36
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 29.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_light_r18_fpn_3x_coco/solov2_light_r18_fpn_3x_coco_20220511_083717-75fa355b.pth
+
+  - Name: solov2_light_r34_fpn_3x_coco
+    In Collection: SOLOv2
+    Config: configs/solov2/solov2_light_r34_fpn_3x_coco.py
+    Metadata:
+      Training Memory (GB): 9.3
+      Epochs: 36
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 31.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_light_r34_fpn_3x_coco/solov2_light_r34_fpn_3x_coco_20220511_091839-e51659d3.pth
+
+  - Name: solov2_light_r50_fpn_3x_coco
+    In Collection: SOLOv2
+    Config: configs/solov2/solov2_light_r50_fpn_3x_coco.py
+    Metadata:
+      Training Memory (GB): 9.9
+      Epochs: 36
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 33.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/solov2/solov2_light_r50_fpn_3x_coco/solov2_light_r50_fpn_3x_coco_20220512_165256-c93a6074.pth
diff --git a/configs/solov2/solov2_light_r18_fpn_3x_coco.py b/configs/solov2/solov2_light_r18_fpn_3x_coco.py
new file mode 100644
index 0000000..6fb33b0
--- /dev/null
+++ b/configs/solov2/solov2_light_r18_fpn_3x_coco.py
@@ -0,0 +1,7 @@
+_base_ = 'solov2_light_r50_fpn_3x_coco.py'
+
+# model settings
+model = dict(
+    backbone=dict(
+        depth=18, init_cfg=dict(checkpoint='torchvision://resnet18')),
+    neck=dict(in_channels=[64, 128, 256, 512]))
diff --git a/configs/solov2/solov2_light_r34_fpn_3x_coco.py b/configs/solov2/solov2_light_r34_fpn_3x_coco.py
new file mode 100644
index 0000000..ea082a1
--- /dev/null
+++ b/configs/solov2/solov2_light_r34_fpn_3x_coco.py
@@ -0,0 +1,7 @@
+_base_ = 'solov2_light_r50_fpn_3x_coco.py'
+
+# model settings
+model = dict(
+    backbone=dict(
+        depth=34, init_cfg=dict(checkpoint='torchvision://resnet34')),
+    neck=dict(in_channels=[64, 128, 256, 512]))
diff --git a/configs/solov2/solov2_light_r50_dcn_fpn_3x_coco.py b/configs/solov2/solov2_light_r50_dcn_fpn_3x_coco.py
new file mode 100644
index 0000000..4d758e2
--- /dev/null
+++ b/configs/solov2/solov2_light_r50_dcn_fpn_3x_coco.py
@@ -0,0 +1,62 @@
+_base_ = 'solov2_r50_fpn_3x_coco.py'
+
+# model settings
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)),
+    mask_head=dict(
+        feat_channels=256,
+        stacked_convs=3,
+        scale_ranges=((1, 64), (32, 128), (64, 256), (128, 512), (256, 2048)),
+        mask_feature_head=dict(out_channels=128),
+        dcn_cfg=dict(type='DCNv2'),
+        dcn_apply_to_all_conv=False))  # light solov2 head
+
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    step=[27, 33])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
+
+# data
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(768, 512), (768, 480), (768, 448), (768, 416), (768, 384),
+                   (768, 352)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(448, 768),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/solov2/solov2_light_r50_fpn_3x_coco.py b/configs/solov2/solov2_light_r50_fpn_3x_coco.py
new file mode 100644
index 0000000..e08f1db
--- /dev/null
+++ b/configs/solov2/solov2_light_r50_fpn_3x_coco.py
@@ -0,0 +1,57 @@
+_base_ = 'solov2_r50_fpn_1x_coco.py'
+
+# model settings
+model = dict(
+    mask_head=dict(
+        stacked_convs=2,
+        feat_channels=256,
+        scale_ranges=((1, 56), (28, 112), (56, 224), (112, 448), (224, 896)),
+        mask_feature_head=dict(out_channels=128)))
+
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    step=[27, 33])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
+
+# data
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(768, 512), (768, 480), (768, 448), (768, 416), (768, 384),
+                   (768, 352)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(448, 768),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/solov2/solov2_r101_dcn_fpn_3x_coco.py b/configs/solov2/solov2_r101_dcn_fpn_3x_coco.py
new file mode 100644
index 0000000..1594118
--- /dev/null
+++ b/configs/solov2/solov2_r101_dcn_fpn_3x_coco.py
@@ -0,0 +1,13 @@
+_base_ = 'solov2_r50_fpn_3x_coco.py'
+
+# model settings
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(checkpoint='torchvision://resnet101'),
+        dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)),
+    mask_head=dict(
+        mask_feature_head=dict(conv_cfg=dict(type='DCNv2')),
+        dcn_cfg=dict(type='DCNv2'),
+        dcn_apply_to_all_conv=True))
diff --git a/configs/solov2/solov2_r101_fpn_3x_coco.py b/configs/solov2/solov2_r101_fpn_3x_coco.py
new file mode 100644
index 0000000..6c248e5
--- /dev/null
+++ b/configs/solov2/solov2_r101_fpn_3x_coco.py
@@ -0,0 +1,6 @@
+_base_ = 'solov2_r50_fpn_3x_coco.py'
+
+# model settings
+model = dict(
+    backbone=dict(
+        depth=101, init_cfg=dict(checkpoint='torchvision://resnet101')))
diff --git a/configs/solov2/solov2_r50_fpn_1x_coco.py b/configs/solov2/solov2_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..9aee571
--- /dev/null
+++ b/configs/solov2/solov2_r50_fpn_1x_coco.py
@@ -0,0 +1,61 @@
+_base_ = [
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# model settings
+model = dict(
+    type='SOLOv2',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50'),
+        style='pytorch'),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=0,
+        num_outs=5),
+    mask_head=dict(
+        type='SOLOV2Head',
+        num_classes=80,
+        in_channels=256,
+        feat_channels=512,
+        stacked_convs=4,
+        strides=[8, 8, 16, 32, 32],
+        scale_ranges=((1, 96), (48, 192), (96, 384), (192, 768), (384, 2048)),
+        pos_scale=0.2,
+        num_grids=[40, 36, 24, 16, 12],
+        cls_down_index=0,
+        mask_feature_head=dict(
+            feat_channels=128,
+            start_level=0,
+            end_level=3,
+            out_channels=256,
+            mask_stride=4,
+            norm_cfg=dict(type='GN', num_groups=32, requires_grad=True)),
+        loss_mask=dict(type='DiceLoss', use_sigmoid=True, loss_weight=3.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0)),
+    # model training and testing settings
+    test_cfg=dict(
+        nms_pre=500,
+        score_thr=0.1,
+        mask_thr=0.5,
+        filter_thr=0.05,
+        kernel='gaussian',  # gaussian/linear
+        sigma=2.0,
+        max_per_img=100))
+
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(
+    _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
diff --git a/configs/solov2/solov2_r50_fpn_3x_coco.py b/configs/solov2/solov2_r50_fpn_3x_coco.py
new file mode 100644
index 0000000..640c730
--- /dev/null
+++ b/configs/solov2/solov2_r50_fpn_3x_coco.py
@@ -0,0 +1,28 @@
+_base_ = 'solov2_r50_fpn_1x_coco.py'
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 800), (1333, 768), (1333, 736), (1333, 704),
+                   (1333, 672), (1333, 640)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=1.0 / 3,
+    step=[27, 33])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/configs/solov2/solov2_x101_dcn_fpn_3x_coco.py b/configs/solov2/solov2_x101_dcn_fpn_3x_coco.py
new file mode 100644
index 0000000..6115fed
--- /dev/null
+++ b/configs/solov2/solov2_x101_dcn_fpn_3x_coco.py
@@ -0,0 +1,17 @@
+_base_ = 'solov2_r50_fpn_3x_coco.py'
+
+# model settings
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')),
+    mask_head=dict(
+        mask_feature_head=dict(conv_cfg=dict(type='DCNv2')),
+        dcn_cfg=dict(type='DCNv2'),
+        dcn_apply_to_all_conv=True))
diff --git a/configs/sparse_rcnn/README.md b/configs/sparse_rcnn/README.md
new file mode 100644
index 0000000..d7912e0
--- /dev/null
+++ b/configs/sparse_rcnn/README.md
@@ -0,0 +1,38 @@
+# Sparse R-CNN
+
+> [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://arxiv.org/abs/2011.12450)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We present Sparse R-CNN, a purely sparse method for object detection in images. Existing works on object detection heavily rely on dense object candidates, such as k anchor boxes pre-defined on all grids of image feature map of size H×W. In our method, however, a fixed sparse set of learned object proposals, total length of N, are provided to object recognition head to perform classification and location. By eliminating HWk (up to hundreds of thousands) hand-designed object candidates to N (e.g. 100) learnable proposals, Sparse R-CNN completely avoids all efforts related to object candidates design and many-to-one label assignment. More importantly, final predictions are directly output without non-maximum suppression post-procedure. Sparse R-CNN demonstrates accuracy, run-time and training convergence performance on par with the well-established detector baselines on the challenging COCO dataset, e.g., achieving 45.0 AP in standard 3× training schedule and running at 22 fps using ResNet-50 FPN model. We hope our work could inspire re-thinking the convention of dense prior in object detectors.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143998489-8a5a687d-ceec-4590-8347-708e427e7dfe.png" height="300"/>
+</div>
+
+## Results and Models
+
+|    Model     | Backbone  |  Style  | Lr schd | Number of Proposals | Multi-Scale | RandomCrop | box AP |                                                                         Config                                                                         |                                                                                                                                                                                                                                 Download                                                                                                                                                                                                                                  |
+| :----------: | :-------: | :-----: | :-----: | :-----------------: | :---------: | :--------: | :----: | :----------------------------------------------------------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| Sparse R-CNN | R-50-FPN  | pytorch |   1x    |         100         |    False    |   False    |  37.9  |                   [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py)                   |                                                                         [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco/sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco/sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.log.json)                                                                         |
+| Sparse R-CNN | R-50-FPN  | pytorch |   3x    |         100         |    True     |   False    |  42.8  |           [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py)           |                                         [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco_20201218_154234-7bc5c054.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco_20201218_154234-7bc5c054.log.json)                                         |
+| Sparse R-CNN | R-50-FPN  | pytorch |   3x    |         300         |    True     |    True    |  45.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py)  |   [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_024605-9fe92701.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_024605-9fe92701.log.json)   |
+| Sparse R-CNN | R-101-FPN | pytorch |   3x    |         100         |    True     |   False    |  44.2  |          [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py)           |                                       [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco_20201223_121552-6c46c9d6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco_20201223_121552-6c46c9d6.log.json)                                       |
+| Sparse R-CNN | R-101-FPN | pytorch |   3x    |         300         |    True     |    True    |  46.2  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_023452-c23c3564.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_023452-c23c3564.log.json) |
+
+### Notes
+
+We observe about 0.3 AP noise especially when using ResNet-101 as the backbone.
+
+## Citation
+
+```latex
+@article{peize2020sparse,
+  title   =  {{SparseR-CNN}: End-to-End Object Detection with Learnable Proposals},
+  author  =  {Peize Sun and Rufeng Zhang and Yi Jiang and Tao Kong and Chenfeng Xu and Wei Zhan and Masayoshi Tomizuka and Lei Li and Zehuan Yuan and Changhu Wang and Ping Luo},
+  journal =  {arXiv preprint arXiv:2011.12450},
+  year    =  {2020}
+}
+```
diff --git a/configs/sparse_rcnn/metafile.yml b/configs/sparse_rcnn/metafile.yml
new file mode 100644
index 0000000..bb1273e
--- /dev/null
+++ b/configs/sparse_rcnn/metafile.yml
@@ -0,0 +1,80 @@
+Collections:
+  - Name: Sparse R-CNN
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - ResNet
+        - Sparse R-CNN
+    Paper:
+      URL: https://arxiv.org/abs/2011.12450
+      Title: 'Sparse R-CNN: End-to-End Object Detection with Learnable Proposals'
+    README: configs/sparse_rcnn/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.9.0/mmdet/models/detectors/sparse_rcnn.py#L6
+      Version: v2.9.0
+
+Models:
+  - Name: sparse_rcnn_r50_fpn_1x_coco
+    In Collection: Sparse R-CNN
+    Config: configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco/sparse_rcnn_r50_fpn_1x_coco_20201222_214453-dc79b137.pth
+
+  - Name: sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco
+    In Collection: Sparse R-CNN
+    Config: configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco_20201218_154234-7bc5c054.pth
+
+  - Name: sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco
+    In Collection: Sparse R-CNN
+    Config: configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 45.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_024605-9fe92701.pth
+
+  - Name: sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco
+    In Collection: Sparse R-CNN
+    Config: configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco_20201223_121552-6c46c9d6.pth
+
+  - Name: sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco
+    In Collection: Sparse R-CNN
+    Config: configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco_20201223_023452-c23c3564.pth
diff --git a/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
new file mode 100644
index 0000000..de323bd
--- /dev/null
+++ b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py'
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py
new file mode 100644
index 0000000..ab4c5f6
--- /dev/null
+++ b/configs/sparse_rcnn/sparse_rcnn_r101_fpn_mstrain_480-800_3x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py'
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..b383ee4
--- /dev/null
+++ b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py
@@ -0,0 +1,95 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+num_stages = 6
+num_proposals = 100
+model = dict(
+    type='SparseRCNN',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=0,
+        add_extra_convs='on_input',
+        num_outs=4),
+    rpn_head=dict(
+        type='EmbeddingRPNHead',
+        num_proposals=num_proposals,
+        proposal_feature_channel=256),
+    roi_head=dict(
+        type='SparseRoIHead',
+        num_stages=num_stages,
+        stage_loss_weights=[1] * num_stages,
+        proposal_feature_channel=256,
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=[
+            dict(
+                type='DIIHead',
+                num_classes=80,
+                num_ffn_fcs=2,
+                num_heads=8,
+                num_cls_fcs=1,
+                num_reg_fcs=3,
+                feedforward_channels=2048,
+                in_channels=256,
+                dropout=0.0,
+                ffn_act_cfg=dict(type='ReLU', inplace=True),
+                dynamic_conv_cfg=dict(
+                    type='DynamicConv',
+                    in_channels=256,
+                    feat_channels=64,
+                    out_channels=256,
+                    input_feat_shape=7,
+                    act_cfg=dict(type='ReLU', inplace=True),
+                    norm_cfg=dict(type='LN')),
+                loss_bbox=dict(type='L1Loss', loss_weight=5.0),
+                loss_iou=dict(type='GIoULoss', loss_weight=2.0),
+                loss_cls=dict(
+                    type='FocalLoss',
+                    use_sigmoid=True,
+                    gamma=2.0,
+                    alpha=0.25,
+                    loss_weight=2.0),
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    clip_border=False,
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.5, 0.5, 1., 1.])) for _ in range(num_stages)
+        ]),
+    # training and testing settings
+    train_cfg=dict(
+        rpn=None,
+        rcnn=[
+            dict(
+                assigner=dict(
+                    type='HungarianAssigner',
+                    cls_cost=dict(type='FocalLossCost', weight=2.0),
+                    reg_cost=dict(type='BBoxL1Cost', weight=5.0),
+                    iou_cost=dict(type='IoUCost', iou_mode='giou',
+                                  weight=2.0)),
+                sampler=dict(type='PseudoSampler'),
+                pos_weight=1) for _ in range(num_stages)
+        ]),
+    test_cfg=dict(rpn=None, rcnn=dict(max_per_img=num_proposals)))
+
+# optimizer
+optimizer = dict(_delete_=True, type='AdamW', lr=0.000025, weight_decay=0.0001)
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=1, norm_type=2))
+# learning policy
+lr_config = dict(policy='step', step=[8, 11])
+runner = dict(type='EpochBasedRunner', max_epochs=12)
diff --git a/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
new file mode 100644
index 0000000..36f1d62
--- /dev/null
+++ b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_300_proposals_crop_mstrain_480-800_3x_coco.py
@@ -0,0 +1,52 @@
+_base_ = './sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py'
+num_proposals = 300
+model = dict(
+    rpn_head=dict(num_proposals=num_proposals),
+    test_cfg=dict(
+        _delete_=True, rpn=None, rcnn=dict(max_per_img=num_proposals)))
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# augmentation strategy originates from DETR.
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='AutoAugment',
+        policies=[[
+            dict(
+                type='Resize',
+                img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                           (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                           (736, 1333), (768, 1333), (800, 1333)],
+                multiscale_mode='value',
+                keep_ratio=True)
+        ],
+                  [
+                      dict(
+                          type='Resize',
+                          img_scale=[(400, 1333), (500, 1333), (600, 1333)],
+                          multiscale_mode='value',
+                          keep_ratio=True),
+                      dict(
+                          type='RandomCrop',
+                          crop_type='absolute_range',
+                          crop_size=(384, 600),
+                          allow_negative_crop=True),
+                      dict(
+                          type='Resize',
+                          img_scale=[(480, 1333), (512, 1333), (544, 1333),
+                                     (576, 1333), (608, 1333), (640, 1333),
+                                     (672, 1333), (704, 1333), (736, 1333),
+                                     (768, 1333), (800, 1333)],
+                          multiscale_mode='value',
+                          override=True,
+                          keep_ratio=True)
+                  ]]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+data = dict(train=dict(pipeline=train_pipeline))
diff --git a/configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py
new file mode 100644
index 0000000..2fa2a80
--- /dev/null
+++ b/configs/sparse_rcnn/sparse_rcnn_r50_fpn_mstrain_480-800_3x_coco.py
@@ -0,0 +1,23 @@
+_base_ = './sparse_rcnn_r50_fpn_1x_coco.py'
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+min_values = (480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, value) for value in min_values],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+data = dict(train=dict(pipeline=train_pipeline))
+lr_config = dict(policy='step', step=[27, 33])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/configs/ssd/README.md b/configs/ssd/README.md
new file mode 100644
index 0000000..463926b
--- /dev/null
+++ b/configs/ssd/README.md
@@ -0,0 +1,62 @@
+# SSD
+
+> [SSD: Single Shot MultiBox Detector](https://arxiv.org/abs/1512.02325)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes. Our SSD model is simple relative to methods that require object proposals because it completely eliminates proposal generation and subsequent pixel or feature resampling stage and encapsulates all computation in a single network. This makes SSD easy to train and straightforward to integrate into systems that require a detection component. Experimental results on the PASCAL VOC, MS COCO, and ILSVRC datasets confirm that SSD has comparable accuracy to methods that utilize an additional object proposal step and is much faster, while providing a unified framework for both training and inference. Compared to other single stage methods, SSD has much better accuracy, even with a smaller input image size. For 300×300 input, SSD achieves 72.1% mAP on VOC2007 test at 58 FPS on a Nvidia Titan X and for 500×500 input, SSD achieves 75.1% mAP, outperforming a comparable state of the art Faster R-CNN model.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143998553-4e12f681-6025-46b4-8410-9e2e1e53a8ec.png"/>
+</div>
+
+## Results and models of SSD
+
+| Backbone | Size | Style | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                           Config                                           |                                                                                                             Download                                                                                                             |
+| :------: | :--: | :---: | :-----: | :------: | :------------: | :----: | :----------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  VGG16   | 300  | caffe |  120e   |   9.9    |      43.7      |  25.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd300_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20210803_015428-d231a06e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20210803_015428.log.json) |
+|  VGG16   | 512  | caffe |  120e   |   19.4   |      30.7      |  29.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssd512_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20210803_022849-0a47a1ca.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20210803_022849.log.json) |
+
+## Results and models of SSD-Lite
+
+|  Backbone   | Size | Training from scratch | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                        Config                                                        |                                                                                                                                                                 Download                                                                                                                                                                 |
+| :---------: | :--: | :-------------------: | :-----: | :------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| MobileNetV2 | 320  |          yes          |  600e   |   4.0    |      69.9      |  21.3  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627.log.json) |
+
+## Notice
+
+### Compatibility
+
+In v2.14.0, [PR5291](https://github.com/open-mmlab/mmdetection/pull/5291) refactored SSD neck and head for more
+flexible usage. If users want to use the SSD checkpoint trained in the older versions, we provide a scripts
+`tools/model_converters/upgrade_ssd_version.py` to convert the model weights.
+
+```bash
+python tools/model_converters/upgrade_ssd_version.py ${OLD_MODEL_PATH} ${NEW_MODEL_PATH}
+
+```
+
+- OLD_MODEL_PATH: the path to load the old version SSD model.
+- NEW_MODEL_PATH: the path to save the converted model weights.
+
+### SSD-Lite training settings
+
+There are some differences between our implementation of MobileNetV2 SSD-Lite and the one in [TensorFlow 1.x detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md) .
+
+1. Use 320x320 as input size instead of 300x300.
+2. The anchor sizes are different.
+3. The C4 feature map is taken from the last layer of stage 4 instead of the middle of the block.
+4. The model in TensorFlow1.x is trained on coco 2014 and validated on coco minival2014, but we trained and validated the model on coco 2017. The mAP on val2017 is usually a little lower than minival2014 (refer to the results in TensorFlow Object Detection API, e.g., MobileNetV2 SSD gets 22 mAP on minival2014 but 20.2 mAP on val2017).
+
+## Citation
+
+```latex
+@article{Liu_2016,
+   title={SSD: Single Shot MultiBox Detector},
+   journal={ECCV},
+   author={Liu, Wei and Anguelov, Dragomir and Erhan, Dumitru and Szegedy, Christian and Reed, Scott and Fu, Cheng-Yang and Berg, Alexander C.},
+   year={2016},
+}
+```
diff --git a/configs/ssd/ascend_ssd300_coco.py b/configs/ssd/ascend_ssd300_coco.py
new file mode 100644
index 0000000..25457ee
--- /dev/null
+++ b/configs/ssd/ascend_ssd300_coco.py
@@ -0,0 +1,72 @@
+_base_ = [
+    '../_base_/models/ascend_ssd300.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(300, 300),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=3,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict(_delete_=True)
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW')
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/ssd/metafile.yml b/configs/ssd/metafile.yml
new file mode 100644
index 0000000..b9ee79c
--- /dev/null
+++ b/configs/ssd/metafile.yml
@@ -0,0 +1,78 @@
+Collections:
+  - Name: SSD
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - VGG
+    Paper:
+      URL: https://arxiv.org/abs/1512.02325
+      Title: 'SSD: Single Shot MultiBox Detector'
+    README: configs/ssd/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.14.0/mmdet/models/dense_heads/ssd_head.py#L16
+      Version: v2.14.0
+
+Models:
+  - Name: ssd300_coco
+    In Collection: SSD
+    Config: configs/ssd/ssd300_coco.py
+    Metadata:
+      Training Memory (GB): 9.9
+      inference time (ms/im):
+        - value: 22.88
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (300, 300)
+      Epochs: 120
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 25.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd300_coco/ssd300_coco_20210803_015428-d231a06e.pth
+
+  - Name: ssd512_coco
+    In Collection: SSD
+    Config: configs/ssd/ssd512_coco.py
+    Metadata:
+      Training Memory (GB): 19.4
+      inference time (ms/im):
+        - value: 32.57
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (512, 512)
+      Epochs: 120
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 29.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssd512_coco/ssd512_coco_20210803_022849-0a47a1ca.pth
+
+  - Name: ssdlite_mobilenetv2_scratch_600e_coco
+    In Collection: SSD
+    Config: configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py
+    Metadata:
+      Training Memory (GB): 4.0
+      inference time (ms/im):
+        - value: 14.3
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (320, 320)
+      Epochs: 600
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 21.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/ssd/ssdlite_mobilenetv2_scratch_600e_coco/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth
diff --git a/configs/ssd/ssd300_coco copy.py b/configs/ssd/ssd300_coco copy.py
new file mode 100644
index 0000000..111737b
--- /dev/null
+++ b/configs/ssd/ssd300_coco copy.py	
@@ -0,0 +1,71 @@
+_base_ = [
+    '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = '/data/dongzhiwei1/dataset/coco2017/'
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(300, 300),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=80,
+    workers_per_gpu=16,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict(_delete_=True)
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(type='CheckInvalidLossHook', interval=800, priority='VERY_LOW')
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/ssd/ssd300_coco_quant_w2a2.py b/configs/ssd/ssd300_coco_quant_w2a2.py
new file mode 100644
index 0000000..ff0d2a8
--- /dev/null
+++ b/configs/ssd/ssd300_coco_quant_w2a2.py
@@ -0,0 +1,76 @@
+_base_ = [
+    '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_qat_w2a2.py', '../_base_/default_runtime.py', 'ssd300_quant_general.py'
+]
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = '/data/dongzhiwei1/dataset/coco2017/'
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(300, 300),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=16,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+# optimizer = dict(type='SGD', lr=1e-2, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict(_delete_=True)
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(type='CheckInvalidLossHook', interval=800, priority='VERY_LOW')
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
+
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)], metric='bbox')
+checkpoint_config = dict(interval=9)
+
+load_from = '../long_used_pretrained/ssd300_coco_20210803_015428-d231a06e.pth'
diff --git a/configs/ssd/ssd300_coco_quant_w4a4.py b/configs/ssd/ssd300_coco_quant_w4a4.py
new file mode 100644
index 0000000..87108fc
--- /dev/null
+++ b/configs/ssd/ssd300_coco_quant_w4a4.py
@@ -0,0 +1,76 @@
+_base_ = [
+    '../_base_/models/ssd300.py', '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_qat_w4a4.py', '../_base_/default_runtime.py', 'ssd300_quant_general.py'
+]
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = '/data/dongzhiwei1/dataset/coco2017/'
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(300, 300),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=64,
+    workers_per_gpu=16,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+# optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict(_delete_=True)
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(type='CheckInvalidLossHook', interval=800, priority='VERY_LOW')
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
+
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)], metric='bbox')
+checkpoint_config = dict(interval=9)
+
+load_from = '../long_used_pretrained/ssd300_coco_20210803_015428-d231a06e.pth'
diff --git a/configs/ssd/ssd300_fp16_coco.py b/configs/ssd/ssd300_fp16_coco.py
new file mode 100644
index 0000000..7c53af4
--- /dev/null
+++ b/configs/ssd/ssd300_fp16_coco.py
@@ -0,0 +1,9 @@
+_base_ = ['./ssd300_coco.py']
+
+fp16 = dict(loss_scale='dynamic')
+
+# learning policy
+# In order to avoid non-convergence in the early stage of
+# mixed-precision training, the warmup in the lr_config is set to linear,
+# warmup_iters increases and warmup_ratio decreases.
+lr_config = dict(warmup='linear', warmup_iters=1000, warmup_ratio=1.0 / 10)
diff --git a/configs/ssd/ssd300_quant_general.py b/configs/ssd/ssd300_quant_general.py
new file mode 100644
index 0000000..4abc076
--- /dev/null
+++ b/configs/ssd/ssd300_quant_general.py
@@ -0,0 +1,40 @@
+trace_config = dict(
+    backbone_detail = dict(
+        input_concrete_args = dict(),
+        preserve_attr = ['act_cfg', 'arch_settings', 'conv_cfg', 'bn_eval', 'is_init', 'bn_frozen', 'extra_setting', 'frozen_stages', 'init_cfg', 'inplanes'
+        , 'out_feature_indices', 'out_indices', 'range_sub_modules', 'stage_blocks'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = [],  
+        specified_general_quantizers = [],
+        last_8bit_module = []
+    )),
+    neck_detail = dict(
+        input_concrete_args = dict(in_num = 2),
+        preserve_attr = ['init_cfg', 'is_init', 'l2_norm'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = [],  
+        specified_general_quantizers = ['getitem_1_post_act_fake_quantizer'],
+        last_8bit_module = []
+    )),
+    bbox_head_detail = dict(
+        input_concrete_args = dict(in_num = 6),
+        preserve_attr = ['simple_test', 'num_classes', 'assigner', 'bbox_coder', 'cls_focal_loss', 'cls_out_channels', 'conv_cfg', 'feat_channels', 
+        'fp16_enabled', 'in_channels', 'init_cfg', 'is_init', 'norm_cfg', 'num_base_priors', 'prior_generator', 'reg_decoded_bbox'
+        , 'sampler', 'sampling', 'stacked_convs', 'test_cfg', 'train_cfg', 'use_depthwise', 'use_sigmoid_cls', 'async_simple_test_rpn', 'aug_test', 
+        'aug_test_bboxes', 'aug_test_rpn', 'forward_single', 'forward_train', 'get_bboxes', 'get_targets', 'loss', 'loss_single', 'merge_aug_bboxes', 
+        'simple_test_bboxes', 'simple_test_rpn', '_get_bboxes_single', '_bbox_post_process', 'get_anchors', '_get_targets_single'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(
+        # exclude_prefixes = ['cls_convs.0.0', 'cls_convs.1.0', 'cls_convs.2.0', 'cls_convs.3.0', 'cls_convs.4.0', 'cls_convs.5.0'],   # 只禁用cls分支 
+        # exclude_prefixes = ['reg_convs.0.0', 'reg_convs.1.0', 'reg_convs.2.0', 'reg_convs.3.0', 'reg_convs.4.0', 'reg_convs.5.0'], # 只禁用reg分支 
+        
+        qloss_flag = True, 
+        specified_general_quantizers = ['getitem_post_act_fake_quantizer', 'getitem_1_post_act_fake_quantizer', 'getitem_2_post_act_fake_quantizer'
+                         , 'getitem_3_post_act_fake_quantizer', 'getitem_4_post_act_fake_quantizer', 'getitem_5_post_act_fake_quantizer'],
+        last_8bit_module = ['cls_convs.0.0', 'cls_convs.1.0', 'cls_convs.2.0', 'cls_convs.3.0', 'cls_convs.4.0'
+                            , 'cls_convs.5.0', 'reg_convs.0.0', 'reg_convs.1.0',  'reg_convs.2.0', 'reg_convs.3.0', 'reg_convs.4.0',  'reg_convs.5.0']
+    )))
\ No newline at end of file
diff --git a/configs/ssd/ssd300_voc0712.py b/configs/ssd/ssd300_voc0712.py
new file mode 100644
index 0000000..ae13b69
--- /dev/null
+++ b/configs/ssd/ssd300_voc0712.py
@@ -0,0 +1,94 @@
+_base_ = [
+    '../_base_/models/ssd300_20.py', '../_base_/datasets/voc0712.py',
+    '../_base_/schedules/schedule_2x.py', '../_base_/default_runtime.py'
+]
+# dataset settings
+dataset_type = 'VOCDataset'
+data_root = '/workspace/share/datasets/VOC/VOCdevkit/'
+classes = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
+               'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
+               'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
+               'tvmonitor')
+
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(300, 300),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=80,
+    workers_per_gpu=16,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=[
+                data_root + 'VOC2007/ImageSets/Main/trainval.txt',
+                data_root + 'VOC2012/ImageSets/Main/trainval.txt'
+            ],
+            img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
+            pipeline=train_pipeline)),
+    val=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',  # 注意这里走的是COCO格式
+        img_prefix=data_root,
+        classes=classes,
+        pipeline=test_pipeline),
+    test=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',
+        img_prefix=data_root,
+        classes=classes,
+        pipeline=test_pipeline))
+# optimizer
+# optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict(_delete_=True)
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(type='CheckInvalidLossHook', interval=800, priority='VERY_LOW')
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
+
+# evaluation = dict(interval=1, metric='mAP')  # 这个只有AP50，或者叫mAP
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
diff --git a/configs/ssd/ssd300_voc0712_harmony.py b/configs/ssd/ssd300_voc0712_harmony.py
new file mode 100644
index 0000000..338c8b9
--- /dev/null
+++ b/configs/ssd/ssd300_voc0712_harmony.py
@@ -0,0 +1,95 @@
+_base_ = [
+    '../_base_/models/ssd300_20.py', '../_base_/datasets/voc0712.py',
+    '../_base_/schedules/schedule_fp32_fine_tune_general.py', '../_base_/default_runtime.py'
+]
+# dataset settings
+dataset_type = 'VOCDataset'
+data_root = '/workspace/share/datasets/VOC/VOCdevkit/'
+classes = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
+               'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
+               'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
+               'tvmonitor')
+
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(300, 300),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=[
+                data_root + 'VOC2007/ImageSets/Main/trainval.txt',
+                data_root + 'VOC2012/ImageSets/Main/trainval.txt'
+            ],
+            img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
+            pipeline=train_pipeline)),
+    val=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',  # 注意这里走的是COCO格式
+        img_prefix=data_root,
+        classes=classes,
+        pipeline=test_pipeline),
+    test=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',
+        img_prefix=data_root,
+        classes=classes,
+        pipeline=test_pipeline))
+# optimizer
+# optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict(_delete_=True)
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(type='CheckInvalidLossHook', interval=800, priority='VERY_LOW')
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
+checkpoint_config = dict(interval=10)
+# evaluation = dict(interval=1, metric='mAP')  # 这个只有AP50，或者叫mAP
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+load_from = '/workspace/share/long_dir/ssd300_voc0712/epoch_23.pth'
diff --git a/configs/ssd/ssd300_voc0712_quant_w4a4.py b/configs/ssd/ssd300_voc0712_quant_w4a4.py
new file mode 100644
index 0000000..d64dc34
--- /dev/null
+++ b/configs/ssd/ssd300_voc0712_quant_w4a4.py
@@ -0,0 +1,94 @@
+_base_ = [
+    '../_base_/models/ssd300_20.py', '../_base_/datasets/voc0712.py',
+    '../_base_/schedules/schedule_qat_w4a4.py', '../_base_/default_runtime.py', 'ssd300_quant_general.py'
+]
+# dataset settings
+dataset_type = 'VOCDataset'
+data_root = '/workspace/share/datasets/VOC/VOCdevkit/'
+classes = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
+               'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
+               'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
+               'tvmonitor')
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(300, 300), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(300, 300),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=48,
+    workers_per_gpu=8,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=[
+                data_root + 'VOC2007/ImageSets/Main/trainval.txt',
+                data_root + 'VOC2012/ImageSets/Main/trainval.txt'
+            ],
+            img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
+            pipeline=train_pipeline)),
+    val=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',  # 注意这里走的是COCO格式
+        img_prefix=data_root,
+        classes=classes,
+        pipeline=test_pipeline),
+    test=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',
+        img_prefix=data_root,
+        classes=classes,
+        pipeline=test_pipeline))
+# optimizer
+# optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict(_delete_=True)
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(type='CheckInvalidLossHook', interval=800, priority='VERY_LOW')
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
+
+# evaluation = dict(interval=1, metric='mAP')  # 这个只有AP50，或者叫mAP
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+load_from = '/workspace/share/long_dir/ssd300_voc0712/epoch_23.pth'
diff --git a/configs/ssd/ssd512_coco.py b/configs/ssd/ssd512_coco.py
new file mode 100644
index 0000000..117777f
--- /dev/null
+++ b/configs/ssd/ssd512_coco.py
@@ -0,0 +1,84 @@
+_base_ = 'ssd300_coco.py'
+input_size = 512
+model = dict(
+    neck=dict(
+        out_channels=(512, 1024, 512, 256, 256, 256, 256),
+        level_strides=(2, 2, 2, 2, 1),
+        level_paddings=(1, 1, 1, 1, 1),
+        last_kernel_size=4),
+    bbox_head=dict(
+        in_channels=(512, 1024, 512, 256, 256, 256, 256),
+        anchor_generator=dict(
+            type='SSDAnchorGenerator',
+            scale_major=False,
+            input_size=input_size,
+            basesize_ratio_range=(0.1, 0.9),
+            strides=[8, 16, 32, 64, 128, 256, 512],
+            ratios=[[2], [2, 3], [2, 3], [2, 3], [2, 3], [2], [2]])))
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(512, 512), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(512, 512),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=3,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=2e-3, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict(_delete_=True)
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW')
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/ssd/ssd512_fp16_coco.py b/configs/ssd/ssd512_fp16_coco.py
new file mode 100644
index 0000000..a74434e
--- /dev/null
+++ b/configs/ssd/ssd512_fp16_coco.py
@@ -0,0 +1,9 @@
+_base_ = ['./ssd512_coco.py']
+# fp16 settings
+fp16 = dict(loss_scale='dynamic')
+
+# learning policy
+# In order to avoid non-convergence in the early stage of
+# mixed-precision training, the warmup in the lr_config is set to linear,
+# warmup_iters increases and warmup_ratio decreases.
+lr_config = dict(warmup='linear', warmup_iters=1000, warmup_ratio=1.0 / 10)
diff --git a/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py b/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py
new file mode 100644
index 0000000..5eb6b03
--- /dev/null
+++ b/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py
@@ -0,0 +1,150 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    type='SingleStageDetector',
+    backbone=dict(
+        type='MobileNetV2',
+        out_indices=(4, 7),
+        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
+        init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
+    neck=dict(
+        type='SSDNeck',
+        in_channels=(96, 1280),
+        out_channels=(96, 1280, 512, 256, 256, 128),
+        level_strides=(2, 2, 2, 2),
+        level_paddings=(1, 1, 1, 1),
+        l2_norm_scale=None,
+        use_depthwise=True,
+        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
+        act_cfg=dict(type='ReLU6'),
+        init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
+    bbox_head=dict(
+        type='SSDHead',
+        in_channels=(96, 1280, 512, 256, 256, 128),
+        num_classes=80,
+        use_depthwise=True,
+        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
+        act_cfg=dict(type='ReLU6'),
+        init_cfg=dict(type='Normal', layer='Conv2d', std=0.001),
+
+        # set anchor size manually instead of using the predefined
+        # SSD300 setting.
+        anchor_generator=dict(
+            type='SSDAnchorGenerator',
+            scale_major=False,
+            strides=[16, 32, 64, 107, 160, 320],
+            ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]],
+            min_sizes=[48, 100, 150, 202, 253, 304],
+            max_sizes=[100, 150, 202, 253, 304, 320]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2])),
+    # model training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.5,
+            min_pos_iou=0.,
+            ignore_iof_thr=-1,
+            gt_max_assign_all=False),
+        smoothl1_beta=1.,
+        allowed_border=-1,
+        pos_weight=-1,
+        neg_pos_ratio=3,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        nms=dict(type='nms', iou_threshold=0.45),
+        min_bbox_size=0,
+        score_thr=0.02,
+        max_per_img=200))
+# cudnn_benchmark = True
+
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(320, 320), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=320),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(320, 320),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=320),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=24,
+    workers_per_gpu=4,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',  # use RepeatDataset to speed up training
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+# optimizer
+optimizer = dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=4.0e-5)
+optimizer_config = dict(grad_clip=None)
+
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    min_lr=0)
+runner = dict(type='EpochBasedRunner', max_epochs=120)
+
+# Avoid evaluation and saving weights too frequently
+evaluation = dict(interval=5, metric='bbox')
+checkpoint_config = dict(interval=10)
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW')
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (24 samples per GPU)
+auto_scale_lr = dict(base_batch_size=192)
diff --git a/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco_quant_w4a4.py b/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco_quant_w4a4.py
new file mode 100644
index 0000000..59f4310
--- /dev/null
+++ b/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco_quant_w4a4.py
@@ -0,0 +1,152 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_qat_w4a4.py'
+    , 'ssd300_quant_general.py'
+]
+
+model = dict(
+    type='SingleStageDetector',
+    backbone=dict(
+        type='MobileNetV2',
+        out_indices=(4, 7),
+        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
+        init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
+    neck=dict(
+        type='SSDNeck',
+        in_channels=(96, 1280),
+        out_channels=(96, 1280, 512, 256, 256, 128),
+        level_strides=(2, 2, 2, 2),
+        level_paddings=(1, 1, 1, 1),
+        l2_norm_scale=None,
+        use_depthwise=True,
+        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
+        act_cfg=dict(type='ReLU6'),
+        init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
+    bbox_head=dict(
+        type='SSDHead',
+        in_channels=(96, 1280, 512, 256, 256, 128),
+        num_classes=80,
+        use_depthwise=True,
+        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
+        act_cfg=dict(type='ReLU6'),
+        init_cfg=dict(type='Normal', layer='Conv2d', std=0.001),
+
+        # set anchor size manually instead of using the predefined
+        # SSD300 setting.
+        anchor_generator=dict(
+            type='SSDAnchorGenerator',
+            scale_major=False,
+            strides=[16, 32, 64, 107, 160, 320],
+            ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]],
+            min_sizes=[48, 100, 150, 202, 253, 304],
+            max_sizes=[100, 150, 202, 253, 304, 320]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2])),
+    # model training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.5,
+            min_pos_iou=0.,
+            ignore_iof_thr=-1,
+            gt_max_assign_all=False),
+        smoothl1_beta=1.,
+        allowed_border=-1,
+        pos_weight=-1,
+        neg_pos_ratio=3,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        nms=dict(type='nms', iou_threshold=0.45),
+        min_bbox_size=0,
+        score_thr=0.02,
+        max_per_img=200))
+# cudnn_benchmark = True
+
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = '/data/dongzhiwei1/dataset/coco2017/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(320, 320), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=320),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(320, 320),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=320),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=40,
+    workers_per_gpu=8,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',  # use RepeatDataset to speed up training
+        times=2,  # repeat，相当于训了好几次
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+# optimizer
+# optimizer = dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=4.0e-5)
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+
+# # learning policy
+# lr_config = dict(
+#     policy='CosineAnnealing',
+#     warmup='linear',
+#     warmup_iters=500,
+#     warmup_ratio=0.001,
+#     min_lr=0)
+# runner = dict(type='EpochBasedRunner', max_epochs=120)
+
+# Avoid evaluation and saving weights too frequently
+evaluation = dict(save_best='auto', interval=10, dynamic_intervals=[(11, 1)], metric='bbox')
+checkpoint_config = dict(interval=9)
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW')
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (24 samples per GPU)
+auto_scale_lr = dict(base_batch_size=192)
+load_from = '../long_used_pretrained/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth'
diff --git a/configs/ssd/ssdlite_mobilenetv2_scratch_600e_voc.py b/configs/ssd/ssdlite_mobilenetv2_scratch_600e_voc.py
new file mode 100644
index 0000000..5cf49b7
--- /dev/null
+++ b/configs/ssd/ssdlite_mobilenetv2_scratch_600e_voc.py
@@ -0,0 +1,168 @@
+_base_ = [
+    '../_base_/datasets/voc0712.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    type='SingleStageDetector',
+    backbone=dict(
+        type='MobileNetV2',
+        out_indices=(4, 7),
+        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
+        init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
+    neck=dict(
+        type='SSDNeck',
+        in_channels=(96, 1280),
+        out_channels=(96, 1280, 512, 256, 256, 128),
+        level_strides=(2, 2, 2, 2),
+        level_paddings=(1, 1, 1, 1),
+        l2_norm_scale=None,
+        use_depthwise=True,
+        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
+        act_cfg=dict(type='ReLU6'),
+        init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
+    bbox_head=dict(
+        type='SSDHead',
+        in_channels=(96, 1280, 512, 256, 256, 128),
+        num_classes=20,
+        use_depthwise=True,
+        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
+        act_cfg=dict(type='ReLU6'),
+        init_cfg=dict(type='Normal', layer='Conv2d', std=0.001),
+
+        # set anchor size manually instead of using the predefined
+        # SSD300 setting.
+        anchor_generator=dict(
+            type='SSDAnchorGenerator',
+            scale_major=False,
+            strides=[16, 32, 64, 107, 160, 320],
+            ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]],
+            min_sizes=[48, 100, 150, 202, 253, 304],
+            max_sizes=[100, 150, 202, 253, 304, 320]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2])),
+    # model training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.5,
+            min_pos_iou=0.,
+            ignore_iof_thr=-1,
+            gt_max_assign_all=False),
+        smoothl1_beta=1.,
+        allowed_border=-1,
+        pos_weight=-1,
+        neg_pos_ratio=3,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        nms=dict(type='nms', iou_threshold=0.45),
+        min_bbox_size=0,
+        score_thr=0.02,
+        max_per_img=200))
+# cudnn_benchmark = True
+
+# dataset settings
+dataset_type = 'VOCDataset'
+data_root = '/workspace/share/datasets/VOC/VOCdevkit/'
+classes = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
+               'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
+               'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
+               'tvmonitor')
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(320, 320), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=320),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(320, 320),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=320),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=120,
+    workers_per_gpu=24,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',  # use RepeatDataset to speed up training
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=[
+                data_root + 'VOC2007/ImageSets/Main/trainval.txt',
+                data_root + 'VOC2012/ImageSets/Main/trainval.txt'
+            ],
+            img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
+            pipeline=train_pipeline)),
+    val=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',  # 注意这里走的是COCO格式
+        img_prefix=data_root,
+        classes=classes,
+        pipeline=test_pipeline),
+    test=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',
+        img_prefix=data_root,
+        classes=classes,
+        pipeline=test_pipeline))
+
+# optimizer
+optimizer = dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=4.0e-5)
+optimizer_config = dict(grad_clip=None)
+
+# learning policy
+lr_config = dict(
+    policy='CosineAnnealing',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    min_lr=0)
+runner = dict(type='EpochBasedRunner', max_epochs=30)
+
+# Avoid evaluation and saving weights too frequently
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(type='CheckInvalidLossHook', interval=50, priority='VERY_LOW')
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (24 samples per GPU)
+auto_scale_lr = dict(base_batch_size=192)
+# load_from = '../long_used_pretrained/ssdlite_mobilenetv2_scratch_600e_coco_20210629_110627-974d9307.pth'
diff --git a/configs/ssd/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4.py b/configs/ssd/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4.py
new file mode 100644
index 0000000..86048f0
--- /dev/null
+++ b/configs/ssd/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4.py
@@ -0,0 +1,169 @@
+_base_ = [
+    '../_base_/datasets/voc0712.py', '../_base_/default_runtime.py', '../_base_/schedules/schedule_qat_w4a4.py'
+    , 'ssdlite_quant_general.py'
+]
+
+model = dict(
+    type='SingleStageDetector',
+    backbone=dict(
+        type='MobileNetV2',
+        out_indices=(4, 7),
+        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
+        init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
+    neck=dict(
+        type='SSDNeck',
+        in_channels=(96, 1280),
+        out_channels=(96, 1280, 512, 256, 256, 128),
+        level_strides=(2, 2, 2, 2),
+        level_paddings=(1, 1, 1, 1),
+        l2_norm_scale=None,
+        use_depthwise=True,
+        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
+        act_cfg=dict(type='ReLU6'),
+        init_cfg=dict(type='TruncNormal', layer='Conv2d', std=0.03)),
+    bbox_head=dict(
+        type='SSDHead',
+        in_channels=(96, 1280, 512, 256, 256, 128),
+        num_classes=20,
+        use_depthwise=True,
+        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
+        act_cfg=dict(type='ReLU6'),
+        init_cfg=dict(type='Normal', layer='Conv2d', std=0.001),
+
+        # set anchor size manually instead of using the predefined
+        # SSD300 setting.
+        anchor_generator=dict(
+            type='SSDAnchorGenerator',
+            scale_major=False,
+            strides=[16, 32, 64, 107, 160, 320],
+            ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]],
+            min_sizes=[48, 100, 150, 202, 253, 304],
+            max_sizes=[100, 150, 202, 253, 304, 320]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2])),
+    # model training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.5,
+            min_pos_iou=0.,
+            ignore_iof_thr=-1,
+            gt_max_assign_all=False),
+        smoothl1_beta=1.,
+        allowed_border=-1,
+        pos_weight=-1,
+        neg_pos_ratio=3,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        nms=dict(type='nms', iou_threshold=0.45),
+        min_bbox_size=0,
+        score_thr=0.02,
+        max_per_img=200))
+# cudnn_benchmark = True
+
+# dataset settings
+dataset_type = 'VOCDataset'
+data_root = '/workspace/share/datasets/VOC/VOCdevkit/'
+classes = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
+               'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
+               'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
+               'tvmonitor')
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(320, 320), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=320),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(320, 320),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=320),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=80,
+    workers_per_gpu=16,
+    train=dict(
+        _delete_=True,
+        type='RepeatDataset',  # use RepeatDataset to speed up training
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=[
+                data_root + 'VOC2007/ImageSets/Main/trainval.txt',
+                data_root + 'VOC2012/ImageSets/Main/trainval.txt'
+            ],
+            img_prefix=[data_root + 'VOC2007/', data_root + 'VOC2012/'],
+            pipeline=train_pipeline)),
+    val=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',  # 注意这里走的是COCO格式
+        img_prefix=data_root,
+        classes=classes,
+        pipeline=test_pipeline),
+    test=dict(
+        type='CocoDataset',
+        ann_file=data_root + 'coco_format/voc07_test.json',
+        img_prefix=data_root,
+        classes=classes,
+        pipeline=test_pipeline))
+
+# optimizer
+# optimizer = dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=4.0e-5)
+optimizer_config = dict(grad_clip=None)
+
+# # learning policy
+# lr_config = dict(
+#     policy='CosineAnnealing',
+#     warmup='linear',
+#     warmup_iters=500,
+#     warmup_ratio=0.001,
+#     min_lr=0)
+# runner = dict(type='EpochBasedRunner', max_epochs=120)
+
+# Avoid evaluation and saving weights too frequently
+evaluation = dict(save_best='auto', interval=1, metric='bbox')
+checkpoint_config = dict(interval=10)
+custom_hooks = [
+    dict(type='NumClassCheckHook'),
+    dict(type='CheckInvalidLossHook', interval=200, priority='VERY_LOW')
+]
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (24 samples per GPU)
+auto_scale_lr = dict(base_batch_size=192)
+load_from = '/workspace/share/long_dir/ssdlite_mobilenetv2_scratch_600e_voc/best_bbox_mAP_epoch_30.pth'
diff --git a/configs/ssd/ssdlite_quant_general.py b/configs/ssd/ssdlite_quant_general.py
new file mode 100644
index 0000000..5ed7516
--- /dev/null
+++ b/configs/ssd/ssdlite_quant_general.py
@@ -0,0 +1,40 @@
+trace_config = dict(
+    backbone_detail = dict(
+        input_concrete_args = dict(),
+        preserve_attr = ['act_cfg', 'arch_settings', 'conv_cfg', 'bn_eval', 'is_init', 'bn_frozen', 'extra_setting', 'frozen_stages', 'init_cfg', 'inplanes'
+        , 'out_feature_indices', 'out_indices', 'range_sub_modules', 'stage_blocks'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = [],  
+        specified_general_quantizers = [],
+        last_8bit_module = []
+    )),
+    neck_detail = dict(
+        input_concrete_args = dict(in_num = 2),
+        preserve_attr = ['init_cfg', 'is_init', 'l2_norm'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = ['getitem_0_post_act_fake_quantizer'],  
+        specified_general_quantizers = ['getitem_1_post_act_fake_quantizer'],  # 0号有负
+        last_8bit_module = []
+    )),
+    bbox_head_detail = dict(
+        input_concrete_args = dict(in_num = 6),
+        preserve_attr = ['simple_test', 'num_classes', 'assigner', 'bbox_coder', 'cls_focal_loss', 'cls_out_channels', 'conv_cfg', 'feat_channels', 
+        'fp16_enabled', 'in_channels', 'init_cfg', 'is_init', 'norm_cfg', 'num_base_priors', 'prior_generator', 'reg_decoded_bbox'
+        , 'sampler', 'sampling', 'stacked_convs', 'test_cfg', 'train_cfg', 'use_depthwise', 'use_sigmoid_cls', 'async_simple_test_rpn', 'aug_test', 
+        'aug_test_bboxes', 'aug_test_rpn', 'forward_single', 'forward_train', 'get_bboxes', 'get_targets', 'loss', 'loss_single', 'merge_aug_bboxes', 
+        'simple_test_bboxes', 'simple_test_rpn', '_get_bboxes_single', '_bbox_post_process', 'get_anchors', '_get_targets_single'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = ['getitem_post_act_fake_quantizer','getitem_1_post_act_fake_quantizer','getitem_2_post_act_fake_quantizer','getitem_3_post_act_fake_quantizer','getitem_4_post_act_fake_quantizer'],  
+        qloss_flag = True, 
+        # specified_general_quantizers = ['getitem_post_act_fake_quantizer', 'getitem_1_post_act_fake_quantizer', 'getitem_2_post_act_fake_quantizer'
+        #                  , 'getitem_3_post_act_fake_quantizer', 'getitem_4_post_act_fake_quantizer', 'getitem_5_post_act_fake_quantizer'],
+        specified_general_quantizers = ['getitem_5_post_act_fake_quantizer'],  # 0号有负的
+        last_8bit_module = ['cls_convs.0.1', 'cls_convs.1.1', 'cls_convs.2.1', 'cls_convs.3.1', 'cls_convs.4.1'
+                            , 'cls_convs.5.1', 'reg_convs.0.1', 'reg_convs.1.1',  'reg_convs.2.1', 'reg_convs.3.1', 'reg_convs.4.1',  'reg_convs.5.1']  # fold不会影响 似乎
+    )))
\ No newline at end of file
diff --git a/configs/strong_baselines/README.md b/configs/strong_baselines/README.md
new file mode 100644
index 0000000..aa2550d
--- /dev/null
+++ b/configs/strong_baselines/README.md
@@ -0,0 +1,20 @@
+# Strong Baselines
+
+<!-- [OTHERS] -->
+
+We train Mask R-CNN with large-scale jitter and longer schedule as strong baselines.
+The modifications follow those in [Detectron2](https://github.com/facebookresearch/detectron2/tree/master/configs/new_baselines).
+
+## Results and Models
+
+| Backbone |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP | mask AP |                                  Config                                   |         Download         |
+| :------: | :-----: | :-----: | :------: | :------------: | :----: | :-----: | :-----------------------------------------------------------------------: | :----------------------: |
+| R-50-FPN | pytorch |   50e   |          |                |        |         |    [config](./mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_50e_coco.py)     | [model](<>) \| [log](<>) |
+| R-50-FPN | pytorch |  100e   |          |                |        |         |    [config](./mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py)    | [model](<>) \| [log](<>) |
+| R-50-FPN |  caffe  |  100e   |          |                |  44.7  |  40.4   | [config](./mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py) | [model](<>) \| [log](<>) |
+| R-50-FPN |  caffe  |  400e   |          |                |        |         | [config](./mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_400e_coco.py) | [model](<>) \| [log](<>) |
+
+## Notice
+
+When using large-scale jittering, there are sometimes empty proposals in the box and mask heads during training.
+This requires MMSyncBN that allows empty tensors. Therefore, please use mmcv-full>=1.3.14 to train models supported in this directory.
diff --git a/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py b/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py
new file mode 100644
index 0000000..a40d6a0
--- /dev/null
+++ b/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py
@@ -0,0 +1,80 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../common/lsj_100e_coco_instance.py'
+]
+
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+# Use MMSyncBN that handles empty tensor in head. It can be changed to
+# SyncBN after https://github.com/pytorch/pytorch/issues/36530 is fixed
+# Requires MMCV-full after  https://github.com/open-mmlab/mmcv/pull/1205.
+head_norm_cfg = dict(type='MMSyncBN', requires_grad=True)
+model = dict(
+    backbone=dict(
+        frozen_stages=-1,
+        norm_eval=False,
+        norm_cfg=norm_cfg,
+        init_cfg=None,
+        style='caffe'),
+    neck=dict(norm_cfg=norm_cfg),
+    rpn_head=dict(num_convs=2),
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            conv_out_channels=256,
+            norm_cfg=head_norm_cfg),
+        mask_head=dict(norm_cfg=head_norm_cfg)))
+
+file_client_args = dict(backend='disk')
+# file_client_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection/',
+#         'data/': 's3://openmmlab/datasets/detection/'
+#     }))
+
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+image_size = (1024, 1024)
+train_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=image_size,
+        ratio_range=(0.1, 2.0),
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(
+        type='RandomCrop',
+        crop_type='absolute_range',
+        crop_size=image_size,
+        recompute_bbox=True,
+        allow_negative_crop=True),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size=image_size),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', file_client_args=file_client_args),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+
+# Use RepeatDataset to speed up training
+data = dict(
+    train=dict(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py b/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py
new file mode 100644
index 0000000..31824eb
--- /dev/null
+++ b/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py
@@ -0,0 +1,2 @@
+_base_ = 'mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
+fp16 = dict(loss_scale=512.)
diff --git a/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_400e_coco.py b/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_400e_coco.py
new file mode 100644
index 0000000..1211925
--- /dev/null
+++ b/configs/strong_baselines/mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_400e_coco.py
@@ -0,0 +1,6 @@
+_base_ = './mask_rcnn_r50_caffe_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
+
+# Use RepeatDataset to speed up training
+# change repeat time from 4 (for 100 epochs) to 16 (for 400 epochs)
+data = dict(train=dict(times=4 * 4))
+lr_config = dict(warmup_iters=500 * 4)
diff --git a/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py b/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py
new file mode 100644
index 0000000..4a15d69
--- /dev/null
+++ b/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py
@@ -0,0 +1,22 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../common/lsj_100e_coco_instance.py'
+]
+
+norm_cfg = dict(type='SyncBN', requires_grad=True)
+# Use MMSyncBN that handles empty tensor in head. It can be changed to
+# SyncBN after https://github.com/pytorch/pytorch/issues/36530 is fixed
+# Requires MMCV-full after  https://github.com/open-mmlab/mmcv/pull/1205.
+head_norm_cfg = dict(type='MMSyncBN', requires_grad=True)
+model = dict(
+    # the model is trained from scratch, so init_cfg is None
+    backbone=dict(
+        frozen_stages=-1, norm_eval=False, norm_cfg=norm_cfg, init_cfg=None),
+    neck=dict(norm_cfg=norm_cfg),
+    rpn_head=dict(num_convs=2),  # leads to 0.1+ mAP
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared4Conv1FCBBoxHead',
+            conv_out_channels=256,
+            norm_cfg=head_norm_cfg),
+        mask_head=dict(norm_cfg=head_norm_cfg)))
diff --git a/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py b/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py
new file mode 100644
index 0000000..7b97960
--- /dev/null
+++ b/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_fp16_coco.py
@@ -0,0 +1,3 @@
+_base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
+# use FP16
+fp16 = dict(loss_scale=512.)
diff --git a/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_50e_coco.py b/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_50e_coco.py
new file mode 100644
index 0000000..922579a
--- /dev/null
+++ b/configs/strong_baselines/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_50e_coco.py
@@ -0,0 +1,5 @@
+_base_ = 'mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_lsj_100e_coco.py'
+
+# Use RepeatDataset to speed up training
+# change repeat time from 4 (for 100 epochs) to 2 (for 50 epochs)
+data = dict(train=dict(times=2))
diff --git a/configs/swin/README.md b/configs/swin/README.md
new file mode 100644
index 0000000..2136134
--- /dev/null
+++ b/configs/swin/README.md
@@ -0,0 +1,41 @@
+# Swin
+
+> [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://arxiv.org/abs/2103.14030)
+
+<!-- [BACKBONE] -->
+
+## Abstract
+
+This paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such as large variations in the scale of visual entities and the high resolution of pixels in images compared to words in text. To address these differences, we propose a hierarchical Transformer whose representation is computed with Shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local windows while also allowing for cross-window connection. This hierarchical architecture has the flexibility to model at various scales and has linear computational complexity with respect to image size. These qualities of Swin Transformer make it compatible with a broad range of vision tasks, including image classification (87.3 top-1 accuracy on ImageNet-1K) and dense prediction tasks such as object detection (58.7 box AP and 51.1 mask AP on COCO test-dev) and semantic segmentation (53.5 mIoU on ADE20K val). Its performance surpasses the previous state-of-the-art by a large margin of +2.7 box AP and +2.6 mask AP on COCO, and +3.2 mIoU on ADE20K, demonstrating the potential of Transformer-based models as vision backbones. The hierarchical design and the shifted window approach also prove beneficial for all-MLP architectures.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143999551-6a527048-de38-485c-a1b6-3133ffa5bfaa.png"/>
+</div>
+
+## Results and Models
+
+### Mask R-CNN
+
+| Backbone |  Pretrain   | Lr schd | Multi-scale crop | FP16 | Mem (GB) | Inf time (fps) | box AP | mask AP |                             Config                             |                                                                                                                                                                                      Download                                                                                                                                                                                       |
+| :------: | :---------: | :-----: | :--------------: | :--: | :------: | :------------: | :----: | :-----: | :------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|  Swin-T  | ImageNet-1K |   1x    |        no        |  no  |   7.6    |                |  42.7  |  39.3   |       [config](./mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py)        |                           [model](https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco/mask_rcnn_swin-t-p4-w7_fpn_1x_coco_20210902_120937-9d6b7cfa.pth)  \| [log](https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco/mask_rcnn_swin-t-p4-w7_fpn_1x_coco_20210902_120937.log.json)                           |
+|  Swin-T  | ImageNet-1K |   3x    |       yes        |  no  |   10.2   |                |  46.0  |  41.6   |   [config](./mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py)    |           [model](https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco_20210906_131725-bacf6f7b.pth)  \| [log](https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco_20210906_131725.log.json)           |
+|  Swin-T  | ImageNet-1K |   3x    |       yes        | yes  |   7.8    |                |  46.0  |  41.7   | [config](./mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco_20210908_165006-90a4008c.pth)  \| [log](https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco_20210908_165006.log.json) |
+|  Swin-S  | ImageNet-1K |   3x    |       yes        | yes  |   11.9   |                |  48.2  |  43.2   | [config](./mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco_20210903_104808-b92c91f1.pth)  \| [log](https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco_20210903_104808.log.json) |
+
+### Notice
+
+Please follow the example
+of `retinanet_swin-t-p4-w7_fpn_1x_coco.py` when you want to combine Swin Transformer with
+the one-stage detector. Because there is a layer norm at the outs of Swin Transformer, you must set `start_level` as 0 in FPN, so we have to set the `out_indices` of backbone as `[1,2,3]`.
+
+## Citation
+
+```latex
+@article{liu2021Swin,
+    title={Swin Transformer: Hierarchical Vision Transformer using Shifted Windows},
+    author={Liu, Ze and Lin, Yutong and Cao, Yue and Hu, Han and Wei, Yixuan and Zhang, Zheng and Lin, Stephen and Guo, Baining},
+    journal={arXiv preprint arXiv:2103.14030},
+    year={2021}
+}
+```
diff --git a/configs/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py b/configs/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py
new file mode 100644
index 0000000..15d50a0
--- /dev/null
+++ b/configs/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py'
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_small_patch4_window7_224.pth'  # noqa
+model = dict(
+    backbone=dict(
+        depths=[2, 2, 18, 2],
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)))
diff --git a/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py b/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py
new file mode 100644
index 0000000..337e858
--- /dev/null
+++ b/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py
@@ -0,0 +1,42 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'  # noqa
+model = dict(
+    type='MaskRCNN',
+    backbone=dict(
+        _delete_=True,
+        type='SwinTransformer',
+        embed_dims=96,
+        depths=[2, 2, 6, 2],
+        num_heads=[3, 6, 12, 24],
+        window_size=7,
+        mlp_ratio=4,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.,
+        attn_drop_rate=0.,
+        drop_path_rate=0.2,
+        patch_norm=True,
+        out_indices=(0, 1, 2, 3),
+        with_cp=False,
+        convert_weights=True,
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    neck=dict(in_channels=[96, 192, 384, 768]))
+
+optimizer = dict(
+    _delete_=True,
+    type='AdamW',
+    lr=0.0001,
+    betas=(0.9, 0.999),
+    weight_decay=0.05,
+    paramwise_cfg=dict(
+        custom_keys={
+            'absolute_pos_embed': dict(decay_mult=0.),
+            'relative_position_bias_table': dict(decay_mult=0.),
+            'norm': dict(decay_mult=0.)
+        }))
+lr_config = dict(warmup_iters=1000, step=[8, 11])
+runner = dict(max_epochs=12)
diff --git a/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py b/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py
new file mode 100644
index 0000000..2be3114
--- /dev/null
+++ b/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py
@@ -0,0 +1,3 @@
+_base_ = './mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py'
+# you need to set mode='dynamic' if you are using pytorch<=1.5.0
+fp16 = dict(loss_scale=dict(init_scale=512))
diff --git a/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py b/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py
new file mode 100644
index 0000000..2612f6e
--- /dev/null
+++ b/configs/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py
@@ -0,0 +1,91 @@
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_instance.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'  # noqa
+
+model = dict(
+    type='MaskRCNN',
+    backbone=dict(
+        _delete_=True,
+        type='SwinTransformer',
+        embed_dims=96,
+        depths=[2, 2, 6, 2],
+        num_heads=[3, 6, 12, 24],
+        window_size=7,
+        mlp_ratio=4,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.,
+        attn_drop_rate=0.,
+        drop_path_rate=0.2,
+        patch_norm=True,
+        out_indices=(0, 1, 2, 3),
+        with_cp=False,
+        convert_weights=True,
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    neck=dict(in_channels=[96, 192, 384, 768]))
+
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+# augmentation strategy originates from DETR / Sparse RCNN
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='AutoAugment',
+        policies=[[
+            dict(
+                type='Resize',
+                img_scale=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
+                           (608, 1333), (640, 1333), (672, 1333), (704, 1333),
+                           (736, 1333), (768, 1333), (800, 1333)],
+                multiscale_mode='value',
+                keep_ratio=True)
+        ],
+                  [
+                      dict(
+                          type='Resize',
+                          img_scale=[(400, 1333), (500, 1333), (600, 1333)],
+                          multiscale_mode='value',
+                          keep_ratio=True),
+                      dict(
+                          type='RandomCrop',
+                          crop_type='absolute_range',
+                          crop_size=(384, 600),
+                          allow_negative_crop=True),
+                      dict(
+                          type='Resize',
+                          img_scale=[(480, 1333), (512, 1333), (544, 1333),
+                                     (576, 1333), (608, 1333), (640, 1333),
+                                     (672, 1333), (704, 1333), (736, 1333),
+                                     (768, 1333), (800, 1333)],
+                          multiscale_mode='value',
+                          override=True,
+                          keep_ratio=True)
+                  ]]),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
+
+optimizer = dict(
+    _delete_=True,
+    type='AdamW',
+    lr=0.0001,
+    betas=(0.9, 0.999),
+    weight_decay=0.05,
+    paramwise_cfg=dict(
+        custom_keys={
+            'absolute_pos_embed': dict(decay_mult=0.),
+            'relative_position_bias_table': dict(decay_mult=0.),
+            'norm': dict(decay_mult=0.)
+        }))
+lr_config = dict(warmup_iters=1000, step=[27, 33])
+runner = dict(max_epochs=36)
diff --git a/configs/swin/metafile.yml b/configs/swin/metafile.yml
new file mode 100644
index 0000000..6c07f17
--- /dev/null
+++ b/configs/swin/metafile.yml
@@ -0,0 +1,120 @@
+Models:
+  - Name: mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco.py
+    Metadata:
+      Training Memory (GB): 11.9
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - AdamW
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Swin Transformer
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 48.2
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 43.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco/mask_rcnn_swin-s-p4-w7_fpn_fp16_ms-crop-3x_coco_20210903_104808-b92c91f1.pth
+    Paper:
+      URL: https://arxiv.org/abs/2107.08430
+      Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
+    README: configs/swin/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/backbones/swin.py#L465
+      Version: v2.16.0
+
+  - Name: mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco.py
+    Metadata:
+      Training Memory (GB): 10.2
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - AdamW
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Swin Transformer
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco/mask_rcnn_swin-t-p4-w7_fpn_ms-crop-3x_coco_20210906_131725-bacf6f7b.pth
+    Paper:
+      URL: https://arxiv.org/abs/2107.08430
+      Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
+    README: configs/swin/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/backbones/swin.py#L465
+      Version: v2.16.0
+
+  - Name: mask_rcnn_swin-t-p4-w7_fpn_1x_coco
+    In Collection: Mask R-CNN
+    Config: configs/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 7.6
+      Epochs: 12
+      Training Data: COCO
+      Training Techniques:
+        - AdamW
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Swin Transformer
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.7
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 39.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco/mask_rcnn_swin-t-p4-w7_fpn_1x_coco_20210902_120937-9d6b7cfa.pth
+    Paper:
+      URL: https://arxiv.org/abs/2107.08430
+      Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
+    README: configs/swin/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/backbones/swin.py#L465
+      Version: v2.16.0
+
+  - Name: mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco
+    In Collection: Mask R-CNN
+    Config: configs/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco.py
+    Metadata:
+      Training Memory (GB): 7.8
+      Epochs: 36
+      Training Data: COCO
+      Training Techniques:
+        - AdamW
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Swin Transformer
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.0
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 41.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/swin/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco/mask_rcnn_swin-t-p4-w7_fpn_fp16_ms-crop-3x_coco_20210908_165006-90a4008c.pth
+    Paper:
+      URL: https://arxiv.org/abs/2107.08430
+      Title: 'Swin Transformer: Hierarchical Vision Transformer using Shifted Windows'
+    README: configs/swin/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.16.0/mmdet/models/backbones/swin.py#L465
+      Version: v2.16.0
diff --git a/configs/swin/retinanet_swin-t-p4-w7_fpn_1x_coco.py b/configs/swin/retinanet_swin-t-p4-w7_fpn_1x_coco.py
new file mode 100644
index 0000000..3315093
--- /dev/null
+++ b/configs/swin/retinanet_swin-t-p4-w7_fpn_1x_coco.py
@@ -0,0 +1,30 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_tiny_patch4_window7_224.pth'  # noqa
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='SwinTransformer',
+        embed_dims=96,
+        depths=[2, 2, 6, 2],
+        num_heads=[3, 6, 12, 24],
+        window_size=7,
+        mlp_ratio=4,
+        qkv_bias=True,
+        qk_scale=None,
+        drop_rate=0.,
+        attn_drop_rate=0.,
+        drop_path_rate=0.2,
+        patch_norm=True,
+        out_indices=(1, 2, 3),
+        # Please only add indices that would be used
+        # in FPN, otherwise some parameter will not be used
+        with_cp=False,
+        convert_weights=True,
+        init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
+    neck=dict(in_channels=[192, 384, 768], start_level=0, num_outs=5))
+
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/timm_example/README.md b/configs/timm_example/README.md
new file mode 100644
index 0000000..4374855
--- /dev/null
+++ b/configs/timm_example/README.md
@@ -0,0 +1,62 @@
+# Timm Example
+
+> [PyTorch Image Models](https://github.com/rwightman/pytorch-image-models)
+
+<!-- [OTHERS] -->
+
+## Abstract
+
+Py**T**orch **Im**age **M**odels (`timm`) is a collection of image models, layers, utilities, optimizers, schedulers, data-loaders / augmentations, and reference training / validation scripts that aim to pull together a wide variety of SOTA models with ability to reproduce ImageNet training results.
+
+<!--
+<div align=center>
+<img src="" height="400" />
+</div>
+-->
+
+## Results and Models
+
+### RetinaNet
+
+|    Backbone     |  Style  | Lr schd | Mem (GB) | Inf time (fps) | box AP |                          Config                           | Download |
+| :-------------: | :-----: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------: | :------: |
+|      R-50       | pytorch |   1x    |          |                |        |   [config](./retinanet_timm_tv_resnet50_fpn_1x_coco.py)   |          |
+| EfficientNet-B1 |    -    |   1x    |          |                |        | [config](./retinanet_timm_efficientnet_b1_fpn_1x_coco.py) |          |
+
+## Usage
+
+### Install additional requirements
+
+MMDetection supports timm backbones via `TIMMBackbone`, a wrapper class in MMClassification.
+Thus, you need to install `mmcls` in addition to timm.
+If you have already installed requirements for mmdet, run
+
+```shell
+pip install 'dataclasses; python_version<"3.7"'
+pip install timm
+pip install 'mmcls>=0.20.0'
+```
+
+See [this document](https://mmclassification.readthedocs.io/en/latest/install.html) for the details of MMClassification installation.
+
+### Edit config
+
+- See example configs for basic usage.
+- See the documents of [timm feature extraction](https://rwightman.github.io/pytorch-image-models/feature_extraction/#multi-scale-feature-maps-feature-pyramid) and [TIMMBackbone](https://mmclassification.readthedocs.io/en/latest/api.html#mmcls.models.backbones.TIMMBackbone) for details.
+- Which feature map is output depends on the backbone.
+  Please check `backbone out_channels` and `backbone out_strides` in your log, and modify `model.neck.in_channels` and `model.backbone.out_indices` if necessary.
+- If you use Vision Transformer models that do not support `features_only=True`, add `custom_hooks = []` to your config to disable `NumClassCheckHook`.
+
+## Citation
+
+```latex
+@misc{rw2019timm,
+  author = {Ross Wightman},
+  title = {PyTorch Image Models},
+  year = {2019},
+  publisher = {GitHub},
+  journal = {GitHub repository},
+  doi = {10.5281/zenodo.4414861},
+  howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
+}
+```
diff --git a/configs/timm_example/retinanet_timm_efficientnet_b1_fpn_1x_coco.py b/configs/timm_example/retinanet_timm_efficientnet_b1_fpn_1x_coco.py
new file mode 100644
index 0000000..6500116
--- /dev/null
+++ b/configs/timm_example/retinanet_timm_efficientnet_b1_fpn_1x_coco.py
@@ -0,0 +1,20 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# please install mmcls>=0.20.0
+# import mmcls.models to trigger register_module in mmcls
+custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='mmcls.TIMMBackbone',
+        model_name='efficientnet_b1',
+        features_only=True,
+        pretrained=True,
+        out_indices=(1, 2, 3, 4)),
+    neck=dict(in_channels=[24, 40, 112, 320]))
+
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/timm_example/retinanet_timm_tv_resnet50_fpn_1x_coco.py b/configs/timm_example/retinanet_timm_tv_resnet50_fpn_1x_coco.py
new file mode 100644
index 0000000..0c5b7a8
--- /dev/null
+++ b/configs/timm_example/retinanet_timm_tv_resnet50_fpn_1x_coco.py
@@ -0,0 +1,19 @@
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# please install mmcls>=0.20.0
+# import mmcls.models to trigger register_module in mmcls
+custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
+model = dict(
+    backbone=dict(
+        _delete_=True,
+        type='mmcls.TIMMBackbone',
+        model_name='tv_resnet50',  # ResNet-50 with torchvision weights
+        features_only=True,
+        pretrained=True,
+        out_indices=(1, 2, 3, 4)))
+
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
diff --git a/configs/tood/README.md b/configs/tood/README.md
new file mode 100644
index 0000000..925f0ed
--- /dev/null
+++ b/configs/tood/README.md
@@ -0,0 +1,40 @@
+# TOOD
+
+> [TOOD: Task-aligned One-stage Object Detection](https://arxiv.org/abs/2108.07755)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+One-stage object detection is commonly implemented by optimizing two sub-tasks: object classification and localization, using heads with two parallel branches, which might lead to a certain level of spatial misalignment in predictions between the two tasks. In this work, we propose a Task-aligned One-stage Object Detection (TOOD) that explicitly aligns the two tasks in a learning-based manner. First, we design a novel Task-aligned Head (T-Head) which offers a better balance between learning task-interactive and task-specific features, as well as a greater flexibility to learn the alignment via a task-aligned predictor. Second, we propose Task Alignment Learning (TAL) to explicitly pull closer (or even unify) the optimal anchors for the two tasks during training via a designed sample assignment scheme and a task-aligned loss. Extensive experiments are conducted on MS-COCO, where TOOD achieves a 51.1 AP at single-model single-scale testing. This surpasses the recent one-stage detectors by a large margin, such as ATSS (47.7 AP), GFL (48.2 AP), and PAA (49.0 AP), with fewer parameters and FLOPs. Qualitative results also demonstrate the effectiveness of TOOD for better aligning the tasks of object classification and localization.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/12907710/145400075-e08191f5-8afa-4335-9b3b-27926fc9a26e.png"/>
+</div>
+
+## Results and Models
+
+|     Backbone      |  Style  | Anchor Type  | Lr schd | Multi-scale Training | Mem (GB) | Inf time (fps) | box AP |                             Config                             |                                                                                                                                                                       Download                                                                                                                                                                        |
+| :---------------: | :-----: | :----------: | :-----: | :------------------: | :------: | :------------: | :----: | :------------------------------------------------------------: | :---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|       R-50        | pytorch | Anchor-free  |   1x    |          N           |   4.1    |                |  42.4  |              [config](./tood_r50_fpn_1x_coco.py)               |                                           [model](https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r50_fpn_1x_coco/tood_r50_fpn_1x_coco_20211210_103425-20e20746.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r50_fpn_1x_coco/tood_r50_fpn_1x_coco_20211210_103425.log)                                           |
+|       R-50        | pytorch | Anchor-based |   1x    |          N           |   4.1    |                |  42.4  |        [config](./tood_r50_fpn_anchor_based_1x_coco.py)        |                 [model](https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r50_fpn_anchor_based_1x_coco/tood_r50_fpn_anchor_based_1x_coco_20211214_100105-b776c134.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r50_fpn_anchor_based_1x_coco/tood_r50_fpn_anchor_based_1x_coco_20211214_100105.log)                 |
+|       R-50        | pytorch | Anchor-free  |   2x    |          Y           |   4.1    |                |  44.5  |          [config](./tood_r50_fpn_mstrain_2x_coco.py)           |                           [model](https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r50_fpn_mstrain_2x_coco/tood_r50_fpn_mstrain_2x_coco_20211210_144231-3b23174c.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r50_fpn_mstrain_2x_coco/tood_r50_fpn_mstrain_2x_coco_20211210_144231.log)                           |
+|       R-101       | pytorch | Anchor-free  |   2x    |          Y           |   6.0    |                |  46.1  |          [config](./tood_r101_fpn_mstrain_2x_coco.py)          |                         [model](https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r101_fpn_mstrain_2x_coco/tood_r101_fpn_mstrain_2x_coco_20211210_144232-a18f53c8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r101_fpn_mstrain_2x_coco/tood_r101_fpn_mstrain_2x_coco_20211210_144232.log)                         |
+|    R-101-dcnv2    | pytorch | Anchor-free  |   2x    |          Y           |   6.2    |                |  49.3  |    [config](./tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py)    | [model](https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20211210_213728-4a824142.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20211210_213728.log) |
+|    X-101-64x4d    | pytorch | Anchor-free  |   2x    |          Y           |   10.2   |                |  47.6  |       [config](./tood_x101_64x4d_fpn_mstrain_2x_coco.py)       |             [model](https://download.openmmlab.com/mmdetection/v2.0/tood/tood_x101_64x4d_fpn_mstrain_2x_coco/tood_x101_64x4d_fpn_mstrain_2x_coco_20211211_003519-a4f36113.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/tood/tood_x101_64x4d_fpn_mstrain_2x_coco/tood_x101_64x4d_fpn_mstrain_2x_coco_20211211_003519.log)             |
+| X-101-64x4d-dcnv2 | pytorch | Anchor-free  |   2x    |          Y           |          |                |        | [config](./tood_x101_64x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py) |                                                                                                                                                               [model](<>) \| [log](<>)                                                                                                                                                                |
+
+\[1\] *1x and 2x mean the model is trained for 90K and 180K iterations, respectively.* \
+\[2\] *All results are obtained with a single model and without any test time data augmentation such as multi-scale, flipping and etc..* \
+\[3\] *`dcnv2` denotes deformable convolutional networks v2.* \\
+
+## Citation
+
+```latex
+@inproceedings{feng2021tood,
+    title={TOOD: Task-aligned One-stage Object Detection},
+    author={Feng, Chengjian and Zhong, Yujie and Gao, Yu and Scott, Matthew R and Huang, Weilin},
+    booktitle={ICCV},
+    year={2021}
+}
+```
diff --git a/configs/tood/metafile.yml b/configs/tood/metafile.yml
new file mode 100644
index 0000000..27a0f8d
--- /dev/null
+++ b/configs/tood/metafile.yml
@@ -0,0 +1,95 @@
+Collections:
+  - Name: TOOD
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - TOOD
+    Paper:
+      URL: https://arxiv.org/abs/2108.07755
+      Title: 'TOOD: Task-aligned One-stage Object Detection'
+    README: configs/tood/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.20.0/mmdet/models/detectors/tood.py#L7
+      Version: v2.20.0
+
+Models:
+  - Name: tood_r101_fpn_mstrain_2x_coco
+    In Collection: TOOD
+    Config: configs/tood/tood_r101_fpn_mstrain_2x_coco.py
+    Metadata:
+      Training Memory (GB): 6.0
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.1
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r101_fpn_mstrain_2x_coco/tood_r101_fpn_mstrain_2x_coco_20211210_144232-a18f53c8.pth
+
+  - Name: tood_x101_64x4d_fpn_mstrain_2x_coco
+    In Collection: TOOD
+    Config: configs/tood/tood_x101_64x4d_fpn_mstrain_2x_coco.py
+    Metadata:
+      Training Memory (GB): 10.2
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 47.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/tood/tood_x101_64x4d_fpn_mstrain_2x_coco/tood_x101_64x4d_fpn_mstrain_2x_coco_20211211_003519-a4f36113.pth
+
+  - Name: tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco
+    In Collection: TOOD
+    Config: configs/tood/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
+    Metadata:
+      Training Memory (GB): 6.2
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 49.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco_20211210_213728-4a824142.pth
+
+  - Name: tood_r50_fpn_anchor_based_1x_coco
+    In Collection: TOOD
+    Config: configs/tood/tood_r50_fpn_anchor_based_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.1
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r50_fpn_anchor_based_1x_coco/tood_r50_fpn_anchor_based_1x_coco_20211214_100105-b776c134.pth
+
+  - Name: tood_r50_fpn_1x_coco
+    In Collection: TOOD
+    Config: configs/tood/tood_r50_fpn_1x_coco.py
+    Metadata:
+      Training Memory (GB): 4.1
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 42.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r50_fpn_1x_coco/tood_r50_fpn_1x_coco_20211210_103425-20e20746.pth
+
+  - Name: tood_r50_fpn_mstrain_2x_coco
+    In Collection: TOOD
+    Config: configs/tood/tood_r50_fpn_mstrain_2x_coco.py
+    Metadata:
+      Training Memory (GB): 4.1
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/tood/tood_r50_fpn_mstrain_2x_coco/tood_r50_fpn_mstrain_2x_coco_20211210_144231-3b23174c.pth
diff --git a/configs/tood/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py b/configs/tood/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
new file mode 100644
index 0000000..c7f1bbc
--- /dev/null
+++ b/configs/tood/tood_r101_fpn_dconv_c3-c5_mstrain_2x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './tood_r101_fpn_mstrain_2x_coco.py'
+
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)),
+    bbox_head=dict(num_dcn=2))
diff --git a/configs/tood/tood_r101_fpn_mstrain_2x_coco.py b/configs/tood/tood_r101_fpn_mstrain_2x_coco.py
new file mode 100644
index 0000000..d9d2c32
--- /dev/null
+++ b/configs/tood/tood_r101_fpn_mstrain_2x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './tood_r50_fpn_mstrain_2x_coco.py'
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/tood/tood_r50_fpn_1x_coco.py b/configs/tood/tood_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..35a77a4
--- /dev/null
+++ b/configs/tood/tood_r50_fpn_1x_coco.py
@@ -0,0 +1,74 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='TOOD',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',
+        num_outs=5),
+    bbox_head=dict(
+        type='TOODHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=6,
+        feat_channels=256,
+        anchor_type='anchor_free',
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        initial_loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            activated=True,  # use probability instead of logit as input
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_cls=dict(
+            type='QualityFocalLoss',
+            use_sigmoid=True,
+            activated=True,  # use probability instead of logit as input
+            beta=2.0,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0)),
+    train_cfg=dict(
+        initial_epoch=4,
+        initial_assigner=dict(type='ATSSAssigner', topk=9),
+        assigner=dict(type='TaskAlignedAssigner', topk=13),
+        alpha=1,
+        beta=6,
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+# optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+
+# custom hooks
+custom_hooks = [dict(type='SetEpochInfoHook')]
diff --git a/configs/tood/tood_r50_fpn_anchor_based_1x_coco.py b/configs/tood/tood_r50_fpn_anchor_based_1x_coco.py
new file mode 100644
index 0000000..c7fbf6a
--- /dev/null
+++ b/configs/tood/tood_r50_fpn_anchor_based_1x_coco.py
@@ -0,0 +1,2 @@
+_base_ = './tood_r50_fpn_1x_coco.py'
+model = dict(bbox_head=dict(anchor_type='anchor_based'))
diff --git a/configs/tood/tood_r50_fpn_mstrain_2x_coco.py b/configs/tood/tood_r50_fpn_mstrain_2x_coco.py
new file mode 100644
index 0000000..157d13a
--- /dev/null
+++ b/configs/tood/tood_r50_fpn_mstrain_2x_coco.py
@@ -0,0 +1,22 @@
+_base_ = './tood_r50_fpn_1x_coco.py'
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
+# multi-scale training
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 480), (1333, 800)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+data = dict(train=dict(pipeline=train_pipeline))
diff --git a/configs/tood/tood_x101_64x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py b/configs/tood/tood_x101_64x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py
new file mode 100644
index 0000000..47c9269
--- /dev/null
+++ b/configs/tood/tood_x101_64x4d_fpn_dconv_c4-c5_mstrain_2x_coco.py
@@ -0,0 +1,7 @@
+_base_ = './tood_x101_64x4d_fpn_mstrain_2x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCNv2', deformable_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, False, True, True),
+    ),
+    bbox_head=dict(num_dcn=2))
diff --git a/configs/tood/tood_x101_64x4d_fpn_mstrain_2x_coco.py b/configs/tood/tood_x101_64x4d_fpn_mstrain_2x_coco.py
new file mode 100644
index 0000000..842f320
--- /dev/null
+++ b/configs/tood/tood_x101_64x4d_fpn_mstrain_2x_coco.py
@@ -0,0 +1,16 @@
+_base_ = './tood_r50_fpn_mstrain_2x_coco.py'
+
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/tridentnet/README.md b/configs/tridentnet/README.md
new file mode 100644
index 0000000..b972b3a
--- /dev/null
+++ b/configs/tridentnet/README.md
@@ -0,0 +1,38 @@
+# TridentNet
+
+> [Scale-Aware Trident Networks for Object Detection](https://arxiv.org/abs/1901.01892)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Scale variation is one of the key challenges in object detection. In this work, we first present a controlled experiment to investigate the effect of receptive fields for scale variation in object detection. Based on the findings from the exploration experiments, we propose a novel Trident Network (TridentNet) aiming to generate scale-specific feature maps with a uniform representational power. We construct a parallel multi-branch architecture in which each branch shares the same transformation parameters but with different receptive fields. Then, we adopt a scale-aware training scheme to specialize each branch by sampling object instances of proper scales for training. As a bonus, a fast approximation version of TridentNet could achieve significant improvements without any additional parameters and computational cost compared with the vanilla detector. On the COCO dataset, our TridentNet with ResNet-101 backbone achieves state-of-the-art single-model results of 48.4 mAP.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/143999668-0927922e-efc2-45fa-8bfc-1e3df18720f5.png"/>
+</div>
+
+## Results and Models
+
+We reports the test results using only one branch for inference.
+
+| Backbone | Style | mstrain | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                                                                                                                                      Download                                                                                                                                                                      |
+| :------: | :---: | :-----: | :-----: | :------: | :------------: | :----: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|   R-50   | caffe |    N    |   1x    |          |                |  37.7  |                 [model](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_1x_coco/tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_1x_coco/tridentnet_r50_caffe_1x_coco_20201230_141838.log.json)                 |
+|   R-50   | caffe |    Y    |   1x    |          |                |  37.6  | [model](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco/tridentnet_r50_caffe_mstrain_1x_coco_20201230_141839-6ce55ccb.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco/tridentnet_r50_caffe_mstrain_1x_coco_20201230_141839.log.json) |
+|   R-50   | caffe |    Y    |   3x    |          |                |  40.3  | [model](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco/tridentnet_r50_caffe_mstrain_3x_coco_20201130_100539-46d227ba.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco/tridentnet_r50_caffe_mstrain_3x_coco_20201130_100539.log.json) |
+
+**Note**
+
+Similar to [Detectron2](https://github.com/facebookresearch/detectron2/tree/master/projects/TridentNet), we haven't implemented the Scale-aware Training Scheme in section 4.2 of the paper.
+
+## Citation
+
+```latex
+@InProceedings{li2019scale,
+  title={Scale-Aware Trident Networks for Object Detection},
+  author={Li, Yanghao and Chen, Yuntao and Wang, Naiyan and Zhang, Zhaoxiang},
+  journal={The International Conference on Computer Vision (ICCV)},
+  year={2019}
+}
+```
diff --git a/configs/tridentnet/metafile.yml b/configs/tridentnet/metafile.yml
new file mode 100644
index 0000000..2536f97
--- /dev/null
+++ b/configs/tridentnet/metafile.yml
@@ -0,0 +1,55 @@
+Collections:
+  - Name: TridentNet
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - ResNet
+        - TridentNet Block
+    Paper:
+      URL: https://arxiv.org/abs/1901.01892
+      Title: 'Scale-Aware Trident Networks for Object Detection'
+    README: configs/tridentnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.8.0/mmdet/models/detectors/trident_faster_rcnn.py#L6
+      Version: v2.8.0
+
+Models:
+  - Name: tridentnet_r50_caffe_1x_coco
+    In Collection: TridentNet
+    Config: configs/tridentnet/tridentnet_r50_caffe_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_1x_coco/tridentnet_r50_caffe_1x_coco_20201230_141838-2ec0b530.pth
+
+  - Name: tridentnet_r50_caffe_mstrain_1x_coco
+    In Collection: TridentNet
+    Config: configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco/tridentnet_r50_caffe_mstrain_1x_coco_20201230_141839-6ce55ccb.pth
+
+  - Name: tridentnet_r50_caffe_mstrain_3x_coco
+    In Collection: TridentNet
+    Config: configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py
+    Metadata:
+      Epochs: 36
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.3
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco/tridentnet_r50_caffe_mstrain_3x_coco_20201130_100539-46d227ba.pth
diff --git a/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py b/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py
new file mode 100644
index 0000000..d779f75
--- /dev/null
+++ b/configs/tridentnet/tridentnet_r50_caffe_1x_coco.py
@@ -0,0 +1,55 @@
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_caffe_c4.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    type='TridentFasterRCNN',
+    backbone=dict(
+        type='TridentResNet',
+        trident_dilations=(1, 2, 3),
+        num_branch=3,
+        test_branch_idx=1,
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron2/resnet50_caffe')),
+    roi_head=dict(type='TridentRoIHead', num_branch=3, test_branch_idx=1),
+    train_cfg=dict(
+        rpn_proposal=dict(max_per_img=500),
+        rcnn=dict(
+            sampler=dict(num=128, pos_fraction=0.5,
+                         add_gt_as_proposals=False))))
+
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py b/configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py
new file mode 100644
index 0000000..c73d9ea
--- /dev/null
+++ b/configs/tridentnet/tridentnet_r50_caffe_mstrain_1x_coco.py
@@ -0,0 +1,22 @@
+_base_ = 'tridentnet_r50_caffe_1x_coco.py'
+
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode='value',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+data = dict(train=dict(pipeline=train_pipeline))
diff --git a/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py b/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py
new file mode 100644
index 0000000..0f40282
--- /dev/null
+++ b/configs/tridentnet/tridentnet_r50_caffe_mstrain_3x_coco.py
@@ -0,0 +1,4 @@
+_base_ = 'tridentnet_r50_caffe_mstrain_1x_coco.py'
+
+lr_config = dict(step=[28, 34])
+runner = dict(type='EpochBasedRunner', max_epochs=36)
diff --git a/configs/vfnet/README.md b/configs/vfnet/README.md
new file mode 100644
index 0000000..a492bec
--- /dev/null
+++ b/configs/vfnet/README.md
@@ -0,0 +1,48 @@
+# VarifocalNet
+
+> [VarifocalNet: An IoU-aware Dense Object Detector](https://arxiv.org/abs/2008.13367)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Accurately ranking the vast number of candidate detections is crucial for dense object detectors to achieve high performance. Prior work uses the classification score or a combination of classification and predicted localization scores to rank candidates. However, neither option results in a reliable ranking, thus degrading detection performance. In this paper, we propose to learn an Iou-aware Classification Score (IACS) as a joint representation of object presence confidence and localization accuracy. We show that dense object detectors can achieve a more accurate ranking of candidate detections based on the IACS. We design a new loss function, named Varifocal Loss, to train a dense object detector to predict the IACS, and propose a new star-shaped bounding box feature representation for IACS prediction and bounding box refinement. Combining these two new components and a bounding box refinement branch, we build an IoU-aware dense object detector based on the FCOS+ATSS architecture, that we call VarifocalNet or VFNet for short. Extensive experiments on MS COCO show that our VFNet consistently surpasses the strong baseline by ∼2.0 AP with different backbones. Our best model VFNet-X-1200 with Res2Net-101-DCN achieves a single-model single-scale AP of 55.1 on COCO test-dev, which is state-of-the-art among various object detectors.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/9102141/97464778-4b9ab000-197c-11eb-9283-ab2907ee0252.png"/>
+</div>
+
+## Introduction
+
+**VarifocalNet (VFNet)** learns to predict the IoU-aware classification score which mixes the object presence confidence and localization accuracy together as the detection score for a bounding box. The learning is supervised by the proposed Varifocal Loss (VFL), based on a new star-shaped bounding box feature representation (the features at nine yellow sampling points). Given the new representation, the object localization accuracy is further improved by refining the initially regressed bounding box. The full paper is available at: [https://arxiv.org/abs/2008.13367](https://arxiv.org/abs/2008.13367).
+
+## Results and Models
+
+|  Backbone   |  Style  | DCN | MS train | Lr schd | Inf time (fps) | box AP (val) | box AP (test-dev) |                                                               Config                                                               |                                                                                                                                                                               Download                                                                                                                                                                               |
+| :---------: | :-----: | :-: | :------: | :-----: | :------------: | :----------: | :---------------: | :--------------------------------------------------------------------------------------------------------------------------------: | :------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+|    R-50     | pytorch |  N  |    N     |   1x    |       -        |     41.6     |       41.6        |               [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r50_fpn_1x_coco.py)               |                                                          [model](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco.json)                                                           |
+|    R-50     | pytorch |  N  |    Y     |   2x    |       -        |     44.5     |       44.8        |           [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py)           |                                          [model](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco_20201027-7cc75bd2.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco.json)                                           |
+|    R-50     | pytorch |  Y  |    Y     |   2x    |       -        |     47.8     |       48.0        |    [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py)     |               [model](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-6879c318.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.json)               |
+|    R-101    | pytorch |  N  |    N     |   1x    |       -        |     43.0     |       43.6        |              [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r101_fpn_1x_coco.py)               |                                                       [model](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco_20201027pth-c831ece7.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco.json)                                                       |
+|    R-101    | pytorch |  N  |    Y     |   2x    |       -        |     46.2     |       46.7        |          [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py)           |                                       [model](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco_20201027pth-4a5d53f1.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco.json)                                       |
+|    R-101    | pytorch |  Y  |    Y     |   2x    |       -        |     49.0     |       49.2        |    [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py)    |             [model](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-7729adb5.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.json)             |
+| X-101-32x4d | pytorch |  Y  |    Y     |   2x    |       -        |     49.7     |       50.0        | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-d300a6fc.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.json) |
+| X-101-64x4d | pytorch |  Y  |    Y     |   2x    |       -        |     50.4     |       50.8        | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-b5f6da5e.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.json) |
+
+**Notes:**
+
+- The MS-train scale range is 1333x\[480:960\] (`range` mode) and the inference scale keeps 1333x800.
+- DCN means using `DCNv2` in both backbone and head.
+- Inference time will be updated soon.
+- More results and pre-trained models can be found in [VarifocalNet-Github](https://github.com/hyz-xmaster/VarifocalNet)
+
+## Citation
+
+```latex
+@article{zhang2020varifocalnet,
+  title={VarifocalNet: An IoU-aware Dense Object Detector},
+  author={Zhang, Haoyang and Wang, Ying and Dayoub, Feras and S{\"u}nderhauf, Niko},
+  journal={arXiv preprint arXiv:2008.13367},
+  year={2020}
+}
+```
diff --git a/configs/vfnet/metafile.yml b/configs/vfnet/metafile.yml
new file mode 100644
index 0000000..bcbe576
--- /dev/null
+++ b/configs/vfnet/metafile.yml
@@ -0,0 +1,116 @@
+Collections:
+  - Name: VFNet
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - ResNet
+        - Varifocal Loss
+    Paper:
+      URL: https://arxiv.org/abs/2008.13367
+      Title: 'VarifocalNet: An IoU-aware Dense Object Detector'
+    README: configs/vfnet/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.6.0/mmdet/models/detectors/vfnet.py#L6
+      Version: v2.6.0
+
+Models:
+  - Name: vfnet_r50_fpn_1x_coco
+    In Collection: VFNet
+    Config: configs/vfnet/vfnet_r50_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 41.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_1x_coco/vfnet_r50_fpn_1x_coco_20201027-38db6f58.pth
+
+  - Name: vfnet_r50_fpn_mstrain_2x_coco
+    In Collection: VFNet
+    Config: configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 44.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mstrain_2x_coco/vfnet_r50_fpn_mstrain_2x_coco_20201027-7cc75bd2.pth
+
+  - Name: vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco
+    In Collection: VFNet
+    Config: configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 48.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-6879c318.pth
+
+  - Name: vfnet_r101_fpn_1x_coco
+    In Collection: VFNet
+    Config: configs/vfnet/vfnet_r101_fpn_1x_coco.py
+    Metadata:
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 43.6
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_1x_coco/vfnet_r101_fpn_1x_coco_20201027pth-c831ece7.pth
+
+  - Name: vfnet_r101_fpn_mstrain_2x_coco
+    In Collection: VFNet
+    Config: configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 46.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mstrain_2x_coco/vfnet_r101_fpn_mstrain_2x_coco_20201027pth-4a5d53f1.pth
+
+  - Name: vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco
+    In Collection: VFNet
+    Config: configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 49.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-7729adb5.pth
+
+  - Name: vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco
+    In Collection: VFNet
+    Config: configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 50.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-d300a6fc.pth
+
+  - Name: vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco
+    In Collection: VFNet
+    Config: configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py
+    Metadata:
+      Epochs: 24
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 50.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco_20201027pth-b5f6da5e.pth
diff --git a/configs/vfnet/vfnet_r101_fpn_1x_coco.py b/configs/vfnet/vfnet_r101_fpn_1x_coco.py
new file mode 100644
index 0000000..b296a07
--- /dev/null
+++ b/configs/vfnet/vfnet_r101_fpn_1x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './vfnet_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/vfnet/vfnet_r101_fpn_2x_coco.py b/configs/vfnet/vfnet_r101_fpn_2x_coco.py
new file mode 100644
index 0000000..27962f3
--- /dev/null
+++ b/configs/vfnet/vfnet_r101_fpn_2x_coco.py
@@ -0,0 +1,8 @@
+_base_ = './vfnet_r50_fpn_1x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
new file mode 100644
index 0000000..e438c24
--- /dev/null
+++ b/configs/vfnet/vfnet_r101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
@@ -0,0 +1,15 @@
+_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNet',
+        depth=101,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True),
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py
new file mode 100644
index 0000000..eae69a0
--- /dev/null
+++ b/configs/vfnet/vfnet_r101_fpn_mstrain_2x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
new file mode 100644
index 0000000..815a36e
--- /dev/null
+++ b/configs/vfnet/vfnet_r2_101_fpn_mdconv_c3-c5_mstrain_2x_coco.py
@@ -0,0 +1,18 @@
+_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='Res2Net',
+        depth=101,
+        scales=4,
+        base_width=26,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True),
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
diff --git a/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py
new file mode 100644
index 0000000..58022e0
--- /dev/null
+++ b/configs/vfnet/vfnet_r2_101_fpn_mstrain_2x_coco.py
@@ -0,0 +1,16 @@
+_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='Res2Net',
+        depth=101,
+        scales=4,
+        base_width=26,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://res2net101_v1d_26w_4s')))
diff --git a/configs/vfnet/vfnet_r50_fpn_1x_coco.py b/configs/vfnet/vfnet_r50_fpn_1x_coco.py
new file mode 100644
index 0000000..7de6429
--- /dev/null
+++ b/configs/vfnet/vfnet_r50_fpn_1x_coco.py
@@ -0,0 +1,107 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# model settings
+model = dict(
+    type='VFNet',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_output',  # use P5
+        num_outs=5,
+        relu_before_extra_convs=True),
+    bbox_head=dict(
+        type='VFNetHead',
+        num_classes=80,
+        in_channels=256,
+        stacked_convs=3,
+        feat_channels=256,
+        strides=[8, 16, 32, 64, 128],
+        center_sampling=False,
+        dcn_on_last_conv=False,
+        use_atss=True,
+        use_vfl=True,
+        loss_cls=dict(
+            type='VarifocalLoss',
+            use_sigmoid=True,
+            alpha=0.75,
+            gamma=2.0,
+            iou_weighted=True,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.5),
+        loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(type='ATSSAssigner', topk=9),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+
+# data setting
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+# optimizer
+optimizer = dict(
+    lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.1,
+    step=[8, 11])
+runner = dict(type='EpochBasedRunner', max_epochs=12)
diff --git a/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py
new file mode 100644
index 0000000..24d2093
--- /dev/null
+++ b/configs/vfnet/vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py
@@ -0,0 +1,6 @@
+_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
+model = dict(
+    backbone=dict(
+        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True)),
+    bbox_head=dict(dcn_on_last_conv=True))
diff --git a/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py
new file mode 100644
index 0000000..6078bb9
--- /dev/null
+++ b/configs/vfnet/vfnet_r50_fpn_mstrain_2x_coco.py
@@ -0,0 +1,39 @@
+_base_ = './vfnet_r50_fpn_1x_coco.py'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 480), (1333, 960)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+# learning policy
+lr_config = dict(step=[16, 22])
+runner = dict(type='EpochBasedRunner', max_epochs=24)
diff --git a/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py
new file mode 100644
index 0000000..7efa051
--- /dev/null
+++ b/configs/vfnet/vfnet_x101_32x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py
@@ -0,0 +1,17 @@
+_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py
new file mode 100644
index 0000000..49a4312
--- /dev/null
+++ b/configs/vfnet/vfnet_x101_32x4d_fpn_mstrain_2x_coco.py
@@ -0,0 +1,15 @@
+_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=32,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_32x4d')))
diff --git a/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py
new file mode 100644
index 0000000..7e1ee42
--- /dev/null
+++ b/configs/vfnet/vfnet_x101_64x4d_fpn_mdconv_c3-c5_mstrain_2x_coco.py
@@ -0,0 +1,17 @@
+_base_ = './vfnet_r50_fpn_mdconv_c3-c5_mstrain_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
+        stage_with_dcn=(False, True, True, True),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py b/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py
new file mode 100644
index 0000000..e51064e
--- /dev/null
+++ b/configs/vfnet/vfnet_x101_64x4d_fpn_mstrain_2x_coco.py
@@ -0,0 +1,15 @@
+_base_ = './vfnet_r50_fpn_mstrain_2x_coco.py'
+model = dict(
+    backbone=dict(
+        type='ResNeXt',
+        depth=101,
+        groups=64,
+        base_width=4,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')))
diff --git a/configs/wider_face/README.md b/configs/wider_face/README.md
new file mode 100644
index 0000000..1904506
--- /dev/null
+++ b/configs/wider_face/README.md
@@ -0,0 +1,57 @@
+# WIDER FACE
+
+> [WIDER FACE: A Face Detection Benchmark](https://arxiv.org/abs/1511.06523)
+
+<!-- [DATASET] -->
+
+## Abstract
+
+Face detection is one of the most studied topics in the computer vision community. Much of the progresses have been made by the availability of face detection benchmark datasets. We show that there is a gap between current face detection performance and the real world requirements. To facilitate future face detection research, we introduce the WIDER FACE dataset, which is 10 times larger than existing datasets. The dataset contains rich annotations, including occlusions, poses, event categories, and face bounding boxes. Faces in the proposed dataset are extremely challenging due to large variations in scale, pose and occlusion, as shown in Fig. 1. Furthermore, we show that WIDER FACE dataset is an effective training source for face detection. We benchmark several representative detection systems, providing an overview of state-of-the-art performance and propose a solution to deal with large scale variation. Finally, we discuss common failure cases that worth to be further investigated.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/144000364-3320de79-34fc-40a6-938f-bb512f05a4bb.png" height="400"/>
+</div>
+
+## Introduction
+
+To use the WIDER Face dataset you need to download it
+and extract to the `data/WIDERFace` folder. Annotation in the VOC format
+can be found in this [repo](https://github.com/sovrasov/wider-face-pascal-voc-annotations.git).
+You should move the annotation files from `WIDER_train_annotations` and `WIDER_val_annotations` folders
+to the `Annotation` folders inside the corresponding directories `WIDER_train` and `WIDER_val`.
+Also annotation lists `val.txt` and `train.txt` should be copied to `data/WIDERFace` from `WIDER_train_annotations` and `WIDER_val_annotations`.
+The directory should be like this:
+
+```
+mmdetection
+├── mmdet
+├── tools
+├── configs
+├── data
+│   ├── WIDERFace
+│   │   ├── WIDER_train
+│   |   │   ├──0--Parade
+│   |   │   ├── ...
+│   |   │   ├── Annotations
+│   │   ├── WIDER_val
+│   |   │   ├──0--Parade
+│   |   │   ├── ...
+│   |   │   ├── Annotations
+│   │   ├── val.txt
+│   │   ├── train.txt
+
+```
+
+After that you can train the SSD300 on WIDER by launching training with the `ssd300_wider_face.py` config or
+create your own config based on the presented one.
+
+## Citation
+
+```latex
+@inproceedings{yang2016wider,
+   Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
+   Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
+   Title = {WIDER FACE: A Face Detection Benchmark},
+   Year = {2016}
+}
+```
diff --git a/configs/wider_face/ssd300_wider_face.py b/configs/wider_face/ssd300_wider_face.py
new file mode 100644
index 0000000..98d820a
--- /dev/null
+++ b/configs/wider_face/ssd300_wider_face.py
@@ -0,0 +1,18 @@
+_base_ = [
+    '../_base_/models/ssd300.py', '../_base_/datasets/wider_face.py',
+    '../_base_/default_runtime.py'
+]
+model = dict(bbox_head=dict(num_classes=1))
+# optimizer
+optimizer = dict(type='SGD', lr=0.012, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=0.001,
+    step=[16, 20])
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=24)
+log_config = dict(interval=1)
diff --git a/configs/yolact/README.md b/configs/yolact/README.md
new file mode 100644
index 0000000..9eb51b4
--- /dev/null
+++ b/configs/yolact/README.md
@@ -0,0 +1,75 @@
+# YOLACT
+
+> [YOLACT: Real-time Instance Segmentation](https://arxiv.org/abs/1904.02689)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We present a simple, fully-convolutional model for real-time instance segmentation that achieves 29.8 mAP on MS COCO at 33.5 fps evaluated on a single Titan Xp, which is significantly faster than any previous competitive approach. Moreover, we obtain this result after training on only one GPU. We accomplish this by breaking instance segmentation into two parallel subtasks: (1) generating a set of prototype masks and (2) predicting per-instance mask coefficients. Then we produce instance masks by linearly combining the prototypes with the mask coefficients. We find that because this process doesn't depend on repooling, this approach produces very high-quality masks and exhibits temporal stability for free. Furthermore, we analyze the emergent behavior of our prototypes and show they learn to localize instances on their own in a translation variant manner, despite being fully-convolutional. Finally, we also propose Fast NMS, a drop-in 12 ms faster replacement for standard NMS that only has a marginal performance penalty.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/144001225-8c62cad7-a330-4f60-873f-61aa17e99223.png"/>
+</div>
+
+## Introduction
+
+A simple, fully convolutional model for real-time instance segmentation. This is the code for our paper:
+
+- [YOLACT: Real-time Instance Segmentation](https://arxiv.org/abs/1904.02689)
+
+<!-- - [YOLACT++: Better Real-time Instance Segmentation](https://arxiv.org/abs/1912.06218) -->
+
+For a real-time demo, check out our ICCV video:
+[![IMAGE ALT TEXT HERE](https://img.youtube.com/vi/0pMfmo8qfpQ/0.jpg)](https://www.youtube.com/watch?v=0pMfmo8qfpQ)
+
+## Evaluation
+
+Here are our YOLACT models along with their FPS on a Titan Xp and mAP on COCO's `val`:
+
+| Image Size | GPU x BS |   Backbone    | \*FPS | mAP  | Weights |                                                Configs                                                 |                                                            Download                                                             |
+| :--------: | :------: | :-----------: | :---: | :--: | :-----: | :----------------------------------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------------------------------------: |
+|    550     |   1x8    | Resnet50-FPN  | 42.5  | 29.0 |         | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact/yolact_r50_1x8_coco.py)  |  [model](https://download.openmmlab.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco/yolact_r50_1x8_coco_20200908-f38d58df.pth)  |
+|    550     |   8x8    | Resnet50-FPN  | 42.5  | 28.4 |         | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact/yolact_r50_8x8_coco.py)  |  [model](https://download.openmmlab.com/mmdetection/v2.0/yolact/yolact_r50_8x8_coco/yolact_r50_8x8_coco_20200908-ca34f5db.pth)  |
+|    550     |   1x8    | Resnet101-FPN | 33.5  | 30.4 |         | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolact/yolact_r101_1x8_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolact/yolact_r101_1x8_coco/yolact_r101_1x8_coco_20200908-4cbe9101.pth) |
+
+\*Note: The FPS is evaluated by the [original implementation](https://github.com/dbolya/yolact). When calculating FPS, only the model inference time is taken into account. Data loading and post-processing operations such as converting masks to RLE code, generating COCO JSON results, image rendering are not included.
+
+## Training
+
+All the aforementioned models are trained with a single GPU. It typically takes ~12GB VRAM when using resnet-101 as the backbone. If you want to try multiple GPUs training, you may have to modify the configuration files accordingly, such as adjusting the training schedule and freezing batch norm.
+
+```Shell
+# Trains using the resnet-101 backbone with a batch size of 8 on a single GPU.
+./tools/dist_train.sh configs/yolact/yolact_r101.py 1
+```
+
+## Testing
+
+Please refer to [mmdetection/docs/getting_started.md](https://mmdetection.readthedocs.io/en/latest/1_exist_data_model.html#test-existing-models).
+
+## Citation
+
+If you use YOLACT or this code base in your work, please cite
+
+```latex
+@inproceedings{yolact-iccv2019,
+  author    = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
+  title     = {YOLACT: {Real-time} Instance Segmentation},
+  booktitle = {ICCV},
+  year      = {2019},
+}
+```
+
+<!-- For YOLACT++, please cite
+
+```latex
+@misc{yolact-plus-arxiv2019,
+  title         = {YOLACT++: Better Real-time Instance Segmentation},
+  author        = {Daniel Bolya and Chong Zhou and Fanyi Xiao and Yong Jae Lee},
+  year          = {2019},
+  eprint        = {1912.06218},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.CV}
+}
+``` -->
diff --git a/configs/yolact/metafile.yml b/configs/yolact/metafile.yml
new file mode 100644
index 0000000..e7019ae
--- /dev/null
+++ b/configs/yolact/metafile.yml
@@ -0,0 +1,78 @@
+Collections:
+  - Name: YOLACT
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - FPN
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/1904.02689
+      Title: 'YOLACT: Real-time Instance Segmentation'
+    README: configs/yolact/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.5.0/mmdet/models/detectors/yolact.py#L9
+      Version: v2.5.0
+
+Models:
+  - Name: yolact_r50_1x8_coco
+    In Collection: YOLACT
+    Config: configs/yolact/yolact_r50_1x8_coco.py
+    Metadata:
+      Training Resources: 1x V100 GPU
+      Batch Size: 8
+      inference time (ms/im):
+        - value: 23.53
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (550, 550)
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 29.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/yolact/yolact_r50_1x8_coco/yolact_r50_1x8_coco_20200908-f38d58df.pth
+
+  - Name: yolact_r50_8x8_coco
+    In Collection: YOLACT
+    Config: configs/yolact/yolact_r50_8x8_coco.py
+    Metadata:
+      Batch Size: 64
+      inference time (ms/im):
+        - value: 23.53
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (550, 550)
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 28.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/yolact/yolact_r50_8x8_coco/yolact_r50_8x8_coco_20200908-ca34f5db.pth
+
+  - Name: yolact_r101_1x8_coco
+    In Collection: YOLACT
+    Config: configs/yolact/yolact_r101_1x8_coco.py
+    Metadata:
+      Training Resources: 1x V100 GPU
+      Batch Size: 8
+      inference time (ms/im):
+        - value: 29.85
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (550, 550)
+    Results:
+      - Task: Instance Segmentation
+        Dataset: COCO
+        Metrics:
+          mask AP: 30.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/yolact/yolact_r101_1x8_coco/yolact_r101_1x8_coco_20200908-4cbe9101.pth
diff --git a/configs/yolact/yolact_r101_1x8_coco.py b/configs/yolact/yolact_r101_1x8_coco.py
new file mode 100644
index 0000000..532631d
--- /dev/null
+++ b/configs/yolact/yolact_r101_1x8_coco.py
@@ -0,0 +1,7 @@
+_base_ = './yolact_r50_1x8_coco.py'
+
+model = dict(
+    backbone=dict(
+        depth=101,
+        init_cfg=dict(type='Pretrained',
+                      checkpoint='torchvision://resnet101')))
diff --git a/configs/yolact/yolact_r50_1x8_coco.py b/configs/yolact/yolact_r50_1x8_coco.py
new file mode 100644
index 0000000..dc11613
--- /dev/null
+++ b/configs/yolact/yolact_r50_1x8_coco.py
@@ -0,0 +1,165 @@
+_base_ = '../_base_/default_runtime.py'
+
+# model settings
+img_size = 550
+model = dict(
+    type='YOLACT',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=-1,  # do not freeze stem
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=False,  # update the statistics of bn
+        zero_init_residual=False,
+        style='pytorch',
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        start_level=1,
+        add_extra_convs='on_input',
+        num_outs=5,
+        upsample_cfg=dict(mode='bilinear')),
+    bbox_head=dict(
+        type='YOLACTHead',
+        num_classes=80,
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=3,
+            scales_per_octave=1,
+            base_sizes=[8, 16, 32, 64, 128],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[550.0 / x for x in [69, 35, 18, 9, 5]],
+            centers=[(550 * 0.5 / x, 550 * 0.5 / x)
+                     for x in [69, 35, 18, 9, 5]]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=False,
+            reduction='none',
+            loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.5),
+        num_head_convs=1,
+        num_protos=32,
+        use_ohem=True),
+    mask_head=dict(
+        type='YOLACTProtonet',
+        in_channels=256,
+        num_protos=32,
+        num_classes=80,
+        max_masks_to_train=100,
+        loss_mask_weight=6.125),
+    segm_head=dict(
+        type='YOLACTSegmHead',
+        num_classes=80,
+        in_channels=256,
+        loss_segm=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='MaxIoUAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.4,
+            min_pos_iou=0.,
+            ignore_iof_thr=-1,
+            gt_max_assign_all=False),
+        # smoothl1_beta=1.,
+        allowed_border=-1,
+        pos_weight=-1,
+        neg_pos_ratio=3,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        iou_thr=0.5,
+        top_k=200,
+        max_per_img=100))
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.68, 116.78, 103.94], std=[58.40, 57.12, 57.38], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(4.0, 4.0)),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 4)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(img_size, img_size), keep_ratio=False),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(
+        type='PhotoMetricDistortion',
+        brightness_delta=32,
+        contrast_range=(0.5, 1.5),
+        saturation_range=(0.5, 1.5),
+        hue_delta=18),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(img_size, img_size),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=False),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=1e-3, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict()
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.1,
+    step=[20, 42, 49, 52])
+runner = dict(type='EpochBasedRunner', max_epochs=55)
+# cudnn_benchmark = True
+evaluation = dict(metric=['bbox', 'segm'])
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (1 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=8)
diff --git a/configs/yolact/yolact_r50_8x8_coco.py b/configs/yolact/yolact_r50_8x8_coco.py
new file mode 100644
index 0000000..41003ab
--- /dev/null
+++ b/configs/yolact/yolact_r50_8x8_coco.py
@@ -0,0 +1,16 @@
+_base_ = 'yolact_r50_1x8_coco.py'
+
+optimizer = dict(type='SGD', lr=8e-3, momentum=0.9, weight_decay=5e-4)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=1000,
+    warmup_ratio=0.1,
+    step=[20, 42, 49, 52])
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/yolo/README.md b/configs/yolo/README.md
new file mode 100644
index 0000000..c9eb8a6
--- /dev/null
+++ b/configs/yolo/README.md
@@ -0,0 +1,55 @@
+# YOLOv3
+
+> [YOLOv3: An Incremental Improvement](https://arxiv.org/abs/1804.02767)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, similar performance but 3.8x faster.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/144001433-b4f7fb5e-3b7a-414b-b949-93733213b670.png" height="300"/>
+</div>
+
+## Results and Models
+
+|  Backbone  | Scale | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                      Config                                                      |                                                                                                                                                        Download                                                                                                                                                        |
+| :--------: | :---: | :-----: | :------: | :------------: | :----: | :--------------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| DarkNet-53 |  320  |  273e   |   2.7    |      63.9      |  27.9  |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_320_273e_coco.py)     |                         [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-421362b6.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-20200819_172101.log.json)                         |
+| DarkNet-53 |  416  |  273e   |   3.8    |      61.2      |  30.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_mstrain-416_273e_coco.py) |         [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-2b60fcd9.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-20200819_173424.log.json)         |
+| DarkNet-53 |  608  |  273e   |   7.4    |      48.1      |  33.7  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco_20210518_115020.log.json) |
+
+## Mixed Precision Training
+
+We also train YOLOv3 with mixed precision training.
+
+|  Backbone  | Scale | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                        Config                                                         |                                                                                                                                                                  Download                                                                                                                                                                  |
+| :--------: | :---: | :-----: | :------: | :------------: | :----: | :-------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| DarkNet-53 |  608  |  273e   |   4.7    |      48.1      |  33.8  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_fp16_mstrain-608_273e_coco/yolov3_d53_fp16_mstrain-608_273e_coco_20210517_213542-4bc34944.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_fp16_mstrain-608_273e_coco/yolov3_d53_fp16_mstrain-608_273e_coco_20210517_213542.log.json) |
+
+## Lightweight models
+
+|  Backbone   | Scale | Lr schd | Mem (GB) | Inf time (fps) | box AP |                                                          Config                                                          |                                                                                                                                                                        Download                                                                                                                                                                        |
+| :---------: | :---: | :-----: | :------: | :------------: | :----: | :----------------------------------------------------------------------------------------------------------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| MobileNetV2 |  416  |  300e   |   5.3    |                |  23.9  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco/yolov3_mobilenetv2_mstrain-416_300e_coco_20210718_010823-f68a07b3.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco/yolov3_mobilenetv2_mstrain-416_300e_coco_20210718_010823.log.json) |
+| MobileNetV2 |  320  |  300e   |   3.2    |                |  22.2  |     [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolo/yolov3_mobilenetv2_320_300e_coco.py)     |                 [model](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_mobilenetv2_320_300e_coco/yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_mobilenetv2_320_300e_coco/yolov3_mobilenetv2_320_300e_coco_20210719_215349.log.json)                 |
+
+Notice: We reduce the number of channels to 96 in both head and neck. It can reduce the flops and parameters, which makes these models more suitable for edge devices.
+
+## Credit
+
+This implementation originates from the project of Haoyu Wu(@wuhy08) at Western Digital.
+
+## Citation
+
+```latex
+@misc{redmon2018yolov3,
+    title={YOLOv3: An Incremental Improvement},
+    author={Joseph Redmon and Ali Farhadi},
+    year={2018},
+    eprint={1804.02767},
+    archivePrefix={arXiv},
+    primaryClass={cs.CV}
+}
+```
diff --git a/configs/yolo/metafile.yml b/configs/yolo/metafile.yml
new file mode 100644
index 0000000..22c35da
--- /dev/null
+++ b/configs/yolo/metafile.yml
@@ -0,0 +1,124 @@
+Collections:
+  - Name: YOLOv3
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - DarkNet
+    Paper:
+      URL: https://arxiv.org/abs/1804.02767
+      Title: 'YOLOv3: An Incremental Improvement'
+    README: configs/yolo/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.4.0/mmdet/models/detectors/yolo.py#L8
+      Version: v2.4.0
+
+Models:
+  - Name: yolov3_d53_320_273e_coco
+    In Collection: YOLOv3
+    Config: configs/yolo/yolov3_d53_320_273e_coco.py
+    Metadata:
+      Training Memory (GB): 2.7
+      inference time (ms/im):
+        - value: 15.65
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (320, 320)
+      Epochs: 273
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 27.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_320_273e_coco/yolov3_d53_320_273e_coco-421362b6.pth
+
+  - Name: yolov3_d53_mstrain-416_273e_coco
+    In Collection: YOLOv3
+    Config: configs/yolo/yolov3_d53_mstrain-416_273e_coco.py
+    Metadata:
+      Training Memory (GB): 3.8
+      inference time (ms/im):
+        - value: 16.34
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (416, 416)
+      Epochs: 273
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 30.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-416_273e_coco/yolov3_d53_mstrain-416_273e_coco-2b60fcd9.pth
+
+  - Name: yolov3_d53_mstrain-608_273e_coco
+    In Collection: YOLOv3
+    Config: configs/yolo/yolov3_d53_mstrain-608_273e_coco.py
+    Metadata:
+      Training Memory (GB): 7.4
+      inference time (ms/im):
+        - value: 20.79
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP32
+          resolution: (608, 608)
+      Epochs: 273
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 33.7
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_mstrain-608_273e_coco/yolov3_d53_mstrain-608_273e_coco_20210518_115020-a2c3acb8.pth
+
+  - Name: yolov3_d53_fp16_mstrain-608_273e_coco
+    In Collection: YOLOv3
+    Config: configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py
+    Metadata:
+      Training Memory (GB): 4.7
+      inference time (ms/im):
+        - value: 20.79
+          hardware: V100
+          backend: PyTorch
+          batch size: 1
+          mode: FP16
+          resolution: (608, 608)
+      Epochs: 273
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 33.8
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_d53_fp16_mstrain-608_273e_coco/yolov3_d53_fp16_mstrain-608_273e_coco_20210517_213542-4bc34944.pth
+
+  - Name: yolov3_mobilenetv2_320_300e_coco
+    In Collection: YOLOv3
+    Config: configs/yolo/yolov3_mobilenetv2_320_300e_coco.py
+    Metadata:
+      Training Memory (GB): 3.2
+      Epochs: 300
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 22.2
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_mobilenetv2_320_300e_coco/yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth
+
+  - Name: yolov3_mobilenetv2_mstrain-416_300e_coco
+    In Collection: YOLOv3
+    Config: configs/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco.py
+    Metadata:
+      Training Memory (GB): 5.3
+      Epochs: 300
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 23.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco/yolov3_mobilenetv2_mstrain-416_300e_coco_20210718_010823-f68a07b3.pth
diff --git a/configs/yolo/yolov3_d53_320_273e_coco.py b/configs/yolo/yolov3_d53_320_273e_coco.py
new file mode 100644
index 0000000..d4785e3
--- /dev/null
+++ b/configs/yolo/yolov3_d53_320_273e_coco.py
@@ -0,0 +1,42 @@
+_base_ = './yolov3_d53_mstrain-608_273e_coco.py'
+# dataset settings
+img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 2)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(320, 320), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(320, 320),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py b/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py
new file mode 100644
index 0000000..4ef2422
--- /dev/null
+++ b/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py
@@ -0,0 +1,3 @@
+_base_ = './yolov3_d53_mstrain-608_273e_coco.py'
+# fp16 settings
+fp16 = dict(loss_scale='dynamic')
diff --git a/configs/yolo/yolov3_d53_mstrain-416_273e_coco.py b/configs/yolo/yolov3_d53_mstrain-416_273e_coco.py
new file mode 100644
index 0000000..94325c5
--- /dev/null
+++ b/configs/yolo/yolov3_d53_mstrain-416_273e_coco.py
@@ -0,0 +1,42 @@
+_base_ = './yolov3_d53_mstrain-608_273e_coco.py'
+# dataset settings
+img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 2)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=[(320, 320), (416, 416)], keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(416, 416),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py b/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py
new file mode 100644
index 0000000..58a9387
--- /dev/null
+++ b/configs/yolo/yolov3_d53_mstrain-608_273e_coco.py
@@ -0,0 +1,132 @@
+_base_ = '../_base_/default_runtime.py'
+# model settings
+model = dict(
+    type='YOLOV3',
+    backbone=dict(
+        type='Darknet',
+        depth=53,
+        out_indices=(3, 4, 5),
+        init_cfg=dict(type='Pretrained', checkpoint='open-mmlab://darknet53')),
+    neck=dict(
+        type='YOLOV3Neck',
+        num_scales=3,
+        in_channels=[1024, 512, 256],
+        out_channels=[512, 256, 128]),
+    bbox_head=dict(
+        type='YOLOV3Head',
+        num_classes=80,
+        in_channels=[512, 256, 128],
+        out_channels=[1024, 512, 256],
+        anchor_generator=dict(
+            type='YOLOAnchorGenerator',
+            base_sizes=[[(116, 90), (156, 198), (373, 326)],
+                        [(30, 61), (62, 45), (59, 119)],
+                        [(10, 13), (16, 30), (33, 23)]],
+            strides=[32, 16, 8]),
+        bbox_coder=dict(type='YOLOBBoxCoder'),
+        featmap_strides=[32, 16, 8],
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=True,
+            loss_weight=1.0,
+            reduction='sum'),
+        loss_conf=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=True,
+            loss_weight=1.0,
+            reduction='sum'),
+        loss_xy=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=True,
+            loss_weight=2.0,
+            reduction='sum'),
+        loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='GridAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.5,
+            min_pos_iou=0)),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        conf_thr=0.005,
+        nms=dict(type='nms', iou_threshold=0.45),
+        max_per_img=100))
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = '/data/dongzhiwei1/dataset/coco2017/'
+img_norm_cfg = dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile', to_float32=True),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 2)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=[(320, 320), (608, 608)], keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(608, 608),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=2000,  # same as burn-in in darknet
+    warmup_ratio=0.1,
+    step=[218, 246])
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=273)
+evaluation = dict(interval=1, metric=['bbox'])
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/yolo/yolov3_mobilenetv2_320_300e_coco.py b/configs/yolo/yolov3_mobilenetv2_320_300e_coco.py
new file mode 100644
index 0000000..477d253
--- /dev/null
+++ b/configs/yolo/yolov3_mobilenetv2_320_300e_coco.py
@@ -0,0 +1,53 @@
+_base_ = ['./yolov3_mobilenetv2_mstrain-416_300e_coco.py']
+
+# yapf:disable
+model = dict(
+    bbox_head=dict(
+        anchor_generator=dict(
+            base_sizes=[[(220, 125), (128, 222), (264, 266)],
+                        [(35, 87), (102, 96), (60, 170)],
+                        [(10, 15), (24, 36), (72, 42)]])))
+# yapf:enable
+
+# dataset settings
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 2)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
+        min_crop_size=0.3),
+    dict(type='Resize', img_scale=(320, 320), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(320, 320),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    train=dict(dataset=dict(pipeline=train_pipeline)),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
diff --git a/configs/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco.py b/configs/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco.py
new file mode 100644
index 0000000..18e0622
--- /dev/null
+++ b/configs/yolo/yolov3_mobilenetv2_mstrain-416_300e_coco.py
@@ -0,0 +1,142 @@
+_base_ = '../_base_/default_runtime.py'
+# model settings
+model = dict(
+    type='YOLOV3',
+    backbone=dict(
+        type='MobileNetV2',
+        out_indices=(2, 4, 6),
+        act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
+        init_cfg=dict(
+            type='Pretrained', checkpoint='open-mmlab://mmdet/mobilenet_v2')),
+    neck=dict(
+        type='YOLOV3Neck',
+        num_scales=3,
+        in_channels=[320, 96, 32],
+        out_channels=[96, 96, 96]),
+    bbox_head=dict(
+        type='YOLOV3Head',
+        num_classes=80,
+        in_channels=[96, 96, 96],
+        out_channels=[96, 96, 96],
+        anchor_generator=dict(
+            type='YOLOAnchorGenerator',
+            base_sizes=[[(116, 90), (156, 198), (373, 326)],
+                        [(30, 61), (62, 45), (59, 119)],
+                        [(10, 13), (16, 30), (33, 23)]],
+            strides=[32, 16, 8]),
+        bbox_coder=dict(type='YOLOBBoxCoder'),
+        featmap_strides=[32, 16, 8],
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=True,
+            loss_weight=1.0,
+            reduction='sum'),
+        loss_conf=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=True,
+            loss_weight=1.0,
+            reduction='sum'),
+        loss_xy=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=True,
+            loss_weight=2.0,
+            reduction='sum'),
+        loss_wh=dict(type='MSELoss', loss_weight=2.0, reduction='sum')),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='GridAssigner',
+            pos_iou_thr=0.5,
+            neg_iou_thr=0.5,
+            min_pos_iou=0)),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        conf_thr=0.005,
+        nms=dict(type='nms', iou_threshold=0.45),
+        max_per_img=100))
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = 'data/coco/'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(
+        type='Expand',
+        mean=img_norm_cfg['mean'],
+        to_rgb=img_norm_cfg['to_rgb'],
+        ratio_range=(1, 2)),
+    dict(
+        type='MinIoURandomCrop',
+        min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
+        min_crop_size=0.3),
+    dict(
+        type='Resize',
+        img_scale=[(320, 320), (416, 416)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='PhotoMetricDistortion'),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(416, 416),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=24,
+    workers_per_gpu=4,
+    train=dict(
+        type='RepeatDataset',  # use RepeatDataset to speed up training
+        times=10,
+        dataset=dict(
+            type=dataset_type,
+            ann_file=data_root + 'annotations/instances_train2017.json',
+            img_prefix=data_root + 'train2017/',
+            pipeline=train_pipeline)),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+# optimizer
+optimizer = dict(type='SGD', lr=0.003, momentum=0.9, weight_decay=0.0005)
+optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=4000,
+    warmup_ratio=0.0001,
+    step=[24, 28])
+# runtime settings
+runner = dict(type='EpochBasedRunner', max_epochs=30)
+evaluation = dict(interval=1, metric=['bbox'])
+find_unused_parameters = True
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (24 samples per GPU)
+auto_scale_lr = dict(base_batch_size=192)
diff --git a/configs/yolof/README.md b/configs/yolof/README.md
new file mode 100644
index 0000000..e88da02
--- /dev/null
+++ b/configs/yolof/README.md
@@ -0,0 +1,35 @@
+# YOLOF
+
+> [You Only Look One-level Feature](https://arxiv.org/abs/2103.09460)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+This paper revisits feature pyramids networks (FPN) for one-stage detectors and points out that the success of FPN is due to its divide-and-conquer solution to the optimization problem in object detection rather than multi-scale feature fusion. From the perspective of optimization, we introduce an alternative way to address the problem instead of adopting the complex feature pyramids - {\\em utilizing only one-level feature for detection}. Based on the simple and efficient solution, we present You Only Look One-level Feature (YOLOF). In our method, two key components, Dilated Encoder and Uniform Matching, are proposed and bring considerable improvements. Extensive experiments on the COCO benchmark prove the effectiveness of the proposed model. Our YOLOF achieves comparable results with its feature pyramids counterpart RetinaNet while being 2.5× faster. Without transformer layers, YOLOF can match the performance of DETR in a single-level feature manner with 7× less training epochs. With an image size of 608×608, YOLOF achieves 44.3 mAP running at 60 fps on 2080Ti, which is 13% faster than YOLOv4.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/144001639-257374ef-7d4f-412b-a783-88abdd22f277.png"/>
+</div>
+
+## Results and Models
+
+| Backbone | Style | Epoch | Lr schd | Mem (GB) | box AP |                                                  Config                                                   |                                                                                                                                         Download                                                                                                                                         |
+| :------: | :---: | :---: | :-----: | :------: | :----: | :-------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| R-50-C5  | caffe |   Y   |   1x    |   8.3    |  37.5  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolof/yolof_r50_c5_8x8_1x_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolof/yolof_r50_c5_8x8_1x_coco/yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/yolof/yolof_r50_c5_8x8_1x_coco/yolof_r50_c5_8x8_1x_coco_20210425_024427.log.json) |
+
+**Note**:
+
+1. We find that the performance is unstable and may fluctuate by about 0.3 mAP. mAP 37.4 ~ 37.7 is acceptable in YOLOF_R_50_C5_1x. Such fluctuation can also be found in the [original implementation](https://github.com/chensnathan/YOLOF).
+2. In addition to instability issues, sometimes there are large loss fluctuations and NAN, so there may still be problems with this project, which will be improved subsequently.
+
+## Citation
+
+```latex
+@inproceedings{chen2021you,
+  title={You Only Look One-level Feature},
+  author={Chen, Qiang and Wang, Yingming and Yang, Tong and Zhang, Xiangyu and Cheng, Jian and Sun, Jian},
+  booktitle={IEEE Conference on Computer Vision and Pattern Recognition},
+  year={2021}
+}
+```
diff --git a/configs/yolof/metafile.yml b/configs/yolof/metafile.yml
new file mode 100644
index 0000000..9436fee
--- /dev/null
+++ b/configs/yolof/metafile.yml
@@ -0,0 +1,32 @@
+Collections:
+  - Name: YOLOF
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Momentum
+        - Weight Decay
+      Training Resources: 8x V100 GPUs
+      Architecture:
+        - Dilated Encoder
+        - ResNet
+    Paper:
+      URL: https://arxiv.org/abs/2103.09460
+      Title: 'You Only Look One-level Feature'
+    README: configs/yolof/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.12.0/mmdet/models/detectors/yolof.py#L6
+      Version: v2.12.0
+
+Models:
+  - Name: yolof_r50_c5_8x8_1x_coco
+    In Collection: YOLOF
+    Config: configs/yolof/yolof_r50_c5_8x8_1x_coco.py
+    Metadata:
+      Training Memory (GB): 8.3
+      Epochs: 12
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 37.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/yolof/yolof_r50_c5_8x8_1x_coco/yolof_r50_c5_8x8_1x_coco_20210425_024427-8e864411.pth
diff --git a/configs/yolof/yolof_r50_c5_8x8_1x_coco.py b/configs/yolof/yolof_r50_c5_8x8_1x_coco.py
new file mode 100644
index 0000000..d0b9649
--- /dev/null
+++ b/configs/yolof/yolof_r50_c5_8x8_1x_coco.py
@@ -0,0 +1,111 @@
+_base_ = [
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    type='YOLOF',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(3, ),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=False),
+        norm_eval=True,
+        style='caffe',
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint='open-mmlab://detectron/resnet50_caffe')),
+    neck=dict(
+        type='DilatedEncoder',
+        in_channels=2048,
+        out_channels=512,
+        block_mid_channels=128,
+        num_residual_blocks=4,
+        block_dilations=[2, 4, 6, 8]),
+    bbox_head=dict(
+        type='YOLOFHead',
+        num_classes=80,
+        in_channels=512,
+        reg_decoded_bbox=True,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[1, 2, 4, 8, 16],
+            strides=[32]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1., 1., 1., 1.],
+            add_ctr_clamp=True,
+            ctr_clamp=32),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.0)),
+    # training and testing settings
+    train_cfg=dict(
+        assigner=dict(
+            type='UniformAssigner', pos_ignore_thr=0.15, neg_ignore_thr=0.7),
+        allowed_border=-1,
+        pos_weight=-1,
+        debug=False),
+    test_cfg=dict(
+        nms_pre=1000,
+        min_bbox_size=0,
+        score_thr=0.05,
+        nms=dict(type='nms', iou_threshold=0.6),
+        max_per_img=100))
+# optimizer
+optimizer = dict(
+    type='SGD',
+    lr=0.12,
+    momentum=0.9,
+    weight_decay=0.0001,
+    paramwise_cfg=dict(
+        norm_decay_mult=0., custom_keys={'backbone': dict(lr_mult=1. / 3)}))
+lr_config = dict(warmup_iters=1500, warmup_ratio=0.00066667)
+
+# use caffe img_norm
+img_norm_cfg = dict(
+    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='RandomShift', shift_ratio=0.5, max_shift_px=32),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=8,
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/yolof/yolof_r50_c5_8x8_iter-1x_coco.py b/configs/yolof/yolof_r50_c5_8x8_iter-1x_coco.py
new file mode 100644
index 0000000..c95c02d
--- /dev/null
+++ b/configs/yolof/yolof_r50_c5_8x8_iter-1x_coco.py
@@ -0,0 +1,14 @@
+_base_ = './yolof_r50_c5_8x8_1x_coco.py'
+
+# We implemented the iter-based config according to the source code.
+# COCO dataset has 117266 images after filtering. We use 8 gpu and
+# 8 batch size training, so 22500 is equivalent to
+# 22500/(117266/(8x8))=12.3 epoch, 15000 is equivalent to 8.2 epoch,
+# 20000 is equivalent to 10.9 epoch. Due to lr(0.12) is large,
+# the iter-based and epoch-based setting have about 0.2 difference on
+# the mAP evaluation value.
+lr_config = dict(step=[15000, 20000])
+runner = dict(_delete_=True, type='IterBasedRunner', max_iters=22500)
+checkpoint_config = dict(interval=2500)
+evaluation = dict(interval=4500)
+log_config = dict(interval=20)
diff --git a/configs/yolox/README.md b/configs/yolox/README.md
new file mode 100644
index 0000000..4890fbd
--- /dev/null
+++ b/configs/yolox/README.md
@@ -0,0 +1,39 @@
+# YOLOX
+
+> [YOLOX: Exceeding YOLO Series in 2021](https://arxiv.org/abs/2107.08430)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX. We switch the YOLO detector to an anchor-free manner and conduct other advanced detection techniques, i.e., a decoupled head and the leading label assignment strategy SimOTA to achieve state-of-the-art results across a large scale range of models: For YOLO-Nano with only 0.91M parameters and 1.08G FLOPs, we get 25.3% AP on COCO, surpassing NanoDet by 1.8% AP; for YOLOv3, one of the most widely used detectors in industry, we boost it to 47.3% AP on COCO, outperforming the current best practice by 3.0% AP; for YOLOX-L with roughly the same amount of parameters as YOLOv4-CSP, YOLOv5-L, we achieve 50.0% AP on COCO at a speed of 68.9 FPS on Tesla V100, exceeding YOLOv5-L by 1.8% AP. Further, we won the 1st Place on Streaming Perception Challenge (Workshop on Autonomous Driving at CVPR 2021) using a single YOLOX-L model. We hope this report can provide useful experience for developers and researchers in practical scenes, and we also provide deploy versions with ONNX, TensorRT, NCNN, and Openvino supported.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/40661020/144001736-9fb303dd-eac7-46b0-ad45-214cfa51e928.png"/>
+</div>
+
+## Results and Models
+
+|  Backbone  | size | Mem (GB) | box AP |                                                  Config                                                   |                                                                                                                                         Download                                                                                                                                         |
+| :--------: | :--: | :------: | :----: | :-------------------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| YOLOX-tiny | 416  |   3.5    |  32.0  | [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_tiny_8x8_300e_coco.py) | [model](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20211124_171234.log.json) |
+|  YOLOX-s   | 640  |   7.6    |  40.5  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_s_8x8_300e_coco.py)   |       [model](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211121_095711.log.json)       |
+|  YOLOX-l   | 640  |   19.9   |  49.4  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_l_8x8_300e_coco.py)   |       [model](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236.log.json)       |
+|  YOLOX-x   | 640  |   28.1   |  50.9  |  [config](https://github.com/open-mmlab/mmdetection/tree/master/configs/yolox/yolox_x_8x8_300e_coco.py)   |       [model](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254.log.json)       |
+
+**Note**:
+
+1. The test score threshold is 0.001, and the box AP indicates the best AP.
+2. Due to the need for pre-training weights, we cannot reproduce the performance of the `yolox-nano` model. Please refer to https://github.com/Megvii-BaseDetection/YOLOX/issues/674 for more information.
+3. We also trained the model by the official release of YOLOX based on [Megvii-BaseDetection/YOLOX#735](https://github.com/Megvii-BaseDetection/YOLOX/issues/735) with commit ID [38c633](https://github.com/Megvii-BaseDetection/YOLOX/tree/38c633bf176462ee42b110c70e4ffe17b5753208). We found that the best AP of `YOLOX-tiny`, `YOLOX-s`, `YOLOX-l`, and `YOLOX-x` is 31.8, 40.3, 49.2, and 50.9, respectively. The performance is consistent with that of our re-implementation (see Table above) but still has a gap (0.3~0.8 AP) in comparison with the reported performance in their [README](https://github.com/Megvii-BaseDetection/YOLOX/blob/38c633bf176462ee42b110c70e4ffe17b5753208/README.md#benchmark).
+
+## Citation
+
+```latex
+@article{yolox2021,
+  title={{YOLOX}: Exceeding YOLO Series in 2021},
+  author={Ge, Zheng and Liu, Songtao and Wang, Feng and Li, Zeming and Sun, Jian},
+  journal={arXiv preprint arXiv:2107.08430},
+  year={2021}
+}
+```
diff --git a/configs/yolox/metafile.yml b/configs/yolox/metafile.yml
new file mode 100644
index 0000000..845cb0a
--- /dev/null
+++ b/configs/yolox/metafile.yml
@@ -0,0 +1,70 @@
+Collections:
+  - Name: YOLOX
+    Metadata:
+      Training Data: COCO
+      Training Techniques:
+        - SGD with Nesterov
+        - Weight Decay
+        - Cosine Annealing Lr Updater
+      Training Resources: 8x TITANXp GPUs
+      Architecture:
+        - CSPDarkNet
+        - PAFPN
+    Paper:
+      URL: https://arxiv.org/abs/2107.08430
+      Title: 'YOLOX: Exceeding YOLO Series in 2021'
+    README: configs/yolox/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection/blob/v2.15.1/mmdet/models/detectors/yolox.py#L6
+      Version: v2.15.1
+
+
+Models:
+  - Name: yolox_s_8x8_300e_coco
+    In Collection: YOLOX
+    Config: configs/yolox/yolox_s_8x8_300e_coco.py
+    Metadata:
+      Training Memory (GB): 7.6
+      Epochs: 300
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 40.5
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_s_8x8_300e_coco/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth
+  - Name: yolox_l_8x8_300e_coco
+    In Collection: YOLOX
+    Config: configs/yolox/yolox_l_8x8_300e_coco.py
+    Metadata:
+      Training Memory (GB): 19.9
+      Epochs: 300
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 49.4
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_l_8x8_300e_coco/yolox_l_8x8_300e_coco_20211126_140236-d3bd2b23.pth
+  - Name: yolox_x_8x8_300e_coco
+    In Collection: YOLOX
+    Config: configs/yolox/yolox_x_8x8_300e_coco.py
+    Metadata:
+      Training Memory (GB): 28.1
+      Epochs: 300
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 50.9
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_x_8x8_300e_coco/yolox_x_8x8_300e_coco_20211126_140254-1ef88d67.pth
+  - Name: yolox_tiny_8x8_300e_coco
+    In Collection: YOLOX
+    Config: configs/yolox/yolox_tiny_8x8_300e_coco.py
+    Metadata:
+      Training Memory (GB): 3.5
+      Epochs: 300
+    Results:
+      - Task: Object Detection
+        Dataset: COCO
+        Metrics:
+          box AP: 32.0
+    Weights: https://download.openmmlab.com/mmdetection/v2.0/yolox/yolox_tiny_8x8_300e_coco/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth
diff --git a/configs/yolox/yolox_l_8x8_300e_coco.py b/configs/yolox/yolox_l_8x8_300e_coco.py
new file mode 100644
index 0000000..dcbfa18
--- /dev/null
+++ b/configs/yolox/yolox_l_8x8_300e_coco.py
@@ -0,0 +1,8 @@
+_base_ = './yolox_s_8x8_300e_coco.py'
+
+# model settings
+model = dict(
+    backbone=dict(deepen_factor=1.0, widen_factor=1.0),
+    neck=dict(
+        in_channels=[256, 512, 1024], out_channels=256, num_csp_blocks=3),
+    bbox_head=dict(in_channels=256, feat_channels=256))
diff --git a/configs/yolox/yolox_m_8x8_300e_coco.py b/configs/yolox/yolox_m_8x8_300e_coco.py
new file mode 100644
index 0000000..3048c95
--- /dev/null
+++ b/configs/yolox/yolox_m_8x8_300e_coco.py
@@ -0,0 +1,8 @@
+_base_ = './yolox_s_8x8_300e_coco.py'
+
+# model settings
+model = dict(
+    backbone=dict(deepen_factor=0.67, widen_factor=0.75),
+    neck=dict(in_channels=[192, 384, 768], out_channels=192, num_csp_blocks=2),
+    bbox_head=dict(in_channels=192, feat_channels=192),
+)
diff --git a/configs/yolox/yolox_nano_8x8_300e_coco.py b/configs/yolox/yolox_nano_8x8_300e_coco.py
new file mode 100644
index 0000000..d33ed04
--- /dev/null
+++ b/configs/yolox/yolox_nano_8x8_300e_coco.py
@@ -0,0 +1,11 @@
+_base_ = './yolox_tiny_8x8_300e_coco.py'
+
+# model settings
+model = dict(
+    backbone=dict(deepen_factor=0.33, widen_factor=0.25, use_depthwise=True),
+    neck=dict(
+        in_channels=[64, 128, 256],
+        out_channels=64,
+        num_csp_blocks=1,
+        use_depthwise=True),
+    bbox_head=dict(in_channels=64, feat_channels=64, use_depthwise=True))
diff --git a/configs/yolox/yolox_quant_general.py b/configs/yolox/yolox_quant_general.py
new file mode 100644
index 0000000..fe68e91
--- /dev/null
+++ b/configs/yolox/yolox_quant_general.py
@@ -0,0 +1,39 @@
+trace_config = dict(
+    backbone_detail = dict(
+        input_concrete_args = dict(),
+        preserve_attr = ['arch_settings', 'dump_patches', 'frozen_stages', 'init_cfg', 'is_init', 'layers', 'norm_eval', 'out_indices', 'use_depthwise'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = [],  # 用于取消某些算子的量化  len(set(x[0].flatten().tolist()))
+        specified_general_quantizers = [],
+        last_8bit_module = ['stem.conv.conv']
+    )),
+    neck_detail = dict(
+        input_concrete_args = dict(in_num = 3),
+        preserve_attr = ['in_channels', 'init_cfg', 'is_init', 'out_channels'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(  # 'getitem_post_act_fake_quantizer'
+        exclude_prefixes = [],  
+        removed_quantizer_names = ['getitem_post_act_fake_quantizer', 'getitem_1_post_act_fake_quantizer'],  
+        specified_general_quantizers = [], # 输入全是对称的
+        last_8bit_module = []
+    )),
+    bbox_head_detail = dict(
+        input_concrete_args = dict(in_num = 3),
+        preserve_attr = ['act_cfg', 'assigner', 'cls_out_channels', 'conv_bias', 'conv_cfg', 'dcn_on_last_conv', 'dump_patches', 'feat_channels', 'fp16_enabled', 
+        'in_channels', 'init_cfg', 'is_init', 'loss_bbox', 'loss_cls', 'loss_l1', 'loss_obj', 'norm_cfg'
+        , 'num_classes', 'prior_generator', 'sampler', 'sampling', 'stacked_convs', 'strides', 'test_cfg', 'train_cfg', 'use_depthwise', 'use_l1', 'use_sigmoid_cls'
+        
+        , '_bbox_decode', '_bbox_post_process', '_bboxes_nms', '_get_backward_hooks', '_get_bboxes_single', '_get_l1_target', '_get_target_single'
+        
+        , 'simple_test', 'async_simple_test_rpn', 'aug_test_bboxes', 'aug_test_rpn', 'forward_single', 'forward_train', 'get_bboxes', 'get_targets', 'loss', 'loss_single', 'merge_aug_bboxes', 
+        'simple_test_bboxes', 'simple_test_rpn'],
+        not_duplicated_prefixes = [],
+        further_detail = dict(
+        exclude_prefixes = [],  
+        removed_quantizer_names = [],  
+        qloss_flag = True, 
+        specified_general_quantizers = [], # 输入全是对称的
+        last_8bit_module = ['multi_level_conv_cls.0', 'multi_level_conv_reg.0', 'multi_level_conv_obj.0', 'multi_level_conv_cls.1', 'multi_level_conv_reg.1', 'multi_level_conv_obj.1', 'multi_level_conv_cls.2', 'multi_level_conv_reg.2', 'multi_level_conv_obj.2']
+    )))
\ No newline at end of file
diff --git a/configs/yolox/yolox_s_8x8_300e_coco.py b/configs/yolox/yolox_s_8x8_300e_coco.py
new file mode 100644
index 0000000..97ff23e
--- /dev/null
+++ b/configs/yolox/yolox_s_8x8_300e_coco.py
@@ -0,0 +1,165 @@
+_base_ = ['../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py']
+
+img_scale = (640, 640)  # height, width
+
+# model settings
+model = dict(
+    type='YOLOX',
+    input_size=img_scale,
+    random_size_range=(15, 25),
+    random_size_interval=10,
+    backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5),
+    neck=dict(
+        type='YOLOXPAFPN',
+        in_channels=[128, 256, 512],
+        out_channels=128,
+        num_csp_blocks=1),
+    bbox_head=dict(
+        type='YOLOXHead', num_classes=80, in_channels=128, feat_channels=128),
+    train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
+    # In order to align the source code, the threshold of the val phase is
+    # 0.01, and the threshold of the test phase is 0.001.
+    test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
+
+# dataset settings
+data_root = 'data/coco/'
+dataset_type = 'CocoDataset'
+
+train_pipeline = [
+    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.1, 2),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
+    dict(
+        type='MixUp',
+        img_scale=img_scale,
+        ratio_range=(0.8, 1.6),
+        pad_val=114.0),
+    dict(type='YOLOXHSVRandomAug'),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    # According to the official implementation, multi-scale
+    # training is not considered here but in the
+    # 'mmdet/models/detectors/yolox.py'.
+    dict(type='Resize', img_scale=img_scale, keep_ratio=True),
+    dict(
+        type='Pad',
+        pad_to_square=True,
+        # If the image is three-channel, the pad value needs
+        # to be set separately for each channel.
+        pad_val=dict(img=(114.0, 114.0, 114.0))),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+train_dataset = dict(
+    type='MultiImageMixDataset',
+    dataset=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ],
+        filter_empty_gt=False,
+    ),
+    pipeline=train_pipeline)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=img_scale,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Pad',
+                pad_to_square=True,
+                pad_val=dict(img=(114.0, 114.0, 114.0))),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    persistent_workers=True,
+    train=train_dataset,
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+
+# optimizer
+# default 8 gpu
+optimizer = dict(
+    type='SGD',
+    lr=0.01,
+    momentum=0.9,
+    weight_decay=5e-4,
+    nesterov=True,
+    paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.))
+optimizer_config = dict(grad_clip=None)
+
+max_epochs = 300
+num_last_epochs = 15
+resume_from = None
+interval = 10
+
+# learning policy
+lr_config = dict(
+    _delete_=True,
+    policy='YOLOX',
+    warmup='exp',
+    by_epoch=False,
+    warmup_by_epoch=True,
+    warmup_ratio=1,
+    warmup_iters=5,  # 5 epoch
+    num_last_epochs=num_last_epochs,
+    min_lr_ratio=0.05)
+
+runner = dict(type='EpochBasedRunner', max_epochs=max_epochs)
+
+custom_hooks = [
+    dict(
+        type='YOLOXModeSwitchHook',
+        num_last_epochs=num_last_epochs,
+        priority=48),
+    dict(
+        type='SyncNormHook',
+        num_last_epochs=num_last_epochs,
+        interval=interval,
+        priority=48),
+    dict(
+        type='ExpMomentumEMAHook',
+        resume_from=resume_from,
+        momentum=0.0001,
+        priority=49)
+]
+checkpoint_config = dict(interval=interval)
+evaluation = dict(
+    save_best='auto',
+    # The evaluation interval is 'interval' when running epoch is
+    # less than ‘max_epochs - num_last_epochs’.
+    # The evaluation interval is 1 when running epoch is greater than
+    # or equal to ‘max_epochs - num_last_epochs’.
+    interval=interval,
+    dynamic_intervals=[(max_epochs - num_last_epochs, 1)],
+    metric='bbox')
+log_config = dict(interval=50)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/yolox/yolox_s_8x8_300e_coco_quant_w2a2.py b/configs/yolox/yolox_s_8x8_300e_coco_quant_w2a2.py
new file mode 100644
index 0000000..8a72879
--- /dev/null
+++ b/configs/yolox/yolox_s_8x8_300e_coco_quant_w2a2.py
@@ -0,0 +1,169 @@
+_base_ = ['../_base_/schedules/schedule_qat_w4a4.py', '../_base_/default_runtime.py'
+          , 'yolox_quant_general.py']
+
+img_scale = (640, 640)  # height, width
+
+# model settings
+model = dict(
+    type='YOLOX',
+    input_size=img_scale,
+    random_size_range=(15, 25),
+    random_size_interval=10,
+    backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5),
+    neck=dict(
+        type='YOLOXPAFPN',
+        in_channels=[128, 256, 512],
+        out_channels=128,
+        num_csp_blocks=1),
+    bbox_head=dict(
+        type='YOLOXHead', num_classes=80, in_channels=128, feat_channels=128),
+    train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
+    # In order to align the source code, the threshold of the val phase is
+    # 0.01, and the threshold of the test phase is 0.001.
+    test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
+
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = '/data/dongzhiwei1/dataset/coco2017/'
+
+train_pipeline = [
+    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.1, 2),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
+    dict(
+        type='MixUp',
+        img_scale=img_scale,
+        ratio_range=(0.8, 1.6),
+        pad_val=114.0),
+    dict(type='YOLOXHSVRandomAug'),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    # According to the official implementation, multi-scale
+    # training is not considered here but in the
+    # 'mmdet/models/detectors/yolox.py'.
+    dict(type='Resize', img_scale=img_scale, keep_ratio=True),
+    dict(
+        type='Pad',
+        pad_to_square=True,
+        # If the image is three-channel, the pad value needs
+        # to be set separately for each channel.
+        pad_val=dict(img=(114.0, 114.0, 114.0))),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+train_dataset = dict(
+    type='MultiImageMixDataset',
+    dataset=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ],
+        filter_empty_gt=False,
+    ),
+    pipeline=train_pipeline)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=img_scale,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Pad',
+                pad_to_square=True,
+                pad_val=dict(img=(114.0, 114.0, 114.0))),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+data = dict(
+    samples_per_gpu=32,
+    workers_per_gpu=8,
+    # persistent_workers=True,
+    train=train_dataset,
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+
+# optimizer
+# default 8 gpu
+optimizer = dict(
+    type='SGD',
+    lr=0.008,
+    momentum=0.9,
+    weight_decay=5e-4,
+    nesterov=True,
+    paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.)
+    )
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=18, norm_type=2))
+
+
+max_epochs = 18
+num_last_epochs = 6
+resume_from = None
+interval = 1
+
+# learning policy
+lr_config = dict(
+    _delete_=True,
+    policy='YOLOX',
+    warmup='exp',
+    by_epoch=False,
+    warmup_by_epoch=True,
+    warmup_ratio=1,
+    warmup_iters=1,  # 1 epoch
+    num_last_epochs=num_last_epochs,
+    min_lr_ratio=0.01)  # 要让l1启动时为4e-5
+
+runner = dict(type='EpochBasedRunner', max_epochs=max_epochs)
+
+custom_hooks = [
+    dict(
+        type='YOLOXModeSwitchHook',  # 这玩意注释了有效！可是为啥呢
+        num_last_epochs=num_last_epochs,
+        priority=48),
+    dict(
+        type='SyncNormHook',
+        num_last_epochs=num_last_epochs,
+        interval=interval,
+        priority=48),
+    dict(
+        type='ExpMomentumEMAHook',
+        resume_from=resume_from,
+        momentum=0.0001,
+        priority=49)
+]
+checkpoint_config = dict(interval=12)
+evaluation = dict(
+    save_best='auto',
+    # The evaluation interval is 'interval' when running epoch is
+    # less than ‘max_epochs - num_last_epochs’.
+    # The evaluation interval is 1 when running epoch is greater than
+    # or equal to ‘max_epochs - num_last_epochs’.
+    interval=interval,
+    dynamic_intervals=[(max_epochs - num_last_epochs, 1)],
+    metric='bbox')
+log_config = dict(interval=200)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
+load_from = '../long_used_pretrained/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth'
diff --git a/configs/yolox/yolox_s_8x8_300e_coco_quant_w4a4.py b/configs/yolox/yolox_s_8x8_300e_coco_quant_w4a4.py
new file mode 100644
index 0000000..ac07886
--- /dev/null
+++ b/configs/yolox/yolox_s_8x8_300e_coco_quant_w4a4.py
@@ -0,0 +1,169 @@
+_base_ = ['../_base_/schedules/schedule_qat_w4a4.py', '../_base_/default_runtime.py'
+          , 'yolox_quant_general.py']
+
+img_scale = (640, 640)  # height, width
+
+# model settings
+model = dict(
+    type='YOLOX',
+    input_size=img_scale,
+    random_size_range=(15, 25),
+    random_size_interval=10,
+    backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5),
+    neck=dict(
+        type='YOLOXPAFPN',
+        in_channels=[128, 256, 512],
+        out_channels=128,
+        num_csp_blocks=1),
+    bbox_head=dict(
+        type='YOLOXHead', num_classes=80, in_channels=128, feat_channels=128),
+    train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
+    # In order to align the source code, the threshold of the val phase is
+    # 0.01, and the threshold of the test phase is 0.001.
+    test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
+
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = '/data/dongzhiwei1/dataset/coco2017/'
+
+train_pipeline = [
+    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.1, 2),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
+    dict(
+        type='MixUp',
+        img_scale=img_scale,
+        ratio_range=(0.8, 1.6),
+        pad_val=114.0),
+    dict(type='YOLOXHSVRandomAug'),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    # According to the official implementation, multi-scale
+    # training is not considered here but in the
+    # 'mmdet/models/detectors/yolox.py'.
+    dict(type='Resize', img_scale=img_scale, keep_ratio=True),
+    dict(
+        type='Pad',
+        pad_to_square=True,
+        # If the image is three-channel, the pad value needs
+        # to be set separately for each channel.
+        pad_val=dict(img=(114.0, 114.0, 114.0))),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+train_dataset = dict(
+    type='MultiImageMixDataset',
+    dataset=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ],
+        filter_empty_gt=False,
+    ),
+    pipeline=train_pipeline)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=img_scale,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Pad',
+                pad_to_square=True,
+                pad_val=dict(img=(114.0, 114.0, 114.0))),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8,
+    # persistent_workers=True,
+    train=train_dataset,
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+
+# optimizer
+# default 8 gpu
+optimizer = dict(
+    type='SGD',
+    lr=0.002,
+    momentum=0.9,
+    weight_decay=5e-4,
+    nesterov=True,
+    paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.)
+    )
+optimizer_config = dict(_delete_=True, grad_clip=dict(max_norm=18, norm_type=2))
+
+
+max_epochs = 16
+num_last_epochs = 6
+resume_from = None
+interval = 1
+
+# learning policy
+lr_config = dict(
+    _delete_=True,
+    policy='YOLOX',
+    warmup='exp',
+    by_epoch=False,
+    warmup_by_epoch=True,
+    warmup_ratio=1,
+    warmup_iters=1,  # 1 epoch
+    num_last_epochs=num_last_epochs,
+    min_lr_ratio=0.1)  # 要让l1启动时为4e-5
+
+runner = dict(type='EpochBasedRunner', max_epochs=max_epochs)
+
+custom_hooks = [
+    dict(
+        type='YOLOXModeSwitchHook',  # 这玩意注释了有效！可是为啥呢
+        num_last_epochs=num_last_epochs,
+        priority=48),
+    dict(
+        type='SyncNormHook',
+        num_last_epochs=num_last_epochs,
+        interval=interval,
+        priority=48),
+    dict(
+        type='ExpMomentumEMAHook',
+        resume_from=resume_from,
+        momentum=0.0001,
+        priority=49)
+]
+checkpoint_config = dict(interval=12)
+evaluation = dict(
+    save_best='auto',
+    # The evaluation interval is 'interval' when running epoch is
+    # less than ‘max_epochs - num_last_epochs’.
+    # The evaluation interval is 1 when running epoch is greater than
+    # or equal to ‘max_epochs - num_last_epochs’.
+    interval=interval,
+    dynamic_intervals=[(max_epochs - num_last_epochs, 1)],
+    metric='bbox')
+log_config = dict(interval=200)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
+load_from = '../long_used_pretrained/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth'
diff --git a/configs/yolox/yolox_s_8x8_300e_voc_quant_w4a4.py b/configs/yolox/yolox_s_8x8_300e_voc_quant_w4a4.py
new file mode 100644
index 0000000..786b6b0
--- /dev/null
+++ b/configs/yolox/yolox_s_8x8_300e_voc_quant_w4a4.py
@@ -0,0 +1,167 @@
+_base_ = ['../_base_/schedules/schedule_qat_w4a4.py', '../_base_/default_runtime.py'
+          , 'yolox_quant_general.py']
+
+img_scale = (640, 640)  # height, width
+
+# model settings
+model = dict(
+    type='YOLOX',
+    input_size=img_scale,
+    random_size_range=(15, 25),
+    random_size_interval=10,
+    backbone=dict(type='CSPDarknet', deepen_factor=0.33, widen_factor=0.5),
+    neck=dict(
+        type='YOLOXPAFPN',
+        in_channels=[128, 256, 512],
+        out_channels=128,
+        num_csp_blocks=1),
+    bbox_head=dict(
+        type='YOLOXHead', num_classes=80, in_channels=128, feat_channels=128),
+    train_cfg=dict(assigner=dict(type='SimOTAAssigner', center_radius=2.5)),
+    # In order to align the source code, the threshold of the val phase is
+    # 0.01, and the threshold of the test phase is 0.001.
+    test_cfg=dict(score_thr=0.01, nms=dict(type='nms', iou_threshold=0.65)))
+
+# dataset settings
+dataset_type = 'CocoDataset'
+data_root = '/data/dongzhiwei1/dataset/coco2017/'
+
+train_pipeline = [
+    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.1, 2),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
+    dict(
+        type='MixUp',
+        img_scale=img_scale,
+        ratio_range=(0.8, 1.6),
+        pad_val=114.0),
+    dict(type='YOLOXHSVRandomAug'),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    # According to the official implementation, multi-scale
+    # training is not considered here but in the
+    # 'mmdet/models/detectors/yolox.py'.
+    dict(type='Resize', img_scale=img_scale, keep_ratio=True),
+    dict(
+        type='Pad',
+        pad_to_square=True,
+        # If the image is three-channel, the pad value needs
+        # to be set separately for each channel.
+        pad_val=dict(img=(114.0, 114.0, 114.0))),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+train_dataset = dict(
+    type='MultiImageMixDataset',
+    dataset=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'images/train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ],
+        filter_empty_gt=False,
+    ),
+    pipeline=train_pipeline)
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=img_scale,
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Pad',
+                pad_to_square=True,
+                pad_val=dict(img=(114.0, 114.0, 114.0))),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+data = dict(
+    samples_per_gpu=8,
+    workers_per_gpu=4,
+    persistent_workers=True,
+    train=train_dataset,
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'images/val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'images/val2017/',
+        pipeline=test_pipeline))
+
+# optimizer
+# default 8 gpu
+optimizer = dict(
+    type='SGD',
+    lr=0.01,
+    momentum=0.9,
+    weight_decay=5e-4,
+    nesterov=True,
+    paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.))
+optimizer_config = dict(grad_clip=None)
+
+max_epochs = 300
+num_last_epochs = 15
+resume_from = None
+interval = 10
+
+# learning policy
+lr_config = dict(
+    _delete_=True,
+    policy='YOLOX',
+    warmup='exp',
+    by_epoch=False,
+    warmup_by_epoch=True,
+    warmup_ratio=1,
+    warmup_iters=5,  # 5 epoch
+    num_last_epochs=num_last_epochs,
+    min_lr_ratio=0.05)
+
+runner = dict(type='EpochBasedRunner', max_epochs=max_epochs)
+
+custom_hooks = [
+    dict(
+        type='YOLOXModeSwitchHook',
+        num_last_epochs=num_last_epochs,
+        priority=48),
+    dict(
+        type='SyncNormHook',
+        num_last_epochs=num_last_epochs,
+        interval=interval,
+        priority=48),
+    dict(
+        type='ExpMomentumEMAHook',
+        resume_from=resume_from,
+        momentum=0.0001,
+        priority=49)
+]
+checkpoint_config = dict(interval=interval)
+evaluation = dict(
+    save_best='auto',
+    # The evaluation interval is 'interval' when running epoch is
+    # less than ‘max_epochs - num_last_epochs’.
+    # The evaluation interval is 1 when running epoch is greater than
+    # or equal to ‘max_epochs - num_last_epochs’.
+    interval=interval,
+    dynamic_intervals=[(max_epochs - num_last_epochs, 1)],
+    metric='bbox')
+log_config = dict(interval=50)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
+# load_from = '../long_used_pretrained/yolox_s_8x8_300e_coco_20211121_095711-4592a793.pth'
diff --git a/configs/yolox/yolox_tiny_8x8_300e_coco.py b/configs/yolox/yolox_tiny_8x8_300e_coco.py
new file mode 100644
index 0000000..75931ba
--- /dev/null
+++ b/configs/yolox/yolox_tiny_8x8_300e_coco.py
@@ -0,0 +1,58 @@
+_base_ = './yolox_s_8x8_300e_coco.py'
+
+# model settings
+model = dict(
+    random_size_range=(10, 20),
+    backbone=dict(deepen_factor=0.33, widen_factor=0.375),
+    neck=dict(in_channels=[96, 192, 384], out_channels=96),
+    bbox_head=dict(in_channels=96, feat_channels=96))
+
+img_scale = (640, 640)  # height, width
+
+train_pipeline = [
+    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.5, 1.5),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
+    dict(type='YOLOXHSVRandomAug'),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Resize', img_scale=img_scale, keep_ratio=True),
+    dict(
+        type='Pad',
+        pad_to_square=True,
+        pad_val=dict(img=(114.0, 114.0, 114.0))),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(416, 416),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Pad',
+                pad_to_square=True,
+                pad_val=dict(img=(114.0, 114.0, 114.0))),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+train_dataset = dict(pipeline=train_pipeline)
+
+data = dict(
+    train=train_dataset,
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
diff --git a/configs/yolox/yolox_tiny_8x8_300e_coco_quant_w2a2.py b/configs/yolox/yolox_tiny_8x8_300e_coco_quant_w2a2.py
new file mode 100644
index 0000000..19a2eca
--- /dev/null
+++ b/configs/yolox/yolox_tiny_8x8_300e_coco_quant_w2a2.py
@@ -0,0 +1,129 @@
+_base_ = './yolox_s_8x8_300e_coco_quant_w4a4.py'
+
+# model settings
+model = dict(
+    random_size_range=(10, 20),
+    backbone=dict(deepen_factor=0.33, widen_factor=0.375),
+    neck=dict(in_channels=[96, 192, 384], out_channels=96),
+    bbox_head=dict(in_channels=96, feat_channels=96))
+
+img_scale = (640, 640)  # height, width
+
+train_pipeline = [
+    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.5, 1.5),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
+    dict(type='YOLOXHSVRandomAug'),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Resize', img_scale=img_scale, keep_ratio=True),
+    dict(
+        type='Pad',
+        pad_to_square=True,
+        pad_val=dict(img=(114.0, 114.0, 114.0))),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(416, 416),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Pad',
+                pad_to_square=True,
+                pad_val=dict(img=(114.0, 114.0, 114.0))),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+train_dataset = dict(pipeline=train_pipeline)
+
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8,
+    train=train_dataset,
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+# optimizer
+# default 8 gpu
+optimizer = dict(
+    type='SGD',
+    lr=0.002,  # 确认之后，应该决定0.004开始
+    momentum=0.9,
+    weight_decay=5e-4,
+    nesterov=True,
+    paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0., 
+                    # custom_keys={ 
+
+                    #         # 'norm': dict(lr_mult=0.),
+                    #         # 'bn': dict(lr_mult=0.)
+                    #         'quant': dict(lr_mult=0.04)  # 还真是这个的问题，破案了
+                    #         }
+    ))
+optimizer_config = dict(grad_clip=None)
+
+max_epochs = 16
+num_last_epochs = 6
+resume_from = None
+interval = 1
+
+# learning policy
+lr_config = dict(
+    _delete_=True,
+    policy='YOLOX',
+    warmup='exp',
+    by_epoch=False,
+    warmup_by_epoch=True,
+    warmup_ratio=1,
+    warmup_iters=1,  # 1 epoch
+    num_last_epochs=num_last_epochs,
+    min_lr_ratio=0.1)  # 要让l1启动时为4e-5 但是hqod一来就得更低
+
+runner = dict(type='EpochBasedRunner', max_epochs=max_epochs)
+
+custom_hooks = [
+    dict(
+        type='YOLOXModeSwitchHook',
+        num_last_epochs=num_last_epochs,
+        priority=48),
+    dict(
+        type='SyncNormHook',
+        num_last_epochs=num_last_epochs,
+        interval=interval,
+        priority=48),
+    dict(
+        type='ExpMomentumEMAHook',
+        resume_from=resume_from,
+        momentum=0.0001,
+        priority=49)
+]
+checkpoint_config = dict(interval=12)
+evaluation = dict(
+    save_best='auto',
+    # The evaluation interval is 'interval' when running epoch is
+    # less than ‘max_epochs - num_last_epochs’.
+    # The evaluation interval is 1 when running epoch is greater than
+    # or equal to ‘max_epochs - num_last_epochs’.
+    interval=10,
+    dynamic_intervals=[(max_epochs - num_last_epochs - 1, 1)],
+    metric='bbox')
+log_config = dict(interval=200)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
+load_from = '/workspace/whole_world/bdata1/long.huang/temp/pretrained/long_used/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth'
+# tune_from = 'work_dirs/yolox_tiny_coco_w4a4_LSQ_LEGA/best_bbox_mAP_epoch_16.pth'
+# tune_from = 'work_dirs/yolox_tiny_coco_w4a4_LSQ_HQOD_no_correlationLoss_LEGA/best_bbox_mAP_epoch_15.pth'
+# tune_from = 'work_dirs/yolox_tiny_coco_w4a4_LSQ_HQOD/best_bbox_mAP_epoch_15.pth'
diff --git a/configs/yolox/yolox_tiny_8x8_300e_coco_quant_w4a4.py b/configs/yolox/yolox_tiny_8x8_300e_coco_quant_w4a4.py
new file mode 100644
index 0000000..b46f0b9
--- /dev/null
+++ b/configs/yolox/yolox_tiny_8x8_300e_coco_quant_w4a4.py
@@ -0,0 +1,129 @@
+_base_ = './yolox_s_8x8_300e_coco_quant_w4a4.py'
+
+# model settings
+model = dict(
+    random_size_range=(10, 20),
+    backbone=dict(deepen_factor=0.33, widen_factor=0.375),
+    neck=dict(in_channels=[96, 192, 384], out_channels=96),
+    bbox_head=dict(in_channels=96, feat_channels=96))
+
+img_scale = (640, 640)  # height, width
+
+train_pipeline = [
+    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.5, 1.5),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
+    dict(type='YOLOXHSVRandomAug'),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Resize', img_scale=img_scale, keep_ratio=True),
+    dict(
+        type='Pad',
+        pad_to_square=True,
+        pad_val=dict(img=(114.0, 114.0, 114.0))),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(416, 416),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Pad',
+                pad_to_square=True,
+                pad_val=dict(img=(114.0, 114.0, 114.0))),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+train_dataset = dict(pipeline=train_pipeline)
+
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8,
+    train=train_dataset,
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+# optimizer
+# default 8 gpu
+optimizer = dict(
+    type='SGD',
+    lr=0.002,  # 确认之后，应该决定0.004开始
+    momentum=0.9,
+    weight_decay=5e-4,
+    nesterov=True,
+    paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0., 
+                    # custom_keys={ 
+
+                    #         # 'norm': dict(lr_mult=0.),
+                    #         # 'bn': dict(lr_mult=0.)
+                    #         'quant': dict(lr_mult=0.04)  # 还真是这个的问题，破案了
+                    #         }
+    ))
+optimizer_config = dict(grad_clip=None)
+
+max_epochs = 16
+num_last_epochs = 6
+resume_from = None
+interval = 1
+
+# learning policy
+lr_config = dict(
+    _delete_=True,
+    policy='YOLOX',
+    warmup='exp',
+    by_epoch=False,
+    warmup_by_epoch=True,
+    warmup_ratio=1,
+    warmup_iters=1,  # 1 epoch
+    num_last_epochs=num_last_epochs,
+    min_lr_ratio=0.1)  # 要让l1启动时为4e-5 但是hqod一来就得更低
+
+runner = dict(type='EpochBasedRunner', max_epochs=max_epochs)
+
+custom_hooks = [
+    dict(
+        type='YOLOXModeSwitchHook',
+        num_last_epochs=num_last_epochs,
+        priority=48),
+    dict(
+        type='SyncNormHook',
+        num_last_epochs=num_last_epochs,
+        interval=interval,
+        priority=48),
+    dict(
+        type='ExpMomentumEMAHook',
+        resume_from=resume_from,
+        momentum=0.0001,
+        priority=49)
+]
+checkpoint_config = dict(interval=12)
+evaluation = dict(
+    save_best='auto',
+    # The evaluation interval is 'interval' when running epoch is
+    # less than ‘max_epochs - num_last_epochs’.
+    # The evaluation interval is 1 when running epoch is greater than
+    # or equal to ‘max_epochs - num_last_epochs’.
+    interval=interval,
+    dynamic_intervals=[(max_epochs - num_last_epochs, 1)],
+    metric='bbox')
+log_config = dict(interval=200)
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
+load_from = '/workspace/whole_world/bdata1/long.huang/temp/pretrained/long_used/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth'
+# tune_from = 'work_dirs/yolox_tiny_coco_w4a4_LSQ_LEGA/best_bbox_mAP_epoch_16.pth'
+# tune_from = 'work_dirs/yolox_tiny_coco_w4a4_LSQ_HQOD_no_correlationLoss_LEGA/best_bbox_mAP_epoch_15.pth'
+# tune_from = 'work_dirs/yolox_tiny_coco_w4a4_LSQ_HQOD/best_bbox_mAP_epoch_15.pth'
diff --git a/configs/yolox/yolox_tiny_8x8_300e_coco_quant_w8a8.py b/configs/yolox/yolox_tiny_8x8_300e_coco_quant_w8a8.py
new file mode 100644
index 0000000..1b6cc4f
--- /dev/null
+++ b/configs/yolox/yolox_tiny_8x8_300e_coco_quant_w8a8.py
@@ -0,0 +1,90 @@
+_base_ = './yolox_s_8x8_300e_coco_quant_w4a4.py'
+
+# model settings
+model = dict(
+    random_size_range=(10, 20),
+    backbone=dict(deepen_factor=0.33, widen_factor=0.375),
+    neck=dict(in_channels=[96, 192, 384], out_channels=96),
+    bbox_head=dict(in_channels=96, feat_channels=96))
+
+img_scale = (640, 640)  # height, width
+
+train_pipeline = [
+    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.5, 1.5),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)),
+    dict(type='YOLOXHSVRandomAug'),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Resize', img_scale=img_scale, keep_ratio=True),
+    dict(
+        type='Pad',
+        pad_to_square=True,
+        pad_val=dict(img=(114.0, 114.0, 114.0))),
+    dict(type='FilterAnnotations', min_gt_bbox_wh=(1, 1), keep_empty=False),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(416, 416),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(
+                type='Pad',
+                pad_to_square=True,
+                pad_val=dict(img=(114.0, 114.0, 114.0))),
+            dict(type='DefaultFormatBundle'),
+            dict(type='Collect', keys=['img'])
+        ])
+]
+
+train_dataset = dict(pipeline=train_pipeline)
+
+data = dict(
+    samples_per_gpu=16,
+    workers_per_gpu=8,
+    train=train_dataset,
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+
+# optimizer
+# default 8 gpu
+optimizer = dict(
+    type='SGD',
+    lr=0.0001,
+    momentum=0.9,
+    weight_decay=5e-4,
+    nesterov=True,
+    paramwise_cfg=dict(norm_decay_mult=0., bias_decay_mult=0.)
+    )
+optimizer_config = dict(grad_clip=None)
+
+max_epochs = 6
+num_last_epochs = 3
+resume_from = None
+interval = 1
+
+# learning policy
+lr_config = dict(
+    _delete_=True,
+    policy='YOLOX',
+    warmup='exp',
+    by_epoch=False,
+    warmup_by_epoch=True,
+    warmup_ratio=1,
+    warmup_iters=1,  # 1 epoch
+    num_last_epochs=num_last_epochs,
+    min_lr_ratio=0.4)  # 要让l1启动时为4e-5 但是hqod一来就得更低
+
+# NOTE: `auto_scale_lr` is for automatically scaling LR,
+# USER SHOULD NOT CHANGE ITS VALUES.
+# base_batch_size = (8 GPUs) x (8 samples per GPU)
+auto_scale_lr = dict(base_batch_size=64)
+load_from = '../long_used_pretrained/yolox_tiny_8x8_300e_coco_20211124_171234-b4047906.pth'
diff --git a/configs/yolox/yolox_x_8x8_300e_coco.py b/configs/yolox/yolox_x_8x8_300e_coco.py
new file mode 100644
index 0000000..65c0b75
--- /dev/null
+++ b/configs/yolox/yolox_x_8x8_300e_coco.py
@@ -0,0 +1,8 @@
+_base_ = './yolox_s_8x8_300e_coco.py'
+
+# model settings
+model = dict(
+    backbone=dict(deepen_factor=1.33, widen_factor=1.25),
+    neck=dict(
+        in_channels=[320, 640, 1280], out_channels=320, num_csp_blocks=4),
+    bbox_head=dict(in_channels=320, feat_channels=320))
diff --git a/demo/MMDet_InstanceSeg_Tutorial.ipynb b/demo/MMDet_InstanceSeg_Tutorial.ipynb
new file mode 100644
index 0000000..7d08f8c
--- /dev/null
+++ b/demo/MMDet_InstanceSeg_Tutorial.ipynb
@@ -0,0 +1,2087 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aGYwt_UjIrqp"
+      },
+      "source": [
+        "# Instance Segmentation\n",
+        "\n",
+        "In this tutorial, you will learn:\n",
+        "- the basic structure of Mask R-CNN.\n",
+        "- to perform inference with a MMDetection detector.\n",
+        "- to train a new instance segmentation model with a new dataset.\n",
+        "\n",
+        "Let's start!\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tJxJHruNLb7Y"
+      },
+      "source": [
+        "## Install MMDetection"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Wi4LPmsR66sy",
+        "outputId": "76e1e1d7-5a14-428f-de09-ce652480a5bd"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "nvcc: NVIDIA (R) Cuda compiler driver\n",
+            "Copyright (c) 2005-2020 NVIDIA Corporation\n",
+            "Built on Mon_Oct_12_20:09:46_PDT_2020\n",
+            "Cuda compilation tools, release 11.1, V11.1.105\n",
+            "Build cuda_11.1.TC455_06.29190527_0\n",
+            "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
+            "Copyright (C) 2017 Free Software Foundation, Inc.\n",
+            "This is free software; see the source for copying conditions.  There is NO\n",
+            "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Check nvcc version\n",
+        "!nvcc -V\n",
+        "# Check GCC version\n",
+        "!gcc --version"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gkGnB9WyHSXB",
+        "outputId": "c36e9f21-ae04-4c91-f0fa-b22d7ba8a439"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
+            "Requirement already satisfied: torch==1.9.0+cu111 in /usr/local/lib/python3.7/dist-packages (1.9.0+cu111)\n",
+            "Requirement already satisfied: torchvision==0.10.0+cu111 in /usr/local/lib/python3.7/dist-packages (0.10.0+cu111)\n",
+            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.9.0+cu111) (3.10.0.2)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision==0.10.0+cu111) (1.19.5)\n",
+            "Requirement already satisfied: pillow>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.10.0+cu111) (7.1.2)\n",
+            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n",
+            "Requirement already satisfied: mmcv-full in /usr/local/lib/python3.7/dist-packages (1.4.4)\n",
+            "Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (4.1.2.30)\n",
+            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (7.1.2)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (1.19.5)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (21.3)\n",
+            "Requirement already satisfied: addict in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (2.4.0)\n",
+            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (3.13)\n",
+            "Requirement already satisfied: yapf in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (0.32.0)\n",
+            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->mmcv-full) (3.0.7)\n",
+            "Cloning into 'mmdetection'...\n",
+            "remote: Enumerating objects: 23075, done.\u001b[K\n",
+            "remote: Counting objects: 100% (6/6), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (6/6), done.\u001b[K\n",
+            "remote: Total 23075 (delta 1), reused 2 (delta 0), pack-reused 23069\u001b[K\n",
+            "Receiving objects: 100% (23075/23075), 25.84 MiB | 13.75 MiB/s, done.\n",
+            "Resolving deltas: 100% (16145/16145), done.\n",
+            "/content/mmdetection\n",
+            "Obtaining file:///content/mmdetection\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmdet==2.21.0) (3.2.2)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmdet==2.21.0) (1.19.5)\n",
+            "Requirement already satisfied: pycocotools in /usr/local/lib/python3.7/dist-packages (from mmdet==2.21.0) (2.0.4)\n",
+            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from mmdet==2.21.0) (1.15.0)\n",
+            "Requirement already satisfied: terminaltables in /usr/local/lib/python3.7/dist-packages (from mmdet==2.21.0) (3.1.10)\n",
+            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet==2.21.0) (2.8.2)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet==2.21.0) (1.3.2)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet==2.21.0) (0.11.0)\n",
+            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet==2.21.0) (3.0.7)\n",
+            "Installing collected packages: mmdet\n",
+            "  Running setup.py develop for mmdet\n",
+            "Successfully installed mmdet-2.21.0\n"
+          ]
+        }
+      ],
+      "source": [
+        "# install dependencies: (use cu111 because colab has CUDA 11.1)\n",
+        "!pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html\n",
+        "\n",
+        "# install mmcv-full thus we could use CUDA operators\n",
+        "!pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n",
+        "\n",
+        "# Install mmdetection\n",
+        "!rm -rf mmdetection\n",
+        "!git clone https://github.com/open-mmlab/mmdetection.git\n",
+        "%cd mmdetection\n",
+        "\n",
+        "!pip install -e ."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6hD0mmMixT0p",
+        "outputId": "3fdfddc5-9314-4d11-ed2b-2833795e1cb6"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "1.9.0+cu111 True\n",
+            "2.21.0\n",
+            "11.1\n",
+            "GCC 7.3\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Check Pytorch installation\n",
+        "import torch, torchvision\n",
+        "print(torch.__version__, torch.cuda.is_available())\n",
+        "\n",
+        "# Check MMDetection installation\n",
+        "import mmdet\n",
+        "print(mmdet.__version__)\n",
+        "\n",
+        "# Check mmcv installation\n",
+        "from mmcv.ops import get_compiling_cuda_version, get_compiler_version\n",
+        "print(get_compiling_cuda_version())\n",
+        "print(get_compiler_version())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gi9zw03oM4CH"
+      },
+      "source": [
+        "## Perform Inference with An MMDetection Detector"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3pFYLerc0we1"
+      },
+      "source": [
+        "### A two-stage detector\n",
+        "\n",
+        "In this tutorial, we use Mask R-CNN, a simple two-stage detector as an example.\n",
+        "\n",
+        "The high-level architecture of Mask R-CNN is shown in the following picture. More details can be found in the [paper](https://arxiv.org/abs/1703.06870).\n",
+        "\n",
+        "<img src=\"https://user-images.githubusercontent.com/40661020/143967081-c2552bed-9af2-46c4-ae44-5b3b74e5679f.png\" alt=\"mask rcnn\" align=\"bottom\" />\n",
+        "\n",
+        "Mask R-CNN adds a mask branch based on the original Faster R-CNN. It also uses RoIAlign, a more precise version of RoIPooling for RoI feature extraction to improve the performance.\n",
+        "\n",
+        "\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "j4doHX4exvS1",
+        "outputId": "a61a514e-6408-4972-e78d-4b4ec70ec918"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "--2022-02-13 11:40:48--  https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth\n",
+            "Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.28\n",
+            "Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.28|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 177867103 (170M) [application/octet-stream]\n",
+            "Saving to: ‘checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth’\n",
+            "\n",
+            "checkpoints/mask_rc 100%[===================>] 169.63M  7.40MB/s    in 23s     \n",
+            "\n",
+            "2022-02-13 11:41:13 (7.28 MB/s) - ‘checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth’ saved [177867103/177867103]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "!mkdir checkpoints\n",
+        "!wget -c https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth \\\n",
+        "      -O checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8M5KUnX7Np3h",
+        "outputId": "ef343a81-a46b-4041-8f6c-a4049a5c8a4e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "load checkpoint from local path: checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "MaskRCNN(\n",
+              "  (backbone): ResNet(\n",
+              "    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+              "    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "    (relu): ReLU(inplace=True)\n",
+              "    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+              "    (layer1): ResLayer(\n",
+              "      (0): Bottleneck(\n",
+              "        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "        (downsample): Sequential(\n",
+              "          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        )\n",
+              "      )\n",
+              "      (1): Bottleneck(\n",
+              "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (2): Bottleneck(\n",
+              "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "    )\n",
+              "    (layer2): ResLayer(\n",
+              "      (0): Bottleneck(\n",
+              "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "        (downsample): Sequential(\n",
+              "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        )\n",
+              "      )\n",
+              "      (1): Bottleneck(\n",
+              "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (2): Bottleneck(\n",
+              "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (3): Bottleneck(\n",
+              "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "    )\n",
+              "    (layer3): ResLayer(\n",
+              "      (0): Bottleneck(\n",
+              "        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "        (downsample): Sequential(\n",
+              "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        )\n",
+              "      )\n",
+              "      (1): Bottleneck(\n",
+              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (2): Bottleneck(\n",
+              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (3): Bottleneck(\n",
+              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (4): Bottleneck(\n",
+              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (5): Bottleneck(\n",
+              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "    )\n",
+              "    (layer4): ResLayer(\n",
+              "      (0): Bottleneck(\n",
+              "        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "        (downsample): Sequential(\n",
+              "          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        )\n",
+              "      )\n",
+              "      (1): Bottleneck(\n",
+              "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (2): Bottleneck(\n",
+              "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "    )\n",
+              "  )\n",
+              "  init_cfg={'type': 'Pretrained', 'checkpoint': 'open-mmlab://detectron2/resnet50_caffe'}\n",
+              "  (neck): FPN(\n",
+              "    (lateral_convs): ModuleList(\n",
+              "      (0): ConvModule(\n",
+              "        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
+              "      )\n",
+              "      (1): ConvModule(\n",
+              "        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
+              "      )\n",
+              "      (2): ConvModule(\n",
+              "        (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
+              "      )\n",
+              "      (3): ConvModule(\n",
+              "        (conv): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
+              "      )\n",
+              "    )\n",
+              "    (fpn_convs): ModuleList(\n",
+              "      (0): ConvModule(\n",
+              "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "      )\n",
+              "      (1): ConvModule(\n",
+              "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "      )\n",
+              "      (2): ConvModule(\n",
+              "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "      )\n",
+              "      (3): ConvModule(\n",
+              "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "      )\n",
+              "    )\n",
+              "  )\n",
+              "  init_cfg={'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}\n",
+              "  (rpn_head): RPNHead(\n",
+              "    (loss_cls): CrossEntropyLoss()\n",
+              "    (loss_bbox): L1Loss()\n",
+              "    (rpn_conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (rpn_cls): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n",
+              "    (rpn_reg): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n",
+              "  )\n",
+              "  init_cfg={'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01}\n",
+              "  (roi_head): StandardRoIHead(\n",
+              "    (bbox_roi_extractor): SingleRoIExtractor(\n",
+              "      (roi_layers): ModuleList(\n",
+              "        (0): RoIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
+              "        (1): RoIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
+              "        (2): RoIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
+              "        (3): RoIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
+              "      )\n",
+              "    )\n",
+              "    (bbox_head): Shared2FCBBoxHead(\n",
+              "      (loss_cls): CrossEntropyLoss()\n",
+              "      (loss_bbox): L1Loss()\n",
+              "      (fc_cls): Linear(in_features=1024, out_features=81, bias=True)\n",
+              "      (fc_reg): Linear(in_features=1024, out_features=320, bias=True)\n",
+              "      (shared_convs): ModuleList()\n",
+              "      (shared_fcs): ModuleList(\n",
+              "        (0): Linear(in_features=12544, out_features=1024, bias=True)\n",
+              "        (1): Linear(in_features=1024, out_features=1024, bias=True)\n",
+              "      )\n",
+              "      (cls_convs): ModuleList()\n",
+              "      (cls_fcs): ModuleList()\n",
+              "      (reg_convs): ModuleList()\n",
+              "      (reg_fcs): ModuleList()\n",
+              "      (relu): ReLU(inplace=True)\n",
+              "    )\n",
+              "    init_cfg=[{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]\n",
+              "    (mask_roi_extractor): SingleRoIExtractor(\n",
+              "      (roi_layers): ModuleList(\n",
+              "        (0): RoIAlign(output_size=(14, 14), spatial_scale=0.25, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
+              "        (1): RoIAlign(output_size=(14, 14), spatial_scale=0.125, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
+              "        (2): RoIAlign(output_size=(14, 14), spatial_scale=0.0625, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
+              "        (3): RoIAlign(output_size=(14, 14), spatial_scale=0.03125, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
+              "      )\n",
+              "    )\n",
+              "    (mask_head): FCNMaskHead(\n",
+              "      (loss_mask): CrossEntropyLoss()\n",
+              "      (convs): ModuleList(\n",
+              "        (0): ConvModule(\n",
+              "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "          (activate): ReLU(inplace=True)\n",
+              "        )\n",
+              "        (1): ConvModule(\n",
+              "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "          (activate): ReLU(inplace=True)\n",
+              "        )\n",
+              "        (2): ConvModule(\n",
+              "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "          (activate): ReLU(inplace=True)\n",
+              "        )\n",
+              "        (3): ConvModule(\n",
+              "          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "          (activate): ReLU(inplace=True)\n",
+              "        )\n",
+              "      )\n",
+              "      (upsample): ConvTranspose2d(256, 256, kernel_size=(2, 2), stride=(2, 2))\n",
+              "      (conv_logits): Conv2d(256, 80, kernel_size=(1, 1), stride=(1, 1))\n",
+              "      (relu): ReLU(inplace=True)\n",
+              "    )\n",
+              "  )\n",
+              ")"
+            ]
+          },
+          "execution_count": 5,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import mmcv\n",
+        "from mmcv.runner import load_checkpoint\n",
+        "\n",
+        "from mmdet.apis import inference_detector, show_result_pyplot\n",
+        "from mmdet.models import build_detector\n",
+        "\n",
+        "# Choose to use a config and initialize the detector\n",
+        "config = 'configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco.py'\n",
+        "# Setup a checkpoint file to load\n",
+        "checkpoint = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'\n",
+        "\n",
+        "# Set the device to be used for evaluation\n",
+        "device='cuda:0'\n",
+        "\n",
+        "# Load the config\n",
+        "config = mmcv.Config.fromfile(config)\n",
+        "# Set pretrained to be None since we do not need pretrained model here\n",
+        "config.model.pretrained = None\n",
+        "\n",
+        "# Initialize the detector\n",
+        "model = build_detector(config.model)\n",
+        "\n",
+        "# Load checkpoint\n",
+        "checkpoint = load_checkpoint(model, checkpoint, map_location=device)\n",
+        "\n",
+        "# Set the classes of models for inference\n",
+        "model.CLASSES = checkpoint['meta']['CLASSES']\n",
+        "\n",
+        "# We need to set the model's cfg for inference\n",
+        "model.cfg = config\n",
+        "\n",
+        "# Convert the model to GPU\n",
+        "model.to(device)\n",
+        "# Convert the model into evaluation mode\n",
+        "model.eval()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pVqDQAOiKkJK"
+      },
+      "source": [
+        "From the printed model, we will find that the model does consist of the components that we described earlier. It uses ResNet as its CNN backbone, and has a RPN head and RoI Head. \n",
+        "The RoI Head includes box head and mask head. In addition, the model has a neural network module, named neck, directly after the CNN backbone. It is a [feature pyramid network (FPN)](https://arxiv.org/abs/1612.03144) for enhancing the multi-scale features.\n",
+        "\n",
+        "\n",
+        "### Inference with the detector\n",
+        "\n",
+        "The model is successfully created and loaded, let's see how good it is. We use the high-level API `inference_detector` implemented in the MMDetection. This API is created to ease the inference process. The details of the codes can be found [here](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/apis/inference.py#L15)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Wi6DRpsQPEmV",
+        "outputId": "8670eb7c-7e35-4c6d-edf8-9599c296fd01"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/content/mmdetection/mmdet/datasets/utils.py:69: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n",
+            "  'data pipeline in your config file.', UserWarning)\n",
+            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
+            "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Use the detector to do inference\n",
+        "img = 'demo/demo.jpg'\n",
+        "result = inference_detector(model, img)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 474
+        },
+        "id": "UsJU5D-QPX8L",
+        "outputId": "61e8ec08-2e3a-49eb-fb15-67af66657f7b"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640.01x427.01 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Let's plot the result\n",
+        "show_result_pyplot(model, img, result, score_thr=0.3)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7GrWIJywLV-V"
+      },
+      "source": [
+        "## Train a Detector on A Customized Dataset\n",
+        "\n",
+        "To train a new detector, there are usually three things to do:\n",
+        "1. Support a new dataset\n",
+        "2. Modify the config\n",
+        "3. Train a new detector\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E73y5Lru-wBx"
+      },
+      "source": [
+        "### Support a new dataset\n",
+        "\n",
+        "There are three ways to support a new dataset in MMDetection: \n",
+        "  1. Reorganize the dataset into a COCO format\n",
+        "  2. Reorganize the dataset into a middle format\n",
+        "  3. Implement a new dataset\n",
+        "\n",
+        "We recommend the first two methods, as they are usually easier than the third.\n",
+        "\n",
+        "In this tutorial, we give an example that converts the data into COCO format because MMDetection **only support evaluating mask AP of dataset in COCO format for now**. Other methods and more advanced usages can be found in the [doc](https://mmdetection.readthedocs.io/en/latest/tutorials/customize_dataset.html).\n",
+        "\n",
+        "First, let's download the [the balloon dataset](https://github.com/matterport/Mask_RCNN/tree/master/samples/balloon)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rHnw5Q_nARXq",
+        "outputId": "5993532c-3a6f-46d2-e9ad-428cf44dae60"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "--2022-02-13 11:41:21--  https://github.com/matterport/Mask_RCNN/releases/download/v2.1/balloon_dataset.zip\n",
+            "Resolving github.com (github.com)... 52.192.72.89\n",
+            "Connecting to github.com (github.com)|52.192.72.89|:443... connected.\n",
+            "HTTP request sent, awaiting response... 302 Found\n",
+            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/107595270/737339e2-2b83-11e8-856a-188034eb3468?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220213%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220213T114121Z&X-Amz-Expires=300&X-Amz-Signature=cb1ba2ce6a619db5efdfb4e96bf705e7c37d7d65971b436ae970c0422623f3cf&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=107595270&response-content-disposition=attachment%3B%20filename%3Dballoon_dataset.zip&response-content-type=application%2Foctet-stream [following]\n",
+            "--2022-02-13 11:41:21--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/107595270/737339e2-2b83-11e8-856a-188034eb3468?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220213%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220213T114121Z&X-Amz-Expires=300&X-Amz-Signature=cb1ba2ce6a619db5efdfb4e96bf705e7c37d7d65971b436ae970c0422623f3cf&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=107595270&response-content-disposition=attachment%3B%20filename%3Dballoon_dataset.zip&response-content-type=application%2Foctet-stream\n",
+            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
+            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 38741381 (37M) [application/octet-stream]\n",
+            "Saving to: ‘balloon_dataset.zip’\n",
+            "\n",
+            "balloon_dataset.zip 100%[===================>]  36.95M  8.75MB/s    in 4.2s    \n",
+            "\n",
+            "2022-02-13 11:41:26 (8.75 MB/s) - ‘balloon_dataset.zip’ saved [38741381/38741381]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# download and unzip the data\n",
+        "!wget https://github.com/matterport/Mask_RCNN/releases/download/v2.1/balloon_dataset.zip\n",
+        "!unzip balloon_dataset.zip > /dev/null"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Wuwxw1oZRtVZ",
+        "outputId": "4ee508e8-5acb-450d-c06b-69ceffdc85dd"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Reading package lists...\n",
+            "Building dependency tree...\n",
+            "Reading state information...\n",
+            "tree is already the newest version (1.7.0-5).\n",
+            "The following packages were automatically installed and are no longer required:\n",
+            "  cuda-command-line-tools-10-0 cuda-command-line-tools-10-1\n",
+            "  cuda-command-line-tools-11-0 cuda-compiler-10-0 cuda-compiler-10-1\n",
+            "  cuda-compiler-11-0 cuda-cuobjdump-10-0 cuda-cuobjdump-10-1\n",
+            "  cuda-cuobjdump-11-0 cuda-cupti-10-0 cuda-cupti-10-1 cuda-cupti-11-0\n",
+            "  cuda-cupti-dev-11-0 cuda-documentation-10-0 cuda-documentation-10-1\n",
+            "  cuda-documentation-11-0 cuda-documentation-11-1 cuda-gdb-10-0 cuda-gdb-10-1\n",
+            "  cuda-gdb-11-0 cuda-gpu-library-advisor-10-0 cuda-gpu-library-advisor-10-1\n",
+            "  cuda-libraries-10-0 cuda-libraries-10-1 cuda-libraries-11-0\n",
+            "  cuda-memcheck-10-0 cuda-memcheck-10-1 cuda-memcheck-11-0 cuda-nsight-10-0\n",
+            "  cuda-nsight-10-1 cuda-nsight-11-0 cuda-nsight-11-1 cuda-nsight-compute-10-0\n",
+            "  cuda-nsight-compute-10-1 cuda-nsight-compute-11-0 cuda-nsight-compute-11-1\n",
+            "  cuda-nsight-systems-10-1 cuda-nsight-systems-11-0 cuda-nsight-systems-11-1\n",
+            "  cuda-nvcc-10-0 cuda-nvcc-10-1 cuda-nvcc-11-0 cuda-nvdisasm-10-0\n",
+            "  cuda-nvdisasm-10-1 cuda-nvdisasm-11-0 cuda-nvml-dev-10-0 cuda-nvml-dev-10-1\n",
+            "  cuda-nvml-dev-11-0 cuda-nvprof-10-0 cuda-nvprof-10-1 cuda-nvprof-11-0\n",
+            "  cuda-nvprune-10-0 cuda-nvprune-10-1 cuda-nvprune-11-0 cuda-nvtx-10-0\n",
+            "  cuda-nvtx-10-1 cuda-nvtx-11-0 cuda-nvvp-10-0 cuda-nvvp-10-1 cuda-nvvp-11-0\n",
+            "  cuda-nvvp-11-1 cuda-samples-10-0 cuda-samples-10-1 cuda-samples-11-0\n",
+            "  cuda-samples-11-1 cuda-sanitizer-11-0 cuda-sanitizer-api-10-1\n",
+            "  cuda-toolkit-10-0 cuda-toolkit-10-1 cuda-toolkit-11-0 cuda-toolkit-11-1\n",
+            "  cuda-tools-10-0 cuda-tools-10-1 cuda-tools-11-0 cuda-tools-11-1\n",
+            "  cuda-visual-tools-10-0 cuda-visual-tools-10-1 cuda-visual-tools-11-0\n",
+            "  cuda-visual-tools-11-1 default-jre dkms freeglut3 freeglut3-dev\n",
+            "  keyboard-configuration libargon2-0 libcap2 libcryptsetup12\n",
+            "  libdevmapper1.02.1 libfontenc1 libidn11 libip4tc0 libjansson4\n",
+            "  libnvidia-cfg1-510 libnvidia-common-460 libnvidia-common-510\n",
+            "  libnvidia-extra-510 libnvidia-fbc1-510 libnvidia-gl-510 libpam-systemd\n",
+            "  libpolkit-agent-1-0 libpolkit-backend-1-0 libpolkit-gobject-1-0 libxfont2\n",
+            "  libxi-dev libxkbfile1 libxmu-dev libxmu-headers libxnvctrl0 libxtst6\n",
+            "  nsight-compute-2020.2.1 nsight-compute-2022.1.0 nsight-systems-2020.3.2\n",
+            "  nsight-systems-2020.3.4 nsight-systems-2021.5.2 nvidia-dkms-510\n",
+            "  nvidia-kernel-common-510 nvidia-kernel-source-510 nvidia-modprobe\n",
+            "  nvidia-settings openjdk-11-jre policykit-1 policykit-1-gnome python3-xkit\n",
+            "  screen-resolution-extra systemd systemd-sysv udev x11-xkb-utils\n",
+            "  xserver-common xserver-xorg-core-hwe-18.04 xserver-xorg-video-nvidia-510\n",
+            "Use 'apt autoremove' to remove them.\n",
+            "0 upgraded, 0 newly installed, 0 to remove and 39 not upgraded.\n",
+            "balloon\n",
+            "├── train\n",
+            "│   ├── 10464445726_6f1e3bbe6a_k.jpg\n",
+            "│   ├── 12037308314_e16fb3a0f7_k.jpg\n",
+            "│   ├── 120853323_d4788431b9_b.jpg\n",
+            "│   ├── 12288043903_fe1ea17a4e_k.jpg\n",
+            "│   ├── 12288355124_5e340d3de3_k.jpg\n",
+            "│   ├── 12288446656_2c6a90e6f5_k.jpg\n",
+            "│   ├── 126700562_8e27720147_b.jpg\n",
+            "│   ├── 1297451346_5b92bdac08_b.jpg\n",
+            "│   ├── 14321263043_b76ef054d3_k.jpg\n",
+            "│   ├── 145053828_e0e748717c_b.jpg\n",
+            "│   ├── 14666848163_8be8e37562_k.jpg\n",
+            "│   ├── 15290896925_884ab33fd3_k.jpg\n",
+            "│   ├── 15331928994_d5b82eb368_k.jpg\n",
+            "│   ├── 154446334_5d41cd1375_b.jpg\n",
+            "│   ├── 155815494_800fc9aa32_b.jpg\n",
+            "│   ├── 15717689633_5f7f78c28e_k.jpg\n",
+            "│   ├── 16435593892_2aa8118f4a_k.jpg\n",
+            "│   ├── 17156759330_5af4f5a5b8_k.jpg\n",
+            "│   ├── 17178818589_16e58fc1e5_k.jpg\n",
+            "│   ├── 18849792632_aad23ad513_k.jpg\n",
+            "│   ├── 2311771643_f46392fcc0_b.jpg\n",
+            "│   ├── 2354829160_3f65a6bf6f_b.jpg\n",
+            "│   ├── 2385899600_94b68350af_b.jpg\n",
+            "│   ├── 24362039530_b151b41a52_k.jpg\n",
+            "│   ├── 25899693952_7c8b8b9edc_k.jpg\n",
+            "│   ├── 2685563244_b0d5f7eb67_b.jpg\n",
+            "│   ├── 2937599387_80e7d6e050_b.jpg\n",
+            "│   ├── 321888854_3723b6f10b_b.jpg\n",
+            "│   ├── 332344155_71be3a3b22_b.jpg\n",
+            "│   ├── 3342804367_f43682bb80_b.jpg\n",
+            "│   ├── 34020010494_e5cb88e1c4_k.jpg\n",
+            "│   ├── 351678851_e2aeebdafd_b.jpg\n",
+            "│   ├── 3646097131_e3e1215843_b.jpg\n",
+            "│   ├── 3927754171_9011487133_b.jpg\n",
+            "│   ├── 3945575930_ce99a7e98d_b.jpg\n",
+            "│   ├── 4057490235_2ffdf7d68b_b.jpg\n",
+            "│   ├── 4543126482_92254ef046_b.jpg\n",
+            "│   ├── 4552737035_3a0a4105fb_b.jpg\n",
+            "│   ├── 485227412_e335662bb5_b.jpg\n",
+            "│   ├── 4864857993_edb62f16ef_b.jpg\n",
+            "│   ├── 4887227769_acd2e6127d_b.jpg\n",
+            "│   ├── 489752654_777853a0ba_b.jpg\n",
+            "│   ├── 4955354786_337a598e4a_b.jpg\n",
+            "│   ├── 5013250607_26359229b6_b.jpg\n",
+            "│   ├── 5178670692_63a4365c9c_b.jpg\n",
+            "│   ├── 518678836_94d58e3839_b.jpg\n",
+            "│   ├── 5253122239_38b1e7f61c_b.jpg\n",
+            "│   ├── 53500107_d24b11b3c2_b.jpg\n",
+            "│   ├── 5560377994_cb597a4af5_b.jpg\n",
+            "│   ├── 5674044810_2d9e2243ff_b.jpg\n",
+            "│   ├── 605521662_a470fef77f_b.jpg\n",
+            "│   ├── 6483318883_21facf57cd_b.jpg\n",
+            "│   ├── 699765866_abaad7274d_b.jpg\n",
+            "│   ├── 7178882742_f090f3ce56_k.jpg\n",
+            "│   ├── 7308740338_591f27b631_k.jpg\n",
+            "│   ├── 7488015492_0583857ca0_k.jpg\n",
+            "│   ├── 8436015314_3a678c1005_k.jpg\n",
+            "│   ├── 873768102_7d931e5fa3_b.jpg\n",
+            "│   ├── 8758393087_76fcd56bd3_k.jpg\n",
+            "│   ├── 9210739293_2b0e0d991e_k.jpg\n",
+            "│   ├── 9330497995_4cf0438cb6_k.jpg\n",
+            "│   └── via_region_data.json\n",
+            "└── val\n",
+            "    ├── 14898532020_ba6199dd22_k.jpg\n",
+            "    ├── 16335852991_f55de7958d_k.jpg\n",
+            "    ├── 24631331976_defa3bb61f_k.jpg\n",
+            "    ├── 2917282960_06beee649a_b.jpg\n",
+            "    ├── 3800636873_ace2c2795f_b.jpg\n",
+            "    ├── 3825919971_93fb1ec581_b.jpg\n",
+            "    ├── 410488422_5f8991f26e_b.jpg\n",
+            "    ├── 4581425993_72b9b15fc0_b.jpg\n",
+            "    ├── 4838031651_3e7b5ea5c7_b.jpg\n",
+            "    ├── 5555705118_3390d70abe_b.jpg\n",
+            "    ├── 5603212091_2dfe16ea72_b.jpg\n",
+            "    ├── 6810773040_3d81036d05_k.jpg\n",
+            "    ├── 8053085540_a72bd21a64_k.jpg\n",
+            "    └── via_region_data.json\n",
+            "\n",
+            "2 directories, 76 files\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Check the directory structure of the tiny data\n",
+        "\n",
+        "# Install tree first\n",
+        "!apt-get -q install tree\n",
+        "!tree balloon"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 594
+        },
+        "id": "YnQQqzOWzE91",
+        "outputId": "befa7aae-a21b-42c8-c3ee-5cb4f5bf3e57"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1080x720 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Let's take a look at the dataset image\n",
+        "import mmcv\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "img = mmcv.imread('balloon/train/10464445726_6f1e3bbe6a_k.jpg')\n",
+        "plt.figure(figsize=(15, 10))\n",
+        "plt.imshow(mmcv.bgr2rgb(img))\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PMZvtSIl71qi"
+      },
+      "source": [
+        "After downloading the data, we need to implement a function to convert the annotation format into the COCO format. Then we can use implemented `COCODataset` to load the data and perform training and evaluation.\n",
+        "Let's take a look at the annotation json file.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "id": "n7rwalnPd6e1"
+      },
+      "outputs": [],
+      "source": [
+        "# Check the label of a single image\n",
+        "annotation = mmcv.load('balloon/train/via_region_data.json')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "keLW7uqJM54Y",
+        "outputId": "d71b98a7-516b-48d4-852d-373f33b881f4"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'base64_img_data': '',\n",
+              " 'file_attributes': {},\n",
+              " 'filename': '34020010494_e5cb88e1c4_k.jpg',\n",
+              " 'fileref': '',\n",
+              " 'regions': {'0': {'region_attributes': {},\n",
+              "   'shape_attributes': {'all_points_x': [1020,\n",
+              "     1000,\n",
+              "     994,\n",
+              "     1003,\n",
+              "     1023,\n",
+              "     1050,\n",
+              "     1089,\n",
+              "     1134,\n",
+              "     1190,\n",
+              "     1265,\n",
+              "     1321,\n",
+              "     1361,\n",
+              "     1403,\n",
+              "     1428,\n",
+              "     1442,\n",
+              "     1445,\n",
+              "     1441,\n",
+              "     1427,\n",
+              "     1400,\n",
+              "     1361,\n",
+              "     1316,\n",
+              "     1269,\n",
+              "     1228,\n",
+              "     1198,\n",
+              "     1207,\n",
+              "     1210,\n",
+              "     1190,\n",
+              "     1177,\n",
+              "     1172,\n",
+              "     1174,\n",
+              "     1170,\n",
+              "     1153,\n",
+              "     1127,\n",
+              "     1104,\n",
+              "     1061,\n",
+              "     1032,\n",
+              "     1020],\n",
+              "    'all_points_y': [963,\n",
+              "     899,\n",
+              "     841,\n",
+              "     787,\n",
+              "     738,\n",
+              "     700,\n",
+              "     663,\n",
+              "     638,\n",
+              "     621,\n",
+              "     619,\n",
+              "     643,\n",
+              "     672,\n",
+              "     720,\n",
+              "     765,\n",
+              "     800,\n",
+              "     860,\n",
+              "     896,\n",
+              "     942,\n",
+              "     990,\n",
+              "     1035,\n",
+              "     1079,\n",
+              "     1112,\n",
+              "     1129,\n",
+              "     1134,\n",
+              "     1144,\n",
+              "     1153,\n",
+              "     1166,\n",
+              "     1166,\n",
+              "     1150,\n",
+              "     1136,\n",
+              "     1129,\n",
+              "     1122,\n",
+              "     1112,\n",
+              "     1084,\n",
+              "     1037,\n",
+              "     989,\n",
+              "     963],\n",
+              "    'name': 'polygon'}}},\n",
+              " 'size': 1115004}"
+            ]
+          },
+          "execution_count": 12,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "# The annotation is a dict, and its values looks like the following\n",
+        "annotation['34020010494_e5cb88e1c4_k.jpg1115004']"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QA1pFg-FeO3l"
+      },
+      "source": [
+        "According to the above observation, each single image has a corresponding annotation containing keys `filename` and `regions` that are necessary for training.\n",
+        "We need to read annotations of each image and convert them into COCO format as below:\n",
+        "\n",
+        "```python\n",
+        "{\n",
+        "    \"images\": [image],\n",
+        "    \"annotations\": [annotation], \n",
+        "    \"categories\": [category]\n",
+        "}\n",
+        "\n",
+        "\n",
+        "image = {\n",
+        "    \"id\": int,\n",
+        "    \"width\": int,\n",
+        "    \"height\": int,\n",
+        "    \"file_name\": str,\n",
+        "}\n",
+        "\n",
+        "annotation = {\n",
+        "    \"id\": int,\n",
+        "    \"image_id\": int,\n",
+        "    \"category_id\": int,\n",
+        "    \"segmentation\": RLE or [polygon],\n",
+        "    \"area\": float,\n",
+        "    \"bbox\": [x,y,width,height],\n",
+        "    \"iscrowd\": 0 or 1,\n",
+        "}\n",
+        "\n",
+        "categories = [{\n",
+        "    \"id\": int,\n",
+        "    \"name\": str,\n",
+        "    \"supercategory\": str,\n",
+        "}]\n",
+        "```\n",
+        "**Note**: We only list the necessary keys for training, as shown above. For a full COCO format, please see [here](https://cocodataset.org/#format-data)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "GdSaB2ad0EdX"
+      },
+      "outputs": [],
+      "source": [
+        "import os.path as osp\n",
+        "\n",
+        "def convert_balloon_to_coco(ann_file, out_file, image_prefix):\n",
+        "    data_infos = mmcv.load(ann_file)\n",
+        "\n",
+        "    annotations = []\n",
+        "    images = []\n",
+        "    obj_count = 0\n",
+        "    for idx, v in enumerate(mmcv.track_iter_progress(data_infos.values())):\n",
+        "        filename = v['filename']\n",
+        "        img_path = osp.join(image_prefix, filename)\n",
+        "        height, width = mmcv.imread(img_path).shape[:2]\n",
+        "\n",
+        "        images.append(dict(\n",
+        "            id=idx,\n",
+        "            file_name=filename,\n",
+        "            height=height,\n",
+        "            width=width))\n",
+        "\n",
+        "        bboxes = []\n",
+        "        labels = []\n",
+        "        masks = []\n",
+        "        for _, obj in v['regions'].items():\n",
+        "            assert not obj['region_attributes']\n",
+        "            obj = obj['shape_attributes']\n",
+        "            px = obj['all_points_x']\n",
+        "            py = obj['all_points_y']\n",
+        "            poly = [(x + 0.5, y + 0.5) for x, y in zip(px, py)]\n",
+        "            poly = [p for x in poly for p in x]\n",
+        "\n",
+        "            x_min, y_min, x_max, y_max = (\n",
+        "                min(px), min(py), max(px), max(py))\n",
+        "\n",
+        "\n",
+        "            data_anno = dict(\n",
+        "                image_id=idx,\n",
+        "                id=obj_count,\n",
+        "                category_id=0,\n",
+        "                bbox=[x_min, y_min, x_max - x_min, y_max - y_min],\n",
+        "                area=(x_max - x_min) * (y_max - y_min),\n",
+        "                segmentation=[poly],\n",
+        "                iscrowd=0)\n",
+        "            annotations.append(data_anno)\n",
+        "            obj_count += 1\n",
+        "\n",
+        "    coco_format_json = dict(\n",
+        "        images=images,\n",
+        "        annotations=annotations,\n",
+        "        categories=[{'id':0, 'name': 'balloon'}])\n",
+        "    mmcv.dump(coco_format_json, out_file)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "G3xV5ktqlpFu",
+        "outputId": "af264997-25d1-4fc1-91bb-f9f1ff2c68c9"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 61/61, 29.6 task/s, elapsed: 2s, ETA:     0s\n",
+            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 13/13, 28.8 task/s, elapsed: 0s, ETA:     0s\n"
+          ]
+        }
+      ],
+      "source": [
+        "convert_balloon_to_coco(\n",
+        "    'balloon/train/via_region_data.json',\n",
+        "    'balloon/train/annotation_coco.json',\n",
+        "    'balloon/train/')\n",
+        "convert_balloon_to_coco(\n",
+        "    'balloon/val/via_region_data.json',\n",
+        "    'balloon/val/annotation_coco.json',\n",
+        "    'balloon/val/')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PwqJOpBe-bMj"
+      },
+      "source": [
+        "### Modify the config\n",
+        "\n",
+        "In the next step, we need to modify the config for the training.\n",
+        "To accelerate the process, we finetune a detector using a pre-trained detector."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "hamZrlnH-YDD"
+      },
+      "outputs": [],
+      "source": [
+        "from mmcv import Config\n",
+        "cfg = Config.fromfile('./configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HntziLGq-92Z"
+      },
+      "source": [
+        "Given a config that trains a Mask R-CNN on COCO dataset, we need to modify some values to use it for training on the balloon dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pUbwD8uV0PR8",
+        "outputId": "0c9ba286-1111-407d-bda4-14d6a262a3e3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Config:\n",
+            "model = dict(\n",
+            "    type='MaskRCNN',\n",
+            "    backbone=dict(\n",
+            "        type='ResNet',\n",
+            "        depth=50,\n",
+            "        num_stages=4,\n",
+            "        out_indices=(0, 1, 2, 3),\n",
+            "        frozen_stages=1,\n",
+            "        norm_cfg=dict(type='BN', requires_grad=False),\n",
+            "        norm_eval=True,\n",
+            "        style='caffe',\n",
+            "        init_cfg=dict(\n",
+            "            type='Pretrained',\n",
+            "            checkpoint='open-mmlab://detectron2/resnet50_caffe')),\n",
+            "    neck=dict(\n",
+            "        type='FPN',\n",
+            "        in_channels=[256, 512, 1024, 2048],\n",
+            "        out_channels=256,\n",
+            "        num_outs=5),\n",
+            "    rpn_head=dict(\n",
+            "        type='RPNHead',\n",
+            "        in_channels=256,\n",
+            "        feat_channels=256,\n",
+            "        anchor_generator=dict(\n",
+            "            type='AnchorGenerator',\n",
+            "            scales=[8],\n",
+            "            ratios=[0.5, 1.0, 2.0],\n",
+            "            strides=[4, 8, 16, 32, 64]),\n",
+            "        bbox_coder=dict(\n",
+            "            type='DeltaXYWHBBoxCoder',\n",
+            "            target_means=[0.0, 0.0, 0.0, 0.0],\n",
+            "            target_stds=[1.0, 1.0, 1.0, 1.0]),\n",
+            "        loss_cls=dict(\n",
+            "            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n",
+            "        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n",
+            "    roi_head=dict(\n",
+            "        type='StandardRoIHead',\n",
+            "        bbox_roi_extractor=dict(\n",
+            "            type='SingleRoIExtractor',\n",
+            "            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n",
+            "            out_channels=256,\n",
+            "            featmap_strides=[4, 8, 16, 32]),\n",
+            "        bbox_head=dict(\n",
+            "            type='Shared2FCBBoxHead',\n",
+            "            in_channels=256,\n",
+            "            fc_out_channels=1024,\n",
+            "            roi_feat_size=7,\n",
+            "            num_classes=1,\n",
+            "            bbox_coder=dict(\n",
+            "                type='DeltaXYWHBBoxCoder',\n",
+            "                target_means=[0.0, 0.0, 0.0, 0.0],\n",
+            "                target_stds=[0.1, 0.1, 0.2, 0.2]),\n",
+            "            reg_class_agnostic=False,\n",
+            "            loss_cls=dict(\n",
+            "                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n",
+            "            loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n",
+            "        mask_roi_extractor=dict(\n",
+            "            type='SingleRoIExtractor',\n",
+            "            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),\n",
+            "            out_channels=256,\n",
+            "            featmap_strides=[4, 8, 16, 32]),\n",
+            "        mask_head=dict(\n",
+            "            type='FCNMaskHead',\n",
+            "            num_convs=4,\n",
+            "            in_channels=256,\n",
+            "            conv_out_channels=256,\n",
+            "            num_classes=1,\n",
+            "            loss_mask=dict(\n",
+            "                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),\n",
+            "    train_cfg=dict(\n",
+            "        rpn=dict(\n",
+            "            assigner=dict(\n",
+            "                type='MaxIoUAssigner',\n",
+            "                pos_iou_thr=0.7,\n",
+            "                neg_iou_thr=0.3,\n",
+            "                min_pos_iou=0.3,\n",
+            "                match_low_quality=True,\n",
+            "                ignore_iof_thr=-1),\n",
+            "            sampler=dict(\n",
+            "                type='RandomSampler',\n",
+            "                num=256,\n",
+            "                pos_fraction=0.5,\n",
+            "                neg_pos_ub=-1,\n",
+            "                add_gt_as_proposals=False),\n",
+            "            allowed_border=-1,\n",
+            "            pos_weight=-1,\n",
+            "            debug=False),\n",
+            "        rpn_proposal=dict(\n",
+            "            nms_pre=2000,\n",
+            "            max_per_img=1000,\n",
+            "            nms=dict(type='nms', iou_threshold=0.7),\n",
+            "            min_bbox_size=0),\n",
+            "        rcnn=dict(\n",
+            "            assigner=dict(\n",
+            "                type='MaxIoUAssigner',\n",
+            "                pos_iou_thr=0.5,\n",
+            "                neg_iou_thr=0.5,\n",
+            "                min_pos_iou=0.5,\n",
+            "                match_low_quality=True,\n",
+            "                ignore_iof_thr=-1),\n",
+            "            sampler=dict(\n",
+            "                type='RandomSampler',\n",
+            "                num=512,\n",
+            "                pos_fraction=0.25,\n",
+            "                neg_pos_ub=-1,\n",
+            "                add_gt_as_proposals=True),\n",
+            "            mask_size=28,\n",
+            "            pos_weight=-1,\n",
+            "            debug=False)),\n",
+            "    test_cfg=dict(\n",
+            "        rpn=dict(\n",
+            "            nms_pre=1000,\n",
+            "            max_per_img=1000,\n",
+            "            nms=dict(type='nms', iou_threshold=0.7),\n",
+            "            min_bbox_size=0),\n",
+            "        rcnn=dict(\n",
+            "            score_thr=0.05,\n",
+            "            nms=dict(type='nms', iou_threshold=0.5),\n",
+            "            max_per_img=100,\n",
+            "            mask_thr_binary=0.5)))\n",
+            "dataset_type = 'COCODataset'\n",
+            "data_root = 'data/coco/'\n",
+            "img_norm_cfg = dict(\n",
+            "    mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)\n",
+            "train_pipeline = [\n",
+            "    dict(type='LoadImageFromFile'),\n",
+            "    dict(\n",
+            "        type='LoadAnnotations',\n",
+            "        with_bbox=True,\n",
+            "        with_mask=True,\n",
+            "        poly2mask=False),\n",
+            "    dict(\n",
+            "        type='Resize',\n",
+            "        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),\n",
+            "                   (1333, 768), (1333, 800)],\n",
+            "        multiscale_mode='value',\n",
+            "        keep_ratio=True),\n",
+            "    dict(type='RandomFlip', flip_ratio=0.5),\n",
+            "    dict(\n",
+            "        type='Normalize',\n",
+            "        mean=[103.53, 116.28, 123.675],\n",
+            "        std=[1.0, 1.0, 1.0],\n",
+            "        to_rgb=False),\n",
+            "    dict(type='Pad', size_divisor=32),\n",
+            "    dict(type='DefaultFormatBundle'),\n",
+            "    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n",
+            "]\n",
+            "test_pipeline = [\n",
+            "    dict(type='LoadImageFromFile'),\n",
+            "    dict(\n",
+            "        type='MultiScaleFlipAug',\n",
+            "        img_scale=(1333, 800),\n",
+            "        flip=False,\n",
+            "        transforms=[\n",
+            "            dict(type='Resize', keep_ratio=True),\n",
+            "            dict(type='RandomFlip'),\n",
+            "            dict(\n",
+            "                type='Normalize',\n",
+            "                mean=[103.53, 116.28, 123.675],\n",
+            "                std=[1.0, 1.0, 1.0],\n",
+            "                to_rgb=False),\n",
+            "            dict(type='Pad', size_divisor=32),\n",
+            "            dict(type='ImageToTensor', keys=['img']),\n",
+            "            dict(type='Collect', keys=['img'])\n",
+            "        ])\n",
+            "]\n",
+            "data = dict(\n",
+            "    samples_per_gpu=2,\n",
+            "    workers_per_gpu=2,\n",
+            "    train=dict(\n",
+            "        type='CocoDataset',\n",
+            "        ann_file='balloon/train/annotation_coco.json',\n",
+            "        img_prefix='balloon/train/',\n",
+            "        pipeline=[\n",
+            "            dict(type='LoadImageFromFile'),\n",
+            "            dict(\n",
+            "                type='LoadAnnotations',\n",
+            "                with_bbox=True,\n",
+            "                with_mask=True,\n",
+            "                poly2mask=False),\n",
+            "            dict(\n",
+            "                type='Resize',\n",
+            "                img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),\n",
+            "                           (1333, 768), (1333, 800)],\n",
+            "                multiscale_mode='value',\n",
+            "                keep_ratio=True),\n",
+            "            dict(type='RandomFlip', flip_ratio=0.5),\n",
+            "            dict(\n",
+            "                type='Normalize',\n",
+            "                mean=[103.53, 116.28, 123.675],\n",
+            "                std=[1.0, 1.0, 1.0],\n",
+            "                to_rgb=False),\n",
+            "            dict(type='Pad', size_divisor=32),\n",
+            "            dict(type='DefaultFormatBundle'),\n",
+            "            dict(\n",
+            "                type='Collect',\n",
+            "                keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])\n",
+            "        ],\n",
+            "        classes=('balloon', )),\n",
+            "    val=dict(\n",
+            "        type='CocoDataset',\n",
+            "        ann_file='balloon/val/annotation_coco.json',\n",
+            "        img_prefix='balloon/val/',\n",
+            "        pipeline=[\n",
+            "            dict(type='LoadImageFromFile'),\n",
+            "            dict(\n",
+            "                type='MultiScaleFlipAug',\n",
+            "                img_scale=(1333, 800),\n",
+            "                flip=False,\n",
+            "                transforms=[\n",
+            "                    dict(type='Resize', keep_ratio=True),\n",
+            "                    dict(type='RandomFlip'),\n",
+            "                    dict(\n",
+            "                        type='Normalize',\n",
+            "                        mean=[103.53, 116.28, 123.675],\n",
+            "                        std=[1.0, 1.0, 1.0],\n",
+            "                        to_rgb=False),\n",
+            "                    dict(type='Pad', size_divisor=32),\n",
+            "                    dict(type='ImageToTensor', keys=['img']),\n",
+            "                    dict(type='Collect', keys=['img'])\n",
+            "                ])\n",
+            "        ],\n",
+            "        classes=('balloon', )),\n",
+            "    test=dict(\n",
+            "        type='CocoDataset',\n",
+            "        ann_file='balloon/val/annotation_coco.json',\n",
+            "        img_prefix='balloon/val/',\n",
+            "        pipeline=[\n",
+            "            dict(type='LoadImageFromFile'),\n",
+            "            dict(\n",
+            "                type='MultiScaleFlipAug',\n",
+            "                img_scale=(1333, 800),\n",
+            "                flip=False,\n",
+            "                transforms=[\n",
+            "                    dict(type='Resize', keep_ratio=True),\n",
+            "                    dict(type='RandomFlip'),\n",
+            "                    dict(\n",
+            "                        type='Normalize',\n",
+            "                        mean=[103.53, 116.28, 123.675],\n",
+            "                        std=[1.0, 1.0, 1.0],\n",
+            "                        to_rgb=False),\n",
+            "                    dict(type='Pad', size_divisor=32),\n",
+            "                    dict(type='ImageToTensor', keys=['img']),\n",
+            "                    dict(type='Collect', keys=['img'])\n",
+            "                ])\n",
+            "        ],\n",
+            "        classes=('balloon', )))\n",
+            "evaluation = dict(metric=['bbox', 'segm'], interval=12)\n",
+            "optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)\n",
+            "optimizer_config = dict(grad_clip=None)\n",
+            "lr_config = dict(\n",
+            "    policy='step',\n",
+            "    warmup=None,\n",
+            "    warmup_iters=500,\n",
+            "    warmup_ratio=0.001,\n",
+            "    step=[8, 11])\n",
+            "runner = dict(type='EpochBasedRunner', max_epochs=12)\n",
+            "checkpoint_config = dict(interval=12)\n",
+            "log_config = dict(\n",
+            "    interval=10,\n",
+            "    hooks=[dict(type='TextLoggerHook'),\n",
+            "           dict(type='TensorboardLoggerHook')])\n",
+            "custom_hooks = [dict(type='NumClassCheckHook')]\n",
+            "dist_params = dict(backend='nccl')\n",
+            "log_level = 'INFO'\n",
+            "load_from = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'\n",
+            "resume_from = None\n",
+            "workflow = [('train', 1)]\n",
+            "opencv_num_threads = 0\n",
+            "mp_start_method = 'fork'\n",
+            "work_dir = './tutorial_exps'\n",
+            "seed = 0\n",
+            "gpu_ids = range(0, 1)\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "from mmdet.apis import set_random_seed\n",
+        "\n",
+        "# Modify dataset type and path\n",
+        "cfg.dataset_type = 'COCODataset'\n",
+        "\n",
+        "cfg.data.test.ann_file = 'balloon/val/annotation_coco.json'\n",
+        "cfg.data.test.img_prefix = 'balloon/val/'\n",
+        "cfg.data.test.classes = ('balloon',)\n",
+        "\n",
+        "cfg.data.train.ann_file = 'balloon/train/annotation_coco.json'\n",
+        "cfg.data.train.img_prefix = 'balloon/train/'\n",
+        "cfg.data.train.classes = ('balloon',)\n",
+        "\n",
+        "\n",
+        "cfg.data.val.ann_file = 'balloon/val/annotation_coco.json'\n",
+        "cfg.data.val.img_prefix = 'balloon/val/'\n",
+        "cfg.data.val.classes = ('balloon',)\n",
+        "\n",
+        "# modify num classes of the model in box head and mask head\n",
+        "cfg.model.roi_head.bbox_head.num_classes = 1\n",
+        "cfg.model.roi_head.mask_head.num_classes = 1\n",
+        "\n",
+        "# We can still the pre-trained Mask RCNN model to obtain a higher performance\n",
+        "cfg.load_from = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'\n",
+        "\n",
+        "# Set up working dir to save files and logs.\n",
+        "cfg.work_dir = './tutorial_exps'\n",
+        "\n",
+        "# The original learning rate (LR) is set for 8-GPU training.\n",
+        "# We divide it by 8 since we only use one GPU.\n",
+        "cfg.optimizer.lr = 0.02 / 8\n",
+        "cfg.lr_config.warmup = None\n",
+        "cfg.log_config.interval = 10\n",
+        "\n",
+        "# We can set the evaluation interval to reduce the evaluation times\n",
+        "cfg.evaluation.interval = 12\n",
+        "# We can set the checkpoint saving interval to reduce the storage cost\n",
+        "cfg.checkpoint_config.interval = 12\n",
+        "\n",
+        "# Set seed thus the results are more reproducible\n",
+        "cfg.seed = 0\n",
+        "set_random_seed(0, deterministic=False)\n",
+        "cfg.gpu_ids = range(1)\n",
+        "\n",
+        "# We can also use tensorboard to log the training process\n",
+        "cfg.log_config.hooks = [\n",
+        "    dict(type='TextLoggerHook'),\n",
+        "    dict(type='TensorboardLoggerHook')]\n",
+        "\n",
+        "# We can initialize the logger for training and have a look\n",
+        "# at the final config used for training\n",
+        "print(f'Config:\\n{cfg.pretty_text}')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "111W_oZV_3wa"
+      },
+      "source": [
+        "### Train a new detector\n",
+        "\n",
+        "Finally, lets initialize the dataset and detector, then train a new detector!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7WBWHu010PN3",
+        "outputId": "5c9b1ed6-393a-42fc-e16a-b5a805f394a9"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "loading annotations into memory...\n",
+            "Done (t=0.01s)\n",
+            "creating index...\n",
+            "index created!\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2022-02-13 11:41:34,851 - mmdet - INFO - load checkpoint from local path: checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth\n",
+            "2022-02-13 11:41:35,014 - mmdet - WARNING - The model and loaded state dict do not match exactly\n",
+            "\n",
+            "size mismatch for roi_head.bbox_head.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([2, 1024]).\n",
+            "size mismatch for roi_head.bbox_head.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([2]).\n",
+            "size mismatch for roi_head.bbox_head.fc_reg.weight: copying a param with shape torch.Size([320, 1024]) from checkpoint, the shape in current model is torch.Size([4, 1024]).\n",
+            "size mismatch for roi_head.bbox_head.fc_reg.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([4]).\n",
+            "size mismatch for roi_head.mask_head.conv_logits.weight: copying a param with shape torch.Size([80, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 256, 1, 1]).\n",
+            "size mismatch for roi_head.mask_head.conv_logits.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]).\n",
+            "2022-02-13 11:41:35,024 - mmdet - INFO - Start running, host: root@5e282d87a36d, work_dir: /content/mmdetection/tutorial_exps\n",
+            "2022-02-13 11:41:35,026 - mmdet - INFO - Hooks will be executed in the following order:\n",
+            "before_run:\n",
+            "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
+            "(NORMAL      ) CheckpointHook                     \n",
+            "(LOW         ) EvalHook                           \n",
+            "(VERY_LOW    ) TextLoggerHook                     \n",
+            "(VERY_LOW    ) TensorboardLoggerHook              \n",
+            " -------------------- \n",
+            "before_train_epoch:\n",
+            "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
+            "(NORMAL      ) NumClassCheckHook                  \n",
+            "(LOW         ) IterTimerHook                      \n",
+            "(LOW         ) EvalHook                           \n",
+            "(VERY_LOW    ) TextLoggerHook                     \n",
+            "(VERY_LOW    ) TensorboardLoggerHook              \n",
+            " -------------------- \n",
+            "before_train_iter:\n",
+            "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
+            "(LOW         ) IterTimerHook                      \n",
+            "(LOW         ) EvalHook                           \n",
+            " -------------------- \n",
+            "after_train_iter:\n",
+            "(ABOVE_NORMAL) OptimizerHook                      \n",
+            "(NORMAL      ) CheckpointHook                     \n",
+            "(LOW         ) IterTimerHook                      \n",
+            "(LOW         ) EvalHook                           \n",
+            "(VERY_LOW    ) TextLoggerHook                     \n",
+            "(VERY_LOW    ) TensorboardLoggerHook              \n",
+            " -------------------- \n",
+            "after_train_epoch:\n",
+            "(NORMAL      ) CheckpointHook                     \n",
+            "(LOW         ) EvalHook                           \n",
+            "(VERY_LOW    ) TextLoggerHook                     \n",
+            "(VERY_LOW    ) TensorboardLoggerHook              \n",
+            " -------------------- \n",
+            "before_val_epoch:\n",
+            "(NORMAL      ) NumClassCheckHook                  \n",
+            "(LOW         ) IterTimerHook                      \n",
+            "(VERY_LOW    ) TextLoggerHook                     \n",
+            "(VERY_LOW    ) TensorboardLoggerHook              \n",
+            " -------------------- \n",
+            "before_val_iter:\n",
+            "(LOW         ) IterTimerHook                      \n",
+            " -------------------- \n",
+            "after_val_iter:\n",
+            "(LOW         ) IterTimerHook                      \n",
+            " -------------------- \n",
+            "after_val_epoch:\n",
+            "(VERY_LOW    ) TextLoggerHook                     \n",
+            "(VERY_LOW    ) TensorboardLoggerHook              \n",
+            " -------------------- \n",
+            "after_run:\n",
+            "(VERY_LOW    ) TextLoggerHook                     \n",
+            "(VERY_LOW    ) TensorboardLoggerHook              \n",
+            " -------------------- \n",
+            "2022-02-13 11:41:35,030 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs\n",
+            "2022-02-13 11:41:35,031 - mmdet - INFO - Checkpoints will be saved to /content/mmdetection/tutorial_exps by HardDiskBackend.\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "loading annotations into memory...\n",
+            "Done (t=0.00s)\n",
+            "creating index...\n",
+            "index created!\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2022-02-13 11:41:56,214 - mmdet - INFO - Epoch [1][10/31]\tlr: 2.500e-03, eta: 0:10:53, time: 1.804, data_time: 0.245, memory: 3642, loss_rpn_cls: 0.0300, loss_rpn_bbox: 0.0167, loss_cls: 0.3630, acc: 84.1797, loss_bbox: 0.4075, loss_mask: 0.4678, loss: 1.2850\n",
+            "2022-02-13 11:42:11,324 - mmdet - INFO - Epoch [1][20/31]\tlr: 2.500e-03, eta: 0:09:44, time: 1.517, data_time: 0.091, memory: 3692, loss_rpn_cls: 0.0335, loss_rpn_bbox: 0.0144, loss_cls: 0.1382, acc: 96.0742, loss_bbox: 0.3001, loss_mask: 0.1131, loss: 0.5993\n",
+            "2022-02-13 11:42:26,183 - mmdet - INFO - Epoch [1][30/31]\tlr: 2.500e-03, eta: 0:09:07, time: 1.478, data_time: 0.085, memory: 3692, loss_rpn_cls: 0.0210, loss_rpn_bbox: 0.0104, loss_cls: 0.0643, acc: 97.6172, loss_bbox: 0.1328, loss_mask: 0.1230, loss: 0.3515\n",
+            "2022-02-13 11:42:44,524 - mmdet - INFO - Epoch [2][10/31]\tlr: 2.500e-03, eta: 0:08:42, time: 1.666, data_time: 0.245, memory: 3692, loss_rpn_cls: 0.0078, loss_rpn_bbox: 0.0075, loss_cls: 0.0546, acc: 97.8125, loss_bbox: 0.1013, loss_mask: 0.0632, loss: 0.2345\n",
+            "2022-02-13 11:42:59,439 - mmdet - INFO - Epoch [2][20/31]\tlr: 2.500e-03, eta: 0:08:21, time: 1.500, data_time: 0.090, memory: 3692, loss_rpn_cls: 0.0151, loss_rpn_bbox: 0.0125, loss_cls: 0.0680, acc: 97.6172, loss_bbox: 0.1259, loss_mask: 0.1011, loss: 0.3225\n",
+            "2022-02-13 11:43:14,733 - mmdet - INFO - Epoch [2][30/31]\tlr: 2.500e-03, eta: 0:08:03, time: 1.526, data_time: 0.081, memory: 3692, loss_rpn_cls: 0.0103, loss_rpn_bbox: 0.0118, loss_cls: 0.0805, acc: 96.9727, loss_bbox: 0.1343, loss_mask: 0.1026, loss: 0.3394\n",
+            "2022-02-13 11:43:33,227 - mmdet - INFO - Epoch [3][10/31]\tlr: 2.500e-03, eta: 0:07:44, time: 1.666, data_time: 0.239, memory: 3692, loss_rpn_cls: 0.0068, loss_rpn_bbox: 0.0052, loss_cls: 0.0446, acc: 98.4961, loss_bbox: 0.0672, loss_mask: 0.0573, loss: 0.1810\n",
+            "2022-02-13 11:43:47,886 - mmdet - INFO - Epoch [3][20/31]\tlr: 2.500e-03, eta: 0:07:26, time: 1.467, data_time: 0.084, memory: 3692, loss_rpn_cls: 0.0047, loss_rpn_bbox: 0.0108, loss_cls: 0.0605, acc: 97.5293, loss_bbox: 0.1027, loss_mask: 0.0806, loss: 0.2593\n",
+            "2022-02-13 11:44:03,271 - mmdet - INFO - Epoch [3][30/31]\tlr: 2.500e-03, eta: 0:07:11, time: 1.539, data_time: 0.083, memory: 3692, loss_rpn_cls: 0.0051, loss_rpn_bbox: 0.0159, loss_cls: 0.0767, acc: 96.8652, loss_bbox: 0.1379, loss_mask: 0.0835, loss: 0.3191\n",
+            "2022-02-13 11:44:21,725 - mmdet - INFO - Epoch [4][10/31]\tlr: 2.500e-03, eta: 0:06:53, time: 1.679, data_time: 0.244, memory: 3692, loss_rpn_cls: 0.0037, loss_rpn_bbox: 0.0110, loss_cls: 0.0645, acc: 97.5586, loss_bbox: 0.1028, loss_mask: 0.0741, loss: 0.2560\n",
+            "2022-02-13 11:44:36,092 - mmdet - INFO - Epoch [4][20/31]\tlr: 2.500e-03, eta: 0:06:35, time: 1.434, data_time: 0.081, memory: 3692, loss_rpn_cls: 0.0039, loss_rpn_bbox: 0.0103, loss_cls: 0.0516, acc: 97.9102, loss_bbox: 0.0752, loss_mask: 0.0608, loss: 0.2018\n",
+            "2022-02-13 11:44:51,949 - mmdet - INFO - Epoch [4][30/31]\tlr: 2.500e-03, eta: 0:06:21, time: 1.579, data_time: 0.084, memory: 3796, loss_rpn_cls: 0.0052, loss_rpn_bbox: 0.0103, loss_cls: 0.0556, acc: 97.8516, loss_bbox: 0.0947, loss_mask: 0.0708, loss: 0.2367\n",
+            "2022-02-13 11:45:10,505 - mmdet - INFO - Epoch [5][10/31]\tlr: 2.500e-03, eta: 0:06:04, time: 1.691, data_time: 0.237, memory: 3796, loss_rpn_cls: 0.0037, loss_rpn_bbox: 0.0089, loss_cls: 0.0433, acc: 98.2715, loss_bbox: 0.0773, loss_mask: 0.0637, loss: 0.1970\n",
+            "2022-02-13 11:45:25,370 - mmdet - INFO - Epoch [5][20/31]\tlr: 2.500e-03, eta: 0:05:48, time: 1.490, data_time: 0.082, memory: 3855, loss_rpn_cls: 0.0036, loss_rpn_bbox: 0.0108, loss_cls: 0.0528, acc: 97.9395, loss_bbox: 0.0800, loss_mask: 0.0566, loss: 0.2038\n",
+            "2022-02-13 11:45:40,909 - mmdet - INFO - Epoch [5][30/31]\tlr: 2.500e-03, eta: 0:05:33, time: 1.536, data_time: 0.078, memory: 3855, loss_rpn_cls: 0.0035, loss_rpn_bbox: 0.0119, loss_cls: 0.0467, acc: 98.2715, loss_bbox: 0.0945, loss_mask: 0.0845, loss: 0.2412\n",
+            "2022-02-13 11:45:59,548 - mmdet - INFO - Epoch [6][10/31]\tlr: 2.500e-03, eta: 0:05:17, time: 1.700, data_time: 0.244, memory: 3855, loss_rpn_cls: 0.0028, loss_rpn_bbox: 0.0062, loss_cls: 0.0324, acc: 98.8477, loss_bbox: 0.0624, loss_mask: 0.0499, loss: 0.1536\n",
+            "2022-02-13 11:46:14,173 - mmdet - INFO - Epoch [6][20/31]\tlr: 2.500e-03, eta: 0:05:01, time: 1.474, data_time: 0.087, memory: 3855, loss_rpn_cls: 0.0061, loss_rpn_bbox: 0.0104, loss_cls: 0.0569, acc: 97.7441, loss_bbox: 0.0969, loss_mask: 0.0968, loss: 0.2671\n",
+            "2022-02-13 11:46:29,226 - mmdet - INFO - Epoch [6][30/31]\tlr: 2.500e-03, eta: 0:04:45, time: 1.503, data_time: 0.076, memory: 3855, loss_rpn_cls: 0.0040, loss_rpn_bbox: 0.0092, loss_cls: 0.0419, acc: 98.5352, loss_bbox: 0.0766, loss_mask: 0.0748, loss: 0.2066\n",
+            "2022-02-13 11:46:48,076 - mmdet - INFO - Epoch [7][10/31]\tlr: 2.500e-03, eta: 0:04:28, time: 1.690, data_time: 0.238, memory: 3867, loss_rpn_cls: 0.0026, loss_rpn_bbox: 0.0057, loss_cls: 0.0278, acc: 98.9355, loss_bbox: 0.0514, loss_mask: 0.0636, loss: 0.1511\n",
+            "2022-02-13 11:47:02,801 - mmdet - INFO - Epoch [7][20/31]\tlr: 2.500e-03, eta: 0:04:13, time: 1.487, data_time: 0.092, memory: 3867, loss_rpn_cls: 0.0026, loss_rpn_bbox: 0.0049, loss_cls: 0.0327, acc: 98.6133, loss_bbox: 0.0656, loss_mask: 0.0596, loss: 0.1654\n",
+            "2022-02-13 11:47:18,660 - mmdet - INFO - Epoch [7][30/31]\tlr: 2.500e-03, eta: 0:03:58, time: 1.577, data_time: 0.076, memory: 3867, loss_rpn_cls: 0.0033, loss_rpn_bbox: 0.0145, loss_cls: 0.0619, acc: 97.5000, loss_bbox: 0.1103, loss_mask: 0.0882, loss: 0.2781\n",
+            "2022-02-13 11:47:37,321 - mmdet - INFO - Epoch [8][10/31]\tlr: 2.500e-03, eta: 0:03:41, time: 1.696, data_time: 0.234, memory: 3867, loss_rpn_cls: 0.0019, loss_rpn_bbox: 0.0073, loss_cls: 0.0365, acc: 98.6426, loss_bbox: 0.0668, loss_mask: 0.0605, loss: 0.1730\n",
+            "2022-02-13 11:47:52,276 - mmdet - INFO - Epoch [8][20/31]\tlr: 2.500e-03, eta: 0:03:26, time: 1.509, data_time: 0.093, memory: 3867, loss_rpn_cls: 0.0013, loss_rpn_bbox: 0.0068, loss_cls: 0.0320, acc: 98.7695, loss_bbox: 0.0625, loss_mask: 0.0638, loss: 0.1663\n",
+            "2022-02-13 11:48:07,563 - mmdet - INFO - Epoch [8][30/31]\tlr: 2.500e-03, eta: 0:03:10, time: 1.519, data_time: 0.076, memory: 3867, loss_rpn_cls: 0.0024, loss_rpn_bbox: 0.0103, loss_cls: 0.0368, acc: 98.5938, loss_bbox: 0.0723, loss_mask: 0.0679, loss: 0.1897\n",
+            "2022-02-13 11:48:25,899 - mmdet - INFO - Epoch [9][10/31]\tlr: 2.500e-04, eta: 0:02:54, time: 1.670, data_time: 0.236, memory: 3867, loss_rpn_cls: 0.0025, loss_rpn_bbox: 0.0095, loss_cls: 0.0372, acc: 98.5449, loss_bbox: 0.0633, loss_mask: 0.0541, loss: 0.1666\n",
+            "2022-02-13 11:48:40,549 - mmdet - INFO - Epoch [9][20/31]\tlr: 2.500e-04, eta: 0:02:38, time: 1.473, data_time: 0.081, memory: 3867, loss_rpn_cls: 0.0015, loss_rpn_bbox: 0.0080, loss_cls: 0.0378, acc: 98.6035, loss_bbox: 0.0641, loss_mask: 0.0609, loss: 0.1724\n",
+            "2022-02-13 11:48:55,935 - mmdet - INFO - Epoch [9][30/31]\tlr: 2.500e-04, eta: 0:02:23, time: 1.526, data_time: 0.072, memory: 3867, loss_rpn_cls: 0.0020, loss_rpn_bbox: 0.0075, loss_cls: 0.0281, acc: 99.0137, loss_bbox: 0.0551, loss_mask: 0.0658, loss: 0.1585\n",
+            "2022-02-13 11:49:14,390 - mmdet - INFO - Epoch [10][10/31]\tlr: 2.500e-04, eta: 0:02:06, time: 1.681, data_time: 0.244, memory: 3867, loss_rpn_cls: 0.0013, loss_rpn_bbox: 0.0065, loss_cls: 0.0251, acc: 99.1309, loss_bbox: 0.0526, loss_mask: 0.0564, loss: 0.1418\n",
+            "2022-02-13 11:49:29,412 - mmdet - INFO - Epoch [10][20/31]\tlr: 2.500e-04, eta: 0:01:51, time: 1.505, data_time: 0.087, memory: 3867, loss_rpn_cls: 0.0024, loss_rpn_bbox: 0.0101, loss_cls: 0.0444, acc: 98.1543, loss_bbox: 0.0755, loss_mask: 0.0656, loss: 0.1979\n",
+            "2022-02-13 11:49:44,314 - mmdet - INFO - Epoch [10][30/31]\tlr: 2.500e-04, eta: 0:01:35, time: 1.488, data_time: 0.083, memory: 3867, loss_rpn_cls: 0.0020, loss_rpn_bbox: 0.0041, loss_cls: 0.0262, acc: 98.8672, loss_bbox: 0.0439, loss_mask: 0.0496, loss: 0.1258\n",
+            "2022-02-13 11:50:02,842 - mmdet - INFO - Epoch [11][10/31]\tlr: 2.500e-04, eta: 0:01:19, time: 1.688, data_time: 0.252, memory: 3867, loss_rpn_cls: 0.0028, loss_rpn_bbox: 0.0125, loss_cls: 0.0416, acc: 98.4570, loss_bbox: 0.0738, loss_mask: 0.0673, loss: 0.1980\n",
+            "2022-02-13 11:50:17,905 - mmdet - INFO - Epoch [11][20/31]\tlr: 2.500e-04, eta: 0:01:03, time: 1.510, data_time: 0.086, memory: 3867, loss_rpn_cls: 0.0013, loss_rpn_bbox: 0.0052, loss_cls: 0.0269, acc: 99.0234, loss_bbox: 0.0567, loss_mask: 0.0561, loss: 0.1461\n",
+            "2022-02-13 11:50:33,036 - mmdet - INFO - Epoch [11][30/31]\tlr: 2.500e-04, eta: 0:00:48, time: 1.500, data_time: 0.082, memory: 3867, loss_rpn_cls: 0.0013, loss_rpn_bbox: 0.0038, loss_cls: 0.0232, acc: 99.1113, loss_bbox: 0.0437, loss_mask: 0.0547, loss: 0.1268\n",
+            "2022-02-13 11:50:51,703 - mmdet - INFO - Epoch [12][10/31]\tlr: 2.500e-05, eta: 0:00:31, time: 1.700, data_time: 0.245, memory: 3867, loss_rpn_cls: 0.0016, loss_rpn_bbox: 0.0074, loss_cls: 0.0321, acc: 98.8379, loss_bbox: 0.0612, loss_mask: 0.0629, loss: 0.1653\n",
+            "2022-02-13 11:51:06,711 - mmdet - INFO - Epoch [12][20/31]\tlr: 2.500e-05, eta: 0:00:16, time: 1.509, data_time: 0.091, memory: 3867, loss_rpn_cls: 0.0013, loss_rpn_bbox: 0.0067, loss_cls: 0.0397, acc: 98.5059, loss_bbox: 0.0688, loss_mask: 0.0661, loss: 0.1825\n",
+            "2022-02-13 11:51:21,732 - mmdet - INFO - Epoch [12][30/31]\tlr: 2.500e-05, eta: 0:00:01, time: 1.503, data_time: 0.081, memory: 3867, loss_rpn_cls: 0.0012, loss_rpn_bbox: 0.0056, loss_cls: 0.0224, acc: 99.1504, loss_bbox: 0.0376, loss_mask: 0.0485, loss: 0.1153\n",
+            "2022-02-13 11:51:22,692 - mmdet - INFO - Saving checkpoint at 12 epochs\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 13/13, 2.0 task/s, elapsed: 6s, ETA:     0s"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2022-02-13 11:51:32,064 - mmdet - INFO - Evaluating bbox...\n",
+            "2022-02-13 11:51:32,116 - mmdet - INFO - \n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.796\n",
+            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.904\n",
+            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.879\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.202\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.662\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.873\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.830\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.830\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.830\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.200\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.725\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.900\n",
+            "\n",
+            "2022-02-13 11:51:32,117 - mmdet - INFO - Evaluating segm...\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loading and preparing results...\n",
+            "DONE (t=0.00s)\n",
+            "creating index...\n",
+            "index created!\n",
+            "Running per image evaluation...\n",
+            "Evaluate annotation type *bbox*\n",
+            "DONE (t=0.03s).\n",
+            "Accumulating evaluation results...\n",
+            "DONE (t=0.01s).\n",
+            "Loading and preparing results...\n",
+            "DONE (t=0.00s)\n",
+            "creating index...\n",
+            "index created!\n",
+            "Running per image evaluation...\n",
+            "Evaluate annotation type *segm*\n",
+            "DONE (t=0.03s).\n",
+            "Accumulating evaluation results...\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/content/mmdetection/mmdet/datasets/coco.py:478: UserWarning: The key \"bbox\" is deleted for more accurate mask AP of small/medium/large instances since v2.12.0. This does not change the overall mAP calculation.\n",
+            "  UserWarning)\n",
+            "2022-02-13 11:51:32,181 - mmdet - INFO - \n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.815\n",
+            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=1000 ] = 0.904\n",
+            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=1000 ] = 0.857\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.126\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.707\n",
+            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.886\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.852\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=300 ] = 0.852\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=1000 ] = 0.852\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=1000 ] = 0.250\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=1000 ] = 0.783\n",
+            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=1000 ] = 0.908\n",
+            "\n",
+            "2022-02-13 11:51:32,183 - mmdet - INFO - Epoch(val) [12][13]\tbbox_mAP: 0.7960, bbox_mAP_50: 0.9040, bbox_mAP_75: 0.8790, bbox_mAP_s: 0.2020, bbox_mAP_m: 0.6620, bbox_mAP_l: 0.8730, bbox_mAP_copypaste: 0.796 0.904 0.879 0.202 0.662 0.873, segm_mAP: 0.8150, segm_mAP_50: 0.9040, segm_mAP_75: 0.8570, segm_mAP_s: 0.1260, segm_mAP_m: 0.7070, segm_mAP_l: 0.8860, segm_mAP_copypaste: 0.815 0.904 0.857 0.126 0.707 0.886\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "DONE (t=0.02s).\n"
+          ]
+        }
+      ],
+      "source": [
+        "from mmdet.datasets import build_dataset\n",
+        "from mmdet.models import build_detector\n",
+        "from mmdet.apis import train_detector\n",
+        "\n",
+        "\n",
+        "# Build dataset\n",
+        "datasets = [build_dataset(cfg.data.train)]\n",
+        "\n",
+        "# Build the detector\n",
+        "model = build_detector(cfg.model)\n",
+        "\n",
+        "# Add an attribute for visualization convenience\n",
+        "model.CLASSES = datasets[0].CLASSES\n",
+        "\n",
+        "# Create work_dir\n",
+        "mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n",
+        "train_detector(model, datasets, cfg, distributed=False, validate=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_vYQF5K2NqqI"
+      },
+      "source": [
+        "### Understand the log\n",
+        "From the log, we can have a basic understanding on the training process and know how well the detector is trained.\n",
+        "\n",
+        "First, since the dataset we are using is small, we loaded a Mask R-CNN model and finetune it for detection. Because the original Mask R-CNN is trained on COCO dataset that contains 80 classes but KITTI Tiny dataset only have 3 classes. Therefore, the last FC layers of the pre-trained Mask R-CNN for classification and regression have different weight shape and are not used. The pre-trained weights of mask prediction layer `mask_head.conv_logits` also does not matches the current model and is not used due to similar reason.\n",
+        "\n",
+        "Third, after training, the detector is evaluated by the default COCO-style evaluation. The results show that the detector achieves 79.6 bbox AP and 81.5 mask AP on the val dataset, not bad!\n",
+        "\n",
+        " We can also check the tensorboard to see the curves."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 840,
+          "resources": {
+            "https://localhost:6006/?tensorboardColab=true": {
+              "data": "<!doctype html><meta name="tb-relative-root" content="./"><!doctype html><!--
@license
Copyright 2019 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
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See the License for the specific language governing permissions and
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--><html><head><meta charset="utf-8">
<title>TensorBoard</title>
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">
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">

<style>
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/uYECMKoHcO9x1wdmbyHIm3-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/sTdaA6j0Psb920Vjv-mrzH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/_VYFx-s824kXq_Ul2BHqYH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/tnj4SB6DNbdaQnsM8CFqBX-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/oMMgfZMQthOryQo9n22dcuvvDin1pK8aKteLpeZ5c0A.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/Ks_cVxiCiwUWVsFWFA3Bjn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/NJ4vxlgWwWbEsv18dAhqnn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/isZ-wbCXNKAbnjo6_TwHToX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/77FXFjRbGzN4aCrSFhlh3oX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/jSN2CGVDbcVyCnfJfjSdfIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/UX6i4JxQDm3fVTc1CPuwqoX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
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@font-face {
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}
</style>



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<style>
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    padding: 0;
    height: 100%;
    font-family: Roboto, sans-serif;
    color: var(--primary-text-color);

    /* Legacy mechanism to avoid issues with subpixel anti-aliasing on macOS.
     *
     * In the past [1], macOS subpixel AA caused excessive bolding for light-on-dark text; this rule
     * avoids that by requesting non-subpixel AA always, rather than the default behavior, which is
     * to use subpixel AA when available. The original issue was "fixed" by removing subpixel AA in
     * macOS 14 (Mojave), but for legacy reasons they preserved the bolding effect as an option.
     * Chrome then in turn updated its font rendering to apply that bolding effect [2], which means
     * that even though the `-webkit-font-smoothing` docs [3] suggest that setting `antialiased`
     * would have no effect for recent versions of macOS, it still is needed to avoid the bolding.
     *
     * [1]: http://www.lighterra.com/articles/macosxtextaabug/
     * [2]: https://bugs.chromium.org/p/chromium/issues/detail?id=858861
     * [3]: https://developer.mozilla.org/en-US/docs/Web/CSS/font-smooth
     *
     */

    -webkit-font-smoothing: antialiased;
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    max-width: 600px;
    padding: 10px;
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+            },
+            "https://localhost:6006/data/plugins_listing": {
+              "data": "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",
+              "headers": [
+                [
+                  "content-type",
+                  "application/json"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/data/runs": {
+              "data": "W10=",
+              "headers": [
+                [
+                  "content-type",
+                  "application/json"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/font-roboto/RxZJdnzeo3R5zSexge8UUZBw1xU1rKptJj_0jans920.woff2": {
+              "data": 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",
+              "headers": [
+                [
+                  "content-type",
+                  "font/woff2"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/font-roboto/d-6IYplOFocCacKzxwXSOJBw1xU1rKptJj_0jans920.woff2": {
+              "data": 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",
+              "headers": [
+                [
+                  "content-type",
+                  "font/woff2"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/font-roboto/oMMgfZMQthOryQo9n22dcuvvDin1pK8aKteLpeZ5c0A.woff2": {
+              "data": 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",
+              "headers": [
+                [
+                  "content-type",
+                  "font/woff2"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/font-roboto/vPcynSL0qHq_6dX7lKVByXYhjbSpvc47ee6xR_80Hnw.woff2": {
+              "data": 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/** vim: et:ts=4:sw=4:sts=4
 * @license RequireJS 2.3.6 Copyright jQuery Foundation and other contributors.
 * Released under MIT license, https://github.com/requirejs/requirejs/blob/master/LICENSE
 */
//Not using strict: uneven strict support in browsers, #392, and causes
//problems with requirejs.exec()/transpiler plugins that may not be strict.
/*jslint regexp: true, nomen: true, sloppy: true */
/*global window, navigator, document, importScripts, setTimeout, opera */

var requirejs, require, define;
(function (global, setTimeout) {
    var req, s, head, baseElement, dataMain, src,
        interactiveScript, currentlyAddingScript, mainScript, subPath,
        version = '2.3.6',
        commentRegExp = /\/\*[\s\S]*?\*\/|([^:"'=]|^)\/\/.*$/mg,
        cjsRequireRegExp = /[^.]\s*require\s*\(\s*["']([^'"\s]+)["']\s*\)/g,
        jsSuffixRegExp = /\.js$/,
        currDirRegExp = /^\.\//,
        op = Object.prototype,
        ostring = op.toString,
        hasOwn = op.hasOwnProperty,
        isBrowser = !!(typeof window !== 'undefined' && typeof navigator !== 'undefined' && window.document),
        isWebWorker = !isBrowser && typeof importScripts !== 'undefined',
        //PS3 indicates loaded and complete, but need to wait for complete
        //specifically. Sequence is 'loading', 'loaded', execution,
        // then 'complete'. The UA check is unfortunate, but not sure how
        //to feature test w/o causing perf issues.
        readyRegExp = isBrowser && navigator.platform === 'PLAYSTATION 3' ?
                      /^complete$/ : /^(complete|loaded)$/,
        defContextName = '_',
        //Oh the tragedy, detecting opera. See the usage of isOpera for reason.
        isOpera = typeof opera !== 'undefined' && opera.toString() === '[object Opera]',
        contexts = {},
        cfg = {},
        globalDefQueue = [],
        useInteractive = false;

    //Could match something like ')//comment', do not lose the prefix to comment.
    function commentReplace(match, singlePrefix) {
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    }

    function isFunction(it) {
        return ostring.call(it) === '[object Function]';
    }

    function isArray(it) {
        return ostring.call(it) === '[object Array]';
    }

    /**
     * Helper function for iterating over an array. If the func returns
     * a true value, it will break out of the loop.
     */
    function each(ary, func) {
        if (ary) {
            var i;
            for (i = 0; i < ary.length; i += 1) {
                if (ary[i] && func(ary[i], i, ary)) {
                    break;
                }
            }
        }
    }

    /**
     * Helper function for iterating over an array backwards. If the func
     * returns a true value, it will break out of the loop.
     */
    function eachReverse(ary, func) {
        if (ary) {
            var i;
            for (i = ary.length - 1; i > -1; i -= 1) {
                if (ary[i] && func(ary[i], i, ary)) {
                    break;
                }
            }
        }
    }

    function hasProp(obj, prop) {
        return hasOwn.call(obj, prop);
    }

    function getOwn(obj, prop) {
        return hasProp(obj, prop) && obj[prop];
    }

    /**
     * Cycles over properties in an object and calls a function for each
     * property value. If the function returns a truthy value, then the
     * iteration is stopped.
     */
    function eachProp(obj, func) {
        var prop;
        for (prop in obj) {
            if (hasProp(obj, prop)) {
                if (func(obj[prop], prop)) {
                    break;
                }
            }
        }
    }

    /**
     * Simple function to mix in properties from source into target,
     * but only if target does not already have a property of the same name.
     */
    function mixin(target, source, force, deepStringMixin) {
        if (source) {
            eachProp(source, function (value, prop) {
                if (force || !hasProp(target, prop)) {
                    if (deepStringMixin && typeof value === 'object' && value &&
                        !isArray(value) && !isFunction(value) &&
                        !(value instanceof RegExp)) {

                        if (!target[prop]) {
                            target[prop] = {};
                        }
                        mixin(target[prop], value, force, deepStringMixin);
                    } else {
                        target[prop] = value;
                    }
                }
            });
        }
        return target;
    }

    //Similar to Function.prototype.bind, but the 'this' object is specified
    //first, since it is easier to read/figure out what 'this' will be.
    function bind(obj, fn) {
        return function () {
            return fn.apply(obj, arguments);
        };
    }

    function scripts() {
        return document.getElementsByTagName('script');
    }

    function defaultOnError(err) {
        throw err;
    }

    //Allow getting a global that is expressed in
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    function getGlobal(value) {
        if (!value) {
            return value;
        }
        var g = global;
        each(value.split('.'), function (part) {
            g = g[part];
        });
        return g;
    }

    /**
     * Constructs an error with a pointer to an URL with more information.
     * @param {String} id the error ID that maps to an ID on a web page.
     * @param {String} message human readable error.
     * @param {Error} [err] the original error, if there is one.
     *
     * @returns {Error}
     */
    function makeError(id, msg, err, requireModules) {
        var e = new Error(msg + '\nhttps://requirejs.org/docs/errors.html#' + id);
        e.requireType = id;
        e.requireModules = requireModules;
        if (err) {
            e.originalError = err;
        }
        return e;
    }

    if (typeof define !== 'undefined') {
        //If a define is already in play via another AMD loader,
        //do not overwrite.
        return;
    }

    if (typeof requirejs !== 'undefined') {
        if (isFunction(requirejs)) {
            //Do not overwrite an existing requirejs instance.
            return;
        }
        cfg = requirejs;
        requirejs = undefined;
    }

    //Allow for a require config object
    if (typeof require !== 'undefined' && !isFunction(require)) {
        //assume it is a config object.
        cfg = require;
        require = undefined;
    }

    function newContext(contextName) {
        var inCheckLoaded, Module, context, handlers,
            checkLoadedTimeoutId,
            config = {
                //Defaults. Do not set a default for map
                //config to speed up normalize(), which
                //will run faster if there is no default.
                waitSeconds: 7,
                baseUrl: './',
                paths: {},
                bundles: {},
                pkgs: {},
                shim: {},
                config: {}
            },
            registry = {},
            //registry of just enabled modules, to speed
            //cycle breaking code when lots of modules
            //are registered, but not activated.
            enabledRegistry = {},
            undefEvents = {},
            defQueue = [],
            defined = {},
            urlFetched = {},
            bundlesMap = {},
            requireCounter = 1,
            unnormalizedCounter = 1;

        /**
         * Trims the . and .. from an array of path segments.
         * It will keep a leading path segment if a .. will become
         * the first path segment, to help with module name lookups,
         * which act like paths, but can be remapped. But the end result,
         * all paths that use this function should look normalized.
         * NOTE: this method MODIFIES the input array.
         * @param {Array} ary the array of path segments.
         */
        function trimDots(ary) {
            var i, part;
            for (i = 0; i < ary.length; i++) {
                part = ary[i];
                if (part === '.') {
                    ary.splice(i, 1);
                    i -= 1;
                } else if (part === '..') {
                    // If at the start, or previous value is still ..,
                    // keep them so that when converted to a path it may
                    // still work when converted to a path, even though
                    // as an ID it is less than ideal. In larger point
                    // releases, may be better to just kick out an error.
                    if (i === 0 || (i === 1 && ary[2] === '..') || ary[i - 1] === '..') {
                        continue;
                    } else if (i > 0) {
                        ary.splice(i - 1, 2);
                        i -= 2;
                    }
                }
            }
        }

        /**
         * Given a relative module name, like ./something, normalize it to
         * a real name that can be mapped to a path.
         * @param {String} name the relative name
         * @param {String} baseName a real name that the name arg is relative
         * to.
         * @param {Boolean} applyMap apply the map config to the value. Should
         * only be done if this normalization is for a dependency ID.
         * @returns {String} normalized name
         */
        function normalize(name, baseName, applyMap) {
            var pkgMain, mapValue, nameParts, i, j, nameSegment, lastIndex,
                foundMap, foundI, foundStarMap, starI, normalizedBaseParts,
                baseParts = (baseName && baseName.split('/')),
                map = config.map,
                starMap = map && map['*'];

            //Adjust any relative paths.
            if (name) {
                name = name.split('/');
                lastIndex = name.length - 1;

                // If wanting node ID compatibility, strip .js from end
                // of IDs. Have to do this here, and not in nameToUrl
                // because node allows either .js or non .js to map
                // to same file.
                if (config.nodeIdCompat && jsSuffixRegExp.test(name[lastIndex])) {
                    name[lastIndex] = name[lastIndex].replace(jsSuffixRegExp, '');
                }

                // Starts with a '.' so need the baseName
                if (name[0].charAt(0) === '.' && baseParts) {
                    //Convert baseName to array, and lop off the last part,
                    //so that . matches that 'directory' and not name of the baseName's
                    //module. For instance, baseName of 'one/two/three', maps to
                    //'one/two/three.js', but we want the directory, 'one/two' for
                    //this normalization.
                    normalizedBaseParts = baseParts.slice(0, baseParts.length - 1);
                    name = normalizedBaseParts.concat(name);
                }

                trimDots(name);
                name = name.join('/');
            }

            //Apply map config if available.
            if (applyMap && map && (baseParts || starMap)) {
                nameParts = name.split('/');

                outerLoop: for (i = nameParts.length; i > 0; i -= 1) {
                    nameSegment = nameParts.slice(0, i).join('/');

                    if (baseParts) {
                        //Find the longest baseName segment match in the config.
                        //So, do joins on the biggest to smallest lengths of baseParts.
                        for (j = baseParts.length; j > 0; j -= 1) {
                            mapValue = getOwn(map, baseParts.slice(0, j).join('/'));

                            //baseName segment has config, find if it has one for
                            //this name.
                            if (mapValue) {
                                mapValue = getOwn(mapValue, nameSegment);
                                if (mapValue) {
                                    //Match, update name to the new value.
                                    foundMap = mapValue;
                                    foundI = i;
                                    break outerLoop;
                                }
                            }
                        }
                    }

                    //Check for a star map match, but just hold on to it,
                    //if there is a shorter segment match later in a matching
                    //config, then favor over this star map.
                    if (!foundStarMap && starMap && getOwn(starMap, nameSegment)) {
                        foundStarMap = getOwn(starMap, nameSegment);
                        starI = i;
                    }
                }

                if (!foundMap && foundStarMap) {
                    foundMap = foundStarMap;
                    foundI = starI;
                }

                if (foundMap) {
                    nameParts.splice(0, foundI, foundMap);
                    name = nameParts.join('/');
                }
            }

            // If the name points to a package's name, use
            // the package main instead.
            pkgMain = getOwn(config.pkgs, name);

            return pkgMain ? pkgMain : name;
        }

        function removeScript(name) {
            if (isBrowser) {
                each(scripts(), function (scriptNode) {
                    if (scriptNode.getAttribute('data-requiremodule') === name &&
                            scriptNode.getAttribute('data-requirecontext') === context.contextName) {
                        scriptNode.parentNode.removeChild(scriptNode);
                        return true;
                    }
                });
            }
        }

        function hasPathFallback(id) {
            var pathConfig = getOwn(config.paths, id);
            if (pathConfig && isArray(pathConfig) && pathConfig.length > 1) {
                //Pop off the first array value, since it failed, and
                //retry
                pathConfig.shift();
                context.require.undef(id);

                //Custom require that does not do map translation, since
                //ID is "absolute", already mapped/resolved.
                context.makeRequire(null, {
                    skipMap: true
                })([id]);

                return true;
            }
        }

        //Turns a plugin!resource to [plugin, resource]
        //with the plugin being undefined if the name
        //did not have a plugin prefix.
        function splitPrefix(name) {
            var prefix,
                index = name ? name.indexOf('!') : -1;
            if (index > -1) {
                prefix = name.substring(0, index);
                name = name.substring(index + 1, name.length);
            }
            return [prefix, name];
        }

        /**
         * Creates a module mapping that includes plugin prefix, module
         * name, and path. If parentModuleMap is provided it will
         * also normalize the name via require.normalize()
         *
         * @param {String} name the module name
         * @param {String} [parentModuleMap] parent module map
         * for the module name, used to resolve relative names.
         * @param {Boolean} isNormalized: is the ID already normalized.
         * This is true if this call is done for a define() module ID.
         * @param {Boolean} applyMap: apply the map config to the ID.
         * Should only be true if this map is for a dependency.
         *
         * @returns {Object}
         */
        function makeModuleMap(name, parentModuleMap, isNormalized, applyMap) {
            var url, pluginModule, suffix, nameParts,
                prefix = null,
                parentName = parentModuleMap ? parentModuleMap.name : null,
                originalName = name,
                isDefine = true,
                normalizedName = '';

            //If no name, then it means it is a require call, generate an
            //internal name.
            if (!name) {
                isDefine = false;
                name = '_@r' + (requireCounter += 1);
            }

            nameParts = splitPrefix(name);
            prefix = nameParts[0];
            name = nameParts[1];

            if (prefix) {
                prefix = normalize(prefix, parentName, applyMap);
                pluginModule = getOwn(defined, prefix);
            }

            //Account for relative paths if there is a base name.
            if (name) {
                if (prefix) {
                    if (isNormalized) {
                        normalizedName = name;
                    } else if (pluginModule && pluginModule.normalize) {
                        //Plugin is loaded, use its normalize method.
                        normalizedName = pluginModule.normalize(name, function (name) {
                            return normalize(name, parentName, applyMap);
                        });
                    } else {
                        // If nested plugin references, then do not try to
                        // normalize, as it will not normalize correctly. This
                        // places a restriction on resourceIds, and the longer
                        // term solution is not to normalize until plugins are
                        // loaded and all normalizations to allow for async
                        // loading of a loader plugin. But for now, fixes the
                        // common uses. Details in #1131
                        normalizedName = name.indexOf('!') === -1 ?
                                         normalize(name, parentName, applyMap) :
                                         name;
                    }
                } else {
                    //A regular module.
                    normalizedName = normalize(name, parentName, applyMap);

                    //Normalized name may be a plugin ID due to map config
                    //application in normalize. The map config values must
                    //already be normalized, so do not need to redo that part.
                    nameParts = splitPrefix(normalizedName);
                    prefix = nameParts[0];
                    normalizedName = nameParts[1];
                    isNormalized = true;

                    url = context.nameToUrl(normalizedName);
                }
            }

            //If the id is a plugin id that cannot be determined if it needs
            //normalization, stamp it with a unique ID so two matching relative
            //ids that may conflict can be separate.
            suffix = prefix && !pluginModule && !isNormalized ?
                     '_unnormalized' + (unnormalizedCounter += 1) :
                     '';

            return {
                prefix: prefix,
                name: normalizedName,
                parentMap: parentModuleMap,
                unnormalized: !!suffix,
                url: url,
                originalName: originalName,
                isDefine: isDefine,
                id: (prefix ?
                        prefix + '!' + normalizedName :
                        normalizedName) + suffix
            };
        }

        function getModule(depMap) {
            var id = depMap.id,
                mod = getOwn(registry, id);

            if (!mod) {
                mod = registry[id] = new context.Module(depMap);
            }

            return mod;
        }

        function on(depMap, name, fn) {
            var id = depMap.id,
                mod = getOwn(registry, id);

            if (hasProp(defined, id) &&
                    (!mod || mod.defineEmitComplete)) {
                if (name === 'defined') {
                    fn(defined[id]);
                }
            } else {
                mod = getModule(depMap);
                if (mod.error && name === 'error') {
                    fn(mod.error);
                } else {
                    mod.on(name, fn);
                }
            }
        }

        function onError(err, errback) {
            var ids = err.requireModules,
                notified = false;

            if (errback) {
                errback(err);
            } else {
                each(ids, function (id) {
                    var mod = getOwn(registry, id);
                    if (mod) {
                        //Set error on module, so it skips timeout checks.
                        mod.error = err;
                        if (mod.events.error) {
                            notified = true;
                            mod.emit('error', err);
                        }
                    }
                });

                if (!notified) {
                    req.onError(err);
                }
            }
        }

        /**
         * Internal method to transfer globalQueue items to this context's
         * defQueue.
         */
        function takeGlobalQueue() {
            //Push all the globalDefQueue items into the context's defQueue
            if (globalDefQueue.length) {
                each(globalDefQueue, function(queueItem) {
                    var id = queueItem[0];
                    if (typeof id === 'string') {
                        context.defQueueMap[id] = true;
                    }
                    defQueue.push(queueItem);
                });
                globalDefQueue = [];
            }
        }

        handlers = {
            'require': function (mod) {
                if (mod.require) {
                    return mod.require;
                } else {
                    return (mod.require = context.makeRequire(mod.map));
                }
            },
            'exports': function (mod) {
                mod.usingExports = true;
                if (mod.map.isDefine) {
                    if (mod.exports) {
                        return (defined[mod.map.id] = mod.exports);
                    } else {
                        return (mod.exports = defined[mod.map.id] = {});
                    }
                }
            },
            'module': function (mod) {
                if (mod.module) {
                    return mod.module;
                } else {
                    return (mod.module = {
                        id: mod.map.id,
                        uri: mod.map.url,
                        config: function () {
                            return getOwn(config.config, mod.map.id) || {};
                        },
                        exports: mod.exports || (mod.exports = {})
                    });
                }
            }
        };

        function cleanRegistry(id) {
            //Clean up machinery used for waiting modules.
            delete registry[id];
            delete enabledRegistry[id];
        }

        function breakCycle(mod, traced, processed) {
            var id = mod.map.id;

            if (mod.error) {
                mod.emit('error', mod.error);
            } else {
                traced[id] = true;
                each(mod.depMaps, function (depMap, i) {
                    var depId = depMap.id,
                        dep = getOwn(registry, depId);

                    //Only force things that have not completed
                    //being defined, so still in the registry,
                    //and only if it has not been matched up
                    //in the module already.
                    if (dep && !mod.depMatched[i] && !processed[depId]) {
                        if (getOwn(traced, depId)) {
                            mod.defineDep(i, defined[depId]);
                            mod.check(); //pass false?
                        } else {
                            breakCycle(dep, traced, processed);
                        }
                    }
                });
                processed[id] = true;
            }
        }

        function checkLoaded() {
            var err, usingPathFallback,
                waitInterval = config.waitSeconds * 1000,
                //It is possible to disable the wait interval by using waitSeconds of 0.
                expired = waitInterval && (context.startTime + waitInterval) < new Date().getTime(),
                noLoads = [],
                reqCalls = [],
                stillLoading = false,
                needCycleCheck = true;

            //Do not bother if this call was a result of a cycle break.
            if (inCheckLoaded) {
                return;
            }

            inCheckLoaded = true;

            //Figure out the state of all the modules.
            eachProp(enabledRegistry, function (mod) {
                var map = mod.map,
                    modId = map.id;

                //Skip things that are not enabled or in error state.
                if (!mod.enabled) {
                    return;
                }

                if (!map.isDefine) {
                    reqCalls.push(mod);
                }

                if (!mod.error) {
                    //If the module should be executed, and it has not
                    //been inited and time is up, remember it.
                    if (!mod.inited && expired) {
                        if (hasPathFallback(modId)) {
                            usingPathFallback = true;
                            stillLoading = true;
                        } else {
                            noLoads.push(modId);
                            removeScript(modId);
                        }
                    } else if (!mod.inited && mod.fetched && map.isDefine) {
                        stillLoading = true;
                        if (!map.prefix) {
                            //No reason to keep looking for unfinished
                            //loading. If the only stillLoading is a
                            //plugin resource though, keep going,
                            //because it may be that a plugin resource
                            //is waiting on a non-plugin cycle.
                            return (needCycleCheck = false);
                        }
                    }
                }
            });

            if (expired && noLoads.length) {
                //If wait time expired, throw error of unloaded modules.
                err = makeError('timeout', 'Load timeout for modules: ' + noLoads, null, noLoads);
                err.contextName = context.contextName;
                return onError(err);
            }

            //Not expired, check for a cycle.
            if (needCycleCheck) {
                each(reqCalls, function (mod) {
                    breakCycle(mod, {}, {});
                });
            }

            //If still waiting on loads, and the waiting load is something
            //other than a plugin resource, or there are still outstanding
            //scripts, then just try back later.
            if ((!expired || usingPathFallback) && stillLoading) {
                //Something is still waiting to load. Wait for it, but only
                //if a timeout is not already in effect.
                if ((isBrowser || isWebWorker) && !checkLoadedTimeoutId) {
                    checkLoadedTimeoutId = setTimeout(function () {
                        checkLoadedTimeoutId = 0;
                        checkLoaded();
                    }, 50);
                }
            }

            inCheckLoaded = false;
        }

        Module = function (map) {
            this.events = getOwn(undefEvents, map.id) || {};
            this.map = map;
            this.shim = getOwn(config.shim, map.id);
            this.depExports = [];
            this.depMaps = [];
            this.depMatched = [];
            this.pluginMaps = {};
            this.depCount = 0;

            /* this.exports this.factory
               this.depMaps = [],
               this.enabled, this.fetched
            */
        };

        Module.prototype = {
            init: function (depMaps, factory, errback, options) {
                options = options || {};

                //Do not do more inits if already done. Can happen if there
                //are multiple define calls for the same module. That is not
                //a normal, common case, but it is also not unexpected.
                if (this.inited) {
                    return;
                }

                this.factory = factory;

                if (errback) {
                    //Register for errors on this module.
                    this.on('error', errback);
                } else if (this.events.error) {
                    //If no errback already, but there are error listeners
                    //on this module, set up an errback to pass to the deps.
                    errback = bind(this, function (err) {
                        this.emit('error', err);
                    });
                }

                //Do a copy of the dependency array, so that
                //source inputs are not modified. For example
                //"shim" deps are passed in here directly, and
                //doing a direct modification of the depMaps array
                //would affect that config.
                this.depMaps = depMaps && depMaps.slice(0);

                this.errback = errback;

                //Indicate this module has be initialized
                this.inited = true;

                this.ignore = options.ignore;

                //Could have option to init this module in enabled mode,
                //or could have been previously marked as enabled. However,
                //the dependencies are not known until init is called. So
                //if enabled previously, now trigger dependencies as enabled.
                if (options.enabled || this.enabled) {
                    //Enable this module and dependencies.
                    //Will call this.check()
                    this.enable();
                } else {
                    this.check();
                }
            },

            defineDep: function (i, depExports) {
                //Because of cycles, defined callback for a given
                //export can be called more than once.
                if (!this.depMatched[i]) {
                    this.depMatched[i] = true;
                    this.depCount -= 1;
                    this.depExports[i] = depExports;
                }
            },

            fetch: function () {
                if (this.fetched) {
                    return;
                }
                this.fetched = true;

                context.startTime = (new Date()).getTime();

                var map = this.map;

                //If the manager is for a plugin managed resource,
                //ask the plugin to load it now.
                if (this.shim) {
                    context.makeRequire(this.map, {
                        enableBuildCallback: true
                    })(this.shim.deps || [], bind(this, function () {
                        return map.prefix ? this.callPlugin() : this.load();
                    }));
                } else {
                    //Regular dependency.
                    return map.prefix ? this.callPlugin() : this.load();
                }
            },

            load: function () {
                var url = this.map.url;

                //Regular dependency.
                if (!urlFetched[url]) {
                    urlFetched[url] = true;
                    context.load(this.map.id, url);
                }
            },

            /**
             * Checks if the module is ready to define itself, and if so,
             * define it.
             */
            check: function () {
                if (!this.enabled || this.enabling) {
                    return;
                }

                var err, cjsModule,
                    id = this.map.id,
                    depExports = this.depExports,
                    exports = this.exports,
                    factory = this.factory;

                if (!this.inited) {
                    // Only fetch if not already in the defQueue.
                    if (!hasProp(context.defQueueMap, id)) {
                        this.fetch();
                    }
                } else if (this.error) {
                    this.emit('error', this.error);
                } else if (!this.defining) {
                    //The factory could trigger another require call
                    //that would result in checking this module to
                    //define itself again. If already in the process
                    //of doing that, skip this work.
                    this.defining = true;

                    if (this.depCount < 1 && !this.defined) {
                        if (isFunction(factory)) {
                            //If there is an error listener, favor passing
                            //to that instead of throwing an error. However,
                            //only do it for define()'d  modules. require
                            //errbacks should not be called for failures in
                            //their callbacks (#699). However if a global
                            //onError is set, use that.
                            if ((this.events.error && this.map.isDefine) ||
                                req.onError !== defaultOnError) {
                                try {
                                    exports = context.execCb(id, factory, depExports, exports);
                                } catch (e) {
                                    err = e;
                                }
                            } else {
                                exports = context.execCb(id, factory, depExports, exports);
                            }

                            // Favor return value over exports. If node/cjs in play,
                            // then will not have a return value anyway. Favor
                            // module.exports assignment over exports object.
                            if (this.map.isDefine && exports === undefined) {
                                cjsModule = this.module;
                                if (cjsModule) {
                                    exports = cjsModule.exports;
                                } else if (this.usingExports) {
                                    //exports already set the defined value.
                                    exports = this.exports;
                                }
                            }

                            if (err) {
                                err.requireMap = this.map;
                                err.requireModules = this.map.isDefine ? [this.map.id] : null;
                                err.requireType = this.map.isDefine ? 'define' : 'require';
                                return onError((this.error = err));
                            }

                        } else {
                            //Just a literal value
                            exports = factory;
                        }

                        this.exports = exports;

                        if (this.map.isDefine && !this.ignore) {
                            defined[id] = exports;

                            if (req.onResourceLoad) {
                                var resLoadMaps = [];
                                each(this.depMaps, function (depMap) {
                                    resLoadMaps.push(depMap.normalizedMap || depMap);
                                });
                                req.onResourceLoad(context, this.map, resLoadMaps);
                            }
                        }

                        //Clean up
                        cleanRegistry(id);

                        this.defined = true;
                    }

                    //Finished the define stage. Allow calling check again
                    //to allow define notifications below in the case of a
                    //cycle.
                    this.defining = false;

                    if (this.defined && !this.defineEmitted) {
                        this.defineEmitted = true;
                        this.emit('defined', this.exports);
                        this.defineEmitComplete = true;
                    }

                }
            },

            callPlugin: function () {
                var map = this.map,
                    id = map.id,
                    //Map already normalized the prefix.
                    pluginMap = makeModuleMap(map.prefix);

                //Mark this as a dependency for this plugin, so it
                //can be traced for cycles.
                this.depMaps.push(pluginMap);

                on(pluginMap, 'defined', bind(this, function (plugin) {
                    var load, normalizedMap, normalizedMod,
                        bundleId = getOwn(bundlesMap, this.map.id),
                        name = this.map.name,
                        parentName = this.map.parentMap ? this.map.parentMap.name : null,
                        localRequire = context.makeRequire(map.parentMap, {
                            enableBuildCallback: true
                        });

                    //If current map is not normalized, wait for that
                    //normalized name to load instead of continuing.
                    if (this.map.unnormalized) {
                        //Normalize the ID if the plugin allows it.
                        if (plugin.normalize) {
                            name = plugin.normalize(name, function (name) {
                                return normalize(name, parentName, true);
                            }) || '';
                        }

                        //prefix and name should already be normalized, no need
                        //for applying map config again either.
                        normalizedMap = makeModuleMap(map.prefix + '!' + name,
                                                      this.map.parentMap,
                                                      true);
                        on(normalizedMap,
                            'defined', bind(this, function (value) {
                                this.map.normalizedMap = normalizedMap;
                                this.init([], function () { return value; }, null, {
                                    enabled: true,
                                    ignore: true
                                });
                            }));

                        normalizedMod = getOwn(registry, normalizedMap.id);
                        if (normalizedMod) {
                            //Mark this as a dependency for this plugin, so it
                            //can be traced for cycles.
                            this.depMaps.push(normalizedMap);

                            if (this.events.error) {
                                normalizedMod.on('error', bind(this, function (err) {
                                    this.emit('error', err);
                                }));
                            }
                            normalizedMod.enable();
                        }

                        return;
                    }

                    //If a paths config, then just load that file instead to
                    //resolve the plugin, as it is built into that paths layer.
                    if (bundleId) {
                        this.map.url = context.nameToUrl(bundleId);
                        this.load();
                        return;
                    }

                    load = bind(this, function (value) {
                        this.init([], function () { return value; }, null, {
                            enabled: true
                        });
                    });

                    load.error = bind(this, function (err) {
                        this.inited = true;
                        this.error = err;
                        err.requireModules = [id];

                        //Remove temp unnormalized modules for this module,
                        //since they will never be resolved otherwise now.
                        eachProp(registry, function (mod) {
                            if (mod.map.id.indexOf(id + '_unnormalized') === 0) {
                                cleanRegistry(mod.map.id);
                            }
                        });

                        onError(err);
                    });

                    //Allow plugins to load other code without having to know the
                    //context or how to 'complete' the load.
                    load.fromText = bind(this, function (text, textAlt) {
                        /*jslint evil: true */
                        var moduleName = map.name,
                            moduleMap = makeModuleMap(moduleName),
                            hasInteractive = useInteractive;

                        //As of 2.1.0, support just passing the text, to reinforce
                        //fromText only being called once per resource. Still
                        //support old style of passing moduleName but discard
                        //that moduleName in favor of the internal ref.
                        if (textAlt) {
                            text = textAlt;
                        }

                        //Turn off interactive script matching for IE for any define
                        //calls in the text, then turn it back on at the end.
                        if (hasInteractive) {
                            useInteractive = false;
                        }

                        //Prime the system by creating a module instance for
                        //it.
                        getModule(moduleMap);

                        //Transfer any config to this other module.
                        if (hasProp(config.config, id)) {
                            config.config[moduleName] = config.config[id];
                        }

                        try {
                            req.exec(text);
                        } catch (e) {
                            return onError(makeError('fromtexteval',
                                             'fromText eval for ' + id +
                                            ' failed: ' + e,
                                             e,
                                             [id]));
                        }

                        if (hasInteractive) {
                            useInteractive = true;
                        }

                        //Mark this as a dependency for the plugin
                        //resource
                        this.depMaps.push(moduleMap);

                        //Support anonymous modules.
                        context.completeLoad(moduleName);

                        //Bind the value of that module to the value for this
                        //resource ID.
                        localRequire([moduleName], load);
                    });

                    //Use parentName here since the plugin's name is not reliable,
                    //could be some weird string with no path that actually wants to
                    //reference the parentName's path.
                    plugin.load(map.name, localRequire, load, config);
                }));

                context.enable(pluginMap, this);
                this.pluginMaps[pluginMap.id] = pluginMap;
            },

            enable: function () {
                enabledRegistry[this.map.id] = this;
                this.enabled = true;

                //Set flag mentioning that the module is enabling,
                //so that immediate calls to the defined callbacks
                //for dependencies do not trigger inadvertent load
                //with the depCount still being zero.
                this.enabling = true;

                //Enable each dependency
                each(this.depMaps, bind(this, function (depMap, i) {
                    var id, mod, handler;

                    if (typeof depMap === 'string') {
                        //Dependency needs to be converted to a depMap
                        //and wired up to this module.
                        depMap = makeModuleMap(depMap,
                                               (this.map.isDefine ? this.map : this.map.parentMap),
                                               false,
                                               !this.skipMap);
                        this.depMaps[i] = depMap;

                        handler = getOwn(handlers, depMap.id);

                        if (handler) {
                            this.depExports[i] = handler(this);
                            return;
                        }

                        this.depCount += 1;

                        on(depMap, 'defined', bind(this, function (depExports) {
                            if (this.undefed) {
                                return;
                            }
                            this.defineDep(i, depExports);
                            this.check();
                        }));

                        if (this.errback) {
                            on(depMap, 'error', bind(this, this.errback));
                        } else if (this.events.error) {
                            // No direct errback on this module, but something
                            // else is listening for errors, so be sure to
                            // propagate the error correctly.
                            on(depMap, 'error', bind(this, function(err) {
                                this.emit('error', err);
                            }));
                        }
                    }

                    id = depMap.id;
                    mod = registry[id];

                    //Skip special modules like 'require', 'exports', 'module'
                    //Also, don't call enable if it is already enabled,
                    //important in circular dependency cases.
                    if (!hasProp(handlers, id) && mod && !mod.enabled) {
                        context.enable(depMap, this);
                    }
                }));

                //Enable each plugin that is used in
                //a dependency
                eachProp(this.pluginMaps, bind(this, function (pluginMap) {
                    var mod = getOwn(registry, pluginMap.id);
                    if (mod && !mod.enabled) {
                        context.enable(pluginMap, this);
                    }
                }));

                this.enabling = false;

                this.check();
            },

            on: function (name, cb) {
                var cbs = this.events[name];
                if (!cbs) {
                    cbs = this.events[name] = [];
                }
                cbs.push(cb);
            },

            emit: function (name, evt) {
                each(this.events[name], function (cb) {
                    cb(evt);
                });
                if (name === 'error') {
                    //Now that the error handler was triggered, remove
                    //the listeners, since this broken Module instance
                    //can stay around for a while in the registry.
                    delete this.events[name];
                }
            }
        };

        function callGetModule(args) {
            //Skip modules already defined.
            if (!hasProp(defined, args[0])) {
                getModule(makeModuleMap(args[0], null, true)).init(args[1], args[2]);
            }
        }

        function removeListener(node, func, name, ieName) {
            //Favor detachEvent because of IE9
            //issue, see attachEvent/addEventListener comment elsewhere
            //in this file.
            if (node.detachEvent && !isOpera) {
                //Probably IE. If not it will throw an error, which will be
                //useful to know.
                if (ieName) {
                    node.detachEvent(ieName, func);
                }
            } else {
                node.removeEventListener(name, func, false);
            }
        }

        /**
         * Given an event from a script node, get the requirejs info from it,
         * and then removes the event listeners on the node.
         * @param {Event} evt
         * @returns {Object}
         */
        function getScriptData(evt) {
            //Using currentTarget instead of target for Firefox 2.0's sake. Not
            //all old browsers will be supported, but this one was easy enough
            //to support and still makes sense.
            var node = evt.currentTarget || evt.srcElement;

            //Remove the listeners once here.
            removeListener(node, context.onScriptLoad, 'load', 'onreadystatechange');
            removeListener(node, context.onScriptError, 'error');

            return {
                node: node,
                id: node && node.getAttribute('data-requiremodule')
            };
        }

        function intakeDefines() {
            var args;

            //Any defined modules in the global queue, intake them now.
            takeGlobalQueue();

            //Make sure any remaining defQueue items get properly processed.
            while (defQueue.length) {
                args = defQueue.shift();
                if (args[0] === null) {
                    return onError(makeError('mismatch', 'Mismatched anonymous define() module: ' +
                        args[args.length - 1]));
                } else {
                    //args are id, deps, factory. Should be normalized by the
                    //define() function.
                    callGetModule(args);
                }
            }
            context.defQueueMap = {};
        }

        context = {
            config: config,
            contextName: contextName,
            registry: registry,
            defined: defined,
            urlFetched: urlFetched,
            defQueue: defQueue,
            defQueueMap: {},
            Module: Module,
            makeModuleMap: makeModuleMap,
            nextTick: req.nextTick,
            onError: onError,

            /**
             * Set a configuration for the context.
             * @param {Object} cfg config object to integrate.
             */
            configure: function (cfg) {
                //Make sure the baseUrl ends in a slash.
                if (cfg.baseUrl) {
                    if (cfg.baseUrl.charAt(cfg.baseUrl.length - 1) !== '/') {
                        cfg.baseUrl += '/';
                    }
                }

                // Convert old style urlArgs string to a function.
                if (typeof cfg.urlArgs === 'string') {
                    var urlArgs = cfg.urlArgs;
                    cfg.urlArgs = function(id, url) {
                        return (url.indexOf('?') === -1 ? '?' : '&') + urlArgs;
                    };
                }

                //Save off the paths since they require special processing,
                //they are additive.
                var shim = config.shim,
                    objs = {
                        paths: true,
                        bundles: true,
                        config: true,
                        map: true
                    };

                eachProp(cfg, function (value, prop) {
                    if (objs[prop]) {
                        if (!config[prop]) {
                            config[prop] = {};
                        }
                        mixin(config[prop], value, true, true);
                    } else {
                        config[prop] = value;
                    }
                });

                //Reverse map the bundles
                if (cfg.bundles) {
                    eachProp(cfg.bundles, function (value, prop) {
                        each(value, function (v) {
                            if (v !== prop) {
                                bundlesMap[v] = prop;
                            }
                        });
                    });
                }

                //Merge shim
                if (cfg.shim) {
                    eachProp(cfg.shim, function (value, id) {
                        //Normalize the structure
                        if (isArray(value)) {
                            value = {
                                deps: value
                            };
                        }
                        if ((value.exports || value.init) && !value.exportsFn) {
                            value.exportsFn = context.makeShimExports(value);
                        }
                        shim[id] = value;
                    });
                    config.shim = shim;
                }

                //Adjust packages if necessary.
                if (cfg.packages) {
                    each(cfg.packages, function (pkgObj) {
                        var location, name;

                        pkgObj = typeof pkgObj === 'string' ? {name: pkgObj} : pkgObj;

                        name = pkgObj.name;
                        location = pkgObj.location;
                        if (location) {
                            config.paths[name] = pkgObj.location;
                        }

                        //Save pointer to main module ID for pkg name.
                        //Remove leading dot in main, so main paths are normalized,
                        //and remove any trailing .js, since different package
                        //envs have different conventions: some use a module name,
                        //some use a file name.
                        config.pkgs[name] = pkgObj.name + '/' + (pkgObj.main || 'main')
                                     .replace(currDirRegExp, '')
                                     .replace(jsSuffixRegExp, '');
                    });
                }

                //If there are any "waiting to execute" modules in the registry,
                //update the maps for them, since their info, like URLs to load,
                //may have changed.
                eachProp(registry, function (mod, id) {
                    //If module already has init called, since it is too
                    //late to modify them, and ignore unnormalized ones
                    //since they are transient.
                    if (!mod.inited && !mod.map.unnormalized) {
                        mod.map = makeModuleMap(id, null, true);
                    }
                });

                //If a deps array or a config callback is specified, then call
                //require with those args. This is useful when require is defined as a
                //config object before require.js is loaded.
                if (cfg.deps || cfg.callback) {
                    context.require(cfg.deps || [], cfg.callback);
                }
            },

            makeShimExports: function (value) {
                function fn() {
                    var ret;
                    if (value.init) {
                        ret = value.init.apply(global, arguments);
                    }
                    return ret || (value.exports && getGlobal(value.exports));
                }
                return fn;
            },

            makeRequire: function (relMap, options) {
                options = options || {};

                function localRequire(deps, callback, errback) {
                    var id, map, requireMod;

                    if (options.enableBuildCallback && callback && isFunction(callback)) {
                        callback.__requireJsBuild = true;
                    }

                    if (typeof deps === 'string') {
                        if (isFunction(callback)) {
                            //Invalid call
                            return onError(makeError('requireargs', 'Invalid require call'), errback);
                        }

                        //If require|exports|module are requested, get the
                        //value for them from the special handlers. Caveat:
                        //this only works while module is being defined.
                        if (relMap && hasProp(handlers, deps)) {
                            return handlers[deps](registry[relMap.id]);
                        }

                        //Synchronous access to one module. If require.get is
                        //available (as in the Node adapter), prefer that.
                        if (req.get) {
                            return req.get(context, deps, relMap, localRequire);
                        }

                        //Normalize module name, if it contains . or ..
                        map = makeModuleMap(deps, relMap, false, true);
                        id = map.id;

                        if (!hasProp(defined, id)) {
                            return onError(makeError('notloaded', 'Module name "' +
                                        id +
                                        '" has not been loaded yet for context: ' +
                                        contextName +
                                        (relMap ? '' : '. Use require([])')));
                        }
                        return defined[id];
                    }

                    //Grab defines waiting in the global queue.
                    intakeDefines();

                    //Mark all the dependencies as needing to be loaded.
                    context.nextTick(function () {
                        //Some defines could have been added since the
                        //require call, collect them.
                        intakeDefines();

                        requireMod = getModule(makeModuleMap(null, relMap));

                        //Store if map config should be applied to this require
                        //call for dependencies.
                        requireMod.skipMap = options.skipMap;

                        requireMod.init(deps, callback, errback, {
                            enabled: true
                        });

                        checkLoaded();
                    });

                    return localRequire;
                }

                mixin(localRequire, {
                    isBrowser: isBrowser,

                    /**
                     * Converts a module name + .extension into an URL path.
                     * *Requires* the use of a module name. It does not support using
                     * plain URLs like nameToUrl.
                     */
                    toUrl: function (moduleNamePlusExt) {
                        var ext,
                            index = moduleNamePlusExt.lastIndexOf('.'),
                            segment = moduleNamePlusExt.split('/')[0],
                            isRelative = segment === '.' || segment === '..';

                        //Have a file extension alias, and it is not the
                        //dots from a relative path.
                        if (index !== -1 && (!isRelative || index > 1)) {
                            ext = moduleNamePlusExt.substring(index, moduleNamePlusExt.length);
                            moduleNamePlusExt = moduleNamePlusExt.substring(0, index);
                        }

                        return context.nameToUrl(normalize(moduleNamePlusExt,
                                                relMap && relMap.id, true), ext,  true);
                    },

                    defined: function (id) {
                        return hasProp(defined, makeModuleMap(id, relMap, false, true).id);
                    },

                    specified: function (id) {
                        id = makeModuleMap(id, relMap, false, true).id;
                        return hasProp(defined, id) || hasProp(registry, id);
                    }
                });

                //Only allow undef on top level require calls
                if (!relMap) {
                    localRequire.undef = function (id) {
                        //Bind any waiting define() calls to this context,
                        //fix for #408
                        takeGlobalQueue();

                        var map = makeModuleMap(id, relMap, true),
                            mod = getOwn(registry, id);

                        mod.undefed = true;
                        removeScript(id);

                        delete defined[id];
                        delete urlFetched[map.url];
                        delete undefEvents[id];

                        //Clean queued defines too. Go backwards
                        //in array so that the splices do not
                        //mess up the iteration.
                        eachReverse(defQueue, function(args, i) {
                            if (args[0] === id) {
                                defQueue.splice(i, 1);
                            }
                        });
                        delete context.defQueueMap[id];

                        if (mod) {
                            //Hold on to listeners in case the
                            //module will be attempted to be reloaded
                            //using a different config.
                            if (mod.events.defined) {
                                undefEvents[id] = mod.events;
                            }

                            cleanRegistry(id);
                        }
                    };
                }

                return localRequire;
            },

            /**
             * Called to enable a module if it is still in the registry
             * awaiting enablement. A second arg, parent, the parent module,
             * is passed in for context, when this method is overridden by
             * the optimizer. Not shown here to keep code compact.
             */
            enable: function (depMap) {
                var mod = getOwn(registry, depMap.id);
                if (mod) {
                    getModule(depMap).enable();
                }
            },

            /**
             * Internal method used by environment adapters to complete a load event.
             * A load event could be a script load or just a load pass from a synchronous
             * load call.
             * @param {String} moduleName the name of the module to potentially complete.
             */
            completeLoad: function (moduleName) {
                var found, args, mod,
                    shim = getOwn(config.shim, moduleName) || {},
                    shExports = shim.exports;

                takeGlobalQueue();

                while (defQueue.length) {
                    args = defQueue.shift();
                    if (args[0] === null) {
                        args[0] = moduleName;
                        //If already found an anonymous module and bound it
                        //to this name, then this is some other anon module
                        //waiting for its completeLoad to fire.
                        if (found) {
                            break;
                        }
                        found = true;
                    } else if (args[0] === moduleName) {
                        //Found matching define call for this script!
                        found = true;
                    }

                    callGetModule(args);
                }
                context.defQueueMap = {};

                //Do this after the cycle of callGetModule in case the result
                //of those calls/init calls changes the registry.
                mod = getOwn(registry, moduleName);

                if (!found && !hasProp(defined, moduleName) && mod && !mod.inited) {
                    if (config.enforceDefine && (!shExports || !getGlobal(shExports))) {
                        if (hasPathFallback(moduleName)) {
                            return;
                        } else {
                            return onError(makeError('nodefine',
                                             'No define call for ' + moduleName,
                                             null,
                                             [moduleName]));
                        }
                    } else {
                        //A script that does not call define(), so just simulate
                        //the call for it.
                        callGetModule([moduleName, (shim.deps || []), shim.exportsFn]);
                    }
                }

                checkLoaded();
            },

            /**
             * Converts a module name to a file path. Supports cases where
             * moduleName may actually be just an URL.
             * Note that it **does not** call normalize on the moduleName,
             * it is assumed to have already been normalized. This is an
             * internal API, not a public one. Use toUrl for the public API.
             */
            nameToUrl: function (moduleName, ext, skipExt) {
                var paths, syms, i, parentModule, url,
                    parentPath, bundleId,
                    pkgMain = getOwn(config.pkgs, moduleName);

                if (pkgMain) {
                    moduleName = pkgMain;
                }

                bundleId = getOwn(bundlesMap, moduleName);

                if (bundleId) {
                    return context.nameToUrl(bundleId, ext, skipExt);
                }

                //If a colon is in the URL, it indicates a protocol is used and it is just
                //an URL to a file, or if it starts with a slash, contains a query arg (i.e. ?)
                //or ends with .js, then assume the user meant to use an url and not a module id.
                //The slash is important for protocol-less URLs as well as full paths.
                if (req.jsExtRegExp.test(moduleName)) {
                    //Just a plain path, not module name lookup, so just return it.
                    //Add extension if it is included. This is a bit wonky, only non-.js things pass
                    //an extension, this method probably needs to be reworked.
                    url = moduleName + (ext || '');
                } else {
                    //A module that needs to be converted to a path.
                    paths = config.paths;

                    syms = moduleName.split('/');
                    //For each module name segment, see if there is a path
                    //registered for it. Start with most specific name
                    //and work up from it.
                    for (i = syms.length; i > 0; i -= 1) {
                        parentModule = syms.slice(0, i).join('/');

                        parentPath = getOwn(paths, parentModule);
                        if (parentPath) {
                            //If an array, it means there are a few choices,
                            //Choose the one that is desired
                            if (isArray(parentPath)) {
                                parentPath = parentPath[0];
                            }
                            syms.splice(0, i, parentPath);
                            break;
                        }
                    }

                    //Join the path parts together, then figure out if baseUrl is needed.
                    url = syms.join('/');
                    url += (ext || (/^data\:|^blob\:|\?/.test(url) || skipExt ? '' : '.js'));
                    url = (url.charAt(0) === '/' || url.match(/^[\w\+\.\-]+:/) ? '' : config.baseUrl) + url;
                }

                return config.urlArgs && !/^blob\:/.test(url) ?
                       url + config.urlArgs(moduleName, url) : url;
            },

            //Delegates to req.load. Broken out as a separate function to
            //allow overriding in the optimizer.
            load: function (id, url) {
                req.load(context, id, url);
            },

            /**
             * Executes a module callback function. Broken out as a separate function
             * solely to allow the build system to sequence the files in the built
             * layer in the right sequence.
             *
             * @private
             */
            execCb: function (name, callback, args, exports) {
                return callback.apply(exports, args);
            },

            /**
             * callback for script loads, used to check status of loading.
             *
             * @param {Event} evt the event from the browser for the script
             * that was loaded.
             */
            onScriptLoad: function (evt) {
                //Using currentTarget instead of target for Firefox 2.0's sake. Not
                //all old browsers will be supported, but this one was easy enough
                //to support and still makes sense.
                if (evt.type === 'load' ||
                        (readyRegExp.test((evt.currentTarget || evt.srcElement).readyState))) {
                    //Reset interactive script so a script node is not held onto for
                    //to long.
                    interactiveScript = null;

                    //Pull out the name of the module and the context.
                    var data = getScriptData(evt);
                    context.completeLoad(data.id);
                }
            },

            /**
             * Callback for script errors.
             */
            onScriptError: function (evt) {
                var data = getScriptData(evt);
                if (!hasPathFallback(data.id)) {
                    var parents = [];
                    eachProp(registry, function(value, key) {
                        if (key.indexOf('_@r') !== 0) {
                            each(value.depMaps, function(depMap) {
                                if (depMap.id === data.id) {
                                    parents.push(key);
                                    return true;
                                }
                            });
                        }
                    });
                    return onError(makeError('scripterror', 'Script error for "' + data.id +
                                             (parents.length ?
                                             '", needed by: ' + parents.join(', ') :
                                             '"'), evt, [data.id]));
                }
            }
        };

        context.require = context.makeRequire();
        return context;
    }

    /**
     * Main entry point.
     *
     * If the only argument to require is a string, then the module that
     * is represented by that string is fetched for the appropriate context.
     *
     * If the first argument is an array, then it will be treated as an array
     * of dependency string names to fetch. An optional function callback can
     * be specified to execute when all of those dependencies are available.
     *
     * Make a local req variable to help Caja compliance (it assumes things
     * on a require that are not standardized), and to give a short
     * name for minification/local scope use.
     */
    req = requirejs = function (deps, callback, errback, optional) {

        //Find the right context, use default
        var context, config,
            contextName = defContextName;

        // Determine if have config object in the call.
        if (!isArray(deps) && typeof deps !== 'string') {
            // deps is a config object
            config = deps;
            if (isArray(callback)) {
                // Adjust args if there are dependencies
                deps = callback;
                callback = errback;
                errback = optional;
            } else {
                deps = [];
            }
        }

        if (config && config.context) {
            contextName = config.context;
        }

        context = getOwn(contexts, contextName);
        if (!context) {
            context = contexts[contextName] = req.s.newContext(contextName);
        }

        if (config) {
            context.configure(config);
        }

        return context.require(deps, callback, errback);
    };

    /**
     * Support require.config() to make it easier to cooperate with other
     * AMD loaders on globally agreed names.
     */
    req.config = function (config) {
        return req(config);
    };

    /**
     * Execute something after the current tick
     * of the event loop. Override for other envs
     * that have a better solution than setTimeout.
     * @param  {Function} fn function to execute later.
     */
    req.nextTick = typeof setTimeout !== 'undefined' ? function (fn) {
        setTimeout(fn, 4);
    } : function (fn) { fn(); };

    /**
     * Export require as a global, but only if it does not already exist.
     */
    if (!require) {
        require = req;
    }

    req.version = version;

    //Used to filter out dependencies that are already paths.
    req.jsExtRegExp = /^\/|:|\?|\.js$/;
    req.isBrowser = isBrowser;
    s = req.s = {
        contexts: contexts,
        newContext: newContext
    };

    //Create default context.
    req({});

    //Exports some context-sensitive methods on global require.
    each([
        'toUrl',
        'undef',
        'defined',
        'specified'
    ], function (prop) {
        //Reference from contexts instead of early binding to default context,
        //so that during builds, the latest instance of the default context
        //with its config gets used.
        req[prop] = function () {
            var ctx = contexts[defContextName];
            return ctx.require[prop].apply(ctx, arguments);
        };
    });

    if (isBrowser) {
        head = s.head = document.getElementsByTagName('head')[0];
        //If BASE tag is in play, using appendChild is a problem for IE6.
        //When that browser dies, this can be removed. Details in this jQuery bug:
        //http://dev.jquery.com/ticket/2709
        baseElement = document.getElementsByTagName('base')[0];
        if (baseElement) {
            head = s.head = baseElement.parentNode;
        }
    }

    /**
     * Any errors that require explicitly generates will be passed to this
     * function. Intercept/override it if you want custom error handling.
     * @param {Error} err the error object.
     */
    req.onError = defaultOnError;

    /**
     * Creates the node for the load command. Only used in browser envs.
     */
    req.createNode = function (config, moduleName, url) {
        var node = config.xhtml ?
                document.createElementNS('http://www.w3.org/1999/xhtml', 'html:script') :
                document.createElement('script');
        node.type = config.scriptType || 'text/javascript';
        node.charset = 'utf-8';
        node.async = true;
        return node;
    };

    /**
     * Does the request to load a module for the browser case.
     * Make this a separate function to allow other environments
     * to override it.
     *
     * @param {Object} context the require context to find state.
     * @param {String} moduleName the name of the module.
     * @param {Object} url the URL to the module.
     */
    req.load = function (context, moduleName, url) {
        var config = (context && context.config) || {},
            node;
        if (isBrowser) {
            //In the browser so use a script tag
            node = req.createNode(config, moduleName, url);

            node.setAttribute('data-requirecontext', context.contextName);
            node.setAttribute('data-requiremodule', moduleName);

            //Set up load listener. Test attachEvent first because IE9 has
            //a subtle issue in its addEventListener and script onload firings
            //that do not match the behavior of all other browsers with
            //addEventListener support, which fire the onload event for a
            //script right after the script execution. See:
            //https://connect.microsoft.com/IE/feedback/details/648057/script-onload-event-is-not-fired-immediately-after-script-execution
            //UNFORTUNATELY Opera implements attachEvent but does not follow the script
            //script execution mode.
            if (node.attachEvent &&
                    //Check if node.attachEvent is artificially added by custom script or
                    //natively supported by browser
                    //read https://github.com/requirejs/requirejs/issues/187
                    //if we can NOT find [native code] then it must NOT natively supported.
                    //in IE8, node.attachEvent does not have toString()
                    //Note the test for "[native code" with no closing brace, see:
                    //https://github.com/requirejs/requirejs/issues/273
                    !(node.attachEvent.toString && node.attachEvent.toString().indexOf('[native code') < 0) &&
                    !isOpera) {
                //Probably IE. IE (at least 6-8) do not fire
                //script onload right after executing the script, so
                //we cannot tie the anonymous define call to a name.
                //However, IE reports the script as being in 'interactive'
                //readyState at the time of the define call.
                useInteractive = true;

                node.attachEvent('onreadystatechange', context.onScriptLoad);
                //It would be great to add an error handler here to catch
                //404s in IE9+. However, onreadystatechange will fire before
                //the error handler, so that does not help. If addEventListener
                //is used, then IE will fire error before load, but we cannot
                //use that pathway given the connect.microsoft.com issue
                //mentioned above about not doing the 'script execute,
                //then fire the script load event listener before execute
                //next script' that other browsers do.
                //Best hope: IE10 fixes the issues,
                //and then destroys all installs of IE 6-9.
                //node.attachEvent('onerror', context.onScriptError);
            } else {
                node.addEventListener('load', context.onScriptLoad, false);
                node.addEventListener('error', context.onScriptError, false);
            }
            node.src = url;

            //Calling onNodeCreated after all properties on the node have been
            //set, but before it is placed in the DOM.
            if (config.onNodeCreated) {
                config.onNodeCreated(node, config, moduleName, url);
            }

            //For some cache cases in IE 6-8, the script executes before the end
            //of the appendChild execution, so to tie an anonymous define
            //call to the module name (which is stored on the node), hold on
            //to a reference to this node, but clear after the DOM insertion.
            currentlyAddingScript = node;
            if (baseElement) {
                head.insertBefore(node, baseElement);
            } else {
                head.appendChild(node);
            }
            currentlyAddingScript = null;

            return node;
        } else if (isWebWorker) {
            try {
                //In a web worker, use importScripts. This is not a very
                //efficient use of importScripts, importScripts will block until
                //its script is downloaded and evaluated. However, if web workers
                //are in play, the expectation is that a build has been done so
                //that only one script needs to be loaded anyway. This may need
                //to be reevaluated if other use cases become common.

                // Post a task to the event loop to work around a bug in WebKit
                // where the worker gets garbage-collected after calling
                // importScripts(): https://webkit.org/b/153317
                setTimeout(function() {}, 0);
                importScripts(url);

                //Account for anonymous modules
                context.completeLoad(moduleName);
            } catch (e) {
                context.onError(makeError('importscripts',
                                'importScripts failed for ' +
                                    moduleName + ' at ' + url,
                                e,
                                [moduleName]));
            }
        }
    };

    function getInteractiveScript() {
        if (interactiveScript && interactiveScript.readyState === 'interactive') {
            return interactiveScript;
        }

        eachReverse(scripts(), function (script) {
            if (script.readyState === 'interactive') {
                return (interactiveScript = script);
            }
        });
        return interactiveScript;
    }

    //Look for a data-main script attribute, which could also adjust the baseUrl.
    if (isBrowser && !cfg.skipDataMain) {
        //Figure out baseUrl. Get it from the script tag with require.js in it.
        eachReverse(scripts(), function (script) {
            //Set the 'head' where we can append children by
            //using the script's parent.
            if (!head) {
                head = script.parentNode;
            }

            //Look for a data-main attribute to set main script for the page
            //to load. If it is there, the path to data main becomes the
            //baseUrl, if it is not already set.
            dataMain = script.getAttribute('data-main');
            if (dataMain) {
                //Preserve dataMain in case it is a path (i.e. contains '?')
                mainScript = dataMain;

                //Set final baseUrl if there is not already an explicit one,
                //but only do so if the data-main value is not a loader plugin
                //module ID.
                if (!cfg.baseUrl && mainScript.indexOf('!') === -1) {
                    //Pull off the directory of data-main for use as the
                    //baseUrl.
                    src = mainScript.split('/');
                    mainScript = src.pop();
                    subPath = src.length ? src.join('/')  + '/' : './';

                    cfg.baseUrl = subPath;
                }

                //Strip off any trailing .js since mainScript is now
                //like a module name.
                mainScript = mainScript.replace(jsSuffixRegExp, '');

                //If mainScript is still a path, fall back to dataMain
                if (req.jsExtRegExp.test(mainScript)) {
                    mainScript = dataMain;
                }

                //Put the data-main script in the files to load.
                cfg.deps = cfg.deps ? cfg.deps.concat(mainScript) : [mainScript];

                return true;
            }
        });
    }

    /**
     * The function that handles definitions of modules. Differs from
     * require() in that a string for the module should be the first argument,
     * and the function to execute after dependencies are loaded should
     * return a value to define the module corresponding to the first argument's
     * name.
     */
    define = function (name, deps, callback) {
        var node, context;

        //Allow for anonymous modules
        if (typeof name !== 'string') {
            //Adjust args appropriately
            callback = deps;
            deps = name;
            name = null;
        }

        //This module may not have dependencies
        if (!isArray(deps)) {
            callback = deps;
            deps = null;
        }

        //If no name, and callback is a function, then figure out if it a
        //CommonJS thing with dependencies.
        if (!deps && isFunction(callback)) {
            deps = [];
            //Remove comments from the callback string,
            //look for require calls, and pull them into the dependencies,
            //but only if there are function args.
            if (callback.length) {
                callback
                    .toString()
                    .replace(commentRegExp, commentReplace)
                    .replace(cjsRequireRegExp, function (match, dep) {
                        deps.push(dep);
                    });

                //May be a CommonJS thing even without require calls, but still
                //could use exports, and module. Avoid doing exports and module
                //work though if it just needs require.
                //REQUIRES the function to expect the CommonJS variables in the
                //order listed below.
                deps = (callback.length === 1 ? ['require'] : ['require', 'exports', 'module']).concat(deps);
            }
        }

        //If in IE 6-8 and hit an anonymous define() call, do the interactive
        //work.
        if (useInteractive) {
            node = currentlyAddingScript || getInteractiveScript();
            if (node) {
                if (!name) {
                    name = node.getAttribute('data-requiremodule');
                }
                context = contexts[node.getAttribute('data-requirecontext')];
            }
        }

        //Always save off evaluating the def call until the script onload handler.
        //This allows multiple modules to be in a file without prematurely
        //tracing dependencies, and allows for anonymous module support,
        //where the module name is not known until the script onload event
        //occurs. If no context, use the global queue, and get it processed
        //in the onscript load callback.
        if (context) {
            context.defQueue.push([name, deps, callback]);
            context.defQueueMap[name] = true;
        } else {
            globalDefQueue.push([name, deps, callback]);
        }
    };

    define.amd = {
        jQuery: true
    };

    /**
     * Executes the text. Normally just uses eval, but can be modified
     * to use a better, environment-specific call. Only used for transpiling
     * loader plugins, not for plain JS modules.
     * @param {String} text the text to execute/evaluate.
     */
    req.exec = function (text) {
        /*jslint evil: true */
        return eval(text);
    };

    //Set up with config info.
    req(cfg);
}(this, (typeof setTimeout === 'undefined' ? undefined : setTimeout)));

// Copyright 2014 Google Inc. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
//     You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
//     See the License for the specific language governing permissions and
// limitations under the License.

!function(){var a={},b={},c={};!function(a,b){function c(a){if("number"==typeof a)return a;var b={};for(var c in a)b[c]=a[c];return b}function d(){this._delay=0,this._endDelay=0,this._fill="none",this._iterationStart=0,this._iterations=1,this._duration=0,this._playbackRate=1,this._direction="normal",this._easing="linear",this._easingFunction=x}function e(){return a.isDeprecated("Invalid timing inputs","2016-03-02","TypeError exceptions will be thrown instead.",!0)}function f(b,c,e){var f=new d;return c&&(f.fill="both",f.duration="auto"),"number"!=typeof b||isNaN(b)?void 0!==b&&Object.getOwnPropertyNames(b).forEach(function(c){if("auto"!=b[c]){if(("number"==typeof f[c]||"duration"==c)&&("number"!=typeof b[c]||isNaN(b[c])))return;if("fill"==c&&-1==v.indexOf(b[c]))return;if("direction"==c&&-1==w.indexOf(b[c]))return;if("playbackRate"==c&&1!==b[c]&&a.isDeprecated("AnimationEffectTiming.playbackRate","2014-11-28","Use Animation.playbackRate 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/*! *****************************************************************************
    Copyright (c) Microsoft Corporation.

    Permission to use, copy, modify, and/or distribute this software for any
    purpose with or without fee is hereby granted.

    THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
    REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
    AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
    INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
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     * at http://polymer.github.io/AUTHORS.txt The complete set of contributors may
     * be found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by
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/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
function e(t){const e={};for(let n in t){const i=t[n];e[n]="function"==typeof i?{type:i}:i}return e})(n))}t.__ownProperties=e}return t.__ownProperties}class r extends e{static get observedAttributes(){if(!this.hasOwnProperty(JSCompiler_renameProperty("__observedAttributes",this))){const t=this._properties;this.__observedAttributes=t?Object.keys(t).map((t=>this.prototype._addPropertyToAttributeMap(t))):[]}return this.__observedAttributes}static finalize(){if(!this.hasOwnProperty(JSCompiler_renameProperty("__finalized",this))){const t=n(this);t&&t.finalize(),this.__finalized=!0,this._finalizeClass()}}static _finalizeClass(){const t=i(this);t&&this.createProperties(t)}static get _properties(){if(!this.hasOwnProperty(JSCompiler_renameProperty("__properties",this))){const t=n(this);this.__properties=Object.assign({},t&&t._properties,i(this))}return this.__properties}static typeForProperty(t){const e=this._properties[t];return 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/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
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    */
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/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
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    */var ve="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{};function be(t){if(t.__esModule)return t;var e=Object.defineProperty({},"__esModule",{value:!0});return Object.keys(t).forEach((function(n){var i=Object.getOwnPropertyDescriptor(t,n);Object.defineProperty(e,n,i.get?i:{enumerable:!0,get:function(){return t[n]}})})),e}var xe,we,Se={exports:{}};
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/**
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/**
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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/**
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    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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/**
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/**
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    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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this.__filterFn&&(e=e.filter(((e,n,i)=>this.__filterFn(t[e],n,i)))),this.__sortFn&&e.sort(((e,n)=>this.__sortFn(t[e],t[n]))),e}__calculateLimit(t){let e=t;const n=this.__instances.length;if(this.initialCount){let i;!this.__chunkCount||this.__itemsArrayChanged&&!this.reuseChunkedInstances?(e=Math.min(t,this.initialCount),i=Math.max(e-n,0),this.__chunkCount=i||1):(i=Math.min(Math.max(t-n,0),this.__chunkCount),e=Math.min(n+i,t)),this.__shouldMeasureChunk=i===this.__chunkCount,this.__shouldContinueChunking=e<t,this.__renderStartTime=performance.now()}return this.__itemsArrayChanged=!1,e}__continueChunking(){if(this.__shouldMeasureChunk){const t=performance.now()-this.__renderStartTime;this.__chunkCount=Math.round(this.__chunkCount*(this._targetFrameTime/t))||1}this.__shouldContinueChunking&&this.__debounceRender(this.__render)}__updateInstances(t,e,n){const i=this.__itemsIdxToInstIdx={};let r;for(r=0;r<e;r++){let e=this.__instances[r],o=n[r],a=t[o];i[o]=r,e?(e._setPendingProperty(this.as,a),e._setPendingProperty(this.indexAs,r),e._setPendingProperty(this.itemsIndexAs,o),e._flushProperties()):this.__insertInstance(a,r,o)}for(let t=this.__instances.length-1;t>=r;t--)this.__detachAndRemoveInstance(t)}__detachInstance(t){let e=this.__instances[t];const n=Y(e.root);for(let t=0;t<e.children.length;t++)n.appendChild(e.children[t]);return e}__attachInstance(t,e){e.insertBefore(this.__instances[t].root,this)}__detachAndRemoveInstance(t){this.__detachInstance(t),this.__instances.splice(t,1)}__stampInstance(t,e,n){let i={};return i[this.as]=t,i[this.indexAs]=e,i[this.itemsIndexAs]=n,new this.__ctor(i)}__insertInstance(t,e,n){const i=this.__stampInstance(t,e,n);let r=this.__instances[e+1],o=r?r.children[0]:this;return Y(Y(this).parentNode).insertBefore(i.root,o),this.__instances[e]=i,i}_showHideChildren(t){for(let e=0;e<this.__instances.length;e++)this.__instances[e]._showHideChildren(t)}__handleItemPath(t,e){let n=t.slice(6),i=n.indexOf("."),r=i<0?n:n.substring(0,i);if(r==parseInt(r,10)){let t=i<0?"":n.substring(i+1);this.__handleObservedPaths(t);let o=this.__instances[this.__itemsIdxToInstIdx[r]];return o&&(o._setPendingPropertyOrPath(this.as+(t?"."+t:""),e,!1,!0),o._flushProperties()),!0}}itemForElement(t){let e=this.modelForElement(t);return e&&e[this.as]}indexForElement(t){let e=this.modelForElement(t);return e&&e[this.indexAs]}modelForElement(t){return Kr(this.template,t)}}customElements.define(no.is,no);
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
class io extends ye{static get is(){return"dom-if"}static get template(){return null}static get properties(){return{if:{type:Boolean,observer:"__debounceRender"},restamp:{type:Boolean,observer:"__debounceRender"},notifyDomChange:{type:Boolean}}}constructor(){super(),this.__renderDebouncer=null,this._lastIf=!1,this.__hideTemplateChildren__=!1}__debounceRender(){this.__renderDebouncer=Tn.debounce(this.__renderDebouncer,vt,(()=>this.__render())),An(this.__renderDebouncer)}disconnectedCallback(){super.disconnectedCallback();const t=Y(this).parentNode;t&&(t.nodeType!=Node.DOCUMENT_FRAGMENT_NODE||Y(t).host)||this.__teardownInstance()}connectedCallback(){super.connectedCallback(),Qr()||(this.style.display="none"),this.if&&this.__debounceRender()}__ensureTemplate(){if(!this.__template){const t=this;let e=t._templateInfo?t:Y(t).querySelector("template");if(!e){let t=new MutationObserver((()=>{if(!Y(this).querySelector("template"))throw new Error("dom-if requires a <template> child");t.disconnect(),this.__render()}));return t.observe(this,{childList:!0}),!1}this.__template=e}return!0}__ensureInstance(){let t=Y(this).parentNode;if(this.__hasInstance()){let e=this.__getInstanceNodes();if(e&&e.length&&Y(this).previousSibling!==e[e.length-1])for(let n,i=0;i<e.length&&(n=e[i]);i++)Y(t).insertBefore(n,this)}else{if(!t)return!1;if(!this.__ensureTemplate())return!1;this.__createAndInsertInstance(t)}return!0}render(){Di()}__render(){if(this.if){if(!this.__ensureInstance())return}else this.restamp&&this.__teardownInstance();this._showHideChildren(),k&&!this.notifyDomChange||this.if==this._lastIf||(this.dispatchEvent(new CustomEvent("dom-change",{bubbles:!0,composed:!0})),this._lastIf=this.if)}__hasInstance(){}__getInstanceNodes(){}__createAndInsertInstance(t){}__teardownInstance(){}_showHideChildren(){}}const ro=A?class extends io{constructor(){super(),this.__instance=null,this.__syncInfo=null}__hasInstance(){return Boolean(this.__instance)}__getInstanceNodes(){return this.__instance.templateInfo.childNodes}__createAndInsertInstance(t){const e=this.__dataHost||this;if(b&&!this.__dataHost)throw new Error("strictTemplatePolicy: template owner not trusted");const n=e._bindTemplate(this.__template,!0);n.runEffects=(t,e,n)=>{let i=this.__syncInfo;if(this.if)i&&(this.__syncInfo=null,this._showHideChildren(),e=Object.assign(i.changedProps,e)),t(e,n);else if(this.__instance)if(i||(i=this.__syncInfo={runEffects:t,changedProps:{}}),n)for(const t in e){const e=$(t);i.changedProps[e]=this.__dataHost[e]}else Object.assign(i.changedProps,e)},this.__instance=e._stampTemplate(this.__template,n),Y(t).insertBefore(this.__instance,this)}__syncHostProperties(){const t=this.__syncInfo;t&&(this.__syncInfo=null,t.runEffects(t.changedProps,!1))}__teardownInstance(){this.__instance&&((this.__dataHost||this)._removeBoundDom(this.__instance),this.__instance=null,this.__syncInfo=null)}_showHideChildren(){const t=this.__hideTemplateChildren__||!this.if;this.__instance&&Boolean(this.__instance.__hidden)!==t&&(this.__instance.__hidden=t,jr(t,this.__instance.templateInfo.childNodes)),t||this.__syncHostProperties()}}:class extends io{constructor(){super(),this.__ctor=null,this.__instance=null,this.__invalidProps=null}__hasInstance(){return Boolean(this.__instance)}__getInstanceNodes(){return this.__instance.children}__createAndInsertInstance(t){this.__ctor||(this.__ctor=$r(this.__template,this,{mutableData:!0,forwardHostProp:function(t,e){this.__instance&&(this.if?this.__instance.forwardHostProp(t,e):(this.__invalidProps=this.__invalidProps||Object.create(null),this.__invalidProps[$(t)]=!0))}})),this.__instance=new this.__ctor,Y(t).insertBefore(this.__instance.root,this)}__teardownInstance(){if(this.__instance){let t=this.__instance.children;if(t&&t.length){let e=Y(t[0]).parentNode;if(e){e=Y(e);for(let n,i=0;i<t.length&&(n=t[i]);i++)e.removeChild(n)}}this.__invalidProps=null,this.__instance=null}}__syncHostProperties(){let t=this.__invalidProps;if(t){this.__invalidProps=null;for(let e in t)this.__instance._setPendingProperty(e,this.__dataHost[e]);this.__instance._flushProperties()}}_showHideChildren(){const t=this.__hideTemplateChildren__||!this.if;this.__instance&&Boolean(this.__instance.__hidden)!==t&&(this.__instance.__hidden=t,this.__instance._showHideChildren(t)),t||this.__syncHostProperties()}};customElements.define(ro.is,ro);
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
let oo=I((t=>{let e=me(t);return class extends e{static get properties(){return{items:{type:Array},multi:{type:Boolean,value:!1},selected:{type:Object,notify:!0},selectedItem:{type:Object,notify:!0},toggle:{type:Boolean,value:!1}}}static get observers(){return["__updateSelection(multi, items.*)"]}constructor(){super(),this.__lastItems=null,this.__lastMulti=null,this.__selectedMap=null}__updateSelection(t,e){let n=e.path;if(n==JSCompiler_renameProperty("items",this)){let n=e.base||[],i=this.__lastItems;if(t!==this.__lastMulti&&this.clearSelection(),i){let t=Ii(n,i);this.__applySplices(t)}this.__lastItems=n,this.__lastMulti=t}else if(e.path==`${JSCompiler_renameProperty("items",this)}.splices`)this.__applySplices(e.value.indexSplices);else{let t=n.slice(`${JSCompiler_renameProperty("items",this)}.`.length),e=parseInt(t,10);t.indexOf(".")<0&&t==e&&this.__deselectChangedIdx(e)}}__applySplices(t){let e=this.__selectedMap;for(let n=0;n<t.length;n++){let i=t[n];e.forEach(((t,n)=>{t<i.index||e.set(n,t>=i.index+i.removed.length?t+i.addedCount-i.removed.length:-1)}));for(let t=0;t<i.addedCount;t++){let n=i.index+t;e.has(this.items[n])&&e.set(this.items[n],n)}}this.__updateLinks();let n=0;e.forEach(((t,i)=>{t<0?(this.multi?this.splice(JSCompiler_renameProperty("selected",this),n,1):this.selected=this.selectedItem=null,e.delete(i)):n++}))}__updateLinks(){if(this.__dataLinkedPaths={},this.multi){let t=0;this.__selectedMap.forEach((e=>{e>=0&&this.linkPaths(`${JSCompiler_renameProperty("items",this)}.${e}`,`${JSCompiler_renameProperty("selected",this)}.${t++}`)}))}else this.__selectedMap.forEach((t=>{this.linkPaths(JSCompiler_renameProperty("selected",this),`${JSCompiler_renameProperty("items",this)}.${t}`),this.linkPaths(JSCompiler_renameProperty("selectedItem",this),`${JSCompiler_renameProperty("items",this)}.${t}`)}))}clearSelection(){this.__dataLinkedPaths={},this.__selectedMap=new Map,this.selected=this.multi?[]:null,this.selectedItem=null}isSelected(t){return this.__selectedMap.has(t)}isIndexSelected(t){return this.isSelected(this.items[t])}__deselectChangedIdx(t){let e=this.__selectedIndexForItemIndex(t);if(e>=0){let t=0;this.__selectedMap.forEach(((n,i)=>{e==t++&&this.deselect(i)}))}}__selectedIndexForItemIndex(t){let e=this.__dataLinkedPaths[`${JSCompiler_renameProperty("items",this)}.${t}`];if(e)return parseInt(e.slice(`${JSCompiler_renameProperty("selected",this)}.`.length),10)}deselect(t){let e=this.__selectedMap.get(t);if(e>=0){let n;this.__selectedMap.delete(t),this.multi&&(n=this.__selectedIndexForItemIndex(e)),this.__updateLinks(),this.multi?this.splice(JSCompiler_renameProperty("selected",this),n,1):this.selected=this.selectedItem=null}}deselectIndex(t){this.deselect(this.items[t])}select(t){this.selectIndex(this.items.indexOf(t))}selectIndex(t){let e=this.items[t];this.isSelected(e)?this.toggle&&this.deselectIndex(t):(this.multi||this.__selectedMap.clear(),this.__selectedMap.set(e,t),this.__updateLinks(),this.multi?this.push(JSCompiler_renameProperty("selected",this),e):this.selected=this.selectedItem=e)}}}))(ye);class ao extends oo{static get is(){return"array-selector"}static get template(){return null}}customElements.define(ao.is,ao);
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const so=new Sn;window.ShadyCSS||(window.ShadyCSS={prepareTemplate(t,e,n){},prepareTemplateDom(t,e){},prepareTemplateStyles(t,e,n){},styleSubtree(t,e){so.processStyles(),Qe(t,e)},styleElement(t){so.processStyles()},styleDocument(t){so.processStyles(),Qe(document.body,t)},getComputedStyleValue:(t,e)=>tn(t,e),flushCustomStyles(){},nativeCss:ke,nativeShadow:Me,cssBuild:Te,disableRuntime:Ae}),window.ShadyCSS.CustomStyleInterface=so;
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const lo="include",co=window.ShadyCSS.CustomStyleInterface;class uo extends HTMLElement{constructor(){super(),this._style=null,co.addCustomStyle(this)}getStyle(){if(this._style)return this._style;const t=this.querySelector("style");if(!t)return null;this._style=t;const e=t.getAttribute(lo);return e&&(t.removeAttribute(lo),t.textContent=(function n(t){let e=t.trim().split(/\s+/),n="";for(let t=0;t<e.length;t++)n+=q(e[t]);return n})(e)+t.textContent),this.ownerDocument!==window.document&&window.document.head.appendChild(this),this._style}}
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
let ho;window.customElements.define("custom-style",uo),ho=zr._mutablePropertyChange;const po={properties:{mutableData:Boolean},_shouldPropertyChange(t,e,n){return ho(this,t,e,n,this.mutableData)}},fo=er(HTMLElement).prototype;
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var mo=new Set;const go={properties:{_parentResizable:{type:Object,observer:"_parentResizableChanged"},_notifyingDescendant:{type:Boolean,value:!1}},listeners:{"iron-request-resize-notifications":"_onIronRequestResizeNotifications"},created:function(){this._interestedResizables=[],this._boundNotifyResize=this.notifyResize.bind(this),this._boundOnDescendantIronResize=this._onDescendantIronResize.bind(this)},attached:function(){this._requestResizeNotifications()},detached:function(){this._parentResizable?this._parentResizable.stopResizeNotificationsFor(this):(mo.delete(this),window.removeEventListener("resize",this._boundNotifyResize)),this._parentResizable=null},notifyResize:function(){this.isAttached&&(this._interestedResizables.forEach((function(t){this.resizerShouldNotify(t)&&this._notifyDescendant(t)}),this),this._fireResize())},assignParentResizable:function(t){this._parentResizable&&this._parentResizable.stopResizeNotificationsFor(this),this._parentResizable=t,t&&-1===t._interestedResizables.indexOf(this)&&(t._interestedResizables.push(this),t._subscribeIronResize(this))},stopResizeNotificationsFor:function(t){var e=this._interestedResizables.indexOf(t);e>-1&&(this._interestedResizables.splice(e,1),this._unsubscribeIronResize(t))},_subscribeIronResize:function(t){t.addEventListener("iron-resize",this._boundOnDescendantIronResize)},_unsubscribeIronResize:function(t){t.removeEventListener("iron-resize",this._boundOnDescendantIronResize)},resizerShouldNotify:function(t){return!0},_onDescendantIronResize:function(t){this._notifyingDescendant?t.stopPropagation():m||this._fireResize()},_fireResize:function(){this.fire("iron-resize",null,{node:this,bubbles:!1})},_onIronRequestResizeNotifications:function(t){var e=Yi(t).rootTarget;e!==this&&(e.assignParentResizable(this),this._notifyDescendant(e),t.stopPropagation())},_parentResizableChanged:function(t){t&&window.removeEventListener("resize",this._boundNotifyResize)},_notifyDescendant:function(t){this.isAttached&&(this._notifyingDescendant=!0,t.notifyResize(),this._notifyingDescendant=!1)},_requestResizeNotifications:function(){if(this.isAttached)if("loading"===document.readyState){var t=this._requestResizeNotifications.bind(this);document.addEventListener("readystatechange",(function e(){document.removeEventListener("readystatechange",e),t()}))}else this._findParent(),this._parentResizable?this._parentResizable._interestedResizables.forEach((function(t){t!==this&&t._findParent()}),this):(mo.forEach((function(t){t!==this&&t._findParent()}),this),window.addEventListener("resize",this._boundNotifyResize),this.notifyResize())},_findParent:function(){this.assignParentResizable(null),this.fire("iron-request-resize-notifications",null,{node:this,bubbles:!0,cancelable:!0}),this._parentResizable?mo.delete(this):mo.add(this)}};var _o=Object.freeze({__proto__:null,IronResizableBehavior:go});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */Rr({_template:_e`
    <style>
      :host {
        display: block;
        transition-duration: var(--iron-collapse-transition-duration, 300ms);
        /* Safari 10 needs this property prefixed to correctly apply the custom property */
        -webkit-transition-duration: var(--iron-collapse-transition-duration, 300ms);
        overflow: visible;
      }

      :host(.iron-collapse-closed) {
        display: none;
      }

      :host(:not(.iron-collapse-opened)) {
        overflow: hidden;
      }
    </style>

    <slot></slot>
`,is:"iron-collapse",behaviors:[go],properties:{horizontal:{type:Boolean,value:!1,observer:"_horizontalChanged"},opened:{type:Boolean,value:!1,notify:!0,observer:"_openedChanged"},transitioning:{type:Boolean,notify:!0,readOnly:!0},noAnimation:{type:Boolean},_desiredSize:{type:String,value:""}},get dimension(){return this.horizontal?"width":"height"},get _dimensionMax(){return this.horizontal?"maxWidth":"maxHeight"},get _dimensionMaxCss(){return this.horizontal?"max-width":"max-height"},hostAttributes:{role:"group","aria-hidden":"true"},listeners:{transitionend:"_onTransitionEnd"},toggle:function(){this.opened=!this.opened},show:function(){this.opened=!0},hide:function(){this.opened=!1},updateSize:function(t,e){t="auto"===t?"":t;var n=e&&!this.noAnimation&&this.isAttached&&this._desiredSize!==t;if(this._desiredSize=t,this._updateTransition(!1),n){var i=this._calcSize();""===t&&(this.style[this._dimensionMax]="",t=this._calcSize()),this.style[this._dimensionMax]=i,this.scrollTop=this.scrollTop,this._updateTransition(!0),n=t!==i}this.style[this._dimensionMax]=t,n||this._transitionEnd()},enableTransition:function(t){fo._warn("`enableTransition()` is deprecated, use `noAnimation` instead."),this.noAnimation=!t},_updateTransition:function(t){this.style.transitionDuration=t&&!this.noAnimation?"":"0s"},_horizontalChanged:function(){this.style.transitionProperty=this._dimensionMaxCss,this.style["maxWidth"===this._dimensionMax?"maxHeight":"maxWidth"]="",this.updateSize(this.opened?"auto":"0px",!1)},_openedChanged:function(){this.setAttribute("aria-hidden",!this.opened),this._setTransitioning(!0),this.toggleClass("iron-collapse-closed",!1),this.toggleClass("iron-collapse-opened",!1),this.updateSize(this.opened?"auto":"0px",!0),this.opened&&this.focus()},_transitionEnd:function(){this.style[this._dimensionMax]=this._desiredSize,this.toggleClass("iron-collapse-closed",!this.opened),this.toggleClass("iron-collapse-opened",this.opened),this._updateTransition(!1),this.notifyResize(),this._setTransitioning(!1)},_onTransitionEnd:function(t){Yi(t).rootTarget===this&&this._transitionEnd()},_calcSize:function(){return this.getBoundingClientRect()[this.dimension]+"px"}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const yo=_e`
/* Most common used flex styles*/
<dom-module id="iron-flex">
  <template>
    <style>
      .layout.horizontal,
      .layout.vertical {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      }

      .layout.inline {
        display: -ms-inline-flexbox;
        display: -webkit-inline-flex;
        display: inline-flex;
      }

      .layout.horizontal {
        -ms-flex-direction: row;
        -webkit-flex-direction: row;
        flex-direction: row;
      }

      .layout.vertical {
        -ms-flex-direction: column;
        -webkit-flex-direction: column;
        flex-direction: column;
      }

      .layout.wrap {
        -ms-flex-wrap: wrap;
        -webkit-flex-wrap: wrap;
        flex-wrap: wrap;
      }

      .layout.no-wrap {
        -ms-flex-wrap: nowrap;
        -webkit-flex-wrap: nowrap;
        flex-wrap: nowrap;
      }

      .layout.center,
      .layout.center-center {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      }

      .layout.center-justified,
      .layout.center-center {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      }

      .flex {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      }

      .flex-auto {
        -ms-flex: 1 1 auto;
        -webkit-flex: 1 1 auto;
        flex: 1 1 auto;
      }

      .flex-none {
        -ms-flex: none;
        -webkit-flex: none;
        flex: none;
      }
    </style>
  </template>
</dom-module>
/* Basic flexbox reverse styles */
<dom-module id="iron-flex-reverse">
  <template>
    <style>
      .layout.horizontal-reverse,
      .layout.vertical-reverse {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      }

      .layout.horizontal-reverse {
        -ms-flex-direction: row-reverse;
        -webkit-flex-direction: row-reverse;
        flex-direction: row-reverse;
      }

      .layout.vertical-reverse {
        -ms-flex-direction: column-reverse;
        -webkit-flex-direction: column-reverse;
        flex-direction: column-reverse;
      }

      .layout.wrap-reverse {
        -ms-flex-wrap: wrap-reverse;
        -webkit-flex-wrap: wrap-reverse;
        flex-wrap: wrap-reverse;
      }
    </style>
  </template>
</dom-module>
/* Flexbox alignment */
<dom-module id="iron-flex-alignment">
  <template>
    <style>
      /**
       * Alignment in cross axis.
       */
      .layout.start {
        -ms-flex-align: start;
        -webkit-align-items: flex-start;
        align-items: flex-start;
      }

      .layout.center,
      .layout.center-center {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      }

      .layout.end {
        -ms-flex-align: end;
        -webkit-align-items: flex-end;
        align-items: flex-end;
      }

      .layout.baseline {
        -ms-flex-align: baseline;
        -webkit-align-items: baseline;
        align-items: baseline;
      }

      /**
       * Alignment in main axis.
       */
      .layout.start-justified {
        -ms-flex-pack: start;
        -webkit-justify-content: flex-start;
        justify-content: flex-start;
      }

      .layout.center-justified,
      .layout.center-center {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      }

      .layout.end-justified {
        -ms-flex-pack: end;
        -webkit-justify-content: flex-end;
        justify-content: flex-end;
      }

      .layout.around-justified {
        -ms-flex-pack: distribute;
        -webkit-justify-content: space-around;
        justify-content: space-around;
      }

      .layout.justified {
        -ms-flex-pack: justify;
        -webkit-justify-content: space-between;
        justify-content: space-between;
      }

      /**
       * Self alignment.
       */
      .self-start {
        -ms-align-self: flex-start;
        -webkit-align-self: flex-start;
        align-self: flex-start;
      }

      .self-center {
        -ms-align-self: center;
        -webkit-align-self: center;
        align-self: center;
      }

      .self-end {
        -ms-align-self: flex-end;
        -webkit-align-self: flex-end;
        align-self: flex-end;
      }

      .self-stretch {
        -ms-align-self: stretch;
        -webkit-align-self: stretch;
        align-self: stretch;
      }

      .self-baseline {
        -ms-align-self: baseline;
        -webkit-align-self: baseline;
        align-self: baseline;
      }

      /**
       * multi-line alignment in main axis.
       */
      .layout.start-aligned {
        -ms-flex-line-pack: start;  /* IE10 */
        -ms-align-content: flex-start;
        -webkit-align-content: flex-start;
        align-content: flex-start;
      }

      .layout.end-aligned {
        -ms-flex-line-pack: end;  /* IE10 */
        -ms-align-content: flex-end;
        -webkit-align-content: flex-end;
        align-content: flex-end;
      }

      .layout.center-aligned {
        -ms-flex-line-pack: center;  /* IE10 */
        -ms-align-content: center;
        -webkit-align-content: center;
        align-content: center;
      }

      .layout.between-aligned {
        -ms-flex-line-pack: justify;  /* IE10 */
        -ms-align-content: space-between;
        -webkit-align-content: space-between;
        align-content: space-between;
      }

      .layout.around-aligned {
        -ms-flex-line-pack: distribute;  /* IE10 */
        -ms-align-content: space-around;
        -webkit-align-content: space-around;
        align-content: space-around;
      }
    </style>
  </template>
</dom-module>
/* Non-flexbox positioning helper styles */
<dom-module id="iron-flex-factors">
  <template>
    <style>
      .flex,
      .flex-1 {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      }

      .flex-2 {
        -ms-flex: 2;
        -webkit-flex: 2;
        flex: 2;
      }

      .flex-3 {
        -ms-flex: 3;
        -webkit-flex: 3;
        flex: 3;
      }

      .flex-4 {
        -ms-flex: 4;
        -webkit-flex: 4;
        flex: 4;
      }

      .flex-5 {
        -ms-flex: 5;
        -webkit-flex: 5;
        flex: 5;
      }

      .flex-6 {
        -ms-flex: 6;
        -webkit-flex: 6;
        flex: 6;
      }

      .flex-7 {
        -ms-flex: 7;
        -webkit-flex: 7;
        flex: 7;
      }

      .flex-8 {
        -ms-flex: 8;
        -webkit-flex: 8;
        flex: 8;
      }

      .flex-9 {
        -ms-flex: 9;
        -webkit-flex: 9;
        flex: 9;
      }

      .flex-10 {
        -ms-flex: 10;
        -webkit-flex: 10;
        flex: 10;
      }

      .flex-11 {
        -ms-flex: 11;
        -webkit-flex: 11;
        flex: 11;
      }

      .flex-12 {
        -ms-flex: 12;
        -webkit-flex: 12;
        flex: 12;
      }
    </style>
  </template>
</dom-module>
<dom-module id="iron-positioning">
  <template>
    <style>
      .block {
        display: block;
      }

      [hidden] {
        display: none !important;
      }

      .invisible {
        visibility: hidden !important;
      }

      .relative {
        position: relative;
      }

      .fit {
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      }

      body.fullbleed {
        margin: 0;
        height: 100vh;
      }

      .scroll {
        -webkit-overflow-scrolling: touch;
        overflow: auto;
      }

      /* fixed position */
      .fixed-bottom,
      .fixed-left,
      .fixed-right,
      .fixed-top {
        position: fixed;
      }

      .fixed-top {
        top: 0;
        left: 0;
        right: 0;
      }

      .fixed-right {
        top: 0;
        right: 0;
        bottom: 0;
      }

      .fixed-bottom {
        right: 0;
        bottom: 0;
        left: 0;
      }

      .fixed-left {
        top: 0;
        bottom: 0;
        left: 0;
      }
    </style>
  </template>
</dom-module>
`;yo.setAttribute("style","display: none;"),document.head.appendChild(yo.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const vo=_e`
<custom-style>
  <style is="custom-style">
    [hidden] {
      display: none !important;
    }
  </style>
</custom-style>
<custom-style>
  <style is="custom-style">
    html {

      --layout: {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      };

      --layout-inline: {
        display: -ms-inline-flexbox;
        display: -webkit-inline-flex;
        display: inline-flex;
      };

      --layout-horizontal: {
        @apply --layout;

        -ms-flex-direction: row;
        -webkit-flex-direction: row;
        flex-direction: row;
      };

      --layout-horizontal-reverse: {
        @apply --layout;

        -ms-flex-direction: row-reverse;
        -webkit-flex-direction: row-reverse;
        flex-direction: row-reverse;
      };

      --layout-vertical: {
        @apply --layout;

        -ms-flex-direction: column;
        -webkit-flex-direction: column;
        flex-direction: column;
      };

      --layout-vertical-reverse: {
        @apply --layout;

        -ms-flex-direction: column-reverse;
        -webkit-flex-direction: column-reverse;
        flex-direction: column-reverse;
      };

      --layout-wrap: {
        -ms-flex-wrap: wrap;
        -webkit-flex-wrap: wrap;
        flex-wrap: wrap;
      };

      --layout-wrap-reverse: {
        -ms-flex-wrap: wrap-reverse;
        -webkit-flex-wrap: wrap-reverse;
        flex-wrap: wrap-reverse;
      };

      --layout-flex-auto: {
        -ms-flex: 1 1 auto;
        -webkit-flex: 1 1 auto;
        flex: 1 1 auto;
      };

      --layout-flex-none: {
        -ms-flex: none;
        -webkit-flex: none;
        flex: none;
      };

      --layout-flex: {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      };

      --layout-flex-2: {
        -ms-flex: 2;
        -webkit-flex: 2;
        flex: 2;
      };

      --layout-flex-3: {
        -ms-flex: 3;
        -webkit-flex: 3;
        flex: 3;
      };

      --layout-flex-4: {
        -ms-flex: 4;
        -webkit-flex: 4;
        flex: 4;
      };

      --layout-flex-5: {
        -ms-flex: 5;
        -webkit-flex: 5;
        flex: 5;
      };

      --layout-flex-6: {
        -ms-flex: 6;
        -webkit-flex: 6;
        flex: 6;
      };

      --layout-flex-7: {
        -ms-flex: 7;
        -webkit-flex: 7;
        flex: 7;
      };

      --layout-flex-8: {
        -ms-flex: 8;
        -webkit-flex: 8;
        flex: 8;
      };

      --layout-flex-9: {
        -ms-flex: 9;
        -webkit-flex: 9;
        flex: 9;
      };

      --layout-flex-10: {
        -ms-flex: 10;
        -webkit-flex: 10;
        flex: 10;
      };

      --layout-flex-11: {
        -ms-flex: 11;
        -webkit-flex: 11;
        flex: 11;
      };

      --layout-flex-12: {
        -ms-flex: 12;
        -webkit-flex: 12;
        flex: 12;
      };

      /* alignment in cross axis */

      --layout-start: {
        -ms-flex-align: start;
        -webkit-align-items: flex-start;
        align-items: flex-start;
      };

      --layout-center: {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      };

      --layout-end: {
        -ms-flex-align: end;
        -webkit-align-items: flex-end;
        align-items: flex-end;
      };

      --layout-baseline: {
        -ms-flex-align: baseline;
        -webkit-align-items: baseline;
        align-items: baseline;
      };

      /* alignment in main axis */

      --layout-start-justified: {
        -ms-flex-pack: start;
        -webkit-justify-content: flex-start;
        justify-content: flex-start;
      };

      --layout-center-justified: {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      };

      --layout-end-justified: {
        -ms-flex-pack: end;
        -webkit-justify-content: flex-end;
        justify-content: flex-end;
      };

      --layout-around-justified: {
        -ms-flex-pack: distribute;
        -webkit-justify-content: space-around;
        justify-content: space-around;
      };

      --layout-justified: {
        -ms-flex-pack: justify;
        -webkit-justify-content: space-between;
        justify-content: space-between;
      };

      --layout-center-center: {
        @apply --layout-center;
        @apply --layout-center-justified;
      };

      /* self alignment */

      --layout-self-start: {
        -ms-align-self: flex-start;
        -webkit-align-self: flex-start;
        align-self: flex-start;
      };

      --layout-self-center: {
        -ms-align-self: center;
        -webkit-align-self: center;
        align-self: center;
      };

      --layout-self-end: {
        -ms-align-self: flex-end;
        -webkit-align-self: flex-end;
        align-self: flex-end;
      };

      --layout-self-stretch: {
        -ms-align-self: stretch;
        -webkit-align-self: stretch;
        align-self: stretch;
      };

      --layout-self-baseline: {
        -ms-align-self: baseline;
        -webkit-align-self: baseline;
        align-self: baseline;
      };

      /* multi-line alignment in main axis */

      --layout-start-aligned: {
        -ms-flex-line-pack: start;  /* IE10 */
        -ms-align-content: flex-start;
        -webkit-align-content: flex-start;
        align-content: flex-start;
      };

      --layout-end-aligned: {
        -ms-flex-line-pack: end;  /* IE10 */
        -ms-align-content: flex-end;
        -webkit-align-content: flex-end;
        align-content: flex-end;
      };

      --layout-center-aligned: {
        -ms-flex-line-pack: center;  /* IE10 */
        -ms-align-content: center;
        -webkit-align-content: center;
        align-content: center;
      };

      --layout-between-aligned: {
        -ms-flex-line-pack: justify;  /* IE10 */
        -ms-align-content: space-between;
        -webkit-align-content: space-between;
        align-content: space-between;
      };

      --layout-around-aligned: {
        -ms-flex-line-pack: distribute;  /* IE10 */
        -ms-align-content: space-around;
        -webkit-align-content: space-around;
        align-content: space-around;
      };

      /*******************************
                Other Layout
      *******************************/

      --layout-block: {
        display: block;
      };

      --layout-invisible: {
        visibility: hidden !important;
      };

      --layout-relative: {
        position: relative;
      };

      --layout-fit: {
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      };

      --layout-scroll: {
        -webkit-overflow-scrolling: touch;
        overflow: auto;
      };

      --layout-fullbleed: {
        margin: 0;
        height: 100vh;
      };

      /* fixed position */

      --layout-fixed-top: {
        position: fixed;
        top: 0;
        left: 0;
        right: 0;
      };

      --layout-fixed-right: {
        position: fixed;
        top: 0;
        right: 0;
        bottom: 0;
      };

      --layout-fixed-bottom: {
        position: fixed;
        right: 0;
        bottom: 0;
        left: 0;
      };

      --layout-fixed-left: {
        position: fixed;
        top: 0;
        bottom: 0;
        left: 0;
      };

    }
  </style>
</custom-style>`;vo.setAttribute("style","display: none;"),document.head.appendChild(vo.content);var bo=document.createElement("style");bo.textContent="[hidden] { display: none !important; }",document.head.appendChild(bo);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
class xo{constructor(t){xo[" "](t),this.type=t&&t.type||"default",this.key=t&&t.key,t&&"value"in t&&(this.value=t.value)}get value(){var t=this.type,e=this.key;if(t&&e)return xo.types[t]&&xo.types[t][e]}set value(t){var e=this.type,n=this.key;e&&n&&(e=xo.types[e]=xo.types[e]||{},null==t?delete e[n]:e[n]=t)}get list(){if(this.type){var t=xo.types[this.type];return t?Object.keys(t).map((function(t){return wo[this.type][t]}),this):[]}}byKey(t){return this.key=t,this.value}}xo[" "]=function(){},xo.types={};var wo=xo.types;Rr({is:"iron-meta",properties:{type:{type:String,value:"default"},key:{type:String},value:{type:String,notify:!0},self:{type:Boolean,observer:"_selfChanged"},__meta:{type:Boolean,computed:"__computeMeta(type, key, value)"}},hostAttributes:{hidden:!0},__computeMeta:function(t,e,n){var i=new xo({type:t,key:e});return void 0!==n&&n!==i.value?i.value=n:this.value!==i.value&&(this.value=i.value),i},get list(){return this.__meta&&this.__meta.list},_selfChanged:function(t){t&&(this.value=this)},byKey:function(t){return new xo({type:this.type,key:t}).value}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        @apply --layout-inline;
        @apply --layout-center-center;
        position: relative;

        vertical-align: middle;

        fill: var(--iron-icon-fill-color, currentcolor);
        stroke: var(--iron-icon-stroke-color, none);

        width: var(--iron-icon-width, 24px);
        height: var(--iron-icon-height, 24px);
        @apply --iron-icon;
      }

      :host([hidden]) {
        display: none;
      }
    </style>
`,is:"iron-icon",properties:{icon:{type:String},theme:{type:String},src:{type:String},_meta:{value:fo.create("iron-meta",{type:"iconset"})}},observers:["_updateIcon(_meta, isAttached)","_updateIcon(theme, isAttached)","_srcChanged(src, isAttached)","_iconChanged(icon, isAttached)"],_DEFAULT_ICONSET:"icons",_iconChanged:function(t){var e=(t||"").split(":");this._iconName=e.pop(),this._iconsetName=e.pop()||this._DEFAULT_ICONSET,this._updateIcon()},_srcChanged:function(t){this._updateIcon()},_usesIconset:function(){return this.icon||!this.src},_updateIcon:function(){this._usesIconset()?(this._img&&this._img.parentNode&&Yi(this.root).removeChild(this._img),""===this._iconName?this._iconset&&this._iconset.removeIcon(this):this._iconsetName&&this._meta&&(this._iconset=this._meta.byKey(this._iconsetName),this._iconset?(this._iconset.applyIcon(this,this._iconName,this.theme),this.unlisten(window,"iron-iconset-added","_updateIcon")):this.listen(window,"iron-iconset-added","_updateIcon"))):(this._iconset&&this._iconset.removeIcon(this),this._img||(this._img=document.createElement("img"),this._img.style.width="100%",this._img.style.height="100%",this._img.draggable=!1),this._img.src=this.src,Yi(this.root).appendChild(this._img))}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({is:"iron-iconset-svg",properties:{name:{type:String,observer:"_nameChanged"},size:{type:Number,value:24},rtlMirroring:{type:Boolean,value:!1},useGlobalRtlAttribute:{type:Boolean,value:!1}},created:function(){this._meta=new xo({type:"iconset",key:null,value:null})},attached:function(){this.style.display="none"},getIconNames:function(){return this._icons=this._createIconMap(),Object.keys(this._icons).map((function(t){return this.name+":"+t}),this)},applyIcon:function(t,e){this.removeIcon(t);var n=this._cloneIcon(e,this.rtlMirroring&&this._targetIsRTL(t));if(n){var i=Yi(t.root||t);return i.insertBefore(n,i.childNodes[0]),t._svgIcon=n}return null},removeIcon:function(t){t._svgIcon&&(Yi(t.root||t).removeChild(t._svgIcon),t._svgIcon=null)},_targetIsRTL:function(t){if(null==this.__targetIsRTL)if(this.useGlobalRtlAttribute){var e=document.body&&document.body.hasAttribute("dir")?document.body:document.documentElement;this.__targetIsRTL="rtl"===e.getAttribute("dir")}else t&&t.nodeType!==Node.ELEMENT_NODE&&(t=t.host),this.__targetIsRTL=t&&"rtl"===window.getComputedStyle(t).direction;return this.__targetIsRTL},_nameChanged:function(){this._meta.value=null,this._meta.key=this.name,this._meta.value=this,this.async((function(){this.fire("iron-iconset-added",this,{node:window})}))},_createIconMap:function(){var t=Object.create(null);return Yi(this).querySelectorAll("[id]").forEach((function(e){t[e.id]=e})),t},_cloneIcon:function(t,e){return this._icons=this._icons||this._createIconMap(),this._prepareSvgClone(this._icons[t],this.size,e)},_prepareSvgClone:function(t,e,n){if(t){var i=t.cloneNode(!0),r=document.createElementNS("http://www.w3.org/2000/svg","svg"),o=i.getAttribute("viewBox")||"0 0 "+e+" "+e,a="pointer-events: none; display: block; width: 100%; height: 100%;";return n&&i.hasAttribute("mirror-in-rtl")&&(a+="-webkit-transform:scale(-1,1);transform:scale(-1,1);transform-origin:center;"),r.setAttribute("viewBox",o),r.setAttribute("preserveAspectRatio","xMidYMid meet"),r.setAttribute("focusable","false"),r.style.cssText=a,r.appendChild(i).removeAttribute("id"),r}return null}});
/**
    @license
    Copyright (c) 2014 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const So=_e`<iron-iconset-svg name="image" size="24">
<svg><defs>
<g id="add-a-photo"><path d="M3 4V1h2v3h3v2H5v3H3V6H0V4h3zm3 6V7h3V4h7l1.83 2H21c1.1 0 2 .9 2 2v12c0 1.1-.9 2-2 2H5c-1.1 0-2-.9-2-2V10h3zm7 9c2.76 0 5-2.24 5-5s-2.24-5-5-5-5 2.24-5 5 2.24 5 5 5zm-3.2-5c0 1.77 1.43 3.2 3.2 3.2s3.2-1.43 3.2-3.2-1.43-3.2-3.2-3.2-3.2 1.43-3.2 3.2z"></path></g>
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/**
    @license
    Copyright (c) 2014 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
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/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var Eo={"U+0008":"backspace","U+0009":"tab","U+001B":"esc","U+0020":"space","U+007F":"del"},To={8:"backspace",9:"tab",13:"enter",27:"esc",33:"pageup",34:"pagedown",35:"end",36:"home",32:"space",37:"left",38:"up",39:"right",40:"down",46:"del",106:"*"},Co={shift:"shiftKey",ctrl:"ctrlKey",alt:"altKey",meta:"metaKey"},Ao=/[a-z0-9*]/,ko=/U\+/,Lo=/^arrow/,Po=/^space(bar)?/,No=/^escape$/;function Io(t,e){var n="";if(t){var i=t.toLowerCase();" "===i||Po.test(i)?n="space":No.test(i)?n="esc":1==i.length?e&&!Ao.test(i)||(n=i):n=Lo.test(i)?i.replace("arrow",""):"multiply"==i?"*":i}return n}function Ro(t,e){return(function n(t,e){return t.key?Io(t.key,e):t.detail&&t.detail.key?Io(t.detail.key,e):(function n(t){var e="";return t&&(t in Eo?e=Eo[t]:ko.test(t)?(t=parseInt(t.replace("U+","0x"),16),e=String.fromCharCode(t).toLowerCase()):e=t.toLowerCase()),e})(t.keyIdentifier)||(function i(t){var e="";return Number(t)&&(e=t>=65&&t<=90?String.fromCharCode(32+t):t>=112&&t<=123?"f"+(t-112+1):t>=48&&t<=57?String(t-48):t>=96&&t<=105?String(t-96):To[t]),e})(t.keyCode)||""})(e,t.hasModifiers)===t.key&&(!t.hasModifiers||!!e.shiftKey==!!t.shiftKey&&!!e.ctrlKey==!!t.ctrlKey&&!!e.altKey==!!t.altKey&&!!e.metaKey==!!t.metaKey)}function Oo(t){return t.trim().split(" ").map((function(t){return(function e(t){return 1===t.length?{combo:t,key:t,event:"keydown"}:t.split("+").reduce((function(t,e){var n=e.split(":"),i=n[0],r=n[1];return i in Co?(t[Co[i]]=!0,t.hasModifiers=!0):(t.key=i,t.event=r||"keydown"),t}),{combo:t.split(":").shift()})})(t)}))}const zo={properties:{keyEventTarget:{type:Object,value:function(){return this}},stopKeyboardEventPropagation:{type:Boolean,value:!1},_boundKeyHandlers:{type:Array,value:function(){return[]}},_imperativeKeyBindings:{type:Object,value:function(){return{}}}},observers:["_resetKeyEventListeners(keyEventTarget, _boundKeyHandlers)"],keyBindings:{},registered:function(){this._prepKeyBindings()},attached:function(){this._listenKeyEventListeners()},detached:function(){this._unlistenKeyEventListeners()},addOwnKeyBinding:function(t,e){this._imperativeKeyBindings[t]=e,this._prepKeyBindings(),this._resetKeyEventListeners()},removeOwnKeyBindings:function(){this._imperativeKeyBindings={},this._prepKeyBindings(),this._resetKeyEventListeners()},keyboardEventMatchesKeys:function(t,e){for(var n=Oo(e),i=0;i<n.length;++i)if(Ro(n[i],t))return!0;return!1},_collectKeyBindings:function(){var t=this.behaviors.map((function(t){return t.keyBindings}));return-1===t.indexOf(this.keyBindings)&&t.push(this.keyBindings),t},_prepKeyBindings:function(){for(var t in this._keyBindings={},this._collectKeyBindings().forEach((function(t){for(var e in t)this._addKeyBinding(e,t[e])}),this),this._imperativeKeyBindings)this._addKeyBinding(t,this._imperativeKeyBindings[t]);for(var e in this._keyBindings)this._keyBindings[e].sort((function(t,e){var n=t[0].hasModifiers;return n===e[0].hasModifiers?0:n?-1:1}))},_addKeyBinding:function(t,e){Oo(t).forEach((function(t){this._keyBindings[t.event]=this._keyBindings[t.event]||[],this._keyBindings[t.event].push([t,e])}),this)},_resetKeyEventListeners:function(){this._unlistenKeyEventListeners(),this.isAttached&&this._listenKeyEventListeners()},_listenKeyEventListeners:function(){this.keyEventTarget&&Object.keys(this._keyBindings).forEach((function(t){var e=this._onKeyBindingEvent.bind(this,this._keyBindings[t]);this._boundKeyHandlers.push([this.keyEventTarget,t,e]),this.keyEventTarget.addEventListener(t,e)}),this)},_unlistenKeyEventListeners:function(){for(var t;this._boundKeyHandlers.length;)(t=this._boundKeyHandlers.pop())[0].removeEventListener(t[1],t[2])},_onKeyBindingEvent:function(t,e){if(this.stopKeyboardEventPropagation&&e.stopPropagation(),!e.defaultPrevented)for(var n=0;n<t.length;n++){var 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this.ownerDocument.documentElement},get _scrollTop(){return this._isValidScrollTarget()?this.scrollTarget===this._doc?window.pageYOffset:this.scrollTarget.scrollTop:0},get _scrollLeft(){return this._isValidScrollTarget()?this.scrollTarget===this._doc?window.pageXOffset:this.scrollTarget.scrollLeft:0},set _scrollTop(t){this.scrollTarget===this._doc?window.scrollTo(window.pageXOffset,t):this._isValidScrollTarget()&&(this.scrollTarget.scrollTop=t)},set _scrollLeft(t){this.scrollTarget===this._doc?window.scrollTo(t,window.pageYOffset):this._isValidScrollTarget()&&(this.scrollTarget.scrollLeft=t)},scroll:function(t,e){var n;"object"==typeof t?(n=t.left,e=t.top):n=t,n=n||0,e=e||0,this.scrollTarget===this._doc?window.scrollTo(n,e):this._isValidScrollTarget()&&(this.scrollTarget.scrollLeft=n,this.scrollTarget.scrollTop=e)},get _scrollTargetWidth(){return this._isValidScrollTarget()?this.scrollTarget===this._doc?window.innerWidth:this.scrollTarget.offsetWidth:0},get _scrollTargetHeight(){return 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/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var Bo=navigator.userAgent.match(/iP(?:hone|ad;(?: U;)? CPU) OS (\d+)/),Ho=Bo&&Bo[1]>=8,Fo="-10000px",Vo=-100;Rr({_template:_e`
    <style>
      :host {
        display: block;
      }

      @media only screen and (-webkit-max-device-pixel-ratio: 1) {
        :host {
          will-change: transform;
        }
      }

      #items {
        @apply --iron-list-items-container;
        position: relative;
      }

      :host(:not([grid])) #items > ::slotted(*) {
        width: 100%;
      }

      #items > ::slotted(*) {
        box-sizing: border-box;
        margin: 0;
        position: absolute;
        top: 0;
        will-change: transform;
      }
    </style>

    <array-selector id="selector" items="{{items}}" selected="{{selectedItems}}" selected-item="{{selectedItem}}"></array-selector>

    <div id="items">
      <slot></slot>
    </div>
`,is:"iron-list",properties:{items:{type:Array},as:{type:String,value:"item"},indexAs:{type:String,value:"index"},selectedAs:{type:String,value:"selected"},grid:{type:Boolean,value:!1,reflectToAttribute:!0,observer:"_gridChanged"},selectionEnabled:{type:Boolean,value:!1},selectedItem:{type:Object,notify:!0},selectedItems:{type:Object,notify:!0},multiSelection:{type:Boolean,value:!1},scrollOffset:{type:Number,value:0}},observers:["_itemsChanged(items.*)","_selectionEnabledChanged(selectionEnabled)","_multiSelectionChanged(multiSelection)","_setOverflow(scrollTarget, scrollOffset)"],behaviors:[Zr,go,Do,po],_ratio:.5,_scrollerPaddingTop:0,_scrollPosition:0,_physicalSize:0,_physicalAverage:0,_physicalAverageCount:0,_physicalTop:0,_virtualCount:0,_estScrollHeight:0,_scrollHeight:0,_viewportHeight:0,_viewportWidth:0,_physicalItems:null,_physicalSizes:null,_firstVisibleIndexVal:null,_lastVisibleIndexVal:null,_maxPages:2,_focusedItem:null,_focusedVirtualIndex:-1,_focusedPhysicalIndex:-1,_offscreenFocusedItem:null,_focusBackfillItem:null,_itemsPerRow:1,_itemWidth:0,_rowHeight:0,_templateCost:0,_parentModel:!0,get _physicalBottom(){return this._physicalTop+this._physicalSize},get _scrollBottom(){return this._scrollPosition+this._viewportHeight},get _virtualEnd(){return this._virtualStart+this._physicalCount-1},get _hiddenContentSize(){return(this.grid?this._physicalRows*this._rowHeight:this._physicalSize)-this._viewportHeight},get _itemsParent(){return Yi(Yi(this._userTemplate).parentNode)},get _maxScrollTop(){return this._estScrollHeight-this._viewportHeight+this._scrollOffset},get _maxVirtualStart(){var t=this._convertIndexToCompleteRow(this._virtualCount);return Math.max(0,t-this._physicalCount)},set _virtualStart(t){t=this._clamp(t,0,this._maxVirtualStart),this.grid&&(t-=t%this._itemsPerRow),this._virtualStartVal=t},get _virtualStart(){return this._virtualStartVal||0},set _physicalStart(t){(t%=this._physicalCount)<0&&(t=this._physicalCount+t),this.grid&&(t-=t%this._itemsPerRow),this._physicalStartVal=t},get _physicalStart(){return this._physicalStartVal||0},get _physicalEnd(){return(this._physicalStart+this._physicalCount-1)%this._physicalCount},set _physicalCount(t){this._physicalCountVal=t},get _physicalCount(){return this._physicalCountVal||0},get _optPhysicalSize(){return 0===this._viewportHeight?1/0:this._viewportHeight*this._maxPages},get _isVisible(){return Boolean(this.offsetWidth||this.offsetHeight)},get firstVisibleIndex(){var t=this._firstVisibleIndexVal;if(null==t){var e=this._physicalTop+this._scrollOffset;t=this._iterateItems((function(t,n){return(e+=this._getPhysicalSizeIncrement(t))>this._scrollPosition?this.grid?n-n%this._itemsPerRow:n:this.grid&&this._virtualCount-1===n?n-n%this._itemsPerRow:void 0}))||0,this._firstVisibleIndexVal=t}return t},get lastVisibleIndex(){var t=this._lastVisibleIndexVal;if(null==t){if(this.grid)t=Math.min(this._virtualCount,this.firstVisibleIndex+this._estRowsInView*this._itemsPerRow-1);else{var e=this._physicalTop+this._scrollOffset;this._iterateItems((function(n,i){e<this._scrollBottom&&(t=i),e+=this._getPhysicalSizeIncrement(n)}))}this._lastVisibleIndexVal=t}return t},get _defaultScrollTarget(){return this},get _virtualRowCount(){return Math.ceil(this._virtualCount/this._itemsPerRow)},get _estRowsInView(){return Math.ceil(this._viewportHeight/this._rowHeight)},get _physicalRows(){return Math.ceil(this._physicalCount/this._itemsPerRow)},get _scrollOffset(){return this._scrollerPaddingTop+this.scrollOffset},ready:function(){this.addEventListener("focus",this._didFocus.bind(this),!0)},attached:function(){this._debounce("_render",this._render,_t),this.listen(this,"iron-resize","_resizeHandler"),this.listen(this,"keydown","_keydownHandler")},detached:function(){this.unlisten(this,"iron-resize","_resizeHandler"),this.unlisten(this,"keydown","_keydownHandler")},_setOverflow:function(t){this.style.webkitOverflowScrolling=t===this?"touch":"",this.style.overflowY=t===this?"auto":"",this._lastVisibleIndexVal=null,this._firstVisibleIndexVal=null,this._debounce("_render",this._render,_t)},updateViewportBoundaries:function(){var t=window.getComputedStyle(this);this._scrollerPaddingTop=this.scrollTarget===this?0:parseInt(t["padding-top"],10),this._isRTL=Boolean("rtl"===t.direction),this._viewportWidth=this.$.items.offsetWidth,this._viewportHeight=this._scrollTargetHeight,this.grid&&this._updateGridMetrics()},_scrollHandler:function(){var t=Math.max(0,Math.min(this._maxScrollTop,this._scrollTop)),e=t-this._scrollPosition,n=e>=0;if(this._scrollPosition=t,this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,Math.abs(e)>this._physicalSize&&this._physicalSize>0){e-=this._scrollOffset;var i=Math.round(e/this._physicalAverage)*this._itemsPerRow;this._virtualStart=this._virtualStart+i,this._physicalStart=this._physicalStart+i,this._physicalTop=Math.min(Math.floor(this._virtualStart/this._itemsPerRow)*this._physicalAverage,this._scrollPosition),this._update()}else if(this._physicalCount>0){var r=this._getReusables(n);n?(this._physicalTop=r.physicalTop,this._virtualStart=this._virtualStart+r.indexes.length,this._physicalStart=this._physicalStart+r.indexes.length):(this._virtualStart=this._virtualStart-r.indexes.length,this._physicalStart=this._physicalStart-r.indexes.length),this._update(r.indexes,n?null:r.indexes),this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,0),vt)}},_getReusables:function(t){var e,n,i,r=[],o=this._hiddenContentSize*this._ratio,a=this._virtualStart,s=this._virtualEnd,l=this._physicalCount,c=this._physicalTop+this._scrollOffset,u=this._scrollPosition,h=this._scrollBottom;for(t?(e=this._physicalStart,n=u-c):(e=this._physicalEnd,n=this._physicalBottom+this._scrollOffset-h);n-=i=this._getPhysicalSizeIncrement(e),!(r.length>=l||n<=o);)if(t){if(s+r.length+1>=this._virtualCount)break;if(c+i>=u-this._scrollOffset)break;r.push(e),c+=i,e=(e+1)%l}else{if(a-r.length<=0)break;if(c+this._physicalSize-i<=h)break;r.push(e),c-=i,e=0===e?l-1:e-1}return{indexes:r,physicalTop:c-this._scrollOffset}},_update:function(t,e){if(!(t&&0===t.length||0===this._physicalCount)){if(this._manageFocus(),this._assignModels(t),this._updateMetrics(t),e)for(;e.length;){var n=e.pop();this._physicalTop-=this._getPhysicalSizeIncrement(n)}this._positionItems(),this._updateScrollerSize()}},_createPool:function(t){var e,n;this._ensureTemplatized();var i=new Array(t);for(e=0;e<t;e++)n=this.stamp(null),i[e]=n.root.querySelector("*"),this._itemsParent.appendChild(n.root);return i},_isClientFull:function(){return 0!=this._scrollBottom&&this._physicalBottom-1>=this._scrollBottom&&this._physicalTop<=this._scrollPosition},_increasePoolIfNeeded:function(t){var e=this._clamp(this._physicalCount+t,3,this._virtualCount-this._virtualStart);if(e=this._convertIndexToCompleteRow(e),this.grid){var n=e%this._itemsPerRow;n&&e-n<=this._physicalCount&&(e+=this._itemsPerRow),e-=n}var i=e-this._physicalCount,r=Math.round(.5*this._physicalCount);if(!(i<0)){if(i>0){var o=window.performance.now();[].push.apply(this._physicalItems,this._createPool(i));for(var a=0;a<i;a++)this._physicalSizes.push(0);this._physicalCount=this._physicalCount+i,this._physicalStart>this._physicalEnd&&this._isIndexRendered(this._focusedVirtualIndex)&&this._getPhysicalIndex(this._focusedVirtualIndex)<this._physicalEnd&&(this._physicalStart=this._physicalStart+i),this._update(),this._templateCost=(window.performance.now()-o)/i,r=Math.round(.5*this._physicalCount)}this._virtualEnd>=this._virtualCount-1||0===r||(this._isClientFull()?this._physicalSize<this._optPhysicalSize&&this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,this._clamp(Math.round(50/this._templateCost),1,r)),yt):this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,r),vt))}},_render:function(){if(this.isAttached&&this._isVisible)if(0!==this._physicalCount){var t=this._getReusables(!0);this._physicalTop=t.physicalTop,this._virtualStart=this._virtualStart+t.indexes.length,this._physicalStart=this._physicalStart+t.indexes.length,this._update(t.indexes),this._update(),this._increasePoolIfNeeded(0)}else this._virtualCount>0&&(this.updateViewportBoundaries(),this._increasePoolIfNeeded(3))},_ensureTemplatized:function(){if(!this.ctor){this._userTemplate=this.queryEffectiveChildren("template"),this._userTemplate||console.warn("iron-list requires a template to be provided in light-dom");var t={__key__:!0};t[this.as]=!0,t[this.indexAs]=!0,t[this.selectedAs]=!0,t.tabIndex=!0,this._instanceProps=t,this.templatize(this._userTemplate,this.mutableData)}},_gridChanged:function(t,e){void 0!==e&&(this.notifyResize(),Di(),t&&this._updateGridMetrics())},_itemsChanged:function(t){if("items"===t.path)this._virtualStart=0,this._physicalTop=0,this._virtualCount=this.items?this.items.length:0,this._physicalIndexForKey={},this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,this._physicalCount=this._physicalCount||0,this._physicalItems=this._physicalItems||[],this._physicalSizes=this._physicalSizes||[],this._physicalStart=0,this._scrollTop>this._scrollOffset&&this._resetScrollPosition(0),this._removeFocusedItem(),this._debounce("_render",this._render,_t);else if("items.splices"===t.path){if(this._adjustVirtualIndex(t.value.indexSplices),this._virtualCount=this.items?this.items.length:0,t.value.indexSplices.some((function(t){return t.addedCount>0||t.removed.length>0}))){var e=this._getActiveElement();this.contains(e)&&e.blur()}var n=t.value.indexSplices.some((function(t){return t.index+t.addedCount>=this._virtualStart&&t.index<=this._virtualEnd}),this);this._isClientFull()&&!n||this._debounce("_render",this._render,_t)}else"items.length"!==t.path&&this._forwardItemPath(t.path,t.value)},_forwardItemPath:function(t,e){var n,i,r,o=(t=t.slice(6)).indexOf(".");-1===o&&(o=t.length);var a=this.modelForElement(this._offscreenFocusedItem),s=parseInt(t.substring(0,o),10);(n=this._isIndexRendered(s))?(i=this._getPhysicalIndex(s),r=this.modelForElement(this._physicalItems[i])):a&&(r=a),r&&r[this.indexAs]===s&&(t=t.substring(o+1),r._setPendingPropertyOrPath(t=this.as+(t?"."+t:""),e,!1,!0),r._flushProperties&&r._flushProperties(),n&&(this._updateMetrics([i]),this._positionItems(),this._updateScrollerSize()))},_adjustVirtualIndex:function(t){t.forEach((function(t){if(t.removed.forEach(this._removeItem,this),t.index<this._virtualStart){var e=Math.max(t.addedCount-t.removed.length,t.index-this._virtualStart);this._virtualStart=this._virtualStart+e,this._focusedVirtualIndex>=0&&(this._focusedVirtualIndex=this._focusedVirtualIndex+e)}}),this)},_removeItem:function(t){this.$.selector.deselect(t),this._focusedItem&&this.modelForElement(this._focusedItem)[this.as]===t&&this._removeFocusedItem()},_iterateItems:function(t,e){var n,i,r,o;if(2===arguments.length&&e){for(o=0;o<e.length;o++)if(i=this._computeVidx(n=e[o]),null!=(r=t.call(this,n,i)))return r}else{for(n=this._physicalStart,i=this._virtualStart;n<this._physicalCount;n++,i++)if(null!=(r=t.call(this,n,i)))return r;for(n=0;n<this._physicalStart;n++,i++)if(null!=(r=t.call(this,n,i)))return r}},_computeVidx:function(t){return t>=this._physicalStart?this._virtualStart+(t-this._physicalStart):this._virtualStart+(this._physicalCount-this._physicalStart)+t},_assignModels:function(t){this._iterateItems((function(t,e){var n=this._physicalItems[t],i=this.items&&this.items[e];if(null!=i){var r=this.modelForElement(n);r.__key__=null,this._forwardProperty(r,this.as,i),this._forwardProperty(r,this.selectedAs,this.$.selector.isSelected(i)),this._forwardProperty(r,this.indexAs,e),this._forwardProperty(r,"tabIndex",this._focusedVirtualIndex===e?0:-1),this._physicalIndexForKey[r.__key__]=t,r._flushProperties&&r._flushProperties(!0),n.removeAttribute("hidden")}else n.setAttribute("hidden","")}),t)},_updateMetrics:function(t){Di();var e=0,n=0,i=this._physicalAverageCount,r=this._physicalAverage;this._iterateItems((function(t,i){n+=this._physicalSizes[t],this._physicalSizes[t]=this._physicalItems[t].offsetHeight,e+=this._physicalSizes[t],this._physicalAverageCount+=this._physicalSizes[t]?1:0}),t),this.grid?(this._updateGridMetrics(),this._physicalSize=Math.ceil(this._physicalCount/this._itemsPerRow)*this._rowHeight):(n=1===this._itemsPerRow?n:Math.ceil(this._physicalCount/this._itemsPerRow)*this._rowHeight,this._physicalSize=this._physicalSize+e-n,this._itemsPerRow=1),this._physicalAverageCount!==i&&(this._physicalAverage=Math.round((r*i+e)/this._physicalAverageCount))},_updateGridMetrics:function(){this._itemWidth=this._physicalCount>0?this._physicalItems[0].getBoundingClientRect().width:200,this._rowHeight=this._physicalCount>0?this._physicalItems[0].offsetHeight:200,this._itemsPerRow=this._itemWidth?Math.floor(this._viewportWidth/this._itemWidth):this._itemsPerRow},_positionItems:function(){this._adjustScrollPosition();var t=this._physicalTop;if(this.grid){var e=(this._viewportWidth-this._itemsPerRow*this._itemWidth)/2;this._iterateItems((function(n,i){var r=Math.floor(i%this._itemsPerRow*this._itemWidth+e);this._isRTL&&(r*=-1),this.translate3d(r+"px",t+"px",0,this._physicalItems[n]),this._shouldRenderNextRow(i)&&(t+=this._rowHeight)}))}else{const e=[];this._iterateItems((function(n,i){const r=this._physicalItems[n];this.translate3d(0,t+"px",0,r),t+=this._physicalSizes[n];const o=r.id;o&&e.push(o)})),e.length&&this.setAttribute("aria-owns",e.join(" "))}},_getPhysicalSizeIncrement:function(t){return this.grid?this._computeVidx(t)%this._itemsPerRow!=this._itemsPerRow-1?0:this._rowHeight:this._physicalSizes[t]},_shouldRenderNextRow:function(t){return t%this._itemsPerRow==this._itemsPerRow-1},_adjustScrollPosition:function(){var t=0===this._virtualStart?this._physicalTop:Math.min(this._scrollPosition+this._physicalTop,0);if(0!==t){this._physicalTop=this._physicalTop-t;var e=this._scrollPosition;!Ho&&e>0&&this._resetScrollPosition(e-t)}},_resetScrollPosition:function(t){this.scrollTarget&&t>=0&&(this._scrollTop=t,this._scrollPosition=this._scrollTop)},_updateScrollerSize:function(t){this._estScrollHeight=this.grid?this._virtualRowCount*this._rowHeight:this._physicalBottom+Math.max(this._virtualCount-this._physicalCount-this._virtualStart,0)*this._physicalAverage,((t=(t=(t=t||0===this._scrollHeight)||this._scrollPosition>=this._estScrollHeight-this._physicalSize)||this.grid&&this.$.items.style.height<this._estScrollHeight)||Math.abs(this._estScrollHeight-this._scrollHeight)>=this._viewportHeight)&&(this.$.items.style.height=this._estScrollHeight+"px",this._scrollHeight=this._estScrollHeight)},scrollToItem:function(t){return this.scrollToIndex(this.items.indexOf(t))},scrollToIndex:function(t){if(!("number"!=typeof t||t<0||t>this.items.length-1)&&(Di(),0!==this._physicalCount)){t=this._clamp(t,0,this._virtualCount-1),(!this._isIndexRendered(t)||t>=this._maxVirtualStart)&&(this._virtualStart=this.grid?t-2*this._itemsPerRow:t-1),this._manageFocus(),this._assignModels(),this._updateMetrics(),this._physicalTop=Math.floor(this._virtualStart/this._itemsPerRow)*this._physicalAverage;for(var e=this._physicalStart,n=this._virtualStart,i=0,r=this._hiddenContentSize;n<t&&i<=r;)i+=this._getPhysicalSizeIncrement(e),e=(e+1)%this._physicalCount,n++;this._updateScrollerSize(!0),this._positionItems(),this._resetScrollPosition(this._physicalTop+this._scrollOffset+i),this._increasePoolIfNeeded(0),this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null}},_resetAverage:function(){this._physicalAverage=0,this._physicalAverageCount=0},_resizeHandler:function(){this._debounce("_render",(function(){this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,this._isVisible?(this.updateViewportBoundaries(),this.toggleScrollListener(!0),this._resetAverage(),this._render()):this.toggleScrollListener(!1)}),_t)},selectItem:function(t){return this.selectIndex(this.items.indexOf(t))},selectIndex:function(t){if(!(t<0||t>=this._virtualCount)){if(!this.multiSelection&&this.selectedItem&&this.clearSelection(),this._isIndexRendered(t)){var e=this.modelForElement(this._physicalItems[this._getPhysicalIndex(t)]);e&&(e[this.selectedAs]=!0),this.updateSizeForIndex(t)}this.$.selector.selectIndex(t)}},deselectItem:function(t){return this.deselectIndex(this.items.indexOf(t))},deselectIndex:function(t){t<0||t>=this._virtualCount||(this._isIndexRendered(t)&&(this.modelForElement(this._physicalItems[this._getPhysicalIndex(t)])[this.selectedAs]=!1,this.updateSizeForIndex(t)),this.$.selector.deselectIndex(t))},toggleSelectionForItem:function(t){return this.toggleSelectionForIndex(this.items.indexOf(t))},toggleSelectionForIndex:function(t){(this.$.selector.isIndexSelected?this.$.selector.isIndexSelected(t):this.$.selector.isSelected(this.items[t]))?this.deselectIndex(t):this.selectIndex(t)},clearSelection:function(){this._iterateItems((function(t,e){this.modelForElement(this._physicalItems[t])[this.selectedAs]=!1})),this.$.selector.clearSelection()},_selectionEnabledChanged:function(t){(t?this.listen:this.unlisten).call(this,this,"tap","_selectionHandler")},_selectionHandler:function(t){var e=this.modelForElement(t.target);if(e){var n,i,r=Yi(t).path[0],o=this._getActiveElement(),a=this._physicalItems[this._getPhysicalIndex(e[this.indexAs])];"input"!==r.localName&&"button"!==r.localName&&"select"!==r.localName&&(n=e.tabIndex,e.tabIndex=Vo,i=o?o.tabIndex:-1,e.tabIndex=n,o&&a!==o&&a.contains(o)&&i!==Vo||this.toggleSelectionForItem(e[this.as]))}},_multiSelectionChanged:function(t){this.clearSelection(),this.$.selector.multi=t},updateSizeForItem:function(t){return this.updateSizeForIndex(this.items.indexOf(t))},updateSizeForIndex:function(t){return this._isIndexRendered(t)?(this._updateMetrics([this._getPhysicalIndex(t)]),this._positionItems(),null):null},_manageFocus:function(){var t=this._focusedVirtualIndex;t>=0&&t<this._virtualCount?this._isIndexRendered(t)?this._restoreFocusedItem():this._createFocusBackfillItem():this._virtualCount>0&&this._physicalCount>0&&(this._focusedPhysicalIndex=this._physicalStart,this._focusedVirtualIndex=this._virtualStart,this._focusedItem=this._physicalItems[this._physicalStart])},_convertIndexToCompleteRow:function(t){return this._itemsPerRow=this._itemsPerRow||1,this.grid?Math.ceil(t/this._itemsPerRow)*this._itemsPerRow:t},_isIndexRendered:function(t){return t>=this._virtualStart&&t<=this._virtualEnd},_isIndexVisible:function(t){return t>=this.firstVisibleIndex&&t<=this.lastVisibleIndex},_getPhysicalIndex:function(t){return(this._physicalStart+(t-this._virtualStart))%this._physicalCount},focusItem:function(t){this._focusPhysicalItem(t)},_focusPhysicalItem:function(t){if(!(t<0||t>=this._virtualCount)){this._restoreFocusedItem(),this._isIndexRendered(t)||this.scrollToIndex(t);var e,n=this._physicalItems[this._getPhysicalIndex(t)],i=this.modelForElement(n);i.tabIndex=Vo,n.tabIndex===Vo&&(e=n),e||(e=Yi(n).querySelector('[tabindex="-100"]')),i.tabIndex=0,this._focusedVirtualIndex=t,e&&e.focus()}},_removeFocusedItem:function(){this._offscreenFocusedItem&&this._itemsParent.removeChild(this._offscreenFocusedItem),this._offscreenFocusedItem=null,this._focusBackfillItem=null,this._focusedItem=null,this._focusedVirtualIndex=-1,this._focusedPhysicalIndex=-1},_createFocusBackfillItem:function(){var t=this._focusedPhysicalIndex;if(!(this._offscreenFocusedItem||this._focusedVirtualIndex<0)){if(!this._focusBackfillItem){var e=this.stamp(null);this._focusBackfillItem=e.root.querySelector("*"),this._itemsParent.appendChild(e.root)}this._offscreenFocusedItem=this._physicalItems[t],this.modelForElement(this._offscreenFocusedItem).tabIndex=0,this._physicalItems[t]=this._focusBackfillItem,this._focusedPhysicalIndex=t,this.translate3d(0,Fo,0,this._offscreenFocusedItem)}},_restoreFocusedItem:function(){if(this._offscreenFocusedItem&&!(this._focusedVirtualIndex<0)){this._assignModels();var t=this._focusedPhysicalIndex=this._getPhysicalIndex(this._focusedVirtualIndex),e=this._physicalItems[t];if(e){var n=this.modelForElement(e),i=this.modelForElement(this._offscreenFocusedItem);n[this.as]===i[this.as]?(this._focusBackfillItem=e,n.tabIndex=-1,this._physicalItems[t]=this._offscreenFocusedItem,this.translate3d(0,Fo,0,this._focusBackfillItem)):(this._removeFocusedItem(),this._focusBackfillItem=null),this._offscreenFocusedItem=null}}},_didFocus:function(t){var e=this.modelForElement(t.target),n=this.modelForElement(this._focusedItem),i=null!==this._offscreenFocusedItem,r=this._focusedVirtualIndex;e&&(n===e?this._isIndexVisible(r)||this.scrollToIndex(r):(this._restoreFocusedItem(),n&&(n.tabIndex=-1),e.tabIndex=0,this._focusedVirtualIndex=r=e[this.indexAs],this._focusedPhysicalIndex=this._getPhysicalIndex(r),this._focusedItem=this._physicalItems[this._focusedPhysicalIndex],i&&!this._offscreenFocusedItem&&this._update()))},_keydownHandler:function(t){switch(t.keyCode){case 40:this._focusedVirtualIndex<this._virtualCount-1&&t.preventDefault(),this._focusPhysicalItem(this._focusedVirtualIndex+(this.grid?this._itemsPerRow:1));break;case 39:this.grid&&this._focusPhysicalItem(this._focusedVirtualIndex+(this._isRTL?-1:1));break;case 38:this._focusedVirtualIndex>0&&t.preventDefault(),this._focusPhysicalItem(this._focusedVirtualIndex-(this.grid?this._itemsPerRow:1));break;case 37:this.grid&&this._focusPhysicalItem(this._focusedVirtualIndex+(this._isRTL?1:-1));break;case 13:this._focusPhysicalItem(this._focusedVirtualIndex),this.selectionEnabled&&this._selectionHandler(t)}},_clamp:function(t,e,n){return Math.min(n,Math.max(e,t))},_debounce:function(t,e,n){this._debouncers=this._debouncers||{},this._debouncers[t]=Tn.debounce(this._debouncers[t],n,e.bind(this)),An(this._debouncers[t])},_forwardProperty:function(t,e,n){t._setPendingProperty(e,n)},_forwardHostPropV2:function(t,e){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach((function(n){n&&this.modelForElement(n).forwardHostProp(t,e)}),this)},_notifyInstancePropV2:function(t,e,n){if(Q(this.as,e)){var i=t[this.indexAs];e==this.as&&(this.items[i]=n),this.notifyPath(J(this.as,"items."+i,e),n)}},_getStampedChildren:function(){return this._physicalItems},_forwardInstancePath:function(t,e,n){0===e.indexOf(this.as+".")&&this.notifyPath("items."+t.__key__+"."+e.slice(this.as.length+1),n)},_forwardParentPath:function(t,e){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach((function(n){n&&this.modelForElement(n).notifyPath(t,e)}),this)},_forwardParentProp:function(t,e){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach((function(n){n&&(this.modelForElement(n)[t]=e)}),this)},_getActiveElement:function(){var t=this._itemsParent.node.domHost;return Yi(t?t.root:document).activeElement}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
class Uo{constructor(t){this.selection=[],this.selectCallback=t}get(){return this.multi?this.selection.slice():this.selection[0]}clear(t){this.selection.slice().forEach((function(e){(!t||t.indexOf(e)<0)&&this.setItemSelected(e,!1)}),this)}isSelected(t){return this.selection.indexOf(t)>=0}setItemSelected(t,e){if(null!=t&&e!==this.isSelected(t)){if(e)this.selection.push(t);else{var n=this.selection.indexOf(t);n>=0&&this.selection.splice(n,1)}this.selectCallback&&this.selectCallback(t,e)}}select(t){this.multi?this.toggle(t):this.get()!==t&&(this.setItemSelected(this.get(),!1),this.setItemSelected(t,!0))}toggle(t){this.setItemSelected(t,!this.isSelected(t))}}
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */const jo={properties:{attrForSelected:{type:String,value:null},selected:{type:String,notify:!0},selectedItem:{type:Object,readOnly:!0,notify:!0},activateEvent:{type:String,value:"tap",observer:"_activateEventChanged"},selectable:String,selectedClass:{type:String,value:"iron-selected"},selectedAttribute:{type:String,value:null},fallbackSelection:{type:String,value:null},items:{type:Array,readOnly:!0,notify:!0,value:function(){return[]}},_excludedLocalNames:{type:Object,value:function(){return{template:1,"dom-bind":1,"dom-if":1,"dom-repeat":1}}}},observers:["_updateAttrForSelected(attrForSelected)","_updateSelected(selected)","_checkFallback(fallbackSelection)"],created:function(){this._bindFilterItem=this._filterItem.bind(this),this._selection=new Uo(this._applySelection.bind(this))},attached:function(){this._observer=this._observeItems(this),this._addListener(this.activateEvent)},detached:function(){this._observer&&Yi(this).unobserveNodes(this._observer),this._removeListener(this.activateEvent)},indexOf:function(t){return this.items?this.items.indexOf(t):-1},select:function(t){this.selected=t},selectPrevious:function(){var t=this.items.length,e=t-1;void 0!==this.selected&&(e=(Number(this._valueToIndex(this.selected))-1+t)%t),this.selected=this._indexToValue(e)},selectNext:function(){var t=0;void 0!==this.selected&&(t=(Number(this._valueToIndex(this.selected))+1)%this.items.length),this.selected=this._indexToValue(t)},selectIndex:function(t){this.select(this._indexToValue(t))},forceSynchronousItemUpdate:function(){this._observer&&"function"==typeof this._observer.flush?this._observer.flush():this._updateItems()},get _shouldUpdateSelection(){return null!=this.selected},_checkFallback:function(){this._updateSelected()},_addListener:function(t){this.listen(this,t,"_activateHandler")},_removeListener:function(t){this.unlisten(this,t,"_activateHandler")},_activateEventChanged:function(t,e){this._removeListener(e),this._addListener(t)},_updateItems:function(){var t=Yi(this).queryDistributedElements(this.selectable||"*");t=Array.prototype.filter.call(t,this._bindFilterItem),this._setItems(t)},_updateAttrForSelected:function(){this.selectedItem&&(this.selected=this._valueForItem(this.selectedItem))},_updateSelected:function(){this._selectSelected(this.selected)},_selectSelected:function(t){if(this.items){var e=this._valueToItem(this.selected);e?this._selection.select(e):this._selection.clear(),this.fallbackSelection&&this.items.length&&void 0===this._selection.get()&&(this.selected=this.fallbackSelection)}},_filterItem:function(t){return!this._excludedLocalNames[t.localName]},_valueToItem:function(t){return null==t?null:this.items[this._valueToIndex(t)]},_valueToIndex:function(t){if(!this.attrForSelected)return Number(t);for(var e,n=0;e=this.items[n];n++)if(this._valueForItem(e)==t)return n},_indexToValue:function(t){if(!this.attrForSelected)return t;var e=this.items[t];return e?this._valueForItem(e):void 0},_valueForItem:function(t){if(!t)return null;if(!this.attrForSelected){var e=this.indexOf(t);return-1===e?null:e}var n=t[st(this.attrForSelected)];return null!=n?n:t.getAttribute(this.attrForSelected)},_applySelection:function(t,e){this.selectedClass&&this.toggleClass(this.selectedClass,e,t),this.selectedAttribute&&this.toggleAttribute(this.selectedAttribute,e,t),this._selectionChange(),this.fire("iron-"+(e?"select":"deselect"),{item:t})},_selectionChange:function(){this._setSelectedItem(this._selection.get())},_observeItems:function(t){return Yi(t).observeNodes((function(t){this._updateItems(),this._updateSelected(),this.fire("iron-items-changed",t,{bubbles:!1,cancelable:!1})}))},_activateHandler:function(t){for(var e=t.target,n=this.items;e&&e!=this;){var i=n.indexOf(e);if(i>=0){var r=this._indexToValue(i);return void this._itemActivate(r,e)}e=e.parentNode}},_itemActivate:function(t,e){this.fire("iron-activate",{selected:t,item:e},{cancelable:!0}).defaultPrevented||this.select(t)}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */Rr({_template:_e`
    <style>
      :host {
        display: block;
      }

      :host > ::slotted(:not(slot):not(.iron-selected)) {
        display: none !important;
      }
    </style>

    <slot></slot>
`,is:"iron-pages",behaviors:[go,jo],properties:{activateEvent:{type:String,value:null}},observers:["_selectedPageChanged(selected)"],_selectedPageChanged:function(t,e){this.async(this.notifyResize)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Go=_e`
<custom-style>
  <style is="custom-style">
    html {

      --shadow-transition: {
        transition: box-shadow 0.28s cubic-bezier(0.4, 0, 0.2, 1);
      };

      --shadow-none: {
        box-shadow: none;
      };

      /* from http://codepen.io/shyndman/pen/c5394ddf2e8b2a5c9185904b57421cdb */

      --shadow-elevation-2dp: {
        box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.14),
                    0 1px 5px 0 rgba(0, 0, 0, 0.12),
                    0 3px 1px -2px rgba(0, 0, 0, 0.2);
      };

      --shadow-elevation-3dp: {
        box-shadow: 0 3px 4px 0 rgba(0, 0, 0, 0.14),
                    0 1px 8px 0 rgba(0, 0, 0, 0.12),
                    0 3px 3px -2px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-4dp: {
        box-shadow: 0 4px 5px 0 rgba(0, 0, 0, 0.14),
                    0 1px 10px 0 rgba(0, 0, 0, 0.12),
                    0 2px 4px -1px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-6dp: {
        box-shadow: 0 6px 10px 0 rgba(0, 0, 0, 0.14),
                    0 1px 18px 0 rgba(0, 0, 0, 0.12),
                    0 3px 5px -1px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-8dp: {
        box-shadow: 0 8px 10px 1px rgba(0, 0, 0, 0.14),
                    0 3px 14px 2px rgba(0, 0, 0, 0.12),
                    0 5px 5px -3px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-12dp: {
        box-shadow: 0 12px 16px 1px rgba(0, 0, 0, 0.14),
                    0 4px 22px 3px rgba(0, 0, 0, 0.12),
                    0 6px 7px -4px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-16dp: {
        box-shadow: 0 16px 24px 2px rgba(0, 0, 0, 0.14),
                    0  6px 30px 5px rgba(0, 0, 0, 0.12),
                    0  8px 10px -5px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-24dp: {
        box-shadow: 0 24px 38px 3px rgba(0, 0, 0, 0.14),
                    0 9px 46px 8px rgba(0, 0, 0, 0.12),
                    0 11px 15px -7px rgba(0, 0, 0, 0.4);
      };
    }
  </style>
</custom-style>`;Go.setAttribute("style","display: none;"),document.head.appendChild(Go.content);
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Wo=_e`
<dom-module id="paper-material-styles">
  <template>
    <style>
      html {
        --paper-material: {
          display: block;
          position: relative;
        };
        --paper-material-elevation-1: {
          @apply --shadow-elevation-2dp;
        };
        --paper-material-elevation-2: {
          @apply --shadow-elevation-4dp;
        };
        --paper-material-elevation-3: {
          @apply --shadow-elevation-6dp;
        };
        --paper-material-elevation-4: {
          @apply --shadow-elevation-8dp;
        };
        --paper-material-elevation-5: {
          @apply --shadow-elevation-16dp;
        };
      }
      .paper-material {
        @apply --paper-material;
      }
      .paper-material[elevation="1"] {
        @apply --paper-material-elevation-1;
      }
      .paper-material[elevation="2"] {
        @apply --paper-material-elevation-2;
      }
      .paper-material[elevation="3"] {
        @apply --paper-material-elevation-3;
      }
      .paper-material[elevation="4"] {
        @apply --paper-material-elevation-4;
      }
      .paper-material[elevation="5"] {
        @apply --paper-material-elevation-5;
      }

      /* Duplicate the styles because of https://github.com/webcomponents/shadycss/issues/193 */
      :host {
        --paper-material: {
          display: block;
          position: relative;
        };
        --paper-material-elevation-1: {
          @apply --shadow-elevation-2dp;
        };
        --paper-material-elevation-2: {
          @apply --shadow-elevation-4dp;
        };
        --paper-material-elevation-3: {
          @apply --shadow-elevation-6dp;
        };
        --paper-material-elevation-4: {
          @apply --shadow-elevation-8dp;
        };
        --paper-material-elevation-5: {
          @apply --shadow-elevation-16dp;
        };
      }
      :host(.paper-material) {
        @apply --paper-material;
      }
      :host(.paper-material[elevation="1"]) {
        @apply --paper-material-elevation-1;
      }
      :host(.paper-material[elevation="2"]) {
        @apply --paper-material-elevation-2;
      }
      :host(.paper-material[elevation="3"]) {
        @apply --paper-material-elevation-3;
      }
      :host(.paper-material[elevation="4"]) {
        @apply --paper-material-elevation-4;
      }
      :host(.paper-material[elevation="5"]) {
        @apply --paper-material-elevation-5;
      }
    </style>
  </template>
</dom-module>`;Wo.setAttribute("style","display: none;"),document.head.appendChild(Wo.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const qo={properties:{focused:{type:Boolean,value:!1,notify:!0,readOnly:!0,reflectToAttribute:!0},disabled:{type:Boolean,value:!1,notify:!0,observer:"_disabledChanged",reflectToAttribute:!0},_oldTabIndex:{type:String},_boundFocusBlurHandler:{type:Function,value:function(){return this._focusBlurHandler.bind(this)}}},observers:["_changedControlState(focused, disabled)"],ready:function(){this.addEventListener("focus",this._boundFocusBlurHandler,!0),this.addEventListener("blur",this._boundFocusBlurHandler,!0)},_focusBlurHandler:function(t){this._setFocused("focus"===t.type)},_disabledChanged:function(t,e){this.setAttribute("aria-disabled",t?"true":"false"),this.style.pointerEvents=t?"none":"",t?(this._oldTabIndex=this.getAttribute("tabindex"),this._setFocused(!1),this.tabIndex=-1,this.blur()):void 0!==this._oldTabIndex&&(null===this._oldTabIndex?this.removeAttribute("tabindex"):this.setAttribute("tabindex",this._oldTabIndex))},_changedControlState:function(){this._controlStateChanged&&this._controlStateChanged()}},Yo={properties:{pressed:{type:Boolean,readOnly:!0,value:!1,reflectToAttribute:!0,observer:"_pressedChanged"},toggles:{type:Boolean,value:!1,reflectToAttribute:!0},active:{type:Boolean,value:!1,notify:!0,reflectToAttribute:!0},pointerDown:{type:Boolean,readOnly:!0,value:!1},receivedFocusFromKeyboard:{type:Boolean,readOnly:!0},ariaActiveAttribute:{type:String,value:"aria-pressed",observer:"_ariaActiveAttributeChanged"}},listeners:{down:"_downHandler",up:"_upHandler",tap:"_tapHandler"},observers:["_focusChanged(focused)","_activeChanged(active, ariaActiveAttribute)"],keyBindings:{"enter:keydown":"_asyncClick","space:keydown":"_spaceKeyDownHandler","space:keyup":"_spaceKeyUpHandler"},_mouseEventRe:/^mouse/,_tapHandler:function(){this.toggles?this._userActivate(!this.active):this.active=!1},_focusChanged:function(t){this._detectKeyboardFocus(t),t||this._setPressed(!1)},_detectKeyboardFocus:function(t){this._setReceivedFocusFromKeyboard(!this.pointerDown&&t)},_userActivate:function(t){this.active!==t&&(this.active=t,this.fire("change"))},_downHandler:function(t){this._setPointerDown(!0),this._setPressed(!0),this._setReceivedFocusFromKeyboard(!1)},_upHandler:function(){this._setPointerDown(!1),this._setPressed(!1)},_spaceKeyDownHandler:function(t){var e=t.detail.keyboardEvent,n=Yi(e).localTarget;this.isLightDescendant(n)||(e.preventDefault(),e.stopImmediatePropagation(),this._setPressed(!0))},_spaceKeyUpHandler:function(t){var e=Yi(t.detail.keyboardEvent).localTarget;this.isLightDescendant(e)||(this.pressed&&this._asyncClick(),this._setPressed(!1))},_asyncClick:function(){this.async((function(){this.click()}),1)},_pressedChanged:function(t){this._changedButtonState()},_ariaActiveAttributeChanged:function(t,e){e&&e!=t&&this.hasAttribute(e)&&this.removeAttribute(e)},_activeChanged:function(t,e){this.toggles?this.setAttribute(this.ariaActiveAttribute,t?"true":"false"):this.removeAttribute(this.ariaActiveAttribute),this._changedButtonState()},_controlStateChanged:function(){this.disabled?this._setPressed(!1):this._changedButtonState()},_changedButtonState:function(){this._buttonStateChanged&&this._buttonStateChanged()}},Xo=[zo,Yo];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2014 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var $o={distance:function(t,e,n,i){var r=t-n,o=e-i;return Math.sqrt(r*r+o*o)},now:window.performance&&window.performance.now?window.performance.now.bind(window.performance):Date.now};function Ko(t){this.element=t,this.width=this.boundingRect.width,this.height=this.boundingRect.height,this.size=Math.max(this.width,this.height)}function Zo(t){this.element=t,this.color=window.getComputedStyle(t).color,this.wave=document.createElement("div"),this.waveContainer=document.createElement("div"),this.wave.style.backgroundColor=this.color,this.wave.classList.add("wave"),this.waveContainer.classList.add("wave-container"),Yi(this.waveContainer).appendChild(this.wave),this.resetInteractionState()}Ko.prototype={get boundingRect(){return this.element.getBoundingClientRect()},furthestCornerDistanceFrom:function(t,e){var n=$o.distance(t,e,0,0),i=$o.distance(t,e,this.width,0),r=$o.distance(t,e,0,this.height),o=$o.distance(t,e,this.width,this.height);return Math.max(n,i,r,o)}},Zo.MAX_RADIUS=300,Zo.prototype={get recenters(){return this.element.recenters},get center(){return this.element.center},get mouseDownElapsed(){var t;return this.mouseDownStart?(t=$o.now()-this.mouseDownStart,this.mouseUpStart&&(t-=this.mouseUpElapsed),t):0},get mouseUpElapsed(){return this.mouseUpStart?$o.now()-this.mouseUpStart:0},get mouseDownElapsedSeconds(){return this.mouseDownElapsed/1e3},get mouseUpElapsedSeconds(){return this.mouseUpElapsed/1e3},get mouseInteractionSeconds(){return this.mouseDownElapsedSeconds+this.mouseUpElapsedSeconds},get initialOpacity(){return this.element.initialOpacity},get opacityDecayVelocity(){return this.element.opacityDecayVelocity},get radius(){var t=1.1*Math.min(Math.sqrt(this.containerMetrics.width*this.containerMetrics.width+this.containerMetrics.height*this.containerMetrics.height),Zo.MAX_RADIUS)+5,e=t*(1-Math.pow(80,-this.mouseInteractionSeconds/(1.1-t/Zo.MAX_RADIUS*.2)));return Math.abs(e)},get opacity(){return this.mouseUpStart?Math.max(0,this.initialOpacity-this.mouseUpElapsedSeconds*this.opacityDecayVelocity):this.initialOpacity},get outerOpacity(){return Math.max(0,Math.min(.3*this.mouseUpElapsedSeconds,this.opacity))},get isOpacityFullyDecayed(){return this.opacity<.01&&this.radius>=Math.min(this.maxRadius,Zo.MAX_RADIUS)},get isRestingAtMaxRadius(){return this.opacity>=this.initialOpacity&&this.radius>=Math.min(this.maxRadius,Zo.MAX_RADIUS)},get isAnimationComplete(){return this.mouseUpStart?this.isOpacityFullyDecayed:this.isRestingAtMaxRadius},get translationFraction(){return Math.min(1,this.radius/this.containerMetrics.size*2/Math.sqrt(2))},get xNow(){return this.xEnd?this.xStart+this.translationFraction*(this.xEnd-this.xStart):this.xStart},get yNow(){return this.yEnd?this.yStart+this.translationFraction*(this.yEnd-this.yStart):this.yStart},get isMouseDown(){return this.mouseDownStart&&!this.mouseUpStart},resetInteractionState:function(){this.maxRadius=0,this.mouseDownStart=0,this.mouseUpStart=0,this.xStart=0,this.yStart=0,this.xEnd=0,this.yEnd=0,this.slideDistance=0,this.containerMetrics=new Ko(this.element)},draw:function(){var t,e,n;this.wave.style.opacity=this.opacity,t=this.radius/(this.containerMetrics.size/2),this.waveContainer.style.webkitTransform="translate("+(e=this.xNow-this.containerMetrics.width/2)+"px, "+(n=this.yNow-this.containerMetrics.height/2)+"px)",this.waveContainer.style.transform="translate3d("+e+"px, "+n+"px, 0)",this.wave.style.webkitTransform="scale("+t+","+t+")",this.wave.style.transform="scale3d("+t+","+t+",1)"},downAction:function(t){var e=this.containerMetrics.width/2,n=this.containerMetrics.height/2;this.resetInteractionState(),this.mouseDownStart=$o.now(),this.center?(this.xStart=e,this.yStart=n,this.slideDistance=$o.distance(this.xStart,this.yStart,this.xEnd,this.yEnd)):(this.xStart=t?t.detail.x-this.containerMetrics.boundingRect.left:this.containerMetrics.width/2,this.yStart=t?t.detail.y-this.containerMetrics.boundingRect.top:this.containerMetrics.height/2),this.recenters&&(this.xEnd=e,this.yEnd=n,this.slideDistance=$o.distance(this.xStart,this.yStart,this.xEnd,this.yEnd)),this.maxRadius=this.containerMetrics.furthestCornerDistanceFrom(this.xStart,this.yStart),this.waveContainer.style.top=(this.containerMetrics.height-this.containerMetrics.size)/2+"px",this.waveContainer.style.left=(this.containerMetrics.width-this.containerMetrics.size)/2+"px",this.waveContainer.style.width=this.containerMetrics.size+"px",this.waveContainer.style.height=this.containerMetrics.size+"px"},upAction:function(t){this.isMouseDown&&(this.mouseUpStart=$o.now())},remove:function(){Yi(Yi(this.waveContainer).parentNode).removeChild(this.waveContainer)}},Rr({_template:_e`
    <style>
      :host {
        display: block;
        position: absolute;
        border-radius: inherit;
        overflow: hidden;
        top: 0;
        left: 0;
        right: 0;
        bottom: 0;

        /* See PolymerElements/paper-behaviors/issues/34. On non-Chrome browsers,
         * creating a node (with a position:absolute) in the middle of an event
         * handler "interrupts" that event handler (which happens when the
         * ripple is created on demand) */
        pointer-events: none;
      }

      :host([animating]) {
        /* This resolves a rendering issue in Chrome (as of 40) where the
           ripple is not properly clipped by its parent (which may have
           rounded corners). See: http://jsbin.com/temexa/4

           Note: We only apply this style conditionally. Otherwise, the browser
           will create a new compositing layer for every ripple element on the
           page, and that would be bad. */
        -webkit-transform: translate(0, 0);
        transform: translate3d(0, 0, 0);
      }

      #background,
      #waves,
      .wave-container,
      .wave {
        pointer-events: none;
        position: absolute;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
      }

      #background,
      .wave {
        opacity: 0;
      }

      #waves,
      .wave {
        overflow: hidden;
      }

      .wave-container,
      .wave {
        border-radius: 50%;
      }

      :host(.circle) #background,
      :host(.circle) #waves {
        border-radius: 50%;
      }

      :host(.circle) .wave-container {
        overflow: hidden;
      }
    </style>

    <div id="background"></div>
    <div id="waves"></div>
`,is:"paper-ripple",behaviors:[zo],properties:{initialOpacity:{type:Number,value:.25},opacityDecayVelocity:{type:Number,value:.8},recenters:{type:Boolean,value:!1},center:{type:Boolean,value:!1},ripples:{type:Array,value:function(){return[]}},animating:{type:Boolean,readOnly:!0,reflectToAttribute:!0,value:!1},holdDown:{type:Boolean,value:!1,observer:"_holdDownChanged"},noink:{type:Boolean,value:!1},_animating:{type:Boolean},_boundAnimate:{type:Function,value:function(){return this.animate.bind(this)}}},get target(){return this.keyEventTarget},keyBindings:{"enter:keydown":"_onEnterKeydown","space:keydown":"_onSpaceKeydown","space:keyup":"_onSpaceKeyup"},attached:function(){this.keyEventTarget=11==Yi(this).parentNode.nodeType?Yi(this).getOwnerRoot().host:Yi(this).parentNode;var t=this.keyEventTarget;this.listen(t,"up","uiUpAction"),this.listen(t,"down","uiDownAction")},detached:function(){this.unlisten(this.keyEventTarget,"up","uiUpAction"),this.unlisten(this.keyEventTarget,"down","uiDownAction"),this.keyEventTarget=null},get shouldKeepAnimating(){for(var t=0;t<this.ripples.length;++t)if(!this.ripples[t].isAnimationComplete)return!0;return!1},simulatedRipple:function(){this.downAction(null),this.async((function(){this.upAction()}),1)},uiDownAction:function(t){this.noink||this.downAction(t)},downAction:function(t){this.holdDown&&this.ripples.length>0||(this.addRipple().downAction(t),this._animating||(this._animating=!0,this.animate()))},uiUpAction:function(t){this.noink||this.upAction(t)},upAction:function(t){this.holdDown||(this.ripples.forEach((function(e){e.upAction(t)})),this._animating=!0,this.animate())},onAnimationComplete:function(){this._animating=!1,this.$.background.style.backgroundColor="",this.fire("transitionend")},addRipple:function(){var t=new Zo(this);return Yi(this.$.waves).appendChild(t.waveContainer),this.$.background.style.backgroundColor=t.color,this.ripples.push(t),this._setAnimating(!0),t},removeRipple:function(t){var e=this.ripples.indexOf(t);e<0||(this.ripples.splice(e,1),t.remove(),this.ripples.length||this._setAnimating(!1))},animate:function(){if(this._animating){var t,e;for(t=0;t<this.ripples.length;++t)(e=this.ripples[t]).draw(),this.$.background.style.opacity=e.outerOpacity,e.isOpacityFullyDecayed&&!e.isRestingAtMaxRadius&&this.removeRipple(e);this.shouldKeepAnimating||0!==this.ripples.length?window.requestAnimationFrame(this._boundAnimate):this.onAnimationComplete()}},animateRipple:function(){return this.animate()},_onEnterKeydown:function(){this.uiDownAction(),this.async(this.uiUpAction,1)},_onSpaceKeydown:function(){this.uiDownAction()},_onSpaceKeyup:function(){this.uiUpAction()},_holdDownChanged:function(t,e){void 0!==e&&(t?this.downAction():this.upAction())}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Jo={properties:{noink:{type:Boolean,observer:"_noinkChanged"},_rippleContainer:{type:Object}},_buttonStateChanged:function(){this.focused&&this.ensureRipple()},_downHandler:function(t){Yo._downHandler.call(this,t),this.pressed&&this.ensureRipple(t)},ensureRipple:function(t){if(!this.hasRipple()){this._ripple=this._createRipple(),this._ripple.noink=this.noink;var e=this._rippleContainer||this.root;if(e&&Yi(e).appendChild(this._ripple),t){var n=Yi(this._rippleContainer||this),i=Yi(t).rootTarget;n.deepContains(i)&&this._ripple.uiDownAction(t)}}},getRipple:function(){return this.ensureRipple(),this._ripple},hasRipple:function(){return Boolean(this._ripple)},_createRipple:function(){return document.createElement("paper-ripple")},_noinkChanged:function(t){this.hasRipple()&&(this._ripple.noink=t)}},Qo={properties:{elevation:{type:Number,reflectToAttribute:!0,readOnly:!0}},observers:["_calculateElevation(focused, disabled, active, pressed, receivedFocusFromKeyboard)","_computeKeyboardClass(receivedFocusFromKeyboard)"],hostAttributes:{role:"button",tabindex:"0",animated:!0},_calculateElevation:function(){var t=1;this.disabled?t=0:this.active||this.pressed?t=4:this.receivedFocusFromKeyboard&&(t=3),this._setElevation(t)},_computeKeyboardClass:function(t){this.toggleClass("keyboard-focus",t)},_spaceKeyDownHandler:function(t){Yo._spaceKeyDownHandler.call(this,t),this.hasRipple()&&this.getRipple().ripples.length<1&&this._ripple.uiDownAction()},_spaceKeyUpHandler:function(t){Yo._spaceKeyUpHandler.call(this,t),this.hasRipple()&&this._ripple.uiUpAction()}},ta=[Xo,qo,Jo,Qo],ea=_e`
  <style include="paper-material-styles">
    /* Need to specify the same specificity as the styles imported from paper-material. */
    :host {
      @apply --layout-inline;
      @apply --layout-center-center;
      position: relative;
      box-sizing: border-box;
      min-width: 5.14em;
      margin: 0 0.29em;
      background: transparent;
      -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
      -webkit-tap-highlight-color: transparent;
      font: inherit;
      text-transform: uppercase;
      outline-width: 0;
      border-radius: 3px;
      -moz-user-select: none;
      -ms-user-select: none;
      -webkit-user-select: none;
      user-select: none;
      cursor: pointer;
      z-index: 0;
      padding: 0.7em 0.57em;

      @apply --paper-font-common-base;
      @apply --paper-button;
    }

    :host([elevation="1"]) {
      @apply --paper-material-elevation-1;
    }

    :host([elevation="2"]) {
      @apply --paper-material-elevation-2;
    }

    :host([elevation="3"]) {
      @apply --paper-material-elevation-3;
    }

    :host([elevation="4"]) {
      @apply --paper-material-elevation-4;
    }

    :host([elevation="5"]) {
      @apply --paper-material-elevation-5;
    }

    :host([hidden]) {
      display: none !important;
    }

    :host([raised].keyboard-focus) {
      font-weight: bold;
      @apply --paper-button-raised-keyboard-focus;
    }

    :host(:not([raised]).keyboard-focus) {
      font-weight: bold;
      @apply --paper-button-flat-keyboard-focus;
    }

    :host([disabled]) {
      background: none;
      color: #a8a8a8;
      cursor: auto;
      pointer-events: none;

      @apply --paper-button-disabled;
    }

    :host([disabled][raised]) {
      background: #eaeaea;
    }


    :host([animated]) {
      @apply --shadow-transition;
    }

    paper-ripple {
      color: var(--paper-button-ink-color);
    }
  </style>

  <slot></slot>`;
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */ea.setAttribute("strip-whitespace",""),Rr({_template:ea,is:"paper-button",behaviors:[ta],properties:{raised:{type:Boolean,reflectToAttribute:!0,value:!1,observer:"_calculateElevation"}},_calculateElevation:function(){this.raised?Qo._calculateElevation.apply(this):this._setElevation(0)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const na=_e`
<custom-style>
  <style is="custom-style">
    html {

      /* Material Design color palette for Google products */

      --google-red-100: #f4c7c3;
      --google-red-300: #e67c73;
      --google-red-500: #db4437;
      --google-red-700: #c53929;

      --google-blue-100: #c6dafc;
      --google-blue-300: #7baaf7;
      --google-blue-500: #4285f4;
      --google-blue-700: #3367d6;

      --google-green-100: #b7e1cd;
      --google-green-300: #57bb8a;
      --google-green-500: #0f9d58;
      --google-green-700: #0b8043;

      --google-yellow-100: #fce8b2;
      --google-yellow-300: #f7cb4d;
      --google-yellow-500: #f4b400;
      --google-yellow-700: #f09300;

      --google-grey-100: #f5f5f5;
      --google-grey-300: #e0e0e0;
      --google-grey-500: #9e9e9e;
      --google-grey-700: #616161;

      /* Material Design color palette from online spec document */

      --paper-red-50: #ffebee;
      --paper-red-100: #ffcdd2;
      --paper-red-200: #ef9a9a;
      --paper-red-300: #e57373;
      --paper-red-400: #ef5350;
      --paper-red-500: #f44336;
      --paper-red-600: #e53935;
      --paper-red-700: #d32f2f;
      --paper-red-800: #c62828;
      --paper-red-900: #b71c1c;
      --paper-red-a100: #ff8a80;
      --paper-red-a200: #ff5252;
      --paper-red-a400: #ff1744;
      --paper-red-a700: #d50000;

      --paper-pink-50: #fce4ec;
      --paper-pink-100: #f8bbd0;
      --paper-pink-200: #f48fb1;
      --paper-pink-300: #f06292;
      --paper-pink-400: #ec407a;
      --paper-pink-500: #e91e63;
      --paper-pink-600: #d81b60;
      --paper-pink-700: #c2185b;
      --paper-pink-800: #ad1457;
      --paper-pink-900: #880e4f;
      --paper-pink-a100: #ff80ab;
      --paper-pink-a200: #ff4081;
      --paper-pink-a400: #f50057;
      --paper-pink-a700: #c51162;

      --paper-purple-50: #f3e5f5;
      --paper-purple-100: #e1bee7;
      --paper-purple-200: #ce93d8;
      --paper-purple-300: #ba68c8;
      --paper-purple-400: #ab47bc;
      --paper-purple-500: #9c27b0;
      --paper-purple-600: #8e24aa;
      --paper-purple-700: #7b1fa2;
      --paper-purple-800: #6a1b9a;
      --paper-purple-900: #4a148c;
      --paper-purple-a100: #ea80fc;
      --paper-purple-a200: #e040fb;
      --paper-purple-a400: #d500f9;
      --paper-purple-a700: #aa00ff;

      --paper-deep-purple-50: #ede7f6;
      --paper-deep-purple-100: #d1c4e9;
      --paper-deep-purple-200: #b39ddb;
      --paper-deep-purple-300: #9575cd;
      --paper-deep-purple-400: #7e57c2;
      --paper-deep-purple-500: #673ab7;
      --paper-deep-purple-600: #5e35b1;
      --paper-deep-purple-700: #512da8;
      --paper-deep-purple-800: #4527a0;
      --paper-deep-purple-900: #311b92;
      --paper-deep-purple-a100: #b388ff;
      --paper-deep-purple-a200: #7c4dff;
      --paper-deep-purple-a400: #651fff;
      --paper-deep-purple-a700: #6200ea;

      --paper-indigo-50: #e8eaf6;
      --paper-indigo-100: #c5cae9;
      --paper-indigo-200: #9fa8da;
      --paper-indigo-300: #7986cb;
      --paper-indigo-400: #5c6bc0;
      --paper-indigo-500: #3f51b5;
      --paper-indigo-600: #3949ab;
      --paper-indigo-700: #303f9f;
      --paper-indigo-800: #283593;
      --paper-indigo-900: #1a237e;
      --paper-indigo-a100: #8c9eff;
      --paper-indigo-a200: #536dfe;
      --paper-indigo-a400: #3d5afe;
      --paper-indigo-a700: #304ffe;

      --paper-blue-50: #e3f2fd;
      --paper-blue-100: #bbdefb;
      --paper-blue-200: #90caf9;
      --paper-blue-300: #64b5f6;
      --paper-blue-400: #42a5f5;
      --paper-blue-500: #2196f3;
      --paper-blue-600: #1e88e5;
      --paper-blue-700: #1976d2;
      --paper-blue-800: #1565c0;
      --paper-blue-900: #0d47a1;
      --paper-blue-a100: #82b1ff;
      --paper-blue-a200: #448aff;
      --paper-blue-a400: #2979ff;
      --paper-blue-a700: #2962ff;

      --paper-light-blue-50: #e1f5fe;
      --paper-light-blue-100: #b3e5fc;
      --paper-light-blue-200: #81d4fa;
      --paper-light-blue-300: #4fc3f7;
      --paper-light-blue-400: #29b6f6;
      --paper-light-blue-500: #03a9f4;
      --paper-light-blue-600: #039be5;
      --paper-light-blue-700: #0288d1;
      --paper-light-blue-800: #0277bd;
      --paper-light-blue-900: #01579b;
      --paper-light-blue-a100: #80d8ff;
      --paper-light-blue-a200: #40c4ff;
      --paper-light-blue-a400: #00b0ff;
      --paper-light-blue-a700: #0091ea;

      --paper-cyan-50: #e0f7fa;
      --paper-cyan-100: #b2ebf2;
      --paper-cyan-200: #80deea;
      --paper-cyan-300: #4dd0e1;
      --paper-cyan-400: #26c6da;
      --paper-cyan-500: #00bcd4;
      --paper-cyan-600: #00acc1;
      --paper-cyan-700: #0097a7;
      --paper-cyan-800: #00838f;
      --paper-cyan-900: #006064;
      --paper-cyan-a100: #84ffff;
      --paper-cyan-a200: #18ffff;
      --paper-cyan-a400: #00e5ff;
      --paper-cyan-a700: #00b8d4;

      --paper-teal-50: #e0f2f1;
      --paper-teal-100: #b2dfdb;
      --paper-teal-200: #80cbc4;
      --paper-teal-300: #4db6ac;
      --paper-teal-400: #26a69a;
      --paper-teal-500: #009688;
      --paper-teal-600: #00897b;
      --paper-teal-700: #00796b;
      --paper-teal-800: #00695c;
      --paper-teal-900: #004d40;
      --paper-teal-a100: #a7ffeb;
      --paper-teal-a200: #64ffda;
      --paper-teal-a400: #1de9b6;
      --paper-teal-a700: #00bfa5;

      --paper-green-50: #e8f5e9;
      --paper-green-100: #c8e6c9;
      --paper-green-200: #a5d6a7;
      --paper-green-300: #81c784;
      --paper-green-400: #66bb6a;
      --paper-green-500: #4caf50;
      --paper-green-600: #43a047;
      --paper-green-700: #388e3c;
      --paper-green-800: #2e7d32;
      --paper-green-900: #1b5e20;
      --paper-green-a100: #b9f6ca;
      --paper-green-a200: #69f0ae;
      --paper-green-a400: #00e676;
      --paper-green-a700: #00c853;

      --paper-light-green-50: #f1f8e9;
      --paper-light-green-100: #dcedc8;
      --paper-light-green-200: #c5e1a5;
      --paper-light-green-300: #aed581;
      --paper-light-green-400: #9ccc65;
      --paper-light-green-500: #8bc34a;
      --paper-light-green-600: #7cb342;
      --paper-light-green-700: #689f38;
      --paper-light-green-800: #558b2f;
      --paper-light-green-900: #33691e;
      --paper-light-green-a100: #ccff90;
      --paper-light-green-a200: #b2ff59;
      --paper-light-green-a400: #76ff03;
      --paper-light-green-a700: #64dd17;

      --paper-lime-50: #f9fbe7;
      --paper-lime-100: #f0f4c3;
      --paper-lime-200: #e6ee9c;
      --paper-lime-300: #dce775;
      --paper-lime-400: #d4e157;
      --paper-lime-500: #cddc39;
      --paper-lime-600: #c0ca33;
      --paper-lime-700: #afb42b;
      --paper-lime-800: #9e9d24;
      --paper-lime-900: #827717;
      --paper-lime-a100: #f4ff81;
      --paper-lime-a200: #eeff41;
      --paper-lime-a400: #c6ff00;
      --paper-lime-a700: #aeea00;

      --paper-yellow-50: #fffde7;
      --paper-yellow-100: #fff9c4;
      --paper-yellow-200: #fff59d;
      --paper-yellow-300: #fff176;
      --paper-yellow-400: #ffee58;
      --paper-yellow-500: #ffeb3b;
      --paper-yellow-600: #fdd835;
      --paper-yellow-700: #fbc02d;
      --paper-yellow-800: #f9a825;
      --paper-yellow-900: #f57f17;
      --paper-yellow-a100: #ffff8d;
      --paper-yellow-a200: #ffff00;
      --paper-yellow-a400: #ffea00;
      --paper-yellow-a700: #ffd600;

      --paper-amber-50: #fff8e1;
      --paper-amber-100: #ffecb3;
      --paper-amber-200: #ffe082;
      --paper-amber-300: #ffd54f;
      --paper-amber-400: #ffca28;
      --paper-amber-500: #ffc107;
      --paper-amber-600: #ffb300;
      --paper-amber-700: #ffa000;
      --paper-amber-800: #ff8f00;
      --paper-amber-900: #ff6f00;
      --paper-amber-a100: #ffe57f;
      --paper-amber-a200: #ffd740;
      --paper-amber-a400: #ffc400;
      --paper-amber-a700: #ffab00;

      --paper-orange-50: #fff3e0;
      --paper-orange-100: #ffe0b2;
      --paper-orange-200: #ffcc80;
      --paper-orange-300: #ffb74d;
      --paper-orange-400: #ffa726;
      --paper-orange-500: #ff9800;
      --paper-orange-600: #fb8c00;
      --paper-orange-700: #f57c00;
      --paper-orange-800: #ef6c00;
      --paper-orange-900: #e65100;
      --paper-orange-a100: #ffd180;
      --paper-orange-a200: #ffab40;
      --paper-orange-a400: #ff9100;
      --paper-orange-a700: #ff6500;

      --paper-deep-orange-50: #fbe9e7;
      --paper-deep-orange-100: #ffccbc;
      --paper-deep-orange-200: #ffab91;
      --paper-deep-orange-300: #ff8a65;
      --paper-deep-orange-400: #ff7043;
      --paper-deep-orange-500: #ff5722;
      --paper-deep-orange-600: #f4511e;
      --paper-deep-orange-700: #e64a19;
      --paper-deep-orange-800: #d84315;
      --paper-deep-orange-900: #bf360c;
      --paper-deep-orange-a100: #ff9e80;
      --paper-deep-orange-a200: #ff6e40;
      --paper-deep-orange-a400: #ff3d00;
      --paper-deep-orange-a700: #dd2c00;

      --paper-brown-50: #efebe9;
      --paper-brown-100: #d7ccc8;
      --paper-brown-200: #bcaaa4;
      --paper-brown-300: #a1887f;
      --paper-brown-400: #8d6e63;
      --paper-brown-500: #795548;
      --paper-brown-600: #6d4c41;
      --paper-brown-700: #5d4037;
      --paper-brown-800: #4e342e;
      --paper-brown-900: #3e2723;

      --paper-grey-50: #fafafa;
      --paper-grey-100: #f5f5f5;
      --paper-grey-200: #eeeeee;
      --paper-grey-300: #e0e0e0;
      --paper-grey-400: #bdbdbd;
      --paper-grey-500: #9e9e9e;
      --paper-grey-600: #757575;
      --paper-grey-700: #616161;
      --paper-grey-800: #424242;
      --paper-grey-900: #212121;

      --paper-blue-grey-50: #eceff1;
      --paper-blue-grey-100: #cfd8dc;
      --paper-blue-grey-200: #b0bec5;
      --paper-blue-grey-300: #90a4ae;
      --paper-blue-grey-400: #78909c;
      --paper-blue-grey-500: #607d8b;
      --paper-blue-grey-600: #546e7a;
      --paper-blue-grey-700: #455a64;
      --paper-blue-grey-800: #37474f;
      --paper-blue-grey-900: #263238;

      /* opacity for dark text on a light background */
      --dark-divider-opacity: 0.12;
      --dark-disabled-opacity: 0.38; /* or hint text or icon */
      --dark-secondary-opacity: 0.54;
      --dark-primary-opacity: 0.87;

      /* opacity for light text on a dark background */
      --light-divider-opacity: 0.12;
      --light-disabled-opacity: 0.3; /* or hint text or icon */
      --light-secondary-opacity: 0.7;
      --light-primary-opacity: 1.0;

    }

  </style>
</custom-style>
`;na.setAttribute("style","display: none;"),document.head.appendChild(na.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ia=_e`
<custom-style>
  <style is="custom-style">
    html {
      /*
       * You can use these generic variables in your elements for easy theming.
       * For example, if all your elements use \`--primary-text-color\` as its main
       * color, then switching from a light to a dark theme is just a matter of
       * changing the value of \`--primary-text-color\` in your application.
       */
      --primary-text-color: var(--light-theme-text-color);
      --primary-background-color: var(--light-theme-background-color);
      --secondary-text-color: var(--light-theme-secondary-color);
      --disabled-text-color: var(--light-theme-disabled-color);
      --divider-color: var(--light-theme-divider-color);
      --error-color: var(--paper-deep-orange-a700);

      /*
       * Primary and accent colors. Also see color.js for more colors.
       */
      --primary-color: var(--paper-indigo-500);
      --light-primary-color: var(--paper-indigo-100);
      --dark-primary-color: var(--paper-indigo-700);

      --accent-color: var(--paper-pink-a200);
      --light-accent-color: var(--paper-pink-a100);
      --dark-accent-color: var(--paper-pink-a400);


      /*
       * Material Design Light background theme
       */
      --light-theme-background-color: #ffffff;
      --light-theme-base-color: #000000;
      --light-theme-text-color: var(--paper-grey-900);
      --light-theme-secondary-color: #737373;  /* for secondary text and icons */
      --light-theme-disabled-color: #9b9b9b;  /* disabled/hint text */
      --light-theme-divider-color: #dbdbdb;

      /*
       * Material Design Dark background theme
       */
      --dark-theme-background-color: var(--paper-grey-900);
      --dark-theme-base-color: #ffffff;
      --dark-theme-text-color: #ffffff;
      --dark-theme-secondary-color: #bcbcbc;  /* for secondary text and icons */
      --dark-theme-disabled-color: #646464;  /* disabled/hint text */
      --dark-theme-divider-color: #3c3c3c;

      /*
       * Deprecated values because of their confusing names.
       */
      --text-primary-color: var(--dark-theme-text-color);
      --default-primary-color: var(--primary-color);
    }
  </style>
</custom-style>`;ia.setAttribute("style","display: none;"),document.head.appendChild(ia.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ra={properties:{name:{type:String},value:{notify:!0,type:String},required:{type:Boolean,value:!1}},attached:function(){},detached:function(){}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */let oa=null;const aa={properties:{validator:{type:String},invalid:{notify:!0,reflectToAttribute:!0,type:Boolean,value:!1,observer:"_invalidChanged"}},registered:function(){oa=new xo({type:"validator"})},_invalidChanged:function(){this.invalid?this.setAttribute("aria-invalid","true"):this.removeAttribute("aria-invalid")},get _validator(){return oa&&oa.byKey(this.validator)},hasValidator:function(){return null!=this._validator},validate:function(t){return this.invalid=void 0===t&&void 0!==this.value?!this._getValidity(this.value):!this._getValidity(t),!this.invalid},_getValidity:function(t){return!this.hasValidator()||this._validator.validate(t)}},sa={properties:{checked:{type:Boolean,value:!1,reflectToAttribute:!0,notify:!0,observer:"_checkedChanged"},toggles:{type:Boolean,value:!0,reflectToAttribute:!0},value:{type:String,value:"on",observer:"_valueChanged"}},observers:["_requiredChanged(required)"],created:function(){this._hasIronCheckedElementBehavior=!0},_getValidity:function(t){return this.disabled||!this.required||this.checked},_requiredChanged:function(){this.required?this.setAttribute("aria-required","true"):this.removeAttribute("aria-required")},_checkedChanged:function(){this.active=this.checked,this.fire("iron-change")},_valueChanged:function(){null==this.value&&(this.value="on")}},la={observers:["_focusedChanged(receivedFocusFromKeyboard)"],_focusedChanged:function(t){t&&this.ensureRipple(),this.hasRipple()&&(this._ripple.holdDown=t)},_createRipple:function(){var t=Jo._createRipple();return t.id="ink",t.setAttribute("center",""),t.classList.add("circle"),t}},ca=[Xo,qo,Jo,la],ua=[ca,[ra,aa,sa],{_checkedChanged:function(){sa._checkedChanged.call(this),this.hasRipple()&&(this.checked?this._ripple.setAttribute("checked",""):this._ripple.removeAttribute("checked"))},_buttonStateChanged:function(){Jo._buttonStateChanged.call(this),this.disabled||this.isAttached&&(this.checked=this.active)}}],ha=_e`<style>
  :host {
    display: inline-block;
    white-space: nowrap;
    cursor: pointer;
    --calculated-paper-checkbox-size: var(--paper-checkbox-size, 18px);
    /* -1px is a sentinel for the default and is replaced in \`attached\`. */
    --calculated-paper-checkbox-ink-size: var(--paper-checkbox-ink-size, -1px);
    @apply --paper-font-common-base;
    line-height: 0;
    -webkit-tap-highlight-color: transparent;
  }

  :host([hidden]) {
    display: none !important;
  }

  :host(:focus) {
    outline: none;
  }

  .hidden {
    display: none;
  }

  #checkboxContainer {
    display: inline-block;
    position: relative;
    width: var(--calculated-paper-checkbox-size);
    height: var(--calculated-paper-checkbox-size);
    min-width: var(--calculated-paper-checkbox-size);
    margin: var(--paper-checkbox-margin, initial);
    vertical-align: var(--paper-checkbox-vertical-align, middle);
    background-color: var(--paper-checkbox-unchecked-background-color, transparent);
  }

  #ink {
    position: absolute;

    /* Center the ripple in the checkbox by negative offsetting it by
     * (inkWidth - rippleWidth) / 2 */
    top: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
    left: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
    width: var(--calculated-paper-checkbox-ink-size);
    height: var(--calculated-paper-checkbox-ink-size);
    color: var(--paper-checkbox-unchecked-ink-color, var(--primary-text-color));
    opacity: 0.6;
    pointer-events: none;
  }

  #ink:dir(rtl) {
    right: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
    left: auto;
  }

  #ink[checked] {
    color: var(--paper-checkbox-checked-ink-color, var(--primary-color));
  }

  #checkbox {
    position: relative;
    box-sizing: border-box;
    height: 100%;
    border: solid 2px;
    border-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
    border-radius: 2px;
    pointer-events: none;
    -webkit-transition: background-color 140ms, border-color 140ms;
    transition: background-color 140ms, border-color 140ms;

    -webkit-transition-duration: var(--paper-checkbox-animation-duration, 140ms);
    transition-duration: var(--paper-checkbox-animation-duration, 140ms);
  }

  /* checkbox checked animations */
  #checkbox.checked #checkmark {
    -webkit-animation: checkmark-expand 140ms ease-out forwards;
    animation: checkmark-expand 140ms ease-out forwards;

    -webkit-animation-duration: var(--paper-checkbox-animation-duration, 140ms);
    animation-duration: var(--paper-checkbox-animation-duration, 140ms);
  }

  @-webkit-keyframes checkmark-expand {
    0% {
      -webkit-transform: scale(0, 0) rotate(45deg);
    }
    100% {
      -webkit-transform: scale(1, 1) rotate(45deg);
    }
  }

  @keyframes checkmark-expand {
    0% {
      transform: scale(0, 0) rotate(45deg);
    }
    100% {
      transform: scale(1, 1) rotate(45deg);
    }
  }

  #checkbox.checked {
    background-color: var(--paper-checkbox-checked-color, var(--primary-color));
    border-color: var(--paper-checkbox-checked-color, var(--primary-color));
  }

  #checkmark {
    position: absolute;
    width: 36%;
    height: 70%;
    border-style: solid;
    border-top: none;
    border-left: none;
    border-right-width: calc(2/15 * var(--calculated-paper-checkbox-size));
    border-bottom-width: calc(2/15 * var(--calculated-paper-checkbox-size));
    border-color: var(--paper-checkbox-checkmark-color, white);
    -webkit-transform-origin: 97% 86%;
    transform-origin: 97% 86%;
    box-sizing: content-box; /* protect against page-level box-sizing */
  }

  #checkmark:dir(rtl) {
    -webkit-transform-origin: 50% 14%;
    transform-origin: 50% 14%;
  }

  /* label */
  #checkboxLabel {
    position: relative;
    display: inline-block;
    vertical-align: middle;
    padding-left: var(--paper-checkbox-label-spacing, 8px);
    white-space: normal;
    line-height: normal;
    color: var(--paper-checkbox-label-color, var(--primary-text-color));
    @apply --paper-checkbox-label;
  }

  :host([checked]) #checkboxLabel {
    color: var(--paper-checkbox-label-checked-color, var(--paper-checkbox-label-color, var(--primary-text-color)));
    @apply --paper-checkbox-label-checked;
  }

  #checkboxLabel:dir(rtl) {
    padding-right: var(--paper-checkbox-label-spacing, 8px);
    padding-left: 0;
  }

  #checkboxLabel[hidden] {
    display: none;
  }

  /* disabled state */

  :host([disabled]) #checkbox {
    opacity: 0.5;
    border-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
  }

  :host([disabled][checked]) #checkbox {
    background-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
    opacity: 0.5;
  }

  :host([disabled]) #checkboxLabel  {
    opacity: 0.65;
  }

  /* invalid state */
  #checkbox.invalid:not(.checked) {
    border-color: var(--paper-checkbox-error-color, var(--error-color));
  }
</style>

<div id="checkboxContainer">
  <div id="checkbox" class$="[[_computeCheckboxClass(checked, invalid)]]">
    <div id="checkmark" class$="[[_computeCheckmarkClass(checked)]]"></div>
  </div>
</div>

<div id="checkboxLabel"><slot></slot></div>`;
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
let da;ha.setAttribute("strip-whitespace",""),Rr({_template:ha,is:"paper-checkbox",behaviors:[ua],hostAttributes:{role:"checkbox","aria-checked":!1,tabindex:0},properties:{ariaActiveAttribute:{type:String,value:"aria-checked"}},attached:function(){Li(this,(function(){if("-1px"===this.getComputedStyleValue("--calculated-paper-checkbox-ink-size").trim()){var t=this.getComputedStyleValue("--calculated-paper-checkbox-size").trim(),e="px",n=t.match(/[A-Za-z]+$/);null!==n&&(e=n[0]);var i=parseFloat(t),r=8/3*i;"px"===e&&(r=Math.floor(r))%2!=i%2&&r++,this.updateStyles({"--paper-checkbox-ink-size":r+e})}}))},_computeCheckboxClass:function(t,e){var n="";return t&&(n+="checked "),e&&(n+="invalid"),n},_computeCheckmarkClass:function(t){return t?"":"hidden"},_createRipple:function(){return this._rippleContainer=this.$.checkboxContainer,la._createRipple.call(this)}});const pa={properties:{sizingTarget:{type:Object,value:function(){return this}},fitInto:{type:Object,value:window},noOverlap:{type:Boolean},positionTarget:{type:Element},horizontalAlign:{type:String},verticalAlign:{type:String},dynamicAlign:{type:Boolean},horizontalOffset:{type:Number,value:0,notify:!0},verticalOffset:{type:Number,value:0,notify:!0},autoFitOnAttach:{type:Boolean,value:!1},expandSizingTargetForScrollbars:{type:Boolean,value:!1},_fitInfo:{type:Object}},get _fitWidth(){return this.fitInto===window?this.fitInto.innerWidth:this.fitInto.getBoundingClientRect().width},get _fitHeight(){return this.fitInto===window?this.fitInto.innerHeight:this.fitInto.getBoundingClientRect().height},get _fitLeft(){return this.fitInto===window?0:this.fitInto.getBoundingClientRect().left},get _fitTop(){return this.fitInto===window?0:this.fitInto.getBoundingClientRect().top},get _defaultPositionTarget(){var t=Yi(this).parentNode;return t&&t.nodeType===Node.DOCUMENT_FRAGMENT_NODE&&(t=t.host),t},get _localeHorizontalAlign(){if(this._isRTL){if("right"===this.horizontalAlign)return"left";if("left"===this.horizontalAlign)return"right"}return this.horizontalAlign},get __shouldPosition(){return(this.horizontalAlign||this.verticalAlign)&&this.positionTarget},get _isRTL(){return void 0===this._memoizedIsRTL&&(this._memoizedIsRTL="rtl"==window.getComputedStyle(this).direction),this._memoizedIsRTL},attached:function(){this.positionTarget=this.positionTarget||this._defaultPositionTarget,this.autoFitOnAttach&&("none"===window.getComputedStyle(this).display?setTimeout(function(){this.fit()}.bind(this)):(window.ShadyDOM&&ShadyDOM.flush(),this.fit()))},detached:function(){this.__deferredFit&&(clearTimeout(this.__deferredFit),this.__deferredFit=null)},fit:function(){this.position(),this.constrain(),this.center()},_discoverInfo:function(){if(!this._fitInfo){var t=window.getComputedStyle(this),e=window.getComputedStyle(this.sizingTarget);this._fitInfo={inlineStyle:{top:this.style.top||"",left:this.style.left||"",position:this.style.position||""},sizerInlineStyle:{maxWidth:this.sizingTarget.style.maxWidth||"",maxHeight:this.sizingTarget.style.maxHeight||"",boxSizing:this.sizingTarget.style.boxSizing||""},positionedBy:{vertically:"auto"!==t.top?"top":"auto"!==t.bottom?"bottom":null,horizontally:"auto"!==t.left?"left":"auto"!==t.right?"right":null},sizedBy:{height:"none"!==e.maxHeight,width:"none"!==e.maxWidth,minWidth:parseInt(e.minWidth,10)||0,minHeight:parseInt(e.minHeight,10)||0},margin:{top:parseInt(t.marginTop,10)||0,right:parseInt(t.marginRight,10)||0,bottom:parseInt(t.marginBottom,10)||0,left:parseInt(t.marginLeft,10)||0}}}},resetFit:function(){var t=this._fitInfo||{};for(var e in t.sizerInlineStyle)this.sizingTarget.style[e]=t.sizerInlineStyle[e];for(var e in t.inlineStyle)this.style[e]=t.inlineStyle[e];this._fitInfo=null},refit:function(){var t=this.sizingTarget.scrollLeft,e=this.sizingTarget.scrollTop;this.resetFit(),this.fit(),this.sizingTarget.scrollLeft=t,this.sizingTarget.scrollTop=e},position:function(){if(!this.__shouldPosition)return;this._discoverInfo(),window.ShadyDOM&&window.ShadyDOM.flush(),this.style.position="fixed",this.sizingTarget.style.boxSizing="border-box",this.style.left="0px",this.style.top="0px";var t=this.getBoundingClientRect(),e=this.__getNormalizedRect(this.positionTarget),n=this.__getNormalizedRect(this.fitInto);let i,r,o,a;this.expandSizingTargetForScrollbars&&(i=this.sizingTarget.offsetWidth,r=this.sizingTarget.offsetHeight,o=this.sizingTarget.clientWidth,a=this.sizingTarget.clientHeight);var s=this._fitInfo.margin,l=this.__getPosition(this._localeHorizontalAlign,this.verticalAlign,{width:t.width+s.left+s.right,height:t.height+s.top+s.bottom},t,e,n),c=l.left+s.left,u=l.top+s.top,h=Math.min(n.right-s.right,c+t.width),d=Math.min(n.bottom-s.bottom,u+t.height);c=Math.max(n.left+s.left,Math.min(c,h-this._fitInfo.sizedBy.minWidth)),u=Math.max(n.top+s.top,Math.min(u,d-this._fitInfo.sizedBy.minHeight));const p=Math.max(h-c,this._fitInfo.sizedBy.minWidth),f=Math.max(d-u,this._fitInfo.sizedBy.minHeight);this.sizingTarget.style.maxWidth=p+"px",this.sizingTarget.style.maxHeight=f+"px";const m=c-t.left,g=u-t.top;if(this.style.left=`${m}px`,this.style.top=`${g}px`,this.expandSizingTargetForScrollbars){const t=this.sizingTarget.offsetHeight,e=t-this.sizingTarget.clientHeight-(r-a);if(e>0){const i=Math.min(n.height-s.top-s.bottom,f+e);this.sizingTarget.style.maxHeight=`${i}px`;const r=this.sizingTarget.offsetHeight,o=r-t;let a;"top"===l.verticalAlign?a=g:"middle"===l.verticalAlign?a=g-o/2:"bottom"===l.verticalAlign&&(a=g-o),a=Math.max(n.top+s.top,Math.min(a,n.bottom-s.bottom-r)),this.style.top=`${a}px`}const c=this.sizingTarget.offsetWidth,u=c-this.sizingTarget.clientWidth-(i-o);if(u>0){const t=(()=>{if(void 0!==da)return da;const t=document.createElement("div");Object.assign(t.style,{overflow:"auto",position:"fixed",left:"0px",top:"0px",maxWidth:"100px",maxHeight:"100px"});const e=document.createElement("div");return e.style.width="200px",e.style.height="200px",t.appendChild(e),document.body.appendChild(t),da=Math.abs(t.offsetWidth-100)>1?t.offsetWidth-t.clientWidth:0,document.body.removeChild(t),da})(),e=Math.min(n.width-s.left-s.right,p+u-t);this.sizingTarget.style.maxWidth=`${e}px`;const i=this.sizingTarget.offsetWidth+t,r=i-c;let o;"left"===l.horizontalAlign?o=m:"center"===l.horizontalAlign?o=m-r/2:"right"===l.horizontalAlign&&(o=m-r),o=Math.max(n.left+s.left,Math.min(o,n.right-s.right-i)),this.style.left=`${o}px`}}},constrain:function(){if(!this.__shouldPosition){this._discoverInfo();var t=this._fitInfo;t.positionedBy.vertically||(this.style.position="fixed",this.style.top="0px"),t.positionedBy.horizontally||(this.style.position="fixed",this.style.left="0px"),this.sizingTarget.style.boxSizing="border-box";var e=this.getBoundingClientRect();t.sizedBy.height||this.__sizeDimension(e,t.positionedBy.vertically,"top","bottom","Height"),t.sizedBy.width||this.__sizeDimension(e,t.positionedBy.horizontally,"left","right","Width")}},_sizeDimension:function(t,e,n,i,r){this.__sizeDimension(t,e,n,i,r)},__sizeDimension:function(t,e,n,i,r){var o=this._fitInfo,a=this.__getNormalizedRect(this.fitInto),s="Width"===r?a.width:a.height,l=e===i,c="offset"+r;this.sizingTarget.style["max"+r]=s-o.margin[l?n:i]-(l?s-t[i]:t[n])-(this[c]-this.sizingTarget[c])+"px"},center:function(){if(!this.__shouldPosition){this._discoverInfo();var t=this._fitInfo.positionedBy;if(!t.vertically||!t.horizontally){this.style.position="fixed",t.vertically||(this.style.top="0px"),t.horizontally||(this.style.left="0px");var e=this.getBoundingClientRect(),n=this.__getNormalizedRect(this.fitInto);t.vertically||(this.style.top=n.top-e.top+(n.height-e.height)/2+"px"),t.horizontally||(this.style.left=n.left-e.left+(n.width-e.width)/2+"px")}}},__getNormalizedRect:function(t){return t===document.documentElement||t===window?{top:0,left:0,width:window.innerWidth,height:window.innerHeight,right:window.innerWidth,bottom:window.innerHeight}:t.getBoundingClientRect()},__getOffscreenArea:function(t,e,n){var i=Math.min(0,t.top)+Math.min(0,n.bottom-(t.top+e.height)),r=Math.min(0,t.left)+Math.min(0,n.right-(t.left+e.width));return Math.abs(i)*e.width+Math.abs(r)*e.height},__getPosition:function(t,e,n,i,r,o){var a,s=[{verticalAlign:"top",horizontalAlign:"left",top:r.top+this.verticalOffset,left:r.left+this.horizontalOffset},{verticalAlign:"top",horizontalAlign:"right",top:r.top+this.verticalOffset,left:r.right-n.width-this.horizontalOffset},{verticalAlign:"bottom",horizontalAlign:"left",top:r.bottom-n.height-this.verticalOffset,left:r.left+this.horizontalOffset},{verticalAlign:"bottom",horizontalAlign:"right",top:r.bottom-n.height-this.verticalOffset,left:r.right-n.width-this.horizontalOffset}];if(this.noOverlap){for(var l=0,c=s.length;l<c;l++){var u={};for(var h in s[l])u[h]=s[l][h];s.push(u)}s[0].top=s[1].top+=r.height,s[2].top=s[3].top-=r.height,s[4].left=s[6].left+=r.width,s[5].left=s[7].left-=r.width}for(e="auto"===e?null:e,(t="auto"===t?null:t)&&"center"!==t||(s.push({verticalAlign:"top",horizontalAlign:"center",top:r.top+this.verticalOffset+(this.noOverlap?r.height:0),left:r.left-i.width/2+r.width/2+this.horizontalOffset}),s.push({verticalAlign:"bottom",horizontalAlign:"center",top:r.bottom-n.height-this.verticalOffset-(this.noOverlap?r.height:0),left:r.left-i.width/2+r.width/2+this.horizontalOffset})),e&&"middle"!==e||(s.push({verticalAlign:"middle",horizontalAlign:"left",top:r.top-i.height/2+r.height/2+this.verticalOffset,left:r.left+this.horizontalOffset+(this.noOverlap?r.width:0)}),s.push({verticalAlign:"middle",horizontalAlign:"right",top:r.top-i.height/2+r.height/2+this.verticalOffset,left:r.right-n.width-this.horizontalOffset-(this.noOverlap?r.width:0)})),"middle"===e&&"center"===t&&s.push({verticalAlign:"middle",horizontalAlign:"center",top:r.top-i.height/2+r.height/2+this.verticalOffset,left:r.left-i.width/2+r.width/2+this.horizontalOffset}),l=0;l<s.length;l++){var d=s[l],p=d.verticalAlign===e,f=d.horizontalAlign===t;if(!this.dynamicAlign&&!this.noOverlap&&p&&f){a=d;break}var m=(!e||p)&&(!t||f);if(this.dynamicAlign||m){if(d.offscreenArea=this.__getOffscreenArea(d,n,o),0===d.offscreenArea&&m){a=d;break}var g=d.offscreenArea-(a=a||d).offscreenArea;(g<0||0===g&&(p||f))&&(a=d)}}return a}};
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var fa=Element.prototype,ma=fa.matches||fa.matchesSelector||fa.mozMatchesSelector||fa.msMatchesSelector||fa.oMatchesSelector||fa.webkitMatchesSelector;const ga=new class{getTabbableNodes(t){var e=[];return this._collectTabbableNodes(t,e)?this._sortByTabIndex(e):e}isFocusable(t){return ma.call(t,"input, select, textarea, button, object")?ma.call(t,":not([disabled])"):ma.call(t,"a[href], area[href], iframe, [tabindex], [contentEditable]")}isTabbable(t){return this.isFocusable(t)&&ma.call(t,':not([tabindex="-1"])')&&this._isVisible(t)}_normalizedTabIndex(t){if(this.isFocusable(t)){var e=t.getAttribute("tabindex")||0;return Number(e)}return-1}_collectTabbableNodes(t,e){if(t.nodeType!==Node.ELEMENT_NODE)return!1;var n=t;if(!this._isVisible(n))return!1;var i,r=this._normalizedTabIndex(n),o=r>0;r>=0&&e.push(n),i="content"===n.localName||"slot"===n.localName?Yi(n).getDistributedNodes():Yi(n.root||n).children;for(var a=0;a<i.length;a++)o=this._collectTabbableNodes(i[a],e)||o;return o}_isVisible(t){var e=t.style;return"hidden"!==e.visibility&&"none"!==e.display&&"hidden"!==(e=window.getComputedStyle(t)).visibility&&"none"!==e.display}_sortByTabIndex(t){var e=t.length;if(e<2)return t;var n=Math.ceil(e/2),i=this._sortByTabIndex(t.slice(0,n)),r=this._sortByTabIndex(t.slice(n));return this._mergeSortByTabIndex(i,r)}_mergeSortByTabIndex(t,e){for(var n=[];t.length>0&&e.length>0;)this._hasLowerTabOrder(t[0],e[0])?n.push(e.shift()):n.push(t.shift());return n.concat(t,e)}_hasLowerTabOrder(t,e){var n=Math.max(t.tabIndex,0),i=Math.max(e.tabIndex,0);return 0===n||0===i?i>n:n>i}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */Rr({_template:_e`
    <style>
      :host {
        position: fixed;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
        background-color: var(--iron-overlay-backdrop-background-color, #000);
        opacity: 0;
        transition: opacity 0.2s;
        pointer-events: none;
        @apply --iron-overlay-backdrop;
      }

      :host(.opened) {
        opacity: var(--iron-overlay-backdrop-opacity, 0.6);
        pointer-events: auto;
        @apply --iron-overlay-backdrop-opened;
      }
    </style>

    <slot></slot>
`,is:"iron-overlay-backdrop",properties:{opened:{reflectToAttribute:!0,type:Boolean,value:!1,observer:"_openedChanged"}},listeners:{transitionend:"_onTransitionend"},created:function(){this.__openedRaf=null},attached:function(){this.opened&&this._openedChanged(this.opened)},prepare:function(){this.opened&&!this.parentNode&&Yi(document.body).appendChild(this)},open:function(){this.opened=!0},close:function(){this.opened=!1},complete:function(){this.opened||this.parentNode!==document.body||Yi(this.parentNode).removeChild(this)},_onTransitionend:function(t){t&&t.target===this&&this.complete()},_openedChanged:function(t){if(t)this.prepare();else{var e=window.getComputedStyle(this);"0s"!==e.transitionDuration&&0!=e.opacity||this.complete()}this.isAttached&&(this.__openedRaf&&(window.cancelAnimationFrame(this.__openedRaf),this.__openedRaf=null),this.scrollTop=this.scrollTop,this.__openedRaf=window.requestAnimationFrame(function(){this.__openedRaf=null,this.toggleClass("opened",this.opened)}.bind(this)))}});const _a=new
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
class{constructor(){this._overlays=[],this._minimumZ=101,this._backdropElement=null,ii(document.documentElement,"tap",(function(){})),document.addEventListener("tap",this._onCaptureClick.bind(this),!0),document.addEventListener("focus",this._onCaptureFocus.bind(this),!0),document.addEventListener("keydown",this._onCaptureKeyDown.bind(this),!0)}get backdropElement(){return this._backdropElement||(this._backdropElement=document.createElement("iron-overlay-backdrop")),this._backdropElement}get deepActiveElement(){var t=document.activeElement;for(t&&t instanceof Element!=0||(t=document.body);t.root&&Yi(t.root).activeElement;)t=Yi(t.root).activeElement;return t}_bringOverlayAtIndexToFront(t){var e=this._overlays[t];if(e){var n=this._overlays.length-1,i=this._overlays[n];if(i&&this._shouldBeBehindOverlay(e,i)&&n--,!(t>=n)){var r=Math.max(this.currentOverlayZ(),this._minimumZ);for(this._getZ(e)<=r&&this._applyOverlayZ(e,r);t<n;)this._overlays[t]=this._overlays[t+1],t++;this._overlays[n]=e}}}addOrRemoveOverlay(t){t.opened?this.addOverlay(t):this.removeOverlay(t)}addOverlay(t){var e=this._overlays.indexOf(t);if(e>=0)return this._bringOverlayAtIndexToFront(e),void this.trackBackdrop();var n=this._overlays.length,i=this._overlays[n-1],r=Math.max(this._getZ(i),this._minimumZ),o=this._getZ(t);i&&this._shouldBeBehindOverlay(t,i)&&(this._applyOverlayZ(i,r),n--,r=Math.max(this._getZ(this._overlays[n-1]),this._minimumZ)),o<=r&&this._applyOverlayZ(t,r),this._overlays.splice(n,0,t),this.trackBackdrop()}removeOverlay(t){var e=this._overlays.indexOf(t);-1!==e&&(this._overlays.splice(e,1),this.trackBackdrop())}currentOverlay(){return this._overlays[this._overlays.length-1]}currentOverlayZ(){return this._getZ(this.currentOverlay())}ensureMinimumZ(t){this._minimumZ=Math.max(this._minimumZ,t)}focusOverlay(){var t=this.currentOverlay();t&&t._applyFocus()}trackBackdrop(){var t=this._overlayWithBackdrop();(t||this._backdropElement)&&(this.backdropElement.style.zIndex=this._getZ(t)-1,this.backdropElement.opened=!!t,this.backdropElement.prepare())}getBackdrops(){for(var t=[],e=0;e<this._overlays.length;e++)this._overlays[e].withBackdrop&&t.push(this._overlays[e]);return t}backdropZ(){return this._getZ(this._overlayWithBackdrop())-1}_overlayWithBackdrop(){for(var t=this._overlays.length-1;t>=0;t--)if(this._overlays[t].withBackdrop)return this._overlays[t]}_getZ(t){var e=this._minimumZ;if(t){var n=Number(t.style.zIndex||window.getComputedStyle(t).zIndex);n==n&&(e=n)}return e}_setZ(t,e){t.style.zIndex=e}_applyOverlayZ(t,e){this._setZ(t,e+2)}_overlayInPath(t){t=t||[];for(var e=0;e<t.length;e++)if(t[e]._manager===this)return t[e]}_onCaptureClick(t){var e=this._overlays.length-1;if(-1!==e)for(var n,i=Yi(t).path;(n=this._overlays[e])&&this._overlayInPath(i)!==n&&(n._onCaptureClick(t),n.allowClickThrough);)e--}_onCaptureFocus(t){var e=this.currentOverlay();e&&e._onCaptureFocus(t)}_onCaptureKeyDown(t){var e=this.currentOverlay();e&&(zo.keyboardEventMatchesKeys(t,"esc")?e._onCaptureEsc(t):zo.keyboardEventMatchesKeys(t,"tab")&&e._onCaptureTab(t))}_shouldBeBehindOverlay(t,e){return!t.alwaysOnTop&&e.alwaysOnTop}};
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */var ya,va,ba={pageX:0,pageY:0},xa=null,wa=[],Sa=["wheel","mousewheel","DOMMouseScroll","touchstart","touchmove"];function Ma(t){Ea.indexOf(t)>=0||(0===Ea.length&&(function e(){ya=ya||Ta.bind(void 0);for(var t=0,e=Sa.length;t<e;t++)document.addEventListener(Sa[t],ya,{capture:!0,passive:!1})})(),Ea.push(t),va=Ea[Ea.length-1])}const Ea=[];function Ta(t){if(t.cancelable&&(function e(t){var e=Yi(t).rootTarget;if("touchmove"!==t.type&&xa!==e&&(xa=e,wa=(function n(t){for(var e=[],n=t.indexOf(va),i=0;i<=n;i++)if(t[i].nodeType===Node.ELEMENT_NODE){var r=t[i],o=r.style;"scroll"!==o.overflow&&"auto"!==o.overflow&&(o=window.getComputedStyle(r)),"scroll"!==o.overflow&&"auto"!==o.overflow||e.push(r)}return e})(Yi(t).path)),!wa.length)return!0;if("touchstart"===t.type)return!1;var i=(function r(t){var e={deltaX:t.deltaX,deltaY:t.deltaY};if("deltaX"in t);else if("wheelDeltaX"in t&&"wheelDeltaY"in t)e.deltaX=-t.wheelDeltaX,e.deltaY=-t.wheelDeltaY;else if("wheelDelta"in t)e.deltaX=0,e.deltaY=-t.wheelDelta;else if("axis"in t)e.deltaX=1===t.axis?t.detail:0,e.deltaY=2===t.axis?t.detail:0;else if(t.targetTouches){var n=t.targetTouches[0];e.deltaX=ba.pageX-n.pageX,e.deltaY=ba.pageY-n.pageY}return e}
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */)(t);return!(function o(t,e,n){if(e||n)for(var i=Math.abs(n)>=Math.abs(e),r=0;r<t.length;r++){var o=t[r];if(i?n<0?o.scrollTop>0:o.scrollTop<o.scrollHeight-o.clientHeight:e<0?o.scrollLeft>0:o.scrollLeft<o.scrollWidth-o.clientWidth)return o}})(wa,i.deltaX,i.deltaY)})(t)&&t.preventDefault(),t.targetTouches){var n=t.targetTouches[0];ba.pageX=n.pageX,ba.pageY=n.pageY}}const Ca={properties:{opened:{observer:"_openedChanged",type:Boolean,value:!1,notify:!0},canceled:{observer:"_canceledChanged",readOnly:!0,type:Boolean,value:!1},withBackdrop:{observer:"_withBackdropChanged",type:Boolean},noAutoFocus:{type:Boolean,value:!1},noCancelOnEscKey:{type:Boolean,value:!1},noCancelOnOutsideClick:{type:Boolean,value:!1},closingReason:{type:Object},restoreFocusOnClose:{type:Boolean,value:!1},allowClickThrough:{type:Boolean},alwaysOnTop:{type:Boolean},scrollAction:{type:String},_manager:{type:Object,value:_a},_focusedChild:{type:Object}},listeners:{"iron-resize":"_onIronResize"},observers:["__updateScrollObservers(isAttached, opened, scrollAction)"],get backdropElement(){return this._manager.backdropElement},get _focusNode(){return this._focusedChild||Yi(this).querySelector("[autofocus]")||this},get _focusableNodes(){return ga.getTabbableNodes(this)},ready:function(){this.__isAnimating=!1,this.__shouldRemoveTabIndex=!1,this.__firstFocusableNode=this.__lastFocusableNode=null,this.__rafs={},this.__restoreFocusNode=null,this.__scrollTop=this.__scrollLeft=null,this.__onCaptureScroll=this.__onCaptureScroll.bind(this),this.__rootNodes=null,this._ensureSetup()},attached:function(){this.opened&&this._openedChanged(this.opened),this._observer=Yi(this).observeNodes(this._onNodesChange)},detached:function(){for(var t in this._observer&&Yi(this).unobserveNodes(this._observer),this._observer=null,this.__rafs)null!==this.__rafs[t]&&cancelAnimationFrame(this.__rafs[t]);this.__rafs={},this._manager.removeOverlay(this),this.__isAnimating&&(this.opened?this._finishRenderOpened():(this._applyFocus(),this._finishRenderClosed()))},toggle:function(){this._setCanceled(!1),this.opened=!this.opened},open:function(){this._setCanceled(!1),this.opened=!0},close:function(){this._setCanceled(!1),this.opened=!1},cancel:function(t){this.fire("iron-overlay-canceled",t,{cancelable:!0}).defaultPrevented||(this._setCanceled(!0),this.opened=!1)},invalidateTabbables:function(){this.__firstFocusableNode=this.__lastFocusableNode=null},_ensureSetup:function(){this._overlaySetup||(this._overlaySetup=!0,this.style.outline="none",this.style.display="none")},_openedChanged:function(t){t?this.removeAttribute("aria-hidden"):this.setAttribute("aria-hidden","true"),this.isAttached&&(this.__isAnimating=!0,this.__deraf("__openedChanged",this.__openedChanged))},_canceledChanged:function(){this.closingReason=this.closingReason||{},this.closingReason.canceled=this.canceled},_withBackdropChanged:function(){this.withBackdrop&&!this.hasAttribute("tabindex")?(this.setAttribute("tabindex","-1"),this.__shouldRemoveTabIndex=!0):this.__shouldRemoveTabIndex&&(this.removeAttribute("tabindex"),this.__shouldRemoveTabIndex=!1),this.opened&&this.isAttached&&this._manager.trackBackdrop()},_prepareRenderOpened:function(){this.__restoreFocusNode=this._manager.deepActiveElement,this._preparePositioning(),this.refit(),this._finishPositioning(),this.noAutoFocus&&document.activeElement===this._focusNode&&(this._focusNode.blur(),this.__restoreFocusNode.focus())},_renderOpened:function(){this._finishRenderOpened()},_renderClosed:function(){this._finishRenderClosed()},_finishRenderOpened:function(){this.notifyResize(),this.__isAnimating=!1,this.fire("iron-overlay-opened")},_finishRenderClosed:function(){this.style.display="none",this.style.zIndex="",this.notifyResize(),this.__isAnimating=!1,this.fire("iron-overlay-closed",this.closingReason)},_preparePositioning:function(){this.style.transition=this.style.webkitTransition="none",this.style.transform=this.style.webkitTransform="none",this.style.display=""},_finishPositioning:function(){this.style.display="none",this.scrollTop=this.scrollTop,this.style.transition=this.style.webkitTransition="",this.style.transform=this.style.webkitTransform="",this.style.display="",this.scrollTop=this.scrollTop},_applyFocus:function(){if(this.opened)this.noAutoFocus||this._focusNode.focus();else{if(this.restoreFocusOnClose&&this.__restoreFocusNode){var t=this._manager.deepActiveElement;(t===document.body||Aa(this,t))&&this.__restoreFocusNode.focus()}this.__restoreFocusNode=null,this._focusNode.blur(),this._focusedChild=null}},_onCaptureClick:function(t){this.noCancelOnOutsideClick||this.cancel(t)},_onCaptureFocus:function(t){if(this.withBackdrop){var e=Yi(t).path;-1===e.indexOf(this)?(t.stopPropagation(),this._applyFocus()):this._focusedChild=e[0]}},_onCaptureEsc:function(t){this.noCancelOnEscKey||this.cancel(t)},_onCaptureTab:function(t){if(this.withBackdrop){this.__ensureFirstLastFocusables();var e=t.shiftKey,n=e?this.__firstFocusableNode:this.__lastFocusableNode,i=e?this.__lastFocusableNode:this.__firstFocusableNode,r=!1;if(n===i)r=!0;else{var o=this._manager.deepActiveElement;r=o===n||o===this}r&&(t.preventDefault(),this._focusedChild=i,this._applyFocus())}},_onIronResize:function(){this.opened&&!this.__isAnimating&&this.__deraf("refit",this.refit)},_onNodesChange:function(){this.opened&&!this.__isAnimating&&(this.invalidateTabbables(),this.notifyResize())},__ensureFirstLastFocusables:function(){var t=this._focusableNodes;this.__firstFocusableNode=t[0],this.__lastFocusableNode=t[t.length-1]},__openedChanged:function(){this.opened?(this._prepareRenderOpened(),this._manager.addOverlay(this),this._applyFocus(),this._renderOpened()):(this._manager.removeOverlay(this),this._applyFocus(),this._renderClosed())},__deraf:function(t,e){var n=this.__rafs;null!==n[t]&&cancelAnimationFrame(n[t]),n[t]=requestAnimationFrame(function i(){n[t]=null,e.call(this)}.bind(this))},__updateScrollObservers:function(t,e,n){t&&e&&this.__isValidScrollAction(n)?("lock"===n&&(this.__saveScrollPosition(),Ma(this)),this.__addScrollListeners()):((function i(t){var e=Ea.indexOf(t);-1!==e&&(Ea.splice(e,1),va=Ea[Ea.length-1],0===Ea.length&&(function n(){for(var t=0,e=Sa.length;t<e;t++)document.removeEventListener(Sa[t],ya,{capture:!0,passive:!1})})())})(this),this.__removeScrollListeners())},__addScrollListeners:function(){if(!this.__rootNodes){if(this.__rootNodes=[],m)for(var t=this;t;)t.nodeType===Node.DOCUMENT_FRAGMENT_NODE&&t.host&&this.__rootNodes.push(t),t=t.host||t.assignedSlot||t.parentNode;this.__rootNodes.push(document)}this.__rootNodes.forEach((function(t){t.addEventListener("scroll",this.__onCaptureScroll,{capture:!0,passive:!0})}),this)},__removeScrollListeners:function(){this.__rootNodes&&this.__rootNodes.forEach((function(t){t.removeEventListener("scroll",this.__onCaptureScroll,{capture:!0,passive:!0})}),this),this.isAttached||(this.__rootNodes=null)},__isValidScrollAction:function(t){return"lock"===t||"refit"===t||"cancel"===t},__onCaptureScroll:function(t){if(!(this.__isAnimating||Yi(t).path.indexOf(this)>=0))switch(this.scrollAction){case"lock":this.__restoreScrollPosition();break;case"refit":this.__deraf("refit",this.refit);break;case"cancel":this.cancel(t)}},__saveScrollPosition:function(){document.scrollingElement?(this.__scrollTop=document.scrollingElement.scrollTop,this.__scrollLeft=document.scrollingElement.scrollLeft):(this.__scrollTop=Math.max(document.documentElement.scrollTop,document.body.scrollTop),this.__scrollLeft=Math.max(document.documentElement.scrollLeft,document.body.scrollLeft))},__restoreScrollPosition:function(){document.scrollingElement?(document.scrollingElement.scrollTop=this.__scrollTop,document.scrollingElement.scrollLeft=this.__scrollLeft):(document.documentElement.scrollTop=document.body.scrollTop=this.__scrollTop,document.documentElement.scrollLeft=document.body.scrollLeft=this.__scrollLeft)}},Aa=(t,e)=>{for(let i=e;i;i=(n=i).assignedSlot||n.parentNode||n.host)if(i===t)return!0;var n;return!1},ka=[pa,go,Ca],La={hostAttributes:{role:"dialog",tabindex:"-1"},properties:{modal:{type:Boolean,value:!1},__readied:{type:Boolean,value:!1}},observers:["_modalChanged(modal, __readied)"],listeners:{tap:"_onDialogClick"},ready:function(){this.__prevNoCancelOnOutsideClick=this.noCancelOnOutsideClick,this.__prevNoCancelOnEscKey=this.noCancelOnEscKey,this.__prevWithBackdrop=this.withBackdrop,this.__readied=!0},_modalChanged:function(t,e){e&&(t?(this.__prevNoCancelOnOutsideClick=this.noCancelOnOutsideClick,this.__prevNoCancelOnEscKey=this.noCancelOnEscKey,this.__prevWithBackdrop=this.withBackdrop,this.noCancelOnOutsideClick=!0,this.noCancelOnEscKey=!0,this.withBackdrop=!0):(this.noCancelOnOutsideClick=this.noCancelOnOutsideClick&&this.__prevNoCancelOnOutsideClick,this.noCancelOnEscKey=this.noCancelOnEscKey&&this.__prevNoCancelOnEscKey,this.withBackdrop=this.withBackdrop&&this.__prevWithBackdrop))},_updateClosingReasonConfirmed:function(t){this.closingReason=this.closingReason||{},this.closingReason.confirmed=t},_onDialogClick:function(t){for(var e=Yi(t).path,n=0,i=e.indexOf(this);n<i;n++){var r=e[n];if(r.hasAttribute&&(r.hasAttribute("dialog-dismiss")||r.hasAttribute("dialog-confirm"))){this._updateClosingReasonConfirmed(r.hasAttribute("dialog-confirm")),this.close(),t.stopPropagation();break}}}},Pa=[ka,La];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
if(
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>

      :host {
        display: block;
        @apply --layout-relative;
      }

      :host(.is-scrolled:not(:first-child))::before {
        content: '';
        position: absolute;
        top: 0;
        left: 0;
        right: 0;
        height: 1px;
        background: var(--divider-color);
      }

      :host(.can-scroll:not(.scrolled-to-bottom):not(:last-child))::after {
        content: '';
        position: absolute;
        bottom: 0;
        left: 0;
        right: 0;
        height: 1px;
        background: var(--divider-color);
      }

      .scrollable {
        padding: 0 24px;

        @apply --layout-scroll;
        @apply --paper-dialog-scrollable;
      }

      .fit {
        @apply --layout-fit;
      }
    </style>

    <div id="scrollable" class="scrollable" on-scroll="updateScrollState">
      <slot></slot>
    </div>
`,is:"paper-dialog-scrollable",properties:{dialogElement:{type:Object}},get scrollTarget(){return this.$.scrollable},ready:function(){this._ensureTarget(),this.classList.add("no-padding")},attached:function(){this._ensureTarget(),requestAnimationFrame(this.updateScrollState.bind(this))},updateScrollState:function(){this.toggleClass("is-scrolled",this.scrollTarget.scrollTop>0),this.toggleClass("can-scroll",this.scrollTarget.offsetHeight<this.scrollTarget.scrollHeight),this.toggleClass("scrolled-to-bottom",this.scrollTarget.scrollTop+this.scrollTarget.offsetHeight>=this.scrollTarget.scrollHeight)},_ensureTarget:function(){this.dialogElement=this.dialogElement||this.parentElement,this.dialogElement&&this.dialogElement.behaviors&&this.dialogElement.behaviors.indexOf(La)>=0?(this.dialogElement.sizingTarget=this.scrollTarget,this.scrollTarget.classList.remove("fit")):this.dialogElement&&this.scrollTarget.classList.add("fit")}}),!window.polymerSkipLoadingFontRoboto){const t=document.createElement("link");t.rel="stylesheet",t.type="text/css",t.crossOrigin="anonymous",t.href="https://fonts.googleapis.com/css?family=Roboto+Mono:400,700|Roboto:400,300,300italic,400italic,500,500italic,700,700italic",document.head.appendChild(t)}
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */const Na=_e`<custom-style>
  <style is="custom-style">
    html {

      /* Shared Styles */
      --paper-font-common-base: {
        font-family: 'Roboto', 'Noto', sans-serif;
        -webkit-font-smoothing: antialiased;
      };

      --paper-font-common-code: {
        font-family: 'Roboto Mono', 'Consolas', 'Menlo', monospace;
        -webkit-font-smoothing: antialiased;
      };

      --paper-font-common-expensive-kerning: {
        text-rendering: optimizeLegibility;
      };

      --paper-font-common-nowrap: {
        white-space: nowrap;
        overflow: hidden;
        text-overflow: ellipsis;
      };

      /* Material Font Styles */

      --paper-font-display4: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 112px;
        font-weight: 300;
        letter-spacing: -.044em;
        line-height: 120px;
      };

      --paper-font-display3: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 56px;
        font-weight: 400;
        letter-spacing: -.026em;
        line-height: 60px;
      };

      --paper-font-display2: {
        @apply --paper-font-common-base;

        font-size: 45px;
        font-weight: 400;
        letter-spacing: -.018em;
        line-height: 48px;
      };

      --paper-font-display1: {
        @apply --paper-font-common-base;

        font-size: 34px;
        font-weight: 400;
        letter-spacing: -.01em;
        line-height: 40px;
      };

      --paper-font-headline: {
        @apply --paper-font-common-base;

        font-size: 24px;
        font-weight: 400;
        letter-spacing: -.012em;
        line-height: 32px;
      };

      --paper-font-title: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 20px;
        font-weight: 500;
        line-height: 28px;
      };

      --paper-font-subhead: {
        @apply --paper-font-common-base;

        font-size: 16px;
        font-weight: 400;
        line-height: 24px;
      };

      --paper-font-body2: {
        @apply --paper-font-common-base;

        font-size: 14px;
        font-weight: 500;
        line-height: 24px;
      };

      --paper-font-body1: {
        @apply --paper-font-common-base;

        font-size: 14px;
        font-weight: 400;
        line-height: 20px;
      };

      --paper-font-caption: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 12px;
        font-weight: 400;
        letter-spacing: 0.011em;
        line-height: 20px;
      };

      --paper-font-menu: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 13px;
        font-weight: 500;
        line-height: 24px;
      };

      --paper-font-button: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 14px;
        font-weight: 500;
        letter-spacing: 0.018em;
        line-height: 24px;
        text-transform: uppercase;
      };

      --paper-font-code2: {
        @apply --paper-font-common-code;

        font-size: 14px;
        font-weight: 700;
        line-height: 20px;
      };

      --paper-font-code1: {
        @apply --paper-font-common-code;

        font-size: 14px;
        font-weight: 500;
        line-height: 20px;
      };

    }

  </style>
</custom-style>`;Na.setAttribute("style","display: none;"),document.head.appendChild(Na.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ia=document.createElement("template");Ia.setAttribute("style","display: none;"),Ia.innerHTML='<dom-module id="paper-dialog-shared-styles">\n  <template>\n    <style>\n      :host {\n        display: block;\n        margin: 24px 40px;\n\n        background: var(--paper-dialog-background-color, var(--primary-background-color));\n        color: var(--paper-dialog-color, var(--primary-text-color));\n\n        @apply --paper-font-body1;\n        @apply --shadow-elevation-16dp;\n        @apply --paper-dialog;\n      }\n\n      :host > ::slotted(*) {\n        margin-top: 20px;\n        padding: 0 24px;\n      }\n\n      :host > ::slotted(.no-padding) {\n        padding: 0;\n      }\n\n      \n      :host > ::slotted(*:first-child) {\n        margin-top: 24px;\n      }\n\n      :host > ::slotted(*:last-child) {\n        margin-bottom: 24px;\n      }\n\n      /* In 1.x, this selector was `:host > ::content h2`. In 2.x <slot> allows\n      to select direct children only, which increases the weight of this\n      selector, so we have to re-define first-child/last-child margins below. */\n      :host > ::slotted(h2) {\n        position: relative;\n        margin: 0;\n\n        @apply --paper-font-title;\n        @apply --paper-dialog-title;\n      }\n\n      /* Apply mixin again, in case it sets margin-top. */\n      :host > ::slotted(h2:first-child) {\n        margin-top: 24px;\n        @apply --paper-dialog-title;\n      }\n\n      /* Apply mixin again, in case it sets margin-bottom. */\n      :host > ::slotted(h2:last-child) {\n        margin-bottom: 24px;\n        @apply --paper-dialog-title;\n      }\n\n      :host > ::slotted(.paper-dialog-buttons),\n      :host > ::slotted(.buttons) {\n        position: relative;\n        padding: 8px 8px 8px 24px;\n        margin: 0;\n\n        color: var(--paper-dialog-button-color, var(--primary-color));\n\n        @apply --layout-horizontal;\n        @apply --layout-end-justified;\n      }\n    </style>\n  </template>\n</dom-module>',document.head.appendChild(Ia.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ra=[{properties:{animationConfig:{type:Object},entryAnimation:{observer:"_entryAnimationChanged",type:String},exitAnimation:{observer:"_exitAnimationChanged",type:String}},_entryAnimationChanged:function(){this.animationConfig=this.animationConfig||{},this.animationConfig.entry=[{name:this.entryAnimation,node:this}]},_exitAnimationChanged:function(){this.animationConfig=this.animationConfig||{},this.animationConfig.exit=[{name:this.exitAnimation,node:this}]},_copyProperties:function(t,e){for(var n in e)t[n]=e[n]},_cloneConfig:function(t){var e={isClone:!0};return this._copyProperties(e,t),e},_getAnimationConfigRecursive:function(t,e,n){var i;if(this.animationConfig)if(this.animationConfig.value&&"function"==typeof this.animationConfig.value)this._warn(this._logf("playAnimation","Please put 'animationConfig' inside of your components 'properties' object instead of outside of it."));else if(i=t?this.animationConfig[t]:this.animationConfig,Array.isArray(i)||(i=[i]),i)for(var r,o=0;r=i[o];o++)if(r.animatable)r.animatable._getAnimationConfigRecursive(r.type||t,e,n);else if(r.id){var a=e[r.id];a?(a.isClone||(e[r.id]=this._cloneConfig(a),a=e[r.id]),this._copyProperties(a,r)):e[r.id]=r}else n.push(r)},getAnimationConfig:function(t){var e={},n=[];for(var i in this._getAnimationConfigRecursive(t,e,n),e)n.push(e[i]);return n}},{_configureAnimations:function(t){var e=[],n=[];if(t.length>0)for(let e,i=0;e=t[i];i++){let t=document.createElement(e.name);if(t.isNeonAnimation){let i=null;t.configure||(t.configure=function(t){return null}),i=t.configure(e),n.push({result:i,config:e,neonAnimation:t})}else console.warn(this.is+":",e.name,"not found!")}for(var i=0;i<n.length;i++){let t=n[i].result,r=n[i].config,o=n[i].neonAnimation;try{"function"!=typeof t.cancel&&(t=document.timeline.play(t))}catch(e){t=null,console.warn("Couldnt play","(",r.name,").",e)}t&&e.push({neonAnimation:o,config:r,animation:t})}return e},_shouldComplete:function(t){for(var e=!0,n=0;n<t.length;n++)if("finished"!=t[n].animation.playState){e=!1;break}return e},_complete:function(t){for(var e=0;e<t.length;e++)t[e].neonAnimation.complete(t[e].config);for(e=0;e<t.length;e++)t[e].animation.cancel()},playAnimation:function(t,e){var n=this.getAnimationConfig(t);if(n){this._active=this._active||{},this._active[t]&&(this._complete(this._active[t]),delete this._active[t]);var i=this._configureAnimations(n);if(0!=i.length){this._active[t]=i;for(var r=0;r<i.length;r++)i[r].animation.onfinish=function(){this._shouldComplete(i)&&(this._complete(i),delete this._active[t],this.fire("neon-animation-finish",e,{bubbles:!1}))}.bind(this)}else this.fire("neon-animation-finish",e,{bubbles:!1})}},cancelAnimation:function(){for(var t in this._active){var e=this._active[t];for(var n in e)e[n].animation.cancel()}this._active={}}}];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style include="paper-dialog-shared-styles"></style>
    <slot></slot>
`,is:"paper-dialog",behaviors:[Pa,Ra],listeners:{"neon-animation-finish":"_onNeonAnimationFinish"},_renderOpened:function(){this.cancelAnimation(),this.playAnimation("entry")},_renderClosed:function(){this.cancelAnimation(),this.playAnimation("exit")},_onNeonAnimationFinish:function(){this.opened?this._finishRenderOpened():this._finishRenderClosed()}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Oa=Rr({_template:_e`
    <style>
      :host {
        display: inline-block;
        position: fixed;
        clip: rect(0px,0px,0px,0px);
      }
    </style>
    <div aria-live$="[[mode]]">[[_text]]</div>
`,is:"iron-a11y-announcer",properties:{mode:{type:String,value:"polite"},timeout:{type:Number,value:150},_text:{type:String,value:""}},created:function(){Oa.instance||(Oa.instance=this),document.addEventListener("iron-announce",this._onIronAnnounce.bind(this))},announce:function(t){this._text="",this.async((function(){this._text=t}),this.timeout)},_onIronAnnounce:function(t){t.detail&&t.detail.text&&this.announce(t.detail.text)}});Oa.instance=null,Oa.requestAvailability=function(){Oa.instance||(Oa.instance=document.createElement("iron-a11y-announcer")),document.body?document.body.appendChild(Oa.instance):document.addEventListener("load",(function(){document.body.appendChild(Oa.instance)}))},
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        display: inline-block;
      }
    </style>
    <slot id="content"></slot>
`,is:"iron-input",behaviors:[aa],properties:{bindValue:{type:String,value:""},value:{type:String,computed:"_computeValue(bindValue)"},allowedPattern:{type:String},autoValidate:{type:Boolean,value:!1},_inputElement:Object},observers:["_bindValueChanged(bindValue, _inputElement)"],listeners:{input:"_onInput",keypress:"_onKeypress"},created:function(){Oa.requestAvailability(),this._previousValidInput="",this._patternAlreadyChecked=!1},attached:function(){this._observer=Yi(this).observeNodes(function(t){this._initSlottedInput()}.bind(this))},detached:function(){this._observer&&(Yi(this).unobserveNodes(this._observer),this._observer=null)},get inputElement(){return this._inputElement},_initSlottedInput:function(){this._inputElement=this.getEffectiveChildren()[0],this.inputElement&&this.inputElement.value&&(this.bindValue=this.inputElement.value),this.fire("iron-input-ready")},get _patternRegExp(){var t;if(this.allowedPattern)t=new RegExp(this.allowedPattern);else switch(this.inputElement.type){case"number":t=/[0-9.,e-]/}return t},_bindValueChanged:function(t,e){e&&(void 0===t?e.value=null:t!==e.value&&(this.inputElement.value=t),this.autoValidate&&this.validate(),this.fire("bind-value-changed",{value:t}))},_onInput:function(){this.allowedPattern&&!this._patternAlreadyChecked&&(this._checkPatternValidity()||(this._announceInvalidCharacter("Invalid string of characters not entered."),this.inputElement.value=this._previousValidInput)),this.bindValue=this._previousValidInput=this.inputElement.value,this._patternAlreadyChecked=!1},_isPrintable:function(t){return!(8==t.keyCode||9==t.keyCode||13==t.keyCode||27==t.keyCode||0==t.charCode&&(19==t.keyCode||20==t.keyCode||45==t.keyCode||46==t.keyCode||144==t.keyCode||145==t.keyCode||t.keyCode>32&&t.keyCode<41||t.keyCode>111&&t.keyCode<124))},_onKeypress:function(t){if(this.allowedPattern||"number"===this.inputElement.type){var e=this._patternRegExp;if(e&&!(t.metaKey||t.ctrlKey||t.altKey)){this._patternAlreadyChecked=!0;var n=String.fromCharCode(t.charCode);this._isPrintable(t)&&!e.test(n)&&(t.preventDefault(),this._announceInvalidCharacter("Invalid character "+n+" not entered."))}}},_checkPatternValidity:function(){var t=this._patternRegExp;if(!t)return!0;for(var e=0;e<this.inputElement.value.length;e++)if(!t.test(this.inputElement.value[e]))return!1;return!0},validate:function(){if(!this.inputElement)return this.invalid=!1,!0;var t=this.inputElement.checkValidity();return t&&(this.required&&""===this.bindValue?t=!1:this.hasValidator()&&(t=aa.validate.call(this,this.bindValue))),this.invalid=!t,this.fire("iron-input-validate"),t},_announceInvalidCharacter:function(t){this.fire("iron-announce",{text:t})},_computeValue:function(t){return t}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const za={attached:function(){this.fire("addon-attached")},update:function(t){}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */Rr({_template:_e`
    <style>
      :host {
        display: inline-block;
        float: right;

        @apply --paper-font-caption;
        @apply --paper-input-char-counter;
      }

      :host([hidden]) {
        display: none !important;
      }

      :host(:dir(rtl)) {
        float: left;
      }
    </style>

    <span>[[_charCounterStr]]</span>
`,is:"paper-input-char-counter",behaviors:[za],properties:{_charCounterStr:{type:String,value:"0"}},update:function(t){if(t.inputElement){t.value=t.value||"";var e=t.value.toString().length.toString();t.inputElement.hasAttribute("maxlength")&&(e+="/"+t.inputElement.getAttribute("maxlength")),this._charCounterStr=e}}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Da=_e`
<custom-style>
  <style is="custom-style">
    html {
      --paper-input-container-shared-input-style: {
        position: relative; /* to make a stacking context */
        outline: none;
        box-shadow: none;
        padding: 0;
        margin: 0;
        width: 100%;
        max-width: 100%;
        background: transparent;
        border: none;
        color: var(--paper-input-container-input-color, var(--primary-text-color));
        -webkit-appearance: none;
        text-align: inherit;
        vertical-align: var(--paper-input-container-input-align, bottom);

        @apply --paper-font-subhead;
      };
    }
  </style>
</custom-style>
`;Da.setAttribute("style","display: none;"),document.head.appendChild(Da.content),Rr({_template:_e`
    <style>
      :host {
        display: block;
        padding: 8px 0;
        @apply --paper-input-container;
      }

      :host([inline]) {
        display: inline-block;
      }

      :host([disabled]) {
        pointer-events: none;
        opacity: 0.33;

        @apply --paper-input-container-disabled;
      }

      :host([hidden]) {
        display: none !important;
      }

      [hidden] {
        display: none !important;
      }

      .floated-label-placeholder {
        @apply --paper-font-caption;
      }

      .underline {
        height: 2px;
        position: relative;
      }

      .focused-line {
        @apply --layout-fit;
        border-bottom: 2px solid var(--paper-input-container-focus-color, var(--primary-color));

        -webkit-transform-origin: center center;
        transform-origin: center center;
        -webkit-transform: scale3d(0,1,1);
        transform: scale3d(0,1,1);

        @apply --paper-input-container-underline-focus;
      }

      .underline.is-highlighted .focused-line {
        -webkit-transform: none;
        transform: none;
        -webkit-transition: -webkit-transform 0.25s;
        transition: transform 0.25s;

        @apply --paper-transition-easing;
      }

      .underline.is-invalid .focused-line {
        border-color: var(--paper-input-container-invalid-color, var(--error-color));
        -webkit-transform: none;
        transform: none;
        -webkit-transition: -webkit-transform 0.25s;
        transition: transform 0.25s;

        @apply --paper-transition-easing;
      }

      .unfocused-line {
        @apply --layout-fit;
        border-bottom: 1px solid var(--paper-input-container-color, var(--secondary-text-color));
        @apply --paper-input-container-underline;
      }

      :host([disabled]) .unfocused-line {
        border-bottom: 1px dashed;
        border-color: var(--paper-input-container-color, var(--secondary-text-color));
        @apply --paper-input-container-underline-disabled;
      }

      .input-wrapper {
        @apply --layout-horizontal;
        @apply --layout-center;
        position: relative;
      }

      .input-content {
        @apply --layout-flex-auto;
        @apply --layout-relative;
        max-width: 100%;
      }

      .input-content ::slotted(label),
      .input-content ::slotted(.paper-input-label) {
        position: absolute;
        top: 0;
        left: 0;
        width: 100%;
        font: inherit;
        color: var(--paper-input-container-color, var(--secondary-text-color));
        -webkit-transition: -webkit-transform 0.25s, width 0.25s;
        transition: transform 0.25s, width 0.25s;
        -webkit-transform-origin: left top;
        transform-origin: left top;
        /* Fix for safari not focusing 0-height date/time inputs with -webkit-apperance: none; */
        min-height: 1px;

        @apply --paper-font-common-nowrap;
        @apply --paper-font-subhead;
        @apply --paper-input-container-label;
        @apply --paper-transition-easing;
      }


      .input-content ::slotted(label):before,
      .input-content ::slotted(.paper-input-label):before {
        @apply --paper-input-container-label-before;
      }

      .input-content ::slotted(label):after,
      .input-content ::slotted(.paper-input-label):after {
        @apply --paper-input-container-label-after;
      }

      .input-content.label-is-floating ::slotted(label),
      .input-content.label-is-floating ::slotted(.paper-input-label) {
        -webkit-transform: translateY(-75%) scale(0.75);
        transform: translateY(-75%) scale(0.75);

        /* Since we scale to 75/100 of the size, we actually have 100/75 of the
        original space now available */
        width: 133%;

        @apply --paper-input-container-label-floating;
      }

      :host(:dir(rtl)) .input-content.label-is-floating ::slotted(label),
      :host(:dir(rtl)) .input-content.label-is-floating ::slotted(.paper-input-label) {
        right: 0;
        left: auto;
        -webkit-transform-origin: right top;
        transform-origin: right top;
      }

      .input-content.label-is-highlighted ::slotted(label),
      .input-content.label-is-highlighted ::slotted(.paper-input-label) {
        color: var(--paper-input-container-focus-color, var(--primary-color));

        @apply --paper-input-container-label-focus;
      }

      .input-content.is-invalid ::slotted(label),
      .input-content.is-invalid ::slotted(.paper-input-label) {
        color: var(--paper-input-container-invalid-color, var(--error-color));
      }

      .input-content.label-is-hidden ::slotted(label),
      .input-content.label-is-hidden ::slotted(.paper-input-label) {
        visibility: hidden;
      }

      .input-content ::slotted(input),
      .input-content ::slotted(iron-input),
      .input-content ::slotted(textarea),
      .input-content ::slotted(iron-autogrow-textarea),
      .input-content ::slotted(.paper-input-input) {
        @apply --paper-input-container-shared-input-style;
        /* The apply shim doesn't apply the nested color custom property,
          so we have to re-apply it here. */
        color: var(--paper-input-container-input-color, var(--primary-text-color));
        @apply --paper-input-container-input;
      }

      .input-content ::slotted(input)::-webkit-outer-spin-button,
      .input-content ::slotted(input)::-webkit-inner-spin-button {
        @apply --paper-input-container-input-webkit-spinner;
      }

      .input-content.focused ::slotted(input),
      .input-content.focused ::slotted(iron-input),
      .input-content.focused ::slotted(textarea),
      .input-content.focused ::slotted(iron-autogrow-textarea),
      .input-content.focused ::slotted(.paper-input-input) {
        @apply --paper-input-container-input-focus;
      }

      .input-content.is-invalid ::slotted(input),
      .input-content.is-invalid ::slotted(iron-input),
      .input-content.is-invalid ::slotted(textarea),
      .input-content.is-invalid ::slotted(iron-autogrow-textarea),
      .input-content.is-invalid ::slotted(.paper-input-input) {
        @apply --paper-input-container-input-invalid;
      }

      .prefix ::slotted(*) {
        display: inline-block;
        @apply --paper-font-subhead;
        @apply --layout-flex-none;
        @apply --paper-input-prefix;
      }

      .suffix ::slotted(*) {
        display: inline-block;
        @apply --paper-font-subhead;
        @apply --layout-flex-none;

        @apply --paper-input-suffix;
      }

      /* Firefox sets a min-width on the input, which can cause layout issues */
      .input-content ::slotted(input) {
        min-width: 0;
      }

      .input-content ::slotted(textarea) {
        resize: none;
      }

      .add-on-content {
        position: relative;
      }

      .add-on-content.is-invalid ::slotted(*) {
        color: var(--paper-input-container-invalid-color, var(--error-color));
      }

      .add-on-content.is-highlighted ::slotted(*) {
        color: var(--paper-input-container-focus-color, var(--primary-color));
      }
    </style>

    <div class="floated-label-placeholder" aria-hidden="true" hidden="[[noLabelFloat]]">&nbsp;</div>

    <div class="input-wrapper">
      <span class="prefix"><slot name="prefix"></slot></span>

      <div class$="[[_computeInputContentClass(noLabelFloat,alwaysFloatLabel,focused,invalid,_inputHasContent)]]" id="labelAndInputContainer">
        <slot name="label"></slot>
        <slot name="input"></slot>
      </div>

      <span class="suffix"><slot name="suffix"></slot></span>
    </div>

    <div class$="[[_computeUnderlineClass(focused,invalid)]]">
      <div class="unfocused-line"></div>
      <div class="focused-line"></div>
    </div>

    <div class$="[[_computeAddOnContentClass(focused,invalid)]]">
      <slot name="add-on"></slot>
    </div>
`,is:"paper-input-container",properties:{noLabelFloat:{type:Boolean,value:!1},alwaysFloatLabel:{type:Boolean,value:!1},attrForValue:{type:String,value:"bind-value"},autoValidate:{type:Boolean,value:!1},invalid:{observer:"_invalidChanged",type:Boolean,value:!1},focused:{readOnly:!0,type:Boolean,value:!1,notify:!0},_addons:{type:Array},_inputHasContent:{type:Boolean,value:!1},_inputSelector:{type:String,value:"input,iron-input,textarea,.paper-input-input"},_boundOnFocus:{type:Function,value:function(){return this._onFocus.bind(this)}},_boundOnBlur:{type:Function,value:function(){return this._onBlur.bind(this)}},_boundOnInput:{type:Function,value:function(){return this._onInput.bind(this)}},_boundValueChanged:{type:Function,value:function(){return this._onValueChanged.bind(this)}}},listeners:{"addon-attached":"_onAddonAttached","iron-input-validate":"_onIronInputValidate"},get _valueChangedEvent(){return this.attrForValue+"-changed"},get _propertyForValue(){return st(this.attrForValue)},get _inputElement(){return Yi(this).querySelector(this._inputSelector)},get _inputElementValue(){return this._inputElement[this._propertyForValue]||this._inputElement.value},ready:function(){this.__isFirstValueUpdate=!0,this._addons||(this._addons=[]),this.addEventListener("focus",this._boundOnFocus,!0),this.addEventListener("blur",this._boundOnBlur,!0)},attached:function(){this.attrForValue?this._inputElement.addEventListener(this._valueChangedEvent,this._boundValueChanged):this.addEventListener("input",this._onInput),this._inputElementValue&&""!=this._inputElementValue?this._handleValueAndAutoValidate(this._inputElement):this._handleValue(this._inputElement)},_onAddonAttached:function(t){this._addons||(this._addons=[]);var e=t.target;-1===this._addons.indexOf(e)&&(this._addons.push(e),this.isAttached&&this._handleValue(this._inputElement))},_onFocus:function(){this._setFocused(!0)},_onBlur:function(){this._setFocused(!1),this._handleValueAndAutoValidate(this._inputElement)},_onInput:function(t){this._handleValueAndAutoValidate(t.target)},_onValueChanged:function(t){var e=t.target;this.__isFirstValueUpdate&&(this.__isFirstValueUpdate=!1,void 0===e.value||""===e.value)||this._handleValueAndAutoValidate(t.target)},_handleValue:function(t){var e=this._inputElementValue;this._inputHasContent=!(!e&&0!==e&&("number"!==t.type||t.checkValidity())),this.updateAddons({inputElement:t,value:e,invalid:this.invalid})},_handleValueAndAutoValidate:function(t){var e;this.autoValidate&&t&&(e=t.validate?t.validate(this._inputElementValue):t.checkValidity(),this.invalid=!e),this._handleValue(t)},_onIronInputValidate:function(t){this.invalid=this._inputElement.invalid},_invalidChanged:function(){this._addons&&this.updateAddons({invalid:this.invalid})},updateAddons:function(t){for(var e,n=0;e=this._addons[n];n++)e.update(t)},_computeInputContentClass:function(t,e,n,i,r){var o="input-content";if(t)r&&(o+=" label-is-hidden"),i&&(o+=" is-invalid");else{var a=this.querySelector("label");e||r?(o+=" label-is-floating",this.$.labelAndInputContainer.style.position="static",i?o+=" is-invalid":n&&(o+=" label-is-highlighted")):(a&&(this.$.labelAndInputContainer.style.position="relative"),i&&(o+=" is-invalid"))}return n&&(o+=" focused"),o},_computeUnderlineClass:function(t,e){var n="underline";return e?n+=" is-invalid":t&&(n+=" is-highlighted"),n},_computeAddOnContentClass:function(t,e){var n="add-on-content";return e?n+=" is-invalid":t&&(n+=" is-highlighted"),n}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        display: inline-block;
        visibility: hidden;

        color: var(--paper-input-container-invalid-color, var(--error-color));

        @apply --paper-font-caption;
        @apply --paper-input-error;
        position: absolute;
        left:0;
        right:0;
      }

      :host([invalid]) {
        visibility: visible;
      }

      #a11yWrapper {
        visibility: hidden;
      }

      :host([invalid]) #a11yWrapper {
        visibility: visible;
      }
    </style>

    <!--
    If the paper-input-error element is directly referenced by an
    \`aria-describedby\` attribute, such as when used as a paper-input add-on,
    then applying \`visibility: hidden;\` to the paper-input-error element itself
    does not hide the error.

    For more information, see:
    https://www.w3.org/TR/accname-1.1/#mapping_additional_nd_description
    -->
    <div id="a11yWrapper">
      <slot></slot>
    </div>
`,is:"paper-input-error",behaviors:[za],properties:{invalid:{readOnly:!0,reflectToAttribute:!0,type:Boolean}},update:function(t){this._setInvalid(t.invalid)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ba={NextLabelID:1,NextAddonID:1,NextInputID:1},Ha={properties:{label:{type:String},value:{notify:!0,type:String},disabled:{type:Boolean,value:!1},invalid:{type:Boolean,value:!1,notify:!0},allowedPattern:{type:String},type:{type:String},list:{type:String},pattern:{type:String},required:{type:Boolean,value:!1},errorMessage:{type:String},charCounter:{type:Boolean,value:!1},noLabelFloat:{type:Boolean,value:!1},alwaysFloatLabel:{type:Boolean,value:!1},autoValidate:{type:Boolean,value:!1},validator:{type:String},autocomplete:{type:String,value:"off"},autofocus:{type:Boolean,observer:"_autofocusChanged"},inputmode:{type:String},minlength:{type:Number},maxlength:{type:Number},min:{type:String},max:{type:String},step:{type:String},name:{type:String},placeholder:{type:String,value:""},readonly:{type:Boolean,value:!1},size:{type:Number},autocapitalize:{type:String,value:"none"},autocorrect:{type:String,value:"off"},autosave:{type:String},results:{type:Number},accept:{type:String},multiple:{type:Boolean},_ariaDescribedBy:{type:String,value:""},_ariaLabelledBy:{type:String,value:""},_inputId:{type:String,value:""}},listeners:{"addon-attached":"_onAddonAttached"},keyBindings:{"shift+tab:keydown":"_onShiftTabDown"},hostAttributes:{tabindex:0},get inputElement(){return this.$||(this.$={}),this.$.input||(this._generateInputId(),this.$.input=this.$$("#"+this._inputId)),this.$.input},get _focusableElement(){return this.inputElement},created:function(){this._typesThatHaveText=["date","datetime","datetime-local","month","time","week","file"]},attached:function(){this._updateAriaLabelledBy(),!ye&&this.inputElement&&-1!==this._typesThatHaveText.indexOf(this.inputElement.type)&&(this.alwaysFloatLabel=!0)},_appendStringWithSpace:function(t,e){return t?t+" "+e:e},_onAddonAttached:function(t){var e=Yi(t).rootTarget;if(e.id)this._ariaDescribedBy=this._appendStringWithSpace(this._ariaDescribedBy,e.id);else{var n="paper-input-add-on-"+Ba.NextAddonID++;e.id=n,this._ariaDescribedBy=this._appendStringWithSpace(this._ariaDescribedBy,n)}},validate:function(){return this.inputElement.validate()},_focusBlurHandler:function(t){qo._focusBlurHandler.call(this,t),this.focused&&!this._shiftTabPressed&&this._focusableElement&&this._focusableElement.focus()},_onShiftTabDown:function(t){var e=this.getAttribute("tabindex");this._shiftTabPressed=!0,this.setAttribute("tabindex","-1"),this.async((function(){this.setAttribute("tabindex",e),this._shiftTabPressed=!1}),1)},_handleAutoValidate:function(){this.autoValidate&&this.validate()},updateValueAndPreserveCaret:function(t){try{var e=this.inputElement.selectionStart;this.value=t,this.inputElement.selectionStart=e,this.inputElement.selectionEnd=e}catch(e){this.value=t}},_computeAlwaysFloatLabel:function(t,e){return e||t},_updateAriaLabelledBy:function(){var t,e=Yi(this.root).querySelector("label");e?(e.id?t=e.id:(t="paper-input-label-"+Ba.NextLabelID++,e.id=t),this._ariaLabelledBy=t):this._ariaLabelledBy=""},_generateInputId:function(){this._inputId&&""!==this._inputId||(this._inputId="input-"+Ba.NextInputID++)},_onChange:function(t){this.shadowRoot&&this.fire(t.type,{sourceEvent:t},{node:this,bubbles:t.bubbles,cancelable:t.cancelable})},_autofocusChanged:function(){if(this.autofocus&&this._focusableElement){var t=document.activeElement;t instanceof HTMLElement&&t!==document.body&&t!==document.documentElement||this._focusableElement.focus()}}},Fa=[qo,zo,Ha];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({is:"paper-input",_template:_e`
    <style>
      :host {
        display: block;
      }

      :host([focused]) {
        outline: none;
      }

      :host([hidden]) {
        display: none !important;
      }

      input {
        /* Firefox sets a min-width on the input, which can cause layout issues */
        min-width: 0;
      }

      /* In 1.x, the <input> is distributed to paper-input-container, which styles it.
      In 2.x the <iron-input> is distributed to paper-input-container, which styles
      it, but in order for this to work correctly, we need to reset some
      of the native input's properties to inherit (from the iron-input) */
      iron-input > input {
        @apply --paper-input-container-shared-input-style;
        font-family: inherit;
        font-weight: inherit;
        font-size: inherit;
        letter-spacing: inherit;
        word-spacing: inherit;
        line-height: inherit;
        text-shadow: inherit;
        color: inherit;
        cursor: inherit;
      }

      input:disabled {
        @apply --paper-input-container-input-disabled;
      }

      input::-webkit-outer-spin-button,
      input::-webkit-inner-spin-button {
        @apply --paper-input-container-input-webkit-spinner;
      }

      input::-webkit-clear-button {
        @apply --paper-input-container-input-webkit-clear;
      }

      input::-webkit-calendar-picker-indicator {
        @apply --paper-input-container-input-webkit-calendar-picker-indicator;
      }

      input::-webkit-input-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input:-moz-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input::-moz-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input::-ms-clear {
        @apply --paper-input-container-ms-clear;
      }

      input::-ms-reveal {
        @apply --paper-input-container-ms-reveal;
      }

      input:-ms-input-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      label {
        pointer-events: none;
      }
    </style>

    <paper-input-container id="container" no-label-float="[[noLabelFloat]]" always-float-label="[[_computeAlwaysFloatLabel(alwaysFloatLabel,placeholder)]]" auto-validate$="[[autoValidate]]" disabled$="[[disabled]]" invalid="[[invalid]]">

      <slot name="prefix" slot="prefix"></slot>

      <label hidden$="[[!label]]" aria-hidden="true" for$="[[_inputId]]" slot="label">[[label]]</label>

      <!-- Need to bind maxlength so that the paper-input-char-counter works correctly -->
      <iron-input bind-value="{{value}}" slot="input" class="input-element" id$="[[_inputId]]" maxlength$="[[maxlength]]" allowed-pattern="[[allowedPattern]]" invalid="{{invalid}}" validator="[[validator]]">
        <input aria-labelledby$="[[_ariaLabelledBy]]" aria-describedby$="[[_ariaDescribedBy]]" disabled$="[[disabled]]" title$="[[title]]" type$="[[type]]" pattern$="[[pattern]]" required$="[[required]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" inputmode$="[[inputmode]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]" min$="[[min]]" max$="[[max]]" step$="[[step]]" name$="[[name]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" list$="[[list]]" size$="[[size]]" autocapitalize$="[[autocapitalize]]" autocorrect$="[[autocorrect]]" on-change="_onChange" tabindex$="[[tabIndex]]" autosave$="[[autosave]]" results$="[[results]]" accept$="[[accept]]" multiple$="[[multiple]]" role$="[[inputRole]]" aria-haspopup$="[[inputAriaHaspopup]]">
      </iron-input>

      <slot name="suffix" slot="suffix"></slot>

      <template is="dom-if" if="[[errorMessage]]">
        <paper-input-error aria-live="assertive" slot="add-on">[[errorMessage]]</paper-input-error>
      </template>

      <template is="dom-if" if="[[charCounter]]">
        <paper-input-char-counter slot="add-on"></paper-input-char-counter>
      </template>

    </paper-input-container>
  `,behaviors:[Fa,ra],properties:{value:{type:String},inputRole:{type:String,value:void 0},inputAriaHaspopup:{type:String,value:void 0}},get _focusableElement(){return this.inputElement._inputElement},listeners:{"iron-input-ready":"_onIronInputReady"},_onIronInputReady:function(){this.$.nativeInput||(this.$.nativeInput=this.$$("input")),this.inputElement&&-1!==this._typesThatHaveText.indexOf(this.$.nativeInput.type)&&(this.alwaysFloatLabel=!0),this.inputElement.bindValue&&this.$.container._handleValueAndAutoValidate(this.inputElement)}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        position: fixed;
      }

      #contentWrapper ::slotted(*) {
        overflow: auto;
      }

      #contentWrapper.animating ::slotted(*) {
        overflow: hidden;
        pointer-events: none;
      }
    </style>

    <div id="contentWrapper">
      <slot id="content" name="dropdown-content"></slot>
    </div>
`,is:"iron-dropdown",behaviors:[qo,zo,ka,Ra],properties:{horizontalAlign:{type:String,value:"left",reflectToAttribute:!0},verticalAlign:{type:String,value:"top",reflectToAttribute:!0},openAnimationConfig:{type:Object},closeAnimationConfig:{type:Object},focusTarget:{type:Object},noAnimations:{type:Boolean,value:!1},allowOutsideScroll:{type:Boolean,value:!1,observer:"_allowOutsideScrollChanged"}},listeners:{"neon-animation-finish":"_onNeonAnimationFinish"},observers:["_updateOverlayPosition(positionTarget, verticalAlign, horizontalAlign, verticalOffset, horizontalOffset)"],get containedElement(){for(var t=Yi(this.$.content).getDistributedNodes(),e=0,n=t.length;e<n;e++)if(t[e].nodeType===Node.ELEMENT_NODE)return t[e]},ready:function(){this.scrollAction||(this.scrollAction=this.allowOutsideScroll?"refit":"lock"),this._readied=!0},attached:function(){this.sizingTarget&&this.sizingTarget!==this||(this.sizingTarget=this.containedElement||this)},detached:function(){this.cancelAnimation()},_openedChanged:function(){this.opened&&this.disabled?this.cancel():(this.cancelAnimation(),this._updateAnimationConfig(),Ca._openedChanged.apply(this,arguments))},_renderOpened:function(){!this.noAnimations&&this.animationConfig.open?(this.$.contentWrapper.classList.add("animating"),this.playAnimation("open")):Ca._renderOpened.apply(this,arguments)},_renderClosed:function(){!this.noAnimations&&this.animationConfig.close?(this.$.contentWrapper.classList.add("animating"),this.playAnimation("close")):Ca._renderClosed.apply(this,arguments)},_onNeonAnimationFinish:function(){this.$.contentWrapper.classList.remove("animating"),this.opened?this._finishRenderOpened():this._finishRenderClosed()},_updateAnimationConfig:function(){for(var t=this.containedElement,e=[].concat(this.openAnimationConfig||[]).concat(this.closeAnimationConfig||[]),n=0;n<e.length;n++)e[n].node=t;this.animationConfig={open:this.openAnimationConfig,close:this.closeAnimationConfig}},_updateOverlayPosition:function(){this.isAttached&&this.notifyResize()},_allowOutsideScrollChanged:function(t){this._readied&&(t?this.scrollAction&&"lock"!==this.scrollAction||(this.scrollAction="refit"):this.scrollAction="lock")},_applyFocus:function(){var t=this.focusTarget||this.containedElement;t&&this.opened&&!this.noAutoFocus?t.focus():Ca._applyFocus.apply(this,arguments)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Va={properties:{animationTiming:{type:Object,value:function(){return{duration:500,easing:"cubic-bezier(0.4, 0, 0.2, 1)",fill:"both"}}}},isNeonAnimation:!0,created:function(){document.body.animate||console.warn("No web animations detected. This element will not function without a web animations polyfill.")},timingFromConfig:function(t){if(t.timing)for(var e in t.timing)this.animationTiming[e]=t.timing[e];return this.animationTiming},setPrefixedProperty:function(t,e,n){for(var i,r={transform:["webkitTransform"],transformOrigin:["mozTransformOrigin","webkitTransformOrigin"]}[e],o=0;i=r[o];o++)t.style[i]=n;t.style[e]=n},complete:function(t){}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */Rr({is:"fade-in-animation",behaviors:[Va],configure:function(t){return this._effect=new KeyframeEffect(t.node,[{opacity:"0"},{opacity:"1"}],this.timingFromConfig(t)),this._effect}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({is:"fade-out-animation",behaviors:[Va],configure:function(t){return this._effect=new KeyframeEffect(t.node,[{opacity:"1"},{opacity:"0"}],this.timingFromConfig(t)),this._effect}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({is:"paper-menu-grow-height-animation",_template:null,behaviors:[Va],configure:function(t){var e=t.node,n=e.getBoundingClientRect().height;return this._effect=new KeyframeEffect(e,[{height:n/2+"px"},{height:n+"px"}],this.timingFromConfig(t)),this._effect}}),Rr({is:"paper-menu-grow-width-animation",_template:null,behaviors:[Va],configure:function(t){var e=t.node,n=e.getBoundingClientRect().width;return this._effect=new KeyframeEffect(e,[{width:n/2+"px"},{width:n+"px"}],this.timingFromConfig(t)),this._effect}}),Rr({is:"paper-menu-shrink-width-animation",_template:null,behaviors:[Va],configure:function(t){var e=t.node,n=e.getBoundingClientRect().width;return this._effect=new KeyframeEffect(e,[{width:n+"px"},{width:n-n/20+"px"}],this.timingFromConfig(t)),this._effect}}),Rr({is:"paper-menu-shrink-height-animation",_template:null,behaviors:[Va],configure:function(t){var e=t.node,n=e.getBoundingClientRect().height;return this.setPrefixedProperty(e,"transformOrigin","0 0"),this._effect=new KeyframeEffect(e,[{height:n+"px",transform:"translateY(0)"},{height:n/2+"px",transform:"translateY(-20px)"}],this.timingFromConfig(t)),this._effect}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var Ua={ANIMATION_CUBIC_BEZIER:"cubic-bezier(.3,.95,.5,1)",MAX_ANIMATION_TIME_MS:400};const ja=Rr({_template:_e`
    <style>
      :host {
        display: inline-block;
        position: relative;
        padding: 8px;
        outline: none;

        @apply --paper-menu-button;
      }

      :host([disabled]) {
        cursor: auto;
        color: var(--disabled-text-color);

        @apply --paper-menu-button-disabled;
      }

      iron-dropdown {
        @apply --paper-menu-button-dropdown;
      }

      .dropdown-content {
        @apply --shadow-elevation-2dp;

        position: relative;
        border-radius: 2px;
        background-color: var(--paper-menu-button-dropdown-background, var(--primary-background-color));

        @apply --paper-menu-button-content;
      }

      :host([vertical-align="top"]) .dropdown-content {
        margin-bottom: 20px;
        margin-top: -10px;
        top: 10px;
      }

      :host([vertical-align="bottom"]) .dropdown-content {
        bottom: 10px;
        margin-bottom: -10px;
        margin-top: 20px;
      }

      #trigger {
        cursor: pointer;
      }
    </style>

    <div id="trigger" on-tap="toggle">
      <slot name="dropdown-trigger"></slot>
    </div>

    <iron-dropdown id="dropdown" opened="{{opened}}" horizontal-align="[[horizontalAlign]]" vertical-align="[[verticalAlign]]" dynamic-align="[[dynamicAlign]]" horizontal-offset="[[horizontalOffset]]" vertical-offset="[[verticalOffset]]" no-overlap="[[noOverlap]]" open-animation-config="[[openAnimationConfig]]" close-animation-config="[[closeAnimationConfig]]" no-animations="[[noAnimations]]" focus-target="[[_dropdownContent]]" allow-outside-scroll="[[allowOutsideScroll]]" restore-focus-on-close="[[restoreFocusOnClose]]" on-iron-overlay-canceled="__onIronOverlayCanceled" expand-sizing-target-for-scrollbars="[[expandSizingTargetForScrollbars]]">
      <div slot="dropdown-content" class="dropdown-content">
        <slot id="content" name="dropdown-content"></slot>
      </div>
    </iron-dropdown>
`,is:"paper-menu-button",behaviors:[zo,qo],properties:{opened:{type:Boolean,value:!1,notify:!0,observer:"_openedChanged"},horizontalAlign:{type:String,value:"left",reflectToAttribute:!0},verticalAlign:{type:String,value:"top",reflectToAttribute:!0},dynamicAlign:{type:Boolean},horizontalOffset:{type:Number,value:0,notify:!0},verticalOffset:{type:Number,value:0,notify:!0},noOverlap:{type:Boolean},noAnimations:{type:Boolean,value:!1},ignoreSelect:{type:Boolean,value:!1},closeOnActivate:{type:Boolean,value:!1},openAnimationConfig:{type:Object,value:function(){return[{name:"fade-in-animation",timing:{delay:100,duration:200}},{name:"paper-menu-grow-width-animation",timing:{delay:100,duration:150,easing:Ua.ANIMATION_CUBIC_BEZIER}},{name:"paper-menu-grow-height-animation",timing:{delay:100,duration:275,easing:Ua.ANIMATION_CUBIC_BEZIER}}]}},closeAnimationConfig:{type:Object,value:function(){return[{name:"fade-out-animation",timing:{duration:150}},{name:"paper-menu-shrink-width-animation",timing:{delay:100,duration:50,easing:Ua.ANIMATION_CUBIC_BEZIER}},{name:"paper-menu-shrink-height-animation",timing:{duration:200,easing:"ease-in"}}]}},allowOutsideScroll:{type:Boolean,value:!1},restoreFocusOnClose:{type:Boolean,value:!0},expandSizingTargetForScrollbars:{type:Boolean,value:!1},_dropdownContent:{type:Object}},hostAttributes:{role:"group","aria-haspopup":"true"},listeners:{"iron-activate":"_onIronActivate","iron-select":"_onIronSelect"},get contentElement(){for(var t=Yi(this.$.content).getDistributedNodes(),e=0,n=t.length;e<n;e++)if(t[e].nodeType===Node.ELEMENT_NODE)return t[e]},toggle:function(){this.opened?this.close():this.open()},open:function(){this.disabled||this.$.dropdown.open()},close:function(){this.$.dropdown.close()},_onIronSelect:function(t){this.ignoreSelect||this.close()},_onIronActivate:function(t){this.closeOnActivate&&this.close()},_openedChanged:function(t,e){t?(this._dropdownContent=this.contentElement,this.fire("paper-dropdown-open")):null!=e&&this.fire("paper-dropdown-close")},_disabledChanged:function(t){qo._disabledChanged.apply(this,arguments),t&&this.opened&&this.close()},__onIronOverlayCanceled:function(t){var e=this.$.trigger;Yi(t.detail).path.indexOf(e)>-1&&t.preventDefault()}});Object.keys(Ua).forEach((function(t){ja[t]=Ua[t]}));
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ga=document.createElement("template");Ga.setAttribute("style","display: none;"),Ga.innerHTML='<iron-iconset-svg name="paper-dropdown-menu" size="24">\n<svg><defs>\n<g id="arrow-drop-down"><path d="M7 10l5 5 5-5z"></path></g>\n</defs></svg>\n</iron-iconset-svg>',document.head.appendChild(Ga.content);
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Wa=document.createElement("template");Wa.setAttribute("style","display: none;"),Wa.innerHTML='<dom-module id="paper-dropdown-menu-shared-styles">\n  <template>\n    <style>\n      :host {\n        display: inline-block;\n        position: relative;\n        text-align: left;\n\n        /* NOTE(cdata): Both values are needed, since some phones require the\n         * value to be `transparent`.\n         */\n        -webkit-tap-highlight-color: rgba(0,0,0,0);\n        -webkit-tap-highlight-color: transparent;\n\n        --paper-input-container-input: {\n          overflow: hidden;\n          white-space: nowrap;\n          text-overflow: ellipsis;\n          max-width: 100%;\n          box-sizing: border-box;\n          cursor: pointer;\n        };\n\n        @apply --paper-dropdown-menu;\n      }\n\n      /* paper-dropdown-menu and paper-dropdown-menu-light both delegate focus\n       * to other internal elements which manage focus styling. */\n      :host(:focus) {\n        outline: none;\n      }\n\n      :host(:dir(rtl)) {\n        text-align: right;\n\n        @apply(--paper-dropdown-menu);\n      }\n\n      :host([disabled]) {\n        @apply --paper-dropdown-menu-disabled;\n      }\n\n      :host([noink]) paper-ripple {\n        display: none;\n      }\n\n      :host([no-label-float]) paper-ripple {\n        top: 8px;\n      }\n\n      paper-ripple {\n        top: 12px;\n        left: 0px;\n        bottom: 8px;\n        right: 0px;\n\n        @apply --paper-dropdown-menu-ripple;\n      }\n\n      paper-menu-button {\n        display: block;\n        padding: 0;\n\n        @apply --paper-dropdown-menu-button;\n      }\n\n      paper-input {\n        @apply --paper-dropdown-menu-input;\n      }\n\n      iron-icon {\n        color: var(--disabled-text-color);\n\n        @apply --paper-dropdown-menu-icon;\n      }\n    </style>\n  </template>\n</dom-module>',document.head.appendChild(Wa.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const qa=er(HTMLElement);Rr({_template:_e`
    <style include="paper-dropdown-menu-shared-styles"></style>

    <paper-menu-button id="menuButton" vertical-align="[[verticalAlign]]" horizontal-align="[[horizontalAlign]]" dynamic-align="[[dynamicAlign]]" vertical-offset="[[_computeMenuVerticalOffset(noLabelFloat, verticalOffset)]]" disabled="[[disabled]]" no-animations="[[noAnimations]]" on-iron-select="_onIronSelect" on-iron-deselect="_onIronDeselect" opened="{{opened}}" close-on-activate allow-outside-scroll="[[allowOutsideScroll]]" restore-focus-on-close="[[restoreFocusOnClose]]" expand-sizing-target-for-scrollbars="[[expandSizingTargetForScrollbars]]">
      <!-- support hybrid mode: user might be using paper-menu-button 1.x which distributes via <content> -->
      <div class="dropdown-trigger" slot="dropdown-trigger">
        <paper-ripple></paper-ripple>
        <!-- paper-input has type="text" for a11y, do not remove -->
        <paper-input id="input" type="text" invalid="[[invalid]]" readonly disabled="[[disabled]]" value="[[value]]" placeholder="[[placeholder]]" error-message="[[errorMessage]]" always-float-label="[[alwaysFloatLabel]]" no-label-float="[[noLabelFloat]]" label="[[label]]" input-role="button" input-aria-haspopup="listbox" autocomplete="off">
          <!-- support hybrid mode: user might be using paper-input 1.x which distributes via <content> -->
          <iron-icon icon="paper-dropdown-menu:arrow-drop-down" suffix slot="suffix"></iron-icon>
        </paper-input>
      </div>
      <slot id="content" name="dropdown-content" slot="dropdown-content"></slot>
    </paper-menu-button>
`,is:"paper-dropdown-menu",behaviors:[Xo,qo,ra,aa],properties:{selectedItemLabel:{type:String,notify:!0,readOnly:!0},selectedItem:{type:Object,notify:!0,readOnly:!0},value:{type:String,notify:!0},label:{type:String},placeholder:{type:String},errorMessage:{type:String},opened:{type:Boolean,notify:!0,value:!1,observer:"_openedChanged"},allowOutsideScroll:{type:Boolean,value:!1},noLabelFloat:{type:Boolean,value:!1,reflectToAttribute:!0},alwaysFloatLabel:{type:Boolean,value:!1},noAnimations:{type:Boolean,value:!1},horizontalAlign:{type:String,value:"right"},verticalAlign:{type:String,value:"top"},verticalOffset:Number,dynamicAlign:{type:Boolean},restoreFocusOnClose:{type:Boolean,value:!0},expandSizingTargetForScrollbars:{type:Boolean,value:!1}},listeners:{tap:"_onTap"},keyBindings:{"up down":"open",esc:"close"},observers:["_selectedItemChanged(selectedItem)"],_attachDom(t){const e=Y(this);return e.attachShadow({mode:"open",delegatesFocus:!0,shadyUpgradeFragment:t}),e.shadowRoot.appendChild(t),qa.prototype._attachDom.call(this,t)},focus(){this.$.input._focusableElement.focus()},attached:function(){var t=this.contentElement;t&&t.selectedItem&&this._setSelectedItem(t.selectedItem)},get contentElement(){for(var t=Yi(this.$.content).getDistributedNodes(),e=0,n=t.length;e<n;e++)if(t[e].nodeType===Node.ELEMENT_NODE)return t[e]},open:function(){this.$.menuButton.open()},close:function(){this.$.menuButton.close()},_onIronSelect:function(t){this._setSelectedItem(t.detail.item)},_onIronDeselect:function(t){this._setSelectedItem(null)},_onTap:function(t){di(t)===this&&this.open()},_selectedItemChanged:function(t){var e;e=t?t.label||t.getAttribute("label")||t.textContent.trim():"",this.value=e,this._setSelectedItemLabel(e)},_computeMenuVerticalOffset:function(t,e){return e||(t?-4:8)},_getValidity:function(t){return this.disabled||!this.required||this.required&&!!this.value},_openedChanged:function(){var t=this.contentElement;t&&t.setAttribute("aria-expanded",this.opened?"true":"false")}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var Ya={outerScroll:{scroll:!0},shadowMode:{standard:2,waterfall:1,"waterfall-tall":1},tallMode:{"waterfall-tall":!0}};Rr({_template:_e`
    <style>
      :host {
        @apply --layout-vertical;
        position: relative;
        height: 100%;
        @apply --paper-header-panel;
      }

      #mainContainer {
        @apply --layout-flex;
        position: relative;
        overflow-y: auto;
        overflow-x: hidden;
        -webkit-overflow-scrolling: touch;
      }

      #mainPanel {
        @apply --layout-vertical;
        @apply --layout-flex;
        position: relative;
        min-height: 0;
        @apply --paper-header-panel-body;
      }

      #mainContainer {
        @apply --paper-header-panel-container;
      }

      /*
       * mode: scroll
       */
      :host([mode=scroll]) #mainContainer {
        @apply --paper-header-panel-scroll-container;
        overflow: visible;
      }

      :host([mode=scroll]) {
        overflow-y: auto;
        overflow-x: hidden;
        -webkit-overflow-scrolling: touch;
      }

      /*
       * mode: cover
       */
      :host([mode=cover]) #mainContainer {
        @apply --paper-header-panel-cover-container;
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      }

      :host([mode=cover]) #mainPanel {
        position: static;
      }

      /*
       * mode: standard
       */
      :host([mode=standard]) #mainContainer {
        @apply --paper-header-panel-standard-container;
      }

      /*
       * mode: seamed
       */
      :host([mode=seamed]) #mainContainer {
        @apply --paper-header-panel-seamed-container;
      }


      /*
       * mode: waterfall
       */
      :host([mode=waterfall]) #mainContainer {
        @apply --paper-header-panel-waterfall-container;
      }

      /*
       * mode: waterfall-tall
       */
      :host([mode=waterfall-tall]) #mainContainer {
        @apply --paper-header-panel-waterfall-tall-container;
      }

      #dropShadow {
        transition: opacity 0.5s;
        height: 6px;
        box-shadow: inset 0px 5px 6px -3px rgba(0, 0, 0, 0.4);
        @apply --paper-header-panel-shadow;
        position: absolute;
        top: 0;
        left: 0;
        right: 0;
        opacity: 0;
        pointer-events: none;
      }

      #dropShadow.has-shadow {
        opacity: 1;
      }

      #mainContainer > ::slotted(.fit) {
        @apply --layout-fit;
      }

    </style>

    <slot id="headerSlot" name="header"></slot>

    <div id="mainPanel">
      <div id="mainContainer" class\$="[[_computeMainContainerClass(mode)]]">
        <slot></slot>
      </div>
      <div id="dropShadow"></div>
    </div>
`,is:"paper-header-panel",properties:{mode:{type:String,value:"standard",observer:"_modeChanged",reflectToAttribute:!0},shadow:{type:Boolean,value:!1},tallClass:{type:String,value:"tall"},atTop:{type:Boolean,value:!0,notify:!0,readOnly:!0,reflectToAttribute:!0}},observers:["_computeDropShadowHidden(atTop, mode, shadow)"],attached:function(){this._addListener(),this._keepScrollingState()},detached:function(){this._removeListener()},ready:function(){this.scrollHandler=this._scroll.bind(this),console.warn(this.is,"is deprecated. Please use app-layout instead!")},get header(){return Yi(this.$.headerSlot).getDistributedNodes()[0]},get scroller(){return this._getScrollerForMode(this.mode)},get visibleShadow(){return this.$.dropShadow.classList.contains("has-shadow")},_computeDropShadowHidden:function(t,e,n){var i=Ya.shadowMode[e];this.toggleClass("has-shadow",!!this.shadow||2===i||1===i&&!t,this.$.dropShadow)},_computeMainContainerClass:function(t){var e={};return e.flex="cover"!==t,Object.keys(e).filter((function(t){return e[t]})).join(" ")},_addListener:function(){this.scroller.addEventListener("scroll",this.scrollHandler)},_removeListener:function(){this.scroller.removeEventListener("scroll",this.scrollHandler)},_modeChanged:function(t,e){var n=Ya,i=this.header;i&&(n.tallMode[e]&&!n.tallMode[t]?(i.classList.remove(this.tallClass),this.async((function(){i.classList.remove("animate")}),200)):this.toggleClass("animate",n.tallMode[t],i)),this._keepScrollingState()},_keepScrollingState:function(){var t=this.scroller,e=this.header;this._setAtTop(0===t.scrollTop),e&&this.tallClass&&Ya.tallMode[this.mode]&&this.toggleClass(this.tallClass,this.atTop||e.classList.contains(this.tallClass)&&t.scrollHeight<this.offsetHeight,e)},_scroll:function(){this._keepScrollingState(),this.fire("content-scroll",{target:this.scroller},{bubbles:!1})},_getScrollerForMode:function(t){return Ya.outerScroll[t]?this:this.$.mainContainer}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({is:"paper-icon-button",_template:_e`
    <style>
      :host {
        display: inline-block;
        position: relative;
        padding: 8px;
        outline: none;
        -webkit-user-select: none;
        -moz-user-select: none;
        -ms-user-select: none;
        user-select: none;
        cursor: pointer;
        z-index: 0;
        line-height: 1;

        width: 40px;
        height: 40px;

        /*
          NOTE: Both values are needed, since some phones require the value to
          be \`transparent\`.
        */
        -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
        -webkit-tap-highlight-color: transparent;

        /* Because of polymer/2558, this style has lower specificity than * */
        box-sizing: border-box !important;

        @apply --paper-icon-button;
      }

      :host #ink {
        color: var(--paper-icon-button-ink-color, var(--primary-text-color));
        opacity: 0.6;
      }

      :host([disabled]) {
        color: var(--paper-icon-button-disabled-text, var(--disabled-text-color));
        pointer-events: none;
        cursor: auto;

        @apply --paper-icon-button-disabled;
      }

      :host([hidden]) {
        display: none !important;
      }

      :host(:hover) {
        @apply --paper-icon-button-hover;
      }

      iron-icon {
        --iron-icon-width: 100%;
        --iron-icon-height: 100%;
      }
    </style>

    <iron-icon id="icon" src="[[src]]" icon="[[icon]]"
               alt$="[[alt]]"></iron-icon>
  `,hostAttributes:{role:"button",tabindex:"0"},behaviors:[ca],registered:function(){this._template.setAttribute("strip-whitespace","")},properties:{src:{type:String},icon:{type:String},alt:{type:String,observer:"_altChanged"}},_altChanged:function(t,e){var n=this.getAttribute("aria-label");n&&e!=n||this.setAttribute("aria-label",t)}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        display: inline-block;
        position: relative;
        width: 400px;
        border: 1px solid;
        padding: 2px;
        -moz-appearance: textarea;
        -webkit-appearance: textarea;
        overflow: hidden;
      }

      .mirror-text {
        visibility: hidden;
        word-wrap: break-word;
        @apply --iron-autogrow-textarea;
      }

      .fit {
        @apply --layout-fit;
      }

      textarea {
        position: relative;
        outline: none;
        border: none;
        resize: none;
        background: inherit;
        color: inherit;
        /* see comments in template */
        width: 100%;
        height: 100%;
        font-size: inherit;
        font-family: inherit;
        line-height: inherit;
        text-align: inherit;
        @apply --iron-autogrow-textarea;
      }

      textarea::-webkit-input-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea:-moz-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea::-moz-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea:-ms-input-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }
    </style>

    <!-- the mirror sizes the input/textarea so it grows with typing -->
    <!-- use &#160; instead &nbsp; of to allow this element to be used in XHTML -->
    <div id="mirror" class="mirror-text" aria-hidden="true">&nbsp;</div>

    <!-- size the input/textarea with a div, because the textarea has intrinsic size in ff -->
    <div class="textarea-container fit">
      <textarea id="textarea" name$="[[name]]" aria-label$="[[label]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" autocapitalize$="[[autocapitalize]]" inputmode$="[[inputmode]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" required$="[[required]]" disabled$="[[disabled]]" rows$="[[rows]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]"></textarea>
    </div>
`,is:"iron-autogrow-textarea",behaviors:[aa,qo],properties:{value:{observer:"_valueChanged",type:String,notify:!0},bindValue:{observer:"_bindValueChanged",type:String,notify:!0},rows:{type:Number,value:1,observer:"_updateCached"},maxRows:{type:Number,value:0,observer:"_updateCached"},autocomplete:{type:String,value:"off"},autofocus:{type:Boolean,value:!1},autocapitalize:{type:String,value:"none"},inputmode:{type:String},placeholder:{type:String},readonly:{type:String},required:{type:Boolean},minlength:{type:Number},maxlength:{type:Number},label:{type:String}},listeners:{input:"_onInput"},get textarea(){return this.$.textarea},get selectionStart(){return this.$.textarea.selectionStart},get selectionEnd(){return this.$.textarea.selectionEnd},set selectionStart(t){this.$.textarea.selectionStart=t},set selectionEnd(t){this.$.textarea.selectionEnd=t},attached:function(){navigator.userAgent.match(/iP(?:[oa]d|hone)/)&&!navigator.userAgent.match(/OS 1[3456789]/)&&(this.$.textarea.style.marginLeft="-3px")},validate:function(){var t=this.$.textarea.validity.valid;return t&&(this.required&&""===this.value?t=!1:this.hasValidator()&&(t=aa.validate.call(this,this.value))),this.invalid=!t,this.fire("iron-input-validate"),t},_bindValueChanged:function(t){this.value=t},_valueChanged:function(t){var e=this.textarea;e&&(e.value!==t&&(e.value=t||0===t?t:""),this.bindValue=t,this.$.mirror.innerHTML=this._valueForMirror(),this.fire("bind-value-changed",{value:this.bindValue}))},_onInput:function(t){var e=Yi(t).path;this.value=e?e[0].value:t.target.value},_constrain:function(t){var e;for(t=t||[""],e=this.maxRows>0&&t.length>this.maxRows?t.slice(0,this.maxRows):t.slice(0);this.rows>0&&e.length<this.rows;)e.push("");return e.join("<br/>")+"&#160;"},_valueForMirror:function(){var t=this.textarea;if(t)return this.tokens=t&&t.value?t.value.replace(/&/gm,"&amp;").replace(/"/gm,"&quot;").replace(/'/gm,"&#39;").replace(/</gm,"&lt;").replace(/>/gm,"&gt;").split("\n"):[""],this._constrain(this.tokens)},_updateCached:function(){this.$.mirror.innerHTML=this._constrain(this.tokens)}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        display: block;
      }

      :host([hidden]) {
        display: none !important;
      }

      label {
        pointer-events: none;
      }
    </style>

    <paper-input-container no-label-float$="[[noLabelFloat]]" always-float-label="[[_computeAlwaysFloatLabel(alwaysFloatLabel,placeholder)]]" auto-validate$="[[autoValidate]]" disabled$="[[disabled]]" invalid="[[invalid]]">

      <label hidden$="[[!label]]" aria-hidden="true" for$="[[_inputId]]" slot="label">[[label]]</label>

      <iron-autogrow-textarea class="paper-input-input" slot="input" id$="[[_inputId]]" aria-labelledby$="[[_ariaLabelledBy]]" aria-describedby$="[[_ariaDescribedBy]]" bind-value="{{value}}" invalid="{{invalid}}" validator$="[[validator]]" disabled$="[[disabled]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" inputmode$="[[inputmode]]" name$="[[name]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" required$="[[required]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]" autocapitalize$="[[autocapitalize]]" rows$="[[rows]]" max-rows$="[[maxRows]]" on-change="_onChange"></iron-autogrow-textarea>

      <template is="dom-if" if="[[errorMessage]]">
        <paper-input-error aria-live="assertive" slot="add-on">[[errorMessage]]</paper-input-error>
      </template>

      <template is="dom-if" if="[[charCounter]]">
        <paper-input-char-counter slot="add-on"></paper-input-char-counter>
      </template>

    </paper-input-container>
`,is:"paper-textarea",behaviors:[Fa,ra],properties:{_ariaLabelledBy:{observer:"_ariaLabelledByChanged",type:String},_ariaDescribedBy:{observer:"_ariaDescribedByChanged",type:String},value:{type:String},rows:{type:Number,value:1},maxRows:{type:Number,value:0}},get selectionStart(){return this.$.input.textarea.selectionStart},set selectionStart(t){this.$.input.textarea.selectionStart=t},get selectionEnd(){return this.$.input.textarea.selectionEnd},set selectionEnd(t){this.$.input.textarea.selectionEnd=t},_ariaLabelledByChanged:function(t){this._focusableElement.setAttribute("aria-labelledby",t)},_ariaDescribedByChanged:function(t){this._focusableElement.setAttribute("aria-describedby",t)},get _focusableElement(){return this.inputElement.textarea}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Xa=document.createElement("template");Xa.setAttribute("style","display: none;"),Xa.innerHTML="<dom-module id=\"paper-item-shared-styles\">\n  <template>\n    <style>\n      :host, .paper-item {\n        display: block;\n        position: relative;\n        min-height: var(--paper-item-min-height, 48px);\n        padding: 0px 16px;\n      }\n\n      .paper-item {\n        @apply --paper-font-subhead;\n        border:none;\n        outline: none;\n        background: white;\n        width: 100%;\n        text-align: left;\n      }\n\n      :host([hidden]), .paper-item[hidden] {\n        display: none !important;\n      }\n\n      :host(.iron-selected), .paper-item.iron-selected {\n        font-weight: var(--paper-item-selected-weight, bold);\n\n        @apply --paper-item-selected;\n      }\n\n      :host([disabled]), .paper-item[disabled] {\n        color: var(--paper-item-disabled-color, var(--disabled-text-color));\n\n        @apply --paper-item-disabled;\n      }\n\n      :host(:focus), .paper-item:focus {\n        position: relative;\n        outline: 0;\n\n        @apply --paper-item-focused;\n      }\n\n      :host(:focus):before, .paper-item:focus:before {\n        @apply --layout-fit;\n\n        background: currentColor;\n        content: '';\n        opacity: var(--dark-divider-opacity);\n        pointer-events: none;\n\n        @apply --paper-item-focused-before;\n      }\n    </style>\n  </template>\n</dom-module>",document.head.appendChild(Xa.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const $a=[Xo,qo,{hostAttributes:{role:"option",tabindex:"0"}}];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style include="paper-item-shared-styles">
      :host {
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-font-subhead;

        @apply --paper-item;
      }
    </style>
    <slot></slot>
`,is:"paper-item",behaviors:[$a]}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        overflow: hidden; /* needed for text-overflow: ellipsis to work on ff */
        @apply --layout-vertical;
        @apply --layout-center-justified;
        @apply --layout-flex;
      }

      :host([two-line]) {
        min-height: var(--paper-item-body-two-line-min-height, 72px);
      }

      :host([three-line]) {
        min-height: var(--paper-item-body-three-line-min-height, 88px);
      }

      :host > ::slotted(*) {
        overflow: hidden;
        text-overflow: ellipsis;
        white-space: nowrap;
      }

      :host > ::slotted([secondary]) {
        @apply --paper-font-body1;

        color: var(--paper-item-body-secondary-color, var(--secondary-text-color));

        @apply --paper-item-body-secondary;
      }
    </style>

    <slot></slot>
`,is:"paper-item-body"}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style include="paper-item-shared-styles"></style>
    <style>
      :host {
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-font-subhead;

        @apply --paper-item;
        @apply --paper-icon-item;
      }

      .content-icon {
        @apply --layout-horizontal;
        @apply --layout-center;

        width: var(--paper-item-icon-width, 56px);
        @apply --paper-item-icon;
      }
    </style>

    <div id="contentIcon" class="content-icon">
      <slot name="item-icon"></slot>
    </div>
    <slot></slot>
`,is:"paper-icon-item",behaviors:[$a]});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ka={properties:{multi:{type:Boolean,value:!1,observer:"multiChanged"},selectedValues:{type:Array,notify:!0,value:function(){return[]}},selectedItems:{type:Array,readOnly:!0,notify:!0,value:function(){return[]}}},observers:["_updateSelected(selectedValues.splices)"],select:function(t){this.multi?this._toggleSelected(t):this.selected=t},multiChanged:function(t){this._selection.multi=t,this._updateSelected()},get _shouldUpdateSelection(){return null!=this.selected||null!=this.selectedValues&&this.selectedValues.length},_updateAttrForSelected:function(){this.multi?this.selectedItems&&this.selectedItems.length>0&&(this.selectedValues=this.selectedItems.map((function(t){return this._indexToValue(this.indexOf(t))}),this).filter((function(t){return null!=t}),this)):jo._updateAttrForSelected.apply(this)},_updateSelected:function(){this.multi?this._selectMulti(this.selectedValues):this._selectSelected(this.selected)},_selectMulti:function(t){var e=(this._valuesToItems(t=t||[])||[]).filter((function(t){return null!=t}));this._selection.clear(e);for(var n=0;n<e.length;n++)this._selection.setItemSelected(e[n],!0);this.fallbackSelection&&!this._selection.get().length&&this._valueToItem(this.fallbackSelection)&&this.select(this.fallbackSelection)},_selectionChange:function(){var t=this._selection.get();this.multi?(this._setSelectedItems(t),this._setSelectedItem(t.length?t[0]:null)):null!=t?(this._setSelectedItems([t]),this._setSelectedItem(t)):(this._setSelectedItems([]),this._setSelectedItem(null))},_toggleSelected:function(t){var e=this.selectedValues.indexOf(t);e<0?this.push("selectedValues",t):this.splice("selectedValues",e,1)},_valuesToItems:function(t){return null==t?null:t.map((function(t){return this._valueToItem(t)}),this)}},Za={properties:{focusedItem:{observer:"_focusedItemChanged",readOnly:!0,type:Object},attrForItemTitle:{type:String},disabled:{type:Boolean,value:!1,observer:"_disabledChanged"}},_MODIFIER_KEYS:["Alt","AltGraph","CapsLock","Control","Fn","FnLock","Hyper","Meta","NumLock","OS","ScrollLock","Shift","Super","Symbol","SymbolLock"],_SEARCH_RESET_TIMEOUT_MS:1e3,_previousTabIndex:0,hostAttributes:{role:"menu"},observers:["_updateMultiselectable(multi)"],listeners:{focus:"_onFocus",keydown:"_onKeydown","iron-items-changed":"_onIronItemsChanged"},keyBindings:{up:"_onUpKey",down:"_onDownKey",esc:"_onEscKey","shift+tab:keydown":"_onShiftTabDown"},attached:function(){this._resetTabindices()},select:function(t){this._defaultFocusAsync&&(this.cancelAsync(this._defaultFocusAsync),this._defaultFocusAsync=null);var e=this._valueToItem(t);e&&e.hasAttribute("disabled")||(this._setFocusedItem(e),Ka.select.apply(this,arguments))},_resetTabindices:function(){var t=this.multi?this.selectedItems&&this.selectedItems[0]:this.selectedItem;this.items.forEach((function(e){e.setAttribute("tabindex",e===t?"0":"-1"),e.setAttribute("aria-selected",this._selection.isSelected(e))}),this)},_updateMultiselectable:function(t){t?this.setAttribute("aria-multiselectable","true"):this.removeAttribute("aria-multiselectable")},_focusWithKeyboardEvent:function(t){if(-1===this._MODIFIER_KEYS.indexOf(t.key)){this.cancelDebouncer("_clearSearchText");for(var e,n=this._searchText||"",i=(n+=(t.key&&1==t.key.length?t.key:String.fromCharCode(t.keyCode)).toLocaleLowerCase()).length,r=0;e=this.items[r];r++)if(!e.hasAttribute("disabled")){var o=this.attrForItemTitle||"textContent",a=(e[o]||e.getAttribute(o)||"").trim();if(!(a.length<i)&&a.slice(0,i).toLocaleLowerCase()==n){this._setFocusedItem(e);break}}this._searchText=n,this.debounce("_clearSearchText",this._clearSearchText,this._SEARCH_RESET_TIMEOUT_MS)}},_clearSearchText:function(){this._searchText=""},_focusPrevious:function(){for(var t=this.items.length,e=Number(this.indexOf(this.focusedItem)),n=1;n<t+1;n++){var i=this.items[(e-n+t)%t];if(!i.hasAttribute("disabled")){var r=Yi(i).getOwnerRoot()||document;if(this._setFocusedItem(i),Yi(r).activeElement==i)return}}},_focusNext:function(){for(var t=this.items.length,e=Number(this.indexOf(this.focusedItem)),n=1;n<t+1;n++){var i=this.items[(e+n)%t];if(!i.hasAttribute("disabled")){var r=Yi(i).getOwnerRoot()||document;if(this._setFocusedItem(i),Yi(r).activeElement==i)return}}},_applySelection:function(t,e){t.setAttribute("aria-selected",e?"true":"false"),jo._applySelection.apply(this,arguments)},_focusedItemChanged:function(t,e){e&&e.setAttribute("tabindex","-1"),!t||t.hasAttribute("disabled")||this.disabled||(t.setAttribute("tabindex","0"),t.focus())},_onIronItemsChanged:function(t){t.detail.addedNodes.length&&this._resetTabindices()},_onShiftTabDown:function(t){var e=this.getAttribute("tabindex");Za._shiftTabPressed=!0,this._setFocusedItem(null),this.setAttribute("tabindex","-1"),this.async((function(){this.setAttribute("tabindex",e),Za._shiftTabPressed=!1}),1)},_onFocus:function(t){if(!Za._shiftTabPressed){var e=Yi(t).rootTarget;(e===this||void 0===e.tabIndex||this.isLightDescendant(e))&&(this._defaultFocusAsync=this.async((function(){var t=this.multi?this.selectedItems&&this.selectedItems[0]:this.selectedItem;this._setFocusedItem(null),t?this._setFocusedItem(t):this.items[0]&&this._focusNext()})))}},_onUpKey:function(t){this._focusPrevious(),t.detail.keyboardEvent.preventDefault()},_onDownKey:function(t){this._focusNext(),t.detail.keyboardEvent.preventDefault()},_onEscKey:function(t){var e=this.focusedItem;e&&e.blur()},_onKeydown:function(t){this.keyboardEventMatchesKeys(t,"up down esc")||this._focusWithKeyboardEvent(t),t.stopPropagation()},_activateHandler:function(t){jo._activateHandler.call(this,t),t.stopPropagation()},_disabledChanged:function(t){t?(this._previousTabIndex=this.hasAttribute("tabindex")?this.tabIndex:0,this.removeAttribute("tabindex")):this.hasAttribute("tabindex")||this.setAttribute("tabindex",this._previousTabIndex)},_shiftTabPressed:!1},Ja=[[jo,Ka],zo,Za];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        display: block;
        padding: 8px 0;

        background: var(--paper-listbox-background-color, var(--primary-background-color));
        color: var(--paper-listbox-color, var(--primary-text-color));

        @apply --paper-listbox;
      }
    </style>

    <slot></slot>
`,is:"paper-listbox",behaviors:[Ja],hostAttributes:{role:"listbox"}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Qa=_e`
<dom-module id="paper-material-shared-styles">
  <template>
    <style>
      :host {
        display: block;
        position: relative;
      }

      :host([elevation="1"]) {
        @apply --shadow-elevation-2dp;
      }

      :host([elevation="2"]) {
        @apply --shadow-elevation-4dp;
      }

      :host([elevation="3"]) {
        @apply --shadow-elevation-6dp;
      }

      :host([elevation="4"]) {
        @apply --shadow-elevation-8dp;
      }

      :host([elevation="5"]) {
        @apply --shadow-elevation-16dp;
      }
    </style>
  </template>
</dom-module>
`;Qa.setAttribute("style","display: none;"),document.body.appendChild(Qa.content),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style include="paper-material-shared-styles"></style>
    <style>
      :host([animated]) {
        @apply --shadow-transition;
      }
      :host {
        @apply --paper-material;
      }
    </style>

    <slot></slot>
`,is:"paper-material",properties:{elevation:{type:Number,reflectToAttribute:!0,value:1},animated:{type:Boolean,reflectToAttribute:!0,value:!1}}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ts={properties:{value:{type:Number,value:0,notify:!0,reflectToAttribute:!0},min:{type:Number,value:0,notify:!0},max:{type:Number,value:100,notify:!0},step:{type:Number,value:1,notify:!0},ratio:{type:Number,value:0,readOnly:!0,notify:!0}},observers:["_update(value, min, max, step)"],_calcRatio:function(t){return(this._clampValue(t)-this.min)/(this.max-this.min)},_clampValue:function(t){return Math.min(this.max,Math.max(this.min,this._calcStep(t)))},_calcStep:function(t){if(t=parseFloat(t),!this.step)return t;var e=Math.round((t-this.min)/this.step);return this.step<1?e/(1/this.step)+this.min:e*this.step+this.min},_validateValue:function(){var t=this._clampValue(this.value);return this.value=this.oldValue=isNaN(t)?this.oldValue:t,this.value!==t},_update:function(){this._validateValue(),this._setRatio(100*this._calcRatio(this.value))}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */Rr({_template:_e`
    <style>
      :host {
        display: block;
        width: 200px;
        position: relative;
        overflow: hidden;
      }

      :host([hidden]), [hidden] {
        display: none !important;
      }

      #progressContainer {
        @apply --paper-progress-container;
        position: relative;
      }

      #progressContainer,
      /* the stripe for the indeterminate animation*/
      .indeterminate::after {
        height: var(--paper-progress-height, 4px);
      }

      #primaryProgress,
      #secondaryProgress,
      .indeterminate::after {
        @apply --layout-fit;
      }

      #progressContainer,
      .indeterminate::after {
        background: var(--paper-progress-container-color, var(--google-grey-300));
      }

      :host(.transiting) #primaryProgress,
      :host(.transiting) #secondaryProgress {
        -webkit-transition-property: -webkit-transform;
        transition-property: transform;

        /* Duration */
        -webkit-transition-duration: var(--paper-progress-transition-duration, 0.08s);
        transition-duration: var(--paper-progress-transition-duration, 0.08s);

        /* Timing function */
        -webkit-transition-timing-function: var(--paper-progress-transition-timing-function, ease);
        transition-timing-function: var(--paper-progress-transition-timing-function, ease);

        /* Delay */
        -webkit-transition-delay: var(--paper-progress-transition-delay, 0s);
        transition-delay: var(--paper-progress-transition-delay, 0s);
      }

      #primaryProgress,
      #secondaryProgress {
        @apply --layout-fit;
        -webkit-transform-origin: left center;
        transform-origin: left center;
        -webkit-transform: scaleX(0);
        transform: scaleX(0);
        will-change: transform;
      }

      #primaryProgress {
        background: var(--paper-progress-active-color, var(--google-green-500));
      }

      #secondaryProgress {
        background: var(--paper-progress-secondary-color, var(--google-green-100));
      }

      :host([disabled]) #primaryProgress {
        background: var(--paper-progress-disabled-active-color, var(--google-grey-500));
      }

      :host([disabled]) #secondaryProgress {
        background: var(--paper-progress-disabled-secondary-color, var(--google-grey-300));
      }

      :host(:not([disabled])) #primaryProgress.indeterminate {
        -webkit-transform-origin: right center;
        transform-origin: right center;
        -webkit-animation: indeterminate-bar var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
        animation: indeterminate-bar var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
      }

      :host(:not([disabled])) #primaryProgress.indeterminate::after {
        content: "";
        -webkit-transform-origin: center center;
        transform-origin: center center;

        -webkit-animation: indeterminate-splitter var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
        animation: indeterminate-splitter var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
      }

      @-webkit-keyframes indeterminate-bar {
        0% {
          -webkit-transform: scaleX(1) translateX(-100%);
        }
        50% {
          -webkit-transform: scaleX(1) translateX(0%);
        }
        75% {
          -webkit-transform: scaleX(1) translateX(0%);
          -webkit-animation-timing-function: cubic-bezier(.28,.62,.37,.91);
        }
        100% {
          -webkit-transform: scaleX(0) translateX(0%);
        }
      }

      @-webkit-keyframes indeterminate-splitter {
        0% {
          -webkit-transform: scaleX(.75) translateX(-125%);
        }
        30% {
          -webkit-transform: scaleX(.75) translateX(-125%);
          -webkit-animation-timing-function: cubic-bezier(.42,0,.6,.8);
        }
        90% {
          -webkit-transform: scaleX(.75) translateX(125%);
        }
        100% {
          -webkit-transform: scaleX(.75) translateX(125%);
        }
      }

      @keyframes indeterminate-bar {
        0% {
          transform: scaleX(1) translateX(-100%);
        }
        50% {
          transform: scaleX(1) translateX(0%);
        }
        75% {
          transform: scaleX(1) translateX(0%);
          animation-timing-function: cubic-bezier(.28,.62,.37,.91);
        }
        100% {
          transform: scaleX(0) translateX(0%);
        }
      }

      @keyframes indeterminate-splitter {
        0% {
          transform: scaleX(.75) translateX(-125%);
        }
        30% {
          transform: scaleX(.75) translateX(-125%);
          animation-timing-function: cubic-bezier(.42,0,.6,.8);
        }
        90% {
          transform: scaleX(.75) translateX(125%);
        }
        100% {
          transform: scaleX(.75) translateX(125%);
        }
      }
    </style>

    <div id="progressContainer">
      <div id="secondaryProgress" hidden\$="[[_hideSecondaryProgress(secondaryRatio)]]"></div>
      <div id="primaryProgress"></div>
    </div>
`,is:"paper-progress",behaviors:[ts],properties:{secondaryProgress:{type:Number,value:0},secondaryRatio:{type:Number,value:0,readOnly:!0},indeterminate:{type:Boolean,value:!1,observer:"_toggleIndeterminate"},disabled:{type:Boolean,value:!1,reflectToAttribute:!0,observer:"_disabledChanged"}},observers:["_progressChanged(secondaryProgress, value, min, max, indeterminate)"],hostAttributes:{role:"progressbar"},_toggleIndeterminate:function(t){this.toggleClass("indeterminate",t,this.$.primaryProgress)},_transformProgress:function(t,e){t.style.transform=t.style.webkitTransform="scaleX("+e/100+")"},_mainRatioChanged:function(t){this._transformProgress(this.$.primaryProgress,t)},_progressChanged:function(t,e,n,i,r){t=this._clampValue(t),e=this._clampValue(e);var o=100*this._calcRatio(t),a=100*this._calcRatio(e);this._setSecondaryRatio(o),this._transformProgress(this.$.secondaryProgress,o),this._transformProgress(this.$.primaryProgress,a),this.secondaryProgress=t,r?this.removeAttribute("aria-valuenow"):this.setAttribute("aria-valuenow",e),this.setAttribute("aria-valuemin",n),this.setAttribute("aria-valuemax",i)},_disabledChanged:function(t){this.setAttribute("aria-disabled",t?"true":"false")},_hideSecondaryProgress:function(t){return 0===t}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const es=_e`
<style>
  :host {
    display: inline-block;
    line-height: 0;
    white-space: nowrap;
    cursor: pointer;
    @apply --paper-font-common-base;
    --calculated-paper-radio-button-size: var(--paper-radio-button-size, 16px);
    /* -1px is a sentinel for the default and is replace in \`attached\`. */
    --calculated-paper-radio-button-ink-size: var(--paper-radio-button-ink-size, -1px);
  }

  :host(:focus) {
    outline: none;
  }

  #radioContainer {
    @apply --layout-inline;
    @apply --layout-center-center;
    position: relative;
    width: var(--calculated-paper-radio-button-size);
    height: var(--calculated-paper-radio-button-size);
    vertical-align: middle;

    @apply --paper-radio-button-radio-container;
  }

  #ink {
    position: absolute;
    top: 50%;
    left: 50%;
    right: auto;
    width: var(--calculated-paper-radio-button-ink-size);
    height: var(--calculated-paper-radio-button-ink-size);
    color: var(--paper-radio-button-unchecked-ink-color, var(--primary-text-color));
    opacity: 0.6;
    pointer-events: none;
    -webkit-transform: translate(-50%, -50%);
    transform: translate(-50%, -50%);
  }

  #ink[checked] {
    color: var(--paper-radio-button-checked-ink-color, var(--primary-color));
  }

  #offRadio, #onRadio {
    position: absolute;
    box-sizing: border-box;
    top: 0;
    left: 0;
    width: 100%;
    height: 100%;
    border-radius: 50%;
  }

  #offRadio {
    border: 2px solid var(--paper-radio-button-unchecked-color, var(--primary-text-color));
    background-color: var(--paper-radio-button-unchecked-background-color, transparent);
    transition: border-color 0.28s;
  }

  #onRadio {
    background-color: var(--paper-radio-button-checked-color, var(--primary-color));
    -webkit-transform: scale(0);
    transform: scale(0);
    transition: -webkit-transform ease 0.28s;
    transition: transform ease 0.28s;
    will-change: transform;
  }

  :host([checked]) #offRadio {
    border-color: var(--paper-radio-button-checked-color, var(--primary-color));
  }

  :host([checked]) #onRadio {
    -webkit-transform: scale(0.5);
    transform: scale(0.5);
  }

  #radioLabel {
    line-height: normal;
    position: relative;
    display: inline-block;
    vertical-align: middle;
    margin-left: var(--paper-radio-button-label-spacing, 10px);
    white-space: normal;
    color: var(--paper-radio-button-label-color, var(--primary-text-color));

    @apply --paper-radio-button-label;
  }

  :host([checked]) #radioLabel {
    @apply --paper-radio-button-label-checked;
  }

  #radioLabel:dir(rtl) {
    margin-left: 0;
    margin-right: var(--paper-radio-button-label-spacing, 10px);
  }

  #radioLabel[hidden] {
    display: none;
  }

  /* disabled state */

  :host([disabled]) #offRadio {
    border-color: var(--paper-radio-button-unchecked-color, var(--primary-text-color));
    opacity: 0.5;
  }

  :host([disabled][checked]) #onRadio {
    background-color: var(--paper-radio-button-unchecked-color, var(--primary-text-color));
    opacity: 0.5;
  }

  :host([disabled]) #radioLabel {
    /* slightly darker than the button, so that it's readable */
    opacity: 0.65;
  }
</style>

<div id="radioContainer">
  <div id="offRadio"></div>
  <div id="onRadio"></div>
</div>

<div id="radioLabel"><slot></slot></div>`;es.setAttribute("strip-whitespace",""),Rr({_template:es,is:"paper-radio-button",behaviors:[ua],hostAttributes:{role:"radio","aria-checked":!1,tabindex:0},properties:{ariaActiveAttribute:{type:String,value:"aria-checked"}},ready:function(){this._rippleContainer=this.$.radioContainer},attached:function(){Li(this,(function(){if("-1px"===this.getComputedStyleValue("--calculated-paper-radio-button-ink-size").trim()){var t=parseFloat(this.getComputedStyleValue("--calculated-paper-radio-button-size").trim()),e=Math.floor(3*t);e%2!=t%2&&e++,this.updateStyles({"--paper-radio-button-ink-size":e+"px"})}}))}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ns={hostAttributes:{role:"menubar"},keyBindings:{left:"_onLeftKey",right:"_onRightKey"},_onUpKey:function(t){this.focusedItem.click(),t.detail.keyboardEvent.preventDefault()},_onDownKey:function(t){this.focusedItem.click(),t.detail.keyboardEvent.preventDefault()},get _isRTL(){return"rtl"===window.getComputedStyle(this).direction},_onLeftKey:function(t){this._isRTL?this._focusNext():this._focusPrevious(),t.detail.keyboardEvent.preventDefault()},_onRightKey:function(t){this._isRTL?this._focusPrevious():this._focusNext(),t.detail.keyboardEvent.preventDefault()},_onKeydown:function(t){this.keyboardEventMatchesKeys(t,"up down left right esc")||this._focusWithKeyboardEvent(t)}},is=[Ja,ns];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        display: inline-block;
      }

      :host ::slotted(*) {
        padding: var(--paper-radio-group-item-padding, 12px);
      }
    </style>

    <slot></slot>
`,is:"paper-radio-group",behaviors:[is],hostAttributes:{role:"radiogroup"},properties:{attrForSelected:{type:String,value:"name"},selectedAttribute:{type:String,value:"checked"},selectable:{type:String,value:"paper-radio-button"},allowEmptySelection:{type:Boolean,value:!1}},select:function(t){var e=this._valueToItem(t);if(!e||!e.hasAttribute("disabled")){if(this.selected){var n=this._valueToItem(this.selected);if(this.selected==t){if(!this.allowEmptySelection)return void(n&&(n.checked=!0));t=""}n&&(n.checked=!1)}jo.select.apply(this,[t]),this.fire("paper-radio-group-changed")}},_activateFocusedItem:function(){this._itemActivate(this._valueForItem(this.focusedItem),this.focusedItem)},_onUpKey:function(t){this._focusPrevious(),t.preventDefault(),this._activateFocusedItem()},_onDownKey:function(t){this._focusNext(),t.preventDefault(),this._activateFocusedItem()},_onLeftKey:function(t){ns._onLeftKey.apply(this,arguments),this._activateFocusedItem()},_onRightKey:function(t){ns._onRightKey.apply(this,arguments),this._activateFocusedItem()}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const rs=_e`
  <style>
    :host {
      @apply --layout;
      @apply --layout-justified;
      @apply --layout-center;
      width: 200px;
      cursor: default;
      -webkit-user-select: none;
      -moz-user-select: none;
      -ms-user-select: none;
      user-select: none;
      -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
      --paper-progress-active-color: var(--paper-slider-active-color, var(--google-blue-700));
      --paper-progress-secondary-color: var(--paper-slider-secondary-color, var(--google-blue-300));
      --paper-progress-disabled-active-color: var(--paper-slider-disabled-active-color, var(--paper-grey-400));
      --paper-progress-disabled-secondary-color: var(--paper-slider-disabled-secondary-color, var(--paper-grey-400));
      --calculated-paper-slider-height: var(--paper-slider-height, 2px);
    }

    /* focus shows the ripple */
    :host(:focus) {
      outline: none;
    }

    /**
      * NOTE(keanulee): Though :host-context is not universally supported, some pages
      * still rely on paper-slider being flipped when dir="rtl" is set on body. For full
      * compatibility, dir="rtl" must be explicitly set on paper-slider.
      */
    :dir(rtl) #sliderContainer {
      -webkit-transform: scaleX(-1);
      transform: scaleX(-1);
    }

    /**
      * NOTE(keanulee): This is separate from the rule above because :host-context may
      * not be recognized.
      */
    :host([dir="rtl"]) #sliderContainer {
      -webkit-transform: scaleX(-1);
      transform: scaleX(-1);
    }

    /**
      * NOTE(keanulee): Needed to override the :host-context rule (where supported)
      * to support LTR sliders in RTL pages.
      */
    :host([dir="ltr"]) #sliderContainer {
      -webkit-transform: scaleX(1);
      transform: scaleX(1);
    }

    #sliderContainer {
      position: relative;
      width: 100%;
      height: calc(30px + var(--calculated-paper-slider-height));
      margin-left: calc(15px + var(--calculated-paper-slider-height)/2);
      margin-right: calc(15px + var(--calculated-paper-slider-height)/2);
    }

    #sliderContainer:focus {
      outline: 0;
    }

    #sliderContainer.editable {
      margin-top: 12px;
      margin-bottom: 12px;
    }

    .bar-container {
      position: absolute;
      top: 0;
      bottom: 0;
      left: 0;
      right: 0;
      overflow: hidden;
    }

    .ring > .bar-container {
      left: calc(5px + var(--calculated-paper-slider-height)/2);
      transition: left 0.18s ease;
    }

    .ring.expand.dragging > .bar-container {
      transition: none;
    }

    .ring.expand:not(.pin) > .bar-container {
      left: calc(8px + var(--calculated-paper-slider-height)/2);
    }

    #sliderBar {
      padding: 15px 0;
      width: 100%;
      background-color: var(--paper-slider-bar-color, transparent);
      --paper-progress-container-color: var(--paper-slider-container-color, var(--paper-grey-400));
      --paper-progress-height: var(--calculated-paper-slider-height);
    }

    .slider-markers {
      position: absolute;
      /* slider-knob is 30px + the slider-height so that the markers should start at a offset of 15px*/
      top: 15px;
      height: var(--calculated-paper-slider-height);
      left: 0;
      right: -1px;
      box-sizing: border-box;
      pointer-events: none;
      @apply --layout-horizontal;
    }

    .slider-marker {
      @apply --layout-flex;
    }
    .slider-markers::after,
    .slider-marker::after {
      content: "";
      display: block;
      margin-left: -1px;
      width: 2px;
      height: var(--calculated-paper-slider-height);
      border-radius: 50%;
      background-color: var(--paper-slider-markers-color, #000);
    }

    .slider-knob {
      position: absolute;
      left: 0;
      top: 0;
      margin-left: calc(-15px - var(--calculated-paper-slider-height)/2);
      width: calc(30px + var(--calculated-paper-slider-height));
      height: calc(30px + var(--calculated-paper-slider-height));
    }

    .transiting > .slider-knob {
      transition: left 0.08s ease;
    }

    .slider-knob:focus {
      outline: none;
    }

    .slider-knob.dragging {
      transition: none;
    }

    .snaps > .slider-knob.dragging {
      transition: -webkit-transform 0.08s ease;
      transition: transform 0.08s ease;
    }

    .slider-knob-inner {
      margin: 10px;
      width: calc(100% - 20px);
      height: calc(100% - 20px);
      background-color: var(--paper-slider-knob-color, var(--google-blue-700));
      border: 2px solid var(--paper-slider-knob-color, var(--google-blue-700));
      border-radius: 50%;

      -moz-box-sizing: border-box;
      box-sizing: border-box;

      transition-property: -webkit-transform, background-color, border;
      transition-property: transform, background-color, border;
      transition-duration: 0.18s;
      transition-timing-function: ease;
    }

    .expand:not(.pin) > .slider-knob > .slider-knob-inner {
      -webkit-transform: scale(1.5);
      transform: scale(1.5);
    }

    .ring > .slider-knob > .slider-knob-inner {
      background-color: var(--paper-slider-knob-start-color, transparent);
      border: 2px solid var(--paper-slider-knob-start-border-color, var(--paper-grey-400));
    }

    .slider-knob-inner::before {
      background-color: var(--paper-slider-pin-color, var(--google-blue-700));
    }

    .pin > .slider-knob > .slider-knob-inner::before {
      content: "";
      position: absolute;
      top: 0;
      left: 50%;
      margin-left: -13px;
      width: 26px;
      height: 26px;
      border-radius: 50% 50% 50% 0;

      -webkit-transform: rotate(-45deg) scale(0) translate(0);
      transform: rotate(-45deg) scale(0) translate(0);
    }

    .slider-knob-inner::before,
    .slider-knob-inner::after {
      transition: -webkit-transform .18s ease, background-color .18s ease;
      transition: transform .18s ease, background-color .18s ease;
    }

    .pin.ring > .slider-knob > .slider-knob-inner::before {
      background-color: var(--paper-slider-pin-start-color, var(--paper-grey-400));
    }

    .pin.expand > .slider-knob > .slider-knob-inner::before {
      -webkit-transform: rotate(-45deg) scale(1) translate(17px, -17px);
      transform: rotate(-45deg) scale(1) translate(17px, -17px);
    }

    .pin > .slider-knob > .slider-knob-inner::after {
      content: attr(value);
      position: absolute;
      top: 0;
      left: 50%;
      margin-left: -16px;
      width: 32px;
      height: 26px;
      text-align: center;
      color: var(--paper-slider-font-color, #fff);
      font-size: 10px;

      -webkit-transform: scale(0) translate(0);
      transform: scale(0) translate(0);
    }

    .pin.expand > .slider-knob > .slider-knob-inner::after {
      -webkit-transform: scale(1) translate(0, -17px);
      transform: scale(1) translate(0, -17px);
    }

    /* paper-input */
    .slider-input {
      width: 50px;
      overflow: hidden;
      --paper-input-container-input: {
        text-align: center;
        @apply --paper-slider-input-container-input;
      };
      @apply --paper-slider-input;
    }

    /* disabled state */
    #sliderContainer.disabled {
      pointer-events: none;
    }

    .disabled > .slider-knob > .slider-knob-inner {
      background-color: var(--paper-slider-disabled-knob-color, var(--paper-grey-400));
      border: 2px solid var(--paper-slider-disabled-knob-color, var(--paper-grey-400));
      -webkit-transform: scale3d(0.75, 0.75, 1);
      transform: scale3d(0.75, 0.75, 1);
    }

    .disabled.ring > .slider-knob > .slider-knob-inner {
      background-color: var(--paper-slider-knob-start-color, transparent);
      border: 2px solid var(--paper-slider-knob-start-border-color, var(--paper-grey-400));
    }

    paper-ripple {
      color: var(--paper-slider-knob-color, var(--google-blue-700));
    }
  </style>

  <div id="sliderContainer" class\$="[[_getClassNames(disabled, pin, snaps, immediateValue, min, expand, dragging, transiting, editable)]]">
    <div class="bar-container">
      <paper-progress disabled\$="[[disabled]]" id="sliderBar" aria-hidden="true" min="[[min]]" max="[[max]]" step="[[step]]" value="[[immediateValue]]" secondary-progress="[[secondaryProgress]]" on-down="_bardown" on-up="_resetKnob" on-track="_bartrack" on-tap="_barclick">
      </paper-progress>
    </div>

    <template is="dom-if" if="[[snaps]]">
      <div class="slider-markers">
        <template is="dom-repeat" items="[[markers]]">
          <div class="slider-marker"></div>
        </template>
      </div>
    </template>

    <div id="sliderKnob" class="slider-knob" on-down="_knobdown" on-up="_resetKnob" on-track="_onTrack" on-transitionend="_knobTransitionEnd">
        <div class="slider-knob-inner" value\$="[[immediateValue]]"></div>
    </div>
  </div>

  <template is="dom-if" if="[[editable]]">
    <paper-input id="input" type="number" step="[[step]]" min="[[min]]" max="[[max]]" class="slider-input" disabled\$="[[disabled]]" value="[[immediateValue]]" on-change="_changeValue" on-keydown="_inputKeyDown" no-label-float>
    </paper-input>
  </template>
`;rs.setAttribute("strip-whitespace",""),Rr({_template:rs,is:"paper-slider",behaviors:[zo,ra,ca,ts],properties:{value:{type:Number,value:0},snaps:{type:Boolean,value:!1,notify:!0},pin:{type:Boolean,value:!1,notify:!0},secondaryProgress:{type:Number,value:0,notify:!0,observer:"_secondaryProgressChanged"},editable:{type:Boolean,value:!1},immediateValue:{type:Number,value:0,readOnly:!0,notify:!0},maxMarkers:{type:Number,value:0,notify:!0},expand:{type:Boolean,value:!1,readOnly:!0},ignoreBarTouch:{type:Boolean,value:!1},dragging:{type:Boolean,value:!1,readOnly:!0,notify:!0},transiting:{type:Boolean,value:!1,readOnly:!0},markers:{type:Array,readOnly:!0,value:function(){return[]}}},observers:["_updateKnob(value, min, max, snaps, step)","_valueChanged(value)","_immediateValueChanged(immediateValue)","_updateMarkers(maxMarkers, min, max, snaps)"],hostAttributes:{role:"slider",tabindex:0},keyBindings:{left:"_leftKey",right:"_rightKey","down pagedown home":"_decrementKey","up pageup end":"_incrementKey"},ready:function(){this.ignoreBarTouch&&oi(this.$.sliderBar,"auto")},increment:function(){this.value=this._clampValue(this.value+this.step)},decrement:function(){this.value=this._clampValue(this.value-this.step)},_updateKnob:function(t,e,n,i,r){this.setAttribute("aria-valuemin",e),this.setAttribute("aria-valuemax",n),this.setAttribute("aria-valuenow",t),this._positionKnob(100*this._calcRatio(t))},_valueChanged:function(){this.fire("value-change",{composed:!0})},_immediateValueChanged:function(){this.dragging?this.fire("immediate-value-change",{composed:!0}):this.value=this.immediateValue},_secondaryProgressChanged:function(){this.secondaryProgress=this._clampValue(this.secondaryProgress)},_expandKnob:function(){this._setExpand(!0)},_resetKnob:function(){this.cancelDebouncer("expandKnob"),this._setExpand(!1)},_positionKnob:function(t){this._setImmediateValue(this._calcStep(this._calcKnobPosition(t))),this._setRatio(100*this._calcRatio(this.immediateValue)),this.$.sliderKnob.style.left=this.ratio+"%",this.dragging&&(this._knobstartx=this.ratio*this._w/100,this.translate3d(0,0,0,this.$.sliderKnob))},_calcKnobPosition:function(t){return(this.max-this.min)*t/100+this.min},_onTrack:function(t){switch(t.stopPropagation(),t.detail.state){case"start":this._trackStart(t);break;case"track":this._trackX(t);break;case"end":this._trackEnd()}},_trackStart:function(t){this._setTransiting(!1),this._w=this.$.sliderBar.offsetWidth,this._x=this.ratio*this._w/100,this._startx=this._x,this._knobstartx=this._startx,this._minx=-this._startx,this._maxx=this._w-this._startx,this.$.sliderKnob.classList.add("dragging"),this._setDragging(!0)},_trackX:function(t){this.dragging||this._trackStart(t);var e=Math.min(this._maxx,Math.max(this._minx,t.detail.dx*(this._isRTL?-1:1)));this._x=this._startx+e;var n=this._calcStep(this._calcKnobPosition(this._x/this._w*100));this._setImmediateValue(n);var i=this._calcRatio(this.immediateValue)*this._w-this._knobstartx;this.translate3d(i+"px",0,0,this.$.sliderKnob)},_trackEnd:function(){var t=this.$.sliderKnob.style;this.$.sliderKnob.classList.remove("dragging"),this._setDragging(!1),this._resetKnob(),this.value=this.immediateValue,t.transform=t.webkitTransform="",this.fire("change",{composed:!0})},_knobdown:function(t){this._expandKnob(),t.preventDefault(),this.focus()},_bartrack:function(t){this._allowBarEvent(t)&&this._onTrack(t)},_barclick:function(t){this._w=this.$.sliderBar.offsetWidth;var e=this.$.sliderBar.getBoundingClientRect(),n=(t.detail.x-e.left)/this._w*100;this._isRTL&&(n=100-n);var i=this.ratio;this._setTransiting(!0),this._positionKnob(n),i===this.ratio&&this._setTransiting(!1),this.async((function(){this.fire("change",{composed:!0})})),t.preventDefault(),this.focus()},_bardown:function(t){this._allowBarEvent(t)&&(this.debounce("expandKnob",this._expandKnob,60),this._barclick(t))},_knobTransitionEnd:function(t){t.target===this.$.sliderKnob&&this._setTransiting(!1)},_updateMarkers:function(t,e,n,i){i||this._setMarkers([]);var r=Math.round((n-e)/this.step);r>t&&(r=t),(r<0||!isFinite(r))&&(r=0),this._setMarkers(new Array(r))},_mergeClasses:function(t){return Object.keys(t).filter((function(e){return t[e]})).join(" ")},_getClassNames:function(){return this._mergeClasses({disabled:this.disabled,pin:this.pin,snaps:this.snaps,ring:this.immediateValue<=this.min,expand:this.expand,dragging:this.dragging,transiting:this.transiting,editable:this.editable})},_allowBarEvent:function(t){return!this.ignoreBarTouch||t.detail.sourceEvent instanceof MouseEvent},get _isRTL(){return void 0===this.__isRTL&&(this.__isRTL="rtl"===window.getComputedStyle(this).direction),this.__isRTL},_leftKey:function(t){this._isRTL?this._incrementKey(t):this._decrementKey(t)},_rightKey:function(t){this._isRTL?this._decrementKey(t):this._incrementKey(t)},_incrementKey:function(t){this.disabled||("end"===t.detail.key?this.value=this.max:this.increment(),this.fire("change"),t.preventDefault())},_decrementKey:function(t){this.disabled||("home"===t.detail.key?this.value=this.min:this.decrement(),this.fire("change"),t.preventDefault())},_changeValue:function(t){this.value=t.target.value,this.fire("change",{composed:!0})},_inputKeyDown:function(t){t.stopPropagation()},_createRipple:function(){return this._rippleContainer=this.$.sliderKnob,la._createRipple.call(this)},_focusedChanged:function(t){t&&this.ensureRipple(),this.hasRipple()&&(this._ripple.style.display=t?"":"none",this._ripple.holdDown=t)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const os=document.createElement("template");os.setAttribute("style","display: none;"),os.innerHTML="<dom-module id=\"paper-spinner-styles\">\n  <template>\n    <style>\n      /*\n      /**************************/\n      /* STYLES FOR THE SPINNER */\n      /**************************/\n\n      /*\n       * Constants:\n       *      ARCSIZE     = 270 degrees (amount of circle the arc takes up)\n       *      ARCTIME     = 1333ms (time it takes to expand and contract arc)\n       *      ARCSTARTROT = 216 degrees (how much the start location of the arc\n       *                                should rotate each time, 216 gives us a\n       *                                5 pointed star shape (it's 360/5 * 3).\n       *                                For a 7 pointed star, we might do\n       *                                360/7 * 3 = 154.286)\n       *      SHRINK_TIME = 400ms\n       */\n\n      :host {\n        display: inline-block;\n        position: relative;\n        width: 28px;\n        height: 28px;\n\n        /* 360 * ARCTIME / (ARCSTARTROT + (360-ARCSIZE)) */\n        --paper-spinner-container-rotation-duration: 1568ms;\n\n        /* ARCTIME */\n        --paper-spinner-expand-contract-duration: 1333ms;\n\n        /* 4 * ARCTIME */\n        --paper-spinner-full-cycle-duration: 5332ms;\n\n        /* SHRINK_TIME */\n        --paper-spinner-cooldown-duration: 400ms;\n      }\n\n      #spinnerContainer {\n        width: 100%;\n        height: 100%;\n\n        /* The spinner does not have any contents that would have to be\n         * flipped if the direction changes. Always use ltr so that the\n         * style works out correctly in both cases. */\n        direction: ltr;\n      }\n\n      #spinnerContainer.active {\n        -webkit-animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite;\n        animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite;\n      }\n\n      @-webkit-keyframes container-rotate {\n        to { -webkit-transform: rotate(360deg) }\n      }\n\n      @keyframes container-rotate {\n        to { transform: rotate(360deg) }\n      }\n\n      .spinner-layer {\n        position: absolute;\n        width: 100%;\n        height: 100%;\n        opacity: 0;\n        white-space: nowrap;\n        color: var(--paper-spinner-color, var(--google-blue-500));\n      }\n\n      .layer-1 {\n        color: var(--paper-spinner-layer-1-color, var(--google-blue-500));\n      }\n\n      .layer-2 {\n        color: var(--paper-spinner-layer-2-color, var(--google-red-500));\n      }\n\n      .layer-3 {\n        color: var(--paper-spinner-layer-3-color, var(--google-yellow-500));\n      }\n\n      .layer-4 {\n        color: var(--paper-spinner-layer-4-color, var(--google-green-500));\n      }\n\n      /**\n       * IMPORTANT NOTE ABOUT CSS ANIMATION PROPERTIES (keanulee):\n       *\n       * iOS Safari (tested on iOS 8.1) does not handle animation-delay very well - it doesn't\n       * guarantee that the animation will start _exactly_ after that value. So we avoid using\n       * animation-delay and instead set custom keyframes for each color (as layer-2undant as it\n       * seems).\n       */\n      .active .spinner-layer {\n        -webkit-animation-name: fill-unfill-rotate;\n        -webkit-animation-duration: var(--paper-spinner-full-cycle-duration);\n        -webkit-animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);\n        -webkit-animation-iteration-count: infinite;\n        animation-name: fill-unfill-rotate;\n        animation-duration: var(--paper-spinner-full-cycle-duration);\n        animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);\n        animation-iteration-count: infinite;\n        opacity: 1;\n      }\n\n      .active .spinner-layer.layer-1 {\n        -webkit-animation-name: fill-unfill-rotate, layer-1-fade-in-out;\n        animation-name: fill-unfill-rotate, layer-1-fade-in-out;\n      }\n\n      .active .spinner-layer.layer-2 {\n        -webkit-animation-name: fill-unfill-rotate, layer-2-fade-in-out;\n        animation-name: fill-unfill-rotate, layer-2-fade-in-out;\n      }\n\n      .active .spinner-layer.layer-3 {\n        -webkit-animation-name: fill-unfill-rotate, layer-3-fade-in-out;\n        animation-name: fill-unfill-rotate, layer-3-fade-in-out;\n      }\n\n      .active .spinner-layer.layer-4 {\n        -webkit-animation-name: fill-unfill-rotate, layer-4-fade-in-out;\n        animation-name: fill-unfill-rotate, layer-4-fade-in-out;\n      }\n\n      @-webkit-keyframes fill-unfill-rotate {\n        12.5% { -webkit-transform: rotate(135deg) } /* 0.5 * ARCSIZE */\n        25%   { -webkit-transform: rotate(270deg) } /* 1   * ARCSIZE */\n        37.5% { -webkit-transform: rotate(405deg) } /* 1.5 * ARCSIZE */\n        50%   { -webkit-transform: rotate(540deg) } /* 2   * ARCSIZE */\n        62.5% { -webkit-transform: rotate(675deg) } /* 2.5 * ARCSIZE */\n        75%   { -webkit-transform: rotate(810deg) } /* 3   * ARCSIZE */\n        87.5% { -webkit-transform: rotate(945deg) } /* 3.5 * ARCSIZE */\n        to    { -webkit-transform: rotate(1080deg) } /* 4   * ARCSIZE */\n      }\n\n      @keyframes fill-unfill-rotate {\n        12.5% { transform: rotate(135deg) } /* 0.5 * ARCSIZE */\n        25%   { transform: rotate(270deg) } /* 1   * ARCSIZE */\n        37.5% { transform: rotate(405deg) } /* 1.5 * ARCSIZE */\n        50%   { transform: rotate(540deg) } /* 2   * ARCSIZE */\n        62.5% { transform: rotate(675deg) } /* 2.5 * ARCSIZE */\n        75%   { transform: rotate(810deg) } /* 3   * ARCSIZE */\n        87.5% { transform: rotate(945deg) } /* 3.5 * ARCSIZE */\n        to    { transform: rotate(1080deg) } /* 4   * ARCSIZE */\n      }\n\n      @-webkit-keyframes layer-1-fade-in-out {\n        0% { opacity: 1 }\n        25% { opacity: 1 }\n        26% { opacity: 0 }\n        89% { opacity: 0 }\n        90% { opacity: 1 }\n        to { opacity: 1 }\n      }\n\n      @keyframes layer-1-fade-in-out {\n        0% { opacity: 1 }\n        25% { opacity: 1 }\n        26% { opacity: 0 }\n        89% { opacity: 0 }\n        90% { opacity: 1 }\n        to { opacity: 1 }\n      }\n\n      @-webkit-keyframes layer-2-fade-in-out {\n        0% { opacity: 0 }\n        15% { opacity: 0 }\n        25% { opacity: 1 }\n        50% { opacity: 1 }\n        51% { opacity: 0 }\n        to { opacity: 0 }\n      }\n\n      @keyframes layer-2-fade-in-out {\n        0% { opacity: 0 }\n        15% { opacity: 0 }\n        25% { opacity: 1 }\n        50% { opacity: 1 }\n        51% { opacity: 0 }\n        to { opacity: 0 }\n      }\n\n      @-webkit-keyframes layer-3-fade-in-out {\n        0% { opacity: 0 }\n        40% { opacity: 0 }\n        50% { opacity: 1 }\n        75% { opacity: 1 }\n        76% { opacity: 0 }\n        to { opacity: 0 }\n      }\n\n      @keyframes layer-3-fade-in-out {\n        0% { opacity: 0 }\n        40% { opacity: 0 }\n        50% { opacity: 1 }\n        75% { opacity: 1 }\n        76% { opacity: 0 }\n        to { opacity: 0 }\n      }\n\n      @-webkit-keyframes layer-4-fade-in-out {\n        0% { opacity: 0 }\n        65% { opacity: 0 }\n        75% { opacity: 1 }\n        90% { opacity: 1 }\n        to { opacity: 0 }\n      }\n\n      @keyframes layer-4-fade-in-out {\n        0% { opacity: 0 }\n        65% { opacity: 0 }\n        75% { opacity: 1 }\n        90% { opacity: 1 }\n        to { opacity: 0 }\n      }\n\n      .circle-clipper {\n        display: inline-block;\n        position: relative;\n        width: 50%;\n        height: 100%;\n        overflow: hidden;\n      }\n\n      /**\n       * Patch the gap that appear between the two adjacent div.circle-clipper while the\n       * spinner is rotating (appears on Chrome 50, Safari 9.1.1, and Edge).\n       */\n      .spinner-layer::after {\n        content: '';\n        left: 45%;\n        width: 10%;\n        border-top-style: solid;\n      }\n\n      .spinner-layer::after,\n      .circle-clipper .circle {\n        box-sizing: border-box;\n        position: absolute;\n        top: 0;\n        border-width: var(--paper-spinner-stroke-width, 3px);\n        border-radius: 50%;\n      }\n\n      .circle-clipper .circle {\n        bottom: 0;\n        width: 200%;\n        border-style: solid;\n        border-bottom-color: transparent !important;\n      }\n\n      .circle-clipper.left .circle {\n        left: 0;\n        border-right-color: transparent !important;\n        -webkit-transform: rotate(129deg);\n        transform: rotate(129deg);\n      }\n\n      .circle-clipper.right .circle {\n        left: -100%;\n        border-left-color: transparent !important;\n        -webkit-transform: rotate(-129deg);\n        transform: rotate(-129deg);\n      }\n\n      .active .gap-patch::after,\n      .active .circle-clipper .circle {\n        -webkit-animation-duration: var(--paper-spinner-expand-contract-duration);\n        -webkit-animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);\n        -webkit-animation-iteration-count: infinite;\n        animation-duration: var(--paper-spinner-expand-contract-duration);\n        animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);\n        animation-iteration-count: infinite;\n      }\n\n      .active .circle-clipper.left .circle {\n        -webkit-animation-name: left-spin;\n        animation-name: left-spin;\n      }\n\n      .active .circle-clipper.right .circle {\n        -webkit-animation-name: right-spin;\n        animation-name: right-spin;\n      }\n\n      @-webkit-keyframes left-spin {\n        0% { -webkit-transform: rotate(130deg) }\n        50% { -webkit-transform: rotate(-5deg) }\n        to { -webkit-transform: rotate(130deg) }\n      }\n\n      @keyframes left-spin {\n        0% { transform: rotate(130deg) }\n        50% { transform: rotate(-5deg) }\n        to { transform: rotate(130deg) }\n      }\n\n      @-webkit-keyframes right-spin {\n        0% { -webkit-transform: rotate(-130deg) }\n        50% { -webkit-transform: rotate(5deg) }\n        to { -webkit-transform: rotate(-130deg) }\n      }\n\n      @keyframes right-spin {\n        0% { transform: rotate(-130deg) }\n        50% { transform: rotate(5deg) }\n        to { transform: rotate(-130deg) }\n      }\n\n      #spinnerContainer.cooldown {\n        -webkit-animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite, fade-out var(--paper-spinner-cooldown-duration) cubic-bezier(0.4, 0.0, 0.2, 1);\n        animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite, fade-out var(--paper-spinner-cooldown-duration) cubic-bezier(0.4, 0.0, 0.2, 1);\n      }\n\n      @-webkit-keyframes fade-out {\n        0% { opacity: 1 }\n        to { opacity: 0 }\n      }\n\n      @keyframes fade-out {\n        0% { opacity: 1 }\n        to { opacity: 0 }\n      }\n    </style>\n  </template>\n</dom-module>",document.head.appendChild(os.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const as={properties:{active:{type:Boolean,value:!1,reflectToAttribute:!0,observer:"__activeChanged"},alt:{type:String,value:"loading",observer:"__altChanged"},__coolingDown:{type:Boolean,value:!1}},__computeContainerClasses:function(t,e){return[t||e?"active":"",e?"cooldown":""].join(" ")},__activeChanged:function(t,e){this.__setAriaHidden(!t),this.__coolingDown=!t&&e},__altChanged:function(t){"loading"===t?this.alt=this.getAttribute("aria-label")||t:(this.__setAriaHidden(""===t),this.setAttribute("aria-label",t))},__setAriaHidden:function(t){var e="aria-hidden";t?this.setAttribute(e,"true"):this.removeAttribute(e)},__reset:function(){this.active=!1,this.__coolingDown=!1}},ss=_e`
  <style include="paper-spinner-styles"></style>

  <div id="spinnerContainer" class-name="[[__computeContainerClasses(active, __coolingDown)]]" on-animationend="__reset" on-webkit-animation-end="__reset">
    <div class="spinner-layer">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>
  </div>
`;
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */ss.setAttribute("strip-whitespace",""),Rr({_template:ss,is:"paper-spinner-lite",behaviors:[as]});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ls=_e`
  <style include="paper-spinner-styles"></style>

  <div id="spinnerContainer" class-name="[[__computeContainerClasses(active, __coolingDown)]]" on-animationend="__reset" on-webkit-animation-end="__reset">
    <div class="spinner-layer layer-1">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>

    <div class="spinner-layer layer-2">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>

    <div class="spinner-layer layer-3">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>

    <div class="spinner-layer layer-4">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>
  </div>
`;ls.setAttribute("strip-whitespace",""),Rr({_template:ls,is:"paper-spinner",behaviors:[as]});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const cs=_e`<iron-iconset-svg name="paper-tabs" size="24">
<svg><defs>
<g id="chevron-left"><path d="M15.41 7.41L14 6l-6 6 6 6 1.41-1.41L10.83 12z"></path></g>
<g id="chevron-right"><path d="M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z"></path></g>
</defs></svg>
</iron-iconset-svg>`;document.head.appendChild(cs.content),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        @apply --layout-inline;
        @apply --layout-center;
        @apply --layout-center-justified;
        @apply --layout-flex-auto;

        position: relative;
        padding: 0 12px;
        overflow: hidden;
        cursor: pointer;
        vertical-align: middle;

        @apply --paper-font-common-base;
        @apply --paper-tab;
      }

      :host(:focus) {
        outline: none;
      }

      :host([link]) {
        padding: 0;
      }

      .tab-content {
        height: 100%;
        transform: translateZ(0);
          -webkit-transform: translateZ(0);
        transition: opacity 0.1s cubic-bezier(0.4, 0.0, 1, 1);
        @apply --layout-horizontal;
        @apply --layout-center-center;
        @apply --layout-flex-auto;
        @apply --paper-tab-content;
      }

      :host(:not(.iron-selected)) > .tab-content {
        opacity: 0.8;

        @apply --paper-tab-content-unselected;
      }

      :host(:focus) .tab-content {
        opacity: 1;
        font-weight: 700;

        @apply --paper-tab-content-focused;
      }

      paper-ripple {
        color: var(--paper-tab-ink, var(--paper-yellow-a100));
      }

      .tab-content > ::slotted(a) {
        @apply --layout-flex-auto;

        height: 100%;
      }
    </style>

    <div class="tab-content">
      <slot></slot>
    </div>
`,is:"paper-tab",behaviors:[qo,Xo,Jo],properties:{link:{type:Boolean,value:!1,reflectToAttribute:!0}},hostAttributes:{role:"tab"},listeners:{down:"_updateNoink",tap:"_onTap"},attached:function(){this._updateNoink()},get _parentNoink(){var t=Yi(this).parentNode;return!!t&&!!t.noink},_updateNoink:function(){this.noink=!!this.noink||!!this._parentNoink},_onTap:function(t){if(this.link){var e=this.queryEffectiveChildren("a");if(!e)return;if(t.target===e)return;e.click()}}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        @apply --layout;
        @apply --layout-center;

        height: 48px;
        font-size: 14px;
        font-weight: 500;
        overflow: hidden;
        -moz-user-select: none;
        -ms-user-select: none;
        -webkit-user-select: none;
        user-select: none;

        /* NOTE: Both values are needed, since some phones require the value to be \`transparent\`. */
        -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
        -webkit-tap-highlight-color: transparent;

        @apply --paper-tabs;
      }

      :host(:dir(rtl)) {
        @apply --layout-horizontal-reverse;
      }

      #tabsContainer {
        position: relative;
        height: 100%;
        white-space: nowrap;
        overflow: hidden;
        @apply --layout-flex-auto;
        @apply --paper-tabs-container;
      }

      #tabsContent {
        height: 100%;
        -moz-flex-basis: auto;
        -ms-flex-basis: auto;
        flex-basis: auto;
        @apply --paper-tabs-content;
      }

      #tabsContent.scrollable {
        position: absolute;
        white-space: nowrap;
      }

      #tabsContent:not(.scrollable),
      #tabsContent.scrollable.fit-container {
        @apply --layout-horizontal;
      }

      #tabsContent.scrollable.fit-container {
        min-width: 100%;
      }

      #tabsContent.scrollable.fit-container > ::slotted(*) {
        /* IE - prevent tabs from compressing when they should scroll. */
        -ms-flex: 1 0 auto;
        -webkit-flex: 1 0 auto;
        flex: 1 0 auto;
      }

      .hidden {
        display: none;
      }

      .not-visible {
        opacity: 0;
        cursor: default;
      }

      paper-icon-button {
        width: 48px;
        height: 48px;
        padding: 12px;
        margin: 0 4px;
      }

      #selectionBar {
        position: absolute;
        height: 0;
        bottom: 0;
        left: 0;
        right: 0;
        border-bottom: 2px solid var(--paper-tabs-selection-bar-color, var(--paper-yellow-a100));
          -webkit-transform: scale(0);
        transform: scale(0);
          -webkit-transform-origin: left center;
        transform-origin: left center;
          transition: -webkit-transform;
        transition: transform;

        @apply --paper-tabs-selection-bar;
      }

      #selectionBar.align-bottom {
        top: 0;
        bottom: auto;
      }

      #selectionBar.expand {
        transition-duration: 0.15s;
        transition-timing-function: cubic-bezier(0.4, 0.0, 1, 1);
      }

      #selectionBar.contract {
        transition-duration: 0.18s;
        transition-timing-function: cubic-bezier(0.0, 0.0, 0.2, 1);
      }

      #tabsContent > ::slotted(:not(#selectionBar)) {
        height: 100%;
      }
    </style>

    <paper-icon-button icon="paper-tabs:chevron-left" class$="[[_computeScrollButtonClass(_leftHidden, scrollable, hideScrollButtons)]]" on-up="_onScrollButtonUp" on-down="_onLeftScrollButtonDown" tabindex="-1"></paper-icon-button>

    <div id="tabsContainer" on-track="_scroll" on-down="_down">
      <div id="tabsContent" class$="[[_computeTabsContentClass(scrollable, fitContainer)]]">
        <div id="selectionBar" class$="[[_computeSelectionBarClass(noBar, alignBottom)]]" on-transitionend="_onBarTransitionEnd"></div>
        <slot></slot>
      </div>
    </div>

    <paper-icon-button icon="paper-tabs:chevron-right" class$="[[_computeScrollButtonClass(_rightHidden, scrollable, hideScrollButtons)]]" on-up="_onScrollButtonUp" on-down="_onRightScrollButtonDown" tabindex="-1"></paper-icon-button>
`,is:"paper-tabs",behaviors:[go,is],properties:{noink:{type:Boolean,value:!1,observer:"_noinkChanged"},noBar:{type:Boolean,value:!1},noSlide:{type:Boolean,value:!1},scrollable:{type:Boolean,value:!1},fitContainer:{type:Boolean,value:!1},disableDrag:{type:Boolean,value:!1},hideScrollButtons:{type:Boolean,value:!1},alignBottom:{type:Boolean,value:!1},selectable:{type:String,value:"paper-tab"},autoselect:{type:Boolean,value:!1},autoselectDelay:{type:Number,value:0},_step:{type:Number,value:10},_holdDelay:{type:Number,value:1},_leftHidden:{type:Boolean,value:!1},_rightHidden:{type:Boolean,value:!1},_previousTab:{type:Object}},hostAttributes:{role:"tablist"},listeners:{"iron-resize":"_onTabSizingChanged","iron-items-changed":"_onTabSizingChanged","iron-select":"_onIronSelect","iron-deselect":"_onIronDeselect"},keyBindings:{"left:keyup right:keyup":"_onArrowKeyup"},created:function(){this._holdJob=null,this._pendingActivationItem=void 0,this._pendingActivationTimeout=void 0,this._bindDelayedActivationHandler=this._delayedActivationHandler.bind(this),this.addEventListener("blur",this._onBlurCapture.bind(this),!0)},ready:function(){this.setScrollDirection("y",this.$.tabsContainer)},detached:function(){this._cancelPendingActivation()},_noinkChanged:function(t){Yi(this).querySelectorAll("paper-tab").forEach(t?this._setNoinkAttribute:this._removeNoinkAttribute)},_setNoinkAttribute:function(t){t.setAttribute("noink","")},_removeNoinkAttribute:function(t){t.removeAttribute("noink")},_computeScrollButtonClass:function(t,e,n){return!e||n?"hidden":t?"not-visible":""},_computeTabsContentClass:function(t,e){return t?"scrollable"+(e?" fit-container":""):" fit-container"},_computeSelectionBarClass:function(t,e){return t?"hidden":e?"align-bottom":""},_onTabSizingChanged:function(){this.debounce("_onTabSizingChanged",(function(){this._scroll(),this._tabChanged(this.selectedItem)}),10)},_onIronSelect:function(t){this._tabChanged(t.detail.item,this._previousTab),this._previousTab=t.detail.item,this.cancelDebouncer("tab-changed")},_onIronDeselect:function(t){this.debounce("tab-changed",(function(){this._tabChanged(null,this._previousTab),this._previousTab=null}),1)},_activateHandler:function(){this._cancelPendingActivation(),Za._activateHandler.apply(this,arguments)},_scheduleActivation:function(t,e){this._pendingActivationItem=t,this._pendingActivationTimeout=this.async(this._bindDelayedActivationHandler,e)},_delayedActivationHandler:function(){var t=this._pendingActivationItem;this._pendingActivationItem=void 0,this._pendingActivationTimeout=void 0,t.fire(this.activateEvent,null,{bubbles:!0,cancelable:!0})},_cancelPendingActivation:function(){void 0!==this._pendingActivationTimeout&&(this.cancelAsync(this._pendingActivationTimeout),this._pendingActivationItem=void 0,this._pendingActivationTimeout=void 0)},_onArrowKeyup:function(t){this.autoselect&&this._scheduleActivation(this.focusedItem,this.autoselectDelay)},_onBlurCapture:function(t){t.target===this._pendingActivationItem&&this._cancelPendingActivation()},get _tabContainerScrollSize(){return Math.max(0,this.$.tabsContainer.scrollWidth-this.$.tabsContainer.offsetWidth)},_scroll:function(t,e){this.scrollable&&this._affectScroll(e&&-e.ddx||0)},_down:function(t){this.async((function(){this._defaultFocusAsync&&(this.cancelAsync(this._defaultFocusAsync),this._defaultFocusAsync=null)}),1)},_affectScroll:function(t){this.$.tabsContainer.scrollLeft+=t;var e=this.$.tabsContainer.scrollLeft;this._leftHidden=0===e,this._rightHidden=e===this._tabContainerScrollSize},_onLeftScrollButtonDown:function(){this._scrollToLeft(),this._holdJob=setInterval(this._scrollToLeft.bind(this),this._holdDelay)},_onRightScrollButtonDown:function(){this._scrollToRight(),this._holdJob=setInterval(this._scrollToRight.bind(this),this._holdDelay)},_onScrollButtonUp:function(){clearInterval(this._holdJob),this._holdJob=null},_scrollToLeft:function(){this._affectScroll(-this._step)},_scrollToRight:function(){this._affectScroll(this._step)},_tabChanged:function(t,e){if(!t)return this.$.selectionBar.classList.remove("expand"),this.$.selectionBar.classList.remove("contract"),void this._positionBar(0,0);var n=this.$.tabsContent.getBoundingClientRect(),i=n.width,r=t.getBoundingClientRect(),o=r.left-n.left;if(this._pos={width:this._calcPercent(r.width,i),left:this._calcPercent(o,i)},this.noSlide||null==e)return this.$.selectionBar.classList.remove("expand"),this.$.selectionBar.classList.remove("contract"),void this._positionBar(this._pos.width,this._pos.left);var a=e.getBoundingClientRect(),s=this.items.indexOf(e),l=this.items.indexOf(t);this.$.selectionBar.classList.add("expand");var c=s<l;this._isRTL&&(c=!c),c?this._positionBar(this._calcPercent(r.left+r.width-a.left,i)-5,this._left):this._positionBar(this._calcPercent(a.left+a.width-r.left,i)-5,this._calcPercent(o,i)+5),this.scrollable&&this._scrollToSelectedIfNeeded(r.width,o)},_scrollToSelectedIfNeeded:function(t,e){var n=e-this.$.tabsContainer.scrollLeft;(n<0||(n+=t-this.$.tabsContainer.offsetWidth)>0)&&(this.$.tabsContainer.scrollLeft+=n)},_calcPercent:function(t,e){return 100*t/e},_positionBar:function(t,e){e=e||0,this._width=t=t||0,this._left=e,this.transform("translateX("+e+"%) scaleX("+t/100+")",this.$.selectionBar)},_onBarTransitionEnd:function(t){var e=this.$.selectionBar.classList;e.contains("expand")?(e.remove("expand"),e.add("contract"),this._positionBar(this._pos.width,this._pos.left)):e.contains("contract")&&e.remove("contract")}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var us=null;Rr({_template:_e`
    <style>
      :host {
        display: block;
        position: fixed;
        background-color: var(--paper-toast-background-color, #323232);
        color: var(--paper-toast-color, #f1f1f1);
        min-height: 48px;
        min-width: 288px;
        padding: 16px 24px;
        box-sizing: border-box;
        box-shadow: 0 2px 5px 0 rgba(0, 0, 0, 0.26);
        border-radius: 2px;
        margin: 12px;
        font-size: 14px;
        cursor: default;
        -webkit-transition: -webkit-transform 0.3s, opacity 0.3s;
        transition: transform 0.3s, opacity 0.3s;
        opacity: 0;
        -webkit-transform: translateY(100px);
        transform: translateY(100px);
        @apply --paper-font-common-base;
      }

      :host(.capsule) {
        border-radius: 24px;
      }

      :host(.fit-bottom) {
        width: 100%;
        min-width: 0;
        border-radius: 0;
        margin: 0;
      }

      :host(.paper-toast-open) {
        opacity: 1;
        -webkit-transform: translateY(0px);
        transform: translateY(0px);
      }
    </style>

    <span id="label">{{text}}</span>
    <slot></slot>
`,is:"paper-toast",behaviors:[ka],properties:{fitInto:{type:Object,value:window,observer:"_onFitIntoChanged"},horizontalAlign:{type:String,value:"left"},verticalAlign:{type:String,value:"bottom"},duration:{type:Number,value:3e3},text:{type:String,value:""},noCancelOnOutsideClick:{type:Boolean,value:!0},noAutoFocus:{type:Boolean,value:!0}},listeners:{transitionend:"__onTransitionEnd"},get visible(){return fo._warn("`visible` is deprecated, use `opened` instead"),this.opened},get _canAutoClose(){return this.duration>0&&this.duration!==1/0},created:function(){this._autoClose=null,Oa.requestAvailability()},show:function(t){for(var e in"string"==typeof t&&(t={text:t}),t)0===e.indexOf("_")?fo._warn('The property "'+e+'" is private and was not set.'):e in this?this[e]=t[e]:fo._warn('The property "'+e+'" is not valid.');this.open()},hide:function(){this.close()},__onTransitionEnd:function(t){t&&t.target===this&&"opacity"===t.propertyName&&(this.opened?this._finishRenderOpened():this._finishRenderClosed())},_openedChanged:function(){null!==this._autoClose&&(this.cancelAsync(this._autoClose),this._autoClose=null),this.opened?(us&&us!==this&&us.close(),us=this,this.fire("iron-announce",{text:this.text}),this._canAutoClose&&(this._autoClose=this.async(this.close,this.duration))):us===this&&(us=null),Ca._openedChanged.apply(this,arguments)},_renderOpened:function(){this.classList.add("paper-toast-open")},_renderClosed:function(){this.classList.remove("paper-toast-open")},_onFitIntoChanged:function(t){this.positionTarget=t}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const hs=_e`

    <style>
      :host {
        display: inline-block;
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-font-common-base;
      }

      :host([disabled]) {
        pointer-events: none;
      }

      :host(:focus) {
        outline:none;
      }

      .toggle-bar {
        position: absolute;
        height: 100%;
        width: 100%;
        border-radius: 8px;
        pointer-events: none;
        opacity: 0.4;
        transition: background-color linear .08s;
        background-color: var(--paper-toggle-button-unchecked-bar-color, #000000);

        @apply --paper-toggle-button-unchecked-bar;
      }

      .toggle-button {
        position: absolute;
        top: -3px;
        left: 0;
        height: 20px;
        width: 20px;
        border-radius: 50%;
        box-shadow: 0 1px 5px 0 rgba(0, 0, 0, 0.6);
        transition: -webkit-transform linear .08s, background-color linear .08s;
        transition: transform linear .08s, background-color linear .08s;
        will-change: transform;
        background-color: var(--paper-toggle-button-unchecked-button-color, var(--paper-grey-50));

        @apply --paper-toggle-button-unchecked-button;
      }

      .toggle-button.dragging {
        -webkit-transition: none;
        transition: none;
      }

      :host([checked]:not([disabled])) .toggle-bar {
        opacity: 0.5;
        background-color: var(--paper-toggle-button-checked-bar-color, var(--primary-color));

        @apply --paper-toggle-button-checked-bar;
      }

      :host([disabled]) .toggle-bar {
        background-color: #000;
        opacity: 0.12;
      }

      :host([checked]) .toggle-button {
        -webkit-transform: translate(16px, 0);
        transform: translate(16px, 0);
      }

      :host([checked]:not([disabled])) .toggle-button {
        background-color: var(--paper-toggle-button-checked-button-color, var(--primary-color));

        @apply --paper-toggle-button-checked-button;
      }

      :host([disabled]) .toggle-button {
        background-color: #bdbdbd;
        opacity: 1;
      }

      .toggle-ink {
        position: absolute;
        top: -14px;
        left: -14px;
        right: auto;
        bottom: auto;
        width: 48px;
        height: 48px;
        opacity: 0.5;
        pointer-events: none;
        color: var(--paper-toggle-button-unchecked-ink-color, var(--primary-text-color));

        @apply --paper-toggle-button-unchecked-ink;
      }

      :host([checked]) .toggle-ink {
        color: var(--paper-toggle-button-checked-ink-color, var(--primary-color));

        @apply --paper-toggle-button-checked-ink;
      }

      .toggle-container {
        display: inline-block;
        position: relative;
        width: 36px;
        height: 14px;
        /* The toggle button has an absolute position of -3px; The extra 1px
        /* accounts for the toggle button shadow box. */
        margin: 4px 1px;
      }

      .toggle-label {
        position: relative;
        display: inline-block;
        vertical-align: middle;
        padding-left: var(--paper-toggle-button-label-spacing, 8px);
        pointer-events: none;
        color: var(--paper-toggle-button-label-color, var(--primary-text-color));
      }

      /* invalid state */
      :host([invalid]) .toggle-bar {
        background-color: var(--paper-toggle-button-invalid-bar-color, var(--error-color));
      }

      :host([invalid]) .toggle-button {
        background-color: var(--paper-toggle-button-invalid-button-color, var(--error-color));
      }

      :host([invalid]) .toggle-ink {
        color: var(--paper-toggle-button-invalid-ink-color, var(--error-color));
      }
    </style>

    <div class="toggle-container">
      <div id="toggleBar" class="toggle-bar"></div>
      <div id="toggleButton" class="toggle-button"></div>
    </div>

    <div class="toggle-label"><slot></slot></div>

  `;hs.setAttribute("strip-whitespace",""),Rr({_template:hs,is:"paper-toggle-button",behaviors:[ua],hostAttributes:{role:"button","aria-pressed":"false",tabindex:0},properties:{},listeners:{track:"_ontrack"},attached:function(){Li(this,(function(){oi(this,"pan-y")}))},_ontrack:function(t){var e=t.detail;"start"===e.state?this._trackStart(e):"track"===e.state?this._trackMove(e):"end"===e.state&&this._trackEnd(e)},_trackStart:function(t){this._width=this.$.toggleBar.offsetWidth/2,this._trackChecked=this.checked,this.$.toggleButton.classList.add("dragging")},_trackMove:function(t){var e=t.dx;this._x=Math.min(this._width,Math.max(0,this._trackChecked?this._width+e:e)),this.translate3d(this._x+"px",0,0,this.$.toggleButton),this._userActivate(this._x>this._width/2)},_trackEnd:function(t){this.$.toggleButton.classList.remove("dragging"),this.transform("",this.$.toggleButton)},_createRipple:function(){this._rippleContainer=this.$.toggleButton;var t=Jo._createRipple();return t.id="ink",t.setAttribute("recenters",""),t.classList.add("circle","toggle-ink"),t}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        --calculated-paper-toolbar-height: var(--paper-toolbar-height, 64px);
        --calculated-paper-toolbar-sm-height: var(--paper-toolbar-sm-height, 56px);
        display: block;
        position: relative;
        box-sizing: border-box;
        -moz-box-sizing: border-box;
        height: var(--calculated-paper-toolbar-height);
        background: var(--paper-toolbar-background, var(--primary-color));
        color: var(--paper-toolbar-color, var(--dark-theme-text-color));
        @apply --paper-toolbar;
      }

      :host(.animate) {
        transition: var(--paper-toolbar-transition, height 0.18s ease-in);
      }

      :host(.medium-tall) {
        height: calc(var(--calculated-paper-toolbar-height) * 2);
        @apply --paper-toolbar-medium;
      }

      :host(.tall) {
        height: calc(var(--calculated-paper-toolbar-height) * 3);
        @apply --paper-toolbar-tall;
      }

      .toolbar-tools {
        position: relative;
        height: var(--calculated-paper-toolbar-height);
        padding: 0 16px;
        pointer-events: none;
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-toolbar-content;
      }

      /*
       * TODO: Where should media query breakpoints live so they can be shared between elements?
       */

      @media (max-width: 600px) {
        :host {
          height: var(--calculated-paper-toolbar-sm-height);
        }

        :host(.medium-tall) {
          height: calc(var(--calculated-paper-toolbar-sm-height) * 2);
        }

        :host(.tall) {
          height: calc(var(--calculated-paper-toolbar-sm-height) * 3);
        }

        .toolbar-tools {
          height: var(--calculated-paper-toolbar-sm-height);
        }
      }

      #topBar {
        position: relative;
      }

      /* middle bar */
      #middleBar {
        position: absolute;
        top: 0;
        right: 0;
        left: 0;
      }

      :host(.tall) #middleBar,
      :host(.medium-tall) #middleBar {
        -webkit-transform: translateY(100%);
        transform: translateY(100%);
      }

      /* bottom bar */
      #bottomBar {
        position: absolute;
        right: 0;
        bottom: 0;
        left: 0;
      }

      /*
       * make elements (e.g. buttons) respond to mouse/touch events
       *
       * \`.toolbar-tools\` disables touch events so multiple toolbars can stack and not
       * absorb events. All children must have pointer events re-enabled to work as
       * expected.
       */
      .toolbar-tools > ::slotted(*:not([disabled])) {
        pointer-events: auto;
      }

      .toolbar-tools > ::slotted(.title) {
        @apply --paper-font-common-base;
        white-space: nowrap;
        overflow: hidden;
        text-overflow: ellipsis;
        font-size: 20px;
        font-weight: 400;
        line-height: 1;
        pointer-events: none;
        @apply --layout-flex;
      }

      .toolbar-tools > ::slotted(.title) {
        margin-left: 56px;
      }

      .toolbar-tools > ::slotted(paper-icon-button + .title) {
        margin-left: 0;
      }

      /**
       * The --paper-toolbar-title mixin is applied here instead of above to
       * fix the issue with margin-left being ignored due to css ordering.
       */
      .toolbar-tools > ::slotted(.title) {
        @apply --paper-toolbar-title;
      }

      .toolbar-tools > ::slotted(paper-icon-button[icon=menu]) {
        margin-right: 24px;
      }

      .toolbar-tools > ::slotted(.fit) {
        position: absolute;
        top: auto;
        right: 0;
        bottom: 0;
        left: 0;
        width: auto;
        margin: 0;
      }

      /* TODO(noms): Until we have a better solution for classes that don't use
       * /deep/ create our own.
       */
      .start-justified {
        @apply --layout-start-justified;
      }

      .center-justified {
        @apply --layout-center-justified;
      }

      .end-justified {
        @apply --layout-end-justified;
      }

      .around-justified {
        @apply --layout-around-justified;
      }

      .justified {
        @apply --layout-justified;
      }
    </style>

    <div id="topBar" class\$="toolbar-tools [[_computeBarExtraClasses(justify)]]">
      <slot name="top"></slot>
    </div>

    <div id="middleBar" class\$="toolbar-tools [[_computeBarExtraClasses(middleJustify)]]">
      <slot name="middle"></slot>
    </div>

    <div id="bottomBar" class\$="toolbar-tools [[_computeBarExtraClasses(bottomJustify)]]">
      <slot name="bottom"></slot>
    </div>
`,is:"paper-toolbar",hostAttributes:{role:"toolbar"},properties:{bottomJustify:{type:String,value:""},justify:{type:String,value:""},middleJustify:{type:String,value:""}},ready:function(){console.warn(this.is,"is deprecated. Please use app-layout instead!")},attached:function(){this._observer=this._observe(this),this._updateAriaLabelledBy()},detached:function(){this._observer&&this._observer.disconnect()},_observe:function(t){var e=new MutationObserver(function(){this._updateAriaLabelledBy()}.bind(this));return e.observe(t,{childList:!0,subtree:!0}),e},_updateAriaLabelledBy:function(){Di();for(var t,e=[],n=Array.prototype.slice.call(Yi(this.root).querySelectorAll("slot")).concat(Array.prototype.slice.call(Yi(this.root).querySelectorAll("content"))),i=0;t=n[i];i++)for(var r,o=Yi(t).getDistributedNodes(),a=0;r=o[a];a++)if(r.classList&&r.classList.contains("title"))if(r.id)e.push(r.id);else{var s="paper-toolbar-label-"+Math.floor(1e4*Math.random());r.id=s,e.push(s)}e.length>0&&this.setAttribute("aria-labelledby",e.join(" "))},_computeBarExtraClasses:function(t){return t?t+("justified"===t?"":"-justified"):""}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        display: block;
        position: absolute;
        outline: none;
        z-index: 1002;
        -moz-user-select: none;
        -ms-user-select: none;
        -webkit-user-select: none;
        user-select: none;
        cursor: default;
      }

      #tooltip {
        display: block;
        outline: none;
        @apply --paper-font-common-base;
        font-size: 10px;
        line-height: 1;
        background-color: var(--paper-tooltip-background, #616161);
        color: var(--paper-tooltip-text-color, white);
        padding: 8px;
        border-radius: 2px;
        @apply --paper-tooltip;
      }

      @keyframes keyFrameScaleUp {
        0% {
          transform: scale(0.0);
        }
        100% {
          transform: scale(1.0);
        }
      }

      @keyframes keyFrameScaleDown {
        0% {
          transform: scale(1.0);
        }
        100% {
          transform: scale(0.0);
        }
      }

      @keyframes keyFrameFadeInOpacity {
        0% {
          opacity: 0;
        }
        100% {
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
      }

      @keyframes keyFrameFadeOutOpacity {
        0% {
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
        100% {
          opacity: 0;
        }
      }

      @keyframes keyFrameSlideDownIn {
        0% {
          transform: translateY(-2000px);
          opacity: 0;
        }
        10% {
          opacity: 0.2;
        }
        100% {
          transform: translateY(0);
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
      }

      @keyframes keyFrameSlideDownOut {
        0% {
          transform: translateY(0);
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
        10% {
          opacity: 0.2;
        }
        100% {
          transform: translateY(-2000px);
          opacity: 0;
        }
      }

      .fade-in-animation {
        opacity: 0;
        animation-delay: var(--paper-tooltip-delay-in, 500ms);
        animation-name: keyFrameFadeInOpacity;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-in, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .fade-out-animation {
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 0ms);
        animation-name: keyFrameFadeOutOpacity;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .scale-up-animation {
        transform: scale(0);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-in, 500ms);
        animation-name: keyFrameScaleUp;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-in, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .scale-down-animation {
        transform: scale(1);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameScaleDown;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .slide-down-animation {
        transform: translateY(-2000px);
        opacity: 0;
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameSlideDownIn;
        animation-iteration-count: 1;
        animation-timing-function: cubic-bezier(0.0, 0.0, 0.2, 1);
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .slide-down-animation-out {
        transform: translateY(0);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameSlideDownOut;
        animation-iteration-count: 1;
        animation-timing-function: cubic-bezier(0.4, 0.0, 1, 1);
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .cancel-animation {
        animation-delay: -30s !important;
      }

      /* Thanks IE 10. */

      .hidden {
        display: none !important;
      }
    </style>

    <div id="tooltip" class="hidden">
      <slot></slot>
    </div>
`,is:"paper-tooltip",hostAttributes:{role:"tooltip",tabindex:-1},properties:{for:{type:String,observer:"_findTarget"},manualMode:{type:Boolean,value:!1,observer:"_manualModeChanged"},position:{type:String,value:"bottom"},fitToVisibleBounds:{type:Boolean,value:!1},offset:{type:Number,value:14},marginTop:{type:Number,value:14},animationDelay:{type:Number,value:500,observer:"_delayChange"},animationEntry:{type:String,value:""},animationExit:{type:String,value:""},animationConfig:{type:Object,value:function(){return{entry:[{name:"fade-in-animation",node:this,timing:{delay:0}}],exit:[{name:"fade-out-animation",node:this}]}}},_showing:{type:Boolean,value:!1}},listeners:{webkitAnimationEnd:"_onAnimationEnd"},get target(){var t=Yi(this).parentNode,e=Yi(this).getOwnerRoot();return this.for?Yi(e).querySelector("#"+this.for):t.nodeType==Node.DOCUMENT_FRAGMENT_NODE?e.host:t},attached:function(){this._findTarget()},detached:function(){this.manualMode||this._removeListeners()},playAnimation:function(t){"entry"===t?this.show():"exit"===t&&this.hide()},cancelAnimation:function(){this.$.tooltip.classList.add("cancel-animation")},show:function(){if(!this._showing){if(""===Yi(this).textContent.trim()){for(var t=!0,e=Yi(this).getEffectiveChildNodes(),n=0;n<e.length;n++)if(""!==e[n].textContent.trim()){t=!1;break}if(t)return}this._showing=!0,this.$.tooltip.classList.remove("hidden"),this.$.tooltip.classList.remove("cancel-animation"),this.$.tooltip.classList.remove(this._getAnimationType("exit")),this.updatePosition(),this._animationPlaying=!0,this.$.tooltip.classList.add(this._getAnimationType("entry"))}},hide:function(){if(this._showing){if(this._animationPlaying)return this._showing=!1,void this._cancelAnimation();this._onAnimationFinish(),this._showing=!1,this._animationPlaying=!0}},updatePosition:function(){if(this._target&&this.offsetParent){var t=this.offset;14!=this.marginTop&&14==this.offset&&(t=this.marginTop);var e,n,i=this.offsetParent.getBoundingClientRect(),r=this._target.getBoundingClientRect(),o=this.getBoundingClientRect(),a=(r.width-o.width)/2,s=(r.height-o.height)/2,l=r.left-i.left,c=r.top-i.top;switch(this.position){case"top":e=l+a,n=c-o.height-t;break;case"bottom":e=l+a,n=c+r.height+t;break;case"left":e=l-o.width-t,n=c+s;break;case"right":e=l+r.width+t,n=c+s}this.fitToVisibleBounds?(i.left+e+o.width>window.innerWidth?(this.style.right="0px",this.style.left="auto"):(this.style.left=Math.max(0,e)+"px",this.style.right="auto"),i.top+n+o.height>window.innerHeight?(this.style.bottom=i.height-c+t+"px",this.style.top="auto"):(this.style.top=Math.max(-i.top,n)+"px",this.style.bottom="auto")):(this.style.left=e+"px",this.style.top=n+"px")}},_addListeners:function(){this._target&&(this.listen(this._target,"mouseenter","show"),this.listen(this._target,"focus","show"),this.listen(this._target,"mouseleave","hide"),this.listen(this._target,"blur","hide"),this.listen(this._target,"tap","hide")),this.listen(this.$.tooltip,"animationend","_onAnimationEnd"),this.listen(this,"mouseenter","hide")},_findTarget:function(){this.manualMode||this._removeListeners(),this._target=this.target,this.manualMode||this._addListeners()},_delayChange:function(t){500!==t&&this.updateStyles({"--paper-tooltip-delay-in":t+"ms"})},_manualModeChanged:function(){this.manualMode?this._removeListeners():this._addListeners()},_cancelAnimation:function(){this.$.tooltip.classList.remove(this._getAnimationType("entry")),this.$.tooltip.classList.remove(this._getAnimationType("exit")),this.$.tooltip.classList.remove("cancel-animation"),this.$.tooltip.classList.add("hidden")},_onAnimationFinish:function(){this._showing&&(this.$.tooltip.classList.remove(this._getAnimationType("entry")),this.$.tooltip.classList.remove("cancel-animation"),this.$.tooltip.classList.add(this._getAnimationType("exit")))},_onAnimationEnd:function(){this._animationPlaying=!1,this._showing||(this.$.tooltip.classList.remove(this._getAnimationType("exit")),this.$.tooltip.classList.add("hidden"))},_getAnimationType:function(t){if("entry"===t&&""!==this.animationEntry)return this.animationEntry;if("exit"===t&&""!==this.animationExit)return this.animationExit;if(this.animationConfig[t]&&"string"==typeof this.animationConfig[t][0].name){if(this.animationConfig[t][0].timing&&this.animationConfig[t][0].timing.delay&&0!==this.animationConfig[t][0].timing.delay){var e=this.animationConfig[t][0].timing.delay;"entry"===t?this.updateStyles({"--paper-tooltip-delay-in":e+"ms"}):"exit"===t&&this.updateStyles({"--paper-tooltip-delay-out":e+"ms"})}return this.animationConfig[t][0].name}},_removeListeners:function(){this._target&&(this.unlisten(this._target,"mouseenter","show"),this.unlisten(this._target,"focus","show"),this.unlisten(this._target,"mouseleave","hide"),this.unlisten(this._target,"blur","hide"),this.unlisten(this._target,"tap","hide")),this.unlisten(this.$.tooltip,"animationend","_onAnimationEnd"),this.unlisten(this,"mouseenter","hide")}});class ds{constructor(t){this.listener=t}}const ps=new Set,fs=new Set;function ms(t){const e=new ds(t);return 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ye{constructor(){super(...arguments),this._tagFilter=Rs("tagFilter",{defaultValue:"",useLocalStorage:!1,polymerProperty:"_tagFilter"}).call(this),this._tagFilterObserver=Os("tagFilter",{defaultValue:"",useLocalStorage:!1,polymerProperty:"_tagFilter"})}_computeTagFilter(){return this._tagFilter}};function el(t){const{moduleName:e,styleContent:n}=t,i=document.createElement("dom-module"),r=document.createElement("template"),o=[];t.styleDependencies&&t.styleDependencies.forEach((t=>{const e=document.createElement("style");e.setAttribute("include",t),o.push(e)}));const a=document.createElement("style");Object.assign(a,{textContent:n}),o.forEach((t=>{r.content.appendChild(t)})),r.content.appendChild(a),i.appendChild(r),i.register(e)}tl.template=_e`
    <paper-input
      no-label-float=""
      label="Filter tags (regular expressions supported)"
      value="{{_tagFilter}}"
      class="search-input"
    >
      <iron-icon prefix="" icon="search" slot="prefix"></iron-icon>
    </paper-input>
    <style>
      :host {
        display: block;
        margin: 10px 5px 10px 10px;
      }
    </style>
  `,t([o({type:String,notify:!0,computed:"_computeTagFilter(_tagFilter)"}),e("design:type",String)],tl.prototype,"tagFilter",void 0),t([o({type:String,observer:"_tagFilterObserver"}),e("design:type",String)],tl.prototype,"_tagFilter",void 0),tl=t([i("tf-tag-filterer")],tl),el({moduleName:"dashboard-style",styleDependencies:["iron-flex"],styleContent:"\n      :host {\n        --sidebar-vertical-padding: 15px;\n        --sidebar-left-padding: 30px;\n      }\n\n      [slot='sidebar'] {\n        box-sizing: border-box;\n        display: flex;\n        flex-direction: column;\n        height: 100%;\n        margin-right: 10px;\n        overflow-x: hidden;\n        padding: 5px 0;\n        text-overflow: ellipsis;\n      }\n\n      .settings {\n        min-height: 50px;\n        overflow-x: hidden;\n        overflow-y: auto;\n        will-change: transform;\n      }\n\n      .runs-selector {\n        display: flex;\n        flex-grow: 1;\n        min-height: 200px;\n      }\n\n      tf-runs-selector {\n        flex-grow: 1;\n        flex-shrink: 1;\n        left: var(--sidebar-left-padding);\n        max-height: calc(100% - var(--sidebar-vertical-padding) * 2);\n        overflow: hidden;\n        position: absolute;\n        right: 0;\n      }\n\n      .search-input {\n        margin: 10px 5px 0 10px;\n      }\n\n      .sidebar-section {\n        border-top: solid 1px var(--tb-ui-border);\n        margin-right: 10px;\n        padding: var(--sidebar-vertical-padding) 0\n          var(--sidebar-vertical-padding) var(--sidebar-left-padding);\n        position: relative;\n        overflow: hidden;\n      }\n\n      .sidebar-section:first-of-type {\n        border: none;\n      }\n\n      .sidebar-section paper-button {\n        margin: 5px;\n      }\n\n      .sidebar-section paper-button:first-of-type {\n        margin-left: 0 !important;\n      }\n\n      .sidebar-section paper-button:last-of-type {\n        margin-right: 0 !important;\n      }\n\n      .sidebar-section > :first-child {\n        margin-top: 0;\n        padding-top: 0;\n      }\n\n      .sidebar-section > :last-child {\n        margin-bottom: 0;\n        padding-bottom: 0;\n      }\n\n      .sidebar-section h3 {\n        color: var(--tb-secondary-text-color);\n        display: block;\n        font-size: 14px;\n        font-weight: normal;\n        margin: 10px 0 5px;\n        pointer-events: none;\n      }\n\n      paper-checkbox {\n        --paper-checkbox-checked-color: var(--tb-ui-dark-accent);\n        --paper-checkbox-unchecked-color: var(--tb-ui-dark-accent);\n        font-size: 15px;\n        margin-top: 5px;\n      }\n\n      a {\n        color: var(--tb-link);\n      }\n\n      a:visited {\n        color: var(--tb-link-visited);\n      }\n  "});const nl=document.createElement("style");function il(t){return class extends t{connectedCallback(){super.connectedCallback(),this._maybeSetDarkMode(),this.observer=new MutationObserver((t=>{t.some((t=>"class"===t.attributeName))&&this._maybeSetDarkMode()})),this.observer.observe(document.body,{attributes:!0})}disconnectedCallback(){var t;super.disconnectedCallback(),null===(t=this.observer)||void 0===t||t.disconnect()}_maybeSetDarkMode(){this.classList.toggle("dark-mode",document.body.classList.contains("dark-mode"))}}}nl.setAttribute("is","custom-style"),nl.textContent="\n  :root {\n    --tb-orange-weak: #ffa726;\n    --tb-orange-strong: #f57c00;\n    --tb-orange-dark: #dc7320;\n    --tb-grey-darker: #e2e2e2;\n    --tb-grey-lighter: #f3f3f3;\n    --tb-ui-dark-accent: #757575;\n    --tb-ui-light-accent: #e0e0e0;\n    --tb-ui-border: var(--paper-grey-300);\n    --tb-graph-faded: #e0d4b3;\n    --tb-secondary-text-color: var(--paper-grey-800);\n    --tb-raised-button-shadow-color: rgba(0, 0, 0, 0.2);\n    --primary-background-color: #fff;\n    --secondary-background-color: #e9e9e9;\n    --tb-layout-background-color: #f5f5f5;\n    --tb-link: #1976d2; /* material blue 700. */\n    --tb-link-visited: #7b1fa2; /* material purple 700. */\n  }\n\n  :root .dark-mode {\n    --tb-ui-border: var(--paper-grey-700);\n    --tb-ui-dark-accent: var(--paper-grey-400);\n    --tb-ui-light-accent: var(--paper-grey-600);\n    --tb-secondary-text-color: var(--paper-grey-400);\n    --tb-raised-button-shadow-color: rgba(255, 255, 255, 0.5);\n    --primary-text-color: #fff;\n    --secondary-text-color: var(--paper-grey-400);\n    --primary-background-color: #303030;  /* material grey A400. */\n    --secondary-background-color: #3a3a3a;\n    --tb-layout-background-color: #3a3a3a;\n    --tb-link: #42a5f5; /* material blue 400. */\n    --tb-link-visited: #ba68c8; /* material purple 300. */\n    /* Overrides paper-material */\n    --shadow-elevation-2dp_-_box-shadow: 0 2px 2px 0 rgba(255, 255, 255, 0.14),\n      0 1px 5px 0 rgba(255, 255, 255, 0.12),\n      0 3px 1px -2px rgba(255, 255, 255, 0.2);\n  }\n",document.head.appendChild(nl),el({moduleName:"scrollbar-style",styleContent:"\n    .scrollbar::-webkit-scrollbar-track {\n      visibility: hidden;\n    }\n\n    .scrollbar::-webkit-scrollbar {\n      width: 10px;\n    }\n\n    .scrollbar::-webkit-scrollbar-thumb {\n      border-radius: 10px;\n      -webkit-box-shadow: inset 0 0 2px rgba(0, 0, 0, 0.3);\n      background-color: var(--paper-grey-500);\n      color: var(--paper-grey-900);\n    }\n    .scrollbar {\n      box-sizing: border-box;\n    }\n  "});let rl=class extends(il(ye)){};rl.template=_e`
    <div id="sidebar">
      <slot name="sidebar"></slot>
    </div>

    <div id="center">
      <slot name="center" class="scollbar"></slot>
    </div>
    <style include="scrollbar-style"></style>
    <style>
      :host {
        background-color: #f5f5f5;
        display: flex;
        flex-direction: row;
        height: 100%;
      }

      :host(.dark-mode) {
        background-color: var(--secondary-background-color);
      }

      #sidebar {
        flex: 0 0 var(--tf-dashboard-layout-sidebar-basis, 25%);
        height: 100%;
        max-width: var(--tf-dashboard-layout-sidebar-max-width, 350px);
        min-width: var(--tf-dashboard-layout-sidebar-min-width, 270px);
        overflow-y: auto;
        text-overflow: ellipsis;
      }

      #center {
        flex-grow: 1;
        flex-shrink: 1;
        height: 100%;
        overflow: hidden;
      }

      ::slotted([slot='center']) {
        contain: strict;
        height: 100%;
        overflow-x: hidden;
        overflow-y: auto;
        width: 100%;
        will-change: transform;
      }

      .tf-graph-dashboard #center {
        background: #fff;
      }
    </style>
  `,rl=t([i("tf-dashboard-layout")],rl);const ol="TF.TensorBoard.PaginatedView.limit";let al=null;const sl=new Set;function ll(t){sl.add(t)}function cl(t){sl.delete(t)}function ul(){return null==al&&(al=Us(ol,{useLocalStorage:!0}),(null==al||!isFinite(al)||al<=0)&&(al=12)),al}class hl extends ye{updateArrayProp(t,e,n){let i=this.get(t);const r=e;if(!Array.isArray(r))throw RangeError(`Expected new value to '${t}' to be an array.`);Array.isArray(i)||(i=[],this.set(t,i));const o=new Set(r.map(((t,e)=>n(t,e))));let a=0,s=0;for(;a<i.length&&s<r.length;)o.has(n(i[a],a))?(n(i[a],a)==n(r[s],s)?this.set(`${t}.${a}`,r[s]):this.splice(t,a,0,r[s]),s++,a++):this.splice(t,a,1);a<i.length&&this.splice(t,a),s<r.length&&this.push(t,...r.slice(s))}}class dl extends hl{constructor(){super(...arguments),this.as="item",this._contentActive=!0,this._domBootstrapped=!1,this._ctor=null,this._renderedItems=[],this._renderedTemplateInst=new Map,this._lruCachedItems=new Map,this._cacheSize=10,this._getItemKey=t=>JSON.stringify(t),this._isConnected=!1}connectedCallback(){super.connectedCallback(),this._isConnected=!0}setCacheSize(t){this._cacheSize=t}setGetItemKey(t){this._getItemKey=t}updateDom(t){this.updateArrayProp("_renderedItems",t,this._getItemKey)}_ensureTemplatized(){if(!this.isConnected)return!1;if(!this._ctor){const t=this.querySelector("template");this._ctor=$r(t,this,{parentModel:!0,instanceProps:{[this.as]:!0,active:this._contentActive},forwardHostProp:function(t,e){this._renderedTemplateInst.forEach((n=>{n.forwardHostProp(t,e)}))}})}return!0}_bootstrapDom(){this._ensureTemplatized()&&!this._domBootstrapped&&(new MutationObserver((t=>{for(const e of t)if("childList"===e.type)for(const t of Array.from(e.addedNodes))t instanceof Element&&t.setAttribute("slot","items")})).observe(this,{childList:!0}),Array.from(this.children).forEach((t=>{this.removeChild(t)})),this._lruCachedItems.clear(),this._renderedItems.forEach(((t,e)=>this._insertItem(t,e))),this._domBootstrapped=!0)}_updateActive(){this._domBootstrapped&&Array.from(this._renderedTemplateInst.values()).forEach((t=>{t.notifyPath("active",this._contentActive)}))}_updateDom(t){if(this._domBootstrapped&&"_renderedItems"!=t.path&&"_renderedItems.length"!=t.path)if("_renderedItems.splices"===t.path)t.value.indexSplices.forEach((t=>{const{index:e,addedCount:n,object:i,removed:r}=t;r.forEach((t=>{this._removeItem(t,this.children[e])})),i.slice(e,e+n).forEach(((t,n)=>this._insertItem(t,e+n))),this._trimCache()}));else{const e=this._getItemKey(t.value);this._renderedTemplateInst.has(e)?this._renderedTemplateInst.get(e).notifyPath(this.as,t.value):console.warn(`Expected '${e}' to exist in the DOM but could not find one.`)}}_insertItem(t,e){if(!this._ensureTemplatized())throw new Error("Expected templatized before inserting an item");let n;const i=this._getItemKey(t);if(this._lruCachedItems.has(i))n=this._lruCachedItems.get(i),this._lruCachedItems.delete(i),this._renderedTemplateInst.get(i).notifyPath("active",this._contentActive);else{const e=new this._ctor({[this.as]:t,active:this._contentActive});n=e.root,this._renderedTemplateInst.set(i,e)}this.children[e]?this.insertBefore(n,this.children[e]):((n.nodeType==Node.DOCUMENT_FRAGMENT_NODE?Array.from(n.children):[n]).forEach((t=>t.setAttribute("slot","items"))),this.appendChild(n))}_removeItem(t,e){e.parentNode&&e.parentNode.removeChild(e);const n=this._getItemKey(t);this._lruCachedItems.set(n,e),this._renderedTemplateInst.get(n).notifyPath("active",!1)}_trimCache(){for(;this._lruCachedItems.size>this._cacheSize;){const[t]=this._lruCachedItems.keys();this._lruCachedItems.delete(t),this._renderedTemplateInst.delete(t)}}}t([o({type:String}),e("design:type",Object)],dl.prototype,"as",void 0),t([o({type:Array}),e("design:type",Array)],dl.prototype,"items",void 0),t([o({type:Boolean}),e("design:type",Boolean)],dl.prototype,"_contentActive",void 0),t([o({type:Boolean}),e("design:type",Object)],dl.prototype,"_domBootstrapped",void 0),t([o({type:Object}),e("design:type",Object)],dl.prototype,"_ctor",void 0),t([o({type:Array}),e("design:type",Array)],dl.prototype,"_renderedItems",void 0),t([o({type:Object}),e("design:type",Object)],dl.prototype,"_renderedTemplateInst",void 0),t([o({type:Object}),e("design:type",Object)],dl.prototype,"_lruCachedItems",void 0),t([o({type:Number}),e("design:type",Object)],dl.prototype,"_cacheSize",void 0),t([o({type:Object}),e("design:type",Object)],dl.prototype,"_getItemKey",void 0),t([o({type:Boolean}),e("design:type",Object)],dl.prototype,"_isConnected",void 0),t([a("_isConnected"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],dl.prototype,"_bootstrapDom",null),t([a("_contentActive"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],dl.prototype,"_updateActive",null),t([a("_renderedItems.*","_domBootstrapped"),e("design:type",Function),e("design:paramtypes",[Object]),e("design:returntype",void 0)],dl.prototype,"_updateDom",null),t([a("_cacheSize"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],dl.prototype,"_trimCache",null);let pl=class extends dl{constructor(){super(...arguments),this.disablePagination=!1,this.getCategoryItemKey=t=>JSON.stringify(t),this._limit=12,this._activeIndex=0,this._pageInputRawValue="",this._pageInputFocused=!1}_computeCount(){return this.category.items.length}get _hasMultiple(){return this._count>1}_togglePane(){this.opened=!this.opened}_changeContentActive(t){this._contentActive=t}_onPaneRenderedChanged(t,e){t&&t!==e&&this.$.ifRendered.render()}_computePaneRendered(t){return!(t.metadata.type===br.SEARCH_RESULTS&&""===t.name)}get _itemsRendered(){return this._paneRendered&&this.opened}_computeIsSearchResults(t){return t===br.SEARCH_RESULTS}_computeIsInvalidSearchResults(t){return t.type===br.SEARCH_RESULTS&&!t.validRegex}_computeIsUniversalSearchQuery(t){return t.type===br.SEARCH_RESULTS&&t.universalRegex}_isCompositeSearch(){const{type:t,compositeSearch:e}=this.category.metadata;return e&&t===br.SEARCH_RESULTS}ready(){super.ready(),this.opened=null==this.initialOpened||this.initialOpened,this._limitListener=()=>{this.set("_limit",ul())},ll(this._limitListener),this._limitListener()}detached(){cl(this._limitListener)}_updateRenderedItems(){var t=this._limit,e=this.disablePagination;if(!this._itemsRendered)return;const n=Math.floor(this._activeIndex/t),i=this.category.items||[],r=e?i:i.slice(n*t,(n+1)*t);this.updateDom(r)}_limitChanged(t){this.setCacheSize(2*t)}_getCategoryItemKeyChanged(){this.setGetItemKey(this.getCategoryItemKey)}get _currentPage(){return Math.floor(this._activeIndex/this._limit)+1}_computePageCount(t,e){return this.category?Math.ceil(this.category.items.length/e):0}get _multiplePagesExist(){return!this.disablePagination&&this._pageCount>1}get _hasPreviousPage(){return this._currentPage>1}get _hasNextPage(){return this._currentPage<this._pageCount}_computeInputWidth(t){return`calc(${t.toString().length}em + 20px)`}_setActiveIndex(t){const e=(this.category.items||[]).length-1;t>e&&(t=e),t<0&&(t=0),this.set("_activeIndex",t)}_clampActiveIndex(){this._setActiveIndex(this._activeIndex)}_performPreviousPage(){this._setActiveIndex(this._activeIndex-this._limit)}_performNextPage(){this._setActiveIndex(this._activeIndex+this._limit)}_computePageInputValue(t,e,n){return t?e:n.toString()}_handlePageInputEvent(t){this.set("_pageInputRawValue",t.target.value);const e=Number(t.target.value||NaN);if(isNaN(e))return;const n=Math.max(1,Math.min(e,this._pageCount))-1;this._setActiveIndex(this._limit*n)}_handlePageChangeEvent(){this.set("_pageInputRawValue",this._currentPage.toString())}_handlePageFocusEvent(){this.set("_pageInputRawValue",this._pageInputValue),this.set("_pageInputFocused",!0)}_handlePageBlurEvent(){this.set("_pageInputFocused",!1)}_updatePageInputValue(t){const e=this.shadowRoot.querySelector("#page-input input");e&&(e.value=t)}_updateInputWidth(){this.updateStyles({"--tf-category-paginated-view-page-input-width":this._inputWidth})}};pl.template=_e`
    <template is="dom-if" if="[[_paneRendered]]" id="ifRendered">
      <button class="heading" on-tap="_togglePane" open-button$="[[opened]]">
        <span class="name">
          <template is="dom-if" if="[[_isSearchResults]]">
            <template is="dom-if" if="[[_isCompositeSearch(category)]]">
              <span>Tags matching multiple experiments</span>
              <template is="dom-if" if="[[_isInvalidSearchResults]]">
                <span
                  >&nbsp;<strong>(malformed regular expression)</strong></span
                >
              </template>
            </template>
            <template is="dom-if" if="[[!_isCompositeSearch(category)]]">
              <span class="light">Tags matching /</span>
              <span class="category-name" title$="[[category.name]]"
                >[[category.name]]</span
              >
              <span class="light">/</span>
              <template is="dom-if" if="[[_isUniversalSearchQuery]]">
                <span> (all tags)</span>
              </template>
              <template is="dom-if" if="[[_isInvalidSearchResults]]">
                <span> <strong>(malformed regular expression)</strong></span>
              </template>
            </template>
          </template>
          <template is="dom-if" if="[[!_isSearchResults]]">
            <span class="category-name" title$="[[category.name]]"
              >[[category.name]]</span
            >
          </template>
        </span>
        <span class="count">
          <template is="dom-if" if="[[_hasMultiple]]">
            <span>[[_count]]</span>
          </template>
          <iron-icon icon="expand-more" class="expand-arrow"></iron-icon>
        </span>
      </button>
      <!-- TODO(stephanwlee): investigate further. For some reason,
        transitionend that the iron-collapse relies on sometimes does not
        trigger when rendering a chart with a spinner. A toy example cannot
        reproduce this bug. -->
      <iron-collapse opened="[[opened]]" no-animation="">
        <div class="content">
          <span id="top-of-container"></span>
          <template is="dom-if" if="[[_multiplePagesExist]]">
            <div class="big-page-buttons" style="margin-bottom: 10px;">
              <paper-button
                on-tap="_performPreviousPage"
                disabled$="[[!_hasPreviousPage]]"
                >Previous page</paper-button
              >
              <paper-button
                on-tap="_performNextPage"
                disabled$="[[!_hasNextPage]]"
                >Next page</paper-button
              >
            </div>
          </template>

          <div id="items">
            <slot name="items"></slot>
          </div>
          <template is="dom-if" if="[[_multiplePagesExist]]">
            <div id="controls-container">
              <div style="display: inline-block; padding: 0 5px">
                Page
                <paper-input
                  id="page-input"
                  type="number"
                  no-label-float=""
                  min="1"
                  max="[[_pageCount]]"
                  value="[[_pageInputValue]]"
                  on-input="_handlePageInputEvent"
                  on-change="_handlePageChangeEvent"
                  on-focus="_handlePageFocusEvent"
                  on-blur="_handlePageBlurEvent"
                ></paper-input>
                of [[_pageCount]]
              </div>
            </div>

            <div class="big-page-buttons" style="margin-top: 10px;">
              <paper-button
                on-tap="_performPreviousPage"
                disabled$="[[!_hasPreviousPage]]"
                >Previous page</paper-button
              >
              <paper-button
                on-tap="_performNextPage"
                disabled$="[[!_hasNextPage]]"
                >Next page</paper-button
              >
            </div>
          </template>
        </div>
      </iron-collapse>
    </template>
    <style>
      :host {
        display: block;
        margin: 0 5px 1px 10px;
      }

      :host(:first-of-type) {
        margin-top: 10px;
      }

      :host(:last-of-type) {
        margin-bottom: 20px;
      }

      .heading {
        background-color: var(--primary-background-color);
        border: none;
        color: inherit;
        cursor: pointer;
        width: 100%;
        font-size: 15px;
        line-height: 1;
        box-shadow: 0 1px 5px var(--tb-raised-button-shadow-color);
        padding: 10px 15px;
        display: flex;
        align-items: center;
        justify-content: space-between;
      }

      .heading::-moz-focus-inner {
        padding: 10px 15px;
      }

      [open-button] {
        border-bottom-left-radius: 0 !important;
        border-bottom-right-radius: 0 !important;
      }

      [open-button] .expand-arrow {
        transform: rotateZ(180deg);
      }

      .name {
        display: inline-flex;
        overflow: hidden;
      }

      .light {
        color: var(--paper-grey-500);
      }

      .category-name {
        white-space: pre;
        overflow: hidden;
        text-overflow: ellipsis;
        padding: 2px 0;
      }

      .count {
        margin: 0 5px;
        font-size: 12px;
        color: var(--paper-grey-500);
        display: flex;
        align-items: center;
        flex: none;
      }

      .heading::-moz-focus-inner {
        padding: 10px 15px;
      }

      .content {
        display: flex;
        flex-direction: column;
        background-color: var(--primary-background-color);
        border-bottom-left-radius: 2px;
        border-bottom-right-radius: 2px;
        border-top: none;
        border: 1px solid #dedede;
        padding: 15px;
      }

      .light {
        color: var(--paper-grey-500);
      }

      #controls-container {
        justify-content: center;
        display: flex;
        flex-direction: row;
        flex-grow: 0;
        flex-shrink: 0;
        width: 100%;
      }

      #controls-container paper-button {
        display: inline-block;
      }

      .big-page-buttons {
        display: flex;
      }

      .big-page-buttons paper-button {
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
        display: inline-block;
        flex-basis: 0;
        flex-grow: 1;
        flex-shrink: 1;
        font-size: 13px;
      }

      .big-page-buttons paper-button[disabled] {
        background: none;
      }

      slot {
        display: flex;
        flex-direction: row;
        flex-wrap: wrap;
      }

      ::slotted([slot='items']) {
        /* Tooltip for descriptions and others break with more strict ones. */
        contain: style;
      }

      #page-input {
        display: inline-block;
        width: var(--tf-category-paginated-view-page-input-width, 100%);
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],pl.prototype,"category",void 0),t([o({type:Boolean}),e("design:type",Boolean)],pl.prototype,"initialOpened",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],pl.prototype,"opened",void 0),t([o({type:Boolean}),e("design:type",Boolean)],pl.prototype,"disablePagination",void 0),t([o({type:Number,computed:"_computeCount(category.items.*)"}),e("design:type",Number)],pl.prototype,"_count",void 0),t([o({type:Boolean,computed:"_computePaneRendered(category)",observer:"_onPaneRenderedChanged"}),e("design:type",Boolean)],pl.prototype,"_paneRendered",void 0),t([o({type:Boolean,computed:"_computeIsSearchResults(category.metadata.type)"}),e("design:type",Boolean)],pl.prototype,"_isSearchResults",void 0),t([o({type:Boolean,computed:"_computeIsInvalidSearchResults(category.metadata)"}),e("design:type",Boolean)],pl.prototype,"_isInvalidSearchResults",void 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arguments.length?(n=null==t?null:[[+t[0][0],+t[0][1]],[+t[1][0],+t[1][1]]],i):n&&[[n[0][0],n[0][1]],[n[1][0],n[1][1]]]},i.size=function(t){return arguments.length?(n=null==t?null:[[0,0],[+t[0],+t[1]]],i):n&&[n[1][0]-n[0][0],n[1][1]-n[0][1]]},i},zoom:kN,zoomTransform:bN,zoomIdentity:vN});const HR=["#ff7043","#0077bb","#cc3311","#33bbee","#ee3377","#009988","#bbbbbb"];class FR extends ml{constructor(){super(...arguments),this._experiments=[]}load(){const t=_r().experiments();return this.requestManager.request(t).then((t=>{Se.exports.isEqual(this._experiments,t)||(this._experiments=t,this.emitChange())}))}getExperiments(){return this._experiments.slice()}}const VR=new FR;class UR{constructor(t=HR){this.palette=t,this.identifiers=wg()}setDomain(t){return this.identifiers=wg(),t.forEach(((t,e)=>{this.identifiers.set(t,this.palette[e%this.palette.length])})),this}getColor(t){if(!this.identifiers.has(t))throw new Error(`String ${t} was not in the domain.`);return this.identifiers.get(t)}}function jR(t,e){const n=new UR;function i(){n.setDomain(e())}return t.addListener(i),i(),t=>n.getColor(t)}const GR=jR(vl,(()=>vl.getRuns()));jR(VR,(()=>VR.getExperiments().map((({name:t})=>t)))),el({moduleName:"run-color-style",styleContent:"\n    [color-class='light-blue'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-light-blue-500);\n      --paper-checkbox-checked-ink-color: var(--paper-light-blue-500);\n      --paper-checkbox-unchecked-color: var(--paper-light-blue-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-light-blue-900);\n    }\n    [color-class='red'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-red-500);\n      --paper-checkbox-checked-ink-color: var(--paper-red-500);\n      --paper-checkbox-unchecked-color: var(--paper-red-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-red-900);\n    }\n    [color-class='green'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-green-500);\n      --paper-checkbox-checked-ink-color: var(--paper-green-500);\n      --paper-checkbox-unchecked-color: var(--paper-green-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-green-900);\n    }\n    [color-class='purple'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-purple-500);\n      --paper-checkbox-checked-ink-color: var(--paper-purple-500);\n      --paper-checkbox-unchecked-color: var(--paper-purple-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-purple-900);\n    }\n    [color-class='teal'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-teal-500);\n      --paper-checkbox-checked-ink-color: var(--paper-teal-500);\n      --paper-checkbox-unchecked-color: var(--paper-teal-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-teal-900);\n    }\n    [color-class='pink'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-pink-500);\n      --paper-checkbox-checked-ink-color: var(--paper-pink-500);\n      --paper-checkbox-unchecked-color: var(--paper-pink-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-pink-900);\n    }\n    [color-class='orange'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-orange-500);\n      --paper-checkbox-checked-ink-color: var(--paper-orange-500);\n      --paper-checkbox-unchecked-color: var(--paper-orange-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-orange-900);\n    }\n    [color-class='brown'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-brown-500);\n      --paper-checkbox-checked-ink-color: var(--paper-brown-500);\n      --paper-checkbox-unchecked-color: var(--paper-brown-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-brown-900);\n    }\n    [color-class='indigo'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-indigo-500);\n      --paper-checkbox-checked-ink-color: var(--paper-indigo-500);\n      --paper-checkbox-unchecked-color: var(--paper-indigo-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-indigo-900);\n    }\n  "});let WR=class extends(er(ye)){constructor(){super(...arguments),this.names=[],this.coloring={getColor:()=>""},this.regex="",this.selectionState={},this.maxNamesToEnableByDefault=40,this._debouncedRegexChange=this._debouncedRegexChangeImpl()}_debouncedRegexChangeImpl(){var t=Se.exports.debounce((t=>{this.regex=t}),150,{leading:!1});return function(){var e=this.$$("#names-regex").value;""==e?this.async((()=>{this.regex=e}),30):t(e)}}get _regex(){var t=this.regex;try{return new RegExp(t)}catch(t){return null}}_setIsolatorIcon(){var t=this.selectionState,e=Se.exports.filter(Se.exports.values(t)).length;Array.prototype.slice.call(this.root.querySelectorAll(".isolator")).forEach((function(n){n.icon=1===e&&t[n.name]?"radio-button-checked":"radio-button-unchecked"}))}computeNamesMatchingRegex(t,e){const n=this._regex;return n?this.names.filter((t=>n.test(t))):this.names}computeOutSelected(t,e){var n=this.selectionState,i=this.namesMatchingRegex.length<=this.maxNamesToEnableByDefault;return this.namesMatchingRegex.filter((t=>null==n[t]?i:n[t]))}synchronizeColors(t){this._setIsolatorIcon(),this.root.querySelectorAll("paper-checkbox").forEach((t=>{const e=this.coloring.getColor(t.name);t.updateStyles({"--paper-checkbox-checked-color":e,"--paper-checkbox-checked-ink-color":e,"--paper-checkbox-unchecked-color":e,"--paper-checkbox-unchecked-ink-color":e})})),this.root.querySelectorAll(".isolator").forEach((t=>{const e=this.coloring.getColor(t.name);t.style.color=e})),window.requestAnimationFrame((()=>{this.updateStyles()}))}_isolateName(t){var e=t.target.name,n={};this.names.forEach((function(t){n[t]=t==e})),this.selectionState=n}_checkboxChange(t){var e=t.target;const n=Se.exports.clone(this.selectionState);n[e.name]=e.checked,this.selectionState=n}_isChecked(t,e){return-1!=this.outSelected.indexOf(t)}toggleAll(){const t=this.namesMatchingRegex.some((t=>this.outSelected.includes(t))),e={};this.names.forEach((n=>{e[n]=!t})),this.selectionState=e}};WR.template=_e`
    <style include="scrollbar-style"></style>
    <style include="run-color-style"></style>

    <paper-input
      id="names-regex"
      no-label-float=""
      label="Write a regex to filter runs"
      value="[[regex]]"
      on-bind-value-changed="_debouncedRegexChange"
    ></paper-input>
    <div id="outer-container" class="scrollbar">
      <template
        is="dom-repeat"
        items="[[namesMatchingRegex]]"
        on-dom-change="synchronizeColors"
      >
        <div class="name-row">
          <div
            class="icon-container checkbox-container vertical-align-container"
          >
            <paper-checkbox
              class="checkbox vertical-align-center"
              id$="checkbox-[[item]]"
              name="[[item]]"
              checked$="[[_isChecked(item, selectionState.*)]]"
              on-change="_checkboxChange"
            ></paper-checkbox>
          </div>
          <div
            class="icon-container isolator-container vertical-align-container"
          >
            <paper-icon-button
              icon="radio-button-unchecked"
              class="isolator vertical-align-center"
              on-tap="_isolateName"
              name="[[item]]"
            ></paper-icon-button>
          </div>
          <div class="item-label-container">
            <span>[[item]]</span>
          </div>
        </div>
      </template>
    </div>
    <style>
      paper-input {
        --paper-input-container-focus-color: var(--tb-orange-strong);
        --paper-input-container-input: {
          font-size: 14px;
        }
        --paper-input-container-label: {
          font-size: 14px;
        }
      }
      :host {
        display: flex;
        flex-direction: column;
        height: 100%;
        overflow: hidden;
      }
      #outer-container {
        contain: content;
        flex-grow: 1;
        flex-shrink: 1;
        overflow-x: hidden;
        overflow-y: auto;
        width: 100%;
        will-change: transform;
        word-wrap: break-word;
      }
      .name-row {
        contain: content;
        padding-top: 5px;
        padding-bottom: 5px;
        display: flex;
        flex-direction: row;
        font-size: 13px;
        word-break: break-all; /* makes wrapping of hyperparam strings better */
      }
      .icon-container {
        flex-grow: 0;
        flex-shrink: 0;
        padding-left: 2px;
      }
      .checkbox {
        padding-left: 2px;
        width: 18px;
        height: 18px;
      }
      .isolator {
        width: 18px;
        height: 18px;
        padding: 0px;
      }
      .isolator-container {
        padding-left: 6px;
        padding-right: 3px;
      }
      .checkbox-container {
        padding-left: 2px;
      }
      .item-label-container {
        padding-left: 5px;
        flex-grow: 1;
        flex-shrink: 1;
        width: 0px; /* hack to get the flex-grow to work properly */
      }
      .tooltip-value-container {
        display: flex;
        justify-content: center;
        flex-grow: 0;
        flex-shrink: 0;
        text-align: right;
        padding-left: 2px;
      }
      .vertical-align-container {
        display: flex;
        justify-content: center;
      }
      .vertical-align-container .vertical-align-center {
        align-self: center;
      }
      .vertical-align-container .vertical-align-top {
        align-self: start;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],WR.prototype,"names",void 0),t([o({type:Object}),e("design:type",Object)],WR.prototype,"coloring",void 0),t([o({type:String,notify:!0}),e("design:type",String)],WR.prototype,"regex",void 0),t([o({type:Array,computed:"computeNamesMatchingRegex(names.*, _regex)"}),e("design:type",Array)],WR.prototype,"namesMatchingRegex",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],WR.prototype,"selectionState",void 0),t([o({type:Array,notify:!0,computed:"computeOutSelected(namesMatchingRegex.*, selectionState.*)"}),e("design:type",Array)],WR.prototype,"outSelected",void 0),t([o({type:Number}),e("design:type",Number)],WR.prototype,"maxNamesToEnableByDefault",void 0),t([o({type:Object}),e("design:type",Object)],WR.prototype,"_debouncedRegexChange",void 0),t([s("regex"),e("design:type",RegExp),e("design:paramtypes",[])],WR.prototype,"_regex",null),t([a("selectionState","names"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],WR.prototype,"_setIsolatorIcon",null),WR=t([i("tf-multi-checkbox")],WR);let qR=class extends ye{get _parts(){var t=this.value,e=this.delimiterPattern;const n=[];for(;;){const i=new RegExp(e,"g");if(i.test(t),0===i.lastIndex){n.push(t);break}n.push(t.slice(0,i.lastIndex)),t=t.slice(i.lastIndex)}return n}};qR.template=_e`
    <!--
      This ugly formatting is required to prevent spaces from slipping
      into the HTML.
    -->
    <template is="dom-repeat" items="[[_parts]]" as="part"
      >[[part]]<wbr
    /></template>
  `,t([o({type:String}),e("design:type",String)],qR.prototype,"value",void 0),t([o({type:String}),e("design:type",String)],qR.prototype,"delimiterPattern",void 0),t([s("value","delimiterPattern"),e("design:type",Array),e("design:paramtypes",[])],qR.prototype,"_parts",null),qR=t([i("tf-wbr-string")],qR);let YR=class extends(er(ye)){constructor(){super(...arguments),this.runSelectionState=$s("runSelectionState",{defaultValue:{}}).call(this),this.regexInput=Rs("regexInput",{defaultValue:""}).call(this),this._dataLocationClipLength=250,this._dataLocationDelimiterPattern="[/=_,-]",this.coloring={getColor:GR},this._storeRunSelectionState=Ks("runSelectionState",{defaultValue:{}}),this._regexObserver=Os("regexInput",{defaultValue:""})}attached(){this._runStoreListener=vl.addListener((()=>{this.set("runs",vl.getRuns())})),this.set("runs",vl.getRuns()),this._envStoreListener=_l.addListener((()=>{this.set("dataLocation",_l.getDataLocation())})),this.set("dataLocation",_l.getDataLocation())}detached(){vl.removeListenerByKey(this._runStoreListener),_l.removeListenerByKey(this._envStoreListener)}_toggleAll(){this.$.multiCheckbox.toggleAll()}get _clippedDataLocation(){var t=this.dataLocation,e=this._dataLocationClipLength;if(void 0!==t)return t.length>e?t.substring(0,e):t}_openDataLocationDialog(t){t.preventDefault(),this.$$("#data-location-dialog").open()}_shouldShowExpandDataLocationButton(t,e){return t&&t.length>e}};YR.template=_e`
    <paper-dialog with-backdrop="" id="data-location-dialog">
      <h2>Data Location</h2>
      <tf-wbr-string
        value="[[dataLocation]]"
        delimiter-pattern="[[_dataLocationDelimiterPattern]]"
      >
      </tf-wbr-string
    ></paper-dialog>
    <div id="top-text">
      <h3 id="tooltip-help" class="tooltip-container">Runs</h3>
    </div>
    <tf-multi-checkbox
      id="multiCheckbox"
      names="[[runs]]"
      selection-state="{{runSelectionState}}"
      out-selected="{{selectedRuns}}"
      regex="{{regexInput}}"
      coloring="[[coloring]]"
    ></tf-multi-checkbox>
    <paper-button class="x-button" id="toggle-all" on-tap="_toggleAll">
      Toggle All Runs
    </paper-button>
    <template is="dom-if" if="[[dataLocation]]">
      <div id="data-location">
        <tf-wbr-string
          value="[[_clippedDataLocation]]"
          delimiter-pattern="[[_dataLocationDelimiterPattern]]"
        ></tf-wbr-string
        ><!--
          We use HTML comments to remove spaces before the ellipsis.
        --><template
          is="dom-if"
          if="[[_shouldShowExpandDataLocationButton(dataLocation, _dataLocationClipLength)]]"
          ><!--
          --><a href="" on-click="_openDataLocationDialog">…</a>
        </template>
      </div>
    </template>
    <style>
      :host {
        box-sizing: border-box;
        display: flex;
        flex-direction: column;
        padding-bottom: 10px;
      }
      #top-text {
        color: var(--tb-secondary-text-color);
        width: 100%;
        flex-grow: 0;
        flex-shrink: 0;
        padding-right: 16px;
        box-sizing: border-box;
      }
      tf-wbr-string {
        overflow-wrap: break-word;
      }
      tf-multi-checkbox {
        display: flex;
        flex-grow: 1;
        flex-shrink: 1;
        overflow: hidden;
      }
      .x-button {
        font-size: 13px;
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
      }
      #tooltip-help {
        color: var(--tb-secondary-text-color);
        margin: 0;
        font-weight: normal;
        font-size: 14px;
        margin-bottom: 5px;
      }
      paper-button {
        margin-left: 0;
      }
      #data-location {
        color: var(--tb-ui-dark-accent);
        font-size: 13px;
        margin: 5px 0 0 0;
        max-width: 288px;
      }
    </style>
  `,t([o({type:Object,observer:"_storeRunSelectionState"}),e("design:type",Object)],YR.prototype,"runSelectionState",void 0),t([o({type:String,observer:"_regexObserver"}),e("design:type",String)],YR.prototype,"regexInput",void 0),t([o({type:Array,notify:!0}),e("design:type",Array)],YR.prototype,"selectedRuns",void 0),t([o({type:Array}),e("design:type",Array)],YR.prototype,"runs",void 0),t([o({type:String,notify:!0}),e("design:type",String)],YR.prototype,"dataLocation",void 0),t([o({type:Number}),e("design:type",Number)],YR.prototype,"_dataLocationClipLength",void 0),t([o({type:String}),e("design:type",String)],YR.prototype,"_dataLocationDelimiterPattern",void 0),t([o({type:Object}),e("design:type",Object)],YR.prototype,"coloring",void 0),t([s("dataLocation","_dataLocationClipLength"),e("design:type",String),e("design:paramtypes",[])],YR.prototype,"_clippedDataLocation",null),YR=t([i("tf-runs-selector")],YR);class XR{constructor(){this.cancellationCount=0}cancellable(t){const e=this.cancellationCount;return n=>t({value:n,cancelled:this.cancellationCount!==e})}cancelAll(){this.cancellationCount++}}let $R=class extends(er(ye)){constructor(){super(...arguments),this.html=""}get sanitizedHtml(){return this.html}attached(){window.requestAnimationFrame((()=>{this.scopeSubtree(this.$.markdown,!0)}))}};function KR(t){return t?t.toString().replace(/GMT-\d+ \(([^)]+)\)/,"$1"):""}$R.template=_e`
    <div id="markdown" inner-h-t-m-l="[[sanitizedHtml]]"></div>
    <style>
      /*
       * Reduce topmost and bottommost margins from 16px to 0.3em (renders
       * at about 4.8px) to keep the layout compact. This improves the
       * appearance when there is only one line of text; standard Markdown
       * renderers will still include a \`<p>\` element.
       *
       * By targeting only the top-level, extremal elements, we preserve any
       * actual paragraph breaks and only change the padding against the
       * component edges.
       */
      #markdown > p:first-child {
        margin-top: 0.3em;
      }
      #markdown > p:last-child {
        margin-bottom: 0.3em;
      }

      /* Pleasant styles for Markdown tables. */
      #markdown table {
        border-collapse: collapse;
      }
      #markdown table th {
        font-weight: 600;
      }
      #markdown table th,
      #markdown table td {
        padding: 6px 13px;
        border: 1px solid var(--tb-ui-border, #dfe2e5);
      }
      #markdown table tr {
        background-color: inherit;
        border-top: 1px solid var(--tb-ui-border, #c6cbd1);
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],$R.prototype,"html",void 0),t([s("html"),e("design:type",Object),e("design:paramtypes",[])],$R.prototype,"sanitizedHtml",null),$R=t([i("tf-markdown-view")],$R),el({moduleName:"tf-card-heading-style",styleContent:"\n    figcaption {\n      width: 100%;\n    }\n\n    /** Horizontal line of labels. */\n    .heading-row {\n      margin-top: -4px;\n      display: flex;\n      flex-direction: row;\n      flex-wrap: wrap;\n    }\n\n    /** Piece of text in the figure caption. */\n    .heading-label {\n      flex-grow: 1;\n      margin-top: 4px;\n      max-width: 100%;\n      word-wrap: break-word;\n    }\n\n    /** Makes label show on the right. */\n    .heading-right {\n      flex-grow: 0;\n    }\n  "});let ZR=class extends ye{constructor(){super(...arguments),this.displayName=null,this.tag=null,this.run=null,this.description=null,this.color=null}_updateHeadingStyle(){this.updateStyles({"--tf-card-heading-background-color":this._runBackground,"--tf-card-heading-color":this._runColor})}_computeRunBackground(t){return t||"none"}_computeRunColor(t){return(function e(t){const e=(function n(t){if(!t)return null;let e=t.match(/^#([0-9a-f]{1,2})([0-9a-f]{1,2})([0-9a-f]{1,2})$/);if(!e)return null;if(4==t.length)for(var n=1;n<=3;n++)e[n]=e[n]+e[n];return[parseInt(e[1],16),parseInt(e[2],16),parseInt(e[3],16)]})(t);return e?Math.round((299*e[0]+587*e[1]+114*e[2])/1e3)>125?"inherit":"#eee":"inherit"})(t)}get _nameLabel(){return this.displayName||this.tag||""}get _tagLabel(){var t=this.tag;return t&&t!==this.displayName?t:""}_toggleDescriptionDialog(t){const e=this.$.descriptionDialog;e.positionTarget=t.target,e.toggle()}};ZR.template=_e`
    <div class="container">
      <figcaption class="content">
        <div class="heading-row">
          <template is="dom-if" if="[[_nameLabel]]">
            <div itemprop="name" class="heading-label name">[[_nameLabel]]</div>
          </template>
          <template is="dom-if" if="[[run]]">
            <!-- Extra wrapping span needed to avoid flexbox blockification. -->
            <!-- (see flexbox spec, section 4 "Flex Items") -->
            <span>
              <span
                itemprop="run"
                id="heading-run"
                class="heading-label heading-right run"
                >[[run]]</span
              >
            </span>
          </template>
        </div>
        <template is="dom-if" if="[[_tagLabel]]">
          <div class="heading-row">
            <div class="heading-label">
              tag: <span itemprop="tag">[[_tagLabel]]</span>
            </div>
          </div>
        </template>
        <slot></slot>
      </figcaption>
      <template is="dom-if" if="[[description]]">
        <paper-icon-button
          icon="info"
          on-tap="_toggleDescriptionDialog"
          title="Show summary description"
        ></paper-icon-button>
      </template>
      <paper-dialog
        id="descriptionDialog"
        no-overlap=""
        horizontal-align="auto"
        vertical-align="auto"
      >
        <paper-dialog-scrollable>
          <tf-markdown-view html="[[description]]"></tf-markdown-view>
        </paper-dialog-scrollable>
      </paper-dialog>
    </div>
    <style include="tf-card-heading-style">
      .container {
        display: flex;
      }
      .content {
        font-size: 12px;
        flex-grow: 1;
      }
      .name {
        font-size: 14px;
      }
      .run {
        font-size: 11px;
        width: auto;
        border-radius: 3px;
        font-weight: bold;
        padding: 1px 4px 2px;
      }
      paper-icon-button {
        flex-grow: 0;
      }
      paper-dialog-scrollable {
        max-width: 640px;
      }
      #heading-run {
        background: var(--tf-card-heading-background-color);
        color: var(--tf-card-heading-color);
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],ZR.prototype,"displayName",void 0),t([o({type:String}),e("design:type",String)],ZR.prototype,"tag",void 0),t([o({type:String}),e("design:type",String)],ZR.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],ZR.prototype,"description",void 0),t([o({type:String}),e("design:type",String)],ZR.prototype,"color",void 0),t([o({type:String,computed:"_computeRunBackground(color)",readOnly:!0,observer:"_updateHeadingStyle"}),e("design:type",String)],ZR.prototype,"_runBackground",void 0),t([o({type:String,computed:"_computeRunColor(color)",readOnly:!0,observer:"_updateHeadingStyle"}),e("design:type",String)],ZR.prototype,"_runColor",void 0),t([s("displayName","tag"),e("design:type",String),e("design:paramtypes",[])],ZR.prototype,"_nameLabel",null),t([s("displayName","tag"),e("design:type",String),e("design:paramtypes",[])],ZR.prototype,"_tagLabel",null),ZR=t([i("tf-card-heading")],ZR);let JR=class extends(er(ye)){constructor(){super(...arguments),this._metadataCanceller=new XR,this._steps=[],this._attached=!1}get _runColor(){return GR(this.run)}get _hasAtLeastOneStep(){var t=this._steps;return!!t&&t.length>0}get _hasMultipleSteps(){var t=this._steps;return!!t&&t.length>1}get _maxStepIndex(){return this._steps.length-1}get _currentDatum(){return this._steps[this._stepIndex]}get _sampleText(){return`${this.sample+1}`}get _hasMultipleSamples(){return this.totalSamples>1}attached(){this._attached=!0,this.reload()}_reloadOnRunTagChange(){this.reload()}reload(){if(!this._attached)return;this._metadataCanceller.cancelAll();const t=_r().pluginRoute("audio","/audio",new URLSearchParams({tag:this.tag,run:this.run,sample:String(this.sample)})),e=this._metadataCanceller.cancellable((t=>{if(t.cancelled)return;const e=t.value.map(this._createStepDatum.bind(this));this.set("_steps",e),this.set("_stepIndex",e.length-1)}));this.requestManager.request(t).then(e)}_createStepDatum(t){const e=new URLSearchParams(t.query);e.append("ts",String(t.wall_time));const n=_r().pluginRoute("audio","/individualAudio",e);return{wall_time:KR(new Date(1e3*t.wall_time)),step:t.step,label:t.label,contentType:t.contentType,url:n}}};JR.template=_e`
    <tf-card-heading
      tag="[[tag]]"
      run="[[run]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
      color="[[_runColor]]"
    >
      <template is="dom-if" if="[[_hasMultipleSamples]]">
        <div class="heading-row">
          <div class="heading-label">
            sample: [[_sampleText]] of [[totalSamples]]
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep]]">
        <div class="heading-row">
          <div class="heading-label">
            step <strong>[[_currentDatum.step]]</strong>
          </div>
          <template is="dom-if" if="[[_currentDatum.wall_time]]">
            <div class="heading-label heading-right">
              [[_currentDatum.wall_time]]
            </div>
          </template>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps]]">
        <div class="heading-row">
          <paper-slider
            id="steps"
            immediate-value="{{_stepIndex}}"
            max="[[_maxStepIndex]]"
            max-markers="[[_maxStepIndex]]"
            snaps=""
            step="1"
            value="{{_stepIndex}}"
          ></paper-slider>
        </div>
      </template>
    </tf-card-heading>
    <template is="dom-if" if="[[_hasAtLeastOneStep]]">
      <audio
        controls=""
        src$="[[_currentDatum.url]]"
        type$="[[_currentDatum.contentType]]"
      ></audio>
      <tf-markdown-view html="[[_currentDatum.label]]"></tf-markdown-view>
    </template>
    <div id="main-audio-container"></div>

    <style include="tf-card-heading-style">
      :host {
        display: block;
        width: 350px;
        height: auto;
        position: relative;
        --step-slider-knob-color: #424242;
        margin-right: 15px;
        margin-bottom: 15px;
      }

      #steps {
        height: 15px;
        margin: 0 0 0 -15px;
        width: 100%;
        box-sizing: border-box;
        padding: 0 5px; /* so the slider knob doesn't butt out */
        margin-top: 5px;
        --paper-slider-active-color: var(--step-slider-knob-color);
        --paper-slider-knob-color: var(--step-slider-knob-color);
        --paper-slider-pin-color: var(--step-slider-knob-color);
        --paper-slider-knob-start-color: var(--step-slider-knob-color);
        --paper-slider-knob-start-border-color: var(--step-slider-knob-color);
        --paper-slider-pin-start-color: var(--step-slider-knob-color);
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],JR.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],JR.prototype,"tag",void 0),t([o({type:Number}),e("design:type",Number)],JR.prototype,"sample",void 0),t([o({type:Number}),e("design:type",Number)],JR.prototype,"totalSamples",void 0),t([o({type:Object}),e("design:type",Object)],JR.prototype,"tagMetadata",void 0),t([o({type:Object}),e("design:type",dr)],JR.prototype,"requestManager",void 0),t([o({type:Object}),e("design:type",XR)],JR.prototype,"_metadataCanceller",void 0),t([o({type:Array}),e("design:type",Array)],JR.prototype,"_steps",void 0),t([o({type:Number}),e("design:type",Number)],JR.prototype,"_stepIndex",void 0),t([s("run"),e("design:type",String),e("design:paramtypes",[])],JR.prototype,"_runColor",null),t([s("_steps"),e("design:type",Boolean),e("design:paramtypes",[])],JR.prototype,"_hasAtLeastOneStep",null),t([s("_steps"),e("design:type",Boolean),e("design:paramtypes",[])],JR.prototype,"_hasMultipleSteps",null),t([s("_steps"),e("design:type",Number),e("design:paramtypes",[])],JR.prototype,"_maxStepIndex",null),t([s("_steps","_stepIndex"),e("design:type",Object),e("design:paramtypes",[])],JR.prototype,"_currentDatum",null),t([s("sample"),e("design:type",String),e("design:paramtypes",[])],JR.prototype,"_sampleText",null),t([s("totalSamples"),e("design:type",Boolean),e("design:paramtypes",[])],JR.prototype,"_hasMultipleSamples",null),t([a("run","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],JR.prototype,"_reloadOnRunTagChange",null),JR=t([i("tf-audio-loader")],JR);let QR=class extends(er(ye)){constructor(){super(...arguments),this.reloadOnReady=!0,this._tagFilter="",this._requestManager=new dr}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){this._fetchTags().then((()=>{this._reloadAudio()}))}_fetchTags(){const t=_r().pluginRoute("audio","/tags");return this._requestManager.request(t).then((t=>{if(Se.exports.isEqual(t,this._runToTagInfo))return;const e=ar(Se.exports.mapValues(t,(t=>Object.keys(t))));this.set("_dataNotFound",0===e.length),this.set("_runToTagInfo",t)}))}_reloadAudio(){this.root.querySelectorAll("tf-audio-loader").forEach((t=>{t.reload()}))}_shouldOpen(t){return t<=2}get _categories(){var t=this._runToTagInfo,e=this._selectedRuns,n=this._tagFilter;function i(e){const n=t[e.run][e.tag].samples;return Se.exports.range(n).map((t=>Object.assign({},e,{sample:t,totalSamples:n})))}return Mr(Se.exports.mapValues(t,(t=>Object.keys(t))),e,n).map((t=>Object.assign({},t,{items:[].concat.apply([],t.items.map(i))})))}_tagMetadata(t,e,n){return t[e][n]}};QR.template=_e`
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section runs-selector">
          <tf-runs-selector
            id="runs-selector"
            selected-runs="{{_selectedRuns}}"
          ></tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No audio data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>You haven’t written any audio data to your event files.</li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
            >
              <template>
                <tf-audio-loader
                  active="[[active]]"
                  run="[[item.run]]"
                  tag="[[item.tag]]"
                  sample="[[item.sample]]"
                  total-samples="[[item.totalSamples]]"
                  tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]"
                  request-manager="[[_requestManager]]"
                ></tf-audio-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>
    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  `,t([o({type:Boolean}),e("design:type",Boolean)],QR.prototype,"reloadOnReady",void 0),t([o({type:Array}),e("design:type",Array)],QR.prototype,"_selectedRuns",void 0),t([o({type:Object}),e("design:type",Object)],QR.prototype,"_runToTagInfo",void 0),t([o({type:Boolean}),e("design:type",Boolean)],QR.prototype,"_dataNotFound",void 0),t([o({type:String}),e("design:type",String)],QR.prototype,"_tagFilter",void 0),t([o({type:Object}),e("design:type",dr)],QR.prototype,"_requestManager",void 0),t([s("_runToTagInfo","_selectedRuns","_tagFilter"),e("design:type",Array),e("design:paramtypes",[])],QR.prototype,"_categories",null),QR=t([i("tf-audio-dashboard")],QR);let tO=class extends(er(ye)){attached(){this.async((function(){this.getEffectiveChildren().forEach(function(t){this.listen(t,"tap","_selectTarget")}.bind(this))}))}_selectTarget(t){this.selectedId=t.currentTarget.id}_selectedIdChanged(){var t=this.queryEffectiveChildren("#"+this.selectedId);t&&(this.getEffectiveChildren().forEach((function(t){t.classList.remove("selected")})),t.classList.add("selected"))}};function eO(t,e){let n;const i={};return Object.keys(t).forEach((e=>{const r=t[e];void 0===n&&(n=r.displayName),n!==r.displayName&&(n=null),void 0===i[r.description]&&(i[r.description]=[]),i[r.description].push(e)})),{displayName:null!=n?n:e,description:(()=>{const t=Object.keys(i);return 0===t.length?"":1===t.length?t[0]:`<p><strong>Multiple descriptions:</strong></p><ul>${t.map((t=>{const e=i[t].map((t=>`<code>${t.replace(/</g,"&lt;").replace(/>/g,"&gt;").replace(/&/g,"&amp;")}</code>`)),n=e.length>2?e.slice(0,e.length-1).join(", ")+", and "+e[e.length-1]:e.join(" and ");return`<li><p>For ${(function r(t,e,n){return 1===t?e:n})(e.length,"run","runs")} ${n}:</p>${t}</li>`})).join("")}</ul>`})()}}tO.template=_e`
    <div id="wrap">
      <h3>[[name]]</h3>
      <div class="content-wrapper"><slot></slot></div>
    </div>
    <style>
      .content-wrapper ::slotted(*) {
        background: none;
        color: var(--tb-ui-dark-accent);
        font-size: 13px;
        margin-top: 10px;
      }

      .content-wrapper ::slotted(*) {
        background: none;
        color: var(--tb-ui-dark-accent);
        font-size: 13px;
        margin-top: 10px;
      }

      .content-wrapper ::slotted(.selected) {
        background-color: var(--tb-ui-dark-accent);
        color: white !important;
      }

      h3 {
        color: var(--tb-secondary-text-color);
        display: block;
        font-size: 14px;
        font-weight: normal;
        margin: 0 0 5px;
        pointer-events: none;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],tO.prototype,"name",void 0),t([o({type:String,notify:!0,observer:"_selectedIdChanged"}),e("design:type",String)],tO.prototype,"selectedId",void 0),tO=t([i("tf-option-selector")],tO);let nO=class extends ye{constructor(){super(...arguments),this.weight=.6,this._updateWeight=Se.exports.debounce((function(t){this.weight=t}),250)}_immediateWeightNumberForPaperSliderChanged(){this._inputWeightStringForPaperInput=this._immediateWeightNumberForPaperSlider.toString(),this._updateWeight.call(this,this._immediateWeightNumberForPaperSlider)}_inputWeightStringForPaperInputChanged(){+this._inputWeightStringForPaperInput<0?this._inputWeightStringForPaperInput="0":+this._inputWeightStringForPaperInput>1&&(this._inputWeightStringForPaperInput="1");var t=+this._inputWeightStringForPaperInput;isNaN(t)||this._updateWeight.call(this,t)}};function iO(t,e){const n=Object.keys(e).sort().filter((t=>void 0!==e[t]));if(!n.length)return t;const i=-1!==t.indexOf("?")?"&":"?";return t+i+[].concat(...n.map((t=>{const n=e[t];return(Array.isArray(n)?n:[n]).map((e=>`${t}=${(function n(t){return encodeURIComponent(t).replace(/\(/g,"%28").replace(/\)/g,"%29")})(e)}`))}))).join("&")}nO.template=_e`
    <h3 class="title">Smoothing</h3>
    <div class="smoothing-block">
      <paper-slider
        id="slider"
        immediate-value="{{_immediateWeightNumberForPaperSlider}}"
        max="[[max]]"
        min="[[min]]"
        pin
        step="[[step]]"
        type="number"
        value="{{weight}}"
      ></paper-slider>
      <paper-input
        id="input"
        label="weight"
        no-label-float
        value="{{_inputWeightStringForPaperInput}}"
        type="number"
        step="[[step]]"
        min="[[min]]"
        max="[[max]]"
      ></paper-input>
    </div>
    <style>
      .title {
        color: var(--tb-secondary-text-color);
        margin: 0;
        font-weight: normal;
        font-size: 14px;
        margin-bottom: 5px;
      }

      .smoothing-block {
        display: flex;
      }

      paper-slider {
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
        flex-grow: 2;
      }

      paper-input {
        --paper-input-container-focus-color: var(--tb-orange-strong);
        --paper-input-container-input: {
          font-size: 14px;
        }
        --paper-input-container-label: {
          font-size: 14px;
        }
        width: 60px;
      }
    </style>
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/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     *
     * @fileoverview manually add d3-selection-multi to d3 default bundle. Most of this code is
     * copied from d3-selection-multi@1.0.0.
     * See https://github.com/d3/d3-selection-multi/issues/11 for why we have to do this
     */
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/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Smt,"__esModule",{value:!0}),Smt.makeEnum=function Mmt(t){return t.reduce((function(t,e){return t[e]=e,t}),{})},
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(ymt,"__esModule",{value:!0});var Emt=bmt,Tmt={linear:vmt.easeLinear,quad:vmt.easeQuad,quadIn:vmt.easeQuadIn,quadOut:vmt.easeQuadOut,quadInOut:vmt.easeQuadInOut,cubic:vmt.easeCubic,cubicIn:vmt.easeCubicIn,cubicOut:vmt.easeCubicOut,cubicInOut:vmt.easeCubicInOut,poly:vmt.easePoly,polyIn:vmt.easePolyIn,polyOut:vmt.easePolyOut,polyInOut:vmt.easePolyInOut,sin:vmt.easeSin,sinIn:vmt.easeSinIn,sinOut:vmt.easeSinOut,sinInOut:vmt.easeSinInOut,exp:vmt.easeExp,expIn:vmt.easeExpIn,expOut:vmt.easeExpOut,expInOut:vmt.easeExpInOut,circle:vmt.easeCircle,circleIn:vmt.easeCircleIn,circleOut:vmt.easeCircleOut,circleInOut:vmt.easeCircleInOut,bounce:vmt.easeBounce,bounceIn:vmt.easeBounceIn,bounceOut:vmt.easeBounceOut,bounceInOut:vmt.easeBounceInOut,back:vmt.easeBack,backIn:vmt.easeBackIn,backOut:vmt.easeBackOut,backInOut:vmt.easeBackInOut,elastic:vmt.easeElastic,elasticIn:vmt.easeElasticIn,elasticOut:vmt.easeElasticOut,elasticInOut:vmt.easeElasticInOut};ymt.EaseName=Smt.makeEnum(["linear","quad","quadIn","quadOut","quadInOut","cubic","cubicIn","cubicOut","cubicInOut","poly","polyIn","polyOut","polyInOut","sin","sinIn","sinOut","sinInOut","exp","expIn","expOut","expInOut","circle","circleIn","circleOut","circleInOut","bounce","bounceIn","bounceOut","bounceInOut","back","backIn","backOut","backInOut","elastic","elasticIn","elasticOut","elasticInOut"]);var Cmt=(function(){function t(){this._startDelay=t._DEFAULT_START_DELAY_MILLISECONDS,this._stepDuration=t._DEFAULT_STEP_DURATION_MILLISECONDS,this._stepDelay=t._DEFAULT_ITERATIVE_DELAY_MILLISECONDS,this._maxTotalDuration=t._DEFAULT_MAX_TOTAL_DURATION_MILLISECONDS,this._easingMode=t._DEFAULT_EASING_MODE}return t.prototype.totalTime=function(t){var e=this._getAdjustedIterativeDelay(t);return this.startDelay()+e*Math.max(t-1,0)+this.stepDuration()},t.prototype.animate=function(t,e){var n=this,i=(t=Emt.coerceExternalD3(t)).size(),r=this._getAdjustedIterativeDelay(i);return t.transition().ease(this._getEaseFactory()).duration(this.stepDuration()).delay((function(t,e){return n.startDelay()+r*e})).attrs(e)},t.prototype.startDelay=function(t){return null==t?this._startDelay:(this._startDelay=t,this)},t.prototype.stepDuration=function(t){return null==t?Math.min(this._stepDuration,this._maxTotalDuration):(this._stepDuration=t,this)},t.prototype.stepDelay=function(t){return null==t?this._stepDelay:(this._stepDelay=t,this)},t.prototype.maxTotalDuration=function(t){return null==t?this._maxTotalDuration:(this._maxTotalDuration=t,this)},t.prototype.easingMode=function(t){return null==t?this._easingMode:(this._easingMode=t,this)},t.prototype._getEaseFactory=function(){var t=this.easingMode();if("string"==typeof t){var e=Tmt[t];return null==e?Tmt.linear:e}return t},t.prototype._getAdjustedIterativeDelay=function(t){var e=this.maxTotalDuration()-this.stepDuration(),n=(e=Math.max(e,0))/Math.max(t-1,1);return Math.min(this.stepDelay(),n)},t._DEFAULT_START_DELAY_MILLISECONDS=0,t._DEFAULT_STEP_DURATION_MILLISECONDS=300,t._DEFAULT_ITERATIVE_DELAY_MILLISECONDS=15,t._DEFAULT_MAX_TOTAL_DURATION_MILLISECONDS=1/0,t._DEFAULT_EASING_MODE="expOut",t})();ymt.Easing=Cmt;var Amt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Amt,"__esModule",{value:!0});var kmt=bmt,Lmt=(function(){function t(){}return t.prototype.totalTime=function(t){return 0},t.prototype.animate=function(t,e){return(t=kmt.coerceExternalD3(t)).attrs(e)},t})();Amt.Null=Lmt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(ymt,t),e.__exportStar(Amt,t)})(_mt);var Pmt={},Nmt={},Imt={},Rmt={},Omt={},zmt={},Dmt={},Bmt={},Hmt={};Object.defineProperty(Hmt,"__esModule",{value:!0});var Fmt=(function(){function t(t){this.cache={},this.compute=t}return t.prototype.get=function(t){return this.cache.hasOwnProperty(t)||(this.cache[t]=this.compute(t)),this.cache[t]},t.prototype.clear=function(){return this.cache={},this},t})();Hmt.Cache=Fmt;var Vmt={};Object.defineProperty(Vmt,"__esModule",{value:!0});var Umt=(function(){function t(){}return t.arrayEq=function(t,e){if(null==t||null==e)return t===e;if(t.length!==e.length)return!1;for(var n=0;n<t.length;n++)if(t[n]!==e[n])return!1;return!0},t.objEq=function(e,n){if(null==e||null==n)return e===n;var i=Object.keys(e).sort(),r=Object.keys(n).sort(),o=i.map((function(t){return e[t]})),a=r.map((function(t){return n[t]}));return t.arrayEq(i,r)&&t.arrayEq(o,a)},t.strictEq=function(t,e){return t===e},t.defaults=function(t){for(var e=[],n=1;n<arguments.length;n++)e[n-1]=arguments[n];if(null==t)throw new TypeError("Cannot convert undefined or null to object");var i=Object(t);return e.forEach((function(t){if(null!=t)for(var e in t)Object.prototype.hasOwnProperty.call(t,e)&&(i[e]=t[e])})),i},t})();Vmt.Methods=Umt;var jmt={};Object.defineProperty(jmt,"__esModule",{value:!0});var Gmt=(function(){function t(){}return t.combineWhitespace=function(t){return t.replace(/[ \t]+/g," ")},t.isNotEmptyString=function(t){return t&&""!==t.trim()},t.trimStart=function(e,n){if(!e)return e;var i=e.split(""),r=n?function(e){return e.split(n).some(t.isNotEmptyString)}:t.isNotEmptyString;return i.reduce((function(t,e){return r(t+e)?t+e:t}),"")},t.trimEnd=function(e,n){if(!e)return e;var i=e.split("");return i.reverse(),(i=t.trimStart(i.join(""),n).split("")).reverse(),i.join("")},t})();jmt.StringMethods=Gmt;var Wmt={};Object.defineProperty(Wmt,"__esModule",{value:!0});var qmt=(function(){function t(){this.WordDividerRegExp=new RegExp("\\W"),this.WhitespaceRegExp=new RegExp("\\s")}return t.prototype.tokenize=function(t){var e=this;return t.split("").reduce((function(t,n){return t.slice(0,-1).concat(e.shouldCreateNewToken(t[t.length-1],n))}),[""])},t.prototype.shouldCreateNewToken=function(t,e){if(!t)return[e];var n=t[t.length-1];return this.WhitespaceRegExp.test(n)&&this.WhitespaceRegExp.test(e)?[t+e]:this.WhitespaceRegExp.test(n)||this.WhitespaceRegExp.test(e)?[t,e]:this.WordDividerRegExp.test(n)?n===e?[t+e]:[t,e]:[t+e]},t})();Wmt.Tokenizer=qmt,(function(t){function e(e){for(var n in e)t.hasOwnProperty(n)||(t[n]=e[n])}Object.defineProperty(t,"__esModule",{value:!0}),e(Hmt),e(Vmt),e(jmt),e(Wmt)})(Bmt),Object.defineProperty(Dmt,"__esModule",{value:!0});var Ymt=Bmt,Xmt={textRotation:0,textShear:0,xAlign:"left",yAlign:"top"},$mt=(function(){function t(t,e,n){this._measurer=t,this._penFactory=e,this._wrapper=n}return t.prototype.measurer=function(t){return this._measurer=t,this},t.prototype.wrapper=function(t){return this._wrapper=t,this},t.prototype.penFactory=function(t){return this._penFactory=t,this},t.prototype.write=function(e,n,i,r,o){if(void 0===r&&(r={}),r=Ymt.Methods.defaults({},Xmt,r),-1===t.SupportedRotation.indexOf(r.textRotation))throw new Error("unsupported rotation - "+r.textRotation+". 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     * @license MIT
     */
Object.defineProperty(Bgt,"__esModule",{value:!0});var lyt=Edt,cyt=Hgt,uyt=Fgt,hyt=Se.exports,dyt=bmt,pyt=Smt;Bgt.XAlignment=pyt.makeEnum(["left","center","right"]),Bgt.YAlignment=pyt.makeEnum(["top","center","bottom"]);var fyt=(function(){function t(){this._overflowHidden=!1,this._origin={x:0,y:0},this._xAlignment="left",this._yAlignment="top",this._isSetup=!1,this._isAnchored=!1,this._cssClasses=new uyt.Set,this._destroyed=!1,this._onAnchorCallbacks=new uyt.CallbackSet,this._onDetachCallbacks=new uyt.CallbackSet,this._cssClasses.add("component")}return t.prototype.anchor=function(t){if(t=dyt.coerceExternalD3(t),this._destroyed)throw new Error("Can't reuse destroy()-ed Components!");return this.isRoot()&&(this._rootElement=t,this._rootElement.classed("plottable",!0)),null!=this._element?t.node().appendChild(this._element.node()):(this._element=t.append("div"),this._setup()),this._isAnchored=!0,this._onAnchorCallbacks.callCallbacks(this),this},t.prototype.onAnchor=function(t){return this._isAnchored&&t(this),this._onAnchorCallbacks.add(t),this},t.prototype.offAnchor=function(t){return this._onAnchorCallbacks.delete(t),this},t.prototype._setup=function(){var t=this;this._isSetup||(this._cssClasses.forEach((function(e){t._element.classed(e,!0)})),this._cssClasses=new uyt.Set,this._backgroundContainer=this._element.append("svg").classed("background-container",!0),this._content=this._element.append("svg").classed("content",!0),this._foregroundContainer=this._element.append("svg").classed("foreground-container",!0),this._content.classed(this._overflowHidden?"component-overflow-hidden":"component-overflow-visible",!0),this._isSetup=!0)},t.prototype.requestedSpace=function(t,e){return{minWidth:0,minHeight:0}},t.prototype.computeLayout=function(e,n,i){if(null==e||null==n||null==i){if(null==this._element)throw new Error("anchor() must be called before computeLayout()");if(null==this._rootElement)throw new Error("null arguments cannot be passed to computeLayout() on a non-root, unanchored node");e={x:0,y:0};var r=this._rootElement.node();n=uyt.DOM.elementWidth(r),i=uyt.DOM.elementHeight(r)}var o=this._sizeFromOffer(n,i),a=o.height,s=o.width;return this.setBounds(s,a,e.x+(n-s)*t._xAlignToProportion[this._xAlignment],e.y+(i-a)*t._yAlignToProportion[this._yAlignment]),this},t.prototype.setBounds=function(t,e,n,i){return void 0===n&&(n=0),void 0===i&&(i=0),this._width=t,this._height=e,this._origin={x:n,y:i},null!=this._element&&this._element.styles({left:n+"px",height:e+"px",top:i+"px",width:t+"px"}),null!=this._resizeHandler&&this._resizeHandler({width:t,height:e}),this},t.prototype._sizeFromOffer=function(t,e){var n=this.requestedSpace(t,e);return{width:this.fixedWidth()?Math.min(t,n.minWidth):t,height:this.fixedHeight()?Math.min(e,n.minHeight):e}},t.prototype.render=function(){return this._isAnchored&&this._isSetup&&this.width()>=0&&this.height()>=0&&cyt.registerToRender(this),this},t.prototype.renderLowPriority=function(){return this.render()},t.prototype._scheduleComputeLayout=function(){this._isAnchored&&this._isSetup&&cyt.registerToComputeLayoutAndRender(this)},t.prototype.onResize=function(t){return this._resizeHandler=t,this},t.prototype.renderImmediately=function(){return this},t.prototype.redraw=function(){return this._isAnchored&&this._isSetup&&(this.isRoot()?this._scheduleComputeLayout():this.parent().redraw()),this},t.prototype.invalidateCache=function(){},t.prototype.renderTo=function(t){if(this.detach(),null!=t){var e=void 0;if(!(e="string"==typeof t||hyt.isElement(t)?lyt.select(t):dyt.coerceExternalD3(t)).node()||null==e.node().nodeName)throw new Error("Plottable requires a valid Element to renderTo");if("svg"===e.node().nodeName)throw new Error("Plottable 3.x and later can only renderTo an HTML component; pass a div instead!");this.anchor(e)}if(null==this._element)throw new Error("If a Component has never been rendered before, then renderTo must be given a node to render to, or a d3.Selection, or a selector string");return cyt.registerToComputeLayoutAndRender(this),cyt.flush(),this},t.prototype.xAlignment=function(e){if(null==e)return this._xAlignment;if(e=e.toLowerCase(),null==t._xAlignToProportion[e])throw new Error("Unsupported alignment: "+e);return this._xAlignment=e,this.redraw(),this},t.prototype.yAlignment=function(e){if(null==e)return this._yAlignment;if(e=e.toLowerCase(),null==t._yAlignToProportion[e])throw new Error("Unsupported alignment: "+e);return this._yAlignment=e,this.redraw(),this},t.prototype.hasClass=function(t){return null!=t&&(null==this._element?this._cssClasses.has(t):this._element.classed(t))},t.prototype.addClass=function(t){return null==t||(null==this._element?this._cssClasses.add(t):this._element.classed(t,!0)),this},t.prototype.removeClass=function(t){return null==t||(null==this._element?this._cssClasses.delete(t):this._element.classed(t,!1)),this},t.prototype.fixedWidth=function(){return!1},t.prototype.fixedHeight=function(){return!1},t.prototype.detach=function(){return this.parent(null),this._isAnchored&&this._element.remove(),this._isAnchored=!1,this._onDetachCallbacks.callCallbacks(this),this},t.prototype.onDetach=function(t){return this._onDetachCallbacks.add(t),this},t.prototype.offDetach=function(t){return this._onDetachCallbacks.delete(t),this},t.prototype.parent=function(t){if(void 0===t)return this._parent;if(null!==t&&!t.has(this))throw new Error("Passed invalid parent");return this._parent=t,this},t.prototype.bounds=function(){var t=this.origin();return{topLeft:t,bottomRight:{x:t.x+this.width(),y:t.y+this.height()}}},t.prototype.destroy=function(){this._destroyed=!0,this.detach()},t.prototype.width=function(){return this._width},t.prototype.height=function(){return this._height},t.prototype.origin=function(){return{x:this._origin.x,y:this._origin.y}},t.prototype.originToRoot=function(){for(var t=this.origin(),e=this.parent();null!=e;){var n=e.origin();t.x+=n.x,t.y+=n.y,e=e.parent()}return t},t.prototype.root=function(){for(var t=this;!t.isRoot();)t=t.parent();return t},t.prototype.isRoot=function(){return null==this.parent()},t.prototype.foreground=function(){return this._foregroundContainer},t.prototype.content=function(){return this._content},t.prototype.element=function(){return this._element},t.prototype.rootElement=function(){return this.root()._rootElement},t.prototype.background=function(){return this._backgroundContainer},t._xAlignToProportion={left:0,center:.5,right:1},t._yAlignToProportion={top:0,center:.5,bottom:1},t})();Bgt.Component=fyt;var myt={},gyt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(gyt,"__esModule",{value:!0});var _yt=Edt;function yyt(t){return void 0===t&&(t=3),vyt(t),function(e){return e.toFixed(t)}}function vyt(t){if(t<0||t>20)throw new RangeError("Formatter precision must be between 0 and 20");if(t!==Math.floor(t))throw new RangeError("Formatter precision must be an integer")}
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */gyt.currency=function byt(t,e,n){void 0===t&&(t=2),void 0===e&&(e="$"),void 0===n&&(n=!0);var i=yyt(t);return function(t){var r=i(Math.abs(t));return""!==r&&(n?r=e+r:r+=e,t<0&&(r="-"+r)),r}},gyt.fixed=yyt,gyt.general=function xyt(t){return void 0===t&&(t=3),vyt(t),function(e){if("number"==typeof e){var n=Math.pow(10,t);return String(Math.round(e*n)/n)}return String(e)}},gyt.identity=function wyt(){return function(t){return String(t)}},gyt.percentage=function Syt(t){void 0===t&&(t=0);var e=yyt(t);return function(t){var n=100*t,i=t.toString(),r=Math.pow(10,i.length-(i.indexOf(".")+1));return n=parseInt((n*r).toString(),10)/r,e(n)+"%"}},gyt.siSuffix=function Myt(t){return void 0===t&&(t=3),vyt(t),function(e){return _yt.format("."+t+"s")(e)}},gyt.shortScale=function Eyt(t){void 0===t&&(t=3),vyt(t);var e="KMBTQ",n=_yt.format("."+t+"e"),i=_yt.format("."+t+"f"),r=Math.pow(10,3*(e.length+1)),o=Math.pow(10,-t);return function(t){var a=Math.abs(t);if((a<o||a>=r)&&0!==a)return n(t);for(var s=-1;a>=Math.pow(1e3,s+2)&&s<e.length-1;)s++;var l="";return l=-1===s?i(t):i(t/Math.pow(1e3,s+1))+e[s],(t>0&&"1000"===l.substr(0,4)||t<0&&"-1000"===l.substr(0,5))&&(s<e.length-1?(s++,l=i(t/Math.pow(1e3,s+1))+e[s]):l=n(t)),l}},gyt.multiTime=function Tyt(){var t=[{specifier:".%L",predicate:function(t){return 0!==t.getMilliseconds()}},{specifier:":%S",predicate:function(t){return 0!==t.getSeconds()}},{specifier:"%I:%M",predicate:function(t){return 0!==t.getMinutes()}},{specifier:"%I %p",predicate:function(t){return 0!==t.getHours()}},{specifier:"%a %d",predicate:function(t){return 0!==t.getDay()&&1!==t.getDate()}},{specifier:"%b %d",predicate:function(t){return 1!==t.getDate()}},{specifier:"%b",predicate:function(t){return 0!==t.getMonth()}}];return function(e){var n=t.filter((function(t){return t.predicate(e)}));return _yt.timeFormat(n.length>0?n[0].specifier:"%Y")(e)}},gyt.time=function Cyt(t,e){return void 0===e&&(e=!1),e?_yt.utcFormat(t):_yt.timeFormat(t)},Object.defineProperty(myt,"__esModule",{value:!0});var Ayt=uO,kyt=Edt,Lyt=Imt,Pyt=Bgt,Nyt=gyt,Iyt=Fgt;myt.AxisOrientation=Smt.makeEnum(["bottom","left","right","top"]);var Ryt=(function(t){function e(e,n){var i=t.call(this)||this;if(i._endTickLength=5,i._innerTickLength=5,i._tickLabelPadding=10,i._margin=15,i._showEndTickLabels=!1,i._annotationsEnabled=!1,i._annotationTierCount=1,null==e||null==n)throw new Error("Axis requires a scale and orientation");return i._scale=e,i.orientation(n),i._setDefaultAlignment(),i.addClass("axis"),i.isHorizontal()?i.addClass("x-axis"):i.addClass("y-axis"),i.formatter(Nyt.identity()),i._rescaleCallback=function(t){return i._rescale()},i._scale.onUpdate(i._rescaleCallback),i._annotatedTicks=[],i._annotationFormatter=Nyt.identity(),i}return Ayt.__extends(e,t),e.prototype.destroy=function(){t.prototype.destroy.call(this),this._scale.offUpdate(this._rescaleCallback)},e.prototype.tickLabelDataOnElement=function(t){if(null!=t){for(var n;null!=t&&t.classList&&void 0===n;)t.classList.contains(e.TICK_LABEL_CLASS)?n=t:t=t.parentNode;return void 0===t?void 0:kyt.select(t).datum()}},e.prototype._computeWidth=function(){return this._maxLabelTickLength()},e.prototype._computeHeight=function(){return this._maxLabelTickLength()},e.prototype.requestedSpace=function(t,n){var i=0,r=0;return this.isHorizontal()?(r=this._computeHeight()+this._margin,this.annotationsEnabled()&&(r+=(this._annotationMeasurer.measure().height+2*e._ANNOTATION_LABEL_PADDING)*this.annotationTierCount())):(i=this._computeWidth()+this._margin,this.annotationsEnabled()&&(i+=(this._annotationMeasurer.measure().height+2*e._ANNOTATION_LABEL_PADDING)*this.annotationTierCount())),{minWidth:i,minHeight:r}},e.prototype.fixedHeight=function(){return this.isHorizontal()},e.prototype.fixedWidth=function(){return!this.isHorizontal()},e.prototype._rescale=function(){this.render()},e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),this.isHorizontal()?this._scale.range([0,this.width()]):this._scale.range([this.height(),0]),this},e.prototype._sizeFromOffer=function(t,e){var n=this.requestedSpace(t,e);return this.isHorizontal()?{width:t,height:n.minHeight}:{height:e,width:n.minWidth}},e.prototype._setup=function(){t.prototype._setup.call(this),this._tickMarkContainer=this.content().append("g").classed(e.TICK_MARK_CLASS+"-container",!0),this._tickLabelContainer=this.content().append("g").classed(e.TICK_LABEL_CLASS+"-container",!0),this._baseline=this.content().append("line").classed("baseline",!0),this._annotationContainer=this.content().append("g").classed("annotation-container",!0),this._annotationContainer.append("g").classed("annotation-line-container",!0),this._annotationContainer.append("g").classed("annotation-circle-container",!0),this._annotationContainer.append("g").classed("annotation-rect-container",!0);var n=this._annotationContainer.append("g").classed("annotation-label-container",!0),i=new Lyt.SvgContext(n.node());this._annotationMeasurer=new Lyt.CacheMeasurer(i),this._annotationWriter=new Lyt.Writer(this._annotationMeasurer,i)},e.prototype._getTickValues=function(){return[]},e.prototype.renderImmediately=function(){var t=this._getTickValues(),n=this._tickMarkContainer.selectAll("."+e.TICK_MARK_CLASS).data(t),i=n.enter().append("line").classed(e.TICK_MARK_CLASS,!0).merge(n);return i.attrs(this._generateTickMarkAttrHash()),kyt.select(i.nodes()[0]).classed(e.END_TICK_MARK_CLASS,!0).attrs(this._generateTickMarkAttrHash(!0)),kyt.select(i.nodes()[t.length-1]).classed(e.END_TICK_MARK_CLASS,!0).attrs(this._generateTickMarkAttrHash(!0)),n.exit().remove(),this._baseline.attrs(this._generateBaselineAttrHash()),this.annotationsEnabled()?this._drawAnnotations():this._removeAnnotations(),this},e.prototype.annotatedTicks=function(t){return null==t?this._annotatedTicks:(this._annotatedTicks=t,this.render(),this)},e.prototype.annotationFormatter=function(t){return null==t?this._annotationFormatter:(this._annotationFormatter=t,this.render(),this)},e.prototype.annotationsEnabled=function(t){return null==t?this._annotationsEnabled:(this._annotationsEnabled=t,this.redraw(),this)},e.prototype.annotationTierCount=function(t){if(null==t)return this._annotationTierCount;if(t<0)throw new Error("annotationTierCount cannot be negative");return this._annotationTierCount=t,this.redraw(),this},e.prototype._drawAnnotations=function(){var t=this,n=e._ANNOTATION_LABEL_PADDING,i=new Iyt.Map,r=this._annotatedTicksToRender();r.forEach((function(e){var r=t._annotationMeasurer.measure(t.annotationFormatter()(e));i.set(e,{width:r.width+2*n,height:r.height+2*n})}));var o=this._annotationMeasurer.measure().height+2*n,a=this._annotationToTier(i),s=new Iyt.Set,l=this.isHorizontal()?this.height():this.width(),c=this._coreSize(),u=Math.min(this.annotationTierCount(),Math.floor((l-c)/o));a.forEach((function(t,e){(-1===t||t>=u)&&s.add(e)}));var h,d=function(t,e,n){var i=t.selectAll("."+n).data(r),o=i.enter().append(e).classed(n,!0).merge(i);return i.exit().remove(),o},p=function(e){switch(t.orientation()){case"bottom":case"right":return a.get(e)*o+c;case"top":case"left":return l-c-a.get(e)*o}},f=function(e){return t._scale.scale(e)},m=function(t){return s.has(t)?"hidden":"visible"};switch(this.orientation()){case"bottom":case"right":h=0;break;case"top":h=this.height();break;case"left":h=this.width()}var g=this.isHorizontal();d(this._annotationContainer.select(".annotation-line-container"),"line",e.ANNOTATION_LINE_CLASS).attrs({x1:g?f:h,x2:g?f:p,y1:g?h:f,y2:g?p:f,visibility:m}),d(this._annotationContainer.select(".annotation-circle-container"),"circle",e.ANNOTATION_CIRCLE_CLASS).attrs({cx:g?f:h,cy:g?h:f,r:3});var _=function(e){switch(t.orientation()){case"bottom":case"right":return p(e);case"top":case"left":return p(e)-i.get(e).height}};d(this._annotationContainer.select(".annotation-rect-container"),"rect",e.ANNOTATION_RECT_CLASS).attrs({x:g?f:_,y:g?_:f,width:g?function(t){return i.get(t).width}:function(t){return i.get(t).height},height:g?function(t){return i.get(t).height}:function(t){return i.get(t).width},visibility:m});var y=this._annotationWriter,v=this.annotationFormatter(),b=d(this._annotationContainer.select(".annotation-label-container"),"g",e.ANNOTATION_LABEL_CLASS);b.selectAll(".text-container").remove(),b.attrs({transform:function(t){return"translate("+(g?f(t):_(t))+","+(g?_(t):f(t))+")"},visibility:m}).each((function(t){y.write(v(t),g?i.get(t).width:i.get(t).height,g?i.get(t).height:i.get(t).width,{xAlign:"center",yAlign:"center",textRotation:g?0:90},kyt.select(this).node())}))},e.prototype._annotatedTicksToRender=function(){var t=this,e=this._scale.range();return Iyt.Array.uniq(this.annotatedTicks().filter((function(n){return null!=n&&Iyt.Math.inRange(t._scale.scale(n),e[0],e[1])})))},e.prototype._coreSize=function(){var t=this.isHorizontal()?this.height():this.width(),e=this.isHorizontal()?this._computeHeight():this._computeWidth();return Math.min(e,t)},e.prototype._annotationTierHeight=function(){return this._annotationMeasurer.measure().height+2*e._ANNOTATION_LABEL_PADDING},e.prototype._annotationToTier=function(t){var e=this,n=[[]],i=new Iyt.Map,r=this.isHorizontal()?this.width():this.height();return this._annotatedTicksToRender().forEach((function(o){var a=e._scale.scale(o),s=t.get(o).width;if(a<0||a+s>r)i.set(o,-1);else{for(var l=0;n[l].some((function(n){var i=e._scale.scale(n),r=t.get(n).width;return a+s>=i&&a<=i+r}));)l++,n.length===l&&n.push([]);n[l].push(o),i.set(o,l)}})),i},e.prototype._removeAnnotations=function(){this._annotationContainer.selectAll(".annotation-line").remove(),this._annotationContainer.selectAll(".annotation-circle").remove(),this._annotationContainer.selectAll(".annotation-rect").remove(),this._annotationContainer.selectAll(".annotation-label").remove()},e.prototype._generateBaselineAttrHash=function(){var t={x1:0,y1:0,x2:0,y2:0};switch(this._orientation){case"bottom":t.x2=this.width();break;case"top":t.x2=this.width(),t.y1=this.height(),t.y2=this.height();break;case"left":t.x1=this.width(),t.x2=this.width(),t.y2=this.height();break;case"right":t.y2=this.height()}return t},e.prototype._generateTickMarkAttrHash=function(t){var e=this;void 0===t&&(t=!1);var n={x1:0,y1:0,x2:0,y2:0},i=function(t){return e._scale.scale(t)};this.isHorizontal()?(n.x1=i,n.x2=i):(n.y1=i,n.y2=i);var r=t?this._endTickLength:this._innerTickLength;switch(this._orientation){case"bottom":n.y2=r;break;case"top":n.y1=this.height(),n.y2=this.height()-r;break;case"left":n.x1=this.width(),n.x2=this.width()-r;break;case"right":n.x2=r}return n},e.prototype._setDefaultAlignment=function(){switch(this._orientation){case"bottom":this.yAlignment("top");break;case"top":this.yAlignment("bottom");break;case"left":this.xAlignment("right");break;case"right":this.xAlignment("left")}},e.prototype.isHorizontal=function(){return"top"===this._orientation||"bottom"===this._orientation},e.prototype.getScale=function(){return this._scale},e.prototype.formatter=function(t){return null==t?this._formatter:(this._formatter=t,this.redraw(),this)},e.prototype.innerTickLength=function(t){if(null==t)return this._innerTickLength;if(t<0)throw new Error("inner tick length must be positive");return this._innerTickLength=t,this.redraw(),this},e.prototype.endTickLength=function(t){if(null==t)return this._endTickLength;if(t<0)throw new Error("end tick length must be positive");return this._endTickLength=t,this.redraw(),this},e.prototype._maxLabelTickLength=function(){return this.showEndTickLabels()?Math.max(this.innerTickLength(),this.endTickLength()):this.innerTickLength()},e.prototype.tickLabelPadding=function(t){if(null==t)return this._tickLabelPadding;if(t<0)throw new Error("tick label padding must be positive");return this._tickLabelPadding=t,this.redraw(),this},e.prototype.margin=function(t){if(null==t)return this._margin;if(t<0)throw new Error("margin size must be positive");return this._margin=t,this.redraw(),this},e.prototype.orientation=function(t){if(null==t)return this._orientation;var e=t.toLowerCase();if("top"!==e&&"bottom"!==e&&"left"!==e&&"right"!==e)throw new Error("unsupported orientation");return this._orientation=e,this.redraw(),this},e.prototype.showEndTickLabels=function(t){return null==t?this._showEndTickLabels:(this._showEndTickLabels=t,this.render(),this)},e.prototype._showAllTickMarks=function(){this._tickMarkContainer.selectAll("."+e.TICK_MARK_CLASS).each((function(){kyt.select(this).style("visibility","inherit")}))},e.prototype._showAllTickLabels=function(){this._tickLabelContainer.selectAll("."+e.TICK_LABEL_CLASS).each((function(){kyt.select(this).style("visibility","inherit")}))},e.prototype._hideOverflowingTickLabels=function(){var t=this.element().node().getBoundingClientRect(),n=this._tickLabelContainer.selectAll("."+e.TICK_LABEL_CLASS);n.empty()||n.each((function(e,n){Iyt.DOM.clientRectInside(this.getBoundingClientRect(),t)||kyt.select(this).style("visibility","hidden")}))},e.prototype._hideTickMarksWithoutLabel=function(){var t=this._tickMarkContainer.selectAll("."+e.TICK_MARK_CLASS),n=this._tickLabelContainer.selectAll("."+e.TICK_LABEL_CLASS).filter((function(t,e){var n=kyt.select(this).style("visibility");return"inherit"===n||"visible"===n})).data();t.each((function(t,e){-1===n.indexOf(t)&&kyt.select(this).style("visibility","hidden")}))},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._annotationMeasurer.reset()},e.END_TICK_MARK_CLASS="end-tick-mark",e.TICK_MARK_CLASS="tick-mark",e.TICK_LABEL_CLASS="tick-label",e.ANNOTATION_LINE_CLASS="annotation-line",e.ANNOTATION_RECT_CLASS="annotation-rect",e.ANNOTATION_CIRCLE_CLASS="annotation-circle",e.ANNOTATION_LABEL_CLASS="annotation-label",e._ANNOTATION_LABEL_PADDING=4,e})(Pyt.Component);myt.Axis=Ryt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Nmt,"__esModule",{value:!0});var Oyt=uO,zyt=Edt,Dyt=Imt,Byt=Bgt,Hyt=Fgt,Fyt=myt,Vyt=(function(t){function e(e,n){void 0===n&&(n="bottom");var i=t.call(this,e,n)||this;return i._tickLabelAngle=0,i._tickLabelShearAngle=0,i.addClass("category-axis"),i}return Oyt.__extends(e,t),Object.defineProperty(e.prototype,"_wrapper",{get:function(){var t=new Dyt.Wrapper;return null!=this._tickLabelMaxLines&&t.maxLines(this._tickLabelMaxLines),t},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"_writer",{get:function(){return new Dyt.Writer(this._measurer,this._typesetterContext,this._wrapper)},enumerable:!0,configurable:!0}),e.prototype._setup=function(){t.prototype._setup.call(this),this._typesetterContext=new Dyt.SvgContext(this._tickLabelContainer.node()),this._measurer=new Dyt.CacheMeasurer(this._typesetterContext)},e.prototype._rescale=function(){return this.redraw()},e.prototype.requestedSpace=function(t,e){var n=this.isHorizontal()?0:this._tickSpaceRequired()+this.margin(),i=this.isHorizontal()?this._tickSpaceRequired()+this.margin():0;if(0===this._scale.domain().length)return{minWidth:0,minHeight:0};if(this.annotationsEnabled()){var r=this._annotationTierHeight()*this.annotationTierCount();this.isHorizontal()?i+=r:n+=r}var o=this._measureTickLabels(t,e);return{minWidth:o.usedWidth+n,minHeight:o.usedHeight+i}},e.prototype._coreSize=function(){var t=this.isHorizontal()?this.height():this.width(),e=this.isHorizontal()?this.requestedSpace(this.width(),this.height()).minHeight:this.requestedSpace(this.width(),this.height()).minWidth,n=this.margin()+this._annotationTierHeight();return Math.min(e-n,t)},e.prototype._getTickValues=function(){return this.getDownsampleInfo().domain},e.prototype._sizeFromOffer=function(t,e){return Byt.Component.prototype._sizeFromOffer.call(this,t,e)},e.prototype.getDownsampleInfo=function(t,n){void 0===t&&(t=this._scale),void 0===n&&(n=t.invertRange());var i=0===this._tickLabelAngle?1:1/Math.cos(this._tickLabelShearAngle/180*Math.PI),r=Math.ceil(e._MINIMUM_WIDTH_PER_LABEL_PX*i/t.stepWidth());return{domain:n.filter((function(t,e){return e%r==0})),stepWidth:r*t.stepWidth()}},e.prototype.tickLabelAngle=function(t){if(null==t)return this._tickLabelAngle;if(0!==t&&90!==t&&-90!==t)throw new Error("Angle "+t+" not supported; only 0, 90, and -90 are valid values");return this._tickLabelAngle=t,this.redraw(),this},e.prototype.tickLabelShearAngle=function(t){if(null==t)return this._tickLabelShearAngle;if(t<-80||t>80)throw new Error("Angle "+t+" not supported; Must be between [-80, 80]");return this._tickLabelShearAngle=t,this.redraw(),this},e.prototype.tickLabelMaxWidth=function(t){return 0===arguments.length?this._tickLabelMaxWidth:(this._tickLabelMaxWidth=t,this.redraw(),this)},e.prototype.tickLabelMaxLines=function(t){return 0===arguments.length?this._tickLabelMaxLines:(this._tickLabelMaxLines=t,this.redraw(),this)},e.prototype._tickSpaceRequired=function(){return this._maxLabelTickLength()+this.tickLabelPadding()},e.prototype._drawTicks=function(t,e){var n,i,r=this;switch(this.tickLabelAngle()){case 0:n={left:"right",right:"left",top:"center",bottom:"center"},i={left:"center",right:"center",top:"bottom",bottom:"top"};break;case 90:n={left:"center",right:"center",top:"right",bottom:"left"},i={left:"top",right:"bottom",top:"center",bottom:"center"};break;case-90:n={left:"center",right:"center",top:"left",bottom:"right"},i={left:"bottom",right:"top",top:"center",bottom:"center"}}e.each((function(e){var o=zyt.select(this),a=r.isHorizontal()?t:r.width()-r._tickSpaceRequired(),s=r.isHorizontal()?r.height()-r._tickSpaceRequired():t,l={xAlign:n[r.orientation()],yAlign:i[r.orientation()],textRotation:r.tickLabelAngle(),textShear:r.tickLabelShearAngle()};if(null!=r._tickLabelMaxWidth){if("left"===r.orientation()&&a>r._tickLabelMaxWidth){var c=a-r._tickLabelMaxWidth,u=o.attr("transform")+" translate("+c+", 0)";o.attr("transform",u)}a=Math.min(a,r._tickLabelMaxWidth)}r._writer.write(r.formatter()(e),a,s,l,o.node())}))},e.prototype._measureTickLabels=function(t,e){var n=this,i=this._scale.cloneWithoutProviders().range([0,this.isHorizontal()?t:e]),r=this.getDownsampleInfo(i),o=r.domain,a=r.stepWidth,s=t-this._tickSpaceRequired();this.isHorizontal()&&(s=a,0!==this._tickLabelAngle&&(s=e-this._tickSpaceRequired()),s=Math.max(s,0));var l=a;this.isHorizontal()&&(l=e-this._tickSpaceRequired(),0!==this._tickLabelAngle&&(l=t-this._tickSpaceRequired()),l=Math.max(l,0)),null!=this._tickLabelMaxWidth&&(s=Math.min(s,this._tickLabelMaxWidth));var c,u=o.map((function(t){return n._wrapper.wrap(n.formatter()(t),n._measurer,s,l)})),h=this.isHorizontal()&&0===this._tickLabelAngle?zyt.sum:Hyt.Math.max,d=this.isHorizontal()&&0===this._tickLabelAngle?Hyt.Math.max:zyt.sum,p=h(u,(function(t){return n._measurer.measure(t.wrappedText).width}),0),f=d(u,(function(t){return n._measurer.measure(t.wrappedText).height}),0);return 0!==this._tickLabelAngle&&(p=(c=[f,p])[0],f=c[1]),{usedWidth:p,usedHeight:f}},e.prototype.renderImmediately=function(){var e=this;t.prototype.renderImmediately.call(this);var n=this._scale,i=this.getDownsampleInfo(n),r=i.domain,o=i.stepWidth,a=o;this.isHorizontal()&&null!=this._tickLabelMaxWidth&&(a=Math.min(a,this._tickLabelMaxWidth));var s=this._tickLabelContainer.selectAll("."+Fyt.Axis.TICK_LABEL_CLASS).data(r),l=s.enter().append("g").classed(Fyt.Axis.TICK_LABEL_CLASS,!0).merge(s);s.exit().remove(),l.attr("transform",(function(t,i){var r=n.scale(t)-a/2;return"translate("+(e.isHorizontal()?r:0)+","+(e.isHorizontal()?0:r)+")"})),l.text(""),this._drawTicks(o,l);var c="right"===this.orientation()?this._tickSpaceRequired():0,u="bottom"===this.orientation()?this._tickSpaceRequired():0;return this._tickLabelContainer.attr("transform","translate("+c+","+u+")"),this._showAllTickMarks(),this._showAllTickLabels(),this._hideTickMarksWithoutLabel(),this},e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),this.isHorizontal()||this._scale.range([0,this.height()]),this},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e._MINIMUM_WIDTH_PER_LABEL_PX=15,e})(Fyt.Axis);Nmt.Category=Vyt;var Uyt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Uyt,"__esModule",{value:!0});var jyt=uO,Gyt=Edt,Wyt=Imt,qyt=gyt,Yyt=Fgt,Xyt=myt,$yt=(function(t){function e(e,n){var i=t.call(this,e,n)||this;return i._tickLabelPositioning="center",i._usesTextWidthApproximation=!1,i.formatter(qyt.general()),i}return jyt.__extends(e,t),e.prototype._setup=function(){t.prototype._setup.call(this);var e=new Wyt.SvgContext(this._tickLabelContainer.node(),Xyt.Axis.TICK_LABEL_CLASS);this._measurer=new Wyt.CacheMeasurer(e),this._wrapper=(new Wyt.Wrapper).maxLines(1)},e.prototype._computeWidth=function(){var t=this._usesTextWidthApproximation?this._computeApproximateTextWidth():this._computeExactTextWidth();return"center"===this._tickLabelPositioning?this._maxLabelTickLength()+this.tickLabelPadding()+t:Math.max(this._maxLabelTickLength(),this.tickLabelPadding()+t)},e.prototype._computeExactTextWidth=function(){var t=this,e=this._getTickValues().map((function(e){var n=t.formatter()(e);return t._measurer.measure(n).width}));return Yyt.Math.max(e,0)},e.prototype._computeApproximateTextWidth=function(){var t=this,e=this._getTickValues(),n=this._measurer.measure("M").width,i=e.map((function(e){return t.formatter()(e).length*n}));return Yyt.Math.max(i,0)},e.prototype._computeHeight=function(){var t=this._measurer.measure().height;return"center"===this._tickLabelPositioning?this._maxLabelTickLength()+this.tickLabelPadding()+t:Math.max(this._maxLabelTickLength(),this.tickLabelPadding()+t)},e.prototype._getTickValues=function(){var t=this._scale,e=t.domain(),n=e[0]<=e[1]?e[0]:e[1],i=e[0]>=e[1]?e[0]:e[1];return t.ticks().filter((function(t){return t>=n&&t<=i}))},e.prototype._rescale=function(){if(this._isSetup){if(!this.isHorizontal()){var t=this._computeWidth();if(t>this.width()||t<this.width()-this.margin())return void this.redraw()}this.render()}},e.prototype.renderImmediately=function(){var e=this;t.prototype.renderImmediately.call(this);var n={x:0,y:0,dx:"0em",dy:"0.3em"},i=this._maxLabelTickLength(),r=this.tickLabelPadding(),o="middle",a=0,s=0,l=0,c=0;if(this.isHorizontal())switch(this._tickLabelPositioning){case"left":o="end",a=-r,c=r;break;case"center":c=i+r;break;case"right":o="start",a=r,c=r}else switch(this._tickLabelPositioning){case"top":n.dy="-0.3em",l=r,s=-r;break;case"center":l=i+r;break;case"bottom":n.dy="1em",l=r,s=r}var u=this._generateTickMarkAttrHash();switch(this.orientation()){case"bottom":n.x=u.x1,n.dy="0.95em",s=u.y1+c;break;case"top":n.x=u.x1,n.dy="-.25em",s=u.y1-c;break;case"left":o="end",a=u.x1-l,n.y=u.y1;break;case"right":o="start",a=u.x1+l,n.y=u.y1}var h=this._getTickValues(),d=this._tickLabelContainer.selectAll("."+Xyt.Axis.TICK_LABEL_CLASS).data(h);return d.exit().remove(),d.enter().append("text").classed(Xyt.Axis.TICK_LABEL_CLASS,!0).merge(d).style("text-anchor",o).style("visibility","inherit").attrs(n).text((function(t){return e.formatter()(t)})),this._tickLabelContainer.attr("transform","translate("+a+", "+s+")"),this._showAllTickMarks(),this.showEndTickLabels()||this._hideEndTickLabels(),this._hideOverflowingTickLabels(),this._hideOverlappingTickLabels(),"center"!==this._tickLabelPositioning&&this._hideTickMarksWithoutLabel(),this},e.prototype.tickLabelPosition=function(t){if(null==t)return this._tickLabelPositioning;var e=t.toLowerCase();if(this.isHorizontal()){if("left"!==e&&"center"!==e&&"right"!==e)throw new Error(e+" is not a valid tick label position for a horizontal NumericAxis")}else if("top"!==e&&"center"!==e&&"bottom"!==e)throw new Error(e+" is not a valid tick label position for a vertical NumericAxis");return this._tickLabelPositioning=e,this.redraw(),this},e.prototype.usesTextWidthApproximation=function(t){return null==t?this._usesTextWidthApproximation:(this._usesTextWidthApproximation=t,this)},e.prototype._hideEndTickLabels=function(){var t=this.element().node().getBoundingClientRect(),e=this._tickLabelContainer.selectAll("."+Xyt.Axis.TICK_LABEL_CLASS);if(0!==e.size()){var n=e.nodes()[0];Yyt.DOM.clientRectInside(n.getBoundingClientRect(),t)||Gyt.select(n).style("visibility","hidden");var i=e.nodes()[e.size()-1];Yyt.DOM.clientRectInside(i.getBoundingClientRect(),t)||Gyt.select(i).style("visibility","hidden")}},e.prototype._hideOverlappingTickLabels=function(){for(var t=this._tickLabelContainer.selectAll("."+Xyt.Axis.TICK_LABEL_CLASS).filter((function(t,e){var n=Gyt.select(this).style("visibility");return"inherit"===n||"visible"===n})),e=t.nodes().map((function(t){return t.getBoundingClientRect()})),n=1;!this._hasOverlapWithInterval(n,e)&&n<e.length;)n+=1;t.each((function(t,e){var i=Gyt.select(this);e%n!=0&&i.style("visibility","hidden")}))},e.prototype._hasOverlapWithInterval=function(t,e){for(var n="center"===this._tickLabelPositioning?this.tickLabelPadding():3*this.tickLabelPadding(),i=e.map((function(t){return Yyt.DOM.expandRect(t,n)})),r=0;r<i.length-t;r+=t)if(Yyt.DOM.clientRectsOverlap(i[r],i[r+t]))return!1;return!0},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e})(Xyt.Axis);Uyt.Numeric=$yt;var Kyt={},Zyt={},Jyt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Jyt,"__esModule",{value:!0});var Qyt=Fgt;Jyt.intervalTickGenerator=function tvt(t){if(t<=0)throw new Error("interval must be positive number");return function(e){var n=e.domain(),i=Math.min(n[0],n[1]),r=Math.max(n[0],n[1]),o=Math.ceil(i/t)*t,a=Math.floor((r-o)/t)+1,s=i%t==0?[]:[i],l=Qyt.Math.range(0,a).map((function(e){return o+e*t})),c=r%t==0?[]:[r];return s.concat(l).concat(c)}},Jyt.integerTickGenerator=function evt(){return function(t){var e=t.defaultTicks();return e.filter((function(t,n){return t%1==0||0===n||n===e.length-1}))}};var nvt={},ivt={};function rvt(t,e,n){return n-(n-t)*e}function ovt(t,e,n){return(t*e-n)/(e-1)}function avt(t,e,n,i){var r=e>1,o=r?i:n;if(null==o)return e;var a=t.getTransformationDomain(),s=Math.abs(a[1]-a[0]);return(r?Math.min:Math.max)(e,o/s)}function svt(t,e,n,i,r){if(e<=1)return{centerPoint:n,zoomAmount:e};if(null==i&&null==r)return{centerPoint:n,zoomAmount:e};var o=lvt(t),a=(function s(t){var e=t.getTransformationDomain();return e[1]<e[0]})(t);i=null==i?a?1/0:-1/0:i,r=null==r?a?-1/0:1/0:r;var l=t.getTransformationDomain(),c=l[0],u=l[1],h=t.scaleTransformation(r),d=t.scaleTransformation(u),p=rvt(d,e,n),f=t.scaleTransformation(i),m=t.scaleTransformation(c),g=rvt(m,e,n),_=Math.abs(h-f);if(Math.abs(p-g)>_){var y=(h-f)/(d-m);return 1!==y?{centerPoint:ovt(d,y,h),zoomAmount:y}:{centerPoint:n,zoomAmount:y}}return p>h!=o?{centerPoint:ovt(d,e,h),zoomAmount:e}:g<f!=o?{centerPoint:ovt(m,e,f),zoomAmount:e}:{centerPoint:n,zoomAmount:e}}function lvt(t){var e=t.range();return e[1]<e[0]}
/**
     * Copyright 2017-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(ivt,"__esModule",{value:!0}),ivt.zoomOut=rvt,ivt.constrainedZoom=function cvt(t,e,n,i,r,o,a){return svt(t,e=avt(t,e,i,r),n,o,a)},ivt.constrainZoomExtents=avt,ivt.constrainZoomValues=svt,ivt.constrainedTranslation=function uvt(t,e,n,i){var r,o=t.getTransformationDomain(),a=o[0],s=o[1],l=lvt(t);if(e>0!==l){if(null!=(r=i)){var c=t.scaleTransformation(s),u=t.scaleTransformation(r);e=(l?Math.max:Math.min)(c+e,u)-c}}else if(null!=(r=n)){var h=t.scaleTransformation(a),d=t.scaleTransformation(r);e=(l?Math.min:Math.max)(h+e,d)-h}return e};var hvt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(hvt,"__esModule",{value:!0});var dvt=Fgt,pvt=(function(){function t(){this._autoDomainAutomatically=!0,this._domainModificationInProgress=!1,this._updateId=0,this._callbacks=new dvt.CallbackSet,this._includedValuesProviders=new dvt.Set}return t.prototype.extentOfValues=function(t){return[]},t.prototype._getAllIncludedValues=function(t){var e=this;void 0===t&&(t=!1);var n=[];return this._includedValuesProviders.forEach((function(i){var r=i(e,t);n=n.concat(r)})),n},t.prototype._getExtent=function(){return[]},t.prototype.onUpdate=function(t){return this._callbacks.add(t),this},t.prototype.offUpdate=function(t){return this._callbacks.delete(t),this},t.prototype._dispatchUpdate=function(){this._updateId++,this._callbacks.callCallbacks(this)},t.prototype.autoDomain=function(){return this._autoDomainAutomatically=!0,this._setDomain(this._getExtent()),this},t.prototype.autoDomainIfAutomaticMode=function(){this._autoDomainAutomatically&&this.autoDomain()},t.prototype.scale=function(t){throw new Error("Subclasses should override scale")},t.prototype.ticks=function(){return this.domain()},t.prototype.domain=function(t){return null==t?this._getDomain():(this._autoDomainAutomatically=!1,this._setDomain(t),this)},t.prototype._getDomain=function(){throw new Error("Subclasses should override _getDomain")},t.prototype._setDomain=function(t){this._domainModificationInProgress||(this._domainModificationInProgress=!0,this._backingScaleDomain(t),this._dispatchUpdate(),this._domainModificationInProgress=!1)},t.prototype._backingScaleDomain=function(t){throw new Error("Subclasses should override _backingDomain")},t.prototype.range=function(t){return null==t?this._getRange():(this._setRange(t),this)},t.prototype._getRange=function(){throw new Error("Subclasses should override _getRange")},t.prototype._setRange=function(t){throw new Error("Subclasses should override _setRange")},t.prototype.addIncludedValuesProvider=function(t){return this._includedValuesProviders.add(t),this.autoDomainIfAutomaticMode(),this},t.prototype.removeIncludedValuesProvider=function(t){return this._includedValuesProviders.delete(t),this.autoDomainIfAutomaticMode(),this},t.prototype.updateId=function(){return this._updateId},t})();hvt.Scale=pvt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(nvt,"__esModule",{value:!0});var fvt=uO,mvt=Edt,gvt=ivt,_vt=Fgt,yvt=[0,1],vvt=(function(t){function e(){var n=t.call(this)||this;return n._range=[0,1],n._d3Scale=mvt.scaleBand(),n._d3Scale.range(yvt),n._d3TransformationScale=mvt.scaleLinear(),n._d3TransformationScale.domain(yvt),n._innerPadding=e._convertToPlottableInnerPadding(.3),n._outerPadding=e._convertToPlottableOuterPadding(.5,.3),n}return fvt.__extends(e,t),e.prototype.cloneWithoutProviders=function(){var t=(new e).domain(this.domain()).range(this.range()).innerPadding(this.innerPadding()).outerPadding(this.outerPadding());return t._d3TransformationScale.domain(this._d3TransformationScale.domain()),t},e.prototype.extentOfValues=function(t){return _vt.Array.uniq(t)},e.prototype._getExtent=function(){return _vt.Array.uniq(this._getAllIncludedValues())},e.prototype.domain=function(e){return t.prototype.domain.call(this,e)},e.prototype.invertRange=function(t){var e=this;void 0===t&&(t=this.range());var n=this._d3Scale.bandwidth(),i=this.invertedTransformation(t[0]),r=this.invertedTransformation(t[1]),o=this._d3Scale.domain(),a=o.map((function(t){return e._d3Scale(t)+n/2})),s=mvt.bisect(a,i),l=mvt.bisect(a,r);return o.slice(s,l)},e.prototype.range=function(e){return t.prototype.range.call(this,e)},e._convertToPlottableInnerPadding=function(t){return 1/(1-t)-1},e._convertToPlottableOuterPadding=function(t,e){return t/(1-e)},e.prototype._setBands=function(){var t=1-1/(1+this.innerPadding()),e=this.outerPadding()/(1+this.innerPadding());this._d3Scale.paddingInner(t),this._d3Scale.paddingOuter(e)},e.prototype.rangeBand=function(){return this._rescaleBand(this._d3Scale.bandwidth())},e.prototype.stepWidth=function(){return this._rescaleBand(this._d3Scale.bandwidth()*(1+this.innerPadding()))},e.prototype.ticks=function(){return this.domain()},e.prototype.innerPadding=function(t){return null==t?this._innerPadding:(this._innerPadding=t,this.range(this.range()),this._dispatchUpdate(),this)},e.prototype.outerPadding=function(t){return null==t?this._outerPadding:(this._outerPadding=t,this.range(this.range()),this._dispatchUpdate(),this)},e.prototype.scale=function(t){var e=this._d3Scale(t)+this._d3Scale.bandwidth()/2;return this._d3TransformationScale(e)},e.prototype.zoom=function(t,e){var n=this;this._d3TransformationScale.domain(this._d3TransformationScale.range().map((function(i){return n._d3TransformationScale.invert(gvt.zoomOut(i,t,e))}))),this._dispatchUpdate()},e.prototype.pan=function(t){var e=this;this._d3TransformationScale.domain(this._d3TransformationScale.range().map((function(n){return e._d3TransformationScale.invert(n+t)}))),this._dispatchUpdate()},e.prototype.scaleTransformation=function(t){return this._d3TransformationScale(t)},e.prototype.invertedTransformation=function(t){return this._d3TransformationScale.invert(t)},e.prototype.getTransformationExtent=function(){return yvt},e.prototype.getTransformationDomain=function(){return this._d3TransformationScale.domain()},e.prototype.setTransformationDomain=function(t){this._d3TransformationScale.domain(t),this._dispatchUpdate()},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this._setBands(),this)},e.prototype._getRange=function(){return this._range},e.prototype._setRange=function(t){this._range=t,this._d3TransformationScale.range(t),this._setBands()},e.prototype._rescaleBand=function(t){return Math.abs(this._d3TransformationScale(t)-this._d3TransformationScale(0))},e})(hvt.Scale);nvt.Category=vvt;var bvt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(bvt,"__esModule",{value:!0});var xvt=uO,wvt=Edt,Svt=Fgt,Mvt=hvt,Evt=(function(){function t(){this.count=0,this.tracker={}}return t.prototype.getIndex=function(t){if(null!=this.tracker[t])return this.tracker[t];var e=this.count;return this.tracker[t]=e,this.count+=1,e},t.prototype.clear=function(){this.count=0,this.tracker={}},t})(),Tvt=(function(t){function e(n){var i,r=t.call(this)||this;switch(r._rangeLength=1,r._tracker=new Evt,n){case null:case void 0:null==e._plottableColorCache&&(e._plottableColorCache=e._getPlottableColors()),i=wvt.scaleOrdinal().range(e._plottableColorCache);break;case"Category10":case"category10":case"10":i=wvt.scaleOrdinal(wvt.schemeCategory10);break;case"Category20":case"category20":case"20":i=wvt.scaleOrdinal(wvt.schemeCategory20);break;case"Category20b":case"category20b":case"20b":i=wvt.scaleOrdinal(wvt.schemeCategory20b);break;case"Category20c":case"category20c":case"20c":i=wvt.scaleOrdinal(wvt.schemeCategory20c);break;default:throw new Error("Unsupported ColorScale type")}return r._d3Scale=i,r._rangeLength=r._d3Scale.range().length,r}return xvt.__extends(e,t),e.prototype.extentOfValues=function(t){return Svt.Array.uniq(t)},e.prototype._getExtent=function(){return Svt.Array.uniq(this._getAllIncludedValues())},e.invalidateColorCache=function(){e._plottableColorCache=null},e._getPlottableColors=function(){for(var t=[],e=wvt.select("body").append("plottable-color-tester"),n=Svt.Color.colorTest(e,""),i=0,r=Svt.Color.colorTest(e,"plottable-colors-0");null!=r&&i<this._MAXIMUM_COLORS_FROM_CSS&&(r!==n||r!==t[t.length-1]);)t.push(r),i++,r=Svt.Color.colorTest(e,"plottable-colors-"+i);return e.remove(),t},e.prototype.scale=function(t){var n=this._d3Scale(t),i=this._tracker.getIndex(t),r=Math.floor(i/this._rangeLength);if(0===r)return n;var o=Math.log(r*e._LOOP_LIGHTEN_FACTOR+1);return Svt.Color.lightenColor(n,o)},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this._tracker.clear(),this)},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t),this._rangeLength=t.length},e._LOOP_LIGHTEN_FACTOR=1.6,e._MAXIMUM_COLORS_FROM_CSS=256,e})(Mvt.Scale);bvt.Color=Tvt;var Cvt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Cvt,"__esModule",{value:!0});var Avt=uO,kvt=Edt,Lvt=Fgt,Pvt=(function(t){function e(n){void 0===n&&(n="linear");var i=t.call(this)||this;switch(n){case"linear":i._colorScale=kvt.scaleLinear();break;case"log":i._colorScale=kvt.scaleLog();break;case"sqrt":i._colorScale=kvt.scaleSqrt();break;case"pow":i._colorScale=kvt.scalePow()}if(null==i._colorScale)throw new Error("unknown QuantitativeScale scale type "+n);return i.range(e.REDS),i}return Avt.__extends(e,t),e.prototype.extentOfValues=function(t){var e=kvt.extent(t);return null==e[0]||null==e[1]?[]:e},e.prototype._d3InterpolatedScale=function(){return this._colorScale.range([0,1]).interpolate(this._interpolateColors())},e.prototype._interpolateColors=function(){var t=this._colorRange;if(t.length<2)throw new Error("Color scale arrays must have at least two elements.");return function(e,n){return function(e){var n=(e=Math.max(0,Math.min(1,e)))*(t.length-1),i=Math.floor(n),r=Math.ceil(n),o=n-i;return kvt.interpolateLab(t[i],t[r])(o)}}},e.prototype._resetScale=function(){this._d3Scale=this._d3InterpolatedScale(),this.autoDomainIfAutomaticMode(),this._dispatchUpdate()},e.prototype.autoDomain=function(){var t=this._getAllIncludedValues();return t.length>0&&this._setDomain([Lvt.Math.min(t,0),Lvt.Math.max(t,0)]),this},e.prototype.scale=function(t){return this._d3Scale(t)},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._getRange=function(){return this._colorRange},e.prototype._setRange=function(t){this._colorRange=t,this._resetScale()},e.REDS=["#FFFFFF","#FFF6E1","#FEF4C0","#FED976","#FEB24C","#FD8D3C","#FC4E2A","#E31A1C","#B10026"],e.BLUES=["#FFFFFF","#CCFFFF","#A5FFFD","#85F7FB","#6ED3EF","#55A7E0","#417FD0","#2545D3","#0B02E1"],e.POSNEG=["#0B02E1","#2545D3","#417FD0","#55A7E0","#6ED3EF","#85F7FB","#A5FFFD","#CCFFFF","#FFFFFF","#FFF6E1","#FEF4C0","#FED976","#FEB24C","#FD8D3C","#FC4E2A","#E31A1C","#B10026"],e})(hvt.Scale);Cvt.InterpolatedColor=Pvt;var Nvt={},Ivt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Ivt,"__esModule",{value:!0});var Rvt=uO,Ovt=Edt,zvt=ivt,Dvt=Fgt,Bvt=(function(t){function e(){var e=t.call(this)||this;return e._tickGenerator=function(t){return t.defaultTicks()},e._padProportion=.05,e._snappingDomainEnabled=!0,e._paddingExceptionsProviders=new Dvt.Set,e}return Rvt.__extends(e,t),e.prototype.autoDomain=function(){return this._domainMin=null,this._domainMax=null,t.prototype.autoDomain.call(this),this},e.prototype.autoDomainIfAutomaticMode=function(){if(null==this._domainMin||null==this._domainMax){var e=this._getExtent();if(null!=this._domainMin){var n=e[1];return this._domainMin>=n&&(n=this._expandSingleValueDomain([this._domainMin,this._domainMin])[1]),void this._setDomain([this._domainMin,n])}if(null!=this._domainMax){var i=e[0];return this._domainMax<=i&&(i=this._expandSingleValueDomain([this._domainMax,this._domainMax])[0]),void this._setDomain([i,this._domainMax])}t.prototype.autoDomainIfAutomaticMode.call(this)}else this._setDomain([this._domainMin,this._domainMax])},e.prototype._getUnboundedExtent=function(t){void 0===t&&(t=!1);var e=this._getAllIncludedValues(t),n=this._defaultExtent();if(0!==e.length){var i=[Dvt.Math.min(e,n[0]),Dvt.Math.max(e,n[1])];n=this._padDomain(i)}return n},e.prototype._getExtent=function(){var t=this._getUnboundedExtent();return null!=this._domainMin&&(t[0]=this._domainMin),null!=this._domainMax&&(t[1]=this._domainMax),t},e.prototype.addPaddingExceptionsProvider=function(t){return this._paddingExceptionsProviders.add(t),this.autoDomainIfAutomaticMode(),this},e.prototype.removePaddingExceptionsProvider=function(t){return this._paddingExceptionsProviders.delete(t),this.autoDomainIfAutomaticMode(),this},e.prototype.padProportion=function(t){if(null==t)return this._padProportion;if(t<0)throw new Error("padProportion must be non-negative");return this._padProportion=t,this.autoDomainIfAutomaticMode(),this},e.prototype._padDomain=function(t){var e=this;if(t[0].valueOf()===t[1].valueOf())return this._expandSingleValueDomain(t);if(0===this._padProportion)return t;var n=this._padProportion/2,i=t[0],r=t[1],o=!1,a=!1;this._paddingExceptionsProviders.forEach((function(t){t(e).forEach((function(t){t.valueOf()===i.valueOf()&&(o=!0),t.valueOf()===r.valueOf()&&(a=!0)}))}));var s=this._backingScaleDomain();this._backingScaleDomain(t);var l=o?i:this.invert(this.scale(i)-(this.scale(r)-this.scale(i))*n),c=a?r:this.invert(this.scale(r)+(this.scale(r)-this.scale(i))*n);return this._backingScaleDomain(s),this._snappingDomainEnabled?this._niceDomain([l,c]):[l,c]},e.prototype.snappingDomainEnabled=function(t){return null==t?this._snappingDomainEnabled:(this._snappingDomainEnabled=t,this.autoDomainIfAutomaticMode(),this)},e.prototype._expandSingleValueDomain=function(t){return t},e.prototype.invert=function(t){throw new Error("Subclasses should override invert")},e.prototype.domain=function(e){return null!=e&&(this._domainMin=e[0],this._domainMax=e[1]),t.prototype.domain.call(this,e)},e.prototype.domainMin=function(t){return null==t?this.domain()[0]:(this._domainMin=t,this.autoDomainIfAutomaticMode(),this)},e.prototype.domainMax=function(t){return null==t?this.domain()[1]:(this._domainMax=t,this.autoDomainIfAutomaticMode(),this)},e.prototype.extentOfValues=function(t){var e=Ovt.extent(t.filter((function(t){return Dvt.Math.isValidNumber(+t)})));return null==e[0]||null==e[1]?[]:e},e.prototype.zoom=function(t,e){var n=this;this.domain(this.range().map((function(i){return n.invert(zvt.zoomOut(i,t,e))})))},e.prototype.pan=function(t){var e=this;this.domain(this.range().map((function(n){return e.invert(n+t)})))},e.prototype.scaleTransformation=function(t){throw new Error("Subclasses should override scaleTransformation")},e.prototype.invertedTransformation=function(t){throw new Error("Subclasses should override invertedTransformation")},e.prototype.getTransformationExtent=function(){throw new Error("Subclasses should override getTransformationExtent")},e.prototype.getTransformationDomain=function(){throw new Error("Subclasses should override getTransformationDomain")},e.prototype.setTransformationDomain=function(t){throw new Error("Subclasses should override setTransformationDomain")},e.prototype._setDomain=function(e){var n=function(t){return Dvt.Math.isNaN(t)||t===1/0||t===-1/0};n(e[0])||n(e[1])?Dvt.Window.warn("Warning: QuantitativeScales cannot take NaN or Infinity as a domain value. Ignoring."):t.prototype._setDomain.call(this,e)},e.prototype.defaultTicks=function(){throw new Error("Subclasses should override _getDefaultTicks")},e.prototype.ticks=function(){return this._tickGenerator(this)},e.prototype._niceDomain=function(t,e){throw new Error("Subclasses should override _niceDomain")},e.prototype._defaultExtent=function(){throw new Error("Subclasses should override _defaultExtent")},e.prototype.tickGenerator=function(t){return null==t?this._tickGenerator:(this._tickGenerator=t,this)},e._DEFAULT_NUM_TICKS=10,e})(hvt.Scale);Ivt.QuantitativeScale=Bvt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Nvt,"__esModule",{value:!0});var Hvt=uO,Fvt=Edt,Vvt=(function(t){function e(){var e=t.call(this)||this;return e._d3Scale=Fvt.scaleLinear(),e}return Hvt.__extends(e,t),e.prototype._defaultExtent=function(){return[0,1]},e.prototype._expandSingleValueDomain=function(t){return t[0]===t[1]?[t[0]-1,t[1]+1]:t},e.prototype.scale=function(t){return this._d3Scale(t)},e.prototype.scaleTransformation=function(t){return this.scale(t)},e.prototype.invertedTransformation=function(t){return this.invert(t)},e.prototype.getTransformationExtent=function(){return this._getUnboundedExtent(!0)},e.prototype.getTransformationDomain=function(){return this.domain()},e.prototype.setTransformationDomain=function(t){this.domain(t)},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t)},e.prototype.invert=function(t){return this._d3Scale.invert(t)},e.prototype.defaultTicks=function(){return this._d3Scale.ticks(e._DEFAULT_NUM_TICKS)},e.prototype._niceDomain=function(t,e){return this._d3Scale.copy().domain(t).nice(e).domain()},e})(Ivt.QuantitativeScale);Nvt.Linear=Vvt;var Uvt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Uvt,"__esModule",{value:!0});var jvt=uO,Gvt=Edt,Wvt=(function(t){function e(e){void 0===e&&(e=10);var n=t.call(this)||this;return n._d3Scale=Gvt.scaleLog().base(e),n._setDomain(n._defaultExtent()),n}return jvt.__extends(e,t),e.prototype._defaultExtent=function(){return[1,this._d3Scale.base()]},e.prototype._expandSingleValueDomain=function(t){return t[0]===t[1]?[t[0]/this._d3Scale.base(),t[1]*this._d3Scale.base()]:t},e.prototype.scale=function(t){return this._d3Scale(t)},e.prototype.scaleTransformation=function(t){return this.scale(t)},e.prototype.invertedTransformation=function(t){return this.invert(t)},e.prototype.getTransformationExtent=function(){return this._getUnboundedExtent(!0)},e.prototype.getTransformationDomain=function(){return this.domain()},e.prototype.setTransformationDomain=function(t){this.domain(t)},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t)},e.prototype.invert=function(t){return this._d3Scale.invert(t)},e.prototype.defaultTicks=function(){return this._d3Scale.ticks(e._DEFAULT_NUM_TICKS)},e.prototype._niceDomain=function(t,e){return this._d3Scale.copy().domain(t).nice().domain()},e})(Ivt.QuantitativeScale);Uvt.Log=Wvt;var qvt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(qvt,"__esModule",{value:!0});var Yvt=uO,Xvt=Edt,$vt=Fgt,Kvt=Zyt,Zvt=(function(t){function e(e){void 0===e&&(e=10);var n=t.call(this)||this;if(n._logTickGenerator=function(t){var e=function(t,e,n){return[t,e,n].sort((function(t,e){return t-e}))[1]},i=$vt.Math.min(n._untransformedDomain,0),r=$vt.Math.max(n._untransformedDomain,0),o=i,a=e(i,r,-n._pivot),s=e(i,r,n._pivot),l=r,c=n._logTicks(-a,-o).map((function(t){return-t})).reverse(),u=n._logTicks(s,l),h=Math.max(i,-n._pivot),d=Math.min(r,n._pivot),p=Xvt.scaleLinear().domain([h,d]).ticks(n._howManyTicks(h,d)),f=c.concat(p).concat(u);return f.length<=1&&(f=Xvt.scaleLinear().domain([i,r]).ticks(Kvt.ModifiedLog._DEFAULT_NUM_TICKS)),f},n._d3Scale=Xvt.scaleLinear(),n._base=e,n._pivot=n._base,n._setDomain(n._defaultExtent()),n.tickGenerator(n._logTickGenerator),e<=1)throw new Error("ModifiedLogScale: The base must be > 1");return n}return Yvt.__extends(e,t),e.prototype._adjustedLog=function(t){var e=t<0?-1:1;return(t*=e)<this._pivot&&(t+=(this._pivot-t)/this._pivot),(t=Math.log(t)/Math.log(this._base))*e},e.prototype._invertedAdjustedLog=function(t){var e=t<0?-1:1;return t*=e,(t=Math.pow(this._base,t))<this._pivot&&(t=this._pivot*(t-1)/(this._pivot-1)),t*e},e.prototype.scale=function(t){return this._d3Scale(this._adjustedLog(t))},e.prototype.invert=function(t){return this._invertedAdjustedLog(this._d3Scale.invert(t))},e.prototype.scaleTransformation=function(t){return this.scale(t)},e.prototype.invertedTransformation=function(t){return this.invert(t)},e.prototype.getTransformationExtent=function(){return this._getUnboundedExtent(!0)},e.prototype.getTransformationDomain=function(){return this.domain()},e.prototype.setTransformationDomain=function(t){this.domain(t)},e.prototype._getDomain=function(){return this._untransformedDomain},e.prototype._setDomain=function(e){this._untransformedDomain=e;var n=[this._adjustedLog(e[0]),this._adjustedLog(e[1])];t.prototype._setDomain.call(this,n)},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._logTicks=function(t,e){var n=this,i=this._howManyTicks(t,e);if(0===i)return[];var r=Math.floor(Math.log(t)/Math.log(this._base)),o=Math.ceil(Math.log(e)/Math.log(this._base)),a=Xvt.range(o,r,-Math.ceil((o-r)/i)),s=Xvt.range(this._base,1,-(this._base-1)).map(Math.floor),l=$vt.Array.uniq(s),c=a.map((function(t){return l.map((function(e){return Math.pow(n._base,t-1)*e}))}));return $vt.Array.flatten(c).filter((function(n){return t<=n&&n<=e})).sort((function(t,e){return t-e}))},e.prototype._howManyTicks=function(t,e){var n=this._adjustedLog($vt.Math.min(this._untransformedDomain,0)),i=this._adjustedLog($vt.Math.max(this._untransformedDomain,0)),r=this._adjustedLog(t),o=this._adjustedLog(e);return Math.ceil((o-r)/(i-n)*Kvt.ModifiedLog._DEFAULT_NUM_TICKS)},e.prototype._niceDomain=function(t,e){return t},e.prototype._defaultExtent=function(){return[0,this._base]},e.prototype._expandSingleValueDomain=function(t){if(t[0]===t[1]){var e=t[0];return e>0?[e/this._base,e*this._base]:0===e?[-this._base,this._base]:[e*this._base,e/this._base]}return t},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t)},e.prototype.defaultTicks=function(){return this._d3Scale.ticks(Kvt.ModifiedLog._DEFAULT_NUM_TICKS)},e})(Ivt.QuantitativeScale);qvt.ModifiedLog=Zvt;var Jvt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Jvt,"__esModule",{value:!0});var Qvt=uO,tbt=Edt,ebt=Kyt,nbt=(function(t){function e(){var e=t.call(this)||this;return e._d3Scale=tbt.scaleTime(),e.autoDomain(),e}return Qvt.__extends(e,t),e.prototype.tickInterval=function(t,n,i){void 0===n&&(n=1),void 0===i&&(i=!1);var r=tbt.scaleTime(),o=e.timeIntervalToD3Time(t,i).every(n);return r.domain(this.domain()),r.range(this.range()),r.ticks(o)},e.prototype._setDomain=function(e){if(e[1]<e[0])throw new Error("Scale.Time domain values must be in chronological order");return t.prototype._setDomain.call(this,e)},e.prototype._defaultExtent=function(){return[new Date("1970-01-01"),new Date("1970-01-02")]},e.prototype._expandSingleValueDomain=function(t){var e=t[0].getTime(),n=t[1].getTime();if(e===n){var i=new Date(e);i.setDate(i.getDate()-1);var r=new Date(n);return r.setDate(r.getDate()+1),[i,r]}return t},e.prototype.scale=function(t){return this._d3Scale(t)},e.prototype.scaleTransformation=function(t){return this.scale(new Date(t))},e.prototype.invertedTransformation=function(t){return this.invert(t).getTime()},e.prototype.getTransformationExtent=function(){var t=this._getUnboundedExtent(!0);return[t[0].valueOf(),t[1].valueOf()]},e.prototype.getTransformationDomain=function(){var t=this.domain();return[t[0].valueOf(),t[1].valueOf()]},e.prototype.setTransformationDomain=function(t){var e=t[1];this.domain([new Date(t[0]),new Date(e)])},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t)},e.prototype.invert=function(t){return this._d3Scale.invert(t)},e.prototype.defaultTicks=function(){return this._d3Scale.ticks(e._DEFAULT_NUM_TICKS)},e.prototype._niceDomain=function(t){return this._d3Scale.copy().domain(t).nice().domain()},e.timeIntervalToD3Time=function(t,e){switch(t){case ebt.TimeInterval.second:return e?tbt.utcSecond:tbt.timeSecond;case ebt.TimeInterval.minute:return e?tbt.utcMinute:tbt.timeMinute;case ebt.TimeInterval.hour:return e?tbt.utcHour:tbt.timeHour;case ebt.TimeInterval.day:return e?tbt.utcDay:tbt.timeDay;case ebt.TimeInterval.week:return e?tbt.utcWeek:tbt.timeWeek;case ebt.TimeInterval.month:return e?tbt.utcMonth:tbt.timeMonth;case ebt.TimeInterval.year:return e?tbt.utcYear:tbt.timeYear;default:throw Error("TimeInterval specified does not exist: "+t)}},e})(Ivt.QuantitativeScale);Jvt.Time=nbt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;t.TickGenerators=Jyt,e.__exportStar(nvt,t),e.__exportStar(bvt,t),e.__exportStar(Cvt,t),e.__exportStar(Nvt,t),e.__exportStar(Uvt,t),e.__exportStar(qvt,t),e.__exportStar(Jvt,t);var n=nvt,i=Ivt;t.isTransformable=function r(t){return t instanceof i.QuantitativeScale||t instanceof n.Category}})(Zyt),(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO,n=Edt,i=Imt,r=gyt,o=Zyt,a=Fgt,s=Smt,l=myt;t.TimeInterval=s.makeEnum(["second","minute","hour","day","week","month","year"]),t.TimeAxisOrientation=s.makeEnum(["top","bottom"]),t.TierLabelPosition=s.makeEnum(["between","center"]);var c,u=(function(s){function u(t,e,n){var i=s.call(this,t,e)||this;return i._maxTimeIntervalPrecision=null,i._tierLabelPositions=[],i._useUTC=n,i.addClass("time-axis"),i.tickLabelPadding(5),i.axisConfigurations(u._DEFAULT_TIME_AXIS_CONFIGURATIONS(i._useUTC)),i.annotationFormatter(r.time("%a %b %d, %Y",i._useUTC)),i}return e.__extends(u,s),u.prototype.tierLabelPositions=function(t){if(null==t)return this._tierLabelPositions;if(!t.every((function(t){return"between"===t.toLowerCase()||"center"===t.toLowerCase()})))throw new Error("Unsupported position for tier labels");return this._tierLabelPositions=t,this.redraw(),this},u.prototype.maxTimeIntervalPrecision=function(t){return null==t?this._maxTimeIntervalPrecision:(this._maxTimeIntervalPrecision=t,this.redraw(),this)},u.prototype.currentAxisConfiguration=function(){return this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex]},u.prototype.axisConfigurations=function(t){if(null==t)return this._possibleTimeAxisConfigurations;this._possibleTimeAxisConfigurations=t,this._numTiers=a.Math.max(this._possibleTimeAxisConfigurations.map((function(t){return t.length})),0),this._isAnchored&&this._setupDomElements();for(var e=this.tierLabelPositions(),n=[],i=0;i<this._numTiers;i++)n.push(e[i]||"between");return this.tierLabelPositions(n),this.redraw(),this},u.prototype._getMostPreciseConfigurationIndex=function(){var t=this,e=this._possibleTimeAxisConfigurations.length;return this._possibleTimeAxisConfigurations.forEach((function(n,i){i<e&&n.every((function(e){return t._checkTimeAxisTierConfiguration(e)}))&&(e=i)})),e===this._possibleTimeAxisConfigurations.length&&(a.Window.warn("zoomed out too far: could not find suitable interval to display labels"),--e),e},u.prototype.orientation=function(t){if(t&&("right"===t.toLowerCase()||"left"===t.toLowerCase()))throw new Error(t+" is not a supported orientation for TimeAxis - only horizontal orientations are supported");return s.prototype.orientation.call(this,t)},u.prototype._computeHeight=function(){var t=this._measurer.measure().height;this._tierHeights=[];for(var e=0;e<this._numTiers;e++)this._tierHeights.push(t+this.tickLabelPadding()+("between"===this._tierLabelPositions[e]?0:this._maxLabelTickLength()));return n.sum(this._tierHeights)},u.prototype._getIntervalLength=function(t){var e=this._scale.domain()[0],n=o.Time.timeIntervalToD3Time(t.interval,this._useUTC).offset(e,t.step);return n>this._scale.domain()[1]?this.width():Math.abs(this._scale.scale(n)-this._scale.scale(e))},u.prototype._maxWidthForInterval=function(t){return this._measurer.measure(t.formatter(u._LONG_DATE)).width},u.prototype._checkTimeAxisTierConfiguration=function(t){if(null!=this._maxTimeIntervalPrecision){var e=u._SORTED_TIME_INTERVAL_INDEX[this._maxTimeIntervalPrecision],n=u._SORTED_TIME_INTERVAL_INDEX[t.interval];if(null!=e&&null!=n&&n<e)return!1}var i=this._maxWidthForInterval(t)+2*this.tickLabelPadding();return Math.min(this._getIntervalLength(t),this.width())>=i},u.prototype._sizeFromOffer=function(t,e){var n=s.prototype._sizeFromOffer.call(this,t,e),i=this._tierHeights.reduce((function(t,e,i,r){return t+e>n.height?t:t+e})),r=this.margin()+(this.annotationsEnabled()?this.annotationTierCount()*this._annotationTierHeight():0);return n.height=Math.min(n.height,i+r),n},u.prototype._setup=function(){s.prototype._setup.call(this),this._setupDomElements()},u.prototype._setupDomElements=function(){this.content().selectAll("."+u.TIME_AXIS_TIER_CLASS).remove(),this._tierLabelContainers=[],this._tierMarkContainers=[],this._tierBaselines=[],this._tickLabelContainer.remove(),this._baseline.remove();for(var t=0;t<this._numTiers;++t){var e=this.content().append("g").classed(u.TIME_AXIS_TIER_CLASS,!0);this._tierLabelContainers.push(e.append("g").classed(l.Axis.TICK_LABEL_CLASS+"-container",!0)),this._tierMarkContainers.push(e.append("g").classed(l.Axis.TICK_MARK_CLASS+"-container",!0)),this._tierBaselines.push(e.append("line").classed("baseline",!0))}var n=new i.SvgContext(this._tierLabelContainers[0].node());this._measurer=new i.CacheMeasurer(n)},u.prototype._getTickIntervalValues=function(t){return this._scale.tickInterval(t.interval,t.step,this._useUTC)},u.prototype._getTickValues=function(){var t=this;return this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex].reduce((function(e,n){return e.concat(t._getTickIntervalValues(n))}),[])},u.prototype._cleanTiers=function(){for(var t=0;t<this._tierLabelContainers.length;t++)this._tierLabelContainers[t].selectAll("."+l.Axis.TICK_LABEL_CLASS).remove(),this._tierMarkContainers[t].selectAll("."+l.Axis.TICK_MARK_CLASS).remove(),this._tierBaselines[t].style("visibility","hidden")},u.prototype._getTickValuesForConfiguration=function(t){var e=this._scale.tickInterval(t.interval,t.step,this._useUTC),n=this._scale.domain(),i=e.map((function(t){return t.valueOf()}));return-1===i.indexOf(n[0].valueOf())&&e.unshift(n[0]),-1===i.indexOf(n[1].valueOf())&&e.push(n[1]),e},u.prototype._renderTierLabels=function(t,e,i){var r=this,o=this._getTickValuesForConfiguration(e),a=[];"between"===this._tierLabelPositions[i]&&1===e.step?o.map((function(t,e){e+1>=o.length||a.push(new Date((o[e+1].valueOf()-o[e].valueOf())/2+o[e].valueOf()))})):a=o;var s=t.selectAll("."+l.Axis.TICK_LABEL_CLASS).data(a,(function(t){return String(t.valueOf())})),c=s.enter().append("g").classed(l.Axis.TICK_LABEL_CLASS,!0);c.append("text");var u,h="center"===this._tierLabelPositions[i]||1===e.step?0:this.tickLabelPadding();u="bottom"===this.orientation()?n.sum(this._tierHeights.slice(0,i+1))-this.tickLabelPadding():"center"===this._tierLabelPositions[i]?this.height()-n.sum(this._tierHeights.slice(0,i))-this.tickLabelPadding()-this._maxLabelTickLength():this.height()-n.sum(this._tierHeights.slice(0,i))-this.tickLabelPadding();var d=s.merge(c),p=d.selectAll("text");p.size()>0&&p.attr("transform","translate("+h+","+u+")"),s.exit().remove(),d.attr("transform",(function(t){return"translate("+r._scale.scale(t)+",0)"}));var f="center"===this._tierLabelPositions[i]||1===e.step?"middle":"start";d.selectAll("text").text(e.formatter).style("text-anchor",f)},u.prototype._renderTickMarks=function(t,e){var i=this._tierMarkContainers[e].selectAll("."+l.Axis.TICK_MARK_CLASS).data(t),r=i.enter().append("line").classed(l.Axis.TICK_MARK_CLASS,!0).merge(i),o=this._generateTickMarkAttrHash(),a=this._tierHeights.slice(0,e).reduce((function(t,e){return t+e}),0);"bottom"===this.orientation()?(o.y1=a,o.y2=a+("center"===this._tierLabelPositions[e]?this.innerTickLength():this._tierHeights[e])):(o.y1=this.height()-a,o.y2=this.height()-(a+("center"===this._tierLabelPositions[e]?this.innerTickLength():this._tierHeights[e]))),r.attrs(o),"bottom"===this.orientation()?(o.y1=a,o.y2=a+("center"===this._tierLabelPositions[e]?this.endTickLength():this._tierHeights[e])):(o.y1=this.height()-a,o.y2=this.height()-(a+("center"===this._tierLabelPositions[e]?this.endTickLength():this._tierHeights[e]))),n.select(r.nodes()[0]).attrs(o),n.select(r.nodes()[r.size()-1]).attrs(o),n.select(r.nodes()[0]).classed(l.Axis.END_TICK_MARK_CLASS,!0),n.select(r.nodes()[r.size()-1]).classed(l.Axis.END_TICK_MARK_CLASS,!0),i.exit().remove()},u.prototype._renderLabellessTickMarks=function(t){var e=this._tickMarkContainer.selectAll("."+l.Axis.TICK_MARK_CLASS).data(t),n=e.enter().append("line").classed(l.Axis.TICK_MARK_CLASS,!0).merge(e),i=this._generateTickMarkAttrHash();i.y2="bottom"===this.orientation()?this.tickLabelPadding():this.height()-this.tickLabelPadding(),n.attrs(i),e.exit().remove()},u.prototype._generateLabellessTicks=function(){return this._mostPreciseConfigIndex<1?[]:this._getTickIntervalValues(this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex-1][0])},u.prototype.renderImmediately=function(){var t=this;this._mostPreciseConfigIndex=this._getMostPreciseConfigurationIndex();var e=this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex];this._cleanTiers(),e.forEach((function(e,n){return t._renderTierLabels(t._tierLabelContainers[n],e,n)}));for(var n=e.map((function(e,n){return t._getTickValuesForConfiguration(e)})),i=0,r=0;r<Math.max(e.length,1);++r){var o=this._generateBaselineAttrHash();o.y1+="bottom"===this.orientation()?i:-i,o.y2=o.y1,this._tierBaselines[r].attrs(o).style("visibility","inherit"),i+=this._tierHeights[r]}var a=[],s=this._scale.domain(),l=this._scale.scale(s[1])-this._scale.scale(s[0]);for(1.5*this._getIntervalLength(e[0])>=l&&(a=this._generateLabellessTicks()),this._renderLabellessTickMarks(a),this._hideOverflowingTiers(),r=0;r<e.length;++r)this._renderTickMarks(n[r],r),this._hideOverlappingAndCutOffLabels(r);return this.annotationsEnabled()?this._drawAnnotations():this._removeAnnotations(),this},u.prototype._hideOverflowingTiers=function(){var t=this,e=this.height(),n=0;this.content().selectAll("."+u.TIME_AXIS_TIER_CLASS).attr("visibility",(function(i,r){return(n+=t._tierHeights[r])<=e?"inherit":"hidden"}))},u.prototype._hideOverlappingAndCutOffLabels=function(t){var e,i=this,r=this.element().node().getBoundingClientRect(),o=this._tierMarkContainers[t].selectAll("."+l.Axis.TICK_MARK_CLASS).filter((function(t,e){var i=n.select(this).style("visibility");return"visible"===i||"inherit"===i})).nodes().map((function(t){return t.getBoundingClientRect()}));this._tierLabelContainers[t].selectAll("."+l.Axis.TICK_LABEL_CLASS).filter((function(t,e){var i=n.select(this).style("visibility");return"visible"===i||"inherit"===i})).each((function(t,s){var l,c=this.getBoundingClientRect(),u=n.select(this),h=o[s],d=o[s+1],p=null!=e&&a.DOM.clientRectsOverlap(c,e),f=null!=h&&a.DOM.clientRectsOverlap(c,h),m=null!=d&&a.DOM.clientRectsOverlap(c,d);l=c,!(Math.floor(r.left)<=Math.ceil(l.left)&&Math.floor(r.top)<=Math.ceil(l.top)&&Math.floor(l.right)<=Math.ceil(r.left+i.width())&&Math.floor(l.bottom)<=Math.ceil(r.top+i.height()))||p||f||m?u.style("visibility","hidden"):(e=c,u.style("visibility","inherit"))}))},u.prototype.invalidateCache=function(){s.prototype.invalidateCache.call(this),this._measurer.reset()},u.TIME_AXIS_TIER_CLASS="time-axis-tier",u._SORTED_TIME_INTERVAL_INDEX=((c={})[t.TimeInterval.second]=0,c[t.TimeInterval.minute]=1,c[t.TimeInterval.hour]=2,c[t.TimeInterval.day]=3,c[t.TimeInterval.week]=4,c[t.TimeInterval.month]=5,c[t.TimeInterval.year]=6,c),u._DEFAULT_TIME_AXIS_CONFIGURATIONS=function(e){var n=function(t){return r.time(t,e)};return[[{interval:t.TimeInterval.second,step:1,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.second,step:5,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.second,step:10,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.second,step:15,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.second,step:30,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:1,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:5,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:10,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:15,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:30,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.hour,step:1,formatter:n("%I %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.hour,step:3,formatter:n("%I %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.hour,step:6,formatter:n("%I %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.hour,step:12,formatter:n("%I %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.day,step:1,formatter:n("%a %e")},{interval:t.TimeInterval.month,step:1,formatter:n("%B %Y")}],[{interval:t.TimeInterval.day,step:1,formatter:n("%e")},{interval:t.TimeInterval.month,step:1,formatter:n("%B %Y")}],[{interval:t.TimeInterval.month,step:1,formatter:n("%B")},{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.month,step:1,formatter:n("%b")},{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.month,step:3,formatter:n("%b")},{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.month,step:6,formatter:n("%b")},{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:1,formatter:n("%y")}],[{interval:t.TimeInterval.year,step:5,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:25,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:50,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:100,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:200,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:500,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:1e3,formatter:n("%Y")}]]},u._LONG_DATE=new Date(9999,8,29,12,59,9999),u})(l.Axis);t.Time=u})(Kyt),(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(Nmt,t),e.__exportStar(Uyt,t),e.__exportStar(Kyt,t)})(Pmt);var ibt={},rbt={},obt={},abt={},sbt={},lbt={},cbt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(cbt,"__esModule",{value:!0});var ubt=Fgt,hbt=(function(){function t(){this._eventToProcessingFunction={},this._eventTarget=document,this._eventNameToCallbackSet={},this._connected=!1}return t.prototype._hasNoCallbacks=function(){for(var t=Object.keys(this._eventNameToCallbackSet),e=0;e<t.length;e++)if(0!==this._eventNameToCallbackSet[t[e]].size)return!1;return!0},t.prototype._connect=function(){var t=this;this._connected||(Object.keys(this._eventToProcessingFunction).forEach((function(e){t._eventTarget.addEventListener(e,t._eventToProcessingFunction[e],"wheel"===e?{passive:!1}:void 0)})),this._connected=!0)},t.prototype._disconnect=function(){var t=this;this._connected&&this._hasNoCallbacks()&&(Object.keys(this._eventToProcessingFunction).forEach((function(e){t._eventTarget.removeEventListener(e,t._eventToProcessingFunction[e])})),this._connected=!1)},t.prototype._addCallbackForEvent=function(t,e){null==this._eventNameToCallbackSet[t]&&(this._eventNameToCallbackSet[t]=new ubt.CallbackSet),this._eventNameToCallbackSet[t].add(e),this._connect()},t.prototype._removeCallbackForEvent=function(t,e){null!=this._eventNameToCallbackSet[t]&&this._eventNameToCallbackSet[t].delete(e),this._disconnect()},t.prototype._callCallbacksForEvent=function(t){for(var e=[],n=1;n<arguments.length;n++)e[n-1]=arguments[n];var i=this._eventNameToCallbackSet[t];null!=i&&i.callCallbacks.apply(i,e)},t})();cbt.Dispatcher=hbt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(lbt,"__esModule",{value:!0});var dbt=uO,pbt=(function(t){function e(){var n=t.call(this)||this;return n._eventToProcessingFunction[e._KEYDOWN_EVENT_NAME]=function(t){return n._processKeydown(t)},n._eventToProcessingFunction[e._KEYUP_EVENT_NAME]=function(t){return n._processKeyup(t)},n}return dbt.__extends(e,t),e.getDispatcher=function(){var t=document[e._DISPATCHER_KEY];return null==t&&(t=new e,document[e._DISPATCHER_KEY]=t),t},e.prototype._processKeydown=function(t){this._callCallbacksForEvent(e._KEYDOWN_EVENT_NAME,t.keyCode,t)},e.prototype._processKeyup=function(t){this._callCallbacksForEvent(e._KEYUP_EVENT_NAME,t.keyCode,t)},e.prototype.onKeyDown=function(t){return this._addCallbackForEvent(e._KEYDOWN_EVENT_NAME,t),this},e.prototype.offKeyDown=function(t){return this._removeCallbackForEvent(e._KEYDOWN_EVENT_NAME,t),this},e.prototype.onKeyUp=function(t){return this._addCallbackForEvent(e._KEYUP_EVENT_NAME,t),this},e.prototype.offKeyUp=function(t){return this._removeCallbackForEvent(e._KEYUP_EVENT_NAME,t),this},e._DISPATCHER_KEY="__Plottable_Dispatcher_Key",e._KEYDOWN_EVENT_NAME="keydown",e._KEYUP_EVENT_NAME="keyup",e})(cbt.Dispatcher);lbt.Key=pbt;var fbt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(fbt,"__esModule",{value:!0});var mbt=uO,gbt=Fgt,_bt=(function(t){function e(n){var i=t.call(this)||this;i._lastMousePosition={x:-1,y:-1},i._translator=gbt.getTranslator(n);var r=function(t){return i._measureAndDispatch(n,t,e._MOUSEMOVE_EVENT_NAME,"page")};return i._eventToProcessingFunction[e._MOUSEOVER_EVENT_NAME]=r,i._eventToProcessingFunction[e._MOUSEMOVE_EVENT_NAME]=r,i._eventToProcessingFunction[e._MOUSEOUT_EVENT_NAME]=r,i._eventToProcessingFunction[e._MOUSEDOWN_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._MOUSEDOWN_EVENT_NAME)},i._eventToProcessingFunction[e._MOUSEUP_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._MOUSEUP_EVENT_NAME,"page")},i._eventToProcessingFunction[e._WHEEL_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._WHEEL_EVENT_NAME)},i._eventToProcessingFunction[e._DBLCLICK_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._DBLCLICK_EVENT_NAME)},i}return mbt.__extends(e,t),e.getDispatcher=function(t){var n=t.root().rootElement(),i=n[e._DISPATCHER_KEY];return null==i&&(i=new e(t),n[e._DISPATCHER_KEY]=i),i},e.prototype.onMouseMove=function(t){return this._addCallbackForEvent(e._MOUSEMOVE_EVENT_NAME,t),this},e.prototype.offMouseMove=function(t){return this._removeCallbackForEvent(e._MOUSEMOVE_EVENT_NAME,t),this},e.prototype.onMouseDown=function(t){return this._addCallbackForEvent(e._MOUSEDOWN_EVENT_NAME,t),this},e.prototype.offMouseDown=function(t){return this._removeCallbackForEvent(e._MOUSEDOWN_EVENT_NAME,t),this},e.prototype.onMouseUp=function(t){return this._addCallbackForEvent(e._MOUSEUP_EVENT_NAME,t),this},e.prototype.offMouseUp=function(t){return this._removeCallbackForEvent(e._MOUSEUP_EVENT_NAME,t),this},e.prototype.onWheel=function(t){return this._addCallbackForEvent(e._WHEEL_EVENT_NAME,t),this},e.prototype.offWheel=function(t){return this._removeCallbackForEvent(e._WHEEL_EVENT_NAME,t),this},e.prototype.onDblClick=function(t){return this._addCallbackForEvent(e._DBLCLICK_EVENT_NAME,t),this},e.prototype.offDblClick=function(t){return this._removeCallbackForEvent(e._DBLCLICK_EVENT_NAME,t),this},e.prototype._measureAndDispatch=function(t,e,n,i){if(void 0===i&&(i="element"),"page"!==i&&"element"!==i)throw new Error("Invalid scope '"+i+"', must be 'element' or 'page'");if("page"===i||this.eventInside(t,e)){var r=this._translator.computePosition(e.clientX,e.clientY);this._lastMousePosition=r,this._callCallbacksForEvent(n,this.lastMousePosition(),e)}},e.prototype.eventInside=function(t,e){return gbt.Translator.isEventInside(t,e)},e.prototype.lastMousePosition=function(){return this._lastMousePosition},e._DISPATCHER_KEY="__Plottable_Dispatcher_Mouse",e._MOUSEOVER_EVENT_NAME="mouseover",e._MOUSEMOVE_EVENT_NAME="mousemove",e._MOUSEOUT_EVENT_NAME="mouseout",e._MOUSEDOWN_EVENT_NAME="mousedown",e._MOUSEUP_EVENT_NAME="mouseup",e._WHEEL_EVENT_NAME="wheel",e._DBLCLICK_EVENT_NAME="dblclick",e})(cbt.Dispatcher);fbt.Mouse=_bt;var ybt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(ybt,"__esModule",{value:!0});var vbt=uO,bbt=Fgt,xbt=(function(t){function e(n){var i=t.call(this)||this;return i._translator=bbt.getTranslator(n),i._eventToProcessingFunction[e._TOUCHSTART_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._TOUCHSTART_EVENT_NAME,"page")},i._eventToProcessingFunction[e._TOUCHMOVE_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._TOUCHMOVE_EVENT_NAME,"page")},i._eventToProcessingFunction[e._TOUCHEND_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._TOUCHEND_EVENT_NAME,"page")},i._eventToProcessingFunction[e._TOUCHCANCEL_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._TOUCHCANCEL_EVENT_NAME,"page")},i}return vbt.__extends(e,t),e.getDispatcher=function(t){var n=t.root().rootElement(),i=n[e._DISPATCHER_KEY];return null==i&&(i=new e(t),n[e._DISPATCHER_KEY]=i),i},e.prototype.onTouchStart=function(t){return this._addCallbackForEvent(e._TOUCHSTART_EVENT_NAME,t),this},e.prototype.offTouchStart=function(t){return this._removeCallbackForEvent(e._TOUCHSTART_EVENT_NAME,t),this},e.prototype.onTouchMove=function(t){return this._addCallbackForEvent(e._TOUCHMOVE_EVENT_NAME,t),this},e.prototype.offTouchMove=function(t){return this._removeCallbackForEvent(e._TOUCHMOVE_EVENT_NAME,t),this},e.prototype.onTouchEnd=function(t){return this._addCallbackForEvent(e._TOUCHEND_EVENT_NAME,t),this},e.prototype.offTouchEnd=function(t){return this._removeCallbackForEvent(e._TOUCHEND_EVENT_NAME,t),this},e.prototype.onTouchCancel=function(t){return this._addCallbackForEvent(e._TOUCHCANCEL_EVENT_NAME,t),this},e.prototype.offTouchCancel=function(t){return this._removeCallbackForEvent(e._TOUCHCANCEL_EVENT_NAME,t),this},e.prototype._measureAndDispatch=function(t,e,n,i){if(void 0===i&&(i="element"),"page"!==i&&"element"!==i)throw new Error("Invalid scope '"+i+"', must be 'element' or 'page'");if("element"!==i||this.eventInside(t,e)){for(var r=e.changedTouches,o={},a=[],s=0;s<r.length;s++){var l=r[s],c=l.identifier,u=this._translator.computePosition(l.clientX,l.clientY);null!=u&&(o[c]=u,a.push(c))}a.length>0&&this._callCallbacksForEvent(n,a,o,e)}},e.prototype.eventInside=function(t,e){return bbt.Translator.isEventInside(t,e)},e._DISPATCHER_KEY="__Plottable_Dispatcher_Touch",e._TOUCHSTART_EVENT_NAME="touchstart",e._TOUCHMOVE_EVENT_NAME="touchmove",e._TOUCHEND_EVENT_NAME="touchend",e._TOUCHCANCEL_EVENT_NAME="touchcancel",e})(cbt.Dispatcher);ybt.Touch=xbt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(lbt,t),e.__exportStar(fbt,t),e.__exportStar(ybt,t)})(sbt);var wbt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(wbt,"__esModule",{value:!0});var Sbt=(function(){function t(){var t=this;this._anchorCallback=function(e){return t._anchor(e)},this._enabled=!0}return t.prototype.attachTo=function(t){return this._disconnect(),this._componentAttachedTo=t,this._connect(),this},t.prototype.detachFrom=function(t){return this.detach()},t.prototype.detach=function(){return this._disconnect(),this._componentAttachedTo=null,this},t.prototype.enabled=function(t){return null==t?this._enabled:(this._enabled=t,this._enabled?this._connect():this._disconnect(),this)},t.prototype._anchor=function(t){this._isAnchored=!0},t.prototype._unanchor=function(){this._isAnchored=!1},t.prototype._translateToComponentSpace=function(t){var e=this._componentAttachedTo.originToRoot();return{x:t.x-e.x,y:t.y-e.y}},t.prototype._isInsideComponent=function(t){return 0<=t.x&&0<=t.y&&t.x<=this._componentAttachedTo.width()&&t.y<=this._componentAttachedTo.height()},t.prototype._connect=function(){this.enabled()&&null!=this._componentAttachedTo&&!this._isAnchored&&this._componentAttachedTo.onAnchor(this._anchorCallback)},t.prototype._disconnect=function(){this._isAnchored&&this._unanchor(),null!=this._componentAttachedTo&&this._componentAttachedTo.offAnchor(this._anchorCallback)},t})();wbt.Interaction=Sbt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(abt,"__esModule",{value:!0});var Mbt=uO,Ebt=sbt,Tbt=Fgt,Cbt=(function(t){function e(){var e=null!==t&&t.apply(this,arguments)||this;return e._clickedDown=!1,e._doubleClicking=!1,e._onClickCallbacks=new Tbt.CallbackSet,e._onDoubleClickCallbacks=new Tbt.CallbackSet,e._mouseDownCallback=function(t,n){return e._handleClickDown(t,n)},e._mouseUpCallback=function(t,n){return e._handleClickUp(t,n)},e._dblClickCallback=function(t,n){return e._handleDblClick(t,n)},e._touchStartCallback=function(t,n,i){return e._handleClickDown(n[t[0]],i)},e._touchEndCallback=function(t,n,i){return e._handleClickUp(n[t[0]],i)},e._touchCancelCallback=function(t,n){return e._clickedDown=!1},e}return Mbt.__extends(e,t),e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._mouseDispatcher=Ebt.Mouse.getDispatcher(e),this._mouseDispatcher.onMouseDown(this._mouseDownCallback),this._mouseDispatcher.onMouseUp(this._mouseUpCallback),this._mouseDispatcher.onDblClick(this._dblClickCallback),this._touchDispatcher=Ebt.Touch.getDispatcher(e),this._touchDispatcher.onTouchStart(this._touchStartCallback),this._touchDispatcher.onTouchEnd(this._touchEndCallback),this._touchDispatcher.onTouchCancel(this._touchCancelCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._mouseDispatcher.offMouseDown(this._mouseDownCallback),this._mouseDispatcher.offMouseUp(this._mouseUpCallback),this._mouseDispatcher.offDblClick(this._dblClickCallback),this._mouseDispatcher=null,this._touchDispatcher.offTouchStart(this._touchStartCallback),this._touchDispatcher.offTouchEnd(this._touchEndCallback),this._touchDispatcher.offTouchCancel(this._touchCancelCallback),this._touchDispatcher=null},e.prototype._handleClickDown=function(t,e){var n=this._translateToComponentSpace(t);this._isInsideComponent(n)&&(this._clickedDown=!0,this._clickedPoint=n)},e.prototype._handleClickUp=function(t,n){var i=this,r=this._translateToComponentSpace(t);this._clickedDown&&e._pointsEqual(r,this._clickedPoint)&&setTimeout((function(){i._doubleClicking||i._onClickCallbacks.callCallbacks(r,n)}),0),this._clickedDown=!1},e.prototype._handleDblClick=function(t,e){var n=this,i=this._translateToComponentSpace(t);this._doubleClicking=!0,this._onDoubleClickCallbacks.callCallbacks(i,e),setTimeout((function(){return n._doubleClicking=!1}),0)},e._pointsEqual=function(t,e){return t.x===e.x&&t.y===e.y},e.prototype.onClick=function(t){return this._onClickCallbacks.add(t),this},e.prototype.offClick=function(t){return this._onClickCallbacks.delete(t),this},e.prototype.onDoubleClick=function(t){return this._onDoubleClickCallbacks.add(t),this},e.prototype.offDoubleClick=function(t){return this._onDoubleClickCallbacks.delete(t),this},e})(wbt.Interaction);abt.Click=Cbt;var Abt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Abt,"__esModule",{value:!0});var kbt=uO,Lbt=sbt,Pbt=Fgt,Nbt=(function(t){function e(n){var i=t.call(this)||this;return i._dragging=!1,i._constrainedToComponent=!0,i._mouseFilter=e._DEFAULT_MOUSE_FILTER,i._dragStartCallbacks=new Pbt.CallbackSet,i._dragCallbacks=new Pbt.CallbackSet,i._dragEndCallbacks=new Pbt.CallbackSet,i._mouseDownCallback=function(t,e){return i._startDrag(t,e)},i._mouseMoveCallback=function(t,e){return i._doDrag(t,e)},i._mouseUpCallback=function(t,e){return i._endDrag(t,e)},i._touchStartCallback=function(t,e,n){return i._startDrag(e[t[0]],n)},i._touchMoveCallback=function(t,e,n){return i._doDrag(e[t[0]],n)},i._touchEndCallback=function(t,e,n){return i._endDrag(e[t[0]],n)},i._mouseButton=void 0!==n?n:0,i}return kbt.__extends(e,t),e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._mouseDispatcher=Lbt.Mouse.getDispatcher(this._componentAttachedTo),this._mouseDispatcher.onMouseDown(this._mouseDownCallback),this._mouseDispatcher.onMouseMove(this._mouseMoveCallback),this._mouseDispatcher.onMouseUp(this._mouseUpCallback),this._touchDispatcher=Lbt.Touch.getDispatcher(this._componentAttachedTo),this._touchDispatcher.onTouchStart(this._touchStartCallback),this._touchDispatcher.onTouchMove(this._touchMoveCallback),this._touchDispatcher.onTouchEnd(this._touchEndCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._mouseDispatcher.offMouseDown(this._mouseDownCallback),this._mouseDispatcher.offMouseMove(this._mouseMoveCallback),this._mouseDispatcher.offMouseUp(this._mouseUpCallback),this._mouseDispatcher=null,this._touchDispatcher.offTouchStart(this._touchStartCallback),this._touchDispatcher.offTouchMove(this._touchMoveCallback),this._touchDispatcher.offTouchEnd(this._touchEndCallback),this._touchDispatcher=null},e.prototype._translateAndConstrain=function(t){var e=this._translateToComponentSpace(t);return this._constrainedToComponent?{x:Pbt.Math.clamp(e.x,0,this._componentAttachedTo.width()),y:Pbt.Math.clamp(e.y,0,this._componentAttachedTo.height())}:e},e.prototype._startDrag=function(t,e){if(!(e instanceof MouseEvent)||this._mouseFilter(e)){var n=this._translateToComponentSpace(t);this._isInsideComponent(n)&&(e.preventDefault(),this._dragging=!0,this._dragOrigin=n,this._dragStartCallbacks.callCallbacks(this._dragOrigin))}},e.prototype._doDrag=function(t,e){this._dragging&&this._dragCallbacks.callCallbacks(this._dragOrigin,this._translateAndConstrain(t))},e.prototype._endDrag=function(t,e){e instanceof MouseEvent&&e.button!==this._mouseButton||this._dragging&&(this._dragging=!1,this._dragEndCallbacks.callCallbacks(this._dragOrigin,this._translateAndConstrain(t)))},e.prototype.constrainedToComponent=function(t){return null==t?this._constrainedToComponent:(this._constrainedToComponent=t,this)},e.prototype.mouseFilter=function(t){return 0===arguments.length?this._mouseFilter:(this._mouseFilter=t,this)},e.prototype.onDragStart=function(t){return this._dragStartCallbacks.add(t),this},e.prototype.offDragStart=function(t){return this._dragStartCallbacks.delete(t),this},e.prototype.onDrag=function(t){return this._dragCallbacks.add(t),this},e.prototype.offDrag=function(t){return this._dragCallbacks.delete(t),this},e.prototype.onDragEnd=function(t){return this._dragEndCallbacks.add(t),this},e.prototype.offDragEnd=function(t){return this._dragEndCallbacks.delete(t),this},e._DEFAULT_MOUSE_FILTER=function(t){return 0===t.button},e})(wbt.Interaction);Abt.Drag=Nbt;var Ibt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Ibt,"__esModule",{value:!0});var Rbt=uO,Obt=sbt,zbt=Fgt,Dbt=(function(t){function e(){var e=null!==t&&t.apply(this,arguments)||this;return e._keyPressCallbacks={},e._keyReleaseCallbacks={},e._mouseMoveCallback=function(t){return!1},e._downedKeys=new zbt.Set,e._keyDownCallback=function(t,n){return e._handleKeyDownEvent(t,n)},e._keyUpCallback=function(t){return e._handleKeyUpEvent(t)},e}return Rbt.__extends(e,t),e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._positionDispatcher=Obt.Mouse.getDispatcher(this._componentAttachedTo),this._positionDispatcher.onMouseMove(this._mouseMoveCallback),this._keyDispatcher=Obt.Key.getDispatcher(),this._keyDispatcher.onKeyDown(this._keyDownCallback),this._keyDispatcher.onKeyUp(this._keyUpCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._positionDispatcher.offMouseMove(this._mouseMoveCallback),this._positionDispatcher=null,this._keyDispatcher.offKeyDown(this._keyDownCallback),this._keyDispatcher.offKeyUp(this._keyUpCallback),this._keyDispatcher=null},e.prototype._handleKeyDownEvent=function(t,e){var n=this._translateToComponentSpace(this._positionDispatcher.lastMousePosition());this._isInsideComponent(n)&&!e.repeat&&(this._keyPressCallbacks[t]&&this._keyPressCallbacks[t].callCallbacks(t),this._downedKeys.add(t))},e.prototype._handleKeyUpEvent=function(t){this._downedKeys.has(t)&&this._keyReleaseCallbacks[t]&&this._keyReleaseCallbacks[t].callCallbacks(t),this._downedKeys.delete(t)},e.prototype.onKeyPress=function(t,e){return this._keyPressCallbacks[t]||(this._keyPressCallbacks[t]=new zbt.CallbackSet),this._keyPressCallbacks[t].add(e),this},e.prototype.offKeyPress=function(t,e){return this._keyPressCallbacks[t].delete(e),0===this._keyPressCallbacks[t].size&&delete this._keyPressCallbacks[t],this},e.prototype.onKeyRelease=function(t,e){return this._keyReleaseCallbacks[t]||(this._keyReleaseCallbacks[t]=new zbt.CallbackSet),this._keyReleaseCallbacks[t].add(e),this},e.prototype.offKeyRelease=function(t,e){return this._keyReleaseCallbacks[t].delete(e),0===this._keyReleaseCallbacks[t].size&&delete this._keyReleaseCallbacks[t],this},e})(wbt.Interaction);Ibt.Key=Dbt;var Bbt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Bbt,"__esModule",{value:!0});var Hbt=uO,Fbt=Edt,Vbt=sbt,Ubt=Zyt,jbt=Fgt,Gbt=obt,Wbt=ivt,qbt=(function(t){function e(e,n){var i=t.call(this)||this;return i._wheelFilter=function(t){return!0},i._wheelCallback=function(t,e){return i._handleWheelEvent(t,e)},i._touchStartCallback=function(t,e,n){return i._handleTouchStart(t,e,n)},i._touchMoveCallback=function(t,e,n){return i._handlePinch(t,e,n)},i._touchEndCallback=function(t,e,n){return i._handleTouchEnd(t,e,n)},i._touchCancelCallback=function(t,e,n){return i._handleTouchEnd(t,e,n)},i._panEndCallbacks=new jbt.CallbackSet,i._zoomEndCallbacks=new jbt.CallbackSet,i._panZoomUpdateCallbacks=new jbt.CallbackSet,i._xScales=new jbt.Set,i._yScales=new jbt.Set,i._dragInteraction=new Gbt.Drag,i._setupDragInteraction(),i._touchIds=Fbt.map(),i._minDomainExtents=new jbt.Map,i._maxDomainExtents=new jbt.Map,i._minDomainValues=new jbt.Map,i._maxDomainValues=new jbt.Map,null!=e&&i.addXScale(e),null!=n&&i.addYScale(n),i}return Hbt.__extends(e,t),e.prototype.dragInteraction=function(){return this._dragInteraction},e.prototype.wheelFilter=function(t){return 0===arguments.length?this._wheelFilter:(this._wheelFilter=t,this)},e.prototype.pan=function(t){var e=this;this.xScales().forEach((function(n){n.pan(e._constrainedTranslation(n,t.x))})),this.yScales().forEach((function(n){n.pan(e._constrainedTranslation(n,t.y))})),this._panZoomUpdateCallbacks.callCallbacks()},e.prototype.zoom=function(t,e,n){var i,r,o=this;return void 0===n&&(n=!0),null!=e&&(i=e.x,r=e.y,n&&(this.xScales().forEach((function(e){var n=o._constrainedZoom(e,t,i);i=n.centerPoint,t=n.zoomAmount})),this.yScales().forEach((function(e){var n=o._constrainedZoom(e,t,r);r=n.centerPoint,t=n.zoomAmount})))),this.xScales().forEach((function(e){var n=e.range();e.zoom(t,null==i?(n[1]+n[0])/2:i)})),this.yScales().forEach((function(e){var n=e.range();e.zoom(t,null==r?(n[1]+n[0])/2:r)})),this._panZoomUpdateCallbacks.callCallbacks(),{zoomAmount:t,centerValue:{centerX:i,centerY:r}}},e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._dragInteraction.attachTo(e),this._mouseDispatcher=Vbt.Mouse.getDispatcher(this._componentAttachedTo),this._mouseDispatcher.onWheel(this._wheelCallback),this._touchDispatcher=Vbt.Touch.getDispatcher(this._componentAttachedTo),this._touchDispatcher.onTouchStart(this._touchStartCallback),this._touchDispatcher.onTouchMove(this._touchMoveCallback),this._touchDispatcher.onTouchEnd(this._touchEndCallback),this._touchDispatcher.onTouchCancel(this._touchCancelCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._mouseDispatcher.offWheel(this._wheelCallback),this._mouseDispatcher=null,this._touchDispatcher.offTouchStart(this._touchStartCallback),this._touchDispatcher.offTouchMove(this._touchMoveCallback),this._touchDispatcher.offTouchEnd(this._touchEndCallback),this._touchDispatcher.offTouchCancel(this._touchCancelCallback),this._touchDispatcher=null,this._dragInteraction.detach()},e.prototype._handleTouchStart=function(t,e,n){for(var i=0;i<t.length&&this._touchIds.size()<2;i++){var r=t[i];this._touchIds.set(r.toString(),this._translateToComponentSpace(e[r]))}},e.prototype._handlePinch=function(t,n,i){var r=this;if(!(this._touchIds.size()<2)){var o=this._touchIds.values();if(this._isInsideComponent(this._translateToComponentSpace(o[0]))&&this._isInsideComponent(this._translateToComponentSpace(o[1]))){var a=e._pointDistance(o[0],o[1]);if(0!==a){t.forEach((function(t){r._touchIds.has(t.toString())&&r._touchIds.set(t.toString(),r._translateToComponentSpace(n[t]))}));var s=this._touchIds.values(),l=e._pointDistance(s[0],s[1]);if(0!==l){var c=a/l,u=s.map((function(t,e){return{x:(t.x-o[e].x)/c,y:(t.y-o[e].y)/c}})),h=e.centerPoint(o[0],o[1]),d=this.zoom(c,h),p=d.centerValue,f=d.zoomAmount,m=p.centerX,g=p.centerY,_=o.map((function(t,e){return{x:u[e].x*f+t.x,y:u[e].y*f+t.y}}));this.pan({x:m-(_[0].x+_[1].x)/2,y:g-(_[0].y+_[1].y)/2})}}}}},e.centerPoint=function(t,e){var n=Math.min(t.x,e.x),i=Math.max(t.x,e.x),r=Math.min(t.y,e.y);return{x:(n+i)/2,y:(Math.max(t.y,e.y)+r)/2}},e._pointDistance=function(t,e){var n=Math.min(t.x,e.x),i=Math.max(t.x,e.x),r=Math.min(t.y,e.y),o=Math.max(t.y,e.y);return Math.sqrt(Math.pow(i-n,2)+Math.pow(o-r,2))},e.prototype._handleTouchEnd=function(t,e,n){var i=this;t.forEach((function(t){i._touchIds.remove(t.toString())})),this._touchIds.size()>0&&this._zoomEndCallbacks.callCallbacks()},e.prototype._handleWheelEvent=function(t,n){if(this._wheelFilter(n)){var i=this._translateToComponentSpace(t);if(this._isInsideComponent(i)){n.preventDefault();var r=Math.pow(2,(0!==n.deltaY?n.deltaY:n.deltaX)*(n.deltaMode?e._PIXELS_PER_LINE:1)*.002);this.zoom(r,i),this._zoomEndCallbacks.callCallbacks()}}},e.prototype._constrainedZoom=function(t,e,n){return Wbt.constrainedZoom(t,e,n,this.minDomainExtent(t),this.maxDomainExtent(t),this.minDomainValue(t),this.maxDomainValue(t))},e.prototype._constrainedTranslation=function(t,e){return Wbt.constrainedTranslation(t,e,this.minDomainValue(t),this.maxDomainValue(t))},e.prototype._setupDragInteraction=function(){var t,e=this;this._dragInteraction.constrainedToComponent(!1),this._dragInteraction.onDragStart((function(){return t=null})),this._dragInteraction.onDrag((function(n,i){e._touchIds.size()>=2||(e.pan({x:(null==t?n.x:t.x)-i.x,y:(null==t?n.y:t.y)-i.y}),t=i)})),this._dragInteraction.onDragEnd((function(){return e._panEndCallbacks.callCallbacks()}))},e.prototype._nonLinearScaleWithExtents=function(t){return!(null==this.minDomainExtent(t)||null==this.maxDomainExtent(t)||t instanceof Ubt.Linear||t instanceof Ubt.Time)},e.prototype.xScales=function(t){var e=this;if(null==t){var n=[];return this._xScales.forEach((function(t){n.push(t)})),n}return this._xScales=new jbt.Set,t.forEach((function(t){e.addXScale(t)})),this},e.prototype.yScales=function(t){var e=this;if(null==t){var n=[];return this._yScales.forEach((function(t){n.push(t)})),n}return this._yScales=new jbt.Set,t.forEach((function(t){e.addYScale(t)})),this},e.prototype.addXScale=function(t){return this._xScales.add(t),this},e.prototype.removeXScale=function(t){return this._xScales.delete(t),this._minDomainExtents.delete(t),this._maxDomainExtents.delete(t),this._minDomainValues.delete(t),this._maxDomainValues.delete(t),this},e.prototype.addYScale=function(t){return this._yScales.add(t),this},e.prototype.removeYScale=function(t){return this._yScales.delete(t),this._minDomainExtents.delete(t),this._maxDomainExtents.delete(t),this._minDomainValues.delete(t),this._maxDomainValues.delete(t),this},e.prototype.minDomainExtent=function(t,e){if(null==e)return this._minDomainExtents.get(t);if(e.valueOf()<0)throw new Error("extent must be non-negative");var n=this.maxDomainExtent(t);if(null!=n&&n.valueOf()<e.valueOf())throw new Error("minDomainExtent must be smaller than maxDomainExtent for the same Scale");return this._nonLinearScaleWithExtents(t)&&jbt.Window.warn("Panning and zooming with extents on a nonlinear scale may have unintended behavior."),this._minDomainExtents.set(t,e),this},e.prototype.maxDomainExtent=function(t,e){if(null==e)return this._maxDomainExtents.get(t);if(e.valueOf()<=0)throw new Error("extent must be positive");var n=this.minDomainExtent(t);if(null!=n&&e.valueOf()<n.valueOf())throw new Error("maxDomainExtent must be larger than minDomainExtent for the same Scale");return this._nonLinearScaleWithExtents(t)&&jbt.Window.warn("Panning and zooming with extents on a nonlinear scale may have unintended behavior."),this._maxDomainExtents.set(t,e),this},e.prototype.minDomainValue=function(t,e){return null==e?this._minDomainValues.get(t):(this._minDomainValues.set(t,e),this)},e.prototype.maxDomainValue=function(t,e){return null==e?this._maxDomainValues.get(t):(this._maxDomainValues.set(t,e),this)},e.prototype.setMinMaxDomainValuesTo=function(t){this._minDomainValues.delete(t),this._maxDomainValues.delete(t);var e=t.getTransformationDomain(),n=e[1];return this.minDomainValue(t,e[0]),this.maxDomainValue(t,n),this},e.prototype.onPanEnd=function(t){return this._panEndCallbacks.add(t),this},e.prototype.offPanEnd=function(t){return this._panEndCallbacks.delete(t),this},e.prototype.onZoomEnd=function(t){return this._zoomEndCallbacks.add(t),this},e.prototype.offZoomEnd=function(t){return this._zoomEndCallbacks.delete(t),this},e.prototype.onPanZoomUpdate=function(t){return this._panZoomUpdateCallbacks.add(t),this},e.prototype.offPanZoomUpdate=function(t){return this._panZoomUpdateCallbacks.delete(t),this},e._PIXELS_PER_LINE=120,e})(wbt.Interaction);Bbt.PanZoom=qbt;var Ybt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Ybt,"__esModule",{value:!0});var Xbt=uO,$bt=sbt,Kbt=Fgt,Zbt=(function(t){function e(){var e=null!==t&&t.apply(this,arguments)||this;return e._overComponent=!1,e._pointerEnterCallbacks=new Kbt.CallbackSet,e._pointerMoveCallbacks=new Kbt.CallbackSet,e._pointerExitCallbacks=new Kbt.CallbackSet,e._mouseMoveCallback=function(t,n){return e._handleMouseEvent(t,n)},e._touchStartCallback=function(t,n,i){return e._handleTouchEvent(n[t[0]],i)},e}return Xbt.__extends(e,t),e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._mouseDispatcher=$bt.Mouse.getDispatcher(this._componentAttachedTo),this._mouseDispatcher.onMouseMove(this._mouseMoveCallback),this._touchDispatcher=$bt.Touch.getDispatcher(this._componentAttachedTo),this._touchDispatcher.onTouchStart(this._touchStartCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._mouseDispatcher.offMouseMove(this._mouseMoveCallback),this._mouseDispatcher=null,this._touchDispatcher.offTouchStart(this._touchStartCallback),this._touchDispatcher=null},e.prototype._handleMouseEvent=function(t,e){var n=this._mouseDispatcher.eventInside(this._componentAttachedTo,e);this._handlePointerEvent(t,n)},e.prototype._handleTouchEvent=function(t,e){var n=this._touchDispatcher.eventInside(this._componentAttachedTo,e);this._handlePointerEvent(t,n)},e.prototype._handlePointerEvent=function(t,e){var n=this._translateToComponentSpace(t),i=this._isInsideComponent(n);i&&e?(this._overComponent||this._pointerEnterCallbacks.callCallbacks(n),this._pointerMoveCallbacks.callCallbacks(n)):this._overComponent&&this._pointerExitCallbacks.callCallbacks(n),this._overComponent=i&&e},e.prototype.onPointerEnter=function(t){return this._pointerEnterCallbacks.add(t),this},e.prototype.offPointerEnter=function(t){return this._pointerEnterCallbacks.delete(t),this},e.prototype.onPointerMove=function(t){return this._pointerMoveCallbacks.add(t),this},e.prototype.offPointerMove=function(t){return this._pointerMoveCallbacks.delete(t),this},e.prototype.onPointerExit=function(t){return this._pointerExitCallbacks.add(t),this},e.prototype.offPointerExit=function(t){return this._pointerExitCallbacks.delete(t),this},e})(wbt.Interaction);Ybt.Pointer=Zbt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(abt,t),e.__exportStar(Abt,t),e.__exportStar(Ibt,t),e.__exportStar(Bbt,t),e.__exportStar(Ybt,t),t.zoomOut=ivt.zoomOut})(obt);var Jbt={};!(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e,n=uO,i=Fgt,r=Bgt;!(function(t){t[t.VALUE=0]="VALUE",t[t.PIXEL=1]="PIXEL"})(e=t.PropertyMode||(t.PropertyMode={}));var o=(function(t){function r(){var n=t.call(this)||this;return n._boxVisible=!1,n._boxBounds={topLeft:{x:0,y:0},bottomRight:{x:0,y:0}},n._xBoundsMode=e.PIXEL,n._yBoundsMode=e.PIXEL,n.addClass("selection-box-layer"),n._adjustBoundsCallback=function(){n.render()},n._overflowHidden=!0,n._xExtent=[void 0,void 0],n._yExtent=[void 0,void 0],n}return n.__extends(r,t),r.prototype._setup=function(){t.prototype._setup.call(this),this._box=this.content().append("g").classed("selection-box",!0).remove(),this._boxArea=this._box.append("rect").classed("selection-area",!0)},r.prototype._sizeFromOffer=function(t,e){return{width:t,height:e}},r.prototype.bounds=function(t){return null==t?this._getBounds():(this._setBounds(t),this._xBoundsMode=e.PIXEL,this._yBoundsMode=e.PIXEL,this.render(),this)},r.prototype._setBounds=function(t){var e={x:Math.min(t.topLeft.x,t.bottomRight.x),y:Math.min(t.topLeft.y,t.bottomRight.y)},n={x:Math.max(t.topLeft.x,t.bottomRight.x),y:Math.max(t.topLeft.y,t.bottomRight.y)};this._boxBounds={topLeft:e,bottomRight:n}},r.prototype._getBounds=function(){return{topLeft:{x:this._xBoundsMode===e.PIXEL?this._boxBounds.topLeft.x:null==this._xScale?0:Math.min(this.xScale().scale(this.xExtent()[0]),this.xScale().scale(this.xExtent()[1])),y:this._yBoundsMode===e.PIXEL?this._boxBounds.topLeft.y:null==this._yScale?0:Math.min(this.yScale().scale(this.yExtent()[0]),this.yScale().scale(this.yExtent()[1]))},bottomRight:{x:this._xBoundsMode===e.PIXEL?this._boxBounds.bottomRight.x:null==this._xScale?0:Math.max(this.xScale().scale(this.xExtent()[0]),this.xScale().scale(this.xExtent()[1])),y:this._yBoundsMode===e.PIXEL?this._boxBounds.bottomRight.y:null==this._yScale?0:Math.max(this.yScale().scale(this.yExtent()[0]),this.yScale().scale(this.yExtent()[1]))}}},r.prototype.renderImmediately=function(){if(t.prototype.renderImmediately.call(this),this._boxVisible){var e=this.bounds(),n=e.topLeft.y,r=e.bottomRight.y,o=e.topLeft.x,a=e.bottomRight.x;if(!(i.Math.isValidNumber(n)&&i.Math.isValidNumber(r)&&i.Math.isValidNumber(o)&&i.Math.isValidNumber(a)))throw new Error("bounds have not been properly set");this._boxArea.attrs({x:o,y:n,width:a-o,height:r-n}),this.content().node().appendChild(this._box.node())}else this._box.remove();return this},r.prototype.boxVisible=function(t){return null==t?this._boxVisible:(this._boxVisible=t,this.render(),this)},r.prototype.fixedWidth=function(){return!0},r.prototype.fixedHeight=function(){return!0},r.prototype.xScale=function(t){return null==t?this._xScale:(null!=this._xScale&&this._xScale.offUpdate(this._adjustBoundsCallback),this._xScale=t,this._xBoundsMode=e.VALUE,this._xScale.onUpdate(this._adjustBoundsCallback),this.render(),this)},r.prototype.yScale=function(t){return null==t?this._yScale:(null!=this._yScale&&this._yScale.offUpdate(this._adjustBoundsCallback),this._yScale=t,this._yBoundsMode=e.VALUE,this._yScale.onUpdate(this._adjustBoundsCallback),this.render(),this)},r.prototype.xExtent=function(t){return null==t?this._getXExtent():(this._setXExtent(t),this._xBoundsMode=e.VALUE,this.render(),this)},r.prototype._getXExtent=function(){return this._xBoundsMode===e.VALUE?this._xExtent:null==this._xScale?[void 0,void 0]:[this._xScale.invert(this._boxBounds.topLeft.x),this._xScale.invert(this._boxBounds.bottomRight.x)]},r.prototype._setXExtent=function(t){this._xExtent=t},r.prototype.yExtent=function(t){return null==t?this._getYExtent():(this._setYExtent(t),this._yBoundsMode=e.VALUE,this.render(),this)},r.prototype._getYExtent=function(){return this._yBoundsMode===e.VALUE?this._yExtent:null==this._yScale?[void 0,void 0]:[this._yScale.invert(this._boxBounds.topLeft.y),this._yScale.invert(this._boxBounds.bottomRight.y)]},r.prototype._setYExtent=function(t){this._yExtent=t},r.prototype.destroy=function(){t.prototype.destroy.call(this),null!=this._xScale&&this.xScale().offUpdate(this._adjustBoundsCallback),null!=this._yScale&&this.yScale().offUpdate(this._adjustBoundsCallback)},r})(r.Component);t.SelectionBoxLayer=o})(Jbt),
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(rbt,"__esModule",{value:!0});var Qbt=uO,txt=obt,ext=Fgt,nxt=bmt,ixt=ibt,rxt=(function(t){function e(){var e=t.call(this)||this;return e._detectionRadius=3,e._resizable=!1,e._movable=!1,e._hasCorners=!0,e.addClass("drag-box-layer"),e._dragInteraction=new txt.Drag,e._setUpCallbacks(),e._dragInteraction.attachTo(e),e._dragStartCallbacks=new ext.CallbackSet,e._dragCallbacks=new ext.CallbackSet,e._dragEndCallbacks=new ext.CallbackSet,e}return Qbt.__extends(e,t),e.prototype._setUpCallbacks=function(){var t,e,n,i,r=this,o=0,a=function(a){t=r._getResizingEdges(a);var s=r.bounds(),l=s.topLeft.x<=a.x&&a.x<=s.bottomRight.x&&s.topLeft.y<=a.y&&a.y<=s.bottomRight.y;r.boxVisible()&&(t.top||t.bottom||t.left||t.right)?o=1:r.boxVisible()&&r.movable()&&l?o=2:(o=0,r._setBounds({topLeft:a,bottomRight:a}),r._xBoundsMode===ixt.PropertyMode.VALUE&&null!=r.xScale()&&r._setXExtent([r.xScale().invert(a.x),r.xScale().invert(a.x)]),r._yBoundsMode===ixt.PropertyMode.VALUE&&null!=r.yScale()&&r._setYExtent([r.yScale().invert(a.y),r.yScale().invert(a.y)]),r.render()),r.boxVisible(!0),s=r.bounds(),e={x:s.topLeft.x,y:s.topLeft.y},n={x:s.bottomRight.x,y:s.bottomRight.y},i=a,r._dragStartCallbacks.callCallbacks(s)},s=function(a,s){switch(o){case 0:n.x=s.x,n.y=s.y;break;case 1:t.bottom?n.y=s.y:t.top&&(e.y=s.y),t.right?n.x=s.x:t.left&&(e.x=s.x);break;case 2:var l=s.x-i.x,c=s.y-i.y;e.x+=l,e.y+=c,n.x+=l,n.y+=c,i=s}r._setBounds({topLeft:e,bottomRight:n}),r._xBoundsMode===ixt.PropertyMode.VALUE&&null!=r.xScale()&&r._setXExtent([r.xScale().invert(e.x),r.xScale().invert(n.x)]),r._yBoundsMode===ixt.PropertyMode.VALUE&&null!=r.yScale()&&r._setYExtent([r.yScale().invert(e.y),r.yScale().invert(n.y)]),r.render(),r._dragCallbacks.callCallbacks(r.bounds())},l=function(t,e){0===o&&t.x===e.x&&t.y===e.y&&r.boxVisible(!1),r._dragEndCallbacks.callCallbacks(r.bounds())};this._dragInteraction.onDragStart(a),this._dragInteraction.onDrag(s),this._dragInteraction.onDragEnd(l),this._disconnectInteraction=function(){r._dragInteraction.offDragStart(a),r._dragInteraction.offDrag(s),r._dragInteraction.offDragEnd(l),r._dragInteraction.detach()}},e.prototype._setup=function(){var e=this;t.prototype._setup.call(this);var n=function(){return e._box.append("line").styles({opacity:0,stroke:"pink","pointer-events":"visibleStroke"})};if(this._detectionEdgeT=n().classed("drag-edge-tb",!0),this._detectionEdgeB=n().classed("drag-edge-tb",!0),this._detectionEdgeL=n().classed("drag-edge-lr",!0),this._detectionEdgeR=n().classed("drag-edge-lr",!0),this._hasCorners){var i=function(){return e._box.append("circle").styles({opacity:0,fill:"pink","pointer-events":"visibleFill"})};this._detectionCornerTL=i().classed("drag-corner-tl",!0),this._detectionCornerTR=i().classed("drag-corner-tr",!0),this._detectionCornerBL=i().classed("drag-corner-bl",!0),this._detectionCornerBR=i().classed("drag-corner-br",!0)}},e.prototype._getResizingEdges=function(t){var e={top:!1,bottom:!1,left:!1,right:!1};if(!this.resizable())return e;var n=this.bounds(),i=n.topLeft.y,r=n.bottomRight.y,o=n.topLeft.x,a=n.bottomRight.x,s=this._detectionRadius;return o-s<=t.x&&t.x<=a+s&&(e.top=i-s<=t.y&&t.y<=i+s,e.bottom=r-s<=t.y&&t.y<=r+s),i-s<=t.y&&t.y<=r+s&&(e.left=o-s<=t.x&&t.x<=o+s,e.right=a-s<=t.x&&t.x<=a+s),e},e.prototype.renderImmediately=function(){if(t.prototype.renderImmediately.call(this),this.boxVisible()){var e=this.bounds(),n=e.topLeft.y,i=e.bottomRight.y,r=e.topLeft.x,o=e.bottomRight.x;this._detectionEdgeT.attrs({x1:r,y1:n,x2:o,y2:n,"stroke-width":2*this._detectionRadius}),this._detectionEdgeB.attrs({x1:r,y1:i,x2:o,y2:i,"stroke-width":2*this._detectionRadius}),this._detectionEdgeL.attrs({x1:r,y1:n,x2:r,y2:i,"stroke-width":2*this._detectionRadius}),this._detectionEdgeR.attrs({x1:o,y1:n,x2:o,y2:i,"stroke-width":2*this._detectionRadius}),this._hasCorners&&(this._detectionCornerTL.attrs({cx:r,cy:n,r:this._detectionRadius}),this._detectionCornerTR.attrs({cx:o,cy:n,r:this._detectionRadius}),this._detectionCornerBL.attrs({cx:r,cy:i,r:this._detectionRadius}),this._detectionCornerBR.attrs({cx:o,cy:i,r:this._detectionRadius}))}return this},e.prototype.detectionRadius=function(t){if(null==t)return this._detectionRadius;if(t<0)throw new Error("detection radius cannot be negative.");return this._detectionRadius=t,this.render(),this},e.prototype.resizable=function(t){return null==t?this._resizable:(this._resizable=t,this._setResizableClasses(t),this)},e.prototype._setResizableClasses=function(t){t&&this.enabled()?(this.addClass("x-resizable"),this.addClass("y-resizable")):(this.removeClass("x-resizable"),this.removeClass("y-resizable"))},e.prototype.movable=function(t){return null==t?this._movable:(this._movable=t,this._setMovableClass(),this)},e.prototype._setMovableClass=function(){this.movable()&&this.enabled()?this.addClass("movable"):this.removeClass("movable")},e.prototype.onDragStart=function(t){return this._dragStartCallbacks.add(t),this},e.prototype.offDragStart=function(t){return this._dragStartCallbacks.delete(t),this},e.prototype.onDrag=function(t){return this._dragCallbacks.add(t),this},e.prototype.offDrag=function(t){return this._dragCallbacks.delete(t),this},e.prototype.onDragEnd=function(t){return this._dragEndCallbacks.add(t),this},e.prototype.offDragEnd=function(t){return this._dragEndCallbacks.delete(t),this},e.prototype.dragInteraction=function(){return this._dragInteraction},e.prototype.enabled=function(t){return null==t?this._dragInteraction.enabled():(this._dragInteraction.enabled(t),this._setResizableClasses(this.resizable()),this._setMovableClass(),this)},e.prototype.destroy=function(){var e=this;t.prototype.destroy.call(this),this._dragStartCallbacks.forEach((function(t){return e._dragCallbacks.delete(t)})),this._dragCallbacks.forEach((function(t){return e._dragCallbacks.delete(t)})),this._dragEndCallbacks.forEach((function(t){return e._dragEndCallbacks.delete(t)})),this._disconnectInteraction()},e.prototype.detach=function(){return this._resetState(),this._dragInteraction.detach(),t.prototype.detach.call(this),this},e.prototype.anchor=function(e){return e=nxt.coerceExternalD3(e),this._dragInteraction.attachTo(this),t.prototype.anchor.call(this,e),this},e.prototype._resetState=function(){this.bounds({topLeft:{x:0,y:0},bottomRight:{x:0,y:0}})},e})(Jbt.SelectionBoxLayer);rbt.DragBoxLayer=rxt;var oxt={},axt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(axt,"__esModule",{value:!0});var sxt,lxt=uO,cxt=Fgt,uxt=Bgt;!(function(t){t[t.VALUE=0]="VALUE",t[t.PIXEL=1]="PIXEL"})(sxt||(sxt={}));var hxt=(function(t){function e(n){var i=t.call(this)||this;if(i._mode=sxt.VALUE,n!==e.ORIENTATION_VERTICAL&&n!==e.ORIENTATION_HORIZONTAL)throw new Error(n+" is not a valid orientation for GuideLineLayer");return i._orientation=n,i._overflowHidden=!0,i.addClass("guide-line-layer"),i._isVertical()?i.addClass("vertical"):i.addClass("horizontal"),i._scaleUpdateCallback=function(){i._syncPixelPositionAndValue(),i.render()},i}return lxt.__extends(e,t),e.prototype._setup=function(){t.prototype._setup.call(this),this._guideLine=this.content().append("line").classed("guide-line",!0)},e.prototype._sizeFromOffer=function(t,e){return{width:t,height:e}},e.prototype._isVertical=function(){return this._orientation===e.ORIENTATION_VERTICAL},e.prototype.fixedWidth=function(){return!0},e.prototype.fixedHeight=function(){return!0},e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),null!=this.scale()&&(this._isVertical()?this.scale().range([0,this.width()]):this.scale().range([this.height(),0])),this},e.prototype.renderImmediately=function(){return t.prototype.renderImmediately.call(this),this._syncPixelPositionAndValue(),this._guideLine.attrs({x1:this._isVertical()?this.pixelPosition():0,y1:this._isVertical()?0:this.pixelPosition(),x2:this._isVertical()?this.pixelPosition():this.width(),y2:this._isVertical()?this.height():this.pixelPosition()}),this},e.prototype._syncPixelPositionAndValue=function(){null!=this.scale()&&(this._mode===sxt.VALUE&&null!=this.value()?this._pixelPosition=this.scale().scale(this.value()):this._mode===sxt.PIXEL&&null!=this.pixelPosition()&&(this._value=this.scale().invert(this.pixelPosition())))},e.prototype._setPixelPositionWithoutChangingMode=function(t){this._pixelPosition=t,null!=this.scale()&&(this._value=this.scale().invert(this.pixelPosition())),this.render()},e.prototype.scale=function(t){if(null==t)return this._scale;var e=this._scale;return null!=e&&e.offUpdate(this._scaleUpdateCallback),this._scale=t,this._scale.onUpdate(this._scaleUpdateCallback),this._syncPixelPositionAndValue(),this.redraw(),this},e.prototype.value=function(t){return null==t?this._value:(this._value=t,this._mode=sxt.VALUE,this._syncPixelPositionAndValue(),this.render(),this)},e.prototype.pixelPosition=function(t){if(null==t)return this._pixelPosition;if(!cxt.Math.isValidNumber(t))throw new Error("pixelPosition must be a finite number");return this._pixelPosition=t,this._mode=sxt.PIXEL,this._syncPixelPositionAndValue(),this.render(),this},e.prototype.destroy=function(){t.prototype.destroy.call(this),null!=this.scale()&&this.scale().offUpdate(this._scaleUpdateCallback)},e.ORIENTATION_VERTICAL="vertical",e.ORIENTATION_HORIZONTAL="horizontal",e})(uxt.Component);axt.GuideLineLayer=hxt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(oxt,"__esModule",{value:!0});var dxt=uO,pxt=obt,fxt=Fgt,mxt=(function(t){function e(e){var n=t.call(this,e)||this;n._detectionRadius=3,n._enabled=!0,n.addClass("drag-line-layer"),n.addClass("enabled"),n._dragInteraction=new pxt.Drag,n._dragInteraction.attachTo(n);var i=!1,r=function(t){(function(t){return n._isVertical()&&n.pixelPosition()-n.detectionRadius()<=t.x&&t.x<=n.pixelPosition()+n.detectionRadius()||!n._isVertical()&&n.pixelPosition()-n.detectionRadius()<=t.y&&t.y<=n.pixelPosition()+n.detectionRadius()})(t)&&(i=!0,n._dragStartCallbacks.callCallbacks(n))};n._dragInteraction.onDragStart(r);var o=function(t,e){i&&(n._setPixelPositionWithoutChangingMode(n._isVertical()?e.x:e.y),n._dragCallbacks.callCallbacks(n))};n._dragInteraction.onDrag(o);var a=function(t,e){i&&(i=!1,n._dragEndCallbacks.callCallbacks(n))};return n._dragInteraction.onDragEnd(a),n._disconnectInteraction=function(){n._dragInteraction.offDragStart(r),n._dragInteraction.offDrag(o),n._dragInteraction.offDragEnd(a),n._dragInteraction.detach()},n._dragStartCallbacks=new fxt.CallbackSet,n._dragCallbacks=new fxt.CallbackSet,n._dragEndCallbacks=new fxt.CallbackSet,n}return dxt.__extends(e,t),e.prototype._setup=function(){t.prototype._setup.call(this),this._detectionEdge=this.content().append("line").styles({opacity:0,stroke:"pink","pointer-events":"visibleStroke"}).classed("drag-edge",!0)},e.prototype.renderImmediately=function(){return t.prototype.renderImmediately.call(this),this._detectionEdge.attrs({x1:this._isVertical()?this.pixelPosition():0,y1:this._isVertical()?0:this.pixelPosition(),x2:this._isVertical()?this.pixelPosition():this.width(),y2:this._isVertical()?this.height():this.pixelPosition(),"stroke-width":2*this._detectionRadius}),this},e.prototype.detectionRadius=function(t){if(null==t)return this._detectionRadius;if(t<0)throw new Error("detection radius cannot be negative.");return this._detectionRadius=t,this.render(),this},e.prototype.enabled=function(t){return null==t?this._enabled:(this._enabled=t,t?this.addClass("enabled"):this.removeClass("enabled"),this._dragInteraction.enabled(t),this)},e.prototype.onDragStart=function(t){return this._dragStartCallbacks.add(t),this},e.prototype.offDragStart=function(t){return this._dragStartCallbacks.delete(t),this},e.prototype.onDrag=function(t){return this._dragCallbacks.add(t),this},e.prototype.offDrag=function(t){return this._dragCallbacks.delete(t),this},e.prototype.onDragEnd=function(t){return this._dragEndCallbacks.add(t),this},e.prototype.offDragEnd=function(t){return this._dragEndCallbacks.delete(t),this},e.prototype.destroy=function(){var e=this;t.prototype.destroy.call(this),this._dragStartCallbacks.forEach((function(t){return e._dragStartCallbacks.delete(t)})),this._dragCallbacks.forEach((function(t){return e._dragCallbacks.delete(t)})),this._dragEndCallbacks.forEach((function(t){return e._dragEndCallbacks.delete(t)})),this._disconnectInteraction()},e})(axt.GuideLineLayer);oxt.DragLineLayer=mxt;var gxt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(gxt,"__esModule",{value:!0});var _xt=uO;function yxt(t,e,n){var i={};if(void 0!==n)for(var r=0;r<n.length;r++)i[n[r]]=n[r-1];return function(n){var r,o=t.scale(n);if(!e)return o;var a=void 0===i[n]?void 0:t.scale(i[n]);return void 0!==a&&(r=a+(o-a)/2),r}}var vxt=(function(t){function e(e,n){var i=t.call(this)||this;return i.addClass("gridlines"),i._xScale=e,i._yScale=n,i._renderCallback=function(t){return i.render()},i._xScale&&i._xScale.onUpdate(i._renderCallback),i._yScale&&i._yScale.onUpdate(i._renderCallback),i}return _xt.__extends(e,t),e.prototype.betweenX=function(t){return void 0===t?this._betweenX:(t!==this._betweenX&&(this._betweenX=t,this.render()),this)},e.prototype.betweenY=function(t){return void 0===t?this._betweenY:(t!==this._betweenY&&(this._betweenY=t,this.render()),this)},e.prototype.destroy=function(){return t.prototype.destroy.call(this),this._xScale&&this._xScale.offUpdate(this._renderCallback),this._yScale&&this._yScale.offUpdate(this._renderCallback),this},e.prototype._setup=function(){t.prototype._setup.call(this),this._xLinesContainer=this.content().append("g").classed("x-gridlines",!0),this._yLinesContainer=this.content().append("g").classed("y-gridlines",!0)},e.prototype.renderImmediately=function(){return t.prototype.renderImmediately.call(this),this._redrawXLines(),this._redrawYLines(),this},e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),null!=this._xScale&&this._xScale.range([0,this.width()]),null!=this._yScale&&this._yScale.range([this.height(),0]),this},e.prototype._redrawXLines=function(){if(this._xScale){var t=this.betweenX(),e=this._xScale.ticks().slice(t?1:0),n=this._xLinesContainer.selectAll("line").data(e);n.enter().append("line").merge(n).attr("x1",yxt(this._xScale,t,this._xScale.ticks())).attr("y1",0).attr("x2",yxt(this._xScale,t,this._xScale.ticks())).attr("y2",this.height()).classed("betweenline",t).classed("zeroline",(function(t){return 0===t})),n.exit().remove()}},e.prototype._redrawYLines=function(){if(this._yScale){var t=this.betweenY(),e=this._yScale.ticks().slice(t?1:0),n=this._yLinesContainer.selectAll("line").data(e);n.enter().append("line").merge(n).attr("x1",0).attr("y1",yxt(this._yScale,t,this._yScale.ticks())).attr("x2",this.width()).attr("y2",yxt(this._yScale,t,this._yScale.ticks())).classed("betweenline",t).classed("zeroline",(function(t){return 0===t})),n.exit().remove()}},e})(Bgt.Component);gxt.Gridlines=vxt;var bxt={},xxt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(xxt,"__esModule",{value:!0});var wxt=uO,Sxt=bmt,Mxt=(function(t){function e(){var e=t.call(this)||this;return e._detachCallback=function(t){return e.remove(t)},e}return wxt.__extends(e,t),e.prototype.anchor=function(e){var n=this;return e=Sxt.coerceExternalD3(e),t.prototype.anchor.call(this,e),this._forEach((function(t){return t.anchor(n.element())})),this},e.prototype.render=function(){return this._forEach((function(t){return t.render()})),this},e.prototype.has=function(t){throw new Error("has() is not implemented on ComponentContainer")},e.prototype._adoptAndAnchor=function(t){t.parent(this),t.onDetach(this._detachCallback),this._isAnchored&&t.anchor(this.element())},e.prototype.remove=function(t){return this.has(t)&&(t.offDetach(this._detachCallback),this._remove(t),t.detach(),this.redraw()),this},e.prototype._remove=function(t){return!1},e.prototype._forEach=function(t){throw new Error("_forEach() is not implemented on ComponentContainer")},e.prototype.destroy=function(){t.prototype.destroy.call(this),this._forEach((function(t){return t.destroy()}))},e.prototype.invalidateCache=function(){this._forEach((function(t){return t.invalidateCache()}))},e})(Bgt.Component);xxt.ComponentContainer=Mxt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(bxt,"__esModule",{value:!0});var Ext=uO,Txt=Fgt,Cxt=(function(t){function e(e){void 0===e&&(e=[]);var n=t.call(this)||this;return n._components=[],n.addClass("component-group"),e.forEach((function(t){return n.append(t)})),n}return Ext.__extends(e,t),e.prototype._forEach=function(t){this.components().forEach(t)},e.prototype.has=function(t){return this._components.indexOf(t)>=0},e.prototype.requestedSpace=function(t,e){var n=this._components.map((function(n){return n.requestedSpace(t,e)}));return{minWidth:Txt.Math.max(n,(function(t){return t.minWidth}),0),minHeight:Txt.Math.max(n,(function(t){return t.minHeight}),0)}},e.prototype.computeLayout=function(e,n,i){var r=this;return t.prototype.computeLayout.call(this,e,n,i),this._forEach((function(t){t.computeLayout({x:0,y:0},r.width(),r.height())})),this},e.prototype._sizeFromOffer=function(t,e){return{width:t,height:e}},e.prototype.fixedWidth=function(){return this._components.every((function(t){return t.fixedWidth()}))},e.prototype.fixedHeight=function(){return this._components.every((function(t){return t.fixedHeight()}))},e.prototype.components=function(){return this._components.slice()},e.prototype.append=function(t){return null==t||this.has(t)||(t.detach(),this._components.push(t),this._adoptAndAnchor(t),this.redraw()),this},e.prototype._remove=function(t){var e=this._components.indexOf(t);return e>=0&&(this._components.splice(e,1),!0)},e})(xxt.ComponentContainer);bxt.Group=Cxt;var Axt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Axt,"__esModule",{value:!0});var kxt=uO,Lxt=Imt,Pxt=L_t,Nxt=gyt,Ixt=Fgt,Rxt=(function(t){function e(e){var n=t.call(this)||this;if(n._textPadding=5,null==e)throw new Error("InterpolatedColorLegend requires a interpolatedColorScale");return n._scale=e,n._redrawCallback=function(t){return n.redraw()},n._scale.onUpdate(n._redrawCallback),n._formatter=Nxt.general(),n._orientation="horizontal",n._expands=!1,n.addClass("legend"),n.addClass("interpolated-color-legend"),n}return kxt.__extends(e,t),e.prototype.destroy=function(){t.prototype.destroy.call(this),this._scale.offUpdate(this._redrawCallback)},e.prototype.formatter=function(t){return void 0===t?this._formatter:(this._formatter=t,this.redraw(),this)},e.prototype.expands=function(t){return null==t?this._expands:(this._expands=t,this.redraw(),this)},e._ensureOrientation=function(t){if("horizontal"===(t=t.toLowerCase())||"left"===t||"right"===t)return t;throw new Error('"'+t+'" is not a valid orientation for InterpolatedColorLegend')},e.prototype.orientation=function(t){return null==t?this._orientation:(this._orientation=e._ensureOrientation(t),this.redraw(),this)},e.prototype.fixedWidth=function(){return!this.expands()||this._isVertical()},e.prototype.fixedHeight=function(){return!this.expands()||!this._isVertical()},e.prototype._generateTicks=function(t){void 0===t&&(t=e._DEFAULT_NUM_SWATCHES);var n=this._scale.domain();if(1===t)return[n[0]];for(var i=(n[1]-n[0])/(t-1),r=[],o=0;o<t;o++)r.push(n[0]+i*o);return r},e.prototype._setup=function(){t.prototype._setup.call(this),this._swatchContainer=this.content().append("g").classed("swatch-container",!0),this._swatchBoundingBox=this.content().append("rect").classed("swatch-bounding-box",!0),this._lowerLabel=this.content().append("g").classed(e.LEGEND_LABEL_CLASS,!0),this._upperLabel=this.content().append("g").classed(e.LEGEND_LABEL_CLASS,!0);var n=new Lxt.SvgContext(this.content().node());this._measurer=new Lxt.Measurer(n),this._wrapper=new Lxt.Wrapper,this._writer=new Lxt.Writer(this._measurer,n,this._wrapper)},e.prototype.requestedSpace=function(t,n){var i,r,o=this,a=this._measurer.measure().height,s=a,l=this._scale.domain().map((function(t){return o._measurer.measure(o._formatter(t)).width})),c=e._DEFAULT_NUM_SWATCHES;if(this._isVertical()){var u=Ixt.Math.max(l,0);r=s+a+this._textPadding+u+this._textPadding,i=c*a}else i=s+a+s,r=this._textPadding+l[0]+c*a+l[1]+this._textPadding;return{minWidth:r,minHeight:i}},e.prototype._isVertical=function(){return"horizontal"!==this._orientation},e.prototype.renderImmediately=function(){var e=this;t.prototype.renderImmediately.call(this);var n,i,r,o,a,s,l=this._scale.domain(),c=this._formatter(l[0]),u=this._measurer.measure(c).width,h=this._formatter(l[1]),d=this._measurer.measure(h).width,p=this._measurer.measure().height,f=this._textPadding,m={x:0,y:0},g={x:0,y:0},_={xAlign:"center",yAlign:"center",textRotation:0},y={xAlign:"center",yAlign:"center",textRotation:0},v={x:0,y:0,width:0,height:0};if(this._isVertical()){s=Math.floor(this.height());var b=Math.max(u,d);a=(this.width()-b-2*this._textPadding)/2,n=Math.max(this.width()-a-2*f-b,0),i=1,o=function(t,n){return e.height()-(n+1)},y.yAlign="top",m.y=0,_.yAlign="bottom",g.y=0,"left"===this._orientation?(r=function(t,e){return f+b+f},y.xAlign="right",m.x=-(a+n+f),_.xAlign="right",g.x=-(a+n+f)):(r=function(t,e){return a},y.xAlign="left",m.x=a+n+f,_.xAlign="left",g.x=a+n+f),v.width=n,v.height=s*i}else a=Math.max(f,(this.height()-p)/2),s=Math.max(Math.floor(this.width()-4*f-u-d),0),n=1,i=Math.max(this.height()-2*a,0),r=function(t,e){return Math.floor(u+2*f)+e},o=function(t,e){return a},y.xAlign="right",m.x=-f,_.xAlign="left",g.x=f,v.y=a,v.width=s*n,v.height=i;v.x=r(null,0),this._upperLabel.text(""),this._writer.write(h,this.width(),this.height(),y,this._upperLabel.node()),this._upperLabel.attr("transform","translate("+m.x+", "+m.y+")"),this._lowerLabel.text(""),this._writer.write(c,this.width(),this.height(),_,this._lowerLabel.node()),this._lowerLabel.attr("transform","translate("+g.x+", "+g.y+")"),this._swatchBoundingBox.attrs(v);var x=this._generateTicks(s),w=this._swatchContainer.selectAll("rect.swatch").data(x),S=w.enter().append("rect").classed("swatch",!0),M=w.merge(S);return w.exit().remove(),M.attrs({fill:function(t,n){return e._scale.scale(t)},width:n,height:i,x:r,y:o,"shape-rendering":"crispEdges"}),Pxt.ADD_TITLE_ELEMENTS&&S.append("title").text((function(t){return e._formatter(t)})),this},e._DEFAULT_NUM_SWATCHES=11,e.LEGEND_LABEL_CLASS="legend-label",e})(Bgt.Component);Axt.InterpolatedColorLegend=Rxt;var Oxt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Oxt,"__esModule",{value:!0});var zxt=uO,Dxt=Imt,Bxt=(function(t){function e(e,n){void 0===e&&(e=""),void 0===n&&(n=0);var i=t.call(this)||this;return i.addClass("label"),i.text(e),i.angle(n),i.xAlignment("center").yAlignment("center"),i._padding=0,i}return zxt.__extends(e,t),e.prototype.requestedSpace=function(t,e){var n=this._measurer.measure(this._text);return{minWidth:(0===this.angle()?n.width:n.height)+2*this.padding(),minHeight:(0===this.angle()?n.height:n.width)+2*this.padding()}},e.prototype._setup=function(){t.prototype._setup.call(this),this._textContainer=this.content().append("g");var e=new Dxt.SvgContext(this._textContainer.node());this._measurer=new Dxt.CacheMeasurer(e),this._wrapper=new Dxt.Wrapper,this._writer=new Dxt.Writer(this._measurer,e,this._wrapper),this.text(this._text)},e.prototype.text=function(t){if(null==t)return this._text;if("string"!=typeof t)throw new Error("Label.text() only takes strings as input");return this._text=t,this.redraw(),this},e.prototype.angle=function(t){if(null==t)return this._angle;if((t%=360)>180?t-=360:t<-180&&(t+=360),-90!==t&&0!==t&&90!==t)throw new Error(t+" is not a valid angle for Label");return this._angle=t,this.redraw(),this},e.prototype.padding=function(t){if(null==t)return this._padding;if((t=+t)<0)throw new Error(t+" is not a valid padding value. Cannot be less than 0.");return this._padding=t,this.redraw(),this},e.prototype.fixedWidth=function(){return!0},e.prototype.fixedHeight=function(){return!0},e.prototype.renderImmediately=function(){t.prototype.renderImmediately.call(this),this._textContainer.selectAll("g").remove();var e=this._measurer.measure(this._text),n=Math.max(Math.min((this.height()-e.height)/2,this.padding()),0),i=Math.max(Math.min((this.width()-e.width)/2,this.padding()),0);this._textContainer.attr("transform","translate("+i+","+n+")");var r=this.width()-2*i,o=this.height()-2*n,a={xAlign:this.xAlignment(),yAlign:this.yAlignment(),textRotation:this.angle()};return this._writer.write(this._text,r,o,a),this},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e})(Bgt.Component);Oxt.Label=Bxt;var Hxt=(function(t){function e(n,i){var r=t.call(this,n,i)||this;return r.addClass(e.TITLE_LABEL_CLASS),r}return zxt.__extends(e,t),e.TITLE_LABEL_CLASS="title-label",e})(Bxt);Oxt.TitleLabel=Hxt;var Fxt=(function(t){function e(n,i){var r=t.call(this,n,i)||this;return r.addClass(e.AXIS_LABEL_CLASS),r}return zxt.__extends(e,t),e.AXIS_LABEL_CLASS="axis-label",e})(Bxt);Oxt.AxisLabel=Fxt;var Vxt={},Uxt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Uxt,"__esModule",{value:!0});var jxt=Edt;Uxt.circle=function Gxt(){return function(t){return jxt.symbol().type(jxt.symbolCircle).size(Math.PI*Math.pow(t/2,2))}},Uxt.square=function Wxt(){return function(t){return jxt.symbol().type(jxt.symbolSquare).size(Math.pow(t,2))}},Uxt.cross=function qxt(){return function(t){return jxt.symbol().type(jxt.symbolCross).size(5/9*Math.pow(t,2))}},Uxt.diamond=function Yxt(){return function(t){return jxt.symbol().type(jxt.symbolDiamond).size(Math.tan(Math.PI/6)*Math.pow(t,2)/2)}},Uxt.triangle=function Xxt(){return function(t){return jxt.symbol().type(jxt.symbolTriangle).size(Math.sqrt(3)*Math.pow(t/2,2))}},Uxt.star=function $xt(){return function(t){return jxt.symbol().type(jxt.symbolStar).size(.8908130915292852*Math.pow(t/2,2))}};var Kxt=3*(1/Math.sqrt(12)/2+1);Uxt.wye=function Zxt(){return function(t){return jxt.symbol().type(jxt.symbolWye).size(Kxt*Math.pow(t/2.4,2))}},
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Vxt,"__esModule",{value:!0});var Jxt=uO,Qxt=Edt,twt=Imt,ewt=L_t,nwt=gyt,iwt=Uxt,rwt=Fgt,owt=Bgt,awt=(function(){function t(t,e,n){void 0===t&&(t=[]),void 0===e&&(e=0),void 0===n&&(n=1/0),this.columns=t,this.bottomPadding=e,this.maxWidth=n}return t.prototype.addColumn=function(t){var e=t.width,n=this.getWidthAvailable();t.width=Math.min(n,e),this.columns.push(t)},t.prototype.getBounds=function(t){for(var e=this.columns[t],n=0,i=0;i<t;i++)n+=this.columns[i].width;return{topLeft:{x:n,y:0},bottomRight:{x:n+e.width,y:e.height}}},t.prototype.getHeight=function(){return rwt.Math.max(this.columns.map((function(t){return t.height})),0)+this.bottomPadding},t.prototype.getWidth=function(){return Math.min(this.columns.reduce((function(t,e){return t+e.width}),0),this.maxWidth)},t.prototype.getWidthAvailable=function(){var t=this.getWidth();return Math.max(this.maxWidth-t,0)},t})(),swt=(function(){function t(t,e,n,i){void 0===t&&(t=1/0),void 0===e&&(e=1/0),void 0===n&&(n=0),void 0===i&&(i=[]),this.maxWidth=t,this.maxHeight=e,this.padding=n,this.rows=i}return t.prototype.addRow=function(t){t.maxWidth=this.maxWidth-2*this.padding,this.rows.push(t)},t.prototype.getColumnBounds=function(t,e){var n=this.getRowBounds(t),i=this.rows[t].getBounds(e);return i.topLeft.x+=n.topLeft.x,i.bottomRight.x+=n.topLeft.x,i.topLeft.y+=n.topLeft.y,i.bottomRight.y+=n.topLeft.y,i},t.prototype.getRowBounds=function(t){for(var e=this.padding,n=this.padding,i=0;i<t;i++)n+=this.rows[i].getHeight();return{topLeft:{x:e,y:n},bottomRight:{x:e+this.rows[t].getWidth(),y:n+this.rows[t].getHeight()}}},t.prototype.getHeight=function(){return Math.min(this.rows.reduce((function(t,e){return t+e.getHeight()}),0)+2*this.padding,this.maxHeight)},t.prototype.getWidth=function(){return Math.min(rwt.Math.max(this.rows.map((function(t){return t.getWidth()})),0)+2*this.padding,this.maxWidth)},t})(),lwt=(function(t){function e(e){var n=t.call(this)||this;if(n._padding=5,n._rowBottomPadding=3,n.addClass("legend"),n.maxEntriesPerRow(1),null==e)throw new Error("Legend requires a colorScale");return n._colorScale=e,n._redrawCallback=function(t){return n.redraw()},n._colorScale.onUpdate(n._redrawCallback),n._formatter=nwt.identity(),n.maxLinesPerEntry(1),n.xAlignment("right").yAlignment("top"),n.comparator((function(t,e){var i=n._colorScale.domain().slice().map((function(t){return n._formatter(t)}));return i.indexOf(t)-i.indexOf(e)})),n._symbolFactoryAccessor=function(){return iwt.circle()},n._symbolOpacityAccessor=function(){return 1},n}return Jxt.__extends(e,t),e.prototype._setup=function(){t.prototype._setup.call(this);var n=this.content().append("g").classed(e.LEGEND_ROW_CLASS,!0);n.append("g").classed(e.LEGEND_ENTRY_CLASS,!0).append("text");var i=new twt.SvgContext(n.node(),null,ewt.ADD_TITLE_ELEMENTS);this._measurer=new twt.CacheMeasurer(i),this._wrapper=(new twt.Wrapper).maxLines(this.maxLinesPerEntry()),this._writer=new twt.Writer(this._measurer,i,this._wrapper)},e.prototype.formatter=function(t){return null==t?this._formatter:(this._formatter=t,this.redraw(),this)},e.prototype.maxEntriesPerRow=function(t){return null==t?this._maxEntriesPerRow:(this._maxEntriesPerRow=t,this.redraw(),this)},e.prototype.maxLinesPerEntry=function(t){return null==t?this._maxLinesPerEntry:(this._maxLinesPerEntry=t,this.redraw(),this)},e.prototype.maxWidth=function(t){return null==t?this._maxWidth:(this._maxWidth=t,this.redraw(),this)},e.prototype.comparator=function(t){return null==t?this._comparator:(this._comparator=t,this.redraw(),this)},e.prototype.colorScale=function(t){return null!=t?(this._colorScale.offUpdate(this._redrawCallback),this._colorScale=t,this._colorScale.onUpdate(this._redrawCallback),this.redraw(),this):this._colorScale},e.prototype.destroy=function(){t.prototype.destroy.call(this),this._colorScale.offUpdate(this._redrawCallback)},e.prototype._buildLegendTable=function(t,e){var n=this,i=this._measurer.measure().height,r=new swt(t,e,this._padding),o=this._colorScale.domain().slice().sort((function(t,e){return n._comparator(n._formatter(t),n._formatter(e))})),a=new awt;return r.addRow(a),a.bottomPadding=this._rowBottomPadding,o.forEach((function(t,e){a.columns.length/2===n.maxEntriesPerRow()&&((a=new awt).bottomPadding=n._rowBottomPadding,r.addRow(a));var o=a.getWidthAvailable(),s=n._formatter(t),l=n._measurer.measure(s).width;o-i-l<0&&a.columns.length>1&&((a=new awt).bottomPadding=n._rowBottomPadding,r.addRow(a)),a.addColumn({width:i,height:i,data:{name:t,type:"symbol"}}),o=a.getWidthAvailable();var c=Math.min(o,l);n._wrapper.maxLines(n.maxLinesPerEntry());var u=n._wrapper.wrap(s,n._measurer,c).noLines;a.addColumn({width:c,height:u*i,data:{name:t,type:"text"}})})),r},e.prototype.requestedSpace=function(t,e){var n=this._buildLegendTable(rwt.Math.min([this.maxWidth(),t],t),e);return{minHeight:n.getHeight(),minWidth:n.getWidth()}},e.prototype.entitiesAt=function(t){var n=this;if(!this._isSetup)return[];var i=this._buildLegendTable(this.width(),this.height());return i.rows.reduce((function(r,o,a){if(0!==r.length)return r;var s=i.getRowBounds(a);return rwt.Math.within(t,s)?o.columns.reduce((function(r,o,s){var l=i.getColumnBounds(a,s);if(rwt.Math.within(t,l)){var c=n.content().selectAll("."+e.LEGEND_ROW_CLASS).nodes()[a],u=Qxt.select(c).selectAll("."+e.LEGEND_ENTRY_CLASS).nodes()[Math.floor(s/2)],h=Qxt.select(u).select("."+e.LEGEND_SYMBOL_CLASS),d=rwt.DOM.getTranslateValues(Qxt.select(c)),p=rwt.DOM.getTranslateValues(h);return[{bounds:rwt.DOM.elementBBox(Qxt.select(c)),datum:o.data.name,position:{x:d[0]+p[0],y:d[1]+p[1]},selection:Qxt.select(u),component:n}]}return r}),r):r}),[])},e.prototype.renderImmediately=function(){t.prototype.renderImmediately.call(this);var n=this._buildLegendTable(this.width(),this.height());this.content().selectAll("*").remove();var i=this.content().selectAll("g."+e.LEGEND_ROW_CLASS).data(n.rows),r=i.enter().append("g").classed(e.LEGEND_ROW_CLASS,!0).merge(i);i.exit().remove(),r.attr("transform",(function(t,e){var i=n.getRowBounds(e);return"translate("+i.topLeft.x+", "+i.topLeft.y+")"}));var o=this;return r.each((function(t,i){for(var r=[],a=0;a<t.columns.length;a+=2)r.push([t.columns[a],t.columns[a+1]]);var s=Qxt.select(this).selectAll("g."+e.LEGEND_ENTRY_CLASS).data(r),l=s.enter().append("g").classed(e.LEGEND_ENTRY_CLASS,!0).merge(s);l.append("path").attr("d",(function(t,e){var n=t[0];return o.symbol()(n.data.name,i)(.6*n.height)(null)})).attr("transform",(function(t,e){var r=t[0],o=n.rows[i].columns.indexOf(r);return"translate("+(n.getColumnBounds(i,o).topLeft.x+r.width/2)+", "+r.height/2+")"})).attr("fill",(function(t){return o._colorScale.scale(t[0].data.name)})).attr("opacity",(function(t,e){return o.symbolOpacity()(t[0].data.name,i)})).classed(e.LEGEND_SYMBOL_CLASS,!0),l.append("g").classed("text-container",!0).attr("transform",(function(t,e){var r=n.rows[i].columns.indexOf(t[1]);return"translate("+n.getColumnBounds(i,r).topLeft.x+", 0)"})).each((function(t,e,n){var i=Qxt.select(this),r=t[1];o._writer.write(o._formatter(r.data.name),r.width,o.height(),{xAlign:"left",yAlign:"top",textRotation:0},i.node())})),s.exit().remove()})),this},e.prototype.symbol=function(t){return null==t?this._symbolFactoryAccessor:(this._symbolFactoryAccessor=t,this.render(),this)},e.prototype.symbolOpacity=function(t){return null==t?this._symbolOpacityAccessor:(this._symbolOpacityAccessor="number"==typeof t?function(){return t}:t,this.render(),this)},e.prototype.fixedWidth=function(){return!0},e.prototype.fixedHeight=function(){return!0},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e.LEGEND_ROW_CLASS="legend-row",e.LEGEND_ENTRY_CLASS="legend-entry",e.LEGEND_SYMBOL_CLASS="legend-symbol",e})(owt.Component);Vxt.Legend=lwt;var cwt={},uwt={},hwt={};!(function(t){var e;
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0}),(e=t.Animator||(t.Animator={})).MAIN="main",e.RESET="reset"})(hwt);var dwt={},pwt={},fwt={};function mwt(t){return!0===(function t(e){return null!=e&&"object"==typeof e&&!1===Array.isArray(e)})(t)&&"[object Object]"===Object.prototype.toString.call(t)}var gwt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(gwt,"__esModule",{value:!0});var _wt=Fgt,ywt=0,vwt=(function(){function t(t,e){void 0===t&&(t=[]),void 0===e&&(e={}),this._updateId=ywt++,this._data=t,this._metadata=e,this._callbacks=new _wt.CallbackSet}return t.prototype.onUpdate=function(t){return this._callbacks.add(t),this},t.prototype.offUpdate=function(t){return this._callbacks.delete(t),this},t.prototype.data=function(t){return null==t?this._data:(this._data=t,this._dispatchUpdate(),this)},t.prototype.metadata=function(t){return null==t?this._metadata:(this._metadata=t,this._dispatchUpdate(),this)},t.prototype.updateId=function(){return this._updateId},t.prototype._dispatchUpdate=function(){this._updateId=ywt++,this._callbacks.callCallbacks(this)},t})();gwt.Dataset=vwt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     * @fileoverview Implements the Signature API to help in comparing when two
     * Plottable objects have "changed".
     *
     * Memoization in Plottable is complicated by mutable scales and datasets. We cannot simply
     * reference compare two e.g. scales since it may have internally mutated. To resolve this,
     * we write a recursive Signature interface that holds an immutable snapshot of whatever
     * state the scale/data was in at the time. Then on memoized function invocation we sign the
     * new inputs and compare the signatures to decide if we should recompute.
     *
     * We must hand-write a signature for each custom class we wish to support.
     */
Object.defineProperty(fwt,"__esModule",{value:!0});var bwt=uO,xwt=gwt,wwt=hvt;function Swt(t){return t instanceof kwt?t:t instanceof Date?Twt(t.valueOf()):t instanceof wwt.Scale?Mwt(t):t instanceof xwt.Dataset?Ewt(t):(function t(e){var n,i;return!1!==mwt(e)&&"function"==typeof(n=e.constructor)&&!1!==mwt(i=n.prototype)&&!1!==i.hasOwnProperty("isPrototypeOf")})(t)?Awt(t):Array.isArray(t)?Cwt(t):Twt(t)}function Mwt(t){return Awt({domain:t.domain(),range:t.range(),updateId:t.updateId(),ref:Twt(t)})}function Ewt(t){return Awt({ref:Twt(t),updateId:t.updateId()})}function Twt(t){return new Pwt(t)}function Cwt(t){return new Lwt(t.map((function(t){return Swt(t)})))}function Awt(t){var e={};for(var n in t)t.hasOwnProperty(n)&&(e[n]=Swt(t[n]));return new Nwt(e)}fwt.sign=Swt,fwt.signScale=Mwt,fwt.signDataset=Ewt,fwt.signRef=Twt,fwt.signArray=Cwt,fwt.signObj=Awt;var kwt=(function(){function t(){}return t.prototype.isDifferent=function(t){return!(t instanceof this.constructor)||this.isSignatureDifferent(t)},t})();fwt.Signature=kwt;var Lwt=(function(t){function e(e){var n=t.call(this)||this;return n.array=e,n}return bwt.__extends(e,t),e.prototype.isSignatureDifferent=function(t){if(t.array.length!==this.array.length)return!0;for(var e=0;e<this.array.length;e++)if(this.array[e].isDifferent(t.array[e]))return!0;return!1},e})(kwt);fwt.ArraySignature=Lwt;var Pwt=(function(t){function e(e){var n=t.call(this)||this;return n.ref=e,n}return bwt.__extends(e,t),e.prototype.isSignatureDifferent=function(t){return this.ref!==t.ref},e})(kwt);fwt.ReferenceSignature=Pwt;var Nwt=(function(t){function e(e){var n=t.call(this)||this;return n.obj=e,n}return bwt.__extends(e,t),e.prototype.isSignatureDifferent=function(t){var e=Object.keys(this.obj),n=Object.keys(t.obj);if(e.length!==n.length)return!0;for(var i=0,r=e;i<r.length;i++){var o=r[i];if(!t.obj.hasOwnProperty(o))return!0;if(this.obj[o].isDifferent(t.obj[o]))return!0}return!1},e})(kwt);fwt.ObjectSignature=Nwt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     * @fileoverview Implements a function memoizer using the Signature API.
     */
Object.defineProperty(pwt,"__esModule",{value:!0});var Iwt=fwt;pwt.memoize=function Rwt(t){var e,n=void 0,i=!1,r=!1,o=function(){for(var o=[],a=0;a<arguments.length;a++)o[a]=arguments[a];if(i)return e;var s=Iwt.signArray(o);return void 0===n||n.isDifferent(s)?(r&&console.warn("cache miss! computing"),n=s,e=t.apply(this,o)):r&&console.warn("cache hit!"),e};return o.doLocked=function(t){if(i)throw new Error("Locking an already locked memoize function!");i=!0;var e=t.apply(this);return i=!1,e},o.logPerformance=function(t){return void 0===t&&(t=!0),r=t,this},o};var Owt={};Object.defineProperty(Owt,"__esModule",{value:!0});var zwt=Se.exports,Dwt=(function(){function t(){this.map=Object.create(null),this.exists=Object.create(null)}return t.prototype.delete=function(t){return delete this.map[t],delete this.exists[t],!0},t.prototype.get=function(t){return this.map[t]},t.prototype.has=function(t){return!!this.exists[t]},t.prototype.set=function(t,e){return this.map[t]=e,this.exists[t]=!0,this},t})(),Bwt=(function(){function t(){this.map=new Dwt}return t.prototype.get=function(t){return this.map.get(t[0]).get(t[1])},t.prototype.has=function(t){return this.map.has(t[0])&&this.map.get(t[0]).has(t[1])},t.prototype.set=function(t,e){return this.map.has(t[0])||this.map.set(t[0],new Dwt),this.map.get(t[0]).set(t[1],e),this},t.prototype.delete=function(t){return this.map.has(t[0])&&this.map.get(t[0]).delete(t[1]),!0},t.prototype.clear=function(){this.map=new Dwt},t.resolver=function(t,e,n){return[n.updateId(),e]},t})();function Hwt(t){var e=zwt.memoize(t,Bwt.resolver);return e.cache=new Bwt,e}Owt.memoizeProjector=Hwt,Owt.memoizeProjectors=function Fwt(t){return Object.keys(t).forEach((function(e){t[e]=Hwt(t[e])})),t};var Vwt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     * @fileoverview Implements a convenient thunk function to handle the common case
     * of creating a memoized function that takes its inputs from mutable class properties.
     */Object.defineProperty(Vwt,"__esModule",{value:!0});var Uwt=dwt;Vwt.memThunk=function jwt(){for(var t=[],e=0;e<arguments.length;e++)t[e]=arguments[e];var n=t.slice(0,-1),i=t[t.length-1],r=Uwt.memoize(i),o=function(){var t=this,e=n.map((function(e){return e.apply(t)}));return r.apply(void 0,e)};return o},(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(pwt,t),e.__exportStar(Owt,t),e.__exportStar(Vwt,t),t.sign=fwt.sign})(dwt);var Gwt={};!(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=Edt,n=(function(){function t(t,e){this._context=t,this._drawStep=e}return t.prototype.getDrawStep=function(){return this._drawStep},t.prototype.draw=function(t,e){var n=e[e.length-1].attrToAppliedProjector;this._context.save(),this._drawStep(this._context,t,n),this._context.restore()},t.prototype.getVisualPrimitives=function(){return[]},t.prototype.getVisualPrimitiveAtIndex=function(t){return null},t.prototype.remove=function(){},t})();function i(t,e,n,i){for(var r={},o=0,a=e;o<a.length;o++){var s=a[o];t.hasOwnProperty(s)&&(r[s]=t[s](n,i))}return r}function r(t){return null!=t["stroke-width"]?parseFloat(t["stroke-width"]):1}function o(t){var e=t["stroke-dasharray"];if(null!=e)try{return e.split(/[ ,]+/).map((function(t){return parseInt(t,10)}))}catch(t){return console.error("getStrokeDashArray failed with: "+t),[]}return[]}function a(t,n){if(n.stroke){t.lineWidth=r(n);var i=e.color(n.stroke),a=o(n);t.setLineDash(a),i.opacity*=(function s(t){var e=null!=t.opacity?parseFloat(t.opacity):1;return(null!=t["stroke-opacity"]?parseFloat(t["stroke-opacity"]):1)*e})(n),t.strokeStyle=i.toString(),t.stroke()}if(n.fill){var l=e.color(n.fill);l.opacity*=(function c(t){var e=null!=t.opacity?parseFloat(t.opacity):1;return(null!=t["fill-opacity"]?parseFloat(t["fill-opacity"]):1)*e})(n),t.fillStyle=l.toString(),t.fill()}}t.CanvasDrawer=n,t.ContextStyleAttrs=["fill-opacity","fill","opacity","stroke-opacity","stroke-width","stroke","stroke-dasharray"],t.resolveAttributesSubsetWithStyles=function s(e,n,r,o){return i(e,t.ContextStyleAttrs.concat(n),r,o)},t.resolveAttributes=i,t.getStrokeWidth=r,t.getStrokeDashArray=o,t.renderArea=function l(t,e,n,i){t.save(),t.beginPath(),e.context(t),e(n),t.lineJoin="round",a(t,i),t.restore()},t.renderLine=function c(t,e,n,i){t.save(),t.beginPath(),e.context(t),e(n),t.lineJoin="round",a(t,i),t.restore()},t.renderPathWithStyle=a})(Gwt);var Wwt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Wwt,"__esModule",{value:!0});var qwt=(function(){function t(t,e){this._svgDrawerFactory=t,this._canvasDrawerFactory=e}return t.prototype.useSVG=function(t){null!=this._currentDrawer&&this._currentDrawer.remove();var e=this._svgDrawerFactory();e.attachTo(t),this._currentDrawer=e},t.prototype.useCanvas=function(t){null!=this._currentDrawer&&this._currentDrawer.remove(),this._currentDrawer=this._canvasDrawerFactory(t.node().getContext("2d"))},t.prototype.getDrawer=function(){return this._currentDrawer},t.prototype.remove=function(){null!=this._currentDrawer&&this._currentDrawer.remove()},t.prototype.draw=function(t,e){this._currentDrawer.draw(t,e)},t.prototype.getVisualPrimitives=function(){return this._currentDrawer.getVisualPrimitives()},t.prototype.getVisualPrimitiveAtIndex=function(t){return this._currentDrawer.getVisualPrimitiveAtIndex(t)},t})();Wwt.ProxyDrawer=qwt;var Ywt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Ywt,"__esModule",{value:!0});var Xwt=Edt,$wt=Fgt,Kwt=(function(){function t(t,e){this._root=Xwt.select(document.createElementNS("http://www.w3.org/2000/svg","g")),this._className=e,this._svgElementName=t}return t.prototype.draw=function(t,e){var n=this;this._createAndDestroyDOMElements(t);for(var i=0,r=e.length,o=function(r){var o=e[r];$wt.Window.setTimeout((function(){return n._drawStep(o)}),i),i+=o.animator.totalTime(t.length)},a=0;a<r;a++)o(a)},t.prototype.getVisualPrimitives=function(){return null==this._cachedVisualPrimitivesNodes&&(this._cachedVisualPrimitivesNodes=this._selection.nodes()),this._cachedVisualPrimitivesNodes},t.prototype.getVisualPrimitiveAtIndex=function(t){return null==this._cachedVisualPrimitivesNodeMap?null:this._cachedVisualPrimitivesNodeMap.get(t)},t.prototype.remove=function(){this._root.remove()},t.prototype.attachTo=function(t){t.node().appendChild(this._root.node())},t.prototype.getRoot=function(){return this._root},t.prototype.selector=function(){return this._svgElementName},t.prototype._applyDefaultAttributes=function(t){},t.prototype._createAndDestroyDOMElements=function(t){var e=t.map((function(t,e){return null!=t?{d:t,i:e}:null})).filter((function(t){return null!=t})),n=this._root.selectAll(this.selector()).data(e);this._selection=n.enter().append(this._svgElementName).merge(n),n.exit().remove();var i=new $wt.Map;this._selection.each((function(t){i.set(t.i,this)})),this._cachedVisualPrimitivesNodeMap=i,this._cachedVisualPrimitivesNodes=null,this._selection.data(this._selection.data().map((function(t){return t.d}))),null!=this._className&&this._selection.classed(this._className,!0),this._applyDefaultAttributes(this._selection)},t.prototype._drawStep=function(t){var e=this;["fill","stroke"].forEach((function(n){null!=t.attrToAppliedProjector[n]&&e._selection.attr(n,t.attrToAppliedProjector[n])})),t.animator.animate(this._selection,t.attrToAppliedProjector),null!=this._className&&this._selection.classed(this._className,!0)},t})();Ywt.SVGDrawer=Kwt;var Zwt={};
/**
     * Copyright 2017-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Zwt,"__esModule",{value:!0});var Jwt=(function(){function t(){var t=this;this.scale=0,this.translate=0,this.cachedDomain=[null,null],this.lastSeenDomain=[null,null],this.updateDomain=function(e){t.lastSeenDomain=e.getTransformationDomain();var n=e.scaleTransformation(t.cachedDomain[1])-e.scaleTransformation(t.cachedDomain[0]),i=e.scaleTransformation(t.lastSeenDomain[1])-e.scaleTransformation(t.lastSeenDomain[0]);t.scale=n/i||1,t.translate=e.scaleTransformation(t.cachedDomain[0])-e.scaleTransformation(t.lastSeenDomain[0])*t.scale||0}}return t.prototype.reset=function(){this.scale=1,this.translate=0,this.cachedDomain=this.lastSeenDomain},t.prototype.setDomain=function(t){this.cachedDomain=t.getTransformationDomain()},t})(),Qwt=(function(){function t(e,n){var i=this;this.renderCallback=e,this.applyTransformCallback=n,this.domainTransformX=new Jwt,this.domainTransformY=new Jwt,this.renderDeferred=function(){i.applyTransform(),clearTimeout(i.timeoutToken),i.timeoutToken=setTimeout((function(){i.renderCallback()}),t.DEFERRED_RENDERING_DELAY)}}return t.prototype.setDomains=function(t,e){t&&this.domainTransformX.setDomain(t),e&&this.domainTransformY.setDomain(e),this.renderDeferred()},t.prototype.updateDomains=function(t,e){t&&this.domainTransformX.updateDomain(t),e&&this.domainTransformY.updateDomain(e),this.renderDeferred()},t.prototype.resetTransforms=function(){this.domainTransformX.reset(),this.domainTransformY.reset(),this.applyTransform()},t.prototype.applyTransform=function(){this.applyTransformCallback(this.domainTransformX.translate,this.domainTransformY.translate,this.domainTransformX.scale,this.domainTransformY.scale)},t.DEFERRED_RENDERING_DELAY=200,t})();Zwt.DeferredRenderer=Qwt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(uwt,"__esModule",{value:!0});var tSt=uO,eSt=Edt,nSt=_mt,iSt=Fgt,rSt=hwt,oSt=dwt,aSt=Bgt,sSt=Gwt,lSt=Wwt,cSt=Ywt,uSt=bmt,hSt=Zwt;uwt.Renderer=Smt.makeEnum(["svg","canvas"]);var dSt=(function(t){function e(){var n=t.call(this)||this;n._dataChanged=!1,n._attrExtents={},n._animate=!1,n._animators={},n._propertyExtents={},n._resetEntityStore=function(){n._cachedEntityStore=void 0},n._overflowHidden=!0,n.addClass("plot"),n._datasetToDrawer=new iSt.Map,n._attrBindings=eSt.map(),n._includedValuesProvider=function(t,e){return n._includedValuesForScale(t,e)},n._renderCallback=function(){return n.render()},n._onDatasetUpdateCallback=function(){return n._onDatasetUpdate()},n._propertyBindings=eSt.map();var i=(new nSt.Easing).maxTotalDuration(e._ANIMATION_MAX_DURATION);return n.animator(rSt.Animator.MAIN,i),n.animator(rSt.Animator.RESET,new nSt.Null),n._deferredResetEntityStore=iSt.Window.debounce(hSt.DeferredRenderer.DEFERRED_RENDERING_DELAY,n._resetEntityStore),n}return 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t=this.element().select(".plot-canvas-container");t.empty()&&(t=this.element().append("div").classed("plot-canvas-container",!0)).node().appendChild(this._canvas.node())},e.prototype.setBounds=function(e,n,i,r){if(t.prototype.setBounds.call(this,e,n,i,r),this._updateExtents(),null!=this._canvas){if(this._bufferCanvas&&!this._bufferCanvasValid){this._bufferCanvas.attr("width",this._canvas.attr("width")),this._bufferCanvas.attr("height",this._canvas.attr("height"));var o=this._bufferCanvas.node().getContext("2d");if(o){var a=this._canvas.node();a.width>0&&a.height>0?o.canvas.width>0&&o.canvas.height>0&&o.drawImage(a,0,0):console.warn("Failed to fill buffer canvas with with 0x0 canvas")}this._bufferCanvasValid=!0}var s=null!=window.devicePixelRatio?window.devicePixelRatio:1;this._canvas.attr("width",e*s),this._canvas.attr("height",n*s);var l=this._canvas.node().getContext("2d");if(l&&(l.setTransform(s,0,0,s,0,0),this._bufferCanvas)){var c=this._bufferCanvas.node();c.width>0&&c.height>0?l.canvas.width>0&&l.canvas.height>0&&l.drawImage(c,0,0,e,n):console.warn("Failed to fill canvas with 0x0 buffer canvas")}}return this},e.prototype.destroy=function(){var e=this;t.prototype.destroy.call(this),this._scales().forEach((function(t){return t.offUpdate(e._renderCallback)})),this.datasets([])},e.prototype._createNodesForDataset=function(t){var e=this._datasetToDrawer.get(t);return"svg"===this.renderer()?e.useSVG(this._renderArea):e.useCanvas(this._canvas),e},e.prototype._createDrawer=function(t){return new lSt.ProxyDrawer((function(){return new cSt.SVGDrawer("path","")}),(function(t){return new sSt.CanvasDrawer(t,(function(){}))}))},e.prototype._getAnimator=function(t){return this._animateOnNextRender()&&this._animators[t]||new nSt.Null},e.prototype._onDatasetUpdate=function(){this._updateExtents(),this._dataChanged=!0,this._resetEntityStore(),this.renderLowPriority()},e.prototype.attr=function(t,e,n){return null==e?this._attrBindings.get(t):(this._bindAttr(t,e,n),this.render(),this)},e.prototype._bindProperty=function(t,e,n,i){var r=this._propertyBindings.get(t),o=null!=r?r.scale:null;this._propertyBindings.set(t,{accessor:"function"==typeof e?e:function(){return e},scale:n,postScale:i}),null!=o&&this._uninstallScaleForKey(o,t),null!=n&&this._installScaleForKey(n,t),this._clearAttrToProjectorCache()},e.prototype._bindAttr=function(t,e,n){var i=this._attrBindings.get(t),r=null!=i?i.scale:null;this._attrBindings.set(t,{accessor:"function"==typeof e?e:function(){return e},scale:n}),null!=r&&this._uninstallScaleForKey(r,t),null!=n&&this._installScaleForKey(n,t),this._clearAttrToProjectorCache()},e.prototype._clearAttrToProjectorCache=function(){delete this._cachedAttrToProjector},e.prototype._getAttrToProjector=function(){if(null==this._cachedAttrToProjector){var t=this._generateAttrToProjector();e.OPTIMIZE_MEMOIZE_PROJECTORS&&(t=oSt.memoizeProjectors(t)),this._cachedAttrToProjector=t}return iSt.assign({},this._cachedAttrToProjector)},e.prototype._generateAttrToProjector=function(){var t={};this._attrBindings.each((function(n,i){t[i]=e._scaledAccessor(n)}));var n=this._propertyProjectors();return Object.keys(n).forEach((function(e){null==t[e]&&(t[e]=n[e])})),t},e.prototype.renderImmediately=function(){return t.prototype.renderImmediately.call(this),this._isAnchored&&(this._paint(),this._dataChanged=!1),this},e.prototype.renderLowPriority=function(){return this._renderCallback(),this},e.prototype.animated=function(t){return null==t?this._animate:(this._animate=t,this)},e.prototype.detach=function(){return t.prototype.detach.call(this),this._updateExtents(),this},e.prototype._scales=function(){var t=[];return this._attrBindings.each((function(e,n){var i=e.scale;null!=i&&-1===t.indexOf(i)&&t.push(i)})),this._propertyBindings.each((function(e,n){var i=e.scale;null!=i&&-1===t.indexOf(i)&&t.push(i)})),t},e.prototype._updateExtents=function(){var t=this;this._resetEntityStore(),this._scales().forEach((function(e){return e.addIncludedValuesProvider(t._includedValuesProvider)}))},e.prototype._filterForProperty=function(t){return null},e.prototype.getExtentsForAttr=function(t){var e=this;if(null==this._attrExtents[t]){var n=oSt.memThunk((function(){return e.datasets()}),(function(){return e._attrBindings.get(t)}),(function(t,e){return null==e||null==e.accessor?null:t.map((function(t){return pSt(t,e,null)}))}));this._attrExtents[t]=n}return this._attrExtents[t]()},e.prototype.getExtentsForProperty=function(t){var e=this;if(null==this._propertyExtents[t]){var n=oSt.memThunk((function(){return e.datasets()}),(function(){return e._propertyBindings.get(t)}),(function(){return e._filterForProperty(t)}),(function(t,e,n){return null==e||null==e.accessor?null:t.map((function(t){return pSt(t,e,n)}))}));this._propertyExtents[t]=n}return this._propertyExtents[t]()},e.prototype._includedValuesForScale=function(t,e){var n=this;if(!this._isAnchored&&!e)return[];var i=[];return this._attrBindings.each((function(e,r){if(e.scale===t){var o=n.getExtentsForAttr(r);null!=o&&(i=i.concat(eSt.merge(o)))}})),this._propertyBindings.each((function(e,r){if(e.scale===t){var o=n.getExtentsForProperty(r);null!=o&&(i=i.concat(eSt.merge(o)))}})),i},e.prototype.animator=function(t,e){return void 0===e?this._animators[t]:(this._animators[t]=e,this)},e.prototype.renderer=function(t){var e=this;return void 0===t?null==this._canvas?"svg":"canvas":(null==this._canvas&&"canvas"===t?(this._canvas=eSt.select(document.createElement("canvas")).classed("plot-canvas",!0),this._bufferCanvas=eSt.select(document.createElement("canvas")),null!=this.element()&&this._appendCanvasNode(),this._datasetToDrawer.forEach((function(t){t.useCanvas(e._canvas)})),this.render()):null!=this._canvas&&"svg"==t&&(this._canvas.remove(),this._canvas=null,this._bufferCanvas=null,this._datasetToDrawer.forEach((function(t){t.useSVG(e._renderArea)})),this.render()),this)},e.prototype.addDataset=function(t){return this._addDataset(t),this._onDatasetUpdate(),this},e.prototype._addDataset=function(t){this._removeDataset(t);var e=this._createDrawer(t);return this._datasetToDrawer.set(t,e),this._isSetup&&this._createNodesForDataset(t),t.onUpdate(this._onDatasetUpdateCallback),this},e.prototype.removeDataset=function(t){return this._removeDataset(t),this._onDatasetUpdate(),this},e.prototype._removeDataset=function(t){return-1===this.datasets().indexOf(t)||(this._removeDatasetNodes(t),t.offUpdate(this._onDatasetUpdateCallback),this._datasetToDrawer.delete(t)),this},e.prototype._removeDatasetNodes=function(t){this._datasetToDrawer.get(t).remove()},e.prototype.datasets=function(t){var e=this,n=[];return this._datasetToDrawer.forEach((function(t,e){return n.push(e)})),null==t?n:(n.forEach((function(t){return e._removeDataset(t)})),t.forEach((function(t){return e._addDataset(t)})),this._onDatasetUpdate(),this)},e.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new nSt.Null}]},e.prototype._additionalPaint=function(t){},e.prototype._buildLightweightPlotEntities=function(t){var e=this,n=[];return t.forEach((function(t,i){for(var r=e._datasetToDrawer.get(t),o=0,a=t.data(),s=a.length,l=function(s){var 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e.getTotalDrawTime(i.get(t),n)})),s=iSt.Math.max(a,0);this._additionalPaint(s)},e.prototype.selections=function(t){var e=this;if(void 0===t&&(t=this.datasets()),"canvas"===this.renderer())return eSt.selectAll();var n=[];return t.forEach((function(t){var i=e._datasetToDrawer.get(t);if(null!=i){var r=i.getVisualPrimitives();n.push.apply(n,r)}})),eSt.selectAll(n)},e.prototype.entities=function(t){var e=this;return this._getEntityStore(t).entities().map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype.filterEntities=function(t){var e=this;return this._getEntityStore().entities().filter(t).map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype._getEntityStore=function(t){var e,n=this,i=function(t){return n._entityBounds(t)};return void 0!==t?((e=new iSt.EntityStore).addAll(this._buildLightweightPlotEntities(t),i,this._localOriginBounds()),e):(void 0===this._cachedEntityStore&&((e=new iSt.EntityStore).addAll(this._buildLightweightPlotEntities(this.datasets()),i,this._localOriginBounds()),this._cachedEntityStore=e),this._cachedEntityStore)},e.prototype._localOriginBounds=function(){return{topLeft:{x:0,y:0},bottomRight:{x:this.width(),y:this.height()}}},e.prototype._entityBounds=function(t){var e=this._pixelPoint(t.datum,t.index,t.dataset);return{x:e.x,y:e.y,width:0,height:0}},e.prototype._lightweightPlotEntityToPlotEntity=function(t){return{bounds:this._entityBounds(t),component:t.component,dataset:t.dataset,datasetIndex:t.datasetIndex,datum:t.datum,index:t.index,position:t.position,selection:eSt.select(t.drawer.getVisualPrimitiveAtIndex(t.validDatumIndex))}},e.prototype.entitiesAt=function(t){throw new Error("plots must implement entitiesAt")},e.prototype.entityNearest=function(t){var e=this._getEntityStore().entityNearest(t);return void 0===e?void 0:this._lightweightPlotEntityToPlotEntity(e)},e.prototype.entitiesIn=function(t,e){return this.entitiesInBounds(null==e?{x:t.topLeft.x,y:t.topLeft.y,width:t.bottomRight.x-t.topLeft.x,height:t.bottomRight.y-t.topLeft.y}:{x:t.min,y:e.min,width:t.max-t.min,height:e.max-e.min})},e.prototype.entitiesInBounds=function(t){var e=this,n=this._getEntityStore().entitiesInBounds(t);if(n)return n.map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype.entitiesInXBounds=function(t){var e=this,n=this._getEntityStore().entitiesInXBounds(t);if(n)return n.map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype.entitiesInYBounds=function(t){var e=this,n=this._getEntityStore().entitiesInYBounds(t);if(n)return n.map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype._uninstallScaleForKey=function(t,e){t.offUpdate(this._renderCallback),t.offUpdate(this._deferredResetEntityStore),t.removeIncludedValuesProvider(this._includedValuesProvider)},e.prototype._installScaleForKey=function(t,e){t.onUpdate(this._renderCallback),t.onUpdate(this._deferredResetEntityStore),t.addIncludedValuesProvider(this._includedValuesProvider)},e.prototype._propertyProjectors=function(){return{}},e._scaledAccessor=function(t){var e=t.scale,n=t.accessor,i=t.postScale,r=null==e?n:function(t,i,r){return e.scale(n(t,i,r))};return null==i?r:function(t,e,n){return i(r(t,e,n),t,e,n)}},e.prototype._pixelPoint=function(t,e,n){return{x:0,y:0}},e.prototype._animateOnNextRender=function(){return this._animate&&this._dataChanged},e.OPTIMIZE_MEMOIZE_PROJECTORS=!1,e._ANIMATION_MAX_DURATION=600,e})(aSt.Component);function pSt(t,e,n){var i=e.accessor,r=e.scale;if(null==r)return[];var o=t.data();null!=n&&(o=o.filter((function(e,i){return 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/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */uwt.Plot=dSt,Object.defineProperty(cwt,"__esModule",{value:!0});var fSt=uO,mSt=uwt,gSt=Fgt,_St=(function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return fSt.__extends(e,t),e.prototype.entityNearest=function(t){var e,n=1/0;return this.components().forEach((function(i){var r=i.entityNearest(t);if(null!=r){var o=gSt.Math.distanceSquared(r.position,t);o<=n&&(n=o,e=r)}})),e},e.prototype.append=function(e){if(null!=e&&!(e instanceof mSt.Plot))throw new Error("Plot Group only accepts plots");return t.prototype.append.call(this,e),this},e})(bxt.Group);cwt.PlotGroup=_St;var ySt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(ySt,"__esModule",{value:!0});var vSt=uO,bSt=Edt,xSt=Fgt,wSt=(function(t){function e(e){void 0===e&&(e=[]);var n=t.call(this)||this;return n._rowPadding=0,n._columnPadding=0,n._rows=[],n._rowWeights=[],n._columnWeights=[],n._nRows=0,n._nCols=0,n._calculatedLayout=null,n.addClass("table"),e.forEach((function(t,e){t.forEach((function(t,i){null!=t&&n.add(t,e,i)}))})),n}return vSt.__extends(e,t),e.prototype._forEach=function(t){for(var e=0;e<this._nRows;e++)for(var n=0;n<this._nCols;n++)null!=this._rows[e][n]&&t(this._rows[e][n])},e.prototype.has=function(t){for(var e=0;e<this._nRows;e++)for(var n=0;n<this._nCols;n++)if(this._rows[e][n]===t)return!0;return!1},e.prototype.componentAt=function(t,e){return t<0||t>=this._nRows||e<0||e>=this._nCols?null:this._rows[t][e]},e.prototype.add=function(t,e,n){if(null==t)throw Error("Cannot add null to a table cell");if(!this.has(t)){if(null!=(this._rows[e]&&this._rows[e][n]))throw new Error("cell is occupied");t.detach(),this._nRows=Math.max(e+1,this._nRows),this._nCols=Math.max(n+1,this._nCols),this._padTableToSize(this._nRows,this._nCols),this._rows[e][n]=t,this._adoptAndAnchor(t),this.redraw()}return this},e.prototype._remove=function(t){for(var e=0;e<this._nRows;e++)for(var n=0;n<this._nCols;n++)if(this._rows[e][n]===t)return this._rows[e][n]=null,!0;return!1},e.prototype._iterateLayout=function(t,n,i){void 0===i&&(i=!1);for(var r,o,a,s,l,c=this._rows,u=bSt.transpose(this._rows),h=t-this._columnPadding*(this._nCols-1),d=n-this._rowPadding*(this._nRows-1),p=e._calcComponentWeights(this._rowWeights,c,(function(t){return null==t||t.fixedHeight()})),f=e._calcComponentWeights(this._columnWeights,u,(function(t){return null==t||t.fixedWidth()})),m=f.map((function(t){return 0===t?.5:t})),g=p.map((function(t){return 0===t?.5:t})),_=e._calcProportionalSpace(m,h),y=e._calcProportionalSpace(g,d),v=xSt.Array.createFilledArray(0,this._nCols),b=xSt.Array.createFilledArray(0,this._nRows),x=0;;){var w=xSt.Array.add(b,y),S=xSt.Array.add(v,_);v=(a=this._determineGuarantees(S,w,i)).guaranteedWidths,b=a.guaranteedHeights,s=a.wantsWidthArr.some((function(t){return t})),l=a.wantsHeightArr.some((function(t){return t}));var M=r,E=o;r=h-bSt.sum(a.guaranteedWidths),o=d-bSt.sum(a.guaranteedHeights);var T=void 0;s?(T=a.wantsWidthArr.map((function(t){return t?.1:0})),T=xSt.Array.add(T,f)):T=f;var C=void 0;if(l?(C=a.wantsHeightArr.map((function(t){return t?.1:0})),C=xSt.Array.add(C,p)):C=p,_=e._calcProportionalSpace(T,r),y=e._calcProportionalSpace(C,o),x++,!(r>0&&r!==M||o>0&&o!==E))break;if(x>5)break}return r=h-bSt.sum(a.guaranteedWidths),o=d-bSt.sum(a.guaranteedHeights),{colProportionalSpace:_=e._calcProportionalSpace(f,r),rowProportionalSpace:y=e._calcProportionalSpace(p,o),guaranteedWidths:a.guaranteedWidths,guaranteedHeights:a.guaranteedHeights,wantsWidth:s,wantsHeight:l}},e.prototype._determineGuarantees=function(t,e,n){void 0===n&&(n=!1);var i=xSt.Array.createFilledArray(0,this._nCols),r=xSt.Array.createFilledArray(0,this._nRows),o=xSt.Array.createFilledArray(!1,this._nCols),a=xSt.Array.createFilledArray(!1,this._nRows);return this._rows.forEach((function(s,l){s.forEach((function(s,c){var u;u=null!=s?s.requestedSpace(t[c],e[l]):{minWidth:0,minHeight:0};var h=n?Math.min(u.minWidth,t[c]):u.minWidth;i[c]=Math.max(i[c],h);var d=n?Math.min(u.minHeight,e[l]):u.minHeight;r[l]=Math.max(r[l],d),o[c]=o[c]||u.minWidth>t[c],a[l]=a[l]||u.minHeight>e[l]}))})),{guaranteedWidths:i,guaranteedHeights:r,wantsWidthArr:o,wantsHeightArr:a}},e.prototype.requestedSpace=function(t,e){return this._calculatedLayout=this._iterateLayout(t,e),{minWidth:bSt.sum(this._calculatedLayout.guaranteedWidths),minHeight:bSt.sum(this._calculatedLayout.guaranteedHeights)}},e.prototype.computeLayout=function(e,n,i){var r=this;t.prototype.computeLayout.call(this,e,n,i);var o=bSt.sum(this._calculatedLayout.guaranteedWidths),a=bSt.sum(this._calculatedLayout.guaranteedHeights),s=this._calculatedLayout;(o>this.width()||a>this.height())&&(s=this._iterateLayout(this.width(),this.height(),!0));var l=0,c=xSt.Array.add(s.rowProportionalSpace,s.guaranteedHeights),u=xSt.Array.add(s.colProportionalSpace,s.guaranteedWidths);return this._rows.forEach((function(t,e){var n=0;t.forEach((function(t,i){null!=t&&t.computeLayout({x:n,y:l},u[i],c[e]),n+=u[i]+r._columnPadding})),l+=c[e]+r._rowPadding})),this},e.prototype.rowPadding=function(t){if(null==t)return this._rowPadding;if(!xSt.Math.isValidNumber(t)||t<0)throw Error("rowPadding must be a non-negative finite value");return this._rowPadding=t,this.redraw(),this},e.prototype.columnPadding=function(t){if(null==t)return this._columnPadding;if(!xSt.Math.isValidNumber(t)||t<0)throw Error("columnPadding must be a non-negative finite value");return this._columnPadding=t,this.redraw(),this},e.prototype.rowWeight=function(t,e){if(null==e)return this._rowWeights[t];if(!xSt.Math.isValidNumber(e)||e<0)throw Error("rowWeight must be a non-negative finite value");return this._rowWeights[t]=e,this.redraw(),this},e.prototype.columnWeight=function(t,e){if(null==e)return this._columnWeights[t];if(!xSt.Math.isValidNumber(e)||e<0)throw Error("columnWeight must be a non-negative finite value");return this._columnWeights[t]=e,this.redraw(),this},e.prototype.fixedWidth=function(){var t=bSt.transpose(this._rows);return e._fixedSpace(t,(function(t){return null==t||t.fixedWidth()}))},e.prototype.fixedHeight=function(){return e._fixedSpace(this._rows,(function(t){return null==t||t.fixedHeight()}))},e.prototype._padTableToSize=function(t,e){for(var n=0;n<t;n++){void 0===this._rows[n]&&(this._rows[n]=[],this._rowWeights[n]=null);for(var i=0;i<e;i++)void 0===this._rows[n][i]&&(this._rows[n][i]=null)}for(i=0;i<e;i++)void 0===this._columnWeights[i]&&(this._columnWeights[i]=null)},e._calcComponentWeights=function(t,e,n){return t.map((function(t,i){return null!=t?t:e[i].map(n).reduce((function(t,e){return t&&e}),!0)?0:1}))},e._calcProportionalSpace=function(t,e){var n=bSt.sum(t);return 0===n?xSt.Array.createFilledArray(0,t.length):t.map((function(t){return e*t/n}))},e._fixedSpace=function(t,e){var n=function(t){return t.reduce((function(t,e){return t&&e}),!0)};return n(t.map((function(t){return n(t.map(e))})))},e})(xxt.ComponentContainer);ySt.Table=wSt;var SSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(SSt,"__esModule",{value:!0});var MSt=uO,ESt=(function(t){function e(){var e=null!==t&&t.apply(this,arguments)||this;return e._maxLines=2,e}return MSt.__extends(e,t),e.prototype.requestedSpace=function(t,e){this._wrapper.maxLines(this._maxLines);var n=0===this.angle()?t:e;0===n&&(n=1/0);var i=this._wrapper.wrap(this._text,this._measurer,n),r=this._measurer.measure(i.wrappedText);return{minWidth:(0===this.angle()?r.width:r.height)+2*this.padding(),minHeight:(0===this.angle()?r.height:r.width)+2*this.padding()}},e.prototype.maxLines=function(t){return 0===arguments.length?this._maxLines:(this._maxLines=t,this.redraw(),this)},e})(Oxt.Label);SSt.WrappedLabel=ESt;var TSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(TSt,"__esModule",{value:!0});var CSt=uO,ASt=(function(t){function e(){var e=t.call(this)||this;return e.addClass("x-drag-box-layer"),e._hasCorners=!1,e}return CSt.__extends(e,t),e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),this._setBounds(this.bounds()),this},e.prototype._setBounds=function(e){t.prototype._setBounds.call(this,{topLeft:{x:e.topLeft.x,y:0},bottomRight:{x:e.bottomRight.x,y:this.height()}})},e.prototype._setResizableClasses=function(t){t&&this.enabled()?this.addClass("x-resizable"):this.removeClass("x-resizable")},e.prototype.yScale=function(e){if(null==e)return t.prototype.yScale.call(this);throw new Error("yScales cannot be set on an XDragBoxLayer")},e.prototype.yExtent=function(e){if(null==e)return t.prototype.yExtent.call(this);throw new Error("XDragBoxLayer has no yExtent")},e})(rbt.DragBoxLayer);TSt.XDragBoxLayer=ASt;var kSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(kSt,"__esModule",{value:!0});var LSt=uO,PSt=(function(t){function e(){var e=t.call(this)||this;return e.addClass("y-drag-box-layer"),e._hasCorners=!1,e}return LSt.__extends(e,t),e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),this._setBounds(this.bounds()),this},e.prototype._setBounds=function(e){t.prototype._setBounds.call(this,{topLeft:{x:0,y:e.topLeft.y},bottomRight:{x:this.width(),y:e.bottomRight.y}})},e.prototype._setResizableClasses=function(t){t&&this.enabled()?this.addClass("y-resizable"):this.removeClass("y-resizable")},e.prototype.xScale=function(e){if(null==e)return t.prototype.xScale.call(this);throw new Error("xScales cannot be set on an YDragBoxLayer")},e.prototype.xExtent=function(e){if(null==e)return t.prototype.xExtent.call(this);throw new Error("YDragBoxLayer has no xExtent")},e})(rbt.DragBoxLayer);kSt.YDragBoxLayer=PSt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(rbt,t),e.__exportStar(oxt,t),e.__exportStar(gxt,t),e.__exportStar(bxt,t),e.__exportStar(axt,t),e.__exportStar(Axt,t),e.__exportStar(Oxt,t),e.__exportStar(Vxt,t),e.__exportStar(cwt,t),e.__exportStar(Jbt,t),e.__exportStar(ySt,t),e.__exportStar(SSt,t),e.__exportStar(TSt,t),e.__exportStar(kSt,t)})(ibt);var NSt={},ISt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(ISt,"__esModule",{value:!0});var RSt=uO,OSt=(function(t){function e(){return t.call(this,"path","arc fill")||this}return RSt.__extends(e,t),e.prototype._applyDefaultAttributes=function(t){t.style("stroke","none")},e})(Ywt.SVGDrawer);ISt.ArcSVGDrawer=OSt;var zSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(zSt,"__esModule",{value:!0});var DSt=uO,BSt=(function(t){function e(){return t.call(this,"path","arc outline")||this}return DSt.__extends(e,t),e.prototype._applyDefaultAttributes=function(t){t.style("fill","none")},e})(Ywt.SVGDrawer);zSt.ArcOutlineSVGDrawer=BSt;var HSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(HSt,"__esModule",{value:!0});var FSt=uO,VSt=Gwt,USt=(function(t){function e(){return t.call(this,"path","area")||this}return FSt.__extends(e,t),e.prototype._applyDefaultAttributes=function(t){t.style("stroke","none")},e.prototype.getVisualPrimitiveAtIndex=function(e){return t.prototype.getVisualPrimitiveAtIndex.call(this,0)},e})(Ywt.SVGDrawer);HSt.AreaSVGDrawer=USt;var jSt=["opacity","fill","fill-opacity"],GSt=["opacity","stroke","stroke-width"];HSt.makeAreaCanvasDrawStep=function WSt(t,e){return function(n,i,r){var o=VSt.resolveAttributes(r,jSt,i[0],0);VSt.renderArea(n,t(),i[0],o);var a=VSt.resolveAttributes(r,GSt,i[0],0);VSt.renderLine(n,e(),i[0],a)}};var qSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(qSt,"__esModule",{value:!0});var YSt=uO,XSt=Gwt,$St=(function(t){function e(){return t.call(this,"path","line")||this}return YSt.__extends(e,t),e.prototype._applyDefaultAttributes=function(t){t.style("fill","none")},e.prototype.getVisualPrimitiveAtIndex=function(e){return t.prototype.getVisualPrimitiveAtIndex.call(this,0)},e})(Ywt.SVGDrawer);qSt.LineSVGDrawer=$St;var KSt=["opacity","stroke-opacity","stroke-width","stroke","stroke-dasharray"];qSt.makeLineCanvasDrawStep=function ZSt(t){return function(e,n,i){var r=XSt.resolveAttributes(i,KSt,n[0],0);XSt.renderLine(e,t(),n[0],r)}};var JSt={};!(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO,n=Gwt,i=(function(t){function n(e){void 0===e&&(e="");var n=t.call(this,"rect","")||this;return n._rootClassName=e,n._root.classed(n._rootClassName,!0),n}return e.__extends(n,t),n})(Ywt.SVGDrawer);t.RectangleSVGDrawer=i;var r=n.ContextStyleAttrs.concat(["x","y","width","height"]);t.RectangleCanvasDrawStep=function(t,e,i){t.save();for(var o=e.length,a=0;a<o;a++){var s=e[a];if(null!=s){var l=n.resolveAttributes(i,r,s,a);t.beginPath(),t.rect(l.x,l.y,l.width,l.height),n.renderPathWithStyle(t,l)}}t.restore()};var o=(function(n){function i(e){return n.call(this,e,t.RectangleCanvasDrawStep)||this}return e.__extends(i,n),i})(n.CanvasDrawer);t.RectangleCanvasDrawer=o})(JSt);var QSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(QSt,"__esModule",{value:!0});var tMt=uO,eMt=(function(t){function e(){return t.call(this,"line","")||this}return tMt.__extends(e,t),e})(Ywt.SVGDrawer);QSt.SegmentSVGDrawer=eMt;var nMt={},iMt={};
/**
     * Copyright 2017-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(iMt,"__esModule",{value:!0});var rMt=(function(){function t(e,n,i){void 0===i&&(i=window.devicePixelRatio),this.screenWidth=e,this.screenHeight=n,this.devicePixelRatio=i,this.pixelWidth=e*i,this.pixelHeight=n*i,this.canvas=document.createElement("canvas"),this.ctx=this.canvas.getContext("2d"),t.sizePixels(this.ctx,e,n,i)}return t.sizePixels=function(t,e,n,i){var r=t.canvas;r.width=e*i,r.height=n*i,r.style.width=e+"px",r.style.height=n+"px",t.setTransform(1,0,0,1,0,0),t.scale(i,i)},t.prototype.blit=function(t,e,n){void 0===e&&(e=0),void 0===n&&(n=0),t.drawImage(this.canvas,e,n,this.screenWidth,this.screenHeight)},t.prototype.blitCenter=function(t,e,n){void 0===e&&(e=0),void 0===n&&(n=0),this.blit(t,Math.floor(e-this.screenWidth/2),Math.floor(n-this.screenHeight/2))},t.prototype.resize=function(e,n,i){void 0===i&&(i=!1);var r=this.devicePixelRatio;return this.screenWidth=e,this.screenHeight=n,this.pixelWidth=e*r,this.pixelHeight=n*r,t.sizePixels(this.ctx,e,n,r),i&&this.ctx.translate(e/2,e/2),this},t.prototype.clear=function(t){var e=this,n=e.pixelWidth,i=e.pixelHeight,r=e.ctx;return r.save(),r.setTransform(1,0,0,1,0,0),null==t?r.clearRect(0,0,n,i):(r.fillStyle=t,r.fillRect(0,0,n,i)),r.restore(),this},t.prototype.getImageData=function(){return this.ctx.getImageData(0,0,this.pixelWidth,this.pixelHeight)},t})();iMt.CanvasBuffer=rMt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(nMt,"__esModule",{value:!0});var oMt=uO,aMt=Gwt,sMt=iMt,lMt=(function(t){function e(){return t.call(this,"path","symbol")||this}return oMt.__extends(e,t),e})(Ywt.SVGDrawer);nMt.SymbolSVGDrawer=lMt;var cMt=aMt.ContextStyleAttrs.concat(["x","y"]);function uMt(t,e,n,i,r){return n+r>=0&&n-r<=t&&i+r>=0&&i-r<=e}function hMt(t,e,n){if(null==t)return!1;for(var i=0;i<n.length;i++){var r=n[i];if(t[r]!=e[r])return!1}return!0}nMt.makeSymbolCanvasDrawStep=function dMt(t,e,n,i){var r=this;return function(o,a,s){for(var l=o.canvas,c=l.clientWidth,u=l.clientHeight,h=void 0===i?new sMt.CanvasBuffer(0,0):i,d=e(),p=n(),f=null,m=null,g=null,_=0;_<a.length;_++){var y=a[_];if(null!=y){var v=aMt.resolveAttributes(s,cMt,y,_),b=p(y,_,t);if(uMt(c,u,v.x,v.y,b)){var x=hMt(f,v,aMt.ContextStyleAttrs),w=d(y,_,r._dataset);if(x&&g==b&&m==w);else{var S=b+aMt.getStrokeWidth(v)+1;(S>h.screenWidth||S>h.screenHeight)&&h.resize(S,S,!0),h.clear();var M=h.ctx;M.beginPath(),w(b).context(M)(null),M.closePath(),aMt.renderPathWithStyle(M,v),m=w,g=b,f=v}h.blitCenter(o,v.x,v.y)}}}}},(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(ISt,t),e.__exportStar(zSt,t),e.__exportStar(HSt,t),e.__exportStar(Gwt,t),e.__exportStar(Wwt,t),e.__exportStar(qSt,t),e.__exportStar(JSt,t),e.__exportStar(QSt,t),e.__exportStar(Ywt,t),e.__exportStar(nMt,t)})(NSt);var pMt={},fMt={},mMt={},gMt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(gMt,"__esModule",{value:!0});var _Mt=uO,yMt=Zyt,vMt=Fgt,bMt=Zwt,xMt=uwt,wMt=(function(t){function e(){var e=t.call(this)||this;return e._autoAdjustXScaleDomain=!1,e._autoAdjustYScaleDomain=!1,e._deferredRendering=!1,e._applyDeferredRenderingTransform=function(t,n,i,r){e._isAnchored&&(null!=e._renderArea&&e._renderArea.attr("transform","translate("+t+", "+n+") scale("+i+", "+r+")"),null!=e._canvas&&e._canvas.style("transform","translate("+t+"px, "+n+"px) scale("+i+", "+r+")"))},e.addClass("xy-plot"),e._adjustYDomainOnChangeFromXCallback=function(t){return e._adjustYDomainOnChangeFromX()},e._adjustXDomainOnChangeFromYCallback=function(t){return e._adjustXDomainOnChangeFromY()},e._renderCallback=function(){if(e.deferredRendering()){var t=e.x()&&e.x().scale,n=e.y()&&e.y().scale;e._deferredRenderer.updateDomains(t,n)}else e.render()},e._deferredRenderer=new bMt.DeferredRenderer((function(){return e.render()}),e._applyDeferredRenderingTransform),e}return _Mt.__extends(e,t),e.prototype.render=function(){return this.deferredRendering()&&this._deferredRenderer.resetTransforms(),t.prototype.render.call(this)},e.prototype.deferredRendering=function(t){if(null==t)return this._deferredRendering;if(t){var e=this.x()&&this.x().scale,n=this.y()&&this.y().scale;this._deferredRenderer.setDomains(e,n)}return this._deferredRendering=t,this},e.prototype.x=function(t,n,i){if(null==t)return this._propertyBindings.get(e._X_KEY);this._bindProperty(e._X_KEY,t,n,i);var r=this.width();return null!=n&&null!=r&&n.range([0,r]),this._autoAdjustYScaleDomain&&this._updateYExtentsAndAutodomain(),this.render(),this},e.prototype.y=function(t,n,i){if(null==t)return this._propertyBindings.get(e._Y_KEY);this._bindProperty(e._Y_KEY,t,n,i);var r=this.height();return null!=n&&null!=r&&n.range(n instanceof yMt.Category?[0,r]:[r,0]),this._autoAdjustXScaleDomain&&this._updateXExtentsAndAutodomain(),this.render(),this},e.prototype._filterForProperty=function(t){return"x"===t&&this._autoAdjustXScaleDomain?this._makeFilterByProperty("y"):"y"!==t&&"y0"!==t||!this._autoAdjustYScaleDomain?null:this._makeFilterByProperty("x")},e.prototype._makeFilterByProperty=function(t){var e=this._propertyBindings.get(t);if(null!=e){var n=e.accessor,i=e.scale;if(null!=i)return function(t,e,r){var o=i.range();return vMt.Math.inRange(i.scale(n(t,e,r)),o[0],o[1])}}return null},e.prototype._uninstallScaleForKey=function(n,i){t.prototype._uninstallScaleForKey.call(this,n,i),n.offUpdate(i===e._X_KEY?this._adjustYDomainOnChangeFromXCallback:this._adjustXDomainOnChangeFromYCallback)},e.prototype._installScaleForKey=function(n,i){t.prototype._installScaleForKey.call(this,n,i),n.onUpdate(i===e._X_KEY?this._adjustYDomainOnChangeFromXCallback:this._adjustXDomainOnChangeFromYCallback)},e.prototype.destroy=function(){return t.prototype.destroy.call(this),this.x().scale&&this.x().scale.offUpdate(this._adjustYDomainOnChangeFromXCallback),this.y().scale&&this.y().scale.offUpdate(this._adjustXDomainOnChangeFromYCallback),this},e.prototype.autorangeMode=function(t){if(null==t)return this._autoAdjustXScaleDomain?"x":this._autoAdjustYScaleDomain?"y":"none";switch(t){case"x":this._autoAdjustXScaleDomain=!0,this._autoAdjustYScaleDomain=!1,this._adjustXDomainOnChangeFromY();break;case"y":this._autoAdjustXScaleDomain=!1,this._autoAdjustYScaleDomain=!0,this._adjustYDomainOnChangeFromX();break;case"none":this._autoAdjustXScaleDomain=!1,this._autoAdjustYScaleDomain=!1;break;default:throw new Error("Invalid scale name '"+t+"', must be 'x', 'y' or 'none'")}return this},e.prototype.computeLayout=function(e,n,i){t.prototype.computeLayout.call(this,e,n,i);var r=this.x(),o=r&&r.scale;null!=o&&o.range([0,this.width()]);var a=this.y(),s=a&&a.scale;return null!=s&&s.range(s instanceof yMt.Category?[0,this.height()]:[this.height(),0]),this},e.prototype._updateXExtentsAndAutodomain=function(){var t=this.x().scale;null!=t&&t.autoDomain()},e.prototype._updateYExtentsAndAutodomain=function(){var t=this.y().scale;null!=t&&t.autoDomain()},e.prototype.showAllData=function(){return this._updateXExtentsAndAutodomain(),this._updateYExtentsAndAutodomain(),this},e.prototype._adjustYDomainOnChangeFromX=function(){this._projectorsReady()&&this._autoAdjustYScaleDomain&&this._updateYExtentsAndAutodomain()},e.prototype._adjustXDomainOnChangeFromY=function(){this._projectorsReady()&&this._autoAdjustXScaleDomain&&this._updateXExtentsAndAutodomain()},e.prototype._projectorsReady=function(){var t=this.x(),e=this.y();return null!=t&&null!=t.accessor&&null!=e&&null!=e.accessor},e.prototype._pixelPoint=function(t,e,n){var i=xMt.Plot._scaledAccessor(this.x()),r=xMt.Plot._scaledAccessor(this.y());return{x:i(t,e,n),y:r(t,e,n)}},e.prototype._getDataToDraw=function(){var e=this,n=t.prototype._getDataToDraw.call(this),i=this.attr("defined");return this.datasets().forEach((function(t){n.set(t,n.get(t).filter((function(n,r){return(function(t,n,r){var o=xMt.Plot._scaledAccessor(e.x())(t,n,r),a=xMt.Plot._scaledAccessor(e.y())(t,n,r);return(!i||!1!==i.accessor(t,n,r))&&vMt.Math.isValidNumber(o)&&vMt.Math.isValidNumber(a)})(n,r,t)})))})),n},e._X_KEY="x",e._Y_KEY="y",e})(xMt.Plot);gMt.XYPlot=wMt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(mMt,"__esModule",{value:!0});var SMt=uO,MMt=Edt,EMt=_mt,TMt=NSt,CMt=Wwt,AMt=qSt,kMt=Zyt,LMt=Ivt,PMt=Fgt,NMt=pMt,IMt=uwt,RMt=gMt,OMt={linear:MMt.curveLinear,linearClosed:MMt.curveLinearClosed,step:MMt.curveStep,stepBefore:MMt.curveStepBefore,stepAfter:MMt.curveStepAfter,basis:MMt.curveBasis,basisOpen:MMt.curveBasisOpen,basisClosed:MMt.curveBasisClosed,bundle:MMt.curveBundle,cardinal:MMt.curveCardinal,cardinalOpen:MMt.curveCardinalOpen,cardinalClosed:MMt.curveCardinalClosed,monotone:MMt.curveMonotoneX};mMt.CurveName=Smt.makeEnum(["linear","linearClosed","step","stepBefore","stepAfter","basis","basisOpen","basisClosed","bundle","cardinal","cardinalOpen","cardinalClosed","monotone"]);var zMt=(function(t){function e(){var e=t.call(this)||this;e._curve="linear",e._autorangeSmooth=!1,e._croppedRenderingEnabled=!0,e._collapseDenseVerticalLinesEnabled=!1,e._downsamplingEnabled=!1,e.addClass("line-plot");var n=new EMt.Easing;return n.stepDuration(IMt.Plot._ANIMATION_MAX_DURATION),n.easingMode("expInOut"),n.maxTotalDuration(IMt.Plot._ANIMATION_MAX_DURATION),e.animator(NMt.Animator.MAIN,n),e.attr("stroke",(new kMt.Color).range()[0]),e.attr("stroke-width","2px"),e}return SMt.__extends(e,t),e.prototype.x=function(e,n,i){return null==e?t.prototype.x.call(this):(t.prototype.x.call(this,e,n,i),this._setScaleSnapping(),this)},e.prototype.y=function(e,n,i){return null==e?t.prototype.y.call(this):(t.prototype.y.call(this,e,n,i),this._setScaleSnapping(),this)},e.prototype.autorangeMode=function(e){return null==e?t.prototype.autorangeMode.call(this):(t.prototype.autorangeMode.call(this,e),this._setScaleSnapping(),this)},e.prototype.autorangeSmooth=function(t){return null==t?this._autorangeSmooth:(this._autorangeSmooth=t,this._setScaleSnapping(),this)},e.prototype._setScaleSnapping=function(){"x"===this.autorangeMode()&&this.x()&&this.x().scale&&this.x().scale instanceof LMt.QuantitativeScale&&this.x().scale.snappingDomainEnabled(!this.autorangeSmooth()),"y"===this.autorangeMode()&&this.y()&&this.y().scale&&this.y().scale instanceof LMt.QuantitativeScale&&this.y().scale.snappingDomainEnabled(!this.autorangeSmooth())},e.prototype.curve=function(t){return null==t?this._curve:(this._curve=t,this.render(),this)},e.prototype.downsamplingEnabled=function(t){return null==t?this._downsamplingEnabled:(this._downsamplingEnabled=t,this)},e.prototype.croppedRenderingEnabled=function(t){return null==t?this._croppedRenderingEnabled:(this._croppedRenderingEnabled=t,this.render(),this)},e.prototype.collapseDenseLinesEnabled=function(t){return null==t?this._collapseDenseVerticalLinesEnabled:(this._collapseDenseVerticalLinesEnabled=t,this.render(),this)},e.prototype._createDrawer=function(t){var e=this;return new CMt.ProxyDrawer((function(){return new AMt.LineSVGDrawer}),(function(n){return new TMt.CanvasDrawer(n,AMt.makeLineCanvasDrawStep((function(){return e._d3LineFactory(t)})))}))},e.prototype.getExtentsForProperty=function(e){var n=t.prototype.getExtentsForProperty.call(this,e);if(!this._autorangeSmooth)return n;if(this.autorangeMode()!==e)return n;if("x"!==this.autorangeMode()&&"y"!==this.autorangeMode())return n;var i,r=this._getEdgeIntersectionPoints();return i="y"===this.autorangeMode()?r.left.concat(r.right).map((function(t){return t.y})):r.top.concat(r.bottom).map((function(t){return t.x})),n.map((function(t){return MMt.extent(MMt.merge([t,i]))}))},e.prototype._getEdgeIntersectionPoints=function(){var t=this;if(!(this.y().scale instanceof LMt.QuantitativeScale&&this.x().scale instanceof LMt.QuantitativeScale))return{left:[],right:[],top:[],bottom:[]};var e=this.y().scale,n=this.x().scale,i={left:[],right:[],top:[],bottom:[]},r=n.scale(n.domain()[0]),o=n.scale(n.domain()[1]),a=e.scale(e.domain()[0]),s=e.scale(e.domain()[1]);return this.datasets().forEach((function(l){for(var c,u,h,d,p=l.data(),f=1;f<p.length;f++)c=h||n.scale(t.x().accessor(p[f-1],f-1,l)),u=d||e.scale(t.y().accessor(p[f-1],f-1,l)),h=n.scale(t.x().accessor(p[f],f,l)),d=e.scale(t.y().accessor(p[f],f,l)),c<r==r<=h&&i.left.push({x:r,y:e.invert(u+(r-c)*(d-u)/(h-c))}),c<o==o<=h&&i.right.push({x:o,y:e.invert(u+(o-c)*(d-u)/(h-c))}),u<s==s<=d&&i.top.push({x:n.invert(c+(s-u)*(h-c)/(d-u)),y:s}),u<a==a<=d&&i.bottom.push({x:n.invert(c+(a-u)*(h-c)/(d-u)),y:a})})),i},e.prototype._getResetYFunction=function(){var t=this.y().scale.domain(),e=Math.max(t[0],t[1]),n=Math.min(t[0],t[1]),i=e<0&&e||n>0&&n||0,r=this.y().scale.scale(i);return function(t,e,n){return r}},e.prototype._generateDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._getAttrToProjector();e.d=this._constructLineProjector(IMt.Plot._scaledAccessor(this.x()),this._getResetYFunction()),t.push({attrToProjector:e,animator:this._getAnimator(NMt.Animator.RESET)})}return t.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(NMt.Animator.MAIN)}),t},e.prototype._generateAttrToProjector=function(){var e=t.prototype._generateAttrToProjector.call(this);return Object.keys(e).forEach((function(t){if("d"!==t){var n=e[t];e[t]=function(t,e,i){return t.length>0?n(t[0],e,i):null}}})),e},e.prototype.entitiesAt=function(t){var e=this.entityNearestByXThenY(t);return null!=e?[e]:[]},e.prototype.entityNearestByXThenY=function(t){for(var e,n=1/0,i=1/0,r=this.bounds(),o=this.entities(),a=o.length,s=0;s<a;s++){var l=o[s];if(PMt.Math.within(l.position,r)){var c=Math.abs(t.x-l.position.x),u=Math.abs(t.y-l.position.y);(c<n||c===n&&u<i)&&(e=l,n=c,i=u)}}return e},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this);return e.d=this._constructLineProjector(IMt.Plot._scaledAccessor(this.x()),IMt.Plot._scaledAccessor(this.y())),e},e.prototype._constructLineProjector=function(t,e){var n=this;return function(i,r,o){return n._d3LineFactory(o,t,e)(i)}},e.prototype._d3LineFactory=function(t,e,n){return void 0===e&&(e=IMt.Plot._scaledAccessor(this.x())),void 0===n&&(n=IMt.Plot._scaledAccessor(this.y())),MMt.line().x((function(n,i){return e(n,i,t)})).y((function(e,i){return n(e,i,t)})).curve(this._getCurveFactory()).defined((function(i,r){return(function(t,i,r){var o=e(t,i,r),a=n(t,i,r);return PMt.Math.isValidNumber(o)&&PMt.Math.isValidNumber(a)})(i,r,t)}))},e.prototype._getCurveFactory=function(){var t=this.curve();if("string"==typeof t){var e=OMt[t];return null==e?OMt.linear:e}return t},e.prototype._getDataToDraw=function(){var t=this,e=new PMt.Map;return this.datasets().forEach((function(n){var i=n.data();if(t._croppedRenderingEnabled||t._downsamplingEnabled){for(var r=[],o=i.length,a=0;a<o;a++)r[a]=a;t._croppedRenderingEnabled&&(r=t._filterCroppedRendering(n,r)),t._downsamplingEnabled&&(r=t._filterDownsampling(n,r)),t._collapseDenseVerticalLinesEnabled&&(r=t._filterDenseLines(n,r));var s=[],l=r.length;for(a=0;a<l;a++)s[a]=i[r[a]];e.set(n,[s])}else e.set(n,[i])})),e},e.prototype._filterCroppedRendering=function(t,e){for(var n=this,i=IMt.Plot._scaledAccessor(this.x()),r=IMt.Plot._scaledAccessor(this.y()),o=t.data(),a=[],s=function(t,e){return PMt.Math.inRange(t,0,n.width())&&PMt.Math.inRange(e,0,n.height())},l=0;l<e.length;l++){var c=s(i(o[e[l]],e[l],t),r(o[e[l]],e[l],t));if(!c&&null!=e[l-1]&&null!=o[e[l-1]]){var u=i(o[e[l-1]],e[l-1],t),h=r(o[e[l-1]],e[l-1],t);c=c||s(u,h)}if(!c&&null!=e[l+1]&&null!=o[e[l+1]]){var d=i(o[e[l+1]],e[l+1],t),p=r(o[e[l+1]],e[l+1],t);c=c||s(d,p)}c&&a.push(e[l])}return a},e.prototype._filterDownsampling=function(t,e){if(0===e.length)return[];for(var n=t.data(),i=IMt.Plot._scaledAccessor(this.x()),r=IMt.Plot._scaledAccessor(this.y()),o=[e[0]],a=function(o,a){var s=i(n[e[o]],e[o],t),l=r(n[e[o]],e[o],t),c=i(n[e[o+1]],e[o+1],t),u=r(n[e[o+1]],e[o+1],t);if(a===1/0)return Math.floor(s)===Math.floor(c);var h=l+(c-s)*a;return Math.floor(u)===Math.floor(h)},s=0;s<e.length-1;){for(var l=e[s],c=i(n[e[s]],e[s],t),u=r(n[e[s]],e[s],t),h=i(n[e[s+1]],e[s+1],t),d=r(n[e[s+1]],e[s+1],t),p=Math.floor(c)===Math.floor(h)?1/0:(d-u)/(h-c),f=e[s],m=p===1/0?u:c,g=f,_=m,y=!0;s<e.length-1&&(y||a(s,p));){s++,y=!1;var v=p===1/0?r(n[e[s]],e[s],t):i(n[e[s]],e[s],t);v>_&&(_=v,g=e[s]),v<m&&(m=v,f=e[s])}var b=e[s];f!==l&&o.push(f),g!==f&&g!==l&&o.push(g),b!==l&&b!==f&&b!==g&&o.push(b)}return o},e.prototype._filterDenseLines=function(t,e){if(0===e.length)return[];var n=t.data(),i=IMt.Plot._scaledAccessor(this.x()),r=IMt.Plot._scaledAccessor(this.y());return this._bucketByX(t,e,(function(e){return i(n[e],e,t)}),(function(e){return r(n[e],e,t)}))},e.prototype._bucketByX=function(t,e,n,i){for(var r=[],o=t.data(),a=null,s=e.length,l=0;l<=s;++l){var c=e[l];if(null!=o[c]){var u=Math.floor(n(c)),h=i(c);null==a?a=new PMt.Bucket(c,u,h):a.isInBucket(u)?a.addToBucket(h,c):(r.push.apply(r,a.getUniqueIndices()),a=new PMt.Bucket(c,u,h))}}return null!=a&&r.push.apply(r,a.getUniqueIndices()),r},e})(RMt.XYPlot);mMt.Line=zMt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(fMt,"__esModule",{value:!0});var DMt=uO,BMt=Edt,HMt=Zyt,FMt=Fgt,VMt=NSt,UMt=HSt,jMt=Wwt,GMt=qSt,WMt=pMt,qMt=uwt,YMt=(function(t){function e(){var e=t.call(this)||this;return e.addClass("area-plot"),e.y0(0),e.attr("fill-opacity",.25),e.attr("fill",(new HMt.Color).range()[0]),e._lineDrawers=new FMt.Map,e}return DMt.__extends(e,t),e.prototype.y=function(n,i){if(null==n)return t.prototype.y.call(this);if(null==i?t.prototype.y.call(this,n):t.prototype.y.call(this,n,i),null!=i){var r=this.y0().accessor;null!=r&&this._bindProperty(e._Y0_KEY,r,i),this._updateYScale()}return this},e.prototype.y0=function(t){if(null==t)return this._propertyBindings.get(e._Y0_KEY);var n=this.y();return this._bindProperty(e._Y0_KEY,t,n&&n.scale),this._updateYScale(),this.render(),this},e.prototype._onDatasetUpdate=function(){t.prototype._onDatasetUpdate.call(this),this._updateYScale()},e.prototype._addDataset=function(e){var n=this;return this._lineDrawers.set(e,new VMt.ProxyDrawer((function(){return new GMt.LineSVGDrawer}),(function(t){return new VMt.CanvasDrawer(t,GMt.makeLineCanvasDrawStep((function(){var t=qMt.Plot._scaledAccessor(n.x()),i=qMt.Plot._scaledAccessor(n.y());return n._d3LineFactory(e,t,i)})))}))),t.prototype._addDataset.call(this,e),this},e.prototype._createNodesForDataset=function(e){t.prototype._createNodesForDataset.call(this,e);var n=this._lineDrawers.get(e);return"svg"===this.renderer()?n.useSVG(this._renderArea):n.useCanvas(this._canvas),n},e.prototype._removeDatasetNodes=function(e){t.prototype._removeDatasetNodes.call(this,e),this._lineDrawers.get(e).remove()},e.prototype._additionalPaint=function(){var t=this,e=this._generateLineDrawSteps(),n=this._getDataToDraw();this.datasets().forEach((function(i){var r=qMt.Plot.applyDrawSteps(e,i);t._lineDrawers.get(i).draw(n.get(i),r)}))},e.prototype._generateLineDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._generateLineAttrToProjector();e.d=this._constructLineProjector(qMt.Plot._scaledAccessor(this.x()),this._getResetYFunction()),t.push({attrToProjector:e,animator:this._getAnimator(WMt.Animator.RESET)})}return t.push({attrToProjector:this._generateLineAttrToProjector(),animator:this._getAnimator(WMt.Animator.MAIN)}),t},e.prototype._generateLineAttrToProjector=function(){var t=this._getAttrToProjector();return t.d=this._constructLineProjector(qMt.Plot._scaledAccessor(this.x()),qMt.Plot._scaledAccessor(this.y())),t},e.prototype._createDrawer=function(t){var e=this;return new jMt.ProxyDrawer((function(){return new UMt.AreaSVGDrawer}),(function(n){return new VMt.CanvasDrawer(n,UMt.makeAreaCanvasDrawStep((function(){var n=e._coordinateProjectors(),i=n[0],r=n[1],o=n[2],a=e._createDefinedProjector(i,r);return e._createAreaGenerator(i,r,o,a,t)}),(function(){var n=e._coordinateProjectors(),i=n[0],r=n[1],o=e._createDefinedProjector(i,r);return e._createTopLineGenerator(i,r,o,t)})))}))},e.prototype._generateDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._getAttrToProjector();e.d=this._constructAreaProjector(qMt.Plot._scaledAccessor(this.x()),this._getResetYFunction(),qMt.Plot._scaledAccessor(this.y0())),t.push({attrToProjector:e,animator:this._getAnimator(WMt.Animator.RESET)})}return t.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(WMt.Animator.MAIN)}),t},e.prototype._updateYScale=function(){var t=this.getExtentsForProperty("y0"),e=FMt.Array.flatten(t),n=FMt.Array.uniq(e),i=1===n.length?n[0]:null,r=this.y(),o=r&&r.scale;null!=o&&(null!=this._constantBaselineValueProvider&&(o.removePaddingExceptionsProvider(this._constantBaselineValueProvider),this._constantBaselineValueProvider=null),null!=i&&(this._constantBaselineValueProvider=function(){return[i]},o.addPaddingExceptionsProvider(this._constantBaselineValueProvider)))},e.prototype._getResetYFunction=function(){return qMt.Plot._scaledAccessor(this.y0())},e.prototype._coordinateProjectors=function(){return[qMt.Plot._scaledAccessor(this.x()),qMt.Plot._scaledAccessor(this.y()),qMt.Plot._scaledAccessor(this.y0())]},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this),n=this._coordinateProjectors();return e.d=this._constructAreaProjector(n[0],n[1],n[2]),e},e.prototype.selections=function(e){var n=this;if(void 0===e&&(e=this.datasets()),"canvas"===this.renderer())return BMt.selectAll();var i=t.prototype.selections.call(this,e).nodes();return e.map((function(t){return n._lineDrawers.get(t)})).filter((function(t){return null!=t})).forEach((function(t){return i.push.apply(i,t.getVisualPrimitives())})),BMt.selectAll(i)},e.prototype._constructAreaProjector=function(t,e,n){var i=this,r=this._createDefinedProjector(qMt.Plot._scaledAccessor(this.x()),qMt.Plot._scaledAccessor(this.y()));return function(o,a,s){return i._createAreaGenerator(t,e,n,r,s)(o)}},e.prototype._createDefinedProjector=function(t,e){return function(n,i,r){var o=t(n,i,r),a=e(n,i,r);return FMt.Math.isValidNumber(o)&&FMt.Math.isValidNumber(a)}},e.prototype._createAreaGenerator=function(t,e,n,i,r){var o=this._getCurveFactory();return BMt.area().x((function(e,n){return t(e,n,r)})).y1((function(t,n){return e(t,n,r)})).y0((function(t,e){return n(t,e,r)})).curve(o).defined((function(t,e){return i(t,e,r)}))},e.prototype._createTopLineGenerator=function(t,e,n,i){var r=this._getCurveFactory();return BMt.line().x((function(e,n){return t(e,n,i)})).y((function(t,n){return e(t,n,i)})).curve(r).defined((function(t,e){return n(t,e,i)}))},e._Y0_KEY="y0",e})(mMt.Line);fMt.Area=YMt;var XMt={};!(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO,n=Edt,i=Imt,r=_mt,o=gyt,a=NSt,s=Wwt,l=JSt,c=dwt,u=Zyt,h=Ivt,d=Fgt,p=Smt,f=pMt,m=uwt,g=gMt;t.BarOrientation=p.makeEnum(["vertical","horizontal"]),t.LabelsPosition=p.makeEnum(["start","middle","end","outside"]),t.BarAlignment=p.makeEnum(["start","middle","end"]);var _=(function(p){function g(e){void 0===e&&(e="vertical");var n=p.call(this)||this;if(n._labelFormatter=o.identity(),n._labelsEnabled=!1,n._labelsPosition=t.LabelsPosition.end,n._hideBarsIfAnyAreTooWide=!0,n._barAlignment="middle",n._computeBarPixelThickness=c.memoize(y),n._fixedBarPixelThickness=!0,n.addClass("bar-plot"),"vertical"!==e&&"horizontal"!==e)throw new Error(e+" is not a valid orientation for Plots.Bar");return n._isVertical="vertical"===e,n.animator("baseline",new r.Null),n.attr("fill",(new u.Color).range()[0]),n.attr(g._BAR_THICKNESS_KEY,(function(){return n._barPixelThickness()})),n._labelConfig=new d.Map,n._baselineValueProvider=function(){return[n.baselineValue()]},n}return e.__extends(g,p),g.prototype.computeLayout=function(t,e,n){return p.prototype.computeLayout.call(this,t,e,n),this._updateExtents(),this},g.prototype.x=function(t,e){return null==t?p.prototype.x.call(this):(null==e?p.prototype.x.call(this,t):p.prototype.x.call(this,t,e),this._updateThicknessAttr(),this._updateLengthScale(),this)},g.prototype.y=function(t,e){return null==t?p.prototype.y.call(this):(null==e?p.prototype.y.call(this,t):p.prototype.y.call(this,t,e),this._updateLengthScale(),this)},g.prototype.length=function(){return this._isVertical?this.y():this.x()},g.prototype.position=function(){return this._isVertical?this.x():this.y()},g.prototype.barEnd=function(t){if(null==t)return this._propertyBindings.get(g._BAR_END_KEY);var e=this.position();return this._bindProperty(g._BAR_END_KEY,t,e&&e.scale),this._updateThicknessAttr(),this._updateLengthScale(),this.render(),this},g.prototype.barAlignment=function(t){return null==t?this._barAlignment:(this._barAlignment=t,this._clearAttrToProjectorCache(),this.render(),this)},g.prototype.orientation=function(){return this._isVertical?"vertical":"horizontal"},g.prototype._createDrawer=function(){return new s.ProxyDrawer((function(){return new l.RectangleSVGDrawer(g._BAR_AREA_CLASS)}),(function(t){return new a.RectangleCanvasDrawer(t)}))},g.prototype._setup=function(){p.prototype._setup.call(this),this._baseline=this._renderArea.append("line").classed("baseline",!0)},g.prototype.baselineValue=function(t){if(null==t){if(null!=this._baselineValue)return this._baselineValue;if(!this._projectorsReady())return 0;var e=this.length().scale;return e&&e instanceof u.Time?new Date(0):0}return this._baselineValue=t,this._updateLengthScale(),this._clearAttrToProjectorCache(),this.render(),this},g.prototype.addDataset=function(t){return p.prototype.addDataset.call(this,t),this},g.prototype._addDataset=function(t){return p.prototype._addDataset.call(this,t),this},g.prototype.removeDataset=function(t){return p.prototype.removeDataset.call(this,t),this},g.prototype._removeDataset=function(t){return p.prototype._removeDataset.call(this,t),this},g.prototype.datasets=function(t){return null==t?p.prototype.datasets.call(this):(p.prototype.datasets.call(this,t),this)},g.prototype.labelsEnabled=function(t,e){return null==t?this._labelsEnabled:(this._labelsEnabled=t,null!=e&&(this._labelsPosition=e),this._clearAttrToProjectorCache(),this.render(),this)},g.prototype.labelFormatter=function(t){return null==t?this._labelFormatter:(this._labelFormatter=t,this._clearAttrToProjectorCache(),this.render(),this)},g.prototype._createNodesForDataset=function(t){var e=p.prototype._createNodesForDataset.call(this,t),n=this._renderArea.append("g").classed(g._LABEL_AREA_CLASS,!0),r=new i.SvgContext(n.node()),o=new i.CacheMeasurer(r),a=new i.Writer(o,r);return this._labelConfig.set(t,{labelArea:n,measurer:o,writer:a}),e},g.prototype._removeDatasetNodes=function(t){p.prototype._removeDatasetNodes.call(this,t);var e=this._labelConfig.get(t);null!=e&&(e.labelArea.remove(),this._labelConfig.delete(t))},g.prototype.entityNearest=function(t){var e=this,n=function(){var n=e._isVertical?e._getEntityStore().entityNearestX(t):e._getEntityStore().entityNearestY(t);return void 0===n?void 0:e._lightweightPlotEntityToPlotEntity(n)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype.entitiesAt=function(t){var e=this,n=function(){return e._entitiesIntersecting(t.x,t.y)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype.entitiesInBounds=function(t){var e=this,n=function(){return p.prototype.entitiesInBounds.call(e,t)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype.entitiesInXBounds=function(t){var e=this,n=function(){return p.prototype.entitiesInXBounds.call(e,t)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype.entitiesInYBounds=function(t){var e=this,n=function(){return p.prototype.entitiesInYBounds.call(e,t)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype._entitiesIntersecting=function(t,e){for(var n=[],i=this._getEntityStore().entities(),r=i.length,o=0;o<r;o++){var a=i[o];d.DOM.intersectsBBox(t,e,this._entityBounds(a))&&n.push(this._lightweightPlotEntityToPlotEntity(a))}return n},g.prototype._updateLengthScale=function(){if(this._projectorsReady()){var t=this.length().scale;t instanceof h.QuantitativeScale&&(t.addPaddingExceptionsProvider(this._baselineValueProvider),t.addIncludedValuesProvider(this._baselineValueProvider))}},g.prototype.renderImmediately=function(){var t=this;return this._barPixelThickness(),this._computeBarPixelThickness.doLocked((function(){return p.prototype.renderImmediately.call(t)}))},g.prototype._additionalPaint=function(t){var e=this,n=this.length().scale.scale(this.baselineValue()),i={x1:this._isVertical?0:n,y1:this._isVertical?n:0,x2:this._isVertical?this.width():n,y2:this._isVertical?n:this.height()};this._getAnimator("baseline").animate(this._baseline,i),this.datasets().forEach((function(t){return e._labelConfig.get(t).labelArea.selectAll("g").remove()})),this._labelsEnabled&&d.Window.setTimeout((function(){return e._drawLabels()}),t)},g.prototype.getExtentsForProperty=function(t){var e,i=this,r=p.prototype.getExtentsForProperty.call(this,t);if("x"===t&&this._isVertical)e=this.x();else{if("y"!==t||this._isVertical)return r;e=this.y()}if(!(e&&e.scale&&e.scale instanceof h.QuantitativeScale))return r;var o=e.scale,a=this._barPixelThickness();return r.map((function(t){return n.extent([o.invert(i._getPositionAttr(o.scale(t[0]),a)),o.invert(i._getPositionAttr(o.scale(t[0]),a)+a),o.invert(i._getPositionAttr(o.scale(t[1]),a)),o.invert(i._getPositionAttr(o.scale(t[1]),a)+a)])}))},g.prototype._getPositionAttr=function(t,e){switch(this._isVertical||(t-=e,e*=-1),this._barAlignment){case"start":return t;case"end":return t-e;case"middle":default:return t-e/2}},g.prototype._drawLabels=function(){var t=this,e=this._getDataToDraw(),n=this._getAttrToProjector(),i=this.datasets().some((function(i){return e.get(i).some((function(e,r){return null!=e&&t._drawLabel(e,r,i,n)}))}));this._hideBarsIfAnyAreTooWide&&i&&this.datasets().forEach((function(e){return t._labelConfig.get(e).labelArea.selectAll("g").remove()}))},g.prototype._drawLabel=function(t,e,n,i){var r=this._labelConfig.get(n),o=r.labelArea,a=r.measurer,s=r.writer,l=(0,this.length().accessor)(t,e,n),c=this.length().scale,u=null!=c?c.scale(l):l,h=null!=c?c.scale(this.baselineValue()):this.baselineValue(),d={x:i.x(t,e,n),y:i.y(t,e,n)},p={width:i.width(t,e,n),height:i.height(t,e,n)},f=this._labelFormatter(l,t,e,n),m=a.measure(f),g=this._shouldShowLabelOnBar(d,p,m),_=this._calculateLabelProperties(d,p,m,g,this._isVertical?u<=h:u<h),y=_.containerDimensions,v=_.labelContainerOrigin,b=_.labelOrigin,x=_.alignment,w=i.fill(t,e,n),S=this._createLabelContainer(o,v,b,m,g,w);return s.write(f,y.width,y.height,{xAlign:x.x,yAlign:x.y},S.node()),this._isVertical?p.width<m.width:p.height<m.height},g.prototype._shouldShowLabelOnBar=function(e,n,i){if(this._labelsPosition===t.LabelsPosition.outside)return!1;var r=this._isVertical?e.y:e.x,o=this._isVertical?n.height:n.width,a=this._isVertical?this.height():this.width(),s=r+o,l=o;return s>a?l=a-r:r<0&&(l=s),(this._isVertical?i.height:i.width)+g._LABEL_MARGIN_INSIDE_BAR<=l},g.prototype._calculateLabelProperties=function(e,n,i,r,o){var a=this,s=this._isVertical?e.y:e.x,l=this._isVertical?n.height:n.width,c=this._isVertical?i.height:i.width,u="center",h=l,d=s,p=s,f=function(t){switch(t){case"topLeft":return u=a._isVertical?"top":"left",d+=g._LABEL_MARGIN_INSIDE_BAR,void(p+=g._LABEL_MARGIN_INSIDE_BAR);case"center":return void(p+=(l+c)/2);case"bottomRight":return u=a._isVertical?"bottom":"right",d-=g._LABEL_MARGIN_INSIDE_BAR,void(p+=h-g._LABEL_MARGIN_INSIDE_BAR-c)}};if(r)switch(this._labelsPosition){case t.LabelsPosition.start:f(o?"bottomRight":"topLeft");break;case t.LabelsPosition.middle:f("center");break;case t.LabelsPosition.end:f(o?"topLeft":"bottomRight")}else o?(u=this._isVertical?"top":"left",h=l+g._LABEL_MARGIN_INSIDE_BAR+c,d-=g._LABEL_MARGIN_INSIDE_BAR+c,p-=g._LABEL_MARGIN_INSIDE_BAR+c):(u=this._isVertical?"bottom":"right",h=l+g._LABEL_MARGIN_INSIDE_BAR+c,p+=l+g._LABEL_MARGIN_INSIDE_BAR);return{containerDimensions:{width:this._isVertical?n.width:h,height:this._isVertical?h:n.height},labelContainerOrigin:{x:this._isVertical?e.x:d,y:this._isVertical?d:e.y},labelOrigin:{x:this._isVertical?e.x+n.width/2-i.width/2:p,y:this._isVertical?p:e.y+n.height/2-i.height/2},alignment:{x:this._isVertical?"center":u,y:this._isVertical?u:"center"}}},g.prototype._createLabelContainer=function(t,e,n,i,r,o){var a=t.append("g").attr("transform","translate("+e.x+", "+e.y+")");if(r){a.classed("on-bar-label",!0);var s=1.6*d.Color.contrast("white",o)<d.Color.contrast("black",o);a.classed(s?"dark-label":"light-label",!0)}else a.classed("off-bar-label",!0);return a},g.prototype._generateDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._getAttrToProjector(),n=this.length().scale.scale(this.baselineValue()),i=this._isVertical?"height":"width";e[this._isVertical?"y":"x"]=function(){return n},e[i]=function(){return 0},t.push({attrToProjector:e,animator:this._getAnimator(f.Animator.RESET)})}return t.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(f.Animator.MAIN)}),t},g.prototype._generateAttrToProjector=function(){var t=this,e=p.prototype._generateAttrToProjector.call(this),n=this.length().scale.scale(this.baselineValue()),i=this._isVertical?"y":"x",r=this._isVertical?"x":"y",o=m.Plot._scaledAccessor(this.position()),a=m.Plot._scaledAccessor(this.length()),s=function(t,e,i){return Math.abs(n-a(t,e,i))},l=e[g._BAR_THICKNESS_KEY],c=e.gap,u=null==c?l:function(t,e,n){var i=l(t,e,n);return i<g._BAR_GAPLESS_THRESHOLD_PX?i:i-c(t,e,n)};return e.width=this._isVertical?u:s,e.height=this._isVertical?s:u,e[i]=function(t,e,i){var r=a(t,e,i);return r>n?n:r},e[r]=function(e,n,i){return t._getPositionAttr(o(e,n,i),l(e,n,i))},e},g.prototype._updateThicknessAttr=function(){var t=this,e=this.position(),n=this.barEnd();null!=e&&null!=n?(this._fixedBarPixelThickness=!1,this.attr(g._BAR_THICKNESS_KEY,(function(t,i,r){var o=e.accessor(t,i,r),a=n.accessor(t,i,r);return o=e.scale?e.scale.scale(o):o,a=n.scale?n.scale.scale(a):a,Math.abs(a-o)}))):(this._fixedBarPixelThickness=!0,this.attr(g._BAR_THICKNESS_KEY,(function(){return t._barPixelThickness()})))},g.prototype._barPixelThickness=function(){return this._fixedBarPixelThickness&&this._projectorsReady()?this._computeBarPixelThickness(this.position(),this.datasets(),this._isVertical?this.width():this.height()):0},g.prototype.entities=function(t){return void 0===t&&(t=this.datasets()),this._projectorsReady()?p.prototype.entities.call(this,t):[]},g.prototype._entityBounds=function(t){return this._pixelBounds(t.datum,t.index,t.dataset)},g.prototype._pixelBounds=function(t,e,n){var i=this._getAttrToProjector();return{x:i.x(t,e,n),y:i.y(t,e,n),width:i.width(t,e,n),height:i.height(t,e,n)}},g.prototype._pixelPoint=function(t,e,n){var i=this._pixelBounds(t,e,n),r=m.Plot._scaledAccessor(this._isVertical?this.y():this.x())(t,e,n),o=(this._isVertical?this.y().scale:this.x().scale).scale(this.baselineValue());return this._pixelPointBar(r,o,i)},g.prototype._pixelPointBar=function(t,e,n){var i,r;return this._isVertical?(i=n.x+n.width/2,r=t<=e?n.y:n.y+n.height):(i=t>=e?n.x+n.width:n.x,r=n.y+n.height/2),{x:i,y:r}},g.prototype._uninstallScaleForKey=function(t,e){p.prototype._uninstallScaleForKey.call(this,t,e)},g.prototype._getDataToDraw=function(){var t=this,e=new d.Map,n=this._getAttrToProjector(),i=this.width(),r=this.height();return this.datasets().forEach((function(o){var a=o.data().map((function(e,a){return t._isDatumOnScreen(n,i,r,e,a,o)?e:null}));e.set(o,a)})),e},g.prototype._isDatumOnScreen=function(t,e,n,i,r,o){var a=t.x(i,r,o),s=t.y(i,r,o),l=t.width(i,r,o),c=t.height(i,r,o);return!!(d.Math.isValidNumber(a)&&d.Math.isValidNumber(s)&&d.Math.isValidNumber(l)&&d.Math.isValidNumber(c))&&d.Math.boundsIntersects(a,s,l,c,0,0,e,n)},g.prototype.invalidateCache=function(){var t=this;p.prototype.invalidateCache.call(this),this.datasets().forEach((function(e){return t._labelConfig.get(e).measurer.reset()}))},g._BAR_THICKNESS_RATIO=.95,g._BAR_GAPLESS_THRESHOLD_PX=3,g._SINGLE_BAR_DIMENSION_RATIO=.4,g._BAR_AREA_CLASS="bar-area",g._BAR_END_KEY="barEnd",g._BAR_THICKNESS_KEY="width",g._LABEL_AREA_CLASS="bar-label-text-area",g._LABEL_MARGIN_INSIDE_BAR=10,g})(g.XYPlot);function y(t,e,i){var r,o=t.scale;if(o instanceof u.Category)r=o.rangeBand();else{var a=t.accessor,s=n.set(d.Array.flatten(e.map((function(t){return t.data().map((function(e,n){return a(e,n,t)})).filter((function(t){return null!=t})).map((function(t){return t.valueOf()}))})))).values().map((function(t){return+t}));s.sort((function(t,e){return t-e}));var l=s.map((function(t){return o.scale(t)})),c=n.pairs(l);r=d.Math.min(c,(function(t,e){return Math.abs(t[1]-t[0])}),i*_._SINGLE_BAR_DIMENSION_RATIO),r*=_._BAR_THICKNESS_RATIO}return r}t.Bar=_})(XMt);var $Mt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty($Mt,"__esModule",{value:!0});var KMt=uO,ZMt=Zyt,JMt=Fgt,QMt=XMt,tEt=uwt,eEt=(function(t){function e(e){void 0===e&&(e="vertical");var n=t.call(this,e)||this;return n._clusterOffsets=new JMt.Map,n}return KMt.__extends(e,t),e.prototype._generateAttrToProjector=function(){var e=this,n=t.prototype._generateAttrToProjector.call(this),i=this._makeInnerScale(),r=function(t,e){return i.rangeBand()};n.width=this._isVertical?r:n.width,n.height=this._isVertical?n.height:r;var o=n.x,a=n.y;return n.x=this._isVertical?function(t,n,i){return o(t,n,i)+e._clusterOffsets.get(i)}:function(t,e,n){return o(t,e,n)},n.y=this._isVertical?function(t,e,n){return a(t,e,n)}:function(t,n,i){return a(t,n,i)+e._clusterOffsets.get(i)},n},e.prototype._updateClusterPosition=function(){var t=this,e=this._makeInnerScale();this.datasets().forEach((function(n,i){return t._clusterOffsets.set(n,e.scale(String(i))-e.rangeBand()/2)}))},e.prototype._makeInnerScale=function(){var t=new ZMt.Category;t.domain(this.datasets().map((function(t,e){return String(e)})));var e=tEt.Plot._scaledAccessor(this.attr(QMt.Bar._BAR_THICKNESS_KEY));return t.range([0,e(null,0,null)]),t},e.prototype._getDataToDraw=function(){return this._updateClusterPosition(),t.prototype._getDataToDraw.call(this)},e})(QMt.Bar);$Mt.ClusteredBar=eEt;var nEt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(nEt,"__esModule",{value:!0});var iEt=uO,rEt=Edt,oEt=Imt,aEt=_mt,sEt=gyt,lEt=Zyt,cEt=Fgt,uEt=ISt,hEt=zSt,dEt=Wwt,pEt=k_t,fEt=uwt,mEt=(function(t){function e(){var e=t.call(this)||this;return e._startAngle=0,e._endAngle=2*Math.PI,e._labelFormatter=sEt.identity(),e._labelsEnabled=!1,e.innerRadius(0),e.outerRadius((function(){var t=e._pieCenter();return Math.min(Math.max(e.width()-t.x,t.x),Math.max(e.height()-t.y,t.y))})),e.addClass("pie-plot"),e.attr("fill",(function(t,e){return String(e)}),new lEt.Color),e._strokeDrawers=new cEt.Map,e}return iEt.__extends(e,t),e.prototype._setup=function(){var e=this;t.prototype._setup.call(this),this._strokeDrawers.forEach((function(t){return t.attachTo(e._renderArea)}))},e.prototype.computeLayout=function(e,n,i){t.prototype.computeLayout.call(this,e,n,i);var r=this._pieCenter();this._renderArea.attr("transform","translate("+r.x+","+r.y+")");var o=Math.min(Math.max(this.width()-r.x,r.x),Math.max(this.height()-r.y,r.y));return null!=this.innerRadius().scale&&this.innerRadius().scale.range([0,o]),null!=this.outerRadius().scale&&this.outerRadius().scale.range([0,o]),this},e.prototype.addDataset=function(e){return t.prototype.addDataset.call(this,e),this},e.prototype._addDataset=function(e){if(1===this.datasets().length)return cEt.Window.warn("Only one dataset is supported in Pie plots"),this;this._updatePieAngles(),t.prototype._addDataset.call(this,e);var n=new hEt.ArcOutlineSVGDrawer;return this._isSetup&&n.attachTo(this._renderArea),this._strokeDrawers.set(e,n),this},e.prototype.removeDataset=function(e){return t.prototype.removeDataset.call(this,e),this},e.prototype._removeDatasetNodes=function(e){t.prototype._removeDatasetNodes.call(this,e),this._strokeDrawers.get(e).remove()},e.prototype._removeDataset=function(e){return t.prototype._removeDataset.call(this,e),this._strokeDrawers.delete(e),this._startAngles=[],this._endAngles=[],this},e.prototype.selections=function(e){var n=this;void 0===e&&(e=this.datasets());var i=t.prototype.selections.call(this,e).nodes();return e.forEach((function(t){var e=n._strokeDrawers.get(t);null!=e&&i.push.apply(i,e.getVisualPrimitives())})),rEt.selectAll(i)},e.prototype._onDatasetUpdate=function(){t.prototype._onDatasetUpdate.call(this),this._updatePieAngles(),this.render()},e.prototype._createDrawer=function(){return new dEt.ProxyDrawer((function(){return new uEt.ArcSVGDrawer}),(function(){return pEt.warn("canvas renderer is not supported on Pie Plot!"),null}))},e.prototype.entities=function(e){var n=this;return void 0===e&&(e=this.datasets()),t.prototype.entities.call(this,e).map((function(t){t.position.x+=n.width()/2,t.position.y+=n.height()/2;var e=rEt.select(n._strokeDrawers.get(t.dataset).getVisualPrimitiveAtIndex(t.index)),i=t;return i.strokeSelection=e,i}))},e.prototype.sectorValue=function(t,n){return null==t?this._propertyBindings.get(e._SECTOR_VALUE_KEY):(this._bindProperty(e._SECTOR_VALUE_KEY,t,n),this._updatePieAngles(),this.render(),this)},e.prototype.innerRadius=function(t,n){return null==t?this._propertyBindings.get(e._INNER_RADIUS_KEY):(this._bindProperty(e._INNER_RADIUS_KEY,t,n),this.render(),this)},e.prototype.outerRadius=function(t,n){return null==t?this._propertyBindings.get(e._OUTER_RADIUS_KEY):(this._bindProperty(e._OUTER_RADIUS_KEY,t,n),this.render(),this)},e.prototype.startAngle=function(t){return null==t?this._startAngle:(this._startAngle=t,this._updatePieAngles(),this.render(),this)},e.prototype.endAngle=function(t){return null==t?this._endAngle:(this._endAngle=t,this._updatePieAngles(),this.render(),this)},e.prototype.labelsEnabled=function(t){return null==t?this._labelsEnabled:(this._labelsEnabled=t,this.render(),this)},e.prototype.labelFormatter=function(t){return null==t?this._labelFormatter:(this._labelFormatter=t,this.render(),this)},e.prototype.entitiesAt=function(t){var e=this.width()/2,n=this.height()/2,i=this._sliceIndexForPoint({x:t.x-e,y:t.y-n});return null==i?[]:[this.entities()[i]]},e.prototype._propertyProjectors=function(){var e=this,n=t.prototype._propertyProjectors.call(this),i=fEt.Plot._scaledAccessor(this.innerRadius()),r=fEt.Plot._scaledAccessor(this.outerRadius());return n.d=function(t,n,o){return rEt.arc().innerRadius(i(t,n,o)).outerRadius(r(t,n,o)).startAngle(e._startAngles[n]).endAngle(e._endAngles[n])(t,n)},n},e.prototype._updatePieAngles=function(){if(null!=this.sectorValue()&&0!==this.datasets().length){var t=fEt.Plot._scaledAccessor(this.sectorValue()),e=this.datasets()[0],n=this._getDataToDraw().get(e),i=rEt.pie().sort(null).startAngle(this._startAngle).endAngle(this._endAngle).value((function(n,i){return t(n,i,e)}))(n);this._startAngles=i.map((function(t){return t.startAngle})),this._endAngles=i.map((function(t){return t.endAngle}))}},e.prototype._pieCenter=function(){var t,e,n,i,r=this._startAngle<this._endAngle?this._startAngle:this._endAngle,o=this._startAngle<this._endAngle?this._endAngle:this._startAngle,a=Math.sin(r),s=Math.cos(r),l=Math.sin(o),c=Math.cos(o);return a>=0&&l>=0?s>=0&&c>=0?(t=s,e=0,i=0,n=l):s<0&&c<0?(t=0,e=-c,i=0,n=a):s>=0&&c<0?(t=s,e=-c,i=0,n=a):s<0&&c>=0&&(t=1,e=1,i=1,n=Math.max(a,l)):a>=0&&l<0?s>=0&&c>=0?(t=Math.max(s,c),e=1,i=1,n=1):s<0&&c<0?(t=0,e=1,i=-l,n=a):s>=0&&c<0?(t=s,e=1,i=-l,n=1):s<0&&c>=0&&(t=c,e=1,i=1,n=a):a<0&&l>=0?s>=0&&c>=0?(t=1,e=0,i=-a,n=l):s<0&&c<0?(t=1,e=Math.max(-s,-c),i=1,n=1):s>=0&&c<0?(t=1,e=-c,i=-a,n=1):s<0&&c>=0&&(t=1,e=-s,i=1,n=l):a<0&&l<0&&(s>=0&&c>=0?(t=c,e=0,i=-a,n=0):s<0&&c<0?(t=0,e=-s,i=-l,n=0):s>=0&&c<0?(t=1,e=1,i=Math.max(s,-c),n=1):s<0&&c>=0&&(t=c,e=-s,i=1,n=0)),{x:i+n==0?0:i/(i+n)*this.width(),y:t+e==0?0:t/(t+e)*this.height()}},e.prototype._getDataToDraw=function(){var n=t.prototype._getDataToDraw.call(this);if(0===this.datasets().length)return n;var i=fEt.Plot._scaledAccessor(this.sectorValue()),r=this.datasets()[0],o=n.get(r).filter((function(t,n){return e._isValidData(i(t,n,r))}));return n.set(r,o),n},e._isValidData=function(t){return cEt.Math.isValidNumber(t)&&t>=0},e.prototype._pixelPoint=function(t,n,i){var r=fEt.Plot._scaledAccessor(this.sectorValue());if(!e._isValidData(r(t,n,i)))return{x:NaN,y:NaN};var o=(fEt.Plot._scaledAccessor(this.innerRadius())(t,n,i)+fEt.Plot._scaledAccessor(this.outerRadius())(t,n,i))/2,a=rEt.pie().sort(null).value((function(t,n){var o=r(t,n,i);return e._isValidData(o)?o:0})).startAngle(this._startAngle).endAngle(this._endAngle)(i.data()),s=(a[n].startAngle+a[n].endAngle)/2;return{x:o*Math.sin(s),y:-o*Math.cos(s)}},e.prototype._additionalPaint=function(t){var e=this;this._renderArea.select(".label-area").remove(),this._labelsEnabled&&cEt.Window.setTimeout((function(){return e._drawLabels()}),t);var n=this._generateStrokeDrawSteps(),i=this._getDataToDraw();this.datasets().forEach((function(t){var r=fEt.Plot.applyDrawSteps(n,t);e._strokeDrawers.get(t).draw(i.get(t),r)}))},e.prototype._generateStrokeDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new aEt.Null}]},e.prototype._sliceIndexForPoint=function(t){var e,n=Math.sqrt(Math.pow(t.x,2)+Math.pow(t.y,2)),i=Math.acos(-t.y/n);t.x<0&&(i=2*Math.PI-i);for(var r=0;r<this._startAngles.length;r++)if(this._startAngles[r]<i&&this._endAngles[r]>i){e=r;break}if(void 0!==e){var o=this.datasets()[0],a=o.data()[e],s=this.innerRadius().accessor(a,e,o),l=this.outerRadius().accessor(a,e,o);if(n>s&&n<l)return e}return null},e.prototype._drawLabels=function(){for(var t=this,e=this._getAttrToProjector(),n=this._renderArea.append("g").classed("label-area",!0),i=new oEt.SvgContext(n.node()),r=new oEt.CacheMeasurer(i),o=new oEt.Writer(r,i),a=this.datasets()[0],s=this._getDataToDraw().get(a),l=s.length,c=function(i){var l=s[i],c=u.sectorValue().accessor(l,i,a);if(!cEt.Math.isValidNumber(c))return"continue";c=u._labelFormatter(c,l,i,a);var h=r.measure(c),d=(u._endAngles[i]+u._startAngles[i])/2,p=u.outerRadius().accessor(l,i,a);u.outerRadius().scale&&(p=u.outerRadius().scale.scale(p));var f=u.innerRadius().accessor(l,i,a);u.innerRadius().scale&&(f=u.innerRadius().scale.scale(f));var m=(p+f)/2,g=Math.sin(d)*m-h.width/2,_=-Math.cos(d)*m-h.height/2,y=[{x:g,y:_},{x:g,y:_+h.height},{x:g+h.width,y:_},{x:g+h.width,y:_+h.height}],v=y.every((function(e){return Math.abs(e.x)<=t.width()/2&&Math.abs(e.y)<=t.height()/2}));v&&(v=y.map((function(e){return t._sliceIndexForPoint(e)})).every((function(t){return t===i})));var b=e.fill(l,i,a),x=1.6*cEt.Color.contrast("white",b)<cEt.Color.contrast("black",b),w=n.append("g").attr("transform","translate("+g+","+_+")");w.classed(x?"dark-label":"light-label",!0),w.style("visibility",v?"inherit":"hidden"),o.write(c,h.width,h.height,{xAlign:"center",yAlign:"center"},w.node())},u=this,h=0;h<l;h++)c(h)},e._INNER_RADIUS_KEY="inner-radius",e._OUTER_RADIUS_KEY="outer-radius",e._SECTOR_VALUE_KEY="sector-value",e})(fEt.Plot);nEt.Pie=mEt;var gEt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(gEt,"__esModule",{value:!0});var _Et=uO,yEt=Edt,vEt=Imt,bEt=_mt,xEt=NSt,wEt=Wwt,SEt=JSt,MEt=Zyt,EEt=Fgt,TEt=uwt,CEt=(function(t){function e(){var e=t.call(this)||this;return e._labelsEnabled=!1,e._label=null,e.animator("rectangles",new bEt.Null),e.addClass("rectangle-plot"),e.attr("fill",(new MEt.Color).range()[0]),e}return _Et.__extends(e,t),e.prototype._createDrawer=function(){return new wEt.ProxyDrawer((function(){return new SEt.RectangleSVGDrawer}),(function(t){return new xEt.RectangleCanvasDrawer(t)}))},e.prototype._generateAttrToProjector=function(){var n=this,i=t.prototype._generateAttrToProjector.call(this),r=TEt.Plot._scaledAccessor(this.x()),o=i[e._X2_KEY],a=TEt.Plot._scaledAccessor(this.y()),s=i[e._Y2_KEY],l=this.x().scale,c=this.y().scale;return null!=o?(i.width=function(t,e,n){return Math.abs(o(t,e,n)-r(t,e,n))},i.x=function(t,e,n){return Math.min(o(t,e,n),r(t,e,n))}):(i.width=function(t,e,i){return n._rectangleWidth(l)},i.x=function(t,e,n){return r(t,e,n)-.5*i.width(t,e,n)}),null!=s?(i.height=function(t,e,n){return Math.abs(s(t,e,n)-a(t,e,n))},i.y=function(t,e,n){return Math.max(s(t,e,n),a(t,e,n))-i.height(t,e,n)}):(i.height=function(t,e,i){return n._rectangleWidth(c)},i.y=function(t,e,n){return a(t,e,n)-.5*i.height(t,e,n)}),delete i[e._X2_KEY],delete i[e._Y2_KEY],i},e.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:this._getAnimator("rectangles")}]},e.prototype._filterForProperty=function(e){return t.prototype._filterForProperty.call(this,"x2"===e?"x":"y2"===e?"y":e)},e.prototype.x=function(n,i,r){if(null==n)return t.prototype.x.call(this);if(null==i?t.prototype.x.call(this,n):t.prototype.x.call(this,n,i,r),null!=i){var o=this.x2(),a=o&&o.accessor;null!=a&&this._bindProperty(e._X2_KEY,a,i,o.postScale)}return i instanceof MEt.Category&&i.innerPadding(0).outerPadding(0),this},e.prototype.x2=function(t,n){if(null==t)return this._propertyBindings.get(e._X2_KEY);var i=this.x();return this._bindProperty(e._X2_KEY,t,i&&i.scale,n),this.render(),this},e.prototype.y=function(n,i,r){if(null==n)return t.prototype.y.call(this);if(null==i?t.prototype.y.call(this,n):t.prototype.y.call(this,n,i,r),null!=i){var o=this.y2(),a=o&&o.accessor;null!=a&&this._bindProperty(e._Y2_KEY,a,i,o.postScale)}return i instanceof MEt.Category&&i.innerPadding(0).outerPadding(0),this},e.prototype.y2=function(t,n){if(null==t)return this._propertyBindings.get(e._Y2_KEY);var i=this.y();return this._bindProperty(e._Y2_KEY,t,i&&i.scale,n),this.render(),this},e.prototype.entitiesAt=function(t){var e=this._getAttrToProjector();return this.entities().filter((function(n){var i=n.datum,r=n.index,o=n.dataset,a=e.x(i,r,o),s=e.y(i,r,o),l=e.width(i,r,o),c=e.height(i,r,o);return a<=t.x&&t.x<=a+l&&s<=t.y&&t.y<=s+c}))},e.prototype._entityBounds=function(t){return this._entityBBox(t.datum,t.index,t.dataset,this._getAttrToProjector())},e.prototype._entityBBox=function(t,e,n,i){return{x:i.x(t,e,n),y:i.y(t,e,n),width:i.width(t,e,n),height:i.height(t,e,n)}},e.prototype.label=function(t){return null==t?this._label:(this._label=t,this.render(),this)},e.prototype.labelsEnabled=function(t){return null==t?this._labelsEnabled:(this._labelsEnabled=t,this.render(),this)},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this);return null!=this.x2()&&(e.x2=TEt.Plot._scaledAccessor(this.x2())),null!=this.y2()&&(e.y2=TEt.Plot._scaledAccessor(this.y2())),e},e.prototype._pixelPoint=function(t,e,n){var i=this._getAttrToProjector(),r=i.x(t,e,n),o=i.y(t,e,n);return{x:r+i.width(t,e,n)/2,y:o+i.height(t,e,n)/2}},e.prototype._rectangleWidth=function(t){if(t instanceof MEt.Category)return t.rangeBand();var e=t===this.x().scale?this.x().accessor:this.y().accessor,n=yEt.set(EEt.Array.flatten(this.datasets().map((function(t){return t.data().map((function(n,i){return e(n,i,t).valueOf()}))})))).values().map((function(t){return+t})),i=EEt.Math.min(n,0),r=EEt.Math.max(n,0),o=t.scale(i);return(t.scale(r)-o)/Math.abs(r-i)},e.prototype._getDataToDraw=function(){var t=new EEt.Map,e=this._getAttrToProjector();return this.datasets().forEach((function(n){var i=n.data().map((function(t,i){return EEt.Math.isValidNumber(e.x(t,i,n))&&EEt.Math.isValidNumber(e.y(t,i,n))&&EEt.Math.isValidNumber(e.width(t,i,n))&&EEt.Math.isValidNumber(e.height(t,i,n))?t:null}));t.set(n,i)})),t},e.prototype._additionalPaint=function(t){var e=this;this._renderArea.selectAll(".label-area").remove(),this._labelsEnabled&&null!=this.label()&&EEt.Window.setTimeout((function(){return e._drawLabels()}),t)},e.prototype._drawLabels=function(){var t=this,e=this._getDataToDraw();this.datasets().forEach((function(n,i){return t._drawLabel(e,n,i)}))},e.prototype._drawLabel=function(t,e,n){for(var i=this._getAttrToProjector(),r=this._renderArea.append("g").classed("label-area",!0),o=new vEt.SvgContext(r.node()),a=new vEt.CacheMeasurer(o),s=new vEt.Writer(a,o),l=this.x().scale.range(),c=this.y().scale.range(),u=Math.min.apply(null,l),h=Math.max.apply(null,l),d=Math.min.apply(null,c),p=Math.max.apply(null,c),f=t.get(e),m=f.length,g=0;g<m;g++){var _=f[g];if(null!=_){var y=""+this.label()(_,g,e),v=a.measure(y),b=i.x(_,g,e),x=i.y(_,g,e),w=i.width(_,g,e),S=i.height(_,g,e);if(v.height<=S&&v.width<=w){var M={min:b+=(w-v.width)/2,max:b+v.width},E={min:x+=(S-v.height)/2,max:x+v.height};if(M.min<u||M.max>h||E.min<d||E.max>p)continue;if(this._overlayLabel(M,E,g,n,t))continue;var T=i.fill(_,g,e),C=1.6*EEt.Color.contrast("white",T)<EEt.Color.contrast("black",T),A=r.append("g").attr("transform","translate("+b+","+x+")");A.classed(C?"dark-label":"light-label",!0),s.write(y,v.width,v.height,{xAlign:"center",yAlign:"center"},A.node())}}}},e.prototype._overlayLabel=function(t,e,n,i,r){for(var o=this._getAttrToProjector(),a=this.datasets(),s=i;s<a.length;s++)for(var l=a[s],c=r.get(l),u=c.length,h=s===i?n+1:0;h<u;h++)if(EEt.DOM.intersectsBBox(t,e,this._entityBBox(c[h],h,l,o)))return!0;return!1},e._X2_KEY="x2",e._Y2_KEY="y2",e})(gMt.XYPlot);gEt.Rectangle=CEt;var AEt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(AEt,"__esModule",{value:!0});var kEt=uO,LEt=Imt,PEt=gyt,NEt=Uxt,IEt=Wwt,REt=nMt,OEt=_mt,zEt=NSt,DEt=Zyt,BEt=Fgt,HEt=pMt,FEt=uwt,VEt=(function(t){function e(){var e=t.call(this)||this;e._labelFormatter=PEt.identity(),e._labelsEnabled=!1,e.addClass("scatter-plot");var n=new OEt.Easing;n.startDelay(5),n.stepDuration(250),n.maxTotalDuration(FEt.Plot._ANIMATION_MAX_DURATION),e.animator(HEt.Animator.MAIN,n),e.attr("opacity",.6),e.attr("fill",(new DEt.Color).range()[0]),e.size(6);var i=NEt.circle();return e.symbol((function(){return i})),e._labelConfig=new BEt.Map,e}return kEt.__extends(e,t),e.prototype._buildLightweightPlotEntities=function(e){var n=this;return t.prototype._buildLightweightPlotEntities.call(this,e).map((function(t){var e=FEt.Plot._scaledAccessor(n.size())(t.datum,t.index,t.dataset);return t.diameter=e,t}))},e.prototype._createDrawer=function(t){var e=this;return new IEt.ProxyDrawer((function(){return new REt.SymbolSVGDrawer}),(function(n){return new zEt.CanvasDrawer(n,REt.makeSymbolCanvasDrawStep(t,(function(){return FEt.Plot._scaledAccessor(e.symbol())}),(function(){return FEt.Plot._scaledAccessor(e.size())})))}))},e.prototype.size=function(t,n){return null==t?this._propertyBindings.get(e._SIZE_KEY):(this._bindProperty(e._SIZE_KEY,t,n),this.render(),this)},e.prototype.symbol=function(t){return null==t?this._propertyBindings.get(e._SYMBOL_KEY):(this._propertyBindings.set(e._SYMBOL_KEY,{accessor:t}),this.render(),this)},e.prototype._generateDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._getAttrToProjector(),n=FEt.Plot._scaledAccessor(this.symbol());e.d=function(t,e,i){return n(t,e,i)(0)(null)},t.push({attrToProjector:e,animator:this._getAnimator(HEt.Animator.RESET)})}return t.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(HEt.Animator.MAIN)}),t},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this),n=FEt.Plot._scaledAccessor(this.x()),i=FEt.Plot._scaledAccessor(this.y());return e.x=n,e.y=i,e.transform=function(t,e,r){return"translate("+n(t,e,r)+","+i(t,e,r)+")"},e.d=this._constructSymbolGenerator(),e},e.prototype._constructSymbolGenerator=function(){var t=FEt.Plot._scaledAccessor(this.symbol()),e=FEt.Plot._scaledAccessor(this.size());return function(n,i,r){return t(n,i,r)(e(n,i,r))(null)}},e.prototype._entityBounds=function(t){return{x:t.position.x-t.diameter/2,y:t.position.y-t.diameter/2,width:t.diameter,height:t.diameter}},e.prototype._entityVisibleOnPlot=function(t,e){var n={min:e.topLeft.x,max:e.bottomRight.x},i={min:e.topLeft.y,max:e.bottomRight.y},r=this._entityBounds(t);return BEt.DOM.intersectsBBox(n,i,r)},e.prototype.entitiesAt=function(t){var e=FEt.Plot._scaledAccessor(this.x()),n=FEt.Plot._scaledAccessor(this.y()),i=FEt.Plot._scaledAccessor(this.size());return this.entities().filter((function(r){var o=r.datum,a=r.index,s=r.dataset,l=e(o,a,s),c=n(o,a,s),u=i(o,a,s);return l-u/2<=t.x&&t.x<=l+u/2&&c-u/2<=t.y&&t.y<=c+u/2}))},e.prototype.labelsEnabled=function(t){return null==t?this._labelsEnabled:(this._labelsEnabled=t,this._clearAttrToProjectorCache(),this.render(),this)},e.prototype._createNodesForDataset=function(n){var i=t.prototype._createNodesForDataset.call(this,n),r=this._renderArea.append("g").classed(e._LABEL_AREA_CLASS,!0),o=new LEt.SvgContext(r.node()),a=new LEt.CacheMeasurer(o),s=new LEt.Writer(a,o);return this._labelConfig.set(n,{labelArea:r,measurer:a,writer:s}),i},e.prototype._removeDatasetNodes=function(e){t.prototype._removeDatasetNodes.call(this,e);var n=this._labelConfig.get(e);null!=n&&(n.labelArea.remove(),this._labelConfig.delete(e))},e.prototype._additionalPaint=function(t){var e=this;this.datasets().forEach((function(t){return e._labelConfig.get(t).labelArea.selectAll("g").remove()})),this._labelsEnabled&&BEt.Window.setTimeout((function(){return e._drawLabels()}),t)},e.prototype._drawLabels=function(){var t=this,e=this._getDataToDraw(),n=this._getAttrToProjector();this.datasets().forEach((function(i){for(var r=e.get(i),o=r.length,a=0;a<o;a++){var s=r[a];null!=s&&t._drawLabel(s,a,i,n)}}))},e.prototype._drawLabel=function(t,e,n,i){if(null!=t.label){var r=this._labelConfig.get(n),o=r.labelArea,a=r.measurer,s=r.writer,l={x:i.x(t,e,n),y:i.y(t,e,n)},c=FEt.Plot._scaledAccessor(this.size())(t,e,n),u=this._labelFormatter(t.label,t,e,n),h=a.measure(u),d=this._calculateLabelProperties(l,c,h),p=d.containerDimensions,f=d.alignment,m=this._createLabelContainer(o,d.labelContainerOrigin,d.labelOrigin,h);s.write(u,p.width,p.height,{xAlign:f.x,yAlign:f.y},m.node())}},e.prototype._calculateLabelProperties=function(t,n,i){return{containerDimensions:{width:i.width,height:i.height},labelContainerOrigin:{x:t.x-i.width/2,y:t.y-i.height/2+(n<i.height?n/2+e._LABEL_MARGIN_FROM_BUBBLE:0)},labelOrigin:{x:t.x,y:t.y},alignment:{x:"center",y:"center"}}},e.prototype._createLabelContainer=function(t,e,n,i){var r=t.append("g").attr("transform","translate("+e.x+", "+e.y+")");return r.classed("on-bar-label",!0),r},e._SIZE_KEY="size",e._SYMBOL_KEY="symbol",e._LABEL_AREA_CLASS="scatter-label-text-area",e._LABEL_MARGIN_FROM_BUBBLE=15,e})(gMt.XYPlot);AEt.Scatter=VEt;var UEt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(UEt,"__esModule",{value:!0});var jEt=uO,GEt=_mt,WEt=Wwt,qEt=QSt,YEt=Zyt,XEt=k_t,$Et=uwt,KEt=(function(t){function e(){var e=t.call(this)||this;return e.addClass("segment-plot"),e.attr("stroke",(new YEt.Color).range()[0]),e.attr("stroke-width","2px"),e}return jEt.__extends(e,t),e.prototype._createDrawer=function(){return new WEt.ProxyDrawer((function(){return new qEt.SegmentSVGDrawer}),(function(){return XEt.warn("canvas renderer is not supported on Segment Plot!"),null}))},e.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new GEt.Null}]},e.prototype._filterForProperty=function(e){return t.prototype._filterForProperty.call(this,"x2"===e?"x":"y2"===e?"y":e)},e.prototype.x=function(n,i){if(null==n)return t.prototype.x.call(this);if(null==i)t.prototype.x.call(this,n);else{t.prototype.x.call(this,n,i);var r=this.x2(),o=r&&r.accessor;null!=o&&this._bindProperty(e._X2_KEY,o,i)}return this},e.prototype.x2=function(t){if(null==t)return this._propertyBindings.get(e._X2_KEY);var n=this.x();return this._bindProperty(e._X2_KEY,t,n&&n.scale),this.render(),this},e.prototype.y=function(n,i){if(null==n)return t.prototype.y.call(this);if(null==i)t.prototype.y.call(this,n);else{t.prototype.y.call(this,n,i);var r=this.y2(),o=r&&r.accessor;null!=o&&this._bindProperty(e._Y2_KEY,o,i)}return this},e.prototype.y2=function(t){if(null==t)return this._propertyBindings.get(e._Y2_KEY);var n=this.y();return this._bindProperty(e._Y2_KEY,t,n&&n.scale),this.render(),this},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this);return e.x1=$Et.Plot._scaledAccessor(this.x()),e.x2=null==this.x2()?$Et.Plot._scaledAccessor(this.x()):$Et.Plot._scaledAccessor(this.x2()),e.y1=$Et.Plot._scaledAccessor(this.y()),e.y2=null==this.y2()?$Et.Plot._scaledAccessor(this.y()):$Et.Plot._scaledAccessor(this.y2()),e},e.prototype.entitiesAt=function(t){var e=this.entityNearest(t);return null!=e?[e]:[]},e.prototype.entitiesIn=function(t,e){var n,i;return null==e?(n={min:t.topLeft.x,max:t.bottomRight.x},i={min:t.topLeft.y,max:t.bottomRight.y}):(n=t,i=e),this._entitiesIntersecting(n,i)},e.prototype._entitiesIntersecting=function(t,e){for(var n=[],i=this._getAttrToProjector(),r=this.entities(),o=r.length,a=0;a<o;a++){var s=r[a];this._lineIntersectsBox(s,t,e,i)&&n.push(s)}return n},e.prototype._lineIntersectsBox=function(t,e,n,i){var r=this,o=i.x1(t.datum,t.index,t.dataset),a=i.x2(t.datum,t.index,t.dataset),s=i.y1(t.datum,t.index,t.dataset),l=i.y2(t.datum,t.index,t.dataset);if(e.min<=o&&o<=e.max&&n.min<=s&&s<=n.max||e.min<=a&&a<=e.max&&n.min<=l&&l<=n.max)return!0;var c={x:o,y:s},u={x:a,y:l},h=[{x:e.min,y:n.min},{x:e.min,y:n.max},{x:e.max,y:n.max},{x:e.max,y:n.min}];return h.filter((function(t,e){return 0!==e&&r._lineIntersectsSegment(c,u,t,h[e-1])&&r._lineIntersectsSegment(t,h[e-1],c,u)})).length>0},e.prototype._lineIntersectsSegment=function(t,e,n,i){var r=function(t,e,n){return(e.x-t.x)*(n.y-e.y)-(e.y-t.y)*(n.x-e.x)};return r(t,e,n)*r(t,e,i)<0},e._X2_KEY="x2",e._Y2_KEY="y2",e})(gMt.XYPlot);UEt.Segment=KEt;var ZEt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(ZEt,"__esModule",{value:!0});var JEt=uO,QEt=Edt,tTt=_mt,eTt=dwt,nTt=Fgt,iTt=uwt,rTt=(function(t){function e(){var e=t.call(this)||this;return e._stackingResult=eTt.memThunk((function(){return e.datasets()}),(function(){return e.x().accessor}),(function(){return e.y().accessor}),(function(){return e._stackingOrder}),(function(t,e,n,i){return nTt.Stacking.stack(t,e,n,i)})),e._stackedExtent=eTt.memThunk(e._stackingResult,(function(){return e.x().accessor}),(function(){return e._filterForProperty("y")}),(function(t,e,n){return nTt.Stacking.stackedExtent(t,e,n)})),e._baselineValue=0,e._stackingOrder="bottomup",e.addClass("stacked-area-plot"),e._baselineValueProvider=function(){return[e._baselineValue]},e.croppedRenderingEnabled(!1),e}return JEt.__extends(e,t),e.prototype.croppedRenderingEnabled=function(e){return null==e?t.prototype.croppedRenderingEnabled.call(this):e?(nTt.Window.warn("Warning: Stacked Area Plot does not support cropped rendering."),this):t.prototype.croppedRenderingEnabled.call(this,e)},e.prototype._getAnimator=function(t){return new tTt.Null},e.prototype._setup=function(){t.prototype._setup.call(this),this._baseline=this._renderArea.append("line").classed("baseline",!0)},e.prototype.x=function(e,n){return null==e?t.prototype.x.call(this):(null==n?t.prototype.x.call(this,e):t.prototype.x.call(this,e,n),this._checkSameDomain(),this)},e.prototype.y=function(e,n){return null==e?t.prototype.y.call(this):(null==n?t.prototype.y.call(this,e):t.prototype.y.call(this,e,n),this._checkSameDomain(),this)},e.prototype.yOffset=function(t,e){var n=this._stackingResult();if(null!=n){var i=n.get(t);if(null!=i){var r=i.get(String(e));if(null!=r)return r.offset}}},e.prototype.stackingOrder=function(t){return null==t?this._stackingOrder:(this._stackingOrder=t,this._onDatasetUpdate(),this)},e.prototype.downsamplingEnabled=function(e){return null==e?t.prototype.downsamplingEnabled.call(this):(nTt.Window.warn("Warning: Stacked Area Plot does not support downsampling"),this)},e.prototype._additionalPaint=function(){var t=this.y().scale.scale(this._baselineValue),e={x1:0,y1:t,x2:this.width(),y2:t};this._getAnimator("baseline").animate(this._baseline,e)},e.prototype._updateYScale=function(){var t=this.y(),e=t&&t.scale;null!=e&&(e.addPaddingExceptionsProvider(this._baselineValueProvider),e.addIncludedValuesProvider(this._baselineValueProvider))},e.prototype._onDatasetUpdate=function(){return this._checkSameDomain(),t.prototype._onDatasetUpdate.call(this),this},e.prototype.getExtentsForProperty=function(e){return"y"===e?[this._stackedExtent()]:t.prototype.getExtentsForProperty.call(this,e)},e.prototype._checkSameDomain=function(){if(this._projectorsReady()){var t=this.datasets(),n=this.x().accessor,i=t.map((function(t){return QEt.set(t.data().map((function(e,i){return nTt.Stacking.normalizeKey(n(e,i,t))}))).values()})),r=e._domainKeys(t,n);i.some((function(t){return t.length!==r.length}))&&nTt.Window.warn("the domains across the datasets are not the same. Plot may produce unintended behavior.")}},e._domainKeys=function(t,e){var n=QEt.set();return t.forEach((function(t){for(var i=t.data(),r=i.length,o=0;o<r;o++)n.add(e(i[o],o,t))})),n.values()},e.prototype._coordinateProjectors=function(){var t=this,e=iTt.Plot._scaledAccessor(this.x()),n=this.y().accessor,i=this.x().accessor,r=function(t,e,n){return nTt.Stacking.normalizeKey(i(t,e,n))},o=this._stackingResult();return[e,function(e,i,a){var s=+n(e,i,a),l=o.get(a).get(r(e,i,a)).offset;return t.y().scale.scale(s+l)},function(e,n,i){var a=o.get(i).get(r(e,n,i)).offset;return t.y().scale.scale(a)}]},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this),n=this._coordinateProjectors();return e.d=this._constructAreaProjector(n[0],n[1],n[2]),e},e.prototype._pixelPoint=function(e,n,i){var r=t.prototype._pixelPoint.call(this,e,n,i),o=this.x().accessor(e,n,i),a=this.y().accessor(e,n,i),s=this.y().scale.scale(+a+this._stackingResult().get(i).get(nTt.Stacking.normalizeKey(o)).offset);return{x:r.x,y:s}},e})(fMt.Area);ZEt.StackedArea=rTt;var oTt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(oTt,"__esModule",{value:!0});var aTt=uO,sTt=Imt,lTt=gyt,cTt=dwt,uTt=Fgt,hTt=XMt,dTt=uwt,pTt=(function(t){function e(e){void 0===e&&(e="vertical");var n=t.call(this,e)||this;return n._extremaFormatter=lTt.identity(),n._stackingResult=cTt.memThunk((function(){return n.datasets()}),(function(){return n.position().accessor}),(function(){return n.length().accessor}),(function(){return n._stackingOrder}),(function(t,e,n,i){return uTt.Stacking.stack(t,e,n,i)})),n._stackedExtent=cTt.memThunk(n._stackingResult,(function(){return n.position().accessor}),(function(){return n._filterForProperty(n._isVertical?"y":"x")}),(function(t,e,n){return uTt.Stacking.stackedExtent(t,e,n)})),n.addClass("stacked-bar-plot"),n._stackingOrder="bottomup",n}return aTt.__extends(e,t),e.prototype.stackingOrder=function(t){return null==t?this._stackingOrder:(this._stackingOrder=t,this._onDatasetUpdate(),this)},e.prototype.extremaFormatter=function(t){return 0===arguments.length?this._extremaFormatter:(this._extremaFormatter=t,this.render(),this)},e.prototype._setup=function(){t.prototype._setup.call(this),this._labelArea=this._renderArea.append("g").classed(hTt.Bar._LABEL_AREA_CLASS,!0);var e=new sTt.SvgContext(this._labelArea.node());this._measurer=new sTt.CacheMeasurer(e),this._writer=new sTt.Writer(this._measurer,e)},e.prototype._drawLabels=function(){var n=this;t.prototype._drawLabels.call(this),this._labelArea.selectAll("g").remove();var i=+this.baselineValue(),r=this.position().scale,o=this.length().scale,a=uTt.Stacking.stackedExtents(this._stackingResult()),s=a.minimumExtents,l=[],c=function(t,e){var a=n._generateAttrToProjector(),s=n.width(),c=n.height();t.forEach((function(t){if(t.extent!==i){var u=n.extremaFormatter()(t.extent),h=n._measurer.measure(u),d=t.stackedDatum,p=d.originalDatum,f=d.originalIndex,m=d.originalDataset;if(!n._isDatumOnScreen(a,s,c,p,f,m))return;var g=dTt.Plot._scaledAccessor(n.attr(hTt.Bar._BAR_THICKNESS_KEY))(p,f,m),_=o.scale(t.extent),y=n._getPositionAttr(r.scale(t.axisValue),g)+g/2,v=e(n._isVertical?{x:y,y:_}:{x:_,y:y},h,g),b=(function(t,e,i){var r=e.topLeft,o=r.x,a=r.y,s=e.bottomRight.x-e.topLeft.x,l=e.bottomRight.y-e.topLeft.y,c=n._isVertical?s>i:l>i;if(!c){var u=n._labelArea.append("g").attr("transform","translate("+o+", "+a+")");u.classed("stacked-bar-label",!0),n._writer.write(t,s,l,{xAlign:"center",yAlign:"center"},u.node())}return c})(u,{topLeft:v,bottomRight:{x:v.x+h.width,y:v.y+h.height}},g);l.push(b)}}))};c(a.maximumExtents,(function(t,i,r){var o=n._isVertical?i.width:i.height;return{x:n._isVertical?t.x-o/2:t.x+e._EXTREMA_LABEL_MARGIN_FROM_BAR,y:n._isVertical?t.y-(n._isVertical?i.height:i.width):t.y-o/2}})),c(s,(function(t,i,r){var o=n._isVertical?i.width:i.height;return{x:n._isVertical?t.x-o/2:t.x-(n._isVertical?i.height:i.width),y:n._isVertical?t.y+e._EXTREMA_LABEL_MARGIN_FROM_BAR:t.y-o/2}})),l.some((function(t){return t}))&&this._labelArea.selectAll("g").remove()},e.prototype._generateAttrToProjector=function(){var e=this,n=t.prototype._generateAttrToProjector.call(this),i=this._isVertical?"y":"x",r=this.length().scale,o=this.length().accessor,a=this.position().accessor,s=function(t,e,n){return uTt.Stacking.normalizeKey(a(t,e,n))},l=this._stackingResult(),c=function(t,e,n){return r.scale(l.get(n).get(s(t,e,n)).offset)},u=function(t,e,n){return r.scale(+o(t,e,n)+l.get(n).get(s(t,e,n)).offset)},h=function(t,e,n){return Math.abs(u(t,e,n)-c(t,e,n))};n[this._isVertical?"height":"width"]=h;var d=function(t,e,n){return+o(t,e,n)<0?c(t,e,n):u(t,e,n)};return n[i]=function(t,n,i){return e._isVertical?d(t,n,i):d(t,n,i)-h(t,n,i)},n},e.prototype.getExtentsForProperty=function(e){return e===(this._isVertical?"y":"x")?[this._stackedExtent()]:t.prototype.getExtentsForProperty.call(this,e)},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e._EXTREMA_LABEL_MARGIN_FROM_BAR=5,e})(hTt.Bar);oTt.StackedBar=pTt;var fTt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(fTt,"__esModule",{value:!0});var mTt=uO,gTt=Fgt,_Tt=uwt,yTt=(function(t){function e(){var e=t.call(this)||this;return e._connectorsEnabled=!1,e.addClass("waterfall-plot"),e}return mTt.__extends(e,t),e.prototype.connectorsEnabled=function(t){return null==t?this._connectorsEnabled:(this._connectorsEnabled=t,this)},e.prototype.total=function(t){return null==t?this._propertyBindings.get(e._TOTAL_KEY):(this._bindProperty(e._TOTAL_KEY,t,null),this)},e.prototype._additionalPaint=function(t){var e=this;this._connectorArea.selectAll("line").remove(),this._connectorsEnabled&&gTt.Window.setTimeout((function(){return e._drawConnectors()}),t)},e.prototype._createNodesForDataset=function(n){var i=t.prototype._createNodesForDataset.call(this,n);return this._connectorArea=this._renderArea.append("g").classed(e._CONNECTOR_AREA_CLASS,!0),i},e.prototype.getExtentsForProperty=function(e){return"y"===e?[this._extent]:t.prototype.getExtentsForProperty.call(this,e)},e.prototype._generateAttrToProjector=function(){var n=this,i=t.prototype._generateAttrToProjector.call(this),r=this.y().scale,o=_Tt.Plot._scaledAccessor(this.total());return null==this.attr("y")&&(i.y=function(t,e,i){var a=n.y().accessor(t,e,i);if(o(t,e,i))return Math.min(r.scale(a),r.scale(0));var s=n._subtotals[e];if(0===e)return r.scale(a<0?s-a:s);var l=n._subtotals[e-1];return r.scale(s>l?s:l)}),null==this.attr("height")&&(i.height=function(t,e,i){var a=o(t,e,i),s=n.y().accessor(t,e,i);if(a)return Math.abs(r.scale(s)-r.scale(0));var l=n._subtotals[e];if(0===e)return Math.abs(r.scale(l)-r.scale(l-s));var c=n._subtotals[e-1];return Math.abs(r.scale(l)-r.scale(c))}),i.class=function(t,i,r){var a="";return null!=n.attr("class")&&(a=n.attr("class").accessor(t,i,r)+" "),o(t,i,r)?a+e._BAR_TOTAL_CLASS:a+(n.y().accessor(t,i,r)>0?e._BAR_GROWTH_CLASS:e._BAR_DECLINE_CLASS)},i},e.prototype._onDatasetUpdate=function(){return this._updateSubtotals(),t.prototype._onDatasetUpdate.call(this),this},e.prototype._calculateSubtotalsAndExtent=function(t){for(var e=Number.MAX_VALUE,n=Number.MIN_VALUE,i=0,r=!1,o=t.data(),a=o.length,s=0;s<a;s++){var l=o[s],c=this.y().accessor(l,s,t),u=this.total().accessor(l,s,t);if(u&&0!==s||(i+=c),this._subtotals.push(i),i<e&&(e=i),i>n&&(n=i),u&&(c<e&&(e=c),c>n&&(n=c)),!r&&u){for(var h=c-i,d=0;d<this._subtotals.length;d++)this._subtotals[d]+=h;r=!0,i+=h,e+=h,n+=h}}this._extent=[e,n]},e.prototype._drawConnectors=function(){for(var t=this._getAttrToProjector(),n=this.datasets()[0],i=1;i<n.data().length;i++){var r=i-1,o=n.data()[i],a=n.data()[r],s=t.x(a,r,n),l=t.x(o,i,n)+t.width(o,i,n),c=t.y(o,i,n);(this._subtotals[i]>0&&this._subtotals[i]>this._subtotals[r]||this._subtotals[i]<0&&this._subtotals[i]>=this._subtotals[r])&&(c=t.y(o,i,n)+t.height(o,i,n)),this._connectorArea.append("line").classed(e._CONNECTOR_CLASS,!0).attr("x1",s).attr("x2",l).attr("y1",c).attr("y2",c)}},e.prototype._updateSubtotals=function(){var t=this.datasets();if(t.length>0){var e=t[t.length-1];this._subtotals=new Array,this._calculateSubtotalsAndExtent(e)}},e._BAR_DECLINE_CLASS="waterfall-decline",e._BAR_GROWTH_CLASS="waterfall-growth",e._BAR_TOTAL_CLASS="waterfall-total",e._CONNECTOR_CLASS="connector",e._CONNECTOR_AREA_CLASS="connector-area",e._TOTAL_KEY="total",e})(XMt.Bar);fTt.Waterfall=yTt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(fMt,t),e.__exportStar(XMt,t),e.__exportStar(hwt,t),e.__exportStar($Mt,t),e.__exportStar(mMt,t),e.__exportStar(nEt,t),e.__exportStar(gEt,t),e.__exportStar(AEt,t),e.__exportStar(UEt,t),e.__exportStar(ZEt,t),e.__exportStar(oTt,t),e.__exportStar(fTt,t)})(pMt);var vTt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(vTt,"__esModule",{value:!0}),vTt.version="3.9.0",(function(t){Object.defineProperty(t,"__esModule",{value:!0});var e=uO;t.Animators=_mt,t.Axes=Pmt,t.Components=ibt,t.Configs=L_t,t.Formatters=gyt,t.RenderController=Hgt,t.RenderPolicies=nyt,t.SymbolFactories=Uxt,t.Dispatchers=sbt,t.Drawers=NSt,t.Interactions=obt,t.Plots=pMt,t.Scales=Zyt,t.Utils=Fgt,e.__exportStar(myt,t),t.TimeInterval=Kyt.TimeInterval,e.__exportStar(Bgt,t),e.__exportStar(xxt,t),e.__exportStar(gwt,t),t.version=vTt.version,e.__exportStar(cbt,t),e.__exportStar(Wwt,t),e.__exportStar(wbt,t),e.__exportStar(Ibt,t),e.__exportStar(gMt,t),e.__exportStar(uwt,t),e.__exportStar(Ivt,t),e.__exportStar(hvt,t)})(rO);const bTt=[{character:"◼",method:rO.SymbolFactories.square},{character:"◆",method:rO.SymbolFactories.diamond},{character:"▲",method:rO.SymbolFactories.triangle},{character:"★",method:rO.SymbolFactories.star},{character:"✚",method:rO.SymbolFactories.cross}];var xTt;function wTt(t){return e=>{let n,i=Math.abs(e);return i<1e-15&&(i=0),n=my(i>=1e4||i>0&&i<.01?"."+t+"~e":"."+t+"~g"),n(e)}}!(function(t){t.STEP="step",t.RELATIVE="relative",t.WALL_TIME="wall_time"})(xTt||(xTt={}));const STt=my(".4~s");function MTt(){let t=new rO.Scales.Linear;t.tickGenerator(rO.Scales.TickGenerators.integerTickGenerator());let e=new rO.Axes.Numeric(t,"bottom");return e.formatter(STt),{scale:t,axis:e,accessor:t=>t.step}}let ETt=rO.Formatters.time("%a %b %e, %H:%M:%S"),TTt=(t,e,n)=>{if(null!=t.relative)return t.relative;let i=n.data();return(+t.wall_time-(i.length>0?+i[0].wall_time:0))/36e5},CTt=t=>{let e="",n=Math.floor(t/24);t-=24*n,n&&(e+=n+"d ");let i=Math.floor(t);t-=i,t*=60,(i||n)&&(e+=i+"h ");let r=Math.floor(t);return t-=r,t*=60,(r||i||n)&&(e+=r+"m "),e+Math.floor(t)+"s"};function ATt(t){switch(t){case xTt.STEP:return MTt();case xTt.WALL_TIME:return(function e(){let t=new rO.Scales.Time;return{scale:t,axis:new rO.Axes.Time(t,"bottom"),accessor:t=>t.wall_time}})();case xTt.RELATIVE:return(function n(){let t=new rO.Scales.Linear;return{scale:t,axis:new rO.Axes.Numeric(t,"bottom"),accessor:TTt}})();default:throw new Error("invalid xType: "+t)}}var kTt;function LTt(t){return class extends t{constructor(){super(...arguments),this.loadKey="",this.dataToLoad=[],this.getDataLoadName=t=>String(t),this.dataLoading=!1,this.dataLoadedAtLeastOnce=!1,this._isConnected=!1,this._dataLoadState=new Map,this._canceller=new XR,this._loadDataAsync=null,this._loadData=Se.exports.throttle(this._loadDataImpl,100,{leading:!0,trailing:!0})}connectedCallback(){super.connectedCallback(),this._isConnected=!0}disconnectedCallback(){super.disconnectedCallback(),this._isConnected=!1}static get properties(){return{active:{type:Boolean,observer:"_loadDataIfActive"},_isConnected:{type:Boolean},loadKey:{type:String},dataToLoad:{type:Array},getDataLoadName:{type:Object},loadDataCallback:{type:Object},requestData:{type:Object}}}static get observers(){return["_dataToLoadChanged(_isConnected, dataToLoad.*)"]}onLoadFinish(){}reload(){this._dataLoadState.clear(),this._loadData()}reset(){null!=this._loadDataAsync&&(clearTimeout(this._loadDataAsync),this._loadDataAsync=null),this._canceller&&this._canceller.cancelAll(),this._dataLoadState&&this._dataLoadState.clear(),this._isConnected&&this._loadData()}_dataToLoadChanged(){this._isConnected&&this._loadData()}detached(){null!=this._loadDataAsync&&(clearTimeout(this._loadDataAsync),this._loadDataAsync=null)}_loadDataIfActive(){this.active&&this._loadData()}_loadDataImpl(){this.active&&(null!==this._loadDataAsync&&clearTimeout(this._loadDataAsync),this._loadDataAsync=setTimeout(this._canceller.cancellable((t=>{if(t.cancelled)return;this.dataLoading=!0;const e=this.dataToLoad.filter((t=>{const e=this.getDataLoadName(t);return!this._dataLoadState.has(e)}));for(const t of e){const e=this.getDataLoadName(t);this._dataLoadState.set(e,kTt.LOADING)}const n=this._canceller.cancellable((t=>{if(t.cancelled)return;const{item:e,data:n}=t.value,i=this.getDataLoadName(e);this._dataLoadState.set(i,kTt.LOADED),this.loadDataCallback(this,e,n)})),i=this._canceller.cancellable((t=>{if(!t.cancelled){const t=new Set(e.map((t=>this.getDataLoadName(t))));this.dataToLoad.some((e=>t.has(this.getDataLoadName(e))))&&this.onLoadFinish(),this._loadDataAsync=null,this.dataLoadedAtLeastOnce=!0}Array.from(this._dataLoadState.values()).includes(kTt.LOADING)||(this.dataLoading=!1)}));this.requestData(e,n,(()=>i(void 0)))}))))}}}!(function(t){t[t.LOADING=0]="LOADING",t[t.LOADED=1]="LOADED"})(kTt||(kTt={})),el({moduleName:"plottable-style",styleContent:"\n    \n.plottable-colors-0 {\n  background-color: #5279c7; /* INDIGO */\n}\n\n.plottable-colors-1 {\n  background-color: #fd373e; /* CORAL_RED */\n}\n\n.plottable-colors-2 {\n  background-color: #63c261; /* FERN */\n}\n\n.plottable-colors-3 {\n  background-color: #fad419; /* BRIGHT_SUN */\n}\n\n.plottable-colors-4 {\n  background-color: #2c2b6f; /* JACARTA */\n}\n\n.plottable-colors-5 {\n  background-color: #ff7939; /* BURNING_ORANGE */\n}\n\n.plottable-colors-6 {\n  background-color: #db2e65; /* CERISE_RED */\n}\n\n.plottable-colors-7 {\n  background-color: #99ce50; /* CONIFER */\n}\n\n.plottable-colors-8 {\n  background-color: #962565; /* ROYAL_HEATH */\n}\n\n.plottable-colors-9 {\n  background-color: #06cccc; /* ROBINS_EGG_BLUE */\n}\n\n/**\n * User-supplied renderTo element.\n */\n.plottable {\n  display: block; /* must be block elements for width/height calculations to work in Firefox. */\n  pointer-events: visibleFill;\n  position: relative;\n  /**\n   * Pre 3.0, users could set the dimension of the root element in two ways: either using CSS\n   * (inline or through a stylesheet), or using the SVG width/height attributes. By default, we\n   * set the SVG width/height attributes to 100%.\n   *\n   * Post 3.0 the root element is always a normal div and the only way to set the dimensions is\n   * to use CSS. To replicate the \"100%-by-default\" behavior, we apply width/height 100%.\n   */\n  width: 100%;\n  height: 100%;\n}\n\n/**\n * The _element that roots each Component's DOM.\n */\n.plottable .component {\n  /* Allow components to be positioned with explicit left/top/width/height styles */\n  position: absolute;\n}\n\n.plottable .background-container,\n.plottable .content,\n.plottable .foreground-container {\n  position: absolute;\n  width: 100%;\n  height: 100%;\n}\n\n/**\n * Don't allow svg elements above the content to steal events\n */\n.plottable .foreground-container {\n  pointer-events: none;\n}\n\n.plottable .component-overflow-hidden {\n  overflow: hidden;\n}\n\n.plottable .component-overflow-visible {\n  overflow: visible;\n}\n\n.plottable .plot-canvas-container {\n  width: 100%;\n  height: 100%;\n  overflow: hidden;\n}\n\n.plottable .plot-canvas {\n  width: 100%;\n  height: 100%;\n  /**\n   * Play well with deferred rendering.\n   */\n  transform-origin: 0px 0px 0px;\n}\n\n.plottable text {\n  text-rendering: geometricPrecision;\n}\n\n.plottable .label text {\n  fill: #32313F;\n}\n\n.plottable .bar-label-text-area text,\n.plottable .scatter-label-text-area text {\n  font-size: 12px;\n}\n\n.plottable .label-area text {\n  fill: #32313F;\n  font-size: 14px;\n}\n\n.plottable .light-label text {\n  fill: white;\n}\n\n.plottable .dark-label text {\n  fill: #32313F;\n}\n\n.plottable .off-bar-label text {\n  fill: #32313F;\n}\n\n.plottable .stacked-bar-label text {\n  fill: #32313F;\n  font-style: normal;\n}\n\n.plottable .stacked-bar-plot .off-bar-label {\n  /* HACKHACK #2795: correct off-bar label logic to be implemented on StackedBar */\n  visibility: hidden !important;\n}\n\n.plottable .axis-label text {\n  font-size: 10px;\n  font-weight: bold;\n  letter-spacing: 1px;\n  line-height: normal;\n  text-transform: uppercase;\n}\n\n.plottable .title-label text {\n  font-size: 20px;\n  font-weight: bold;\n}\n\n.plottable .axis line.baseline {\n  stroke: #CCC;\n  stroke-width: 1px;\n}\n\n.plottable .axis line.tick-mark {\n  stroke: #CCC;\n  stroke-width: 1px;\n}\n\n.plottable .axis text {\n  fill: #32313F;\n  font-size: 12px;\n  font-weight: 200;\n  line-height: normal;\n}\n\n.plottable .axis .annotation-circle {\n  fill: white;\n  stroke-width: 1px;\n  stroke: #CCC;\n}\n\n.plottable .axis .annotation-line {\n  stroke: #CCC;\n  stroke-width: 1px;\n}\n\n.plottable .axis .annotation-rect {\n  stroke: #CCC;\n  stroke-width: 1px;\n  fill: white;\n}\n\n.plottable .bar-plot .baseline {\n  stroke: #999;\n}\n\n.plottable .gridlines line {\n  stroke: #3C3C3C; /* hackhack: gridlines should be solid; see #820 */\n  opacity: 0.25;\n  stroke-width: 1px;\n}\n\n.plottable .selection-box-layer .selection-area {\n  fill: black;\n  fill-opacity: 0.03;\n  stroke: #CCC;\n}\n/* DragBoxLayer */\n.plottable .drag-box-layer.x-resizable .drag-edge-lr {\n  cursor: ew-resize;\n}\n.plottable .drag-box-layer.y-resizable .drag-edge-tb {\n  cursor: ns-resize;\n}\n\n.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-tl {\n  cursor: nwse-resize;\n}\n.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-tr {\n  cursor: nesw-resize;\n}\n.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-bl {\n  cursor: nesw-resize;\n}\n.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-br {\n  cursor: nwse-resize;\n}\n\n.plottable .drag-box-layer.movable .selection-area {\n  cursor: move; /* IE fallback */\n  cursor: -moz-grab;\n  cursor: -webkit-grab;\n  cursor: grab;\n}\n\n.plottable .drag-box-layer.movable .selection-area:active {\n  cursor: -moz-grabbing;\n  cursor: -webkit-grabbing;\n  cursor: grabbing;\n}\n/* /DragBoxLayer */\n\n.plottable .guide-line-layer line.guide-line {\n  stroke: #CCC;\n  stroke-width: 1px;\n}\n\n.plottable .drag-line-layer.enabled.vertical line.drag-edge {\n  cursor: ew-resize;\n}\n\n.plottable .drag-line-layer.enabled.horizontal line.drag-edge {\n  cursor: ns-resize;\n}\n\n.plottable .legend text {\n  fill: #32313F;\n  font-size: 12px;\n  font-weight: bold;\n  line-height: normal;\n}\n\n.plottable .interpolated-color-legend rect.swatch-bounding-box {\n  fill: none;\n  stroke: #CCC;\n  stroke-width: 1px;\n  pointer-events: none;\n}\n\n.plottable .waterfall-plot line.connector {\n  stroke: #CCC;\n  stroke-width: 1px;\n}\n\n.plottable .pie-plot .arc.outline {\n  stroke-linejoin: round;\n}\n\n"});const PTt=[1,0,0,1,0,0];class NTt extends rO.Utils.Translator{computePosition(t,e){const n={x:t,y:e},i=(function r(t){const e=(function n(t){const e=[];for(;t&&t instanceof HTMLElement;)if(e.push(t),t.assignedSlot)t=t.assignedSlot;else if(t.parentElement)t=t.parentElement;else{const e=t.parentNode;t=e instanceof DocumentFragment?e.host:e!==t?e:null}return e})(t);let i=PTt,r=null;for(const t of e){const e=rO.Utils.DOM.getElementTransform(t);if(null!=e){const n=t.clientWidth/2,r=t.clientHeight/2;i=rO.Utils.Math.multiplyTranslate(i,[n,r]),i=rO.Utils.Math.multiplyMatrix(i,rO.Utils.Math.invertMatrix(e)),i=rO.Utils.Math.multiplyTranslate(i,[-n,-r])}let n=t.scrollLeft,o=t.scrollTop;null!==r&&t!==r||(n-=t.offsetLeft+t.clientLeft,o-=t.offsetTop+t.clientTop,r=t.offsetParent),i=rO.Utils.Math.multiplyTranslate(i,[n,o])}return i})(this._rootElement);return null==i?n:rO.Utils.Math.applyTransform(i,n)}}class ITt extends rO.Dispatchers.Mouse{constructor(t){super(t),this._eventTarget=t.root().rootElement().node(),this._translator=new NTt(t.root().rootElement().node())}static getDispatcher(t){const e=t.root().rootElement();let n=e[ITt._DISPATCHER_KEY];return n||(n=new ITt(t),e[ITt._DISPATCHER_KEY]=n),n}}class RTt extends rO.Dispatchers.Touch{constructor(t){super(t),this._eventTarget=t.root().rootElement().node(),this._translator=new NTt(t.root().rootElement().node())}static getDispatcher(t){const e=t.root().rootElement();let n=e[RTt._DISPATCHER_KEY];return n||(n=new RTt(t),e[RTt._DISPATCHER_KEY]=n),n}}rO.Interaction.prototype._isInsideComponent=function(t){return 0<=t.x&&0<=t.y&&t.x<this._componentAttachedTo.width()&&t.y<this._componentAttachedTo.height()};class OTt extends rO.Interactions.Pointer{_anchor(t){const e=this;e._isAnchored=!0,e._mouseDispatcher=ITt.getDispatcher(e._componentAttachedTo),e._mouseDispatcher.onMouseMove(e._mouseMoveCallback),e._touchDispatcher=RTt.getDispatcher(e._componentAttachedTo),e._touchDispatcher.onTouchStart(e._touchStartCallback)}}var zTt;!(function(t){t.AUTO="auto",t.BOTTOM="bottom",t.RIGHT="right"})(zTt||(zTt={}));const DTt={boxShadow:"0 1px 4px rgba(0, 0, 0, .3)",opacity:0,position:"fixed",willChange:"transform",zIndex:5};let BTt=class extends(er(ye)){constructor(){super(...arguments),this.position=zTt.AUTO,this.minDistFromEdge=15,this._styleCache=null,this._raf=null,this._tunnel=null}ready(){this._styleCache=null,this._raf=null,this._tunnel=null}attached(){this._tunnel=this._createTunnel(),this._hideOnBlur=()=>{document.hidden&&this.hide()},window.addEventListener("visibilitychange",this._hideOnBlur)}detached(){this.hide(),this._removeTunnel(this._tunnel),this._tunnel=null,window.removeEventListener("visibilitychange",this._hideOnBlur)}content(){return this._tunnel.shadowRoot}hide(){window.cancelAnimationFrame(this._raf),this._styleCache=null,this._tunnel.style.opacity=0}updateAndPosition(t){window.cancelAnimationFrame(this._raf),this._raf=window.requestAnimationFrame((()=>{this.isAttached&&this._repositionImpl(t)}))}_repositionImpl(t){const e=this._tunnel,n=t.getBoundingClientRect(),i=e.getBoundingClientRect(),r=window.innerHeight,o=document.body.clientWidth,a=n.top,s=a+n.height,l=i.height+20;let c=null,u=Math.max(this.minDistFromEdge,n.left),h=null,d=a;this.position==zTt.RIGHT?u=n.right:(d=s+20,o<u+i.width+this.minDistFromEdge&&(u=null,h=this.minDistFromEdge)),this.position==zTt.AUTO&&n.top-l>0&&r<n.top+n.height+l&&(d=null,c=r-a+20);const p={contain:"content",opacity:1,left:u?`${u}px`:null,right:h?`${h}px`:null,top:d?`${d}px`:null,bottom:c?`${c}px`:null};Se.exports.isEqual(this._styleCache,p)||(Object.assign(e.style,p),this._styleCache=p)}_createTunnel(){if(!this.contentComponentName)throw new RangeError("Require `contentComponentName` to be a name of a Polymer component");const t=document.createElement(this.contentComponentName);return Object.assign(t.style,DTt),document.body.appendChild(t),t}_removeTunnel(t){document.body.removeChild(t)}};t([o({type:String}),e("design:type",String)],BTt.prototype,"contentComponentName",void 0),t([o({type:String}),e("design:type",String)],BTt.prototype,"position",void 0),t([o({type:Number}),e("design:type",Number)],BTt.prototype,"minDistFromEdge",void 0),BTt=t([i("vz-chart-tooltip")],BTt);const HTt=my(".2~e"),FTt=my(".4~r"),VTt=my(",~");function UTt(t){if(0===t)return"0";const e=Math.abs(t);return e>=1e4||e<.001?HTt(t):FTt(t)}const jTt={formatTick:UTt,formatShort:UTt,formatReadable(t){const e=Math.abs(t);return e>=1e4||e<.001?HTt(t):VTt(t)},formatLong:VTt};my("0.3~s"),my(",.3~f"),my(".4~");const GTt=cA().tickFormat();let WTt;const qTt={formatTick:t=>GTt(new Date(t)),formatShort:t=>new Date(t).toLocaleString(WTt,{year:"numeric",month:"short",day:"numeric",hour:"numeric",minute:"numeric",second:"numeric"}),formatReadable:t=>new Date(t).toLocaleString(WTt,{year:"numeric",month:"short",day:"numeric",hour:"numeric",minute:"numeric",second:"numeric",timeZoneName:"short"}),formatLong:t=>new Date(t).toLocaleString(WTt,{year:"numeric",month:"long",day:"numeric",hour:"numeric",minute:"numeric",second:"numeric",timeZoneName:"short",fractionalSecondDigits:3})};var YTt;!(function(t){t[t.LINEAR=0]="LINEAR",t[t.LOG10=1]="LOG10",t[t.TIME=2]="TIME"})(YTt||(YTt={}));class XTt{constructor(){this.defaultFormatter=jTt}transform(t,e,n){const[i,r]=t,o=r-i,[a,s]=e;return 0===o?a:(s-a)/o*(n-i)+a}forward(t,e,n){return this.transform(t,e,n)}reverse(t,e,n){return this.transform(e,t,n)}niceDomain(t){let[e,n]=t;if(n<e)throw new Error("Unexpected input: min is larger than max");if(n===e)return 0===e?[-1,1]:e<0?[2*e,0]:[0,2*e];const i=VM(),r=.05*(n-e+Number.EPSILON),[o,a]=i.domain([e-r,n+r]).nice().domain();return[o,a]}ticks(t,e){return VM().domain(t).ticks(e)}isSafeNumber(t){return Number.isFinite(t)}}class $Tt{constructor(){this.defaultFormatter=jTt}transform(t){return Math.log10(t>0?t:Number.MIN_VALUE)}untransform(t){return Math.exp(t/Math.LOG10E)}forward(t,e,n){if(n<=0)return e[0];const[i,r]=t,[o,a]=e,s=this.transform(i),l=this.transform(r)-s,c=a-o;return n=this.transform(n),c/(l+Number.EPSILON)*(n-s)+o}reverse(t,e,n){const[i,r]=t,[o,a]=e,s=this.transform(i),l=this.transform(r);return this.untransform((l-s)/(a-o+Number.EPSILON)*(n-o)+s)}niceDomain(t){const[e,n]=t;if(e>n)throw new Error("Unexpected input: min is larger than max");const i=Math.max(e,Number.MIN_VALUE),r=Math.max(n,Number.MIN_VALUE);return n<=0?[Number.MIN_VALUE,1]:[Math.max(Number.MIN_VALUE,.5*i),2*r]}ticks(t,e){const n=t[0]<=0?Number.MIN_VALUE:t[0],i=t[1]<=0?Number.MIN_VALUE:t[1],r=KM().domain([n,i]).ticks(e);return r.length?r:t}isSafeNumber(t){return Number.isFinite(t)&&t>0}}class KTt{constructor(){this.scale=cA(),this.defaultFormatter=qTt}forward(t,e,n){return this.scale.domain(t).range(e)(n)}reverse(t,e,n){return this.scale.domain(t).range(e).invert(n).getTime()}niceDomain(t){const[e,n]=this.scale.domain(t).nice().domain();return[e.getTime(),n.getTime()]}ticks(t,e){return this.scale.domain(t).ticks(e).map((t=>t.getTime()))}isSafeNumber(t){return Number.isFinite(t)}}class ZTt extends rO.Scales.Linear{constructor(){super(),this._ignoreOutlier=!1,this.padProportion(.2)}setValueProviderForDomain(t){return this._valueProviderForDomain=t,this}_niceDomain(t,e){const[n,i]=t;return(function r(t){switch(t){case YTt.LINEAR:return new XTt;case YTt.LOG10:return new $Tt;case YTt.TIME:return new KTt;default:throw new RangeError(`ScaleType ${t} not supported.`)}})(YTt.LINEAR).niceDomain([n,i])}_getUnboundedExtent(t){const e=this._getAllIncludedValues(t);let n=this._defaultExtent();if(0!==e.length){const t=[rO.Utils.Math.min(e,n[0]),rO.Utils.Math.max(e,n[1])];n=this._niceDomain(t)}return n}_getAllIncludedValues(t=!1){const e=this._valueProviderForDomain?this._valueProviderForDomain():[];return this.extentOfValues(e)}extentOfValues(t){const e=t.filter((t=>rO.Utils.Math.isValidNumber(t)));let n=e;if(this.ignoreOutlier()){const t=e.sort(((t,e)=>t-e)),i=Gl(t,.05),r=Gl(t,.95);n=e.filter((t=>t>=i&&t<=r))}const i=Ll(n);return null==i[0]||null==i[1]?[]:i}ignoreOutlier(t){return"boolean"==typeof t?(this._ignoreOutlier=t,this):this._ignoreOutlier}}class JTt extends rO.QuantitativeScale{constructor(){super(...arguments),this._ignoreOutlier=!1}setValueProviderForDomain(t){return this._valueProviderForDomain=t,this}ignoreOutlier(t){return"boolean"==typeof t?(this._ignoreOutlier=t,this):this._ignoreOutlier}_getAllIncludedValues(t=!1){const e=this._valueProviderForDomain?this._valueProviderForDomain():[];return this.extentOfValues(e)}}const QTt=Math.pow(2,-1074);function tCt(t){return Math.log10(t)}function eCt(t){return Math.pow(10,t)}class nCt extends JTt{constructor(){super(),this._d3LogScale=KM(),this.padProportion(.2)}scale(t){return t<=0?NaN:this._d3LogScale(t)}invert(t){return this._d3LogScale.invert(t)}scaleTransformation(t){return this.scale(t)}invertedTransformation(t){return this.invert(t)}getTransformationDomain(){return this.domain()}setTransformationDomain(t){this.domain(t)}getTransformationExtent(){return this._getUnboundedExtent(!0)}_getDomain(){return this._untransformedDomain}_setDomain(t){this._untransformedDomain=t;const[e,n]=t;super._setDomain([Math.max(QTt,e),n])}_niceDomain(t,e){const[n,i]=t,r=Math.max(tCt(QTt),tCt(n)),o=tCt(i),a=o-r,s=a?a*this.padProportion():1;return[eCt(Math.max(tCt(QTt),r-s)),eCt(o+s)]}_getUnboundedExtent(t){const e=this._getAllIncludedValues(t);let n=this._defaultExtent();if(0!==e.length){const t=[rO.Utils.Math.min(e,n[0]),rO.Utils.Math.max(e,n[1])];n=this._niceDomain(t)}return n}_getAllIncludedValues(t=!1){return super._getAllIncludedValues().map((t=>t>0?t:QTt))}_defaultExtent(){return[1,10]}_backingScaleDomain(t){return null==t?this._d3LogScale.domain():(this._d3LogScale.domain(t),this)}_getRange(){return this._d3LogScale.range()}_setRange(t){this._d3LogScale.range(t)}defaultTicks(){return this._d3LogScale.ticks(1)}ticks(){return this._d3LogScale.ticks()}extentOfValues(t){const e=t.filter((t=>rO.Utils.Math.isValidNumber(t)&&t>0));let n=e;if(this.ignoreOutlier()){const t=e.map(tCt).sort(((t,e)=>t-e)),i=Gl(t,.05),r=Gl(t,.95);n=t.filter((t=>t>=i&&t<=r)).map(eCt)}const i=Ll(n);return null==i[0]||null==i[1]?[]:i}}class iCt extends rO.Components.SelectionBoxLayer{constructor(t,e,n){super(),this.easeFn=Tf,this._animationTime=750,this.xScale(t),this.yScale(e),this._dragInteraction=new rO.Interactions.Drag,this._doubleClickInteraction=new rO.Interactions.Click,this.setupCallbacks(),this.unzoomMethod=n,this.onDetach((()=>{this._doubleClickInteraction.detachFrom(this),this._dragInteraction.detachFrom(this)})),this.onAnchor((()=>{this._doubleClickInteraction.attachTo(this),this._dragInteraction.attachTo(this)}))}interactionStart(t){this.onStart=t}interactionEnd(t){this.onEnd=t}dragInteraction(){return this._dragInteraction}setupCallbacks(){let t=!1;this._dragInteraction.onDragStart((t=>{this.bounds({topLeft:t,bottomRight:t}),this.onStart()})),this._dragInteraction.onDrag(((e,n)=>{this.bounds({topLeft:e,bottomRight:n}),this.boxVisible(!0),t=!0})),this._dragInteraction.onDragEnd(((e,n)=>{this.boxVisible(!1),this.bounds({topLeft:e,bottomRight:n}),t?this.zoom():this.onEnd(),t=!1})),this._doubleClickInteraction.onDoubleClick(this.unzoom.bind(this))}animationTime(t){if(null==t)return this._animationTime;if(t<0)throw new Error("animationTime cannot be negative");return this._animationTime=t,this}ease(t){if("function"!=typeof t)throw new Error("ease function must be a function");return 0===t(0)&&1===t(1)||rO.Utils.Window.warn("Easing function does not maintain invariant f(0)==0 && f(1)==1. Bad behavior may result."),this.easeFn=t,this}zoom(){let t=this.xExtent()[0].valueOf(),e=this.xExtent()[1].valueOf(),n=this.yExtent()[1].valueOf(),i=this.yExtent()[0].valueOf();t!==e&&n!==i&&this.interpolateZoom(t,e,n,i)}unzoom(){let t=this.xScale();t._domainMin=null,t._domainMax=null;let e=t._getExtent();this.xScale().domain(e),this.unzoomMethod()}isZooming(t){this._dragInteraction.enabled(!t),this._doubleClickInteraction.enabled(!t)}interpolateZoom(t,e,n,i){let r=this.xScale().domain()[0].valueOf(),o=this.xScale().domain()[1].valueOf(),a=this.yScale().domain()[0].valueOf(),s=this.yScale().domain()[1].valueOf(),l=this.easeFn,c=(t,e,n)=>Bd(t,e)(l(n));this.isZooming(!0);let u=Date.now(),h=()=>{let l=Date.now(),d=0===this._animationTime?1:Math.min(1,(l-u)/this._animationTime),p=c(r,t,d),f=c(o,e,d),m=c(a,n,d),g=c(s,i,d);this.xScale().domain([p,f]),this.yScale().domain([m,g]),d<1?rO.Utils.DOM.requestAnimationFramePolyfill(h):(this.onEnd(),this.isZooming(!1))};h()}}var rCt,oCt,aCt,sCt;!(function(t){t[t.NONE=0]="NONE",t[t.DRAG_ZOOMING=1]="DRAG_ZOOMING",t[t.PANNING=2]="PANNING"})(rCt||(rCt={}));class lCt extends rO.Components.Group{constructor(t,e,n){super(),this.state=rCt.NONE,this.panStartCallback=new rO.Utils.CallbackSet,this.panEndCallback=new rO.Utils.CallbackSet,this.panZoom=new rO.Interactions.PanZoom(t,e),this.panZoom.dragInteraction().mouseFilter((t=>lCt.isPanKey(t)&&0===t.button)),this.panZoom.wheelFilter(this.canScrollZoom),this.dragZoomLayer=new iCt(t,e,n),this.dragZoomLayer.dragInteraction().mouseFilter((t=>!lCt.isPanKey(t)&&0===t.button)),this.append(this.dragZoomLayer);const i=this.onWheel.bind(this);this.onAnchor((()=>{this._mouseDispatcher=rO.Dispatchers.Mouse.getDispatcher(this),this._mouseDispatcher.onWheel(i),this.panZoom.attachTo(this)})),this.onDetach((()=>{this.panZoom.detachFrom(this),this._mouseDispatcher&&(this._mouseDispatcher.offWheel(i),this._mouseDispatcher=null)})),this.panZoom.dragInteraction().onDragStart((()=>{this.state==rCt.NONE&&this.setState(rCt.PANNING)})),this.panZoom.dragInteraction().onDragEnd((()=>{this.state==rCt.PANNING&&this.setState(rCt.NONE)})),this.dragZoomLayer.dragInteraction().onDragStart((()=>{this.state==rCt.NONE&&this.setState(rCt.DRAG_ZOOMING)})),this.dragZoomLayer.dragInteraction().onDragEnd((()=>{this.state==rCt.DRAG_ZOOMING&&this.setState(rCt.NONE)}))}onWheel(t,e){if(this.canScrollZoom(e))return;const n=this.element();if(!n.select(".help").empty())return;const i=n.append("div").classed("help",!0);i.append("span").text("Alt + Scroll to Zoom"),i.on("animationend",(()=>{i.remove()}))}static isPanKey(t){return Boolean(t.altKey)||Boolean(t.shiftKey)}canScrollZoom(t){return t.altKey}setState(t){if(this.state==t)return;const e=this.state;this.state=t,this.root().removeClass(this.stateClassName(e)),this.root().addClass(this.stateClassName(t)),e==rCt.PANNING&&this.panEndCallback.callCallbacks(),t==rCt.PANNING&&this.panStartCallback.callCallbacks()}stateClassName(t){switch(t){case rCt.PANNING:return"panning";case rCt.DRAG_ZOOMING:return"drag-zooming";case rCt.NONE:default:return""}}onPanStart(t){this.panStartCallback.add(t)}onPanEnd(t){this.panEndCallback.add(t)}onScrollZoom(t){this.panZoom.onZoomEnd(t)}onDragZoomStart(t){this.dragZoomLayer.interactionStart(t)}onDragZoomEnd(t){this.dragZoomLayer.interactionEnd(t)}}!(function(t){t[t.TEXT=0]="TEXT",t[t.DOM=1]="DOM"})(oCt||(oCt={})),(function(t){t.LOG="log",t.LINEAR="linear"})(aCt||(aCt={}));class cCt{constructor(t,e,n,i,r,o,a,s,l,c,u){this.dirtyDatasets=new Set,this.seriesNames=[],this.name2datasets={},this.colorScale=i,this.tooltip=r,this.datasets=[],this._ignoreYOutliers=!1,this.lastPointsDataset=new rO.Dataset,this.nanDataset=new rO.Dataset,this.yValueAccessor=e,this.symbolFunction=c,this._defaultXRange=s,this._defaultYRange=l,this.tooltipColumns=o,this.buildChart(t,e,n,a,u)}buildChart(t,e,n,i,r){this.destroy();const o=t();this.xAccessor=o.accessor,this.xScale=o.scale,this.xAxis=o.axis,this.xAxis.margin(1).tickLabelPadding(3),r&&this.xAxis.formatter(r),this.yScale=cCt.getYScaleFromType(n),this.yScale.setValueProviderForDomain((()=>this.getValuesForYAxisDomainCompute())),this.yAxis=new rO.Axes.Numeric(this.yScale,"left");let a=wTt(3);this.yAxis.margin(0).tickLabelPadding(5).formatter(a),this.yAxis.usesTextWidthApproximation(!0),this.fillArea=i;const s=new lCt(this.xScale,this.yScale,(()=>this.resetDomain()));this.tooltipInteraction=this.createTooltipInteraction(s),this.tooltipPointsComponent=new rO.Component;const l=this.buildPlot(this.xScale,this.yScale,i);this.gridlines=new rO.Components.Gridlines(this.xScale,this.yScale);let c=null;n!==aCt.LOG&&(c=new rO.Components.GuideLineLayer("horizontal"),c.scale(this.yScale).value(0));let u=new rO.Components.GuideLineLayer("vertical");u.scale(this.xScale).value(0),this.center=new rO.Components.Group([this.gridlines,c,u,l,this.tooltipPointsComponent,s]),this.center.addClass("main"),this.outer=new rO.Components.Table([[this.yAxis,this.center],[null,this.xAxis]])}buildPlot(t,e,n){n&&(this.marginAreaPlot=new rO.Plots.Area,this.marginAreaPlot.x(this.xAccessor,t),this.marginAreaPlot.y(n.higherAccessor,e),this.marginAreaPlot.y0(n.lowerAccessor),this.marginAreaPlot.attr("fill",((t,e,n)=>this.colorScale.scale(n.metadata().name))),this.marginAreaPlot.attr("fill-opacity",.3),this.marginAreaPlot.attr("stroke-width",0)),this.smoothedAccessor=t=>t.smoothed;let i=new rO.Plots.Line;i.x(this.xAccessor,t),i.y(this.yValueAccessor,e),i.attr("stroke",((t,e,n)=>this.colorScale.scale(n.metadata().name))),this.linePlot=i,this.setupTooltips(i);let r=new rO.Plots.Line;if(r.x(this.xAccessor,t),r.y(this.smoothedAccessor,e),r.attr("stroke",((t,e,n)=>this.colorScale.scale(n.metadata().name))),this.smoothLinePlot=r,this.symbolFunction){const n=new rO.Plots.Scatter;n.x(this.xAccessor,t),n.y(this.yValueAccessor,e),n.attr("fill",((t,e,n)=>this.colorScale.scale(n.metadata().name))),n.attr("opacity",1),n.size(8),n.symbol(((t,e,n)=>this.symbolFunction(n.metadata().name))),this.markersScatterPlot=n}let o=new rO.Plots.Scatter;o.x(this.xAccessor,t),o.y(this.yValueAccessor,e),o.attr("fill",(t=>this.colorScale.scale(t.name))),o.attr("opacity",1),o.size(8),o.datasets([this.lastPointsDataset]),this.scatterPlot=o;let a=new rO.Plots.Scatter;a.x(this.xAccessor,t),a.y((t=>t.displayY),e),a.attr("fill",(t=>this.colorScale.scale(t.name))),a.attr("opacity",1),a.size(12),a.datasets([this.nanDataset]),a.symbol(rO.SymbolFactories.triangle),this.nanDisplay=a;const s=[a,o,r,i];return this.marginAreaPlot&&s.push(this.marginAreaPlot),this.markersScatterPlot&&s.push(this.markersScatterPlot),new rO.Components.Group(s)}ignoreYOutliers(t){t!==this._ignoreYOutliers&&(this._ignoreYOutliers=t,this.updateSpecialDatasets(),this.yScale.ignoreOutlier(t),this.resetYDomain())}getValuesForYAxisDomainCompute(){const t=this.getAccessorsForComputingYRange();return Se.exports.flattenDeep(this.datasets.map((e=>t.map((t=>e.data().map((n=>t(n,-1,e)))))))).filter(isFinite)}updateSpecialDatasets(){const t=this.getYAxisAccessor();let e=this.datasets.map((e=>{let n=null,i=e.data().filter((n=>!isNaN(t(n,-1,e))));return i.length>0&&(n=i[i.length-1],n.name=e.metadata().name,n.relative=TTt(n,0,e)),n})).filter((t=>null!=t));this.lastPointsDataset.data(e),this.markersScatterPlot&&this.markersScatterPlot.datasets(this.datasets.map(this.createSampledDatasetForMarkers));let n=Se.exports.flatten(this.datasets.map((e=>{let n=null,i=e.data(),r=0;for(;r<i.length&&null==n;)isNaN(t(i[r],-1,e))||(n=t(i[r],-1,e)),r++;null==n&&(n=0);let o=[];for(r=0;r<i.length;r++)isNaN(t(i[r],-1,e))?(i[r].name=e.metadata().name,i[r].displayY=n,i[r].relative=TTt(i[r],0,e),o.push(i[r])):n=t(i[r],-1,e);return o})));this.nanDataset.data(n)}resetDomain(){this.resetXDomain(),this.resetYDomain()}resetXDomain(){let t;if(null!=this._defaultXRange)t=this._defaultXRange;else{const e=this.xScale;e._domainMin=null,e._domainMax=null,t=e._getExtent()}this.xScale.domain(t)}resetYDomain(){null!=this._defaultYRange?this.yScale.domain(this._defaultYRange):(this.yScale.autoDomain(),this.yScale.domain(this.yScale.domain()))}getAccessorsForComputingYRange(){const t=[this.getYAxisAccessor()];return this.fillArea&&t.push(this.fillArea.lowerAccessor,this.fillArea.higherAccessor),t}getYAxisAccessor(){return this.smoothingEnabled?this.smoothedAccessor:this.yValueAccessor}createTooltipInteraction(t){const e=new OTt,n=()=>{e.enabled(!1),this.hideTooltips()},i=()=>e.enabled(!0);return t.onPanStart(n),t.onDragZoomStart(n),t.onPanEnd(i),t.onDragZoomEnd(i),t.onScrollZoom((()=>this.updateTooltipContent(this._lastMousePosition))),e.onPointerMove((t=>{this._lastMousePosition=t,this.updateTooltipContent(t)})),e.onPointerExit((()=>this.hideTooltips())),e}updateTooltipContent(t){this.linePlot&&(window.cancelAnimationFrame(this._tooltipUpdateAnimationFrame),this._tooltipUpdateAnimationFrame=window.requestAnimationFrame((()=>{let e={x:t.x,y:t.y,datum:null,dataset:null},n=this.gridlines.content().node().getBBox(),i=this.linePlot.datasets().map((t=>this.findClosestPoint(e,t))).filter(Boolean),r=rO.Utils.DOM.intersectsBBox,o=i.filter((t=>r(t.x,t.y,n)||isNaN(this.yValueAccessor(t.datum,0,t.dataset)))),a=o.filter((t=>!isNaN(this.yValueAccessor(t.datum,0,t.dataset))));if(0!==i.length){this.scatterPlot.attr("display","none");const t=this.tooltipPointsComponent.content().selectAll(".point").data(a,(t=>t.dataset.metadata().name));t.enter().append("circle").classed("point",!0),t.attr("r",4).attr("cx",(t=>t.x)).attr("cy",(t=>t.y)).style("stroke","none").attr("fill",(t=>this.colorScale.scale(t.dataset.metadata().name))),t.exit().remove(),this.drawTooltips(o,e,this.tooltipColumns)}else this.hideTooltips()})))}hideTooltips(){window.cancelAnimationFrame(this._tooltipUpdateAnimationFrame),this.tooltip.hide(),this.scatterPlot.attr("display","block"),this.tooltipPointsComponent.content().selectAll(".point").remove()}setupTooltips(t){t.onDetach((()=>{this.tooltipInteraction.detachFrom(t),this.tooltipInteraction.enabled(!1)})),t.onAnchor((()=>{this.tooltipInteraction.attachTo(t),this.tooltipInteraction.enabled(!0)}))}drawTooltips(t,e,n){if(!t.length)return void this.tooltip.hide();const{colorScale:i}=this;n=[{title:"",static:!1,evalType:oCt.DOM,evaluate(t){return Su(this).select("span").style("background-color",(()=>i.scale(t.dataset.metadata().name))),""},enter(t){Su(this).append("span").classed("swatch",!0).style("background-color",(()=>i.scale(t.dataset.metadata().name)))}},...n];const r=t=>Math.pow(t.x-e.x,2)+Math.pow(t.y-e.y,2),o=Se.exports.min(t.map(r)),a=this.smoothingEnabled?this.smoothedAccessor:this.yValueAccessor;t="ascending"===this.tooltipSortingMethod?Se.exports.sortBy(t,(t=>a(t.datum,-1,t.dataset))):"descending"===this.tooltipSortingMethod?Se.exports.sortBy(t,(t=>a(t.datum,-1,t.dataset))).reverse():"nearest"===this.tooltipSortingMethod?Se.exports.sortBy(t,r):t.slice(0).reverse();const s=this,l=Su(this.tooltip.content()).select("table"),c=l.select("thead").selectAll("th").data(n,((t,e,n)=>t.title));c.enter().append("th").text((t=>t.title)).nodes(),c.exit().remove();const u=l.select("tbody").selectAll("tr").data(t,((t,e,n)=>t.dataset.metadata().name));u.classed("distant",(t=>{let n=t.dataset.data()[0],i=Se.exports.last(t.dataset.data()),r=this.xScale.scale(this.xAccessor(n,0,t.dataset)),o=this.xScale.scale(this.xAccessor(i,0,t.dataset)),a=this.smoothingEnabled?t.datum.smoothed:this.yValueAccessor(t.datum,0,t.dataset);return e.x<r||e.x>o||isNaN(a)})).classed("closest",(t=>r(t)===o)).each((function(t){s.drawTooltipRow(this,n,t)})).order(),u.exit().remove(),u.enter().append("tr").each((function(t){s.drawTooltipRow(this,n,t)})).nodes(),this.tooltip.updateAndPosition(this.targetSVG.node())}drawTooltipRow(t,e,n){const i=this,r=Su(t).selectAll("td").data(e);r.each((function(t){t.static||i.drawTooltipColumn.call(i,this,t,n)})),r.exit().remove(),r.enter().append("td").each((function(t){"enter"in t&&t.enter&&t.enter.call(this,n),i.drawTooltipColumn.call(i,this,t,n)}))}drawTooltipColumn(t,e,n){const{smoothingEnabled:i}=this;"evalType"in e&&e.evalType==oCt.DOM?e.evaluate.call(t,n,{smoothingEnabled:i}):Su(t).text(e.evaluate.call(t,n,{smoothingEnabled:i}))}findClosestPoint(t,e){const n=e.data().map(((t,n)=>this.xScale.scale(this.xAccessor(t,n,e))));let i=Se.exports.sortedIndex(n,t.x);if(0==n.length)return null;i===n.length?i-=1:0!==i&&(i=Math.abs(n[i-1]-t.x)<Math.abs(n[i]-t.x)?i-1:i);const r=e.data()[i],o=this.smoothingEnabled?this.smoothedAccessor(r,i,e):this.yValueAccessor(r,i,e);return{x:n[i],y:this.yScale.scale(o),datum:r,dataset:e}}resmoothDataset(t){let e=t.data();const n=this.smoothingWeight;let i=e.length>0?0:NaN,r=0;const o=e.map(((e,n)=>this.yValueAccessor(e,n,t))),a=o.every((t=>t==o[0]));e.forEach(((t,e)=>{const s=o[e];if(a||!Number.isFinite(s))t.smoothed=s;else{i=i*n+(1-n)*s,r++;let e=1;1!==n&&(e=1-Math.pow(n,r)),t.smoothed=i/e}}))}getDataset(t){return void 0===this.name2datasets[t]&&(this.name2datasets[t]=new rO.Dataset([],{name:t,meta:null})),this.name2datasets[t]}static getYScaleFromType(t){if(t===aCt.LOG)return new nCt;if(t===aCt.LINEAR)return new ZTt;throw new Error("Unrecognized yScale type "+t)}setVisibleSeries(t){this.disableChanges(),(t=t.sort()).reverse(),this.seriesNames=t}disableChanges(){this.dirtyDatasets.size||(this.linePlot.datasets([]),this.smoothLinePlot&&this.smoothLinePlot.datasets([]),this.marginAreaPlot&&this.marginAreaPlot.datasets([]))}commitChanges(){this.datasets=this.seriesNames.map((t=>this.getDataset(t))),[...this.dirtyDatasets].forEach((t=>{this.smoothingEnabled&&this.resmoothDataset(this.getDataset(t))})),this.updateSpecialDatasets(),this.linePlot.datasets(this.datasets),this.smoothingEnabled&&this.smoothLinePlot.datasets(this.datasets),this.marginAreaPlot&&this.marginAreaPlot.datasets(this.datasets),this.measureBBoxAndMaybeInvalidateLayoutInRaf(),this.dirtyDatasets.clear()}createSampledDatasetForMarkers(t){const e=t.data();if(e.length<=20)return t;const n=Math.ceil(e.length/20),i=new Array(Math.floor(e.length/n));for(let t=0,r=0;t<i.length;t++,r+=n)i[t]=e[r];return new rO.Dataset(i,t.metadata())}setSeriesData(t,e){this.disableChanges(),this.getDataset(t).data(e),this.dirtyDatasets.add(t)}setSeriesMetadata(t,e){this.disableChanges(),this.getDataset(t).metadata(Object.assign(Object.assign({},this.getDataset(t).metadata()),{meta:e})),this.dirtyDatasets.add(t)}smoothingUpdate(t){this.smoothingWeight=t,this.datasets.forEach((t=>this.resmoothDataset(t))),this.smoothingEnabled||(this.linePlot.addClass("ghost"),this.scatterPlot.y(this.smoothedAccessor,this.yScale),this.smoothingEnabled=!0,this.smoothLinePlot.datasets(this.datasets)),this.markersScatterPlot&&this.markersScatterPlot.y(this.getYAxisAccessor(),this.yScale),this.updateSpecialDatasets()}smoothingDisable(){this.smoothingEnabled&&(this.linePlot.removeClass("ghost"),this.scatterPlot.y(this.yValueAccessor,this.yScale),this.smoothLinePlot.datasets([]),this.smoothingEnabled=!1,this.updateSpecialDatasets()),this.markersScatterPlot&&this.markersScatterPlot.y(this.getYAxisAccessor(),this.yScale)}setColorScale(t){this.colorScale=t}setTooltipColumns(t){this.tooltipColumns=t}setTooltipSortingMethod(t){this.tooltipSortingMethod=t}renderTo(t){this.targetSVG=t,this.outer.renderTo(t),null!=this._defaultXRange&&this.resetXDomain(),null!=this._defaultYRange&&this.resetYDomain(),this.measureBBoxAndMaybeInvalidateLayoutInRaf()}redraw(){window.cancelAnimationFrame(this._redrawRaf),this._redrawRaf=window.requestAnimationFrame((()=>{this.measureBBoxAndMaybeInvalidateLayout(),this.outer.redraw()}))}measureBBoxAndMaybeInvalidateLayoutInRaf(){window.cancelAnimationFrame(this._invalidateLayoutRaf),this._invalidateLayoutRaf=window.requestAnimationFrame((()=>{this.measureBBoxAndMaybeInvalidateLayout()}))}measureBBoxAndMaybeInvalidateLayout(){if(this._lastDrawBBox){const{width:t}=this._lastDrawBBox,{width:e}=this.targetSVG.node().getBoundingClientRect();0==t&&t<e&&this.outer.invalidateCache()}this._lastDrawBBox=this.targetSVG.node().getBoundingClientRect()}destroy(){window.cancelAnimationFrame(this._redrawRaf),window.cancelAnimationFrame(this._invalidateLayoutRaf),this.outer&&this.outer.destroy()}onAnchor(t){this.outer&&this.outer.onAnchor(t)}isDataFitToDomain(){return t(this.xAxis.getScale())&&t(this.yAxis.getScale());function t(t){const e=t.getTransformationDomain(),n=t.getTransformationExtent();return n[0]===e[0]&&n[1]===e[1]}}}!(function(t){t.GROUP="G",t.DIV="DIV",t.SVG="SVG",t.TEXT="TEXT"})(sCt||(sCt={}));class uCt extends class{constructor(t){this.uniqueId=0,this.root=t}exportAsString(){const t=this.convert(this.root);if(!t)return"";const e=this.createRootSvg();return e.appendChild(t),e.outerHTML}createUniqueId(t){return`${t}_${this.uniqueId++}`}getSize(){return this.root.getBoundingClientRect()}createRootSvg(){const t=document.createElement("svg"),e=this.getSize();return t.setAttributeNS("svg","viewBox",`0 0 ${e.width} ${e.height}`),t.setAttribute("xmlns","http://www.w3.org/2000/svg"),t}convert(t){let e=null;const n=t.nodeName.toUpperCase();if(t.nodeType!=Node.ELEMENT_NODE||n!=sCt.DIV&&n!=sCt.SVG)e=t.cloneNode();else{e=document.createElement(sCt.GROUP);const n=window.getComputedStyle(t),i=parseInt(n.left,10),r=parseInt(n.top,10);if(i||r){const t=this.createUniqueId("clip");e.setAttribute("transform",`translate(${i}, ${r})`),e.setAttribute("clip-path",`url(#${t})`);const o=parseInt(n.width,10),a=parseInt(n.height,10),s=document.createElement("rect");s.setAttribute("width",String(o)),s.setAttribute("height",String(a));const l=document.createElementNS("svg","clipPath");l.id=t,l.appendChild(s),e.appendChild(l)}}return Array.from(t.childNodes).map((t=>this.convert(t))).filter(Boolean).forEach((t=>e.appendChild(t))),e.nodeName.toUpperCase()==sCt.GROUP&&!e.hasChildNodes()||this.shouldOmitNode(t)?null:this.stripClass(this.transferStyle(t,e))}stripClass(t){return t.nodeType==Node.ELEMENT_NODE&&t.removeAttribute("class"),t}transferStyle(t,e){if(e.nodeType!=Node.ELEMENT_NODE)return e;const n=e,i=e.nodeName.toUpperCase(),r=window.getComputedStyle(t);return i==sCt.TEXT&&Object.assign(n.style,{fontFamily:r.fontFamily,fontSize:r.fontSize,fontWeight:r.fontWeight}),i!=sCt.GROUP&&(n.setAttribute("fill",r.fill),n.setAttribute("stroke",r.stroke),n.setAttribute("stroke-width",r.strokeWidth)),"1"!=r.opacity&&n.setAttribute("opacity",r.opacity),e}shouldOmitNode(t){return!1}}{shouldOmitNode(t){return t.nodeType==Node.ELEMENT_NODE&&t.classList.contains("scatter-plot")}}el({moduleName:"vz-pan-zoom-style",styleContent:"\n    .help {\n      align-items: center;\n      animation-delay: 1s;\n      animation-duration: 1s;\n      animation-name: fade-out;\n      background: rgba(30, 30, 30, 0.6);\n      bottom: 0;\n      color: #fff;\n      display: flex;\n      justify-content: center;\n      left: 0;\n      opacity: 1;\n      padding: 20px;\n      pointer-events: none;\n      position: absolute;\n      right: 0;\n      top: 0;\n    }\n\n    .help > span {\n      white-space: normal;\n    }\n\n    @keyframes fade-out {\n      0% {\n        opacity: 1;\n      }\n\n      100% {\n        opacity: 0;\n      }\n    }\n  "});const hCt=wTt(4),dCt=t=>isNaN(t)?"NaN":hCt(t),pCt=[{title:"Name",evaluate:t=>t.dataset.metadata().name},{title:"Smoothed",evaluate(t,e){const{smoothingEnabled:n}=e;return dCt(n?t.datum.smoothed:t.datum.scalar)}},{title:"Value",evaluate:t=>dCt(t.datum.scalar)},{title:"Step",evaluate:t=>STt(t.datum.step)},{title:"Time",evaluate:t=>ETt(t.datum.wall_time)},{title:"Relative",evaluate:t=>CTt(TTt(t.datum,0,t.dataset))}];let fCt=class extends(er(ye)){constructor(){super(...arguments),this.colorScale=(new rO.Scales.Color).range(gA.slice(0)),this.smoothingEnabled=!1,this.smoothingWeight=.6,this.xType=null,this.xComponentsCreationMethod=null,this.yValueAccessor=t=>t.scalar,this.tooltipColumns=pCt,this.yScaleType=aCt.LINEAR,this.ignoreYOutliers=!1,this.tooltipSortingMethod="default",this.tooltipPosition=zTt.BOTTOM,this._visibleSeriesCache=[],this._seriesDataCache={},this._seriesMetadataCache={},this._makeChartAsyncCallbackId=null}ready(){super.ready(),this.scopeSubtree(this.$.chartdiv,!0)}attached(){const t={capture:!0,passive:!0};this._listen(this,"mousedown",this._onMouseDown.bind(this),t),this._listen(this,"mouseup",this._onMouseUp.bind(this),t),this._listen(window,"keydown",this._onKeyDown.bind(this),t),this._listen(window,"keyup",this._onKeyUp.bind(this),t)}detached(){this.cancelAsync(this._makeChartAsyncCallbackId),this._chart&&(this._chart.destroy(),this._chart=void 0),this._listeners&&(this._listeners.forEach((({node:t,eventName:e,func:n,option:i})=>{t.removeEventListener(e,n,i)})),this._listeners.clear())}_listen(t,e,n,i={}){this._listeners||(this._listeners=new Set),this._listeners.add({node:t,eventName:e,func:n,option:i}),t.addEventListener(e,n,i)}_onKeyDown(t){this.toggleClass("pankey",lCt.isPanKey(t))}_onKeyUp(t){this.toggleClass("pankey",lCt.isPanKey(t))}_onMouseDown(t){this.toggleClass("mousedown",!0)}_onMouseUp(t){this.toggleClass("mousedown",!1)}isDataFitToDomain(){return!this._chart||this._chart.isDataFitToDomain()}setVisibleSeries(t){Se.exports.isEqual(this._visibleSeriesCache,t)||(this._visibleSeriesCache=t)}setSeriesData(t,e){this._seriesDataCache[t]=e,this._chart&&this._chart.setSeriesData(t,e)}setSeriesMetadata(t,e){this._seriesMetadataCache[t]=e,this._chart&&this._chart.setSeriesMetadata(t,e)}commitChanges(){this._chart&&this._chart.commitChanges()}resetDomain(){this._chart&&this._chart.resetDomain()}redraw(){this._chart&&this._chart.redraw()}_makeChart(){null!==this._makeChartAsyncCallbackId&&(this.cancelAsync(this._makeChartAsyncCallbackId),this._makeChartAsyncCallbackId=null),this._makeChartAsyncCallbackId=this.async((function(){this._makeChartAsyncCallbackId=null;let t=this.xComponentsCreationMethod;if(this.xType||t?this.xType&&(t=()=>ATt(this.xType)):t=MTt,t&&this.yValueAccessor&&this.tooltipColumns){var e=new cCt(t,this.yValueAccessor,this.yScaleType,this.colorScale,this.$.tooltip,this.tooltipColumns,this.fillArea,this.defaultXRange,this.defaultYRange,this.symbolFunction,this.xAxisFormatter),n=Su(this.$.chartdiv);e.renderTo(n),this._chart&&this._chart.destroy(),this._chart=e,this._chart.onAnchor((()=>this.fire("chart-attached")))}}),350)}_reloadFromCache(){this._chart&&(this._visibleSeriesCache.forEach((t=>{this._chart.setSeriesData(t,this._seriesDataCache[t]||[])})),this._visibleSeriesCache.filter((t=>this._seriesMetadataCache[t])).forEach((t=>{this._chart.setSeriesMetadata(t,this._seriesMetadataCache[t])})),this._chart.setVisibleSeries(this._visibleSeriesCache),this._chart.commitChanges())}_smoothingChanged(){this._chart&&(this.smoothingEnabled?this._chart.smoothingUpdate(this.smoothingWeight):this._chart.smoothingDisable())}_outliersChanged(){this._chart&&this._chart.ignoreYOutliers(this.ignoreYOutliers)}_colorScaleChanged(){this._chart&&(this._chart.setColorScale(this.colorScale),this._chart.redraw())}_tooltipColumnsChanged(){this._chart&&this._chart.setTooltipColumns(this.tooltipColumns)}_tooltipSortingMethodChanged(){this._chart&&this._chart.setTooltipSortingMethod(this.tooltipSortingMethod)}getExporter(){return new uCt(this.$.chartdiv)}};fCt.template=_e`
    <div id="chartdiv"></div>
    <vz-chart-tooltip
      id="tooltip"
      position="[[tooltipPosition]]"
      content-component-name="vz-line-chart-tooltip"
    ></vz-chart-tooltip>
    <style include="plottable-style"></style>
    <style include="vz-pan-zoom-style"></style>
    <style>
      :host {
        -moz-user-select: none;
        -webkit-user-select: none;
        display: flex;
        flex-direction: column;
        flex-grow: 1;
        flex-shrink: 1;
        outline: none;
        position: relative;
        white-space: nowrap;
      }
      div {
        -webkit-user-select: none;
        -moz-user-select: none;
        flex-grow: 1;
        flex-shrink: 1;
      }

      #chartdiv .main {
        contain: strict;
        cursor: crosshair;
      }

      :host(.pankey) #chartdiv :not(.drag-zooming) .main {
        cursor: -webkit-grab;
        cursor: grab;
      }

      :host(.mousedown) #chartdiv .panning .main {
        cursor: -webkit-grabbing;
        cursor: grabbing;
      }

      #chartdiv {
        contain: strict;
      }

      #chartdiv line.guide-line {
        stroke: #999;
        stroke-width: 1.5px;
      }
      #chartdiv:hover .main {
        will-change: transform;
      }

      .ghost {
        opacity: 0.2;
        stroke-width: 1px;
      }

      .plottable .axis text {
        fill: currentColor;
      }

      .plottable .gridlines line {
        stroke: var(--tb-secondary-text-color);
      }
    </style>
  `,t([o({type:Object}),e("design:type",rO.Scales.Color)],fCt.prototype,"colorScale",void 0),t([o({type:Object}),e("design:type",Function)],fCt.prototype,"symbolFunction",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],fCt.prototype,"smoothingEnabled",void 0),t([o({type:Number}),e("design:type",Number)],fCt.prototype,"smoothingWeight",void 0),t([o({type:String}),e("design:type",String)],fCt.prototype,"xType",void 0),t([o({type:Object}),e("design:type",Function)],fCt.prototype,"xComponentsCreationMethod",void 0),t([o({type:Object}),e("design:type",Function)],fCt.prototype,"xAxisFormatter",void 0),t([o({type:Object}),e("design:type",Function)],fCt.prototype,"yValueAccessor",void 0),t([o({type:Array}),e("design:type",Array)],fCt.prototype,"tooltipColumns",void 0),t([o({type:Object}),e("design:type",Object)],fCt.prototype,"fillArea",void 0),t([o({type:Array}),e("design:type",Array)],fCt.prototype,"defaultXRange",void 0),t([o({type:Array}),e("design:type",Array)],fCt.prototype,"defaultYRange",void 0),t([o({type:String}),e("design:type",String)],fCt.prototype,"yScaleType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],fCt.prototype,"ignoreYOutliers",void 0),t([o({type:String}),e("design:type",String)],fCt.prototype,"tooltipSortingMethod",void 0),t([o({type:String}),e("design:type",String)],fCt.prototype,"tooltipPosition",void 0),t([o({type:Object}),e("design:type",Object)],fCt.prototype,"_chart",void 0),t([o({type:Array}),e("design:type",Array)],fCt.prototype,"_visibleSeriesCache",void 0),t([o({type:Object}),e("design:type",Object)],fCt.prototype,"_seriesDataCache",void 0),t([o({type:Object}),e("design:type",Object)],fCt.prototype,"_seriesMetadataCache",void 0),t([o({type:Number}),e("design:type",Number)],fCt.prototype,"_makeChartAsyncCallbackId",void 0),t([a("xComponentsCreationMethod","xType","yValueAccessor","yScaleType","isAttached"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fCt.prototype,"_makeChart",null),t([a("_chart","_visibleSeriesCache"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fCt.prototype,"_reloadFromCache",null),t([a("smoothingEnabled","smoothingWeight","_chart"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fCt.prototype,"_smoothingChanged",null),t([a("ignoreYOutliers","_chart"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fCt.prototype,"_outliersChanged",null),t([a("colorScale"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fCt.prototype,"_colorScaleChanged",null),t([a("tooltipColumns"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fCt.prototype,"_tooltipColumnsChanged",null),t([a("tooltipSortingMethod","_chart"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fCt.prototype,"_tooltipSortingMethodChanged",null),fCt=t([i("vz-line-chart2")],fCt);let mCt=class extends ye{};mCt.template=_e`
    <div class="content">
      <table>
        <thead></thead>
        <tbody></tbody>
      </table>
    </div>
    <style>
      :host {
        pointer-events: none;
      }

      .content {
        background: rgba(0, 0, 0, 0.8);
        border-radius: 4px;
        color: #fff;
        overflow: hidden;
        pointer-events: none;
      }

      table {
        font-size: 13px;
        line-height: 1.4em;
        margin-top: 10px;
        padding: 8px;
      }

      thead {
        font-size: 14px;
      }

      tbody {
        font-size: 13px;
        line-height: 21px;
        white-space: nowrap;
      }

      td {
        padding: 0 5px;
      }

      .swatch {
        border-radius: 50%;
        display: block;
        height: 18px;
        width: 18px;
      }

      .closest .swatch {
        box-shadow: inset 0 0 0 2px #fff;
      }

      th {
        padding: 0 5px;
        text-align: left;
      }

      .distant td:not(.swatch) {
        opacity: 0.8;
      }

      .ghost {
        opacity: 0.2;
        stroke-width: 1px;
      }
    </style>
  `,mCt=t([i("vz-line-chart-tooltip")],mCt);const gCt=[],_Ct=Se.exports.throttle((function t(){if(0==gCt.length)return;const e=gCt.shift();e&&e.active&&(e.redraw(),e._maybeRenderedInBadState=!1),window.cancelAnimationFrame(0),window.requestAnimationFrame(t)}),100);let yCt=class extends(LTt(er(ye))){constructor(){super(...arguments),this._redrawRaf=null,this.active=!1,this.logScaleActive=!1,this.colorScale={scale:GR},this._resetDomainOnNextLoad=!0,this._maybeRenderedInBadState=!1}onLoadFinish(){this.commitChanges(),this.dataToLoad.length>0&&this._resetDomainOnNextLoad&&(this._resetDomainOnNextLoad=!1,this.getChart().resetDomain()),this.redraw()}disconnectedCallback(){super.disconnectedCallback(),null!==this._redrawRaf&&cancelAnimationFrame(this._redrawRaf)}exportAsSvgString(){return this.getChart().getExporter().exportAsString()}getChart(){return this.$.chart}resetDomain(){this.getChart().resetDomain()}setSeriesData(t,e){this.getChart().setSeriesData(t,e)}setSeriesMetadata(t,e){this.getChart().setSeriesMetadata(t,e)}commitChanges(){this.getChart().commitChanges()}redraw(){null!==this._redrawRaf&&cancelAnimationFrame(this._redrawRaf),this._redrawRaf=window.requestAnimationFrame((()=>{this.active?this.getChart().redraw():this._maybeRenderedInBadState=!0}))}_loadKeyChanged(){this.reset(),this._resetDomainOnNextLoad=!0}_dataSeriesChanged(){this.getChart().setVisibleSeries(this.dataSeries)}_logScaleChanged(t){this.getChart().yScaleType=t?aCt.LOG:aCt.LINEAR,this.redraw()}_fixBadStateWhenActive(){this.active&&this._maybeRenderedInBadState&&(gCt.push(this),_Ct())}_onChartAttached(){this.active||(this._maybeRenderedInBadState=!0)}};yCt.template=_e`
    <div id="chart-and-spinner-container">
      <vz-line-chart2
        id="chart"
        data-loading$="[[dataLoading]]"
        data-loaded-once$="[[dataLoadedAtLeastOnce]]"
        color-scale="[[colorScale]]"
        default-x-range="[[defaultXRange]]"
        default-y-range="[[defaultYRange]]"
        fill-area="[[fillArea]]"
        ignore-y-outliers="[[ignoreYOutliers]]"
        on-chart-attached="_onChartAttached"
        smoothing-enabled="[[smoothingEnabled]]"
        smoothing-weight="[[smoothingWeight]]"
        symbol-function="[[symbolFunction]]"
        tooltip-columns="[[tooltipColumns]]"
        tooltip-position="[[tooltipPosition]]"
        tooltip-sorting-method="[[tooltipSortingMethod]]"
        x-components-creation-method="[[xComponentsCreationMethod]]"
        x-type="[[xType]]"
        y-value-accessor="[[yValueAccessor]]"
      ></vz-line-chart2>
      <template is="dom-if" if="[[dataLoading]]">
        <div id="loading-spinner-container">
          <paper-spinner-lite active=""></paper-spinner-lite>
        </div>
      </template>
    </div>
    <style>
      :host {
        height: 100%;
        width: 100%;
        display: flex;
        flex-direction: column;
      }

      :host([_maybe-rendered-in-bad-state]) vz-line-chart {
        visibility: hidden;
      }

      #chart-and-spinner-container {
        display: flex;
        flex-grow: 1;
        position: relative;
      }

      #loading-spinner-container {
        align-items: center;
        bottom: 0;
        display: flex;
        display: flex;
        justify-content: center;
        left: 0;
        pointer-events: none;
        position: absolute;
        right: 0;
        top: 0;
      }

      vz-line-chart2 {
        -webkit-user-select: none;
        -moz-user-select: none;
      }

      vz-line-chart2[data-loading] {
        opacity: 0.3;
      }
    </style>
  `,t([o({type:Boolean,observer:"_fixBadStateWhenActive"}),e("design:type",Boolean)],yCt.prototype,"active",void 0),t([o({type:Array}),e("design:type",Array)],yCt.prototype,"dataSeries",void 0),t([o({type:Object}),e("design:type",dr)],yCt.prototype,"requestManager",void 0),t([o({type:Boolean,observer:"_logScaleChanged"}),e("design:type",Boolean)],yCt.prototype,"logScaleActive",void 0),t([o({type:Object}),e("design:type",Object)],yCt.prototype,"xComponentsCreationMethod",void 0),t([o({type:String}),e("design:type",String)],yCt.prototype,"xType",void 0),t([o({type:Object}),e("design:type",Function)],yCt.prototype,"yValueAccessor",void 0),t([o({type:Object}),e("design:type",Object)],yCt.prototype,"fillArea",void 0),t([o({type:Boolean}),e("design:type",Boolean)],yCt.prototype,"smoothingEnabled",void 0),t([o({type:Number}),e("design:type",Number)],yCt.prototype,"smoothingWeight",void 0),t([o({type:Array}),e("design:type",Array)],yCt.prototype,"tooltipColumns",void 0),t([o({type:String}),e("design:type",Object)],yCt.prototype,"tooltipSortingMethod",void 0),t([o({type:String}),e("design:type",String)],yCt.prototype,"tooltipPosition",void 0),t([o({type:Boolean}),e("design:type",Boolean)],yCt.prototype,"ignoreYOutliers",void 0),t([o({type:Array}),e("design:type",Array)],yCt.prototype,"defaultXRange",void 0),t([o({type:Array}),e("design:type",Array)],yCt.prototype,"defaultYRange",void 0),t([o({type:Object}),e("design:type",Function)],yCt.prototype,"symbolFunction",void 0),t([o({type:Object}),e("design:type",Object)],yCt.prototype,"colorScale",void 0),t([o({type:Boolean}),e("design:type",Boolean)],yCt.prototype,"_resetDomainOnNextLoad",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],yCt.prototype,"_maybeRenderedInBadState",void 0),t([a("loadKey"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],yCt.prototype,"_loadKeyChanged",null),t([a("dataSeries.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],yCt.prototype,"_dataSeriesChanged",null),yCt=t([i("tf-line-chart-data-loader")],yCt),el({moduleName:"tf-custom-scalar-card-style",styleContent:"\n    :host {\n      margin: 5px 10px;\n      display: inline-block;\n      width: 330px;\n      vertical-align: text-top;\n    }\n\n    :host([_expanded]) {\n      width: 100%;\n    }\n\n    :host([_expanded]) #tf-line-chart-data-loader-container {\n      height: 400px;\n    }\n\n    h1 {\n      font-size: 19px;\n      font-weight: normal;\n    }\n\n    #tf-line-chart-data-loader-container {\n      height: 200px;\n      width: 100%;\n    }\n\n    #buttons {\n      display: flex;\n      flex-direction: row;\n    }\n\n    paper-icon-button {\n      color: #2196f3;\n      border-radius: 100%;\n      width: 32px;\n      height: 32px;\n      padding: 4px;\n    }\n\n    paper-icon-button[selected] {\n      background: var(--tb-ui-light-accent);\n    }\n\n    .download-links {\n      display: flex;\n      height: 32px;\n    }\n\n    .download-links a {\n      font-size: 10px;\n      align-self: center;\n      margin: 2px;\n    }\n\n    .download-links paper-dropdown-menu {\n      width: 100px;\n      --paper-input-container-label: {\n        font-size: 10px;\n      }\n      --paper-input-container-input: {\n        font-size: 10px;\n      }\n    }\n  "});class vCt{constructor(t,e,n,i,r){this.run=t,this.tag=e,this.name=n,this.scalarData=i,this.symbol=r}getName(){return this.name}setData(t){this.scalarData=t}getData(){return this.scalarData}getRun(){return this.run}getTag(){return this.tag}getSymbol(){return this.symbol}}function bCt(t,e){return`${e} (${t})`}class xCt{constructor(t){this.runBasedColorScale=t}scale(t){return this.runBasedColorScale.scale(this.parseRunName(t))}parseRunName(t){const e=t.match(/\((.*)\)$/);return e?e[1]:""}}let wCt=class extends(er(ye)){constructor(){super(...arguments),this.active=!0,this._colorScale=new xCt({scale:GR}),this._nameToDataSeries={},this._expanded=!1,this._requestData=(t,e,n)=>{const i=_r().pluginRoute("custom_scalars","/scalars");Promise.all(t.map((t=>{const n=iO(i,{tag:this._tagFilter,run:t});return this.requestManager.request(n).then((n=>{e({item:t,data:n})}))}))).finally((()=>{n()}))},this._runToNextAvailableSymbolIndex={},this._matchesListOpened=!1,this._fillArea={lowerAccessor:t=>t.lower,higherAccessor:t=>t.upper},this._tooltipColumns=(()=>{const t=wTt(4),e=e=>isNaN(e)?"NaN":t(e);return[{title:"Name",evaluate:t=>t.dataset.metadata().name},{title:"Value",evaluate:t=>e(t.datum.scalar)},{title:"Lower Margin",evaluate:t=>e(t.datum.lower)},{title:"Upper Margin",evaluate:t=>e(t.datum.upper)},{title:"Step",evaluate:t=>STt(t.datum.step)},{title:"Time",evaluate:t=>ETt(t.datum.wall_time)},{title:"Relative",evaluate:t=>CTt(TTt(t.datum,0,t.dataset))}]})(),this._missingTags=[],this._missingTagsCollapsibleOpened=!1}reload(){this.$.loader.reload()}redraw(){this.$.loader.redraw()}_toggleExpanded(t){this.set("_expanded",!this._expanded),this.redraw()}_toggleLogScale(){this.set("_logScaleActive",!this._logScaleActive)}_resetDomain(){const t=this.$.loader;t&&t.resetDomain()}_csvUrl(t,e){return e?iO(this._downloadDataUrl(t,e),{format:"csv"}):""}_jsonUrl(t,e){return e?iO(this._downloadDataUrl(t,e),{format:"json"}):""}_downloadDataUrl(t,e){const n=t[e],i={tag:n.getTag(),run:n.getRun()};return iO(_r().pluginRoute("custom_scalars","/download_data"),i)}_createProcessDataFunction(t){return(e,n,i)=>{if(!i.regex_valid)return void this.set("_tagFilterInvalid",!0);const r=Se.exports.clone(this._nameToDataSeries),o=[];Se.exports.forEach(t,(t=>{let e=!1;const a=i.tag_to_events[t.value],s=i.tag_to_events[t.lower],l=i.tag_to_events[t.upper];if(Se.exports.isUndefined(a)&&(o.push(t.value),e=!0),Se.exports.isUndefined(s)&&(o.push(t.lower),e=!0),Se.exports.isUndefined(l)&&(o.push(t.upper),e=!0),e)return;const c=t=>t[1],u=this._findStepMismatch(t,a.map(c),s.map(c),l.map(c));if(u)return void this.set("_stepsMismatch",u);const h=t=>t[2],d=a.map(((t,e)=>({wall_time:new Date(1e3*t[0]),step:c(t),scalar:h(t),lower:h(s[e]),upper:h(l[e])}))),p=bCt(n,t.value),f=r[p];if(f)f.setData(d);else{const e=this._createNewDataSeries(n,t.value,p,d);r[p]=e}})),this.set("_nameToDataSeries",r);const a=Se.exports.findIndex(this._missingTags,(t=>t.run===n));if(o.length&&3!=o.length){const t={run:n,tags:o};a>=0?this.splice("_missingTags",a,1,t):this.push("_missingTags",t)}else a>=0&&this.splice("_missingTags",a,1)}}_findStepMismatch(t,e,n,i){return Se.exports.isEqual(n,e)&&Se.exports.isEqual(i,e)?null:{seriesObject:t,valueSteps:e,lowerSteps:n,upperSteps:i}}_createNewDataSeries(t,e,n,i){this._runToNextAvailableSymbolIndex[t]|=0;const r=new vCt(t,e,n,i,bTt[this._runToNextAvailableSymbolIndex[t]]);return this._runToNextAvailableSymbolIndex[t]=(this._runToNextAvailableSymbolIndex[t]+1)%bTt.length,r}_updateChart(){Se.exports.forOwn(this._nameToDataSeries,(t=>{this.$.loader.setSeriesData(t.getName(),t.getData())})),this.$.loader.commitChanges()}get _seriesNames(){const t=new Set(this.runs);return Object.entries(this._nameToDataSeries).filter((([e,n])=>t.has(n.run))).map((([t])=>t))}_determineColor(t,e){return t.scale(e)}_refreshDataSeries(){this.set("_nameToDataSeries",{})}_createSymbolFunction(){return t=>this._nameToDataSeries[t].getSymbol().method()}_determineSymbol(t,e){return t[e].getSymbol().character}get _tagFilter(){return Se.exports.flatten(this.marginChartSeries.map((t=>[t.value,t.lower,t.upper]))).map((t=>"("+this._escapeRegexCharacters(t)+")")).join("|")}_escapeRegexCharacters(t){return t.replace(/[.*+?^${}()|[\]\\]/g,"\\$&")}_getToggleCollapsibleIcon(t){return t?"expand-less":"expand-more"}_toggleMatchesOpen(){this.set("_matchesListOpened",!this._matchesListOpened)}get _titleDisplayString(){return this.title||"untitled"}_separateWithCommas(t){return t.join(", ")}_toggleMissingTagsCollapsibleOpen(){this.set("_missingTagsCollapsibleOpened",!this._missingTagsCollapsibleOpened)}_matchListEntryColorUpdated(){const t=this.$$("#match-list-repeat");t&&this.root.querySelectorAll(".match-list-entry").forEach((e=>{const n=t.itemForElement(e);e.style.color=this._determineColor(this._colorScale,n)}))}};wCt.template=_e`
    <tf-card-heading display-name="[[_titleDisplayString]]"></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader
        id="loader"
        active="[[active]]"
        color-scale="[[_colorScale]]"
        data-series="[[_seriesNames]]"
        fill-area="[[_fillArea]]"
        ignore-y-outliers="[[ignoreYOutliers]]"
        load-key="[[_tagFilter]]"
        data-to-load="[[runs]]"
        request-data="[[_requestData]]"
        log-scale-active="[[_logScaleActive]]"
        load-data-callback="[[_createProcessDataFunction(marginChartSeries)]]"
        request-manager="[[requestManager]]"
        symbol-function="[[_createSymbolFunction()]]"
        tooltip-columns="[[_tooltipColumns]]"
        tooltip-sorting-method="[[tooltipSortingMethod]]"
        x-type="[[xType]]"
      >
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
      <paper-icon-button
        selected$="[[_logScaleActive]]"
        icon="line-weight"
        on-tap="_toggleLogScale"
        title="Toggle y-axis log scale"
      ></paper-icon-button>
      <paper-icon-button
        icon="settings-overscan"
        on-tap="_resetDomain"
        title="Fit domain to data"
      ></paper-icon-button>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <paper-dropdown-menu
            no-label-float="true"
            label="series to download"
            selected-item-label="{{_dataSeriesNameToDownload}}"
          >
            <paper-listbox class="dropdown-content" slot="dropdown-content">
              <template
                is="dom-repeat"
                items="[[_seriesNames]]"
                as="dataSeriesName"
              >
                <paper-item no-label-float="true"
                  >[[dataSeriesName]]</paper-item
                >
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
          <a
            download="[[_dataSeriesNameToDownload]].csv"
            href="[[_csvUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]"
            >CSV</a
          >
          <a
            download="[[_dataSeriesNameToDownload]].json"
            href="[[_jsonUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]"
            >JSON</a
          >
        </div>
      </template>
    </div>

    <!-- here -->
    <template is="dom-if" if="[[_missingTags.length]]">
      <div class="collapsible-list-title">
        <paper-icon-button
          icon="[[_getToggleCollapsibleIcon(_missingTagsCollapsibleOpened)]]"
          on-click="_toggleMissingTagsCollapsibleOpen"
          class="toggle-collapsible-button"
        >
        </paper-icon-button>
        <span class="collapsible-title-text">
          <iron-icon icon="icons:error"></iron-icon> Missing Tags
        </span>
      </div>
      <iron-collapse opened="[[_missingTagsCollapsibleOpened]]">
        <div class="error-content">
          <iron-icon class="error-icon" icon="icons:error"></iron-icon>
          <template is="dom-repeat" items="[[_missingTags]]" as="missingEntry">
            <div class="missing-tags-for-run-container">
              Run "[[missingEntry.run]]" lacks data for tags
              <ul>
                <template
                  is="dom-repeat"
                  items="[[missingEntry.tags]]"
                  as="tag"
                >
                  <li>[[tag]]</li>
                </template>
              </ul>
            </div>
          </template>
        </div>
      </iron-collapse>
    </template>

    <template is="dom-if" if="[[_tagFilterInvalid]]">
      <div class="error-content">
        <iron-icon class="error-icon" icon="icons:error"></iron-icon>
        This regular expresion is invalid:<br />
        <span class="invalid-regex">[[_tagFilter]]</span>
      </div>
    </template>

    <template is="dom-if" if="[[_stepsMismatch]]">
      <div class="error-content">
        <iron-icon class="error-icon" icon="icons:error"></iron-icon>
        The steps for value, lower, and upper tags do not match:
        <ul>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.value]]</span>:
            [[_separateWithCommas(_stepsMismatch.valueSteps)]]
          </li>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.lower]]</span>:
            [[_separateWithCommas(_stepsMismatch.lowerSteps)]]
          </li>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.upper]]</span>:
            [[_separateWithCommas(_stepsMismatch.upperSteps)]]
          </li>
        </ul>
      </div>
    </template>

    <div id="matches-container">
      <div class="collapsible-list-title">
        <template is="dom-if" if="[[_seriesNames.length]]">
          <paper-icon-button
            icon="[[_getToggleCollapsibleIcon(_matchesListOpened)]]"
            on-click="_toggleMatchesOpen"
            class="toggle-matches-button"
          >
          </paper-icon-button>
        </template>

        <span class="collapsible-title-text">
          Matches ([[_seriesNames.length]])
        </span>
      </div>
      <template is="dom-if" if="[[_seriesNames.length]]">
        <iron-collapse opened="[[_matchesListOpened]]">
          <div id="matches-list">
            <template
              is="dom-repeat"
              items="[[_seriesNames]]"
              as="seriesName"
              id="match-list-repeat"
              on-dom-change="_matchListEntryColorUpdated"
            >
              <div class="match-list-entry">
                <span class="match-entry-symbol">
                  [[_determineSymbol(_nameToDataSeries, seriesName)]]
                </span>
                [[seriesName]]
              </div>
            </template>
          </div>
        </iron-collapse>
      </template>
    </div>

    <style include="tf-custom-scalar-card-style"></style>
    <style>
      .error-content {
        background: #f00;
        border-radius: 5px;
        color: #fff;
        margin: 10px 0 0 0;
        padding: 10px;
      }

      .error-icon {
        display: block;
        fill: #fff;
        margin: 0 auto 5px auto;
      }

      .invalid-regex {
        font-weight: bold;
      }

      .error-content ul {
        margin: 1px 0 0 0;
        padding: 0 0 0 19px;
      }

      .tag-name {
        font-weight: bold;
      }

      .collapsible-list-title {
        margin: 10px 0 5px 0;
      }

      .collapsible-title-text {
        vertical-align: middle;
      }

      #matches-list {
        max-height: 200px;
        overflow-y: auto;
      }

      .match-list-entry {
        margin: 0 0 5px 0;
      }

      .match-entry-symbol {
        font-family: arial, sans-serif;
        display: inline-block;
        width: 10px;
      }

      .missing-tags-for-run-container {
        margin: 8px 0 0 0;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],wCt.prototype,"runs",void 0),t([o({type:String}),e("design:type",String)],wCt.prototype,"xType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wCt.prototype,"active",void 0),t([o({type:String}),e("design:type",String)],wCt.prototype,"title",void 0),t([o({type:Array}),e("design:type",Array)],wCt.prototype,"marginChartSeries",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wCt.prototype,"ignoreYOutliers",void 0),t([o({type:Object}),e("design:type",dr)],wCt.prototype,"requestManager",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wCt.prototype,"showDownloadLinks",void 0),t([o({type:Object}),e("design:type",Object)],wCt.prototype,"tagMetadata",void 0),t([o({type:String}),e("design:type",String)],wCt.prototype,"tooltipSortingMethod",void 0),t([o({type:Object}),e("design:type",Object)],wCt.prototype,"_colorScale",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wCt.prototype,"_tagFilterInvalid",void 0),t([o({type:Object}),e("design:type",Object)],wCt.prototype,"_nameToDataSeries",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],wCt.prototype,"_expanded",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wCt.prototype,"_logScaleActive",void 0),t([o({type:Object}),e("design:type",Function)],wCt.prototype,"_requestData",void 0),t([o({type:Object}),e("design:type",Object)],wCt.prototype,"_runToNextAvailableSymbolIndex",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wCt.prototype,"_matchesListOpened",void 0),t([o({type:Object}),e("design:type",Object)],wCt.prototype,"_fillArea",void 0),t([o({type:Array}),e("design:type",Array)],wCt.prototype,"_tooltipColumns",void 0),t([o({type:Array}),e("design:type",Array)],wCt.prototype,"_missingTags",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wCt.prototype,"_missingTagsCollapsibleOpened",void 0),t([o({type:Object}),e("design:type",Object)],wCt.prototype,"_stepsMismatch",void 0),t([a("_nameToDataSeries"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],wCt.prototype,"_updateChart",null),t([s("_nameToDataSeries","runs"),e("design:type",Object),e("design:paramtypes",[])],wCt.prototype,"_seriesNames",null),t([a("_tagFilter"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],wCt.prototype,"_refreshDataSeries",null),t([s("marginChartSeries"),e("design:type",String),e("design:paramtypes",[])],wCt.prototype,"_tagFilter",null),t([s("title"),e("design:type",String),e("design:paramtypes",[])],wCt.prototype,"_titleDisplayString",null),wCt=t([i("tf-custom-scalar-margin-chart-card")],wCt);var SCt=Object.freeze({__proto__:null,TYPES:[],getRunsNamed:function MCt(t){return Se.exports.keys(t).sort(nr)},getTags:ar,filterTags:function ECt(t,e){let n=[];return e.forEach((e=>n=n.concat(t[e]))),Se.exports.uniq(n).sort(nr)},ListenKey:fl,BaseStore:ml,Canceller:XR,EnvironmentStore:gl,environmentStore:_l,ExperimentsStore:FR,experimentsStore:VR,RequestCancellationError:sr,InvalidRequestOptionsError:lr,RequestNetworkError:cr,get HttpMethodType(){return ur},RequestOptions:hr,RequestManager:dr,createRouter:gr,getRouter:_r,setRouter:function TCt(t){if(null==t)throw new Error("Router required, but got: "+t);mr=t},createSearchParam:vr,RunsStore:yl,runsStore:vl,addParams:iO});let CCt=class extends(er(ye)){constructor(){super(...arguments),this.active=!0,this._colorScale=new xCt({scale:GR}),this._nameToDataSeries={},this._expanded=!1,this._requestData=(t,e,n)=>{const i=_r().pluginRoute("custom_scalars","/scalars");Promise.all(t.map((t=>{const n=iO(i,{tag:this._tagFilter,run:t});return this.requestManager.request(n).then((n=>{e({item:t,data:n})}))}))).finally((()=>{n()}))},this._runToNextAvailableSymbolIndex={},this._matchesListOpened=!1}reload(){this.$.loader.reload()}redraw(){this.$.loader.redraw()}_toggleExpanded(t){this.set("_expanded",!this._expanded),this.redraw()}_toggleLogScale(){this.set("_logScaleActive",!this._logScaleActive)}_resetDomain(){const t=this.$.loader;t&&t.resetDomain()}_csvUrl(t,e){return e?iO(this._downloadDataUrl(t,e),{format:"csv"}):""}_jsonUrl(t,e){return e?iO(this._downloadDataUrl(t,e),{format:"json"}):""}_downloadDataUrl(t,e){const n=t[e],i={tag:n.getTag(),run:n.getRun()};return iO(_r().pluginRoute("custom_scalars","/download_data"),i)}_createProcessDataFunction(){return(t,e,n)=>{if(n.regex_valid){const t=Se.exports.clone(this._nameToDataSeries);Se.exports.forOwn(n.tag_to_events,((n,i)=>{const r=n.map((t=>({wall_time:new Date(1e3*t[0]),step:t[1],scalar:t[2]}))),o=bCt(e,i),a=t[o];if(a)a.setData(r);else{Se.exports.isUndefined(this._runToNextAvailableSymbolIndex[e])&&(this._runToNextAvailableSymbolIndex[e]=0);const n=new vCt(e,i,o,r,bTt[this._runToNextAvailableSymbolIndex[e]]);t[o]=n,this._runToNextAvailableSymbolIndex[e]=(this._runToNextAvailableSymbolIndex[e]+1)%bTt.length}})),this.set("_nameToDataSeries",t)}}}_updateChart(){Object.entries(this._nameToDataSeries).forEach((([t,e])=>{this.$.loader.setSeriesData(t,e.getData())})),this.$.loader.commitChanges()}_computeSelectedRunsSet(t){const e={};return Se.exports.forEach(t,(t=>{e[t]=1})),e}get _seriesNames(){const t=new Set(this.runs);return Object.entries(this._nameToDataSeries).filter((([e,n])=>t.has(n.run))).map((([t])=>t))}_determineColor(t,e){return t.scale(e)}_refreshDataSeries(){this.set("_nameToDataSeries",{})}_createSymbolFunction(){return t=>this._nameToDataSeries[t].getSymbol().method()}_determineSymbol(t,e){return t[e].getSymbol().character}get _tagFilter(){var t=this.tagRegexes;return 1===t.length?t[0]:t.map((t=>"("+t+")")).join("|")}_getToggleMatchesIcon(t){return t?"expand-less":"expand-more"}_toggleMatchesOpen(){this.set("_matchesListOpened",!this._matchesListOpened)}get _titleDisplayString(){return this.title||"untitled"}_matchListEntryColorUpdated(t){const e=this.$$("#match-list-repeat");e&&this.root.querySelectorAll(".match-list-entry").forEach((t=>{const n=e.itemForElement(t);t.style.color=this._determineColor(this._colorScale,n)}))}};CCt.template=_e`
    <tf-card-heading display-name="[[_titleDisplayString]]"></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader
        id="loader"
        active="[[active]]"
        color-scale="[[_colorScale]]"
        data-series="[[_seriesNames]]"
        ignore-y-outliers="[[ignoreYOutliers]]"
        load-key="[[_tagFilter]]"
        data-to-load="[[runs]]"
        request-data="[[_requestData]]"
        log-scale-active="[[_logScaleActive]]"
        load-data-callback="[[_createProcessDataFunction()]]"
        request-manager="[[requestManager]]"
        smoothing-enabled="[[smoothingEnabled]]"
        smoothing-weight="[[smoothingWeight]]"
        symbol-function="[[_createSymbolFunction()]]"
        tooltip-sorting-method="[[tooltipSortingMethod]]"
        x-type="[[xType]]"
      >
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
      <paper-icon-button
        selected$="[[_logScaleActive]]"
        icon="line-weight"
        on-tap="_toggleLogScale"
        title="Toggle y-axis log scale"
      ></paper-icon-button>
      <paper-icon-button
        icon="settings-overscan"
        on-tap="_resetDomain"
        title="Fit domain to data"
      ></paper-icon-button>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <paper-dropdown-menu
            no-label-float="true"
            label="series to download"
            selected-item-label="{{_dataSeriesNameToDownload}}"
          >
            <paper-listbox class="dropdown-content" slot="dropdown-content">
              <template
                is="dom-repeat"
                items="[[_seriesNames]]"
                as="dataSeriesName"
              >
                <paper-item no-label-float="true"
                  >[[dataSeriesName]]</paper-item
                >
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
          <a
            download="[[_dataSeriesNameToDownload]].csv"
            href="[[_csvUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]"
            >CSV</a
          >
          <a
            download="[[_dataSeriesNameToDownload]].json"
            href="[[_jsonUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]"
            >JSON</a
          >
        </div>
      </template>
    </div>
    <div id="matches-container">
      <div id="matches-list-title">
        <template is="dom-if" if="[[_seriesNames.length]]">
          <paper-icon-button
            icon="[[_getToggleMatchesIcon(_matchesListOpened)]]"
            on-click="_toggleMatchesOpen"
            class="toggle-matches-button"
          >
          </paper-icon-button>
        </template>

        <span class="matches-text"> Matches ([[_seriesNames.length]]) </span>
      </div>
      <template is="dom-if" if="[[_seriesNames.length]]">
        <iron-collapse opened="[[_matchesListOpened]]">
          <div id="matches-list">
            <template
              is="dom-repeat"
              items="[[_seriesNames]]"
              as="seriesName"
              id="match-list-repeat"
              on-dom-change="_matchListEntryColorUpdated"
            >
              <div class="match-list-entry">
                <span class="match-entry-symbol">
                  [[_determineSymbol(_nameToDataSeries, seriesName)]]
                </span>
                [[seriesName]]
              </div>
            </template>
          </div>
        </iron-collapse>
      </template>
    </div>

    <style include="tf-custom-scalar-card-style"></style>
    <style>
      #matches-list-title {
        margin: 10px 0 5px 0;
      }

      #matches-list {
        max-height: 200px;
        overflow-y: auto;
      }

      .match-list-entry {
        margin: 0 0 5px 0;
      }

      .match-entry-symbol {
        font-family: arial, sans-serif;
        display: inline-block;
        width: 10px;
      }

      .matches-text {
        vertical-align: middle;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],CCt.prototype,"runs",void 0),t([o({type:String}),e("design:type",String)],CCt.prototype,"xType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CCt.prototype,"active",void 0),t([o({type:String}),e("design:type",String)],CCt.prototype,"title",void 0),t([o({type:Array}),e("design:type",Array)],CCt.prototype,"tagRegexes",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CCt.prototype,"ignoreYOutliers",void 0),t([o({type:Object}),e("design:type",dr)],CCt.prototype,"requestManager",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CCt.prototype,"showDownloadLinks",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CCt.prototype,"smoothingEnabled",void 0),t([o({type:Number}),e("design:type",Number)],CCt.prototype,"smoothingWeight",void 0),t([o({type:Object}),e("design:type",Object)],CCt.prototype,"tagMetadata",void 0),t([o({type:String}),e("design:type",String)],CCt.prototype,"tooltipSortingMethod",void 0),t([o({type:Object}),e("design:type",xCt)],CCt.prototype,"_colorScale",void 0),t([o({type:Object}),e("design:type",Object)],CCt.prototype,"_nameToDataSeries",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],CCt.prototype,"_expanded",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CCt.prototype,"_logScaleActive",void 0),t([o({type:Object}),e("design:type",Function)],CCt.prototype,"_requestData",void 0),t([o({type:Object}),e("design:type",Object)],CCt.prototype,"_runToNextAvailableSymbolIndex",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CCt.prototype,"_matchesListOpened",void 0),t([a("_nameToDataSeries"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],CCt.prototype,"_updateChart",null),t([s("_nameToDataSeries","runs"),e("design:type",Object),e("design:paramtypes",[])],CCt.prototype,"_seriesNames",null),t([a("_tagFilter"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],CCt.prototype,"_refreshDataSeries",null),t([s("tagRegexes"),e("design:type",String),e("design:paramtypes",[])],CCt.prototype,"_tagFilter",null),t([s("title"),e("design:type",String),e("design:paramtypes",[])],CCt.prototype,"_titleDisplayString",null),CCt=t([i("tf-custom-scalar-multi-line-chart-card")],CCt);let ACt=class extends ye{constructor(){super(...arguments),this._requestManager=new dr(50),this._canceller=new XR,this._showDownloadLinks=Hs("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}).call(this),this._smoothingWeight=Gs("_smoothingWeight",{defaultValue:.6}).call(this),this._ignoreYOutliers=Hs("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0}).call(this),this._xType="step",this._active=!0,this.reloadOnReady=!0,this._showDownloadLinksObserver=Fs("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}),this._smoothingWeightObserver=Ws("_smoothingWeight",{defaultValue:.6}),this._ignoreYOutliersObserver=Fs("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0})}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){const t=_r().pluginsListing(),e=this._canceller.cancellable((t=>{t.cancelled||(this.set("_dataNotFound",!t.value.custom_scalars),this._dataNotFound||this._retrieveLayoutAndData())}));this._requestManager.request(t).then(e)}_reloadCharts(){this.root.querySelectorAll("tf-custom-scalar-margin-chart-card, tf-custom-scalar-multi-line-chart-card").forEach((t=>{t.reload()}))}_retrieveLayoutAndData(){const t=_r().pluginRoute("custom_scalars","/layout"),e=this._canceller.cancellable((t=>{t.cancelled||(this.set("_layout",t.value),this._dataNotFound||this._reloadCharts())}));this._requestManager.request(t).then(e)}get _smoothingEnabled(){return this._smoothingWeight>0}get _categories(){var t=this._layout;if(!t.category)return[];let e=!1;return this._openedCategories||(e=!0,this._openedCategories={}),t.category.map((t=>(e&&!t.closed&&(this._openedCategories[t.title]=!0),{name:t.title,items:t.chart,metadata:{type:br.PREFIX_GROUP,opened:!!this._openedCategories[t.title]}})))}_categoryOpenedToggled(t){const e=t.target;e.opened?this._openedCategories[e.category.name]=!0:delete this._openedCategories[e.category.name]}};ACt.template=_e`
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="settings">
          <div class="sidebar-section">
            <div class="line-item">
              <paper-checkbox checked="{{_showDownloadLinks}}"
                >Show data download links</paper-checkbox
              >
            </div>
            <div class="line-item">
              <paper-checkbox checked="{{_ignoreYOutliers}}"
                >Ignore outliers in chart scaling</paper-checkbox
              >
            </div>
            <div id="tooltip-sorting">
              <div id="tooltip-sorting-label">Tooltip sorting method:</div>
              <paper-dropdown-menu
                no-label-float=""
                selected-item-label="{{_tooltipSortingMethod}}"
              >
                <paper-listbox
                  class="dropdown-content"
                  selected="0"
                  slot="dropdown-content"
                >
                  <paper-item>default</paper-item>
                  <paper-item>descending</paper-item>
                  <paper-item>ascending</paper-item>
                  <paper-item>nearest</paper-item>
                </paper-listbox>
              </paper-dropdown-menu>
            </div>
          </div>
          <div class="sidebar-section">
            <tf-smoothing-input
              weight="{{_smoothingWeight}}"
              step="0.001"
              min="0"
              max="1"
            ></tf-smoothing-input>
          </div>
          <div class="sidebar-section">
            <tf-option-selector
              id="x-type-selector"
              name="Horizontal Axis"
              selected-id="{{_xType}}"
            >
              <paper-button id="step">step</paper-button
              ><!--
            --><paper-button id="relative">relative</paper-button
              ><!--
            --><paper-button id="wall_time">wall</paper-button>
            </tf-option-selector>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center" id="categories-container">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>The custom scalars dashboard is inactive.</h3>
            <p>Probable causes:</p>
            <ol>
              <li>You haven't laid out the dashboard.</li>
              <li>You haven’t written any scalar data to your event files.</li>
            </ol>

            <p>
              To lay out the dashboard, pass a <code>Layout</code> protobuffer
              to the <code>set_layout</code> method. For example,
            </p>
            <pre>
from tensorboard import summary
from tensorboard.plugins.custom_scalar import layout_pb2
...
# This action does not have to be performed at every step, so the action is not
# taken care of by an op in the graph. We only need to specify the layout once
# (instead of per step).
layout_summary = summary_lib.custom_scalar_pb(layout_pb2.Layout(
  category=[
    layout_pb2.Category(
      title='losses',
      chart=[
          layout_pb2.Chart(
              title='losses',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'loss.*'],
              )),
          layout_pb2.Chart(
              title='baz',
              margin=layout_pb2.MarginChartContent(
                series=[
                  layout_pb2.MarginChartContent.Series(
                    value='loss/baz/scalar_summary',
                    lower='baz_lower/baz/scalar_summary',
                    upper='baz_upper/baz/scalar_summary'),
                ],
              )),
      ]),
    layout_pb2.Category(
      title='trig functions',
      chart=[
          layout_pb2.Chart(
              title='wave trig functions',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'trigFunctions/cosine', r'trigFunctions/sine'],
              )),
          # The range of tangent is different. Let's give it its own chart.
          layout_pb2.Chart(
              title='tan',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'trigFunctions/tangent'],
              )),
      ],
      # This category we care less about. Let's make it initially closed.
      closed=True),
  ]))
writer.add_summary(layout_summary)
</pre
            >
            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              as="chart"
              category="[[category]]"
              disable-pagination
              initial-opened="[[category.metadata.opened]]"
            >
              <template>
                <template is="dom-if" if="[[chart.multiline]]">
                  <tf-custom-scalar-multi-line-chart-card
                    active="[[active]]"
                    request-manager="[[_requestManager]]"
                    runs="[[_selectedRuns]]"
                    title="[[chart.title]]"
                    x-type="[[_xType]]"
                    smoothing-enabled="[[_smoothingEnabled]]"
                    smoothing-weight="[[_smoothingWeight]]"
                    tooltip-sorting-method="[[tooltipSortingMethod]]"
                    ignore-y-outliers="[[_ignoreYOutliers]]"
                    show-download-links="[[_showDownloadLinks]]"
                    tag-regexes="[[chart.multiline.tag]]"
                  ></tf-custom-scalar-multi-line-chart-card>
                </template>
                <template is="dom-if" if="[[chart.margin]]">
                  <tf-custom-scalar-margin-chart-card
                    active="[[active]]"
                    request-manager="[[_requestManager]]"
                    runs="[[_selectedRuns]]"
                    title="[[chart.title]]"
                    x-type="[[_xType]]"
                    tooltip-sorting-method="[[tooltipSortingMethod]]"
                    ignore-y-outliers="[[_ignoreYOutliers]]"
                    show-download-links="[[_showDownloadLinks]]"
                    margin-chart-series="[[chart.margin.series]]"
                  ></tf-custom-scalar-margin-chart-card>
                </template>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      #tooltip-sorting {
        align-items: center;
        display: flex;
        font-size: 14px;
        margin-top: 15px;
      }
      #tooltip-sorting paper-dropdown-menu {
        margin-left: 10px;
        --paper-input-container-focus-color: var(--tb-orange-strong);
        width: 105px;
      }
      .line-item {
        display: block;
        padding-top: 5px;
      }
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  `,t([o({type:Object}),e("design:type",dr)],ACt.prototype,"_requestManager",void 0),t([o({type:Object}),e("design:type",XR)],ACt.prototype,"_canceller",void 0),t([o({type:Array}),e("design:type",Array)],ACt.prototype,"_selectedRuns",void 0),t([o({type:Boolean,notify:!0,observer:"_showDownloadLinksObserver"}),e("design:type",Boolean)],ACt.prototype,"_showDownloadLinks",void 0),t([o({type:Number,notify:!0,observer:"_smoothingWeightObserver"}),e("design:type",Number)],ACt.prototype,"_smoothingWeight",void 0),t([o({type:Boolean,observer:"_ignoreYOutliersObserver"}),e("design:type",Boolean)],ACt.prototype,"_ignoreYOutliers",void 0),t([o({type:String}),e("design:type",String)],ACt.prototype,"_xType",void 0),t([o({type:Object}),e("design:type",Object)],ACt.prototype,"_layout",void 0),t([o({type:Boolean}),e("design:type",Boolean)],ACt.prototype,"_dataNotFound",void 0),t([o({type:Object}),e("design:type",Object)],ACt.prototype,"_openedCategories",void 0),t([o({type:Boolean}),e("design:type",Boolean)],ACt.prototype,"_active",void 0),t([o({type:Boolean}),e("design:type",Boolean)],ACt.prototype,"reloadOnReady",void 0),t([s("_smoothingWeight"),e("design:type",Boolean),e("design:paramtypes",[])],ACt.prototype,"_smoothingEnabled",null),t([s("_layout"),e("design:type",Array),e("design:paramtypes",[])],ACt.prototype,"_categories",null),ACt=t([i("tf-custom-scalar-dashboard")],ACt);class kCt{constructor(t,e){this.run2datasets={},this.colorScale=e,this.buildChart(t)}getDataset(t){return void 0===this.run2datasets[t]&&(this.run2datasets[t]=new rO.Dataset([],{run:t})),this.run2datasets[t]}buildChart(t){this.outer&&this.outer.destroy();let e=ATt(t);this.xAccessor=e.accessor,this.xScale=e.scale,this.xAxis=e.axis,this.xAxis.margin(0),this.xAxis.tickLabelPadding(3),this.yScale=new rO.Scales.Linear,this.yAxis=new rO.Axes.Numeric(this.yScale,"left");let n=wTt(3);this.yAxis.margin(0).tickLabelPadding(5).formatter(n),this.yAxis.usesTextWidthApproximation(!0);let i=this.buildPlot(this.xAccessor,this.xScale,this.yScale);this.gridlines=new rO.Components.Gridlines(this.xScale,this.yScale),this.center=new rO.Components.Group([this.gridlines,i]),this.outer=new rO.Components.Table([[this.yAxis,this.center],[null,this.xAxis]])}buildPlot(t,e,n){let i=[0,228,1587,3085,5e3,6915,8413,9772,1e4],r=Se.exports.range(i.length-1).map((t=>(i[t+1]-i[t])/2500)),o=i.map(((t,e)=>t=>t[e][1])),a=o[4],s=Se.exports.range(o.length-1).map((i=>{let a=new rO.Plots.Area;a.x(t,e);let s=i>4?o[i]:o[i+1];return a.y(i>4?o[i+1]:o[i],n),a.y0(s),a.attr("fill",((t,e,n)=>this.colorScale.scale(n.metadata().run))),a.attr("stroke",((t,e,n)=>this.colorScale.scale(n.metadata().run))),a.attr("stroke-weight",((t,e,n)=>"0.5px")),a.attr("stroke-opacity",(()=>r[i])),a.attr("fill-opacity",(()=>r[i])),a})),l=new rO.Plots.Line;return l.x(t,e),l.y(a,n),l.attr("stroke",((t,e,n)=>this.colorScale.scale(n.run))),this.plots=s,new rO.Components.Group(s)}setVisibleSeries(t){this.runs=t;let e=t.map((t=>this.getDataset(t)));this.plots.forEach((t=>t.datasets(e)))}setSeriesData(t,e){this.getDataset(t).data(e)}renderTo(t){this.targetSVG=t,this.outer.renderTo(t)}redraw(){this.outer.redraw()}destroy(){this.outer.destroy()}}let LCt=class extends(er(ye)){constructor(){super(...arguments),this.colorScale=(new rO.Scales.Color).range(gA.slice()),this.xType="step",this._visibleSeriesCache=[],this._seriesDataCache={},this._makeChartAsyncCallbackId=null}setVisibleSeries(t){this._visibleSeriesCache=t,this._chart&&(this._chart.setVisibleSeries(t),this.redraw())}setSeriesData(t,e){this._seriesDataCache[t]=e,this._chart&&this._chart.setSeriesData(t,e)}redraw(){this._chart.redraw()}_makeChart(){var t=this.xType,e=this.colorScale,n=this._attached;null===this._makeChartAsyncCallbackId&&this.cancelAsync(this._makeChartAsyncCallbackId),this._makeChartAsyncCallbackId=this.async((function(){if(this._makeChartAsyncCallbackId=null,n){this._chart&&this._chart.destroy();var i=new kCt(t,e),r=Su(this.$.chartdiv);i.renderTo(r),this._chart=i}}),350)}_reloadFromCache(){this._chart&&(this._chart.setVisibleSeries(this._visibleSeriesCache),this._visibleSeriesCache.forEach(function(t){this._chart.setSeriesData(t,this._seriesDataCache[t]||[])}.bind(this)))}attached(){this._attached=!0}detached(){this._attached=!1}};LCt.template=_e`
    <style include="plottable-style"></style>
    <div id="chartdiv"></div>
    <style>
      :host {
        -webkit-user-select: none;
        -moz-user-select: none;
        display: flex;
        flex-direction: column;
        flex-grow: 1;
        flex-shrink: 1;
        position: relative;
      }
      #chartdiv {
        -webkit-user-select: none;
        -moz-user-select: none;
        flex-grow: 1;
        flex-shrink: 1;
      }
      .plottable .axis text {
        fill: currentColor;
      }
    </style>
  `,t([o({type:Object}),e("design:type",rO.Scales.Color)],LCt.prototype,"colorScale",void 0),t([o({type:String}),e("design:type",String)],LCt.prototype,"xType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],LCt.prototype,"_attached",void 0),t([o({type:Object}),e("design:type",kCt)],LCt.prototype,"_chart",void 0),t([o({type:Array}),e("design:type",Array)],LCt.prototype,"_visibleSeriesCache",void 0),t([o({type:Object}),e("design:type",Object)],LCt.prototype,"_seriesDataCache",void 0),t([o({type:Number}),e("design:type",Number)],LCt.prototype,"_makeChartAsyncCallbackId",void 0),t([a("xType","colorScale","_attached"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],LCt.prototype,"_makeChart",null),t([a("_chart"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],LCt.prototype,"_reloadFromCache",null),LCt=t([i("vz-distribution-chart")],LCt);let PCt=class extends(LTt(er(ye))){constructor(){super(...arguments),this.getDataLoadName=({run:t})=>t,this.requestData=(t,e,n)=>{const i=_r().pluginRoute("distributions","/distributions");Promise.all(t.map((t=>{const n=iO(i,{tag:t.tag,run:t.run});return this.requestManager.request(n).then((n=>{e({item:t,data:n})}))}))).finally((()=>{n()}))},this.loadDataCallback=(t,e,n)=>{const i=n.map((t=>{const[e,n,i]=t;return i.wall_time=new Date(1e3*e),i.step=n,i})),r=this.getDataLoadName(e);this.$.chart.setSeriesData(r,i),this.$.chart.setVisibleSeries([r])},this._colorScale={scale:GR},this._expanded=!1,this._canceller=new XR}_reloadOnRunTagChange(){this.reload()}_updateDataToLoad(){this.dataToLoad=[{run:this.run,tag:this.tag}]}get _runColor(){return this._colorScale.scale(this.run)}redraw(){this.$.chart.redraw()}_toggleExpanded(t){this.set("_expanded",!this._expanded),this.redraw()}};PCt.template=_e`
    <tf-card-heading
      tag="[[tag]]"
      run="[[run]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
      color="[[_runColor]]"
    ></tf-card-heading>
    <!--
      The main distribution that we render. Data is set directly with
      \`setSeriesData\`, not with a bound property.
    -->
    <vz-distribution-chart
      id="chart"
      x-type="[[xType]]"
      color-scale="[[_colorScale]]"
    ></vz-distribution-chart>
    <div style="display: flex; flex-direction: row;">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
    </div>
    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 330px;
        height: 235px;
        margin-right: 10px;
        margin-bottom: 15px;
      }
      :host([_expanded]) {
        width: 700px;
        height: 500px;
      }

      vz-histogram-timeseries {
        -moz-user-select: none;
        -webkit-user-select: none;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }
      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      tf-card-heading {
        margin-bottom: 10px;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],PCt.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],PCt.prototype,"tag",void 0),t([o({type:Object}),e("design:type",Object)],PCt.prototype,"tagMetadata",void 0),t([o({type:String}),e("design:type",String)],PCt.prototype,"xType",void 0),t([o({type:Object}),e("design:type",Object)],PCt.prototype,"getDataLoadName",void 0),t([o({type:Object}),e("design:type",Object)],PCt.prototype,"loadDataCallback",void 0),t([o({type:Object}),e("design:type",Object)],PCt.prototype,"_colorScale",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],PCt.prototype,"_expanded",void 0),t([o({type:Object}),e("design:type",dr)],PCt.prototype,"requestManager",void 0),t([o({type:Object}),e("design:type",XR)],PCt.prototype,"_canceller",void 0),t([a("run","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],PCt.prototype,"_reloadOnRunTagChange",null),t([a("run","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],PCt.prototype,"_updateDataToLoad",null),t([s("run"),e("design:type",String),e("design:paramtypes",[])],PCt.prototype,"_runColor",null),PCt=t([i("tf-distribution-loader")],PCt);let NCt=class extends(er(ye)){constructor(){super(...arguments),this.reloadOnReady=!0,this._xType="step",this._requestManager=new dr}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){this._fetchTags().then((()=>{this._reloadDistributions()}))}_fetchTags(){const t=_r().pluginRoute("distributions","/tags");return this._requestManager.request(t).then((t=>{if(Se.exports.isEqual(t,this._runToTagInfo))return;const e=Se.exports.mapValues(t,(t=>Object.keys(t))),n=ar(e);this.set("_dataNotFound",0===n.length),this.set("_runToTag",e),this.set("_runToTagInfo",t),this.async((()=>{this.set("_categoriesDomReady",!0)}))}))}_reloadDistributions(){this.root.querySelectorAll("tf-distribution-loader").forEach((t=>{t.reload()}))}_shouldOpen(t){return t<=2}get _categories(){return Mr(this._runToTag,this._selectedRuns,this._tagFilter)}_tagMetadata(t,e,n){return t[e][n]}};NCt.template=_e`
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="settings">
          <div class="sidebar-section">
            <tf-option-selector
              id="xTypeSelector"
              name="Horizontal axis"
              selected-id="{{_xType}}"
            >
              <paper-button id="step">step</paper-button>
              <paper-button id="relative">relative</paper-button>
              <paper-button id="wall_time">wall</paper-button>
            </tf-option-selector>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>

      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No distribution data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any histogram data to your event files.
                (Histograms and distributions both use the histogram summary
                operation.)
              </li>

              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
            >
              <template>
                <tf-distribution-loader
                  active="[[active]]"
                  run="[[item.run]]"
                  tag="[[item.tag]]"
                  tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]"
                  x-type="[[_xType]]"
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e=this._sucs[t];if(e)return eBt.keys(e)},rBt.prototype.neighbors=function(t){var e=this.predecessors(t);if(e)return eBt.union(e,this.successors(t))},rBt.prototype.isLeaf=function(t){return 0===(this.isDirected()?this.successors(t):this.neighbors(t)).length},rBt.prototype.filterNodes=function(t){var e=new this.constructor({directed:this._isDirected,multigraph:this._isMultigraph,compound:this._isCompound});e.setGraph(this.graph());var n=this;eBt.each(this._nodes,(function(n,i){t(i)&&e.setNode(i,n)})),eBt.each(this._edgeObjs,(function(t){e.hasNode(t.v)&&e.hasNode(t.w)&&e.setEdge(t,n.edge(t))}));var i={};function r(t){var o=n.parent(t);return void 0===o||e.hasNode(o)?(i[t]=o,o):o in i?i[o]:r(o)}return this._isCompound&&eBt.each(e.nodes(),(function(t){e.setParent(t,r(t))})),e},rBt.prototype.setDefaultEdgeLabel=function(t){return eBt.isFunction(t)||(t=eBt.constant(t)),this._defaultEdgeLabelFn=t,this},rBt.prototype.edgeCount=function(){return 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null})(t.attr),this.compatible=!1,this.type=jGt.OP,this.isGroupNode=!1,this.cardinality=1,this.inEmbeddings=[],this.outEmbeddings=[],this.parentNode=null,this.include=GGt.UNSPECIFIED,this.owningSeries=null}}function $Gt(t,e={}){return new KGt(t,e)}class KGt{constructor(t,e={}){this.name=t,this.type=jGt.META,this.depth=1,this.isGroupNode=!0,this.cardinality=0,this.metagraph=oWt(t,UGt.META,e),this.bridgegraph=null,this.opHistogram={},this.deviceHistogram={},this.xlaClusterHistogram={},this.compatibilityHistogram={compatible:0,incompatible:0},this.templateId=null,this.parentNode=null,this.hasNonControlEdges=!1,this.include=GGt.UNSPECIFIED,this.associatedFunction=""}getFirstChild(){return this.metagraph.node(this.metagraph.nodes()[0])}getRootOp(){let t=this.name.split("/");return this.metagraph.node(this.name+"/("+t[t.length-1]+")")}leaves(){let t,e=[],n=[this];for(;n.length;){let i=n.shift();i.isGroupNode?(t=i.metagraph,Se.exports.each(t.nodes(),(e=>n.push(t.node(e))))):e.push(i.name)}return e}}function ZGt(t,e){return new JGt(t,e)}class JGt{constructor(t,e){this.v=t,this.w=e,this.baseEdgeList=[],this.inbound=null,this.numRegularEdges=0,this.numControlEdges=0,this.numRefEdges=0,this.totalSize=0}addBaseEdge(t,e){this.baseEdgeList.push(t),t.isControlDependency?this.numControlEdges+=1:this.numRegularEdges+=1,t.isReferenceEdge&&(this.numRefEdges+=1),this.totalSize+=JGt.computeSizeOfEdge(t,e),e.maxMetaEdgeSize=Math.max(e.maxMetaEdgeSize,this.totalSize)}static computeSizeOfEdge(t,e){let n=e.node(t.v);if(!n.outputShapes)return 1;e.hasShapeInfo=!0;const i=Object.keys(n.outputShapes).map((t=>n.outputShapes[t])).map((t=>null==t?1:t.reduce(((t,e)=>(-1===e&&(e=1),t*e)),1)));return Se.exports.sum(i)}}function QGt(t,e,n,i,r,o){return new eWt(t,e,n,i,r,o)}function tWt(t,e,n,i,r){return(n?n+"/":"")+t+(void 0!==i&&void 0!==r?"["+i+"-"+r+"]":"#")+e}class eWt{constructor(t,e,n,i,r,o){this.name=r||tWt(t,e,n),this.type=jGt.SERIES,this.hasLoop=!1,this.prefix=t,this.suffix=e,this.clusterId=i,this.ids=[],this.parent=n,this.isGroupNode=!0,this.cardinality=0,this.metagraph=oWt(r,UGt.SERIES,o),this.bridgegraph=null,this.parentNode=null,this.deviceHistogram={},this.xlaClusterHistogram={},this.compatibilityHistogram={compatible:0,incompatible:0},this.hasNonControlEdges=!1,this.include=GGt.UNSPECIFIED}}const nWt=/^([^:]+):((\w+:|)\d+)$/;function iWt(t,e,n,i,r,o){e!==n.name&&t.edges.push({v:e,w:n.name,outputTensorKey:i.outputTensorKey,isControlDependency:i.isControlDependency,isReferenceEdge:!0===r.refEdges[n.op+" "+o]})}const rWt={enableEmbedding:!0,inEmbeddingTypes:["Const"],outEmbeddingTypes:["^[a-zA-Z]+Summary$"],refEdges:{"Assign 0":!0,"AssignAdd 0":!0,"AssignSub 0":!0,"assign 0":!0,"assign_add 0":!0,"assign_sub 0":!0,"count_up_to 0":!0,"ScatterAdd 0":!0,"ScatterSub 0":!0,"ScatterUpdate 0":!0,"scatter_add 0":!0,"scatter_sub 0":!0,"scatter_update 0":!0}};function oWt(t,e,n={}){const i=new fGt.graphlib.Graph(n);return i.setGraph({name:t,rankdir:n.rankdir||"BT",type:e}),i}function aWt(t){return function(e){for(let n=0;n<t.length;n++){let i=new RegExp(t[n]);if("string"==typeof e.op&&e.op.match(i))return!0}return!1}}function sWt(t){let e=t.split(HGt);return t+HGt+"("+e[e.length-1]+")"}function lWt(t){let e=t.nodes().map((function(e){return t.neighbors(e).length}));return e.sort(),e}function cWt(t,e){let n=[],i=t.indexOf(HGt);for(;i>=0;)n.push(t.substring(0,i)),i=t.indexOf(HGt,i+1);if(e){let i=e[t];i&&n.push(i)}return n.push(t),n}function uWt(t){return t===GGt.EXCLUDE?"Add to main graph":"Remove from main graph"}let hWt="#0f9d58",dWt="#db4437",pWt={DEFAULT_FILL:"#d9d9d9",DEFAULT_STROKE:"#a6a6a6",SATURATION:.6,LIGHTNESS:.85,EXPANDED_COLOR:"#f0f0f0",HUES:[220,100,180,40,20,340,260,300,140,60],STRUCTURE_PALETTE(t,e){let n=pWt.HUES,i=n[t%n.length],r=Math.sin(i*Math.PI/360);return $h(i,.01*(e?30:90-60*r),.01*(e?95:80)).toString()},DEVICE_PALETTE:t=>pWt.STRUCTURE_PALETTE(t),XLA_CLUSTER_PALETTE:t=>pWt.STRUCTURE_PALETTE(t),UNKNOWN:"#eee",GRADIENT_OUTLINE:"#888"};const fWt=["NoOp"],mWt=[],gWt=["#fff5f0","#fb6a4a"],_Wt=new RegExp("^(?:__function_library__)?(\\w+)_[a-z0-9]{8}(?:_\\d+)?$");class yWt{constructor(t,e,n){this.hierarchy=t,this.displayingStats=e,this.autoExtractNodes=n,this.index={},this.renderedOpNames=[],this.computeScales(),this.hasSubhierarchy={},this.root=new TWt(t.root,t.graphOptions),this.index[t.root.name]=this.root,this.renderedOpNames.push(t.root.name),this.buildSubhierarchy(t.root.name),this.root.expanded=!0,this.traceInputs=!1}computeScales(){this.deviceColorMap=EM().domain(this.hierarchy.devices).range(Se.exports.map(zl(this.hierarchy.devices.length),pWt.DEVICE_PALETTE)),this.xlaClusterColorMap=EM().domain(this.hierarchy.xlaClusters).range(Se.exports.map(zl(this.hierarchy.xlaClusters.length),pWt.XLA_CLUSTER_PALETTE));let t=this.hierarchy.root.metagraph,e=Wl(t.nodes(),((e,n)=>{let i=t.node(e);if(null!=i.stats)return i.stats.totalBytes}));this.memoryUsageScale=VM().domain([0,e]).range(gWt);let n=Wl(t.nodes(),((e,n)=>{let i=t.node(e);if(null!=i.stats)return i.stats.getTotalMicros()}));this.computeTimeScale=VM().domain([0,n]).range(gWt),this.edgeWidthSizedBasedScale=this.hierarchy.hasShapeInfo?EGt:VM().domain([1,this.hierarchy.maxMetaEdgeSize]).range([MGt,12])}getRenderNodeByName(t){return this.index[t]}getNodeByName(t){return this.hierarchy.node(t)}colorHistogram(t,e){if(Object.keys(t).length>0){const n=Se.exports.sum(Object.keys(t).map((e=>t[e])));return Object.keys(t).map((i=>({color:e(i),proportion:t[i]/n})))}return null}getOrCreateRenderNodeByName(t){if(!t)return null;if(t in this.index)return this.index[t];let e=this.hierarchy.node(t);if(!e)return null;let n=e.isGroupNode?new TWt(e,this.hierarchy.graphOptions):new wWt(e);this.index[t]=n,this.renderedOpNames.push(t),e.stats&&(n.memoryColor=this.memoryUsageScale(e.stats.totalBytes),n.computeTimeColor=this.computeTimeScale(e.stats.getTotalMicros())),n.isFadedOut=this.displayingStats&&!OGt(e.stats);var i=null,r=null,o=null;if(e.isGroupNode){i=e.deviceHistogram,r=e.xlaClusterHistogram;let t=e.compatibilityHistogram.compatible,n=e.compatibilityHistogram.incompatible;0==t&&0==n||(o=t/(t+n))}else{let t=n.node.device;t&&(i={[t]:1});let e=n.node.xlaCluster;e&&(r={[e]:1}),n.node.type===jGt.OP&&(o=n.node.compatible?1:0)}return i&&(n.deviceColors=this.colorHistogram(i,this.deviceColorMap)),r&&(n.xlaClusterColors=this.colorHistogram(r,this.xlaClusterColorMap)),null!=o&&(n.compatibilityColors=[{color:hWt,proportion:o},{color:dWt,proportion:1-o}]),this.index[t]}getNearestVisibleAncestor(t){let e=cWt(t),n=0,i=null,r=t;for(;n<e.length&&(r=e[n],i=this.getRenderNodeByName(r),i.expanded);n++);if(n==e.length-2){let t=e[n+1];if(i.inAnnotations.nodeNames[t])return t;if(i.outAnnotations.nodeNames[t])return t}return r}setDepth(t){CWt(this.root,+t)}isNodeAuxiliary(t){let e=this.getRenderNodeByName(t.node.parentNode.name),n=Se.exports.find(e.isolatedInExtract,(e=>e.node.name===t.node.name));return!!n||(n=Se.exports.find(e.isolatedOutExtract,(e=>e.node.name===t.node.name)),!!n)}getNamesOfRenderedOps(){return this.renderedOpNames}cloneAndAddFunctionOpNode(t,e,n,i){const r=n.name.replace(e,i);let o=t.metagraph.node(r);if(o)return o;o=new XGt({name:r,input:[],device:n.device,op:n.op,attr:Se.exports.cloneDeep(n.attr)}),o.cardinality=n.cardinality,o.include=n.include,o.outputShapes=Se.exports.cloneDeep(n.outputShapes),o.xlaCluster=n.xlaCluster,o.functionInputIndex=n.functionInputIndex,o.functionOutputIndex=n.functionOutputIndex,o.inputs=n.inputs.map((t=>{const n=Se.exports.clone(t);return n.name=t.name.replace(e,i),n})),o.parentNode=t,t.metagraph.setNode(o.name,o),this.hierarchy.setNode(o.name,o);const a=n=>this.cloneAndAddFunctionOpNode(t,e,n,i);return o.inEmbeddings=n.inEmbeddings.map(a),o.outEmbeddings=n.outEmbeddings.map(a),o}cloneFunctionLibraryMetanode(t,e,n,i,r){const o={},a=this.cloneFunctionLibraryMetanodeHelper(t,e,n,i,r,o);return Se.exports.isEmpty(o)||this.patchEdgesFromFunctionOutputs(e,o),a}cloneFunctionLibraryMetanodeHelper(t,e,n,i,r,o){const a=$Gt(n.name.replace(i,r));return a.depth=n.depth,a.cardinality=n.cardinality,a.templateId=n.templateId,a.opHistogram=Se.exports.clone(n.opHistogram),a.deviceHistogram=Se.exports.clone(n.deviceHistogram),a.xlaClusterHistogram=Se.exports.clone(n.xlaClusterHistogram),a.hasNonControlEdges=n.hasNonControlEdges,a.include=n.include,a.nodeAttributes=Se.exports.clone(n.nodeAttributes),a.associatedFunction=n.associatedFunction,Se.exports.each(n.metagraph.nodes(),(s=>{const l=n.metagraph.node(s);switch(l.type){case jGt.META:const n=this.cloneFunctionLibraryMetanodeHelper(t,e,l,i,r,o);n.parentNode=a,a.metagraph.setNode(n.name,n),this.hierarchy.setNode(n.name,n);break;case jGt.OP:const s=this.cloneAndAddFunctionOpNode(a,i,l,r);Se.exports.isNumber(s.functionInputIndex)&&this.patchEdgesIntoFunctionInputs(e,s),Se.exports.isNumber(s.functionOutputIndex)&&(o[s.functionOutputIndex]=s);break;default:console.warn(l.name+" is oddly neither a metanode nor an opnode.")}})),this.cloneLibraryMetanodeEdges(n,a,i,r),a}cloneLibraryMetanodeEdges(t,e,n,i){Se.exports.each(t.metagraph.edges(),(r=>{const o=t.metagraph.edge(r),a=o.v.replace(n,i),s=o.w.replace(n,i),l=new JGt(a,s);l.inbound=o.inbound,l.numRegularEdges=o.numRegularEdges,l.numControlEdges=o.numControlEdges,l.numRefEdges=o.numRefEdges,l.totalSize=o.totalSize,o.baseEdgeList&&(l.baseEdgeList=o.baseEdgeList.map((t=>{const e=Se.exports.clone(t);return e.v=t.v.replace(n,i),e.w=t.w.replace(n,i),e}))),e.metagraph.node(s)?e.metagraph.setEdge(a,s,l):e.metagraph.setEdge(s,a,l)}))}patchEdgesIntoFunctionInputs(t,e){let n=Math.min(e.functionInputIndex,t.inputs.length-1),i=Se.exports.clone(t.inputs[n]);for(;i.isControlDependency;)n++,i=t.inputs[n];e.inputs.push(i);const r=this.hierarchy.getPredecessors(t.name);let o,a=0;Se.exports.each(r.regular,(t=>{if(a+=t.numRegularEdges,a>n)return o=t,!1})),Se.exports.each(o.baseEdgeList,(n=>{n.w===t.name&&(n.w=e.name),n.v===t.name&&(n.v=e.name)}))}patchEdgesFromFunctionOutputs(t,e){const n=this.hierarchy.getSuccessors(t.name);Se.exports.each(n.regular,(n=>{Se.exports.each(n.baseEdgeList,(n=>{const i=this.hierarchy.node(n.w);Se.exports.each(i.inputs,(i=>{i.name===t.name&&(i.name=e[i.outputTensorKey].name,i.outputTensorKey=n.outputTensorKey)}))})),Se.exports.each(n.baseEdgeList,(t=>{t.v=e[t.outputTensorKey].name,t.outputTensorKey="0"}))}))}buildSubhierarchy(t){if(t in this.hasSubhierarchy)return;this.hasSubhierarchy[t]=!0;let e=this.index[t];if(e.node.type!==jGt.META&&e.node.type!==jGt.SERIES)return;let n=e,i=n.node.metagraph,r=n.coreGraph;const o=[],a=[];Se.exports.isEmpty(this.hierarchy.libraryFunctions)||(Se.exports.each(i.nodes(),(t=>{const e=i.node(t),n=this.hierarchy.libraryFunctions[e.op];if(!n)return;if(0===t.indexOf(VGt))return;const r=this.cloneFunctionLibraryMetanode(i,e,n.node,n.node.name,e.name);o.push(e),a.push(r)})),Se.exports.each(a,((t,e)=>{const n=o[e];t.parentNode=n.parentNode,i.setNode(n.name,t),this.hierarchy.setNode(n.name,t)}))),Se.exports.each(i.nodes(),(t=>{let e=this.getOrCreateRenderNodeByName(t),n=e.node;r.setNode(t,e),n.isGroupNode||(Se.exports.each(n.inEmbeddings,(t=>{let n=new SWt(null),i=new wWt(t);MWt(e,t,i,n,bWt.CONSTANT),this.index[t.name]=i})),Se.exports.each(n.outEmbeddings,(t=>{let n=new SWt(null),i=new wWt(t);EWt(e,t,i,n,bWt.SUMMARY),this.index[t.name]=i})))})),Se.exports.each(i.edges(),(t=>{let e=i.edge(t),n=new SWt(e);n.isFadedOut=this.index[t.v].isFadedOut||this.index[t.w].isFadedOut,r.setEdge(t.v,t.w,n)})),n.node.type===jGt.META&&(function s(t,e){(function n(t){let e=t.coreGraph;Se.exports.each(e.nodes(),(n=>{e.node(n).node.include!==GGt.EXCLUDE||n.startsWith(VGt)||(t.coreGraph.outEdges(n).length>t.coreGraph.inEdges(n).length?kWt(t,n):LWt(t,n))}))})(t),fWt.length&&(function i(t){let e=t.coreGraph;Se.exports.each(e.nodes(),(n=>{let i=e.node(n);i.node.include===GGt.UNSPECIFIED&&PWt(i.node,fWt)&&kWt(t,n)}))})(t),mWt.length&&(function r(t){let e=t.coreGraph;Se.exports.each(e.nodes(),(n=>{let i=e.node(n);i.node.include===GGt.UNSPECIFIED&&PWt(i.node,mWt)&&LWt(t,n)}))})(t),e&&(function o(t){let e=t.coreGraph,n={},i={},r=0;if(Se.exports.each(e.nodes(),(t=>{if(e.node(t).node.include!==GGt.UNSPECIFIED)return;let o=Se.exports.reduce(e.predecessors(t),((n,i)=>n+(e.edge(i,t).metaedge.numRegularEdges?1:0)),0);0===o&&e.predecessors(t).length>0&&(o=e.predecessors(t).length);let a=Se.exports.reduce(e.successors(t),((n,i)=>n+(e.edge(t,i).metaedge.numRegularEdges?1:0)),0);0===a&&e.successors(t).length>0&&(a=e.successors(t).length),n[t]=o,i[t]=a,r++})),r<15)return;let o=Math.round(.75*r),a=Math.round(.25*r),s=Object.keys(n).sort(((t,e)=>n[t]-n[e])),l=n[s[o]],c=l+l-n[s[a]];c=Math.max(c,4);for(let e=r-1;n[s[e]]>c;e--)LWt(t,s[e]);let u=Object.keys(i).sort(((t,e)=>i[t]-i[e])),h=i[u[o]],d=h+4*(h-i[u[a]]);d=Math.max(d,4);for(let n=r-1;i[u[n]]>d;n--){let i=e.node(u[n]);i&&!i.isInExtract&&kWt(t,u[n])}})(t),(function a(t){let e=t.coreGraph,n={};Se.exports.each(e.edges(),(t=>{e.edge(t).metaedge.numRegularEdges||((n[t.v]=n[t.v]||[]).push(t),(n[t.w]=n[t.w]||[]).push(t))})),Se.exports.each(n,((t,n)=>{t.length>4&&Se.exports.each(t,(t=>AWt(e,t.v,t.w)))}))})(t);let s=t.coreGraph;Se.exports.each(s.nodes(),(e=>{let n=s.node(e),i=s.neighbors(e).length;if(n.node.include===GGt.UNSPECIFIED&&0===i){let i=n.outAnnotations.list.length>0,r=n.inAnnotations.list.length>0;n.isInExtract?(t.isolatedInExtract.push(n),n.node.include=GGt.EXCLUDE,s.removeNode(e)):n.isOutExtract?(t.isolatedOutExtract.push(n),n.node.include=GGt.EXCLUDE,s.removeNode(e)):i&&!r?(n.isInExtract=!0,t.isolatedInExtract.push(n),n.node.include=GGt.EXCLUDE,s.removeNode(e)):r&&!i&&(n.isOutExtract=!0,t.isolatedOutExtract.push(n),n.node.include=GGt.EXCLUDE,s.removeNode(e))}}))})(n,this.autoExtractNodes),Se.exports.isEmpty(this.hierarchy.libraryFunctions)||this.buildSubhierarchiesForNeededFunctions(i),t===FGt&&Se.exports.forOwn(this.hierarchy.libraryFunctions,((t,e)=>{const i=t.node,o=this.getOrCreateRenderNodeByName(i.name);n.libraryFunctionsExtract.push(o),o.node.include=GGt.EXCLUDE,r.removeNode(i.name)}));let l=n.node.parentNode;if(!l)return;let c=this.index[l.name],u=(t,...e)=>e.concat([t?"IN":"OUT"]).join("~~"),h=this.hierarchy.getBridgegraph(t),d={in:{},out:{},control:{}};Se.exports.each(h.edges(),(t=>{let e=!!i.node(t.w),n=e?t.v:t.w;h.edge(t).numRegularEdges?e?d.out[n]=(d.out[n]||0)+1:d.in[n]=(d.in[n]||0)+1:d.control[n]=(d.control[n]||0)+1}));let p=this.hierarchy.getNodeMap();Se.exports.each(h.edges(),(n=>{let o=h.edge(n),a=!!i.node(n.w),[s,f]=a?[n.w,n.v]:[n.v,n.w],m=this.index[s],g=this.index[f],_=g?g.node:p[f],y=!o.numRegularEdges&&d.control[f]>4,[,v]=a?[e.inAnnotations,m.inAnnotations]:[e.outAnnotations,m.outAnnotations],b=null,x=!1;if(!((a?d.out:d.in)[f]>4)&&!y&&m.isInCore()){let e=e=>c.coreGraph.edge(a?{v:e,w:t}:{v:t,w:e});b=e(f),b||(b=e(u(a,f,l.name))),x=!!b}let w=!1;if(b&&!o.numRegularEdges){let t=b,e=c.node;for(;t.adjoiningMetaedge;)t=t.adjoiningMetaedge,e=e.parentNode;let n=this.hierarchy.getTopologicalOrdering(e.name),i=t.metaedge;w=n[i.v]>n[i.w]}if(x=x&&!w,!x)return void v.push(new vWt(_,g,new SWt(o),bWt.SHORTCUT,a));let S=u(a,t),M=u(a,f,t),E=r.node(M);if(!E){let t=r.node(S);t||(t=new wWt({name:S,type:jGt.BRIDGE,isGroupNode:!1,cardinality:0,parentNode:null,stats:null,include:GGt.UNSPECIFIED,inbound:a,nodeAttributes:{}}),this.index[S]=t,r.setNode(S,t)),E=new wWt({name:M,type:jGt.BRIDGE,isGroupNode:!1,cardinality:1,parentNode:null,stats:null,include:GGt.UNSPECIFIED,inbound:a,nodeAttributes:{}}),this.index[M]=E,r.setNode(M,E),r.setParent(M,S),t.node.cardinality++}let T=new SWt(o);T.adjoiningMetaedge=b,a?r.setEdge(M,s,T):r.setEdge(s,M,T)})),Se.exports.each([!0,!1],(e=>{let n=u(e,t),i=r.node(n);i&&Se.exports.each(r.nodes(),(o=>{if(r.node(o).node.type===jGt.BRIDGE)return;if(!(e?!r.predecessors(o).length:!r.successors(o).length))return;let a=u(e,t,"STRUCTURAL_TARGET"),s=r.node(a);s||(s=new wWt({name:a,type:jGt.BRIDGE,isGroupNode:!1,cardinality:1,parentNode:null,stats:null,include:GGt.UNSPECIFIED,inbound:e,nodeAttributes:{}}),s.structural=!0,this.index[a]=s,r.setNode(a,s),i.node.cardinality++,r.setParent(a,n));let l=new SWt(null);l.structural=!0,l.weight--,e?r.setEdge(a,o,l):r.setEdge(o,a,l)}))}))}buildSubhierarchiesForNeededFunctions(t){Se.exports.each(t.edges(),(e=>{let n=t.edge(e),i=new SWt(n);Se.exports.forEach(i.metaedge.baseEdgeList,(t=>{const e=t.v.split(HGt);for(let t=e.length;t>=0;t--){const n=e.slice(0,t),i=this.hierarchy.node(n.join(HGt));if(i){if(i.type===jGt.OP&&this.hierarchy.libraryFunctions[i.op])for(let t=1;t<n.length;t++){const e=n.slice(0,t).join(HGt);e&&this.buildSubhierarchy(e)}break}}}))}))}}class vWt{constructor(t,e,n,i,r){this.node=t,this.renderNodeInfo=e,this.renderMetaedgeInfo=n,this.annotationType=i,this.dx=0,this.dy=0,this.width=0,this.height=0,n&&n.metaedge&&(this.v=n.metaedge.v,this.w=n.metaedge.w),this.isIn=r,this.points=[]}}var bWt;!(function(t){t[t.SHORTCUT=0]="SHORTCUT",t[t.CONSTANT=1]="CONSTANT",t[t.SUMMARY=2]="SUMMARY",t[t.ELLIPSIS=3]="ELLIPSIS"})(bWt||(bWt={}));class xWt{constructor(){this.list=[],this.nodeNames={}}push(t){if(t.node.name in this.nodeNames)return;if(this.nodeNames[t.node.name]=!0,this.list.length<5)return void this.list.push(t);let e=this.list[this.list.length-1];if(e.annotationType===bWt.ELLIPSIS){let t=e.node;return void t.setNumMoreNodes(++t.numMoreNodes)}let n=new YGt(1);this.list.push(new vWt(n,new wWt(n),null,bWt.ELLIPSIS,t.isIn))}}class wWt{constructor(t){if(this.node=t,this.expanded=!1,this.inAnnotations=new xWt,this.outAnnotations=new xWt,this.x=0,this.y=0,this.width=0,this.height=0,this.inboxWidth=0,this.outboxWidth=0,this.excluded=!1,this.structural=!1,this.labelOffset=0,this.radius=0,this.labelHeight=0,this.paddingTop=0,this.paddingLeft=0,this.paddingRight=0,this.paddingBottom=0,this.isInExtract=!1,this.isOutExtract=!1,this.coreBox={width:0,height:0},this.isFadedOut=!1,this.displayName=t.name.substring(t.name.lastIndexOf(HGt)+1),t.type===jGt.META&&t.associatedFunction){const t=this.displayName.match(_Wt);t?this.displayName=t[1]:Se.exports.startsWith(this.displayName,VGt)&&(this.displayName=this.displayName.substring(VGt.length))}}isInCore(){return!this.isInExtract&&!this.isOutExtract&&!this.isLibraryFunction}}class SWt{constructor(t){this.metaedge=t,this.adjoiningMetaedge=null,this.structural=!1,this.weight=1,this.isFadedOut=!1}}function MWt(t,e,n,i,r){let o=new vWt(e,n,i,r,!0);t.inAnnotations.push(o)}function EWt(t,e,n,i,r){let o=new vWt(e,n,i,r,!1);t.outAnnotations.push(o)}class TWt extends wWt{constructor(t,e){super(t);let n=t.metagraph.graph();this.coreGraph=oWt(n.name,UGt.CORE,e),this.inExtractBox={width:0,height:0},this.outExtractBox={width:0,height:0},this.libraryFunctionsBox={width:0,height:0},this.isolatedInExtract=[],this.isolatedOutExtract=[],this.libraryFunctionsExtract=[]}}function CWt(t,e){t.coreGraph&&(function n(t,e){Se.exports.each(t.nodes(),(n=>{let i=t.node(n);if(i.expanded=e>1,e>0)switch(i.node.type){case jGt.META:case jGt.SERIES:CWt(i,e-1)}}))})(t.coreGraph,e)}function AWt(t,e,n){let i=t.node(e),r=t.node(n),o=t.edge(e,n);(i.node.include!==GGt.INCLUDE&&r.node.include!==GGt.INCLUDE||i.node.include===GGt.EXCLUDE||r.node.include===GGt.EXCLUDE)&&(EWt(i,r.node,r,o,bWt.SHORTCUT),MWt(r,i.node,i,o,bWt.SHORTCUT),t.removeEdge(e,n))}function kWt(t,e,n){let i=t.coreGraph,r=i.node(e);r.isOutExtract=!0,Se.exports.each(i.predecessors(e),((t,n)=>{AWt(i,t,e)})),Se.exports.each(i.successors(e),((t,n)=>{AWt(i,e,t)})),0===i.neighbors(e).length&&(r.node.include=GGt.EXCLUDE,t.isolatedOutExtract.push(r),i.removeNode(e))}function LWt(t,e,n){let i=t.coreGraph,r=i.node(e);r.isInExtract=!0,Se.exports.each(i.successors(e),((t,n)=>{AWt(i,e,t)})),Se.exports.each(i.predecessors(e),((t,n)=>{AWt(i,t,e)})),0===i.neighbors(e).length&&(r.node.include=GGt.EXCLUDE,t.isolatedInExtract.push(r),i.removeNode(e))}function PWt(t,e){if(t.type===jGt.OP){for(let n=0;n<e.length;n++)if(t.op===e[n])return!0}else if(t.type===jGt.META){let n=t.getRootOp();if(n)for(let t=0;t<e.length;t++)if(n.op===e[t])return!0}return!1}const NWt={animation:{duration:250},graph:{meta:{nodeSep:5,rankSep:25,edgeSep:5},series:{nodeSep:5,rankSep:25,edgeSep:5},padding:{paddingTop:40,paddingLeft:20}},subscene:{meta:{paddingTop:10,paddingBottom:10,paddingLeft:10,paddingRight:10,labelHeight:20,extractXOffset:15,extractYOffset:20},series:{paddingTop:10,paddingBottom:10,paddingLeft:10,paddingRight:10,labelHeight:10}},nodeSize:{meta:{radius:5,width:60,maxLabelWidth:52,height:VM().domain([1,200]).range([15,60]).clamp(!0),expandButtonRadius:3},op:{width:15,height:6,radius:3,labelOffset:-8,maxLabelWidth:30},series:{expanded:{radius:10,labelOffset:0},vertical:{width:16,height:13,labelOffset:-13},horizontal:{width:24,height:8,radius:10,labelOffset:-10}},bridge:{width:20,height:20,radius:2,labelOffset:0}},shortcutSize:{op:{width:10,height:4},meta:{width:12,height:4,radius:1},series:{width:14,height:4}},annotations:{inboxWidth:50,outboxWidth:50,xOffset:10,yOffset:3,labelOffset:2,maxLabelWidth:40},constant:{size:{width:4,height:4}},series:{maxStackCount:3,parallelStackOffsetRatio:.2,towerStackOffsetRatio:.5},minimap:{size:150}},IWt=140;function RWt(t){t.node.isGroupNode&&(function e(t){let e=t.coreGraph.nodes().map((e=>t.coreGraph.node(e))).concat(t.isolatedInExtract,t.isolatedOutExtract,t.libraryFunctionsExtract);Se.exports.each(e,(t=>{switch(t.node.type){case jGt.OP:Se.exports.extend(t,NWt.nodeSize.op);break;case jGt.BRIDGE:Se.exports.extend(t,NWt.nodeSize.bridge);break;case jGt.META:t.expanded?RWt(t):(Se.exports.extend(t,NWt.nodeSize.meta),t.height=NWt.nodeSize.meta.height(t.node.cardinality));break;case jGt.SERIES:t.expanded?(Se.exports.extend(t,NWt.nodeSize.series.expanded),RWt(t)):Se.exports.extend(t,t.node.hasNonControlEdges?NWt.nodeSize.series.vertical:NWt.nodeSize.series.horizontal);break;default:throw Error("Unrecognized node type: "+t.node.type)}t.expanded||(function e(t){t.inboxWidth=t.inAnnotations.list.length>0?NWt.annotations.inboxWidth:0,t.outboxWidth=t.outAnnotations.list.length>0?NWt.annotations.outboxWidth:0,t.coreBox.width=t.width,t.coreBox.height=t.height,t.width=Math.max(t.coreBox.width+t.inboxWidth+t.outboxWidth,3*t.displayName.length)})(t),(function n(t){if(t.expanded)return;let e=t.inAnnotations.list,n=t.outAnnotations.list;Se.exports.each(e,(t=>zWt(t))),Se.exports.each(n,(t=>zWt(t)));let i=NWt.annotations,r=Se.exports.reduce(e,((e,n,r)=>{let o=r>0?i.yOffset:0;return n.dx=-(t.coreBox.width+n.width)/2-i.xOffset,n.dy=e+o+n.height/2,e+o+n.height}),0);Se.exports.each(e,(t=>{t.dy-=r/2,t.labelOffset=i.labelOffset}));let o=Se.exports.reduce(n,((e,n,r)=>{let o=r>0?i.yOffset:0;return n.dx=(t.coreBox.width+n.width)/2+i.xOffset,n.dy=e+o+n.height/2,e+o+n.height}),0);Se.exports.each(n,(t=>{t.dy-=o/2,t.labelOffset=i.labelOffset}));let a=Math.min(t.height/2-t.radius,r/2);a=a<0?0:a;let s=VM().domain([0,e.length-1]).range([-a,a]);Se.exports.each(e,((n,i)=>{n.points=[{dx:n.dx+n.width/2,dy:n.dy},{dx:-t.coreBox.width/2,dy:e.length>1?s(i):0}]}));let l=Math.min(t.height/2-t.radius,o/2);l=l<0?0:l;let c=VM().domain([0,n.length-1]).range([-l,l]);Se.exports.each(n,((e,i)=>{e.points=[{dx:t.coreBox.width/2,dy:n.length>1?c(i):0},{dx:e.dx-e.width/2,dy:e.dy}]})),t.height=Math.max(t.height,r,o)})(t)}))})(t),t.node.type===jGt.META?(function n(t){let e=NWt.subscene.meta;Se.exports.extend(t,e),Se.exports.extend(t.coreBox,OWt(t.coreGraph,NWt.graph.meta));let n=t.isolatedInExtract.length?Se.exports.maxBy(t.isolatedInExtract,(t=>t.width)).width:null;t.inExtractBox.width=null!=n?n:0,t.inExtractBox.height=Se.exports.reduce(t.isolatedInExtract,((t,n,i)=>{let r=i>0?e.extractYOffset:0;return n.x=0,n.y=t+r+n.height/2,t+r+n.height}),0);let i=t.isolatedOutExtract.length?Se.exports.maxBy(t.isolatedOutExtract,(t=>t.width)).width:null;t.outExtractBox.width=null!=i?i:0,t.outExtractBox.height=Se.exports.reduce(t.isolatedOutExtract,((t,n,i)=>{let r=i>0?e.extractYOffset:0;return n.x=0,n.y=t+r+n.height/2,t+r+n.height}),0);let r=t.libraryFunctionsExtract.length?Se.exports.maxBy(t.libraryFunctionsExtract,(t=>t.width)).width:null;t.libraryFunctionsBox.width=null!=r?r:0,t.libraryFunctionsBox.height=Se.exports.reduce(t.libraryFunctionsExtract,((t,n,i)=>{let r=i>0?e.extractYOffset:0;return n.x=0,n.y=t+r+n.height/2,t+r+n.height}),0);let o=0;t.isolatedInExtract.length>0&&o++,t.isolatedOutExtract.length>0&&o++,t.libraryFunctionsExtract.length>0&&o++,t.coreGraph.nodeCount()>0&&o++;let a=o<=1?0:o*NWt.subscene.meta.extractXOffset;const s=Math.max(IWt,t.inExtractBox.width+t.outExtractBox.width);t.coreBox.width+=s+a+t.libraryFunctionsBox.width+a,t.coreBox.height=e.labelHeight+Math.max(t.inExtractBox.height,t.coreBox.height,t.libraryFunctionsBox.height,t.outExtractBox.height),t.width=t.coreBox.width+e.paddingLeft+e.paddingRight,t.height=t.paddingTop+t.coreBox.height+t.paddingBottom})(t):t.node.type===jGt.SERIES&&(function i(t){let e=t.coreGraph,n=NWt.subscene.series;Se.exports.extend(t,n),Se.exports.extend(t.coreBox,OWt(t.coreGraph,NWt.graph.series)),Se.exports.each(e.nodes(),(t=>{e.node(t).excluded=!1})),t.width=t.coreBox.width+n.paddingLeft+n.paddingRight,t.height=t.coreBox.height+n.paddingTop+n.paddingBottom})(t)}function OWt(t,e){Se.exports.extend(t.graph(),{nodesep:e.nodeSep,ranksep:e.rankSep,edgesep:e.edgeSep});let n=[];if(Se.exports.each(t.nodes(),(e=>{t.node(e).node.type===jGt.BRIDGE||n.push(e)})),!n.length)return{width:0,height:0};fGt.layout(t);let i=1/0,r=1/0,o=-1/0,a=-1/0;return Se.exports.each(n,(e=>{let n=t.node(e),s=.5*n.width,l=n.x-s,c=n.x+s;i=l<i?l:i,o=c>o?c:o;let u=.5*n.height,h=n.y-u,d=n.y+u;r=h<r?h:r,a=d>a?d:a})),Se.exports.each(t.edges(),(e=>{let n=t.edge(e);if(n.structural)return;let s=t.node(n.metaedge.v),l=t.node(n.metaedge.w);if(3===n.points.length&&(function c(t){let e=BWt(t[0],t[1]);for(let n=1;n<t.length-1;n++){let i=BWt(t[n],t[n+1]);if(Math.abs(i-e)>1)return!1;e=i}return!0})(n.points)){if(null!=s){let t=s.expanded?s.x:DWt(s);n.points[0].x=t}if(null!=l){let t=l.expanded?l.x:DWt(l);n.points[2].x=t}n.points=[n.points[0],n.points[1]]}null!=l&&(n.points[n.points.length-1]=HWt(n.points[n.points.length-2],l)),null!=s&&(n.points[0]=HWt(n.points[1],s)),Se.exports.each(n.points,(t=>{i=t.x<i?t.x:i,o=t.x>o?t.x:o,r=t.y<r?t.y:r,a=t.y>a?t.y:a}))})),Se.exports.each(t.nodes(),(e=>{let n=t.node(e);n.x-=i,n.y-=r})),Se.exports.each(t.edges(),(e=>{Se.exports.each(t.edge(e).points,(t=>{t.x-=i,t.y-=r}))})),{width:o-i,height:a-r}}function zWt(t){switch(t.annotationType){case bWt.CONSTANT:Se.exports.extend(t,NWt.constant.size);break;case bWt.SHORTCUT:if(t.node.type===jGt.OP)Se.exports.extend(t,NWt.shortcutSize.op);else if(t.node.type===jGt.META)Se.exports.extend(t,NWt.shortcutSize.meta);else{if(t.node.type!==jGt.SERIES)throw Error("Invalid node type: "+t.node.type);Se.exports.extend(t,NWt.shortcutSize.series)}break;case bWt.SUMMARY:Se.exports.extend(t,NWt.constant.size)}}function DWt(t){return t.expanded?t.x:t.x-t.width/2+(t.inAnnotations.list.length?t.inboxWidth:0)+t.coreBox.width/2}function BWt(t,e){return 180*Math.atan((e.y-t.y)/(e.x-t.x))/Math.PI}function HWt(t,e){let n,i,r=e.expanded?e.x:DWt(e),o=e.y,a=t.x-r,s=t.y-o,l=e.expanded?e.width:e.coreBox.width,c=e.expanded?e.height:e.coreBox.height;return Math.abs(s)*l/2>Math.abs(a)*c/2?(s<0&&(c=-c),n=0===s?0:c/2*a/s,i=c/2):(a<0&&(l=-l),n=l/2,i=0===a?0:l/2*s/a),{x:r+n,y:o+i}}const FWt=xGt,VWt=vGt;let UWt=[{background_color:"#CC2F2C",label:"NaN"},{background_color:"#FF8D00",label:"-∞"},{background_color:"#EAEAEA",label:"-"},{background_color:"#A5A5A5",label:"0"},{background_color:"#262626",label:"+"},{background_color:"#003ED4",label:"+∞"}];function jWt(t,e,n){null!=t.attr("transform")&&(t=t.transition("position")),t.attr("transform","translate("+e+","+n+")")}function GWt(t,e,n,i,r){t.transition().attr("x",e-i/2).attr("y",n-r/2).attr("width",i).attr("height",r)}function WWt(t,e,n,i,r){t.transition().attr("cx",e).attr("cy",n).attr("rx",i/2).attr("ry",r/2)}function qWt(t,e){return e?t.toFixed(0):Math.abs(t)>=1?t.toFixed(1):t.toExponential(1)}let YWt=class extends(er(ye)){constructor(){super(...arguments),this.specificHealthPillStep=0,this.healthPillEntries=UWt}ready(){super.ready();var t=document.getElementById("mainContainer"),e=document.querySelector("tf-dashboard-layout .scrollbar");t&&e&&(t.style.overflow="hidden",e.style.overflow="hidden")}_healthPillsAvailable(t,e){return t&&e}_computeTensorCountString(t,e){return t?t[e].toFixed(0):""}get healthPillValuesForSelectedNode(){var t=this.selectedNode;if(this.areHealthPillsLoading)return null;if(!t)return null;const e=this.nodeNamesToHealthPills[t];if(!e)return null;const n=e[this.allStepsModeEnabled?0:this.healthPillStepIndex];return n?n.value.slice(2,8):null}get _currentStepDisplayValue(){var t=this.nodeNamesToHealthPills,e=this.healthPillStepIndex,n=this.areHealthPillsLoading;if(this.allStepsModeEnabled)return this.specificHealthPillStep.toFixed(0);if(n)return 0;for(let n in t)return t[n][e].step.toFixed(0);return 0}get _biggestStepEverSeen(){var t=this.nodeNamesToHealthPills;for(let n in t){var e=t[n];return Math.max(this._biggestStepEverSeen,e[e.length-1].step)}return this._biggestStepEverSeen||0}get _maxStepIndex(){var t=this.nodeNamesToHealthPills;for(let e in t)return t[e].length-1;return 0}_hasDebuggerNumericAlerts(t){return t&&t.length}_updateAlertsList(){var t=this.debuggerNumericAlerts,e=this.$$("#numeric-alerts-body");if(e){e.innerText="";for(var n=0;n<t.length;n++){var i=t[n],r=document.createElement("tr"),o=document.createElement("td");o.innerText=(f=i.first_timestamp,(m=+new Date-+new Date(f/1e3))<3e4?"just now":m<6e4?Math.floor(m/1e3)+" seconds ago":m<12e4?"a minute ago":m<36e5?Math.floor(m/6e4)+" minutes ago":1==Math.floor(m/36e5)?"an hour ago":m<864e5?Math.floor(m/36e5)+" hours ago":m<1728e5?"yesterday":Math.floor(m/864e5)+" days ago"),o.classList.add("first-offense-td"),r.appendChild(o);var a=document.createElement("td");a.classList.add("tensor-device-td");var s=document.createElement("div");s.classList.add("tensor-section-within-table"),s.innerText=i.tensor_name,this._addOpExpansionListener(s,i.tensor_name),a.appendChild(s);var l=document.createElement("div");l.classList.add("device-section-within-table"),l.innerText="("+i.device_name+")",a.appendChild(l),r.appendChild(a);var c=document.createElement("div");c.classList.add("mini-health-pill");var u=document.createElement("td");if(u.classList.add("mini-health-pill-td"),u.appendChild(c),r.appendChild(u),i.neg_inf_event_count){var h=document.createElement("div");h.classList.add("negative-inf-mini-health-pill-section"),h.innerText=i.neg_inf_event_count,h.setAttribute("title",i.neg_inf_event_count+" events with -∞"),c.appendChild(h)}if(i.pos_inf_event_count){var d=document.createElement("div");d.classList.add("positive-inf-mini-health-pill-section"),d.innerText=i.pos_inf_event_count,d.setAttribute("title",i.pos_inf_event_count+" events with +∞"),c.appendChild(d)}if(i.nan_event_count){var p=document.createElement("div");p.classList.add("nan-mini-health-pill-section"),p.innerText=i.nan_event_count,p.setAttribute("title",i.nan_event_count+" events with NaN"),c.appendChild(p)}Yi(e).appendChild(r)}var f,m}}_addOpExpansionListener(t,e){t.addEventListener("click",(()=>{var t,n=(function i(t,e,n){const i=n.split("/"),r=i[i.length-1].match(/(.*):\w+/);2===r.length&&(i[i.length-1]=r[1]);let o=i[0],a=e.getRenderNodeByName(o);for(let n=1;n<i.length&&a.node.type!==jGt.OP;n++)e.buildSubhierarchy(o),a.expanded=!0,t.setNodeExpanded(a),o+="/"+i[n],a=e.getRenderNodeByName(o);return a.node.name})(document.getElementById("scene"),this.renderHierarchy,e),r=document.querySelector("tf-graph-info#graph-info");r&&(t=r.scrollHeight-r.scrollTop);var o=this.selectedNode;this.set("selectedNode",n);var a=()=>{r.scrollTop=r.scrollHeight-t};r&&(o?a():window.setTimeout(a,20))}))}};var XWt;YWt.template=_e`
    <style>
      :host {
        font-size: 12px;
        margin: 0;
        padding: 0;
        display: block;
      }

      h2 {
        padding: 0;
        text-align: center;
        margin: 0;
      }

      .health-pill-legend {
        padding: 15px;
      }

      .health-pill-legend h2 {
        text-align: left;
      }

      .health-pill-entry {
        margin: 10px 10px 10px 0;
      }

      .health-pill-entry .color-preview {
        width: 26px;
        height: 26px;
        border-radius: 3px;
        display: inline-block;
        margin: 0 10px 0 0;
      }

      .health-pill-entry .color-label,
      .health-pill-entry .tensor-count {
        color: #777;
        display: inline-block;
        height: 26px;
        font-size: 22px;
        line-height: 26px;
        vertical-align: top;
      }

      .health-pill-entry .tensor-count {
        float: right;
      }

      #health-pill-step-slider {
        width: 100%;
        margin: 0 0 0 -15px;
        /* 31 comes from adding a padding of 15px from both sides of the paper-slider, subtracting
   * 1px so that the slider width aligns with the image (the last slider marker takes up 1px),
   * and adding 2px to account for a border of 1px on both sides of the image. 30 - 1 + 2.
   * Apparently, the paper-slider lacks a mixin for those padding values. */
        width: calc(100% + 31px);
      }

      #health-pills-loading-spinner {
        width: 20px;
        height: 20px;
        vertical-align: top;
      }

      #health-pill-step-number-input {
        text-align: center;
        vertical-align: top;
      }

      #numeric-alerts-table-container {
        max-height: 400px;
        overflow-x: hidden;
        overflow-y: auto;
      }

      #numeric-alerts-table {
        text-align: left;
      }

      #numeric-alerts-table td {
        vertical-align: top;
      }

      #numeric-alerts-table .first-offense-td {
        display: inline-block;
      }

      .first-offense-td {
        width: 80px;
      }

      .tensor-device-td {
        max-width: 140px;
        word-wrap: break-word;
      }

      .tensor-section-within-table {
        color: #266236;
        cursor: pointer;
        opacity: 0.8;
        text-decoration: underline;
      }

      .tensor-section-within-table:hover {
        opacity: 1;
      }

      .device-section-within-table {
        color: #666;
      }

      .mini-health-pill {
        width: 130px;
      }

      .mini-health-pill > div {
        height: 100%;
        width: 60px;
        border-radius: 3px;
      }

      #event-counts-th {
        padding: 0 0 0 10px;
      }

      .negative-inf-mini-health-pill-section {
        background: rgb(255, 141, 0);
        width: 20px;
      }

      .positive-inf-mini-health-pill-section {
        background: rgb(0, 62, 212);
        width: 20px;
      }

      .nan-mini-health-pill-section {
        background: rgb(204, 47, 44);
        width: 20px;
      }

      .negative-inf-mini-health-pill-section,
      .positive-inf-mini-health-pill-section,
      .nan-mini-health-pill-section {
        color: #fff;
        display: inline-block;
        height: 100%;
        line-height: 20px;
        margin: 0 0 0 10px;
        text-align: center;
      }

      .no-numeric-alerts-notification {
        margin: 0;
      }
    </style>
    <paper-material elevation="1" class="card health-pill-legend">
      <div class="title">
        Enable all (not just sampled) steps. Requires slow disk read.
      </div>
      <paper-toggle-button
        id="enableAllStepsModeToggle"
        checked="{{allStepsModeEnabled}}"
      >
      </paper-toggle-button>
      <h2>
        Step of Health Pills:
        <template is="dom-if" if="[[allStepsModeEnabled]]">
          <input
            type="number"
            id="health-pill-step-number-input"
            min="0"
            max="[[_biggestStepEverSeen]]"
            value="{{specificHealthPillStep::input}}"
          />
        </template>
        <template is="dom-if" if="[[!allStepsModeEnabled]]">
          [[_currentStepDisplayValue]]
        </template>
        <paper-spinner-lite
          active
          hidden$="[[!areHealthPillsLoading]]"
          id="health-pills-loading-spinner"
        ></paper-spinner-lite>
      </h2>
      <template is="dom-if" if="[[allStepsModeEnabled]]">
        <paper-slider
          id="health-pill-step-slider"
          immediate-value="{{specificHealthPillStep}}"
          max="[[_biggestStepEverSeen]]"
          snaps
          step="1"
          value="{{specificHealthPillStep}}"
        ></paper-slider>
      </template>
      <template is="dom-if" if="[[!allStepsModeEnabled]]">
        <template is="dom-if" if="[[_maxStepIndex]]">
          <paper-slider
            id="health-pill-step-slider"
            immediate-value="{{healthPillStepIndex}}"
            max="[[_maxStepIndex]]"
            snaps
            step="1"
            value="{{healthPillStepIndex}}"
          ></paper-slider>
        </template>
      </template>
      <h2>
        Health Pill
        <template is="dom-if" if="[[healthPillValuesForSelectedNode]]">
          Counts for Selected Node
        </template>
        <template is="dom-if" if="[[!healthPillValuesForSelectedNode]]">
          Legend
        </template>
      </h2>
      <template is="dom-repeat" items="[[healthPillEntries]]">
        <div class="health-pill-entry">
          <div
            class="color-preview"
            style="background:[[item.background_color]]"
          ></div>
          <div class="color-label">[[item.label]]</div>
          <div class="tensor-count">
            [[_computeTensorCountString(healthPillValuesForSelectedNode,
            index)]]
          </div>
        </div>
      </template>
      <div hidden$="[[!_hasDebuggerNumericAlerts(debuggerNumericAlerts)]]">
        <h2 id="numeric-alerts-header">Numeric Alerts</h2>
        <p>Alerts are sorted from top to bottom by increasing timestamp.</p>
        <div id="numeric-alerts-table-container">
          <table id="numeric-alerts-table">
            <thead>
              <tr>
                <th>First Offense</th>
                <th>Tensor (Device)</th>
                <th id="event-counts-th">Event Counts</th>
              </tr>
            </thead>
            <tbody id="numeric-alerts-body"></tbody>
          </table>
        </div>
      </div>
      <template
        is="dom-if"
        if="[[!_hasDebuggerNumericAlerts(debuggerNumericAlerts)]]"
      >
        <p class="no-numeric-alerts-notification">
          No numeric alerts so far. That is likely good. Alerts indicate the
          presence of NaN or (+/-) Infinity values, which may be concerning.
        </p>
      </template>
    </paper-material>
  `,t([o({type:Object}),e("design:type",yWt)],YWt.prototype,"renderHierarchy",void 0),t([o({type:Array,notify:!0}),e("design:type",Object)],YWt.prototype,"debuggerNumericAlerts",void 0),t([o({type:Object}),e("design:type",Object)],YWt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Number,notify:!0}),e("design:type",Object)],YWt.prototype,"healthPillStepIndex",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],YWt.prototype,"specificHealthPillStep",void 0),t([o({type:String,notify:!0}),e("design:type",Object)],YWt.prototype,"selectedNode",void 0),t([o({type:String,notify:!0}),e("design:type",Object)],YWt.prototype,"highlightedNode",void 0),t([o({type:Number,notify:!0}),e("design:type",Object)],YWt.prototype,"selectedNodeInclude",void 0),t([o({type:Boolean}),e("design:type",Object)],YWt.prototype,"areHealthPillsLoading",void 0),t([o({type:Array}),e("design:type",Array)],YWt.prototype,"healthPillEntries",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Object)],YWt.prototype,"allStepsModeEnabled",void 0),t([s("nodeNamesToHealthPills","healthPillStepIndex","selectedNode","allStepsModeEnabled","areHealthPillsLoading"),e("design:type",Array),e("design:paramtypes",[])],YWt.prototype,"healthPillValuesForSelectedNode",null),t([s("nodeNamesToHealthPills","healthPillStepIndex","allStepsModeEnabled","specificHealthPillStep","areHealthPillsLoading"),e("design:type",Object),e("design:paramtypes",[])],YWt.prototype,"_currentStepDisplayValue",null),t([s("nodeNamesToHealthPills"),e("design:type",Number),e("design:paramtypes",[])],YWt.prototype,"_biggestStepEverSeen",null),t([s("nodeNamesToHealthPills"),e("design:type",Number),e("design:paramtypes",[])],YWt.prototype,"_maxStepIndex",null),t([a("debuggerNumericAlerts"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],YWt.prototype,"_updateAlertsList",null),YWt=t([i("tf-graph-debugger-data-card")],YWt),(function(t){t.CONST="CONST",t.META="META",t.OP="OP",t.SERIES="SERIES",t.SUMMARY="SUMMARY"})(XWt||(XWt={}));let $Wt=class extends(er(il(ye))){constructor(){super(...arguments),this.vertical=!1,this.fillOverride=null,this.strokeOverride=null,this.height=20,this.faded=!1}getSvgDefinableElement(){return this.$.svgDefs}get _fill(){var t=this.fillOverride;if(null!=t)return t;switch(this.type){case XWt.META:return pWt.DEFAULT_FILL;case XWt.SERIES:return"white";default:return"#ffffff"}}get _stroke(){var t=this.strokeOverride;if(null!=t)return t;switch(this.type){case XWt.META:return pWt.DEFAULT_STROKE;case XWt.SERIES:default:return"#b2b2b2"}}_isType(t,e){return t===e}_fadedClass(t,e){return t?"faded-"+e:""}};$Wt.template=_e`
    <style>
      :host {
        font-size: 0;
      }

      :host(.dark-mode) svg {
        filter: invert(1);
      }

      .faded-rect {
        fill: url(#rectHatch);
      }

      .faded-ellipse {
        fill: url(#ellipseHatch);
      }

      .faded-rect,
      .faded-ellipse,
      .faded-series {
        stroke: var(--tb-graph-faded) !important;
      }
      #rectHatch line,
      #ellipseHatch line {
        color: #e0d4b3 !important;
        fill: white;
        stroke: #e0d4b3 !important;
      }
    </style>
    <!-- SVG for definitions -->
    <svg height="0" width="0" id="svgDefs">
      <defs>
        <!-- Hatch patterns for faded out nodes. -->
        <pattern
          id="rectHatch"
          patternTransform="rotate(45 0 0)"
          width="5"
          height="5"
          patternUnits="userSpaceOnUse"
        >
          <line x1="0" y1="0" x2="0" y2="5" style="stroke-width: 1"></line>
        </pattern>
        <pattern
          id="ellipseHatch"
          patternTransform="rotate(45 0 0)"
          width="2"
          height="2"
          patternUnits="userSpaceOnUse"
        >
          <line x1="0" y1="0" x2="0" y2="2" style="stroke-width: 1"></line>
        </pattern>
        <!-- Template for an Op node ellipse. -->
        <ellipse
          id="op-node-stamp"
          rx="7.5"
          ry="3"
          stroke="inherit"
          fill="inherit"
        ></ellipse>
        <!-- Template for an Op node annotation ellipse (smaller). -->
        <ellipse
          id="op-node-annotation-stamp"
          rx="5"
          ry="2"
          stroke="inherit"
          fill="inherit"
        ></ellipse>
        <!-- Vertically stacked series of Op nodes when unexpanded. -->
        <g id="op-series-vertical-stamp">
          <use xlink:href="#op-node-stamp" x="8" y="9"></use>
          <use xlink:href="#op-node-stamp" x="8" y="6"></use>
          <use xlink:href="#op-node-stamp" x="8" y="3"></use>
        </g>
        <g id="op-series-horizontal-stamp">
          <use xlink:href="#op-node-stamp" x="16" y="4"></use>
          <use xlink:href="#op-node-stamp" x="12" y="4"></use>
          <use xlink:href="#op-node-stamp" x="8" y="4"></use>
        </g>
        <g
          id="summary-icon"
          fill="#848484"
          height="12"
          viewBox="0 0 24 24"
          width="12"
        >
          <path
            d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z"
          ></path>
        </g>
      </defs>
    </svg>
    <template is="dom-if" if="[[_isType(type, 'CONST')]]">
      <svg
        height$="[[height]]"
        preserveAspectRatio="xMinYMid meet"
        viewBox="0 0 10 10"
      >
        <circle
          cx="5"
          cy="5"
          r="3"
          fill$="[[_fill]]"
          stroke$="[[_stroke]]"
        ></circle>
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'SUMMARY')]]">
      <svg
        width$="[[height]]"
        height$="[[height]]"
        viewBox="0 0 24 24"
        fill="#848484"
      >
        <path
          d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z"
        ></path>
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'OP')]]">
      <svg
        height$="[[height]]"
        preserveAspectRatio="xMinYMid meet"
        viewBox="0 0 16 8"
      >
        <use
          xmlns:xlink="http://www.w3.org/1999/xlink"
          xlink:href="#op-node-stamp"
          fill$="[[_fill]]"
          stroke$="[[_stroke]]"
          class$="{{_fadedClass(faded, 'ellipse')}}"
          x="8"
          y="4"
        ></use>
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'META')]]">
      <svg
        height$="[[height]]"
        preserveAspectRatio="xMinYMid meet"
        viewBox="0 0 37 16"
      >
        <rect
          x="1"
          y="1"
          fill$="[[_fill]]"
          stroke$="[[_stroke]]"
          class$="{{_fadedClass(faded, 'rect')}}"
          stroke-width="2px"
          height="14"
          width="35"
          rx="5"
          ry="5"
        ></rect>
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'SERIES')]]">
      <template is="dom-if" if="[[vertical]]">
        <svg
          height$="[[height]]"
          preserveAspectRatio="xMinYMid meet"
          viewBox="0 0 16 15"
        >
          <use
            xmlns:xlink="http://www.w3.org/1999/xlink"
            xlink:href="#op-series-vertical-stamp"
            fill$="[[_fill]]"
            stroke$="[[_stroke]]"
            class$="{{_fadedClass(faded, 'series')}}"
            x="0"
            y="2"
          ></use>
        </svg>
      </template>
      <template is="dom-if" if="[[!vertical]]">
        <svg
          height$="[[height]]"
          preserveAspectRatio="xMinYMid meet"
          viewBox="0 0 24 10"
        >
          <use
            xmlns:xlink="http://www.w3.org/1999/xlink"
            xlink:href="#op-series-horizontal-stamp"
            fill$="[[_fill]]"
            stroke$="[[_stroke]]"
            class$="{{_fadedClass(faded, 'series')}}"
            x="0"
            y="1"
          ></use>
        </svg>
      </template>
    </template>
  `,t([o({type:String}),e("design:type",String)],$Wt.prototype,"type",void 0),t([o({type:Boolean}),e("design:type",Boolean)],$Wt.prototype,"vertical",void 0),t([o({type:String}),e("design:type",String)],$Wt.prototype,"fillOverride",void 0),t([o({type:String}),e("design:type",String)],$Wt.prototype,"strokeOverride",void 0),t([o({type:Number}),e("design:type",Number)],$Wt.prototype,"height",void 0),t([o({type:Boolean}),e("design:type",Boolean)],$Wt.prototype,"faded",void 0),t([s("type","fillOverride"),e("design:type",String),e("design:paramtypes",[])],$Wt.prototype,"_fill",null),t([s("type","strokeOverride"),e("design:type",String),e("design:paramtypes",[])],$Wt.prototype,"_stroke",null),$Wt=t([i("tf-graph-icon")],$Wt);var KWt=Object.freeze({__proto__:null,get GraphIconType(){return XWt}});function ZWt(t,e){const n=t.getContextMenu(),i=Su(t.getContextMenu());return function(r,o){let a=uu;const s=(function l(t){let e=0,n=0,i=t;for(;i&&i.offsetLeft>=0&&i.offsetTop>=0;)e+=i.offsetLeft-i.scrollLeft,n+=i.offsetTop-i.scrollTop,i=i.offsetParent;return{left:e,top:n}})(t);function c(t){t&&t.composedPath().includes(n)||(i.style("display","none"),document.body.removeEventListener("mousedown",c,{capture:!0}))}i.style("display","block").style("left",a.clientX-s.left+1+"px").style("top",a.clientY-s.top+1+"px"),a.preventDefault(),a.stopPropagation(),document.body.addEventListener("mousedown",c,{capture:!0}),i.html(""),i.append("ul").selectAll("li").data(e).enter().append("li").on("click",((t,e)=>{t.action(this,r,o),c()})).html((function(t){return t.title(r)}))}}let JWt=aE().domain([MGt,12]).range(["small","medium","large","xlarge"]);function QWt(t){return t.v+"--"+t.w}function tqt(t,e){let n=e.getNodeByName(t.v);if(null==n.outputShapes||Se.exports.isEmpty(n.outputShapes))return null;let i=n.outputShapes[t.outputTensorKey];return null==i?null:0===i.length?"scalar":i.map((t=>-1===t?"?":t)).join("×")}function eqt(t,e){return e.edgeLabelFunction?e.edgeLabelFunction(t,e):t.baseEdgeList.length>1?t.baseEdgeList.length+" tensors":tqt(t.baseEdgeList[0],e)}function nqt(t,e,n){const i=document.createElementNS(bGt,"path");for(let r=1;r<t.length;r++)if(i.setAttribute("d",n(t.slice(0,r))),i.getTotalLength()>e)return r-1;return t.length-1}function iqt(t,e,n){let i=uL().x((t=>t.x)).y((t=>t.y)),r=Su(document.createElementNS("http://www.w3.org/2000/svg","path")).attr("d",i(t)),o=+e.attr("markerWidth"),a=e.attr("viewBox").split(" ").map(Number),s=a[2]-a[0],l=+e.attr("refX"),c=r.node();if(n){const e=o*(1-l/s),n=c.getPointAtLength(e),r=nqt(t,e,i);return t[r-1]={x:n.x,y:n.y},t.slice(r-1)}{const e=1-l/s,n=c.getTotalLength()-o*e,r=c.getPointAtLength(n),a=nqt(t,n,i);return t[a]={x:r.x,y:r.y},t.slice(0,a+1)}}function rqt(t,e,n,i){i=i||vGt.Edge.LINE,e.label&&e.label.structural&&(i+=" "+vGt.Edge.STRUCTURAL),e.label&&e.label.metaedge&&e.label.metaedge.numRefEdges&&(i+=" "+vGt.Edge.REFERENCE_EDGE),n.handleEdgeSelected&&(i+=" "+vGt.Edge.SELECTABLE);let r,o="path_"+QWt(e);if(n.renderHierarchy.edgeWidthFunction)r=n.renderHierarchy.edgeWidthFunction(e,i);else{let t=1;null!=e.label&&null!=e.label.metaedge&&(t=e.label.metaedge.totalSize),r=n.renderHierarchy.edgeWidthSizedBasedScale(t)}let a=t.append("path").attr("id",o).attr("class",i).style("stroke-width",r+"px");if(e.label&&e.label.metaedge)if(e.label.metaedge.numRefEdges){const t=`reference-arrowhead-${JWt(r)}`;a.style("marker-start",`url(#${t})`),e.label.startMarkerId=t}else{const t=`dataflow-arrowhead-${JWt(r)}`;a.style("marker-end",`url(#${t})`),e.label.endMarkerId=t}if(null==e.label||null==e.label.metaedge)return;let s=eqt(e.label.metaedge,n.renderHierarchy);null!=s&&t.append("text").append("textPath").attr("xlink:href","#"+o).attr("startOffset","50%").attr("text-anchor","middle").attr("dominant-baseline","central").text(s)}let oqt=uL().curve($L).x((t=>t.x)).y((t=>t.y));var aqt;function sqt(t,e,n){let i=wGt(t,"g",vGt.Node.CONTAINER).selectAll((function(){return this.childNodes})).data(e,(t=>t.node.name+":"+t.node.type));return i.enter().append("g").attr("data-name",(t=>t.node.name)).each((function(t){let e=Su(this);n.addNodeGroup(t.node.name,e)})).merge(i).attr("class",(t=>vGt.Node.GROUP+" "+yqt(t))).each((function(t){let e=Su(this);Aqt(wGt(e,"g",vGt.Annotation.INBOX),t.inAnnotations,t,n),Aqt(wGt(e,"g",vGt.Annotation.OUTBOX),t.outAnnotations,t,n);let i=_qt(e,t,vGt.Node.SHAPE);t.node.isGroupNode&&(function r(t,e,n){let i=wGt(t,"g",vGt.Node.BUTTON_CONTAINER);wGt(i,"circle",vGt.Node.BUTTON_CIRCLE),wGt(i,"path",vGt.Node.EXPAND_BUTTON).attr("d","M0,-2.2 V2.2 M-2.2,0 H2.2"),wGt(i,"path",vGt.Node.COLLAPSE_BUTTON).attr("d","M-2.2,0 H2.2"),i.on("click",(t=>{uu.stopPropagation(),n.fire("node-toggle-expand",{name:t.node.name})})),(function r(t,e){let n=DWt(e)+(e.expanded?e.width:e.coreBox.width)/2-6,i=e.y-(e.expanded?e.height:e.coreBox.height)/2+6;e.node.type!==jGt.SERIES||e.expanded||(n+=10,i-=2);let r="translate("+n+","+i+")";t.selectAll("path").transition().attr("transform",r),t.select("circle").transition().attr({cx:n,cy:i,r:NWt.nodeSize.meta.expandButtonRadius})})(i,e)})(i,t,n),cqt(i,t,n),(function o(t,e,n){if(e.node.isGroupNode){if(e.expanded)return Lqt(t,e,n,vGt.Subscene.GROUP);FWt(t,"g",vGt.Subscene.GROUP).remove()}})(e,t,n),cqt((function a(t,e,n){let i=e.displayName,r=e.node.type===jGt.META&&!e.expanded,o=wGt(t,"text",vGt.Node.LABEL),a=o.node();a.parentNode.appendChild(a),o.attr("dy",".35em").attr("text-anchor","middle");let s=8;switch(e.node.type){case jGt.META:s=e.expanded?9:8;break;case jGt.OP:s=6}if(r){i.length>n.maxMetanodeLabelLength&&(i=i.substr(0,n.maxMetanodeLabelLength-2)+"…");let t=(function l(t){return mqt||(mqt=VM().domain([t.maxMetanodeLabelLengthLargeFont,t.maxMetanodeLabelLength]).range([t.maxMetanodeLabelLengthFontSize,t.minMetanodeLabelLengthFontSize]).clamp(!0)),mqt})(n);o.attr("font-size",t(i.length)+"px"),s=t(i.length)}return fqt(o.text(i),e.node.type,s,e),o})(e,t,n),t,n,t.node.type===jGt.META),wqt(e,t,n),(function s(t,e){let n=FWt(t,"g",vGt.Node.SHAPE),i=DWt(e);switch(e.node.type){case jGt.OP:{const r=e.node;Se.exports.isNumber(r.functionInputIndex)||Se.exports.isNumber(r.functionOutputIndex)?(function r(t,e,n,i,o){const a=o/2,s=i/2,l=[[e,n-a],[e+s,n+a],[e-s,n+a]];t.transition().attr("points",l.map((t=>t.join(","))).join(" "))})(FWt(n,"polygon"),e.x,e.y,e.coreBox.width,e.coreBox.height):WWt(FWt(n,"ellipse"),i,e.y,e.coreBox.width,e.coreBox.height),gqt(t,i,e.y,e.labelOffset);break}case jGt.META:{let r=n.selectAll("rect");e.expanded?(GWt(r,e.x,e.y,e.width,e.height),lqt(t,e),gqt(t,i,e.y,-e.height/2+e.labelHeight/2)):(GWt(r,i,e.y,e.coreBox.width,e.coreBox.height),gqt(t,i,e.y,0));break}case jGt.SERIES:{let r=FWt(n,"use");e.expanded?(GWt(r,e.x,e.y,e.width,e.height),lqt(t,e),gqt(t,i,e.y,-e.height/2+e.labelHeight/2)):(GWt(r,i,e.y,e.coreBox.width,e.coreBox.height),gqt(t,i,e.y,e.labelOffset));break}case jGt.BRIDGE:GWt(FWt(n,"rect"),e.x,e.y,e.width,e.height);break;default:throw Error("Unrecognized node type: "+e.node.type)}})(e,t)})),i.exit().each((function(t){n.removeNodeGroup(t.node.name);let e=Su(this);t.inAnnotations.list.length>0&&e.select("."+vGt.Annotation.INBOX).selectAll("."+vGt.Annotation.GROUP).each((e=>{n.removeAnnotationGroup(e,t)})),t.outAnnotations.list.length>0&&e.select("."+vGt.Annotation.OUTBOX).selectAll("."+vGt.Annotation.GROUP).each((e=>{n.removeAnnotationGroup(e,t)}))})).remove(),i}function lqt(t,e){let n=e.x-e.width/2+e.paddingLeft,i=e.y-e.height/2+e.paddingTop;jWt(FWt(t,"g",vGt.Subscene.GROUP),n,i)}function cqt(t,e,n,i){if(i)return void t.attr("pointer-events","none");let r=ZWt(n,uqt(e.node,n));t.on("dblclick",(t=>{n.fire("node-toggle-expand",{name:t.node.name})})).on("mouseover",(t=>{n.isNodeExpanded(t)||n.fire("node-highlight",{name:t.node.name})})).on("mouseout",(t=>{n.isNodeExpanded(t)||n.fire("node-unhighlight",{name:t.node.name})})).on("click",(t=>{uu.stopPropagation(),n.fire("node-select",{name:t.node.name})})).on("contextmenu",((t,e)=>{n.fire("node-select",{name:t.node.name}),r.call(t,e)}))}function uqt(t,e){let n=[{title:e=>uWt(t.include),action:(n,i,r)=>{e.fire("node-toggle-extract",{name:t.name})}}];return e.nodeContextMenuItems&&(n=n.concat(e.nodeContextMenuItems)),hqt(t)&&n.push({title:e=>pqt(t),action:(n,i,r)=>{e.fire("node-toggle-seriesgroup",{name:dqt(t)})}}),n}function hqt(t){return null!==dqt(t)}function dqt(t){return t?t.type===jGt.SERIES?t.name:t.type===jGt.OP?t.owningSeries:null:null}function pqt(t){return(function n(t){return t===WGt.GROUP?"Ungroup this series of nodes":"Group this series of nodes"})(null!==(function e(t){let e=null;return t?(t.type===jGt.SERIES?e=t:t.parentNode&&t.parentNode.type===jGt.SERIES&&(e=t.parentNode),e):null})(t)?WGt.GROUP:WGt.UNGROUP)}function fqt(t,e,n,i){let r=t.node(),o=r.textContent,a=null;switch(e){case jGt.META:i&&!i.expanded&&(a=NWt.nodeSize.meta.maxLabelWidth);break;case jGt.OP:a=NWt.nodeSize.op.maxLabelWidth;break;case-1:a=NWt.annotations.maxLabelWidth}if(null!==a)return r.textContent=(function s(t,e,n){if(!t)return"";if(BGt(t,e)<=n)return t;let i=0,r=t.length;for(;i<r;){const o=i+Math.round((r-i)/2);BGt(t.slice(0,o)+"…",e)<=n?i=o:r=o-1}return 0===i?t[0]:t.slice(0,i)+"…"})(r.textContent,n,a),t.append("title").text(o)}!(function(t){t.NONE="none",t.COMPUTE_TIME="compute_time",t.DEVICE="device",t.MEMORY="memory",t.OP_COMPATIBILITY="op_compatibility",t.STRUCTURE="structure",t.XLA_CLUSTER="xla_cluster"})(aqt||(aqt={}));let mqt=null;function gqt(t,e,n,i){FWt(t,"text",vGt.Node.LABEL).transition().attr("x",e).attr("y",n+i)}function _qt(t,e,n){let i=wGt(t,"g",n);switch(e.node.type){case jGt.OP:const t=e.node;if(Se.exports.isNumber(t.functionInputIndex)||Se.exports.isNumber(t.functionOutputIndex)){wGt(i,"polygon",vGt.Node.COLOR_TARGET);break}wGt(i,"ellipse",vGt.Node.COLOR_TARGET);break;case jGt.SERIES:let n="annotation",r=e;r.coreGraph&&(n=r.node.hasNonControlEdges?"vertical":"horizontal");let o=[vGt.Node.COLOR_TARGET];r.isFadedOut&&o.push("faded-ellipse"),wGt(i,"use",o).attr("xlink:href","#op-series-"+n+"-stamp"),wGt(i,"rect",vGt.Node.COLOR_TARGET).attr("rx",e.radius).attr("ry",e.radius);break;case jGt.BRIDGE:case jGt.META:wGt(i,"rect",vGt.Node.COLOR_TARGET).attr("rx",e.radius).attr("ry",e.radius);break;default:throw Error("Unrecognized node type: "+e.node.type)}return i}function yqt(t){switch(t.node.type){case jGt.OP:return vGt.OPNODE;case jGt.META:return vGt.METANODE;case jGt.SERIES:return vGt.SERIESNODE;case jGt.BRIDGE:return vGt.BRIDGENODE;case jGt.ELLIPSIS:return vGt.ELLIPSISNODE}throw Error("Unrecognized node type: "+t.node.type)}function vqt(t,e,n){let i=(function r(t){return t.replace(/([:.\[\],/\\\(\)])/g,"\\$1")})(t);if(!n)return`url(#${i})`;let o=Su(n),a=o.select("defs#_graph-gradients");a.empty()&&(a=o.append("defs").attr("id","_graph-gradients"));let s=a.select("linearGradient#"+i);if(s.empty()){s=a.append("linearGradient").attr("id",t),s.selectAll("*").remove();let n=0;Se.exports.each(e,(t=>{let e=t.color;s.append("stop").attr("offset",n).attr("stop-color",e),s.append("stop").attr("offset",n+t.proportion).attr("stop-color",e),n+=t.proportion}))}return`url(#${i})`}function bqt(t){Su(t).select("defs#_graph-gradients").remove()}function xqt(t,e,n,i,r){let o=pWt;switch(t=t||(()=>0),e){case aqt.NONE:case aqt.STRUCTURE:if(n.node.type===jGt.META){let r=n.node.templateId;return e===aqt.STRUCTURE&&null!==r?o.STRUCTURE_PALETTE(t(r),i):o.UNKNOWN}return n.node.type===jGt.SERIES?i?o.EXPANDED_COLOR:"white":n.node.type===jGt.BRIDGE?n.structural?"#f0e":n.node.inbound?"#0ef":"#fe0":Se.exports.isNumber(n.node.functionInputIndex)?"#795548":Se.exports.isNumber(n.node.functionOutputIndex)?"#009688":"white";case aqt.DEVICE:return null==n.deviceColors?o.UNKNOWN:i?o.EXPANDED_COLOR:vqt("device-"+n.node.name,n.deviceColors,r);case aqt.XLA_CLUSTER:return null==n.xlaClusterColors?o.UNKNOWN:i?o.EXPANDED_COLOR:vqt("xla-"+n.node.name,n.xlaClusterColors,r);case aqt.COMPUTE_TIME:return i?o.EXPANDED_COLOR:n.computeTimeColor||o.UNKNOWN;case aqt.MEMORY:return i?o.EXPANDED_COLOR:n.memoryColor||o.UNKNOWN;case aqt.OP_COMPATIBILITY:return null==n.compatibilityColors?o.UNKNOWN:i?o.EXPANDED_COLOR:vqt("op-compat-"+n.node.name,n.compatibilityColors,r);default:throw new Error("Unknown case to color nodes by")}}function wqt(t,e,n,i){i=i||vGt.Node.SHAPE;const r=n.isNodeHighlighted(e.node.name),o=n.isNodeSelected(e.node.name),a=e.isInExtract||e.isOutExtract||e.isLibraryFunction,s=e.expanded&&i!==vGt.Annotation.NODE,l=e.isFadedOut;t.classed("highlighted",r),t.classed("selected",o),t.classed("extract",a),t.classed("expanded",s),t.classed("faded",l);const c=t.select("."+i+" ."+vGt.Node.COLOR_TARGET),u=xqt(n.templateIndex,n.colorBy,e,s,n.getGraphSvgRoot());c.style("fill",u),c.style("stroke",o?null:Sqt(u))}function Sqt(t){return"url"===t.substring(0,3)?pWt.GRADIENT_OUTLINE:Uh(t).darker().toString()}function Mqt(t,e,n,i){const r=Su(t);if(r.selectAll(".input-highlight").classed("input-highlight",!1),r.selectAll(".non-input").classed("non-input",!1),r.selectAll(".input-parent").classed("input-parent",!1),r.selectAll(".input-child").classed("input-child",!1),r.selectAll(".input-edge-highlight").classed("input-edge-highlight",!1),r.selectAll(".non-input-edge-highlight").classed("non-input-edge-highlight",!1),r.selectAll(".input-highlight-selected").classed("input-highlight-selected",!1),!e||!i||!n)return;let o=Eqt(n,e),a={};Se.exports.each(o,(function(n){a=Tqt(t,e,n,a)}));let s=Object.keys(a),l=(function c(t,e){let n={};return Se.exports.each(e,(function(e){let i=t.getNodeByName(e),r=Cqt(t,i);n[r.name]=r})),n})(e,s);!(function u(t,e){Se.exports.forOwn(e,(function(e){let n=e;for(;n.name!==FGt;){const e=Su(t).select(`.node[data-name="${n.name}"]`);!e.nodes().length||e.classed("input-highlight")||e.classed("selected")||e.classed("op")||e.classed("input-parent",!0),n=n.parentNode}}))})(t,l),r.selectAll("g.node:not(.selected):not(.input-highlight):not(.input-parent):not(.input-children)").classed("non-input",!0).each((function(t){r.selectAll(`[data-name="${t.node.name}"]`).classed("non-input",!0)})),r.selectAll("g.edge:not(.input-edge-highlight)").classed("non-input-edge-highlight",!0)}function Eqt(t,e){let n=[],i=e.getNodeByName(t);if(i instanceof XGt)return[i].concat(i.inEmbeddings);let r=i.metagraph.nodes();return Se.exports.each(r,(function(t){n=n.concat(Eqt(t,e))})),n}function Tqt(t,e,n,i){if(i[n.name])return i;i[n.name]=!0;let r=n.inputs,o=Cqt(e,n);Su(t).select(`.node[data-name="${o.name}"]`).classed("input-highlight",!0);let a={};Se.exports.each(r,(function(t){let n=e.getNodeByName(t.name);if(void 0===n)return;if(n instanceof KGt){let t=sWt(n.name);n=e.getNodeByName(t)}let i=Cqt(e,n),r=a[i.name];r?r.opNodes.push(n):a[i.name]={visibleParent:i,opNodes:[n]}}));let s={},l=[o];s[o.name]={traced:!1,index:0,connectionEndpoints:[]};let c=o;for(let t=1;c.name!==FGt;t++)c=c.parentNode,s[c.name]={traced:!1,index:t,connectionEndpoints:[]},l[t]=c;return Se.exports.forOwn(a,(function(n,r){let a=n.visibleParent;Se.exports.each(n.opNodes,(function(n){i=Tqt(t,e,n,i)})),a.name!==o.name&&(function c(t,e,n,i){let r=e,o=e,a=[];for(;!n[r.name];)o.name!==r.name&&a.push([o,r]),o=r,r=r.parentNode;let s=n[r.name].index,l=i[Math.max(s-1,0)].name,c=l,u=o.name,h=o.name;const d=Su(t);d.selectAll(`[data-edge="${h}--${l}"]`).classed("input-edge-highlight",!0),Se.exports.each(a,(function(t){d.selectAll(`[data-edge="${t[0].name}--${c}~~${t[1].name}~~OUT"]`).classed("input-edge-highlight",!0)}));for(let t=1;t<s;t++)d.selectAll(`[data-edge="${u}~~${i[t].name}~~IN--${i[t-1].name}"]`).classed("input-edge-highlight",!0)})(t,a,s,l)})),i}function Cqt(t,e){let n=!1,i=e;for(;!n;)if(i=(e=i).parentNode,void 0===i)n=!0;else{let e=t.getRenderNodeByName(i.name);e&&(e.expanded||i instanceof XGt)&&(n=!0)}return e}function Aqt(t,e,n,i){let r=t.selectAll((function(){return this.childNodes})).data(e.list,(t=>t.node.name));return r.enter().append("g").attr("data-name",(t=>t.node.name)).each((function(t){let e=Su(this);i.addAnnotationGroup(t,n,e);let r=vGt.Annotation.EDGE,o=t.renderMetaedgeInfo&&t.renderMetaedgeInfo.metaedge;o&&!o.numRegularEdges&&(r+=" "+vGt.Annotation.CONTROL_EDGE),o&&o.numRefEdges&&(r+=" "+vGt.Edge.REF_LINE),rqt(e,t,i,r),t.annotationType!==bWt.ELLIPSIS?((function a(t,e){let n=e.node.name.split("/");kqt(t,n[n.length-1],e,null)})(e,t),(function s(t,e){e.annotationType===bWt.SUMMARY?wGt(t,"use").attr("class","summary").attr("xlink:href","#summary-icon").attr("cursor","pointer"):wGt(_qt(t,e,vGt.Annotation.NODE),"title").text(e.node.name)})(e,t)):kqt(e,t.node.name,t,vGt.Annotation.ELLIPSIS)})).merge(r).attr("class",(t=>vGt.Annotation.GROUP+" "+(function e(t){return(bWt[t]||"").toLowerCase()||null})(t.annotationType)+" "+yqt(t))).each((function(t){let e=Su(this);!(function r(t,e,n,i){let r=DWt(e);n.renderNodeInfo&&n.annotationType!==bWt.ELLIPSIS&&wqt(t,n.renderNodeInfo,i,vGt.Annotation.NODE),n.annotationType===bWt.SUMMARY&&(n.width+=10),t.select("text."+vGt.Annotation.LABEL).transition().attr("x",r+n.dx+(n.isIn?-1:1)*(n.width/2+n.labelOffset)).attr("y",e.y+n.dy),t.select("use.summary").transition().attr("x",r+n.dx-3).attr("y",e.y+n.dy-6),WWt(t.select("."+vGt.Annotation.NODE+" ellipse"),r+n.dx,e.y+n.dy,n.width,n.height),GWt(t.select("."+vGt.Annotation.NODE+" rect"),r+n.dx,e.y+n.dy,n.width,n.height),GWt(t.select("."+vGt.Annotation.NODE+" use"),r+n.dx,e.y+n.dy,n.width,n.height),t.select("path."+vGt.Annotation.EDGE).transition().attr("d",(t=>{let n=t.points.map((t=>({x:t.dx+r,y:t.dy+e.y})));return oqt(n)}))})(e,n,t,i),t.annotationType!==bWt.ELLIPSIS&&(function o(t,e,n,i){t.on("mouseover",(t=>{i.fire("annotation-highlight",{name:t.node.name,hostName:e.node.name})})).on("mouseout",(t=>{i.fire("annotation-unhighlight",{name:t.node.name,hostName:e.node.name})})).on("click",(t=>{uu.stopPropagation(),i.fire("annotation-select",{name:t.node.name,hostName:e.node.name})})),n.annotationType!==bWt.SUMMARY&&n.annotationType!==bWt.CONSTANT&&t.on("contextmenu",ZWt(i,uqt(n.node,i)))})(e,n,t,i)})),r.exit().each((function(t){i.removeAnnotationGroup(t,n)})).remove(),r}function kqt(t,e,n,i){let r=vGt.Annotation.LABEL;return i&&(r+=" "+i),fqt(t.append("text").attr("class",r).attr("dy",".35em").attr("text-anchor",n.isIn?"end":"start").text(e),-1,5)}function Lqt(t,e,n,i){let r=xGt(t,"g",i=i||vGt.Scene.GROUP).empty(),o=wGt(t,"g",i),a=wGt(o,"g",vGt.Scene.CORE),s=Se.exports.reduce(e.coreGraph.nodes(),((t,n)=>{let i=e.coreGraph.node(n);return i.excluded||t.push(i),t}),[]);return e.node.type===jGt.SERIES&&s.reverse(),(function l(t,e,n){const i=n;let r=[];r=Se.exports.reduce(e.edges(),((t,n)=>{let i=e.edge(n);return t.push({v:n.v,w:n.w,label:i}),t}),r);let o=wGt(t,"g",vGt.Edge.CONTAINER).selectAll((function(){return this.childNodes})).data(r,QWt);o.enter().append("g").attr("class",vGt.Edge.GROUP).attr("data-edge",QWt).each((function(t){let e=Su(this);t.label.edgeGroup=e,i._edgeGroupIndex[QWt(t)]=e,i.handleEdgeSelected&&e.on("click",(t=>{uu.stopPropagation(),i.fire("edge-select",{edgeData:t,edgeGroup:e})})),rqt(e,t,i)})).merge(o).each((function(){!(function t(e,n){Su(n).select("path."+vGt.Edge.LINE).transition().attrTween("d",(function(t,n,i){return(function r(t,e,n,i,o){let a=n.label,s=a.adjoiningMetaedge,l=a.points;const{shadowRoot:c}=t;if(n.label.startMarkerId&&(l=iqt(l,Su(c.querySelector("#"+n.label.startMarkerId)),!0)),n.label.endMarkerId&&(l=iqt(l,Su(c.querySelector("#"+n.label.endMarkerId)),!1)),!s)return jd(o,oqt(l));let u=s.edgeGroup.node().firstChild,h=a.metaedge.inbound;return function(t){let n=u.getPointAtLength(h?u.getTotalLength():0).matrixTransform(u.getCTM()).matrixTransform(e.getCTM().inverse()),i=h?0:l.length-1;return l[i].x=n.x,l[i].y=n.y,oqt(l)}})(e,this,t,0,i)}))})(n,this)})).each((function(t){!(function e(t,n,i){t.classed("faded",n.label.isFadedOut);let r=n.label.metaedge;t.select("path."+vGt.Edge.LINE).classed("control-dep",r&&!r.numRegularEdges)})(Su(this),t)})),o.exit().each((t=>{delete i._edgeGroupIndex[QWt(t)]})).remove()})(a,e.coreGraph,n),sqt(a,s,n),e.isolatedInExtract.length>0?sqt(wGt(o,"g",vGt.Scene.INEXTRACT),e.isolatedInExtract,n):xGt(o,"g",vGt.Scene.INEXTRACT).remove(),e.isolatedOutExtract.length>0?sqt(wGt(o,"g",vGt.Scene.OUTEXTRACT),e.isolatedOutExtract,n):xGt(o,"g",vGt.Scene.OUTEXTRACT).remove(),e.libraryFunctionsExtract.length>0?sqt(wGt(o,"g",vGt.Scene.FUNCTION_LIBRARY),e.libraryFunctionsExtract,n):xGt(o,"g",vGt.Scene.FUNCTION_LIBRARY).remove(),(function c(t,e){let n=e.node.type===jGt.SERIES?0:NWt.subscene.meta.labelHeight;jWt(FWt(t,"g",VWt.Scene.CORE),0,n);let i=e.isolatedInExtract.length>0,r=e.isolatedOutExtract.length>0,o=e.libraryFunctionsExtract.length>0,a=NWt.subscene.meta.extractXOffset,s=0;if(i&&(s+=e.outExtractBox.width),r&&(s+=e.outExtractBox.width),i){let i=e.coreBox.width;i=s<IWt?i-IWt+e.inExtractBox.width/2:i-e.inExtractBox.width/2-e.outExtractBox.width-(r?a:0),i=i-e.libraryFunctionsBox.width-(o?a:0),jWt(FWt(t,"g",VWt.Scene.INEXTRACT),i,n)}if(r){let i=e.coreBox.width;s<IWt?i=i-IWt+e.outExtractBox.width/2:i-=e.outExtractBox.width/2,i=i-e.libraryFunctionsBox.width-(o?a:0),jWt(FWt(t,"g",VWt.Scene.OUTEXTRACT),i,n)}if(o){let i=e.coreBox.width-e.libraryFunctionsBox.width/2;jWt(FWt(t,"g",VWt.Scene.FUNCTION_LIBRARY),i,n)}})(o,e),r&&o.attr("opacity",0).transition().attr("opacity",1),o}let Pqt=class extends(er(ye)){constructor(){super(...arguments),this.node=null,this.renderInfo=null,this.colorBy=aqt.STRUCTURE,this.templateIndex=null,this.type=null,this.vertical=!1,this.const=!1,this.summary=!1,this.fill=null,this.height=20}_computeFillOverride(t,e,n,i,r){return t&&e&&i?xqt(i,n,e,!1):r}_getStrokeOverride(t){return t?Sqt(t):null}_getType(t,e,n,i){const{GraphIconType:r}=KWt;if(t)switch(t.type){case jGt.OP:{const i=t.op;return"string"!=typeof i?r.OP:"Const"===i||n?r.CONST:i.endsWith("Summary")||e?r.SUMMARY:r.OP}case jGt.META:return r.META;case jGt.SERIES:return r.SERIES}return i}_isVertical(t,e){return t?t.hasNonControlEdges:!!e}_getFaded(t){return t&&t.isFadedOut}_onFillOverrideChanged(t,e){const{node:n,renderInfo:i,colorBy:r,templateIndex:o}=this;t!==e&&bqt(this.$.icon.getSvgDefinableElement()),n&&i&&o&&xqt(o,r,i,!1,this.$.icon.getSvgDefinableElement())}};Pqt.template=_e`
    <style>
      tf-graph-icon {
        --tb-graph-faded: var(--tb-graph-faded);
      }
    </style>
    <tf-graph-icon
      id="icon"
      type="[[_getType(node, summary, const, type)]]"
      height="[[height]]"
      fill-override="[[_fillOverride]]"
      stroke-override="[[_getStrokeOverride(_fillOverride)]]"
      faded="[[_getFaded(renderInfo)]]"
      vertical="[[_isVertical(node, vertical)]]"
    ></tf-graph-icon>
  `,t([o({type:Object}),e("design:type",Object)],Pqt.prototype,"node",void 0),t([o({type:Object}),e("design:type",Object)],Pqt.prototype,"renderInfo",void 0),t([o({type:Object}),e("design:type",String)],Pqt.prototype,"colorBy",void 0),t([o({type:Object}),e("design:type",Function)],Pqt.prototype,"templateIndex",void 0),t([o({type:String}),e("design:type",String)],Pqt.prototype,"type",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Pqt.prototype,"vertical",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Pqt.prototype,"const",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Pqt.prototype,"summary",void 0),t([o({type:String}),e("design:type",String)],Pqt.prototype,"fill",void 0),t([o({type:Number}),e("design:type",Number)],Pqt.prototype,"height",void 0),t([o({type:String,computed:"_computeFillOverride(node, renderInfo, colorBy, templateIndex, fill)",observer:"_onFillOverrideChanged"}),e("design:type",String)],Pqt.prototype,"_fillOverride",void 0),Pqt=t([i("tf-node-icon")],Pqt);let Nqt=class extends(er(ye)){_itemTypeChanged(){"subnode"!==this.itemType?this.$["list-item"].classList.add("clickable"):this.$["list-item"].classList.remove("clickable")}_nodeListener(t){this.fire("node-list-item-"+t.type,{nodeName:this.name,type:this.itemType})}_fadedClass(t){return t&&t.isFadedOut?"faded":""}};function Iqt(t,e,n){return Se.exports.sortBy(t,[t=>e.node(t).op,t=>e.node(t).templateId,t=>e.neighbors(t).length,t=>e.predecessors(t).length,t=>e.successors(t).length,t=>t.substr(n.length)])}function Rqt(t,e){if(!(function n(t,e){let n=lWt(t),i=lWt(e);for(let t=0;t<n.length;t++)if(n[t]!==i[t])return!1;return!0})(t,e))return!1;let i=t.graph().name,r=e.graph().name,o={},a={},s=[];function l(t,e){let n=t.substr(i.length),l=e.substr(r.length);return o[n]^a[l]?(console.warn("different visit pattern","["+i+"]",n,"["+r+"]",l),!0):(o[n]||(o[n]=a[l]=!0,s.push({n1:t,n2:e})),!1)}let c=t.sources(),u=e.sources();if(c.length!==u.length)return console.log("different source length"),!1;c=Iqt(c,t,i),u=Iqt(u,e,r);for(let t=0;t<c.length;t++)if(l(c[t],u[t]))return!1;for(;s.length>0;){let n=s.pop();if(!Oqt(t.node(n.n1),e.node(n.n2)))return!1;let o=t.successors(n.n1),a=e.successors(n.n2);if(o.length!==a.length)return console.log("# of successors mismatch",o,a),!1;o=Iqt(o,t,i),a=Iqt(a,e,r);for(let t=0;t<o.length;t++)if(l(o[t],a[t]))return!1}return!0}function Oqt(t,e){if(t.type===jGt.META){let n=t,i=e;return n.templateId&&i.templateId&&n.templateId===i.templateId}if(t.type===jGt.OP&&e.type===jGt.OP)return t.op===e.op;if(t.type===jGt.SERIES&&e.type===jGt.SERIES){let n=t,i=e,r=n.metagraph.nodeCount();return r===i.metagraph.nodeCount()&&(0===r||n.metagraph.node(n.metagraph.nodes()[0]).op===i.metagraph.node(i.metagraph.nodes()[0]).op)}return!1}var zqt;Nqt.template=_e`
    <style>
      #list-item {
        width: 100%;
        color: var(--secondary-text-color);
        font-size: 11pt;
        font-weight: 400;
        position: relative;
        display: inline-block;
      }

      #list-item:hover {
        background-color: var(--google-yellow-100);
      }

      .clickable {
        cursor: pointer;
      }

      #list-item span {
        margin-left: 40px;
      }

      #list-item.excluded span {
        color: #999;
      }

      #list-item span.edge-label {
        float: right;
        font-size: 10px;
        margin-left: 3px;
        margin-right: 5px;
      }

      .node-icon {
        position: absolute;
        top: 1px;
        left: 2px;
      }

      .faded span {
        color: var(--tb-graph-faded);
      }
    </style>

    <div
      id="list-item"
      on-mouseover="_nodeListener"
      on-mouseout="_nodeListener"
      on-click="_nodeListener"
    >
      <div class$="{{_fadedClass(itemRenderInfo)}}">
        <tf-node-icon
          class="node-icon"
          height="12"
          color-by="[[colorBy]]"
          color-by-params="[[colorByParams]]"
          node="[[itemNode]]"
          render-info="[[itemRenderInfo]]"
          template-index="[[templateIndex]]"
        >
        </tf-node-icon>
        <span title$="[[name]]">[[name]]</span>
      </div>
    </div>
  `,t([o({type:Object}),e("design:type",Object)],Nqt.prototype,"cardNode",void 0),t([o({type:Object}),e("design:type",Object)],Nqt.prototype,"itemNode",void 0),t([o({type:String}),e("design:type",String)],Nqt.prototype,"edgeLabel",void 0),t([o({type:Object}),e("design:type",Object)],Nqt.prototype,"itemRenderInfo",void 0),t([o({type:String}),e("design:type",String)],Nqt.prototype,"name",void 0),t([o({type:String,observer:"_itemTypeChanged"}),e("design:type",String)],Nqt.prototype,"itemType",void 0),t([o({type:String}),e("design:type",String)],Nqt.prototype,"colorBy",void 0),t([o({type:Object}),e("design:type",Object)],Nqt.prototype,"colorByParams",void 0),t([o({type:Object}),e("design:type",Function)],Nqt.prototype,"templateIndex",void 0),Nqt=t([i("tf-graph-op-compat-list-item")],Nqt),(function(t){t[t.TEMPLATES_UPDATED=0]="TEMPLATES_UPDATED"})(zqt||(zqt={}));class Dqt extends class{constructor(){this.eventTypeToListeners=new Map}getListeners(t){return this.eventTypeToListeners.has(t)||this.eventTypeToListeners.set(t,[]),this.eventTypeToListeners.get(t)}addListener(t,e){this.getListeners(t).push(e)}removeListener(t,e){const n=this.getListeners(t).filter((t=>t!==e));this.eventTypeToListeners.set(t,n)}dispatchEvent(t,e){for(const n of this.getListeners(t))n(e)}}{constructor(t){super(),this.hasShapeInfo=!1,this.maxMetaEdgeSize=1,this.graphOptions={},this.templates=null,this.graphOptions.compound=!0,this.graphOptions.rankdir=t.rankDirection,this.root=$Gt(FGt,this.graphOptions),this.libraryFunctions={},this.seriesGroupMap=new Map(t.seriesMap),this.devices=null,this.xlaClusters=null,this.verifyTemplate=t.verifyTemplate,this.index={},this.index.__root__=this.root,this.orderings={}}getSeriesGroupType(t){var e;return null!==(e=this.seriesGroupMap.get(t))&&void 0!==e?e:WGt.GROUP}setSeriesGroupType(t,e){return this.seriesGroupMap.set(t,e)}buildSeriesGroupMapToggled(t){const e=this.getSeriesGroupType(t)===WGt.GROUP?WGt.UNGROUP:WGt.GROUP;return new Map([...this.seriesGroupMap,[t,e]])}getNodeMap(){return this.index}node(t){return this.index[t]}setNode(t,e){this.index[t]=e}getBridgegraph(t){let e=this.index[t];if(!e)throw Error("Could not find node in hierarchy: "+t);if(!("metagraph"in e))return null;if(e.bridgegraph)return e.bridgegraph;let n=e.bridgegraph=oWt("BRIDGEGRAPH",UGt.BRIDGE,this.graphOptions);if(!e.parentNode||!("metagraph"in e.parentNode))return n;let i=e.parentNode,r=i.metagraph,o=this.getBridgegraph(i.name);return Se.exports.each([r,o],(e=>{e.edges().filter((e=>e.v===t||e.w===t)).forEach((i=>{let r=i.w===t,o=e.edge(i);Se.exports.each(o.baseEdgeList,(e=>{let[o,a]=r?[e.w,i.v]:[e.v,i.w],s=this.getChildName(t,o),l={v:r?a:s,w:r?s:a},c=n.edge(l);c||(c=ZGt(l.v,l.w),c.inbound=r,n.setEdge(l.v,l.w,c)),c.addBaseEdge(e,this)}))}))})),n}getChildName(t,e){let n=this.index[e];for(;n;){if(n.parentNode&&n.parentNode.name===t)return n.name;n=n.parentNode}throw Error("Could not find immediate child for descendant: "+e)}getPredecessors(t){let e=this.index[t];if(!e)throw Error("Could not find node with name: "+t);let n=this.getOneWayEdges(e,!0);return e.isGroupNode||Se.exports.each(e.inEmbeddings,(i=>{Se.exports.each(e.inputs,(e=>{if(e.name===i.name){let r=new JGt(i.name,t);r.addBaseEdge({isControlDependency:e.isControlDependency,outputTensorKey:e.outputTensorKey,isReferenceEdge:!1,v:i.name,w:t},this),n.regular.push(r)}}))})),n}getSuccessors(t){let e=this.index[t];if(!e)throw Error("Could not find node with name: "+t);let n=this.getOneWayEdges(e,!1);return e.isGroupNode||Se.exports.each(e.outEmbeddings,(e=>{Se.exports.each(e.inputs,(i=>{if(i.name===t){let r=new JGt(t,e.name);r.addBaseEdge({isControlDependency:i.isControlDependency,outputTensorKey:i.outputTensorKey,isReferenceEdge:!1,v:t,w:e.name},this),n.regular.push(r)}}))})),n}getOneWayEdges(t,e){let n={control:[],regular:[]};if(!t.parentNode||!t.parentNode.isGroupNode)return n;let i=t.parentNode,r=i.metagraph,o=this.getBridgegraph(i.name);return Bqt(r,t,e,n),Bqt(o,t,e,n),n}getTopologicalOrdering(t){let e=this.index[t];if(!e)throw Error("Could not find node with name: "+t);if(!e.isGroupNode)return null;if(t in this.orderings)return this.orderings[t];let n={},i={},r=e.metagraph;Se.exports.each(r.edges(),(t=>{r.edge(t).numRegularEdges&&(t.v in n||(n[t.v]=[]),n[t.v].push(t.w),i[t.w]=!0)}));let o=Se.exports.difference(Se.exports.keys(n),Se.exports.keys(i)),a=this.orderings[t]={},s=0;for(;o.length;){let t=o.shift();a[t]=s++,Se.exports.each(n[t],(t=>o.push(t))),delete n[t]}return a}getTemplateIndex(){if(!this.templates)return null;let t=Lg(this.templates);if(!t.length)return null;let e=EM().domain(t).range(zl(0,t.length));return t=>e(t)}updateTemplates(){CGt("Finding similar subgraphs",(()=>{this.templates=(function t(e,n){let i=(function o(t,e){return Se.exports.reduce(t,(function(t,n){let i=n[0],r=[];return n[1].nodes.forEach((function(t){for(let n=0;n<r.length;n++)if(!e||Rqt(r[n].metanode.metagraph,t.metagraph))return t.templateId=r[n].metanode.templateId,void r[n].members.push(t.name);t.templateId=i+"["+r.length+"]",r.push({metanode:t,members:[t.name]})})),r.forEach((function(e){t[e.metanode.templateId]={level:n[1].level,nodes:e.members}})),t}),{})})((function r(t){const e=t.getNodeMap();let n=Object.keys(e).reduce(((t,n)=>{const i=e[n];if(i.type!==jGt.META)return t;let r=n.split("/").length-1,o=(function a(t){return Se.exports.map({depth:t.depth,"|V|":t.metagraph.nodes().length,"|E|":t.metagraph.edges().length},(function(t,e){return e+"="+t})).join(" ")+" [ops] "+Se.exports.map(t.opHistogram,(function(t,e){return e+"="+t})).join(",")})(i),s=t[o]||{nodes:[],level:r};return t[o]=s,s.nodes.push(i),s.level>r&&(s.level=r),t}),{});return Object.keys(n).map((t=>[t,n[t]])).filter((([t,e])=>{const{nodes:n}=e;if(n.length>1)return!0;const i=n[0];return i.type===jGt.META&&i.associatedFunction})).sort((([t,e])=>e.nodes[0].depth))})(e),n);return Object.keys(i).sort((t=>i[t].level)).reduce(((t,e)=>(t[e]=i[e],t)),{})})(this,this.verifyTemplate),this.dispatchEvent(zqt.TEMPLATES_UPDATED)}),_Gt.HIERARCHY_FIND_SIMILAR_SUBGRAPHS)}}function Bqt(t,e,n,i){let r=n?t.inEdges(e.name):t.outEdges(e.name);Se.exports.each(r,(e=>{let n=t.edge(e);(n.numRegularEdges?i.regular:i.control).push(n)}))}const Hqt={verifyTemplate:!0,seriesNodeMinSize:5,seriesMap:new Map,rankDirection:"BT",useGeneralizedSeriesPatterns:!1};function Fqt(t,e,n){const i=new Dqt(e),r={};return LGt("Adding nodes",30,(()=>{let e={},n={};Se.exports.each(t.nodes,((t,i)=>{t.device&&(e[t.device]=!0),t.xlaCluster&&(n[t.xlaCluster]=!0)})),i.devices=Se.exports.keys(e),i.xlaClusters=Se.exports.keys(n),(function r(t,e){const n={};Se.exports.each(e.nodes,((e,i)=>{let r=cWt(e.name),o=t.root;o.depth=Math.max(r.length,o.depth),n[e.op]||(n[e.op]=[]),n[e.op].push(e);for(let i=0;i<r.length&&(o.depth=Math.max(o.depth,r.length-i),o.cardinality+=e.cardinality,o.opHistogram[e.op]=(o.opHistogram[e.op]||0)+1,null!=e.device&&(o.deviceHistogram[e.device]=(o.deviceHistogram[e.device]||0)+1),null!=e.xlaCluster&&(o.xlaClusterHistogram[e.xlaCluster]=(o.xlaClusterHistogram[e.xlaCluster]||0)+1),e.compatible?o.compatibilityHistogram.compatible=(o.compatibilityHistogram.compatible||0)+1:o.compatibilityHistogram.incompatible=(o.compatibilityHistogram.incompatible||0)+1,Se.exports.each(e.inEmbeddings,(t=>{t.compatible?o.compatibilityHistogram.compatible=(o.compatibilityHistogram.compatible||0)+1:o.compatibilityHistogram.incompatible=(o.compatibilityHistogram.incompatible||0)+1})),Se.exports.each(e.outEmbeddings,(t=>{t.compatible?o.compatibilityHistogram.compatible=(o.compatibilityHistogram.compatible||0)+1:o.compatibilityHistogram.incompatible=(o.compatibilityHistogram.incompatible||0)+1})),i!==r.length-1);i++){let e=r[i],a=t.node(e);if(!a&&(a=$Gt(e,t.graphOptions),a.parentNode=o,t.setNode(e,a),o.metagraph.setNode(e,a),0===e.indexOf(VGt)&&o.name===FGt)){const i=e.substring(VGt.length);n[i]||(n[i]=[]),t.libraryFunctions[i]={node:a,usages:n[i]},a.associatedFunction=i}o=a}t.setNode(e.name,e),e.parentNode=o,o.metagraph.setNode(e.name,e),Se.exports.each(e.inEmbeddings,(function(n){t.setNode(n.name,n),n.parentNode=e})),Se.exports.each(e.outEmbeddings,(function(n){t.setNode(n.name,n),n.parentNode=e}))}))})(i,t)}),n,_Gt.HIERARCHY_ADD_NODES).then((()=>LGt("Detect series",30,(()=>{e.seriesNodeMinSize>0&&Vqt(i.root,i,r,e.seriesNodeMinSize,e.seriesMap,e.useGeneralizedSeriesPatterns)}),n,_Gt.HIERARCHY_DETECT_SERIES))).then((()=>LGt("Adding edges",40,(()=>{!(function e(t,n,i){let r=t.getNodeMap(),o=[],a=[],s=(t,e)=>{let n=0;for(;t;)e[n++]=t.name,t=t.parentNode;return n-1};Se.exports.each(n.edges,(e=>{let i=s(n.nodes[e.v],o),l=s(n.nodes[e.w],a);if(-1===i||-1===l)return;for(;o[i]===a[l];)if(i--,l--,i<0||l<0)throw Error("No difference found between ancestor paths.");let c=r[o[i+1]],u=o[i],h=a[l],d=c.metagraph.edge(u,h);d||(d=ZGt(u,h),c.metagraph.setEdge(u,h,d)),c.hasNonControlEdges||e.isControlDependency||(c.hasNonControlEdges=!0),d.addBaseEdge(e,t)}))})(i,t)}),n,_Gt.HIERARCHY_ADD_EDGES))).then((()=>i))}function Vqt(t,e,n,i,r,o){let a=t.metagraph;Se.exports.each(a.nodes(),(t=>{let s=a.node(t);s.type===jGt.META&&Vqt(s,e,n,i,r,o)}));let s=(function l(t){return Se.exports.reduce(t.nodes(),((e,n)=>{let i=t.node(n);if(i.type===jGt.META)return e;let r=i.op;return r&&(e[r]=e[r]||[],e[r].push(i.name)),e}),{})})(a),c=(o?jqt:Uqt)(s,a,e.graphOptions);Se.exports.each(c,(function(t,r){let o=t.metagraph.nodes();Se.exports.each(o,(t=>{let e=a.node(t);e.owningSeries||(e.owningSeries=r)})),o.length<i&&e.getSeriesGroupType(t.name)===WGt.GROUP&&e.setSeriesGroupType(t.name,WGt.UNGROUP),e.getSeriesGroupType(t.name)!==WGt.UNGROUP&&(e.setNode(r,t),a.setNode(r,t),Se.exports.each(o,(e=>{let i=a.node(e);t.metagraph.setNode(e,i),t.parentNode=i.parentNode,t.cardinality++,null!=i.device&&(t.deviceHistogram[i.device]=(t.deviceHistogram[i.device]||0)+1),null!=i.xlaCluster&&(t.xlaClusterHistogram[i.xlaCluster]=(t.xlaClusterHistogram[i.xlaCluster]||0)+1),i.compatible?t.compatibilityHistogram.compatible=(t.compatibilityHistogram.compatible||0)+1:t.compatibilityHistogram.incompatible=(t.compatibilityHistogram.incompatible||0)+1,Se.exports.each(i.inEmbeddings,(e=>{e.compatible?t.compatibilityHistogram.compatible=(t.compatibilityHistogram.compatible||0)+1:t.compatibilityHistogram.incompatible=(t.compatibilityHistogram.incompatible||0)+1})),Se.exports.each(i.outEmbeddings,(e=>{e.compatible?t.compatibilityHistogram.compatible=(t.compatibilityHistogram.compatible||0)+1:t.compatibilityHistogram.incompatible=(t.compatibilityHistogram.incompatible||0)+1})),i.parentNode=t,n[e]=r,a.removeNode(e)})))}))}function Uqt(t,e,n){let i={};return Se.exports.each(t,(function(t,r){if(t.length<=1)return;let o={};Se.exports.each(t,(function(t){const e="*"===t.charAt(t.length-1),i=t.split("/"),r=i[i.length-1],a=i.slice(0,i.length-1).join("/"),s=r.match(/^(\D*)(\d+)$/);let l,c,u="";s?(l=s[1],c=s[2]):(l=e?r.substr(0,r.length-1):r,c=0,u=e?"*":"");const h=tWt(l,u,a);o[h]=o[h]||[];const d=QGt(l,u,a,+c,t,n);o[h].push(d)})),Se.exports.each(o,(function(t,o){if(t.length<2)return;t.sort((function(t,e){return+t.clusterId-+e.clusterId}));let a=[t[0]];for(let o=1;o<t.length;o++){let s=t[o];s.clusterId!==a[a.length-1].clusterId+1?(Gqt(a,i,+r,e,n),a=[s]):a.push(s)}Gqt(a,i,+r,e,n)}))})),i}function jqt(t,e,n){let i={};return Se.exports.each(t,(function(t,r){if(t.length<=1)return;let o={},a={};Se.exports.each(t,(function(t){let e="*"===t.charAt(t.length-1),i=t.split("/"),r=i[i.length-1],s=i.slice(0,i.length-1).join("/");const l=/(\d+)/g;let c,u,h,d,p,f=0;for(;c=l.exec(r);)++f,u=r.slice(0,c.index),h=c[0],d=r.slice(c.index+c[0].length),p=tWt(u,d,s),o[p]=o[p],o[p]||(o[p]=QGt(u,d,s,+h,t,n)),o[p].ids.push(h),a[t]=a[t]||[],a[t].push([p,h]);f<1&&(u=e?r.substr(0,r.length-1):r,h=0,d=e?"*":"",p=tWt(u,d,s),o[p]=o[p],o[p]||(o[p]=QGt(u,d,s,+h,t,n)),o[p].ids.push(h),a[t]=a[t]||[],a[t].push([p,h]))}));var s={};Se.exports.each(a,(function(t,e){t.sort((function(t,e){return o[e[0]].ids.length-o[t[0]].ids.length}));var i=t[0][0],r=t[0][1];s[i]=s[i]||[];const a=e.split("/"),l=a.slice(0,a.length-1).join("/");var c=QGt(o[i].prefix,o[i].suffix,l,+r,e,n);s[i].push(c)})),Se.exports.each(s,(function(t,o){if(t.length<2)return;t.sort((function(t,e){return+t.clusterId-+e.clusterId}));let a=[t[0]];for(let o=1;o<t.length;o++){let s=t[o];s.clusterId!==a[a.length-1].clusterId+1?(Gqt(a,i,+r,e,n),a=[s]):a.push(s)}Gqt(a,i,+r,e,n)}))})),i}function Gqt(t,e,n,i,r){if(t.length>1){let o=tWt(t[0].prefix,t[0].suffix,t[0].parent,t[0].clusterId,t[t.length-1].clusterId),a=QGt(t[0].prefix,t[0].suffix,t[0].parent,n,o,r);Se.exports.each(t,(function(t){a.ids.push(t.clusterId),a.metagraph.setNode(t.name,i.node(t.name))})),e[o]=a}}let Wqt=class extends(er(il(ye))){constructor(){super(...arguments),this._expanded=!0,this._opCompatColor=hWt,this._opIncompatColor=dWt,this._templateIndex=null}_getNode(t,e){return e.node(t)}_getRenderInfo(t,e){return this.renderHierarchy.getOrCreateRenderNodeByName(t)}_toggleExpanded(){this._expanded=!this._expanded}_getToggleIcon(t){return t?"expand-less":"expand-more"}_resizeList(t){var e=document.querySelector(t);e&&e.fire("iron-resize")}get _incompatibleOpNodes(){const t=this.graphHierarchy;return t&&t.root?(this.async(this._resizeList.bind(this,"#incompatibleOpsList")),(function e(t){const e=[],n={};return Se.exports.each(t.root.leaves(),(i=>{let r=t.node(i);if(r.type==jGt.OP){let i=r;if(!i.compatible)if(i.owningSeries){if(t.getSeriesGroupType(i.owningSeries)===WGt.UNGROUP)e.push(i);else if(!n[i.owningSeries]){let r=t.node(i.owningSeries);r&&(n[i.owningSeries]=r,e.push(r))}}else e.push(i);Se.exports.each(i.inEmbeddings,(t=>{t.compatible||e.push(t)})),Se.exports.each(i.outEmbeddings,(t=>{t.compatible||e.push(t)}))}})),e})(t)):[]}get _opCompatScore(){var t=this.graphHierarchy;if(t&&t.root){var e=t.root,n=e.compatibilityHistogram.compatible,i=e.compatibilityHistogram.incompatible;return 0==n&&0==i?0:Math.floor(100*n/(n+i))/100}return 0}get _opCompatScoreLabel(){var t=this._opCompatScore;return my(".0%")(t)}get _totalIncompatOps(){var t=this.graphHierarchy;return t&&t.root?t.root.compatibilityHistogram.incompatible:0}_graphHierarchyChanged(){this._templateIndex=this.graphHierarchy.getTemplateIndex(),this.graphHierarchy.addListener(zqt.TEMPLATES_UPDATED,(()=>{this._templateIndex=this.graphHierarchy.getTemplateIndex()}))}};Wqt.template=_e`
    <style>
      :host {
        max-height: 500px;
      }

      .incompatible-ops-list {
        height: 350px;
        max-height: 400px;
        overflow-y: scroll;
        display: flex;
        flex-direction: column;
      }

      iron-list {
        flex: 1 1 auto;
      }

      paper-item {
        padding: 0;
        background: var(--secondary-background-color);
      }

      paper-item-body[two-line] {
        min-height: 0;
        padding: 8px 12px 4px;
      }

      .expandedInfo {
        padding: 8px 12px;
        font-weight: 500;
        font-size: 12pt;
        width: 100%;
      }

      .node-name {
        white-space: normal;
        word-wrap: break-word;
        font-size: 14pt;
        font-weight: 500;
      }

      .subtitle {
        color: var(--secondary-text-color);
        font-size: 12pt;
      }

      .toggle-button {
        float: right;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .non-control-list-item {
        padding-left: 10px;
      }

      div.op-compat-display {
        margin-top: 10px;
        display: inline-block;
      }

      /**
       * Sadly, because the whole body is inverted in color, legends also need
       * to be inverted.
       **/
      :host(.dark-mode) div.op-compat-display {
        filter: invert(1);
      }

      svg.op-compat {
        width: 250px;
        height: 25px;
        float: left;
      }

      div.op-compat-value {
        float: right;
        height: 100%;
        font-size: 14px;
        color: black;
        margin-left: 10px;
      }
    </style>

    <paper-item>
      <paper-item-body two-line>
        <div>
          <paper-icon-button
            icon="{{_getToggleIcon(_expanded)}}"
            on-click="_toggleExpanded"
            class="toggle-button"
          >
          </paper-icon-button>
          <div class="node-name" id="nodetitle">[[nodeTitle]]</div>
        </div>
        <div secondary>
          <div class="subtitle">
            <div class="op-compat-display">
              <svg
                class="op-compat"
                preserveAspectRatio="xMinYMid meet"
                viewBox="0 0 250 25"
              >
                <defs>
                  <linearGradient id="op-compat-fill">
                    <stop offset="0" stop-color$="[[_opCompatColor]]"></stop>
                    <stop
                      offset$="[[_opCompatScore]]"
                      stop-color$="[[_opCompatColor]]"
                    ></stop>
                    <stop
                      offset$="[[_opCompatScore]]"
                      stop-color$="[[_opIncompatColor]]"
                    ></stop>
                    <stop offset="1" stop-color$="[[_opIncompatColor ]]"></stop>
                  </linearGradient>
                </defs>
                <rect
                  height="25"
                  width="250"
                  rx="5"
                  ry="5"
                  style="fill: url('#op-compat-fill');"
                ></rect>
              </svg>
              <div class="op-compat-value">[[_opCompatScoreLabel]]</div>
            </div>
          </div>
        </div>
      </paper-item-body>
    </paper-item>

    <iron-collapse opened="{{_expanded}}">
      <template is="dom-if" if="{{_expanded}}" restamp="true">
        <div class="expandedInfo">
          Incompatible Operations: (<span>[[_totalIncompatOps]]</span>)
          <iron-list
            class="incompatible-ops-list"
            id="incompatibleOpsList"
            items="[[_incompatibleOpNodes]]"
          >
            <template>
              <tf-graph-op-compat-list-item
                class="non-control-list-item"
                item-node="[[item]]"
                item-render-info="[[_getRenderInfo(item.name, renderHierarchy)]]"
                name="[[item.name]]"
                template-index="[[_templateIndex]]"
                color-by="[[colorBy]]"
                item-type="incompatible-ops"
              >
              </tf-graph-op-compat-list-item>
            </template>
          </iron-list>
        </div>
      </template>
    </iron-collapse>
  `,t([o({type:Object}),e("design:type",Dqt)],Wqt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",yWt)],Wqt.prototype,"renderHierarchy",void 0),t([o({type:String}),e("design:type",String)],Wqt.prototype,"nodeTitle",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Wqt.prototype,"_expanded",void 0),t([o({type:String}),e("design:type",String)],Wqt.prototype,"_opCompatColor",void 0),t([o({type:String}),e("design:type",String)],Wqt.prototype,"_opIncompatColor",void 0),t([o({type:Object}),e("design:type",Function)],Wqt.prototype,"_templateIndex",void 0),t([s("graphHierarchy"),e("design:type",Array),e("design:paramtypes",[])],Wqt.prototype,"_incompatibleOpNodes",null),t([s("graphHierarchy"),e("design:type",Number),e("design:paramtypes",[])],Wqt.prototype,"_opCompatScore",null),t([s("_opCompatScore"),e("design:type",String),e("design:paramtypes",[])],Wqt.prototype,"_opCompatScoreLabel",null),t([s("graphHierarchy"),e("design:type",Number),e("design:paramtypes",[])],Wqt.prototype,"_totalIncompatOps",null),t([a("graphHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Wqt.prototype,"_graphHierarchyChanged",null),Wqt=t([i("tf-graph-op-compat-card")],Wqt);let qqt=class extends(er(il(ye))){_itemTypeChanged(){"subnode"!==this.itemType?this.$["list-item"].classList.add("clickable"):this.$["list-item"].classList.remove("clickable")}_nodeListener(t){this.fire("node-list-item-"+t.type,{cardNode:this.cardNode.name,nodeName:this.name,type:this.itemType})}_fadedClass(t){return t&&t.isFadedOut?"faded":""}};qqt.template=_e`
    <style>
      #list-item {
        width: 100%;
        color: var(--secondary-text-color);
        font-size: 11pt;
        font-weight: 400;
        position: relative;
        display: inline-block;
      }

      #list-item:hover {
        background-color: var(--google-yellow-100);
      }

      :host(.dark-mode) #list-item:hover {
        background-color: var(--paper-yellow-900);
        color: #fff;
      }

      .clickable {
        cursor: pointer;
      }

      #list-item span {
        margin-left: 40px;
      }

      #list-item.excluded span {
        color: #999;
      }

      #list-item span.edge-label {
        float: right;
        font-size: 10px;
        margin-left: 3px;
        margin-right: 5px;
      }

      .node-icon {
        position: absolute;
        top: 1px;
        left: 2px;
      }

      .faded span {
        color: var(--tb-graph-faded);
      }
    </style>
    <div
      id="list-item"
      on-mouseover="_nodeListener"
      on-mouseout="_nodeListener"
      on-click="_nodeListener"
    >
      <div class$="{{_fadedClass(itemRenderInfo)}}">
        <tf-node-icon
          class="node-icon"
          height="12"
          color-by="[[colorBy]]"
          color-by-params="[[colorByParams]]"
          node="[[itemNode]]"
          render-info="[[itemRenderInfo]]"
          template-index="[[templateIndex]]"
        ></tf-node-icon>
        <span title$="[[name]]">[[name]]</span>
        <span class="edge-label">[[edgeLabel]]</span>
      </div>
    </div>
  `,t([o({type:Object}),e("design:type",Object)],qqt.prototype,"cardNode",void 0),t([o({type:Object}),e("design:type",Object)],qqt.prototype,"itemNode",void 0),t([o({type:String}),e("design:type",String)],qqt.prototype,"edgeLabel",void 0),t([o({type:Object}),e("design:type",Object)],qqt.prototype,"itemRenderInfo",void 0),t([o({type:String}),e("design:type",String)],qqt.prototype,"name",void 0),t([o({type:String,observer:"_itemTypeChanged"}),e("design:type",String)],qqt.prototype,"itemType",void 0),t([o({type:String}),e("design:type",String)],qqt.prototype,"colorBy",void 0),t([o({type:Object}),e("design:type",Object)],qqt.prototype,"colorByParams",void 0),t([o({type:Object}),e("design:type",Object)],qqt.prototype,"templateIndex",void 0),qqt=t([i("tf-node-list-item")],qqt);let Yqt=class extends(er(ye)){constructor(){super(...arguments),this._expanded=!0,this._openedControlPred=!1,this._openedControlSucc=!1,this._templateIndex=null}expandNode(){this.fire("_node.expand",this.node)}_getNode(t,e){return e.node(t)}_getNodeStats(t,e){var n=this._getNode(t,e);return n?n.stats:null}_getTotalMicros(t){return t?t.getTotalMicros():0}get _hasDisplayableNodeStats(){return OGt(this._nodeStats)}get _nodeStatsFormattedBytes(){var t=this._nodeStats;if(t&&t.totalBytes)return RGt(t.totalBytes,NGt)}get _nodeStatsFormattedComputeTime(){var t=this._nodeStats;if(t&&t.getTotalMicros())return RGt(t.getTotalMicros(),IGt)}get _nodeStatsFormattedOutputSizes(){var t=this._nodeStats;if(t&&t.outputSize&&t.outputSize.length)return Se.exports.map(t.outputSize,(function(t){return 0===t.length?"scalar":"["+t.join(", ")+"]"}))}_getRenderInfo(t,e){return this.renderHierarchy.getOrCreateRenderNodeByName(t)}get _attributes(){var t=this._node;if(this.async(this._resizeList.bind(this,"#attributesList")),!t||!t.attr)return[];var e=[];return Se.exports.each(t.attr,(function(t){"_too_large_attrs"===t.key?e=e.concat(t.value.list.s.map((function(t){return{key:t,value:"Too large to show..."}}))):e.push({key:t.key,value:JSON.stringify(t.value)})})),e}get _device(){var t=this._node;return t?t.device:null}get _successors(){var t=this._node,e=this.graphHierarchy;return this._refreshNodeItemList("inputsList"),t?this._convertEdgeListToEdgeInfoList(e.getSuccessors(t.name),!1,t.isGroupNode):{regular:[],control:[]}}get _predecessors(){var t=this._node,e=this.graphHierarchy;return this._refreshNodeItemList("outputsList"),t?this._convertEdgeListToEdgeInfoList(e.getPredecessors(t.name),!0,t.isGroupNode):{regular:[],control:[]}}get _functionUsages(){var t=this._node,e=this.graphHierarchy;if(this._refreshNodeItemList("functionUsagesList"),!t||t.type!==jGt.META)return[];const n=e.libraryFunctions[t.associatedFunction];return n?n.usages:[]}_refreshNodeItemList(t){this.async(this._resizeList.bind(this,`#${t}`))}_convertEdgeListToEdgeInfoList(t,e,n){var i=t=>Se.exports.map(t.baseEdgeList,(t=>{var n=e?t.v:t.w;return{name:n,node:this._getNode(n,this.graphHierarchy),edgeLabel:tqt(t,this.renderHierarchy),renderInfo:this._getRenderInfo(n,this.renderHierarchy)}})),r=function(t){var r=[];return Se.exports.each(t,(t=>{var o=e?t.v:t.w;n&&1!=t.baseEdgeList.length?r.push({name:o,node:this._getNode(o,this.graphHierarchy),edgeLabel:eqt(t,this.renderHierarchy),renderInfo:this._getRenderInfo(o,this.renderHierarchy)}):r=r.concat(i(t))})),r}.bind(this);return{regular:r(t.regular),control:r(t.control)}}get _subnodes(){var t=this._node;return t&&t.metagraph?t.metagraph.nodes():null}get _totalPredecessors(){var t=this._predecessors;return t.regular.length+t.control.length}get _totalSuccessors(){var t=this._successors;return t.regular.length+t.control.length}_toggleControlPred(){this._openedControlPred=!this._openedControlPred}_toggleControlSucc(){this._openedControlSucc=!this._openedControlSucc}_toggleExpanded(){this._expanded=!this._expanded}_getToggleIcon(t){return t?"expand-less":"expand-more"}_resetState(){this._openedControlPred=!1,this._openedControlSucc=!1,this.set("_groupButtonText",pqt(this._node))}_resizeList(t){var e=document.querySelector(t);e&&e.fire("iron-resize")}_toggleInclude(){this.fire("node-toggle-inclusion",{name:this.graphNodeName})}_nodeIncludeStateChanged(t,e){this.set("_auxButtonText",uWt(t))}_toggleGroup(){var t=dqt(this._node);this.fire("node-toggle-seriesgroup",{name:t})}_isLibraryFunction(t){return t&&t.name.startsWith(VGt)}_isInSeries(t){return hqt(t)}_graphHierarchyChanged(){this._templateIndex=this.graphHierarchy.getTemplateIndex(),this.graphHierarchy.addListener(zqt.TEMPLATES_UPDATED,(()=>{this._templateIndex=this.graphHierarchy.getTemplateIndex()}))}};Yqt.template=_e`
    <style>
      .sub-list-group {
        font-weight: 500;
        font-size: 12pt;
        padding-bottom: 8px;
        width: 100%;
      }

      .sub-list {
        max-height: 300px;
        overflow-y: scroll;
      }

      .attr-left {
        float: left;
        width: 30%;
        word-wrap: break-word;
        color: var(--secondary-text-color);
        font-size: 11pt;
        font-weight: 400;
      }

      .attr-right {
        margin-left: 30%;
        word-wrap: break-word;
        color: var(--secondary-text-color);
        font-weight: 400;
      }

      .sub-list-table {
        display: table;
        width: 100%;
      }

      .sub-list-table-row {
        display: table-row;
      }

      .sub-list-table-row .sub-list-table-cell:last-child {
        text-align: right;
      }

      .sub-list-table-cell {
        color: var(--secondary-text-color);
        display: table-cell;
        font-size: 11pt;
        font-weight: 400;
        max-width: 200px;
        padding: 0 4px;
      }

      paper-item {
        padding: 0;
        background: var(--primary-background-color);
      }

      paper-item-body[two-line] {
        min-height: 0;
        padding: 8px 12px 4px;
      }

      .expandedInfo {
        padding: 8px 12px;
      }

      .controlDeps {
        padding: 0 0 0 8px;
      }

      .node-name {
        white-space: normal;
        word-wrap: break-word;
        font-size: 14pt;
        font-weight: 500;
      }

      .node-icon {
        float: right;
      }

      .subtitle {
        color: var(--secondary-text-color);
        font-size: 12pt;
      }

      .controlLine {
        font-size: 11pt;
        font-weight: 400;
      }

      .toggle-button {
        float: right;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .control-toggle-button {
        float: left;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .toggle-include-group {
        padding-top: 4px;
      }

      .toggle-include {
        margin: 5px 6px;
        text-transform: none;
        padding: 4px 6px;
        font-size: 10pt;
        background-color: #fafafa;
        color: #666;
      }

      .toggle-include:hover {
        background-color: var(--google-yellow-100);
      }

      .non-control-list-item {
        padding-left: 10px;
      }
    </style>
    <paper-item>
      <paper-item-body two-line>
        <div>
          <paper-icon-button
            icon="{{_getToggleIcon(_expanded)}}"
            on-click="_toggleExpanded"
            class="toggle-button"
          >
          </paper-icon-button>
          <div class="node-name">
            <tf-wbr-string value="[[_node.name]]" delimiter-pattern="/">
            </tf-wbr-string>
          </div>
        </div>
        <div secondary>
          <tf-node-icon
            class="node-icon"
            node="[[_node]]"
            render-info="[[_getRenderInfo(graphNodeName, renderHierarchy)]]"
            color-by="[[colorBy]]"
            template-index="[[_templateIndex]]"
          ></tf-node-icon>
          <template is="dom-if" if="{{_node.op}}">
            <div class="subtitle">
              Operation:
              <span>[[_node.op]]</span>
            </div>
          </template>
          <template is="dom-if" if="{{_node.metagraph}}">
            <div class="subtitle">
              Subgraph:
              <span>[[_node.cardinality]]</span> nodes
            </div>
          </template>
        </div>
      </paper-item-body>
    </paper-item>
    <iron-collapse opened="{{_expanded}}">
      <template is="dom-if" if="{{_expanded}}" restamp="true">
        <div class="expandedInfo">
          <div class="sub-list-group attributes">
            Attributes (<span>[[_attributes.length]]</span>)
            <iron-list
              class="sub-list"
              id="attributesList"
              items="[[_attributes]]"
            >
              <template>
                <div>
                  <div class="attr-left">[[item.key]]</div>
                  <div class="attr-right">[[item.value]]</div>
                </div>
              </template>
            </iron-list>
          </div>

          <template is="dom-if" if="{{_device}}">
            <div class="sub-list-group device">
              <div class="attr-left">Device</div>
              <div class="attr-right">[[_device]]</div>
            </div>
          </template>

          <div class="sub-list-group predecessors">
            Inputs (<span>[[_totalPredecessors]]</span>)
            <iron-list
              class="sub-list"
              id="inputsList"
              items="[[_predecessors.regular]]"
            >
              <template>
                <tf-node-list-item
                  class="non-control-list-item"
                  card-node="[[_node]]"
                  item-node="[[item.node]]"
                  edge-label="[[item.edgeLabel]]"
                  item-render-info="[[item.renderInfo]]"
                  name="[[item.name]]"
                  item-type="predecessors"
                  color-by="[[colorBy]]"
                  template-index="[[_templateIndex]]"
                >
                </tf-node-list-item>
              </template>
            </iron-list>
            <template is="dom-if" if="[[_predecessors.control.length]]">
              <div class="controlDeps">
                <div class="controlLine">
                  <paper-icon-button
                    icon="{{_getToggleIcon(_openedControlPred)}}"
                    on-click="_toggleControlPred"
                    class="control-toggle-button"
                  >
                  </paper-icon-button>
                  Control dependencies
                </div>
                <iron-collapse opened="{{_openedControlPred}}" no-animation>
                  <template
                    is="dom-if"
                    if="{{_openedControlPred}}"
                    restamp="true"
                  >
                    <iron-list
                      class="sub-list"
                      items="[[_predecessors.control]]"
                    >
                      <template>
                        <tf-node-list-item
                          card-node="[[_node]]"
                          item-node="[[item.node]]"
                          item-render-info="[[item.renderInfo]]"
                          name="[[item.name]]"
                          item-type="predecessors"
                          color-by="[[colorBy]]"
                          template-index="[[_templateIndex]]"
                        >
                        </tf-node-list-item>
                      </template>
                    </iron-list>
                  </template>
                </iron-collapse>
              </div>
            </template>
          </div>

          <div class="sub-list-group successors">
            Outputs (<span>[[_totalSuccessors]]</span>)
            <iron-list
              class="sub-list"
              id="outputsList"
              items="[[_successors.regular]]"
            >
              <template>
                <tf-node-list-item
                  class="non-control-list-item"
                  card-node="[[_node]]"
                  item-node="[[item.node]]"
                  edge-label="[[item.edgeLabel]]"
                  item-render-info="[[item.renderInfo]]"
                  name="[[item.name]]"
                  item-type="successor"
                  color-by="[[colorBy]]"
                  template-index="[[_templateIndex]]"
                >
                </tf-node-list-item>
              </template>
            </iron-list>
            <template is="dom-if" if="[[_successors.control.length]]">
              <div class="controlDeps">
                <div class="controlLine">
                  <paper-icon-button
                    icon="{{_getToggleIcon(_openedControlSucc)}}"
                    on-click="_toggleControlSucc"
                    class="control-toggle-button"
                  >
                  </paper-icon-button>
                  Control dependencies
                </div>
                <iron-collapse opened="{{_openedControlSucc}}" no-animation>
                  <template
                    is="dom-if"
                    if="{{_openedControlSucc}}"
                    restamp="true"
                  >
                    <iron-list class="sub-list" items="[[_successors.control]]">
                      <template>
                        <tf-node-list-item
                          card-node="[[_node]]"
                          item-node="[[item.node]]"
                          item-render-info="[[item.renderInfo]]"
                          name="[[item.name]]"
                          item-type="successors"
                          color-by="[[colorBy]]"
                          template-index="[[_templateIndex]]"
                        >
                        </tf-node-list-item>
                      </template>
                    </iron-list>
                  </template>
                </iron-collapse>
              </div>
            </template>
          </div>
          <template is="dom-if" if="{{_hasDisplayableNodeStats}}">
            <div class="sub-list-group node-stats">
              Node Stats
              <div class="sub-list-table">
                <template is="dom-if" if="{{_nodeStats.totalBytes}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Memory</div>
                    <div class="sub-list-table-cell">
                      [[_nodeStatsFormattedBytes]]
                    </div>
                  </div>
                </template>
                <template is="dom-if" if="{{_getTotalMicros(_nodeStats)}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Compute Time</div>
                    <div class="sub-list-table-cell">
                      [[_nodeStatsFormattedComputeTime]]
                    </div>
                  </div>
                </template>
                <template is="dom-if" if="{{_nodeStats.outputSize}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Tensor Output Sizes</div>
                    <div class="sub-list-table-cell">
                      <template
                        is="dom-repeat"
                        items="{{_nodeStatsFormattedOutputSizes}}"
                      >
                        [[item]] <br />
                      </template>
                    </div>
                  </div>
                </template>
              </div>
            </div>
          </template>

          <template is="dom-if" if="[[_functionUsages.length]]">
            <div class="sub-list-group predecessors">
              Usages of the Function (<span>[[_functionUsages.length]]</span>)
              <iron-list
                class="sub-list"
                id="functionUsagesList"
                items="[[_functionUsages]]"
              >
                <template>
                  <tf-node-list-item
                    class="non-control-list-item"
                    card-node="[[_node]]"
                    item-node="[[item]]"
                    name="[[item.name]]"
                    item-type="functionUsages"
                    color-by="[[colorBy]]"
                    template-index="[[_templateIndex]]"
                  >
                  </tf-node-list-item>
                </template>
              </iron-list>
            </div>
          </template>

          <template is="dom-if" if="[[!_isLibraryFunction(_node)]]">
            <div class="toggle-include-group">
              <paper-button
                raised
                class="toggle-include"
                on-click="_toggleInclude"
              >
                <span>[[_auxButtonText]]</span>
              </paper-button>
            </div>
          </template>

          <template is="dom-if" if="{{_isInSeries(_node)}}">
            <div class="toggle-include-group">
              <paper-button
                raised
                class="toggle-include"
                on-click="_toggleGroup"
              >
                <span>[[_groupButtonText]]</span>
              </paper-button>
            </div>
          </template>
        </div>
      </template>
    </iron-collapse>
  `,t([o({type:String}),e("design:type",String)],Yqt.prototype,"graphNodeName",void 0),t([o({type:Object}),e("design:type",Dqt)],Yqt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",Object)],Yqt.prototype,"renderHierarchy",void 0),t([o({type:String}),e("design:type",String)],Yqt.prototype,"colorBy",void 0),t([o({type:Object,computed:"_getNode(graphNodeName, graphHierarchy)",observer:"_resetState"}),e("design:type",Object)],Yqt.prototype,"_node",void 0),t([o({type:Object,computed:"_getNodeStats(graphNodeName, graphHierarchy)",observer:"_resetState"}),e("design:type",Object)],Yqt.prototype,"_nodeStats",void 0),t([o({type:Number,observer:"_nodeIncludeStateChanged"}),e("design:type",Number)],Yqt.prototype,"nodeInclude",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Yqt.prototype,"_expanded",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Yqt.prototype,"_openedControlPred",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Yqt.prototype,"_openedControlSucc",void 0),t([o({type:String}),e("design:type",String)],Yqt.prototype,"_auxButtonText",void 0),t([o({type:String}),e("design:type",String)],Yqt.prototype,"_groupButtonText",void 0),t([o({type:Object}),e("design:type",Function)],Yqt.prototype,"_templateIndex",void 0),t([s("_nodeStats"),e("design:type",Boolean),e("design:paramtypes",[])],Yqt.prototype,"_hasDisplayableNodeStats",null),t([s("_nodeStats"),e("design:type",String),e("design:paramtypes",[])],Yqt.prototype,"_nodeStatsFormattedBytes",null),t([s("_nodeStats"),e("design:type",String),e("design:paramtypes",[])],Yqt.prototype,"_nodeStatsFormattedComputeTime",null),t([s("_nodeStats"),e("design:type",Array),e("design:paramtypes",[])],Yqt.prototype,"_nodeStatsFormattedOutputSizes",null),t([s("_node"),e("design:type",Array),e("design:paramtypes",[])],Yqt.prototype,"_attributes",null),t([s("_node"),e("design:type",String),e("design:paramtypes",[])],Yqt.prototype,"_device",null),t([s("_node","graphHierarchy"),e("design:type",Object),e("design:paramtypes",[])],Yqt.prototype,"_successors",null),t([s("_node","graphHierarchy"),e("design:type",Object),e("design:paramtypes",[])],Yqt.prototype,"_predecessors",null),t([s("_node","graphHierarchy"),e("design:type",Array),e("design:paramtypes",[])],Yqt.prototype,"_functionUsages",null),t([s("_node"),e("design:type",Array),e("design:paramtypes",[])],Yqt.prototype,"_subnodes",null),t([s("_predecessors"),e("design:type",Number),e("design:paramtypes",[])],Yqt.prototype,"_totalPredecessors",null),t([s("_successors"),e("design:type",Number),e("design:paramtypes",[])],Yqt.prototype,"_totalSuccessors",null),t([a("graphHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Yqt.prototype,"_graphHierarchyChanged",null),Yqt=t([i("tf-node-info")],Yqt);let Xqt=class extends(er(ye)){ready(){super.ready(),this.addEventListener("node-list-item-click",this._nodeListItemClicked.bind(this)),this.addEventListener("node-list-item-mouseover",this._nodeListItemMouseover.bind(this)),this.addEventListener("node-list-item-mouseout",this._nodeListItemMouseout.bind(this))}_nodeListItemClicked(t){this.selectedNode=t.detail.nodeName}_nodeListItemMouseover(t){this.highlightedNode=t.detail.nodeName}_nodeListItemMouseout(){this.highlightedNode=null}_healthPillsAvailable(t,e){return t&&e&&Object.keys(e).length>0}_equals(t,e){return t===e}};Xqt.template=_e`
    <style>
      :host {
        background: var(--secondary-background-color);
        font-size: 12px;
        margin: 0;
        padding: 0;
        display: block;
        max-height: 650px;
        overflow-x: hidden;
        overflow-y: auto;
      }

      h2 {
        padding: 0;
        text-align: center;
        margin: 0;
      }
    </style>
    <template is="dom-if" if="{{selectedNode}}">
      <paper-material elevation="1" class="card">
        <tf-node-info
          graph-hierarchy="[[graphHierarchy]]"
          render-hierarchy="[[renderHierarchy]]"
          flat-graph="[[graph]]"
          graph-node-name="[[selectedNode]]"
          node-include="[[selectedNodeInclude]]"
          highlighted-node="{{highlightedNode}}"
          color-by="[[colorBy]]"
        >
        </tf-node-info>
      </paper-material>
    </template>
    <template is="dom-if" if="[[_equals(colorBy, 'op_compatibility')]]">
      <tf-graph-op-compat-card
        graph-hierarchy="[[graphHierarchy]]"
        render-hierarchy="[[renderHierarchy]]"
        color-by="[[colorBy]]"
        node-title="[[compatNodeTitle]]"
      >
      </tf-graph-op-compat-card>
    </template>
    <template
      is="dom-if"
      if="[[_healthPillsAvailable(debuggerDataEnabled, nodeNamesToHealthPills)]]"
    >
      <tf-graph-debugger-data-card
        render-hierarchy="[[renderHierarchy]]"
        debugger-numeric-alerts="[[debuggerNumericAlerts]]"
        node-names-to-health-pills="[[nodeNamesToHealthPills]]"
        selected-node="{{selectedNode}}"
        highlighted-node="{{highlightedNode}}"
        are-health-pills-loading="[[areHealthPillsLoading]]"
        all-steps-mode-enabled="{{allStepsModeEnabled}}"
        specific-health-pill-step="{{specificHealthPillStep}}"
        health-pill-step-index="{{healthPillStepIndex}}"
      >
      </tf-graph-debugger-data-card>
    </template>
  `,t([o({type:String}),e("design:type",String)],Xqt.prototype,"title",void 0),t([o({type:Object}),e("design:type",Dqt)],Xqt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",qGt)],Xqt.prototype,"graph",void 0),t([o({type:Object}),e("design:type",yWt)],Xqt.prototype,"renderHierarchy",void 0),t([o({type:Object}),e("design:type",Object)],Xqt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],Xqt.prototype,"healthPillStepIndex",void 0),t([o({type:String}),e("design:type",String)],Xqt.prototype,"colorBy",void 0),t([o({type:String}),e("design:type",String)],Xqt.prototype,"compatNodeTitle",void 0),t([o({type:String,notify:!0}),e("design:type",String)],Xqt.prototype,"selectedNode",void 0),t([o({type:String,notify:!0}),e("design:type",String)],Xqt.prototype,"highlightedNode",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],Xqt.prototype,"selectedNodeInclude",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Xqt.prototype,"debuggerDataEnabled",void 0),Xqt=t([i("tf-graph-info")],Xqt);class $qt{constructor(t,e,n,i,r,o){this.svg=t,this.labelPadding=o,this.zoomG=e,this.mainZoom=n,this.maxWandH=r;let a=Su(i.shadowRoot),s=a.select("svg"),l=s.select("rect");this.viewpointCoord={x:0,y:0};let c=vh().subject(Object).on("drag",(t=>{this.viewpointCoord.x=uu.x,this.viewpointCoord.y=uu.y,this.updateViewpoint()}));l.datum(this.viewpointCoord).call(c),s.on("click",(()=>{if(uu.defaultPrevented)return;let t=Number(l.attr("width")),e=Number(l.attr("height")),n=ah(s.node());this.viewpointCoord.x=n[0]-t/2,this.viewpointCoord.y=n[1]-e/2,this.updateViewpoint()})),this.viewpoint=l.node(),this.minimapSvg=s.node(),this.minimap=i,this.canvas=a.select("canvas.first").node(),this.canvasBuffer=a.select("canvas.second").node(),this.downloadCanvas=a.select("canvas.download").node(),Su(this.downloadCanvas).style("display","none"),this.update()}updateViewpoint(){Su(this.viewpoint).attr("x",this.viewpointCoord.x).attr("y",this.viewpointCoord.y);let t=-this.viewpointCoord.x*this.scaleMain/this.scaleMinimap,e=-this.viewpointCoord.y*this.scaleMain/this.scaleMinimap;Su(this.svg).call(this.mainZoom.transform,vN.translate(t,e).scale(this.scaleMain))}getImageBlob(){return new Promise((t=>{this.downloadCanvas.toBlob((e=>{t(e)}),"image/png")}))}update(){let t=null;try{if(t=this.zoomG.getBBox(),0===t.width)return}catch(t){return}let e=Su(this.svg),n="";const i=this.svg,r=(i.getRootNode?i.getRootNode():this.svg.parentNode).styleSheets;for(let t=0;t<r.length;t++)try{let e=r[t].cssRules||r[t].rules;if(null==e)continue;for(let t=0;t<e.length;t++)n+=e[t].cssText.replace(/ ?tf-[\w-]+ ?/g,"")+"\n"}catch(t){if("SecurityError"!==t.name)throw t}let o=e.append("style");o.text(n);let a=Su(this.zoomG),s=a.attr("transform");a.attr("transform",null),t.height+=t.y,t.width+=t.x,t.height+=2*this.labelPadding,t.width+=2*this.labelPadding,e.attr("width",t.width).attr("height",t.height),this.scaleMinimap=this.maxWandH/Math.max(t.width,t.height),this.minimapSize={width:t.width*this.scaleMinimap,height:t.height*this.scaleMinimap},Su(this.minimapSvg).attr(this.minimapSize),Su(this.canvasBuffer).attr(this.minimapSize);const l=Su(this.downloadCanvas);l.style("width",t.width),l.style("height",t.height),l.attr("width",3*t.width),l.attr("height",3*t.height),null!=this.translate&&null!=this.zoom&&requestAnimationFrame((()=>this.zoom()));let c=(new XMLSerializer).serializeToString(this.svg);o.remove(),e.attr("width",null).attr("height",null),a.attr("transform",s);let u=new Image;u.onload=()=>{let t=this.canvasBuffer.getContext("2d");t.clearRect(0,0,this.canvasBuffer.width,this.canvasBuffer.height),t.drawImage(u,0,0,this.minimapSize.width,this.minimapSize.height),requestAnimationFrame((()=>{Su(this.canvasBuffer).style("display",null),Su(this.canvas).style("display","none"),[this.canvas,this.canvasBuffer]=[this.canvasBuffer,this.canvas]}));let e=this.downloadCanvas.getContext("2d");e.clearRect(0,0,this.downloadCanvas.width,this.downloadCanvas.height),e.drawImage(u,0,0,this.downloadCanvas.width,this.downloadCanvas.height)},u.onerror=()=>{let t=new Blob([c],{type:"image/svg+xml;charset=utf-8"});u.src=URL.createObjectURL(t)},u.src="data:image/svg+xml;charset=utf-8,"+encodeURIComponent(c)}zoom(t){if(null==this.scaleMinimap)return;t&&(this.translate=[t.x,t.y],this.scaleMain=t.k);let e=this.svg.getBoundingClientRect(),n=Su(this.viewpoint);this.viewpointCoord.x=-this.translate[0]*this.scaleMinimap/this.scaleMain,this.viewpointCoord.y=-this.translate[1]*this.scaleMinimap/this.scaleMain;let i=e.width*this.scaleMinimap/this.scaleMain,r=e.height*this.scaleMinimap/this.scaleMain;n.attr("x",this.viewpointCoord.x).attr("y",this.viewpointCoord.y).attr("width",i).attr("height",r);let o=this.minimapSize.width,a=this.minimapSize.height,s=this.viewpointCoord.x,l=this.viewpointCoord.y;(Math.min(Math.max(0,s+i),o)-Math.min(Math.max(0,s),o))*(Math.min(Math.max(0,l+r),a)-Math.min(Math.max(0,l),a))/(o*a)<.8?this.minimap.classList.remove("hidden"):this.minimap.classList.add("hidden")}}let Kqt=class extends ye{init(t,e,n,i,r){return new $qt(t,e,n,this,i,r)}};Kqt.template=_e`
    <style>
      :host {
        background-color: white;
        transition: opacity 0.3s linear;
        pointer-events: auto;
      }

      :host(.hidden) {
        opacity: 0;
        pointer-events: none;
      }

      canvas {
        border: 1px solid #999;
      }

      rect {
        fill: white;
        stroke: #111111;
        stroke-width: 1px;
        fill-opacity: 0;
        filter: url(#minimapDropShadow);
        cursor: move;
      }

      svg {
        position: absolute;
      }
    </style>
    <svg>
      <defs>
        <filter
          id="minimapDropShadow"
          x="-20%"
          y="-20%"
          width="150%"
          height="150%"
        >
          <feOffset result="offOut" in="SourceGraphic" dx="1" dy="1"></feOffset>
          <feColorMatrix
            result="matrixOut"
            in="offOut"
            type="matrix"
            values="0.1 0 0 0 0 0 0.1 0 0 0 0 0 0.1 0 0 0 0 0 0.5 0"
          ></feColorMatrix>
          <feGaussianBlur
            result="blurOut"
            in="matrixOut"
            stdDeviation="2"
          ></feGaussianBlur>
          <feBlend in="SourceGraphic" in2="blurOut" mode="normal"></feBlend>
        </filter>
      </defs>
      <rect></rect>
    </svg>
    <canvas class="first"></canvas>
    <!-- Additional canvas to use as buffer to avoid flickering between updates -->
    <canvas class="second"></canvas>
    <canvas class="download"></canvas>
  `,Kqt=t([i("tf-graph-minimap")],Kqt);const Zqt=_e`
  <style>
    :host(.dark-mode) {
      filter: invert(1);
    }

    :host {
      display: flex;
      font-size: 20px;
      height: 100%;
      width: 100%;
    }

    #svg {
      flex: 1;
      font-family: Roboto, sans-serif;
      height: 100%;
      overflow: hidden;
      width: 100%;
    }

    #hidden {
      position: fixed;
      top: 0px;
      visibility: hidden;
    }

    text {
      user-select: none;
    }

    /* --- Node and annotation-node for Metanode --- */

    .meta > .nodeshape > rect,
    .meta > .annotation-node > rect {
      cursor: pointer;
      fill: hsl(0, 0%, 70%);
    }
    .node.meta.highlighted > .nodeshape > rect,
    .node.meta.highlighted > .annotation-node > rect {
      stroke-width: 2;
    }
    .annotation.meta.highlighted > .nodeshape > rect,
    .annotation.meta.highlighted > .annotation-node > rect {
      stroke-width: 1;
    }
    .meta.selected > .nodeshape > rect,
    .meta.selected > .annotation-node > rect {
      stroke: red;
      stroke-width: 2;
    }
    .node.meta.selected.expanded > .nodeshape > rect,
    .node.meta.selected.expanded > .annotation-node > rect {
      stroke: red;
      stroke-width: 3;
    }
    .annotation.meta.selected > .nodeshape > rect,
    .annotation.meta.selected > .annotation-node > rect {
      stroke: red;
      stroke-width: 2;
    }
    .node.meta.selected.expanded.highlighted > .nodeshape > rect,
    .node.meta.selected.expanded.highlighted > .annotation-node > rect {
      stroke: red;
      stroke-width: 4;
    }

    .faded,
    .faded rect,
    .faded ellipse,
    .faded path,
    .faded use,
    #rectHatch line,
    #ellipseHatch line {
      color: #e0d4b3 !important;
      fill: white;
      stroke: #e0d4b3 !important;
    }

    .faded path {
      stroke-width: 1px !important;
    }

    .faded rect {
      fill: url(#rectHatch) !important;
    }

    .faded ellipse,
    .faded use {
      fill: url(#ellipseHatch) !important;
    }

    .faded text {
      opacity: 0;
    }

    /* Rules used for input-tracing. */
    .input-highlight > * > rect,
    .input-highlight > * > ellipse,
    .input-highlight > * > use {
      fill: white;
      stroke: #ff9800 !important;
    }

    /*  - Faded non-input styling */
    .non-input > * > rect,
.non-input > * > ellipse,
.non-input > * > use,
/* For Const nodes. */
.non-input > * > .constant:not([class*="input-highlight"]) >
  .annotation-node > ellipse,
/* For styling of annotation nodes of non-input nodes. */
.non-input > g > .annotation > .annotation-node > rect {
      stroke: #e0d4b3 !important;
      stroke-width: inherit;
      stroke-dasharray: inherit;
    }

    .non-input path {
      visibility: hidden;
    }

    .non-input > .nodeshape > rect,
.non-input > .annotation-node > rect,
/* For styling of annotation nodes of non-input nodes. */
.non-input > g > .annotation > .annotation-node > rect {
      fill: url(#rectHatch) !important;
    }

    .non-input ellipse,
    .non-input use {
      fill: url(#ellipseHatch) !important;
    }

    .non-input > text {
      opacity: 0;
    }

    .non-input .annotation > .annotation-edge {
      marker-end: url(#annotation-arrowhead-faded);
    }

    .non-input .annotation > .annotation-edge.refline {
      marker-start: url(#ref-annotation-arrowhead-faded);
    }

    /* Input edges. */
    .input-edge-highlight > text {
      fill: black !important;
    }
    .input-highlight > .in-annotations > .annotation > .annotation-edge,
    .input-highlight-selected
      > .in-annotations
      > .annotation
      > .annotation-edge {
      stroke: #999 !important;
    }

    /* Non-input edges. */
    .non-input-edge-highlight,
.non-input > g > .annotation > path,
/* Annotation styles (label and edges respectively). */
.non-input > g >
.annotation:not(.input-highlight):not(.input-highlight-selected) >
.annotation-label
/*.annotation-edge*/ {
      visibility: hidden;
    }

    /* --- Op Node --- */

    .op > .nodeshape > .nodecolortarget,
    .op > .annotation-node > .nodecolortarget {
      cursor: pointer;
      fill: #fff;
      stroke: #ccc;
    }

    .op.selected > .nodeshape > .nodecolortarget,
    .op.selected > .annotation-node > .nodecolortarget {
      stroke: red;
      stroke-width: 2;
    }

    .op.highlighted > .nodeshape > .nodecolortarget,
    .op.highlighted > .annotation-node > .nodecolortarget {
      stroke-width: 2;
    }

    /* --- Series Node --- */

    /* By default, don't show the series background <rect>. */
    .series > .nodeshape > rect {
      fill: hsl(0, 0%, 70%);
      fill-opacity: 0;
      stroke-dasharray: 5, 5;
      stroke-opacity: 0;
      cursor: pointer;
    }

    /* Once expanded, show the series background <rect> and hide the <use>. */
    .series.expanded > .nodeshape > rect {
      fill-opacity: 0.15;
      stroke: hsl(0, 0%, 70%);
      stroke-opacity: 1;
    }
    .series.expanded > .nodeshape > use {
      visibility: hidden;
    }

    /**
 * TODO: Simplify this by applying a stable class name to all <g>
 * elements that currently have either the nodeshape or annotation-node classes.
 */
    .series > .nodeshape > use,
    .series > .annotation-node > use {
      stroke: #ccc;
    }
    .series.highlighted > .nodeshape > use,
    .series.highlighted > .annotation-node > use {
      stroke-width: 2;
    }
    .series.selected > .nodeshape > use,
    .series.selected > .annotation-node > use {
      stroke: red;
      stroke-width: 2;
    }

    .series.selected > .nodeshape > rect {
      stroke: red;
      stroke-width: 2;
    }

    .annotation.series.selected > .annotation-node > use {
      stroke: red;
      stroke-width: 2;
    }

    /* --- Bridge Node --- */
    .bridge > .nodeshape > rect {
      stroke: #f0f;
      opacity: 0.2;
      display: none;
    }

    /* --- Structural Elements --- */
    .edge > path.edgeline.structural {
      stroke: #f0f;
      opacity: 0.2;
      display: none;
    }

    /* Reference Edge */
    .edge > path.edgeline.referenceedge {
      stroke: #ffb74d;
      opacity: 1;
    }

    /* --- Series Nodes --- */

    /* Hide the rect for a series' annotation. */
    .series > .annotation-node > rect {
      display: none;
    }

    /* --- Node label --- */

    .node {
      /* Provide a hint to browsers to avoid using their static rasterization
      at initial scale, which looks very pixelated on Chromium when zoomed in.
      Note that we intentionally do *not* use 'will-change: transform' and
      'translateZ(0) here, which introduce blurriness on Firefox.
      See https://github.com/tensorflow/tensorboard/issues/4744 */
      transform: translateZ(1px);
    }

    .node > text.nodelabel {
      cursor: pointer;
      fill: #444;
    }

    .meta.expanded > text.nodelabel {
      font-size: 9px;
    }

    .series > text.nodelabel {
      font-size: 8px;
    }

    .op > text.nodelabel {
      font-size: 6px;
    }

    .bridge > text.nodelabel {
      display: none;
    }

    .node.meta.expanded > text.nodelabel {
      cursor: normal;
    }

    .annotation.meta.highlighted > text.annotation-label {
      fill: #50a3f7;
    }

    .annotation.meta.selected > text.annotation-label {
      fill: #4285f4;
    }

    /* --- Annotation --- */

    /* only applied for annotations that are not summary or constant.
(.summary, .constant gets overridden below) */
    .annotation > .annotation-node > * {
      stroke-width: 0.5;
      stroke-dasharray: 1, 1;
    }

    .annotation.summary > .annotation-node > *,
    .annotation.constant > .annotation-node > * {
      stroke-width: 1;
      stroke-dasharray: none;
    }

    .annotation > .annotation-edge {
      fill: none;
      stroke: #aaa;
      stroke-width: 0.5;
      marker-end: url(#annotation-arrowhead);
    }

    .faded .annotation > .annotation-edge {
      marker-end: url(#annotation-arrowhead-faded);
    }

    .annotation > .annotation-edge.refline {
      marker-start: url(#ref-annotation-arrowhead);
    }

    .faded .annotation > .annotation-edge.refline {
      marker-start: url(#ref-annotation-arrowhead-faded);
    }

    .annotation > .annotation-control-edge {
      stroke-dasharray: 1, 1;
    }

    #annotation-arrowhead {
      fill: #aaa;
    }

    #annotation-arrowhead-faded {
      fill: #e0d4b3;
    }

    #ref-annotation-arrowhead {
      fill: #aaa;
    }

    #ref-annotation-arrowhead-faded {
      fill: #e0d4b3;
    }

    .annotation > .annotation-label {
      font-size: 5px;
      cursor: pointer;
    }
    .annotation > .annotation-label.annotation-ellipsis {
      cursor: default;
    }

    /* Hide annotations on expanded meta nodes since they're redundant. */
    .expanded > .in-annotations,
    .expanded > .out-annotations {
      display: none;
    }

    /* --- Annotation: Constant --- */

    .constant > .annotation-node > ellipse {
      cursor: pointer;
      fill: white;
      stroke: #848484;
    }

    .constant.selected > .annotation-node > ellipse {
      fill: white;
      stroke: red;
    }

    .constant.highlighted > .annotation-node > ellipse {
      stroke-width: 1.5;
    }

    /* --- Annotation: Summary --- */

    .summary > .annotation-node > ellipse {
      cursor: pointer;
      fill: #db4437;
      stroke: #db4437;
    }

    .summary.selected > .annotation-node > ellipse {
      fill: #a52714;
      stroke: #a52714;
    }

    .summary.highlighted > .annotation-node > ellipse {
      stroke-width: 1.5;
    }

    /* --- Edge --- */

    .edge > path.edgeline {
      fill: none;
      stroke: #bbb;
      stroke-linecap: round;
      stroke-width: 0.75;
    }

    .edge .selectableedge {
      cursor: pointer;
    }

    .selectededge > path.edgeline {
      cursor: default;
      stroke: #f00;
    }

    .edge.selectededge text {
      fill: #000;
    }

    /* Labels showing tensor shapes on edges */
    .edge > text {
      font-size: 3.5px;
      fill: #666;
    }

    .dataflow-arrowhead {
      fill: #bbb;
    }

    .reference-arrowhead {
      fill: #ffb74d;
    }

    .selected-arrowhead {
      fill: #f00;
    }

    .edge .control-dep {
      stroke-dasharray: 2, 2;
    }

    /* --- Group node expand/collapse button --- */

    /* Hides expand/collapse buttons when a node isn't expanded or highlighted. Using
   incredibly small opacity so that the bounding box of the <g> parent still takes
   this container into account even when it isn't visible */
    .node:not(.highlighted):not(.expanded) > .nodeshape > .buttoncontainer {
      opacity: 0.01;
    }
    .node.highlighted > .nodeshape > .buttoncontainer {
      cursor: pointer;
    }
    .buttoncircle {
      fill: #e7811d;
    }
    .buttoncircle:hover {
      fill: #b96717;
    }
    .expandbutton,
    .collapsebutton {
      stroke: white;
    }
    /* Do not let the path elements in the button take pointer focus */
    .node > .nodeshape > .buttoncontainer > .expandbutton,
    .node > .nodeshape > .buttoncontainer > .collapsebutton {
      pointer-events: none;
    }
    /* Only show the expand button when a node is collapsed and only show the
   collapse button when a node is expanded. */
    .node.expanded > .nodeshape > .buttoncontainer > .expandbutton {
      display: none;
    }
    .node:not(.expanded) > .nodeshape > .buttoncontainer > .collapsebutton {
      display: none;
    }

    .health-pill-stats {
      font-size: 4px;
      text-anchor: middle;
    }

    .health-pill rect {
      filter: url(#health-pill-shadow);
      rx: 3;
      ry: 3;
    }

    .titleContainer {
      position: relative;
      top: 20px;
    }

    .title,
    .auxTitle,
    .functionLibraryTitle {
      position: absolute;
    }

    #minimap {
      position: absolute;
      right: 20px;
      bottom: 20px;
    }

    .context-menu {
      position: absolute;
      display: none;
      background-color: #e2e2e2;
      border-radius: 2px;
      font-size: 14px;
      min-width: 150px;
      border: 1px solid #d4d4d4;
    }

    .context-menu ul {
      list-style-type: none;
      margin: 0;
      padding: 0;
      cursor: default;
    }

    .context-menu ul li {
      padding: 4px 16px;
    }

    .context-menu ul li:hover {
      background-color: #f3913e;
      color: white;
    }
  </style>
  <div class="titleContainer">
    <div id="title" class="title">Main Graph</div>
    <div id="auxTitle" class="auxTitle">Auxiliary Nodes</div>
    <div id="functionLibraryTitle" class="functionLibraryTitle">Functions</div>
  </div>
  <svg id="svg">
    <defs>
      <!-- Arrow heads for reference edge paths of different predefined sizes per color. -->
      <path
        id="reference-arrowhead-path"
        d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0"
      ></path>
      <marker
        class="reference-arrowhead"
        id="reference-arrowhead-small"
        viewBox="0 0 10 10"
        markerWidth="5"
        markerHeight="5"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#reference-arrowhead-path"></use>
      </marker>
      <marker
        class="reference-arrowhead"
        id="reference-arrowhead-medium"
        viewBox="0 0 10 10"
        markerWidth="13"
        markerHeight="13"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#reference-arrowhead-path"></use>
      </marker>
      <marker
        class="reference-arrowhead"
        id="reference-arrowhead-large"
        viewBox="0 0 10 10"
        markerWidth="16"
        markerHeight="16"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#reference-arrowhead-path"></use>
      </marker>
      <marker
        class="reference-arrowhead"
        id="reference-arrowhead-xlarge"
        viewBox="0 0 10 10"
        markerWidth="20"
        markerHeight="20"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#reference-arrowhead-path"></use>
      </marker>

      <!-- Arrow heads for dataflow edge paths of different predefined sizes per color. -->
      <path
        id="dataflow-arrowhead-path"
        d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0"
      ></path>
      <marker
        class="dataflow-arrowhead"
        id="dataflow-arrowhead-small"
        viewBox="0 0 10 10"
        markerWidth="5"
        markerHeight="5"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#dataflow-arrowhead-path"></use>
      </marker>
      <marker
        class="dataflow-arrowhead"
        id="dataflow-arrowhead-medium"
        viewBox="0 0 10 10"
        markerWidth="13"
        markerHeight="13"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#dataflow-arrowhead-path"></use>
      </marker>
      <marker
        class="dataflow-arrowhead"
        id="dataflow-arrowhead-large"
        viewBox="0 0 10 10"
        markerWidth="16"
        markerHeight="16"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#dataflow-arrowhead-path"></use>
      </marker>
      <marker
        class="dataflow-arrowhead"
        id="dataflow-arrowhead-xlarge"
        viewBox="0 0 10 10"
        markerWidth="20"
        markerHeight="20"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#dataflow-arrowhead-path"></use>
      </marker>

      <!-- Arrow head for annotation edge paths. -->
      <marker
        id="annotation-arrowhead"
        markerWidth="5"
        markerHeight="5"
        refX="5"
        refY="2.5"
        orient="auto"
      >
        <path d="M 0,0 L 5,2.5 L 0,5 L 0,0"></path>
      </marker>
      <marker
        id="annotation-arrowhead-faded"
        markerWidth="5"
        markerHeight="5"
        refX="5"
        refY="2.5"
        orient="auto"
      >
        <path d="M 0,0 L 5,2.5 L 0,5 L 0,0"></path>
      </marker>
      <marker
        id="ref-annotation-arrowhead"
        markerWidth="5"
        markerHeight="5"
        refX="0"
        refY="2.5"
        orient="auto"
      >
        <path d="M 5,0 L 0,2.5 L 5,5 L 5,0"></path>
      </marker>
      <marker
        id="ref-annotation-arrowhead-faded"
        markerWidth="5"
        markerHeight="5"
        refX="0"
        refY="2.5"
        orient="auto"
      >
        <path d="M 5,0 L 0,2.5 L 5,5 L 5,0"></path>
      </marker>
      <!-- Template for an Op node ellipse. -->
      <ellipse
        id="op-node-stamp"
        rx="7.5"
        ry="3"
        stroke="inherit"
        fill="inherit"
      ></ellipse>
      <!-- Template for an Op node annotation ellipse (smaller). -->
      <ellipse
        id="op-node-annotation-stamp"
        rx="5"
        ry="2"
        stroke="inherit"
        fill="inherit"
      ></ellipse>
      <!-- Vertically stacked series of Op nodes when unexpanded. -->
      <g id="op-series-vertical-stamp">
        <use xlink:href="#op-node-stamp" x="8" y="9"></use>
        <use xlink:href="#op-node-stamp" x="8" y="6"></use>
        <use xlink:href="#op-node-stamp" x="8" y="3"></use>
      </g>
      <!-- Horizontally stacked series of Op nodes when unexpanded. -->
      <g id="op-series-horizontal-stamp">
        <use xlink:href="#op-node-stamp" x="16" y="4"></use>
        <use xlink:href="#op-node-stamp" x="12" y="4"></use>
        <use xlink:href="#op-node-stamp" x="8" y="4"></use>
      </g>
      <!-- Horizontally stacked series of Op nodes for annotation. -->
      <g id="op-series-annotation-stamp">
        <use xlink:href="#op-node-annotation-stamp" x="9" y="2"></use>
        <use xlink:href="#op-node-annotation-stamp" x="7" y="2"></use>
        <use xlink:href="#op-node-annotation-stamp" x="5" y="2"></use>
      </g>
      <svg
        id="summary-icon"
        fill="#848484"
        height="12"
        viewBox="0 0 24 24"
        width="12"
      >
        <path
          d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z"
        ></path>
      </svg>

      <!-- Hatch patterns for faded out nodes. -->
      <pattern
        id="rectHatch"
        patternTransform="rotate(45 0 0)"
        width="5"
        height="5"
        patternUnits="userSpaceOnUse"
      >
        <line x1="0" y1="0" x2="0" y2="5" style="stroke-width: 1"></line>
      </pattern>
      <pattern
        id="ellipseHatch"
        patternTransform="rotate(45 0 0)"
        width="2"
        height="2"
        patternUnits="userSpaceOnUse"
      >
        <line x1="0" y1="0" x2="0" y2="2" style="stroke-width: 1"></line>
      </pattern>

      <!-- A shadow for health pills. -->
      <filter
        id="health-pill-shadow"
        x="-40%"
        y="-40%"
        width="180%"
        height="180%"
      >
        <feGaussianBlur in="SourceAlpha" stdDeviation="0.8"></feGaussianBlur>
        <feOffset dx="0" dy="0" result="offsetblur"></feOffset>
        <feFlood flood-color="#000000"></feFlood>
        <feComposite in2="offsetblur" operator="in"></feComposite>
        <feMerge>
          <feMergeNode></feMergeNode>
          <feMergeNode in="SourceGraphic"></feMergeNode>
        </feMerge>
      </filter>
    </defs>
    <!-- Make a large rectangle that fills the svg space so that
  zoom events get captured on safari -->
    <rect fill="white" width="10000" height="10000"></rect>
    <g id="root"></g>
  </svg>
  <tf-graph-minimap id="minimap"></tf-graph-minimap>
  <div id="contextMenu" class="context-menu"></div>
`;let Jqt=class extends(er(il(ye))){constructor(){super(...arguments),this._zoomed=!1,this._zoomStartCoords=null,this._zoomTransform=null,this._maxZoomDistanceForClick=20,this._nodeGroupIndex={},this._annotationGroupIndex={},this._edgeGroupIndex={},this.maxMetanodeLabelLengthFontSize=9,this.minMetanodeLabelLengthFontSize=6,this.maxMetanodeLabelLengthLargeFont=11,this.maxMetanodeLabelLength=18}getNode(t){return this.renderHierarchy.getRenderNodeByName(t)}isNodeExpanded(t){return t.expanded}setNodeExpanded(t){this._build(this.renderHierarchy),this._updateLabels(!this._zoomed)}panToNode(t){(function e(t,n,i,r){const o=Su(n).select(`[data-name="${t}"]`).node();if(!o)return console.warn(`panToNode() failed for node name "${t}"`),!1;let a=o.getBBox(),s=o.getScreenCTM(),l=n.createSVGPoint(),c=n.createSVGPoint();l.x=a.x,l.y=a.y,c.x=a.x+a.width,c.y=a.y+a.height,l=l.matrixTransform(s),c=c.matrixTransform(s);let u=(t,e,n,i)=>!(t>n&&e<i),h=n.getBoundingClientRect();const d=h.top+h.height-150;if(u(l.x,c.x,h.left,h.left+h.width-320)||u(l.y,c.y,h.top,d)){let t=h.left+h.width/2-(l.x+c.x)/2,e=h.top+h.height/2-(l.y+c.y)/2;const i=bN(n);return Su(n).transition().duration(500).call(r.translateBy,t/i.k,e/i.k),!0}return!1})(t,this.$.svg,0,this._zoom)&&(this._zoomed=!0)}getGraphSvgRoot(){return this.$.svg}getContextMenu(){return this.$.contextMenu}_resetState(){this._nodeGroupIndex={},this._annotationGroupIndex={},this._edgeGroupIndex={},this._updateLabels(!1),Su(this.$.svg).select("#root").selectAll("*").remove(),bqt(this.$.svg)}_build(t){this.templateIndex=t.hierarchy.getTemplateIndex(),CGt("tf-graph-scene (layout):",function(){RWt(t.root)}.bind(this),_Gt.RENDER_SCENE_LAYOUT),CGt("tf-graph-scene (build scene):",function(){Lqt(Su(this.$.root),t.root,this),(function e(t,n){Su(t).on("click",(()=>{n.fire("graph-select")}))})(this.$.svg,this),this._updateInputTrace()}.bind(this),_Gt.RENDER_SCENE_BUILD_SCENE),setTimeout(function(){this._updateHealthPills(this.nodeNamesToHealthPills,this.healthPillStepIndex),this.minimap.update()}.bind(this),NWt.animation.duration)}ready(){super.ready(),this._zoom=kN().on("end",function(){this._zoomStartCoords&&(Math.sqrt(Math.pow(this._zoomStartCoords.x-this._zoomTransform.x,2)+Math.pow(this._zoomStartCoords.y-this._zoomTransform.y,2))<this._maxZoomDistanceForClick?this._fireEnableClick():setTimeout(this._fireEnableClick.bind(this),50)),this._zoomStartCoords=null}.bind(this)).on("zoom",function(){this._zoomTransform=uu.transform,this._zoomStartCoords||(this._zoomStartCoords=this._zoomTransform,this.fire("disable-click")),this._zoomed=!0,Su(this.$.root).attr("transform",uu.transform),this.minimap.zoom(uu.transform)}.bind(this)),Su(this.$.svg).call(this._zoom).on("dblclick.zoom",null),Su(window).on("resize",function(){this.minimap.zoom()}.bind(this)),this.minimap=this.$.minimap.init(this.$.svg,this.$.root,this._zoom,NWt.minimap.size,NWt.subscene.meta.labelHeight)}attached(){this.set("_isAttached",!0)}detached(){this.set("_isAttached",!1)}_renderHierarchyChanged(){var t=this.renderHierarchy;this._hasRenderHierarchyBeenFitOnce=!1,this._resetState(),this._build(t)}_animateAndFit(){!this._hasRenderHierarchyBeenFitOnce&&this._isAttached&&setTimeout(this.fit.bind(this),NWt.animation.duration)}_updateLabels(t){var e=this.$$(".title"),n=e.style,i=this.$$(".auxTitle"),r=i.style,o=this.$$(".functionLibraryTitle").style;const a=Su(this.$.svg);var s=a.select("."+VWt.Scene.GROUP+">."+VWt.Scene.CORE).node();if(t&&s&&this.progress&&100===this.progress.value){var l=a.select("."+VWt.Scene.GROUP+">."+VWt.Scene.INEXTRACT).node()||a.select("."+VWt.Scene.GROUP+">."+VWt.Scene.OUTEXTRACT).node(),c=s.getCTM().e,u=l?l.getCTM().e:null;n.display="inline",n.left=c+"px",null!==u&&u!==c?(r.display="inline",u=Math.max(c+e.getBoundingClientRect().width,u),r.left=u+"px"):r.display="none";let t=a.select("."+VWt.Scene.GROUP+">."+VWt.Scene.FUNCTION_LIBRARY).node(),h=t?t.getCTM().e:null;null!==h&&h!==u?(o.display="inline",h=Math.max(u+i.getBoundingClientRect().width,h),o.left=h+"px"):o.display="none"}else n.display="none",r.display="none",o.display="none"}nodeColorsChanged(){null!=this.renderHierarchy&&(this.templateIndex=this.renderHierarchy.hierarchy.getTemplateIndex(),Se.exports.each(this._nodeGroupIndex,((t,e)=>{this._updateNodeState(e)})),this.minimap.update())}fit(){this._hasRenderHierarchyBeenFitOnce=!0,(function t(e,n,i,r){let o=e.getBoundingClientRect(),a=null;try{if(a=n.getBBox(),0===a.width)return}catch(t){return}let s=.9*Math.min(o.width/a.width,o.height/a.height,2),l=NWt.graph;const c=vN.scale(s).translate(l.padding.paddingLeft,l.padding.paddingTop);Su(e).transition().duration(500).call(i.transform,c).on("end.fitted",(()=>{i.on("end.fitted",null),r()}))})(this.$.svg,this.$.root,this._zoom,function(){this._zoomed=!1}.bind(this))}getImageBlob(){return this.minimap.getImageBlob()}isNodeSelected(t){return t===this.selectedNode}isNodeHighlighted(t){return t===this.highlightedNode}addAnnotationGroup(t,e,n){var i=t.node.name;this._annotationGroupIndex[i]=this._annotationGroupIndex[i]||{},this._annotationGroupIndex[i][e.node.name]=n}getAnnotationGroupsIndex(t){return this._annotationGroupIndex[t]}removeAnnotationGroup(t,e){delete this._annotationGroupIndex[t.node.name][e.node.name]}addNodeGroup(t,e){this._nodeGroupIndex[t]=e}getNodeGroup(t){return this._nodeGroupIndex[t]}removeNodeGroup(t){delete this._nodeGroupIndex[t]}addEdgeGroup(t,e){this._edgeGroupIndex[t]=e}getEdgeGroup(t){return this._edgeGroupIndex[t]}_updateHealthPills(){!(function t(e,n,i){if(!n)return;let r=1;Su(e).selectAll("g.nodeshape").each((function(t){const e=n[t.node.name];!(function o(t,e,n,i,r=60,a=10,s=0,l){if(Su(t.parentNode).selectAll(".health-pill").remove(),!e)return;const c=e.value,u=c.slice(2,8),h=u[0],d=u[1],p=u[5];let f=c[1];const m={min:c[8],max:c[9],mean:c[10],stddev:Math.sqrt(c[11])};null==r&&(r=60),null==a&&(a=10),null==s&&(s=0),null!=n&&n.node.type===jGt.OP&&(r/=2,a/=2);let g=document.createElementNS(bGt,"g");g.classList.add("health-pill");let _=document.createElementNS(bGt,"defs");g.appendChild(_);let y=document.createElementNS(bGt,"linearGradient");const v="health-pill-gradient-"+i;y.setAttribute("id",v);let b=0,x="0%";for(let t=0;t<u.length;t++){if(!u[t])continue;b+=u[t];let e=document.createElementNS(bGt,"stop");e.setAttribute("offset",x),e.setAttribute("stop-color",UWt[t].background_color),y.appendChild(e);let n=document.createElementNS(bGt,"stop"),i=100*b/f+"%";n.setAttribute("offset",i),n.setAttribute("stop-color",UWt[t].background_color),y.appendChild(n),x=i}_.appendChild(y);let w=document.createElementNS(bGt,"rect");w.setAttribute("fill","url(#"+v+")"),w.setAttribute("width",String(r)),w.setAttribute("height",String(a)),w.setAttribute("y",String(s)),g.appendChild(w);let S=document.createElementNS(bGt,"title");S.textContent=(function M(t,e,n,i){let r="Device: "+t.device_name+"\n";r+="dtype: "+t.dtype+"\n";let o="(scalar)";t.shape.length>0&&(o="("+t.shape.join(",")+")"),r+="\nshape: "+o+"\n\n",r+="#(elements): "+e+"\n";const a=[];for(let t=0;t<n.length;t++)n[t]>0&&a.push("#("+UWt[t].label+"): "+n[t]);return r+=a.join(", ")+"\n\n",i.max>=i.min&&(r+="min: "+i.min+", max: "+i.max+"\n",r+="mean: "+i.mean+", stddev: "+i.stddev),r})(e,f,u,m),g.appendChild(S);let E=!1;if(null!=n){let t=n.y-a-n.height/2-2;if(n.labelOffset<0&&(t+=n.labelOffset),g.setAttribute("transform","translate("+(n.x-r/2)+", "+t+")"),u[2]||u[3]||u[4]){let t=n.node.attr;if(t&&t.length)for(let e=0;e<t.length;e++)if("T"===t[e].key){let n=t[e].value.type;E=n&&/^DT_(BOOL|INT|UINT)/.test(n);break}}}let T=document.createElementNS(bGt,"text");if(Number.isFinite(m.min)&&Number.isFinite(m.max)){const t=qWt(m.min,E),e=qWt(m.max,E);if(T.textContent=f>1?t+" ~ "+e:t,h>0||d>0||p>0){T.textContent+=" (";const t=[];h>0&&t.push(`NaN×${h}`),d>0&&t.push(`-∞×${d}`),p>0&&t.push(`+∞×${p}`),T.textContent+=t.join("; ")+")"}}else T.textContent="(No finite elements)";T.classList.add("health-pill-stats"),null==l&&(l=r/2),T.setAttribute("x",String(l)),T.setAttribute("y",String(s-2)),g.appendChild(T),Yi(t.parentNode).appendChild(g)})(this,e?e[i]:null,t,r++)}))})(this.$.svg,this.nodeNamesToHealthPills,this.healthPillStepIndex)}_updateNodeState(t){var e=this.getNode(t),n=this.getNodeGroup(t);n&&wqt(n,e,this),e.node.type===jGt.META&&e.node.associatedFunction&&!e.isLibraryFunction&&wqt(Su("."+VWt.Scene.GROUP+">."+VWt.Scene.FUNCTION_LIBRARY+' g[data-name="'+(VGt+e.node.associatedFunction)+'"]'),e,this);var i=this.getAnnotationGroupsIndex(t);Se.exports.each(i,((t,n)=>{wqt(t,e,this,VWt.Annotation.NODE)}))}_selectedNodeChanged(t,e){if(t!==e&&(e&&this._updateNodeState(e),t)){this.minimap.update();for(var n,i=this.renderHierarchy.hierarchy.node(t),r=[];null!=i.parentNode&&i.parentNode.name!=FGt;)r.push((i=i.parentNode).name);Se.exports.forEachRight(r,(t=>{this.renderHierarchy.buildSubhierarchy(t);var e=this.renderHierarchy.getRenderNodeByName(t);e.node.isGroupNode&&!e.expanded&&(e.expanded=!0,n||(n=e))})),n&&(this.setNodeExpanded(n),this._zoomed=!0),t&&this._updateNodeState(t),setTimeout((()=>{this.panToNode(t)}),NWt.animation.duration)}}_highlightedNodeChanged(t,e){t!==e&&(t&&this._updateNodeState(t),e&&this._updateNodeState(e))}_onZoomChanged(){this._updateLabels(!this._zoomed)}_fireEnableClick(){this.fire("enable-click")}_updateInputTrace(){Mqt(this.getGraphSvgRoot(),this.renderHierarchy,this.selectedNode,this.traceInputs)}};Jqt.template=Zqt,t([o({type:Object}),e("design:type",yWt)],Jqt.prototype,"renderHierarchy",void 0),t([o({type:String}),e("design:type",String)],Jqt.prototype,"name",void 0),t([o({type:String}),e("design:type",String)],Jqt.prototype,"colorBy",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Jqt.prototype,"traceInputs",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Jqt.prototype,"_hasRenderHierarchyBeenFitOnce",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Jqt.prototype,"_isAttached",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"_zoom",void 0),t([o({type:String,observer:"_highlightedNodeChanged"}),e("design:type",String)],Jqt.prototype,"highlightedNode",void 0),t([o({type:String,observer:"_selectedNodeChanged"}),e("design:type",String)],Jqt.prototype,"selectedNode",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"handleEdgeSelected",void 0),t([o({type:Boolean,observer:"_onZoomChanged"}),e("design:type",Boolean)],Jqt.prototype,"_zoomed",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"_zoomStartCoords",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"_zoomTransform",void 0),t([o({type:Number}),e("design:type",Number)],Jqt.prototype,"_maxZoomDistanceForClick",void 0),t([o({type:Object}),e("design:type",Function)],Jqt.prototype,"templateIndex",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"_nodeGroupIndex",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"_annotationGroupIndex",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"_edgeGroupIndex",void 0),t([o({type:Number}),e("design:type",Number)],Jqt.prototype,"maxMetanodeLabelLengthFontSize",void 0),t([o({type:Number}),e("design:type",Number)],Jqt.prototype,"minMetanodeLabelLengthFontSize",void 0),t([o({type:Number}),e("design:type",Number)],Jqt.prototype,"maxMetanodeLabelLengthLargeFont",void 0),t([o({type:Number}),e("design:type",Number)],Jqt.prototype,"maxMetanodeLabelLength",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"progress",void 0),t([o({type:Array}),e("design:type",Array)],Jqt.prototype,"nodeContextMenuItems",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Number}),e("design:type",Number)],Jqt.prototype,"healthPillStepIndex",void 0),t([a("renderHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Jqt.prototype,"_renderHierarchyChanged",null),t([a("_isAttached","renderHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Jqt.prototype,"_animateAndFit",null),t([a("colorBy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Jqt.prototype,"nodeColorsChanged",null),t([a("nodeNamesToHealthPills","healthPillStepIndex"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Jqt.prototype,"_updateHealthPills",null),t([a("traceInputs","selectedNode"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Jqt.prototype,"_updateInputTrace",null),Jqt=t([i("tf-graph-scene")],Jqt);let Qqt=class extends(er(ye)){constructor(){super(...arguments),this._renderDepth=1,this._allowGraphSelect=!0,this.edgeWidthFunction="",this.handleNodeSelected="",this.edgeLabelFunction="",this.handleEdgeSelected=""}panToNode(t){this.$$("tf-graph-scene").panToNode(t)}_autoExtractNodesChanged(){var t=this.graphHierarchy;if(t){for(const e of Object.values(t.getNodeMap()))e.include=GGt.UNSPECIFIED;this._buildRenderHierarchy(t)}}_buildNewRenderHierarchy(){var t=this.graphHierarchy;t&&this._buildRenderHierarchy(t)}_statsChanged(){var t=this.stats,e=this.devicesForStats;this.graphHierarchy&&(t&&e&&((function n(t,e,i){Se.exports.each(t.nodes,(t=>{t.stats=null})),Se.exports.each(e.dev_stats,(e=>{i&&!i[e.device]||Se.exports.each(e.node_stats,(n=>{let i=n.node_name in t.nodes?n.node_name:sWt(n.node_name);if(!(i in t.nodes))return;let r=0;n.memory&&Se.exports.each(n.memory,(t=>{t.total_bytes&&(t.total_bytes>0?r+=Number(t.total_bytes):console.log("ignoring negative memory allocation for "+i))}));let o=null;n.output&&(o=Se.exports.map(n.output,(t=>Se.exports.map(t.tensor_description.shape.dim,(t=>Number(t.size)))))),t.nodes[i].device=e.device,null==t.nodes[i].stats&&(t.nodes[i].stats=new SGt(o)),t.nodes[i].stats.addBytesAllocation(r),n.all_end_rel_micros&&(n.all_end_rel_micros>0?t.nodes[i].stats.addExecutionTime(n.all_start_micros,n.all_start_micros+n.all_end_rel_micros):console.log("ignoring negative runtime for "+i))}))}))})(this.basicGraph,t,e),(function i(t,e){let n={},i={};Se.exports.each(t.root.leaves(),(e=>{let r=t.node(e);null!=r.device&&(n[r.device]=!0),null!=r.xlaCluster&&(i[r.xlaCluster]=!0)})),t.devices=Se.exports.keys(n),t.xlaClusters=Se.exports.keys(i),Se.exports.each(t.getNodeMap(),((t,e)=>{t.isGroupNode&&(t.stats=new SGt(null),t.deviceHistogram={})})),Se.exports.each(t.root.leaves(),(e=>{let n=t.node(e),i=n;for(;null!=i.parentNode;){if(null!=n.device){let t=i.parentNode.deviceHistogram;t[n.device]=(t[n.device]||0)+1}if(null!=n.xlaCluster){let t=i.parentNode.xlaClusterHistogram;t[n.xlaCluster]=(t[n.xlaCluster]||0)+1}null!=n.stats&&i.parentNode.stats.combine(n.stats),i=i.parentNode}}))})(this.graphHierarchy)),this._buildRenderHierarchy(this.graphHierarchy))}ready(){super.ready(),this.addEventListener("graph-select",this._graphSelected.bind(this)),this.addEventListener("disable-click",this._disableClick.bind(this)),this.addEventListener("enable-click",this._enableClick.bind(this)),this.addEventListener("node-toggle-expand",this._nodeToggleExpand.bind(this)),this.addEventListener("node-select",this._nodeSelected.bind(this)),this.addEventListener("node-highlight",this._nodeHighlighted.bind(this)),this.addEventListener("node-unhighlight",this._nodeUnhighlighted.bind(this)),this.addEventListener("node-toggle-extract",this._nodeToggleExtract.bind(this)),this.addEventListener("node-toggle-seriesgroup",this._nodeToggleSeriesGroup.bind(this)),this.addEventListener("edge-select",this._edgeSelected.bind(this)),this.addEventListener("annotation-select",this._nodeSelected.bind(this)),this.addEventListener("annotation-highlight",this._nodeHighlighted.bind(this)),this.addEventListener("annotation-unhighlight",this._nodeUnhighlighted.bind(this))}_buildRenderHierarchy(t){if(t.root.type!==jGt.META)return;const e=this,n=CGt("new tf_graph_render.Hierarchy",(()=>{const n=new yWt(t,!!this.stats,this.autoExtractNodes);function i(t){return{minValue:t.domain()[0],maxValue:t.domain()[1],startColor:t.range()[0],endColor:t.range()[1]}}return n.edgeLabelFunction=this.edgeLabelFunction,n.edgeWidthFunction=this.edgeWidthFunction,e._setColorByParams({compute_time:i(n.computeTimeScale),memory:i(n.memoryUsageScale),device:Se.exports.map(n.deviceColorMap.domain(),(function(t){return{device:t,color:n.deviceColorMap(t)}})),xla_cluster:Se.exports.map(n.xlaClusterColorMap.domain(),(function(t){return{xla_cluster:t,color:n.xlaClusterColorMap(t)}}))}),n}),_Gt.RENDER_BUILD_HIERARCHY);e._setRenderHierarchy(n)}_getVisible(t){return t?this.renderHierarchy.getNearestVisibleAncestor(t):t}fit(){this.$.scene.fit()}getImageBlob(){return this.$.scene.getImageBlob()}_graphChanged(){this.graphHierarchy&&(this.graphHierarchy.addListener(zqt.TEMPLATES_UPDATED,(()=>{this.$.scene.nodeColorsChanged()})),this.fire("graph-select"))}_graphSelected(t){this._allowGraphSelect&&(this.set("selectedNode",null),this.set("selectedEdge",null)),this._allowGraphSelect=!0}_disableClick(t){this._allowGraphSelect=!1}_enableClick(t){this._allowGraphSelect=!0}_selectedNodeChanged(){this.handleNodeSelected&&this.handleNodeSelected(this.selectedNode)}_selectedEdgeChanged(){var t=this.selectedEdge;this._deselectPreviousEdge(),t&&(this._lastSelectedEdgeGroup.classed(VWt.Edge.SELECTED,!0),this._updateMarkerOfSelectedEdge(t)),this.handleEdgeSelected&&this.handleEdgeSelected(t)}_nodeSelected(t){this._allowGraphSelect&&this.set("selectedNode",t.detail.name),this._allowGraphSelect=!0}_edgeSelected(t){this._allowGraphSelect&&(this.set("_lastSelectedEdgeGroup",t.detail.edgeGroup),this.set("selectedEdge",t.detail.edgeData)),this._allowGraphSelect=!0}_nodeHighlighted(t){this.set("highlightedNode",t.detail.name)}_nodeUnhighlighted(t){this.set("highlightedNode",null)}_nodeToggleExpand(t){this._nodeSelected(t);var e=t.detail.name,n=this.renderHierarchy.getRenderNodeByName(e);n.node.type!==jGt.OP&&(this.renderHierarchy.buildSubhierarchy(e),n.expanded=!n.expanded,this.async((function(){this.$.scene.setNodeExpanded(n)}),75),TGt({actionId:_Gt.NODE_EXPANSION_TOGGLED,eventLabel:n.expanded?"expanded":"collapsed"}))}_nodeToggleExtract(t){this.nodeToggleExtract(t.detail.name)}nodeToggleExtract(t){const e=this.renderHierarchy.getRenderNodeByName(t);e.node.include=e.node.include==GGt.INCLUDE?GGt.EXCLUDE:e.node.include==GGt.EXCLUDE||this.renderHierarchy.isNodeAuxiliary(e)?GGt.INCLUDE:GGt.EXCLUDE,this._buildRenderHierarchy(this.graphHierarchy),TGt({actionId:_Gt.NODE_AUXILIARY_EXTRACTION_CHANGED,eventLabel:e.node.include===GGt.INCLUDE?"Auxiliary to Main":"Main to Auxiliary"})}_nodeToggleSeriesGroup(t){this.nodeToggleSeriesGroup(t.detail.name)}nodeToggleSeriesGroup(t){this.set("progress",{value:0,msg:""});var e=kGt(AGt(this),100,"Namespace hierarchy");const n=Object.assign(Object.assign({},this.hierarchyParams),{seriesMap:this.graphHierarchy.buildSeriesGroupMapToggled(t)});Fqt(this.basicGraph,n,e).then(function(t){this.set("graphHierarchy",t),this._buildRenderHierarchy(this.graphHierarchy)}.bind(this))}_deselectPreviousEdge(){Su("."+VWt.Edge.SELECTED).classed(VWt.Edge.SELECTED,!1).each(((t,e)=>{if(t.label){const e=Su(this).selectAll("path.edgeline");t.label.startMarkerId&&e.style("marker-start",`url(#${t.label.startMarkerId})`),t.label.endMarkerId&&e.style("marker-end",`url(#${t.label.endMarkerId})`)}}))}_updateMarkerOfSelectedEdge(t){if(t.label){const e=t.label.startMarkerId||t.label.endMarkerId;if(e){const n=e.replace("dataflow-","selected-");let i=this.$$("#"+n);if(!i){const t=this.$.scene.querySelector("#"+e);i=t.cloneNode(!0),i.setAttribute("id",n),i.classList.add("selected-arrowhead"),t.parentNode.appendChild(i)}const r=t.label.startMarkerId?"marker-start":"marker-end";this._lastSelectedEdgeGroup.selectAll("path.edgeline").style(r,`url(#${n})`)}}}not(t){return!t}};Qqt.template=_e`
    <style>
      .container {
        width: 100%;
        height: 100%;
        background: white;
        box-shadow: 0 1px 5px rgba(0, 0, 0, 0.2);
      }

      .vertical {
        width: 100%;
        height: 100%;
        @apply --layout-vertical;
      }

      .auto {
        @apply --layout-flex-auto;
        @apply --layout-vertical;
      }

      h2 {
        text-align: center;
      }

      paper-button {
        text-transform: none;
      }
    </style>
    <div class="container">
      <div class="vertical">
        <template is="dom-if" if="[[title]]">
          <h2>[[title]]</h2>
        </template>
        <tf-graph-scene
          id="scene"
          class="auto"
          render-hierarchy="[[renderHierarchy]]"
          highlighted-node="[[_getVisible(highlightedNode)]]"
          selected-node="{{selectedNode}}"
          selected-edge="{{selectedEdge}}"
          color-by="[[colorBy]]"
          progress="[[progress]]"
          node-context-menu-items="[[nodeContextMenuItems]]"
          node-names-to-health-pills="[[nodeNamesToHealthPills]]"
          health-pill-step-index="{{healthPillStepIndex}}"
          handle-edge-selected="[[handleEdgeSelected]]"
          trace-inputs="[[traceInputs]]"
        ></tf-graph-scene>
      </div>
    </div>
  `,t([o({type:Object,notify:!0,observer:"_graphChanged"}),e("design:type",Dqt)],Qqt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",qGt)],Qqt.prototype,"basicGraph",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"stats",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"devicesForStats",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"hierarchyParams",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],Qqt.prototype,"progress",void 0),t([o({type:String}),e("design:type",String)],Qqt.prototype,"title",void 0),t([o({type:String,notify:!0}),e("design:type",String)],Qqt.prototype,"selectedNode",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],Qqt.prototype,"selectedEdge",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"_lastSelectedEdgeGroup",void 0),t([o({type:String,notify:!0}),e("design:type",String)],Qqt.prototype,"highlightedNode",void 0),t([o({type:String}),e("design:type",String)],Qqt.prototype,"colorBy",void 0),t([o({type:Object,notify:!0,readOnly:!0}),e("design:type",Object)],Qqt.prototype,"colorByParams",void 0),t([o({type:Object,readOnly:!0,notify:!0}),e("design:type",yWt)],Qqt.prototype,"renderHierarchy",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Qqt.prototype,"traceInputs",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Qqt.prototype,"autoExtractNodes",void 0),t([o({type:Array}),e("design:type",Array)],Qqt.prototype,"nodeContextMenuItems",void 0),t([o({type:Number}),e("design:type",Number)],Qqt.prototype,"_renderDepth",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Qqt.prototype,"_allowGraphSelect",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Number}),e("design:type",Number)],Qqt.prototype,"healthPillStepIndex",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"edgeWidthFunction",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"handleNodeSelected",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"edgeLabelFunction",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"handleEdgeSelected",void 0),t([a("autoExtractNodes"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Qqt.prototype,"_autoExtractNodesChanged",null),t([a("graphHierarchy","edgeWidthFunction","handleNodeSelected","edgeLabelFunction","handleEdgeSelected"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Qqt.prototype,"_buildNewRenderHierarchy",null),t([a("stats","devicesForStats"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Qqt.prototype,"_statsChanged",null),t([a("selectedNode"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Qqt.prototype,"_selectedNodeChanged",null),t([a("selectedEdge"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Qqt.prototype,"_selectedEdgeChanged",null),Qqt=t([i("tf-graph")],Qqt);const tYt={MAX_NODE_COUNT:1e4,MAX_EDGE_COUNT:1e4};let eYt=class extends(er(ye)){constructor(){super(...arguments),this.hierarchyParams=Hqt,this.allStepsModeEnabled=!1,this.specificHealthPillStep=0,this.compatNodeTitle="TPU Compatibility"}fit(){this.$.graph.fit()}downloadAsImage(t){return n(this,void 0,void 0,(function*(){const e=yield this.$.graph.getImageBlob(),n=document.createElement("a");n.href=URL.createObjectURL(e),n.download=t,n.click(),URL.revokeObjectURL(n.href)}))}_isNotComplete(t){return t.value<100}_getContainerClass(t){var e="container";return t.error&&(e+=" error"),this._isNotComplete(t)&&(e+=" loading"),e}_onNodeInclusionToggled(t){this.$.graph.nodeToggleExtract(t.detail.name)}_onNodeSeriesGroupToggled(t){this.$.graph.nodeToggleSeriesGroup(t.detail.name)}_updateNodeInclude(){const t=this.renderHierarchy?this.renderHierarchy.getNodeByName(this.selectedNode):null;this._selectedNodeInclude=t?t.include:GGt.UNSPECIFIED}_slimGraphChanged(){if(!this.graph)return;const{MAX_NODE_COUNT:t,MAX_EDGE_COUNT:e}=tYt;Object.keys(this.graph.nodes).length>t&&this.graph.edges.length>e&&this.colorBy===aqt.STRUCTURE&&(this.colorBy=aqt.NONE)}_ensureTemplates(){this.graphHierarchy&&this.colorBy===aqt.STRUCTURE&&(this.graphHierarchy.getTemplateIndex()||this.graphHierarchy.updateTemplates())}};eYt.template=_e`
    <style>
      ::host {
        display: block;
      }

      /deep/ .close {
        position: absolute;
        cursor: pointer;
        left: 15px;
        bottom: 15px;
      }

      .container {
        width: 100%;
        height: 100%;
        opacity: 1;
      }

      .container.loading {
        cursor: progress;
        opacity: 0.1;
      }

      .container.loading.error {
        cursor: auto;
      }

      #info {
        position: absolute;
        right: 5px;
        top: 5px;
        padding: 0px;
        max-width: 380px;
        min-width: 320px;
        background-color: rgba(255, 255, 255, 0.9);
        @apply --shadow-elevation-2dp;
      }

      #main {
        width: 100%;
        height: 100%;
      }

      #progress-bar {
        display: flex;
        flex-direction: column;
        align-items: center;
        justify-content: center;
        width: 100%;
        position: absolute;
        top: 40px;
        left: 0;
        font-size: 13px;
      }

      #progress-msg {
        margin-bottom: 5px;
        white-space: pre-wrap;
        width: 400px;
      }

      paper-progress {
        width: 400px;
        --paper-progress-height: 6px;
        --paper-progress-active-color: #f3913e;
      }

      .context-menu {
        position: absolute;
        display: none;
        background-color: #e2e2e2;
        border-radius: 2px;
        font-size: 14px;
        min-width: 150px;
        border: 1px solid #d4d4d4;
      }

      /deep/ .context-menu ul {
        list-style-type: none;
        margin: 0;
        padding: 0;
        cursor: default;
      }

      /deep/ .context-menu ul li {
        padding: 4px 16px;
      }

      /deep/ .context-menu ul li:hover {
        background-color: #f3913e;
        color: white;
      }
    </style>
    <template is="dom-if" if="[[_isNotComplete(progress)]]">
      <div id="progress-bar">
        <div id="progress-msg">[[progress.msg]]</div>
        <paper-progress value="[[progress.value]]"></paper-progress>
      </div>
    </template>
    <div class$="[[_getContainerClass(progress)]]">
      <div id="main">
        <tf-graph
          id="graph"
          graph-hierarchy="{{graphHierarchy}}"
          basic-graph="[[graph]]"
          hierarchy-params="[[hierarchyParams]]"
          render-hierarchy="{{renderHierarchy}}"
          devices-for-stats="[[devicesForStats]]"
          stats="[[stats]]"
          selected-node="{{selectedNode}}"
          highlighted-node="{{_highlightedNode}}"
          color-by="[[colorBy]]"
          color-by-params="{{colorByParams}}"
          progress="{{progress}}"
          edge-label-function="[[edgeLabelFunction]]"
          edge-width-function="[[edgeWidthFunction]]"
          node-names-to-health-pills="[[nodeNamesToHealthPills]]"
          health-pill-step-index="[[healthPillStepIndex]]"
          handle-node-selected="[[handleNodeSelected]]"
          handle-edge-selected="[[handleEdgeSelected]]"
          trace-inputs="[[traceInputs]]"
          auto-extract-nodes="[[autoExtractNodes]]"
        ></tf-graph>
      </div>
      <div id="info">
        <tf-graph-info
          id="graph-info"
          title="selected"
          graph-hierarchy="[[graphHierarchy]]"
          render-hierarchy="[[renderHierarchy]]"
          graph="[[graph]]"
          selected-node="{{selectedNode}}"
          selected-node-include="{{_selectedNodeInclude}}"
          highlighted-node="{{_highlightedNode}}"
          color-by="[[colorBy]]"
          color-by-params="[[colorByParams]]"
          debugger-data-enabled="[[debuggerDataEnabled]]"
          are-health-pills-loading="[[areHealthPillsLoading]]"
          debugger-numeric-alerts="[[debuggerNumericAlerts]]"
          node-names-to-health-pills="[[nodeNamesToHealthPills]]"
          all-steps-mode-enabled="{{allStepsModeEnabled}}"
          specific-health-pill-step="{{specificHealthPillStep}}"
          health-pill-step-index="{{healthPillStepIndex}}"
          compat-node-title="[[compatNodeTitle]]"
          on-node-toggle-inclusion="_onNodeInclusionToggled"
          on-node-toggle-seriesgroup="_onNodeSeriesGroupToggled"
        ></tf-graph-info>
      </div>
    </div>
  `,t([o({type:Object}),e("design:type",Dqt)],eYt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",qGt)],eYt.prototype,"graph",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"hierarchyParams",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"stats",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"progress",void 0),t([o({type:Boolean}),e("design:type",Boolean)],eYt.prototype,"traceInputs",void 0),t([o({type:Boolean}),e("design:type",Boolean)],eYt.prototype,"autoExtractNodes",void 0),t([o({type:String,notify:!0}),e("design:type",String)],eYt.prototype,"colorBy",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],eYt.prototype,"colorByParams",void 0),t([o({type:Object,notify:!0}),e("design:type",yWt)],eYt.prototype,"renderHierarchy",void 0),t([o({type:Boolean}),e("design:type",Boolean)],eYt.prototype,"debuggerDataEnabled",void 0),t([o({type:Boolean}),e("design:type",Boolean)],eYt.prototype,"areHealthPillsLoading",void 0),t([o({type:Array,notify:!0}),e("design:type",Array)],eYt.prototype,"debuggerNumericAlerts",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],eYt.prototype,"allStepsModeEnabled",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],eYt.prototype,"specificHealthPillStep",void 0),t([o({type:Number}),e("design:type",Number)],eYt.prototype,"healthPillStepIndex",void 0),t([o({type:String,notify:!0}),e("design:type",String)],eYt.prototype,"selectedNode",void 0),t([o({type:String}),e("design:type",String)],eYt.prototype,"compatNodeTitle",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"edgeWidthFunction",void 0),t([o({type:Number}),e("design:type",Number)],eYt.prototype,"_selectedNodeInclude",void 0),t([o({type:String}),e("design:type",String)],eYt.prototype,"_highlightedNode",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"handleNodeSelected",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"edgeLabelFunction",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"handleEdgeSelected",void 0),t([a("selectedNode","renderHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],eYt.prototype,"_updateNodeInclude",null),t([a("graph"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],eYt.prototype,"_slimGraphChanged",null),t([a("colorBy","graphHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],eYt.prototype,"_ensureTemplates",null),eYt=t([i("tf-graph-board")],eYt);let nYt=class extends(er(ye)){constructor(){super(...arguments),this._rawRegexInput="",this._previousRegexInput="",this._searchTimeoutDelay=150,this._maxRegexResults=42}get _regexInput(){return this._rawRegexInput.trim()}_regexInputChanged(){this._requestSearch()}_clearSearchResults(){this.set("_regexMatches",[])}_requestSearch(){this._searchPending||(this._regexInput!==this._previousRegexInput?(this._searchPending=!0,this._executeSearch(),this.async((()=>{this._searchPending=!1,this._requestSearch()}),this._searchTimeoutDelay)):this._searchPending=!1)}_executeSearch(){if(this._previousRegexInput=this._regexInput,!this._regexInput)return void this._clearSearchResults();try{var t=new RegExp(this._regexInput)}catch(t){return void this._clearSearchResults()}const e=[],n=this.renderHierarchy.hierarchy.getNodeMap();Se.exports.each(n,((n,i)=>{if(e.length>=this._maxRegexResults)return!1;t.test(i)&&e.push(i)})),this.set("_regexMatches",e)}_matchClicked(t){this.set("selectedNode",t.model.item),TGt({actionId:_Gt.NODE_SEARCH_RESULT_FOCUSED})}};nYt.template=_e`
    <div id="search-container">
      <paper-input
        id="runs-regex"
        label="Search nodes (regex)"
        value="{{_rawRegexInput}}"
      >
      </paper-input>
      <div id="search-results-anchor">
        <div id="search-results">
          <template is="dom-repeat" items="[[_regexMatches]]">
            <div id="search-match" on-click="_matchClicked">[[item]]</div>
          </template>
        </div>
      </div>
    </div>
    <style>
      #search-container {
        width: 100%;
        overflow: visible;
      }

      #runs-regex {
        width: 100%;
      }

      #search-results-anchor {
        position: relative;
      }

      #search-results {
        color: #fff;
        position: absolute;
        max-height: 200px;
        overflow-x: hidden;
        overflow-y: auto;
        text-align: right;
        max-width: 100%;
        box-sizing: border-box;
      }

      #search-match {
        background: var(--tb-orange-strong);
        padding: 3px;
        float: right;
        width: 100%;
        box-sizing: border-box;
        direction: rtl;
      }

      #search-match:hover {
        background: var(--tb-orange-weak);
        cursor: pointer;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],nYt.prototype,"renderHierarchy",void 0),t([o({type:String,notify:!0}),e("design:type",String)],nYt.prototype,"selectedNode",void 0),t([o({type:String}),e("design:type",String)],nYt.prototype,"_rawRegexInput",void 0),t([o({type:String}),e("design:type",String)],nYt.prototype,"_previousRegexInput",void 0),t([o({type:Number}),e("design:type",Number)],nYt.prototype,"_searchTimeoutDelay",void 0),t([o({type:Boolean}),e("design:type",Boolean)],nYt.prototype,"_searchPending",void 0),t([o({type:Number}),e("design:type",Number)],nYt.prototype,"_maxRegexResults",void 0),t([o({type:Array}),e("design:type",Array)],nYt.prototype,"_regexMatches",void 0),t([s("renderHierarchy","_rawRegexInput"),e("design:type",String),e("design:paramtypes",[])],nYt.prototype,"_regexInput",null),t([a("_regexInput"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],nYt.prototype,"_regexInputChanged",null),nYt=t([i("tf-graph-node-search")],nYt);const iYt=/device:([^:]+:[0-9]+)$/,rYt=[{regex:iYt}],oYt=[],aYt=new Set([aqt.COMPUTE_TIME,aqt.MEMORY]);let sYt=class extends(er(il(ye))){constructor(){super(...arguments),this.ColorBy=aqt,this.stats=null,this.devicesForStats=null,this.colorBy=aqt.STRUCTURE,this.datasets=[],this._selectedRunIndex=0,this.traceInputs=!1,this.autoExtractNodes=!0,this._selectedTagIndex=0,this._selectedGraphType=yGt.OP_GRAPH,this.showSessionRunsDropdown=!0,this.showUploadButton=!0,this._legendOpened=!0,this._downloadFilename="graph.png"}_onGraphTypeChangedByUserGesture(){TGt({actionId:_Gt.GRAPH_TYPE_CHANGED,eventLabel:this._selectedGraphType})}_onColorByChangedByUserGesture(){TGt({actionId:_Gt.NODE_COLOR_MODE_CHANGED,eventLabel:this.colorBy})}_onTraceInputsChangedByUserGesture(){TGt({actionId:_Gt.TRACE_INPUT_MODE_TOGGLED})}_xlaClustersProvided(t){return t&&t.hierarchy&&t.hierarchy.xlaClusters.length>0}_statsChanged(t){if(null!=t){var e={};Se.exports.each(t.dev_stats,(function(t){var n=Se.exports.some(rYt,(function(e){return e.regex.test(t.device)})),i=Se.exports.some(oYt,(function(e){return e.regex.test(t.device)}));n&&!i&&(e[t.device]=!0)})),this.set("devicesForStats",e)}}get _currentDevices(){var t=this.devicesForStats;const e=this.stats,n=(e?e.dev_stats:[]).map((t=>t.device)).filter((t=>rYt.some((e=>e.regex.test(t))))),i=zGt(n);if(1==i.length){const t=i[0].match(iYt);t&&(i[0]=t[1])}return n.map(((e,n)=>{let r=null;return oYt.forEach((t=>{t.regex.test(e)&&(r=t.msg)})),{device:e,suffix:i[n],used:t[e],ignoredMsg:r}}))}_deviceCheckboxClicked(t){const e=t.target,n=Object.assign({},this.devicesForStats),i=e.value;e.checked?n[i]=!0:delete n[i],this.set("devicesForStats",n)}_numTags(t,e){return this._getTags(t,e).length}_getTags(t,e){return t&&t[e]?t[e].tags:[]}_fit(){this.fire("fit-tap")}_isGradientColoring(t,e){return aYt.has(e)&&null!=t}_equals(t,e){return t===e}get _currentDeviceParams(){const t=this.colorByParams.device.filter((t=>rYt.some((e=>e.regex.test(t.device))))),e=zGt(t.map((t=>t.device)));if(1==e.length){var n=e[0].match(iYt);n&&(e[0]=n[1])}return t.map(((t,n)=>({device:e[n],color:t.color})))}get _currentXlaClusterParams(){return this.colorByParams.xla_cluster}get _currentGradientParams(){var t=this.colorByParams,e=this.colorBy;if(!this._isGradientColoring(this.stats,e))return;const n=t[e];let i=n.minValue,r=n.maxValue;return e===aqt.MEMORY?(i=RGt(i,NGt),r=RGt(r,NGt)):e===aqt.COMPUTE_TIME&&(i=RGt(i,IGt),r=RGt(r,IGt)),{minValue:i,maxValue:r,startColor:n.startColor,endColor:n.endColor}}download(){this.fire("download-image-requested",this._downloadFilename)}_updateFileInput(t){const e=t.target.files[0];if(!e)return;let n=e.name;const i=n.lastIndexOf(".");i>=0&&(n=n.substring(0,i));const r=n.lastIndexOf("/");r>=0&&(n=n.substring(r+1)),this._setDownloadFilename(n),this.set("selectedFile",t),TGt({actionId:_Gt.UPLOADED_GRAPH_FROM_FILESYSTEM})}_datasetsChanged(t,e){var n;null!=e&&(this._selectedRunIndex=0),this._setDownloadFilename(null===(n=this.datasets[this._selectedRunIndex])||void 0===n?void 0:n.name)}_computeSelection(t,e,n,i){return t[e]&&t[e].tags[n]?{run:t[e].name,tag:t[e].tags[n].tag,type:i}:null}_selectedRunIndexChanged(t){var e;this.datasets&&(this.colorBy=aqt.STRUCTURE,this._selectedTagIndex=0,this._selectedGraphType=this._getDefaultSelectionType(),this.traceInputs=!1,this._setDownloadFilename(null===(e=this.datasets[t])||void 0===e?void 0:e.name))}_selectedTagIndexChanged(){this._selectedGraphType=this._getDefaultSelectionType()}_getDefaultSelectionType(){const{datasets:t,_selectedRunIndex:e,_selectedTagIndex:n}=this;if(!t||!t[e]||!t[e].tags[n]||t[e].tags[n].opGraph)return yGt.OP_GRAPH;const i=t[e];return i.tags[n].profile?yGt.PROFILE:i.tags[n].conceptualGraph?yGt.CONCEPTUAL_GRAPH:yGt.OP_GRAPH}_getFile(){this.$$("#file").click()}_setDownloadFilename(t){this._downloadFilename=(t||"graph")+".png"}_statsNotNull(t){return null!==t}_toggleLegendOpen(){this.set("_legendOpened",!this._legendOpened)}_getToggleLegendIcon(t){return t?"expand-more":"expand-less"}_getSelectionOpGraphDisabled(t,e,n){return!t[e]||!t[e].tags[n]||!t[e].tags[n].opGraph}_getSelectionProfileDisabled(t,e,n){return!t[e]||!t[e].tags[n]||!t[e].tags[n].profile}_getSelectionConceptualGraphDisabled(t,e,n){return!t[e]||!t[e].tags[n]||!t[e].tags[n].conceptualGraph}};sYt.template=_e`
    <style>
      :host {
        color: #555;
        display: flex;
        flex-direction: column;
        font-size: 12px;
        width: 100%;
        --tb-graph-controls-title-color: #000;
        --tb-graph-controls-legend-text-color: #000;
        --tb-graph-controls-text-color: #555;
        --tb-graph-controls-title-font-size: 14px;
        --tb-graph-controls-subtitle-font-size: 14px;
        --paper-input-container-shared-input-style_-_font-size: 14px;
        --paper-font-subhead_-_font-size: 14px;
      }

      :host(.dark-mode) {
        --tb-graph-controls-title-color: #fff;
        --tb-graph-controls-legend-text-color: #f3f3f3;
        --tb-graph-controls-text-color: #eee;
      }

      paper-dropdown-menu {
        --paper-dropdown-menu-input: {
          padding: 0;
          color: gray;
        }
        --iron-icon-width: 15px;
        --iron-icon-height: 15px;
        --primary-text-color: gray;
        --paper-item-min-height: 30px;
      }

      paper-button[raised].keyboard-focus {
        font-weight: normal;
      }

      .run-dropdown {
        --paper-input-container: {
          padding: 5px 0 5px 5px;
        }
      }

      table {
        border-collapse: collapse;
        border-spacing: 0;
      }

      table tr {
        height: 20px;
      }

      table td {
        padding: 0;
        margin: 0;
      }

      .allcontrols {
        padding: 0 20px 20px;
        flex-grow: 1;
        overflow-y: auto;
      }

      .legend-holder {
        background: var(--secondary-background-color);
        box-sizing: border-box;
        color: var(--tb-graph-controls-text-color);
        width: 100%;
      }

      .legend-toolbar {
        appearance: none;
        background-color: inherit;
        border-top: 1px solid #ccc;
        border-bottom: 1px solid #ccc;
        border-right: none;
        border-left: none;
        cursor: pointer;
        color: var(--tb-graph-controls-legend-text-color);
        font: inherit;
        display: flex;
        align-items: center;
        justify-content: space-between;
        width: 100%;
      }

      .legend-toolbar,
      .legend-content {
        padding: 8px 20px;
      }

      .toggle-legend-button {
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .toggle-legend-text {
        font-size: var(--tb-graph-controls-subtitle-font-size);
      }

      paper-radio-button {
        display: block;
        padding: 5px;
      }
      svg.icon,
      tf-graph-icon {
        width: 60px;
        height: 18px;
      }
      .domainValues {
        margin-bottom: 10px;
        width: 165px;
      }
      .domainStart {
        float: left;
      }
      .domainEnd {
        float: right;
      }
      .colorBox {
        width: 20px;
      }

      .image-icon {
        width: 24px;
        height: 24px;
      }

      .help-icon {
        height: 15px;
        margin: 0;
        padding: 0;
      }

      .gray {
        color: #666;
      }

      .title {
        font-size: var(--tb-graph-controls-title-font-size);
        margin: 8px 5px 8px 0;
        color: var(--tb-graph-controls-title-color);
      }
      .title small {
        font-weight: normal;
      }
      .deviceList,
      .xlaClusterList {
        max-height: 200px;
        overflow-y: auto;
      }

      #file {
        padding: 8px 0;
      }

      .color-legend-row {
        align-items: center;
        clear: both;
        display: flex;
        height: 20px;
        margin-top: 5px;
      }

      .color-legend-row .label,
      .color-legend-row svg,
      .color-legend-row tf-graph-icon {
        flex: 0 0 40px;
        margin-right: 20px;
      }

      .devices-checkbox input {
        text-align: left;
        vertical-align: middle;
      }

      .control-holder .icon-button {
        font-size: var(--tb-graph-controls-subtitle-font-size);
        margin: 0 -5px;
        padding: 5px;
        display: flex;
        justify-content: flex-start;
        color: var(--tb-graph-controls-text-color);
      }

      .button-text {
        padding-left: 20px;
        text-transform: none;
      }

      .upload-button {
        width: 165px;
        height: 25px;
        text-transform: none;
        margin-top: 4px;
      }

      .button-icon {
        width: 26px;
        height: 26px;
        color: var(--paper-orange-500);
      }

      .hidden-input {
        display: none;
      }

      .allcontrols .control-holder {
        clear: both;
        display: flex;
        justify-content: space-between;
      }

      .allcontrols .control-holder.control-options {
        padding: 0 0 15px 15px;
        flex-direction: column;
      }

      .allcontrols .control-holder paper-toggle-button {
        margin-bottom: 5px;
      }

      span.counter {
        font-size: var(--tb-graph-controls-subtitle-font-size);
        color: gray;
        margin-left: 4px;
      }

      .runs-row .title,
      .tags-row .title {
        display: flex;
        align-items: baseline;
      }

      .runs-row paper-item,
      .tags-row paper-item {
        --paper-item: {
          white-space: nowrap;
        }
      }

      table.control-holder {
        border: 0;
        border-collapse: collapse;
      }

      table.tf-graph-controls td.input-element-table-data {
        padding: 0 0 0 20px;
      }

      .spacer {
        flex-grow: 1;
      }

      .color-text {
        overflow: hidden;
      }

      .color-text.gradient-container {
        margin: 0 5px;
      }

      /** Override inline styles that suppress pointer events for disabled buttons. Otherwise, the */
      /*  tooltips do not appear. */
      paper-radio-group paper-radio-button {
        pointer-events: auto !important;
      }

      .legend-clarifier {
        color: #266236;
        cursor: help;
        display: inline-block;
        text-decoration: underline;
      }

      .legend-clarifier paper-tooltip {
        width: 150px;
      }

      /** Otherwise, polymer UI controls appear atop node search. */
      tf-graph-node-search {
        z-index: 1;
        width: 100%;
      }

      paper-dropdown-menu {
        flex-grow: 1;
      }
    </style>

    <div class="allcontrols">
      <div class="control-holder">
        <tf-graph-node-search
          selected-node="{{selectedNode}}"
          render-hierarchy="[[renderHierarchy]]"
        ></tf-graph-node-search>
      </div>
      <div class="control-holder">
        <paper-button class="icon-button" on-tap="_fit" alt="Fit to screen">
          <iron-icon icon="aspect-ratio" class="button-icon"></iron-icon>
          <span class="button-text">Fit to screen</span>
        </paper-button>
      </div>
      <div class="control-holder">
        <paper-button
          class="icon-button"
          on-click="download"
          alt="Download PNG"
        >
          <iron-icon icon="file-download" class="button-icon"></iron-icon>
          <span class="button-text">Download PNG</span>
        </paper-button>
      </div>
      <template is="dom-if" if="[[showUploadButton]]">
        <div class="control-holder">
          <paper-button
            class="icon-button"
            on-click="_getFile"
            alt="Upload file"
            title="Upload a pbtxt file to view a graph from the local filesystem"
          >
            <iron-icon icon="file-upload" class="button-icon"></iron-icon>
            <span class="button-text">Upload file</span>
          </paper-button>

          <div class="hidden-input">
            <input
              type="file"
              id="file"
              name="file"
              on-change="_updateFileInput"
              accept=".pbtxt"
            />
          </div>
        </div>
      </template>
      <div class="control-holder runs-row">
        <div class="title">
          Run <span class="counter">([[datasets.length]])</span>
        </div>
        <paper-dropdown-menu
          no-label-float
          no-animations
          noink
          horizontal-align="left"
          class="run-dropdown"
        >
          <paper-listbox
            class="dropdown-content"
            selected="{{_selectedRunIndex}}"
            slot="dropdown-content"
          >
            <template is="dom-repeat" items="[[datasets]]">
              <paper-item>[[item.name]]</paper-item>
            </template>
          </paper-listbox>
        </paper-dropdown-menu>
      </div>
      <template is="dom-if" if="[[showSessionRunsDropdown]]">
        <div class="control-holder tags-row">
          <div class="title">
            Tag
            <span class="counter"
              >([[_numTags(datasets, _selectedRunIndex)]])</span
            >
          </div>
          <paper-dropdown-menu
            no-label-float
            no-animations
            horizontal-align="left"
            noink
            class="run-dropdown"
          >
            <paper-listbox
              class="dropdown-content"
              selected="{{_selectedTagIndex}}"
              slot="dropdown-content"
            >
              <template
                is="dom-repeat"
                items="[[_getTags(datasets, _selectedRunIndex)]]"
              >
                <paper-item>[[item.displayName]]</paper-item>
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
        </div>
      </template>
      <div class="title">Graph type</div>
      <div class="control-holder control-options">
        <paper-radio-group
          selected="{{_selectedGraphType}}"
          on-paper-radio-group-changed="_onGraphTypeChangedByUserGesture"
        >
          <!-- Note that the name has to match that of tf_graph_common.SelectionType. -->
          <paper-radio-button
            name="op_graph"
            disabled="[[_getSelectionOpGraphDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]"
            >Op graph</paper-radio-button
          >
          <paper-radio-button
            name="conceptual_graph"
            disabled="[[_getSelectionConceptualGraphDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]"
            >Conceptual graph</paper-radio-button
          >
          <paper-radio-button
            name="profile"
            disabled="[[_getSelectionProfileDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]"
            >Profile</paper-radio-button
          >
        </paper-radio-group>
      </div>
      <div class="title">Node options</div>
      <div class="control-holder control-options">
        <paper-toggle-button
          checked="{{traceInputs}}"
          on-change="_onTraceInputsChangedByUserGesture"
        >
          Trace inputs
        </paper-toggle-button>
        <paper-toggle-button checked="{{autoExtractNodes}}">
          Auto-extract high-degree nodes
        </paper-toggle-button>
      </div>
      <template is="dom-if" if="[[healthPillsFeatureEnabled]]">
        <div class="control-holder">
          <paper-toggle-button checked="{{healthPillsToggledOn}}"
            >Show health pills</paper-toggle-button
          >
        </div>
      </template>
      <div class="title">Color by</div>
      <div class="control-holder control-options">
        <paper-radio-group
          selected="{{colorBy}}"
          on-paper-radio-group-changed="_onColorByChangedByUserGesture"
        >
          <paper-radio-button name="[[ColorBy.NONE]]">None</paper-radio-button>

          <paper-radio-button name="[[ColorBy.STRUCTURE]]"
            >Structure</paper-radio-button
          >

          <paper-radio-button name="[[ColorBy.DEVICE]]"
            >Device</paper-radio-button
          >

          <paper-radio-button
            id="xla-cluster-radio-button"
            name="[[ColorBy.XLA_CLUSTER]]"
            disabled="[[!_xlaClustersProvided(renderHierarchy)]]"
          >
            XLA cluster
          </paper-radio-button>
          <paper-tooltip
            animation-delay="0"
            for="xla-cluster-radio-button"
            position="right"
            offset="0"
          >
            Coloring by XLA cluster is only enabled if at least 1 op specifies
            an XLA cluster.
          </paper-tooltip>

          <paper-radio-button
            id="compute-time-radio-button"
            name="[[ColorBy.COMPUTE_TIME]]"
            disabled="[[!stats]]"
          >
            Compute time
          </paper-radio-button>
          <paper-tooltip
            animation-delay="0"
            for="compute-time-radio-button"
            position="right"
            offset="0"
          >
            Coloring by compute time is only enabled if the RunMetadata proto is
            passed to the FileWriter when a specific session is run.
          </paper-tooltip>

          <paper-radio-button
            id="memory-radio-button"
            name="[[ColorBy.MEMORY]]"
            disabled="[[!stats]]"
          >
            Memory
          </paper-radio-button>
          <paper-tooltip
            animation-delay="0"
            for="memory-radio-button"
            position="right"
            offset="0"
          >
            Coloring by memory is only enabled if the RunMetadata proto is
            passed to the FileWriter when a specific session is run.
          </paper-tooltip>

          <paper-radio-button
            id="tpu-compatibility-radio-button"
            name="[[ColorBy.OP_COMPATIBILITY]]"
          >
            TPU compatibility
          </paper-radio-button>
          <paper-tooltip
            animation-delay="0"
            for="tpu-compatibility-radio-button"
            position="right"
            offset="0"
          >
            Coloring by whether an operation is compatible for the TPU device.
          </paper-tooltip>
        </paper-radio-group>
        <span class="spacer"></span>
      </div>
    </div>
    <div class="legend-holder">
      <button class="legend-toolbar" on-click="_toggleLegendOpen">
        <span class="toggle-legend-text">Legend</span>
        <iron-icon
          icon="[[_getToggleLegendIcon(_legendOpened)]]"
          class="toggle-legend-button"
        >
        </iron-icon>
      </button>
      <iron-collapse opened="[[_legendOpened]]" class="legend-content">
        <!-- Color-mode-specific legend items -->
        <div>
          <template is="dom-if" if="[[_isGradientColoring(stats, colorBy)]]">
            <svg width="140" height="20" class="color-text gradient-container">
              <defs>
                <linearGradient
                  id="linearGradient"
                  x1="0%"
                  y1="0%"
                  x2="100%"
                  y2="0%"
                >
                  <stop
                    class="start"
                    offset="0%"
                    stop-color$="[[_currentGradientParams.startColor]]"
                  ></stop>
                  <stop
                    class="end"
                    offset="100%"
                    stop-color$="[[_currentGradientParams.endColor]]"
                  ></stop>
                </linearGradient>
              </defs>
              <rect
                x="0"
                y="0"
                width="135"
                height="20"
                fill="url(#linearGradient)"
                stroke="black"
              ></rect>
            </svg>
            <div class="domainValues color-text">
              <div class="domainStart">[[_currentGradientParams.minValue]]</div>
              <div class="domainEnd">[[_currentGradientParams.maxValue]]</div>
            </div>
            <br style="clear: both" />
            <div>Devices included in stats:</div>
            <div class="deviceList">
              <template is="dom-repeat" items="[[_currentDevices]]">
                <div class="color-legend-row devices-checkbox">
                  <span
                    ><input
                      type="checkbox"
                      value$="[[item.device]]"
                      checked$="[[item.used]]"
                      on-click="_deviceCheckboxClicked"
                  /></span>
                  <span>[[item.suffix]]</span>
                  <template is="dom-if" if="[[item.ignoredMsg]]">
                    <paper-icon-button
                      icon="help"
                      class="help-icon"
                    ></paper-icon-button>
                    <paper-tooltip
                      position="right"
                      offset="0"
                      animation-delay="0"
                      >[[item.ignoredMsg]]</paper-tooltip
                    >
                  </template>
                </div>
              </template>
            </div>
          </template>
          <template is="dom-if" if="[[_equals(colorBy, 'structure')]]">
            <div class="color-text">
              <div class="color-legend-row">
                <span class="label"> colors </span>
                <span class="color-legend-value">same substructure</span>
              </div>
              <div class="color-legend-row">
                <tf-graph-icon
                  type="META"
                  height="16"
                  fill-override="#eee"
                  stroke-override="#a6a6a6"
                ></tf-graph-icon>
                <span class="color-legend-value">unique substructure</span>
              </div>
            </div>
          </template>
          <template is="dom-if" if="[[_equals(colorBy, 'device')]]">
            <div>
              <template is="dom-repeat" items="[[_currentDeviceParams]]">
                <div class="color-legend-row">
                  <tf-graph-icon
                    type="META"
                    height="16"
                    fill-override="[[item.color]]"
                    stroke-override="#a6a6a6"
                  ></tf-graph-icon>
                  <span class="color-legend-value">[[item.device]]</span>
                </div>
              </template>
              <div class="color-legend-row">
                <tf-graph-icon
                  type="META"
                  height="16"
                  fill-override="#eee"
                  stroke-override="#a6a6a6"
                ></tf-graph-icon>
                <span class="color-legend-value">unknown device</span>
              </div>
            </div>
          </template>
          <template is="dom-if" if="[[_equals(colorBy, 'xla_cluster')]]">
            <div>
              <template is="dom-repeat" items="[[_currentXlaClusterParams]]">
                <div class="color-legend-row">
                  <svg>
                    <use
                      xmlns:xlink="http://www.w3.org/1999/xlink"
                      xlink:href="#unfilled-rect"
                      x="0"
                      y="0"
                      style="fill:[[item.color]]"
                    ></use>
                  </svg>
                  <span class="color-legend-value">[[item.xla_cluster]]</span>
                </div>
              </template>
              <div class="color-legend-row">
                <svg>
                  <use
                    xmlns:xlink="http://www.w3.org/1999/xlink"
                    xlink:href="#grey-rect"
                    x="0"
                    y="0"
                  ></use>
                </svg>
                <span class="color-legend-value">unknown XLA cluster</span>
              </div>
            </div>
          </template>
          <template is="dom-if" if="[[_equals(colorBy, 'op_compatibility')]]">
            <div class="color-text">
              <div class="color-legend-row">
                <tf-graph-icon
                  type="OP"
                  height="16"
                  fill-override="#0f9d58"
                  stroke-override="#ccc"
                ></tf-graph-icon>
                <span class="color-legend-value">Valid Op</span>
              </div>
              <div class="color-legend-row">
                <tf-graph-icon
                  type="OP"
                  height="16"
                  fill-override="#db4437"
                  stroke-override="#ccc"
                ></tf-graph-icon>
                <span class="color-legend-value">Invalid Op</span>
              </div>
            </div>
          </template>
          <template is="dom-if" if="[[_statsNotNull(stats)]]">
            <div class="color-legend-row">
              <tf-graph-icon type="META" height="16" faded></tf-graph-icon>
              <span class="color-legend-value">unused substructure</span>
            </div>
          </template>
        </div>

        <!-- Common legend items -->
        <div>
          <table>
            <tbody>
              <tr>
                <td></td>
                <td>(* = expandable)</td>
              </tr>
              <tr>
                <td>
                  <tf-graph-icon
                    type="META"
                    height="16"
                    fill-override="#d9d9d9"
                    stroke-override="#ccc"
                  ></tf-graph-icon>
                </td>
                <td>
                  Namespace<span class="gray">*</span>
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Encapsulates a set of nodes. Namespace is hierarchical and
                      based on scope.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <tf-graph-icon type="OP" height="16"></tf-graph-icon>
                </td>
                <td>
                  OpNode
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Node that performs an operation. These nodes cannot
                      expand.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <tf-graph-icon type="SERIES" height="16"></tf-graph-icon>
                </td>
                <td>
                  Unconnected series<span class="gray">*</span>
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Sequence of numbered nodes that are not connected to each
                      other.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <tf-graph-icon
                    type="SERIES"
                    height="16"
                    vertical
                  ></tf-graph-icon>
                </td>
                <td>
                  Connected series<span class="gray">*</span>
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Sequence of numbered nodes that are connected to each
                      other.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <svg class="icon">
                    <circle
                      fill="white"
                      stroke="#848484"
                      cx="10"
                      cy="10"
                      r="5"
                    ></circle>
                  </svg>
                </td>
                <td>
                  Constant
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Node that outputs a constant value.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <tf-graph-icon type="SUMMARY" height="20"></tf-graph-icon>
                </td>
                <td>
                  Summary
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Node that collects data for visualization within
                      TensorBoard.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <svg
                    class="icon"
                    height="15px"
                    preserveAspectRatio="xMinYMid meet"
                    viewBox="0 0 15 15"
                  >
                    <defs>
                      <marker
                        id="dataflow-arrowhead-legend"
                        fill="#bbb"
                        markerWidth="10"
                        markerHeight="10"
                        refX="9"
                        refY="5"
                        orient="auto-start-reverse"
                      >
                        <path d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0"></path>
                      </marker>
                    </defs>
                    <path
                      marker-end="url(#dataflow-arrowhead-legend)"
                      stroke="#bbb"
                      d="M2 9 l 29 0"
                      stroke-linecap="round"
                    ></path>
                  </svg>
                </td>
                <td>
                  Dataflow edge
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Edge showing the data flow between operations. Edges flow
                      upwards unless arrowheads specify otherwise.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <svg
                    class="icon"
                    height="15px"
                    preserveAspectRatio="xMinYMid meet"
                    viewBox="0 0 15 15"
                  >
                    <path
                      stroke="#bbb"
                      d="M2 9 l 29 0"
                      stroke-linecap="round"
                      stroke-dasharray="2, 2"
                    ></path>
                  </svg>
                </td>
                <td>
                  Control dependency edge
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Edge showing the control dependency between operations.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <svg
                    class="icon"
                    height="15px"
                    preserveAspectRatio="xMinYMid meet"
                    viewBox="0 0 15 15"
                  >
                    <defs>
                      <marker
                        id="reference-arrowhead-legend"
                        fill="#FFB74D"
                        markerWidth="10"
                        markerHeight="10"
                        refX="9"
                        refY="5"
                        orient="auto-start-reverse"
                      >
                        <path d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0"></path>
                      </marker>
                    </defs>
                    <path
                      marker-end="url(#reference-arrowhead-legend)"
                      stroke="#FFB74D"
                      d="M2 9 l 29 0"
                      stroke-linecap="round"
                    ></path>
                  </svg>
                </td>
                <td>
                  Reference edge
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Edge showing that the outgoing operation node can mutate
                      the incoming tensor.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
            </tbody>
          </table>
        </div>
      </iron-collapse>
    </div>
  `,t([o({type:Object,observer:"_statsChanged"}),e("design:type",Object)],sYt.prototype,"stats",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],sYt.prototype,"devicesForStats",void 0),t([o({type:String,notify:!0}),e("design:type",String)],sYt.prototype,"colorBy",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],sYt.prototype,"colorByParams",void 0),t([o({type:Array,observer:"_datasetsChanged"}),e("design:type",Object)],sYt.prototype,"datasets",void 0),t([o({type:Object}),e("design:type",yWt)],sYt.prototype,"renderHierarchy",void 0),t([o({type:Object,notify:!0,readOnly:!0,computed:"_computeSelection(datasets, _selectedRunIndex, _selectedTagIndex, _selectedGraphType)"}),e("design:type",Object)],sYt.prototype,"selection",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],sYt.prototype,"selectedFile",void 0),t([o({type:Number,observer:"_selectedRunIndexChanged"}),e("design:type",Number)],sYt.prototype,"_selectedRunIndex",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],sYt.prototype,"traceInputs",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],sYt.prototype,"autoExtractNodes",void 0),t([o({type:Number,observer:"_selectedTagIndexChanged"}),e("design:type",Number)],sYt.prototype,"_selectedTagIndex",void 0),t([o({type:String}),e("design:type",String)],sYt.prototype,"_selectedGraphType",void 0),t([o({type:String,notify:!0}),e("design:type",String)],sYt.prototype,"selectedNode",void 0),t([o({type:Boolean}),e("design:type",Boolean)],sYt.prototype,"showSessionRunsDropdown",void 0),t([o({type:Boolean}),e("design:type",Boolean)],sYt.prototype,"showUploadButton",void 0),t([o({type:Boolean}),e("design:type",Boolean)],sYt.prototype,"healthPillsFeatureEnabled",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],sYt.prototype,"healthPillsToggledOn",void 0),t([o({type:Boolean}),e("design:type",Boolean)],sYt.prototype,"_legendOpened",void 0),t([s("devicesForStats"),e("design:type",Array),e("design:paramtypes",[])],sYt.prototype,"_currentDevices",null),t([s("colorByParams"),e("design:type",Array),e("design:paramtypes",[])],sYt.prototype,"_currentDeviceParams",null),t([s("colorByParams"),e("design:type",Array),e("design:paramtypes",[])],sYt.prototype,"_currentXlaClusterParams",null),t([s("colorByParams","colorBy"),e("design:type",Object),e("design:paramtypes",[])],sYt.prototype,"_currentGradientParams",null),sYt=t([i("tf-graph-controls")],sYt);class lYt{isNotTpuOp(t){return-1!=t.toLowerCase().search("cpu:")||-1!=t.toLowerCase().search("gpu:")||-1==t.toLowerCase().search("tpu")}opValid(t){return 0==t.name.search(VGt)||!t.op||!(!t.device||!this.isNotTpuOp(t.device))||!(!t.device||-1==t.device.search("TPU_SYSTEM"))||Se.exports.includes(lYt.WHITELIST,t.op)}}function cYt(t){return new Promise(((e,n)=>{fetch(t).then((t=>{t.ok?t.arrayBuffer().then(e,n):t.text().then(n,n)}))}))}lYt.WHITELIST=["Abs","Acos","Acosh","Add","AddN","AddV2","AdjustContrastv2","AdjustHue","AdjustSaturation","All","AllToAll","Angle","Any","ApproximateEqual","ArgMax","ArgMin","Asin","Asinh","Assert","AssignAddVariableOp","AssignSubVariableOp","AssignVariableOp","Atan","Atan2","Atanh","AvgPool","AvgPool3D","AvgPool3DGrad","AvgPoolGrad","BatchMatMul","BatchMatMulV2","BatchToSpace","BatchToSpaceND","BesselI0e","BesselI1e","Betainc","BiasAdd","BiasAddGrad","BiasAddV1","Bitcast","BitwiseAnd","BitwiseOr","BitwiseXor","BroadcastArgs","BroadcastGradientArgs","BroadcastTo","Bucketize","Case","Cast","Ceil","CheckNumerics","Cholesky","ClipByValue","CollectivePermute","CollectiveReduceV2","Complex","ComplexAbs","Concat","ConcatOffset","ConcatV2","Conj","ConjugateTranspose","Const","ControlTrigger","Conv2D","Conv2DBackpropFilter","Conv2DBackpropInput","Conv3D","Conv3DBackpropFilterV2","Conv3DBackpropInputV2","Cos","Cosh","Cross","CrossReplicaSum","Cumprod","Cumsum","DataFormatDimMap","DataFormatVecPermute","DepthToSpace","DepthwiseConv2dNative","DepthwiseConv2dNativeBackpropFilter","DepthwiseConv2dNativeBackpropInput","Dequantize","DeviceIndex","Diag","DiagPart","Digamma","Div","DivNoNan","DynamicStitch","Einsum","Elu","EluGrad","Empty","EmptyTensorList","EnsureShape","Equal","Erf","Erfc","Erfinv","Exp","ExpandDims","Expm1","ExtractImagePatches","FFT","FFT2D","FFT3D","FakeParam","FakeQuantWithMinMaxArgs","FakeQuantWithMinMaxArgsGradient","FakeQuantWithMinMaxVars","FakeQuantWithMinMaxVarsGradient","Fill","Floor","FloorDiv","FloorMod","FusedBatchNorm","FusedBatchNormGrad","FusedBatchNormGradV2","FusedBatchNormGradV3","FusedBatchNormV2","FusedBatchNormV3","Gather","GatherNd","GatherV2","GetItem","Greater","GreaterEqual","HSVToRGB","IFFT","IFFT2D","IFFT3D","IRFFT","IRFFT2D","IRFFT3D","Identity","IdentityN","If","Igamma","IgammaGradA","Igammac","Imag","InTopKV2","InfeedDequeue","InfeedDequeueTuple","InplaceAdd","InplaceUpdate","Inv","Invert","InvertPermutation","IsFinite","IsInf","IsNan","KthOrderStatistic","L2Loss","LRN","LRNGrad","LeakyRelu","LeakyReluGrad","LeftShift","Less","LessEqual","Lgamma","LinSpace","ListDiff","Log","Log1p","LogSoftmax","LogicalAnd","LogicalNot","LogicalOr","LowerBound","MakeUnique","MatMul","MatrixBandPart","MatrixDiag","MatrixDiagPart","MatrixDiagPartV2","MatrixDiagPartV3","MatrixDiagV2","MatrixDiagV3","MatrixInverse","MatrixSetDiag","MatrixSetDiagV2","MatrixSetDiagV3","MatrixSolve","MatrixTriangularSolve","Max","MaxPool","MaxPool3D","MaxPool3DGrad","MaxPool3DGradGrad","MaxPoolGrad","MaxPoolGradGrad","MaxPoolGradGradV2","MaxPoolGradV2","MaxPoolV2","Maximum","Mean","Min","Minimum","MirrorPad","MirrorPadGrad","Mod","Mul","MulNoNan","Multinomial","Ndtri","Neg","NextAfter","NoOp","NonMaxSuppressionV4","NotEqual","OneHot","OnesLike","OutfeedEnqueue","OutfeedEnqueueTuple","Pack","Pad","PadV2","ParallelDynamicStitch","ParameterizedTruncatedNormal","PartitionedCall","PlaceholderWithDefault","Polygamma","PopulationCount","Pow","PreventGradient","Prod","Qr","QuantizeAndDequantizeV2","QuantizeAndDequantizeV3","RFFT","RFFT2D","RFFT3D","RGBToHSV","RandomGammaGrad","RandomShuffle","RandomStandardNormal","RandomUniform","RandomUniformInt","Range","Rank","ReadVariableOp","Real","RealDiv","Reciprocal","ReciprocalGrad","Relu","Relu6","Relu6Grad","ReluGrad","Reshape","ResizeBilinear","ResizeBilinearGrad","ResizeNearestNeighbor","ResizeNearestNeighborGrad","ResourceApplyAdaMax","ResourceApplyAdadelta","ResourceApplyAdagrad","ResourceApplyAdagradDA","ResourceApplyAdagradV2","ResourceApplyAdam","ResourceApplyAddSign","ResourceApplyCenteredRMSProp","ResourceApplyFtrl","ResourceApplyFtrlV2","ResourceApplyGradientDescent","ResourceApplyKerasMomentum","ResourceApplyMomentum","ResourceApplyPowerSign","ResourceApplyProximalAdagrad","ResourceApplyProximalGradientDescent","ResourceApplyRMSProp","ResourceGather","ResourceScatterAdd","ResourceScatterDiv","ResourceScatterMax","ResourceScatterMin","ResourceScatterMul","ResourceScatterNdAdd","ResourceScatterNdSub","ResourceScatterNdUpdate","ResourceScatterSub","ResourceScatterUpdate","ResourceStridedSliceAssign","Reverse","ReverseSequence","ReverseV2","RightShift","Rint","RngReadAndSkip","RngSkip","Roll","Round","Rsqrt","RsqrtGrad","ScatterNd","Select","SelectV2","SelfAdjointEigV2","Selu","SeluGrad","Shape","ShapeN","Sigmoid","SigmoidGrad","Sign","Sin","Sinh","Size","Slice","Snapshot","Softmax","SoftmaxCrossEntropyWithLogits","Softplus","SoftplusGrad","Softsign","SoftsignGrad","SpaceToBatch","SpaceToBatchND","SpaceToDepth","SparseMatMul","SparseSoftmaxCrossEntropyWithLogits","SparseToDense","Split","SplitV","Sqrt","SqrtGrad","Square","SquaredDifference","Squeeze","StackCloseV2","StackPopV2","StackPushV2","StackV2","StatefulPartitionedCall","StatefulStandardNormalV2","StatefulTruncatedNormal","StatefulUniform","StatefulUniformFullInt","StatefulUniformInt","StatelessCase","StatelessIf","State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this._requestManager.request(_r().pluginRoute("graphs","/info"))}_fetchHealthPills(t,e){const n={node_names:JSON.stringify(t),run:"__debugger_data__"};void 0!==e&&(n.step=e);const i=_r().pluginRoute("debugger","/health_pills");return this._requestManager.request(i,n)}_fetchDebuggerNumericsAlerts(){return this._requestManager.request(_r().pluginRoute("debugger","/numerics_alert_report"))}_graphUrl(t,e,n){return _r().pluginRoute("graphs","/graph",new URLSearchParams({run:t,limit_attr_size:e,large_attrs_key:n}))}_shouldRequestHealthPills(){return this._debuggerDataEnabled&&this.healthPillsToggledOn&&this._renderHierarchy&&this._datasetsState(this._datasetsFetched,this._datasets,"PRESENT")}_maybeInitializeDashboard(){!this._initialized&&this._isAttached&&(this.set("_compatibilityProvider",new lYt),this._initialized=!0,this._fetchDataset().then((t=>{const e=Object.keys(t);this._datasets=e.sort(nr).map((e=>{const n=t[e],i=Object.keys(n.tags).sort(nr).map((t=>n.tags[t])).map((({tag:t,conceptual_graph:e,op_graph:n,profile:i})=>({tag:t,displayName:t,conceptualGraph:e,opGraph:n,profile:i})));return{name:e,tags:n.run_graph?[{tag:null,displayName:"Default",conceptualGraph:!1,opGraph:!0,profile:!1},...i]:i}})),this._datasetsFetched=!0})))}_determineSelectedDataset(){var t=this._datasetsFetched,e=this._datasets,n=this.run;if(!n)return void this.set("_selectedDataset",0);const i=e.findIndex((t=>t.name===n));if(-1!==i)this.set("_selectedDataset",i);else if(t){const t=this.$$("#error-dialog");t.textContent=`No dataset named "${n}" could be found.`,t.open()}}_updateSelectedDatasetName(){var t=this._datasets,e=this._selectedDataset;this._datasetsFetched&&(t.length<=e||this.set("run",t[e].name))}_requestHealthPills(){this.set("_areHealthPillsLoading",!0);var 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          selection="[[_selection]]"
          selected-file="[[_selectedFile]]"
          out-graph-hierarchy="{{_graphHierarchy}}"
          out-graph="{{_graph}}"
          out-stats="{{_stats}}"
          progress="{{_progress}}"
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            <a href="https://www.tensorflow.org/tensorboard/graphs"
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            >.
          </p>

          <p>
            If you’re new to using TensorBoard, and want to find out how to add
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              >README</a
            >
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            <a
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    <div id="tooltip"><span></span></div>
    <svg id="svg">
      <g>
        <g class="axis x"></g>
        <g class="axis y"></g>
        <g class="axis y slice"></g>
        <g class="stage">
          <rect class="background"></rect>
        </g>
        <g class="x-axis-hover"></g>
        <g class="y-axis-hover"></g>
        <g class="y-slice-axis-hover"></g>
      </g>
    </svg>

    <style>
      :host {
        color: #aaa;
        display: flex;
        flex-direction: column;
        flex-grow: 1;
        flex-shrink: 1;
        position: relative;
        --vz-histogram-timeseries-hover-bg-color: #fff;
        --vz-histogram-timeseries-outline-color: #fff;
        --vz-histogram-timeseries-hover-outline-color: #000;
      }

      :host(.dark-mode) {
        --vz-histogram-timeseries-hover-bg-color: var(
          --primary-background-color
        );
        --vz-histogram-timeseries-outline-color: var(--paper-grey-600);
        --vz-histogram-timeseries-hover-outline-color: #fff;
      }

      svg {
        font-family: roboto, sans-serif;
        overflow: visible;
        display: block;
        width: 100%;
        flex-grow: 1;
        flex-shrink: 1;
      }

      text {
        fill: currentColor;
      }

      #tooltip {
        position: absolute;
        display: block;
        opacity: 0;
        font-weight: bold;
        font-size: 11px;
      }

      .background {
        fill-opacity: 0;
        fill: red;
      }

      .histogram {
        pointer-events: none;
      }

      .hover {
        font-size: 9px;
        dominant-baseline: middle;
        opacity: 0;
      }

      .hover circle {
        stroke: white;
        stroke-opacity: 0.5;
        stroke-width: 1px;
      }

      .hover text {
        fill: black;
        opacity: 0;
      }

      .hover.hover-closest circle {
        fill: var(--vz-histogram-timeseries-hover-outline-color) !important;
      }

      .hover.hover-closest text {
        opacity: 1;
      }

      .baseline {
        stroke: black;
        stroke-opacity: 0.1;
      }

      .outline {
        fill: none;
        stroke: var(--vz-histogram-timeseries-outline-color);
        stroke-opacity: 0.5;
      }

      .outline.outline-hover {
        stroke: var(--vz-histogram-timeseries-hover-outline-color) !important;
        stroke-opacity: 1;
      }

      .x-axis-hover,
      .y-axis-hover,
      .y-slice-axis-hover {
        pointer-events: none;
      }

      .x-axis-hover .label,
      .y-axis-hover .label,
      .y-slice-axis-hover .label {
        opacity: 0;
        font-weight: bold;
        font-size: 11px;
        text-anchor: end;
      }

      .x-axis-hover text {
        text-anchor: middle;
      }

      .y-axis-hover text,
      .y-slice-axis-hover text {
        text-anchor: start;
      }

      .x-axis-hover line,
      .y-axis-hover line,
      .y-slice-axis-hover line {
        stroke: currentColor;
      }

      .x-axis-hover rect,
      .y-axis-hover rect,
      .y-slice-axis-hover rect {
        fill: var(--vz-histogram-timeseries-hover-bg-color);
      }

      #tooltip,
      .x-axis-hover text,
      .y-axis-hover text,
      .y-slice-axis-hover text {
        color: var(--vz-histogram-timeseries-hover-outline-color);
      }

      .axis {
        font-size: 11px;
      }

      .axis path.domain {
        fill: none;
      }

      .axis .tick line {
        stroke: #ddd;
      }

      .axis.slice {
        opacity: 0;
      }

      .axis.slice .tick line {
        stroke-dasharray: 2;
      }

      .small .axis text {
        display: none;
      }
      .small .axis .tick:first-of-type text {
        display: block;
      }
      .small .axis .tick:last-of-type text {
        display: block;
      }
      .medium .axis text {
        display: none;
      }
      .medium .axis .tick:nth-child(2n + 1) text {
        display: block;
      }
      .large .axis text {
        display: none;
      }
      .large .axis .tick:nth-child(2n + 1) text {
        display: block;
      }
    </style>
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    <tf-card-heading
      tag="[[tag]]"
      run="[[run]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
      color="[[_runColor]]"
    ></tf-card-heading>
    <!--
      The main histogram that we render. Data is set directly with
      \`setSeriesData\`, not with a bound property.
    -->
    <vz-histogram-timeseries
      id="chart"
      time-property="[[timeProperty]]"
      mode="[[histogramMode]]"
      color-scale="[[_colorScaleFunction]]"
    ></vz-histogram-timeseries>
    <div style="display: flex; flex-direction: row;">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
    </div>
    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 330px;
        height: 235px;
        margin-right: 10px;
        margin-bottom: 15px;
      }
      :host([_expanded]) {
        width: 700px;
        height: 500px;
      }

      vz-histogram-timeseries {
        -moz-user-select: none;
        -webkit-user-select: none;
        will-change: transform;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }

      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      tf-card-heading {
        margin-bottom: 10px;
        width: 90%;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],vYt.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],vYt.prototype,"tag",void 0),t([o({type:Object}),e("design:type",Object)],vYt.prototype,"getDataLoadName",void 0),t([o({type:Object}),e("design:type",dr)],vYt.prototype,"requestManager",void 0),t([o({type:Object}),e("design:type",Object)],vYt.prototype,"loadDataCallback",void 0),t([o({type:Object}),e("design:type",Object)],vYt.prototype,"tagMetadata",void 0),t([o({type:String}),e("design:type",String)],vYt.prototype,"timeProperty",void 0),t([o({type:String}),e("design:type",String)],vYt.prototype,"histogramMode",void 0),t([o({type:Object}),e("design:type",Function)],vYt.prototype,"_colorScaleFunction",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],vYt.prototype,"_expanded",void 0),t([a("run","tag","requestManager"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],vYt.prototype,"_reloadOnRunTagRequestManagerChange",null),t([a("run","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],vYt.prototype,"_updateDataToLoad",null),t([s("run"),e("design:type",String),e("design:paramtypes",[])],vYt.prototype,"_runColor",null),vYt=t([i("tf-histogram-loader")],vYt);let bYt=class extends(er(ye)){constructor(){super(...arguments),this.reloadOnReady=!0,this._histogramMode="offset",this._timeProperty="step",this._restamp=!1,this._requestManager=new dr}_redrawCategoryPane(t,e){e&&t.target.querySelectorAll("tf-histogram-loader").forEach((t=>t.redraw()))}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){this._fetchTags().then((()=>{this._reloadHistograms()}))}_fetchTags(){const t=_r().pluginRoute("histograms","/tags");return this._requestManager.request(t).then((t=>{if(Se.exports.isEqual(t,this._runToTagInfo))return;const e=Se.exports.mapValues(t,(t=>Object.keys(t))),n=ar(e);this.set("_dataNotFound",0===n.length),this.set("_runToTag",e),this.set("_runToTagInfo",t),this.async((()=>{this.set("_categoriesDomReady",!0)}))}))}_reloadHistograms(){this.root.querySelectorAll("tf-histogram-loader").forEach((t=>{t.reload()}))}_shouldOpen(t){return t<=2}get _categories(){return Mr(this._runToTag,this._selectedRuns,this._tagFilter)}_tagMetadata(t,e,n){return t[e][n]}};function xYt(t){return""!==t.displayName&&void 0!==t.displayName?t.displayName:t.name}function wYt(t){if(""!==t.displayName&&void 0!==t.displayName)return t.displayName;let e=t.name.group,n=t.name.tag;return void 0===e&&(e=""),void 0===n&&(n=""),""===e?n:e+"."+n}function SYt(t,e){return e<t.hparamColumns.length?xYt(t.hparamColumns[e].hparamInfo):wYt(t.metricColumns[e-t.hparamColumns.length].metricInfo)}function MYt(t){return t.hparamColumns.length}function EYt(t){return t.metricColumns.length}function TYt(t){return MYt(t)+EYt(t)}function CYt(t,e){return t[e]}function AYt(t,e){return t.find((t=>Se.exports.isEqual(t.name,e)))}function kYt(t,e,n){return e.hparams[t.hparamColumns[n].hparamInfo.name]}function LYt(t,e,n){const i=AYt(e.metricValues,t.metricColumns[n].metricInfo.name);return void 0===i?void 0:i.value}function PYt(t,e,n){return n<t.hparamColumns.length?kYt(t,e,n):LYt(t,e,n-t.hparamColumns.length)}function NYt(t,e,n){return Ll(e,(e=>PYt(t,e,n)))}function IYt(t,e,n){let i;if(n<e.hparamInfos.length)i=t.hparamColumns.findIndex((t=>t.hparamInfo.name===e.hparamInfos[n].name));else{const r=e.metricInfos[n-e.hparamInfos.length].name;i=t.hparamColumns.length+t.metricColumns.findIndex((t=>t.metricInfo.name===r))}return console.assert(-1!==i),i}function RYt(t){return t.hparamInfos.length}function OYt(t){return t.metricInfos.length}function zYt(t,e,n){return Ll(e,(e=>FYt(t,e,n)))}function DYt(t,e){return t.find((t=>t.name===e))}function BYt(t,e,n){return e.hparams[t.hparamInfos[n].name]}function HYt(t,e,n){const i=AYt(e.metricValues,t.metricInfos[n].name);return void 0===i?void 0:i.value}function FYt(t,e,n){return n<t.hparamInfos.length?BYt(t,e,n):HYt(t,e,n-t.hparamInfos.length)}function VYt(t){return Se.exports.isNumber(t)?t.toPrecision(5):void 0===t?"":t.toString()}function UYt(t,e){return t*t+e*e}function jYt(t,e,n,i){return Math.sqrt(UYt(t-n,e-i))}function GYt(t,e,n,i,r,o){if(t<n&&e<i)return jYt(t,e,n,i);if(n<=t&&t<r&&e<i)return i-e;if(r<=t&&e<i)return jYt(t,e,r,i);if(t<n&&i<=e&&e<o)return n-t;if(n<=t&&t<r&&i<=e&&e<o)return 0;if(r<=t&&i<=e&&e<o)return t-r;if(t<n&&o<=e)return jYt(t,e,n,o);if(n<=t&&t<r&&o<=e)return e-o;if(r<=t&&o<=e)return jYt(t,e,r,o);throw"Point (x,y) must be in one of the regions defined above."}function WYt(t,e){return void 0===e?"translate("+t+")":"translate("+t+","+e+")"}function qYt(t,e,n){const i=t.get(e,t);Array.isArray(i)?t.splice.apply(t,[e,0,i.length].concat(n)):t.set(e,n)}function YYt(t){let e=0;for(let n=0;n<t.length;++n)e=31*e+t.charCodeAt(n)&4294967295;return e+Math.pow(2,31)}bYt.template=_e`
    <tf-dashboard-layout>
      <div slot="sidebar">
        <div class="settings">
          <div class="sidebar-section">
            <tf-option-selector
              id="histogramModeSelector"
              name="Histogram mode"
              selected-id="{{_histogramMode}}"
            >
              <paper-button id="overlay">overlay</paper-button>
              <paper-button id="offset">offset</paper-button>
            </tf-option-selector>
          </div>
          <div class="sidebar-section">
            <tf-option-selector
              id="timePropertySelector"
              name="Offset time axis"
              selected-id="{{_timeProperty}}"
            >
              <paper-button id="step">step</paper-button>
              <paper-button id="relative">relative</paper-button>
              <paper-button id="wall_time">wall</paper-button>
            </tf-option-selector>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No histogram data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any histogram data to your event files.
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
            >
              <template>
                <tf-histogram-loader
                  run="[[item.run]]"
                  tag="[[item.tag]]"
                  active="[[active]]"
                  tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]"
                  time-property="[[_timeProperty]]"
                  histogram-mode="[[_histogramMode]]"
                  request-manager="[[_requestManager]]"
                ></tf-histogram-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  `,t([o({type:Boolean}),e("design:type",Boolean)],bYt.prototype,"reloadOnReady",void 0),t([o({type:String}),e("design:type",String)],bYt.prototype,"_histogramMode",void 0),t([o({type:String}),e("design:type",String)],bYt.prototype,"_timeProperty",void 0),t([o({type:Array}),e("design:type",Array)],bYt.prototype,"_selectedRuns",void 0),t([o({type:Object}),e("design:type",Object)],bYt.prototype,"_runToTag",void 0),t([o({type:Object}),e("design:type",Object)],bYt.prototype,"_runToTagInfo",void 0),t([o({type:Boolean}),e("design:type",Boolean)],bYt.prototype,"_dataNotFound",void 0),t([o({type:String}),e("design:type",String)],bYt.prototype,"_tagFilter",void 0),t([o({type:Boolean}),e("design:type",Boolean)],bYt.prototype,"_restamp",void 0),t([o({type:Boolean}),e("design:type",Boolean)],bYt.prototype,"_categoriesDomReady",void 0),t([o({type:Object}),e("design:type",dr)],bYt.prototype,"_requestManager",void 0),t([s("_runToTag","_selectedRuns","_tagFilter","_categoriesDomReady"),e("design:type",Array),e("design:paramtypes",[])],bYt.prototype,"_categories",null),bYt=t([i("tf-histogram-dashboard")],bYt);var XYt=Object.freeze({__proto__:null,hparamName:xYt,metricName:wYt,schemaColumnName:SYt,numHParams:MYt,numMetrics:EYt,numColumns:TYt,hparamValueByName:CYt,metricValueByName:AYt,hparamValueByIndex:kYt,metricValueByIndex:LYt,columnValueByIndex:PYt,numericColumnExtent:NYt,getAbsoluteColumnIndex:IYt,schemaVisibleColumnName:function $Yt(t,e){return e<t.hparamInfos.length?xYt(t.hparamInfos[e]):wYt(t.metricInfos[e-t.hparamInfos.length])},numVisibleHParams:RYt,numVisibleMetrics:OYt,numVisibleColumns:function KYt(t){return RYt(t)+OYt(t)},visibleNumericColumnExtent:zYt,prettyPrintHParamValueByName:function ZYt(t,e){return VYt(CYt(t,e))},prettyPrintMetricValueByName:function JYt(t,e){return VYt(AYt(t,e))},sessionGroupWithName:DYt,hparamValueByVisibleIndex:BYt,metricValueByVisibleIndex:HYt,columnValueByVisibleIndex:FYt,prettyPrint:VYt,l2NormSquared:UYt,euclideanDist:jYt,pointToRectangleDist:GYt,translateStr:WYt,rotateStr:function QYt(t,e,n){let i="rotate("+t;return void 0!==e&&void 0!==n&&(i=i+","+e+","+n),i+=")",i},isNullOrUndefined:function tXt(t){return null==t},quadTreeVisitPointsInRect:function eXt(t,e,n,i,r,o){t.visit(((a,s,l,c,u)=>{if(void 0===a.length){do{const s=t.x()(a.data),l=t.y()(a.data);e<=s&&s<i&&n<=l&&l<r&&o(a.data)}while(a=a.next);return!0}return s>=i||c<=e||l>=r||u<=n}))},quadTreeVisitPointsInDisk:function nXt(t,e,n,i,r){t.visit(((o,a,s,l,c)=>{if(void 0===o.length){do{const a=t.x()(o.data),s=t.y()(o.data),l=jYt(e,n,a,s);l<=i&&r(o.data,l)}while(o=o.next);return!0}return GYt(e,n,a,s,l,c)>i}))},filterSet:function iXt(t,e){const n=new Set;return t.forEach((t=>{e(t)&&n.add(t)})),n},setArrayObservably:qYt,hashOfString:YYt});let rXt=class extends ye{constructor(){super(...arguments),this.orientation="horizontal"}};rXt.template=_e`
    <slot name="content"></slot>

    <style>
      :host {
        display: block;
      }

      :host slot {
        display: flex;
        height: 100%;
        width: 100%;
      }

      :host ::slotted(*) {
        flex: 0 0 auto;
      }

      :host([orientation='horizontal']) slot {
        flex-direction: row;
        overflow-x: auto;
      }

      :host([orientation='vertical']) slot {
        flex-direction: column;
        overflow-y: auto;
      }

      :host ::slotted(*:not(:last-child)) {
        border: 0 solid var(--divider-color, #ccc);
      }

      :host([orientation='vertical']) ::slotted(*:not(:last-child)) {
        border-bottom-width: 5px;
      }

      :host([orientation='horizontal']) ::slotted(*:not(:last-child)) {
        border-right-width: 5px;
      }
    </style>
  `,t([o({type:String,reflectToAttribute:!0}),e("design:type",String)],rXt.prototype,"orientation",void 0),rXt=t([i("hparams-split-layout")],rXt);let oXt=class extends(er(ye)){constructor(){super(...arguments),this.configuration={schema:{hparamColumns:[],metricColumns:[]},columnsVisibility:[],visibleSchema:{hparamInfos:[],metricInfos:[]}},this.sessionGroups=[],this.dataLoadedWithNonEmptyHparams=!1,this.dataLoadedWithEmptyHparams=!1,this._statuses=[{value:"STATUS_UNKNOWN",displayName:"Unknown",allowed:!0},{value:"STATUS_SUCCESS",displayName:"Success",allowed:!0},{value:"STATUS_FAILURE",displayName:"Failure",allowed:!0},{value:"STATUS_RUNNING",displayName:"Running",allowed:!0}],this._getExperimentResolved=new Promise((t=>{this._resolveGetExperiment=t})),this._listSessionGroupsCanceller=new XR,this._pageSizeInput={value:"100",invalid:!1},this._pageNumberInput={value:"1",invalid:!1},this._pageCountStr="?",this._hparamName=xYt,this._metricName=wYt,this._prettyPrint=VYt}reload(){this._queryServer()}_csvUrl(t,e){return this._downloadDataUrl(t,e,"csv")}_jsonUrl(t,e){return this._downloadDataUrl(t,e,"json")}_latexUrl(t,e){return this._downloadDataUrl(t,e,"latex")}_downloadDataUrl(t,e,n){return this.backend.getDownloadUrl(n,t,e.columnsVisibility)}_computeExperimentAndRelatedProps(){const t=XYt;t.isNullOrUndefined(this.backend)||t.isNullOrUndefined(this.experimentName)||this.backend.getExperiment({experimentName:this.experimentName}).then((t=>{Se.exports.isEqual(t,this._experiment)||(this.set("_experiment",t),this._computeHParams(),this._computeMetrics(),this._queryServer(),this._resolveGetExperiment())})).finally((()=>{this._computeDataFound()}))}_computeDataFound(){const t=Boolean(this._experiment&&this._experiment.hparamInfos&&this._experiment.hparamInfos.length>0&&this._experiment.metricInfos&&this._experiment.metricInfos.length>0);this.set("dataLoadedWithNonEmptyHparams",t),this.set("dataLoadedWithEmptyHparams",!t)}_computeHParams(){const t=[];this._experiment.hparamInfos.forEach(((e,n)=>{const i={info:e,displayed:n<5,filter:{}};i.info.hasOwnProperty("domainDiscrete")?(i.filter.domainDiscrete=[],i.info.domainDiscrete.forEach((t=>{i.filter.domainDiscrete.push({value:t,checked:!0})}))):"DATA_TYPE_BOOL"===i.info.type?i.filter.domainDiscrete=[{value:!1,checked:!0},{value:!0,checked:!0}]:"DATA_TYPE_FLOAT64"===i.info.type?i.filter.interval={min:{value:"",invalid:!1},max:{value:"",invalid:!1}}:"DATA_TYPE_STRING"===i.info.type?i.filter.regexp="":console.warn("unknown hparam.info.type: %s",i.info.type),t.push(i)})),this.set("_hparams",t)}_computeMetrics(){const t=[];this._experiment.metricInfos.forEach(((e,n)=>{t.push({info:e,filter:{interval:{min:{value:"",invalid:!1},max:{value:"",invalid:!1}}},displayed:n<5})})),this.set("_metrics",t)}_computeSchema(){return this._hparams&&this._metrics?{hparamColumns:this._hparams.map((t=>({hparamInfo:t.info}))),metricColumns:this._metrics.map((t=>({metricInfo:t.info})))}:{hparamColumns:[],metricColumns:[]}}_updateConfiguration(){this.debounce("_updateConfiguration",(()=>{this.configuration={schema:this._computeSchema(),columnsVisibility:this._computeColumnsVisibility(),visibleSchema:this._computeVisibleSchema()}}))}_computeColumnsVisibility(){return this._hparams&&this._metrics?this._hparams.map((t=>t.displayed)).concat(this._metrics.map((t=>t.displayed))):[]}_computeVisibleSchema(){return this._hparams&&this._metrics?{hparamInfos:this._hparams.filter((t=>t.displayed)).map((t=>t.info)),metricInfos:this._metrics.filter((t=>t.displayed)).map((t=>t.info))}:{hparamInfos:[],metricInfos:[]}}_queryServer(){this.debounce("queryServer",(()=>this._queryServerNoDebounce()),100)}_queryServerNoDebounce(){if(this._hparams&&this._metrics)return this._sendListSessionGroupsRequest().then(this._listSessionGroupsCanceller.cancellable((({value:t,cancelled:e})=>{e||(t.totalSize>=0?(this.set("_pageCountStr",String(Math.ceil(t.totalSize/+this._pageSizeInput.value))),this.set("_totalSessionGroupsCountStr",t.totalSize)):(this.set("_pageCountStr","?"),this.set("_totalSessionGroupsCountStr","Unknown")),qYt(this,"sessionGroups",t.sessionGroups))})))}_sendListSessionGroupsRequest(){const t=this._buildListSessionGroupsRequest();if(null!==t)return this.set("_sessionGroupsRequest",t),this._listSessionGroupsCanceller.cancelAll(),this.backend.listSessionGroups(t)}_buildListSessionGroupsRequest(){const t=this;let e=!0;function n(n){const i=t.get(n+".min.value");console.assert(void 0!==i);const r=""===i?"-Infinity":+i;t.set(n+".min.invalid",isNaN(r)),e=e&&!isNaN(r);const o=t.get(n+".max.value");console.assert(void 0!==o);const a=""===o?"Infinity":+o;return t.set(n+".max.invalid",isNaN(a)),e=e&&!isNaN(a),isNaN(r)||isNaN(a)?null:{minValue:r,maxValue:a}}function i(n){const i=t.get(n+".value");console.assert(void 0!==i);const r=+i,o=Number.isInteger(r)&&r>0;return t.set(n+".invalid",!o),e=e&&o,o?r:null}const r=this._statuses.filter((t=>t.allowed)).map((t=>t.value));let o=[];if(this._hparams.forEach(((t,e)=>{let i={hparam:t.info.name};if(t.filter.domainDiscrete)i.filterDiscrete=[],t.filter.domainDiscrete.forEach((t=>{t.checked&&i.filterDiscrete.push(t.value)}));else if(t.filter.interval)i.filterInterval=n("_hparams."+e+".filter.interval");else{if(!t.filter.regexp)return console.error("hparam.filter with no domainDiscrete, interval or regexp properties set: %s",t),null;i.filterRegexp=t.filter.regexp}o.push(i)})),this._metrics.forEach(((t,e)=>{let i={metric:t.info.name,filterInterval:n("_metrics."+e+".filter.interval")};o.push(i)})),void 0!==this._sortByIndex&&void 0!==this._sortDirection){if(!(this._sortByIndex in o))return console.error("No column in colParams with index sortByIndex: %s",this._sortByIndex),null;o[this._sortByIndex].order=0===this._sortDirection?"ORDER_ASC":"ORDER_DESC"}const a=i("_pageNumberInput"),s=i("_pageSizeInput");return e?{experimentName:this.experimentName,allowedStatuses:r,colParams:o,startIndex:s*(a-1),sliceSize:s}:null}_metricSortByIndex(t){return t+this._hparams.length}};oXt.template=_e`
    <hparams-split-layout orientation="vertical">
      <div slot="content" class="section hyperparameters">
        <div class="section-title">Hyperparameters</div>
        <template is="dom-repeat" items="{{_hparams}}" as="hparam">
          <div class="hparam">
            <paper-checkbox
              checked="{{hparam.displayed}}"
              class="hparam-checkbox"
            >
              [[_hparamName(hparam.info)]]
            </paper-checkbox>
            <!-- Precisely one of the templates below will be stamped.-->
            <!-- 1. A list of checkboxes -->
            <template is="dom-if" if="[[hparam.filter.domainDiscrete]]">
              <template
                is="dom-repeat"
                items="[[hparam.filter.domainDiscrete]]"
              >
                <paper-checkbox
                  checked="{{item.checked}}"
                  class="discrete-value-checkbox"
                  on-change="_queryServer"
                >
                  [[_prettyPrint(item.value)]]
                </paper-checkbox>
              </template>
            </template>
            <!-- 2. A numeric interval -->
            <template is="dom-if" if="[[hparam.filter.interval]]">
              <paper-input
                label="Min"
                value="{{hparam.filter.interval.min.value}}"
                allowed_pattern="[0-9.e\\-]"
                on-value-changed="_queryServer"
                error-message="Invalid input"
                invalid="[[hparam.filter.interval.min.invalid]]"
                placeholder="-infinity"
              >
              </paper-input>
              <paper-input
                label="Max"
                value="{{hparam.filter.interval.max.value}}"
                allowed_pattern="[0-9.e\\-]"
                on-value-changed="_queryServer"
                error-message="Invalid input"
                invalid="[[hparam.filter.interval.max.invalid]]"
                placeholder="+infinity"
              >
              </paper-input>
            </template>
            <!-- 3. A regexp -->
            <template is="dom-if" if="[[hparam.filter.regexp]]">
              <paper-input
                label="Regular expression"
                value="{{hparam.filter.regexp}}"
                on-value-changed="_queryServer"
              >
              </paper-input>
            </template>
          </div>
        </template>
      </div>
      <div slot="content" class="section metrics">
        <div class="section-title">Metrics</div>
        <template is="dom-repeat" items="{{_metrics}}" as="metric">
          <div class="metric">
            <!-- TODO(erez): Make it easier to handle a large number of
                  metrics:
                  1. Add an 'isolator' radio-button to select just one
                  metric and
                  hide all the rest
                  2. Add a 'toggle-all' button that will hide/unhide
                    all the
                  metrics.
                  Use similar logic/appearance to the run-selector of
                  scalars.-->
            <paper-checkbox
              checked="{{metric.displayed}}"
              class="metric-checkbox"
            >
              [[_metricName(metric.info)]]
            </paper-checkbox>
            <div class="inline-element">
              <paper-input
                label="Min"
                value="{{metric.filter.interval.min.value}}"
                allowed-pattern="[0-9.e\\-]"
                on-value-changed="_queryServer"
                error-message="Invalid input"
                invalid="{{metric.filter.interval.min.invalid}}"
                placeholder="-infinity"
              >
              </paper-input>
            </div>
            <div class="inline-element">
              <paper-input
                label="Max"
                allowed-pattern="[0-9.e\\-]"
                value="{{metric.filter.interval.max.value}}"
                on-value-changed="_queryServer"
                error-message="Invalid input"
                invalid="{{metric.filter.interval.max.invalid}}"
                placeholder="+infinity"
              >
              </paper-input>
            </div>
          </div>
        </template>
      </div>
      <div slot="content" class="section status">
        <div class="section-title">Status</div>
        <template is="dom-repeat" items="[[_statuses]]" as="status">
          <paper-checkbox checked="{{status.allowed}}" on-change="_queryServer">
            [[status.displayName]]
          </paper-checkbox>
        </template>
      </div>
      <div slot="content" class="section sorting">
        <div class="section-title">Sorting</div>
        <paper-dropdown-menu
          label="Sort by"
          on-selected-item-changed="_queryServer"
          horizontal-align="left"
        >
          <paper-listbox
            class="dropdown-content"
            slot="dropdown-content"
            selected="{{_sortByIndex}}"
            on-selected-item-changed="_queryServer"
          >
            <template is="dom-repeat" items="[[_hparams]]" as="hparam">
              <paper-item> [[_hparamName(hparam.info)]] </paper-item>
            </template>
            <template is="dom-repeat" items="[[_metrics]]" as="metric">
              <paper-item> [[_metricName(metric.info)]] </paper-item>
            </template>
          </paper-listbox>
        </paper-dropdown-menu>
        <paper-dropdown-menu
          label="Direction"
          on-selected-item-changed="_queryServer"
          horizontal-align="left"
        >
          <paper-listbox
            class="dropdown-content"
            slot="dropdown-content"
            selected="{{_sortDirection}}"
          >
            <paper-item>Ascending</paper-item>
            <paper-item>Descending</paper-item>
          </paper-listbox>
        </paper-dropdown-menu>
      </div>
      <div slot="content" class="section paging">
        <div class="section-title">Paging</div>
        <div>
          Number of matching session groups: [[_totalSessionGroupsCountStr]]
        </div>
        <div class="inline-element page-number-input">
          <paper-input
            label="Page #"
            value="{{_pageNumberInput.value}}"
            allowed-pattern="[0-9]"
            error-message="Invalid input"
            invalid="[[_pageNumberInput.invalid]]"
            on-value-changed="_queryServer"
          >
            <div slot="suffix" class="page-suffix">/ [[_pageCountStr]]</div>
          </paper-input>
        </div>
        <div class="inline-element page-size-input">
          <paper-input
            label="Max # of session groups per page:"
            value="{{_pageSizeInput.value}}"
            allowed-pattern="[0-9]"
            error-message="Invalid input"
            invalid="[[_pageSizeInput.invalid]]"
            on-value-changed="_queryServer"
          >
          </paper-input>
        </div>
      </div>
      <div slot="content" class="section download">
        <template is="dom-if" if="[[_sessionGroupsRequest]]">
          Download data as
          <span>
            <a
              id="csvLink"
              download="hparams_table.csv"
              href="[[_csvUrl(_sessionGroupsRequest, configuration)]]"
              >CSV</a
            >
            <a
              id="jsonLink"
              download="hparams_table.json"
              href="[[_jsonUrl(_sessionGroupsRequest, configuration)]]"
              >JSON</a
            >
            <a
              id="latexLink"
              download="hparams_table.tex"
              href="[[_latexUrl(_sessionGroupsRequest, configuration)]]"
              >LaTeX</a
            >
          </span>
        </template>
      </div>
    </hparams-split-layout>
    <style>
      .section {
        padding: 10px;
      }
      .section-title {
        display: block;
        font-weight: bold;
        text-decoration: underline;
        margin-bottom: 7px;
      }
      .discrete-value-checkbox,
      .metric-checkbox,
      .hparam-checkbox {
        display: block;
      }
      .discrete-value-checkbox {
        margin-left: 20px;
      }
      .hparam,
      .metric {
        display: block;
      }
      .inline-element {
        display: inline-block;
        width: 40%;
        margin-left: 10px;
      }
      .page-number-input {
        width: 20%;
      }
      .page-size-input {
        width: 60%;
      }
      vaadin-split-layout {
        height: 100%;
      }
      paper-listbox {
        max-height: 15em;
      }
      .page-suffix {
        white-space: nowrap;
      }
    </style>
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    <div class="control-panel">
      <!-- 'Color by' drop down menu -->
      <paper-dropdown-menu
        label="Color by"
        id="colorByDropDownMenu"
        horizontal-align="left"
      >
        <paper-listbox
          class="dropdown-content"
          slot="dropdown-content"
          selected="{{options.colorByColumnIndex}}"
          id="colorByListBox"
        >
          <template
            is="dom-repeat"
            items="[[options.columns]]"
            as="column"
            id="colorByColumnTemplate"
          >
            <paper-item disabled="[[!_isNumericColumn(column.index)]]">
              [[column.name]]
            </paper-item>
          </template>
        </paper-listbox>
      </paper-dropdown-menu>

      <!-- Columns scales -->
      <div class="columns-container">
        <!-- Scale options for each numeric feature -->
        <template is="dom-repeat" items="{{options.columns}}" as="column">
          <template is="dom-if" if="[[_isNumericColumn(column.index)]]">
            <div class="column">
              <div class="column-title">[[column.name]]</div>
              <div>
                <paper-radio-group
                  class="scale-radio-group"
                  selected="{{column.scale}}"
                >
                  <paper-radio-button name="LINEAR">
                    Linear
                  </paper-radio-button>
                  <!-- The id here is used to access this button in unit
                       tests.-->
                  <paper-radio-button
                    id="logScaleButton_[[column.name]]"
                    name="LOG"
                    disabled="[[!_allowLogScale(column, sessionGroups.*)]]"
                  >
                    Logarithmic
                  </paper-radio-button>
                  <paper-radio-button name="QUANTILE">
                    Quantile
                  </paper-radio-button>
                </paper-radio-group>
              </div>
            </div>
          </template>
        </template>
      </div>
    </div>

    <style>
      :host {
        display: block;
      }
      .control-panel {
        overflow: auto;
      }
      .column {
        flex-grow: 1;
        flex-shrink: 1;
        margin-right: 5px;
        border: solid 1px darkgray;
        padding: 3px;
      }
      .column-title {
        /* Fit every title in one line so the radio boxes align vertically. */
        white-space: nowrap;
        text-decoration: underline;
      }
      .columns-container {
        display: flex;
        flex-direction: row;
      }
      .scale-radio-group paper-radio-button {
        padding: 2px;
        display: block;
      }
      paper-listbox {
        max-height: 15em;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],aXt.prototype,"configuration",void 0),t([o({type:Array}),e("design:type",Array)],aXt.prototype,"sessionGroups",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],aXt.prototype,"options",void 0),t([a("configuration.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],aXt.prototype,"_configurationChanged",null),t([a("sessionGroups.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],aXt.prototype,"_unselectDisabledLogScales",null),aXt=t([i("tf-hparams-scale-and-color-controls")],aXt);
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
const sXt="undefined"!=typeof window&&null!=window.customElements&&void 0!==window.customElements.polyfillWrapFlushCallback,lXt=(t,e,n=null)=>{for(;e!==n;){const n=e.nextSibling;t.removeChild(e),e=n}},cXt=`{{lit-${String(Math.random()).slice(2)}}}`,uXt=`\x3c!--${cXt}--\x3e`,hXt=new RegExp(`${cXt}|${uXt}`),dXt="$lit$";class pXt{constructor(t,e){this.parts=[],this.element=e;const n=[],i=[],r=document.createTreeWalker(e.content,133,null,!1);let o=0,a=-1,s=0;const{strings:l,values:{length:c}}=t;for(;s<c;){const t=r.nextNode();if(null!==t){if(a++,1===t.nodeType){if(t.hasAttributes()){const e=t.attributes,{length:n}=e;let i=0;for(let t=0;t<n;t++)fXt(e[t].name,dXt)&&i++;for(;i-- >0;){const e=_Xt.exec(l[s])[2],n=e.toLowerCase()+dXt,i=t.getAttribute(n);t.removeAttribute(n);const r=i.split(hXt);this.parts.push({type:"attribute",index:a,name:e,strings:r}),s+=r.length-1}}"TEMPLATE"===t.tagName&&(i.push(t),r.currentNode=t.content)}else if(3===t.nodeType){const e=t.data;if(e.indexOf(cXt)>=0){const i=t.parentNode,r=e.split(hXt),o=r.length-1;for(let e=0;e<o;e++){let n,o=r[e];if(""===o)n=gXt();else{const t=_Xt.exec(o);null!==t&&fXt(t[2],dXt)&&(o=o.slice(0,t.index)+t[1]+t[2].slice(0,-dXt.length)+t[3]),n=document.createTextNode(o)}i.insertBefore(n,t),this.parts.push({type:"node",index:++a})}""===r[o]?(i.insertBefore(gXt(),t),n.push(t)):t.data=r[o],s+=o}}else if(8===t.nodeType)if(t.data===cXt){const e=t.parentNode;null!==t.previousSibling&&a!==o||(a++,e.insertBefore(gXt(),t)),o=a,this.parts.push({type:"node",index:a}),null===t.nextSibling?t.data="":(n.push(t),a--),s++}else{let e=-1;for(;-1!==(e=t.data.indexOf(cXt,e+1));)this.parts.push({type:"node",index:-1}),s++}}else r.currentNode=i.pop()}for(const t of n)t.parentNode.removeChild(t)}}const fXt=(t,e)=>{const n=t.length-e.length;return n>=0&&t.slice(n)===e},mXt=t=>-1!==t.index,gXt=()=>document.createComment(""),_Xt=/([ \x09\x0a\x0c\x0d])([^\0-\x1F\x7F-\x9F "'>=/]+)([ \x09\x0a\x0c\x0d]*=[ \x09\x0a\x0c\x0d]*(?:[^ \x09\x0a\x0c\x0d"'`<>=]*|"[^"]*|'[^']*))$/;function yXt(t,e){const{element:{content:n},parts:i}=t,r=document.createTreeWalker(n,133,null,!1);let o=bXt(i),a=i[o],s=-1,l=0;const c=[];let u=null;for(;r.nextNode();){s++;const t=r.currentNode;for(t.previousSibling===u&&(u=null),e.has(t)&&(c.push(t),null===u&&(u=t)),null!==u&&l++;void 0!==a&&a.index===s;)a.index=null!==u?-1:a.index-l,o=bXt(i,o),a=i[o]}c.forEach((t=>t.parentNode.removeChild(t)))}const vXt=t=>{let e=11===t.nodeType?0:1;const n=document.createTreeWalker(t,133,null,!1);for(;n.nextNode();)e++;return e},bXt=(t,e=-1)=>{for(let n=e+1;n<t.length;n++)if(mXt(t[n]))return n;return-1},xXt=new WeakMap,wXt={},SXt={};
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
class MXt{constructor(t,e,n){this.__parts=[],this.template=t,this.processor=e,this.options=n}update(t){let e=0;for(const n of this.__parts)void 0!==n&&n.setValue(t[e]),e++;for(const t of this.__parts)void 0!==t&&t.commit()}_clone(){const t=sXt?this.template.element.content.cloneNode(!0):document.importNode(this.template.element.content,!0),e=[],n=this.template.parts,i=document.createTreeWalker(t,133,null,!1);let r,o=0,a=0,s=i.nextNode();for(;o<n.length;)if(r=n[o],mXt(r)){for(;a<r.index;)a++,"TEMPLATE"===s.nodeName&&(e.push(s),i.currentNode=s.content),null===(s=i.nextNode())&&(i.currentNode=e.pop(),s=i.nextNode());if("node"===r.type){const t=this.processor.handleTextExpression(this.options);t.insertAfterNode(s.previousSibling),this.__parts.push(t)}else this.__parts.push(...this.processor.handleAttributeExpressions(s,r.name,r.strings,this.options));o++}else this.__parts.push(void 0),o++;return sXt&&(document.adoptNode(t),customElements.upgrade(t)),t}}
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */const EXt=window.trustedTypes&&trustedTypes.createPolicy("lit-html",{createHTML:t=>t}),TXt=` ${cXt} `;class CXt{constructor(t){this.value=void 0,this.__pendingValue=void 0,this.options=t}appendInto(t){this.startNode=t.appendChild(gXt()),this.endNode=t.appendChild(gXt())}insertAfterNode(t){this.startNode=t,this.endNode=t.nextSibling}appendIntoPart(t){t.__insert(this.startNode=gXt()),t.__insert(this.endNode=gXt())}insertAfterPart(t){t.__insert(this.startNode=gXt()),this.endNode=t.endNode,t.endNode=this.startNode}setValue(t){this.__pendingValue=t}commit(){if(null===this.startNode.parentNode)return;for(;"function"==typeof(t=this.__pendingValue)&&xXt.has(t);){const t=this.__pendingValue;this.__pendingValue=wXt,t(this)}var t;const e=this.__pendingValue;e!==wXt&&((t=>null===t||!("object"==typeof t||"function"==typeof t))(e)?e!==this.value&&this.__commitText(e):e instanceof class{constructor(t,e,n,i){this.strings=t,this.values=e,this.type=n,this.processor=i}getHTML(){const t=this.strings.length-1;let e="",n=!1;for(let i=0;i<t;i++){const t=this.strings[i],r=t.lastIndexOf("\x3c!--");n=(r>-1||n)&&-1===t.indexOf("--\x3e",r+1);const o=_Xt.exec(t);e+=null===o?t+(n?TXt:uXt):t.substr(0,o.index)+o[1]+o[2]+dXt+o[3]+cXt}return e+=this.strings[t],e}getTemplateElement(){const t=document.createElement("template");let e=this.getHTML();return void 0!==EXt&&(e=EXt.createHTML(e)),t.innerHTML=e,t}}
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */?this.__commitTemplateResult(e):e instanceof Node?this.__commitNode(e):(t=>Array.isArray(t)||!(!t||!t[Symbol.iterator]))(e)?this.__commitIterable(e):e===SXt?(this.value=SXt,this.clear()):this.__commitText(e))}__insert(t){this.endNode.parentNode.insertBefore(t,this.endNode)}__commitNode(t){this.value!==t&&(this.clear(),this.__insert(t),this.value=t)}__commitText(t){const e=this.startNode.nextSibling,n="string"==typeof(t=null==t?"":t)?t:String(t);e===this.endNode.previousSibling&&3===e.nodeType?e.data=n:this.__commitNode(document.createTextNode(n)),this.value=t}__commitTemplateResult(t){const e=this.options.templateFactory(t);if(this.value instanceof MXt&&this.value.template===e)this.value.update(t.values);else{const n=new MXt(e,t.processor,this.options),i=n._clone();n.update(t.values),this.__commitNode(i),this.value=n}}__commitIterable(t){Array.isArray(this.value)||(this.value=[],this.clear());const e=this.value;let n,i=0;for(const r of t)n=e[i],void 0===n&&(n=new CXt(this.options),e.push(n),0===i?n.appendIntoPart(this):n.insertAfterPart(e[i-1])),n.setValue(r),n.commit(),i++;i<e.length&&(e.length=i,this.clear(n&&n.endNode))}clear(t=this.startNode){lXt(this.startNode.parentNode,t.nextSibling,this.endNode)}}let AXt=!1;
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
function kXt(t){let e=LXt.get(t.type);void 0===e&&(e={stringsArray:new WeakMap,keyString:new Map},LXt.set(t.type,e));let n=e.stringsArray.get(t.strings);if(void 0!==n)return n;const i=t.strings.join(cXt);return n=e.keyString.get(i),void 0===n&&(n=new pXt(t,t.getTemplateElement()),e.keyString.set(i,n)),e.stringsArray.set(t.strings,n),n}(()=>{try{const t={get capture(){return AXt=!0,!1}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){}})();const LXt=new Map,PXt=new WeakMap;
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
"undefined"!=typeof window&&(window.litHtmlVersions||(window.litHtmlVersions=[])).push("1.4.1");
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */const NXt=(t,e)=>`${t}--${e}`;let IXt=!0;void 0===window.ShadyCSS?IXt=!1:void 0===window.ShadyCSS.prepareTemplateDom&&(console.warn("Incompatible ShadyCSS version detected. Please update to at least @webcomponents/webcomponentsjs@2.0.2 and @webcomponents/shadycss@1.3.1."),IXt=!1);const RXt=t=>e=>{const n=NXt(e.type,t);let i=LXt.get(n);void 0===i&&(i={stringsArray:new WeakMap,keyString:new Map},LXt.set(n,i));let r=i.stringsArray.get(e.strings);if(void 0!==r)return r;const o=e.strings.join(cXt);if(r=i.keyString.get(o),void 0===r){const n=e.getTemplateElement();IXt&&window.ShadyCSS.prepareTemplateDom(n,t),r=new pXt(e,n),i.keyString.set(o,r)}return i.stringsArray.set(e.strings,r),r},OXt=["html","svg"],zXt=new Set;window.JSCompiler_renameProperty=(t,e)=>t;const DXt={toAttribute(t,e){switch(e){case Boolean:return t?"":null;case Object:case Array:return null==t?t:JSON.stringify(t)}return t},fromAttribute(t,e){switch(e){case Boolean:return null!==t;case Number:return null===t?null:Number(t);case Object:case Array:return JSON.parse(t)}return t}},BXt=(t,e)=>e!==t&&(e==e||t==t),HXt={attribute:!0,type:String,converter:DXt,reflect:!1,hasChanged:BXt};class FXt extends HTMLElement{constructor(){super(),this.initialize()}static get observedAttributes(){this.finalize();const t=[];return this._classProperties.forEach(((e,n)=>{const i=this._attributeNameForProperty(n,e);void 0!==i&&(this._attributeToPropertyMap.set(i,n),t.push(i))})),t}static _ensureClassProperties(){if(!this.hasOwnProperty(JSCompiler_renameProperty("_classProperties",this))){this._classProperties=new Map;const t=Object.getPrototypeOf(this)._classProperties;void 0!==t&&t.forEach(((t,e)=>this._classProperties.set(e,t)))}}static createProperty(t,e=HXt){if(this._ensureClassProperties(),this._classProperties.set(t,e),e.noAccessor||this.prototype.hasOwnProperty(t))return;const n="symbol"==typeof t?Symbol():`__${t}`,i=this.getPropertyDescriptor(t,n,e);void 0!==i&&Object.defineProperty(this.prototype,t,i)}static getPropertyDescriptor(t,e,n){return{get(){return this[e]},set(i){const r=this[t];this[e]=i,this.requestUpdateInternal(t,r,n)},configurable:!0,enumerable:!0}}static getPropertyOptions(t){return this._classProperties&&this._classProperties.get(t)||HXt}static finalize(){const t=Object.getPrototypeOf(this);if(t.hasOwnProperty("finalized")||t.finalize(),this.finalized=!0,this._ensureClassProperties(),this._attributeToPropertyMap=new Map,this.hasOwnProperty(JSCompiler_renameProperty("properties",this))){const t=this.properties,e=[...Object.getOwnPropertyNames(t),..."function"==typeof Object.getOwnPropertySymbols?Object.getOwnPropertySymbols(t):[]];for(const n of e)this.createProperty(n,t[n])}}static _attributeNameForProperty(t,e){const n=e.attribute;return!1===n?void 0:"string"==typeof n?n:"string"==typeof t?t.toLowerCase():void 0}static _valueHasChanged(t,e,n=BXt){return n(t,e)}static _propertyValueFromAttribute(t,e){const n=e.converter||DXt,i="function"==typeof n?n:n.fromAttribute;return i?i(t,e.type):t}static _propertyValueToAttribute(t,e){if(void 0===e.reflect)return;const n=e.converter;return(n&&n.toAttribute||DXt.toAttribute)(t,e.type)}initialize(){this._updateState=0,this._updatePromise=new Promise((t=>this._enableUpdatingResolver=t)),this._changedProperties=new Map,this._saveInstanceProperties(),this.requestUpdateInternal()}_saveInstanceProperties(){this.constructor._classProperties.forEach(((t,e)=>{if(this.hasOwnProperty(e)){const t=this[e];delete this[e],this._instanceProperties||(this._instanceProperties=new Map),this._instanceProperties.set(e,t)}}))}_applyInstanceProperties(){this._instanceProperties.forEach(((t,e)=>this[e]=t)),this._instanceProperties=void 0}connectedCallback(){this.enableUpdating()}enableUpdating(){void 0!==this._enableUpdatingResolver&&(this._enableUpdatingResolver(),this._enableUpdatingResolver=void 0)}disconnectedCallback(){}attributeChangedCallback(t,e,n){e!==n&&this._attributeToProperty(t,n)}_propertyToAttribute(t,e,n=HXt){const i=this.constructor,r=i._attributeNameForProperty(t,n);if(void 0!==r){const t=i._propertyValueToAttribute(e,n);if(void 0===t)return;this._updateState=8|this._updateState,null==t?this.removeAttribute(r):this.setAttribute(r,t),this._updateState=-9&this._updateState}}_attributeToProperty(t,e){if(8&this._updateState)return;const n=this.constructor,i=n._attributeToPropertyMap.get(t);if(void 0!==i){const t=n.getPropertyOptions(i);this._updateState=16|this._updateState,this[i]=n._propertyValueFromAttribute(e,t),this._updateState=-17&this._updateState}}requestUpdateInternal(t,e,n){let i=!0;if(void 0!==t){const r=this.constructor;n=n||r.getPropertyOptions(t),r._valueHasChanged(this[t],e,n.hasChanged)?(this._changedProperties.has(t)||this._changedProperties.set(t,e),!0!==n.reflect||16&this._updateState||(void 0===this._reflectingProperties&&(this._reflectingProperties=new Map),this._reflectingProperties.set(t,n))):i=!1}!this._hasRequestedUpdate&&i&&(this._updatePromise=this._enqueueUpdate())}requestUpdate(t,e){return this.requestUpdateInternal(t,e),this.updateComplete}async _enqueueUpdate(){this._updateState=4|this._updateState;try{await this._updatePromise}catch(t){}const t=this.performUpdate();return null!=t&&await t,!this._hasRequestedUpdate}get _hasRequestedUpdate(){return 4&this._updateState}get hasUpdated(){return 1&this._updateState}performUpdate(){if(!this._hasRequestedUpdate)return;this._instanceProperties&&this._applyInstanceProperties();let t=!1;const e=this._changedProperties;try{t=this.shouldUpdate(e),t?this.update(e):this._markUpdated()}catch(e){throw t=!1,this._markUpdated(),e}t&&(1&this._updateState||(this._updateState=1|this._updateState,this.firstUpdated(e)),this.updated(e))}_markUpdated(){this._changedProperties=new Map,this._updateState=-5&this._updateState}get updateComplete(){return this._getUpdateComplete()}_getUpdateComplete(){return this.getUpdateComplete()}getUpdateComplete(){return this._updatePromise}shouldUpdate(t){return!0}update(t){void 0!==this._reflectingProperties&&this._reflectingProperties.size>0&&(this._reflectingProperties.forEach(((t,e)=>this._propertyToAttribute(e,this[e],t))),this._reflectingProperties=void 0),this._markUpdated()}updated(t){}firstUpdated(t){}}FXt.finalized=!0;
/**
    @license
    Copyright (c) 2019 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const VXt=window.ShadowRoot&&(void 0===window.ShadyCSS||window.ShadyCSS.nativeShadow)&&"adoptedStyleSheets"in Document.prototype&&"replace"in CSSStyleSheet.prototype,UXt=Symbol();class jXt{constructor(t,e){if(e!==UXt)throw new Error("CSSResult is not constructable. Use `unsafeCSS` or `css` instead.");this.cssText=t}get styleSheet(){return void 0===this._styleSheet&&(VXt?(this._styleSheet=new CSSStyleSheet,this._styleSheet.replaceSync(this.cssText)):this._styleSheet=null),this._styleSheet}toString(){return this.cssText}}const GXt=(t,...e)=>{const n=e.reduce(((e,n,i)=>e+(t=>{if(t instanceof jXt)return t.cssText;if("number"==typeof t)return t;throw new Error(`Value passed to 'css' function must be a 'css' function result: ${t}. Use 'unsafeCSS' to pass non-literal values, but\n            take care to ensure page security.`)})(n)+t[i+1]),t[0]);return new jXt(n,UXt)};
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
(window.litElementVersions||(window.litElementVersions=[])).push("2.5.1");const WXt={};class qXt extends FXt{static getStyles(){return this.styles}static _getUniqueStyles(){if(this.hasOwnProperty(JSCompiler_renameProperty("_styles",this)))return;const t=this.getStyles();if(Array.isArray(t)){const e=(t,n)=>t.reduceRight(((t,n)=>Array.isArray(n)?e(n,t):(t.add(n),t)),n),n=e(t,new Set),i=[];n.forEach((t=>i.unshift(t))),this._styles=i}else this._styles=void 0===t?[]:[t];this._styles=this._styles.map((t=>t instanceof CSSStyleSheet&&!VXt?(t=>new jXt(String(t),UXt))(Array.prototype.slice.call(t.cssRules).reduce(((t,e)=>t+e.cssText),"")):t))}initialize(){super.initialize(),this.constructor._getUniqueStyles(),this.renderRoot=this.createRenderRoot(),window.ShadowRoot&&this.renderRoot instanceof window.ShadowRoot&&this.adoptStyles()}createRenderRoot(){return this.attachShadow(this.constructor.shadowRootOptions)}adoptStyles(){const t=this.constructor._styles;0!==t.length&&(void 0===window.ShadyCSS||window.ShadyCSS.nativeShadow?VXt?this.renderRoot.adoptedStyleSheets=t.map((t=>t instanceof CSSStyleSheet?t:t.styleSheet)):this._needsShimAdoptedStyleSheets=!0:window.ShadyCSS.ScopingShim.prepareAdoptedCssText(t.map((t=>t.cssText)),this.localName))}connectedCallback(){super.connectedCallback(),this.hasUpdated&&void 0!==window.ShadyCSS&&window.ShadyCSS.styleElement(this)}update(t){const e=this.render();super.update(t),e!==WXt&&this.constructor.render(e,this.renderRoot,{scopeName:this.localName,eventContext:this}),this._needsShimAdoptedStyleSheets&&(this._needsShimAdoptedStyleSheets=!1,this.constructor._styles.forEach((t=>{const e=document.createElement("style");e.textContent=t.cssText,this.renderRoot.appendChild(e)})))}render(){return WXt}}qXt.finalized=!0,qXt.render=(t,e,n)=>{if(!n||"object"!=typeof n||!n.scopeName)throw new Error("The `scopeName` option is required.");const i=n.scopeName,r=PXt.has(e),o=IXt&&11===e.nodeType&&!!e.host,a=o&&!zXt.has(i),s=a?document.createDocumentFragment():e;if(((t,e,n)=>{let i=PXt.get(e);void 0===i&&(lXt(e,e.firstChild),PXt.set(e,i=new CXt(Object.assign({templateFactory:kXt},n))),i.appendInto(e)),i.setValue(t),i.commit()})(t,s,Object.assign({templateFactory:RXt(i)},n)),a){const t=PXt.get(s);PXt.delete(s),((t,e,n)=>{zXt.add(t);const i=n?n.element:document.createElement("template"),r=e.querySelectorAll("style"),{length:o}=r;if(0===o)return void window.ShadyCSS.prepareTemplateStyles(i,t);const a=document.createElement("style");for(let t=0;t<o;t++){const e=r[t];e.parentNode.removeChild(e),a.textContent+=e.textContent}(t=>{OXt.forEach((e=>{const n=LXt.get(NXt(e,t));void 0!==n&&n.keyString.forEach((t=>{const{element:{content:e}}=t,n=new Set;Array.from(e.querySelectorAll("style")).forEach((t=>{n.add(t)})),yXt(t,n)}))}))})(t);const s=i.content;n?(function l(t,e,n=null){const{element:{content:i},parts:r}=t;if(null==n)return void i.appendChild(e);const o=document.createTreeWalker(i,133,null,!1);let a=bXt(r),s=0,l=-1;for(;o.nextNode();)for(l++,o.currentNode===n&&(s=vXt(e),n.parentNode.insertBefore(e,n));-1!==a&&r[a].index===l;){if(s>0){for(;-1!==a;)r[a].index+=s,a=bXt(r,a);return}a=bXt(r,a)}}
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */)(n,a,s.firstChild):s.insertBefore(a,s.firstChild),window.ShadyCSS.prepareTemplateStyles(i,t);const c=s.querySelector("style");if(window.ShadyCSS.nativeShadow&&null!==c)e.insertBefore(c.cloneNode(!0),e.firstChild);else if(n){s.insertBefore(a,s.firstChild);const t=new Set;t.add(a),yXt(n,t)}})(i,s,t.value instanceof MXt?t.value.template:void 0),lXt(e,e.firstChild),e.appendChild(s),PXt.set(e,t)}!r&&o&&window.ShadyCSS.styleElement(e.host)},qXt.shadowRootOptions={mode:"open"};let YXt=0;const XXt={},$Xt=(t,e,n)=>{const i=n&&n.moduleId||"custom-style-module-"+YXt++;Array.isArray(e)||(e=e?[e]:[]),e.forEach((t=>{if(!(t instanceof jXt))throw new Error("An item in styles is not of type CSSResult. Use `unsafeCSS` or `css`.");if(!XXt[t]){const e=document.createElement("dom-module");e.innerHTML=`\n        <template>\n          <style>${t.toString()}</style>\n        </template>\n      `;const n="custom-style-module-"+YXt++;e.register(n),XXt[t]=n}}));const r=document.createElement("dom-module");if(t){const e=customElements.get(t);e&&Object.prototype.hasOwnProperty.call(e,"__finalized")&&console.warn(`The custom element definition for "${t}"\n      was finalized before a style module was registered.\n      Make sure to add component specific style modules before\n      importing the corresponding custom element.`),r.setAttribute("theme-for",t)}r.innerHTML=`\n    <template>\n      ${(n&&n.include||[]).map((t=>`<style include=${t}></style>`))}\n      ${e.map((t=>`<style include=${XXt[t]}></style>`))}\n    </template>\n  `,r.register(i)};
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
class KXt extends HTMLElement{static get version(){return"20.0.2"}}customElements.define("vaadin-lumo-styles",KXt);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const ZXt=GXt`
  :host {
    /* Base (background) */
    --lumo-base-color: #fff;

    /* Tint */
    --lumo-tint-5pct: hsla(0, 0%, 100%, 0.3);
    --lumo-tint-10pct: hsla(0, 0%, 100%, 0.37);
    --lumo-tint-20pct: hsla(0, 0%, 100%, 0.44);
    --lumo-tint-30pct: hsla(0, 0%, 100%, 0.5);
    --lumo-tint-40pct: hsla(0, 0%, 100%, 0.57);
    --lumo-tint-50pct: hsla(0, 0%, 100%, 0.64);
    --lumo-tint-60pct: hsla(0, 0%, 100%, 0.7);
    --lumo-tint-70pct: hsla(0, 0%, 100%, 0.77);
    --lumo-tint-80pct: hsla(0, 0%, 100%, 0.84);
    --lumo-tint-90pct: hsla(0, 0%, 100%, 0.9);
    --lumo-tint: #fff;

    /* Shade */
    --lumo-shade-5pct: hsla(214, 61%, 25%, 0.05);
    --lumo-shade-10pct: hsla(214, 57%, 24%, 0.1);
    --lumo-shade-20pct: hsla(214, 53%, 23%, 0.16);
    --lumo-shade-30pct: hsla(214, 50%, 22%, 0.26);
    --lumo-shade-40pct: hsla(214, 47%, 21%, 0.38);
    --lumo-shade-50pct: hsla(214, 45%, 20%, 0.5);
    --lumo-shade-60pct: hsla(214, 43%, 19%, 0.61);
    --lumo-shade-70pct: hsla(214, 42%, 18%, 0.72);
    --lumo-shade-80pct: hsla(214, 41%, 17%, 0.83);
    --lumo-shade-90pct: hsla(214, 40%, 16%, 0.94);
    --lumo-shade: hsl(214, 35%, 15%);

    /* Contrast */
    --lumo-contrast-5pct: var(--lumo-shade-5pct);
    --lumo-contrast-10pct: var(--lumo-shade-10pct);
    --lumo-contrast-20pct: var(--lumo-shade-20pct);
    --lumo-contrast-30pct: var(--lumo-shade-30pct);
    --lumo-contrast-40pct: var(--lumo-shade-40pct);
    --lumo-contrast-50pct: var(--lumo-shade-50pct);
    --lumo-contrast-60pct: var(--lumo-shade-60pct);
    --lumo-contrast-70pct: var(--lumo-shade-70pct);
    --lumo-contrast-80pct: var(--lumo-shade-80pct);
    --lumo-contrast-90pct: var(--lumo-shade-90pct);
    --lumo-contrast: var(--lumo-shade);

    /* Text */
    --lumo-header-text-color: var(--lumo-contrast);
    --lumo-body-text-color: var(--lumo-contrast-90pct);
    --lumo-secondary-text-color: var(--lumo-contrast-70pct);
    --lumo-tertiary-text-color: var(--lumo-contrast-50pct);
    --lumo-disabled-text-color: var(--lumo-contrast-30pct);

    /* Primary */
    --lumo-primary-color: hsl(214, 90%, 52%);
    --lumo-primary-color-50pct: hsla(214, 90%, 52%, 0.5);
    --lumo-primary-color-10pct: hsla(214, 90%, 52%, 0.1);
    --lumo-primary-text-color: var(--lumo-primary-color);
    --lumo-primary-contrast-color: #fff;

    /* Error */
    --lumo-error-color: hsl(3, 100%, 61%);
    --lumo-error-color-50pct: hsla(3, 100%, 60%, 0.5);
    --lumo-error-color-10pct: hsla(3, 100%, 60%, 0.1);
    --lumo-error-text-color: hsl(3, 92%, 53%);
    --lumo-error-contrast-color: #fff;

    /* Success */
    --lumo-success-color: hsl(145, 80%, 42%); /* hsl(144,82%,37%); */
    --lumo-success-color-50pct: hsla(145, 76%, 44%, 0.55);
    --lumo-success-color-10pct: hsla(145, 76%, 44%, 0.12);
    --lumo-success-text-color: hsl(145, 100%, 32%);
    --lumo-success-contrast-color: #fff;
  }
`,JXt=document.createElement("template");JXt.innerHTML=`<style>${ZXt.toString().replace(":host","html")}</style>`,document.head.appendChild(JXt.content),$Xt("",GXt`
  [theme~='dark'] {
    /* Base (background) */
    --lumo-base-color: hsl(214, 35%, 21%);

    /* Tint */
    --lumo-tint-5pct: hsla(214, 65%, 85%, 0.06);
    --lumo-tint-10pct: hsla(214, 60%, 80%, 0.14);
    --lumo-tint-20pct: hsla(214, 64%, 82%, 0.23);
    --lumo-tint-30pct: hsla(214, 69%, 84%, 0.32);
    --lumo-tint-40pct: hsla(214, 73%, 86%, 0.41);
    --lumo-tint-50pct: hsla(214, 78%, 88%, 0.5);
    --lumo-tint-60pct: hsla(214, 82%, 90%, 0.6);
    --lumo-tint-70pct: hsla(214, 87%, 92%, 0.7);
    --lumo-tint-80pct: hsla(214, 91%, 94%, 0.8);
    --lumo-tint-90pct: hsla(214, 96%, 96%, 0.9);
    --lumo-tint: hsl(214, 100%, 98%);

    /* Shade */
    --lumo-shade-5pct: hsla(214, 0%, 0%, 0.07);
    --lumo-shade-10pct: hsla(214, 4%, 2%, 0.15);
    --lumo-shade-20pct: hsla(214, 8%, 4%, 0.23);
    --lumo-shade-30pct: hsla(214, 12%, 6%, 0.32);
    --lumo-shade-40pct: hsla(214, 16%, 8%, 0.41);
    --lumo-shade-50pct: hsla(214, 20%, 10%, 0.5);
    --lumo-shade-60pct: hsla(214, 24%, 12%, 0.6);
    --lumo-shade-70pct: hsla(214, 28%, 13%, 0.7);
    --lumo-shade-80pct: hsla(214, 32%, 13%, 0.8);
    --lumo-shade-90pct: hsla(214, 33%, 13%, 0.9);
    --lumo-shade: hsl(214, 33%, 13%);

    /* Contrast */
    --lumo-contrast-5pct: var(--lumo-tint-5pct);
    --lumo-contrast-10pct: var(--lumo-tint-10pct);
    --lumo-contrast-20pct: var(--lumo-tint-20pct);
    --lumo-contrast-30pct: var(--lumo-tint-30pct);
    --lumo-contrast-40pct: var(--lumo-tint-40pct);
    --lumo-contrast-50pct: var(--lumo-tint-50pct);
    --lumo-contrast-60pct: var(--lumo-tint-60pct);
    --lumo-contrast-70pct: var(--lumo-tint-70pct);
    --lumo-contrast-80pct: var(--lumo-tint-80pct);
    --lumo-contrast-90pct: var(--lumo-tint-90pct);
    --lumo-contrast: var(--lumo-tint);

    /* Text */
    --lumo-header-text-color: var(--lumo-contrast);
    --lumo-body-text-color: var(--lumo-contrast-90pct);
    --lumo-secondary-text-color: var(--lumo-contrast-70pct);
    --lumo-tertiary-text-color: var(--lumo-contrast-50pct);
    --lumo-disabled-text-color: var(--lumo-contrast-30pct);

    /* Primary */
    --lumo-primary-color: hsl(214, 86%, 55%);
    --lumo-primary-color-50pct: hsla(214, 86%, 55%, 0.5);
    --lumo-primary-color-10pct: hsla(214, 90%, 63%, 0.1);
    --lumo-primary-text-color: hsl(214, 100%, 70%);
    --lumo-primary-contrast-color: #fff;

    /* Error */
    --lumo-error-color: hsl(3, 90%, 63%);
    --lumo-error-color-50pct: hsla(3, 90%, 63%, 0.5);
    --lumo-error-color-10pct: hsla(3, 90%, 63%, 0.1);
    --lumo-error-text-color: hsl(3, 100%, 67%);

    /* Success */
    --lumo-success-color: hsl(145, 65%, 42%);
    --lumo-success-color-50pct: hsla(145, 65%, 42%, 0.5);
    --lumo-success-color-10pct: hsla(145, 65%, 42%, 0.1);
    --lumo-success-text-color: hsl(145, 85%, 47%);
  }

  html {
    color: var(--lumo-body-text-color);
    background-color: var(--lumo-base-color);
  }

  [theme~='dark'] {
    color: var(--lumo-body-text-color);
    background-color: var(--lumo-base-color);
  }

  h1,
  h2,
  h3,
  h4,
  h5,
  h6 {
    color: var(--lumo-header-text-color);
  }

  a {
    color: var(--lumo-primary-text-color);
  }

  blockquote {
    color: var(--lumo-secondary-text-color);
  }

  code,
  pre {
    background-color: var(--lumo-contrast-10pct);
    border-radius: var(--lumo-border-radius-m);
  }
`,{moduleId:"lumo-color"}),$Xt("",GXt`
  :host {
    color: var(--lumo-body-text-color) !important;
    background-color: var(--lumo-base-color) !important;
  }
`,{moduleId:"lumo-color-legacy",include:["lumo-color"]});
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const QXt=document.createElement("template");QXt.innerHTML='\n  <style>\n    @font-face {\n      font-family: \'lumo-icons\';\n      src: url(data:application/font-woff;charset=utf-8;base64,d09GRgABAAAAABEcAAsAAAAAIiwAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAABHU1VCAAABCAAAADsAAABUIIslek9TLzIAAAFEAAAAQwAAAFZAIUuKY21hcAAAAYgAAAD4AAADrsCU8d5nbHlmAAACgAAAC2MAABd4h9To2WhlYWQAAA3kAAAAMAAAADZa/6SsaGhlYQAADhQAAAAdAAAAJAbpA35obXR4AAAONAAAABAAAACspBAAAGxvY2EAAA5EAAAAWAAAAFh55IAsbWF4cAAADpwAAAAfAAAAIAFKAXBuYW1lAAAOvAAAATEAAAIuUUJZCHBvc3QAAA/wAAABKwAAAelm8SzVeJxjYGRgYOBiMGCwY2BycfMJYeDLSSzJY5BiYGGAAJA8MpsxJzM9kYEDxgPKsYBpDiBmg4gCACY7BUgAeJxjYGS+yDiBgZWBgamKaQ8DA0MPhGZ8wGDIyAQUZWBlZsAKAtJcUxgcXjG+0mIO+p/FEMUcxDANKMwIkgMABn8MLQB4nO3SWW6DMABF0UtwCEnIPM/zhLK8LqhfXRybSP14XUYtHV9hGYQwQBNIo3cUIPkhQeM7rib1ekqnXg981XuC1qvy84lzojleh3puxL0hPjGjRU473teloEefAUNGjJkwZcacBUtWrNmwZceeA0dOnLlw5cadB09elPGhGf+j0NTI/65KfXerT6JhqKnpRKtgOpuqaTrtKjPUlqHmhto21I7pL6i6hlqY3q7qGWrfUAeGOjTUkaGODXViqFNDnRnq3FAXhro01JWhrg11Y6hbQ90Z6t5QD4Z6NNSToZ4N9WKoV0O9GerdUB+G+jTUl6GWRvkL24BkEXictVh9bFvVFb/nxvbz+7Rf/N6zHcd2bCfP+Wgc1Z9N0jpNnEL6kbRVS6HA2hQYGh9TGR1CbCqa2rXrWOkQE/sHNJgmtZvoVNZqE1B1DNHxzTQxCehUTYiJTQyENui0qSLezr3PduyQfgmRWOfde8+9551z7rnn/O4jLoJ/bRP0UaKQMLFJjpBAvphLZC3Dk0ok7WBzR2/upJs7Ryw/nfFbln/uuN/apCvwrKLrSvUqRufbm5pn0fs0w4gYxnGVP6qHnO4bWiDQGQgwtS6lm3lB3QoX1M2vwEmuzirF39y+Es2+DJ8d1pkyqBIqoze3D1+Zz4DrFoazxI8dWwMrDlZ2DMqQAR9AROsJU+2cmlTPazTco52F1xTa2a2+K8vvq92dVHmtLoPeQX/AZPRYGthDYOeZjBjKoFsVGulR3lWU95WeCK44qHU7MhWUGUKZDT3oKUcG2GWuh+EDDfUYA/jhAhl0TOsJNYSEu7mQmi3UzfXwZKA4BsVsHLXQYGgRW95uEtpJ1Vfn9XiLriRBlFEqxsDjA09yCNUoQxxwd7KWSTt2y3GTKiflqHRSoWZc3m11Wa/fJdFgXD4sSYfleJBKd8GMz7J8dZn/cGRCcKGDnA2Ge3fKzcvlnTDNthGWLXzX/WaXtUAmRgeLlHSr30r0G9UTXMb0AtmwzOoy73fkSlHZkduw/TYuU9cAD4YutPoxTTsA3797wVr4Z/1NC5zARHr4vtxJjxIfiZMhMkbWk+14BnJZKwqGZwDfswLyxWDSg11rFLJF7Nopxjd1h1/QOT+oezgfu3Yq+Hk+duf5x+40o1GTkaIgikK/IEnC6aYxCUBaZJSN4XTYFjU/YMNIKqJwhDGOCCI8FDXnXmXjtGhGJyShqjAOnBOkW2JG9S7GgYeMWAU5JzhnWmBOaOM+CKEPoqSfFDC2Unq+DLlUgUVUFFLZGJg6jtlojsdsa8kPObPuJdi5dnBdBsLJMGTWDa4t2JvtwuPo9s+Y86suv/W33QG1rAaOAUV+vx4K6f2D04PVKlC7WLSrZzAi45ZV6lIC7WoXqmRyvUqoVwrzUoVsIjeTXWQv+RH5GTlBXiB/In8ln0IbBCAFOajAJrgZYyOHWqOfUe/aHjI12R6OQo1jCgt215l+4f6XPb+0MNou0V+43n2F77tSfRb24d7zitgnKmvYHs69zugaPvBwv6ioXkb2LdL65Atw51uLkXlu1bhMMRcXSPcYoqKIRlh34lQP8/5JbuUFye4vxD6/6MxFF11C0uVLr9Ulgw44tS3pMViNLUExbycFgLIct+QDMibRimx1ydUz8FXZiuOIDBOMVX2nUZc+huNE5XUJ81uiJoiabwqaVF0uacKbau/pl4R2VW0XXlJra6boVrYG646TF5NYzwy4vjENVrDlcNpZPl8DH6XX8XWCx0mvWVZY6KFLrvsY66/zPict5FnxaNUR/juvZCM3TvD60E2W1tZizbXTPDuabcm0nbbzpWKpmA1ayBQ8giedLUM+A0kNjBjQjmuYz7YrgIXYvmF63ZLBwSXrpn9Tb9wwdd/U1H0PMQK3XcO8ul3WT7PyPPdpy0TemKxNRcJNauiXJnnUDpUppQWs4SnUIy0EESGYqJYQLGHxzaGWwVIaS6Y7mQFM8ZjYDQ3axjf61SWjU33JwOZA1pwaG1L9mzf71aHRdX1JHw6Fp0aXhNwbqyeGNg4NbdzGCBxoz4ZXjy4Nu69Zr6sDY6vMrLU5nA1P8JkbdWXJ6ERfMryvNh1JfQ9+T4dIhGvK9w3dxjBBzatsQ/MlOHVIDnYpDz6odAXlQ01t2Pa5Iafd8MMpxAeDKP0C6CjgVLT5osB6icUx01lWjXxzT/GyRF2welEM5Z/7jG3VjQ1SrNn5IbyzOG5dobB3/QHxyZvsXcoz8IoEwS7plCg+zxHQk424q9BfEpkESJbFHQusDBSWFkuBkoPO0kLKwRVYjxGXlHTcTDQMJ/H6TX9afkO7mnraTO1feTnZAXLu4cp7HAXMmNG1yeFk9TgS/NHhZR/4QoBTr/ZB+6hCgyl15Nq1UbN6nE1/ZnP1U2cizCBpvs8cJQZJ4LkYx5N/yZPAUZNQQ0V4f3BQllWrK3YRzl30dOT6RVn2upNur6woSa8CqpdT/aKnBM4o3jNur9d9xqtUT6veBEt9Ca9at+ERzEEhUkR8sa5mQ4aVvJoVeEA8zI4ei5mULXFGyU7z/6TAeYLVcpzSWZY8PYYF5yrTV60sT0+XV141vX++Wf16V2bFeGVPZXxFpkvyeKTWLlzfW0mnKxsY6Y3294/0998SCfX1blm5pbcvFGlq/r07MRAMhYIDiW5JFKWW3vdrEpCsZSJG+om7Zu/PSScZJhNkLbmW5Wsr12pWqW5zKtlwRS4bFOxUw17mCzy6lskCDl1WYOGWDYrADrMA7BDDweWWNd5koiJnR1dz+ytLP2q0SqPB1lnK2ccB7RYe4FSoPks3iB3t4txTSHctb2sy1ivk0pvHuCNm6w1f6wxv3+OCgN78LqdQnUVh7R0oTAp0zOf2rbW770Vu5C2dIyGdTnHo8zSji7dppj0USoVCz+lhRMTh53Teq9VbGfbjuSbAooSdXayY4PYHg374C6f7gl1B/DXuJ4/QXxOBdJFJspFsI3egpoWUUCjlTIFnNYNl+ZyZKmBeYKGHkD1QyDlhaKbKwKcIJqJ4TLJ2OmdY/JWXae4DdGBw8HZ7eXcgFF2zr2SoalDry5iKqoa0Puhe3hPQ2s3elTYM+MI+n3rK0KgL7/La3GeMLt6m7u912vGnvtORiIa0qBmhqVi+XW9XNBmqb8eVgKzIHfGI5bNoG7X0UCzeISmqIcO/nY8FH7U8avX9fx/ST+hx0sezPw9Qy8Mum3GWf2N4Uy/yIYGVBXbJHWIZp7dfTcptdMTr9Qmq7DaiK/ukqCL4kt4RUfS5XPnMtmT22/mQFqF7emSqtrlu8SVElxDRJrZODkpuwe0VfTfjdEp1f7A7v+fozNBXUJ/6WTuK2TtFlpFVZAZ3LcFvUi1Z2p2YT+EMAkGJVStOzLTAPg4IqWIAlzRSjOBkl2zxj3TKycpzT/MnvX3uaSMWM+gU0rkXjohhefVRMaps3/kLMSKv23lT23uxQrkQjyOJleMDsdhAnD6ZGElWZ5MjCXzCE/hkWX+WF4knzGhVOyK2eQZekV3eyo0zL8kuYWCnDCvjjhAkcTPOBDXVdoav3HVcFnQjLvtV9S2p0zA6JegPwMQxt+yFb3ll9zGlq/5dRKb3cEyQYoaNYpharJ7xCB7AWxsLY3jjZXY0XsZj0Wjwc9I6PP/dKABnCZaqHpaZEACxk4ZeLZSKNgZABl+lYQX1sJQOSX3n6r410evcoud5JeAGUXVP9H1tZOKejTq4Ono0z0erro1FrnOpohva1d/hTdtVsQdKN5W9RlT3NjD0nznyKNTgKAMfWNWcyodV0IGLPIHOF0o4JyqufaK4z6WIIzuGh3d8c8cwQg8ER+OVxyrjdm8vNuhts4LoOihGxIMuUdgzwiYN7xhh1+oZnJNuTG7gQZvu4XWZ9GAZZjGEubwePqYhtKDTH+9VQkl17/iGybsnJ+8+sKtyPrcll9ty65Zsdst/9iqpEKh7M5VdBxh3csOdNc6tW3I1uyM1PzOXegSOrLFsFNI2O27M+TF2ApnN9MUv5ud6LjxIvEQnHRzxIu4IsA9MLFkJn2tcZoZ7ON7dXe7ujrc8HrusPKamlqXwd77lQUuLpilau4PUMapueBb7irU4RoUXEYXuVuIGlRGmOp+2lNkaRPVziOqmlaZvaqG4dFgSj0jxEJWrv12IUWntmw+rfQarRE0Aph4ocI6nlUlGqs+u3/+T/ethW62PpHp2eHbZstnh/wOO95yDAHicY2BkYGAA4pmJ6QHx/DZfGbiZXwBFGGpUNzQi6P+vmacy3QJyORiYQKIANoULVXicY2BkYGAO+p8FJF8wAAHzVAZGBlSgDQBW9gNvAAAAeJxjYGBgYH4xNDAAzwQmjwAAAAAATgCaAOgBCgEsAU4BcAGaAcQB7gIaApwC6ASaBLwE1gTyBQ4FKgV6BdAF/gZEBmYGtgcYB5AIGAhSCGoI/glGCb4J2goECjwKggq4CvALUAuWC7x4nGNgZGBg0GZMYRBlAAEmIOYCQgaG/2A+AwAYlAG8AHicbZE9TsMwGIbf9A/RSggEYmHxAgtq+jN2ZGj3Dt3T1GlTOXHkuBW9AyfgEByCgTNwCA7BW/NJlVBtyd/jx+8XKwmAa3whwnFE6Ib1OBq44O6Pm6Qb4Rb5QbiNHh6FO/RD4S6eMRHu4RaaT4halzR3eBVu4Apvwk36d+EW+UO4jXt8Cnfov4W7WOBHuIen6MXsCtvPU1vWc73emcSdxIkW2tW5LdUoHp7kTJfaJV6v1PKg6v167H2mMmcLNbWl18ZYVTm71amPN95Xk8EgEx+ntoDBDgUs+siRspaoMef7rukNEriziXNuwS7Hmoe9wggxv+e55IzJMqQTeNYV00scuNbY8+YxrUfGfcaMZb/CNPQe04bT0lThbEuT0sfYhK6K/23Amf3Lx+H24hcj4GScAAAAeJxtjtlugzAQRbkJUEJIuu/7vqR8lGNPAcWx0YAb5e/LklR96EgenSufGY038PqKvf9rhgGG8BEgxA4ijBBjjAQTTLGLPezjAIc4wjFOcIoznOMCl7jCNW5wizvc4wGPeMIzXvCKN7zjAzN8eonQRWZSSaYmjvug6ase98hFltexMJmmVNmV2WBvdNgZUc+ujAWzXW3UDnu1w43asStHc8GpzAXX/py0jqTQZJTgkcxJLpaCF0lD32xNt+43tAsn29Dft02uDKS2cjGUNgsk26qK2lFthYoU27INPqmiDqg5goe0pqR5qSoqMdek/CUZFywL46rEsiImleqiqoMyt4baXlu/1GLdNFf5zbcNmdr1YUWCZe47o+zUmb/DoStbw3cVsef9ALjjiPQA) format(\'woff\');\n      font-weight: normal;\n      font-style: normal;\n    }\n\n    html {\n      --lumo-icons-align-center: "\\ea01";\n      --lumo-icons-align-left: "\\ea02";\n      --lumo-icons-align-right: "\\ea03";\n      --lumo-icons-angle-down: "\\ea04";\n      --lumo-icons-angle-left: "\\ea05";\n      --lumo-icons-angle-right: "\\ea06";\n      --lumo-icons-angle-up: "\\ea07";\n      --lumo-icons-arrow-down: "\\ea08";\n      --lumo-icons-arrow-left: "\\ea09";\n      --lumo-icons-arrow-right: "\\ea0a";\n      --lumo-icons-arrow-up: "\\ea0b";\n      --lumo-icons-bar-chart: "\\ea0c";\n      --lumo-icons-bell: "\\ea0d";\n      --lumo-icons-calendar: "\\ea0e";\n      --lumo-icons-checkmark: "\\ea0f";\n      --lumo-icons-chevron-down: "\\ea10";\n      --lumo-icons-chevron-left: "\\ea11";\n      --lumo-icons-chevron-right: "\\ea12";\n      --lumo-icons-chevron-up: "\\ea13";\n      --lumo-icons-clock: "\\ea14";\n      --lumo-icons-cog: "\\ea15";\n      --lumo-icons-cross: "\\ea16";\n      --lumo-icons-download: "\\ea17";\n      --lumo-icons-dropdown: "\\ea18";\n      --lumo-icons-edit: "\\ea19";\n      --lumo-icons-error: "\\ea1a";\n      --lumo-icons-eye: "\\ea1b";\n      --lumo-icons-eye-disabled: "\\ea1c";\n      --lumo-icons-menu: "\\ea1d";\n      --lumo-icons-minus: "\\ea1e";\n      --lumo-icons-ordered-list: "\\ea1f";\n      --lumo-icons-phone: "\\ea20";\n      --lumo-icons-photo: "\\ea21";\n      --lumo-icons-play: "\\ea22";\n      --lumo-icons-plus: "\\ea23";\n      --lumo-icons-redo: "\\ea24";\n      --lumo-icons-reload: "\\ea25";\n      --lumo-icons-search: "\\ea26";\n      --lumo-icons-undo: "\\ea27";\n      --lumo-icons-unordered-list: "\\ea28";\n      --lumo-icons-upload: "\\ea29";\n      --lumo-icons-user: "\\ea2a";\n    }\n  </style>\n',document.head.appendChild(QXt.content);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const t$t=GXt`
  :host {
    --lumo-size-xs: 1.625rem;
    --lumo-size-s: 1.875rem;
    --lumo-size-m: 2.25rem;
    --lumo-size-l: 2.75rem;
    --lumo-size-xl: 3.5rem;

    /* Icons */
    --lumo-icon-size-s: 1.25em;
    --lumo-icon-size-m: 1.5em;
    --lumo-icon-size-l: 2.25em;
    /* For backwards compatibility */
    --lumo-icon-size: var(--lumo-icon-size-m);
  }
`,e$t=document.createElement("template");e$t.innerHTML=`<style>${t$t.toString().replace(":host","html")}</style>`,document.head.appendChild(e$t.content);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const n$t=GXt`
  :host {
    /* Square */
    --lumo-space-xs: 0.25rem;
    --lumo-space-s: 0.5rem;
    --lumo-space-m: 1rem;
    --lumo-space-l: 1.5rem;
    --lumo-space-xl: 2.5rem;

    /* Wide */
    --lumo-space-wide-xs: calc(var(--lumo-space-xs) / 2) var(--lumo-space-xs);
    --lumo-space-wide-s: calc(var(--lumo-space-s) / 2) var(--lumo-space-s);
    --lumo-space-wide-m: calc(var(--lumo-space-m) / 2) var(--lumo-space-m);
    --lumo-space-wide-l: calc(var(--lumo-space-l) / 2) var(--lumo-space-l);
    --lumo-space-wide-xl: calc(var(--lumo-space-xl) / 2) var(--lumo-space-xl);

    /* Tall */
    --lumo-space-tall-xs: var(--lumo-space-xs) calc(var(--lumo-space-xs) / 2);
    --lumo-space-tall-s: var(--lumo-space-s) calc(var(--lumo-space-s) / 2);
    --lumo-space-tall-m: var(--lumo-space-m) calc(var(--lumo-space-m) / 2);
    --lumo-space-tall-l: var(--lumo-space-l) calc(var(--lumo-space-l) / 2);
    --lumo-space-tall-xl: var(--lumo-space-xl) calc(var(--lumo-space-xl) / 2);
  }
`,i$t=document.createElement("template");i$t.innerHTML=`<style>${n$t.toString().replace(":host","html")}</style>`,document.head.appendChild(i$t.content);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const r$t=GXt`
  :host {
    /* Border radius */
    --lumo-border-radius-s: 0.25em; /* Checkbox, badge, date-picker year indicator, etc */
    --lumo-border-radius-m: var(--lumo-border-radius, 0.25em); /* Button, text field, menu overlay, etc */
    --lumo-border-radius-l: 0.5em; /* Dialog, notification, etc */
    --lumo-border-radius: 0.25em; /* Deprecated */

    /* Shadow */
    --lumo-box-shadow-xs: 0 1px 4px -1px var(--lumo-shade-50pct);
    --lumo-box-shadow-s: 0 2px 4px -1px var(--lumo-shade-20pct), 0 3px 12px -1px var(--lumo-shade-30pct);
    --lumo-box-shadow-m: 0 2px 6px -1px var(--lumo-shade-20pct), 0 8px 24px -4px var(--lumo-shade-40pct);
    --lumo-box-shadow-l: 0 3px 18px -2px var(--lumo-shade-20pct), 0 12px 48px -6px var(--lumo-shade-40pct);
    --lumo-box-shadow-xl: 0 4px 24px -3px var(--lumo-shade-20pct), 0 18px 64px -8px var(--lumo-shade-40pct);

    /* Clickable element cursor */
    --lumo-clickable-cursor: default;
  }
`,o$t=document.createElement("template");o$t.innerHTML=`<style>${r$t.toString().replace(":host","html")}</style>`,document.head.appendChild(o$t.content);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const a$t=GXt`
  :host {
    /* prettier-ignore */
    --lumo-font-family: -apple-system, BlinkMacSystemFont, 'Roboto', 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';

    /* Font sizes */
    --lumo-font-size-xxs: 0.75rem;
    --lumo-font-size-xs: 0.8125rem;
    --lumo-font-size-s: 0.875rem;
    --lumo-font-size-m: 1rem;
    --lumo-font-size-l: 1.125rem;
    --lumo-font-size-xl: 1.375rem;
    --lumo-font-size-xxl: 1.75rem;
    --lumo-font-size-xxxl: 2.5rem;

    /* Line heights */
    --lumo-line-height-xs: 1.25;
    --lumo-line-height-s: 1.375;
    --lumo-line-height-m: 1.625;
  }
`,s$t=document.createElement("template");s$t.innerHTML=`<style>${a$t.toString().replace(":host","html")}</style>`,document.head.appendChild(s$t.content),$Xt("",GXt`
  html {
    font-family: var(--lumo-font-family);
    font-size: var(--lumo-font-size, var(--lumo-font-size-m));
    line-height: var(--lumo-line-height-m);
    -webkit-text-size-adjust: 100%;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
  }

  /* Can’t combine with the above selector because that doesn’t work in browsers without native shadow dom */
  :host {
    font-family: var(--lumo-font-family);
    font-size: var(--lumo-font-size, var(--lumo-font-size-m));
    line-height: var(--lumo-line-height-m);
    -webkit-text-size-adjust: 100%;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
  }

  small,
  [theme~='font-size-s'] {
    font-size: var(--lumo-font-size-s);
    line-height: var(--lumo-line-height-s);
  }

  [theme~='font-size-xs'] {
    font-size: var(--lumo-font-size-xs);
    line-height: var(--lumo-line-height-xs);
  }

  h1,
  h2,
  h3,
  h4,
  h5,
  h6 {
    font-weight: 600;
    line-height: var(--lumo-line-height-xs);
    margin-top: 1.25em;
  }

  h1 {
    font-size: var(--lumo-font-size-xxxl);
    margin-bottom: 0.75em;
  }

  h2 {
    font-size: var(--lumo-font-size-xxl);
    margin-bottom: 0.5em;
  }

  h3 {
    font-size: var(--lumo-font-size-xl);
    margin-bottom: 0.5em;
  }

  h4 {
    font-size: var(--lumo-font-size-l);
    margin-bottom: 0.5em;
  }

  h5 {
    font-size: var(--lumo-font-size-m);
    margin-bottom: 0.25em;
  }

  h6 {
    font-size: var(--lumo-font-size-xs);
    margin-bottom: 0;
    text-transform: uppercase;
    letter-spacing: 0.03em;
  }

  p,
  blockquote {
    margin-top: 0.5em;
    margin-bottom: 0.75em;
  }

  a {
    text-decoration: none;
  }

  a:hover {
    text-decoration: underline;
  }

  hr {
    display: block;
    align-self: stretch;
    height: 1px;
    border: 0;
    padding: 0;
    margin: var(--lumo-space-s) calc(var(--lumo-border-radius-m) / 2);
    background-color: var(--lumo-contrast-10pct);
  }

  blockquote {
    border-left: 2px solid var(--lumo-contrast-30pct);
  }

  b,
  strong {
    font-weight: 600;
  }

  /* RTL specific styles */

  blockquote[dir='rtl'] {
    border-left: none;
    border-right: 2px solid var(--lumo-contrast-30pct);
  }
`,{moduleId:"lumo-typography"}),$Xt("vaadin-checkbox",GXt`
    :host {
      -webkit-tap-highlight-color: transparent;
      -webkit-user-select: none;
      -moz-user-select: none;
      user-select: none;
      cursor: default;
      outline: none;
    }

    [part='label']:not([empty]) {
      margin: 0.1875em 0.875em 0.1875em 0.375em;
    }

    [part='checkbox'] {
      width: calc(1em + 2px);
      height: calc(1em + 2px);
      margin: 0.1875em;
      position: relative;
      border-radius: var(--lumo-border-radius-s);
      background-color: var(--lumo-contrast-20pct);
      transition: transform 0.2s cubic-bezier(0.12, 0.32, 0.54, 2), background-color 0.15s;
      pointer-events: none;
      line-height: 1.2;
    }

    :host([indeterminate]) [part='checkbox'],
    :host([checked]) [part='checkbox'] {
      background-color: var(--lumo-primary-color);
    }

    /* Needed to align the checkbox nicely on the baseline */
    [part='checkbox']::before {
      content: '\\2003';
    }

    /* Checkmark */
    [part='checkbox']::after {
      content: '';
      display: inline-block;
      width: 0;
      height: 0;
      border: 0 solid var(--lumo-primary-contrast-color);
      border-width: 0.1875em 0 0 0.1875em;
      box-sizing: border-box;
      transform-origin: 0 0;
      position: absolute;
      top: 0.8125em;
      left: 0.5em;
      transform: scale(0.55) rotate(-135deg);
      opacity: 0;
    }

    :host([checked]) [part='checkbox']::after {
      opacity: 1;
      width: 0.625em;
      height: 1.0625em;
    }

    /* Indeterminate checkmark */
    :host([indeterminate]) [part='checkbox']::after {
      transform: none;
      opacity: 1;
      top: 45%;
      height: 10%;
      left: 22%;
      right: 22%;
      width: auto;
      border: 0;
      background-color: var(--lumo-primary-contrast-color);
      transition: opacity 0.25s;
    }

    /* Focus ring */
    :host([focus-ring]) [part='checkbox'] {
      box-shadow: 0 0 0 3px var(--lumo-primary-color-50pct);
    }

    /* Disabled */
    :host([disabled]) {
      pointer-events: none;
      color: var(--lumo-disabled-text-color);
    }

    :host([disabled]) [part='label'] ::slotted(*) {
      color: inherit;
    }

    :host([disabled]) [part='checkbox'] {
      background-color: var(--lumo-contrast-10pct);
    }

    :host([disabled]) [part='checkbox']::after {
      border-color: var(--lumo-contrast-30pct);
    }

    :host([indeterminate][disabled]) [part='checkbox']::after {
      background-color: var(--lumo-contrast-30pct);
    }

    /* RTL specific styles */
    :host([dir='rtl']) [part='label']:not([empty]) {
      margin: 0.1875em 0.375em 0.1875em 0.875em;
    }

    /* Transition the checkmark if activated with the mouse (disabled for grid select-all this way) */
    :host(:hover) [part='checkbox']::after {
      transition: width 0.1s, height 0.25s;
    }

    /* Used for activation "halo" */
    [part='checkbox']::before {
      color: transparent;
      display: inline-block;
      width: 100%;
      height: 100%;
      border-radius: inherit;
      background-color: inherit;
      transform: scale(1.4);
      opacity: 0;
      transition: transform 0.1s, opacity 0.8s;
    }

    /* Hover */
    :host(:not([checked]):not([indeterminate]):not([disabled]):hover) [part='checkbox'] {
      background-color: var(--lumo-contrast-30pct);
    }

    /* Disable hover for touch devices */
    @media (pointer: coarse) {
      :host(:not([checked]):not([indeterminate]):not([disabled]):hover) [part='checkbox'] {
        background-color: var(--lumo-contrast-20pct);
      }
    }

    /* Active */
    :host([active]) [part='checkbox'] {
      transform: scale(0.9);
      transition-duration: 0.05s;
    }

    :host([active][checked]) [part='checkbox'] {
      transform: scale(1.1);
    }

    :host([active]:not([checked])) [part='checkbox']::before {
      transition-duration: 0.01s, 0.01s;
      transform: scale(0);
      opacity: 0.4;
    }
  `,{moduleId:"lumo-checkbox"});const l$t=t=>class extends t{static get properties(){return{theme:{type:String,readOnly:!0}}}attributeChangedCallback(t,e,n){super.attributeChangedCallback(t,e,n),"theme"===t&&this._setTheme(n)}},c$t=t=>class extends(l$t(t)){static finalize(){super.finalize();const t=this.prototype._template,e=Object.getPrototypeOf(this.prototype)._template;e&&Array.from(e.content.querySelectorAll("style[include]")).forEach((e=>{this._includeStyle(e.getAttribute("include"),t)})),this._includeMatchingThemes(t)}static _includeMatchingThemes(t){const e=B.prototype.modules;let n=!1;const i=this.is+"-default-theme";Object.keys(e).sort(((t,e)=>{const n=0===t.indexOf("vaadin-"),i=0===e.indexOf("vaadin-"),r=["lumo-","material-"],o=r.filter((e=>0===t.indexOf(e))).length>0,a=r.filter((t=>0===e.indexOf(t))).length>0;return n!==i?n?-1:1:o!==a?o?-1:1:0})).forEach((r=>{if(r!==i){const i=e[r].getAttribute("theme-for");i&&i.split(" ").forEach((e=>{new RegExp("^"+e.split("*").join(".*")+"$").test(this.is)&&(n=!0,this._includeStyle(r,t))}))}})),!n&&e[i]&&this._includeStyle(i,t)}static _includeStyle(t,e){if(e&&!e.content.querySelector(`style[include="${t}"]`)){const n=document.createElement("style");n.setAttribute("include",t),e.content.appendChild(n)}}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */;let u$t=!1;window.addEventListener("keydown",(()=>{u$t=!0}),{capture:!0}),window.addEventListener("mousedown",(()=>{u$t=!1}),{capture:!0});const h$t=t=>class extends((t=>class extends t{static get properties(){return{tabindex:{type:Number,value:0,reflectToAttribute:!0,observer:"_tabindexChanged"}}}})(t)){static get properties(){return{autofocus:{type:Boolean},_previousTabIndex:{type:Number},disabled:{type:Boolean,observer:"_disabledChanged",reflectToAttribute:!0},_isShiftTabbing:{type:Boolean}}}ready(){this.addEventListener("focusin",(t=>{t.composedPath()[0]===this?this.contains(t.relatedTarget)||this._focus():-1===t.composedPath().indexOf(this.focusElement)||this.disabled||this._setFocused(!0)})),this.addEventListener("focusout",(()=>this._setFocused(!1))),super.ready(),this.addEventListener("keydown",(t=>{!t.defaultPrevented&&9===t.keyCode&&t.shiftKey&&(this._isShiftTabbing=!0,HTMLElement.prototype.focus.apply(this),this._setFocused(!1),setTimeout((()=>this._isShiftTabbing=!1),0))})),this.autofocus&&!this.disabled&&window.requestAnimationFrame((()=>{this._focus(),this._setFocused(!0),this.setAttribute("focus-ring","")}))}disconnectedCallback(){super.disconnectedCallback(),this.hasAttribute("focused")&&this._setFocused(!1)}_setFocused(t){t?this.setAttribute("focused",""):this.removeAttribute("focused"),t&&u$t?this.setAttribute("focus-ring",""):this.removeAttribute("focus-ring")}get focusElement(){return window.console.warn(`Please implement the 'focusElement' property in <${this.localName}>`),this}_focus(){this.focusElement&&!this._isShiftTabbing&&(this.focusElement.focus(),this._setFocused(!0))}focus(){this.focusElement&&!this.disabled&&(this.focusElement.focus(),this._setFocused(!0))}blur(){this.focusElement&&(this.focusElement.blur(),this._setFocused(!1))}_disabledChanged(t){this.focusElement.disabled=t,t?(this.blur(),this._previousTabIndex=this.tabindex,this.tabindex=-1,this.setAttribute("aria-disabled","true")):(void 0!==this._previousTabIndex&&(this.tabindex=this._previousTabIndex),this.removeAttribute("aria-disabled"))}_tabindexChanged(t){void 0!==t&&(this.focusElement.tabIndex=t),this.disabled&&this.tabindex&&(-1!==this.tabindex&&(this._previousTabIndex=this.tabindex),this.tabindex=t=void 0)}click(){this.disabled||super.click()}},d$t=/\/\*\*\s+vaadin-dev-mode:start([\s\S]*)vaadin-dev-mode:end\s+\*\*\//i,p$t=window.Vaadin&&window.Vaadin.Flow&&window.Vaadin.Flow.clients;function f$t(t,e){if("function"!=typeof t)return;const n=d$t.exec(t.toString());if(n)try{t=new Function(n[1])}catch(t){console.log("vaadin-development-mode-detector: uncommentAndRun() failed",t)}return t(e)}function m$t(){}window.Vaadin=window.Vaadin||{},void 0===window.Vaadin.developmentMode&&(window.Vaadin.developmentMode=(function g$t(){try{return!!(function t(){return localStorage.getItem("vaadin.developmentmode.force")})()||!!(function e(){return["localhost","127.0.0.1"].indexOf(window.location.hostname)>=0})()&&(p$t?!(function n(){return!!(p$t&&Object.keys(p$t).map((t=>p$t[t])).filter((t=>t.productionMode)).length>0)})():!(function i(){return f$t((function t(){return!0}))})())}catch(t){return!1}})());
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
class _$t{static detectScrollType(){const t=document.createElement("div");t.textContent="ABCD",t.dir="rtl",t.style.fontSize="14px",t.style.width="4px",t.style.height="1px",t.style.position="absolute",t.style.top="-1000px",t.style.overflow="scroll",document.body.appendChild(t);let e="reverse";return t.scrollLeft>0?e="default":(t.scrollLeft=2,t.scrollLeft<2&&(e="negative")),document.body.removeChild(t),e}static getNormalizedScrollLeft(t,e,n){const{scrollLeft:i}=n;if("rtl"!==e||!t)return i;switch(t){case"negative":return n.scrollWidth-n.clientWidth+i;case"reverse":return n.scrollWidth-n.clientWidth-i}return i}static setNormalizedScrollLeft(t,e,n,i){if("rtl"===e&&t)switch(t){case"negative":n.scrollLeft=n.clientWidth-n.scrollWidth+i;break;case"reverse":n.scrollLeft=n.scrollWidth-n.clientWidth-i;break;default:n.scrollLeft=i}else n.scrollLeft=i}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */const y$t=[];let v$t;new MutationObserver((function(){const t=x$t();y$t.forEach((e=>{b$t(e,t)}))})).observe(document.documentElement,{attributes:!0,attributeFilter:["dir"]});const b$t=function(t,e,n=t.getAttribute("dir")){e?t.setAttribute("dir",e):null!=n&&t.removeAttribute("dir")},x$t=function(){return document.documentElement.getAttribute("dir")},w$t=t=>class extends t{static get properties(){return{dir:{type:String,value:"",reflectToAttribute:!0}}}static finalize(){super.finalize(),v$t||(v$t=_$t.detectScrollType())}connectedCallback(){super.connectedCallback(),this.hasAttribute("dir")||(this.__subscribe(),b$t(this,x$t(),null))}attributeChangedCallback(t,e,n){if(super.attributeChangedCallback(t,e,n),"dir"!==t)return;const i=x$t(),r=n===i&&-1===y$t.indexOf(this),o=!n&&e&&-1===y$t.indexOf(this),a=n!==i&&e===i;r||o?(this.__subscribe(),b$t(this,i,n)):a&&this.__subscribe(!1)}disconnectedCallback(){super.disconnectedCallback(),this.__subscribe(!1),this.removeAttribute("dir")}_valueToNodeAttribute(t,e,n){("dir"!==n||""!==e||t.hasAttribute("dir"))&&super._valueToNodeAttribute(t,e,n)}_attributeToProperty(t,e,n){"dir"!==t||e?super._attributeToProperty(t,e,n):this.dir=""}__subscribe(t=!0){t?-1===y$t.indexOf(this)&&y$t.push(this):y$t.indexOf(this)>-1&&y$t.splice(y$t.indexOf(this),1)}__getNormalizedScrollLeft(t){return _$t.getNormalizedScrollLeft(v$t,this.getAttribute("dir")||"ltr",t)}__setNormalizedScrollLeft(t,e){return _$t.setNormalizedScrollLeft(v$t,this.getAttribute("dir")||"ltr",t,e)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */;let S$t;window.Vaadin=window.Vaadin||{},window.Vaadin.registrations=window.Vaadin.registrations||[],window.Vaadin.developmentModeCallback=window.Vaadin.developmentModeCallback||{},window.Vaadin.developmentModeCallback["vaadin-usage-statistics"]=function(){var t;t=m$t,window.Vaadin.developmentMode&&f$t(t,void 0)};const M$t=new Set,E$t=t=>class extends(w$t(t)){static finalize(){super.finalize();const{is:t}=this;t&&!M$t.has(t)&&(window.Vaadin.registrations.push(this),M$t.add(t),window.Vaadin.developmentModeCallback&&(S$t=Tn.debounce(S$t,yt,(()=>{window.Vaadin.developmentModeCallback["vaadin-usage-statistics"]()})),An(S$t)))}constructor(){super(),null===document.doctype&&console.warn('Vaadin components require the "standards mode" declaration. Please add <!DOCTYPE html> to the HTML document.')}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */;class T$t extends(E$t(h$t(c$t(pi(ye))))){static get template(){return _e`
      <style>
        :host {
          display: inline-block;
        }

        :host([hidden]) {
          display: none !important;
        }

        label {
          display: inline-flex;
          align-items: baseline;
          outline: none;
        }

        [part='checkbox'] {
          position: relative;
          display: inline-block;
          flex: none;
        }

        input[type='checkbox'] {
          position: absolute;
          top: 0;
          left: 0;
          right: 0;
          width: 100%;
          height: 100%;
          opacity: 0;
          cursor: inherit;
          margin: 0;
        }

        :host([disabled]) {
          -webkit-tap-highlight-color: transparent;
        }
      </style>

      <label>
        <span part="checkbox">
          <input
            type="checkbox"
            checked="{{checked::change}}"
            disabled$="[[disabled]]"
            indeterminate="{{indeterminate::change}}"
            role="presentation"
            tabindex="-1"
          />
        </span>

        <span part="label">
          <slot></slot>
        </span>
      </label>
    `}static get is(){return"vaadin-checkbox"}static get version(){return"20.0.2"}static get properties(){return{checked:{type:Boolean,value:!1,notify:!0,observer:"_checkedChanged",reflectToAttribute:!0},indeterminate:{type:Boolean,notify:!0,observer:"_indeterminateChanged",reflectToAttribute:!0,value:!1},value:{type:String,value:"on"},_nativeCheckbox:{type:Object}}}constructor(){super()}get name(){return this.checked?this._storedName:""}set name(t){this._storedName=t}ready(){super.ready(),this.setAttribute("role","checkbox"),this._nativeCheckbox=this.shadowRoot.querySelector('input[type="checkbox"]'),this.addEventListener("click",this._handleClick.bind(this)),this._addActiveListeners();const t=this.getAttribute("name");t&&(this.name=t),this.shadowRoot.querySelector('[part~="label"]').querySelector("slot").addEventListener("slotchange",this._updateLabelAttribute.bind(this)),this._updateLabelAttribute()}_updateLabelAttribute(){const t=this.shadowRoot.querySelector('[part~="label"]'),e=t.firstElementChild.assignedNodes();this._isAssignedNodesEmpty(e)?t.setAttribute("empty",""):t.removeAttribute("empty")}_isAssignedNodesEmpty(t){return 0===t.length||1==t.length&&t[0].nodeType==Node.TEXT_NODE&&""===t[0].textContent.trim()}_checkedChanged(t){this.setAttribute("aria-checked",this.indeterminate?"mixed":Boolean(t))}_indeterminateChanged(t){this.setAttribute("aria-checked",t?"mixed":this.checked)}_addActiveListeners(){this._addEventListenerToNode(this,"down",(t=>{this.__interactionsAllowed(t)&&this.setAttribute("active","")})),this._addEventListenerToNode(this,"up",(()=>this.removeAttribute("active"))),this.addEventListener("keydown",(t=>{this.__interactionsAllowed(t)&&32===t.keyCode&&(t.preventDefault(),this.setAttribute("active",""))})),this.addEventListener("keyup",(t=>{this.__interactionsAllowed(t)&&32===t.keyCode&&(t.preventDefault(),this._toggleChecked(),this.removeAttribute("active"),this.indeterminate&&(this.indeterminate=!1))}))}get focusElement(){return this.shadowRoot.querySelector("input")}__interactionsAllowed(t){return!this.disabled&&"a"!==t.target.localName}_handleClick(t){this.__interactionsAllowed(t)&&(this.indeterminate?(this.indeterminate=!1,t.preventDefault(),this._toggleChecked()):t.composedPath()[0]!==this._nativeCheckbox&&(t.preventDefault(),this._toggleChecked()))}_toggleChecked(){this.checked=!this.checked,this.dispatchEvent(new CustomEvent("change",{composed:!1,bubbles:!0}))}}customElements.define(T$t.is,T$t),$Xt("vaadin-grid",GXt`
    :host {
      font-family: var(--lumo-font-family);
      font-size: var(--lumo-font-size-m);
      line-height: var(--lumo-line-height-s);
      color: var(--lumo-body-text-color);
      background-color: var(--lumo-base-color);
      box-sizing: border-box;
      -webkit-text-size-adjust: 100%;
      -webkit-tap-highlight-color: transparent;
      -webkit-font-smoothing: antialiased;
      -moz-osx-font-smoothing: grayscale;

      /* For internal use only */
      --_lumo-grid-border-color: var(--lumo-contrast-20pct);
      --_lumo-grid-secondary-border-color: var(--lumo-contrast-10pct);
      --_lumo-grid-border-width: 1px;
      --_lumo-grid-selected-row-color: var(--lumo-primary-color-10pct);
    }

    /* No (outer) border */

    :host(:not([theme~='no-border'])) {
      border: var(--_lumo-grid-border-width) solid var(--_lumo-grid-border-color);
    }

    /* Cell styles */

    [part~='cell'] {
      min-height: var(--lumo-size-m);
      background-color: var(--lumo-base-color);
    }

    [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      cursor: default;
      padding: var(--lumo-space-xs) var(--lumo-space-m);
    }

    /* Apply row borders by default and introduce the "no-row-borders" variant */
    :host(:not([theme~='no-row-borders'])) [part~='cell']:not([part~='details-cell']) {
      border-top: var(--_lumo-grid-border-width) solid var(--_lumo-grid-secondary-border-color);
    }

    /* Hide first body row top border */
    :host(:not([theme~='no-row-borders'])) [part='row'][first] [part~='cell']:not([part~='details-cell']) {
      border-top: 0;
      min-height: calc(var(--lumo-size-m) - var(--_lumo-grid-border-width));
    }

    /* Focus-ring */

    [part~='cell']:focus {
      outline: none;
    }

    :host([navigating]) [part~='cell']:focus::before {
      content: '';
      position: absolute;
      top: 0;
      right: 0;
      bottom: 0;
      left: 0;
      pointer-events: none;
      box-shadow: inset 0 0 0 2px var(--lumo-primary-color-50pct);
    }

    /* Drag and Drop styles */
    :host([dragover])::after {
      content: '';
      position: absolute;
      z-index: 100;
      top: 0;
      right: 0;
      bottom: 0;
      left: 0;
      pointer-events: none;
      box-shadow: inset 0 0 0 2px var(--lumo-primary-color-50pct);
    }

    [part~='row'][dragover] {
      z-index: 100 !important;
    }

    [part~='row'][dragover] [part~='cell'] {
      overflow: visible;
    }

    [part~='row'][dragover] [part~='cell']::after {
      content: '';
      position: absolute;
      top: 0;
      right: 0;
      bottom: 0;
      left: 0;
      height: calc(var(--_lumo-grid-border-width) + 2px);
      pointer-events: none;
      background: var(--lumo-primary-color-50pct);
    }

    :host([theme~='no-row-borders']) [dragover] [part~='cell']::after {
      height: 2px;
    }

    [part~='row'][dragover='below'] [part~='cell']::after {
      top: 100%;
      bottom: auto;
      margin-top: -1px;
    }

    [part~='row'][dragover='above'] [part~='cell']::after {
      top: auto;
      bottom: 100%;
      margin-bottom: -1px;
    }

    [part~='row'][details-opened][dragover='below'] [part~='cell']:not([part~='details-cell'])::after,
    [part~='row'][details-opened][dragover='above'] [part~='details-cell']::after {
      display: none;
    }

    [part~='row'][dragover][dragover='on-top'] [part~='cell']::after {
      height: 100%;
    }

    [part~='row'][dragstart] {
      /* Add bottom-space to the row so the drag number doesn't get clipped. Needed for IE/Edge */
      border-bottom: 100px solid transparent;
      z-index: 100 !important;
      opacity: 0.9;
    }

    [part~='row'][dragstart] [part~='cell'] {
      border: none !important;
      box-shadow: none !important;
    }

    [part~='row'][dragstart] [part~='cell'][last-column] {
      border-radius: 0 var(--lumo-border-radius-s) var(--lumo-border-radius-s) 0;
    }

    [part~='row'][dragstart] [part~='cell'][first-column] {
      border-radius: var(--lumo-border-radius-s) 0 0 var(--lumo-border-radius-s);
    }

    [ios] [part~='row'][dragstart] [part~='cell'] {
      background: var(--lumo-primary-color-50pct);
    }

    #scroller:not([ios]) [part~='row'][dragstart]:not([dragstart=''])::after {
      display: block;
      position: absolute;
      left: var(--_grid-drag-start-x);
      top: var(--_grid-drag-start-y);
      z-index: 100;
      content: attr(dragstart);
      align-items: center;
      justify-content: center;
      box-sizing: border-box;
      padding: calc(var(--lumo-space-xs) * 0.8);
      color: var(--lumo-error-contrast-color);
      background-color: var(--lumo-error-color);
      border-radius: var(--lumo-border-radius-m);
      font-family: var(--lumo-font-family);
      font-size: var(--lumo-font-size-xxs);
      line-height: 1;
      font-weight: 500;
      text-transform: initial;
      letter-spacing: initial;
      min-width: calc(var(--lumo-size-s) * 0.7);
      text-align: center;
    }

    /* Headers and footers */

    [part~='header-cell'] ::slotted(vaadin-grid-cell-content),
    [part~='footer-cell'] ::slotted(vaadin-grid-cell-content),
    [part~='reorder-ghost'] {
      font-size: var(--lumo-font-size-s);
      font-weight: 500;
    }

    [part~='footer-cell'] ::slotted(vaadin-grid-cell-content) {
      font-weight: 400;
    }

    [part='row']:only-child [part~='header-cell'] {
      min-height: var(--lumo-size-xl);
    }

    /* Header borders */

    /* Hide first header row top border */
    :host(:not([theme~='no-row-borders'])) [part='row']:first-child [part~='header-cell'] {
      border-top: 0;
    }

    [part='row']:last-child [part~='header-cell'] {
      border-bottom: var(--_lumo-grid-border-width) solid transparent;
    }

    :host(:not([theme~='no-row-borders'])) [part='row']:last-child [part~='header-cell'] {
      border-bottom-color: var(--_lumo-grid-secondary-border-color);
    }

    /* Overflow uses a stronger border color */
    :host([overflow~='top']) [part='row']:last-child [part~='header-cell'] {
      border-bottom-color: var(--_lumo-grid-border-color);
    }

    /* Footer borders */

    [part='row']:first-child [part~='footer-cell'] {
      border-top: var(--_lumo-grid-border-width) solid transparent;
    }

    :host(:not([theme~='no-row-borders'])) [part='row']:first-child [part~='footer-cell'] {
      border-top-color: var(--_lumo-grid-secondary-border-color);
    }

    /* Overflow uses a stronger border color */
    :host([overflow~='bottom']) [part='row']:first-child [part~='footer-cell'] {
      border-top-color: var(--_lumo-grid-border-color);
    }

    /* Column reordering */

    :host([reordering]) [part~='cell'] {
      background: linear-gradient(var(--lumo-shade-20pct), var(--lumo-shade-20pct)) var(--lumo-base-color);
    }

    :host([reordering]) [part~='cell'][reorder-status='allowed'] {
      background: var(--lumo-base-color);
    }

    :host([reordering]) [part~='cell'][reorder-status='dragging'] {
      background: linear-gradient(var(--lumo-contrast-5pct), var(--lumo-contrast-5pct)) var(--lumo-base-color);
    }

    [part~='reorder-ghost'] {
      opacity: 0.85;
      box-shadow: var(--lumo-box-shadow-s);
      /* TODO Use the same styles as for the cell element (reorder-ghost copies styles from the cell element) */
      padding: var(--lumo-space-s) var(--lumo-space-m) !important;
    }

    /* Column resizing */

    [part='resize-handle'] {
      width: 3px;
      background-color: var(--lumo-primary-color-50pct);
      opacity: 0;
      transition: opacity 0.2s;
    }

    :host(:not([reordering])) *:not([column-resizing]) [part~='cell']:hover [part='resize-handle'],
    [part='resize-handle']:active {
      opacity: 1;
      transition-delay: 0.15s;
    }

    /* Column borders */

    :host([theme~='column-borders']) [part~='cell']:not([last-column]):not([part~='details-cell']) {
      border-right: var(--_lumo-grid-border-width) solid var(--_lumo-grid-secondary-border-color);
    }

    /* Frozen columns */

    [last-frozen] {
      border-right: var(--_lumo-grid-border-width) solid transparent;
      overflow: hidden;
    }

    :host([overflow~='left']) [part~='cell'][last-frozen]:not([part~='details-cell']) {
      border-right-color: var(--_lumo-grid-border-color);
    }

    /* Row stripes */

    :host([theme~='row-stripes']) [part~='row']:not([odd]) [part~='body-cell'],
    :host([theme~='row-stripes']) [part~='row']:not([odd]) [part~='details-cell'] {
      background-image: linear-gradient(var(--lumo-contrast-5pct), var(--lumo-contrast-5pct));
      background-repeat: repeat-x;
    }

    /* Selected row */

    /* Raise the selected rows above unselected rows (so that box-shadow can cover unselected rows) */
    :host(:not([reordering])) [part~='row'][selected] {
      z-index: 1;
    }

    :host(:not([reordering])) [part~='row'][selected] [part~='body-cell']:not([part~='details-cell']) {
      background-image: linear-gradient(var(--_lumo-grid-selected-row-color), var(--_lumo-grid-selected-row-color));
      background-repeat: repeat;
    }

    /* Cover the border of an unselected row */
    :host(:not([theme~='no-row-borders'])) [part~='row'][selected] [part~='cell']:not([part~='details-cell']) {
      box-shadow: 0 var(--_lumo-grid-border-width) 0 0 var(--_lumo-grid-selected-row-color);
    }

    /* Compact */

    :host([theme~='compact']) [part='row']:only-child [part~='header-cell'] {
      min-height: var(--lumo-size-m);
    }

    :host([theme~='compact']) [part~='cell'] {
      min-height: var(--lumo-size-s);
    }

    :host([theme~='compact']) [part='row'][first] [part~='cell']:not([part~='details-cell']) {
      min-height: calc(var(--lumo-size-s) - var(--_lumo-grid-border-width));
    }

    :host([theme~='compact']) [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      padding: var(--lumo-space-xs) var(--lumo-space-s);
    }

    /* Wrap cell contents */

    :host([theme~='wrap-cell-content']) [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      white-space: normal;
    }

    /* RTL specific styles */

    :host([dir='rtl']) [part~='row'][dragstart] [part~='cell'][last-column] {
      border-radius: var(--lumo-border-radius-s) 0 0 var(--lumo-border-radius-s);
    }

    :host([dir='rtl']) [part~='row'][dragstart] [part~='cell'][first-column] {
      border-radius: 0 var(--lumo-border-radius-s) var(--lumo-border-radius-s) 0;
    }

    :host([dir='rtl'][theme~='column-borders']) [part~='cell']:not([last-column]):not([part~='details-cell']) {
      border-right: none;
      border-left: var(--_lumo-grid-border-width) solid var(--_lumo-grid-secondary-border-color);
    }

    :host([dir='rtl']) [last-frozen] {
      border-right: none;
      border-left: var(--_lumo-grid-border-width) solid transparent;
    }

    :host([dir='rtl'][overflow~='right']) [part~='cell'][last-frozen]:not([part~='details-cell']) {
      border-left-color: var(--_lumo-grid-border-color);
    }
  `,{moduleId:"lumo-grid"}),$Xt("vaadin-checkbox",GXt`
    :host(.vaadin-grid-select-all-checkbox) {
      font-size: var(--lumo-font-size-m);
    }
  `,{moduleId:"vaadin-grid-select-all-checkbox-lumo"});
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const C$t=navigator.userAgent.match(/iP(?:hone|ad;(?: U;)? CPU) OS (\d+)/),A$t=C$t&&C$t[1]>=8,k$t=Ir({behaviors:[go,Do],_ratio:.5,_scrollerPaddingTop:0,_scrollPosition:0,_physicalSize:0,_physicalAverage:0,_physicalAverageCount:0,_physicalTop:0,_virtualCount:0,_estScrollHeight:0,_scrollHeight:0,_viewportHeight:0,_viewportWidth:0,_physicalItems:null,_physicalSizes:null,_firstVisibleIndexVal:null,_lastVisibleIndexVal:null,_maxPages:2,_focusedVirtualIndex:-1,_templateCost:0,get _physicalBottom(){return this._physicalTop+this._physicalSize},get _scrollBottom(){return this._scrollPosition+this._viewportHeight},get _virtualEnd(){return this._virtualStart+this._physicalCount-1},get _hiddenContentSize(){return this._physicalSize-this._viewportHeight},get _maxScrollTop(){return this._estScrollHeight-this._viewportHeight+this._scrollOffset},get _maxVirtualStart(){return Math.max(0,this._virtualCount-this._physicalCount)},set _virtualStart(t){t=this._clamp(t,0,this._maxVirtualStart),this._virtualStartVal=t},get _virtualStart(){return this._virtualStartVal||0},set _physicalStart(t){(t%=this._physicalCount)<0&&(t=this._physicalCount+t),this._physicalStartVal=t},get _physicalStart(){return this._physicalStartVal||0},get _physicalEnd(){return(this._physicalStart+this._physicalCount-1)%this._physicalCount},set _physicalCount(t){this._physicalCountVal=t},get _physicalCount(){return this._physicalCountVal||0},get _optPhysicalSize(){return 0===this._viewportHeight?1/0:this._viewportHeight*this._maxPages},get _isVisible(){return Boolean(this.offsetWidth||this.offsetHeight)},get firstVisibleIndex(){let t=this._firstVisibleIndexVal;if(null==t){let e=this._physicalTop+this._scrollOffset;t=this._iterateItems((function(t,n){if(e+=this._physicalSizes[t],e>this._scrollPosition)return n}))||0,this._firstVisibleIndexVal=t}return t},get lastVisibleIndex(){let t=this._lastVisibleIndexVal;if(null==t){let e=this._physicalTop+this._scrollOffset;this._iterateItems((function(n,i){e<this._scrollBottom&&(t=i),e+=this._physicalSizes[n]})),this._lastVisibleIndexVal=t}return t},get _scrollOffset(){return this._scrollerPaddingTop},attached:function(){this._debounce("_render",this._render,_t),this.listen(this,"iron-resize","_resizeHandler")},detached:function(){this.unlisten(this,"iron-resize","_resizeHandler")},updateViewportBoundaries:function(){const t=window.getComputedStyle(this);this._scrollerPaddingTop=this.scrollTarget===this?0:parseInt(t["padding-top"],10),this._isRTL=Boolean("rtl"===t.direction),this._viewportWidth=this.$.items.offsetWidth,this._viewportHeight=this._scrollTargetHeight},_scrollHandler:function(){const t=Math.max(0,Math.min(this._maxScrollTop,this._scrollTop));let e=t-this._scrollPosition;const n=e>=0;if(this._scrollPosition=t,this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,Math.abs(e)>this._physicalSize&&this._physicalSize>0){e-=this._scrollOffset;const t=Math.round(e/this._physicalAverage);this._virtualStart=this._virtualStart+t,this._physicalStart=this._physicalStart+t,this._physicalTop=Math.floor(this._virtualStart)*this._physicalAverage,this._update()}else if(this._physicalCount>0){const{physicalTop:t,indexes:e}=this._getReusables(n);n?(this._physicalTop=t,this._virtualStart=this._virtualStart+e.length,this._physicalStart=this._physicalStart+e.length):(this._virtualStart=this._virtualStart-e.length,this._physicalStart=this._physicalStart-e.length),this._update(e,n?null:e),this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,0),vt)}},_getReusables:function(t){let e,n,i;const r=[],o=this._hiddenContentSize*this._ratio,a=this._virtualStart,s=this._virtualEnd,l=this._physicalCount;let c=this._physicalTop+this._scrollOffset;const u=this._scrollTop,h=this._scrollBottom;for(t?(e=this._physicalStart,n=u-c):(e=this._physicalEnd,n=this._physicalBottom+this._scrollOffset-h);i=this._physicalSizes[e],n-=i,!(r.length>=l||n<=o);)if(t){if(s+r.length+1>=this._virtualCount)break;if(c+i>=u-this._scrollOffset)break;r.push(e),c+=i,e=(e+1)%l}else{if(a-r.length<=0)break;if(c+this._physicalSize-i<=h)break;r.push(e),c-=i,e=0===e?l-1:e-1}return{indexes:r,physicalTop:c-this._scrollOffset}},_update:function(t,e){if(!(t&&0===t.length||0===this._physicalCount)){if(this._assignModels(t),this._updateMetrics(t),e)for(;e.length;){const t=e.pop();this._physicalTop-=this._physicalSizes[t]}this._positionItems(),this._updateScrollerSize()}},_isClientFull:function(){return 0!=this._scrollBottom&&this._physicalBottom-1>=this._scrollBottom&&this._physicalTop<=this._scrollPosition},_increasePoolIfNeeded:function(t){const e=this._clamp(this._physicalCount+t,3,this._virtualCount-this._virtualStart)-this._physicalCount;let n=Math.round(.5*this._physicalCount);if(!(e<0)){if(e>0){const t=window.performance.now();[].push.apply(this._physicalItems,this._createPool(e));for(let t=0;t<e;t++)this._physicalSizes.push(0);this._physicalCount=this._physicalCount+e,this._physicalStart>this._physicalEnd&&this._isIndexRendered(this._focusedVirtualIndex)&&this._getPhysicalIndex(this._focusedVirtualIndex)<this._physicalEnd&&(this._physicalStart=this._physicalStart+e),this._update(),this._templateCost=(window.performance.now()-t)/e,n=Math.round(.5*this._physicalCount)}this._virtualEnd>=this._virtualCount-1||0===n||(this._isClientFull()?this._physicalSize<this._optPhysicalSize&&this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,this._clamp(Math.round(50/this._templateCost),1,n)),yt):this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,n),vt))}},_render:function(){if(this.isAttached&&this._isVisible)if(0!==this._physicalCount){const{physicalTop:t,indexes:e}=this._getReusables(!0);this._physicalTop=t,this._virtualStart=this._virtualStart+e.length,this._physicalStart=this._physicalStart+e.length,this._update(e),this._update(),this._increasePoolIfNeeded(0)}else this._virtualCount>0&&(this.updateViewportBoundaries(),this._increasePoolIfNeeded(3))},_itemsChanged:function(t){"items"===t.path&&(this._virtualStart=0,this._physicalTop=0,this._virtualCount=this.items?this.items.length:0,this._physicalIndexForKey={},this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,this._physicalCount=this._physicalCount||0,this._physicalItems=this._physicalItems||[],this._physicalSizes=this._physicalSizes||[],this._physicalStart=0,this._scrollTop>this._scrollOffset&&this._resetScrollPosition(0),this._debounce("_render",this._render,_t))},_iterateItems:function(t,e){let n,i,r,o;if(2===arguments.length&&e){for(o=0;o<e.length;o++)if(n=e[o],i=this._computeVidx(n),null!=(r=t.call(this,n,i)))return r}else{for(n=this._physicalStart,i=this._virtualStart;n<this._physicalCount;n++,i++)if(null!=(r=t.call(this,n,i)))return r;for(n=0;n<this._physicalStart;n++,i++)if(null!=(r=t.call(this,n,i)))return r}},_computeVidx:function(t){return t>=this._physicalStart?this._virtualStart+(t-this._physicalStart):this._virtualStart+(this._physicalCount-this._physicalStart)+t},_updateMetrics:function(t){if(!this._isVisible)return;Di();let e=0,n=0;const i=this._physicalAverageCount,r=this._physicalAverage;this._iterateItems((function(t){n+=this._physicalSizes[t],this._physicalSizes[t]=this._physicalItems[t].offsetHeight,e+=this._physicalSizes[t],this._physicalAverageCount+=this._physicalSizes[t]?1:0}),t),this._physicalSize=this._physicalSize+e-n,this._physicalAverageCount!==i&&(this._physicalAverage=Math.round((r*i+e)/this._physicalAverageCount))},_positionItems:function(){this._adjustScrollPosition();let t=this._physicalTop;this._iterateItems((function(e){this.translate3d(0,t+"px",0,this._physicalItems[e]),t+=this._physicalSizes[e]}))},_adjustScrollPosition:function(){const t=0===this._virtualStart?this._physicalTop:Math.min(this._scrollPosition+this._physicalTop,0);if(0!==t){this._physicalTop=this._physicalTop-t;const e=this._scrollTop;!A$t&&e>0&&this._resetScrollPosition(e-t)}},_resetScrollPosition:function(t){this.scrollTarget&&t>=0&&(this._scrollTop=t,this._scrollPosition=this._scrollTop)},_updateScrollerSize:function(t){this._estScrollHeight=this._physicalBottom+Math.max(this._virtualCount-this._physicalCount-this._virtualStart,0)*this._physicalAverage,((t=(t=t||0===this._scrollHeight)||this._scrollPosition>=this._estScrollHeight-this._physicalSize)||Math.abs(this._estScrollHeight-this._scrollHeight)>=this._viewportHeight)&&(this.$.items.style.height=this._estScrollHeight+"px",this._scrollHeight=this._estScrollHeight)},scrollToIndex:function(t){if("number"!=typeof t||t<0||t>this.items.length-1)return;if(Di(),0===this._physicalCount)return;t=this._clamp(t,0,this._virtualCount-1),(!this._isIndexRendered(t)||t>=this._maxVirtualStart)&&(this._virtualStart=t-1),this._assignModels(),this._updateMetrics(),this._physicalTop=Math.floor(this._virtualStart)*this._physicalAverage;let e=this._physicalStart,n=this._virtualStart,i=0;const r=this._hiddenContentSize;for(;n<t&&i<=r;)i+=this._physicalSizes[e],e=(e+1)%this._physicalCount,n++;this._updateScrollerSize(!0),this._positionItems(),this._resetScrollPosition(this._physicalTop+this._scrollOffset+i),this._increasePoolIfNeeded(0),this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null},_resetAverage:function(){this._physicalAverage=0,this._physicalAverageCount=0},_resizeHandler:function(){this._debounce("_render",(function(){this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,this.updateViewportBoundaries(),this._isVisible?(this.toggleScrollListener(!0),this._resetAverage(),this._render()):this.toggleScrollListener(!1)}),_t)},_isIndexRendered:function(t){return t>=this._virtualStart&&t<=this._virtualEnd},_getPhysicalIndex:function(t){return(this._physicalStart+(t-this._virtualStart))%this._physicalCount},_clamp:function(t,e,n){return Math.min(n,Math.max(e,t))},_debounce:function(t,e,n){this._debouncers=this._debouncers||{},this._debouncers[t]=Tn.debounce(this._debouncers[t],n,e.bind(this)),An(this._debouncers[t])}});
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
class L$t extends k$t{static get properties(){return{size:{type:Number,notify:!0},_vidxOffset:{type:Number,value:0}}}static get observers(){return["_effectiveSizeChanged(_effectiveSize)"]}connectedCallback(){super.connectedCallback(),this._scrollHandler()}_updateScrollerItem(){}_afterScroll(){}_getRowTarget(){}_createScrollerRows(){}_canPopulate(){}scrollToIndex(t){this._warnPrivateAPIAccess("scrollToIndex"),this._scrollingToIndex=!0,t=Math.min(Math.max(t,0),this._effectiveSize-1),this.$.table.scrollTop=t/this._effectiveSize*(this.$.table.scrollHeight-this.$.table.offsetHeight),this._scrollHandler(),this._accessIronListAPI((()=>this._maxScrollTop))&&this._virtualCount<this._effectiveSize&&this._adjustVirtualIndexOffset(1e6),this._accessIronListAPI((()=>super.scrollToIndex(t-this._vidxOffset))),this._scrollHandler();const e=Array.from(this.$.items.children).filter((e=>e.index===t))[0];if(e){const t=e.getBoundingClientRect().top-this.$.header.getBoundingClientRect().bottom;Math.abs(t)>1&&(this.$.table.scrollTop+=t,this._scrollHandler())}this._scrollingToIndex=!1}_effectiveSizeChanged(t){let e,n=0;this._iterateItems(((t,i)=>{if(i===this._firstVisibleIndex){const i=this._physicalItems[t];e=i.index,n=i.getBoundingClientRect().top}})),this.items&&t<this.items.length&&(this._scrollTop=0),Array.isArray(this.items)||(this.items={length:Math.min(t,1e5)}),this._accessIronListAPI((()=>super._itemsChanged({path:"items"}))),this._virtualCount=Math.min(this.items.length,t)||0,0===this._scrollTop&&(this._accessIronListAPI((()=>this._scrollToIndex(Math.min(t-1,e)))),this._iterateItems((t=>{const i=this._physicalItems[t];if(i.index===e&&(this.$.table.scrollTop+=Math.round(i.getBoundingClientRect().top-n)),i.index===this._focusedItemIndex&&this._itemsFocusable&&this.$.items.contains(this.shadowRoot.activeElement)){const t=Array.from(this._itemsFocusable.parentElement.children).indexOf(this._itemsFocusable);i.children[t].focus()}}))),this._assignModels(),requestAnimationFrame((()=>this._update())),this.__updateFooterPositioning()}_positionItems(){let t;this._adjustScrollPosition(),isNaN(this._physicalTop)&&(t=!0,this._physicalTop=0);let e=this._physicalTop;this._iterateItems((t=>{this._physicalItems[t].style.transform=`translateY(${e}px)`,e+=this._physicalSizes[t]})),t&&this._scrollToIndex(0)}_increasePoolIfNeeded(t){0===t&&this._scrollingToIndex||!this._canPopulate()||!this._effectiveSize||(this._initialPoolCreated?this._optPhysicalSize!==1/0&&(this._debounceIncreasePool=Tn.debounce(this._debounceIncreasePool,_t,(()=>{this._updateMetrics();let t=Math.ceil((this._optPhysicalSize-this._physicalSize)/this._physicalAverage);this._physicalCount+t>this._effectiveSize&&(t=Math.max(0,this._effectiveSize-this._physicalCount)),this._physicalSize&&t>0&&this._optPhysicalSize!==1/0&&(super._increasePoolIfNeeded(t),this.__reorderChildNodes())}))):(this._initialPoolCreated=!0,super._increasePoolIfNeeded(25)))}__reorderChildNodes(){const t=Array.from(this.$.items.childNodes);t.reduce(((t,e,n,i)=>{if(0===n||i[n-1].index===e.index-1)return t}),!0)||t.sort(((t,e)=>t.index-e.index)).forEach((t=>this.$.items.appendChild(t)))}_createPool(t){const e=document.createDocumentFragment(),n=this._createScrollerRows(t);n.forEach((t=>e.appendChild(t))),this._getRowTarget().appendChild(e);const i=this.querySelector("[slot]");if(i){const t=i.getAttribute("slot");i.setAttribute("slot","foo-bar"),i.setAttribute("slot",t)}return Li(this,(()=>this.notifyResize())),n}_assignModels(t){this._iterateItems(((t,e)=>{const n=this._physicalItems[t];this._toggleAttribute("hidden",e>=this._effectiveSize,n),this._updateScrollerItem(n,e+(this._vidxOffset||0))}),t)}_scrollHandler(){const t=this.$.table.scrollTop-this._scrollPosition;this._accessIronListAPI(super._scrollHandler);const e=this._vidxOffset;this._accessIronListAPI((()=>this._maxScrollTop))&&this._virtualCount<this._effectiveSize?this._adjustVirtualIndexOffset(t):this._vidxOffset=0,this._vidxOffset!==e&&this._update(),this._afterScroll()}_adjustVirtualIndexOffset(t){if(Math.abs(t)>1e4){if(this._noScale)return void(this._noScale=!1);const t=this.$.table.scrollTop/(this.$.table.scrollHeight-this.$.table.offsetHeight);this._vidxOffset=Math.round(t*this._effectiveSize-t*this._virtualCount)}else{const t=this._vidxOffset||0,e=1e3,n=100;0===this._scrollTop?(this._vidxOffset=0,t!==this._vidxOffset&&super.scrollToIndex(0)):this.firstVisibleIndex<e&&this._vidxOffset>0&&(this._vidxOffset-=Math.min(this._vidxOffset,n),t!==this._vidxOffset&&super.scrollToIndex(this.firstVisibleIndex+(t-this._vidxOffset)),this._noScale=!0);const i=this._effectiveSize-this._virtualCount;this._scrollTop>=this._maxScrollTop&&this._maxScrollTop>0?(this._vidxOffset=i,t!==this._vidxOffset&&super.scrollToIndex(this._virtualCount)):this.firstVisibleIndex>this._virtualCount-e&&this._vidxOffset<i&&(this._vidxOffset+=Math.min(i-this._vidxOffset,n),t!==this._vidxOffset&&super.scrollToIndex(this.firstVisibleIndex-(this._vidxOffset-t)),this._noScale=!0)}}_accessIronListAPI(t){this._warnPrivateAPIAccessAsyncEnabled=!1;const e=t.apply(this);return this._debouncerWarnPrivateAPIAccess=Tn.debounce(this._debouncerWarnPrivateAPIAccess,_t,(()=>this._warnPrivateAPIAccessAsyncEnabled=!0)),e}_debounceRender(t,e){super._debounceRender((()=>this._accessIronListAPI(t)),e)}_warnPrivateAPIAccess(t){this._warnPrivateAPIAccessAsyncEnabled&&console.warn(`Accessing private API (${t})!`)}_render(){this._accessIronListAPI(super._render)}_itemsChanged(){}get _firstVisibleIndex(){return this._accessIronListAPI((()=>super.firstVisibleIndex))}get _lastVisibleIndex(){return this._accessIronListAPI((()=>super.lastVisibleIndex))}_scrollToIndex(t){this._accessIronListAPI((()=>this.scrollToIndex(t)))}get firstVisibleIndex(){return this._warnPrivateAPIAccess("firstVisibleIndex"),super.firstVisibleIndex}set firstVisibleIndex(t){this._warnPrivateAPIAccess("firstVisibleIndex"),super.firstVisibleIndex=t}get lastVisibleIndex(){return this._warnPrivateAPIAccess("lastVisibleIndex"),super.lastVisibleIndex}set lastVisibleIndex(t){this._warnPrivateAPIAccess("lastVisibleIndex"),super.lastVisibleIndex=t}updateViewportBoundaries(){this._warnPrivateAPIAccess("updateViewportBoundaries"),super.updateViewportBoundaries.apply(this,arguments)}_resizeHandler(){super._resizeHandler(),Di()}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */const P$t=t=>class extends t{static get observers(){return["_a11yUpdateGridSize(size, _columnTree, _columnTree.*)"]}_a11yGetHeaderRowCount(t){return t.filter((t=>t.some((t=>t._headerTemplate||t.headerRenderer||t.path||t.header)))).length}_a11yGetFooterRowCount(t){return t.filter((t=>t.some((t=>t._headerTemplate||t.headerRenderer)))).length}_a11yUpdateGridSize(t,e){if(void 0===t||void 0===e)return;const n=e[e.length-1];this.$.table.setAttribute("aria-rowcount",t+this._a11yGetHeaderRowCount(e)+this._a11yGetFooterRowCount(e)),this.$.table.setAttribute("aria-colcount",n&&n.length||0),this._a11yUpdateHeaderRows(),this._a11yUpdateFooterRows()}_a11yUpdateHeaderRows(){Array.from(this.$.header.children).forEach(((t,e)=>t.setAttribute("aria-rowindex",e+1)))}_a11yUpdateFooterRows(){Array.from(this.$.footer.children).forEach(((t,e)=>t.setAttribute("aria-rowindex",this._a11yGetHeaderRowCount(this._columnTree)+this.size+e+1)))}_a11yUpdateRowRowindex(t,e){t.setAttribute("aria-rowindex",e+this._a11yGetHeaderRowCount(this._columnTree)+1)}_a11yUpdateRowSelected(t,e){t.setAttribute("aria-selected",Boolean(e)),Array.from(t.children).forEach((t=>t.setAttribute("aria-selected",Boolean(e))))}_a11yUpdateRowLevel(t,e){t.setAttribute("aria-level",e+1)}_a11yUpdateRowDetailsOpened(t,e){Array.from(t.children).forEach((t=>{"boolean"==typeof e?t.setAttribute("aria-expanded",e):t.hasAttribute("aria-expanded")&&t.removeAttribute("aria-expanded")}))}_a11ySetRowDetailsCell(t,e){Array.from(t.children).forEach((t=>{t!==e&&t.setAttribute("aria-controls",e.id)}))}_a11yUpdateCellColspan(t,e){t.setAttribute("aria-colspan",Number(e))}_a11yUpdateSorters(){Array.from(this.querySelectorAll("vaadin-grid-sorter")).forEach((t=>{let e=t.parentNode;for(;e&&"vaadin-grid-cell-content"!==e.localName;)e=e.parentNode;e&&e.assignedSlot&&e.assignedSlot.parentNode.setAttribute("aria-sort",{asc:"ascending",desc:"descending"}[String(t.direction)]||"none")}))}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,N$t=t=>class extends t{static get properties(){return{activeItem:{type:Object,notify:!0,value:null}}}ready(){super.ready(),this.$.scroller.addEventListener("click",this._onClick.bind(this)),this.addEventListener("cell-activate",this._activateItem.bind(this))}_activateItem(t){const e=t.detail.model,n=e?e.item:null;n&&(this.activeItem=this._itemsEqual(this.activeItem,n)?null:n)}_onClick(t){if(t.defaultPrevented)return;const e=t.composedPath(),n=e[e.indexOf(this.$.table)-3];if(!n||n.getAttribute("part").indexOf("details-cell")>-1)return;const i=n._content,r=this.getRootNode().activeElement;i.contains(r)||this._isFocusable(t.target)||this.dispatchEvent(new CustomEvent("cell-activate",{detail:{model:this.__getRowModel(n.parentElement)}}))}_isFocusable(t){return I$t(t)}},I$t=t=>{if(!t.parentNode)return!1;const e=-1!==Array.from(t.parentNode.querySelectorAll("[tabindex], button, input, select, textarea, object, iframe, label, a[href], area[href]")).filter((t=>"cell body-cell"!==t.getAttribute("part"))).indexOf(t);return!t.disabled&&e},R$t=t=>class extends t{static get properties(){return{items:Array}}static get observers(){return["_itemsChanged(items, items.*, isAttached)"]}_itemsChanged(t,e,n){if(n){if(!Array.isArray(t))return null==t&&(this.size=0),void(this.dataProvider===this._arrayDataProvider&&(this.dataProvider=void 0));this.size=t.length,this.dataProvider=this.dataProvider||this._arrayDataProvider,this.clearCache(),this._ensureFirstPageLoaded()}}_arrayDataProvider(t,e){let n=(Array.isArray(this.items)?this.items:[]).slice(0);this._filters&&this._checkPaths(this._filters,"filtering",n)&&(n=this._filter(n)),this.size=n.length,t.sortOrders.length&&this._checkPaths(this._sorters,"sorting",n)&&(n=n.sort(this._multiSort.bind(this)));const i=t.page*t.pageSize;e(n.slice(i,i+t.pageSize),n.length)}_checkPaths(t,e,n){if(!n.length)return!1;let i=!0;for(let r in t){const o=t[r].path;if(!o||-1===o.indexOf("."))continue;const a=o.replace(/\.[^.]*$/,"");void 0===fo.get(a,n[0])&&(console.warn(`Path "${o}" used for ${e} does not exist in all of the items, ${e} is disabled.`),i=!1)}return i}_multiSort(t,e){return this._sorters.map((n=>"asc"===n.direction?this._compare(fo.get(n.path,t),fo.get(n.path,e)):"desc"===n.direction?this._compare(fo.get(n.path,e),fo.get(n.path,t)):0)).reduce(((t,e)=>t||e),0)}_normalizeEmptyValue(t){return[void 0,null].indexOf(t)>=0?"":isNaN(t)?t.toString():t}_compare(t,e){return(t=this._normalizeEmptyValue(t))<(e=this._normalizeEmptyValue(e))?-1:t>e?1:0}_filter(t){return t.filter((t=>0===this._filters.filter((e=>{const n=this._normalizeEmptyValue(fo.get(e.path,t)),i=this._normalizeEmptyValue(e.value).toString().toLowerCase();return-1===n.toString().toLowerCase().indexOf(i)})).length))}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,O$t=t=>class extends(pi(t)){ready(){super.ready();const t=this.$.scroller;ii(t,"track",this._onHeaderTrack.bind(this)),t.addEventListener("touchmove",(e=>t.hasAttribute("column-resizing")&&e.preventDefault())),t.addEventListener("contextmenu",(t=>"resize-handle"==t.target.getAttribute("part")&&t.preventDefault())),t.addEventListener("mousedown",(t=>"resize-handle"===t.target.getAttribute("part")&&t.preventDefault()))}_onHeaderTrack(t){const e=t.target;if("resize-handle"===e.getAttribute("part")){let n=e.parentElement._column;for(this._toggleAttribute("column-resizing",!0,this.$.scroller);"vaadin-grid-column-group"===n.localName;)n=Array.prototype.slice.call(n._childColumns,0).sort((function(t,e){return t._order-e._order})).filter((function(t){return!t.hidden})).pop();const i=Array.from(this.$.header.querySelectorAll('[part~="row"]:last-child [part~="cell"]')),r=i.filter((t=>t._column===n))[0];if(r.offsetWidth){const e=window.getComputedStyle(r),i=10+parseInt(e.paddingLeft)+parseInt(e.paddingRight)+parseInt(e.borderLeftWidth)+parseInt(e.borderRightWidth)+parseInt(e.marginLeft)+parseInt(e.marginRight),o=r.offsetWidth+(this.__isRTL?r.getBoundingClientRect().left-t.detail.x:t.detail.x-r.getBoundingClientRect().right);n.width=Math.max(i,o)+"px",n.flexGrow=0}i.sort((function(t,e){return t._column._order-e._column._order})).forEach((function(t,e,n){e<n.indexOf(r)&&(t._column.width=t.offsetWidth+"px",t._column.flexGrow=0)})),"end"===t.detail.state&&(this._toggleAttribute("column-resizing",!1,this.$.scroller),this.dispatchEvent(new CustomEvent("column-resize",{detail:{resizedColumn:n}}))),this._resizeHandler()}}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,z$t=class t{constructor(t,e,n){this.grid=t,this.parentCache=e,this.parentItem=n,this.itemCaches={},this.items={},this.effectiveSize=0,this.size=0,this.pendingRequests={}}isLoading(){return Boolean(Object.keys(this.pendingRequests).length||Object.keys(this.itemCaches).filter((t=>this.itemCaches[t].isLoading()))[0])}getItemForIndex(t){const{cache:e,scaledIndex:n}=this.getCacheAndIndex(t);return e.items[n]}updateSize(){this.effectiveSize=!this.parentItem||this.grid._isExpanded(this.parentItem)?this.size+Object.keys(this.itemCaches).reduce(((t,e)=>{const n=this.itemCaches[e];return n.updateSize(),t+n.effectiveSize}),0):0}ensureSubCacheForScaledIndex(e){if(!this.itemCaches[e]){const n=new t(this.grid,this,this.items[e]);this.itemCaches[e]=n,this.grid._loadPage(0,n)}}getCacheAndIndex(t){let e=t;const n=Object.keys(this.itemCaches);for(let t=0;t<n.length;t++){const i=Number(n[t]),r=this.itemCaches[i];if(e<=i)return{cache:this,scaledIndex:e};if(e<=i+r.effectiveSize)return r.getCacheAndIndex(e-i-1);e-=r.effectiveSize}return{cache:this,scaledIndex:e}}},D$t=t=>class extends t{static get properties(){return{pageSize:{type:Number,value:50,observer:"_pageSizeChanged"},dataProvider:{type:Object,notify:!0,observer:"_dataProviderChanged"},loading:{type:Boolean,notify:!0,readOnly:!0,reflectToAttribute:!0},_cache:{type:Object,value:function(){return new z$t(this)}},itemIdPath:{type:String,value:null},expandedItems:{type:Object,notify:!0,value:()=>[]}}}static get observers(){return["_sizeChanged(size)","_itemIdPathChanged(itemIdPath)","_expandedItemsChanged(expandedItems.*)"]}_sizeChanged(t){const e=t-this._cache.size;this._cache.size+=e,this._cache.effectiveSize+=e,this._effectiveSize=this._cache.effectiveSize,this._increasePoolIfNeeded(0),this._debounceIncreasePool&&this._debounceIncreasePool.flush()}_getItem(t,e){if(t>=this._effectiveSize)return;e.index=t;const{cache:n,scaledIndex:i}=this._cache.getCacheAndIndex(t),r=n.items[i];r?(this._toggleAttribute("loading",!1,e),this._updateItem(e,r),this._isExpanded(r)&&n.ensureSubCacheForScaledIndex(i)):(this._toggleAttribute("loading",!0,e),this._loadPage(this._getPageForIndex(i),n))}_expandedInstanceChangedCallback(t,e){void 0!==t.item&&(e?this.expandItem(t.item):this.collapseItem(t.item))}getItemId(t){return this.itemIdPath?this.get(this.itemIdPath,t):t}_isExpanded(t){return this.__expandedKeys.has(this.getItemId(t))}_expandedItemsChanged(){this.__cacheExpandedKeys(),this._cache.updateSize(),this._effectiveSize=this._cache.effectiveSize,this._assignModels()}_itemIdPathChanged(){this.__cacheExpandedKeys()}__cacheExpandedKeys(){this.expandedItems&&(this.__expandedKeys=new Set,this.expandedItems.forEach((t=>{this.__expandedKeys.add(this.getItemId(t))})))}expandItem(t){this._isExpanded(t)||(this.expandedItems=[...this.expandedItems,t])}collapseItem(t){this._isExpanded(t)&&(this.expandedItems=this.expandedItems.filter((e=>!this._itemsEqual(e,t))))}_getIndexLevel(t){let{cache:e}=this._cache.getCacheAndIndex(t),n=0;for(;e.parentCache;)e=e.parentCache,n++;return n}_canPopulate(){return Boolean(this._hasData&&this._columnTree)}_loadPage(t,e){if(!e.pendingRequests[t]&&this.dataProvider){this._setLoading(!0),e.pendingRequests[t]=!0;const n={page:t,pageSize:this.pageSize,sortOrders:this._mapSorters(),filters:this._mapFilters(),parentItem:e.parentItem};this._debounceIncreasePool&&this._debounceIncreasePool.flush(),this.dataProvider(n,((i,r)=>{void 0!==r?e.size=r:n.parentItem&&(e.size=i.length);const o=Array.from(this.$.items.children).map((t=>t._item));i.forEach(((n,i)=>{const r=t*this.pageSize+i;e.items[r]=n,this._isExpanded(n)&&o.indexOf(n)>-1&&e.ensureSubCacheForScaledIndex(r)})),this._hasData=!0,delete e.pendingRequests[t],this._debouncerApplyCachedData=Tn.debounce(this._debouncerApplyCachedData,gt.after(0),(()=>{this._setLoading(!1),this._cache.updateSize(),this._effectiveSize=this._cache.effectiveSize,Array.from(this.$.items.children).filter((t=>!t.hidden)).forEach((t=>{this._cache.getItemForIndex(t.index)&&this._getItem(t.index,t)})),this._increasePoolIfNeeded(0),this.__scrollToPendingIndex()})),this._cache.isLoading()||this._debouncerApplyCachedData.flush(),this.__itemsReceived()}))}}_getPageForIndex(t){return Math.floor(t/this.pageSize)}clearCache(){this._cache=new z$t(this),Array.from(this.$.items.children).forEach((t=>{Array.from(t.children).forEach((t=>{t._instance&&t._instance._setPendingProperty("item",{},!1)}))})),this._cache.size=this.size||0,this._cache.updateSize(),this._hasData=!1,this._assignModels(),this._effectiveSize&&this._initialPoolCreated||this._loadPage(0,this._cache)}_pageSizeChanged(t,e){void 0!==e&&t!==e&&this.clearCache()}_checkSize(){void 0===this.size&&0===this._effectiveSize&&console.warn("The <vaadin-grid> needs the total number of items in order to display rows. Set the total number of items to the `size` property, or provide the total number of items in the second argument of the `dataProvider`’s `callback` call.")}_dataProviderChanged(t,e){void 0!==e&&this.clearCache(),t&&this.items&&this.items.length&&this._scrollToIndex(this._firstVisibleIndex),this._ensureFirstPageLoaded(),this._debouncerCheckSize=Tn.debounce(this._debouncerCheckSize,gt.after(2e3),this._checkSize.bind(this)),this._scrollHandler()}_ensureFirstPageLoaded(){this._hasData||this._loadPage(0,this._cache)}_itemsEqual(t,e){return this.getItemId(t)===this.getItemId(e)}_getItemIndexInArray(t,e){let n=-1;return e.forEach(((e,i)=>{this._itemsEqual(e,t)&&(n=i)})),n}scrollToIndex(t){super.scrollToIndex(t),isNaN(t)||!this._cache.isLoading()&&this.clientHeight||(this.__pendingScrollToIndex=t)}__scrollToPendingIndex(){if(this.__pendingScrollToIndex&&this.$.items.children.length){const t=this.__pendingScrollToIndex;delete this.__pendingScrollToIndex,this._debounceIncreasePool&&this._debounceIncreasePool.flush(),this.scrollToIndex(t)}}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,B$t=t=>class extends t{ready(){super.ready(),this._addNodeObserver()}_hasColumnGroups(t){for(let e=0;e<t.length;e++)if("vaadin-grid-column-group"===t[e].localName)return!0;return!1}_getChildColumns(t){return zi.getFlattenedNodes(t).filter(this._isColumnElement)}_flattenColumnGroups(t){return t.map((t=>"vaadin-grid-column-group"===t.localName?this._getChildColumns(t):[t])).reduce(((t,e)=>t.concat(e)),[])}_getColumnTree(){const t=[];for(let e=zi.getFlattenedNodes(this).filter(this._isColumnElement);t.push(e),this._hasColumnGroups(e);)e=this._flattenColumnGroups(e);return t}_updateColumnTree(){const t=this._getColumnTree();this._arrayEquals(t,this._columnTree)||(this._columnTree=t)}_addNodeObserver(){this._observer=new zi(this,(t=>{const e=t.addedNodes.filter((t=>"template"===t.localName&&t.classList.contains("row-details")))[0];e&&this._rowDetailsTemplate!==e&&(this._rowDetailsTemplate=e);const n=t=>t.filter(this._isColumnElement).length>0;if(n(t.addedNodes)||n(t.removedNodes)){const e=t.removedNodes.flatMap((t=>t._allCells)),n=t=>e.filter((e=>e._content.contains(t))).length;this.__removeSorters(this._sorters.filter(n)),this.__removeFilters(this._filters.filter(n)),this._updateColumnTree()}this._debouncerCheckImports=Tn.debounce(this._debouncerCheckImports,gt.after(2e3),this._checkImports.bind(this)),this._ensureFirstPageLoaded()}))}_arrayEquals(t,e){if(!t||!e||t.length!=e.length)return!1;for(let n=0,i=t.length;n<i;n++)if(t[n]instanceof Array&&e[n]instanceof Array){if(!this._arrayEquals(t[n],e[n]))return!1}else if(t[n]!=e[n])return!1;return!0}_checkImports(){["vaadin-grid-column-group","vaadin-grid-filter","vaadin-grid-filter-column","vaadin-grid-tree-toggle","vaadin-grid-selection-column","vaadin-grid-sort-column","vaadin-grid-sorter"].forEach((t=>{const e=this.querySelector(t);!e||e instanceof ye||console.warn(`Make sure you have imported the required module for <${t}> element.`)}))}_updateFirstAndLastColumn(){Array.from(this.shadowRoot.querySelectorAll("tr")).forEach((t=>this._updateFirstAndLastColumnForRow(t)))}_updateFirstAndLastColumnForRow(t){Array.from(t.querySelectorAll('[part~="cell"]:not([part~="details-cell"])')).sort(((t,e)=>t._column._order-e._column._order)).forEach(((t,e,n)=>{this._toggleAttribute("first-column",0===e,t),this._toggleAttribute("last-column",e===n.length-1,t)}))}_isColumnElement(t){return t.nodeType===Node.ELEMENT_NODE&&/\bcolumn\b/.test(t.localName)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,H$t=t=>class extends t{getEventContext(t){const e={},n=t.composedPath(),i=n[n.indexOf(this.$.table)-3];return i?(e.section=["body","header","footer","details"].filter((t=>i.getAttribute("part").indexOf(t)>-1))[0],i._column&&(e.column=i._column),"body"!==e.section&&"details"!==e.section||Object.assign(e,this.__getRowModel(i.parentElement)),e):e}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,F$t=t=>class extends t{static get properties(){return{_filters:{type:Array,value:function(){return[]}}}}ready(){super.ready(),this.addEventListener("filter-changed",this._filterChanged.bind(this))}_filterChanged(t){t.stopPropagation(),this.__addFilter(t.target),this.__applyFilters()}__removeFilters(t){0!=t.length&&(this._filters=this._filters.filter((e=>t.indexOf(e)<0)),this.__applyFilters())}__addFilter(t){-1===this._filters.indexOf(t)&&this._filters.push(t)}__applyFilters(){this.dataProvider&&this.isAttached&&this.clearCache()}_mapFilters(){return this._filters.map((t=>({path:t.path,value:t.value})))}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */;class V$t extends ye{static get is(){return"vaadin-grid-templatizer"}static get properties(){return{dataHost:Object,template:Object,_templateInstances:{type:Array,value:function(){return[]}},_parentPathValues:{value:function(){return{}}},_grid:Object}}static get observers(){return["_templateInstancesChanged(_templateInstances.*, _parentPathValues.*)"]}constructor(){super(),this._instanceProps={detailsOpened:!0,index:!0,item:!0,selected:!0,expanded:!0,level:!0}}createInstance(){this._ensureTemplatized();const t=new this._TemplateClass({});return this.addInstance(t),t}addInstance(t){-1===this._templateInstances.indexOf(t)&&(this._templateInstances.push(t),requestAnimationFrame((()=>this.notifyPath("_templateInstances.*",this._templateInstances))))}removeInstance(t){const e=this._templateInstances.indexOf(t);this.splice("_templateInstances",e,1)}_ensureTemplatized(){this._TemplateClass||(this._TemplateClass=$r(this.template,this,{instanceProps:this._instanceProps,parentModel:!0,forwardHostProp:function(t,e){this._forwardParentProp(t,e),this._templateInstances&&this._templateInstances.forEach((n=>n.notifyPath(t,e)))},notifyInstanceProp:function(t,e,n){if("index"===e||"item"===e)return;const i=`__${e}__`;if(t[i]===n)return;t[i]=n;const r=Array.from(this._grid.$.items.children).filter((e=>this._grid._itemsEqual(e._item,t.item)))[0];r&&Array.from(r.children).forEach((t=>{t._instance&&(t._instance[i]=n,t._instance.notifyPath(e,n))}));const o="item.";if(Array.isArray(this._grid.items)&&0===e.indexOf(o)){const i=this._grid.items.indexOf(t.item),r=e.slice(o.length);this._grid.notifyPath(`items.${i}.${r}`,n)}const a=`_${e}InstanceChangedCallback`;this._grid&&this._grid[a]&&this._grid[a](t,n)}}))}_forwardParentProp(t,e){this._parentPathValues[t]=e,this._templateInstances.forEach((n=>n.notifyPath(t,e)))}_templateInstancesChanged(t){let e,n;if("_templateInstances"===t.path)e=0,n=this._templateInstances.length;else{if("_templateInstances.splices"!==t.path)return;e=t.value.index,n=t.value.addedCount}Object.keys(this._parentPathValues||{}).forEach((t=>{for(let i=e;i<e+n;i++)this._templateInstances[i].set(t,this._parentPathValues[t])}))}}customElements.define(V$t.is,V$t);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const U$t=t=>class extends t{static get properties(){return{detailsOpenedItems:{type:Array,value:function(){return[]}},_rowDetailsTemplate:Object,rowDetailsRenderer:Function,_detailsCells:{type:Array}}}static get observers(){return["_detailsOpenedItemsChanged(detailsOpenedItems.*, _rowDetailsTemplate, rowDetailsRenderer)","_rowDetailsTemplateOrRendererChanged(_rowDetailsTemplate, rowDetailsRenderer)"]}_rowDetailsTemplateOrRendererChanged(t,e){if(t&&e)throw new Error("You should only use either a renderer or a template for row details");if(t||e){if(t&&!t.templatizer){const e=new V$t;e._grid=this,e.dataHost=this.dataHost,e.template=t,t.templatizer=e}this._columnTree&&Array.from(this.$.items.children).forEach((t=>{t.querySelector("[part~=details-cell]")||(this._updateRow(t,this._columnTree[this._columnTree.length-1]),this._a11yUpdateRowDetailsOpened(t,!1)),delete t.querySelector("[part~=details-cell]")._instance})),this.detailsOpenedItems.length&&(Array.from(this.$.items.children).forEach(this._toggleDetailsCell,this),this._update())}}_detailsOpenedItemsChanged(t){"detailsOpenedItems.length"!==t.path&&t.value&&Array.from(this.$.items.children).forEach((t=>{this._toggleDetailsCell(t,t._item),this._a11yUpdateRowDetailsOpened(t,this._isDetailsOpened(t._item)),this._toggleAttribute("details-opened",this._isDetailsOpened(t._item),t)}))}_configureDetailsCell(t){t.setAttribute("part","cell details-cell"),this._toggleAttribute("frozen",!0,t)}_toggleDetailsCell(t,e){const n=t.querySelector('[part~="details-cell"]');if(!n)return;const i=!this._isDetailsOpened(e),r=!!n.hidden!==i;(n._instance||n._renderer)&&n.hidden===i||(n.hidden=i,i?t.style.removeProperty("padding-bottom"):(this.rowDetailsRenderer?(n._renderer=this.rowDetailsRenderer,n._renderer.call(this,n._content,this,{index:t.index,item:e})):this._rowDetailsTemplate&&!n._instance&&(n._instance=this._rowDetailsTemplate.templatizer.createInstance(),n._content.innerHTML="",n._content.appendChild(n._instance.root),this._updateItem(t,e)),Di(),t.style.setProperty("padding-bottom",`${n.offsetHeight}px`),requestAnimationFrame((()=>this.notifyResize())))),r&&(this._updateMetrics(),this._positionItems())}_updateDetailsCellHeights(){Array.from(this.$.items.querySelectorAll('[part~="details-cell"]:not([hidden])')).forEach((t=>{t.parentElement.style.setProperty("padding-bottom",`${t.offsetHeight}px`)}))}_isDetailsOpened(t){return this.detailsOpenedItems&&-1!==this._getItemIndexInArray(t,this.detailsOpenedItems)}openItemDetails(t){this._isDetailsOpened(t)||(this.detailsOpenedItems=[...this.detailsOpenedItems,t])}closeItemDetails(t){this._isDetailsOpened(t)&&(this.detailsOpenedItems=this.detailsOpenedItems.filter((e=>!this._itemsEqual(e,t))))}_detailsOpenedInstanceChangedCallback(t,e){e?this.openItemDetails(t.item):this.closeItemDetails(t.item)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,j$t=t=>class extends t{static get properties(){return{_frozenCells:{type:Array,value:()=>[]},_rowWithFocusedElement:Element,_deltaYAcc:{type:Number,value:0},_useSticky:{type:Boolean,value:window.CSS&&window.CSS.supports&&(window.CSS.supports("position","sticky")||window.CSS.supports("position","-webkit-sticky"))}}}static get observers(){return["_scrollViewportHeightUpdated(_viewportHeight)"]}set _scrollTop(t){this.$.table.scrollTop=t}get _scrollTop(){return this.$.table.scrollTop}constructor(){super(),this._scrollLineHeight=this._getScrollLineHeight()}_getScrollLineHeight(){const t=document.createElement("div");t.style.fontSize="initial",t.style.display="none",document.body.appendChild(t);const e=window.getComputedStyle(t).fontSize;return document.body.removeChild(t),e?window.parseInt(e):void 0}_scrollViewportHeightUpdated(t){this._scrollPageHeight=t-this.$.header.clientHeight-this.$.footer.clientHeight-this._scrollLineHeight}ready(){super.ready(),this.$.outerscroller=document.createElement("div"),this.scrollTarget=this.$.table,this.addEventListener("wheel",this._onWheel),this.$.items.addEventListener("focusin",(t=>{const e=t.composedPath().indexOf(this.$.items);this._rowWithFocusedElement=t.composedPath()[e-1]})),this.$.items.addEventListener("focusout",(()=>this._rowWithFocusedElement=void 0)),this.scrollTarget.addEventListener("mousedown",(()=>this.__mouseDown=!0)),this.scrollTarget.addEventListener("mouseup",(()=>{this.__mouseDown=!1,this.__pendingReorder&&(this.__pendingReorder=!1,setTimeout((()=>this._reorderRows()),500))}))}scrollToIndex(t){this._accessIronListAPI((()=>super.scrollToIndex(t)))}_onWheel(t){if(t.ctrlKey||this._hasScrolledAncestor(t.target,t.deltaX,t.deltaY))return;const e=this.$.table;let n=t.deltaY;if(t.deltaMode===WheelEvent.DOM_DELTA_LINE?n*=this._scrollLineHeight:t.deltaMode===WheelEvent.DOM_DELTA_PAGE&&(n*=this._scrollPageHeight),this._wheelAnimationFrame)return this._deltaYAcc+=n,void t.preventDefault();n+=this._deltaYAcc,this._deltaYAcc=0,this._wheelAnimationFrame=!0,this._debouncerWheelAnimationFrame=Tn.debounce(this._debouncerWheelAnimationFrame,_t,(()=>this._wheelAnimationFrame=!1));const i=Math.abs(t.deltaX)+Math.abs(n);this._canScroll(e,t.deltaX,n)?(t.preventDefault(),e.scrollTop+=n,e.scrollLeft+=t.deltaX,this._scrollHandler(),this._hasResidualMomentum=!0,this._ignoreNewWheel=!0,this._debouncerIgnoreNewWheel=Tn.debounce(this._debouncerIgnoreNewWheel,gt.after(500),(()=>this._ignoreNewWheel=!1))):this._hasResidualMomentum&&i<=this._previousMomentum||this._ignoreNewWheel?t.preventDefault():i>this._previousMomentum&&(this._hasResidualMomentum=!1),this._previousMomentum=i}_hasScrolledAncestor(t,e,n){return"vaadin-grid-cell-content"!==t.localName&&(!(!this._canScroll(t,e,n)||-1===["auto","scroll"].indexOf(getComputedStyle(t).overflow))||(t!==this&&t.parentElement?this._hasScrolledAncestor(t.parentElement,e,n):void 0))}_canScroll(t,e,n){return n>0&&t.scrollTop<t.scrollHeight-t.offsetHeight||n<0&&t.scrollTop>0||e>0&&t.scrollLeft<t.scrollWidth-t.offsetWidth||e<0&&t.scrollLeft>0}_scheduleScrolling(){this._scrollingFrame||(this._scrollingFrame=requestAnimationFrame((()=>this._toggleAttribute("scrolling",!0,this.$.scroller)))),this._debounceScrolling=Tn.debounce(this._debounceScrolling,gt.after(500),(()=>{cancelAnimationFrame(this._scrollingFrame),delete this._scrollingFrame,this._toggleAttribute("scrolling",!1,this.$.scroller),this._reorderRows()}))}_afterScroll(){this._translateStationaryElements(),this.hasAttribute("reordering")||this._scheduleScrolling(),this._updateOverflow()}_updateOverflow(){let t="";const e=this.$.table;e.scrollTop<e.scrollHeight-e.clientHeight&&(t+=" bottom"),e.scrollTop>0&&(t+=" top"),e.scrollLeft<e.scrollWidth-e.clientWidth&&(t+=" right"),e.scrollLeft>0&&(t+=" left"),this._debounceOverflow=Tn.debounce(this._debounceOverflow,_t,(()=>{const e=t.trim();e.length>0&&this.getAttribute("overflow")!==e?this.setAttribute("overflow",e):0==e.length&&this.hasAttribute("overflow")&&this.removeAttribute("overflow")}))}_reorderRows(){if(this.__mouseDown)return void(this.__pendingReorder=!0);const t=this.$.items,e=t.querySelectorAll("tr");if(!e.length)return;const n=this._virtualStart+this._vidxOffset,i=this._rowWithFocusedElement||Array.from(e).filter((t=>!t.hidden))[0];if(!i)return;const r=i.index-n,o=Array.from(e).indexOf(i)-r;if(o>0)for(let n=0;n<o;n++)t.appendChild(e[n]);else if(o<0)for(let n=e.length+o;n<e.length;n++)t.insertBefore(e[n],e[0]);if(this._safari){const{transform:t}=this.$.header.style;this.$.header.style.transform="",setTimeout((()=>this.$.header.style.transform=t))}}_frozenCellsChanged(){this._debouncerCacheElements=Tn.debounce(this._debouncerCacheElements,vt,(()=>{Array.from(this.shadowRoot.querySelectorAll('[part~="cell"]')).forEach((function(t){t.style.transform=""})),this._frozenCells=Array.prototype.slice.call(this.$.table.querySelectorAll("[frozen]")),this._updateScrollerMeasurements(),this._translateStationaryElements()})),this._updateLastFrozen()}_updateScrollerMeasurements(){this._frozenCells.length>0&&this.__isRTL&&(this.__scrollerMetrics={scrollWidth:this.$.table.scrollWidth,clientWidth:this.$.table.clientWidth})}_updateLastFrozen(){if(!this._columnTree)return;const t=this._columnTree[this._columnTree.length-1].slice(0);t.sort(((t,e)=>t._order-e._order));const e=t.reduce(((t,e,n)=>(e._lastFrozen=!1,e.frozen&&!e.hidden?n:t)),void 0);void 0!==e&&(t[e]._lastFrozen=!0)}_translateStationaryElements(){const t=Math.max(0,this._scrollLeft),e=Math.max(0,this._scrollTop);let n=0,i=0,r=0;if(this._useSticky||(n=t,i=e,r=this.$.table.clientHeight-this.$.footer.offsetHeight-this.$.footer.offsetTop),this.$.header.style.transform=this._getTranslate(-t+n,i),this.$.footer.style.transform=this._getTranslate(-t+n,i+r),this.$.items.style.transform=this._getTranslate(-t+n,0),this._frozenCells.length>0){const t=this.__isRTL?this.__getNormalizedScrollLeft(this.$.table)+this.__scrollerMetrics.clientWidth-this.__scrollerMetrics.scrollWidth:this._scrollLeft,e=this._getTranslate(t,0);for(let t=0;t<this._frozenCells.length;t++)this._frozenCells[t].style.transform=e}}_getTranslate(t,e){return`translate(${t}px, ${e}px)`}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,G$t=t=>class extends t{static get properties(){return{selectedItems:{type:Object,notify:!0,value:()=>[]}}}static get observers(){return["_selectedItemsChanged(selectedItems.*)"]}_isSelected(t){return this.selectedItems&&this._getItemIndexInArray(t,this.selectedItems)>-1}selectItem(t){this._isSelected(t)||(this.selectedItems=[...this.selectedItems,t])}deselectItem(t){this._isSelected(t)&&(this.selectedItems=this.selectedItems.filter((e=>!this._itemsEqual(e,t))))}_toggleItem(t){-1===this._getItemIndexInArray(t,this.selectedItems)?this.selectItem(t):this.deselectItem(t)}_selectedItemsChanged(t){!this.$.items.children.length||"selectedItems"!==t.path&&"selectedItems.splices"!==t.path||Array.from(this.$.items.children).forEach((t=>{this._updateItem(t,t._item)}))}_selectedInstanceChangedCallback(t,e){e?this.selectItem(t.item):this.deselectItem(t.item)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,W$t=t=>class extends t{static get properties(){return{multiSort:{type:Boolean,value:!1},_sorters:{type:Array,value:function(){return[]}},_previousSorters:{type:Array,value:function(){return[]}}}}ready(){super.ready(),this.addEventListener("sorter-changed",this._onSorterChanged)}_onSorterChanged(t){const e=t.target;t.stopPropagation(),this.__updateSorter(e),this.__applySorters()}__removeSorters(t){0!=t.length&&(this._sorters=this._sorters.filter((e=>t.indexOf(e)<0)),this.multiSort&&this.__updateSortOrders(),this.__applySorters())}__updateSortOrders(){this._sorters.forEach(((t,e)=>t._order=this._sorters.length>1?e:null),this)}__updateSorter(t){if(t.direction||-1!==this._sorters.indexOf(t))if(t._order=null,this.multiSort)this._removeArrayItem(this._sorters,t),t.direction&&this._sorters.unshift(t),this.__updateSortOrders();else if(t.direction){const e=this._sorters.filter((e=>e!=t));this._sorters=[t],e.forEach((t=>{t._order=null,t.direction=null}))}}__applySorters(){this.dataProvider&&this.isAttached&&JSON.stringify(this._previousSorters)!==JSON.stringify(this._mapSorters())&&this.clearCache(),this._a11yUpdateSorters(),this._previousSorters=this._mapSorters()}_mapSorters(){return this._sorters.map((t=>({path:t.path,direction:t.direction})))}_removeArrayItem(t,e){const n=t.indexOf(e);n>-1&&t.splice(n,1)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,q$t=t=>class extends t{static get properties(){return{cellClassNameGenerator:Function}}static get observers(){return["__cellClassNameGeneratorChanged(cellClassNameGenerator)"]}__cellClassNameGeneratorChanged(){this.generateCellClassNames()}generateCellClassNames(){Array.from(this.$.items.children).filter((t=>!t.hidden)).forEach((t=>this._generateCellClassNames(t,this.__getRowModel(t))))}_generateCellClassNames(t,e){Array.from(t.children).forEach((t=>{if(t.__generatedClasses&&t.__generatedClasses.forEach((e=>t.classList.remove(e))),this.cellClassNameGenerator){const n=this.cellClassNameGenerator(t._column,e);t.__generatedClasses=n&&n.split(" ").filter((t=>t.length>0)),t.__generatedClasses&&t.__generatedClasses.forEach((e=>t.classList.add(e)))}}))}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,Y$t="between",X$t="on-top-or-between",$$t="above",K$t="below",Z$t="empty",J$t=t=>class extends t{static get properties(){return{dropMode:String,rowsDraggable:Boolean,dragFilter:Function,dropFilter:Function,__dndAutoScrollThreshold:{value:50}}}static get observers(){return["_dragDropAccessChanged(rowsDraggable, dropMode, dragFilter, dropFilter)"]}ready(){super.ready(),this.$.table.addEventListener("dragstart",this._onDragStart.bind(this)),this.$.table.addEventListener("dragend",this._onDragEnd.bind(this)),this.$.table.addEventListener("dragover",this._onDragOver.bind(this)),this.$.table.addEventListener("dragleave",this._onDragLeave.bind(this)),this.$.table.addEventListener("drop",this._onDrop.bind(this)),this.$.table.addEventListener("dragenter",(t=>{this.dropMode&&(t.preventDefault(),t.stopPropagation())}))}_onDragStart(t){if(this.rowsDraggable){let e=t.target;if("vaadin-grid-cell-content"===e.localName&&(e=e.assignedSlot.parentNode.parentNode),e.parentNode!==this.$.items)return;if(t.stopPropagation(),this._toggleAttribute("dragging-rows",!0,this),this._safari){const t=e.style.transform;e.style.top=/translateY\((.*)\)/.exec(t)[1],e.style.transform="none",requestAnimationFrame((()=>{e.style.top="",e.style.transform=t}))}const n=e.getBoundingClientRect();this._ios?t.dataTransfer.setDragImage(e):t.dataTransfer.setDragImage(e,t.clientX-n.left,t.clientY-n.top);let i=[e];this._isSelected(e._item)&&(i=this.__getViewportRows().filter((t=>this._isSelected(t._item))).filter((t=>!this.dragFilter||this.dragFilter(this.__getRowModel(t))))),t.dataTransfer.setData("text",this.__formatDefaultTransferData(i)),e.setAttribute("dragstart",i.length>1?i.length:""),this.updateStyles({"--_grid-drag-start-x":t.clientX-n.left+20+"px","--_grid-drag-start-y":t.clientY-n.top+10+"px"}),requestAnimationFrame((()=>{e.removeAttribute("dragstart"),this.updateStyles({"--_grid-drag-start-x":"","--_grid-drag-start-y":""})}));const r=new CustomEvent("grid-dragstart",{detail:{draggedItems:i.map((t=>t._item)),setDragData:(e,n)=>t.dataTransfer.setData(e,n),setDraggedItemsCount:t=>e.setAttribute("dragstart",t)}});r.originalEvent=t,this.dispatchEvent(r)}}_onDragEnd(t){this._toggleAttribute("dragging-rows",!1,this),t.stopPropagation();const e=new CustomEvent("grid-dragend");e.originalEvent=t,this.dispatchEvent(e)}_onDragLeave(t){t.stopPropagation(),this._clearDragStyles()}_onDragOver(t){if(this.dropMode){if(this._dropLocation=void 0,this._dragOverItem=void 0,this.__dndAutoScroll(t.clientY))return void this._clearDragStyles();let e=t.composedPath().filter((t=>"tr"===t.localName))[0];if(this._effectiveSize&&"on-grid"!==this.dropMode)if(e&&e.parentNode===this.$.items){const n=e.getBoundingClientRect();this._dropLocation="on-top",this.dropMode===Y$t?this._dropLocation=t.clientY-n.top<n.bottom-t.clientY?$$t:K$t:this.dropMode===X$t&&(t.clientY-n.top<n.height/3?this._dropLocation=$$t:t.clientY-n.top>n.height/3*2&&(this._dropLocation=K$t))}else{if(e)return;if(this.dropMode!==Y$t&&this.dropMode!==X$t)return;e=Array.from(this.$.items.children).filter((t=>!t.hidden)).pop(),this._dropLocation=K$t}else this._dropLocation=Z$t;if(e&&e.hasAttribute("drop-disabled"))return void(this._dropLocation=void 0);t.stopPropagation(),t.preventDefault(),this._dropLocation===Z$t?this._toggleAttribute("dragover",!0,this):e?(this._dragOverItem=e._item,e.getAttribute("dragover")!==this._dropLocation&&e.setAttribute("dragover",this._dropLocation)):this._clearDragStyles()}}__dndAutoScroll(t){if(this.__dndAutoScrolling)return!0;const e=this.$.header.getBoundingClientRect().bottom,n=this.$.footer.getBoundingClientRect().top,i=e-t+this.__dndAutoScrollThreshold,r=t-n+this.__dndAutoScrollThreshold;let o=0;if(r>0?o=2*r:i>0&&(o=2*-i),o){const t=this.$.table.scrollTop;if(this.$.table.scrollTop+=o,t!==this.$.table.scrollTop)return this.__dndAutoScrolling=!0,setTimeout((()=>this.__dndAutoScrolling=!1),20),this._scrollHandler(),!0}}__getViewportRows(){const t=this.$.header.getBoundingClientRect().bottom,e=this.$.footer.getBoundingClientRect().top;return Array.from(this.$.items.children).filter((n=>{const i=n.getBoundingClientRect();return i.bottom>t&&i.top<e}))}_clearDragStyles(){this.removeAttribute("dragover"),Array.from(this.$.items.children).forEach((t=>t.removeAttribute("dragover")))}_onDrop(t){if(this.dropMode){t.stopPropagation(),t.preventDefault();const e=t.dataTransfer.types&&Array.from(t.dataTransfer.types).map((e=>({type:e,data:t.dataTransfer.getData(e)})));this._clearDragStyles();const n=new CustomEvent("grid-drop",{bubbles:t.bubbles,cancelable:t.cancelable,detail:{dropTargetItem:this._dragOverItem,dropLocation:this._dropLocation,dragData:e}});n.originalEvent=t,this.dispatchEvent(n)}}__formatDefaultTransferData(t){return t.map((t=>Array.from(t.children).filter((t=>!t.hidden&&-1===t.getAttribute("part").indexOf("details-cell"))).sort(((t,e)=>t._column._order>e._column._order?1:-1)).map((t=>t._content.textContent.trim())).filter((t=>t)).join("\t"))).join("\n")}_dragDropAccessChanged(){this.filterDragAndDrop()}filterDragAndDrop(){Array.from(this.$.items.children).filter((t=>!t.hidden)).forEach((t=>{this._filterDragAndDrop(t,this.__getRowModel(t))}))}_filterDragAndDrop(t,e){const n=!this.rowsDraggable||this.dragFilter&&!this.dragFilter(e),i=!this.dropMode||this.dropFilter&&!this.dropFilter(e);Array.from(t.children).map((t=>t._content)).forEach((t=>{n?t.removeAttribute("draggable"):t.setAttribute("draggable",!0)})),this._toggleAttribute("drag-disabled",n,t),this._toggleAttribute("drop-disabled",i,t)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,Q$t=t=>class extends t{static get properties(){return{_headerFocusable:{type:Object,observer:"_focusableChanged"},_itemsFocusable:{type:Object,observer:"_focusableChanged"},_footerFocusable:{type:Object,observer:"_focusableChanged"},_navigatingIsHidden:Boolean,_focusedItemIndex:{type:Number,value:0},_focusedColumnOrder:Number,interacting:{type:Boolean,value:!1,reflectToAttribute:!0,readOnly:!0,observer:"_interactingChanged"}}}ready(){super.ready(),this._ios||this._android||(this.addEventListener("keydown",this._onKeyDown),this.addEventListener("keyup",this._onKeyUp),this.addEventListener("focusin",this._onFocusIn),this.addEventListener("focusout",this._onFocusOut),this.$.table.addEventListener("focusin",this._onCellFocusIn.bind(this)),this.$.table.addEventListener("focusout",this._onCellFocusOut.bind(this)),this.addEventListener("mousedown",(()=>{this._toggleAttribute("navigating",!1,this),this._isMousedown=!0})),this.addEventListener("mouseup",(()=>this._isMousedown=!1)))}_focusableChanged(t,e){e&&e.setAttribute("tabindex","-1"),t&&this._updateGridSectionFocusTarget(t)}_interactingChanged(){this._updateGridSectionFocusTarget(this._headerFocusable),this._updateGridSectionFocusTarget(this._itemsFocusable),this._updateGridSectionFocusTarget(this._footerFocusable)}_onKeyDown(t){const e=t.key;let n;switch(e){case"ArrowUp":case"ArrowDown":case"ArrowLeft":case"ArrowRight":case"PageUp":case"PageDown":case"Home":case"End":n="Navigation";break;case"Enter":case"Escape":case"F2":n="Interaction";break;case"Tab":n="Tab";break;case" ":n="Space"}this._detectInteracting(t),this.interacting&&"Interaction"!==n&&(n=void 0),n&&this[`_on${n}KeyDown`](t,e)}_ensureScrolledToIndex(t){Array.from(this.$.items.children).filter((e=>e.index===t))[0]||this._scrollToIndex(t)}_onNavigationKeyDown(t,e){function n(t){return Array.prototype.indexOf.call(t.parentNode.children,t)}this._scrollHandler(),t.preventDefault();const i=this._lastVisibleIndex-this._firstVisibleIndex-1;let r=0,o=0;switch(e){case"ArrowRight":r=this.__isRTL?-1:1;break;case"ArrowLeft":r=this.__isRTL?1:-1;break;case"Home":r=-1/0,t.ctrlKey&&(o=-1/0);break;case"End":r=1/0,t.ctrlKey&&(o=1/0);break;case"ArrowDown":o=1;break;case"ArrowUp":o=-1;break;case"PageDown":o=i;break;case"PageUp":o=-i}const a=t.composedPath()[0],s=n(a),l=this._elementMatches(a,'[part~="details-cell"]'),c=a.parentNode,u=c.parentNode,h=(u===this.$.items?this._effectiveSize:u.children.length)-1,d=u===this.$.items?void 0!==this._focusedItemIndex?this._focusedItemIndex:c.index:n(c);let p=Math.max(0,Math.min(d+o,h)),f=!1;if(u===this.$.items){const t=c._item,e=this._cache.getItemForIndex(p);f=l?0===o:1===o&&this._isDetailsOpened(t)||-1===o&&p!==d&&this._isDetailsOpened(e),f!==l&&(1===o&&f||-1===o&&!f)&&(p=d)}if(u!==this.$.items)if(p>d)for(;p<h&&u.children[p].hidden;)p++;else if(p<d)for(;p>0&&u.children[p].hidden;)p--;void 0===this._focusedColumnOrder&&(this._focusedColumnOrder=l?0:this._getColumns(u,d).filter((t=>!t.hidden))[s]._order);const m=this._getColumns(u,p).filter((t=>!t.hidden)),g=m.map((t=>t._order)).sort(((t,e)=>t-e)),_=g.length-1,y=g.indexOf(g.slice(0).sort(((t,e)=>Math.abs(t-this._focusedColumnOrder)-Math.abs(e-this._focusedColumnOrder)))[0]),v=0===o&&l?y:Math.max(0,Math.min(y+r,_));v!==y&&(this._focusedColumnOrder=void 0),u===this.$.items&&this._ensureScrolledToIndex(p),this._toggleAttribute("navigating",!0,this);const b=m.reduce(((t,e,n)=>(t[e._order]=n,t)),{})[g[v]],x=u===this.$.items?Array.from(u.children).filter((t=>t.index===p))[0]:u.children[p];if(!x)return;const w=f?Array.from(x.children).filter((t=>this._elementMatches(t,'[part~="details-cell"]')))[0]:x.children[b];if(this._scrollHorizontallyToCell(w),u===this.$.items&&(this._focusedItemIndex=p),u===this.$.items){const t=w.getBoundingClientRect(),e=this.$.footer.getBoundingClientRect().top,n=this.$.header.getBoundingClientRect().bottom;t.bottom>e?(this.$.table.scrollTop+=t.bottom-e,this._scrollHandler()):t.top<n&&(this.$.table.scrollTop-=n-t.top,this._scrollHandler())}w.focus()}_onInteractionKeyDown(t,e){const n=t.composedPath()[0],i="input"===n.localName&&!/^(button|checkbox|color|file|image|radio|range|reset|submit)$/i.test(n.type);let r;switch(e){case"Enter":r=!this.interacting||!i;break;case"Escape":r=!1;break;case"F2":r=!this.interacting}const{cell:o}=this._getGridEventLocation(t);if(this.interacting!==r&&null!==o)if(r){const e=o._content.querySelector("[focus-target]")||o._content.firstElementChild;e&&(t.preventDefault(),e.focus(),this._setInteracting(!0),this._toggleAttribute("navigating",!1,this))}else t.preventDefault(),this._focusedColumnOrder=void 0,o.focus(),this._setInteracting(!1),this._toggleAttribute("navigating",!0,this)}_predictFocusStepTarget(t,e){const n=[this.$.table,this._headerFocusable,this._itemsFocusable,this._footerFocusable,this.$.focusexit];let i=n.indexOf(t);for(i+=e;i>=0&&i<=n.length-1&&(!n[i]||n[i].parentNode.hidden);)i+=e;return n[i]}_onTabKeyDown(t){const e=this._predictFocusStepTarget(t.composedPath()[0],t.shiftKey?-1:1);if(e===this.$.table)this.$.table.focus();else if(e===this.$.focusexit)this.$.focusexit.focus();else if(e===this._itemsFocusable){let n=e;const i=this._itemsFocusable.parentNode;if(this._ensureScrolledToIndex(this._focusedItemIndex),i.index!==this._focusedItemIndex){const t=Array.from(i.children).indexOf(this._itemsFocusable),e=Array.from(this.$.items.children).filter((t=>t.index===this._focusedItemIndex))[0];e&&(n=e.children[t])}t.preventDefault(),n.focus()}else t.preventDefault(),e.focus();this._toggleAttribute("navigating",!0,this)}_onSpaceKeyDown(t){t.preventDefault();const e=t.composedPath()[0];e._content&&e._content.firstElementChild||this.dispatchEvent(new CustomEvent("cell-activate",{detail:{model:this.__getRowModel(e.parentElement)}}))}_onKeyUp(t){if(!/^( |SpaceBar)$/.test(t.key))return;t.preventDefault();const e=t.composedPath()[0];if(e._content&&e._content.firstElementChild){const t=this.hasAttribute("navigating");e._content.firstElementChild.click(),this._toggleAttribute("navigating",t,this)}}_onFocusIn(t){this._isMousedown||this._toggleAttribute("navigating",!0,this);const e=t.composedPath()[0];e===this.$.table||e===this.$.focusexit?(this._predictFocusStepTarget(e,e===this.$.table?1:-1).focus(),this._setInteracting(!1)):this._detectInteracting(t)}_onFocusOut(t){this._toggleAttribute("navigating",!1,this),this._detectInteracting(t)}_onCellFocusIn(t){const{section:e,cell:n}=this._getGridEventLocation(t);this._detectInteracting(t),e&&n&&(this._activeRowGroup=e,this.$.header===e?this._headerFocusable=n:this.$.items===e?this._itemsFocusable=n:this.$.footer===e&&(this._footerFocusable=n),n._content.dispatchEvent(new CustomEvent("cell-focusin",{bubbles:!1})),n.dispatchEvent(new CustomEvent("cell-focus",{bubbles:!0,composed:!0}))),this._detectFocusedItemIndex(t)}_onCellFocusOut(t){3===t.composedPath().indexOf(this.$.table)&&t.composedPath()[0]._content.dispatchEvent(new CustomEvent("cell-focusout",{bubbles:!1}))}_detectInteracting(t){const e=t.composedPath().some((t=>"vaadin-grid-cell-content"===t.localName));this._setInteracting(e)}_detectFocusedItemIndex(t){const{section:e,row:n}=this._getGridEventLocation(t);e===this.$.items&&(this._focusedItemIndex=n.index)}_updateGridSectionFocusTarget(t){if(!t)return;const e=this._getGridSectionFromFocusTarget(t);t.tabIndex=this.interacting&&e===this._activeRowGroup?-1:0}_preventScrollerRotatingCellFocus(t,e){t.index===this._focusedItemIndex&&this.hasAttribute("navigating")&&this._activeRowGroup===this.$.items&&(this._navigatingIsHidden=!0,this._toggleAttribute("navigating",!1,this)),e===this._focusedItemIndex&&this._navigatingIsHidden&&(this._navigatingIsHidden=!1,this._toggleAttribute("navigating",!0,this))}_getColumns(t,e){let n=this._columnTree.length-1;return t===this.$.header?n=e:t===this.$.footer&&(n=this._columnTree.length-1-e),this._columnTree[n]}_resetKeyboardNavigation(){if(this.$.header.firstElementChild&&(this._headerFocusable=Array.from(this.$.header.firstElementChild.children).filter((t=>!t.hidden))[0]),this.$.items.firstElementChild){const t=this._iterateItems(((t,e)=>{if(this._firstVisibleIndex===e)return this.$.items.children[t]}));t&&(this._itemsFocusable=Array.from(t.children).filter((t=>!t.hidden))[0])}this.$.footer.firstElementChild&&(this._footerFocusable=Array.from(this.$.footer.firstElementChild.children).filter((t=>!t.hidden))[0])}_scrollHorizontallyToCell(t){if(t.hasAttribute("frozen")||this._elementMatches(t,'[part~="details-cell"]'))return;const e=t.getBoundingClientRect(),n=t.parentNode,i=Array.from(n.children).indexOf(t),r=this.$.table.getBoundingClientRect();let o=r.left,a=r.right;for(let t=i-1;t>=0;t--){const e=n.children[t];if(!e.hasAttribute("hidden")&&!this._elementMatches(e,'[part~="details-cell"]')&&e.hasAttribute("frozen")){o=e.getBoundingClientRect().right;break}}for(let t=i+1;t<n.children.length;t++){const e=n.children[t];if(!e.hasAttribute("hidden")&&!this._elementMatches(e,'[part~="details-cell"]')&&e.hasAttribute("frozen")){a=e.getBoundingClientRect().left;break}}e.left<o&&(this.$.table.scrollLeft+=Math.round(e.left-o)),e.right>a&&(this.$.table.scrollLeft+=Math.round(e.right-a))}_elementMatches(t,e){return t.matches?t.matches(e):-1!==Array.from(t.parentNode.querySelectorAll(e)).indexOf(t)}_getGridEventLocation(t){const e=t.composedPath(),n=e.indexOf(this.$.table);return{section:n>=1?e[n-1]:null,row:n>=2?e[n-2]:null,cell:n>=3?e[n-3]:null}}_getGridSectionFromFocusTarget(t){return t===this._headerFocusable?this.$.header:t===this._itemsFocusable?this.$.items:t===this._footerFocusable?this.$.footer:null}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,tKt=t=>class extends(pi(t)){static get properties(){return{columnReorderingAllowed:{type:Boolean,value:!1},_orderBaseScope:{type:Number,value:1e7}}}static get observers(){return["_updateOrders(_columnTree, _columnTree.*)"]}ready(){super.ready(),ii(this,"track",this._onTrackEvent),this._reorderGhost=this.shadowRoot.querySelector('[part="reorder-ghost"]'),this.addEventListener("touchstart",this._onTouchStart.bind(this)),this.addEventListener("touchmove",this._onTouchMove.bind(this)),this.addEventListener("touchend",this._onTouchEnd.bind(this)),this.addEventListener("contextmenu",this._onContextMenu.bind(this))}_onContextMenu(t){this.hasAttribute("reordering")&&t.preventDefault()}_onTouchStart(t){this._startTouchReorderTimeout=setTimeout((()=>{this._onTrackStart({detail:{x:t.touches[0].clientX,y:t.touches[0].clientY}})}),100)}_onTouchMove(t){this._draggedColumn&&t.preventDefault(),clearTimeout(this._startTouchReorderTimeout)}_onTouchEnd(){clearTimeout(this._startTouchReorderTimeout),this._onTrackEnd()}_onTrackEvent(t){if("start"===t.detail.state){const e=t.composedPath(),n=e[e.indexOf(this.$.header)-2];if(!n||!n._content)return;if(n._content.contains(this.getRootNode().activeElement))return;if(this.$.scroller.hasAttribute("column-resizing"))return;this._touchDevice||this._onTrackStart(t)}else"track"===t.detail.state?this._onTrack(t):"end"===t.detail.state&&this._onTrackEnd(t)}_onTrackStart(t){if(!this.columnReorderingAllowed)return;const e=t.composedPath&&t.composedPath();if(e&&e.filter((t=>t.hasAttribute&&t.hasAttribute("draggable")))[0])return;const n=this._cellFromPoint(t.detail.x,t.detail.y);if(n&&-1!==n.getAttribute("part").indexOf("header-cell")){for(this._toggleAttribute("reordering",!0,this),this._draggedColumn=n._column;1===this._draggedColumn.parentElement.childElementCount;)this._draggedColumn=this._draggedColumn.parentElement;this._setSiblingsReorderStatus(this._draggedColumn,"allowed"),this._draggedColumn._reorderStatus="dragging",this._updateGhost(n),this._reorderGhost.style.visibility="visible",this._updateGhostPosition(t.detail.x,this._touchDevice?t.detail.y-50:t.detail.y),this._autoScroller()}}_onTrack(t){if(!this._draggedColumn)return;const e=this._cellFromPoint(t.detail.x,t.detail.y);if(!e)return;const n=this._getTargetColumn(e,this._draggedColumn);this._isSwapAllowed(this._draggedColumn,n)&&this._isSwappableByPosition(n,t.detail.x)&&this._swapColumnOrders(this._draggedColumn,n),this._updateGhostPosition(t.detail.x,this._touchDevice?t.detail.y-50:t.detail.y),this._lastDragClientX=t.detail.x}_onTrackEnd(){this._draggedColumn&&(this._toggleAttribute("reordering",!1,this),this._draggedColumn._reorderStatus="",this._setSiblingsReorderStatus(this._draggedColumn,""),this._draggedColumn=null,this._lastDragClientX=null,this._reorderGhost.style.visibility="hidden",this.dispatchEvent(new CustomEvent("column-reorder",{detail:{columns:this._getColumnsInOrder()}})))}_getColumnsInOrder(){return this._columnTree.slice(0).pop().filter((t=>!t.hidden)).sort(((t,e)=>t._order-e._order))}_cellFromPoint(t,e){t=t||0,e=e||0,this._draggedColumn||this._toggleAttribute("no-content-pointer-events",!0,this.$.scroller);const n=this.shadowRoot.elementFromPoint(t,e);if(this._toggleAttribute("no-content-pointer-events",!1,this.$.scroller),n&&n._column)return n}_updateGhostPosition(t,e){const n=this._reorderGhost.getBoundingClientRect(),i=t-n.width/2,r=e-n.height/2,o=parseInt(this._reorderGhost._left||0),a=parseInt(this._reorderGhost._top||0);this._reorderGhost._left=o-(n.left-i),this._reorderGhost._top=a-(n.top-r),this._reorderGhost.style.transform=`translate(${this._reorderGhost._left}px, ${this._reorderGhost._top}px)`}_updateGhost(t){const e=this._reorderGhost;e.textContent=t._content.innerText;const n=window.getComputedStyle(t);return["boxSizing","display","width","height","background","alignItems","padding","border","flex-direction","overflow"].forEach((t=>e.style[t]=n[t])),e}_updateOrders(t,e){void 0!==t&&void 0!==e&&(t[0].forEach((t=>t._order=0)),(function n(t,e,i){let r=1;t.forEach((t=>{r%10==0&&r++,t._order=i+r*e,r++}))}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */)(t[0],this._orderBaseScope,0))}_setSiblingsReorderStatus(t,e){Array.from(t.parentNode.children).filter((e=>/column/.test(e.localName)&&this._isSwapAllowed(e,t))).forEach((t=>t._reorderStatus=e))}_autoScroller(){if(this._lastDragClientX){const t=this._lastDragClientX-this.getBoundingClientRect().right+50,e=this.getBoundingClientRect().left-this._lastDragClientX+50;t>0?this.$.table.scrollLeft+=t/10:e>0&&(this.$.table.scrollLeft-=e/10),this._scrollHandler()}this._draggedColumn&&this.async(this._autoScroller,10)}_isSwapAllowed(t,e){if(t&&e){const n=t.parentElement===e.parentElement,i=t.frozen===e.frozen;return t!==e&&n&&i}}_isSwappableByPosition(t,e){const n=Array.from(this.$.header.querySelectorAll('tr:not([hidden]) [part~="cell"]')).filter((e=>t.contains(e._column)))[0],i=this.$.header.querySelector("tr:not([hidden]) [reorder-status=dragging]").getBoundingClientRect(),r=n.getBoundingClientRect();return r.left>i.left?e>r.right-i.width:e<r.left+i.width}_swapColumnOrders(t,e){const n=t._order;t._order=e._order,e._order=n,this._updateLastFrozen(),this._updateFirstAndLastColumn()}_getTargetColumn(t,e){if(t&&e){let n=t._column;for(;n.parentElement!==e.parentElement&&n!==this;)n=n.parentElement;return n.parentElement===e.parentElement?n:t._column}}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,eKt=t=>class extends t{static get properties(){return{resizable:{type:Boolean,value:function(){if("vaadin-grid-column-group"===this.localName)return;const t=this.parentNode;return t&&"vaadin-grid-column-group"===t.localName&&t.resizable||!1}},_headerTemplate:{type:Object},_footerTemplate:{type:Object},frozen:{type:Boolean,value:!1},hidden:{type:Boolean},header:{type:String},textAlign:{type:String},_lastFrozen:{type:Boolean,value:!1},_order:Number,_reorderStatus:Boolean,_emptyCells:Array,_headerCell:Object,_footerCell:Object,_grid:Object,headerRenderer:Function,footerRenderer:Function}}static get observers(){return["_widthChanged(width, _headerCell, _footerCell, _cells.*)","_frozenChanged(frozen, _headerCell, _footerCell, _cells.*)","_flexGrowChanged(flexGrow, _headerCell, _footerCell, _cells.*)","_pathOrHeaderChanged(path, header, _headerCell, _footerCell, _cells.*, renderer, headerRenderer, _bodyTemplate, _headerTemplate)","_textAlignChanged(textAlign, _cells.*, _headerCell, _footerCell)","_orderChanged(_order, _headerCell, _footerCell, _cells.*)","_lastFrozenChanged(_lastFrozen)","_setBodyTemplateOrRenderer(_bodyTemplate, renderer, _cells, _cells.*)","_setHeaderTemplateOrRenderer(_headerTemplate, headerRenderer, _headerCell)","_setFooterTemplateOrRenderer(_footerTemplate, footerRenderer, _footerCell)","_resizableChanged(resizable, _headerCell)","_reorderStatusChanged(_reorderStatus, _headerCell, _footerCell, _cells.*)","_hiddenChanged(hidden, _headerCell, _footerCell, _cells.*)"]}connectedCallback(){super.connectedCallback(),this._bodyTemplate&&(this._bodyTemplate.templatizer._grid=this._grid),this._headerTemplate&&(this._headerTemplate.templatizer._grid=this._grid),this._footerTemplate&&(this._footerTemplate.templatizer._grid=this._grid),this._templateObserver.flush(),this._bodyTemplate||this._templateObserver.callback(),requestAnimationFrame((()=>{this._allCells.forEach((t=>{t._content.parentNode||this._grid&&this._grid.appendChild(t._content)}))}))}disconnectedCallback(){super.disconnectedCallback(),requestAnimationFrame((()=>{this._findHostGrid()||this._allCells.forEach((t=>{t._content.parentNode&&t._content.parentNode.removeChild(t._content)}))})),this._gridValue=void 0}_findHostGrid(){let t=this;for(;t&&!/^vaadin.*grid(-pro)?$/.test(t.localName);)t=t.assignedSlot?t.assignedSlot.parentNode:t.parentNode;return t||void 0}get _grid(){return this._gridValue||(this._gridValue=this._findHostGrid()),this._gridValue}get _allCells(){return[].concat(this._cells||[]).concat(this._emptyCells||[]).concat(this._headerCell).concat(this._footerCell).filter((t=>t))}constructor(){super(),this._templateObserver=new zi(this,(()=>{this._headerTemplate=this._prepareHeaderTemplate(),this._footerTemplate=this._prepareFooterTemplate(),this._bodyTemplate=this._prepareBodyTemplate()}))}_prepareHeaderTemplate(){return this._prepareTemplatizer(this._findTemplate(!0)||null,{})}_prepareFooterTemplate(){return this._prepareTemplatizer(this._findTemplate(!1,!0)||null,{})}_prepareBodyTemplate(){return this._prepareTemplatizer(this._findTemplate()||null)}_prepareTemplatizer(t,e){if(t&&!t.templatizer){const n=new V$t;n._grid=this._grid,n.dataHost=this.dataHost,n._instanceProps=e||n._instanceProps,n.template=t,t.templatizer=n}return t}_renderHeaderAndFooter(){this.headerRenderer&&this._headerCell&&this.__runRenderer(this.headerRenderer,this._headerCell),this.footerRenderer&&this._footerCell&&this.__runRenderer(this.footerRenderer,this._footerCell)}__runRenderer(t,e,n){const i=[e._content,this];n&&n.item&&i.push(n),t.apply(this,i)}__setColumnTemplateOrRenderer(t,e,n){if(!this.hidden){if(t&&e)throw new Error("You should only use either a renderer or a template");n.forEach((n=>{const i=this._grid.__getRowModel(n.parentElement);if(e)n._renderer=e,(i.item||e===this.headerRenderer||e===this.footerRenderer)&&this.__runRenderer(e,n,i);else if(n._template!==t){n._template=t,n._content.innerHTML="",t.templatizer._grid=t.templatizer._grid||this._grid;const e=t.templatizer.createInstance();n._content.appendChild(e.root),n._instance=e,i.item&&n._instance.setProperties(i)}}))}}_setBodyTemplateOrRenderer(t,e,n){(t||e)&&n&&this.__setColumnTemplateOrRenderer(t,e,n)}_setHeaderTemplateOrRenderer(t,e,n){(t||e)&&n&&this.__setColumnTemplateOrRenderer(t,e,[n])}_setFooterTemplateOrRenderer(t,e,n){(t||e)&&n&&(this.__setColumnTemplateOrRenderer(t,e,[n]),this._grid.__updateHeaderFooterRowVisibility(n.parentElement))}_selectFirstTemplate(t=!1,e=!1){return zi.getFlattenedNodes(this).filter((n=>"template"===n.localName&&n.classList.contains("header")===t&&n.classList.contains("footer")===e))[0]}_findTemplate(t,e){const n=this._selectFirstTemplate(t,e);return n&&this.dataHost&&(n._rootDataHost=this.dataHost._rootDataHost||this.dataHost),n}_flexGrowChanged(t){this.parentElement&&this.parentElement._columnPropChanged&&this.parentElement._columnPropChanged("flexGrow"),this._allCells.forEach((e=>e.style.flexGrow=t))}_orderChanged(t){this._allCells.forEach((e=>e.style.order=t))}_widthChanged(t){this.parentElement&&this.parentElement._columnPropChanged&&this.parentElement._columnPropChanged("width"),this._allCells.forEach((e=>e.style.width=t)),this._grid&&this._grid.__forceReflow&&this._grid.__forceReflow()}_frozenChanged(t){this.parentElement&&this.parentElement._columnPropChanged&&this.parentElement._columnPropChanged("frozen",t),this._allCells.forEach((e=>this._toggleAttribute("frozen",t,e))),this._grid&&this._grid._frozenCellsChanged&&this._grid._frozenCellsChanged()}_lastFrozenChanged(t){this._allCells.forEach((e=>this._toggleAttribute("last-frozen",t,e))),this.parentElement&&this.parentElement._columnPropChanged&&(this.parentElement._lastFrozen=t)}_pathOrHeaderChanged(t,e,n,i,r,o,a,s,l){const c=void 0!==e;!a&&!l&&c&&n&&this.__setTextContent(n._content,e),t&&r.value&&(o||s||this.__setColumnTemplateOrRenderer(void 0,((e,n,{item:i})=>this.__setTextContent(e,this.get(t,i))),r.value),a||l||c||!n||null===e||this.__setTextContent(n._content,this._generateHeader(t))),n&&this._grid.__updateHeaderFooterRowVisibility(n.parentElement)}__setTextContent(t,e){t.textContent!==e&&(t.textContent=e)}_generateHeader(t){return t.substr(t.lastIndexOf(".")+1).replace(/([A-Z])/g,"-$1").toLowerCase().replace(/-/g," ").replace(/^./,(t=>t.toUpperCase()))}_toggleAttribute(t,e,n){n.hasAttribute(t)===!e&&(e?n.setAttribute(t,""):n.removeAttribute(t))}_reorderStatusChanged(t){this._allCells.forEach((e=>e.setAttribute("reorder-status",t)))}_resizableChanged(t,e){void 0!==t&&void 0!==e&&e&&[e].concat(this._emptyCells).forEach((e=>{if(e){const n=e.querySelector('[part~="resize-handle"]');if(n&&e.removeChild(n),t){const t=document.createElement("div");t.setAttribute("part","resize-handle"),e.appendChild(t)}}}))}_textAlignChanged(t){if(void 0===t)return;if(-1===["start","end","center"].indexOf(t))return void console.warn('textAlign can only be set as "start", "end" or "center"');let e;"ltr"===getComputedStyle(this._grid).direction?"start"===t?e="left":"end"===t&&(e="right"):"start"===t?e="right":"end"===t&&(e="left"),this._allCells.forEach((n=>{n._content.style.textAlign=t,getComputedStyle(n._content).textAlign!==t&&(n._content.style.textAlign=e)}))}_hiddenChanged(t){this.parentElement&&this.parentElement._columnPropChanged&&this.parentElement._columnPropChanged("hidden",t),!!t!=!!this._previousHidden&&this._grid&&(!0===t&&this._allCells.forEach((t=>{t._content.parentNode&&t._content.parentNode.removeChild(t._content)})),this._grid._debouncerHiddenChanged=Tn.debounce(this._grid._debouncerHiddenChanged,_t,(()=>{this._grid&&this._grid._renderColumnTree&&this._grid._renderColumnTree(this._grid._columnTree)})),this._grid._updateLastFrozen&&this._grid._updateLastFrozen(),this._grid.notifyResize&&this._grid.notifyResize(),this._grid._resetKeyboardNavigation&&this._grid._resetKeyboardNavigation()),this._previousHidden=t}};class nKt extends(eKt(w$t(ye))){static get is(){return"vaadin-grid-column"}static get properties(){return{width:{type:String,value:"100px"},flexGrow:{type:Number,value:1},renderer:Function,path:{type:String},autoWidth:{type:Boolean,value:!1},_bodyTemplate:{type:Object},_cells:Array}}}customElements.define(nKt.is,nKt),
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
$Xt("vaadin-grid",GXt`
    @keyframes vaadin-grid-appear {
      to {
        opacity: 1;
      }
    }

    :host {
      display: block;
      animation: 1ms vaadin-grid-appear;
      height: 400px;
      flex: 1 1 auto;
      align-self: stretch;
      position: relative;
    }

    :host([hidden]) {
      display: none !important;
    }

    #scroller {
      display: block;
      transform: translateY(0);
      width: auto;
      height: auto;
      position: absolute;
      top: 0;
      right: 0;
      bottom: 0;
      left: 0;
    }

    :host([height-by-rows]) {
      height: auto;
      align-self: flex-start;
      flex-grow: 0;
      width: 100%;
    }

    :host([height-by-rows]) #scroller {
      width: 100%;
      height: 100%;
      position: relative;
    }

    #table {
      display: flex;
      flex-direction: column;
      width: 100%;
      height: 100%;
      overflow: auto;
      position: relative;
      outline: none;
      /* Workaround for a Desktop Safari bug: new stacking context here prevents the scrollbar from getting hidden */
      z-index: 0;
    }

    #header,
    #footer {
      display: block;
      position: -webkit-sticky;
      position: sticky;
      left: 0;
      overflow: visible;
      width: 100%;
      z-index: 1;
    }

    #header {
      top: 0;
    }

    th {
      text-align: inherit;
    }

    /* Safari doesn't work with "inherit" */
    [safari] th {
      text-align: initial;
    }

    #footer {
      bottom: 0;
    }

    #items {
      flex-grow: 1;
      flex-shrink: 0;
      display: block;
      position: -webkit-sticky;
      position: sticky;
      width: 100%;
      left: 0;
      overflow: visible;
    }

    [part~='row'] {
      display: flex;
      width: 100%;
      box-sizing: border-box;
      margin: 0;
    }

    [part~='row'][loading] [part~='body-cell'] ::slotted(vaadin-grid-cell-content) {
      opacity: 0;
    }

    #items [part~='row'] {
      position: absolute;
    }

    #items [part~='row']:empty {
      height: 1em;
    }

    [part~='cell']:not([part~='details-cell']) {
      flex-shrink: 0;
      flex-grow: 1;
      box-sizing: border-box;
      display: flex;
      width: 100%;
      position: relative;
      align-items: center;
      padding: 0;
      white-space: nowrap;
    }

    [part~='details-cell'] {
      position: absolute;
      bottom: 0;
      width: 100%;
      box-sizing: border-box;
      padding: 0;
    }

    [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      display: block;
      width: 100%;
      box-sizing: border-box;
      overflow: hidden;
      text-overflow: ellipsis;
    }

    [hidden] {
      display: none !important;
    }

    [frozen] {
      z-index: 2;
      will-change: transform;
    }

    [no-scrollbars][safari] #table,
    [no-scrollbars][firefox] #table {
      overflow: hidden;
    }

    /* Reordering styles */
    :host([reordering]) [part~='cell'] ::slotted(vaadin-grid-cell-content),
    :host([reordering]) [part~='resize-handle'],
    #scroller[no-content-pointer-events] [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      pointer-events: none;
    }

    [part~='reorder-ghost'] {
      visibility: hidden;
      position: fixed;
      pointer-events: none;
      opacity: 0.5;

      /* Prevent overflowing the grid in Firefox */
      top: 0;
      left: 0;
    }

    :host([reordering]) {
      -moz-user-select: none;
      -webkit-user-select: none;
      user-select: none;
    }

    /* Resizing styles */
    [part~='resize-handle'] {
      position: absolute;
      top: 0;
      right: 0;
      height: 100%;
      cursor: col-resize;
      z-index: 1;
    }

    [part~='resize-handle']::before {
      position: absolute;
      content: '';
      height: 100%;
      width: 35px;
      transform: translateX(-50%);
    }

    [last-column] [part~='resize-handle']::before,
    [last-frozen] [part~='resize-handle']::before {
      width: 18px;
      transform: none;
      right: 0;
    }

    #scroller[column-resizing] {
      -ms-user-select: none;
      -moz-user-select: none;
      -webkit-user-select: none;
      user-select: none;
    }

    /* Sizer styles */
    #sizer {
      display: flex;
      position: absolute;
      visibility: hidden;
    }

    #sizer [part~='details-cell'] {
      display: none !important;
    }

    #sizer [part~='cell'][hidden] {
      display: none !important;
    }

    #sizer [part~='cell'] {
      display: block;
      flex-shrink: 0;
      line-height: 0;
      height: 0 !important;
      min-height: 0 !important;
      max-height: 0 !important;
      padding: 0 !important;
      border: none !important;
    }

    #sizer [part~='cell']::before {
      content: '-';
    }

    #sizer [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      display: none !important;
    }

    /* RTL specific styles */

    :host([dir='rtl']) #items,
    :host([dir='rtl']) #header,
    :host([dir='rtl']) #footer {
      left: auto;
    }

    :host([dir='rtl']) [part~='reorder-ghost'] {
      left: auto;
      right: 0;
    }

    :host([dir='rtl']) [part~='resize-handle'] {
      left: 0;
      right: auto;
    }

    :host([dir='rtl']) [part~='resize-handle']::before {
      transform: translateX(50%);
    }

    :host([dir='rtl']) [last-column] [part~='resize-handle']::before,
    :host([dir='rtl']) [last-frozen] [part~='resize-handle']::before {
      left: 0;
      right: auto;
    }
  `,{moduleId:"vaadin-grid-styles"});
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const iKt=(()=>{try{return document.createEvent("TouchEvent"),!0}catch(t){return!1}})();class rKt extends(E$t(c$t(D$t(R$t(B$t(N$t(j$t(G$t(W$t(U$t(Q$t(P$t(F$t(tKt(O$t(H$t(J$t(q$t(L$t))))))))))))))))))){static get template(){return _e`
      <div
        id="scroller"
        safari$="[[_safari]]"
        ios$="[[_ios]]"
        loading$="[[loading]]"
        column-reordering-allowed$="[[columnReorderingAllowed]]"
      >
        <table id="table" role="grid" aria-multiselectable="true" tabindex="0">
          <caption id="sizer" part="row"></caption>
          <thead id="header" role="rowgroup"></thead>
          <tbody id="items" role="rowgroup"></tbody>
          <tfoot id="footer" role="rowgroup"></tfoot>
        </table>

        <div part="reorder-ghost"></div>
      </div>

      <div id="focusexit" tabindex="0"></div>
    `}static get is(){return"vaadin-grid"}static get version(){return"20.0.2"}static get observers(){return["_columnTreeChanged(_columnTree, _columnTree.*)"]}static get properties(){return{_safari:{type:Boolean,value:/^((?!chrome|android).)*safari/i.test(navigator.userAgent)},_ios:{type:Boolean,value:/iPad|iPhone|iPod/.test(navigator.userAgent)&&!window.MSStream||"MacIntel"===navigator.platform&&navigator.maxTouchPoints>1},_firefox:{type:Boolean,value:navigator.userAgent.toLowerCase().indexOf("firefox")>-1},_android:{type:Boolean,value:/android/i.test(navigator.userAgent)},_touchDevice:{type:Boolean,value:iKt},heightByRows:{type:Boolean,value:!1,reflectToAttribute:!0,observer:"_heightByRowsChanged"},_recalculateColumnWidthOnceLoadingFinished:{type:Boolean,value:!0}}}constructor(){super(),this.addEventListener("animationend",this._onAnimationEnd)}connectedCallback(){super.connectedCallback(),this.recalculateColumnWidths()}attributeChangedCallback(t,e,n){super.attributeChangedCallback(t,e,n),"dir"===t&&(this.__isRTL="rtl"===n,this._updateScrollerMeasurements())}__hasRowsWithClientHeight(){return!!Array.from(this.$.items.children).filter((t=>t.clientHeight)).length}__itemsReceived(){this._recalculateColumnWidthOnceLoadingFinished&&!this._cache.isLoading()&&this.__hasRowsWithClientHeight()&&(this._recalculateColumnWidthOnceLoadingFinished=!1,this.recalculateColumnWidths())}_recalculateColumnWidths(t){t.forEach((t=>{t.width="auto",t._origFlexGrow=t.flexGrow,t.flexGrow=0})),t.forEach((t=>{t._currentWidth=0,t._allCells.forEach((e=>{t._currentWidth=Math.max(t._currentWidth,e.offsetWidth+1)}))})),t.forEach((t=>{t.width=`${t._currentWidth}px`,t.flexGrow=t._origFlexGrow,t._currentWidth=void 0,t._origFlexGrow=void 0}))}recalculateColumnWidths(){if(this._columnTree)if(this._cache.isLoading())this._recalculateColumnWidthOnceLoadingFinished=!0;else{const t=this._getColumns().filter((t=>!t.hidden&&t.autoWidth));this._recalculateColumnWidths(t)}}_createScrollerRows(t){const e=[];for(let n=0;n<t;n++){const t=document.createElement("tr");t.setAttribute("part","row"),t.setAttribute("role","row"),this._columnTree&&this._updateRow(t,this._columnTree[this._columnTree.length-1],"body",!1,!0),e.push(t)}return this._columnTree&&this._columnTree[this._columnTree.length-1].forEach((t=>t.isConnected&&t.notifyPath&&t.notifyPath("_cells.*",t._cells))),(function n(t,e,i){Ei||Ai(),Ti.push([t,e,i])})(this,(()=>{this._updateFirstAndLastColumn(),this._resetKeyboardNavigation()})),e}_getRowTarget(){return this.$.items}_createCell(t){const e="vaadin-grid-cell-content-"+(this._contentIndex=this._contentIndex+1||0),n=document.createElement("vaadin-grid-cell-content");n.setAttribute("slot",e);const i=document.createElement(t);i.id=e.replace("-content-","-"),i.setAttribute("tabindex","-1"),i.setAttribute("role","td"===t?"gridcell":"columnheader");const r=document.createElement("slot");return r.setAttribute("name",e),i.appendChild(r),i._content=n,n.addEventListener("mousedown",(()=>{if(window.chrome){const t=()=>{n.contains(this.getRootNode().activeElement)||i.focus(),document.removeEventListener("mouseup",t,!0)};document.addEventListener("mouseup",t,!0)}else setTimeout((()=>{n.contains(this.getRootNode().activeElement)||i.focus()}))})),i}_updateRow(t,e,n,i,r){n=n||"body";const o=document.createDocumentFragment();Array.from(t.children).forEach((t=>t._vacant=!0)),t.innerHTML="","sizer"!==t.id&&(t.hidden=!0),e.filter((t=>!t.hidden)).forEach(((e,a,s)=>{let l;if("body"===n){if(e._cells=e._cells||[],l=e._cells.filter((t=>t._vacant))[0],l||(l=this._createCell("td"),e._cells.push(l)),l.setAttribute("part","cell body-cell"),t.appendChild(l),a===s.length-1&&(this._rowDetailsTemplate||this.rowDetailsRenderer)){this._detailsCells=this._detailsCells||[];const e=this._detailsCells.filter((t=>t._vacant))[0]||this._createCell("td");-1===this._detailsCells.indexOf(e)&&this._detailsCells.push(e),e._content.parentElement||o.appendChild(e._content),this._configureDetailsCell(e),t.appendChild(e),this._a11ySetRowDetailsCell(t,e),e._vacant=!1}e.notifyPath&&!r&&e.notifyPath("_cells.*",e._cells)}else{const r="header"===n?"th":"td";i||"vaadin-grid-column-group"===e.localName?(l=e[`_${n}Cell`]||this._createCell(r),l._column=e,t.appendChild(l),e[`_${n}Cell`]=l):(e._emptyCells=e._emptyCells||[],l=e._emptyCells.filter((t=>t._vacant))[0]||this._createCell(r),l._column=e,t.appendChild(l),-1===e._emptyCells.indexOf(l)&&e._emptyCells.push(l)),l.setAttribute("part",`cell ${n}-cell`),this.__updateHeaderFooterRowVisibility(t)}l._content.parentElement||o.appendChild(l._content),l._vacant=!1,l._column=e})),this.appendChild(o),this._frozenCellsChanged(),this._updateFirstAndLastColumnForRow(t)}__updateHeaderFooterRowVisibility(t){if(!t)return;const e=Array.from(t.children).filter((e=>{const n=e._column;if(n._emptyCells&&n._emptyCells.indexOf(e)>-1)return!1;if(t.parentElement===this.$.header){if(n.headerRenderer||n._headerTemplate)return!0;if(null===n.header)return!1;if(n.path||void 0!==n.header)return!0}else if(n.footerRenderer||n._footerTemplate)return!0}));t.hidden!==!e.length&&(t.hidden=!e.length,this.notifyResize())}_updateScrollerItem(t,e){this._preventScrollerRotatingCellFocus(t,e),this._columnTree&&(this._toggleAttribute("first",0===e,t),this._toggleAttribute("odd",e%2,t),this._a11yUpdateRowRowindex(t,e),this._getItem(e,t))}_columnTreeChanged(t){this._renderColumnTree(t),this.recalculateColumnWidths()}_renderColumnTree(t){for(Array.from(this.$.items.children).forEach((e=>this._updateRow(e,t[t.length-1],null,!1,!0)));this.$.header.children.length<t.length;){const t=document.createElement("tr");t.setAttribute("part","row"),t.setAttribute("role","row"),this.$.header.appendChild(t);const e=document.createElement("tr");e.setAttribute("part","row"),e.setAttribute("role","row"),this.$.footer.appendChild(e)}for(;this.$.header.children.length>t.length;)this.$.header.removeChild(this.$.header.firstElementChild),this.$.footer.removeChild(this.$.footer.firstElementChild);Array.from(this.$.header.children).forEach(((e,n)=>this._updateRow(e,t[n],"header",n===t.length-1))),Array.from(this.$.footer.children).forEach(((e,n)=>this._updateRow(e,t[t.length-1-n],"footer",0===n))),this._updateRow(this.$.sizer,t[t.length-1]),this._resizeHandler(),this._frozenCellsChanged(),this._updateFirstAndLastColumn(),this._resetKeyboardNavigation(),this._a11yUpdateHeaderRows(),this._a11yUpdateFooterRows(),this.__updateFooterPositioning()}__updateFooterPositioning(){this._firefox&&(this.$.items.style.paddingBottom=0,this.heightByRows||(this.$.items.style.paddingBottom=`${this.$.footer.offsetHeight}px`)),this._ios&&!window.CSS.supports("position","sticky")&&(this.$.table.style.height="",this.$.table.style.minHeight="100%",this.$.table.style.maxHeight="100%",setTimeout((()=>this.$.table.style.height=`${this.$.scroller.offsetHeight}px`)))}_updateItem(t,e){t._item=e;const n=this.__getRowModel(t);this._toggleAttribute("selected",n.selected,t),this._a11yUpdateRowSelected(t,n.selected),this._a11yUpdateRowLevel(t,n.level),this._toggleAttribute("expanded",n.expanded,t),this._toggleAttribute("details-opened",this._isDetailsOpened(e),t),(this._rowDetailsTemplate||this.rowDetailsRenderer)&&this._toggleDetailsCell(t,e),this._generateCellClassNames(t,n),this._filterDragAndDrop(t,n),Array.from(t.children).forEach((t=>{if(t._renderer){const e=t._column||this;t._renderer.call(e,t._content,e,n)}else t._instance&&(t._instance.__detailsOpened__=n.detailsOpened,t._instance.__selected__=n.selected,t._instance.__level__=n.level,t._instance.__expanded__=n.expanded,t._instance.setProperties(n))})),this._debouncerUpdateHeights=Tn.debounce(this._debouncerUpdateHeights,gt.after(1),(()=>{this._updateMetrics(),this._positionItems(),this._updateScrollerSize()}))}_resizeHandler(){this._updateDetailsCellHeights(),this._accessIronListAPI(super._resizeHandler,!0),this._updateScrollerMeasurements(),this.__updateFooterPositioning()}_onAnimationEnd(t){0===t.animationName.indexOf("vaadin-grid-appear")&&(this._render(),t.stopPropagation(),this.notifyResize(),this.__itemsReceived(),requestAnimationFrame((()=>{this.__scrollToPendingIndex(),this.$.table.style.webkitOverflowScrolling="touch"})))}_toggleAttribute(t,e,n){n.hasAttribute(t)===!e&&(e?n.setAttribute(t,""):n.removeAttribute(t))}__getRowModel(t){return{index:t.index,item:t._item,level:this._getIndexLevel(t.index),expanded:this._isExpanded(t._item),selected:this._isSelected(t._item),detailsOpened:!(!this._rowDetailsTemplate&&!this.rowDetailsRenderer)&&this._isDetailsOpened(t._item)}}render(){this._columnTree&&(this._columnTree.forEach((t=>{t.forEach((t=>t._renderHeaderAndFooter()))})),this._update())}notifyResize(){super.notifyResize()}_heightByRowsChanged(t,e){(t||e)&&this.notifyResize()}__forceReflow(){this._debouncerForceReflow=Tn.debounce(this._debouncerForceReflow,_t,(()=>{this.$.scroller.style.overflow="hidden",setTimeout((()=>this.$.scroller.style.overflow=""))}))}}customElements.define(rKt.is,rKt);let oKt=class extends ye{constructor(){super(...arguments),this._run=""}_csvUrl(t,e,n){return e?iO(n(t,e),{format:"csv"}):""}_jsonUrl(t,e,n){return e?n(t,e):""}_csvName(t,e){return e?`run-${e}-tag-${t}.csv`:""}_jsonName(t,e){return e?`run-${e}-tag-${t}.json`:""}};oKt.template=_e`
    <paper-dropdown-menu
      no-label-float="true"
      label="run to download"
      selected-item-label="{{_run}}"
    >
      <paper-listbox slot="dropdown-content">
        <template is="dom-repeat" items="[[runs]]">
          <paper-item no-label-float="true">[[item]]</paper-item>
        </template>
      </paper-listbox>
    </paper-dropdown-menu>
    <template is="dom-if" if="[[_run]]">
      <a download="[[_csvName(tag, _run)]]" href="[[_csvUrl(tag, _run, urlFn)]]"
        >CSV</a
      ><!--
      --><a
        download="[[_jsonName(tag, _run)]]"
        href="[[_jsonUrl(tag, _run, urlFn)]]"
        >JSON</a
      >
    </template>
    <style>
      :host {
        display: flex;
        align-items: center;
        height: 32px;
      }
      paper-dropdown-menu {
        width: 100px;
        --paper-input-container-label: {
          font-size: 10px;
        }
        --paper-input-container-input: {
          font-size: 10px;
        }
      }
      a {
        font-size: 10px;
        margin: 0 0.2em;
      }
      paper-input {
        font-size: 22px;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],oKt.prototype,"_run",void 0),t([o({type:Array}),e("design:type",Array)],oKt.prototype,"runs",void 0),t([o({type:String}),e("design:type",String)],oKt.prototype,"tag",void 0),t([o({type:Object}),e("design:type",Object)],oKt.prototype,"urlFn",void 0),oKt=t([i("tf-downloader")],oKt),new URLSearchParams(window.location.search);let aKt=class extends ye{constructor(){super(...arguments),this.colorScale=null,this._loadDataCallback=(t,e,n)=>{if(null==n)return void console.error("Failed to load data for:",e);const i=n.map((t=>({wall_time:new Date(1e3*t[0]),step:t[1],scalar:t[2]}))),r=this._getSeriesNameFromDatum(e);t.setSeriesMetadata(r,e),t.setSeriesData(r,i)},this.getDataLoadUrl=({tag:t,run:e})=>_r().pluginRoute("scalars","/scalars",new URLSearchParams({tag:t,run:e})),this._downloadUrlFn=(t,e)=>this.getDataLoadUrl({tag:t,run:e}),this.requestData=(t,e,n)=>this.inColab?this._requestDataGet(t,e,n):this._requestDataPost(t,e,n),this._requestDataGet=(t,e,n)=>{const i=_r().pluginRoute("scalars","/scalars");Promise.all(t.map((t=>{const n=iO(i,{tag:t.tag,run:t.run});return this.requestManager.request(n).then((n=>{e({item:t,data:n})}))}))).finally((()=>{n()}))},this._requestDataPost=(t,e,n)=>{var i;const r=_r().pluginRoute("scalars","/scalars_multirun"),o=new Map;for(const{tag:e,run:n}of t){let t=o.get(e);null==t&&o.set(e,t=[]),t.push(n)}const a=null!==(i=this.batchSize)&&void 0!==i?i:64,s=[];for(const[t,e]of o)for(let n=0;n<e.length;n+=a)s.push({tag:t,runs:e.slice(n,n+a)});Promise.all(s.map((({tag:t,runs:n})=>this.requestManager.request(r,{tag:t,runs:n}).then((i=>{for(const r of n){const n={tag:t,run:r};Object.prototype.hasOwnProperty.call(i,r)?e({item:n,data:i[r]}):e({item:n,data:null})}}))))).finally((()=>{n()}))},this._getDataLoadName=t=>this._getSeriesNameFromDatum(t),this._expanded=!1,this._tooltipColumns=(()=>{const t=pCt.slice(),e=t.findIndex((t=>"Name"==t.title));return t.splice(e,1,{title:"Name",evaluate:t=>{const e=t.dataset.metadata().meta;return this._getSeriesDisplayNameFromDatum(e)}}),t})()}_getChartDataLoader(){return this.shadowRoot.querySelector("tf-line-chart-data-loader")}reload(){this._getChartDataLoader().reload()}redraw(){this._getChartDataLoader().redraw()}_toggleExpanded(t){this.set("_expanded",!this._expanded),this.redraw()}_toggleLogScale(){this.set("_logScaleActive",!this._logScaleActive)}_resetDomain(){const t=this._getChartDataLoader();t&&t.resetDomain()}_updateDownloadLink(){const t=this._getChartDataLoader().exportAsSvgString();this.shadowRoot.querySelector("#svgLink").href=`data:image/svg+xml;base64,${btoa(t)}`}_runsFromData(t){return t.map((t=>t.run))}_getDataSeries(){return this.dataToLoad.map((t=>this._getSeriesNameFromDatum(t)))}_getSeriesNameFromDatum({run:t,experiment:e={name:"_default"}}){return JSON.stringify([e.name,t])}_getSeriesDisplayNameFromDatum(t){return t.run}_getColorScale(){return null!==this.colorScale?this.colorScale:{scale:t=>{const[,e]=JSON.parse(t);return GR(e)}}}};aKt.template=_e`
    <tf-card-heading
      tag="[[tag]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
    ></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader
        active="[[active]]"
        color-scale="[[_getColorScale(colorScale)]]"
        data-series="[[_getDataSeries(dataToLoad.*)]]"
        data-to-load="[[dataToLoad]]"
        get-data-load-name="[[_getDataLoadName]]"
        get-data-load-url="[[getDataLoadUrl]]"
        request-data="[[requestData]]"
        ignore-y-outliers="[[ignoreYOutliers]]"
        load-data-callback="[[_loadDataCallback]]"
        load-key="[[tag]]"
        log-scale-active="[[_logScaleActive]]"
        request-manager="[[requestManager]]"
        smoothing-enabled="[[smoothingEnabled]]"
        smoothing-weight="[[smoothingWeight]]"
        tag-metadata="[[tagMetadata]]"
        tooltip-columns="[[_tooltipColumns]]"
        tooltip-position="auto"
        tooltip-sorting-method="[[tooltipSortingMethod]]"
        x-type="[[xType]]"
      >
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
      <paper-icon-button
        selected$="[[_logScaleActive]]"
        icon="line-weight"
        on-tap="_toggleLogScale"
        title="Toggle y-axis log scale"
      ></paper-icon-button>
      <paper-icon-button
        icon="settings-overscan"
        on-tap="_resetDomain"
        title="Fit domain to data"
      ></paper-icon-button>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <paper-menu-button on-paper-dropdown-open="_updateDownloadLink">
          <paper-icon-button
            class="dropdown-trigger"
            slot="dropdown-trigger"
            icon="file-download"
          ></paper-icon-button>
          <paper-listbox class="dropdown-content" slot="dropdown-content">
            <paper-item>
              <a id="svgLink" download="[[tag]].svg">
                Download Current Chart as SVG
              </a>
            </paper-item>
          </paper-listbox>
        </paper-menu-button>
      </template>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <tf-downloader
            runs="[[_runsFromData(dataToLoad)]]"
            tag="[[tag]]"
            url-fn="[[_downloadUrlFn]]"
          ></tf-downloader>
        </div>
      </template>
    </div>
    <style>
      :host {
        margin: 5px;
        display: block;
        width: 330px;
      }

      :host([_expanded]) {
        width: 100%;
      }

      :host([_expanded]) #tf-line-chart-data-loader-container {
        height: 400px;
      }

      #tf-line-chart-data-loader-container {
        height: 200px;
        width: 100%;
      }

      tf-card-heading {
        display: block;
        margin-bottom: 10px;
      }

      #buttons {
        display: flex;
        flex-direction: row;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }

      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      .download-links {
        display: flex;
        height: 32px;
      }

      .download-links a {
        align-self: center;
        font-size: 10px;
        margin: 2px;
      }

      .download-links paper-dropdown-menu {
        width: 100px;
        --paper-input-container-label: {
          font-size: 10px;
        }
        --paper-input-container-input: {
          font-size: 10px;
        }
      }

      paper-menu-button {
        padding: 0;
      }
      paper-item a {
        color: inherit;
        text-decoration: none;
        white-space: nowrap;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],aKt.prototype,"tag",void 0),t([o({type:Array}),e("design:type",Array)],aKt.prototype,"dataToLoad",void 0),t([o({type:String}),e("design:type",String)],aKt.prototype,"xType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],aKt.prototype,"active",void 0),t([o({type:Boolean}),e("design:type",Boolean)],aKt.prototype,"ignoreYOutliers",void 0),t([o({type:Object}),e("design:type",dr)],aKt.prototype,"requestManager",void 0),t([o({type:Boolean}),e("design:type",Boolean)],aKt.prototype,"showDownLinks",void 0),t([o({type:Boolean}),e("design:type",Boolean)],aKt.prototype,"smoothingEnabled",void 0),t([o({type:Number}),e("design:type",Number)],aKt.prototype,"smoothingWeight",void 0),t([o({type:Object}),e("design:type",Object)],aKt.prototype,"tagMetadata",void 0),t([o({type:Object}),e("design:type",Object)],aKt.prototype,"colorScale",void 0),t([o({type:String}),e("design:type",String)],aKt.prototype,"tooltipSortingMethod",void 0),t([o({type:Number}),e("design:type",Number)],aKt.prototype,"batchSize",void 0),t([o({type:Boolean}),e("design:type",Number)],aKt.prototype,"inColab",void 0),t([o({type:Object}),e("design:type",Object)],aKt.prototype,"_loadDataCallback",void 0),t([o({type:Object}),e("design:type",Function)],aKt.prototype,"getDataLoadUrl",void 0),t([o({type:Object}),e("design:type",Object)],aKt.prototype,"_downloadUrlFn",void 0),t([o({type:Object}),e("design:type",Function)],aKt.prototype,"requestData",void 0),t([o({type:Object}),e("design:type",Object)],aKt.prototype,"_getDataLoadName",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],aKt.prototype,"_expanded",void 0),t([o({type:Boolean}),e("design:type",Boolean)],aKt.prototype,"_logScaleActive",void 0),t([o({type:Array}),e("design:type",Array)],aKt.prototype,"_tooltipColumns",void 0),aKt=t([i("tf-scalar-card")],aKt);let sKt=class extends((function t(e,n){return(function i(t,e){return Nr({},er(e),t)})(e,n)})([_o],ye)){constructor(){super(...arguments),this.sessionGroup=null,this._xType=xTt.STEP,this._noMultiExperiments=!1,this._requestData=(t,e,n)=>{Promise.all(t.map((t=>this.backend.listMetricEvals({experimentName:this.experimentName,sessionName:t.run,metricName:t.tag}).then((n=>{e({item:t,data:n})}))))).finally((()=>{n()}))},this._colorScale={scale:t=>{const e=JSON.parse(t)[1],n=this._indexOfSession.get(e);return HR[(this._sessionGroupNameHash+n)%HR.length]}}}connectedCallback(){super.connectedCallback(),this.addEventListener("iron-resize",this.redraw.bind(this))}redraw(){this.shadowRoot.querySelectorAll("tf-scalar-card").forEach((t=>t.redraw()))}_sessionGroupChanged(){this.sessionGroup&&0!=Object.keys(this.sessionGroup).length?(this._indexOfSession=new Map(this.sessionGroup.sessions.map(((t,e)=>[t.name,e]))),this._sessionGroupNameHash=YYt(this.sessionGroup.name)):(this._indexOfSession=new Map,this._sessionGroupNameHash=0),this.shadowRoot.querySelectorAll("tf-scalar-card").forEach((t=>{const e=t,n=e.get("tag");e.set("tag",""),e.set("tag",n)}))}_haveMetrics(){return this.visibleSchema&&Array.isArray(this.visibleSchema.metricInfos)&&this.visibleSchema.metricInfos.length>0}_haveMetricsAndSessionGroup(){return this.sessionGroup&&this._haveMetrics()}_computeSeriesForSessionGroupMetric(t,e){return null===t||0==Object.keys(t).length||null===e?[]:t.sessions.filter((t=>void 0!==AYt(t.metricValues,e.name))).map((t=>({tag:e.name,run:t.name})))}_computeTagMetadata(t){return{displayName:wYt(t),description:t.description||""}}};sKt.template=_e`
    <template is="dom-if" if="[[!sessionGroup]]">
      <div>
        <h3>No session group selected</h3>
        <p>Please select a session group to see its metric-graphs here.</p>
      </div>
    </template>
    <template is="dom-if" if="[[!_haveMetrics(visibleSchema.*)]]">
      <div>
        <h3>No metrics are enabled</h3>
        <p>Please enable some metrics to see content here.</p>
      </div>
    </template>
    <div class="layout horizontal wrap session-group-details">
      <template
        is="dom-if"
        if="[[_haveMetricsAndSessionGroup(visibleSchema.*, sessionGroup)]]"
      >
        <template
          is="dom-repeat"
          items="[[visibleSchema.metricInfos]]"
          as="metricInfo"
        >
          <!-- Note that we do not provide a request-manager attribute since
               we provide a function in request-data for calling the backend
               to get the metrics data.
            -->
          <tf-scalar-card
            class="scalar-card"
            color-scale="[[_colorScale]]"
            data-to-load="[[_computeSeriesForSessionGroupMetric(sessionGroup, metricInfo)]]"
            tag="[[metricInfo.name.tag]]"
            tag-metadata="[[_computeTagMetadata(metricInfo)]]"
            x-type="[[_xType]]"
            multi-experiments="[[_noMultiExperiments]]"
            request-data="[[_requestData]]"
            active
          >
          </tf-scalar-card>
        </template>
      </template>
    </div>
    <!-- "iron-flex" is needed to use the layout classes in the div above -->
    <style include="iron-flex">
      :host {
        display: block;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],sKt.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],sKt.prototype,"experimentName",void 0),t([o({type:Object}),e("design:type",Object)],sKt.prototype,"visibleSchema",void 0),t([o({type:Object}),e("design:type",Object)],sKt.prototype,"sessionGroup",void 0),t([o({type:String}),e("design:type",String)],sKt.prototype,"_xType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],sKt.prototype,"_noMultiExperiments",void 0),t([o({type:Object}),e("design:type",Object)],sKt.prototype,"_indexOfSession",void 0),t([o({type:Number}),e("design:type",Number)],sKt.prototype,"_sessionGroupNameHash",void 0),t([o({type:Object}),e("design:type",Function)],sKt.prototype,"_requestData",void 0),t([o({type:Object}),e("design:type",Object)],sKt.prototype,"_colorScale",void 0),t([a("sessionGroup.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],sKt.prototype,"_sessionGroupChanged",null),sKt=t([i("tf-hparams-session-group-details")],sKt);let lKt=class extends(er(il(ye))){constructor(){super(...arguments),this._hparamName=xYt,this._metricName=wYt}_visibleSchemaOrSessionGroupsChanged(){const t=this.$.sessionGroupsTable.get("detailsOpenedItems");this.$.sessionGroupsTable.set("detailsOpenedItems",[]),Di();const e=new Map;this.sessionGroups.forEach((t=>{e.set(t.name,t)})),this.$.sessionGroupsTable.set("detailsOpenedItems",t.map((t=>e.get(t.name))).filter(Boolean))}_sessionGroupHParam(t,e){return null!=t&&0!=Object.keys(t).length&&Object.prototype.hasOwnProperty.call(t.hparams,e)?VYt(t.hparams[e]):""}_sessionGroupMetric(t,e){if(null==t||0==Object.keys(t).length)return"";for(let n=0;n<t.metricValues.length;++n){let i=t.metricValues[n];if(i.name.group===e.group&&i.name.tag==e.tag)return VYt(i.value)}return""}_rowNumber(t){return t+1}};lKt.template=_e`
    <vaadin-grid
      class="session-group-table"
      id="sessionGroupsTable"
      column-reordering-allowed=""
      items="[[sessionGroups]]"
    >
      <vaadin-grid-column flex-grow="0" width="10em" resizable="">
        <template class="header">
          <div class="table-header table-cell">Trial ID</div>
        </template>
        <template>
          <div class="table-cell">[[item.name]]</div>
        </template>
      </vaadin-grid-column>
      <template is="dom-if" if="[[enableShowMetrics]]">
        <vaadin-grid-column flex-grow="0" autoWidth="" resizable="">
          <template class="header">
            <div class="table-header table-cell">Show Metrics</div>
          </template>
          <template>
            <paper-checkbox class="table-cell" checked="{{detailsOpened}}">
            </paper-checkbox>
          </template>
        </vaadin-grid-column>
      </template>
      <template
        is="dom-repeat"
        items="[[visibleSchema.hparamInfos]]"
        as="hparamInfo"
        index-as="hparamIndex"
      >
        <vaadin-grid-column flex-grow="2" width="10em" resizable="">
          <template class="header">
            <div class="table-header table-cell">
              [[_hparamName(hparamInfo)]]
            </div>
          </template>
          <template>
            <div class="table-cell">
              [[_sessionGroupHParam(item, hparamInfo.name)]]
            </div>
          </template>
        </vaadin-grid-column>
      </template>
      <template
        is="dom-repeat"
        items="{{visibleSchema.metricInfos}}"
        as="metricInfo"
        index-as="metricIndex"
      >
        <vaadin-grid-column flex-grow="2" width="10em" resizable="">
          <template class="header">
            <div class="table-header table-cell">
              [[_metricName(metricInfo)]]
            </div>
          </template>
          <template>
            <div class="table-cell">
              [[_sessionGroupMetric(item, metricInfo.name)]]
            </div>
          </template>
        </vaadin-grid-column>
      </template>
      <template class="row-details">
        <tf-hparams-session-group-details
          backend="[[backend]]"
          experiment-name="[[experimentName]]"
          session-group="[[item]]"
          visible-schema="[[visibleSchema]]"
          class="session-group-details"
        >
        </tf-hparams-session-group-details>
      </template>
    </vaadin-grid>

    <style>
      :host {
        display: inline;
      }

      :host(.dark-mode) {
        --lumo-base-color: #303030;
        --lumo-body-text-color: #fff;
      }

      :host(.dark-mode) vaadin-grid {
        --_lumo-grid-secondary-border-color: #505050;
      }

      .table-cell {
        white-space: nowrap;
        text-overflow: ellipsis;
        overflow: hidden;
      }
      .table-header {
        /* line-break overflowing column headers */
        white-space: normal;
        overflow-wrap: break-word;
      }
      .session-group-table {
        height: 100%;
      }
      .session-group-details {
        height: 360px;
        overflow-y: auto;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],lKt.prototype,"visibleSchema",void 0),t([o({type:Array}),e("design:type",Array)],lKt.prototype,"sessionGroups",void 0),t([o({type:Boolean}),e("design:type",Boolean)],lKt.prototype,"enableShowMetrics",void 0),t([o({type:Object}),e("design:type",Object)],lKt.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],lKt.prototype,"experimentName",void 0),t([a("visibleSchema.*","sessionGroups.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],lKt.prototype,"_visibleSchemaOrSessionGroupsChanged",null),lKt=t([i("tf-hparams-table-view")],lKt);let cKt=class extends ye{constructor(){super(...arguments),this.sessionGroup=null,this.visibleSchema=null}_propertiesArePopulated(t,e){return null!=t&&null!=e}_singletonSessionGroups(t){return null==t?[]:[t]}};function uKt(t,e,n){function i(){if(0===t.length)return[1,2];const[e,n]=Ll(t);return e!==n?[e,n]:e>0?[.5*e,1.5*e]:e<0?[1.5*e,.5*e]:[-1,1]}if("LINEAR"===n)return VM().domain(i()).range([e,0]);if("LOG"===n){const n=i();return n[0]<=0&&n[1]>=0?uKt(t,e,"LINEAR"):KM().domain(n).range([e,0])}if("QUANTILE"===n){const n=20,i=zl(n).map((t=>e-t*e/(n-1)));return 0===t.length&&(t=[1]),oE().domain(Se.exports.uniq(t)).range(i)}if("NON_NUMERIC"===n)return AM().domain(Se.exports.uniq(t.sort())).range([e,0]).padding(.1);throw RangeError("Unknown scale: "+n)}var hKt,dKt;cKt.template=_e`
    <!-- If sessionGroup or visibleSchema are not populated, do not display
         anything.
      -->
    <template
      is="dom-if"
      if="[[_propertiesArePopulated(visibleSchema, sessionGroup)]]"
    >
      <!-- Display one row without a "show-metrics" column -->
      <tf-hparams-table-view
        visible-schema="[[visibleSchema]]"
        session-groups="[[_singletonSessionGroups(sessionGroup)]]"
      >
      </tf-hparams-table-view>
    </template>
    <template
      is="dom-if"
      if="[[!_propertiesArePopulated(visibleSchema, sessionGroup)]]"
    >
      <div>Click or hover over a session group to display its values here.</div>
    </template>

    <style>
      :host {
        display: block;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],cKt.prototype,"sessionGroup",void 0),t([o({type:Object}),e("design:type",Object)],cKt.prototype,"visibleSchema",void 0),cKt=t([i("tf-hparams-session-group-values")],cKt),(function(t){t.LINEAR="LINEAR",t.LOG="LOG",t.QUANTILE="QUANTILE",t.NON_NUMERIC="NON_NUMERIC"})(hKt||(hKt={}));class pKt{isPassing(t){return!0}}class fKt{constructor(t,e,n,i){this._lower=t,this._upper=e,this._lowerOpen=n,this._upperOpen=i}isPassing(t){const e=t;return this._before(this._lower,e,!this._lowerOpen)&&this._before(e,this._upper,!this._upperOpen)}_before(t,e,n){return n?t<=e:t<e}}class mKt{constructor(t){this._domainSet=t}isPassing(t){return-1!==this._domainSet.findIndex((e=>e===t))}}class gKt{constructor(t,e,n,i){this._svgProps=t,this._schema=e,this._interactionManager=n,this._colIndex=i,this._isDisplayed=!1,this._yScale=null,this._scaleType=null,this.setBrushSelection(null)}colIndex(){return this._colIndex}yScale(){return this._yScale}scaleType(){return this._scaleType}brushSelection(){return this._brushSelection}isDisplayed(){return this._isDisplayed}setBrushSelection(t){this._brushSelection=t,this._brushFilter=this._buildBrushFilter(this.brushSelection(),this.scaleType(),this.yScale())}setDomainAndScale(t,e){this._scaleType=e,this._yScale=uKt(t.slice(),this._svgProps.height,this.scaleType()),this._brushFilter=this._buildBrushFilter(this.brushSelection(),this.scaleType(),this.yScale())}brushFilter(){return this._brushFilter}updateDOM(t){let e=ac(this.yScale());this.scaleType()===hKt.QUANTILE&&(e=e.tickValues(this.yScale().quantiles()).tickFormat(my("-.6g")));const n=Su(t);n.selectAll("g").remove(),n.append("g").classed("axis",!0).call(e).append("text").classed("axis-title",!0).style("cursor","move").style("text-anchor","middle").attr("y",-9).text((t=>SYt(this._schema,t))),n.call(vh().on("start",(()=>{t.setAttribute("is-dragging",""),this._interactionManager.onDragStart(this.colIndex())})).on("drag",(()=>this._interactionManager.onDrag(uu.x))).on("end",(()=>{this._interactionManager.onDragEnd(),t.removeAttribute("is-dragging")})));const i=Zm().extent([[-8,0],[8,this._svgProps.height+1]]).on("start",(()=>{yKt(uu)&&(t.setAttribute("is-brushing",""),this._interactionManager.onBrushChanged(this.colIndex(),uu.selection))})).on("brush",(()=>{yKt(uu)&&this._interactionManager.onBrushChanged(this.colIndex(),uu.selection)})).on("end",(()=>{yKt(uu)&&(this._interactionManager.onBrushChanged(this.colIndex(),uu.selection),t.removeAttribute("is-brushing"))})),r=Su(t).append("g").classed("brush",!0);r.call(i),i.move(r,this.brushSelection())}setDisplayed(t){this._isDisplayed=t}_buildBrushFilter(t,e,n){if(null===t)return new pKt;if(null===e)return console.error("Scale type is null, but brushSelection isn't: ",t),new pKt;switch(e){case hKt.LINEAR:case hKt.LOG:{const[e,i]=(function r(t,e,n){return[t.invert(e),t.invert(n)].sort(((t,e)=>t-e))})(n,t[0],t[1]);return new fKt(e,i,!1,!1)}case hKt.QUANTILE:{const[e,i]=(function r(t,e,n){const i=t.range(),r=i.filter((t=>e<=t&&t<=n)).map((e=>{const n=t.invertExtent(e);return e===i[i.length-1]?[n[0],n[1]+1]:n}));return 0==r.length?[0,0]:Ll(ql(r))})(n,t[0],t[1]);return new fKt(e,i,!1,!0)}case hKt.NON_NUMERIC:return new mKt((function e(t,n,i){return t.domain().filter((e=>{const r=t(e);return n<=r&&r<=i}))})(n,t[0],t[1]))}return console.error("Unknown scale type: ",e),new pKt}}class _Kt{constructor(t,e,n){this._svgProps=t,this._schema=e,this._axes=this._createAxes(n),this._stationaryAxesPositions=AM().range([1,this._svgProps.width-1]).padding(.5),this._draggedAxis=null,this._svgProps.svgG.selectAll("g.axis-parent").remove(),this._parentsSel=this._svgProps.svgG.selectAll(".axis-parent")}updateAxes(t,e){console.assert(!this.isAxisDragging());const n=new Set;t.columns.forEach((t=>{const i=t.absoluteIndex;let r=this._axes[i];r.setDisplayed(!0);const o=e.map((t=>PYt(this._schema,t,i)));r.setDomainAndScale(o,t.scale),n.add(i)})),this._axes.forEach((t=>{n.has(t.colIndex())||t.setDisplayed(!1)})),this._updateStationaryAxesPositions(n),this._parentsSel=this._parentsSel.data(Array.from(n),(t=>t)),this._parentsSel.exit().remove(),this._parentsSel=this._parentsSel.enter().append("g").classed("axis-parent",!0).merge(this._parentsSel);const i=this;this._parentsSel.call((t=>this._updateAxesPositionsInDOM(t))).each((function(t){i._axes[t].updateDOM(this)}))}mapVisibleAxes(t){return this._stationaryAxesPositions.domain().map((e=>t(this.getAxisPosition(e),this._axes[e])))}allVisibleAxesSatisfy(t){return this._stationaryAxesPositions.domain().every((e=>t(this.getAxisPosition(e),this._axes[e])))}getAxisForColIndex(t){return this._axes[t]}dragStart(t){console.assert(!this.isAxisDragging()),console.assert(this._axes[t].isDisplayed()),this._draggedAxis=this._axes[t],this._draggedAxisPosition=this._stationaryAxesPositions(t)}drag(t){t=Math.min(Math.max(t,0),this._svgProps.width),this._draggedAxisPosition=t;let e=this._stationaryAxesPositions.domain();e.sort(((t,e)=>this.getAxisPosition(t)-this.getAxisPosition(e))),this._stationaryAxesPositions.domain(e),this._updateAxesPositionsInDOM(this._parentsSel)}dragEnd(t){console.assert(this.isAxisDragging()),this._draggedAxisPosition=null,this._draggedAxis=null,this._updateAxesPositionsInDOM(this._parentsSel.transition().duration(t))}isAxisDragging(){return null!==this._draggedAxis}getAxisPosition(t){return null!==this._draggedAxis&&this._draggedAxis.colIndex()===t?this._draggedAxisPosition:this._stationaryAxesPositions(t)}_updateStationaryAxesPositions(t){const e=this._stationaryAxesPositions.domain().filter((e=>t.has(e))),n=Array.from(new Set([...e,...Array.from(t)]));this._stationaryAxesPositions.domain(n)}_updateAxesPositionsInDOM(t){t.attr("transform",(t=>WYt(this.getAxisPosition(t))))}_createAxes(t){return zl(TYt(this._schema)).map((e=>new gKt(this._svgProps,this._schema,t,e)))}}function yKt(t){return null!==t.sourceEvent}!(function(t){t[t.FOREGROUND=0]="FOREGROUND",t[t.BACKGROUND=1]="BACKGROUND"})(dKt||(dKt={}));class vKt{constructor(t){void 0===t&&(t=sh(null)),console.assert(t.size()<=1),this._sessionGroupSel=t}sessionGroup(){return 1===this._sessionGroupSel.size()?this._sessionGroupSel.datum():null}isNull(){return null===this.sessionGroup()}selection(){return this._sessionGroupSel}equalsTo(t){return this.isNull()?t.isNull():!t.isNull()&&t.sessionGroup().name==this.sessionGroup().name}}class bKt{constructor(t,e,n){this._svgProps=t,this._schema=e,this._axesCollection=n,this._sessionGroups=[],this._svgProps.svgG.selectAll("g.background").remove(),this._svgProps.svgG.selectAll("g.foreground").remove(),this._bgPathsSel=this._svgProps.svgG.append("g").classed("background",!0).selectAll("path"),this._fgPathsSel=this._svgProps.svgG.append("g").classed("foreground",!0).selectAll("path"),this._updateVisibleFgPathsSel(),this._peakedSessionGroupHandle=new vKt,this._selectedSessionGroupHandle=new vKt,this._d3line=uL().curve(sL)}getSessionGroupHandle(t){return null==t?new vKt:new vKt(this._fgPathsSel.filter((e=>e.name===t.name)))}hideBackgroundLines(){this._bgPathsSel.attr("visibility","hidden")}showBackgroundLines(){this._bgPathsSel.attr("visibility",null)}peakedSessionGroupHandle(){return this._peakedSessionGroupHandle}selectedSessionGroupHandle(){return this._selectedSessionGroupHandle}recomputeControlPoints(t,e=0){(t===dKt.FOREGROUND?this._fgPathsSel:this._bgPathsSel).transition().duration(e).attr("d",(t=>this._pathDAttribute(t))),t===dKt.FOREGROUND&&window.setTimeout((()=>{const t=this;this._fgPathsSel.each((function(e){t._setControlPointsProperty(this,e)}))}))}recomputeForegroundLinesVisibility(){this._fgPathsSel.classed("invisible-path",(t=>!this._axesCollection.allVisibleAxesSatisfy(((e,n)=>n.brushFilter().isPassing(PYt(this._schema,t,n.colIndex())))))),this._updateVisibleFgPathsSel()}setForegroundLinesColor(t,e,n){const i=this._createLineColorFunction(t,e,n);this._fgPathsSel.attr("stroke",i)}redraw(t,e,n,i){const r=this._peakedSessionGroupHandle.sessionGroup(),o=this._selectedSessionGroupHandle.sessionGroup();this._sessionGroups=t,this._fgPathsSel=this._recomputePathSelection(this._fgPathsSel),this._bgPathsSel=this._recomputePathSelection(this._bgPathsSel),this._peakedSessionGroupHandle=this.getSessionGroupHandle(r),this._selectedSessionGroupHandle=this.getSessionGroupHandle(o),this.recomputeControlPoints(dKt.FOREGROUND),this.recomputeControlPoints(dKt.BACKGROUND),this.recomputeForegroundLinesVisibility(),this.setForegroundLinesColor(e,n,i)}updatePeakedSessionGroup(t){this._peakedSessionGroupHandle.selection().classed("peaked-path",!1),this._peakedSessionGroupHandle=t,this._peakedSessionGroupHandle.selection().classed("peaked-path",!0)}clearPeakedSessionGroup(){this.updatePeakedSessionGroup(new vKt)}updateSelectedSessionGroup(t){this._selectedSessionGroupHandle.selection().classed("selected-path",!1),this._selectedSessionGroupHandle=t,this._selectedSessionGroupHandle.selection().classed("selected-path",!0)}findClosestSessionGroup(t,e){const n=this._axesCollection.mapVisibleAxes(((t,e)=>t)),i=(function r(t,e,n,i){if(e.length<2)return console.error("Less than two axes in parallel coordinates plot."),null;const r=n[0],o=n[1];if(r<=e[0]||r>=e[e.length-1])return null;const a=Se.exports.sortedIndex(e,r);console.assert(a>0),console.assert(a<e.length);const s=a-1;let l=null,c=null;return t.forEach((t=>{const e=(function n(t,e,i,a){const s=t-i,l=e-a,c=r-i,u=o-a,h=(s*c+l*u)/(s*s+l*l);return h<=0?UYt(c,u):h>=1?UYt(t-r,e-o):UYt(c-h*s,u-h*l)})(t.controlPoints[s][0],t.controlPoints[s][1],t.controlPoints[a][0],t.controlPoints[a][1]);e>i||(null===l||e<l)&&(l=e,c=t)})),c})(this._visibleFgPathsSel.nodes(),n,[t,e],100);return null===i?new vKt:new vKt(Su(i))}_createLineColorFunction(t,e,n){if(null===t)return()=>"red";const i=VM().domain(NYt(this._schema,this._sessionGroups,t)).range([e,n]).interpolate(cp);return e=>i(PYt(this._schema,e,t))}_recomputePathSelection(t){return(t=t.data(this._sessionGroups,(t=>t.name))).exit().remove(),t.enter().append("path").merge(t)}_setControlPointsProperty(t,e){t.controlPoints=this._computeControlPoints(e)}_computeControlPoints(t){return this._axesCollection.mapVisibleAxes(((e,n)=>[e,n.yScale()(PYt(this._schema,t,n.colIndex()))]))}_pathDAttribute(t){return this._d3line(this._computeControlPoints(t))}_updateVisibleFgPathsSel(){this._visibleFgPathsSel=this._fgPathsSel.filter(":not(.invisible-path)")}}class xKt{constructor(t,e){this.svg=Su(t);const n=100*e+10+10;this.svg.attr("viewBox",`0 0 ${n} 240`),this.svg.attr("preserveAspectRatio","xMidYMid"),this.svg.style("min-width",n+"px"),this.svg.style("min-height","240px"),this.width=n-10-10,this.height=200,this.svgG=this.svg.append("g").attr("transform",WYt(10,30))}}class wKt{constructor(t,e,n,i){this._svgProps=t,this._schema=e,this._peakedSessionGroupChangedCB=n,this._selectedSessionGroupChangedCB=i,this._axesCollection=new _Kt(t,e,this),this._linesCollection=new bKt(t,e,this._axesCollection),this._svgProps.svg.on("click",(()=>this.onClick())).on("mousemove mouseenter",(()=>{const[t,e]=ah(this._svgProps.svgG.node());this.onMouseMoved(t,e)})).on("mouseleave",(()=>this.onMouseLeave()))}onDragStart(t){this._axesCollection.dragStart(t),this._linesCollection.hideBackgroundLines()}onDrag(t){this._axesCollection.drag(t),this._linesCollection.recomputeControlPoints(dKt.FOREGROUND)}onDragEnd(){this._axesCollection.dragEnd(500),this._linesCollection.recomputeControlPoints(dKt.FOREGROUND,500),window.setTimeout((()=>{this._linesCollection.recomputeControlPoints(dKt.BACKGROUND),this._linesCollection.showBackgroundLines()}),500)}onBrushChanged(t,e){this._axesCollection.getAxisForColIndex(t).setBrushSelection(e),this._linesCollection.recomputeForegroundLinesVisibility()}onMouseMoved(t,e){this._linesCollection.updatePeakedSessionGroup(this._linesCollection.findClosestSessionGroup(t,e)),this._peakedSessionGroupChangedCB(this._linesCollection.peakedSessionGroupHandle().sessionGroup())}onMouseLeave(){this._linesCollection.peakedSessionGroupHandle().isNull()||(this._linesCollection.clearPeakedSessionGroup(),this._peakedSessionGroupChangedCB(null))}onClick(){this._linesCollection.peakedSessionGroupHandle().sessionGroup()===this._linesCollection.selectedSessionGroupHandle().sessionGroup()?this._linesCollection.updateSelectedSessionGroup(new vKt):this._linesCollection.updateSelectedSessionGroup(this._linesCollection.peakedSessionGroupHandle()),this._selectedSessionGroupChangedCB(this._linesCollection.selectedSessionGroupHandle().sessionGroup())}onOptionsOrSessionGroupsChanged(t,e){this._axesCollection.updateAxes(t,e);const n=this._linesCollection.peakedSessionGroupHandle(),i=this._linesCollection.selectedSessionGroupHandle();this._linesCollection.redraw(e,void 0!==t.colorByColumnIndex?t.columns[t.colorByColumnIndex].absoluteIndex:null,t.minColor,t.maxColor),n.equalsTo(this._linesCollection.peakedSessionGroupHandle())||this._peakedSessionGroupChangedCB(this._linesCollection.peakedSessionGroupHandle().sessionGroup()),i.equalsTo(this._linesCollection.selectedSessionGroupHandle())||this._selectedSessionGroupChangedCB(this._linesCollection.selectedSessionGroupHandle().sessionGroup())}schema(){return this._schema}}let SKt=class extends(er(il(ye))){constructor(){super(...arguments),this.selectedSessionGroup=null,this.closestSessionGroup=null,this.redrawCount=0}_optionsOrSessionGroupsChanged(){var t;if(!this.options)return;const{configuration:e}=null!==(t=this._prevOptions)&&void 0!==t?t:{},{configuration:n}=this.options;if(void 0===this._interactionManager||!Se.exports.isEqual(e.schema,n.schema)||!Se.exports.isEqual(e.columnsVisibility,n.columnsVisibility)){Su(this.$.svg).selectAll("*").remove();const t=new xKt(this.$.svg,n.columnsVisibility.filter(Boolean).length);this.scopeSubtree(this.$.svg,!0),this._interactionManager=new wKt(t,n.schema,(t=>this.closestSessionGroupChanged(t)),(t=>this.selectedSessionGroupChanged(t)))}this._computeValidSessionGroups(),this._interactionManager.onOptionsOrSessionGroupsChanged(this.options,this._validSessionGroups),this.redrawCount++,this._prevOptions=this.options}closestSessionGroupChanged(t){this.closestSessionGroup=t}selectedSessionGroupChanged(t){this.selectedSessionGroup=t}_computeValidSessionGroups(){const t=XYt;if(void 0===this.sessionGroups)return void(this._validSessionGroups=void 0);const e=this.options.configuration.schema;this._validSessionGroups=this.sessionGroups.filter((n=>{for(let i=0;i<t.numColumns(e);++i)if(this.options.configuration.columnsVisibility[i]&&void 0===t.columnValueByIndex(e,n,i))return!1;return!0}))}};SKt.template=_e`
    <div id="container">
      <svg id="svg"></svg>
    </div>
    <style>
      :host {
        display: block;
        --tf-hparams-parallel-coords-plot-axis-shadow: 0 1px 0 #fff,
          1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;
      }
      :host(.dark-mode) {
        --tf-hparams-parallel-coords-plot-axis-shadow: 0 1px 0 #000,
          1px 0 0 #000, 0 -1px 0 #000, -1px 0 0 #000;
      }
      svg {
        font: 10px sans-serif;
      }

      .background path {
        fill: none;
        stroke: #ddd;
        shape-rendering: crispEdges;
      }

      .foreground path {
        fill: none;
        stroke-opacity: 0.7;
        stroke-width: 1;
      }

      /* Will be set on foreground paths that are not "contained" in the current
         axes brushes. If no brushes are set, no path will have this class. */
      .foreground .invisible-path {
        display: none;
      }

      /* Style for the path closest to the mouse pointer (typically will become
      the selected path when the user clicks). */
      .foreground .peaked-path {
        stroke-width: 3;
      }

      /* The currently selected path class. We use !important to override the
         inline style that sets the regular color of a path. */
      .foreground .selected-path {
        stroke-width: 3 !important;
        stroke: #0f0 !important;
      }

      #container {
        height: 100%;
        width: 100%;
      }

      svg {
        width: 100%;
        height: 100%;
      }

      .axis text {
        text-shadow: var(--tf-hparams-parallel-coords-plot-axis-shadow);
        fill: currentColor;
        cursor: move;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],SKt.prototype,"sessionGroups",void 0),t([o({type:Object}),e("design:type",Object)],SKt.prototype,"options",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],SKt.prototype,"selectedSessionGroup",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],SKt.prototype,"closestSessionGroup",void 0),t([o({type:Number}),e("design:type",Number)],SKt.prototype,"redrawCount",void 0),t([o({type:Array}),e("design:type",Array)],SKt.prototype,"_validSessionGroups",void 0),t([o({type:Object}),e("design:type",Object)],SKt.prototype,"_interactionManager",void 0),t([a("options.*","sessionGroups.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],SKt.prototype,"_optionsOrSessionGroupsChanged",null),SKt=t([i("tf-hparams-parallel-coords-plot")],SKt);let MKt=class extends ye{_closestOrSelected(t,e){return null!==t?t:e}};MKt.template=_e`
    <!-- Controls behavior of parallel coordinates plot
         outputs set options to the _options property.
      -->
    <hparams-split-layout orientation="vertical">
      <!-- The scale and color controls. -->
      <tf-hparams-scale-and-color-controls
        id="controls"
        slot="content"
        class="section"
        configuration="[[configuration]]"
        session-groups="[[sessionGroups]]"
        options="{{_options}}"
      >
      </tf-hparams-scale-and-color-controls>
      <!-- The actual parallel coordinates plot -->
      <tf-hparams-parallel-coords-plot
        id="plot"
        slot="content"
        class="section"
        session-groups="[[sessionGroups]]"
        selected-session-group="{{_selectedGroup}}"
        closest-session-group="{{_closestGroup}}"
        options="[[_options]]"
      >
      </tf-hparams-parallel-coords-plot>
      <tf-hparams-session-group-values
        id="values"
        slot="content"
        class="section"
        visible-schema="[[configuration.visibleSchema]]"
        session-group="[[_closestOrSelected(
                             _closestGroup, _selectedGroup)]]"
      >
      </tf-hparams-session-group-values>
      <tf-hparams-session-group-details
        id="details"
        slot="content"
        class="section"
        backend="[[backend]]"
        experiment-name="[[experimentName]]"
        session-group="[[_selectedGroup]]"
        visible-schema="[[configuration.visibleSchema]]"
      >
      </tf-hparams-session-group-details>
    </hparams-split-layout>

    <style>
      .section {
        padding: 10px;
      }
      #values {
        height: 115px;
      }
      #details {
        flex-grow: 1;
        max-height: fit-content;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],MKt.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],MKt.prototype,"experimentName",void 0),t([o({type:Object}),e("design:type",Object)],MKt.prototype,"configuration",void 0),t([o({type:Array}),e("design:type",Array)],MKt.prototype,"sessionGroups",void 0),MKt=t([i("tf-hparams-parallel-coords-view")],MKt);let EKt=class extends(er(ye)){constructor(){super(...arguments),this.selectedSessionGroup=null,this.closestSessionGroup=null,this._container=null,this._svg=null,this.width=0,this.height=0,this._brushedCellIndex=null,this._brushSelection=null}ready(){super.ready(),this._container=this.$.container,this._svg=Su(this.$.svg),this._redraw()}_sessionGroupsChanged(){null!==this.selectedSessionGroup&&(this.selectedSessionGroup=DYt(this.sessionGroups,this.selectedSessionGroup.name)||null),this._redraw()}_visibleSchemaChanged(){this._brushedCellIndex=null,this._brushSelection=null,this._redraw()}_redraw(){this.debounce("_redraw",(()=>{const t=XYt;this.width=Math.max(150*t.numVisibleColumns(this.visibleSchema),1200),this.height=Math.max(112.5*t.numVisibleMetrics(this.visibleSchema),480),this._container.style.width=this.width+"px",this._container.style.height=this.height+"px",this._svg.attr("width",this.width).attr("height",this.height),this._svg.selectAll("g").remove(),this._draw()}),100)}_draw(){const t=XYt,e=this;if(!this.sessionGroups||0==this.sessionGroups.length||!this.visibleSchema||0==this.visibleSchema.metricInfos.length)return;const n=zl(t.numVisibleColumns(e.visibleSchema)),i=zl(t.numVisibleMetrics(e.visibleSchema)),r=TM().domain(n).range([85,this.width-1-5]).paddingInner(.1),o=TM().domain(i).range([this.height-1-5-50,5]).paddingInner(.1),a=r.bandwidth(),s=o.bandwidth(),l=n.map((t=>e._cellScale(t,[0,a-1]))),c=i.map((n=>e._cellScale(n+t.numVisibleHParams(e.visibleSchema),[s-1,0]))),u=this._svg.selectAll(".x-axis").data(n).enter().append("g").classed("x-axis",!0).attr("transform",(e=>t.translateStr(r(e),0)));function h(t){return"x-axis-clip-path-"+t}function d(t){return"x-label-clip-path-"+t}u.append("clipPath").attr("id",h).append("rect").attr("x",-5).attr("y",0).attr("width",a+10).attr("height",e.height-25),u.append("clipPath").attr("id",d).append("rect").attr("x",0).attr("y",e.height-25).attr("width",a).attr("height",25),u.append("g").attr("clip-path",(t=>"url(#"+h(t)+")")).each((function(t){Su(this).call(g,oc(l[t]).tickSize(e.height-50),a,40,e.options.columns[t].scale)})),u.append("g").classed("x-axis-label",!0).attr("clip-path",(t=>"url(#"+d(t)+")")).append("text").attr("text-anchor","middle").attr("x",a/2).attr("y",e.height-1-12.5).text((n=>t.schemaVisibleColumnName(e.visibleSchema,n))).append("title").text((n=>t.schemaVisibleColumnName(e.visibleSchema,n)));const p=this._svg.selectAll(".y-axis").data(i).enter().append("g").classed("y-axis",!0).attr("transform",(n=>t.translateStr(e.width-1,o(n))));function f(t){return"y-axis-clip-path-"+t}function m(t){return"y-label-clip-path-"+t}function g(t,e,n,i,r){const o=Math.floor(n/i),a=e.scale();if("QUANTILE"===r){let t=a.quantiles();const n=Math.ceil(t.length/o);t=zl(0,t.length,n).map((e=>t[e])),e.tickValues(t).tickFormat(my("-.2g"))}"LINEAR"!==r&&"LOG"!==r||e.ticks(o),t.call(e),t.selectAll(".domain").remove(),t.selectAll(".tick line").attr("stroke","#ddd")}p.append("clipPath").attr("id",f).append("rect").attr("x",-(e.width-40-1)).attr("y",-5).attr("width",e.width-40).attr("height",s+10),p.append("clipPath").attr("id",m).append("rect").attr("x",-(e.width-1)).attr("y",0).attr("width",40).attr("height",s),p.append("g").attr("clip-path",(t=>"url(#"+f(t)+")")).each((function(n){Su(this).call(g,ac(c[n]).tickSize(e.width-80),s,20,e.options.columns[n+t.numVisibleHParams(e.visibleSchema)].scale)})),p.append("g").classed("y-axis-label",!0).attr("clip-path",(t=>"url(#"+m(t)+")")).append("text").attr("text-anchor","middle").attr("x",-(e.width-20-1)).attr("y",s/2).attr("transform",t.rotateStr(90,-(e.width-20-1),s/2)).text((n=>t.metricName(e.visibleSchema.metricInfos[n]))).append("title").text((n=>t.metricName(e.visibleSchema.metricInfos[n])));const _=this._svg.selectAll(".cell").data(Tl(n,i)).enter().append("g").classed("cell",!0).attr("transform",(([e,n])=>t.translateStr(r(e),o(n))));_.append("g").classed("frame",!0).append("rect").attr("x",-5).attr("y",-5).attr("width",a+10).attr("height",s+10).attr("stroke","#000").attr("fill","none").attr("shape-rendering","crispEdges");let y=null;void 0!==e.options.colorByColumnIndex&&(y=VM().domain(this._colExtent(this.options.colorByColumnIndex)).range([this.options.minColor,this.options.maxColor]).interpolate(cp));const v=void 0===e.options.colorByColumnIndex?()=>"red":({sessionGroup:t})=>y(this._colValue(t,e.options.colorByColumnIndex));function b(t,n){return l[n](e._colValue(t,n))}function x(t,n){return c[n](e._metricValue(t,n))}const[w,S,M]=(function E(t,r){const o=t.selectAll(".data-marker").data((([t,n])=>e.sessionGroups.filter((i=>void 0!==e._colValue(i,t)&&void 0!==e._metricValue(i,n))).map((e=>({col:t,metric:n,sessionGroup:e,x:b(e,t),y:x(e,n),sessionGroupMarkers:null}))))).enter().append("circle").classed("data-marker",!0).attr("cx",(({x:t})=>t)).attr("cy",(({y:t})=>t)).attr("r",2).attr("fill",r),a=new Map;e.sessionGroups.forEach((t=>{a.set(t,[])})),o.each((function(t){a.get(t.sessionGroup).push(this)})),o.each((t=>{const e=a.get(t.sessionGroup);t.sessionGroupMarkers=new Set(e)}));const s=n.map((t=>i.map((e=>o.filter((n=>n.col==t&&n.metric==e))))));return[o,s,a]})(_.append("g"),v),T=n.map((t=>i.map((e=>(function n(t,e){const n=[];return S[t][e].each((function(){n.push(this)})),N_().x((t=>Su(t).datum().x)).y((t=>Su(t).datum().y)).addAll(n)})(t,e)))));let C=null;N()&&(C=_.filter((t=>Se.exports.isEqual(t,e._brushedCellIndex))),console.assert(1==C.size(),C));let A=new Set(w.nodes());function k(){let n=new Set(w.nodes());(function i(){return!N()||e._brushSelection[0][0]===e._brushSelection[1][0]||e._brushSelection[0][1]===e._brushSelection[1][1]})()||(n=(function r(e,n){console.assert(null!==e),console.assert(null!==n);const[i,r]=e,o=new Set;return t.quadTreeVisitPointsInRect(T[i][r],n[0][0],n[0][1],n[1][0],n[1][1],(t=>{Su(t).datum().sessionGroupMarkers.forEach((t=>{o.add(t)}))})),o})(e._brushedCellIndex,e._brushSelection)),sh(Array.from(t.filterSet(n,(t=>!A.has(t))))).attr("fill",v),sh(Array.from(t.filterSet(A,(t=>!n.has(t))))).attr("fill","#ddd"),A=n}k();const L=Jm().extent([[-4,-4],[a-1+5-1,s-1+5-1]]).on("start",(function(){N()&&C.node()!=this&&L.move(C,null),P(this)})).on("brush",(function(){P(this)})).on("end",(function(){P(this)}));function P(t){const n=Km(t);!N()&&null===n||N()&&t===C.node()&&Se.exports.isEqual(n,e._brushSelection)||(e._brushSelection=n,null!==n?(C=Su(t),e._brushedCellIndex=C.datum()):(C=null,e._brushedCellIndex=null),k())}function N(){return null!==e._brushedCellIndex&&null!==e._brushSelection}_.call(L),N()&&L.move(C,e._brushSelection);let I=null,R=null;null!==this.selectedSessionGroup&&(R=sh(M.get(this.selectedSessionGroup)).classed("selected-marker",!0)),_.on("click",(function(){const t=I===R?null:I;if(t===R)return;null!==R&&R.classed("selected-marker",!1),R=t,null!==R&&R.classed("selected-marker",!0);const n=null===R?null:R.datum().sessionGroup;e.selectedSessionGroup=n})).on("mousemove mouseenter",(function([n,i]){const[r,o]=ah(this),a=(function s(e,n,i,r,o){let a=1/0,s=null;return t.quadTreeVisitPointsInDisk(T[e][n],i,r,o,((t,e)=>{if(A.has(t)&&e<a){const n=Su(t).datum();a=e,s=n.sessionGroup}})),null===s?null:sh(M.get(s))})(n,i,r,o,20);I!==a&&(null!==I&&I.classed("closest-marker",!1),I=a,null!==I?(I.classed("closest-marker",!0),e.closestSessionGroup=I.datum().sessionGroup):e.closestSessionGroup=null)})).on("mouseleave",(function([t,n]){null!==I&&(I.classed("closest-marker",!1),I=null,e.closestSessionGroup=null)})),this._svg.selectAll("*").classed("tf-hparams-scatter-plot-matrix-plot",!0)}_cellScale(t,e){const n=this._colExtent(t),i=VM().domain(n).range(e);if("LINEAR"===this.options.columns[t].scale)return i;if("LOG"===this.options.columns[t].scale)return n[0]<=0&&n[1]>=0?i:KM().domain(n).range(e);if("QUANTILE"===this.options.columns[t].scale){const n=(e[1]-e[0])/19,i=zl(20).map((t=>e[0]+n*t));return oE().domain(Se.exports.uniq(this.sessionGroups.map((e=>this._colValue(e,t))))).range(i)}if("NON_NUMERIC"===this.options.columns[t].scale)return AM().domain(Se.exports.uniq(this.sessionGroups.map((e=>this._colValue(e,t))).sort())).range(e).padding(.1);throw"Unknown scale for column: "+t+". options: "+this.options}_colValue(t,e){return FYt(this.visibleSchema,t,e)}_metricValue(t,e){return HYt(this.visibleSchema,t,e)}_colExtent(t){return zYt(this.visibleSchema,this.sessionGroups,t)}};EKt.template=_e`
    <div id="container">
      <svg id="svg"></svg>
    </div>

    <style>
      :host {
        display: block;
      }
      svg {
        font: 10px sans-serif;
      }

      text {
        fill: currentColor;
      }

      .frame rect {
        stroke: currentColor;
      }

      /* The closest data point marker to the mouse pointer. We use !important
         to override the inline style that sets the regular style of a marker.
      */
      .closest-marker {
        r: 6 !important;
      }

      /* The currently selected data point marker. We use !important to
         override the inline style that sets the regular style of a marker. */
      .selected-marker {
        r: 6 !important;
        fill: #0f0 !important;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],EKt.prototype,"visibleSchema",void 0),t([o({type:Array}),e("design:type",Array)],EKt.prototype,"sessionGroups",void 0),t([o({type:Object}),e("design:type",Object)],EKt.prototype,"options",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],EKt.prototype,"selectedSessionGroup",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],EKt.prototype,"closestSessionGroup",void 0),t([o({type:Object}),e("design:type",HTMLElement)],EKt.prototype,"_container",void 0),t([o({type:Object}),e("design:type",Object)],EKt.prototype,"_svg",void 0),t([o({type:Number}),e("design:type",Number)],EKt.prototype,"width",void 0),t([o({type:Number}),e("design:type",Number)],EKt.prototype,"height",void 0),t([o({type:Object}),e("design:type",Object)],EKt.prototype,"_brushedCellIndex",void 0),t([o({type:Object}),e("design:type",Object)],EKt.prototype,"_brushSelection",void 0),t([a("sessionGroups.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],EKt.prototype,"_sessionGroupsChanged",null),t([a("visibleSchema.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],EKt.prototype,"_visibleSchemaChanged",null),t([a("options.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],EKt.prototype,"_redraw",null),EKt=t([i("tf-hparams-scatter-plot-matrix-plot")],EKt);let TKt=class extends ye{_closestOrSelected(t,e){return null!==t?t:e}};TKt.template=_e`
    <hparams-split-layout orientation="vertical">
      <!-- Controls behavior of the scatter plot matrix
             outputs the configured options to the _options property. -->
      <tf-hparams-scale-and-color-controls
        slot="content"
        class="section"
        id="controls"
        configuration="[[configuration]]"
        session-groups="[[sessionGroups]]"
        options="{{_options}}"
      >
      </tf-hparams-scale-and-color-controls>
      <!-- The actual scatter plot matrix -->
      <tf-hparams-scatter-plot-matrix-plot
        slot="content"
        class="section"
        id="plot"
        visible-schema="[[configuration.visibleSchema]]"
        session-groups="[[sessionGroups]]"
        selected-session-group="{{_selectedGroup}}"
        closest-session-group="{{_closestGroup}}"
        options="[[_options]]"
      >
      </tf-hparams-scatter-plot-matrix-plot>
      <tf-hparams-session-group-values
        slot="content"
        class="section"
        id="values"
        visible-schema="[[configuration.visibleSchema]]"
        session-group="[[_closestOrSelected(
                                 _closestGroup, _selectedGroup)]]"
      >
      </tf-hparams-session-group-values>
      <!-- Shows session group details for the clicked marker. -->
      <tf-hparams-session-group-details
        slot="content"
        class="section"
        id="details"
        backend="[[backend]]"
        experiment-name="[[experimentName]]"
        session-group="[[_selectedGroup]]"
        visible-schema="[[configuration.visibleSchema]]"
      >
      </tf-hparams-session-group-details>
    </hparams-split-layout>
    <style>
      .section {
        padding: 10px;
      }
      #controls {
        flex-grow: 0;
        flex-shrink: 0;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      #plot {
        flex-grow: 1;
        flex-shrink: 1;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      #values {
        flex-grow: 0;
        flex-shrink: 0;
        flex-basis: auto;
        height: 115px;
        overflow-y: auto;
        max-height: fit-content;
      }
      #details {
        flex-grow: 0;
        flex-shrink: 1;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      vaadin-split-layout {
        height: 100%;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],TKt.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],TKt.prototype,"experimentName",void 0),t([o({type:Object}),e("design:type",Object)],TKt.prototype,"configuration",void 0),t([o({type:Array}),e("design:type",Array)],TKt.prototype,"sessionGroups",void 0),TKt=t([i("tf-hparams-scatter-plot-matrix-view")],TKt);let CKt=class extends ye{constructor(){super(...arguments),this._selectedTab=0}};CKt.template=_e`
    <paper-header-panel>
      <paper-toolbar slot="header" class="tab-bar">
        <paper-tabs selected="{{_selectedTab}}" slot="top">
          <!-- view-id can be used by integration tests to locate a tab.
               It should be the name of the root element implementing the view
               without the 'tf-hparams-' prefix. -->
          <paper-tab view-id="table-view"> TABLE VIEW </paper-tab>
          <paper-tab view-id="parallel-coords-view">
            PARALLEL COORDINATES VIEW
          </paper-tab>
          <paper-tab view-id="scatter-plot-matrix-view">
            SCATTER PLOT MATRIX VIEW
          </paper-tab>
          <div class="help-and-feedback">
            <template is="dom-if" if="[[bugReportUrl]]">
              <a
                href$="[[bugReportUrl]]"
                target="_blank"
                rel="noopener noreferrer"
              >
                <paper-button
                  id="bug-report"
                  raised
                  title="Send a bug report or feature request"
                >
                  Bug Report / Feature Request
                </paper-button>
              </a>
            </template>
            <template is="dom-if" if="[[helpUrl]]">
              <a href$="[[helpUrl]]" target="_blank" rel="noopener noreferrer">
                <paper-icon-button
                  icon="help-outline"
                  title="View documentation"
                >
                </paper-icon-button>
              </a>
            </template>
          </div>
        </paper-tabs>
      </paper-toolbar>
      <iron-pages selected="[[_selectedTab]]" class="fit tab-view">
        <div id="0" class="tab">
          <tf-hparams-table-view
            backend="[[backend]]"
            experiment-name="[[experimentName]]"
            visible-schema="[[configuration.visibleSchema]]"
            session-groups="[[sessionGroups]]"
            enable-show-metrics
          >
          </tf-hparams-table-view>
        </div>
        <div id="1" class="tab">
          <tf-hparams-parallel-coords-view
            backend="[[backend]]"
            experiment-name="[[experimentName]]"
            configuration="[[configuration]]"
            session-groups="[[sessionGroups]]"
          >
          </tf-hparams-parallel-coords-view>
        </div>
        <div id="2" class="tab">
          <tf-hparams-scatter-plot-matrix-view
            backend="[[backend]]"
            experiment-name="[[experimentName]]"
            configuration="[[configuration]]"
            session-groups="[[sessionGroups]]"
          >
          </tf-hparams-scatter-plot-matrix-view>
        </div>
      </iron-pages>
    </paper-header-panel>

    <style>
      .tab-view {
        height: 100%;
      }
      .tab-bar {
        overflow-y: auto;
        color: white;
        background-color: var(
          --tb-toolbar-background-color,
          var(--tb-orange-strong)
        );
      }
      .tab {
        height: 100%;
      }
      paper-tabs {
        flex-grow: 1;
        width: 100%;
        height: 100%;
        --paper-tabs-selection-bar-color: white;
        --paper-tabs-content: {
          -webkit-font-smoothing: antialiased;
        }
      }
      tf-hparams-table-view {
        width: 100%;
        height: 100%;
      }
      .help-and-feedback {
        display: inline-flex; /* Ensure that icons stay aligned */
        justify-content: flex-end;
        align-items: center;
        text-align: right;
        color: white;
      }
      #bug-report {
        border: solid black;
        background: red;
        white-space: normal;
        word-break: break-words;
        font-size: 12px;
        max-width: 150px;
        text-align: left;
      }
      .help-and-feedback a {
        color: white;
        text-decoration: none;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],CKt.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],CKt.prototype,"helpUrl",void 0),t([o({type:String}),e("design:type",String)],CKt.prototype,"bugReportUrl",void 0),t([o({type:String}),e("design:type",String)],CKt.prototype,"experimentName",void 0),t([o({type:Object}),e("design:type",Object)],CKt.prototype,"configuration",void 0),t([o({type:Array}),e("design:type",Array)],CKt.prototype,"sessionGroups",void 0),t([o({type:Number}),e("design:type",Number)],CKt.prototype,"_selectedTab",void 0),CKt=t([i("tf-hparams-sessions-pane")],CKt);class AKt{constructor(t,e,n=!0){this._apiUrl=t,this._requestManager=e,this._useHttpGet=n}getExperiment(t){return this._sendRequest("experiment",t)}getDownloadUrl(t,e,n){return this._apiUrl+"/download_data?"+new URLSearchParams({format:t,columnsVisibility:JSON.stringify(n),request:JSON.stringify(e)})}listSessionGroups(t){return this._sendRequest("session_groups",t)}listMetricEvals(t){return this._sendRequest("metric_evals",t)}_sendRequest(t,e){if(this._useHttpGet){const n=encodeURIComponent(JSON.stringify(e));return this._requestManager.request(this._apiUrl+"/"+t+"?request="+n)}const n=new hr;return n.withCredentials=!0,n.methodType=ur.POST,n.contentType="text/plain",n.body=JSON.stringify(e),this._requestManager.requestWithOptions(this._apiUrl+"/"+t,n)}}let kKt=class extends(er(ye)){reload(){this.$["query-pane"].reload()}};kKt.template=_e`
    <hparams-split-layout>
      <div slot="content" class="sidebar">
        <tf-hparams-query-pane
          id="query-pane"
          backend="[[backend]]"
          experiment-name="[[experimentName]]"
          configuration="{{_configuration}}"
          session-groups="{{_sessionGroups}}"
          data-loaded-with-non-empty-hparams="{{_dataLoadedWithNonEmptyHparams}}"
          data-loaded-with-empty-hparams="{{_dataLoadedWithEmptyHparams}}"
        >
        </tf-hparams-query-pane>
      </div>
      <div slot="content" class="center">
        <template is="dom-if" if="[[_dataLoadedWithEmptyHparams]]">
          <div class="no-data-warning">
            <h3>No hparams data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>You haven’t written any hparams data to your event files.</li>
              <li>
                Event files are still being loaded (try reloading this page).
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>

        <template is="dom-if" if="[[_dataLoadedWithNonEmptyHparams]]">
          <tf-hparams-sessions-pane
            id="sessions-pane"
            backend="[[backend]]"
            help-url="[[helpUrl]]"
            bug-report-url="[[bugReportUrl]]"
            experiment-name="[[experimentName]]"
            configuration="[[_configuration]]"
            session-groups="[[_sessionGroups]]"
          >
          </tf-hparams-sessions-pane>
        </template>
      </div>
    </hparams-split-layout>
    <style>
      hparams-split-layout {
        width: 100%;
      }

      .sidebar {
        width: 20%;
        height: 100%;
        overflow: auto;
        flex-grow: 0;
        flex-shrink: 0;
        min-width: 10%;
      }

      .center {
        height: 100%;
        overflow-y: auto;
        flex-grow: 1;
        flex-shrink: 1;
        width: 80%;
      }

      :host {
        display: flex;
        flex-direction: row;
        height: 100%;
        width: 100%;
      }

      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }

      a {
        color: var(--tb-link);
      }

      a:visited {
        color: var(--tb-link-visited);
      }
    </style>
  `,t([o({type:Object}),e("design:type",AKt)],kKt.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],kKt.prototype,"experimentName",void 0),t([o({type:String}),e("design:type",String)],kKt.prototype,"helpUrl",void 0),t([o({type:String}),e("design:type",String)],kKt.prototype,"bugReportUrl",void 0),t([o({type:Object}),e("design:type",Object)],kKt.prototype,"_configuration",void 0),t([o({type:Array}),e("design:type",Array)],kKt.prototype,"_sessionGroups",void 0),t([o({type:Boolean}),e("design:type",Boolean)],kKt.prototype,"_dataLoadedWithNonEmptyHparams",void 0),t([o({type:Boolean}),e("design:type",Boolean)],kKt.prototype,"_dataLoadedWithEmptyHparams",void 0),kKt=t([i("tf-hparams-main")],kKt);const LKt="true"===new URLSearchParams(window.location.search).get("tensorboardColab");let PKt=class extends(er(ye)){constructor(){super(...arguments),this._backend=new AKt(_r().pluginRoute("hparams",""),new dr,LKt)}reload(){this.$["hparams-main"].reload()}};PKt.template=_e`
    <!-- TensorBoard does not specify an experimentName. Currently it only
         supports one experiment per invocation. -->
    <tf-hparams-main
      id="hparams-main"
      backend="[[_backend]]"
      experiment-name=""
    >
    </tf-hparams-main>
  `,t([o({type:Object}),e("design:type",Object)],PKt.prototype,"_backend",void 0),PKt=t([i("tf-hparams-dashboard")],PKt);let NKt=class extends(er(ye)){constructor(){super(...arguments),this.actualSize=!1,this.brightnessAdjustment=.5,this.contrastPercentage=0,this._metadataCanceller=new XR,this._imageCanceller=new XR,this._steps=[],this._isImageLoading=!1}get _runColor(){return GR(this.run)}get _hasAtLeastOneStep(){var t=this._steps;return!!t&&t.length>0}get _hasMultipleSteps(){var t=this._steps;return!!t&&t.length>1}get _currentStep(){return this._steps[this._stepIndex]||null}get _stepValue(){var t=this._currentStep;return t?t.step:0}get _currentWallTime(){var t=this._currentStep;return t?KR(t.wall_time):""}get _maxStepIndex(){return this._steps.length-1}get _sampleText(){return`${this.sample+1}`}get _hasMultipleSamples(){return this.ofSamples>1}_getAriaExpanded(){return this.actualSize?"true":"false"}attached(){this.reload()}reload(){if(!this.isAttached)return;this._metadataCanceller.cancelAll();const t=iO(_r().pluginRoute("images","/images"),{tag:this.tag,run:this.run,sample:this.sample}),e=this._metadataCanceller.cancellable((t=>{if(t.cancelled)return;const e=t.value.map(this._createStepDatum.bind(this));this.set("_steps",e),this.set("_stepIndex",e.length-1)}));this.requestManager.request(t).then(e)}_createStepDatum(t){let e=_r().pluginRoute("images","/individualImage");return e=iO(e,{ts:t.wall_time}),e+="&"+t.query,{wall_time:new Date(1e3*t.wall_time),step:t.step,url:e}}_updateImageUrl(){var t=this._currentStep,e=this.brightnessAdjustment,n=this.contrastPercentage;if(!t)return;const i=new Image;this._imageCanceller.cancelAll(),i.onload=i.onerror=this._imageCanceller.cancellable((t=>{if(t.cancelled)return;const e=this.$$("#main-image-container");e.textContent="",Yi(e).appendChild(i),this.set("_isImageLoading",!1)})).bind(this),i.style.filter=`contrast(${n}%) `,i.style.filter+=`brightness(${e})`,this.set("_isImageLoading",!0),i.src=t.url}_handleTap(t){this.set("actualSize",!this.actualSize)}_toLocaleString(t){return t.toLocaleString()}};NKt.template=_e`
    <tf-card-heading
      tag="[[tag]]"
      run="[[run]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
      color="[[_runColor]]"
    >
      <template is="dom-if" if="[[_hasMultipleSamples]]">
        <div>sample: [[_sampleText]] of [[ofSamples]]</div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep]]">
        <div class="heading-row">
          <div class="heading-label">
            step
            <span style="font-weight: bold"
              >[[_toLocaleString(_stepValue)]]</span
            >
          </div>
          <div class="heading-label heading-right datetime">
            <template is="dom-if" if="[[_currentWallTime]]">
              [[_currentWallTime]]
            </template>
          </div>
          <div class="label right">
            <paper-spinner-lite active hidden$="[[!_isImageLoading]]">
            </paper-spinner-lite>
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps]]">
        <div>
          <paper-slider
            id="steps"
            immediate-value="{{_stepIndex}}"
            max="[[_maxStepIndex]]"
            max-markers="[[_maxStepIndex]]"
            snaps
            step="1"
            value="{{_stepIndex}}"
          ></paper-slider>
        </div>
      </template>
    </tf-card-heading>

    <!-- Semantically a button but <img> inside a <button> disallows user to do
    an interesting operation like "Copy Image" in non-Chromium browsers. -->
    <a
      id="main-image-container"
      role="button"
      aria-label="Toggle actual size"
      aria-expanded$="[[_getAriaExpanded(actualSize)]]"
      on-tap="_handleTap"
    ></a>

    <style include="tf-card-heading-style">
      /** Make button a div. */
      button {
        width: 100%;
        display: block;
        background: none;
        border: 0;
        padding: 0;
      }

      /** Firefox: Get rid of dotted line inside button. */
      button::-moz-focus-inner {
        border: 0;
        padding: 0;
      }

      /** Firefox: Simulate Chrome's outer glow on button when focused. */
      button:-moz-focusring {
        outline: none;
        box-shadow: 0px 0px 1px 2px Highlight;
      }

      :host {
        display: block;
        width: 350px;
        height: auto;
        position: relative;
        margin: 0 15px 40px 0;
        overflow-x: auto;
      }

      /** When actual size shown is on, use the actual image width. */
      :host([actual-size]) {
        max-width: 100%;
        width: auto;
      }

      :host([actual-size]) #main-image-container {
        max-height: none;
        width: auto;
      }

      :host([actual-size]) #main-image-container img {
        width: auto;
      }

      paper-spinner-lite {
        width: 14px;
        height: 14px;
        vertical-align: text-bottom;
        --paper-spinner-color: var(--tb-orange-strong);
      }

      #steps {
        height: 15px;
        margin: 0 0 0 -15px;
        /*
         * 31 comes from adding a padding of 15px from both sides of the
         * paper-slider, subtracting 1px so that the slider width aligns
         * with the image (the last slider marker takes up 1px), and
         * adding 2px to account for a border of 1px on both sides of
         * the image. 30 - 1 + 2.
         */
        width: calc(100% + 31px);
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
      }

      #main-image-container {
        max-height: 1024px;
        overflow: auto;
      }

      #main-image-container img {
        cursor: pointer;
        display: block;
        image-rendering: -moz-crisp-edges;
        image-rendering: pixelated;
        width: 100%;
        height: auto;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }
      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }
      [hidden] {
        display: none;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],NKt.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],NKt.prototype,"tag",void 0),t([o({type:Number}),e("design:type",Number)],NKt.prototype,"sample",void 0),t([o({type:Number}),e("design:type",Number)],NKt.prototype,"ofSamples",void 0),t([o({type:Object}),e("design:type",Object)],NKt.prototype,"tagMetadata",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],NKt.prototype,"actualSize",void 0),t([o({type:Number}),e("design:type",Number)],NKt.prototype,"brightnessAdjustment",void 0),t([o({type:Number}),e("design:type",Number)],NKt.prototype,"contrastPercentage",void 0),t([o({type:Object}),e("design:type",dr)],NKt.prototype,"requestManager",void 0),t([o({type:Object}),e("design:type",Object)],NKt.prototype,"_metadataCanceller",void 0),t([o({type:Object}),e("design:type",Object)],NKt.prototype,"_imageCanceller",void 0),t([o({type:Array,notify:!0}),e("design:type",Array)],NKt.prototype,"_steps",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],NKt.prototype,"_stepIndex",void 0),t([o({type:Boolean}),e("design:type",Boolean)],NKt.prototype,"_isImageLoading",void 0),t([s("run"),e("design:type",String),e("design:paramtypes",[])],NKt.prototype,"_runColor",null),t([s("_steps"),e("design:type",Boolean),e("design:paramtypes",[])],NKt.prototype,"_hasAtLeastOneStep",null),t([s("_steps"),e("design:type",Boolean),e("design:paramtypes",[])],NKt.prototype,"_hasMultipleSteps",null),t([s("_steps","_stepIndex"),e("design:type",Object),e("design:paramtypes",[])],NKt.prototype,"_currentStep",null),t([s("_currentStep"),e("design:type",Number),e("design:paramtypes",[])],NKt.prototype,"_stepValue",null),t([s("_currentStep"),e("design:type",String),e("design:paramtypes",[])],NKt.prototype,"_currentWallTime",null),t([s("_steps"),e("design:type",Number),e("design:paramtypes",[])],NKt.prototype,"_maxStepIndex",null),t([s("sample"),e("design:type",String),e("design:paramtypes",[])],NKt.prototype,"_sampleText",null),t([s("ofSamples"),e("design:type",Boolean),e("design:paramtypes",[])],NKt.prototype,"_hasMultipleSamples",null),t([a("run","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],NKt.prototype,"reload",null),t([a("_currentStep","brightnessAdjustment","contrastPercentage"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],NKt.prototype,"_updateImageUrl",null),NKt=t([i("tf-image-loader")],NKt);let IKt=class extends(er(ye)){constructor(){super(...arguments),this.reloadOnReady=!0,this._defaultBrightnessAdjustment=1,this._defaultContrastPercentage=100,this._brightnessAdjustment=1,this._contrastPercentage=100,this._requestManager=new dr}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){this._fetchTags().then((()=>{this._reloadImages()}))}_fetchTags(){const t=_r().pluginRoute("images","/tags");return this._requestManager.request(t).then((t=>{if(Se.exports.isEqual(t,this._runToTagInfo))return;const e=ar(Se.exports.mapValues(t,(t=>Object.keys(t))));this.set("_dataNotFound",0===e.length),this.set("_runToTagInfo",t),this.async((()=>{this.set("_categoriesDomReady",!0)}))}))}_reloadImages(){this.root.querySelectorAll("tf-image-loader").forEach((t=>{t.reload()}))}_shouldOpen(t){return t<=2}_resetBrightness(){this._brightnessAdjustment=this._defaultBrightnessAdjustment}_resetContrast(){this._contrastPercentage=this._defaultContrastPercentage}get _brightnessIsDefault(){return this._brightnessAdjustment===this._defaultBrightnessAdjustment}get _contrastIsDefault(){return this._contrastPercentage===this._defaultContrastPercentage}get _categories(){var t=this._runToTagInfo,e=this._selectedRuns,n=this._tagFilter;function i(e){const n=t[e.run][e.tag].samples;return Se.exports.range(n).map((t=>Object.assign({},e,{sample:t,ofSamples:n})))}return Mr(Se.exports.mapValues(t,(t=>Object.keys(t))),e,n).map((t=>Object.assign({},t,{items:[].concat.apply([],t.items.map(i))})))}_tagMetadata(t,e,n){return t[e][n]}};var RKt,OKt,zKt;IKt.template=_e`
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="settings">
          <div class="sidebar-section">
            <div class="line-item">
              <paper-checkbox checked="{{_actualSize}}"
                >Show actual image size</paper-checkbox
              >
            </div>
          </div>
          <div class="sidebar-section">
            <h3 class="tooltip-container">Brightness adjustment</h3>
            <div class="resettable-slider-container">
              <paper-slider
                min="0"
                max="2"
                snaps
                pin
                step="0.01"
                value="{{_brightnessAdjustment}}"
                immediate-value="{{_brightnessAdjustment}}"
              ></paper-slider>
              <paper-button
                class="x-button"
                on-tap="_resetBrightness"
                disabled="[[_brightnessIsDefault]]"
                >Reset</paper-button
              >
            </div>
          </div>
          <div class="sidebar-section">
            <h3 class="tooltip-container">Contrast adjustment</h3>
            <div class="resettable-slider-container">
              <paper-slider
                min="0"
                max="500"
                snaps
                pin
                step="1"
                value="{{_contrastPercentage}}"
                immediate-value="{{_contrastPercentage}}"
              ></paper-slider>
              <paper-button
                class="x-button"
                on-tap="_resetContrast"
                disabled="[[_contrastIsDefault]]"
                >Reset</paper-button
              >
            </div>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector
            id="runs-selector"
            selected-runs="{{_selectedRuns}}"
          ></tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No image data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>You haven’t written any image data to your event files.</li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
            >
              <template>
                <tf-image-loader
                  active="[[active]]"
                  run="[[item.run]]"
                  tag="[[item.tag]]"
                  sample="[[item.sample]]"
                  of-samples="[[item.ofSamples]]"
                  tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]"
                  request-manager="[[_requestManager]]"
                  actual-size="[[_actualSize]]"
                  brightness-adjustment="[[_brightnessAdjustment]]"
                  contrast-percentage="[[_contrastPercentage]]"
                ></tf-image-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>
    <style include="dashboard-style"></style>
    <style>
      .resettable-slider-container {
        display: flex;
      }
      .resettable-slider-container paper-slider {
        flex-grow: 1;
      }
      .resettable-slider-container paper-button {
        flex-grow: 0;
      }
      .resettable-slider-container paper-button[disabled] {
        background-color: unset;
      }
      .x-button {
        font-size: 13px;
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
      }
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      paper-slider {
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
      }
    </style>
  `,t([o({type:Boolean}),e("design:type",Boolean)],IKt.prototype,"reloadOnReady",void 0),t([o({type:Array}),e("design:type",Array)],IKt.prototype,"_selectedRuns",void 0),t([o({type:Object}),e("design:type",Object)],IKt.prototype,"_runToTagInfo",void 0),t([o({type:Boolean}),e("design:type",Boolean)],IKt.prototype,"_dataNotFound",void 0),t([o({type:Boolean}),e("design:type",Boolean)],IKt.prototype,"_actualSize",void 0),t([o({type:Number}),e("design:type",Number)],IKt.prototype,"_defaultBrightnessAdjustment",void 0),t([o({type:Number}),e("design:type",Number)],IKt.prototype,"_defaultContrastPercentage",void 0),t([o({type:Number}),e("design:type",Number)],IKt.prototype,"_brightnessAdjustment",void 0),t([o({type:Number}),e("design:type",Number)],IKt.prototype,"_contrastPercentage",void 0),t([o({type:String}),e("design:type",String)],IKt.prototype,"_tagFilter",void 0),t([o({type:Boolean}),e("design:type",Boolean)],IKt.prototype,"_categoriesDomReady",void 0),t([o({type:Object}),e("design:type",Object)],IKt.prototype,"_requestManager",void 0),t([s("_brightnessAdjustment"),e("design:type",Boolean),e("design:paramtypes",[])],IKt.prototype,"_brightnessIsDefault",null),t([s("_contrastPercentage"),e("design:type",Boolean),e("design:paramtypes",[])],IKt.prototype,"_contrastIsDefault",null),t([s("_runToTagInfo","_selectedRuns","_tagFilter","_categoriesDomReady"),e("design:type",Array),e("design:paramtypes",[])],IKt.prototype,"_categories",null),IKt=t([i("tf-image-dashboard")],IKt),(function(t){t[t.CANCELLED=1]="CANCELLED"})(RKt||(RKt={})),(function(t){t[t.VERTEX=1]="VERTEX",t[t.FACE=2]="FACE",t[t.COLOR=3]="COLOR"})(OKt||(OKt={})),(function(t){t.VERTEX="float32",t.FACE="int32",t.COLOR="uint8"})(zKt||(zKt={}));class DKt{constructor(t){this._canceller=new XR,this._requestManager=t}reload(t,e,n){return this._canceller.cancelAll(),this._fetchMetadata(t,e,n)}_fetchDataByStep(t,e,n,i,r,o){const a=_r().pluginRoute("mesh","/data",new URLSearchParams({tag:e,run:t,content_type:n,sample:String(i),step:String(r)})),s=function(t){let e=[];for(let n=0;n<t.length/3;n++){let i=[];for(let e=0;e<3;e++)i.push(t[3*n+e]);e.push(i)}return e},l=this._canceller.cancellable((t=>{if(t.cancelled)return Promise.reject({code:RKt.CANCELLED,message:"Response was invalidated."});let e=t.value;switch(n){case"VERTEX":o.vertices=s(new Float32Array(e));break;case"FACE":o.faces=s(new Int32Array(e));break;case"COLOR":o.colors=s(new Uint8Array(e))}return o}));return this._requestManager.fetch(a,{method:"GET",headers:{responseType:"arraybuffer",contentType:zKt[n]}}).then((t=>t.arrayBuffer())).then(l)}fetchData(t,e,n,i){let r=[],o=new Map;return Object.keys(OKt).forEach((a=>{t.components&1<<OKt[a]&&r.push(this._fetchDataByStep(e,n,a,i,t.step,o))})),Promise.all(r)}_fetchMetadata(t,e,n){this._canceller.cancelAll();const i=_r().pluginRoute("mesh","/meshes",new URLSearchParams({tag:e,run:t,sample:n})),r=this._canceller.cancellable((t=>t.cancelled?Promise.reject({code:RKt.CANCELLED,message:"Response was invalidated."}):t.value));return this._requestManager.fetch(i).then((t=>t.json())).then(r).then(this._processMetadata.bind(this))}_processMetadata(t){if(!t)return;const e=new Map;for(let n=0;n<t.length;n++){let i=t[n];e.has(i.step)||e.set(i.step,[]),e.get(i.step).push(i)}let n=[];return e.forEach((t=>{let e=this._createStepDatum(t[0]);n.push(e)})),n}_createStepDatum(t){return{wall_time:new Date(1e3*t.wall_time),step:t.step,config:t.config,content_type:t.content_type,components:t.components}}}
/**
     * @license
     * Copyright 2010-2021 Three.js Authors
     * SPDX-License-Identifier: MIT
     */const BKt="131",HKt={LEFT:0,MIDDLE:1,RIGHT:2,ROTATE:0,DOLLY:1,PAN:2},FKt={ROTATE:0,PAN:1,DOLLY_PAN:2,DOLLY_ROTATE:3},VKt=100,UKt=300,jKt=301,GKt=302,WKt=303,qKt=304,YKt=306,XKt=307,$Kt=1e3,KKt=1001,ZKt=1002,JKt=1003,QKt=1004,tZt=1005,eZt=1006,nZt=1007,iZt=1008,rZt=1009,oZt=1012,aZt=1014,sZt=1015,lZt=1016,cZt=1020,uZt=1022,hZt=1023,dZt=1026,pZt=1027,fZt=33776,mZt=33777,gZt=33778,_Zt=33779,yZt=35840,vZt=35841,bZt=35842,xZt=35843,wZt=37492,SZt=37496,MZt=2300,EZt=2301,TZt=2302,CZt=2400,AZt=2401,kZt=2402,LZt=2500,PZt=2501,NZt=3e3,IZt=3001,RZt=3007,OZt=3002,zZt=3004,DZt=3005,BZt=3006,HZt=7680,FZt=35044,VZt=35048,UZt="300 es";class jZt{addEventListener(t,e){void 0===this._listeners&&(this._listeners={});const n=this._listeners;void 0===n[t]&&(n[t]=[]),-1===n[t].indexOf(e)&&n[t].push(e)}hasEventListener(t,e){if(void 0===this._listeners)return!1;const n=this._listeners;return void 0!==n[t]&&-1!==n[t].indexOf(e)}removeEventListener(t,e){if(void 0===this._listeners)return;const n=this._listeners[t];if(void 0!==n){const t=n.indexOf(e);-1!==t&&n.splice(t,1)}}dispatchEvent(t){if(void 0===this._listeners)return;const e=this._listeners[t.type];if(void 0!==e){t.target=this;const n=e.slice(0);for(let e=0,i=n.length;e<i;e++)n[e].call(this,t);t.target=null}}}const GZt=[];for(let t=0;t<256;t++)GZt[t]=(t<16?"0":"")+t.toString(16);let WZt=1234567;const qZt=Math.PI/180,YZt=180/Math.PI;function XZt(){const t=4294967295*Math.random()|0,e=4294967295*Math.random()|0,n=4294967295*Math.random()|0,i=4294967295*Math.random()|0;return(GZt[255&t]+GZt[t>>8&255]+GZt[t>>16&255]+GZt[t>>24&255]+"-"+GZt[255&e]+GZt[e>>8&255]+"-"+GZt[e>>16&15|64]+GZt[e>>24&255]+"-"+GZt[63&n|128]+GZt[n>>8&255]+"-"+GZt[n>>16&255]+GZt[n>>24&255]+GZt[255&i]+GZt[i>>8&255]+GZt[i>>16&255]+GZt[i>>24&255]).toUpperCase()}function $Zt(t,e,n){return Math.max(e,Math.min(n,t))}function KZt(t,e){return(t%e+e)%e}function ZZt(t,e,n){return(1-n)*t+n*e}function JZt(t){return 0==(t&t-1)&&0!==t}function QZt(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.LN2))}function tJt(t){return Math.pow(2,Math.floor(Math.log(t)/Math.LN2))}var eJt=Object.freeze({__proto__:null,DEG2RAD:qZt,RAD2DEG:YZt,generateUUID:XZt,clamp:$Zt,euclideanModulo:KZt,mapLinear:function nJt(t,e,n,i,r){return i+(t-e)*(r-i)/(n-e)},inverseLerp:function iJt(t,e,n){return t!==e?(n-t)/(e-t):0},lerp:ZZt,damp:function rJt(t,e,n,i){return ZZt(t,e,1-Math.exp(-n*i))},pingpong:function oJt(t,e=1){return e-Math.abs(KZt(t,2*e)-e)},smoothstep:function aJt(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*(3-2*t)},smootherstep:function sJt(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*t*(t*(6*t-15)+10)},randInt:function lJt(t,e){return t+Math.floor(Math.random()*(e-t+1))},randFloat:function cJt(t,e){return t+Math.random()*(e-t)},randFloatSpread:function uJt(t){return t*(.5-Math.random())},seededRandom:function hJt(t){return void 0!==t&&(WZt=t%2147483647),WZt=16807*WZt%2147483647,(WZt-1)/2147483646},degToRad:function dJt(t){return t*qZt},radToDeg:function pJt(t){return t*YZt},isPowerOfTwo:JZt,ceilPowerOfTwo:QZt,floorPowerOfTwo:tJt,setQuaternionFromProperEuler:function fJt(t,e,n,i,r){const o=Math.cos,a=Math.sin,s=o(n/2),l=a(n/2),c=o((e+i)/2),u=a((e+i)/2),h=o((e-i)/2),d=a((e-i)/2),p=o((i-e)/2),f=a((i-e)/2);switch(r){case"XYX":t.set(s*u,l*h,l*d,s*c);break;case"YZY":t.set(l*d,s*u,l*h,s*c);break;case"ZXZ":t.set(l*h,l*d,s*u,s*c);break;case"XZX":t.set(s*u,l*f,l*p,s*c);break;case"YXY":t.set(l*p,s*u,l*f,s*c);break;case"ZYZ":t.set(l*f,l*p,s*u,s*c);break;default:console.warn("THREE.MathUtils: .setQuaternionFromProperEuler() encountered an unknown order: "+r)}}});class mJt{constructor(t=0,e=0){this.x=t,this.y=e}get width(){return this.x}set width(t){this.x=t}get height(){return this.y}set height(t){this.y=t}set(t,e){return this.x=t,this.y=e,this}setScalar(t){return this.x=t,this.y=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y)}copy(t){return this.x=t.x,this.y=t.y,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector2: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this)}addScalar(t){return this.x+=t,this.y+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this}sub(t,e){return void 0!==e?(console.warn("THREE.Vector2: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this)}subScalar(t){return this.x-=t,this.y-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this}multiply(t){return this.x*=t.x,this.y*=t.y,this}multiplyScalar(t){return this.x*=t,this.y*=t,this}divide(t){return this.x/=t.x,this.y/=t.y,this}divideScalar(t){return this.multiplyScalar(1/t)}applyMatrix3(t){const e=this.x,n=this.y,i=t.elements;return this.x=i[0]*e+i[3]*n+i[6],this.y=i[1]*e+i[4]*n+i[7],this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this}negate(){return this.x=-this.x,this.y=-this.y,this}dot(t){return this.x*t.x+this.y*t.y}cross(t){return this.x*t.y-this.y*t.x}lengthSq(){return this.x*this.x+this.y*this.y}length(){return Math.sqrt(this.x*this.x+this.y*this.y)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)}normalize(){return this.divideScalar(this.length()||1)}angle(){return Math.atan2(-this.y,-this.x)+Math.PI}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y;return e*e+n*n}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this}equals(t){return t.x===this.x&&t.y===this.y}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector2: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this}rotateAround(t,e){const n=Math.cos(e),i=Math.sin(e),r=this.x-t.x,o=this.y-t.y;return this.x=r*n-o*i+t.x,this.y=r*i+o*n+t.y,this}random(){return this.x=Math.random(),this.y=Math.random(),this}}mJt.prototype.isVector2=!0;class gJt{constructor(){this.elements=[1,0,0,0,1,0,0,0,1],arguments.length>0&&console.error("THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.")}set(t,e,n,i,r,o,a,s,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=a,c[3]=e,c[4]=r,c[5]=s,c[6]=n,c[7]=o,c[8]=l,this}identity(){return 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e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],o=t[4],a=t[5],s=t[6],l=t[7],c=t[8];return e*o*c-e*a*l-n*r*c+n*a*s+i*r*l-i*o*s}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],o=t[4],a=t[5],s=t[6],l=t[7],c=t[8],u=c*o-a*l,h=a*s-c*r,d=l*r-o*s,p=e*u+n*h+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const f=1/p;return t[0]=u*f,t[1]=(i*l-c*n)*f,t[2]=(a*n-i*o)*f,t[3]=h*f,t[4]=(c*e-i*s)*f,t[5]=(i*r-a*e)*f,t[6]=d*f,t[7]=(n*s-l*e)*f,t[8]=(o*e-n*r)*f,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,r,o,a){const s=Math.cos(r),l=Math.sin(r);return 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t.src;if("undefined"==typeof HTMLCanvasElement)return t.src;let e;if(t instanceof HTMLCanvasElement)e=t;else{void 0===_Jt&&(_Jt=document.createElementNS("http://www.w3.org/1999/xhtml","canvas")),_Jt.width=t.width,_Jt.height=t.height;const n=_Jt.getContext("2d");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=_Jt}return e.width>2048||e.height>2048?(console.warn("THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons",t),e.toDataURL("image/jpeg",.6)):e.toDataURL("image/png")}}let vJt=0;class bJt extends jZt{constructor(t=bJt.DEFAULT_IMAGE,e=bJt.DEFAULT_MAPPING,n=1001,i=1001,r=1006,o=1008,a=1023,s=1009,l=1,c=3e3){super(),Object.defineProperty(this,"id",{value:vJt++}),this.uuid=XZt(),this.name="",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=n,this.wrapT=i,this.magFilter=r,this.minFilter=o,this.anisotropy=l,this.format=a,this.internalFormat=null,this.type=s,this.offset=new mJt(0,0),this.repeat=new mJt(1,1),this.center=new mJt(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new gJt,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.encoding=c,this.version=0,this.onUpdate=null,this.isRenderTargetTexture=!1}updateMatrix(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}clone(){return(new this.constructor).copy(this)}copy(t){return this.name=t.name,this.image=t.image,this.mipmaps=t.mipmaps.slice(0),this.mapping=t.mapping,this.wrapS=t.wrapS,this.wrapT=t.wrapT,this.magFilter=t.magFilter,this.minFilter=t.minFilter,this.anisotropy=t.anisotropy,this.format=t.format,this.internalFormat=t.internalFormat,this.type=t.type,this.offset.copy(t.offset),this.repeat.copy(t.repeat),this.center.copy(t.center),this.rotation=t.rotation,this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrix.copy(t.matrix),this.generateMipmaps=t.generateMipmaps,this.premultiplyAlpha=t.premultiplyAlpha,this.flipY=t.flipY,this.unpackAlignment=t.unpackAlignment,this.encoding=t.encoding,this}toJSON(t){const e=void 0===t||"string"==typeof t;if(!e&&void 0!==t.textures[this.uuid])return t.textures[this.uuid];const n={metadata:{version:4.5,type:"Texture",generator:"Texture.toJSON"},uuid:this.uuid,name:this.name,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){const i=this.image;if(void 0===i.uuid&&(i.uuid=XZt()),!e&&void 0===t.images[i.uuid]){let e;if(Array.isArray(i)){e=[];for(let t=0,n=i.length;t<n;t++)e.push(xJt(i[t].isDataTexture?i[t].image:i[t]))}else e=xJt(i);t.images[i.uuid]={uuid:i.uuid,url:e}}n.image=i.uuid}return e||(t.textures[this.uuid]=n),n}dispose(){this.dispatchEvent({type:"dispose"})}transformUv(t){if(this.mapping!==UKt)return t;if(t.applyMatrix3(this.matrix),t.x<0||t.x>1)switch(this.wrapS){case $Kt:t.x=t.x-Math.floor(t.x);break;case KKt:t.x=t.x<0?0:1;break;case ZKt:t.x=1===Math.abs(Math.floor(t.x)%2)?Math.ceil(t.x)-t.x:t.x-Math.floor(t.x)}if(t.y<0||t.y>1)switch(this.wrapT){case $Kt:t.y=t.y-Math.floor(t.y);break;case KKt:t.y=t.y<0?0:1;break;case ZKt:t.y=1===Math.abs(Math.floor(t.y)%2)?Math.ceil(t.y)-t.y:t.y-Math.floor(t.y)}return this.flipY&&(t.y=1-t.y),t}set needsUpdate(t){!0===t&&this.version++}}function xJt(t){return"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof ImageBitmap&&t instanceof ImageBitmap?yJt.getDataURL(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn("THREE.Texture: Unable to serialize Texture."),{})}bJt.DEFAULT_IMAGE=void 0,bJt.DEFAULT_MAPPING=UKt,bJt.prototype.isTexture=!0;class wJt{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn("THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=this.w,o=t.elements;return this.x=o[0]*e+o[4]*n+o[8]*i+o[12]*r,this.y=o[1]*e+o[5]*n+o[9]*i+o[13]*r,this.z=o[2]*e+o[6]*n+o[10]*i+o[14]*r,this.w=o[3]*e+o[7]*n+o[11]*i+o[15]*r,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,r;const o=.01,a=.1,s=t.elements,l=s[0],c=s[4],u=s[8],h=s[1],d=s[5],p=s[9],f=s[2],m=s[6],g=s[10];if(Math.abs(c-h)<o&&Math.abs(u-f)<o&&Math.abs(p-m)<o){if(Math.abs(c+h)<a&&Math.abs(u+f)<a&&Math.abs(p+m)<a&&Math.abs(l+d+g-3)<a)return this.set(1,0,0,0),this;e=Math.PI;const t=(l+1)/2,s=(d+1)/2,_=(g+1)/2,y=(c+h)/4,v=(u+f)/4,b=(p+m)/4;return t>s&&t>_?t<o?(n=0,i=.707106781,r=.707106781):(n=Math.sqrt(t),i=y/n,r=v/n):s>_?s<o?(n=.707106781,i=0,r=.707106781):(i=Math.sqrt(s),n=y/i,r=b/i):_<o?(n=.707106781,i=.707106781,r=0):(r=Math.sqrt(_),n=v/r,i=b/r),this.set(n,i,r,e),this}let _=Math.sqrt((m-p)*(m-p)+(u-f)*(u-f)+(h-c)*(h-c));return Math.abs(_)<.001&&(_=1),this.x=(m-p)/_,this.y=(u-f)/_,this.z=(h-c)/_,this.w=Math.acos((l+d+g-1)/2),this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this.w=Math.min(this.w,t.w),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this.w=Math.max(this.w,t.w),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this.w=Math.max(t.w,Math.min(e.w,this.w)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this.w=Math.max(t,Math.min(e,this.w)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this.w=Math.floor(this.w),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this.w=Math.ceil(this.w),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this.w=Math.round(this.w),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this.w=this.w<0?Math.ceil(this.w):Math.floor(this.w),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this.w=-this.w,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z+this.w*t.w}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)+Math.abs(this.w)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this.w+=(t.w-this.w)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this.w=t.w+(e.w-t.w)*n,this}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z&&t.w===this.w}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this.w=t[e+3],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t[e+3]=this.w,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector4: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this.w=t.getW(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this.w=Math.random(),this}}wJt.prototype.isVector4=!0;class SJt extends jZt{constructor(t,e,n={}){super(),this.width=t,this.height=e,this.depth=1,this.scissor=new wJt(0,0,t,e),this.scissorTest=!1,this.viewport=new wJt(0,0,t,e),this.texture=new bJt(void 0,n.mapping,n.wrapS,n.wrapT,n.magFilter,n.minFilter,n.format,n.type,n.anisotropy,n.encoding),this.texture.isRenderTargetTexture=!0,this.texture.image={width:t,height:e,depth:1},this.texture.generateMipmaps=void 0!==n.generateMipmaps&&n.generateMipmaps,this.texture.minFilter=void 0!==n.minFilter?n.minFilter:eZt,this.depthBuffer=void 0===n.depthBuffer||n.depthBuffer,this.stencilBuffer=void 0!==n.stencilBuffer&&n.stencilBuffer,this.depthTexture=void 0!==n.depthTexture?n.depthTexture:null}setTexture(t){t.image={width:this.width,height:this.height,depth:this.depth},this.texture=t}setSize(t,e,n=1){this.width===t&&this.height===e&&this.depth===n||(this.width=t,this.height=e,this.depth=n,this.texture.image.width=t,this.texture.image.height=e,this.texture.image.depth=n,this.dispose()),this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e)}clone(){return(new this.constructor).copy(this)}copy(t){return this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.copy(t.viewport),this.texture=t.texture.clone(),this.texture.image={...this.texture.image},this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this}dispose(){this.dispatchEvent({type:"dispose"})}}SJt.prototype.isWebGLRenderTarget=!0;class MJt extends SJt{constructor(t,e,n){super(t,e);const i=this.texture;this.texture=[];for(let t=0;t<n;t++)this.texture[t]=i.clone()}setSize(t,e,n=1){if(this.width!==t||this.height!==e||this.depth!==n){this.width=t,this.height=e,this.depth=n;for(let i=0,r=this.texture.length;i<r;i++)this.texture[i].image.width=t,this.texture[i].image.height=e,this.texture[i].image.depth=n;this.dispose()}return this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e),this}copy(t){this.dispose(),this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.set(0,0,this.width,this.height),this.scissor.set(0,0,this.width,this.height),this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this.texture.length=0;for(let e=0,n=t.texture.length;e<n;e++)this.texture[e]=t.texture[e].clone();return this}}MJt.prototype.isWebGLMultipleRenderTargets=!0;class EJt extends SJt{constructor(t,e,n){super(t,e,n),this.samples=4}copy(t){return super.copy.call(this,t),this.samples=t.samples,this}}EJt.prototype.isWebGLMultisampleRenderTarget=!0;class TJt{constructor(t=0,e=0,n=0,i=1){this._x=t,this._y=e,this._z=n,this._w=i}static slerp(t,e,n,i){return console.warn("THREE.Quaternion: Static .slerp() has been deprecated. Use qm.slerpQuaternions( qa, qb, t ) instead."),n.slerpQuaternions(t,e,i)}static slerpFlat(t,e,n,i,r,o,a){let s=n[i+0],l=n[i+1],c=n[i+2],u=n[i+3];const h=r[o+0],d=r[o+1],p=r[o+2],f=r[o+3];if(0===a)return t[e+0]=s,t[e+1]=l,t[e+2]=c,void(t[e+3]=u);if(1===a)return t[e+0]=h,t[e+1]=d,t[e+2]=p,void(t[e+3]=f);if(u!==f||s!==h||l!==d||c!==p){let t=1-a;const e=s*h+l*d+c*p+u*f,n=e>=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const r=Math.sqrt(i),o=Math.atan2(r,e*n);t=Math.sin(t*o)/r,a=Math.sin(a*o)/r}const r=a*n;if(s=s*t+h*r,l=l*t+d*r,c=c*t+p*r,u=u*t+f*r,t===1-a){const t=1/Math.sqrt(s*s+l*l+c*c+u*u);s*=t,l*=t,c*=t,u*=t}}t[e]=s,t[e+1]=l,t[e+2]=c,t[e+3]=u}static multiplyQuaternionsFlat(t,e,n,i,r,o){const a=n[i],s=n[i+1],l=n[i+2],c=n[i+3],u=r[o],h=r[o+1],d=r[o+2],p=r[o+3];return t[e]=a*p+c*u+s*d-l*h,t[e+1]=s*p+c*h+l*u-a*d,t[e+2]=l*p+c*d+a*h-s*u,t[e+3]=c*p-a*u-s*h-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error("THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.");const n=t._x,i=t._y,r=t._z,o=t._order,a=Math.cos,s=Math.sin,l=a(n/2),c=a(i/2),u=a(r/2),h=s(n/2),d=s(i/2),p=s(r/2);switch(o){case"XYZ":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case"YXZ":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case"ZXY":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case"ZYX":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case"YZX":this._x=h*c*u+l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u-h*d*p;break;case"XZY":this._x=h*c*u-l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u+h*d*p;break;default:console.warn("THREE.Quaternion: .setFromEuler() encountered an unknown order: "+o)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],r=e[8],o=e[1],a=e[5],s=e[9],l=e[2],c=e[6],u=e[10],h=n+a+u;if(h>0){const t=.5/Math.sqrt(h+1);this._w=.25/t,this._x=(c-s)*t,this._y=(r-l)*t,this._z=(o-i)*t}else if(n>a&&n>u){const t=2*Math.sqrt(1+n-a-u);this._w=(c-s)/t,this._x=.25*t,this._y=(i+o)/t,this._z=(r+l)/t}else if(a>u){const t=2*Math.sqrt(1+a-n-u);this._w=(r-l)/t,this._x=(i+o)/t,this._y=.25*t,this._z=(s+c)/t}else{const t=2*Math.sqrt(1+u-n-a);this._w=(o-i)/t,this._x=(r+l)/t,this._y=(s+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return n<Number.EPSILON?(n=0,Math.abs(t.x)>Math.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs($Zt(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn("THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead."),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,r=t._z,o=t._w,a=e._x,s=e._y,l=e._z,c=e._w;return this._x=n*c+o*a+i*l-r*s,this._y=i*c+o*s+r*a-n*l,this._z=r*c+o*l+n*s-i*a,this._w=o*c-n*a-i*s-r*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,r=this._z,o=this._w;let a=o*t._w+n*t._x+i*t._y+r*t._z;if(a<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,a=-a):this.copy(t),a>=1)return this._w=o,this._x=n,this._y=i,this._z=r,this;const s=1-a*a;if(s<=Number.EPSILON){const t=1-e;return this._w=t*o+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*r+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(s),c=Math.atan2(l,a),u=Math.sin((1-e)*c)/l,h=Math.sin(e*c)/l;return this._w=o*u+this._w*h,this._x=n*u+this._x*h,this._y=i*u+this._y*h,this._z=r*u+this._z*h,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}TJt.prototype.isQuaternion=!0;class CJt{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}set(t,e,n){return void 0===n&&(n=this.z),this.x=t,this.y=e,this.z=n,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this}sub(t,e){return void 0!==e?(console.warn("THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiply(t,e){return void 0!==e?(console.warn("THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead."),this.multiplyVectors(t,e)):(this.x*=t.x,this.y*=t.y,this.z*=t.z,this)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}applyEuler(t){return t&&t.isEuler||console.error("THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order."),this.applyQuaternion(kJt.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(kJt.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[3]*n+r[6]*i,this.y=r[1]*e+r[4]*n+r[7]*i,this.z=r[2]*e+r[5]*n+r[8]*i,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=t.elements,o=1/(r[3]*e+r[7]*n+r[11]*i+r[15]);return this.x=(r[0]*e+r[4]*n+r[8]*i+r[12])*o,this.y=(r[1]*e+r[5]*n+r[9]*i+r[13])*o,this.z=(r[2]*e+r[6]*n+r[10]*i+r[14])*o,this}applyQuaternion(t){const e=this.x,n=this.y,i=this.z,r=t.x,o=t.y,a=t.z,s=t.w,l=s*e+o*i-a*n,c=s*n+a*e-r*i,u=s*i+r*n-o*e,h=-r*e-o*n-a*i;return this.x=l*s+h*-r+c*-a-u*-o,this.y=c*s+h*-o+u*-r-l*-a,this.z=u*s+h*-a+l*-o-c*-r,this}project(t){return this.applyMatrix4(t.matrixWorldInverse).applyMatrix4(t.projectionMatrix)}unproject(t){return this.applyMatrix4(t.projectionMatrixInverse).applyMatrix4(t.matrixWorld)}transformDirection(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i,this.y=r[1]*e+r[5]*n+r[9]*i,this.z=r[2]*e+r[6]*n+r[10]*i,this.normalize()}divide(t){return this.x/=t.x,this.y/=t.y,this.z/=t.z,this}divideScalar(t){return this.multiplyScalar(1/t)}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this}cross(t,e){return void 0!==e?(console.warn("THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead."),this.crossVectors(t,e)):this.crossVectors(this,t)}crossVectors(t,e){const n=t.x,i=t.y,r=t.z,o=e.x,a=e.y,s=e.z;return this.x=i*s-r*a,this.y=r*o-n*s,this.z=n*a-i*o,this}projectOnVector(t){const e=t.lengthSq();if(0===e)return this.set(0,0,0);const n=t.dot(this)/e;return this.copy(t).multiplyScalar(n)}projectOnPlane(t){return AJt.copy(this).projectOnVector(t),this.sub(AJt)}reflect(t){return this.sub(AJt.copy(t).multiplyScalar(2*this.dot(t)))}angleTo(t){const e=Math.sqrt(this.lengthSq()*t.lengthSq());if(0===e)return Math.PI/2;const n=this.dot(t)/e;return Math.acos($Zt(n,-1,1))}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y,i=this.z-t.z;return e*e+n*n+i*i}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)+Math.abs(this.z-t.z)}setFromSpherical(t){return this.setFromSphericalCoords(t.radius,t.phi,t.theta)}setFromSphericalCoords(t,e,n){const i=Math.sin(e)*t;return this.x=i*Math.sin(n),this.y=Math.cos(e)*t,this.z=i*Math.cos(n),this}setFromCylindrical(t){return this.setFromCylindricalCoords(t.radius,t.theta,t.y)}setFromCylindricalCoords(t,e,n){return this.x=t*Math.sin(e),this.y=n,this.z=t*Math.cos(e),this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}setFromMatrixScale(t){const e=this.setFromMatrixColumn(t,0).length(),n=this.setFromMatrixColumn(t,1).length(),i=this.setFromMatrixColumn(t,2).length();return this.x=e,this.y=n,this.z=i,this}setFromMatrixColumn(t,e){return this.fromArray(t.elements,4*e)}setFromMatrix3Column(t,e){return this.fromArray(t.elements,3*e)}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector3: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}}CJt.prototype.isVector3=!0;const AJt=new CJt,kJt=new TJt;class LJt{constructor(t=new CJt(1/0,1/0,1/0),e=new CJt(-1/0,-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromArray(t){let e=1/0,n=1/0,i=1/0,r=-1/0,o=-1/0,a=-1/0;for(let s=0,l=t.length;s<l;s+=3){const l=t[s],c=t[s+1],u=t[s+2];l<e&&(e=l),c<n&&(n=c),u<i&&(i=u),l>r&&(r=l),c>o&&(o=c),u>a&&(a=u)}return this.min.set(e,n,i),this.max.set(r,o,a),this}setFromBufferAttribute(t){let e=1/0,n=1/0,i=1/0,r=-1/0,o=-1/0,a=-1/0;for(let s=0,l=t.count;s<l;s++){const l=t.getX(s),c=t.getY(s),u=t.getZ(s);l<e&&(e=l),c<n&&(n=c),u<i&&(i=u),l>r&&(r=l),c>o&&(o=c),u>a&&(a=u)}return this.min.set(e,n,i),this.max.set(r,o,a),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;e<n;e++)this.expandByPoint(t[e]);return this}setFromCenterAndSize(t,e){const n=NJt.copy(e).multiplyScalar(.5);return this.min.copy(t).sub(n),this.max.copy(t).add(n),this}setFromObject(t){return this.makeEmpty(),this.expandByObject(t)}clone(){return(new this.constructor).copy(this)}copy(t){return this.min.copy(t.min),this.max.copy(t.max),this}makeEmpty(){return this.min.x=this.min.y=this.min.z=1/0,this.max.x=this.max.y=this.max.z=-1/0,this}isEmpty(){return this.max.x<this.min.x||this.max.y<this.min.y||this.max.z<this.min.z}getCenter(t){return this.isEmpty()?t.set(0,0,0):t.addVectors(this.min,this.max).multiplyScalar(.5)}getSize(t){return this.isEmpty()?t.set(0,0,0):t.subVectors(this.max,this.min)}expandByPoint(t){return this.min.min(t),this.max.max(t),this}expandByVector(t){return this.min.sub(t),this.max.add(t),this}expandByScalar(t){return this.min.addScalar(-t),this.max.addScalar(t),this}expandByObject(t){t.updateWorldMatrix(!1,!1);const e=t.geometry;void 0!==e&&(null===e.boundingBox&&e.computeBoundingBox(),IJt.copy(e.boundingBox),IJt.applyMatrix4(t.matrixWorld),this.union(IJt));const n=t.children;for(let t=0,e=n.length;t<e;t++)this.expandByObject(n[t]);return this}containsPoint(t){return!(t.x<this.min.x||t.x>this.max.x||t.y<this.min.y||t.y>this.max.y||t.z<this.min.z||t.z>this.max.z)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y&&this.min.z<=t.min.z&&t.max.z<=this.max.z}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y),(t.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(t){return!(t.max.x<this.min.x||t.min.x>this.max.x||t.max.y<this.min.y||t.min.y>this.max.y||t.max.z<this.min.z||t.min.z>this.max.z)}intersectsSphere(t){return this.clampPoint(t.center,NJt),NJt.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,n;return t.normal.x>0?(e=t.normal.x*this.min.x,n=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,n=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,n+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,n+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,n+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,n+=t.normal.z*this.min.z),e<=-t.constant&&n>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(FJt),VJt.subVectors(this.max,FJt),RJt.subVectors(t.a,FJt),OJt.subVectors(t.b,FJt),zJt.subVectors(t.c,FJt),DJt.subVectors(OJt,RJt),BJt.subVectors(zJt,OJt),HJt.subVectors(RJt,zJt);let e=[0,-DJt.z,DJt.y,0,-BJt.z,BJt.y,0,-HJt.z,HJt.y,DJt.z,0,-DJt.x,BJt.z,0,-BJt.x,HJt.z,0,-HJt.x,-DJt.y,DJt.x,0,-BJt.y,BJt.x,0,-HJt.y,HJt.x,0];return!!GJt(e,RJt,OJt,zJt,VJt)&&(e=[1,0,0,0,1,0,0,0,1],!!GJt(e,RJt,OJt,zJt,VJt)&&(UJt.crossVectors(DJt,BJt),e=[UJt.x,UJt.y,UJt.z],GJt(e,RJt,OJt,zJt,VJt)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return NJt.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(NJt).length(),t}intersect(t){return this.min.max(t.min),this.max.min(t.max),this.isEmpty()&&this.makeEmpty(),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}applyMatrix4(t){return this.isEmpty()||(PJt[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),PJt[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),PJt[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),PJt[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),PJt[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),PJt[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),PJt[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),PJt[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(PJt)),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}LJt.prototype.isBox3=!0;const PJt=[new CJt,new CJt,new CJt,new CJt,new CJt,new CJt,new CJt,new CJt],NJt=new CJt,IJt=new LJt,RJt=new CJt,OJt=new CJt,zJt=new CJt,DJt=new CJt,BJt=new CJt,HJt=new CJt,FJt=new CJt,VJt=new CJt,UJt=new CJt,jJt=new CJt;function GJt(t,e,n,i,r){for(let o=0,a=t.length-3;o<=a;o+=3){jJt.fromArray(t,o);const a=r.x*Math.abs(jJt.x)+r.y*Math.abs(jJt.y)+r.z*Math.abs(jJt.z),s=e.dot(jJt),l=n.dot(jJt),c=i.dot(jJt);if(Math.max(-Math.max(s,l,c),Math.min(s,l,c))>a)return!1}return!0}const WJt=new LJt,qJt=new CJt,YJt=new CJt,XJt=new CJt;class $Jt{constructor(t=new CJt,e=-1){this.center=t,this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}setFromPoints(t,e){const n=this.center;void 0!==e?n.copy(e):WJt.setFromPoints(t).getCenter(n);let i=0;for(let e=0,r=t.length;e<r;e++)i=Math.max(i,n.distanceToSquared(t[e]));return this.radius=Math.sqrt(i),this}copy(t){return this.center.copy(t.center),this.radius=t.radius,this}isEmpty(){return this.radius<0}makeEmpty(){return this.center.set(0,0,0),this.radius=-1,this}containsPoint(t){return t.distanceToSquared(this.center)<=this.radius*this.radius}distanceToPoint(t){return t.distanceTo(this.center)-this.radius}intersectsSphere(t){const e=this.radius+t.radius;return t.center.distanceToSquared(this.center)<=e*e}intersectsBox(t){return t.intersectsSphere(this)}intersectsPlane(t){return Math.abs(t.distanceToPoint(this.center))<=this.radius}clampPoint(t,e){const n=this.center.distanceToSquared(t);return e.copy(t),n>this.radius*this.radius&&(e.sub(this.center).normalize(),e.multiplyScalar(this.radius).add(this.center)),e}getBoundingBox(t){return this.isEmpty()?(t.makeEmpty(),t):(t.set(this.center,this.center),t.expandByScalar(this.radius),t)}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}expandByPoint(t){XJt.subVectors(t,this.center);const e=XJt.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(XJt.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return YJt.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(qJt.copy(t.center).add(YJt)),this.expandByPoint(qJt.copy(t.center).sub(YJt)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const KJt=new CJt,ZJt=new CJt,JJt=new CJt,QJt=new CJt,tQt=new CJt,eQt=new CJt,nQt=new CJt;class iQt{constructor(t=new CJt,e=new CJt(0,0,-1)){this.origin=t,this.direction=e}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return e.copy(this.direction).multiplyScalar(t).add(this.origin)}lookAt(t){return this.direction.copy(t).sub(this.origin).normalize(),this}recast(t){return this.origin.copy(this.at(t,KJt)),this}closestPointToPoint(t,e){e.subVectors(t,this.origin);const n=e.dot(this.direction);return n<0?e.copy(this.origin):e.copy(this.direction).multiplyScalar(n).add(this.origin)}distanceToPoint(t){return Math.sqrt(this.distanceSqToPoint(t))}distanceSqToPoint(t){const e=KJt.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(KJt.copy(this.direction).multiplyScalar(e).add(this.origin),KJt.distanceToSquared(t))}distanceSqToSegment(t,e,n,i){ZJt.copy(t).add(e).multiplyScalar(.5),JJt.copy(e).sub(t).normalize(),QJt.copy(this.origin).sub(ZJt);const r=.5*t.distanceTo(e),o=-this.direction.dot(JJt),a=QJt.dot(this.direction),s=-QJt.dot(JJt),l=QJt.lengthSq(),c=Math.abs(1-o*o);let u,h,d,p;if(c>0)if(u=o*s-a,h=o*a-s,p=r*c,u>=0)if(h>=-p)if(h<=p){const t=1/c;u*=t,h*=t,d=u*(u+o*h+2*a)+h*(o*u+h+2*s)+l}else h=r,u=Math.max(0,-(o*h+a)),d=-u*u+h*(h+2*s)+l;else h=-r,u=Math.max(0,-(o*h+a)),d=-u*u+h*(h+2*s)+l;else h<=-p?(u=Math.max(0,-(-o*r+a)),h=u>0?-r:Math.min(Math.max(-r,-s),r),d=-u*u+h*(h+2*s)+l):h<=p?(u=0,h=Math.min(Math.max(-r,-s),r),d=h*(h+2*s)+l):(u=Math.max(0,-(o*r+a)),h=u>0?r:Math.min(Math.max(-r,-s),r),d=-u*u+h*(h+2*s)+l);else h=o>0?-r:r,u=Math.max(0,-(o*h+a)),d=-u*u+h*(h+2*s)+l;return n&&n.copy(this.direction).multiplyScalar(u).add(this.origin),i&&i.copy(JJt).multiplyScalar(h).add(ZJt),d}intersectSphere(t,e){KJt.subVectors(t.center,this.origin);const n=KJt.dot(this.direction),i=KJt.dot(KJt)-n*n,r=t.radius*t.radius;if(i>r)return null;const o=Math.sqrt(r-i),a=n-o,s=n+o;return a<0&&s<0?null:this.at(a<0?s:a,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);return 0===e||t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,r,o,a,s;const l=1/this.direction.x,c=1/this.direction.y,u=1/this.direction.z,h=this.origin;return l>=0?(n=(t.min.x-h.x)*l,i=(t.max.x-h.x)*l):(n=(t.max.x-h.x)*l,i=(t.min.x-h.x)*l),c>=0?(r=(t.min.y-h.y)*c,o=(t.max.y-h.y)*c):(r=(t.max.y-h.y)*c,o=(t.min.y-h.y)*c),n>o||r>i?null:((r>n||n!=n)&&(n=r),(o<i||i!=i)&&(i=o),u>=0?(a=(t.min.z-h.z)*u,s=(t.max.z-h.z)*u):(a=(t.max.z-h.z)*u,s=(t.min.z-h.z)*u),n>s||a>i?null:((a>n||n!=n)&&(n=a),(s<i||i!=i)&&(i=s),i<0?null:this.at(n>=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,KJt)}intersectTriangle(t,e,n,i,r){tQt.subVectors(e,t),eQt.subVectors(n,t),nQt.crossVectors(tQt,eQt);let o,a=this.direction.dot(nQt);if(a>0){if(i)return null;o=1}else{if(!(a<0))return null;o=-1,a=-a}QJt.subVectors(this.origin,t);const s=o*this.direction.dot(eQt.crossVectors(QJt,eQt));if(s<0)return null;const l=o*this.direction.dot(tQt.cross(QJt));if(l<0)return null;if(s+l>a)return null;const c=-o*QJt.dot(nQt);return c<0?null:this.at(c/a,r)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}class rQt{constructor(){this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],arguments.length>0&&console.error("THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.")}set(t,e,n,i,r,o,a,s,l,c,u,h,d,p,f,m){const g=this.elements;return g[0]=t,g[4]=e,g[8]=n,g[12]=i,g[1]=r,g[5]=o,g[9]=a,g[13]=s,g[2]=l,g[6]=c,g[10]=u,g[14]=h,g[3]=d,g[7]=p,g[11]=f,g[15]=m,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new rQt).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return this.set(e[0],e[3],e[6],0,e[1],e[4],e[7],0,e[2],e[5],e[8],0,0,0,0,1),this}extractBasis(t,e,n){return t.setFromMatrixColumn(this,0),e.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(t,e,n){return this.set(t.x,e.x,n.x,0,t.y,e.y,n.y,0,t.z,e.z,n.z,0,0,0,0,1),this}extractRotation(t){const e=this.elements,n=t.elements,i=1/oQt.setFromMatrixColumn(t,0).length(),r=1/oQt.setFromMatrixColumn(t,1).length(),o=1/oQt.setFromMatrixColumn(t,2).length();return e[0]=n[0]*i,e[1]=n[1]*i,e[2]=n[2]*i,e[3]=0,e[4]=n[4]*r,e[5]=n[5]*r,e[6]=n[6]*r,e[7]=0,e[8]=n[8]*o,e[9]=n[9]*o,e[10]=n[10]*o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error("THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.");const e=this.elements,n=t.x,i=t.y,r=t.z,o=Math.cos(n),a=Math.sin(n),s=Math.cos(i),l=Math.sin(i),c=Math.cos(r),u=Math.sin(r);if("XYZ"===t.order){const t=o*c,n=o*u,i=a*c,r=a*u;e[0]=s*c,e[4]=-s*u,e[8]=l,e[1]=n+i*l,e[5]=t-r*l,e[9]=-a*s,e[2]=r-t*l,e[6]=i+n*l,e[10]=o*s}else if("YXZ"===t.order){const t=s*c,n=s*u,i=l*c,r=l*u;e[0]=t+r*a,e[4]=i*a-n,e[8]=o*l,e[1]=o*u,e[5]=o*c,e[9]=-a,e[2]=n*a-i,e[6]=r+t*a,e[10]=o*s}else if("ZXY"===t.order){const t=s*c,n=s*u,i=l*c,r=l*u;e[0]=t-r*a,e[4]=-o*u,e[8]=i+n*a,e[1]=n+i*a,e[5]=o*c,e[9]=r-t*a,e[2]=-o*l,e[6]=a,e[10]=o*s}else if("ZYX"===t.order){const t=o*c,n=o*u,i=a*c,r=a*u;e[0]=s*c,e[4]=i*l-n,e[8]=t*l+r,e[1]=s*u,e[5]=r*l+t,e[9]=n*l-i,e[2]=-l,e[6]=a*s,e[10]=o*s}else if("YZX"===t.order){const t=o*s,n=o*l,i=a*s,r=a*l;e[0]=s*c,e[4]=r-t*u,e[8]=i*u+n,e[1]=u,e[5]=o*c,e[9]=-a*c,e[2]=-l*c,e[6]=n*u+i,e[10]=t-r*u}else if("XZY"===t.order){const t=o*s,n=o*l,i=a*s,r=a*l;e[0]=s*c,e[4]=-u,e[8]=l*c,e[1]=t*u+r,e[5]=o*c,e[9]=n*u-i,e[2]=i*u-n,e[6]=a*c,e[10]=r*u+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(sQt,t,lQt)}lookAt(t,e,n){const i=this.elements;return hQt.subVectors(t,e),0===hQt.lengthSq()&&(hQt.z=1),hQt.normalize(),cQt.crossVectors(n,hQt),0===cQt.lengthSq()&&(1===Math.abs(n.z)?hQt.x+=1e-4:hQt.z+=1e-4,hQt.normalize(),cQt.crossVectors(n,hQt)),cQt.normalize(),uQt.crossVectors(hQt,cQt),i[0]=cQt.x,i[4]=uQt.x,i[8]=hQt.x,i[1]=cQt.y,i[5]=uQt.y,i[9]=hQt.y,i[2]=cQt.z,i[6]=uQt.z,i[10]=hQt.z,this}multiply(t,e){return void 0!==e?(console.warn("THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead."),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,o=n[0],a=n[4],s=n[8],l=n[12],c=n[1],u=n[5],h=n[9],d=n[13],p=n[2],f=n[6],m=n[10],g=n[14],_=n[3],y=n[7],v=n[11],b=n[15],x=i[0],w=i[4],S=i[8],M=i[12],E=i[1],T=i[5],C=i[9],A=i[13],k=i[2],L=i[6],P=i[10],N=i[14],I=i[3],R=i[7],O=i[11],z=i[15];return r[0]=o*x+a*E+s*k+l*I,r[4]=o*w+a*T+s*L+l*R,r[8]=o*S+a*C+s*P+l*O,r[12]=o*M+a*A+s*N+l*z,r[1]=c*x+u*E+h*k+d*I,r[5]=c*w+u*T+h*L+d*R,r[9]=c*S+u*C+h*P+d*O,r[13]=c*M+u*A+h*N+d*z,r[2]=p*x+f*E+m*k+g*I,r[6]=p*w+f*T+m*L+g*R,r[10]=p*S+f*C+m*P+g*O,r[14]=p*M+f*A+m*N+g*z,r[3]=_*x+y*E+v*k+b*I,r[7]=_*w+y*T+v*L+b*R,r[11]=_*S+y*C+v*P+b*O,r[15]=_*M+y*A+v*N+b*z,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],r=t[12],o=t[1],a=t[5],s=t[9],l=t[13],c=t[2],u=t[6],h=t[10],d=t[14];return t[3]*(+r*s*u-i*l*u-r*a*h+n*l*h+i*a*d-n*s*d)+t[7]*(+e*s*d-e*l*h+r*o*h-i*o*d+i*l*c-r*s*c)+t[11]*(+e*l*u-e*a*d-r*o*u+n*o*d+r*a*c-n*l*c)+t[15]*(-i*a*c-e*s*u+e*a*h+i*o*u-n*o*h+n*s*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],o=t[4],a=t[5],s=t[6],l=t[7],c=t[8],u=t[9],h=t[10],d=t[11],p=t[12],f=t[13],m=t[14],g=t[15],_=u*m*l-f*h*l+f*s*d-a*m*d-u*s*g+a*h*g,y=p*h*l-c*m*l-p*s*d+o*m*d+c*s*g-o*h*g,v=c*f*l-p*u*l+p*a*d-o*f*d-c*a*g+o*u*g,b=p*u*s-c*f*s-p*a*h+o*f*h+c*a*m-o*u*m,x=e*_+n*y+i*v+r*b;if(0===x)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/x;return t[0]=_*w,t[1]=(f*h*r-u*m*r-f*i*d+n*m*d+u*i*g-n*h*g)*w,t[2]=(a*m*r-f*s*r+f*i*l-n*m*l-a*i*g+n*s*g)*w,t[3]=(u*s*r-a*h*r-u*i*l+n*h*l+a*i*d-n*s*d)*w,t[4]=y*w,t[5]=(c*m*r-p*h*r+p*i*d-e*m*d-c*i*g+e*h*g)*w,t[6]=(p*s*r-o*m*r-p*i*l+e*m*l+o*i*g-e*s*g)*w,t[7]=(o*h*r-c*s*r+c*i*l-e*h*l-o*i*d+e*s*d)*w,t[8]=v*w,t[9]=(p*u*r-c*f*r-p*n*d+e*f*d+c*n*g-e*u*g)*w,t[10]=(o*f*r-p*a*r+p*n*l-e*f*l-o*n*g+e*a*g)*w,t[11]=(c*a*r-o*u*r-c*n*l+e*u*l+o*n*d-e*a*d)*w,t[12]=b*w,t[13]=(c*f*i-p*u*i+p*n*h-e*f*h-c*n*m+e*u*m)*w,t[14]=(p*a*i-o*f*i-p*n*s+e*f*s+o*n*m-e*a*m)*w,t[15]=(o*u*i-c*a*i+c*n*s-e*u*s-o*n*h+e*a*h)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,r=t.z;return e[0]*=n,e[4]*=i,e[8]*=r,e[1]*=n,e[5]*=i,e[9]*=r,e[2]*=n,e[6]*=i,e[10]*=r,e[3]*=n,e[7]*=i,e[11]*=r,this}getMaxScaleOnAxis(){const t=this.elements;return Math.sqrt(Math.max(t[0]*t[0]+t[1]*t[1]+t[2]*t[2],t[4]*t[4]+t[5]*t[5]+t[6]*t[6],t[8]*t[8]+t[9]*t[9]+t[10]*t[10]))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),r=1-n,o=t.x,a=t.y,s=t.z,l=r*o,c=r*a;return this.set(l*o+n,l*a-i*s,l*s+i*a,0,l*a+i*s,c*a+n,c*s-i*o,0,l*s-i*a,c*s+i*o,r*s*s+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,r,o){return this.set(1,n,r,0,t,1,o,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,r=e._x,o=e._y,a=e._z,s=e._w,l=r+r,c=o+o,u=a+a,h=r*l,d=r*c,p=r*u,f=o*c,m=o*u,g=a*u,_=s*l,y=s*c,v=s*u,b=n.x,x=n.y,w=n.z;return i[0]=(1-(f+g))*b,i[1]=(d+v)*b,i[2]=(p-y)*b,i[3]=0,i[4]=(d-v)*x,i[5]=(1-(h+g))*x,i[6]=(m+_)*x,i[7]=0,i[8]=(p+y)*w,i[9]=(m-_)*w,i[10]=(1-(h+f))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let r=oQt.set(i[0],i[1],i[2]).length();const o=oQt.set(i[4],i[5],i[6]).length(),a=oQt.set(i[8],i[9],i[10]).length();this.determinant()<0&&(r=-r),t.x=i[12],t.y=i[13],t.z=i[14],aQt.copy(this);const s=1/r,l=1/o,c=1/a;return aQt.elements[0]*=s,aQt.elements[1]*=s,aQt.elements[2]*=s,aQt.elements[4]*=l,aQt.elements[5]*=l,aQt.elements[6]*=l,aQt.elements[8]*=c,aQt.elements[9]*=c,aQt.elements[10]*=c,e.setFromRotationMatrix(aQt),n.x=r,n.y=o,n.z=a,this}makePerspective(t,e,n,i,r,o){void 0===o&&console.warn("THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.");const a=this.elements,s=2*r/(n-i),l=(e+t)/(e-t),c=(n+i)/(n-i),u=-(o+r)/(o-r),h=-2*o*r/(o-r);return a[0]=2*r/(e-t),a[4]=0,a[8]=l,a[12]=0,a[1]=0,a[5]=s,a[9]=c,a[13]=0,a[2]=0,a[6]=0,a[10]=u,a[14]=h,a[3]=0,a[7]=0,a[11]=-1,a[15]=0,this}makeOrthographic(t,e,n,i,r,o){const a=this.elements,s=1/(e-t),l=1/(n-i),c=1/(o-r),u=(e+t)*s,h=(n+i)*l,d=(o+r)*c;return a[0]=2*s,a[4]=0,a[8]=0,a[12]=-u,a[1]=0,a[5]=2*l,a[9]=0,a[13]=-h,a[2]=0,a[6]=0,a[10]=-2*c,a[14]=-d,a[3]=0,a[7]=0,a[11]=0,a[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}rQt.prototype.isMatrix4=!0;const oQt=new CJt,aQt=new rQt,sQt=new CJt(0,0,0),lQt=new CJt(1,1,1),cQt=new CJt,uQt=new CJt,hQt=new CJt,dQt=new rQt,pQt=new TJt;class fQt{constructor(t=0,e=0,n=0,i=fQt.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=i}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,i=this._order){return this._x=t,this._y=e,this._z=n,this._order=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const i=t.elements,r=i[0],o=i[4],a=i[8],s=i[1],l=i[5],c=i[9],u=i[2],h=i[6],d=i[10];switch(e){case"XYZ":this._y=Math.asin($Zt(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-c,d),this._z=Math.atan2(-o,r)):(this._x=Math.atan2(h,l),this._z=0);break;case"YXZ":this._x=Math.asin(-$Zt(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(a,d),this._z=Math.atan2(s,l)):(this._y=Math.atan2(-u,r),this._z=0);break;case"ZXY":this._x=Math.asin($Zt(h,-1,1)),Math.abs(h)<.9999999?(this._y=Math.atan2(-u,d),this._z=Math.atan2(-o,l)):(this._y=0,this._z=Math.atan2(s,r));break;case"ZYX":this._y=Math.asin(-$Zt(u,-1,1)),Math.abs(u)<.9999999?(this._x=Math.atan2(h,d),this._z=Math.atan2(s,r)):(this._x=0,this._z=Math.atan2(-o,l));break;case"YZX":this._z=Math.asin($Zt(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-u,r)):(this._x=0,this._y=Math.atan2(a,d));break;case"XZY":this._z=Math.asin(-$Zt(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(h,l),this._y=Math.atan2(a,r)):(this._x=Math.atan2(-c,d),this._y=0);break;default:console.warn("THREE.Euler: .setFromRotationMatrix() encountered an unknown order: "+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return dQt.makeRotationFromQuaternion(t),this.setFromRotationMatrix(dQt,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return pQt.setFromEuler(this),this.setFromQuaternion(pQt,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new CJt(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}fQt.prototype.isEuler=!0,fQt.DefaultOrder="XYZ",fQt.RotationOrders=["XYZ","YZX","ZXY","XZY","YXZ","ZYX"];class mQt{constructor(){this.mask=1}set(t){this.mask=1<<t|0}enable(t){this.mask|=1<<t|0}enableAll(){this.mask=-1}toggle(t){this.mask^=1<<t|0}disable(t){this.mask&=~(1<<t|0)}disableAll(){this.mask=0}test(t){return 0!=(this.mask&t.mask)}}let gQt=0;const _Qt=new CJt,yQt=new TJt,vQt=new rQt,bQt=new CJt,xQt=new CJt,wQt=new CJt,SQt=new TJt,MQt=new CJt(1,0,0),EQt=new CJt(0,1,0),TQt=new CJt(0,0,1),CQt={type:"added"},AQt={type:"removed"};class kQt extends jZt{constructor(){super(),Object.defineProperty(this,"id",{value:gQt++}),this.uuid=XZt(),this.name="",this.type="Object3D",this.parent=null,this.children=[],this.up=kQt.DefaultUp.clone();const t=new CJt,e=new fQt,n=new TJt,i=new CJt(1,1,1);e._onChange((function r(){n.setFromEuler(e,!1)})),n._onChange((function o(){e.setFromQuaternion(n,void 0,!1)})),Object.defineProperties(this,{position:{configurable:!0,enumerable:!0,value:t},rotation:{configurable:!0,enumerable:!0,value:e},quaternion:{configurable:!0,enumerable:!0,value:n},scale:{configurable:!0,enumerable:!0,value:i},modelViewMatrix:{value:new rQt},normalMatrix:{value:new gJt}}),this.matrix=new rQt,this.matrixWorld=new rQt,this.matrixAutoUpdate=kQt.DefaultMatrixAutoUpdate,this.matrixWorldNeedsUpdate=!1,this.layers=new mQt,this.visible=!0,this.castShadow=!1,this.receiveShadow=!1,this.frustumCulled=!0,this.renderOrder=0,this.animations=[],this.userData={}}onBeforeRender(){}onAfterRender(){}applyMatrix4(t){this.matrixAutoUpdate&&this.updateMatrix(),this.matrix.premultiply(t),this.matrix.decompose(this.position,this.quaternion,this.scale)}applyQuaternion(t){return this.quaternion.premultiply(t),this}setRotationFromAxisAngle(t,e){this.quaternion.setFromAxisAngle(t,e)}setRotationFromEuler(t){this.quaternion.setFromEuler(t,!0)}setRotationFromMatrix(t){this.quaternion.setFromRotationMatrix(t)}setRotationFromQuaternion(t){this.quaternion.copy(t)}rotateOnAxis(t,e){return yQt.setFromAxisAngle(t,e),this.quaternion.multiply(yQt),this}rotateOnWorldAxis(t,e){return yQt.setFromAxisAngle(t,e),this.quaternion.premultiply(yQt),this}rotateX(t){return this.rotateOnAxis(MQt,t)}rotateY(t){return this.rotateOnAxis(EQt,t)}rotateZ(t){return this.rotateOnAxis(TQt,t)}translateOnAxis(t,e){return _Qt.copy(t).applyQuaternion(this.quaternion),this.position.add(_Qt.multiplyScalar(e)),this}translateX(t){return this.translateOnAxis(MQt,t)}translateY(t){return this.translateOnAxis(EQt,t)}translateZ(t){return this.translateOnAxis(TQt,t)}localToWorld(t){return t.applyMatrix4(this.matrixWorld)}worldToLocal(t){return t.applyMatrix4(vQt.copy(this.matrixWorld).invert())}lookAt(t,e,n){t.isVector3?bQt.copy(t):bQt.set(t,e,n);const i=this.parent;this.updateWorldMatrix(!0,!1),xQt.setFromMatrixPosition(this.matrixWorld),this.isCamera||this.isLight?vQt.lookAt(xQt,bQt,this.up):vQt.lookAt(bQt,xQt,this.up),this.quaternion.setFromRotationMatrix(vQt),i&&(vQt.extractRotation(i.matrixWorld),yQt.setFromRotationMatrix(vQt),this.quaternion.premultiply(yQt.invert()))}add(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.add(arguments[t]);return this}return t===this?(console.error("THREE.Object3D.add: object can't be added as a child of itself.",t),this):(t&&t.isObject3D?(null!==t.parent&&t.parent.remove(t),t.parent=this,this.children.push(t),t.dispatchEvent(CQt)):console.error("THREE.Object3D.add: object not an instance of THREE.Object3D.",t),this)}remove(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.remove(arguments[t]);return this}const e=this.children.indexOf(t);return-1!==e&&(t.parent=null,this.children.splice(e,1),t.dispatchEvent(AQt)),this}removeFromParent(){const t=this.parent;return null!==t&&t.remove(this),this}clear(){for(let t=0;t<this.children.length;t++){const e=this.children[t];e.parent=null,e.dispatchEvent(AQt)}return this.children.length=0,this}attach(t){return this.updateWorldMatrix(!0,!1),vQt.copy(this.matrixWorld).invert(),null!==t.parent&&(t.parent.updateWorldMatrix(!0,!1),vQt.multiply(t.parent.matrixWorld)),t.applyMatrix4(vQt),this.add(t),t.updateWorldMatrix(!1,!0),this}getObjectById(t){return this.getObjectByProperty("id",t)}getObjectByName(t){return this.getObjectByProperty("name",t)}getObjectByProperty(t,e){if(this[t]===e)return this;for(let n=0,i=this.children.length;n<i;n++){const i=this.children[n].getObjectByProperty(t,e);if(void 0!==i)return i}}getWorldPosition(t){return this.updateWorldMatrix(!0,!1),t.setFromMatrixPosition(this.matrixWorld)}getWorldQuaternion(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(xQt,t,wQt),t}getWorldScale(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(xQt,SQt,t),t}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(e[8],e[9],e[10]).normalize()}raycast(){}traverse(t){t(this);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].traverse(t)}traverseVisible(t){if(!1===this.visible)return;t(this);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].traverseVisible(t)}traverseAncestors(t){const e=this.parent;null!==e&&(t(e),e.traverseAncestors(t))}updateMatrix(){this.matrix.compose(this.position,this.quaternion,this.scale),this.matrixWorldNeedsUpdate=!0}updateMatrixWorld(t){this.matrixAutoUpdate&&this.updateMatrix(),(this.matrixWorldNeedsUpdate||t)&&(null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),this.matrixWorldNeedsUpdate=!1,t=!0);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].updateMatrixWorld(t)}updateWorldMatrix(t,e){const n=this.parent;if(!0===t&&null!==n&&n.updateWorldMatrix(!0,!1),this.matrixAutoUpdate&&this.updateMatrix(),null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),!0===e){const t=this.children;for(let e=0,n=t.length;e<n;e++)t[e].updateWorldMatrix(!1,!0)}}toJSON(t){const e=void 0===t||"string"==typeof t,n={};e&&(t={geometries:{},materials:{},textures:{},images:{},shapes:{},skeletons:{},animations:{}},n.metadata={version:4.5,type:"Object",generator:"Object3D.toJSON"});const i={};function r(e,n){return void 0===e[n.uuid]&&(e[n.uuid]=n.toJSON(t)),n.uuid}if(i.uuid=this.uuid,i.type=this.type,""!==this.name&&(i.name=this.name),!0===this.castShadow&&(i.castShadow=!0),!0===this.receiveShadow&&(i.receiveShadow=!0),!1===this.visible&&(i.visible=!1),!1===this.frustumCulled&&(i.frustumCulled=!1),0!==this.renderOrder&&(i.renderOrder=this.renderOrder),"{}"!==JSON.stringify(this.userData)&&(i.userData=this.userData),i.layers=this.layers.mask,i.matrix=this.matrix.toArray(),!1===this.matrixAutoUpdate&&(i.matrixAutoUpdate=!1),this.isInstancedMesh&&(i.type="InstancedMesh",i.count=this.count,i.instanceMatrix=this.instanceMatrix.toJSON(),null!==this.instanceColor&&(i.instanceColor=this.instanceColor.toJSON())),this.isScene)this.background&&(this.background.isColor?i.background=this.background.toJSON():this.background.isTexture&&(i.background=this.background.toJSON(t).uuid)),this.environment&&this.environment.isTexture&&(i.environment=this.environment.toJSON(t).uuid);else if(this.isMesh||this.isLine||this.isPoints){i.geometry=r(t.geometries,this.geometry);const e=this.geometry.parameters;if(void 0!==e&&void 0!==e.shapes){const n=e.shapes;if(Array.isArray(n))for(let e=0,i=n.length;e<i;e++)r(t.shapes,n[e]);else r(t.shapes,n)}}if(this.isSkinnedMesh&&(i.bindMode=this.bindMode,i.bindMatrix=this.bindMatrix.toArray(),void 0!==this.skeleton&&(r(t.skeletons,this.skeleton),i.skeleton=this.skeleton.uuid)),void 0!==this.material)if(Array.isArray(this.material)){const e=[];for(let n=0,i=this.material.length;n<i;n++)e.push(r(t.materials,this.material[n]));i.material=e}else i.material=r(t.materials,this.material);if(this.children.length>0){i.children=[];for(let e=0;e<this.children.length;e++)i.children.push(this.children[e].toJSON(t).object)}if(this.animations.length>0){i.animations=[];for(let e=0;e<this.animations.length;e++)i.animations.push(r(t.animations,this.animations[e]))}if(e){const e=o(t.geometries),i=o(t.materials),r=o(t.textures),a=o(t.images),s=o(t.shapes),l=o(t.skeletons),c=o(t.animations);e.length>0&&(n.geometries=e),i.length>0&&(n.materials=i),r.length>0&&(n.textures=r),a.length>0&&(n.images=a),s.length>0&&(n.shapes=s),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function o(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e<t.children.length;e++)this.add(t.children[e].clone());return this}}kQt.DefaultUp=new CJt(0,1,0),kQt.DefaultMatrixAutoUpdate=!0,kQt.prototype.isObject3D=!0;const LQt=new CJt,PQt=new CJt,NQt=new CJt,IQt=new CJt,RQt=new CJt,OQt=new CJt,zQt=new CJt,DQt=new CJt,BQt=new CJt,HQt=new CJt;class FQt{constructor(t=new CJt,e=new CJt,n=new CJt){this.a=t,this.b=e,this.c=n}static getNormal(t,e,n,i){i.subVectors(n,e),LQt.subVectors(t,e),i.cross(LQt);const r=i.lengthSq();return r>0?i.multiplyScalar(1/Math.sqrt(r)):i.set(0,0,0)}static getBarycoord(t,e,n,i,r){LQt.subVectors(i,e),PQt.subVectors(n,e),NQt.subVectors(t,e);const o=LQt.dot(LQt),a=LQt.dot(PQt),s=LQt.dot(NQt),l=PQt.dot(PQt),c=PQt.dot(NQt),u=o*l-a*a;if(0===u)return r.set(-2,-1,-1);const h=1/u,d=(l*s-a*c)*h,p=(o*c-a*s)*h;return r.set(1-d-p,p,d)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,IQt),IQt.x>=0&&IQt.y>=0&&IQt.x+IQt.y<=1}static getUV(t,e,n,i,r,o,a,s){return this.getBarycoord(t,e,n,i,IQt),s.set(0,0),s.addScaledVector(r,IQt.x),s.addScaledVector(o,IQt.y),s.addScaledVector(a,IQt.z),s}static isFrontFacing(t,e,n,i){return LQt.subVectors(n,e),PQt.subVectors(t,e),LQt.cross(PQt).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return LQt.subVectors(this.c,this.b),PQt.subVectors(this.a,this.b),.5*LQt.cross(PQt).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return FQt.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return FQt.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,r){return FQt.getUV(t,this.a,this.b,this.c,e,n,i,r)}containsPoint(t){return FQt.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return FQt.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,r=this.c;let o,a;RQt.subVectors(i,n),OQt.subVectors(r,n),DQt.subVectors(t,n);const s=RQt.dot(DQt),l=OQt.dot(DQt);if(s<=0&&l<=0)return e.copy(n);BQt.subVectors(t,i);const c=RQt.dot(BQt),u=OQt.dot(BQt);if(c>=0&&u<=c)return e.copy(i);const h=s*u-c*l;if(h<=0&&s>=0&&c<=0)return o=s/(s-c),e.copy(n).addScaledVector(RQt,o);HQt.subVectors(t,r);const d=RQt.dot(HQt),p=OQt.dot(HQt);if(p>=0&&d<=p)return e.copy(r);const f=d*l-s*p;if(f<=0&&l>=0&&p<=0)return a=l/(l-p),e.copy(n).addScaledVector(OQt,a);const m=c*p-d*u;if(m<=0&&u-c>=0&&d-p>=0)return zQt.subVectors(r,i),a=(u-c)/(u-c+(d-p)),e.copy(i).addScaledVector(zQt,a);const g=1/(m+f+h);return o=f*g,a=h*g,e.copy(n).addScaledVector(RQt,o).addScaledVector(OQt,a)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let VQt=0;class UQt extends jZt{constructor(){super(),Object.defineProperty(this,"id",{value:VQt++}),this.uuid=XZt(),this.name="",this.type="Material",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=VKt,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=3,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=519,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=HZt,this.stencilZFail=HZt,this.stencilZPass=HZt,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaTest=0,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0}onBuild(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn("THREE.Material: '"+e+"' parameter is undefined.");continue}if("shading"===e){console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead."),this.flatShading=1===n;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn("THREE."+this.type+": '"+e+"' is not a property of this material.")}}toJSON(t){const e=void 0===t||"string"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:"Material",generator:"Material.toJSON"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,""!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),this.sheen&&this.sheen.isColor&&(n.sheen=this.sheen.getHex()),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),1!==this.blending&&(n.blending=this.blending),0!==this.side&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),"round"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),"round"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),"{}"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),r=i(t.images);e.length>0&&(n.textures=e),r.length>0&&(n.images=r)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:"dispose"})}set needsUpdate(t){!0===t&&this.version++}}UQt.prototype.isMaterial=!0;const jQt={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},GQt={h:0,s:0,l:0},WQt={h:0,s:0,l:0};function qQt(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function YQt(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function XQt(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class $Qt{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):"number"==typeof t?this.setHex(t):"string"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=KZt(t,1),e=$Zt(e,0,1),n=$Zt(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,r=2*n-i;this.r=qQt(r,i,t+1/3),this.g=qQt(r,i,t),this.b=qQt(r,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn("THREE.Color: Alpha component of "+t+" will be ignored.")}let n;if(n=/^((?:rgb|hsl)a?)\(([^\)]*)\)/.exec(t)){let t;const i=n[2];switch(n[1]){case"rgb":case"rgba":if(t=/^\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(i))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\s*(\d+)\%\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(i))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case"hsl":case"hsla":if(t=/^\s*(\d*\.?\d+)\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(i)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,r=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,r)}}}else if(n=/^\#([A-Fa-f\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=jQt[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn("THREE.Color: Unknown color "+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=YQt(t.r),this.g=YQt(t.g),this.b=YQt(t.b),this}copyLinearToSRGB(t){return this.r=XQt(t.r),this.g=XQt(t.g),this.b=XQt(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return("000000"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,r=Math.max(e,n,i),o=Math.min(e,n,i);let a,s;const l=(o+r)/2;if(o===r)a=0,s=0;else{const t=r-o;switch(s=l<=.5?t/(r+o):t/(2-r-o),r){case e:a=(n-i)/t+(n<i?6:0);break;case n:a=(i-e)/t+2;break;case i:a=(e-n)/t+4}a/=6}return t.h=a,t.s=s,t.l=l,t}getStyle(){return"rgb("+(255*this.r|0)+","+(255*this.g|0)+","+(255*this.b|0)+")"}offsetHSL(t,e,n){return this.getHSL(GQt),GQt.h+=t,GQt.s+=e,GQt.l+=n,this.setHSL(GQt.h,GQt.s,GQt.l),this}add(t){return this.r+=t.r,this.g+=t.g,this.b+=t.b,this}addColors(t,e){return this.r=t.r+e.r,this.g=t.g+e.g,this.b=t.b+e.b,this}addScalar(t){return this.r+=t,this.g+=t,this.b+=t,this}sub(t){return this.r=Math.max(0,this.r-t.r),this.g=Math.max(0,this.g-t.g),this.b=Math.max(0,this.b-t.b),this}multiply(t){return this.r*=t.r,this.g*=t.g,this.b*=t.b,this}multiplyScalar(t){return this.r*=t,this.g*=t,this.b*=t,this}lerp(t,e){return this.r+=(t.r-this.r)*e,this.g+=(t.g-this.g)*e,this.b+=(t.b-this.b)*e,this}lerpColors(t,e,n){return this.r=t.r+(e.r-t.r)*n,this.g=t.g+(e.g-t.g)*n,this.b=t.b+(e.b-t.b)*n,this}lerpHSL(t,e){this.getHSL(GQt),t.getHSL(WQt);const n=ZZt(GQt.h,WQt.h,e),i=ZZt(GQt.s,WQt.s,e),r=ZZt(GQt.l,WQt.l,e);return this.setHSL(n,i,r),this}equals(t){return t.r===this.r&&t.g===this.g&&t.b===this.b}fromArray(t,e=0){return this.r=t[e],this.g=t[e+1],this.b=t[e+2],this}toArray(t=[],e=0){return t[e]=this.r,t[e+1]=this.g,t[e+2]=this.b,t}fromBufferAttribute(t,e){return this.r=t.getX(e),this.g=t.getY(e),this.b=t.getZ(e),!0===t.normalized&&(this.r/=255,this.g/=255,this.b/=255),this}toJSON(){return this.getHex()}}$Qt.NAMES=jQt,$Qt.prototype.isColor=!0,$Qt.prototype.r=1,$Qt.prototype.g=1,$Qt.prototype.b=1;class KQt extends UQt{constructor(t){super(),this.type="MeshBasicMaterial",this.color=new $Qt(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}KQt.prototype.isMeshBasicMaterial=!0;const ZQt=new CJt,JQt=new mJt;class QQt{constructor(t,e,n){if(Array.isArray(t))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");this.name="",this.array=t,this.itemSize=e,this.count=void 0!==t?t.length/e:0,this.normalized=!0===n,this.usage=FZt,this.updateRange={offset:0,count:-1},this.version=0}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.name=t.name,this.array=new t.array.constructor(t.array),this.itemSize=t.itemSize,this.count=t.count,this.normalized=t.normalized,this.usage=t.usage,this}copyAt(t,e,n){t*=this.itemSize,n*=e.itemSize;for(let i=0,r=this.itemSize;i<r;i++)this.array[t+i]=e.array[n+i];return this}copyArray(t){return this.array.set(t),this}copyColorsArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn("THREE.BufferAttribute.copyColorsArray(): color is undefined",i),r=new $Qt),e[n++]=r.r,e[n++]=r.g,e[n++]=r.b}return this}copyVector2sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn("THREE.BufferAttribute.copyVector2sArray(): vector is undefined",i),r=new mJt),e[n++]=r.x,e[n++]=r.y}return this}copyVector3sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn("THREE.BufferAttribute.copyVector3sArray(): vector is undefined",i),r=new CJt),e[n++]=r.x,e[n++]=r.y,e[n++]=r.z}return this}copyVector4sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn("THREE.BufferAttribute.copyVector4sArray(): vector is undefined",i),r=new wJt),e[n++]=r.x,e[n++]=r.y,e[n++]=r.z,e[n++]=r.w}return this}applyMatrix3(t){if(2===this.itemSize)for(let e=0,n=this.count;e<n;e++)JQt.fromBufferAttribute(this,e),JQt.applyMatrix3(t),this.setXY(e,JQt.x,JQt.y);else if(3===this.itemSize)for(let e=0,n=this.count;e<n;e++)ZQt.fromBufferAttribute(this,e),ZQt.applyMatrix3(t),this.setXYZ(e,ZQt.x,ZQt.y,ZQt.z);return this}applyMatrix4(t){for(let e=0,n=this.count;e<n;e++)ZQt.x=this.getX(e),ZQt.y=this.getY(e),ZQt.z=this.getZ(e),ZQt.applyMatrix4(t),this.setXYZ(e,ZQt.x,ZQt.y,ZQt.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)ZQt.x=this.getX(e),ZQt.y=this.getY(e),ZQt.z=this.getZ(e),ZQt.applyNormalMatrix(t),this.setXYZ(e,ZQt.x,ZQt.y,ZQt.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)ZQt.x=this.getX(e),ZQt.y=this.getY(e),ZQt.z=this.getZ(e),ZQt.transformDirection(t),this.setXYZ(e,ZQt.x,ZQt.y,ZQt.z);return this}set(t,e=0){return this.array.set(t,e),this}getX(t){return this.array[t*this.itemSize]}setX(t,e){return this.array[t*this.itemSize]=e,this}getY(t){return this.array[t*this.itemSize+1]}setY(t,e){return this.array[t*this.itemSize+1]=e,this}getZ(t){return this.array[t*this.itemSize+2]}setZ(t,e){return this.array[t*this.itemSize+2]=e,this}getW(t){return this.array[t*this.itemSize+3]}setW(t,e){return this.array[t*this.itemSize+3]=e,this}setXY(t,e,n){return this.array[0+(t*=this.itemSize)]=e,this.array[t+1]=n,this}setXYZ(t,e,n,i){return this.array[0+(t*=this.itemSize)]=e,this.array[t+1]=n,this.array[t+2]=i,this}setXYZW(t,e,n,i,r){return this.array[0+(t*=this.itemSize)]=e,this.array[t+1]=n,this.array[t+2]=i,this.array[t+3]=r,this}onUpload(t){return this.onUploadCallback=t,this}clone(){return new this.constructor(this.array,this.itemSize).copy(this)}toJSON(){const t={itemSize:this.itemSize,type:this.array.constructor.name,array:Array.prototype.slice.call(this.array),normalized:this.normalized};return""!==this.name&&(t.name=this.name),this.usage!==FZt&&(t.usage=this.usage),0===this.updateRange.offset&&-1===this.updateRange.count||(t.updateRange=this.updateRange),t}}QQt.prototype.isBufferAttribute=!0;class t1t extends QQt{constructor(t,e,n){super(new Int8Array(t),e,n)}}class e1t extends QQt{constructor(t,e,n){super(new Uint8Array(t),e,n)}}class n1t extends QQt{constructor(t,e,n){super(new Uint8ClampedArray(t),e,n)}}class i1t extends QQt{constructor(t,e,n){super(new Int16Array(t),e,n)}}class r1t extends QQt{constructor(t,e,n){super(new Uint16Array(t),e,n)}}class o1t extends QQt{constructor(t,e,n){super(new Int32Array(t),e,n)}}class a1t extends QQt{constructor(t,e,n){super(new Uint32Array(t),e,n)}}class s1t extends QQt{constructor(t,e,n){super(new Uint16Array(t),e,n)}}s1t.prototype.isFloat16BufferAttribute=!0;class l1t extends QQt{constructor(t,e,n){super(new Float32Array(t),e,n)}}class c1t extends QQt{constructor(t,e,n){super(new Float64Array(t),e,n)}}function u1t(t){if(0===t.length)return-1/0;let e=t[0];for(let n=1,i=t.length;n<i;++n)t[n]>e&&(e=t[n]);return e}const h1t={Int8Array:Int8Array,Uint8Array:Uint8Array,Uint8ClampedArray:Uint8ClampedArray,Int16Array:Int16Array,Uint16Array:Uint16Array,Int32Array:Int32Array,Uint32Array:Uint32Array,Float32Array:Float32Array,Float64Array:Float64Array};function d1t(t,e){return new h1t[t](e)}let p1t=0;const f1t=new rQt,m1t=new kQt,g1t=new CJt,_1t=new LJt,y1t=new LJt,v1t=new CJt;class b1t extends jZt{constructor(){super(),Object.defineProperty(this,"id",{value:p1t++}),this.uuid=XZt(),this.name="",this.type="BufferGeometry",this.index=null,this.attributes={},this.morphAttributes={},this.morphTargetsRelative=!1,this.groups=[],this.boundingBox=null,this.boundingSphere=null,this.drawRange={start:0,count:1/0},this.userData={}}getIndex(){return this.index}setIndex(t){return this.index=Array.isArray(t)?new(u1t(t)>65535?a1t:r1t)(t,1):t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new gJt).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return f1t.makeRotationFromQuaternion(t),this.applyMatrix4(f1t),this}rotateX(t){return f1t.makeRotationX(t),this.applyMatrix4(f1t),this}rotateY(t){return f1t.makeRotationY(t),this.applyMatrix4(f1t),this}rotateZ(t){return f1t.makeRotationZ(t),this.applyMatrix4(f1t),this}translate(t,e,n){return f1t.makeTranslation(t,e,n),this.applyMatrix4(f1t),this}scale(t,e,n){return f1t.makeScale(t,e,n),this.applyMatrix4(f1t),this}lookAt(t){return m1t.lookAt(t),m1t.updateMatrix(),this.applyMatrix4(m1t.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(g1t).negate(),this.translate(g1t.x,g1t.y,g1t.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n<i;n++){const i=t[n];e.push(i.x,i.y,i.z||0)}return this.setAttribute("position",new l1t(e,3)),this}computeBoundingBox(){null===this.boundingBox&&(this.boundingBox=new LJt);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingBox.set(new CJt(-1/0,-1/0,-1/0),new CJt(1/0,1/0,1/0));if(void 0!==t){if(this.boundingBox.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++)_1t.setFromBufferAttribute(e[t]),this.morphTargetsRelative?(v1t.addVectors(this.boundingBox.min,_1t.min),this.boundingBox.expandByPoint(v1t),v1t.addVectors(this.boundingBox.max,_1t.max),this.boundingBox.expandByPoint(v1t)):(this.boundingBox.expandByPoint(_1t.min),this.boundingBox.expandByPoint(_1t.max))}else this.boundingBox.makeEmpty();(isNaN(this.boundingBox.min.x)||isNaN(this.boundingBox.min.y)||isNaN(this.boundingBox.min.z))&&console.error('THREE.BufferGeometry.computeBoundingBox(): Computed min/max have NaN values. The "position" attribute is likely to have NaN values.',this)}computeBoundingSphere(){null===this.boundingSphere&&(this.boundingSphere=new $Jt);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingSphere(): GLBufferAttribute requires a manual bounding sphere. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingSphere.set(new CJt,1/0);if(t){const n=this.boundingSphere.center;if(_1t.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++)y1t.setFromBufferAttribute(e[t]),this.morphTargetsRelative?(v1t.addVectors(_1t.min,y1t.min),_1t.expandByPoint(v1t),v1t.addVectors(_1t.max,y1t.max),_1t.expandByPoint(v1t)):(_1t.expandByPoint(y1t.min),_1t.expandByPoint(y1t.max));_1t.getCenter(n);let i=0;for(let e=0,r=t.count;e<r;e++)v1t.fromBufferAttribute(t,e),i=Math.max(i,n.distanceToSquared(v1t));if(e)for(let r=0,o=e.length;r<o;r++){const o=e[r],a=this.morphTargetsRelative;for(let e=0,r=o.count;e<r;e++)v1t.fromBufferAttribute(o,e),a&&(g1t.fromBufferAttribute(t,e),v1t.add(g1t)),i=Math.max(i,n.distanceToSquared(v1t))}this.boundingSphere.radius=Math.sqrt(i),isNaN(this.boundingSphere.radius)&&console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The "position" attribute is likely to have NaN values.',this)}}computeFaceNormals(){}computeTangents(){const t=this.index,e=this.attributes;if(null===t||void 0===e.position||void 0===e.normal||void 0===e.uv)return void console.error("THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)");const n=t.array,i=e.position.array,r=e.normal.array,o=e.uv.array,a=i.length/3;void 0===e.tangent&&this.setAttribute("tangent",new QQt(new Float32Array(4*a),4));const s=e.tangent.array,l=[],c=[];for(let t=0;t<a;t++)l[t]=new CJt,c[t]=new CJt;const u=new CJt,h=new CJt,d=new CJt,p=new mJt,f=new mJt,m=new mJt,g=new CJt,_=new CJt;function y(t,e,n){u.fromArray(i,3*t),h.fromArray(i,3*e),d.fromArray(i,3*n),p.fromArray(o,2*t),f.fromArray(o,2*e),m.fromArray(o,2*n),h.sub(u),d.sub(u),f.sub(p),m.sub(p);const r=1/(f.x*m.y-m.x*f.y);isFinite(r)&&(g.copy(h).multiplyScalar(m.y).addScaledVector(d,-f.y).multiplyScalar(r),_.copy(d).multiplyScalar(f.x).addScaledVector(h,-m.x).multiplyScalar(r),l[t].add(g),l[e].add(g),l[n].add(g),c[t].add(_),c[e].add(_),c[n].add(_))}let v=this.groups;0===v.length&&(v=[{start:0,count:n.length}]);for(let t=0,e=v.length;t<e;++t){const e=v[t],i=e.start;for(let t=i,r=i+e.count;t<r;t+=3)y(n[t+0],n[t+1],n[t+2])}const b=new CJt,x=new CJt,w=new CJt,S=new CJt;function M(t){w.fromArray(r,3*t),S.copy(w);const e=l[t];b.copy(e),b.sub(w.multiplyScalar(w.dot(e))).normalize(),x.crossVectors(S,e);const n=x.dot(c[t])<0?-1:1;s[4*t]=b.x,s[4*t+1]=b.y,s[4*t+2]=b.z,s[4*t+3]=n}for(let t=0,e=v.length;t<e;++t){const e=v[t],i=e.start;for(let t=i,r=i+e.count;t<r;t+=3)M(n[t+0]),M(n[t+1]),M(n[t+2])}}computeVertexNormals(){const t=this.index,e=this.getAttribute("position");if(void 0!==e){let n=this.getAttribute("normal");if(void 0===n)n=new QQt(new Float32Array(3*e.count),3),this.setAttribute("normal",n);else for(let t=0,e=n.count;t<e;t++)n.setXYZ(t,0,0,0);const i=new CJt,r=new CJt,o=new CJt,a=new CJt,s=new CJt,l=new CJt,c=new CJt,u=new CJt;if(t)for(let h=0,d=t.count;h<d;h+=3){const d=t.getX(h+0),p=t.getX(h+1),f=t.getX(h+2);i.fromBufferAttribute(e,d),r.fromBufferAttribute(e,p),o.fromBufferAttribute(e,f),c.subVectors(o,r),u.subVectors(i,r),c.cross(u),a.fromBufferAttribute(n,d),s.fromBufferAttribute(n,p),l.fromBufferAttribute(n,f),a.add(c),s.add(c),l.add(c),n.setXYZ(d,a.x,a.y,a.z),n.setXYZ(p,s.x,s.y,s.z),n.setXYZ(f,l.x,l.y,l.z)}else for(let t=0,a=e.count;t<a;t+=3)i.fromBufferAttribute(e,t+0),r.fromBufferAttribute(e,t+1),o.fromBufferAttribute(e,t+2),c.subVectors(o,r),u.subVectors(i,r),c.cross(u),n.setXYZ(t+0,c.x,c.y,c.z),n.setXYZ(t+1,c.x,c.y,c.z),n.setXYZ(t+2,c.x,c.y,c.z);this.normalizeNormals(),n.needsUpdate=!0}}merge(t,e){if(!t||!t.isBufferGeometry)return void console.error("THREE.BufferGeometry.merge(): geometry not an instance of THREE.BufferGeometry.",t);void 0===e&&(e=0,console.warn("THREE.BufferGeometry.merge(): Overwriting original geometry, starting at offset=0. Use BufferGeometryUtils.mergeBufferGeometries() for lossless merge."));const n=this.attributes;for(const i in n){if(void 0===t.attributes[i])continue;const r=n[i].array,o=t.attributes[i],a=o.array,s=o.itemSize*e,l=Math.min(a.length,r.length-s);for(let t=0,e=s;t<l;t++,e++)r[e]=a[t]}return this}normalizeNormals(){const t=this.attributes.normal;for(let e=0,n=t.count;e<n;e++)v1t.fromBufferAttribute(t,e),v1t.normalize(),t.setXYZ(e,v1t.x,v1t.y,v1t.z)}toNonIndexed(){function t(t,e){const n=t.array,i=t.itemSize,r=t.normalized,o=new n.constructor(e.length*i);let a=0,s=0;for(let r=0,l=e.length;r<l;r++){a=t.isInterleavedBufferAttribute?e[r]*t.data.stride+t.offset:e[r]*i;for(let t=0;t<i;t++)o[s++]=n[a++]}return new QQt(o,i,r)}if(null===this.index)return console.warn("THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed."),this;const e=new b1t,n=this.index.array,i=this.attributes;for(const r in i){const o=t(i[r],n);e.setAttribute(r,o)}const r=this.morphAttributes;for(const i in r){const o=[],a=r[i];for(let e=0,i=a.length;e<i;e++){const i=t(a[e],n);o.push(i)}e.morphAttributes[i]=o}e.morphTargetsRelative=this.morphTargetsRelative;const o=this.groups;for(let t=0,n=o.length;t<n;t++){const n=o[t];e.addGroup(n.start,n.count,n.materialIndex)}return e}toJSON(){const t={metadata:{version:4.5,type:"BufferGeometry",generator:"BufferGeometry.toJSON"}};if(t.uuid=this.uuid,t.type=this.type,""!==this.name&&(t.name=this.name),Object.keys(this.userData).length>0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n)t.data.attributes[e]=n[e].toJSON(t.data);const i={};let r=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],o=[];for(let e=0,i=n.length;e<i;e++)o.push(n[e].toJSON(t.data));o.length>0&&(i[e]=o,r=!0)}r&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const o=this.groups;o.length>0&&(t.data.groups=JSON.parse(JSON.stringify(o)));const a=this.boundingSphere;return null!==a&&(t.data.boundingSphere={center:a.center.toArray(),radius:a.radius}),t}clone(){return(new b1t).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i)this.setAttribute(t,i[t].clone(e));const r=t.morphAttributes;for(const t in r){const n=[],i=r[t];for(let t=0,r=i.length;t<r;t++)n.push(i[t].clone(e));this.morphAttributes[t]=n}this.morphTargetsRelative=t.morphTargetsRelative;const o=t.groups;for(let t=0,e=o.length;t<e;t++){const e=o[t];this.addGroup(e.start,e.count,e.materialIndex)}const a=t.boundingBox;null!==a&&(this.boundingBox=a.clone());const s=t.boundingSphere;return null!==s&&(this.boundingSphere=s.clone()),this.drawRange.start=t.drawRange.start,this.drawRange.count=t.drawRange.count,this.userData=t.userData,this}dispose(){this.dispatchEvent({type:"dispose"})}}b1t.prototype.isBufferGeometry=!0;const x1t=new rQt,w1t=new iQt,S1t=new $Jt,M1t=new CJt,E1t=new CJt,T1t=new CJt,C1t=new CJt,A1t=new CJt,k1t=new CJt,L1t=new CJt,P1t=new CJt,N1t=new CJt,I1t=new mJt,R1t=new mJt,O1t=new mJt,z1t=new CJt,D1t=new CJt;class B1t extends kQt{constructor(t=new b1t,e=new KQt){super(),this.type="Mesh",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),void 0!==t.morphTargetInfluences&&(this.morphTargetInfluences=t.morphTargetInfluences.slice()),void 0!==t.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},t.morphTargetDictionary)),this.material=t.material,this.geometry=t.geometry,this}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}}raycast(t,e){const n=this.geometry,i=this.material,r=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),S1t.copy(n.boundingSphere),S1t.applyMatrix4(r),!1===t.ray.intersectsSphere(S1t))return;if(x1t.copy(r).invert(),w1t.copy(t.ray).applyMatrix4(x1t),null!==n.boundingBox&&!1===w1t.intersectsBox(n.boundingBox))return;let o;if(n.isBufferGeometry){const r=n.index,a=n.attributes.position,s=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,u=n.attributes.uv2,h=n.groups,d=n.drawRange;if(null!==r)if(Array.isArray(i))for(let n=0,p=h.length;n<p;n++){const p=h[n],f=i[p.materialIndex];for(let n=Math.max(p.start,d.start),i=Math.min(p.start+p.count,d.start+d.count);n<i;n+=3){const i=r.getX(n),h=r.getX(n+1),d=r.getX(n+2);o=H1t(this,f,t,w1t,a,s,l,c,u,i,h,d),o&&(o.faceIndex=Math.floor(n/3),o.face.materialIndex=p.materialIndex,e.push(o))}}else for(let n=Math.max(0,d.start),h=Math.min(r.count,d.start+d.count);n<h;n+=3){const h=r.getX(n),d=r.getX(n+1),p=r.getX(n+2);o=H1t(this,i,t,w1t,a,s,l,c,u,h,d,p),o&&(o.faceIndex=Math.floor(n/3),e.push(o))}else if(void 0!==a)if(Array.isArray(i))for(let n=0,r=h.length;n<r;n++){const r=h[n],p=i[r.materialIndex];for(let n=Math.max(r.start,d.start),i=Math.min(r.start+r.count,d.start+d.count);n<i;n+=3)o=H1t(this,p,t,w1t,a,s,l,c,u,n,n+1,n+2),o&&(o.faceIndex=Math.floor(n/3),o.face.materialIndex=r.materialIndex,e.push(o))}else for(let n=Math.max(0,d.start),r=Math.min(a.count,d.start+d.count);n<r;n+=3)o=H1t(this,i,t,w1t,a,s,l,c,u,n,n+1,n+2),o&&(o.faceIndex=Math.floor(n/3),e.push(o))}else n.isGeometry&&console.error("THREE.Mesh.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}}function H1t(t,e,n,i,r,o,a,s,l,c,u,h){M1t.fromBufferAttribute(r,c),E1t.fromBufferAttribute(r,u),T1t.fromBufferAttribute(r,h);const d=t.morphTargetInfluences;if(o&&d){L1t.set(0,0,0),P1t.set(0,0,0),N1t.set(0,0,0);for(let t=0,e=o.length;t<e;t++){const e=d[t],n=o[t];0!==e&&(C1t.fromBufferAttribute(n,c),A1t.fromBufferAttribute(n,u),k1t.fromBufferAttribute(n,h),a?(L1t.addScaledVector(C1t,e),P1t.addScaledVector(A1t,e),N1t.addScaledVector(k1t,e)):(L1t.addScaledVector(C1t.sub(M1t),e),P1t.addScaledVector(A1t.sub(E1t),e),N1t.addScaledVector(k1t.sub(T1t),e)))}M1t.add(L1t),E1t.add(P1t),T1t.add(N1t)}t.isSkinnedMesh&&(t.boneTransform(c,M1t),t.boneTransform(u,E1t),t.boneTransform(h,T1t));const p=(function f(t,e,n,i,r,o,a,s){let l;if(l=1===e.side?i.intersectTriangle(a,o,r,!0,s):i.intersectTriangle(r,o,a,2!==e.side,s),null===l)return null;D1t.copy(s),D1t.applyMatrix4(t.matrixWorld);const c=n.ray.origin.distanceTo(D1t);return c<n.near||c>n.far?null:{distance:c,point:D1t.clone(),object:t}})(t,e,n,i,M1t,E1t,T1t,z1t);if(p){s&&(I1t.fromBufferAttribute(s,c),R1t.fromBufferAttribute(s,u),O1t.fromBufferAttribute(s,h),p.uv=FQt.getUV(z1t,M1t,E1t,T1t,I1t,R1t,O1t,new mJt)),l&&(I1t.fromBufferAttribute(l,c),R1t.fromBufferAttribute(l,u),O1t.fromBufferAttribute(l,h),p.uv2=FQt.getUV(z1t,M1t,E1t,T1t,I1t,R1t,O1t,new mJt));const t={a:c,b:u,c:h,normal:new CJt,materialIndex:0};FQt.getNormal(M1t,E1t,T1t,t.normal),p.face=t}return p}B1t.prototype.isMesh=!0;class F1t extends b1t{constructor(t=1,e=1,n=1,i=1,r=1,o=1){super(),this.type="BoxGeometry",this.parameters={width:t,height:e,depth:n,widthSegments:i,heightSegments:r,depthSegments:o};const a=this;i=Math.floor(i),r=Math.floor(r),o=Math.floor(o);const s=[],l=[],c=[],u=[];let h=0,d=0;function p(t,e,n,i,r,o,p,f,m,g,_){const y=o/m,v=p/g,b=o/2,x=p/2,w=f/2,S=m+1,M=g+1;let E=0,T=0;const C=new CJt;for(let o=0;o<M;o++){const a=o*v-x;for(let s=0;s<S;s++)C[t]=(s*y-b)*i,C[e]=a*r,C[n]=w,l.push(C.x,C.y,C.z),C[t]=0,C[e]=0,C[n]=f>0?1:-1,c.push(C.x,C.y,C.z),u.push(s/m),u.push(1-o/g),E+=1}for(let t=0;t<g;t++)for(let e=0;e<m;e++){const n=h+e+S*(t+1),i=h+(e+1)+S*(t+1),r=h+(e+1)+S*t;s.push(h+e+S*t,n,r),s.push(n,i,r),T+=6}a.addGroup(d,T,_),d+=T,h+=E}p("z","y","x",-1,-1,n,e,t,o,r,0),p("z","y","x",1,-1,n,e,-t,o,r,1),p("x","z","y",1,1,t,n,e,i,o,2),p("x","z","y",1,-1,t,n,-e,i,o,3),p("x","y","z",1,-1,t,e,n,i,r,4),p("x","y","z",-1,-1,t,e,-n,i,r,5),this.setIndex(s),this.setAttribute("position",new l1t(l,3)),this.setAttribute("normal",new l1t(c,3)),this.setAttribute("uv",new l1t(u,2))}static fromJSON(t){return new F1t(t.width,t.height,t.depth,t.widthSegments,t.heightSegments,t.depthSegments)}}function V1t(t){const e={};for(const n in t){e[n]={};for(const i in t[n]){const r=t[n][i];e[n][i]=r&&(r.isColor||r.isMatrix3||r.isMatrix4||r.isVector2||r.isVector3||r.isVector4||r.isTexture||r.isQuaternion)?r.clone():Array.isArray(r)?r.slice():r}}return e}function U1t(t){const e={};for(let n=0;n<t.length;n++){const i=V1t(t[n]);for(const t in i)e[t]=i[t]}return e}const j1t={clone:V1t,merge:U1t};class G1t extends UQt{constructor(t){super(),this.type="ShaderMaterial",this.defines={},this.uniforms={},this.vertexShader="void main() {\n\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n}",this.fragmentShader="void main() {\n\tgl_FragColor = vec4( 1.0, 0.0, 0.0, 1.0 );\n}",this.linewidth=1,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.lights=!1,this.clipping=!1,this.extensions={derivatives:!1,fragDepth:!1,drawBuffers:!1,shaderTextureLOD:!1},this.defaultAttributeValues={color:[1,1,1],uv:[0,0],uv2:[0,0]},this.index0AttributeName=void 0,this.uniformsNeedUpdate=!1,this.glslVersion=null,void 0!==t&&(void 0!==t.attributes&&console.error("THREE.ShaderMaterial: attributes should now be defined in THREE.BufferGeometry instead."),this.setValues(t))}copy(t){return super.copy(t),this.fragmentShader=t.fragmentShader,this.vertexShader=t.vertexShader,this.uniforms=V1t(t.uniforms),this.defines=Object.assign({},t.defines),this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.lights=t.lights,this.clipping=t.clipping,this.extensions=Object.assign({},t.extensions),this.glslVersion=t.glslVersion,this}toJSON(t){const e=super.toJSON(t);e.glslVersion=this.glslVersion,e.uniforms={};for(const n in this.uniforms){const i=this.uniforms[n].value;e.uniforms[n]=i&&i.isTexture?{type:"t",value:i.toJSON(t).uuid}:i&&i.isColor?{type:"c",value:i.getHex()}:i&&i.isVector2?{type:"v2",value:i.toArray()}:i&&i.isVector3?{type:"v3",value:i.toArray()}:i&&i.isVector4?{type:"v4",value:i.toArray()}:i&&i.isMatrix3?{type:"m3",value:i.toArray()}:i&&i.isMatrix4?{type:"m4",value:i.toArray()}:{value:i}}Object.keys(this.defines).length>0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}G1t.prototype.isShaderMaterial=!0;class W1t extends kQt{constructor(){super(),this.type="Camera",this.matrixWorldInverse=new rQt,this.projectionMatrix=new rQt,this.projectionMatrixInverse=new rQt}copy(t,e){return super.copy(t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()}updateMatrixWorld(t){super.updateMatrixWorld(t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(t,e){super.updateWorldMatrix(t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return(new this.constructor).copy(this)}}W1t.prototype.isCamera=!0;class q1t extends W1t{constructor(t=50,e=1,n=.1,i=2e3){super(),this.type="PerspectiveCamera",this.fov=t,this.zoom=1,this.near=n,this.far=i,this.focus=10,this.aspect=e,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.fov=t.fov,this.zoom=t.zoom,this.near=t.near,this.far=t.far,this.focus=t.focus,this.aspect=t.aspect,this.view=null===t.view?null:Object.assign({},t.view),this.filmGauge=t.filmGauge,this.filmOffset=t.filmOffset,this}setFocalLength(t){const e=.5*this.getFilmHeight()/t;this.fov=2*YZt*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*qZt*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*YZt*Math.atan(Math.tan(.5*qZt*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}setViewOffset(t,e,n,i,r,o){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=o,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*qZt*this.fov)/this.zoom,n=2*e,i=this.aspect*n,r=-.5*i;const o=this.view;if(null!==this.view&&this.view.enabled){const t=o.fullWidth,a=o.fullHeight;r+=o.offsetX*i/t,e-=o.offsetY*n/a,i*=o.width/t,n*=o.height/a}const a=this.filmOffset;0!==a&&(r+=t*a/this.getFilmWidth()),this.projectionMatrix.makePerspective(r,r+i,e,e-n,t,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.fov=this.fov,e.object.zoom=this.zoom,e.object.near=this.near,e.object.far=this.far,e.object.focus=this.focus,e.object.aspect=this.aspect,null!==this.view&&(e.object.view=Object.assign({},this.view)),e.object.filmGauge=this.filmGauge,e.object.filmOffset=this.filmOffset,e}}q1t.prototype.isPerspectiveCamera=!0;const Y1t=90;class X1t extends kQt{constructor(t,e,n){if(super(),this.type="CubeCamera",!0!==n.isWebGLCubeRenderTarget)return void console.error("THREE.CubeCamera: The constructor now expects an instance of WebGLCubeRenderTarget as third parameter.");this.renderTarget=n;const i=new q1t(Y1t,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new CJt(1,0,0)),this.add(i);const r=new q1t(Y1t,1,t,e);r.layers=this.layers,r.up.set(0,-1,0),r.lookAt(new CJt(-1,0,0)),this.add(r);const o=new q1t(Y1t,1,t,e);o.layers=this.layers,o.up.set(0,0,1),o.lookAt(new CJt(0,1,0)),this.add(o);const a=new q1t(Y1t,1,t,e);a.layers=this.layers,a.up.set(0,0,-1),a.lookAt(new CJt(0,-1,0)),this.add(a);const s=new q1t(Y1t,1,t,e);s.layers=this.layers,s.up.set(0,-1,0),s.lookAt(new CJt(0,0,1)),this.add(s);const l=new q1t(Y1t,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new CJt(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[i,r,o,a,s,l]=this.children,c=t.xr.enabled,u=t.getRenderTarget();t.xr.enabled=!1;const h=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,i),t.setRenderTarget(n,1),t.render(e,r),t.setRenderTarget(n,2),t.render(e,o),t.setRenderTarget(n,3),t.render(e,a),t.setRenderTarget(n,4),t.render(e,s),n.texture.generateMipmaps=h,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(u),t.xr.enabled=c}}class $1t extends bJt{constructor(t,e,n,i,r,o,a,s,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:jKt,n,i,r,o,a=void 0!==a?a:uZt,s,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}$1t.prototype.isCubeTexture=!0;class K1t extends SJt{constructor(t,e,n){Number.isInteger(e)&&(console.warn("THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )"),e=n),super(t,t,e),this.texture=new $1t(void 0,(e=e||{}).mapping,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.format,e.type,e.anisotropy,e.encoding),this.texture.isRenderTargetTexture=!0,this.texture.generateMipmaps=void 0!==e.generateMipmaps&&e.generateMipmaps,this.texture.minFilter=void 0!==e.minFilter?e.minFilter:eZt,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=hZt,this.texture.encoding=e.encoding,this.texture.generateMipmaps=e.generateMipmaps,this.texture.minFilter=e.minFilter,this.texture.magFilter=e.magFilter;const n=new F1t(5,5,5),i=new G1t({name:"CubemapFromEquirect",uniforms:V1t({tEquirect:{value:null}}),vertexShader:"\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\n\t\t\t\t\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n\n\t\t\t\t}\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvWorldDirection = transformDirection( position, modelMatrix );\n\n\t\t\t\t\t#include <begin_vertex>\n\t\t\t\t\t#include <project_vertex>\n\n\t\t\t\t}\n\t\t\t",fragmentShader:"\n\n\t\t\t\tuniform sampler2D tEquirect;\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\t#include <common>\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvec3 direction = normalize( vWorldDirection );\n\n\t\t\t\t\tvec2 sampleUV = equirectUv( direction );\n\n\t\t\t\t\tgl_FragColor = texture2D( tEquirect, sampleUV );\n\n\t\t\t\t}\n\t\t\t",side:1,blending:0});i.uniforms.tEquirect.value=e;const r=new B1t(n,i),o=e.minFilter;return e.minFilter===iZt&&(e.minFilter=eZt),new X1t(1,10,this).update(t,r),e.minFilter=o,r.geometry.dispose(),r.material.dispose(),this}clear(t,e,n,i){const r=t.getRenderTarget();for(let r=0;r<6;r++)t.setRenderTarget(this,r),t.clear(e,n,i);t.setRenderTarget(r)}}K1t.prototype.isWebGLCubeRenderTarget=!0;const Z1t=new CJt,J1t=new CJt,Q1t=new gJt;class t0t{constructor(t=new CJt(1,0,0),e=0){this.normal=t,this.constant=e}set(t,e){return this.normal.copy(t),this.constant=e,this}setComponents(t,e,n,i){return this.normal.set(t,e,n),this.constant=i,this}setFromNormalAndCoplanarPoint(t,e){return this.normal.copy(t),this.constant=-e.dot(this.normal),this}setFromCoplanarPoints(t,e,n){const i=Z1t.subVectors(n,e).cross(J1t.subVectors(t,e)).normalize();return this.setFromNormalAndCoplanarPoint(i,t),this}copy(t){return this.normal.copy(t.normal),this.constant=t.constant,this}normalize(){const t=1/this.normal.length();return this.normal.multiplyScalar(t),this.constant*=t,this}negate(){return this.constant*=-1,this.normal.negate(),this}distanceToPoint(t){return this.normal.dot(t)+this.constant}distanceToSphere(t){return this.distanceToPoint(t.center)-t.radius}projectPoint(t,e){return e.copy(this.normal).multiplyScalar(-this.distanceToPoint(t)).add(t)}intersectLine(t,e){const n=t.delta(Z1t),i=this.normal.dot(n);if(0===i)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const r=-(t.start.dot(this.normal)+this.constant)/i;return r<0||r>1?null:e.copy(n).multiplyScalar(r).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||Q1t.getNormalMatrix(t),i=this.coplanarPoint(Z1t).applyMatrix4(t),r=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(r),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}t0t.prototype.isPlane=!0;const e0t=new $Jt,n0t=new CJt;class i0t{constructor(t=new t0t,e=new t0t,n=new t0t,i=new t0t,r=new t0t,o=new t0t){this.planes=[t,e,n,i,r,o]}set(t,e,n,i,r,o){const a=this.planes;return a[0].copy(t),a[1].copy(e),a[2].copy(n),a[3].copy(i),a[4].copy(r),a[5].copy(o),this}copy(t){const e=this.planes;for(let n=0;n<6;n++)e[n].copy(t.planes[n]);return this}setFromProjectionMatrix(t){const e=this.planes,n=t.elements,i=n[0],r=n[1],o=n[2],a=n[3],s=n[4],l=n[5],c=n[6],u=n[7],h=n[8],d=n[9],p=n[10],f=n[11],m=n[12],g=n[13],_=n[14],y=n[15];return e[0].setComponents(a-i,u-s,f-h,y-m).normalize(),e[1].setComponents(a+i,u+s,f+h,y+m).normalize(),e[2].setComponents(a+r,u+l,f+d,y+g).normalize(),e[3].setComponents(a-r,u-l,f-d,y-g).normalize(),e[4].setComponents(a-o,u-c,f-p,y-_).normalize(),e[5].setComponents(a+o,u+c,f+p,y+_).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),e0t.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(e0t)}intersectsSprite(t){return e0t.center.set(0,0,0),e0t.radius=.7071067811865476,e0t.applyMatrix4(t.matrixWorld),this.intersectsSphere(e0t)}intersectsSphere(t){const e=this.planes,n=t.center,i=-t.radius;for(let t=0;t<6;t++)if(e[t].distanceToPoint(n)<i)return!1;return!0}intersectsBox(t){const e=this.planes;for(let n=0;n<6;n++){const i=e[n];if(n0t.x=i.normal.x>0?t.max.x:t.min.x,n0t.y=i.normal.y>0?t.max.y:t.min.y,n0t.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(n0t)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let n=0;n<6;n++)if(e[n].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}function r0t(){let t=null,e=!1,n=null,i=null;function r(e,o){n(e,o),i=t.requestAnimationFrame(r)}return{start:function(){!0!==e&&null!==n&&(i=t.requestAnimationFrame(r),e=!0)},stop:function(){t.cancelAnimationFrame(i),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function o0t(t,e){const n=e.isWebGL2,i=new WeakMap;return{get:function r(t){return t.isInterleavedBufferAttribute&&(t=t.data),i.get(t)},remove:function o(e){e.isInterleavedBufferAttribute&&(e=e.data);const n=i.get(e);n&&(t.deleteBuffer(n.buffer),i.delete(e))},update:function a(e,r){if(e.isGLBufferAttribute){const t=i.get(e);return void((!t||t.version<e.version)&&i.set(e,{buffer:e.buffer,type:e.type,bytesPerElement:e.elementSize,version:e.version}))}e.isInterleavedBufferAttribute&&(e=e.data);const o=i.get(e);void 0===o?i.set(e,(function a(e,i){const r=e.array,o=e.usage,a=t.createBuffer();t.bindBuffer(i,a),t.bufferData(i,r,o),e.onUploadCallback();let s=5126;return r instanceof Float32Array?s=5126:r instanceof Float64Array?console.warn("THREE.WebGLAttributes: Unsupported data buffer format: Float64Array."):r instanceof Uint16Array?e.isFloat16BufferAttribute?n?s=5131:console.warn("THREE.WebGLAttributes: Usage of Float16BufferAttribute requires WebGL2."):s=5123:r instanceof Int16Array?s=5122:r instanceof Uint32Array?s=5125:r instanceof Int32Array?s=5124:r instanceof Int8Array?s=5120:(r instanceof Uint8Array||r instanceof Uint8ClampedArray)&&(s=5121),{buffer:a,type:s,bytesPerElement:r.BYTES_PER_ELEMENT,version:e.version}})(e,r)):o.version<e.version&&((function s(e,i,r){const o=i.array,a=i.updateRange;t.bindBuffer(r,e),-1===a.count?t.bufferSubData(r,0,o):(n?t.bufferSubData(r,a.offset*o.BYTES_PER_ELEMENT,o,a.offset,a.count):t.bufferSubData(r,a.offset*o.BYTES_PER_ELEMENT,o.subarray(a.offset,a.offset+a.count)),a.count=-1)})(o.buffer,e,r),o.version=e.version)}}}class a0t extends b1t{constructor(t=1,e=1,n=1,i=1){super(),this.type="PlaneGeometry",this.parameters={width:t,height:e,widthSegments:n,heightSegments:i};const r=t/2,o=e/2,a=Math.floor(n),s=Math.floor(i),l=a+1,c=s+1,u=t/a,h=e/s,d=[],p=[],f=[],m=[];for(let t=0;t<c;t++){const e=t*h-o;for(let n=0;n<l;n++)p.push(n*u-r,-e,0),f.push(0,0,1),m.push(n/a),m.push(1-t/s)}for(let t=0;t<s;t++)for(let e=0;e<a;e++){const n=e+l*(t+1),i=e+1+l*(t+1),r=e+1+l*t;d.push(e+l*t,n,r),d.push(n,i,r)}this.setIndex(d),this.setAttribute("position",new l1t(p,3)),this.setAttribute("normal",new l1t(f,3)),this.setAttribute("uv",new l1t(m,2))}static fromJSON(t){return new a0t(t.width,t.height,t.widthSegments,t.heightSegments)}}const s0t={alphamap_fragment:"#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\n#endif",alphamap_pars_fragment:"#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",alphatest_fragment:"#ifdef ALPHATEST\n\tif ( diffuseColor.a < ALPHATEST ) discard;\n#endif",aomap_fragment:"#ifdef USE_AOMAP\n\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\n\treflectedLight.indirectDiffuse *= ambientOcclusion;\n\t#if defined( USE_ENVMAP ) && defined( STANDARD )\n\t\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.specularRoughness );\n\t#endif\n#endif",aomap_pars_fragment:"#ifdef USE_AOMAP\n\tuniform sampler2D aoMap;\n\tuniform float aoMapIntensity;\n#endif",begin_vertex:"vec3 transformed = vec3( position );",beginnormal_vertex:"vec3 objectNormal = vec3( normal );\n#ifdef USE_TANGENT\n\tvec3 objectTangent = vec3( tangent.xyz );\n#endif",bsdfs:"vec2 integrateSpecularBRDF( const in float dotNV, const in float roughness ) {\n\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\n\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\n\tvec4 r = roughness * c0 + c1;\n\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\n\treturn vec2( -1.04, 1.04 ) * a004 + r.zw;\n}\nfloat punctualLightIntensityToIrradianceFactor( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\n#if defined ( PHYSICALLY_CORRECT_LIGHTS )\n\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\n\tif( cutoffDistance > 0.0 ) {\n\t\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\n\t}\n\treturn distanceFalloff;\n#else\n\tif( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\n\t\treturn pow( saturate( -lightDistance / cutoffDistance + 1.0 ), decayExponent );\n\t}\n\treturn 1.0;\n#endif\n}\nvec3 BRDF_Diffuse_Lambert( const in vec3 diffuseColor ) {\n\treturn RECIPROCAL_PI * diffuseColor;\n}\nvec3 F_Schlick( const in vec3 f0, const in vec3 f90, const in float dotVH ) {\n\tfloat fresnel = exp2( ( -5.55473 * dotVH - 6.98316 ) * dotVH );\n\treturn ( f90 - f0 ) * fresnel + f0;\n}\nvec3 F_Schlick_RoughnessDependent( const in vec3 F0, const in float dotNV, const in float roughness ) {\n\tfloat fresnel = exp2( ( -5.55473 * dotNV - 6.98316 ) * dotNV );\n\tvec3 Fr = max( vec3( 1.0 - roughness ), F0 ) - F0;\n\treturn Fr * fresnel + F0;\n}\nfloat G_GGX_Smith( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 = pow2( alpha );\n\tfloat gl = dotNL + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\tfloat gv = dotNV + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\treturn 1.0 / ( gl * gv );\n}\nfloat G_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 = pow2( alpha );\n\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\treturn 0.5 / max( gv + gl, EPSILON );\n}\nfloat D_GGX( const in float alpha, const in float dotNH ) {\n\tfloat a2 = pow2( alpha );\n\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;\n\treturn RECIPROCAL_PI * a2 / pow2( denom );\n}\nvec3 BRDF_Specular_GGX( const in IncidentLight incidentLight, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in vec3 f90, const in float roughness ) {\n\tfloat alpha = pow2( roughness );\n\tvec3 halfDir = normalize( incidentLight.direction + viewDir );\n\tfloat dotNL = saturate( dot( normal, incidentLight.direction ) );\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F = F_Schlick( f0, f90, dotLH );\n\tfloat G = G_GGX_SmithCorrelated( alpha, dotNL, dotNV );\n\tfloat D = D_GGX( alpha, dotNH );\n\treturn F * ( G * D );\n}\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\n\tconst float LUT_SIZE = 64.0;\n\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\n\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\n\tfloat dotNV = saturate( dot( N, V ) );\n\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\n\tuv = uv * LUT_SCALE + LUT_BIAS;\n\treturn uv;\n}\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\n\tfloat l = length( f );\n\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\n}\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\n\tfloat x = dot( v1, v2 );\n\tfloat y = abs( x );\n\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\n\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\n\tfloat v = a / b;\n\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\n\treturn cross( v1, v2 ) * theta_sintheta;\n}\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\n\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\n\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\n\tvec3 lightNormal = cross( v1, v2 );\n\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\n\tvec3 T1, T2;\n\tT1 = normalize( V - N * dot( V, N ) );\n\tT2 = - cross( N, T1 );\n\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\n\tvec3 coords[ 4 ];\n\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\n\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\n\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\n\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\n\tcoords[ 0 ] = normalize( coords[ 0 ] );\n\tcoords[ 1 ] = normalize( coords[ 1 ] );\n\tcoords[ 2 ] = normalize( coords[ 2 ] );\n\tcoords[ 3 ] = normalize( coords[ 3 ] );\n\tvec3 vectorFormFactor = vec3( 0.0 );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\n\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\n\treturn vec3( result );\n}\nvec3 BRDF_Specular_GGX_Environment( const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float roughness ) {\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\n\treturn specularColor * brdf.x + brdf.y;\n}\nvoid BRDF_Specular_Multiscattering_Environment( const in GeometricContext geometry, const in vec3 specularColor, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\n\tvec3 F = F_Schlick_RoughnessDependent( specularColor, dotNV, roughness );\n\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\n\tvec3 FssEss = F * brdf.x + brdf.y;\n\tfloat Ess = brdf.x + brdf.y;\n\tfloat Ems = 1.0 - Ess;\n\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619;\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\n\tsingleScatter += FssEss;\n\tmultiScatter += Fms * Ems;\n}\nfloat G_BlinnPhong_Implicit( ) {\n\treturn 0.25;\n}\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\n\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\n}\nvec3 BRDF_Specular_BlinnPhong( const in IncidentLight incidentLight, const in GeometricContext geometry, const in vec3 specularColor, const in float shininess ) {\n\tvec3 halfDir = normalize( incidentLight.direction + geometry.viewDir );\n\tfloat dotNH = saturate( dot( geometry.normal, halfDir ) );\n\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F = F_Schlick( specularColor, vec3( 1.0 ), dotLH );\n\tfloat G = G_BlinnPhong_Implicit( );\n\tfloat D = D_BlinnPhong( shininess, dotNH );\n\treturn F * ( G * D );\n}\nfloat GGXRoughnessToBlinnExponent( const in float ggxRoughness ) {\n\treturn ( 2.0 / pow2( ggxRoughness + 0.0001 ) - 2.0 );\n}\nfloat BlinnExponentToGGXRoughness( const in float blinnExponent ) {\n\treturn sqrt( 2.0 / ( blinnExponent + 2.0 ) );\n}\n#if defined( USE_SHEEN )\nfloat D_Charlie(float roughness, float NoH) {\n\tfloat invAlpha = 1.0 / roughness;\n\tfloat cos2h = NoH * NoH;\n\tfloat sin2h = max(1.0 - cos2h, 0.0078125);\treturn (2.0 + invAlpha) * pow(sin2h, invAlpha * 0.5) / (2.0 * PI);\n}\nfloat V_Neubelt(float NoV, float NoL) {\n\treturn saturate(1.0 / (4.0 * (NoL + NoV - NoL * NoV)));\n}\nvec3 BRDF_Specular_Sheen( const in float roughness, const in vec3 L, const in GeometricContext geometry, vec3 specularColor ) {\n\tvec3 N = geometry.normal;\n\tvec3 V = geometry.viewDir;\n\tvec3 H = normalize( V + L );\n\tfloat dotNH = saturate( dot( N, H ) );\n\treturn specularColor * D_Charlie( roughness, dotNH ) * V_Neubelt( dot(N, V), dot(N, L) );\n}\n#endif",bumpmap_pars_fragment:"#ifdef USE_BUMPMAP\n\tuniform sampler2D bumpMap;\n\tuniform float bumpScale;\n\tvec2 dHdxy_fwd() {\n\t\tvec2 dSTdx = dFdx( vUv );\n\t\tvec2 dSTdy = dFdy( vUv );\n\t\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\n\t\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\n\t\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\n\t\treturn vec2( dBx, dBy );\n\t}\n\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\n\t\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\n\t\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\n\t\tvec3 vN = surf_norm;\n\t\tvec3 R1 = cross( vSigmaY, vN );\n\t\tvec3 R2 = cross( vN, vSigmaX );\n\t\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\n\t\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\n\t\treturn normalize( abs( fDet ) * surf_norm - vGrad );\n\t}\n#endif",clipping_planes_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvec4 plane;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\n\t\tplane = clippingPlanes[ i ];\n\t\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\n\t}\n\t#pragma unroll_loop_end\n\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\n\t\tbool clipped = true;\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\n\t\t\tplane = clippingPlanes[ i ];\n\t\t\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t\tif ( clipped ) discard;\n\t#endif\n#endif",clipping_planes_pars_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\n#endif",clipping_planes_pars_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n#endif",clipping_planes_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvClipPosition = - mvPosition.xyz;\n#endif",color_fragment:"#if defined( USE_COLOR_ALPHA )\n\tdiffuseColor *= vColor;\n#elif defined( USE_COLOR )\n\tdiffuseColor.rgb *= vColor;\n#endif",color_pars_fragment:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR )\n\tvarying vec3 vColor;\n#endif",color_pars_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvarying vec3 vColor;\n#endif",color_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvColor = vec4( 1.0 );\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvColor = vec3( 1.0 );\n#endif\n#ifdef USE_COLOR\n\tvColor *= color;\n#endif\n#ifdef USE_INSTANCING_COLOR\n\tvColor.xyz *= instanceColor.xyz;\n#endif",common:"#define PI 3.141592653589793\n#define PI2 6.283185307179586\n#define PI_HALF 1.5707963267948966\n#define RECIPROCAL_PI 0.3183098861837907\n#define RECIPROCAL_PI2 0.15915494309189535\n#define EPSILON 1e-6\n#ifndef saturate\n#define saturate(a) clamp( a, 0.0, 1.0 )\n#endif\n#define whiteComplement(a) ( 1.0 - saturate( a ) )\nfloat pow2( const in float x ) { return x*x; }\nfloat pow3( const in float x ) { return x*x*x; }\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\nhighp float rand( const in vec2 uv ) {\n\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\n\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\n\treturn fract(sin(sn) * c);\n}\n#ifdef HIGH_PRECISION\n\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\n#else\n\tfloat max3( vec3 v ) { return max( max( v.x, v.y ), v.z ); }\n\tfloat precisionSafeLength( vec3 v ) {\n\t\tfloat maxComponent = max3( abs( v ) );\n\t\treturn length( v / maxComponent ) * maxComponent;\n\t}\n#endif\nstruct IncidentLight {\n\tvec3 color;\n\tvec3 direction;\n\tbool visible;\n};\nstruct ReflectedLight {\n\tvec3 directDiffuse;\n\tvec3 directSpecular;\n\tvec3 indirectDiffuse;\n\tvec3 indirectSpecular;\n};\nstruct GeometricContext {\n\tvec3 position;\n\tvec3 normal;\n\tvec3 viewDir;\n#ifdef CLEARCOAT\n\tvec3 clearcoatNormal;\n#endif\n};\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n}\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\n}\nvec3 projectOnPlane(in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\tfloat distance = dot( planeNormal, point - pointOnPlane );\n\treturn - distance * planeNormal + point;\n}\nfloat sideOfPlane( in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn sign( dot( point - pointOnPlane, planeNormal ) );\n}\nvec3 linePlaneIntersect( in vec3 pointOnLine, in vec3 lineDirection, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn lineDirection * ( dot( planeNormal, pointOnPlane - pointOnLine ) / dot( planeNormal, lineDirection ) ) + pointOnLine;\n}\nmat3 transposeMat3( const in mat3 m ) {\n\tmat3 tmp;\n\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\n\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\n\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\n\treturn tmp;\n}\nfloat linearToRelativeLuminance( const in vec3 color ) {\n\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\n\treturn dot( weights, color.rgb );\n}\nbool isPerspectiveMatrix( mat4 m ) {\n\treturn m[ 2 ][ 3 ] == - 1.0;\n}\nvec2 equirectUv( in vec3 dir ) {\n\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\n\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\treturn vec2( u, v );\n}",cube_uv_reflection_fragment:"#ifdef ENVMAP_TYPE_CUBE_UV\n\t#define cubeUV_maxMipLevel 8.0\n\t#define cubeUV_minMipLevel 4.0\n\t#define cubeUV_maxTileSize 256.0\n\t#define cubeUV_minTileSize 16.0\n\tfloat getFace( vec3 direction ) {\n\t\tvec3 absDirection = abs( direction );\n\t\tfloat face = - 1.0;\n\t\tif ( absDirection.x > absDirection.z ) {\n\t\t\tif ( absDirection.x > absDirection.y )\n\t\t\t\tface = direction.x > 0.0 ? 0.0 : 3.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t} else {\n\t\t\tif ( absDirection.z > absDirection.y )\n\t\t\t\tface = direction.z > 0.0 ? 2.0 : 5.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t}\n\t\treturn face;\n\t}\n\tvec2 getUV( vec3 direction, float face ) {\n\t\tvec2 uv;\n\t\tif ( face == 0.0 ) {\n\t\t\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 1.0 ) {\n\t\t\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\n\t\t} else if ( face == 2.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\n\t\t} else if ( face == 3.0 ) {\n\t\t\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 4.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\n\t\t} else {\n\t\t\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\n\t\t}\n\t\treturn 0.5 * ( uv + 1.0 );\n\t}\n\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\n\t\tfloat face = getFace( direction );\n\t\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\n\t\tmipInt = max( mipInt, cubeUV_minMipLevel );\n\t\tfloat faceSize = exp2( mipInt );\n\t\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\n\t\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\n\t\tvec2 f = fract( uv );\n\t\tuv += 0.5 - f;\n\t\tif ( face > 2.0 ) {\n\t\t\tuv.y += faceSize;\n\t\t\tface -= 3.0;\n\t\t}\n\t\tuv.x += face * faceSize;\n\t\tif ( mipInt < cubeUV_maxMipLevel ) {\n\t\t\tuv.y += 2.0 * cubeUV_maxTileSize;\n\t\t}\n\t\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\n\t\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\n\t\tuv *= texelSize;\n\t\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.x += texelSize;\n\t\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.y += texelSize;\n\t\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.x -= texelSize;\n\t\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tvec3 tm = mix( tl, tr, f.x );\n\t\tvec3 bm = mix( bl, br, f.x );\n\t\treturn mix( tm, bm, f.y );\n\t}\n\t#define r0 1.0\n\t#define v0 0.339\n\t#define m0 - 2.0\n\t#define r1 0.8\n\t#define v1 0.276\n\t#define m1 - 1.0\n\t#define r4 0.4\n\t#define v4 0.046\n\t#define m4 2.0\n\t#define r5 0.305\n\t#define v5 0.016\n\t#define m5 3.0\n\t#define r6 0.21\n\t#define v6 0.0038\n\t#define m6 4.0\n\tfloat roughnessToMip( float roughness ) {\n\t\tfloat mip = 0.0;\n\t\tif ( roughness >= r1 ) {\n\t\t\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\n\t\t} else if ( roughness >= r4 ) {\n\t\t\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\n\t\t} else if ( roughness >= r5 ) {\n\t\t\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\n\t\t} else if ( roughness >= r6 ) {\n\t\t\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\n\t\t} else {\n\t\t\tmip = - 2.0 * log2( 1.16 * roughness );\t\t}\n\t\treturn mip;\n\t}\n\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\n\t\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\n\t\tfloat mipF = fract( mip );\n\t\tfloat mipInt = floor( mip );\n\t\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\n\t\tif ( mipF == 0.0 ) {\n\t\t\treturn vec4( color0, 1.0 );\n\t\t} else {\n\t\t\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\n\t\t\treturn vec4( mix( color0, color1, mipF ), 1.0 );\n\t\t}\n\t}\n#endif",defaultnormal_vertex:"vec3 transformedNormal = objectNormal;\n#ifdef USE_INSTANCING\n\tmat3 m = mat3( instanceMatrix );\n\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\n\ttransformedNormal = m * transformedNormal;\n#endif\ntransformedNormal = normalMatrix * transformedNormal;\n#ifdef FLIP_SIDED\n\ttransformedNormal = - transformedNormal;\n#endif\n#ifdef USE_TANGENT\n\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#ifdef FLIP_SIDED\n\t\ttransformedTangent = - transformedTangent;\n\t#endif\n#endif",displacementmap_pars_vertex:"#ifdef USE_DISPLACEMENTMAP\n\tuniform sampler2D displacementMap;\n\tuniform float displacementScale;\n\tuniform float displacementBias;\n#endif",displacementmap_vertex:"#ifdef USE_DISPLACEMENTMAP\n\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\n#endif",emissivemap_fragment:"#ifdef USE_EMISSIVEMAP\n\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\n\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\n\ttotalEmissiveRadiance *= emissiveColor.rgb;\n#endif",emissivemap_pars_fragment:"#ifdef USE_EMISSIVEMAP\n\tuniform sampler2D emissiveMap;\n#endif",encodings_fragment:"gl_FragColor = linearToOutputTexel( gl_FragColor );",encodings_pars_fragment:"\nvec4 LinearToLinear( in vec4 value ) {\n\treturn value;\n}\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\n}\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\n}\nvec4 sRGBToLinear( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\n}\nvec4 LinearTosRGB( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\n}\nvec4 RGBEToLinear( in vec4 value ) {\n\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\n}\nvec4 LinearToRGBE( in vec4 value ) {\n\tfloat maxComponent = max( max( value.r, value.g ), value.b );\n\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\n\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\n}\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\n}\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\n\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\n\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\n\tM = ceil( M * 255.0 ) / 255.0;\n\treturn vec4( value.rgb / ( M * maxRange ), M );\n}\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\n}\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\n\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\n\tfloat D = max( maxRange / maxRGB, 1.0 );\n\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\n\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\n}\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\nvec4 LinearToLogLuv( in vec4 value ) {\n\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\n\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\n\tvec4 vResult;\n\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\n\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\n\tvResult.w = fract( Le );\n\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\n\treturn vResult;\n}\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\nvec4 LogLuvToLinear( in vec4 value ) {\n\tfloat Le = value.z * 255.0 + value.w;\n\tvec3 Xp_Y_XYZp;\n\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\n\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\n\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\n\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\n\treturn vec4( max( vRGB, 0.0 ), 1.0 );\n}",envmap_fragment:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvec3 cameraToFrag;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\n\t\t#endif\n\t#else\n\t\tvec3 reflectVec = vReflect;\n\t#endif\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\n\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\n\t#else\n\t\tvec4 envColor = vec4( 0.0 );\n\t#endif\n\t#ifndef ENVMAP_TYPE_CUBE_UV\n\t\tenvColor = envMapTexelToLinear( envColor );\n\t#endif\n\t#ifdef ENVMAP_BLENDING_MULTIPLY\n\t\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_MIX )\n\t\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_ADD )\n\t\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\n\t#endif\n#endif",envmap_common_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float envMapIntensity;\n\tuniform float flipEnvMap;\n\tuniform int maxMipLevel;\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tuniform samplerCube envMap;\n\t#else\n\t\tuniform sampler2D envMap;\n\t#endif\n\t\n#endif",envmap_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float reflectivity;\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\tvarying vec3 vWorldPosition;\n\t\tuniform float refractionRatio;\n\t#else\n\t\tvarying vec3 vReflect;\n\t#endif\n#endif",envmap_pars_vertex:"#ifdef USE_ENVMAP\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\t\n\t\tvarying vec3 vWorldPosition;\n\t#else\n\t\tvarying vec3 vReflect;\n\t\tuniform float refractionRatio;\n\t#endif\n#endif",envmap_physical_pars_fragment:"#if defined( USE_ENVMAP )\n\t#ifdef ENVMAP_MODE_REFRACTION\n\t\tuniform float refractionRatio;\n\t#endif\n\tvec3 getLightProbeIndirectIrradiance( const in GeometricContext geometry, const in int maxMIPLevel ) {\n\t\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryVec = vec3( flipEnvMap * worldNormal.x, worldNormal.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor = textureCubeLodEXT( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor = textureCube( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\n\t\t#else\n\t\t\tvec4 envMapColor = vec4( 0.0 );\n\t\t#endif\n\t\treturn PI * envMapColor.rgb * envMapIntensity;\n\t}\n\tfloat getSpecularMIPLevel( const in float roughness, const in int maxMIPLevel ) {\n\t\tfloat maxMIPLevelScalar = float( maxMIPLevel );\n\t\tfloat sigma = PI * roughness * roughness / ( 1.0 + roughness );\n\t\tfloat desiredMIPLevel = maxMIPLevelScalar + log2( sigma );\n\t\treturn clamp( desiredMIPLevel, 0.0, maxMIPLevelScalar );\n\t}\n\tvec3 getLightProbeIndirectRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness, const in int maxMIPLevel ) {\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( -viewDir, normal );\n\t\t\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( -viewDir, normal, refractionRatio );\n\t\t#endif\n\t\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\n\t\tfloat specularMIPLevel = getSpecularMIPLevel( roughness, maxMIPLevel );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryReflectVec = vec3( flipEnvMap * reflectVec.x, reflectVec.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor = textureCubeLodEXT( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor = textureCube( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\n\t\t#endif\n\t\treturn envMapColor.rgb * envMapIntensity;\n\t}\n#endif",envmap_vertex:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvWorldPosition = worldPosition.xyz;\n\t#else\n\t\tvec3 cameraToVertex;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvReflect = reflect( cameraToVertex, worldNormal );\n\t\t#else\n\t\t\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\n\t\t#endif\n\t#endif\n#endif",fog_vertex:"#ifdef USE_FOG\n\tfogDepth = - mvPosition.z;\n#endif",fog_pars_vertex:"#ifdef USE_FOG\n\tvarying float fogDepth;\n#endif",fog_fragment:"#ifdef USE_FOG\n\t#ifdef FOG_EXP2\n\t\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * fogDepth * fogDepth );\n\t#else\n\t\tfloat fogFactor = smoothstep( fogNear, fogFar, fogDepth );\n\t#endif\n\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\n#endif",fog_pars_fragment:"#ifdef USE_FOG\n\tuniform vec3 fogColor;\n\tvarying float fogDepth;\n\t#ifdef FOG_EXP2\n\t\tuniform float fogDensity;\n\t#else\n\t\tuniform float fogNear;\n\t\tuniform float fogFar;\n\t#endif\n#endif",gradientmap_pars_fragment:"#ifdef USE_GRADIENTMAP\n\tuniform sampler2D gradientMap;\n#endif\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\n\tfloat dotNL = dot( normal, lightDirection );\n\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\n\t#ifdef USE_GRADIENTMAP\n\t\treturn texture2D( gradientMap, coord ).rgb;\n\t#else\n\t\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\n\t#endif\n}",lightmap_fragment:"#ifdef USE_LIGHTMAP\n\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\treflectedLight.indirectDiffuse += PI * lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n#endif",lightmap_pars_fragment:"#ifdef USE_LIGHTMAP\n\tuniform sampler2D lightMap;\n\tuniform float lightMapIntensity;\n#endif",lights_lambert_vertex:"vec3 diffuse = vec3( 1.0 );\nGeometricContext geometry;\ngeometry.position = mvPosition.xyz;\ngeometry.normal = normalize( transformedNormal );\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\nGeometricContext backGeometry;\nbackGeometry.position = geometry.position;\nbackGeometry.normal = -geometry.normal;\nbackGeometry.viewDir = geometry.viewDir;\nvLightFront = vec3( 0.0 );\nvIndirectFront = vec3( 0.0 );\n#ifdef DOUBLE_SIDED\n\tvLightBack = vec3( 0.0 );\n\tvIndirectBack = vec3( 0.0 );\n#endif\nIncidentLight directLight;\nfloat dotNL;\nvec3 directLightColor_Diffuse;\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry );\n#ifdef DOUBLE_SIDED\n\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\n\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry );\n#endif\n#if NUM_POINT_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tgetPointDirectLightIrradiance( pointLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tgetSpotDirectLightIrradiance( spotLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_DIR_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tgetDirectionalDirectLightIrradiance( directionalLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry );\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif",lights_pars_begin:"uniform bool receiveShadow;\nuniform vec3 ambientLightColor;\nuniform vec3 lightProbe[ 9 ];\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\n\tfloat x = normal.x, y = normal.y, z = normal.z;\n\tvec3 result = shCoefficients[ 0 ] * 0.886227;\n\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\n\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\n\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\n\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\n\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\n\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\n\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\n\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\n\treturn result;\n}\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in GeometricContext geometry ) {\n\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\n\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\n\treturn irradiance;\n}\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\n\tvec3 irradiance = ambientLightColor;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treturn irradiance;\n}\n#if NUM_DIR_LIGHTS > 0\n\tstruct DirectionalLight {\n\t\tvec3 direction;\n\t\tvec3 color;\n\t};\n\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n\tvoid getDirectionalDirectLightIrradiance( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tdirectLight.color = directionalLight.color;\n\t\tdirectLight.direction = directionalLight.direction;\n\t\tdirectLight.visible = true;\n\t}\n#endif\n#if NUM_POINT_LIGHTS > 0\n\tstruct PointLight {\n\t\tvec3 position;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t};\n\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n\tvoid getPointDirectLightIrradiance( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tvec3 lVector = pointLight.position - geometry.position;\n\t\tdirectLight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tdirectLight.color = pointLight.color;\n\t\tdirectLight.color *= punctualLightIntensityToIrradianceFactor( lightDistance, pointLight.distance, pointLight.decay );\n\t\tdirectLight.visible = ( directLight.color != vec3( 0.0 ) );\n\t}\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\tstruct SpotLight {\n\t\tvec3 position;\n\t\tvec3 direction;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t\tfloat coneCos;\n\t\tfloat penumbraCos;\n\t};\n\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\n\tvoid getSpotDirectLightIrradiance( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tvec3 lVector = spotLight.position - geometry.position;\n\t\tdirectLight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tfloat angleCos = dot( directLight.direction, spotLight.direction );\n\t\tif ( angleCos > spotLight.coneCos ) {\n\t\t\tfloat spotEffect = smoothstep( spotLight.coneCos, spotLight.penumbraCos, angleCos );\n\t\t\tdirectLight.color = spotLight.color;\n\t\t\tdirectLight.color *= spotEffect * punctualLightIntensityToIrradianceFactor( lightDistance, spotLight.distance, spotLight.decay );\n\t\t\tdirectLight.visible = true;\n\t\t} else {\n\t\t\tdirectLight.color = vec3( 0.0 );\n\t\t\tdirectLight.visible = false;\n\t\t}\n\t}\n#endif\n#if NUM_RECT_AREA_LIGHTS > 0\n\tstruct RectAreaLight {\n\t\tvec3 color;\n\t\tvec3 position;\n\t\tvec3 halfWidth;\n\t\tvec3 halfHeight;\n\t};\n\tuniform sampler2D ltc_1;\tuniform sampler2D ltc_2;\n\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\tstruct HemisphereLight {\n\t\tvec3 direction;\n\t\tvec3 skyColor;\n\t\tvec3 groundColor;\n\t};\n\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\n\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in GeometricContext geometry ) {\n\t\tfloat dotNL = dot( geometry.normal, hemiLight.direction );\n\t\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\n\t\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tirradiance *= PI;\n\t\t#endif\n\t\treturn irradiance;\n\t}\n#endif",lights_toon_fragment:"ToonMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;",lights_toon_pars_fragment:"varying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\nstruct ToonMaterial {\n\tvec3 diffuseColor;\n};\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_Toon\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Toon\n#define Material_LightProbeLOD( material )\t(0)",lights_phong_fragment:"BlinnPhongMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;\nmaterial.specularColor = specular;\nmaterial.specularShininess = shininess;\nmaterial.specularStrength = specularStrength;",lights_phong_pars_fragment:"varying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\nstruct BlinnPhongMaterial {\n\tvec3 diffuseColor;\n\tvec3 specularColor;\n\tfloat specularShininess;\n\tfloat specularStrength;\n};\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n\treflectedLight.directSpecular += irradiance * BRDF_Specular_BlinnPhong( directLight, geometry, material.specularColor, material.specularShininess ) * material.specularStrength;\n}\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_BlinnPhong\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_BlinnPhong\n#define Material_LightProbeLOD( material )\t(0)",lights_physical_fragment:"PhysicalMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\nmaterial.specularRoughness = max( roughnessFactor, 0.0525 );material.specularRoughness += geometryRoughness;\nmaterial.specularRoughness = min( material.specularRoughness, 1.0 );\n#ifdef REFLECTIVITY\n\t#ifdef SPECULAR\n\t\tvec3 specularIntensityFactor = vec3( specularIntensity );\n\t\tvec3 specularTintFactor = specularTint;\n\t\t#ifdef USE_SPECULARINTENSITYMAP\n\t\t\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\n\t\t#endif\n\t\t#ifdef USE_SPECULARTINTMAP\n\t\t\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\n\t\t#endif\n\t\tmaterial.specularColorF90 = mix( specularIntensityFactor, vec3( 1.0 ), metalnessFactor );\n\t#else\n\t\tvec3 specularIntensityFactor = vec3( 1.0 );\n\t\tvec3 specularTintFactor = vec3( 1.0 );\n\t\tmaterial.specularColorF90 = vec3( 1.0 );\n\t#endif\n\tmaterial.specularColor = mix( min( vec3( MAXIMUM_SPECULAR_COEFFICIENT * pow2( reflectivity ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\n#else\n\tmaterial.specularColor = mix( vec3( DEFAULT_SPECULAR_COEFFICIENT ), diffuseColor.rgb, metalnessFactor );\n\tmaterial.specularColorF90 = vec3( 1.0 );\n#endif\n#ifdef CLEARCOAT\n\tmaterial.clearcoat = clearcoat;\n\tmaterial.clearcoatRoughness = clearcoatRoughness;\n\t#ifdef USE_CLEARCOATMAP\n\t\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\n\t#endif\n\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\t\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\n\t#endif\n\tmaterial.clearcoat = saturate( material.clearcoat );\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\n\tmaterial.clearcoatRoughness += geometryRoughness;\n\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\n#endif\n#ifdef USE_SHEEN\n\tmaterial.sheenColor = sheen;\n#endif",lights_physical_pars_fragment:"struct PhysicalMaterial {\n\tvec3 diffuseColor;\n\tfloat specularRoughness;\n\tvec3 specularColor;\n\tvec3 specularColorF90;\n#ifdef CLEARCOAT\n\tfloat clearcoat;\n\tfloat clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tvec3 sheenColor;\n#endif\n};\n#define MAXIMUM_SPECULAR_COEFFICIENT 0.16\n#define DEFAULT_SPECULAR_COEFFICIENT 0.04\nfloat clearcoatDHRApprox( const in float roughness, const in float dotNL ) {\n\treturn DEFAULT_SPECULAR_COEFFICIENT + ( 1.0 - DEFAULT_SPECULAR_COEFFICIENT ) * ( pow( 1.0 - dotNL, 5.0 ) * pow( 1.0 - roughness, 2.0 ) );\n}\n#if NUM_RECT_AREA_LIGHTS > 0\n\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\t\tvec3 normal = geometry.normal;\n\t\tvec3 viewDir = geometry.viewDir;\n\t\tvec3 position = geometry.position;\n\t\tvec3 lightPos = rectAreaLight.position;\n\t\tvec3 halfWidth = rectAreaLight.halfWidth;\n\t\tvec3 halfHeight = rectAreaLight.halfHeight;\n\t\tvec3 lightColor = rectAreaLight.color;\n\t\tfloat roughness = material.specularRoughness;\n\t\tvec3 rectCoords[ 4 ];\n\t\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\t\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\n\t\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\n\t\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\n\t\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\n\t\tvec4 t1 = texture2D( ltc_1, uv );\n\t\tvec4 t2 = texture2D( ltc_2, uv );\n\t\tmat3 mInv = mat3(\n\t\t\tvec3( t1.x, 0, t1.y ),\n\t\t\tvec3(    0, 1,    0 ),\n\t\t\tvec3( t1.z, 0, t1.w )\n\t\t);\n\t\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\n\t\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\n\t\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\n\t}\n#endif\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNL = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\n\t\tvec3 ccIrradiance = ccDotNL * directLight.color;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tccIrradiance *= PI;\n\t\t#endif\n\t\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t\treflectedLight.directSpecular += ccIrradiance * material.clearcoat * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), vec3( 1.0 ), material.clearcoatRoughness );\n\t#else\n\t\tfloat clearcoatDHR = 0.0;\n\t#endif\n\t#ifdef USE_SHEEN\n\t\treflectedLight.directSpecular += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_Sheen(\n\t\t\tmaterial.specularRoughness,\n\t\t\tdirectLight.direction,\n\t\t\tgeometry,\n\t\t\tmaterial.sheenColor\n\t\t);\n\t#else\n\t\treflectedLight.directSpecular += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.normal, material.specularColor, material.specularColorF90, material.specularRoughness);\n\t#endif\n\treflectedLight.directDiffuse += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNV = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular += clearcoatRadiance * material.clearcoat * BRDF_Specular_GGX_Environment( geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearcoatRoughness );\n\t\tfloat ccDotNL = ccDotNV;\n\t\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t#else\n\t\tfloat clearcoatDHR = 0.0;\n\t#endif\n\tfloat clearcoatInv = 1.0 - clearcoatDHR;\n\tvec3 singleScattering = vec3( 0.0 );\n\tvec3 multiScattering = vec3( 0.0 );\n\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\n\tBRDF_Specular_Multiscattering_Environment( geometry, material.specularColor, material.specularRoughness, singleScattering, multiScattering );\n\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\n\treflectedLight.indirectSpecular += clearcoatInv * radiance * singleScattering;\n\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\n\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\n}\n#define RE_Direct\t\t\t\tRE_Direct_Physical\n#define RE_Direct_RectArea\t\tRE_Direct_RectArea_Physical\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Physical\n#define RE_IndirectSpecular\t\tRE_IndirectSpecular_Physical\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\n\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\n}",lights_fragment_begin:"\nGeometricContext geometry;\ngeometry.position = - vViewPosition;\ngeometry.normal = normal;\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\n#ifdef CLEARCOAT\n\tgeometry.clearcoatNormal = clearcoatNormal;\n#endif\nIncidentLight directLight;\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\n\tPointLight pointLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tpointLight = pointLights[ i ];\n\t\tgetPointDirectLightIrradiance( pointLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\n\t\tpointLightShadow = pointLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\n\tSpotLight spotLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tspotLight = spotLights[ i ];\n\t\tgetSpotDirectLightIrradiance( spotLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\tspotLightShadow = spotLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\n\tDirectionalLight directionalLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tdirectionalLight = directionalLights[ i ];\n\t\tgetDirectionalDirectLightIrradiance( directionalLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\n\t\tdirectionalLightShadow = directionalLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\n\tRectAreaLight rectAreaLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\n\t\trectAreaLight = rectAreaLights[ i ];\n\t\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if defined( RE_IndirectDiffuse )\n\tvec3 iblIrradiance = vec3( 0.0 );\n\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\n\tirradiance += getLightProbeIrradiance( lightProbe, geometry );\n\t#if ( NUM_HEMI_LIGHTS > 0 )\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\t\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n#endif\n#if defined( RE_IndirectSpecular )\n\tvec3 radiance = vec3( 0.0 );\n\tvec3 clearcoatRadiance = vec3( 0.0 );\n#endif",lights_fragment_maps:"#if defined( RE_IndirectDiffuse )\n\t#ifdef USE_LIGHTMAP\n\t\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\t\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tlightMapIrradiance *= PI;\n\t\t#endif\n\t\tirradiance += lightMapIrradiance;\n\t#endif\n\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\n\t\tiblIrradiance += getLightProbeIndirectIrradiance( geometry, maxMipLevel );\n\t#endif\n#endif\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\n\tradiance += getLightProbeIndirectRadiance( geometry.viewDir, geometry.normal, material.specularRoughness, maxMipLevel );\n\t#ifdef CLEARCOAT\n\t\tclearcoatRadiance += getLightProbeIndirectRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness, maxMipLevel );\n\t#endif\n#endif",lights_fragment_end:"#if defined( RE_IndirectDiffuse )\n\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\n#endif\n#if defined( RE_IndirectSpecular )\n\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\n#endif",logdepthbuf_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\n#endif",logdepthbuf_pars_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tuniform float logDepthBufFC;\n\tvarying float vFragDepth;\n\tvarying float vIsPerspective;\n#endif",logdepthbuf_pars_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvarying float vFragDepth;\n\t\tvarying float vIsPerspective;\n\t#else\n\t\tuniform float logDepthBufFC;\n\t#endif\n#endif",logdepthbuf_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvFragDepth = 1.0 + gl_Position.w;\n\t\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\n\t#else\n\t\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\n\t\t\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\n\t\t\tgl_Position.z *= gl_Position.w;\n\t\t}\n\t#endif\n#endif",map_fragment:"#ifdef USE_MAP\n\tvec4 texelColor = texture2D( map, vUv );\n\ttexelColor = mapTexelToLinear( texelColor );\n\tdiffuseColor *= texelColor;\n#endif",map_pars_fragment:"#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif",map_particle_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\n#endif\n#ifdef USE_MAP\n\tvec4 mapTexel = texture2D( map, uv );\n\tdiffuseColor *= mapTexelToLinear( mapTexel );\n#endif\n#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\n#endif",map_particle_pars_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tuniform mat3 uvTransform;\n#endif\n#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif\n#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",metalnessmap_fragment:"float metalnessFactor = metalness;\n#ifdef USE_METALNESSMAP\n\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\n\tmetalnessFactor *= texelMetalness.b;\n#endif",metalnessmap_pars_fragment:"#ifdef USE_METALNESSMAP\n\tuniform sampler2D metalnessMap;\n#endif",morphnormal_vertex:"#ifdef USE_MORPHNORMALS\n\tobjectNormal *= morphTargetBaseInfluence;\n\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\n\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\n\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\n\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\n#endif",morphtarget_pars_vertex:"#ifdef USE_MORPHTARGETS\n\tuniform float morphTargetBaseInfluence;\n\t#ifndef USE_MORPHNORMALS\n\t\tuniform float morphTargetInfluences[ 8 ];\n\t#else\n\t\tuniform float morphTargetInfluences[ 4 ];\n\t#endif\n#endif",morphtarget_vertex:"#ifdef USE_MORPHTARGETS\n\ttransformed *= morphTargetBaseInfluence;\n\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\n\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\n\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\n\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\n\t#ifndef USE_MORPHNORMALS\n\t\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\n\t\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\n\t\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\n\t\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\n\t#endif\n#endif",normal_fragment_begin:"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\n#ifdef FLAT_SHADED\n\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\n\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\n\tvec3 normal = normalize( cross( fdx, fdy ) );\n#else\n\tvec3 normal = normalize( vNormal );\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\t#ifdef USE_TANGENT\n\t\tvec3 tangent = normalize( vTangent );\n\t\tvec3 bitangent = normalize( vBitangent );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\ttangent = tangent * faceDirection;\n\t\t\tbitangent = bitangent * faceDirection;\n\t\t#endif\n\t\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\n\t\t\tmat3 vTBN = mat3( tangent, bitangent, normal );\n\t\t#endif\n\t#endif\n#endif\nvec3 geometryNormal = normal;",normal_fragment_maps:"#ifdef OBJECTSPACE_NORMALMAP\n\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t#ifdef FLIP_SIDED\n\t\tnormal = - normal;\n\t#endif\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\tnormal = normalize( normalMatrix * normal );\n#elif defined( TANGENTSPACE_NORMALMAP )\n\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\tmapN.xy *= normalScale;\n\t#ifdef USE_TANGENT\n\t\tnormal = normalize( vTBN * mapN );\n\t#else\n\t\tnormal = perturbNormal2Arb( -vViewPosition, normal, mapN, faceDirection );\n\t#endif\n#elif defined( USE_BUMPMAP )\n\tnormal = perturbNormalArb( -vViewPosition, normal, dHdxy_fwd(), faceDirection );\n#endif",normalmap_pars_fragment:"#ifdef USE_NORMALMAP\n\tuniform sampler2D normalMap;\n\tuniform vec2 normalScale;\n#endif\n#ifdef OBJECTSPACE_NORMALMAP\n\tuniform mat3 normalMatrix;\n#endif\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\n\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\n\t\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\n\t\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\n\t\tvec2 st0 = dFdx( vUv.st );\n\t\tvec2 st1 = dFdy( vUv.st );\n\t\tvec3 N = surf_norm;\n\t\tvec3 q1perp = cross( q1, N );\n\t\tvec3 q0perp = cross( N, q0 );\n\t\tvec3 T = q1perp * st0.x + q0perp * st1.x;\n\t\tvec3 B = q1perp * st0.y + q0perp * st1.y;\n\t\tfloat det = max( dot( T, T ), dot( B, B ) );\n\t\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\n\t\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\n\t}\n#endif",clearcoat_normal_fragment_begin:"#ifdef CLEARCOAT\n\tvec3 clearcoatNormal = geometryNormal;\n#endif",clearcoat_normal_fragment_maps:"#ifdef USE_CLEARCOAT_NORMALMAP\n\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\n\tclearcoatMapN.xy *= clearcoatNormalScale;\n\t#ifdef USE_TANGENT\n\t\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\n\t#else\n\t\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\n\t#endif\n#endif",clearcoat_pars_fragment:"#ifdef USE_CLEARCOATMAP\n\tuniform sampler2D clearcoatMap;\n#endif\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\tuniform sampler2D clearcoatRoughnessMap;\n#endif\n#ifdef USE_CLEARCOAT_NORMALMAP\n\tuniform sampler2D clearcoatNormalMap;\n\tuniform vec2 clearcoatNormalScale;\n#endif",packing:"vec3 packNormalToRGB( const in vec3 normal ) {\n\treturn normalize( normal ) * 0.5 + 0.5;\n}\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\n\treturn 2.0 * rgb.xyz - 1.0;\n}\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\nconst float ShiftRight8 = 1. / 256.;\nvec4 packDepthToRGBA( const in float v ) {\n\tvec4 r = vec4( fract( v * PackFactors ), v );\n\tr.yzw -= r.xyz * ShiftRight8;\treturn r * PackUpscale;\n}\nfloat unpackRGBAToDepth( const in vec4 v ) {\n\treturn dot( v, UnpackFactors );\n}\nvec4 pack2HalfToRGBA( vec2 v ) {\n\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ));\n\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w);\n}\nvec2 unpackRGBATo2Half( vec4 v ) {\n\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\n}\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn ( viewZ + near ) / ( near - far );\n}\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\n\treturn linearClipZ * ( near - far ) - near;\n}\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn (( near + viewZ ) * far ) / (( far - near ) * viewZ );\n}\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\n\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\n}",premultiplied_alpha_fragment:"#ifdef PREMULTIPLIED_ALPHA\n\tgl_FragColor.rgb *= gl_FragColor.a;\n#endif",project_vertex:"vec4 mvPosition = vec4( transformed, 1.0 );\n#ifdef USE_INSTANCING\n\tmvPosition = instanceMatrix * mvPosition;\n#endif\nmvPosition = modelViewMatrix * mvPosition;\ngl_Position = projectionMatrix * mvPosition;",dithering_fragment:"#ifdef DITHERING\n\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\n#endif",dithering_pars_fragment:"#ifdef DITHERING\n\tvec3 dithering( vec3 color ) {\n\t\tfloat grid_position = rand( gl_FragCoord.xy );\n\t\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\n\t\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\n\t\treturn color + dither_shift_RGB;\n\t}\n#endif",roughnessmap_fragment:"float roughnessFactor = roughness;\n#ifdef USE_ROUGHNESSMAP\n\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\n\troughnessFactor *= texelRoughness.g;\n#endif",roughnessmap_pars_fragment:"#ifdef USE_ROUGHNESSMAP\n\tuniform sampler2D roughnessMap;\n#endif",shadowmap_pars_fragment:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\n\t\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\n\t}\n\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\n\t\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\n\t}\n\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\n\t\tfloat occlusion = 1.0;\n\t\tvec2 distribution = texture2DDistribution( shadow, uv );\n\t\tfloat hard_shadow = step( compare , distribution.x );\n\t\tif (hard_shadow != 1.0 ) {\n\t\t\tfloat distance = compare - distribution.x ;\n\t\t\tfloat variance = max( 0.00000, distribution.y * distribution.y );\n\t\t\tfloat softness_probability = variance / (variance + distance * distance );\t\t\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\t\t\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\n\t\t}\n\t\treturn occlusion;\n\t}\n\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\n\t\tfloat shadow = 1.0;\n\t\tshadowCoord.xyz /= shadowCoord.w;\n\t\tshadowCoord.z += shadowBias;\n\t\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\n\t\tbool inFrustum = all( inFrustumVec );\n\t\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\n\t\tbool frustumTest = all( frustumTestVec );\n\t\tif ( frustumTest ) {\n\t\t#if defined( SHADOWMAP_TYPE_PCF )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx0 = - texelSize.x * shadowRadius;\n\t\t\tfloat dy0 = - texelSize.y * shadowRadius;\n\t\t\tfloat dx1 = + texelSize.x * shadowRadius;\n\t\t\tfloat dy1 = + texelSize.y * shadowRadius;\n\t\t\tfloat dx2 = dx0 / 2.0;\n\t\t\tfloat dy2 = dy0 / 2.0;\n\t\t\tfloat dx3 = dx1 / 2.0;\n\t\t\tfloat dy3 = dy1 / 2.0;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\n\t\t\t) * ( 1.0 / 17.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx = texelSize.x;\n\t\t\tfloat dy = texelSize.y;\n\t\t\tvec2 uv = shadowCoord.xy;\n\t\t\tvec2 f = fract( uv * shadowMapSize + 0.5 );\n\t\t\tuv -= f * texelSize;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t f.y )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_VSM )\n\t\t\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#else\n\t\t\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#endif\n\t\t}\n\t\treturn shadow;\n\t}\n\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\n\t\tvec3 absV = abs( v );\n\t\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\n\t\tabsV *= scaleToCube;\n\t\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\n\t\tvec2 planar = v.xy;\n\t\tfloat almostATexel = 1.5 * texelSizeY;\n\t\tfloat almostOne = 1.0 - almostATexel;\n\t\tif ( absV.z >= almostOne ) {\n\t\t\tif ( v.z > 0.0 )\n\t\t\t\tplanar.x = 4.0 - v.x;\n\t\t} else if ( absV.x >= almostOne ) {\n\t\t\tfloat signX = sign( v.x );\n\t\t\tplanar.x = v.z * signX + 2.0 * signX;\n\t\t} else if ( absV.y >= almostOne ) {\n\t\t\tfloat signY = sign( v.y );\n\t\t\tplanar.x = v.x + 2.0 * signY + 2.0;\n\t\t\tplanar.y = v.z * signY - 2.0;\n\t\t}\n\t\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\n\t}\n\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\n\t\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\n\t\tvec3 lightToPosition = shadowCoord.xyz;\n\t\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\t\tdp += shadowBias;\n\t\tvec3 bd3D = normalize( lightToPosition );\n\t\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\n\t\t\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\n\t\t\treturn (\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#else\n\t\t\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\n\t\t#endif\n\t}\n#endif",shadowmap_pars_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n#endif",shadowmap_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\n\t\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\tvec4 shadowWorldPosition;\n\t#endif\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n#endif",shadowmask_pars_fragment:"float getShadowMask() {\n\tfloat shadow = 1.0;\n\t#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tdirectionalLight = directionalLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tspotLight = spotLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tpointLight = pointLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#endif\n\treturn shadow;\n}",skinbase_vertex:"#ifdef USE_SKINNING\n\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\n\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\n\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\n\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\n#endif",skinning_pars_vertex:"#ifdef USE_SKINNING\n\tuniform mat4 bindMatrix;\n\tuniform mat4 bindMatrixInverse;\n\t#ifdef BONE_TEXTURE\n\t\tuniform highp sampler2D boneTexture;\n\t\tuniform int boneTextureSize;\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tfloat j = i * 4.0;\n\t\t\tfloat x = mod( j, float( boneTextureSize ) );\n\t\t\tfloat y = floor( j / float( boneTextureSize ) );\n\t\t\tfloat dx = 1.0 / float( boneTextureSize );\n\t\t\tfloat dy = 1.0 / float( boneTextureSize );\n\t\t\ty = dy * ( y + 0.5 );\n\t\t\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\n\t\t\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\n\t\t\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\n\t\t\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\n\t\t\tmat4 bone = mat4( v1, v2, v3, v4 );\n\t\t\treturn bone;\n\t\t}\n\t#else\n\t\tuniform mat4 boneMatrices[ MAX_BONES ];\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tmat4 bone = boneMatrices[ int(i) ];\n\t\t\treturn bone;\n\t\t}\n\t#endif\n#endif",skinning_vertex:"#ifdef USE_SKINNING\n\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\n\tvec4 skinned = vec4( 0.0 );\n\tskinned += boneMatX * skinVertex * skinWeight.x;\n\tskinned += boneMatY * skinVertex * skinWeight.y;\n\tskinned += boneMatZ * skinVertex * skinWeight.z;\n\tskinned += boneMatW * skinVertex * skinWeight.w;\n\ttransformed = ( bindMatrixInverse * skinned ).xyz;\n#endif",skinnormal_vertex:"#ifdef USE_SKINNING\n\tmat4 skinMatrix = mat4( 0.0 );\n\tskinMatrix += skinWeight.x * boneMatX;\n\tskinMatrix += skinWeight.y * boneMatY;\n\tskinMatrix += skinWeight.z * boneMatZ;\n\tskinMatrix += skinWeight.w * boneMatW;\n\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\n\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\n\t#ifdef USE_TANGENT\n\t\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#endif\n#endif",specularmap_fragment:"float specularStrength;\n#ifdef USE_SPECULARMAP\n\tvec4 texelSpecular = texture2D( specularMap, vUv );\n\tspecularStrength = texelSpecular.r;\n#else\n\tspecularStrength = 1.0;\n#endif",specularmap_pars_fragment:"#ifdef USE_SPECULARMAP\n\tuniform sampler2D specularMap;\n#endif",tonemapping_fragment:"#if defined( TONE_MAPPING )\n\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\n#endif",tonemapping_pars_fragment:"#ifndef saturate\n#define saturate(a) clamp( a, 0.0, 1.0 )\n#endif\nuniform float toneMappingExposure;\nvec3 LinearToneMapping( vec3 color ) {\n\treturn toneMappingExposure * color;\n}\nvec3 ReinhardToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\treturn saturate( color / ( vec3( 1.0 ) + color ) );\n}\nvec3 OptimizedCineonToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\tcolor = max( vec3( 0.0 ), color - 0.004 );\n\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\n}\nvec3 RRTAndODTFit( vec3 v ) {\n\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\n\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\n\treturn a / b;\n}\nvec3 ACESFilmicToneMapping( vec3 color ) {\n\tconst mat3 ACESInputMat = mat3(\n\t\tvec3( 0.59719, 0.07600, 0.02840 ),\t\tvec3( 0.35458, 0.90834, 0.13383 ),\n\t\tvec3( 0.04823, 0.01566, 0.83777 )\n\t);\n\tconst mat3 ACESOutputMat = mat3(\n\t\tvec3(  1.60475, -0.10208, -0.00327 ),\t\tvec3( -0.53108,  1.10813, -0.07276 ),\n\t\tvec3( -0.07367, -0.00605,  1.07602 )\n\t);\n\tcolor *= toneMappingExposure / 0.6;\n\tcolor = ACESInputMat * color;\n\tcolor = RRTAndODTFit( color );\n\tcolor = ACESOutputMat * color;\n\treturn saturate( color );\n}\nvec3 CustomToneMapping( vec3 color ) { return color; }",transmission_fragment:"#ifdef USE_TRANSMISSION\n\tfloat transmissionFactor = transmission;\n\tfloat thicknessFactor = thickness;\n\t#ifdef USE_TRANSMISSIONMAP\n\t\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\n\t#endif\n\tvec3 pos = vWorldPosition.xyz / vWorldPosition.w;\n\tvec3 v = normalize( cameraPosition - pos );\n\tvec3 n = inverseTransformDirection( normal, viewMatrix );\n\tfloat ior = ( 1.0 + 0.4 * reflectivity ) / ( 1.0 - 0.4 * reflectivity );\n\tvec3 transmission = transmissionFactor * getIBLVolumeRefraction(\n\t\tn, v, roughnessFactor, material.diffuseColor, material.specularColor,\n\t\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\n\t\tattenuationTint, attenuationDistance );\n\ttotalDiffuse = mix( totalDiffuse, transmission, transmissionFactor );\n#endif",transmission_pars_fragment:"#ifdef USE_TRANSMISSION\n\t#ifdef USE_TRANSMISSIONMAP\n\t\tuniform sampler2D transmissionMap;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tuniform sampler2D thicknessMap;\n\t#endif\n\tuniform vec2 transmissionSamplerSize;\n\tuniform sampler2D transmissionSamplerMap;\n\tuniform mat4 modelMatrix;\n\tuniform mat4 projectionMatrix;\n\tvarying vec4 vWorldPosition;\n\tvec3 getVolumeTransmissionRay(vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix) {\n\t\tvec3 refractionVector = refract(-v, normalize(n), 1.0 / ior);\n\t\tvec3 modelScale;\n\t\tmodelScale.x = length(vec3(modelMatrix[0].xyz));\n\t\tmodelScale.y = length(vec3(modelMatrix[1].xyz));\n\t\tmodelScale.z = length(vec3(modelMatrix[2].xyz));\n\t\treturn normalize(refractionVector) * thickness * modelScale;\n\t}\n\tfloat applyIorToRoughness(float roughness, float ior) {\n\t\treturn roughness * clamp(ior * 2.0 - 2.0, 0.0, 1.0);\n\t}\n\tvec3 getTransmissionSample(vec2 fragCoord, float roughness, float ior) {\n\t\tfloat framebufferLod = log2(transmissionSamplerSize.x) * applyIorToRoughness(roughness, ior);\n\t\treturn texture2DLodEXT(transmissionSamplerMap, fragCoord.xy, framebufferLod).rgb;\n\t}\n\tvec3 applyVolumeAttenuation(vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance) {\n\t\tif (attenuationDistance == 0.0) {\n\t\t\treturn radiance;\n\t\t} else {\n\t\t\tvec3 attenuationCoefficient = -log(attenuationColor) / attenuationDistance;\n\t\t\tvec3 transmittance = exp(-attenuationCoefficient * transmissionDistance);\t\t\treturn transmittance * radiance;\n\t\t}\n\t}\n\tvec3 getIBLVolumeRefraction(vec3 n, vec3 v, float perceptualRoughness, vec3 baseColor, vec3 specularColor,\n\t\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\n\t\tvec3 attenuationColor, float attenuationDistance) {\n\t\tvec3 transmissionRay = getVolumeTransmissionRay(n, v, thickness, ior, modelMatrix);\n\t\tvec3 refractedRayExit = position + transmissionRay;\n\t\tvec4 ndcPos = projMatrix * viewMatrix * vec4(refractedRayExit, 1.0);\n\t\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\n\t\trefractionCoords += 1.0;\n\t\trefractionCoords /= 2.0;\n\t\tvec3 transmittedLight = getTransmissionSample(refractionCoords, perceptualRoughness, ior);\n\t\tvec3 attenuatedColor = applyVolumeAttenuation(transmittedLight, length(transmissionRay), attenuationColor, attenuationDistance);\n\t\treturn (1.0 - specularColor) * attenuatedColor * baseColor;\n\t}\n#endif",uv_pars_fragment:"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\n\tvarying vec2 vUv;\n#endif",uv_pars_vertex:"#ifdef USE_UV\n\t#ifdef UVS_VERTEX_ONLY\n\t\tvec2 vUv;\n\t#else\n\t\tvarying vec2 vUv;\n\t#endif\n\tuniform mat3 uvTransform;\n#endif",uv_vertex:"#ifdef USE_UV\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n#endif",uv2_pars_fragment:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvarying vec2 vUv2;\n#endif",uv2_pars_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tattribute vec2 uv2;\n\tvarying vec2 vUv2;\n\tuniform mat3 uv2Transform;\n#endif",uv2_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\n#endif",worldpos_vertex:"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\n\tvec4 worldPosition = vec4( transformed, 1.0 );\n\t#ifdef USE_INSTANCING\n\t\tworldPosition = instanceMatrix * worldPosition;\n\t#endif\n\tworldPosition = modelMatrix * worldPosition;\n#endif",background_frag:"uniform sampler2D t2D;\nvarying vec2 vUv;\nvoid main() {\n\tvec4 texColor = texture2D( t2D, vUv );\n\tgl_FragColor = mapTexelToLinear( texColor );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",background_vert:"varying vec2 vUv;\nuniform mat3 uvTransform;\nvoid main() {\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n\tgl_Position = vec4( position.xy, 1.0, 1.0 );\n}",cube_frag:"#include <envmap_common_pars_fragment>\nuniform float opacity;\nvarying vec3 vWorldDirection;\n#include <cube_uv_reflection_fragment>\nvoid main() {\n\tvec3 vReflect = vWorldDirection;\n\t#include <envmap_fragment>\n\tgl_FragColor = envColor;\n\tgl_FragColor.a *= opacity;\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",cube_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\tgl_Position.z = gl_Position.w;\n}",depth_frag:"#if DEPTH_PACKING == 3200\n\tuniform float opacity;\n#endif\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#if DEPTH_PACKING == 3200\n\t\tdiffuseColor.a = opacity;\n\t#endif\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <logdepthbuf_fragment>\n\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\n\t#if DEPTH_PACKING == 3200\n\t\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\n\t#elif DEPTH_PACKING == 3201\n\t\tgl_FragColor = packDepthToRGBA( fragCoordZ );\n\t#endif\n}",depth_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvHighPrecisionZW = gl_Position.zw;\n}",distanceRGBA_frag:"#define DISTANCE\nuniform vec3 referencePosition;\nuniform float nearDistance;\nuniform float farDistance;\nvarying vec3 vWorldPosition;\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main () {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\tfloat dist = length( vWorldPosition - referencePosition );\n\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\n\tdist = saturate( dist );\n\tgl_FragColor = packDepthToRGBA( dist );\n}",distanceRGBA_vert:"#define DISTANCE\nvarying vec3 vWorldPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <clipping_planes_vertex>\n\tvWorldPosition = worldPosition.xyz;\n}",equirect_frag:"uniform sampler2D tEquirect;\nvarying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvec3 direction = normalize( vWorldDirection );\n\tvec2 sampleUV = equirectUv( direction );\n\tvec4 texColor = texture2D( tEquirect, sampleUV );\n\tgl_FragColor = mapTexelToLinear( texColor );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",equirect_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n}",linedashed_frag:"uniform vec3 diffuse;\nuniform float opacity;\nuniform float dashSize;\nuniform float totalSize;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\n\t\tdiscard;\n\t}\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <color_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",linedashed_vert:"uniform float scale;\nattribute float lineDistance;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\tvLineDistance = scale * lineDistance;\n\t#include <color_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n}",meshbasic_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <fog_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\t#ifdef USE_LIGHTMAP\n\t\n\t\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\t\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n\t#else\n\t\treflectedLight.indirectDiffuse += vec3( 1.0 );\n\t#endif\n\t#include <aomap_fragment>\n\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\n\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshbasic_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinbase_vertex>\n\t\t#include <skinnormal_vertex>\n\t\t#include <defaultnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <fog_vertex>\n}",meshlambert_frag:"uniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <fog_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <shadowmask_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <emissivemap_fragment>\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\n\t#else\n\t\treflectedLight.indirectDiffuse += vIndirectFront;\n\t#endif\n\t#include <lightmap_fragment>\n\treflectedLight.indirectDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb );\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\n\t#else\n\t\treflectedLight.directDiffuse = vLightFront;\n\t#endif\n\treflectedLight.directDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb ) * getShadowMask();\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshlambert_vert:"#define LAMBERT\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <envmap_pars_vertex>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <lights_lambert_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshmatcap_frag:"#define MATCAP\nuniform vec3 diffuse;\nuniform float opacity;\nuniform sampler2D matcap;\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tvec3 viewDir = normalize( vViewPosition );\n\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\n\tvec3 y = cross( viewDir, x );\n\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\n\t#ifdef USE_MATCAP\n\t\tvec4 matcapColor = texture2D( matcap, uv );\n\t\tmatcapColor = matcapTexelToLinear( matcapColor );\n\t#else\n\t\tvec4 matcapColor = vec4( 1.0 );\n\t#endif\n\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshmatcap_vert:"#define MATCAP\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <color_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#ifndef FLAT_SHADED\n\t\tvNormal = normalize( transformedNormal );\n\t\t#ifdef USE_TANGENT\n\t\t\tvTangent = normalize( transformedTangent );\n\t\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t\t#endif\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n\tvViewPosition = - mvPosition.xyz;\n}",meshtoon_frag:"#define TOON\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <gradientmap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <lights_toon_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_toon_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshtoon_vert:"#define TOON\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshphong_frag:"#define PHONG\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform vec3 specular;\nuniform float shininess;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <lights_phong_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_phong_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshphong_vert:"#define PHONG\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshphysical_frag:"#define STANDARD\n#ifdef PHYSICAL\n\t#define REFLECTIVITY\n\t#define CLEARCOAT\n\t#define SPECULAR\n#endif\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float roughness;\nuniform float metalness;\nuniform float opacity;\n#ifdef USE_TRANSMISSION\n\tuniform float transmission;\n\tuniform float thickness;\n\tuniform float attenuationDistance;\n\tuniform vec3 attenuationTint;\n#endif\n#ifdef REFLECTIVITY\n\tuniform float reflectivity;\n#endif\n#ifdef SPECULAR\n\tuniform float specularIntensity;\n\tuniform vec3 specularTint;\n\t#ifdef USE_SPECULARINTENSITYMAP\n\t\tuniform sampler2D specularIntensityMap;\n\t#endif\n\t#ifdef USE_SPECULARTINTMAP\n\t\tuniform sampler2D specularTintMap;\n\t#endif\n#endif\n#ifdef CLEARCOAT\n\tuniform float clearcoat;\n\tuniform float clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tuniform vec3 sheen;\n#endif\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <bsdfs>\n#include <transmission_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_physical_pars_fragment>\n#include <fog_pars_fragment>\n#include <lights_pars_begin>\n#include <lights_physical_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <clearcoat_pars_fragment>\n#include <roughnessmap_pars_fragment>\n#include <metalnessmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <roughnessmap_fragment>\n\t#include <metalnessmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <clearcoat_normal_fragment_begin>\n\t#include <clearcoat_normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_physical_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\n\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\n\t#include <transmission_fragment>\n\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshphysical_vert:"#define STANDARD\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#ifdef USE_TRANSMISSION\n\tvarying vec4 vWorldPosition;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n#ifdef USE_TRANSMISSION\n\tvWorldPosition = worldPosition;\n#endif\n}",normal_frag:"#define NORMAL\nuniform float opacity;\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <packing>\n#include <uv_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\t#include <logdepthbuf_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\n}",normal_vert:"#define NORMAL\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvViewPosition = - mvPosition.xyz;\n#endif\n}",points_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include <common>\n#include <color_pars_fragment>\n#include <map_particle_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_particle_fragment>\n\t#include <color_fragment>\n\t#include <alphatest_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",points_vert:"uniform float size;\nuniform float scale;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <color_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\tgl_PointSize = size;\n\t#ifdef USE_SIZEATTENUATION\n\t\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\n\t\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\n\t#endif\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <fog_vertex>\n}",shadow_frag:"uniform vec3 color;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <shadowmap_pars_fragment>\n#include <shadowmask_pars_fragment>\nvoid main() {\n\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n}",shadow_vert:"#include <common>\n#include <fog_pars_vertex>\n#include <shadowmap_pars_vertex>\nvoid main() {\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",sprite_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include <common>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n}",sprite_vert:"uniform float rotation;\nuniform vec2 center;\n#include <common>\n#include <uv_pars_vertex>\n#include <fog_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\n\tvec2 scale;\n\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\n\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\n\t#ifndef USE_SIZEATTENUATION\n\t\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\n\t\tif ( isPerspective ) scale *= - mvPosition.z;\n\t#endif\n\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\n\tvec2 rotatedPosition;\n\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\n\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\n\tmvPosition.xy += rotatedPosition;\n\tgl_Position = projectionMatrix * mvPosition;\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n}"},l0t={common:{diffuse:{value:new $Qt(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new gJt},uv2Transform:{value:new 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$Qt(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},uvTransform:{value:new gJt}},sprite:{diffuse:{value:new $Qt(16777215)},opacity:{value:1},center:{value:new mJt(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},uvTransform:{value:new gJt}}},c0t={basic:{uniforms:U1t([l0t.common,l0t.specularmap,l0t.envmap,l0t.aomap,l0t.lightmap,l0t.fog]),vertexShader:s0t.meshbasic_vert,fragmentShader:s0t.meshbasic_frag},lambert:{uniforms:U1t([l0t.common,l0t.specularmap,l0t.envmap,l0t.aomap,l0t.lightmap,l0t.emissivemap,l0t.fog,l0t.lights,{emissive:{value:new $Qt(0)}}]),vertexShader:s0t.meshlambert_vert,fragmentShader:s0t.meshlambert_frag},phong:{uniforms:U1t([l0t.common,l0t.specularmap,l0t.envmap,l0t.aomap,l0t.lightmap,l0t.emissivemap,l0t.bumpmap,l0t.normalmap,l0t.displacementmap,l0t.fog,l0t.lights,{emissive:{value:new $Qt(0)},specular:{value:new $Qt(1118481)},shininess:{value:30}}]),vertexShader:s0t.meshphong_vert,fragmentShader:s0t.meshphong_frag},standard:{uniforms:U1t([l0t.common,l0t.envmap,l0t.aomap,l0t.lightmap,l0t.emissivemap,l0t.bumpmap,l0t.normalmap,l0t.displacementmap,l0t.roughnessmap,l0t.metalnessmap,l0t.fog,l0t.lights,{emissive:{value:new $Qt(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:s0t.meshphysical_vert,fragmentShader:s0t.meshphysical_frag},toon:{uniforms:U1t([l0t.common,l0t.aomap,l0t.lightmap,l0t.emissivemap,l0t.bumpmap,l0t.normalmap,l0t.displacementmap,l0t.gradientmap,l0t.fog,l0t.lights,{emissive:{value:new 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h0t(t,e,n,i){const r=t.getParameter(34921),o=i.isWebGL2?null:e.get("OES_vertex_array_object"),a=i.isWebGL2||null!==o,s={},l=d(null);let c=l;function u(e){return i.isWebGL2?t.bindVertexArray(e):o.bindVertexArrayOES(e)}function h(e){return i.isWebGL2?t.deleteVertexArray(e):o.deleteVertexArrayOES(e)}function d(t){const e=[],n=[],i=[];for(let t=0;t<r;t++)e[t]=0,n[t]=0,i[t]=0;return{geometry:null,program:null,wireframe:!1,newAttributes:e,enabledAttributes:n,attributeDivisors:i,object:t,attributes:{},index:null}}function p(){const t=c.newAttributes;for(let e=0,n=t.length;e<n;e++)t[e]=0}function f(t){m(t,0)}function m(n,r){const o=c.enabledAttributes,a=c.attributeDivisors;c.newAttributes[n]=1,0===o[n]&&(t.enableVertexAttribArray(n),o[n]=1),a[n]!==r&&((i.isWebGL2?t:e.get("ANGLE_instanced_arrays"))[i.isWebGL2?"vertexAttribDivisor":"vertexAttribDivisorANGLE"](n,r),a[n]=r)}function g(){const e=c.newAttributes,n=c.enabledAttributes;for(let 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mJt(1,1)},clearcoatNormalMap:{value:null},sheen:{value:new $Qt(0)},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new mJt},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new $Qt(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new $Qt(1,1,1)},specularTintMap:{value:null}}]),vertexShader:s0t.meshphysical_vert,fragmentShader:s0t.meshphysical_frag};class g0t extends W1t{constructor(t=-1,e=1,n=1,i=-1,r=.1,o=2e3){super(),this.type="OrthographicCamera",this.zoom=1,this.view=null,this.left=t,this.right=e,this.top=n,this.bottom=i,this.near=r,this.far=o,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.left=t.left,this.right=t.right,this.top=t.top,this.bottom=t.bottom,this.near=t.near,this.far=t.far,this.zoom=t.zoom,this.view=null===t.view?null:Object.assign({},t.view),this}setViewOffset(t,e,n,i,r,o){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=o,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=(this.right-this.left)/(2*this.zoom),e=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,i=(this.top+this.bottom)/2;let r=n-t,o=n+t,a=i+e,s=i-e;if(null!==this.view&&this.view.enabled){const t=(this.right-this.left)/this.view.fullWidth/this.zoom,e=(this.top-this.bottom)/this.view.fullHeight/this.zoom;r+=t*this.view.offsetX,o=r+t*this.view.width,a-=e*this.view.offsetY,s=a-e*this.view.height}this.projectionMatrix.makeOrthographic(r,o,a,s,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.zoom=this.zoom,e.object.left=this.left,e.object.right=this.right,e.object.top=this.top,e.object.bottom=this.bottom,e.object.near=this.near,e.object.far=this.far,null!==this.view&&(e.object.view=Object.assign({},this.view)),e}}g0t.prototype.isOrthographicCamera=!0;class _0t extends G1t{constructor(t){super(t),this.type="RawShaderMaterial"}}_0t.prototype.isRawShaderMaterial=!0;const y0t=Math.pow(2,8),v0t=[.125,.215,.35,.446,.526,.582],b0t=5+v0t.length,x0t={[NZt]:0,[IZt]:1,[OZt]:2,[zZt]:3,[DZt]:4,[BZt]:5,[RZt]:6},w0t=new KQt({side:1,depthWrite:!1,depthTest:!1}),S0t=new B1t(new F1t,w0t),M0t=new g0t,{_lodPlanes:E0t,_sizeLods:T0t,_sigmas:C0t}=z0t(),A0t=new $Qt;let k0t=null;const L0t=(1+Math.sqrt(5))/2,P0t=1/L0t,N0t=[new CJt(1,1,1),new CJt(-1,1,1),new CJt(1,1,-1),new CJt(-1,1,-1),new CJt(0,L0t,P0t),new CJt(0,L0t,-P0t),new CJt(P0t,0,L0t),new CJt(-P0t,0,L0t),new CJt(L0t,P0t,0),new CJt(-L0t,P0t,0)];function I0t(t){const e=Math.max(t.r,t.g,t.b),n=Math.min(Math.max(Math.ceil(Math.log2(e)),-128),127);return t.multiplyScalar(Math.pow(2,-n)),(n+128)/255}class R0t{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=(function e(t){const e=new Float32Array(t),n=new CJt(0,1,0);return new _0t({name:"SphericalGaussianBlur",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:x0t[3e3]},outputEncoding:{value:x0t[3e3]}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute vec3 position;\n\t\tattribute vec2 uv;\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\t\t\tuniform int samples;\n\t\t\tuniform float weights[ n ];\n\t\t\tuniform bool latitudinal;\n\t\t\tuniform float dTheta;\n\t\t\tuniform float mipInt;\n\t\t\tuniform vec3 poleAxis;\n\n\t\t\t\n\n\t\tuniform int inputEncoding;\n\t\tuniform int outputEncoding;\n\n\t\t#include <encodings_pars_fragment>\n\n\t\tvec4 inputTexelToLinear( vec4 value ) {\n\n\t\t\tif ( inputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( inputEncoding == 1 ) {\n\n\t\t\t\treturn sRGBToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 2 ) {\n\n\t\t\t\treturn RGBEToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 3 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 7.0 );\n\n\t\t\t} else if ( inputEncoding == 4 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 16.0 );\n\n\t\t\t} else if ( inputEncoding == 5 ) {\n\n\t\t\t\treturn RGBDToLinear( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn GammaToLinear( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 linearToOutputTexel( vec4 value ) {\n\n\t\t\tif ( outputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( outputEncoding == 1 ) {\n\n\t\t\t\treturn LinearTosRGB( value );\n\n\t\t\t} else if ( outputEncoding == 2 ) {\n\n\t\t\t\treturn LinearToRGBE( value );\n\n\t\t\t} else if ( outputEncoding == 3 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 7.0 );\n\n\t\t\t} else if ( outputEncoding == 4 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 16.0 );\n\n\t\t\t} else if ( outputEncoding == 5 ) {\n\n\t\t\t\treturn LinearToRGBD( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn LinearToGamma( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 envMapTexelToLinear( vec4 color ) {\n\n\t\t\treturn inputTexelToLinear( color );\n\n\t\t}\n\t\n\n\t\t\t#define ENVMAP_TYPE_CUBE_UV\n\t\t\t#include <cube_uv_reflection_fragment>\n\n\t\t\tvec3 getSample( float theta, vec3 axis ) {\n\n\t\t\t\tfloat cosTheta = cos( theta );\n\t\t\t\t// Rodrigues' axis-angle rotation\n\t\t\t\tvec3 sampleDirection = vOutputDirection * cosTheta\n\t\t\t\t\t+ cross( axis, vOutputDirection ) * sin( theta )\n\t\t\t\t\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\n\n\t\t\t\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\n\n\t\t\t}\n\n\t\t\tvoid main() {\n\n\t\t\t\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\n\n\t\t\t\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\n\n\t\t\t\t\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\n\n\t\t\t\t}\n\n\t\t\t\taxis = normalize( axis );\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t\t\t\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\n\n\t\t\t\tfor ( int i = 1; i < n; i++ ) {\n\n\t\t\t\t\tif ( i >= samples ) {\n\n\t\t\t\t\t\tbreak;\n\n\t\t\t\t\t}\n\n\t\t\t\t\tfloat theta = dTheta * float( i );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\n\n\t\t\t\t}\n\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t",blending:0,depthTest:!1,depthWrite:!1})})(20),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){k0t=this._renderer.getRenderTarget();const r=this._allocateTargets();return this._sceneToCubeUV(t,n,i,r),e>0&&this._blur(r,0,0,e),this._applyPMREM(r),this._cleanup(r),r}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=F0t(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=H0t(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t<E0t.length;t++)E0t[t].dispose()}_cleanup(t){this._pingPongRenderTarget.dispose(),this._renderer.setRenderTarget(k0t),t.scissorTest=!1,B0t(t,0,0,t.width,t.height)}_fromTexture(t){k0t=this._renderer.getRenderTarget();const e=this._allocateTargets(t);return this._textureToCubeUV(t,e),this._applyPMREM(e),this._cleanup(e),e}_allocateTargets(t){const e={magFilter:JKt,minFilter:JKt,generateMipmaps:!1,type:rZt,format:1023,encoding:O0t(t)?t.encoding:OZt,depthBuffer:!1},n=D0t(e);return n.depthBuffer=!t,this._pingPongRenderTarget=D0t(e),n}_compileMaterial(t){const e=new B1t(E0t[0],t);this._renderer.compile(e,M0t)}_sceneToCubeUV(t,e,n,i){const r=new q1t(90,1,e,n),o=[1,-1,1,1,1,1],a=[1,1,1,-1,-1,-1],s=this._renderer,l=s.autoClear,c=s.outputEncoding,u=s.toneMapping;s.getClearColor(A0t),s.toneMapping=0,s.outputEncoding=NZt,s.autoClear=!1;let h=!1;const d=t.background;if(d){if(d.isColor){w0t.color.copy(d).convertSRGBToLinear(),t.background=null;const e=I0t(w0t.color);w0t.opacity=e,h=!0}}else{w0t.color.copy(A0t).convertSRGBToLinear();const t=I0t(w0t.color);w0t.opacity=t,h=!0}for(let e=0;e<6;e++){const n=e%3;0==n?(r.up.set(0,o[e],0),r.lookAt(a[e],0,0)):1==n?(r.up.set(0,0,o[e]),r.lookAt(0,a[e],0)):(r.up.set(0,o[e],0),r.lookAt(0,0,a[e])),B0t(i,n*y0t,e>2?y0t:0,y0t,y0t),s.setRenderTarget(i),h&&s.render(S0t,r),s.render(t,r)}s.toneMapping=u,s.outputEncoding=c,s.autoClear=l}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=F0t()):null==this._equirectShader&&(this._equirectShader=H0t());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,r=new B1t(E0t[0],i),o=i.uniforms;o.envMap.value=t,t.isCubeTexture||o.texelSize.value.set(1/t.image.width,1/t.image.height),o.inputEncoding.value=x0t[t.encoding],o.outputEncoding.value=x0t[e.texture.encoding],B0t(e,0,0,3*y0t,2*y0t),n.setRenderTarget(e),n.render(r,M0t)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;e<b0t;e++){const n=Math.sqrt(C0t[e]*C0t[e]-C0t[e-1]*C0t[e-1]);this._blur(t,e-1,e,n,N0t[(e-1)%N0t.length])}e.autoClear=n}_blur(t,e,n,i,r){const o=this._pingPongRenderTarget;this._halfBlur(t,o,e,n,i,"latitudinal",r),this._halfBlur(o,t,n,n,i,"longitudinal",r)}_halfBlur(t,e,n,i,r,o,a){const s=this._renderer,l=this._blurMaterial;"latitudinal"!==o&&"longitudinal"!==o&&console.error("blur direction must be either latitudinal or longitudinal!");const c=new B1t(E0t[i],l),u=l.uniforms,h=T0t[n]-1,d=isFinite(r)?Math.PI/(2*h):2*Math.PI/39,p=r/d,f=isFinite(r)?1+Math.floor(3*p):20;f>20&&console.warn(`sigmaRadians, ${r}, is too large and will clip, as it requested ${f} samples when the maximum is set to 20`);const m=[];let g=0;for(let t=0;t<20;++t){const e=t/p,n=Math.exp(-e*e/2);m.push(n),0==t?g+=n:t<f&&(g+=2*n)}for(let t=0;t<m.length;t++)m[t]=m[t]/g;u.envMap.value=t.texture,u.samples.value=f,u.weights.value=m,u.latitudinal.value="latitudinal"===o,a&&(u.poleAxis.value=a),u.dTheta.value=d,u.mipInt.value=8-n,u.inputEncoding.value=x0t[t.texture.encoding],u.outputEncoding.value=x0t[t.texture.encoding];const _=T0t[i];B0t(e,3*Math.max(0,y0t-2*_),(0===i?0:2*y0t)+2*_*(i>4?i-8+4:0),3*_,2*_),s.setRenderTarget(e),s.render(c,M0t)}}function O0t(t){return void 0!==t&&t.type===rZt&&(t.encoding===NZt||t.encoding===IZt||t.encoding===RZt)}function z0t(){const t=[],e=[],n=[];let i=8;for(let r=0;r<b0t;r++){const o=Math.pow(2,i);e.push(o);let a=1/o;r>4?a=v0t[r-8+4-1]:0==r&&(a=0),n.push(a);const s=1/(o-1),l=-s/2,c=1+s/2,u=[l,l,c,l,c,c,l,l,c,c,l,c],h=6,d=6,p=3,f=2,m=1,g=new Float32Array(p*d*h),_=new Float32Array(f*d*h),y=new Float32Array(m*d*h);for(let t=0;t<h;t++){const e=t%3*2/3-1,n=t>2?0:-1;g.set([e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0],p*d*t),_.set(u,f*d*t),y.set([t,t,t,t,t,t],m*d*t)}const v=new b1t;v.setAttribute("position",new QQt(g,p)),v.setAttribute("uv",new QQt(_,f)),v.setAttribute("faceIndex",new QQt(y,m)),t.push(v),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function D0t(t){const e=new SJt(3*y0t,3*y0t,t);return e.texture.mapping=YKt,e.texture.name="PMREM.cubeUv",e.scissorTest=!0,e}function B0t(t,e,n,i,r){t.viewport.set(e,n,i,r),t.scissor.set(e,n,i,r)}function H0t(){const t=new mJt(1,1);return new _0t({name:"EquirectangularToCubeUV",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:x0t[3e3]},outputEncoding:{value:x0t[3e3]}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute vec3 position;\n\t\tattribute vec2 uv;\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\t\t\tuniform vec2 texelSize;\n\n\t\t\t\n\n\t\tuniform int inputEncoding;\n\t\tuniform int outputEncoding;\n\n\t\t#include <encodings_pars_fragment>\n\n\t\tvec4 inputTexelToLinear( vec4 value ) {\n\n\t\t\tif ( inputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( inputEncoding == 1 ) {\n\n\t\t\t\treturn sRGBToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 2 ) {\n\n\t\t\t\treturn RGBEToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 3 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 7.0 );\n\n\t\t\t} else if ( inputEncoding == 4 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 16.0 );\n\n\t\t\t} else if ( inputEncoding == 5 ) {\n\n\t\t\t\treturn RGBDToLinear( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn GammaToLinear( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 linearToOutputTexel( vec4 value ) {\n\n\t\t\tif ( outputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( outputEncoding == 1 ) {\n\n\t\t\t\treturn LinearTosRGB( value );\n\n\t\t\t} else if ( outputEncoding == 2 ) {\n\n\t\t\t\treturn LinearToRGBE( value );\n\n\t\t\t} else if ( outputEncoding == 3 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 7.0 );\n\n\t\t\t} else if ( outputEncoding == 4 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 16.0 );\n\n\t\t\t} else if ( outputEncoding == 5 ) {\n\n\t\t\t\treturn LinearToRGBD( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn LinearToGamma( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 envMapTexelToLinear( vec4 color ) {\n\n\t\t\treturn inputTexelToLinear( color );\n\n\t\t}\n\t\n\n\t\t\t#include <common>\n\n\t\t\tvoid main() {\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\n\t\t\t\tvec3 outputDirection = normalize( vOutputDirection );\n\t\t\t\tvec2 uv = equirectUv( outputDirection );\n\n\t\t\t\tvec2 f = fract( uv / texelSize - 0.5 );\n\t\t\t\tuv -= f * texelSize;\n\t\t\t\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.x += texelSize.x;\n\t\t\t\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.y += texelSize.y;\n\t\t\t\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.x -= texelSize.x;\n\t\t\t\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\n\t\t\t\tvec3 tm = mix( tl, tr, f.x );\n\t\t\t\tvec3 bm = mix( bl, br, f.x );\n\t\t\t\tgl_FragColor.rgb = mix( tm, bm, f.y );\n\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t",blending:0,depthTest:!1,depthWrite:!1})}function F0t(){return new _0t({name:"CubemapToCubeUV",uniforms:{envMap:{value:null},inputEncoding:{value:x0t[3e3]},outputEncoding:{value:x0t[3e3]}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute vec3 position;\n\t\tattribute vec2 uv;\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform samplerCube envMap;\n\n\t\t\t\n\n\t\tuniform int inputEncoding;\n\t\tuniform int outputEncoding;\n\n\t\t#include <encodings_pars_fragment>\n\n\t\tvec4 inputTexelToLinear( vec4 value ) {\n\n\t\t\tif ( inputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( inputEncoding == 1 ) {\n\n\t\t\t\treturn sRGBToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 2 ) {\n\n\t\t\t\treturn RGBEToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 3 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 7.0 );\n\n\t\t\t} else if ( inputEncoding == 4 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 16.0 );\n\n\t\t\t} else if ( inputEncoding == 5 ) {\n\n\t\t\t\treturn RGBDToLinear( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn GammaToLinear( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 linearToOutputTexel( vec4 value ) {\n\n\t\t\tif ( outputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( outputEncoding == 1 ) {\n\n\t\t\t\treturn LinearTosRGB( value );\n\n\t\t\t} else if ( outputEncoding == 2 ) {\n\n\t\t\t\treturn LinearToRGBE( value );\n\n\t\t\t} else if ( outputEncoding == 3 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 7.0 );\n\n\t\t\t} else if ( outputEncoding == 4 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 16.0 );\n\n\t\t\t} else if ( outputEncoding == 5 ) {\n\n\t\t\t\treturn LinearToRGBD( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn LinearToGamma( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 envMapTexelToLinear( vec4 color ) {\n\n\t\t\treturn inputTexelToLinear( color );\n\n\t\t}\n\t\n\n\t\t\tvoid main() {\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t\t\t\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t",blending:0,depthTest:!1,depthWrite:!1})}function V0t(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener("dispose",i);const r=e.get(n);void 0!==r&&(r.delete(n),r.dispose())}return{get:function r(o){if(o&&o.isTexture&&!1===o.isRenderTargetTexture){const r=o.mapping,a=r===WKt||r===qKt,s=r===jKt||r===GKt;if(a||s){if(e.has(o))return e.get(o).texture;{const r=o.image;if(a&&r&&r.height>0||s&&r&&(function a(t){let e=0;for(let n=0;n<6;n++)void 0!==t[n]&&e++;return 6===e})(r)){const r=t.getRenderTarget();null===n&&(n=new R0t(t));const s=a?n.fromEquirectangular(o):n.fromCubemap(o);return e.set(o,s),t.setRenderTarget(r),o.addEventListener("dispose",i),s.texture}return null}}}return o},dispose:function o(){e=new WeakMap,null!==n&&(n.dispose(),n=null)}}}function U0t(t){const e={};function n(n){if(void 0!==e[n])return e[n];let 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Use their .texture property instead."),e=t.texture.encoding):e=NZt,e}return{getParameters:function _(o,s,m,y,v){const b=y.fog,x=(o.isMeshStandardMaterial?n:e).get(o.envMap||(o.isMeshStandardMaterial?y.environment:null)),w=f[o.type],S=v.isSkinnedMesh?(function M(t){const e=t.skeleton.bones;if(u)return 1024;{const t=Math.floor((h-20)/4),n=Math.min(t,e.length);return n<e.length?(console.warn("THREE.WebGLRenderer: Skeleton has "+e.length+" bones. This GPU supports "+n+"."),0):n}})(v):0;let E,T;if(null!==o.precision&&(p=r.getMaxPrecision(o.precision),p!==o.precision&&console.warn("THREE.WebGLProgram.getParameters:",o.precision,"not supported, using",p,"instead.")),w){const t=c0t[w];E=t.vertexShader,T=t.fragmentShader}else E=o.vertexShader,T=o.fragmentShader;const C=t.getRenderTarget();return{isWebGL2:l,shaderID:w,shaderName:o.type,vertexShader:E,fragmentShader:T,defines:o.defines,isRawShaderMaterial:!0===o.isRawShaderMaterial,glslVersion:o.glslVersion,precision:p,instancing:!0===v.isInstancedMesh,instancingColor:!0===v.isInstancedMesh&&null!==v.instanceColor,supportsVertexTextures:d,outputEncoding:null!==C?g(C.texture):t.outputEncoding,map:!!o.map,mapEncoding:g(o.map),matcap:!!o.matcap,matcapEncoding:g(o.matcap),envMap:!!x,envMapMode:x&&x.mapping,envMapEncoding:g(x),envMapCubeUV:!!x&&(x.mapping===YKt||x.mapping===XKt),lightMap:!!o.lightMap,lightMapEncoding:g(o.lightMap),aoMap:!!o.aoMap,emissiveMap:!!o.emissiveMap,emissiveMapEncoding:g(o.emissiveMap),bumpMap:!!o.bumpMap,normalMap:!!o.normalMap,objectSpaceNormalMap:1===o.normalMapType,tangentSpaceNormalMap:0===o.normalMapType,clearcoatMap:!!o.clearcoatMap,clearcoatRoughnessMap:!!o.clearcoatRoughnessMap,clearcoatNormalMap:!!o.clearcoatNormalMap,displacementMap:!!o.displacementMap,roughnessMap:!!o.roughnessMap,metalnessMap:!!o.metalnessMap,specularMap:!!o.specularMap,specularIntensityMap:!!o.specularIntensityMap,specularTintMap:!!o.specularTintMap,specularTintMapEncoding:g(o.specularTintMap),alphaMap:!!o.alphaMap,gradientMap:!!o.gradientMap,sheen:!!o.sheen,transmission:!!o.transmission,transmissionMap:!!o.transmissionMap,thicknessMap:!!o.thicknessMap,combine:o.combine,vertexTangents:!!o.normalMap&&!!v.geometry&&!!v.geometry.attributes.tangent,vertexColors:o.vertexColors,vertexAlphas:!0===o.vertexColors&&!!v.geometry&&!!v.geometry.attributes.color&&4===v.geometry.attributes.color.itemSize,vertexUvs:!!(o.map||o.bumpMap||o.normalMap||o.specularMap||o.alphaMap||o.emissiveMap||o.roughnessMap||o.metalnessMap||o.clearcoatMap||o.clearcoatRoughnessMap||o.clearcoatNormalMap||o.displacementMap||o.transmissionMap||o.thicknessMap||o.specularIntensityMap||o.specularTintMap),uvsVertexOnly:!(o.map||o.bumpMap||o.normalMap||o.specularMap||o.alphaMap||o.emissiveMap||o.roughnessMap||o.metalnessMap||o.clearcoatNormalMap||o.transmission||o.transmissionMap||o.thicknessMap||o.specularIntensityMap||o.specularTintMap||!o.displacementMap),fog:!!b,useFog:o.fog,fogExp2:b&&b.isFogExp2,flatShading:!!o.flatShading,sizeAttenuation:o.sizeAttenuation,logarithmicDepthBuffer:c,skinning:!0===v.isSkinnedMesh&&S>0,maxBones:S,useVertexTexture:u,morphTargets:!!v.geometry&&!!v.geometry.morphAttributes.position,morphNormals:!!v.geometry&&!!v.geometry.morphAttributes.normal,numDirLights:s.directional.length,numPointLights:s.point.length,numSpotLights:s.spot.length,numRectAreaLights:s.rectArea.length,numHemiLights:s.hemi.length,numDirLightShadows:s.directionalShadowMap.length,numPointLightShadows:s.pointShadowMap.length,numSpotLightShadows:s.spotShadowMap.length,numClippingPlanes:a.numPlanes,numClipIntersection:a.numIntersection,dithering:o.dithering,shadowMapEnabled:t.shadowMap.enabled&&m.length>0,shadowMapType:t.shadowMap.type,toneMapping:o.toneMapped?t.toneMapping:0,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:o.premultipliedAlpha,alphaTest:o.alphaTest,doubleSided:2===o.side,flipSided:1===o.side,depthPacking:void 0!==o.depthPacking&&o.depthPacking,index0AttributeName:o.index0AttributeName,extensionDerivatives:o.extensions&&o.extensions.derivatives,extensionFragDepth:o.extensions&&o.extensions.fragDepth,extensionDrawBuffers:o.extensions&&o.extensions.drawBuffers,extensionShaderTextureLOD:o.extensions&&o.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||i.has("EXT_frag_depth"),rendererExtensionDrawBuffers:l||i.has("WEBGL_draw_buffers"),rendererExtensionShaderTextureLod:l||i.has("EXT_shader_texture_lod"),customProgramCacheKey:o.customProgramCacheKey()}},getProgramCacheKey:function y(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t<m.length;t++)n.push(e[m[t]]);n.push(t.outputEncoding),n.push(t.gammaFactor)}return n.push(e.customProgramCacheKey),n.join()},getUniforms:function v(t){const e=f[t.type];let n;return n=e?j1t.clone(c0t[e].uniforms):t.uniforms,n},acquireProgram:function b(e,n){let i;for(let t=0,e=s.length;t<e;t++){const e=s[t];if(e.cacheKey===n){i=e,++i.usedTimes;break}}return void 0===i&&(i=new v5t(t,n,e,o),s.push(i)),i},releaseProgram:function x(t){if(0==--t.usedTimes){const e=s.indexOf(t);s[e]=s[s.length-1],s.pop(),t.destroy()}},programs:s}}function x5t(){let t=new WeakMap;return{get:function e(n){let i=t.get(n);return void 0===i&&(i={},t.set(n,i)),i},remove:function n(e){t.delete(e)},update:function i(e,n,r){t.get(e)[n]=r},dispose:function r(){t=new WeakMap}}}function w5t(t,e){return t.groupOrder!==e.groupOrder?t.groupOrder-e.groupOrder:t.renderOrder!==e.renderOrder?t.renderOrder-e.renderOrder:t.program!==e.program?t.program.id-e.program.id:t.material.id!==e.material.id?t.material.id-e.material.id:t.z!==e.z?t.z-e.z:t.id-e.id}function S5t(t,e){return t.groupOrder!==e.groupOrder?t.groupOrder-e.groupOrder:t.renderOrder!==e.renderOrder?t.renderOrder-e.renderOrder:t.z!==e.z?e.z-t.z:t.id-e.id}function M5t(t){const e=[];let n=0;const i=[],r=[],o=[],a={id:-1};function s(i,r,o,s,l,c){let u=e[n];const h=t.get(o);return void 0===u?(u={id:i.id,object:i,geometry:r,material:o,program:h.program||a,groupOrder:s,renderOrder:i.renderOrder,z:l,group:c},e[n]=u):(u.id=i.id,u.object=i,u.geometry=r,u.material=o,u.program=h.program||a,u.groupOrder=s,u.renderOrder=i.renderOrder,u.z=l,u.group=c),n++,u}return{opaque:i,transmissive:r,transparent:o,init:function l(){n=0,i.length=0,r.length=0,o.length=0},push:function c(t,e,n,a,l,u){const h=s(t,e,n,a,l,u);n.transmission>0?r.push(h):!0===n.transparent?o.push(h):i.push(h)},unshift:function u(t,e,n,a,l,c){const u=s(t,e,n,a,l,c);n.transmission>0?r.unshift(u):!0===n.transparent?o.unshift(u):i.unshift(u)},finish:function h(){for(let t=n,i=e.length;t<i;t++){const n=e[t];if(null===n.id)break;n.id=null,n.object=null,n.geometry=null,n.material=null,n.program=null,n.group=null}},sort:function d(t,e){i.length>1&&i.sort(t||w5t),r.length>1&&r.sort(e||S5t),o.length>1&&o.sort(e||S5t)}}}function E5t(t){let e=new WeakMap;return{get:function n(i,r){let o;return!1===e.has(i)?(o=new M5t(t),e.set(i,[o])):r>=e.get(i).length?(o=new M5t(t),e.get(i).push(o)):o=e.get(i)[r],o},dispose:function i(){e=new WeakMap}}}function T5t(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case"DirectionalLight":n={direction:new CJt,color:new $Qt};break;case"SpotLight":n={position:new CJt,direction:new CJt,color:new $Qt,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case"PointLight":n={position:new CJt,color:new $Qt,distance:0,decay:0};break;case"HemisphereLight":n={direction:new CJt,skyColor:new $Qt,groundColor:new $Qt};break;case"RectAreaLight":n={color:new $Qt,position:new CJt,halfWidth:new CJt,halfHeight:new CJt}}return t[e.id]=n,n}}}let C5t=0;function A5t(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function k5t(t,e){const n=new T5t,i=(function r(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case"DirectionalLight":case"SpotLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new mJt};break;case"PointLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new mJt,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}})(),o={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)o.probe.push(new CJt);const a=new CJt,s=new rQt,l=new rQt;return{setup:function c(r){let a=0,s=0,l=0;for(let t=0;t<9;t++)o.probe[t].set(0,0,0);let c=0,u=0,h=0,d=0,p=0,f=0,m=0,g=0;r.sort(A5t);for(let t=0,e=r.length;t<e;t++){const e=r[t],_=e.color,y=e.intensity,v=e.distance,b=e.shadow&&e.shadow.map?e.shadow.map.texture:null;if(e.isAmbientLight)a+=_.r*y,s+=_.g*y,l+=_.b*y;else if(e.isLightProbe)for(let t=0;t<9;t++)o.probe[t].addScaledVector(e.sh.coefficients[t],y);else if(e.isDirectionalLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity),e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,o.directionalShadow[c]=n,o.directionalShadowMap[c]=b,o.directionalShadowMatrix[c]=e.shadow.matrix,f++}o.directional[c]=t,c++}else if(e.isSpotLight){const t=n.get(e);if(t.position.setFromMatrixPosition(e.matrixWorld),t.color.copy(_).multiplyScalar(y),t.distance=v,t.coneCos=Math.cos(e.angle),t.penumbraCos=Math.cos(e.angle*(1-e.penumbra)),t.decay=e.decay,e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,o.spotShadow[h]=n,o.spotShadowMap[h]=b,o.spotShadowMatrix[h]=e.shadow.matrix,g++}o.spot[h]=t,h++}else if(e.isRectAreaLight){const t=n.get(e);t.color.copy(_).multiplyScalar(y),t.halfWidth.set(.5*e.width,0,0),t.halfHeight.set(0,.5*e.height,0),o.rectArea[d]=t,d++}else if(e.isPointLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity),t.distance=e.distance,t.decay=e.decay,e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,n.shadowCameraNear=t.camera.near,n.shadowCameraFar=t.camera.far,o.pointShadow[u]=n,o.pointShadowMap[u]=b,o.pointShadowMatrix[u]=e.shadow.matrix,m++}o.point[u]=t,u++}else if(e.isHemisphereLight){const t=n.get(e);t.skyColor.copy(e.color).multiplyScalar(y),t.groundColor.copy(e.groundColor).multiplyScalar(y),o.hemi[p]=t,p++}}d>0&&(e.isWebGL2||!0===t.has("OES_texture_float_linear")?(o.rectAreaLTC1=l0t.LTC_FLOAT_1,o.rectAreaLTC2=l0t.LTC_FLOAT_2):!0===t.has("OES_texture_half_float_linear")?(o.rectAreaLTC1=l0t.LTC_HALF_1,o.rectAreaLTC2=l0t.LTC_HALF_2):console.error("THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.")),o.ambient[0]=a,o.ambient[1]=s,o.ambient[2]=l;const _=o.hash;_.directionalLength===c&&_.pointLength===u&&_.spotLength===h&&_.rectAreaLength===d&&_.hemiLength===p&&_.numDirectionalShadows===f&&_.numPointShadows===m&&_.numSpotShadows===g||(o.directional.length=c,o.spot.length=h,o.rectArea.length=d,o.point.length=u,o.hemi.length=p,o.directionalShadow.length=f,o.directionalShadowMap.length=f,o.pointShadow.length=m,o.pointShadowMap.length=m,o.spotShadow.length=g,o.spotShadowMap.length=g,o.directionalShadowMatrix.length=f,o.pointShadowMatrix.length=m,o.spotShadowMatrix.length=g,_.directionalLength=c,_.pointLength=u,_.spotLength=h,_.rectAreaLength=d,_.hemiLength=p,_.numDirectionalShadows=f,_.numPointShadows=m,_.numSpotShadows=g,o.version=C5t++)},setupView:function u(t,e){let n=0,i=0,r=0,c=0,u=0;const h=e.matrixWorldInverse;for(let e=0,d=t.length;e<d;e++){const d=t[e];if(d.isDirectionalLight){const t=o.directional[n];t.direction.setFromMatrixPosition(d.matrixWorld),a.setFromMatrixPosition(d.target.matrixWorld),t.direction.sub(a),t.direction.transformDirection(h),n++}else if(d.isSpotLight){const t=o.spot[r];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),t.direction.setFromMatrixPosition(d.matrixWorld),a.setFromMatrixPosition(d.target.matrixWorld),t.direction.sub(a),t.direction.transformDirection(h),r++}else if(d.isRectAreaLight){const t=o.rectArea[c];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),l.identity(),s.copy(d.matrixWorld),s.premultiply(h),l.extractRotation(s),t.halfWidth.set(.5*d.width,0,0),t.halfHeight.set(0,.5*d.height,0),t.halfWidth.applyMatrix4(l),t.halfHeight.applyMatrix4(l),c++}else if(d.isPointLight){const t=o.point[i];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),i++}else if(d.isHemisphereLight){const t=o.hemi[u];t.direction.setFromMatrixPosition(d.matrixWorld),t.direction.transformDirection(h),t.direction.normalize(),u++}}},state:o}}function L5t(t,e){const n=new k5t(t,e),i=[],r=[];return{init:function o(){i.length=0,r.length=0},state:{lightsArray:i,shadowsArray:r,lights:n},setupLights:function a(){n.setup(i)},setupLightsView:function s(t){n.setupView(i,t)},pushLight:function l(t){i.push(t)},pushShadow:function c(t){r.push(t)}}}function P5t(t,e){let n=new WeakMap;return{get:function i(r,o=0){let a;return!1===n.has(r)?(a=new L5t(t,e),n.set(r,[a])):o>=n.get(r).length?(a=new L5t(t,e),n.get(r).push(a)):a=n.get(r)[o],a},dispose:function r(){n=new WeakMap}}}class N5t extends UQt{constructor(t){super(),this.type="MeshDepthMaterial",this.depthPacking=3200,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}N5t.prototype.isMeshDepthMaterial=!0;class I5t extends UQt{constructor(t){super(),this.type="MeshDistanceMaterial",this.referencePosition=new CJt,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}function R5t(t,e,n){let i=new i0t;const r=new mJt,o=new mJt,a=new wJt,s=new N5t({depthPacking:3201}),l=new I5t,c={},u=n.maxTextureSize,h={0:1,1:0,2:2},d=new G1t({defines:{SAMPLE_RATE:2/8,HALF_SAMPLE_RATE:1/8},uniforms:{shadow_pass:{value:null},resolution:{value:new mJt},radius:{value:4}},vertexShader:"void main() {\n\tgl_Position = vec4( position, 1.0 );\n}",fragmentShader:"uniform sampler2D shadow_pass;\nuniform vec2 resolution;\nuniform float radius;\n#include <packing>\nvoid main() {\n\tfloat mean = 0.0;\n\tfloat squared_mean = 0.0;\n\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\n\tfor ( float i = -1.0; i < 1.0 ; i += SAMPLE_RATE) {\n\t\t#ifdef HORIZONTAL_PASS\n\t\t\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( i, 0.0 ) * radius ) / resolution ) );\n\t\t\tmean += distribution.x;\n\t\t\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\n\t\t#else\n\t\t\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, i ) * radius ) / resolution ) );\n\t\t\tmean += depth;\n\t\t\tsquared_mean += depth * depth;\n\t\t#endif\n\t}\n\tmean = mean * HALF_SAMPLE_RATE;\n\tsquared_mean = squared_mean * HALF_SAMPLE_RATE;\n\tfloat std_dev = sqrt( squared_mean - mean * mean );\n\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\n}"}),p=d.clone();p.defines.HORIZONTAL_PASS=1;const f=new b1t;f.setAttribute("position",new QQt(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const m=new B1t(f,d),g=this;function _(n,i){const r=e.update(m);d.uniforms.shadow_pass.value=n.map.texture,d.uniforms.resolution.value=n.mapSize,d.uniforms.radius.value=n.radius,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,r,d,m,null),p.uniforms.shadow_pass.value=n.mapPass.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,r,p,m,null)}function y(e,n,i,r,o,a,u){let d=null;const p=!0===r.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===r.isPointLight?l:s,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let r=n[e];void 0===r&&(r=d.clone(),n[e]=r),d=r}return d.visible=i.visible,d.wireframe=i.wireframe,d.side=3===u?null!==i.shadowSide?i.shadowSide:i.side:null!==i.shadowSide?i.shadowSide:h[i.side],d.clipShadows=i.clipShadows,d.clippingPlanes=i.clippingPlanes,d.clipIntersection=i.clipIntersection,d.wireframeLinewidth=i.wireframeLinewidth,d.linewidth=i.linewidth,!0===r.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(r.matrixWorld),d.nearDistance=o,d.farDistance=a),d}function v(n,r,o,a,s){if(!1===n.visible)return;if(n.layers.test(r.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&3===s)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(o.matrixWorldInverse,n.matrixWorld);const i=e.update(n),r=n.material;if(Array.isArray(r)){const e=i.groups;for(let l=0,c=e.length;l<c;l++){const c=e[l],u=r[c.materialIndex];if(u&&u.visible){const e=y(n,0,u,a,o.near,o.far,s);t.renderBufferDirect(o,null,i,e,n,c)}}}else if(r.visible){const e=y(n,0,r,a,o.near,o.far,s);t.renderBufferDirect(o,null,i,e,n,null)}}const l=n.children;for(let t=0,e=l.length;t<e;t++)v(l[t],r,o,a,s)}this.enabled=!1,this.autoUpdate=!0,this.needsUpdate=!1,this.type=1,this.render=function(e,n,s){if(!1===g.enabled)return;if(!1===g.autoUpdate&&!1===g.needsUpdate)return;if(0===e.length)return;const 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Texture.minFilter should be set to THREE.NearestFilter or THREE.LinearFilter.")),!0===e.has("EXT_texture_filter_anisotropic")){const a=e.get("EXT_texture_filter_anisotropic");if(o.type===sZt&&!1===e.has("OES_texture_float_linear"))return;if(!1===s&&o.type===lZt&&!1===e.has("OES_texture_half_float_linear"))return;(o.anisotropy>1||i.get(o).__currentAnisotropy)&&(t.texParameterf(n,a.TEXTURE_MAX_ANISOTROPY_EXT,Math.min(o.anisotropy,r.getMaxAnisotropy())),i.get(o).__currentAnisotropy=o.anisotropy)}}function L(e,n){void 0===e.__webglInit&&(e.__webglInit=!0,n.addEventListener("dispose",w),e.__webglTexture=t.createTexture(),a.memory.textures++)}function P(e,i,r){let a=3553;i.isDataTexture2DArray&&(a=35866),i.isDataTexture3D&&(a=32879),L(e,i),n.activeTexture(33984+r),n.bindTexture(a,e.__webglTexture),t.pixelStorei(37440,i.flipY),t.pixelStorei(37441,i.premultiplyAlpha),t.pixelStorei(3317,i.unpackAlignment),t.pixelStorei(37443,0);const l=(function c(t){return!s&&(t.wrapS!==KKt||t.wrapT!==KKt||t.minFilter!==JKt&&t.minFilter!==eZt)})(i)&&!1===_(i.image),h=g(i.image,l,!1,u),d=_(h)||s,p=o.convert(i.format);let f,m=o.convert(i.type),x=b(i.internalFormat,p,m);k(a,i,d);const w=i.mipmaps;if(i.isDepthTexture)x=6402,s?x=i.type===sZt?36012:i.type===aZt?33190:i.type===cZt?35056:33189:i.type===sZt&&console.error("WebGLRenderer: Floating point depth texture requires WebGL2."),i.format===dZt&&6402===x&&i.type!==oZt&&i.type!==aZt&&(console.warn("THREE.WebGLRenderer: Use UnsignedShortType or UnsignedIntType for DepthFormat DepthTexture."),i.type=oZt,m=o.convert(i.type)),i.format===pZt&&6402===x&&(x=34041,i.type!==cZt&&(console.warn("THREE.WebGLRenderer: Use UnsignedInt248Type for DepthStencilFormat DepthTexture."),i.type=cZt,m=o.convert(i.type))),n.texImage2D(3553,0,x,h.width,h.height,0,p,m,null);else if(i.isDataTexture)if(w.length>0&&d){for(let t=0,e=w.length;t<e;t++)f=w[t],n.texImage2D(3553,t,x,f.width,f.height,0,p,m,f.data);i.generateMipmaps=!1,e.__maxMipLevel=w.length-1}else n.texImage2D(3553,0,x,h.width,h.height,0,p,m,h.data),e.__maxMipLevel=0;else if(i.isCompressedTexture){for(let t=0,e=w.length;t<e;t++)f=w[t],i.format!==hZt&&i.format!==uZt?null!==p?n.compressedTexImage2D(3553,t,x,f.width,f.height,0,f.data):console.warn("THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .uploadTexture()"):n.texImage2D(3553,t,x,f.width,f.height,0,p,m,f.data);e.__maxMipLevel=w.length-1}else if(i.isDataTexture2DArray)n.texImage3D(35866,0,x,h.width,h.height,h.depth,0,p,m,h.data),e.__maxMipLevel=0;else if(i.isDataTexture3D)n.texImage3D(32879,0,x,h.width,h.height,h.depth,0,p,m,h.data),e.__maxMipLevel=0;else if(w.length>0&&d){for(let t=0,e=w.length;t<e;t++)f=w[t],n.texImage2D(3553,t,x,p,m,f);i.generateMipmaps=!1,e.__maxMipLevel=w.length-1}else n.texImage2D(3553,0,x,p,m,h),e.__maxMipLevel=0;y(i,d)&&v(a,i,h.width,h.height),e.__version=i.version,i.onUpdate&&i.onUpdate(i)}function N(e,r,a,s,l){const c=o.convert(a.format),u=o.convert(a.type),h=b(a.internalFormat,c,u);32879===l||35866===l?n.texImage3D(l,0,h,r.width,r.height,r.depth,0,c,u,null):n.texImage2D(l,0,h,r.width,r.height,0,c,u,null),n.bindFramebuffer(36160,e),t.framebufferTexture2D(36160,s,l,i.get(a).__webglTexture,0),n.bindFramebuffer(36160,null)}function I(e,n,i){if(t.bindRenderbuffer(36161,e),n.depthBuffer&&!n.stencilBuffer){let r=33189;if(i){const e=n.depthTexture;e&&e.isDepthTexture&&(e.type===sZt?r=36012:e.type===aZt&&(r=33190));const i=R(n);t.renderbufferStorageMultisample(36161,i,r,n.width,n.height)}else t.renderbufferStorage(36161,r,n.width,n.height);t.framebufferRenderbuffer(36160,36096,36161,e)}else if(n.depthBuffer&&n.stencilBuffer){if(i){const e=R(n);t.renderbufferStorageMultisample(36161,e,35056,n.width,n.height)}else t.renderbufferStorage(36161,34041,n.width,n.height);t.framebufferRenderbuffer(36160,33306,36161,e)}else{const e=!0===n.isWebGLMultipleRenderTargets?n.texture[0]:n.texture,r=o.convert(e.format),a=o.convert(e.type),s=b(e.internalFormat,r,a);if(i){const e=R(n);t.renderbufferStorageMultisample(36161,e,s,n.width,n.height)}else t.renderbufferStorage(36161,s,n.width,n.height)}t.bindRenderbuffer(36161,null)}function R(t){return s&&t.isWebGLMultisampleRenderTarget?Math.min(h,t.samples):0}let O=!1,z=!1;this.allocateTextureUnit=function D(){const t=M;return t>=l&&console.warn("THREE.WebGLTextures: Trying to use "+t+" texture units while this GPU supports only "+l),M+=1,t},this.resetTextureUnits=function B(){M=0},this.setTexture2D=E,this.setTexture2DArray=function H(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?P(r,t,e):(n.activeTexture(33984+e),n.bindTexture(35866,r.__webglTexture))},this.setTexture3D=function F(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?P(r,t,e):(n.activeTexture(33984+e),n.bindTexture(32879,r.__webglTexture))},this.setTextureCube=T,this.setupRenderTarget=function V(e){const l=e.texture,c=i.get(e),u=i.get(l);e.addEventListener("dispose",S),!0!==e.isWebGLMultipleRenderTargets&&(u.__webglTexture=t.createTexture(),u.__version=l.version,a.memory.textures++);const h=!0===e.isWebGLCubeRenderTarget,d=!0===e.isWebGLMultipleRenderTargets,p=!0===e.isWebGLMultisampleRenderTarget,f=l.isDataTexture3D||l.isDataTexture2DArray,m=_(e)||s;if(!s||l.format!==uZt||l.type!==sZt&&l.type!==lZt||(l.format=hZt,console.warn("THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.")),h){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),d)if(r.drawBuffers){const n=e.texture;for(let e=0,r=n.length;e<r;e++){const r=i.get(n[e]);void 0===r.__webglTexture&&(r.__webglTexture=t.createTexture(),a.memory.textures++)}}else console.warn("THREE.WebGLRenderer: WebGLMultipleRenderTargets can only be used with WebGL2 or WEBGL_draw_buffers extension.");else if(p)if(s){c.__webglMultisampledFramebuffer=t.createFramebuffer(),c.__webglColorRenderbuffer=t.createRenderbuffer(),t.bindRenderbuffer(36161,c.__webglColorRenderbuffer);const i=o.convert(l.format),r=o.convert(l.type),a=b(l.internalFormat,i,r),s=R(e);t.renderbufferStorageMultisample(36161,s,a,e.width,e.height),n.bindFramebuffer(36160,c.__webglMultisampledFramebuffer),t.framebufferRenderbuffer(36160,36064,36161,c.__webglColorRenderbuffer),t.bindRenderbuffer(36161,null),e.depthBuffer&&(c.__webglDepthRenderbuffer=t.createRenderbuffer(),I(c.__webglDepthRenderbuffer,e,!0)),n.bindFramebuffer(36160,null)}else console.warn("THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.");if(h){n.bindTexture(34067,u.__webglTexture),k(34067,l,m);for(let t=0;t<6;t++)N(c.__webglFramebuffer[t],e,l,36064,34069+t);y(l,m)&&v(34067,l,e.width,e.height),n.bindTexture(34067,null)}else if(d){const t=e.texture;for(let r=0,o=t.length;r<o;r++){const o=t[r],a=i.get(o);n.bindTexture(3553,a.__webglTexture),k(3553,o,m),N(c.__webglFramebuffer,e,o,36064+r,3553),y(o,m)&&v(3553,o,e.width,e.height)}n.bindTexture(3553,null)}else{let t=3553;f&&(s?t=l.isDataTexture3D?32879:35866:console.warn("THREE.DataTexture3D and THREE.DataTexture2DArray only supported with WebGL2.")),n.bindTexture(t,u.__webglTexture),k(t,l,m),N(c.__webglFramebuffer,e,l,36064,t),y(l,m)&&v(t,l,e.width,e.height,e.depth),n.bindTexture(t,null)}e.depthBuffer&&(function g(e){const r=i.get(e),o=!0===e.isWebGLCubeRenderTarget;if(e.depthTexture){if(o)throw new Error("target.depthTexture not supported in Cube render targets");!(function a(e,r){if(r&&r.isWebGLCubeRenderTarget)throw new Error("Depth Texture with cube render targets is not supported");if(n.bindFramebuffer(36160,e),!r.depthTexture||!r.depthTexture.isDepthTexture)throw new Error("renderTarget.depthTexture must be an instance of THREE.DepthTexture");i.get(r.depthTexture).__webglTexture&&r.depthTexture.image.width===r.width&&r.depthTexture.image.height===r.height||(r.depthTexture.image.width=r.width,r.depthTexture.image.height=r.height,r.depthTexture.needsUpdate=!0),E(r.depthTexture,0);const o=i.get(r.depthTexture).__webglTexture;if(r.depthTexture.format===dZt)t.framebufferTexture2D(36160,36096,3553,o,0);else{if(r.depthTexture.format!==pZt)throw new Error("Unknown depthTexture format");t.framebufferTexture2D(36160,33306,3553,o,0)}})(r.__webglFramebuffer,e)}else if(o){r.__webglDepthbuffer=[];for(let i=0;i<6;i++)n.bindFramebuffer(36160,r.__webglFramebuffer[i]),r.__webglDepthbuffer[i]=t.createRenderbuffer(),I(r.__webglDepthbuffer[i],e,!1)}else n.bindFramebuffer(36160,r.__webglFramebuffer),r.__webglDepthbuffer=t.createRenderbuffer(),I(r.__webglDepthbuffer,e,!1);n.bindFramebuffer(36160,null)})(e)},this.updateRenderTargetMipmap=function U(t){const e=_(t)||s,r=!0===t.isWebGLMultipleRenderTargets?t.texture:[t.texture];for(let o=0,a=r.length;o<a;o++){const a=r[o];if(y(a,e)){const e=t.isWebGLCubeRenderTarget?34067:3553,r=i.get(a).__webglTexture;n.bindTexture(e,r),v(e,a,t.width,t.height),n.bindTexture(e,null)}}},this.updateMultisampleRenderTarget=function j(e){if(e.isWebGLMultisampleRenderTarget)if(s){const r=e.width,o=e.height;let a=16384;e.depthBuffer&&(a|=256),e.stencilBuffer&&(a|=1024);const s=i.get(e);n.bindFramebuffer(36008,s.__webglMultisampledFramebuffer),n.bindFramebuffer(36009,s.__webglFramebuffer),t.blitFramebuffer(0,0,r,o,0,0,r,o,a,9728),n.bindFramebuffer(36008,null),n.bindFramebuffer(36009,s.__webglMultisampledFramebuffer)}else console.warn("THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.")},this.safeSetTexture2D=function G(t,e){t&&t.isWebGLRenderTarget&&(!1===O&&(console.warn("THREE.WebGLTextures.safeSetTexture2D: don't use render targets as textures. Use their .texture property instead."),O=!0),t=t.texture),E(t,e)},this.safeSetTextureCube=function W(t,e){t&&t.isWebGLCubeRenderTarget&&(!1===z&&(console.warn("THREE.WebGLTextures.safeSetTextureCube: don't use cube render targets as textures. Use their .texture property instead."),z=!0),t=t.texture),T(t,e)}}function D5t(t,e,n){const i=n.isWebGL2;return{convert:function r(t){let n;if(t===rZt)return 5121;if(1017===t)return 32819;if(1018===t)return 32820;if(1019===t)return 33635;if(1010===t)return 5120;if(1011===t)return 5122;if(t===oZt)return 5123;if(1013===t)return 5124;if(t===aZt)return 5125;if(t===sZt)return 5126;if(t===lZt)return i?5131:(n=e.get("OES_texture_half_float"),null!==n?n.HALF_FLOAT_OES:null);if(1021===t)return 6406;if(t===uZt)return 6407;if(t===hZt)return 6408;if(1024===t)return 6409;if(1025===t)return 6410;if(t===dZt)return 6402;if(t===pZt)return 34041;if(1028===t)return 6403;if(1029===t)return 36244;if(1030===t)return 33319;if(1031===t)return 33320;if(1032===t)return 36248;if(1033===t)return 36249;if(t===fZt||t===mZt||t===gZt||t===_Zt){if(n=e.get("WEBGL_compressed_texture_s3tc"),null===n)return null;if(t===fZt)return n.COMPRESSED_RGB_S3TC_DXT1_EXT;if(t===mZt)return n.COMPRESSED_RGBA_S3TC_DXT1_EXT;if(t===gZt)return n.COMPRESSED_RGBA_S3TC_DXT3_EXT;if(t===_Zt)return n.COMPRESSED_RGBA_S3TC_DXT5_EXT}if(t===yZt||t===vZt||t===bZt||t===xZt){if(n=e.get("WEBGL_compressed_texture_pvrtc"),null===n)return null;if(t===yZt)return n.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(t===vZt)return n.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(t===bZt)return n.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(t===xZt)return n.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}if(36196===t)return n=e.get("WEBGL_compressed_texture_etc1"),null!==n?n.COMPRESSED_RGB_ETC1_WEBGL:null;if((t===wZt||t===SZt)&&(n=e.get("WEBGL_compressed_texture_etc"),null!==n)){if(t===wZt)return n.COMPRESSED_RGB8_ETC2;if(t===SZt)return n.COMPRESSED_RGBA8_ETC2_EAC}return 37808===t||37809===t||37810===t||37811===t||37812===t||37813===t||37814===t||37815===t||37816===t||37817===t||37818===t||37819===t||37820===t||37821===t||37840===t||37841===t||37842===t||37843===t||37844===t||37845===t||37846===t||37847===t||37848===t||37849===t||37850===t||37851===t||37852===t||37853===t?(n=e.get("WEBGL_compressed_texture_astc"),null!==n?t:null):36492===t?(n=e.get("EXT_texture_compression_bptc"),null!==n?t:null):t===cZt?i?34042:(n=e.get("WEBGL_depth_texture"),null!==n?n.UNSIGNED_INT_24_8_WEBGL:null):void 0}}}I5t.prototype.isMeshDistanceMaterial=!0;class B5t extends q1t{constructor(t=[]){super(),this.cameras=t}}B5t.prototype.isArrayCamera=!0;class H5t extends kQt{constructor(){super(),this.type="Group"}}H5t.prototype.isGroup=!0;const F5t={type:"move"};class V5t{constructor(){this._targetRay=null,this._grip=null,this._hand=null}getHandSpace(){return null===this._hand&&(this._hand=new H5t,this._hand.matrixAutoUpdate=!1,this._hand.visible=!1,this._hand.joints={},this._hand.inputState={pinching:!1}),this._hand}getTargetRaySpace(){return null===this._targetRay&&(this._targetRay=new H5t,this._targetRay.matrixAutoUpdate=!1,this._targetRay.visible=!1,this._targetRay.hasLinearVelocity=!1,this._targetRay.linearVelocity=new CJt,this._targetRay.hasAngularVelocity=!1,this._targetRay.angularVelocity=new CJt),this._targetRay}getGripSpace(){return null===this._grip&&(this._grip=new H5t,this._grip.matrixAutoUpdate=!1,this._grip.visible=!1,this._grip.hasLinearVelocity=!1,this._grip.linearVelocity=new CJt,this._grip.hasAngularVelocity=!1,this._grip.angularVelocity=new CJt),this._grip}dispatchEvent(t){return null!==this._targetRay&&this._targetRay.dispatchEvent(t),null!==this._grip&&this._grip.dispatchEvent(t),null!==this._hand&&this._hand.dispatchEvent(t),this}disconnect(t){return this.dispatchEvent({type:"disconnected",data:t}),null!==this._targetRay&&(this._targetRay.visible=!1),null!==this._grip&&(this._grip.visible=!1),null!==this._hand&&(this._hand.visible=!1),this}update(t,e,n){let i=null,r=null,o=null;const a=this._targetRay,s=this._grip,l=this._hand;if(t&&"visible-blurred"!==e.session.visibilityState)if(null!==a&&(i=e.getPose(t.targetRaySpace,n),null!==i&&(a.matrix.fromArray(i.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),i.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(i.linearVelocity)):a.hasLinearVelocity=!1,i.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(i.angularVelocity)):a.hasAngularVelocity=!1,this.dispatchEvent(F5t))),l&&t.hand){o=!0;for(const i of t.hand.values()){const t=e.getJointPose(i,n);if(void 0===l.joints[i.jointName]){const t=new H5t;t.matrixAutoUpdate=!1,t.visible=!1,l.joints[i.jointName]=t,l.add(t)}const r=l.joints[i.jointName];null!==t&&(r.matrix.fromArray(t.transform.matrix),r.matrix.decompose(r.position,r.rotation,r.scale),r.jointRadius=t.radius),r.visible=null!==t}const i=l.joints["index-finger-tip"].position.distanceTo(l.joints["thumb-tip"].position),r=.02,a=.005;l.inputState.pinching&&i>r+a?(l.inputState.pinching=!1,this.dispatchEvent({type:"pinchend",handedness:t.handedness,target:this})):!l.inputState.pinching&&i<=r-a&&(l.inputState.pinching=!0,this.dispatchEvent({type:"pinchstart",handedness:t.handedness,target:this}))}else null!==s&&t.gripSpace&&(r=e.getPose(t.gripSpace,n),null!==r&&(s.matrix.fromArray(r.transform.matrix),s.matrix.decompose(s.position,s.rotation,s.scale),r.linearVelocity?(s.hasLinearVelocity=!0,s.linearVelocity.copy(r.linearVelocity)):s.hasLinearVelocity=!1,r.angularVelocity?(s.hasAngularVelocity=!0,s.angularVelocity.copy(r.angularVelocity)):s.hasAngularVelocity=!1));return null!==a&&(a.visible=null!==i),null!==s&&(s.visible=null!==r),null!==l&&(l.visible=null!==o),this}}class U5t extends jZt{constructor(t,e){super();const n=this,i=t.state;let r=null,o=1,a=null,s="local-floor",l=null,c=null,u=null,h=null,d=null;const p=[],f=new Map,m=new q1t;m.layers.enable(1),m.viewport=new wJt;const g=new q1t;g.layers.enable(2),g.viewport=new wJt;const _=[m,g],y=new B5t;y.layers.enable(1),y.layers.enable(2);let v=null,b=null;function x(t){const e=f.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function w(){f.forEach((function(t,e){t.disconnect(e)})),f.clear(),v=null,b=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),A.stop(),n.isPresenting=!1,n.dispatchEvent({type:"sessionend"})}function S(t){const e=r.inputSources;for(let t=0;t<p.length;t++)f.set(e[t],p[t]);for(let e=0;e<t.removed.length;e++){const n=t.removed[e],i=f.get(n);i&&(i.dispatchEvent({type:"disconnected",data:n}),f.delete(n))}for(let e=0;e<t.added.length;e++){const n=t.added[e],i=f.get(n);i&&i.dispatchEvent({type:"connected",data:n})}}this.cameraAutoUpdate=!0,this.enabled=!1,this.isPresenting=!1,this.getController=function(t){let e=p[t];return void 0===e&&(e=new V5t,p[t]=e),e.getTargetRaySpace()},this.getControllerGrip=function(t){let e=p[t];return void 0===e&&(e=new V5t,p[t]=e),e.getGripSpace()},this.getHand=function(t){let e=p[t];return void 0===e&&(e=new V5t,p[t]=e),e.getHandSpace()},this.setFramebufferScaleFactor=function(t){o=t,!0===n.isPresenting&&console.warn("THREE.WebXRManager: Cannot change framebuffer scale while presenting.")},this.setReferenceSpaceType=function(t){s=t,!0===n.isPresenting&&console.warn("THREE.WebXRManager: Cannot change reference space type while presenting.")},this.getReferenceSpace=function(){return a},this.getSession=function(){return r},this.setSession=async function(t){if(r=t,null!==r){r.addEventListener("select",x),r.addEventListener("selectstart",x),r.addEventListener("selectend",x),r.addEventListener("squeeze",x),r.addEventListener("squeezestart",x),r.addEventListener("squeezeend",x),r.addEventListener("end",w),r.addEventListener("inputsourceschange",S);const t=e.getContextAttributes();if(!0!==t.xrCompatible&&await e.makeXRCompatible(),void 0===r.renderState.layers)d=new XRWebGLLayer(r,e,{antialias:t.antialias,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:o}),r.updateRenderState({baseLayer:d});else{let n=0;if(t.antialias)d=new XRWebGLLayer(r,e,{antialias:!0,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:o}),r.updateRenderState({layers:[d]});else{t.depth&&(n=t.stencil?34041:6402);const i={colorFormat:t.alpha?6408:6407,depthFormat:n,scaleFactor:o};c=new XRWebGLBinding(r,e),h=c.createProjectionLayer(i),u=e.createFramebuffer(),r.updateRenderState({layers:[h]})}}a=await r.requestReferenceSpace(s),A.setContext(r),A.start(),n.isPresenting=!0,n.dispatchEvent({type:"sessionstart"})}};const M=new CJt,E=new CJt;function T(t,e){null===e?t.matrixWorld.copy(t.matrix):t.matrixWorld.multiplyMatrices(e.matrixWorld,t.matrix),t.matrixWorldInverse.copy(t.matrixWorld).invert()}this.updateCamera=function(t){if(null===r)return;y.near=g.near=m.near=t.near,y.far=g.far=m.far=t.far,v===y.near&&b===y.far||(r.updateRenderState({depthNear:y.near,depthFar:y.far}),v=y.near,b=y.far);const e=t.parent,n=y.cameras;T(y,e);for(let t=0;t<n.length;t++)T(n[t],e);y.matrixWorld.decompose(y.position,y.quaternion,y.scale),t.position.copy(y.position),t.quaternion.copy(y.quaternion),t.scale.copy(y.scale),t.matrix.copy(y.matrix),t.matrixWorld.copy(y.matrixWorld);const i=t.children;for(let t=0,e=i.length;t<e;t++)i[t].updateMatrixWorld(!0);2===n.length?(function o(t,e,n){M.setFromMatrixPosition(e.matrixWorld),E.setFromMatrixPosition(n.matrixWorld);const 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n=!1;t.length!==y.cameras.length&&(y.cameras.length=0,n=!0);for(let r=0;r<t.length;r++){const o=t[r];let a=null;if(null!==d)a=d.getViewport(o);else{const t=c.getViewSubImage(h,o);i.bindXRFramebuffer(u),void 0!==t.depthStencilTexture&&e.framebufferTexture2D(36160,36096,3553,t.depthStencilTexture,0),e.framebufferTexture2D(36160,36064,3553,t.colorTexture,0),a=t.viewport}const s=_[r];s.matrix.fromArray(o.transform.matrix),s.projectionMatrix.fromArray(o.projectionMatrix),s.viewport.set(a.x,a.y,a.width,a.height),0===r&&y.matrix.copy(s.matrix),!0===n&&y.cameras.push(s)}}const o=r.inputSources;for(let t=0;t<p.length;t++)p[t].update(o[t],n,a);C&&C(t,n)})),this.setAnimationLoop=function(t){C=t},this.dispose=function(){}}}function j5t(t){function e(e,n){e.opacity.value=n.opacity,n.color&&e.diffuse.value.copy(n.color),n.emissive&&e.emissive.value.copy(n.emissive).multiplyScalar(n.emissiveIntensity),n.map&&(e.map.value=n.map),n.alphaMap&&(e.alphaMap.value=n.alphaMap),n.specularMap&&(e.specularMap.value=n.specularMap);const i=t.get(n).envMap;if(i){e.envMap.value=i,e.flipEnvMap.value=i.isCubeTexture&&!1===i.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.refractionRatio.value=n.refractionRatio;const r=t.get(i).__maxMipLevel;void 0!==r&&(e.maxMipLevel.value=r)}let r,o;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?r=n.map:n.specularMap?r=n.specularMap:n.displacementMap?r=n.displacementMap:n.normalMap?r=n.normalMap:n.bumpMap?r=n.bumpMap:n.roughnessMap?r=n.roughnessMap:n.metalnessMap?r=n.metalnessMap:n.alphaMap?r=n.alphaMap:n.emissiveMap?r=n.emissiveMap:n.clearcoatMap?r=n.clearcoatMap:n.clearcoatNormalMap?r=n.clearcoatNormalMap:n.clearcoatRoughnessMap?r=n.clearcoatRoughnessMap:n.specularIntensityMap?r=n.specularIntensityMap:n.specularTintMap&&(r=n.specularTintMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uvTransform.value.copy(r.matrix)),n.aoMap?o=n.aoMap:n.lightMap&&(o=n.lightMap),void 0!==o&&(o.isWebGLRenderTarget&&(o=o.texture),!0===o.matrixAutoUpdate&&o.updateMatrix(),e.uv2Transform.value.copy(o.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,1===n.side&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),1===n.side&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias),t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function i(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function r(t,i,o,a,s){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),(function l(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)})(t,i)):i.isMeshToonMaterial?(e(t,i),(function c(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap),e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1)),e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate()),e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,i)):i.isMeshPhongMaterial?(e(t,i),(function u(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap),e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1)),e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate()),e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?(function h(t,e,i){n(t,e),t.reflectivity.value=e.reflectivity,t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.sheen&&t.sheen.value.copy(e.sheen),e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,1===e.side&&t.clearcoatNormalScale.value.negate()),t.transmission.value=e.transmission,e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),e.transmission>0&&(t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height)),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint),t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap),e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)})(t,i,s):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),(function d(t,e){e.matcap&&(t.matcap.value=e.matcap),e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1)),e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate()),e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,i)):i.isMeshDepthMaterial?(e(t,i),(function p(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,i)):i.isMeshDistanceMaterial?(e(t,i),(function 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i=e.getAttributes();t.hasPositions&&(dt.bindBuffer(34962,n.position),dt.bufferData(34962,t.positionArray,35048),ht.enableAttribute(i.position),dt.vertexAttribPointer(i.position,3,5126,!1,0,0)),t.hasNormals&&(dt.bindBuffer(34962,n.normal),dt.bufferData(34962,t.normalArray,35048),ht.enableAttribute(i.normal),dt.vertexAttribPointer(i.normal,3,5126,!1,0,0)),t.hasUvs&&(dt.bindBuffer(34962,n.uv),dt.bufferData(34962,t.uvArray,35048),ht.enableAttribute(i.uv),dt.vertexAttribPointer(i.uv,2,5126,!1,0,0)),t.hasColors&&(dt.bindBuffer(34962,n.color),dt.bufferData(34962,t.colorArray,35048),ht.enableAttribute(i.color),dt.vertexAttribPointer(i.color,3,5126,!1,0,0)),ht.disableUnusedAttributes(),dt.drawArrays(4,0,t.count),t.count=0},this.renderBufferDirect=function(t,e,n,i,r,o){null===e&&(e=V);const a=r.isMesh&&r.matrixWorld.determinant()<0,s=At(t,e,i,r);W.setMaterial(i,a);let l=n.index;const c=n.attributes.position;if(null===l){if(void 0===c||0===c.count)return}else if(0===l.count)return;let 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if((t.isMesh||t.isLine||t.isPoints)&&(t.isSkinnedMesh&&t.skeleton.frame!==q.render.frame&&(t.skeleton.update(),t.skeleton.frame=q.render.frame),!t.frustumCulled||O.intersectsObject(t))){i&&F.setFromMatrixPosition(t.matrixWorld).applyMatrix4(H);const e=Q.update(t),r=t.material;if(Array.isArray(r)){const i=e.groups;for(let o=0,a=i.length;o<a;o++){const a=i[o],s=r[a.materialIndex];s&&s.visible&&d.push(t,e,s,n,F.z,a)}}else r.visible&&d.push(t,e,r,n,F.z,null)}const r=t.children;for(let t=0,o=r.length;t<o;t++)St(r[t],e,n,i)}function Mt(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;if(n.isArrayCamera){const r=n.cameras;for(let n=0,o=r.length;n<o;n++){const o=r[n];W.viewport(S.copy(o.viewport)),p.setupLightsView(o);for(let n=0,r=t.length;n<r;n++){const r=t[n],a=r.object,s=r.geometry,l=null===i?r.material:i,c=r.group;a.layers.test(o.layers)&&Et(a,e,o,s,l,c)}}}else for(let r=0,o=t.length;r<o;r++){const o=t[r];Et(o.object,e,n,o.geometry,null===i?o.material:i,o.group)}}function Et(t,e,n,i,r,o){if(t.onBeforeRender(g,e,n,i,r,o),t.modelViewMatrix.multiplyMatrices(n.matrixWorldInverse,t.matrixWorld),t.normalMatrix.getNormalMatrix(t.modelViewMatrix),t.isImmediateRenderObject){const i=At(n,e,r,t);W.setMaterial(r),ht.reset(),(function a(t,e){t.render((function(t){g.renderBufferImmediate(t,e)}))})(t,i)}else!0===r.transparent&&2===r.side?(r.side=1,r.needsUpdate=!0,g.renderBufferDirect(n,e,i,r,t,o),r.side=0,r.needsUpdate=!0,g.renderBufferDirect(n,e,i,r,t,o),r.side=2):g.renderBufferDirect(n,e,i,r,t,o);t.onAfterRender(g,e,n,i,r,o)}function Tt(t,e,n){!0!==e.isScene&&(e=V);const i=Y.get(t),r=p.state.lights,o=r.state.version,a=tt.getParameters(t,r.state,p.state.shadowsArray,e,n),s=tt.getProgramCacheKey(a);let l=i.programs;i.environment=t.isMeshStandardMaterial?e.environment:null,i.fog=e.fog,i.envMap=(t.isMeshStandardMaterial?K:$).get(t.envMap||i.environment),void 0===l&&(t.addEventListener("dispose",yt),l=new Map,i.programs=l);let c=l.get(s);if(void 0!==c){if(i.currentProgram===c&&i.lightsStateVersion===o)return Ct(t,a),c}else a.uniforms=tt.getUniforms(t),t.onBuild(a,g),t.onBeforeCompile(a,g),c=tt.acquireProgram(a,s),l.set(s,c),i.uniforms=a.uniforms;const u=i.uniforms;(t.isShaderMaterial||t.isRawShaderMaterial)&&!0!==t.clipping||(u.clippingPlanes=rt.uniform),Ct(t,a),i.needsLights=(function h(t){return t.isMeshLambertMaterial||t.isMeshToonMaterial||t.isMeshPhongMaterial||t.isMeshStandardMaterial||t.isShadowMaterial||t.isShaderMaterial&&!0===t.lights})(t),i.lightsStateVersion=o,i.needsLights&&(u.ambientLightColor.value=r.state.ambient,u.lightProbe.value=r.state.probe,u.directionalLights.value=r.state.directional,u.directionalLightShadows.value=r.state.directionalShadow,u.spotLights.value=r.state.spot,u.spotLightShadows.value=r.state.spotShadow,u.rectAreaLights.value=r.state.rectArea,u.ltc_1.value=r.state.rectAreaLTC1,u.ltc_2.value=r.state.rectAreaLTC2,u.pointLights.value=r.state.point,u.pointLightShadows.value=r.state.pointShadow,u.hemisphereLights.value=r.state.hemi,u.directionalShadowMap.value=r.state.directionalShadowMap,u.directionalShadowMatrix.value=r.state.directionalShadowMatrix,u.spotShadowMap.value=r.state.spotShadowMap,u.spotShadowMatrix.value=r.state.spotShadowMatrix,u.pointShadowMap.value=r.state.pointShadowMap,u.pointShadowMatrix.value=r.state.pointShadowMatrix);const d=c.getUniforms(),f=Q2t.seqWithValue(d.seq,u);return i.currentProgram=c,i.uniformsList=f,c}function Ct(t,e){const n=Y.get(t);n.outputEncoding=e.outputEncoding,n.instancing=e.instancing,n.skinning=e.skinning,n.morphTargets=e.morphTargets,n.morphNormals=e.morphNormals,n.numClippingPlanes=e.numClippingPlanes,n.numIntersection=e.numClipIntersection,n.vertexAlphas=e.vertexAlphas,n.vertexTangents=e.vertexTangents}function At(t,e,n,i){!0!==e.isScene&&(e=V),X.resetTextureUnits();const r=e.fog,o=null===b?g.outputEncoding:b.texture.encoding,a=(n.isMeshStandardMaterial?K:$).get(n.envMap||(n.isMeshStandardMaterial?e.environment:null)),s=!0===n.vertexColors&&!!i.geometry&&!!i.geometry.attributes.color&&4===i.geometry.attributes.color.itemSize,l=!!i.geometry&&!!i.geometry.attributes.tangent,c=!!i.geometry&&!!i.geometry.morphAttributes.position,u=!!i.geometry&&!!i.geometry.morphAttributes.normal,h=Y.get(n),d=p.state.lights;!0!==z||!0!==D&&t===w||rt.setState(n,t,t===w&&n.id===x);let f=!1;n.version===h.__version?h.needsLights&&h.lightsStateVersion!==d.state.version||h.outputEncoding!==o||i.isInstancedMesh&&!1===h.instancing?f=!0:i.isInstancedMesh||!0!==h.instancing?i.isSkinnedMesh&&!1===h.skinning?f=!0:i.isSkinnedMesh||!0!==h.skinning?h.envMap!==a||n.fog&&h.fog!==r?f=!0:void 0===h.numClippingPlanes||h.numClippingPlanes===rt.numPlanes&&h.numIntersection===rt.numIntersection?(h.vertexAlphas!==s||h.vertexTangents!==l||h.morphTargets!==c||h.morphNormals!==u)&&(f=!0):f=!0:f=!0:f=!0:(f=!0,h.__version=n.version);let m=h.currentProgram;!0===f&&(m=Tt(n,e,i));let _=!1,y=!1,v=!1;const S=m.getUniforms(),M=h.uniforms;if(W.useProgram(m.program)&&(_=!0,y=!0,v=!0),n.id!==x&&(x=n.id,y=!0),_||w!==t){if(S.setValue(dt,"projectionMatrix",t.projectionMatrix),G.logarithmicDepthBuffer&&S.setValue(dt,"logDepthBufFC",2/(Math.log(t.far+1)/Math.LN2)),w!==t&&(w=t,y=!0,v=!0),n.isShaderMaterial||n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshStandardMaterial||n.envMap){const e=S.map.cameraPosition;void 0!==e&&e.setValue(dt,F.setFromMatrixPosition(t.matrixWorld))}(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial)&&S.setValue(dt,"isOrthographic",!0===t.isOrthographicCamera),(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial||n.isShadowMaterial||i.isSkinnedMesh)&&S.setValue(dt,"viewMatrix",t.matrixWorldInverse)}if(i.isSkinnedMesh){S.setOptional(dt,i,"bindMatrix"),S.setOptional(dt,i,"bindMatrixInverse");const t=i.skeleton;t&&(G.floatVertexTextures?(null===t.boneTexture&&t.computeBoneTexture(),S.setValue(dt,"boneTexture",t.boneTexture,X),S.setValue(dt,"boneTextureSize",t.boneTextureSize)):S.setOptional(dt,t,"boneMatrices"))}return(y||h.receiveShadow!==i.receiveShadow)&&(h.receiveShadow=i.receiveShadow,S.setValue(dt,"receiveShadow",i.receiveShadow)),y&&(S.setValue(dt,"toneMappingExposure",g.toneMappingExposure),h.needsLights&&(function E(t,e){t.ambientLightColor.needsUpdate=e,t.lightProbe.needsUpdate=e,t.directionalLights.needsUpdate=e,t.directionalLightShadows.needsUpdate=e,t.pointLights.needsUpdate=e,t.pointLightShadows.needsUpdate=e,t.spotLights.needsUpdate=e,t.spotLightShadows.needsUpdate=e,t.rectAreaLights.needsUpdate=e,t.hemisphereLights.needsUpdate=e})(M,v),r&&n.fog&&et.refreshFogUniforms(M,r),et.refreshMaterialUniforms(M,n,A,C,B),Q2t.upload(dt,h.uniformsList,M,X)),n.isShaderMaterial&&!0===n.uniformsNeedUpdate&&(Q2t.upload(dt,h.uniformsList,M,X),n.uniformsNeedUpdate=!1),n.isSpriteMaterial&&S.setValue(dt,"center",i.center),S.setValue(dt,"modelViewMatrix",i.modelViewMatrix),S.setValue(dt,"normalMatrix",i.normalMatrix),S.setValue(dt,"modelMatrix",i.matrixWorld),m}wt.setAnimationLoop((function kt(t){vt&&vt(t)})),"undefined"!=typeof window&&wt.setContext(window),this.setAnimationLoop=function(t){vt=t,mt.setAnimationLoop(t),null===t?wt.stop():wt.start()},mt.addEventListener("sessionstart",bt),mt.addEventListener("sessionend",xt),this.render=function(t,e){if(void 0!==e&&!0!==e.isCamera)return void console.error("THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera.");if(!0===_)return;!0===t.autoUpdate&&t.updateMatrixWorld(),null===e.parent&&e.updateMatrixWorld(),!0===mt.enabled&&!0===mt.isPresenting&&(!0===mt.cameraAutoUpdate&&mt.updateCamera(e),e=mt.getCamera()),!0===t.isScene&&t.onBeforeRender(g,t,e,b),p=it.get(t,m.length),p.init(),m.push(p),H.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),O.setFromProjectionMatrix(H),D=this.localClippingEnabled,z=rt.init(this.clippingPlanes,D,e),d=nt.get(t,f.length),d.init(),f.push(d),St(t,e,0,g.sortObjects),d.finish(),!0===g.sortObjects&&d.sort(k,L),!0===z&&rt.beginShadows(),ot.render(p.state.shadowsArray,t,e),p.setupLights(),p.setupLightsView(e),!0===z&&rt.endShadows(),!0===this.info.autoReset&&this.info.reset(),at.render(d,t);const n=d.opaque,i=d.transmissive,r=d.transparent;n.length>0&&Mt(n,t,e),i.length>0&&(function o(t,e,n,i){null===B&&(B=new(!0===s&&!0===G.isWebGL2?EJt:SJt)(1024,1024,{generateMipmaps:!0,type:null!==ut.convert(lZt)?lZt:rZt,minFilter:iZt,magFilter:JKt,wrapS:KKt,wrapT:KKt}));const r=g.getRenderTarget();g.setRenderTarget(B),g.clear();const o=g.toneMapping;g.toneMapping=0,Mt(t,n,i),g.toneMapping=o,X.updateMultisampleRenderTarget(B),X.updateRenderTargetMipmap(B),g.setRenderTarget(r),Mt(e,n,i)})(n,i,t,e),r.length>0&&Mt(r,t,e),null!==b&&(X.updateMultisampleRenderTarget(b),X.updateRenderTargetMipmap(b)),!0===t.isScene&&t.onAfterRender(g,t,e),W.buffers.depth.setTest(!0),W.buffers.depth.setMask(!0),W.buffers.color.setMask(!0),W.setPolygonOffset(!1),ht.resetDefaultState(),x=-1,w=null,m.pop(),p=m.length>0?m[m.length-1]:null,f.pop(),d=f.length>0?f[f.length-1]:null},this.getActiveCubeFace=function(){return y},this.getActiveMipmapLevel=function(){return v},this.getRenderTarget=function(){return b},this.setRenderTarget=function(t,e=0,n=0){b=t,y=e,v=n,t&&void 0===Y.get(t).__webglFramebuffer&&X.setupRenderTarget(t);let i=null,r=!1,o=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(o=!0);const a=Y.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=a[e],r=!0):i=t.isWebGLMultisampleRenderTarget?Y.get(t).__webglMultisampledFramebuffer:a,S.copy(t.viewport),M.copy(t.scissor),E=t.scissorTest}else S.copy(P).multiplyScalar(A).floor(),M.copy(N).multiplyScalar(A).floor(),E=I;if(W.bindFramebuffer(36160,i)&&G.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(R.length!==n.length||36064!==R[0]){for(let t=0,e=n.length;t<e;t++)R[t]=36064+t;R.length=n.length,e=!0}}else 1===R.length&&36064===R[0]||(R[0]=36064,R.length=1,e=!0);else 1===R.length&&1029===R[0]||(R[0]=1029,R.length=1,e=!0);e&&(G.isWebGL2?dt.drawBuffers(R):j.get("WEBGL_draw_buffers").drawBuffersWEBGL(R))}if(W.viewport(S),W.scissor(M),W.setScissorTest(E),r){const i=Y.get(t.texture);dt.framebufferTexture2D(36160,36064,34069+e,i.__webglTexture,n)}else if(o){const i=Y.get(t.texture);dt.framebufferTextureLayer(36160,36064,i.__webglTexture,n||0,e||0)}},this.readRenderTargetPixels=function(t,e,n,i,r,o,a){if(!t||!t.isWebGLRenderTarget)return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.");let s=Y.get(t).__webglFramebuffer;if(t.isWebGLCubeRenderTarget&&void 0!==a&&(s=s[a]),s){W.bindFramebuffer(36160,s);try{const a=t.texture,s=a.format,l=a.type;if(s!==hZt&&ut.convert(s)!==dt.getParameter(35739))return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format.");const c=l===lZt&&(j.has("EXT_color_buffer_half_float")||G.isWebGL2&&j.has("EXT_color_buffer_float"));if(!(l===rZt||ut.convert(l)===dt.getParameter(35738)||l===sZt&&(G.isWebGL2||j.has("OES_texture_float")||j.has("WEBGL_color_buffer_float"))||c))return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.");36053===dt.checkFramebufferStatus(36160)?e>=0&&e<=t.width-i&&n>=0&&n<=t.height-r&&dt.readPixels(e,n,i,r,ut.convert(s),ut.convert(l),o):console.error("THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.")}finally{const t=null!==b?Y.get(b).__webglFramebuffer:null;W.bindFramebuffer(36160,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),r=Math.floor(e.image.width*i),o=Math.floor(e.image.height*i);let a=ut.convert(e.format);G.isWebGL2&&(6407===a&&(a=32849),6408===a&&(a=32856)),X.setTexture2D(e,0),dt.copyTexImage2D(3553,n,a,t.x,t.y,r,o,0),W.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const r=e.image.width,o=e.image.height,a=ut.convert(n.format),s=ut.convert(n.type);X.setTexture2D(n,0),dt.pixelStorei(37440,n.flipY),dt.pixelStorei(37441,n.premultiplyAlpha),dt.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?dt.texSubImage2D(3553,i,t.x,t.y,r,o,a,s,e.image.data):e.isCompressedTexture?dt.compressedTexSubImage2D(3553,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,a,e.mipmaps[0].data):dt.texSubImage2D(3553,i,t.x,t.y,a,s,e.image),0===i&&n.generateMipmaps&&dt.generateMipmap(3553),W.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,r=0){if(g.isWebGL1Renderer)return void console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.");const o=t.max.x-t.min.x+1,a=t.max.y-t.min.y+1,s=t.max.z-t.min.z+1,l=ut.convert(i.format),c=ut.convert(i.type);let u;if(i.isDataTexture3D)X.setTexture3D(i,0),u=32879;else{if(!i.isDataTexture2DArray)return void console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.");X.setTexture2DArray(i,0),u=35866}dt.pixelStorei(37440,i.flipY),dt.pixelStorei(37441,i.premultiplyAlpha),dt.pixelStorei(3317,i.unpackAlignment);const h=dt.getParameter(3314),d=dt.getParameter(32878),p=dt.getParameter(3316),f=dt.getParameter(3315),m=dt.getParameter(32877),_=n.isCompressedTexture?n.mipmaps[0]:n.image;dt.pixelStorei(3314,_.width),dt.pixelStorei(32878,_.height),dt.pixelStorei(3316,t.min.x),dt.pixelStorei(3315,t.min.y),dt.pixelStorei(32877,t.min.z),n.isDataTexture||n.isDataTexture3D?dt.texSubImage3D(u,r,e.x,e.y,e.z,o,a,s,l,c,_.data):n.isCompressedTexture?(console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture."),dt.compressedTexSubImage3D(u,r,e.x,e.y,e.z,o,a,s,l,_.data)):dt.texSubImage3D(u,r,e.x,e.y,e.z,o,a,s,l,c,_),dt.pixelStorei(3314,h),dt.pixelStorei(32878,d),dt.pixelStorei(3316,p),dt.pixelStorei(3315,f),dt.pixelStorei(32877,m),0===r&&i.generateMipmaps&&dt.generateMipmap(u),W.unbindTexture()},this.initTexture=function(t){X.setTexture2D(t,0),W.unbindTexture()},this.resetState=function(){y=0,v=0,b=null,W.reset(),ht.reset()},"undefined"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}class W5t extends G5t{}W5t.prototype.isWebGL1Renderer=!0;class q5t{constructor(t,e=25e-5){this.name="",this.color=new $Qt(t),this.density=e}clone(){return new q5t(this.color,this.density)}toJSON(){return{type:"FogExp2",color:this.color.getHex(),density:this.density}}}q5t.prototype.isFogExp2=!0;class Y5t{constructor(t,e=1,n=1e3){this.name="",this.color=new $Qt(t),this.near=e,this.far=n}clone(){return new Y5t(this.color,this.near,this.far)}toJSON(){return{type:"Fog",color:this.color.getHex(),near:this.near,far:this.far}}}Y5t.prototype.isFog=!0;class X5t extends kQt{constructor(){super(),this.type="Scene",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,"undefined"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}X5t.prototype.isScene=!0;class $5t{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=FZt,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=XZt()}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.array=new t.array.constructor(t.array),this.count=t.count,this.stride=t.stride,this.usage=t.usage,this}copyAt(t,e,n){t*=this.stride,n*=e.stride;for(let i=0,r=this.stride;i<r;i++)this.array[t+i]=e.array[n+i];return this}set(t,e=0){return this.array.set(t,e),this}clone(t){void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=XZt()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=this.array.slice(0).buffer);const e=new this.array.constructor(t.arrayBuffers[this.array.buffer._uuid]),n=new this.constructor(e,this.stride);return n.setUsage(this.usage),n}onUpload(t){return this.onUploadCallback=t,this}toJSON(t){return void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=XZt()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=Array.prototype.slice.call(new Uint32Array(this.array.buffer))),{uuid:this.uuid,buffer:this.array.buffer._uuid,type:this.array.constructor.name,stride:this.stride}}}$5t.prototype.isInterleavedBuffer=!0;const K5t=new CJt;class Z5t{constructor(t,e,n,i=!1){this.name="",this.data=t,this.itemSize=e,this.offset=n,this.normalized=!0===i}get count(){return this.data.count}get array(){return this.data.array}set needsUpdate(t){this.data.needsUpdate=t}applyMatrix4(t){for(let e=0,n=this.data.count;e<n;e++)K5t.x=this.getX(e),K5t.y=this.getY(e),K5t.z=this.getZ(e),K5t.applyMatrix4(t),this.setXYZ(e,K5t.x,K5t.y,K5t.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)K5t.x=this.getX(e),K5t.y=this.getY(e),K5t.z=this.getZ(e),K5t.applyNormalMatrix(t),this.setXYZ(e,K5t.x,K5t.y,K5t.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)K5t.x=this.getX(e),K5t.y=this.getY(e),K5t.z=this.getZ(e),K5t.transformDirection(t),this.setXYZ(e,K5t.x,K5t.y,K5t.z);return this}setX(t,e){return this.data.array[t*this.data.stride+this.offset]=e,this}setY(t,e){return this.data.array[t*this.data.stride+this.offset+1]=e,this}setZ(t,e){return this.data.array[t*this.data.stride+this.offset+2]=e,this}setW(t,e){return this.data.array[t*this.data.stride+this.offset+3]=e,this}getX(t){return this.data.array[t*this.data.stride+this.offset]}getY(t){return this.data.array[t*this.data.stride+this.offset+1]}getZ(t){return this.data.array[t*this.data.stride+this.offset+2]}getW(t){return this.data.array[t*this.data.stride+this.offset+3]}setXY(t,e,n){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this}setXYZ(t,e,n,i){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=i,this}setXYZW(t,e,n,i,r){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=i,this.data.array[t+3]=r,this}clone(t){if(void 0===t){console.log("THREE.InterleavedBufferAttribute.clone(): Cloning an interlaved buffer attribute will deinterleave buffer data.");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return new QQt(new this.array.constructor(t),this.itemSize,this.normalized)}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.clone(t)),new Z5t(t.interleavedBuffers[this.data.uuid],this.itemSize,this.offset,this.normalized)}toJSON(t){if(void 0===t){console.log("THREE.InterleavedBufferAttribute.toJSON(): Serializing an interlaved buffer attribute will deinterleave buffer data.");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return{itemSize:this.itemSize,type:this.array.constructor.name,array:t,normalized:this.normalized}}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.toJSON(t)),{isInterleavedBufferAttribute:!0,itemSize:this.itemSize,data:this.data.uuid,offset:this.offset,normalized:this.normalized}}}Z5t.prototype.isInterleavedBufferAttribute=!0;class J5t extends UQt{constructor(t){super(),this.type="SpriteMaterial",this.color=new $Qt(16777215),this.map=null,this.alphaMap=null,this.rotation=0,this.sizeAttenuation=!0,this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.rotation=t.rotation,this.sizeAttenuation=t.sizeAttenuation,this}}let Q5t;J5t.prototype.isSpriteMaterial=!0;const t3t=new CJt,e3t=new CJt,n3t=new CJt,i3t=new mJt,r3t=new mJt,o3t=new rQt,a3t=new CJt,s3t=new CJt,l3t=new CJt,c3t=new mJt,u3t=new mJt,h3t=new mJt;class d3t extends kQt{constructor(t){if(super(),this.type="Sprite",void 0===Q5t){Q5t=new b1t;const t=new Float32Array([-.5,-.5,0,0,0,.5,-.5,0,1,0,.5,.5,0,1,1,-.5,.5,0,0,1]),e=new $5t(t,5);Q5t.setIndex([0,1,2,0,2,3]),Q5t.setAttribute("position",new Z5t(e,3,0,!1)),Q5t.setAttribute("uv",new Z5t(e,2,3,!1))}this.geometry=Q5t,this.material=void 0!==t?t:new J5t,this.center=new mJt(.5,.5)}raycast(t,e){null===t.camera&&console.error('THREE.Sprite: "Raycaster.camera" needs to be set in order to raycast against sprites.'),e3t.setFromMatrixScale(this.matrixWorld),o3t.copy(t.camera.matrixWorld),this.modelViewMatrix.multiplyMatrices(t.camera.matrixWorldInverse,this.matrixWorld),n3t.setFromMatrixPosition(this.modelViewMatrix),t.camera.isPerspectiveCamera&&!1===this.material.sizeAttenuation&&e3t.multiplyScalar(-n3t.z);const n=this.material.rotation;let i,r;0!==n&&(r=Math.cos(n),i=Math.sin(n));const o=this.center;p3t(a3t.set(-.5,-.5,0),n3t,o,e3t,i,r),p3t(s3t.set(.5,-.5,0),n3t,o,e3t,i,r),p3t(l3t.set(.5,.5,0),n3t,o,e3t,i,r),c3t.set(0,0),u3t.set(1,0),h3t.set(1,1);let a=t.ray.intersectTriangle(a3t,s3t,l3t,!1,t3t);if(null===a&&(p3t(s3t.set(-.5,.5,0),n3t,o,e3t,i,r),u3t.set(0,1),a=t.ray.intersectTriangle(a3t,l3t,s3t,!1,t3t),null===a))return;const s=t.ray.origin.distanceTo(t3t);s<t.near||s>t.far||e.push({distance:s,point:t3t.clone(),uv:FQt.getUV(t3t,a3t,s3t,l3t,c3t,u3t,h3t,new mJt),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function p3t(t,e,n,i,r,o){i3t.subVectors(t,n).addScalar(.5).multiply(i),void 0!==r?(r3t.x=o*i3t.x-r*i3t.y,r3t.y=r*i3t.x+o*i3t.y):r3t.copy(i3t),t.copy(e),t.x+=r3t.x,t.y+=r3t.y,t.applyMatrix4(o3t)}d3t.prototype.isSprite=!0;const f3t=new CJt,m3t=new CJt;class g3t extends kQt{constructor(){super(),this._currentLevel=0,this.type="LOD",Object.defineProperties(this,{levels:{enumerable:!0,value:[]},isLOD:{value:!0}}),this.autoUpdate=!0}copy(t){super.copy(t,!1);const e=t.levels;for(let t=0,n=e.length;t<n;t++){const n=e[t];this.addLevel(n.object.clone(),n.distance)}return this.autoUpdate=t.autoUpdate,this}addLevel(t,e=0){e=Math.abs(e);const n=this.levels;let i;for(i=0;i<n.length&&!(e<n[i].distance);i++);return n.splice(i,0,{distance:e,object:t}),this.add(t),this}getCurrentLevel(){return this._currentLevel}getObjectForDistance(t){const e=this.levels;if(e.length>0){let n,i;for(n=1,i=e.length;n<i&&!(t<e[n].distance);n++);return e[n-1].object}return null}raycast(t,e){if(this.levels.length>0){f3t.setFromMatrixPosition(this.matrixWorld);const n=t.ray.origin.distanceTo(f3t);this.getObjectForDistance(n).raycast(t,e)}}update(t){const e=this.levels;if(e.length>1){f3t.setFromMatrixPosition(t.matrixWorld),m3t.setFromMatrixPosition(this.matrixWorld);const n=f3t.distanceTo(m3t)/t.zoom;let i,r;for(e[0].object.visible=!0,i=1,r=e.length;i<r&&n>=e[i].distance;i++)e[i-1].object.visible=!1,e[i].object.visible=!0;for(this._currentLevel=i-1;i<r;i++)e[i].object.visible=!1}}toJSON(t){const e=super.toJSON(t);!1===this.autoUpdate&&(e.object.autoUpdate=!1),e.object.levels=[];const n=this.levels;for(let t=0,i=n.length;t<i;t++){const i=n[t];e.object.levels.push({object:i.object.uuid,distance:i.distance})}return e}}const _3t=new CJt,y3t=new wJt,v3t=new wJt,b3t=new CJt,x3t=new rQt;class w3t extends B1t{constructor(t,e){super(t,e),this.type="SkinnedMesh",this.bindMode="attached",this.bindMatrix=new rQt,this.bindMatrixInverse=new rQt}copy(t){return super.copy(t),this.bindMode=t.bindMode,this.bindMatrix.copy(t.bindMatrix),this.bindMatrixInverse.copy(t.bindMatrixInverse),this.skeleton=t.skeleton,this}bind(t,e){this.skeleton=t,void 0===e&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),e=this.matrixWorld),this.bindMatrix.copy(e),this.bindMatrixInverse.copy(e).invert()}pose(){this.skeleton.pose()}normalizeSkinWeights(){const t=new wJt,e=this.geometry.attributes.skinWeight;for(let n=0,i=e.count;n<i;n++){t.x=e.getX(n),t.y=e.getY(n),t.z=e.getZ(n),t.w=e.getW(n);const i=1/t.manhattanLength();i!==1/0?t.multiplyScalar(i):t.set(1,0,0,0),e.setXYZW(n,t.x,t.y,t.z,t.w)}}updateMatrixWorld(t){super.updateMatrixWorld(t),"attached"===this.bindMode?this.bindMatrixInverse.copy(this.matrixWorld).invert():"detached"===this.bindMode?this.bindMatrixInverse.copy(this.bindMatrix).invert():console.warn("THREE.SkinnedMesh: Unrecognized bindMode: "+this.bindMode)}boneTransform(t,e){const n=this.skeleton,i=this.geometry;y3t.fromBufferAttribute(i.attributes.skinIndex,t),v3t.fromBufferAttribute(i.attributes.skinWeight,t),_3t.fromBufferAttribute(i.attributes.position,t).applyMatrix4(this.bindMatrix),e.set(0,0,0);for(let t=0;t<4;t++){const i=v3t.getComponent(t);if(0!==i){const r=y3t.getComponent(t);x3t.multiplyMatrices(n.bones[r].matrixWorld,n.boneInverses[r]),e.addScaledVector(b3t.copy(_3t).applyMatrix4(x3t),i)}}return e.applyMatrix4(this.bindMatrixInverse)}}w3t.prototype.isSkinnedMesh=!0;class S3t extends kQt{constructor(){super(),this.type="Bone"}}S3t.prototype.isBone=!0;class M3t extends bJt{constructor(t=null,e=1,n=1,i,r,o,a,s,l=1003,c=1003,u,h){super(null,o,a,s,l,c,i,r,u,h),this.image={data:t,width:e,height:n},this.magFilter=l,this.minFilter=c,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}M3t.prototype.isDataTexture=!0;const E3t=new rQt,T3t=new rQt;class C3t{constructor(t=[],e=[]){this.uuid=XZt(),this.bones=t.slice(0),this.boneInverses=e,this.boneMatrices=null,this.boneTexture=null,this.boneTextureSize=0,this.frame=-1,this.init()}init(){const t=this.bones,e=this.boneInverses;if(this.boneMatrices=new Float32Array(16*t.length),0===e.length)this.calculateInverses();else if(t.length!==e.length){console.warn("THREE.Skeleton: Number of inverse bone matrices does not match amount of bones."),this.boneInverses=[];for(let t=0,e=this.bones.length;t<e;t++)this.boneInverses.push(new rQt)}}calculateInverses(){this.boneInverses.length=0;for(let t=0,e=this.bones.length;t<e;t++){const e=new rQt;this.bones[t]&&e.copy(this.bones[t].matrixWorld).invert(),this.boneInverses.push(e)}}pose(){for(let t=0,e=this.bones.length;t<e;t++){const e=this.bones[t];e&&e.matrixWorld.copy(this.boneInverses[t]).invert()}for(let t=0,e=this.bones.length;t<e;t++){const e=this.bones[t];e&&(e.parent&&e.parent.isBone?(e.matrix.copy(e.parent.matrixWorld).invert(),e.matrix.multiply(e.matrixWorld)):e.matrix.copy(e.matrixWorld),e.matrix.decompose(e.position,e.quaternion,e.scale))}}update(){const t=this.bones,e=this.boneInverses,n=this.boneMatrices,i=this.boneTexture;for(let i=0,r=t.length;i<r;i++)E3t.multiplyMatrices(t[i]?t[i].matrixWorld:T3t,e[i]),E3t.toArray(n,16*i);null!==i&&(i.needsUpdate=!0)}clone(){return new C3t(this.bones,this.boneInverses)}computeBoneTexture(){let t=Math.sqrt(4*this.bones.length);t=QZt(t),t=Math.max(t,4);const e=new Float32Array(t*t*4);e.set(this.boneMatrices);const n=new M3t(e,t,t,hZt,sZt);return this.boneMatrices=e,this.boneTexture=n,this.boneTextureSize=t,this}getBoneByName(t){for(let e=0,n=this.bones.length;e<n;e++){const n=this.bones[e];if(n.name===t)return n}}dispose(){null!==this.boneTexture&&(this.boneTexture.dispose(),this.boneTexture=null)}fromJSON(t,e){this.uuid=t.uuid;for(let n=0,i=t.bones.length;n<i;n++){const i=t.bones[n];let r=e[i];void 0===r&&(console.warn("THREE.Skeleton: No bone found with UUID:",i),r=new S3t),this.bones.push(r),this.boneInverses.push((new rQt).fromArray(t.boneInverses[n]))}return this.init(),this}toJSON(){const t={metadata:{version:4.5,type:"Skeleton",generator:"Skeleton.toJSON"},bones:[],boneInverses:[]};t.uuid=this.uuid;const e=this.bones,n=this.boneInverses;for(let i=0,r=e.length;i<r;i++)t.bones.push(e[i].uuid),t.boneInverses.push(n[i].toArray());return t}}const A3t=new rQt,k3t=new rQt,L3t=[],P3t=new B1t;class N3t extends B1t{constructor(t,e,n){super(t,e),this.instanceMatrix=new QQt(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}copy(t){return super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,i=this.count;if(P3t.geometry=this.geometry,P3t.material=this.material,void 0!==P3t.material)for(let r=0;r<i;r++){this.getMatrixAt(r,A3t),k3t.multiplyMatrices(n,A3t),P3t.matrixWorld=k3t,P3t.raycast(t,L3t);for(let t=0,n=L3t.length;t<n;t++){const n=L3t[t];n.instanceId=r,n.object=this,e.push(n)}L3t.length=0}}setColorAt(t,e){null===this.instanceColor&&(this.instanceColor=new QQt(new Float32Array(3*this.instanceMatrix.count),3)),e.toArray(this.instanceColor.array,3*t)}setMatrixAt(t,e){e.toArray(this.instanceMatrix.array,16*t)}updateMorphTargets(){}dispose(){this.dispatchEvent({type:"dispose"})}}N3t.prototype.isInstancedMesh=!0;class I3t extends UQt{constructor(t){super(),this.type="LineBasicMaterial",this.color=new $Qt(16777215),this.linewidth=1,this.linecap="round",this.linejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.linewidth=t.linewidth,this.linecap=t.linecap,this.linejoin=t.linejoin,this}}I3t.prototype.isLineBasicMaterial=!0;const R3t=new CJt,O3t=new CJt,z3t=new rQt,D3t=new iQt,B3t=new $Jt;class H3t extends kQt{constructor(t=new b1t,e=new I3t){super(),this.type="Line",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[0];for(let t=1,i=e.count;t<i;t++)R3t.fromBufferAttribute(e,t-1),O3t.fromBufferAttribute(e,t),n[t]=n[t-1],n[t]+=R3t.distanceTo(O3t);t.setAttribute("lineDistance",new l1t(n,1))}else console.warn("THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else t.isGeometry&&console.error("THREE.Line.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");return this}raycast(t,e){const n=this.geometry,i=this.matrixWorld,r=t.params.Line.threshold,o=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),B3t.copy(n.boundingSphere),B3t.applyMatrix4(i),B3t.radius+=r,!1===t.ray.intersectsSphere(B3t))return;z3t.copy(i).invert(),D3t.copy(t.ray).applyMatrix4(z3t);const a=r/((this.scale.x+this.scale.y+this.scale.z)/3),s=a*a,l=new CJt,c=new CJt,u=new CJt,h=new CJt,d=this.isLineSegments?2:1;if(n.isBufferGeometry){const i=n.index,r=n.attributes.position;if(null!==i)for(let n=Math.max(0,o.start),a=Math.min(i.count,o.start+o.count)-1;n<a;n+=d){const o=i.getX(n),a=i.getX(n+1);if(l.fromBufferAttribute(r,o),c.fromBufferAttribute(r,a),D3t.distanceSqToSegment(l,c,h,u)>s)continue;h.applyMatrix4(this.matrixWorld);const d=t.ray.origin.distanceTo(h);d<t.near||d>t.far||e.push({distance:d,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}else for(let n=Math.max(0,o.start),i=Math.min(r.count,o.start+o.count)-1;n<i;n+=d){if(l.fromBufferAttribute(r,n),c.fromBufferAttribute(r,n+1),D3t.distanceSqToSegment(l,c,h,u)>s)continue;h.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(h);i<t.near||i>t.far||e.push({distance:i,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else n.isGeometry&&console.error("THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.")}}}H3t.prototype.isLine=!0;const F3t=new CJt,V3t=new CJt;class U3t extends H3t{constructor(t,e){super(t,e),this.type="LineSegments"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t<i;t+=2)F3t.fromBufferAttribute(e,t),V3t.fromBufferAttribute(e,t+1),n[t]=0===t?0:n[t-1],n[t+1]=n[t]+F3t.distanceTo(V3t);t.setAttribute("lineDistance",new l1t(n,1))}else console.warn("THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else t.isGeometry&&console.error("THREE.LineSegments.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");return this}}U3t.prototype.isLineSegments=!0;class j3t extends H3t{constructor(t,e){super(t,e),this.type="LineLoop"}}j3t.prototype.isLineLoop=!0;class G3t extends UQt{constructor(t){super(),this.type="PointsMaterial",this.color=new $Qt(16777215),this.map=null,this.alphaMap=null,this.size=1,this.sizeAttenuation=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.size=t.size,this.sizeAttenuation=t.sizeAttenuation,this}}G3t.prototype.isPointsMaterial=!0;const W3t=new rQt,q3t=new iQt,Y3t=new $Jt,X3t=new CJt;class $3t extends kQt{constructor(t=new b1t,e=new G3t){super(),this.type="Points",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}raycast(t,e){const n=this.geometry,i=this.matrixWorld,r=t.params.Points.threshold,o=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),Y3t.copy(n.boundingSphere),Y3t.applyMatrix4(i),Y3t.radius+=r,!1===t.ray.intersectsSphere(Y3t))return;W3t.copy(i).invert(),q3t.copy(t.ray).applyMatrix4(W3t);const a=r/((this.scale.x+this.scale.y+this.scale.z)/3),s=a*a;if(n.isBufferGeometry){const r=n.index,a=n.attributes.position;if(null!==r)for(let n=Math.max(0,o.start),l=Math.min(r.count,o.start+o.count);n<l;n++){const o=r.getX(n);X3t.fromBufferAttribute(a,o),K3t(X3t,o,s,i,t,e,this)}else for(let n=Math.max(0,o.start),r=Math.min(a.count,o.start+o.count);n<r;n++)X3t.fromBufferAttribute(a,n),K3t(X3t,n,s,i,t,e,this)}else console.error("THREE.Points.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.")}}}function K3t(t,e,n,i,r,o,a){const s=q3t.distanceSqToPoint(t);if(s<n){const n=new CJt;q3t.closestPointToPoint(t,n),n.applyMatrix4(i);const l=r.ray.origin.distanceTo(n);if(l<r.near||l>r.far)return;o.push({distance:l,distanceToRay:Math.sqrt(s),point:n,index:e,face:null,object:a})}}$3t.prototype.isPoints=!0;class Z3t extends bJt{constructor(t,e,n,i,r,o,a,s,l){super(t,e,n,i,r,o,a,s,l),this.format=void 0!==a?a:uZt,this.minFilter=void 0!==o?o:eZt,this.magFilter=void 0!==r?r:eZt,this.generateMipmaps=!1;const c=this;"requestVideoFrameCallback"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;0=="requestVideoFrameCallback"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}Z3t.prototype.isVideoTexture=!0;class J3t extends bJt{constructor(t,e,n,i,r,o,a,s,l,c,u,h){super(null,o,a,s,l,c,i,r,u,h),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}J3t.prototype.isCompressedTexture=!0;class Q3t extends bJt{constructor(t,e,n,i,r,o,a,s,l){super(t,e,n,i,r,o,a,s,l),this.needsUpdate=!0}}Q3t.prototype.isCanvasTexture=!0;class t4t extends bJt{constructor(t,e,n,i,r,o,a,s,l,c){if((c=void 0!==c?c:dZt)!==dZt&&c!==pZt)throw new Error("DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat");void 0===n&&c===dZt&&(n=oZt),void 0===n&&c===pZt&&(n=cZt),super(null,i,r,o,a,s,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==a?a:JKt,this.minFilter=void 0!==s?s:JKt,this.flipY=!1,this.generateMipmaps=!1}}t4t.prototype.isDepthTexture=!0;class e4t extends b1t{constructor(t=1,e=8,n=0,i=2*Math.PI){super(),this.type="CircleGeometry",this.parameters={radius:t,segments:e,thetaStart:n,thetaLength:i},e=Math.max(3,e);const r=[],o=[],a=[],s=[],l=new CJt,c=new mJt;o.push(0,0,0),a.push(0,0,1),s.push(.5,.5);for(let r=0,u=3;r<=e;r++,u+=3){const h=n+r/e*i;l.x=t*Math.cos(h),l.y=t*Math.sin(h),o.push(l.x,l.y,l.z),a.push(0,0,1),c.x=(o[u]/t+1)/2,c.y=(o[u+1]/t+1)/2,s.push(c.x,c.y)}for(let t=1;t<=e;t++)r.push(t,t+1,0);this.setIndex(r),this.setAttribute("position",new l1t(o,3)),this.setAttribute("normal",new l1t(a,3)),this.setAttribute("uv",new l1t(s,2))}static fromJSON(t){return new e4t(t.radius,t.segments,t.thetaStart,t.thetaLength)}}class n4t extends b1t{constructor(t=1,e=1,n=1,i=8,r=1,o=!1,a=0,s=2*Math.PI){super(),this.type="CylinderGeometry",this.parameters={radiusTop:t,radiusBottom:e,height:n,radialSegments:i,heightSegments:r,openEnded:o,thetaStart:a,thetaLength:s};const l=this;i=Math.floor(i),r=Math.floor(r);const c=[],u=[],h=[],d=[];let p=0;const f=[],m=n/2;let g=0;function _(n){const r=p,o=new mJt,f=new CJt;let _=0;const y=!0===n?t:e,v=!0===n?1:-1;for(let t=1;t<=i;t++)u.push(0,m*v,0),h.push(0,v,0),d.push(.5,.5),p++;const b=p;for(let t=0;t<=i;t++){const e=t/i*s+a,n=Math.cos(e),r=Math.sin(e);f.x=y*r,f.y=m*v,f.z=y*n,u.push(f.x,f.y,f.z),h.push(0,v,0),o.x=.5*n+.5,o.y=.5*r*v+.5,d.push(o.x,o.y),p++}for(let t=0;t<i;t++){const e=r+t,i=b+t;!0===n?c.push(i,i+1,e):c.push(i+1,i,e),_+=3}l.addGroup(g,_,!0===n?1:2),g+=_}!(function y(){const o=new CJt,_=new CJt;let y=0;const v=(e-t)/n;for(let l=0;l<=r;l++){const c=[],g=l/r,y=g*(e-t)+t;for(let t=0;t<=i;t++){const e=t/i,r=e*s+a,l=Math.sin(r),f=Math.cos(r);_.x=y*l,_.y=-g*n+m,_.z=y*f,u.push(_.x,_.y,_.z),o.set(l,v,f).normalize(),h.push(o.x,o.y,o.z),d.push(e,1-g),c.push(p++)}f.push(c)}for(let t=0;t<i;t++)for(let e=0;e<r;e++){const n=f[e+1][t],i=f[e+1][t+1],r=f[e][t+1];c.push(f[e][t],n,r),c.push(n,i,r),y+=6}l.addGroup(g,y,0),g+=y})(),!1===o&&(t>0&&_(!0),e>0&&_(!1)),this.setIndex(c),this.setAttribute("position",new l1t(u,3)),this.setAttribute("normal",new l1t(h,3)),this.setAttribute("uv",new l1t(d,2))}static fromJSON(t){return new n4t(t.radiusTop,t.radiusBottom,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class i4t extends n4t{constructor(t=1,e=1,n=8,i=1,r=!1,o=0,a=2*Math.PI){super(0,t,e,n,i,r,o,a),this.type="ConeGeometry",this.parameters={radius:t,height:e,radialSegments:n,heightSegments:i,openEnded:r,thetaStart:o,thetaLength:a}}static fromJSON(t){return new i4t(t.radius,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class r4t extends b1t{constructor(t,e,n=1,i=0){super(),this.type="PolyhedronGeometry",this.parameters={vertices:t,indices:e,radius:n,detail:i};const r=[],o=[];function a(t,e,n,i){const r=i+1,o=[];for(let i=0;i<=r;i++){o[i]=[];const a=t.clone().lerp(n,i/r),s=e.clone().lerp(n,i/r),l=r-i;for(let t=0;t<=l;t++)o[i][t]=0===t&&i===r?a:a.clone().lerp(s,t/l)}for(let t=0;t<r;t++)for(let e=0;e<2*(r-t)-1;e++){const n=Math.floor(e/2);e%2==0?(s(o[t][n+1]),s(o[t+1][n]),s(o[t][n])):(s(o[t][n+1]),s(o[t+1][n+1]),s(o[t+1][n]))}}function s(t){r.push(t.x,t.y,t.z)}function l(e,n){const i=3*e;n.x=t[i+0],n.y=t[i+1],n.z=t[i+2]}function c(t,e,n,i){i<0&&1===t.x&&(o[e]=t.x-1),0===n.x&&0===n.z&&(o[e]=i/2/Math.PI+.5)}function u(t){return Math.atan2(t.z,-t.x)}!(function h(t){const n=new CJt,i=new CJt,r=new CJt;for(let o=0;o<e.length;o+=3)l(e[o+0],n),l(e[o+1],i),l(e[o+2],r),a(n,i,r,t)})(i),(function d(t){const e=new CJt;for(let n=0;n<r.length;n+=3)e.x=r[n+0],e.y=r[n+1],e.z=r[n+2],e.normalize().multiplyScalar(t),r[n+0]=e.x,r[n+1]=e.y,r[n+2]=e.z})(n),(function p(){const t=new CJt;for(let n=0;n<r.length;n+=3){t.x=r[n+0],t.y=r[n+1],t.z=r[n+2];const i=u(t)/2/Math.PI+.5,a=(e=t,Math.atan2(-e.y,Math.sqrt(e.x*e.x+e.z*e.z))/Math.PI+.5);o.push(i,1-a)}var e;(function n(){const t=new CJt,e=new CJt,n=new CJt,i=new CJt,a=new mJt,s=new mJt,l=new mJt;for(let h=0,d=0;h<r.length;h+=9,d+=6){t.set(r[h+0],r[h+1],r[h+2]),e.set(r[h+3],r[h+4],r[h+5]),n.set(r[h+6],r[h+7],r[h+8]),a.set(o[d+0],o[d+1]),s.set(o[d+2],o[d+3]),l.set(o[d+4],o[d+5]),i.copy(t).add(e).add(n).divideScalar(3);const p=u(i);c(a,d+0,t,p),c(s,d+2,e,p),c(l,d+4,n,p)}})(),(function i(){for(let t=0;t<o.length;t+=6){const e=o[t+0],n=o[t+2],i=o[t+4],r=Math.max(e,n,i),a=Math.min(e,n,i);r>.9&&a<.1&&(e<.2&&(o[t+0]+=1),n<.2&&(o[t+2]+=1),i<.2&&(o[t+4]+=1))}})()})(),this.setAttribute("position",new l1t(r,3)),this.setAttribute("normal",new l1t(r.slice(),3)),this.setAttribute("uv",new l1t(o,2)),0===i?this.computeVertexNormals():this.normalizeNormals()}static fromJSON(t){return new r4t(t.vertices,t.indices,t.radius,t.details)}}class o4t extends r4t{constructor(t=1,e=0){const n=(1+Math.sqrt(5))/2,i=1/n;super([-1,-1,-1,-1,-1,1,-1,1,-1,-1,1,1,1,-1,-1,1,-1,1,1,1,-1,1,1,1,0,-i,-n,0,-i,n,0,i,-n,0,i,n,-i,-n,0,-i,n,0,i,-n,0,i,n,0,-n,0,-i,n,0,-i,-n,0,i,n,0,i],[3,11,7,3,7,15,3,15,13,7,19,17,7,17,6,7,6,15,17,4,8,17,8,10,17,10,6,8,0,16,8,16,2,8,2,10,0,12,1,0,1,18,0,18,16,6,10,2,6,2,13,6,13,15,2,16,18,2,18,3,2,3,13,18,1,9,18,9,11,18,11,3,4,14,12,4,12,0,4,0,8,11,9,5,11,5,19,11,19,7,19,5,14,19,14,4,19,4,17,1,12,14,1,14,5,1,5,9],t,e),this.type="DodecahedronGeometry",this.parameters={radius:t,detail:e}}static fromJSON(t){return new o4t(t.radius,t.detail)}}const a4t=new CJt,s4t=new CJt,l4t=new CJt,c4t=new FQt;class u4t extends b1t{constructor(t,e){if(super(),this.type="EdgesGeometry",this.parameters={thresholdAngle:e},e=void 0!==e?e:1,!0===t.isGeometry)return void console.error("THREE.EdgesGeometry no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");const n=Math.cos(qZt*e),i=t.getIndex(),r=t.getAttribute("position"),o=i?i.count:r.count,a=[0,0,0],s=["a","b","c"],l=new Array(3),c={},u=[];for(let t=0;t<o;t+=3){i?(a[0]=i.getX(t),a[1]=i.getX(t+1),a[2]=i.getX(t+2)):(a[0]=t,a[1]=t+1,a[2]=t+2);const{a:e,b:o,c:h}=c4t;if(e.fromBufferAttribute(r,a[0]),o.fromBufferAttribute(r,a[1]),h.fromBufferAttribute(r,a[2]),c4t.getNormal(l4t),l[0]=`${e.x},${e.y},${e.z}`,l[1]=`${o.x},${o.y},${o.z}`,l[2]=`${h.x},${h.y},${h.z}`,l[0]!==l[1]&&l[1]!==l[2]&&l[2]!==l[0])for(let t=0;t<3;t++){const e=(t+1)%3,i=l[t],r=l[e],o=c4t[s[t]],h=c4t[s[e]],d=`${i}_${r}`,p=`${r}_${i}`;p in c&&c[p]?(l4t.dot(c[p].normal)<=n&&(u.push(o.x,o.y,o.z),u.push(h.x,h.y,h.z)),c[p]=null):d in c||(c[d]={index0:a[t],index1:a[e],normal:l4t.clone()})}}for(const t in c)if(c[t]){const{index0:e,index1:n}=c[t];a4t.fromBufferAttribute(r,e),s4t.fromBufferAttribute(r,n),u.push(a4t.x,a4t.y,a4t.z),u.push(s4t.x,s4t.y,s4t.z)}this.setAttribute("position",new l1t(u,3))}}class h4t{constructor(){this.type="Curve",this.arcLengthDivisions=200}getPoint(){return console.warn("THREE.Curve: .getPoint() not implemented."),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,i=this.getPoint(0),r=0;e.push(0);for(let o=1;o<=t;o++)n=this.getPoint(o/t),r+=n.distanceTo(i),e.push(r),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const r=n.length;let o;o=e||t*n[r-1];let a,s=0,l=r-1;for(;s<=l;)if(i=Math.floor(s+(l-s)/2),a=n[i]-o,a<0)s=i+1;else{if(!(a>0)){l=i;break}l=i-1}if(i=l,n[i]===o)return i/(r-1);const c=n[i];return(i+(o-c)/(n[i+1]-c))/(r-1)}getTangent(t,e){const n=1e-4;let i=t-n,r=t+n;i<0&&(i=0),r>1&&(r=1);const o=this.getPoint(i),a=this.getPoint(r),s=e||(o.isVector2?new mJt:new CJt);return s.copy(a).sub(o).normalize(),s}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new CJt,i=[],r=[],o=[],a=new CJt,s=new rQt;for(let e=0;e<=t;e++)i[e]=this.getTangentAt(e/t,new CJt),i[e].normalize();r[0]=new CJt,o[0]=new CJt;let l=Number.MAX_VALUE;const c=Math.abs(i[0].x),u=Math.abs(i[0].y),h=Math.abs(i[0].z);c<=l&&(l=c,n.set(1,0,0)),u<=l&&(l=u,n.set(0,1,0)),h<=l&&n.set(0,0,1),a.crossVectors(i[0],n).normalize(),r[0].crossVectors(i[0],a),o[0].crossVectors(i[0],r[0]);for(let e=1;e<=t;e++){if(r[e]=r[e-1].clone(),o[e]=o[e-1].clone(),a.crossVectors(i[e-1],i[e]),a.length()>Number.EPSILON){a.normalize();const t=Math.acos($Zt(i[e-1].dot(i[e]),-1,1));r[e].applyMatrix4(s.makeRotationAxis(a,t))}o[e].crossVectors(i[e],r[e])}if(!0===e){let e=Math.acos($Zt(r[0].dot(r[t]),-1,1));e/=t,i[0].dot(a.crossVectors(r[0],r[t]))>0&&(e=-e);for(let n=1;n<=t;n++)r[n].applyMatrix4(s.makeRotationAxis(i[n],e*n)),o[n].crossVectors(i[n],r[n])}return{tangents:i,normals:r,binormals:o}}clone(){return(new this.constructor).copy(this)}copy(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}toJSON(){const t={metadata:{version:4.5,type:"Curve",generator:"Curve.toJSON"}};return t.arcLengthDivisions=this.arcLengthDivisions,t.type=this.type,t}fromJSON(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}}class d4t extends h4t{constructor(t=0,e=0,n=1,i=1,r=0,o=2*Math.PI,a=!1,s=0){super(),this.type="EllipseCurve",this.aX=t,this.aY=e,this.xRadius=n,this.yRadius=i,this.aStartAngle=r,this.aEndAngle=o,this.aClockwise=a,this.aRotation=s}getPoint(t,e){const n=e||new mJt,i=2*Math.PI;let r=this.aEndAngle-this.aStartAngle;const o=Math.abs(r)<Number.EPSILON;for(;r<0;)r+=i;for(;r>i;)r-=i;r<Number.EPSILON&&(r=o?0:i),!0!==this.aClockwise||o||(r===i?r=-i:r-=i);const a=this.aStartAngle+t*r;let s=this.aX+this.xRadius*Math.cos(a),l=this.aY+this.yRadius*Math.sin(a);if(0!==this.aRotation){const t=Math.cos(this.aRotation),e=Math.sin(this.aRotation),n=s-this.aX,i=l-this.aY;s=n*t-i*e+this.aX,l=n*e+i*t+this.aY}return n.set(s,l)}copy(t){return super.copy(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}toJSON(){const t=super.toJSON();return t.aX=this.aX,t.aY=this.aY,t.xRadius=this.xRadius,t.yRadius=this.yRadius,t.aStartAngle=this.aStartAngle,t.aEndAngle=this.aEndAngle,t.aClockwise=this.aClockwise,t.aRotation=this.aRotation,t}fromJSON(t){return super.fromJSON(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}}d4t.prototype.isEllipseCurve=!0;class p4t extends d4t{constructor(t,e,n,i,r,o){super(t,e,n,n,i,r,o),this.type="ArcCurve"}}function f4t(){let t=0,e=0,n=0,i=0;function r(r,o,a,s){t=r,e=a,n=-3*r+3*o-2*a-s,i=2*r-2*o+a+s}return{initCatmullRom:function(t,e,n,i,o){r(e,n,o*(n-t),o*(i-e))},initNonuniformCatmullRom:function(t,e,n,i,o,a,s){let l=(e-t)/o-(n-t)/(o+a)+(n-e)/a,c=(n-e)/a-(i-e)/(a+s)+(i-n)/s;l*=a,c*=a,r(e,n,l,c)},calc:function(r){const o=r*r;return t+e*r+n*o+i*(o*r)}}}p4t.prototype.isArcCurve=!0;const m4t=new CJt,g4t=new f4t,_4t=new f4t,y4t=new f4t;class v4t extends h4t{constructor(t=[],e=!1,n="centripetal",i=.5){super(),this.type="CatmullRomCurve3",this.points=t,this.closed=e,this.curveType=n,this.tension=i}getPoint(t,e=new CJt){const n=e,i=this.points,r=i.length,o=(r-(this.closed?0:1))*t;let a,s,l=Math.floor(o),c=o-l;this.closed?l+=l>0?0:(Math.floor(Math.abs(l)/r)+1)*r:0===c&&l===r-1&&(l=r-2,c=1),this.closed||l>0?a=i[(l-1)%r]:(m4t.subVectors(i[0],i[1]).add(i[0]),a=m4t);const u=i[l%r],h=i[(l+1)%r];if(this.closed||l+2<r?s=i[(l+2)%r]:(m4t.subVectors(i[r-1],i[r-2]).add(i[r-1]),s=m4t),"centripetal"===this.curveType||"chordal"===this.curveType){const t="chordal"===this.curveType?.5:.25;let e=Math.pow(a.distanceToSquared(u),t),n=Math.pow(u.distanceToSquared(h),t),i=Math.pow(h.distanceToSquared(s),t);n<1e-4&&(n=1),e<1e-4&&(e=n),i<1e-4&&(i=n),g4t.initNonuniformCatmullRom(a.x,u.x,h.x,s.x,e,n,i),_4t.initNonuniformCatmullRom(a.y,u.y,h.y,s.y,e,n,i),y4t.initNonuniformCatmullRom(a.z,u.z,h.z,s.z,e,n,i)}else"catmullrom"===this.curveType&&(g4t.initCatmullRom(a.x,u.x,h.x,s.x,this.tension),_4t.initCatmullRom(a.y,u.y,h.y,s.y,this.tension),y4t.initCatmullRom(a.z,u.z,h.z,s.z,this.tension));return n.set(g4t.calc(c),_4t.calc(c),y4t.calc(c)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++)this.points.push(t.points[e].clone());return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++)t.points.push(this.points[e].toArray());return t.closed=this.closed,t.curveType=this.curveType,t.tension=this.tension,t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new CJt).fromArray(n))}return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}}function b4t(t,e,n,i,r){const o=.5*(i-e),a=.5*(r-n),s=t*t;return(2*n-2*i+o+a)*(t*s)+(-3*n+3*i-2*o-a)*s+o*t+n}function x4t(t,e,n,i){return(function r(t,e){const n=1-t;return n*n*e})(t,e)+(function o(t,e){return 2*(1-t)*t*e})(t,n)+(function a(t,e){return t*t*e})(t,i)}function w4t(t,e,n,i,r){return(function o(t,e){const n=1-t;return n*n*n*e})(t,e)+(function a(t,e){const n=1-t;return 3*n*n*t*e})(t,n)+(function s(t,e){return 3*(1-t)*t*t*e})(t,i)+(function l(t,e){return t*t*t*e})(t,r)}v4t.prototype.isCatmullRomCurve3=!0;class S4t extends h4t{constructor(t=new mJt,e=new mJt,n=new mJt,i=new mJt){super(),this.type="CubicBezierCurve",this.v0=t,this.v1=e,this.v2=n,this.v3=i}getPoint(t,e=new mJt){const n=e,i=this.v0,r=this.v1,o=this.v2,a=this.v3;return n.set(w4t(t,i.x,r.x,o.x,a.x),w4t(t,i.y,r.y,o.y,a.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}S4t.prototype.isCubicBezierCurve=!0;class M4t extends h4t{constructor(t=new CJt,e=new CJt,n=new CJt,i=new CJt){super(),this.type="CubicBezierCurve3",this.v0=t,this.v1=e,this.v2=n,this.v3=i}getPoint(t,e=new CJt){const n=e,i=this.v0,r=this.v1,o=this.v2,a=this.v3;return n.set(w4t(t,i.x,r.x,o.x,a.x),w4t(t,i.y,r.y,o.y,a.y),w4t(t,i.z,r.z,o.z,a.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}M4t.prototype.isCubicBezierCurve3=!0;class E4t extends h4t{constructor(t=new mJt,e=new mJt){super(),this.type="LineCurve",this.v1=t,this.v2=e}getPoint(t,e=new mJt){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}getTangent(t,e){const n=e||new mJt;return n.copy(this.v2).sub(this.v1).normalize(),n}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}E4t.prototype.isLineCurve=!0;class T4t extends h4t{constructor(t=new CJt,e=new CJt){super(),this.type="LineCurve3",this.isLineCurve3=!0,this.v1=t,this.v2=e}getPoint(t,e=new CJt){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}class C4t extends h4t{constructor(t=new mJt,e=new mJt,n=new mJt){super(),this.type="QuadraticBezierCurve",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new mJt){const n=e,i=this.v0,r=this.v1,o=this.v2;return n.set(x4t(t,i.x,r.x,o.x),x4t(t,i.y,r.y,o.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}C4t.prototype.isQuadraticBezierCurve=!0;class A4t extends h4t{constructor(t=new CJt,e=new CJt,n=new CJt){super(),this.type="QuadraticBezierCurve3",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new CJt){const n=e,i=this.v0,r=this.v1,o=this.v2;return n.set(x4t(t,i.x,r.x,o.x),x4t(t,i.y,r.y,o.y),x4t(t,i.z,r.z,o.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}A4t.prototype.isQuadraticBezierCurve3=!0;class k4t extends h4t{constructor(t=[]){super(),this.type="SplineCurve",this.points=t}getPoint(t,e=new mJt){const n=e,i=this.points,r=(i.length-1)*t,o=Math.floor(r),a=r-o,s=i[0===o?o:o-1],l=i[o],c=i[o>i.length-2?i.length-1:o+1],u=i[o>i.length-3?i.length-1:o+2];return n.set(b4t(a,s.x,l.x,c.x,u.x),b4t(a,s.y,l.y,c.y,u.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++)this.points.push(t.points[e].clone());return this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++)t.points.push(this.points[e].toArray());return t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new mJt).fromArray(n))}return this}}k4t.prototype.isSplineCurve=!0;var L4t=Object.freeze({__proto__:null,ArcCurve:p4t,CatmullRomCurve3:v4t,CubicBezierCurve:S4t,CubicBezierCurve3:M4t,EllipseCurve:d4t,LineCurve:E4t,LineCurve3:T4t,QuadraticBezierCurve:C4t,QuadraticBezierCurve3:A4t,SplineCurve:k4t});function P4t(t,e,n,i,r){let o,a;if(r===(function s(t,e,n,i){let r=0;for(let o=e,a=n-i;o<n;o+=i)r+=(t[a]-t[o])*(t[o+1]+t[a+1]),a=o;return r})(t,e,n,i)>0)for(o=e;o<n;o+=i)a=J4t(o,t[o],t[o+1],a);else for(o=n-i;o>=e;o-=i)a=J4t(o,t[o],t[o+1],a);return a&&q4t(a,a.next)&&(Q4t(a),a=a.next),a}function N4t(t,e){if(!t)return t;e||(e=t);let n,i=t;do{if(n=!1,i.steiner||!q4t(i,i.next)&&0!==W4t(i.prev,i,i.next))i=i.next;else{if(Q4t(i),i=e=i.prev,i===i.next)break;n=!0}}while(n||i!==e);return e}function I4t(t,e,n,i,r,o,a){if(!t)return;!a&&o&&(function s(t,e,n,i){let r=t;do{null===r.z&&(r.z=V4t(r.x,r.y,e,n,i)),r.prevZ=r.prev,r.nextZ=r.next,r=r.next}while(r!==t);r.prevZ.nextZ=null,r.prevZ=null,(function o(t){let e,n,i,r,o,a,s,l,c=1;do{for(n=t,t=null,o=null,a=0;n;){for(a++,i=n,s=0,e=0;e<c&&(s++,i=i.nextZ,i);e++);for(l=c;s>0||l>0&&i;)0!==s&&(0===l||!i||n.z<=i.z)?(r=n,n=n.nextZ,s--):(r=i,i=i.nextZ,l--),o?o.nextZ=r:t=r,r.prevZ=o,o=r;n=i}o.nextZ=null,c*=2}while(a>1)})(r)})(t,i,r,o);let l,c,u=t;for(;t.prev!==t.next;)if(l=t.prev,c=t.next,o?O4t(t,i,r,o):R4t(t))e.push(l.i/n),e.push(t.i/n),e.push(c.i/n),Q4t(t),t=c.next,u=c.next;else if((t=c)===u){a?1===a?I4t(t=z4t(N4t(t),e,n),e,n,i,r,o,2):2===a&&D4t(t,e,n,i,r,o):I4t(N4t(t),e,n,i,r,o,1);break}}function R4t(t){const e=t.prev,n=t,i=t.next;if(W4t(e,n,i)>=0)return!1;let r=t.next.next;for(;r!==t.prev;){if(j4t(e.x,e.y,n.x,n.y,i.x,i.y,r.x,r.y)&&W4t(r.prev,r,r.next)>=0)return!1;r=r.next}return!0}function O4t(t,e,n,i){const r=t.prev,o=t,a=t.next;if(W4t(r,o,a)>=0)return!1;const 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Q4t(t){t.next.prev=t.prev,t.prev.next=t.next,t.prevZ&&(t.prevZ.nextZ=t.nextZ),t.nextZ&&(t.nextZ.prevZ=t.prevZ)}function t6t(t,e,n){this.i=t,this.x=e,this.y=n,this.prev=null,this.next=null,this.z=null,this.prevZ=null,this.nextZ=null,this.steiner=!1}class e6t{static area(t){const e=t.length;let n=0;for(let i=e-1,r=0;r<e;i=r++)n+=t[i].x*t[r].y-t[r].x*t[i].y;return.5*n}static isClockWise(t){return e6t.area(t)<0}static triangulateShape(t,e){const n=[],i=[],r=[];n6t(t),i6t(n,t);let o=t.length;e.forEach(n6t);for(let t=0;t<e.length;t++)i.push(o),o+=e[t].length,i6t(n,e[t]);const a=(function(t,e,n=2){const i=e&&e.length,r=i?e[0]*n:t.length;let o=P4t(t,0,r,n,!0);const a=[];if(!o||o.next===o.prev)return a;let s,l,c,u,h,d,p;if(i&&(o=(function f(t,e,n,i){const r=[];let o,a,s,l,c;for(o=0,a=e.length;o<a;o++)s=e[o]*i,l=o<a-1?e[o+1]*i:t.length,c=P4t(t,s,l,i,!1),c===c.next&&(c.steiner=!0),r.push(U4t(c));for(r.sort(B4t),o=0;o<r.length;o++)H4t(r[o],n),n=N4t(n,n.next);return 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e(t,n,i){if(i.shapes=[],Array.isArray(t))for(let e=0,n=t.length;e<n;e++)i.shapes.push(t[e].uuid);else i.shapes.push(t.uuid);return void 0!==n.extrudePath&&(i.options.extrudePath=n.extrudePath.toJSON()),i})(this.parameters.shapes,this.parameters.options,t)}static fromJSON(t,e){const n=[];for(let i=0,r=t.shapes.length;i<r;i++)n.push(e[t.shapes[i]]);const i=t.options.extrudePath;return void 0!==i&&(t.options.extrudePath=(new L4t[i.type]).fromJSON(i)),new r6t(n,t.options)}}const o6t={generateTopUV:function(t,e,n,i,r){const o=e[3*i],a=e[3*i+1],s=e[3*r],l=e[3*r+1];return[new mJt(e[3*n],e[3*n+1]),new mJt(o,a),new mJt(s,l)]},generateSideWallUV:function(t,e,n,i,r,o){const a=e[3*n],s=e[3*n+1],l=e[3*n+2],c=e[3*i],u=e[3*i+1],h=e[3*i+2],d=e[3*r],p=e[3*r+1],f=e[3*r+2],m=e[3*o],g=e[3*o+1],_=e[3*o+2];return Math.abs(s-u)<Math.abs(a-c)?[new mJt(a,1-l),new mJt(c,1-h),new mJt(d,1-f),new mJt(m,1-_)]:[new mJt(s,1-l),new mJt(u,1-h),new mJt(p,1-f),new mJt(g,1-_)]}};class a6t extends r4t{constructor(t=1,e=0){const n=(1+Math.sqrt(5))/2;super([-1,n,0,1,n,0,-1,-n,0,1,-n,0,0,-1,n,0,1,n,0,-1,-n,0,1,-n,n,0,-1,n,0,1,-n,0,-1,-n,0,1],[0,11,5,0,5,1,0,1,7,0,7,10,0,10,11,1,5,9,5,11,4,11,10,2,10,7,6,7,1,8,3,9,4,3,4,2,3,2,6,3,6,8,3,8,9,4,9,5,2,4,11,6,2,10,8,6,7,9,8,1],t,e),this.type="IcosahedronGeometry",this.parameters={radius:t,detail:e}}static fromJSON(t){return new a6t(t.radius,t.detail)}}class s6t extends b1t{constructor(t,e=12,n=0,i=2*Math.PI){super(),this.type="LatheGeometry",this.parameters={points:t,segments:e,phiStart:n,phiLength:i},e=Math.floor(e),i=$Zt(i,0,2*Math.PI);const r=[],o=[],a=[],s=1/e,l=new CJt,c=new mJt;for(let r=0;r<=e;r++){const u=n+r*s*i,h=Math.sin(u),d=Math.cos(u);for(let n=0;n<=t.length-1;n++)l.x=t[n].x*h,l.y=t[n].y,l.z=t[n].x*d,o.push(l.x,l.y,l.z),c.x=r/e,c.y=n/(t.length-1),a.push(c.x,c.y)}for(let n=0;n<e;n++)for(let e=0;e<t.length-1;e++){const i=e+n*t.length,o=i+t.length,a=i+t.length+1,s=i+1;r.push(i,o,s),r.push(o,a,s)}if(this.setIndex(r),this.setAttribute("position",new l1t(o,3)),this.setAttribute("uv",new l1t(a,2)),this.computeVertexNormals(),i===2*Math.PI){const n=this.attributes.normal.array,i=new CJt,r=new CJt,o=new CJt,a=e*t.length*3;for(let e=0,s=0;e<t.length;e++,s+=3)i.x=n[s+0],i.y=n[s+1],i.z=n[s+2],r.x=n[a+s+0],r.y=n[a+s+1],r.z=n[a+s+2],o.addVectors(i,r).normalize(),n[s+0]=n[a+s+0]=o.x,n[s+1]=n[a+s+1]=o.y,n[s+2]=n[a+s+2]=o.z}}static fromJSON(t){return new s6t(t.points,t.segments,t.phiStart,t.phiLength)}}class l6t extends r4t{constructor(t=1,e=0){super([1,0,0,-1,0,0,0,1,0,0,-1,0,0,0,1,0,0,-1],[0,2,4,0,4,3,0,3,5,0,5,2,1,2,5,1,5,3,1,3,4,1,4,2],t,e),this.type="OctahedronGeometry",this.parameters={radius:t,detail:e}}static fromJSON(t){return new l6t(t.radius,t.detail)}}class c6t extends b1t{constructor(t,e,n){super(),this.type="ParametricGeometry",this.parameters={func:t,slices:e,stacks:n};const i=[],r=[],o=[],a=[],s=1e-5,l=new CJt,c=new CJt,u=new CJt,h=new CJt,d=new CJt;t.length<3&&console.error("THREE.ParametricGeometry: Function must now modify a Vector3 as third parameter.");const p=e+1;for(let i=0;i<=n;i++){const p=i/n;for(let n=0;n<=e;n++){const i=n/e;t(i,p,c),r.push(c.x,c.y,c.z),i-s>=0?(t(i-s,p,u),h.subVectors(c,u)):(t(i+s,p,u),h.subVectors(u,c)),p-s>=0?(t(i,p-s,u),d.subVectors(c,u)):(t(i,p+s,u),d.subVectors(u,c)),l.crossVectors(h,d).normalize(),o.push(l.x,l.y,l.z),a.push(i,p)}}for(let t=0;t<n;t++)for(let n=0;n<e;n++){const e=t*p+n+1,r=(t+1)*p+n+1,o=(t+1)*p+n;i.push(t*p+n,e,o),i.push(e,r,o)}this.setIndex(i),this.setAttribute("position",new l1t(r,3)),this.setAttribute("normal",new l1t(o,3)),this.setAttribute("uv",new l1t(a,2))}}class u6t extends b1t{constructor(t=.5,e=1,n=8,i=1,r=0,o=2*Math.PI){super(),this.type="RingGeometry",this.parameters={innerRadius:t,outerRadius:e,thetaSegments:n,phiSegments:i,thetaStart:r,thetaLength:o},n=Math.max(3,n);const a=[],s=[],l=[],c=[];let u=t;const h=(e-t)/(i=Math.max(1,i)),d=new CJt,p=new mJt;for(let t=0;t<=i;t++){for(let t=0;t<=n;t++){const i=r+t/n*o;d.x=u*Math.cos(i),d.y=u*Math.sin(i),s.push(d.x,d.y,d.z),l.push(0,0,1),p.x=(d.x/e+1)/2,p.y=(d.y/e+1)/2,c.push(p.x,p.y)}u+=h}for(let t=0;t<i;t++){const e=t*(n+1);for(let t=0;t<n;t++){const i=t+e,r=i+n+1,o=i+n+2,s=i+1;a.push(i,r,s),a.push(r,o,s)}}this.setIndex(a),this.setAttribute("position",new l1t(s,3)),this.setAttribute("normal",new l1t(l,3)),this.setAttribute("uv",new l1t(c,2))}static fromJSON(t){return new u6t(t.innerRadius,t.outerRadius,t.thetaSegments,t.phiSegments,t.thetaStart,t.thetaLength)}}class h6t extends b1t{constructor(t,e=12){super(),this.type="ShapeGeometry",this.parameters={shapes:t,curveSegments:e};const n=[],i=[],r=[],o=[];let a=0,s=0;if(!1===Array.isArray(t))l(t);else for(let e=0;e<t.length;e++)l(t[e]),this.addGroup(a,s,e),a+=s,s=0;function l(t){const a=i.length/3,l=t.extractPoints(e);let c=l.shape;const u=l.holes;!1===e6t.isClockWise(c)&&(c=c.reverse());for(let t=0,e=u.length;t<e;t++){const e=u[t];!0===e6t.isClockWise(e)&&(u[t]=e.reverse())}const h=e6t.triangulateShape(c,u);for(let t=0,e=u.length;t<e;t++)c=c.concat(u[t]);for(let t=0,e=c.length;t<e;t++){const e=c[t];i.push(e.x,e.y,0),r.push(0,0,1),o.push(e.x,e.y)}for(let t=0,e=h.length;t<e;t++){const e=h[t];n.push(e[0]+a,e[1]+a,e[2]+a),s+=3}}this.setIndex(n),this.setAttribute("position",new l1t(i,3)),this.setAttribute("normal",new l1t(r,3)),this.setAttribute("uv",new l1t(o,2))}toJSON(){const t=super.toJSON();return(function e(t,n){if(n.shapes=[],Array.isArray(t))for(let e=0,i=t.length;e<i;e++)n.shapes.push(t[e].uuid);else n.shapes.push(t.uuid);return n})(this.parameters.shapes,t)}static fromJSON(t,e){const n=[];for(let i=0,r=t.shapes.length;i<r;i++)n.push(e[t.shapes[i]]);return new h6t(n,t.curveSegments)}}class d6t extends b1t{constructor(t=1,e=32,n=16,i=0,r=2*Math.PI,o=0,a=Math.PI){super(),this.type="SphereGeometry",this.parameters={radius:t,widthSegments:e,heightSegments:n,phiStart:i,phiLength:r,thetaStart:o,thetaLength:a},e=Math.max(3,Math.floor(e)),n=Math.max(2,Math.floor(n));const s=Math.min(o+a,Math.PI);let l=0;const c=[],u=new CJt,h=new CJt,d=[],p=[],f=[],m=[];for(let d=0;d<=n;d++){const g=[],_=d/n;let y=0;0==d&&0==o?y=.5/e:d==n&&s==Math.PI&&(y=-.5/e);for(let n=0;n<=e;n++){const s=n/e;u.x=-t*Math.cos(i+s*r)*Math.sin(o+_*a),u.y=t*Math.cos(o+_*a),u.z=t*Math.sin(i+s*r)*Math.sin(o+_*a),p.push(u.x,u.y,u.z),h.copy(u).normalize(),f.push(h.x,h.y,h.z),m.push(s+y,1-_),g.push(l++)}c.push(g)}for(let t=0;t<n;t++)for(let i=0;i<e;i++){const e=c[t][i+1],r=c[t][i],a=c[t+1][i],l=c[t+1][i+1];(0!==t||o>0)&&d.push(e,r,l),(t!==n-1||s<Math.PI)&&d.push(r,a,l)}this.setIndex(d),this.setAttribute("position",new l1t(p,3)),this.setAttribute("normal",new l1t(f,3)),this.setAttribute("uv",new l1t(m,2))}static fromJSON(t){return new d6t(t.radius,t.widthSegments,t.heightSegments,t.phiStart,t.phiLength,t.thetaStart,t.thetaLength)}}class p6t extends r4t{constructor(t=1,e=0){super([1,1,1,-1,-1,1,-1,1,-1,1,-1,-1],[2,1,0,0,3,2,1,3,0,2,3,1],t,e),this.type="TetrahedronGeometry",this.parameters={radius:t,detail:e}}static fromJSON(t){return new p6t(t.radius,t.detail)}}class f6t extends r6t{constructor(t,e={}){const n=e.font;if(!n||!n.isFont)return console.error("THREE.TextGeometry: font parameter is not an instance of THREE.Font."),new b1t;const i=n.generateShapes(t,e.size);e.depth=void 0!==e.height?e.height:50,void 0===e.bevelThickness&&(e.bevelThickness=10),void 0===e.bevelSize&&(e.bevelSize=8),void 0===e.bevelEnabled&&(e.bevelEnabled=!1),super(i,e),this.type="TextGeometry"}}class m6t extends b1t{constructor(t=1,e=.4,n=8,i=6,r=2*Math.PI){super(),this.type="TorusGeometry",this.parameters={radius:t,tube:e,radialSegments:n,tubularSegments:i,arc:r},n=Math.floor(n),i=Math.floor(i);const o=[],a=[],s=[],l=[],c=new CJt,u=new CJt,h=new CJt;for(let o=0;o<=n;o++)for(let d=0;d<=i;d++){const p=d/i*r,f=o/n*Math.PI*2;u.x=(t+e*Math.cos(f))*Math.cos(p),u.y=(t+e*Math.cos(f))*Math.sin(p),u.z=e*Math.sin(f),a.push(u.x,u.y,u.z),c.x=t*Math.cos(p),c.y=t*Math.sin(p),h.subVectors(u,c).normalize(),s.push(h.x,h.y,h.z),l.push(d/i),l.push(o/n)}for(let t=1;t<=n;t++)for(let e=1;e<=i;e++){const n=(i+1)*(t-1)+e-1,r=(i+1)*(t-1)+e,a=(i+1)*t+e;o.push((i+1)*t+e-1,n,a),o.push(n,r,a)}this.setIndex(o),this.setAttribute("position",new l1t(a,3)),this.setAttribute("normal",new l1t(s,3)),this.setAttribute("uv",new l1t(l,2))}static fromJSON(t){return new m6t(t.radius,t.tube,t.radialSegments,t.tubularSegments,t.arc)}}class g6t extends b1t{constructor(t=1,e=.4,n=64,i=8,r=2,o=3){super(),this.type="TorusKnotGeometry",this.parameters={radius:t,tube:e,tubularSegments:n,radialSegments:i,p:r,q:o},n=Math.floor(n),i=Math.floor(i);const a=[],s=[],l=[],c=[],u=new CJt,h=new CJt,d=new CJt,p=new CJt,f=new CJt,m=new CJt,g=new CJt;for(let a=0;a<=n;++a){const y=a/n*r*Math.PI*2;_(y,r,o,t,d),_(y+.01,r,o,t,p),m.subVectors(p,d),g.addVectors(p,d),f.crossVectors(m,g),g.crossVectors(f,m),f.normalize(),g.normalize();for(let t=0;t<=i;++t){const r=t/i*Math.PI*2,o=-e*Math.cos(r),p=e*Math.sin(r);u.x=d.x+(o*g.x+p*f.x),u.y=d.y+(o*g.y+p*f.y),u.z=d.z+(o*g.z+p*f.z),s.push(u.x,u.y,u.z),h.subVectors(u,d).normalize(),l.push(h.x,h.y,h.z),c.push(a/n),c.push(t/i)}}for(let t=1;t<=n;t++)for(let e=1;e<=i;e++){const n=(i+1)*t+(e-1),r=(i+1)*t+e,o=(i+1)*(t-1)+e;a.push((i+1)*(t-1)+(e-1),n,o),a.push(n,r,o)}function _(t,e,n,i,r){const o=Math.cos(t),a=Math.sin(t),s=n/e*t,l=Math.cos(s);r.x=i*(2+l)*.5*o,r.y=i*(2+l)*a*.5,r.z=i*Math.sin(s)*.5}this.setIndex(a),this.setAttribute("position",new l1t(s,3)),this.setAttribute("normal",new l1t(l,3)),this.setAttribute("uv",new l1t(c,2))}static fromJSON(t){return new g6t(t.radius,t.tube,t.tubularSegments,t.radialSegments,t.p,t.q)}}class _6t extends b1t{constructor(t,e=64,n=1,i=8,r=!1){super(),this.type="TubeGeometry",this.parameters={path:t,tubularSegments:e,radius:n,radialSegments:i,closed:r};const o=t.computeFrenetFrames(e,r);this.tangents=o.tangents,this.normals=o.normals,this.binormals=o.binormals;const a=new CJt,s=new CJt,l=new mJt;let c=new CJt;const u=[],h=[],d=[],p=[];function f(r){c=t.getPointAt(r/e,c);const l=o.normals[r],d=o.binormals[r];for(let t=0;t<=i;t++){const e=t/i*Math.PI*2,r=Math.sin(e),o=-Math.cos(e);s.x=o*l.x+r*d.x,s.y=o*l.y+r*d.y,s.z=o*l.z+r*d.z,s.normalize(),h.push(s.x,s.y,s.z),a.x=c.x+n*s.x,a.y=c.y+n*s.y,a.z=c.z+n*s.z,u.push(a.x,a.y,a.z)}}!(function m(){for(let t=0;t<e;t++)f(t);f(!1===r?e:0),(function t(){for(let t=0;t<=e;t++)for(let n=0;n<=i;n++)l.x=t/e,l.y=n/i,d.push(l.x,l.y)})(),(function n(){for(let t=1;t<=e;t++)for(let e=1;e<=i;e++){const n=(i+1)*t+(e-1),r=(i+1)*t+e,o=(i+1)*(t-1)+e;p.push((i+1)*(t-1)+(e-1),n,o),p.push(n,r,o)}})()})(),this.setIndex(p),this.setAttribute("position",new l1t(u,3)),this.setAttribute("normal",new l1t(h,3)),this.setAttribute("uv",new l1t(d,2))}toJSON(){const t=super.toJSON();return t.path=this.parameters.path.toJSON(),t}static fromJSON(t){return new _6t((new L4t[t.path.type]).fromJSON(t.path),t.tubularSegments,t.radius,t.radialSegments,t.closed)}}class y6t extends b1t{constructor(t){if(super(),this.type="WireframeGeometry",!0===t.isGeometry)return void console.error("THREE.WireframeGeometry no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");const e=[],n=new Set,i=new CJt,r=new CJt;if(null!==t.index){const o=t.attributes.position,a=t.index;let s=t.groups;0===s.length&&(s=[{start:0,count:a.count,materialIndex:0}]);for(let t=0,l=s.length;t<l;++t){const l=s[t],c=l.start;for(let t=c,s=c+l.count;t<s;t+=3)for(let s=0;s<3;s++){const l=a.getX(t+s),c=a.getX(t+(s+1)%3);i.fromBufferAttribute(o,l),r.fromBufferAttribute(o,c),!0===v6t(i,r,n)&&(e.push(i.x,i.y,i.z),e.push(r.x,r.y,r.z))}}}else{const o=t.attributes.position;for(let t=0,a=o.count/3;t<a;t++)for(let a=0;a<3;a++){const s=3*t+(a+1)%3;i.fromBufferAttribute(o,3*t+a),r.fromBufferAttribute(o,s),!0===v6t(i,r,n)&&(e.push(i.x,i.y,i.z),e.push(r.x,r.y,r.z))}}this.setAttribute("position",new l1t(e,3))}}function v6t(t,e,n){const i=`${t.x},${t.y},${t.z}-${e.x},${e.y},${e.z}`,r=`${e.x},${e.y},${e.z}-${t.x},${t.y},${t.z}`;return!0!==n.has(i)&&!0!==n.has(r)&&(n.add(i,r),!0)}var b6t=Object.freeze({__proto__:null,BoxGeometry:F1t,BoxBufferGeometry:F1t,CircleGeometry:e4t,CircleBufferGeometry:e4t,ConeGeometry:i4t,ConeBufferGeometry:i4t,CylinderGeometry:n4t,CylinderBufferGeometry:n4t,DodecahedronGeometry:o4t,DodecahedronBufferGeometry:o4t,EdgesGeometry:u4t,ExtrudeGeometry:r6t,ExtrudeBufferGeometry:r6t,IcosahedronGeometry:a6t,IcosahedronBufferGeometry:a6t,LatheGeometry:s6t,LatheBufferGeometry:s6t,OctahedronGeometry:l6t,OctahedronBufferGeometry:l6t,ParametricGeometry:c6t,ParametricBufferGeometry:c6t,PlaneGeometry:a0t,PlaneBufferGeometry:a0t,PolyhedronGeometry:r4t,PolyhedronBufferGeometry:r4t,RingGeometry:u6t,RingBufferGeometry:u6t,ShapeGeometry:h6t,ShapeBufferGeometry:h6t,SphereGeometry:d6t,SphereBufferGeometry:d6t,TetrahedronGeometry:p6t,TetrahedronBufferGeometry:p6t,TextGeometry:f6t,TextBufferGeometry:f6t,TorusGeometry:m6t,TorusBufferGeometry:m6t,TorusKnotGeometry:g6t,TorusKnotBufferGeometry:g6t,TubeGeometry:_6t,TubeBufferGeometry:_6t,WireframeGeometry:y6t});class x6t extends UQt{constructor(t){super(),this.type="ShadowMaterial",this.color=new $Qt(0),this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this}}x6t.prototype.isShadowMaterial=!0;class w6t extends UQt{constructor(t){super(),this.defines={STANDARD:""},this.type="MeshStandardMaterial",this.color=new $Qt(16777215),this.roughness=1,this.metalness=0,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new $Qt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new mJt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.roughnessMap=null,this.metalnessMap=null,this.alphaMap=null,this.envMap=null,this.envMapIntensity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:""},this.color.copy(t.color),this.roughness=t.roughness,this.metalness=t.metalness,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.roughnessMap=t.roughnessMap,this.metalnessMap=t.metalnessMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.envMapIntensity=t.envMapIntensity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}w6t.prototype.isMeshStandardMaterial=!0;class S6t extends w6t{constructor(t){super(),this.defines={STANDARD:"",PHYSICAL:""},this.type="MeshPhysicalMaterial",this.clearcoat=0,this.clearcoatMap=null,this.clearcoatRoughness=0,this.clearcoatRoughnessMap=null,this.clearcoatNormalScale=new mJt(1,1),this.clearcoatNormalMap=null,this.reflectivity=.5,Object.defineProperty(this,"ior",{get:function(){return(1+.4*this.reflectivity)/(1-.4*this.reflectivity)},set:function(t){this.reflectivity=$Zt(2.5*(t-1)/(t+1),0,1)}}),this.sheen=null,this.transmission=0,this.transmissionMap=null,this.thickness=.01,this.thicknessMap=null,this.attenuationDistance=0,this.attenuationTint=new $Qt(1,1,1),this.specularIntensity=1,this.specularIntensityMap=null,this.specularTint=new $Qt(1,1,1),this.specularTintMap=null,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:"",PHYSICAL:""},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.reflectivity=t.reflectivity,this.sheen=t.sheen?(this.sheen||new $Qt).copy(t.sheen):null,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}S6t.prototype.isMeshPhysicalMaterial=!0;class M6t extends UQt{constructor(t){super(),this.type="MeshPhongMaterial",this.color=new $Qt(16777215),this.specular=new $Qt(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new $Qt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new mJt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.specular.copy(t.specular),this.shininess=t.shininess,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}M6t.prototype.isMeshPhongMaterial=!0;class E6t extends UQt{constructor(t){super(),this.defines={TOON:""},this.type="MeshToonMaterial",this.color=new $Qt(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new $Qt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new mJt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.gradientMap=t.gradientMap,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}E6t.prototype.isMeshToonMaterial=!0;class T6t extends UQt{constructor(t){super(),this.type="MeshNormalMaterial",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new mJt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.flatShading=t.flatShading,this}}T6t.prototype.isMeshNormalMaterial=!0;class C6t extends UQt{constructor(t){super(),this.type="MeshLambertMaterial",this.color=new $Qt(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new $Qt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}C6t.prototype.isMeshLambertMaterial=!0;class A6t extends UQt{constructor(t){super(),this.defines={MATCAP:""},this.type="MeshMatcapMaterial",this.color=new $Qt(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new mJt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={MATCAP:""},this.color.copy(t.color),this.matcap=t.matcap,this.map=t.map,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.flatShading=t.flatShading,this}}A6t.prototype.isMeshMatcapMaterial=!0;class k6t extends I3t{constructor(t){super(),this.type="LineDashedMaterial",this.scale=1,this.dashSize=3,this.gapSize=1,this.setValues(t)}copy(t){return super.copy(t),this.scale=t.scale,this.dashSize=t.dashSize,this.gapSize=t.gapSize,this}}k6t.prototype.isLineDashedMaterial=!0;var L6t=Object.freeze({__proto__:null,ShadowMaterial:x6t,SpriteMaterial:J5t,RawShaderMaterial:_0t,ShaderMaterial:G1t,PointsMaterial:G3t,MeshPhysicalMaterial:S6t,MeshStandardMaterial:w6t,MeshPhongMaterial:M6t,MeshToonMaterial:E6t,MeshNormalMaterial:T6t,MeshLambertMaterial:C6t,MeshDepthMaterial:N5t,MeshDistanceMaterial:I5t,MeshBasicMaterial:KQt,MeshMatcapMaterial:A6t,LineDashedMaterial:k6t,LineBasicMaterial:I3t,Material:UQt});const P6t={arraySlice:function(t,e,n){return P6t.isTypedArray(t)?new t.constructor(t.subarray(e,void 0!==n?n:t.length)):t.slice(e,n)},convertArray:function(t,e,n){return!t||!n&&t.constructor===e?t:"number"==typeof e.BYTES_PER_ELEMENT?new e(t):Array.prototype.slice.call(t)},isTypedArray:function(t){return ArrayBuffer.isView(t)&&!(t instanceof DataView)},getKeyframeOrder:function(t){const e=t.length,n=new Array(e);for(let t=0;t!==e;++t)n[t]=t;return n.sort((function i(e,n){return t[e]-t[n]})),n},sortedArray:function(t,e,n){const i=t.length,r=new t.constructor(i);for(let o=0,a=0;a!==i;++o){const i=n[o]*e;for(let n=0;n!==e;++n)r[a++]=t[i+n]}return r},flattenJSON:function(t,e,n,i){let r=1,o=t[0];for(;void 0!==o&&void 0===o[i];)o=t[r++];if(void 0===o)return;let a=o[i];if(void 0!==a)if(Array.isArray(a))do{a=o[i],void 0!==a&&(e.push(o.time),n.push.apply(n,a)),o=t[r++]}while(void 0!==o);else if(void 0!==a.toArray)do{a=o[i],void 0!==a&&(e.push(o.time),a.toArray(n,n.length)),o=t[r++]}while(void 0!==o);else do{a=o[i],void 0!==a&&(e.push(o.time),n.push(a)),o=t[r++]}while(void 0!==o)},subclip:function(t,e,n,i,r=30){const o=t.clone();o.name=e;const a=[];for(let t=0;t<o.tracks.length;++t){const e=o.tracks[t],s=e.getValueSize(),l=[],c=[];for(let t=0;t<e.times.length;++t){const o=e.times[t]*r;if(!(o<n||o>=i)){l.push(e.times[t]);for(let n=0;n<s;++n)c.push(e.values[t*s+n])}}0!==l.length&&(e.times=P6t.convertArray(l,e.times.constructor),e.values=P6t.convertArray(c,e.values.constructor),a.push(e))}o.tracks=a;let s=1/0;for(let t=0;t<o.tracks.length;++t)s>o.tracks[t].times[0]&&(s=o.tracks[t].times[0]);for(let t=0;t<o.tracks.length;++t)o.tracks[t].shift(-1*s);return o.resetDuration(),o},makeClipAdditive:function(t,e=0,n=t,i=30){i<=0&&(i=30);const r=n.tracks.length,o=e/i;for(let e=0;e<r;++e){const i=n.tracks[e],r=i.ValueTypeName;if("bool"===r||"string"===r)continue;const a=t.tracks.find((function(t){return t.name===i.name&&t.ValueTypeName===r}));if(void 0===a)continue;let s=0;const l=i.getValueSize();i.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(s=l/3);let c=0;const u=a.getValueSize();a.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(c=u/3);const h=i.times.length-1;let d;if(o<=i.times[0])d=P6t.arraySlice(i.values,s,l-s);else if(o>=i.times[h]){const t=h*l+s;d=P6t.arraySlice(i.values,t,t+l-s)}else{const t=i.createInterpolant(),e=s,n=l-s;t.evaluate(o),d=P6t.arraySlice(t.resultBuffer,e,n)}"quaternion"===r&&(new TJt).fromArray(d).normalize().conjugate().toArray(d);const p=a.times.length;for(let t=0;t<p;++t){const e=t*u+c;if("quaternion"===r)TJt.multiplyQuaternionsFlat(a.values,e,d,0,a.values,e);else{const t=u-2*c;for(let n=0;n<t;++n)a.values[e+n]-=d[n]}}}return t.blendMode=PZt,t}};class N6t{constructor(t,e,n,i){this.parameterPositions=t,this._cachedIndex=0,this.resultBuffer=void 0!==i?i:new e.constructor(n),this.sampleValues=e,this.valueSize=n,this.settings=null,this.DefaultSettings_={}}evaluate(t){const e=this.parameterPositions;let n=this._cachedIndex,i=e[n],r=e[n-1];t:{e:{let o;n:{i:if(!(t<i)){for(let o=n+2;;){if(void 0===i){if(t<r)break i;return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,t,r)}if(n===o)break;if(r=i,i=e[++n],t<i)break e}o=e.length;break n}if(t>=r)break t;{const a=e[1];t<a&&(n=2,r=a);for(let o=n-2;;){if(void 0===r)return this._cachedIndex=0,this.beforeStart_(0,t,i);if(n===o)break;if(i=r,r=e[--n-1],t>=r)break e}o=n,n=0}}for(;n<o;){const i=n+o>>>1;t<e[i]?o=i:n=i+1}if(i=e[n],r=e[n-1],void 0===r)return this._cachedIndex=0,this.beforeStart_(0,t,i);if(void 0===i)return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,r,t)}this._cachedIndex=n,this.intervalChanged_(n,r,i)}return this.interpolate_(n,r,t,i)}getSettings_(){return this.settings||this.DefaultSettings_}copySampleValue_(t){const e=this.resultBuffer,n=this.sampleValues,i=this.valueSize,r=t*i;for(let t=0;t!==i;++t)e[t]=n[r+t];return e}interpolate_(){throw new Error("call to abstract method")}intervalChanged_(){}}N6t.prototype.beforeStart_=N6t.prototype.copySampleValue_,N6t.prototype.afterEnd_=N6t.prototype.copySampleValue_;class I6t extends N6t{constructor(t,e,n,i){super(t,e,n,i),this._weightPrev=-0,this._offsetPrev=-0,this._weightNext=-0,this._offsetNext=-0,this.DefaultSettings_={endingStart:CZt,endingEnd:CZt}}intervalChanged_(t,e,n){const i=this.parameterPositions;let r=t-2,o=t+1,a=i[r],s=i[o];if(void 0===a)switch(this.getSettings_().endingStart){case AZt:r=t,a=2*e-n;break;case kZt:r=i.length-2,a=e+i[r]-i[r+1];break;default:r=t,a=n}if(void 0===s)switch(this.getSettings_().endingEnd){case AZt:o=t,s=2*n-e;break;case kZt:o=1,s=n+i[1]-i[0];break;default:o=t-1,s=e}const l=.5*(n-e),c=this.valueSize;this._weightPrev=l/(e-a),this._weightNext=l/(s-n),this._offsetPrev=r*c,this._offsetNext=o*c}interpolate_(t,e,n,i){const r=this.resultBuffer,o=this.sampleValues,a=this.valueSize,s=t*a,l=s-a,c=this._offsetPrev,u=this._offsetNext,h=this._weightPrev,d=this._weightNext,p=(n-e)/(i-e),f=p*p,m=f*p,g=-h*m+2*h*f-h*p,_=(1+h)*m+(-1.5-2*h)*f+(-.5+h)*p+1,y=(-1-d)*m+(1.5+d)*f+.5*p,v=d*m-d*f;for(let t=0;t!==a;++t)r[t]=g*o[c+t]+_*o[l+t]+y*o[s+t]+v*o[u+t];return r}}class R6t extends N6t{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const r=this.resultBuffer,o=this.sampleValues,a=this.valueSize,s=t*a,l=s-a,c=(n-e)/(i-e),u=1-c;for(let t=0;t!==a;++t)r[t]=o[l+t]*u+o[s+t]*c;return r}}class O6t extends N6t{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t){return this.copySampleValue_(t-1)}}class z6t{constructor(t,e,n,i){if(void 0===t)throw new Error("THREE.KeyframeTrack: track name is undefined");if(void 0===e||0===e.length)throw new Error("THREE.KeyframeTrack: no keyframes in track named "+t);this.name=t,this.times=P6t.convertArray(e,this.TimeBufferType),this.values=P6t.convertArray(n,this.ValueBufferType),this.setInterpolation(i||this.DefaultInterpolation)}static toJSON(t){const e=t.constructor;let n;if(e.toJSON!==this.toJSON)n=e.toJSON(t);else{n={name:t.name,times:P6t.convertArray(t.times,Array),values:P6t.convertArray(t.values,Array)};const e=t.getInterpolation();e!==t.DefaultInterpolation&&(n.interpolation=e)}return n.type=t.ValueTypeName,n}InterpolantFactoryMethodDiscrete(t){return new O6t(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodLinear(t){return new R6t(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodSmooth(t){return new I6t(this.times,this.values,this.getValueSize(),t)}setInterpolation(t){let e;switch(t){case MZt:e=this.InterpolantFactoryMethodDiscrete;break;case EZt:e=this.InterpolantFactoryMethodLinear;break;case TZt:e=this.InterpolantFactoryMethodSmooth}if(void 0===e){const e="unsupported interpolation for "+this.ValueTypeName+" keyframe track named "+this.name;if(void 0===this.createInterpolant){if(t===this.DefaultInterpolation)throw new Error(e);this.setInterpolation(this.DefaultInterpolation)}return console.warn("THREE.KeyframeTrack:",e),this}return this.createInterpolant=e,this}getInterpolation(){switch(this.createInterpolant){case this.InterpolantFactoryMethodDiscrete:return MZt;case this.InterpolantFactoryMethodLinear:return EZt;case this.InterpolantFactoryMethodSmooth:return TZt}}getValueSize(){return this.values.length/this.times.length}shift(t){if(0!==t){const e=this.times;for(let n=0,i=e.length;n!==i;++n)e[n]+=t}return this}scale(t){if(1!==t){const e=this.times;for(let n=0,i=e.length;n!==i;++n)e[n]*=t}return this}trim(t,e){const n=this.times,i=n.length;let r=0,o=i-1;for(;r!==i&&n[r]<t;)++r;for(;-1!==o&&n[o]>e;)--o;if(++o,0!==r||o!==i){r>=o&&(o=Math.max(o,1),r=o-1);const t=this.getValueSize();this.times=P6t.arraySlice(n,r,o),this.values=P6t.arraySlice(this.values,r*t,o*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error("THREE.KeyframeTrack: Invalid value size in track.",this),t=!1);const n=this.times,i=this.values,r=n.length;0===r&&(console.error("THREE.KeyframeTrack: Track is empty.",this),t=!1);let o=null;for(let e=0;e!==r;e++){const i=n[e];if("number"==typeof i&&isNaN(i)){console.error("THREE.KeyframeTrack: Time is not a valid number.",this,e,i),t=!1;break}if(null!==o&&o>i){console.error("THREE.KeyframeTrack: Out of order keys.",this,e,i,o),t=!1;break}o=i}if(void 0!==i&&P6t.isTypedArray(i))for(let e=0,n=i.length;e!==n;++e){const n=i[e];if(isNaN(n)){console.error("THREE.KeyframeTrack: Value is not a valid number.",this,e,n),t=!1;break}}return t}optimize(){const t=P6t.arraySlice(this.times),e=P6t.arraySlice(this.values),n=this.getValueSize(),i=this.getInterpolation()===TZt,r=t.length-1;let o=1;for(let a=1;a<r;++a){let r=!1;const s=t[a];if(s!==t[a+1]&&(1!==a||s!==t[0]))if(i)r=!0;else{const t=a*n,i=t-n,o=t+n;for(let a=0;a!==n;++a){const n=e[t+a];if(n!==e[i+a]||n!==e[o+a]){r=!0;break}}}if(r){if(a!==o){t[o]=t[a];const i=a*n,r=o*n;for(let t=0;t!==n;++t)e[r+t]=e[i+t]}++o}}if(r>0){t[o]=t[r];for(let t=r*n,i=o*n,a=0;a!==n;++a)e[i+a]=e[t+a];++o}return o!==t.length?(this.times=P6t.arraySlice(t,0,o),this.values=P6t.arraySlice(e,0,o*n)):(this.times=t,this.values=e),this}clone(){const t=P6t.arraySlice(this.times,0),e=P6t.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}z6t.prototype.TimeBufferType=Float32Array,z6t.prototype.ValueBufferType=Float32Array,z6t.prototype.DefaultInterpolation=EZt;class D6t extends z6t{}D6t.prototype.ValueTypeName="bool",D6t.prototype.ValueBufferType=Array,D6t.prototype.DefaultInterpolation=MZt,D6t.prototype.InterpolantFactoryMethodLinear=void 0,D6t.prototype.InterpolantFactoryMethodSmooth=void 0;class B6t extends z6t{}B6t.prototype.ValueTypeName="color";class H6t extends z6t{}H6t.prototype.ValueTypeName="number";class F6t extends N6t{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const r=this.resultBuffer,o=this.sampleValues,a=this.valueSize,s=(n-e)/(i-e);let l=t*a;for(let t=l+a;l!==t;l+=4)TJt.slerpFlat(r,0,o,l-a,o,l,s);return r}}class V6t extends z6t{InterpolantFactoryMethodLinear(t){return new F6t(this.times,this.values,this.getValueSize(),t)}}V6t.prototype.ValueTypeName="quaternion",V6t.prototype.DefaultInterpolation=EZt,V6t.prototype.InterpolantFactoryMethodSmooth=void 0;class U6t extends z6t{}U6t.prototype.ValueTypeName="string",U6t.prototype.ValueBufferType=Array,U6t.prototype.DefaultInterpolation=MZt,U6t.prototype.InterpolantFactoryMethodLinear=void 0,U6t.prototype.InterpolantFactoryMethodSmooth=void 0;class j6t extends z6t{}j6t.prototype.ValueTypeName="vector";class G6t{constructor(t,e=-1,n,i=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=XZt(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,i=1/(t.fps||1);for(let t=0,r=n.length;t!==r;++t)e.push(W6t(n[t]).scale(i));const r=new this(t.name,t.duration,e,t.blendMode);return r.uuid=t.uuid,r}static toJSON(t){const e=[],n=t.tracks,i={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,i=n.length;t!==i;++t)e.push(z6t.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,i){const r=e.length,o=[];for(let t=0;t<r;t++){let a=[],s=[];a.push((t+r-1)%r,t,(t+1)%r),s.push(0,1,0);const l=P6t.getKeyframeOrder(a);a=P6t.sortedArray(a,1,l),s=P6t.sortedArray(s,1,l),i||0!==a[0]||(a.push(r),s.push(s[0])),o.push(new H6t(".morphTargetInfluences["+e[t].name+"]",a,s).scale(1/n))}return new this(t,-1,o)}static findByName(t,e){let n=t;if(!Array.isArray(t)){const e=t;n=e.geometry&&e.geometry.animations||e.animations}for(let t=0;t<n.length;t++)if(n[t].name===e)return n[t];return null}static CreateClipsFromMorphTargetSequences(t,e,n){const i={},r=/^([\w-]*?)([\d]+)$/;for(let e=0,n=t.length;e<n;e++){const n=t[e],o=n.name.match(r);if(o&&o.length>1){const t=o[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const o=[];for(const t in i)o.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return o}static parseAnimation(t,e){if(!t)return console.error("THREE.AnimationClip: No animation in JSONLoader data."),null;const n=function(t,e,n,i,r){if(0!==n.length){const o=[],a=[];P6t.flattenJSON(n,o,a,i),0!==o.length&&r.push(new t(e,o,a))}},i=[],r=t.name||"default",o=t.fps||30,a=t.blendMode;let s=t.length||-1;const l=t.hierarchy||[];for(let t=0;t<l.length;t++){const r=l[t].keys;if(r&&0!==r.length)if(r[0].morphTargets){const t={};let e;for(e=0;e<r.length;e++)if(r[e].morphTargets)for(let n=0;n<r[e].morphTargets.length;n++)t[r[e].morphTargets[n]]=-1;for(const n in t){const t=[],o=[];for(let i=0;i!==r[e].morphTargets.length;++i){const i=r[e];t.push(i.time),o.push(i.morphTarget===n?1:0)}i.push(new H6t(".morphTargetInfluence["+n+"]",t,o))}s=t.length*(o||1)}else{const o=".bones["+e[t].name+"]";n(j6t,o+".position",r,"pos",i),n(V6t,o+".quaternion",r,"rot",i),n(j6t,o+".scale",r,"scl",i)}}return 0===i.length?null:new this(r,s,i,a)}resetDuration(){let t=0;for(let e=0,n=this.tracks.length;e!==n;++e){const n=this.tracks[e];t=Math.max(t,n.times[n.times.length-1])}return this.duration=t,this}trim(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].trim(0,this.duration);return this}validate(){let t=!0;for(let e=0;e<this.tracks.length;e++)t=t&&this.tracks[e].validate();return t}optimize(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].optimize();return this}clone(){const t=[];for(let e=0;e<this.tracks.length;e++)t.push(this.tracks[e].clone());return new this.constructor(this.name,this.duration,t,this.blendMode)}toJSON(){return this.constructor.toJSON(this)}}function W6t(t){if(void 0===t.type)throw new Error("THREE.KeyframeTrack: track type undefined, can not parse");const e=(function n(t){switch(t.toLowerCase()){case"scalar":case"double":case"float":case"number":case"integer":return H6t;case"vector":case"vector2":case"vector3":case"vector4":return j6t;case"color":return B6t;case"quaternion":return V6t;case"bool":case"boolean":return D6t;case"string":return U6t}throw new Error("THREE.KeyframeTrack: Unsupported typeName: "+t)})(t.type);if(void 0===t.times){const e=[],n=[];P6t.flattenJSON(t.keys,e,n,"value"),t.times=e,t.values=n}return void 0!==e.parse?e.parse(t):new e(t.name,t.times,t.values,t.interpolation)}const q6t={enabled:!1,files:{},add:function(t,e){!1!==this.enabled&&(this.files[t]=e)},get:function(t){if(!1!==this.enabled)return this.files[t]},remove:function(t){delete this.files[t]},clear:function(){this.files={}}};class Y6t{constructor(t,e,n){const i=this;let r,o=!1,a=0,s=0;const l=[];this.onStart=void 0,this.onLoad=t,this.onProgress=e,this.onError=n,this.itemStart=function(t){s++,!1===o&&void 0!==i.onStart&&i.onStart(t,a,s),o=!0},this.itemEnd=function(t){a++,void 0!==i.onProgress&&i.onProgress(t,a,s),a===s&&(o=!1,void 0!==i.onLoad&&i.onLoad())},this.itemError=function(t){void 0!==i.onError&&i.onError(t)},this.resolveURL=function(t){return r?r(t):t},this.setURLModifier=function(t){return r=t,this},this.addHandler=function(t,e){return l.push(t,e),this},this.removeHandler=function(t){const e=l.indexOf(t);return-1!==e&&l.splice(e,2),this},this.getHandler=function(t){for(let e=0,n=l.length;e<n;e+=2){const n=l[e],i=l[e+1];if(n.global&&(n.lastIndex=0),n.test(t))return i}return null}}}const X6t=new Y6t;class $6t{constructor(t){this.manager=void 0!==t?t:X6t,this.crossOrigin="anonymous",this.withCredentials=!1,this.path="",this.resourcePath="",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(i,r){n.load(t,i,e,r)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}const K6t={};class Z6t extends $6t{constructor(t){super(t)}load(t,e,n,i){void 0===t&&(t=""),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,o=q6t.get(t);if(void 0!==o)return r.manager.itemStart(t),setTimeout((function(){e&&e(o),r.manager.itemEnd(t)}),0),o;if(void 0!==K6t[t])return void K6t[t].push({onLoad:e,onProgress:n,onError:i});const a=t.match(/^data:(.*?)(;base64)?,(.*)$/);let s;if(a){const n=a[1],o=!!a[2];let s=a[3];s=decodeURIComponent(s),o&&(s=atob(s));try{let i;const o=(this.responseType||"").toLowerCase();switch(o){case"arraybuffer":case"blob":const t=new Uint8Array(s.length);for(let e=0;e<s.length;e++)t[e]=s.charCodeAt(e);i="blob"===o?new Blob([t.buffer],{type:n}):t.buffer;break;case"document":const e=new DOMParser;i=e.parseFromString(s,n);break;case"json":i=JSON.parse(s);break;default:i=s}setTimeout((function(){e&&e(i),r.manager.itemEnd(t)}),0)}catch(e){setTimeout((function(){i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)}),0)}}else{K6t[t]=[],K6t[t].push({onLoad:e,onProgress:n,onError:i}),s=new XMLHttpRequest,s.open("GET",t,!0),s.addEventListener("load",(function(e){const n=this.response,i=K6t[t];if(delete K6t[t],200===this.status||0===this.status){0===this.status&&console.warn("THREE.FileLoader: HTTP Status 0 received."),q6t.add(t,n);for(let t=0,e=i.length;t<e;t++){const e=i[t];e.onLoad&&e.onLoad(n)}r.manager.itemEnd(t)}else{for(let t=0,n=i.length;t<n;t++){const n=i[t];n.onError&&n.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}}),!1),s.addEventListener("progress",(function(e){const n=K6t[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onProgress&&i.onProgress(e)}}),!1),s.addEventListener("error",(function(e){const n=K6t[t];delete K6t[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onError&&i.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}),!1),s.addEventListener("abort",(function(e){const n=K6t[t];delete K6t[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onError&&i.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}),!1),void 0!==this.responseType&&(s.responseType=this.responseType),void 0!==this.withCredentials&&(s.withCredentials=this.withCredentials),s.overrideMimeType&&s.overrideMimeType(void 0!==this.mimeType?this.mimeType:"text/plain");for(const t in this.requestHeader)s.setRequestHeader(t,this.requestHeader[t]);s.send(null)}return r.manager.itemStart(t),s}setResponseType(t){return this.responseType=t,this}setMimeType(t){return this.mimeType=t,this}}class J6t extends $6t{constructor(t){super(t)}load(t,e,n,i){void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,o=q6t.get(t);if(void 0!==o)return r.manager.itemStart(t),setTimeout((function(){e&&e(o),r.manager.itemEnd(t)}),0),o;const a=document.createElementNS("http://www.w3.org/1999/xhtml","img");function s(){a.removeEventListener("load",s,!1),a.removeEventListener("error",l,!1),q6t.add(t,this),e&&e(this),r.manager.itemEnd(t)}function l(e){a.removeEventListener("load",s,!1),a.removeEventListener("error",l,!1),i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)}return a.addEventListener("load",s,!1),a.addEventListener("error",l,!1),"data:"!==t.substr(0,5)&&void 0!==this.crossOrigin&&(a.crossOrigin=this.crossOrigin),r.manager.itemStart(t),a.src=t,a}}class Q6t extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=new $1t,o=new J6t(this.manager);o.setCrossOrigin(this.crossOrigin),o.setPath(this.path);let a=0;function s(n){o.load(t[n],(function(t){r.images[n]=t,a++,6===a&&(r.needsUpdate=!0,e&&e(r))}),void 0,i)}for(let e=0;e<t.length;++e)s(e);return r}}class t9t extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new M3t,a=new Z6t(this.manager);return a.setResponseType("arraybuffer"),a.setRequestHeader(this.requestHeader),a.setPath(this.path),a.setWithCredentials(r.withCredentials),a.load(t,(function(t){const n=r.parse(t);n&&(void 0!==n.image?o.image=n.image:void 0!==n.data&&(o.image.width=n.width,o.image.height=n.height,o.image.data=n.data),o.wrapS=void 0!==n.wrapS?n.wrapS:KKt,o.wrapT=void 0!==n.wrapT?n.wrapT:KKt,o.magFilter=void 0!==n.magFilter?n.magFilter:eZt,o.minFilter=void 0!==n.minFilter?n.minFilter:eZt,o.anisotropy=void 0!==n.anisotropy?n.anisotropy:1,void 0!==n.encoding&&(o.encoding=n.encoding),void 0!==n.flipY&&(o.flipY=n.flipY),void 0!==n.format&&(o.format=n.format),void 0!==n.type&&(o.type=n.type),void 0!==n.mipmaps&&(o.mipmaps=n.mipmaps,o.minFilter=iZt),1===n.mipmapCount&&(o.minFilter=eZt),void 0!==n.generateMipmaps&&(o.generateMipmaps=n.generateMipmaps),o.needsUpdate=!0,e&&e(o,n))}),n,i),o}}class e9t extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=new bJt,o=new J6t(this.manager);return o.setCrossOrigin(this.crossOrigin),o.setPath(this.path),o.load(t,(function(n){r.image=n;const i=t.search(/\.jpe?g($|\?)/i)>0||0===t.search(/^data\:image\/jpeg/);r.format=i?uZt:hZt,r.needsUpdate=!0,void 0!==e&&e(r)}),n,i),r}}class n9t extends h4t{constructor(){super(),this.type="CurvePath",this.curves=[],this.autoClose=!1}add(t){this.curves.push(t)}closePath(){const t=this.curves[0].getPoint(0),e=this.curves[this.curves.length-1].getPoint(1);t.equals(e)||this.curves.push(new E4t(e,t))}getPoint(t){const e=t*this.getLength(),n=this.getCurveLengths();let i=0;for(;i<n.length;){if(n[i]>=e){const t=n[i]-e,r=this.curves[i],o=r.getLength();return r.getPointAt(0===o?0:1-t/o)}i++}return null}getLength(){const t=this.getCurveLengths();return t[t.length-1]}updateArcLengths(){this.needsUpdate=!0,this.cacheLengths=null,this.getCurveLengths()}getCurveLengths(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;const t=[];let e=0;for(let n=0,i=this.curves.length;n<i;n++)e+=this.curves[n].getLength(),t.push(e);return this.cacheLengths=t,t}getSpacedPoints(t=40){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return this.autoClose&&e.push(e[0]),e}getPoints(t=12){const e=[];let n;for(let i=0,r=this.curves;i<r.length;i++){const o=r[i],a=o.getPoints(o&&o.isEllipseCurve?2*t:o&&(o.isLineCurve||o.isLineCurve3)?1:o&&o.isSplineCurve?t*o.points.length:t);for(let t=0;t<a.length;t++){const i=a[t];n&&n.equals(i)||(e.push(i),n=i)}}return this.autoClose&&e.length>1&&!e[e.length-1].equals(e[0])&&e.push(e[0]),e}copy(t){super.copy(t),this.curves=[];for(let e=0,n=t.curves.length;e<n;e++)this.curves.push(t.curves[e].clone());return this.autoClose=t.autoClose,this}toJSON(){const t=super.toJSON();t.autoClose=this.autoClose,t.curves=[];for(let e=0,n=this.curves.length;e<n;e++)t.curves.push(this.curves[e].toJSON());return t}fromJSON(t){super.fromJSON(t),this.autoClose=t.autoClose,this.curves=[];for(let e=0,n=t.curves.length;e<n;e++){const n=t.curves[e];this.curves.push((new L4t[n.type]).fromJSON(n))}return this}}class i9t extends n9t{constructor(t){super(),this.type="Path",this.currentPoint=new mJt,t&&this.setFromPoints(t)}setFromPoints(t){this.moveTo(t[0].x,t[0].y);for(let e=1,n=t.length;e<n;e++)this.lineTo(t[e].x,t[e].y);return this}moveTo(t,e){return this.currentPoint.set(t,e),this}lineTo(t,e){const n=new E4t(this.currentPoint.clone(),new mJt(t,e));return this.curves.push(n),this.currentPoint.set(t,e),this}quadraticCurveTo(t,e,n,i){const r=new C4t(this.currentPoint.clone(),new mJt(t,e),new mJt(n,i));return this.curves.push(r),this.currentPoint.set(n,i),this}bezierCurveTo(t,e,n,i,r,o){const a=new S4t(this.currentPoint.clone(),new mJt(t,e),new mJt(n,i),new mJt(r,o));return this.curves.push(a),this.currentPoint.set(r,o),this}splineThru(t){const e=[this.currentPoint.clone()].concat(t),n=new k4t(e);return this.curves.push(n),this.currentPoint.copy(t[t.length-1]),this}arc(t,e,n,i,r,o){return this.absarc(t+this.currentPoint.x,e+this.currentPoint.y,n,i,r,o),this}absarc(t,e,n,i,r,o){return this.absellipse(t,e,n,n,i,r,o),this}ellipse(t,e,n,i,r,o,a,s){return this.absellipse(t+this.currentPoint.x,e+this.currentPoint.y,n,i,r,o,a,s),this}absellipse(t,e,n,i,r,o,a,s){const l=new d4t(t,e,n,i,r,o,a,s);if(this.curves.length>0){const t=l.getPoint(0);t.equals(this.currentPoint)||this.lineTo(t.x,t.y)}this.curves.push(l);const c=l.getPoint(1);return this.currentPoint.copy(c),this}copy(t){return super.copy(t),this.currentPoint.copy(t.currentPoint),this}toJSON(){const t=super.toJSON();return t.currentPoint=this.currentPoint.toArray(),t}fromJSON(t){return super.fromJSON(t),this.currentPoint.fromArray(t.currentPoint),this}}class r9t extends i9t{constructor(t){super(t),this.uuid=XZt(),this.type="Shape",this.holes=[]}getPointsHoles(t){const e=[];for(let n=0,i=this.holes.length;n<i;n++)e[n]=this.holes[n].getPoints(t);return e}extractPoints(t){return{shape:this.getPoints(t),holes:this.getPointsHoles(t)}}copy(t){super.copy(t),this.holes=[];for(let e=0,n=t.holes.length;e<n;e++)this.holes.push(t.holes[e].clone());return this}toJSON(){const t=super.toJSON();t.uuid=this.uuid,t.holes=[];for(let e=0,n=this.holes.length;e<n;e++)t.holes.push(this.holes[e].toJSON());return t}fromJSON(t){super.fromJSON(t),this.uuid=t.uuid,this.holes=[];for(let e=0,n=t.holes.length;e<n;e++){const n=t.holes[e];this.holes.push((new i9t).fromJSON(n))}return this}}class o9t extends kQt{constructor(t,e=1){super(),this.type="Light",this.color=new $Qt(t),this.intensity=e}dispose(){}copy(t){return super.copy(t),this.color.copy(t.color),this.intensity=t.intensity,this}toJSON(t){const e=super.toJSON(t);return e.object.color=this.color.getHex(),e.object.intensity=this.intensity,void 0!==this.groundColor&&(e.object.groundColor=this.groundColor.getHex()),void 0!==this.distance&&(e.object.distance=this.distance),void 0!==this.angle&&(e.object.angle=this.angle),void 0!==this.decay&&(e.object.decay=this.decay),void 0!==this.penumbra&&(e.object.penumbra=this.penumbra),void 0!==this.shadow&&(e.object.shadow=this.shadow.toJSON()),e}}o9t.prototype.isLight=!0;class a9t extends o9t{constructor(t,e,n){super(t,n),this.type="HemisphereLight",this.position.copy(kQt.DefaultUp),this.updateMatrix(),this.groundColor=new $Qt(e)}copy(t){return o9t.prototype.copy.call(this,t),this.groundColor.copy(t.groundColor),this}}a9t.prototype.isHemisphereLight=!0;const s9t=new rQt,l9t=new CJt,c9t=new CJt;class u9t{constructor(t){this.camera=t,this.bias=0,this.normalBias=0,this.radius=1,this.mapSize=new mJt(512,512),this.map=null,this.mapPass=null,this.matrix=new rQt,this.autoUpdate=!0,this.needsUpdate=!1,this._frustum=new i0t,this._frameExtents=new mJt(1,1),this._viewportCount=1,this._viewports=[new wJt(0,0,1,1)]}getViewportCount(){return this._viewportCount}getFrustum(){return this._frustum}updateMatrices(t){const e=this.camera,n=this.matrix;l9t.setFromMatrixPosition(t.matrixWorld),e.position.copy(l9t),c9t.setFromMatrixPosition(t.target.matrixWorld),e.lookAt(c9t),e.updateMatrixWorld(),s9t.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),this._frustum.setFromProjectionMatrix(s9t),n.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),n.multiply(e.projectionMatrix),n.multiply(e.matrixWorldInverse)}getViewport(t){return this._viewports[t]}getFrameExtents(){return this._frameExtents}dispose(){this.map&&this.map.dispose(),this.mapPass&&this.mapPass.dispose()}copy(t){return this.camera=t.camera.clone(),this.bias=t.bias,this.radius=t.radius,this.mapSize.copy(t.mapSize),this}clone(){return(new this.constructor).copy(this)}toJSON(){const t={};return 0!==this.bias&&(t.bias=this.bias),0!==this.normalBias&&(t.normalBias=this.normalBias),1!==this.radius&&(t.radius=this.radius),512===this.mapSize.x&&512===this.mapSize.y||(t.mapSize=this.mapSize.toArray()),t.camera=this.camera.toJSON(!1).object,delete t.camera.matrix,t}}class h9t extends u9t{constructor(){super(new q1t(50,1,.5,500)),this.focus=1}updateMatrices(t){const e=this.camera,n=2*YZt*t.angle*this.focus,i=this.mapSize.width/this.mapSize.height,r=t.distance||e.far;n===e.fov&&i===e.aspect&&r===e.far||(e.fov=n,e.aspect=i,e.far=r,e.updateProjectionMatrix()),super.updateMatrices(t)}copy(t){return super.copy(t),this.focus=t.focus,this}}h9t.prototype.isSpotLightShadow=!0;class d9t extends o9t{constructor(t,e,n=0,i=Math.PI/3,r=0,o=1){super(t,e),this.type="SpotLight",this.position.copy(kQt.DefaultUp),this.updateMatrix(),this.target=new kQt,this.distance=n,this.angle=i,this.penumbra=r,this.decay=o,this.shadow=new h9t}get power(){return this.intensity*Math.PI}set power(t){this.intensity=t/Math.PI}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.angle=t.angle,this.penumbra=t.penumbra,this.decay=t.decay,this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}d9t.prototype.isSpotLight=!0;const p9t=new rQt,f9t=new CJt,m9t=new CJt;class g9t extends u9t{constructor(){super(new q1t(90,1,.5,500)),this._frameExtents=new mJt(4,2),this._viewportCount=6,this._viewports=[new wJt(2,1,1,1),new wJt(0,1,1,1),new wJt(3,1,1,1),new wJt(1,1,1,1),new wJt(3,0,1,1),new wJt(1,0,1,1)],this._cubeDirections=[new CJt(1,0,0),new CJt(-1,0,0),new CJt(0,0,1),new CJt(0,0,-1),new CJt(0,1,0),new CJt(0,-1,0)],this._cubeUps=[new CJt(0,1,0),new CJt(0,1,0),new CJt(0,1,0),new CJt(0,1,0),new CJt(0,0,1),new CJt(0,0,-1)]}updateMatrices(t,e=0){const n=this.camera,i=this.matrix,r=t.distance||n.far;r!==n.far&&(n.far=r,n.updateProjectionMatrix()),f9t.setFromMatrixPosition(t.matrixWorld),n.position.copy(f9t),m9t.copy(n.position),m9t.add(this._cubeDirections[e]),n.up.copy(this._cubeUps[e]),n.lookAt(m9t),n.updateMatrixWorld(),i.makeTranslation(-f9t.x,-f9t.y,-f9t.z),p9t.multiplyMatrices(n.projectionMatrix,n.matrixWorldInverse),this._frustum.setFromProjectionMatrix(p9t)}}g9t.prototype.isPointLightShadow=!0;class _9t extends o9t{constructor(t,e,n=0,i=1){super(t,e),this.type="PointLight",this.distance=n,this.decay=i,this.shadow=new g9t}get power(){return 4*this.intensity*Math.PI}set power(t){this.intensity=t/(4*Math.PI)}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.decay=t.decay,this.shadow=t.shadow.clone(),this}}_9t.prototype.isPointLight=!0;class y9t extends u9t{constructor(){super(new g0t(-5,5,5,-5,.5,500))}}y9t.prototype.isDirectionalLightShadow=!0;class v9t extends o9t{constructor(t,e){super(t,e),this.type="DirectionalLight",this.position.copy(kQt.DefaultUp),this.updateMatrix(),this.target=new kQt,this.shadow=new y9t}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}v9t.prototype.isDirectionalLight=!0;class b9t extends o9t{constructor(t,e){super(t,e),this.type="AmbientLight"}}b9t.prototype.isAmbientLight=!0;class x9t extends o9t{constructor(t,e,n=10,i=10){super(t,e),this.type="RectAreaLight",this.width=n,this.height=i}copy(t){return super.copy(t),this.width=t.width,this.height=t.height,this}toJSON(t){const e=super.toJSON(t);return e.object.width=this.width,e.object.height=this.height,e}}x9t.prototype.isRectAreaLight=!0;class w9t{constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new CJt)}set(t){for(let e=0;e<9;e++)this.coefficients[e].copy(t[e]);return this}zero(){for(let t=0;t<9;t++)this.coefficients[t].set(0,0,0);return this}getAt(t,e){const n=t.x,i=t.y,r=t.z,o=this.coefficients;return e.copy(o[0]).multiplyScalar(.282095),e.addScaledVector(o[1],.488603*i),e.addScaledVector(o[2],.488603*r),e.addScaledVector(o[3],.488603*n),e.addScaledVector(o[4],n*i*1.092548),e.addScaledVector(o[5],i*r*1.092548),e.addScaledVector(o[6],.315392*(3*r*r-1)),e.addScaledVector(o[7],n*r*1.092548),e.addScaledVector(o[8],.546274*(n*n-i*i)),e}getIrradianceAt(t,e){const n=t.x,i=t.y,r=t.z,o=this.coefficients;return e.copy(o[0]).multiplyScalar(.886227),e.addScaledVector(o[1],1.023328*i),e.addScaledVector(o[2],1.023328*r),e.addScaledVector(o[3],1.023328*n),e.addScaledVector(o[4],.858086*n*i),e.addScaledVector(o[5],.858086*i*r),e.addScaledVector(o[6],.743125*r*r-.247708),e.addScaledVector(o[7],.858086*n*r),e.addScaledVector(o[8],.429043*(n*n-i*i)),e}add(t){for(let e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].fromArray(t,e+3*i);return this}toArray(t=[],e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].toArray(t,e+3*i);return t}static getBasisAt(t,e){const n=t.x,i=t.y,r=t.z;e[0]=.282095,e[1]=.488603*i,e[2]=.488603*r,e[3]=.488603*n,e[4]=1.092548*n*i,e[5]=1.092548*i*r,e[6]=.315392*(3*r*r-1),e[7]=1.092548*n*r,e[8]=.546274*(n*n-i*i)}}w9t.prototype.isSphericalHarmonics3=!0;class S9t extends o9t{constructor(t=new w9t,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}S9t.prototype.isLightProbe=!0;class M9t extends $6t{constructor(t){super(t),this.textures={}}load(t,e,n,i){const r=this,o=new Z6t(r.manager);o.setPath(r.path),o.setRequestHeader(r.requestHeader),o.setWithCredentials(r.withCredentials),o.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e=this.textures;function n(t){return void 0===e[t]&&console.warn("THREE.MaterialLoader: Undefined texture",t),e[t]}const i=new L6t[t.type];if(void 0!==t.uuid&&(i.uuid=t.uuid),void 0!==t.name&&(i.name=t.name),void 0!==t.color&&void 0!==i.color&&i.color.setHex(t.color),void 0!==t.roughness&&(i.roughness=t.roughness),void 0!==t.metalness&&(i.metalness=t.metalness),void 0!==t.sheen&&(i.sheen=(new $Qt).setHex(t.sheen)),void 0!==t.emissive&&void 0!==i.emissive&&i.emissive.setHex(t.emissive),void 0!==t.specular&&void 0!==i.specular&&i.specular.setHex(t.specular),void 0!==t.specularIntensity&&(i.specularIntensity=t.specularIntensity),void 0!==t.specularTint&&void 0!==i.specularTint&&i.specularTint.setHex(t.specularTint),void 0!==t.shininess&&(i.shininess=t.shininess),void 0!==t.clearcoat&&(i.clearcoat=t.clearcoat),void 0!==t.clearcoatRoughness&&(i.clearcoatRoughness=t.clearcoatRoughness),void 0!==t.transmission&&(i.transmission=t.transmission),void 0!==t.thickness&&(i.thickness=t.thickness),void 0!==t.attenuationDistance&&(i.attenuationDistance=t.attenuationDistance),void 0!==t.attenuationTint&&void 0!==i.attenuationTint&&i.attenuationTint.setHex(t.attenuationTint),void 0!==t.fog&&(i.fog=t.fog),void 0!==t.flatShading&&(i.flatShading=t.flatShading),void 0!==t.blending&&(i.blending=t.blending),void 0!==t.combine&&(i.combine=t.combine),void 0!==t.side&&(i.side=t.side),void 0!==t.shadowSide&&(i.shadowSide=t.shadowSide),void 0!==t.opacity&&(i.opacity=t.opacity),void 0!==t.transparent&&(i.transparent=t.transparent),void 0!==t.alphaTest&&(i.alphaTest=t.alphaTest),void 0!==t.depthTest&&(i.depthTest=t.depthTest),void 0!==t.depthWrite&&(i.depthWrite=t.depthWrite),void 0!==t.colorWrite&&(i.colorWrite=t.colorWrite),void 0!==t.stencilWrite&&(i.stencilWrite=t.stencilWrite),void 0!==t.stencilWriteMask&&(i.stencilWriteMask=t.stencilWriteMask),void 0!==t.stencilFunc&&(i.stencilFunc=t.stencilFunc),void 0!==t.stencilRef&&(i.stencilRef=t.stencilRef),void 0!==t.stencilFuncMask&&(i.stencilFuncMask=t.stencilFuncMask),void 0!==t.stencilFail&&(i.stencilFail=t.stencilFail),void 0!==t.stencilZFail&&(i.stencilZFail=t.stencilZFail),void 0!==t.stencilZPass&&(i.stencilZPass=t.stencilZPass),void 0!==t.wireframe&&(i.wireframe=t.wireframe),void 0!==t.wireframeLinewidth&&(i.wireframeLinewidth=t.wireframeLinewidth),void 0!==t.wireframeLinecap&&(i.wireframeLinecap=t.wireframeLinecap),void 0!==t.wireframeLinejoin&&(i.wireframeLinejoin=t.wireframeLinejoin),void 0!==t.rotation&&(i.rotation=t.rotation),1!==t.linewidth&&(i.linewidth=t.linewidth),void 0!==t.dashSize&&(i.dashSize=t.dashSize),void 0!==t.gapSize&&(i.gapSize=t.gapSize),void 0!==t.scale&&(i.scale=t.scale),void 0!==t.polygonOffset&&(i.polygonOffset=t.polygonOffset),void 0!==t.polygonOffsetFactor&&(i.polygonOffsetFactor=t.polygonOffsetFactor),void 0!==t.polygonOffsetUnits&&(i.polygonOffsetUnits=t.polygonOffsetUnits),void 0!==t.dithering&&(i.dithering=t.dithering),void 0!==t.alphaToCoverage&&(i.alphaToCoverage=t.alphaToCoverage),void 0!==t.premultipliedAlpha&&(i.premultipliedAlpha=t.premultipliedAlpha),void 0!==t.visible&&(i.visible=t.visible),void 0!==t.toneMapped&&(i.toneMapped=t.toneMapped),void 0!==t.userData&&(i.userData=t.userData),void 0!==t.vertexColors&&(i.vertexColors="number"==typeof t.vertexColors?t.vertexColors>0:t.vertexColors),void 0!==t.uniforms)for(const e in t.uniforms){const r=t.uniforms[e];switch(i.uniforms[e]={},r.type){case"t":i.uniforms[e].value=n(r.value);break;case"c":i.uniforms[e].value=(new $Qt).setHex(r.value);break;case"v2":i.uniforms[e].value=(new mJt).fromArray(r.value);break;case"v3":i.uniforms[e].value=(new CJt).fromArray(r.value);break;case"v4":i.uniforms[e].value=(new wJt).fromArray(r.value);break;case"m3":i.uniforms[e].value=(new gJt).fromArray(r.value);break;case"m4":i.uniforms[e].value=(new rQt).fromArray(r.value);break;default:i.uniforms[e].value=r.value}}if(void 0!==t.defines&&(i.defines=t.defines),void 0!==t.vertexShader&&(i.vertexShader=t.vertexShader),void 0!==t.fragmentShader&&(i.fragmentShader=t.fragmentShader),void 0!==t.extensions)for(const e in t.extensions)i.extensions[e]=t.extensions[e];if(void 0!==t.shading&&(i.flatShading=1===t.shading),void 0!==t.size&&(i.size=t.size),void 0!==t.sizeAttenuation&&(i.sizeAttenuation=t.sizeAttenuation),void 0!==t.map&&(i.map=n(t.map)),void 0!==t.matcap&&(i.matcap=n(t.matcap)),void 0!==t.alphaMap&&(i.alphaMap=n(t.alphaMap)),void 0!==t.bumpMap&&(i.bumpMap=n(t.bumpMap)),void 0!==t.bumpScale&&(i.bumpScale=t.bumpScale),void 0!==t.normalMap&&(i.normalMap=n(t.normalMap)),void 0!==t.normalMapType&&(i.normalMapType=t.normalMapType),void 0!==t.normalScale){let e=t.normalScale;!1===Array.isArray(e)&&(e=[e,e]),i.normalScale=(new mJt).fromArray(e)}return void 0!==t.displacementMap&&(i.displacementMap=n(t.displacementMap)),void 0!==t.displacementScale&&(i.displacementScale=t.displacementScale),void 0!==t.displacementBias&&(i.displacementBias=t.displacementBias),void 0!==t.roughnessMap&&(i.roughnessMap=n(t.roughnessMap)),void 0!==t.metalnessMap&&(i.metalnessMap=n(t.metalnessMap)),void 0!==t.emissiveMap&&(i.emissiveMap=n(t.emissiveMap)),void 0!==t.emissiveIntensity&&(i.emissiveIntensity=t.emissiveIntensity),void 0!==t.specularMap&&(i.specularMap=n(t.specularMap)),void 0!==t.specularIntensityMap&&(i.specularIntensityMap=n(t.specularIntensityMap)),void 0!==t.specularTintMap&&(i.specularTintMap=n(t.specularTintMap)),void 0!==t.envMap&&(i.envMap=n(t.envMap)),void 0!==t.envMapIntensity&&(i.envMapIntensity=t.envMapIntensity),void 0!==t.reflectivity&&(i.reflectivity=t.reflectivity),void 0!==t.refractionRatio&&(i.refractionRatio=t.refractionRatio),void 0!==t.lightMap&&(i.lightMap=n(t.lightMap)),void 0!==t.lightMapIntensity&&(i.lightMapIntensity=t.lightMapIntensity),void 0!==t.aoMap&&(i.aoMap=n(t.aoMap)),void 0!==t.aoMapIntensity&&(i.aoMapIntensity=t.aoMapIntensity),void 0!==t.gradientMap&&(i.gradientMap=n(t.gradientMap)),void 0!==t.clearcoatMap&&(i.clearcoatMap=n(t.clearcoatMap)),void 0!==t.clearcoatRoughnessMap&&(i.clearcoatRoughnessMap=n(t.clearcoatRoughnessMap)),void 0!==t.clearcoatNormalMap&&(i.clearcoatNormalMap=n(t.clearcoatNormalMap)),void 0!==t.clearcoatNormalScale&&(i.clearcoatNormalScale=(new mJt).fromArray(t.clearcoatNormalScale)),void 0!==t.transmissionMap&&(i.transmissionMap=n(t.transmissionMap)),void 0!==t.thicknessMap&&(i.thicknessMap=n(t.thicknessMap)),i}setTextures(t){return this.textures=t,this}}class E9t{static decodeText(t){if("undefined"!=typeof TextDecoder)return(new TextDecoder).decode(t);let e="";for(let n=0,i=t.length;n<i;n++)e+=String.fromCharCode(t[n]);try{return decodeURIComponent(escape(e))}catch(t){return e}}static extractUrlBase(t){const e=t.lastIndexOf("/");return-1===e?"./":t.substr(0,e+1)}}class T9t extends b1t{constructor(){super(),this.type="InstancedBufferGeometry",this.instanceCount=1/0}copy(t){return super.copy(t),this.instanceCount=t.instanceCount,this}clone(){return(new this.constructor).copy(this)}toJSON(){const t=super.toJSON(this);return t.instanceCount=this.instanceCount,t.isInstancedBufferGeometry=!0,t}}T9t.prototype.isInstancedBufferGeometry=!0;class C9t extends QQt{constructor(t,e,n,i=1){"number"==typeof n&&(i=n,n=!1,console.error("THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument.")),super(t,e,n),this.meshPerAttribute=i}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}toJSON(){const t=super.toJSON();return t.meshPerAttribute=this.meshPerAttribute,t.isInstancedBufferAttribute=!0,t}}C9t.prototype.isInstancedBufferAttribute=!0;class A9t extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new Z6t(r.manager);o.setPath(r.path),o.setRequestHeader(r.requestHeader),o.setWithCredentials(r.withCredentials),o.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e={},n={};function i(t,i){if(void 0!==e[i])return e[i];const r=t.interleavedBuffers[i],o=(function a(t,e){if(void 0!==n[e])return n[e];const i=new Uint32Array(t.arrayBuffers[e]).buffer;return n[e]=i,i})(t,r.buffer),s=d1t(r.type,o),l=new $5t(s,r.stride);return l.uuid=r.uuid,e[i]=l,l}const r=t.isInstancedBufferGeometry?new T9t:new b1t,o=t.data.index;if(void 0!==o){const t=d1t(o.type,o.array);r.setIndex(new QQt(t,1))}const a=t.data.attributes;for(const e in a){const n=a[e];let o;if(n.isInterleavedBufferAttribute){const e=i(t.data,n.data);o=new Z5t(e,n.itemSize,n.offset,n.normalized)}else{const t=d1t(n.type,n.array);o=new(n.isInstancedBufferAttribute?C9t:QQt)(t,n.itemSize,n.normalized)}void 0!==n.name&&(o.name=n.name),void 0!==n.usage&&o.setUsage(n.usage),void 0!==n.updateRange&&(o.updateRange.offset=n.updateRange.offset,o.updateRange.count=n.updateRange.count),r.setAttribute(e,o)}const s=t.data.morphAttributes;if(s)for(const e in s){const n=s[e],o=[];for(let e=0,r=n.length;e<r;e++){const r=n[e];let a;if(r.isInterleavedBufferAttribute){const e=i(t.data,r.data);a=new Z5t(e,r.itemSize,r.offset,r.normalized)}else{const t=d1t(r.type,r.array);a=new QQt(t,r.itemSize,r.normalized)}void 0!==r.name&&(a.name=r.name),o.push(a)}r.morphAttributes[e]=o}t.data.morphTargetsRelative&&(r.morphTargetsRelative=!0);const l=t.data.groups||t.data.drawcalls||t.data.offsets;if(void 0!==l)for(let t=0,e=l.length;t!==e;++t){const e=l[t];r.addGroup(e.start,e.count,e.materialIndex)}const c=t.data.boundingSphere;if(void 0!==c){const t=new CJt;void 0!==c.center&&t.fromArray(c.center),r.boundingSphere=new $Jt(t,c.radius)}return t.name&&(r.name=t.name),t.userData&&(r.userData=t.userData),r}}const k9t={UVMapping:UKt,CubeReflectionMapping:jKt,CubeRefractionMapping:GKt,EquirectangularReflectionMapping:WKt,EquirectangularRefractionMapping:qKt,CubeUVReflectionMapping:YKt,CubeUVRefractionMapping:XKt},L9t={RepeatWrapping:$Kt,ClampToEdgeWrapping:KKt,MirroredRepeatWrapping:ZKt},P9t={NearestFilter:JKt,NearestMipmapNearestFilter:QKt,NearestMipmapLinearFilter:tZt,LinearFilter:eZt,LinearMipmapNearestFilter:nZt,LinearMipmapLinearFilter:iZt};class N9t extends $6t{constructor(t){super(t),"undefined"==typeof createImageBitmap&&console.warn("THREE.ImageBitmapLoader: createImageBitmap() not supported."),"undefined"==typeof fetch&&console.warn("THREE.ImageBitmapLoader: fetch() not supported."),this.options={premultiplyAlpha:"none"}}setOptions(t){return this.options=t,this}load(t,e,n,i){void 0===t&&(t=""),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,o=q6t.get(t);if(void 0!==o)return r.manager.itemStart(t),setTimeout((function(){e&&e(o),r.manager.itemEnd(t)}),0),o;const a={};a.credentials="anonymous"===this.crossOrigin?"same-origin":"include",a.headers=this.requestHeader,fetch(t,a).then((function(t){return t.blob()})).then((function(t){return createImageBitmap(t,Object.assign(r.options,{colorSpaceConversion:"none"}))})).then((function(n){q6t.add(t,n),e&&e(n),r.manager.itemEnd(t)})).catch((function(e){i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)})),r.manager.itemStart(t)}}N9t.prototype.isImageBitmapLoader=!0;class I9t{constructor(){this.type="ShapePath",this.color=new $Qt,this.subPaths=[],this.currentPath=null}moveTo(t,e){return this.currentPath=new i9t,this.subPaths.push(this.currentPath),this.currentPath.moveTo(t,e),this}lineTo(t,e){return this.currentPath.lineTo(t,e),this}quadraticCurveTo(t,e,n,i){return this.currentPath.quadraticCurveTo(t,e,n,i),this}bezierCurveTo(t,e,n,i,r,o){return this.currentPath.bezierCurveTo(t,e,n,i,r,o),this}splineThru(t){return this.currentPath.splineThru(t),this}toShapes(t,e){function n(t){const e=[];for(let n=0,i=t.length;n<i;n++){const i=t[n],r=new r9t;r.curves=i.curves,e.push(r)}return e}function i(t,e){const n=e.length;let i=!1;for(let r=n-1,o=0;o<n;r=o++){let n=e[r],a=e[o],s=a.x-n.x,l=a.y-n.y;if(Math.abs(l)>Number.EPSILON){if(l<0&&(n=e[o],s=-s,a=e[r],l=-l),t.y<n.y||t.y>a.y)continue;if(t.y===n.y){if(t.x===n.x)return!0}else{const e=l*(t.x-n.x)-s*(t.y-n.y);if(0===e)return!0;if(e<0)continue;i=!i}}else{if(t.y!==n.y)continue;if(a.x<=t.x&&t.x<=n.x||n.x<=t.x&&t.x<=a.x)return!0}}return i}const r=e6t.isClockWise,o=this.subPaths;if(0===o.length)return[];if(!0===e)return n(o);let a,s,l;const c=[];if(1===o.length)return s=o[0],l=new r9t,l.curves=s.curves,c.push(l),c;let u=!r(o[0].getPoints());u=t?!u:u;const h=[],d=[];let p,f,m=[],g=0;d[g]=void 0,m[g]=[];for(let e=0,n=o.length;e<n;e++)s=o[e],p=s.getPoints(),a=r(p),a=t?!a:a,a?(!u&&d[g]&&g++,d[g]={s:new r9t,p:p},d[g].s.curves=s.curves,u&&g++,m[g]=[]):m[g].push({h:s,p:p[0]});if(!d[0])return n(o);if(d.length>1){let t=!1;const e=[];for(let t=0,e=d.length;t<e;t++)h[t]=[];for(let n=0,r=d.length;n<r;n++){const r=m[n];for(let o=0;o<r.length;o++){const a=r[o];let s=!0;for(let r=0;r<d.length;r++)i(a.p,d[r].p)&&(n!==r&&e.push({froms:n,tos:r,hole:o}),s?(s=!1,h[r].push(a)):t=!0);s&&h[n].push(a)}}e.length>0&&(t||(m=h))}for(let t=0,e=d.length;t<e;t++){l=d[t].s,c.push(l),f=m[t];for(let t=0,e=f.length;t<e;t++)l.holes.push(f[t].h)}return c}}class R9t{constructor(t){this.type="Font",this.data=t}generateShapes(t,e=100){const n=[],i=(function r(t,e,n){const i=Array.from(t),r=e/n.resolution,o=(n.boundingBox.yMax-n.boundingBox.yMin+n.underlineThickness)*r,a=[];let s=0,l=0;for(let t=0;t<i.length;t++){const e=i[t];if("\n"===e)s=0,l-=o;else{const t=O9t(e,r,s,l,n);s+=t.offsetX,a.push(t.path)}}return a})(t,e,this.data);for(let t=0,e=i.length;t<e;t++)Array.prototype.push.apply(n,i[t].toShapes());return n}}function O9t(t,e,n,i,r){const o=r.glyphs[t]||r.glyphs["?"];if(!o)return void console.error('THREE.Font: character "'+t+'" does not exists in font family '+r.familyName+".");const a=new I9t;let s,l,c,u,h,d,p,f;if(o.o){const t=o._cachedOutline||(o._cachedOutline=o.o.split(" "));for(let r=0,o=t.length;r<o;)switch(t[r++]){case"m":s=t[r++]*e+n,l=t[r++]*e+i,a.moveTo(s,l);break;case"l":s=t[r++]*e+n,l=t[r++]*e+i,a.lineTo(s,l);break;case"q":c=t[r++]*e+n,u=t[r++]*e+i,h=t[r++]*e+n,d=t[r++]*e+i,a.quadraticCurveTo(h,d,c,u);break;case"b":c=t[r++]*e+n,u=t[r++]*e+i,h=t[r++]*e+n,d=t[r++]*e+i,p=t[r++]*e+n,f=t[r++]*e+i,a.bezierCurveTo(h,d,p,f,c,u)}}return{offsetX:o.ha*e,path:a}}let z9t;R9t.prototype.isFont=!0;const D9t={getContext:function(){return void 0===z9t&&(z9t=new(window.AudioContext||window.webkitAudioContext)),z9t},setContext:function(t){z9t=t}};class B9t extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new Z6t(this.manager);o.setResponseType("arraybuffer"),o.setPath(this.path),o.setRequestHeader(this.requestHeader),o.setWithCredentials(this.withCredentials),o.load(t,(function(n){try{const t=n.slice(0);D9t.getContext().decodeAudioData(t,(function(t){e(t)}))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}}class H9t extends S9t{constructor(t,e,n=1){super(void 0,n);const i=(new $Qt).set(t),r=(new $Qt).set(e),o=new CJt(i.r,i.g,i.b),a=new CJt(r.r,r.g,r.b),s=Math.sqrt(Math.PI),l=s*Math.sqrt(.75);this.sh.coefficients[0].copy(o).add(a).multiplyScalar(s),this.sh.coefficients[1].copy(o).sub(a).multiplyScalar(l)}}H9t.prototype.isHemisphereLightProbe=!0;class F9t extends S9t{constructor(t,e=1){super(void 0,e);const n=(new $Qt).set(t);this.sh.coefficients[0].set(n.r,n.g,n.b).multiplyScalar(2*Math.sqrt(Math.PI))}}F9t.prototype.isAmbientLightProbe=!0;const V9t=new rQt,U9t=new rQt;class j9t{constructor(t=!0){this.autoStart=t,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}start(){this.startTime=G9t(),this.oldTime=this.startTime,this.elapsedTime=0,this.running=!0}stop(){this.getElapsedTime(),this.running=!1,this.autoStart=!1}getElapsedTime(){return this.getDelta(),this.elapsedTime}getDelta(){let t=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){const e=G9t();t=(e-this.oldTime)/1e3,this.oldTime=e,this.elapsedTime+=t}return t}}function G9t(){return("undefined"==typeof performance?Date:performance).now()}const W9t=new CJt,q9t=new TJt,Y9t=new CJt,X9t=new CJt;class $9t extends kQt{constructor(t){super(),this.type="Audio",this.listener=t,this.context=t.context,this.gain=this.context.createGain(),this.gain.connect(t.getInput()),this.autoplay=!1,this.buffer=null,this.detune=0,this.loop=!1,this.loopStart=0,this.loopEnd=0,this.offset=0,this.duration=void 0,this.playbackRate=1,this.isPlaying=!1,this.hasPlaybackControl=!0,this.source=null,this.sourceType="empty",this._startedAt=0,this._progress=0,this._connected=!1,this.filters=[]}getOutput(){return this.gain}setNodeSource(t){return this.hasPlaybackControl=!1,this.sourceType="audioNode",this.source=t,this.connect(),this}setMediaElementSource(t){return this.hasPlaybackControl=!1,this.sourceType="mediaNode",this.source=this.context.createMediaElementSource(t),this.connect(),this}setMediaStreamSource(t){return this.hasPlaybackControl=!1,this.sourceType="mediaStreamNode",this.source=this.context.createMediaStreamSource(t),this.connect(),this}setBuffer(t){return this.buffer=t,this.sourceType="buffer",this.autoplay&&this.play(),this}play(t=0){if(!0===this.isPlaying)return void console.warn("THREE.Audio: Audio is already playing.");if(!1===this.hasPlaybackControl)return void console.warn("THREE.Audio: this Audio has no playback control.");this._startedAt=this.context.currentTime+t;const e=this.context.createBufferSource();return e.buffer=this.buffer,e.loop=this.loop,e.loopStart=this.loopStart,e.loopEnd=this.loopEnd,e.onended=this.onEnded.bind(this),e.start(this._startedAt,this._progress+this.offset,this.duration),this.isPlaying=!0,this.source=e,this.setDetune(this.detune),this.setPlaybackRate(this.playbackRate),this.connect()}pause(){if(!1!==this.hasPlaybackControl)return!0===this.isPlaying&&(this._progress+=Math.max(this.context.currentTime-this._startedAt,0)*this.playbackRate,!0===this.loop&&(this._progress=this._progress%(this.duration||this.buffer.duration)),this.source.stop(),this.source.onended=null,this.isPlaying=!1),this;console.warn("THREE.Audio: this Audio has no playback control.")}stop(){if(!1!==this.hasPlaybackControl)return this._progress=0,this.source.stop(),this.source.onended=null,this.isPlaying=!1,this;console.warn("THREE.Audio: this Audio has no playback control.")}connect(){if(this.filters.length>0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].connect(this.filters[t]);this.filters[this.filters.length-1].connect(this.getOutput())}else this.source.connect(this.getOutput());return this._connected=!0,this}disconnect(){if(this.filters.length>0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].disconnect(this.filters[t]);this.filters[this.filters.length-1].disconnect(this.getOutput())}else this.source.disconnect(this.getOutput());return this._connected=!1,this}getFilters(){return this.filters}setFilters(t){return t||(t=[]),!0===this._connected?(this.disconnect(),this.filters=t.slice(),this.connect()):this.filters=t.slice(),this}setDetune(t){if(this.detune=t,void 0!==this.source.detune)return!0===this.isPlaying&&this.source.detune.setTargetAtTime(this.detune,this.context.currentTime,.01),this}getDetune(){return this.detune}getFilter(){return this.getFilters()[0]}setFilter(t){return this.setFilters(t?[t]:[])}setPlaybackRate(t){if(!1!==this.hasPlaybackControl)return this.playbackRate=t,!0===this.isPlaying&&this.source.playbackRate.setTargetAtTime(this.playbackRate,this.context.currentTime,.01),this;console.warn("THREE.Audio: this Audio has no playback control.")}getPlaybackRate(){return this.playbackRate}onEnded(){this.isPlaying=!1}getLoop(){return!1===this.hasPlaybackControl?(console.warn("THREE.Audio: this Audio has no playback control."),!1):this.loop}setLoop(t){if(!1!==this.hasPlaybackControl)return this.loop=t,!0===this.isPlaying&&(this.source.loop=this.loop),this;console.warn("THREE.Audio: this Audio has no playback control.")}setLoopStart(t){return this.loopStart=t,this}setLoopEnd(t){return this.loopEnd=t,this}getVolume(){return this.gain.gain.value}setVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}}const K9t=new CJt,Z9t=new TJt,J9t=new CJt,Q9t=new CJt;class t8t{constructor(t,e=2048){this.analyser=t.context.createAnalyser(),this.analyser.fftSize=e,this.data=new Uint8Array(this.analyser.frequencyBinCount),t.getOutput().connect(this.analyser)}getFrequencyData(){return this.analyser.getByteFrequencyData(this.data),this.data}getAverageFrequency(){let t=0;const e=this.getFrequencyData();for(let n=0;n<e.length;n++)t+=e[n];return t/e.length}}class e8t{constructor(t,e,n){let i,r,o;switch(this.binding=t,this.valueSize=n,e){case"quaternion":i=this._slerp,r=this._slerpAdditive,o=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case"string":case"bool":i=this._select,r=this._select,o=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:i=this._lerp,r=this._lerpAdditive,o=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=i,this._mixBufferRegionAdditive=r,this._setIdentity=o,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(t,e){const n=this.buffer,i=this.valueSize,r=t*i+i;let o=this.cumulativeWeight;if(0===o){for(let t=0;t!==i;++t)n[r+t]=n[t];o=e}else o+=e,this._mixBufferRegion(n,r,0,e/o,i);this.cumulativeWeight=o}accumulateAdditive(t){const e=this.buffer,n=this.valueSize,i=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(e,i,0,t,n),this.cumulativeWeightAdditive+=t}apply(t){const e=this.valueSize,n=this.buffer,i=t*e+e,r=this.cumulativeWeight,o=this.cumulativeWeightAdditive,a=this.binding;this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,r<1&&this._mixBufferRegion(n,i,e*this._origIndex,1-r,e),o>0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,r=e+e;t!==r;++t)if(n[t]!==n[t+e]){a.setValue(n,i);break}}saveOriginalState(){const t=this.buffer,e=this.valueSize,n=e*this._origIndex;this.binding.getValue(t,n);for(let i=e,r=n;i!==r;++i)t[i]=t[n+i%e];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){this.binding.setValue(this.buffer,3*this.valueSize)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n<e;n++)this.buffer[n]=0}_setAdditiveIdentityQuaternion(){this._setAdditiveIdentityNumeric(),this.buffer[this._addIndex*this.valueSize+3]=1}_setAdditiveIdentityOther(){const t=this._origIndex*this.valueSize,e=this._addIndex*this.valueSize;for(let n=0;n<this.valueSize;n++)this.buffer[e+n]=this.buffer[t+n]}_select(t,e,n,i,r){if(i>=.5)for(let i=0;i!==r;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){TJt.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,r){const o=this._workIndex*r;TJt.multiplyQuaternionsFlat(t,o,t,e,t,n),TJt.slerpFlat(t,e,t,e,t,o,i)}_lerp(t,e,n,i,r){const o=1-i;for(let a=0;a!==r;++a){const r=e+a;t[r]=t[r]*o+t[n+a]*i}}_lerpAdditive(t,e,n,i,r){for(let o=0;o!==r;++o){const r=e+o;t[r]=t[r]+t[n+o]*i}}}const n8t=new RegExp("[\\[\\]\\.:\\/]","g"),i8t="[^\\[\\]\\.:\\/]",r8t="[^"+"\\[\\]\\.:\\/".replace("\\.","")+"]",o8t=/((?:WC+[\/:])*)/.source.replace("WC",i8t),a8t=/(WCOD+)?/.source.replace("WCOD",r8t),s8t=/(?:\.(WC+)(?:\[(.+)\])?)?/.source.replace("WC",i8t),l8t=/\.(WC+)(?:\[(.+)\])?/.source.replace("WC",i8t),c8t=new RegExp("^"+o8t+a8t+s8t+l8t+"$"),u8t=["material","materials","bones"];class h8t{constructor(t,e,n){this.path=e,this.parsedPath=n||h8t.parseTrackName(e),this.node=h8t.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new h8t.Composite(t,e,n):new h8t(t,e,n)}static sanitizeNodeName(t){return t.replace(/\s/g,"_").replace(n8t,"")}static parseTrackName(t){const e=c8t.exec(t);if(!e)throw new Error("PropertyBinding: Cannot parse trackName: "+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},i=n.nodeName&&n.nodeName.lastIndexOf(".");if(void 0!==i&&-1!==i){const t=n.nodeName.substring(i+1);-1!==u8t.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,i),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error("PropertyBinding: can not parse propertyName from trackName: "+t);return n}static findNode(t,e){if(!e||""===e||"."===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let i=0;i<t.length;i++){const r=t[i];if(r.name===e||r.uuid===e)return r;const o=n(r.children);if(o)return o}return null},i=n(t.children);if(i)return i}return null}_getValue_unavailable(){}_setValue_unavailable(){}_getValue_direct(t,e){t[e]=this.node[this.propertyName]}_getValue_array(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)t[e++]=n[i]}_getValue_arrayElement(t,e){t[e]=this.resolvedProperty[this.propertyIndex]}_getValue_toArray(t,e){this.resolvedProperty.toArray(t,e)}_setValue_direct(t,e){this.targetObject[this.propertyName]=t[e]}_setValue_direct_setNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.needsUpdate=!0}_setValue_direct_setMatrixWorldNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_array(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++]}_setValue_array_setNeedsUpdate(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++];this.targetObject.needsUpdate=!0}_setValue_array_setMatrixWorldNeedsUpdate(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++];this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_arrayElement(t,e){this.resolvedProperty[this.propertyIndex]=t[e]}_setValue_arrayElement_setNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.needsUpdate=!0}_setValue_arrayElement_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_fromArray(t,e){this.resolvedProperty.fromArray(t,e)}_setValue_fromArray_setNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.needsUpdate=!0}_setValue_fromArray_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.matrixWorldNeedsUpdate=!0}_getValue_unbound(t,e){this.bind(),this.getValue(t,e)}_setValue_unbound(t,e){this.bind(),this.setValue(t,e)}bind(){let t=this.node;const e=this.parsedPath,n=e.objectName,i=e.propertyName;let r=e.propertyIndex;if(t||(t=h8t.findNode(this.rootNode,e.nodeName)||this.rootNode,this.node=t),this.getValue=this._getValue_unavailable,this.setValue=this._setValue_unavailable,!t)return void console.error("THREE.PropertyBinding: Trying to update node for track: "+this.path+" but it wasn't found.");if(n){let i=e.objectIndex;switch(n){case"materials":if(!t.material)return void console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.",this);if(!t.material.materials)return void console.error("THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.",this);t=t.material.materials;break;case"bones":if(!t.skeleton)return void console.error("THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.",this);t=t.skeleton.bones;for(let e=0;e<t.length;e++)if(t[e].name===i){i=e;break}break;default:if(void 0===t[n])return void console.error("THREE.PropertyBinding: Can not bind to objectName of node undefined.",this);t=t[n]}if(void 0!==i){if(void 0===t[i])return void console.error("THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.",this,t);t=t[i]}}const o=t[i];if(void 0===o)return void console.error("THREE.PropertyBinding: Trying to update property for track: "+e.nodeName+"."+i+" but it wasn't found.",t);let a=this.Versioning.None;this.targetObject=t,void 0!==t.needsUpdate?a=this.Versioning.NeedsUpdate:void 0!==t.matrixWorldNeedsUpdate&&(a=this.Versioning.MatrixWorldNeedsUpdate);let s=this.BindingType.Direct;if(void 0!==r){if("morphTargetInfluences"===i){if(!t.geometry)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.",this);if(!t.geometry.isBufferGeometry)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences on THREE.Geometry. Use THREE.BufferGeometry instead.",this);if(!t.geometry.morphAttributes)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.",this);void 0!==t.morphTargetDictionary[r]&&(r=t.morphTargetDictionary[r])}s=this.BindingType.ArrayElement,this.resolvedProperty=o,this.propertyIndex=r}else void 0!==o.fromArray&&void 0!==o.toArray?(s=this.BindingType.HasFromToArray,this.resolvedProperty=o):Array.isArray(o)?(s=this.BindingType.EntireArray,this.resolvedProperty=o):this.propertyName=i;this.getValue=this.GetterByBindingType[s],this.setValue=this.SetterByBindingTypeAndVersioning[s][a]}unbind(){this.node=null,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}}h8t.Composite=class{constructor(t,e,n){const i=n||h8t.parseTrackName(e);this._targetGroup=t,this._bindings=t.subscribe_(e,i)}getValue(t,e){this.bind();const n=this._bindings[this._targetGroup.nCachedObjects_];void 0!==n&&n.getValue(t,e)}setValue(t,e){const n=this._bindings;for(let i=this._targetGroup.nCachedObjects_,r=n.length;i!==r;++i)n[i].setValue(t,e)}bind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].bind()}unbind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].unbind()}},h8t.prototype.BindingType={Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3},h8t.prototype.Versioning={None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2},h8t.prototype.GetterByBindingType=[h8t.prototype._getValue_direct,h8t.prototype._getValue_array,h8t.prototype._getValue_arrayElement,h8t.prototype._getValue_toArray],h8t.prototype.SetterByBindingTypeAndVersioning=[[h8t.prototype._setValue_direct,h8t.prototype._setValue_direct_setNeedsUpdate,h8t.prototype._setValue_direct_setMatrixWorldNeedsUpdate],[h8t.prototype._setValue_array,h8t.prototype._setValue_array_setNeedsUpdate,h8t.prototype._setValue_array_setMatrixWorldNeedsUpdate],[h8t.prototype._setValue_arrayElement,h8t.prototype._setValue_arrayElement_setNeedsUpdate,h8t.prototype._setValue_arrayElement_setMatrixWorldNeedsUpdate],[h8t.prototype._setValue_fromArray,h8t.prototype._setValue_fromArray_setNeedsUpdate,h8t.prototype._setValue_fromArray_setMatrixWorldNeedsUpdate]];class d8t{constructor(){this.uuid=XZt(),this._objects=Array.prototype.slice.call(arguments),this.nCachedObjects_=0;const t={};this._indicesByUUID=t;for(let e=0,n=arguments.length;e!==n;++e)t[arguments[e].uuid]=e;this._paths=[],this._parsedPaths=[],this._bindings=[],this._bindingsIndicesByPath={};const e=this;this.stats={objects:{get total(){return e._objects.length},get inUse(){return this.total-e.nCachedObjects_}},get bindingsPerObject(){return e._bindings.length}}}add(){const t=this._objects,e=this._indicesByUUID,n=this._paths,i=this._parsedPaths,r=this._bindings,o=r.length;let a,s=t.length,l=this.nCachedObjects_;for(let c=0,u=arguments.length;c!==u;++c){const u=arguments[c],h=u.uuid;let d=e[h];if(void 0===d){d=s++,e[h]=d,t.push(u);for(let t=0,e=o;t!==e;++t)r[t].push(new h8t(u,n[t],i[t]))}else if(d<l){a=t[d];const s=--l,c=t[s];e[c.uuid]=d,t[d]=c,e[h]=s,t[s]=u;for(let t=0,e=o;t!==e;++t){const e=r[t];let o=e[d];e[d]=e[s],void 0===o&&(o=new h8t(u,n[t],i[t])),e[s]=o}}else t[d]!==a&&console.error("THREE.AnimationObjectGroup: Different objects with the same UUID detected. Clean the caches or recreate your infrastructure when reloading scenes.")}this.nCachedObjects_=l}remove(){const t=this._objects,e=this._indicesByUUID,n=this._bindings,i=n.length;let r=this.nCachedObjects_;for(let o=0,a=arguments.length;o!==a;++o){const a=arguments[o],s=a.uuid,l=e[s];if(void 0!==l&&l>=r){const o=r++,c=t[o];e[c.uuid]=l,t[l]=c,e[s]=o,t[o]=a;for(let t=0,e=i;t!==e;++t){const e=n[t],i=e[l];e[l]=e[o],e[o]=i}}}this.nCachedObjects_=r}uncache(){const t=this._objects,e=this._indicesByUUID,n=this._bindings,i=n.length;let r=this.nCachedObjects_,o=t.length;for(let a=0,s=arguments.length;a!==s;++a){const s=arguments[a].uuid,l=e[s];if(void 0!==l)if(delete e[s],l<r){const a=--r,s=t[a],c=--o,u=t[c];e[s.uuid]=l,t[l]=s,e[u.uuid]=a,t[a]=u,t.pop();for(let t=0,e=i;t!==e;++t){const e=n[t],i=e[c];e[l]=e[a],e[a]=i,e.pop()}}else{const r=--o,a=t[r];r>0&&(e[a.uuid]=l),t[l]=a,t.pop();for(let t=0,e=i;t!==e;++t){const e=n[t];e[l]=e[r],e.pop()}}}this.nCachedObjects_=r}subscribe_(t,e){const n=this._bindingsIndicesByPath;let i=n[t];const r=this._bindings;if(void 0!==i)return r[i];const o=this._paths,a=this._parsedPaths,s=this._objects,l=this.nCachedObjects_,c=new Array(s.length);i=r.length,n[t]=i,o.push(t),a.push(e),r.push(c);for(let n=l,i=s.length;n!==i;++n)c[n]=new h8t(s[n],t,e);return c}unsubscribe_(t){const e=this._bindingsIndicesByPath,n=e[t];if(void 0!==n){const i=this._paths,r=this._parsedPaths,o=this._bindings,a=o.length-1,s=o[a];e[t[a]]=n,o[n]=s,o.pop(),r[n]=r[a],r.pop(),i[n]=i[a],i.pop()}}}d8t.prototype.isAnimationObjectGroup=!0;class p8t{constructor(t,e,n=null,i=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=i;const r=e.tracks,o=r.length,a=new Array(o),s={endingStart:CZt,endingEnd:CZt};for(let t=0;t!==o;++t){const e=r[t].createInterpolant(null);a[t]=e,e.settings=s}this._interpolantSettings=s,this._interpolants=a,this._propertyBindings=new Array(o),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=2201,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(t){return this._startTime=t,this}setLoop(t,e){return this.loop=t,this.repetitions=e,this}setEffectiveWeight(t){return this.weight=t,this._effectiveWeight=this.enabled?t:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(t){return this._scheduleFading(t,0,1)}fadeOut(t){return this._scheduleFading(t,1,0)}crossFadeFrom(t,e,n){if(t.fadeOut(e),this.fadeIn(e),n){const n=this._clip.duration,i=t._clip.duration,r=n/i;t.warp(1,i/n,e),this.warp(r,1,e)}return this}crossFadeTo(t,e,n){return t.crossFadeFrom(this,e,n)}stopFading(){const t=this._weightInterpolant;return null!==t&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}setEffectiveTimeScale(t){return this.timeScale=t,this._effectiveTimeScale=this.paused?0:t,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(t){return this.timeScale=this._clip.duration/t,this.stopWarping()}syncWith(t){return this.time=t.time,this.timeScale=t.timeScale,this.stopWarping()}halt(t){return this.warp(this._effectiveTimeScale,0,t)}warp(t,e,n){const i=this._mixer,r=i.time,o=this.timeScale;let a=this._timeScaleInterpolant;null===a&&(a=i._lendControlInterpolant(),this._timeScaleInterpolant=a);const s=a.parameterPositions,l=a.sampleValues;return s[0]=r,s[1]=r+n,l[0]=t/o,l[1]=e/o,this}stopWarping(){const t=this._timeScaleInterpolant;return null!==t&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(t,e,n,i){if(!this.enabled)return void this._updateWeight(t);const r=this._startTime;if(null!==r){const i=(t-r)*n;if(i<0||0===n)return;this._startTime=null,e=n*i}e*=this._updateTimeScale(t);const o=this._updateTime(e),a=this._updateWeight(t);if(a>0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case PZt:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(o),e[n].accumulateAdditive(a);break;case LZt:default:for(let n=0,r=t.length;n!==r;++n)t[n].evaluate(o),e[n].accumulate(i,a)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;null!==n&&(e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e))}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,r=this._loopCount;const o=2202===n;if(0===t)return-1===r?i:o&&1==(1&r)?e-i:i;if(2200===n){-1===r&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:"finished",action:this,direction:t<0?-1:1})}}else{if(-1===r&&(t>=0?(r=0,this._setEndings(!0,0===this.repetitions,o)):this._setEndings(0===this.repetitions,!0,o)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,r+=Math.abs(n);const a=this.repetitions-r;if(a<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:"finished",action:this,direction:t>0?1:-1});else{if(1===a){const e=t<0;this._setEndings(e,!e,o)}else this._setEndings(!1,!1,o);this._loopCount=r,this.time=i,this._mixer.dispatchEvent({type:"loop",action:this,loopDelta:n})}}else this.time=i;if(o&&1==(1&r))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=AZt,i.endingEnd=AZt):(i.endingStart=t?this.zeroSlopeAtStart?AZt:CZt:kZt,i.endingEnd=e?this.zeroSlopeAtEnd?AZt:CZt:kZt)}_scheduleFading(t,e,n){const i=this._mixer,r=i.time;let o=this._weightInterpolant;null===o&&(o=i._lendControlInterpolant(),this._weightInterpolant=o);const a=o.parameterPositions,s=o.sampleValues;return a[0]=r,s[0]=e,a[1]=r+t,s[1]=n,this}}class f8t extends jZt{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,r=i.length,o=t._propertyBindings,a=t._interpolants,s=n.uuid,l=this._bindingsByRootAndName;let c=l[s];void 0===c&&(c={},l[s]=c);for(let t=0;t!==r;++t){const r=i[t],l=r.name;let u=c[l];if(void 0!==u)o[t]=u;else{if(u=o[t],void 0!==u){null===u._cacheIndex&&(++u.referenceCount,this._addInactiveBinding(u,s,l));continue}u=new e8t(h8t.create(n,l,e&&e._propertyBindings[t].binding.parsedPath),r.ValueTypeName,r.getValueSize()),++u.referenceCount,this._addInactiveBinding(u,s,l),o[t]=u}a[t].resultBuffer=u.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e<this._nActiveActions}_addInactiveAction(t,e,n){const i=this._actions,r=this._actionsByClip;let o=r[e];if(void 0===o)o={knownActions:[t],actionByRoot:{}},t._byClipCacheIndex=0,r[e]=o;else{const e=o.knownActions;t._byClipCacheIndex=e.length,e.push(t)}t._cacheIndex=i.length,i.push(t),o.actionByRoot[n]=t}_removeInactiveAction(t){const e=this._actions,n=e[e.length-1],i=t._cacheIndex;n._cacheIndex=i,e[i]=n,e.pop(),t._cacheIndex=null;const r=t._clip.uuid,o=this._actionsByClip,a=o[r],s=a.knownActions,l=s[s.length-1],c=t._byClipCacheIndex;l._byClipCacheIndex=c,s[c]=l,s.pop(),t._byClipCacheIndex=null,delete a.actionByRoot[(t._localRoot||this._root).uuid],0===s.length&&delete o[r],this._removeInactiveBindingsForAction(t)}_removeInactiveBindingsForAction(t){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.referenceCount&&this._removeInactiveBinding(n)}}_lendAction(t){const e=this._actions,n=t._cacheIndex,i=this._nActiveActions++,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_takeBackAction(t){const e=this._actions,n=t._cacheIndex,i=--this._nActiveActions,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_addInactiveBinding(t,e,n){const i=this._bindingsByRootAndName,r=this._bindings;let o=i[e];void 0===o&&(o={},i[e]=o),o[n]=t,t._cacheIndex=r.length,r.push(t)}_removeInactiveBinding(t){const e=this._bindings,n=t.binding,i=n.rootNode.uuid,r=n.path,o=this._bindingsByRootAndName,a=o[i],s=e[e.length-1],l=t._cacheIndex;s._cacheIndex=l,e[l]=s,e.pop(),delete a[r],0===Object.keys(a).length&&delete o[i]}_lendBinding(t){const e=this._bindings,n=t._cacheIndex,i=this._nActiveBindings++,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_takeBackBinding(t){const e=this._bindings,n=t._cacheIndex,i=--this._nActiveBindings,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_lendControlInterpolant(){const t=this._controlInterpolants,e=this._nActiveControlInterpolants++;let n=t[e];return void 0===n&&(n=new R6t(new Float32Array(2),new Float32Array(2),1,this._controlInterpolantsResultBuffer),n.__cacheIndex=e,t[e]=n),n}_takeBackControlInterpolant(t){const e=this._controlInterpolants,n=t.__cacheIndex,i=--this._nActiveControlInterpolants,r=e[i];t.__cacheIndex=i,e[i]=t,r.__cacheIndex=n,e[n]=r}clipAction(t,e,n){const i=e||this._root,r=i.uuid;let o="string"==typeof t?G6t.findByName(i,t):t;const a=null!==o?o.uuid:t,s=this._actionsByClip[a];let l=null;if(void 0===n&&(n=null!==o?o.blendMode:LZt),void 0!==s){const t=s.actionByRoot[r];if(void 0!==t&&t.blendMode===n)return t;l=s.knownActions[0],null===o&&(o=l._clip)}if(null===o)return null;const c=new p8t(this,o,e,n);return this._bindAction(c,l),this._addInactiveAction(c,a,r),c}existingAction(t,e){const n=e||this._root,i=n.uuid,r="string"==typeof t?G6t.findByName(n,t):t,o=this._actionsByClip[r?r.uuid:t];return void 0!==o&&o.actionByRoot[i]||null}stopAllAction(){const t=this._actions;for(let e=this._nActiveActions-1;e>=0;--e)t[e].stop();return this}update(t){const e=this._actions,n=this._nActiveActions,i=this.time+=t*=this.timeScale,r=Math.sign(t),o=this._accuIndex^=1;for(let a=0;a!==n;++a)e[a]._update(i,t,r,o);const a=this._bindings,s=this._nActiveBindings;for(let t=0;t!==s;++t)a[t].apply(o);return this}setTime(t){this.time=0;for(let t=0;t<this._actions.length;t++)this._actions[t].time=0;return this.update(t)}getRoot(){return this._root}uncacheClip(t){const e=this._actions,n=t.uuid,i=this._actionsByClip,r=i[n];if(void 0!==r){const t=r.knownActions;for(let n=0,i=t.length;n!==i;++n){const i=t[n];this._deactivateAction(i);const r=i._cacheIndex,o=e[e.length-1];i._cacheIndex=null,i._byClipCacheIndex=null,o._cacheIndex=r,e[r]=o,e.pop(),this._removeInactiveBindingsForAction(i)}delete i[n]}}uncacheRoot(t){const e=t.uuid,n=this._actionsByClip;for(const t in n){const i=n[t].actionByRoot[e];void 0!==i&&(this._deactivateAction(i),this._removeInactiveAction(i))}const i=this._bindingsByRootAndName[e];if(void 0!==i)for(const t in i){const e=i[t];e.restoreOriginalState(),this._removeInactiveBinding(e)}}uncacheAction(t,e){const n=this.existingAction(t,e);null!==n&&(this._deactivateAction(n),this._removeInactiveAction(n))}}f8t.prototype._controlInterpolantsResultBuffer=new Float32Array(1);class m8t{constructor(t){"string"==typeof t&&(console.warn("THREE.Uniform: Type parameter is no longer needed."),t=arguments[1]),this.value=t}clone(){return new m8t(void 0===this.value.clone?this.value:this.value.clone())}}class g8t extends $5t{constructor(t,e,n=1){super(t,e),this.meshPerAttribute=n}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}clone(t){const e=super.clone(t);return e.meshPerAttribute=this.meshPerAttribute,e}toJSON(t){const e=super.toJSON(t);return e.isInstancedInterleavedBuffer=!0,e.meshPerAttribute=this.meshPerAttribute,e}}g8t.prototype.isInstancedInterleavedBuffer=!0;class _8t{constructor(t,e,n,i,r){this.buffer=t,this.type=e,this.itemSize=n,this.elementSize=i,this.count=r,this.version=0}set needsUpdate(t){!0===t&&this.version++}setBuffer(t){return this.buffer=t,this}setType(t,e){return this.type=t,this.elementSize=e,this}setItemSize(t){return this.itemSize=t,this}setCount(t){return this.count=t,this}}function y8t(t,e){return t.distance-e.distance}function v8t(t,e,n,i){if(t.layers.test(e.layers)&&t.raycast(e,n),!0===i){const i=t.children;for(let t=0,r=i.length;t<r;t++)v8t(i[t],e,n,!0)}}_8t.prototype.isGLBufferAttribute=!0;class b8t{constructor(t=1,e=0,n=0){return this.radius=t,this.phi=e,this.theta=n,this}set(t,e,n){return this.radius=t,this.phi=e,this.theta=n,this}copy(t){return this.radius=t.radius,this.phi=t.phi,this.theta=t.theta,this}makeSafe(){const t=1e-6;return this.phi=Math.max(t,Math.min(Math.PI-t,this.phi)),this}setFromVector3(t){return this.setFromCartesianCoords(t.x,t.y,t.z)}setFromCartesianCoords(t,e,n){return this.radius=Math.sqrt(t*t+e*e+n*n),0===this.radius?(this.theta=0,this.phi=0):(this.theta=Math.atan2(t,n),this.phi=Math.acos($Zt(e/this.radius,-1,1))),this}clone(){return(new this.constructor).copy(this)}}const x8t=new mJt;class w8t{constructor(t=new mJt(1/0,1/0),e=new mJt(-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;e<n;e++)this.expandByPoint(t[e]);return this}setFromCenterAndSize(t,e){const n=x8t.copy(e).multiplyScalar(.5);return this.min.copy(t).sub(n),this.max.copy(t).add(n),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.min.copy(t.min),this.max.copy(t.max),this}makeEmpty(){return this.min.x=this.min.y=1/0,this.max.x=this.max.y=-1/0,this}isEmpty(){return this.max.x<this.min.x||this.max.y<this.min.y}getCenter(t){return this.isEmpty()?t.set(0,0):t.addVectors(this.min,this.max).multiplyScalar(.5)}getSize(t){return this.isEmpty()?t.set(0,0):t.subVectors(this.max,this.min)}expandByPoint(t){return this.min.min(t),this.max.max(t),this}expandByVector(t){return this.min.sub(t),this.max.add(t),this}expandByScalar(t){return this.min.addScalar(-t),this.max.addScalar(t),this}containsPoint(t){return!(t.x<this.min.x||t.x>this.max.x||t.y<this.min.y||t.y>this.max.y)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y))}intersectsBox(t){return!(t.max.x<this.min.x||t.min.x>this.max.x||t.max.y<this.min.y||t.min.y>this.max.y)}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return x8t.copy(t).clamp(this.min,this.max).sub(t).length()}intersect(t){return this.min.max(t.min),this.max.min(t.max),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}w8t.prototype.isBox2=!0;const S8t=new CJt,M8t=new CJt;class E8t{constructor(t=new CJt,e=new CJt){this.start=t,this.end=e}set(t,e){return this.start.copy(t),this.end.copy(e),this}copy(t){return this.start.copy(t.start),this.end.copy(t.end),this}getCenter(t){return t.addVectors(this.start,this.end).multiplyScalar(.5)}delta(t){return t.subVectors(this.end,this.start)}distanceSq(){return this.start.distanceToSquared(this.end)}distance(){return this.start.distanceTo(this.end)}at(t,e){return this.delta(e).multiplyScalar(t).add(this.start)}closestPointToPointParameter(t,e){S8t.subVectors(t,this.start),M8t.subVectors(this.end,this.start);const n=M8t.dot(M8t);let i=M8t.dot(S8t)/n;return e&&(i=$Zt(i,0,1)),i}closestPointToPoint(t,e,n){const i=this.closestPointToPointParameter(t,e);return this.delta(n).multiplyScalar(i).add(this.start)}applyMatrix4(t){return this.start.applyMatrix4(t),this.end.applyMatrix4(t),this}equals(t){return t.start.equals(this.start)&&t.end.equals(this.end)}clone(){return(new this.constructor).copy(this)}}class T8t extends kQt{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}T8t.prototype.isImmediateRenderObject=!0;const C8t=new CJt,A8t=new CJt,k8t=new rQt,L8t=new rQt;class P8t extends U3t{constructor(t){const e=N8t(t),n=new b1t,i=[],r=[],o=new $Qt(0,0,1),a=new $Qt(0,1,0);for(let t=0;t<e.length;t++){const n=e[t];n.parent&&n.parent.isBone&&(i.push(0,0,0),i.push(0,0,0),r.push(o.r,o.g,o.b),r.push(a.r,a.g,a.b))}n.setAttribute("position",new l1t(i,3)),n.setAttribute("color",new l1t(r,3)),super(n,new I3t({vertexColors:!0,depthTest:!1,depthWrite:!1,toneMapped:!1,transparent:!0})),this.type="SkeletonHelper",this.isSkeletonHelper=!0,this.root=t,this.bones=e,this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1}updateMatrixWorld(t){const e=this.bones,n=this.geometry,i=n.getAttribute("position");L8t.copy(this.root.matrixWorld).invert();for(let t=0,n=0;t<e.length;t++){const r=e[t];r.parent&&r.parent.isBone&&(k8t.multiplyMatrices(L8t,r.matrixWorld),A8t.setFromMatrixPosition(k8t),i.setXYZ(n,A8t.x,A8t.y,A8t.z),k8t.multiplyMatrices(L8t,r.parent.matrixWorld),A8t.setFromMatrixPosition(k8t),i.setXYZ(n+1,A8t.x,A8t.y,A8t.z),n+=2)}n.getAttribute("position").needsUpdate=!0,super.updateMatrixWorld(t)}}function N8t(t){const e=[];t&&t.isBone&&e.push(t);for(let n=0;n<t.children.length;n++)e.push.apply(e,N8t(t.children[n]));return e}const I8t=new CJt,R8t=new $Qt,O8t=new $Qt;class z8t extends U3t{constructor(t=10,e=10,n=4473924,i=8947848){n=new $Qt(n),i=new $Qt(i);const r=e/2,o=t/e,a=t/2,s=[],l=[];for(let t=0,c=0,u=-a;t<=e;t++,u+=o){s.push(-a,0,u,a,0,u),s.push(u,0,-a,u,0,a);const e=t===r?n:i;e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3}const c=new b1t;c.setAttribute("position",new l1t(s,3)),c.setAttribute("color",new l1t(l,3)),super(c,new I3t({vertexColors:!0,toneMapped:!1})),this.type="GridHelper"}}const D8t=new CJt,B8t=new CJt,H8t=new CJt,F8t=new CJt,V8t=new W1t;function U8t(t,e,n,i,r,o,a){F8t.set(r,o,a).unproject(i);const s=e[t];if(void 0!==s){const t=n.getAttribute("position");for(let e=0,n=s.length;e<n;e++)t.setXYZ(s[e],F8t.x,F8t.y,F8t.z)}}const j8t=new LJt;class G8t extends U3t{constructor(t,e=16776960){const n=new Uint16Array([0,1,1,2,2,3,3,0,4,5,5,6,6,7,7,4,0,4,1,5,2,6,3,7]),i=new Float32Array(24),r=new b1t;r.setIndex(new QQt(n,1)),r.setAttribute("position",new QQt(i,3)),super(r,new I3t({color:e,toneMapped:!1})),this.object=t,this.type="BoxHelper",this.matrixAutoUpdate=!1,this.update()}update(t){if(void 0!==t&&console.warn("THREE.BoxHelper: .update() has no longer arguments."),void 0!==this.object&&j8t.setFromObject(this.object),j8t.isEmpty())return;const e=j8t.min,n=j8t.max,i=this.geometry.attributes.position,r=i.array;r[0]=n.x,r[1]=n.y,r[2]=n.z,r[3]=e.x,r[4]=n.y,r[5]=n.z,r[6]=e.x,r[7]=e.y,r[8]=n.z,r[9]=n.x,r[10]=e.y,r[11]=n.z,r[12]=n.x,r[13]=n.y,r[14]=e.z,r[15]=e.x,r[16]=n.y,r[17]=e.z,r[18]=e.x,r[19]=e.y,r[20]=e.z,r[21]=n.x,r[22]=e.y,r[23]=e.z,i.needsUpdate=!0,this.geometry.computeBoundingSphere()}setFromObject(t){return this.object=t,this.update(),this}copy(t){return U3t.prototype.copy.call(this,t),this.object=t.object,this}}const W8t=new CJt;let q8t,Y8t;class X8t extends U3t{constructor(t=1){const e=[0,0,0,t,0,0,0,0,0,0,t,0,0,0,0,0,0,t],n=new b1t;n.setAttribute("position",new l1t(e,3)),n.setAttribute("color",new l1t([1,0,0,1,.6,0,0,1,0,.6,1,0,0,0,1,0,.6,1],3)),super(n,new I3t({vertexColors:!0,toneMapped:!1})),this.type="AxesHelper"}setColors(t,e,n){const i=new $Qt,r=this.geometry.attributes.color.array;return i.set(t),i.toArray(r,0),i.toArray(r,3),i.set(e),i.toArray(r,6),i.toArray(r,9),i.set(n),i.toArray(r,12),i.toArray(r,15),this.geometry.attributes.color.needsUpdate=!0,this}dispose(){this.geometry.dispose(),this.material.dispose()}}const $8t=new Float32Array(1),K8t=new Int32Array($8t.buffer);h4t.create=function(t,e){return console.log("THREE.Curve.create() has been deprecated"),t.prototype=Object.create(h4t.prototype),t.prototype.constructor=t,t.prototype.getPoint=e,t},i9t.prototype.fromPoints=function(t){return console.warn("THREE.Path: .fromPoints() has been renamed to .setFromPoints()."),this.setFromPoints(t)},z8t.prototype.setColors=function(){console.error("THREE.GridHelper: setColors() has been deprecated, pass them in the constructor instead.")},P8t.prototype.update=function(){console.error("THREE.SkeletonHelper: update() no longer needs to be called.")},$6t.prototype.extractUrlBase=function(t){return console.warn("THREE.Loader: .extractUrlBase() has been deprecated. Use THREE.LoaderUtils.extractUrlBase() instead."),E9t.extractUrlBase(t)},$6t.Handlers={add:function(){console.error("THREE.Loader: Handlers.add() has been removed. Use LoadingManager.addHandler() instead.")},get:function(){console.error("THREE.Loader: Handlers.get() has been removed. Use LoadingManager.getHandler() instead.")}},w8t.prototype.center=function(t){return console.warn("THREE.Box2: .center() has been renamed to .getCenter()."),this.getCenter(t)},w8t.prototype.empty=function(){return console.warn("THREE.Box2: .empty() has been renamed to .isEmpty()."),this.isEmpty()},w8t.prototype.isIntersectionBox=function(t){return console.warn("THREE.Box2: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},w8t.prototype.size=function(t){return console.warn("THREE.Box2: .size() has been renamed to .getSize()."),this.getSize(t)},LJt.prototype.center=function(t){return console.warn("THREE.Box3: .center() has been renamed to .getCenter()."),this.getCenter(t)},LJt.prototype.empty=function(){return console.warn("THREE.Box3: .empty() has been renamed to .isEmpty()."),this.isEmpty()},LJt.prototype.isIntersectionBox=function(t){return console.warn("THREE.Box3: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},LJt.prototype.isIntersectionSphere=function(t){return console.warn("THREE.Box3: .isIntersectionSphere() has been renamed to .intersectsSphere()."),this.intersectsSphere(t)},LJt.prototype.size=function(t){return console.warn("THREE.Box3: .size() has been renamed to .getSize()."),this.getSize(t)},$Jt.prototype.empty=function(){return console.warn("THREE.Sphere: .empty() has been renamed to .isEmpty()."),this.isEmpty()},i0t.prototype.setFromMatrix=function(t){return console.warn("THREE.Frustum: .setFromMatrix() has been renamed to .setFromProjectionMatrix()."),this.setFromProjectionMatrix(t)},E8t.prototype.center=function(t){return console.warn("THREE.Line3: .center() has been renamed to .getCenter()."),this.getCenter(t)},gJt.prototype.flattenToArrayOffset=function(t,e){return console.warn("THREE.Matrix3: .flattenToArrayOffset() has been deprecated. Use .toArray() instead."),this.toArray(t,e)},gJt.prototype.multiplyVector3=function(t){return console.warn("THREE.Matrix3: .multiplyVector3() has been removed. Use vector.applyMatrix3( matrix ) instead."),t.applyMatrix3(this)},gJt.prototype.multiplyVector3Array=function(){console.error("THREE.Matrix3: .multiplyVector3Array() has been removed.")},gJt.prototype.applyToBufferAttribute=function(t){return console.warn("THREE.Matrix3: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix3( matrix ) instead."),t.applyMatrix3(this)},gJt.prototype.applyToVector3Array=function(){console.error("THREE.Matrix3: .applyToVector3Array() has been removed.")},gJt.prototype.getInverse=function(t){return console.warn("THREE.Matrix3: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead."),this.copy(t).invert()},rQt.prototype.extractPosition=function(t){return console.warn("THREE.Matrix4: .extractPosition() has been renamed to .copyPosition()."),this.copyPosition(t)},rQt.prototype.flattenToArrayOffset=function(t,e){return console.warn("THREE.Matrix4: .flattenToArrayOffset() has been deprecated. Use .toArray() instead."),this.toArray(t,e)},rQt.prototype.getPosition=function(){return console.warn("THREE.Matrix4: .getPosition() has been removed. Use Vector3.setFromMatrixPosition( matrix ) instead."),(new CJt).setFromMatrixColumn(this,3)},rQt.prototype.setRotationFromQuaternion=function(t){return console.warn("THREE.Matrix4: .setRotationFromQuaternion() has been renamed to .makeRotationFromQuaternion()."),this.makeRotationFromQuaternion(t)},rQt.prototype.multiplyToArray=function(){console.warn("THREE.Matrix4: .multiplyToArray() has been removed.")},rQt.prototype.multiplyVector3=function(t){return console.warn("THREE.Matrix4: .multiplyVector3() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},rQt.prototype.multiplyVector4=function(t){return console.warn("THREE.Matrix4: .multiplyVector4() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},rQt.prototype.multiplyVector3Array=function(){console.error("THREE.Matrix4: .multiplyVector3Array() has been removed.")},rQt.prototype.rotateAxis=function(t){console.warn("THREE.Matrix4: .rotateAxis() has been removed. Use Vector3.transformDirection( matrix ) instead."),t.transformDirection(this)},rQt.prototype.crossVector=function(t){return console.warn("THREE.Matrix4: .crossVector() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},rQt.prototype.translate=function(){console.error("THREE.Matrix4: .translate() has been removed.")},rQt.prototype.rotateX=function(){console.error("THREE.Matrix4: .rotateX() has been removed.")},rQt.prototype.rotateY=function(){console.error("THREE.Matrix4: .rotateY() has been removed.")},rQt.prototype.rotateZ=function(){console.error("THREE.Matrix4: .rotateZ() has been removed.")},rQt.prototype.rotateByAxis=function(){console.error("THREE.Matrix4: .rotateByAxis() has been removed.")},rQt.prototype.applyToBufferAttribute=function(t){return console.warn("THREE.Matrix4: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},rQt.prototype.applyToVector3Array=function(){console.error("THREE.Matrix4: .applyToVector3Array() has been removed.")},rQt.prototype.makeFrustum=function(t,e,n,i,r,o){return console.warn("THREE.Matrix4: .makeFrustum() has been removed. Use .makePerspective( left, right, top, bottom, near, far ) instead."),this.makePerspective(t,e,i,n,r,o)},rQt.prototype.getInverse=function(t){return console.warn("THREE.Matrix4: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead."),this.copy(t).invert()},t0t.prototype.isIntersectionLine=function(t){return console.warn("THREE.Plane: .isIntersectionLine() has been renamed to .intersectsLine()."),this.intersectsLine(t)},TJt.prototype.multiplyVector3=function(t){return console.warn("THREE.Quaternion: .multiplyVector3() has been removed. Use is now vector.applyQuaternion( quaternion ) instead."),t.applyQuaternion(this)},TJt.prototype.inverse=function(){return console.warn("THREE.Quaternion: .inverse() has been renamed to invert()."),this.invert()},iQt.prototype.isIntersectionBox=function(t){return console.warn("THREE.Ray: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},iQt.prototype.isIntersectionPlane=function(t){return console.warn("THREE.Ray: .isIntersectionPlane() has been renamed to .intersectsPlane()."),this.intersectsPlane(t)},iQt.prototype.isIntersectionSphere=function(t){return console.warn("THREE.Ray: .isIntersectionSphere() has been renamed to .intersectsSphere()."),this.intersectsSphere(t)},FQt.prototype.area=function(){return console.warn("THREE.Triangle: .area() has been renamed to .getArea()."),this.getArea()},FQt.prototype.barycoordFromPoint=function(t,e){return console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord()."),this.getBarycoord(t,e)},FQt.prototype.midpoint=function(t){return console.warn("THREE.Triangle: .midpoint() has been renamed to .getMidpoint()."),this.getMidpoint(t)},FQt.prototypenormal=function(t){return console.warn("THREE.Triangle: .normal() has been renamed to .getNormal()."),this.getNormal(t)},FQt.prototype.plane=function(t){return console.warn("THREE.Triangle: .plane() has been renamed to .getPlane()."),this.getPlane(t)},FQt.barycoordFromPoint=function(t,e,n,i,r){return console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord()."),FQt.getBarycoord(t,e,n,i,r)},FQt.normal=function(t,e,n,i){return console.warn("THREE.Triangle: .normal() has been renamed to .getNormal()."),FQt.getNormal(t,e,n,i)},r9t.prototype.extractAllPoints=function(t){return console.warn("THREE.Shape: .extractAllPoints() has been removed. Use .extractPoints() instead."),this.extractPoints(t)},r9t.prototype.extrude=function(t){return console.warn("THREE.Shape: .extrude() has been removed. Use ExtrudeGeometry() instead."),new r6t(this,t)},r9t.prototype.makeGeometry=function(t){return console.warn("THREE.Shape: .makeGeometry() has been removed. Use ShapeGeometry() instead."),new h6t(this,t)},mJt.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector2: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},mJt.prototype.distanceToManhattan=function(t){return console.warn("THREE.Vector2: .distanceToManhattan() has been renamed to .manhattanDistanceTo()."),this.manhattanDistanceTo(t)},mJt.prototype.lengthManhattan=function(){return console.warn("THREE.Vector2: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},CJt.prototype.setEulerFromRotationMatrix=function(){console.error("THREE.Vector3: .setEulerFromRotationMatrix() has been removed. Use Euler.setFromRotationMatrix() instead.")},CJt.prototype.setEulerFromQuaternion=function(){console.error("THREE.Vector3: .setEulerFromQuaternion() has been removed. Use Euler.setFromQuaternion() instead.")},CJt.prototype.getPositionFromMatrix=function(t){return console.warn("THREE.Vector3: .getPositionFromMatrix() has been renamed to .setFromMatrixPosition()."),this.setFromMatrixPosition(t)},CJt.prototype.getScaleFromMatrix=function(t){return console.warn("THREE.Vector3: .getScaleFromMatrix() has been renamed to .setFromMatrixScale()."),this.setFromMatrixScale(t)},CJt.prototype.getColumnFromMatrix=function(t,e){return console.warn("THREE.Vector3: .getColumnFromMatrix() has been renamed to .setFromMatrixColumn()."),this.setFromMatrixColumn(e,t)},CJt.prototype.applyProjection=function(t){return console.warn("THREE.Vector3: .applyProjection() has been removed. Use .applyMatrix4( m ) instead."),this.applyMatrix4(t)},CJt.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector3: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},CJt.prototype.distanceToManhattan=function(t){return console.warn("THREE.Vector3: .distanceToManhattan() has been renamed to .manhattanDistanceTo()."),this.manhattanDistanceTo(t)},CJt.prototype.lengthManhattan=function(){return console.warn("THREE.Vector3: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},wJt.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector4: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},wJt.prototype.lengthManhattan=function(){return console.warn("THREE.Vector4: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},kQt.prototype.getChildByName=function(t){return console.warn("THREE.Object3D: .getChildByName() has been renamed to .getObjectByName()."),this.getObjectByName(t)},kQt.prototype.renderDepth=function(){console.warn("THREE.Object3D: .renderDepth has been removed. Use .renderOrder, instead.")},kQt.prototype.translate=function(t,e){return console.warn("THREE.Object3D: .translate() has been removed. Use .translateOnAxis( axis, distance ) instead."),this.translateOnAxis(e,t)},kQt.prototype.getWorldRotation=function(){console.error("THREE.Object3D: .getWorldRotation() has been removed. Use THREE.Object3D.getWorldQuaternion( target ) instead.")},kQt.prototype.applyMatrix=function(t){return console.warn("THREE.Object3D: .applyMatrix() has been renamed to .applyMatrix4()."),this.applyMatrix4(t)},Object.defineProperties(kQt.prototype,{eulerOrder:{get:function(){return console.warn("THREE.Object3D: .eulerOrder is now .rotation.order."),this.rotation.order},set:function(t){console.warn("THREE.Object3D: .eulerOrder is now .rotation.order."),this.rotation.order=t}},useQuaternion:{get:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")},set:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")}}}),B1t.prototype.setDrawMode=function(){console.error("THREE.Mesh: .setDrawMode() has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.")},Object.defineProperties(B1t.prototype,{drawMode:{get:function(){return console.error("THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode."),0},set:function(){console.error("THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.")}}}),w3t.prototype.initBones=function(){console.error("THREE.SkinnedMesh: initBones() has been removed.")},q1t.prototype.setLens=function(t,e){console.warn("THREE.PerspectiveCamera.setLens is deprecated. Use .setFocalLength and .filmGauge for a photographic setup."),void 0!==e&&(this.filmGauge=e),this.setFocalLength(t)},Object.defineProperties(o9t.prototype,{onlyShadow:{set:function(){console.warn("THREE.Light: .onlyShadow has been removed.")}},shadowCameraFov:{set:function(t){console.warn("THREE.Light: .shadowCameraFov is now .shadow.camera.fov."),this.shadow.camera.fov=t}},shadowCameraLeft:{set:function(t){console.warn("THREE.Light: .shadowCameraLeft is now .shadow.camera.left."),this.shadow.camera.left=t}},shadowCameraRight:{set:function(t){console.warn("THREE.Light: .shadowCameraRight is now .shadow.camera.right."),this.shadow.camera.right=t}},shadowCameraTop:{set:function(t){console.warn("THREE.Light: .shadowCameraTop is now .shadow.camera.top."),this.shadow.camera.top=t}},shadowCameraBottom:{set:function(t){console.warn("THREE.Light: .shadowCameraBottom is now .shadow.camera.bottom."),this.shadow.camera.bottom=t}},shadowCameraNear:{set:function(t){console.warn("THREE.Light: .shadowCameraNear is now .shadow.camera.near."),this.shadow.camera.near=t}},shadowCameraFar:{set:function(t){console.warn("THREE.Light: .shadowCameraFar is now .shadow.camera.far."),this.shadow.camera.far=t}},shadowCameraVisible:{set:function(){console.warn("THREE.Light: .shadowCameraVisible has been removed. Use new THREE.CameraHelper( light.shadow.camera ) instead.")}},shadowBias:{set:function(t){console.warn("THREE.Light: .shadowBias is now .shadow.bias."),this.shadow.bias=t}},shadowDarkness:{set:function(){console.warn("THREE.Light: .shadowDarkness has been removed.")}},shadowMapWidth:{set:function(t){console.warn("THREE.Light: .shadowMapWidth is now .shadow.mapSize.width."),this.shadow.mapSize.width=t}},shadowMapHeight:{set:function(t){console.warn("THREE.Light: .shadowMapHeight is now .shadow.mapSize.height."),this.shadow.mapSize.height=t}}}),Object.defineProperties(QQt.prototype,{length:{get:function(){return console.warn("THREE.BufferAttribute: .length has been deprecated. Use .count instead."),this.array.length}},dynamic:{get:function(){return console.warn("THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead."),this.usage===VZt},set:function(){console.warn("THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead."),this.setUsage(VZt)}}}),QQt.prototype.setDynamic=function(t){return console.warn("THREE.BufferAttribute: .setDynamic() has been deprecated. Use .setUsage() instead."),this.setUsage(!0===t?VZt:FZt),this},QQt.prototype.copyIndicesArray=function(){console.error("THREE.BufferAttribute: .copyIndicesArray() has been removed.")},QQt.prototype.setArray=function(){console.error("THREE.BufferAttribute: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers")},b1t.prototype.addIndex=function(t){console.warn("THREE.BufferGeometry: .addIndex() has been renamed to .setIndex()."),this.setIndex(t)},b1t.prototype.addAttribute=function(t,e){return console.warn("THREE.BufferGeometry: .addAttribute() has been renamed to .setAttribute()."),e&&e.isBufferAttribute||e&&e.isInterleavedBufferAttribute?"index"===t?(console.warn("THREE.BufferGeometry.addAttribute: Use .setIndex() for index attribute."),this.setIndex(e),this):this.setAttribute(t,e):(console.warn("THREE.BufferGeometry: .addAttribute() now expects ( name, attribute )."),this.setAttribute(t,new QQt(arguments[1],arguments[2])))},b1t.prototype.addDrawCall=function(t,e,n){void 0!==n&&console.warn("THREE.BufferGeometry: .addDrawCall() no longer supports indexOffset."),console.warn("THREE.BufferGeometry: .addDrawCall() is now .addGroup()."),this.addGroup(t,e)},b1t.prototype.clearDrawCalls=function(){console.warn("THREE.BufferGeometry: .clearDrawCalls() is now .clearGroups()."),this.clearGroups()},b1t.prototype.computeOffsets=function(){console.warn("THREE.BufferGeometry: .computeOffsets() has been removed.")},b1t.prototype.removeAttribute=function(t){return console.warn("THREE.BufferGeometry: .removeAttribute() has been renamed to .deleteAttribute()."),this.deleteAttribute(t)},b1t.prototype.applyMatrix=function(t){return console.warn("THREE.BufferGeometry: .applyMatrix() has been renamed to .applyMatrix4()."),this.applyMatrix4(t)},Object.defineProperties(b1t.prototype,{drawcalls:{get:function(){return console.error("THREE.BufferGeometry: .drawcalls has been renamed to .groups."),this.groups}},offsets:{get:function(){return console.warn("THREE.BufferGeometry: .offsets has been renamed to .groups."),this.groups}}}),$5t.prototype.setDynamic=function(t){return console.warn("THREE.InterleavedBuffer: .setDynamic() has been deprecated. Use .setUsage() instead."),this.setUsage(!0===t?VZt:FZt),this},$5t.prototype.setArray=function(){console.error("THREE.InterleavedBuffer: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers")},r6t.prototype.getArrays=function(){console.error("THREE.ExtrudeGeometry: .getArrays() has been removed.")},r6t.prototype.addShapeList=function(){console.error("THREE.ExtrudeGeometry: .addShapeList() has been removed.")},r6t.prototype.addShape=function(){console.error("THREE.ExtrudeGeometry: .addShape() has been removed.")},X5t.prototype.dispose=function(){console.error("THREE.Scene: .dispose() has been removed.")},m8t.prototype.onUpdate=function(){return console.warn("THREE.Uniform: .onUpdate() has been removed. Use object.onBeforeRender() instead."),this},Object.defineProperties(UQt.prototype,{wrapAround:{get:function(){console.warn("THREE.Material: .wrapAround has been removed.")},set:function(){console.warn("THREE.Material: .wrapAround has been removed.")}},overdraw:{get:function(){console.warn("THREE.Material: .overdraw has been removed.")},set:function(){console.warn("THREE.Material: .overdraw has been removed.")}},wrapRGB:{get:function(){return console.warn("THREE.Material: .wrapRGB has been removed."),new $Qt}},shading:{get:function(){console.error("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead.")},set:function(t){console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead."),this.flatShading=1===t}},stencilMask:{get:function(){return console.warn("THREE."+this.type+": .stencilMask has been removed. Use .stencilFuncMask instead."),this.stencilFuncMask},set:function(t){console.warn("THREE."+this.type+": .stencilMask has been removed. Use .stencilFuncMask instead."),this.stencilFuncMask=t}},vertexTangents:{get:function(){console.warn("THREE."+this.type+": .vertexTangents has been removed.")},set:function(){console.warn("THREE."+this.type+": .vertexTangents has been removed.")}}}),Object.defineProperties(G1t.prototype,{derivatives:{get:function(){return console.warn("THREE.ShaderMaterial: .derivatives has been moved to .extensions.derivatives."),this.extensions.derivatives},set:function(t){console.warn("THREE. ShaderMaterial: .derivatives has been moved to .extensions.derivatives."),this.extensions.derivatives=t}}}),G5t.prototype.clearTarget=function(t,e,n,i){console.warn("THREE.WebGLRenderer: .clearTarget() has been deprecated. Use .setRenderTarget() and .clear() instead."),this.setRenderTarget(t),this.clear(e,n,i)},G5t.prototype.animate=function(t){console.warn("THREE.WebGLRenderer: .animate() is now .setAnimationLoop()."),this.setAnimationLoop(t)},G5t.prototype.getCurrentRenderTarget=function(){return console.warn("THREE.WebGLRenderer: .getCurrentRenderTarget() is now .getRenderTarget()."),this.getRenderTarget()},G5t.prototype.getMaxAnisotropy=function(){return console.warn("THREE.WebGLRenderer: .getMaxAnisotropy() is now .capabilities.getMaxAnisotropy()."),this.capabilities.getMaxAnisotropy()},G5t.prototype.getPrecision=function(){return console.warn("THREE.WebGLRenderer: .getPrecision() is now .capabilities.precision."),this.capabilities.precision},G5t.prototype.resetGLState=function(){return console.warn("THREE.WebGLRenderer: .resetGLState() is now .state.reset()."),this.state.reset()},G5t.prototype.supportsFloatTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsFloatTextures() is now .extensions.get( 'OES_texture_float' )."),this.extensions.get("OES_texture_float")},G5t.prototype.supportsHalfFloatTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsHalfFloatTextures() is now .extensions.get( 'OES_texture_half_float' )."),this.extensions.get("OES_texture_half_float")},G5t.prototype.supportsStandardDerivatives=function(){return console.warn("THREE.WebGLRenderer: .supportsStandardDerivatives() is now .extensions.get( 'OES_standard_derivatives' )."),this.extensions.get("OES_standard_derivatives")},G5t.prototype.supportsCompressedTextureS3TC=function(){return console.warn("THREE.WebGLRenderer: .supportsCompressedTextureS3TC() is now .extensions.get( 'WEBGL_compressed_texture_s3tc' )."),this.extensions.get("WEBGL_compressed_texture_s3tc")},G5t.prototype.supportsCompressedTexturePVRTC=function(){return console.warn("THREE.WebGLRenderer: .supportsCompressedTexturePVRTC() is now .extensions.get( 'WEBGL_compressed_texture_pvrtc' )."),this.extensions.get("WEBGL_compressed_texture_pvrtc")},G5t.prototype.supportsBlendMinMax=function(){return console.warn("THREE.WebGLRenderer: .supportsBlendMinMax() is now .extensions.get( 'EXT_blend_minmax' )."),this.extensions.get("EXT_blend_minmax")},G5t.prototype.supportsVertexTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsVertexTextures() is now .capabilities.vertexTextures."),this.capabilities.vertexTextures},G5t.prototype.supportsInstancedArrays=function(){return console.warn("THREE.WebGLRenderer: .supportsInstancedArrays() is now .extensions.get( 'ANGLE_instanced_arrays' )."),this.extensions.get("ANGLE_instanced_arrays")},G5t.prototype.enableScissorTest=function(t){console.warn("THREE.WebGLRenderer: .enableScissorTest() is now .setScissorTest()."),this.setScissorTest(t)},G5t.prototype.initMaterial=function(){console.warn("THREE.WebGLRenderer: .initMaterial() has been removed.")},G5t.prototype.addPrePlugin=function(){console.warn("THREE.WebGLRenderer: .addPrePlugin() has been removed.")},G5t.prototype.addPostPlugin=function(){console.warn("THREE.WebGLRenderer: .addPostPlugin() has been removed.")},G5t.prototype.updateShadowMap=function(){console.warn("THREE.WebGLRenderer: .updateShadowMap() has been removed.")},G5t.prototype.setFaceCulling=function(){console.warn("THREE.WebGLRenderer: .setFaceCulling() has been removed.")},G5t.prototype.allocTextureUnit=function(){console.warn("THREE.WebGLRenderer: .allocTextureUnit() has been removed.")},G5t.prototype.setTexture=function(){console.warn("THREE.WebGLRenderer: .setTexture() has been removed.")},G5t.prototype.setTexture2D=function(){console.warn("THREE.WebGLRenderer: .setTexture2D() has been removed.")},G5t.prototype.setTextureCube=function(){console.warn("THREE.WebGLRenderer: .setTextureCube() has been removed.")},G5t.prototype.getActiveMipMapLevel=function(){return console.warn("THREE.WebGLRenderer: .getActiveMipMapLevel() is now .getActiveMipmapLevel()."),this.getActiveMipmapLevel()},Object.defineProperties(G5t.prototype,{shadowMapEnabled:{get:function(){return this.shadowMap.enabled},set:function(t){console.warn("THREE.WebGLRenderer: .shadowMapEnabled is now .shadowMap.enabled."),this.shadowMap.enabled=t}},shadowMapType:{get:function(){return this.shadowMap.type},set:function(t){console.warn("THREE.WebGLRenderer: .shadowMapType is now .shadowMap.type."),this.shadowMap.type=t}},shadowMapCullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")}},context:{get:function(){return console.warn("THREE.WebGLRenderer: .context has been removed. Use .getContext() instead."),this.getContext()}},vr:{get:function(){return console.warn("THREE.WebGLRenderer: .vr has been renamed to .xr"),this.xr}},gammaInput:{get:function(){return console.warn("THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead."),!1},set:function(){console.warn("THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead.")}},gammaOutput:{get:function(){return console.warn("THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead."),!1},set:function(t){console.warn("THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead."),this.outputEncoding=!0===t?IZt:NZt}},toneMappingWhitePoint:{get:function(){return console.warn("THREE.WebGLRenderer: .toneMappingWhitePoint has been removed."),1},set:function(){console.warn("THREE.WebGLRenderer: .toneMappingWhitePoint has been removed.")}}}),Object.defineProperties(R5t.prototype,{cullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")}},renderReverseSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")}},renderSingleSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")}}}),Object.defineProperties(SJt.prototype,{wrapS:{get:function(){return console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS."),this.texture.wrapS},set:function(t){console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS."),this.texture.wrapS=t}},wrapT:{get:function(){return console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT."),this.texture.wrapT},set:function(t){console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT."),this.texture.wrapT=t}},magFilter:{get:function(){return console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter."),this.texture.magFilter},set:function(t){console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter."),this.texture.magFilter=t}},minFilter:{get:function(){return console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter."),this.texture.minFilter},set:function(t){console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter."),this.texture.minFilter=t}},anisotropy:{get:function(){return console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy."),this.texture.anisotropy},set:function(t){console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy."),this.texture.anisotropy=t}},offset:{get:function(){return console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset."),this.texture.offset},set:function(t){console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset."),this.texture.offset=t}},repeat:{get:function(){return console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat."),this.texture.repeat},set:function(t){console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat."),this.texture.repeat=t}},format:{get:function(){return console.warn("THREE.WebGLRenderTarget: .format is now .texture.format."),this.texture.format},set:function(t){console.warn("THREE.WebGLRenderTarget: .format is now .texture.format."),this.texture.format=t}},type:{get:function(){return console.warn("THREE.WebGLRenderTarget: .type is now .texture.type."),this.texture.type},set:function(t){console.warn("THREE.WebGLRenderTarget: .type is now .texture.type."),this.texture.type=t}},generateMipmaps:{get:function(){return console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps."),this.texture.generateMipmaps},set:function(t){console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps."),this.texture.generateMipmaps=t}}}),$9t.prototype.load=function(t){console.warn("THREE.Audio: .load has been deprecated. Use THREE.AudioLoader instead.");const e=this;return(new B9t).load(t,(function(t){e.setBuffer(t)})),this},t8t.prototype.getData=function(){return console.warn("THREE.AudioAnalyser: .getData() is now .getFrequencyData()."),this.getFrequencyData()},X1t.prototype.updateCubeMap=function(t,e){return console.warn("THREE.CubeCamera: .updateCubeMap() is now .update()."),this.update(t,e)},X1t.prototype.clear=function(t,e,n,i){return console.warn("THREE.CubeCamera: .clear() is now .renderTarget.clear()."),this.renderTarget.clear(t,e,n,i)},yJt.crossOrigin=void 0,yJt.loadTexture=function(t,e,n,i){console.warn("THREE.ImageUtils.loadTexture has been deprecated. Use THREE.TextureLoader() instead.");const r=new e9t;r.setCrossOrigin(this.crossOrigin);const o=r.load(t,n,void 0,i);return e&&(o.mapping=e),o},yJt.loadTextureCube=function(t,e,n,i){console.warn("THREE.ImageUtils.loadTextureCube has been deprecated. Use THREE.CubeTextureLoader() instead.");const r=new Q6t;r.setCrossOrigin(this.crossOrigin);const o=r.load(t,n,void 0,i);return e&&(o.mapping=e),o},yJt.loadCompressedTexture=function(){console.error("THREE.ImageUtils.loadCompressedTexture has been removed. Use THREE.DDSLoader instead.")},yJt.loadCompressedTextureCube=function(){console.error("THREE.ImageUtils.loadCompressedTextureCube has been removed. Use THREE.DDSLoader instead.")};const Z8t={createMultiMaterialObject:function(){console.error("THREE.SceneUtils has been moved to /examples/jsm/utils/SceneUtils.js")},detach:function(){console.error("THREE.SceneUtils has been moved to /examples/jsm/utils/SceneUtils.js")},attach:function(){console.error("THREE.SceneUtils has been moved to /examples/jsm/utils/SceneUtils.js")}};"undefined"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("register",{detail:{revision:BKt}})),"undefined"!=typeof window&&(window.__THREE__?console.warn("WARNING: Multiple instances of Three.js being imported."):window.__THREE__=BKt);var J8t=Object.freeze({__proto__:null,ACESFilmicToneMapping:4,AddEquation:VKt,AddOperation:2,AdditiveAnimationBlendMode:PZt,AdditiveBlending:2,AlphaFormat:1021,AlwaysDepth:1,AlwaysStencilFunc:519,AmbientLight:b9t,AmbientLightProbe:F9t,AnimationClip:G6t,AnimationLoader:class extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new Z6t(this.manager);o.setPath(this.path),o.setRequestHeader(this.requestHeader),o.setWithCredentials(this.withCredentials),o.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e=[];for(let n=0;n<t.length;n++){const i=G6t.parse(t[n]);e.push(i)}return e}},AnimationMixer:f8t,AnimationObjectGroup:d8t,AnimationUtils:P6t,ArcCurve:p4t,ArrayCamera:B5t,ArrowHelper:class extends kQt{constructor(t=new CJt(0,0,1),e=new CJt(0,0,0),n=1,i=16776960,r=.2*n,o=.2*r){super(),this.type="ArrowHelper",void 0===q8t&&(q8t=new b1t,q8t.setAttribute("position",new l1t([0,0,0,0,1,0],3)),Y8t=new n4t(0,.5,1,5,1),Y8t.translate(0,-.5,0)),this.position.copy(e),this.line=new H3t(q8t,new I3t({color:i,toneMapped:!1})),this.line.matrixAutoUpdate=!1,this.add(this.line),this.cone=new B1t(Y8t,new KQt({color:i,toneMapped:!1})),this.cone.matrixAutoUpdate=!1,this.add(this.cone),this.setDirection(t),this.setLength(n,r,o)}setDirection(t){if(t.y>.99999)this.quaternion.set(0,0,0,1);else if(t.y<-.99999)this.quaternion.set(1,0,0,0);else{W8t.set(t.z,0,-t.x).normalize();const e=Math.acos(t.y);this.quaternion.setFromAxisAngle(W8t,e)}}setLength(t,e=.2*t,n=.2*e){this.line.scale.set(1,Math.max(1e-4,t-e),1),this.line.updateMatrix(),this.cone.scale.set(n,e,n),this.cone.position.y=t,this.cone.updateMatrix()}setColor(t){this.line.material.color.set(t),this.cone.material.color.set(t)}copy(t){return super.copy(t,!1),this.line.copy(t.line),this.cone.copy(t.cone),this}},Audio:$9t,AudioAnalyser:t8t,AudioContext:D9t,AudioListener:class extends kQt{constructor(){super(),this.type="AudioListener",this.context=D9t.getContext(),this.gain=this.context.createGain(),this.gain.connect(this.context.destination),this.filter=null,this.timeDelta=0,this._clock=new j9t}getInput(){return this.gain}removeFilter(){return null!==this.filter&&(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination),this.gain.connect(this.context.destination),this.filter=null),this}getFilter(){return this.filter}setFilter(t){return null!==this.filter?(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination)):this.gain.disconnect(this.context.destination),this.filter=t,this.gain.connect(this.filter),this.filter.connect(this.context.destination),this}getMasterVolume(){return this.gain.gain.value}setMasterVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}updateMatrixWorld(t){super.updateMatrixWorld(t);const e=this.context.listener,n=this.up;if(this.timeDelta=this._clock.getDelta(),this.matrixWorld.decompose(W9t,q9t,Y9t),X9t.set(0,0,-1).applyQuaternion(q9t),e.positionX){const t=this.context.currentTime+this.timeDelta;e.positionX.linearRampToValueAtTime(W9t.x,t),e.positionY.linearRampToValueAtTime(W9t.y,t),e.positionZ.linearRampToValueAtTime(W9t.z,t),e.forwardX.linearRampToValueAtTime(X9t.x,t),e.forwardY.linearRampToValueAtTime(X9t.y,t),e.forwardZ.linearRampToValueAtTime(X9t.z,t),e.upX.linearRampToValueAtTime(n.x,t),e.upY.linearRampToValueAtTime(n.y,t),e.upZ.linearRampToValueAtTime(n.z,t)}else e.setPosition(W9t.x,W9t.y,W9t.z),e.setOrientation(X9t.x,X9t.y,X9t.z,n.x,n.y,n.z)}},AudioLoader:B9t,AxesHelper:X8t,AxisHelper:function Q8t(t){return console.warn("THREE.AxisHelper has been renamed to THREE.AxesHelper."),new X8t(t)},BackSide:1,BasicDepthPacking:3200,BasicShadowMap:0,BinaryTextureLoader:function t7t(t){return console.warn("THREE.BinaryTextureLoader has been renamed to THREE.DataTextureLoader."),new t9t(t)},Bone:S3t,BooleanKeyframeTrack:D6t,BoundingBoxHelper:function e7t(t,e){return console.warn("THREE.BoundingBoxHelper has been deprecated. Creating a THREE.BoxHelper instead."),new G8t(t,e)},Box2:w8t,Box3:LJt,Box3Helper:class extends U3t{constructor(t,e=16776960){const n=new Uint16Array([0,1,1,2,2,3,3,0,4,5,5,6,6,7,7,4,0,4,1,5,2,6,3,7]),i=new b1t;i.setIndex(new QQt(n,1)),i.setAttribute("position",new l1t([1,1,1,-1,1,1,-1,-1,1,1,-1,1,1,1,-1,-1,1,-1,-1,-1,-1,1,-1,-1],3)),super(i,new I3t({color:e,toneMapped:!1})),this.box=t,this.type="Box3Helper",this.geometry.computeBoundingSphere()}updateMatrixWorld(t){const e=this.box;e.isEmpty()||(e.getCenter(this.position),e.getSize(this.scale),this.scale.multiplyScalar(.5),super.updateMatrixWorld(t))}},BoxBufferGeometry:F1t,BoxGeometry:F1t,BoxHelper:G8t,BufferAttribute:QQt,BufferGeometry:b1t,BufferGeometryLoader:A9t,ByteType:1010,Cache:q6t,Camera:W1t,CameraHelper:class extends U3t{constructor(t){const e=new b1t,n=new I3t({color:16777215,vertexColors:!0,toneMapped:!1}),i=[],r=[],o={},a=new $Qt(16755200),s=new $Qt(16711680),l=new $Qt(43775),c=new $Qt(16777215),u=new $Qt(3355443);function h(t,e,n){d(t,n),d(e,n)}function d(t,e){i.push(0,0,0),r.push(e.r,e.g,e.b),void 0===o[t]&&(o[t]=[]),o[t].push(i.length/3-1)}h("n1","n2",a),h("n2","n4",a),h("n4","n3",a),h("n3","n1",a),h("f1","f2",a),h("f2","f4",a),h("f4","f3",a),h("f3","f1",a),h("n1","f1",a),h("n2","f2",a),h("n3","f3",a),h("n4","f4",a),h("p","n1",s),h("p","n2",s),h("p","n3",s),h("p","n4",s),h("u1","u2",l),h("u2","u3",l),h("u3","u1",l),h("c","t",c),h("p","c",u),h("cn1","cn2",u),h("cn3","cn4",u),h("cf1","cf2",u),h("cf3","cf4",u),e.setAttribute("position",new l1t(i,3)),e.setAttribute("color",new l1t(r,3)),super(e,n),this.type="CameraHelper",this.camera=t,this.camera.updateProjectionMatrix&&this.camera.updateProjectionMatrix(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.pointMap=o,this.update()}update(){const t=this.geometry,e=this.pointMap;V8t.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse),U8t("c",e,t,V8t,0,0,-1),U8t("t",e,t,V8t,0,0,1),U8t("n1",e,t,V8t,-1,-1,-1),U8t("n2",e,t,V8t,1,-1,-1),U8t("n3",e,t,V8t,-1,1,-1),U8t("n4",e,t,V8t,1,1,-1),U8t("f1",e,t,V8t,-1,-1,1),U8t("f2",e,t,V8t,1,-1,1),U8t("f3",e,t,V8t,-1,1,1),U8t("f4",e,t,V8t,1,1,1),U8t("u1",e,t,V8t,.7,1.1,-1),U8t("u2",e,t,V8t,-.7,1.1,-1),U8t("u3",e,t,V8t,0,2,-1),U8t("cf1",e,t,V8t,-1,0,1),U8t("cf2",e,t,V8t,1,0,1),U8t("cf3",e,t,V8t,0,-1,1),U8t("cf4",e,t,V8t,0,1,1),U8t("cn1",e,t,V8t,-1,0,-1),U8t("cn2",e,t,V8t,1,0,-1),U8t("cn3",e,t,V8t,0,-1,-1),U8t("cn4",e,t,V8t,0,1,-1),t.getAttribute("position").needsUpdate=!0}dispose(){this.geometry.dispose(),this.material.dispose()}},CanvasRenderer:function n7t(){console.error("THREE.CanvasRenderer has been removed")},CanvasTexture:Q3t,CatmullRomCurve3:v4t,CineonToneMapping:3,CircleBufferGeometry:e4t,CircleGeometry:e4t,ClampToEdgeWrapping:KKt,Clock:j9t,Color:$Qt,ColorKeyframeTrack:B6t,CompressedTexture:J3t,CompressedTextureLoader:class extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=this,o=[],a=new J3t,s=new Z6t(this.manager);s.setPath(this.path),s.setResponseType("arraybuffer"),s.setRequestHeader(this.requestHeader),s.setWithCredentials(r.withCredentials);let l=0;function c(c){s.load(t[c],(function(t){const n=r.parse(t,!0);o[c]={width:n.width,height:n.height,format:n.format,mipmaps:n.mipmaps},l+=1,6===l&&(1===n.mipmapCount&&(a.minFilter=eZt),a.image=o,a.format=n.format,a.needsUpdate=!0,e&&e(a))}),n,i)}if(Array.isArray(t))for(let e=0,n=t.length;e<n;++e)c(e);else s.load(t,(function(t){const n=r.parse(t,!0);if(n.isCubemap){const t=n.mipmaps.length/n.mipmapCount;for(let e=0;e<t;e++){o[e]={mipmaps:[]};for(let t=0;t<n.mipmapCount;t++)o[e].mipmaps.push(n.mipmaps[e*n.mipmapCount+t]),o[e].format=n.format,o[e].width=n.width,o[e].height=n.height}a.image=o}else a.image.width=n.width,a.image.height=n.height,a.mipmaps=n.mipmaps;1===n.mipmapCount&&(a.minFilter=eZt),a.format=n.format,a.needsUpdate=!0,e&&e(a)}),n,i);return a}},ConeBufferGeometry:i4t,ConeGeometry:i4t,CubeCamera:X1t,CubeReflectionMapping:jKt,CubeRefractionMapping:GKt,CubeTexture:$1t,CubeTextureLoader:Q6t,CubeUVReflectionMapping:YKt,CubeUVRefractionMapping:XKt,CubicBezierCurve:S4t,CubicBezierCurve3:M4t,CubicInterpolant:I6t,CullFaceBack:1,CullFaceFront:2,CullFaceFrontBack:3,CullFaceNone:0,Curve:h4t,CurvePath:n9t,CustomBlending:5,CustomToneMapping:5,CylinderBufferGeometry:n4t,CylinderGeometry:n4t,Cylindrical:class{constructor(t=1,e=0,n=0){return this.radius=t,this.theta=e,this.y=n,this}set(t,e,n){return this.radius=t,this.theta=e,this.y=n,this}copy(t){return this.radius=t.radius,this.theta=t.theta,this.y=t.y,this}setFromVector3(t){return this.setFromCartesianCoords(t.x,t.y,t.z)}setFromCartesianCoords(t,e,n){return this.radius=Math.sqrt(t*t+n*n),this.theta=Math.atan2(t,n),this.y=e,this}clone(){return(new this.constructor).copy(this)}},DataTexture:M3t,DataTexture2DArray:K0t,DataTexture3D:Z0t,DataTextureLoader:t9t,DataUtils:class{static toHalfFloat(t){$8t[0]=t;const e=K8t[0];let n=e>>16&32768,i=e>>12&2047;const r=e>>23&255;return r<103?n:r>142?(n|=31744,n|=(255==r?0:1)&&8388607&e,n):r<113?(i|=2048,n|=(i>>114-r)+(i>>113-r&1),n):(n|=r-112<<10|i>>1,n+=1&i,n)}},DecrementStencilOp:7683,DecrementWrapStencilOp:34056,DefaultLoadingManager:X6t,DepthFormat:dZt,DepthStencilFormat:pZt,DepthTexture:t4t,DirectionalLight:v9t,DirectionalLightHelper:class extends kQt{constructor(t,e,n){super(),this.light=t,this.light.updateMatrixWorld(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.color=n,void 0===e&&(e=1);let i=new b1t;i.setAttribute("position",new l1t([-e,e,0,e,e,0,e,-e,0,-e,-e,0,-e,e,0],3));const r=new I3t({fog:!1,toneMapped:!1});this.lightPlane=new H3t(i,r),this.add(this.lightPlane),i=new b1t,i.setAttribute("position",new l1t([0,0,0,0,0,1],3)),this.targetLine=new H3t(i,r),this.add(this.targetLine),this.update()}dispose(){this.lightPlane.geometry.dispose(),this.lightPlane.material.dispose(),this.targetLine.geometry.dispose(),this.targetLine.material.dispose()}update(){D8t.setFromMatrixPosition(this.light.matrixWorld),B8t.setFromMatrixPosition(this.light.target.matrixWorld),H8t.subVectors(B8t,D8t),this.lightPlane.lookAt(B8t),void 0!==this.color?(this.lightPlane.material.color.set(this.color),this.targetLine.material.color.set(this.color)):(this.lightPlane.material.color.copy(this.light.color),this.targetLine.material.color.copy(this.light.color)),this.targetLine.lookAt(B8t),this.targetLine.scale.z=H8t.length()}},DiscreteInterpolant:O6t,DodecahedronBufferGeometry:o4t,DodecahedronGeometry:o4t,DoubleSide:2,DstAlphaFactor:206,DstColorFactor:208,DynamicBufferAttribute:function i7t(t,e){return console.warn("THREE.DynamicBufferAttribute has been removed. Use new THREE.BufferAttribute().setUsage( THREE.DynamicDrawUsage ) instead."),new QQt(t,e).setUsage(VZt)},DynamicCopyUsage:35050,DynamicDrawUsage:VZt,DynamicReadUsage:35049,EdgesGeometry:u4t,EdgesHelper:function r7t(t,e){return console.warn("THREE.EdgesHelper has been removed. Use THREE.EdgesGeometry instead."),new U3t(new u4t(t.geometry),new I3t({color:void 0!==e?e:16777215}))},EllipseCurve:d4t,EqualDepth:4,EqualStencilFunc:514,EquirectangularReflectionMapping:WKt,EquirectangularRefractionMapping:qKt,Euler:fQt,EventDispatcher:jZt,ExtrudeBufferGeometry:r6t,ExtrudeGeometry:r6t,FaceColors:1,FileLoader:Z6t,FlatShading:1,Float16BufferAttribute:s1t,Float32Attribute:function o7t(t,e){return console.warn("THREE.Float32Attribute has been removed. Use new THREE.Float32BufferAttribute() instead."),new l1t(t,e)},Float32BufferAttribute:l1t,Float64Attribute:function a7t(t,e){return console.warn("THREE.Float64Attribute has been removed. Use new THREE.Float64BufferAttribute() instead."),new c1t(t,e)},Float64BufferAttribute:c1t,FloatType:sZt,Fog:Y5t,FogExp2:q5t,Font:R9t,FontLoader:class extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new Z6t(this.manager);o.setPath(this.path),o.setRequestHeader(this.requestHeader),o.setWithCredentials(r.withCredentials),o.load(t,(function(t){let n;try{n=JSON.parse(t)}catch(e){console.warn("THREE.FontLoader: typeface.js support is being deprecated. Use typeface.json instead."),n=JSON.parse(t.substring(65,t.length-2))}const i=r.parse(n);e&&e(i)}),n,i)}parse(t){return new R9t(t)}},FrontSide:0,Frustum:i0t,GLBufferAttribute:_8t,GLSL1:"100",GLSL3:UZt,GammaEncoding:RZt,GreaterDepth:6,GreaterEqualDepth:5,GreaterEqualStencilFunc:518,GreaterStencilFunc:516,GridHelper:z8t,Group:H5t,HalfFloatType:lZt,HemisphereLight:a9t,HemisphereLightHelper:class extends kQt{constructor(t,e,n){super(),this.light=t,this.light.updateMatrixWorld(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.color=n;const i=new l6t(e);i.rotateY(.5*Math.PI),this.material=new KQt({wireframe:!0,fog:!1,toneMapped:!1}),void 0===this.color&&(this.material.vertexColors=!0);const r=i.getAttribute("position"),o=new Float32Array(3*r.count);i.setAttribute("color",new QQt(o,3)),this.add(new B1t(i,this.material)),this.update()}dispose(){this.children[0].geometry.dispose(),this.children[0].material.dispose()}update(){const t=this.children[0];if(void 0!==this.color)this.material.color.set(this.color);else{const e=t.geometry.getAttribute("color");R8t.copy(this.light.color),O8t.copy(this.light.groundColor);for(let t=0,n=e.count;t<n;t++){const i=t<n/2?R8t:O8t;e.setXYZ(t,i.r,i.g,i.b)}e.needsUpdate=!0}t.lookAt(I8t.setFromMatrixPosition(this.light.matrixWorld).negate())}},HemisphereLightProbe:H9t,IcosahedronBufferGeometry:a6t,IcosahedronGeometry:a6t,ImageBitmapLoader:N9t,ImageLoader:J6t,ImageUtils:yJt,ImmediateRenderObject:T8t,IncrementStencilOp:7682,IncrementWrapStencilOp:34055,InstancedBufferAttribute:C9t,InstancedBufferGeometry:T9t,InstancedInterleavedBuffer:g8t,InstancedMesh:N3t,Int16Attribute:function s7t(t,e){return console.warn("THREE.Int16Attribute has been removed. Use new THREE.Int16BufferAttribute() instead."),new i1t(t,e)},Int16BufferAttribute:i1t,Int32Attribute:function l7t(t,e){return console.warn("THREE.Int32Attribute has been removed. Use new THREE.Int32BufferAttribute() instead."),new o1t(t,e)},Int32BufferAttribute:o1t,Int8Attribute:function c7t(t,e){return console.warn("THREE.Int8Attribute has been removed. 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Use an Array instead."),t.isMultiMaterial=!0,t.materials=t,t.clone=function(){return t.slice()},t},MultiplyBlending:4,MultiplyOperation:0,NearestFilter:JKt,NearestMipMapLinearFilter:1005,NearestMipMapNearestFilter:1004,NearestMipmapLinearFilter:tZt,NearestMipmapNearestFilter:QKt,NeverDepth:0,NeverStencilFunc:512,NoBlending:0,NoColors:0,NoToneMapping:0,NormalAnimationBlendMode:LZt,NormalBlending:1,NotEqualDepth:7,NotEqualStencilFunc:517,NumberKeyframeTrack:H6t,Object3D:kQt,ObjectLoader:class extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=this,o=""===this.path?E9t.extractUrlBase(t):this.path;this.resourcePath=this.resourcePath||o;const a=new Z6t(this.manager);a.setPath(this.path),a.setRequestHeader(this.requestHeader),a.setWithCredentials(this.withCredentials),a.load(t,(function(n){let o=null;try{o=JSON.parse(n)}catch(e){return void 0!==i&&i(e),void console.error("THREE:ObjectLoader: Can't parse "+t+".",e.message)}const a=o.metadata;void 0!==a&&void 0!==a.type&&"geometry"!==a.type.toLowerCase()?r.parse(o,e):console.error("THREE.ObjectLoader: Can't load "+t)}),n,i)}async loadAsync(t,e){const n=""===this.path?E9t.extractUrlBase(t):this.path;this.resourcePath=this.resourcePath||n;const i=new Z6t(this.manager);i.setPath(this.path),i.setRequestHeader(this.requestHeader),i.setWithCredentials(this.withCredentials);const r=await i.loadAsync(t,e),o=JSON.parse(r),a=o.metadata;if(void 0===a||void 0===a.type||"geometry"===a.type.toLowerCase())throw new Error("THREE.ObjectLoader: Can't load "+t);return await this.parseAsync(o)}parse(t,e){const n=this.parseAnimations(t.animations),i=this.parseShapes(t.shapes),r=this.parseGeometries(t.geometries,i),o=this.parseImages(t.images,(function(){void 0!==e&&e(l)})),a=this.parseTextures(t.textures,o),s=this.parseMaterials(t.materials,a),l=this.parseObject(t.object,r,s,a,n),c=this.parseSkeletons(t.skeletons,l);if(this.bindSkeletons(l,c),void 0!==e){let t=!1;for(const e in o)if(o[e]instanceof HTMLImageElement){t=!0;break}!1===t&&e(l)}return l}async parseAsync(t){const e=this.parseAnimations(t.animations),n=this.parseShapes(t.shapes),i=this.parseGeometries(t.geometries,n),r=await this.parseImagesAsync(t.images),o=this.parseTextures(t.textures,r),a=this.parseMaterials(t.materials,o),s=this.parseObject(t.object,i,a,o,e),l=this.parseSkeletons(t.skeletons,s);return this.bindSkeletons(s,l),s}parseShapes(t){const e={};if(void 0!==t)for(let n=0,i=t.length;n<i;n++){const i=(new r9t).fromJSON(t[n]);e[i.uuid]=i}return e}parseSkeletons(t,e){const n={},i={};if(e.traverse((function(t){t.isBone&&(i[t.uuid]=t)})),void 0!==t)for(let e=0,r=t.length;e<r;e++){const r=(new C3t).fromJSON(t[e],i);n[r.uuid]=r}return n}parseGeometries(t,e){const n={};if(void 0!==t){const i=new A9t;for(let r=0,o=t.length;r<o;r++){let o;const a=t[r];switch(a.type){case"BufferGeometry":case"InstancedBufferGeometry":o=i.parse(a);break;case"Geometry":console.error("THREE.ObjectLoader: The legacy Geometry type is no longer supported.");break;default:a.type in b6t?o=b6t[a.type].fromJSON(a,e):console.warn(`THREE.ObjectLoader: Unsupported geometry type "${a.type}"`)}o.uuid=a.uuid,void 0!==a.name&&(o.name=a.name),!0===o.isBufferGeometry&&void 0!==a.userData&&(o.userData=a.userData),n[a.uuid]=o}}return n}parseMaterials(t,e){const n={},i={};if(void 0!==t){const r=new M9t;r.setTextures(e);for(let e=0,o=t.length;e<o;e++){const o=t[e];if("MultiMaterial"===o.type){const t=[];for(let e=0;e<o.materials.length;e++){const i=o.materials[e];void 0===n[i.uuid]&&(n[i.uuid]=r.parse(i)),t.push(n[i.uuid])}i[o.uuid]=t}else void 0===n[o.uuid]&&(n[o.uuid]=r.parse(o)),i[o.uuid]=n[o.uuid]}}return i}parseAnimations(t){const e={};if(void 0!==t)for(let n=0;n<t.length;n++){const i=G6t.parse(t[n]);e[i.uuid]=i}return e}parseImages(t,e){const n=this,i={};let r;function o(t){if("string"==typeof t){const e=t;return(function i(t){return n.manager.itemStart(t),r.load(t,(function(){n.manager.itemEnd(t)}),void 0,(function(){n.manager.itemError(t),n.manager.itemEnd(t)}))})(/^(\/\/)|([a-z]+:(\/\/)?)/i.test(e)?e:n.resourcePath+e)}return t.data?{data:d1t(t.type,t.data),width:t.width,height:t.height}:null}if(void 0!==t&&t.length>0){const n=new Y6t(e);r=new J6t(n),r.setCrossOrigin(this.crossOrigin);for(let e=0,n=t.length;e<n;e++){const n=t[e],r=n.url;if(Array.isArray(r)){i[n.uuid]=[];for(let t=0,e=r.length;t<e;t++){const e=o(r[t]);null!==e&&(e instanceof HTMLImageElement?i[n.uuid].push(e):i[n.uuid].push(new M3t(e.data,e.width,e.height)))}}else{const t=o(n.url);null!==t&&(i[n.uuid]=t)}}}return i}async parseImagesAsync(t){const e=this,n={};let i;async function r(t){if("string"==typeof t){const n=t,r=/^(\/\/)|([a-z]+:(\/\/)?)/i.test(n)?n:e.resourcePath+n;return await i.loadAsync(r)}return t.data?{data:d1t(t.type,t.data),width:t.width,height:t.height}:null}if(void 0!==t&&t.length>0){i=new J6t(this.manager),i.setCrossOrigin(this.crossOrigin);for(let e=0,i=t.length;e<i;e++){const i=t[e],o=i.url;if(Array.isArray(o)){n[i.uuid]=[];for(let t=0,e=o.length;t<e;t++){const e=o[t],a=await r(e);null!==a&&(a instanceof HTMLImageElement?n[i.uuid].push(a):n[i.uuid].push(new M3t(a.data,a.width,a.height)))}}else{const t=await r(i.url);null!==t&&(n[i.uuid]=t)}}}return n}parseTextures(t,e){function n(t,e){return"number"==typeof t?t:(console.warn("THREE.ObjectLoader.parseTexture: Constant should be in numeric form.",t),e[t])}const i={};if(void 0!==t)for(let r=0,o=t.length;r<o;r++){const o=t[r];let a;void 0===o.image&&console.warn('THREE.ObjectLoader: No "image" specified for',o.uuid),void 0===e[o.image]&&console.warn("THREE.ObjectLoader: Undefined image",o.image);const s=e[o.image];Array.isArray(s)?(a=new $1t(s),6===s.length&&(a.needsUpdate=!0)):(a=s&&s.data?new M3t(s.data,s.width,s.height):new bJt(s),s&&(a.needsUpdate=!0)),a.uuid=o.uuid,void 0!==o.name&&(a.name=o.name),void 0!==o.mapping&&(a.mapping=n(o.mapping,k9t)),void 0!==o.offset&&a.offset.fromArray(o.offset),void 0!==o.repeat&&a.repeat.fromArray(o.repeat),void 0!==o.center&&a.center.fromArray(o.center),void 0!==o.rotation&&(a.rotation=o.rotation),void 0!==o.wrap&&(a.wrapS=n(o.wrap[0],L9t),a.wrapT=n(o.wrap[1],L9t)),void 0!==o.format&&(a.format=o.format),void 0!==o.type&&(a.type=o.type),void 0!==o.encoding&&(a.encoding=o.encoding),void 0!==o.minFilter&&(a.minFilter=n(o.minFilter,P9t)),void 0!==o.magFilter&&(a.magFilter=n(o.magFilter,P9t)),void 0!==o.anisotropy&&(a.anisotropy=o.anisotropy),void 0!==o.flipY&&(a.flipY=o.flipY),void 0!==o.premultiplyAlpha&&(a.premultiplyAlpha=o.premultiplyAlpha),void 0!==o.unpackAlignment&&(a.unpackAlignment=o.unpackAlignment),i[o.uuid]=a}return i}parseObject(t,e,n,i,r){let o,a,s;function l(t){return void 0===e[t]&&console.warn("THREE.ObjectLoader: Undefined geometry",t),e[t]}function c(t){if(void 0!==t){if(Array.isArray(t)){const e=[];for(let i=0,r=t.length;i<r;i++){const r=t[i];void 0===n[r]&&console.warn("THREE.ObjectLoader: Undefined material",r),e.push(n[r])}return e}return void 0===n[t]&&console.warn("THREE.ObjectLoader: Undefined material",t),n[t]}}function u(t){return void 0===i[t]&&console.warn("THREE.ObjectLoader: Undefined texture",t),i[t]}switch(t.type){case"Scene":o=new X5t,void 0!==t.background&&(o.background=Number.isInteger(t.background)?new $Qt(t.background):u(t.background)),void 0!==t.environment&&(o.environment=u(t.environment)),void 0!==t.fog&&("Fog"===t.fog.type?o.fog=new Y5t(t.fog.color,t.fog.near,t.fog.far):"FogExp2"===t.fog.type&&(o.fog=new q5t(t.fog.color,t.fog.density)));break;case"PerspectiveCamera":o=new q1t(t.fov,t.aspect,t.near,t.far),void 0!==t.focus&&(o.focus=t.focus),void 0!==t.zoom&&(o.zoom=t.zoom),void 0!==t.filmGauge&&(o.filmGauge=t.filmGauge),void 0!==t.filmOffset&&(o.filmOffset=t.filmOffset),void 0!==t.view&&(o.view=Object.assign({},t.view));break;case"OrthographicCamera":o=new g0t(t.left,t.right,t.top,t.bottom,t.near,t.far),void 0!==t.zoom&&(o.zoom=t.zoom),void 0!==t.view&&(o.view=Object.assign({},t.view));break;case"AmbientLight":o=new b9t(t.color,t.intensity);break;case"DirectionalLight":o=new v9t(t.color,t.intensity);break;case"PointLight":o=new _9t(t.color,t.intensity,t.distance,t.decay);break;case"RectAreaLight":o=new x9t(t.color,t.intensity,t.width,t.height);break;case"SpotLight":o=new d9t(t.color,t.intensity,t.distance,t.angle,t.penumbra,t.decay);break;case"HemisphereLight":o=new a9t(t.color,t.groundColor,t.intensity);break;case"LightProbe":o=(new S9t).fromJSON(t);break;case"SkinnedMesh":a=l(t.geometry),s=c(t.material),o=new w3t(a,s),void 0!==t.bindMode&&(o.bindMode=t.bindMode),void 0!==t.bindMatrix&&o.bindMatrix.fromArray(t.bindMatrix),void 0!==t.skeleton&&(o.skeleton=t.skeleton);break;case"Mesh":a=l(t.geometry),s=c(t.material),o=new B1t(a,s);break;case"InstancedMesh":a=l(t.geometry),s=c(t.material);const e=t.instanceMatrix,n=t.instanceColor;o=new N3t(a,s,t.count),o.instanceMatrix=new QQt(new Float32Array(e.array),16),void 0!==n&&(o.instanceColor=new QQt(new Float32Array(n.array),n.itemSize));break;case"LOD":o=new g3t;break;case"Line":o=new H3t(l(t.geometry),c(t.material));break;case"LineLoop":o=new j3t(l(t.geometry),c(t.material));break;case"LineSegments":o=new U3t(l(t.geometry),c(t.material));break;case"PointCloud":case"Points":o=new $3t(l(t.geometry),c(t.material));break;case"Sprite":o=new d3t(c(t.material));break;case"Group":o=new H5t;break;case"Bone":o=new S3t;break;default:o=new kQt}if(o.uuid=t.uuid,void 0!==t.name&&(o.name=t.name),void 0!==t.matrix?(o.matrix.fromArray(t.matrix),void 0!==t.matrixAutoUpdate&&(o.matrixAutoUpdate=t.matrixAutoUpdate),o.matrixAutoUpdate&&o.matrix.decompose(o.position,o.quaternion,o.scale)):(void 0!==t.position&&o.position.fromArray(t.position),void 0!==t.rotation&&o.rotation.fromArray(t.rotation),void 0!==t.quaternion&&o.quaternion.fromArray(t.quaternion),void 0!==t.scale&&o.scale.fromArray(t.scale)),void 0!==t.castShadow&&(o.castShadow=t.castShadow),void 0!==t.receiveShadow&&(o.receiveShadow=t.receiveShadow),t.shadow&&(void 0!==t.shadow.bias&&(o.shadow.bias=t.shadow.bias),void 0!==t.shadow.normalBias&&(o.shadow.normalBias=t.shadow.normalBias),void 0!==t.shadow.radius&&(o.shadow.radius=t.shadow.radius),void 0!==t.shadow.mapSize&&o.shadow.mapSize.fromArray(t.shadow.mapSize),void 0!==t.shadow.camera&&(o.shadow.camera=this.parseObject(t.shadow.camera))),void 0!==t.visible&&(o.visible=t.visible),void 0!==t.frustumCulled&&(o.frustumCulled=t.frustumCulled),void 0!==t.renderOrder&&(o.renderOrder=t.renderOrder),void 0!==t.userData&&(o.userData=t.userData),void 0!==t.layers&&(o.layers.mask=t.layers),void 0!==t.children){const a=t.children;for(let t=0;t<a.length;t++)o.add(this.parseObject(a[t],e,n,i,r))}if(void 0!==t.animations){const e=t.animations;for(let t=0;t<e.length;t++)o.animations.push(r[e[t]])}if("LOD"===t.type){void 0!==t.autoUpdate&&(o.autoUpdate=t.autoUpdate);const e=t.levels;for(let t=0;t<e.length;t++){const n=e[t],i=o.getObjectByProperty("uuid",n.object);void 0!==i&&o.addLevel(i,n.distance)}}return o}bindSkeletons(t,e){0!==Object.keys(e).length&&t.traverse((function(t){if(!0===t.isSkinnedMesh&&void 0!==t.skeleton){const n=e[t.skeleton];void 0===n?console.warn("THREE.ObjectLoader: No skeleton found with UUID:",t.skeleton):t.bind(n,t.bindMatrix)}}))}setTexturePath(t){return console.warn("THREE.ObjectLoader: .setTexturePath() has been renamed to .setResourcePath()."),this.setResourcePath(t)}},ObjectSpaceNormalMap:1,OctahedronBufferGeometry:l6t,OctahedronGeometry:l6t,OneFactor:201,OneMinusDstAlphaFactor:207,OneMinusDstColorFactor:209,OneMinusSrcAlphaFactor:205,OneMinusSrcColorFactor:203,OrthographicCamera:g0t,PCFShadowMap:1,PCFSoftShadowMap:2,PMREMGenerator:R0t,ParametricBufferGeometry:c6t,ParametricGeometry:c6t,Particle:function f7t(t){return console.warn("THREE.Particle has been renamed to THREE.Sprite."),new d3t(t)},ParticleBasicMaterial:function m7t(t){return console.warn("THREE.ParticleBasicMaterial has been renamed to THREE.PointsMaterial."),new G3t(t)},ParticleSystem:function g7t(t,e){return console.warn("THREE.ParticleSystem has been renamed to THREE.Points."),new $3t(t,e)},ParticleSystemMaterial:function _7t(t){return console.warn("THREE.ParticleSystemMaterial has been renamed to THREE.PointsMaterial."),new G3t(t)},Path:i9t,PerspectiveCamera:q1t,Plane:t0t,PlaneBufferGeometry:a0t,PlaneGeometry:a0t,PlaneHelper:class extends H3t{constructor(t,e=1,n=16776960){const i=n,r=new b1t;r.setAttribute("position",new l1t([1,-1,1,-1,1,1,-1,-1,1,1,1,1,-1,1,1,-1,-1,1,1,-1,1,1,1,1,0,0,1,0,0,0],3)),r.computeBoundingSphere(),super(r,new I3t({color:i,toneMapped:!1})),this.type="PlaneHelper",this.plane=t,this.size=e;const o=new b1t;o.setAttribute("position",new l1t([1,1,1,-1,1,1,-1,-1,1,1,1,1,-1,-1,1,1,-1,1],3)),o.computeBoundingSphere(),this.add(new B1t(o,new KQt({color:i,opacity:.2,transparent:!0,depthWrite:!1,toneMapped:!1})))}updateMatrixWorld(t){let e=-this.plane.constant;Math.abs(e)<1e-8&&(e=1e-8),this.scale.set(.5*this.size,.5*this.size,e),this.children[0].material.side=e<0?1:0,this.lookAt(this.plane.normal),super.updateMatrixWorld(t)}},PointCloud:function y7t(t,e){return console.warn("THREE.PointCloud has been renamed to THREE.Points."),new $3t(t,e)},PointCloudMaterial:function v7t(t){return console.warn("THREE.PointCloudMaterial has been renamed to THREE.PointsMaterial."),new G3t(t)},PointLight:_9t,PointLightHelper:class extends B1t{constructor(t,e,n){super(new d6t(e,4,2),new KQt({wireframe:!0,fog:!1,toneMapped:!1})),this.light=t,this.light.updateMatrixWorld(),this.color=n,this.type="PointLightHelper",this.matrix=this.light.matrixWorld,this.matrixAutoUpdate=!1,this.update()}dispose(){this.geometry.dispose(),this.material.dispose()}update(){void 0!==this.color?this.material.color.set(this.color):this.material.color.copy(this.light.color)}},Points:$3t,PointsMaterial:G3t,PolarGridHelper:class extends U3t{constructor(t=10,e=16,n=8,i=64,r=4473924,o=8947848){r=new $Qt(r),o=new $Qt(o);const a=[],s=[];for(let n=0;n<=e;n++){const i=n/e*(2*Math.PI),l=Math.sin(i)*t,c=Math.cos(i)*t;a.push(0,0,0),a.push(l,0,c);const u=1&n?r:o;s.push(u.r,u.g,u.b),s.push(u.r,u.g,u.b)}for(let e=0;e<=n;e++){const l=1&e?r:o,c=t-t/n*e;for(let t=0;t<i;t++){let e=t/i*(2*Math.PI),n=Math.sin(e)*c,r=Math.cos(e)*c;a.push(n,0,r),s.push(l.r,l.g,l.b),e=(t+1)/i*(2*Math.PI),n=Math.sin(e)*c,r=Math.cos(e)*c,a.push(n,0,r),s.push(l.r,l.g,l.b)}}const l=new b1t;l.setAttribute("position",new l1t(a,3)),l.setAttribute("color",new l1t(s,3)),super(l,new I3t({vertexColors:!0,toneMapped:!1})),this.type="PolarGridHelper"}},PolyhedronBufferGeometry:r4t,PolyhedronGeometry:r4t,PositionalAudio:class extends $9t{constructor(t){super(t),this.panner=this.context.createPanner(),this.panner.panningModel="HRTF",this.panner.connect(this.gain)}getOutput(){return this.panner}getRefDistance(){return this.panner.refDistance}setRefDistance(t){return this.panner.refDistance=t,this}getRolloffFactor(){return this.panner.rolloffFactor}setRolloffFactor(t){return this.panner.rolloffFactor=t,this}getDistanceModel(){return this.panner.distanceModel}setDistanceModel(t){return this.panner.distanceModel=t,this}getMaxDistance(){return this.panner.maxDistance}setMaxDistance(t){return this.panner.maxDistance=t,this}setDirectionalCone(t,e,n){return this.panner.coneInnerAngle=t,this.panner.coneOuterAngle=e,this.panner.coneOuterGain=n,this}updateMatrixWorld(t){if(super.updateMatrixWorld(t),!0===this.hasPlaybackControl&&!1===this.isPlaying)return;this.matrixWorld.decompose(K9t,Z9t,J9t),Q9t.set(0,0,1).applyQuaternion(Z9t);const e=this.panner;if(e.positionX){const t=this.context.currentTime+this.listener.timeDelta;e.positionX.linearRampToValueAtTime(K9t.x,t),e.positionY.linearRampToValueAtTime(K9t.y,t),e.positionZ.linearRampToValueAtTime(K9t.z,t),e.orientationX.linearRampToValueAtTime(Q9t.x,t),e.orientationY.linearRampToValueAtTime(Q9t.y,t),e.orientationZ.linearRampToValueAtTime(Q9t.z,t)}else e.setPosition(K9t.x,K9t.y,K9t.z),e.setOrientation(Q9t.x,Q9t.y,Q9t.z)}},PropertyBinding:h8t,PropertyMixer:e8t,QuadraticBezierCurve:C4t,QuadraticBezierCurve3:A4t,Quaternion:TJt,QuaternionKeyframeTrack:V6t,QuaternionLinearInterpolant:F6t,REVISION:BKt,RGBADepthPacking:3201,RGBAFormat:hZt,RGBAIntegerFormat:1033,RGBA_ASTC_10x10_Format:37819,RGBA_ASTC_10x5_Format:37816,RGBA_ASTC_10x6_Format:37817,RGBA_ASTC_10x8_Format:37818,RGBA_ASTC_12x10_Format:37820,RGBA_ASTC_12x12_Format:37821,RGBA_ASTC_4x4_Format:37808,RGBA_ASTC_5x4_Format:37809,RGBA_ASTC_5x5_Format:37810,RGBA_ASTC_6x5_Format:37811,RGBA_ASTC_6x6_Format:37812,RGBA_ASTC_8x5_Format:37813,RGBA_ASTC_8x6_Format:37814,RGBA_ASTC_8x8_Format:37815,RGBA_BPTC_Format:36492,RGBA_ETC2_EAC_Format:SZt,RGBA_PVRTC_2BPPV1_Format:xZt,RGBA_PVRTC_4BPPV1_Format:bZt,RGBA_S3TC_DXT1_Format:mZt,RGBA_S3TC_DXT3_Format:gZt,RGBA_S3TC_DXT5_Format:_Zt,RGBDEncoding:BZt,RGBEEncoding:OZt,RGBEFormat:1023,RGBFormat:uZt,RGBIntegerFormat:1032,RGBM16Encoding:DZt,RGBM7Encoding:zZt,RGB_ETC1_Format:36196,RGB_ETC2_Format:wZt,RGB_PVRTC_2BPPV1_Format:vZt,RGB_PVRTC_4BPPV1_Format:yZt,RGB_S3TC_DXT1_Format:fZt,RGFormat:1030,RGIntegerFormat:1031,RawShaderMaterial:_0t,Ray:iQt,Raycaster:class{constructor(t,e,n=0,i=1/0){this.ray=new iQt(t,e),this.near=n,this.far=i,this.camera=null,this.layers=new mQt,this.params={Mesh:{},Line:{threshold:1},LOD:{},Points:{threshold:1},Sprite:{}}}set(t,e){this.ray.set(t,e)}setFromCamera(t,e){e&&e.isPerspectiveCamera?(this.ray.origin.setFromMatrixPosition(e.matrixWorld),this.ray.direction.set(t.x,t.y,.5).unproject(e).sub(this.ray.origin).normalize(),this.camera=e):e&&e.isOrthographicCamera?(this.ray.origin.set(t.x,t.y,(e.near+e.far)/(e.near-e.far)).unproject(e),this.ray.direction.set(0,0,-1).transformDirection(e.matrixWorld),this.camera=e):console.error("THREE.Raycaster: Unsupported camera type: "+e.type)}intersectObject(t,e=!1,n=[]){return v8t(t,this,n,e),n.sort(y8t),n}intersectObjects(t,e=!1,n=[]){for(let i=0,r=t.length;i<r;i++)v8t(t[i],this,n,e);return n.sort(y8t),n}},RectAreaLight:x9t,RedFormat:1028,RedIntegerFormat:1029,ReinhardToneMapping:2,RepeatWrapping:$Kt,ReplaceStencilOp:7681,ReverseSubtractEquation:102,RingBufferGeometry:u6t,RingGeometry:u6t,SRGB8_ALPHA8_ASTC_10x10_Format:37851,SRGB8_ALPHA8_ASTC_10x5_Format:37848,SRGB8_ALPHA8_ASTC_10x6_Format:37849,SRGB8_ALPHA8_ASTC_10x8_Format:37850,SRGB8_ALPHA8_ASTC_12x10_Format:37852,SRGB8_ALPHA8_ASTC_12x12_Format:37853,SRGB8_ALPHA8_ASTC_4x4_Format:37840,SRGB8_ALPHA8_ASTC_5x4_Format:37841,SRGB8_ALPHA8_ASTC_5x5_Format:37842,SRGB8_ALPHA8_ASTC_6x5_Format:37843,SRGB8_ALPHA8_ASTC_6x6_Format:37844,SRGB8_ALPHA8_ASTC_8x5_Format:37845,SRGB8_ALPHA8_ASTC_8x6_Format:37846,SRGB8_ALPHA8_ASTC_8x8_Format:37847,Scene:X5t,SceneUtils:Z8t,ShaderChunk:s0t,ShaderLib:c0t,ShaderMaterial:G1t,ShadowMaterial:x6t,Shape:r9t,ShapeBufferGeometry:h6t,ShapeGeometry:h6t,ShapePath:I9t,ShapeUtils:e6t,ShortType:1011,Skeleton:C3t,SkeletonHelper:P8t,SkinnedMesh:w3t,SmoothShading:2,Sphere:$Jt,SphereBufferGeometry:d6t,SphereGeometry:d6t,Spherical:b8t,SphericalHarmonics3:w9t,SplineCurve:k4t,SpotLight:d9t,SpotLightHelper:class extends kQt{constructor(t,e){super(),this.light=t,this.light.updateMatrixWorld(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.color=e;const n=new b1t,i=[0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,-1,0,1,0,0,0,0,1,1,0,0,0,0,-1,1];for(let t=0,e=1,n=32;t<n;t++,e++){const r=t/n*Math.PI*2,o=e/n*Math.PI*2;i.push(Math.cos(r),Math.sin(r),1,Math.cos(o),Math.sin(o),1)}n.setAttribute("position",new l1t(i,3));const r=new I3t({fog:!1,toneMapped:!1});this.cone=new U3t(n,r),this.add(this.cone),this.update()}dispose(){this.cone.geometry.dispose(),this.cone.material.dispose()}update(){this.light.updateMatrixWorld();const t=this.light.distance?this.light.distance:1e3,e=t*Math.tan(this.light.angle);this.cone.scale.set(e,e,t),C8t.setFromMatrixPosition(this.light.target.matrixWorld),this.cone.lookAt(C8t),void 0!==this.color?this.cone.material.color.set(this.color):this.cone.material.color.copy(this.light.color)}},Sprite:d3t,SpriteMaterial:J5t,SrcAlphaFactor:204,SrcAlphaSaturateFactor:210,SrcColorFactor:202,StaticCopyUsage:35046,StaticDrawUsage:FZt,StaticReadUsage:35045,StereoCamera:class{constructor(){this.type="StereoCamera",this.aspect=1,this.eyeSep=.064,this.cameraL=new q1t,this.cameraL.layers.enable(1),this.cameraL.matrixAutoUpdate=!1,this.cameraR=new q1t,this.cameraR.layers.enable(2),this.cameraR.matrixAutoUpdate=!1,this._cache={focus:null,fov:null,aspect:null,near:null,far:null,zoom:null,eyeSep:null}}update(t){const e=this._cache;if(e.focus!==t.focus||e.fov!==t.fov||e.aspect!==t.aspect*this.aspect||e.near!==t.near||e.far!==t.far||e.zoom!==t.zoom||e.eyeSep!==this.eyeSep){e.focus=t.focus,e.fov=t.fov,e.aspect=t.aspect*this.aspect,e.near=t.near,e.far=t.far,e.zoom=t.zoom,e.eyeSep=this.eyeSep;const n=t.projectionMatrix.clone(),i=e.eyeSep/2,r=i*e.near/e.focus,o=e.near*Math.tan(qZt*e.fov*.5)/e.zoom;let a,s;U9t.elements[12]=-i,V9t.elements[12]=i,a=-o*e.aspect+r,s=o*e.aspect+r,n.elements[0]=2*e.near/(s-a),n.elements[8]=(s+a)/(s-a),this.cameraL.projectionMatrix.copy(n),a=-o*e.aspect-r,s=o*e.aspect-r,n.elements[0]=2*e.near/(s-a),n.elements[8]=(s+a)/(s-a),this.cameraR.projectionMatrix.copy(n)}this.cameraL.matrixWorld.copy(t.matrixWorld).multiply(U9t),this.cameraR.matrixWorld.copy(t.matrixWorld).multiply(V9t)}},StreamCopyUsage:35042,StreamDrawUsage:35040,StreamReadUsage:35041,StringKeyframeTrack:U6t,SubtractEquation:101,SubtractiveBlending:3,TOUCH:FKt,TangentSpaceNormalMap:0,TetrahedronBufferGeometry:p6t,TetrahedronGeometry:p6t,TextBufferGeometry:f6t,TextGeometry:f6t,Texture:bJt,TextureLoader:e9t,TorusBufferGeometry:m6t,TorusGeometry:m6t,TorusKnotBufferGeometry:g6t,TorusKnotGeometry:g6t,Triangle:FQt,TriangleFanDrawMode:2,TriangleStripDrawMode:1,TrianglesDrawMode:0,TubeBufferGeometry:_6t,TubeGeometry:_6t,UVMapping:UKt,Uint16Attribute:function b7t(t,e){return console.warn("THREE.Uint16Attribute has been removed. Use new THREE.Uint16BufferAttribute() instead."),new r1t(t,e)},Uint16BufferAttribute:r1t,Uint32Attribute:function x7t(t,e){return console.warn("THREE.Uint32Attribute has been removed. Use new THREE.Uint32BufferAttribute() instead."),new a1t(t,e)},Uint32BufferAttribute:a1t,Uint8Attribute:function w7t(t,e){return console.warn("THREE.Uint8Attribute has been removed. Use new THREE.Uint8BufferAttribute() instead."),new e1t(t,e)},Uint8BufferAttribute:e1t,Uint8ClampedAttribute:function S7t(t,e){return console.warn("THREE.Uint8ClampedAttribute has been removed. Use new THREE.Uint8ClampedBufferAttribute() instead."),new n1t(t,e)},Uint8ClampedBufferAttribute:n1t,Uniform:m8t,UniformsLib:l0t,UniformsUtils:j1t,UnsignedByteType:rZt,UnsignedInt248Type:cZt,UnsignedIntType:aZt,UnsignedShort4444Type:1017,UnsignedShort5551Type:1018,UnsignedShort565Type:1019,UnsignedShortType:oZt,VSMShadowMap:3,Vector2:mJt,Vector3:CJt,Vector4:wJt,VectorKeyframeTrack:j6t,Vertex:function M7t(t,e,n){return console.warn("THREE.Vertex has been removed. Use THREE.Vector3 instead."),new CJt(t,e,n)},VertexColors:2,VideoTexture:Z3t,WebGL1Renderer:W5t,WebGLCubeRenderTarget:K1t,WebGLMultipleRenderTargets:MJt,WebGLMultisampleRenderTarget:EJt,WebGLRenderTarget:SJt,WebGLRenderTargetCube:function E7t(t,e,n){return console.warn("THREE.WebGLRenderTargetCube( width, height, options ) is now WebGLCubeRenderTarget( size, options )."),new K1t(t,n)},WebGLRenderer:G5t,WebGLUtils:D5t,WireframeGeometry:y6t,WireframeHelper:function T7t(t,e){return console.warn("THREE.WireframeHelper has been removed. Use THREE.WireframeGeometry instead."),new U3t(new y6t(t.geometry),new I3t({color:void 0!==e?e:16777215}))},WrapAroundEnding:kZt,XHRLoader:function C7t(t){return console.warn("THREE.XHRLoader has been renamed to THREE.FileLoader."),new Z6t(t)},ZeroCurvatureEnding:CZt,ZeroFactor:200,ZeroSlopeEnding:AZt,ZeroStencilOp:0,sRGBEncoding:IZt});const A7t={type:"change"},k7t={type:"start"},L7t={type:"end"};class P7t extends jZt{constructor(t,e){super(),void 0===e&&console.warn('THREE.OrbitControls: The second parameter "domElement" is now mandatory.'),e===document&&console.error('THREE.OrbitControls: "document" should not be used as the target "domElement". Please use "renderer.domElement" instead.'),this.object=t,this.domElement=e,this.domElement.style.touchAction="none",this.enabled=!0,this.target=new CJt,this.minDistance=0,this.maxDistance=1/0,this.minZoom=0,this.maxZoom=1/0,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.minAzimuthAngle=-1/0,this.maxAzimuthAngle=1/0,this.enableDamping=!1,this.dampingFactor=.05,this.enableZoom=!0,this.zoomSpeed=1,this.enableRotate=!0,this.rotateSpeed=1,this.enablePan=!0,this.panSpeed=1,this.screenSpacePanning=!0,this.keyPanSpeed=7,this.autoRotate=!1,this.autoRotateSpeed=2,this.keys={LEFT:"ArrowLeft",UP:"ArrowUp",RIGHT:"ArrowRight",BOTTOM:"ArrowDown"},this.mouseButtons={LEFT:HKt.ROTATE,MIDDLE:HKt.DOLLY,RIGHT:HKt.PAN},this.touches={ONE:FKt.ROTATE,TWO:FKt.DOLLY_PAN},this.target0=this.target.clone(),this.position0=this.object.position.clone(),this.zoom0=this.object.zoom,this._domElementKeyEvents=null,this.getPolarAngle=function(){return a.phi},this.getAzimuthalAngle=function(){return a.theta},this.getDistance=function(){return this.object.position.distanceTo(this.target)},this.listenToKeyEvents=function(t){t.addEventListener("keydown",j),this._domElementKeyEvents=t},this.saveState=function(){n.target0.copy(n.target),n.position0.copy(n.object.position),n.zoom0=n.object.zoom},this.reset=function(){n.target.copy(n.target0),n.object.position.copy(n.position0),n.object.zoom=n.zoom0,n.object.updateProjectionMatrix(),n.dispatchEvent(A7t),n.update(),r=i.NONE},this.update=(function(){const e=new CJt,h=(new TJt).setFromUnitVectors(t.up,new CJt(0,1,0)),d=h.clone().invert(),p=new CJt,f=new TJt,m=2*Math.PI;return function t(){const g=n.object.position;e.copy(g).sub(n.target),e.applyQuaternion(h),a.setFromVector3(e),n.autoRotate&&r===i.NONE&&S((function _(){return 2*Math.PI/60/60*n.autoRotateSpeed})()),n.enableDamping?(a.theta+=s.theta*n.dampingFactor,a.phi+=s.phi*n.dampingFactor):(a.theta+=s.theta,a.phi+=s.phi);let y=n.minAzimuthAngle,v=n.maxAzimuthAngle;return isFinite(y)&&isFinite(v)&&(y<-Math.PI?y+=m:y>Math.PI&&(y-=m),v<-Math.PI?v+=m:v>Math.PI&&(v-=m),a.theta=y<=v?Math.max(y,Math.min(v,a.theta)):a.theta>(y+v)/2?Math.max(y,a.theta):Math.min(v,a.theta)),a.phi=Math.max(n.minPolarAngle,Math.min(n.maxPolarAngle,a.phi)),a.makeSafe(),a.radius*=l,a.radius=Math.max(n.minDistance,Math.min(n.maxDistance,a.radius)),!0===n.enableDamping?n.target.addScaledVector(c,n.dampingFactor):n.target.add(c),e.setFromSpherical(a),e.applyQuaternion(d),g.copy(n.target).add(e),n.object.lookAt(n.target),!0===n.enableDamping?(s.theta*=1-n.dampingFactor,s.phi*=1-n.dampingFactor,c.multiplyScalar(1-n.dampingFactor)):(s.set(0,0,0),c.set(0,0,0)),l=1,!!(u||p.distanceToSquared(n.object.position)>o||8*(1-f.dot(n.object.quaternion))>o)&&(n.dispatchEvent(A7t),p.copy(n.object.position),f.copy(n.object.quaternion),u=!1,!0)}})(),this.dispose=function(){n.domElement.removeEventListener("contextmenu",G),n.domElement.removeEventListener("pointerdown",B),n.domElement.removeEventListener("pointercancel",V),n.domElement.removeEventListener("wheel",U),n.domElement.removeEventListener("pointermove",H),n.domElement.removeEventListener("pointerup",F),null!==n._domElementKeyEvents&&n._domElementKeyEvents.removeEventListener("keydown",j)};const n=this,i={NONE:-1,ROTATE:0,DOLLY:1,PAN:2,TOUCH_ROTATE:3,TOUCH_PAN:4,TOUCH_DOLLY_PAN:5,TOUCH_DOLLY_ROTATE:6};let r=i.NONE;const o=1e-6,a=new b8t,s=new b8t;let l=1;const c=new CJt;let u=!1;const h=new mJt,d=new mJt,p=new mJt,f=new mJt,m=new mJt,g=new mJt,_=new mJt,y=new mJt,v=new mJt,b=[],x={};function w(){return Math.pow(.95,n.zoomSpeed)}function S(t){s.theta-=t}function M(t){s.phi-=t}const E=(function(){const t=new CJt;return function e(n,i){t.setFromMatrixColumn(i,0),t.multiplyScalar(-n),c.add(t)}})(),T=(function(){const t=new CJt;return function e(i,r){!0===n.screenSpacePanning?t.setFromMatrixColumn(r,1):(t.setFromMatrixColumn(r,0),t.crossVectors(n.object.up,t)),t.multiplyScalar(i),c.add(t)}})(),C=(function(){const t=new CJt;return function e(i,r){const o=n.domElement;if(n.object.isPerspectiveCamera){t.copy(n.object.position).sub(n.target);let e=t.length();e*=Math.tan(n.object.fov/2*Math.PI/180),E(2*i*e/o.clientHeight,n.object.matrix),T(2*r*e/o.clientHeight,n.object.matrix)}else n.object.isOrthographicCamera?(E(i*(n.object.right-n.object.left)/n.object.zoom/o.clientWidth,n.object.matrix),T(r*(n.object.top-n.object.bottom)/n.object.zoom/o.clientHeight,n.object.matrix)):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - pan disabled."),n.enablePan=!1)}})();function A(t){n.object.isPerspectiveCamera?l/=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom*t)),n.object.updateProjectionMatrix(),u=!0):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled."),n.enableZoom=!1)}function k(t){n.object.isPerspectiveCamera?l*=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom/t)),n.object.updateProjectionMatrix(),u=!0):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled."),n.enableZoom=!1)}function L(t){h.set(t.clientX,t.clientY)}function P(t){f.set(t.clientX,t.clientY)}function N(){1===b.length?h.set(b[0].pageX,b[0].pageY):h.set(.5*(b[0].pageX+b[1].pageX),.5*(b[0].pageY+b[1].pageY))}function I(){1===b.length?f.set(b[0].pageX,b[0].pageY):f.set(.5*(b[0].pageX+b[1].pageX),.5*(b[0].pageY+b[1].pageY))}function R(){const t=b[0].pageX-b[1].pageX,e=b[0].pageY-b[1].pageY,n=Math.sqrt(t*t+e*e);_.set(0,n)}function O(t){if(1==b.length)d.set(t.pageX,t.pageY);else{const e=Y(t);d.set(.5*(t.pageX+e.x),.5*(t.pageY+e.y))}p.subVectors(d,h).multiplyScalar(n.rotateSpeed);const e=n.domElement;S(2*Math.PI*p.x/e.clientHeight),M(2*Math.PI*p.y/e.clientHeight),h.copy(d)}function z(t){if(1===b.length)m.set(t.pageX,t.pageY);else{const e=Y(t);m.set(.5*(t.pageX+e.x),.5*(t.pageY+e.y))}g.subVectors(m,f).multiplyScalar(n.panSpeed),C(g.x,g.y),f.copy(m)}function D(t){const e=Y(t),i=t.pageX-e.x,r=t.pageY-e.y,o=Math.sqrt(i*i+r*r);y.set(0,o),v.set(0,Math.pow(y.y/_.y,n.zoomSpeed)),A(v.y),_.copy(y)}function B(t){!1!==n.enabled&&(0===b.length&&(n.domElement.setPointerCapture(t.pointerId),n.domElement.addEventListener("pointermove",H),n.domElement.addEventListener("pointerup",F)),(function e(t){b.push(t)})(t),"touch"===t.pointerType?(function o(t){switch(q(t),b.length){case 1:switch(n.touches.ONE){case FKt.ROTATE:if(!1===n.enableRotate)return;N(),r=i.TOUCH_ROTATE;break;case FKt.PAN:if(!1===n.enablePan)return;I(),r=i.TOUCH_PAN;break;default:r=i.NONE}break;case 2:switch(n.touches.TWO){case FKt.DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;!(function t(){n.enableZoom&&R(),n.enablePan&&I()})(),r=i.TOUCH_DOLLY_PAN;break;case FKt.DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;!(function e(){n.enableZoom&&R(),n.enableRotate&&N()})(),r=i.TOUCH_DOLLY_ROTATE;break;default:r=i.NONE}break;default:r=i.NONE}r!==i.NONE&&n.dispatchEvent(k7t)})(t):(function a(t){let e;switch(t.button){case 0:e=n.mouseButtons.LEFT;break;case 1:e=n.mouseButtons.MIDDLE;break;case 2:e=n.mouseButtons.RIGHT;break;default:e=-1}switch(e){case HKt.DOLLY:if(!1===n.enableZoom)return;!(function e(t){_.set(t.clientX,t.clientY)})(t),r=i.DOLLY;break;case HKt.ROTATE:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enablePan)return;P(t),r=i.PAN}else{if(!1===n.enableRotate)return;L(t),r=i.ROTATE}break;case HKt.PAN:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enableRotate)return;L(t),r=i.ROTATE}else{if(!1===n.enablePan)return;P(t),r=i.PAN}break;default:r=i.NONE}r!==i.NONE&&n.dispatchEvent(k7t)})(t))}function H(t){!1!==n.enabled&&("touch"===t.pointerType?(function e(t){switch(q(t),r){case i.TOUCH_ROTATE:if(!1===n.enableRotate)return;O(t),n.update();break;case i.TOUCH_PAN:if(!1===n.enablePan)return;z(t),n.update();break;case i.TOUCH_DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;!(function e(t){n.enableZoom&&D(t),n.enablePan&&z(t)})(t),n.update();break;case i.TOUCH_DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;!(function o(t){n.enableZoom&&D(t),n.enableRotate&&O(t)})(t),n.update();break;default:r=i.NONE}})(t):(function o(t){if(!1!==n.enabled)switch(r){case i.ROTATE:if(!1===n.enableRotate)return;!(function e(t){d.set(t.clientX,t.clientY),p.subVectors(d,h).multiplyScalar(n.rotateSpeed);const e=n.domElement;S(2*Math.PI*p.x/e.clientHeight),M(2*Math.PI*p.y/e.clientHeight),h.copy(d),n.update()})(t);break;case i.DOLLY:if(!1===n.enableZoom)return;!(function r(t){y.set(t.clientX,t.clientY),v.subVectors(y,_),v.y>0?A(w()):v.y<0&&k(w()),_.copy(y),n.update()})(t);break;case i.PAN:if(!1===n.enablePan)return;!(function o(t){m.set(t.clientX,t.clientY),g.subVectors(m,f).multiplyScalar(n.panSpeed),C(g.x,g.y),f.copy(m),n.update()})(t)}})(t))}function F(t){!1!==n.enabled&&("touch"===t.pointerType?(function e(t){n.dispatchEvent(L7t),r=i.NONE})():(function o(t){n.dispatchEvent(L7t),r=i.NONE})(),W(t),0===b.length&&(n.domElement.releasePointerCapture(t.pointerId),n.domElement.removeEventListener("pointermove",H),n.domElement.removeEventListener("pointerup",F)))}function V(t){W(t)}function U(t){!1===n.enabled||!1===n.enableZoom||r!==i.NONE&&r!==i.ROTATE||(t.preventDefault(),n.dispatchEvent(k7t),(function e(t){t.deltaY<0?k(w()):t.deltaY>0&&A(w()),n.update()})(t),n.dispatchEvent(L7t))}function j(t){!1!==n.enabled&&!1!==n.enablePan&&(function e(t){let e=!1;switch(t.code){case n.keys.UP:C(0,n.keyPanSpeed),e=!0;break;case n.keys.BOTTOM:C(0,-n.keyPanSpeed),e=!0;break;case n.keys.LEFT:C(n.keyPanSpeed,0),e=!0;break;case n.keys.RIGHT:C(-n.keyPanSpeed,0),e=!0}e&&(t.preventDefault(),n.update())})(t)}function G(t){!1!==n.enabled&&t.preventDefault()}function W(t){delete x[t.pointerId];for(let e=0;e<b.length;e++)if(b[e].pointerId==t.pointerId)return void b.splice(e,1)}function q(t){let e=x[t.pointerId];void 0===e&&(e=new mJt,x[t.pointerId]=e),e.set(t.pageX,t.pageY)}function Y(t){return x[(t.pointerId===b[0].pointerId?b[1]:b[0]).pointerId]}n.domElement.addEventListener("contextmenu",G),n.domElement.addEventListener("pointerdown",B),n.domElement.addEventListener("pointercancel",V),n.domElement.addEventListener("wheel",U,{passive:!1}),this.update()}}class N7t extends jZt{constructor(t){super(),this._lastMesh=null,this._clock=new j9t,this._canvasSize=null,this._layersConfig=null,this._runColor=t}_isObject(t){return"object"==typeof t&&null!=t&&!Array.isArray(t)}_applyDefaults(t,e){let n={};const i=[t,e];for(let t=0;t<i.length;t++){const e=i[t];for(let t in e){const i=t in n;this._isObject(e[t])?n[t]=this._applyDefaults(n[t]||{},e[t]):i||(n[t]=e[t])}}return n}_createLayers(){if(this._layersConfig&&this._scene&&this._lastMesh){if(this._layersConfig.showBoundingBox){var t=new G8t(this._lastMesh,new $Qt("rgb(0, 0, 255)"));this._scene.add(t)}if(this._layersConfig.showAxes){var e=new X8t(5);this._scene.add(e)}}}setLayersConfig(t){this._layersConfig=this._applyDefaults(t,this._layersConfig||{})}_createWorld(t,e){if(this.isReady())return;this._scene=new X5t;var n=new J8t[t.camera.cls](t.camera.fov,this._canvasSize.width/this._canvasSize.height,t.camera.near,t.camera.far);this._camera=n;var i=new P7t(n,e);const r=i;r.lookSpeed=.4,r.movementSpeed=20,r.noFly=!0,r.lookVertical=!0,r.constrainVertical=!0,r.verticalMin=1,r.verticalMax=2,r.addEventListener("change",this._onCameraPositionChange.bind(this)),this._cameraControls=i,this._renderer=new G5t({antialias:!0}),this._renderer.setPixelRatio(window.devicePixelRatio),this._renderer.setSize(this._canvasSize.width,this._canvasSize.height),this._renderer.setClearColor(16777215,1)}_clearScene(){for(;this._scene.children.length>0;)this._scene.remove(this._scene.children[0])}getRenderer(){return this._renderer}getCameraControls(){return this._cameraControls}isReady(){return!!this._camera&&!!this._cameraControls}getCameraPosition(){return{far:this._camera.far,position:this._camera.position.clone(),target:this._cameraControls.target.clone()}}setCanvasSize(t){this._canvasSize=t}draw(){this._animationFrameIndex&&cancelAnimationFrame(this._animationFrameIndex),this._camera.aspect=this._canvasSize.width/this._canvasSize.height,this._camera.updateProjectionMatrix(),this._renderer.setSize(this._canvasSize.width,this._canvasSize.height);const t=function(){var e=this._clock.getDelta();this._cameraControls.update(e),this._animationFrameIndex=requestAnimationFrame(t),this._renderer.render(this._scene,this._camera)}.bind(this);t()}updateScene(t,e){let n={};"config"in t&&t.config&&(n=JSON.parse(t.config)),this.dispatchEvent({type:"beforeUpdateScene"}),n=this._applyDefaults(n,{camera:{cls:"PerspectiveCamera",fov:75,near:.1,far:1e3},lights:[{cls:"AmbientLight",color:"#ffffff",intensity:.75},{cls:"DirectionalLight",color:"#ffffff",intensity:.75,position:[0,-1,2]}]}),this._createWorld(n,e),this._clearScene(),this._createLights(this._scene,n),this._createGeometry(t,n),this._createLayers(),this.draw()}resetView(t){if(!this.isReady())return;let e;this._cameraControls.reset(),!t&&this._lastMesh&&(e=this._lastMesh),e&&(this._fitObjectToViewport(e),this._lastMesh=e),this._cameraControls.update()}_createGeometry(t,e){const n=t.mesh;n.vertices&&n.faces&&n.faces.length?this._createMesh(n,e):this._createPointCloud(n,e)}_createPointCloud(t,e){const n=t.vertices,i=t.colors;let r={material:{cls:"PointsMaterial",size:.005}};i&&i.length==n.length?r.material.vertexColors=!0:r.material.color=this._runColor;const o=this._applyDefaults(e,r),a=new b1t,s=new Float32Array(n.flat());if(a.setAttribute("position",new QQt(s,3)),i&&i.length==n.length){const t=new Float32Array(i.flat());for(let e=0;e<t.length;e++)t[e]=t[e]/255;a.setAttribute("color",new QQt(t,3))}var l=new J8t[o.material.cls](o.material),c=new $3t(a,l);this._scene.add(c),this._lastMesh=c}setCameraViewpoint(t,e,n){this._silent=!0,this._camera.far=e,this._camera.position.set(t.x,t.y,t.z),this._camera.lookAt(n.clone()),this._camera.updateProjectionMatrix(),this._cameraControls.target=n.clone(),this._cameraControls.update(),this._silent=!1}_onCameraPositionChange(t){this._silent||this.dispatchEvent({type:"cameraPositionChange",event:t})}_fitObjectToViewport(t){const e=new LJt,n=new CJt,i=new CJt;e.setFromObject(t),e.getCenter(n),e.getSize(i);const r=Math.max(i.x,i.y,i.z),o=this._camera.fov*(Math.PI/180);let a=1.25*Math.abs(r/(2*Math.tan(o/2)));const s=e.min.z;this.setCameraViewpoint({x:n.x,y:n.y,z:a},3*(s<0?-s+a:a-s),n)}_createMesh(t,e){const n=t.vertices,i=t.faces,r=t.colors,o=this._applyDefaults(e,{material:{cls:"MeshStandardMaterial",color:"#a0a0a0",roughness:1,metalness:0}}),a=new b1t,s=new Float32Array(n.flat());a.setAttribute("position",new QQt(s,3));const l=new Uint16Array(i.flat());if(r&&r.length){const t=r.flat();for(let e=0;e<t.length;e++)t[e]=t[e]/255;a.setAttribute("color",new QQt(new Float32Array(t),3)),o.material=o.material||{},o.material.vertexColors=!0}a.center(),a.computeBoundingSphere(),a.setIndex(new QQt(l,1)),a.computeVertexNormals();let c=new J8t[o.material.cls](o.material),u=new B1t(a,c);u.castShadow=!0,u.receiveShadow=!0,this._scene.add(u),this._lastMesh=u}_createLights(t,e){for(let n=0;n<e.lights.length;n++){const i=e.lights[n];let r=new J8t[i.cls](i.color,i.intensity);i.position&&r.position.set(i.position[0],i.position[1],i.position[2]),t.add(r)}}}let I7t=class extends(er(ye)){constructor(){super(...arguments),this.selectedView="all",this.active=!1,this._colorScaleFunction=GR,this._steps=[],this._meshViewerAttached=!1,this._cameraPositionInitialized=!1,this._isMeshLoading=!1}get _runColor(){return this._colorScaleFunction(this.run)}connectedCallback(){super.connectedCallback(),this._dataProvider=new DKt(this.requestManager);const t=new N7t(this._runColor);t.addEventListener("beforeUpdateScene",this._updateCanvasSize.bind(this)),t.addEventListener("cameraPositionChange",this._onCameraPositionChange.bind(this)),this._meshViewer=t}reload(){this.active&&this._dataProvider&&(this._isMeshLoading=!0,this._dataProvider.reload(this.run,this.tag,this.sample).then((t=>{t&&(this._steps=t,this._stepIndex=t.length-1)})).catch((t=>{if(!t||!t.code||t.code!=RKt.CANCELLED)throw t=t||"Response processing failed.",new Error(t)})))}_updateScene(){const t=this._currentStep;t&&t.mesh&&(this._meshViewer.updateScene(t,this),this._cameraPositionInitialized||(this._meshViewer.resetView(),this._cameraPositionInitialized=!0),this._meshViewerAttached||(this.shadowRoot.appendChild(this._meshViewer.getRenderer().domElement),this._meshViewerAttached=!0))}_debouncedFetchMesh(){this.debounce("fetchMesh",(()=>this._maybeFetchMesh()),100)}_maybeFetchMesh(){return n(this,void 0,void 0,(function*(){const t=this._currentStep;if(t&&!t.mesh&&!t.meshFetching){t.meshFetching=!0,this._isMeshLoading=!0;try{const e=yield this._dataProvider.fetchData(t,this.run,this.tag,this.sample);t.mesh=e[0],this.notifyPath("_currentStep.mesh")}catch(t){if(!t||!t.code||t.code!=RKt.CANCELLED)throw t=t||"Response processing failed.",new Error(t)}finally{this._isMeshLoading=!1,t.meshFetching=!1}}}))}_onCameraPositionChange(){if(!this._meshViewer.isReady())return;const t=new CustomEvent("camera-position-change",{detail:this._meshViewer.getCameraPosition()});this.dispatchEvent(t)}setCameraViewpoint(t,e,n){this._meshViewer.setCameraViewpoint(t,e,n)}_updateCanvasSize(){const t=this.offsetWidth,e=t,n=this.$$(".tf-mesh-loader-header").offsetHeight;this._meshViewer.setCanvasSize({width:t,height:e-n})}redraw(){this._updateCanvasSize(),this.isConnected&&this._meshViewer.draw()}_hasAtLeastOneStep(t){return!!t&&t.length>0}_hasMultipleSteps(t){return!!t&&t.length>1}get _currentStep(){return this._steps[this._stepIndex]||null}get _stepValue(){const t=this._currentStep;return t?t.step:0}get _currentWallTime(){const t=this._currentStep;return t?KR(t.wall_time):""}_getMaxStepIndex(t){return t.length-1}_getSampleText(t){return String(t+1)}_hasMultipleSamples(t){return t>1}_updateView(){this._meshViewer&&"all"==this.selectedView&&this._meshViewer.resetView()}toLocaleString_(t){return t.toLocaleString()}};I7t.template=_e`
    <tf-card-heading color="[[_runColor]]" class="tf-mesh-loader-header">
      <template is="dom-if" if="[[_hasMultipleSamples(ofSamples)]]">
        <div>sample: [[_getSampleText(sample)]] of [[ofSamples]]</div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep(_steps)]]">
        <div class="heading-row">
          <div class="heading-label">
            step
            <span style="font-weight: bold"
              >[[toLocaleString_(_stepValue)]]</span
            >
          </div>
          <div class="heading-label heading-right">
            <template is="dom-if" if="[[_currentWallTime]]">
              [[_currentWallTime]]
            </template>
          </div>
          <div class="label right">
            <paper-spinner-lite active hidden$="[[!_isMeshLoading]]">
            </paper-spinner-lite>
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps(_steps)]]">
        <div>
          <paper-slider
            id="steps"
            immediate-value="{{_stepIndex}}"
            max="[[_getMaxStepIndex(_steps)]]"
            max-markers="[[_getMaxStepIndex(_steps)]]"
            snaps
            step="1"
            value="{{_stepIndex}}"
          ></paper-slider>
        </div>
      </template>
    </tf-card-heading>
    <style>
      paper-slider {
        width: 100%;
        margin-left: 1px;
        margin-right: 1px;
      }
      .tf-mesh-loader-header {
        display: block;
        height: 105px;
      }
      [hidden] {
        display: none;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],I7t.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],I7t.prototype,"tag",void 0),t([o({type:Number}),e("design:type",Number)],I7t.prototype,"sample",void 0),t([o({type:Number}),e("design:type",Number)],I7t.prototype,"ofSamples",void 0),t([o({type:String}),e("design:type",String)],I7t.prototype,"selectedView",void 0),t([o({type:Boolean}),e("design:type",Boolean)],I7t.prototype,"active",void 0),t([o({type:Object}),e("design:type",dr)],I7t.prototype,"requestManager",void 0),t([o({type:Object}),e("design:type",N7t)],I7t.prototype,"_meshViewer",void 0),t([o({type:Object}),e("design:type",DKt)],I7t.prototype,"_dataProvider",void 0),t([o({type:Object}),e("design:type",Object)],I7t.prototype,"_colorScaleFunction",void 0),t([o({type:Array,notify:!0}),e("design:type",Array)],I7t.prototype,"_steps",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],I7t.prototype,"_stepIndex",void 0),t([o({type:Boolean}),e("design:type",Boolean)],I7t.prototype,"_meshViewerAttached",void 0),t([o({type:Boolean}),e("design:type",Boolean)],I7t.prototype,"_cameraPositionInitialized",void 0),t([o({type:Boolean}),e("design:type",Boolean)],I7t.prototype,"_isMeshLoading",void 0),t([s("run"),e("design:type",String),e("design:paramtypes",[])],I7t.prototype,"_runColor",null),t([a("run","tag","active","_dataProvider","_meshViewer"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],I7t.prototype,"reload",null),t([a("_currentStep.*","_meshViewer"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],I7t.prototype,"_updateScene",null),t([a("_currentStep"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],I7t.prototype,"_debouncedFetchMesh",null),t([s("_steps","_stepIndex"),e("design:type",Object),e("design:paramtypes",[])],I7t.prototype,"_currentStep",null),t([s("_currentStep"),e("design:type",Number),e("design:paramtypes",[])],I7t.prototype,"_stepValue",null),t([s("_currentStep"),e("design:type",String),e("design:paramtypes",[])],I7t.prototype,"_currentWallTime",null),t([a("selectedView"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],I7t.prototype,"_updateView",null),I7t=t([i("tf-mesh-loader")],I7t);let R7t=class extends ye{constructor(){super(),this.reloadOnReady=!0,this._tagFilter=".*",this._selectedView="all",this._requestManager=new dr,window.addEventListener("resize",(()=>{this._handleWindowResize()}),!1),this.reloadOnReady&&this.reload()}_getAllChildren(){return Array.from(this.shadowRoot.querySelectorAll("tf-mesh-loader"))}_onCameraPositionChanged(t){"share"==this._selectedView&&this._getAllChildren().forEach((e=>{t.target!=e&&e.setCameraViewpoint(t.detail.position,t.detail.far,t.detail.target)}))}_shouldOpen(t){return t<=2}reload(){this._fetchTags().then(this._reloadMeshes.bind(this))}_handleWindowResize(){this._getAllChildren().forEach((t=>{t.redraw()}))}_fetchTags(){const t=_r().pluginRoute("mesh","/tags");return this._requestManager.request(t).then((t=>{if(Se.exports.isEqual(t,this._runToTagInfo))return;const e=ar(Se.exports.mapValues(t,(t=>Object.keys(t))));this._dataNotFound=0===e.length,this._runToTagInfo=t}))}_reloadMeshes(){this._getAllChildren().forEach((t=>{t.reload()}))}get _categories(){var t=this._runToTagInfo,e=this._selectedRuns,n=this._tagFilter;function i(e){const n=t[e.run][e.tag].samples;return Se.exports.range(n).map((t=>Object.assign({},e,{sample:t,ofSamples:n})))}return Mr(Se.exports.mapValues(t,(t=>Object.keys(t))),e,n).map((t=>Object.assign({},t,{items:[].concat.apply([],t.items.map(i))})))}};R7t.template=_e`
    <tf-dashboard-layout>
      <div slot="sidebar" class="all-controls">
        <div class="settings">
          <div class="sidebar-section view-control">
            <h3 class="title">Point of view</h3>
            <div>
              <paper-radio-group
                id="view-radio-group"
                selected="{{_selectedView}}"
              >
                <paper-radio-button id="all-radio-button" name="all">
                  Display all points
                </paper-radio-button>
                <paper-tooltip
                  animation-delay="0"
                  for="all-radio-button"
                  position="right"
                  offset="0"
                >
                  Zoom and center camera to display all points at once. Note,
                  that some points could be too far (i.e. too small) to be
                  visible.
                </paper-tooltip>
                <paper-radio-button id="user-radio-button" name="user">
                  Current view
                </paper-radio-button>
                <paper-tooltip
                  animation-delay="0"
                  for="user-radio-button"
                  position="right"
                  offset="0"
                >
                  Keep current camera position and zoom level.
                </paper-tooltip>
                <paper-radio-button id="share-radio-button" name="share">
                  Share viewpoint
                </paper-radio-button>
                <paper-tooltip
                  animation-delay="0"
                  for="share-radio-button"
                  position="right"
                  offset="0"
                >
                  Share viewpoint among all cameras.
                </paper-tooltip>
              </paper-radio-group>
            </div>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No point cloud data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any point cloud data to your event files.
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
            >
              <template>
                <tf-mesh-loader
                  active="[[active]]"
                  selected-view="[[_selectedView]]"
                  run="[[item.run]]"
                  tag="[[item.tag]]"
                  sample="[[item.sample]]"
                  of-samples="[[item.ofSamples]]"
                  request-manager="[[_requestManager]]"
                  class="tf-mesh-loader-container"
                  on-camera-position-change="_onCameraPositionChanged"
                >
                </tf-mesh-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      paper-radio-button {
        display: block;
        padding: 5px;
      }
      .sidebar-section h3 {
        margin: 0;
        font-weight: normal;
        font-size: 14px;
        margin-bottom: 5px;
      }

      .runs-selector {
        flex-grow: 1;
      }

      tf-runs-selector {
        display: flex;
      }

      .view-control {
        display: block !important;
      }

      .view-control h3.title {
        padding-top: 16px;
        padding-bottom: 16px;
      }

      .allcontrols .view-control paper-radio-group {
        margin-top: 5px;
      }
      /* Layout must be horizontal, i.e. items arranged in a row. If items cannot fit in a row,
       * they should be moved to next line. All items must be square at all times. Minimum size of
       * the item is 480px. This means that maximum size of the item must be 480px + 479px = 959px.
       * */
      .horizontal {
        display: flex;
        flex-direction: row;
        flex-wrap: wrap;
      }
      tf-mesh-loader {
        width: 480px;
        flex-basis: 480px;
        flex-grow: 1;
        display: block;
      }
    </style>
  `,t([o({type:Boolean}),e("design:type",Boolean)],R7t.prototype,"reloadOnReady",void 0),t([o({type:Array}),e("design:type",Array)],R7t.prototype,"_selectedRuns",void 0),t([o({type:Object}),e("design:type",Object)],R7t.prototype,"_runToTagInfo",void 0),t([o({type:Boolean}),e("design:type",Boolean)],R7t.prototype,"_dataNotFound",void 0),t([o({type:String}),e("design:type",String)],R7t.prototype,"_tagFilter",void 0),t([o({type:String,notify:!0}),e("design:type",String)],R7t.prototype,"_selectedView",void 0),t([o({type:Object}),e("design:type",Object)],R7t.prototype,"_requestManager",void 0),t([s("_runToTagInfo","_selectedRuns","_tagFilter"),e("design:type",Array),e("design:paramtypes",[])],R7t.prototype,"_categories",null),R7t=t([i("mesh-dashboard"),e("design:paramtypes",[])],R7t);let O7t=class extends ye{constructor(){super(...arguments),this._expanded=!1,this._runToPrCurveEntry={},this._previousRunToPrCurveEntry={},this._colorScaleFunction={scale:GR},this._canceller=new XR,this._xComponentsCreationMethod=()=>{const t=new rO.Scales.Linear;return{scale:t,axis:new rO.Axes.Numeric(t,"bottom"),accessor:t=>t.recall}},this._yValueAccessor=t=>t.precision,this._tooltipColumns=(()=>{const t=wTt(4),e=e=>isNaN(e)?"NaN":t(e);return[{title:"Run",evaluate:t=>t.dataset.metadata().name},{title:"Threshold",evaluate:t=>e(t.datum.thresholds)},{title:"Precision",evaluate:t=>e(t.datum.precision)},{title:"Recall",evaluate:t=>e(t.datum.recall)},{title:"TP",evaluate:t=>t.datum.true_positives},{title:"FP",evaluate:t=>t.datum.false_positives},{title:"TN",evaluate:t=>t.datum.true_negatives},{title:"FN",evaluate:t=>t.datum.false_negatives}]})(),this._seriesDataFields=["thresholds","precision","recall","true_positives","false_positives","true_negatives","false_negatives"],this._defaultXRange=[-.05,1.05],this._defaultYRange=[-.05,1.05],this._requestData=(t,e,n)=>{const i=_r().pluginRoute("pr_curves","/pr_curves");Promise.all(t.map((t=>{const n=iO(i,{tag:this.tag,run:t});return this.requestManager.request(n).then((n=>{e({item:t,data:n})}))}))).finally((()=>{n()}))},this._smoothingEnabled=!1}_createProcessDataFunction(){return(t,e,n)=>{this.set("_runToDataOverTime",Object.assign({},this._runToDataOverTime,n))}}_computeRunColor(t){return GR(t)}connectedCallback(){super.connectedCallback(),this._attached=!0,this.reload()}_getChartDataLoader(){return this.shadowRoot.querySelector("tf-line-chart-data-loader")}reload(){this._attached&&(0!==this.runs.length?this._getChartDataLoader().reload():this.set("_runToDataOverTime",{}))}_setChartData(){var t=this._runToPrCurveEntry,e=this._previousRunToPrCurveEntry,n=this._setOfRelevantRuns;Se.exports.forOwn(t,((i,r)=>{const o=e[r];o&&t[r].step===o.step||(n[r]?this._updateSeriesDataForRun(r,i):this._clearSeriesData(r))}))}_updateSeriesDataForRun(t,e){const n=Se.exports.reduce(this._seriesDataFields,((t,n)=>(t[n]=e[n].slice().reverse(),t)),{}),i=new Array(n[this._seriesDataFields[0]].length);for(let t=0;t<i.length;t++)i[t]=Se.exports.mapValues(n,(e=>e[t]));const r=this._getChartDataLoader();r.setSeriesData(t,i),r.commitChanges()}_clearSeriesData(t){const e=this._getChartDataLoader();e.setSeriesData(t,[]),e.commitChanges()}_updateRunToPrCurveEntry(){var t=this.runToStepCap;const e={};Se.exports.forOwn(this._runToDataOverTime,((n,i)=>{n&&n.length&&(e[i]=this._computeEntryClosestOrEqualToStepCap(t[i],n))})),this.set("_previousRunToPrCurveEntry",this._runToPrCurveEntry),this.set("_runToPrCurveEntry",e)}_notifyDataChange(){this.onDataChange&&this.onDataChange(this._runToDataOverTime)}_computeEntryClosestOrEqualToStepCap(t,e){const n=Math.min(Se.exports.sortedIndex(e.map((t=>t.step)),t),e.length-1);return e[n]}get _runsWithStepAvailable(){var t=this._runToPrCurveEntry;return Se.exports.filter(this.runs,(e=>t[e])).sort()}get _setOfRelevantRuns(){const t={};return Se.exports.forEach(this._runsWithStepAvailable,(e=>{t[e]=!0})),t}_computeCurrentStepForRun(t,e){const n=t[e];return n?n.step:null}_computeCurrentWallTimeForRun(t,e){const n=t[e];return n?new Date(1e3*n.wall_time).toString():null}_toggleExpanded(t){this.set("_expanded",!this._expanded),this.redraw()}_resetDomain(){this._getChartDataLoader().resetDomain()}redraw(){this._getChartDataLoader().redraw()}};O7t.template=_e`
    <tf-card-heading
      tag="[[tag]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
    ></tf-card-heading>

    <tf-line-chart-data-loader
      x-components-creation-method="[[_xComponentsCreationMethod]]"
      y-value-accessor="[[_yValueAccessor]]"
      tooltip-columns="[[_tooltipColumns]]"
      color-scale="[[_colorScaleFunction]]"
      default-x-range="[[_defaultXRange]]"
      default-y-range="[[_defaultYRange]]"
      smoothing-enabled="[[_smoothingEnabled]]"
      request-manager="[[requestManager]]"
      data-to-load="[[runs]]"
      data-series="[[runs]]"
      load-key="[[tag]]"
      request-data="[[_requestData]]"
      load-data-callback="[[_createProcessDataFunction()]]"
      active="[[active]]"
    ></tf-line-chart-data-loader>

    <div id="buttons-row">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
      <paper-icon-button
        icon="settings-overscan"
        on-tap="_resetDomain"
        title="Reset axes to [0, 1]."
      ></paper-icon-button>
    </div>

    <div id="step-legend">
      <template is="dom-repeat" items="[[_runsWithStepAvailable]]" as="run">
        <div class="legend-row">
          <div
            class="color-box"
            style="background: [[_computeRunColor(run)]];"
          ></div>
          [[run]] is at
          <span class="step-label-text">
            step [[_computeCurrentStepForRun(_runToPrCurveEntry, run)]] </span
          ><br />
          <span class="wall-time-label-text">
            ([[_computeCurrentWallTimeForRun(_runToPrCurveEntry, run)]])
          </span>
        </div>
      </template>
    </div>

    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 500px;
        margin-right: 10px;
        margin-bottom: 25px;
      }
      :host([_expanded]) {
        width: 100%;
      }
      tf-line-chart-data-loader {
        height: 300px;
        position: relative;
      }
      :host([_expanded]) tf-line-chart-data-loader {
        height: 600px;
      }
      #buttons-row {
        display: flex;
        flex-direction: row;
      }
      #buttons-row paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }
      #buttons-row paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }
      #step-legend {
        box-sizing: border-box;
        font-size: 0.8em;
        max-height: 200px;
        overflow-y: auto;
        padding: 0 0 0 10px;
        width: 100%;
      }
      .legend-row {
        margin: 5px 0 5px 0;
        width: 100%;
      }
      .color-box {
        display: inline-block;
        border-radius: 1px;
        width: 10px;
        height: 10px;
      }
      .step-label-text {
        font-weight: bold;
      }
      .wall-time-label-text {
        color: #888;
        font-size: 0.8em;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],O7t.prototype,"runs",void 0),t([o({type:String}),e("design:type",String)],O7t.prototype,"tag",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"tagMetadata",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"runToStepCap",void 0),t([o({type:Object}),e("design:type",dr)],O7t.prototype,"requestManager",void 0),t([o({type:Boolean}),e("design:type",Boolean)],O7t.prototype,"active",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],O7t.prototype,"_expanded",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"_runToPrCurveEntry",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"_previousRunToPrCurveEntry",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"_runToDataOverTime",void 0),t([o({type:Object}),e("design:type",Function)],O7t.prototype,"onDataChange",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"_colorScaleFunction",void 0),t([o({type:Object}),e("design:type",XR)],O7t.prototype,"_canceller",void 0),t([o({type:Boolean}),e("design:type",Boolean)],O7t.prototype,"_attached",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"_xComponentsCreationMethod",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"_yValueAccessor",void 0),t([o({type:Array}),e("design:type",Array)],O7t.prototype,"_tooltipColumns",void 0),t([o({type:Array}),e("design:type",Array)],O7t.prototype,"_seriesDataFields",void 0),t([o({type:Array}),e("design:type",Array)],O7t.prototype,"_defaultXRange",void 0),t([o({type:Array}),e("design:type",Array)],O7t.prototype,"_defaultYRange",void 0),t([o({type:Object}),e("design:type",Function)],O7t.prototype,"_requestData",void 0),t([o({type:Boolean}),e("design:type",Boolean)],O7t.prototype,"_smoothingEnabled",void 0),t([a("runs","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],O7t.prototype,"reload",null),t([a("_runToPrCurveEntry","_previousRunToPrCurveEntry","_setOfRelevantRuns"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],O7t.prototype,"_setChartData",null),t([a("_runToDataOverTime","runToStepCap"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],O7t.prototype,"_updateRunToPrCurveEntry",null),t([a("_runToDataOverTime"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],O7t.prototype,"_notifyDataChange",null),t([s("runs","_runToPrCurveEntry"),e("design:type",Array),e("design:paramtypes",[])],O7t.prototype,"_runsWithStepAvailable",null),t([s("_runsWithStepAvailable"),e("design:type",Object),e("design:paramtypes",[])],O7t.prototype,"_setOfRelevantRuns",null),O7t=t([i("tf-pr-curve-card")],O7t);let z7t=class extends ye{constructor(){super(...arguments),this._runToStepIndex={}}_computeColorForRun(t){return GR(t)}_computeTimeTextForRun(t,e,n,i){const r=e[n];if(!Se.exports.isNumber(r))return"";const o=t[n];if(!o)return"";const a=o[r][i];if("step"===i)return`step ${a}`;if("relative"===i)return a<1?`${(1e3*a).toFixed(2)} ms`:`${a.toFixed(2)} s`;if("wall_time"===i)return new Date(1e3*a).toString();throw new Error(`The display type of ${i} is not recognized.`)}_sliderValueChanged(t){const e=t.target.dataset.run,n=t.target.immediateValue,i=Object.assign({},this._runToStepIndex);isNaN(n)?delete i[e]:i[e]=t.target.immediateValue,this._runToStepIndex=i}_computeMaxStepIndexForRun(t,e){const n=t[e];return n&&n.length?n.length-1:0}_updateStepsForNewRuns(){var t=this.runToAvailableTimeEntries;const e=Object.assign({},this._runToStepIndex);Se.exports.forOwn(t,((t,n)=>{Se.exports.isNumber(e[n])||(e[n]=t.length-1)})),this._runToStepIndex=e}_getStep(t,e){return this._runToStepIndex?this._runToStepIndex[e]:0}_computeRunToStep(t,e){const n={};return Se.exports.forOwn(e,((e,i)=>{const r=t[i];r&&(n[i]=r[e].step)})),n}get _runsWithSliders(){var t=this.runToAvailableTimeEntries;return this.runs.filter((e=>t[e]))}};z7t.template=_e`
    <template is="dom-repeat" items="[[_runsWithSliders]]" as="run">
      <div class="run-widget">
        <div class="run-display-container">
          <div
            class="run-color-box"
            style="background:[[_computeColorForRun(run)]];"
          ></div>
          <div class="run-text">[[run]]</div>
        </div>
        <div class="step-display-container">
          [[_computeTimeTextForRun(runToAvailableTimeEntries, _runToStepIndex,
          run, timeDisplayType)]]
        </div>
        <paper-slider
          data-run$="[[run]]"
          step="1"
          type="number"
          min="0"
          max="[[_computeMaxStepIndexForRun(runToAvailableTimeEntries, run)]]"
          value="[[_getStep(_runToStepIndex, run)]]"
          on-immediate-value-changed="_sliderValueChanged"
        ></paper-slider>
      </div>
    </template>
    <style>
      .run-widget {
        margin: 10px 0 0 0;
      }
      paper-slider {
        margin: -8px 0 0 -15px;
        width: 100%;
      }
      .step-display-container {
        font-size: 0.9em;
        margin: 0 15px 0 0;
      }
      .run-text {
        display: inline-block;
      }
      .run-color-box {
        width: 12px;
        height: 12px;
        border-radius: 3px;
        display: inline-block;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],z7t.prototype,"runs",void 0),t([o({type:Object}),e("design:type",Object)],z7t.prototype,"runToAvailableTimeEntries",void 0),t([o({type:Object,notify:!0,computed:"_computeRunToStep(runToAvailableTimeEntries, _runToStepIndex)"}),e("design:type",Object)],z7t.prototype,"runToStep",void 0),t([o({type:String}),e("design:type",String)],z7t.prototype,"timeDisplayType",void 0),t([o({type:Object}),e("design:type",Object)],z7t.prototype,"_runToStepIndex",void 0),t([a("runToAvailableTimeEntries"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],z7t.prototype,"_updateStepsForNewRuns",null),t([s("runs","runToAvailableTimeEntries"),e("design:type",Array),e("design:paramtypes",[])],z7t.prototype,"_runsWithSliders",null),z7t=t([i("tf-pr-curve-steps-selector")],z7t);let D7t=class extends(er(ye)){constructor(){super(...arguments),this.reloadOnReady=!0,this._timeDisplayType="step",this._selectedRuns=[],this._runToTagInfo={},this._tagToRunToData={},this._getCategoryItemKey=t=>t.tag,this._requestManager=new dr,this._step=0}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){Promise.all([this._fetchTags()]).then((()=>{this._reloadCards()}))}_shouldOpen(t){return t<=2}_fetchTags(){const t=_r().pluginRoute("pr_curves","/tags");return this._requestManager.request(t).then((t=>{if(Se.exports.isEqual(t,this._runToTagInfo))return;const e=ar(Se.exports.mapValues(t,(t=>Se.exports.keys(t))));this.set("_dataNotFound",0===e.length),this.set("_runToTagInfo",t),this.async((()=>{this.set("_categoriesDomReady",!0)}))}))}_reloadCards(){Se.exports.forEach(this.root.querySelectorAll("tf-pr-curve-card"),(t=>{t.reload()}))}get _categories(){var t=this._selectedRuns,e=this._tagFilter;return wr(Se.exports.mapValues(this._runToTagInfo,(t=>Object.keys(t))),t,e)}get _relevantSelectedRuns(){var t=this._runToTagInfo;return this._selectedRuns.filter((e=>t[e]))}_tagMetadata(t,e,n){const i={};e.forEach((e=>{i[e]=t[e][n]}));const r=n.replace(/\/pr_curves$/,"");return eO(i,r)}_createDataChangeCallback(t){return e=>{this.set("_tagToRunToData",Object.assign(Object.assign({},this._tagToRunToData),{[t]:e}))}}get _runToAvailableTimeEntries(){var t=this._tagToRunToData;const e={};for(const[n,i]of Object.entries(t))for(const[t]of Object.entries(i))(null==e[t]||n<e[t])&&(e[t]=n);const n={};for(const[i,r]of Object.entries(e)){const e=t[r][i];n[i]=e.map((t=>({step:t.step,wall_time:t.wall_time,relative:t.wall_time-e[0].wall_time})))}return n}};D7t.template=_e`
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="settings">
          <div class="sidebar-section">
            <tf-option-selector
              id="time-type-selector"
              name="Time Display Type"
              selected-id="{{_timeDisplayType}}"
            >
              <paper-button id="step">step</paper-button>
              <!--
            -->
              <paper-button id="relative">relative</paper-button>
              <!--
            -->
              <paper-button id="wall_time">wall</paper-button>
            </tf-option-selector>
          </div>
          <template is="dom-if" if="[[_runToAvailableTimeEntries]]">
            <div class="sidebar-section" id="steps-selector-container">
              <tf-pr-curve-steps-selector
                runs="[[_relevantSelectedRuns]]"
                run-to-step="{{_runToStep}}"
                run-to-available-time-entries="[[_runToAvailableTimeEntries]]"
                time-display-type="[[_timeDisplayType]]"
              >
              </tf-pr-curve-steps-selector>
            </div>
          </template>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No precision–recall curve data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any precision–recall data to your event
                files.
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>
            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
              get-category-item-key="[[_getCategoryItemKey]]"
            >
              <template>
                <tf-pr-curve-card
                  active="[[active]]"
                  runs="[[item.runs]]"
                  tag="[[item.tag]]"
                  tag-metadata="[[_tagMetadata(_runToTagInfo, item.runs, item.tag)]]"
                  request-manager="[[_requestManager]]"
                  run-to-step-cap="[[_runToStep]]"
                  on-data-change="[[_createDataChangeCallback(item.tag)]]"
                ></tf-pr-curve-card>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }

      /** Do not let the steps selector occlude the run selector. */
      #steps-selector-container {
        max-height: 60%;
        overflow-y: auto;
      }
    </style>
  `,t([o({type:Boolean}),e("design:type",Boolean)],D7t.prototype,"reloadOnReady",void 0),t([o({type:String}),e("design:type",String)],D7t.prototype,"_timeDisplayType",void 0),t([o({type:Array}),e("design:type",Array)],D7t.prototype,"_selectedRuns",void 0),t([o({type:Object}),e("design:type",Object)],D7t.prototype,"_runToTagInfo",void 0),t([o({type:Object}),e("design:type",Object)],D7t.prototype,"_tagToRunToData",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],D7t.prototype,"_runToStep",void 0),t([o({type:Boolean}),e("design:type",Boolean)],D7t.prototype,"_dataNotFound",void 0),t([o({type:String}),e("design:type",String)],D7t.prototype,"_tagFilter",void 0),t([o({type:Boolean}),e("design:type",Boolean)],D7t.prototype,"_categoriesDomReady",void 0),t([o({type:Object}),e("design:type",Object)],D7t.prototype,"_getCategoryItemKey",void 0),t([o({type:Object}),e("design:type",dr)],D7t.prototype,"_requestManager",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],D7t.prototype,"_step",void 0),t([s("_runToTagInfo","_selectedRuns","_tagFilter","_categoriesDomReady"),e("design:type",Array),e("design:paramtypes",[])],D7t.prototype,"_categories",null),t([s("_selectedRuns","_runToTagInfo"),e("design:type",Array),e("design:paramtypes",[])],D7t.prototype,"_relevantSelectedRuns",null),t([s("_tagToRunToData"),e("design:type",Object),e("design:paramtypes",[])],D7t.prototype,"_runToAvailableTimeEntries",null),D7t=t([i("tf-pr-curve-dashboard")],D7t);let B7t=class extends(er(ye)){constructor(){super(...arguments),this._installCommand="pip install -U tensorboard-plugin-profile"}_copyInstallCommand(){return n(this,void 0,void 0,(function*(){const t=()=>n(this,void 0,void 0,(function*(){this.$.commandTextarea.select();try{yield navigator.clipboard.writeText(this._installCommand)}catch(t){if(!document.execCommand("copy"))return Promise.reject()}})),e=this.$.copiedMessage;try{yield t(),e.innerText="Copied."}catch(t){e.innerText="Failed to copy to clipboard."}}))}_removeCopiedMessage(){this.$.copiedMessage.innerText=""}};B7t.template=_e`
    <div class="message">
      <h3>The profile plugin has moved.</h3>
      <p>
        Please install the new version of the profile plugin from PyPI by
        running the following command from the machine running TensorBoard:
      </p>
      <textarea
        id="commandTextarea"
        readonly=""
        rows="1"
        on-blur="_removeCopiedMessage"
      >
[[_installCommand]]</textarea
      >
      <div id="copyContainer">
        <span id="copiedMessage"></span>
        <paper-button raised="" on-tap="_copyInstallCommand"
          >Copy to clipboard</paper-button
        >
      </div>
    </div>

    <style>
      :host {
        display: flex;
      }

      .message {
        margin: 80px auto 0 auto;
        max-width: 540px;
      }
      #commandTextarea {
        margin-top: 1ex;
        padding: 1ex 1em;
        resize: vertical;
        width: 100%;
      }
      #copyContainer {
        display: flex;
      }
      #copiedMessage {
        align-self: center;
        flex-grow: 1;
        font-style: italic;
        padding-right: 1em;
        text-align: right;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],B7t.prototype,"_installCommand",void 0),B7t=t([i("tf-profile-redirect-dashboard")],B7t);let H7t=class extends(er(hl)){constructor(){super(...arguments),this.reloadOnReady=!0,this._showDownloadLinks=Hs("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}).call(this),this._smoothingWeight=Gs("_smoothingWeight",{defaultValue:.6}).call(this),this._ignoreYOutliers=Hs("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0}).call(this),this._xType=xTt.STEP,this._selectedRuns=[],this._tagFilter="",this._categories=[],this._getCategoryItemKey=t=>t.tag,this._requestManager=new dr(50),this._showDownloadLinksObserver=Fs("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}),this._smoothingWeightObserver=Ws("_smoothingWeight",{defaultValue:.6}),this._ignoreYOutliersObserver=Fs("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0})}get _smoothingEnabled(){return this._smoothingWeight>0}_getCategoryKey(t){return t.metadata.type==br.SEARCH_RESULTS?"":t.name}_shouldOpen(t){return t<=2}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){this._fetchTags().then((()=>{this._reloadCharts()}))}_fetchTags(){const t=_r().pluginRoute("scalars","/tags");return this._requestManager.request(t).then((t=>{if(Se.exports.isEqual(t,this._runToTagInfo))return;const e=ar(Se.exports.mapValues(t,(t=>Object.keys(t))));this.set("_dataNotFound",0===e.length),this.set("_runToTagInfo",t),this.async((()=>{this.set("_categoriesDomReady",!0)}))}))}_reloadCharts(){this.root.querySelectorAll("tf-scalar-card").forEach((t=>{t.reload()}))}_updateCategories(){var t=this._selectedRuns;let e,n=this._tagFilter;e=wr(Se.exports.mapValues(this._runToTagInfo,(t=>Object.keys(t))),t,n),e.forEach((t=>{t.items=t.items.map((t=>({tag:t.tag,series:t.runs.map((e=>({run:e,tag:t.tag})))})))})),this.updateArrayProp("_categories",e,this._getCategoryKey)}_tagMetadata(t,e,n){const i=t.name,r=n.tag,o={};n.series.forEach((({run:t})=>{o[t]=e[t][r]}));const a=r.replace(/\/scalar_summary$/,"");let{description:s,displayName:l}=eO(o,a);return t.metadata.type==br.PREFIX_GROUP&&l.startsWith(i+"/")&&(l=l.slice(i.length+1)),{description:s,displayName:l}}};H7t.template=_e`
    <tf-dashboard-layout>
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            <div class="line-item">
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                checked="{{_ignoreYOutliers}}"
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              >
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                  smoothing-enabled="[[_smoothingEnabled]]"
                  smoothing-weight="[[_smoothingWeight]]"
                  tag-metadata="[[_tagMetadata(category, _runToTagInfo, item)]]"
                  tag="[[item.tag]]"
                  tooltip-sorting-method="[[_tooltipSortingMethod]]"
                  x-type="[[_xType]]"
                  batch-size="[[featureFlags.scalarsBatchSize]]"
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                ></tf-scalar-card>
              </template>
            </tf-category-paginated-view>
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        </template>
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        margin-top: 15px;
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    </style>
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    </tf-card-heading>
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      elevation="1"
      id="steps-container"
      class="container scrollbar"
    >
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        </paper-material>
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    <style include="scrollbar-style"></style>
    <style>
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        display: flex;
        flex-direction: column;
        width: 100%;
        height: auto;
        margin-right: 10px;
        margin-bottom: 15px;
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        will-change: transform;
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      #steps-container {
        border-radius: 3px;
        border: 2px solid /* color computed and set as inline style */;
        display: block;
        max-height: 500px;
        overflow: auto;
        padding: 10px;
        border-color: var(--tb-text-loader-outline);
      }
      .text {
        background-color: inherit;
        border-radius: 0 3px 3px 3px;
        padding: 5px;
        word-break: break-word;
      }
      .step-container {
        background-color: var(--tb-ui-light-accent);
        border-bottom: none;
        border-radius: 3px 3px 0 0;
        border: 1px solid var(--tb-ui-border);
        display: inline-block;
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        font-style: italic;
        margin-left: -1px; /* to correct for border */
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      }
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        margin-top: 15px;
      }

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          <tf-runs-selector selected-runs="{{_selectedRuns}}">
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            <p>Probable causes:</p>
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            <p>
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                  run="[[item.run]]"
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!(function(){
/*! *****************************************************************************
    Copyright (c) Microsoft Corporation.

    Permission to use, copy, modify, and/or distribute this software for any
    purpose with or without fee is hereby granted.

    THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
    REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
    AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
    INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
    LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
    OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
    PERFORMANCE OF THIS SOFTWARE.
    ***************************************************************************** */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const ea=-1;class na{constructor(t,e,n){this.factory=t,this.resolving=!1,ngDevMode&&hn(t,"Factory not specified"),ngDevMode&&ln(typeof t,"function","Expected factory function."),this.canSeeViewProviders=e,this.injectImpl=n}}function oa(t){let e="";return 1&t&&(e+="|Text"),2&t&&(e+="|Element"),4&t&&(e+="|Container"),8&t&&(e+="|ElementContainer"),16&t&&(e+="|Projection"),32&t&&(e+="|IcuContainer"),64&t&&(e+="|Placeholder"),e.length>0?e.substring(1):e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function ia(t,e,n){hn(t,"should be called with a TNode"),0==(t.type&e)&&bn(n||`Expected [${oa(e)}] but got ${oa(t.type)}.`)}function aa(t,e,n){const o=$o(t);let i=0;for(;i<n.length;){const a=n[i];if("number"==typeof a){if(0!==a)break;i++;const r=n[i++],s=n[i++],l=n[i++];ngDevMode&&ngDevMode.rendererSetAttribute++,o?t.setAttribute(e,s,l,r):e.setAttributeNS(r,s,l)}else{const r=a,s=n[++i];ngDevMode&&ngDevMode.rendererSetAttribute++,sa(r)?o&&t.setProperty(e,r,s):o?t.setAttribute(e,r,s):e.setAttribute(r,s),i++}}return i}function ra(t){return 3===t||4===t||6===t}function sa(t){return 64===t.charCodeAt(0)}function la(t,e){if(null===e||0===e.length);else if(null===t||0===t.length)t=e.slice();else{let n=-1;for(let o=0;o<e.length;o++){const i=e[o];"number"==typeof i?n=i:0===n||ca(t,n,i,null,-1===n||2===n?e[++o]:null)}}return t}function ca(t,e,n,o,i){let a=0,r=t.length;if(-1===e)r=-1;else for(;a<t.length;){const n=t[a++];if("number"==typeof n){if(n===e){r=-1;break}if(n>e){r=a-1;break}}}for(;a<t.length;){const e=t[a];if("number"==typeof e)break;if(e===n){if(null===o)return void(null!==i&&(t[a+1]=i));if(o===t[a+1])return void(t[a+2]=i)}a++,null!==o&&a++,null!==i&&a++}-1!==r&&(t.splice(r,0,e),a=r+1),t.splice(a++,0,n),null!==o&&t.splice(a++,0,o),null!==i&&t.splice(a++,0,i)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function da(t){return t!==ea}function pa(t){return ngDevMode&&an(t,"Number expected"),ngDevMode&&cn(t,-1,"Not a valid state."),ngDevMode&&fn(32767&t,go,"Parent injector must be pointing past HEADER_OFFSET."),32767&t}function ma(t,e){let n=(function o(t){return t>>16})(t),i=e;for(;n>0;)i=i[15],n--;return i}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let ua=!0;function fa(t){const e=ua;return ua=t,e}let ga=0;function ha(t,e){const n=ya(t,e);if(-1!==n)return n;const o=e[1];o.firstCreatePass&&(t.injectorIndex=e.length,ba(o.data,t),ba(e,null),ba(o.blueprint,null));const i=_a(t,e),a=t.injectorIndex;if(da(i)){const t=pa(i),n=ma(i,e),o=n[1].data;for(let i=0;i<8;i++)e[a+i]=n[t+i]|o[t+i]}return e[a+8]=i,a}function ba(t,e){t.push(0,0,0,0,0,0,0,0,e)}function ya(t,e){return-1===t.injectorIndex||t.parent&&t.parent.injectorIndex===t.injectorIndex||null===e[t.injectorIndex+8]?-1:(ngDevMode&&_n(e,t.injectorIndex),t.injectorIndex)}function _a(t,e){if(t.parent&&-1!==t.parent.injectorIndex)return t.parent.injectorIndex;let n=0,o=null,i=e;for(;null!==i;){const t=i[1],e=t.type;if(2===e?(ngDevMode&&hn(t.declTNode,"Embedded TNodes should have declaration parents."),o=t.declTNode):1===e?o=i[6]:(ngDevMode&&ln(t.type,0,"Root type expected"),o=null),null===o)return ea;if(ngDevMode&&o&&Oo(o,i[15]),n++,i=i[15],-1!==o.injectorIndex)return o.injectorIndex|n<<16}return ea}function Ca(t,e,n){!(function o(t,e,n){let o;ngDevMode&&ln(e.firstCreatePass,!0,"expected firstCreatePass to be true"),"string"==typeof n?o=n.charCodeAt(0)||0:n.hasOwnProperty(Qn)&&(o=n[Qn]),null==o&&(o=n[Qn]=ga++);const i=255&o;e.data[t+(i>>5)]|=1<<i})(t,e,n)}function Ma(t,e,n){if(n&En.Optional)return t;on(e,"NodeInjector")}function va(t,e,n,o){if(n&En.Optional&&void 0===o&&(o=null),0==(n&(En.Self|En.Host))){const i=t[9],a=An(void 0);try{return i?i.get(e,o,n&En.Optional):Tn(e,o,n&En.Optional)}finally{An(a)}}return Ma(o,e,n)}function xa(t,e,n,o=En.Default,i){if(null!==t){const a=(function a(t){if(ngDevMode&&hn(t,"token must be defined"),"string"==typeof t)return t.charCodeAt(0)||0;const e=t.hasOwnProperty(Qn)?t[Qn]:void 0;return"number"==typeof e?e>=0?255&e:(ngDevMode&&ln(e,-1,"Expecting to get Special Injector Id"),Pa):e})(n);if("function"==typeof a){if(!Hi(e,t,o))return o&En.Host?Ma(i,n,o):va(e,n,o,i);try{const t=a(o);if(null!=t||o&En.Optional)return t;on(n)}finally{ji()}}else if("number"==typeof a){let i=null,r=ya(t,e),s=ea,l=o&En.Host?e[16][6]:null;for((-1===r||o&En.SkipSelf)&&(s=-1===r?_a(t,e):e[r+8],s!==ea&&Ea(o,!1)?(i=e[1],r=pa(s),e=ma(s,e)):r=-1);-1!==r;){ngDevMode&&Ho(e,r);const t=e[1];if(ngDevMode&&Oo(t.data[r+8],e),Da(a,r,t.data)){const t=wa(r,e,n,i,o,l);if(t!==Oa)return t}s=e[r+8],s!==ea&&Ea(o,e[1].data[r+8]===l)&&Da(a,r,e)?(i=t,r=pa(s),e=ma(s,e)):r=-1}}}return va(e,n,o,i)}const Oa={};function Pa(){return new Ra(bi(),fi())}function wa(t,e,n,o,i,a){const r=e[1],s=r.data[t+8],l=ka(s,r,n,null==o?Mo(s)&&ua:o!=r&&0!=(3&s.type),i&En.Host&&a===s);return null!==l?Sa(e,r,l,s):Oa}function ka(t,e,n,o,i){const a=t.providerIndexes,r=e.data,s=1048575&a,l=t.directiveStart,c=a>>20,d=i?s+c:t.directiveEnd;for(let t=o?s:s+c;t<d;t++){const e=r[t];if(t<l&&n===e||t>=l&&e.type===n)return t}if(i){const t=r[l];if(t&&xo(t)&&t.type===n)return l}return null}function Sa(t,e,n,o){let i=t[n];const a=e.data;if((function r(t){return t instanceof na})(i)){const r=i;r.resolving&&en(tn(a[n]));const s=fa(r.canSeeViewProviders);r.resolving=!0;const l=r.injectImpl?An(r.injectImpl):null,c=Hi(t,o,En.Default);ngDevMode&&ln(c,!0,"Because flags do not contain `SkipSelf' we expect this to always succeed.");try{i=t[n]=r.factory(void 0,a,t,o),e.firstCreatePass&&n>=o.directiveStart&&(ngDevMode&&(function s(t){void 0!==t.type&&null!=t.selectors&&void 0!==t.inputs||bn("Expected a DirectiveDef/ComponentDef and this object does not seem to have the expected shape.")})(a[n]),(
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function l(t,e,n){ngDevMode&&Ao(n);const{ngOnChanges:o,ngOnInit:i,ngDoCheck:a}=e.type.prototype;if(o){const o=Vo(e);(n.preOrderHooks||(n.preOrderHooks=[])).push(t,o),(n.preOrderCheckHooks||(n.preOrderCheckHooks=[])).push(t,o)}i&&(n.preOrderHooks||(n.preOrderHooks=[])).push(0-t,i),a&&((n.preOrderHooks||(n.preOrderHooks=[])).push(t,a),(n.preOrderCheckHooks||(n.preOrderCheckHooks=[])).push(t,a))})(n,a[n],e))}finally{null!==l&&An(l),fa(s),r.resolving=!1,ji()}}return i}function Da(t,e,n){return!!(n[e+(t>>5)]&1<<t)}function Ea(t,e){return!(t&En.Self||t&En.Host&&e)}class Ra{constructor(t,e){this._tNode=t,this._lView=e}get(t,e){return xa(this._tNode,this._lView,t,void 0,e)}}function Aa(t){return Nn((()=>{const e=t.prototype.constructor,n=e[Jn]||Ta(e),o=Object.prototype;let i=Object.getPrototypeOf(t.prototype).constructor;for(;i&&i!==o;){const t=i[Jn]||Ta(i);if(t&&t!==n)return t;i=Object.getPrototypeOf(i)}return t=>new t}))}function Ta(t){return Xe(t)?()=>{const e=Ta(Ze(t));return e&&e()}:Fo(t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Na(t){return(function e(t,n){if(ngDevMode&&ia(t,15),ngDevMode&&hn(t,"expecting tNode"),"class"===n)return t.classes;if("style"===n)return t.styles;const o=t.attrs;if(o){const t=o.length;let e=0;for(;e<t;){const i=o[e];if(ra(i))break;if(0===i)e+=2;else if("number"==typeof i)for(e++;e<t&&"string"==typeof o[e];)e++;else{if(i===n)return o[e+1];e+=2}}}return null})(bi(),t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const za="__annotations__",Ia="__parameters__",Ha="__prop__metadata__";function Fa(t,e,n,o,i){return Nn((()=>{const a=La(e);function r(...t){if(this instanceof r)return a.call(this,...t),this;const e=new r(...t);return function n(a){return i&&i(a,...t),(a.hasOwnProperty(za)?a[za]:Object.defineProperty(a,za,{value:[]})[za]).push(e),o&&o(a),a}}return n&&(r.prototype=Object.create(n.prototype)),r.prototype.ngMetadataName=t,r.annotationCls=r,r}))}function La(t){return function e(...n){if(t){const e=t(...n);for(const t in e)this[t]=e[t]}}}function Ba(t,e,n){return Nn((()=>{const o=La(e);function i(...t){if(this instanceof i)return o.apply(this,t),this;const e=new i(...t);return n.annotation=e,n;function n(t,n,o){const i=t.hasOwnProperty(Ia)?t[Ia]:Object.defineProperty(t,Ia,{value:[]})[Ia];for(;i.length<=o;)i.push(null);return(i[o]=i[o]||[]).push(e),t}}return n&&(i.prototype=Object.create(n.prototype)),i.prototype.ngMetadataName=t,i.annotationCls=i,i}))}function Va(t,e,n,o){return Nn((()=>{const i=La(e);function a(...t){if(this instanceof a)return i.apply(this,t),this;const e=new a(...t);return function n(i,a){const r=i.constructor,s=r.hasOwnProperty(Ha)?r[Ha]:Object.defineProperty(r,Ha,{value:{}})[Ha];s[a]=s.hasOwnProperty(a)&&s[a]||[],s[a].unshift(e),o&&o(i,a,...t)}}return n&&(a.prototype=Object.create(n.prototype)),a.prototype.ngMetadataName=t,a.annotationCls=a,a}))}const ja=(function Ua(){return Ba("Attribute",(t=>({attributeName:t,__NG_ELEMENT_ID__:()=>Na(t)})))})();
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Ga{constructor(t,e){this._desc=t,this.ngMetadataName="InjectionToken",this.ɵprov=void 0,"number"==typeof e?(("undefined"==typeof ngDevMode||ngDevMode)&&mn(e,0,"Only negative numbers are supported here"),this.__NG_ELEMENT_ID__=e):void 0!==e&&(this.ɵprov=Mn({token:this,providedIn:e.providedIn||"root",factory:e.factory}))}toString(){return`InjectionToken ${this._desc}`}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */new Ga("AnalyzeForEntryComponents");class Wa{}const Ya=Va("ContentChildren",((t,e={})=>Object.assign({selector:t,first:!1,isViewQuery:!1,descendants:!1,emitDistinctChangesOnly:!0},e)),Wa),qa=Va("ContentChild",((t,e={})=>Object.assign({selector:t,first:!0,isViewQuery:!1,descendants:!0},e)),Wa);Va("ViewChildren",((t,e={})=>Object.assign({selector:t,first:!1,isViewQuery:!0,descendants:!0,emitDistinctChangesOnly:!0},e)),Wa);const Za=Va("ViewChild",((t,e)=>Object.assign({selector:t,first:!0,isViewQuery:!0,descendants:!0},e)),Wa);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
var Xa,Ka;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function Ja(t){const e=jn.ng;if(e&&e.ɵcompilerFacade)return e.ɵcompilerFacade;if("undefined"==typeof ngDevMode||ngDevMode){console.error(`JIT compilation failed for ${t.kind}`,t.type);let e=`The ${t.kind} '${t.type.name}' needs to be compiled using the JIT compiler, but '@angular/compiler' is not available.\n\n`;throw 1===t.usage?(e+=`The ${t.kind} is part of a library that has been partially compiled.\n`,e+="However, the Angular Linker has not processed the library such that JIT compilation is used as fallback.\n",e+="\n",e+="Ideally, the library is processed using the Angular Linker to become fully AOT compiled.\n"):e+="JIT compilation is discouraged for production use-cases! Consider using AOT mode instead.\n",e+="Alternatively, the JIT compiler should be loaded by bootstrapping using '@angular/platform-browser-dynamic' or '@angular/platform-server',\n",e+="or manually provide the compiler with 'import \"@angular/compiler\";' before bootstrapping.",new Error(e)}throw new Error("JIT compiler unavailable")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t,e,1)}const lr=/^function\s+\S+\(\)\s*{[\s\S]+\.apply\(this,\s*(arguments|(?:[^()]+\(\[\],)?[^()]+\(arguments\))\)/,cr=/^class\s+[A-Za-z\d$_]*\s*extends\s+[^{]+{/,dr=/^class\s+[A-Za-z\d$_]*\s*extends\s+[^{]+{[\s\S]*constructor\s*\(/,pr=/^class\s+[A-Za-z\d$_]*\s*extends\s+[^{]+{[\s\S]*constructor\s*\(\)\s*{\s*super\(\.\.\.arguments\)/;class mr{constructor(t){this._reflect=t||jn.Reflect}isReflectionEnabled(){return!0}factory(t){return(...e)=>new t(...e)}_zipTypesAndAnnotations(t,e){let n;n=ir(void 0===t?e.length:t.length);for(let o=0;o<n.length;o++)n[o]=void 0===t?[]:t[o]&&t[o]!=Object?[t[o]]:[],e&&null!=e[o]&&(n[o]=n[o].concat(e[o]));return n}_ownParameters(t,e){if((function n(t){return lr.test(t)||pr.test(t)||cr.test(t)&&!dr.test(t)})(t.toString()))return null;if(t.parameters&&t.parameters!==e.parameters)return t.parameters;const o=t.ctorParameters;if(o&&o!==e.ctorParameters){const t="function"==typeof o?o():o,e=t.map((t=>t&&t.type)),n=t.map((t=>t&&ur(t.decorators)));return this._zipTypesAndAnnotations(e,n)}const i=t.hasOwnProperty(Ia)&&t[Ia],a=this._reflect&&this._reflect.getOwnMetadata&&this._reflect.getOwnMetadata("design:paramtypes",t);return a||i?this._zipTypesAndAnnotations(a,i):ir(t.length)}parameters(t){if(!$a(t))return[];const e=fr(t);let n=this._ownParameters(t,e);return n||e===Object||(n=this.parameters(e)),n||[]}_ownAnnotations(t,e){if(t.annotations&&t.annotations!==e.annotations){let e=t.annotations;return"function"==typeof e&&e.annotations&&(e=e.annotations),e}return t.decorators&&t.decorators!==e.decorators?ur(t.decorators):t.hasOwnProperty(za)?t[za]:null}annotations(t){if(!$a(t))return[];const e=fr(t),n=this._ownAnnotations(t,e)||[];return(e!==Object?this.annotations(e):[]).concat(n)}_ownPropMetadata(t,e){if(t.propMetadata&&t.propMetadata!==e.propMetadata){let e=t.propMetadata;return"function"==typeof e&&e.propMetadata&&(e=e.propMetadata),e}if(t.propDecorators&&t.propDecorators!==e.propDecorators){const e=t.propDecorators,n={};return Object.keys(e).forEach((t=>{n[t]=ur(e[t])})),n}return t.hasOwnProperty(Ha)?t[Ha]:null}propMetadata(t){if(!$a(t))return{};const e=fr(t),n={};if(e!==Object){const t=this.propMetadata(e);Object.keys(t).forEach((e=>{n[e]=t[e]}))}const o=this._ownPropMetadata(t,e);return o&&Object.keys(o).forEach((t=>{const e=[];n.hasOwnProperty(t)&&e.push(...n[t]),e.push(...o[t]),n[t]=e})),n}ownPropMetadata(t){return $a(t)&&this._ownPropMetadata(t,fr(t))||{}}hasLifecycleHook(t,e){return t instanceof Qa&&e in t.prototype}guards(t){return{}}getter(t){return new Function("o","return o."+t+";")}setter(t){return new Function("o","v","return o."+t+" = v;")}method(t){return new Function("o","args",`if (!o.${t}) throw new Error('"${t}" is undefined');\n        return o.${t}.apply(o, args);`)}importUri(t){return"object"==typeof t&&t.filePath?t.filePath:`./${Ge(t)}`}resourceUri(t){return`./${Ge(t)}`}resolveIdentifier(t,e,n,o){return o}resolveEnum(t,e){return t[e]}}function ur(t){return t?t.map((t=>new(0,t.type.annotationCls)(...t.args?t.args:[]))):[]}function fr(t){const e=t.prototype?Object.getPrototypeOf(t.prototype):null;return(e?e.constructor:null)||Object}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const gr={},hr=/\n/gm,br="__source",yr=je({provide:String,useValue:je});let _r;function Cr(t){const e=_r;return _r=t,e}function Mr(t,e=En.Default){if(void 0===_r)throw new Error("inject() must be called from an injection context");return null===_r?Tn(t,void 0,e):_r.get(t,e&En.Optional?null:void 0,e)}function vr(t,e=En.Default){return((function n(){return Rn})()||Mr)(Ze(t),e)}function xr(t){const e=ngDevMode?`This constructor is not compatible with Angular Dependency Injection because its dependency at index ${t} of the parameter list is invalid.\nThis can happen if the dependency type is a primitive like a string or if an ancestor of this class is missing an Angular decorator.\n\nPlease check that 1) the type for the parameter at index ${t} is correct and 2) the correct Angular decorators are defined for this class and its ancestors.`:"invalid";throw new Error(e)}const Or=vr;function Pr(t){const e=[];for(let n=0;n<t.length;n++){const o=Ze(t[n]);if(Array.isArray(o)){if(0===o.length)throw new Error("Arguments array must have arguments.");let t,n=En.Default;for(let e=0;e<o.length;e++){const i=o[e],a=i.__NG_DI_FLAG__;"number"==typeof a?-1===a?t=i.token:n|=a:t=i}e.push(vr(t,n))}else e.push(vr(o))}return e}function wr(t,e){return t.__NG_DI_FLAG__=e,t.prototype.__NG_DI_FLAG__=e,t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const kr=wr(Ba("Inject",(t=>({token:t}))),-1),Sr=wr(Ba("Optional"),8),Dr=wr(Ba("Self"),2),Er=wr(Ba("SkipSelf"),4),Rr=wr(Ba("Host"),1);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let Ar=null;function Tr(){return Ar=Ar||new mr}function Nr(t){return zr(Tr().parameters(t))}function zr(t){return t.map((t=>(function e(t){const e={token:null,attribute:null,host:!1,optional:!1,self:!1,skipSelf:!1};if(Array.isArray(t)&&t.length>0)for(let n=0;n<t.length;n++){const o=t[n];if(void 0===o)continue;const i=Object.getPrototypeOf(o);if(o instanceof Sr||"Optional"===i.ngMetadataName)e.optional=!0;else if(o instanceof Er||"SkipSelf"===i.ngMetadataName)e.skipSelf=!0;else if(o instanceof Dr||"Self"===i.ngMetadataName)e.self=!0;else if(o instanceof Rr||"Host"===i.ngMetadataName)e.host=!0;else if(o instanceof kr)e.token=o.token;else if(o instanceof ja){if(void 0===o.attributeName)throw new Error("Attribute name must be defined.");e.attribute=o.attributeName}else e.token=o}else e.token=void 0===t||Array.isArray(t)&&0===t.length?null:t;return e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let Br,Vr;function jr(){if(void 0===Br&&(Br=null,jn.trustedTypes))try{Br=jn.trustedTypes.createPolicy("angular",{createHTML:t=>t,createScript:t=>t,createScriptURL:t=>t})}catch(t){}return Br}function Ur(t){var e;return(null===(e=jr())||void 0===e?void 0:e.createHTML(t))||t}function Gr(){if(void 0===Vr&&(Vr=null,jn.trustedTypes))try{Vr=jn.trustedTypes.createPolicy("angular#unsafe-bypass",{createHTML:t=>t,createScript:t=>t,createScriptURL:t=>t})}catch(t){}return Vr}function Wr(t){var e;return(null===(e=Gr())||void 0===e?void 0:e.createHTML(t))||t}function Yr(t){var e;return(null===(e=Gr())||void 0===e?void 0:e.createScript(t))||t}function qr(t){var e;return(null===(e=Gr())||void 0===e?void 0:e.createScriptURL(t))||t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function os(t){const e=new as(t);return(function n(){try{return!!(new window.DOMParser).parseFromString(Ur(""),"text/html")}catch(t){return!1}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function ds(t){const e={};for(const n of t.split(","))e[n]=!0;return e}function ps(...t){const e={};for(const n of t)for(const t in n)n.hasOwnProperty(t)&&(e[t]=!0);return e}const ms=ds("area,br,col,hr,img,wbr"),us=ds("colgroup,dd,dt,li,p,tbody,td,tfoot,th,thead,tr"),fs=ds("rp,rt"),gs=ps(fs,us),hs=ps(us,ds("address,article,aside,blockquote,caption,center,del,details,dialog,dir,div,dl,figure,figcaption,footer,h1,h2,h3,h4,h5,h6,header,hgroup,hr,ins,main,map,menu,nav,ol,pre,section,summary,table,ul")),bs=ps(fs,ds("a,abbr,acronym,audio,b,bdi,bdo,big,br,cite,code,del,dfn,em,font,i,img,ins,kbd,label,map,mark,picture,q,ruby,rp,rt,s,samp,small,source,span,strike,strong,sub,sup,time,track,tt,u,var,video")),ys=ps(ms,hs,bs,gs),_s=ds("background,cite,href,itemtype,longdesc,poster,src,xlink:href"),Cs=ds("srcset"),Ms=ds("abbr,accesskey,align,alt,autoplay,axis,bgcolor,border,cellpadding,cellspacing,class,clear,color,cols,colspan,compact,controls,coords,datetime,default,dir,download,face,headers,height,hidden,hreflang,hspace,ismap,itemscope,itemprop,kind,label,lang,language,loop,media,muted,nohref,nowrap,open,preload,rel,rev,role,rows,rowspan,rules,scope,scrolling,shape,size,sizes,span,srclang,start,summary,tabindex,target,title,translate,type,usemap,valign,value,vspace,width"),vs=ds("aria-activedescendant,aria-atomic,aria-autocomplete,aria-busy,aria-checked,aria-colcount,aria-colindex,aria-colspan,aria-controls,aria-current,aria-describedby,aria-details,aria-disabled,aria-dropeffect,aria-errormessage,aria-expanded,aria-flowto,aria-grabbed,aria-haspopup,aria-hidden,aria-invalid,aria-keyshortcuts,aria-label,aria-labelledby,aria-level,aria-live,aria-modal,aria-multiline,aria-multiselectable,aria-orientation,aria-owns,aria-placeholder,aria-posinset,aria-pressed,aria-readonly,aria-relevant,aria-required,aria-roledescription,aria-rowcount,aria-rowindex,aria-rowspan,aria-selected,aria-setsize,aria-sort,aria-valuemax,aria-valuemin,aria-valuenow,aria-valuetext"),xs=ps(_s,Cs,Ms,vs),Os=ds("script,style,template");class Ps{constructor(){this.sanitizedSomething=!1,this.buf=[]}sanitizeChildren(t){let e=t.firstChild,n=!0;for(;e;)if(e.nodeType===Node.ELEMENT_NODE?n=this.startElement(e):e.nodeType===Node.TEXT_NODE?this.chars(e.nodeValue):this.sanitizedSomething=!0,n&&e.firstChild)e=e.firstChild;else for(;e;){e.nodeType===Node.ELEMENT_NODE&&this.endElement(e);let t=this.checkClobberedElement(e,e.nextSibling);if(t){e=t;break}e=this.checkClobberedElement(e,e.parentNode)}return this.buf.join("")}startElement(t){const e=t.nodeName.toLowerCase();if(!ys.hasOwnProperty(e))return this.sanitizedSomething=!0,!Os.hasOwnProperty(e);this.buf.push("<"),this.buf.push(e);const n=t.attributes;for(let t=0;t<n.length;t++){const e=n.item(t),o=e.name,i=o.toLowerCase();if(!xs.hasOwnProperty(i)){this.sanitizedSomething=!0;continue}let a=e.value;_s[i]&&(a=ls(a)),Cs[i]&&(a=cs(a)),this.buf.push(" ",o,'="',Ss(a),'"')}return this.buf.push(">"),!0}endElement(t){const e=t.nodeName.toLowerCase();ys.hasOwnProperty(e)&&!ms.hasOwnProperty(e)&&(this.buf.push("</"),this.buf.push(e),this.buf.push(">"))}chars(t){this.buf.push(Ss(t))}checkClobberedElement(t,e){if(e&&(t.compareDocumentPosition(e)&Node.DOCUMENT_POSITION_CONTAINED_BY)===Node.DOCUMENT_POSITION_CONTAINED_BY)throw new Error(`Failed to sanitize html because the element is clobbered: ${t.outerHTML}`);return e}}const ws=/[\uD800-\uDBFF][\uDC00-\uDFFF]/g,ks=/([^\#-~ |!])/g;function Ss(t){return t.replace(/&/g,"&amp;").replace(ws,(function(t){return"&#"+(1024*(t.charCodeAt(0)-55296)+(t.charCodeAt(1)-56320)+65536)+";"})).replace(ks,(function(t){return"&#"+t.charCodeAt(0)+";"})).replace(/</g,"&lt;").replace(/>/g,"&gt;")}let Ds;function Es(t,e){let n=null;try{Ds=Ds||os(t);let o=e?String(e):"";n=Ds.getInertBodyElement(o);let i=5,a=o;do{if(0===i)throw new Error("Failed to sanitize html because the input is unstable");i--,o=a,a=n.innerHTML,n=Ds.getInertBodyElement(o)}while(o!==a);const r=new Ps,s=r.sanitizeChildren(Rs(n)||n);return("undefined"==typeof ngDevMode||ngDevMode)&&r.sanitizedSomething&&console.warn("WARNING: sanitizing HTML stripped some content, see https://g.co/ng/security#xss"),Ur(s)}finally{if(n){const t=Rs(n)||n;for(;t.firstChild;)t.removeChild(t.firstChild)}}}function Rs(t){return"content"in t&&(function e(t){return t.nodeType===Node.ELEMENT_NODE&&"TEMPLATE"===t.nodeName}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t)?t.content:null}var As;function Ts(t){const e=zs();return e?e.sanitize(As.URL,t)||"":es(t,"URL")?ts(t):ls($e(t))}function Ns(t){const e=zs();if(e)return qr(e.sanitize(As.RESOURCE_URL,t)||"");if(es(t,"ResourceURL"))return qr(ts(t));throw new Error("unsafe value used in a resource URL context (see https://g.co/ng/security#xss)")}function zs(){const t=fi();return t&&t[12]}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Is(t){let e=Bs(t);if(e){if(Array.isArray(e)){const n=e;let o,i,a;if((function n(t){return t&&t.constructor&&t.constructor.ɵcmp})(t)){if(o=Gs(n,t),-1==o)throw new Error("The provided component was not found in the application");i=t}else if((function o(t){return t&&t.constructor&&t.constructor.ɵdir})(t)){if(o=(function i(t,e){let n=t[1].firstChild;for(;n;){const o=n.directiveEnd;for(let i=n.directiveStart;i<o;i++)if(t[i]===e)return n.index;n=Us(n)}return-1})(n,t),-1==o)throw new Error("The provided directive was not found in the application");a=Ws(o,n,!1)}else if(o=js(n,t),-1==o)return null;const r=ei(n[o]),s=Bs(r),l=s&&!Array.isArray(s)?s:Hs(n,o,r);if(i&&void 0===l.component&&(l.component=i,Ls(l.component,l)),a&&void 0===l.directives){l.directives=a;for(let t=0;t<a.length;t++)Ls(a[t],l)}Ls(l.native,l),e=l}}else{const n=t;ngDevMode&&yn(n);let o=n;for(;o=o.parentNode;){const t=Bs(o);if(t){let o;if(o=Array.isArray(t)?t:t.lView,!o)return null;const i=js(o,n);if(i>=0){const t=ei(o[i]),n=Hs(o,i,t);Ls(t,n),e=n;break}}}}return e||null}function Hs(t,e,n){return{lView:t,nodeIndex:e,native:n,component:void 0,directives:void 0,localRefs:void 0}}function Fs(t){let e,n=Bs(t);if(Array.isArray(n)){const o=Gs(n,t);e=ri(o,n);const i=Hs(n,o,e[0]);i.component=t,Ls(t,i),Ls(i.native,i)}else e=ri(n.nodeIndex,n.lView);return e}function Ls(t,e){ngDevMode&&hn(t,"Target expected"),t.__ngContext__=e}function Bs(t){return ngDevMode&&hn(t,"Target expected"),t.__ngContext__||null}function Vs(t){const e=Bs(t);return e?Array.isArray(e)?e:e.lView:null}function js(t,e){const n=t[1];for(let o=go;o<n.bindingStartIndex;o++)if(ei(t[o])===e)return o;return-1}function Us(t){if(t.child)return t.child;if(t.next)return t.next;for(;t.parent&&!t.parent.next;)t=t.parent;return t.parent&&t.parent.next}function Gs(t,e){const n=t[1].components;if(n)for(let o=0;o<n.length;o++){const i=n[o];if(ri(i,t)[8]===e)return i}else if(ri(go,t)[8]===e)return go;return-1}function Ws(t,e,n){const o=e[1].data[t];let i=o.directiveStart;return 0==i?Wn:(!n&&2&o.flags&&i++,e.slice(i,o.directiveEnd))}function Ys(t){return t.ngOriginalError}function qs(t,...e){t.error(...e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */!(function(t){t[t.NONE=0]="NONE",t[t.HTML=1]="HTML",t[t.STYLE=2]="STYLE",t[t.SCRIPT=3]="SCRIPT",t[t.URL=4]="URL",t[t.RESOURCE_URL=5]="RESOURCE_URL"})(As||(As={}));class Zs{constructor(){this._console=console}handleError(t){const e=this._findOriginalError(t),n=this._findContext(t),o=(function i(t){return t&&t.ngErrorLogger||qs})(t);o(this._console,"ERROR",t),e&&o(this._console,"ORIGINAL ERROR",e),n&&o(this._console,"ERROR CONTEXT",n)}_findContext(t){return t?(function e(t){return t.ngDebugContext})(t)||this._findContext(Ys(t)):null}_findOriginalError(t){let e=t&&Ys(t);for(;e&&Ys(e);)e=Ys(e);return e||null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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var cl;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let dl;function pl(t,e){return dl(t,e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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function ml(t){ngDevMode&&Ro(t);const e=t[3];return _o(e)?e[3]:e}function ul(t){const e=(function n(t){ngDevMode&&hn(t,"component");let e=yo(t)?t:Vs(t);for(;e&&!(512&e[2]);)e=ml(e);return ngDevMode&&Ro(e),e})(t);return ngDevMode&&hn(e[8],"RootView has no context. 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function ql(t,e,n){ngDevMode&&cn(e,"",'can not look for "" string.');let o=t.length;for(;;){const i=t.indexOf(e,n);if(-1===i)return i;if(0===i||t.charCodeAt(i-1)<=32){const n=e.length;if(i+n===o||t.charCodeAt(i+n)<=32)return i}n=i+1}}const Zl="ng-template";function Xl(t,e,n){ngDevMode&&ln(e,e.toLowerCase(),"Class name expected to be lowercase.");let o=0;for(;o<t.length;){let i=t[o++];if(n&&"class"===i){if(i=t[o],-1!==ql(i.toLowerCase(),e,0))return!0}else if(1===i){for(;o<t.length&&"string"==typeof(i=t[o++]);)if(i.toLowerCase()===e)return!0;return!1}}return!1}function Kl(t){return 4===t.type&&t.value!==Zl}function Jl(t,e,n){return e===(4!==t.type||n?t.value:Zl)}function Ql(t,e,n){ngDevMode&&hn(e[0],"Selector should have a tag name");let o=4;const i=t.attrs||[],a=(function r(t){for(let e=0;e<t.length;e++)if(ra(t[e]))return e;return t.length})(i);let s=!1;for(let r=0;r<e.length;r++){const l=e[r];if("number"!=typeof l){if(!s)if(4&o){if(o=2|1&o,""!==l&&!Jl(t,l,n)||""===l&&1===e.length){if($l(o))return!1;s=!0}}else{const c=8&o?l:e[++r];if(8&o&&null!==t.attrs){if(!Xl(t.attrs,c,n)){if($l(o))return!1;s=!0}continue}const d=tc(8&o?"class":l,i,Kl(t),n);if(-1===d){if($l(o))return!1;s=!0;continue}if(""!==c){let t;d>a?t="":(ngDevMode&&cn(i[d],0,"We do not match directives on namespaced attributes"),t=i[d+1].toLowerCase());const e=8&o?t:null;if(e&&-1!==ql(e,c,0)||2&o&&c!==t){if($l(o))return!1;s=!0}}}}else{if(!s&&!$l(o)&&!$l(l))return!1;if(s&&$l(l))continue;s=!1,o=l|1&o}}return $l(o)||s}function $l(t){return 0==(1&t)}function tc(t,e,n,o){if(null===e)return-1;let i=0;if(o||!n){let n=!1;for(;i<e.length;){const o=e[i];if(o===t)return i;if(3===o||6===o)n=!0;else{if(1===o||2===o){let t=e[++i];for(;"string"==typeof t;)t=e[++i];continue}if(4===o)break;if(0===o){i+=4;continue}}i+=n?1:2}return-1}return(function a(t,e){let n=t.indexOf(4);if(n>-1)for(n++;n<t.length;){const o=t[n];if("number"==typeof o)return-1;if(o===e)return n;n++}return-1})(e,t)}function ec(t,e,n=!1){for(let o=0;o<e.length;o++)if(Ql(t,e[o],n))return!0;return!1}function nc(t,e){t:for(let n=0;n<e.length;n++){const o=e[n];if(t.length===o.length){for(let e=0;e<t.length;e++)if(t[e]!==o[e])continue t;return!0}}return!1}function oc(t,e){return t?":not("+e.trim()+")":e}function ic(t){let e=t[0],n=1,o=2,i="",a=!1;for(;n<t.length;){let r=t[n];if("string"==typeof r)if(2&o){const e=t[++n];i+="["+r+(e.length>0?'="'+e+'"':"")+"]"}else 8&o?i+="."+r:4&o&&(i+=" "+r);else""===i||$l(r)||(e+=oc(a,i),i=""),o=r,a=a||!$l(o);n++}return""!==i&&(e+=oc(a,i)),e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const ac="undefined"==typeof ngDevMode||ngDevMode?{__brand__:"NO_CHANGE"}:{};
/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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     * Copyright Google LLC All Rights Reserved.
     *
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     */function lc(t,e){return ngDevMode&&rn(t,0,32767),ngDevMode&&rn(e,0,32767),t<<17|e<<2}function cc(t){return ngDevMode&&an(t,"expected number"),t>>17&32767}function dc(t){return ngDevMode&&an(t,"expected number"),2==(2&t)}function pc(t){return ngDevMode&&an(t,"expected number"),2|t}function mc(t){return ngDevMode&&an(t,"expected number"),(131068&t)>>2}function uc(t,e){return ngDevMode&&an(t,"expected number"),ngDevMode&&rn(e,0,32767),-131069&t|e<<2}function fc(t){return ngDevMode&&an(t,"expected number"),1==(1&t)}function gc(t){return ngDevMode&&an(t,"expected number"),1|t}
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function hc(t,e){if(!ngDevMode)throw new Error("This method should be guarded with `ngDevMode` so that it can be tree shaken in production!");Object.defineProperty(t,"debug",{value:e,enumerable:!1})}function bc(t,e){if(!ngDevMode)throw new Error("This method should be guarded with `ngDevMode` so that it can be tree shaken in production!");Object.defineProperty(t,"debug",{get:e,enumerable:!1})}
/**
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     * Copyright Google LLC All Rights Reserved.
     *
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t}function zc(t,e=!1){const n=ei(t);if(n)switch(n.nodeType){case Node.TEXT_NODE:return n.textContent;case Node.COMMENT_NODE:return`\x3c!--${n.textContent}--\x3e`;case Node.ELEMENT_NODE:const t=n.outerHTML;return e?t:t.split(">"+n.innerHTML+"<")[0]+">"}return null}class Ic{constructor(t){this._raw_lView=t}get flags(){const t=this._raw_lView[2];return{__raw__flags__:t,initPhaseState:3&t,creationMode:!!(4&t),firstViewPass:!!(8&t),checkAlways:!!(16&t),dirty:!!(64&t),attached:!!(128&t),destroyed:!!(256&t),isRoot:!!(512&t),indexWithinInitPhase:t>>11}}get parent(){return Nc(this._raw_lView[3])}get hostHTML(){return zc(this._raw_lView[0],!0)}get html(){return(this.nodes||[]).map(Hc).join("")}get context(){return this._raw_lView[8]}get nodes(){const t=this._raw_lView;return Lc(t[1].firstChild,t)}get template(){return this.tView.template_}get tView(){return this._raw_lView[1]}get cleanup(){return this._raw_lView[7]}get injector(){return this._raw_lView[9]}get rendererFactory(){return 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a=n;a<o;a++)i.push({index:a,t:t.data[a],l:e[a]});return{start:n,end:o,length:o-n,content:i}}function Lc(t,e){if(t){const n=[];let o=t;for(;o;)n.push(Bc(o,e)),o=o.next;return n}return[]}function Bc(t,e){const n=ei(e[t.index]),o=[],i=[],a=e[1];for(let n=t.directiveStart;n<t.directiveEnd;n++)o.push(a.data[n].type),i.push(e[n]);return{html:zc(n),type:oa(t.type),tNode:t,native:n,children:Lc(t.child,e),factories:o,instances:i,injector:Vc(t,a,e),get injectorResolutionPath(){return t.debugNodeInjectorPath(e)}}}function Vc(t,e,n){const o=[];for(let n=t.providerIndexStart_;n<t.providerIndexEnd_;n++)o.push(e.data[n]);const i=[];for(let n=t.providerIndexEnd_;n<t.directiveEnd;n++)i.push(e.data[n]);return{bloom:Uc(n,t.injectorIndex),cumulativeBloom:Uc(e.data,t.injectorIndex),providers:i,viewProviders:o,parentInjectorIndex:n[t.providerIndexStart_-1]}}function jc(t,e){const n=t[e];if("number"!=typeof n)return"????????";const o="00000000"+n.toString(2);return o.substring(o.length-8)}function Uc(t,e){return e<0?"NO_NODE_INJECTOR":`${jc(t,e+7)}_${jc(t,e+6)}_${jc(t,e+5)}_${jc(t,e+4)}_${jc(t,e+3)}_${jc(t,e+2)}_${jc(t,e+1)}_${jc(t,e+0)}`}class Gc{constructor(t){this._raw_lContainer=t}get hasTransplantedViews(){return this._raw_lContainer[2]}get views(){return this._raw_lContainer.slice(bo).map(Nc)}get parent(){return Nc(this._raw_lContainer[3])}get movedViews(){return this._raw_lContainer[9]}get host(){return this._raw_lContainer[0]}get native(){return this._raw_lContainer[7]}get next(){return Nc(this._raw_lContainer[4])}}const Wc=Promise.resolve(null);function Yc(t,e){const n=t.contentQueries;if(null!==n)for(let o=0;o<n.length;o+=2){const i=n[o],a=n[o+1];if(-1!==a){const n=t.data[a];ngDevMode&&hn(n,"DirectiveDef not found."),ngDevMode&&hn(n.contentQueries,"contentQueries function should be defined"),zi(i),n.contentQueries(2,e[a],a)}}}function qc(t,e,n,o,i,a,r,s,l,c){const d=ngDevMode?(function p(t){return(function e(t,n){switch(t){case 0:return void 0===Mc&&(Mc=new(tl("LRootView"))),Mc;case 1:void 0===_c&&(_c=new Map);let t=_c.get(n);return void 0===t&&(t=new(tl("LComponentView"+vc(n))),_c.set(n,t)),t;case 2:void 0===Cc&&(Cc=new Map);let e=Cc.get(n);return void 0===e&&(e=new(tl("LEmbeddedView"+vc(n))),Cc.set(n,e)),e}})(t.type,t.template&&t.template.name).concat(t.blueprint)})(e):e.blueprint.slice();return d[0]=i,d[2]=140|o,di(d),ngDevMode&&e.declTNode&&t&&Oo(e.declTNode,t),d[3]=d[15]=t,d[8]=n,d[10]=r||t&&t[10],ngDevMode&&hn(d[10],"RendererFactory is required"),d[11]=s||t&&t[11],ngDevMode&&hn(d[11],"Renderer is required"),d[12]=l||t&&t[12]||null,d[9]=c||t&&t[9]||null,d[6]=a,ngDevMode&&ln(2!=e.type||null!==t,!0,"Embedded views must have parentLView"),d[16]=2==e.type?t[16]:d,ngDevMode&&(function m(t){hc(t,new Ic(t))})(d),d}function Zc(t,e,n,o,i){ngDevMode&&0!==e&&gn(e,go,"TNodes can't be in the LView header."),ngDevMode&&(function a(t){2!==t&&1!==t&&4!==t&&8!==t&&32!==t&&16!==t&&64!==t&&bn(`Expected TNodeType to have only a single type selected, but got ${oa(t)}.`)})(n);let r=t.data[e];if(null===r)r=Xc(t,e,n,o,i),(function s(){return mi.lFrame.inI18n})()&&(r.flags|=64);else if(64&r.type){r.type=n,r.value=o,r.attrs=i;const a=_i();r.injectorIndex=null===a?-1:a.injectorIndex,ngDevMode&&Po(r,t),ngDevMode&&ln(e,r.index,"Expecting same index")}return Ci(r,!0),r}function Xc(t,e,n,o,i){const a=yi(),r=Mi(),s=t.data[e]=(function l(t,e,n,o,i,a){ngDevMode&&0!==o&&gn(o,go,"TNodes can't be in the LView header."),ngDevMode&&pn(a,void 0,"'undefined' is not valid value for 'attrs'"),ngDevMode&&ngDevMode.tNode++,ngDevMode&&e&&Po(e,t);let r=e?e.injectorIndex:-1;const s=ngDevMode?new class{constructor(t,e,n,o,i,a,r,s,l,c,d,p,m,u,f,g,h,b,y,_,C,M,v,x,O,P,w,k,S,D,E,R){this.tView_=t,this.type=e,this.index=n,this.insertBeforeIndex=o,this.injectorIndex=i,this.directiveStart=a,this.directiveEnd=r,this.directiveStylingLast=s,this.propertyBindings=l,this.flags=c,this.providerIndexes=d,this.value=p,this.attrs=m,this.mergedAttrs=u,this.localNames=f,this.initialInputs=g,this.inputs=h,this.outputs=b,this.tViews=y,this.next=_,this.projectionNext=C,this.child=M,this.parent=v,this.projection=x,this.styles=O,this.stylesWithoutHost=P,this.residualStyles=w,this.classes=k,this.classesWithoutHost=S,this.residualClasses=D,this.classBindings=E,this.styleBindings=R}debugNodeInjectorPath(t){const e=[];let n=ya(this,t);if(-1===n){const e=_a(this,t);e!==ea&&(n=pa(e),t=ma(e,t))}for(;-1!==n;){ngDevMode&&Ho(t,n),e.push(Bc(t[1].data[n+8],t));const o=t[n+8];o===ea?n=-1:(n=pa(o),t=ma(o,t))}return e}get type_(){return oa(this.type)||`TNodeType.?${this.type}?`}get flags_(){const t=[];return 16&this.flags&&t.push("TNodeFlags.hasClassInput"),8&this.flags&&t.push("TNodeFlags.hasContentQuery"),32&this.flags&&t.push("TNodeFlags.hasStyleInput"),128&this.flags&&t.push("TNodeFlags.hasHostBindings"),2&this.flags&&t.push("TNodeFlags.isComponentHost"),1&this.flags&&t.push("TNodeFlags.isDirectiveHost"),64&this.flags&&t.push("TNodeFlags.isDetached"),4&this.flags&&t.push("TNodeFlags.isProjected"),t.join("|")}get template_(){if(1&this.type)return this.value;const t=[],e="string"==typeof this.value&&this.value||this.type_;if(t.push("<",e),this.flags&&t.push(" ",this.flags_),this.attrs)for(let e=0;e<this.attrs.length;){const n=this.attrs[e++];if("number"==typeof n)break;const o=this.attrs[e++];t.push(" ",n,'="',o,'"')}return t.push(">"),Oc(this.child,t),t.push("</",e,">"),t.join("")}get styleBindings_(){return xc(this,!1)}get classBindings_(){return xc(this,!0)}get providerIndexStart_(){return 1048575&this.providerIndexes}get providerIndexEnd_(){return this.providerIndexStart_+(this.providerIndexes>>>20)}}(t,n,o,null,r,-1,-1,-1,null,0,0,i,a,null,null,void 0,null,null,null,null,null,null,e,null,null,null,void 0,null,null,void 0,0,0):{type:n,index:o,insertBeforeIndex:null,injectorIndex:r,directiveStart:-1,directiveEnd:-1,directiveStylingLast:-1,propertyBindings:null,flags:0,providerIndexes:0,value:i,attrs:a,mergedAttrs:null,localNames:null,initialInputs:void 0,inputs:null,outputs:null,tViews:null,next:null,projectionNext:null,child:null,parent:e,projection:null,styles:null,stylesWithoutHost:null,residualStyles:void 0,classes:null,classesWithoutHost:null,residualClasses:void 0,classBindings:0,styleBindings:0};return ngDevMode&&Object.seal(s),s})(t,r?a:a&&a.parent,n,e,o,i);return null===t.firstChild&&(t.firstChild=s),null!==a&&(r?null==a.child&&null!==s.parent&&(a.child=s):null===a.next&&(a.next=s)),s}function Kc(t,e,n,o){if(0===n)return-1;ngDevMode&&(Ao(t),dn(t,e[1],"`LView` must be associated with `TView`!"),ln(t.data.length,e.length,"Expecting LView to be same size as TView"),ln(t.data.length,t.blueprint.length,"Expecting Blueprint to be same size as TView"),To(t));const i=e.length;for(let i=0;i<n;i++)e.push(o),t.blueprint.push(o),t.data.push(null);return i}function Jc(t,e,n){ngDevMode&&ln(si(e),!0,"Should be run in creation mode"),Fi(e);try{const o=t.viewQuery;null!==o&&Nd(1,o,n);const i=t.template;null!==i&&td(t,e,i,1,n),t.firstCreatePass&&(t.firstCreatePass=!1),t.staticContentQueries&&Yc(t,e),t.staticViewQueries&&Nd(2,t.viewQuery,n);const a=t.components;null!==a&&(function o(t,e){for(let n=0;n<e.length;n++)Sd(t,e[n])})(e,a)}catch(e){throw t.firstCreatePass&&(t.incompleteFirstPass=!0,t.firstCreatePass=!1),e}finally{e[2]&=-5,Ui()}}function Qc(t,e,n,o){ngDevMode&&ln(si(e),!1,"Should be run in update mode");const i=e[2];if(256==(256&i))return;Fi(e);const a=xi();try{di(e),ki(t.bindingStartIndex),null!==n&&td(t,e,n,2,o);const r=3==(3&i);if(!a)if(r){const n=t.preOrderCheckHooks;null!==n&&Ki(e,n,null)}else{const n=t.preOrderHooks;null!==n&&Ji(e,n,0,null),Qi(e,0)}if((function r(t){for(let e=fl(t);null!==e;e=gl(e)){if(!e[2])continue;const t=e[9];ngDevMode&&hn(t,"Transplanted View flags set but missing MOVED_VIEWS");for(let e=0;e<t.length;e++){const n=t[e],o=n[3];ngDevMode&&Do(o),0==(1024&n[2])&&pi(o,1),n[2]|=1024}}})(e),(function s(t){for(let e=fl(t);null!==e;e=gl(e))for(let t=bo;t<e.length;t++){const n=e[t],o=n[1];ngDevMode&&hn(o,"TView must be allocated"),li(n)&&Qc(o,n,o.template,n[8])}})(e),null!==t.contentQueries&&Yc(t,e),!a)if(r){const n=t.contentCheckHooks;null!==n&&Ki(e,n)}else{const n=t.contentHooks;null!==n&&Ji(e,n,1),Qi(e,1)}!(function l(t,e){const n=t.hostBindingOpCodes;if(null!==n)try{for(let t=0;t<n.length;t++){const o=n[t];if(o<0)Wi(~o);else{const i=o,a=n[++t],r=n[++t];Ri(a,i),r(2,e[i])}}}finally{Wi(-1)}})(t,e);const s=t.components;null!==s&&(function c(t,e){for(let n=0;n<e.length;n++)wd(t,e[n])})(e,s);const l=t.viewQuery;if(null!==l&&Nd(2,l,o),!a)if(r){const n=t.viewCheckHooks;null!==n&&Ki(e,n)}else{const n=t.viewHooks;null!==n&&Ji(e,n,2),Qi(e,2)}!0===t.firstUpdatePass&&(t.firstUpdatePass=!1),a||(e[2]&=-73),1024&e[2]&&(e[2]&=-1025,pi(e[3],-1))}finally{Ui()}}function $c(t,e,n,o){const i=e[10],a=!xi(),r=si(e);try{a&&!r&&i.begin&&i.begin(),r&&Jc(t,e,o),Qc(t,e,n,o)}finally{a&&!r&&i.end&&i.end()}}function td(t,e,n,o,i){const a=Gi(),r=2&o;try{Wi(-1),r&&e.length>go&&sc(t,e,go,xi()),qo(r?2:0,i),n(o,i)}finally{Wi(a),qo(r?3:1,i)}}function ed(t,e,n){if(Co(e)){const o=e.directiveEnd;for(let i=e.directiveStart;i<o;i++){const e=t.data[i];e.contentQueries&&e.contentQueries(1,n[i],i)}}}function nd(t,e,n){ui()&&((function o(t,e,n,i){const a=n.directiveStart,r=n.directiveEnd;t.firstCreatePass||ha(n,e),Ls(i,e);const s=n.initialInputs;for(let o=a;o<r;o++){const i=t.data[o],r=xo(i);r&&(ngDevMode&&ia(n,3),_d(e,n,i));const l=Sa(e,t,o,n);Ls(l,e),null!==s&&vd(e,o-a,l,i,n,s),r&&(ri(n.index,e)[8]=l)}})(t,e,n,oi(n,e)),128==(128&n.flags)&&(function i(t,e,n){const o=n.directiveStart,i=n.directiveEnd,a=n.index,r=(function s(){return mi.lFrame.currentDirectiveIndex})();try{Wi(a);for(let n=o;n<i;n++){const o=t.data[n],i=e[n];Ai(n),null===o.hostBindings&&0===o.hostVars&&null===o.hostAttrs||fd(o,i)}}finally{Wi(-1),Ai(r)}})(t,e,n))}function od(t,e,n=oi){const o=e.localNames;if(null!==o){let i=e.index+1;for(let a=0;a<o.length;a+=2){const r=o[a+1],s=-1===r?n(e,t):t[r];t[i++]=s}}}function id(t){const e=t.tView;return null===e||e.incompleteFirstPass?t.tView=ad(1,null,t.template,t.decls,t.vars,t.directiveDefs,t.pipeDefs,t.viewQuery,t.schemas,t.consts):e}function ad(t,e,n,o,i,a,r,s,l,c){ngDevMode&&ngDevMode.tView++;const d=go+o,p=d+i,m=(function u(t,e){const n=ngDevMode?new kc:[];for(let o=0;o<e;o++)n.push(o<t?null:ac);return n})(d,p),f="function"==typeof c?c():c,g=m[1]=ngDevMode?new class{constructor(t,e,n,o,i,a,r,s,l,c,d,p,m,u,f,g,h,b,y,_,C,M,v,x,O,P,w,k,S,D,E,R){this.type=t,this.blueprint=e,this.template=n,this.queries=o,this.viewQuery=i,this.declTNode=a,this.data=r,this.bindingStartIndex=s,this.expandoStartIndex=l,this.hostBindingOpCodes=c,this.firstCreatePass=d,this.firstUpdatePass=p,this.staticViewQueries=m,this.staticContentQueries=u,this.preOrderHooks=f,this.preOrderCheckHooks=g,this.contentHooks=h,this.contentCheckHooks=b,this.viewHooks=y,this.viewCheckHooks=_,this.destroyHooks=C,this.cleanup=M,this.contentQueries=v,this.components=x,this.directiveRegistry=O,this.pipeRegistry=P,this.firstChild=w,this.schemas=k,this.consts=S,this.incompleteFirstPass=D,this._decls=E,this._vars=R}get template_(){const t=[];return Oc(this.firstChild,t),t.join("")}get type_(){return ho[this.type]||`TViewType.?${this.type}?`}}(t,m,n,null,s,e,(function h(t){return void 0===wc&&(wc=new Pc),wc.concat(t)})(m).fill(null,d),d,p,null,!0,!0,!1,!1,null,null,null,null,null,null,null,null,null,null,"function"==typeof a?a():a,"function"==typeof r?r():r,null,l,f,!1,o,i):{type:t,blueprint:m,template:n,queries:null,viewQuery:s,declTNode:e,data:m.slice().fill(null,d),bindingStartIndex:d,expandoStartIndex:p,hostBindingOpCodes:null,firstCreatePass:!0,firstUpdatePass:!0,staticViewQueries:!1,staticContentQueries:!1,preOrderHooks:null,preOrderCheckHooks:null,contentHooks:null,contentCheckHooks:null,viewHooks:null,viewCheckHooks:null,destroyHooks:null,cleanup:null,contentQueries:null,components:null,directiveRegistry:"function"==typeof a?a():a,pipeRegistry:"function"==typeof r?r():r,firstChild:null,schemas:l,consts:f,incompleteFirstPass:!1};return ngDevMode&&Object.seal(g),g}function rd(t,e,n,o){const i=Hd(e);null===n?(ngDevMode&&Object.freeze(Fd(t)),i.push(o)):(i.push(n),t.firstCreatePass&&Fd(t).push(o,i.length-1))}function sd(t,e,n){for(let o in t)if(t.hasOwnProperty(o)){const i=t[o];(n=null===n?{}:n).hasOwnProperty(o)?n[o].push(e,i):n[o]=[e,i]}return n}function ld(t,e,n,o,i,a,r,s){ngDevMode&&pn(i,ac,"Incoming value should never be NO_CHANGE.");const l=oi(e,n);let c,d=e.inputs;if(!s&&null!=d&&(c=d[o]))Vd(t,n,c,o,i),Mo(e)&&(function p(t,e){ngDevMode&&Ro(t);const n=ri(e,t);16&n[2]||(n[2]|=64)})(n,e.index),ngDevMode&&(function m(t,e,n,o,i){if(7&n)for(let a=0;a<o.length;a+=2)cd(t,e,n,o[a+1],i)})(n,l,e.type,c,i);else if(3&e.type){if(o=(function u(t){return"class"===t?"className":"for"===t?"htmlFor":"formaction"===t?"formAction":"innerHtml"===t?"innerHTML":"readonly"===t?"readOnly":"tabindex"===t?"tabIndex":t})(o),ngDevMode){if((function f(t){if(t.toLowerCase().startsWith("on")){const e=`Binding to event property '${t}' is disallowed for security reasons, please use (${t.slice(2)})=...\nIf '${t}' is a directive input, make sure the directive is imported by the current module.`;throw new Error(e)}})(o),!(function g(t,e,n,o){return!(null!==t.schemas&&!(dd(t,o.value)||n in e||sa(n))&&"undefined"!=typeof Node&&null!==Node&&e instanceof Node)})(t,l,o,e))return void pd(o,e);ngDevMode.rendererSetProperty++}i=null!=r?r(i,e.value||"",o):i,$o(a)?a.setProperty(l,o,i):sa(o)||(l.setProperty?l.setProperty(o,i):l[o]=i)}else 12&e.type&&ngDevMode&&!dd(t,e.value)&&pd(o,e)}function cd(t,e,n,o,i){const a=t[11];o=(function r(t){return`ng-reflect-${t=(function e(t){return t.replace(el,((...t)=>"-"+t[1].toLowerCase()))})(t.replace(/[$@]/g,"_"))}`})(o);const s=(function l(t){try{return null!=t?t.toString().slice(0,30):t}catch(t){return"[ERROR] Exception while trying to serialize the value"}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(i);if(3&n)null==i?$o(a)?a.removeAttribute(e,o):e.removeAttribute(o):$o(a)?a.setAttribute(e,o,s):e.setAttribute(o,s);else{const t=$s(`bindings=${JSON.stringify({[o]:s},null,2)}`);$o(a)?a.setValue(e,t):e.textContent=t}}function dd(t,e){const n=t.schemas;if(null!==n)for(let t=0;t<n.length;t++){const o=n[t];if(o===Ks||o===Xs&&e&&e.indexOf("-")>-1)return!0}return!1}function pd(t,e){console.error(Qe("303",`Can't bind to '${t}' since it isn't a known property of '${e.value}'.`))}function md(t,e,n,o){ngDevMode&&Ao(t);let i=!1;if(ui()){const a=(function a(t,e,n){ngDevMode&&Ao(t),ngDevMode&&ia(n,15);const o=t.directiveRegistry;let i=null;if(o)for(let a=0;a<o.length;a++){const r=o[a];ec(n,r.selectors,!1)&&(i||(i=ngDevMode?new Sc:[]),Ca(ha(n,e),t,r.type),xo(r)?(ngDevMode&&(ia(n,2,`"${n.value}" tags cannot be used as component hosts. 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Ud(t,e,n){ngDevMode&&Ao(gi(),"Expecting to be called in first template pass only");let o=n?t.styles:null,i=n?t.classes:null,a=0;if(null!==e)for(let t=0;t<e.length;t++){const n=e[t];"number"==typeof n?a=n:1==a?i=We(i,n):2==a&&(o=We(o,n+": "+e[++t]+";"))}n?t.styles=o:t.stylesWithoutHost=o,n?t.classes=i:t.classesWithoutHost=i}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Gd=new Ga("INJECTOR",-1);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Wd{get(t,e=gr){if(e===gr){const e=new Error(`NullInjectorError: No provider for ${Ge(t)}!`);throw e.name="NullInjectorError",e}return e}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Yd=new Ga("Set Injector scope."),qd={},Zd={};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let Xd;function Kd(){return void 0===Xd&&(Xd=new Wd),Xd}function Jd(t,e=null,n=null,o){return new Qd(t,n,e||Kd(),o)}class Qd{constructor(t,e,n,o=null){this.parent=n,this.records=new Map,this.injectorDefTypes=new Set,this.onDestroy=new Set,this._destroyed=!1;const i=[];e&&er(e,(n=>this.processProvider(n,t,e))),er([t],(t=>this.processInjectorType(t,[],i))),this.records.set(Gd,ep(void 0,this));const a=this.records.get(Yd);this.scope=null!=a?a.value:null,this.source=o||("object"==typeof t?null:Ge(t))}get destroyed(){return this._destroyed}destroy(){this.assertNotDestroyed(),this._destroyed=!0;try{this.onDestroy.forEach((t=>t.ngOnDestroy()))}finally{this.records.clear(),this.onDestroy.clear(),this.injectorDefTypes.clear()}}get(t,e=gr,n=En.Default){this.assertNotDestroyed();const o=Cr(this),i=An(void 0);try{if(!(n&En.SkipSelf)){let e=this.records.get(t);if(void 0===e){const n=(function o(t){return"function"==typeof t||"object"==typeof t&&t instanceof Ga})(t)&&xn(t);e=n&&this.injectableDefInScope(n)?ep($d(t),qd):null,this.records.set(t,e)}if(null!=e)return this.hydrate(t,e)}return(n&En.Self?Kd():this.parent).get(t,e=n&En.Optional&&e===gr?null:e)}catch(e){if("NullInjectorError"===e.name){if((e.ngTempTokenPath=e.ngTempTokenPath||[]).unshift(Ge(t)),o)throw e;return(function n(t,e,o,i){const a=t.ngTempTokenPath;throw e[br]&&a.unshift(e[br]),t.message=(function r(t,e,n,o=null){t=t&&"\n"===t.charAt(0)&&"ɵ"==t.charAt(1)?t.substr(2):t;let i=Ge(e);if(Array.isArray(e))i=e.map(Ge).join(" -> ");else if("object"==typeof e){let t=[];for(let n in e)if(e.hasOwnProperty(n)){let o=e[n];t.push(n+":"+("string"==typeof o?JSON.stringify(o):Ge(o)))}i=`{${t.join(", ")}}`}return`${n}${o?"("+o+")":""}[${i}]: ${t.replace(hr,"\n  ")}`})("\n"+t.message,a,o,i),t.ngTokenPath=a,t.ngTempTokenPath=null,t})(e,t,"R3InjectorError",this.source)}throw e}finally{An(i),Cr(o)}}_resolveInjectorDefTypes(){this.injectorDefTypes.forEach((t=>this.get(t)))}toString(){const t=[];return this.records.forEach(((e,n)=>t.push(Ge(n)))),`R3Injector[${t.join(", ")}]`}assertNotDestroyed(){if(this._destroyed)throw new Error("Injector has already been destroyed.")}processInjectorType(t,e,n){if(!(t=Ze(t)))return!1;let o=Pn(t);const i=null==o&&t.ngModule||void 0,a=void 0===i?t:i;ngDevMode&&-1!==e.indexOf(a)&&en(Ge(a),e.map(Ge));const r=-1!==n.indexOf(a);if(void 0!==i&&(o=Pn(i)),null==o)return!1;if(null!=o.imports&&!r){let t;ngDevMode&&e.push(a),n.push(a);try{er(o.imports,(o=>{this.processInjectorType(o,e,n)&&(void 0===t&&(t=[]),t.push(o))}))}finally{ngDevMode&&e.pop()}if(void 0!==t)for(let e=0;e<t.length;e++){const{ngModule:n,providers:o}=t[e];er(o,(t=>this.processProvider(t,n,o||Wn)))}}this.injectorDefTypes.add(a);const s=Fo(a)||(()=>new a);this.records.set(a,ep(s,qd));const l=o.providers;if(null!=l&&!r){const e=t;er(l,(t=>this.processProvider(t,e,l)))}return void 0!==i&&void 0!==t.providers}processProvider(t,e,n){let o=op(t=Ze(t))?t:Ze(t&&t.provide);const i=(function a(t,e,n){return np(t)?ep(void 0,t.useValue):ep(tp(t,e,n),qd)})(t,e,n);if(op(t)||!0!==t.multi){const t=this.records.get(o);ngDevMode&&t&&void 0!==t.multi&&nn()}else{let e=this.records.get(o);e?ngDevMode&&void 0===e.multi&&nn():(e=ep(void 0,qd,!0),e.factory=()=>Pr(e.multi),this.records.set(o,e)),o=t,e.multi.push(t)}this.records.set(o,i)}hydrate(t,e){return ngDevMode&&e.value===Zd?en(Ge(t)):e.value===qd&&(e.value=Zd,e.value=e.factory()),"object"==typeof e.value&&e.value&&(function n(t){return null!==t&&"object"==typeof t&&"function"==typeof t.ngOnDestroy})(e.value)&&this.onDestroy.add(e.value),e.value}injectableDefInScope(t){if(!t.providedIn)return!1;const e=Ze(t.providedIn);return"string"==typeof e?"any"===e||e===this.scope:this.injectorDefTypes.has(e)}}function $d(t){const e=xn(t),n=null!==e?e.factory:Fo(t);if(null!==n)return n;if(t instanceof Ga)throw new Error(`Token ${Ge(t)} is missing a ɵprov definition.`);if(t instanceof Function)return(function o(t){const e=t.length;if(e>0){const n=ir(e,"?");throw new Error(`Can't resolve all parameters for ${Ge(t)}: (${n.join(", ")}).`)}const n=(function o(t){const e=t&&(t[wn]||t[Sn]);if(e){const n=(function n(t){if(t.hasOwnProperty("name"))return t.name;const e=(""+t).match(/^function\s*([^\s(]+)/);return null===e?"":e[1]})(t);return console.warn(`DEPRECATED: DI is instantiating a token "${n}" that inherits its @Injectable decorator but does not provide one itself.\nThis will become an error in a future version of Angular. Please add @Injectable() to the "${n}" class.`),e}return null})(t);return null!==n?()=>n.factory(t):()=>new t})(t);throw new Error("unreachable")}function tp(t,e,n){let o;if(op(t)){const e=Ze(t);return Fo(e)||$d(e)}if(np(t))o=()=>Ze(t.useValue);else if((function i(t){return!(!t||!t.useFactory)})(t))o=()=>t.useFactory(...Pr(t.deps||[]));else if((function a(t){return!(!t||!t.useExisting)})(t))o=()=>vr(Ze(t.useExisting));else{const i=Ze(t&&(t.useClass||t.provide));if(ngDevMode&&!i&&(function r(t,e,n){let o="";throw t&&e&&(o=` - only instances of Provider and Type are allowed, got: [${e.map((t=>t==n?"?"+n+"?":"...")).join(", ")}]`),new Error(`Invalid provider for the NgModule '${Ge(t)}'`+o)})(e,n,t),!(function s(t){return!!t.deps})(t))return Fo(i)||$d(i);o=()=>new i(...Pr(t.deps))}return o}function ep(t,e,n=!1){return{factory:t,value:e,multi:n?[]:void 0}}function np(t){return null!==t&&"object"==typeof t&&yr in t}function op(t){return"function"==typeof t}const ip=function ap(t,e,n){return(function o(t,e=null,n=null,i){const a=Jd(t,e,n,i);return a._resolveInjectorDefTypes(),a})({name:n},e,t,n)};class rp{static create(t,e){return Array.isArray(t)?ip(t,e,""):ip(t.providers,t.parent,t.name||"")}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function sp(t){bp(t);const e=Is(t);return null===e?null:(void 0===e.component&&(e.component=(function n(t,e){const n=e[1].data[t];return 2&n.flags?e[n.directiveStart]:null})(e.nodeIndex,e.lView)),e.component)}function lp(t){bp(t);const e=Is(t);return null===e?null:e.lView[8]}function cp(t){const e=Is(t);if(null===e)return null;let n,o=e.lView;for(ngDevMode&&Ro(o);2===o[1].type&&(n=ml(o));)o=n;return 512&o[2]?null:o[8]}function dp(t){return[...ul(t).components]}function pp(t){const e=Is(t);return null===e?rp.NULL:new Ra(e.lView[1].data[e.nodeIndex],e.lView)}function mp(t){if(t instanceof Text)return[];const e=Is(t);if(null===e)return[];const n=e.lView,o=n[1],i=e.nodeIndex;return(null==o?void 0:o.data[i])?(void 0===e.directives&&(e.directives=Ws(i,n,!1)),null===e.directives?[]:[...e.directives]):[]}function up(t){const{constructor:e}=t;if(!e)throw new Error("Unable to find the instance constructor");const n=po(e);if(n)return{inputs:n.inputs,outputs:n.outputs,encapsulation:n.encapsulation,changeDetection:n.onPush?zn.OnPush:zn.Default};const o=mo(e);return o?{inputs:o.inputs,outputs:o.outputs}:null}function fp(t){return Is(t).native}function gp(t){bp(t);const e=Is(t);if(null===e)return[];const n=e.lView,o=n[7],i=n[1].cleanup,a=[];if(i&&o)for(let e=0;e<i.length;){const r=i[e++],s=i[e++];if("string"==typeof r){const l=r,c=ei(n[s]),d=o[i[e++]],p=i[e++],m="boolean"==typeof p||p>=0?"dom":"output",u="boolean"==typeof p&&p;t==c&&a.push({element:t,name:l,callback:d,useCapture:u,type:m})}}return a.sort(hp),a}function hp(t,e){return t.name==e.name?0:t.name<e.name?-1:1}function bp(t){if("undefined"!=typeof Element&&!(t instanceof Element))throw new Error("Expecting instance of DOM Element")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function yp(t){(function e(t){ngDevMode&&hn(t,"component");const e=Ed(Fs(t));ngDevMode&&hn(e[8],"rootContext should be defined"),(function n(t,e){if(0===t.flags&&t.clean==Wc){let n;t.flags|=e,t.clean=new Promise((t=>n=t)),t.scheduler((()=>{if(1&t.flags&&(t.flags&=-2,Rd(t)),2&t.flags){t.flags&=-3;const e=t.playerHandler;e&&e.flushPlayers()}t.clean=Wc,n(null)}))}})(e[8],1)})(t),dp(t).forEach((t=>(function e(t){const e=Fs(t);Ad(e[1],e,t)})(t)))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */rp.THROW_IF_NOT_FOUND=gr,rp.NULL=new Wd,rp.ɵprov=Mn({token:rp,providedIn:"any",factory:()=>vr(Gd)}),rp.__NG_ELEMENT_ID__=-1;let _p=!1;function Cp(t,e){if("undefined"==typeof COMPILED||!COMPILED){const n=jn;if(ngDevMode&&hn(e,"function not defined"),n){let o=n.ng;o||(o=n.ng={}),o[t]=e}}}function Mp(t,e){const n=Vs(t);ngDevMode&&hn(n,"LView is required");const o=n[1],i=bi();ngDevMode&&hn(i,"TNode is required"),Xi(o,i)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Sp=["providersResolver"],Dp=["template","decls","consts","vars","onPush","ngContentSelectors","styles","encapsulation","schemas"];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let Ep=null;function Rp(){if(!Ep){const t=jn.Symbol;if(t&&t.iterator)Ep=t.iterator;else{const t=Object.getOwnPropertyNames(Map.prototype);for(let e=0;e<t.length;++e){const n=t[e];"entries"!==n&&"size"!==n&&Map.prototype[n]===Map.prototype.entries&&(Ep=n)}}}return Ep}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Ap(t,e){const n=Np(t),o=Np(e);if(n&&o)return(function i(t,e,n){const o=t[Rp()](),i=e[Rp()]();for(;;){const t=o.next(),e=i.next();if(t.done&&e.done)return!0;if(t.done||e.done)return!1;if(!n(t.value,e.value))return!1}})(t,e,Ap);{const i=t&&("object"==typeof t||"function"==typeof t),a=e&&("object"==typeof e||"function"==typeof e);return!(n||!i||o||!a)||Object.is(t,e)}}class Tp{constructor(t){this.wrapped=t}static wrap(t){return new Tp(t)}static unwrap(t){return Tp.isWrapped(t)?t.wrapped:t}static isWrapped(t){return t instanceof Tp}}function Np(t){return!!zp(t)&&(Array.isArray(t)||!(t instanceof Map)&&Rp()in t)}function zp(t){return null!==t&&("function"==typeof t||"object"==typeof t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Ip(t,e,n){return t[e]=n}function Hp(t,e){return ngDevMode&&_n(t,e),ngDevMode&&pn(t[e],ac,"Stored value should never be NO_CHANGE."),t[e]}function Fp(t,e,n){ngDevMode&&pn(n,ac,"Incoming value should never be NO_CHANGE."),ngDevMode&&mn(e,t.length,"Slot should have been initialized to NO_CHANGE");const o=t[e];if(Object.is(o,n))return!1;if(ngDevMode&&xi()){const i=o!==ac?o:void 0;if(!Ap(i,n)){const a=(function i(t,e,n,o){const i=t[1].data,a=i[e];if("string"==typeof a)return a.indexOf(al)>-1?ll(t,e,e,a,o):{propName:a,oldValue:n,newValue:o};if(null===a){let n=e-1;for(;"string"!=typeof i[n]&&null===i[n+1];)n--;const a=i[n];if("string"==typeof a){const i=a.match(new RegExp(al,"g"));if(i&&i.length-1>e-n)return ll(t,n,e,a,o)}}return{propName:void 0,oldValue:n,newValue:o}})(t,e,i,n);!(function a(t,e,n,o){let i=`ExpressionChangedAfterItHasBeenCheckedError: Expression has changed after it was checked. Previous value${o?` for '${o}'`:""}: '${e}'. Current value: '${n}'.`;throw t&&(i+=" It seems like the view has been created after its parent and its children have been dirty checked. Has it been created in a change detection hook?"),new Ke("100",i)})(o===ac,a.oldValue,a.newValue,a.propName)}return!1}return t[e]=n,!0}function Lp(t,e,n,o){const i=Fp(t,e,n);return Fp(t,e+1,o)||i}function Bp(t,e,n,o,i){const a=Lp(t,e,n,o);return Fp(t,e+2,i)||a}function Vp(t,e,n,o,i,a){const r=Lp(t,e,n,o);return Lp(t,e+2,i,a)||r}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function jp(t,e,n,o){const i=fi(),a=Si();if(Fp(i,a,e)){const r=gi(),s=Yi();Cd(s,i,t,e,n,o),ngDevMode&&zd(r.data,s,"attr."+t,a)}return jp}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Up(t,e){ngDevMode&&mn(2,e.length,"should have at least 3 values"),ngDevMode&&ln(e.length%2,1,"should have an odd number of values");let n=!1,o=wi();for(let i=1;i<e.length;i+=2)n=Fp(t,o++,e[i])||n;if(ki(o),!n)return ac;let i=e[0];for(let t=1;t<e.length;t+=2)i+=$e(e[t])+e[t+1];return i}function Gp(t,e,n,o){return Fp(t,Si(),n)?e+$e(n)+o:ac}function Wp(t,e,n,o,i,a){const r=Lp(t,wi(),n,i);return Di(2),r?e+$e(n)+o+$e(i)+a:ac}function Yp(t,e,n,o,i,a,r,s){const l=Bp(t,wi(),n,i,r);return Di(3),l?e+$e(n)+o+$e(i)+a+$e(r)+s:ac}function qp(t,e,n,o,i,a,r,s,l,c){const d=Vp(t,wi(),n,i,r,l);return Di(4),d?e+$e(n)+o+$e(i)+a+$e(r)+s+$e(l)+c:ac}function Zp(t,e,n,o,i,a,r,s,l,c,d,p){const m=wi();let u=Vp(t,m,n,i,r,l);return u=Fp(t,m+4,d)||u,Di(5),u?e+$e(n)+o+$e(i)+a+$e(r)+s+$e(l)+c+$e(d)+p:ac}function Xp(t,e,n,o,i,a,r,s,l,c,d,p,m,u){const f=wi();let g=Vp(t,f,n,i,r,l);return g=Lp(t,f+4,d,m)||g,Di(6),g?e+$e(n)+o+$e(i)+a+$e(r)+s+$e(l)+c+$e(d)+p+$e(m)+u:ac}function Kp(t,e,n,o,i,a,r,s,l,c,d,p,m,u,f,g){const h=wi();let b=Vp(t,h,n,i,r,l);return b=Bp(t,h+4,d,m,f)||b,Di(7),b?e+$e(n)+o+$e(i)+a+$e(r)+s+$e(l)+c+$e(d)+p+$e(m)+u+$e(f)+g:ac}function Jp(t,e,n,o,i,a,r,s,l,c,d,p,m,u,f,g,h,b){const y=wi();let _=Vp(t,y,n,i,r,l);return _=Vp(t,y+4,d,m,f,h)||_,Di(8),_?e+$e(n)+o+$e(i)+a+$e(r)+s+$e(l)+c+$e(d)+p+$e(m)+u+$e(f)+g+$e(h)+b:ac}function Qp(t,e,n,o,i,a,r,s){const l=fi(),c=gi(),d=t+go,p=c.firstCreatePass?(
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function m(t,e,n,o,i,a,r,s,l){ngDevMode&&Ao(e),ngDevMode&&ngDevMode.firstCreatePass++;const c=e.consts,d=Zc(e,t,4,r||null,ci(c,s));md(e,n,d,ci(c,l)),Xi(e,d);const p=d.tViews=ad(2,d,o,i,a,e.directiveRegistry,e.pipeRegistry,null,e.schemas,c);return null!==e.queries&&(e.queries.template(e,d),p.queries=e.queries.embeddedTView(d)),d})(d,c,l,e,n,o,i,a,r):c.data[d];Ci(p,!1);const u=l[11].createComment(ngDevMode?"container":"");Hl(c,l,u,p),Ls(u,l),Dd(l,l[d]=Pd(u,l,u,p)),vo(p)&&nd(c,l,p),null!=r&&od(l,p,s)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function $p(t){return ai((function e(){return mi.lFrame.contextLView})(),go+t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const tm={"ɵɵdefineInjectable":Mn,"ɵɵdefineInjector":vn,"ɵɵinject":vr,"ɵɵinvalidFactoryDep":xr,resolveForwardRef:Ze},em=je({provide:String,useValue:je});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function nm(t){return void 0!==t.useClass}function om(t){return void 0!==t.useFactory}je({provide:String,useValue:je});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const im=Fa("Injectable",void 0,void 0,void 0,((t,e)=>am(t,e))),am=function rm(t,e){let n=null,o=null;t.hasOwnProperty(wn)||Object.defineProperty(t,wn,{get:()=>{if(null===n){const o=Ja({usage:0,kind:"injectable",type:t});n=o.compileInjectable(tm,`ng:///${t.name}/ɵprov.js`,(function o(t,e){const n=e||{providedIn:null},o={name:t.name,type:t,typeArgumentCount:0,providedIn:n.providedIn};return(nm(n)||om(n))&&void 0!==n.deps&&(o.deps=zr(n.deps)),nm(n)?o.useClass=n.useClass:(function i(t){return em in t})(n)?o.useValue=n.useValue:om(n)?o.useFactory=n.useFactory:(function a(t){return void 0!==t.useExisting})(n)&&(o.useExisting=n.useExisting),o}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function e(t){const e=[];for(let n=0;n<t.length;++n){if(e.indexOf(t[n])>-1)return e.push(t[n]),e;e.push(t[n])}return e})(t.slice().reverse()).map((t=>Ge(t.token))).join(" -> ")+")":""}function lm(t,e,n,o){const i=[e],a=n(i),r=o?(function s(t,e){const n=`${t} caused by: ${e instanceof Error?e.message:e}`,o=Error(n);return o.ngOriginalError=e,o})(a,o):Error(a);return r.addKey=cm,r.keys=i,r.injectors=[t],r.constructResolvingMessage=n,r.ngOriginalError=o,r}function cm(t,e){this.injectors.push(t),this.keys.push(e),this.message=this.constructResolvingMessage(this.keys)}function dm(t,e){const n=[];for(let t=0,o=e.length;t<o;t++){const o=e[t];n.push(o&&0!=o.length?o.map(Ge).join(" "):"?")}return Error("Cannot resolve all parameters for '"+Ge(t)+"'("+n.join(", ")+"). Make sure that all the parameters are decorated with Inject or have valid type annotations and that '"+Ge(t)+"' is decorated with Injectable.")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class pm{constructor(t,e){if(this.token=t,this.id=e,!t)throw new Error("Token must be defined!");this.displayName=Ge(this.token)}static get(t){return mm.get(Ze(t))}static get numberOfKeys(){return mm.numberOfKeys}}const mm=new class{constructor(){this._allKeys=new Map}get(t){if(t instanceof pm)return t;if(this._allKeys.has(t))return this._allKeys.get(t);const e=new pm(t,pm.numberOfKeys);return this._allKeys.set(t,e),e}get numberOfKeys(){return this._allKeys.size}},um=new
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class{constructor(t){this.reflectionCapabilities=t}updateCapabilities(t){this.reflectionCapabilities=t}factory(t){return this.reflectionCapabilities.factory(t)}parameters(t){return this.reflectionCapabilities.parameters(t)}annotations(t){return this.reflectionCapabilities.annotations(t)}propMetadata(t){return this.reflectionCapabilities.propMetadata(t)}hasLifecycleHook(t,e){return this.reflectionCapabilities.hasLifecycleHook(t,e)}getter(t){return this.reflectionCapabilities.getter(t)}setter(t){return this.reflectionCapabilities.setter(t)}method(t){return this.reflectionCapabilities.method(t)}importUri(t){return this.reflectionCapabilities.importUri(t)}resourceUri(t){return this.reflectionCapabilities.resourceUri(t)}resolveIdentifier(t,e,n,o){return this.reflectionCapabilities.resolveIdentifier(t,e,n,o)}resolveEnum(t,e){return this.reflectionCapabilities.resolveEnum(t,e)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */(new mr);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class fm{constructor(t,e,n){this.key=t,this.optional=e,this.visibility=n}static fromKey(t){return new fm(t,!1,null)}}const gm=[];class hm{constructor(t,e,n){this.key=t,this.resolvedFactories=e,this.multiProvider=n,this.resolvedFactory=this.resolvedFactories[0]}}class bm{constructor(t,e){this.factory=t,this.dependencies=e}}function ym(t){let e,n;if(t.useClass){const o=Ze(t.useClass);e=um.factory(o),n=vm(o)}else t.useExisting?(e=t=>t,n=[fm.fromKey(pm.get(t.useExisting))]):t.useFactory?(e=t.useFactory,n=(function o(t,e){if(e){const n=e.map((t=>[t]));return e.map((e=>xm(t,e,n)))}return vm(t)})(t.useFactory,t.deps)):(e=()=>t.useValue,n=gm);return new bm(e,n)}function _m(t){return new hm(pm.get(t.provide),[ym(t)],t.multi||!1)}function Cm(t){const e=(function n(t,e){for(let n=0;n<t.length;n++){const o=t[n],i=e.get(o.key.id);if(i){if(o.multiProvider!==i.multiProvider)throw Error(`Cannot mix multi providers and regular providers, got: ${i} ${o}`);if(o.multiProvider)for(let t=0;t<o.resolvedFactories.length;t++)i.resolvedFactories.push(o.resolvedFactories[t]);else e.set(o.key.id,o)}else{let t;t=o.multiProvider?new hm(o.key,o.resolvedFactories.slice(),o.multiProvider):o,e.set(o.key.id,t)}}return e})(Mm(t,[]).map(_m),new Map);return Array.from(e.values())}function Mm(t,e){return t.forEach((t=>{if(t instanceof Qa)e.push({provide:t,useClass:t});else if(t&&"object"==typeof t&&void 0!==t.provide)e.push(t);else{if(!Array.isArray(t))throw(function n(t){return Error(`Invalid provider - only instances of Provider and Type are allowed, got: ${t}`)})(t);Mm(t,e)}})),e}function vm(t){const e=um.parameters(t);if(!e)return[];if(e.some((t=>null==t)))throw dm(t,e);return e.map((n=>xm(t,n,e)))}function xm(t,e,n){let o=null,i=!1;if(!Array.isArray(e))return Om(e instanceof kr?e.token:e,i,null);let a=null;for(let t=0;t<e.length;++t){const n=e[t];n instanceof Qa?o=n:n instanceof kr?o=n.token:n instanceof Sr?i=!0:n instanceof Dr||n instanceof Er?a=n:n instanceof Ga&&(o=n)}if(o=Ze(o),null!=o)return Om(o,i,a);throw dm(t,n)}function Om(t,e,n){return new fm(pm.get(t),e,n)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Pm={};class wm{static resolve(t){return Cm(t)}static resolveAndCreate(t,e){const n=wm.resolve(t);return wm.fromResolvedProviders(n,e)}static fromResolvedProviders(t,e){return new km(t,e)}}class km{constructor(t,e){this._constructionCounter=0,this._providers=t,this.parent=e||null;const n=t.length;this.keyIds=[],this.objs=[];for(let e=0;e<n;e++)this.keyIds[e]=t[e].key.id,this.objs[e]=Pm}get(t,e=gr){return this._getByKey(pm.get(t),null,e)}resolveAndCreateChild(t){const e=wm.resolve(t);return this.createChildFromResolved(e)}createChildFromResolved(t){const e=new km(t);return e.parent=this,e}resolveAndInstantiate(t){return this.instantiateResolved(wm.resolve([t])[0])}instantiateResolved(t){return this._instantiateProvider(t)}getProviderAtIndex(t){if(t<0||t>=this._providers.length)throw(function e(t){return Error(`Index ${t} is out-of-bounds.`)})(t);return this._providers[t]}_new(t){if(this._constructionCounter++>this._getMaxNumberOfObjects())throw(function e(t,n){return lm(t,n,(function(t){return`Cannot instantiate cyclic dependency!${sm(t)}`}))})(this,t.key);return this._instantiateProvider(t)}_getMaxNumberOfObjects(){return this.objs.length}_instantiateProvider(t){if(t.multiProvider){const e=[];for(let n=0;n<t.resolvedFactories.length;++n)e[n]=this._instantiate(t,t.resolvedFactories[n]);return e}return this._instantiate(t,t.resolvedFactories[0])}_instantiate(t,e){const n=e.factory;let o,i;try{o=e.dependencies.map((t=>this._getByReflectiveDependency(t)))}catch(e){throw e.addKey&&e.addKey(this,t.key),e}try{i=n(...o)}catch(e){throw(function n(t,e,o,i){return lm(t,i,(function(t){const n=Ge(t[0].token);return`${e.message}: Error during instantiation of ${n}!${sm(t)}.`}),e)})(this,e,0,t.key)}return i}_getByReflectiveDependency(t){return this._getByKey(t.key,t.visibility,t.optional?null:gr)}_getByKey(t,e,n){return t===km.INJECTOR_KEY?this:e instanceof Dr?this._getByKeySelf(t,n):this._getByKeyDefault(t,n,e)}_getObjByKeyId(t){for(let e=0;e<this.keyIds.length;e++)if(this.keyIds[e]===t)return this.objs[e]===Pm&&(this.objs[e]=this._new(this._providers[e])),this.objs[e];return Pm}_throwOrNull(t,e){if(e!==gr)return e;throw(function n(t,e){return lm(t,e,(function(t){return`No provider for ${Ge(t[0].token)}!${sm(t)}`}))})(this,t)}_getByKeySelf(t,e){const n=this._getObjByKeyId(t.id);return n!==Pm?n:this._throwOrNull(t,e)}_getByKeyDefault(t,e,n){let o;for(o=n instanceof Er?this.parent:this;o instanceof km;){const e=o,n=e._getObjByKeyId(t.id);if(n!==Pm)return n;o=e.parent}return null!==o?o.get(t.token,e):this._throwOrNull(t,e)}get displayName(){return`ReflectiveInjector(providers: [${(function t(e,n){const o=[];for(let t=0;t<e._providers.length;++t)o[t]=n(e.getProviderAtIndex(t));return o}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(this,(t=>' "'+t.key.displayName+'" ')).join(", ")}])`}toString(){return this.displayName}}function Sm(t,e=En.Default){const n=fi();return null===n?(ngDevMode&&(function o(t){ngDevMode&&cn(Rn,t,"Calling ɵɵinject would cause infinite recursion")})(Sm),vr(t,e)):xa(bi(),n,Ze(t),e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function Dm(t,e,n){const o=fi(),i=Si();if(Fp(o,i,e)){const a=gi(),r=Yi();ld(a,r,o,t,e,o[11],n,!1),ngDevMode&&zd(a.data,r,t,i)}return Dm}function Em(t,e,n,o,i){const a=i?"class":"style";Vd(t,n,e.inputs[a],a,o)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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     * Copyright Google LLC All Rights Reserved.
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     * Copyright Google LLC All Rights Reserved.
     *
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     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
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     * Copyright Google LLC All Rights Reserved.
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     * Copyright Google LLC All Rights Reserved.
     *
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     * Copyright Google LLC All Rights Reserved.
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     * Copyright Google LLC All Rights Reserved.
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     * Copyright Google LLC All Rights Reserved.
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
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/**
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     * Copyright Google LLC All Rights Reserved.
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     * found in the LICENSE file at https://angular.io/license
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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     * Copyright Google LLC All Rights Reserved.
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let Zu=Gu;function Xu(t){hn(t,"Expected localeId to be defined"),"string"==typeof t&&(Zu=t.toLowerCase().replace(/_/g,"-"))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function Ku(t,e,n){const o=e.insertBeforeIndex,i=Array.isArray(o)?o[0]:o;return null===i?Tl(t,0,n):(ngDevMode&&_n(n,i),ei(n[i]))}function Ju(t,e,n,o,i){const a=e.insertBeforeIndex;if(Array.isArray(a)){ngDevMode&&yn(o);let r=o,s=null;if(3&e.type||(s=r,r=i),null!==r&&0==(2&e.flags))for(let e=1;e<a.length;e++)Sl(t,r,n[a[e]],s,!1)}}
/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let df=0,pf=0;function mf(t,e,n,o){ngDevMode&&yn(o);const i=n[11];let a,r=null;for(let s=0;s<e.length;s++){const l=e[s];if("string"==typeof l){const t=e[++s];null===n[t]&&(ngDevMode&&ngDevMode.rendererCreateTextNode++,ngDevMode&&_n(n,t),n[t]=yl(i,l))}else if("number"==typeof l)switch(1&l){case 0:const c=sf(l);let d,p;if(null===r&&(r=c,a=Rl(i,o)),c===r?(d=o,p=a):(d=null,p=ei(n[c])),null!==p){ngDevMode&&yn(p);const e=lf(l);ngDevMode&&fn(e,go,"Missing ref");const o=n[e];ngDevMode&&yn(o),Sl(i,p,o,d,!1);const a=of(t,e);if(null!==a&&"object"==typeof a){ngDevMode&&ko(a);const e=rf(a,n);null!==e&&mf(t,a.create[e],n,n[a.anchorIdx])}}break;case 1:const m=e[++s],u=e[++s];Md(i,ni(l>>>1,n),null,null,m,u,null);break;default:throw new Error(`Unable to determine the type of mutate operation for "${l}"`)}else switch(l){case Yu:const t=e[++s],o=e[++s];null===n[o]&&(ngDevMode&&ln(typeof t,"string",`Expected "${t}" to be a comment node value`),ngDevMode&&ngDevMode.rendererCreateComment++,ngDevMode&&No(n,o),Ls(n[o]=Cl(i,t),n));break;case Wu:const a=e[++s],r=e[++s];null===n[r]&&(ngDevMode&&ln(typeof a,"string",`Expected "${a}" to be an element node tag name`),ngDevMode&&ngDevMode.rendererCreateElement++,ngDevMode&&No(n,r),Ls(n[r]=Ml(i,a,null),n));break;default:ngDevMode&&bn(`Unable to determine the type of mutate operation for "${l}"`)}}}function uf(t,e,n,o,i){for(let a=0;a<n.length;a++){const r=n[a],s=n[++a];if(r&i){let i="";for(let r=a+1;r<=a+s;r++){const a=n[r];if("string"==typeof a)i+=a;else if("number"==typeof a)if(a<0)i+=$e(e[o-a]);else{const s=a>>>2;switch(3&a){case 1:const a=n[++r],l=n[++r],c=t.data[s];ngDevMode&&hn(c,"Experting TNode or string"),"string"==typeof c?Md(e[11],e[s],null,c,a,i,l):ld(t,c,e,a,i,e[11],l,!1);break;case 0:const d=e[s];null!==d&&_l(e[11],d,i);break;case 2:gf(t,of(t,s),e,i);break;case 3:ff(t,of(t,s),o,e)}}}}else{const i=n[a+1];if(i>0&&3==(3&i)){const n=of(t,i>>>2);e[n.currentCaseLViewIndex]<0&&ff(t,n,o,e)}}a+=s}}function ff(t,e,n,o){ngDevMode&&_n(o,e.currentCaseLViewIndex);let i=o[e.currentCaseLViewIndex];if(null!==i){let a=df;i<0&&(i=o[e.currentCaseLViewIndex]=~i,a=-1),uf(t,o,e.update[i],n,a)}}function gf(t,e,n,o){const i=(function a(t,e){let n=t.cases.indexOf(e);if(-1===n)switch(t.type){case 1:{const o=(function i(t,e){const n=Bu(e)(parseInt(t,10)),o=Uu[n];return void 0!==o?o:"other"})(e,(function o(){return Zu})());n=t.cases.indexOf(o),-1===n&&"other"!==o&&(n=t.cases.indexOf("other"));break}case 0:n=t.cases.indexOf("other")}return-1===n?null:n}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function yf(t){const e=t||(Array.isArray(this)?this:[]);let n=[];for(let t=0;t<e.length;t++){const o=e[t++],i=(o&qu.APPEND_EAGERLY)===qu.APPEND_EAGERLY,a=o>>>qu.SHIFT;n.push(`lView[${a}] = document.${(o&qu.COMMENT)===qu.COMMENT?"createComment":"createText"}(${JSON.stringify(e[t])});`),i&&n.push(`parent.appendChild(lView[${a}]);`)}return n}function _f(t){const e=new vf(t||(Array.isArray(this)?this:[]));let n=[];function o(t){const n=t>>>2;switch(3&t){case 0:return`(lView[${n}] as Text).textContent = $$$`;case 1:const t=e.consumeString(),o=e.consumeFunction();return`(lView[${n}] as Element).setAttribute('${t}', ${o?`(${o})($$$)`:"$$$"})`;case 2:return`icuSwitchCase(${n}, $$$)`;case 3:return`icuUpdateCase(${n})`}throw new Error("unexpected OpCode")}for(;e.hasMore();){let t=e.consumeNumber(),i=e.consumeNumber();const a=e.i+i,r=[];let s="";for(;e.i<a;){let t=e.consumeNumberOrString();if("string"==typeof t)s+=t;else if(t<0)s+="${lView[i"+t+"]}";else{const e=o(t);r.push(e.replace("$$$","`"+s+"`")+";"),s=""}}n.push(`if (mask & 0b${t.toString(2)}) { ${r.join(" ")} }`)}return n}function Cf(t){const e=new vf(t||(Array.isArray(this)?this:[]));let n=[];function o(t){const n=sf(t),o=lf(t);switch(cf(t)){case 0:return`(lView[${n}] as Element).appendChild(lView[${i}])`;case 1:return`(lView[${o}] as Element).setAttribute("${e.consumeString()}", "${e.consumeString()}")`}throw new Error("Unexpected OpCode: "+cf(t))}let i=-1;for(;e.hasMore();){let t=e.consumeNumberStringOrMarker();if(t===Yu){const t=e.consumeString();i=e.consumeNumber(),n.push(`lView[${i}] = document.createComment("${t}")`)}else if(t===Wu){const t=e.consumeString();i=e.consumeNumber(),n.push(`lView[${i}] = document.createElement("${t}")`)}else if("string"==typeof t)i=e.consumeNumber(),n.push(`lView[${i}] = document.createTextNode("${t}")`);else{if("number"!=typeof t)throw new Error("Unexpected value");{const e=o(t);e&&n.push(e)}}}return n}function Mf(t){const e=t||(Array.isArray(this)?this:[]);let n=[];for(let t=0;t<e.length;t++){const o=e[t];n.push(o>0?`remove(lView[${o}])`:`removeNestedICU(${~o})`)}return n}class vf{constructor(t){this.i=0,this.codes=t}hasMore(){return this.i<this.codes.length}consumeNumber(){let t=this.codes[this.i++];return an(t,"expecting number in OpCode"),t}consumeString(){let t=this.codes[this.i++];return sn(t,"expecting string in OpCode"),t}consumeFunction(){let t=this.codes[this.i++];if(null===t||"function"==typeof t)return t;throw new Error("expecting function in OpCode")}consumeNumberOrString(){let t=this.codes[this.i++];return"string"==typeof t||an(t,"expecting number or string in OpCode"),t}consumeNumberStringOrMarker(){let t=this.codes[this.i++];return"string"==typeof t||"number"==typeof t||t==Yu||t==Wu||an(t,"expecting number, string, ICU_MARKER or ELEMENT_MARKER in OpCode"),t}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function Qf(t,e,n=-1){const o=gi(),i=fi(),a=go+t;ngDevMode&&hn(o,"tView should be defined");const r=ci(o.consts,e),s=_i();o.firstCreatePass&&(function l(t,e,n,o,i,a){const r=_i(),s=[],l=[],c=[[]];ngDevMode&&(bc(s,yf),bc(l,_f));const d=(function m(t){return t.replace(Df," ")})(i=(function p(t,e){if((function n(t){return-1===t})(e))return zf(t);{const n=t.indexOf(`:${e}�`)+2+e.toString().length,o=t.search(new RegExp(`�\\/\\*\\d+:${e}�`));return zf(t.substring(n,o))}})(i,a)).split(Sf);for(let a=0;a<d.length;a++){let p=d[a];if(0==(1&a)){const a=Ff(p);for(let d=0;d<a.length;d++){let p=a[d];if(0==(1&d)){const e=p;ngDevMode&&sn(e,"Parsed ICU part should be string"),""!==e&&Rf(t,r,c[0],s,l,n,e)}else{const a=p;if("object"!=typeof a)throw new Error(`Unable to parse ICU expression in "${i}" message.`);const d=Ef(t,r,c[0],n,s,ngDevMode?`ICU ${o}:${a.mainBinding}`:"",!0).index;ngDevMode&&gn(d,go,"Index must be in absolute LView offset"),If(t,n,l,e,a,d)}}}else{const e=47===p.charCodeAt(0),n=p.charCodeAt(e?1:0);ngDevMode&&Cn(n,42,35);const o=go+Number.parseInt(p.substring(e?2:1));if(e)c.shift(),Ci(_i(),!1);else{const e=af(t,c[0],o);c.unshift([]),Ci(e,!0)}}}t.data[o]={create:s,update:l}})(o,null===s?0:s.index,i,a,r,n);const c=o.data[a],d=kl(o,s===i[6]?null:s,i);!(function p(t,e,n,o){const i=t[11];for(let a=0;a<e.length;a++){const r=e[a++],s=e[a],l=(r&qu.COMMENT)===qu.COMMENT,c=(r&qu.APPEND_EAGERLY)===qu.APPEND_EAGERLY,d=r>>>qu.SHIFT;let p=t[d];null===p&&(p=t[d]=l?i.createComment(s):yl(i,s)),c&&null!==n&&Sl(i,n,p,o,!1)}})(i,c.create,d,s&&8&s.type?i[s.index]:null),Ei(!0)}function $f(){Ei(!1)}function tg(t,e,n){Qf(t,e,n),$f()}function eg(t){return(function e(t){t&&(df|=1<<Math.min(pf,31)),pf++})(Fp(fi(),Si(),t)),eg}function ng(t){!(function e(t,n,o){if(pf>0){ngDevMode&&hn(t,"tView should be defined");const e=t.data[o];uf(t,n,Array.isArray(e)?e:e.update,wi()-pf-1,df)}df=0,pf=0})(gi(),fi(),t+go)}function og(t,e={}){return(function n(t,e={}){let n=t;if(Wf.test(t)){const t={},e=[0];n=n.replace(Yf,((n,o,i)=>{const a=o||i,r=t[a]||[];if(r.length||(a.split("|").forEach((t=>{const e=t.match(Jf),n=e?parseInt(e[1],10):0,o=Kf.test(t);r.push([n,o,t])})),t[a]=r),!r.length)throw new Error(`i18n postprocess: unmatched placeholder - ${a}`);const s=e[e.length-1];let l=0;for(let t=0;t<r.length;t++)if(r[t][0]===s){l=t;break}const[c,d,p]=r[l];return d?e.pop():s!==c&&e.push(c),r.splice(l,1),p}))}return Object.keys(e).length?(n=n.replace(qf,((t,n,o,i,a,r)=>e.hasOwnProperty(o)?`${n}${e[o]}${r}`:t)),n=n.replace(Zf,((t,n)=>e.hasOwnProperty(n)?e[n]:t)),n=n.replace(Xf,((t,n)=>{if(e.hasOwnProperty(n)){const o=e[n];if(!o.length)throw new Error(`i18n postprocess: unmatched ICU - ${t} with key: ${n}`);return o.shift()}return t})),n):n})(t,e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function ig(t,e,n,o,i){if(t=Ze(t),Array.isArray(t))for(let a=0;a<t.length;a++)ig(t[a],e,n,o,i);else{const a=gi(),r=fi();let s=op(t)?t:Ze(t.provide),l=tp(t);const c=bi(),d=1048575&c.providerIndexes,p=c.directiveStart,m=c.providerIndexes>>20;if(op(t)||!t.multi){const o=new na(l,i,Sm),u=sg(s,e,i?d:d+m,p);-1===u?(Ca(ha(c,r),a,s),ag(a,t,e.length),e.push(s),c.directiveStart++,c.directiveEnd++,i&&(c.providerIndexes+=1048576),n.push(o),r.push(o)):(n[u]=o,r[u]=o)}else{const u=sg(s,e,d+m,p),f=sg(s,e,d,d+m),g=u>=0&&n[u],h=f>=0&&n[f];if(i&&!h||!i&&!g){Ca(ha(c,r),a,s);const d=(function a(t,e,n,o,i){const a=new na(t,n,Sm);return a.multi=[],a.index=e,a.componentProviders=0,rg(a,i,o&&!n),a})(i?cg:lg,n.length,i,o,l);!i&&h&&(n[f].providerFactory=d),ag(a,t,e.length,0),e.push(s),c.directiveStart++,c.directiveEnd++,i&&(c.providerIndexes+=1048576),n.push(d),r.push(d)}else ag(a,t,u>-1?u:f,rg(n[i?f:u],l,!i&&o));!i&&o&&h&&n[f].componentProviders++}}}function ag(t,e,n,o){const i=op(e);if(i||(function a(t){return!!t.useClass})(e)){const a=(e.useClass||e).prototype.ngOnDestroy;if(a){const r=t.destroyHooks||(t.destroyHooks=[]);if(!i&&e.multi){ngDevMode&&hn(o,"indexInFactory when registering multi factory destroy hook");const t=r.indexOf(n);-1===t?r.push(n,[o,a]):r[t+1].push(o,a)}else r.push(n,a)}}}function rg(t,e,n){return n&&t.componentProviders++,t.multi.push(e)-1}function sg(t,e,n,o){for(let i=n;i<o;i++)if(e[i]===t)return i;return-1}function lg(t,e,n,o){return dg(this.multi,[])}function cg(t,e,n,o){const i=this.multi;let a;if(this.providerFactory){const t=this.providerFactory.componentProviders,e=Sa(n,n[1],this.providerFactory.index,o);a=e.slice(0,t),dg(i,a);for(let n=t;n<e.length;n++)a.push(e[n])}else a=[],dg(i,a);return a}function dg(t,e){for(let n=0;n<t.length;n++)e.push((0,t[n])());return e}function pg(t,e=[]){return n=>{n.providersResolver=(n,o)=>(function i(t,e,n){const o=gi();if(o.firstCreatePass){const i=xo(t);ig(n,o.data,o.blueprint,i,!0),ig(e,o.data,o.blueprint,i,!1)}})(n,o?o(t):t,e)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class mg{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class ug{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function fg(...t){}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function gg(t,e){return new hg(oi(t,e))}ug.NULL=new class{resolveComponentFactory(t){throw(function e(t){const e=Error(`No component factory found for ${Ge(t)}. Did you add it to @NgModule.entryComponents?`);return e.ngComponent=t,e})(t)}};class hg{constructor(t){this.nativeElement=t}}function bg(t){return t instanceof hg?t.nativeElement:t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */hg.__NG_ELEMENT_ID__=function yg(){return gg(bi(),fi())},new Ga("Renderer2Interceptor");class _g{}class Cg{}Cg.__NG_ELEMENT_ID__=()=>Mg();const Mg=function vg(){const t=fi(),e=ri(bi().index,t);return(function n(t){const e=t[11];if(ngDevMode&&!$o(e))throw new Error("Cannot inject Renderer2 when the application uses Renderer3!");return e})(yo(e)?e:t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */;class xg{}xg.ɵprov=Mn({token:xg,providedIn:"root",factory:()=>null});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Og{constructor(t){this.full=t,this.major=t.split(".")[0],this.minor=t.split(".")[1],this.patch=t.split(".").slice(2).join(".")}}const Pg=new Og("12.2.1");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class wg{constructor(){}supports(t){return Np(t)}create(t){return new Sg(t)}}const kg=(t,e)=>e;class Sg{constructor(t){this.length=0,this._linkedRecords=null,this._unlinkedRecords=null,this._previousItHead=null,this._itHead=null,this._itTail=null,this._additionsHead=null,this._additionsTail=null,this._movesHead=null,this._movesTail=null,this._removalsHead=null,this._removalsTail=null,this._identityChangesHead=null,this._identityChangesTail=null,this._trackByFn=t||kg}forEachItem(t){let e;for(e=this._itHead;null!==e;e=e._next)t(e)}forEachOperation(t){let e=this._itHead,n=this._removalsHead,o=0,i=null;for(;e||n;){const a=!n||e&&e.currentIndex<Ag(n,o,i)?e:n,r=Ag(a,o,i),s=a.currentIndex;if(a===n)o--,n=n._nextRemoved;else if(e=e._next,null==a.previousIndex)o++;else{i||(i=[]);const t=r-o,e=s-o;if(t!=e){for(let n=0;n<t;n++){const o=n<i.length?i[n]:i[n]=0,a=o+n;e<=a&&a<t&&(i[n]=o+1)}i[a.previousIndex]=e-t}}r!==s&&t(a,r,s)}}forEachPreviousItem(t){let e;for(e=this._previousItHead;null!==e;e=e._nextPrevious)t(e)}forEachAddedItem(t){let e;for(e=this._additionsHead;null!==e;e=e._nextAdded)t(e)}forEachMovedItem(t){let e;for(e=this._movesHead;null!==e;e=e._nextMoved)t(e)}forEachRemovedItem(t){let e;for(e=this._removalsHead;null!==e;e=e._nextRemoved)t(e)}forEachIdentityChange(t){let e;for(e=this._identityChangesHead;null!==e;e=e._nextIdentityChange)t(e)}diff(t){if(null==t&&(t=[]),!Np(t))throw new Error(`Error trying to diff '${Ge(t)}'. Only arrays and iterables are allowed`);return this.check(t)?this:null}onDestroy(){}check(t){this._reset();let e,n,o,i=this._itHead,a=!1;if(Array.isArray(t)){this.length=t.length;for(let e=0;e<this.length;e++)n=t[e],o=this._trackByFn(e,n),null!==i&&Object.is(i.trackById,o)?(a&&(i=this._verifyReinsertion(i,n,o,e)),Object.is(i.item,n)||this._addIdentityChange(i,n)):(i=this._mismatch(i,n,o,e),a=!0),i=i._next}else e=0,(function r(t,e){if(Array.isArray(t))for(let n=0;n<t.length;n++)e(t[n]);else{const n=t[Rp()]();let o;for(;!(o=n.next()).done;)e(o.value)}})(t,(t=>{o=this._trackByFn(e,t),null!==i&&Object.is(i.trackById,o)?(a&&(i=this._verifyReinsertion(i,t,o,e)),Object.is(i.item,t)||this._addIdentityChange(i,t)):(i=this._mismatch(i,t,o,e),a=!0),i=i._next,e++})),this.length=e;return this._truncate(i),this.collection=t,this.isDirty}get isDirty(){return null!==this._additionsHead||null!==this._movesHead||null!==this._removalsHead||null!==this._identityChangesHead}_reset(){if(this.isDirty){let t;for(t=this._previousItHead=this._itHead;null!==t;t=t._next)t._nextPrevious=t._next;for(t=this._additionsHead;null!==t;t=t._nextAdded)t.previousIndex=t.currentIndex;for(this._additionsHead=this._additionsTail=null,t=this._movesHead;null!==t;t=t._nextMoved)t.previousIndex=t.currentIndex;this._movesHead=this._movesTail=null,this._removalsHead=this._removalsTail=null,this._identityChangesHead=this._identityChangesTail=null}}_mismatch(t,e,n,o){let i;return null===t?i=this._itTail:(i=t._prev,this._remove(t)),null!==(t=null===this._unlinkedRecords?null:this._unlinkedRecords.get(n,null))?(Object.is(t.item,e)||this._addIdentityChange(t,e),this._reinsertAfter(t,i,o)):null!==(t=null===this._linkedRecords?null:this._linkedRecords.get(n,o))?(Object.is(t.item,e)||this._addIdentityChange(t,e),this._moveAfter(t,i,o)):t=this._addAfter(new Dg(e,n),i,o),t}_verifyReinsertion(t,e,n,o){let i=null===this._unlinkedRecords?null:this._unlinkedRecords.get(n,null);return null!==i?t=this._reinsertAfter(i,t._prev,o):t.currentIndex!=o&&(t.currentIndex=o,this._addToMoves(t,o)),t}_truncate(t){for(;null!==t;){const e=t._next;this._addToRemovals(this._unlink(t)),t=e}null!==this._unlinkedRecords&&this._unlinkedRecords.clear(),null!==this._additionsTail&&(this._additionsTail._nextAdded=null),null!==this._movesTail&&(this._movesTail._nextMoved=null),null!==this._itTail&&(this._itTail._next=null),null!==this._removalsTail&&(this._removalsTail._nextRemoved=null),null!==this._identityChangesTail&&(this._identityChangesTail._nextIdentityChange=null)}_reinsertAfter(t,e,n){null!==this._unlinkedRecords&&this._unlinkedRecords.remove(t);const o=t._prevRemoved,i=t._nextRemoved;return null===o?this._removalsHead=i:o._nextRemoved=i,null===i?this._removalsTail=o:i._prevRemoved=o,this._insertAfter(t,e,n),this._addToMoves(t,n),t}_moveAfter(t,e,n){return this._unlink(t),this._insertAfter(t,e,n),this._addToMoves(t,n),t}_addAfter(t,e,n){return this._insertAfter(t,e,n),this._additionsTail=null===this._additionsTail?this._additionsHead=t:this._additionsTail._nextAdded=t,t}_insertAfter(t,e,n){const o=null===e?this._itHead:e._next;return t._next=o,t._prev=e,null===o?this._itTail=t:o._prev=t,null===e?this._itHead=t:e._next=t,null===this._linkedRecords&&(this._linkedRecords=new Rg),this._linkedRecords.put(t),t.currentIndex=n,t}_remove(t){return this._addToRemovals(this._unlink(t))}_unlink(t){null!==this._linkedRecords&&this._linkedRecords.remove(t);const e=t._prev,n=t._next;return null===e?this._itHead=n:e._next=n,null===n?this._itTail=e:n._prev=e,t}_addToMoves(t,e){return t.previousIndex===e||(this._movesTail=null===this._movesTail?this._movesHead=t:this._movesTail._nextMoved=t),t}_addToRemovals(t){return null===this._unlinkedRecords&&(this._unlinkedRecords=new Rg),this._unlinkedRecords.put(t),t.currentIndex=null,t._nextRemoved=null,null===this._removalsTail?(this._removalsTail=this._removalsHead=t,t._prevRemoved=null):(t._prevRemoved=this._removalsTail,this._removalsTail=this._removalsTail._nextRemoved=t),t}_addIdentityChange(t,e){return t.item=e,this._identityChangesTail=null===this._identityChangesTail?this._identityChangesHead=t:this._identityChangesTail._nextIdentityChange=t,t}}class Dg{constructor(t,e){this.item=t,this.trackById=e,this.currentIndex=null,this.previousIndex=null,this._nextPrevious=null,this._prev=null,this._next=null,this._prevDup=null,this._nextDup=null,this._prevRemoved=null,this._nextRemoved=null,this._nextAdded=null,this._nextMoved=null,this._nextIdentityChange=null}}class Eg{constructor(){this._head=null,this._tail=null}add(t){null===this._head?(this._head=this._tail=t,t._nextDup=null,t._prevDup=null):(this._tail._nextDup=t,t._prevDup=this._tail,t._nextDup=null,this._tail=t)}get(t,e){let n;for(n=this._head;null!==n;n=n._nextDup)if((null===e||e<=n.currentIndex)&&Object.is(n.trackById,t))return n;return null}remove(t){const e=t._prevDup,n=t._nextDup;return null===e?this._head=n:e._nextDup=n,null===n?this._tail=e:n._prevDup=e,null===this._head}}class Rg{constructor(){this.map=new Map}put(t){const e=t.trackById;let n=this.map.get(e);n||(n=new Eg,this.map.set(e,n)),n.add(t)}get(t,e){const n=this.map.get(t);return n?n.get(t,e):null}remove(t){const e=t.trackById;return this.map.get(e).remove(t)&&this.map.delete(e),t}get isEmpty(){return 0===this.map.size}clear(){this.map.clear()}}function Ag(t,e,n){const o=t.previousIndex;if(null===o)return o;let i=0;return n&&o<n.length&&(i=n[o]),o+e+i}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Tg{constructor(){}supports(t){return t instanceof Map||zp(t)}create(){return new Ng}}class Ng{constructor(){this._records=new Map,this._mapHead=null,this._appendAfter=null,this._previousMapHead=null,this._changesHead=null,this._changesTail=null,this._additionsHead=null,this._additionsTail=null,this._removalsHead=null,this._removalsTail=null}get isDirty(){return null!==this._additionsHead||null!==this._changesHead||null!==this._removalsHead}forEachItem(t){let e;for(e=this._mapHead;null!==e;e=e._next)t(e)}forEachPreviousItem(t){let e;for(e=this._previousMapHead;null!==e;e=e._nextPrevious)t(e)}forEachChangedItem(t){let e;for(e=this._changesHead;null!==e;e=e._nextChanged)t(e)}forEachAddedItem(t){let e;for(e=this._additionsHead;null!==e;e=e._nextAdded)t(e)}forEachRemovedItem(t){let e;for(e=this._removalsHead;null!==e;e=e._nextRemoved)t(e)}diff(t){if(t){if(!(t instanceof Map||zp(t)))throw new Error(`Error trying to diff '${Ge(t)}'. Only maps and objects are allowed`)}else t=new Map;return this.check(t)?this:null}onDestroy(){}check(t){this._reset();let e=this._mapHead;if(this._appendAfter=null,this._forEach(t,((t,n)=>{if(e&&e.key===n)this._maybeAddToChanges(e,t),this._appendAfter=e,e=e._next;else{const o=this._getOrCreateRecordForKey(n,t);e=this._insertBeforeOrAppend(e,o)}})),e){e._prev&&(e._prev._next=null),this._removalsHead=e;for(let t=e;null!==t;t=t._nextRemoved)t===this._mapHead&&(this._mapHead=null),this._records.delete(t.key),t._nextRemoved=t._next,t.previousValue=t.currentValue,t.currentValue=null,t._prev=null,t._next=null}return this._changesTail&&(this._changesTail._nextChanged=null),this._additionsTail&&(this._additionsTail._nextAdded=null),this.isDirty}_insertBeforeOrAppend(t,e){if(t){const n=t._prev;return e._next=t,e._prev=n,t._prev=e,n&&(n._next=e),t===this._mapHead&&(this._mapHead=e),this._appendAfter=t,t}return this._appendAfter?(this._appendAfter._next=e,e._prev=this._appendAfter):this._mapHead=e,this._appendAfter=e,null}_getOrCreateRecordForKey(t,e){if(this._records.has(t)){const n=this._records.get(t);this._maybeAddToChanges(n,e);const o=n._prev,i=n._next;return o&&(o._next=i),i&&(i._prev=o),n._next=null,n._prev=null,n}const n=new zg(t);return this._records.set(t,n),n.currentValue=e,this._addToAdditions(n),n}_reset(){if(this.isDirty){let t;for(this._previousMapHead=this._mapHead,t=this._previousMapHead;null!==t;t=t._next)t._nextPrevious=t._next;for(t=this._changesHead;null!==t;t=t._nextChanged)t.previousValue=t.currentValue;for(t=this._additionsHead;null!=t;t=t._nextAdded)t.previousValue=t.currentValue;this._changesHead=this._changesTail=null,this._additionsHead=this._additionsTail=null,this._removalsHead=null}}_maybeAddToChanges(t,e){Object.is(e,t.currentValue)||(t.previousValue=t.currentValue,t.currentValue=e,this._addToChanges(t))}_addToAdditions(t){null===this._additionsHead?this._additionsHead=this._additionsTail=t:(this._additionsTail._nextAdded=t,this._additionsTail=t)}_addToChanges(t){null===this._changesHead?this._changesHead=this._changesTail=t:(this._changesTail._nextChanged=t,this._changesTail=t)}_forEach(t,e){t instanceof Map?t.forEach(e):Object.keys(t).forEach((n=>e(t[n],n)))}}class zg{constructor(t){this.key=t,this.previousValue=null,this.currentValue=null,this._nextPrevious=null,this._next=null,this._prev=null,this._nextAdded=null,this._nextRemoved=null,this._nextChanged=null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Ig(){return new Hg([new wg])}class Hg{constructor(t){this.factories=t}static create(t,e){if(null!=e){const n=e.factories.slice();t=t.concat(n)}return new Hg(t)}static extend(t){return{provide:Hg,useFactory:e=>Hg.create(t,e||Ig()),deps:[[Hg,new Er,new Sr]]}}find(t){const e=this.factories.find((e=>e.supports(t)));if(null!=e)return e;throw new Error(`Cannot find a differ supporting object '${t}' of type '${(function n(t){return t.name||typeof t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t)}'`)}}function Fg(){return new Lg([new Tg])}Hg.ɵprov=Mn({token:Hg,providedIn:"root",factory:Ig});class Lg{constructor(t){this.factories=t}static create(t,e){if(e){const n=e.factories.slice();t=t.concat(n)}return new Lg(t)}static extend(t){return{provide:Lg,useFactory:e=>Lg.create(t,e||Fg()),deps:[[Lg,new Er,new Sr]]}}find(t){const e=this.factories.find((e=>e.supports(t)));if(e)return e;throw new Error(`Cannot find a differ supporting object '${t}'`)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function Bg(t,e,n,o,i=!1){for(;null!==n;){ngDevMode&&ia(n,63);const a=e[n.index];if(null!==a&&o.push(ei(a)),_o(a))for(let t=bo;t<a.length;t++){const e=a[t],n=e[1].firstChild;null!==n&&Bg(e[1],e,n,o)}const r=n.type;if(8&r)Bg(t,e,n.child,o);else if(32&r){const t=pl(n,e);let i;for(;i=t();)o.push(i)}else if(16&r){const t=Ll(e,n);if(Array.isArray(t))o.push(...t);else{const n=ml(e[16]);ngDevMode&&Io(n),Bg(n[1],n,t,o,!0)}}n=i?n.projectionNext:n.next}return o}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */Lg.ɵprov=Mn({token:Lg,providedIn:"root",factory:Fg});class Vg{constructor(t,e){this._lView=t,this._cdRefInjectingView=e,this._appRef=null,this._attachedToViewContainer=!1}get rootNodes(){const t=this._lView,e=t[1];return Bg(e,t,e.firstChild,[])}get context(){return this._lView[8]}set context(t){this._lView[8]=t}get destroyed(){return 256==(256&this._lView[2])}destroy(){if(this._appRef)this._appRef.detachView(this);else if(this._attachedToViewContainer){const t=this._lView[3];if(_o(t)){const e=t[8],n=e?e.indexOf(this):-1;n>-1&&(ngDevMode&&ln(n,t.indexOf(this._lView)-bo,"An attached view should be in the same position within its container as its ViewRef in the VIEW_REFS array."),xl(t,n),or(e,n))}this._attachedToViewContainer=!1}Ol(this._lView[1],this._lView)}onDestroy(t){rd(this._lView[1],this._lView,null,t)}markForCheck(){Ed(this._cdRefInjectingView||this._lView)}detach(){this._lView[2]&=-129}reattach(){this._lView[2]|=128}detectChanges(){Ad(this._lView[1],this._lView,this.context)}checkNoChanges(){!(function t(e,n,o){Oi(!0);try{Ad(e,n,o)}finally{Oi(!1)}})(this._lView[1],this._lView,this.context)}attachToViewContainerRef(){if(this._appRef)throw new Error("This view is already attached directly to the ApplicationRef!");this._attachedToViewContainer=!0}detachFromAppRef(){this._appRef=null,(function t(e,n){Ul(e,n,n[11],2,null,null)})(this._lView[1],this._lView)}attachToAppRef(t){if(this._attachedToViewContainer)throw new Error("This view is already attached to a ViewContainer!");this._appRef=t}}class jg extends Vg{constructor(t){super(t),this._view=t}detectChanges(){Td(this._view)}checkNoChanges(){!(function t(e){Oi(!0);try{Td(e)}finally{Oi(!1)}})(this._view)}get context(){return null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Ug{}Ug.__NG_ELEMENT_ID__=function Gg(t){return(function e(t,n,o){if(Mo(t)&&!o){const e=ri(t.index,n);return new Vg(e,e)}return 47&t.type?new Vg(n[16],n):null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(bi(),fi(),16==(16&t))};const Wg=[new Tg],Yg=[new wg],qg=new Hg(Yg),Zg=new Lg(Wg);class Xg{}Xg.__NG_ELEMENT_ID__=function Kg(){return $g(bi(),fi())};const Jg=Xg,Qg=class extends Jg{constructor(t,e,n){super(),this._declarationLView=t,this._declarationTContainer=e,this.elementRef=n}createEmbeddedView(t){const e=this._declarationTContainer.tViews,n=qc(this._declarationLView,e,t,16,null,e.declTNode,null,null,null,null),o=this._declarationLView[this._declarationTContainer.index];ngDevMode&&Do(o),n[17]=o;const i=this._declarationLView[19];return null!==i&&(n[19]=i.createEmbeddedView(e)),Jc(e,n,t),new Vg(n)}};function $g(t,e){return 4&t.type?(ngDevMode&&hn(t.tViews,"TView must be allocated"),new Qg(e,t,gg(t,e))):null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class th{}class eh{}eh.__NG_ELEMENT_ID__=function nh(){return sh(bi(),fi())};const oh=eh,ih=class extends oh{constructor(t,e,n){super(),this._lContainer=t,this._hostTNode=e,this._hostLView=n}get element(){return gg(this._hostTNode,this._hostLView)}get injector(){return new Ra(this._hostTNode,this._hostLView)}get parentInjector(){const t=_a(this._hostTNode,this._hostLView);if(da(t)){const e=ma(t,this._hostLView),n=pa(t);return ngDevMode&&Ho(e,n),new Ra(e[1].data[n+8],e)}return new Ra(null,this._hostLView)}clear(){for(;this.length>0;)this.remove(this.length-1)}get(t){const e=ah(this._lContainer);return null!==e&&e[t]||null}get length(){return this._lContainer.length-bo}createEmbeddedView(t,e,n){const o=t.createEmbeddedView(e||{});return this.insert(o,n),o}createComponent(t,e,n,o,i){const a=n||this.parentInjector;if(!i&&null==t.ngModule&&a){const t=a.get(th,null);t&&(i=t)}const r=t.create(a,o,void 0,i);return this.insert(r.hostView,e),r}insert(t,e){const n=t._lView,o=n[1];if(ngDevMode&&t.destroyed)throw new Error("Cannot insert a destroyed View in a ViewContainer!");if((function i(t){return _o(t[3])})(n)){const e=this.indexOf(t);if(-1!==e)this.detach(e);else{const e=n[3];ngDevMode&&ln(_o(e),!0,"An attached view should have its PARENT point to a container.");const o=new ih(e,e[6],e[3]);o.detach(o.indexOf(t))}}const a=this._adjustIndex(e),r=this._lContainer;!(function s(t,e,n,o){ngDevMode&&Ro(e),ngDevMode&&Do(n);const i=bo+o,a=n.length;o>0&&(n[i-1][4]=e),o<a-bo?(e[4]=n[i],nr(n,bo+o,e)):(n.push(e),e[4]=null),e[3]=n;const r=e[17];null!==r&&n!==r&&(function s(t,e){ngDevMode&&hn(e,"LView required"),ngDevMode&&Do(t);const n=t[9],o=e[3];ngDevMode&&Do(o);const i=o[3][16];ngDevMode&&hn(i,"Missing insertedComponentLView");const a=e[16];ngDevMode&&hn(a,"Missing declaredComponentLView"),a!==i&&(t[2]=!0),null===n?t[9]=[e]:n.push(e)})(r,e);const l=e[19];null!==l&&l.insertView(t),e[2]|=128})(o,n,r,a);const l=Bl(a,r),c=n[11],d=Rl(c,r[7]);return null!==d&&(function p(t,e,n,o,i,a){o[0]=i,o[6]=e,Ul(t,o,n,1,i,a)})(o,r[6],c,n,d,l),t.attachToViewContainerRef(),nr(rh(r),a,t),t}move(t,e){if(ngDevMode&&t.destroyed)throw new Error("Cannot move a destroyed View in a ViewContainer!");return this.insert(t,e)}indexOf(t){const e=ah(this._lContainer);return null!==e?e.indexOf(t):-1}remove(t){const e=this._adjustIndex(t,-1),n=xl(this._lContainer,e);n&&(or(rh(this._lContainer),e),Ol(n[1],n))}detach(t){const e=this._adjustIndex(t,-1),n=xl(this._lContainer,e);return n&&null!=or(rh(this._lContainer),e)?new Vg(n):null}_adjustIndex(t,e=0){return null==t?this.length+e:(ngDevMode&&(fn(t,-1,`ViewRef index must be positive, got ${t}`),mn(t,this.length+1+e,"index")),t)}};function ah(t){return t[8]}function rh(t){return t[8]||(t[8]=[])}function sh(t,e){let n;ngDevMode&&ia(t,15);const o=e[t.index];if(_o(o))n=o;else{let i;if(8&t.type)i=ei(o);else{const n=e[11];ngDevMode&&ngDevMode.rendererCreateComment++,i=n.createComment(ngDevMode?"container":"");const o=oi(t,e);Sl(n,Rl(n,o),i,(function i(t,e){return $o(t)?t.nextSibling(e):e.nextSibling})(n,o),!1)}e[t.index]=n=Pd(o,e,i,t),Dd(e,n)}return new ih(n,t,e)}const lh=new Map;function ch(t){let e=lh.get(t);return e||(e=Ge(t)+"_"+lh.size,lh.set(t,e)),e}ch(rp),ch(Gd),ch(th),
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
ch(Cg),ch(hg),ch(eh),ch(Xg),ch(Ug),ch(rp),ch(Gd);const dh={};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class ph extends ug{constructor(t){super(),this.ngModule=t}resolveComponentFactory(t){ngDevMode&&(function e(t,n="Type passed in is not ComponentType, it does not have 'ɵcmp' property."){po(t)||bn(n)})(t);const n=po(t);return new fh(n,this.ngModule)}}function mh(t){const e=[];for(let n in t)t.hasOwnProperty(n)&&e.push({propName:t[n],templateName:n});return e}const uh=new Ga("SCHEDULER_TOKEN",{providedIn:"root",factory:()=>nl});class fh extends mg{constructor(t,e){super(),this.componentDef=t,this.ngModule=e,this.componentType=t.type,this.selector=(function n(t){return t.map(ic).join(",")})(t.selectors),this.ngContentSelectors=t.ngContentSelectors?t.ngContentSelectors:[],this.isBoundToModule=!!e}get inputs(){return mh(this.componentDef.inputs)}get outputs(){return mh(this.componentDef.outputs)}create(t,e,n,o){const i=(o=o||this.ngModule)?(function a(t,e){return{get:(n,o,i)=>{const a=t.get(n,dh,i);return a!==dh||o===dh?a:e.get(n,o,i)}}})(t,o.injector):t,r=i.get(_g,ti),s=i.get(xg,null),l=r.createRenderer(null,this.componentDef),c=this.componentDef.selectors[0][0]||"div",d=n?(function p(t,e,n){if($o(t))return t.selectRootElement(e,n===Hn.ShadowDom);let o="string"==typeof e?t.querySelector(e):e;return ngDevMode&&(function i(t,e){if(!t)throw(function n(t,e){return new Error(`Renderer: ${t} [${tn(e)}]`)})("string"==typeof e?"Host node with selector not found:":"Host node is required:",e)})(o,e),o.textContent="",o})(l,n,this.componentDef.encapsulation):Ml(r.createRenderer(null,this.componentDef),c,(function m(t){const e=t.toLowerCase();return"svg"===e?Zo:"math"===e?Xo:null})(c)),u=this.componentDef.onPush?576:528,f=(function g(t,e){return{components:[],scheduler:t||nl,clean:Id,playerHandler:e||null,flags:0}})(),h=ad(0,null,null,1,0,null,null,null,null,null),b=qc(null,h,f,u,null,null,r,l,s,i);let y,_;Fi(b);try{const t=(function o(t,e,n,i,a,r){const s=n[1];ngDevMode&&_n(n,20),n[20]=t;const l=Zc(s,20,2,"#host",null),c=l.mergedAttrs=e.hostAttrs;null!==c&&(Ud(l,c,!0),null!==t&&(aa(a,t,c),null!==l.classes&&Yl(a,t,l.classes),null!==l.styles&&Wl(a,t,l.styles)));const d=i.createRenderer(t,e),p=qc(n,id(e),null,e.onPush?64:16,n[20],l,i,d,r||null,null);return s.firstCreatePass&&(Ca(ha(l,n),s,e.type),gd(s,l),bd(l,n.length,1)),Dd(n,p),n[20]=p})(d,this.componentDef,b,r,l);if(d)if(n)aa(l,d,["ng-version",Pg.full]);else{const{attrs:t,classes:e}=(function n(t){const e=[],n=[];let o=1,i=2;for(;o<t.length;){let a=t[o];if("string"==typeof a)2===i?""!==a&&e.push(a,t[++o]):8===i&&n.push(a);else{if(!$l(i))break;i=a}o++}return{attrs:e,classes:n}})(this.componentDef.selectors[0]);t&&aa(l,d,t),e&&e.length>0&&Yl(l,d,e.join(" "))}if(_=ii(h,go),void 0!==e){const t=_.projection=[];for(let n=0;n<this.ngContentSelectors.length;n++){const o=e[n];t.push(null!=o?Array.from(o):null)}}y=(function i(t,e,n,o,a){const r=n[1],s=(function l(t,e,n){const o=bi();if(t.firstCreatePass){n.providersResolver&&n.providersResolver(n);const i=Kc(t,e,1,null);ngDevMode&&ln(i,o.directiveStart,"Because this is a root component the allocated expando should match the TNode component."),yd(t,o,e,i,n)}const i=Sa(e,t,o.directiveStart,o);Ls(i,e);const a=oi(o,e);return a&&Ls(a,e),i})(r,n,e);if(o.components.push(s),t[8]=s,a&&a.forEach((t=>t(s,e))),e.contentQueries){const t=bi();ngDevMode&&hn(t,"TNode expected"),e.contentQueries(1,s,t.directiveStart)}const c=bi();return ngDevMode&&hn(c,"tNode should have been already created"),!r.firstCreatePass||null===e.hostBindings&&null===e.hostAttrs||(Wi(c.index),ud(n[1],c,0,c.directiveStart,c.directiveEnd,e),fd(e,s)),s})(t,this.componentDef,b,f,[Mp]),Jc(h,b,null)}finally{Ui()}return new gh(this.componentType,y,gg(_,b),b,_)}}new ph;class gh extends class{}{constructor(t,e,n,o,i){super(),this.location=n,this._rootLView=o,this._tNode=i,this.instance=e,this.hostView=this.changeDetectorRef=new jg(o),this.componentType=t}get injector(){return new Ra(this._tNode,this._rootLView)}destroy(){this.hostView.destroy()}onDestroy(t){this.hostView.onDestroy(t)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function hh(t,e,n,o){return Nn((()=>{const i=t;null!==e&&(i.hasOwnProperty("decorators")&&void 0!==i.decorators?i.decorators.push(...e):i.decorators=e),null!==n&&(i.ctorParameters=n),null!==o&&(i.propDecorators=i.hasOwnProperty("propDecorators")&&void 0!==i.propDecorators?Object.assign(Object.assign({},i.propDecorators),o):o)}))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const bh=new Map;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class yh extends th{constructor(t,e){super(),this._parent=e,this._bootstrapComponents=[],this.injector=this,this.destroyCbs=[],this.componentFactoryResolver=new ph(this);const n=fo(t);ngDevMode&&hn(n,`NgModule '${Ge(t)}' is not a subtype of 'NgModuleType'.`);const o=(function i(t){return t[Kn]||null})(t);o&&Xu(o),this._bootstrapComponents=rl(n.bootstrap),this._r3Injector=Jd(t,e,[{provide:th,useValue:this},{provide:ug,useValue:this.componentFactoryResolver}],Ge(t)),this._r3Injector._resolveInjectorDefTypes(),this.instance=this.get(t)}get(t,e=rp.THROW_IF_NOT_FOUND,n=En.Default){return t===rp||t===th||t===Gd?this:this._r3Injector.get(t,e,n)}destroy(){ngDevMode&&hn(this.destroyCbs,"NgModule already destroyed");const t=this._r3Injector;!t.destroyed&&t.destroy(),this.destroyCbs.forEach((t=>t())),this.destroyCbs=null}onDestroy(t){ngDevMode&&hn(this.destroyCbs,"NgModule already destroyed"),this.destroyCbs.push(t)}}class _h extends class{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */{constructor(t){super(),this.moduleType=t,null!==fo(t)&&(function e(t){const e=new Set;!(function t(n){const o=fo(n,!0),i=o.id;null!==i&&((function a(t,e,n){if(e&&e!==n)throw new Error(`Duplicate module registered for ${t} - ${Ge(e)} vs ${Ge(e.name)}`)})(i,bh.get(i),n),bh.set(i,n));const r=rl(o.imports);for(const n of r)e.has(n)||(e.add(n),t(n))})(t)})(t)}create(t){return new yh(this.moduleType,t)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Ch(t,e,n){const o=Pi()+t,i=fi();return i[o]===ac?Ip(i,o,n?e.call(n):e()):Hp(i,o)}function Mh(t,e,n,o){return kh(fi(),Pi(),t,e,n,o)}function vh(t,e,n,o,i){return Sh(fi(),Pi(),t,e,n,o,i)}function xh(t,e,n,o,i,a){return Dh(fi(),Pi(),t,e,n,o,i,a)}function Oh(t,e,n,o,i,a,r){return Eh(fi(),Pi(),t,e,n,o,i,a,r)}function Ph(t,e,n,o,i,a,r,s){const l=Pi()+t,c=fi(),d=Vp(c,l,n,o,i,a);return Fp(c,l+4,r)||d?Ip(c,l+5,s?e.call(s,n,o,i,a,r):e(n,o,i,a,r)):Hp(c,l+5)}function wh(t,e){ngDevMode&&_n(t,e);const n=t[e];return n===ac?void 0:n}function kh(t,e,n,o,i,a){const r=e+n;return Fp(t,r,i)?Ip(t,r+1,a?o.call(a,i):o(i)):wh(t,r+1)}function Sh(t,e,n,o,i,a,r){const s=e+n;return Lp(t,s,i,a)?Ip(t,s+2,r?o.call(r,i,a):o(i,a)):wh(t,s+2)}function Dh(t,e,n,o,i,a,r,s){const l=e+n;return Bp(t,l,i,a,r)?Ip(t,l+3,s?o.call(s,i,a,r):o(i,a,r)):wh(t,l+3)}function Eh(t,e,n,o,i,a,r,s,l){const c=e+n;return Vp(t,c,i,a,r,s)?Ip(t,c+4,l?o.call(l,i,a,r,s):o(i,a,r,s)):wh(t,c+4)}function Rh(t,e,n,o,i,a){let r=e+n,s=!1;for(let e=0;e<i.length;e++)Fp(t,r++,i[e])&&(s=!0);return s?Ip(t,r,o.apply(a,i)):wh(t,r)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Fh(t){return e=>{setTimeout(t,void 0,e)}}const Lh=class extends I{constructor(t=!1){super(),this.__isAsync=t}emit(t){super.next(t)}subscribe(t,e,n){var o,i,a;let r=t,s=e||(()=>null),l=n;if(t&&"object"==typeof t){const e=t;r=null===(o=e.next)||void 0===o?void 0:o.bind(e),s=null===(i=e.error)||void 0===i?void 0:i.bind(e),l=null===(a=e.complete)||void 0===a?void 0:a.bind(e)}this.__isAsync&&(s=Fh(s),r&&(r=Fh(r)),l&&(l=Fh(l)));const c=super.subscribe({next:r,error:s,complete:l});return t instanceof m&&t.add(c),c}};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Bh(){return this._results[Rp()]()}class Vh{constructor(t=!1){this._emitDistinctChangesOnly=t,this.dirty=!0,this._results=[],this._changesDetected=!1,this._changes=null,this.length=0,this.first=void 0,this.last=void 0;const e=Rp(),n=Vh.prototype;n[e]||(n[e]=Bh)}get changes(){return this._changes||(this._changes=new Lh)}get(t){return this._results[t]}map(t){return this._results.map(t)}filter(t){return this._results.filter(t)}find(t){return this._results.find(t)}reduce(t,e){return this._results.reduce(t,e)}forEach(t){this._results.forEach(t)}some(t){return this._results.some(t)}toArray(){return this._results.slice()}toString(){return this._results.toString()}reset(t,e){const n=this;n.dirty=!1;const o=tr(t);(this._changesDetected=!(function i(t,e,n){if(t.length!==e.length)return!1;for(let o=0;o<t.length;o++){let 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function ib(t,e){return $g(t,e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const ab={"ɵɵattribute":jp,"ɵɵattributeInterpolate1":function t(e,n,o,i,a,r){const s=fi(),l=Gp(s,n,o,i);if(l!==ac){const t=Yi();Cd(t,s,e,l,a,r),ngDevMode&&zd(gi().data,t,"attr."+e,wi()-1,n,i)}return t},"ɵɵattributeInterpolate2":function t(e,n,o,i,a,r,s,l){const c=fi(),d=Wp(c,n,o,i,a,r);if(d!==ac){const t=Yi();Cd(t,c,e,d,s,l),ngDevMode&&zd(gi().data,t,"attr."+e,wi()-2,n,i,r)}return t},"ɵɵattributeInterpolate3":function t(e,n,o,i,a,r,s,l,c,d){const p=fi(),m=Yp(p,n,o,i,a,r,s,l);if(m!==ac){const t=Yi();Cd(t,p,e,m,c,d),ngDevMode&&zd(gi().data,t,"attr."+e,wi()-3,n,i,r,l)}return t},"ɵɵattributeInterpolate4":function t(e,n,o,i,a,r,s,l,c,d,p,m){const u=fi(),f=qp(u,n,o,i,a,r,s,l,c,d);if(f!==ac){const t=Yi();Cd(t,u,e,f,p,m),ngDevMode&&zd(gi().data,t,"attr."+e,wi()-4,n,i,r,l,d)}return t},"ɵɵattributeInterpolate5":function t(e,n,o,i,a,r,s,l,c,d,p,m,u,f){const g=fi(),h=Zp(g,n,o,i,a,r,s,l,c,d,p,m);if(h!==ac){const 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
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/**
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     * Copyright Google LLC All Rights Reserved.
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/**
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     * Copyright Google LLC All Rights Reserved.
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     * found in the LICENSE file at https://angular.io/license
     */
class Zy{constructor(t,e){this.ngModuleFactory=t,this.componentFactories=e}}const Xy=function(t){return new _h(t)},Ky=Xy,Jy=function(t){return Promise.resolve(Xy(t))},Qy=function(t){const e=Xy(t),n=rl(fo(t).declarations).reduce(((t,e)=>{const n=po(e);return n&&t.push(new fh(n)),t}),[]);return new Zy(e,n)},$y=Qy,t_=function(t){return Promise.resolve(Qy(t))};class e_{constructor(){this.compileModuleSync=Ky,this.compileModuleAsync=Jy,this.compileModuleAndAllComponentsSync=$y,this.compileModuleAndAllComponentsAsync=t_}clearCache(){}clearCacheFor(t){}getModuleId(t){}}e_.ɵfac=function t(e){return new(e||e_)},e_.ɵprov=Mn({token:e_,factory:e_.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(e_,[{type:im}],(function(){return[]}),null);const n_=new Ga("compilerOptions"),o_=Promise.resolve(0);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function i_(t){"undefined"==typeof Zone?o_.then((()=>{t&&t.apply(null,null)})):Zone.current.scheduleMicroTask("scheduleMicrotask",t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class a_{constructor({enableLongStackTrace:t=!1,shouldCoalesceEventChangeDetection:e=!1,shouldCoalesceRunChangeDetection:n=!1}){if(this.hasPendingMacrotasks=!1,this.hasPendingMicrotasks=!1,this.isStable=!0,this.onUnstable=new Lh(!1),this.onMicrotaskEmpty=new Lh(!1),this.onStable=new Lh(!1),this.onError=new Lh(!1),"undefined"==typeof Zone)throw new Error("In this configuration Angular requires Zone.js");Zone.assertZonePatched();const o=this;o._nesting=0,o._outer=o._inner=Zone.current,Zone.TaskTrackingZoneSpec&&(o._inner=o._inner.fork(new Zone.TaskTrackingZoneSpec)),t&&Zone.longStackTraceZoneSpec&&(o._inner=o._inner.fork(Zone.longStackTraceZoneSpec)),o.shouldCoalesceEventChangeDetection=!n&&e,o.shouldCoalesceRunChangeDetection=n,o.lastRequestAnimationFrameId=-1,o.nativeRequestAnimationFrame=(function i(){let t=jn.requestAnimationFrame,e=jn.cancelAnimationFrame;if("undefined"!=typeof Zone&&t&&e){const n=t[Zone.__symbol__("OriginalDelegate")];n&&(t=n);const o=e[Zone.__symbol__("OriginalDelegate")];o&&(e=o)}return{nativeRequestAnimationFrame:t,nativeCancelAnimationFrame:e}})().nativeRequestAnimationFrame,(function a(t){const e=()=>{!(function e(t){t.isCheckStableRunning||-1!==t.lastRequestAnimationFrameId||(t.lastRequestAnimationFrameId=t.nativeRequestAnimationFrame.call(jn,(()=>{t.fakeTopEventTask||(t.fakeTopEventTask=Zone.root.scheduleEventTask("fakeTopEventTask",(()=>{t.lastRequestAnimationFrameId=-1,l_(t),t.isCheckStableRunning=!0,s_(t),t.isCheckStableRunning=!1}),void 0,(()=>{}),(()=>{}))),t.fakeTopEventTask.invoke()})),l_(t))})(t)};t._inner=t._inner.fork({name:"angular",properties:{isAngularZone:!0},onInvokeTask:(n,o,i,a,r,s)=>{try{return c_(t),n.invokeTask(i,a,r,s)}finally{(t.shouldCoalesceEventChangeDetection&&"eventTask"===a.type||t.shouldCoalesceRunChangeDetection)&&e(),d_(t)}},onInvoke:(n,o,i,a,r,s,l)=>{try{return c_(t),n.invoke(i,a,r,s,l)}finally{t.shouldCoalesceRunChangeDetection&&e(),d_(t)}},onHasTask:(e,n,o,i)=>{e.hasTask(o,i),n===o&&("microTask"==i.change?(t._hasPendingMicrotasks=i.microTask,l_(t),s_(t)):"macroTask"==i.change&&(t.hasPendingMacrotasks=i.macroTask))},onHandleError:(e,n,o,i)=>(e.handleError(o,i),t.runOutsideAngular((()=>t.onError.emit(i))),!1)})})(o)}static isInAngularZone(){return!0===Zone.current.get("isAngularZone")}static assertInAngularZone(){if(!a_.isInAngularZone())throw new Error("Expected to be in Angular Zone, but it is not!")}static assertNotInAngularZone(){if(a_.isInAngularZone())throw new Error("Expected to not be in Angular Zone, but it is!")}run(t,e,n){return this._inner.run(t,e,n)}runTask(t,e,n,o){const i=this._inner,a=i.scheduleEventTask("NgZoneEvent: "+o,t,r_,fg,fg);try{return i.runTask(a,e,n)}finally{i.cancelTask(a)}}runGuarded(t,e,n){return this._inner.runGuarded(t,e,n)}runOutsideAngular(t){return this._outer.run(t)}}const r_={};function s_(t){if(0==t._nesting&&!t.hasPendingMicrotasks&&!t.isStable)try{t._nesting++,t.onMicrotaskEmpty.emit(null)}finally{if(t._nesting--,!t.hasPendingMicrotasks)try{t.runOutsideAngular((()=>t.onStable.emit(null)))}finally{t.isStable=!0}}}function l_(t){t.hasPendingMicrotasks=!!(t._hasPendingMicrotasks||(t.shouldCoalesceEventChangeDetection||t.shouldCoalesceRunChangeDetection)&&-1!==t.lastRequestAnimationFrameId)}function c_(t){t._nesting++,t.isStable&&(t.isStable=!1,t.onUnstable.emit(null))}function d_(t){t._nesting--,s_(t)}class p_{constructor(){this.hasPendingMicrotasks=!1,this.hasPendingMacrotasks=!1,this.isStable=!0,this.onUnstable=new Lh,this.onMicrotaskEmpty=new Lh,this.onStable=new Lh,this.onError=new Lh}run(t,e,n){return t.apply(e,n)}runGuarded(t,e,n){return t.apply(e,n)}runOutsideAngular(t){return t()}runTask(t,e,n,o){return t.apply(e,n)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class m_{constructor(t){this._ngZone=t,this._pendingCount=0,this._isZoneStable=!0,this._didWork=!1,this._callbacks=[],this.taskTrackingZone=null,this._watchAngularEvents(),t.run((()=>{this.taskTrackingZone="undefined"==typeof Zone?null:Zone.current.get("TaskTrackingZone")}))}_watchAngularEvents(){this._ngZone.onUnstable.subscribe({next:()=>{this._didWork=!0,this._isZoneStable=!1}}),this._ngZone.runOutsideAngular((()=>{this._ngZone.onStable.subscribe({next:()=>{a_.assertNotInAngularZone(),i_((()=>{this._isZoneStable=!0,this._runCallbacksIfReady()}))}})}))}increasePendingRequestCount(){return this._pendingCount+=1,this._didWork=!0,this._pendingCount}decreasePendingRequestCount(){if(this._pendingCount-=1,this._pendingCount<0)throw new Error("pending async requests below zero");return this._runCallbacksIfReady(),this._pendingCount}isStable(){return this._isZoneStable&&0===this._pendingCount&&!this._ngZone.hasPendingMacrotasks}_runCallbacksIfReady(){if(this.isStable())i_((()=>{for(;0!==this._callbacks.length;){let t=this._callbacks.pop();clearTimeout(t.timeoutId),t.doneCb(this._didWork)}this._didWork=!1}));else{let t=this.getPendingTasks();this._callbacks=this._callbacks.filter((e=>!e.updateCb||!e.updateCb(t)||(clearTimeout(e.timeoutId),!1))),this._didWork=!0}}getPendingTasks(){return this.taskTrackingZone?this.taskTrackingZone.macroTasks.map((t=>({source:t.source,creationLocation:t.creationLocation,data:t.data}))):[]}addCallback(t,e,n){let o=-1;e&&e>0&&(o=setTimeout((()=>{this._callbacks=this._callbacks.filter((t=>t.timeoutId!==o)),t(this._didWork,this.getPendingTasks())}),e)),this._callbacks.push({doneCb:t,timeoutId:o,updateCb:n})}whenStable(t,e,n){if(n&&!this.taskTrackingZone)throw new Error('Task tracking zone is required when passing an update callback to whenStable(). Is "zone.js/plugins/task-tracking" loaded?');this.addCallback(t,e,n),this._runCallbacksIfReady()}getPendingRequestCount(){return this._pendingCount}findProviders(t,e,n){return[]}}m_.ɵfac=function t(e){return new(e||m_)(vr(a_))},m_.ɵprov=Mn({token:m_,factory:m_.ɵfac}),m_.ctorParameters=()=>[{type:a_}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(m_,[{type:im}],(function(){return[{type:a_}]}),null);class u_{constructor(){this._applications=new Map,g_.addToWindow(this)}registerApplication(t,e){this._applications.set(t,e)}unregisterApplication(t){this._applications.delete(t)}unregisterAllApplications(){this._applications.clear()}getTestability(t){return this._applications.get(t)||null}getAllTestabilities(){return Array.from(this._applications.values())}getAllRootElements(){return Array.from(this._applications.keys())}findTestabilityInTree(t,e=!0){return g_.findTestabilityInTree(this,t,e)}}u_.ɵfac=function t(e){return new(e||u_)},u_.ɵprov=Mn({token:u_,factory:u_.ɵfac}),u_.ctorParameters=()=>[],("undefined"==typeof ngDevMode||ngDevMode)&&hh(u_,[{type:im}],(function(){return[]}),null);let f_,g_=new class{addToWindow(t){}findTestabilityInTree(t,e,n){return null}},h_=!0,b_=!1;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function y_(){return b_=!0,h_}const __=new Ga("AllowMultipleToken");function C_(t,e,n=[]){const o=`Platform: ${e}`,i=new Ga(o);return(e=[])=>{let a=M_();if(!a||a.injector.get(__,!1))if(t)t(n.concat(e).concat({provide:i,useValue:!0}));else{const t=n.concat(e).concat({provide:i,useValue:!0},{provide:Yd,useValue:"platform"});!(function r(t){if(f_&&!f_.destroyed&&!f_.injector.get(__,!1))throw new Error("There can be only one platform. Destroy the previous one to create a new one.");(function e(){ngDevMode&&(function t(){_p||(_p=!0,Cp("ɵsetProfiler",Yo),Cp("getDirectiveMetadata",up),Cp("getComponent",sp),Cp("getContext",lp),Cp("getListeners",gp),Cp("getOwningComponent",cp),Cp("getHostElement",fp),Cp("getInjector",pp),Cp("getRootComponents",dp),Cp("getDirectives",mp),Cp("applyChanges",yp))})()})(),f_=t.get(v_);const n=t.get(Vy,null);n&&n.forEach((t=>t()))})(rp.create({providers:t,name:o}))}return(function s(t){const e=M_();if(!e)throw new Error("No platform exists!");if(!e.injector.get(t,null))throw new Error("A platform with a different configuration has been created. Please destroy it first.");return e})(i)}}function M_(){return f_&&!f_.destroyed?f_:null}class v_{constructor(t){this._injector=t,this._modules=[],this._destroyListeners=[],this._destroyed=!1}bootstrapModuleFactory(t,e){const n=(function o(t,e){let n;return n="noop"===t?new p_:("zone.js"===t?void 0:t)||new a_({enableLongStackTrace:y_(),shouldCoalesceEventChangeDetection:!!(null==e?void 0:e.ngZoneEventCoalescing),shouldCoalesceRunChangeDetection:!!(null==e?void 0:e.ngZoneRunCoalescing)}),n})(e?e.ngZone:void 0,{ngZoneEventCoalescing:e&&e.ngZoneEventCoalescing||!1,ngZoneRunCoalescing:e&&e.ngZoneRunCoalescing||!1}),i=[{provide:a_,useValue:n}];return n.run((()=>{const e=rp.create({providers:i,parent:this.injector,name:t.moduleType.name}),o=t.create(e),a=o.injector.get(Zs,null);if(!a)throw new Error("No ErrorHandler. Is platform module (BrowserModule) included?");return n.runOutsideAngular((()=>{const t=n.onError.subscribe({next:t=>{a.handleError(t)}});o.onDestroy((()=>{P_(this._modules,o),t.unsubscribe()}))})),(function r(t,e,n){try{const o=n();return Fm(o)?o.catch((n=>{throw e.runOutsideAngular((()=>t.handleError(n))),n})):o}catch(n){throw e.runOutsideAngular((()=>t.handleError(n))),n}})(a,n,(()=>{const t=o.injector.get(Iy);return t.runInitializers(),t.donePromise.then((()=>(Xu(o.injector.get(Wy,Gu)||Gu),this._moduleDoBootstrap(o),o)))}))}))}bootstrapModule(t,e=[]){const n=x_({},e);return(function o(t,e,n){ngDevMode&&(function o(t,e="Type passed in is not NgModuleType, it does not have 'ɵmod' property."){fo(t)||bn(e)})(n);const i=new _h(n);if("undefined"!=typeof ngJitMode&&!ngJitMode)return Promise.resolve(i);const a=t.get(n_,[]).concat(e);if((function r(t){if(null!==jb){if(t.defaultEncapsulation!==jb.defaultEncapsulation)return void(ngDevMode&&console.error("Provided value for `defaultEncapsulation` can not be changed once it has been set."));if(t.preserveWhitespaces!==jb.preserveWhitespaces)return void(ngDevMode&&console.error("Provided value for `preserveWhitespaces` can not be changed once it has been set."))}jb=t})({defaultEncapsulation:w_(a.map((t=>t.defaultEncapsulation))),preserveWhitespaces:w_(a.map((t=>t.preserveWhitespaces)))}),(function s(){return 0===Ir.size})())return Promise.resolve(i);const l=(function c(t){const e=[];return t.forEach((t=>t&&e.push(...t))),e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(a.map((t=>t.providers)));if(0===l.length)return Promise.resolve(i);const d=Ja({usage:0,kind:"NgModule",type:n}),p=rp.create({providers:l}).get(d.ResourceLoader);return(function m(t){const e=[],n=new Map;function o(e){let o=n.get(e);if(!o){const i=t(e);n.set(e,o=i.then(Lr))}return o}return Ir.forEach(((t,n)=>{const i=[];t.templateUrl&&i.push(o(t.templateUrl).then((e=>{t.template=e})));const a=t.styleUrls,r=t.styles||(t.styles=[]),s=t.styles.length;a&&a.forEach(((e,n)=>{r.push(""),i.push(o(e).then((o=>{r[s+n]=o,a.splice(a.indexOf(e),1),0==a.length&&(t.styleUrls=void 0)})))}));const l=Promise.all(i).then((()=>(function t(e){Hr.delete(e)})(n)));e.push(l)})),(function i(){Ir=new Map})(),Promise.all(e).then((()=>{}))})((t=>Promise.resolve(p.get(t)))).then((()=>i))})(this.injector,n,t).then((t=>this.bootstrapModuleFactory(t,n)))}_moduleDoBootstrap(t){const e=t.injector.get(O_);if(t._bootstrapComponents.length>0)t._bootstrapComponents.forEach((t=>e.bootstrap(t)));else{if(!t.instance.ngDoBootstrap)throw new Error(`The module ${Ge(t.instance.constructor)} was bootstrapped, but it does not declare "@NgModule.bootstrap" components nor a "ngDoBootstrap" method. Please define one of these.`);t.instance.ngDoBootstrap(e)}this._modules.push(t)}onDestroy(t){this._destroyListeners.push(t)}get injector(){return this._injector}destroy(){if(this._destroyed)throw new Error("The platform has already been destroyed!");this._modules.slice().forEach((t=>t.destroy())),this._destroyListeners.forEach((t=>t())),this._destroyed=!0}get destroyed(){return this._destroyed}}function x_(t,e){return Array.isArray(e)?e.reduce(x_,t):Object.assign(Object.assign({},t),e)}v_.ɵfac=function t(e){return new(e||v_)(vr(rp))},v_.ɵprov=Mn({token:v_,factory:v_.ɵfac}),v_.ctorParameters=()=>[{type:rp}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(v_,[{type:im}],(function(){return[{type:rp}]}),null);class O_{constructor(t,e,n,o,i){this._zone=t,this._injector=e,this._exceptionHandler=n,this._componentFactoryResolver=o,this._initStatus=i,this._bootstrapListeners=[],this._views=[],this._runningTick=!1,this._stable=!0,this.componentTypes=[],this.components=[],this._onMicrotaskEmptySubscription=this._zone.onMicrotaskEmpty.subscribe({next:()=>{this._zone.run((()=>{this.tick()}))}});const a=new D((t=>{this._stable=this._zone.isStable&&!this._zone.hasPendingMacrotasks&&!this._zone.hasPendingMicrotasks,this._zone.runOutsideAngular((()=>{t.next(this._stable),t.complete()}))})),r=new D((t=>{let e;this._zone.runOutsideAngular((()=>{e=this._zone.onStable.subscribe((()=>{a_.assertNotInAngularZone(),i_((()=>{this._stable||this._zone.hasPendingMacrotasks||this._zone.hasPendingMicrotasks||(this._stable=!0,t.next(!0))}))}))}));const n=this._zone.onUnstable.subscribe((()=>{a_.assertInAngularZone(),this._stable&&(this._stable=!1,this._zone.runOutsideAngular((()=>{t.next(!1)})))}));return()=>{e.unsubscribe(),n.unsubscribe()}}));this.isStable=re(a,r.pipe(Ee()))}bootstrap(t,e){if(!this._initStatus.done)throw new Error("Cannot bootstrap as there are still asynchronous initializers running. Bootstrap components in the `ngDoBootstrap` method of the root module.");let n;n=t instanceof mg?t:this._componentFactoryResolver.resolveComponentFactory(t),this.componentTypes.push(n.componentType);const o=(function i(t){return t.isBoundToModule})(n)?void 0:this._injector.get(th),a=n.create(rp.NULL,[],e||n.selector,o),r=a.location.nativeElement,s=a.injector.get(m_,null),l=s&&a.injector.get(u_);return s&&l&&l.registerApplication(r,s),a.onDestroy((()=>{this.detachView(a.hostView),P_(this.components,a),l&&l.unregisterApplication(r)})),this._loadComponent(a),("undefined"==typeof ngDevMode||ngDevMode)&&y_()&&this._injector.get(Gy).log("Angular is running in development mode. Call enableProdMode() to enable production mode."),a}tick(){if(this._runningTick)throw new Error("ApplicationRef.tick is called recursively");try{this._runningTick=!0;for(let t of this._views)t.detectChanges();if(("undefined"==typeof ngDevMode||ngDevMode)&&y_())for(let t of this._views)t.checkNoChanges()}catch(t){this._zone.runOutsideAngular((()=>this._exceptionHandler.handleError(t)))}finally{this._runningTick=!1}}attachView(t){const e=t;this._views.push(e),e.attachToAppRef(this)}detachView(t){const e=t;P_(this._views,e),e.detachFromAppRef()}_loadComponent(t){this.attachView(t.hostView),this.tick(),this.components.push(t),this._injector.get(Uy,[]).concat(this._bootstrapListeners).forEach((e=>e(t)))}ngOnDestroy(){this._views.slice().forEach((t=>t.destroy())),this._onMicrotaskEmptySubscription.unsubscribe()}get viewCount(){return this._views.length}}function P_(t,e){const n=t.indexOf(e);n>-1&&t.splice(n,1)}function w_(t){for(let e=t.length-1;e>=0;e--)if(void 0!==t[e])return t[e]}O_.ɵfac=function t(e){return new(e||O_)(vr(a_),vr(rp),vr(Zs),vr(ug),vr(Iy))},O_.ɵprov=Mn({token:O_,factory:O_.ɵfac}),O_.ctorParameters=()=>[{type:a_},{type:rp},{type:Zs},{type:ug},{type:Iy}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(O_,[{type:im}],(function(){return[{type:a_},{type:rp},{type:Zs},{type:ug},{type:Iy}]}),null);class k_{}const S_={factoryPathPrefix:"",factoryPathSuffix:".ngfactory"};class D_{constructor(t,e){this._compiler=t,this._config=e||S_}load(t){return this.loadAndCompile(t)}loadAndCompile(t){let[e,n]=t.split("#");return void 0===n&&(n="default"),System.import(e).then((t=>t[n])).then((t=>E_(t,e,n))).then((t=>this._compiler.compileModuleAsync(t)))}loadFactory(t){let[e,n]=t.split("#"),o="NgFactory";return void 0===n&&(n="default",o=""),System.import(this._config.factoryPathPrefix+e+this._config.factoryPathSuffix).then((t=>t[n+o])).then((t=>E_(t,e,n)))}}function E_(t,e,n){if(!t)throw new Error(`Cannot find '${n}' in '${e}'`);return t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */D_.ɵfac=function t(e){return new(e||D_)(vr(e_),vr(k_,8))},D_.ɵprov=Mn({token:D_,factory:D_.ɵfac}),D_.ctorParameters=()=>[{type:e_},{type:k_,decorators:[{type:Sr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(D_,[{type:im}],(function(){return[{type:e_},{type:k_,decorators:[{type:Sr}]}]}),null);const R_=C_(null,"core",[{provide:jy,useValue:"unknown"},{provide:v_,deps:[rp]},{provide:u_,deps:[]},{provide:Gy,deps:[]}]),A_=[{provide:O_,useClass:O_,deps:[a_,rp,Zs,ug,Iy]},{provide:uh,deps:[a_],useFactory:function T_(t){let e=[];return t.onStable.subscribe((()=>{for(;e.length;)e.pop()()})),function(t){e.push(t)}}},{provide:Iy,useClass:Iy,deps:[[new Sr,zy]]},{provide:e_,useClass:e_,deps:[]},Fy,{provide:Hg,useFactory:
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function N_(){return qg},deps:[]},{provide:Lg,useFactory:function z_(){return Zg},deps:[]},{provide:Wy,useFactory:function I_(t){return Xu(t=t||(function e(){return"undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode&&"undefined"!=typeof goog&&"en"!==goog.getLocale()?goog.getLocale():"undefined"!=typeof $localize&&$localize.locale||Gu})()),t},deps:[[new kr(Wy),new Sr,new Er]]},{provide:Yy,useValue:"USD"}];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class H_{constructor(t){}}var F_,L_;H_.ɵfac=function t(e){return new(e||H_)(vr(O_))},H_.ɵmod=ao({type:H_}),H_.ɵinj=vn({providers:A_}),H_.ctorParameters=()=>[{type:O_}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(H_,[{type:Ay,args:[{providers:A_}]}],(function(){return[{type:O_}]}),null),(function(t){t[t.CreateViewNodes=0]="CreateViewNodes",t[t.CheckNoChanges=1]="CheckNoChanges",t[t.CheckNoChangesProjectedViews=2]="CheckNoChangesProjectedViews",t[t.CheckAndUpdate=3]="CheckAndUpdate",t[t.CheckAndUpdateProjectedViews=4]="CheckAndUpdateProjectedViews",t[t.Destroy=5]="Destroy"})(F_||(F_={})),(function(t){t[t.create=0]="create",t[t.detectChanges=1]="detectChanges",t[t.checkNoChanges=2]="checkNoChanges",t[t.destroy=3]="destroy",t[t.handleEvent=4]="handleEvent"})(L_||(L_={})),
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
"undefined"!=typeof ngDevMode&&ngDevMode&&(jn.$localize=jn.$localize||function(){throw new Error("It looks like your application or one of its dependencies is using i18n.\nAngular 9 introduced a global `$localize()` function that needs to be loaded.\nPlease run `ng add @angular/localize` from the Angular CLI.\n(For non-CLI projects, add `import '@angular/localize/init';` to your `polyfills.ts` file.\nFor server-side rendering applications add the import to your `main.server.ts` file.)")})
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
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/**
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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     * found in the LICENSE file at https://angular.io/license
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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     * Copyright Google LLC All Rights Reserved.
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     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */var mC,uC,fC,gC,hC,bC,yC;function _C(t,e){return wC(Lu(t)[ju.DateFormat],e)}function CC(t,e){return wC(Lu(t)[ju.TimeFormat],e)}function MC(t,e){return wC(Lu(t)[ju.DateTimeFormat],e)}function vC(t,e){const n=Lu(t),o=n[ju.NumberSymbols][e];if(void 0===o){if(e===bC.CurrencyDecimal)return n[ju.NumberSymbols][bC.Decimal];if(e===bC.CurrencyGroup)return n[ju.NumberSymbols][bC.Group]}return o}function xC(t,e){return Lu(t)[ju.NumberFormats][e]}!(function(t){t[t.Decimal=0]="Decimal",t[t.Percent=1]="Percent",t[t.Currency=2]="Currency",t[t.Scientific=3]="Scientific"})(mC||(mC={})),(function(t){t[t.Zero=0]="Zero",t[t.One=1]="One",t[t.Two=2]="Two",t[t.Few=3]="Few",t[t.Many=4]="Many",t[t.Other=5]="Other"})(uC||(uC={})),(function(t){t[t.Format=0]="Format",t[t.Standalone=1]="Standalone"})(fC||(fC={})),(function(t){t[t.Narrow=0]="Narrow",t[t.Abbreviated=1]="Abbreviated",t[t.Wide=2]="Wide",t[t.Short=3]="Short"})(gC||(gC={})),(function(t){t[t.Short=0]="Short",t[t.Medium=1]="Medium",t[t.Long=2]="Long",t[t.Full=3]="Full"})(hC||(hC={})),(function(t){t[t.Decimal=0]="Decimal",t[t.Group=1]="Group",t[t.List=2]="List",t[t.PercentSign=3]="PercentSign",t[t.PlusSign=4]="PlusSign",t[t.MinusSign=5]="MinusSign",t[t.Exponential=6]="Exponential",t[t.SuperscriptingExponent=7]="SuperscriptingExponent",t[t.PerMille=8]="PerMille",t[t[1/0]=9]="Infinity",t[t.NaN=10]="NaN",t[t.TimeSeparator=11]="TimeSeparator",t[t.CurrencyDecimal=12]="CurrencyDecimal",t[t.CurrencyGroup=13]="CurrencyGroup"})(bC||(bC={})),(function(t){t[t.Sunday=0]="Sunday",t[t.Monday=1]="Monday",t[t.Tuesday=2]="Tuesday",t[t.Wednesday=3]="Wednesday",t[t.Thursday=4]="Thursday",t[t.Friday=5]="Friday",t[t.Saturday=6]="Saturday"})(yC||(yC={}));const OC=Bu;function PC(t){if(!t[ju.ExtraData])throw new Error(`Missing extra locale data for the locale "${t[ju.LocaleId]}". Use "registerLocaleData" to load new data. See the "I18n guide" on angular.io to know more.`)}function wC(t,e){for(let n=e;n>-1;n--)if(void 0!==t[n])return t[n];throw new Error("Locale data API: locale data undefined")}function kC(t){const[e,n]=t.split(":");return{hours:+e,minutes:+n}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const SC=/^(\d{4})-?(\d\d)-?(\d\d)(?:T(\d\d)(?::?(\d\d)(?::?(\d\d)(?:\.(\d+))?)?)?(Z|([+-])(\d\d):?(\d\d))?)?$/,DC={},EC=/((?:[^BEGHLMOSWYZabcdhmswyz']+)|(?:'(?:[^']|'')*')|(?:G{1,5}|y{1,4}|Y{1,4}|M{1,5}|L{1,5}|w{1,2}|W{1}|d{1,2}|E{1,6}|c{1,6}|a{1,5}|b{1,5}|B{1,5}|h{1,2}|H{1,2}|m{1,2}|s{1,2}|S{1,3}|z{1,4}|Z{1,5}|O{1,4}))([\s\S]*)/;var RC,AC,TC;function NC(t,e,n,o){let i=(function a(t){if(qC(t))return t;if("number"==typeof t&&!isNaN(t))return new Date(t);if("string"==typeof t){if(t=t.trim(),/^(\d{4}(-\d{1,2}(-\d{1,2})?)?)$/.test(t)){const[e,n=1,o=1]=t.split("-").map((t=>+t));return zC(e,n-1,o)}const e=parseFloat(t);if(!isNaN(t-e))return new Date(e);let n;if(n=t.match(SC))return(function e(t){const e=new Date(0);let n=0,o=0;const i=t[8]?e.setUTCFullYear:e.setFullYear,a=t[8]?e.setUTCHours:e.setHours;t[9]&&(n=Number(t[9]+t[10]),o=Number(t[9]+t[11])),i.call(e,Number(t[1]),Number(t[2])-1,Number(t[3]));const 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e;switch(t){case"G":case"GG":case"GGG":e=BC(TC.Eras,gC.Abbreviated);break;case"GGGG":e=BC(TC.Eras,gC.Wide);break;case"GGGGG":e=BC(TC.Eras,gC.Narrow);break;case"y":e=LC(AC.FullYear,1,0,!1,!0);break;case"yy":e=LC(AC.FullYear,2,0,!0,!0);break;case"yyy":e=LC(AC.FullYear,3,0,!1,!0);break;case"yyyy":e=LC(AC.FullYear,4,0,!1,!0);break;case"Y":e=GC(1);break;case"YY":e=GC(2,!0);break;case"YYY":e=GC(3);break;case"YYYY":e=GC(4);break;case"M":case"L":e=LC(AC.Month,1,1);break;case"MM":case"LL":e=LC(AC.Month,2,1);break;case"MMM":e=BC(TC.Months,gC.Abbreviated);break;case"MMMM":e=BC(TC.Months,gC.Wide);break;case"MMMMM":e=BC(TC.Months,gC.Narrow);break;case"LLL":e=BC(TC.Months,gC.Abbreviated,fC.Standalone);break;case"LLLL":e=BC(TC.Months,gC.Wide,fC.Standalone);break;case"LLLLL":e=BC(TC.Months,gC.Narrow,fC.Standalone);break;case"w":e=UC(1);break;case"ww":e=UC(2);break;case"W":e=UC(1,!0);break;case"d":e=LC(AC.Date,1);break;case"dd":e=LC(AC.Date,2);break;case"c":case"cc":e=LC(AC.Day,1);break;case"ccc":e=BC(TC.Days,gC.Abbreviated,fC.Standalone);break;case"cccc":e=BC(TC.Days,gC.Wide,fC.Standalone);break;case"ccccc":e=BC(TC.Days,gC.Narrow,fC.Standalone);break;case"cccccc":e=BC(TC.Days,gC.Short,fC.Standalone);break;case"E":case"EE":case"EEE":e=BC(TC.Days,gC.Abbreviated);break;case"EEEE":e=BC(TC.Days,gC.Wide);break;case"EEEEE":e=BC(TC.Days,gC.Narrow);break;case"EEEEEE":e=BC(TC.Days,gC.Short);break;case"a":case"aa":case"aaa":e=BC(TC.DayPeriods,gC.Abbreviated);break;case"aaaa":e=BC(TC.DayPeriods,gC.Wide);break;case"aaaaa":e=BC(TC.DayPeriods,gC.Narrow);break;case"b":case"bb":case"bbb":e=BC(TC.DayPeriods,gC.Abbreviated,fC.Standalone,!0);break;case"bbbb":e=BC(TC.DayPeriods,gC.Wide,fC.Standalone,!0);break;case"bbbbb":e=BC(TC.DayPeriods,gC.Narrow,fC.Standalone,!0);break;case"B":case"BB":case"BBB":e=BC(TC.DayPeriods,gC.Abbreviated,fC.Format,!0);break;case"BBBB":e=BC(TC.DayPeriods,gC.Wide,fC.Format,!0);break;case"BBBBB":e=BC(TC.DayPeriods,gC.Narrow,fC.Format,!0);break;case"h":e=LC(AC.Hours,1,-12);break;case"hh":e=LC(AC.Hours,2,-12);break;case"H":e=LC(AC.Hours,1);break;case"HH":e=LC(AC.Hours,2);break;case"m":e=LC(AC.Minutes,1);break;case"mm":e=LC(AC.Minutes,2);break;case"s":e=LC(AC.Seconds,1);break;case"ss":e=LC(AC.Seconds,2);break;case"S":e=LC(AC.FractionalSeconds,1);break;case"SS":e=LC(AC.FractionalSeconds,2);break;case"SSS":e=LC(AC.FractionalSeconds,3);break;case"Z":case"ZZ":case"ZZZ":e=VC(RC.Short);break;case"ZZZZZ":e=VC(RC.Extended);break;case"O":case"OO":case"OOO":case"z":case"zz":case"zzz":e=VC(RC.ShortGMT);break;case"OOOO":case"ZZZZ":case"zzzz":e=VC(RC.Long);break;default:return null}return WC[t]=e,e})(t);d+=e?e(i,n,l):"''"===t?"'":t.replace(/(^'|'$)/g,"").replace(/''/g,"'")})),d}function zC(t,e,n){const o=new Date(0);return o.setFullYear(t,e,n),o.setHours(0,0,0),o}function IC(t,e){const n=(function o(t){return Lu(t)[ju.LocaleId]})(t);if(DC[n]=DC[n]||{},DC[n][e])return DC[n][e];let i="";switch(e){case"shortDate":i=_C(t,hC.Short);break;case"mediumDate":i=_C(t,hC.Medium);break;case"longDate":i=_C(t,hC.Long);break;case"fullDate":i=_C(t,hC.Full);break;case"shortTime":i=CC(t,hC.Short);break;case"mediumTime":i=CC(t,hC.Medium);break;case"longTime":i=CC(t,hC.Long);break;case"fullTime":i=CC(t,hC.Full);break;case"short":const e=IC(t,"shortTime"),n=IC(t,"shortDate");i=HC(MC(t,hC.Short),[e,n]);break;case"medium":const o=IC(t,"mediumTime"),a=IC(t,"mediumDate");i=HC(MC(t,hC.Medium),[o,a]);break;case"long":const r=IC(t,"longTime"),s=IC(t,"longDate");i=HC(MC(t,hC.Long),[r,s]);break;case"full":const l=IC(t,"fullTime"),c=IC(t,"fullDate");i=HC(MC(t,hC.Full),[l,c])}return i&&(DC[n][e]=i),i}function HC(t,e){return e&&(t=t.replace(/\{([^}]+)}/g,(function(t,n){return null!=e&&n in e?e[n]:t}))),t}function FC(t,e,n="-",o,i){let a="";(t<0||i&&t<=0)&&(i?t=1-t:(t=-t,a=n));let r=String(t);for(;r.length<e;)r="0"+r;return o&&(r=r.substr(r.length-e)),a+r}function LC(t,e,n=0,o=!1,i=!1){return function(a,r){let s=(function l(t,e){switch(t){case AC.FullYear:return e.getFullYear();case AC.Month:return e.getMonth();case AC.Date:return e.getDate();case AC.Hours:return e.getHours();case AC.Minutes:return e.getMinutes();case AC.Seconds:return e.getSeconds();case AC.FractionalSeconds:return e.getMilliseconds();case AC.Day:return e.getDay();default:throw new Error(`Unknown DateType value "${t}".`)}})(t,a);if((n>0||s>-n)&&(s+=n),t===AC.Hours)0===s&&-12===n&&(s=12);else if(t===AC.FractionalSeconds)return(function c(t,e){return FC(t,3).substr(0,e)})(s,e);const d=vC(r,bC.MinusSign);return FC(s,e,d,o,i)}}function BC(t,e,n=fC.Format,o=!1){return function(i,a){return(function r(t,e,n,o,i,a){switch(n){case TC.Months:return(function r(t,e,n){const o=Lu(t),i=wC([o[ju.MonthsFormat],o[ju.MonthsStandalone]],e);return wC(i,n)})(e,i,o)[t.getMonth()];case TC.Days:return(function s(t,e,n){const o=Lu(t),i=wC([o[ju.DaysFormat],o[ju.DaysStandalone]],e);return wC(i,n)})(e,i,o)[t.getDay()];case TC.DayPeriods:const r=t.getHours(),s=t.getMinutes();if(a){const t=(function l(t){const e=Lu(t);return PC(e),(e[ju.ExtraData][2]||[]).map((t=>"string"==typeof t?kC(t):[kC(t[0]),kC(t[1])]))})(e),n=(function c(t,e,n){const o=Lu(t);PC(o);const i=wC([o[ju.ExtraData][0],o[ju.ExtraData][1]],e)||[];return wC(i,n)||[]})(e,i,o),a=t.findIndex((t=>{if(Array.isArray(t)){const[e,n]=t,o=r>=e.hours&&s>=e.minutes,i=r<n.hours||r===n.hours&&s<n.minutes;if(e.hours<n.hours){if(o&&i)return!0}else if(o||i)return!0}else if(t.hours===r&&t.minutes===s)return!0;return!1}));if(-1!==a)return n[a]}return(function d(t,e,n){const o=Lu(t),i=wC([o[ju.DayPeriodsFormat],o[ju.DayPeriodsStandalone]],e);return wC(i,n)})(e,i,o)[r<12?0:1];case TC.Eras:return(function p(t,e){return wC(Lu(t)[ju.Eras],e)})(e,o)[t.getFullYear()<=0?0:1];default:throw new Error(`unexpected translation type ${n}`)}})(i,a,t,e,n,o)}}function VC(t){return function(e,n,o){const i=-1*o,a=vC(n,bC.MinusSign),r=i>0?Math.floor(i/60):Math.ceil(i/60);switch(t){case RC.Short:return(i>=0?"+":"")+FC(r,2,a)+FC(Math.abs(i%60),2,a);case RC.ShortGMT:return"GMT"+(i>=0?"+":"")+FC(r,1,a);case RC.Long:return"GMT"+(i>=0?"+":"")+FC(r,2,a)+":"+FC(Math.abs(i%60),2,a);case RC.Extended:return 0===o?"Z":(i>=0?"+":"")+FC(r,2,a)+":"+FC(Math.abs(i%60),2,a);default:throw new Error(`Unknown zone width "${t}"`)}}}function jC(t){return zC(t.getFullYear(),t.getMonth(),t.getDate()+(4-t.getDay()))}function UC(t,e=!1){return function(n,o){let i;if(e){const t=new Date(n.getFullYear(),n.getMonth(),1).getDay()-1,e=n.getDate();i=1+Math.floor((e+t)/7)}else{const t=jC(n),e=(function a(t){const e=zC(t,0,1).getDay();return zC(t,0,1+(e<=4?4:11)-e)})(t.getFullYear()),o=t.getTime()-e.getTime();i=1+Math.round(o/6048e5)}return FC(i,t,vC(o,bC.MinusSign))}}function GC(t,e=!1){return function(n,o){return FC(jC(n).getFullYear(),t,vC(o,bC.MinusSign),e)}}!(function(t){t[t.Short=0]="Short",t[t.ShortGMT=1]="ShortGMT",t[t.Long=2]="Long",t[t.Extended=3]="Extended"})(RC||(RC={})),(function(t){t[t.FullYear=0]="FullYear",t[t.Month=1]="Month",t[t.Date=2]="Date",t[t.Hours=3]="Hours",t[t.Minutes=4]="Minutes",t[t.Seconds=5]="Seconds",t[t.FractionalSeconds=6]="FractionalSeconds",t[t.Day=7]="Day"})(AC||(AC={})),(function(t){t[t.DayPeriods=0]="DayPeriods",t[t.Days=1]="Days",t[t.Months=2]="Months",t[t.Eras=3]="Eras"})(TC||(TC={}));const WC={};function YC(t,e){t=t.replace(/:/g,"");const n=Date.parse("Jan 01, 1970 00:00:00 "+t)/6e4;return isNaN(n)?e:n}function qC(t){return t instanceof Date&&!isNaN(t.valueOf())}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const ZC=/^(\d+)?\.((\d+)(-(\d+))?)?$/,XC=".",KC="0";function JC(t,e,n,o,i,a,r=!1){let s="",l=!1;if(isFinite(t)){let c=(function c(t){let e,n,o,i,a,r=Math.abs(t)+"",s=0;for((n=r.indexOf(XC))>-1&&(r=r.replace(XC,"")),(o=r.search(/e/i))>0?(n<0&&(n=o),n+=+r.slice(o+1),r=r.substring(0,o)):n<0&&(n=r.length),o=0;r.charAt(o)===KC;o++);if(o===(a=r.length))e=[0],n=1;else{for(a--;r.charAt(a)===KC;)a--;for(n-=o,e=[],i=0;o<=a;o++,i++)e[i]=Number(r.charAt(o))}return n>22&&(e=e.splice(0,21),s=n-1,n=1),{digits:e,exponent:s,integerLen:n}})(t);r&&(c=(function d(t){if(0===t.digits[0])return t;const e=t.digits.length-t.integerLen;return t.exponent?t.exponent+=2:(0===e?t.digits.push(0,0):1===e&&t.digits.push(0),t.integerLen+=2),t})(c));let d=e.minInt,p=e.minFrac,m=e.maxFrac;if(a){const t=a.match(ZC);if(null===t)throw new Error(`${a} is not a valid digit info`);const e=t[1],n=t[3],o=t[5];null!=e&&(d=tM(e)),null!=n&&(p=tM(n)),null!=o?m=tM(o):null!=n&&p>m&&(m=p)}!(function p(t,e,n){if(e>n)throw new Error(`The minimum number of digits after fraction (${e}) is higher than the maximum (${n}).`);let o=t.digits,i=o.length-t.integerLen;const a=Math.min(Math.max(e,i),n);let r=a+t.integerLen,s=o[r];if(r>0){o.splice(Math.max(t.integerLen,r));for(let t=r;t<o.length;t++)o[t]=0}else{i=Math.max(0,i),t.integerLen=1,o.length=Math.max(1,r=a+1),o[0]=0;for(let t=1;t<r;t++)o[t]=0}if(s>=5)if(r-1<0){for(let e=0;e>r;e--)o.unshift(0),t.integerLen++;o.unshift(1),t.integerLen++}else o[r-1]++;for(;i<Math.max(0,a);i++)o.push(0);let l=0!==a;const c=e+t.integerLen,d=o.reduceRight((function(t,e,n,o){return o[n]=(e+=t)<10?e:e-10,l&&(0===o[n]&&n>=c?o.pop():l=!1),e>=10?1:0}),0);d&&(o.unshift(d),t.integerLen++)})(c,p,m);let u=c.digits,f=c.integerLen;const g=c.exponent;let h=[];for(l=u.every((t=>!t));f<d;f++)u.unshift(0);for(;f<0;f++)u.unshift(0);f>0?h=u.splice(f,u.length):(h=u,u=[0]);const b=[];for(u.length>=e.lgSize&&b.unshift(u.splice(-e.lgSize,u.length).join(""));u.length>e.gSize;)b.unshift(u.splice(-e.gSize,u.length).join(""));u.length&&b.unshift(u.join("")),s=b.join(vC(n,o)),h.length&&(s+=vC(n,i)+h.join("")),g&&(s+=vC(n,bC.Exponential)+"+"+g)}else s=vC(n,bC.Infinity);return s=t<0&&!l?e.negPre+s+e.negSuf:e.posPre+s+e.posSuf,s}function QC(t,e,n){return JC(t,$C(xC(e,mC.Decimal),vC(e,bC.MinusSign)),e,bC.Group,bC.Decimal,n)}function $C(t,e="-"){const n={minInt:1,minFrac:0,maxFrac:0,posPre:"",posSuf:"",negPre:"",negSuf:"",gSize:0,lgSize:0},o=t.split(";"),i=o[0],a=o[1],r=-1!==i.indexOf(XC)?i.split(XC):[i.substring(0,i.lastIndexOf(KC)+1),i.substring(i.lastIndexOf(KC)+1)],s=r[0],l=r[1]||"";n.posPre=s.substr(0,s.indexOf("#"));for(let t=0;t<l.length;t++){const e=l.charAt(t);e===KC?n.minFrac=n.maxFrac=t+1:"#"===e?n.maxFrac=t+1:n.posSuf+=e}const c=s.split(",");if(n.gSize=c[1]?c[1].length:0,n.lgSize=c[2]||c[1]?(c[2]||c[1]).length:0,a){const t=i.length-n.posPre.length-n.posSuf.length,e=a.indexOf("#");n.negPre=a.substr(0,e).replace(/'/g,""),n.negSuf=a.substr(e+t).replace(/'/g,"")}else n.negPre=e+n.posPre,n.negSuf=n.posSuf;return n}function tM(t){const e=parseInt(t);if(isNaN(e))throw new Error("Invalid integer literal when parsing "+t);return e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class eM{}function nM(t,e,n,o){let i=`=${t}`;if(e.indexOf(i)>-1)return i;if(i=n.getPluralCategory(t,o),e.indexOf(i)>-1)return i;if(e.indexOf("other")>-1)return"other";throw new Error(`No plural message found for value "${t}"`)}class oM extends eM{constructor(t){super(),this.locale=t}getPluralCategory(t,e){switch(OC(e||this.locale)(t)){case uC.Zero:return"zero";case uC.One:return"one";case uC.Two:return"two";case uC.Few:return"few";case uC.Many:return"many";default:return"other"}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function iM(t,e){e=encodeURIComponent(e);for(const n of t.split(";")){const t=n.indexOf("="),[o,i]=-1==t?[n,""]:[n.slice(0,t),n.slice(t+1)];if(o.trim()===e)return decodeURIComponent(i)}return null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */oM.ɵfac=function t(e){return new(e||oM)(vr(Wy))},oM.ɵprov=Mn({token:oM,factory:oM.ɵfac}),oM.ctorParameters=()=>[{type:String,decorators:[{type:kr,args:[Wy]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(oM,[{type:im}],(function(){return[{type:String,decorators:[{type:kr,args:[Wy]}]}]}),null);class aM{constructor(t,e,n,o){this._iterableDiffers=t,this._keyValueDiffers=e,this._ngEl=n,this._renderer=o,this._iterableDiffer=null,this._keyValueDiffer=null,this._initialClasses=[],this._rawClass=null}set klass(t){this._removeClasses(this._initialClasses),this._initialClasses="string"==typeof t?t.split(/\s+/):[],this._applyClasses(this._initialClasses),this._applyClasses(this._rawClass)}set ngClass(t){this._removeClasses(this._rawClass),this._applyClasses(this._initialClasses),this._iterableDiffer=null,this._keyValueDiffer=null,this._rawClass="string"==typeof t?t.split(/\s+/):t,this._rawClass&&(Np(this._rawClass)?this._iterableDiffer=this._iterableDiffers.find(this._rawClass).create():this._keyValueDiffer=this._keyValueDiffers.find(this._rawClass).create())}ngDoCheck(){if(this._iterableDiffer){const t=this._iterableDiffer.diff(this._rawClass);t&&this._applyIterableChanges(t)}else if(this._keyValueDiffer){const t=this._keyValueDiffer.diff(this._rawClass);t&&this._applyKeyValueChanges(t)}}_applyKeyValueChanges(t){t.forEachAddedItem((t=>this._toggleClass(t.key,t.currentValue))),t.forEachChangedItem((t=>this._toggleClass(t.key,t.currentValue))),t.forEachRemovedItem((t=>{t.previousValue&&this._toggleClass(t.key,!1)}))}_applyIterableChanges(t){t.forEachAddedItem((t=>{if("string"!=typeof t.item)throw new Error(`NgClass can only toggle CSS classes expressed as strings, got ${Ge(t.item)}`);this._toggleClass(t.item,!0)})),t.forEachRemovedItem((t=>this._toggleClass(t.item,!1)))}_applyClasses(t){t&&(Array.isArray(t)||t instanceof Set?t.forEach((t=>this._toggleClass(t,!0))):Object.keys(t).forEach((e=>this._toggleClass(e,!!t[e]))))}_removeClasses(t){t&&(Array.isArray(t)||t instanceof Set?t.forEach((t=>this._toggleClass(t,!1))):Object.keys(t).forEach((t=>this._toggleClass(t,!1))))}_toggleClass(t,e){(t=t.trim())&&t.split(/\s+/g).forEach((t=>{e?this._renderer.addClass(this._ngEl.nativeElement,t):this._renderer.removeClass(this._ngEl.nativeElement,t)}))}}aM.ɵfac=function t(e){return new(e||aM)(Sm(Hg),Sm(Lg),Sm(hg),Sm(Cg))},aM.ɵdir=lo({type:aM,selectors:[["","ngClass",""]],inputs:{klass:["class","klass"],ngClass:"ngClass"}}),aM.ctorParameters=()=>[{type:Hg},{type:Lg},{type:hg},{type:Cg}],aM.propDecorators={klass:[{type:xy,args:["class"]}],ngClass:[{type:xy,args:["ngClass"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(aM,[{type:Cy,args:[{selector:"[ngClass]"}]}],(function(){return[{type:Hg},{type:Lg},{type:hg},{type:Cg}]}),{klass:[{type:xy,args:["class"]}],ngClass:[{type:xy,args:["ngClass"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class rM{constructor(t){this._viewContainerRef=t,this._componentRef=null,this._moduleRef=null}ngOnChanges(t){if(this._viewContainerRef.clear(),this._componentRef=null,this.ngComponentOutlet){const e=this.ngComponentOutletInjector||this._viewContainerRef.parentInjector;if(t.ngComponentOutletNgModuleFactory)if(this._moduleRef&&this._moduleRef.destroy(),this.ngComponentOutletNgModuleFactory){const t=e.get(th);this._moduleRef=this.ngComponentOutletNgModuleFactory.create(t.injector)}else this._moduleRef=null;const n=(this._moduleRef?this._moduleRef.componentFactoryResolver:e.get(ug)).resolveComponentFactory(this.ngComponentOutlet);this._componentRef=this._viewContainerRef.createComponent(n,this._viewContainerRef.length,e,this.ngComponentOutletContent)}}ngOnDestroy(){this._moduleRef&&this._moduleRef.destroy()}}rM.ɵfac=function t(e){return new(e||rM)(Sm(eh))},rM.ɵdir=lo({type:rM,selectors:[["","ngComponentOutlet",""]],inputs:{ngComponentOutlet:"ngComponentOutlet",ngComponentOutletInjector:"ngComponentOutletInjector",ngComponentOutletContent:"ngComponentOutletContent",ngComponentOutletNgModuleFactory:"ngComponentOutletNgModuleFactory"},features:[Bo]}),rM.ctorParameters=()=>[{type:eh}],rM.propDecorators={ngComponentOutlet:[{type:xy}],ngComponentOutletInjector:[{type:xy}],ngComponentOutletContent:[{type:xy}],ngComponentOutletNgModuleFactory:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(rM,[{type:Cy,args:[{selector:"[ngComponentOutlet]"}]}],(function(){return[{type:eh}]}),{ngComponentOutlet:[{type:xy}],ngComponentOutletInjector:[{type:xy}],ngComponentOutletContent:[{type:xy}],ngComponentOutletNgModuleFactory:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class sM{constructor(t,e,n,o){this.$implicit=t,this.ngForOf=e,this.index=n,this.count=o}get first(){return 0===this.index}get last(){return this.index===this.count-1}get even(){return this.index%2==0}get odd(){return!this.even}}class lM{constructor(t,e,n){this._viewContainer=t,this._template=e,this._differs=n,this._ngForOf=null,this._ngForOfDirty=!0,this._differ=null}set ngForOf(t){this._ngForOf=t,this._ngForOfDirty=!0}set ngForTrackBy(t){"undefined"!=typeof ngDevMode&&!ngDevMode||null==t||"function"==typeof t||console&&console.warn&&console.warn(`trackBy must be a function, but received ${JSON.stringify(t)}. See https://angular.io/api/common/NgForOf#change-propagation for more information.`),this._trackByFn=t}get ngForTrackBy(){return this._trackByFn}set ngForTemplate(t){t&&(this._template=t)}ngDoCheck(){if(this._ngForOfDirty){this._ngForOfDirty=!1;const t=this._ngForOf;if(!this._differ&&t)try{this._differ=this._differs.find(t).create(this.ngForTrackBy)}catch(e){throw new Error(`Cannot find a differ supporting object '${t}' of type '${(function n(t){return t.name||typeof t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t)}'. NgFor only supports binding to Iterables such as Arrays.`)}}if(this._differ){const t=this._differ.diff(this._ngForOf);t&&this._applyChanges(t)}}_applyChanges(t){const e=[];t.forEachOperation(((t,n,o)=>{if(null==t.previousIndex){const n=this._viewContainer.createEmbeddedView(this._template,new sM(null,this._ngForOf,-1,-1),null===o?void 0:o),i=new cM(t,n);e.push(i)}else if(null==o)this._viewContainer.remove(null===n?void 0:n);else if(null!==n){const i=this._viewContainer.get(n);this._viewContainer.move(i,o);const a=new cM(t,i);e.push(a)}}));for(let t=0;t<e.length;t++)this._perViewChange(e[t].view,e[t].record);for(let t=0,e=this._viewContainer.length;t<e;t++){const n=this._viewContainer.get(t);n.context.index=t,n.context.count=e,n.context.ngForOf=this._ngForOf}t.forEachIdentityChange((t=>{this._viewContainer.get(t.currentIndex).context.$implicit=t.item}))}_perViewChange(t,e){t.context.$implicit=e.item}static ngTemplateContextGuard(t,e){return!0}}lM.ɵfac=function t(e){return new(e||lM)(Sm(eh),Sm(Xg),Sm(Hg))},lM.ɵdir=lo({type:lM,selectors:[["","ngFor","","ngForOf",""]],inputs:{ngForOf:"ngForOf",ngForTrackBy:"ngForTrackBy",ngForTemplate:"ngForTemplate"}}),lM.ctorParameters=()=>[{type:eh},{type:Xg},{type:Hg}],lM.propDecorators={ngForOf:[{type:xy}],ngForTrackBy:[{type:xy}],ngForTemplate:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(lM,[{type:Cy,args:[{selector:"[ngFor][ngForOf]"}]}],(function(){return[{type:eh},{type:Xg},{type:Hg}]}),{ngForOf:[{type:xy}],ngForTrackBy:[{type:xy}],ngForTemplate:[{type:xy}]});class cM{constructor(t,e){this.record=t,this.view=e}}class dM{constructor(t,e){this._viewContainer=t,this._context=new pM,this._thenTemplateRef=null,this._elseTemplateRef=null,this._thenViewRef=null,this._elseViewRef=null,this._thenTemplateRef=e}set ngIf(t){this._context.$implicit=this._context.ngIf=t,this._updateView()}set ngIfThen(t){mM("ngIfThen",t),this._thenTemplateRef=t,this._thenViewRef=null,this._updateView()}set ngIfElse(t){mM("ngIfElse",t),this._elseTemplateRef=t,this._elseViewRef=null,this._updateView()}_updateView(){this._context.$implicit?this._thenViewRef||(this._viewContainer.clear(),this._elseViewRef=null,this._thenTemplateRef&&(this._thenViewRef=this._viewContainer.createEmbeddedView(this._thenTemplateRef,this._context))):this._elseViewRef||(this._viewContainer.clear(),this._thenViewRef=null,this._elseTemplateRef&&(this._elseViewRef=this._viewContainer.createEmbeddedView(this._elseTemplateRef,this._context)))}static ngTemplateContextGuard(t,e){return!0}}dM.ɵfac=function t(e){return new(e||dM)(Sm(eh),Sm(Xg))},dM.ɵdir=lo({type:dM,selectors:[["","ngIf",""]],inputs:{ngIf:"ngIf",ngIfThen:"ngIfThen",ngIfElse:"ngIfElse"}}),dM.ctorParameters=()=>[{type:eh},{type:Xg}],dM.propDecorators={ngIf:[{type:xy}],ngIfThen:[{type:xy}],ngIfElse:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(dM,[{type:Cy,args:[{selector:"[ngIf]"}]}],(function(){return[{type:eh},{type:Xg}]}),{ngIf:[{type:xy}],ngIfThen:[{type:xy}],ngIfElse:[{type:xy}]});class pM{constructor(){this.$implicit=null,this.ngIf=null}}function mM(t,e){if(e&&!e.createEmbeddedView)throw new Error(`${t} must be a TemplateRef, but received '${Ge(e)}'.`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class uM{constructor(t,e){this._viewContainerRef=t,this._templateRef=e,this._created=!1}create(){this._created=!0,this._viewContainerRef.createEmbeddedView(this._templateRef)}destroy(){this._created=!1,this._viewContainerRef.clear()}enforceState(t){t&&!this._created?this.create():!t&&this._created&&this.destroy()}}class fM{constructor(){this._defaultUsed=!1,this._caseCount=0,this._lastCaseCheckIndex=0,this._lastCasesMatched=!1}set ngSwitch(t){this._ngSwitch=t,0===this._caseCount&&this._updateDefaultCases(!0)}_addCase(){return this._caseCount++}_addDefault(t){this._defaultViews||(this._defaultViews=[]),this._defaultViews.push(t)}_matchCase(t){const e=t==this._ngSwitch;return this._lastCasesMatched=this._lastCasesMatched||e,this._lastCaseCheckIndex++,this._lastCaseCheckIndex===this._caseCount&&(this._updateDefaultCases(!this._lastCasesMatched),this._lastCaseCheckIndex=0,this._lastCasesMatched=!1),e}_updateDefaultCases(t){if(this._defaultViews&&t!==this._defaultUsed){this._defaultUsed=t;for(let e=0;e<this._defaultViews.length;e++)this._defaultViews[e].enforceState(t)}}}fM.ɵfac=function t(e){return new(e||fM)},fM.ɵdir=lo({type:fM,selectors:[["","ngSwitch",""]],inputs:{ngSwitch:"ngSwitch"}}),fM.propDecorators={ngSwitch:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(fM,[{type:Cy,args:[{selector:"[ngSwitch]"}]}],(function(){return[]}),{ngSwitch:[{type:xy}]});class gM{constructor(t,e,n){this.ngSwitch=n,"undefined"!=typeof ngDevMode&&!ngDevMode||n||bM("ngSwitchCase","NgSwitchCase"),n._addCase(),this._view=new uM(t,e)}ngDoCheck(){this._view.enforceState(this.ngSwitch._matchCase(this.ngSwitchCase))}}gM.ɵfac=function t(e){return new(e||gM)(Sm(eh),Sm(Xg),Sm(fM,9))},gM.ɵdir=lo({type:gM,selectors:[["","ngSwitchCase",""]],inputs:{ngSwitchCase:"ngSwitchCase"}}),gM.ctorParameters=()=>[{type:eh},{type:Xg},{type:fM,decorators:[{type:Sr},{type:Rr}]}],gM.propDecorators={ngSwitchCase:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(gM,[{type:Cy,args:[{selector:"[ngSwitchCase]"}]}],(function(){return[{type:eh},{type:Xg},{type:fM,decorators:[{type:Sr},{type:Rr}]}]}),{ngSwitchCase:[{type:xy}]});class hM{constructor(t,e,n){"undefined"!=typeof ngDevMode&&!ngDevMode||n||bM("ngSwitchDefault","NgSwitchDefault"),n._addDefault(new uM(t,e))}}function bM(t,e){throw new Ke("305",`An element with the "${t}" attribute (matching the "${e}" directive) must be located inside an element with the "ngSwitch" attribute (matching "NgSwitch" directive)`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */hM.ɵfac=function t(e){return new(e||hM)(Sm(eh),Sm(Xg),Sm(fM,9))},hM.ɵdir=lo({type:hM,selectors:[["","ngSwitchDefault",""]]}),hM.ctorParameters=()=>[{type:eh},{type:Xg},{type:fM,decorators:[{type:Sr},{type:Rr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(hM,[{type:Cy,args:[{selector:"[ngSwitchDefault]"}]}],(function(){return[{type:eh},{type:Xg},{type:fM,decorators:[{type:Sr},{type:Rr}]}]}),null);class yM{constructor(t){this._localization=t,this._caseViews={}}set ngPlural(t){this._switchValue=t,this._updateView()}addCase(t,e){this._caseViews[t]=e}_updateView(){this._clearViews();const t=Object.keys(this._caseViews),e=nM(this._switchValue,t,this._localization);this._activateView(this._caseViews[e])}_clearViews(){this._activeView&&this._activeView.destroy()}_activateView(t){t&&(this._activeView=t,this._activeView.create())}}yM.ɵfac=function t(e){return new(e||yM)(Sm(eM))},yM.ɵdir=lo({type:yM,selectors:[["","ngPlural",""]],inputs:{ngPlural:"ngPlural"}}),yM.ctorParameters=()=>[{type:eM}],yM.propDecorators={ngPlural:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(yM,[{type:Cy,args:[{selector:"[ngPlural]"}]}],(function(){return[{type:eM}]}),{ngPlural:[{type:xy}]});class _M{constructor(t,e,n,o){this.value=t;const i=!isNaN(Number(t));o.addCase(i?`=${t}`:t,new uM(n,e))}}_M.ɵfac=function t(e){return new(e||_M)(Na("ngPluralCase"),Sm(Xg),Sm(eh),Sm(yM,1))},_M.ɵdir=lo({type:_M,selectors:[["","ngPluralCase",""]]}),_M.ctorParameters=()=>[{type:String,decorators:[{type:ja,args:["ngPluralCase"]}]},{type:Xg},{type:eh},{type:yM,decorators:[{type:Rr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(_M,[{type:Cy,args:[{selector:"[ngPluralCase]"}]}],(function(){return[{type:String,decorators:[{type:ja,args:["ngPluralCase"]}]},{type:Xg},{type:eh},{type:yM,decorators:[{type:Rr}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class CM{constructor(t,e,n){this._ngEl=t,this._differs=e,this._renderer=n,this._ngStyle=null,this._differ=null}set ngStyle(t){this._ngStyle=t,!this._differ&&t&&(this._differ=this._differs.find(t).create())}ngDoCheck(){if(this._differ){const t=this._differ.diff(this._ngStyle);t&&this._applyChanges(t)}}_setStyle(t,e){const[n,o]=t.split(".");null!=(e=null!=e&&o?`${e}${o}`:e)?this._renderer.setStyle(this._ngEl.nativeElement,n,e):this._renderer.removeStyle(this._ngEl.nativeElement,n)}_applyChanges(t){t.forEachRemovedItem((t=>this._setStyle(t.key,null))),t.forEachAddedItem((t=>this._setStyle(t.key,t.currentValue))),t.forEachChangedItem((t=>this._setStyle(t.key,t.currentValue)))}}CM.ɵfac=function t(e){return new(e||CM)(Sm(hg),Sm(Lg),Sm(Cg))},CM.ɵdir=lo({type:CM,selectors:[["","ngStyle",""]],inputs:{ngStyle:"ngStyle"}}),CM.ctorParameters=()=>[{type:hg},{type:Lg},{type:Cg}],CM.propDecorators={ngStyle:[{type:xy,args:["ngStyle"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(CM,[{type:Cy,args:[{selector:"[ngStyle]"}]}],(function(){return[{type:hg},{type:Lg},{type:Cg}]}),{ngStyle:[{type:xy,args:["ngStyle"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class MM{constructor(t){this._viewContainerRef=t,this._viewRef=null,this.ngTemplateOutletContext=null,this.ngTemplateOutlet=null}ngOnChanges(t){if(t.ngTemplateOutlet){const t=this._viewContainerRef;this._viewRef&&t.remove(t.indexOf(this._viewRef)),this._viewRef=this.ngTemplateOutlet?t.createEmbeddedView(this.ngTemplateOutlet,this.ngTemplateOutletContext):null}else this._viewRef&&t.ngTemplateOutletContext&&this.ngTemplateOutletContext&&(this._viewRef.context=this.ngTemplateOutletContext)}}MM.ɵfac=function t(e){return new(e||MM)(Sm(eh))},MM.ɵdir=lo({type:MM,selectors:[["","ngTemplateOutlet",""]],inputs:{ngTemplateOutletContext:"ngTemplateOutletContext",ngTemplateOutlet:"ngTemplateOutlet"},features:[Bo]}),MM.ctorParameters=()=>[{type:eh}],MM.propDecorators={ngTemplateOutletContext:[{type:xy}],ngTemplateOutlet:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(MM,[{type:Cy,args:[{selector:"[ngTemplateOutlet]"}]}],(function(){return[{type:eh}]}),{ngTemplateOutletContext:[{type:xy}],ngTemplateOutlet:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const vM=[aM,rM,lM,dM,MM,CM,fM,gM,hM,yM,_M];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function xM(t,e){return Error(`InvalidPipeArgument: '${e}' for pipe '${Ge(t)}'`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const OM=new class{createSubscription(t,e){return t.then(e,(t=>{throw t}))}dispose(t){}onDestroy(t){}},PM=new class{createSubscription(t,e){return t.subscribe({next:e,error:t=>{throw t}})}dispose(t){t.unsubscribe()}onDestroy(t){t.unsubscribe()}};class wM{constructor(t){this._ref=t,this._latestValue=null,this._subscription=null,this._obj=null,this._strategy=null}ngOnDestroy(){this._subscription&&this._dispose()}transform(t){return this._obj?t!==this._obj?(this._dispose(),this.transform(t)):this._latestValue:(t&&this._subscribe(t),this._latestValue)}_subscribe(t){this._obj=t,this._strategy=this._selectStrategy(t),this._subscription=this._strategy.createSubscription(t,(e=>this._updateLatestValue(t,e)))}_selectStrategy(t){if(Fm(t))return OM;if(Lm(t))return PM;throw xM(wM,t)}_dispose(){this._strategy.dispose(this._subscription),this._latestValue=null,this._subscription=null,this._obj=null}_updateLatestValue(t,e){t===this._obj&&(this._latestValue=e,this._ref.markForCheck())}}wM.ɵfac=function t(e){return new(e||wM)(Sm(Ug,16))},wM.ɵpipe=co({name:"async",type:wM,pure:!1}),wM.ctorParameters=()=>[{type:Ug}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(wM,[{type:vy,args:[{name:"async",pure:!1}]}],(function(){return[{type:Ug}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class kM{transform(t){if(null==t)return null;if("string"!=typeof t)throw xM(kM,t);return t.toLowerCase()}}kM.ɵfac=function t(e){return new(e||kM)},kM.ɵpipe=co({name:"lowercase",type:kM,pure:!0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(kM,[{type:vy,args:[{name:"lowercase"}]}],null,null);const 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class RM{constructor(t){this.locale=t}transform(t,e="mediumDate",n,o){if(null==t||""===t||t!=t)return null;try{return NC(t,e,o||this.locale,n)}catch(t){throw xM(RM,t.message)}}}RM.ɵfac=function t(e){return new(e||RM)(Sm(Wy,16))},RM.ɵpipe=co({name:"date",type:RM,pure:!0}),RM.ctorParameters=()=>[{type:String,decorators:[{type:kr,args:[Wy]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(RM,[{type:vy,args:[{name:"date",pure:!0}]}],(function(){return[{type:String,decorators:[{type:kr,args:[Wy]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const AM=/#/g;class TM{constructor(t){this._localization=t}transform(t,e,n){if(null==t)return"";if("object"!=typeof e||null===e)throw xM(TM,e);return e[nM(t,Object.keys(e),this._localization,n)].replace(AM,t.toString())}}TM.ɵfac=function t(e){return new(e||TM)(Sm(eM,16))},TM.ɵpipe=co({name:"i18nPlural",type:TM,pure:!0}),TM.ctorParameters=()=>[{type:eM}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(TM,[{type:vy,args:[{name:"i18nPlural",pure:!0}]}],(function(){return[{type:eM}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class NM{transform(t,e){if(null==t)return"";if("object"!=typeof e||"string"!=typeof t)throw xM(NM,e);return e.hasOwnProperty(t)?e[t]:e.hasOwnProperty("other")?e.other:""}}NM.ɵfac=function t(e){return new(e||NM)},NM.ɵpipe=co({name:"i18nSelect",type:NM,pure:!0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(NM,[{type:vy,args:[{name:"i18nSelect",pure:!0}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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class zM{transform(t){return JSON.stringify(t,null,2)}}zM.ɵfac=function t(e){return new(e||zM)},zM.ɵpipe=co({name:"json",type:zM,pure:!1}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(zM,[{type:vy,args:[{name:"json",pure:!1}]}],null,null);class IM{constructor(t){this.differs=t,this.keyValues=[],this.compareFn=HM}transform(t,e=HM){if(!t||!(t instanceof Map)&&"object"!=typeof t)return null;this.differ||(this.differ=this.differs.find(t).create());const n=this.differ.diff(t),o=e!==this.compareFn;return n&&(this.keyValues=[],n.forEachItem((t=>{this.keyValues.push((
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */BM.ɵfac=function t(e){return new(e||BM)(Sm(Wy,16),Sm(Yy,16))},BM.ɵpipe=co({name:"currency",type:BM,pure:!0}),BM.ctorParameters=()=>[{type:String,decorators:[{type:kr,args:[Wy]}]},{type:String,decorators:[{type:kr,args:[Yy]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(BM,[{type:vy,args:[{name:"currency"}]}],(function(){return[{type:String,decorators:[{type:kr,args:[Wy]}]},{type:String,decorators:[{type:kr,args:[Yy]}]}]}),null);class UM{transform(t,e,n){if(null==t)return null;if(!this.supports(t))throw xM(UM,t);return t.slice(e,n)}supports(t){return"string"==typeof t||Array.isArray(t)}}UM.ɵfac=function t(e){return new(e||UM)},UM.ɵpipe=co({name:"slice",type:UM,pure:!1}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(UM,[{type:vy,args:[{name:"slice",pure:!1}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const GM=[wM,EM,kM,zM,UM,FM,LM,DM,BM,RM,TM,NM,IM];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class WM{}WM.ɵfac=function t(e){return new(e||WM)},WM.ɵmod=ao({type:WM}),WM.ɵinj=vn({providers:[{provide:eM,useClass:oM}]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(WM,[{type:Ay,args:[{declarations:[vM,GM],exports:[vM,GM],providers:[{provide:eM,useClass:oM}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(WM,{declarations:[aM,rM,lM,dM,MM,CM,fM,gM,hM,yM,_M,wM,EM,kM,zM,UM,FM,LM,DM,BM,RM,TM,NM,IM],exports:[aM,rM,lM,dM,MM,CM,fM,gM,hM,yM,_M,wM,EM,kM,zM,UM,FM,LM,DM,BM,RM,TM,NM,IM]}),
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
new Og("12.2.1");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class YM{}YM.ɵprov=Mn({token:YM,providedIn:"root",factory:()=>new qM(vr(Z_),window)});class qM{constructor(t,e){this.document=t,this.window=e,this.offset=()=>[0,0]}setOffset(t){this.offset=Array.isArray(t)?()=>t:t}getScrollPosition(){return this.supportsScrolling()?[this.window.pageXOffset,this.window.pageYOffset]:[0,0]}scrollToPosition(t){this.supportsScrolling()&&this.window.scrollTo(t[0],t[1])}scrollToAnchor(t){if(!this.supportsScrolling())return;const e=(function n(t,e){const n=t.getElementById(e)||t.getElementsByName(e)[0];if(n)return n;if("function"==typeof t.createTreeWalker&&t.body&&(t.body.createShadowRoot||t.body.attachShadow)){const n=t.createTreeWalker(t.body,NodeFilter.SHOW_ELEMENT);let o=n.currentNode;for(;o;){const t=o.shadowRoot;if(t){const n=t.getElementById(e)||t.querySelector(`[name="${e}"]`);if(n)return n}o=n.nextNode()}}return null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(this.document,t);e&&(this.scrollToElement(e),this.attemptFocus(e))}setHistoryScrollRestoration(t){if(this.supportScrollRestoration()){const e=this.window.history;e&&e.scrollRestoration&&(e.scrollRestoration=t)}}scrollToElement(t){const e=t.getBoundingClientRect(),n=e.left+this.window.pageXOffset,o=e.top+this.window.pageYOffset,i=this.offset();this.window.scrollTo(n-i[0],o-i[1])}attemptFocus(t){return t.focus(),this.document.activeElement===t}supportScrollRestoration(){try{if(!this.supportsScrolling())return!1;const t=ZM(this.window.history)||ZM(Object.getPrototypeOf(this.window.history));return!(!t||!t.writable&&!t.set)}catch(t){return!1}}supportsScrolling(){try{return!!this.window&&!!this.window.scrollTo&&"pageXOffset"in this.window}catch(t){return!1}}}function ZM(t){return Object.getOwnPropertyDescriptor(t,"scrollRestoration")}class XM{}
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class KM extends class extends class{}{constructor(){super(...arguments),this.supportsDOMEvents=!0}}{static makeCurrent(){!(function t(e){Y_||(Y_=e)})(new KM)}onAndCancel(t,e,n){return t.addEventListener(e,n,!1),()=>{t.removeEventListener(e,n,!1)}}dispatchEvent(t,e){t.dispatchEvent(e)}remove(t){t.parentNode&&t.parentNode.removeChild(t)}createElement(t,e){return(e=e||this.getDefaultDocument()).createElement(t)}createHtmlDocument(){return document.implementation.createHTMLDocument("fakeTitle")}getDefaultDocument(){return document}isElementNode(t){return t.nodeType===Node.ELEMENT_NODE}isShadowRoot(t){return t instanceof DocumentFragment}getGlobalEventTarget(t,e){return"window"===e?window:"document"===e?t:"body"===e?t.body:null}getBaseHref(t){const e=(function n(){return QM=QM||document.querySelector("base"),QM?QM.getAttribute("href"):null})();return null==e?null:(function o(t){JM=JM||document.createElement("a"),JM.setAttribute("href",t);const e=JM.pathname;return"/"===e.charAt(0)?e:`/${e}`}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(e)}resetBaseElement(){QM=null}getUserAgent(){return window.navigator.userAgent}getCookie(t){return iM(document.cookie,t)}}let JM,QM=null;const $M=new Ga("TRANSITION_ID"),tv=[{provide:zy,useFactory:function ev(t,e,n){return()=>{n.get(Iy).donePromise.then((()=>{const n=q_();Array.prototype.slice.apply(e.querySelectorAll("style[ng-transition]")).filter((e=>e.getAttribute("ng-transition")===t)).forEach((t=>n.remove(t)))}))}},deps:[$M,Z_,rp],multi:!0}];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class nv{static init(){!(function t(e){g_=e})(new nv)}addToWindow(t){jn.getAngularTestability=(e,n=!0)=>{const o=t.findTestabilityInTree(e,n);if(null==o)throw new Error("Could not find testability for element.");return o},jn.getAllAngularTestabilities=()=>t.getAllTestabilities(),jn.getAllAngularRootElements=()=>t.getAllRootElements(),jn.frameworkStabilizers||(jn.frameworkStabilizers=[]),jn.frameworkStabilizers.push((t=>{const e=jn.getAllAngularTestabilities();let n=e.length,o=!1;const i=function(e){o=o||e,n--,0==n&&t(o)};e.forEach((function(t){t.whenStable(i)}))}))}findTestabilityInTree(t,e,n){if(null==e)return null;const o=t.getTestability(e);return null!=o?o:n?q_().isShadowRoot(e)?this.findTestabilityInTree(t,e.host,!0):this.findTestabilityInTree(t,e.parentElement,!0):null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class ov{build(){return new XMLHttpRequest}}ov.ɵfac=function t(e){return new(e||ov)},ov.ɵprov=Mn({token:ov,factory:ov.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ov,[{type:im}],null,null),new Sr;const iv=new Ga("EventManagerPlugins");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class av{constructor(t,e){this._zone=e,this._eventNameToPlugin=new Map,t.forEach((t=>t.manager=this)),this._plugins=t.slice().reverse()}addEventListener(t,e,n){return this._findPluginFor(e).addEventListener(t,e,n)}addGlobalEventListener(t,e,n){return this._findPluginFor(e).addGlobalEventListener(t,e,n)}getZone(){return this._zone}_findPluginFor(t){const e=this._eventNameToPlugin.get(t);if(e)return e;const n=this._plugins;for(let e=0;e<n.length;e++){const o=n[e];if(o.supports(t))return this._eventNameToPlugin.set(t,o),o}throw new Error(`No event manager plugin found for event ${t}`)}}av.ɵfac=function t(e){return new(e||av)(vr(iv),vr(a_))},av.ɵprov=Mn({token:av,factory:av.ɵfac}),av.ctorParameters=()=>[{type:Array,decorators:[{type:kr,args:[iv]}]},{type:a_}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(av,[{type:im}],(function(){return[{type:Array,decorators:[{type:kr,args:[iv]}]},{type:a_}]}),null);class rv{constructor(t){this._doc=t}addGlobalEventListener(t,e,n){const o=q_().getGlobalEventTarget(this._doc,t);if(!o)throw new Error(`Unsupported event target ${o} for event ${e}`);return this.addEventListener(o,e,n)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class sv{constructor(){this._stylesSet=new Set}addStyles(t){const e=new Set;t.forEach((t=>{this._stylesSet.has(t)||(this._stylesSet.add(t),e.add(t))})),this.onStylesAdded(e)}onStylesAdded(t){}getAllStyles(){return Array.from(this._stylesSet)}}sv.ɵfac=function t(e){return new(e||sv)},sv.ɵprov=Mn({token:sv,factory:sv.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(sv,[{type:im}],(function(){return[]}),null);class lv extends sv{constructor(t){super(),this._doc=t,this._hostNodes=new Map,this._hostNodes.set(t.head,[])}_addStylesToHost(t,e,n){t.forEach((t=>{const o=this._doc.createElement("style");o.textContent=t,n.push(e.appendChild(o))}))}addHost(t){const e=[];this._addStylesToHost(this._stylesSet,t,e),this._hostNodes.set(t,e)}removeHost(t){const e=this._hostNodes.get(t);e&&e.forEach(cv),this._hostNodes.delete(t)}onStylesAdded(t){this._hostNodes.forEach(((e,n)=>{this._addStylesToHost(t,n,e)}))}ngOnDestroy(){this._hostNodes.forEach((t=>t.forEach(cv)))}}function cv(t){q_().remove(t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class vv extends rv{constructor(t){super(t)}supports(t){return!0}addEventListener(t,e,n){return t.addEventListener(e,n,!1),()=>this.removeEventListener(t,e,n)}removeEventListener(t,e,n){return t.removeEventListener(e,n)}}vv.ɵfac=function t(e){return new(e||vv)(vr(Z_))},vv.ɵprov=Mn({token:vv,factory:vv.ɵfac}),vv.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(vv,[{type:im}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const xv={pan:!0,panstart:!0,panmove:!0,panend:!0,pancancel:!0,panleft:!0,panright:!0,panup:!0,pandown:!0,pinch:!0,pinchstart:!0,pinchmove:!0,pinchend:!0,pinchcancel:!0,pinchin:!0,pinchout:!0,press:!0,pressup:!0,rotate:!0,rotatestart:!0,rotatemove:!0,rotateend:!0,rotatecancel:!0,swipe:!0,swipeleft:!0,swiperight:!0,swipeup:!0,swipedown:!0,tap:!0,doubletap:!0},Ov=new Ga("HammerGestureConfig"),Pv=new Ga("HammerLoader");class wv{constructor(){this.events=[],this.overrides={}}buildHammer(t){const e=new Hammer(t,this.options);e.get("pinch").set({enable:!0}),e.get("rotate").set({enable:!0});for(const t in this.overrides)e.get(t).set(this.overrides[t]);return e}}wv.ɵfac=function t(e){return new(e||wv)},wv.ɵprov=Mn({token:wv,factory:wv.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(wv,[{type:im}],(function(){return[]}),null);class kv extends rv{constructor(t,e,n,o){super(t),this._config=e,this.console=n,this.loader=o,this._loaderPromise=null}supports(t){return!(!xv.hasOwnProperty(t.toLowerCase())&&!this.isCustomEvent(t)||!window.Hammer&&!this.loader&&(("undefined"==typeof ngDevMode||ngDevMode)&&this.console.warn(`The "${t}" event cannot be bound because Hammer.JS is not loaded and no custom loader has been specified.`),1))}addEventListener(t,e,n){const o=this.manager.getZone();if(e=e.toLowerCase(),!window.Hammer&&this.loader){this._loaderPromise=this._loaderPromise||this.loader();let o=!1,i=()=>{o=!0};return this._loaderPromise.then((()=>{if(!window.Hammer)return("undefined"==typeof ngDevMode||ngDevMode)&&this.console.warn("The custom HAMMER_LOADER completed, but Hammer.JS is not present."),void(i=()=>{});o||(i=this.addEventListener(t,e,n))})).catch((()=>{("undefined"==typeof ngDevMode||ngDevMode)&&this.console.warn(`The "${e}" event cannot be bound because the custom Hammer.JS loader failed.`),i=()=>{}})),()=>{i()}}return o.runOutsideAngular((()=>{const i=this._config.buildHammer(t),a=function(t){o.runGuarded((function(){n(t)}))};return i.on(e,a),()=>{i.off(e,a),"function"==typeof i.destroy&&i.destroy()}}))}isCustomEvent(t){return this._config.events.indexOf(t)>-1}}kv.ɵfac=function t(e){return new(e||kv)(vr(Z_),vr(Ov),vr(Gy),vr(Pv,8))},kv.ɵprov=Mn({token:kv,factory:kv.ɵfac}),kv.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wv,decorators:[{type:kr,args:[Ov]}]},{type:Gy},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Pv]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(kv,[{type:im}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wv,decorators:[{type:kr,args:[Ov]}]},{type:Gy},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Pv]}]}]}),null);const Sv=[{provide:iv,useClass:kv,multi:!0,deps:[Z_,Ov,Gy,[new Sr,Pv]]},{provide:Ov,useClass:wv,deps:[]}];class Dv{}Dv.ɵfac=function t(e){return new(e||Dv)},Dv.ɵmod=ao({type:Dv}),Dv.ɵinj=vn({providers:Sv}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Dv,[{type:Ay,args:[{providers:Sv}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Ev=["alt","control","meta","shift"],Rv={"\b":"Backspace","\t":"Tab","":"Delete","":"Escape",Del:"Delete",Esc:"Escape",Left:"ArrowLeft",Right:"ArrowRight",Up:"ArrowUp",Down:"ArrowDown",Menu:"ContextMenu",Scroll:"ScrollLock",Win:"OS"},Av={A:"1",B:"2",C:"3",D:"4",E:"5",F:"6",G:"7",H:"8",I:"9",J:"*",K:"+",M:"-",N:".",O:"/","`":"0","":"NumLock"},Tv={alt:t=>t.altKey,control:t=>t.ctrlKey,meta:t=>t.metaKey,shift:t=>t.shiftKey};class Nv extends rv{constructor(t){super(t)}supports(t){return null!=Nv.parseEventName(t)}addEventListener(t,e,n){const o=Nv.parseEventName(e),i=Nv.eventCallback(o.fullKey,n,this.manager.getZone());return this.manager.getZone().runOutsideAngular((()=>q_().onAndCancel(t,o.domEventName,i)))}static parseEventName(t){const e=t.toLowerCase().split("."),n=e.shift();if(0===e.length||"keydown"!==n&&"keyup"!==n)return null;const o=Nv._normalizeKey(e.pop());let i="";if(Ev.forEach((t=>{const n=e.indexOf(t);n>-1&&(e.splice(n,1),i+=t+".")})),i+=o,0!=e.length||0===o.length)return null;const a={};return a.domEventName=n,a.fullKey=i,a}static getEventFullKey(t){let e="",n=(function o(t){let e=t.key;if(null==e){if(e=t.keyIdentifier,null==e)return"Unidentified";e.startsWith("U+")&&(e=String.fromCharCode(parseInt(e.substring(2),16)),3===t.location&&Av.hasOwnProperty(e)&&(e=Av[e]))}return Rv[e]||e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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     * Copyright Google LLC All Rights Reserved.
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     * Copyright Google LLC All Rights Reserved.
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license Angular v12.0.0-next.0
     * (c) 2010-2020 Google LLC. https://angular.io/
     * License: MIT
     */!(function(){
/**
         * @license
         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
         * found in the LICENSE file at https://angular.io/license
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/**
         * @license
         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
         * found in the LICENSE file at https://angular.io/license
         */
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/**
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         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
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/**
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         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
         * found in the LICENSE file at https://angular.io/license
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/**
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         * Copyright Google LLC All Rights Reserved.
         *
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/**
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         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
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/**
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         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
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         * Copyright Google LLC All Rights Reserved.
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/**
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         * Copyright Google LLC All Rights Reserved.
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         * Copyright Google LLC All Rights Reserved.
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         * Copyright Google LLC All Rights Reserved.
         *
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/**
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     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class mx{constructor(t=0,e=0){this._onDoneFns=[],this._onStartFns=[],this._onDestroyFns=[],this._started=!1,this._destroyed=!1,this._finished=!1,this._position=0,this.parentPlayer=null,this.totalTime=t+e}_onFinish(){this._finished||(this._finished=!0,this._onDoneFns.forEach((t=>t())),this._onDoneFns=[])}onStart(t){this._onStartFns.push(t)}onDone(t){this._onDoneFns.push(t)}onDestroy(t){this._onDestroyFns.push(t)}hasStarted(){return this._started}init(){}play(){this.hasStarted()||(this._onStart(),this.triggerMicrotask()),this._started=!0}triggerMicrotask(){px((()=>this._onFinish()))}_onStart(){this._onStartFns.forEach((t=>t())),this._onStartFns=[]}pause(){}restart(){}finish(){this._onFinish()}destroy(){this._destroyed||(this._destroyed=!0,this.hasStarted()||this._onStart(),this.finish(),this._onDestroyFns.forEach((t=>t())),this._onDestroyFns=[])}reset(){this._started=!1}setPosition(t){this._position=this.totalTime?t*this.totalTime:1}getPosition(){return this.totalTime?this._position/this.totalTime:1}triggerCallback(t){const e="start"==t?this._onStartFns:this._onDoneFns;e.forEach((t=>t())),e.length=0}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class ux{constructor(t){this._onDoneFns=[],this._onStartFns=[],this._finished=!1,this._started=!1,this._destroyed=!1,this._onDestroyFns=[],this.parentPlayer=null,this.totalTime=0,this.players=t;let e=0,n=0,o=0;const i=this.players.length;0==i?px((()=>this._onFinish())):this.players.forEach((t=>{t.onDone((()=>{++e==i&&this._onFinish()})),t.onDestroy((()=>{++n==i&&this._onDestroy()})),t.onStart((()=>{++o==i&&this._onStart()}))})),this.totalTime=this.players.reduce(((t,e)=>Math.max(t,e.totalTime)),0)}_onFinish(){this._finished||(this._finished=!0,this._onDoneFns.forEach((t=>t())),this._onDoneFns=[])}init(){this.players.forEach((t=>t.init()))}onStart(t){this._onStartFns.push(t)}_onStart(){this.hasStarted()||(this._started=!0,this._onStartFns.forEach((t=>t())),this._onStartFns=[])}onDone(t){this._onDoneFns.push(t)}onDestroy(t){this._onDestroyFns.push(t)}hasStarted(){return this._started}play(){this.parentPlayer||this.init(),this._onStart(),this.players.forEach((t=>t.play()))}pause(){this.players.forEach((t=>t.pause()))}restart(){this.players.forEach((t=>t.restart()))}finish(){this._onFinish(),this.players.forEach((t=>t.finish()))}destroy(){this._onDestroy()}_onDestroy(){this._destroyed||(this._destroyed=!0,this._onFinish(),this.players.forEach((t=>t.destroy())),this._onDestroyFns.forEach((t=>t())),this._onDestroyFns=[])}reset(){this.players.forEach((t=>t.reset())),this._destroyed=!1,this._finished=!1,this._started=!1}setPosition(t){const e=t*this.totalTime;this.players.forEach((t=>{const n=t.totalTime?Math.min(1,e/t.totalTime):1;t.setPosition(n)}))}getPosition(){const t=this.players.reduce(((t,e)=>null===t||e.totalTime>t.totalTime?e:t),null);return null!=t?t.getPosition():0}beforeDestroy(){this.players.forEach((t=>{t.beforeDestroy&&t.beforeDestroy()}))}triggerCallback(t){const e="start"==t?this._onStartFns:this._onDoneFns;e.forEach((t=>t())),e.length=0}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Nx{validateStyleProperty(t){return Dx(t)}matchesElement(t,e){return Ex(t,e)}containsElement(t,e){return Rx(t,e)}query(t,e,n){return Ax(t,e,n)}computeStyle(t,e,n){return n||""}animate(t,e,n,o,i,a=[],r){return new mx(n,o)}}Nx.ɵfac=function t(e){return new(e||Nx)},Nx.ɵprov=Mn({token:Nx,factory:Nx.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Nx,[{type:im}],null,null);class zx{}zx.NOOP=new Nx;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const vO=new RegExp(":enter","g"),xO=new RegExp(":leave","g");function OO(t,e,n,o,i,a={},r={},s,l,c=[]){return(new PO).buildKeyframes(t,e,n,o,i,a,r,s,l,c)}class PO{buildKeyframes(t,e,n,o,i,a,r,s,l,c=[]){l=l||new MO;const d=new kO(t,e,l,o,i,c,[]);d.options=s,d.currentTimeline.setStyles([a],null,d.errors,s),rO(this,n,d);const p=d.timelines.filter((t=>t.containsAnimation()));if(p.length&&Object.keys(r).length){const t=p[p.length-1];t.allowOnlyTimelineStyles()||t.setStyles([r],null,d.errors,s)}return p.length?p.map((t=>t.buildKeyframes())):[CO(e,[],[],[],0,0,"",!1)]}visitTrigger(t,e){}visitState(t,e){}visitTransition(t,e){}visitAnimateChild(t,e){const n=e.subInstructions.consume(e.element);if(n){const o=e.createSubContext(t.options),i=e.currentTimeline.currentTime,a=this._visitSubInstructions(n,o,o.options);i!=a&&e.transformIntoNewTimeline(a)}e.previousNode=t}visitAnimateRef(t,e){const n=e.createSubContext(t.options);n.transformIntoNewTimeline(),this.visitReference(t.animation,n),e.transformIntoNewTimeline(n.currentTimeline.currentTime),e.previousNode=t}_visitSubInstructions(t,e,n){let o=e.currentTimeline.currentTime;const i=null!=n.duration?jx(n.duration):null,a=null!=n.delay?jx(n.delay):null;return 0!==i&&t.forEach((t=>{const n=e.appendInstructionToTimeline(t,i,a);o=Math.max(o,n.duration+n.delay)})),o}visitReference(t,e){e.updateOptions(t.options,!0),rO(this,t.animation,e),e.previousNode=t}visitSequence(t,e){const n=e.subContextCount;let o=e;const i=t.options;if(i&&(i.params||i.delay)&&(o=e.createSubContext(i),o.transformIntoNewTimeline(),null!=i.delay)){6==o.previousNode.type&&(o.currentTimeline.snapshotCurrentStyles(),o.previousNode=wO);const t=jx(i.delay);o.delayNextStep(t)}t.steps.length&&(t.steps.forEach((t=>rO(this,t,o))),o.currentTimeline.applyStylesToKeyframe(),o.subContextCount>n&&o.transformIntoNewTimeline()),e.previousNode=t}visitGroup(t,e){const n=[];let o=e.currentTimeline.currentTime;const i=t.options&&t.options.delay?jx(t.options.delay):0;t.steps.forEach((a=>{const r=e.createSubContext(t.options);i&&r.delayNextStep(i),rO(this,a,r),o=Math.max(o,r.currentTimeline.currentTime),n.push(r.currentTimeline)})),n.forEach((t=>e.currentTimeline.mergeTimelineCollectedStyles(t))),e.transformIntoNewTimeline(o),e.previousNode=t}_visitTiming(t,e){if(t.dynamic){const n=t.strValue;return Gx(e.params?tO(n,e.params,e.errors):n,e.errors)}return{duration:t.duration,delay:t.delay,easing:t.easing}}visitAnimate(t,e){const n=e.currentAnimateTimings=this._visitTiming(t.timings,e),o=e.currentTimeline;n.delay&&(e.incrementTime(n.delay),o.snapshotCurrentStyles());const i=t.style;5==i.type?this.visitKeyframes(i,e):(e.incrementTime(n.duration),this.visitStyle(i,e),o.applyStylesToKeyframe()),e.currentAnimateTimings=null,e.previousNode=t}visitStyle(t,e){const n=e.currentTimeline,o=e.currentAnimateTimings;!o&&n.getCurrentStyleProperties().length&&n.forwardFrame();const i=o&&o.easing||t.easing;t.isEmptyStep?n.applyEmptyStep(i):n.setStyles(t.styles,i,e.errors,e.options),e.previousNode=t}visitKeyframes(t,e){const n=e.currentAnimateTimings,o=e.currentTimeline.duration,i=n.duration,a=e.createSubContext().currentTimeline;a.easing=n.easing,t.styles.forEach((t=>{a.forwardTime((t.offset||0)*i),a.setStyles(t.styles,t.easing,e.errors,e.options),a.applyStylesToKeyframe()})),e.currentTimeline.mergeTimelineCollectedStyles(a),e.transformIntoNewTimeline(o+i),e.previousNode=t}visitQuery(t,e){const n=e.currentTimeline.currentTime,o=t.options||{},i=o.delay?jx(o.delay):0;i&&(6===e.previousNode.type||0==n&&e.currentTimeline.getCurrentStyleProperties().length)&&(e.currentTimeline.snapshotCurrentStyles(),e.previousNode=wO);let a=n;const r=e.invokeQuery(t.selector,t.originalSelector,t.limit,t.includeSelf,!!o.optional,e.errors);e.currentQueryTotal=r.length;let s=null;r.forEach(((n,o)=>{e.currentQueryIndex=o;const r=e.createSubContext(t.options,n);i&&r.delayNextStep(i),n===e.element&&(s=r.currentTimeline),rO(this,t.animation,r),r.currentTimeline.applyStylesToKeyframe(),a=Math.max(a,r.currentTimeline.currentTime)})),e.currentQueryIndex=0,e.currentQueryTotal=0,e.transformIntoNewTimeline(a),s&&(e.currentTimeline.mergeTimelineCollectedStyles(s),e.currentTimeline.snapshotCurrentStyles()),e.previousNode=t}visitStagger(t,e){const n=e.parentContext,o=e.currentTimeline,i=t.timings,a=Math.abs(i.duration),r=a*(e.currentQueryTotal-1);let s=a*e.currentQueryIndex;switch(i.duration<0?"reverse":i.easing){case"reverse":s=r-s;break;case"full":s=n.currentStaggerTime}const l=e.currentTimeline;s&&l.delayNextStep(s);const c=l.currentTime;rO(this,t.animation,e),e.previousNode=t,n.currentStaggerTime=o.currentTime-c+(o.startTime-n.currentTimeline.startTime)}}const wO={};class kO{constructor(t,e,n,o,i,a,r,s){this._driver=t,this.element=e,this.subInstructions=n,this._enterClassName=o,this._leaveClassName=i,this.errors=a,this.timelines=r,this.parentContext=null,this.currentAnimateTimings=null,this.previousNode=wO,this.subContextCount=0,this.options={},this.currentQueryIndex=0,this.currentQueryTotal=0,this.currentStaggerTime=0,this.currentTimeline=s||new SO(this._driver,e,0),r.push(this.currentTimeline)}get params(){return this.options.params}updateOptions(t,e){if(!t)return;const n=t;let o=this.options;null!=n.duration&&(o.duration=jx(n.duration)),null!=n.delay&&(o.delay=jx(n.delay));const i=n.params;if(i){let t=o.params;t||(t=this.options.params={}),Object.keys(i).forEach((n=>{e&&t.hasOwnProperty(n)||(t[n]=tO(i[n],t,this.errors))}))}}_copyOptions(){const t={};if(this.options){const e=this.options.params;if(e){const n=t.params={};Object.keys(e).forEach((t=>{n[t]=e[t]}))}}return t}createSubContext(t=null,e,n){const o=e||this.element,i=new kO(this._driver,o,this.subInstructions,this._enterClassName,this._leaveClassName,this.errors,this.timelines,this.currentTimeline.fork(o,n||0));return i.previousNode=this.previousNode,i.currentAnimateTimings=this.currentAnimateTimings,i.options=this._copyOptions(),i.updateOptions(t),i.currentQueryIndex=this.currentQueryIndex,i.currentQueryTotal=this.currentQueryTotal,i.parentContext=this,this.subContextCount++,i}transformIntoNewTimeline(t){return this.previousNode=wO,this.currentTimeline=this.currentTimeline.fork(this.element,t),this.timelines.push(this.currentTimeline),this.currentTimeline}appendInstructionToTimeline(t,e,n){const o={duration:null!=e?e:t.duration,delay:this.currentTimeline.currentTime+(null!=n?n:0)+t.delay,easing:""},i=new DO(this._driver,t.element,t.keyframes,t.preStyleProps,t.postStyleProps,o,t.stretchStartingKeyframe);return this.timelines.push(i),o}incrementTime(t){this.currentTimeline.forwardTime(this.currentTimeline.duration+t)}delayNextStep(t){t>0&&this.currentTimeline.delayNextStep(t)}invokeQuery(t,e,n,o,i,a){let r=[];if(o&&r.push(this.element),t.length>0){t=(t=t.replace(vO,"."+this._enterClassName)).replace(xO,"."+this._leaveClassName);let e=this._driver.query(this.element,t,1!=n);0!==n&&(e=n<0?e.slice(e.length+n,e.length):e.slice(0,n)),r.push(...e)}return i||0!=r.length||a.push(`\`query("${e}")\` returned zero elements. (Use \`query("${e}", { optional: true })\` if you wish to allow this.)`),r}}class SO{constructor(t,e,n,o){this._driver=t,this.element=e,this.startTime=n,this._elementTimelineStylesLookup=o,this.duration=0,this._previousKeyframe={},this._currentKeyframe={},this._keyframes=new Map,this._styleSummary={},this._pendingStyles={},this._backFill={},this._currentEmptyStepKeyframe=null,this._elementTimelineStylesLookup||(this._elementTimelineStylesLookup=new Map),this._localTimelineStyles=Object.create(this._backFill,{}),this._globalTimelineStyles=this._elementTimelineStylesLookup.get(e),this._globalTimelineStyles||(this._globalTimelineStyles=this._localTimelineStyles,this._elementTimelineStylesLookup.set(e,this._localTimelineStyles)),this._loadKeyframe()}containsAnimation(){switch(this._keyframes.size){case 0:return!1;case 1:return this.getCurrentStyleProperties().length>0;default:return!0}}getCurrentStyleProperties(){return Object.keys(this._currentKeyframe)}get currentTime(){return this.startTime+this.duration}delayNextStep(t){const e=1==this._keyframes.size&&Object.keys(this._pendingStyles).length;this.duration||e?(this.forwardTime(this.currentTime+t),e&&this.snapshotCurrentStyles()):this.startTime+=t}fork(t,e){return this.applyStylesToKeyframe(),new SO(this._driver,t,e||this.currentTime,this._elementTimelineStylesLookup)}_loadKeyframe(){this._currentKeyframe&&(this._previousKeyframe=this._currentKeyframe),this._currentKeyframe=this._keyframes.get(this.duration),this._currentKeyframe||(this._currentKeyframe=Object.create(this._backFill,{}),this._keyframes.set(this.duration,this._currentKeyframe))}forwardFrame(){this.duration+=1,this._loadKeyframe()}forwardTime(t){this.applyStylesToKeyframe(),this.duration=t,this._loadKeyframe()}_updateStyle(t,e){this._localTimelineStyles[t]=e,this._globalTimelineStyles[t]=e,this._styleSummary[t]={time:this.currentTime,value:e}}allowOnlyTimelineStyles(){return this._currentEmptyStepKeyframe!==this._currentKeyframe}applyEmptyStep(t){t&&(this._previousKeyframe.easing=t),Object.keys(this._globalTimelineStyles).forEach((t=>{this._backFill[t]=this._globalTimelineStyles[t]||ex,this._currentKeyframe[t]=ex})),this._currentEmptyStepKeyframe=this._currentKeyframe}setStyles(t,e,n,o){e&&(this._previousKeyframe.easing=e);const i=o&&o.params||{},a=(function r(t,e){const n={};let o;return t.forEach((t=>{"*"===t?(o=o||Object.keys(e),o.forEach((t=>{n[t]=ex}))):Yx(t,!1,n)})),n}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t,this._globalTimelineStyles);Object.keys(a).forEach((t=>{const e=tO(a[t],i,n);this._pendingStyles[t]=e,this._localTimelineStyles.hasOwnProperty(t)||(this._backFill[t]=this._globalTimelineStyles.hasOwnProperty(t)?this._globalTimelineStyles[t]:ex),this._updateStyle(t,e)}))}applyStylesToKeyframe(){const t=this._pendingStyles,e=Object.keys(t);0!=e.length&&(this._pendingStyles={},e.forEach((e=>{this._currentKeyframe[e]=t[e]})),Object.keys(this._localTimelineStyles).forEach((t=>{this._currentKeyframe.hasOwnProperty(t)||(this._currentKeyframe[t]=this._localTimelineStyles[t])})))}snapshotCurrentStyles(){Object.keys(this._localTimelineStyles).forEach((t=>{const e=this._localTimelineStyles[t];this._pendingStyles[t]=e,this._updateStyle(t,e)}))}getFinalKeyframe(){return this._keyframes.get(this.duration)}get properties(){const t=[];for(let e in this._currentKeyframe)t.push(e);return t}mergeTimelineCollectedStyles(t){Object.keys(t._styleSummary).forEach((e=>{const n=this._styleSummary[e],o=t._styleSummary[e];(!n||o.time>n.time)&&this._updateStyle(e,o.value)}))}buildKeyframes(){this.applyStylesToKeyframe();const t=new Set,e=new Set,n=1===this._keyframes.size&&0===this.duration;let o=[];this._keyframes.forEach(((i,a)=>{const r=Yx(i,!0);Object.keys(r).forEach((n=>{const o=r[n];"!"==o?t.add(n):o==ex&&e.add(n)})),n||(r.offset=a/this.duration),o.push(r)}));const i=t.size?eO(t.values()):[],a=e.size?eO(e.values()):[];if(n){const t=o[0],e=Wx(t);t.offset=0,e.offset=1,o=[t,e]}return CO(this.element,o,i,a,this.duration,this.startTime,this.easing,!1)}}class DO extends SO{constructor(t,e,n,o,i,a,r=!1){super(t,e,a.delay),this.keyframes=n,this.preStyleProps=o,this.postStyleProps=i,this._stretchStartingKeyframe=r,this.timings={duration:a.duration,delay:a.delay,easing:a.easing}}containsAnimation(){return this.keyframes.length>1}buildKeyframes(){let t=this.keyframes,{delay:e,duration:n,easing:o}=this.timings;if(this._stretchStartingKeyframe&&e){const i=[],a=n+e,r=e/a,s=Yx(t[0],!1);s.offset=0,i.push(s);const l=Yx(t[0],!1);l.offset=EO(r),i.push(l);const c=t.length-1;for(let o=1;o<=c;o++){let r=Yx(t[o],!1);r.offset=EO((e+r.offset*n)/a),i.push(r)}n=a,e=0,o="",t=i}return CO(this.element,t,this.preStyleProps,this.postStyleProps,n,e,o,!0)}}function EO(t,e=3){const n=Math.pow(10,e-1);return Math.round(t*n)/n}class RO{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class AO extends RO{normalizePropertyName(t,e){return oO(t)}normalizeStyleValue(t,e,n,o){let i="";const a=n.toString().trim();if(TO[e]&&0!==n&&"0"!==n)if("number"==typeof n)i="px";else{const e=n.match(/^[+-]?[\d\.]+([a-z]*)$/);e&&0==e[1].length&&o.push(`Please provide a CSS unit value for ${t}:${n}`)}return a+i}}const TO=(function NO(t){const e={};return t.forEach((t=>e[t]=!0)),e})("width,height,minWidth,minHeight,maxWidth,maxHeight,left,top,bottom,right,fontSize,outlineWidth,outlineOffset,paddingTop,paddingLeft,paddingBottom,paddingRight,marginTop,marginLeft,marginBottom,marginRight,borderRadius,borderWidth,borderTopWidth,borderLeftWidth,borderRightWidth,borderBottomWidth,textIndent,perspective".split(","));function zO(t,e,n,o,i,a,r,s,l,c,d,p,m){return{type:0,element:t,triggerName:e,isRemovalTransition:i,fromState:n,fromStyles:a,toState:o,toStyles:r,timelines:s,queriedElements:l,preStyleProps:c,postStyleProps:d,totalTime:p,errors:m}}const IO={};class HO{constructor(t,e,n){this._triggerName=t,this.ast=e,this._stateStyles=n}match(t,e,n,o){return(function i(t,e,n,o,a){return t.some((t=>t(e,n,o,a)))})(this.ast.matchers,t,e,n,o)}buildStyles(t,e,n){const o=this._stateStyles["*"],i=this._stateStyles[t],a=o?o.buildStyles(e,n):{};return i?i.buildStyles(e,n):a}build(t,e,n,o,i,a,r,s,l,c){const d=[],p=this.ast.options&&this.ast.options.params||IO,m=this.buildStyles(n,r&&r.params||IO,d),u=s&&s.params||IO,f=this.buildStyles(o,u,d),g=new Set,h=new Map,b=new Map,y="void"===o,_={params:Object.assign(Object.assign({},p),u)},C=c?[]:OO(t,e,this.ast.animation,i,a,m,f,_,l,d);let M=0;if(C.forEach((t=>{M=Math.max(t.duration+t.delay,M)})),d.length)return zO(e,this._triggerName,n,o,y,m,f,[],[],h,b,M,d);C.forEach((t=>{const n=t.element,o=Mx(h,n,{});t.preStyleProps.forEach((t=>o[t]=!0));const i=Mx(b,n,{});t.postStyleProps.forEach((t=>i[t]=!0)),n!==e&&g.add(n)}));const v=eO(g.values());return zO(e,this._triggerName,n,o,y,m,f,C,v,h,b,M)}}class FO{constructor(t,e,n){this.styles=t,this.defaultParams=e,this.normalizer=n}buildStyles(t,e){const n={},o=Wx(this.defaultParams);return Object.keys(t).forEach((e=>{const n=t[e];null!=n&&(o[e]=n)})),this.styles.styles.forEach((t=>{if("string"!=typeof t){const i=t;Object.keys(i).forEach((t=>{let a=i[t];a.length>1&&(a=tO(a,o,e));const r=this.normalizer.normalizePropertyName(t,e);a=this.normalizer.normalizeStyleValue(t,r,a,e),n[r]=a}))}})),n}}class LO{constructor(t,e,n){this.name=t,this.ast=e,this._normalizer=n,this.transitionFactories=[],this.states={},e.states.forEach((t=>{this.states[t.name]=new FO(t.style,t.options&&t.options.params||{},n)})),BO(this.states,"true","1"),BO(this.states,"false","0"),e.transitions.forEach((e=>{this.transitionFactories.push(new HO(t,e,this.states))})),this.fallbackTransition=(function o(t,e,n){return new HO(t,{type:1,animation:{type:2,steps:[],options:null},matchers:[(t,e)=>!0],options:null,queryCount:0,depCount:0},e)})(t,this.states)}get containsQueries(){return this.ast.queryCount>0}matchTransition(t,e,n,o){return this.transitionFactories.find((i=>i.match(t,e,n,o)))||null}matchStyles(t,e,n){return this.fallbackTransition.buildStyles(t,e,n)}}function BO(t,e,n){t.hasOwnProperty(e)?t.hasOwnProperty(n)||(t[n]=t[e]):t.hasOwnProperty(n)&&(t[e]=t[n])}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const VO=new MO;class jO{constructor(t,e,n){this.bodyNode=t,this._driver=e,this._normalizer=n,this._animations={},this._playersById={},this.players=[]}register(t,e){const n=[],o=fO(this._driver,e,n);if(n.length)throw new Error(`Unable to build the animation due to the following errors: ${n.join("\n")}`);this._animations[t]=o}_buildPlayer(t,e,n){const o=t.element,i=bx(0,this._normalizer,0,t.keyframes,e,n);return this._driver.animate(o,i,t.duration,t.delay,t.easing,[],!0)}create(t,e,n={}){const o=[],i=this._animations[t];let a;const r=new Map;if(i?(a=OO(this._driver,e,i,Ix,Hx,{},{},n,VO,o),a.forEach((t=>{const e=Mx(r,t.element,{});t.postStyleProps.forEach((t=>e[t]=null))}))):(o.push("The requested animation doesn't exist or has already been destroyed"),a=[]),o.length)throw new Error(`Unable to create the animation due to the following errors: ${o.join("\n")}`);r.forEach(((t,e)=>{Object.keys(t).forEach((n=>{t[n]=this._driver.computeStyle(e,n,ex)}))}));const s=hx(a.map((t=>{const e=r.get(t.element);return this._buildPlayer(t,{},e)})));return this._playersById[t]=s,s.onDestroy((()=>this.destroy(t))),this.players.push(s),s}destroy(t){const e=this._getPlayer(t);e.destroy(),delete this._playersById[t];const n=this.players.indexOf(e);n>=0&&this.players.splice(n,1)}_getPlayer(t){const e=this._playersById[t];if(!e)throw new Error(`Unable to find the timeline player referenced by ${t}`);return e}listen(t,e,n,o){const i=Cx(e,"","","");return yx(this._getPlayer(t),n,i,o),()=>{}}command(t,e,n,o){if("register"==n)return void this.register(t,o[0]);if("create"==n)return void this.create(t,e,o[0]||{});const i=this._getPlayer(t);switch(n){case"play":i.play();break;case"pause":i.pause();break;case"reset":i.reset();break;case"restart":i.restart();break;case"finish":i.finish();break;case"init":i.init();break;case"setPosition":i.setPosition(parseFloat(o[0]));break;case"destroy":this.destroy(t)}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const UO="ng-animate-queued",GO="ng-animate-disabled",WO=".ng-animate-disabled",YO=[],qO={namespaceId:"",setForRemoval:!1,setForMove:!1,hasAnimation:!1,removedBeforeQueried:!1},ZO={namespaceId:"",setForMove:!1,setForRemoval:!1,hasAnimation:!1,removedBeforeQueried:!0};class XO{constructor(t,e=""){this.namespaceId=e;const n=t&&t.hasOwnProperty("value");if(this.value=(function o(t){return null!=t?t:null})(n?t.value:t),n){const e=Wx(t);delete e.value,this.options=e}else this.options={};this.options.params||(this.options.params={})}get params(){return this.options.params}absorbOptions(t){const e=t.params;if(e){const t=this.options.params;Object.keys(e).forEach((n=>{null==t[n]&&(t[n]=e[n])}))}}}const KO="void",JO=new XO(KO);class QO{constructor(t,e,n){this.id=t,this.hostElement=e,this._engine=n,this.players=[],this._triggers={},this._queue=[],this._elementListeners=new Map,this._hostClassName="ng-tns-"+t,aP(e,this._hostClassName)}listen(t,e,n,o){if(!this._triggers.hasOwnProperty(e))throw new Error(`Unable to listen on the animation trigger event "${n}" because the animation trigger "${e}" doesn't exist!`);if(null==n||0==n.length)throw new Error(`Unable to listen on the animation trigger "${e}" because the provided event is undefined!`);if(!(function i(t){return"start"==t||"done"==t})(n))throw new Error(`The provided animation trigger event "${n}" for the animation trigger "${e}" is not supported!`);const a=Mx(this._elementListeners,t,[]),r={name:e,phase:n,callback:o};a.push(r);const s=Mx(this._engine.statesByElement,t,{});return s.hasOwnProperty(e)||(aP(t,Fx),aP(t,"ng-trigger-"+e),s[e]=JO),()=>{this._engine.afterFlush((()=>{const t=a.indexOf(r);t>=0&&a.splice(t,1),this._triggers[e]||delete s[e]}))}}register(t,e){return!this._triggers[t]&&(this._triggers[t]=e,!0)}_getTrigger(t){const e=this._triggers[t];if(!e)throw new Error(`The provided animation trigger "${t}" has not been registered!`);return e}trigger(t,e,n,o=!0){const i=this._getTrigger(e),a=new tP(this.id,e,t);let r=this._engine.statesByElement.get(t);r||(aP(t,Fx),aP(t,"ng-trigger-"+e),this._engine.statesByElement.set(t,r={}));let s=r[e];const l=new XO(n,this.id);if(!(n&&n.hasOwnProperty("value"))&&s&&l.absorbOptions(s.options),r[e]=l,s||(s=JO),l.value!==KO&&s.value===l.value){if(!(function e(t,n){const o=Object.keys(t),i=Object.keys(n);if(o.length!=i.length)return!1;for(let e=0;e<o.length;e++){const i=o[e];if(!n.hasOwnProperty(i)||t[i]!==n[i])return!1}return!0})(s.params,l.params)){const e=[],n=i.matchStyles(s.value,s.params,e),o=i.matchStyles(l.value,l.params,e);e.length?this._engine.reportError(e):this._engine.afterFlush((()=>{Kx(t,n),Xx(t,o)}))}return}const c=Mx(this._engine.playersByElement,t,[]);c.forEach((t=>{t.namespaceId==this.id&&t.triggerName==e&&t.queued&&t.destroy()}));let d=i.matchTransition(s.value,l.value,t,l.params),p=!1;if(!d){if(!o)return;d=i.fallbackTransition,p=!0}return this._engine.totalQueuedPlayers++,this._queue.push({element:t,triggerName:e,transition:d,fromState:s,toState:l,player:a,isFallbackTransition:p}),p||(aP(t,UO),a.onStart((()=>{rP(t,UO)}))),a.onDone((()=>{let e=this.players.indexOf(a);e>=0&&this.players.splice(e,1);const n=this._engine.playersByElement.get(t);if(n){let t=n.indexOf(a);t>=0&&n.splice(t,1)}})),this.players.push(a),c.push(a),a}deregister(t){delete this._triggers[t],this._engine.statesByElement.forEach(((e,n)=>{delete e[t]})),this._elementListeners.forEach(((e,n)=>{this._elementListeners.set(n,e.filter((e=>e.name!=t)))}))}clearElementCache(t){this._engine.statesByElement.delete(t),this._elementListeners.delete(t);const e=this._engine.playersByElement.get(t);e&&(e.forEach((t=>t.destroy())),this._engine.playersByElement.delete(t))}_signalRemovalForInnerTriggers(t,e){const n=this._engine.driver.query(t,Lx,!0);n.forEach((t=>{if(t.__ng_removed)return;const n=this._engine.fetchNamespacesByElement(t);n.size?n.forEach((n=>n.triggerLeaveAnimation(t,e,!1,!0))):this.clearElementCache(t)})),this._engine.afterFlushAnimationsDone((()=>n.forEach((t=>this.clearElementCache(t)))))}triggerLeaveAnimation(t,e,n,o){const i=this._engine.statesByElement.get(t);if(i){const a=[];if(Object.keys(i).forEach((e=>{if(this._triggers[e]){const n=this.trigger(t,e,KO,o);n&&a.push(n)}})),a.length)return this._engine.markElementAsRemoved(this.id,t,!0,e),n&&hx(a).onDone((()=>this._engine.processLeaveNode(t))),!0}return!1}prepareLeaveAnimationListeners(t){const e=this._elementListeners.get(t),n=this._engine.statesByElement.get(t);if(e&&n){const o=new Set;e.forEach((e=>{const i=e.name;if(o.has(i))return;o.add(i);const a=this._triggers[i].fallbackTransition,r=n[i]||JO,s=new XO(KO),l=new tP(this.id,i,t);this._engine.totalQueuedPlayers++,this._queue.push({element:t,triggerName:i,transition:a,fromState:r,toState:s,player:l,isFallbackTransition:!0})}))}}removeNode(t,e){const n=this._engine;if(t.childElementCount&&this._signalRemovalForInnerTriggers(t,e),this.triggerLeaveAnimation(t,e,!0))return;let o=!1;if(n.totalAnimations){const e=n.players.length?n.playersByQueriedElement.get(t):[];if(e&&e.length)o=!0;else{let e=t;for(;e=e.parentNode;)if(n.statesByElement.get(e)){o=!0;break}}}if(this.prepareLeaveAnimationListeners(t),o)n.markElementAsRemoved(this.id,t,!1,e);else{const o=t.__ng_removed;o&&o!==qO||(n.afterFlush((()=>this.clearElementCache(t))),n.destroyInnerAnimations(t),n._onRemovalComplete(t,e))}}insertNode(t,e){aP(t,this._hostClassName)}drainQueuedTransitions(t){const e=[];return this._queue.forEach((n=>{const o=n.player;if(o.destroyed)return;const i=n.element,a=this._elementListeners.get(i);a&&a.forEach((e=>{if(e.name==n.triggerName){const o=Cx(i,n.triggerName,n.fromState.value,n.toState.value);o._data=t,yx(n.player,e.phase,o,e.callback)}})),o.markedForDestroy?this._engine.afterFlush((()=>{o.destroy()})):e.push(n)})),this._queue=[],e.sort(((t,e)=>{const n=t.transition.ast.depCount,o=e.transition.ast.depCount;return 0==n||0==o?n-o:this._engine.driver.containsElement(t.element,e.element)?1:-1}))}destroy(t){this.players.forEach((t=>t.destroy())),this._signalRemovalForInnerTriggers(this.hostElement,t)}elementContainsData(t){let e=!1;return this._elementListeners.has(t)&&(e=!0),e=!!this._queue.find((e=>e.element===t))||e,e}}class $O{constructor(t,e,n){this.bodyNode=t,this.driver=e,this._normalizer=n,this.players=[],this.newHostElements=new Map,this.playersByElement=new Map,this.playersByQueriedElement=new Map,this.statesByElement=new Map,this.disabledNodes=new Set,this.totalAnimations=0,this.totalQueuedPlayers=0,this._namespaceLookup={},this._namespaceList=[],this._flushFns=[],this._whenQuietFns=[],this.namespacesByHostElement=new Map,this.collectedEnterElements=[],this.collectedLeaveElements=[],this.onRemovalComplete=(t,e)=>{}}_onRemovalComplete(t,e){this.onRemovalComplete(t,e)}get queuedPlayers(){const t=[];return this._namespaceList.forEach((e=>{e.players.forEach((e=>{e.queued&&t.push(e)}))})),t}createNamespace(t,e){const n=new QO(t,e,this);return this.bodyNode&&this.driver.containsElement(this.bodyNode,e)?this._balanceNamespaceList(n,e):(this.newHostElements.set(e,n),this.collectEnterElement(e)),this._namespaceLookup[t]=n}_balanceNamespaceList(t,e){const n=this._namespaceList.length-1;if(n>=0){let o=!1;for(let i=n;i>=0;i--)if(this.driver.containsElement(this._namespaceList[i].hostElement,e)){this._namespaceList.splice(i+1,0,t),o=!0;break}o||this._namespaceList.splice(0,0,t)}else this._namespaceList.push(t);return this.namespacesByHostElement.set(e,t),t}register(t,e){let n=this._namespaceLookup[t];return n||(n=this.createNamespace(t,e)),n}registerTrigger(t,e,n){let o=this._namespaceLookup[t];o&&o.register(e,n)&&this.totalAnimations++}destroy(t,e){if(!t)return;const n=this._fetchNamespace(t);this.afterFlush((()=>{this.namespacesByHostElement.delete(n.hostElement),delete this._namespaceLookup[t];const e=this._namespaceList.indexOf(n);e>=0&&this._namespaceList.splice(e,1)})),this.afterFlushAnimationsDone((()=>n.destroy(e)))}_fetchNamespace(t){return this._namespaceLookup[t]}fetchNamespacesByElement(t){const e=new Set,n=this.statesByElement.get(t);if(n){const t=Object.keys(n);for(let o=0;o<t.length;o++){const i=n[t[o]].namespaceId;if(i){const t=this._fetchNamespace(i);t&&e.add(t)}}}return e}trigger(t,e,n,o){if(eP(e)){const i=this._fetchNamespace(t);if(i)return i.trigger(e,n,o),!0}return!1}insertNode(t,e,n,o){if(!eP(e))return;const i=e.__ng_removed;if(i&&i.setForRemoval){i.setForRemoval=!1,i.setForMove=!0;const t=this.collectedLeaveElements.indexOf(e);t>=0&&this.collectedLeaveElements.splice(t,1)}if(t){const o=this._fetchNamespace(t);o&&o.insertNode(e,n)}o&&this.collectEnterElement(e)}collectEnterElement(t){this.collectedEnterElements.push(t)}markElementAsDisabled(t,e){e?this.disabledNodes.has(t)||(this.disabledNodes.add(t),aP(t,GO)):this.disabledNodes.has(t)&&(this.disabledNodes.delete(t),rP(t,GO))}removeNode(t,e,n,o){if(eP(e)){const i=t?this._fetchNamespace(t):null;if(i?i.removeNode(e,o):this.markElementAsRemoved(t,e,!1,o),n){const n=this.namespacesByHostElement.get(e);n&&n.id!==t&&n.removeNode(e,o)}}else this._onRemovalComplete(e,o)}markElementAsRemoved(t,e,n,o){this.collectedLeaveElements.push(e),e.__ng_removed={namespaceId:t,setForRemoval:o,hasAnimation:n,removedBeforeQueried:!1}}listen(t,e,n,o,i){return eP(e)?this._fetchNamespace(t).listen(e,n,o,i):()=>{}}_buildInstruction(t,e,n,o,i){return t.transition.build(this.driver,t.element,t.fromState.value,t.toState.value,n,o,t.fromState.options,t.toState.options,e,i)}destroyInnerAnimations(t){let e=this.driver.query(t,Lx,!0);e.forEach((t=>this.destroyActiveAnimationsForElement(t))),0!=this.playersByQueriedElement.size&&(e=this.driver.query(t,Vx,!0),e.forEach((t=>this.finishActiveQueriedAnimationOnElement(t))))}destroyActiveAnimationsForElement(t){const e=this.playersByElement.get(t);e&&e.forEach((t=>{t.queued?t.markedForDestroy=!0:t.destroy()}))}finishActiveQueriedAnimationOnElement(t){const e=this.playersByQueriedElement.get(t);e&&e.forEach((t=>t.finish()))}whenRenderingDone(){return new Promise((t=>{if(this.players.length)return hx(this.players).onDone((()=>t()));t()}))}processLeaveNode(t){const e=t.__ng_removed;if(e&&e.setForRemoval){if(t.__ng_removed=qO,e.namespaceId){this.destroyInnerAnimations(t);const n=this._fetchNamespace(e.namespaceId);n&&n.clearElementCache(t)}this._onRemovalComplete(t,e.setForRemoval)}this.driver.matchesElement(t,WO)&&this.markElementAsDisabled(t,!1),this.driver.query(t,WO,!0).forEach((t=>{this.markElementAsDisabled(t,!1)}))}flush(t=-1){let e=[];if(this.newHostElements.size&&(this.newHostElements.forEach(((t,e)=>this._balanceNamespaceList(t,e))),this.newHostElements.clear()),this.totalAnimations&&this.collectedEnterElements.length)for(let t=0;t<this.collectedEnterElements.length;t++)aP(this.collectedEnterElements[t],"ng-star-inserted");if(this._namespaceList.length&&(this.totalQueuedPlayers||this.collectedLeaveElements.length)){const n=[];try{e=this._flushAnimations(n,t)}finally{for(let t=0;t<n.length;t++)n[t]()}}else for(let t=0;t<this.collectedLeaveElements.length;t++)this.processLeaveNode(this.collectedLeaveElements[t]);if(this.totalQueuedPlayers=0,this.collectedEnterElements.length=0,this.collectedLeaveElements.length=0,this._flushFns.forEach((t=>t())),this._flushFns=[],this._whenQuietFns.length){const t=this._whenQuietFns;this._whenQuietFns=[],e.length?hx(e).onDone((()=>{t.forEach((t=>t()))})):t.forEach((t=>t()))}}reportError(t){throw new Error(`Unable to process animations due to the following failed trigger transitions\n ${t.join("\n")}`)}_flushAnimations(t,e){const n=new MO,o=[],i=new Map,a=[],r=new Map,s=new Map,l=new Map,c=new Set;this.disabledNodes.forEach((t=>{c.add(t);const e=this.driver.query(t,".ng-animate-queued",!0);for(let t=0;t<e.length;t++)c.add(e[t])}));const d=this.bodyNode,p=Array.from(this.statesByElement.keys()),m=iP(p,this.collectedEnterElements),u=new Map;let f=0;m.forEach(((t,e)=>{const n=Ix+f++;u.set(e,n),t.forEach((t=>aP(t,n)))}));const g=[],h=new Set,b=new Set;for(let t=0;t<this.collectedLeaveElements.length;t++){const e=this.collectedLeaveElements[t],n=e.__ng_removed;n&&n.setForRemoval&&(g.push(e),h.add(e),n.hasAnimation?this.driver.query(e,".ng-star-inserted",!0).forEach((t=>h.add(t))):b.add(e))}const y=new Map,_=iP(p,Array.from(h));_.forEach(((t,e)=>{const n=Hx+f++;y.set(e,n),t.forEach((t=>aP(t,n)))})),t.push((()=>{m.forEach(((t,e)=>{const n=u.get(e);t.forEach((t=>rP(t,n)))})),_.forEach(((t,e)=>{const n=y.get(e);t.forEach((t=>rP(t,n)))})),g.forEach((t=>{this.processLeaveNode(t)}))}));const C=[],M=[];for(let t=this._namespaceList.length-1;t>=0;t--)this._namespaceList[t].drainQueuedTransitions(e).forEach((t=>{const e=t.player,i=t.element;if(C.push(e),this.collectedEnterElements.length){const t=i.__ng_removed;if(t&&t.setForMove)return void e.destroy()}const c=!d||!this.driver.containsElement(d,i),p=y.get(i),m=u.get(i),f=this._buildInstruction(t,n,m,p,c);if(f.errors&&f.errors.length)M.push(f);else{if(c)return e.onStart((()=>Kx(i,f.fromStyles))),e.onDestroy((()=>Xx(i,f.toStyles))),void o.push(e);if(t.isFallbackTransition)return e.onStart((()=>Kx(i,f.fromStyles))),e.onDestroy((()=>Xx(i,f.toStyles))),void o.push(e);f.timelines.forEach((t=>t.stretchStartingKeyframe=!0)),n.append(i,f.timelines),a.push({instruction:f,player:e,element:i}),f.queriedElements.forEach((t=>Mx(r,t,[]).push(e))),f.preStyleProps.forEach(((t,e)=>{const n=Object.keys(t);if(n.length){let t=s.get(e);t||s.set(e,t=new Set),n.forEach((e=>t.add(e)))}})),f.postStyleProps.forEach(((t,e)=>{const n=Object.keys(t);let o=l.get(e);o||l.set(e,o=new Set),n.forEach((t=>o.add(t)))}))}}));if(M.length){const t=[];M.forEach((e=>{t.push(`@${e.triggerName} has failed due to:\n`),e.errors.forEach((e=>t.push(`- ${e}\n`)))})),C.forEach((t=>t.destroy())),this.reportError(t)}const v=new Map,x=new Map;a.forEach((t=>{const e=t.element;n.has(e)&&(x.set(e,e),this._beforeAnimationBuild(t.player.namespaceId,t.instruction,v))})),o.forEach((t=>{const e=t.element;this._getPreviousPlayers(e,!1,t.namespaceId,t.triggerName,null).forEach((t=>{Mx(v,e,[]).push(t),t.destroy()}))}));const O=g.filter((t=>cP(t,s,l))),P=new Map;oP(P,this.driver,b,l,ex).forEach((t=>{cP(t,s,l)&&O.push(t)}));const w=new Map;m.forEach(((t,e)=>{oP(w,this.driver,new Set(t),s,"!")})),O.forEach((t=>{const e=P.get(t),n=w.get(t);P.set(t,Object.assign(Object.assign({},e),n))}));const k=[],S=[],D={};a.forEach((t=>{const{element:e,player:a,instruction:r}=t;if(n.has(e)){if(c.has(e))return a.onDestroy((()=>Xx(e,r.toStyles))),a.disabled=!0,a.overrideTotalTime(r.totalTime),void o.push(a);let t=D;if(x.size>1){let n=e;const o=[];for(;n=n.parentNode;){const e=x.get(n);if(e){t=e;break}o.push(n)}o.forEach((e=>x.set(e,t)))}const n=this._buildAnimation(a.namespaceId,r,v,i,w,P);if(a.setRealPlayer(n),t===D)k.push(a);else{const e=this.playersByElement.get(t);e&&e.length&&(a.parentPlayer=hx(e)),o.push(a)}}else Kx(e,r.fromStyles),a.onDestroy((()=>Xx(e,r.toStyles))),S.push(a),c.has(e)&&o.push(a)})),S.forEach((t=>{const e=i.get(t.element);if(e&&e.length){const n=hx(e);t.setRealPlayer(n)}})),o.forEach((t=>{t.parentPlayer?t.syncPlayerEvents(t.parentPlayer):t.destroy()}));for(let t=0;t<g.length;t++){const e=g[t],n=e.__ng_removed;if(rP(e,Hx),n&&n.hasAnimation)continue;let o=[];if(r.size){let t=r.get(e);t&&t.length&&o.push(...t);let n=this.driver.query(e,Vx,!0);for(let t=0;t<n.length;t++){let e=r.get(n[t]);e&&e.length&&o.push(...e)}}const i=o.filter((t=>!t.destroyed));i.length?sP(this,e,i):this.processLeaveNode(e)}return g.length=0,k.forEach((t=>{this.players.push(t),t.onDone((()=>{t.destroy();const e=this.players.indexOf(t);this.players.splice(e,1)})),t.play()})),k}elementContainsData(t,e){let n=!1;const o=e.__ng_removed;return o&&o.setForRemoval&&(n=!0),this.playersByElement.has(e)&&(n=!0),this.playersByQueriedElement.has(e)&&(n=!0),this.statesByElement.has(e)&&(n=!0),this._fetchNamespace(t).elementContainsData(e)||n}afterFlush(t){this._flushFns.push(t)}afterFlushAnimationsDone(t){this._whenQuietFns.push(t)}_getPreviousPlayers(t,e,n,o,i){let a=[];if(e){const e=this.playersByQueriedElement.get(t);e&&(a=e)}else{const e=this.playersByElement.get(t);if(e){const t=!i||i==KO;e.forEach((e=>{e.queued||(t||e.triggerName==o)&&a.push(e)}))}}return(n||o)&&(a=a.filter((t=>!(n&&n!=t.namespaceId||o&&o!=t.triggerName)))),a}_beforeAnimationBuild(t,e,n){const o=e.element,i=e.isRemovalTransition?void 0:t,a=e.isRemovalTransition?void 0:e.triggerName;for(const t of e.timelines){const r=t.element,s=r!==o,l=Mx(n,r,[]);this._getPreviousPlayers(r,s,i,a,e.toState).forEach((t=>{const e=t.getRealPlayer();e.beforeDestroy&&e.beforeDestroy(),t.destroy(),l.push(t)}))}Kx(o,e.fromStyles)}_buildAnimation(t,e,n,o,i,a){const r=e.triggerName,s=e.element,l=[],c=new Set,d=new Set,p=e.timelines.map((e=>{const p=e.element;c.add(p);const m=p.__ng_removed;if(m&&m.removedBeforeQueried)return new mx(e.duration,e.delay);const u=p!==s,f=(function g(t){const e=[];return lP(t,e),e})((n.get(p)||YO).map((t=>t.getRealPlayer()))).filter((t=>!!t.element&&t.element===p)),h=i.get(p),b=a.get(p),y=bx(0,this._normalizer,0,e.keyframes,h,b),_=this._buildPlayer(e,y,f);if(e.subTimeline&&o&&d.add(p),u){const e=new tP(t,r,p);e.setRealPlayer(_),l.push(e)}return _}));l.forEach((t=>{Mx(this.playersByQueriedElement,t.element,[]).push(t),t.onDone((()=>(function e(t,n,o){let i;if(t instanceof Map){if(i=t.get(n),i){if(i.length){const t=i.indexOf(o);i.splice(t,1)}0==i.length&&t.delete(n)}}else if(i=t[n],i){if(i.length){const t=i.indexOf(o);i.splice(t,1)}0==i.length&&delete t[n]}return i})(this.playersByQueriedElement,t.element,t)))})),c.forEach((t=>aP(t,Bx)));const m=hx(p);return m.onDestroy((()=>{c.forEach((t=>rP(t,Bx))),Xx(s,e.toStyles)})),d.forEach((t=>{Mx(o,t,[]).push(m)})),m}_buildPlayer(t,e,n){return e.length>0?this.driver.animate(t.element,e,t.duration,t.delay,t.easing,n):new mx(t.duration,t.delay)}}class tP{constructor(t,e,n){this.namespaceId=t,this.triggerName=e,this.element=n,this._player=new mx,this._containsRealPlayer=!1,this._queuedCallbacks={},this.destroyed=!1,this.markedForDestroy=!1,this.disabled=!1,this.queued=!0,this.totalTime=0}setRealPlayer(t){this._containsRealPlayer||(this._player=t,Object.keys(this._queuedCallbacks).forEach((e=>{this._queuedCallbacks[e].forEach((n=>yx(t,e,void 0,n)))})),this._queuedCallbacks={},this._containsRealPlayer=!0,this.overrideTotalTime(t.totalTime),this.queued=!1)}getRealPlayer(){return this._player}overrideTotalTime(t){this.totalTime=t}syncPlayerEvents(t){const e=this._player;e.triggerCallback&&t.onStart((()=>e.triggerCallback("start"))),t.onDone((()=>this.finish())),t.onDestroy((()=>this.destroy()))}_queueEvent(t,e){Mx(this._queuedCallbacks,t,[]).push(e)}onDone(t){this.queued&&this._queueEvent("done",t),this._player.onDone(t)}onStart(t){this.queued&&this._queueEvent("start",t),this._player.onStart(t)}onDestroy(t){this.queued&&this._queueEvent("destroy",t),this._player.onDestroy(t)}init(){this._player.init()}hasStarted(){return!this.queued&&this._player.hasStarted()}play(){!this.queued&&this._player.play()}pause(){!this.queued&&this._player.pause()}restart(){!this.queued&&this._player.restart()}finish(){this._player.finish()}destroy(){this.destroyed=!0,this._player.destroy()}reset(){!this.queued&&this._player.reset()}setPosition(t){this.queued||this._player.setPosition(t)}getPosition(){return this.queued?0:this._player.getPosition()}triggerCallback(t){const e=this._player;e.triggerCallback&&e.triggerCallback(t)}}function eP(t){return t&&1===t.nodeType}function nP(t,e){const n=t.style.display;return t.style.display=null!=e?e:"none",n}function oP(t,e,n,o,i){const a=[];n.forEach((t=>a.push(nP(t))));const r=[];o.forEach(((n,o)=>{const a={};n.forEach((t=>{const n=a[t]=e.computeStyle(o,t,i);n&&0!=n.length||(o.__ng_removed=ZO,r.push(o))})),t.set(o,a)}));let s=0;return n.forEach((t=>nP(t,a[s++]))),r}function iP(t,e){const n=new Map;if(t.forEach((t=>n.set(t,[]))),0==e.length)return n;const o=new Set(e),i=new Map;function a(t){if(!t)return 1;let e=i.get(t);if(e)return e;const r=t.parentNode;return e=n.has(r)?r:o.has(r)?1:a(r),i.set(t,e),e}return e.forEach((t=>{const e=a(t);1!==e&&n.get(e).push(t)})),n}function aP(t,e){if(t.classList)t.classList.add(e);else{let n=t.$$classes;n||(n=t.$$classes={}),n[e]=!0}}function rP(t,e){if(t.classList)t.classList.remove(e);else{let n=t.$$classes;n&&delete n[e]}}function sP(t,e,n){hx(n).onDone((()=>t.processLeaveNode(e)))}function lP(t,e){for(let n=0;n<t.length;n++){const o=t[n];o instanceof ux?lP(o.players,e):e.push(o)}}function cP(t,e,n){const o=n.get(t);if(!o)return!1;let i=e.get(t);return i?o.forEach((t=>i.add(t))):e.set(t,o),n.delete(t),!0}class dP{constructor(t,e,n){this.bodyNode=t,this._driver=e,this._normalizer=n,this._triggerCache={},this.onRemovalComplete=(t,e)=>{},this._transitionEngine=new $O(t,e,n),this._timelineEngine=new jO(t,e,n),this._transitionEngine.onRemovalComplete=(t,e)=>this.onRemovalComplete(t,e)}registerTrigger(t,e,n,o,i){const a=t+"-"+o;let r=this._triggerCache[a];if(!r){const t=[],e=fO(this._driver,i,t);if(t.length)throw new Error(`The animation trigger "${o}" has failed to build due to the following errors:\n - ${t.join("\n - ")}`);r=(function n(t,e,o){return new LO(t,e,o)})(o,e,this._normalizer),this._triggerCache[a]=r}this._transitionEngine.registerTrigger(e,o,r)}register(t,e){this._transitionEngine.register(t,e)}destroy(t,e){this._transitionEngine.destroy(t,e)}onInsert(t,e,n,o){this._transitionEngine.insertNode(t,e,n,o)}onRemove(t,e,n,o){this._transitionEngine.removeNode(t,e,o||!1,n)}disableAnimations(t,e){this._transitionEngine.markElementAsDisabled(t,e)}process(t,e,n,o){if("@"==n.charAt(0)){const[t,i]=vx(n);this._timelineEngine.command(t,e,i,o)}else this._transitionEngine.trigger(t,e,n,o)}listen(t,e,n,o,i){if("@"==n.charAt(0)){const[t,o]=vx(n);return this._timelineEngine.listen(t,e,o,i)}return this._transitionEngine.listen(t,e,n,o,i)}flush(t=-1){this._transitionEngine.flush(t)}get players(){return this._transitionEngine.players.concat(this._timelineEngine.players)}whenRenderingDone(){return this._transitionEngine.whenRenderingDone()}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function pP(t,e){let n=null,o=null;return Array.isArray(e)&&e.length?(n=uP(e[0]),e.length>1&&(o=uP(e[e.length-1]))):e&&(n=uP(e)),n||o?new mP(t,n,o):null}class mP{constructor(t,e,n){this._element=t,this._startStyles=e,this._endStyles=n,this._state=0;let o=mP.initialStylesByElement.get(t);o||mP.initialStylesByElement.set(t,o={}),this._initialStyles=o}start(){this._state<1&&(this._startStyles&&Xx(this._element,this._startStyles,this._initialStyles),this._state=1)}finish(){this.start(),this._state<2&&(Xx(this._element,this._initialStyles),this._endStyles&&(Xx(this._element,this._endStyles),this._endStyles=null),this._state=1)}destroy(){this.finish(),this._state<3&&(mP.initialStylesByElement.delete(this._element),this._startStyles&&(Kx(this._element,this._startStyles),this._endStyles=null),this._endStyles&&(Kx(this._element,this._endStyles),this._endStyles=null),Xx(this._element,this._initialStyles),this._state=3)}}function uP(t){let e=null;const n=Object.keys(t);for(let o=0;o<n.length;o++){const i=n[o];fP(i)&&(e=e||{},e[i]=t[i])}return e}function fP(t){return"display"===t||"position"===t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */mP.initialStylesByElement=new WeakMap;const gP="animation",hP="animationend";class bP{constructor(t,e,n,o,i,a,r){this._element=t,this._name=e,this._duration=n,this._delay=o,this._easing=i,this._fillMode=a,this._onDoneFn=r,this._finished=!1,this._destroyed=!1,this._startTime=0,this._position=0,this._eventFn=t=>this._handleCallback(t)}apply(){!(function t(e,n){const o=xP(e,"").trim();let i=0;o.length&&((function a(t,e){let n=0;for(let o=0;o<t.length;o++)t.charAt(o)===e&&n++;return n})(o,",")+1,n=`${o}, ${n}`),vP(e,"",n)})(this._element,`${this._duration}ms ${this._easing} ${this._delay}ms 1 normal ${this._fillMode} ${this._name}`),MP(this._element,this._eventFn,!1),this._startTime=Date.now()}pause(){yP(this._element,this._name,"paused")}resume(){yP(this._element,this._name,"running")}setPosition(t){const e=_P(this._element,this._name);this._position=t*this._duration,vP(this._element,"Delay",`-${this._position}ms`,e)}getPosition(){return this._position}_handleCallback(t){const e=t._ngTestManualTimestamp||Date.now(),n=1e3*parseFloat(t.elapsedTime.toFixed(3));t.animationName==this._name&&Math.max(e-this._startTime,0)>=this._delay&&n>=this._duration&&this.finish()}finish(){this._finished||(this._finished=!0,this._onDoneFn(),MP(this._element,this._eventFn,!0))}destroy(){this._destroyed||(this._destroyed=!0,this.finish(),(function t(e,n){const o=xP(e,"").split(","),i=CP(o,n);i>=0&&(o.splice(i,1),vP(e,"",o.join(",")))})(this._element,this._name))}}function yP(t,e,n){vP(t,"PlayState",n,_P(t,e))}function _P(t,e){const n=xP(t,"");return n.indexOf(",")>0?CP(n.split(","),e):CP([n],e)}function CP(t,e){for(let n=0;n<t.length;n++)if(t[n].indexOf(e)>=0)return n;return-1}function MP(t,e,n){n?t.removeEventListener(hP,e):t.addEventListener(hP,e)}function vP(t,e,n,o){const i=gP+e;if(null!=o){const e=t.style[i];if(e.length){const t=e.split(",");t[o]=n,n=t.join(",")}}t.style[i]=n}function xP(t,e){return t.style[gP+e]||""}class OP{constructor(t,e,n,o,i,a,r,s){this.element=t,this.keyframes=e,this.animationName=n,this._duration=o,this._delay=i,this._finalStyles=r,this._specialStyles=s,this._onDoneFns=[],this._onStartFns=[],this._onDestroyFns=[],this.currentSnapshot={},this._state=0,this.easing=a||"linear",this.totalTime=o+i,this._buildStyler()}onStart(t){this._onStartFns.push(t)}onDone(t){this._onDoneFns.push(t)}onDestroy(t){this._onDestroyFns.push(t)}destroy(){this.init(),this._state>=4||(this._state=4,this._styler.destroy(),this._flushStartFns(),this._flushDoneFns(),this._specialStyles&&this._specialStyles.destroy(),this._onDestroyFns.forEach((t=>t())),this._onDestroyFns=[])}_flushDoneFns(){this._onDoneFns.forEach((t=>t())),this._onDoneFns=[]}_flushStartFns(){this._onStartFns.forEach((t=>t())),this._onStartFns=[]}finish(){this.init(),this._state>=3||(this._state=3,this._styler.finish(),this._flushStartFns(),this._specialStyles&&this._specialStyles.finish(),this._flushDoneFns())}setPosition(t){this._styler.setPosition(t)}getPosition(){return this._styler.getPosition()}hasStarted(){return this._state>=2}init(){this._state>=1||(this._state=1,this._styler.apply(),this._delay&&this._styler.pause())}play(){this.init(),this.hasStarted()||(this._flushStartFns(),this._state=2,this._specialStyles&&this._specialStyles.start()),this._styler.resume()}pause(){this.init(),this._styler.pause()}restart(){this.reset(),this.play()}reset(){this._state=0,this._styler.destroy(),this._buildStyler(),this._styler.apply()}_buildStyler(){this._styler=new bP(this.element,this.animationName,this._duration,this._delay,this.easing,"forwards",(()=>this.finish()))}triggerCallback(t){const e="start"==t?this._onStartFns:this._onDoneFns;e.forEach((t=>t())),e.length=0}beforeDestroy(){this.init();const t={};if(this.hasStarted()){const e=this._state>=3;Object.keys(this._finalStyles).forEach((n=>{"offset"!=n&&(t[n]=e?this._finalStyles[n]:sO(this.element,n))}))}this.currentSnapshot=t}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class PP extends mx{constructor(t,e){super(),this.element=t,this._startingStyles={},this.__initialized=!1,this._styles=Tx(e)}init(){!this.__initialized&&this._startingStyles&&(this.__initialized=!0,Object.keys(this._styles).forEach((t=>{this._startingStyles[t]=this.element.style[t]})),super.init())}play(){this._startingStyles&&(this.init(),Object.keys(this._styles).forEach((t=>this.element.style.setProperty(t,this._styles[t]))),super.play())}destroy(){this._startingStyles&&(Object.keys(this._startingStyles).forEach((t=>{const e=this._startingStyles[t];e?this.element.style.setProperty(t,e):this.element.style.removeProperty(t)})),this._startingStyles=null,super.destroy())}}class wP{constructor(){this._count=0}validateStyleProperty(t){return Dx(t)}matchesElement(t,e){return Ex(t,e)}containsElement(t,e){return Rx(t,e)}query(t,e,n){return Ax(t,e,n)}computeStyle(t,e,n){return window.getComputedStyle(t)[e]}buildKeyframeElement(t,e,n){n=n.map((t=>Tx(t)));let o=`@keyframes ${e} {\n`,i="";n.forEach((t=>{i=" ";const e=parseFloat(t.offset);o+=`${i}${100*e}% {\n`,i+=" ",Object.keys(t).forEach((e=>{const n=t[e];switch(e){case"offset":return;case"easing":return void(n&&(o+=`${i}animation-timing-function: ${n};\n`));default:return void(o+=`${i}${e}: ${n};\n`)}})),o+=`${i}}\n`})),o+="}\n";const a=document.createElement("style");return a.textContent=o,a}animate(t,e,n,o,i,a=[],r){("undefined"==typeof ngDevMode||ngDevMode)&&r&&(function s(){kP||(console.warn("@angular/animations: please load the web-animations.js polyfill to allow programmatic access...\n","  visit https://bit.ly/IWukam to learn more about using the web-animation-js polyfill."),kP=!0)})();const l=a.filter((t=>t instanceof OP)),c={};iO(n,o)&&l.forEach((t=>{let e=t.currentSnapshot;Object.keys(e).forEach((t=>c[t]=e[t]))}));const d=(function p(t){let e={};return t&&(Array.isArray(t)?t:[t]).forEach((t=>{Object.keys(t).forEach((n=>{"offset"!=n&&"easing"!=n&&(e[n]=t[n])}))})),e})(e=aO(t,e,c));if(0==n)return new PP(t,d);const m="gen_css_kf_"+this._count++,u=this.buildKeyframeElement(t,m,e);(function f(t){var e;const n=null===(e=t.getRootNode)||void 0===e?void 0:e.call(t);return"undefined"!=typeof ShadowRoot&&n instanceof ShadowRoot?n:document.head})(t).appendChild(u);const g=pP(t,e),h=new OP(t,e,m,n,o,i,d,g);return h.onDestroy((()=>(function t(e){e.parentNode.removeChild(e)})(u))),h}}let kP=!1;class SP{constructor(t,e,n,o){this.element=t,this.keyframes=e,this.options=n,this._specialStyles=o,this._onDoneFns=[],this._onStartFns=[],this._onDestroyFns=[],this._initialized=!1,this._finished=!1,this._started=!1,this._destroyed=!1,this.time=0,this.parentPlayer=null,this.currentSnapshot={},this._duration=n.duration,this._delay=n.delay||0,this.time=this._duration+this._delay}_onFinish(){this._finished||(this._finished=!0,this._onDoneFns.forEach((t=>t())),this._onDoneFns=[])}init(){this._buildPlayer(),this._preparePlayerBeforeStart()}_buildPlayer(){if(this._initialized)return;this._initialized=!0;const t=this.keyframes;this.domPlayer=this._triggerWebAnimation(this.element,t,this.options),this._finalKeyframe=t.length?t[t.length-1]:{},this.domPlayer.addEventListener("finish",(()=>this._onFinish()))}_preparePlayerBeforeStart(){this._delay?this._resetDomPlayerState():this.domPlayer.pause()}_triggerWebAnimation(t,e,n){return t.animate(e,n)}onStart(t){this._onStartFns.push(t)}onDone(t){this._onDoneFns.push(t)}onDestroy(t){this._onDestroyFns.push(t)}play(){this._buildPlayer(),this.hasStarted()||(this._onStartFns.forEach((t=>t())),this._onStartFns=[],this._started=!0,this._specialStyles&&this._specialStyles.start()),this.domPlayer.play()}pause(){this.init(),this.domPlayer.pause()}finish(){this.init(),this._specialStyles&&this._specialStyles.finish(),this._onFinish(),this.domPlayer.finish()}reset(){this._resetDomPlayerState(),this._destroyed=!1,this._finished=!1,this._started=!1}_resetDomPlayerState(){this.domPlayer&&this.domPlayer.cancel()}restart(){this.reset(),this.play()}hasStarted(){return this._started}destroy(){this._destroyed||(this._destroyed=!0,this._resetDomPlayerState(),this._onFinish(),this._specialStyles&&this._specialStyles.destroy(),this._onDestroyFns.forEach((t=>t())),this._onDestroyFns=[])}setPosition(t){void 0===this.domPlayer&&this.init(),this.domPlayer.currentTime=t*this.time}getPosition(){return this.domPlayer.currentTime/this.time}get totalTime(){return this._delay+this._duration}beforeDestroy(){const t={};this.hasStarted()&&Object.keys(this._finalKeyframe).forEach((e=>{"offset"!=e&&(t[e]=this._finished?this._finalKeyframe[e]:sO(this.element,e))})),this.currentSnapshot=t}triggerCallback(t){const e="start"==t?this._onStartFns:this._onDoneFns;e.forEach((t=>t())),e.length=0}}class DP{constructor(){this._isNativeImpl=/\{\s*\[native\s+code\]\s*\}/.test(EP().toString()),this._cssKeyframesDriver=new wP}validateStyleProperty(t){return Dx(t)}matchesElement(t,e){return Ex(t,e)}containsElement(t,e){return Rx(t,e)}query(t,e,n){return Ax(t,e,n)}computeStyle(t,e,n){return window.getComputedStyle(t)[e]}overrideWebAnimationsSupport(t){this._isNativeImpl=t}animate(t,e,n,o,i,a=[],r){if(!r&&!this._isNativeImpl)return this._cssKeyframesDriver.animate(t,e,n,o,i,a);const s={duration:n,delay:o,fill:0==o?"both":"forwards"};i&&(s.easing=i);const l={},c=a.filter((t=>t instanceof SP));iO(n,o)&&c.forEach((t=>{let e=t.currentSnapshot;Object.keys(e).forEach((t=>l[t]=e[t]))}));const d=pP(t,e=aO(t,e=e.map((t=>Yx(t,!1))),l));return new SP(t,e,s,d)}}function EP(){return fx()&&Element.prototype.animate||{}}
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */class RP extends tx{constructor(t,e){super(),this._nextAnimationId=0,this._renderer=t.createRenderer(e.body,{id:"0",encapsulation:Hn.None,styles:[],data:{animation:[]}})}build(t){const e=this._nextAnimationId.toString();this._nextAnimationId++;const n=Array.isArray(t)?ix(t):t;return NP(this._renderer,null,e,"register",[n]),new AP(e,this._renderer)}}RP.ɵfac=function t(e){return new(e||RP)(vr(_g),vr(Z_))},RP.ɵprov=Mn({token:RP,factory:RP.ɵfac}),RP.ctorParameters=()=>[{type:_g},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(RP,[{type:im}],(function(){return[{type:_g},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);class AP extends class{}{constructor(t,e){super(),this._id=t,this._renderer=e}create(t,e){return new TP(this._id,t,e||{},this._renderer)}}class TP{constructor(t,e,n,o){this.id=t,this.element=e,this._renderer=o,this.parentPlayer=null,this._started=!1,this.totalTime=0,this._command("create",n)}_listen(t,e){return this._renderer.listen(this.element,`@@${this.id}:${t}`,e)}_command(t,...e){return NP(this._renderer,this.element,this.id,t,e)}onDone(t){this._listen("done",t)}onStart(t){this._listen("start",t)}onDestroy(t){this._listen("destroy",t)}init(){this._command("init")}hasStarted(){return this._started}play(){this._command("play"),this._started=!0}pause(){this._command("pause")}restart(){this._command("restart")}finish(){this._command("finish")}destroy(){this._command("destroy")}reset(){this._command("reset"),this._started=!1}setPosition(t){this._command("setPosition",t)}getPosition(){var t,e;return null!==(e=null===(t=this._renderer.engine.players[+this.id])||void 0===t?void 0:t.getPosition())&&void 0!==e?e:0}}function NP(t,e,n,o,i){return t.setProperty(e,`@@${n}:${o}`,i)}const zP="@",IP="@.disabled";class HP{constructor(t,e,n){this.delegate=t,this.engine=e,this._zone=n,this._currentId=0,this._microtaskId=1,this._animationCallbacksBuffer=[],this._rendererCache=new Map,this._cdRecurDepth=0,this.promise=Promise.resolve(0),e.onRemovalComplete=(t,e)=>{e&&e.parentNode(t)&&e.removeChild(t.parentNode,t)}}createRenderer(t,e){const n=this.delegate.createRenderer(t,e);if(!(t&&e&&e.data&&e.data.animation)){let t=this._rendererCache.get(n);return t||(t=new FP("",n,this.engine),this._rendererCache.set(n,t)),t}const o=e.id,i=e.id+"-"+this._currentId;this._currentId++,this.engine.register(i,t);const a=e=>{Array.isArray(e)?e.forEach(a):this.engine.registerTrigger(o,i,t,e.name,e)};return e.data.animation.forEach(a),new LP(this,i,n,this.engine)}begin(){this._cdRecurDepth++,this.delegate.begin&&this.delegate.begin()}_scheduleCountTask(){this.promise.then((()=>{this._microtaskId++}))}scheduleListenerCallback(t,e,n){t>=0&&t<this._microtaskId?this._zone.run((()=>e(n))):(0==this._animationCallbacksBuffer.length&&Promise.resolve(null).then((()=>{this._zone.run((()=>{this._animationCallbacksBuffer.forEach((t=>{const[e,n]=t;e(n)})),this._animationCallbacksBuffer=[]}))})),this._animationCallbacksBuffer.push([e,n]))}end(){this._cdRecurDepth--,0==this._cdRecurDepth&&this._zone.runOutsideAngular((()=>{this._scheduleCountTask(),this.engine.flush(this._microtaskId)})),this.delegate.end&&this.delegate.end()}whenRenderingDone(){return this.engine.whenRenderingDone()}}HP.ɵfac=function t(e){return new(e||HP)(vr(_g),vr(dP),vr(a_))},HP.ɵprov=Mn({token:HP,factory:HP.ɵfac}),HP.ctorParameters=()=>[{type:_g},{type:dP},{type:a_}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(HP,[{type:im}],(function(){return[{type:_g},{type:dP},{type:a_}]}),null);class FP{constructor(t,e,n){this.namespaceId=t,this.delegate=e,this.engine=n,this.destroyNode=this.delegate.destroyNode?t=>e.destroyNode(t):null}get data(){return this.delegate.data}destroy(){this.engine.destroy(this.namespaceId,this.delegate),this.delegate.destroy()}createElement(t,e){return this.delegate.createElement(t,e)}createComment(t){return this.delegate.createComment(t)}createText(t){return this.delegate.createText(t)}appendChild(t,e){this.delegate.appendChild(t,e),this.engine.onInsert(this.namespaceId,e,t,!1)}insertBefore(t,e,n,o=!0){this.delegate.insertBefore(t,e,n),this.engine.onInsert(this.namespaceId,e,t,o)}removeChild(t,e,n){this.engine.onRemove(this.namespaceId,e,this.delegate,n)}selectRootElement(t,e){return this.delegate.selectRootElement(t,e)}parentNode(t){return this.delegate.parentNode(t)}nextSibling(t){return this.delegate.nextSibling(t)}setAttribute(t,e,n,o){this.delegate.setAttribute(t,e,n,o)}removeAttribute(t,e,n){this.delegate.removeAttribute(t,e,n)}addClass(t,e){this.delegate.addClass(t,e)}removeClass(t,e){this.delegate.removeClass(t,e)}setStyle(t,e,n,o){this.delegate.setStyle(t,e,n,o)}removeStyle(t,e,n){this.delegate.removeStyle(t,e,n)}setProperty(t,e,n){e.charAt(0)==zP&&e==IP?this.disableAnimations(t,!!n):this.delegate.setProperty(t,e,n)}setValue(t,e){this.delegate.setValue(t,e)}listen(t,e,n){return this.delegate.listen(t,e,n)}disableAnimations(t,e){this.engine.disableAnimations(t,e)}}class LP extends FP{constructor(t,e,n,o){super(e,n,o),this.factory=t,this.namespaceId=e}setProperty(t,e,n){e.charAt(0)==zP?"."==e.charAt(1)&&e==IP?this.disableAnimations(t,n=void 0===n||!!n):this.engine.process(this.namespaceId,t,e.substr(1),n):this.delegate.setProperty(t,e,n)}listen(t,e,n){if(e.charAt(0)==zP){const o=(function i(t){switch(t){case"body":return document.body;case"document":return document;case"window":return window;default:return t}})(t);let a=e.substr(1),r="";return a.charAt(0)!=zP&&([a,r]=(function s(t){const e=t.indexOf(".");return[t.substring(0,e),t.substr(e+1)]}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class ZP{static withConfig(t){return{ngModule:ZP,providers:t.disableAnimations?qP:WP}}}ZP.ɵfac=function t(e){return new(e||ZP)},ZP.ɵmod=ao({type:ZP}),ZP.ɵinj=vn({providers:WP,imports:[Uv]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ZP,[{type:Ay,args:[{exports:[Uv],providers:WP}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(ZP,{exports:function(){return[Uv]}});class XP{}XP.ɵfac=function t(e){return new(e||XP)},XP.ɵmod=ao({type:XP}),XP.ɵinj=vn({providers:qP,imports:[Uv]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(XP,[{type:Ay,args:[{exports:[Uv],providers:qP}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(XP,{exports:function(){return[Uv]}});const KP={};function JP(t,e){if(KP[t]=(KP[t]||0)+1,"function"==typeof e)return QP(t,((...n)=>Object.assign(Object.assign({},e(...n)),{type:t})));switch(e?e._as:"empty"){case"empty":return QP(t,(()=>({type:t})));case"props":return QP(t,(e=>Object.assign(Object.assign({},e),{type:t})));default:throw new Error("Unexpected config.")}}function QP(t,e){return Object.defineProperty(e,"type",{value:t,writable:!1})}const $P="@ngrx/store/init";class tw extends F{constructor(){super({type:$P})}next(t){if("function"==typeof t)throw new TypeError("\n        Dispatch expected an object, instead it received a function.\n        If you're using the createAction function, make sure to invoke the function\n        before dispatching the action. 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Array.isArray(e)&&e.length>0&&(t=Ow.apply(null,[...e,t])),(e,n)=>{const o=t(e);return(t,e)=>o(t=void 0===t?n:t,e)}}class ww extends D{}class kw extends tw{}class Sw extends F{constructor(t,e,n,o){super(o(n,e)),this.dispatcher=t,this.initialState=e,this.reducers=n,this.reducerFactory=o}get currentReducers(){return this.reducers}addFeature(t){this.addFeatures([t])}addFeatures(t){const e=t.reduce(((t,{reducers:e,reducerFactory:n,metaReducers:o,initialState:i,key:a})=>{const r="function"==typeof e?(function s(t){const e=Array.isArray(t)&&t.length>0?Ow(...t):t=>t;return(t,n)=>(t=e(t),(e,o)=>t(e=void 0===e?n:e,o))})(o)(e,i):Pw(n,o)(e,i);return 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Ow(wk,xk)(t)}function wk(t){return(function e(t){return t.constructor.hasOwnProperty(Ok)})(t)?t.constructor[Ok]:[]}function kk(t,e,n=10){return t.pipe(pe((o=>(e&&e.handleError(o),n<=1?t:kk(t,e,n-1)))))}class Sk extends D{constructor(t){super(),t&&(this.source=t)}lift(t){const e=new Sk;return e.source=this,e.operator=t,e}}function Dk(...t){return ce((e=>t.some((t=>"string"==typeof t?t===e.type:t.type===e.type))))}function Ek(t){return Rk(t,"ngrxOnInitEffects")}function Rk(t,e){return t&&e in t&&"function"==typeof t[e]}Sk.ɵfac=function t(e){return new(e||Sk)(vr(Ew))},Sk.ɵprov=Mn({token:Sk,factory:Sk.ɵfac}),Sk.ctorParameters=()=>[{type:D,decorators:[{type:kr,args:[Ew]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Sk,[{type:im}],(function(){return[{type:D,decorators:[{type:kr,args:[Ew]}]}]}),null);const Ak=new Ga("@ngrx/effects Internal Root Guard"),Tk=new Ga("@ngrx/effects User Provided Effects"),Nk=new Ga("@ngrx/effects Internal Root Effects"),zk=new Ga("@ngrx/effects Root Effects"),Ik=new Ga("@ngrx/effects Internal Feature Effects"),Hk=new Ga("@ngrx/effects Feature Effects"),Fk=new Ga("@ngrx/effects Effects Error Handler");class Lk extends I{constructor(t,e){super(),this.errorHandler=t,this.effectsErrorHandler=e}addEffects(t){this.next(t)}toActions(){return this.pipe(we(xk),Zt((t=>t.pipe(we(Bk)))),Zt((t=>re(t.pipe(Pe((t=>(function e(t,n){return e=>{const o=(function i(t,e,n){const o=xk(t).constructor.name;return re(...(function i(t){return[Pk,vk].reduce(((e,n)=>e.concat(n(t))),[])})(t).map((({propertyName:i,dispatch:a,useEffectsErrorHandler:r})=>{const s="function"==typeof t[i]?t[i]():t[i],l=r?n(s,e):s;return!1===a?l.pipe(ye()):l.pipe((function c(){return R((function(t,e){t.subscribe(new T(e,(function(t){e.next(At.createNext(t))}),(function(){e.next(At.createComplete()),e.complete()}),(function(t){e.next(At.createError(t)),e.complete()})))}))})()).pipe(It((e=>({effect:t[i],notification:e,propertyName:i,sourceName:o,sourceInstance:t}))))})))})(e,t,n);return(function a(t){return Rk(t,"ngrxOnRunEffects")})(e)?e.ngrxOnRunEffects(o):o}})(this.errorHandler,this.effectsErrorHandler)(t))),It((t=>((function e(t,n){if("N"===t.notification.kind){const e=t.notification.value;!(function o(t){return"function"!=typeof t&&t&&t.type&&"string"==typeof t.type})(e)&&n.handleError(new Error(`Effect ${(function i({propertyName:t,sourceInstance:e,sourceName:n}){const o="function"==typeof e[t];return`"${n}.${String(t)}${o?"()":""}"`})(t)} dispatched an invalid action: ${(function a(t){try{return JSON.stringify(t)}catch(e){return t}})(e)}`))}})(t,this.errorHandler),t.notification))),ce((t=>"N"===t.kind&&null!=t.value)),(function e(){return R((function(t,e){t.subscribe(new T(e,(function(t){return Tt(t,e)})))}))})()),t.pipe(be(1),ce(Ek),It((t=>t.ngrxOnInitEffects())))))))}}function Bk(t){return(function e(t){return Rk(t,"ngrxOnIdentifyEffects")})(t)?t.ngrxOnIdentifyEffects():""}Lk.ɵfac=function t(e){return new(e||Lk)(vr(Zs),vr(Fk))},Lk.ɵprov=Mn({token:Lk,factory:Lk.ɵfac}),Lk.ctorParameters=()=>[{type:Zs},{type:void 0,decorators:[{type:kr,args:[Fk]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Lk,[{type:im}],(function(){return[{type:Zs},{type:void 0,decorators:[{type:kr,args:[Fk]}]}]}),null);class Vk{constructor(t,e){this.effectSources=t,this.store=e,this.effectsSubscription=null}start(){this.effectsSubscription||(this.effectsSubscription=this.effectSources.toActions().subscribe(this.store))}ngOnDestroy(){this.effectsSubscription&&(this.effectsSubscription.unsubscribe(),this.effectsSubscription=null)}}Vk.ɵfac=function t(e){return new(e||Vk)(vr(Lk),vr(Iw))},Vk.ɵprov=Mn({token:Vk,factory:Vk.ɵfac}),Vk.ctorParameters=()=>[{type:Lk},{type:Iw}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Vk,[{type:im}],(function(){return[{type:Lk},{type:Iw}]}),null);const jk="@ngrx/effects/init";JP(jk);class Uk{constructor(t,e,n,o,i,a,r){this.sources=t,e.start(),o.forEach((e=>t.addEffects(e))),n.dispatch({type:jk})}addEffects(t){this.sources.addEffects(t)}}Uk.ɵfac=function t(e){return new(e||Uk)(vr(Lk),vr(Vk),vr(Iw),vr(zk),vr(lk,8),vr(ck,8),vr(Ak,8))},Uk.ɵmod=ao({type:Uk}),Uk.ɵinj=vn({}),Uk.ctorParameters=()=>[{type:Lk},{type:Vk},{type:Iw},{type:Array,decorators:[{type:kr,args:[zk]}]},{type:lk,decorators:[{type:Sr}]},{type:ck,decorators:[{type:Sr}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Ak]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Uk,[{type:Ay,args:[{}]}],(function(){return[{type:Lk},{type:Vk},{type:Iw},{type:Array,decorators:[{type:kr,args:[zk]}]},{type:lk,decorators:[{type:Sr}]},{type:ck,decorators:[{type:Sr}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Ak]}]}]}),null);class Gk{constructor(t,e,n,o){e.forEach((e=>e.forEach((e=>t.addEffects(e)))))}}Gk.ɵfac=function t(e){return new(e||Gk)(vr(Uk),vr(Hk),vr(lk,8),vr(ck,8))},Gk.ɵmod=ao({type:Gk}),Gk.ɵinj=vn({}),Gk.ctorParameters=()=>[{type:Uk},{type:Array,decorators:[{type:kr,args:[Hk]}]},{type:lk,decorators:[{type:Sr}]},{type:ck,decorators:[{type:Sr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Gk,[{type:Ay,args:[{}]}],(function(){return[{type:Uk},{type:Array,decorators:[{type:kr,args:[Hk]}]},{type:lk,decorators:[{type:Sr}]},{type:ck,decorators:[{type:Sr}]}]}),null);class Wk{static forFeature(t=[]){return{ngModule:Gk,providers:[t,{provide:Ik,multi:!0,useValue:t},{provide:Tk,multi:!0,useValue:[]},{provide:Hk,multi:!0,useFactory:Yk,deps:[rp,Ik,Tk]}]}}static forRoot(t=[]){return{ngModule:Uk,providers:[{provide:Fk,useValue:kk},Vk,Lk,Sk,t,{provide:Nk,useValue:[t]},{provide:Ak,useFactory:qk,deps:[[Vk,new Sr,new Er],[Nk,new Dr]]},{provide:Tk,multi:!0,useValue:[]},{provide:zk,useFactory:Yk,deps:[rp,Nk,Tk]}]}}}function Yk(t,e,n){const o=[];for(const t of e)o.push(...t);for(const t of n)o.push(...t);return(function i(t,e){return e.map((e=>t.get(e)))})(t,o)}function qk(t,e){if((1!==e.length||0!==e[0].length)&&t)throw new TypeError("EffectsModule.forRoot() called twice. Feature modules should use EffectsModule.forFeature() instead.");return"guarded"}var Zk;Wk.ɵfac=function t(e){return new(e||Wk)},Wk.ɵmod=ao({type:Wk}),Wk.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Wk,[{type:Ay,args:[{}]}],null,null),(function(t){t[t.UNKNOWN=0]="UNKNOWN",t[t.EXPERIMENTS=1]="EXPERIMENTS",t[t.EXPERIMENT=2]="EXPERIMENT",t[t.COMPARE_EXPERIMENT=3]="COMPARE_EXPERIMENT",t[t.NOT_SET=4]="NOT_SET"})(Zk||(Zk={}));const Xk="defaultExperimentId";function Kk(t){return t.split(",").map((t=>{const e=t.indexOf(":");if(e<0)throw new Error(`Expect colon delimiting name and ID: ${t}`);const n=t.slice(0,e),o=t.slice(e+1);if(!o)throw new Error(`Expect id to be non-falsy: ${t}`);return{name:n,id:o}}))}function Jk(t,e){switch(t){case Zk.EXPERIMENT:return Object.prototype.hasOwnProperty.call(e,"experimentId")?[e.experimentId]:[Xk];case Zk.COMPARE_EXPERIMENT:return Kk(e.experimentIds).map((({id:t})=>t));case Zk.EXPERIMENTS:default:return null}}function Qk(t,e){var n;switch(t){case Zk.COMPARE_EXPERIMENT:case Zk.EXPERIMENT:{const o=null!==(n=Jk(t,e))&&void 0!==n?n:[];return o.sort(),`${t}/${o.join(",")}`}case Zk.EXPERIMENTS:return String(t);case Zk.NOT_SET:return"__not_set";default:return""}}const $k=()=>window.location.href;class tS{getHref(){return $k()}getSearch(){const t=new URLSearchParams(window.location.search),e=[];return t.forEach(((t,n)=>{e.push({key:n,value:t})})),e}getHash(){return window.location.hash}getPath(){return window.location.pathname}replaceState(t){window.history.replaceState(null,"",t)}pushState(t){window.history.pushState(null,"",t)}onPopState(){return oe(window,"popstate").pipe(It((()=>({pathname:this.getPath(),queryParams:this.getSearch()}))))}getResolvedPath(t){return new URL(t,$k()).pathname}getFullPathFromRouteOrNav(t,e){const n=this.getResolvedPath(t.pathname);let o="";return!(function i(t){return t.hasOwnProperty("pathname")&&!t.hasOwnProperty("queryParams")})(t)&&t.queryParams.length&&(o="?"+(function a(t){const e=new URLSearchParams;for(const{key:n,value:o}of t)e.append(n,o);return e})(t.queryParams).toString()),`${n}${o}${e?this.getHash():""}`}}tS.ɵfac=function t(e){return new(e||tS)},tS.ɵprov=Mn({token:tS,factory:tS.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(tS,[{type:im}],null,null);class eS{constructor(t){this.appRoot=this.getAppRootFromMetaElement(t)}getAppRootFromMetaElement(t){const e=document.querySelector('head meta[name="tb-relative-root"]');if(!e)return"/";const{pathname:n}=new URL(e.content,t.getHref());return n.replace(/\/*$/,"/")}getAbsPathnameWithAppRoot(t){return this.appRoot.slice(0,-1)+t}getAppRootlessPathname(t){return t.startsWith(this.appRoot)?"/"+t.slice(this.appRoot.length):t}}eS.ɵfac=function t(e){return new(e||eS)(vr(tS))},eS.ɵprov=Mn({token:eS,factory:eS.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(eS,[{type:im}],(function(){return[{type:tS}]}),null);class nS extends eS{getAppRoot(){return this.appRoot}setAppRoot(t){this.appRoot=t}}nS.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(nS)))(n||nS)}})(),nS.ɵprov=Mn({token:nS,factory:nS.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(nS,[{type:im}],null,null);class oS{}oS.ɵfac=function t(e){return new(e||oS)},oS.ɵmod=ao({type:oS}),oS.ɵinj=vn({providers:[tS]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(oS,[{type:Ay,args:[{providers:[tS]}]}],null,null);class iS{}iS.ɵfac=function t(e){return new(e||iS)},iS.ɵmod=ao({type:iS}),iS.ɵinj=vn({providers:[eS],imports:[[oS]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(iS,[{type:Ay,args:[{imports:[oS],providers:[eS]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(iS,{imports:[oS]});const aS=JP("[App Routing] Discarding Unsaved Updates"),rS=JP("[App Routing] State Rehydrated From Url",{_as:"props",_p:void 0}),sS=JP("[App Routing] Route Config Loaded",{_as:"props",_p:void 0}),lS=JP("[App Routing] In App Navigation Requested",{_as:"props",_p:void 0}),cS=JP("[App Routing] In App Navigating",{_as:"props",_p:void 0}),dS=JP("[App Routing] In App Navigated",{_as:"props",_p:void 0}),pS=new Ga("[App Routing] Dirty Updates");class mS{constructor(t){this.dirtyUpdatesSelectorFactories=t}getDirtyUpdatesSelectors(){var t;return null!==(t=this.dirtyUpdatesSelectorFactories)&&void 0!==t?t:[]}static registerDirtyUpdates(t){return{ngModule:mS,providers:[{provide:pS,multi:!0,useFactory:t}]}}}mS.ɵfac=function t(e){return new(e||mS)(vr(pS,8))},mS.ɵmod=ao({type:mS}),mS.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(mS,[{type:Ay}],(function(){return[{type:Array,decorators:[{type:Sr},{type:kr,args:[pS]}]}]}),null);const uS=new Ga("[App Routing] Programmatical Navigation Provider");class fS{constructor(t){this.providers=new Map;for(const e of t||[]){if(this.providers.has(e.actionCreator.type))throw new RangeError(`"${e.actionCreator.type}" is already registered for nav. Multiple navigations on same kick is not allowed.`);this.providers.set(e.actionCreator.type,e.lambda)}}getNavigation(t){const e=this.providers.get(t.type);return e?e(t):null}static registerProgrammaticalNavigation(t){return{ngModule:fS,providers:[{provide:uS,multi:!0,useFactory:t}]}}}function gS(t){return null!=t.routeKind}function hS(t){return vS(t).map((t=>{const e=t.startsWith(":");return e?{pathPart:t,isParam:!0,paramName:t.slice(1)}:{pathPart:t,isParam:e}}))}fS.ɵfac=function t(e){return new(e||fS)(vr(uS,8))},fS.ɵmod=ao({type:fS}),fS.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(fS,[{type:Ay,args:[{}]}],(function(){return[{type:void 0,decorators:[{type:Sr},{type:kr,args:[uS]}]}]}),null);class bS{constructor(t){this.validateConfig(t),this.pathFragments=hS(t.path),this.pathMatchers=this.getPathMatchers(this.pathFragments)}static getMatcher(t){return gS(t)?new yS(t):(function e(t){return void 0!==t.redirectionPath})(t)?new _S(t):new CS(t)}validateConfig({path:t}){if(!t.startsWith("/"))throw new RangeError(`config.path should start with '/'. ${t}`);let e=0;for(;(e=t.indexOf(":",e+1))>=0;){if("/"!==t[e-1])throw new RangeError(`config.path parameter should come after '/'. ${t}`);if(void 0===t[e+1]||"/"===t[e+1])throw new RangeError(`config.path parameter should have non-empty name. ${t}`)}}getPathMatchers(t){return t.map((t=>{const{pathPart:e}=t;return t.isParam?e=>({isParamPathPart:!0,partMatched:!0,paramName:t.paramName,paramValue:e}):t=>({isParamPathPart:!1,partMatched:t===e})}))}match(t){let e={};if(this.pathMatchers.length!==t.length)return{result:!1};let n=0;for(const o of this.pathMatchers){const i=o(t[n++]);if(!i.partMatched)return{result:!1};i.isParamPathPart&&(e=Object.assign(Object.assign({},e),{[i.paramName]:i.paramValue}))}return{result:!0,params:e,pathParts:t,isRedirection:!1}}matchByParams(t){return{result:!0,params:t,pathParts:this.reprojectPathByParams(this.pathFragments,t),isRedirection:!1}}reprojectPathByParams(t,e){const n=[];for(const o of t)if(o.isParam){const{paramName:t}=o;if(!e.hasOwnProperty(t))throw new RangeError(`Failed to reproject parameter. "${t}" parameter should be present.`);n.push(e[t])}else n.push(o.pathPart);return n}}class yS extends bS{constructor(t){super(t),this.definition=t}}class _S extends bS{constructor(t){super(t),this.definition=t,this.redirectionFragments=hS(t.redirectionPath)}match(t){const e=super.match(t);if(!e.result)return e;const n=this.reprojectPathByParams(this.redirectionFragments,e.params);return{result:!0,params:e.params,pathParts:n,isRedirection:!0}}}class CS extends bS{constructor(t){super(t),this.definition=t}match(t){const e=super.match(t);if(!e.result)return e;const{pathParts:n,queryParams:o}=this.definition.redirector(t);return{result:!0,params:e.params,pathParts:n,isRedirection:!0,redirectionQueryParams:o}}}class MS{constructor(t,e=3){if(this.maxRedirection=e,e<0)throw new RangeError("maxRedirection has to be non-negative number");this.validateRouteConfigs(t),this.defaultRouteConfig=null,this.routeKindToConcreteConfigMatchers=new Map,this.configMatchers=[];for(const e of t){const t=bS.getMatcher(e);this.configMatchers.push(t),t instanceof yS&&(this.routeKindToConcreteConfigMatchers.set(t.definition.routeKind,t),t.definition.defaultRoute&&(this.defaultRouteConfig=t))}}validateRouteConfigs(t){const e=t.filter(gS),n=e.filter((t=>t.defaultRoute));if(n.length>1){const t=n.map((({path:t})=>t)).join(", ");throw new RangeError(`There are more than one defaultRoutes. ${t}`)}if(1===n.length){const{path:t}=n[0];if(Boolean(hS(t).find((({isParam:t})=>t))))throw new RangeError(`A defaultRoute cannot have any params. ${t}`)}const o=new Set;for(const{routeKind:t}of e){if(o.has(t))throw new RangeError(`Multiple route configuration for kind: ${t}. Configurations should have unique routeKinds`);o.add(t)}}match(t){var e;if(!t.pathname.startsWith("/"))throw new RangeError('Navigation has to made with pathname that starts with "/"');let n,o=vS(t.pathname),i=0,a=!1;for(;;){let t=!1;for(const e of this.configMatchers){const i=e.match(o);if(i.result){t=!0;const{params:r,pathParts:s,isRedirection:l}=i;if(l){o=s,a=!0,n=i.redirectionQueryParams;break}if(!(e instanceof yS))throw new RangeError("No concrete route definition `match` return redirection");const{definition:c}=e,d={routeKind:c.routeKind,params:r,pathname:xS(s),deepLinkProvider:c.deepLinkProvider||null};return Object.assign(Object.assign({},d),a?{originateFromRedirection:!0,redirectionOnlyQueryParams:n}:{originateFromRedirection:!1})}}if(a&&i++,!t||i>this.maxRedirection)break}if(i>this.maxRedirection)throw new Error(`Potential redirection loop (redirecting more than ${this.maxRedirection} times. Please do not have cycles in the routes.`);if(this.defaultRouteConfig){const{definition:t}=this.defaultRouteConfig;return{routeKind:t.routeKind,deepLinkProvider:null!==(e=t.deepLinkProvider)&&void 0!==e?e:null,pathname:t.path,params:{},originateFromRedirection:a}}return null}matchByRouteKind(t,e){const n=this.routeKindToConcreteConfigMatchers.get(t);if(!n)throw new RangeError(`Requires configuration for routeKind: ${t}`);return{routeKind:t,params:e,pathname:xS(n.matchByParams(e).pathParts),deepLinkProvider:n.definition.deepLinkProvider||null,originateFromRedirection:!1}}}function vS(t){return t.split("/").slice(1)}function xS(t){return"/"+t.join("/")}const OS=new Ga("[App Routing] Route Config");class PS{constructor(t){if(this.routeConfigs=null,this.routeKindToNgComponent=new Map,!t)return;const e=[];for(const n of t)for(const t of n)e.push(t);this.routeConfigs=new MS(e),e.forEach((t=>{gS(t)&&this.routeKindToNgComponent.set(t.routeKind,t.ngComponent)}))}getRegisteredRouteKinds(){return this.routeKindToNgComponent.keys()}getRouteConfigs(){return this.routeConfigs}getNgComponentByRouteKind(t){return this.routeKindToNgComponent.get(t)||null}static registerRoutes(t){return{ngModule:PS,providers:[{provide:OS,multi:!0,useFactory:t}]}}}PS.ɵfac=function t(e){return new(e||PS)(vr(OS,8))},PS.ɵmod=ao({type:PS}),PS.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(PS,[{type:Ay,args:[{}]}],(function(){return[{type:void 0,decorators:[{type:Sr},{type:kr,args:[OS]}]}]}),null);const wS="app_routing",kS=Kw(wS),SS=Zw(kS,(t=>t.activeRoute)),DS=Zw(kS,(t=>t.nextRoute)),ES=Zw(kS,(t=>t.registeredRouteKeys)),RS=Zw(SS,(t=>t?t.routeKind:Zk.NOT_SET)),AS=Zw(SS,(t=>t?t.params:{})),TS=Zw(RS,AS,((t,e)=>Jk(t,e))),NS=Zw(RS,AS,((t,e)=>Qk(t,e))),zS=Zw(RS,AS,((t,e)=>{if(t!==Zk.COMPARE_EXPERIMENT)return{};const n=(function o(t){const e=new Map,n=Kk(t.experimentIds);for(const{id:t,name:o}of n)o&&e.set(t,o);return e})(e);return Object.fromEntries(n.entries())})),IS=JP("[App Routing] Effects Init");class HS{constructor(t,e,n,o,i,a,r){this.actions$=t,this.store=e,this.location=n,this.dirtyUpdatesRegistry=o,this.registry=i,this.programmaticalNavModule=a,this.appRootProvider=r,this.onNavigationRequested$=this.actions$.pipe(Dk(lS),It((t=>{const e=t.pathname.startsWith("/")?this.appRootProvider.getAbsPathnameWithAppRoot(t.pathname):this.location.getResolvedPath(t.pathname);return Object.assign(Object.assign({},t),{pathname:e})}))),this.bootstrapReducers$=Mk((()=>this.actions$.pipe(Dk(IS),It((()=>sS({routeKinds:new Set(this.registry.getRegisteredRouteKinds())})))))),this.onInit$=this.actions$.pipe(Dk(IS)).pipe(Ce(0),It((()=>({pathname:this.location.getPath(),queryParams:this.location.getSearch(),replaceState:!0,browserInitiated:!0})))),this.userInitNavRoute$=re(this.onNavigationRequested$,this.onInit$,this.location.onPopState().pipe(It((t=>({pathname:t.pathname,replaceState:t.replaceState,browserInitiated:!0}))))).pipe(It((t=>{if(!t.pathname.startsWith("/"))throw new Error(`[App routing] pathname must start with '/'. Got: ${t.pathname}`);return Object.assign(Object.assign({},t),{pathname:this.appRootProvider.getAppRootlessPathname(t.pathname)})})),It((t=>({routeMatch:this.routeConfigs?this.routeConfigs.match(t):null,options:{replaceState:t.replaceState,browserInitiated:t.browserInitiated}})))),this.programmticalNavRoute$=this.actions$.pipe(It((t=>this.programmaticalNavModule.getNavigation(t))),ce((t=>null!==t)),It((t=>{const e=t,n=e.routeKind;let o;switch(e.routeKind){case Zk.COMPARE_EXPERIMENT:o={experimentIds:(i=e.routeParams.aliasAndExperimentIds,i.map((({alias:t,id:e})=>`${t}:${e}`)).join(","))};break;default:o=e.routeParams}var i;return{routeKind:n,routeParams:o}})),It((({routeKind:t,routeParams:e})=>({routeMatch:this.routeConfigs?this.routeConfigs.matchByRouteKind(t,e):null,options:{replaceState:!1,browserInitiated:!1}})))),this.validatedRoute$=re(this.userInitNavRoute$,this.programmticalNavRoute$).pipe(ce((({routeMatch:t})=>Boolean(t))),It((t=>({routeMatch:t.routeMatch,options:t.options})))),this.navigate$=Mk((()=>this.validatedRoute$.pipe(Ve(this.store.select(SS)),Zt((([t,e])=>{const n=null!==e&&Qk(t.routeMatch.routeKind,t.routeMatch.params)===Qk(e.routeKind,e.params),o=this.dirtyUpdatesRegistry.getDirtyUpdatesSelectors();return n||!o.length?Et(t):$t(this.dirtyUpdatesRegistry.getDirtyUpdatesSelectors().map((t=>this.store.select(t).pipe(be(1))))).pipe(It((t=>void 0!==t[0].experimentIds&&t[0].experimentIds.length>0)),ce((t=>{if(t){const t=window.confirm("You have unsaved edits, are you sure you want to discard them?");return t&&this.store.dispatch(aS()),t}return!0})),It((()=>t)))})),Fe((({routeMatch:t,options:e})=>{if(e.browserInitiated&&t.deepLinkProvider){const e=t.originateFromRedirection&&t.redirectionOnlyQueryParams?t.redirectionOnlyQueryParams:this.location.getSearch(),n=t.deepLinkProvider.deserializeQueryParams(e);this.store.dispatch(rS({routeKind:t.routeKind,partialState:n}))}})),ze((({routeMatch:t,options:e})=>{var n;const o={replaceState:null!==(n=e.replaceState)&&void 0!==n&&n},i=Et({routeKind:t.routeKind,params:t.params,pathname:t.pathname,queryParams:[],navigationOptions:o});return null===t.deepLinkProvider?i:t.deepLinkProvider.serializeStateToQueryParams(this.store).pipe(It(((e,n)=>({routeKind:t.routeKind,params:t.params,pathname:t.pathname,queryParams:e,navigationOptions:0===n?o:Object.assign(Object.assign({},o),{replaceState:!0})}))))})),Fe((t=>{this.store.dispatch(cS({after:t}))})),ge(0)).pipe(Ve(this.store.select(SS)),It((([t,e])=>({preserveHash:null===e||null===t||Qk(e.routeKind,e.params)===Qk(t.routeKind,t.params),route:t}))),Fe((({preserveHash:t,route:e})=>{!(function n(t,e){return t.pathname===e.pathname&&t.queryParams.length===e.queryParams.length&&t.queryParams.every(((t,n)=>{const o=e.queryParams[n];return t.key===o.key&&t.value===o.value}))})(e,{pathname:this.appRootProvider.getAppRootlessPathname(this.location.getPath()),queryParams:this.location.getSearch()})&&(e.navigationOptions.replaceState?this.location.replaceState(this.appRootProvider.getAbsPathnameWithAppRoot(this.location.getFullPathFromRouteOrNav(e,t))):this.location.pushState(this.appRootProvider.getAbsPathnameWithAppRoot(this.location.getFullPathFromRouteOrNav(e,t))))}))).pipe(Ve(this.store.select(SS)),It((([{route:t},e])=>dS({before:e,after:t})))))),this.routeConfigs=i.getRouteConfigs()}ngrxOnInitEffects(){return IS()}}HS.ɵfac=function t(e){return new(e||HS)(vr(Sk),vr(Iw),vr(tS),vr(mS),vr(PS),vr(fS),vr(eS))},HS.ɵprov=Mn({token:HS,factory:HS.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(HS,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:tS},{type:mS},{type:PS},{type:fS},{type:eS}]}),null);const FS=yk({activeRoute:null,nextRoute:null,registeredRouteKeys:new Set},bk(cS,((t,{after:e})=>Object.assign(Object.assign({},t),{nextRoute:e}))),bk(dS,((t,{after:e})=>Object.assign(Object.assign({},t),{activeRoute:e,nextRoute:null}))),bk(sS,((t,{routeKinds:e})=>Object.assign(Object.assign({},t),{registeredRouteKeys:e}))));function LS(t,e){return FS(t,e)}class BS{}BS.ɵfac=function t(e){return new(e||BS)},BS.ɵmod=ao({type:BS}),BS.ɵinj=vn({providers:[mS,fS],imports:[[PS,dk.forFeature(wS,LS),Wk.forFeature([HS]),iS,oS]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(BS,[{type:Ay,args:[{imports:[PS,dk.forFeature(wS,LS),Wk.forFeature([HS]),iS,oS],providers:[mS,fS]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(BS,{imports:[PS,ck,Gk,iS,oS]});class VS{}const jS="__tab__";class US{constructor(){this.tfStorage=document.createElement("tf-storage"),document.createElement("tf-globals").tf_globals.setUseHash(!0),this.tfStorage.tf_storage.migrateLegacyURLScheme()}getString(t){return this.tfStorage.tf_storage.getString(t)}setString(t,e,n){this.tfStorage.tf_storage.setString(t,e,n)}getPluginId(){return this.getString(jS)}setPluginId(t,e){this.setString(jS,t,e)}}var GS;US.ɵfac=function t(e){return new(e||US)},US.ɵprov=Mn({token:US,factory:US.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(US,[{type:im}],(function(){return[]}),null),(function(t){t.BROWSER_DEFAULT="browser_default",t.LIGHT="light",t.DARK="dark"})(GS||(GS={}));const WS=JP("[Persistent Settings] Global Settings Loaded",{_as:"props",_p:void 0}),YS=new Ga("[Persistent Settings] Global Settings");class qS{constructor(t){this.globalSettingSelectors=[],t&&(this.globalSettingSelectors=t.map((t=>t())))}getGlobalSettingSelectors(){var t;return null!==(t=this.globalSettingSelectors)&&void 0!==t?t:[]}static defineGlobalSetting(t){return{ngModule:qS,providers:[{provide:YS,multi:!0,useValue:t}]}}}qS.ɵfac=function t(e){return new(e||qS)(vr(YS,8))},qS.ɵmod=ao({type:qS}),qS.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(qS,[{type:Ay}],(function(){return[{type:Array,decorators:[{type:Sr},{type:kr,args:[YS]}]}]}),null);class ZS{setItem(t,e){localStorage.setItem(t,e)}getItem(t){return localStorage.getItem(t)}removeItem(t){localStorage.removeItem(t)}}ZS.ɵfac=function t(e){return new(e||ZS)},ZS.ɵprov=Mn({token:ZS,factory:ZS.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ZS,[{type:im}],null,null);class XS{}XS.ɵfac=function t(e){return new(e||XS)},XS.ɵmod=ao({type:XS}),XS.ɵinj=vn({providers:[ZS]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(XS,[{type:Ay,args:[{providers:[ZS]}]}],null,null);const KS="_tb_global_settings.timeseries",JS="_tb_global_settings",QS="notificationLastReadTimestamp";class $S{}$S.ɵfac=function t(e){return new(e||$S)},$S.ɵprov=Mn({token:$S,factory:$S.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh($S,[{type:im}],null,null);class tD{}tD.ɵfac=function t(e){return new(e||tD)},tD.ɵprov=Mn({token:tD,factory:tD.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(tD,[{type:im}],null,null);class eD extends tD{uiToBackend(t){return{ignoreOutliers:t.ignoreOutliers,scalarSmoothing:t.scalarSmoothing,tooltipSort:t.tooltipSortString,autoReload:t.autoReload,autoReloadPeriodInMs:t.autoReloadPeriodInMs,paginationSize:t.pageSize,theme:t.themeOverride,notificationLastReadTimeInMs:t.notificationLastReadTimeInMs,sideBarWidthInPercent:t.sideBarWidthInPercent,timeSeriesPromotionDismissed:t.timeSeriesPromotionDismissed,timeSeriesSettingsPaneOpened:t.timeSeriesSettingsPaneOpened}}backendToUi(t){const e={};return t.hasOwnProperty("scalarSmoothing")&&"number"==typeof t.scalarSmoothing&&(e.scalarSmoothing=t.scalarSmoothing),t.hasOwnProperty("ignoreOutliers")&&"boolean"==typeof t.ignoreOutliers&&(e.ignoreOutliers=t.ignoreOutliers),t.hasOwnProperty("tooltipSort")&&"string"==typeof t.tooltipSort&&(e.tooltipSortString=t.tooltipSort),t.hasOwnProperty("autoReload")&&"boolean"==typeof t.autoReload&&(e.autoReload=t.autoReload),t.hasOwnProperty("autoReloadPeriodInMs")&&"number"==typeof t.autoReloadPeriodInMs&&(e.autoReloadPeriodInMs=t.autoReloadPeriodInMs),t.hasOwnProperty("paginationSize")&&"number"==typeof t.paginationSize&&(e.pageSize=t.paginationSize),t.hasOwnProperty("theme")&&"string"==typeof t.theme&&new Set(Object.values(GS)).has(t.theme)&&(e.themeOverride=t.theme),t.hasOwnProperty("notificationLastReadTimeInMs")&&"number"==typeof t.notificationLastReadTimeInMs&&(e.notificationLastReadTimeInMs=t.notificationLastReadTimeInMs),t.hasOwnProperty("sideBarWidthInPercent")&&"number"==typeof t.sideBarWidthInPercent&&(e.sideBarWidthInPercent=t.sideBarWidthInPercent),t.hasOwnProperty("timeSeriesPromotionDismissed")&&"boolean"==typeof t.timeSeriesPromotionDismissed&&(e.timeSeriesPromotionDismissed=t.timeSeriesPromotionDismissed),t.hasOwnProperty("timeSeriesSettingsPaneOpened")&&"boolean"==typeof t.timeSeriesSettingsPaneOpened&&(e.timeSeriesSettingsPaneOpened=t.timeSeriesSettingsPaneOpened),e}}eD.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(eD)))(n||eD)}})(),eD.ɵprov=Mn({token:eD,factory:eD.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(eD,[{type:im}],null,null);class nD{constructor(t,e){this.localStorage=t,this.converter=e}setSettings(t){return Object.keys(t)?this.getSettings().pipe(Fe((e=>{this.localStorage.setItem(JS,JSON.stringify(this.converter.uiToBackend(Object.assign(Object.assign({},e),t)))),this.localStorage.removeItem(KS),this.localStorage.removeItem(QS)})),It((()=>{}))):rt}deserialize(t){try{return JSON.parse(t)}catch(t){return{}}}getSettings(){var t,e;const n=this.localStorage.getItem(QS),o=this.converter.backendToUi(this.deserialize(n?JSON.stringify({notificationLastReadTimeInMs:Number(n)}):"{}")),i=this.converter.backendToUi(this.deserialize(null!==(t=this.localStorage.getItem(KS))&&void 0!==t?t:"{}")),a=this.converter.backendToUi(this.deserialize(null!==(e=this.localStorage.getItem(JS))&&void 0!==e?e:"{}"));return Et(Object.assign(Object.assign(Object.assign({},o),i),a))}}nD.ɵfac=function t(e){return new(e||nD)(vr(ZS),vr(tD))},nD.ɵprov=Mn({token:nD,factory:nD.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(nD,[{type:im}],(function(){return[{type:ZS},{type:tD}]}),null);class oD{}oD.ɵfac=function t(e){return new(e||oD)},oD.ɵmod=ao({type:oD}),oD.ɵinj=vn({providers:[{provide:$S,useClass:nD},{provide:tD,useClass:eD}],imports:[[XS]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(oD,[{type:Ay,args:[{imports:[XS],providers:[{provide:$S,useClass:nD},{provide:tD,useClass:eD}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(oD,{imports:[XS]});const iD=JP("[Persistent Settings] Effects Init");class aD{constructor(t,e,n,o){this.actions$=t,this.store=e,this.configModule=n,this.dataSource=o,this.initializeAndUpdateSettings$=Mk((()=>{const t=this.actions$.pipe(Dk(iD),Zt((()=>this.dataSource.getSettings())),Fe((t=>{this.store.dispatch(WS({partialSettings:t}))})),Ce(0),Zt((()=>re(...this.configModule.getGlobalSettingSelectors().map((t=>this.store.select(t).pipe(Me(((t,e)=>{const n=Object.values(t),o=Object.values(e);return n.length===o.length&&n.every(((t,e)=>t===o[e]))})),Te(1))))))),Ee());return t.pipe((function e(t){return R((function(e,n){var o=[];return e.subscribe(new T(n,(function(t){return o.push(t)}),(function(){n.next(o),n.complete()}))),t.subscribe(new T(n,(function(){var t=o;o=[],n.next(t)}),y)),function(){o=null}}))})(t.pipe(ge(500))),Zt((t=>{const e={};for(const n of t)Object.assign(e,n);return this.dataSource.setSettings(e)})))}),{dispatch:!1})}ngrxOnInitEffects(){return iD()}}aD.ɵfac=function t(e){return new(e||aD)(vr(Sk),vr(Iw),vr(qS),vr($S))},aD.ɵprov=Mn({token:aD,factory:aD.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(aD,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:qS},{type:$S}]}),null);class rD{}rD.ɵfac=function t(e){return new(e||rD)},rD.ɵmod=ao({type:rD}),rD.ɵinj=vn({providers:[qS],imports:[[Wk.forFeature([aD]),oD]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(rD,[{type:Ay,args:[{imports:[Wk.forFeature([aD]),oD],providers:[qS]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(rD,{imports:[Gk,oD]});
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
class sD{}class lD{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class cD{constructor(t){this.normalizedNames=new Map,this.lazyUpdate=null,t?this.lazyInit="string"==typeof t?()=>{this.headers=new Map,t.split("\n").forEach((t=>{const e=t.indexOf(":");if(e>0){const n=t.slice(0,e),o=n.toLowerCase(),i=t.slice(e+1).trim();this.maybeSetNormalizedName(n,o),this.headers.has(o)?this.headers.get(o).push(i):this.headers.set(o,[i])}}))}:()=>{this.headers=new Map,Object.keys(t).forEach((e=>{let n=t[e];const o=e.toLowerCase();"string"==typeof n&&(n=[n]),n.length>0&&(this.headers.set(o,n),this.maybeSetNormalizedName(e,o))}))}:this.headers=new Map}has(t){return this.init(),this.headers.has(t.toLowerCase())}get(t){this.init();const e=this.headers.get(t.toLowerCase());return e&&e.length>0?e[0]:null}keys(){return this.init(),Array.from(this.normalizedNames.values())}getAll(t){return this.init(),this.headers.get(t.toLowerCase())||null}append(t,e){return this.clone({name:t,value:e,op:"a"})}set(t,e){return this.clone({name:t,value:e,op:"s"})}delete(t,e){return this.clone({name:t,value:e,op:"d"})}maybeSetNormalizedName(t,e){this.normalizedNames.has(e)||this.normalizedNames.set(e,t)}init(){this.lazyInit&&(this.lazyInit instanceof cD?this.copyFrom(this.lazyInit):this.lazyInit(),this.lazyInit=null,this.lazyUpdate&&(this.lazyUpdate.forEach((t=>this.applyUpdate(t))),this.lazyUpdate=null))}copyFrom(t){t.init(),Array.from(t.headers.keys()).forEach((e=>{this.headers.set(e,t.headers.get(e)),this.normalizedNames.set(e,t.normalizedNames.get(e))}))}clone(t){const e=new cD;return e.lazyInit=this.lazyInit&&this.lazyInit instanceof cD?this.lazyInit:this,e.lazyUpdate=(this.lazyUpdate||[]).concat([t]),e}applyUpdate(t){const e=t.name.toLowerCase();switch(t.op){case"a":case"s":let n=t.value;if("string"==typeof n&&(n=[n]),0===n.length)return;this.maybeSetNormalizedName(t.name,e);const o=("a"===t.op?this.headers.get(e):void 0)||[];o.push(...n),this.headers.set(e,o);break;case"d":const i=t.value;if(i){let t=this.headers.get(e);if(!t)return;t=t.filter((t=>-1===i.indexOf(t))),0===t.length?(this.headers.delete(e),this.normalizedNames.delete(e)):this.headers.set(e,t)}else this.headers.delete(e),this.normalizedNames.delete(e)}}forEach(t){this.init(),Array.from(this.normalizedNames.keys()).forEach((e=>t(this.normalizedNames.get(e),this.headers.get(e))))}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class dD{encodeKey(t){return pD(t)}encodeValue(t){return pD(t)}decodeKey(t){return decodeURIComponent(t)}decodeValue(t){return decodeURIComponent(t)}}function pD(t){return encodeURIComponent(t).replace(/%40/gi,"@").replace(/%3A/gi,":").replace(/%24/gi,"$").replace(/%2C/gi,",").replace(/%3B/gi,";").replace(/%2B/gi,"+").replace(/%3D/gi,"=").replace(/%3F/gi,"?").replace(/%2F/gi,"/")}function mD(t){return`${t}`}class uD{constructor(t={}){if(this.updates=null,this.cloneFrom=null,this.encoder=t.encoder||new dD,t.fromString){if(t.fromObject)throw new Error("Cannot specify both fromString and fromObject.");this.map=(function e(t,n){const o=new Map;return t.length>0&&t.replace(/^\?/,"").split("&").forEach((t=>{const e=t.indexOf("="),[i,a]=-1==e?[n.decodeKey(t),""]:[n.decodeKey(t.slice(0,e)),n.decodeValue(t.slice(e+1))],r=o.get(i)||[];r.push(a),o.set(i,r)})),o})(t.fromString,this.encoder)}else t.fromObject?(this.map=new Map,Object.keys(t.fromObject).forEach((e=>{const n=t.fromObject[e];this.map.set(e,Array.isArray(n)?n:[n])}))):this.map=null}has(t){return this.init(),this.map.has(t)}get(t){this.init();const e=this.map.get(t);return e?e[0]:null}getAll(t){return this.init(),this.map.get(t)||null}keys(){return this.init(),Array.from(this.map.keys())}append(t,e){return this.clone({param:t,value:e,op:"a"})}appendAll(t){const e=[];return Object.keys(t).forEach((n=>{const o=t[n];Array.isArray(o)?o.forEach((t=>{e.push({param:n,value:t,op:"a"})})):e.push({param:n,value:o,op:"a"})})),this.clone(e)}set(t,e){return this.clone({param:t,value:e,op:"s"})}delete(t,e){return this.clone({param:t,value:e,op:"d"})}toString(){return this.init(),this.keys().map((t=>{const e=this.encoder.encodeKey(t);return this.map.get(t).map((t=>e+"="+this.encoder.encodeValue(t))).join("&")})).filter((t=>""!==t)).join("&")}clone(t){const e=new uD({encoder:this.encoder});return e.cloneFrom=this.cloneFrom||this,e.updates=(this.updates||[]).concat(t),e}init(){null===this.map&&(this.map=new Map),null!==this.cloneFrom&&(this.cloneFrom.init(),this.cloneFrom.keys().forEach((t=>this.map.set(t,this.cloneFrom.map.get(t)))),this.updates.forEach((t=>{switch(t.op){case"a":case"s":const e=("a"===t.op?this.map.get(t.param):void 0)||[];e.push(mD(t.value)),this.map.set(t.param,e);break;case"d":if(void 0===t.value){this.map.delete(t.param);break}{let e=this.map.get(t.param)||[];const n=e.indexOf(mD(t.value));-1!==n&&e.splice(n,1),e.length>0?this.map.set(t.param,e):this.map.delete(t.param)}}})),this.cloneFrom=this.updates=null)}}class fD{constructor(){this.map=new Map}set(t,e){return this.map.set(t,e),this}get(t){return this.map.has(t)||this.map.set(t,t.defaultValue()),this.map.get(t)}delete(t){return this.map.delete(t),this}keys(){return this.map.keys()}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function gD(t){return"undefined"!=typeof ArrayBuffer&&t instanceof ArrayBuffer}function hD(t){return"undefined"!=typeof Blob&&t instanceof Blob}function bD(t){return"undefined"!=typeof FormData&&t instanceof FormData}class yD{constructor(t,e,n,o){let i;if(this.url=e,this.body=null,this.reportProgress=!1,this.withCredentials=!1,this.responseType="json",this.method=t.toUpperCase(),(function a(t){switch(t){case"DELETE":case"GET":case"HEAD":case"OPTIONS":case"JSONP":return!1;default:return!0}})(this.method)||o?(this.body=void 0!==n?n:null,i=o):i=n,i&&(this.reportProgress=!!i.reportProgress,this.withCredentials=!!i.withCredentials,i.responseType&&(this.responseType=i.responseType),i.headers&&(this.headers=i.headers),i.context&&(this.context=i.context),i.params&&(this.params=i.params)),this.headers||(this.headers=new cD),this.context||(this.context=new fD),this.params){const t=this.params.toString();if(0===t.length)this.urlWithParams=e;else{const n=e.indexOf("?");this.urlWithParams=e+(-1===n?"?":n<e.length-1?"&":"")+t}}else this.params=new uD,this.urlWithParams=e}serializeBody(){return null===this.body?null:gD(this.body)||hD(this.body)||bD(this.body)||(function t(e){return"undefined"!=typeof URLSearchParams&&e instanceof URLSearchParams})(this.body)||"string"==typeof this.body?this.body:this.body instanceof uD?this.body.toString():"object"==typeof this.body||"boolean"==typeof this.body||Array.isArray(this.body)?JSON.stringify(this.body):this.body.toString()}detectContentTypeHeader(){return null===this.body||bD(this.body)?null:hD(this.body)?this.body.type||null:gD(this.body)?null:"string"==typeof this.body?"text/plain":this.body instanceof uD?"application/x-www-form-urlencoded;charset=UTF-8":"object"==typeof this.body||"number"==typeof this.body||"boolean"==typeof this.body?"application/json":null}clone(t={}){var e;const n=t.method||this.method,o=t.url||this.url,i=t.responseType||this.responseType,a=void 0!==t.body?t.body:this.body,r=void 0!==t.withCredentials?t.withCredentials:this.withCredentials,s=void 0!==t.reportProgress?t.reportProgress:this.reportProgress;let l=t.headers||this.headers,c=t.params||this.params;const d=null!==(e=t.context)&&void 0!==e?e:this.context;return void 0!==t.setHeaders&&(l=Object.keys(t.setHeaders).reduce(((e,n)=>e.set(n,t.setHeaders[n])),l)),t.setParams&&(c=Object.keys(t.setParams).reduce(((e,n)=>e.set(n,t.setParams[n])),c)),new yD(n,o,a,{params:c,headers:l,context:d,reportProgress:s,responseType:i,withCredentials:r})}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */var _D;!(function(t){t[t.Sent=0]="Sent",t[t.UploadProgress=1]="UploadProgress",t[t.ResponseHeader=2]="ResponseHeader",t[t.DownloadProgress=3]="DownloadProgress",t[t.Response=4]="Response",t[t.User=5]="User"})(_D||(_D={}));class CD{constructor(t,e=200,n="OK"){this.headers=t.headers||new cD,this.status=void 0!==t.status?t.status:e,this.statusText=t.statusText||n,this.url=t.url||null,this.ok=this.status>=200&&this.status<300}}class MD extends CD{constructor(t={}){super(t),this.type=_D.ResponseHeader}clone(t={}){return new MD({headers:t.headers||this.headers,status:void 0!==t.status?t.status:this.status,statusText:t.statusText||this.statusText,url:t.url||this.url||void 0})}}class vD extends CD{constructor(t={}){super(t),this.type=_D.Response,this.body=void 0!==t.body?t.body:null}clone(t={}){return new vD({body:void 0!==t.body?t.body:this.body,headers:t.headers||this.headers,status:void 0!==t.status?t.status:this.status,statusText:t.statusText||this.statusText,url:t.url||this.url||void 0})}}class xD extends CD{constructor(t){super(t,0,"Unknown Error"),this.name="HttpErrorResponse",this.ok=!1,this.message=this.status>=200&&this.status<300?`Http failure during parsing for ${t.url||"(unknown url)"}`:`Http failure response for ${t.url||"(unknown url)"}: ${t.status} ${t.statusText}`,this.error=t.error||null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function OD(t,e){return{body:e,headers:t.headers,context:t.context,observe:t.observe,params:t.params,reportProgress:t.reportProgress,responseType:t.responseType,withCredentials:t.withCredentials}}class PD{constructor(t){this.handler=t}request(t,e,n={}){let o;if(t instanceof yD)o=t;else{let i,a;i=n.headers instanceof cD?n.headers:new cD(n.headers),n.params&&(a=n.params instanceof uD?n.params:new uD({fromObject:n.params})),o=new yD(t,e,void 0!==n.body?n.body:null,{headers:i,context:n.context,params:a,reportProgress:n.reportProgress,responseType:n.responseType||"json",withCredentials:n.withCredentials})}const i=Et(o).pipe((function a(t,e){return l(e)?Zt(t,e,1):Zt(t,1)})((t=>this.handler.handle(t))));if(t instanceof yD||"events"===n.observe)return i;const r=i.pipe(ce((t=>t instanceof vD)));switch(n.observe||"body"){case"body":switch(o.responseType){case"arraybuffer":return r.pipe(It((t=>{if(null!==t.body&&!(t.body instanceof ArrayBuffer))throw new Error("Response is not an ArrayBuffer.");return t.body})));case"blob":return r.pipe(It((t=>{if(null!==t.body&&!(t.body instanceof Blob))throw new Error("Response is not a Blob.");return t.body})));case"text":return r.pipe(It((t=>{if(null!==t.body&&"string"!=typeof t.body)throw new Error("Response is not a string.");return t.body})));case"json":default:return r.pipe(It((t=>t.body)))}case"response":return r;default:throw new Error(`Unreachable: unhandled observe type ${n.observe}}`)}}delete(t,e={}){return this.request("DELETE",t,e)}get(t,e={}){return this.request("GET",t,e)}head(t,e={}){return this.request("HEAD",t,e)}jsonp(t,e){return this.request("JSONP",t,{params:(new uD).append(e,"JSONP_CALLBACK"),observe:"body",responseType:"json"})}options(t,e={}){return this.request("OPTIONS",t,e)}patch(t,e,n={}){return this.request("PATCH",t,OD(n,e))}post(t,e,n={}){return this.request("POST",t,OD(n,e))}put(t,e,n={}){return this.request("PUT",t,OD(n,e))}}PD.ɵfac=function t(e){return new(e||PD)(vr(sD))},PD.ɵprov=Mn({token:PD,factory:PD.ɵfac}),PD.ctorParameters=()=>[{type:sD}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(PD,[{type:im}],(function(){return[{type:sD}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class wD{constructor(t,e){this.next=t,this.interceptor=e}handle(t){return this.interceptor.intercept(t,this.next)}}const kD=new Ga("HTTP_INTERCEPTORS");class SD{intercept(t,e){return e.handle(t)}}SD.ɵfac=function t(e){return new(e||SD)},SD.ɵprov=Mn({token:SD,factory:SD.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(SD,[{type:im}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let DD=0;class ED{}class RD{constructor(t,e){this.callbackMap=t,this.document=e,this.resolvedPromise=Promise.resolve()}nextCallback(){return"ng_jsonp_callback_"+DD++}handle(t){if("JSONP"!==t.method)throw new Error("JSONP requests must use JSONP request method.");if("json"!==t.responseType)throw new Error("JSONP requests must use Json response type.");return new D((e=>{const n=this.nextCallback(),o=t.urlWithParams.replace(/=JSONP_CALLBACK(&|$)/,`=${n}$1`),i=this.document.createElement("script");i.src=o;let a=null,r=!1,s=!1;this.callbackMap[n]=t=>{delete this.callbackMap[n],s||(a=t,r=!0)};const l=()=>{i.parentNode&&i.parentNode.removeChild(i),delete this.callbackMap[n]},c=t=>{s||this.resolvedPromise.then((()=>{l(),r?(e.next(new vD({body:a,status:200,statusText:"OK",url:o})),e.complete()):e.error(new xD({url:o,status:0,statusText:"JSONP Error",error:new Error("JSONP injected script did not invoke callback.")}))}))},d=t=>{s||(l(),e.error(new xD({error:t,status:0,statusText:"JSONP Error",url:o})))};return i.addEventListener("load",c),i.addEventListener("error",d),this.document.body.appendChild(i),e.next({type:_D.Sent}),()=>{s=!0,i.removeEventListener("load",c),i.removeEventListener("error",d),l()}}))}}RD.ɵfac=function t(e){return new(e||RD)(vr(ED),vr(Z_))},RD.ɵprov=Mn({token:RD,factory:RD.ɵfac}),RD.ctorParameters=()=>[{type:ED},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(RD,[{type:im}],(function(){return[{type:ED},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);class AD{constructor(t){this.jsonp=t}intercept(t,e){return"JSONP"===t.method?this.jsonp.handle(t):e.handle(t)}}AD.ɵfac=function t(e){return new(e||AD)(vr(RD))},AD.ɵprov=Mn({token:AD,factory:AD.ɵfac}),AD.ctorParameters=()=>[{type:RD}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(AD,[{type:im}],(function(){return[{type:RD}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const TD=/^\)\]\}',?\n/;class ND{constructor(t){this.xhrFactory=t}handle(t){if("JSONP"===t.method)throw new Error("Attempted to construct Jsonp request without HttpClientJsonpModule installed.");return new D((e=>{const n=this.xhrFactory.build();if(n.open(t.method,t.urlWithParams),t.withCredentials&&(n.withCredentials=!0),t.headers.forEach(((t,e)=>n.setRequestHeader(t,e.join(",")))),t.headers.has("Accept")||n.setRequestHeader("Accept","application/json, text/plain, */*"),!t.headers.has("Content-Type")){const e=t.detectContentTypeHeader();null!==e&&n.setRequestHeader("Content-Type",e)}if(t.responseType){const e=t.responseType.toLowerCase();n.responseType="json"!==e?e:"text"}const o=t.serializeBody();let i=null;const a=()=>{if(null!==i)return i;const e=1223===n.status?204:n.status,o=n.statusText||"OK",a=new cD(n.getAllResponseHeaders()),r=(function s(t){return"responseURL"in t&&t.responseURL?t.responseURL:/^X-Request-URL:/m.test(t.getAllResponseHeaders())?t.getResponseHeader("X-Request-URL"):null})(n)||t.url;return i=new MD({headers:a,status:e,statusText:o,url:r}),i},r=()=>{let{headers:o,status:i,statusText:r,url:s}=a(),l=null;204!==i&&(l=void 0===n.response?n.responseText:n.response),0===i&&(i=l?200:0);let c=i>=200&&i<300;if("json"===t.responseType&&"string"==typeof l){const t=l;l=l.replace(TD,"");try{l=""!==l?JSON.parse(l):null}catch(e){l=t,c&&(c=!1,l={error:e,text:l})}}c?(e.next(new vD({body:l,headers:o,status:i,statusText:r,url:s||void 0})),e.complete()):e.error(new xD({error:l,headers:o,status:i,statusText:r,url:s||void 0}))},s=t=>{const{url:o}=a(),i=new xD({error:t,status:n.status||0,statusText:n.statusText||"Unknown Error",url:o||void 0});e.error(i)};let l=!1;const c=o=>{l||(e.next(a()),l=!0);let i={type:_D.DownloadProgress,loaded:o.loaded};o.lengthComputable&&(i.total=o.total),"text"===t.responseType&&n.responseText&&(i.partialText=n.responseText),e.next(i)},d=t=>{let n={type:_D.UploadProgress,loaded:t.loaded};t.lengthComputable&&(n.total=t.total),e.next(n)};return n.addEventListener("load",r),n.addEventListener("error",s),n.addEventListener("timeout",s),n.addEventListener("abort",s),t.reportProgress&&(n.addEventListener("progress",c),null!==o&&n.upload&&n.upload.addEventListener("progress",d)),n.send(o),e.next({type:_D.Sent}),()=>{n.removeEventListener("error",s),n.removeEventListener("abort",s),n.removeEventListener("load",r),n.removeEventListener("timeout",s),t.reportProgress&&(n.removeEventListener("progress",c),null!==o&&n.upload&&n.upload.removeEventListener("progress",d)),n.readyState!==n.DONE&&n.abort()}}))}}ND.ɵfac=function t(e){return new(e||ND)(vr(XM))},ND.ɵprov=Mn({token:ND,factory:ND.ɵfac}),ND.ctorParameters=()=>[{type:XM}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(ND,[{type:im}],(function(){return[{type:XM}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const zD=new Ga("XSRF_COOKIE_NAME"),ID=new Ga("XSRF_HEADER_NAME");class HD{}class FD{constructor(t,e,n){this.doc=t,this.platform=e,this.cookieName=n,this.lastCookieString="",this.lastToken=null,this.parseCount=0}getToken(){if("server"===this.platform)return null;const t=this.doc.cookie||"";return t!==this.lastCookieString&&(this.parseCount++,this.lastToken=iM(t,this.cookieName),this.lastCookieString=t),this.lastToken}}FD.ɵfac=function t(e){return new(e||FD)(vr(Z_),vr(jy),vr(zD))},FD.ɵprov=Mn({token:FD,factory:FD.ɵfac}),FD.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:String,decorators:[{type:kr,args:[jy]}]},{type:String,decorators:[{type:kr,args:[zD]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(FD,[{type:im}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:String,decorators:[{type:kr,args:[jy]}]},{type:String,decorators:[{type:kr,args:[zD]}]}]}),null);class LD{constructor(t,e){this.tokenService=t,this.headerName=e}intercept(t,e){const n=t.url.toLowerCase();if("GET"===t.method||"HEAD"===t.method||n.startsWith("http://")||n.startsWith("https://"))return e.handle(t);const o=this.tokenService.getToken();return null===o||t.headers.has(this.headerName)||(t=t.clone({headers:t.headers.set(this.headerName,o)})),e.handle(t)}}LD.ɵfac=function t(e){return new(e||LD)(vr(HD),vr(ID))},LD.ɵprov=Mn({token:LD,factory:LD.ɵfac}),LD.ctorParameters=()=>[{type:HD},{type:String,decorators:[{type:kr,args:[ID]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(LD,[{type:im}],(function(){return[{type:HD},{type:String,decorators:[{type:kr,args:[ID]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class BD{constructor(t,e){this.backend=t,this.injector=e,this.chain=null}handle(t){if(null===this.chain){const t=this.injector.get(kD,[]);this.chain=t.reduceRight(((t,e)=>new wD(t,e)),this.backend)}return this.chain.handle(t)}}function VD(){return"object"==typeof window?window:{}}BD.ɵfac=function t(e){return new(e||BD)(vr(lD),vr(rp))},BD.ɵprov=Mn({token:BD,factory:BD.ɵfac}),BD.ctorParameters=()=>[{type:lD},{type:rp}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(BD,[{type:im}],(function(){return[{type:lD},{type:rp}]}),null);class jD{static disable(){return{ngModule:jD,providers:[{provide:LD,useClass:SD}]}}static withOptions(t={}){return{ngModule:jD,providers:[t.cookieName?{provide:zD,useValue:t.cookieName}:[],t.headerName?{provide:ID,useValue:t.headerName}:[]]}}}jD.ɵfac=function t(e){return new(e||jD)},jD.ɵmod=ao({type:jD}),jD.ɵinj=vn({providers:[LD,{provide:kD,useExisting:LD,multi:!0},{provide:HD,useClass:FD},{provide:zD,useValue:"XSRF-TOKEN"},{provide:ID,useValue:"X-XSRF-TOKEN"}]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(jD,[{type:Ay,args:[{providers:[LD,{provide:kD,useExisting:LD,multi:!0},{provide:HD,useClass:FD},{provide:zD,useValue:"XSRF-TOKEN"},{provide:ID,useValue:"X-XSRF-TOKEN"}]}]}],null,null);class UD{}UD.ɵfac=function t(e){return new(e||UD)},UD.ɵmod=ao({type:UD}),UD.ɵinj=vn({providers:[PD,{provide:sD,useClass:BD},ND,{provide:lD,useExisting:ND}],imports:[[jD.withOptions({cookieName:"XSRF-TOKEN",headerName:"X-XSRF-TOKEN"})]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(UD,[{type:Ay,args:[{imports:[jD.withOptions({cookieName:"XSRF-TOKEN",headerName:"X-XSRF-TOKEN"})],providers:[PD,{provide:sD,useClass:BD},ND,{provide:lD,useExisting:ND}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(UD,{imports:[jD]});class GD{}GD.ɵfac=function t(e){return new(e||GD)},GD.ɵmod=ao({type:GD}),GD.ɵinj=vn({providers:[RD,{provide:ED,useFactory:VD},{provide:kD,useClass:AD,multi:!0}]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(GD,[{type:Ay,args:[{providers:[RD,{provide:ED,useFactory:VD},{provide:kD,useClass:AD,multi:!0}]}]}],null,null);const WD="feature",YD=Kw(WD),qD=Zw(YD,(t=>t.isFeatureFlagsLoaded)),ZD=Zw(YD,(t=>Object.assign(Object.assign({},t.defaultFlags),t.flagOverrides))),XD=Zw(YD,(t=>t.flagOverrides||{})),KD=Zw(ZD,(t=>t.isAutoDarkModeAllowed)),JD=Zw(ZD,(t=>null!==t.enableDarkModeOverride?t.enableDarkModeOverride:t.defaultEnableDarkMode)),QD=Zw(ZD,(t=>t.enableDarkModeOverride)),$D=Zw(ZD,(t=>t.enabledExperimentalPlugins)),tE=Zw(ZD,(t=>t.inColab)),eE=Zw(ZD,(t=>t.enabledColorGroup)),nE=Zw(ZD,(t=>t.enabledColorGroupByRegex)),oE=Zw(ZD,(t=>t.metricsImageSupportEnabled)),iE=Zw(ZD,(t=>t.enabledLinkedTime)),aE=Zw(ZD,(t=>t.enableTimeSeriesPromotion));function rE(t){const e={};for(const[n,o]of t.entries())e[n]=o;return e}function sE(t){let e=t.headers||new cD;return e=e.append("X-XSRF-Protected","1"),Object.assign(Object.assign({},t),{headers:e})}class lE{constructor(t,e,n){this.appRootProvider=t,this.http=e,this.store=n}resolveAppRoot(t){return t.startsWith("/")?this.appRootProvider.getAbsPathnameWithAppRoot(t):t}get(t,e={}){return this.http.get(this.resolveAppRoot(t),e)}post(t,e,n={}){return n=sE(n),this.store.select(qD).pipe(ce((t=>Boolean(t))),be(1),Ve(this.store.select(tE)),Zt((([,o])=>{const i=this.resolveAppRoot(t);return o?this.http.get(i,{headers:n.headers,params:rE(e)}):this.http.post(i,e,n)})))}put(t,e,n={}){return this.http.put(this.resolveAppRoot(t),e,sE(n))}delete(t,e={}){return this.http.delete(this.resolveAppRoot(t),sE(e))}}lE.ɵfac=function t(e){return new(e||lE)(vr(eS),vr(PD),vr(Iw))},lE.ɵprov=Mn({token:lE,factory:lE.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lE,[{type:im}],(function(){return[{type:eS},{type:PD},{type:Iw}]}),null);class cE{}var dE,pE,mE;cE.ɵfac=function t(e){return new(e||cE)},cE.ɵmod=ao({type:cE}),cE.ɵinj=vn({providers:[lE],imports:[[UD,iS]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(cE,[{type:Ay,args:[{imports:[UD,iS],providers:[lE]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(cE,{imports:[UD,iS]}),(function(t){t.STEP="step",t.WALL_TIME="wall_time",t.RELATIVE="relative"})(dE||(dE={})),(function(t){t.OFFSET="offset",t.OVERLAY="overlay"})(pE||(pE={})),(function(t){t.UNKNOWN="UNKNOWN",t.NOT_FOUND="NOT_FOUND"})(mE||(mE={}));const uE=new Ga("TensorBoard brand name");function fE(t){let e=mE.UNKNOWN;return t instanceof xD&&404===t.status&&(e=mE.NOT_FOUND),Rt(new gE(e))}class gE{constructor(t){this.failureCode=t}}class hE{constructor(t){this.http=t,this.tfBackend=document.createElement("tf-backend").tf_backend}fetchPluginsListing(t){const e=(function n(t){if(!t.length)return null;const e=new URLSearchParams;for(const n of t)e.append("experimentalPlugin",n);return e})(t),o=e?`data/plugins_listing?${e.toString()}`:"data/plugins_listing";return this.http.get(o).pipe(pe(fE))}fetchEnvironment(){return $t([this.http.get("data/environment"),Ct(this.tfBackend.environmentStore.refresh())]).pipe(It((([t])=>t)),pe(fE))}}hE.ɵfac=function t(e){return new(e||hE)(vr(lE))},hE.ɵprov=Mn({token:hE,factory:hE.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(hE,[{type:im}],(function(){return[{type:lE}]}),null);class bE{}var yE;bE.ɵfac=function t(e){return new(e||bE)},bE.ɵmod=ao({type:bE}),bE.ɵinj=vn({providers:[hE],imports:[[cE]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(bE,[{type:Ay,args:[{imports:[cE],providers:[hE]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(bE,{imports:[cE]}),(function(t){t[t.NOT_LOADED=0]="NOT_LOADED",t[t.LOADED=1]="LOADED",t[t.LOADING=2]="LOADING",t[t.FAILED=3]="FAILED"})(yE||(yE={}));const _E=JP("[Core] Plugin Changed",{_as:"props",_p:void 0}),CE=JP("[Core] Plugin Url Hash Changed",{_as:"props",_p:void 0}),ME=JP("[Core] Loaded"),vE=JP("[Core] User Triggered Reload"),xE=JP("[Core] Auto Reload"),OE=JP("[Core] PluginListing Fetch Requested"),PE=JP("[Core] PluginListing Fetch Successful",{_as:"props",_p:void 0}),wE=JP("[Core] PluginListing Fetch Failed",{_as:"props",_p:void 0}),kE=JP("[Core] Polymer Component Runs Fetch Requested"),SE=JP("[Core] Polymer Component Runs Fetch Successful"),DE=JP("[Core] Polymer Component Runs Fetch Failed"),EE=JP("[Core] Environment Fetch Successful",{_as:"props",_p:void 0}),RE=JP("[Core] Run Selection Changed",{_as:"props",_p:void 0}),AE=JP("[Core] Run Fetch Successful",{_as:"props",_p:void 0}),TE=JP("[Core] Side Bar Width Changed",{_as:"props",_p:void 0}),NE=JP("[Metrics] Metrics Settings Pane Closed"),zE=JP("[Metrics] Metrics Settings Pane Toggled"),IE=JP("[Metrics] Metrics Tag Metadata Requested"),HE=JP("[Metrics] Metrics Tag Metadata Loaded",{_as:"props",_p:void 0}),FE=JP("[Metrics] Metrics Tag Metadata Failed"),LE=JP("[Metrics] Metrics Settings Change Tooltip",{_as:"props",_p:void 0});JP("[Metrics] Metrics Settings Toggle Show Data Download");const BE=JP("[Metrics] Metrics Setting Toggle Ignore Outlier"),VE=JP("[Metrics] Metrics Setting Change X Axis Type",{_as:"props",_p:void 0}),jE=JP("[Metrics] Metrics Setting Change Scalar Smoothing",{_as:"props",_p:void 0}),UE=JP("[Metrics] Metrics Setting Partition Non Monotonic X Toggled"),GE=JP("[Metrics] Metrics Setting Change Image Brightness",{_as:"props",_p:void 0}),WE=JP("[Metrics] Metrics Setting Change Image Contrast",{_as:"props",_p:void 0}),YE=JP("[Metrics] Image Brightness Setting Reset"),qE=JP("[Metrics] Image Contrast Setting Reset"),ZE=JP("[Metrics] Metrics Setting Toggle Image Show Actual Size"),XE=JP("[Metrics] Metrics Setting Change Histogram Mode",{_as:"props",_p:void 0}),KE=JP("[Metrics] Multiple Time Series Requested",{_as:"props",_p:void 0}),JE=JP("[Metrics] Fetch Time Series Request Failed",{_as:"props",_p:void 0}),QE=JP("[Metrics] Fetch Time Series Response Loaded",{_as:"props",_p:void 0}),$E=JP("[Metrics] Card Visibility Changed",{_as:"props",_p:void 0}),tR=JP("[Metrics] Card Step Slider Changed",{_as:"props",_p:void 0}),eR=JP("[Metrics] Tag Filter Changed",{_as:"props",_p:void 0}),nR=JP("[Metrics] Metrics Tag Group Expansion Changed",{_as:"props",_p:void 0}),oR=JP("[Metrics] Card Pin State Toggled",{_as:"props",_p:void 0}),iR=JP("[Metrics] Toggle Visible Plugin",{_as:"props",_p:void 0}),aR=JP("[Metrics] Toggle Show All Plugins"),rR=JP("[Metrics] Linked Time Selection Changed",{_as:"props",_p:void 0}),sR=JP("[Metrics] Linked Time Selection Cleared"),lR=JP("[Metrics] Select Time Enable Toggle"),cR=JP("[Metrics] Use Range Select Time Toggle"),dR=JP("[Metrics] Metrics Promo Dismissed"),pR=JP("[Metrics] Metrics Promo Go To Scalars"),mR="core",uR={activePlugin:null,plugins:{},coreDataLoadState:{state:yE.NOT_LOADED,lastLoadedTimeInMs:null},pluginsListLoaded:{state:yE.NOT_LOADED,lastLoadedTimeInMs:null,failureCode:null},environment:{data_location:"",window_title:""},polymerRunsLoadState:{state:yE.NOT_LOADED,lastLoadedTimeInMs:null},polymerInteropRuns:[],polymerInteropRunSelection:new Set,sideBarWidthInPercent:20},fR=yk(uR,bk(_E,CE,((t,{plugin:e})=>Object.assign(Object.assign({},t),{activePlugin:e}))),bk(OE,(t=>Object.assign(Object.assign({},t),{coreDataLoadState:Object.assign(Object.assign({},t.coreDataLoadState),{state:yE.LOADING}),pluginsListLoaded:Object.assign(Object.assign({},t.pluginsListLoaded),{state:yE.LOADING})}))),bk(wE,((t,{failureCode:e})=>Object.assign(Object.assign({},t),{coreDataLoadState:Object.assign(Object.assign({},t.coreDataLoadState),{state:yE.FAILED}),pluginsListLoaded:Object.assign(Object.assign({},t.pluginsListLoaded),{state:yE.FAILED,failureCode:e})}))),bk(PE,((t,{plugins:e})=>{const n=Object.keys(e).find((t=>e[t].enabled))||null,o=t.activePlugin||n,i=Date.now();let a=t.coreDataLoadState;return t.polymerRunsLoadState.state===yE.LOADED&&(a={state:yE.LOADED,lastLoadedTimeInMs:i}),Object.assign(Object.assign({},t),{activePlugin:o,coreDataLoadState:a,plugins:e,pluginsListLoaded:{state:yE.LOADED,lastLoadedTimeInMs:i,failureCode:null}})})),bk(kE,(t=>Object.assign(Object.assign({},t),{coreDataLoadState:Object.assign(Object.assign({},t.coreDataLoadState),{state:yE.LOADING}),polymerRunsLoadState:Object.assign(Object.assign({},t.polymerRunsLoadState),{state:yE.LOADING})}))),bk(SE,(t=>{const e=Date.now();let n=t.coreDataLoadState;return t.pluginsListLoaded.state===yE.LOADED&&(n={state:yE.LOADED,lastLoadedTimeInMs:e}),Object.assign(Object.assign({},t),{coreDataLoadState:n,polymerRunsLoadState:Object.assign(Object.assign({},t.polymerRunsLoadState),{state:yE.LOADED,lastLoadedTimeInMs:e})})})),bk(DE,(t=>Object.assign(Object.assign({},t),{coreDataLoadState:Object.assign(Object.assign({},t.coreDataLoadState),{state:yE.FAILED}),polymerRunsLoadState:Object.assign(Object.assign({},t.polymerRunsLoadState),{state:yE.FAILED})}))),bk(EE,((t,{environment:e})=>Object.assign(Object.assign({},t),{environment:e}))),bk(AE,((t,{runs:e})=>Object.assign(Object.assign({},t),{polymerInteropRuns:e}))),bk(RE,((t,{nextSelection:e})=>Object.assign(Object.assign({},t),{polymerInteropRunSelection:new Set(e)}))),bk(TE,((t,{widthInPercent:e})=>Object.assign(Object.assign({},t),{sideBarWidthInPercent:Math.min(Math.max(0,e),100)}))),bk(WS,((t,{partialSettings:e})=>{const n=Object.assign({},t),o=e.sideBarWidthInPercent;return"number"==typeof o&&o>=0&&o<=100&&(n.sideBarWidthInPercent=o),n})),bk(pR,(t=>Object.assign(Object.assign({},t),{activePlugin:"scalars"}))));function gR(t,e){return fR(t,e)}const hR=Kw(mR),bR=Zw(hR,(t=>t.pluginsListLoaded)),yR=Zw(hR,(t=>t.polymerRunsLoadState)),_R=Zw(hR,(t=>t.coreDataLoadState.state)),CR=Zw(hR,(t=>t.coreDataLoadState.lastLoadedTimeInMs)),MR=Zw(hR,(t=>t.activePlugin)),vR=Zw(hR,(t=>t.plugins)),xR=Zw(hR,(t=>t.environment)),OR=Zw(hR,(t=>t.sideBarWidthInPercent)),PR=new Set([Zk.COMPARE_EXPERIMENT,Zk.EXPERIMENT,Zk.NOT_SET]);class wR{constructor(t,e,n){this.actions$=t,this.store=e,this.webappDataSource=n,this.tfBackend={ref:document.createElement("tf-backend").tf_backend},this.onDashboardLoad$=re(this.actions$.pipe(Dk(ME,dS),Ve(this.store.select(NS)),Me((([,t],[,e])=>t===e))),this.actions$.pipe(Dk(xE,vE))).pipe(Ve(this.store.select(RS)),ce((([,t])=>PR.has(t))),Be(1,void 0,{leading:!0})),this.fetchWebAppData$=Mk((()=>re(this.onDashboardLoad$.pipe(Ve(this.store.select(bR),this.store.select($D)),ce((([,{state:t}])=>t!==yE.LOADING)),Fe((()=>this.store.dispatch(OE()))),Zt((([,,t])=>(function e(){for(var t=[],e=0;e<arguments.length;e++)t[e]=arguments[e];var n=kt(t),o=le(t);return o.length?new D((function(t){var e=o.map((function(){return[]})),r=o.map((function(){return!1}));t.add((function(){e=r=null}));for(var s=function(s){Mt(o[s]).subscribe(new T(t,(function(o){if(e[s].push(o),e.every((function(t){return t.length}))){var l=e.map((function(t){return t.shift()}));t.next(n?n.apply(void 0,a([],i(l))):l),e.some((function(t,e){return!t.length&&r[e]}))&&t.complete()}}),(function(){r[s]=!0,!e[s].length&&t.complete()})))},l=0;!t.closed&&l<o.length;l++)s(l);return function(){e=r=null}})):rt})(this.webappDataSource.fetchPluginsListing(t),this.fetchEnvironment()).pipe(It((([t])=>{this.store.dispatch(PE({plugins:t}))})),pe((t=>(this.store.dispatch(wE(t instanceof gE?{failureCode:t.failureCode}:{failureCode:mE.UNKNOWN})),rt))))))),this.onDashboardLoad$.pipe(It((([,t])=>t)),ze((t=>t!==Zk.COMPARE_EXPERIMENT?Et([]):this.store.select(zS).pipe(Me(((t,e)=>{const n=Object.entries(t),o=new Map(Object.entries(e));if(n.length!==o.size)return!1;for(const[t,e]of n)if(o.get(t)!==e)return!1;return!0})),Ce(0),Be(500,void 0,{leading:!0,trailing:!0})))),Ve(this.store.select(RS),this.store.select(yR)),ce((([,t,e])=>PR.has(t)&&e.state!==yE.LOADING)),Fe((()=>{this.store.dispatch(kE())})),ze((()=>this.refreshPolymerRuns())),Fe((()=>{this.store.dispatch(SE())})),pe((()=>(this.store.dispatch(DE()),rt)))))),{dispatch:!1}),this.dispatchChangePlugin$=Mk((()=>re(this.onDashboardLoad$,this.actions$.pipe(Dk(PE))).pipe(Ve(this.store.select(MR)),It((([,t])=>t)),Me(),ce((t=>null!==t)),be(1),Fe((t=>{this.store.dispatch(_E({plugin:t}))})))),{dispatch:!1})}refreshPolymerRuns(){return Ct(this.tfBackend.ref.runsStore.refresh())}fetchEnvironment(){return this.webappDataSource.fetchEnvironment().pipe(Fe((t=>{this.store.dispatch(EE({environment:t}))})))}}wR.ɵfac=function t(e){return new(e||wR)(vr(Sk),vr(Iw),vr(hE))},wR.ɵprov=Mn({token:wR,factory:wR.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(wR,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:hE}]}),null);const kR=new Ga("Core Feature Config");function SR(t){return{initialState:Object.assign(Object.assign({},uR),{activePlugin:t.getPluginId()||null})}}function DR(){return Zw(OR,(t=>({sideBarWidthInPercent:t})))}class ER{}ER.ɵfac=function t(e){return new(e||ER)},ER.ɵmod=ao({type:ER}),ER.ɵinj=vn({providers:[{provide:kR,deps:[VS],useFactory:SR}],imports:[[Wk.forFeature([wR]),dk.forFeature(mR,gR,kR),bE,qS.defineGlobalSetting(DR)]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ER,[{type:Ay,args:[{imports:[Wk.forFeature([wR]),dk.forFeature(mR,gR,kR),bE,qS.defineGlobalSetting(DR)],providers:[{provide:kR,deps:[VS],useFactory:SR}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(ER,{imports:[Gk,ck,bE,qS]});const RR=new Ga("[Alert] Action-To-Alert Provider");class AR{constructor(t){this.providers=new Map;for(const e of t||[])for(const t of e){if(this.providers.has(t.actionCreator.type))throw new RangeError(`"${t.actionCreator.type}" is already registered for alerts. Multiple alerts for the same action is not allowed.`);this.providers.set(t.actionCreator.type,t.alertFromAction)}}getAlertFromAction(t){const e=this.providers.get(t.type);return e?e(t):null}static registerAlertActions(t){return{ngModule:AR,providers:[{provide:RR,multi:!0,useFactory:t}]}}}AR.ɵfac=function t(e){return new(e||AR)(vr(RR,8))},AR.ɵmod=ao({type:AR}),AR.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(AR,[{type:Ay,args:[{}]}],(function(){return[{type:void 0,decorators:[{type:Sr},{type:kr,args:[RR]}]}]}),null);const TR="hparams",NR=JP("[Runs] Fetch Runs Requested",{_as:"props",_p:void 0}),zR=JP("[Runs] Fetch Runs Succeeded",{_as:"props",_p:void 0}),IR=JP("[Runs] Fetch Runs Failed",{_as:"props",_p:void 0}),HR=JP("[Runs] Run Selection Toggled",{_as:"props",_p:void 0}),FR=JP("[Runs] Run Page Selection Toggled",{_as:"props",_p:void 0}),LR=JP("[Runs] Run Selector Pagination Option Changed",{_as:"props",_p:void 0}),BR=JP("[Runs] Run Selector Sort Changed",{_as:"props",_p:void 0}),VR=JP("[Runs] Run Selector Regex Filter Changed",{_as:"props",_p:void 0}),jR=JP("[Runs] Run Color Changed",{_as:"props",_p:void 0}),UR=JP("[Runs] Run Table Shown",{_as:"props",_p:void 0}),GR=JP("[Runs] Run Group By Changed",{_as:"props",_p:void 0});var WR,YR,qR,ZR;!(function(t){t.DATASET_UNKNOWN="DATASET_UNKNOWN",t.DATASET_TRAINING="DATASET_TRAINING",t.DATASET_VALIDATION="DATASET_VALIDATION"})(WR||(WR={})),(function(t){t.STATUS_UNKNOWN="STATUS_UNKNOWN",t.STATUS_SUCCESS="STATUS_SUCCESS",t.STATUS_FAILURE="STATUS_FAILURE",t.STATUS_RUNNING="STATUS_RUNNING"})(YR||(YR={})),(function(t){t.DATA_TYPE_UNSET="DATA_TYPE_UNSET",t.DATA_TYPE_STRING="DATA_TYPE_STRING",t.DATA_TYPE_BOOL="DATA_TYPE_BOOL",t.DATA_TYPE_FLOAT64="DATA_TYPE_FLOAT64"})(qR||(qR={})),(function(t){t[t.DISCRETE=0]="DISCRETE",t[t.INTERVAL=1]="INTERVAL"})(ZR||(ZR={}));class XR{}XR.ɵfac=function t(e){return new(e||XR)},XR.ɵprov=Mn({token:XR,factory:XR.ɵfac,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(XR,[{type:im,args:[{providedIn:"root"}]}],null,null);const KR=JP("[Hparams] Hparams Discrete Hparam Filter Changed",{_as:"props",_p:void 0}),JR=JP("[Hparams] Hparams Interval Hparam Filter Changed",{_as:"props",_p:void 0}),QR=JP("[Hparams] Hparams Metric Filter Changed",{_as:"props",_p:void 0});function $R(t){return JSON.stringify([...t].sort())}function tA(t){var e,n,o,i;const a=new Map,r=new Map,s=new Map;for(const a of t)for(const[t,l]of a)if(l.type===ZR.DISCRETE){const{possibleValues:e,values:n}=r.get(t)||{possibleValues:new Set,values:new Set};for(const t of l.filterValues)n.add(t);for(const t of l.possibleValues)e.add(t);r.set(t,{possibleValues:e,values:n})}else{const a=s.get(t);s.set(t,{filterLowerValue:Math.min(l.filterLowerValue,null!==(e=null==a?void 0:a.filterLowerValue)&&void 0!==e?e:1/0),filterUpperValue:Math.max(l.filterUpperValue,null!==(n=null==a?void 0:a.filterUpperValue)&&void 0!==n?n:-1/0),minValue:Math.min(l.minValue,null!==(o=null==a?void 0:a.minValue)&&void 0!==o?o:1/0),maxValue:Math.max(l.maxValue,null!==(i=null==a?void 0:a.maxValue)&&void 0!==i?i:-1/0)})}for(const[t,{values:e,possibleValues:n}]of r)a.set(t,{type:ZR.DISCRETE,includeUndefined:!0,possibleValues:[...n],filterValues:[...e]});for(const[t,{minValue:e,maxValue:n,filterLowerValue:o,filterUpperValue:i}]of s){if(a.has(t)){const e=a.get(t);if(e.type===ZR.DISCRETE&&e.possibleValues.some((t=>t)))throw new RangeError(`Cannot combine hparam, ${t}, as it is of mixed types.`)}a.set(t,{type:ZR.INTERVAL,includeUndefined:!0,minValue:e,maxValue:n,filterLowerValue:o,filterUpperValue:i})}return a}function eA(t){var e,n,o,i;const a=new Map;for(const r of t)for(const[t,s]of r){const r=a.get(t);a.set(t,Object.assign(Object.assign({type:ZR.INTERVAL,includeUndefined:!0},r),{minValue:Math.min(s.minValue,null!==(e=null==r?void 0:r.minValue)&&void 0!==e?e:1/0),maxValue:Math.max(s.maxValue,null!==(n=null==r?void 0:r.maxValue)&&void 0!==n?n:-1/0),filterLowerValue:Math.min(s.filterLowerValue,null!==(o=null==r?void 0:r.filterLowerValue)&&void 0!==o?o:1/0),filterUpperValue:Math.max(s.filterUpperValue,null!==(i=null==r?void 0:r.filterUpperValue)&&void 0!==i?i:-1/0)}))}return a}const nA=yk({specs:{},filters:{}},bk(KR,((t,e)=>{var n;const{experimentIds:o,hparamName:i,filterValues:a,includeUndefined:r}=e,s=$R(o),l=null!==(n=t.filters[s])&&void 0!==n?n:{hparams:new Map},c=l.hparams.get(i);if(c&&c.type!==ZR.DISCRETE)throw new RangeError(`New discrete filter of ${i} conflicts existing filter of `+ZR[c.type]);const d=tA(o.filter((e=>Boolean(t.specs[e]))).map((e=>t.specs[e].hparam.defaultFilters))).get(i);if(!d)throw new Error(`Cannot set hparam, ${i}, when it is not known for experimentIds: ${o.join(", ")}`);if(d.type!==ZR.DISCRETE)throw new Error(`Cannot set ${i} when default filter is not of discrete type.`);const p=new Set(d.possibleValues),m=[...a].filter((t=>!p.has(t)));if(m.length)throw new Error(`New filter for ${i} has more than one value that is not present in the spec. Bad values: ${m.join(", ")}`);const u=new Map(l.hparams);return u.set(i,Object.assign(Object.assign({},c),{type:ZR.DISCRETE,includeUndefined:r,possibleValues:[...p],filterValues:a})),Object.assign(Object.assign({},t),{filters:Object.assign(Object.assign({},t.filters),{[s]:Object.assign(Object.assign({},l),{hparams:u})})})})),bk(JR,((t,e)=>{var n;const{experimentIds:o,hparamName:i,filterLowerValue:a,filterUpperValue:r,includeUndefined:s}=e,l=$R(o),c=null!==(n=t.filters[l])&&void 0!==n?n:{metrics:new Map,hparams:new Map},d=c.hparams.get(i);if(d&&d.type!==ZR.INTERVAL)throw new RangeError(`New interval filter of ${i} conflicts existing filter of `+ZR[d.type]);const p=tA(o.filter((e=>Boolean(t.specs[e]))).map((e=>t.specs[e].hparam.defaultFilters))).get(i);if(!p)throw new Error(`Cannot set hpara, ${i}, when it is not known for experimentIds: ${o.join(", ")}`);if(p.type!==ZR.INTERVAL)throw new Error(`Cannot set ${i} when default filter is not of interval type.`);const m=new Map(c.hparams);return m.set(i,Object.assign(Object.assign({},d),{type:ZR.INTERVAL,includeUndefined:s,minValue:p.minValue,maxValue:p.maxValue,filterLowerValue:a,filterUpperValue:r})),Object.assign(Object.assign({},t),{filters:Object.assign(Object.assign({},t.filters),{[l]:Object.assign(Object.assign({},c),{hparams:m})})})})),bk(QR,((t,e)=>{var n;const{experimentIds:o,metricTag:i,filterLowerValue:a,filterUpperValue:r,includeUndefined:s}=e,l=$R(o),c=null!==(n=t.filters[l])&&void 0!==n?n:{metrics:new Map,hparams:new Map},d=eA(o.filter((e=>Boolean(t.specs[e]))).map((e=>t.specs[e].metric.defaultFilters))).get(i);if(!d)throw new Error(`Cannot set metric, ${i}, when it is not known for experimentIds: ${o.join(", ")}`);const p=c.metrics.get(i),m=new Map(c.metrics);return m.set(i,Object.assign(Object.assign({},p),{type:ZR.INTERVAL,includeUndefined:s,minValue:d.minValue,maxValue:d.maxValue,filterLowerValue:a,filterUpperValue:r})),Object.assign(Object.assign({},t),{filters:Object.assign(Object.assign({},t.filters),{[l]:Object.assign(Object.assign({},c),{metrics:m})})})})),bk(zR,((t,e)=>{var n,o,i,a;if(0===Object.keys(e.newRunsAndMetadata).length)return t;const r=Object.assign({},t.specs),s=new Map,l=new Set;for(const t of Object.keys(e.newRunsAndMetadata)){const c=new Map,d=new Map,p=new Map,m=new Map,{runs:u,metadata:f}=e.newRunsAndMetadata[t];for(const t of u){const e=f.runToHparamsAndMetrics[t.id];if(e)for(const t of e.metrics){const e=s.get(t.tag);s.set(t.tag,{min:e?Math.min(e.min,t.value):t.value,max:e?Math.max(e.max,t.value):t.value})}}for(const{name:t,domain:e}of f.hparamSpecs)if(e.type===ZR.DISCRETE){const n=p.get(t)||new Set;for(const t of e.values)n.add(t);p.set(t,n)}else{const n=m.get(t);m.set(t,{minValue:n?Math.min(e.minValue,n.minValue):e.minValue,maxValue:n?Math.max(e.maxValue,n.maxValue):e.maxValue})}for(const t of f.metricSpecs)l.add(t.tag);for(const[t,e]of p)c.set(t,{type:ZR.DISCRETE,includeUndefined:!0,possibleValues:[...e],filterValues:[...e]});for(const[t,{minValue:e,maxValue:n}]of m)c.set(t,{type:ZR.INTERVAL,includeUndefined:!0,minValue:e,maxValue:n,filterLowerValue:e,filterUpperValue:n});for(const t of l){const e=s.get(t),i=null!==(n=null==e?void 0:e.min)&&void 0!==n?n:0,a=null!==(o=null==e?void 0:e.max)&&void 0!==o?o:0;d.set(t,{type:ZR.INTERVAL,includeUndefined:!0,minValue:i,maxValue:a,filterLowerValue:i,filterUpperValue:a})}r[t]={hparam:Object.assign(Object.assign({},null===(i=r[t])||void 0===i?void 0:i.hparam),{specs:f.hparamSpecs,defaultFilters:c}),metric:Object.assign(Object.assign({},null===(a=r[t])||void 0===a?void 0:a.metric),{specs:f.metricSpecs,defaultFilters:d})}}return Object.assign(Object.assign({},t),{specs:r})})));function oA(t,e){return nA(t,e)}class iA{}iA.ɵfac=function t(e){return new(e||iA)},iA.ɵmod=ao({type:iA}),iA.ɵinj=vn({imports:[[dk.forFeature(TR,oA)]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(iA,[{type:Ay,args:[{imports:[dk.forFeature(TR,oA)]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(iA,{imports:[ck]});class aA{}function rA(t,e){return`${e}/${t}`}aA.ɵfac=function t(e){return new(e||aA)},aA.ɵmod=ao({type:aA}),aA.ɵinj=vn({imports:[[iA]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(aA,[{type:Ay,args:[{imports:[iA]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(aA,{imports:[iA]});class sA{constructor(t){this.http=t}fetchRuns(t){return this.http.get("data/runs").pipe(It((e=>e.map((e=>({id:rA(e,t),name:e,startTime:0}))))))}fetchHparamsMetadata(t){return Et({hparamSpecs:[],metricSpecs:[],runToHparamsAndMetrics:{}})}}sA.ɵfac=function t(e){return new(e||sA)(vr(lE))},sA.ɵprov=Mn({token:sA,factory:sA.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(sA,[{type:im}],(function(){return[{type:lE}]}),null);class lA{}lA.ɵfac=function t(e){return new(e||lA)},lA.ɵmod=ao({type:lA}),lA.ɵinj=vn({providers:[{provide:XR,useClass:sA}]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lA,[{type:Ay,args:[{providers:[{provide:XR,useClass:sA}]}]}],null,null);const cA="alerts",dA=Zw(Kw(cA),(t=>t.latestAlert)),pA="experiments",mA=Zw(Kw(pA),(t=>t.data)),uA=Zw(mA,((t,e)=>{const{experimentId:n}=e;return t.experimentMap[n]||null}));
/*! *****************************************************************************
    Copyright (c) Microsoft Corporation.

    Permission to use, copy, modify, and/or distribute this software for any
    purpose with or without fee is hereby granted.

    THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
    REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
    AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
    INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
    LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
    OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
    PERFORMANCE OF THIS SOFTWARE.
    ***************************************************************************** */
function fA(t,e){var n={};for(var o in t)Object.prototype.hasOwnProperty.call(t,o)&&e.indexOf(o)<0&&(n[o]=t[o]);if(null!=t&&"function"==typeof Object.getOwnPropertySymbols){var i=0;for(o=Object.getOwnPropertySymbols(t);i<o.length;i++)e.indexOf(o[i])<0&&Object.prototype.propertyIsEnumerable.call(t,o[i])&&(n[o[i]]=t[o[i]])}return n}function gA(t,e,n,o){return new(n||(n=Promise))((function(i,a){function r(t){try{l(o.next(t))}catch(t){a(t)}}function s(t){try{l(o.throw(t))}catch(t){a(t)}}function l(t){t.done?i(t.value):(function e(t){return t instanceof n?t:new n((function(e){e(t)}))})(t.value).then(r,s)}l((o=o.apply(t,e||[])).next())}))}var hA,bA,yA;!(function(t){t.SCALARS="scalars",t.HISTOGRAMS="histograms",t.IMAGES="images"})(hA||(hA={})),(function(t){t.DEFAULT="default",t.ASCENDING="ascending",t.DESCENDING="descending",t.NEAREST="nearest"})(bA||(bA={})),(function(t){t[t.STEP=0]="STEP",t[t.RELATIVE=1]="RELATIVE",t[t.WALL_TIME=2]="WALL_TIME"})(yA||(yA={}));const _A="timeseries",CA=[hA.IMAGES];function MA(t){return CA.includes(t)}const vA=[hA.HISTOGRAMS,hA.IMAGES];function xA(t){return vA.includes(t)}function OA(t){return xA(t.plugin)}class PA{}function wA(t){return t.hasOwnProperty("error")}const kA="data/plugin/timeseries";function SA(t){const e=t.indexOf("/");return{run:t.substring(e+1),experimentId:t.substring(0,e)}}function DA(t,e){return`${e}/${t}`}function EA(t,e){const{runToSeries:n,run:o}=t,i=fA(t,["runToSeries","run"]),a=Object.assign({},i);return n&&(a.runToSeries=RA(n,e)),o&&(a.runId=DA(o,e)),a}function RA(t,e){const n={};for(const o in t)t.hasOwnProperty(o)&&(n[DA(o,e)]=t[o]);return n}class AA{constructor(t,e){this.http=t,this.store=e}fetchTagMetadata(t){const e=t.map((t=>this.http.get(`/experiment/${t}/${kA}/tags`).pipe(It((e=>(function n(t,e){const n={};for(const o of Object.keys(t)){const i=o;if(MA(i)){const o=t[i],{tagRunSampledInfo:a}=o,r=fA(o,["tagRunSampledInfo"]),s={};for(const t in 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n&&i.push(t.experimentAlias,`${t.experimentAlias}/${t.runName}`),i.some((t=>o.test(t)))}const NN=Zw((t=>{const e=TS(t);return e?mN(t,{experimentIds:e}):null}),gN,(t=>{var e;const n=null!==(e=TS(t))&&void 0!==e?e:[],o=zS(t),i=new Map;for(const e of n){const n=uA(t,{experimentId:e});if(!n)continue;const a=lN(t,{experimentId:e});for(const t of a)i.set(t.id,{runName:t.name,experimentName:n.name,experimentAlias:o[e]})}return i}),RS,((t,e,n,o)=>{if(!t)return null;const i=o===Zk.COMPARE_EXPERIMENT,a=new Map;for(const[o,r]of t.entries()){const t=n.get(o);a.set(o,TN(t,e,i)&&r)}return a})),zN=Zw(AN,CN,_N,JD,((t,e,n,o)=>{const i={};return e.forEach(((e,a)=>{let r=o?t.inactive.darkHex:t.inactive.lightHex;if(n.has(a))r=n.get(a);else if(e>=0){const n=t.colors[e%t.colors.length];r=o?n.darkHex:n.lightHex}i[a]=r})),i}));class IN{constructor(t,e,n){this.actions$=t,this.store=e,this.runsDataSource=n,this.loadRunsOnRunTableShown$=Mk((()=>this.actions$.pipe(Dk(UR),Zt((({experimentIds:t})=>this.getExperimentsWithLoadState(t,(t=>t===yE.FAILED||t===yE.NOT_LOADED)).pipe(ce((t=>!!t.length)),Zt((e=>this.fetchAllRunsList(t,e)))))))),{dispatch:!1}),this.experimentsWithStaleRunsOnRouteChange$=this.actions$.pipe(Dk(dS),Ve(this.store.select(NS)),Me((([,t],[,e])=>t===e)),Ve(this.store.select(TS)),ce((([,t])=>!!t)),It((([,t])=>t)),Zt((t=>this.getExperimentsWithLoadState(t,(t=>t===yE.FAILED||t===yE.NOT_LOADED)).pipe(It((e=>({experimentIds:t,experimentIdsToBeFetched:e}))))))),this.experimentsWithStaleRunsOnReload$=this.actions$.pipe(Dk(xE,vE),Ve(this.store.select(TS)),ce((([,t])=>!!t)),It((([,t])=>t)),Zt((t=>this.getExperimentsWithLoadState(t,(t=>t!==yE.LOADING)).pipe(It((e=>({experimentIds:t,experimentIdsToBeFetched:e}))))))),this.loadRunsOnNavigationOrReload$=Mk((()=>re(this.experimentsWithStaleRunsOnRouteChange$,this.experimentsWithStaleRunsOnReload$).pipe(Zt((({experimentIds:t,experimentIdsToBeFetched:e})=>this.fetchAllRunsList(t,e))))),{dispatch:!1})}getRunsListLoadState(t){return this.store.select(pN,{experimentId:t}).pipe(be(1))}getExperimentsWithLoadState(t,e){return $t(t.map((t=>this.getRunsListLoadState(t)))).pipe(It((n=>t.filter(((t,o)=>e(n[o].state))))))}fetchAllRunsList(t,e){return Et({experimentIds:t,experimentIdsToBeFetched:e}).pipe(Fe((()=>{this.store.dispatch(NR({experimentIds:t,requestedExperimentIds:e}))})),Zt((()=>{const n=new Set(e);return $t(t.map((t=>n.has(t)?this.fetchRunsForExperiment(t):this.maybeWaitForRunsAndGetRuns(t))))})),It((t=>{const e={},n=[];for(const o of t)n.push(...o.runs),o.fromRemote&&(e[o.experimentId]={runs:o.runs,metadata:o.metadata});return{newRunsAndMetadata:e,runsForAllExperiments:n}})),Fe((({newRunsAndMetadata:e,runsForAllExperiments:n})=>{this.store.dispatch(zR({experimentIds:t,newRunsAndMetadata:e,runsForAllExperiments:n}))})),pe((n=>(this.store.dispatch(IR({experimentIds:t,requestedExperimentIds:e})),Et(null)))),It((()=>null)))}maybeWaitForRunsAndGetRuns(t){return this.store.select(pN,{experimentId:t}).pipe(ce((t=>t.state!==yE.LOADING)),be(1),Zt((t=>t.state===yE.FAILED?Rt(new Error("Pending request failed")):Et(t))),Ve(this.store.select(lN,{experimentId:t})),It((([,e])=>({fromRemote:!1,experimentId:t,runs:e}))))}fetchRunsForExperiment(t){return $t([this.runsDataSource.fetchRuns(t),this.runsDataSource.fetchHparamsMetadata(t)]).pipe(It((([e,n])=>({fromRemote:!0,experimentId:t,runs:e,metadata:n}))))}}function HN(t,e,n){const o=Object.keys(t),i=Object.assign(Object.assign(Object.assign({},t),e),{privateRouteContextedState:{}});return{initialState:i,reducers:yk(i,bk(dS,((e,{before:i,after:a})=>{const r=Qk(a.routeKind,a.params),s=i?Qk(i.routeKind,i.params):null;if(s===r)return e;let l=Object.assign({},e.privateRouteContextedState);if(s){const t={};for(const n of o)t[n]=e[n];l=Object.assign(Object.assign({},l),{[s]:t})}let c=e.privateRouteContextedState&&e.privateRouteContextedState[r]?e.privateRouteContextedState[r]:null;s&&null===c&&(c=t);const d=Object.assign(Object.assign(Object.assign({},e),c),{privateRouteContextedState:l});return n?n(d,a):d})))}}var FN,LN;function BN(...t){return(e,n)=>{let o=e;for(const e of t)o=e(o,n);return o}}IN.ɵfac=function t(e){return new(e||IN)(vr(Sk),vr(Iw),vr(XR))},IN.ɵprov=Mn({token:IN,factory:IN.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(IN,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:XR}]}),null),(function(t){t.ASC="asc",t.DESC="desc",t.UNSET=""})(FN||(FN={})),(function(t){t.EXPERIMENT_ID="id",t.USER="user",t.BEFORE="before",t.AFTER="after",t.REGEX="regex"})(LN||(LN={}));const{initialState:VN,reducers:jN}=HN({runColorOverrideForGroupBy:new Map,defaultRunColorIdForGroupBy:new Map,groupKeyToColorId:new Map,initialGroupBy:{key:tN.RUN},userSetGroupByKey:null,colorGroupRegexString:"",regexFilter:""},{runIds:{},runIdToExpId:{},runMetadata:{},runsLoadState:{},selectionState:new Map},((t,e)=>Object.assign(Object.assign({},t),{initialGroupBy:{key:e.routeKind===Zk.COMPARE_EXPERIMENT?tN.EXPERIMENT:tN.RUN}}))),UN=BN(yk(VN,bk(rS,((t,{routeKind:e,partialState:n})=>{var o;if(e!==Zk.COMPARE_EXPERIMENT&&e!==Zk.EXPERIMENT)return t;const i=n.runs.groupBy;if(!i)return t;const a=i.key===tN.REGEX?i.regexString:t.colorGroupRegexString;return Object.assign(Object.assign({},t),{colorGroupRegexString:a,userSetGroupByKey:null!==(o=i.key)&&void 0!==o?o:null})})),bk(NR,((t,e)=>{const n=Object.assign({},t.runsLoadState);for(const t of e.requestedExperimentIds)n[t]=n[t]?Object.assign(Object.assign({},n[t]),{state:yE.LOADING}):{lastLoadedTimeInMs:null,state:yE.LOADING};return Object.assign(Object.assign({},t),{runsLoadState:n})})),bk(zR,((t,e)=>{var n;const o=Object.assign({},t.runIds),i=Object.assign({},t.runMetadata),a=Object.assign({},t.runIdToExpId),r=Object.assign({},t.runsLoadState),s=new Map(t.selectionState);for(const t of Object.keys(e.newRunsAndMetadata)){const{runs:n,metadata:s}=e.newRunsAndMetadata[t];o[t]=n.map((({id:t})=>t)),r[t]=Object.assign(Object.assign({},r[t]),{lastLoadedTimeInMs:Date.now(),state:yE.LOADED});for(const e of n){const n=s.runToHparamsAndMetrics[e.id];i[e.id]=Object.assign(Object.assign({},e),{hparams:n?n.hparams:null,metrics:n?n.metrics:null}),a[e.id]=t}}const l=eN(e.experimentIds),c=new Map(null!==(n=s.get(l))&&void 0!==n?n:[]),d=e.runsForAllExperiments.length<=500;for(const t of e.runsForAllExperiments)c.has(t.id)||c.set(t.id,d);return s.set(l,c),Object.assign(Object.assign({},t),{runIds:o,runIdToExpId:a,runMetadata:i,runsLoadState:r,selectionState:s})})),bk(IR,((t,e)=>{const n=Object.assign({},t.runsLoadState);for(const t of e.requestedExperimentIds)n[t]=n[t]?Object.assign(Object.assign({},n[t]),{state:yE.FAILED}):{lastLoadedTimeInMs:null,state:yE.FAILED};return Object.assign(Object.assign({},t),{runsLoadState:n})})),bk(HR,((t,{experimentIds:e,runId:n})=>{var o;const i=eN(e),a=new Map(t.selectionState),r=new Map(null!==(o=a.get(i))&&void 0!==o?o:[]);return r.set(n,!Boolean(r.get(n))),a.set(i,r),Object.assign(Object.assign({},t),{selectionState:a})})),bk(FR,((t,{experimentIds:e,runIds:n})=>{var o;const i=eN(e),a=new Map(t.selectionState),r=new Map(null!==(o=a.get(i))&&void 0!==o?o:[]),s=!n.every((t=>Boolean(r.get(t))));for(const t of n)r.set(t,s);return a.set(i,r),Object.assign(Object.assign({},t),{selectionState:a})})),bk(zR,((t,{runsForAllExperiments:e})=>{const n=new Map(t.groupKeyToColorId),o=new Map(t.defaultRunColorIdForGroupBy);let i=t.initialGroupBy;null!==t.userSetGroupByKey&&(i=oN(t.userSetGroupByKey,t.colorGroupRegexString));const a=nN(i,e,t.runIdToExpId);Object.entries(a.matches).forEach((([t,e])=>{var i;const a=null!==(i=n.get(t))&&void 0!==i?i:n.size;n.set(t,a);for(const t of e)o.set(t.id,a)}));for(const t of a.nonMatches)o.set(t.id,-1);return Object.assign(Object.assign({},t),{defaultRunColorIdForGroupBy:o,groupKeyToColorId:n})})),bk(GR,((t,{experimentIds:e,groupBy:n})=>{const o=new Map,i=new Map(t.defaultRunColorIdForGroupBy),a=nN(n,e.flatMap((e=>t.runIds[e])).map((e=>t.runMetadata[e])),t.runIdToExpId);Object.entries(a.matches).forEach((([t,e])=>{var n;const a=null!==(n=o.get(t))&&void 0!==n?n:o.size;o.set(t,a);for(const t of e)i.set(t.id,a)}));for(const t of a.nonMatches)i.set(t.id,-1);const r=n.key===tN.REGEX?n.regexString:t.colorGroupRegexString;return Object.assign(Object.assign({},t),{colorGroupRegexString:r,userSetGroupByKey:n.key,defaultRunColorIdForGroupBy:i,groupKeyToColorId:o,runColorOverrideForGroupBy:new Map})})),bk(jR,((t,{runId:e,newColor:n})=>{const o=new Map(t.runColorOverrideForGroupBy);return o.set(e,n),Object.assign(Object.assign({},t),{runColorOverrideForGroupBy:o})})),bk(VR,((t,e)=>Object.assign(Object.assign({},t),{regexFilter:e.regexString})))),jN),GN={key:null,direction:FN.UNSET},{initialState:WN,reducers:YN}=HN({paginationOption:{pageIndex:0,pageSize:10},sort:GN},{}),qN=BN(yk(WN,bk(LR,((t,{pageSize:e,pageIndex:n})=>Object.assign(Object.assign({},t),{paginationOption:{pageSize:e,pageIndex:n}}))),bk(VR,((t,e)=>Object.assign(Object.assign({},t),{paginationOption:Object.assign(Object.assign({},t.paginationOption),{pageIndex:0})}))),bk(BR,((t,e)=>Object.assign(Object.assign({},t),{sort:{key:e.key,direction:e.direction}})))),YN);function ZN(t,e){return xw({data:UN,ui:qN})(t,e)}function XN(){return[{actionCreator:IR,alertFromAction:()=>({localizedMessage:"Failed to fetch runs"})}]}class KN{}var JN;KN.ɵfac=function t(e){return new(e||KN)},KN.ɵmod=ao({type:KN}),KN.ɵinj=vn({imports:[[dk.forFeature(QT,ZN),Wk.forFeature([IN]),lA,AR.registerAlertActions(XN),aA]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(KN,[{type:Ay,args:[{imports:[dk.forFeature(QT,ZN),Wk.forFeature([IN]),lA,AR.registerAlertActions(XN),aA]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(KN,{imports:[ck,Gk,lA,AR,aA]}),(function(t){t.RUNS_CHANGED="experimental.RunsChanged",t.GET_RUNS="experimental.GetRuns",t.GET_URL_DATA="experimental.GetURLPluginData",t.DATA_RELOADED="experimental.DataReloaded"})(JN||(JN={}));class QN{constructor(t){this.port=t,this.id=0,this.responseWaits=new Map,this.listeners=new Map,this.port.addEventListener("message",(t=>this.onMessage(t)))}listen(t,e){this.listeners.set(t,e)}unlisten(t){this.listeners.delete(t)}onMessage(t){return gA(this,void 0,void 0,(function*(){const e=JSON.parse(t.data),n=e.type,o=e.id,i=e.payload,a=e.error;if(e.isReply){if(!this.responseWaits.has(o))return;const{resolve:t,reject:e}=this.responseWaits.get(o);return this.responseWaits.delete(o),void(a?e(new Error(a)):t(i))}let r=null,s=null;if(this.listeners.has(n)){const t=this.listeners.get(n);try{r=yield t(i)}catch(t){s=t}}this.postMessage({type:n,id:o,payload:r,error:s,isReply:!0})}))}postMessage(t){this.port.postMessage(JSON.stringify(t))}sendMessage(t,e){const n=this.id++;return this.postMessage({type:t,id:n,payload:e,error:null,isReply:!1}),new Promise(((t,e)=>{this.responseWaits.set(n,{resolve:t,reject:e})}))}}const $N=new WeakMap,tz=new Set,ez=new Map,nz=new Map;function oz(t,e){return n=>{const o=nz.get(e),i=$N.get(o)||null;return t(i,n)}}window.addEventListener("message",(t=>{if("experimental.bootstrap"!==t.data)return;const e=t.ports[0];if(!e)return;const n=t.source?t.source.frameElement:null;n&&(function o(t,e){const n=new QN(t);tz.add(n),nz.set(n,e),t.start();for(const[t,e]of ez){const o=oz(e,n);n.listen(t,o)}})(e,n)}));class iz{broadcast(t,e){return(function n(t,e){for(const t of tz)nz.get(t).isConnected||(tz.delete(t),nz.delete(t));const n=[...tz].map((n=>n.sendMessage(t,e)));return Promise.all(n)})(t,e)}listen(t,e){!(function n(t,e){ez.set(t,e);for(const n of tz){const o=oz(e,n);n.listen(t,o)}})(t,e)}unlisten(t){!(function e(t){ez.delete(t);for(const e of tz)e.unlisten(t)})(t)}}iz.ɵfac=function t(e){return new(e||iz)},iz.ɵprov=Mn({token:iz,factory:iz.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(iz,[{type:im}],null,null);class az{constructor(t,e){this.ipc=t,this.store=e}init(){const t=document.createElement("tf-storage");this.ipc.listen(JN.GET_URL_DATA,(e=>{if(!e)return;const n=`p.${e.pluginName}.`,o={},i=t.tf_storage.getUrlHashDict();for(let t in i)t.startsWith(n)&&(o[t.substring(n.length)]=i[t]);return o})),this.store.select(CR).pipe(ce((t=>null!==t)),Me()).subscribe((()=>{this.ipc.broadcast(JN.DATA_RELOADED,void 0)}))}}az.ɵfac=function t(e){return new(e||az)(vr(iz),vr(Iw))},az.ɵprov=Mn({token:az,factory:az.ɵfac,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(az,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:iz},{type:Iw}]}),null);class rz{constructor(t,e){this.ipc=t,this.store=e}init(){const t=this.store.select(TS).pipe(Zt((t=>t?Wt(t.map((t=>this.store.select(lN,{experimentId:t})))).pipe(It((t=>t.flat())),Me(((t,e)=>t.length===e.length&&t.every(((t,n)=>e[n].id===t.id)))),It((t=>t.map((({name:t})=>t))))):Et([]))));t.subscribe((t=>{this.ipc.broadcast(JN.RUNS_CHANGED,t)})),this.ipc.listen(JN.GET_RUNS,(()=>t.pipe(be(1)).toPromise()))}}rz.ɵfac=function t(e){return new(e||rz)(vr(iz),vr(Iw))},rz.ɵprov=Mn({token:rz,factory:rz.ɵfac,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(rz,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:iz},{type:Iw}]}),null);class sz{constructor(t,e){e.init(),t.init()}registerPluginIframe(t,e){!(function n(t,e){$N.set(t,{pluginName:e})})(t,e)}}sz.ɵfac=function t(e){return new(e||sz)(vr(rz),vr(az))},sz.ɵmod=ao({type:sz}),sz.ɵinj=vn({providers:[iz,az,rz],imports:[[BS,ER,KN]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(sz,[{type:Ay,args:[{providers:[iz,az,rz],imports:[BS,ER,KN]}]}],(function(){return[{type:rz},{type:az}]}),null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(sz,{imports:[BS,ER,KN]});const lz=JP("[Alert] Alert Reported",{_as:"props",_p:void 0});class cz{constructor(t,e,n){this.actions$=t,this.store=e,this.alertActionModule=n,this.reportRegisteredActionAlerts$=Mk((()=>this.actions$.pipe(Fe((t=>{const e=this.alertActionModule.getAlertFromAction(t);e&&this.store.dispatch(lz(e))})))),{dispatch:!1})}}cz.ɵfac=function t(e){return new(e||cz)(vr(Sk),vr(Iw),vr(AR))},cz.ɵprov=Mn({token:cz,factory:cz.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(cz,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:AR}]}),null);const dz=yk({latestAlert:null},bk(lz,((t,{localizedMessage:e,followupAction:n})=>Object.assign(Object.assign({},t),{latestAlert:{localizedMessage:e,followupAction:n,created:Date.now()}}))));function pz(t,e){return dz(t,e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const mz=13,uz=27,fz=32,gz=38,hz=40;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function bz(t,...e){return e.length?e.some((e=>t[e])):t.altKey||t.shiftKey||t.ctrlKey||t.metaKey}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function yz(t){return null!=t&&"false"!=`${t}`}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function _z(t,e=0){return Cz(t)?Number(t):e}function Cz(t){return!isNaN(parseFloat(t))&&!isNaN(Number(t))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Mz(t){return Array.isArray(t)?t:[t]}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function vz(t){return null==t?"":"string"==typeof t?t:`${t}px`}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function xz(t){return t instanceof hg?t.nativeElement:t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let Oz,Pz;try{Oz="undefined"!=typeof Intl&&Intl.v8BreakIterator}catch(jCt){Oz=!1}class wz{constructor(t){this._platformId=t,this.isBrowser=this._platformId?(function e(t){return"browser"===t})(this._platformId):"object"==typeof document&&!!document,this.EDGE=this.isBrowser&&/(edge)/i.test(navigator.userAgent),this.TRIDENT=this.isBrowser&&/(msie|trident)/i.test(navigator.userAgent),this.BLINK=this.isBrowser&&!(!window.chrome&&!Oz)&&"undefined"!=typeof CSS&&!this.EDGE&&!this.TRIDENT,this.WEBKIT=this.isBrowser&&/AppleWebKit/i.test(navigator.userAgent)&&!this.BLINK&&!this.EDGE&&!this.TRIDENT,this.IOS=this.isBrowser&&/iPad|iPhone|iPod/.test(navigator.userAgent)&&!("MSStream"in window),this.FIREFOX=this.isBrowser&&/(firefox|minefield)/i.test(navigator.userAgent),this.ANDROID=this.isBrowser&&/android/i.test(navigator.userAgent)&&!this.TRIDENT,this.SAFARI=this.isBrowser&&/safari/i.test(navigator.userAgent)&&this.WEBKIT}}wz.ɵfac=function t(e){return new(e||wz)(vr(jy))},wz.ɵprov=Mn({factory:function t(){return new wz(vr(jy))},token:wz,providedIn:"root"}),wz.ctorParameters=()=>[{type:Object,decorators:[{type:kr,args:[jy]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(wz,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:Object,decorators:[{type:kr,args:[jy]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class kz{}kz.ɵfac=function t(e){return new(e||kz)},kz.ɵmod=ao({type:kz}),kz.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(kz,[{type:Ay,args:[{}]}],null,null);const Sz=["color","button","checkbox","date","datetime-local","email","file","hidden","image","month","number","password","radio","range","reset","search","submit","tel","text","time","url","week"];function Dz(){if(Pz)return Pz;if("object"!=typeof document||!document)return Pz=new Set(Sz),Pz;let t=document.createElement("input");return Pz=new Set(Sz.filter((e=>(t.setAttribute("type",e),t.type===e)))),Pz}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let Ez,Rz,Az,Tz;function Nz(t){return(function e(){if(null==Ez&&"undefined"!=typeof window)try{window.addEventListener("test",null,Object.defineProperty({},"passive",{get:()=>Ez=!0}))}finally{Ez=Ez||!1}return Ez})()?t:!!t.capture}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function zz(){if(null==Az){if("object"!=typeof document||!document||"function"!=typeof Element||!Element)return Az=!1,Az;if("scrollBehavior"in document.documentElement.style)Az=!0;else{const t=Element.prototype.scrollTo;Az=!!t&&!/\{\s*\[native code\]\s*\}/.test(t.toString())}}return Az}function Iz(){if("object"!=typeof document||!document)return 0;if(null==Rz){const t=document.createElement("div"),e=t.style;t.dir="rtl",e.width="1px",e.overflow="auto",e.visibility="hidden",e.pointerEvents="none",e.position="absolute";const n=document.createElement("div"),o=n.style;o.width="2px",o.height="1px",t.appendChild(n),document.body.appendChild(t),Rz=0,0===t.scrollLeft&&(t.scrollLeft=1,Rz=0===t.scrollLeft?1:2),t.parentNode.removeChild(t)}return Rz}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Hz(t){if((function e(){if(null==Tz){const t="undefined"!=typeof document?document.head:null;Tz=!(!t||!t.createShadowRoot&&!t.attachShadow)}return Tz})()){const e=t.getRootNode?t.getRootNode():null;if("undefined"!=typeof ShadowRoot&&ShadowRoot&&e instanceof ShadowRoot)return e}return null}function Fz(){let t="undefined"!=typeof document&&document?document.activeElement:null;for(;t&&t.shadowRoot;){const e=t.shadowRoot.activeElement;if(e===t)break;t=e}return t}function Lz(t){return t.composedPath?t.composedPath()[0]:t.target}class Bz{create(t){return"undefined"==typeof MutationObserver?null:new MutationObserver(t)}}Bz.ɵfac=function t(e){return new(e||Bz)},Bz.ɵprov=Mn({factory:function t(){return new Bz},token:Bz,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Bz,[{type:im,args:[{providedIn:"root"}]}],null,null);class Vz{constructor(t){this._mutationObserverFactory=t,this._observedElements=new Map}ngOnDestroy(){this._observedElements.forEach(((t,e)=>this._cleanupObserver(e)))}observe(t){const e=xz(t);return new D((t=>{const n=this._observeElement(e).subscribe(t);return()=>{n.unsubscribe(),this._unobserveElement(e)}}))}_observeElement(t){if(this._observedElements.has(t))this._observedElements.get(t).count++;else{const e=new I,n=this._mutationObserverFactory.create((t=>e.next(t)));n&&n.observe(t,{characterData:!0,childList:!0,subtree:!0}),this._observedElements.set(t,{observer:n,stream:e,count:1})}return this._observedElements.get(t).stream}_unobserveElement(t){this._observedElements.has(t)&&(this._observedElements.get(t).count--,this._observedElements.get(t).count||this._cleanupObserver(t))}_cleanupObserver(t){if(this._observedElements.has(t)){const{observer:e,stream:n}=this._observedElements.get(t);e&&e.disconnect(),n.complete(),this._observedElements.delete(t)}}}Vz.ɵfac=function t(e){return new(e||Vz)(vr(Bz))},Vz.ɵprov=Mn({factory:function t(){return new Vz(vr(Bz))},token:Vz,providedIn:"root"}),Vz.ctorParameters=()=>[{type:Bz}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Vz,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:Bz}]}),null);class jz{constructor(t,e,n){this._contentObserver=t,this._elementRef=e,this._ngZone=n,this.event=new Lh,this._disabled=!1,this._currentSubscription=null}get disabled(){return this._disabled}set disabled(t){this._disabled=yz(t),this._disabled?this._unsubscribe():this._subscribe()}get debounce(){return this._debounce}set debounce(t){this._debounce=_z(t),this._subscribe()}ngAfterContentInit(){this._currentSubscription||this.disabled||this._subscribe()}ngOnDestroy(){this._unsubscribe()}_subscribe(){this._unsubscribe();const t=this._contentObserver.observe(this._elementRef);this._ngZone.runOutsideAngular((()=>{this._currentSubscription=(this.debounce?t.pipe(ge(this.debounce)):t).subscribe(this.event)}))}_unsubscribe(){var t;null===(t=this._currentSubscription)||void 0===t||t.unsubscribe()}}jz.ɵfac=function t(e){return new(e||jz)(Sm(Vz),Sm(hg),Sm(a_))},jz.ɵdir=lo({type:jz,selectors:[["","cdkObserveContent",""]],inputs:{disabled:["cdkObserveContentDisabled","disabled"],debounce:"debounce"},outputs:{event:"cdkObserveContent"},exportAs:["cdkObserveContent"]}),jz.ctorParameters=()=>[{type:Vz},{type:hg},{type:a_}],jz.propDecorators={event:[{type:Oy,args:["cdkObserveContent"]}],disabled:[{type:xy,args:["cdkObserveContentDisabled"]}],debounce:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(jz,[{type:Cy,args:[{selector:"[cdkObserveContent]",exportAs:"cdkObserveContent"}]}],(function(){return[{type:Vz},{type:hg},{type:a_}]}),{event:[{type:Oy,args:["cdkObserveContent"]}],disabled:[{type:xy,args:["cdkObserveContentDisabled"]}],debounce:[{type:xy}]});class Uz{}function Gz(t,e){return(t.getAttribute(e)||"").match(/\S+/g)||[]}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */Uz.ɵfac=function t(e){return new(e||Uz)},Uz.ɵmod=ao({type:Uz}),Uz.ɵinj=vn({providers:[Bz]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Uz,[{type:Ay,args:[{exports:[jz],declarations:[jz],providers:[Bz]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Uz,{declarations:[jz],exports:[jz]});const Wz="cdk-describedby-message-container",Yz="cdk-describedby-host";let qz=0;const Zz=new Map;let Xz=null;class Kz{constructor(t){this._document=t}describe(t,e,n){if(!this._canBeDescribed(t,e))return;const o=Jz(e,n);"string"!=typeof e?(Qz(e),Zz.set(o,{messageElement:e,referenceCount:0})):Zz.has(o)||this._createMessageElement(e,n),this._isElementDescribedByMessage(t,o)||this._addMessageReference(t,o)}removeDescription(t,e,n){if(!e||!this._isElementNode(t))return;const o=Jz(e,n);if(this._isElementDescribedByMessage(t,o)&&this._removeMessageReference(t,o),"string"==typeof e){const t=Zz.get(o);t&&0===t.referenceCount&&this._deleteMessageElement(o)}Xz&&0===Xz.childNodes.length&&this._deleteMessagesContainer()}ngOnDestroy(){const t=this._document.querySelectorAll("[cdk-describedby-host]");for(let e=0;e<t.length;e++)this._removeCdkDescribedByReferenceIds(t[e]),t[e].removeAttribute(Yz);Xz&&this._deleteMessagesContainer(),Zz.clear()}_createMessageElement(t,e){const n=this._document.createElement("div");Qz(n),n.textContent=t,e&&n.setAttribute("role",e),this._createMessagesContainer(),Xz.appendChild(n),Zz.set(Jz(t,e),{messageElement:n,referenceCount:0})}_deleteMessageElement(t){const e=Zz.get(t),n=e&&e.messageElement;Xz&&n&&Xz.removeChild(n),Zz.delete(t)}_createMessagesContainer(){if(!Xz){const t=this._document.getElementById(Wz);t&&t.parentNode&&t.parentNode.removeChild(t),Xz=this._document.createElement("div"),Xz.id=Wz,Xz.style.visibility="hidden",Xz.classList.add("cdk-visually-hidden"),this._document.body.appendChild(Xz)}}_deleteMessagesContainer(){Xz&&Xz.parentNode&&(Xz.parentNode.removeChild(Xz),Xz=null)}_removeCdkDescribedByReferenceIds(t){const e=Gz(t,"aria-describedby").filter((t=>0!=t.indexOf("cdk-describedby-message")));t.setAttribute("aria-describedby",e.join(" "))}_addMessageReference(t,e){const n=Zz.get(e);!(function o(t,e,n){const o=Gz(t,e);o.some((t=>t.trim()==n.trim()))||(o.push(n.trim()),t.setAttribute(e,o.join(" ")))})(t,"aria-describedby",n.messageElement.id),t.setAttribute(Yz,""),n.referenceCount++}_removeMessageReference(t,e){const n=Zz.get(e);n.referenceCount--,(function o(t,e,n){const o=Gz(t,e).filter((t=>t!=n.trim()));o.length?t.setAttribute(e,o.join(" ")):t.removeAttribute(e)})(t,"aria-describedby",n.messageElement.id),t.removeAttribute(Yz)}_isElementDescribedByMessage(t,e){const n=Gz(t,"aria-describedby"),o=Zz.get(e),i=o&&o.messageElement.id;return!!i&&-1!=n.indexOf(i)}_canBeDescribed(t,e){if(!this._isElementNode(t))return!1;if(e&&"object"==typeof e)return!0;const n=null==e?"":`${e}`.trim(),o=t.getAttribute("aria-label");return!(!n||o&&o.trim()===n)}_isElementNode(t){return t.nodeType===this._document.ELEMENT_NODE}}function Jz(t,e){return"string"==typeof t?`${e||""}/${t}`:t}function Qz(t){t.id||(t.id="cdk-describedby-message-"+qz++)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */Kz.ɵfac=function t(e){return new(e||Kz)(vr(Z_))},Kz.ɵprov=Mn({factory:function t(){return new Kz(vr(Z_))},token:Kz,providedIn:"root"}),Kz.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Kz,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);class $z{constructor(t){this._items=t,this._activeItemIndex=-1,this._activeItem=null,this._wrap=!1,this._letterKeyStream=new I,this._typeaheadSubscription=m.EMPTY,this._vertical=!0,this._allowedModifierKeys=[],this._homeAndEnd=!1,this._skipPredicateFn=t=>t.disabled,this._pressedLetters=[],this.tabOut=new I,this.change=new I,t instanceof Vh&&t.changes.subscribe((t=>{if(this._activeItem){const e=t.toArray().indexOf(this._activeItem);e>-1&&e!==this._activeItemIndex&&(this._activeItemIndex=e)}}))}skipPredicate(t){return this._skipPredicateFn=t,this}withWrap(t=!0){return this._wrap=t,this}withVerticalOrientation(t=!0){return this._vertical=t,this}withHorizontalOrientation(t){return this._horizontal=t,this}withAllowedModifierKeys(t){return this._allowedModifierKeys=t,this}withTypeAhead(t=200){if(("undefined"==typeof ngDevMode||ngDevMode)&&this._items.length&&this._items.some((t=>"function"!=typeof t.getLabel)))throw Error("ListKeyManager items in typeahead mode must implement the `getLabel` method.");return this._typeaheadSubscription.unsubscribe(),this._typeaheadSubscription=this._letterKeyStream.pipe(Fe((t=>this._pressedLetters.push(t))),ge(t),ce((()=>this._pressedLetters.length>0)),It((()=>this._pressedLetters.join("")))).subscribe((t=>{const e=this._getItemsArray();for(let n=1;n<e.length+1;n++){const o=(this._activeItemIndex+n)%e.length,i=e[o];if(!this._skipPredicateFn(i)&&0===i.getLabel().toUpperCase().trim().indexOf(t)){this.setActiveItem(o);break}}this._pressedLetters=[]})),this}withHomeAndEnd(t=!0){return this._homeAndEnd=t,this}setActiveItem(t){const e=this._activeItem;this.updateActiveItem(t),this._activeItem!==e&&this.change.next(this._activeItemIndex)}onKeydown(t){const e=t.keyCode,n=["altKey","ctrlKey","metaKey","shiftKey"].every((e=>!t[e]||this._allowedModifierKeys.indexOf(e)>-1));switch(e){case 9:return void this.tabOut.next();case hz:if(this._vertical&&n){this.setNextItemActive();break}return;case gz:if(this._vertical&&n){this.setPreviousItemActive();break}return;case 39:if(this._horizontal&&n){"rtl"===this._horizontal?this.setPreviousItemActive():this.setNextItemActive();break}return;case 37:if(this._horizontal&&n){"rtl"===this._horizontal?this.setNextItemActive():this.setPreviousItemActive();break}return;case 36:if(this._homeAndEnd&&n){this.setFirstItemActive();break}return;case 35:if(this._homeAndEnd&&n){this.setLastItemActive();break}return;default:return void((n||bz(t,"shiftKey"))&&(t.key&&1===t.key.length?this._letterKeyStream.next(t.key.toLocaleUpperCase()):(e>=65&&e<=90||e>=48&&e<=57)&&this._letterKeyStream.next(String.fromCharCode(e))))}this._pressedLetters=[],t.preventDefault()}get activeItemIndex(){return this._activeItemIndex}get activeItem(){return this._activeItem}isTyping(){return this._pressedLetters.length>0}setFirstItemActive(){this._setActiveItemByIndex(0,1)}setLastItemActive(){this._setActiveItemByIndex(this._items.length-1,-1)}setNextItemActive(){this._activeItemIndex<0?this.setFirstItemActive():this._setActiveItemByDelta(1)}setPreviousItemActive(){this._activeItemIndex<0&&this._wrap?this.setLastItemActive():this._setActiveItemByDelta(-1)}updateActiveItem(t){const e=this._getItemsArray(),n="number"==typeof t?t:e.indexOf(t),o=e[n];this._activeItem=null==o?null:o,this._activeItemIndex=n}_setActiveItemByDelta(t){this._wrap?this._setActiveInWrapMode(t):this._setActiveInDefaultMode(t)}_setActiveInWrapMode(t){const e=this._getItemsArray();for(let n=1;n<=e.length;n++){const o=(this._activeItemIndex+t*n+e.length)%e.length;if(!this._skipPredicateFn(e[o]))return void this.setActiveItem(o)}}_setActiveInDefaultMode(t){this._setActiveItemByIndex(this._activeItemIndex+t,t)}_setActiveItemByIndex(t,e){const n=this._getItemsArray();if(n[t]){for(;this._skipPredicateFn(n[t]);)if(!n[t+=e])return;this.setActiveItem(t)}}_getItemsArray(){return this._items instanceof Vh?this._items.toArray():this._items}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class tI extends $z{setActiveItem(t){this.activeItem&&this.activeItem.setInactiveStyles(),super.setActiveItem(t),this.activeItem&&this.activeItem.setActiveStyles()}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class eI extends $z{constructor(){super(...arguments),this._origin="program"}setFocusOrigin(t){return this._origin=t,this}setActiveItem(t){super.setActiveItem(t),this.activeItem&&this.activeItem.focus(this._origin)}}class nI{constructor(t){this._platform=t}isDisabled(t){return t.hasAttribute("disabled")}isVisible(t){return(function e(t){return!!(t.offsetWidth||t.offsetHeight||"function"==typeof t.getClientRects&&t.getClientRects().length)})(t)&&"visible"===getComputedStyle(t).visibility}isTabbable(t){if(!this._platform.isBrowser)return!1;const e=(function o(t){try{return t.frameElement}catch(t){return null}})((function n(t){return t.ownerDocument&&t.ownerDocument.defaultView||window}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class lI extends aI{constructor(t,e,n,o,i,a,r){super(t,e,n,o,r.defer),this._focusTrapManager=i,this._inertStrategy=a,this._focusTrapManager.register(this)}get enabled(){return this._enabled}set enabled(t){this._enabled=t,this._enabled?this._focusTrapManager.register(this):this._focusTrapManager.deregister(this)}destroy(){this._focusTrapManager.deregister(this),super.destroy()}_enable(){this._inertStrategy.preventFocus(this),this.toggleAnchors(!0)}_disable(){this._inertStrategy.allowFocus(this),this.toggleAnchors(!1)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const cI=new Ga("FOCUS_TRAP_INERT_STRATEGY"),dI="undefined"!=typeof Element&&!!Element.prototype.closest;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function pI(t,e){return t.matches?t.matches(e):t.msMatchesSelector(e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class mI{constructor(){this._listener=null}preventFocus(t){this._listener&&t._document.removeEventListener("focus",this._listener,!0),this._listener=e=>this._trapFocus(t,e),t._ngZone.runOutsideAngular((()=>{t._document.addEventListener("focus",this._listener,!0)}))}allowFocus(t){this._listener&&(t._document.removeEventListener("focus",this._listener,!0),this._listener=null)}_trapFocus(t,e){const n=e.target,o=t._element;o.contains(n)||null!==(function i(t,e){if(!(t instanceof Node))return null;let n=t;for(;null!=n&&!(n instanceof Element);)n=n.parentNode;return n&&(dI?n.closest(e):(function o(t,e){let n=t;for(;null!=n&&!(n instanceof Element&&pI(n,e));)n=n.parentNode;return n||null})(n,e))})(n,"div.cdk-overlay-pane")||setTimeout((()=>{t.enabled&&!o.contains(t._document.activeElement)&&t.focusFirstTabbableElement()}))}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class uI{constructor(){this._focusTrapStack=[]}register(t){this._focusTrapStack=this._focusTrapStack.filter((e=>e!==t));let e=this._focusTrapStack;e.length&&e[e.length-1]._disable(),e.push(t),t._enable()}deregister(t){t._disable();const e=this._focusTrapStack,n=e.indexOf(t);-1!==n&&(e.splice(n,1),e.length&&e[e.length-1]._enable())}}uI.ɵfac=function t(e){return new(e||uI)},uI.ɵprov=Mn({factory:function t(){return new uI},token:uI,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(uI,[{type:im,args:[{providedIn:"root"}]}],(function(){return[]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class fI{constructor(t,e,n,o,i){this._checker=t,this._ngZone=e,this._focusTrapManager=n,this._document=o,this._inertStrategy=i||new mI}create(t,e={defer:!1}){let n;return n="boolean"==typeof e?{defer:e}:e,new lI(t,this._checker,this._ngZone,this._document,this._focusTrapManager,this._inertStrategy,n)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function gI(t){return 0===t.offsetX&&0===t.offsetY}function hI(t){const e=t.touches&&t.touches[0]||t.changedTouches&&t.changedTouches[0];return!(!e||-1!==e.identifier||null!=e.radiusX&&1!==e.radiusX||null!=e.radiusY&&1!==e.radiusY)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */fI.ɵfac=function t(e){return new(e||fI)(vr(nI),vr(a_),vr(uI),vr(Z_),vr(cI,8))},fI.ɵprov=Mn({factory:function t(){return new fI(vr(nI),vr(a_),vr(uI),vr(Z_),vr(cI,8))},token:fI,providedIn:"root"}),fI.ctorParameters=()=>[{type:nI},{type:a_},{type:uI},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[cI]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(fI,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:nI},{type:a_},{type:uI},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[cI]}]}]}),null);const bI=new Ga("cdk-input-modality-detector-options"),yI={ignoreKeys:[18,17,224,91,16]},_I=Nz({passive:!0,capture:!0});class CI{constructor(t,e,n,o){this._platform=t,this._mostRecentTarget=null,this._modality=new F(null),this._lastTouchMs=0,this._onKeydown=t=>{var e,n;(null===(n=null===(e=this._options)||void 0===e?void 0:e.ignoreKeys)||void 0===n?void 0:n.some((e=>e===t.keyCode)))||(this._modality.next("keyboard"),this._mostRecentTarget=Lz(t))},this._onMousedown=t=>{Date.now()-this._lastTouchMs<650||(this._modality.next(gI(t)?"keyboard":"mouse"),this._mostRecentTarget=Lz(t))},this._onTouchstart=t=>{hI(t)?this._modality.next("keyboard"):(this._lastTouchMs=Date.now(),this._modality.next("touch"),this._mostRecentTarget=Lz(t))},this._options=Object.assign(Object.assign({},yI),o),this.modalityDetected=this._modality.pipe(Te(1)),this.modalityChanged=this.modalityDetected.pipe(Me()),t.isBrowser&&e.runOutsideAngular((()=>{n.addEventListener("keydown",this._onKeydown,_I),n.addEventListener("mousedown",this._onMousedown,_I),n.addEventListener("touchstart",this._onTouchstart,_I)}))}get mostRecentModality(){return this._modality.value}ngOnDestroy(){this._platform.isBrowser&&(document.removeEventListener("keydown",this._onKeydown,_I),document.removeEventListener("mousedown",this._onMousedown,_I),document.removeEventListener("touchstart",this._onTouchstart,_I))}}CI.ɵfac=function t(e){return new(e||CI)(vr(wz),vr(a_),vr(Z_),vr(bI,8))},CI.ɵprov=Mn({factory:function t(){return new CI(vr(wz),vr(a_),vr(Z_),vr(bI,8))},token:CI,providedIn:"root"}),CI.ctorParameters=()=>[{type:wz},{type:a_},{type:Document,decorators:[{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[bI]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(CI,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:wz},{type:a_},{type:Document,decorators:[{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[bI]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const MI=new Ga("liveAnnouncerElement",{providedIn:"root",factory:function vI(){return null}}),xI=new Ga("LIVE_ANNOUNCER_DEFAULT_OPTIONS");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class OI{constructor(t,e,n,o){this._ngZone=e,this._defaultOptions=o,this._document=n,this._liveElement=t||this._createLiveElement()}announce(t,...e){const n=this._defaultOptions;let o,i;return 1===e.length&&"number"==typeof e[0]?i=e[0]:[o,i]=e,this.clear(),clearTimeout(this._previousTimeout),o||(o=n&&n.politeness?n.politeness:"polite"),null==i&&n&&(i=n.duration),this._liveElement.setAttribute("aria-live",o),this._ngZone.runOutsideAngular((()=>new Promise((e=>{clearTimeout(this._previousTimeout),this._previousTimeout=setTimeout((()=>{this._liveElement.textContent=t,e(),"number"==typeof i&&(this._previousTimeout=setTimeout((()=>this.clear()),i))}),100)}))))}clear(){this._liveElement&&(this._liveElement.textContent="")}ngOnDestroy(){clearTimeout(this._previousTimeout),this._liveElement&&this._liveElement.parentNode&&(this._liveElement.parentNode.removeChild(this._liveElement),this._liveElement=null)}_createLiveElement(){const t="cdk-live-announcer-element",e=this._document.getElementsByClassName(t),n=this._document.createElement("div");for(let t=0;t<e.length;t++)e[t].parentNode.removeChild(e[t]);return n.classList.add(t),n.classList.add("cdk-visually-hidden"),n.setAttribute("aria-atomic","true"),n.setAttribute("aria-live","polite"),this._document.body.appendChild(n),n}}OI.ɵfac=function t(e){return new(e||OI)(vr(MI,8),vr(a_),vr(Z_),vr(xI,8))},OI.ɵprov=Mn({factory:function t(){return new OI(vr(MI,8),vr(a_),vr(Z_),vr(xI,8))},token:OI,providedIn:"root"}),OI.ctorParameters=()=>[{type:void 0,decorators:[{type:Sr},{type:kr,args:[MI]}]},{type:a_},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[xI]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(OI,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:Sr},{type:kr,args:[MI]}]},{type:a_},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[xI]}]}]}),null);class PI{constructor(t,e,n,o){this._elementRef=t,this._liveAnnouncer=e,this._contentObserver=n,this._ngZone=o,this._politeness="polite"}get politeness(){return this._politeness}set politeness(t){this._politeness="off"===t||"assertive"===t?t:"polite","off"===this._politeness?this._subscription&&(this._subscription.unsubscribe(),this._subscription=null):this._subscription||(this._subscription=this._ngZone.runOutsideAngular((()=>this._contentObserver.observe(this._elementRef).subscribe((()=>{const t=this._elementRef.nativeElement.textContent;t!==this._previousAnnouncedText&&(this._liveAnnouncer.announce(t,this._politeness),this._previousAnnouncedText=t)})))))}ngOnDestroy(){this._subscription&&this._subscription.unsubscribe()}}PI.ɵfac=function t(e){return new(e||PI)(Sm(hg),Sm(OI),Sm(Vz),Sm(a_))},PI.ɵdir=lo({type:PI,selectors:[["","cdkAriaLive",""]],inputs:{politeness:["cdkAriaLive","politeness"]},exportAs:["cdkAriaLive"]}),PI.ctorParameters=()=>[{type:hg},{type:OI},{type:Vz},{type:a_}],PI.propDecorators={politeness:[{type:xy,args:["cdkAriaLive"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(PI,[{type:Cy,args:[{selector:"[cdkAriaLive]",exportAs:"cdkAriaLive"}]}],(function(){return[{type:hg},{type:OI},{type:Vz},{type:a_}]}),{politeness:[{type:xy,args:["cdkAriaLive"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const wI=new Ga("cdk-focus-monitor-default-options"),kI=Nz({passive:!0,capture:!0});class SI{constructor(t,e,n,o,i){this._ngZone=t,this._platform=e,this._inputModalityDetector=n,this._origin=null,this._windowFocused=!1,this._originFromTouchInteraction=!1,this._elementInfo=new Map,this._monitoredElementCount=0,this._rootNodeFocusListenerCount=new Map,this._windowFocusListener=()=>{this._windowFocused=!0,this._windowFocusTimeoutId=setTimeout((()=>this._windowFocused=!1))},this._stopInputModalityDetector=new I,this._rootNodeFocusAndBlurListener=t=>{const e=Lz(t),n="focus"===t.type?this._onFocus:this._onBlur;for(let o=e;o;o=o.parentElement)n.call(this,t,o)},this._document=o,this._detectionMode=(null==i?void 0:i.detectionMode)||0}monitor(t,e=!1){const n=xz(t);if(!this._platform.isBrowser||1!==n.nodeType)return Et(null);const o=Hz(n)||this._getDocument(),i=this._elementInfo.get(n);if(i)return e&&(i.checkChildren=!0),i.subject;const a={checkChildren:e,subject:new I,rootNode:o};return this._elementInfo.set(n,a),this._registerGlobalListeners(a),a.subject}stopMonitoring(t){const e=xz(t),n=this._elementInfo.get(e);n&&(n.subject.complete(),this._setClasses(e),this._elementInfo.delete(e),this._removeGlobalListeners(n))}focusVia(t,e,n){const o=xz(t);o===this._getDocument().activeElement?this._getClosestElementsInfo(o).forEach((([t,n])=>this._originChanged(t,e,n))):(this._setOrigin(e),"function"==typeof o.focus&&o.focus(n))}ngOnDestroy(){this._elementInfo.forEach(((t,e)=>this.stopMonitoring(e)))}_getDocument(){return this._document||document}_getWindow(){return this._getDocument().defaultView||window}_toggleClass(t,e,n){n?t.classList.add(e):t.classList.remove(e)}_getFocusOrigin(t){return this._origin?this._originFromTouchInteraction?this._shouldBeAttributedToTouch(t)?"touch":"program":this._origin:this._windowFocused&&this._lastFocusOrigin?this._lastFocusOrigin:"program"}_shouldBeAttributedToTouch(t){return 1===this._detectionMode||!!(null==t?void 0:t.contains(this._inputModalityDetector._mostRecentTarget))}_setClasses(t,e){this._toggleClass(t,"cdk-focused",!!e),this._toggleClass(t,"cdk-touch-focused","touch"===e),this._toggleClass(t,"cdk-keyboard-focused","keyboard"===e),this._toggleClass(t,"cdk-mouse-focused","mouse"===e),this._toggleClass(t,"cdk-program-focused","program"===e)}_setOrigin(t,e=!1){this._ngZone.runOutsideAngular((()=>{this._origin=t,this._originFromTouchInteraction="touch"===t&&e,0===this._detectionMode&&(clearTimeout(this._originTimeoutId),this._originTimeoutId=setTimeout((()=>this._origin=null),this._originFromTouchInteraction?650:1))}))}_onFocus(t,e){const n=this._elementInfo.get(e),o=Lz(t);n&&(n.checkChildren||e===o)&&this._originChanged(e,this._getFocusOrigin(o),n)}_onBlur(t,e){const n=this._elementInfo.get(e);!n||n.checkChildren&&t.relatedTarget instanceof Node&&e.contains(t.relatedTarget)||(this._setClasses(e),this._emitOrigin(n.subject,null))}_emitOrigin(t,e){this._ngZone.run((()=>t.next(e)))}_registerGlobalListeners(t){if(!this._platform.isBrowser)return;const e=t.rootNode,n=this._rootNodeFocusListenerCount.get(e)||0;n||this._ngZone.runOutsideAngular((()=>{e.addEventListener("focus",this._rootNodeFocusAndBlurListener,kI),e.addEventListener("blur",this._rootNodeFocusAndBlurListener,kI)})),this._rootNodeFocusListenerCount.set(e,n+1),1==++this._monitoredElementCount&&(this._ngZone.runOutsideAngular((()=>{this._getWindow().addEventListener("focus",this._windowFocusListener)})),this._inputModalityDetector.modalityDetected.pipe(Ie(this._stopInputModalityDetector)).subscribe((t=>{this._setOrigin(t,!0)})))}_removeGlobalListeners(t){const e=t.rootNode;if(this._rootNodeFocusListenerCount.has(e)){const t=this._rootNodeFocusListenerCount.get(e);t>1?this._rootNodeFocusListenerCount.set(e,t-1):(e.removeEventListener("focus",this._rootNodeFocusAndBlurListener,kI),e.removeEventListener("blur",this._rootNodeFocusAndBlurListener,kI),this._rootNodeFocusListenerCount.delete(e))}--this._monitoredElementCount||(this._getWindow().removeEventListener("focus",this._windowFocusListener),this._stopInputModalityDetector.next(),clearTimeout(this._windowFocusTimeoutId),clearTimeout(this._originTimeoutId))}_originChanged(t,e,n){this._setClasses(t,e),this._emitOrigin(n.subject,e),this._lastFocusOrigin=e}_getClosestElementsInfo(t){const e=[];return this._elementInfo.forEach(((n,o)=>{(o===t||n.checkChildren&&o.contains(t))&&e.push([o,n])})),e}}SI.ɵfac=function t(e){return new(e||SI)(vr(a_),vr(wz),vr(CI),vr(Z_,8),vr(wI,8))},SI.ɵprov=Mn({factory:function t(){return new SI(vr(a_),vr(wz),vr(CI),vr(Z_,8),vr(wI,8))},token:SI,providedIn:"root"}),SI.ctorParameters=()=>[{type:a_},{type:wz},{type:CI},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wI]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(SI,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:a_},{type:wz},{type:CI},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wI]}]}]}),null);class DI{constructor(t,e){this._elementRef=t,this._focusMonitor=e,this.cdkFocusChange=new Lh}ngAfterViewInit(){const t=this._elementRef.nativeElement;this._monitorSubscription=this._focusMonitor.monitor(t,1===t.nodeType&&t.hasAttribute("cdkMonitorSubtreeFocus")).subscribe((t=>this.cdkFocusChange.emit(t)))}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef),this._monitorSubscription&&this._monitorSubscription.unsubscribe()}}DI.ɵfac=function t(e){return new(e||DI)(Sm(hg),Sm(SI))},DI.ɵdir=lo({type:DI,selectors:[["","cdkMonitorElementFocus",""],["","cdkMonitorSubtreeFocus",""]],outputs:{cdkFocusChange:"cdkFocusChange"}}),DI.ctorParameters=()=>[{type:hg},{type:SI}],DI.propDecorators={cdkFocusChange:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(DI,[{type:Cy,args:[{selector:"[cdkMonitorElementFocus], [cdkMonitorSubtreeFocus]"}]}],(function(){return[{type:hg},{type:SI}]}),{cdkFocusChange:[{type:Oy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const EI="cdk-high-contrast-black-on-white",RI="cdk-high-contrast-white-on-black",AI="cdk-high-contrast-active";class TI{constructor(t,e){this._platform=t,this._document=e}getHighContrastMode(){if(!this._platform.isBrowser)return 0;const t=this._document.createElement("div");t.style.backgroundColor="rgb(1,2,3)",t.style.position="absolute",this._document.body.appendChild(t);const e=this._document.defaultView||window,n=e&&e.getComputedStyle?e.getComputedStyle(t):null,o=(n&&n.backgroundColor||"").replace(/ /g,"");switch(this._document.body.removeChild(t),o){case"rgb(0,0,0)":return 2;case"rgb(255,255,255)":return 1}return 0}_applyBodyHighContrastModeCssClasses(){if(!this._hasCheckedHighContrastMode&&this._platform.isBrowser&&this._document.body){const t=this._document.body.classList;t.remove(AI),t.remove(EI),t.remove(RI),this._hasCheckedHighContrastMode=!0;const e=this.getHighContrastMode();1===e?(t.add(AI),t.add(EI)):2===e&&(t.add(AI),t.add(RI))}}}TI.ɵfac=function t(e){return new(e||TI)(vr(wz),vr(Z_))},TI.ɵprov=Mn({factory:function t(){return new TI(vr(wz),vr(Z_))},token:TI,providedIn:"root"}),TI.ctorParameters=()=>[{type:wz},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(TI,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:wz},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class NI{constructor(t){t._applyBodyHighContrastModeCssClasses()}}NI.ɵfac=function t(e){return new(e||NI)(vr(TI))},NI.ɵmod=ao({type:NI}),NI.ɵinj=vn({imports:[[kz,Uz]]}),NI.ctorParameters=()=>[{type:TI}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(NI,[{type:Ay,args:[{imports:[kz,Uz],declarations:[PI,sI,DI],exports:[PI,sI,DI]}]}],(function(){return[{type:TI}]}),null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(NI,{declarations:function(){return[PI,sI,DI]},imports:function(){return[kz,Uz]},exports:function(){return[PI,sI,DI]}});const zI=new Ga("cdk-dir-doc",{providedIn:"root",factory:function II(){return Or(Z_)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */});class HI{constructor(t){if(this.value="ltr",this.change=new Lh,t){const e=t.documentElement?t.documentElement.dir:null,n=(t.body?t.body.dir:null)||e;this.value="ltr"===n||"rtl"===n?n:"ltr"}}ngOnDestroy(){this.change.complete()}}HI.ɵfac=function t(e){return new(e||HI)(vr(zI,8))},HI.ɵprov=Mn({factory:function t(){return new HI(vr(zI,8))},token:HI,providedIn:"root"}),HI.ctorParameters=()=>[{type:void 0,decorators:[{type:Sr},{type:kr,args:[zI]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(HI,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:Sr},{type:kr,args:[zI]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class FI{constructor(){this._dir="ltr",this._isInitialized=!1,this.change=new Lh}get dir(){return this._dir}set dir(t){const e=this._dir,n=t?t.toLowerCase():t;this._rawDir=t,this._dir="ltr"===n||"rtl"===n?n:"ltr",e!==this._dir&&this._isInitialized&&this.change.emit(this._dir)}get value(){return this.dir}ngAfterContentInit(){this._isInitialized=!0}ngOnDestroy(){this.change.complete()}}FI.ɵfac=function t(e){return new(e||FI)},FI.ɵdir=lo({type:FI,selectors:[["","dir",""]],hostVars:1,hostBindings:function t(e,n){2&e&&jp("dir",n._rawDir)},inputs:{dir:"dir"},outputs:{change:"dirChange"},exportAs:["dir"],features:[pg([{provide:HI,useExisting:FI}])]}),FI.propDecorators={change:[{type:Oy,args:["dirChange"]}],dir:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(FI,[{type:Cy,args:[{selector:"[dir]",providers:[{provide:HI,useExisting:FI}],host:{"[attr.dir]":"_rawDir"},exportAs:"dir"}]}],(function(){return[]}),{change:[{type:Oy,args:["dirChange"]}],dir:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class LI{}LI.ɵfac=function t(e){return new(e||LI)},LI.ɵmod=ao({type:LI}),LI.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(LI,[{type:Ay,args:[{exports:[FI],declarations:[FI]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(LI,{declarations:[FI],exports:[FI]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const BI=new Og("12.2.1"),VI=["*",[["mat-option"],["ng-container"]]];function jI(t,e){if(1&t&&Tm(0,"mat-pseudo-checkbox",4),2&t){const t=Ym();Dm("state",t.selected?"checked":"unchecked")("disabled",t.disabled)}}function UI(t,e){if(1&t&&(Rm(0,"span",5),ku(1),Am()),2&t){const t=Ym();rc(1),Du("(",t.group.label,")")}}new Og("12.2.3");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class GI{}GI.STANDARD_CURVE="cubic-bezier(0.4,0.0,0.2,1)",GI.DECELERATION_CURVE="cubic-bezier(0.0,0.0,0.2,1)",GI.ACCELERATION_CURVE="cubic-bezier(0.4,0.0,1,1)",GI.SHARP_CURVE="cubic-bezier(0.4,0.0,0.6,1)";class WI{}WI.COMPLEX="375ms",WI.ENTERING="225ms",WI.EXITING="195ms";
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const YI=new Og("12.2.3"),qI=new Ga("mat-sanity-checks",{providedIn:"root",factory:function ZI(){return!0}});class XI{constructor(t,e,n){this._hasDoneGlobalChecks=!1,this._document=n,t._applyBodyHighContrastModeCssClasses(),this._sanityChecks=e,this._hasDoneGlobalChecks||(this._checkDoctypeIsDefined(),this._checkThemeIsPresent(),this._checkCdkVersionMatch(),this._hasDoneGlobalChecks=!0)}_getWindow(){const t=this._document.defaultView||window;return"object"==typeof t&&t?t:null}_checkIsEnabled(t){return!(!y_()||this._isTestEnv())&&("boolean"==typeof this._sanityChecks?this._sanityChecks:!!this._sanityChecks[t])}_isTestEnv(){const t=this._getWindow();return t&&(t.__karma__||t.jasmine)}_checkDoctypeIsDefined(){this._checkIsEnabled("doctype")&&!this._document.doctype&&console.warn("Current document does not have a doctype. This may cause some Angular Material components not to behave as expected.")}_checkThemeIsPresent(){if(!this._checkIsEnabled("theme")||!this._document.body||"function"!=typeof getComputedStyle)return;const t=this._document.createElement("div");t.classList.add("mat-theme-loaded-marker"),this._document.body.appendChild(t);const e=getComputedStyle(t);e&&"none"!==e.display&&console.warn("Could not find Angular Material core theme. Most Material components may not work as expected. For more info refer to the theming guide: https://material.angular.io/guide/theming"),this._document.body.removeChild(t)}_checkCdkVersionMatch(){this._checkIsEnabled("version")&&YI.full!==BI.full&&console.warn("The Angular Material version ("+YI.full+") does not match the Angular CDK version ("+BI.full+").\nPlease ensure the versions of these two packages exactly match.")}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function KI(t){return class extends t{constructor(...t){super(...t),this._disabled=!1}get disabled(){return this._disabled}set disabled(t){this._disabled=yz(t)}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function JI(t,e){return class extends t{constructor(...t){super(...t),this.defaultColor=e,this.color=e}get color(){return this._color}set color(t){const e=t||this.defaultColor;e!==this._color&&(this._color&&this._elementRef.nativeElement.classList.remove(`mat-${this._color}`),e&&this._elementRef.nativeElement.classList.add(`mat-${e}`),this._color=e)}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function QI(t){return class extends t{constructor(...t){super(...t),this._disableRipple=!1}get disableRipple(){return this._disableRipple}set disableRipple(t){this._disableRipple=yz(t)}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function $I(t,e=0){return class extends t{constructor(...t){super(...t),this._tabIndex=e,this.defaultTabIndex=e}get tabIndex(){return this.disabled?-1:this._tabIndex}set tabIndex(t){this._tabIndex=null!=t?_z(t):this.defaultTabIndex}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function tH(t){return class extends t{constructor(...t){super(...t),this.stateChanges=new I,this.errorState=!1}updateErrorState(){const t=this.errorState,e=(this.errorStateMatcher||this._defaultErrorStateMatcher).isErrorState(this.ngControl?this.ngControl.control:null,this._parentFormGroup||this._parentForm);e!==t&&(this.errorState=e,this.stateChanges.next())}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function eH(t){return class extends t{constructor(...t){super(...t),this._isInitialized=!1,this._pendingSubscribers=[],this.initialized=new D((t=>{this._isInitialized?this._notifySubscriber(t):this._pendingSubscribers.push(t)}))}_markInitialized(){if(this._isInitialized&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("This directive has already been marked as initialized and should not be called twice.");this._isInitialized=!0,this._pendingSubscribers.forEach(this._notifySubscriber),this._pendingSubscribers=null}_notifySubscriber(t){t.next(),t.complete()}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */XI.ɵfac=function t(e){return new(e||XI)(vr(TI),vr(qI,8),vr(Z_))},XI.ɵmod=ao({type:XI}),XI.ɵinj=vn({imports:[[LI],LI]}),XI.ctorParameters=()=>[{type:TI},{type:void 0,decorators:[{type:Sr},{type:kr,args:[qI]}]},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(XI,[{type:Ay,args:[{imports:[LI],exports:[LI]}]}],(function(){return[{type:TI},{type:void 0,decorators:[{type:Sr},{type:kr,args:[qI]}]},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(XI,{imports:function(){return[LI]},exports:function(){return[LI]}});const nH=new Ga("MAT_DATE_LOCALE",{providedIn:"root",factory:function oH(){return Or(Wy)}});class iH{constructor(){this._localeChanges=new I,this.localeChanges=this._localeChanges}getValidDateOrNull(t){return this.isDateInstance(t)&&this.isValid(t)?t:null}deserialize(t){return null==t||this.isDateInstance(t)&&this.isValid(t)?t:this.invalid()}setLocale(t){this.locale=t,this._localeChanges.next()}compareDate(t,e){return this.getYear(t)-this.getYear(e)||this.getMonth(t)-this.getMonth(e)||this.getDate(t)-this.getDate(e)}sameDate(t,e){if(t&&e){let n=this.isValid(t),o=this.isValid(e);return n&&o?!this.compareDate(t,e):n==o}return t==e}clampDate(t,e,n){return e&&this.compareDate(t,e)<0?e:n&&this.compareDate(t,n)>0?n:t}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const aH=new Ga("mat-date-formats");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let rH;try{rH="undefined"!=typeof Intl}catch(jCt){rH=!1}const sH={long:["January","February","March","April","May","June","July","August","September","October","November","December"],short:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],narrow:["J","F","M","A","M","J","J","A","S","O","N","D"]},lH=pH(31,(t=>String(t+1))),cH={long:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],short:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],narrow:["S","M","T","W","T","F","S"]},dH=/^\d{4}-\d{2}-\d{2}(?:T\d{2}:\d{2}:\d{2}(?:\.\d+)?(?:Z|(?:(?:\+|-)\d{2}:\d{2}))?)?$/;function pH(t,e){const n=Array(t);for(let o=0;o<t;o++)n[o]=e(o);return n}class mH extends iH{constructor(t,e){super(),this.useUtcForDisplay=!0,super.setLocale(t),this.useUtcForDisplay=!e.TRIDENT,this._clampDate=e.TRIDENT||e.EDGE}getYear(t){return t.getFullYear()}getMonth(t){return t.getMonth()}getDate(t){return t.getDate()}getDayOfWeek(t){return t.getDay()}getMonthNames(t){if(rH){const e=new Intl.DateTimeFormat(this.locale,{month:t,timeZone:"utc"});return pH(12,(t=>this._stripDirectionalityCharacters(this._format(e,new Date(2017,t,1)))))}return sH[t]}getDateNames(){if(rH){const t=new Intl.DateTimeFormat(this.locale,{day:"numeric",timeZone:"utc"});return pH(31,(e=>this._stripDirectionalityCharacters(this._format(t,new Date(2017,0,e+1)))))}return lH}getDayOfWeekNames(t){if(rH){const e=new Intl.DateTimeFormat(this.locale,{weekday:t,timeZone:"utc"});return pH(7,(t=>this._stripDirectionalityCharacters(this._format(e,new Date(2017,0,t+1)))))}return cH[t]}getYearName(t){if(rH){const e=new Intl.DateTimeFormat(this.locale,{year:"numeric",timeZone:"utc"});return this._stripDirectionalityCharacters(this._format(e,t))}return String(this.getYear(t))}getFirstDayOfWeek(){return 0}getNumDaysInMonth(t){return this.getDate(this._createDateWithOverflow(this.getYear(t),this.getMonth(t)+1,0))}clone(t){return new Date(t.getTime())}createDate(t,e,n){if("undefined"==typeof ngDevMode||ngDevMode){if(e<0||e>11)throw Error(`Invalid month index "${e}". Month index has to be between 0 and 11.`);if(n<1)throw Error(`Invalid date "${n}". Date has to be greater than 0.`)}let o=this._createDateWithOverflow(t,e,n);if(o.getMonth()!=e&&("undefined"==typeof ngDevMode||ngDevMode))throw Error(`Invalid date "${n}" for month with index "${e}".`);return o}today(){return new Date}parse(t){return"number"==typeof t?new Date(t):t?new Date(Date.parse(t)):null}format(t,e){if(!this.isValid(t))throw Error("NativeDateAdapter: Cannot format invalid date.");if(rH){this._clampDate&&(t.getFullYear()<1||t.getFullYear()>9999)&&(t=this.clone(t)).setFullYear(Math.max(1,Math.min(9999,t.getFullYear()))),e=Object.assign(Object.assign({},e),{timeZone:"utc"});const n=new Intl.DateTimeFormat(this.locale,e);return this._stripDirectionalityCharacters(this._format(n,t))}return this._stripDirectionalityCharacters(t.toDateString())}addCalendarYears(t,e){return this.addCalendarMonths(t,12*e)}addCalendarMonths(t,e){let n=this._createDateWithOverflow(this.getYear(t),this.getMonth(t)+e,this.getDate(t));return this.getMonth(n)!=((this.getMonth(t)+e)%12+12)%12&&(n=this._createDateWithOverflow(this.getYear(n),this.getMonth(n),0)),n}addCalendarDays(t,e){return this._createDateWithOverflow(this.getYear(t),this.getMonth(t),this.getDate(t)+e)}toIso8601(t){return[t.getUTCFullYear(),this._2digit(t.getUTCMonth()+1),this._2digit(t.getUTCDate())].join("-")}deserialize(t){if("string"==typeof t){if(!t)return null;if(dH.test(t)){let e=new Date(t);if(this.isValid(e))return e}}return super.deserialize(t)}isDateInstance(t){return t instanceof Date}isValid(t){return!isNaN(t.getTime())}invalid(){return new Date(NaN)}_createDateWithOverflow(t,e,n){const o=new Date;return o.setFullYear(t,e,n),o.setHours(0,0,0,0),o}_2digit(t){return("00"+t).slice(-2)}_stripDirectionalityCharacters(t){return t.replace(/[\u200e\u200f]/g,"")}_format(t,e){const n=new Date;return n.setUTCFullYear(e.getFullYear(),e.getMonth(),e.getDate()),n.setUTCHours(e.getHours(),e.getMinutes(),e.getSeconds(),e.getMilliseconds()),t.format(n)}}mH.ɵfac=function t(e){return new(e||mH)(vr(nH,8),vr(wz))},mH.ɵprov=Mn({token:mH,factory:mH.ɵfac}),mH.ctorParameters=()=>[{type:String,decorators:[{type:Sr},{type:kr,args:[nH]}]},{type:wz}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(mH,[{type:im}],(function(){return[{type:String,decorators:[{type:Sr},{type:kr,args:[nH]}]},{type:wz}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class uH{}uH.ɵfac=function t(e){return new(e||uH)},uH.ɵmod=ao({type:uH}),uH.ɵinj=vn({providers:[{provide:iH,useClass:mH}],imports:[[kz]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(uH,[{type:Ay,args:[{imports:[kz],providers:[{provide:iH,useClass:mH}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(uH,{imports:function(){return[kz]}});const fH={parse:{dateInput:null},display:{dateInput:{year:"numeric",month:"numeric",day:"numeric"},monthYearLabel:{year:"numeric",month:"short"},dateA11yLabel:{year:"numeric",month:"long",day:"numeric"},monthYearA11yLabel:{year:"numeric",month:"long"}}};class gH{}gH.ɵfac=function t(e){return new(e||gH)},gH.ɵmod=ao({type:gH}),gH.ɵinj=vn({providers:[{provide:aH,useValue:fH}],imports:[[uH]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(gH,[{type:Ay,args:[{imports:[uH],providers:[{provide:aH,useValue:fH}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(gH,{imports:[uH]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class hH{isErrorState(t,e){return!!(t&&t.invalid&&(t.dirty||e&&e.submitted))}}hH.ɵfac=function t(e){return new(e||hH)},hH.ɵprov=Mn({token:hH,factory:hH.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(hH,[{type:im}],null,null);class bH{isErrorState(t,e){return!!(t&&t.invalid&&(t.touched||e&&e.submitted))}}bH.ɵfac=function t(e){return new(e||bH)},bH.ɵprov=Mn({factory:function t(){return new bH},token:bH,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(bH,[{type:im,args:[{providedIn:"root"}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class yH{}yH.ɵfac=function t(e){return new(e||yH)},yH.ɵdir=lo({type:yH,selectors:[["","mat-line",""],["","matLine",""]],hostAttrs:[1,"mat-line"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(yH,[{type:Cy,args:[{selector:"[mat-line], [matLine]",host:{class:"mat-line"}}]}],null,null);class _H{}_H.ɵfac=function t(e){return new(e||_H)},_H.ɵmod=ao({type:_H}),_H.ɵinj=vn({imports:[[XI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(_H,[{type:Ay,args:[{imports:[XI],exports:[yH,XI],declarations:[yH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(_H,{declarations:[yH],imports:[XI],exports:[yH,XI]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class CH{constructor(t,e,n){this._renderer=t,this.element=e,this.config=n,this.state=3}fadeOut(){this._renderer.fadeOutRipple(this)}}const MH={enterDuration:225,exitDuration:150},vH=Nz({passive:!0}),xH=["mousedown","touchstart"],OH=["mouseup","mouseleave","touchend","touchcancel"];class PH{constructor(t,e,n,o){this._target=t,this._ngZone=e,this._isPointerDown=!1,this._activeRipples=new Set,this._pointerUpEventsRegistered=!1,o.isBrowser&&(this._containerElement=xz(n))}fadeInRipple(t,e,n={}){const o=this._containerRect=this._containerRect||this._containerElement.getBoundingClientRect(),i=Object.assign(Object.assign({},MH),n.animation);n.centered&&(t=o.left+o.width/2,e=o.top+o.height/2);const a=n.radius||(function r(t,e,n){const o=Math.max(Math.abs(t-n.left),Math.abs(t-n.right)),i=Math.max(Math.abs(e-n.top),Math.abs(e-n.bottom));return Math.sqrt(o*o+i*i)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t,e,o),s=t-o.left,l=e-o.top,c=i.enterDuration,d=document.createElement("div");d.classList.add("mat-ripple-element"),d.style.left=s-a+"px",d.style.top=l-a+"px",d.style.height=2*a+"px",d.style.width=2*a+"px",null!=n.color&&(d.style.backgroundColor=n.color),d.style.transitionDuration=`${c}ms`,this._containerElement.appendChild(d),(function p(t){window.getComputedStyle(t).getPropertyValue("opacity")})(d),d.style.transform="scale(1)";const m=new CH(this,d,n);return m.state=0,this._activeRipples.add(m),n.persistent||(this._mostRecentTransientRipple=m),this._runTimeoutOutsideZone((()=>{const t=m===this._mostRecentTransientRipple;m.state=1,n.persistent||t&&this._isPointerDown||m.fadeOut()}),c),m}fadeOutRipple(t){const e=this._activeRipples.delete(t);if(t===this._mostRecentTransientRipple&&(this._mostRecentTransientRipple=null),this._activeRipples.size||(this._containerRect=null),!e)return;const n=t.element,o=Object.assign(Object.assign({},MH),t.config.animation);n.style.transitionDuration=`${o.exitDuration}ms`,n.style.opacity="0",t.state=2,this._runTimeoutOutsideZone((()=>{t.state=3,n.parentNode.removeChild(n)}),o.exitDuration)}fadeOutAll(){this._activeRipples.forEach((t=>t.fadeOut()))}fadeOutAllNonPersistent(){this._activeRipples.forEach((t=>{t.config.persistent||t.fadeOut()}))}setupTriggerEvents(t){const e=xz(t);e&&e!==this._triggerElement&&(this._removeTriggerEvents(),this._triggerElement=e,this._registerEvents(xH))}handleEvent(t){"mousedown"===t.type?this._onMousedown(t):"touchstart"===t.type?this._onTouchStart(t):this._onPointerUp(),this._pointerUpEventsRegistered||(this._registerEvents(OH),this._pointerUpEventsRegistered=!0)}_onMousedown(t){const e=gI(t),n=this._lastTouchStartEvent&&Date.now()<this._lastTouchStartEvent+800;this._target.rippleDisabled||e||n||(this._isPointerDown=!0,this.fadeInRipple(t.clientX,t.clientY,this._target.rippleConfig))}_onTouchStart(t){if(!this._target.rippleDisabled&&!hI(t)){this._lastTouchStartEvent=Date.now(),this._isPointerDown=!0;const e=t.changedTouches;for(let t=0;t<e.length;t++)this.fadeInRipple(e[t].clientX,e[t].clientY,this._target.rippleConfig)}}_onPointerUp(){this._isPointerDown&&(this._isPointerDown=!1,this._activeRipples.forEach((t=>{!t.config.persistent&&(1===t.state||t.config.terminateOnPointerUp&&0===t.state)&&t.fadeOut()})))}_runTimeoutOutsideZone(t,e=0){this._ngZone.runOutsideAngular((()=>setTimeout(t,e)))}_registerEvents(t){this._ngZone.runOutsideAngular((()=>{t.forEach((t=>{this._triggerElement.addEventListener(t,this,vH)}))}))}_removeTriggerEvents(){this._triggerElement&&(xH.forEach((t=>{this._triggerElement.removeEventListener(t,this,vH)})),this._pointerUpEventsRegistered&&OH.forEach((t=>{this._triggerElement.removeEventListener(t,this,vH)})))}}const wH=new Ga("mat-ripple-global-options");class kH{constructor(t,e,n,o,i){this._elementRef=t,this._animationMode=i,this.radius=0,this._disabled=!1,this._isInitialized=!1,this._globalOptions=o||{},this._rippleRenderer=new PH(this,e,t,n)}get disabled(){return this._disabled}set disabled(t){t&&this.fadeOutAllNonPersistent(),this._disabled=t,this._setupTriggerEventsIfEnabled()}get trigger(){return this._trigger||this._elementRef.nativeElement}set trigger(t){this._trigger=t,this._setupTriggerEventsIfEnabled()}ngOnInit(){this._isInitialized=!0,this._setupTriggerEventsIfEnabled()}ngOnDestroy(){this._rippleRenderer._removeTriggerEvents()}fadeOutAll(){this._rippleRenderer.fadeOutAll()}fadeOutAllNonPersistent(){this._rippleRenderer.fadeOutAllNonPersistent()}get rippleConfig(){return{centered:this.centered,radius:this.radius,color:this.color,animation:Object.assign(Object.assign(Object.assign({},this._globalOptions.animation),"NoopAnimations"===this._animationMode?{enterDuration:0,exitDuration:0}:{}),this.animation),terminateOnPointerUp:this._globalOptions.terminateOnPointerUp}}get rippleDisabled(){return this.disabled||!!this._globalOptions.disabled}_setupTriggerEventsIfEnabled(){!this.disabled&&this._isInitialized&&this._rippleRenderer.setupTriggerEvents(this.trigger)}launch(t,e=0,n){return"number"==typeof t?this._rippleRenderer.fadeInRipple(t,e,Object.assign(Object.assign({},this.rippleConfig),n)):this._rippleRenderer.fadeInRipple(0,0,Object.assign(Object.assign({},this.rippleConfig),t))}}kH.ɵfac=function t(e){return new(e||kH)(Sm(hg),Sm(a_),Sm(wz),Sm(wH,8),Sm(VP,8))},kH.ɵdir=lo({type:kH,selectors:[["","mat-ripple",""],["","matRipple",""]],hostAttrs:[1,"mat-ripple"],hostVars:2,hostBindings:function t(e,n){2&e&&pu("mat-ripple-unbounded",n.unbounded)},inputs:{radius:["matRippleRadius","radius"],disabled:["matRippleDisabled","disabled"],trigger:["matRippleTrigger","trigger"],color:["matRippleColor","color"],unbounded:["matRippleUnbounded","unbounded"],centered:["matRippleCentered","centered"],animation:["matRippleAnimation","animation"]},exportAs:["matRipple"]}),kH.ctorParameters=()=>[{type:hg},{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],kH.propDecorators={color:[{type:xy,args:["matRippleColor"]}],unbounded:[{type:xy,args:["matRippleUnbounded"]}],centered:[{type:xy,args:["matRippleCentered"]}],radius:[{type:xy,args:["matRippleRadius"]}],animation:[{type:xy,args:["matRippleAnimation"]}],disabled:[{type:xy,args:["matRippleDisabled"]}],trigger:[{type:xy,args:["matRippleTrigger"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(kH,[{type:Cy,args:[{selector:"[mat-ripple], [matRipple]",exportAs:"matRipple",host:{class:"mat-ripple","[class.mat-ripple-unbounded]":"unbounded"}}]}],(function(){return[{type:hg},{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{radius:[{type:xy,args:["matRippleRadius"]}],disabled:[{type:xy,args:["matRippleDisabled"]}],trigger:[{type:xy,args:["matRippleTrigger"]}],color:[{type:xy,args:["matRippleColor"]}],unbounded:[{type:xy,args:["matRippleUnbounded"]}],centered:[{type:xy,args:["matRippleCentered"]}],animation:[{type:xy,args:["matRippleAnimation"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class SH{}SH.ɵfac=function t(e){return new(e||SH)},SH.ɵmod=ao({type:SH}),SH.ɵinj=vn({imports:[[XI,kz],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(SH,[{type:Ay,args:[{imports:[XI,kz],exports:[kH,XI],declarations:[kH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(SH,{declarations:function(){return[kH]},imports:function(){return[XI,kz]},exports:function(){return[kH,XI]}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class DH{constructor(t){this._animationMode=t,this.state="unchecked",this.disabled=!1}}DH.ɵfac=function t(e){return new(e||DH)(Sm(VP,8))},DH.ɵcmp=to({type:DH,selectors:[["mat-pseudo-checkbox"]],hostAttrs:[1,"mat-pseudo-checkbox"],hostVars:8,hostBindings:function t(e,n){2&e&&pu("mat-pseudo-checkbox-indeterminate","indeterminate"===n.state)("mat-pseudo-checkbox-checked","checked"===n.state)("mat-pseudo-checkbox-disabled",n.disabled)("_mat-animation-noopable","NoopAnimations"===n._animationMode)},inputs:{state:"state",disabled:"disabled"},decls:0,vars:0,template:function t(e,n){},styles:['.mat-pseudo-checkbox{width:16px;height:16px;border:2px solid;border-radius:2px;cursor:pointer;display:inline-block;vertical-align:middle;box-sizing:border-box;position:relative;flex-shrink:0;transition:border-color 90ms cubic-bezier(0, 0, 0.2, 0.1),background-color 90ms cubic-bezier(0, 0, 0.2, 0.1)}.mat-pseudo-checkbox::after{position:absolute;opacity:0;content:"";border-bottom:2px solid currentColor;transition:opacity 90ms cubic-bezier(0, 0, 0.2, 0.1)}.mat-pseudo-checkbox.mat-pseudo-checkbox-checked,.mat-pseudo-checkbox.mat-pseudo-checkbox-indeterminate{border-color:transparent}._mat-animation-noopable.mat-pseudo-checkbox{transition:none;animation:none}._mat-animation-noopable.mat-pseudo-checkbox::after{transition:none}.mat-pseudo-checkbox-disabled{cursor:default}.mat-pseudo-checkbox-indeterminate::after{top:5px;left:1px;width:10px;opacity:1;border-radius:2px}.mat-pseudo-checkbox-checked::after{top:2.4px;left:1px;width:8px;height:3px;border-left:2px solid currentColor;transform:rotate(-45deg);opacity:1;box-sizing:content-box}\n'],encapsulation:2,changeDetection:0}),DH.ctorParameters=()=>[{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],DH.propDecorators={state:[{type:xy}],disabled:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(DH,[{type:My,args:[{encapsulation:Hn.None,changeDetection:zn.OnPush,selector:"mat-pseudo-checkbox",template:"",host:{class:"mat-pseudo-checkbox","[class.mat-pseudo-checkbox-indeterminate]":'state === "indeterminate"',"[class.mat-pseudo-checkbox-checked]":'state === "checked"',"[class.mat-pseudo-checkbox-disabled]":"disabled","[class._mat-animation-noopable]":'_animationMode === "NoopAnimations"'},styles:['.mat-pseudo-checkbox{width:16px;height:16px;border:2px solid;border-radius:2px;cursor:pointer;display:inline-block;vertical-align:middle;box-sizing:border-box;position:relative;flex-shrink:0;transition:border-color 90ms cubic-bezier(0, 0, 0.2, 0.1),background-color 90ms cubic-bezier(0, 0, 0.2, 0.1)}.mat-pseudo-checkbox::after{position:absolute;opacity:0;content:"";border-bottom:2px solid currentColor;transition:opacity 90ms cubic-bezier(0, 0, 0.2, 0.1)}.mat-pseudo-checkbox.mat-pseudo-checkbox-checked,.mat-pseudo-checkbox.mat-pseudo-checkbox-indeterminate{border-color:transparent}._mat-animation-noopable.mat-pseudo-checkbox{transition:none;animation:none}._mat-animation-noopable.mat-pseudo-checkbox::after{transition:none}.mat-pseudo-checkbox-disabled{cursor:default}.mat-pseudo-checkbox-indeterminate::after{top:5px;left:1px;width:10px;opacity:1;border-radius:2px}.mat-pseudo-checkbox-checked::after{top:2.4px;left:1px;width:8px;height:3px;border-left:2px solid currentColor;transform:rotate(-45deg);opacity:1;box-sizing:content-box}\n']}]}],(function(){return[{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{state:[{type:xy}],disabled:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class EH{}EH.ɵfac=function t(e){return new(e||EH)},EH.ɵmod=ao({type:EH}),EH.ɵinj=vn({imports:[[XI]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(EH,[{type:Ay,args:[{imports:[XI],exports:[DH],declarations:[DH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(EH,{declarations:[DH],imports:[XI],exports:[DH]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const RH=new Ga("MAT_OPTION_PARENT_COMPONENT"),AH=KI(class{});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let TH=0;class NH extends AH{constructor(t){var e;super(),this._labelId="mat-optgroup-label-"+TH++,this._inert=null!==(e=null==t?void 0:t.inertGroups)&&void 0!==e&&e}}NH.ɵfac=function t(e){return new(e||NH)(Sm(RH,8))},NH.ɵdir=lo({type:NH,inputs:{label:"label"},features:[xp]}),NH.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[RH]},{type:Sr}]}],NH.propDecorators={label:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(NH,[{type:Cy}],(function(){return[{type:void 0,decorators:[{type:kr,args:[RH]},{type:Sr}]}]}),{label:[{type:xy}]});const zH=new Ga("MatOptgroup");class IH extends NH{}IH.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(IH)))(n||IH)}})(),IH.ɵcmp=to({type:IH,selectors:[["mat-optgroup"]],hostAttrs:[1,"mat-optgroup"],hostVars:5,hostBindings:function t(e,n){2&e&&(jp("role",n._inert?null:"group")("aria-disabled",n._inert?null:n.disabled.toString())("aria-labelledby",n._inert?null:n._labelId),pu("mat-optgroup-disabled",n.disabled))},inputs:{disabled:"disabled"},exportAs:["matOptgroup"],features:[pg([{provide:zH,useExisting:IH}]),xp],ngContentSelectors:["*","mat-option, ng-container"],decls:4,vars:2,consts:[["aria-hidden","true",1,"mat-optgroup-label",3,"id"]],template:function t(e,n){1&e&&(Zm(VI),Rm(0,"span",0),ku(1),Xm(2),Am(),Xm(3,1)),2&e&&(Dm("id",n._labelId),rc(1),Du("",n.label," "))},styles:[".mat-optgroup-label{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-optgroup-label[disabled]{cursor:default}[dir=rtl] .mat-optgroup-label{text-align:right}.mat-optgroup-label .mat-icon{margin-right:16px;vertical-align:middle}.mat-optgroup-label .mat-icon svg{vertical-align:top}[dir=rtl] .mat-optgroup-label .mat-icon{margin-left:16px;margin-right:0}\n"],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(IH,[{type:My,args:[{selector:"mat-optgroup",exportAs:"matOptgroup",template:'<span class="mat-optgroup-label" aria-hidden="true" [id]="_labelId">{{ label }} <ng-content></ng-content></span>\n<ng-content select="mat-option, ng-container"></ng-content>\n',encapsulation:Hn.None,changeDetection:zn.OnPush,inputs:["disabled"],host:{class:"mat-optgroup","[attr.role]":'_inert ? null : "group"',"[attr.aria-disabled]":"_inert ? null : disabled.toString()","[attr.aria-labelledby]":"_inert ? null : _labelId","[class.mat-optgroup-disabled]":"disabled"},providers:[{provide:zH,useExisting:IH}],styles:[".mat-optgroup-label{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-optgroup-label[disabled]{cursor:default}[dir=rtl] .mat-optgroup-label{text-align:right}.mat-optgroup-label .mat-icon{margin-right:16px;vertical-align:middle}.mat-optgroup-label .mat-icon svg{vertical-align:top}[dir=rtl] .mat-optgroup-label .mat-icon{margin-left:16px;margin-right:0}\n"]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let HH=0;class FH{constructor(t,e=!1){this.source=t,this.isUserInput=e}}class LH{constructor(t,e,n,o){this._element=t,this._changeDetectorRef=e,this._parent=n,this.group=o,this._selected=!1,this._active=!1,this._disabled=!1,this._mostRecentViewValue="",this.id="mat-option-"+HH++,this.onSelectionChange=new Lh,this._stateChanges=new I}get multiple(){return this._parent&&this._parent.multiple}get selected(){return this._selected}get disabled(){return this.group&&this.group.disabled||this._disabled}set disabled(t){this._disabled=yz(t)}get disableRipple(){return this._parent&&this._parent.disableRipple}get active(){return this._active}get viewValue(){return(this._getHostElement().textContent||"").trim()}select(){this._selected||(this._selected=!0,this._changeDetectorRef.markForCheck(),this._emitSelectionChangeEvent())}deselect(){this._selected&&(this._selected=!1,this._changeDetectorRef.markForCheck(),this._emitSelectionChangeEvent())}focus(t,e){const n=this._getHostElement();"function"==typeof n.focus&&n.focus(e)}setActiveStyles(){this._active||(this._active=!0,this._changeDetectorRef.markForCheck())}setInactiveStyles(){this._active&&(this._active=!1,this._changeDetectorRef.markForCheck())}getLabel(){return this.viewValue}_handleKeydown(t){t.keyCode!==mz&&t.keyCode!==fz||bz(t)||(this._selectViaInteraction(),t.preventDefault())}_selectViaInteraction(){this.disabled||(this._selected=!this.multiple||!this._selected,this._changeDetectorRef.markForCheck(),this._emitSelectionChangeEvent(!0))}_getAriaSelected(){return this.selected||!this.multiple&&null}_getTabIndex(){return this.disabled?"-1":"0"}_getHostElement(){return this._element.nativeElement}ngAfterViewChecked(){if(this._selected){const t=this.viewValue;t!==this._mostRecentViewValue&&(this._mostRecentViewValue=t,this._stateChanges.next())}}ngOnDestroy(){this._stateChanges.complete()}_emitSelectionChangeEvent(t=!1){this.onSelectionChange.emit(new FH(this,t))}}LH.ɵfac=function t(e){return new(e||LH)(Sm(hg),Sm(Ug),Sm(void 0),Sm(NH))},LH.ɵdir=lo({type:LH,inputs:{id:"id",disabled:"disabled",value:"value"},outputs:{onSelectionChange:"onSelectionChange"}}),LH.ctorParameters=()=>[{type:hg},{type:Ug},{type:void 0},{type:NH}],LH.propDecorators={value:[{type:xy}],id:[{type:xy}],disabled:[{type:xy}],onSelectionChange:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(LH,[{type:Cy}],(function(){return[{type:hg},{type:Ug},{type:void 0},{type:NH}]}),{id:[{type:xy}],onSelectionChange:[{type:Oy}],disabled:[{type:xy}],value:[{type:xy}]});class BH extends LH{constructor(t,e,n,o){super(t,e,n,o)}}function VH(t,e,n){if(n.length){let o=e.toArray(),i=n.toArray(),a=0;for(let e=0;e<t+1;e++)o[e].group&&o[e].group===i[a]&&a++;return a}return 0}function jH(t,e,n,o){return t<n?t:t+e>n+o?Math.max(0,t-o+e):n}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */BH.ɵfac=function t(e){return new(e||BH)(Sm(hg),Sm(Ug),Sm(RH,8),Sm(zH,8))},BH.ɵcmp=to({type:BH,selectors:[["mat-option"]],hostAttrs:["role","option",1,"mat-option","mat-focus-indicator"],hostVars:12,hostBindings:function t(e,n){1&e&&Vm("click",(function t(){return n._selectViaInteraction()}))("keydown",(function t(e){return n._handleKeydown(e)})),2&e&&(Tu("id",n.id),jp("tabindex",n._getTabIndex())("aria-selected",n._getAriaSelected())("aria-disabled",n.disabled.toString()),pu("mat-selected",n.selected)("mat-option-multiple",n.multiple)("mat-active",n.active)("mat-option-disabled",n.disabled))},exportAs:["matOption"],features:[xp],ngContentSelectors:["*"],decls:5,vars:4,consts:[["class","mat-option-pseudo-checkbox",3,"state","disabled",4,"ngIf"],[1,"mat-option-text"],["class","cdk-visually-hidden",4,"ngIf"],["mat-ripple","",1,"mat-option-ripple",3,"matRippleTrigger","matRippleDisabled"],[1,"mat-option-pseudo-checkbox",3,"state","disabled"],[1,"cdk-visually-hidden"]],template:function t(e,n){1&e&&(Zm(),Qp(0,jI,1,2,"mat-pseudo-checkbox",0),Rm(1,"span",1),Xm(2),Am(),Qp(3,UI,2,1,"span",2),Tm(4,"div",3)),2&e&&(Dm("ngIf",n.multiple),rc(3),Dm("ngIf",n.group&&n.group._inert),rc(1),Dm("matRippleTrigger",n._getHostElement())("matRippleDisabled",n.disabled||n.disableRipple))},directives:[dM,kH,DH],styles:[".mat-option{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;position:relative;cursor:pointer;outline:none;display:flex;flex-direction:row;max-width:100%;box-sizing:border-box;align-items:center;-webkit-tap-highlight-color:transparent}.mat-option[disabled]{cursor:default}[dir=rtl] .mat-option{text-align:right}.mat-option .mat-icon{margin-right:16px;vertical-align:middle}.mat-option .mat-icon svg{vertical-align:top}[dir=rtl] .mat-option .mat-icon{margin-left:16px;margin-right:0}.mat-option[aria-disabled=true]{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-optgroup .mat-option:not(.mat-option-multiple){padding-left:32px}[dir=rtl] .mat-optgroup .mat-option:not(.mat-option-multiple){padding-left:16px;padding-right:32px}.cdk-high-contrast-active .mat-option{margin:0 1px}.cdk-high-contrast-active .mat-option.mat-active{border:solid 1px currentColor;margin:0}.cdk-high-contrast-active .mat-option[aria-disabled=true]{opacity:.5}.mat-option-text{display:inline-block;flex-grow:1;overflow:hidden;text-overflow:ellipsis}.mat-option .mat-option-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none}.mat-option-pseudo-checkbox{margin-right:8px}[dir=rtl] .mat-option-pseudo-checkbox{margin-left:8px;margin-right:0}\n"],encapsulation:2,changeDetection:0}),BH.ctorParameters=()=>[{type:hg},{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[RH]}]},{type:IH,decorators:[{type:Sr},{type:kr,args:[zH]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(BH,[{type:My,args:[{selector:"mat-option",exportAs:"matOption",host:{role:"option","[attr.tabindex]":"_getTabIndex()","[class.mat-selected]":"selected","[class.mat-option-multiple]":"multiple","[class.mat-active]":"active","[id]":"id","[attr.aria-selected]":"_getAriaSelected()","[attr.aria-disabled]":"disabled.toString()","[class.mat-option-disabled]":"disabled","(click)":"_selectViaInteraction()","(keydown)":"_handleKeydown($event)",class:"mat-option mat-focus-indicator"},template:'<mat-pseudo-checkbox *ngIf="multiple" class="mat-option-pseudo-checkbox"\n    [state]="selected ? \'checked\' : \'unchecked\'" [disabled]="disabled"></mat-pseudo-checkbox>\n\n<span class="mat-option-text"><ng-content></ng-content></span>\n\n\x3c!-- See a11y notes inside optgroup.ts for context behind this element. --\x3e\n<span class="cdk-visually-hidden" *ngIf="group && group._inert">({{ group.label }})</span>\n\n<div class="mat-option-ripple" mat-ripple\n     [matRippleTrigger]="_getHostElement()"\n     [matRippleDisabled]="disabled || disableRipple">\n</div>\n',encapsulation:Hn.None,changeDetection:zn.OnPush,styles:[".mat-option{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;position:relative;cursor:pointer;outline:none;display:flex;flex-direction:row;max-width:100%;box-sizing:border-box;align-items:center;-webkit-tap-highlight-color:transparent}.mat-option[disabled]{cursor:default}[dir=rtl] .mat-option{text-align:right}.mat-option .mat-icon{margin-right:16px;vertical-align:middle}.mat-option .mat-icon svg{vertical-align:top}[dir=rtl] .mat-option .mat-icon{margin-left:16px;margin-right:0}.mat-option[aria-disabled=true]{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-optgroup .mat-option:not(.mat-option-multiple){padding-left:32px}[dir=rtl] .mat-optgroup .mat-option:not(.mat-option-multiple){padding-left:16px;padding-right:32px}.cdk-high-contrast-active .mat-option{margin:0 1px}.cdk-high-contrast-active .mat-option.mat-active{border:solid 1px currentColor;margin:0}.cdk-high-contrast-active .mat-option[aria-disabled=true]{opacity:.5}.mat-option-text{display:inline-block;flex-grow:1;overflow:hidden;text-overflow:ellipsis}.mat-option .mat-option-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none}.mat-option-pseudo-checkbox{margin-right:8px}[dir=rtl] .mat-option-pseudo-checkbox{margin-left:8px;margin-right:0}\n"]}]}],(function(){return[{type:hg},{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[RH]}]},{type:IH,decorators:[{type:Sr},{type:kr,args:[zH]}]}]}),null);class UH{}UH.ɵfac=function t(e){return new(e||UH)},UH.ɵmod=ao({type:UH}),UH.ɵinj=vn({imports:[[SH,WM,XI,EH]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(UH,[{type:Ay,args:[{imports:[SH,WM,XI,EH],exports:[BH,IH],declarations:[BH,IH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(UH,{declarations:function(){return[BH,IH]},imports:function(){return[SH,WM,XI,EH]},exports:function(){return[BH,IH]}});const GH=["mat-button",""],WH=["*"],YH=".mat-button .mat-button-focus-overlay,.mat-icon-button .mat-button-focus-overlay{opacity:0}.mat-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay,.mat-stroked-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay{opacity:.04}@media(hover: none){.mat-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay,.mat-stroked-button:hover:not(.mat-button-disabled) 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.mat-button-focus-overlay,.mat-mini-fab.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-mini-fab::-moz-focus-inner{border:0}._mat-animation-noopable.mat-mini-fab{transition:none;animation:none}.mat-mini-fab .mat-button-wrapper{padding:8px 0;display:inline-block;line-height:24px}.mat-icon-button{padding:0;min-width:0;width:40px;height:40px;flex-shrink:0;line-height:40px;border-radius:50%}.mat-icon-button i,.mat-icon-button .mat-icon{line-height:24px}.mat-button-ripple.mat-ripple,.mat-button-focus-overlay{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none;border-radius:inherit}.mat-button-ripple.mat-ripple:not(:empty){transform:translateZ(0)}.mat-button-focus-overlay{opacity:0;transition:opacity 200ms cubic-bezier(0.35, 0, 0.25, 1),background-color 200ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable .mat-button-focus-overlay{transition:none}.mat-button-ripple-round{border-radius:50%;z-index:1}.mat-button .mat-button-wrapper>*,.mat-flat-button .mat-button-wrapper>*,.mat-stroked-button .mat-button-wrapper>*,.mat-raised-button .mat-button-wrapper>*,.mat-icon-button .mat-button-wrapper>*,.mat-fab .mat-button-wrapper>*,.mat-mini-fab .mat-button-wrapper>*{vertical-align:middle}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon-button,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon-button{display:inline-flex;justify-content:center;align-items:center;font-size:inherit;width:2.5em;height:2.5em}.cdk-high-contrast-active .mat-button,.cdk-high-contrast-active .mat-flat-button,.cdk-high-contrast-active .mat-raised-button,.cdk-high-contrast-active .mat-icon-button,.cdk-high-contrast-active .mat-fab,.cdk-high-contrast-active .mat-mini-fab{outline:solid 1px}.cdk-high-contrast-active .mat-button-base.cdk-keyboard-focused,.cdk-high-contrast-active .mat-button-base.cdk-program-focused{outline:solid 3px}\n",qH=["mat-button","mat-flat-button","mat-icon-button","mat-raised-button","mat-stroked-button","mat-mini-fab","mat-fab"],ZH=JI(KI(QI(class{constructor(t){this._elementRef=t}})));class XH extends ZH{constructor(t,e,n){super(t),this._focusMonitor=e,this._animationMode=n,this.isRoundButton=this._hasHostAttributes("mat-fab","mat-mini-fab"),this.isIconButton=this._hasHostAttributes("mat-icon-button");for(const t of qH)this._hasHostAttributes(t)&&this._getHostElement().classList.add(t);t.nativeElement.classList.add("mat-button-base"),this.isRoundButton&&(this.color="accent")}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0)}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef)}focus(t,e){t?this._focusMonitor.focusVia(this._getHostElement(),t,e):this._getHostElement().focus(e)}_getHostElement(){return this._elementRef.nativeElement}_isRippleDisabled(){return this.disableRipple||this.disabled}_hasHostAttributes(...t){return t.some((t=>this._getHostElement().hasAttribute(t)))}}XH.ɵfac=function t(e){return new(e||XH)(Sm(hg),Sm(SI),Sm(VP,8))},XH.ɵcmp=to({type:XH,selectors:[["button","mat-button",""],["button","mat-raised-button",""],["button","mat-icon-button",""],["button","mat-fab",""],["button","mat-mini-fab",""],["button","mat-stroked-button",""],["button","mat-flat-button",""]],viewQuery:function t(e,n){if(1&e&&Qh(kH,5),2&e){let t;Jh(t=tb())&&(n.ripple=t.first)}},hostAttrs:[1,"mat-focus-indicator"],hostVars:5,hostBindings:function t(e,n){2&e&&(jp("disabled",n.disabled||null),pu("_mat-animation-noopable","NoopAnimations"===n._animationMode)("mat-button-disabled",n.disabled))},inputs:{disabled:"disabled",disableRipple:"disableRipple",color:"color"},exportAs:["matButton"],features:[xp],attrs:GH,ngContentSelectors:WH,decls:4,vars:5,consts:[[1,"mat-button-wrapper"],["matRipple","",1,"mat-button-ripple",3,"matRippleDisabled","matRippleCentered","matRippleTrigger"],[1,"mat-button-focus-overlay"]],template:function t(e,n){1&e&&(Zm(),Rm(0,"span",0),Xm(1),Am(),Tm(2,"span",1),Tm(3,"span",2)),2&e&&(rc(2),pu("mat-button-ripple-round",n.isRoundButton||n.isIconButton),Dm("matRippleDisabled",n._isRippleDisabled())("matRippleCentered",n.isIconButton)("matRippleTrigger",n._getHostElement()))},directives:[kH],styles:[YH],encapsulation:2,changeDetection:0}),XH.ctorParameters=()=>[{type:hg},{type:SI},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],XH.propDecorators={ripple:[{type:Za,args:[kH]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(XH,[{type:My,args:[{selector:"button[mat-button], button[mat-raised-button], button[mat-icon-button],\n             button[mat-fab], button[mat-mini-fab], button[mat-stroked-button],\n             button[mat-flat-button]",exportAs:"matButton",host:{"[attr.disabled]":"disabled || null","[class._mat-animation-noopable]":'_animationMode === "NoopAnimations"',"[class.mat-button-disabled]":"disabled",class:"mat-focus-indicator"},template:'<span class="mat-button-wrapper"><ng-content></ng-content></span>\n<span matRipple class="mat-button-ripple"\n      [class.mat-button-ripple-round]="isRoundButton || isIconButton"\n      [matRippleDisabled]="_isRippleDisabled()"\n      [matRippleCentered]="isIconButton"\n      [matRippleTrigger]="_getHostElement()"></span>\n<span class="mat-button-focus-overlay"></span>\n',inputs:["disabled","disableRipple","color"],encapsulation:Hn.None,changeDetection:zn.OnPush,styles:[".mat-button .mat-button-focus-overlay,.mat-icon-button .mat-button-focus-overlay{opacity:0}.mat-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay,.mat-stroked-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay{opacity:.04}@media(hover: none){.mat-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay,.mat-stroked-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay{opacity:0}}.mat-button,.mat-icon-button,.mat-stroked-button,.mat-flat-button{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 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.mat-mini-fab{outline:solid 1px}.cdk-high-contrast-active .mat-button-base.cdk-keyboard-focused,.cdk-high-contrast-active .mat-button-base.cdk-program-focused{outline:solid 3px}\n"]}]}],(function(){return[{type:hg},{type:SI},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{ripple:[{type:Za,args:[kH]}]});class KH extends XH{constructor(t,e,n){super(e,t,n)}_haltDisabledEvents(t){this.disabled&&(t.preventDefault(),t.stopImmediatePropagation())}}KH.ɵfac=function t(e){return new(e||KH)(Sm(SI),Sm(hg),Sm(VP,8))},KH.ɵcmp=to({type:KH,selectors:[["a","mat-button",""],["a","mat-raised-button",""],["a","mat-icon-button",""],["a","mat-fab",""],["a","mat-mini-fab",""],["a","mat-stroked-button",""],["a","mat-flat-button",""]],hostAttrs:[1,"mat-focus-indicator"],hostVars:7,hostBindings:function t(e,n){1&e&&Vm("click",(function t(e){return 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.mat-button-focus-overlay{opacity:.12}.mat-fab::-moz-focus-inner{border:0}._mat-animation-noopable.mat-fab{transition:none;animation:none}.mat-fab .mat-button-wrapper{padding:16px 0;display:inline-block;line-height:24px}.mat-mini-fab{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 16px;border-radius:4px;overflow:visible;transform:translate3d(0, 0, 0);transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);min-width:0;border-radius:50%;width:40px;height:40px;padding:0;flex-shrink:0}.mat-mini-fab::-moz-focus-inner{border:0}.mat-mini-fab.mat-button-disabled{cursor:default}.mat-mini-fab.cdk-keyboard-focused .mat-button-focus-overlay,.mat-mini-fab.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-mini-fab::-moz-focus-inner{border:0}._mat-animation-noopable.mat-mini-fab{transition:none;animation:none}.mat-mini-fab .mat-button-wrapper{padding:8px 0;display:inline-block;line-height:24px}.mat-icon-button{padding:0;min-width:0;width:40px;height:40px;flex-shrink:0;line-height:40px;border-radius:50%}.mat-icon-button i,.mat-icon-button .mat-icon{line-height:24px}.mat-button-ripple.mat-ripple,.mat-button-focus-overlay{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none;border-radius:inherit}.mat-button-ripple.mat-ripple:not(:empty){transform:translateZ(0)}.mat-button-focus-overlay{opacity:0;transition:opacity 200ms cubic-bezier(0.35, 0, 0.25, 1),background-color 200ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable .mat-button-focus-overlay{transition:none}.mat-button-ripple-round{border-radius:50%;z-index:1}.mat-button .mat-button-wrapper>*,.mat-flat-button .mat-button-wrapper>*,.mat-stroked-button .mat-button-wrapper>*,.mat-raised-button .mat-button-wrapper>*,.mat-icon-button .mat-button-wrapper>*,.mat-fab .mat-button-wrapper>*,.mat-mini-fab .mat-button-wrapper>*{vertical-align:middle}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon-button,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon-button{display:inline-flex;justify-content:center;align-items:center;font-size:inherit;width:2.5em;height:2.5em}.cdk-high-contrast-active .mat-button,.cdk-high-contrast-active .mat-flat-button,.cdk-high-contrast-active .mat-raised-button,.cdk-high-contrast-active .mat-icon-button,.cdk-high-contrast-active .mat-fab,.cdk-high-contrast-active .mat-mini-fab{outline:solid 1px}.cdk-high-contrast-active .mat-button-base.cdk-keyboard-focused,.cdk-high-contrast-active .mat-button-base.cdk-program-focused{outline:solid 3px}\n"]}]}],(function(){return[{type:SI},{type:hg},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{tabIndex:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class JH{}JH.ɵfac=function t(e){return new(e||JH)},JH.ɵmod=ao({type:JH}),JH.ɵinj=vn({imports:[[SH,XI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(JH,[{type:Ay,args:[{imports:[SH,XI],exports:[XH,KH,XI],declarations:[XH,KH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(JH,{declarations:function(){return[XH,KH]},imports:function(){return[SH,XI]},exports:function(){return[XH,KH,XI]}});class QH{}function $H(t){return t&&"function"==typeof t.connect}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class tF extends QH{constructor(t){super(),this._data=t}connect(){return Nt(this._data)?this._data:Et(this._data)}disconnect(){}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class eF{applyChanges(t,e,n,o,i){t.forEachOperation(((t,o,a)=>{let r,s;if(null==t.previousIndex){const i=n(t,o,a);r=e.createEmbeddedView(i.templateRef,i.context,i.index),s=1}else null==a?(e.remove(o),s=3):(r=e.get(o),e.move(r,a),s=2);i&&i({context:null==r?void 0:r.context,operation:s,record:t})}))}detach(){}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class nF{constructor(){this.viewCacheSize=20,this._viewCache=[]}applyChanges(t,e,n,o,i){t.forEachOperation(((t,a,r)=>{let s,l;null==t.previousIndex?(s=this._insertView((()=>n(t,a,r)),r,e,o(t)),l=s?1:0):null==r?(this._detachAndCacheView(a,e),l=3):(s=this._moveView(a,r,e,o(t)),l=2),i&&i({context:null==s?void 0:s.context,operation:l,record:t})}))}detach(){for(const t of this._viewCache)t.destroy();this._viewCache=[]}_insertView(t,e,n,o){const i=this._insertViewFromCache(e,n);if(i)return void(i.context.$implicit=o);const a=t();return n.createEmbeddedView(a.templateRef,a.context,a.index)}_detachAndCacheView(t,e){const n=e.detach(t);this._maybeCacheView(n,e)}_moveView(t,e,n,o){const i=n.get(t);return n.move(i,e),i.context.$implicit=o,i}_maybeCacheView(t,e){if(this._viewCache.length<this.viewCacheSize)this._viewCache.push(t);else{const n=e.indexOf(t);-1===n?t.destroy():e.remove(n)}}_insertViewFromCache(t,e){const n=this._viewCache.pop();return n&&e.insert(n,t),n||null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class oF{constructor(t=!1,e,n=!0){this._multiple=t,this._emitChanges=n,this._selection=new Set,this._deselectedToEmit=[],this._selectedToEmit=[],this.changed=new I,e&&e.length&&(t?e.forEach((t=>this._markSelected(t))):this._markSelected(e[0]),this._selectedToEmit.length=0)}get selected(){return this._selected||(this._selected=Array.from(this._selection.values())),this._selected}select(...t){this._verifyValueAssignment(t),t.forEach((t=>this._markSelected(t))),this._emitChangeEvent()}deselect(...t){this._verifyValueAssignment(t),t.forEach((t=>this._unmarkSelected(t))),this._emitChangeEvent()}toggle(t){this.isSelected(t)?this.deselect(t):this.select(t)}clear(){this._unmarkAll(),this._emitChangeEvent()}isSelected(t){return this._selection.has(t)}isEmpty(){return 0===this._selection.size}hasValue(){return!this.isEmpty()}sort(t){this._multiple&&this.selected&&this._selected.sort(t)}isMultipleSelection(){return this._multiple}_emitChangeEvent(){this._selected=null,(this._selectedToEmit.length||this._deselectedToEmit.length)&&(this.changed.next({source:this,added:this._selectedToEmit,removed:this._deselectedToEmit}),this._deselectedToEmit=[],this._selectedToEmit=[])}_markSelected(t){this.isSelected(t)||(this._multiple||this._unmarkAll(),this._selection.add(t),this._emitChanges&&this._selectedToEmit.push(t))}_unmarkSelected(t){this.isSelected(t)&&(this._selection.delete(t),this._emitChanges&&this._deselectedToEmit.push(t))}_unmarkAll(){this.isEmpty()||this._selection.forEach((t=>this._unmarkSelected(t)))}_verifyValueAssignment(t){if(t.length>1&&!this._multiple&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("Cannot pass multiple values into SelectionModel with single-value mode.")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)()}}class iF{constructor(){this._listeners=[]}notify(t,e){for(let n of this._listeners)n(t,e)}listen(t){return this._listeners.push(t),()=>{this._listeners=this._listeners.filter((e=>t!==e))}}ngOnDestroy(){this._listeners=[]}}iF.ɵfac=function t(e){return new(e||iF)},iF.ɵprov=Mn({factory:function t(){return new iF},token:iF,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(iF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const aF=new Ga("_ViewRepeater"),rF=["contentWrapper"],sF=new Ga("VIRTUAL_SCROLL_STRATEGY");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class lF{constructor(t,e,n){this._scrolledIndexChange=new I,this.scrolledIndexChange=this._scrolledIndexChange.pipe(Me()),this._viewport=null,this._itemSize=t,this._minBufferPx=e,this._maxBufferPx=n}attach(t){this._viewport=t,this._updateTotalContentSize(),this._updateRenderedRange()}detach(){this._scrolledIndexChange.complete(),this._viewport=null}updateItemAndBufferSize(t,e,n){if(n<e&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("CDK virtual scroll: maxBufferPx must be greater than or equal to minBufferPx");this._itemSize=t,this._minBufferPx=e,this._maxBufferPx=n,this._updateTotalContentSize(),this._updateRenderedRange()}onContentScrolled(){this._updateRenderedRange()}onDataLengthChanged(){this._updateTotalContentSize(),this._updateRenderedRange()}onContentRendered(){}onRenderedOffsetChanged(){}scrollToIndex(t,e){this._viewport&&this._viewport.scrollToOffset(t*this._itemSize,e)}_updateTotalContentSize(){this._viewport&&this._viewport.setTotalContentSize(this._viewport.getDataLength()*this._itemSize)}_updateRenderedRange(){if(!this._viewport)return;const t=this._viewport.getRenderedRange(),e={start:t.start,end:t.end},n=this._viewport.getViewportSize(),o=this._viewport.getDataLength();let i=this._viewport.measureScrollOffset(),a=this._itemSize>0?i/this._itemSize:0;if(e.end>o){const t=Math.ceil(n/this._itemSize),r=Math.max(0,Math.min(a,o-t));a!=r&&(a=r,i=r*this._itemSize,e.start=Math.floor(a)),e.end=Math.max(0,Math.min(o,e.start+t))}const r=i-e.start*this._itemSize;if(r<this._minBufferPx&&0!=e.start){const t=Math.ceil((this._maxBufferPx-r)/this._itemSize);e.start=Math.max(0,e.start-t),e.end=Math.min(o,Math.ceil(a+(n+this._minBufferPx)/this._itemSize))}else{const t=e.end*this._itemSize-(i+n);if(t<this._minBufferPx&&e.end!=o){const n=Math.ceil((this._maxBufferPx-t)/this._itemSize);n>0&&(e.end=Math.min(o,e.end+n),e.start=Math.max(0,Math.floor(a-this._minBufferPx/this._itemSize)))}}this._viewport.setRenderedRange(e),this._viewport.setRenderedContentOffset(this._itemSize*e.start),this._scrolledIndexChange.next(Math.floor(a))}}function cF(t){return t._scrollStrategy}class dF{constructor(){this._itemSize=20,this._minBufferPx=100,this._maxBufferPx=200,this._scrollStrategy=new lF(this.itemSize,this.minBufferPx,this.maxBufferPx)}get itemSize(){return this._itemSize}set itemSize(t){this._itemSize=_z(t)}get minBufferPx(){return this._minBufferPx}set minBufferPx(t){this._minBufferPx=_z(t)}get maxBufferPx(){return this._maxBufferPx}set maxBufferPx(t){this._maxBufferPx=_z(t)}ngOnChanges(){this._scrollStrategy.updateItemAndBufferSize(this.itemSize,this.minBufferPx,this.maxBufferPx)}}dF.ɵfac=function t(e){return new(e||dF)},dF.ɵdir=lo({type:dF,selectors:[["cdk-virtual-scroll-viewport","itemSize",""]],inputs:{itemSize:"itemSize",minBufferPx:"minBufferPx",maxBufferPx:"maxBufferPx"},features:[pg([{provide:sF,useFactory:cF,deps:[qe((()=>dF))]}]),Bo]}),dF.propDecorators={itemSize:[{type:xy}],minBufferPx:[{type:xy}],maxBufferPx:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(dF,[{type:Cy,args:[{selector:"cdk-virtual-scroll-viewport[itemSize]",providers:[{provide:sF,useFactory:cF,deps:[qe((()=>dF))]}]}]}],(function(){return[]}),{itemSize:[{type:xy}],minBufferPx:[{type:xy}],maxBufferPx:[{type:xy}]});class pF{constructor(t,e,n){this._ngZone=t,this._platform=e,this._scrolled=new I,this._globalSubscription=null,this._scrolledCount=0,this.scrollContainers=new Map,this._document=n}register(t){this.scrollContainers.has(t)||this.scrollContainers.set(t,t.elementScrolled().subscribe((()=>this._scrolled.next(t))))}deregister(t){const e=this.scrollContainers.get(t);e&&(e.unsubscribe(),this.scrollContainers.delete(t))}scrolled(t=20){return this._platform.isBrowser?new D((e=>{this._globalSubscription||this._addGlobalListener();const n=t>0?this._scrolled.pipe(de(t)).subscribe(e):this._scrolled.subscribe(e);return this._scrolledCount++,()=>{n.unsubscribe(),this._scrolledCount--,this._scrolledCount||this._removeGlobalListener()}})):Et()}ngOnDestroy(){this._removeGlobalListener(),this.scrollContainers.forEach(((t,e)=>this.deregister(e))),this._scrolled.complete()}ancestorScrolled(t,e){const n=this.getAncestorScrollContainers(t);return this.scrolled(e).pipe(ce((t=>!t||n.indexOf(t)>-1)))}getAncestorScrollContainers(t){const e=[];return this.scrollContainers.forEach(((n,o)=>{this._scrollableContainsElement(o,t)&&e.push(o)})),e}_getWindow(){return this._document.defaultView||window}_scrollableContainsElement(t,e){let n=xz(e),o=t.getElementRef().nativeElement;do{if(n==o)return!0}while(n=n.parentElement);return!1}_addGlobalListener(){this._globalSubscription=this._ngZone.runOutsideAngular((()=>oe(this._getWindow().document,"scroll").subscribe((()=>this._scrolled.next()))))}_removeGlobalListener(){this._globalSubscription&&(this._globalSubscription.unsubscribe(),this._globalSubscription=null)}}pF.ɵfac=function t(e){return new(e||pF)(vr(a_),vr(wz),vr(Z_,8))},pF.ɵprov=Mn({factory:function t(){return new pF(vr(a_),vr(wz),vr(Z_,8))},token:pF,providedIn:"root"}),pF.ctorParameters=()=>[{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(pF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class mF{constructor(t,e,n,o){this.elementRef=t,this.scrollDispatcher=e,this.ngZone=n,this.dir=o,this._destroyed=new I,this._elementScrolled=new D((t=>this.ngZone.runOutsideAngular((()=>oe(this.elementRef.nativeElement,"scroll").pipe(Ie(this._destroyed)).subscribe(t)))))}ngOnInit(){this.scrollDispatcher.register(this)}ngOnDestroy(){this.scrollDispatcher.deregister(this),this._destroyed.next(),this._destroyed.complete()}elementScrolled(){return this._elementScrolled}getElementRef(){return this.elementRef}scrollTo(t){const e=this.elementRef.nativeElement,n=this.dir&&"rtl"==this.dir.value;null==t.left&&(t.left=n?t.end:t.start),null==t.right&&(t.right=n?t.start:t.end),null!=t.bottom&&(t.top=e.scrollHeight-e.clientHeight-t.bottom),n&&0!=Iz()?(null!=t.left&&(t.right=e.scrollWidth-e.clientWidth-t.left),2==Iz()?t.left=t.right:1==Iz()&&(t.left=t.right?-t.right:t.right)):null!=t.right&&(t.left=e.scrollWidth-e.clientWidth-t.right),this._applyScrollToOptions(t)}_applyScrollToOptions(t){const e=this.elementRef.nativeElement;zz()?e.scrollTo(t):(null!=t.top&&(e.scrollTop=t.top),null!=t.left&&(e.scrollLeft=t.left))}measureScrollOffset(t){const e="left",n="right",o=this.elementRef.nativeElement;if("top"==t)return o.scrollTop;if("bottom"==t)return o.scrollHeight-o.clientHeight-o.scrollTop;const i=this.dir&&"rtl"==this.dir.value;return"start"==t?t=i?n:e:"end"==t&&(t=i?e:n),i&&2==Iz()?t==e?o.scrollWidth-o.clientWidth-o.scrollLeft:o.scrollLeft:i&&1==Iz()?t==e?o.scrollLeft+o.scrollWidth-o.clientWidth:-o.scrollLeft:t==e?o.scrollLeft:o.scrollWidth-o.clientWidth-o.scrollLeft}}mF.ɵfac=function t(e){return new(e||mF)(Sm(hg),Sm(pF),Sm(a_),Sm(HI,8))},mF.ɵdir=lo({type:mF,selectors:[["","cdk-scrollable",""],["","cdkScrollable",""]]}),mF.ctorParameters=()=>[{type:hg},{type:pF},{type:a_},{type:HI,decorators:[{type:Sr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(mF,[{type:Cy,args:[{selector:"[cdk-scrollable], [cdkScrollable]"}]}],(function(){return[{type:hg},{type:pF},{type:a_},{type:HI,decorators:[{type:Sr}]}]}),null);class uF{constructor(t,e,n){this._platform=t,this._change=new I,this._changeListener=t=>{this._change.next(t)},this._document=n,e.runOutsideAngular((()=>{if(t.isBrowser){const t=this._getWindow();t.addEventListener("resize",this._changeListener),t.addEventListener("orientationchange",this._changeListener)}this.change().subscribe((()=>this._viewportSize=null))}))}ngOnDestroy(){if(this._platform.isBrowser){const t=this._getWindow();t.removeEventListener("resize",this._changeListener),t.removeEventListener("orientationchange",this._changeListener)}this._change.complete()}getViewportSize(){this._viewportSize||this._updateViewportSize();const t={width:this._viewportSize.width,height:this._viewportSize.height};return this._platform.isBrowser||(this._viewportSize=null),t}getViewportRect(){const t=this.getViewportScrollPosition(),{width:e,height:n}=this.getViewportSize();return{top:t.top,left:t.left,bottom:t.top+n,right:t.left+e,height:n,width:e}}getViewportScrollPosition(){if(!this._platform.isBrowser)return{top:0,left:0};const t=this._document,e=this._getWindow(),n=t.documentElement,o=n.getBoundingClientRect();return{top:-o.top||t.body.scrollTop||e.scrollY||n.scrollTop||0,left:-o.left||t.body.scrollLeft||e.scrollX||n.scrollLeft||0}}change(t=20){return t>0?this._change.pipe(de(t)):this._change}_getWindow(){return this._document.defaultView||window}_updateViewportSize(){const t=this._getWindow();this._viewportSize=this._platform.isBrowser?{width:t.innerWidth,height:t.innerHeight}:{width:0,height:0}}}uF.ɵfac=function t(e){return new(e||uF)(vr(wz),vr(a_),vr(Z_,8))},uF.ɵprov=Mn({factory:function t(){return new uF(vr(wz),vr(a_),vr(Z_,8))},token:uF,providedIn:"root"}),uF.ctorParameters=()=>[{type:wz},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(uF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:wz},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]}]}),null);const fF="undefined"!=typeof requestAnimationFrame?at:$;class gF extends mF{constructor(t,e,n,o,i,a,r){if(super(t,a,n,i),this.elementRef=t,this._changeDetectorRef=e,this._scrollStrategy=o,this._detachedSubject=new I,this._renderedRangeSubject=new I,this._orientation="vertical",this._appendOnly=!1,this.scrolledIndexChange=new D((t=>this._scrollStrategy.scrolledIndexChange.subscribe((e=>Promise.resolve().then((()=>this.ngZone.run((()=>t.next(e))))))))),this.renderedRangeStream=this._renderedRangeSubject,this._totalContentSize=0,this._totalContentWidth="",this._totalContentHeight="",this._renderedRange={start:0,end:0},this._dataLength=0,this._viewportSize=0,this._renderedContentOffset=0,this._renderedContentOffsetNeedsRewrite=!1,this._isChangeDetectionPending=!1,this._runAfterChangeDetection=[],this._viewportChanges=m.EMPTY,!o&&("undefined"==typeof ngDevMode||ngDevMode))throw Error('Error: cdk-virtual-scroll-viewport requires the "itemSize" property to be set.');this._viewportChanges=r.change().subscribe((()=>{this.checkViewportSize()}))}get orientation(){return this._orientation}set orientation(t){this._orientation!==t&&(this._orientation=t,this._calculateSpacerSize())}get appendOnly(){return this._appendOnly}set appendOnly(t){this._appendOnly=yz(t)}ngOnInit(){super.ngOnInit(),this.ngZone.runOutsideAngular((()=>Promise.resolve().then((()=>{this._measureViewportSize(),this._scrollStrategy.attach(this),this.elementScrolled().pipe(Ne(null),de(0,fF)).subscribe((()=>this._scrollStrategy.onContentScrolled())),this._markChangeDetectionNeeded()}))))}ngOnDestroy(){this.detach(),this._scrollStrategy.detach(),this._renderedRangeSubject.complete(),this._detachedSubject.complete(),this._viewportChanges.unsubscribe(),super.ngOnDestroy()}attach(t){if(this._forOf&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("CdkVirtualScrollViewport is already attached.");this.ngZone.runOutsideAngular((()=>{this._forOf=t,this._forOf.dataStream.pipe(Ie(this._detachedSubject)).subscribe((t=>{const e=t.length;e!==this._dataLength&&(this._dataLength=e,this._scrollStrategy.onDataLengthChanged()),this._doChangeDetection()}))}))}detach(){this._forOf=null,this._detachedSubject.next()}getDataLength(){return this._dataLength}getViewportSize(){return this._viewportSize}getRenderedRange(){return this._renderedRange}setTotalContentSize(t){this._totalContentSize!==t&&(this._totalContentSize=t,this._calculateSpacerSize(),this._markChangeDetectionNeeded())}setRenderedRange(t){(
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function e(t,n){return t.start==n.start&&t.end==n.end})(this._renderedRange,t)||(this.appendOnly&&(t={start:0,end:Math.max(this._renderedRange.end,t.end)}),this._renderedRangeSubject.next(this._renderedRange=t),this._markChangeDetectionNeeded((()=>this._scrollStrategy.onContentRendered())))}getOffsetToRenderedContentStart(){return this._renderedContentOffsetNeedsRewrite?null:this._renderedContentOffset}setRenderedContentOffset(t,e="to-start"){const n="horizontal"==this.orientation,o=n?"X":"Y";let i=`translate${o}(${Number((n&&this.dir&&"rtl"==this.dir.value?-1:1)*t)}px)`;this._renderedContentOffset=t,"to-end"===e&&(i+=` translate${o}(-100%)`,this._renderedContentOffsetNeedsRewrite=!0),this._renderedContentTransform!=i&&(this._renderedContentTransform=i,this._markChangeDetectionNeeded((()=>{this._renderedContentOffsetNeedsRewrite?(this._renderedContentOffset-=this.measureRenderedContentSize(),this._renderedContentOffsetNeedsRewrite=!1,this.setRenderedContentOffset(this._renderedContentOffset)):this._scrollStrategy.onRenderedOffsetChanged()})))}scrollToOffset(t,e="auto"){const n={behavior:e};"horizontal"===this.orientation?n.start=t:n.top=t,this.scrollTo(n)}scrollToIndex(t,e="auto"){this._scrollStrategy.scrollToIndex(t,e)}measureScrollOffset(t){return super.measureScrollOffset(t||("horizontal"===this.orientation?"start":"top"))}measureRenderedContentSize(){const t=this._contentWrapper.nativeElement;return"horizontal"===this.orientation?t.offsetWidth:t.offsetHeight}measureRangeSize(t){return this._forOf?this._forOf.measureRangeSize(t,this.orientation):0}checkViewportSize(){this._measureViewportSize(),this._scrollStrategy.onDataLengthChanged()}_measureViewportSize(){const t=this.elementRef.nativeElement;this._viewportSize="horizontal"===this.orientation?t.clientWidth:t.clientHeight}_markChangeDetectionNeeded(t){t&&this._runAfterChangeDetection.push(t),this._isChangeDetectionPending||(this._isChangeDetectionPending=!0,this.ngZone.runOutsideAngular((()=>Promise.resolve().then((()=>{this._doChangeDetection()})))))}_doChangeDetection(){this._isChangeDetectionPending=!1,this._contentWrapper.nativeElement.style.transform=this._renderedContentTransform,this.ngZone.run((()=>this._changeDetectorRef.markForCheck()));const t=this._runAfterChangeDetection;this._runAfterChangeDetection=[];for(const e of t)e()}_calculateSpacerSize(){this._totalContentHeight="horizontal"===this.orientation?"":`${this._totalContentSize}px`,this._totalContentWidth="horizontal"===this.orientation?`${this._totalContentSize}px`:""}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function hF(t,e,n){if(!n.getBoundingClientRect)return 0;const o=n.getBoundingClientRect();return"horizontal"===t?"start"===e?o.left:o.right:"start"===e?o.top:o.bottom}gF.ɵfac=function t(e){return new(e||gF)(Sm(hg),Sm(Ug),Sm(a_),Sm(sF,8),Sm(HI,8),Sm(pF),Sm(uF))},gF.ɵcmp=to({type:gF,selectors:[["cdk-virtual-scroll-viewport"]],viewQuery:function t(e,n){if(1&e&&Qh(rF,7),2&e){let t;Jh(t=tb())&&(n._contentWrapper=t.first)}},hostAttrs:[1,"cdk-virtual-scroll-viewport"],hostVars:4,hostBindings:function t(e,n){2&e&&pu("cdk-virtual-scroll-orientation-horizontal","horizontal"===n.orientation)("cdk-virtual-scroll-orientation-vertical","horizontal"!==n.orientation)},inputs:{orientation:"orientation",appendOnly:"appendOnly"},outputs:{scrolledIndexChange:"scrolledIndexChange"},features:[pg([{provide:mF,useExisting:gF}]),xp],ngContentSelectors:["*"],decls:4,vars:4,consts:[[1,"cdk-virtual-scroll-content-wrapper"],["contentWrapper",""],[1,"cdk-virtual-scroll-spacer"]],template:function t(e,n){1&e&&(Zm(),Rm(0,"div",0,1),Xm(2),Am(),Tm(3,"div",2)),2&e&&(rc(3),du("width",n._totalContentWidth)("height",n._totalContentHeight))},styles:["cdk-virtual-scroll-viewport{display:block;position:relative;overflow:auto;contain:strict;transform:translateZ(0);will-change:scroll-position;-webkit-overflow-scrolling:touch}.cdk-virtual-scroll-content-wrapper{position:absolute;top:0;left:0;contain:content}[dir=rtl] .cdk-virtual-scroll-content-wrapper{right:0;left:auto}.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper{min-height:100%}.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>dl:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>ol:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>table:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>ul:not([cdkVirtualFor]){padding-left:0;padding-right:0;margin-left:0;margin-right:0;border-left-width:0;border-right-width:0;outline:none}.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper{min-width:100%}.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>dl:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>ol:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>table:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>ul:not([cdkVirtualFor]){padding-top:0;padding-bottom:0;margin-top:0;margin-bottom:0;border-top-width:0;border-bottom-width:0;outline:none}.cdk-virtual-scroll-spacer{position:absolute;top:0;left:0;height:1px;width:1px;transform-origin:0 0}[dir=rtl] .cdk-virtual-scroll-spacer{right:0;left:auto;transform-origin:100% 0}\n"],encapsulation:2,changeDetection:0}),gF.ctorParameters=()=>[{type:hg},{type:Ug},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[sF]}]},{type:HI,decorators:[{type:Sr}]},{type:pF},{type:uF}],gF.propDecorators={orientation:[{type:xy}],appendOnly:[{type:xy}],scrolledIndexChange:[{type:Oy}],_contentWrapper:[{type:Za,args:["contentWrapper",{static:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(gF,[{type:My,args:[{selector:"cdk-virtual-scroll-viewport",template:'\x3c!--\n  Wrap the rendered content in an element that will be used to offset it based on the scroll\n  position.\n--\x3e\n<div #contentWrapper class="cdk-virtual-scroll-content-wrapper">\n  <ng-content></ng-content>\n</div>\n\x3c!--\n  Spacer used to force the scrolling container to the correct size for the *total* number of items\n  so that the scrollbar captures the size of the entire data set.\n--\x3e\n<div class="cdk-virtual-scroll-spacer"\n     [style.width]="_totalContentWidth" [style.height]="_totalContentHeight"></div>\n',host:{class:"cdk-virtual-scroll-viewport","[class.cdk-virtual-scroll-orientation-horizontal]":'orientation === "horizontal"',"[class.cdk-virtual-scroll-orientation-vertical]":'orientation !== "horizontal"'},encapsulation:Hn.None,changeDetection:zn.OnPush,providers:[{provide:mF,useExisting:gF}],styles:["cdk-virtual-scroll-viewport{display:block;position:relative;overflow:auto;contain:strict;transform:translateZ(0);will-change:scroll-position;-webkit-overflow-scrolling:touch}.cdk-virtual-scroll-content-wrapper{position:absolute;top:0;left:0;contain:content}[dir=rtl] .cdk-virtual-scroll-content-wrapper{right:0;left:auto}.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper{min-height:100%}.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>dl:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>ol:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>table:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>ul:not([cdkVirtualFor]){padding-left:0;padding-right:0;margin-left:0;margin-right:0;border-left-width:0;border-right-width:0;outline:none}.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper{min-width:100%}.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>dl:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>ol:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>table:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>ul:not([cdkVirtualFor]){padding-top:0;padding-bottom:0;margin-top:0;margin-bottom:0;border-top-width:0;border-bottom-width:0;outline:none}.cdk-virtual-scroll-spacer{position:absolute;top:0;left:0;height:1px;width:1px;transform-origin:0 0}[dir=rtl] .cdk-virtual-scroll-spacer{right:0;left:auto;transform-origin:100% 0}\n"]}]}],(function(){return[{type:hg},{type:Ug},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[sF]}]},{type:HI,decorators:[{type:Sr}]},{type:pF},{type:uF}]}),{scrolledIndexChange:[{type:Oy}],orientation:[{type:xy}],appendOnly:[{type:xy}],_contentWrapper:[{type:Za,args:["contentWrapper",{static:!0}]}]});class bF{constructor(t,e,n,o,i,a){this._viewContainerRef=t,this._template=e,this._differs=n,this._viewRepeater=o,this._viewport=i,this.viewChange=new I,this._dataSourceChanges=new I,this.dataStream=this._dataSourceChanges.pipe(Ne(null),De(),ze((([t,e])=>this._changeDataSource(t,e))),Ae(1)),this._differ=null,this._needsUpdate=!1,this._destroyed=new I,this.dataStream.subscribe((t=>{this._data=t,this._onRenderedDataChange()})),this._viewport.renderedRangeStream.pipe(Ie(this._destroyed)).subscribe((t=>{this._renderedRange=t,a.run((()=>this.viewChange.next(this._renderedRange))),this._onRenderedDataChange()})),this._viewport.attach(this)}get cdkVirtualForOf(){return this._cdkVirtualForOf}set cdkVirtualForOf(t){this._cdkVirtualForOf=t,$H(t)?this._dataSourceChanges.next(t):this._dataSourceChanges.next(new tF(Nt(t)?t:Array.from(t||[])))}get cdkVirtualForTrackBy(){return this._cdkVirtualForTrackBy}set cdkVirtualForTrackBy(t){this._needsUpdate=!0,this._cdkVirtualForTrackBy=t?(e,n)=>t(e+(this._renderedRange?this._renderedRange.start:0),n):void 0}set cdkVirtualForTemplate(t){t&&(this._needsUpdate=!0,this._template=t)}get cdkVirtualForTemplateCacheSize(){return this._viewRepeater.viewCacheSize}set cdkVirtualForTemplateCacheSize(t){this._viewRepeater.viewCacheSize=_z(t)}measureRangeSize(t,e){if(t.start>=t.end)return 0;if((t.start<this._renderedRange.start||t.end>this._renderedRange.end)&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Error: attempted to measure an item that isn't rendered.");const n=t.start-this._renderedRange.start,o=t.end-t.start;let i,a;for(let t=0;t<o;t++){const e=this._viewContainerRef.get(t+n);if(e&&e.rootNodes.length){i=a=e.rootNodes[0];break}}for(let t=o-1;t>-1;t--){const e=this._viewContainerRef.get(t+n);if(e&&e.rootNodes.length){a=e.rootNodes[e.rootNodes.length-1];break}}return i&&a?hF(e,"end",a)-hF(e,"start",i):0}ngDoCheck(){if(this._differ&&this._needsUpdate){const t=this._differ.diff(this._renderedItems);t?this._applyChanges(t):this._updateContext(),this._needsUpdate=!1}}ngOnDestroy(){this._viewport.detach(),this._dataSourceChanges.next(void 0),this._dataSourceChanges.complete(),this.viewChange.complete(),this._destroyed.next(),this._destroyed.complete(),this._viewRepeater.detach()}_onRenderedDataChange(){this._renderedRange&&(this._renderedItems=this._data.slice(this._renderedRange.start,this._renderedRange.end),this._differ||(this._differ=this._differs.find(this._renderedItems).create(((t,e)=>this.cdkVirtualForTrackBy?this.cdkVirtualForTrackBy(t,e):e))),this._needsUpdate=!0)}_changeDataSource(t,e){return t&&t.disconnect(this),this._needsUpdate=!0,e?e.connect(this):Et()}_updateContext(){const t=this._data.length;let e=this._viewContainerRef.length;for(;e--;){const n=this._viewContainerRef.get(e);n.context.index=this._renderedRange.start+e,n.context.count=t,this._updateComputedContextProperties(n.context),n.detectChanges()}}_applyChanges(t){this._viewRepeater.applyChanges(t,this._viewContainerRef,((t,e,n)=>this._getEmbeddedViewArgs(t,n)),(t=>t.item)),t.forEachIdentityChange((t=>{this._viewContainerRef.get(t.currentIndex).context.$implicit=t.item}));const e=this._data.length;let n=this._viewContainerRef.length;for(;n--;){const t=this._viewContainerRef.get(n);t.context.index=this._renderedRange.start+n,t.context.count=e,this._updateComputedContextProperties(t.context)}}_updateComputedContextProperties(t){t.first=0===t.index,t.last=t.index===t.count-1,t.even=t.index%2==0,t.odd=!t.even}_getEmbeddedViewArgs(t,e){return{templateRef:this._template,context:{$implicit:t.item,cdkVirtualForOf:this._cdkVirtualForOf,index:-1,count:-1,first:!1,last:!1,odd:!1,even:!1},index:e}}}bF.ɵfac=function t(e){return new(e||bF)(Sm(eh),Sm(Xg),Sm(Hg),Sm(aF),Sm(gF,4),Sm(a_))},bF.ɵdir=lo({type:bF,selectors:[["","cdkVirtualFor","","cdkVirtualForOf",""]],inputs:{cdkVirtualForOf:"cdkVirtualForOf",cdkVirtualForTrackBy:"cdkVirtualForTrackBy",cdkVirtualForTemplate:"cdkVirtualForTemplate",cdkVirtualForTemplateCacheSize:"cdkVirtualForTemplateCacheSize"},features:[pg([{provide:aF,useClass:nF}])]}),bF.ctorParameters=()=>[{type:eh},{type:Xg},{type:Hg},{type:nF,decorators:[{type:kr,args:[aF]}]},{type:gF,decorators:[{type:Er}]},{type:a_}],bF.propDecorators={cdkVirtualForOf:[{type:xy}],cdkVirtualForTrackBy:[{type:xy}],cdkVirtualForTemplate:[{type:xy}],cdkVirtualForTemplateCacheSize:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(bF,[{type:Cy,args:[{selector:"[cdkVirtualFor][cdkVirtualForOf]",providers:[{provide:aF,useClass:nF}]}]}],(function(){return[{type:eh},{type:Xg},{type:Hg},{type:nF,decorators:[{type:kr,args:[aF]}]},{type:gF,decorators:[{type:Er}]},{type:a_}]}),{cdkVirtualForOf:[{type:xy}],cdkVirtualForTrackBy:[{type:xy}],cdkVirtualForTemplate:[{type:xy}],cdkVirtualForTemplateCacheSize:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class yF{}yF.ɵfac=function t(e){return new(e||yF)},yF.ɵmod=ao({type:yF}),yF.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(yF,[{type:Ay,args:[{exports:[mF],declarations:[mF]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(yF,{declarations:[mF],exports:[mF]});class _F{}function CF(){throw Error("Host already has a portal attached")}_F.ɵfac=function t(e){return new(e||_F)},_F.ɵmod=ao({type:_F}),_F.ɵinj=vn({imports:[[LI,kz,yF],LI,yF]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(_F,[{type:Ay,args:[{imports:[LI,kz,yF],exports:[LI,yF,dF,bF,gF],declarations:[dF,bF,gF]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(_F,{declarations:function(){return[dF,bF,gF]},imports:function(){return[LI,kz,yF]},exports:function(){return[LI,yF,dF,bF,gF]}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class MF{attach(t){return("undefined"==typeof ngDevMode||ngDevMode)&&(null==t&&(function e(){throw Error("Attempting to attach a portal to a null PortalOutlet")})(),t.hasAttached()&&CF()),this._attachedHost=t,t.attach(this)}detach(){let t=this._attachedHost;null!=t?(this._attachedHost=null,t.detach()):("undefined"==typeof ngDevMode||ngDevMode)&&(function e(){throw Error("Attempting to detach a portal that is not attached to a host")})()}get isAttached(){return null!=this._attachedHost}setAttachedHost(t){this._attachedHost=t}}class vF extends MF{constructor(t,e,n,o){super(),this.component=t,this.viewContainerRef=e,this.injector=n,this.componentFactoryResolver=o}}class xF extends MF{constructor(t,e,n){super(),this.templateRef=t,this.viewContainerRef=e,this.context=n}get origin(){return this.templateRef.elementRef}attach(t,e=this.context){return this.context=e,super.attach(t)}detach(){return this.context=void 0,super.detach()}}class OF extends MF{constructor(t){super(),this.element=t instanceof hg?t.nativeElement:t}}class PF{constructor(){this._isDisposed=!1,this.attachDomPortal=null}hasAttached(){return!!this._attachedPortal}attach(t){return("undefined"==typeof ngDevMode||ngDevMode)&&(t||(function e(){throw Error("Must provide a portal to attach")})(),this.hasAttached()&&CF(),this._isDisposed&&(function n(){throw Error("This PortalOutlet has already been disposed")})()),t instanceof vF?(this._attachedPortal=t,this.attachComponentPortal(t)):t instanceof xF?(this._attachedPortal=t,this.attachTemplatePortal(t)):this.attachDomPortal&&t instanceof OF?(this._attachedPortal=t,this.attachDomPortal(t)):void(("undefined"==typeof ngDevMode||ngDevMode)&&(function o(){throw Error("Attempting to attach an unknown Portal type. BasePortalOutlet accepts either a ComponentPortal or a TemplatePortal.")})())}detach(){this._attachedPortal&&(this._attachedPortal.setAttachedHost(null),this._attachedPortal=null),this._invokeDisposeFn()}dispose(){this.hasAttached()&&this.detach(),this._invokeDisposeFn(),this._isDisposed=!0}setDisposeFn(t){this._disposeFn=t}_invokeDisposeFn(){this._disposeFn&&(this._disposeFn(),this._disposeFn=null)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class wF extends PF{constructor(t,e,n,o,i){super(),this.outletElement=t,this._componentFactoryResolver=e,this._appRef=n,this._defaultInjector=o,this.attachDomPortal=t=>{if(!this._document&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Cannot attach DOM portal without _document constructor parameter");const e=t.element;if(!e.parentNode&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("DOM portal content must be attached to a parent node.");const n=this._document.createComment("dom-portal");e.parentNode.insertBefore(n,e),this.outletElement.appendChild(e),this._attachedPortal=t,super.setDisposeFn((()=>{n.parentNode&&n.parentNode.replaceChild(e,n)}))},this._document=i}attachComponentPortal(t){const e=(t.componentFactoryResolver||this._componentFactoryResolver).resolveComponentFactory(t.component);let n;return t.viewContainerRef?(n=t.viewContainerRef.createComponent(e,t.viewContainerRef.length,t.injector||t.viewContainerRef.injector),this.setDisposeFn((()=>n.destroy()))):(n=e.create(t.injector||this._defaultInjector),this._appRef.attachView(n.hostView),this.setDisposeFn((()=>{this._appRef.detachView(n.hostView),n.destroy()}))),this.outletElement.appendChild(this._getComponentRootNode(n)),this._attachedPortal=t,n}attachTemplatePortal(t){let e=t.viewContainerRef,n=e.createEmbeddedView(t.templateRef,t.context);return n.rootNodes.forEach((t=>this.outletElement.appendChild(t))),n.detectChanges(),this.setDisposeFn((()=>{let t=e.indexOf(n);-1!==t&&e.remove(t)})),this._attachedPortal=t,n}dispose(){super.dispose(),null!=this.outletElement.parentNode&&this.outletElement.parentNode.removeChild(this.outletElement)}_getComponentRootNode(t){return t.hostView.rootNodes[0]}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class kF extends xF{constructor(t,e){super(t,e)}}kF.ɵfac=function t(e){return new(e||kF)(Sm(Xg),Sm(eh))},kF.ɵdir=lo({type:kF,selectors:[["","cdkPortal",""]],exportAs:["cdkPortal"],features:[xp]}),kF.ctorParameters=()=>[{type:Xg},{type:eh}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(kF,[{type:Cy,args:[{selector:"[cdkPortal]",exportAs:"cdkPortal"}]}],(function(){return[{type:Xg},{type:eh}]}),null);class SF extends kF{}SF.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(SF)))(n||SF)}})(),SF.ɵdir=lo({type:SF,selectors:[["","cdk-portal",""],["","portal",""]],exportAs:["cdkPortal"],features:[pg([{provide:kF,useExisting:SF}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(SF,[{type:Cy,args:[{selector:"[cdk-portal], [portal]",exportAs:"cdkPortal",providers:[{provide:kF,useExisting:SF}]}]}],null,null);class DF extends PF{constructor(t,e,n){super(),this._componentFactoryResolver=t,this._viewContainerRef=e,this._isInitialized=!1,this.attached=new Lh,this.attachDomPortal=t=>{if(!this._document&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Cannot attach DOM portal without _document constructor parameter");const e=t.element;if(!e.parentNode&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("DOM portal content must be attached to a parent node.");const n=this._document.createComment("dom-portal");t.setAttachedHost(this),e.parentNode.insertBefore(n,e),this._getRootNode().appendChild(e),this._attachedPortal=t,super.setDisposeFn((()=>{n.parentNode&&n.parentNode.replaceChild(e,n)}))},this._document=n}get portal(){return this._attachedPortal}set portal(t){(!this.hasAttached()||t||this._isInitialized)&&(this.hasAttached()&&super.detach(),t&&super.attach(t),this._attachedPortal=t)}get attachedRef(){return this._attachedRef}ngOnInit(){this._isInitialized=!0}ngOnDestroy(){super.dispose(),this._attachedPortal=null,this._attachedRef=null}attachComponentPortal(t){t.setAttachedHost(this);const e=null!=t.viewContainerRef?t.viewContainerRef:this._viewContainerRef,n=(t.componentFactoryResolver||this._componentFactoryResolver).resolveComponentFactory(t.component),o=e.createComponent(n,e.length,t.injector||e.injector);return e!==this._viewContainerRef&&this._getRootNode().appendChild(o.hostView.rootNodes[0]),super.setDisposeFn((()=>o.destroy())),this._attachedPortal=t,this._attachedRef=o,this.attached.emit(o),o}attachTemplatePortal(t){t.setAttachedHost(this);const e=this._viewContainerRef.createEmbeddedView(t.templateRef,t.context);return super.setDisposeFn((()=>this._viewContainerRef.clear())),this._attachedPortal=t,this._attachedRef=e,this.attached.emit(e),e}_getRootNode(){const t=this._viewContainerRef.element.nativeElement;return t.nodeType===t.ELEMENT_NODE?t:t.parentNode}}DF.ɵfac=function t(e){return new(e||DF)(Sm(ug),Sm(eh),Sm(Z_))},DF.ɵdir=lo({type:DF,selectors:[["","cdkPortalOutlet",""]],inputs:{portal:["cdkPortalOutlet","portal"]},outputs:{attached:"attached"},exportAs:["cdkPortalOutlet"],features:[xp]}),DF.ctorParameters=()=>[{type:ug},{type:eh},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],DF.propDecorators={attached:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(DF,[{type:Cy,args:[{selector:"[cdkPortalOutlet]",exportAs:"cdkPortalOutlet",inputs:["portal: cdkPortalOutlet"]}]}],(function(){return[{type:ug},{type:eh},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),{attached:[{type:Oy}]});class EF extends DF{}EF.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(EF)))(n||EF)}})(),EF.ɵdir=lo({type:EF,selectors:[["","cdkPortalHost",""],["","portalHost",""]],inputs:{portal:["cdkPortalHost","portal"]},exportAs:["cdkPortalHost"],features:[pg([{provide:DF,useExisting:EF}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(EF,[{type:Cy,args:[{selector:"[cdkPortalHost], [portalHost]",exportAs:"cdkPortalHost",inputs:["portal: cdkPortalHost"],providers:[{provide:DF,useExisting:EF}]}]}],null,null);class RF{}RF.ɵfac=function t(e){return new(e||RF)},RF.ɵmod=ao({type:RF}),RF.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(RF,[{type:Ay,args:[{exports:[kF,DF,SF,EF],declarations:[kF,DF,SF,EF]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(RF,{declarations:[kF,DF,SF,EF],exports:[kF,DF,SF,EF]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const AF=zz();class TF{constructor(t,e){this._viewportRuler=t,this._previousHTMLStyles={top:"",left:""},this._isEnabled=!1,this._document=e}attach(){}enable(){if(this._canBeEnabled()){const t=this._document.documentElement;this._previousScrollPosition=this._viewportRuler.getViewportScrollPosition(),this._previousHTMLStyles.left=t.style.left||"",this._previousHTMLStyles.top=t.style.top||"",t.style.left=vz(-this._previousScrollPosition.left),t.style.top=vz(-this._previousScrollPosition.top),t.classList.add("cdk-global-scrollblock"),this._isEnabled=!0}}disable(){if(this._isEnabled){const t=this._document.documentElement,e=t.style,n=this._document.body.style,o=e.scrollBehavior||"",i=n.scrollBehavior||"";this._isEnabled=!1,e.left=this._previousHTMLStyles.left,e.top=this._previousHTMLStyles.top,t.classList.remove("cdk-global-scrollblock"),AF&&(e.scrollBehavior=n.scrollBehavior="auto"),window.scroll(this._previousScrollPosition.left,this._previousScrollPosition.top),AF&&(e.scrollBehavior=o,n.scrollBehavior=i)}}_canBeEnabled(){if(this._document.documentElement.classList.contains("cdk-global-scrollblock")||this._isEnabled)return!1;const t=this._document.body,e=this._viewportRuler.getViewportSize();return t.scrollHeight>e.height||t.scrollWidth>e.width}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function NF(){return Error("Scroll strategy has already been attached.")}class zF{constructor(t,e,n,o){this._scrollDispatcher=t,this._ngZone=e,this._viewportRuler=n,this._config=o,this._scrollSubscription=null,this._detach=()=>{this.disable(),this._overlayRef.hasAttached()&&this._ngZone.run((()=>this._overlayRef.detach()))}}attach(t){if(this._overlayRef&&("undefined"==typeof ngDevMode||ngDevMode))throw NF();this._overlayRef=t}enable(){if(this._scrollSubscription)return;const t=this._scrollDispatcher.scrolled(0);this._config&&this._config.threshold&&this._config.threshold>1?(this._initialScrollPosition=this._viewportRuler.getViewportScrollPosition().top,this._scrollSubscription=t.subscribe((()=>{const t=this._viewportRuler.getViewportScrollPosition().top;Math.abs(t-this._initialScrollPosition)>this._config.threshold?this._detach():this._overlayRef.updatePosition()}))):this._scrollSubscription=t.subscribe(this._detach)}disable(){this._scrollSubscription&&(this._scrollSubscription.unsubscribe(),this._scrollSubscription=null)}detach(){this.disable(),this._overlayRef=null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class IF{enable(){}disable(){}attach(){}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function HF(t,e){return e.some((e=>t.bottom<e.top||t.top>e.bottom||t.right<e.left||t.left>e.right))}function FF(t,e){return e.some((e=>t.top<e.top||t.bottom>e.bottom||t.left<e.left||t.right>e.right))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class LF{constructor(t,e,n,o){this._scrollDispatcher=t,this._viewportRuler=e,this._ngZone=n,this._config=o,this._scrollSubscription=null}attach(t){if(this._overlayRef&&("undefined"==typeof ngDevMode||ngDevMode))throw NF();this._overlayRef=t}enable(){this._scrollSubscription||(this._scrollSubscription=this._scrollDispatcher.scrolled(this._config?this._config.scrollThrottle:0).subscribe((()=>{if(this._overlayRef.updatePosition(),this._config&&this._config.autoClose){const t=this._overlayRef.overlayElement.getBoundingClientRect(),{width:e,height:n}=this._viewportRuler.getViewportSize();HF(t,[{width:e,height:n,bottom:n,right:e,top:0,left:0}])&&(this.disable(),this._ngZone.run((()=>this._overlayRef.detach())))}})))}disable(){this._scrollSubscription&&(this._scrollSubscription.unsubscribe(),this._scrollSubscription=null)}detach(){this.disable(),this._overlayRef=null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class BF{constructor(t,e,n,o){this._scrollDispatcher=t,this._viewportRuler=e,this._ngZone=n,this.noop=()=>new IF,this.close=t=>new zF(this._scrollDispatcher,this._ngZone,this._viewportRuler,t),this.block=()=>new TF(this._viewportRuler,this._document),this.reposition=t=>new LF(this._scrollDispatcher,this._viewportRuler,this._ngZone,t),this._document=o}}BF.ɵfac=function t(e){return new(e||BF)(vr(pF),vr(uF),vr(a_),vr(Z_))},BF.ɵprov=Mn({factory:function t(){return new BF(vr(pF),vr(uF),vr(a_),vr(Z_))},token:BF,providedIn:"root"}),BF.ctorParameters=()=>[{type:pF},{type:uF},{type:a_},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(BF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:pF},{type:uF},{type:a_},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class VF{constructor(t){if(this.scrollStrategy=new IF,this.panelClass="",this.hasBackdrop=!1,this.backdropClass="cdk-overlay-dark-backdrop",this.disposeOnNavigation=!1,t){const e=Object.keys(t);for(const n of e)void 0!==t[n]&&(this[n]=t[n])}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class jF{constructor(t,e,n,o,i){this.offsetX=n,this.offsetY=o,this.panelClass=i,this.originX=t.originX,this.originY=t.originY,this.overlayX=e.overlayX,this.overlayY=e.overlayY}}class UF{}class GF{constructor(t,e){this.connectionPair=t,this.scrollableViewProperties=e}}function WF(t,e){if("top"!==e&&"bottom"!==e&&"center"!==e)throw Error(`ConnectedPosition: Invalid ${t} "${e}". Expected "top", "bottom" or "center".`)}function YF(t,e){if("start"!==e&&"end"!==e&&"center"!==e)throw Error(`ConnectedPosition: Invalid ${t} "${e}". Expected "start", "end" or "center".`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */GF.ctorParameters=()=>[{type:jF},{type:UF,decorators:[{type:Sr}]}];class qF{constructor(t){this._attachedOverlays=[],this._document=t}ngOnDestroy(){this.detach()}add(t){this.remove(t),this._attachedOverlays.push(t)}remove(t){const e=this._attachedOverlays.indexOf(t);e>-1&&this._attachedOverlays.splice(e,1),0===this._attachedOverlays.length&&this.detach()}}qF.ɵfac=function t(e){return new(e||qF)(vr(Z_))},qF.ɵprov=Mn({factory:function t(){return new qF(vr(Z_))},token:qF,providedIn:"root"}),qF.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(qF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class ZF extends qF{constructor(t){super(t),this._keydownListener=t=>{const e=this._attachedOverlays;for(let n=e.length-1;n>-1;n--)if(e[n]._keydownEvents.observers.length>0){e[n]._keydownEvents.next(t);break}}}add(t){super.add(t),this._isAttached||(this._document.body.addEventListener("keydown",this._keydownListener),this._isAttached=!0)}detach(){this._isAttached&&(this._document.body.removeEventListener("keydown",this._keydownListener),this._isAttached=!1)}}ZF.ɵfac=function t(e){return new(e||ZF)(vr(Z_))},ZF.ɵprov=Mn({factory:function t(){return new ZF(vr(Z_))},token:ZF,providedIn:"root"}),ZF.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(ZF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class XF extends qF{constructor(t,e){super(t),this._platform=e,this._cursorStyleIsSet=!1,this._clickListener=t=>{const e=Lz(t),n=this._attachedOverlays.slice();for(let o=n.length-1;o>-1;o--){const i=n[o];if(!(i._outsidePointerEvents.observers.length<1)&&i.hasAttached()){if(i.overlayElement.contains(e))break;i._outsidePointerEvents.next(t)}}}}add(t){if(super.add(t),!this._isAttached){const t=this._document.body;t.addEventListener("click",this._clickListener,!0),t.addEventListener("auxclick",this._clickListener,!0),t.addEventListener("contextmenu",this._clickListener,!0),this._platform.IOS&&!this._cursorStyleIsSet&&(this._cursorOriginalValue=t.style.cursor,t.style.cursor="pointer",this._cursorStyleIsSet=!0),this._isAttached=!0}}detach(){if(this._isAttached){const t=this._document.body;t.removeEventListener("click",this._clickListener,!0),t.removeEventListener("auxclick",this._clickListener,!0),t.removeEventListener("contextmenu",this._clickListener,!0),this._platform.IOS&&this._cursorStyleIsSet&&(t.style.cursor=this._cursorOriginalValue,this._cursorStyleIsSet=!1),this._isAttached=!1}}}XF.ɵfac=function t(e){return new(e||XF)(vr(Z_),vr(wz))},XF.ɵprov=Mn({factory:function t(){return new XF(vr(Z_),vr(wz))},token:XF,providedIn:"root"}),XF.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(XF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const KF="undefined"!=typeof window?window:{},JF=void 0!==KF.__karma__&&!!KF.__karma__||void 0!==KF.jasmine&&!!KF.jasmine||void 0!==KF.jest&&!!KF.jest||void 0!==KF.Mocha&&!!KF.Mocha;class QF{constructor(t,e){this._platform=e,this._document=t}ngOnDestroy(){const t=this._containerElement;t&&t.parentNode&&t.parentNode.removeChild(t)}getContainerElement(){return this._containerElement||this._createContainer(),this._containerElement}_createContainer(){const t="cdk-overlay-container";if(this._platform.isBrowser||JF){const e=this._document.querySelectorAll(`.${t}[platform="server"], .${t}[platform="test"]`);for(let t=0;t<e.length;t++)e[t].parentNode.removeChild(e[t])}const e=this._document.createElement("div");e.classList.add(t),JF?e.setAttribute("platform","test"):this._platform.isBrowser||e.setAttribute("platform","server"),this._document.body.appendChild(e),this._containerElement=e}}QF.ɵfac=function t(e){return new(e||QF)(vr(Z_),vr(wz))},QF.ɵprov=Mn({factory:function t(){return new QF(vr(Z_),vr(wz))},token:QF,providedIn:"root"}),QF.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(QF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class $F{constructor(t,e,n,o,i,a,r,s,l){this._portalOutlet=t,this._host=e,this._pane=n,this._config=o,this._ngZone=i,this._keyboardDispatcher=a,this._document=r,this._location=s,this._outsideClickDispatcher=l,this._backdropElement=null,this._backdropClick=new I,this._attachments=new I,this._detachments=new I,this._locationChanges=m.EMPTY,this._backdropClickHandler=t=>this._backdropClick.next(t),this._keydownEvents=new I,this._outsidePointerEvents=new I,o.scrollStrategy&&(this._scrollStrategy=o.scrollStrategy,this._scrollStrategy.attach(this)),this._positionStrategy=o.positionStrategy}get overlayElement(){return this._pane}get backdropElement(){return this._backdropElement}get hostElement(){return this._host}attach(t){let e=this._portalOutlet.attach(t);return!this._host.parentElement&&this._previousHostParent&&this._previousHostParent.appendChild(this._host),this._positionStrategy&&this._positionStrategy.attach(this),this._updateStackingOrder(),this._updateElementSize(),this._updateElementDirection(),this._scrollStrategy&&this._scrollStrategy.enable(),this._ngZone.onStable.pipe(be(1)).subscribe((()=>{this.hasAttached()&&this.updatePosition()})),this._togglePointerEvents(!0),this._config.hasBackdrop&&this._attachBackdrop(),this._config.panelClass&&this._toggleClasses(this._pane,this._config.panelClass,!0),this._attachments.next(),this._keyboardDispatcher.add(this),this._config.disposeOnNavigation&&(this._locationChanges=this._location.subscribe((()=>this.dispose()))),this._outsideClickDispatcher.add(this),e}detach(){if(!this.hasAttached())return;this.detachBackdrop(),this._togglePointerEvents(!1),this._positionStrategy&&this._positionStrategy.detach&&this._positionStrategy.detach(),this._scrollStrategy&&this._scrollStrategy.disable();const t=this._portalOutlet.detach();return this._detachments.next(),this._keyboardDispatcher.remove(this),this._detachContentWhenStable(),this._locationChanges.unsubscribe(),this._outsideClickDispatcher.remove(this),t}dispose(){const t=this.hasAttached();this._positionStrategy&&this._positionStrategy.dispose(),this._disposeScrollStrategy(),this.detachBackdrop(),this._locationChanges.unsubscribe(),this._keyboardDispatcher.remove(this),this._portalOutlet.dispose(),this._attachments.complete(),this._backdropClick.complete(),this._keydownEvents.complete(),this._outsidePointerEvents.complete(),this._outsideClickDispatcher.remove(this),this._host&&this._host.parentNode&&(this._host.parentNode.removeChild(this._host),this._host=null),this._previousHostParent=this._pane=null,t&&this._detachments.next(),this._detachments.complete()}hasAttached(){return this._portalOutlet.hasAttached()}backdropClick(){return this._backdropClick}attachments(){return this._attachments}detachments(){return this._detachments}keydownEvents(){return this._keydownEvents}outsidePointerEvents(){return this._outsidePointerEvents}getConfig(){return this._config}updatePosition(){this._positionStrategy&&this._positionStrategy.apply()}updatePositionStrategy(t){t!==this._positionStrategy&&(this._positionStrategy&&this._positionStrategy.dispose(),this._positionStrategy=t,this.hasAttached()&&(t.attach(this),this.updatePosition()))}updateSize(t){this._config=Object.assign(Object.assign({},this._config),t),this._updateElementSize()}setDirection(t){this._config=Object.assign(Object.assign({},this._config),{direction:t}),this._updateElementDirection()}addPanelClass(t){this._pane&&this._toggleClasses(this._pane,t,!0)}removePanelClass(t){this._pane&&this._toggleClasses(this._pane,t,!1)}getDirection(){const t=this._config.direction;return t?"string"==typeof t?t:t.value:"ltr"}updateScrollStrategy(t){t!==this._scrollStrategy&&(this._disposeScrollStrategy(),this._scrollStrategy=t,this.hasAttached()&&(t.attach(this),t.enable()))}_updateElementDirection(){this._host.setAttribute("dir",this.getDirection())}_updateElementSize(){if(!this._pane)return;const t=this._pane.style;t.width=vz(this._config.width),t.height=vz(this._config.height),t.minWidth=vz(this._config.minWidth),t.minHeight=vz(this._config.minHeight),t.maxWidth=vz(this._config.maxWidth),t.maxHeight=vz(this._config.maxHeight)}_togglePointerEvents(t){this._pane.style.pointerEvents=t?"":"none"}_attachBackdrop(){const t="cdk-overlay-backdrop-showing";this._backdropElement=this._document.createElement("div"),this._backdropElement.classList.add("cdk-overlay-backdrop"),this._config.backdropClass&&this._toggleClasses(this._backdropElement,this._config.backdropClass,!0),this._host.parentElement.insertBefore(this._backdropElement,this._host),this._backdropElement.addEventListener("click",this._backdropClickHandler),"undefined"!=typeof requestAnimationFrame?this._ngZone.runOutsideAngular((()=>{requestAnimationFrame((()=>{this._backdropElement&&this._backdropElement.classList.add(t)}))})):this._backdropElement.classList.add(t)}_updateStackingOrder(){this._host.nextSibling&&this._host.parentNode.appendChild(this._host)}detachBackdrop(){let t,e=this._backdropElement;if(!e)return;let n=()=>{e&&(e.removeEventListener("click",this._backdropClickHandler),e.removeEventListener("transitionend",n),e.parentNode&&e.parentNode.removeChild(e)),this._backdropElement==e&&(this._backdropElement=null),this._config.backdropClass&&this._toggleClasses(e,this._config.backdropClass,!1),clearTimeout(t)};e.classList.remove("cdk-overlay-backdrop-showing"),this._ngZone.runOutsideAngular((()=>{e.addEventListener("transitionend",n)})),e.style.pointerEvents="none",t=this._ngZone.runOutsideAngular((()=>setTimeout(n,500)))}_toggleClasses(t,e,n){const o=t.classList;Mz(e).forEach((t=>{t&&(n?o.add(t):o.remove(t))}))}_detachContentWhenStable(){this._ngZone.runOutsideAngular((()=>{const t=this._ngZone.onStable.pipe(Ie(re(this._attachments,this._detachments))).subscribe((()=>{this._pane&&this._host&&0!==this._pane.children.length||(this._pane&&this._config.panelClass&&this._toggleClasses(this._pane,this._config.panelClass,!1),this._host&&this._host.parentElement&&(this._previousHostParent=this._host.parentElement,this._previousHostParent.removeChild(this._host)),t.unsubscribe())}))}))}_disposeScrollStrategy(){const t=this._scrollStrategy;t&&(t.disable(),t.detach&&t.detach())}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const tL="cdk-overlay-connected-position-bounding-box",eL=/([A-Za-z%]+)$/;class nL{constructor(t,e,n,o,i){this._viewportRuler=e,this._document=n,this._platform=o,this._overlayContainer=i,this._lastBoundingBoxSize={width:0,height:0},this._isPushed=!1,this._canPush=!0,this._growAfterOpen=!1,this._hasFlexibleDimensions=!0,this._positionLocked=!1,this._viewportMargin=0,this._scrollables=[],this._preferredPositions=[],this._positionChanges=new I,this._resizeSubscription=m.EMPTY,this._offsetX=0,this._offsetY=0,this._appliedPanelClasses=[],this.positionChanges=this._positionChanges,this.setOrigin(t)}get positions(){return this._preferredPositions}attach(t){if(this._overlayRef&&t!==this._overlayRef&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("This position strategy is already attached to an overlay");this._validatePositions(),t.hostElement.classList.add(tL),this._overlayRef=t,this._boundingBox=t.hostElement,this._pane=t.overlayElement,this._isDisposed=!1,this._isInitialRender=!0,this._lastPosition=null,this._resizeSubscription.unsubscribe(),this._resizeSubscription=this._viewportRuler.change().subscribe((()=>{this._isInitialRender=!0,this.apply()}))}apply(){if(this._isDisposed||!this._platform.isBrowser)return;if(!this._isInitialRender&&this._positionLocked&&this._lastPosition)return void this.reapplyLastPosition();this._clearPanelClasses(),this._resetOverlayElementStyles(),this._resetBoundingBoxStyles(),this._viewportRect=this._getNarrowedViewportRect(),this._originRect=this._getOriginRect(),this._overlayRect=this._pane.getBoundingClientRect();const t=this._originRect,e=this._overlayRect,n=this._viewportRect,o=[];let i;for(let a of this._preferredPositions){let r=this._getOriginPoint(t,a),s=this._getOverlayPoint(r,e,a),l=this._getOverlayFit(s,e,n,a);if(l.isCompletelyWithinViewport)return this._isPushed=!1,void this._applyPosition(a,r);this._canFitWithFlexibleDimensions(l,s,n)?o.push({position:a,origin:r,overlayRect:e,boundingBoxRect:this._calculateBoundingBoxRect(r,a)}):(!i||i.overlayFit.visibleArea<l.visibleArea)&&(i={overlayFit:l,overlayPoint:s,originPoint:r,position:a,overlayRect:e})}if(o.length){let t=null,e=-1;for(const n of o){const o=n.boundingBoxRect.width*n.boundingBoxRect.height*(n.position.weight||1);o>e&&(e=o,t=n)}return this._isPushed=!1,void this._applyPosition(t.position,t.origin)}if(this._canPush)return this._isPushed=!0,void this._applyPosition(i.position,i.originPoint);this._applyPosition(i.position,i.originPoint)}detach(){this._clearPanelClasses(),this._lastPosition=null,this._previousPushAmount=null,this._resizeSubscription.unsubscribe()}dispose(){this._isDisposed||(this._boundingBox&&oL(this._boundingBox.style,{top:"",left:"",right:"",bottom:"",height:"",width:"",alignItems:"",justifyContent:""}),this._pane&&this._resetOverlayElementStyles(),this._overlayRef&&this._overlayRef.hostElement.classList.remove(tL),this.detach(),this._positionChanges.complete(),this._overlayRef=this._boundingBox=null,this._isDisposed=!0)}reapplyLastPosition(){if(!this._isDisposed&&(!this._platform||this._platform.isBrowser)){this._originRect=this._getOriginRect(),this._overlayRect=this._pane.getBoundingClientRect(),this._viewportRect=this._getNarrowedViewportRect();const t=this._lastPosition||this._preferredPositions[0],e=this._getOriginPoint(this._originRect,t);this._applyPosition(t,e)}}withScrollableContainers(t){return this._scrollables=t,this}withPositions(t){return this._preferredPositions=t,-1===t.indexOf(this._lastPosition)&&(this._lastPosition=null),this._validatePositions(),this}withViewportMargin(t){return this._viewportMargin=t,this}withFlexibleDimensions(t=!0){return this._hasFlexibleDimensions=t,this}withGrowAfterOpen(t=!0){return this._growAfterOpen=t,this}withPush(t=!0){return this._canPush=t,this}withLockedPosition(t=!0){return this._positionLocked=t,this}setOrigin(t){return this._origin=t,this}withDefaultOffsetX(t){return this._offsetX=t,this}withDefaultOffsetY(t){return this._offsetY=t,this}withTransformOriginOn(t){return this._transformOriginSelector=t,this}_getOriginPoint(t,e){let n,o;if("center"==e.originX)n=t.left+t.width/2;else{const o=this._isRtl()?t.right:t.left,i=this._isRtl()?t.left:t.right;n="start"==e.originX?o:i}return o="center"==e.originY?t.top+t.height/2:"top"==e.originY?t.top:t.bottom,{x:n,y:o}}_getOverlayPoint(t,e,n){let o,i;return o="center"==n.overlayX?-e.width/2:"start"===n.overlayX?this._isRtl()?-e.width:0:this._isRtl()?0:-e.width,i="center"==n.overlayY?-e.height/2:"top"==n.overlayY?0:-e.height,{x:t.x+o,y:t.y+i}}_getOverlayFit(t,e,n,o){const i=aL(e);let{x:a,y:r}=t,s=this._getOffset(o,"x"),l=this._getOffset(o,"y");s&&(a+=s),l&&(r+=l);let c=0-r,d=r+i.height-n.height,p=this._subtractOverflows(i.width,0-a,a+i.width-n.width),m=this._subtractOverflows(i.height,c,d),u=p*m;return{visibleArea:u,isCompletelyWithinViewport:i.width*i.height===u,fitsInViewportVertically:m===i.height,fitsInViewportHorizontally:p==i.width}}_canFitWithFlexibleDimensions(t,e,n){if(this._hasFlexibleDimensions){const o=n.bottom-e.y,i=n.right-e.x,a=iL(this._overlayRef.getConfig().minHeight),r=iL(this._overlayRef.getConfig().minWidth),s=t.fitsInViewportHorizontally||null!=r&&r<=i;return(t.fitsInViewportVertically||null!=a&&a<=o)&&s}return!1}_pushOverlayOnScreen(t,e,n){if(this._previousPushAmount&&this._positionLocked)return{x:t.x+this._previousPushAmount.x,y:t.y+this._previousPushAmount.y};const o=aL(e),i=this._viewportRect,a=Math.max(t.x+o.width-i.width,0),r=Math.max(t.y+o.height-i.height,0),s=Math.max(i.top-n.top-t.y,0),l=Math.max(i.left-n.left-t.x,0);let c=0,d=0;return c=o.width<=i.width?l||-a:t.x<this._viewportMargin?i.left-n.left-t.x:0,d=o.height<=i.height?s||-r:t.y<this._viewportMargin?i.top-n.top-t.y:0,this._previousPushAmount={x:c,y:d},{x:t.x+c,y:t.y+d}}_applyPosition(t,e){if(this._setTransformOrigin(t),this._setOverlayElementStyles(e,t),this._setBoundingBoxStyles(e,t),t.panelClass&&this._addPanelClasses(t.panelClass),this._lastPosition=t,this._positionChanges.observers.length){const e=this._getScrollVisibility(),n=new GF(t,e);this._positionChanges.next(n)}this._isInitialRender=!1}_setTransformOrigin(t){if(!this._transformOriginSelector)return;const e=this._boundingBox.querySelectorAll(this._transformOriginSelector);let n,o=t.overlayY;n="center"===t.overlayX?"center":this._isRtl()?"start"===t.overlayX?"right":"left":"start"===t.overlayX?"left":"right";for(let t=0;t<e.length;t++)e[t].style.transformOrigin=`${n} ${o}`}_calculateBoundingBoxRect(t,e){const n=this._viewportRect,o=this._isRtl();let i,a,r,s,l,c;if("top"===e.overlayY)a=t.y,i=n.height-a+this._viewportMargin;else if("bottom"===e.overlayY)r=n.height-t.y+2*this._viewportMargin,i=n.height-r+this._viewportMargin;else{const e=Math.min(n.bottom-t.y+n.top,t.y),o=this._lastBoundingBoxSize.height;i=2*e,a=t.y-e,i>o&&!this._isInitialRender&&!this._growAfterOpen&&(a=t.y-o/2)}if("end"===e.overlayX&&!o||"start"===e.overlayX&&o)c=n.width-t.x+this._viewportMargin,s=t.x-this._viewportMargin;else if("start"===e.overlayX&&!o||"end"===e.overlayX&&o)l=t.x,s=n.right-t.x;else{const e=Math.min(n.right-t.x+n.left,t.x),o=this._lastBoundingBoxSize.width;s=2*e,l=t.x-e,s>o&&!this._isInitialRender&&!this._growAfterOpen&&(l=t.x-o/2)}return{top:a,left:l,bottom:r,right:c,width:s,height:i}}_setBoundingBoxStyles(t,e){const n=this._calculateBoundingBoxRect(t,e);this._isInitialRender||this._growAfterOpen||(n.height=Math.min(n.height,this._lastBoundingBoxSize.height),n.width=Math.min(n.width,this._lastBoundingBoxSize.width));const o={};if(this._hasExactPosition())o.top=o.left="0",o.bottom=o.right=o.maxHeight=o.maxWidth="",o.width=o.height="100%";else{const t=this._overlayRef.getConfig().maxHeight,i=this._overlayRef.getConfig().maxWidth;o.height=vz(n.height),o.top=vz(n.top),o.bottom=vz(n.bottom),o.width=vz(n.width),o.left=vz(n.left),o.right=vz(n.right),o.alignItems="center"===e.overlayX?"center":"end"===e.overlayX?"flex-end":"flex-start",o.justifyContent="center"===e.overlayY?"center":"bottom"===e.overlayY?"flex-end":"flex-start",t&&(o.maxHeight=vz(t)),i&&(o.maxWidth=vz(i))}this._lastBoundingBoxSize=n,oL(this._boundingBox.style,o)}_resetBoundingBoxStyles(){oL(this._boundingBox.style,{top:"0",left:"0",right:"0",bottom:"0",height:"",width:"",alignItems:"",justifyContent:""})}_resetOverlayElementStyles(){oL(this._pane.style,{top:"",left:"",bottom:"",right:"",position:"",transform:""})}_setOverlayElementStyles(t,e){const n={},o=this._hasExactPosition(),i=this._hasFlexibleDimensions,a=this._overlayRef.getConfig();if(o){const o=this._viewportRuler.getViewportScrollPosition();oL(n,this._getExactOverlayY(e,t,o)),oL(n,this._getExactOverlayX(e,t,o))}else n.position="static";let r="",s=this._getOffset(e,"x"),l=this._getOffset(e,"y");s&&(r+=`translateX(${s}px) `),l&&(r+=`translateY(${l}px)`),n.transform=r.trim(),a.maxHeight&&(o?n.maxHeight=vz(a.maxHeight):i&&(n.maxHeight="")),a.maxWidth&&(o?n.maxWidth=vz(a.maxWidth):i&&(n.maxWidth="")),oL(this._pane.style,n)}_getExactOverlayY(t,e,n){let o={top:"",bottom:""},i=this._getOverlayPoint(e,this._overlayRect,t);this._isPushed&&(i=this._pushOverlayOnScreen(i,this._overlayRect,n));let a=this._overlayContainer.getContainerElement().getBoundingClientRect().top;return i.y-=a,"bottom"===t.overlayY?o.bottom=this._document.documentElement.clientHeight-(i.y+this._overlayRect.height)+"px":o.top=vz(i.y),o}_getExactOverlayX(t,e,n){let o,i={left:"",right:""},a=this._getOverlayPoint(e,this._overlayRect,t);return this._isPushed&&(a=this._pushOverlayOnScreen(a,this._overlayRect,n)),o=this._isRtl()?"end"===t.overlayX?"left":"right":"end"===t.overlayX?"right":"left","right"===o?i.right=this._document.documentElement.clientWidth-(a.x+this._overlayRect.width)+"px":i.left=vz(a.x),i}_getScrollVisibility(){const t=this._getOriginRect(),e=this._pane.getBoundingClientRect(),n=this._scrollables.map((t=>t.getElementRef().nativeElement.getBoundingClientRect()));return{isOriginClipped:FF(t,n),isOriginOutsideView:HF(t,n),isOverlayClipped:FF(e,n),isOverlayOutsideView:HF(e,n)}}_subtractOverflows(t,...e){return e.reduce(((t,e)=>t-Math.max(e,0)),t)}_getNarrowedViewportRect(){const t=this._document.documentElement.clientWidth,e=this._document.documentElement.clientHeight,n=this._viewportRuler.getViewportScrollPosition();return{top:n.top+this._viewportMargin,left:n.left+this._viewportMargin,right:n.left+t-this._viewportMargin,bottom:n.top+e-this._viewportMargin,width:t-2*this._viewportMargin,height:e-2*this._viewportMargin}}_isRtl(){return"rtl"===this._overlayRef.getDirection()}_hasExactPosition(){return!this._hasFlexibleDimensions||this._isPushed}_getOffset(t,e){return"x"===e?null==t.offsetX?this._offsetX:t.offsetX:null==t.offsetY?this._offsetY:t.offsetY}_validatePositions(){if("undefined"==typeof ngDevMode||ngDevMode){if(!this._preferredPositions.length)throw Error("FlexibleConnectedPositionStrategy: At least one position is required.");this._preferredPositions.forEach((t=>{YF("originX",t.originX),WF("originY",t.originY),YF("overlayX",t.overlayX),WF("overlayY",t.overlayY)}))}}_addPanelClasses(t){this._pane&&Mz(t).forEach((t=>{""!==t&&-1===this._appliedPanelClasses.indexOf(t)&&(this._appliedPanelClasses.push(t),this._pane.classList.add(t))}))}_clearPanelClasses(){this._pane&&(this._appliedPanelClasses.forEach((t=>{this._pane.classList.remove(t)})),this._appliedPanelClasses=[])}_getOriginRect(){const t=this._origin;if(t instanceof hg)return t.nativeElement.getBoundingClientRect();if(t instanceof Element)return t.getBoundingClientRect();const e=t.width||0,n=t.height||0;return{top:t.y,bottom:t.y+n,left:t.x,right:t.x+e,height:n,width:e}}}function oL(t,e){for(let n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}function iL(t){if("number"!=typeof t&&null!=t){const[e,n]=t.split(eL);return n&&"px"!==n?null:parseFloat(e)}return t||null}function aL(t){return{top:Math.floor(t.top),right:Math.floor(t.right),bottom:Math.floor(t.bottom),left:Math.floor(t.left),width:Math.floor(t.width),height:Math.floor(t.height)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class rL{constructor(t,e,n,o,i,a,r){this._preferredPositions=[],this._positionStrategy=new nL(n,o,i,a,r).withFlexibleDimensions(!1).withPush(!1).withViewportMargin(0),this.withFallbackPosition(t,e),this.onPositionChange=this._positionStrategy.positionChanges}get positions(){return this._preferredPositions}attach(t){this._overlayRef=t,this._positionStrategy.attach(t),this._direction&&(t.setDirection(this._direction),this._direction=null)}dispose(){this._positionStrategy.dispose()}detach(){this._positionStrategy.detach()}apply(){this._positionStrategy.apply()}recalculateLastPosition(){this._positionStrategy.reapplyLastPosition()}withScrollableContainers(t){this._positionStrategy.withScrollableContainers(t)}withFallbackPosition(t,e,n,o){const i=new jF(t,e,n,o);return this._preferredPositions.push(i),this._positionStrategy.withPositions(this._preferredPositions),this}withDirection(t){return this._overlayRef?this._overlayRef.setDirection(t):this._direction=t,this}withOffsetX(t){return this._positionStrategy.withDefaultOffsetX(t),this}withOffsetY(t){return this._positionStrategy.withDefaultOffsetY(t),this}withLockedPosition(t){return this._positionStrategy.withLockedPosition(t),this}withPositions(t){return this._preferredPositions=t.slice(),this._positionStrategy.withPositions(this._preferredPositions),this}setOrigin(t){return this._positionStrategy.setOrigin(t),this}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const sL="cdk-global-overlay-wrapper";class lL{constructor(){this._cssPosition="static",this._topOffset="",this._bottomOffset="",this._leftOffset="",this._rightOffset="",this._alignItems="",this._justifyContent="",this._width="",this._height=""}attach(t){const e=t.getConfig();this._overlayRef=t,this._width&&!e.width&&t.updateSize({width:this._width}),this._height&&!e.height&&t.updateSize({height:this._height}),t.hostElement.classList.add(sL),this._isDisposed=!1}top(t=""){return this._bottomOffset="",this._topOffset=t,this._alignItems="flex-start",this}left(t=""){return this._rightOffset="",this._leftOffset=t,this._justifyContent="flex-start",this}bottom(t=""){return this._topOffset="",this._bottomOffset=t,this._alignItems="flex-end",this}right(t=""){return this._leftOffset="",this._rightOffset=t,this._justifyContent="flex-end",this}width(t=""){return this._overlayRef?this._overlayRef.updateSize({width:t}):this._width=t,this}height(t=""){return this._overlayRef?this._overlayRef.updateSize({height:t}):this._height=t,this}centerHorizontally(t=""){return this.left(t),this._justifyContent="center",this}centerVertically(t=""){return this.top(t),this._alignItems="center",this}apply(){if(!this._overlayRef||!this._overlayRef.hasAttached())return;const t=this._overlayRef.overlayElement.style,e=this._overlayRef.hostElement.style,n=this._overlayRef.getConfig(),{width:o,height:i,maxWidth:a,maxHeight:r}=n,s=!("100%"!==o&&"100vw"!==o||a&&"100%"!==a&&"100vw"!==a),l=!("100%"!==i&&"100vh"!==i||r&&"100%"!==r&&"100vh"!==r);t.position=this._cssPosition,t.marginLeft=s?"0":this._leftOffset,t.marginTop=l?"0":this._topOffset,t.marginBottom=this._bottomOffset,t.marginRight=this._rightOffset,s?e.justifyContent="flex-start":"center"===this._justifyContent?e.justifyContent="center":"rtl"===this._overlayRef.getConfig().direction?"flex-start"===this._justifyContent?e.justifyContent="flex-end":"flex-end"===this._justifyContent&&(e.justifyContent="flex-start"):e.justifyContent=this._justifyContent,e.alignItems=l?"flex-start":this._alignItems}dispose(){if(this._isDisposed||!this._overlayRef)return;const t=this._overlayRef.overlayElement.style,e=this._overlayRef.hostElement,n=e.style;e.classList.remove(sL),n.justifyContent=n.alignItems=t.marginTop=t.marginBottom=t.marginLeft=t.marginRight=t.position="",this._overlayRef=null,this._isDisposed=!0}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class cL{constructor(t,e,n,o){this._viewportRuler=t,this._document=e,this._platform=n,this._overlayContainer=o}global(){return new lL}connectedTo(t,e,n){return new rL(e,n,t,this._viewportRuler,this._document,this._platform,this._overlayContainer)}flexibleConnectedTo(t){return new nL(t,this._viewportRuler,this._document,this._platform,this._overlayContainer)}}cL.ɵfac=function t(e){return new(e||cL)(vr(uF),vr(Z_),vr(wz),vr(QF))},cL.ɵprov=Mn({factory:function t(){return new cL(vr(uF),vr(Z_),vr(wz),vr(QF))},token:cL,providedIn:"root"}),cL.ctorParameters=()=>[{type:uF},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz},{type:QF}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(cL,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:uF},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz},{type:QF}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let dL=0;class pL{constructor(t,e,n,o,i,a,r,s,l,c,d){this.scrollStrategies=t,this._overlayContainer=e,this._componentFactoryResolver=n,this._positionBuilder=o,this._keyboardDispatcher=i,this._injector=a,this._ngZone=r,this._document=s,this._directionality=l,this._location=c,this._outsideClickDispatcher=d}create(t){const e=this._createHostElement(),n=this._createPaneElement(e),o=this._createPortalOutlet(n),i=new VF(t);return i.direction=i.direction||this._directionality.value,new $F(o,e,n,i,this._ngZone,this._keyboardDispatcher,this._document,this._location,this._outsideClickDispatcher)}position(){return this._positionBuilder}_createPaneElement(t){const e=this._document.createElement("div");return e.id="cdk-overlay-"+dL++,e.classList.add("cdk-overlay-pane"),t.appendChild(e),e}_createHostElement(){const t=this._document.createElement("div");return this._overlayContainer.getContainerElement().appendChild(t),t}_createPortalOutlet(t){return this._appRef||(this._appRef=this._injector.get(O_)),new wF(t,this._componentFactoryResolver,this._appRef,this._injector,this._document)}}pL.ɵfac=function t(e){return new(e||pL)(vr(BF),vr(QF),vr(ug),vr(cL),vr(ZF),vr(rp),vr(a_),vr(Z_),vr(HI),vr(lC),vr(XF))},pL.ɵprov=Mn({token:pL,factory:pL.ɵfac}),pL.ctorParameters=()=>[{type:BF},{type:QF},{type:ug},{type:cL},{type:ZF},{type:rp},{type:a_},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:HI},{type:lC},{type:XF}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(pL,[{type:im}],(function(){return[{type:BF},{type:QF},{type:ug},{type:cL},{type:ZF},{type:rp},{type:a_},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:HI},{type:lC},{type:XF}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const mL=[{originX:"start",originY:"bottom",overlayX:"start",overlayY:"top"},{originX:"start",originY:"top",overlayX:"start",overlayY:"bottom"},{originX:"end",originY:"top",overlayX:"end",overlayY:"bottom"},{originX:"end",originY:"bottom",overlayX:"end",overlayY:"top"}],uL=new Ga("cdk-connected-overlay-scroll-strategy");class fL{constructor(t){this.elementRef=t}}fL.ɵfac=function t(e){return new(e||fL)(Sm(hg))},fL.ɵdir=lo({type:fL,selectors:[["","cdk-overlay-origin",""],["","overlay-origin",""],["","cdkOverlayOrigin",""]],exportAs:["cdkOverlayOrigin"]}),fL.ctorParameters=()=>[{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(fL,[{type:Cy,args:[{selector:"[cdk-overlay-origin], [overlay-origin], [cdkOverlayOrigin]",exportAs:"cdkOverlayOrigin"}]}],(function(){return[{type:hg}]}),null);class gL{constructor(t,e,n,o,i){this._overlay=t,this._dir=i,this._hasBackdrop=!1,this._lockPosition=!1,this._growAfterOpen=!1,this._flexibleDimensions=!1,this._push=!1,this._backdropSubscription=m.EMPTY,this._attachSubscription=m.EMPTY,this._detachSubscription=m.EMPTY,this._positionSubscription=m.EMPTY,this.viewportMargin=0,this.open=!1,this.disableClose=!1,this.backdropClick=new Lh,this.positionChange=new Lh,this.attach=new Lh,this.detach=new Lh,this.overlayKeydown=new Lh,this.overlayOutsideClick=new Lh,this._templatePortal=new xF(e,n),this._scrollStrategyFactory=o,this.scrollStrategy=this._scrollStrategyFactory()}get offsetX(){return this._offsetX}set offsetX(t){this._offsetX=t,this._position&&this._updatePositionStrategy(this._position)}get offsetY(){return this._offsetY}set offsetY(t){this._offsetY=t,this._position&&this._updatePositionStrategy(this._position)}get hasBackdrop(){return this._hasBackdrop}set hasBackdrop(t){this._hasBackdrop=yz(t)}get lockPosition(){return this._lockPosition}set lockPosition(t){this._lockPosition=yz(t)}get flexibleDimensions(){return this._flexibleDimensions}set flexibleDimensions(t){this._flexibleDimensions=yz(t)}get growAfterOpen(){return this._growAfterOpen}set growAfterOpen(t){this._growAfterOpen=yz(t)}get push(){return this._push}set push(t){this._push=yz(t)}get overlayRef(){return this._overlayRef}get dir(){return this._dir?this._dir.value:"ltr"}ngOnDestroy(){this._attachSubscription.unsubscribe(),this._detachSubscription.unsubscribe(),this._backdropSubscription.unsubscribe(),this._positionSubscription.unsubscribe(),this._overlayRef&&this._overlayRef.dispose()}ngOnChanges(t){this._position&&(this._updatePositionStrategy(this._position),this._overlayRef.updateSize({width:this.width,minWidth:this.minWidth,height:this.height,minHeight:this.minHeight}),t.origin&&this.open&&this._position.apply()),t.open&&(this.open?this._attachOverlay():this._detachOverlay())}_createOverlay(){this.positions&&this.positions.length||(this.positions=mL);const t=this._overlayRef=this._overlay.create(this._buildConfig());this._attachSubscription=t.attachments().subscribe((()=>this.attach.emit())),this._detachSubscription=t.detachments().subscribe((()=>this.detach.emit())),t.keydownEvents().subscribe((t=>{this.overlayKeydown.next(t),t.keyCode!==uz||this.disableClose||bz(t)||(t.preventDefault(),this._detachOverlay())})),this._overlayRef.outsidePointerEvents().subscribe((t=>{this.overlayOutsideClick.next(t)}))}_buildConfig(){const t=this._position=this.positionStrategy||this._createPositionStrategy(),e=new VF({direction:this._dir,positionStrategy:t,scrollStrategy:this.scrollStrategy,hasBackdrop:this.hasBackdrop});return(this.width||0===this.width)&&(e.width=this.width),(this.height||0===this.height)&&(e.height=this.height),(this.minWidth||0===this.minWidth)&&(e.minWidth=this.minWidth),(this.minHeight||0===this.minHeight)&&(e.minHeight=this.minHeight),this.backdropClass&&(e.backdropClass=this.backdropClass),this.panelClass&&(e.panelClass=this.panelClass),e}_updatePositionStrategy(t){const e=this.positions.map((t=>({originX:t.originX,originY:t.originY,overlayX:t.overlayX,overlayY:t.overlayY,offsetX:t.offsetX||this.offsetX,offsetY:t.offsetY||this.offsetY,panelClass:t.panelClass||void 0})));return t.setOrigin(this.origin.elementRef).withPositions(e).withFlexibleDimensions(this.flexibleDimensions).withPush(this.push).withGrowAfterOpen(this.growAfterOpen).withViewportMargin(this.viewportMargin).withLockedPosition(this.lockPosition).withTransformOriginOn(this.transformOriginSelector)}_createPositionStrategy(){const t=this._overlay.position().flexibleConnectedTo(this.origin.elementRef);return this._updatePositionStrategy(t),t}_attachOverlay(){this._overlayRef?this._overlayRef.getConfig().hasBackdrop=this.hasBackdrop:this._createOverlay(),this._overlayRef.hasAttached()||this._overlayRef.attach(this._templatePortal),this.hasBackdrop?this._backdropSubscription=this._overlayRef.backdropClick().subscribe((t=>{this.backdropClick.emit(t)})):this._backdropSubscription.unsubscribe(),this._positionSubscription.unsubscribe(),this.positionChange.observers.length>0&&(this._positionSubscription=this._position.positionChanges.pipe(He((()=>this.positionChange.observers.length>0))).subscribe((t=>{this.positionChange.emit(t),0===this.positionChange.observers.length&&this._positionSubscription.unsubscribe()})))}_detachOverlay(){this._overlayRef&&this._overlayRef.detach(),this._backdropSubscription.unsubscribe(),this._positionSubscription.unsubscribe()}}gL.ɵfac=function t(e){return new(e||gL)(Sm(pL),Sm(Xg),Sm(eh),Sm(uL),Sm(HI,8))},gL.ɵdir=lo({type:gL,selectors:[["","cdk-connected-overlay",""],["","connected-overlay",""],["","cdkConnectedOverlay",""]],inputs:{viewportMargin:["cdkConnectedOverlayViewportMargin","viewportMargin"],open:["cdkConnectedOverlayOpen","open"],disableClose:["cdkConnectedOverlayDisableClose","disableClose"],scrollStrategy:["cdkConnectedOverlayScrollStrategy","scrollStrategy"],offsetX:["cdkConnectedOverlayOffsetX","offsetX"],offsetY:["cdkConnectedOverlayOffsetY","offsetY"],hasBackdrop:["cdkConnectedOverlayHasBackdrop","hasBackdrop"],lockPosition:["cdkConnectedOverlayLockPosition","lockPosition"],flexibleDimensions:["cdkConnectedOverlayFlexibleDimensions","flexibleDimensions"],growAfterOpen:["cdkConnectedOverlayGrowAfterOpen","growAfterOpen"],push:["cdkConnectedOverlayPush","push"],positions:["cdkConnectedOverlayPositions","positions"],origin:["cdkConnectedOverlayOrigin","origin"],positionStrategy:["cdkConnectedOverlayPositionStrategy","positionStrategy"],width:["cdkConnectedOverlayWidth","width"],height:["cdkConnectedOverlayHeight","height"],minWidth:["cdkConnectedOverlayMinWidth","minWidth"],minHeight:["cdkConnectedOverlayMinHeight","minHeight"],backdropClass:["cdkConnectedOverlayBackdropClass","backdropClass"],panelClass:["cdkConnectedOverlayPanelClass","panelClass"],transformOriginSelector:["cdkConnectedOverlayTransformOriginOn","transformOriginSelector"]},outputs:{backdropClick:"backdropClick",positionChange:"positionChange",attach:"attach",detach:"detach",overlayKeydown:"overlayKeydown",overlayOutsideClick:"overlayOutsideClick"},exportAs:["cdkConnectedOverlay"],features:[Bo]}),gL.ctorParameters=()=>[{type:pL},{type:Xg},{type:eh},{type:void 0,decorators:[{type:kr,args:[uL]}]},{type:HI,decorators:[{type:Sr}]}],gL.propDecorators={origin:[{type:xy,args:["cdkConnectedOverlayOrigin"]}],positions:[{type:xy,args:["cdkConnectedOverlayPositions"]}],positionStrategy:[{type:xy,args:["cdkConnectedOverlayPositionStrategy"]}],offsetX:[{type:xy,args:["cdkConnectedOverlayOffsetX"]}],offsetY:[{type:xy,args:["cdkConnectedOverlayOffsetY"]}],width:[{type:xy,args:["cdkConnectedOverlayWidth"]}],height:[{type:xy,args:["cdkConnectedOverlayHeight"]}],minWidth:[{type:xy,args:["cdkConnectedOverlayMinWidth"]}],minHeight:[{type:xy,args:["cdkConnectedOverlayMinHeight"]}],backdropClass:[{type:xy,args:["cdkConnectedOverlayBackdropClass"]}],panelClass:[{type:xy,args:["cdkConnectedOverlayPanelClass"]}],viewportMargin:[{type:xy,args:["cdkConnectedOverlayViewportMargin"]}],scrollStrategy:[{type:xy,args:["cdkConnectedOverlayScrollStrategy"]}],open:[{type:xy,args:["cdkConnectedOverlayOpen"]}],disableClose:[{type:xy,args:["cdkConnectedOverlayDisableClose"]}],transformOriginSelector:[{type:xy,args:["cdkConnectedOverlayTransformOriginOn"]}],hasBackdrop:[{type:xy,args:["cdkConnectedOverlayHasBackdrop"]}],lockPosition:[{type:xy,args:["cdkConnectedOverlayLockPosition"]}],flexibleDimensions:[{type:xy,args:["cdkConnectedOverlayFlexibleDimensions"]}],growAfterOpen:[{type:xy,args:["cdkConnectedOverlayGrowAfterOpen"]}],push:[{type:xy,args:["cdkConnectedOverlayPush"]}],backdropClick:[{type:Oy}],positionChange:[{type:Oy}],attach:[{type:Oy}],detach:[{type:Oy}],overlayKeydown:[{type:Oy}],overlayOutsideClick:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(gL,[{type:Cy,args:[{selector:"[cdk-connected-overlay], [connected-overlay], [cdkConnectedOverlay]",exportAs:"cdkConnectedOverlay"}]}],(function(){return[{type:pL},{type:Xg},{type:eh},{type:void 0,decorators:[{type:kr,args:[uL]}]},{type:HI,decorators:[{type:Sr}]}]}),{viewportMargin:[{type:xy,args:["cdkConnectedOverlayViewportMargin"]}],open:[{type:xy,args:["cdkConnectedOverlayOpen"]}],disableClose:[{type:xy,args:["cdkConnectedOverlayDisableClose"]}],backdropClick:[{type:Oy}],positionChange:[{type:Oy}],attach:[{type:Oy}],detach:[{type:Oy}],overlayKeydown:[{type:Oy}],overlayOutsideClick:[{type:Oy}],scrollStrategy:[{type:xy,args:["cdkConnectedOverlayScrollStrategy"]}],offsetX:[{type:xy,args:["cdkConnectedOverlayOffsetX"]}],offsetY:[{type:xy,args:["cdkConnectedOverlayOffsetY"]}],hasBackdrop:[{type:xy,args:["cdkConnectedOverlayHasBackdrop"]}],lockPosition:[{type:xy,args:["cdkConnectedOverlayLockPosition"]}],flexibleDimensions:[{type:xy,args:["cdkConnectedOverlayFlexibleDimensions"]}],growAfterOpen:[{type:xy,args:["cdkConnectedOverlayGrowAfterOpen"]}],push:[{type:xy,args:["cdkConnectedOverlayPush"]}],positions:[{type:xy,args:["cdkConnectedOverlayPositions"]}],origin:[{type:xy,args:["cdkConnectedOverlayOrigin"]}],positionStrategy:[{type:xy,args:["cdkConnectedOverlayPositionStrategy"]}],width:[{type:xy,args:["cdkConnectedOverlayWidth"]}],height:[{type:xy,args:["cdkConnectedOverlayHeight"]}],minWidth:[{type:xy,args:["cdkConnectedOverlayMinWidth"]}],minHeight:[{type:xy,args:["cdkConnectedOverlayMinHeight"]}],backdropClass:[{type:xy,args:["cdkConnectedOverlayBackdropClass"]}],panelClass:[{type:xy,args:["cdkConnectedOverlayPanelClass"]}],transformOriginSelector:[{type:xy,args:["cdkConnectedOverlayTransformOriginOn"]}]});const hL={provide:uL,deps:[pL],useFactory:function bL(t){return()=>t.scrollStrategies.reposition()}};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class yL{}yL.ɵfac=function t(e){return new(e||yL)},yL.ɵmod=ao({type:yL}),yL.ɵinj=vn({providers:[pL,hL],imports:[[LI,RF,_F],_F]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(yL,[{type:Ay,args:[{imports:[LI,RF,_F],exports:[gL,fL,_F],declarations:[gL,fL],providers:[pL,hL]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(yL,{declarations:function(){return[gL,fL]},imports:function(){return[LI,RF,_F]},exports:function(){return[gL,fL,_F]}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class _L extends QF{constructor(t,e){super(t,e)}ngOnDestroy(){super.ngOnDestroy(),this._fullScreenEventName&&this._fullScreenListener&&this._document.removeEventListener(this._fullScreenEventName,this._fullScreenListener)}_createContainer(){super._createContainer(),this._adjustParentForFullscreenChange(),this._addFullscreenChangeListener((()=>this._adjustParentForFullscreenChange()))}_adjustParentForFullscreenChange(){this._containerElement&&(this.getFullscreenElement()||this._document.body).appendChild(this._containerElement)}_addFullscreenChangeListener(t){const e=this._getEventName();e&&(this._fullScreenListener&&this._document.removeEventListener(e,this._fullScreenListener),this._document.addEventListener(e,t),this._fullScreenListener=t)}_getEventName(){if(!this._fullScreenEventName){const t=this._document;t.fullscreenEnabled?this._fullScreenEventName="fullscreenchange":t.webkitFullscreenEnabled?this._fullScreenEventName="webkitfullscreenchange":t.mozFullScreenEnabled?this._fullScreenEventName="mozfullscreenchange":t.msFullscreenEnabled&&(this._fullScreenEventName="MSFullscreenChange")}return this._fullScreenEventName}getFullscreenElement(){const t=this._document;return t.fullscreenElement||t.webkitFullscreenElement||t.mozFullScreenElement||t.msFullscreenElement||null}}_L.ɵfac=function t(e){return new(e||_L)(vr(Z_),vr(wz))},_L.ɵprov=Mn({factory:function t(){return new _L(vr(Z_),vr(wz))},token:_L,providedIn:"root"}),_L.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(_L,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz}]}),null);class CL{}CL.ɵfac=function t(e){return new(e||CL)},CL.ɵmod=ao({type:CL}),CL.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(CL,[{type:Ay,args:[{}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const ML=new Set;let vL;class xL{constructor(t){this._platform=t,this._matchMedia=this._platform.isBrowser&&window.matchMedia?window.matchMedia.bind(window):OL}matchMedia(t){return this._platform.WEBKIT&&(function e(t){if(!ML.has(t))try{vL||(vL=document.createElement("style"),vL.setAttribute("type","text/css"),document.head.appendChild(vL)),vL.sheet&&(vL.sheet.insertRule(`@media ${t} {.fx-query-test{ }}`,0),ML.add(t))}catch(t){console.error(t)}})(t),this._matchMedia(t)}}function OL(t){return{matches:"all"===t||""===t,media:t,addListener:()=>{},removeListener:()=>{}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */xL.ɵfac=function t(e){return new(e||xL)(vr(wz))},xL.ɵprov=Mn({factory:function t(){return new xL(vr(wz))},token:xL,providedIn:"root"}),xL.ctorParameters=()=>[{type:wz}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(xL,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:wz}]}),null);class PL{constructor(t,e){this._mediaMatcher=t,this._zone=e,this._queries=new Map,this._destroySubject=new I}ngOnDestroy(){this._destroySubject.next(),this._destroySubject.complete()}isMatched(t){return wL(Mz(t)).some((t=>this._registerQuery(t).mql.matches))}observe(t){let e=Wt(wL(Mz(t)).map((t=>this._registerQuery(t).observable)));return e=Jt(e.pipe(be(1)),e.pipe(Te(1),ge(0))),e.pipe(It((t=>{const e={matches:!1,breakpoints:{}};return t.forEach((({matches:t,query:n})=>{e.matches=e.matches||t,e.breakpoints[n]=t})),e})))}_registerQuery(t){if(this._queries.has(t))return this._queries.get(t);const e=this._mediaMatcher.matchMedia(t),n={observable:new D((t=>{const n=e=>this._zone.run((()=>t.next(e)));return e.addListener(n),()=>{e.removeListener(n)}})).pipe(Ne(e),It((({matches:e})=>({query:t,matches:e}))),Ie(this._destroySubject)),mql:e};return this._queries.set(t,n),n}}function wL(t){return t.map((t=>t.split(","))).reduce(((t,e)=>t.concat(e))).map((t=>t.trim()))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function kL(t,e){if(1&t){const t=Hm();Rm(0,"div",1),Rm(1,"button",2),Vm("click",(function e(){return hi(t),Ym().action()})),ku(2),Am(),Am()}if(2&t){const t=Ym();rc(2),Su(t.data.action)}}function SL(t,e){}PL.ɵfac=function t(e){return new(e||PL)(vr(xL),vr(a_))},PL.ɵprov=Mn({factory:function t(){return new PL(vr(xL),vr(a_))},token:PL,providedIn:"root"}),PL.ctorParameters=()=>[{type:xL},{type:a_}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(PL,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:xL},{type:a_}]}),null);const DL=new Ga("MatSnackBarData");class EL{constructor(){this.politeness="assertive",this.announcementMessage="",this.duration=0,this.data=null,this.horizontalPosition="center",this.verticalPosition="bottom"}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const RL=Math.pow(2,31)-1;class AL{constructor(t,e){this._overlayRef=e,this._afterDismissed=new I,this._afterOpened=new I,this._onAction=new I,this._dismissedByAction=!1,this.containerInstance=t,this.onAction().subscribe((()=>this.dismiss())),t._onExit.subscribe((()=>this._finishDismiss()))}dismiss(){this._afterDismissed.closed||this.containerInstance.exit(),clearTimeout(this._durationTimeoutId)}dismissWithAction(){this._onAction.closed||(this._dismissedByAction=!0,this._onAction.next(),this._onAction.complete()),clearTimeout(this._durationTimeoutId)}closeWithAction(){this.dismissWithAction()}_dismissAfter(t){this._durationTimeoutId=setTimeout((()=>this.dismiss()),Math.min(t,RL))}_open(){this._afterOpened.closed||(this._afterOpened.next(),this._afterOpened.complete())}_finishDismiss(){this._overlayRef.dispose(),this._onAction.closed||this._onAction.complete(),this._afterDismissed.next({dismissedByAction:this._dismissedByAction}),this._afterDismissed.complete(),this._dismissedByAction=!1}afterDismissed(){return this._afterDismissed}afterOpened(){return this.containerInstance._onEnter}onAction(){return this._onAction}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class TL{constructor(t,e){this.snackBarRef=t,this.data=e}action(){this.snackBarRef.dismissWithAction()}get hasAction(){return!!this.data.action}}TL.ɵfac=function t(e){return new(e||TL)(Sm(AL),Sm(DL))},TL.ɵcmp=to({type:TL,selectors:[["simple-snack-bar"]],hostAttrs:[1,"mat-simple-snackbar"],decls:3,vars:2,consts:[["class","mat-simple-snackbar-action",4,"ngIf"],[1,"mat-simple-snackbar-action"],["mat-button","",3,"click"]],template:function t(e,n){1&e&&(Rm(0,"span"),ku(1),Am(),Qp(2,kL,3,1,"div",0)),2&e&&(rc(1),Su(n.data.message),rc(1),Dm("ngIf",n.hasAction))},directives:[dM,XH],styles:[".mat-simple-snackbar{display:flex;justify-content:space-between;align-items:center;line-height:20px;opacity:1}.mat-simple-snackbar-action{flex-shrink:0;margin:-8px -8px -8px 8px}.mat-simple-snackbar-action button{max-height:36px;min-width:0}[dir=rtl] .mat-simple-snackbar-action{margin-left:-8px;margin-right:8px}\n"],encapsulation:2,changeDetection:0}),TL.ctorParameters=()=>[{type:AL},{type:void 0,decorators:[{type:kr,args:[DL]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(TL,[{type:My,args:[{selector:"simple-snack-bar",template:'<span>{{data.message}}</span>\n<div class="mat-simple-snackbar-action"  *ngIf="hasAction">\n  <button mat-button (click)="action()">{{data.action}}</button>\n</div>\n',encapsulation:Hn.None,changeDetection:zn.OnPush,host:{class:"mat-simple-snackbar"},styles:[".mat-simple-snackbar{display:flex;justify-content:space-between;align-items:center;line-height:20px;opacity:1}.mat-simple-snackbar-action{flex-shrink:0;margin:-8px -8px -8px 8px}.mat-simple-snackbar-action button{max-height:36px;min-width:0}[dir=rtl] .mat-simple-snackbar-action{margin-left:-8px;margin-right:8px}\n"]}]}],(function(){return[{type:AL},{type:void 0,decorators:[{type:kr,args:[DL]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const NL={snackBarState:nx("state",[rx("void, hidden",ax({transform:"scale(0.8)",opacity:0})),rx("visible",ax({transform:"scale(1)",opacity:1})),lx("* => visible",ox("150ms cubic-bezier(0, 0, 0.2, 1)")),lx("* => void, * => hidden",ox("75ms cubic-bezier(0.4, 0.0, 1, 1)",ax({opacity:0})))])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class zL extends PF{constructor(t,e,n,o,i){super(),this._ngZone=t,this._elementRef=e,this._changeDetectorRef=n,this._platform=o,this.snackBarConfig=i,this._announceDelay=150,this._destroyed=!1,this._onAnnounce=new I,this._onExit=new I,this._onEnter=new I,this._animationState="void",this.attachDomPortal=t=>(this._assertNotAttached(),this._applySnackBarClasses(),this._portalOutlet.attachDomPortal(t)),this._live="assertive"!==i.politeness||i.announcementMessage?"off"===i.politeness?"off":"polite":"assertive",this._platform.FIREFOX&&("polite"===this._live&&(this._role="status"),"assertive"===this._live&&(this._role="alert"))}attachComponentPortal(t){return this._assertNotAttached(),this._applySnackBarClasses(),this._portalOutlet.attachComponentPortal(t)}attachTemplatePortal(t){return this._assertNotAttached(),this._applySnackBarClasses(),this._portalOutlet.attachTemplatePortal(t)}onAnimationEnd(t){const{fromState:e,toState:n}=t;if(("void"===n&&"void"!==e||"hidden"===n)&&this._completeExit(),"visible"===n){const t=this._onEnter;this._ngZone.run((()=>{t.next(),t.complete()}))}}enter(){this._destroyed||(this._animationState="visible",this._changeDetectorRef.detectChanges(),this._screenReaderAnnounce())}exit(){return this._animationState="hidden",this._elementRef.nativeElement.setAttribute("mat-exit",""),clearTimeout(this._announceTimeoutId),this._onExit}ngOnDestroy(){this._destroyed=!0,this._completeExit()}_completeExit(){this._ngZone.onMicrotaskEmpty.pipe(be(1)).subscribe((()=>{this._onExit.next(),this._onExit.complete()}))}_applySnackBarClasses(){const t=this._elementRef.nativeElement,e=this.snackBarConfig.panelClass;e&&(Array.isArray(e)?e.forEach((e=>t.classList.add(e))):t.classList.add(e)),"center"===this.snackBarConfig.horizontalPosition&&t.classList.add("mat-snack-bar-center"),"top"===this.snackBarConfig.verticalPosition&&t.classList.add("mat-snack-bar-top")}_assertNotAttached(){if(this._portalOutlet.hasAttached()&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Attempting to attach snack bar content after content is already attached")}_screenReaderAnnounce(){this._announceTimeoutId||this._ngZone.runOutsideAngular((()=>{this._announceTimeoutId=setTimeout((()=>{const t=this._elementRef.nativeElement.querySelector("[aria-hidden]"),e=this._elementRef.nativeElement.querySelector("[aria-live]");if(t&&e){let n=null;this._platform.isBrowser&&document.activeElement instanceof HTMLElement&&t.contains(document.activeElement)&&(n=document.activeElement),t.removeAttribute("aria-hidden"),e.appendChild(t),null==n||n.focus(),this._onAnnounce.next(),this._onAnnounce.complete()}}),this._announceDelay)}))}}zL.ɵfac=function t(e){return new(e||zL)(Sm(a_),Sm(hg),Sm(Ug),Sm(wz),Sm(EL))},zL.ɵcmp=to({type:zL,selectors:[["snack-bar-container"]],viewQuery:function t(e,n){if(1&e&&Qh(DF,7),2&e){let t;Jh(t=tb())&&(n._portalOutlet=t.first)}},hostAttrs:[1,"mat-snack-bar-container"],hostVars:1,hostBindings:function t(e,n){1&e&&jm("@state.done",(function t(e){return n.onAnimationEnd(e)})),2&e&&Nu("@state",n._animationState)},features:[xp],decls:3,vars:2,consts:[["aria-hidden","true"],["cdkPortalOutlet",""]],template:function t(e,n){1&e&&(Rm(0,"div",0),Qp(1,SL,0,0,"ng-template",1),Am(),Tm(2,"div")),2&e&&(rc(2),jp("aria-live",n._live)("role",n._role))},directives:[DF],styles:[".mat-snack-bar-container{border-radius:4px;box-sizing:border-box;display:block;margin:24px;max-width:33vw;min-width:344px;padding:14px 16px;min-height:48px;transform-origin:center}.cdk-high-contrast-active .mat-snack-bar-container{border:solid 1px}.mat-snack-bar-handset{width:100%}.mat-snack-bar-handset .mat-snack-bar-container{margin:8px;max-width:100%;min-width:0;width:100%}\n"],encapsulation:2,data:{animation:[NL.snackBarState]}}),zL.ctorParameters=()=>[{type:a_},{type:hg},{type:Ug},{type:wz},{type:EL}],zL.propDecorators={_portalOutlet:[{type:Za,args:[DF,{static:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(zL,[{type:My,args:[{selector:"snack-bar-container",template:'\x3c!-- Initially holds the snack bar content, will be empty after announcing to screen readers. --\x3e\n<div aria-hidden="true">\n  <ng-template cdkPortalOutlet></ng-template>\n</div>\n\n\x3c!-- Will receive the snack bar content from the non-live div, move will happen a short delay after opening --\x3e\n<div [attr.aria-live]="_live" [attr.role]="_role"></div>\n',changeDetection:zn.Default,encapsulation:Hn.None,animations:[NL.snackBarState],host:{class:"mat-snack-bar-container","[@state]":"_animationState","(@state.done)":"onAnimationEnd($event)"},styles:[".mat-snack-bar-container{border-radius:4px;box-sizing:border-box;display:block;margin:24px;max-width:33vw;min-width:344px;padding:14px 16px;min-height:48px;transform-origin:center}.cdk-high-contrast-active .mat-snack-bar-container{border:solid 1px}.mat-snack-bar-handset{width:100%}.mat-snack-bar-handset .mat-snack-bar-container{margin:8px;max-width:100%;min-width:0;width:100%}\n"]}]}],(function(){return[{type:a_},{type:hg},{type:Ug},{type:wz},{type:EL}]}),{_portalOutlet:[{type:Za,args:[DF,{static:!0}]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class IL{}IL.ɵfac=function t(e){return new(e||IL)},IL.ɵmod=ao({type:IL}),IL.ɵinj=vn({imports:[[yL,RF,WM,JH,XI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(IL,[{type:Ay,args:[{imports:[yL,RF,WM,JH,XI],exports:[zL,XI],declarations:[zL,TL],entryComponents:[zL,TL]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(IL,{declarations:function(){return[zL,TL]},imports:function(){return[yL,RF,WM,JH,XI]},exports:function(){return[zL,XI]}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const HL=new Ga("mat-snack-bar-default-options",{providedIn:"root",factory:function FL(){return new EL}});class LL{constructor(t,e,n,o,i,a){this._overlay=t,this._live=e,this._injector=n,this._breakpointObserver=o,this._parentSnackBar=i,this._defaultConfig=a,this._snackBarRefAtThisLevel=null,this.simpleSnackBarComponent=TL,this.snackBarContainerComponent=zL,this.handsetCssClass="mat-snack-bar-handset"}get _openedSnackBarRef(){const t=this._parentSnackBar;return t?t._openedSnackBarRef:this._snackBarRefAtThisLevel}set _openedSnackBarRef(t){this._parentSnackBar?this._parentSnackBar._openedSnackBarRef=t:this._snackBarRefAtThisLevel=t}openFromComponent(t,e){return this._attach(t,e)}openFromTemplate(t,e){return this._attach(t,e)}open(t,e="",n){const o=Object.assign(Object.assign({},this._defaultConfig),n);return o.data={message:t,action:e},o.announcementMessage===t&&(o.announcementMessage=void 0),this.openFromComponent(this.simpleSnackBarComponent,o)}dismiss(){this._openedSnackBarRef&&this._openedSnackBarRef.dismiss()}ngOnDestroy(){this._snackBarRefAtThisLevel&&this._snackBarRefAtThisLevel.dismiss()}_attachSnackBarContainer(t,e){const n=rp.create({parent:e&&e.viewContainerRef&&e.viewContainerRef.injector||this._injector,providers:[{provide:EL,useValue:e}]}),o=new vF(this.snackBarContainerComponent,e.viewContainerRef,n),i=t.attach(o);return i.instance.snackBarConfig=e,i.instance}_attach(t,e){const n=Object.assign(Object.assign(Object.assign({},new EL),this._defaultConfig),e),o=this._createOverlay(n),i=this._attachSnackBarContainer(o,n),a=new AL(i,o);if(t instanceof Xg){const e=new xF(t,null,{$implicit:n.data,snackBarRef:a});a.instance=i.attachTemplatePortal(e)}else{const e=this._createInjector(n,a),o=new vF(t,void 0,e),r=i.attachComponentPortal(o);a.instance=r.instance}return this._breakpointObserver.observe("(max-width: 599.98px) and (orientation: portrait)").pipe(Ie(o.detachments())).subscribe((t=>{const e=o.overlayElement.classList;t.matches?e.add(this.handsetCssClass):e.remove(this.handsetCssClass)})),n.announcementMessage&&i._onAnnounce.subscribe((()=>{this._live.announce(n.announcementMessage,n.politeness)})),this._animateSnackBar(a,n),this._openedSnackBarRef=a,this._openedSnackBarRef}_animateSnackBar(t,e){t.afterDismissed().subscribe((()=>{this._openedSnackBarRef==t&&(this._openedSnackBarRef=null),e.announcementMessage&&this._live.clear()})),this._openedSnackBarRef?(this._openedSnackBarRef.afterDismissed().subscribe((()=>{t.containerInstance.enter()})),this._openedSnackBarRef.dismiss()):t.containerInstance.enter(),e.duration&&e.duration>0&&t.afterOpened().subscribe((()=>t._dismissAfter(e.duration)))}_createOverlay(t){const e=new VF;e.direction=t.direction;let n=this._overlay.position().global();const o="rtl"===t.direction,i="left"===t.horizontalPosition||"start"===t.horizontalPosition&&!o||"end"===t.horizontalPosition&&o,a=!i&&"center"!==t.horizontalPosition;return i?n.left("0"):a?n.right("0"):n.centerHorizontally(),"top"===t.verticalPosition?n.top("0"):n.bottom("0"),e.positionStrategy=n,this._overlay.create(e)}_createInjector(t,e){return rp.create({parent:t&&t.viewContainerRef&&t.viewContainerRef.injector||this._injector,providers:[{provide:AL,useValue:e},{provide:DL,useValue:t.data}]})}}LL.ɵfac=function t(e){return new(e||LL)(vr(pL),vr(OI),vr(rp),vr(PL),vr(LL,12),vr(HL))},LL.ɵprov=Mn({factory:function t(){return new LL(vr(pL),vr(OI),vr(Gd),vr(PL),vr(LL,12),vr(HL))},token:LL,providedIn:IL}),LL.ctorParameters=()=>[{type:pL},{type:OI},{type:rp},{type:PL},{type:LL,decorators:[{type:Sr},{type:Er}]},{type:EL,decorators:[{type:kr,args:[HL]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(LL,[{type:im,args:[{providedIn:IL}]}],(function(){return[{type:pL},{type:OI},{type:rp},{type:PL},{type:LL,decorators:[{type:Sr},{type:Er}]},{type:EL,decorators:[{type:kr,args:[HL]}]}]}),null);const BL=/[\\^$.*+?()[\]{}|]/g,VL="\\u0000-\\u0020\\u007f-\\u009f",jL=new RegExp("(?:[a-zA-Z][a-zA-Z0-9+.-]{2,}:\\/\\/|data:|www\\.)[^\\s"+VL+'"]{2,}[^\\s'+VL+"\"')}\\],:;.!?]","gu");function UL(t){return(function e(t,n){n.flags.includes("g")||(n=new RegExp(n,n.flags+"g"));const o=[];let i=0;for(const e of t.matchAll(n)){const n=e.index,a=e[0];n>i&&o.push({index:i,text:t.substring(i,n),matchesRegex:!1}),o.push({index:n,text:a,matchesRegex:!0}),i=n+a.length}return t.length>i&&o.push({index:i,text:t.substring(i,t.length),matchesRegex:!1}),o})(t,jL).map((({matchesRegex:t,text:e})=>({isURL:t,text:e})))}function GL(t,e){if(1&t&&(Nm(0),ku(1),zm()),2&t){const t=Ym().$implicit;rc(1),Du(" ",t.text," ")}}function WL(t,e){if(1&t&&(Rm(0,"a",7),ku(1),Am()),2&t){const t=Ym().$implicit;Km("href",t.text,Ts),rc(1),Su(t.text)}}function YL(t,e){if(1&t&&(Nm(0),Qp(1,GL,2,1,"ng-container",5),Qp(2,WL,2,2,"ng-template",null,6,ib),zm()),2&t){const t=e.$implicit,n=$p(3);rc(1),Dm("ngIf",!t.isURL)("ngIfElse",n)}}function qL(t,e){if(1&t){const t=Hm();Rm(0,"button",8),Vm("click",(function e(){return hi(t),Ym().onActionButtonClicked()})),ku(1),Am()}if(2&t){const t=Ym();rc(1),Du(" ",t.alert.followupAction.localizedLabel," ")}}class ZL{constructor(t,e,n){this.snackBarRef=t,this.unknownData=e,this.store=n,this.splitByURL=UL,this.alert=e}onActionButtonClicked(){return gA(this,void 0,void 0,(function*(){this.snackBarRef.dismiss();const t=yield this.alert.followupAction.getFollowupAction(this.store);this.store.dispatch(t)}))}onCloseButtonClicked(){this.snackBarRef.dismiss()}}ZL.ɵfac=function t(e){return new(e||ZL)(Sm(AL),Sm(DL),Sm(Iw))},ZL.ɵcmp=to({type:ZL,selectors:[["alert-display-snackbar"]],decls:6,vars:2,consts:function(){let t;return t="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Dismiss"):$localize`:A button to close the snackbar message␟ea4d9fe61420a3fce81cf54c4c615e3c19c646a6␟1536087519743707362:Dismiss`,[[1,"message"],[4,"ngFor","ngForOf"],[1,"controls"],["mat-button","","class","followup-button",3,"click",4,"ngIf"],["mat-button","","aria-label",t,1,"dismiss-button",3,"click"],[4,"ngIf","ngIfElse"],["linkPiece",""],["rel","noreferrer noopener","target","_blank",3,"href"],["mat-button","",1,"followup-button",3,"click"]]},template:function t(e,n){1&e&&(Rm(0,"div",0),Qp(1,YL,4,2,"ng-container",1),Am(),Rm(2,"div",2),Qp(3,qL,2,1,"button",3),Rm(4,"button",4),Vm("click",(function t(){return n.onCloseButtonClicked()})),ku(5," Dismiss "),Am(),Am()),2&e&&(rc(1),Dm("ngForOf",n.splitByURL(n.alert.localizedMessage)),rc(2),Dm("ngIf",n.alert.followupAction))},directives:[lM,dM,XH],styles:["[_nghost-%COMP%]{display:flex;flex-wrap:wrap}.message[_ngcontent-%COMP%]{font-size:14px;align-self:center;margin:5px 0;word-break:break-word}.message[_ngcontent-%COMP%]   a[_ngcontent-%COMP%]{color:inherit}.controls[_ngcontent-%COMP%]{white-space:nowrap;margin-left:auto}button[_ngcontent-%COMP%]{text-transform:uppercase}"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ZL,[{type:My,args:[{selector:"alert-display-snackbar",templateUrl:"./alert_display_snackbar_container.ng.html",styleUrls:["./alert_display_snackbar_container.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:AL},{type:void 0,decorators:[{type:kr,args:[DL]}]},{type:Iw}]}),null);class XL{constructor(t,e){this.store=t,this.snackBar=e,this.ngUnsubscribe=new I}ngOnInit(){this.store.select(dA).pipe(Ie(this.ngUnsubscribe),ce((t=>Boolean(t)))).subscribe((t=>{this.showAlert(t)}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}showAlert(t){this.snackBar.openFromComponent(ZL,{duration:5e3,horizontalPosition:"start",verticalPosition:"bottom",data:t})}}XL.ɵfac=function t(e){return new(e||XL)(Sm(Iw),Sm(LL))},XL.ɵcmp=to({type:XL,selectors:[["alert-snackbar"]],decls:0,vars:0,template:function t(e,n){},encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(XL,[{type:My,args:[{selector:"alert-snackbar",template:"",changeDetection:zn.OnPush}]}],(function(){return[{type:Iw},{type:LL}]}),null);class KL{}KL.ɵfac=function t(e){return new(e||KL)},KL.ɵmod=ao({type:KL}),KL.ɵinj=vn({imports:[[WM,JH,IL]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(KL,[{type:Ay,args:[{declarations:[XL,ZL],exports:[XL],imports:[WM,JH,IL],entryComponents:[ZL]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(KL,{declarations:[XL,ZL],imports:[WM,JH,IL],exports:[XL]});class JL{}JL.ɵfac=function t(e){return new(e||JL)},JL.ɵmod=ao({type:JL}),JL.ɵinj=vn({imports:[[AR,KL,dk.forFeature(cA,pz),Wk.forFeature([cz])]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(JL,[{type:Ay,args:[{imports:[AR,KL,dk.forFeature(cA,pz),Wk.forFeature([cz])]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(JL,{imports:[AR,KL,ck,Gk]});const QL=["*",[["mat-toolbar-row"]]],$L=JI(class{constructor(t){this._elementRef=t}});class tB{}tB.ɵfac=function t(e){return new(e||tB)},tB.ɵdir=lo({type:tB,selectors:[["mat-toolbar-row"]],hostAttrs:[1,"mat-toolbar-row"],exportAs:["matToolbarRow"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(tB,[{type:Cy,args:[{selector:"mat-toolbar-row",exportAs:"matToolbarRow",host:{class:"mat-toolbar-row"}}]}],null,null);class eB extends $L{constructor(t,e,n){super(t),this._platform=e,this._document=n}ngAfterViewInit(){this._platform.isBrowser&&(this._checkToolbarMixedModes(),this._toolbarRows.changes.subscribe((()=>this._checkToolbarMixedModes())))}_checkToolbarMixedModes(){this._toolbarRows.length&&("undefined"==typeof ngDevMode||ngDevMode)&&Array.from(this._elementRef.nativeElement.childNodes).filter((t=>!(t.classList&&t.classList.contains("mat-toolbar-row")))).filter((t=>t.nodeType!==(this._document?this._document.COMMENT_NODE:8))).some((t=>!(!t.textContent||!t.textContent.trim())))&&(function t(){throw Error("MatToolbar: Attempting to combine different toolbar modes. Either specify multiple `<mat-toolbar-row>` elements explicitly or just place content inside of a `<mat-toolbar>` for a single row.")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)()}}eB.ɵfac=function t(e){return new(e||eB)(Sm(hg),Sm(wz),Sm(Z_))},eB.ɵcmp=to({type:eB,selectors:[["mat-toolbar"]],contentQueries:function t(e,n,o){if(1&e&&$h(o,tB,5),2&e){let t;Jh(t=tb())&&(n._toolbarRows=t)}},hostAttrs:[1,"mat-toolbar"],hostVars:4,hostBindings:function t(e,n){2&e&&pu("mat-toolbar-multiple-rows",n._toolbarRows.length>0)("mat-toolbar-single-row",0===n._toolbarRows.length)},inputs:{color:"color"},exportAs:["matToolbar"],features:[xp],ngContentSelectors:["*","mat-toolbar-row"],decls:2,vars:0,template:function t(e,n){1&e&&(Zm(QL),Xm(0),Xm(1,1))},styles:[".cdk-high-contrast-active .mat-toolbar{outline:solid 1px}.mat-toolbar-row,.mat-toolbar-single-row{display:flex;box-sizing:border-box;padding:0 16px;width:100%;flex-direction:row;align-items:center;white-space:nowrap}.mat-toolbar-multiple-rows{display:flex;box-sizing:border-box;flex-direction:column;width:100%}\n"],encapsulation:2,changeDetection:0}),eB.ctorParameters=()=>[{type:hg},{type:wz},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],eB.propDecorators={_toolbarRows:[{type:Ya,args:[tB,{descendants:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(eB,[{type:My,args:[{selector:"mat-toolbar",exportAs:"matToolbar",template:'<ng-content></ng-content>\n<ng-content select="mat-toolbar-row"></ng-content>\n',inputs:["color"],host:{class:"mat-toolbar","[class.mat-toolbar-multiple-rows]":"_toolbarRows.length > 0","[class.mat-toolbar-single-row]":"_toolbarRows.length === 0"},changeDetection:zn.OnPush,encapsulation:Hn.None,styles:[".cdk-high-contrast-active .mat-toolbar{outline:solid 1px}.mat-toolbar-row,.mat-toolbar-single-row{display:flex;box-sizing:border-box;padding:0 16px;width:100%;flex-direction:row;align-items:center;white-space:nowrap}.mat-toolbar-multiple-rows{display:flex;box-sizing:border-box;flex-direction:column;width:100%}\n"]}]}],(function(){return[{type:hg},{type:wz},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),{_toolbarRows:[{type:Ya,args:[tB,{descendants:!0}]}]});class nB{}function oB(t,e){1&t&&Xm(0)}nB.ɵfac=function t(e){return new(e||nB)},nB.ɵmod=ao({type:nB}),nB.ɵinj=vn({imports:[[XI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(nB,[{type:Ay,args:[{imports:[XI],exports:[eB,tB,XI],declarations:[eB,tB]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(nB,{declarations:function(){return[eB,tB]},imports:function(){return[XI]},exports:function(){return[eB,tB,XI]}});const iB=["*"];function aB(t,e){}const rB=function(t){return{animationDuration:t}},sB=function(t,e){return{value:t,params:e}},lB=["tabBodyWrapper"],cB=["tabHeader"];function dB(t,e){}function pB(t,e){1&t&&Qp(0,dB,0,0,"ng-template",9),2&t&&Dm("cdkPortalOutlet",Ym().$implicit.templateLabel)}function mB(t,e){1&t&&ku(0),2&t&&Su(Ym().$implicit.textLabel)}function uB(t,e){if(1&t){const t=Hm();Rm(0,"div",6),Vm("click",(function e(){const n=hi(t),o=n.$implicit,i=n.index,a=Ym(),r=$p(1);return a._handleClick(o,r,i)}))("cdkFocusChange",(function e(n){const o=hi(t).index;return Ym()._tabFocusChanged(n,o)})),Rm(1,"div",7),Qp(2,pB,1,1,"ng-template",8),Qp(3,mB,1,1,"ng-template",8),Am(),Am()}if(2&t){const t=e.$implicit,n=e.index,o=Ym();pu("mat-tab-label-active",o.selectedIndex==n),Dm("id",o._getTabLabelId(n))("disabled",t.disabled)("matRippleDisabled",t.disabled||o.disableRipple),jp("tabIndex",o._getTabIndex(t,n))("aria-posinset",n+1)("aria-setsize",o._tabs.length)("aria-controls",o._getTabContentId(n))("aria-selected",o.selectedIndex==n)("aria-label",t.ariaLabel||null)("aria-labelledby",!t.ariaLabel&&t.ariaLabelledby?t.ariaLabelledby:null),rc(2),Dm("ngIf",t.templateLabel),rc(1),Dm("ngIf",!t.templateLabel)}}function fB(t,e){if(1&t){const t=Hm();Rm(0,"mat-tab-body",10),Vm("_onCentered",(function e(){return hi(t),Ym()._removeTabBodyWrapperHeight()}))("_onCentering",(function e(n){return hi(t),Ym()._setTabBodyWrapperHeight(n)})),Am()}if(2&t){const t=e.$implicit,n=e.index,o=Ym();pu("mat-tab-body-active",o.selectedIndex===n),Dm("id",o._getTabContentId(n))("content",t.content)("position",t.position)("origin",t.origin)("animationDuration",o.animationDuration),jp("tabindex",null!=o.contentTabIndex&&o.selectedIndex===n?o.contentTabIndex:null)("aria-labelledby",o._getTabLabelId(n))}}const gB=["tabListContainer"],hB=["tabList"],bB=["nextPaginator"],yB=["previousPaginator"],_B=new Ga("MatInkBarPositioner",{providedIn:"root",factory:function CB(){return t=>({left:t?(t.offsetLeft||0)+"px":"0",width:t?(t.offsetWidth||0)+"px":"0"})}});class MB{constructor(t,e,n,o){this._elementRef=t,this._ngZone=e,this._inkBarPositioner=n,this._animationMode=o}alignToElement(t){this.show(),"undefined"!=typeof requestAnimationFrame?this._ngZone.runOutsideAngular((()=>{requestAnimationFrame((()=>this._setStyles(t)))})):this._setStyles(t)}show(){this._elementRef.nativeElement.style.visibility="visible"}hide(){this._elementRef.nativeElement.style.visibility="hidden"}_setStyles(t){const e=this._inkBarPositioner(t),n=this._elementRef.nativeElement;n.style.left=e.left,n.style.width=e.width}}MB.ɵfac=function t(e){return new(e||MB)(Sm(hg),Sm(a_),Sm(_B),Sm(VP,8))},MB.ɵdir=lo({type:MB,selectors:[["mat-ink-bar"]],hostAttrs:[1,"mat-ink-bar"],hostVars:2,hostBindings:function t(e,n){2&e&&pu("_mat-animation-noopable","NoopAnimations"===n._animationMode)}}),MB.ctorParameters=()=>[{type:hg},{type:a_},{type:void 0,decorators:[{type:kr,args:[_B]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(MB,[{type:Cy,args:[{selector:"mat-ink-bar",host:{class:"mat-ink-bar","[class._mat-animation-noopable]":"_animationMode === 'NoopAnimations'"}}]}],(function(){return[{type:hg},{type:a_},{type:void 0,decorators:[{type:kr,args:[_B]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const vB=new Ga("MatTabContent");class xB{constructor(t){this.template=t}}xB.ɵfac=function t(e){return new(e||xB)(Sm(Xg))},xB.ɵdir=lo({type:xB,selectors:[["","matTabContent",""]],features:[pg([{provide:vB,useExisting:xB}])]}),xB.ctorParameters=()=>[{type:Xg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(xB,[{type:Cy,args:[{selector:"[matTabContent]",providers:[{provide:vB,useExisting:xB}]}]}],(function(){return[{type:Xg}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const OB=new Ga("MatTabLabel");class PB extends kF{}PB.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(PB)))(n||PB)}})(),PB.ɵdir=lo({type:PB,selectors:[["","mat-tab-label",""],["","matTabLabel",""]],features:[pg([{provide:OB,useExisting:PB}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(PB,[{type:Cy,args:[{selector:"[mat-tab-label], [matTabLabel]",providers:[{provide:OB,useExisting:PB}]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const wB=KI(class{}),kB=new Ga("MAT_TAB_GROUP");class SB extends wB{constructor(t,e){super(),this._viewContainerRef=t,this._closestTabGroup=e,this.textLabel="",this._contentPortal=null,this._stateChanges=new I,this.position=null,this.origin=null,this.isActive=!1}get templateLabel(){return this._templateLabel}set templateLabel(t){this._setTemplateLabelInput(t)}get content(){return this._contentPortal}ngOnChanges(t){(t.hasOwnProperty("textLabel")||t.hasOwnProperty("disabled"))&&this._stateChanges.next()}ngOnDestroy(){this._stateChanges.complete()}ngOnInit(){this._contentPortal=new xF(this._explicitContent||this._implicitContent,this._viewContainerRef)}_setTemplateLabelInput(t){t&&(this._templateLabel=t)}}SB.ɵfac=function t(e){return new(e||SB)(Sm(eh),Sm(kB,8))},SB.ɵcmp=to({type:SB,selectors:[["mat-tab"]],contentQueries:function t(e,n,o){if(1&e&&($h(o,OB,5),$h(o,vB,7,Xg)),2&e){let t;Jh(t=tb())&&(n.templateLabel=t.first),Jh(t=tb())&&(n._explicitContent=t.first)}},viewQuery:function t(e,n){if(1&e&&Qh(Xg,7),2&e){let t;Jh(t=tb())&&(n._implicitContent=t.first)}},inputs:{disabled:"disabled",textLabel:["label","textLabel"],ariaLabel:["aria-label","ariaLabel"],ariaLabelledby:["aria-labelledby","ariaLabelledby"]},exportAs:["matTab"],features:[xp,Bo],ngContentSelectors:iB,decls:1,vars:0,template:function t(e,n){1&e&&(Zm(),Qp(0,oB,1,0,"ng-template"))},encapsulation:2}),SB.ctorParameters=()=>[{type:eh},{type:void 0,decorators:[{type:kr,args:[kB]},{type:Sr}]}],SB.propDecorators={templateLabel:[{type:qa,args:[OB]}],_explicitContent:[{type:qa,args:[vB,{read:Xg,static:!0}]}],_implicitContent:[{type:Za,args:[Xg,{static:!0}]}],textLabel:[{type:xy,args:["label"]}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(SB,[{type:My,args:[{selector:"mat-tab",template:"\x3c!-- Create a template for the content of the <mat-tab> so that we can grab a reference to this\n    TemplateRef and use it in a Portal to render the tab content in the appropriate place in the\n    tab-group. --\x3e\n<ng-template><ng-content></ng-content></ng-template>\n",inputs:["disabled"],changeDetection:zn.Default,encapsulation:Hn.None,exportAs:"matTab"}]}],(function(){return[{type:eh},{type:void 0,decorators:[{type:kr,args:[kB]},{type:Sr}]}]}),{textLabel:[{type:xy,args:["label"]}],templateLabel:[{type:qa,args:[OB]}],_explicitContent:[{type:qa,args:[vB,{read:Xg,static:!0}]}],_implicitContent:[{type:Za,args:[Xg,{static:!0}]}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const DB={translateTab:nx("translateTab",[rx("center, void, left-origin-center, right-origin-center",ax({transform:"none"})),rx("left",ax({transform:"translate3d(-100%, 0, 0)",minHeight:"1px"})),rx("right",ax({transform:"translate3d(100%, 0, 0)",minHeight:"1px"})),lx("* => left, * => right, left => center, right => center",ox("{{animationDuration}} cubic-bezier(0.35, 0, 0.25, 1)")),lx("void => left-origin-center",[ax({transform:"translate3d(-100%, 0, 0)"}),ox("{{animationDuration}} cubic-bezier(0.35, 0, 0.25, 1)")]),lx("void => right-origin-center",[ax({transform:"translate3d(100%, 0, 0)"}),ox("{{animationDuration}} cubic-bezier(0.35, 0, 0.25, 1)")])])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class EB extends DF{constructor(t,e,n,o){super(t,e,o),this._host=n,this._centeringSub=m.EMPTY,this._leavingSub=m.EMPTY}ngOnInit(){super.ngOnInit(),this._centeringSub=this._host._beforeCentering.pipe(Ne(this._host._isCenterPosition(this._host._position))).subscribe((t=>{t&&!this.hasAttached()&&this.attach(this._host._content)})),this._leavingSub=this._host._afterLeavingCenter.subscribe((()=>{this.detach()}))}ngOnDestroy(){super.ngOnDestroy(),this._centeringSub.unsubscribe(),this._leavingSub.unsubscribe()}}EB.ɵfac=function t(e){return new(e||EB)(Sm(ug),Sm(eh),Sm(qe((()=>AB))),Sm(Z_))},EB.ɵdir=lo({type:EB,selectors:[["","matTabBodyHost",""]],features:[xp]}),EB.ctorParameters=()=>[{type:ug},{type:eh},{type:AB,decorators:[{type:kr,args:[qe((()=>AB))]}]},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(EB,[{type:Cy,args:[{selector:"[matTabBodyHost]"}]}],(function(){return[{type:ug},{type:eh},{type:AB,decorators:[{type:kr,args:[qe((()=>AB))]}]},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);class RB{constructor(t,e,n){this._elementRef=t,this._dir=e,this._dirChangeSubscription=m.EMPTY,this._translateTabComplete=new I,this._onCentering=new Lh,this._beforeCentering=new Lh,this._afterLeavingCenter=new Lh,this._onCentered=new Lh(!0),this.animationDuration="500ms",e&&(this._dirChangeSubscription=e.change.subscribe((t=>{this._computePositionAnimationState(t),n.markForCheck()}))),this._translateTabComplete.pipe(Me(((t,e)=>t.fromState===e.fromState&&t.toState===e.toState))).subscribe((t=>{this._isCenterPosition(t.toState)&&this._isCenterPosition(this._position)&&this._onCentered.emit(),this._isCenterPosition(t.fromState)&&!this._isCenterPosition(this._position)&&this._afterLeavingCenter.emit()}))}set position(t){this._positionIndex=t,this._computePositionAnimationState()}ngOnInit(){"center"==this._position&&null!=this.origin&&(this._position=this._computePositionFromOrigin(this.origin))}ngOnDestroy(){this._dirChangeSubscription.unsubscribe(),this._translateTabComplete.complete()}_onTranslateTabStarted(t){const e=this._isCenterPosition(t.toState);this._beforeCentering.emit(e),e&&this._onCentering.emit(this._elementRef.nativeElement.clientHeight)}_getLayoutDirection(){return this._dir&&"rtl"===this._dir.value?"rtl":"ltr"}_isCenterPosition(t){return"center"==t||"left-origin-center"==t||"right-origin-center"==t}_computePositionAnimationState(t=this._getLayoutDirection()){this._position=this._positionIndex<0?"ltr"==t?"left":"right":this._positionIndex>0?"ltr"==t?"right":"left":"center"}_computePositionFromOrigin(t){const e=this._getLayoutDirection();return"ltr"==e&&t<=0||"rtl"==e&&t>0?"left-origin-center":"right-origin-center"}}RB.ɵfac=function t(e){return new(e||RB)(Sm(hg),Sm(HI,8),Sm(Ug))},RB.ɵdir=lo({type:RB,inputs:{animationDuration:"animationDuration",position:"position",_content:["content","_content"],origin:"origin"},outputs:{_onCentering:"_onCentering",_beforeCentering:"_beforeCentering",_afterLeavingCenter:"_afterLeavingCenter",_onCentered:"_onCentered"}}),RB.ctorParameters=()=>[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:Ug}],RB.propDecorators={_onCentering:[{type:Oy}],_beforeCentering:[{type:Oy}],_afterLeavingCenter:[{type:Oy}],_onCentered:[{type:Oy}],_content:[{type:xy,args:["content"]}],origin:[{type:xy}],animationDuration:[{type:xy}],position:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(RB,[{type:Cy}],(function(){return[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:Ug}]}),{_onCentering:[{type:Oy}],_beforeCentering:[{type:Oy}],_afterLeavingCenter:[{type:Oy}],_onCentered:[{type:Oy}],animationDuration:[{type:xy}],position:[{type:xy}],_content:[{type:xy,args:["content"]}],origin:[{type:xy}]});class AB extends RB{constructor(t,e,n){super(t,e,n)}}AB.ɵfac=function t(e){return new(e||AB)(Sm(hg),Sm(HI,8),Sm(Ug))},AB.ɵcmp=to({type:AB,selectors:[["mat-tab-body"]],viewQuery:function t(e,n){if(1&e&&Qh(DF,5),2&e){let t;Jh(t=tb())&&(n._portalHost=t.first)}},hostAttrs:[1,"mat-tab-body"],features:[xp],decls:3,vars:6,consts:[["cdkScrollable","",1,"mat-tab-body-content"],["content",""],["matTabBodyHost",""]],template:function t(e,n){1&e&&(Rm(0,"div",0,1),Vm("@translateTab.start",(function t(e){return n._onTranslateTabStarted(e)}))("@translateTab.done",(function t(e){return n._translateTabComplete.next(e)})),Qp(2,aB,0,0,"ng-template",2),Am()),2&e&&Dm("@translateTab",vh(3,sB,n._position,Mh(1,rB,n.animationDuration)))},directives:[EB],styles:[".mat-tab-body-content{height:100%;overflow:auto}.mat-tab-group-dynamic-height .mat-tab-body-content{overflow:hidden}\n"],encapsulation:2,data:{animation:[DB.translateTab]}}),AB.ctorParameters=()=>[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:Ug}],AB.propDecorators={_portalHost:[{type:Za,args:[DF]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(AB,[{type:My,args:[{selector:"mat-tab-body",template:'<div class="mat-tab-body-content" #content\n     [@translateTab]="{\n        value: _position,\n        params: {animationDuration: animationDuration}\n     }"\n     (@translateTab.start)="_onTranslateTabStarted($event)"\n     (@translateTab.done)="_translateTabComplete.next($event)"\n     cdkScrollable>\n  <ng-template matTabBodyHost></ng-template>\n</div>\n',encapsulation:Hn.None,changeDetection:zn.Default,animations:[DB.translateTab],host:{class:"mat-tab-body"},styles:[".mat-tab-body-content{height:100%;overflow:auto}.mat-tab-group-dynamic-height .mat-tab-body-content{overflow:hidden}\n"]}]}],(function(){return[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:Ug}]}),{_portalHost:[{type:Za,args:[DF]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const TB=new Ga("MAT_TABS_CONFIG");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let NB=0;class zB{}const IB=JI(QI(class{constructor(t){this._elementRef=t}}),"primary");class HB extends IB{constructor(t,e,n,o){var i;super(t),this._changeDetectorRef=e,this._animationMode=o,this._tabs=new Vh,this._indexToSelect=0,this._tabBodyWrapperHeight=0,this._tabsSubscription=m.EMPTY,this._tabLabelSubscription=m.EMPTY,this._selectedIndex=null,this.headerPosition="above",this.selectedIndexChange=new Lh,this.focusChange=new Lh,this.animationDone=new Lh,this.selectedTabChange=new Lh(!0),this._groupId=NB++,this.animationDuration=n&&n.animationDuration?n.animationDuration:"500ms",this.disablePagination=!(!n||null==n.disablePagination)&&n.disablePagination,this.dynamicHeight=!(!n||null==n.dynamicHeight)&&n.dynamicHeight,this.contentTabIndex=null!==(i=null==n?void 0:n.contentTabIndex)&&void 0!==i?i:null}get dynamicHeight(){return this._dynamicHeight}set dynamicHeight(t){this._dynamicHeight=yz(t)}get selectedIndex(){return this._selectedIndex}set selectedIndex(t){this._indexToSelect=_z(t,null)}get animationDuration(){return this._animationDuration}set animationDuration(t){this._animationDuration=/^\d+$/.test(t)?t+"ms":t}get contentTabIndex(){return this._contentTabIndex}set contentTabIndex(t){this._contentTabIndex=_z(t,null)}get backgroundColor(){return this._backgroundColor}set backgroundColor(t){const e=this._elementRef.nativeElement;e.classList.remove(`mat-background-${this.backgroundColor}`),t&&e.classList.add(`mat-background-${t}`),this._backgroundColor=t}ngAfterContentChecked(){const t=this._indexToSelect=this._clampTabIndex(this._indexToSelect);if(this._selectedIndex!=t){const e=null==this._selectedIndex;if(!e){this.selectedTabChange.emit(this._createChangeEvent(t));const e=this._tabBodyWrapper.nativeElement;e.style.minHeight=e.clientHeight+"px"}Promise.resolve().then((()=>{this._tabs.forEach(((e,n)=>e.isActive=n===t)),e||(this.selectedIndexChange.emit(t),this._tabBodyWrapper.nativeElement.style.minHeight="")}))}this._tabs.forEach(((e,n)=>{e.position=n-t,null==this._selectedIndex||0!=e.position||e.origin||(e.origin=t-this._selectedIndex)})),this._selectedIndex!==t&&(this._selectedIndex=t,this._changeDetectorRef.markForCheck())}ngAfterContentInit(){this._subscribeToAllTabChanges(),this._subscribeToTabLabels(),this._tabsSubscription=this._tabs.changes.subscribe((()=>{if(this._clampTabIndex(this._indexToSelect)===this._selectedIndex){const t=this._tabs.toArray();for(let e=0;e<t.length;e++)if(t[e].isActive){this._indexToSelect=this._selectedIndex=e;break}}this._changeDetectorRef.markForCheck()}))}_subscribeToAllTabChanges(){this._allTabs.changes.pipe(Ne(this._allTabs)).subscribe((t=>{this._tabs.reset(t.filter((t=>t._closestTabGroup===this||!t._closestTabGroup))),this._tabs.notifyOnChanges()}))}ngOnDestroy(){this._tabs.destroy(),this._tabsSubscription.unsubscribe(),this._tabLabelSubscription.unsubscribe()}realignInkBar(){this._tabHeader&&this._tabHeader._alignInkBarToSelectedTab()}focusTab(t){const e=this._tabHeader;e&&(e.focusIndex=t)}_focusChanged(t){this.focusChange.emit(this._createChangeEvent(t))}_createChangeEvent(t){const e=new zB;return e.index=t,this._tabs&&this._tabs.length&&(e.tab=this._tabs.toArray()[t]),e}_subscribeToTabLabels(){this._tabLabelSubscription&&this._tabLabelSubscription.unsubscribe(),this._tabLabelSubscription=re(...this._tabs.map((t=>t._stateChanges))).subscribe((()=>this._changeDetectorRef.markForCheck()))}_clampTabIndex(t){return Math.min(this._tabs.length-1,Math.max(t||0,0))}_getTabLabelId(t){return`mat-tab-label-${this._groupId}-${t}`}_getTabContentId(t){return`mat-tab-content-${this._groupId}-${t}`}_setTabBodyWrapperHeight(t){if(!this._dynamicHeight||!this._tabBodyWrapperHeight)return;const e=this._tabBodyWrapper.nativeElement;e.style.height=this._tabBodyWrapperHeight+"px",this._tabBodyWrapper.nativeElement.offsetHeight&&(e.style.height=t+"px")}_removeTabBodyWrapperHeight(){const t=this._tabBodyWrapper.nativeElement;this._tabBodyWrapperHeight=t.clientHeight,t.style.height="",this.animationDone.emit()}_handleClick(t,e,n){t.disabled||(this.selectedIndex=e.focusIndex=n)}_getTabIndex(t,e){return t.disabled?null:this.selectedIndex===e?0:-1}_tabFocusChanged(t,e){t&&"mouse"!==t&&"touch"!==t&&(this._tabHeader.focusIndex=e)}}HB.ɵfac=function t(e){return new(e||HB)(Sm(hg),Sm(Ug),Sm(TB,8),Sm(VP,8))},HB.ɵdir=lo({type:HB,inputs:{headerPosition:"headerPosition",animationDuration:"animationDuration",disablePagination:"disablePagination",dynamicHeight:"dynamicHeight",contentTabIndex:"contentTabIndex",selectedIndex:"selectedIndex",backgroundColor:"backgroundColor"},outputs:{selectedIndexChange:"selectedIndexChange",focusChange:"focusChange",animationDone:"animationDone",selectedTabChange:"selectedTabChange"},features:[xp]}),HB.ctorParameters=()=>[{type:hg},{type:Ug},{type:void 0,decorators:[{type:kr,args:[TB]},{type:Sr}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],HB.propDecorators={dynamicHeight:[{type:xy}],selectedIndex:[{type:xy}],headerPosition:[{type:xy}],animationDuration:[{type:xy}],contentTabIndex:[{type:xy}],disablePagination:[{type:xy}],backgroundColor:[{type:xy}],selectedIndexChange:[{type:Oy}],focusChange:[{type:Oy}],animationDone:[{type:Oy}],selectedTabChange:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(HB,[{type:Cy}],(function(){return[{type:hg},{type:Ug},{type:void 0,decorators:[{type:kr,args:[TB]},{type:Sr}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{headerPosition:[{type:xy}],selectedIndexChange:[{type:Oy}],focusChange:[{type:Oy}],animationDone:[{type:Oy}],selectedTabChange:[{type:Oy}],animationDuration:[{type:xy}],disablePagination:[{type:xy}],dynamicHeight:[{type:xy}],contentTabIndex:[{type:xy}],selectedIndex:[{type:xy}],backgroundColor:[{type:xy}]});class FB extends HB{constructor(t,e,n,o){super(t,e,n,o)}}FB.ɵfac=function t(e){return new(e||FB)(Sm(hg),Sm(Ug),Sm(TB,8),Sm(VP,8))},FB.ɵcmp=to({type:FB,selectors:[["mat-tab-group"]],contentQueries:function t(e,n,o){if(1&e&&$h(o,SB,5),2&e){let t;Jh(t=tb())&&(n._allTabs=t)}},viewQuery:function t(e,n){if(1&e&&(Qh(lB,5),Qh(cB,5)),2&e){let t;Jh(t=tb())&&(n._tabBodyWrapper=t.first),Jh(t=tb())&&(n._tabHeader=t.first)}},hostAttrs:[1,"mat-tab-group"],hostVars:4,hostBindings:function t(e,n){2&e&&pu("mat-tab-group-dynamic-height",n.dynamicHeight)("mat-tab-group-inverted-header","below"===n.headerPosition)},inputs:{color:"color",disableRipple:"disableRipple"},exportAs:["matTabGroup"],features:[pg([{provide:kB,useExisting:FB}]),xp],decls:6,vars:7,consts:[[3,"selectedIndex","disableRipple","disablePagination","indexFocused","selectFocusedIndex"],["tabHeader",""],["class","mat-tab-label mat-focus-indicator","role","tab","matTabLabelWrapper","","mat-ripple","","cdkMonitorElementFocus","",3,"id","mat-tab-label-active","disabled","matRippleDisabled","click","cdkFocusChange",4,"ngFor","ngForOf"],[1,"mat-tab-body-wrapper"],["tabBodyWrapper",""],["role","tabpanel",3,"id","mat-tab-body-active","content","position","origin","animationDuration","_onCentered","_onCentering",4,"ngFor","ngForOf"],["role","tab","matTabLabelWrapper","","mat-ripple","","cdkMonitorElementFocus","",1,"mat-tab-label","mat-focus-indicator",3,"id","disabled","matRippleDisabled","click","cdkFocusChange"],[1,"mat-tab-label-content"],[3,"ngIf"],[3,"cdkPortalOutlet"],["role","tabpanel",3,"id","content","position","origin","animationDuration","_onCentered","_onCentering"]],template:function t(e,n){1&e&&(Rm(0,"mat-tab-header",0,1),Vm("indexFocused",(function t(e){return n._focusChanged(e)}))("selectFocusedIndex",(function t(e){return n.selectedIndex=e})),Qp(2,uB,4,14,"div",2),Am(),Rm(3,"div",3,4),Qp(5,fB,1,9,"mat-tab-body",5),Am()),2&e&&(Dm("selectedIndex",n.selectedIndex||0)("disableRipple",n.disableRipple)("disablePagination",n.disablePagination),rc(2),Dm("ngForOf",n._tabs),rc(1),pu("_mat-animation-noopable","NoopAnimations"===n._animationMode),rc(2),Dm("ngForOf",n._tabs))},directives:function(){return[GB,lM,BB,kH,DI,dM,DF,AB]},styles:[".mat-tab-group{display:flex;flex-direction:column;max-width:100%}.mat-tab-group.mat-tab-group-inverted-header{flex-direction:column-reverse}.mat-tab-label{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;position:relative}.mat-tab-label:focus{outline:none}.mat-tab-label:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-label:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-label.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-label.mat-tab-disabled{opacity:.5}.mat-tab-label .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-label{opacity:1}@media(max-width: 599px){.mat-tab-label{padding:0 12px}}@media(max-width: 959px){.mat-tab-label{padding:0 12px}}.mat-tab-group[mat-stretch-tabs]>.mat-tab-header .mat-tab-label{flex-basis:0;flex-grow:1}.mat-tab-body-wrapper{position:relative;overflow:hidden;display:flex;transition:height 500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-tab-body-wrapper{transition:none;animation:none}.mat-tab-body{top:0;left:0;right:0;bottom:0;position:absolute;display:block;overflow:hidden;outline:0;flex-basis:100%}.mat-tab-body.mat-tab-body-active{position:relative;overflow-x:hidden;overflow-y:auto;z-index:1;flex-grow:1}.mat-tab-group.mat-tab-group-dynamic-height .mat-tab-body.mat-tab-body-active{overflow-y:hidden}\n"],encapsulation:2}),FB.ctorParameters=()=>[{type:hg},{type:Ug},{type:void 0,decorators:[{type:kr,args:[TB]},{type:Sr}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],FB.propDecorators={_allTabs:[{type:Ya,args:[SB,{descendants:!0}]}],_tabBodyWrapper:[{type:Za,args:["tabBodyWrapper"]}],_tabHeader:[{type:Za,args:["tabHeader"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(FB,[{type:My,args:[{selector:"mat-tab-group",exportAs:"matTabGroup",template:'<mat-tab-header #tabHeader\n               [selectedIndex]="selectedIndex || 0"\n               [disableRipple]="disableRipple"\n               [disablePagination]="disablePagination"\n               (indexFocused)="_focusChanged($event)"\n               (selectFocusedIndex)="selectedIndex = $event">\n  <div class="mat-tab-label mat-focus-indicator" role="tab" matTabLabelWrapper mat-ripple cdkMonitorElementFocus\n       *ngFor="let tab of _tabs; let i = index"\n       [id]="_getTabLabelId(i)"\n       [attr.tabIndex]="_getTabIndex(tab, i)"\n       [attr.aria-posinset]="i + 1"\n       [attr.aria-setsize]="_tabs.length"\n       [attr.aria-controls]="_getTabContentId(i)"\n       [attr.aria-selected]="selectedIndex == i"\n       [attr.aria-label]="tab.ariaLabel || null"\n       [attr.aria-labelledby]="(!tab.ariaLabel && tab.ariaLabelledby) ? tab.ariaLabelledby : null"\n       [class.mat-tab-label-active]="selectedIndex == i"\n       [disabled]="tab.disabled"\n       [matRippleDisabled]="tab.disabled || disableRipple"\n       (click)="_handleClick(tab, tabHeader, i)"\n       (cdkFocusChange)="_tabFocusChanged($event, i)">\n\n\n    <div class="mat-tab-label-content">\n      \x3c!-- If there is a label template, use it. --\x3e\n      <ng-template [ngIf]="tab.templateLabel">\n        <ng-template [cdkPortalOutlet]="tab.templateLabel"></ng-template>\n      </ng-template>\n\n      \x3c!-- If there is not a label template, fall back to the text label. --\x3e\n      <ng-template [ngIf]="!tab.templateLabel">{{tab.textLabel}}</ng-template>\n    </div>\n  </div>\n</mat-tab-header>\n\n<div\n  class="mat-tab-body-wrapper"\n  [class._mat-animation-noopable]="_animationMode === \'NoopAnimations\'"\n  #tabBodyWrapper>\n  <mat-tab-body role="tabpanel"\n               *ngFor="let tab of _tabs; let i = index"\n               [id]="_getTabContentId(i)"\n               [attr.tabindex]="(contentTabIndex != null && selectedIndex === i) ? contentTabIndex : null"\n               [attr.aria-labelledby]="_getTabLabelId(i)"\n               [class.mat-tab-body-active]="selectedIndex === i"\n               [content]="tab.content!"\n               [position]="tab.position!"\n               [origin]="tab.origin"\n               [animationDuration]="animationDuration"\n               (_onCentered)="_removeTabBodyWrapperHeight()"\n               (_onCentering)="_setTabBodyWrapperHeight($event)">\n  </mat-tab-body>\n</div>\n',encapsulation:Hn.None,changeDetection:zn.Default,inputs:["color","disableRipple"],providers:[{provide:kB,useExisting:FB}],host:{class:"mat-tab-group","[class.mat-tab-group-dynamic-height]":"dynamicHeight","[class.mat-tab-group-inverted-header]":'headerPosition === "below"'},styles:[".mat-tab-group{display:flex;flex-direction:column;max-width:100%}.mat-tab-group.mat-tab-group-inverted-header{flex-direction:column-reverse}.mat-tab-label{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;position:relative}.mat-tab-label:focus{outline:none}.mat-tab-label:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-label:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-label.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-label.mat-tab-disabled{opacity:.5}.mat-tab-label .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-label{opacity:1}@media(max-width: 599px){.mat-tab-label{padding:0 12px}}@media(max-width: 959px){.mat-tab-label{padding:0 12px}}.mat-tab-group[mat-stretch-tabs]>.mat-tab-header .mat-tab-label{flex-basis:0;flex-grow:1}.mat-tab-body-wrapper{position:relative;overflow:hidden;display:flex;transition:height 500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-tab-body-wrapper{transition:none;animation:none}.mat-tab-body{top:0;left:0;right:0;bottom:0;position:absolute;display:block;overflow:hidden;outline:0;flex-basis:100%}.mat-tab-body.mat-tab-body-active{position:relative;overflow-x:hidden;overflow-y:auto;z-index:1;flex-grow:1}.mat-tab-group.mat-tab-group-dynamic-height .mat-tab-body.mat-tab-body-active{overflow-y:hidden}\n"]}]}],(function(){return[{type:hg},{type:Ug},{type:void 0,decorators:[{type:kr,args:[TB]},{type:Sr}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{_allTabs:[{type:Ya,args:[SB,{descendants:!0}]}],_tabBodyWrapper:[{type:Za,args:["tabBodyWrapper"]}],_tabHeader:[{type:Za,args:["tabHeader"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const LB=KI(class{});class BB extends LB{constructor(t){super(),this.elementRef=t}focus(){this.elementRef.nativeElement.focus()}getOffsetLeft(){return this.elementRef.nativeElement.offsetLeft}getOffsetWidth(){return this.elementRef.nativeElement.offsetWidth}}BB.ɵfac=function t(e){return new(e||BB)(Sm(hg))},BB.ɵdir=lo({type:BB,selectors:[["","matTabLabelWrapper",""]],hostVars:3,hostBindings:function t(e,n){2&e&&(jp("aria-disabled",!!n.disabled),pu("mat-tab-disabled",n.disabled))},inputs:{disabled:"disabled"},features:[xp]}),BB.ctorParameters=()=>[{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(BB,[{type:Cy,args:[{selector:"[matTabLabelWrapper]",inputs:["disabled"],host:{"[class.mat-tab-disabled]":"disabled","[attr.aria-disabled]":"!!disabled"}}]}],(function(){return[{type:hg}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const VB=Nz({passive:!0});class jB{constructor(t,e,n,o,i,a,r){this._elementRef=t,this._changeDetectorRef=e,this._viewportRuler=n,this._dir=o,this._ngZone=i,this._platform=a,this._animationMode=r,this._scrollDistance=0,this._selectedIndexChanged=!1,this._destroyed=new I,this._showPaginationControls=!1,this._disableScrollAfter=!0,this._disableScrollBefore=!0,this._stopScrolling=new I,this.disablePagination=!1,this._selectedIndex=0,this.selectFocusedIndex=new Lh,this.indexFocused=new Lh,i.runOutsideAngular((()=>{oe(t.nativeElement,"mouseleave").pipe(Ie(this._destroyed)).subscribe((()=>{this._stopInterval()}))}))}get selectedIndex(){return this._selectedIndex}set selectedIndex(t){t=_z(t),this._selectedIndex!=t&&(this._selectedIndexChanged=!0,this._selectedIndex=t,this._keyManager&&this._keyManager.updateActiveItem(t))}ngAfterViewInit(){oe(this._previousPaginator.nativeElement,"touchstart",VB).pipe(Ie(this._destroyed)).subscribe((()=>{this._handlePaginatorPress("before")})),oe(this._nextPaginator.nativeElement,"touchstart",VB).pipe(Ie(this._destroyed)).subscribe((()=>{this._handlePaginatorPress("after")}))}ngAfterContentInit(){const t=this._dir?this._dir.change:Et("ltr"),e=this._viewportRuler.change(150),n=()=>{this.updatePagination(),this._alignInkBarToSelectedTab()};this._keyManager=new eI(this._items).withHorizontalOrientation(this._getLayoutDirection()).withHomeAndEnd().withWrap(),this._keyManager.updateActiveItem(this._selectedIndex),"undefined"!=typeof requestAnimationFrame?requestAnimationFrame(n):n(),re(t,e,this._items.changes).pipe(Ie(this._destroyed)).subscribe((()=>{this._ngZone.run((()=>Promise.resolve().then(n))),this._keyManager.withHorizontalOrientation(this._getLayoutDirection())})),this._keyManager.change.pipe(Ie(this._destroyed)).subscribe((t=>{this.indexFocused.emit(t),this._setTabFocus(t)}))}ngAfterContentChecked(){this._tabLabelCount!=this._items.length&&(this.updatePagination(),this._tabLabelCount=this._items.length,this._changeDetectorRef.markForCheck()),this._selectedIndexChanged&&(this._scrollToLabel(this._selectedIndex),this._checkScrollingControls(),this._alignInkBarToSelectedTab(),this._selectedIndexChanged=!1,this._changeDetectorRef.markForCheck()),this._scrollDistanceChanged&&(this._updateTabScrollPosition(),this._scrollDistanceChanged=!1,this._changeDetectorRef.markForCheck())}ngOnDestroy(){this._destroyed.next(),this._destroyed.complete(),this._stopScrolling.complete()}_handleKeydown(t){if(!bz(t))switch(t.keyCode){case mz:case fz:this.focusIndex!==this.selectedIndex&&(this.selectFocusedIndex.emit(this.focusIndex),this._itemSelected(t));break;default:this._keyManager.onKeydown(t)}}_onContentChanges(){const t=this._elementRef.nativeElement.textContent;t!==this._currentTextContent&&(this._currentTextContent=t||"",this._ngZone.run((()=>{this.updatePagination(),this._alignInkBarToSelectedTab(),this._changeDetectorRef.markForCheck()})))}updatePagination(){this._checkPaginationEnabled(),this._checkScrollingControls(),this._updateTabScrollPosition()}get focusIndex(){return this._keyManager?this._keyManager.activeItemIndex:0}set focusIndex(t){this._isValidIndex(t)&&this.focusIndex!==t&&this._keyManager&&this._keyManager.setActiveItem(t)}_isValidIndex(t){if(!this._items)return!0;const e=this._items?this._items.toArray()[t]:null;return!!e&&!e.disabled}_setTabFocus(t){if(this._showPaginationControls&&this._scrollToLabel(t),this._items&&this._items.length){this._items.toArray()[t].focus();const e=this._tabListContainer.nativeElement,n=this._getLayoutDirection();e.scrollLeft="ltr"==n?0:e.scrollWidth-e.offsetWidth}}_getLayoutDirection(){return this._dir&&"rtl"===this._dir.value?"rtl":"ltr"}_updateTabScrollPosition(){if(this.disablePagination)return;const t=this.scrollDistance,e="ltr"===this._getLayoutDirection()?-t:t;this._tabList.nativeElement.style.transform=`translateX(${Math.round(e)}px)`,(this._platform.TRIDENT||this._platform.EDGE)&&(this._tabListContainer.nativeElement.scrollLeft=0)}get scrollDistance(){return this._scrollDistance}set scrollDistance(t){this._scrollTo(t)}_scrollHeader(t){return this._scrollTo(this._scrollDistance+("before"==t?-1:1)*this._tabListContainer.nativeElement.offsetWidth/3)}_handlePaginatorClick(t){this._stopInterval(),this._scrollHeader(t)}_scrollToLabel(t){if(this.disablePagination)return;const e=this._items?this._items.toArray()[t]:null;if(!e)return;const n=this._tabListContainer.nativeElement.offsetWidth,{offsetLeft:o,offsetWidth:i}=e.elementRef.nativeElement;let a,r;"ltr"==this._getLayoutDirection()?(a=o,r=a+i):(r=this._tabList.nativeElement.offsetWidth-o,a=r-i);const s=this.scrollDistance,l=this.scrollDistance+n;a<s?this.scrollDistance-=s-a+60:r>l&&(this.scrollDistance+=r-l+60)}_checkPaginationEnabled(){if(this.disablePagination)this._showPaginationControls=!1;else{const t=this._tabList.nativeElement.scrollWidth>this._elementRef.nativeElement.offsetWidth;t||(this.scrollDistance=0),t!==this._showPaginationControls&&this._changeDetectorRef.markForCheck(),this._showPaginationControls=t}}_checkScrollingControls(){this.disablePagination?this._disableScrollAfter=this._disableScrollBefore=!0:(this._disableScrollBefore=0==this.scrollDistance,this._disableScrollAfter=this.scrollDistance==this._getMaxScrollDistance(),this._changeDetectorRef.markForCheck())}_getMaxScrollDistance(){return this._tabList.nativeElement.scrollWidth-this._tabListContainer.nativeElement.offsetWidth||0}_alignInkBarToSelectedTab(){const t=this._items&&this._items.length?this._items.toArray()[this.selectedIndex]:null,e=t?t.elementRef.nativeElement:null;e?this._inkBar.alignToElement(e):this._inkBar.hide()}_stopInterval(){this._stopScrolling.next()}_handlePaginatorPress(t,e){e&&null!=e.button&&0!==e.button||(this._stopInterval(),ae(650,100).pipe(Ie(re(this._stopScrolling,this._destroyed))).subscribe((()=>{const{maxScrollDistance:e,distance:n}=this._scrollHeader(t);(0===n||n>=e)&&this._stopInterval()})))}_scrollTo(t){if(this.disablePagination)return{maxScrollDistance:0,distance:0};const e=this._getMaxScrollDistance();return this._scrollDistance=Math.max(0,Math.min(e,t)),this._scrollDistanceChanged=!0,this._checkScrollingControls(),{maxScrollDistance:e,distance:this._scrollDistance}}}jB.ɵfac=function t(e){return new(e||jB)(Sm(hg),Sm(Ug),Sm(uF),Sm(HI,8),Sm(a_),Sm(wz),Sm(VP,8))},jB.ɵdir=lo({type:jB,inputs:{disablePagination:"disablePagination"}}),jB.ctorParameters=()=>[{type:hg},{type:Ug},{type:uF},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],jB.propDecorators={disablePagination:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(jB,[{type:Cy}],(function(){return[{type:hg},{type:Ug},{type:uF},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{disablePagination:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class UB extends jB{constructor(t,e,n,o,i,a,r){super(t,e,n,o,i,a,r),this._disableRipple=!1}get disableRipple(){return this._disableRipple}set disableRipple(t){this._disableRipple=yz(t)}_itemSelected(t){t.preventDefault()}}UB.ɵfac=function t(e){return new(e||UB)(Sm(hg),Sm(Ug),Sm(uF),Sm(HI,8),Sm(a_),Sm(wz),Sm(VP,8))},UB.ɵdir=lo({type:UB,inputs:{disableRipple:"disableRipple"},features:[xp]}),UB.ctorParameters=()=>[{type:hg},{type:Ug},{type:uF},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],UB.propDecorators={disableRipple:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(UB,[{type:Cy}],(function(){return[{type:hg},{type:Ug},{type:uF},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{disableRipple:[{type:xy}]});class GB extends UB{constructor(t,e,n,o,i,a,r){super(t,e,n,o,i,a,r)}}GB.ɵfac=function t(e){return new(e||GB)(Sm(hg),Sm(Ug),Sm(uF),Sm(HI,8),Sm(a_),Sm(wz),Sm(VP,8))},GB.ɵcmp=to({type:GB,selectors:[["mat-tab-header"]],contentQueries:function t(e,n,o){if(1&e&&$h(o,BB,4),2&e){let t;Jh(t=tb())&&(n._items=t)}},viewQuery:function t(e,n){if(1&e&&(Qh(MB,7),Qh(gB,7),Qh(hB,7),Qh(bB,5),Qh(yB,5)),2&e){let t;Jh(t=tb())&&(n._inkBar=t.first),Jh(t=tb())&&(n._tabListContainer=t.first),Jh(t=tb())&&(n._tabList=t.first),Jh(t=tb())&&(n._nextPaginator=t.first),Jh(t=tb())&&(n._previousPaginator=t.first)}},hostAttrs:[1,"mat-tab-header"],hostVars:4,hostBindings:function t(e,n){2&e&&pu("mat-tab-header-pagination-controls-enabled",n._showPaginationControls)("mat-tab-header-rtl","rtl"==n._getLayoutDirection())},inputs:{selectedIndex:"selectedIndex"},outputs:{selectFocusedIndex:"selectFocusedIndex",indexFocused:"indexFocused"},features:[xp],ngContentSelectors:iB,decls:13,vars:8,consts:[["aria-hidden","true","mat-ripple","",1,"mat-tab-header-pagination","mat-tab-header-pagination-before","mat-elevation-z4",3,"matRippleDisabled","click","mousedown","touchend"],["previousPaginator",""],[1,"mat-tab-header-pagination-chevron"],[1,"mat-tab-label-container",3,"keydown"],["tabListContainer",""],["role","tablist",1,"mat-tab-list",3,"cdkObserveContent"],["tabList",""],[1,"mat-tab-labels"],["aria-hidden","true","mat-ripple","",1,"mat-tab-header-pagination","mat-tab-header-pagination-after","mat-elevation-z4",3,"matRippleDisabled","mousedown","click","touchend"],["nextPaginator",""]],template:function t(e,n){1&e&&(Zm(),Rm(0,"div",0,1),Vm("click",(function t(){return n._handlePaginatorClick("before")}))("mousedown",(function t(e){return n._handlePaginatorPress("before",e)}))("touchend",(function t(){return n._stopInterval()})),Tm(2,"div",2),Am(),Rm(3,"div",3,4),Vm("keydown",(function t(e){return n._handleKeydown(e)})),Rm(5,"div",5,6),Vm("cdkObserveContent",(function t(){return n._onContentChanges()})),Rm(7,"div",7),Xm(8),Am(),Tm(9,"mat-ink-bar"),Am(),Am(),Rm(10,"div",8,9),Vm("mousedown",(function t(e){return n._handlePaginatorPress("after",e)}))("click",(function t(){return n._handlePaginatorClick("after")}))("touchend",(function t(){return n._stopInterval()})),Tm(12,"div",2),Am()),2&e&&(pu("mat-tab-header-pagination-disabled",n._disableScrollBefore),Dm("matRippleDisabled",n._disableScrollBefore||n.disableRipple),rc(5),pu("_mat-animation-noopable","NoopAnimations"===n._animationMode),rc(5),pu("mat-tab-header-pagination-disabled",n._disableScrollAfter),Dm("matRippleDisabled",n._disableScrollAfter||n.disableRipple))},directives:[kH,jz,MB],styles:['.mat-tab-header{display:flex;overflow:hidden;position:relative;flex-shrink:0}.mat-tab-header-pagination{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;position:relative;display:none;justify-content:center;align-items:center;min-width:32px;cursor:pointer;z-index:2;-webkit-tap-highlight-color:transparent;touch-action:none}.mat-tab-header-pagination-controls-enabled .mat-tab-header-pagination{display:flex}.mat-tab-header-pagination-before,.mat-tab-header-rtl .mat-tab-header-pagination-after{padding-left:4px}.mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-rtl .mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(-135deg)}.mat-tab-header-rtl .mat-tab-header-pagination-before,.mat-tab-header-pagination-after{padding-right:4px}.mat-tab-header-rtl .mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(45deg)}.mat-tab-header-pagination-chevron{border-style:solid;border-width:2px 2px 0 0;content:"";height:8px;width:8px}.mat-tab-header-pagination-disabled{box-shadow:none;cursor:default}.mat-tab-list{flex-grow:1;position:relative;transition:transform 500ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-ink-bar{position:absolute;bottom:0;height:2px;transition:500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-ink-bar{transition:none;animation:none}.mat-tab-group-inverted-header .mat-ink-bar{bottom:auto;top:0}.cdk-high-contrast-active .mat-ink-bar{outline:solid 2px;height:0}.mat-tab-labels{display:flex}[mat-align-tabs=center]>.mat-tab-header .mat-tab-labels{justify-content:center}[mat-align-tabs=end]>.mat-tab-header .mat-tab-labels{justify-content:flex-end}.mat-tab-label-container{display:flex;flex-grow:1;overflow:hidden;z-index:1}._mat-animation-noopable.mat-tab-list{transition:none;animation:none}.mat-tab-label{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;position:relative}.mat-tab-label:focus{outline:none}.mat-tab-label:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-label:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-label.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-label.mat-tab-disabled{opacity:.5}.mat-tab-label .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-label{opacity:1}@media(max-width: 599px){.mat-tab-label{min-width:72px}}\n'],encapsulation:2}),GB.ctorParameters=()=>[{type:hg},{type:Ug},{type:uF},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],GB.propDecorators={_items:[{type:Ya,args:[BB,{descendants:!1}]}],_inkBar:[{type:Za,args:[MB,{static:!0}]}],_tabListContainer:[{type:Za,args:["tabListContainer",{static:!0}]}],_tabList:[{type:Za,args:["tabList",{static:!0}]}],_nextPaginator:[{type:Za,args:["nextPaginator"]}],_previousPaginator:[{type:Za,args:["previousPaginator"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(GB,[{type:My,args:[{selector:"mat-tab-header",template:'<div class="mat-tab-header-pagination mat-tab-header-pagination-before mat-elevation-z4"\n     #previousPaginator\n     aria-hidden="true"\n     mat-ripple [matRippleDisabled]="_disableScrollBefore || disableRipple"\n     [class.mat-tab-header-pagination-disabled]="_disableScrollBefore"\n     (click)="_handlePaginatorClick(\'before\')"\n     (mousedown)="_handlePaginatorPress(\'before\', $event)"\n     (touchend)="_stopInterval()">\n  <div class="mat-tab-header-pagination-chevron"></div>\n</div>\n\n<div class="mat-tab-label-container" #tabListContainer (keydown)="_handleKeydown($event)">\n  <div\n    #tabList\n    class="mat-tab-list"\n    [class._mat-animation-noopable]="_animationMode === \'NoopAnimations\'"\n    role="tablist"\n    (cdkObserveContent)="_onContentChanges()">\n    <div class="mat-tab-labels">\n      <ng-content></ng-content>\n    </div>\n    <mat-ink-bar></mat-ink-bar>\n  </div>\n</div>\n\n<div class="mat-tab-header-pagination mat-tab-header-pagination-after mat-elevation-z4"\n     #nextPaginator\n     aria-hidden="true"\n     mat-ripple [matRippleDisabled]="_disableScrollAfter || disableRipple"\n     [class.mat-tab-header-pagination-disabled]="_disableScrollAfter"\n     (mousedown)="_handlePaginatorPress(\'after\', $event)"\n     (click)="_handlePaginatorClick(\'after\')"\n     (touchend)="_stopInterval()">\n  <div class="mat-tab-header-pagination-chevron"></div>\n</div>\n',inputs:["selectedIndex"],outputs:["selectFocusedIndex","indexFocused"],encapsulation:Hn.None,changeDetection:zn.Default,host:{class:"mat-tab-header","[class.mat-tab-header-pagination-controls-enabled]":"_showPaginationControls","[class.mat-tab-header-rtl]":"_getLayoutDirection() == 'rtl'"},styles:['.mat-tab-header{display:flex;overflow:hidden;position:relative;flex-shrink:0}.mat-tab-header-pagination{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;position:relative;display:none;justify-content:center;align-items:center;min-width:32px;cursor:pointer;z-index:2;-webkit-tap-highlight-color:transparent;touch-action:none}.mat-tab-header-pagination-controls-enabled .mat-tab-header-pagination{display:flex}.mat-tab-header-pagination-before,.mat-tab-header-rtl .mat-tab-header-pagination-after{padding-left:4px}.mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-rtl .mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(-135deg)}.mat-tab-header-rtl .mat-tab-header-pagination-before,.mat-tab-header-pagination-after{padding-right:4px}.mat-tab-header-rtl .mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(45deg)}.mat-tab-header-pagination-chevron{border-style:solid;border-width:2px 2px 0 0;content:"";height:8px;width:8px}.mat-tab-header-pagination-disabled{box-shadow:none;cursor:default}.mat-tab-list{flex-grow:1;position:relative;transition:transform 500ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-ink-bar{position:absolute;bottom:0;height:2px;transition:500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-ink-bar{transition:none;animation:none}.mat-tab-group-inverted-header .mat-ink-bar{bottom:auto;top:0}.cdk-high-contrast-active .mat-ink-bar{outline:solid 2px;height:0}.mat-tab-labels{display:flex}[mat-align-tabs=center]>.mat-tab-header .mat-tab-labels{justify-content:center}[mat-align-tabs=end]>.mat-tab-header .mat-tab-labels{justify-content:flex-end}.mat-tab-label-container{display:flex;flex-grow:1;overflow:hidden;z-index:1}._mat-animation-noopable.mat-tab-list{transition:none;animation:none}.mat-tab-label{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;position:relative}.mat-tab-label:focus{outline:none}.mat-tab-label:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-label:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-label.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-label.mat-tab-disabled{opacity:.5}.mat-tab-label .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-label{opacity:1}@media(max-width: 599px){.mat-tab-label{min-width:72px}}\n']}]}],(function(){return[{type:hg},{type:Ug},{type:uF},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{_items:[{type:Ya,args:[BB,{descendants:!1}]}],_inkBar:[{type:Za,args:[MB,{static:!0}]}],_tabListContainer:[{type:Za,args:["tabListContainer",{static:!0}]}],_tabList:[{type:Za,args:["tabList",{static:!0}]}],_nextPaginator:[{type:Za,args:["nextPaginator"]}],_previousPaginator:[{type:Za,args:["previousPaginator"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class WB extends jB{constructor(t,e,n,o,i,a,r){super(t,o,i,e,n,a,r),this._disableRipple=!1,this.color="primary"}get backgroundColor(){return this._backgroundColor}set backgroundColor(t){const e=this._elementRef.nativeElement.classList;e.remove(`mat-background-${this.backgroundColor}`),t&&e.add(`mat-background-${t}`),this._backgroundColor=t}get disableRipple(){return this._disableRipple}set disableRipple(t){this._disableRipple=yz(t)}_itemSelected(){}ngAfterContentInit(){this._items.changes.pipe(Ne(null),Ie(this._destroyed)).subscribe((()=>{this.updateActiveLink()})),super.ngAfterContentInit()}updateActiveLink(){if(!this._items)return;const t=this._items.toArray();for(let e=0;e<t.length;e++)if(t[e].active)return this.selectedIndex=e,void this._changeDetectorRef.markForCheck();this.selectedIndex=-1,this._inkBar.hide()}}WB.ɵfac=function t(e){return new(e||WB)(Sm(hg),Sm(HI,8),Sm(a_),Sm(Ug),Sm(uF),Sm(wz),Sm(VP,8))},WB.ɵdir=lo({type:WB,inputs:{color:"color",backgroundColor:"backgroundColor",disableRipple:"disableRipple"},features:[xp]}),WB.ctorParameters=()=>[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:Ug},{type:uF},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],WB.propDecorators={backgroundColor:[{type:xy}],disableRipple:[{type:xy}],color:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(WB,[{type:Cy}],(function(){return[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:Ug},{type:uF},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{color:[{type:xy}],backgroundColor:[{type:xy}],disableRipple:[{type:xy}]});class YB extends WB{constructor(t,e,n,o,i,a,r){super(t,e,n,o,i,a,r)}}YB.ɵfac=function t(e){return new(e||YB)(Sm(hg),Sm(HI,8),Sm(a_),Sm(Ug),Sm(uF),Sm(wz),Sm(VP,8))},YB.ɵcmp=to({type:YB,selectors:[["","mat-tab-nav-bar",""]],contentQueries:function t(e,n,o){if(1&e&&$h(o,XB,5),2&e){let t;Jh(t=tb())&&(n._items=t)}},viewQuery:function t(e,n){if(1&e&&(Qh(MB,7),Qh(gB,7),Qh(hB,7),Qh(bB,5),Qh(yB,5)),2&e){let t;Jh(t=tb())&&(n._inkBar=t.first),Jh(t=tb())&&(n._tabListContainer=t.first),Jh(t=tb())&&(n._tabList=t.first),Jh(t=tb())&&(n._nextPaginator=t.first),Jh(t=tb())&&(n._previousPaginator=t.first)}},hostAttrs:[1,"mat-tab-nav-bar","mat-tab-header"],hostVars:10,hostBindings:function t(e,n){2&e&&pu("mat-tab-header-pagination-controls-enabled",n._showPaginationControls)("mat-tab-header-rtl","rtl"==n._getLayoutDirection())("mat-primary","warn"!==n.color&&"accent"!==n.color)("mat-accent","accent"===n.color)("mat-warn","warn"===n.color)},inputs:{color:"color"},exportAs:["matTabNavBar","matTabNav"],features:[xp],attrs:["mat-tab-nav-bar",""],ngContentSelectors:iB,decls:13,vars:8,consts:[["aria-hidden","true","mat-ripple","",1,"mat-tab-header-pagination","mat-tab-header-pagination-before","mat-elevation-z4",3,"matRippleDisabled","click","mousedown","touchend"],["previousPaginator",""],[1,"mat-tab-header-pagination-chevron"],[1,"mat-tab-link-container",3,"keydown"],["tabListContainer",""],[1,"mat-tab-list",3,"cdkObserveContent"],["tabList",""],[1,"mat-tab-links"],["aria-hidden","true","mat-ripple","",1,"mat-tab-header-pagination","mat-tab-header-pagination-after","mat-elevation-z4",3,"matRippleDisabled","mousedown","click","touchend"],["nextPaginator",""]],template:function t(e,n){1&e&&(Zm(),Rm(0,"div",0,1),Vm("click",(function t(){return n._handlePaginatorClick("before")}))("mousedown",(function t(e){return n._handlePaginatorPress("before",e)}))("touchend",(function t(){return n._stopInterval()})),Tm(2,"div",2),Am(),Rm(3,"div",3,4),Vm("keydown",(function t(e){return n._handleKeydown(e)})),Rm(5,"div",5,6),Vm("cdkObserveContent",(function t(){return n._onContentChanges()})),Rm(7,"div",7),Xm(8),Am(),Tm(9,"mat-ink-bar"),Am(),Am(),Rm(10,"div",8,9),Vm("mousedown",(function t(e){return n._handlePaginatorPress("after",e)}))("click",(function t(){return n._handlePaginatorClick("after")}))("touchend",(function t(){return n._stopInterval()})),Tm(12,"div",2),Am()),2&e&&(pu("mat-tab-header-pagination-disabled",n._disableScrollBefore),Dm("matRippleDisabled",n._disableScrollBefore||n.disableRipple),rc(5),pu("_mat-animation-noopable","NoopAnimations"===n._animationMode),rc(5),pu("mat-tab-header-pagination-disabled",n._disableScrollAfter),Dm("matRippleDisabled",n._disableScrollAfter||n.disableRipple))},directives:[kH,jz,MB],styles:['.mat-tab-header{display:flex;overflow:hidden;position:relative;flex-shrink:0}.mat-tab-header-pagination{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;position:relative;display:none;justify-content:center;align-items:center;min-width:32px;cursor:pointer;z-index:2;-webkit-tap-highlight-color:transparent;touch-action:none}.mat-tab-header-pagination-controls-enabled .mat-tab-header-pagination{display:flex}.mat-tab-header-pagination-before,.mat-tab-header-rtl .mat-tab-header-pagination-after{padding-left:4px}.mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-rtl .mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(-135deg)}.mat-tab-header-rtl .mat-tab-header-pagination-before,.mat-tab-header-pagination-after{padding-right:4px}.mat-tab-header-rtl .mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(45deg)}.mat-tab-header-pagination-chevron{border-style:solid;border-width:2px 2px 0 0;content:"";height:8px;width:8px}.mat-tab-header-pagination-disabled{box-shadow:none;cursor:default}.mat-tab-list{flex-grow:1;position:relative;transition:transform 500ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-tab-links{display:flex}[mat-align-tabs=center]>.mat-tab-link-container .mat-tab-links{justify-content:center}[mat-align-tabs=end]>.mat-tab-link-container .mat-tab-links{justify-content:flex-end}.mat-ink-bar{position:absolute;bottom:0;height:2px;transition:500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-ink-bar{transition:none;animation:none}.mat-tab-group-inverted-header .mat-ink-bar{bottom:auto;top:0}.cdk-high-contrast-active .mat-ink-bar{outline:solid 2px;height:0}.mat-tab-link-container{display:flex;flex-grow:1;overflow:hidden;z-index:1}.mat-tab-link{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;vertical-align:top;text-decoration:none;position:relative;overflow:hidden;-webkit-tap-highlight-color:transparent}.mat-tab-link:focus{outline:none}.mat-tab-link:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-link:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-link.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-link.mat-tab-disabled{opacity:.5}.mat-tab-link .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-link{opacity:1}[mat-stretch-tabs] .mat-tab-link{flex-basis:0;flex-grow:1}.mat-tab-link.mat-tab-disabled{pointer-events:none}@media(max-width: 599px){.mat-tab-link{min-width:72px}}\n'],encapsulation:2}),YB.ctorParameters=()=>[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:Ug},{type:uF},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],YB.propDecorators={_items:[{type:Ya,args:[qe((()=>XB)),{descendants:!0}]}],_inkBar:[{type:Za,args:[MB,{static:!0}]}],_tabListContainer:[{type:Za,args:["tabListContainer",{static:!0}]}],_tabList:[{type:Za,args:["tabList",{static:!0}]}],_nextPaginator:[{type:Za,args:["nextPaginator"]}],_previousPaginator:[{type:Za,args:["previousPaginator"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(YB,[{type:My,args:[{selector:"[mat-tab-nav-bar]",exportAs:"matTabNavBar, matTabNav",inputs:["color"],template:'<div class="mat-tab-header-pagination mat-tab-header-pagination-before mat-elevation-z4"\n     #previousPaginator\n     aria-hidden="true"\n     mat-ripple [matRippleDisabled]="_disableScrollBefore || disableRipple"\n     [class.mat-tab-header-pagination-disabled]="_disableScrollBefore"\n     (click)="_handlePaginatorClick(\'before\')"\n     (mousedown)="_handlePaginatorPress(\'before\', $event)"\n     (touchend)="_stopInterval()">\n  <div class="mat-tab-header-pagination-chevron"></div>\n</div>\n\n<div class="mat-tab-link-container" #tabListContainer (keydown)="_handleKeydown($event)">\n  <div\n    class="mat-tab-list"\n    [class._mat-animation-noopable]="_animationMode === \'NoopAnimations\'"\n    #tabList\n    (cdkObserveContent)="_onContentChanges()">\n    <div class="mat-tab-links">\n      <ng-content></ng-content>\n    </div>\n    <mat-ink-bar></mat-ink-bar>\n  </div>\n</div>\n\n<div class="mat-tab-header-pagination mat-tab-header-pagination-after mat-elevation-z4"\n     #nextPaginator\n     aria-hidden="true"\n     mat-ripple [matRippleDisabled]="_disableScrollAfter || disableRipple"\n     [class.mat-tab-header-pagination-disabled]="_disableScrollAfter"\n     (mousedown)="_handlePaginatorPress(\'after\', $event)"\n     (click)="_handlePaginatorClick(\'after\')"\n     (touchend)="_stopInterval()">\n  <div class="mat-tab-header-pagination-chevron"></div>\n</div>\n',host:{class:"mat-tab-nav-bar mat-tab-header","[class.mat-tab-header-pagination-controls-enabled]":"_showPaginationControls","[class.mat-tab-header-rtl]":"_getLayoutDirection() == 'rtl'","[class.mat-primary]":'color !== "warn" && color !== "accent"',"[class.mat-accent]":'color === "accent"',"[class.mat-warn]":'color === "warn"'},encapsulation:Hn.None,changeDetection:zn.Default,styles:['.mat-tab-header{display:flex;overflow:hidden;position:relative;flex-shrink:0}.mat-tab-header-pagination{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;position:relative;display:none;justify-content:center;align-items:center;min-width:32px;cursor:pointer;z-index:2;-webkit-tap-highlight-color:transparent;touch-action:none}.mat-tab-header-pagination-controls-enabled .mat-tab-header-pagination{display:flex}.mat-tab-header-pagination-before,.mat-tab-header-rtl .mat-tab-header-pagination-after{padding-left:4px}.mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-rtl .mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(-135deg)}.mat-tab-header-rtl .mat-tab-header-pagination-before,.mat-tab-header-pagination-after{padding-right:4px}.mat-tab-header-rtl .mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(45deg)}.mat-tab-header-pagination-chevron{border-style:solid;border-width:2px 2px 0 0;content:"";height:8px;width:8px}.mat-tab-header-pagination-disabled{box-shadow:none;cursor:default}.mat-tab-list{flex-grow:1;position:relative;transition:transform 500ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-tab-links{display:flex}[mat-align-tabs=center]>.mat-tab-link-container .mat-tab-links{justify-content:center}[mat-align-tabs=end]>.mat-tab-link-container .mat-tab-links{justify-content:flex-end}.mat-ink-bar{position:absolute;bottom:0;height:2px;transition:500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-ink-bar{transition:none;animation:none}.mat-tab-group-inverted-header .mat-ink-bar{bottom:auto;top:0}.cdk-high-contrast-active .mat-ink-bar{outline:solid 2px;height:0}.mat-tab-link-container{display:flex;flex-grow:1;overflow:hidden;z-index:1}.mat-tab-link{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;vertical-align:top;text-decoration:none;position:relative;overflow:hidden;-webkit-tap-highlight-color:transparent}.mat-tab-link:focus{outline:none}.mat-tab-link:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-link:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-link.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-link.mat-tab-disabled{opacity:.5}.mat-tab-link .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-link{opacity:1}[mat-stretch-tabs] .mat-tab-link{flex-basis:0;flex-grow:1}.mat-tab-link.mat-tab-disabled{pointer-events:none}@media(max-width: 599px){.mat-tab-link{min-width:72px}}\n']}]}],(function(){return[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:Ug},{type:uF},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{_items:[{type:Ya,args:[qe((()=>XB)),{descendants:!0}]}],_inkBar:[{type:Za,args:[MB,{static:!0}]}],_tabListContainer:[{type:Za,args:["tabListContainer",{static:!0}]}],_tabList:[{type:Za,args:["tabList",{static:!0}]}],_nextPaginator:[{type:Za,args:["nextPaginator"]}],_previousPaginator:[{type:Za,args:["previousPaginator"]}]});const qB=$I(QI(KI(class{})));class ZB extends qB{constructor(t,e,n,o,i,a){super(),this._tabNavBar=t,this.elementRef=e,this._focusMonitor=i,this._isActive=!1,this.rippleConfig=n||{},this.tabIndex=parseInt(o)||0,"NoopAnimations"===a&&(this.rippleConfig.animation={enterDuration:0,exitDuration:0})}get active(){return this._isActive}set active(t){yz(t)!==this._isActive&&(this._isActive=t,this._tabNavBar.updateActiveLink())}get rippleDisabled(){return this.disabled||this.disableRipple||this._tabNavBar.disableRipple||!!this.rippleConfig.disabled}focus(){this.elementRef.nativeElement.focus()}ngAfterViewInit(){this._focusMonitor.monitor(this.elementRef)}ngOnDestroy(){this._focusMonitor.stopMonitoring(this.elementRef)}_handleFocus(){this._tabNavBar.focusIndex=this._tabNavBar._items.toArray().indexOf(this)}}ZB.ɵfac=function t(e){return new(e||ZB)(Sm(WB),Sm(hg),Sm(wH,8),Na("tabindex"),Sm(SI),Sm(VP,8))},ZB.ɵdir=lo({type:ZB,inputs:{active:"active"},features:[xp]}),ZB.ctorParameters=()=>[{type:WB},{type:hg},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:SI},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],ZB.propDecorators={active:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(ZB,[{type:Cy}],(function(){return[{type:WB},{type:hg},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:SI},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{active:[{type:xy}]});class XB extends ZB{constructor(t,e,n,o,i,a,r,s){super(t,e,i,a,r,s),this._tabLinkRipple=new PH(this,n,e,o),this._tabLinkRipple.setupTriggerEvents(e.nativeElement)}ngOnDestroy(){super.ngOnDestroy(),this._tabLinkRipple._removeTriggerEvents()}}XB.ɵfac=function t(e){return new(e||XB)(Sm(YB),Sm(hg),Sm(a_),Sm(wz),Sm(wH,8),Na("tabindex"),Sm(SI),Sm(VP,8))},XB.ɵdir=lo({type:XB,selectors:[["","mat-tab-link",""],["","matTabLink",""]],hostAttrs:[1,"mat-tab-link","mat-focus-indicator"],hostVars:7,hostBindings:function t(e,n){1&e&&Vm("focus",(function t(){return n._handleFocus()})),2&e&&(jp("aria-current",n.active?"page":null)("aria-disabled",n.disabled)("tabIndex",n.tabIndex),pu("mat-tab-disabled",n.disabled)("mat-tab-label-active",n.active))},inputs:{disabled:"disabled",disableRipple:"disableRipple",tabIndex:"tabIndex"},exportAs:["matTabLink"],features:[xp]}),XB.ctorParameters=()=>[{type:YB},{type:hg},{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:SI},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(XB,[{type:Cy,args:[{selector:"[mat-tab-link], [matTabLink]",exportAs:"matTabLink",inputs:["disabled","disableRipple","tabIndex"],host:{class:"mat-tab-link mat-focus-indicator","[attr.aria-current]":'active ? "page" : null',"[attr.aria-disabled]":"disabled","[attr.tabIndex]":"tabIndex","[class.mat-tab-disabled]":"disabled","[class.mat-tab-label-active]":"active","(focus)":"_handleFocus()"}}]}],(function(){return[{type:YB},{type:hg},{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:SI},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class KB{}KB.ɵfac=function t(e){return new(e||KB)},KB.ɵmod=ao({type:KB}),KB.ɵinj=vn({imports:[[WM,XI,RF,SH,Uz,NI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(KB,[{type:Ay,args:[{imports:[WM,XI,RF,SH,Uz,NI],exports:[XI,FB,PB,SB,YB,XB,xB],declarations:[FB,PB,SB,MB,BB,YB,XB,AB,EB,GB,xB]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(KB,{declarations:function(){return[FB,PB,SB,MB,BB,YB,XB,AB,EB,GB,xB]},imports:function(){return[WM,XI,RF,SH,Uz,NI]},exports:function(){return[XI,FB,PB,SB,YB,XB,xB]}});const JB=["underline"],QB=["connectionContainer"],$B=["inputContainer"],tV=["label"];function eV(t,e){1&t&&(Nm(0),Rm(1,"div",14),Tm(2,"div",15),Tm(3,"div",16),Tm(4,"div",17),Am(),Rm(5,"div",18),Tm(6,"div",15),Tm(7,"div",16),Tm(8,"div",17),Am(),zm())}function nV(t,e){1&t&&(Rm(0,"div",19),Xm(1,1),Am())}function oV(t,e){if(1&t&&(Nm(0),Xm(1,2),Rm(2,"span"),ku(3),Am(),zm()),2&t){const t=Ym(2);rc(3),Su(t._control.placeholder)}}function iV(t,e){1&t&&Xm(0,3,["*ngSwitchCase","true"])}function aV(t,e){1&t&&(Rm(0,"span",23),ku(1," *"),Am())}function rV(t,e){if(1&t){const t=Hm();Rm(0,"label",20,21),Vm("cdkObserveContent",(function e(){return hi(t),Ym().updateOutlineGap()})),Qp(2,oV,4,1,"ng-container",12),Qp(3,iV,1,0,"ng-content",12),Qp(4,aV,2,0,"span",22),Am()}if(2&t){const t=Ym();pu("mat-empty",t._control.empty&&!t._shouldAlwaysFloat())("mat-form-field-empty",t._control.empty&&!t._shouldAlwaysFloat())("mat-accent","accent"==t.color)("mat-warn","warn"==t.color),Dm("cdkObserveContentDisabled","outline"!=t.appearance)("id",t._labelId)("ngSwitch",t._hasLabel()),jp("for",t._control.id)("aria-owns",t._control.id),rc(2),Dm("ngSwitchCase",!1),rc(1),Dm("ngSwitchCase",!0),rc(1),Dm("ngIf",!t.hideRequiredMarker&&t._control.required&&!t._control.disabled)}}function sV(t,e){1&t&&(Rm(0,"div",24),Xm(1,4),Am())}function lV(t,e){if(1&t&&(Rm(0,"div",25,26),Tm(2,"span",27),Am()),2&t){const t=Ym();rc(2),pu("mat-accent","accent"==t.color)("mat-warn","warn"==t.color)}}function cV(t,e){1&t&&(Rm(0,"div"),Xm(1,5),Am()),2&t&&Dm("@transitionMessages",Ym()._subscriptAnimationState)}function dV(t,e){if(1&t&&(Rm(0,"div",31),ku(1),Am()),2&t){const t=Ym(2);Dm("id",t._hintLabelId),rc(1),Su(t.hintLabel)}}function pV(t,e){if(1&t&&(Rm(0,"div",28),Qp(1,dV,2,2,"div",29),Xm(2,6),Tm(3,"div",30),Xm(4,7),Am()),2&t){const t=Ym();Dm("@transitionMessages",t._subscriptAnimationState),rc(1),Dm("ngIf",t.hintLabel)}}const mV=["*",[["","matPrefix",""]],[["mat-placeholder"]],[["mat-label"]],[["","matSuffix",""]],[["mat-error"]],[["mat-hint",3,"align","end"]],[["mat-hint","align","end"]]];let uV=0;const fV=new Ga("MatError");class gV{constructor(t,e){this.id="mat-error-"+uV++,t||e.nativeElement.setAttribute("aria-live","polite")}}gV.ɵfac=function t(e){return new(e||gV)(Na("aria-live"),Sm(hg))},gV.ɵdir=lo({type:gV,selectors:[["mat-error"]],hostAttrs:["aria-atomic","true",1,"mat-error"],hostVars:1,hostBindings:function t(e,n){2&e&&jp("id",n.id)},inputs:{id:"id"},features:[pg([{provide:fV,useExisting:gV}])]}),gV.ctorParameters=()=>[{type:String,decorators:[{type:ja,args:["aria-live"]}]},{type:hg}],gV.propDecorators={id:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(gV,[{type:Cy,args:[{selector:"mat-error",host:{class:"mat-error","[attr.id]":"id","aria-atomic":"true"},providers:[{provide:fV,useExisting:gV}]}]}],(function(){return[{type:String,decorators:[{type:ja,args:["aria-live"]}]},{type:hg}]}),{id:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const hV={transitionMessages:nx("transitionMessages",[rx("enter",ax({opacity:1,transform:"translateY(0%)"})),lx("void => enter",[ax({opacity:0,transform:"translateY(-5px)"}),ox("300ms cubic-bezier(0.55, 0, 0.55, 0.2)")])])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class bV{}function yV(t){return Error(`A hint was already declared for 'align="${t}"'.`)}bV.ɵfac=function t(e){return new(e||bV)},bV.ɵdir=lo({type:bV}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(bV,[{type:Cy}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let _V=0;const CV=new Ga("MatHint");class MV{constructor(){this.align="start",this.id="mat-hint-"+_V++}}MV.ɵfac=function t(e){return new(e||MV)},MV.ɵdir=lo({type:MV,selectors:[["mat-hint"]],hostAttrs:[1,"mat-hint"],hostVars:4,hostBindings:function t(e,n){2&e&&(jp("id",n.id)("align",null),pu("mat-form-field-hint-end","end"===n.align))},inputs:{align:"align",id:"id"},features:[pg([{provide:CV,useExisting:MV}])]}),MV.propDecorators={align:[{type:xy}],id:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(MV,[{type:Cy,args:[{selector:"mat-hint",host:{class:"mat-hint","[class.mat-form-field-hint-end]":'align === "end"',"[attr.id]":"id","[attr.align]":"null"},providers:[{provide:CV,useExisting:MV}]}]}],(function(){return[]}),{align:[{type:xy}],id:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class vV{}vV.ɵfac=function t(e){return new(e||vV)},vV.ɵdir=lo({type:vV,selectors:[["mat-label"]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(vV,[{type:Cy,args:[{selector:"mat-label"}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class xV{}xV.ɵfac=function t(e){return new(e||xV)},xV.ɵdir=lo({type:xV,selectors:[["mat-placeholder"]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(xV,[{type:Cy,args:[{selector:"mat-placeholder"}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const OV=new Ga("MatPrefix");class PV{}PV.ɵfac=function t(e){return new(e||PV)},PV.ɵdir=lo({type:PV,selectors:[["","matPrefix",""]],features:[pg([{provide:OV,useExisting:PV}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(PV,[{type:Cy,args:[{selector:"[matPrefix]",providers:[{provide:OV,useExisting:PV}]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const wV=new Ga("MatSuffix");class kV{}kV.ɵfac=function t(e){return new(e||kV)},kV.ɵdir=lo({type:kV,selectors:[["","matSuffix",""]],features:[pg([{provide:wV,useExisting:kV}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(kV,[{type:Cy,args:[{selector:"[matSuffix]",providers:[{provide:wV,useExisting:kV}]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let SV=0;const DV=JI(class{constructor(t){this._elementRef=t}},"primary"),EV=new Ga("MAT_FORM_FIELD_DEFAULT_OPTIONS"),RV=new Ga("MatFormField");class AV extends DV{constructor(t,e,n,o,i,a,r,s){super(t),this._changeDetectorRef=e,this._dir=o,this._defaults=i,this._platform=a,this._ngZone=r,this._outlineGapCalculationNeededImmediately=!1,this._outlineGapCalculationNeededOnStable=!1,this._destroyed=new I,this._showAlwaysAnimate=!1,this._subscriptAnimationState="",this._hintLabel="",this._hintLabelId="mat-hint-"+SV++,this._labelId="mat-form-field-label-"+SV++,this.floatLabel=this._getDefaultFloatLabelState(),this._animationsEnabled="NoopAnimations"!==s,this.appearance=i&&i.appearance?i.appearance:"legacy",this._hideRequiredMarker=!(!i||null==i.hideRequiredMarker)&&i.hideRequiredMarker}get appearance(){return this._appearance}set appearance(t){const e=this._appearance;this._appearance=t||this._defaults&&this._defaults.appearance||"legacy","outline"===this._appearance&&e!==t&&(this._outlineGapCalculationNeededOnStable=!0)}get hideRequiredMarker(){return this._hideRequiredMarker}set hideRequiredMarker(t){this._hideRequiredMarker=yz(t)}_shouldAlwaysFloat(){return"always"===this.floatLabel&&!this._showAlwaysAnimate}_canLabelFloat(){return"never"!==this.floatLabel}get hintLabel(){return this._hintLabel}set hintLabel(t){this._hintLabel=t,this._processHints()}get floatLabel(){return"legacy"!==this.appearance&&"never"===this._floatLabel?"auto":this._floatLabel}set floatLabel(t){t!==this._floatLabel&&(this._floatLabel=t||this._getDefaultFloatLabelState(),this._changeDetectorRef.markForCheck())}get _control(){return this._explicitFormFieldControl||this._controlNonStatic||this._controlStatic}set _control(t){this._explicitFormFieldControl=t}getLabelId(){return this._hasFloatingLabel()?this._labelId:null}getConnectedOverlayOrigin(){return this._connectionContainerRef||this._elementRef}ngAfterContentInit(){this._validateControlChild();const t=this._control;t.controlType&&this._elementRef.nativeElement.classList.add(`mat-form-field-type-${t.controlType}`),t.stateChanges.pipe(Ne(null)).subscribe((()=>{this._validatePlaceholders(),this._syncDescribedByIds(),this._changeDetectorRef.markForCheck()})),t.ngControl&&t.ngControl.valueChanges&&t.ngControl.valueChanges.pipe(Ie(this._destroyed)).subscribe((()=>this._changeDetectorRef.markForCheck())),this._ngZone.runOutsideAngular((()=>{this._ngZone.onStable.pipe(Ie(this._destroyed)).subscribe((()=>{this._outlineGapCalculationNeededOnStable&&this.updateOutlineGap()}))})),re(this._prefixChildren.changes,this._suffixChildren.changes).subscribe((()=>{this._outlineGapCalculationNeededOnStable=!0,this._changeDetectorRef.markForCheck()})),this._hintChildren.changes.pipe(Ne(null)).subscribe((()=>{this._processHints(),this._changeDetectorRef.markForCheck()})),this._errorChildren.changes.pipe(Ne(null)).subscribe((()=>{this._syncDescribedByIds(),this._changeDetectorRef.markForCheck()})),this._dir&&this._dir.change.pipe(Ie(this._destroyed)).subscribe((()=>{"function"==typeof requestAnimationFrame?this._ngZone.runOutsideAngular((()=>{requestAnimationFrame((()=>this.updateOutlineGap()))})):this.updateOutlineGap()}))}ngAfterContentChecked(){this._validateControlChild(),this._outlineGapCalculationNeededImmediately&&this.updateOutlineGap()}ngAfterViewInit(){this._subscriptAnimationState="enter",this._changeDetectorRef.detectChanges()}ngOnDestroy(){this._destroyed.next(),this._destroyed.complete()}_shouldForward(t){const e=this._control?this._control.ngControl:null;return e&&e[t]}_hasPlaceholder(){return!!(this._control&&this._control.placeholder||this._placeholderChild)}_hasLabel(){return!(!this._labelChildNonStatic&&!this._labelChildStatic)}_shouldLabelFloat(){return this._canLabelFloat()&&(this._control&&this._control.shouldLabelFloat||this._shouldAlwaysFloat())}_hideControlPlaceholder(){return"legacy"===this.appearance&&!this._hasLabel()||this._hasLabel()&&!this._shouldLabelFloat()}_hasFloatingLabel(){return this._hasLabel()||"legacy"===this.appearance&&this._hasPlaceholder()}_getDisplayedMessages(){return this._errorChildren&&this._errorChildren.length>0&&this._control.errorState?"error":"hint"}_animateAndLockLabel(){this._hasFloatingLabel()&&this._canLabelFloat()&&(this._animationsEnabled&&this._label&&(this._showAlwaysAnimate=!0,oe(this._label.nativeElement,"transitionend").pipe(be(1)).subscribe((()=>{this._showAlwaysAnimate=!1}))),this.floatLabel="always",this._changeDetectorRef.markForCheck())}_validatePlaceholders(){if(this._control.placeholder&&this._placeholderChild&&("undefined"==typeof ngDevMode||ngDevMode))
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
throw(function t(){return Error("Placeholder attribute and child element were both specified.")})()}_processHints(){this._validateHints(),this._syncDescribedByIds()}_validateHints(){if(this._hintChildren&&("undefined"==typeof ngDevMode||ngDevMode)){let t,e;this._hintChildren.forEach((n=>{if("start"===n.align){if(t||this.hintLabel)throw yV("start");t=n}else if("end"===n.align){if(e)throw yV("end");e=n}}))}}_getDefaultFloatLabelState(){return this._defaults&&this._defaults.floatLabel||"auto"}_syncDescribedByIds(){if(this._control){let t=[];if(this._control.userAriaDescribedBy&&"string"==typeof this._control.userAriaDescribedBy&&t.push(...this._control.userAriaDescribedBy.split(" ")),"hint"===this._getDisplayedMessages()){const e=this._hintChildren?this._hintChildren.find((t=>"start"===t.align)):null,n=this._hintChildren?this._hintChildren.find((t=>"end"===t.align)):null;e?t.push(e.id):this._hintLabel&&t.push(this._hintLabelId),n&&t.push(n.id)}else this._errorChildren&&t.push(...this._errorChildren.map((t=>t.id)));this._control.setDescribedByIds(t)}}_validateControlChild(){if(!this._control&&("undefined"==typeof ngDevMode||ngDevMode))throw(function t(){return Error("mat-form-field must contain a MatFormFieldControl.")})()}updateOutlineGap(){const t=this._label?this._label.nativeElement:null;if("outline"!==this.appearance||!t||!t.children.length||!t.textContent.trim())return;if(!this._platform.isBrowser)return;if(!this._isAttachedToDOM())return void(this._outlineGapCalculationNeededImmediately=!0);let e=0,n=0;const o=this._connectionContainerRef.nativeElement,i=o.querySelectorAll(".mat-form-field-outline-start"),a=o.querySelectorAll(".mat-form-field-outline-gap");if(this._label&&this._label.nativeElement.children.length){const i=o.getBoundingClientRect();if(0===i.width&&0===i.height)return this._outlineGapCalculationNeededOnStable=!0,void(this._outlineGapCalculationNeededImmediately=!1);const a=this._getStartEnd(i),r=t.children,s=this._getStartEnd(r[0].getBoundingClientRect());let l=0;for(let t=0;t<r.length;t++)l+=r[t].offsetWidth;e=Math.abs(s-a)-5,n=l>0?.75*l+10:0}for(let t=0;t<i.length;t++)i[t].style.width=`${e}px`;for(let t=0;t<a.length;t++)a[t].style.width=`${n}px`;this._outlineGapCalculationNeededOnStable=this._outlineGapCalculationNeededImmediately=!1}_getStartEnd(t){return this._dir&&"rtl"===this._dir.value?t.right:t.left}_isAttachedToDOM(){const t=this._elementRef.nativeElement;if(t.getRootNode){const e=t.getRootNode();return e&&e!==t}return document.documentElement.contains(t)}}AV.ɵfac=function t(e){return new(e||AV)(Sm(hg),Sm(Ug),Sm(hg),Sm(HI,8),Sm(EV,8),Sm(wz),Sm(a_),Sm(VP,8))},AV.ɵcmp=to({type:AV,selectors:[["mat-form-field"]],contentQueries:function t(e,n,o){if(1&e&&($h(o,bV,5),$h(o,bV,7),$h(o,vV,5),$h(o,vV,7),$h(o,xV,5),$h(o,fV,5),$h(o,CV,5),$h(o,OV,5),$h(o,wV,5)),2&e){let t;Jh(t=tb())&&(n._controlNonStatic=t.first),Jh(t=tb())&&(n._controlStatic=t.first),Jh(t=tb())&&(n._labelChildNonStatic=t.first),Jh(t=tb())&&(n._labelChildStatic=t.first),Jh(t=tb())&&(n._placeholderChild=t.first),Jh(t=tb())&&(n._errorChildren=t),Jh(t=tb())&&(n._hintChildren=t),Jh(t=tb())&&(n._prefixChildren=t),Jh(t=tb())&&(n._suffixChildren=t)}},viewQuery:function t(e,n){if(1&e&&(Qh(JB,5),Qh(QB,7),Qh($B,5),Qh(tV,5)),2&e){let t;Jh(t=tb())&&(n.underlineRef=t.first),Jh(t=tb())&&(n._connectionContainerRef=t.first),Jh(t=tb())&&(n._inputContainerRef=t.first),Jh(t=tb())&&(n._label=t.first)}},hostAttrs:[1,"mat-form-field"],hostVars:40,hostBindings:function t(e,n){2&e&&pu("mat-form-field-appearance-standard","standard"==n.appearance)("mat-form-field-appearance-fill","fill"==n.appearance)("mat-form-field-appearance-outline","outline"==n.appearance)("mat-form-field-appearance-legacy","legacy"==n.appearance)("mat-form-field-invalid",n._control.errorState)("mat-form-field-can-float",n._canLabelFloat())("mat-form-field-should-float",n._shouldLabelFloat())("mat-form-field-has-label",n._hasFloatingLabel())("mat-form-field-hide-placeholder",n._hideControlPlaceholder())("mat-form-field-disabled",n._control.disabled)("mat-form-field-autofilled",n._control.autofilled)("mat-focused",n._control.focused)("ng-untouched",n._shouldForward("untouched"))("ng-touched",n._shouldForward("touched"))("ng-pristine",n._shouldForward("pristine"))("ng-dirty",n._shouldForward("dirty"))("ng-valid",n._shouldForward("valid"))("ng-invalid",n._shouldForward("invalid"))("ng-pending",n._shouldForward("pending"))("_mat-animation-noopable",!n._animationsEnabled)},inputs:{color:"color",floatLabel:"floatLabel",appearance:"appearance",hideRequiredMarker:"hideRequiredMarker",hintLabel:"hintLabel"},exportAs:["matFormField"],features:[pg([{provide:RV,useExisting:AV}]),xp],ngContentSelectors:["*","[matPrefix]","mat-placeholder","mat-label","[matSuffix]","mat-error","mat-hint:not([align='end'])","mat-hint[align='end']"],decls:15,vars:8,consts:[[1,"mat-form-field-wrapper"],[1,"mat-form-field-flex",3,"click"],["connectionContainer",""],[4,"ngIf"],["class","mat-form-field-prefix",4,"ngIf"],[1,"mat-form-field-infix"],["inputContainer",""],[1,"mat-form-field-label-wrapper"],["class","mat-form-field-label",3,"cdkObserveContentDisabled","id","mat-empty","mat-form-field-empty","mat-accent","mat-warn","ngSwitch","cdkObserveContent",4,"ngIf"],["class","mat-form-field-suffix",4,"ngIf"],["class","mat-form-field-underline",4,"ngIf"],[1,"mat-form-field-subscript-wrapper",3,"ngSwitch"],[4,"ngSwitchCase"],["class","mat-form-field-hint-wrapper",4,"ngSwitchCase"],[1,"mat-form-field-outline"],[1,"mat-form-field-outline-start"],[1,"mat-form-field-outline-gap"],[1,"mat-form-field-outline-end"],[1,"mat-form-field-outline","mat-form-field-outline-thick"],[1,"mat-form-field-prefix"],[1,"mat-form-field-label",3,"cdkObserveContentDisabled","id","ngSwitch","cdkObserveContent"],["label",""],["class","mat-placeholder-required mat-form-field-required-marker","aria-hidden","true",4,"ngIf"],["aria-hidden","true",1,"mat-placeholder-required","mat-form-field-required-marker"],[1,"mat-form-field-suffix"],[1,"mat-form-field-underline"],["underline",""],[1,"mat-form-field-ripple"],[1,"mat-form-field-hint-wrapper"],["class","mat-hint",3,"id",4,"ngIf"],[1,"mat-form-field-hint-spacer"],[1,"mat-hint",3,"id"]],template:function t(e,n){1&e&&(Zm(mV),Rm(0,"div",0),Rm(1,"div",1,2),Vm("click",(function t(e){return n._control.onContainerClick&&n._control.onContainerClick(e)})),Qp(3,eV,9,0,"ng-container",3),Qp(4,nV,2,0,"div",4),Rm(5,"div",5,6),Xm(7),Rm(8,"span",7),Qp(9,rV,5,16,"label",8),Am(),Am(),Qp(10,sV,2,0,"div",9),Am(),Qp(11,lV,3,4,"div",10),Rm(12,"div",11),Qp(13,cV,2,1,"div",12),Qp(14,pV,5,2,"div",13),Am(),Am()),2&e&&(rc(3),Dm("ngIf","outline"==n.appearance),rc(1),Dm("ngIf",n._prefixChildren.length),rc(5),Dm("ngIf",n._hasFloatingLabel()),rc(1),Dm("ngIf",n._suffixChildren.length),rc(1),Dm("ngIf","outline"!=n.appearance),rc(1),Dm("ngSwitch",n._getDisplayedMessages()),rc(1),Dm("ngSwitchCase","error"),rc(1),Dm("ngSwitchCase","hint"))},directives:[dM,fM,gM,jz],styles:[".mat-form-field{display:inline-block;position:relative;text-align:left}[dir=rtl] 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class TV{}TV.ɵfac=function t(e){return new(e||TV)},TV.ɵmod=ao({type:TV}),TV.ɵinj=vn({imports:[[WM,XI,Uz],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(TV,[{type:Ay,args:[{declarations:[gV,AV,MV,vV,xV,PV,kV],imports:[WM,XI,Uz],exports:[XI,gV,AV,MV,vV,xV,PV,kV]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(TV,{declarations:function(){return[gV,AV,MV,vV,xV,PV,kV]},imports:function(){return[WM,XI,Uz]},exports:function(){return[XI,gV,AV,MV,vV,xV,PV,kV]}});
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
class NV{constructor(t,e){this._renderer=t,this._elementRef=e,this.onChange=t=>{},this.onTouched=()=>{}}setProperty(t,e){this._renderer.setProperty(this._elementRef.nativeElement,t,e)}registerOnTouched(t){this.onTouched=t}registerOnChange(t){this.onChange=t}setDisabledState(t){this.setProperty("disabled",t)}}NV.ɵfac=function t(e){return new(e||NV)(Sm(Cg),Sm(hg))},NV.ɵdir=lo({type:NV}),NV.ctorParameters=()=>[{type:Cg},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(NV,[{type:Cy}],(function(){return[{type:Cg},{type:hg}]}),null);class zV extends NV{}zV.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(zV)))(n||zV)}})(),zV.ɵdir=lo({type:zV,features:[xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(zV,[{type:Cy}],null,null);const IV=new Ga("NgValueAccessor"),HV={provide:IV,useExisting:qe((()=>FV)),multi:!0};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class FV extends zV{writeValue(t){this.setProperty("checked",t)}}FV.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(FV)))(n||FV)}})(),FV.ɵdir=lo({type:FV,selectors:[["input","type","checkbox","formControlName",""],["input","type","checkbox","formControl",""],["input","type","checkbox","ngModel",""]],hostBindings:function t(e,n){1&e&&Vm("change",(function t(e){return n.onChange(e.target.checked)}))("blur",(function t(){return n.onTouched()}))},features:[pg([HV]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(FV,[{type:Cy,args:[{selector:"input[type=checkbox][formControlName],input[type=checkbox][formControl],input[type=checkbox][ngModel]",host:{"(change)":"onChange($event.target.checked)","(blur)":"onTouched()"},providers:[HV]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function jV(t){return null==t||0===t.length}function UV(t){return null!=t&&"number"==typeof t.length}VV.ɵfac=function t(e){return new(e||VV)(Sm(Cg),Sm(hg),Sm(BV,8))},VV.ɵdir=lo({type:VV,selectors:[["input","formControlName","",3,"type","checkbox"],["textarea","formControlName",""],["input","formControl","",3,"type","checkbox"],["textarea","formControl",""],["input","ngModel","",3,"type","checkbox"],["textarea","ngModel",""],["","ngDefaultControl",""]],hostBindings:function t(e,n){1&e&&Vm("input",(function t(e){return n._handleInput(e.target.value)}))("blur",(function t(){return n.onTouched()}))("compositionstart",(function t(){return n._compositionStart()}))("compositionend",(function t(e){return n._compositionEnd(e.target.value)}))},features:[pg([LV]),xp]}),VV.ctorParameters=()=>[{type:Cg},{type:hg},{type:Boolean,decorators:[{type:Sr},{type:kr,args:[BV]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(VV,[{type:Cy,args:[{selector:"input:not([type=checkbox])[formControlName],textarea[formControlName],input:not([type=checkbox])[formControl],textarea[formControl],input:not([type=checkbox])[ngModel],textarea[ngModel],[ngDefaultControl]",host:{"(input)":"$any(this)._handleInput($event.target.value)","(blur)":"onTouched()","(compositionstart)":"$any(this)._compositionStart()","(compositionend)":"$any(this)._compositionEnd($event.target.value)"},providers:[LV]}]}],(function(){return[{type:Cg},{type:hg},{type:Boolean,decorators:[{type:Sr},{type:kr,args:[BV]}]}]}),null);const GV=new Ga("NgValidators"),WV=new Ga("NgAsyncValidators"),YV=/^(?=.{1,254}$)(?=.{1,64}@)[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+(?:\.[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+)*@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*$/;class qV{static min(t){return ZV(t)}static max(t){return XV(t)}static required(t){return KV(t)}static requiredTrue(t){return JV(t)}static email(t){return QV(t)}static minLength(t){return $V(t)}static maxLength(t){return tj(t)}static pattern(t){return ej(t)}static nullValidator(t){return null}static compose(t){return lj(t)}static composeAsync(t){return dj(t)}}function ZV(t){return e=>{if(jV(e.value)||jV(t))return null;const n=parseFloat(e.value);return!isNaN(n)&&n<t?{min:{min:t,actual:e.value}}:null}}function XV(t){return e=>{if(jV(e.value)||jV(t))return null;const n=parseFloat(e.value);return!isNaN(n)&&n>t?{max:{max:t,actual:e.value}}:null}}function KV(t){return jV(t.value)?{required:!0}:null}function JV(t){return!0===t.value?null:{required:!0}}function QV(t){return jV(t.value)||YV.test(t.value)?null:{email:!0}}function $V(t){return e=>jV(e.value)||!UV(e.value)?null:e.value.length<t?{minlength:{requiredLength:t,actualLength:e.value.length}}:null}function tj(t){return e=>UV(e.value)&&e.value.length>t?{maxlength:{requiredLength:t,actualLength:e.value.length}}:null}function ej(t){if(!t)return nj;let e,n;return"string"==typeof t?(n="","^"!==t.charAt(0)&&(n+="^"),n+=t,"$"!==t.charAt(t.length-1)&&(n+="$"),e=new RegExp(n)):(n=t.toString(),e=t),t=>{if(jV(t.value))return null;const o=t.value;return e.test(o)?null:{pattern:{requiredPattern:n,actualValue:o}}}}function nj(t){return null}function oj(t){return null!=t}function ij(t){const e=Fm(t)?Ct(t):t;if(!Bm(e)&&("undefined"==typeof ngDevMode||ngDevMode))throw new Error("Expected validator to return Promise or Observable.");return e}function aj(t){let e={};return t.forEach((t=>{e=null!=t?Object.assign(Object.assign({},e),t):e})),0===Object.keys(e).length?null:e}function rj(t,e){return e.map((e=>e(t)))}function sj(t){return t.map((t=>(function e(t){return!t.validate})(t)?t:e=>t.validate(e)))}function lj(t){if(!t)return null;const e=t.filter(oj);return 0==e.length?null:function(t){return aj(rj(t,e))}}function cj(t){return null!=t?lj(sj(t)):null}function dj(t){if(!t)return null;const e=t.filter(oj);return 0==e.length?null:function(t){return $t(rj(t,e).map(ij)).pipe(It(aj))}}function pj(t){return null!=t?dj(sj(t)):null}function mj(t,e){return null===t?[e]:Array.isArray(t)?[...t,e]:[t,e]}function uj(t){return t._rawValidators}function fj(t){return t._rawAsyncValidators}function gj(t){return t?Array.isArray(t)?t:[t]:[]}function hj(t,e){return Array.isArray(t)?t.includes(e):t===e}function bj(t,e){const n=gj(e);return gj(t).forEach((t=>{hj(n,t)||n.push(t)})),n}function yj(t,e){return gj(e).filter((e=>!hj(t,e)))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class _j{constructor(){this._rawValidators=[],this._rawAsyncValidators=[],this._onDestroyCallbacks=[]}get value(){return this.control?this.control.value:null}get valid(){return this.control?this.control.valid:null}get invalid(){return this.control?this.control.invalid:null}get pending(){return this.control?this.control.pending:null}get disabled(){return this.control?this.control.disabled:null}get enabled(){return this.control?this.control.enabled:null}get errors(){return this.control?this.control.errors:null}get pristine(){return this.control?this.control.pristine:null}get dirty(){return this.control?this.control.dirty:null}get touched(){return this.control?this.control.touched:null}get status(){return this.control?this.control.status:null}get untouched(){return this.control?this.control.untouched:null}get statusChanges(){return this.control?this.control.statusChanges:null}get valueChanges(){return this.control?this.control.valueChanges:null}get path(){return null}_setValidators(t){this._rawValidators=t||[],this._composedValidatorFn=cj(this._rawValidators)}_setAsyncValidators(t){this._rawAsyncValidators=t||[],this._composedAsyncValidatorFn=pj(this._rawAsyncValidators)}get validator(){return this._composedValidatorFn||null}get asyncValidator(){return this._composedAsyncValidatorFn||null}_registerOnDestroy(t){this._onDestroyCallbacks.push(t)}_invokeOnDestroyCallbacks(){this._onDestroyCallbacks.forEach((t=>t())),this._onDestroyCallbacks=[]}reset(t){this.control&&this.control.reset(t)}hasError(t,e){return!!this.control&&this.control.hasError(t,e)}getError(t,e){return this.control?this.control.getError(t,e):null}}_j.ɵfac=function t(e){return new(e||_j)},_j.ɵdir=lo({type:_j});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Cj extends _j{get formDirective(){return null}get path(){return null}}Cj.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(Cj)))(n||Cj)}})(),Cj.ɵdir=lo({type:Cj,features:[xp]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Mj extends _j{constructor(){super(...arguments),this._parent=null,this.name=null,this.valueAccessor=null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class vj{constructor(t){this._cd=t}is(t){var e,n,o;return"submitted"===t?!!(null===(e=this._cd)||void 0===e?void 0:e.submitted):!!(null===(o=null===(n=this._cd)||void 0===n?void 0:n.control)||void 0===o?void 0:o[t])}}class xj extends vj{constructor(t){super(t)}}xj.ɵfac=function t(e){return new(e||xj)(Sm(Mj,2))},xj.ɵdir=lo({type:xj,selectors:[["","formControlName",""],["","ngModel",""],["","formControl",""]],hostVars:14,hostBindings:function t(e,n){2&e&&pu("ng-untouched",n.is("untouched"))("ng-touched",n.is("touched"))("ng-pristine",n.is("pristine"))("ng-dirty",n.is("dirty"))("ng-valid",n.is("valid"))("ng-invalid",n.is("invalid"))("ng-pending",n.is("pending"))},features:[xp]}),xj.ctorParameters=()=>[{type:Mj,decorators:[{type:Dr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(xj,[{type:Cy,args:[{selector:"[formControlName],[ngModel],[formControl]",host:{"[class.ng-untouched]":'is("untouched")',"[class.ng-touched]":'is("touched")',"[class.ng-pristine]":'is("pristine")',"[class.ng-dirty]":'is("dirty")',"[class.ng-valid]":'is("valid")',"[class.ng-invalid]":'is("invalid")',"[class.ng-pending]":'is("pending")'}}]}],(function(){return[{type:Mj,decorators:[{type:Dr}]}]}),null);class Oj extends vj{constructor(t){super(t)}}Oj.ɵfac=function t(e){return new(e||Oj)(Sm(Cj,10))},Oj.ɵdir=lo({type:Oj,selectors:[["","formGroupName",""],["","formArrayName",""],["","ngModelGroup",""],["","formGroup",""],["form",3,"ngNoForm",""],["","ngForm",""]],hostVars:16,hostBindings:function t(e,n){2&e&&pu("ng-untouched",n.is("untouched"))("ng-touched",n.is("touched"))("ng-pristine",n.is("pristine"))("ng-dirty",n.is("dirty"))("ng-valid",n.is("valid"))("ng-invalid",n.is("invalid"))("ng-pending",n.is("pending"))("ng-submitted",n.is("submitted"))},features:[xp]}),Oj.ctorParameters=()=>[{type:Cj,decorators:[{type:Sr},{type:Dr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Oj,[{type:Cy,args:[{selector:"[formGroupName],[formArrayName],[ngModelGroup],[formGroup],form:not([ngNoForm]),[ngForm]",host:{"[class.ng-untouched]":'is("untouched")',"[class.ng-touched]":'is("touched")',"[class.ng-pristine]":'is("pristine")',"[class.ng-dirty]":'is("dirty")',"[class.ng-valid]":'is("valid")',"[class.ng-invalid]":'is("invalid")',"[class.ng-pending]":'is("pending")',"[class.ng-submitted]":'is("submitted")'}}]}],(function(){return[{type:Cj,decorators:[{type:Sr},{type:Dr}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Pj='\n    <div [formGroup]="myGroup">\n      <input formControlName="firstName">\n    </div>\n\n    In your class:\n\n    this.myGroup = new FormGroup({\n       firstName: new FormControl()\n    });',wj='\n    <div [formGroup]="myGroup">\n       <div formGroupName="person">\n          <input formControlName="firstName">\n       </div>\n    </div>\n\n    In your class:\n\n    this.myGroup = new FormGroup({\n       person: new FormGroup({ firstName: new FormControl() })\n    });',kj='\n    <form>\n       <div ngModelGroup="person">\n          <input [(ngModel)]="person.name" name="firstName">\n       </div>\n    </form>';
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Sj{static controlParentException(){throw new Error(`formControlName must be used with a parent formGroup directive.  You'll want to add a formGroup\n       directive and pass it an existing FormGroup instance (you can create one in your class).\n\n      Example:\n\n      ${Pj}`)}static ngModelGroupException(){throw new Error(`formControlName cannot be used with an ngModelGroup parent. It is only compatible with parents\n       that also have a "form" prefix: formGroupName, formArrayName, or formGroup.\n\n       Option 1:  Update the parent to be formGroupName (reactive form strategy)\n\n        ${wj}\n\n        Option 2: Use ngModel instead of formControlName (template-driven strategy)\n\n        ${kj}`)}static missingFormException(){throw new Error(`formGroup expects a FormGroup instance. Please pass one in.\n\n       Example:\n\n       ${Pj}`)}static groupParentException(){throw new Error(`formGroupName must be used with a parent formGroup directive.  You'll want to add a formGroup\n      directive and pass it an existing FormGroup instance (you can create one in your class).\n\n      Example:\n\n      ${wj}`)}static arrayParentException(){throw new Error('formArrayName must be used with a parent formGroup directive.  You\'ll want to add a formGroup\n       directive and pass it an existing FormGroup instance (you can create one in your class).\n\n        Example:\n\n        \n    <div [formGroup]="myGroup">\n      <div formArrayName="cities">\n        <div *ngFor="let city of cityArray.controls; index as i">\n          <input [formControlName]="i">\n        </div>\n      </div>\n    </div>\n\n    In your class:\n\n    this.cityArray = new FormArray([new FormControl(\'SF\')]);\n    this.myGroup = new FormGroup({\n      cities: this.cityArray\n    });')}static disabledAttrWarning(){console.warn("\n      It looks like you're using the disabled attribute with a reactive form directive. If you set disabled to true\n      when you set up this control in your component class, the disabled attribute will actually be set in the DOM for\n      you. We recommend using this approach to avoid 'changed after checked' errors.\n\n      Example:\n      form = new FormGroup({\n        first: new FormControl({value: 'Nancy', disabled: true}, Validators.required),\n        last: new FormControl('Drew', Validators.required)\n      });\n    ")}static ngModelWarning(t){console.warn(`\n    It looks like you're using ngModel on the same form field as ${t}.\n    Support for using the ngModel input property and ngModelChange event with\n    reactive form directives has been deprecated in Angular v6 and will be removed\n    in a future version of Angular.\n\n    For more information on this, see our API docs here:\n    https://angular.io/api/forms/${"formControl"===t?"FormControlDirective":"FormControlName"}#use-with-ngmodel\n    `)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Dj(t,e){return[...e.path,t]}function Ej(t,e){("undefined"==typeof ngDevMode||ngDevMode)&&(t||Hj(e,"Cannot find control with"),e.valueAccessor||Hj(e,"No value accessor for form control with")),Tj(t,e),e.valueAccessor.writeValue(t.value),(function n(t,e){e.valueAccessor.registerOnChange((n=>{t._pendingValue=n,t._pendingChange=!0,t._pendingDirty=!0,"change"===t.updateOn&&zj(t,e)}))})(t,e),(function o(t,e){const n=(t,n)=>{e.valueAccessor.writeValue(t),n&&e.viewToModelUpdate(t)};t.registerOnChange(n),e._registerOnDestroy((()=>{t._unregisterOnChange(n)}))})(t,e),(function i(t,e){e.valueAccessor.registerOnTouched((()=>{t._pendingTouched=!0,"blur"===t.updateOn&&t._pendingChange&&zj(t,e),"submit"!==t.updateOn&&t.markAsTouched()}))})(t,e),(function a(t,e){if(e.valueAccessor.setDisabledState){const n=t=>{e.valueAccessor.setDisabledState(t)};t.registerOnDisabledChange(n),e._registerOnDestroy((()=>{t._unregisterOnDisabledChange(n)}))}})(t,e)}function Rj(t,e,n=!0){const o=()=>{n&&("undefined"==typeof ngDevMode||ngDevMode)&&(function t(e){Hj(e,"There is no FormControl instance attached to form control element with")})(e)};e.valueAccessor&&(e.valueAccessor.registerOnChange(o),e.valueAccessor.registerOnTouched(o)),Nj(t,e),t&&(e._invokeOnDestroyCallbacks(),t._registerOnCollectionChange((()=>{})))}function Aj(t,e){t.forEach((t=>{t.registerOnValidatorChange&&t.registerOnValidatorChange(e)}))}function Tj(t,e){const n=uj(t);null!==e.validator?t.setValidators(mj(n,e.validator)):"function"==typeof n&&t.setValidators([n]);const o=fj(t);null!==e.asyncValidator?t.setAsyncValidators(mj(o,e.asyncValidator)):"function"==typeof o&&t.setAsyncValidators([o]);const i=()=>t.updateValueAndValidity();Aj(e._rawValidators,i),Aj(e._rawAsyncValidators,i)}function Nj(t,e){let n=!1;if(null!==t){if(null!==e.validator){const o=uj(t);if(Array.isArray(o)&&o.length>0){const i=o.filter((t=>t!==e.validator));i.length!==o.length&&(n=!0,t.setValidators(i))}}if(null!==e.asyncValidator){const o=fj(t);if(Array.isArray(o)&&o.length>0){const i=o.filter((t=>t!==e.asyncValidator));i.length!==o.length&&(n=!0,t.setAsyncValidators(i))}}}const o=()=>{};return Aj(e._rawValidators,o),Aj(e._rawAsyncValidators,o),n}function zj(t,e){t._pendingDirty&&t.markAsDirty(),t.setValue(t._pendingValue,{emitModelToViewChange:!1}),e.viewToModelUpdate(t._pendingValue),t._pendingChange=!1}function Ij(t,e){null!=t||"undefined"!=typeof ngDevMode&&!ngDevMode||Hj(e,"Cannot find control with"),Tj(t,e)}function Hj(t,e){let n;throw n=t.path.length>1?`path: '${t.path.join(" -> ")}'`:t.path[0]?`name: '${t.path}'`:"unspecified name attribute",new Error(`${e} ${n}`)}function Fj(t,e){if(!t.hasOwnProperty("model"))return!1;const n=t.model;return!!n.isFirstChange()||!Object.is(e,n.currentValue)}function Lj(t,e){t._syncPendingControls(),e.forEach((t=>{const e=t.control;"submit"===e.updateOn&&e._pendingChange&&(t.viewToModelUpdate(e._pendingValue),e._pendingChange=!1)}))}function Bj(t,e){if(!e)return null;let n,o,i;return Array.isArray(e)||"undefined"!=typeof ngDevMode&&!ngDevMode||Hj(t,"Value accessor was not provided as an array for form control with"),e.forEach((e=>{e.constructor===VV?n=e:(function a(t){return Object.getPrototypeOf(t.constructor)===zV})(e)?(o&&("undefined"==typeof ngDevMode||ngDevMode)&&Hj(t,"More than one built-in value accessor matches form control with"),o=e):(i&&("undefined"==typeof ngDevMode||ngDevMode)&&Hj(t,"More than one custom value accessor matches form control with"),i=e)})),i||o||n||(("undefined"==typeof ngDevMode||ngDevMode)&&Hj(t,"No valid value accessor for form control with"),null)}function Vj(t,e){const n=t.indexOf(e);n>-1&&t.splice(n,1)}function jj(t,e,n,o){"never"!==o&&((null!==o&&"once"!==o||e._ngModelWarningSentOnce)&&("always"!==o||n._ngModelWarningSent)||(Sj.ngModelWarning(t),e._ngModelWarningSentOnce=!0,n._ngModelWarningSent=!0))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Uj="VALID",Gj="INVALID",Wj="PENDING",Yj="DISABLED";function qj(t){return(Jj(t)?t.validators:t)||null}function Zj(t){return Array.isArray(t)?cj(t):t||null}function Xj(t,e){return(Jj(e)?e.asyncValidators:t)||null}function Kj(t){return Array.isArray(t)?pj(t):t||null}function Jj(t){return null!=t&&!Array.isArray(t)&&"object"==typeof t}class Qj{constructor(t,e){this._hasOwnPendingAsyncValidator=!1,this._onCollectionChange=()=>{},this._parent=null,this.pristine=!0,this.touched=!1,this._onDisabledChange=[],this._rawValidators=t,this._rawAsyncValidators=e,this._composedValidatorFn=Zj(this._rawValidators),this._composedAsyncValidatorFn=Kj(this._rawAsyncValidators)}get validator(){return this._composedValidatorFn}set validator(t){this._rawValidators=this._composedValidatorFn=t}get asyncValidator(){return this._composedAsyncValidatorFn}set asyncValidator(t){this._rawAsyncValidators=this._composedAsyncValidatorFn=t}get parent(){return this._parent}get valid(){return this.status===Uj}get invalid(){return this.status===Gj}get pending(){return this.status==Wj}get disabled(){return this.status===Yj}get enabled(){return this.status!==Yj}get dirty(){return!this.pristine}get untouched(){return!this.touched}get updateOn(){return this._updateOn?this._updateOn:this.parent?this.parent.updateOn:"change"}setValidators(t){this._rawValidators=t,this._composedValidatorFn=Zj(t)}setAsyncValidators(t){this._rawAsyncValidators=t,this._composedAsyncValidatorFn=Kj(t)}addValidators(t){this.setValidators(bj(t,this._rawValidators))}addAsyncValidators(t){this.setAsyncValidators(bj(t,this._rawAsyncValidators))}removeValidators(t){this.setValidators(yj(t,this._rawValidators))}removeAsyncValidators(t){this.setAsyncValidators(yj(t,this._rawAsyncValidators))}hasValidator(t){return hj(this._rawValidators,t)}hasAsyncValidator(t){return hj(this._rawAsyncValidators,t)}clearValidators(){this.validator=null}clearAsyncValidators(){this.asyncValidator=null}markAsTouched(t={}){this.touched=!0,this._parent&&!t.onlySelf&&this._parent.markAsTouched(t)}markAllAsTouched(){this.markAsTouched({onlySelf:!0}),this._forEachChild((t=>t.markAllAsTouched()))}markAsUntouched(t={}){this.touched=!1,this._pendingTouched=!1,this._forEachChild((t=>{t.markAsUntouched({onlySelf:!0})})),this._parent&&!t.onlySelf&&this._parent._updateTouched(t)}markAsDirty(t={}){this.pristine=!1,this._parent&&!t.onlySelf&&this._parent.markAsDirty(t)}markAsPristine(t={}){this.pristine=!0,this._pendingDirty=!1,this._forEachChild((t=>{t.markAsPristine({onlySelf:!0})})),this._parent&&!t.onlySelf&&this._parent._updatePristine(t)}markAsPending(t={}){this.status=Wj,!1!==t.emitEvent&&this.statusChanges.emit(this.status),this._parent&&!t.onlySelf&&this._parent.markAsPending(t)}disable(t={}){const e=this._parentMarkedDirty(t.onlySelf);this.status=Yj,this.errors=null,this._forEachChild((e=>{e.disable(Object.assign(Object.assign({},t),{onlySelf:!0}))})),this._updateValue(),!1!==t.emitEvent&&(this.valueChanges.emit(this.value),this.statusChanges.emit(this.status)),this._updateAncestors(Object.assign(Object.assign({},t),{skipPristineCheck:e})),this._onDisabledChange.forEach((t=>t(!0)))}enable(t={}){const e=this._parentMarkedDirty(t.onlySelf);this.status=Uj,this._forEachChild((e=>{e.enable(Object.assign(Object.assign({},t),{onlySelf:!0}))})),this.updateValueAndValidity({onlySelf:!0,emitEvent:t.emitEvent}),this._updateAncestors(Object.assign(Object.assign({},t),{skipPristineCheck:e})),this._onDisabledChange.forEach((t=>t(!1)))}_updateAncestors(t){this._parent&&!t.onlySelf&&(this._parent.updateValueAndValidity(t),t.skipPristineCheck||this._parent._updatePristine(),this._parent._updateTouched())}setParent(t){this._parent=t}updateValueAndValidity(t={}){this._setInitialStatus(),this._updateValue(),this.enabled&&(this._cancelExistingSubscription(),this.errors=this._runValidator(),this.status=this._calculateStatus(),this.status!==Uj&&this.status!==Wj||this._runAsyncValidator(t.emitEvent)),!1!==t.emitEvent&&(this.valueChanges.emit(this.value),this.statusChanges.emit(this.status)),this._parent&&!t.onlySelf&&this._parent.updateValueAndValidity(t)}_updateTreeValidity(t={emitEvent:!0}){this._forEachChild((e=>e._updateTreeValidity(t))),this.updateValueAndValidity({onlySelf:!0,emitEvent:t.emitEvent})}_setInitialStatus(){this.status=this._allControlsDisabled()?Yj:Uj}_runValidator(){return this.validator?this.validator(this):null}_runAsyncValidator(t){if(this.asyncValidator){this.status=Wj,this._hasOwnPendingAsyncValidator=!0;const e=ij(this.asyncValidator(this));this._asyncValidationSubscription=e.subscribe((e=>{this._hasOwnPendingAsyncValidator=!1,this.setErrors(e,{emitEvent:t})}))}}_cancelExistingSubscription(){this._asyncValidationSubscription&&(this._asyncValidationSubscription.unsubscribe(),this._hasOwnPendingAsyncValidator=!1)}setErrors(t,e={}){this.errors=t,this._updateControlsErrors(!1!==e.emitEvent)}get(t){return(function e(t,n,o){if(null==n)return null;if(Array.isArray(n)||(n=n.split(o)),Array.isArray(n)&&0===n.length)return null;let i=t;return n.forEach((t=>{i=i instanceof tU?i.controls.hasOwnProperty(t)?i.controls[t]:null:i instanceof eU&&i.at(t)||null})),i})(this,t,".")}getError(t,e){const n=e?this.get(e):this;return n&&n.errors?n.errors[t]:null}hasError(t,e){return!!this.getError(t,e)}get root(){let t=this;for(;t._parent;)t=t._parent;return t}_updateControlsErrors(t){this.status=this._calculateStatus(),t&&this.statusChanges.emit(this.status),this._parent&&this._parent._updateControlsErrors(t)}_initObservables(){this.valueChanges=new Lh,this.statusChanges=new Lh}_calculateStatus(){return this._allControlsDisabled()?Yj:this.errors?Gj:this._hasOwnPendingAsyncValidator||this._anyControlsHaveStatus(Wj)?Wj:this._anyControlsHaveStatus(Gj)?Gj:Uj}_anyControlsHaveStatus(t){return this._anyControls((e=>e.status===t))}_anyControlsDirty(){return this._anyControls((t=>t.dirty))}_anyControlsTouched(){return this._anyControls((t=>t.touched))}_updatePristine(t={}){this.pristine=!this._anyControlsDirty(),this._parent&&!t.onlySelf&&this._parent._updatePristine(t)}_updateTouched(t={}){this.touched=this._anyControlsTouched(),this._parent&&!t.onlySelf&&this._parent._updateTouched(t)}_isBoxedValue(t){return"object"==typeof t&&null!==t&&2===Object.keys(t).length&&"value"in t&&"disabled"in t}_registerOnCollectionChange(t){this._onCollectionChange=t}_setUpdateStrategy(t){Jj(t)&&null!=t.updateOn&&(this._updateOn=t.updateOn)}_parentMarkedDirty(t){return!t&&!(!this._parent||!this._parent.dirty)&&!this._parent._anyControlsDirty()}}class $j extends Qj{constructor(t=null,e,n){super(qj(e),Xj(n,e)),this._onChange=[],this._applyFormState(t),this._setUpdateStrategy(e),this._initObservables(),this.updateValueAndValidity({onlySelf:!0,emitEvent:!!this.asyncValidator})}setValue(t,e={}){this.value=this._pendingValue=t,this._onChange.length&&!1!==e.emitModelToViewChange&&this._onChange.forEach((t=>t(this.value,!1!==e.emitViewToModelChange))),this.updateValueAndValidity(e)}patchValue(t,e={}){this.setValue(t,e)}reset(t=null,e={}){this._applyFormState(t),this.markAsPristine(e),this.markAsUntouched(e),this.setValue(this.value,e),this._pendingChange=!1}_updateValue(){}_anyControls(t){return!1}_allControlsDisabled(){return this.disabled}registerOnChange(t){this._onChange.push(t)}_unregisterOnChange(t){Vj(this._onChange,t)}registerOnDisabledChange(t){this._onDisabledChange.push(t)}_unregisterOnDisabledChange(t){Vj(this._onDisabledChange,t)}_forEachChild(t){}_syncPendingControls(){return!("submit"!==this.updateOn||(this._pendingDirty&&this.markAsDirty(),this._pendingTouched&&this.markAsTouched(),!this._pendingChange)||(this.setValue(this._pendingValue,{onlySelf:!0,emitModelToViewChange:!1}),0))}_applyFormState(t){this._isBoxedValue(t)?(this.value=this._pendingValue=t.value,t.disabled?this.disable({onlySelf:!0,emitEvent:!1}):this.enable({onlySelf:!0,emitEvent:!1})):this.value=this._pendingValue=t}}class tU extends Qj{constructor(t,e,n){super(qj(e),Xj(n,e)),this.controls=t,this._initObservables(),this._setUpdateStrategy(e),this._setUpControls(),this.updateValueAndValidity({onlySelf:!0,emitEvent:!!this.asyncValidator})}registerControl(t,e){return this.controls[t]?this.controls[t]:(this.controls[t]=e,e.setParent(this),e._registerOnCollectionChange(this._onCollectionChange),e)}addControl(t,e,n={}){this.registerControl(t,e),this.updateValueAndValidity({emitEvent:n.emitEvent}),this._onCollectionChange()}removeControl(t,e={}){this.controls[t]&&this.controls[t]._registerOnCollectionChange((()=>{})),delete this.controls[t],this.updateValueAndValidity({emitEvent:e.emitEvent}),this._onCollectionChange()}setControl(t,e,n={}){this.controls[t]&&this.controls[t]._registerOnCollectionChange((()=>{})),delete this.controls[t],e&&this.registerControl(t,e),this.updateValueAndValidity({emitEvent:n.emitEvent}),this._onCollectionChange()}contains(t){return this.controls.hasOwnProperty(t)&&this.controls[t].enabled}setValue(t,e={}){this._checkAllValuesPresent(t),Object.keys(t).forEach((n=>{this._throwIfControlMissing(n),this.controls[n].setValue(t[n],{onlySelf:!0,emitEvent:e.emitEvent})})),this.updateValueAndValidity(e)}patchValue(t,e={}){null!=t&&(Object.keys(t).forEach((n=>{this.controls[n]&&this.controls[n].patchValue(t[n],{onlySelf:!0,emitEvent:e.emitEvent})})),this.updateValueAndValidity(e))}reset(t={},e={}){this._forEachChild(((n,o)=>{n.reset(t[o],{onlySelf:!0,emitEvent:e.emitEvent})})),this._updatePristine(e),this._updateTouched(e),this.updateValueAndValidity(e)}getRawValue(){return this._reduceChildren({},((t,e,n)=>(t[n]=e instanceof $j?e.value:e.getRawValue(),t)))}_syncPendingControls(){let t=this._reduceChildren(!1,((t,e)=>!!e._syncPendingControls()||t));return t&&this.updateValueAndValidity({onlySelf:!0}),t}_throwIfControlMissing(t){if(!Object.keys(this.controls).length)throw new Error("\n        There are no form controls registered with this group yet. If you're using ngModel,\n        you may want to check next tick (e.g. use setTimeout).\n      ");if(!this.controls[t])throw new Error(`Cannot find form control with name: ${t}.`)}_forEachChild(t){Object.keys(this.controls).forEach((e=>{const n=this.controls[e];n&&t(n,e)}))}_setUpControls(){this._forEachChild((t=>{t.setParent(this),t._registerOnCollectionChange(this._onCollectionChange)}))}_updateValue(){this.value=this._reduceValue()}_anyControls(t){for(const e of Object.keys(this.controls)){const n=this.controls[e];if(this.contains(e)&&t(n))return!0}return!1}_reduceValue(){return this._reduceChildren({},((t,e,n)=>((e.enabled||this.disabled)&&(t[n]=e.value),t)))}_reduceChildren(t,e){let n=t;return this._forEachChild(((t,o)=>{n=e(n,t,o)})),n}_allControlsDisabled(){for(const t of Object.keys(this.controls))if(this.controls[t].enabled)return!1;return Object.keys(this.controls).length>0||this.disabled}_checkAllValuesPresent(t){this._forEachChild(((e,n)=>{if(void 0===t[n])throw new Error(`Must supply a value for form control with name: '${n}'.`)}))}}class eU extends Qj{constructor(t,e,n){super(qj(e),Xj(n,e)),this.controls=t,this._initObservables(),this._setUpdateStrategy(e),this._setUpControls(),this.updateValueAndValidity({onlySelf:!0,emitEvent:!!this.asyncValidator})}at(t){return this.controls[t]}push(t,e={}){this.controls.push(t),this._registerControl(t),this.updateValueAndValidity({emitEvent:e.emitEvent}),this._onCollectionChange()}insert(t,e,n={}){this.controls.splice(t,0,e),this._registerControl(e),this.updateValueAndValidity({emitEvent:n.emitEvent})}removeAt(t,e={}){this.controls[t]&&this.controls[t]._registerOnCollectionChange((()=>{})),this.controls.splice(t,1),this.updateValueAndValidity({emitEvent:e.emitEvent})}setControl(t,e,n={}){this.controls[t]&&this.controls[t]._registerOnCollectionChange((()=>{})),this.controls.splice(t,1),e&&(this.controls.splice(t,0,e),this._registerControl(e)),this.updateValueAndValidity({emitEvent:n.emitEvent}),this._onCollectionChange()}get length(){return this.controls.length}setValue(t,e={}){this._checkAllValuesPresent(t),t.forEach(((t,n)=>{this._throwIfControlMissing(n),this.at(n).setValue(t,{onlySelf:!0,emitEvent:e.emitEvent})})),this.updateValueAndValidity(e)}patchValue(t,e={}){null!=t&&(t.forEach(((t,n)=>{this.at(n)&&this.at(n).patchValue(t,{onlySelf:!0,emitEvent:e.emitEvent})})),this.updateValueAndValidity(e))}reset(t=[],e={}){this._forEachChild(((n,o)=>{n.reset(t[o],{onlySelf:!0,emitEvent:e.emitEvent})})),this._updatePristine(e),this._updateTouched(e),this.updateValueAndValidity(e)}getRawValue(){return this.controls.map((t=>t instanceof $j?t.value:t.getRawValue()))}clear(t={}){this.controls.length<1||(this._forEachChild((t=>t._registerOnCollectionChange((()=>{})))),this.controls.splice(0),this.updateValueAndValidity({emitEvent:t.emitEvent}))}_syncPendingControls(){let t=this.controls.reduce(((t,e)=>!!e._syncPendingControls()||t),!1);return t&&this.updateValueAndValidity({onlySelf:!0}),t}_throwIfControlMissing(t){if(!this.controls.length)throw new Error("\n        There are no form controls registered with this array yet. If you're using ngModel,\n        you may want to check next tick (e.g. use setTimeout).\n      ");if(!this.at(t))throw new Error(`Cannot find form control at index ${t}`)}_forEachChild(t){this.controls.forEach(((e,n)=>{t(e,n)}))}_updateValue(){this.value=this.controls.filter((t=>t.enabled||this.disabled)).map((t=>t.value))}_anyControls(t){return this.controls.some((e=>e.enabled&&t(e)))}_setUpControls(){this._forEachChild((t=>this._registerControl(t)))}_checkAllValuesPresent(t){this._forEachChild(((e,n)=>{if(void 0===t[n])throw new Error(`Must supply a value for form control at index: ${n}.`)}))}_allControlsDisabled(){for(const t of this.controls)if(t.enabled)return!1;return this.controls.length>0||this.disabled}_registerControl(t){t.setParent(this),t._registerOnCollectionChange(this._onCollectionChange)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const nU={provide:Cj,useExisting:qe((()=>iU))},oU=Promise.resolve(null);class iU extends Cj{constructor(t,e){super(),this.submitted=!1,this._directives=[],this.ngSubmit=new Lh,this.form=new tU({},cj(t),pj(e))}ngAfterViewInit(){this._setUpdateStrategy()}get formDirective(){return this}get control(){return this.form}get path(){return[]}get controls(){return this.form.controls}addControl(t){oU.then((()=>{const e=this._findContainer(t.path);t.control=e.registerControl(t.name,t.control),Ej(t.control,t),t.control.updateValueAndValidity({emitEvent:!1}),this._directives.push(t)}))}getControl(t){return this.form.get(t.path)}removeControl(t){oU.then((()=>{const e=this._findContainer(t.path);e&&e.removeControl(t.name),Vj(this._directives,t)}))}addFormGroup(t){oU.then((()=>{const e=this._findContainer(t.path),n=new tU({});Ij(n,t),e.registerControl(t.name,n),n.updateValueAndValidity({emitEvent:!1})}))}removeFormGroup(t){oU.then((()=>{const e=this._findContainer(t.path);e&&e.removeControl(t.name)}))}getFormGroup(t){return this.form.get(t.path)}updateModel(t,e){oU.then((()=>{this.form.get(t.path).setValue(e)}))}setValue(t){this.control.setValue(t)}onSubmit(t){return this.submitted=!0,Lj(this.form,this._directives),this.ngSubmit.emit(t),!1}onReset(){this.resetForm()}resetForm(t){this.form.reset(t),this.submitted=!1}_setUpdateStrategy(){this.options&&null!=this.options.updateOn&&(this.form._updateOn=this.options.updateOn)}_findContainer(t){return t.pop(),t.length?this.form.get(t):this.form}}iU.ɵfac=function t(e){return new(e||iU)(Sm(GV,10),Sm(WV,10))},iU.ɵdir=lo({type:iU,selectors:[["form",3,"ngNoForm","",3,"formGroup",""],["ng-form"],["","ngForm",""]],hostBindings:function t(e,n){1&e&&Vm("submit",(function t(e){return n.onSubmit(e)}))("reset",(function t(){return n.onReset()}))},inputs:{options:["ngFormOptions","options"]},outputs:{ngSubmit:"ngSubmit"},exportAs:["ngForm"],features:[pg([nU]),xp]}),iU.ctorParameters=()=>[{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}],iU.propDecorators={options:[{type:xy,args:["ngFormOptions"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(iU,[{type:Cy,args:[{selector:"form:not([ngNoForm]):not([formGroup]),ng-form,[ngForm]",providers:[nU],host:{"(submit)":"onSubmit($event)","(reset)":"onReset()"},outputs:["ngSubmit"],exportAs:"ngForm"}]}],(function(){return[{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}]}),{options:[{type:xy,args:["ngFormOptions"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class aU extends Cj{ngOnInit(){this._checkParentType(),this.formDirective.addFormGroup(this)}ngOnDestroy(){this.formDirective&&this.formDirective.removeFormGroup(this)}get control(){return this.formDirective.getFormGroup(this)}get path(){return Dj(null==this.name?this.name:this.name.toString(),this._parent)}get formDirective(){return this._parent?this._parent.formDirective:null}_checkParentType(){}}aU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(aU)))(n||aU)}})(),aU.ɵdir=lo({type:aU,features:[xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(aU,[{type:Cy}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class rU{static modelParentException(){throw new Error(`\n      ngModel cannot be used to register form controls with a parent formGroup directive.  Try using\n      formGroup's partner directive "formControlName" instead.  Example:\n\n      ${Pj}\n\n      Or, if you'd like to avoid registering this form control, indicate that it's standalone in ngModelOptions:\n\n      Example:\n\n      \n    <div [formGroup]="myGroup">\n       <input formControlName="firstName">\n       <input [(ngModel)]="showMoreControls" [ngModelOptions]="{standalone: true}">\n    </div>\n  `)}static formGroupNameException(){throw new Error(`\n      ngModel cannot be used to register form controls with a parent formGroupName or formArrayName directive.\n\n      Option 1: Use formControlName instead of ngModel (reactive strategy):\n\n      ${wj}\n\n      Option 2:  Update ngModel's parent be ngModelGroup (template-driven strategy):\n\n      ${kj}`)}static missingNameException(){throw new Error('If ngModel is used within a form tag, either the name attribute must be set or the form\n      control must be defined as \'standalone\' in ngModelOptions.\n\n      Example 1: <input [(ngModel)]="person.firstName" name="first">\n      Example 2: <input [(ngModel)]="person.firstName" [ngModelOptions]="{standalone: true}">')}static modelGroupParentException(){throw new Error(`\n      ngModelGroup cannot be used with a parent formGroup directive.\n\n      Option 1: Use formGroupName instead of ngModelGroup (reactive strategy):\n\n      ${wj}\n\n      Option 2:  Use a regular form tag instead of the formGroup directive (template-driven strategy):\n\n      ${kj}`)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const sU={provide:Cj,useExisting:qe((()=>lU))};class lU extends aU{constructor(t,e,n){super(),this._parent=t,this._setValidators(e),this._setAsyncValidators(n)}_checkParentType(){this._parent instanceof lU||this._parent instanceof iU||"undefined"!=typeof ngDevMode&&!ngDevMode||rU.modelGroupParentException()}}lU.ɵfac=function t(e){return new(e||lU)(Sm(Cj,5),Sm(GV,10),Sm(WV,10))},lU.ɵdir=lo({type:lU,selectors:[["","ngModelGroup",""]],inputs:{name:["ngModelGroup","name"]},exportAs:["ngModelGroup"],features:[pg([sU]),xp]}),lU.ctorParameters=()=>[{type:Cj,decorators:[{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}],lU.propDecorators={name:[{type:xy,args:["ngModelGroup"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(lU,[{type:Cy,args:[{selector:"[ngModelGroup]",providers:[sU],exportAs:"ngModelGroup"}]}],(function(){return[{type:Cj,decorators:[{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}]}),{name:[{type:xy,args:["ngModelGroup"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const cU={provide:Mj,useExisting:qe((()=>pU))},dU=Promise.resolve(null);class pU extends Mj{constructor(t,e,n,o){super(),this.control=new $j,this._registered=!1,this.update=new Lh,this._parent=t,this._setValidators(e),this._setAsyncValidators(n),this.valueAccessor=Bj(this,o)}ngOnChanges(t){this._checkForErrors(),this._registered||this._setUpControl(),"isDisabled"in t&&this._updateDisabled(t),Fj(t,this.viewModel)&&(this._updateValue(this.model),this.viewModel=this.model)}ngOnDestroy(){this.formDirective&&this.formDirective.removeControl(this)}get path(){return this._parent?Dj(this.name,this._parent):[this.name]}get formDirective(){return this._parent?this._parent.formDirective:null}viewToModelUpdate(t){this.viewModel=t,this.update.emit(t)}_setUpControl(){this._setUpdateStrategy(),this._isStandalone()?this._setUpStandalone():this.formDirective.addControl(this),this._registered=!0}_setUpdateStrategy(){this.options&&null!=this.options.updateOn&&(this.control._updateOn=this.options.updateOn)}_isStandalone(){return!this._parent||!(!this.options||!this.options.standalone)}_setUpStandalone(){Ej(this.control,this),this.control.updateValueAndValidity({emitEvent:!1})}_checkForErrors(){this._isStandalone()||this._checkParentType(),this._checkName()}_checkParentType(){("undefined"==typeof ngDevMode||ngDevMode)&&(!(this._parent instanceof lU)&&this._parent instanceof aU?rU.formGroupNameException():this._parent instanceof lU||this._parent instanceof iU||rU.modelParentException())}_checkName(){this.options&&this.options.name&&(this.name=this.options.name),this._isStandalone()||this.name||"undefined"!=typeof ngDevMode&&!ngDevMode||rU.missingNameException()}_updateValue(t){dU.then((()=>{this.control.setValue(t,{emitViewToModelChange:!1})}))}_updateDisabled(t){const e=t.isDisabled.currentValue,n=""===e||e&&"false"!==e;dU.then((()=>{n&&!this.control.disabled?this.control.disable():!n&&this.control.disabled&&this.control.enable()}))}}pU.ɵfac=function t(e){return new(e||pU)(Sm(Cj,9),Sm(GV,10),Sm(WV,10),Sm(IV,10))},pU.ɵdir=lo({type:pU,selectors:[["","ngModel","",3,"formControlName","",3,"formControl",""]],inputs:{name:"name",isDisabled:["disabled","isDisabled"],model:["ngModel","model"],options:["ngModelOptions","options"]},outputs:{update:"ngModelChange"},exportAs:["ngModel"],features:[pg([cU]),xp,Bo]}),pU.ctorParameters=()=>[{type:Cj,decorators:[{type:Sr},{type:Rr}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[IV]}]}],pU.propDecorators={name:[{type:xy}],isDisabled:[{type:xy,args:["disabled"]}],model:[{type:xy,args:["ngModel"]}],options:[{type:xy,args:["ngModelOptions"]}],update:[{type:Oy,args:["ngModelChange"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(pU,[{type:Cy,args:[{selector:"[ngModel]:not([formControlName]):not([formControl])",providers:[cU],exportAs:"ngModel"}]}],(function(){return[{type:Cj,decorators:[{type:Sr},{type:Rr}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[IV]}]}]}),{update:[{type:Oy,args:["ngModelChange"]}],name:[{type:xy}],isDisabled:[{type:xy,args:["disabled"]}],model:[{type:xy,args:["ngModel"]}],options:[{type:xy,args:["ngModelOptions"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class mU{}mU.ɵfac=function t(e){return new(e||mU)},mU.ɵdir=lo({type:mU,selectors:[["form",3,"ngNoForm","",3,"ngNativeValidate",""]],hostAttrs:["novalidate",""]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(mU,[{type:Cy,args:[{selector:"form:not([ngNoForm]):not([ngNativeValidate])",host:{novalidate:""}}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const uU={provide:IV,useExisting:qe((()=>fU)),multi:!0};class fU extends zV{writeValue(t){this.setProperty("value",null==t?"":t)}registerOnChange(t){this.onChange=e=>{t(""==e?null:parseFloat(e))}}}fU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(fU)))(n||fU)}})(),fU.ɵdir=lo({type:fU,selectors:[["input","type","number","formControlName",""],["input","type","number","formControl",""],["input","type","number","ngModel",""]],hostBindings:function t(e,n){1&e&&Vm("input",(function t(e){return n.onChange(e.target.value)}))("blur",(function t(){return n.onTouched()}))},features:[pg([uU]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(fU,[{type:Cy,args:[{selector:"input[type=number][formControlName],input[type=number][formControl],input[type=number][ngModel]",host:{"(input)":"onChange($event.target.value)","(blur)":"onTouched()"},providers:[uU]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const _U={provide:IV,useExisting:qe((()=>CU)),multi:!0};class CU extends zV{writeValue(t){this.setProperty("value",parseFloat(t))}registerOnChange(t){this.onChange=e=>{t(""==e?null:parseFloat(e))}}}CU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(CU)))(n||CU)}})(),CU.ɵdir=lo({type:CU,selectors:[["input","type","range","formControlName",""],["input","type","range","formControl",""],["input","type","range","ngModel",""]],hostBindings:function t(e,n){1&e&&Vm("change",(function t(e){return n.onChange(e.target.value)}))("input",(function t(e){return n.onChange(e.target.value)}))("blur",(function t(){return n.onTouched()}))},features:[pg([_U]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(CU,[{type:Cy,args:[{selector:"input[type=range][formControlName],input[type=range][formControl],input[type=range][ngModel]",host:{"(change)":"onChange($event.target.value)","(input)":"onChange($event.target.value)","(blur)":"onTouched()"},providers:[_U]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const MU=new Ga("NgModelWithFormControlWarning"),vU={provide:Mj,useExisting:qe((()=>xU))};class xU extends Mj{constructor(t,e,n,o){super(),this._ngModelWarningConfig=o,this.update=new Lh,this._ngModelWarningSent=!1,this._setValidators(t),this._setAsyncValidators(e),this.valueAccessor=Bj(this,n)}set isDisabled(t){("undefined"==typeof ngDevMode||ngDevMode)&&Sj.disabledAttrWarning()}ngOnChanges(t){if(this._isControlChanged(t)){const e=t.form.previousValue;e&&Rj(e,this,!1),Ej(this.form,this),this.control.disabled&&this.valueAccessor.setDisabledState&&this.valueAccessor.setDisabledState(!0),this.form.updateValueAndValidity({emitEvent:!1})}Fj(t,this.viewModel)&&(("undefined"==typeof ngDevMode||ngDevMode)&&jj("formControl",xU,this,this._ngModelWarningConfig),this.form.setValue(this.model),this.viewModel=this.model)}ngOnDestroy(){this.form&&Rj(this.form,this,!1)}get path(){return[]}get control(){return this.form}viewToModelUpdate(t){this.viewModel=t,this.update.emit(t)}_isControlChanged(t){return t.hasOwnProperty("form")}}xU.ɵfac=function t(e){return new(e||xU)(Sm(GV,10),Sm(WV,10),Sm(IV,10),Sm(MU,8))},xU.ɵdir=lo({type:xU,selectors:[["","formControl",""]],inputs:{isDisabled:["disabled","isDisabled"],form:["formControl","form"],model:["ngModel","model"]},outputs:{update:"ngModelChange"},exportAs:["ngForm"],features:[pg([vU]),xp,Bo]}),xU._ngModelWarningSentOnce=!1,xU.ctorParameters=()=>[{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[IV]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[MU]}]}],xU.propDecorators={form:[{type:xy,args:["formControl"]}],isDisabled:[{type:xy,args:["disabled"]}],model:[{type:xy,args:["ngModel"]}],update:[{type:Oy,args:["ngModelChange"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(xU,[{type:Cy,args:[{selector:"[formControl]",providers:[vU],exportAs:"ngForm"}]}],(function(){return[{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[IV]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[MU]}]}]}),{update:[{type:Oy,args:["ngModelChange"]}],isDisabled:[{type:xy,args:["disabled"]}],form:[{type:xy,args:["formControl"]}],model:[{type:xy,args:["ngModel"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const OU={provide:Cj,useExisting:qe((()=>PU))};class PU extends Cj{constructor(t,e){super(),this.validators=t,this.asyncValidators=e,this.submitted=!1,this._onCollectionChange=()=>this._updateDomValue(),this.directives=[],this.form=null,this.ngSubmit=new Lh,this._setValidators(t),this._setAsyncValidators(e)}ngOnChanges(t){this._checkFormPresent(),t.hasOwnProperty("form")&&(this._updateValidators(),this._updateDomValue(),this._updateRegistrations(),this._oldForm=this.form)}ngOnDestroy(){this.form&&(Nj(this.form,this),this.form._onCollectionChange===this._onCollectionChange&&this.form._registerOnCollectionChange((()=>{})))}get formDirective(){return this}get control(){return this.form}get path(){return[]}addControl(t){const e=this.form.get(t.path);return Ej(e,t),e.updateValueAndValidity({emitEvent:!1}),this.directives.push(t),e}getControl(t){return this.form.get(t.path)}removeControl(t){Rj(t.control||null,t,!1),Vj(this.directives,t)}addFormGroup(t){this._setUpFormContainer(t)}removeFormGroup(t){this._cleanUpFormContainer(t)}getFormGroup(t){return this.form.get(t.path)}addFormArray(t){this._setUpFormContainer(t)}removeFormArray(t){this._cleanUpFormContainer(t)}getFormArray(t){return this.form.get(t.path)}updateModel(t,e){this.form.get(t.path).setValue(e)}onSubmit(t){return this.submitted=!0,Lj(this.form,this.directives),this.ngSubmit.emit(t),!1}onReset(){this.resetForm()}resetForm(t){this.form.reset(t),this.submitted=!1}_updateDomValue(){this.directives.forEach((t=>{const e=t.control,n=this.form.get(t.path);e!==n&&(Rj(e||null,t),n instanceof $j&&(Ej(n,t),t.control=n))})),this.form._updateTreeValidity({emitEvent:!1})}_setUpFormContainer(t){const e=this.form.get(t.path);Ij(e,t),e.updateValueAndValidity({emitEvent:!1})}_cleanUpFormContainer(t){if(this.form){const e=this.form.get(t.path);e&&(function n(t,e){return Nj(t,e)})(e,t)&&e.updateValueAndValidity({emitEvent:!1})}}_updateRegistrations(){this.form._registerOnCollectionChange(this._onCollectionChange),this._oldForm&&this._oldForm._registerOnCollectionChange((()=>{}))}_updateValidators(){Tj(this.form,this),this._oldForm&&Nj(this._oldForm,this)}_checkFormPresent(){this.form||"undefined"!=typeof ngDevMode&&!ngDevMode||Sj.missingFormException()}}PU.ɵfac=function t(e){return new(e||PU)(Sm(GV,10),Sm(WV,10))},PU.ɵdir=lo({type:PU,selectors:[["","formGroup",""]],hostBindings:function t(e,n){1&e&&Vm("submit",(function t(e){return n.onSubmit(e)}))("reset",(function t(){return n.onReset()}))},inputs:{form:["formGroup","form"]},outputs:{ngSubmit:"ngSubmit"},exportAs:["ngForm"],features:[pg([OU]),xp,Bo]}),PU.ctorParameters=()=>[{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}],PU.propDecorators={form:[{type:xy,args:["formGroup"]}],ngSubmit:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(PU,[{type:Cy,args:[{selector:"[formGroup]",providers:[OU],host:{"(submit)":"onSubmit($event)","(reset)":"onReset()"},exportAs:"ngForm"}]}],(function(){return[{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}]}),{form:[{type:xy,args:["formGroup"]}],ngSubmit:[{type:Oy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const wU={provide:Cj,useExisting:qe((()=>kU))};class kU extends aU{constructor(t,e,n){super(),this._parent=t,this._setValidators(e),this._setAsyncValidators(n)}_checkParentType(){EU(this._parent)&&("undefined"==typeof ngDevMode||ngDevMode)&&Sj.groupParentException()}}kU.ɵfac=function t(e){return new(e||kU)(Sm(Cj,13),Sm(GV,10),Sm(WV,10))},kU.ɵdir=lo({type:kU,selectors:[["","formGroupName",""]],inputs:{name:["formGroupName","name"]},features:[pg([wU]),xp]}),kU.ctorParameters=()=>[{type:Cj,decorators:[{type:Sr},{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}],kU.propDecorators={name:[{type:xy,args:["formGroupName"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(kU,[{type:Cy,args:[{selector:"[formGroupName]",providers:[wU]}]}],(function(){return[{type:Cj,decorators:[{type:Sr},{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}]}),{name:[{type:xy,args:["formGroupName"]}]});const SU={provide:Cj,useExisting:qe((()=>DU))};class DU extends Cj{constructor(t,e,n){super(),this._parent=t,this._setValidators(e),this._setAsyncValidators(n)}ngOnInit(){this._checkParentType(),this.formDirective.addFormArray(this)}ngOnDestroy(){this.formDirective&&this.formDirective.removeFormArray(this)}get control(){return this.formDirective.getFormArray(this)}get formDirective(){return this._parent?this._parent.formDirective:null}get path(){return Dj(null==this.name?this.name:this.name.toString(),this._parent)}_checkParentType(){EU(this._parent)&&("undefined"==typeof ngDevMode||ngDevMode)&&Sj.arrayParentException()}}function EU(t){return!(t instanceof kU||t instanceof PU||t instanceof DU)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */DU.ɵfac=function t(e){return new(e||DU)(Sm(Cj,13),Sm(GV,10),Sm(WV,10))},DU.ɵdir=lo({type:DU,selectors:[["","formArrayName",""]],inputs:{name:["formArrayName","name"]},features:[pg([SU]),xp]}),DU.ctorParameters=()=>[{type:Cj,decorators:[{type:Sr},{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}],DU.propDecorators={name:[{type:xy,args:["formArrayName"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(DU,[{type:Cy,args:[{selector:"[formArrayName]",providers:[SU]}]}],(function(){return[{type:Cj,decorators:[{type:Sr},{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}]}),{name:[{type:xy,args:["formArrayName"]}]});const RU={provide:Mj,useExisting:qe((()=>AU))};class AU extends Mj{constructor(t,e,n,o,i){super(),this._ngModelWarningConfig=i,this._added=!1,this.update=new Lh,this._ngModelWarningSent=!1,this._parent=t,this._setValidators(e),this._setAsyncValidators(n),this.valueAccessor=Bj(this,o)}set isDisabled(t){("undefined"==typeof ngDevMode||ngDevMode)&&Sj.disabledAttrWarning()}ngOnChanges(t){this._added||this._setUpControl(),Fj(t,this.viewModel)&&(("undefined"==typeof ngDevMode||ngDevMode)&&jj("formControlName",AU,this,this._ngModelWarningConfig),this.viewModel=this.model,this.formDirective.updateModel(this,this.model))}ngOnDestroy(){this.formDirective&&this.formDirective.removeControl(this)}viewToModelUpdate(t){this.viewModel=t,this.update.emit(t)}get path(){return Dj(null==this.name?this.name:this.name.toString(),this._parent)}get formDirective(){return this._parent?this._parent.formDirective:null}_checkParentType(){("undefined"==typeof ngDevMode||ngDevMode)&&(!(this._parent instanceof kU)&&this._parent instanceof aU?Sj.ngModelGroupException():this._parent instanceof kU||this._parent instanceof PU||this._parent instanceof DU||Sj.controlParentException())}_setUpControl(){this._checkParentType(),this.control=this.formDirective.addControl(this),this.control.disabled&&this.valueAccessor.setDisabledState&&this.valueAccessor.setDisabledState(!0),this._added=!0}}AU.ɵfac=function t(e){return new(e||AU)(Sm(Cj,13),Sm(GV,10),Sm(WV,10),Sm(IV,10),Sm(MU,8))},AU.ɵdir=lo({type:AU,selectors:[["","formControlName",""]],inputs:{isDisabled:["disabled","isDisabled"],name:["formControlName","name"],model:["ngModel","model"]},outputs:{update:"ngModelChange"},features:[pg([RU]),xp,Bo]}),AU._ngModelWarningSentOnce=!1,AU.ctorParameters=()=>[{type:Cj,decorators:[{type:Sr},{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[IV]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[MU]}]}],AU.propDecorators={name:[{type:xy,args:["formControlName"]}],isDisabled:[{type:xy,args:["disabled"]}],model:[{type:xy,args:["ngModel"]}],update:[{type:Oy,args:["ngModelChange"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(AU,[{type:Cy,args:[{selector:"[formControlName]",providers:[RU]}]}],(function(){return[{type:Cj,decorators:[{type:Sr},{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[IV]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[MU]}]}]}),{update:[{type:Oy,args:["ngModelChange"]}],isDisabled:[{type:xy,args:["disabled"]}],name:[{type:xy,args:["formControlName"]}],model:[{type:xy,args:["ngModel"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const TU={provide:IV,useExisting:qe((()=>zU)),multi:!0};function NU(t,e){return null==t?`${e}`:(e&&"object"==typeof e&&(e="Object"),`${t}: ${e}`.slice(0,50))}class zU extends zV{constructor(){super(...arguments),this._optionMap=new Map,this._idCounter=0,this._compareWith=Object.is}set compareWith(t){if("function"!=typeof t&&("undefined"==typeof ngDevMode||ngDevMode))throw new Error(`compareWith must be a function, but received ${JSON.stringify(t)}`);this._compareWith=t}writeValue(t){this.value=t;const e=this._getOptionId(t);null==e&&this.setProperty("selectedIndex",-1);const n=NU(e,t);this.setProperty("value",n)}registerOnChange(t){this.onChange=e=>{this.value=this._getOptionValue(e),t(this.value)}}_registerOption(){return(this._idCounter++).toString()}_getOptionId(t){for(const e of Array.from(this._optionMap.keys()))if(this._compareWith(this._optionMap.get(e),t))return e;return null}_getOptionValue(t){const e=(function n(t){return t.split(":")[0]})(t);return this._optionMap.has(e)?this._optionMap.get(e):t}}zU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(zU)))(n||zU)}})(),zU.ɵdir=lo({type:zU,selectors:[["select","formControlName","",3,"multiple",""],["select","formControl","",3,"multiple",""],["select","ngModel","",3,"multiple",""]],hostBindings:function t(e,n){1&e&&Vm("change",(function t(e){return n.onChange(e.target.value)}))("blur",(function t(){return n.onTouched()}))},inputs:{compareWith:"compareWith"},features:[pg([TU]),xp]}),zU.propDecorators={compareWith:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(zU,[{type:Cy,args:[{selector:"select:not([multiple])[formControlName],select:not([multiple])[formControl],select:not([multiple])[ngModel]",host:{"(change)":"onChange($event.target.value)","(blur)":"onTouched()"},providers:[TU]}]}],null,{compareWith:[{type:xy}]});class IU{constructor(t,e,n){this._element=t,this._renderer=e,this._select=n,this._select&&(this.id=this._select._registerOption())}set ngValue(t){null!=this._select&&(this._select._optionMap.set(this.id,t),this._setElementValue(NU(this.id,t)),this._select.writeValue(this._select.value))}set value(t){this._setElementValue(t),this._select&&this._select.writeValue(this._select.value)}_setElementValue(t){this._renderer.setProperty(this._element.nativeElement,"value",t)}ngOnDestroy(){this._select&&(this._select._optionMap.delete(this.id),this._select.writeValue(this._select.value))}}IU.ɵfac=function t(e){return new(e||IU)(Sm(hg),Sm(Cg),Sm(zU,9))},IU.ɵdir=lo({type:IU,selectors:[["option"]],inputs:{ngValue:"ngValue",value:"value"}}),IU.ctorParameters=()=>[{type:hg},{type:Cg},{type:zU,decorators:[{type:Sr},{type:Rr}]}],IU.propDecorators={ngValue:[{type:xy,args:["ngValue"]}],value:[{type:xy,args:["value"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(IU,[{type:Cy,args:[{selector:"option"}]}],(function(){return[{type:hg},{type:Cg},{type:zU,decorators:[{type:Sr},{type:Rr}]}]}),{ngValue:[{type:xy,args:["ngValue"]}],value:[{type:xy,args:["value"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const HU={provide:IV,useExisting:qe((()=>LU)),multi:!0};function FU(t,e){return null==t?`${e}`:("string"==typeof e&&(e=`'${e}'`),e&&"object"==typeof e&&(e="Object"),`${t}: ${e}`.slice(0,50))}class LU extends zV{constructor(){super(...arguments),this._optionMap=new Map,this._idCounter=0,this._compareWith=Object.is}set compareWith(t){if("function"!=typeof t&&("undefined"==typeof ngDevMode||ngDevMode))throw new Error(`compareWith must be a function, but received ${JSON.stringify(t)}`);this._compareWith=t}writeValue(t){let e;if(this.value=t,Array.isArray(t)){const n=t.map((t=>this._getOptionId(t)));e=(t,e)=>{t._setSelected(n.indexOf(e.toString())>-1)}}else e=(t,e)=>{t._setSelected(!1)};this._optionMap.forEach(e)}registerOnChange(t){this.onChange=e=>{const n=[];if(void 0!==e.selectedOptions){const t=e.selectedOptions;for(let e=0;e<t.length;e++){const o=t.item(e),i=this._getOptionValue(o.value);n.push(i)}}else{const t=e.options;for(let e=0;e<t.length;e++){const o=t.item(e);if(o.selected){const t=this._getOptionValue(o.value);n.push(t)}}}this.value=n,t(n)}}_registerOption(t){const e=(this._idCounter++).toString();return this._optionMap.set(e,t),e}_getOptionId(t){for(const e of Array.from(this._optionMap.keys()))if(this._compareWith(this._optionMap.get(e)._value,t))return e;return null}_getOptionValue(t){const e=(function n(t){return t.split(":")[0]})(t);return this._optionMap.has(e)?this._optionMap.get(e)._value:t}}LU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(LU)))(n||LU)}})(),LU.ɵdir=lo({type:LU,selectors:[["select","multiple","","formControlName",""],["select","multiple","","formControl",""],["select","multiple","","ngModel",""]],hostBindings:function t(e,n){1&e&&Vm("change",(function t(e){return n.onChange(e.target)}))("blur",(function t(){return n.onTouched()}))},inputs:{compareWith:"compareWith"},features:[pg([HU]),xp]}),LU.propDecorators={compareWith:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(LU,[{type:Cy,args:[{selector:"select[multiple][formControlName],select[multiple][formControl],select[multiple][ngModel]",host:{"(change)":"onChange($event.target)","(blur)":"onTouched()"},providers:[HU]}]}],null,{compareWith:[{type:xy}]});class BU{constructor(t,e,n){this._element=t,this._renderer=e,this._select=n,this._select&&(this.id=this._select._registerOption(this))}set ngValue(t){null!=this._select&&(this._value=t,this._setElementValue(FU(this.id,t)),this._select.writeValue(this._select.value))}set value(t){this._select?(this._value=t,this._setElementValue(FU(this.id,t)),this._select.writeValue(this._select.value)):this._setElementValue(t)}_setElementValue(t){this._renderer.setProperty(this._element.nativeElement,"value",t)}_setSelected(t){this._renderer.setProperty(this._element.nativeElement,"selected",t)}ngOnDestroy(){this._select&&(this._select._optionMap.delete(this.id),this._select.writeValue(this._select.value))}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function VU(t){return"number"==typeof t?t:parseInt(t,10)}BU.ɵfac=function t(e){return new(e||BU)(Sm(hg),Sm(Cg),Sm(LU,9))},BU.ɵdir=lo({type:BU,selectors:[["option"]],inputs:{ngValue:"ngValue",value:"value"}}),BU.ctorParameters=()=>[{type:hg},{type:Cg},{type:LU,decorators:[{type:Sr},{type:Rr}]}],BU.propDecorators={ngValue:[{type:xy,args:["ngValue"]}],value:[{type:xy,args:["value"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(BU,[{type:Cy,args:[{selector:"option"}]}],(function(){return[{type:hg},{type:Cg},{type:LU,decorators:[{type:Sr},{type:Rr}]}]}),{ngValue:[{type:xy,args:["ngValue"]}],value:[{type:xy,args:["value"]}]});class jU{constructor(){this._validator=nj}handleChanges(t){if(this.inputName in t){const e=this.normalizeInput(t[this.inputName].currentValue);this._validator=this.createValidator(e),this._onChange&&this._onChange()}}validate(t){return this._validator(t)}registerOnValidatorChange(t){this._onChange=t}}jU.ɵfac=function t(e){return new(e||jU)},jU.ɵdir=lo({type:jU}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(jU,[{type:Cy}],(function(){return[]}),null);const UU={provide:GV,useExisting:qe((()=>GU)),multi:!0};class GU extends jU{constructor(){super(...arguments),this.inputName="max",this.normalizeInput=t=>parseFloat(t),this.createValidator=t=>XV(t)}ngOnChanges(t){this.handleChanges(t)}}GU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(GU)))(n||GU)}})(),GU.ɵdir=lo({type:GU,selectors:[["input","type","number","max","","formControlName",""],["input","type","number","max","","formControl",""],["input","type","number","max","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){if(2&e){let t;jp("max",null!==(t=n.max)&&void 0!==t?t:null)}},inputs:{max:"max"},features:[pg([UU]),xp,Bo]}),GU.propDecorators={max:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(GU,[{type:Cy,args:[{selector:"input[type=number][max][formControlName],input[type=number][max][formControl],input[type=number][max][ngModel]",providers:[UU],host:{"[attr.max]":"max ?? null"}}]}],null,{max:[{type:xy}]});const WU={provide:GV,useExisting:qe((()=>YU)),multi:!0};class YU extends jU{constructor(){super(...arguments),this.inputName="min",this.normalizeInput=t=>parseFloat(t),this.createValidator=t=>ZV(t)}ngOnChanges(t){this.handleChanges(t)}}YU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(YU)))(n||YU)}})(),YU.ɵdir=lo({type:YU,selectors:[["input","type","number","min","","formControlName",""],["input","type","number","min","","formControl",""],["input","type","number","min","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){if(2&e){let t;jp("min",null!==(t=n.min)&&void 0!==t?t:null)}},inputs:{min:"min"},features:[pg([WU]),xp,Bo]}),YU.propDecorators={min:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(YU,[{type:Cy,args:[{selector:"input[type=number][min][formControlName],input[type=number][min][formControl],input[type=number][min][ngModel]",providers:[WU],host:{"[attr.min]":"min ?? null"}}]}],null,{min:[{type:xy}]});const qU={provide:GV,useExisting:qe((()=>XU)),multi:!0},ZU={provide:GV,useExisting:qe((()=>KU)),multi:!0};class XU{constructor(){this._required=!1}get required(){return this._required}set required(t){this._required=null!=t&&!1!==t&&"false"!=`${t}`,this._onChange&&this._onChange()}validate(t){return this.required?KV(t):null}registerOnValidatorChange(t){this._onChange=t}}XU.ɵfac=function t(e){return new(e||XU)},XU.ɵdir=lo({type:XU,selectors:[["","required","","formControlName","",3,"type","checkbox"],["","required","","formControl","",3,"type","checkbox"],["","required","","ngModel","",3,"type","checkbox"]],hostVars:1,hostBindings:function t(e,n){2&e&&jp("required",n.required?"":null)},inputs:{required:"required"},features:[pg([qU])]}),XU.propDecorators={required:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(XU,[{type:Cy,args:[{selector:":not([type=checkbox])[required][formControlName],:not([type=checkbox])[required][formControl],:not([type=checkbox])[required][ngModel]",providers:[qU],host:{"[attr.required]":'required ? "" : null'}}]}],(function(){return[]}),{required:[{type:xy}]});class KU extends XU{validate(t){return this.required?JV(t):null}}KU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(KU)))(n||KU)}})(),KU.ɵdir=lo({type:KU,selectors:[["input","type","checkbox","required","","formControlName",""],["input","type","checkbox","required","","formControl",""],["input","type","checkbox","required","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){2&e&&jp("required",n.required?"":null)},features:[pg([ZU]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(KU,[{type:Cy,args:[{selector:"input[type=checkbox][required][formControlName],input[type=checkbox][required][formControl],input[type=checkbox][required][ngModel]",providers:[ZU],host:{"[attr.required]":'required ? "" : null'}}]}],null,null);const JU={provide:GV,useExisting:qe((()=>QU)),multi:!0};class QU{constructor(){this._enabled=!1}set email(t){this._enabled=""===t||!0===t||"true"===t,this._onChange&&this._onChange()}validate(t){return this._enabled?QV(t):null}registerOnValidatorChange(t){this._onChange=t}}QU.ɵfac=function t(e){return new(e||QU)},QU.ɵdir=lo({type:QU,selectors:[["","email","","formControlName",""],["","email","","formControl",""],["","email","","ngModel",""]],inputs:{email:"email"},features:[pg([JU])]}),QU.propDecorators={email:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(QU,[{type:Cy,args:[{selector:"[email][formControlName],[email][formControl],[email][ngModel]",providers:[JU]}]}],(function(){return[]}),{email:[{type:xy}]});const $U={provide:GV,useExisting:qe((()=>tG)),multi:!0};class tG{constructor(){this._validator=nj}ngOnChanges(t){"minlength"in t&&(this._createValidator(),this._onChange&&this._onChange())}validate(t){return this.enabled()?this._validator(t):null}registerOnValidatorChange(t){this._onChange=t}_createValidator(){this._validator=this.enabled()?$V(VU(this.minlength)):nj}enabled(){return null!=this.minlength}}tG.ɵfac=function t(e){return new(e||tG)},tG.ɵdir=lo({type:tG,selectors:[["","minlength","","formControlName",""],["","minlength","","formControl",""],["","minlength","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){2&e&&jp("minlength",n.enabled()?n.minlength:null)},inputs:{minlength:"minlength"},features:[pg([$U]),Bo]}),tG.propDecorators={minlength:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(tG,[{type:Cy,args:[{selector:"[minlength][formControlName],[minlength][formControl],[minlength][ngModel]",providers:[$U],host:{"[attr.minlength]":"enabled() ? minlength : null"}}]}],(function(){return[]}),{minlength:[{type:xy}]});const eG={provide:GV,useExisting:qe((()=>nG)),multi:!0};class nG{constructor(){this._validator=nj}ngOnChanges(t){"maxlength"in t&&(this._createValidator(),this._onChange&&this._onChange())}validate(t){return this.enabled()?this._validator(t):null}registerOnValidatorChange(t){this._onChange=t}_createValidator(){this._validator=this.enabled()?tj(VU(this.maxlength)):nj}enabled(){return null!=this.maxlength}}nG.ɵfac=function t(e){return new(e||nG)},nG.ɵdir=lo({type:nG,selectors:[["","maxlength","","formControlName",""],["","maxlength","","formControl",""],["","maxlength","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){2&e&&jp("maxlength",n.enabled()?n.maxlength:null)},inputs:{maxlength:"maxlength"},features:[pg([eG]),Bo]}),nG.propDecorators={maxlength:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(nG,[{type:Cy,args:[{selector:"[maxlength][formControlName],[maxlength][formControl],[maxlength][ngModel]",providers:[eG],host:{"[attr.maxlength]":"enabled() ? maxlength : null"}}]}],(function(){return[]}),{maxlength:[{type:xy}]});const oG={provide:GV,useExisting:qe((()=>iG)),multi:!0};class iG{constructor(){this._validator=nj}ngOnChanges(t){"pattern"in t&&(this._createValidator(),this._onChange&&this._onChange())}validate(t){return this._validator(t)}registerOnValidatorChange(t){this._onChange=t}_createValidator(){this._validator=ej(this.pattern)}}iG.ɵfac=function t(e){return new(e||iG)},iG.ɵdir=lo({type:iG,selectors:[["","pattern","","formControlName",""],["","pattern","","formControl",""],["","pattern","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){2&e&&jp("pattern",n.pattern?n.pattern:null)},inputs:{pattern:"pattern"},features:[pg([oG]),Bo]}),iG.propDecorators={pattern:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(iG,[{type:Cy,args:[{selector:"[pattern][formControlName],[pattern][formControl],[pattern][ngModel]",providers:[oG],host:{"[attr.pattern]":"pattern ? pattern : null"}}]}],(function(){return[]}),{pattern:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const aG=[mU,IU,BU,VV,fU,CU,FV,zU,LU,yU,xj,Oj,XU,tG,nG,iG,KU,QU,YU,GU],rG=[pU,lU,iU],sG=[xU,PU,AU,kU,DU];class lG{}lG.ɵfac=function t(e){return new(e||lG)},lG.ɵmod=ao({type:lG}),lG.ɵinj=vn({imports:[[hU]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lG,[{type:Ay,args:[{declarations:aG,imports:[hU],exports:aG}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(lG,{declarations:[mU,IU,BU,VV,fU,CU,FV,zU,LU,yU,xj,Oj,XU,tG,nG,iG,KU,QU,YU,GU],imports:[hU],exports:[mU,IU,BU,VV,fU,CU,FV,zU,LU,yU,xj,Oj,XU,tG,nG,iG,KU,QU,YU,GU]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class cG{}cG.ɵfac=function t(e){return new(e||cG)},cG.ɵmod=ao({type:cG}),cG.ɵinj=vn({imports:[lG]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(cG,[{type:Ay,args:[{declarations:rG,exports:[lG,rG]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(cG,{declarations:[pU,lU,iU],exports:[lG,pU,lU,iU]});class dG{static withConfig(t){return{ngModule:dG,providers:[{provide:MU,useValue:t.warnOnNgModelWithFormControl}]}}}dG.ɵfac=function t(e){return new(e||dG)},dG.ɵmod=ao({type:dG}),dG.ɵinj=vn({imports:[lG]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(dG,[{type:Ay,args:[{declarations:[sG],exports:[lG,sG]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(dG,{declarations:[xU,PU,AU,kU,DU],exports:[lG,xU,PU,AU,kU,DU]});class pG{group(t,e=null){const n=this._reduceControls(t);let o,i=null,a=null;return null!=e&&((
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function r(t){return void 0!==t.asyncValidators||void 0!==t.validators||void 0!==t.updateOn})(e)?(i=null!=e.validators?e.validators:null,a=null!=e.asyncValidators?e.asyncValidators:null,o=null!=e.updateOn?e.updateOn:void 0):(i=null!=e.validator?e.validator:null,a=null!=e.asyncValidator?e.asyncValidator:null)),new tU(n,{asyncValidators:a,updateOn:o,validators:i})}control(t,e,n){return new $j(t,e,n)}array(t,e,n){const o=t.map((t=>this._createControl(t)));return new eU(o,e,n)}_reduceControls(t){const e={};return Object.keys(t).forEach((n=>{e[n]=this._createControl(t[n])})),e}_createControl(t){return t instanceof $j||t instanceof tU||t instanceof eU?t:Array.isArray(t)?this.control(t[0],t.length>1?t[1]:null,t.length>2?t[2]:null):this.control(t)}}pG.ɵfac=function t(e){return new(e||pG)},pG.ɵprov=Mn({factory:function t(){return new pG},token:pG,providedIn:dG}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(pG,[{type:im,args:[{providedIn:dG}]}],null,null),
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
new Og("12.2.1");const mG=["trigger"],uG=["panel"];function fG(t,e){if(1&t&&(Rm(0,"span",8),ku(1),Am()),2&t){const t=Ym();rc(1),Su(t.placeholder)}}function gG(t,e){if(1&t&&(Rm(0,"span",12),ku(1),Am()),2&t){const t=Ym(2);rc(1),Su(t.triggerValue)}}function hG(t,e){1&t&&Xm(0,0,["*ngSwitchCase","true"])}function bG(t,e){1&t&&(Rm(0,"span",9),Qp(1,gG,2,1,"span",10),Qp(2,hG,1,0,"ng-content",11),Am()),2&t&&(Dm("ngSwitch",!!Ym().customTrigger),rc(2),Dm("ngSwitchCase",!0))}function yG(t,e){if(1&t){const t=Hm();Rm(0,"div",13),Rm(1,"div",14,15),Vm("@transformPanel.done",(function e(n){return hi(t),Ym()._panelDoneAnimatingStream.next(n.toState)}))("keydown",(function e(n){return hi(t),Ym()._handleKeydown(n)})),Xm(3,1),Am(),Am()}if(2&t){const t=Ym();Dm("@transformPanelWrap",void 0),rc(1),Au("mat-select-panel ",t._getPanelTheme(),""),du("transform-origin",t._transformOrigin)("font-size",t._triggerFontSize,"px"),Dm("ngClass",t.panelClass)("@transformPanel",t.multiple?"showing-multiple":"showing"),jp("id",t.id+"-panel")("aria-multiselectable",t.multiple)("aria-label",t.ariaLabel||null)("aria-labelledby",t._getPanelAriaLabelledby())}}const _G=[[["mat-select-trigger"]],"*"],CG={transformPanelWrap:nx("transformPanelWrap",[lx("* => void",dx("@transformPanel",[cx()],{optional:!0}))]),transformPanel:nx("transformPanel",[rx("void",ax({transform:"scaleY(0.8)",minWidth:"100%",opacity:0})),rx("showing",ax({opacity:1,minWidth:"calc(100% + 32px)",transform:"scaleY(1)"})),rx("showing-multiple",ax({opacity:1,minWidth:"calc(100% + 64px)",transform:"scaleY(1)"})),lx("void => *",ox("120ms cubic-bezier(0, 0, 0.2, 1)")),lx("* => void",ox("100ms 25ms linear",ax({opacity:0})))])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let MG=0;const vG=256,xG=new Ga("mat-select-scroll-strategy"),OG=new Ga("MAT_SELECT_CONFIG"),PG={provide:xG,deps:[pL],useFactory:function wG(t){return()=>t.scrollStrategies.reposition()}};class kG{constructor(t,e){this.source=t,this.value=e}}const SG=QI($I(KI(tH(class{constructor(t,e,n,o,i){this._elementRef=t,this._defaultErrorStateMatcher=e,this._parentForm=n,this._parentFormGroup=o,this.ngControl=i}})))),DG=new Ga("MatSelectTrigger");class EG{}EG.ɵfac=function t(e){return new(e||EG)},EG.ɵdir=lo({type:EG,selectors:[["mat-select-trigger"]],features:[pg([{provide:DG,useExisting:EG}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(EG,[{type:Cy,args:[{selector:"mat-select-trigger",providers:[{provide:DG,useExisting:EG}]}]}],null,null);class RG extends SG{constructor(t,e,n,o,i,a,r,s,l,c,d,p,m,u){var f,g,h;super(i,o,r,s,c),this._viewportRuler=t,this._changeDetectorRef=e,this._ngZone=n,this._dir=a,this._parentFormField=l,this._liveAnnouncer=m,this._defaultOptions=u,this._panelOpen=!1,this._compareWith=(t,e)=>t===e,this._uid="mat-select-"+MG++,this._triggerAriaLabelledBy=null,this._destroy=new I,this._onChange=()=>{},this._onTouched=()=>{},this._valueId="mat-select-value-"+MG++,this._panelDoneAnimatingStream=new I,this._overlayPanelClass=(null===(f=this._defaultOptions)||void 0===f?void 0:f.overlayPanelClass)||"",this._focused=!1,this.controlType="mat-select",this._required=!1,this._multiple=!1,this._disableOptionCentering=null!==(h=null===(g=this._defaultOptions)||void 0===g?void 0:g.disableOptionCentering)&&void 0!==h&&h,this.ariaLabel="",this.optionSelectionChanges=Qt((()=>{const t=this.options;return t?t.changes.pipe(Ne(t),ze((()=>re(...t.map((t=>t.onSelectionChange)))))):this._ngZone.onStable.pipe(be(1),ze((()=>this.optionSelectionChanges)))})),this.openedChange=new Lh,this._openedStream=this.openedChange.pipe(ce((t=>t)),It((()=>{}))),this._closedStream=this.openedChange.pipe(ce((t=>!t)),It((()=>{}))),this.selectionChange=new Lh,this.valueChange=new Lh,this.ngControl&&(this.ngControl.valueAccessor=this),null!=(null==u?void 0:u.typeaheadDebounceInterval)&&(this._typeaheadDebounceInterval=u.typeaheadDebounceInterval),this._scrollStrategyFactory=p,this._scrollStrategy=this._scrollStrategyFactory(),this.tabIndex=parseInt(d)||0,this.id=this.id}get focused(){return this._focused||this._panelOpen}get placeholder(){return this._placeholder}set placeholder(t){this._placeholder=t,this.stateChanges.next()}get required(){return this._required}set required(t){this._required=yz(t),this.stateChanges.next()}get multiple(){return this._multiple}set multiple(t){if(this._selectionModel&&("undefined"==typeof ngDevMode||ngDevMode))
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
throw(function e(){return Error("Cannot change `multiple` mode of select after initialization.")})();this._multiple=yz(t)}get disableOptionCentering(){return this._disableOptionCentering}set disableOptionCentering(t){this._disableOptionCentering=yz(t)}get compareWith(){return this._compareWith}set compareWith(t){if("function"!=typeof t&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("`compareWith` must be a function.")})();this._compareWith=t,this._selectionModel&&this._initializeSelection()}get value(){return this._value}set value(t){(t!==this._value||this._multiple&&Array.isArray(t))&&(this.options&&this._setSelectionByValue(t),this._value=t)}get typeaheadDebounceInterval(){return this._typeaheadDebounceInterval}set typeaheadDebounceInterval(t){this._typeaheadDebounceInterval=_z(t)}get id(){return this._id}set id(t){this._id=t||this._uid,this.stateChanges.next()}ngOnInit(){this._selectionModel=new oF(this.multiple),this.stateChanges.next(),this._panelDoneAnimatingStream.pipe(Me(),Ie(this._destroy)).subscribe((()=>this._panelDoneAnimating(this.panelOpen)))}ngAfterContentInit(){this._initKeyManager(),this._selectionModel.changed.pipe(Ie(this._destroy)).subscribe((t=>{t.added.forEach((t=>t.select())),t.removed.forEach((t=>t.deselect()))})),this.options.changes.pipe(Ne(null),Ie(this._destroy)).subscribe((()=>{this._resetOptions(),this._initializeSelection()}))}ngDoCheck(){const t=this._getTriggerAriaLabelledby();if(t!==this._triggerAriaLabelledBy){const e=this._elementRef.nativeElement;this._triggerAriaLabelledBy=t,t?e.setAttribute("aria-labelledby",t):e.removeAttribute("aria-labelledby")}this.ngControl&&this.updateErrorState()}ngOnChanges(t){t.disabled&&this.stateChanges.next(),t.typeaheadDebounceInterval&&this._keyManager&&this._keyManager.withTypeAhead(this._typeaheadDebounceInterval)}ngOnDestroy(){this._destroy.next(),this._destroy.complete(),this.stateChanges.complete()}toggle(){this.panelOpen?this.close():this.open()}open(){this._canOpen()&&(this._panelOpen=!0,this._keyManager.withHorizontalOrientation(null),this._highlightCorrectOption(),this._changeDetectorRef.markForCheck())}close(){this._panelOpen&&(this._panelOpen=!1,this._keyManager.withHorizontalOrientation(this._isRtl()?"rtl":"ltr"),this._changeDetectorRef.markForCheck(),this._onTouched())}writeValue(t){this.value=t}registerOnChange(t){this._onChange=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t,this._changeDetectorRef.markForCheck(),this.stateChanges.next()}get panelOpen(){return this._panelOpen}get selected(){return this.multiple?this._selectionModel.selected:this._selectionModel.selected[0]}get triggerValue(){if(this.empty)return"";if(this._multiple){const t=this._selectionModel.selected.map((t=>t.viewValue));return this._isRtl()&&t.reverse(),t.join(", ")}return this._selectionModel.selected[0].viewValue}_isRtl(){return!!this._dir&&"rtl"===this._dir.value}_handleKeydown(t){this.disabled||(this.panelOpen?this._handleOpenKeydown(t):this._handleClosedKeydown(t))}_handleClosedKeydown(t){const e=t.keyCode,n=e===hz||e===gz||37===e||39===e,o=e===mz||e===fz,i=this._keyManager;if(!i.isTyping()&&o&&!bz(t)||(this.multiple||t.altKey)&&n)t.preventDefault(),this.open();else if(!this.multiple){const e=this.selected;i.onKeydown(t);const n=this.selected;n&&e!==n&&this._liveAnnouncer.announce(n.viewValue,1e4)}}_handleOpenKeydown(t){const e=this._keyManager,n=t.keyCode,o=n===hz||n===gz,i=e.isTyping();if(o&&t.altKey)t.preventDefault(),this.close();else if(i||n!==mz&&n!==fz||!e.activeItem||bz(t))if(!i&&this._multiple&&65===n&&t.ctrlKey){t.preventDefault();const e=this.options.some((t=>!t.disabled&&!t.selected));this.options.forEach((t=>{t.disabled||(e?t.select():t.deselect())}))}else{const n=e.activeItemIndex;e.onKeydown(t),this._multiple&&o&&t.shiftKey&&e.activeItem&&e.activeItemIndex!==n&&e.activeItem._selectViaInteraction()}else t.preventDefault(),e.activeItem._selectViaInteraction()}_onFocus(){this.disabled||(this._focused=!0,this.stateChanges.next())}_onBlur(){this._focused=!1,this.disabled||this.panelOpen||(this._onTouched(),this._changeDetectorRef.markForCheck(),this.stateChanges.next())}_onAttached(){this._overlayDir.positionChange.pipe(be(1)).subscribe((()=>{this._changeDetectorRef.detectChanges(),this._positioningSettled()}))}_getPanelTheme(){return this._parentFormField?`mat-${this._parentFormField.color}`:""}get empty(){return!this._selectionModel||this._selectionModel.isEmpty()}_initializeSelection(){Promise.resolve().then((()=>{this._setSelectionByValue(this.ngControl?this.ngControl.value:this._value),this.stateChanges.next()}))}_setSelectionByValue(t){if(this._selectionModel.selected.forEach((t=>t.setInactiveStyles())),this._selectionModel.clear(),this.multiple&&t){if(!Array.isArray(t)&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("Value must be an array in multiple-selection mode.")})();t.forEach((t=>this._selectValue(t))),this._sortValues()}else{const e=this._selectValue(t);e?this._keyManager.updateActiveItem(e):this.panelOpen||this._keyManager.updateActiveItem(-1)}this._changeDetectorRef.markForCheck()}_selectValue(t){const e=this.options.find((e=>{if(this._selectionModel.isSelected(e))return!1;try{return null!=e.value&&this._compareWith(e.value,t)}catch(t){return("undefined"==typeof ngDevMode||ngDevMode)&&console.warn(t),!1}}));return e&&this._selectionModel.select(e),e}_initKeyManager(){this._keyManager=new tI(this.options).withTypeAhead(this._typeaheadDebounceInterval).withVerticalOrientation().withHorizontalOrientation(this._isRtl()?"rtl":"ltr").withHomeAndEnd().withAllowedModifierKeys(["shiftKey"]),this._keyManager.tabOut.pipe(Ie(this._destroy)).subscribe((()=>{this.panelOpen&&(!this.multiple&&this._keyManager.activeItem&&this._keyManager.activeItem._selectViaInteraction(),this.focus(),this.close())})),this._keyManager.change.pipe(Ie(this._destroy)).subscribe((()=>{this._panelOpen&&this.panel?this._scrollOptionIntoView(this._keyManager.activeItemIndex||0):this._panelOpen||this.multiple||!this._keyManager.activeItem||this._keyManager.activeItem._selectViaInteraction()}))}_resetOptions(){const t=re(this.options.changes,this._destroy);this.optionSelectionChanges.pipe(Ie(t)).subscribe((t=>{this._onSelect(t.source,t.isUserInput),t.isUserInput&&!this.multiple&&this._panelOpen&&(this.close(),this.focus())})),re(...this.options.map((t=>t._stateChanges))).pipe(Ie(t)).subscribe((()=>{this._changeDetectorRef.markForCheck(),this.stateChanges.next()}))}_onSelect(t,e){const n=this._selectionModel.isSelected(t);null!=t.value||this._multiple?(n!==t.selected&&(t.selected?this._selectionModel.select(t):this._selectionModel.deselect(t)),e&&this._keyManager.setActiveItem(t),this.multiple&&(this._sortValues(),e&&this.focus())):(t.deselect(),this._selectionModel.clear(),null!=this.value&&this._propagateChanges(t.value)),n!==this._selectionModel.isSelected(t)&&this._propagateChanges(),this.stateChanges.next()}_sortValues(){if(this.multiple){const t=this.options.toArray();this._selectionModel.sort(((e,n)=>this.sortComparator?this.sortComparator(e,n,t):t.indexOf(e)-t.indexOf(n))),this.stateChanges.next()}}_propagateChanges(t){let e=null;e=this.multiple?this.selected.map((t=>t.value)):this.selected?this.selected.value:t,this._value=e,this.valueChange.emit(e),this._onChange(e),this.selectionChange.emit(this._getChangeEvent(e)),this._changeDetectorRef.markForCheck()}_highlightCorrectOption(){this._keyManager&&(this.empty?this._keyManager.setFirstItemActive():this._keyManager.setActiveItem(this._selectionModel.selected[0]))}_canOpen(){var t;return!this._panelOpen&&!this.disabled&&(null===(t=this.options)||void 0===t?void 0:t.length)>0}focus(t){this._elementRef.nativeElement.focus(t)}_getPanelAriaLabelledby(){var t;if(this.ariaLabel)return null;const e=null===(t=this._parentFormField)||void 0===t?void 0:t.getLabelId();return this.ariaLabelledby?(e?e+" ":"")+this.ariaLabelledby:e}_getAriaActiveDescendant(){return this.panelOpen&&this._keyManager&&this._keyManager.activeItem?this._keyManager.activeItem.id:null}_getTriggerAriaLabelledby(){var t;if(this.ariaLabel)return null;const e=null===(t=this._parentFormField)||void 0===t?void 0:t.getLabelId();let n=(e?e+" ":"")+this._valueId;return this.ariaLabelledby&&(n+=" "+this.ariaLabelledby),n}_panelDoneAnimating(t){this.openedChange.emit(t)}setDescribedByIds(t){this._ariaDescribedby=t.join(" ")}onContainerClick(){this.focus(),this.open()}get shouldLabelFloat(){return this._panelOpen||!this.empty||this._focused&&!!this._placeholder}}RG.ɵfac=function t(e){return new(e||RG)(Sm(uF),Sm(Ug),Sm(a_),Sm(bH),Sm(hg),Sm(HI,8),Sm(iU,8),Sm(PU,8),Sm(RV,8),Sm(Mj,10),Na("tabindex"),Sm(xG),Sm(OI),Sm(OG,8))},RG.ɵdir=lo({type:RG,viewQuery:function t(e,n){if(1&e&&(Qh(mG,5),Qh(uG,5),Qh(gL,5)),2&e){let t;Jh(t=tb())&&(n.trigger=t.first),Jh(t=tb())&&(n.panel=t.first),Jh(t=tb())&&(n._overlayDir=t.first)}},inputs:{ariaLabel:["aria-label","ariaLabel"],id:"id",placeholder:"placeholder",required:"required",multiple:"multiple",disableOptionCentering:"disableOptionCentering",compareWith:"compareWith",value:"value",typeaheadDebounceInterval:"typeaheadDebounceInterval",panelClass:"panelClass",ariaLabelledby:["aria-labelledby","ariaLabelledby"],errorStateMatcher:"errorStateMatcher",sortComparator:"sortComparator"},outputs:{openedChange:"openedChange",_openedStream:"opened",_closedStream:"closed",selectionChange:"selectionChange",valueChange:"valueChange"},features:[xp,Bo]}),RG.ctorParameters=()=>[{type:uF},{type:Ug},{type:a_},{type:bH},{type:hg},{type:HI,decorators:[{type:Sr}]},{type:iU,decorators:[{type:Sr}]},{type:PU,decorators:[{type:Sr}]},{type:AV,decorators:[{type:Sr},{type:kr,args:[RV]}]},{type:Mj,decorators:[{type:Dr},{type:Sr}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:void 0,decorators:[{type:kr,args:[xG]}]},{type:OI},{type:void 0,decorators:[{type:Sr},{type:kr,args:[OG]}]}],RG.propDecorators={trigger:[{type:Za,args:["trigger"]}],panel:[{type:Za,args:["panel"]}],_overlayDir:[{type:Za,args:[gL]}],panelClass:[{type:xy}],placeholder:[{type:xy}],required:[{type:xy}],multiple:[{type:xy}],disableOptionCentering:[{type:xy}],compareWith:[{type:xy}],value:[{type:xy}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],errorStateMatcher:[{type:xy}],typeaheadDebounceInterval:[{type:xy}],sortComparator:[{type:xy}],id:[{type:xy}],openedChange:[{type:Oy}],_openedStream:[{type:Oy,args:["opened"]}],_closedStream:[{type:Oy,args:["closed"]}],selectionChange:[{type:Oy}],valueChange:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(RG,[{type:Cy}],(function(){return[{type:uF},{type:Ug},{type:a_},{type:bH},{type:hg},{type:HI,decorators:[{type:Sr}]},{type:iU,decorators:[{type:Sr}]},{type:PU,decorators:[{type:Sr}]},{type:AV,decorators:[{type:Sr},{type:kr,args:[RV]}]},{type:Mj,decorators:[{type:Dr},{type:Sr}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:void 0,decorators:[{type:kr,args:[xG]}]},{type:OI},{type:void 0,decorators:[{type:Sr},{type:kr,args:[OG]}]}]}),{ariaLabel:[{type:xy,args:["aria-label"]}],openedChange:[{type:Oy}],_openedStream:[{type:Oy,args:["opened"]}],_closedStream:[{type:Oy,args:["closed"]}],selectionChange:[{type:Oy}],valueChange:[{type:Oy}],id:[{type:xy}],placeholder:[{type:xy}],required:[{type:xy}],multiple:[{type:xy}],disableOptionCentering:[{type:xy}],compareWith:[{type:xy}],value:[{type:xy}],typeaheadDebounceInterval:[{type:xy}],trigger:[{type:Za,args:["trigger"]}],panel:[{type:Za,args:["panel"]}],_overlayDir:[{type:Za,args:[gL]}],panelClass:[{type:xy}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],errorStateMatcher:[{type:xy}],sortComparator:[{type:xy}]});class AG extends RG{constructor(){super(...arguments),this._scrollTop=0,this._triggerFontSize=0,this._transformOrigin="top",this._offsetY=0,this._positions=[{originX:"start",originY:"top",overlayX:"start",overlayY:"top"},{originX:"start",originY:"bottom",overlayX:"start",overlayY:"bottom"}]}_calculateOverlayScroll(t,e,n){const o=this._getItemHeight();return Math.min(Math.max(0,o*t-e+o/2),n)}ngOnInit(){super.ngOnInit(),this._viewportRuler.change().pipe(Ie(this._destroy)).subscribe((()=>{this.panelOpen&&(this._triggerRect=this.trigger.nativeElement.getBoundingClientRect(),this._changeDetectorRef.markForCheck())}))}open(){super._canOpen()&&(super.open(),this._triggerRect=this.trigger.nativeElement.getBoundingClientRect(),this._triggerFontSize=parseInt(getComputedStyle(this.trigger.nativeElement).fontSize||"0"),this._calculateOverlayPosition(),this._ngZone.onStable.pipe(be(1)).subscribe((()=>{this._triggerFontSize&&this._overlayDir.overlayRef&&this._overlayDir.overlayRef.overlayElement&&(this._overlayDir.overlayRef.overlayElement.style.fontSize=`${this._triggerFontSize}px`)})))}_scrollOptionIntoView(t){const e=VH(t,this.options,this.optionGroups),n=this._getItemHeight();this.panel.nativeElement.scrollTop=0===t&&1===e?0:jH((t+e)*n,n,this.panel.nativeElement.scrollTop,vG)}_positioningSettled(){this._calculateOverlayOffsetX(),this.panel.nativeElement.scrollTop=this._scrollTop}_panelDoneAnimating(t){this.panelOpen?this._scrollTop=0:(this._overlayDir.offsetX=0,this._changeDetectorRef.markForCheck()),super._panelDoneAnimating(t)}_getChangeEvent(t){return new kG(this,t)}_calculateOverlayOffsetX(){const t=this._overlayDir.overlayRef.overlayElement.getBoundingClientRect(),e=this._viewportRuler.getViewportSize(),n=this._isRtl(),o=this.multiple?56:32;let i;if(this.multiple)i=40;else if(this.disableOptionCentering)i=16;else{let t=this._selectionModel.selected[0]||this.options.first;i=t&&t.group?32:16}n||(i*=-1);const a=0-(t.left+i-(n?o:0)),r=t.right+i-e.width+(n?0:o);a>0?i+=a+8:r>0&&(i-=r+8),this._overlayDir.offsetX=Math.round(i),this._overlayDir.overlayRef.updatePosition()}_calculateOverlayOffsetY(t,e,n){const o=this._getItemHeight(),i=(o-this._triggerRect.height)/2,a=Math.floor(vG/o);let r;return this.disableOptionCentering?0:(r=0===this._scrollTop?t*o:this._scrollTop===n?(t-(this._getItemCount()-a))*o+(o-(this._getItemCount()*o-vG)%o):e-o/2,Math.round(-1*r-i))}_checkOverlayWithinViewport(t){const e=this._getItemHeight(),n=this._viewportRuler.getViewportSize(),o=this._triggerRect.top-8,i=n.height-this._triggerRect.bottom-8,a=Math.abs(this._offsetY),r=Math.min(this._getItemCount()*e,vG)-a-this._triggerRect.height;r>i?this._adjustPanelUp(r,i):a>o?this._adjustPanelDown(a,o,t):this._transformOrigin=this._getOriginBasedOnOption()}_adjustPanelUp(t,e){const n=Math.round(t-e);this._scrollTop-=n,this._offsetY-=n,this._transformOrigin=this._getOriginBasedOnOption(),this._scrollTop<=0&&(this._scrollTop=0,this._offsetY=0,this._transformOrigin="50% bottom 0px")}_adjustPanelDown(t,e,n){const o=Math.round(t-e);if(this._scrollTop+=o,this._offsetY+=o,this._transformOrigin=this._getOriginBasedOnOption(),this._scrollTop>=n)return this._scrollTop=n,this._offsetY=0,void(this._transformOrigin="50% top 0px")}_calculateOverlayPosition(){const t=this._getItemHeight(),e=this._getItemCount(),n=Math.min(e*t,vG),o=e*t-n;let i;i=this.empty?0:Math.max(this.options.toArray().indexOf(this._selectionModel.selected[0]),0),i+=VH(i,this.options,this.optionGroups);const a=n/2;this._scrollTop=this._calculateOverlayScroll(i,a,o),this._offsetY=this._calculateOverlayOffsetY(i,a,o),this._checkOverlayWithinViewport(o)}_getOriginBasedOnOption(){const t=this._getItemHeight(),e=(t-this._triggerRect.height)/2;return`50% ${Math.abs(this._offsetY)-e+t/2}px 0px`}_getItemHeight(){return 3*this._triggerFontSize}_getItemCount(){return this.options.length+this.optionGroups.length}}AG.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(AG)))(n||AG)}})(),AG.ɵcmp=to({type:AG,selectors:[["mat-select"]],contentQueries:function t(e,n,o){if(1&e&&($h(o,DG,5),$h(o,BH,5),$h(o,zH,5)),2&e){let t;Jh(t=tb())&&(n.customTrigger=t.first),Jh(t=tb())&&(n.options=t),Jh(t=tb())&&(n.optionGroups=t)}},hostAttrs:["role","combobox","aria-autocomplete","none","aria-haspopup","true",1,"mat-select"],hostVars:20,hostBindings:function t(e,n){1&e&&Vm("keydown",(function t(e){return n._handleKeydown(e)}))("focus",(function t(){return n._onFocus()}))("blur",(function t(){return n._onBlur()})),2&e&&(jp("id",n.id)("tabindex",n.tabIndex)("aria-controls",n.panelOpen?n.id+"-panel":null)("aria-expanded",n.panelOpen)("aria-label",n.ariaLabel||null)("aria-required",n.required.toString())("aria-disabled",n.disabled.toString())("aria-invalid",n.errorState)("aria-describedby",n._ariaDescribedby||null)("aria-activedescendant",n._getAriaActiveDescendant()),pu("mat-select-disabled",n.disabled)("mat-select-invalid",n.errorState)("mat-select-required",n.required)("mat-select-empty",n.empty)("mat-select-multiple",n.multiple))},inputs:{disabled:"disabled",disableRipple:"disableRipple",tabIndex:"tabIndex"},exportAs:["matSelect"],features:[pg([{provide:bV,useExisting:AG},{provide:RH,useExisting:AG}]),xp],ngContentSelectors:["mat-select-trigger","*"],decls:9,vars:12,consts:[["cdk-overlay-origin","",1,"mat-select-trigger",3,"click"],["origin","cdkOverlayOrigin","trigger",""],[1,"mat-select-value",3,"ngSwitch"],["class","mat-select-placeholder mat-select-min-line",4,"ngSwitchCase"],["class","mat-select-value-text",3,"ngSwitch",4,"ngSwitchCase"],[1,"mat-select-arrow-wrapper"],[1,"mat-select-arrow"],["cdk-connected-overlay","","cdkConnectedOverlayLockPosition","","cdkConnectedOverlayHasBackdrop","","cdkConnectedOverlayBackdropClass","cdk-overlay-transparent-backdrop",3,"cdkConnectedOverlayPanelClass","cdkConnectedOverlayScrollStrategy","cdkConnectedOverlayOrigin","cdkConnectedOverlayOpen","cdkConnectedOverlayPositions","cdkConnectedOverlayMinWidth","cdkConnectedOverlayOffsetY","backdropClick","attach","detach"],[1,"mat-select-placeholder","mat-select-min-line"],[1,"mat-select-value-text",3,"ngSwitch"],["class","mat-select-min-line",4,"ngSwitchDefault"],[4,"ngSwitchCase"],[1,"mat-select-min-line"],[1,"mat-select-panel-wrap"],["role","listbox","tabindex","-1",3,"ngClass","keydown"],["panel",""]],template:function t(e,n){if(1&e&&(Zm(_G),Rm(0,"div",0,1),Vm("click",(function t(){return n.toggle()})),Rm(3,"div",2),Qp(4,fG,2,1,"span",3),Qp(5,bG,3,2,"span",4),Am(),Rm(6,"div",5),Tm(7,"div",6),Am(),Am(),Qp(8,yG,4,14,"ng-template",7),Vm("backdropClick",(function t(){return n.close()}))("attach",(function t(){return n._onAttached()}))("detach",(function t(){return n.close()}))),2&e){const t=$p(1);jp("aria-owns",n.panelOpen?n.id+"-panel":null),rc(3),Dm("ngSwitch",n.empty),jp("id",n._valueId),rc(1),Dm("ngSwitchCase",!0),rc(1),Dm("ngSwitchCase",!1),rc(3),Dm("cdkConnectedOverlayPanelClass",n._overlayPanelClass)("cdkConnectedOverlayScrollStrategy",n._scrollStrategy)("cdkConnectedOverlayOrigin",t)("cdkConnectedOverlayOpen",n.panelOpen)("cdkConnectedOverlayPositions",n._positions)("cdkConnectedOverlayMinWidth",null==n._triggerRect?null:n._triggerRect.width)("cdkConnectedOverlayOffsetY",n._offsetY)}},directives:[fL,fM,gM,gL,hM,aM],styles:['.mat-select{display:inline-block;width:100%;outline:none}.mat-select-trigger{display:inline-table;cursor:pointer;position:relative;box-sizing:border-box}.mat-select-disabled .mat-select-trigger{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-select-value{display:table-cell;max-width:0;width:100%;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.mat-select-value-text{white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.mat-select-arrow-wrapper{display:table-cell;vertical-align:middle}.mat-form-field-appearance-fill .mat-select-arrow-wrapper{transform:translateY(-50%)}.mat-form-field-appearance-outline .mat-select-arrow-wrapper{transform:translateY(-25%)}.mat-form-field-appearance-standard.mat-form-field-has-label .mat-select:not(.mat-select-empty) .mat-select-arrow-wrapper{transform:translateY(-50%)}.mat-form-field-appearance-standard .mat-select.mat-select-empty .mat-select-arrow-wrapper{transition:transform 400ms cubic-bezier(0.25, 0.8, 0.25, 1)}._mat-animation-noopable.mat-form-field-appearance-standard .mat-select.mat-select-empty .mat-select-arrow-wrapper{transition:none}.mat-select-arrow{width:0;height:0;border-left:5px solid transparent;border-right:5px solid transparent;border-top:5px solid;margin:0 4px}.mat-select-panel-wrap{flex-basis:100%}.mat-select-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;padding-top:0;padding-bottom:0;max-height:256px;min-width:100%;border-radius:4px;outline:0}.cdk-high-contrast-active .mat-select-panel{outline:solid 1px}.mat-select-panel .mat-optgroup-label,.mat-select-panel .mat-option{font-size:inherit;line-height:3em;height:3em}.mat-form-field-type-mat-select:not(.mat-form-field-disabled) .mat-form-field-flex{cursor:pointer}.mat-form-field-type-mat-select .mat-form-field-label{width:calc(100% - 18px)}.mat-select-placeholder{transition:color 400ms 133.3333333333ms cubic-bezier(0.25, 0.8, 0.25, 1)}._mat-animation-noopable .mat-select-placeholder{transition:none}.mat-form-field-hide-placeholder .mat-select-placeholder{color:transparent;-webkit-text-fill-color:transparent;transition:none;display:block}.mat-select-min-line:empty::before{content:" ";white-space:pre;width:1px;display:inline-block;opacity:0}\n'],encapsulation:2,data:{animation:[CG.transformPanelWrap,CG.transformPanel]},changeDetection:0}),AG.propDecorators={options:[{type:Ya,args:[BH,{descendants:!0}]}],optionGroups:[{type:Ya,args:[zH,{descendants:!0}]}],customTrigger:[{type:qa,args:[DG]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(AG,[{type:My,args:[{selector:"mat-select",exportAs:"matSelect",template:'\x3c!--\n Note that the select trigger element specifies `aria-owns` pointing to the listbox overlay.\n While aria-owns is not required for the ARIA 1.2 `role="combobox"` interaction pattern,\n it fixes an issue with VoiceOver when the select appears inside of an `aria-model="true"`\n element (e.g. a dialog). Without this `aria-owns`, the `aria-modal` on a dialog prevents\n VoiceOver from "seeing" the select\'s listbox overlay for aria-activedescendant.\n Using `aria-owns` re-parents the select overlay so that it works again.\n See https://github.com/angular/components/issues/20694\n--\x3e\n<div cdk-overlay-origin\n     [attr.aria-owns]="panelOpen ? id + \'-panel\' : null"\n     class="mat-select-trigger"\n     (click)="toggle()"\n     #origin="cdkOverlayOrigin"\n     #trigger>\n  <div class="mat-select-value" [ngSwitch]="empty" [attr.id]="_valueId">\n    <span class="mat-select-placeholder mat-select-min-line" *ngSwitchCase="true">{{placeholder}}</span>\n    <span class="mat-select-value-text" *ngSwitchCase="false" [ngSwitch]="!!customTrigger">\n      <span class="mat-select-min-line" *ngSwitchDefault>{{triggerValue}}</span>\n      <ng-content select="mat-select-trigger" *ngSwitchCase="true"></ng-content>\n    </span>\n  </div>\n\n  <div class="mat-select-arrow-wrapper"><div class="mat-select-arrow"></div></div>\n</div>\n\n<ng-template\n  cdk-connected-overlay\n  cdkConnectedOverlayLockPosition\n  cdkConnectedOverlayHasBackdrop\n  cdkConnectedOverlayBackdropClass="cdk-overlay-transparent-backdrop"\n  [cdkConnectedOverlayPanelClass]="_overlayPanelClass"\n  [cdkConnectedOverlayScrollStrategy]="_scrollStrategy"\n  [cdkConnectedOverlayOrigin]="origin"\n  [cdkConnectedOverlayOpen]="panelOpen"\n  [cdkConnectedOverlayPositions]="_positions"\n  [cdkConnectedOverlayMinWidth]="_triggerRect?.width!"\n  [cdkConnectedOverlayOffsetY]="_offsetY"\n  (backdropClick)="close()"\n  (attach)="_onAttached()"\n  (detach)="close()">\n  <div class="mat-select-panel-wrap" [@transformPanelWrap]>\n    <div\n      #panel\n      role="listbox"\n      tabindex="-1"\n      class="mat-select-panel {{ _getPanelTheme() }}"\n      [attr.id]="id + \'-panel\'"\n      [attr.aria-multiselectable]="multiple"\n      [attr.aria-label]="ariaLabel || null"\n      [attr.aria-labelledby]="_getPanelAriaLabelledby()"\n      [ngClass]="panelClass"\n      [@transformPanel]="multiple ? \'showing-multiple\' : \'showing\'"\n      (@transformPanel.done)="_panelDoneAnimatingStream.next($event.toState)"\n      [style.transformOrigin]="_transformOrigin"\n      [style.font-size.px]="_triggerFontSize"\n      (keydown)="_handleKeydown($event)">\n      <ng-content></ng-content>\n    </div>\n  </div>\n</ng-template>\n',inputs:["disabled","disableRipple","tabIndex"],encapsulation:Hn.None,changeDetection:zn.OnPush,host:{role:"combobox","aria-autocomplete":"none","aria-haspopup":"true",class:"mat-select","[attr.id]":"id","[attr.tabindex]":"tabIndex","[attr.aria-controls]":'panelOpen ? id + "-panel" : null',"[attr.aria-expanded]":"panelOpen","[attr.aria-label]":"ariaLabel || null","[attr.aria-required]":"required.toString()","[attr.aria-disabled]":"disabled.toString()","[attr.aria-invalid]":"errorState","[attr.aria-describedby]":"_ariaDescribedby || null","[attr.aria-activedescendant]":"_getAriaActiveDescendant()","[class.mat-select-disabled]":"disabled","[class.mat-select-invalid]":"errorState","[class.mat-select-required]":"required","[class.mat-select-empty]":"empty","[class.mat-select-multiple]":"multiple","(keydown)":"_handleKeydown($event)","(focus)":"_onFocus()","(blur)":"_onBlur()"},animations:[CG.transformPanelWrap,CG.transformPanel],providers:[{provide:bV,useExisting:AG},{provide:RH,useExisting:AG}],styles:['.mat-select{display:inline-block;width:100%;outline:none}.mat-select-trigger{display:inline-table;cursor:pointer;position:relative;box-sizing:border-box}.mat-select-disabled .mat-select-trigger{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-select-value{display:table-cell;max-width:0;width:100%;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.mat-select-value-text{white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.mat-select-arrow-wrapper{display:table-cell;vertical-align:middle}.mat-form-field-appearance-fill .mat-select-arrow-wrapper{transform:translateY(-50%)}.mat-form-field-appearance-outline .mat-select-arrow-wrapper{transform:translateY(-25%)}.mat-form-field-appearance-standard.mat-form-field-has-label .mat-select:not(.mat-select-empty) .mat-select-arrow-wrapper{transform:translateY(-50%)}.mat-form-field-appearance-standard .mat-select.mat-select-empty .mat-select-arrow-wrapper{transition:transform 400ms cubic-bezier(0.25, 0.8, 0.25, 1)}._mat-animation-noopable.mat-form-field-appearance-standard .mat-select.mat-select-empty .mat-select-arrow-wrapper{transition:none}.mat-select-arrow{width:0;height:0;border-left:5px solid transparent;border-right:5px solid transparent;border-top:5px solid;margin:0 4px}.mat-select-panel-wrap{flex-basis:100%}.mat-select-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;padding-top:0;padding-bottom:0;max-height:256px;min-width:100%;border-radius:4px;outline:0}.cdk-high-contrast-active .mat-select-panel{outline:solid 1px}.mat-select-panel .mat-optgroup-label,.mat-select-panel .mat-option{font-size:inherit;line-height:3em;height:3em}.mat-form-field-type-mat-select:not(.mat-form-field-disabled) .mat-form-field-flex{cursor:pointer}.mat-form-field-type-mat-select .mat-form-field-label{width:calc(100% - 18px)}.mat-select-placeholder{transition:color 400ms 133.3333333333ms cubic-bezier(0.25, 0.8, 0.25, 1)}._mat-animation-noopable .mat-select-placeholder{transition:none}.mat-form-field-hide-placeholder .mat-select-placeholder{color:transparent;-webkit-text-fill-color:transparent;transition:none;display:block}.mat-select-min-line:empty::before{content:" ";white-space:pre;width:1px;display:inline-block;opacity:0}\n']}]}],null,{options:[{type:Ya,args:[BH,{descendants:!0}]}],optionGroups:[{type:Ya,args:[zH,{descendants:!0}]}],customTrigger:[{type:qa,args:[DG]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class TG{}function NG(t,e){if(1&t){const t=Hm();Rm(0,"span",5),Vm("click",(function e(n){hi(t);const o=Ym().$implicit;return Ym().onActivePluginSelection(n,o.id)})),ku(1),Am()}if(2&t){const t=Ym().$implicit;jp("data-plugin-id",t.id),rc(1),Du(" ",t.tab_name," ")}}function zG(t,e){1&t&&(Rm(0,"mat-tab",3),Qp(1,NG,2,2,"ng-template",4),Am()),2&t&&Dm("disabled",!e.$implicit.enabled)}function IG(t,e){if(1&t&&(Rm(0,"mat-option",9),ku(1),Am()),2&t){const t=e.$implicit;Dm("value",t.id),jp("data-plugin-id",t.id),rc(1),Du(" ",t.tab_name," ")}}function HG(t,e){if(1&t){const t=Hm();Rm(0,"mat-form-field",6),Rm(1,"mat-label"),ku(2,"Inactive"),Am(),Rm(3,"mat-select",7),Vm("selectionChange",(function e(n){return hi(t),Ym().onDisabledPluginSelectionChanged(n)})),Qp(4,IG,2,3,"mat-option",8),Am(),Am()}if(2&t){const t=Ym();rc(3),Dm("value",t.selectedPlugin),rc(1),Dm("ngForOf",t.disabledPlugins)}}TG.ɵfac=function t(e){return new(e||TG)},TG.ɵmod=ao({type:TG}),TG.ɵinj=vn({providers:[PG],imports:[[WM,yL,UH,XI],yF,TV,UH,XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(TG,[{type:Ay,args:[{imports:[WM,yL,UH,XI],exports:[yF,TV,AG,EG,UH,XI],declarations:[AG,EG],providers:[PG]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(TG,{declarations:function(){return[AG,EG]},imports:function(){return[WM,yL,UH,XI]},exports:function(){return[yF,TV,AG,EG,UH,XI]}});class FG{constructor(){this.onPluginSelectionChanged=new Lh}getActivePluginIndex(){return this.activePlugins.findIndex((({id:t})=>t===this.selectedPlugin))}onActivePluginSelection(t,e){t.stopPropagation(),this.onPluginSelectionChanged.emit(e)}onDisabledPluginSelectionChanged(t){this.onPluginSelectionChanged.emit(t.value)}}FG.ɵfac=function t(e){return new(e||FG)},FG.ɵcmp=to({type:FG,selectors:[["plugin-selector-component"]],inputs:{activePlugins:"activePlugins",disabledPlugins:"disabledPlugins",selectedPlugin:"selectedPlugin"},outputs:{onPluginSelectionChanged:"onPluginSelectionChanged"},decls:3,vars:3,consts:[["animationDuration","100ms",1,"active-plugin-list",3,"selectedIndex"],[3,"disabled",4,"ngFor","ngForOf"],["floatLabel","never",4,"ngIf"],[3,"disabled"],["mat-tab-label",""],[1,"plugin-name",3,"click"],["floatLabel","never"],[3,"value","selectionChange"],[3,"value",4,"ngFor","ngForOf"],[3,"value"]],template:function t(e,n){1&e&&(Rm(0,"mat-tab-group",0),Qp(1,zG,2,1,"mat-tab",1),Am(),Qp(2,HG,5,2,"mat-form-field",2)),2&e&&(Dm("selectedIndex",n.getActivePluginIndex()),rc(1),Dm("ngForOf",n.activePlugins),rc(1),Dm("ngIf",n.disabledPlugins.length>0))},directives:[FB,lM,dM,SB,PB,AV,vV,AG,BH],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", 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.mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}input.mat-input-element[_ngcontent-%COMP%]{margin-top:-0.0625em}.mat-menu-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:400}.mat-paginator[_ngcontent-%COMP%], .mat-paginator-page-size[_ngcontent-%COMP%]   .mat-select-trigger[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px}.mat-radio-button[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action[_ngcontent-%COMP%]{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node[_ngcontent-%COMP%], .mat-nested-tree-node[_ngcontent-%COMP%]{font-weight:400;font-size:14px}.mat-ripple[_ngcontent-%COMP%]{overflow:hidden;position:relative}.mat-ripple[_ngcontent-%COMP%]:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded[_ngcontent-%COMP%]{overflow:visible}.mat-ripple-element[_ngcontent-%COMP%]{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms cubic-bezier(0, 0, 0.2, 1);transform:scale(0)}.cdk-high-contrast-active[_ngcontent-%COMP%]   .mat-ripple-element[_ngcontent-%COMP%]{display:none}.cdk-visually-hidden[_ngcontent-%COMP%]{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container[_ngcontent-%COMP%], .cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{align-items:center;display:flex;flex:1 1 auto;font-size:14px;height:100%;overflow:hidden}mat-form-field[_ngcontent-%COMP%]{flex:0 0;margin-top:5px;width:130px}mat-label[_ngcontent-%COMP%], mat-select[_ngcontent-%COMP%], mat-option[_ngcontent-%COMP%]{font-weight:500;text-transform:uppercase}.active-plugin-list[_ngcontent-%COMP%]{align-self:stretch;flex:1 1 auto;overflow:hidden}.plugin-name[_ngcontent-%COMP%]{align-items:center;display:inline-flex;height:100%;justify-content:center;padding:0 12px;width:100%}[_nghost-%COMP%]     .active-plugin-list.mat-primary .mat-tab-list .mat-ink-bar{background-color:currentColor}[_nghost-%COMP%]     .active-plugin-list .mat-tab-label, [_nghost-%COMP%]     .active-plugin-list .mat-tab-link{color:inherit;opacity:.7}[_nghost-%COMP%]     .active-plugin-list .mat-tab-label.mat-tab-label-active, [_nghost-%COMP%]     .active-plugin-list .mat-tab-link.mat-tab-label-active{opacity:1}[_nghost-%COMP%]     .active-plugin-list .mat-tab-header-pagination-chevron{border-color:currentColor}[_nghost-%COMP%]     .active-plugin-list .mat-tab-header-pagination-disabled{visibility:hidden}[_nghost-%COMP%]     .active-plugin-list .mat-tab-disabled{display:none}[_nghost-%COMP%]     .active-plugin-list mat-tab-list, [_nghost-%COMP%]     .active-plugin-list .mat-tab-header, [_nghost-%COMP%]     .active-plugin-list .mat-tab-labels, [_nghost-%COMP%]     .active-plugin-list .mat-tab-label{height:100%}[_nghost-%COMP%]     .active-plugin-list .mat-tab-label{min-width:48px;padding:0;text-transform:uppercase}[_nghost-%COMP%]     .active-plugin-list .mat-tab-label-content{height:100%}[_nghost-%COMP%]     .active-plugin-list mat-tab-header .mat-tab-list{padding:0 36px}[_nghost-%COMP%]     .active-plugin-list mat-tab-header>:first-child, [_nghost-%COMP%]     .active-plugin-list mat-tab-header>.mat-tab-label-container, [_nghost-%COMP%]     .active-plugin-list mat-tab-header>:last-child{bottom:0;position:absolute;top:0}[_nghost-%COMP%]     .active-plugin-list mat-tab-header>:first-child, [_nghost-%COMP%]     .active-plugin-list mat-tab-header>.mat-tab-label-container{left:0}[_nghost-%COMP%]     .active-plugin-list mat-tab-header>:last-child, [_nghost-%COMP%]     .active-plugin-list mat-tab-header>.mat-tab-label-container{right:0}[_nghost-%COMP%]     .active-plugin-list mat-tab-header>.mat-tab-header-pagination{background-color:#f57c00}body.dark-mode   [_nghost-%COMP%]     .active-plugin-list mat-tab-header>.mat-tab-header-pagination{background-color:#ef6c00}']}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(FG,[{type:My,args:[{selector:"plugin-selector-component",templateUrl:"./plugin_selector_component.ng.html",styleUrls:["./plugin_selector_component.css"]}]}],null,{activePlugins:[{type:xy}],disabledPlugins:[{type:xy}],selectedPlugin:[{type:xy}],onPluginSelectionChanged:[{type:Oy}]});const LG=Zw(vR,(t=>Object.keys(t).map((e=>Object.assign({},{id:e},t[e]))))),BG=Zw(LG,(t=>t.filter((t=>!t.enabled))));class VG{constructor(t){this.store=t,this.activePlugin$=this.store.pipe(Fw(MR)),this.plugins$=this.store.pipe(Fw(LG)),this.disabledPlugins$=this.store.pipe(Fw(BG))}onPluginSelectionChange(t){this.store.dispatch(_E({plugin:t}))}}function jG(t,e){}VG.ɵfac=function t(e){return new(e||VG)(Sm(Iw))},VG.ɵcmp=to({type:VG,selectors:[["plugin-selector"]],decls:4,vars:9,consts:[[3,"activePlugins","disabledPlugins","selectedPlugin","onPluginSelectionChanged"]],template:function t(e,n){1&e&&(Rm(0,"plugin-selector-component",0),Vm("onPluginSelectionChanged",(function t(e){return n.onPluginSelectionChange(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Am()),2&e&&Dm("activePlugins",Th(1,3,n.plugins$))("disabledPlugins",Th(2,5,n.disabledPlugins$))("selectedPlugin",Th(3,7,n.activePlugin$))},directives:[FG],pipes:[wM],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(VG,[{type:My,args:[{selector:"plugin-selector",template:'\n    <plugin-selector-component\n      [activePlugins]="plugins$ | async"\n      [disabledPlugins]="disabledPlugins$ | async"\n      [selectedPlugin]="activePlugin$ | async"\n      (onPluginSelectionChanged)="onPluginSelectionChange($event)"\n    ></plugin-selector-component>\n  '}]}],(function(){return[{type:Iw}]}),null);class UG{constructor(){this.role="dialog",this.panelClass="",this.hasBackdrop=!0,this.backdropClass="",this.disableClose=!1,this.width="",this.height="",this.maxWidth="80vw",this.data=null,this.ariaDescribedBy=null,this.ariaLabelledBy=null,this.ariaLabel=null,this.autoFocus=!0,this.restoreFocus=!0,this.closeOnNavigation=!0}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const GG={dialogContainer:nx("dialogContainer",[rx("void, exit",ax({opacity:0,transform:"scale(0.7)"})),rx("enter",ax({transform:"none"})),lx("* => enter",ox("150ms cubic-bezier(0, 0, 0.2, 1)",ax({transform:"none",opacity:1}))),lx("* => void, * => exit",ox("75ms cubic-bezier(0.4, 0.0, 0.2, 1)",ax({opacity:0})))])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function WG(){throw Error("Attempting to attach dialog content after content is already attached")}class YG extends PF{constructor(t,e,n,o,i,a){super(),this._elementRef=t,this._focusTrapFactory=e,this._changeDetectorRef=n,this._config=i,this._focusMonitor=a,this._animationStateChanged=new Lh,this._elementFocusedBeforeDialogWasOpened=null,this._closeInteractionType=null,this.attachDomPortal=t=>(this._portalOutlet.hasAttached()&&("undefined"==typeof ngDevMode||ngDevMode)&&WG(),this._portalOutlet.attachDomPortal(t)),this._ariaLabelledBy=i.ariaLabelledBy||null,this._document=o}_initializeWithAttachedContent(){this._setupFocusTrap(),this._capturePreviouslyFocusedElement(),this._focusDialogContainer()}attachComponentPortal(t){return this._portalOutlet.hasAttached()&&("undefined"==typeof ngDevMode||ngDevMode)&&WG(),this._portalOutlet.attachComponentPortal(t)}attachTemplatePortal(t){return this._portalOutlet.hasAttached()&&("undefined"==typeof ngDevMode||ngDevMode)&&WG(),this._portalOutlet.attachTemplatePortal(t)}_recaptureFocus(){this._containsFocus()||(!this._config.autoFocus||!this._focusTrap.focusInitialElement())&&this._elementRef.nativeElement.focus()}_trapFocus(){this._config.autoFocus?this._focusTrap.focusInitialElementWhenReady():this._containsFocus()||this._elementRef.nativeElement.focus()}_restoreFocus(){const t=this._elementFocusedBeforeDialogWasOpened;if(this._config.restoreFocus&&t&&"function"==typeof t.focus){const e=Fz(),n=this._elementRef.nativeElement;e&&e!==this._document.body&&e!==n&&!n.contains(e)||(this._focusMonitor?(this._focusMonitor.focusVia(t,this._closeInteractionType),this._closeInteractionType=null):t.focus())}this._focusTrap&&this._focusTrap.destroy()}_setupFocusTrap(){this._focusTrap=this._focusTrapFactory.create(this._elementRef.nativeElement)}_capturePreviouslyFocusedElement(){this._document&&(this._elementFocusedBeforeDialogWasOpened=Fz())}_focusDialogContainer(){this._elementRef.nativeElement.focus&&this._elementRef.nativeElement.focus()}_containsFocus(){const t=this._elementRef.nativeElement,e=Fz();return t===e||t.contains(e)}}YG.ɵfac=function t(e){return new(e||YG)(Sm(hg),Sm(rI),Sm(Ug),Sm(Z_,8),Sm(UG),Sm(SI))},YG.ɵdir=lo({type:YG,viewQuery:function t(e,n){if(1&e&&Qh(DF,7),2&e){let t;Jh(t=tb())&&(n._portalOutlet=t.first)}},features:[xp]}),YG.ctorParameters=()=>[{type:hg},{type:rI},{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:UG},{type:SI}],YG.propDecorators={_portalOutlet:[{type:Za,args:[DF,{static:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(YG,[{type:Cy}],(function(){return[{type:hg},{type:rI},{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:UG},{type:SI}]}),{_portalOutlet:[{type:Za,args:[DF,{static:!0}]}]});class qG extends YG{constructor(){super(...arguments),this._state="enter"}_onAnimationDone({toState:t,totalTime:e}){"enter"===t?(this._trapFocus(),this._animationStateChanged.next({state:"opened",totalTime:e})):"exit"===t&&(this._restoreFocus(),this._animationStateChanged.next({state:"closed",totalTime:e}))}_onAnimationStart({toState:t,totalTime:e}){"enter"===t?this._animationStateChanged.next({state:"opening",totalTime:e}):"exit"!==t&&"void"!==t||this._animationStateChanged.next({state:"closing",totalTime:e})}_startExitAnimation(){this._state="exit",this._changeDetectorRef.markForCheck()}}qG.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(qG)))(n||qG)}})(),qG.ɵcmp=to({type:qG,selectors:[["mat-dialog-container"]],hostAttrs:["tabindex","-1","aria-modal","true",1,"mat-dialog-container"],hostVars:6,hostBindings:function t(e,n){1&e&&jm("@dialogContainer.start",(function t(e){return n._onAnimationStart(e)}))("@dialogContainer.done",(function t(e){return n._onAnimationDone(e)})),2&e&&(Tu("id",n._id),jp("role",n._config.role)("aria-labelledby",n._config.ariaLabel?null:n._ariaLabelledBy)("aria-label",n._config.ariaLabel)("aria-describedby",n._config.ariaDescribedBy||null),Nu("@dialogContainer",n._state))},features:[xp],decls:1,vars:0,consts:[["cdkPortalOutlet",""]],template:function t(e,n){1&e&&Qp(0,jG,0,0,"ng-template",0)},directives:[DF],styles:[".mat-dialog-container{display:block;padding:24px;border-radius:4px;box-sizing:border-box;overflow:auto;outline:0;width:100%;height:100%;min-height:inherit;max-height:inherit}.cdk-high-contrast-active .mat-dialog-container{outline:solid 1px}.mat-dialog-content{display:block;margin:0 -24px;padding:0 24px;max-height:65vh;overflow:auto;-webkit-overflow-scrolling:touch}.mat-dialog-title{margin:0 0 20px;display:block}.mat-dialog-actions{padding:8px 0;display:flex;flex-wrap:wrap;min-height:52px;align-items:center;box-sizing:content-box;margin-bottom:-24px}.mat-dialog-actions[align=end]{justify-content:flex-end}.mat-dialog-actions[align=center]{justify-content:center}.mat-dialog-actions .mat-button-base+.mat-button-base,.mat-dialog-actions .mat-mdc-button-base+.mat-mdc-button-base{margin-left:8px}[dir=rtl] .mat-dialog-actions .mat-button-base+.mat-button-base,[dir=rtl] .mat-dialog-actions .mat-mdc-button-base+.mat-mdc-button-base{margin-left:0;margin-right:8px}\n"],encapsulation:2,data:{animation:[GG.dialogContainer]}}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(qG,[{type:My,args:[{selector:"mat-dialog-container",template:"<ng-template cdkPortalOutlet></ng-template>\n",encapsulation:Hn.None,changeDetection:zn.Default,animations:[GG.dialogContainer],host:{class:"mat-dialog-container",tabindex:"-1","aria-modal":"true","[id]":"_id","[attr.role]":"_config.role","[attr.aria-labelledby]":"_config.ariaLabel ? null : _ariaLabelledBy","[attr.aria-label]":"_config.ariaLabel","[attr.aria-describedby]":"_config.ariaDescribedBy || null","[@dialogContainer]":"_state","(@dialogContainer.start)":"_onAnimationStart($event)","(@dialogContainer.done)":"_onAnimationDone($event)"},styles:[".mat-dialog-container{display:block;padding:24px;border-radius:4px;box-sizing:border-box;overflow:auto;outline:0;width:100%;height:100%;min-height:inherit;max-height:inherit}.cdk-high-contrast-active .mat-dialog-container{outline:solid 1px}.mat-dialog-content{display:block;margin:0 -24px;padding:0 24px;max-height:65vh;overflow:auto;-webkit-overflow-scrolling:touch}.mat-dialog-title{margin:0 0 20px;display:block}.mat-dialog-actions{padding:8px 0;display:flex;flex-wrap:wrap;min-height:52px;align-items:center;box-sizing:content-box;margin-bottom:-24px}.mat-dialog-actions[align=end]{justify-content:flex-end}.mat-dialog-actions[align=center]{justify-content:center}.mat-dialog-actions .mat-button-base+.mat-button-base,.mat-dialog-actions .mat-mdc-button-base+.mat-mdc-button-base{margin-left:8px}[dir=rtl] .mat-dialog-actions .mat-button-base+.mat-button-base,[dir=rtl] .mat-dialog-actions .mat-mdc-button-base+.mat-mdc-button-base{margin-left:0;margin-right:8px}\n"]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let ZG=0;class XG{constructor(t,e,n="mat-dialog-"+ZG++){this._overlayRef=t,this._containerInstance=e,this.id=n,this.disableClose=this._containerInstance._config.disableClose,this._afterOpened=new I,this._afterClosed=new I,this._beforeClosed=new I,this._state=0,e._id=n,e._animationStateChanged.pipe(ce((t=>"opened"===t.state)),be(1)).subscribe((()=>{this._afterOpened.next(),this._afterOpened.complete()})),e._animationStateChanged.pipe(ce((t=>"closed"===t.state)),be(1)).subscribe((()=>{clearTimeout(this._closeFallbackTimeout),this._finishDialogClose()})),t.detachments().subscribe((()=>{this._beforeClosed.next(this._result),this._beforeClosed.complete(),this._afterClosed.next(this._result),this._afterClosed.complete(),this.componentInstance=null,this._overlayRef.dispose()})),t.keydownEvents().pipe(ce((t=>t.keyCode===uz&&!this.disableClose&&!bz(t)))).subscribe((t=>{t.preventDefault(),KG(this,"keyboard")})),t.backdropClick().subscribe((()=>{this.disableClose?this._containerInstance._recaptureFocus():KG(this,"mouse")}))}close(t){this._result=t,this._containerInstance._animationStateChanged.pipe(ce((t=>"closing"===t.state)),be(1)).subscribe((e=>{this._beforeClosed.next(t),this._beforeClosed.complete(),this._overlayRef.detachBackdrop(),this._closeFallbackTimeout=setTimeout((()=>this._finishDialogClose()),e.totalTime+100)})),this._state=1,this._containerInstance._startExitAnimation()}afterOpened(){return this._afterOpened}afterClosed(){return this._afterClosed}beforeClosed(){return this._beforeClosed}backdropClick(){return this._overlayRef.backdropClick()}keydownEvents(){return this._overlayRef.keydownEvents()}updatePosition(t){let e=this._getPositionStrategy();return t&&(t.left||t.right)?t.left?e.left(t.left):e.right(t.right):e.centerHorizontally(),t&&(t.top||t.bottom)?t.top?e.top(t.top):e.bottom(t.bottom):e.centerVertically(),this._overlayRef.updatePosition(),this}updateSize(t="",e=""){return this._overlayRef.updateSize({width:t,height:e}),this._overlayRef.updatePosition(),this}addPanelClass(t){return this._overlayRef.addPanelClass(t),this}removePanelClass(t){return this._overlayRef.removePanelClass(t),this}getState(){return this._state}_finishDialogClose(){this._state=2,this._overlayRef.dispose()}_getPositionStrategy(){return this._overlayRef.getConfig().positionStrategy}}function KG(t,e,n){return void 0!==t._containerInstance&&(t._containerInstance._closeInteractionType=e),t.close(n)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const JG=new Ga("MatDialogData"),QG=new Ga("mat-dialog-default-options"),$G=new Ga("mat-dialog-scroll-strategy"),tW={provide:$G,deps:[pL],useFactory:function eW(t){return()=>t.scrollStrategies.block()}};class nW{constructor(t,e,n,o,i,a,r,s,l){this._overlay=t,this._injector=e,this._defaultOptions=n,this._parentDialog=o,this._overlayContainer=i,this._dialogRefConstructor=r,this._dialogContainerType=s,this._dialogDataToken=l,this._openDialogsAtThisLevel=[],this._afterAllClosedAtThisLevel=new I,this._afterOpenedAtThisLevel=new I,this._ariaHiddenElements=new Map,this.afterAllClosed=Qt((()=>this.openDialogs.length?this._getAfterAllClosed():this._getAfterAllClosed().pipe(Ne(void 0)))),this._scrollStrategy=a}get openDialogs(){return this._parentDialog?this._parentDialog.openDialogs:this._openDialogsAtThisLevel}get afterOpened(){return this._parentDialog?this._parentDialog.afterOpened:this._afterOpenedAtThisLevel}_getAfterAllClosed(){const t=this._parentDialog;return t?t._getAfterAllClosed():this._afterAllClosedAtThisLevel}open(t,e){if((e=(function n(t,e){return Object.assign(Object.assign({},e),t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(e,this._defaultOptions||new UG)).id&&this.getDialogById(e.id)&&("undefined"==typeof ngDevMode||ngDevMode))throw Error(`Dialog with id "${e.id}" exists already. The dialog id must be unique.`);const o=this._createOverlay(e),i=this._attachDialogContainer(o,e),a=this._attachDialogContent(t,i,o,e);return this.openDialogs.length||this._hideNonDialogContentFromAssistiveTechnology(),this.openDialogs.push(a),a.afterClosed().subscribe((()=>this._removeOpenDialog(a))),this.afterOpened.next(a),i._initializeWithAttachedContent(),a}closeAll(){this._closeDialogs(this.openDialogs)}getDialogById(t){return this.openDialogs.find((e=>e.id===t))}ngOnDestroy(){this._closeDialogs(this._openDialogsAtThisLevel),this._afterAllClosedAtThisLevel.complete(),this._afterOpenedAtThisLevel.complete()}_createOverlay(t){const e=this._getOverlayConfig(t);return this._overlay.create(e)}_getOverlayConfig(t){const e=new VF({positionStrategy:this._overlay.position().global(),scrollStrategy:t.scrollStrategy||this._scrollStrategy(),panelClass:t.panelClass,hasBackdrop:t.hasBackdrop,direction:t.direction,minWidth:t.minWidth,minHeight:t.minHeight,maxWidth:t.maxWidth,maxHeight:t.maxHeight,disposeOnNavigation:t.closeOnNavigation});return t.backdropClass&&(e.backdropClass=t.backdropClass),e}_attachDialogContainer(t,e){const n=rp.create({parent:e&&e.viewContainerRef&&e.viewContainerRef.injector||this._injector,providers:[{provide:UG,useValue:e}]}),o=new vF(this._dialogContainerType,e.viewContainerRef,n,e.componentFactoryResolver);return t.attach(o).instance}_attachDialogContent(t,e,n,o){const i=new this._dialogRefConstructor(n,e,o.id);if(t instanceof Xg)e.attachTemplatePortal(new xF(t,null,{$implicit:o.data,dialogRef:i}));else{const n=this._createInjector(o,i,e),a=e.attachComponentPortal(new vF(t,o.viewContainerRef,n));i.componentInstance=a.instance}return i.updateSize(o.width,o.height).updatePosition(o.position),i}_createInjector(t,e,n){const o=t&&t.viewContainerRef&&t.viewContainerRef.injector,i=[{provide:this._dialogContainerType,useValue:n},{provide:this._dialogDataToken,useValue:t.data},{provide:this._dialogRefConstructor,useValue:e}];return!t.direction||o&&o.get(HI,null,En.Optional)||i.push({provide:HI,useValue:{value:t.direction,change:Et()}}),rp.create({parent:o||this._injector,providers:i})}_removeOpenDialog(t){const e=this.openDialogs.indexOf(t);e>-1&&(this.openDialogs.splice(e,1),this.openDialogs.length||(this._ariaHiddenElements.forEach(((t,e)=>{t?e.setAttribute("aria-hidden",t):e.removeAttribute("aria-hidden")})),this._ariaHiddenElements.clear(),this._getAfterAllClosed().next()))}_hideNonDialogContentFromAssistiveTechnology(){const t=this._overlayContainer.getContainerElement();if(t.parentElement){const e=t.parentElement.children;for(let n=e.length-1;n>-1;n--){let o=e[n];o===t||"SCRIPT"===o.nodeName||"STYLE"===o.nodeName||o.hasAttribute("aria-live")||(this._ariaHiddenElements.set(o,o.getAttribute("aria-hidden")),o.setAttribute("aria-hidden","true"))}}}_closeDialogs(t){let e=t.length;for(;e--;)t[e].close()}}nW.ɵfac=function t(e){return new(e||nW)(Sm(pL),Sm(rp),Sm(void 0),Sm(void 0),Sm(QF),Sm(void 0),Sm(Qa),Sm(Qa),Sm(Ga))},nW.ɵdir=lo({type:nW}),nW.ctorParameters=()=>[{type:pL},{type:rp},{type:void 0},{type:void 0},{type:QF},{type:void 0},{type:Qa},{type:Qa},{type:Ga}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(nW,[{type:Cy}],(function(){return[{type:pL},{type:rp},{type:void 0},{type:void 0},{type:QF},{type:void 0},{type:Qa},{type:Qa},{type:Ga}]}),null);class oW extends nW{constructor(t,e,n,o,i,a,r){super(t,e,o,a,r,i,XG,qG,JG)}}oW.ɵfac=function t(e){return new(e||oW)(vr(pL),vr(rp),vr(lC,8),vr(QG,8),vr($G),vr(oW,12),vr(QF))},oW.ɵprov=Mn({token:oW,factory:oW.ɵfac}),oW.ctorParameters=()=>[{type:pL},{type:rp},{type:lC,decorators:[{type:Sr}]},{type:UG,decorators:[{type:Sr},{type:kr,args:[QG]}]},{type:void 0,decorators:[{type:kr,args:[$G]}]},{type:oW,decorators:[{type:Sr},{type:Er}]},{type:QF}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(oW,[{type:im}],(function(){return[{type:pL},{type:rp},{type:lC,decorators:[{type:Sr}]},{type:UG,decorators:[{type:Sr},{type:kr,args:[QG]}]},{type:void 0,decorators:[{type:kr,args:[$G]}]},{type:oW,decorators:[{type:Sr},{type:Er}]},{type:QF}]}),null);let iW=0;class aW{constructor(t,e,n){this.dialogRef=t,this._elementRef=e,this._dialog=n,this.type="button"}ngOnInit(){this.dialogRef||(this.dialogRef=cW(this._elementRef,this._dialog.openDialogs))}ngOnChanges(t){const e=t._matDialogClose||t._matDialogCloseResult;e&&(this.dialogResult=e.currentValue)}_onButtonClick(t){KG(this.dialogRef,0===t.screenX&&0===t.screenY?"keyboard":"mouse",this.dialogResult)}}aW.ɵfac=function t(e){return new(e||aW)(Sm(XG,8),Sm(hg),Sm(oW))},aW.ɵdir=lo({type:aW,selectors:[["","mat-dialog-close",""],["","matDialogClose",""]],hostVars:2,hostBindings:function t(e,n){1&e&&Vm("click",(function t(e){return n._onButtonClick(e)})),2&e&&jp("aria-label",n.ariaLabel||null)("type",n.type)},inputs:{type:"type",dialogResult:["mat-dialog-close","dialogResult"],ariaLabel:["aria-label","ariaLabel"],_matDialogClose:["matDialogClose","_matDialogClose"]},exportAs:["matDialogClose"],features:[Bo]}),aW.ctorParameters=()=>[{type:XG,decorators:[{type:Sr}]},{type:hg},{type:oW}],aW.propDecorators={ariaLabel:[{type:xy,args:["aria-label"]}],type:[{type:xy}],dialogResult:[{type:xy,args:["mat-dialog-close"]}],_matDialogClose:[{type:xy,args:["matDialogClose"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(aW,[{type:Cy,args:[{selector:"[mat-dialog-close], [matDialogClose]",exportAs:"matDialogClose",host:{"(click)":"_onButtonClick($event)","[attr.aria-label]":"ariaLabel || null","[attr.type]":"type"}}]}],(function(){return[{type:XG,decorators:[{type:Sr}]},{type:hg},{type:oW}]}),{type:[{type:xy}],dialogResult:[{type:xy,args:["mat-dialog-close"]}],ariaLabel:[{type:xy,args:["aria-label"]}],_matDialogClose:[{type:xy,args:["matDialogClose"]}]});class rW{constructor(t,e,n){this._dialogRef=t,this._elementRef=e,this._dialog=n,this.id="mat-dialog-title-"+iW++}ngOnInit(){this._dialogRef||(this._dialogRef=cW(this._elementRef,this._dialog.openDialogs)),this._dialogRef&&Promise.resolve().then((()=>{const t=this._dialogRef._containerInstance;t&&!t._ariaLabelledBy&&(t._ariaLabelledBy=this.id)}))}}rW.ɵfac=function t(e){return new(e||rW)(Sm(XG,8),Sm(hg),Sm(oW))},rW.ɵdir=lo({type:rW,selectors:[["","mat-dialog-title",""],["","matDialogTitle",""]],hostAttrs:[1,"mat-dialog-title"],hostVars:1,hostBindings:function t(e,n){2&e&&Tu("id",n.id)},inputs:{id:"id"},exportAs:["matDialogTitle"]}),rW.ctorParameters=()=>[{type:XG,decorators:[{type:Sr}]},{type:hg},{type:oW}],rW.propDecorators={id:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(rW,[{type:Cy,args:[{selector:"[mat-dialog-title], [matDialogTitle]",exportAs:"matDialogTitle",host:{class:"mat-dialog-title","[id]":"id"}}]}],(function(){return[{type:XG,decorators:[{type:Sr}]},{type:hg},{type:oW}]}),{id:[{type:xy}]});class sW{}sW.ɵfac=function t(e){return new(e||sW)},sW.ɵdir=lo({type:sW,selectors:[["","mat-dialog-content",""],["mat-dialog-content"],["","matDialogContent",""]],hostAttrs:[1,"mat-dialog-content"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(sW,[{type:Cy,args:[{selector:"[mat-dialog-content], mat-dialog-content, [matDialogContent]",host:{class:"mat-dialog-content"}}]}],null,null);class lW{}function cW(t,e){let n=t.nativeElement.parentElement;for(;n&&!n.classList.contains("mat-dialog-container");)n=n.parentElement;return n?e.find((t=>t.id===n.id)):null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */lW.ɵfac=function t(e){return new(e||lW)},lW.ɵdir=lo({type:lW,selectors:[["","mat-dialog-actions",""],["mat-dialog-actions"],["","matDialogActions",""]],hostAttrs:[1,"mat-dialog-actions"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lW,[{type:Cy,args:[{selector:"[mat-dialog-actions], mat-dialog-actions, [matDialogActions]",host:{class:"mat-dialog-actions"}}]}],null,null);class dW{}dW.ɵfac=function t(e){return new(e||dW)},dW.ɵmod=ao({type:dW}),dW.ɵinj=vn({providers:[oW,tW],imports:[[yL,RF,XI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(dW,[{type:Ay,args:[{imports:[yL,RF,XI],exports:[qG,aW,rW,sW,lW,XI],declarations:[qG,aW,rW,lW,sW],providers:[oW,tW],entryComponents:[qG]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(dW,{declarations:function(){return[qG,aW,rW,lW,sW]},imports:function(){return[yL,RF,XI]},exports:function(){return[qG,aW,rW,sW,lW,XI]}});class pW{constructor(t,e){this._document=e;const n=this._textarea=this._document.createElement("textarea"),o=n.style;o.position="fixed",o.top=o.opacity="0",o.left="-999em",n.setAttribute("aria-hidden","true"),n.value=t,this._document.body.appendChild(n)}copy(){const t=this._textarea;let e=!1;try{if(t){const n=this._document.activeElement;t.select(),t.setSelectionRange(0,t.value.length),e=this._document.execCommand("copy"),n&&n.focus()}}catch(t){}return e}destroy(){const t=this._textarea;t&&(t.parentNode&&t.parentNode.removeChild(t),this._textarea=void 0)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class mW{constructor(t){this._document=t}copy(t){const e=this.beginCopy(t),n=e.copy();return e.destroy(),n}beginCopy(t){return new pW(t,this._document)}}mW.ɵfac=function t(e){return new(e||mW)(vr(Z_))},mW.ɵprov=Mn({factory:function t(){return new mW(vr(Z_))},token:mW,providedIn:"root"}),mW.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(mW,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const uW=new Ga("CDK_COPY_TO_CLIPBOARD_CONFIG");class fW{constructor(t,e,n){this._clipboard=t,this._ngZone=e,this.text="",this.attempts=1,this.copied=new Lh,this._pending=new Set,n&&null!=n.attempts&&(this.attempts=n.attempts)}copy(t=this.attempts){if(t>1){let e=t;const n=this._clipboard.beginCopy(this.text);this._pending.add(n);const o=()=>{const t=n.copy();t||!--e||this._destroyed?(this._currentTimeout=null,this._pending.delete(n),n.destroy(),this.copied.emit(t)):this._currentTimeout=this._ngZone.runOutsideAngular((()=>setTimeout(o,1)))};o()}else this.copied.emit(this._clipboard.copy(this.text))}ngOnDestroy(){this._currentTimeout&&clearTimeout(this._currentTimeout),this._pending.forEach((t=>t.destroy())),this._pending.clear(),this._destroyed=!0}}fW.ɵfac=function t(e){return new(e||fW)(Sm(mW),Sm(a_),Sm(uW,8))},fW.ɵdir=lo({type:fW,selectors:[["","cdkCopyToClipboard",""]],hostBindings:function t(e,n){1&e&&Vm("click",(function t(){return n.copy()}))},inputs:{text:["cdkCopyToClipboard","text"],attempts:["cdkCopyToClipboardAttempts","attempts"]},outputs:{copied:"cdkCopyToClipboardCopied"}}),fW.ctorParameters=()=>[{type:mW},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[uW]}]}],fW.propDecorators={text:[{type:xy,args:["cdkCopyToClipboard"]}],attempts:[{type:xy,args:["cdkCopyToClipboardAttempts"]}],copied:[{type:Oy,args:["cdkCopyToClipboardCopied"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(fW,[{type:Cy,args:[{selector:"[cdkCopyToClipboard]",host:{"(click)":"copy()"}}]}],(function(){return[{type:mW},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[uW]}]}]}),{text:[{type:xy,args:["cdkCopyToClipboard"]}],attempts:[{type:xy,args:["cdkCopyToClipboardAttempts"]}],copied:[{type:Oy,args:["cdkCopyToClipboardCopied"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class gW{}function hW(t){return Error(`Unable to find icon with the name "${t}"`)}function bW(t){return Error(`The URL provided to MatIconRegistry was not trusted as a resource URL via Angular's DomSanitizer. Attempted URL was "${t}".`)}function yW(t){return Error(`The literal provided to MatIconRegistry was not trusted as safe HTML by Angular's DomSanitizer. Attempted literal was "${t}".`)}gW.ɵfac=function t(e){return new(e||gW)},gW.ɵmod=ao({type:gW}),gW.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(gW,[{type:Ay,args:[{declarations:[fW],exports:[fW]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(gW,{declarations:[fW],exports:[fW]});class _W{constructor(t,e,n){this.url=t,this.svgText=e,this.options=n}}class CW{constructor(t,e,n,o){this._httpClient=t,this._sanitizer=e,this._errorHandler=o,this._svgIconConfigs=new Map,this._iconSetConfigs=new Map,this._cachedIconsByUrl=new Map,this._inProgressUrlFetches=new Map,this._fontCssClassesByAlias=new Map,this._resolvers=[],this._defaultFontSetClass="material-icons",this._document=n}addSvgIcon(t,e,n){return this.addSvgIconInNamespace("",t,e,n)}addSvgIconLiteral(t,e,n){return this.addSvgIconLiteralInNamespace("",t,e,n)}addSvgIconInNamespace(t,e,n,o){return this._addSvgIconConfig(t,e,new _W(n,null,o))}addSvgIconResolver(t){return this._resolvers.push(t),this}addSvgIconLiteralInNamespace(t,e,n,o){const i=this._sanitizer.sanitize(As.HTML,n);if(!i)throw yW(n);return this._addSvgIconConfig(t,e,new _W("",i,o))}addSvgIconSet(t,e){return this.addSvgIconSetInNamespace("",t,e)}addSvgIconSetLiteral(t,e){return this.addSvgIconSetLiteralInNamespace("",t,e)}addSvgIconSetInNamespace(t,e,n){return this._addSvgIconSetConfig(t,new _W(e,null,n))}addSvgIconSetLiteralInNamespace(t,e,n){const o=this._sanitizer.sanitize(As.HTML,e);if(!o)throw yW(e);return this._addSvgIconSetConfig(t,new _W("",o,n))}registerFontClassAlias(t,e=t){return this._fontCssClassesByAlias.set(t,e),this}classNameForFontAlias(t){return this._fontCssClassesByAlias.get(t)||t}setDefaultFontSetClass(t){return this._defaultFontSetClass=t,this}getDefaultFontSetClass(){return this._defaultFontSetClass}getSvgIconFromUrl(t){const e=this._sanitizer.sanitize(As.RESOURCE_URL,t);if(!e)throw bW(t);const n=this._cachedIconsByUrl.get(e);return n?Et(MW(n)):this._loadSvgIconFromConfig(new _W(t,null)).pipe(Fe((t=>this._cachedIconsByUrl.set(e,t))),It((t=>MW(t))))}getNamedSvgIcon(t,e=""){const n=vW(e,t);let o=this._svgIconConfigs.get(n);if(o)return this._getSvgFromConfig(o);if(o=this._getIconConfigFromResolvers(e,t),o)return this._svgIconConfigs.set(n,o),this._getSvgFromConfig(o);const i=this._iconSetConfigs.get(e);return i?this._getSvgFromIconSetConfigs(t,i):Rt(hW(n))}ngOnDestroy(){this._resolvers=[],this._svgIconConfigs.clear(),this._iconSetConfigs.clear(),this._cachedIconsByUrl.clear()}_getSvgFromConfig(t){return t.svgText?Et(MW(this._svgElementFromConfig(t))):this._loadSvgIconFromConfig(t).pipe(It((t=>MW(t))))}_getSvgFromIconSetConfigs(t,e){const n=this._extractIconWithNameFromAnySet(t,e);return n?Et(n):$t(e.filter((t=>!t.svgText)).map((t=>this._loadSvgIconSetFromConfig(t).pipe(pe((e=>{const n=this._sanitizer.sanitize(As.RESOURCE_URL,t.url);return this._errorHandler.handleError(new Error(`Loading icon set URL: ${n} failed: ${e.message}`)),Et(null)})))))).pipe(It((()=>{const n=this._extractIconWithNameFromAnySet(t,e);if(!n)throw hW(t);return n})))}_extractIconWithNameFromAnySet(t,e){for(let n=e.length-1;n>=0;n--){const o=e[n];if(o.svgText&&o.svgText.indexOf(t)>-1){const e=this._svgElementFromConfig(o),n=this._extractSvgIconFromSet(e,t,o.options);if(n)return n}}return null}_loadSvgIconFromConfig(t){return this._fetchIcon(t).pipe(Fe((e=>t.svgText=e)),It((()=>this._svgElementFromConfig(t))))}_loadSvgIconSetFromConfig(t){return t.svgText?Et(null):this._fetchIcon(t).pipe(Fe((e=>t.svgText=e)))}_extractSvgIconFromSet(t,e,n){const o=t.querySelector(`[id="${e}"]`);if(!o)return null;const i=o.cloneNode(!0);if(i.removeAttribute("id"),"svg"===i.nodeName.toLowerCase())return this._setSvgAttributes(i,n);if("symbol"===i.nodeName.toLowerCase())return this._setSvgAttributes(this._toSvgElement(i),n);const a=this._svgElementFromString("<svg></svg>");return a.appendChild(i),this._setSvgAttributes(a,n)}_svgElementFromString(t){const e=this._document.createElement("DIV");e.innerHTML=t;const n=e.querySelector("svg");if(!n)throw Error("<svg> tag not found");return n}_toSvgElement(t){const e=this._svgElementFromString("<svg></svg>"),n=t.attributes;for(let t=0;t<n.length;t++){const{name:o,value:i}=n[t];"id"!==o&&e.setAttribute(o,i)}for(let n=0;n<t.childNodes.length;n++)t.childNodes[n].nodeType===this._document.ELEMENT_NODE&&e.appendChild(t.childNodes[n].cloneNode(!0));return e}_setSvgAttributes(t,e){return t.setAttribute("fit",""),t.setAttribute("height","100%"),t.setAttribute("width","100%"),t.setAttribute("preserveAspectRatio","xMidYMid meet"),t.setAttribute("focusable","false"),e&&e.viewBox&&t.setAttribute("viewBox",e.viewBox),t}_fetchIcon(t){var e;const{url:n,options:o}=t,i=null!==(e=null==o?void 0:o.withCredentials)&&void 0!==e&&e;if(!this._httpClient)throw(function a(){return Error("Could not find HttpClient provider for use with Angular Material icons. Please include the HttpClientModule from @angular/common/http in your app imports.")})();if(null==n)throw Error(`Cannot fetch icon from URL "${n}".`);const r=this._sanitizer.sanitize(As.RESOURCE_URL,n);if(!r)throw bW(n);const s=this._inProgressUrlFetches.get(r);if(s)return s;const l=this._httpClient.get(r,{responseType:"text",withCredentials:i}).pipe((function c(t){return R((function(e,n){try{e.subscribe(n)}finally{n.add(t)}}))})((()=>this._inProgressUrlFetches.delete(r))),Ee());return this._inProgressUrlFetches.set(r,l),l}_addSvgIconConfig(t,e,n){return this._svgIconConfigs.set(vW(t,e),n),this}_addSvgIconSetConfig(t,e){const n=this._iconSetConfigs.get(t);return n?n.push(e):this._iconSetConfigs.set(t,[e]),this}_svgElementFromConfig(t){if(!t.svgElement){const e=this._svgElementFromString(t.svgText);this._setSvgAttributes(e,t.options),t.svgElement=e}return t.svgElement}_getIconConfigFromResolvers(t,e){for(let o=0;o<this._resolvers.length;o++){const i=this._resolvers[o](e,t);if(i)return(n=i).url&&n.options?new _W(i.url,null,i.options):new _W(i,null)}var n;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */}}function MW(t){return t.cloneNode(!0)}function vW(t,e){return t+":"+e}CW.ɵfac=function t(e){return new(e||CW)(vr(PD,8),vr(zv),vr(Z_,8),vr(Zs))},CW.ɵprov=Mn({factory:function t(){return new CW(vr(PD,8),vr(zv),vr(Z_,8),vr(Zs))},token:CW,providedIn:"root"}),CW.ctorParameters=()=>[{type:PD,decorators:[{type:Sr}]},{type:zv},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:Zs}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(CW,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:PD,decorators:[{type:Sr}]},{type:zv},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:Zs}]}),null),new Sr,new Er,new Sr,new Sr;const xW=JI(class{constructor(t){this._elementRef=t}}),OW=new Ga("mat-icon-location",{providedIn:"root",factory:function PW(){const t=Or(Z_),e=t?t.location:null;return{getPathname:()=>e?e.pathname+e.search:""}}}),wW=["clip-path","color-profile","src","cursor","fill","filter","marker","marker-start","marker-mid","marker-end","mask","stroke"],kW=wW.map((t=>`[${t}]`)).join(", "),SW=/^url\(['"]?#(.*?)['"]?\)$/;class DW extends xW{constructor(t,e,n,o,i){super(t),this._iconRegistry=e,this._location=o,this._errorHandler=i,this._inline=!1,this._currentIconFetch=m.EMPTY,n||t.nativeElement.setAttribute("aria-hidden","true")}get inline(){return this._inline}set inline(t){this._inline=yz(t)}get svgIcon(){return this._svgIcon}set svgIcon(t){t!==this._svgIcon&&(t?this._updateSvgIcon(t):this._svgIcon&&this._clearSvgElement(),this._svgIcon=t)}get fontSet(){return this._fontSet}set fontSet(t){const e=this._cleanupFontValue(t);e!==this._fontSet&&(this._fontSet=e,this._updateFontIconClasses())}get fontIcon(){return this._fontIcon}set fontIcon(t){const e=this._cleanupFontValue(t);e!==this._fontIcon&&(this._fontIcon=e,this._updateFontIconClasses())}_splitIconName(t){if(!t)return["",""];const e=t.split(":");switch(e.length){case 1:return["",e[0]];case 2:return e;default:throw Error(`Invalid icon name: "${t}"`)}}ngOnInit(){this._updateFontIconClasses()}ngAfterViewChecked(){const t=this._elementsWithExternalReferences;if(t&&t.size){const t=this._location.getPathname();t!==this._previousPath&&(this._previousPath=t,this._prependPathToReferences(t))}}ngOnDestroy(){this._currentIconFetch.unsubscribe(),this._elementsWithExternalReferences&&this._elementsWithExternalReferences.clear()}_usingFontIcon(){return!this.svgIcon}_setSvgElement(t){this._clearSvgElement();const e=t.querySelectorAll("style");for(let t=0;t<e.length;t++)e[t].textContent+=" ";const n=this._location.getPathname();this._previousPath=n,this._cacheChildrenWithExternalReferences(t),this._prependPathToReferences(n),this._elementRef.nativeElement.appendChild(t)}_clearSvgElement(){const t=this._elementRef.nativeElement;let e=t.childNodes.length;for(this._elementsWithExternalReferences&&this._elementsWithExternalReferences.clear();e--;){const n=t.childNodes[e];1===n.nodeType&&"svg"!==n.nodeName.toLowerCase()||t.removeChild(n)}}_updateFontIconClasses(){if(!this._usingFontIcon())return;const t=this._elementRef.nativeElement,e=this.fontSet?this._iconRegistry.classNameForFontAlias(this.fontSet):this._iconRegistry.getDefaultFontSetClass();e!=this._previousFontSetClass&&(this._previousFontSetClass&&t.classList.remove(this._previousFontSetClass),e&&t.classList.add(e),this._previousFontSetClass=e),this.fontIcon!=this._previousFontIconClass&&(this._previousFontIconClass&&t.classList.remove(this._previousFontIconClass),this.fontIcon&&t.classList.add(this.fontIcon),this._previousFontIconClass=this.fontIcon)}_cleanupFontValue(t){return"string"==typeof t?t.trim().split(" ")[0]:t}_prependPathToReferences(t){const e=this._elementsWithExternalReferences;e&&e.forEach(((e,n)=>{e.forEach((e=>{n.setAttribute(e.name,`url('${t}#${e.value}')`)}))}))}_cacheChildrenWithExternalReferences(t){const e=t.querySelectorAll(kW),n=this._elementsWithExternalReferences=this._elementsWithExternalReferences||new Map;for(let t=0;t<e.length;t++)wW.forEach((o=>{const i=e[t],a=i.getAttribute(o),r=a?a.match(SW):null;if(r){let t=n.get(i);t||(t=[],n.set(i,t)),t.push({name:o,value:r[1]})}}))}_updateSvgIcon(t){if(this._svgNamespace=null,this._svgName=null,this._currentIconFetch.unsubscribe(),t){const[e,n]=this._splitIconName(t);e&&(this._svgNamespace=e),n&&(this._svgName=n),this._currentIconFetch=this._iconRegistry.getNamedSvgIcon(n,e).pipe(be(1)).subscribe((t=>this._setSvgElement(t)),(t=>{this._errorHandler.handleError(new Error(`Error retrieving icon ${e}:${n}! ${t.message}`))}))}}}DW.ɵfac=function t(e){return new(e||DW)(Sm(hg),Sm(CW),Na("aria-hidden"),Sm(OW),Sm(Zs))},DW.ɵcmp=to({type:DW,selectors:[["mat-icon"]],hostAttrs:["role","img",1,"mat-icon","notranslate"],hostVars:7,hostBindings:function t(e,n){2&e&&(jp("data-mat-icon-type",n._usingFontIcon()?"font":"svg")("data-mat-icon-name",n._svgName||n.fontIcon)("data-mat-icon-namespace",n._svgNamespace||n.fontSet),pu("mat-icon-inline",n.inline)("mat-icon-no-color","primary"!==n.color&&"accent"!==n.color&&"warn"!==n.color))},inputs:{color:"color",inline:"inline",svgIcon:"svgIcon",fontSet:"fontSet",fontIcon:"fontIcon"},exportAs:["matIcon"],features:[xp],ngContentSelectors:["*"],decls:1,vars:0,template:function t(e,n){1&e&&(Zm(),Xm(0))},styles:[".mat-icon{background-repeat:no-repeat;display:inline-block;fill:currentColor;height:24px;width:24px}.mat-icon.mat-icon-inline{font-size:inherit;height:inherit;line-height:inherit;width:inherit}[dir=rtl] .mat-icon-rtl-mirror{transform:scale(-1, 1)}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon{display:block}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon-button .mat-icon,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon-button .mat-icon{margin:auto}\n"],encapsulation:2,changeDetection:0}),DW.ctorParameters=()=>[{type:hg},{type:CW},{type:String,decorators:[{type:ja,args:["aria-hidden"]}]},{type:void 0,decorators:[{type:kr,args:[OW]}]},{type:Zs}],DW.propDecorators={inline:[{type:xy}],svgIcon:[{type:xy}],fontSet:[{type:xy}],fontIcon:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(DW,[{type:My,args:[{template:"<ng-content></ng-content>",selector:"mat-icon",exportAs:"matIcon",inputs:["color"],host:{role:"img",class:"mat-icon notranslate","[attr.data-mat-icon-type]":'_usingFontIcon() ? "font" : "svg"',"[attr.data-mat-icon-name]":"_svgName || fontIcon","[attr.data-mat-icon-namespace]":"_svgNamespace || fontSet","[class.mat-icon-inline]":"inline","[class.mat-icon-no-color]":'color !== "primary" && color !== "accent" && color !== "warn"'},encapsulation:Hn.None,changeDetection:zn.OnPush,styles:[".mat-icon{background-repeat:no-repeat;display:inline-block;fill:currentColor;height:24px;width:24px}.mat-icon.mat-icon-inline{font-size:inherit;height:inherit;line-height:inherit;width:inherit}[dir=rtl] .mat-icon-rtl-mirror{transform:scale(-1, 1)}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon{display:block}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon-button .mat-icon,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon-button .mat-icon{margin:auto}\n"]}]}],(function(){return[{type:hg},{type:CW},{type:String,decorators:[{type:ja,args:["aria-hidden"]}]},{type:void 0,decorators:[{type:kr,args:[OW]}]},{type:Zs}]}),{inline:[{type:xy}],svgIcon:[{type:xy}],fontSet:[{type:xy}],fontIcon:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class EW{}EW.ɵfac=function t(e){return new(e||EW)},EW.ɵmod=ao({type:EW}),EW.ɵinj=vn({imports:[[XI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(EW,[{type:Ay,args:[{imports:[XI],exports:[DW,XI],declarations:[DW]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(EW,{declarations:function(){return[DW]},imports:function(){return[XI]},exports:function(){return[DW,XI]}});class RW{constructor(t){this.dialogRef=t,this.tensorboardDotDevUrl="https://tensorboard.dev/?utm_source=tensorboard"}onClose(){this.dialogRef.close()}getCommandText(){return this.logdir?"tensorboard dev upload --logdir \\\n    '"+this.logdir.replace(/'/g,"'\\''")+"'":"tensorboard dev upload --logdir {logdir}"}}RW.ɵfac=function t(e){return new(e||RW)(Sm(XG))},RW.ɵcmp=to({type:RW,selectors:[["tbdev-upload-dialog-component"]],inputs:{logdir:"logdir"},decls:21,vars:4,consts:[["target","_blank","rel","noreferrer noopener",1,"anchor-text",3,"href"],[1,"command"],["mat-icon-button","","title","Click to copy the command",1,"command-copy",3,"cdkCopyToClipboard"],["svgIcon","content_copy_24px"],[1,"bottom-buttons"],["mat-flat-button","",1,"close-button",3,"click"],["mat-flat-button","","target","_blank","rel","noreferrer noopener",1,"learn-more-button",3,"href"]],template:function t(e,n){1&e&&(Rm(0,"h3"),ku(1,"Upload to TensorBoard.dev"),Am(),Rm(2,"p"),Rm(3,"a",0),ku(4," TensorBoard.dev"),Am(),ku(5," enables you to easily host, track, and share your ML experiments with everyone. You can share a link to the uploaded TensorBoard in papers, blog posts, and social media. This can showcase the results more effectively and helps reproducibility.\n"),Am(),Rm(6,"p"),ku(7,"To upload a logdir to TensorBoard.dev, run the command:"),Am(),Rm(8,"div",1),Rm(9,"pre"),Rm(10,"code"),ku(11),Am(),Am(),Rm(12,"button",2),Tm(13,"mat-icon",3),Am(),Am(),Rm(14,"p"),ku(15," Only certain plugins are currently supported. Uploaded TensorBoards are public and visible to everyone; do not upload sensitive data.\n"),Am(),Rm(16,"div",4),Rm(17,"button",5),Vm("click",(function t(){return n.onClose()})),ku(18," Close "),Am(),Rm(19,"a",6),ku(20," Learn more "),Am(),Am()),2&e&&(rc(3),Km("href",n.tensorboardDotDevUrl,Ts),rc(8),Su(n.getCommandText()),rc(1),Dm("cdkCopyToClipboard",n.getCommandText()),rc(7),Km("href",n.tensorboardDotDevUrl,Ts))},directives:[XH,fW,DW,KH],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", 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ngDevMode||ngDevMode)&&hh(IW,[{type:My,args:[{selector:"tbdev-upload-button",templateUrl:"./tbdev_upload_button_component.ng.html",styleUrls:["./tbdev_upload_button_component.css"]}]}],(function(){return[{type:Window,decorators:[{type:kr,args:["window"]}]},{type:oW}]}),{shown:[{type:Py,args:["class.shown"]}]});const FW=["*"];function LW(t,e){if(1&t){const t=Hm();Rm(0,"div",0),Vm("keydown",(function e(n){return hi(t),Ym()._handleKeydown(n)}))("click",(function e(){return hi(t),Ym().closed.emit("click")}))("@transformMenu.start",(function e(n){return hi(t),Ym()._onAnimationStart(n)}))("@transformMenu.done",(function e(n){return hi(t),Ym()._onAnimationDone(n)})),Rm(1,"div",1),Xm(2),Am(),Am()}if(2&t){const t=Ym();Dm("id",t.panelId)("ngClass",t._classList)("@transformMenu",t._panelAnimationState),jp("aria-label",t.ariaLabel||null)("aria-labelledby",t.ariaLabelledby||null)("aria-describedby",t.ariaDescribedby||null)}}const BW={transformMenu:nx("transformMenu",[rx("void",ax({opacity:0,transform:"scale(0.8)"})),lx("void => enter",ox("120ms cubic-bezier(0, 0, 0.2, 1)",ax({opacity:1,transform:"scale(1)"}))),lx("* => void",ox("100ms 25ms linear",ax({opacity:0})))]),fadeInItems:nx("fadeInItems",[rx("showing",ax({opacity:1})),lx("void => *",[ax({opacity:0}),ox("400ms 100ms cubic-bezier(0.55, 0, 0.55, 0.2)")])])},VW=new Ga("MatMenuContent");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class jW{constructor(t,e,n,o,i,a,r){this._template=t,this._componentFactoryResolver=e,this._appRef=n,this._injector=o,this._viewContainerRef=i,this._document=a,this._changeDetectorRef=r,this._attached=new I}attach(t={}){this._portal||(this._portal=new xF(this._template,this._viewContainerRef)),this.detach(),this._outlet||(this._outlet=new wF(this._document.createElement("div"),this._componentFactoryResolver,this._appRef,this._injector));const e=this._template.elementRef.nativeElement;e.parentNode.insertBefore(this._outlet.outletElement,e),this._changeDetectorRef&&this._changeDetectorRef.markForCheck(),this._portal.attach(this._outlet,t),this._attached.next()}detach(){this._portal.isAttached&&this._portal.detach()}ngOnDestroy(){this._outlet&&this._outlet.dispose()}}jW.ɵfac=function t(e){return new(e||jW)(Sm(Xg),Sm(ug),Sm(O_),Sm(rp),Sm(eh),Sm(Z_),Sm(Ug))},jW.ɵdir=lo({type:jW,selectors:[["ng-template","matMenuContent",""]],features:[pg([{provide:VW,useExisting:jW}])]}),jW.ctorParameters=()=>[{type:Xg},{type:ug},{type:O_},{type:rp},{type:eh},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:Ug}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(jW,[{type:Cy,args:[{selector:"ng-template[matMenuContent]",providers:[{provide:VW,useExisting:jW}]}]}],(function(){return[{type:Xg},{type:ug},{type:O_},{type:rp},{type:eh},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:Ug}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const UW=new Ga("MAT_MENU_PANEL"),GW=QI(KI(class{}));
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class WW extends GW{constructor(t,e,n,o,i){super(),this._elementRef=t,this._focusMonitor=n,this._parentMenu=o,this._changeDetectorRef=i,this.role="menuitem",this._hovered=new I,this._focused=new I,this._highlighted=!1,this._triggersSubmenu=!1,o&&o.addItem&&o.addItem(this)}focus(t,e){this._focusMonitor&&t?this._focusMonitor.focusVia(this._getHostElement(),t,e):this._getHostElement().focus(e),this._focused.next(this)}ngAfterViewInit(){this._focusMonitor&&this._focusMonitor.monitor(this._elementRef,!1)}ngOnDestroy(){this._focusMonitor&&this._focusMonitor.stopMonitoring(this._elementRef),this._parentMenu&&this._parentMenu.removeItem&&this._parentMenu.removeItem(this),this._hovered.complete(),this._focused.complete()}_getTabIndex(){return this.disabled?"-1":"0"}_getHostElement(){return this._elementRef.nativeElement}_checkDisabled(t){this.disabled&&(t.preventDefault(),t.stopPropagation())}_handleMouseEnter(){this._hovered.next(this)}getLabel(){var t,e;const n=this._elementRef.nativeElement.cloneNode(!0),o=n.querySelectorAll("mat-icon, .material-icons");for(let e=0;e<o.length;e++){const n=o[e];null===(t=n.parentNode)||void 0===t||t.removeChild(n)}return(null===(e=n.textContent)||void 0===e?void 0:e.trim())||""}_setHighlighted(t){var e;this._highlighted=t,null===(e=this._changeDetectorRef)||void 0===e||e.markForCheck()}}WW.ɵfac=function t(e){return new(e||WW)(Sm(hg),Sm(Z_),Sm(SI),Sm(UW,8),Sm(Ug))},WW.ɵcmp=to({type:WW,selectors:[["","mat-menu-item",""]],hostAttrs:[1,"mat-focus-indicator"],hostVars:10,hostBindings:function t(e,n){1&e&&Vm("click",(function t(e){return n._checkDisabled(e)}))("mouseenter",(function t(){return n._handleMouseEnter()})),2&e&&(jp("role",n.role)("tabindex",n._getTabIndex())("aria-disabled",n.disabled.toString())("disabled",n.disabled||null),pu("mat-menu-item",!0)("mat-menu-item-highlighted",n._highlighted)("mat-menu-item-submenu-trigger",n._triggersSubmenu))},inputs:{disabled:"disabled",disableRipple:"disableRipple",role:"role"},exportAs:["matMenuItem"],features:[xp],attrs:["mat-menu-item",""],ngContentSelectors:FW,decls:3,vars:3,consts:[["matRipple","",1,"mat-menu-ripple",3,"matRippleDisabled","matRippleTrigger"],["class","mat-menu-submenu-icon","viewBox","0 0 5 10","focusable","false",4,"ngIf"],["viewBox","0 0 5 10","focusable","false",1,"mat-menu-submenu-icon"],["points","0,0 5,5 0,10"]],template:function t(e,n){1&e&&(Zm(),Xm(0),Tm(1,"div",0),Qp(2,HW,2,0,"svg",1)),2&e&&(rc(1),Dm("matRippleDisabled",n.disableRipple||n.disabled)("matRippleTrigger",n._getHostElement()),rc(1),Dm("ngIf",n._triggersSubmenu))},directives:[kH,dM],encapsulation:2,changeDetection:0}),WW.ctorParameters=()=>[{type:hg},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:SI},{type:void 0,decorators:[{type:kr,args:[UW]},{type:Sr}]},{type:Ug}],WW.propDecorators={role:[{type:xy}],_checkDisabled:[{type:wy,args:["click",["$event"]]}],_handleMouseEnter:[{type:wy,args:["mouseenter"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(WW,[{type:My,args:[{selector:"[mat-menu-item]",exportAs:"matMenuItem",inputs:["disabled","disableRipple"],host:{"[attr.role]":"role","[class.mat-menu-item]":"true","[class.mat-menu-item-highlighted]":"_highlighted","[class.mat-menu-item-submenu-trigger]":"_triggersSubmenu","[attr.tabindex]":"_getTabIndex()","[attr.aria-disabled]":"disabled.toString()","[attr.disabled]":"disabled || null",class:"mat-focus-indicator"},changeDetection:zn.OnPush,encapsulation:Hn.None,template:'<ng-content></ng-content>\n<div class="mat-menu-ripple" matRipple\n     [matRippleDisabled]="disableRipple || disabled"\n     [matRippleTrigger]="_getHostElement()">\n</div>\n\n<svg\n  *ngIf="_triggersSubmenu"\n  class="mat-menu-submenu-icon"\n  viewBox="0 0 5 10"\n  focusable="false"><polygon points="0,0 5,5 0,10"/></svg>\n'}]}],(function(){return[{type:hg},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:SI},{type:void 0,decorators:[{type:kr,args:[UW]},{type:Sr}]},{type:Ug}]}),{role:[{type:xy}],_checkDisabled:[{type:wy,args:["click",["$event"]]}],_handleMouseEnter:[{type:wy,args:["mouseenter"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const YW=new Ga("mat-menu-default-options",{providedIn:"root",factory:function qW(){return{overlapTrigger:!1,xPosition:"after",yPosition:"below",backdropClass:"cdk-overlay-transparent-backdrop"}}});let ZW=0;class XW{constructor(t,e,n){this._elementRef=t,this._ngZone=e,this._defaultOptions=n,this._xPosition=this._defaultOptions.xPosition,this._yPosition=this._defaultOptions.yPosition,this._directDescendantItems=new Vh,this._tabSubscription=m.EMPTY,this._classList={},this._panelAnimationState="void",this._animationDone=new I,this.overlayPanelClass=this._defaultOptions.overlayPanelClass||"",this.backdropClass=this._defaultOptions.backdropClass,this._overlapTrigger=this._defaultOptions.overlapTrigger,this._hasBackdrop=this._defaultOptions.hasBackdrop,this.closed=new Lh,this.close=this.closed,this.panelId="mat-menu-panel-"+ZW++}get xPosition(){return this._xPosition}set xPosition(t){"before"===t||"after"===t||"undefined"!=typeof ngDevMode&&!ngDevMode||(function e(){throw Error('xPosition value must be either \'before\' or after\'.\n      Example: <mat-menu xPosition="before" #menu="matMenu"></mat-menu>')})(),this._xPosition=t,this.setPositionClasses()}get yPosition(){return this._yPosition}set yPosition(t){"above"===t||"below"===t||"undefined"!=typeof ngDevMode&&!ngDevMode||(function e(){throw Error('yPosition value must be either \'above\' or below\'.\n      Example: <mat-menu yPosition="above" #menu="matMenu"></mat-menu>')})(),this._yPosition=t,this.setPositionClasses()}get overlapTrigger(){return this._overlapTrigger}set overlapTrigger(t){this._overlapTrigger=yz(t)}get hasBackdrop(){return this._hasBackdrop}set hasBackdrop(t){this._hasBackdrop=yz(t)}set panelClass(t){const e=this._previousPanelClass;e&&e.length&&e.split(" ").forEach((t=>{this._classList[t]=!1})),this._previousPanelClass=t,t&&t.length&&(t.split(" ").forEach((t=>{this._classList[t]=!0})),this._elementRef.nativeElement.className="")}get classList(){return this.panelClass}set classList(t){this.panelClass=t}ngOnInit(){this.setPositionClasses()}ngAfterContentInit(){this._updateDirectDescendants(),this._keyManager=new eI(this._directDescendantItems).withWrap().withTypeAhead().withHomeAndEnd(),this._tabSubscription=this._keyManager.tabOut.subscribe((()=>this.closed.emit("tab"))),this._directDescendantItems.changes.pipe(Ne(this._directDescendantItems),ze((t=>re(...t.map((t=>t._focused)))))).subscribe((t=>this._keyManager.updateActiveItem(t)))}ngOnDestroy(){this._directDescendantItems.destroy(),this._tabSubscription.unsubscribe(),this.closed.complete()}_hovered(){return this._directDescendantItems.changes.pipe(Ne(this._directDescendantItems),ze((t=>re(...t.map((t=>t._hovered))))))}addItem(t){}removeItem(t){}_handleKeydown(t){const e=t.keyCode,n=this._keyManager;switch(e){case uz:bz(t)||(t.preventDefault(),this.closed.emit("keydown"));break;case 37:this.parentMenu&&"ltr"===this.direction&&this.closed.emit("keydown");break;case 39:this.parentMenu&&"rtl"===this.direction&&this.closed.emit("keydown");break;default:e!==gz&&e!==hz||n.setFocusOrigin("keyboard"),n.onKeydown(t)}}focusFirstItem(t="program"){this.lazyContent?this._ngZone.onStable.pipe(be(1)).subscribe((()=>this._focusFirstItem(t))):this._focusFirstItem(t)}_focusFirstItem(t){const e=this._keyManager;if(e.setFocusOrigin(t).setFirstItemActive(),!e.activeItem&&this._directDescendantItems.length){let t=this._directDescendantItems.first._getHostElement().parentElement;for(;t;){if("menu"===t.getAttribute("role")){t.focus();break}t=t.parentElement}}}resetActiveItem(){this._keyManager.setActiveItem(-1)}setElevation(t){const e=Math.min(this._baseElevation+t,24),n=`${this._elevationPrefix}${e}`,o=Object.keys(this._classList).find((t=>t.startsWith(this._elevationPrefix)));o&&o!==this._previousElevation||(this._previousElevation&&(this._classList[this._previousElevation]=!1),this._classList[n]=!0,this._previousElevation=n)}setPositionClasses(t=this.xPosition,e=this.yPosition){const n=this._classList;n["mat-menu-before"]="before"===t,n["mat-menu-after"]="after"===t,n["mat-menu-above"]="above"===e,n["mat-menu-below"]="below"===e}_startAnimation(){this._panelAnimationState="enter"}_resetAnimation(){this._panelAnimationState="void"}_onAnimationDone(t){this._animationDone.next(t),this._isAnimating=!1}_onAnimationStart(t){this._isAnimating=!0,"enter"===t.toState&&0===this._keyManager.activeItemIndex&&(t.element.scrollTop=0)}_updateDirectDescendants(){this._allItems.changes.pipe(Ne(this._allItems)).subscribe((t=>{this._directDescendantItems.reset(t.filter((t=>t._parentMenu===this))),this._directDescendantItems.notifyOnChanges()}))}}XW.ɵfac=function t(e){return new(e||XW)(Sm(hg),Sm(a_),Sm(YW))},XW.ɵdir=lo({type:XW,contentQueries:function t(e,n,o){if(1&e&&($h(o,VW,5),$h(o,WW,5),$h(o,WW,4)),2&e){let t;Jh(t=tb())&&(n.lazyContent=t.first),Jh(t=tb())&&(n._allItems=t),Jh(t=tb())&&(n.items=t)}},viewQuery:function t(e,n){if(1&e&&Qh(Xg,5),2&e){let t;Jh(t=tb())&&(n.templateRef=t.first)}},inputs:{backdropClass:"backdropClass",xPosition:"xPosition",yPosition:"yPosition",overlapTrigger:"overlapTrigger",hasBackdrop:"hasBackdrop",panelClass:["class","panelClass"],classList:"classList",ariaLabel:["aria-label","ariaLabel"],ariaLabelledby:["aria-labelledby","ariaLabelledby"],ariaDescribedby:["aria-describedby","ariaDescribedby"]},outputs:{closed:"closed",close:"close"}}),XW.ctorParameters=()=>[{type:hg},{type:a_},{type:void 0,decorators:[{type:kr,args:[YW]}]}],XW.propDecorators={_allItems:[{type:Ya,args:[WW,{descendants:!0}]}],backdropClass:[{type:xy}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],ariaDescribedby:[{type:xy,args:["aria-describedby"]}],xPosition:[{type:xy}],yPosition:[{type:xy}],templateRef:[{type:Za,args:[Xg]}],items:[{type:Ya,args:[WW,{descendants:!1}]}],lazyContent:[{type:qa,args:[VW]}],overlapTrigger:[{type:xy}],hasBackdrop:[{type:xy}],panelClass:[{type:xy,args:["class"]}],classList:[{type:xy}],closed:[{type:Oy}],close:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(XW,[{type:Cy}],(function(){return[{type:hg},{type:a_},{type:void 0,decorators:[{type:kr,args:[YW]}]}]}),{backdropClass:[{type:xy}],closed:[{type:Oy}],close:[{type:Oy}],xPosition:[{type:xy}],yPosition:[{type:xy}],overlapTrigger:[{type:xy}],hasBackdrop:[{type:xy}],panelClass:[{type:xy,args:["class"]}],classList:[{type:xy}],_allItems:[{type:Ya,args:[WW,{descendants:!0}]}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],ariaDescribedby:[{type:xy,args:["aria-describedby"]}],templateRef:[{type:Za,args:[Xg]}],items:[{type:Ya,args:[WW,{descendants:!1}]}],lazyContent:[{type:qa,args:[VW]}]});class KW extends XW{constructor(t,e,n){super(t,e,n),this._elevationPrefix="mat-elevation-z",this._baseElevation=4}}KW.ɵfac=function t(e){return new(e||KW)(Sm(hg),Sm(a_),Sm(YW))},KW.ɵcmp=to({type:KW,selectors:[["mat-menu"]],hostVars:3,hostBindings:function t(e,n){2&e&&jp("aria-label",null)("aria-labelledby",null)("aria-describedby",null)},exportAs:["matMenu"],features:[pg([{provide:UW,useExisting:KW}]),xp],ngContentSelectors:FW,decls:1,vars:0,consts:[["tabindex","-1","role","menu",1,"mat-menu-panel",3,"id","ngClass","keydown","click"],[1,"mat-menu-content"]],template:function t(e,n){1&e&&(Zm(),Qp(0,LW,3,6,"ng-template"))},directives:[aM],styles:["mat-menu{display:none}.mat-menu-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;max-height:calc(100vh - 48px);border-radius:4px;outline:0;min-height:64px}.mat-menu-panel.ng-animating{pointer-events:none}.cdk-high-contrast-active .mat-menu-panel{outline:solid 1px}.mat-menu-content:not(:empty){padding-top:8px;padding-bottom:8px}.mat-menu-item{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;position:relative}.mat-menu-item::-moz-focus-inner{border:0}.mat-menu-item[disabled]{cursor:default}[dir=rtl] .mat-menu-item{text-align:right}.mat-menu-item .mat-icon{margin-right:16px;vertical-align:middle}.mat-menu-item .mat-icon svg{vertical-align:top}[dir=rtl] .mat-menu-item .mat-icon{margin-left:16px;margin-right:0}.mat-menu-item[disabled]{pointer-events:none}.cdk-high-contrast-active .mat-menu-item{margin-top:1px}.cdk-high-contrast-active .mat-menu-item.cdk-program-focused,.cdk-high-contrast-active .mat-menu-item.cdk-keyboard-focused,.cdk-high-contrast-active .mat-menu-item-highlighted{outline:dotted 1px}.mat-menu-item-submenu-trigger{padding-right:32px}[dir=rtl] .mat-menu-item-submenu-trigger{padding-right:16px;padding-left:32px}.mat-menu-submenu-icon{position:absolute;top:50%;right:16px;transform:translateY(-50%);width:5px;height:10px;fill:currentColor}[dir=rtl] .mat-menu-submenu-icon{right:auto;left:16px;transform:translateY(-50%) scaleX(-1)}.cdk-high-contrast-active .mat-menu-submenu-icon{fill:CanvasText}button.mat-menu-item{width:100%}.mat-menu-item .mat-menu-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none}\n"],encapsulation:2,data:{animation:[BW.transformMenu,BW.fadeInItems]},changeDetection:0}),KW.ctorParameters=()=>[{type:hg},{type:a_},{type:void 0,decorators:[{type:kr,args:[YW]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(KW,[{type:My,args:[{selector:"mat-menu",template:'<ng-template>\n  <div\n    class="mat-menu-panel"\n    [id]="panelId"\n    [ngClass]="_classList"\n    (keydown)="_handleKeydown($event)"\n    (click)="closed.emit(\'click\')"\n    [@transformMenu]="_panelAnimationState"\n    (@transformMenu.start)="_onAnimationStart($event)"\n    (@transformMenu.done)="_onAnimationDone($event)"\n    tabindex="-1"\n    role="menu"\n    [attr.aria-label]="ariaLabel || null"\n    [attr.aria-labelledby]="ariaLabelledby || null"\n    [attr.aria-describedby]="ariaDescribedby || null">\n    <div class="mat-menu-content">\n      <ng-content></ng-content>\n    </div>\n  </div>\n</ng-template>\n',changeDetection:zn.OnPush,encapsulation:Hn.None,exportAs:"matMenu",host:{"[attr.aria-label]":"null","[attr.aria-labelledby]":"null","[attr.aria-describedby]":"null"},animations:[BW.transformMenu,BW.fadeInItems],providers:[{provide:UW,useExisting:KW}],styles:["mat-menu{display:none}.mat-menu-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;max-height:calc(100vh - 48px);border-radius:4px;outline:0;min-height:64px}.mat-menu-panel.ng-animating{pointer-events:none}.cdk-high-contrast-active .mat-menu-panel{outline:solid 1px}.mat-menu-content:not(:empty){padding-top:8px;padding-bottom:8px}.mat-menu-item{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;position:relative}.mat-menu-item::-moz-focus-inner{border:0}.mat-menu-item[disabled]{cursor:default}[dir=rtl] .mat-menu-item{text-align:right}.mat-menu-item .mat-icon{margin-right:16px;vertical-align:middle}.mat-menu-item .mat-icon svg{vertical-align:top}[dir=rtl] .mat-menu-item .mat-icon{margin-left:16px;margin-right:0}.mat-menu-item[disabled]{pointer-events:none}.cdk-high-contrast-active .mat-menu-item{margin-top:1px}.cdk-high-contrast-active .mat-menu-item.cdk-program-focused,.cdk-high-contrast-active .mat-menu-item.cdk-keyboard-focused,.cdk-high-contrast-active .mat-menu-item-highlighted{outline:dotted 1px}.mat-menu-item-submenu-trigger{padding-right:32px}[dir=rtl] .mat-menu-item-submenu-trigger{padding-right:16px;padding-left:32px}.mat-menu-submenu-icon{position:absolute;top:50%;right:16px;transform:translateY(-50%);width:5px;height:10px;fill:currentColor}[dir=rtl] .mat-menu-submenu-icon{right:auto;left:16px;transform:translateY(-50%) scaleX(-1)}.cdk-high-contrast-active .mat-menu-submenu-icon{fill:CanvasText}button.mat-menu-item{width:100%}.mat-menu-item .mat-menu-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none}\n"]}]}],(function(){return[{type:hg},{type:a_},{type:void 0,decorators:[{type:kr,args:[YW]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const JW=new Ga("mat-menu-scroll-strategy"),QW={provide:JW,deps:[pL],useFactory:function $W(t){return()=>t.scrollStrategies.reposition()}},tY=Nz({passive:!0});class eY{constructor(t,e,n,o,i,a,r,s){this._overlay=t,this._element=e,this._viewContainerRef=n,this._menuItemInstance=a,this._dir=r,this._focusMonitor=s,this._overlayRef=null,this._menuOpen=!1,this._closingActionsSubscription=m.EMPTY,this._hoverSubscription=m.EMPTY,this._menuCloseSubscription=m.EMPTY,this._handleTouchStart=t=>{hI(t)||(this._openedBy="touch")},this._openedBy=void 0,this.restoreFocus=!0,this.menuOpened=new Lh,this.onMenuOpen=this.menuOpened,this.menuClosed=new Lh,this.onMenuClose=this.menuClosed,this._scrollStrategy=o,this._parentMaterialMenu=i instanceof XW?i:void 0,e.nativeElement.addEventListener("touchstart",this._handleTouchStart,tY),a&&(a._triggersSubmenu=this.triggersSubmenu())}get _deprecatedMatMenuTriggerFor(){return this.menu}set _deprecatedMatMenuTriggerFor(t){this.menu=t}get menu(){return this._menu}set menu(t){t!==this._menu&&(this._menu=t,this._menuCloseSubscription.unsubscribe(),t&&(t!==this._parentMaterialMenu||"undefined"!=typeof ngDevMode&&!ngDevMode||(function e(){throw Error("matMenuTriggerFor: menu cannot contain its own trigger. Assign a menu that is not a parent of the trigger or move the trigger outside of the menu.")})(),this._menuCloseSubscription=t.close.subscribe((t=>{this._destroyMenu(t),"click"!==t&&"tab"!==t||!this._parentMaterialMenu||this._parentMaterialMenu.closed.emit(t)}))))}ngAfterContentInit(){this._checkMenu(),this._handleHover()}ngOnDestroy(){this._overlayRef&&(this._overlayRef.dispose(),this._overlayRef=null),this._element.nativeElement.removeEventListener("touchstart",this._handleTouchStart,tY),this._menuCloseSubscription.unsubscribe(),this._closingActionsSubscription.unsubscribe(),this._hoverSubscription.unsubscribe()}get menuOpen(){return this._menuOpen}get dir(){return this._dir&&"rtl"===this._dir.value?"rtl":"ltr"}triggersSubmenu(){return!(!this._menuItemInstance||!this._parentMaterialMenu)}toggleMenu(){return this._menuOpen?this.closeMenu():this.openMenu()}openMenu(){if(this._menuOpen)return;this._checkMenu();const t=this._createOverlay(),e=t.getConfig();this._setPosition(e.positionStrategy),e.hasBackdrop=null==this.menu.hasBackdrop?!this.triggersSubmenu():this.menu.hasBackdrop,t.attach(this._getPortal()),this.menu.lazyContent&&this.menu.lazyContent.attach(this.menuData),this._closingActionsSubscription=this._menuClosingActions().subscribe((()=>this.closeMenu())),this._initMenu(),this.menu instanceof XW&&this.menu._startAnimation()}closeMenu(){this.menu.close.emit()}focus(t,e){this._focusMonitor&&t?this._focusMonitor.focusVia(this._element,t,e):this._element.nativeElement.focus(e)}updatePosition(){var t;null===(t=this._overlayRef)||void 0===t||t.updatePosition()}_destroyMenu(t){if(!this._overlayRef||!this.menuOpen)return;const e=this.menu;this._closingActionsSubscription.unsubscribe(),this._overlayRef.detach(),!this.restoreFocus||"keydown"!==t&&this._openedBy&&this.triggersSubmenu()||this.focus(this._openedBy),this._openedBy=void 0,e instanceof XW?(e._resetAnimation(),e.lazyContent?e._animationDone.pipe(ce((t=>"void"===t.toState)),be(1),Ie(e.lazyContent._attached)).subscribe({next:()=>e.lazyContent.detach(),complete:()=>this._setIsMenuOpen(!1)}):this._setIsMenuOpen(!1)):(this._setIsMenuOpen(!1),e.lazyContent&&e.lazyContent.detach())}_initMenu(){this.menu.parentMenu=this.triggersSubmenu()?this._parentMaterialMenu:void 0,this.menu.direction=this.dir,this._setMenuElevation(),this.menu.focusFirstItem(this._openedBy||"program"),this._setIsMenuOpen(!0)}_setMenuElevation(){if(this.menu.setElevation){let t=0,e=this.menu.parentMenu;for(;e;)t++,e=e.parentMenu;this.menu.setElevation(t)}}_setIsMenuOpen(t){this._menuOpen=t,this._menuOpen?this.menuOpened.emit():this.menuClosed.emit(),this.triggersSubmenu()&&this._menuItemInstance._setHighlighted(t)}_checkMenu(){this.menu||"undefined"!=typeof ngDevMode&&!ngDevMode||(
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function t(){throw Error('matMenuTriggerFor: must pass in an mat-menu instance.\n\n    Example:\n      <mat-menu #menu="matMenu"></mat-menu>\n      <button [matMenuTriggerFor]="menu"></button>')})()}_createOverlay(){if(!this._overlayRef){const t=this._getOverlayConfig();this._subscribeToPositions(t.positionStrategy),this._overlayRef=this._overlay.create(t),this._overlayRef.keydownEvents().subscribe()}return this._overlayRef}_getOverlayConfig(){return new VF({positionStrategy:this._overlay.position().flexibleConnectedTo(this._element).withLockedPosition().withGrowAfterOpen().withTransformOriginOn(".mat-menu-panel, .mat-mdc-menu-panel"),backdropClass:this.menu.backdropClass||"cdk-overlay-transparent-backdrop",panelClass:this.menu.overlayPanelClass,scrollStrategy:this._scrollStrategy(),direction:this._dir})}_subscribeToPositions(t){this.menu.setPositionClasses&&t.positionChanges.subscribe((t=>{this.menu.setPositionClasses("start"===t.connectionPair.overlayX?"after":"before","top"===t.connectionPair.overlayY?"below":"above")}))}_setPosition(t){let[e,n]="before"===this.menu.xPosition?["end","start"]:["start","end"],[o,i]="above"===this.menu.yPosition?["bottom","top"]:["top","bottom"],[a,r]=[o,i],[s,l]=[e,n],c=0;this.triggersSubmenu()?(l=e="before"===this.menu.xPosition?"start":"end",n=s="end"===e?"start":"end",c="bottom"===o?8:-8):this.menu.overlapTrigger||(a="top"===o?"bottom":"top",r="top"===i?"bottom":"top"),t.withPositions([{originX:e,originY:a,overlayX:s,overlayY:o,offsetY:c},{originX:n,originY:a,overlayX:l,overlayY:o,offsetY:c},{originX:e,originY:r,overlayX:s,overlayY:i,offsetY:-c},{originX:n,originY:r,overlayX:l,overlayY:i,offsetY:-c}])}_menuClosingActions(){const t=this._overlayRef.backdropClick(),e=this._overlayRef.detachments();return re(t,this._parentMaterialMenu?this._parentMaterialMenu.closed:Et(),this._parentMaterialMenu?this._parentMaterialMenu._hovered().pipe(ce((t=>t!==this._menuItemInstance)),ce((()=>this._menuOpen))):Et(),e)}_handleMousedown(t){gI(t)||(this._openedBy=0===t.button?"mouse":void 0,this.triggersSubmenu()&&t.preventDefault())}_handleKeydown(t){const e=t.keyCode;e!==mz&&e!==fz||(this._openedBy="keyboard"),this.triggersSubmenu()&&(39===e&&"ltr"===this.dir||37===e&&"rtl"===this.dir)&&(this._openedBy="keyboard",this.openMenu())}_handleClick(t){this.triggersSubmenu()?(t.stopPropagation(),this.openMenu()):this.toggleMenu()}_handleHover(){this.triggersSubmenu()&&this._parentMaterialMenu&&(this._hoverSubscription=this._parentMaterialMenu._hovered().pipe(ce((t=>t===this._menuItemInstance&&!t.disabled)),Ce(0,$)).subscribe((()=>{this._openedBy="mouse",this.menu instanceof XW&&this.menu._isAnimating?this.menu._animationDone.pipe(be(1),Ce(0,$),Ie(this._parentMaterialMenu._hovered())).subscribe((()=>this.openMenu())):this.openMenu()})))}_getPortal(){return this._portal&&this._portal.templateRef===this.menu.templateRef||(this._portal=new xF(this.menu.templateRef,this._viewContainerRef)),this._portal}}eY.ɵfac=function t(e){return new(e||eY)(Sm(pL),Sm(hg),Sm(eh),Sm(JW),Sm(UW,8),Sm(WW,10),Sm(HI,8),Sm(SI))},eY.ɵdir=lo({type:eY,selectors:[["","mat-menu-trigger-for",""],["","matMenuTriggerFor",""]],hostAttrs:["aria-haspopup","true",1,"mat-menu-trigger"],hostVars:2,hostBindings:function t(e,n){1&e&&Vm("mousedown",(function t(e){return n._handleMousedown(e)}))("keydown",(function t(e){return n._handleKeydown(e)}))("click",(function t(e){return n._handleClick(e)})),2&e&&jp("aria-expanded",n.menuOpen||null)("aria-controls",n.menuOpen?n.menu.panelId:null)},inputs:{restoreFocus:["matMenuTriggerRestoreFocus","restoreFocus"],_deprecatedMatMenuTriggerFor:["mat-menu-trigger-for","_deprecatedMatMenuTriggerFor"],menu:["matMenuTriggerFor","menu"],menuData:["matMenuTriggerData","menuData"]},outputs:{menuOpened:"menuOpened",onMenuOpen:"onMenuOpen",menuClosed:"menuClosed",onMenuClose:"onMenuClose"},exportAs:["matMenuTrigger"]}),eY.ctorParameters=()=>[{type:pL},{type:hg},{type:eh},{type:void 0,decorators:[{type:kr,args:[JW]}]},{type:void 0,decorators:[{type:kr,args:[UW]},{type:Sr}]},{type:WW,decorators:[{type:Sr},{type:Dr}]},{type:HI,decorators:[{type:Sr}]},{type:SI}],eY.propDecorators={_deprecatedMatMenuTriggerFor:[{type:xy,args:["mat-menu-trigger-for"]}],menu:[{type:xy,args:["matMenuTriggerFor"]}],menuData:[{type:xy,args:["matMenuTriggerData"]}],restoreFocus:[{type:xy,args:["matMenuTriggerRestoreFocus"]}],menuOpened:[{type:Oy}],onMenuOpen:[{type:Oy}],menuClosed:[{type:Oy}],onMenuClose:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(eY,[{type:Cy,args:[{selector:"[mat-menu-trigger-for], [matMenuTriggerFor]",host:{class:"mat-menu-trigger","aria-haspopup":"true","[attr.aria-expanded]":"menuOpen || null","[attr.aria-controls]":"menuOpen ? menu.panelId : null","(mousedown)":"_handleMousedown($event)","(keydown)":"_handleKeydown($event)","(click)":"_handleClick($event)"},exportAs:"matMenuTrigger"}]}],(function(){return[{type:pL},{type:hg},{type:eh},{type:void 0,decorators:[{type:kr,args:[JW]}]},{type:void 0,decorators:[{type:kr,args:[UW]},{type:Sr}]},{type:WW,decorators:[{type:Sr},{type:Dr}]},{type:HI,decorators:[{type:Sr}]},{type:SI}]}),{restoreFocus:[{type:xy,args:["matMenuTriggerRestoreFocus"]}],menuOpened:[{type:Oy}],onMenuOpen:[{type:Oy}],menuClosed:[{type:Oy}],onMenuClose:[{type:Oy}],_deprecatedMatMenuTriggerFor:[{type:xy,args:["mat-menu-trigger-for"]}],menu:[{type:xy,args:["matMenuTriggerFor"]}],menuData:[{type:xy,args:["matMenuTriggerData"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class nY{}nY.ɵfac=function t(e){return new(e||nY)},nY.ɵmod=ao({type:nY}),nY.ɵinj=vn({providers:[QW],imports:[XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(nY,[{type:Ay,args:[{exports:[eY,jW,XI],declarations:[eY,jW],providers:[QW]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(nY,{declarations:function(){return[eY,jW]},exports:function(){return[eY,jW,XI]}});class oY{}function iY(t,e){1&t&&Tm(0,"mat-icon",8)}function aY(t,e){1&t&&Tm(0,"mat-icon",9)}function rY(t,e){1&t&&Tm(0,"mat-icon",10)}var sY;oY.ɵfac=function t(e){return new(e||oY)},oY.ɵmod=ao({type:oY}),oY.ɵinj=vn({providers:[QW],imports:[[WM,XI,SH,yL,nY],yF,XI,nY]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(oY,[{type:Ay,args:[{imports:[WM,XI,SH,yL,nY],exports:[yF,XI,KW,WW,nY],declarations:[KW,WW],providers:[QW]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(oY,{declarations:function(){return[KW,WW]},imports:function(){return[WM,XI,SH,yL,nY]},exports:function(){return[yF,XI,KW,WW,nY]}}),(function(t){t[t.DEFAULT=0]="DEFAULT",t[t.DARK_MODE_ON=1]="DARK_MODE_ON",t[t.DARK_MODE_OFF=2]="DARK_MODE_OFF"})(sY||(sY={}));class lY{constructor(){this.DarkModeOverride=sY,this.onOverrideChanged=new Lh}getButtonTitle(){let t;switch(this.darkModeOverride){case sY.DEFAULT:t="Browser default";break;case sY.DARK_MODE_ON:t="Dark mode";break;case sY.DARK_MODE_OFF:t="Light mode"}return`Current mode: [${t}]. Switch between browser default, light, or dark theme.`}}lY.ɵfac=function t(e){return new(e||lY)},lY.ɵcmp=to({type:lY,selectors:[["app-header-dark-mode-toggle-component"]],inputs:{darkModeOverride:"darkModeOverride"},outputs:{onOverrideChanged:"onOverrideChanged"},decls:15,vars:6,consts:[["mat-icon-button","","aria-label","Menu for changing light or dark theme",3,"matMenuTriggerFor","ngSwitch","title"],["svgIcon","brightness_6_24px",4,"ngSwitchCase"],["svgIcon","light_mode_24px",4,"ngSwitchCase"],["svgIcon","dark_mode_24px",4,"ngSwitchCase"],["menu","matMenu"],["mat-menu-item","","title","Set the theme to match the default mode in the browser.",3,"click"],["mat-menu-item","","title","Force light TensorBoard theme.",3,"click"],["mat-menu-item","","title","Force dark TensorBoard theme.",3,"click"],["svgIcon","brightness_6_24px"],["svgIcon","light_mode_24px"],["svgIcon","dark_mode_24px"]],template:function t(e,n){1&e&&(Rm(0,"button",0),Qp(1,iY,1,0,"mat-icon",1),Qp(2,aY,1,0,"mat-icon",2),Qp(3,rY,1,0,"mat-icon",3),Am(),Rm(4,"mat-menu",null,4),Rm(6,"button",5),Vm("click",(function t(){return n.onOverrideChanged.emit(n.DarkModeOverride.DEFAULT)})),Rm(7,"label"),ku(8,"Browser default"),Am(),Am(),Rm(9,"button",6),Vm("click",(function t(){return n.onOverrideChanged.emit(n.DarkModeOverride.DARK_MODE_OFF)})),Rm(10,"label"),ku(11,"Light"),Am(),Am(),Rm(12,"button",7),Vm("click",(function t(){return n.onOverrideChanged.emit(n.DarkModeOverride.DARK_MODE_ON)})),Rm(13,"label"),ku(14,"Dark"),Am(),Am(),Am()),2&e&&(Dm("matMenuTriggerFor",$p(5))("ngSwitch",n.darkModeOverride)("title",n.getButtonTitle()),rc(1),Dm("ngSwitchCase",n.DarkModeOverride.DEFAULT),rc(1),Dm("ngSwitchCase",n.DarkModeOverride.DARK_MODE_OFF),rc(1),Dm("ngSwitchCase",n.DarkModeOverride.DARK_MODE_ON))},directives:[XH,eY,fM,gM,KW,WW,DW],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lY,[{type:My,args:[{selector:"app-header-dark-mode-toggle-component",template:'\n    <button\n      mat-icon-button\n      [matMenuTriggerFor]="menu"\n      aria-label="Menu for changing light or dark theme"\n      [ngSwitch]="darkModeOverride"\n      [title]="getButtonTitle()"\n    >\n      <mat-icon\n        *ngSwitchCase="DarkModeOverride.DEFAULT"\n        svgIcon="brightness_6_24px"\n      ></mat-icon>\n      <mat-icon\n        *ngSwitchCase="DarkModeOverride.DARK_MODE_OFF"\n        svgIcon="light_mode_24px"\n      ></mat-icon>\n      <mat-icon\n        *ngSwitchCase="DarkModeOverride.DARK_MODE_ON"\n        svgIcon="dark_mode_24px"\n      ></mat-icon>\n    </button>\n    <mat-menu #menu="matMenu">\n      <button\n        mat-menu-item\n        title="Set the theme to match the default mode in the browser."\n        (click)="onOverrideChanged.emit(DarkModeOverride.DEFAULT)"\n      >\n        <label>Browser default</label>\n      </button>\n      <button\n        mat-menu-item\n        title="Force light TensorBoard theme."\n        (click)="onOverrideChanged.emit(DarkModeOverride.DARK_MODE_OFF)"\n      >\n        <label>Light</label>\n      </button>\n      <button\n        mat-menu-item\n        title="Force dark TensorBoard theme."\n        (click)="onOverrideChanged.emit(DarkModeOverride.DARK_MODE_ON)"\n      >\n        <label>Dark</label>\n      </button>\n    </mat-menu>\n  '}]}],null,{darkModeOverride:[{type:xy}],onOverrideChanged:[{type:Oy}]});class cY{constructor(t){this.store=t,this.darkModeOverride$=this.store.select(QD).pipe(It((t=>null===t?sY.DEFAULT:t?sY.DARK_MODE_ON:sY.DARK_MODE_OFF)))}changeDarkMode(t){let e=null;switch(t){case sY.DEFAULT:e=null;break;case sY.DARK_MODE_OFF:e=!1;break;case sY.DARK_MODE_ON:e=!0}this.store.dispatch(WA({enableDarkMode:e}))}}cY.ɵfac=function t(e){return new(e||cY)(Sm(Iw))},cY.ɵcmp=to({type:cY,selectors:[["app-header-dark-mode-toggle"]],decls:2,vars:3,consts:[[3,"darkModeOverride","onOverrideChanged"]],template:function t(e,n){1&e&&(Rm(0,"app-header-dark-mode-toggle-component",0),Vm("onOverrideChanged",(function t(e){return n.changeDarkMode(e)})),Ah(1,"async"),Am()),2&e&&Dm("darkModeOverride",Th(1,1,n.darkModeOverride$))},directives:[lY],pipes:[wM],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(cY,[{type:My,args:[{selector:"app-header-dark-mode-toggle",template:'\n    <app-header-dark-mode-toggle-component\n      [darkModeOverride]="darkModeOverride$ | async"\n      (onOverrideChanged)="changeDarkMode($event)"\n    >\n    </app-header-dark-mode-toggle-component>\n  '}]}],(function(){return[{type:Iw}]}),null);const dY=Zw(vR,MR,((t,e)=>!(!e||!t[e])&&t[e].disable_reload));class pY{constructor(t){this.store=t,this.reloadDisabled$=this.store.select(dY),this.isReloading$=this.store.select(_R).pipe(fe(this.reloadDisabled$),It((([t,e])=>!e&&t===yE.LOADING))),this.lastLoadedTimeInMs$=this.store.select(CR)}triggerReload(){this.store.dispatch(vE())}getReloadTitle(t){return t?`Last Updated: ${t}`:"Loading..."}}function mY(t){return t.state!==yE.NOT_LOADED&&t.state!==yE.LOADING}pY.ɵfac=function t(e){return new(e||pY)(Sm(Iw))},pY.ɵcmp=to({type:pY,selectors:[["app-header-reload"]],decls:6,vars:13,consts:[["mat-icon-button","",1,"reload-button",3,"title","disabled","click"],["svgIcon","refresh_24px",1,"refresh-icon"]],template:function t(e,n){1&e&&(Rm(0,"button",0),Vm("click",(function t(){return n.triggerReload()})),Ah(1,"async"),Ah(2,"date"),Ah(3,"async"),Ah(4,"async"),Tm(5,"mat-icon",1),Am()),2&e&&(pu("loading",Th(1,4,n.isReloading$)),Dm("title",n.getReloadTitle(Nh(2,6,Th(3,9,n.lastLoadedTimeInMs$),"medium")))("disabled",Th(4,11,n.reloadDisabled$)))},directives:[XH,DW],pipes:[wM,RM],styles:[".reload-button[_ngcontent-%COMP%], .refresh-icon[_ngcontent-%COMP%] {\n        align-items: center;\n        display: flex;\n        justify-content: center;\n      }\n\n      .reload-button.loading[_ngcontent-%COMP%] {\n        animation: rotate 2s linear infinite;\n      }\n\n      @keyframes rotate {\n        0% {\n          transform: rotate(0deg);\n        }\n        50% {\n          transform: rotate(180deg);\n        }\n        100% {\n          transform: rotate(360deg);\n        }\n      }"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(pY,[{type:My,args:[{selector:"app-header-reload",template:'\n    <button\n      class="reload-button"\n      [class.loading]="isReloading$ | async"\n      mat-icon-button\n      (click)="triggerReload()"\n      [title]="getReloadTitle(lastLoadedTimeInMs$ | async | date: \'medium\')"\n      [disabled]="reloadDisabled$ | async"\n    >\n      <mat-icon class="refresh-icon" svgIcon="refresh_24px"></mat-icon>\n    </button>\n  ',styles:["\n      .reload-button,\n      .refresh-icon {\n        align-items: center;\n        display: flex;\n        justify-content: center;\n      }\n\n      .reload-button.loading {\n        animation: rotate 2s linear infinite;\n      }\n\n      @keyframes rotate {\n        0% {\n          transform: rotate(0deg);\n        }\n        50% {\n          transform: rotate(180deg);\n        }\n        100% {\n          transform: rotate(360deg);\n        }\n      }\n    "]}]}],(function(){return[{type:Iw}]}),null);const uY=yk(wN,bk(vN,(t=>mY(t)?Object.assign(Object.assign({},t),{settings:Object.assign(Object.assign({},t.settings),{reloadEnabled:!t.settings.reloadEnabled})}):t)),bk(xN,((t,{periodInMs:e})=>{if(!mY(t))return t;const n=e>=3e4?e:t.settings.reloadPeriodInMs;return Object.assign(Object.assign({},t),{settings:Object.assign(Object.assign({},t.settings),{reloadPeriodInMs:n})})})),bk(ON,((t,{size:e})=>{if(!mY(t))return t;const n=e>0?e:t.settings.pageSize;return Object.assign(Object.assign({},t),{settings:Object.assign(Object.assign({},t.settings),{pageSize:n})})})),bk(WS,((t,{partialSettings:e})=>{const n={};return Number.isFinite(e.pageSize)&&e.pageSize>0&&(n.pageSize=e.pageSize),"boolean"==typeof e.autoReload&&(n.reloadEnabled=e.autoReload),Number.isFinite(e.autoReloadPeriodInMs)&&e.autoReloadPeriodInMs>3e4&&(n.reloadPeriodInMs=e.autoReloadPeriodInMs),Object.assign(Object.assign({},t),{settings:Object.assign(Object.assign({},t.settings),n)})})));function fY(t,e){return uY(t,e)}const gY=["input"],hY=function(t){return{enterDuration:t}},bY=new Ga("mat-checkbox-default-options",{providedIn:"root",factory:function yY(){return{color:"accent",clickAction:"check-indeterminate"}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */});let _Y=0;const CY={color:"accent",clickAction:"check-indeterminate"},MY={provide:IV,useExisting:qe((()=>OY)),multi:!0};class vY{}const xY=$I(JI(QI(KI(class{constructor(t){this._elementRef=t}}))));class OY extends xY{constructor(t,e,n,o,i,a,r){super(t),this._changeDetectorRef=e,this._focusMonitor=n,this._ngZone=o,this._animationMode=a,this._options=r,this.ariaLabel="",this.ariaLabelledby=null,this._uniqueId="mat-checkbox-"+ ++_Y,this.id=this._uniqueId,this.labelPosition="after",this.name=null,this.change=new Lh,this.indeterminateChange=new Lh,this._onTouched=()=>{},this._currentAnimationClass="",this._currentCheckState=0,this._controlValueAccessorChangeFn=()=>{},this._checked=!1,this._disabled=!1,this._indeterminate=!1,this._options=this._options||CY,this.color=this.defaultColor=this._options.color||CY.color,this.tabIndex=parseInt(i)||0}get inputId(){return`${this.id||this._uniqueId}-input`}get required(){return this._required}set required(t){this._required=yz(t)}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0).subscribe((t=>{t||Promise.resolve().then((()=>{this._onTouched(),this._changeDetectorRef.markForCheck()}))})),this._syncIndeterminate(this._indeterminate)}ngAfterViewChecked(){}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef)}get checked(){return this._checked}set checked(t){t!=this.checked&&(this._checked=t,this._changeDetectorRef.markForCheck())}get disabled(){return this._disabled}set disabled(t){const e=yz(t);e!==this.disabled&&(this._disabled=e,this._changeDetectorRef.markForCheck())}get indeterminate(){return this._indeterminate}set indeterminate(t){const e=t!=this._indeterminate;this._indeterminate=yz(t),e&&(this._transitionCheckState(this._indeterminate?3:this.checked?1:2),this.indeterminateChange.emit(this._indeterminate)),this._syncIndeterminate(this._indeterminate)}_isRippleDisabled(){return this.disableRipple||this.disabled}_onLabelTextChange(){this._changeDetectorRef.detectChanges()}writeValue(t){this.checked=!!t}registerOnChange(t){this._controlValueAccessorChangeFn=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t}_getAriaChecked(){return this.checked?"true":this.indeterminate?"mixed":"false"}_transitionCheckState(t){let e=this._currentCheckState,n=this._elementRef.nativeElement;if(e!==t&&(this._currentAnimationClass.length>0&&n.classList.remove(this._currentAnimationClass),this._currentAnimationClass=this._getAnimationClassForCheckStateTransition(e,t),this._currentCheckState=t,this._currentAnimationClass.length>0)){n.classList.add(this._currentAnimationClass);const t=this._currentAnimationClass;this._ngZone.runOutsideAngular((()=>{setTimeout((()=>{n.classList.remove(t)}),1e3)}))}}_emitChangeEvent(){const t=new 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n="";switch(t){case 0:if(1===e)n="unchecked-checked";else{if(3!=e)return"";n="unchecked-indeterminate"}break;case 2:n=1===e?"unchecked-checked":"unchecked-indeterminate";break;case 1:n=2===e?"checked-unchecked":"checked-indeterminate";break;case 3:n=1===e?"indeterminate-checked":"indeterminate-unchecked"}return`mat-checkbox-anim-${n}`}_syncIndeterminate(t){const e=this._inputElement;e&&(e.nativeElement.indeterminate=t)}}OY.ɵfac=function t(e){return new(e||OY)(Sm(hg),Sm(Ug),Sm(SI),Sm(a_),Na("tabindex"),Sm(VP,8),Sm(bY,8))},OY.ɵcmp=to({type:OY,selectors:[["mat-checkbox"]],viewQuery:function t(e,n){if(1&e&&(Qh(gY,5),Qh(kH,5)),2&e){let t;Jh(t=tb())&&(n._inputElement=t.first),Jh(t=tb())&&(n.ripple=t.first)}},hostAttrs:[1,"mat-checkbox"],hostVars:12,hostBindings:function 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0 24 24",0,"xml","space","preserve","aria-hidden","true",1,"mat-checkbox-checkmark"],["fill","none","stroke","white","d","M4.1,12.7 9,17.6 20.3,6.3",1,"mat-checkbox-checkmark-path"],[1,"mat-checkbox-mixedmark"],[1,"mat-checkbox-label",3,"cdkObserveContent"],["checkboxLabel",""],[2,"display","none"]],template:function t(e,n){if(1&e&&(Zm(),Rm(0,"label",0,1),Rm(2,"span",2),Rm(3,"input",3,4),Vm("change",(function t(e){return n._onInteractionEvent(e)}))("click",(function t(e){return n._onInputClick(e)})),Am(),Rm(5,"span",5),Tm(6,"span",6),Am(),Tm(7,"span",7),Rm(8,"span",8),qi(),Rm(9,"svg",9),Tm(10,"path",10),Am(),Zi(),Tm(11,"span",11),Am(),Am(),Rm(12,"span",12,13),Vm("cdkObserveContent",(function t(){return n._onLabelTextChange()})),Rm(14,"span",14),ku(15," "),Am(),Xm(16),Am(),Am()),2&e){const t=$p(1),e=$p(13);jp("for",n.inputId),rc(2),pu("mat-checkbox-inner-container-no-side-margin",!e.textContent||!e.textContent.trim()),rc(1),Dm("id",n.inputId)("required",n.required)("checked",n.checked)("disabled",n.disabled)("tabIndex",n.tabIndex),jp("value",n.value)("name",n.name)("aria-label",n.ariaLabel||null)("aria-labelledby",n.ariaLabelledby)("aria-checked",n._getAriaChecked())("aria-describedby",n.ariaDescribedby),rc(2),Dm("matRippleTrigger",t)("matRippleDisabled",n._isRippleDisabled())("matRippleRadius",20)("matRippleCentered",!0)("matRippleAnimation",Mh(19,hY,"NoopAnimations"===n._animationMode?0:150))}},directives:[kH,jz],styles:["@keyframes mat-checkbox-fade-in-background{0%{opacity:0}50%{opacity:1}}@keyframes mat-checkbox-fade-out-background{0%,50%{opacity:1}100%{opacity:0}}@keyframes mat-checkbox-unchecked-checked-checkmark-path{0%,50%{stroke-dashoffset:22.910259}50%{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1)}100%{stroke-dashoffset:0}}@keyframes mat-checkbox-unchecked-indeterminate-mixedmark{0%,68.2%{transform:scaleX(0)}68.2%{animation-timing-function:cubic-bezier(0, 0, 0, 1)}100%{transform:scaleX(1)}}@keyframes mat-checkbox-checked-unchecked-checkmark-path{from{animation-timing-function:cubic-bezier(0.4, 0, 1, 1);stroke-dashoffset:0}to{stroke-dashoffset:-22.910259}}@keyframes mat-checkbox-checked-indeterminate-checkmark{from{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1);opacity:1;transform:rotate(0deg)}to{opacity:0;transform:rotate(45deg)}}@keyframes mat-checkbox-indeterminate-checked-checkmark{from{animation-timing-function:cubic-bezier(0.14, 0, 0, 1);opacity:0;transform:rotate(45deg)}to{opacity:1;transform:rotate(360deg)}}@keyframes mat-checkbox-checked-indeterminate-mixedmark{from{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1);opacity:0;transform:rotate(-45deg)}to{opacity:1;transform:rotate(0deg)}}@keyframes mat-checkbox-indeterminate-checked-mixedmark{from{animation-timing-function:cubic-bezier(0.14, 0, 0, 1);opacity:1;transform:rotate(0deg)}to{opacity:0;transform:rotate(315deg)}}@keyframes mat-checkbox-indeterminate-unchecked-mixedmark{0%{animation-timing-function:linear;opacity:1;transform:scaleX(1)}32.8%,100%{opacity:0;transform:scaleX(0)}}.mat-checkbox-background,.mat-checkbox-frame{top:0;left:0;right:0;bottom:0;position:absolute;border-radius:2px;box-sizing:border-box;pointer-events:none}.mat-checkbox{display:inline-block;transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);cursor:pointer;-webkit-tap-highlight-color:transparent}._mat-animation-noopable.mat-checkbox{transition:none;animation:none}.mat-checkbox .mat-ripple-element:not(.mat-checkbox-persistent-ripple){opacity:.16}.mat-checkbox .mat-checkbox-ripple{position:absolute;left:calc(50% - 20px);top:calc(50% - 20px);height:40px;width:40px;z-index:1;pointer-events:none}.cdk-high-contrast-active .mat-checkbox.cdk-keyboard-focused .mat-checkbox-ripple{outline:solid 3px}.mat-checkbox-layout{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:inherit;align-items:baseline;vertical-align:middle;display:inline-flex;white-space:nowrap}.mat-checkbox-label{-webkit-user-select:auto;-moz-user-select:auto;-ms-user-select:auto;user-select:auto}.mat-checkbox-inner-container{display:inline-block;height:16px;line-height:0;margin:auto;margin-right:8px;order:0;position:relative;vertical-align:middle;white-space:nowrap;width:16px;flex-shrink:0}[dir=rtl] .mat-checkbox-inner-container{margin-left:8px;margin-right:auto}.mat-checkbox-inner-container-no-side-margin{margin-left:0;margin-right:0}.mat-checkbox-frame{background-color:transparent;transition:border-color 90ms cubic-bezier(0, 0, 0.2, 0.1);border-width:2px;border-style:solid}._mat-animation-noopable .mat-checkbox-frame{transition:none}.mat-checkbox-background{align-items:center;display:inline-flex;justify-content:center;transition:background-color 90ms cubic-bezier(0, 0, 0.2, 0.1),opacity 90ms cubic-bezier(0, 0, 0.2, 0.1);-webkit-print-color-adjust:exact;color-adjust:exact}._mat-animation-noopable .mat-checkbox-background{transition:none}.cdk-high-contrast-active .mat-checkbox .mat-checkbox-background{background:none}.mat-checkbox-persistent-ripple{display:block;width:100%;height:100%;transform:none}.mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{opacity:.04}.mat-checkbox.cdk-keyboard-focused .mat-checkbox-persistent-ripple{opacity:.12}.mat-checkbox-persistent-ripple,.mat-checkbox.mat-checkbox-disabled .mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{opacity:0}@media(hover: none){.mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{display:none}}.mat-checkbox-checkmark{top:0;left:0;right:0;bottom:0;position:absolute;width:100%}.mat-checkbox-checkmark-path{stroke-dashoffset:22.910259;stroke-dasharray:22.910259;stroke-width:2.1333333333px}.cdk-high-contrast-black-on-white .mat-checkbox-checkmark-path{stroke:#000 !important}.mat-checkbox-mixedmark{width:calc(100% - 6px);height:2px;opacity:0;transform:scaleX(0) rotate(0deg);border-radius:2px}.cdk-high-contrast-active .mat-checkbox-mixedmark{height:0;border-top:solid 2px;margin-top:2px}.mat-checkbox-label-before .mat-checkbox-inner-container{order:1;margin-left:8px;margin-right:auto}[dir=rtl] .mat-checkbox-label-before .mat-checkbox-inner-container{margin-left:auto;margin-right:8px}.mat-checkbox-checked .mat-checkbox-checkmark{opacity:1}.mat-checkbox-checked .mat-checkbox-checkmark-path{stroke-dashoffset:0}.mat-checkbox-checked .mat-checkbox-mixedmark{transform:scaleX(1) rotate(-45deg)}.mat-checkbox-indeterminate .mat-checkbox-checkmark{opacity:0;transform:rotate(45deg)}.mat-checkbox-indeterminate .mat-checkbox-checkmark-path{stroke-dashoffset:0}.mat-checkbox-indeterminate .mat-checkbox-mixedmark{opacity:1;transform:scaleX(1) rotate(0deg)}.mat-checkbox-unchecked .mat-checkbox-background{background-color:transparent}.mat-checkbox-disabled{cursor:default}.cdk-high-contrast-active .mat-checkbox-disabled{opacity:.5}.mat-checkbox-anim-unchecked-checked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-in-background}.mat-checkbox-anim-unchecked-checked .mat-checkbox-checkmark-path{animation:180ms linear 0ms mat-checkbox-unchecked-checked-checkmark-path}.mat-checkbox-anim-unchecked-indeterminate .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-in-background}.mat-checkbox-anim-unchecked-indeterminate .mat-checkbox-mixedmark{animation:90ms linear 0ms mat-checkbox-unchecked-indeterminate-mixedmark}.mat-checkbox-anim-checked-unchecked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-out-background}.mat-checkbox-anim-checked-unchecked .mat-checkbox-checkmark-path{animation:90ms linear 0ms mat-checkbox-checked-unchecked-checkmark-path}.mat-checkbox-anim-checked-indeterminate .mat-checkbox-checkmark{animation:90ms linear 0ms mat-checkbox-checked-indeterminate-checkmark}.mat-checkbox-anim-checked-indeterminate .mat-checkbox-mixedmark{animation:90ms linear 0ms mat-checkbox-checked-indeterminate-mixedmark}.mat-checkbox-anim-indeterminate-checked .mat-checkbox-checkmark{animation:500ms linear 0ms mat-checkbox-indeterminate-checked-checkmark}.mat-checkbox-anim-indeterminate-checked .mat-checkbox-mixedmark{animation:500ms linear 0ms mat-checkbox-indeterminate-checked-mixedmark}.mat-checkbox-anim-indeterminate-unchecked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-out-background}.mat-checkbox-anim-indeterminate-unchecked .mat-checkbox-mixedmark{animation:300ms linear 0ms mat-checkbox-indeterminate-unchecked-mixedmark}.mat-checkbox-input{bottom:0;left:50%}\n"],encapsulation:2,changeDetection:0}),OY.ctorParameters=()=>[{type:hg},{type:Ug},{type:SI},{type:a_},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[bY]}]}],OY.propDecorators={ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],ariaDescribedby:[{type:xy,args:["aria-describedby"]}],id:[{type:xy}],required:[{type:xy}],labelPosition:[{type:xy}],name:[{type:xy}],change:[{type:Oy}],indeterminateChange:[{type:Oy}],value:[{type:xy}],_inputElement:[{type:Za,args:["input"]}],ripple:[{type:Za,args:[kH]}],checked:[{type:xy}],disabled:[{type:xy}],indeterminate:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(OY,[{type:My,args:[{selector:"mat-checkbox",template:'<label [attr.for]="inputId" class="mat-checkbox-layout" #label>\n  <span class="mat-checkbox-inner-container"\n       [class.mat-checkbox-inner-container-no-side-margin]="!checkboxLabel.textContent || !checkboxLabel.textContent.trim()">\n    <input #input\n           class="mat-checkbox-input cdk-visually-hidden" type="checkbox"\n           [id]="inputId"\n           [required]="required"\n           [checked]="checked"\n           [attr.value]="value"\n           [disabled]="disabled"\n           [attr.name]="name"\n           [tabIndex]="tabIndex"\n           [attr.aria-label]="ariaLabel || null"\n           [attr.aria-labelledby]="ariaLabelledby"\n           [attr.aria-checked]="_getAriaChecked()"\n           [attr.aria-describedby]="ariaDescribedby"\n           (change)="_onInteractionEvent($event)"\n           (click)="_onInputClick($event)">\n    <span matRipple class="mat-checkbox-ripple mat-focus-indicator"\n         [matRippleTrigger]="label"\n         [matRippleDisabled]="_isRippleDisabled()"\n         [matRippleRadius]="20"\n         [matRippleCentered]="true"\n         [matRippleAnimation]="{enterDuration: _animationMode === \'NoopAnimations\' ? 0 : 150}">\n      <span class="mat-ripple-element mat-checkbox-persistent-ripple"></span>\n    </span>\n    <span class="mat-checkbox-frame"></span>\n    <span class="mat-checkbox-background">\n      <svg version="1.1"\n           focusable="false"\n           class="mat-checkbox-checkmark"\n           viewBox="0 0 24 24"\n           xml:space="preserve"\n           aria-hidden="true">\n        <path class="mat-checkbox-checkmark-path"\n              fill="none"\n              stroke="white"\n              d="M4.1,12.7 9,17.6 20.3,6.3"/>\n      </svg>\n      \x3c!-- Element for rendering the indeterminate state checkbox. --\x3e\n      <span class="mat-checkbox-mixedmark"></span>\n    </span>\n  </span>\n  <span class="mat-checkbox-label" #checkboxLabel (cdkObserveContent)="_onLabelTextChange()">\n    \x3c!-- Add an invisible span so JAWS can read the label --\x3e\n    <span style="display:none">&nbsp;</span>\n    <ng-content></ng-content>\n  </span>\n</label>\n',exportAs:"matCheckbox",host:{class:"mat-checkbox","[id]":"id","[attr.tabindex]":"null","[class.mat-checkbox-indeterminate]":"indeterminate","[class.mat-checkbox-checked]":"checked","[class.mat-checkbox-disabled]":"disabled","[class.mat-checkbox-label-before]":'labelPosition == "before"',"[class._mat-animation-noopable]":"_animationMode === 'NoopAnimations'"},providers:[MY],inputs:["disableRipple","color","tabIndex"],encapsulation:Hn.None,changeDetection:zn.OnPush,styles:["@keyframes mat-checkbox-fade-in-background{0%{opacity:0}50%{opacity:1}}@keyframes mat-checkbox-fade-out-background{0%,50%{opacity:1}100%{opacity:0}}@keyframes mat-checkbox-unchecked-checked-checkmark-path{0%,50%{stroke-dashoffset:22.910259}50%{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1)}100%{stroke-dashoffset:0}}@keyframes mat-checkbox-unchecked-indeterminate-mixedmark{0%,68.2%{transform:scaleX(0)}68.2%{animation-timing-function:cubic-bezier(0, 0, 0, 1)}100%{transform:scaleX(1)}}@keyframes mat-checkbox-checked-unchecked-checkmark-path{from{animation-timing-function:cubic-bezier(0.4, 0, 1, 1);stroke-dashoffset:0}to{stroke-dashoffset:-22.910259}}@keyframes mat-checkbox-checked-indeterminate-checkmark{from{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1);opacity:1;transform:rotate(0deg)}to{opacity:0;transform:rotate(45deg)}}@keyframes mat-checkbox-indeterminate-checked-checkmark{from{animation-timing-function:cubic-bezier(0.14, 0, 0, 1);opacity:0;transform:rotate(45deg)}to{opacity:1;transform:rotate(360deg)}}@keyframes mat-checkbox-checked-indeterminate-mixedmark{from{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1);opacity:0;transform:rotate(-45deg)}to{opacity:1;transform:rotate(0deg)}}@keyframes mat-checkbox-indeterminate-checked-mixedmark{from{animation-timing-function:cubic-bezier(0.14, 0, 0, 1);opacity:1;transform:rotate(0deg)}to{opacity:0;transform:rotate(315deg)}}@keyframes mat-checkbox-indeterminate-unchecked-mixedmark{0%{animation-timing-function:linear;opacity:1;transform:scaleX(1)}32.8%,100%{opacity:0;transform:scaleX(0)}}.mat-checkbox-background,.mat-checkbox-frame{top:0;left:0;right:0;bottom:0;position:absolute;border-radius:2px;box-sizing:border-box;pointer-events:none}.mat-checkbox{display:inline-block;transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);cursor:pointer;-webkit-tap-highlight-color:transparent}._mat-animation-noopable.mat-checkbox{transition:none;animation:none}.mat-checkbox .mat-ripple-element:not(.mat-checkbox-persistent-ripple){opacity:.16}.mat-checkbox .mat-checkbox-ripple{position:absolute;left:calc(50% - 20px);top:calc(50% - 20px);height:40px;width:40px;z-index:1;pointer-events:none}.cdk-high-contrast-active .mat-checkbox.cdk-keyboard-focused .mat-checkbox-ripple{outline:solid 3px}.mat-checkbox-layout{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:inherit;align-items:baseline;vertical-align:middle;display:inline-flex;white-space:nowrap}.mat-checkbox-label{-webkit-user-select:auto;-moz-user-select:auto;-ms-user-select:auto;user-select:auto}.mat-checkbox-inner-container{display:inline-block;height:16px;line-height:0;margin:auto;margin-right:8px;order:0;position:relative;vertical-align:middle;white-space:nowrap;width:16px;flex-shrink:0}[dir=rtl] .mat-checkbox-inner-container{margin-left:8px;margin-right:auto}.mat-checkbox-inner-container-no-side-margin{margin-left:0;margin-right:0}.mat-checkbox-frame{background-color:transparent;transition:border-color 90ms cubic-bezier(0, 0, 0.2, 0.1);border-width:2px;border-style:solid}._mat-animation-noopable .mat-checkbox-frame{transition:none}.mat-checkbox-background{align-items:center;display:inline-flex;justify-content:center;transition:background-color 90ms cubic-bezier(0, 0, 0.2, 0.1),opacity 90ms cubic-bezier(0, 0, 0.2, 0.1);-webkit-print-color-adjust:exact;color-adjust:exact}._mat-animation-noopable .mat-checkbox-background{transition:none}.cdk-high-contrast-active .mat-checkbox .mat-checkbox-background{background:none}.mat-checkbox-persistent-ripple{display:block;width:100%;height:100%;transform:none}.mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{opacity:.04}.mat-checkbox.cdk-keyboard-focused .mat-checkbox-persistent-ripple{opacity:.12}.mat-checkbox-persistent-ripple,.mat-checkbox.mat-checkbox-disabled .mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{opacity:0}@media(hover: none){.mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{display:none}}.mat-checkbox-checkmark{top:0;left:0;right:0;bottom:0;position:absolute;width:100%}.mat-checkbox-checkmark-path{stroke-dashoffset:22.910259;stroke-dasharray:22.910259;stroke-width:2.1333333333px}.cdk-high-contrast-black-on-white .mat-checkbox-checkmark-path{stroke:#000 !important}.mat-checkbox-mixedmark{width:calc(100% - 6px);height:2px;opacity:0;transform:scaleX(0) rotate(0deg);border-radius:2px}.cdk-high-contrast-active .mat-checkbox-mixedmark{height:0;border-top:solid 2px;margin-top:2px}.mat-checkbox-label-before .mat-checkbox-inner-container{order:1;margin-left:8px;margin-right:auto}[dir=rtl] .mat-checkbox-label-before .mat-checkbox-inner-container{margin-left:auto;margin-right:8px}.mat-checkbox-checked .mat-checkbox-checkmark{opacity:1}.mat-checkbox-checked .mat-checkbox-checkmark-path{stroke-dashoffset:0}.mat-checkbox-checked .mat-checkbox-mixedmark{transform:scaleX(1) rotate(-45deg)}.mat-checkbox-indeterminate .mat-checkbox-checkmark{opacity:0;transform:rotate(45deg)}.mat-checkbox-indeterminate .mat-checkbox-checkmark-path{stroke-dashoffset:0}.mat-checkbox-indeterminate .mat-checkbox-mixedmark{opacity:1;transform:scaleX(1) rotate(0deg)}.mat-checkbox-unchecked .mat-checkbox-background{background-color:transparent}.mat-checkbox-disabled{cursor:default}.cdk-high-contrast-active .mat-checkbox-disabled{opacity:.5}.mat-checkbox-anim-unchecked-checked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-in-background}.mat-checkbox-anim-unchecked-checked .mat-checkbox-checkmark-path{animation:180ms linear 0ms mat-checkbox-unchecked-checked-checkmark-path}.mat-checkbox-anim-unchecked-indeterminate .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-in-background}.mat-checkbox-anim-unchecked-indeterminate .mat-checkbox-mixedmark{animation:90ms linear 0ms mat-checkbox-unchecked-indeterminate-mixedmark}.mat-checkbox-anim-checked-unchecked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-out-background}.mat-checkbox-anim-checked-unchecked .mat-checkbox-checkmark-path{animation:90ms linear 0ms mat-checkbox-checked-unchecked-checkmark-path}.mat-checkbox-anim-checked-indeterminate .mat-checkbox-checkmark{animation:90ms linear 0ms mat-checkbox-checked-indeterminate-checkmark}.mat-checkbox-anim-checked-indeterminate .mat-checkbox-mixedmark{animation:90ms linear 0ms mat-checkbox-checked-indeterminate-mixedmark}.mat-checkbox-anim-indeterminate-checked .mat-checkbox-checkmark{animation:500ms linear 0ms mat-checkbox-indeterminate-checked-checkmark}.mat-checkbox-anim-indeterminate-checked .mat-checkbox-mixedmark{animation:500ms linear 0ms mat-checkbox-indeterminate-checked-mixedmark}.mat-checkbox-anim-indeterminate-unchecked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-out-background}.mat-checkbox-anim-indeterminate-unchecked .mat-checkbox-mixedmark{animation:300ms linear 0ms mat-checkbox-indeterminate-unchecked-mixedmark}.mat-checkbox-input{bottom:0;left:50%}\n"]}]}],(function(){return[{type:hg},{type:Ug},{type:SI},{type:a_},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[bY]}]}]}),{ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],id:[{type:xy}],labelPosition:[{type:xy}],name:[{type:xy}],change:[{type:Oy}],indeterminateChange:[{type:Oy}],required:[{type:xy}],checked:[{type:xy}],disabled:[{type:xy}],indeterminate:[{type:xy}],ariaDescribedby:[{type:xy,args:["aria-describedby"]}],value:[{type:xy}],_inputElement:[{type:Za,args:["input"]}],ripple:[{type:Za,args:[kH]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const PY={provide:GV,useExisting:qe((()=>wY)),multi:!0};class wY extends KU{}wY.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(wY)))(n||wY)}})(),wY.ɵdir=lo({type:wY,selectors:[["mat-checkbox","required","","formControlName",""],["mat-checkbox","required","","formControl",""],["mat-checkbox","required","","ngModel",""]],features:[pg([PY]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(wY,[{type:Cy,args:[{selector:"mat-checkbox[required][formControlName],\n             mat-checkbox[required][formControl], mat-checkbox[required][ngModel]",providers:[PY]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class kY{}kY.ɵfac=function t(e){return new(e||kY)},kY.ɵmod=ao({type:kY}),kY.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(kY,[{type:Ay,args:[{exports:[wY],declarations:[wY]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(kY,{declarations:[wY],exports:[wY]});class SY{}SY.ɵfac=function t(e){return new(e||SY)},SY.ɵmod=ao({type:SY}),SY.ɵinj=vn({imports:[[SH,XI,Uz,kY],XI,kY]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(SY,[{type:Ay,args:[{imports:[SH,XI,Uz,kY],exports:[OY,XI,kY],declarations:[OY]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(SY,{declarations:function(){return[OY]},imports:function(){return[SH,XI,Uz,kY]},exports:function(){return[OY,XI,kY]}});const DY=Nz({passive:!0});class EY{constructor(t,e){this._platform=t,this._ngZone=e,this._monitoredElements=new Map}monitor(t){if(!this._platform.isBrowser)return rt;const e=xz(t),n=this._monitoredElements.get(e);if(n)return n.subject;const o=new I,i="cdk-text-field-autofilled",a=t=>{"cdk-text-field-autofill-start"!==t.animationName||e.classList.contains(i)?"cdk-text-field-autofill-end"===t.animationName&&e.classList.contains(i)&&(e.classList.remove(i),this._ngZone.run((()=>o.next({target:t.target,isAutofilled:!1})))):(e.classList.add(i),this._ngZone.run((()=>o.next({target:t.target,isAutofilled:!0}))))};return this._ngZone.runOutsideAngular((()=>{e.addEventListener("animationstart",a,DY),e.classList.add("cdk-text-field-autofill-monitored")})),this._monitoredElements.set(e,{subject:o,unlisten:()=>{e.removeEventListener("animationstart",a,DY)}}),o}stopMonitoring(t){const e=xz(t),n=this._monitoredElements.get(e);n&&(n.unlisten(),n.subject.complete(),e.classList.remove("cdk-text-field-autofill-monitored"),e.classList.remove("cdk-text-field-autofilled"),this._monitoredElements.delete(e))}ngOnDestroy(){this._monitoredElements.forEach(((t,e)=>this.stopMonitoring(e)))}}EY.ɵfac=function t(e){return new(e||EY)(vr(wz),vr(a_))},EY.ɵprov=Mn({factory:function t(){return new EY(vr(wz),vr(a_))},token:EY,providedIn:"root"}),EY.ctorParameters=()=>[{type:wz},{type:a_}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(EY,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:wz},{type:a_}]}),null);class RY{constructor(t,e){this._elementRef=t,this._autofillMonitor=e,this.cdkAutofill=new Lh}ngOnInit(){this._autofillMonitor.monitor(this._elementRef).subscribe((t=>this.cdkAutofill.emit(t)))}ngOnDestroy(){this._autofillMonitor.stopMonitoring(this._elementRef)}}RY.ɵfac=function t(e){return new(e||RY)(Sm(hg),Sm(EY))},RY.ɵdir=lo({type:RY,selectors:[["","cdkAutofill",""]],outputs:{cdkAutofill:"cdkAutofill"}}),RY.ctorParameters=()=>[{type:hg},{type:EY}],RY.propDecorators={cdkAutofill:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(RY,[{type:Cy,args:[{selector:"[cdkAutofill]"}]}],(function(){return[{type:hg},{type:EY}]}),{cdkAutofill:[{type:Oy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class AY{constructor(t,e,n,o){this._elementRef=t,this._platform=e,this._ngZone=n,this._destroyed=new I,this._enabled=!0,this._previousMinRows=-1,this._isViewInited=!1,this._handleFocusEvent=t=>{this._hasFocus="focus"===t.type},this._document=o,this._textareaElement=this._elementRef.nativeElement}get minRows(){return this._minRows}set minRows(t){this._minRows=_z(t),this._setMinHeight()}get maxRows(){return this._maxRows}set maxRows(t){this._maxRows=_z(t),this._setMaxHeight()}get enabled(){return this._enabled}set enabled(t){t=yz(t),this._enabled!==t&&((this._enabled=t)?this.resizeToFitContent(!0):this.reset())}get placeholder(){return this._textareaElement.placeholder}set placeholder(t){this._cachedPlaceholderHeight=void 0,this._textareaElement.placeholder=t,this._cacheTextareaPlaceholderHeight()}_setMinHeight(){const t=this.minRows&&this._cachedLineHeight?this.minRows*this._cachedLineHeight+"px":null;t&&(this._textareaElement.style.minHeight=t)}_setMaxHeight(){const t=this.maxRows&&this._cachedLineHeight?this.maxRows*this._cachedLineHeight+"px":null;t&&(this._textareaElement.style.maxHeight=t)}ngAfterViewInit(){this._platform.isBrowser&&(this._initialHeight=this._textareaElement.style.height,this.resizeToFitContent(),this._ngZone.runOutsideAngular((()=>{oe(this._getWindow(),"resize").pipe(de(16),Ie(this._destroyed)).subscribe((()=>this.resizeToFitContent(!0))),this._textareaElement.addEventListener("focus",this._handleFocusEvent),this._textareaElement.addEventListener("blur",this._handleFocusEvent)})),this._isViewInited=!0,this.resizeToFitContent(!0))}ngOnDestroy(){this._textareaElement.removeEventListener("focus",this._handleFocusEvent),this._textareaElement.removeEventListener("blur",this._handleFocusEvent),this._destroyed.next(),this._destroyed.complete()}_cacheTextareaLineHeight(){if(this._cachedLineHeight)return;let t=this._textareaElement.cloneNode(!1);t.rows=1,t.style.position="absolute",t.style.visibility="hidden",t.style.border="none",t.style.padding="0",t.style.height="",t.style.minHeight="",t.style.maxHeight="",t.style.overflow="hidden",this._textareaElement.parentNode.appendChild(t),this._cachedLineHeight=t.clientHeight,this._textareaElement.parentNode.removeChild(t),this._setMinHeight(),this._setMaxHeight()}_measureScrollHeight(){const t=this._textareaElement,e=t.style.marginBottom||"",n=this._platform.FIREFOX,o=n&&this._hasFocus,i=n?"cdk-textarea-autosize-measuring-firefox":"cdk-textarea-autosize-measuring";o&&(t.style.marginBottom=`${t.clientHeight}px`),t.classList.add(i);const a=t.scrollHeight-4;return t.classList.remove(i),o&&(t.style.marginBottom=e),a}_cacheTextareaPlaceholderHeight(){if(!this._isViewInited||null!=this._cachedPlaceholderHeight)return;if(!this.placeholder)return void(this._cachedPlaceholderHeight=0);const t=this._textareaElement.value;this._textareaElement.value=this._textareaElement.placeholder,this._cachedPlaceholderHeight=this._measureScrollHeight(),this._textareaElement.value=t}ngDoCheck(){this._platform.isBrowser&&this.resizeToFitContent()}resizeToFitContent(t=!1){if(!this._enabled)return;if(this._cacheTextareaLineHeight(),this._cacheTextareaPlaceholderHeight(),!this._cachedLineHeight)return;const e=this._elementRef.nativeElement,n=e.value;if(!t&&this._minRows===this._previousMinRows&&n===this._previousValue)return;const o=this._measureScrollHeight(),i=Math.max(o,this._cachedPlaceholderHeight||0);e.style.height=`${i}px`,this._ngZone.runOutsideAngular((()=>{"undefined"!=typeof requestAnimationFrame?requestAnimationFrame((()=>this._scrollToCaretPosition(e))):setTimeout((()=>this._scrollToCaretPosition(e)))})),this._previousValue=n,this._previousMinRows=this._minRows}reset(){void 0!==this._initialHeight&&(this._textareaElement.style.height=this._initialHeight)}_noopInputHandler(){}_getDocument(){return this._document||document}_getWindow(){return this._getDocument().defaultView||window}_scrollToCaretPosition(t){const{selectionStart:e,selectionEnd:n}=t;!this._destroyed.isStopped&&this._hasFocus&&t.setSelectionRange(e,n)}}AY.ɵfac=function t(e){return new(e||AY)(Sm(hg),Sm(wz),Sm(a_),Sm(Z_,8))},AY.ɵdir=lo({type:AY,selectors:[["textarea","cdkTextareaAutosize",""]],hostAttrs:["rows","1",1,"cdk-textarea-autosize"],hostBindings:function t(e,n){1&e&&Vm("input",(function t(){return n._noopInputHandler()}))},inputs:{minRows:["cdkAutosizeMinRows","minRows"],maxRows:["cdkAutosizeMaxRows","maxRows"],enabled:["cdkTextareaAutosize","enabled"],placeholder:"placeholder"},exportAs:["cdkTextareaAutosize"]}),AY.ctorParameters=()=>[{type:hg},{type:wz},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]}],AY.propDecorators={minRows:[{type:xy,args:["cdkAutosizeMinRows"]}],maxRows:[{type:xy,args:["cdkAutosizeMaxRows"]}],enabled:[{type:xy,args:["cdkTextareaAutosize"]}],placeholder:[{type:xy}],_noopInputHandler:[{type:wy,args:["input"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(AY,[{type:Cy,args:[{selector:"textarea[cdkTextareaAutosize]",exportAs:"cdkTextareaAutosize",host:{class:"cdk-textarea-autosize",rows:"1"}}]}],(function(){return[{type:hg},{type:wz},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]}]}),{minRows:[{type:xy,args:["cdkAutosizeMinRows"]}],maxRows:[{type:xy,args:["cdkAutosizeMaxRows"]}],enabled:[{type:xy,args:["cdkTextareaAutosize"]}],placeholder:[{type:xy}],_noopInputHandler:[{type:wy,args:["input"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class TY{}TY.ɵfac=function t(e){return new(e||TY)},TY.ɵmod=ao({type:TY}),TY.ɵinj=vn({imports:[[kz]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(TY,[{type:Ay,args:[{declarations:[RY,AY],imports:[kz],exports:[RY,AY]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(TY,{declarations:function(){return[RY,AY]},imports:function(){return[kz]},exports:function(){return[RY,AY]}});class NY extends AY{get matAutosizeMinRows(){return this.minRows}set matAutosizeMinRows(t){this.minRows=t}get matAutosizeMaxRows(){return this.maxRows}set matAutosizeMaxRows(t){this.maxRows=t}get matAutosize(){return this.enabled}set matAutosize(t){this.enabled=t}get matTextareaAutosize(){return this.enabled}set matTextareaAutosize(t){this.enabled=t}}NY.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(NY)))(n||NY)}})(),NY.ɵdir=lo({type:NY,selectors:[["textarea","mat-autosize",""],["textarea","matTextareaAutosize",""]],hostAttrs:["rows","1",1,"cdk-textarea-autosize","mat-autosize"],inputs:{cdkAutosizeMinRows:"cdkAutosizeMinRows",cdkAutosizeMaxRows:"cdkAutosizeMaxRows",matAutosizeMinRows:"matAutosizeMinRows",matAutosizeMaxRows:"matAutosizeMaxRows",matAutosize:["mat-autosize","matAutosize"],matTextareaAutosize:"matTextareaAutosize"},exportAs:["matTextareaAutosize"],features:[xp]}),NY.propDecorators={matAutosizeMinRows:[{type:xy}],matAutosizeMaxRows:[{type:xy}],matAutosize:[{type:xy,args:["mat-autosize"]}],matTextareaAutosize:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(NY,[{type:Cy,args:[{selector:"textarea[mat-autosize], textarea[matTextareaAutosize]",exportAs:"matTextareaAutosize",inputs:["cdkAutosizeMinRows","cdkAutosizeMaxRows"],host:{class:"cdk-textarea-autosize mat-autosize",rows:"1"}}]}],null,{matAutosizeMinRows:[{type:xy}],matAutosizeMaxRows:[{type:xy}],matAutosize:[{type:xy,args:["mat-autosize"]}],matTextareaAutosize:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const zY=new Ga("MAT_INPUT_VALUE_ACCESSOR"),IY=["button","checkbox","file","hidden","image","radio","range","reset","submit"];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let HY=0;const FY=tH(class{constructor(t,e,n,o){this._defaultErrorStateMatcher=t,this._parentForm=e,this._parentFormGroup=n,this.ngControl=o}});class LY extends FY{constructor(t,e,n,o,i,a,r,s,l,c){super(a,o,i,n),this._elementRef=t,this._platform=e,this._autofillMonitor=s,this._formField=c,this._uid="mat-input-"+HY++,this.focused=!1,this.stateChanges=new I,this.controlType="mat-input",this.autofilled=!1,this._disabled=!1,this._required=!1,this._type="text",this._readonly=!1,this._neverEmptyInputTypes=["date","datetime","datetime-local","month","time","week"].filter((t=>Dz().has(t)));const d=this._elementRef.nativeElement,p=d.nodeName.toLowerCase();this._inputValueAccessor=r||d,this._previousNativeValue=this.value,this.id=this.id,e.IOS&&l.runOutsideAngular((()=>{t.nativeElement.addEventListener("keyup",(t=>{const e=t.target;e.value||0!==e.selectionStart||0!==e.selectionEnd||(e.setSelectionRange(1,1),e.setSelectionRange(0,0))}))})),this._isServer=!this._platform.isBrowser,this._isNativeSelect="select"===p,this._isTextarea="textarea"===p,this._isInFormField=!!c,this._isNativeSelect&&(this.controlType=d.multiple?"mat-native-select-multiple":"mat-native-select")}get disabled(){return this.ngControl&&null!==this.ngControl.disabled?this.ngControl.disabled:this._disabled}set disabled(t){this._disabled=yz(t),this.focused&&(this.focused=!1,this.stateChanges.next())}get id(){return this._id}set id(t){this._id=t||this._uid}get required(){return this._required}set required(t){this._required=yz(t)}get type(){return this._type}set type(t){this._type=t||"text",this._validateType(),!this._isTextarea&&Dz().has(this._type)&&(this._elementRef.nativeElement.type=this._type)}get value(){return this._inputValueAccessor.value}set value(t){t!==this.value&&(this._inputValueAccessor.value=t,this.stateChanges.next())}get readonly(){return this._readonly}set readonly(t){this._readonly=yz(t)}ngAfterViewInit(){this._platform.isBrowser&&this._autofillMonitor.monitor(this._elementRef.nativeElement).subscribe((t=>{this.autofilled=t.isAutofilled,this.stateChanges.next()}))}ngOnChanges(){this.stateChanges.next()}ngOnDestroy(){this.stateChanges.complete(),this._platform.isBrowser&&this._autofillMonitor.stopMonitoring(this._elementRef.nativeElement)}ngDoCheck(){this.ngControl&&this.updateErrorState(),this._dirtyCheckNativeValue(),this._dirtyCheckPlaceholder()}focus(t){this._elementRef.nativeElement.focus(t)}_focusChanged(t){t!==this.focused&&(this.focused=t,this.stateChanges.next())}_onInput(){}_dirtyCheckPlaceholder(){var t,e;const n=(null===(e=null===(t=this._formField)||void 0===t?void 0:t._hideControlPlaceholder)||void 0===e?void 0:e.call(t))?null:this.placeholder;if(n!==this._previousPlaceholder){const t=this._elementRef.nativeElement;this._previousPlaceholder=n,n?t.setAttribute("placeholder",n):t.removeAttribute("placeholder")}}_dirtyCheckNativeValue(){const t=this._elementRef.nativeElement.value;this._previousNativeValue!==t&&(this._previousNativeValue=t,this.stateChanges.next())}_validateType(){if(IY.indexOf(this._type)>-1&&("undefined"==typeof ngDevMode||ngDevMode))
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
throw(function t(e){return Error(`Input type "${e}" isn't supported by matInput.`)})(this._type)}_isNeverEmpty(){return this._neverEmptyInputTypes.indexOf(this._type)>-1}_isBadInput(){let t=this._elementRef.nativeElement.validity;return t&&t.badInput}get empty(){return!(this._isNeverEmpty()||this._elementRef.nativeElement.value||this._isBadInput()||this.autofilled)}get shouldLabelFloat(){if(this._isNativeSelect){const t=this._elementRef.nativeElement,e=t.options[0];return this.focused||t.multiple||!this.empty||!!(t.selectedIndex>-1&&e&&e.label)}return this.focused||!this.empty}setDescribedByIds(t){t.length?this._elementRef.nativeElement.setAttribute("aria-describedby",t.join(" ")):this._elementRef.nativeElement.removeAttribute("aria-describedby")}onContainerClick(){this.focused||this.focus()}}LY.ɵfac=function t(e){return new(e||LY)(Sm(hg),Sm(wz),Sm(Mj,10),Sm(iU,8),Sm(PU,8),Sm(bH),Sm(zY,10),Sm(EY),Sm(a_),Sm(RV,8))},LY.ɵdir=lo({type:LY,selectors:[["input","matInput",""],["textarea","matInput",""],["select","matNativeControl",""],["input","matNativeControl",""],["textarea","matNativeControl",""]],hostAttrs:[1,"mat-input-element","mat-form-field-autofill-control"],hostVars:9,hostBindings:function t(e,n){1&e&&Vm("focus",(function t(){return n._focusChanged(!0)}))("blur",(function t(){return n._focusChanged(!1)}))("input",(function t(){return n._onInput()})),2&e&&(Tu("disabled",n.disabled)("required",n.required),jp("id",n.id)("data-placeholder",n.placeholder)("readonly",n.readonly&&!n._isNativeSelect||null)("aria-invalid",n.empty&&n.required?null:n.errorState)("aria-required",n.required),pu("mat-input-server",n._isServer))},inputs:{id:"id",disabled:"disabled",required:"required",type:"type",value:"value",readonly:"readonly",placeholder:"placeholder",errorStateMatcher:"errorStateMatcher",userAriaDescribedBy:["aria-describedby","userAriaDescribedBy"]},exportAs:["matInput"],features:[pg([{provide:bV,useExisting:LY}]),xp,Bo]}),LY.ctorParameters=()=>[{type:hg},{type:wz},{type:Mj,decorators:[{type:Sr},{type:Dr}]},{type:iU,decorators:[{type:Sr}]},{type:PU,decorators:[{type:Sr}]},{type:bH},{type:void 0,decorators:[{type:Sr},{type:Dr},{type:kr,args:[zY]}]},{type:EY},{type:a_},{type:AV,decorators:[{type:Sr},{type:kr,args:[RV]}]}],LY.propDecorators={disabled:[{type:xy}],id:[{type:xy}],placeholder:[{type:xy}],required:[{type:xy}],type:[{type:xy}],errorStateMatcher:[{type:xy}],userAriaDescribedBy:[{type:xy,args:["aria-describedby"]}],value:[{type:xy}],readonly:[{type:xy}],_focusChanged:[{type:wy,args:["focus",["true"]]},{type:wy,args:["blur",["false"]]}],_onInput:[{type:wy,args:["input"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(LY,[{type:Cy,args:[{selector:"input[matInput], textarea[matInput], select[matNativeControl],\n      input[matNativeControl], textarea[matNativeControl]",exportAs:"matInput",host:{class:"mat-input-element mat-form-field-autofill-control","[class.mat-input-server]":"_isServer","[attr.id]":"id","[attr.data-placeholder]":"placeholder","[disabled]":"disabled","[required]":"required","[attr.readonly]":"readonly && !_isNativeSelect || null","[attr.aria-invalid]":"(empty && required) ? null : errorState","[attr.aria-required]":"required"},providers:[{provide:bV,useExisting:LY}]}]}],(function(){return[{type:hg},{type:wz},{type:Mj,decorators:[{type:Sr},{type:Dr}]},{type:iU,decorators:[{type:Sr}]},{type:PU,decorators:[{type:Sr}]},{type:bH},{type:void 0,decorators:[{type:Sr},{type:Dr},{type:kr,args:[zY]}]},{type:EY},{type:a_},{type:AV,decorators:[{type:Sr},{type:kr,args:[RV]}]}]}),{id:[{type:xy}],disabled:[{type:xy}],required:[{type:xy}],type:[{type:xy}],value:[{type:xy}],readonly:[{type:xy}],_focusChanged:[{type:wy,args:["focus",["true"]]},{type:wy,args:["blur",["false"]]}],_onInput:[{type:wy,args:["input"]}],placeholder:[{type:xy}],errorStateMatcher:[{type:xy}],userAriaDescribedBy:[{type:xy,args:["aria-describedby"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class BY{}function VY(t,e){if(1&t&&(Rm(0,"mat-error"),ku(1),Am()),2&t){const t=Ym();rc(1),Du(" Reload period has to be minimum of ",t.MIN_RELOAD_PERIOD_IN_S," seconds. ")}}function jY(t,e){1&t&&(Rm(0,"mat-error"),ku(1," Page size has to be a positive integer. "),Am())}BY.ɵfac=function t(e){return new(e||BY)},BY.ɵmod=ao({type:BY}),BY.ɵinj=vn({providers:[bH],imports:[[TY,TV,XI],TY,TV]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(BY,[{type:Ay,args:[{declarations:[LY,NY],imports:[TY,TV,XI],exports:[TY,TV,LY,NY],providers:[bH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(BY,{declarations:function(){return[LY,NY]},imports:function(){return[TY,TV,XI]},exports:function(){return[TY,TV,LY,NY]}});class UY{constructor(){this.reloadToggled=new Lh,this.reloadPeriodInMsChanged=new Lh,this.pageSizeChanged=new Lh,this.MIN_RELOAD_PERIOD_IN_S=30,this.reloadPeriodControl=new $j(this.MIN_RELOAD_PERIOD_IN_S,[qV.required,qV.min(this.MIN_RELOAD_PERIOD_IN_S)]),this.paginationControl=new $j(1,[qV.required,qV.min(1),t=>{const e=Number(t.value);return Math.round(e)===t.value?null:{integer:{value:t.value}}}]),this.ngUnsubscribe=new I}ngOnInit(){this.reloadPeriodControl.valueChanges.pipe(Ie(this.ngUnsubscribe),ge(500),ce((()=>this.reloadPeriodControl.valid))).subscribe((()=>{this.reloadPeriodControl.valid&&this.reloadPeriodInMsChanged.emit(1e3*this.reloadPeriodControl.value)})),this.paginationControl.valueChanges.pipe(Ie(this.ngUnsubscribe),ge(500),ce((()=>this.paginationControl.valid))).subscribe((()=>{this.pageSizeChanged.emit(this.paginationControl.value)}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}ngOnChanges(t){if(t.reloadPeriodInMs){const e=t.reloadPeriodInMs;e.previousValue!==e.currentValue&&this.reloadPeriodControl.setValue(e.currentValue/1e3)}if(t.reloadEnabled&&(t.reloadEnabled.currentValue?this.reloadPeriodControl.enable():this.reloadPeriodControl.disable()),t.pageSize){const e=t.pageSize;e.previousValue!==e.currentValue&&this.paginationControl.setValue(e.currentValue)}}onReloadToggle(){this.reloadToggled.emit()}}UY.ɵfac=function t(e){return new(e||UY)},UY.ɵcmp=to({type:UY,selectors:[["settings-dialog-component"]],inputs:{reloadEnabled:"reloadEnabled",reloadPeriodInMs:"reloadPeriodInMs",pageSize:"pageSize"},outputs:{reloadToggled:"reloadToggled",reloadPeriodInMsChanged:"reloadPeriodInMsChanged",pageSizeChanged:"pageSizeChanged"},features:[Bo],decls:14,vars:5,consts:[[1,"reload-toggle"],[3,"checked","change"],["matInput","","type","number","placeholder","Reload Period",1,"reload-period",3,"formControl"],[4,"ngIf"],["matInput","","type","number","placeholder","Pagination Limit",1,"page-size",3,"formControl"]],template:function t(e,n){1&e&&(Rm(0,"h3"),ku(1,"Settings"),Am(),Rm(2,"div"),Rm(3,"div",0),Rm(4,"mat-checkbox",1),Vm("change",(function t(){return n.onReloadToggle()})),ku(5,"Reload data"),Am(),Am(),Rm(6,"div"),Rm(7,"mat-form-field"),Tm(8,"input",2),Am(),Qp(9,VY,2,1,"mat-error",3),Am(),Am(),Rm(10,"div"),Rm(11,"mat-form-field"),Tm(12,"input",4),Am(),Qp(13,jY,2,0,"mat-error",3),Am()),2&e&&(rc(4),Dm("checked",n.reloadEnabled),rc(4),Dm("formControl",n.reloadPeriodControl),rc(1),Dm("ngIf",n.reloadPeriodControl.hasError("min")||n.reloadPeriodControl.hasError("required")),rc(3),Dm("formControl",n.paginationControl),rc(1),Dm("ngIf",n.paginationControl.invalid))},directives:[OY,AV,LY,fU,VV,xj,xU,dM,gV],styles:["[_nghost-%COMP%] {\n  font-size: 15px;\n}\n\n[_nghost-%COMP%]    > div[_ngcontent-%COMP%] {\n  margin: 10px 0;\n}\n\n[_nghost-%COMP%]    > [_ngcontent-%COMP%]:first-child {\n  margin-top: 0;\n}\n\n[_nghost-%COMP%]    > [_ngcontent-%COMP%]:last-child {\n  margin-bottom: 0;\n}\n\nh3[_ngcontent-%COMP%] {\n  font-size: 20px;\n}\n\n.reload-toggle[_ngcontent-%COMP%] {\n  margin-bottom: 10px;\n}"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(UY,[{type:My,args:[{selector:"settings-dialog-component",templateUrl:"settings_dialog_component.ng.html",styleUrls:["./settings_dialog_component.css"]}]}],null,{reloadEnabled:[{type:xy}],reloadPeriodInMs:[{type:xy}],pageSize:[{type:xy}],reloadToggled:[{type:Oy}],reloadPeriodInMsChanged:[{type:Oy}],pageSizeChanged:[{type:Oy}]});class GY{constructor(t){this.store=t,this.reloadEnabled$=this.store.select(DN),this.reloadPeriodInMs$=this.store.select(EN),this.pageSize$=this.store.select(RN)}onReloadToggled(){this.store.dispatch(vN())}onReloadPeriodInMsChanged(t){this.store.dispatch(xN({periodInMs:t}))}onPageSizeChanged(t){this.store.dispatch(ON({size:t}))}}GY.ɵfac=function t(e){return new(e||GY)(Sm(Iw))},GY.ɵcmp=to({type:GY,selectors:[["settings-dialog"]],decls:4,vars:9,consts:[[3,"reloadEnabled","reloadPeriodInMs","pageSize","reloadToggled","reloadPeriodInMsChanged","pageSizeChanged"]],template:function t(e,n){1&e&&(Rm(0,"settings-dialog-component",0),Vm("reloadToggled",(function t(){return n.onReloadToggled()}))("reloadPeriodInMsChanged",(function t(e){return n.onReloadPeriodInMsChanged(e)}))("pageSizeChanged",(function t(e){return n.onPageSizeChanged(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Am()),2&e&&Dm("reloadEnabled",Th(1,3,n.reloadEnabled$))("reloadPeriodInMs",Th(2,5,n.reloadPeriodInMs$))("pageSize",Th(3,7,n.pageSize$))},directives:[UY],pipes:[wM],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(GY,[{type:My,args:[{selector:"settings-dialog",template:'\n    <settings-dialog-component\n      [reloadEnabled]="reloadEnabled$ | async"\n      [reloadPeriodInMs]="reloadPeriodInMs$ | async"\n      [pageSize]="pageSize$ | async"\n      (reloadToggled)="onReloadToggled()"\n      (reloadPeriodInMsChanged)="onReloadPeriodInMsChanged($event)"\n      (pageSizeChanged)="onPageSizeChanged($event)"\n    ></settings-dialog-component>\n  '}]}],(function(){return[{type:Iw}]}),null);class WY{constructor(t){this.dialog=t}isButtonDisabled(){return this.settingsLoadState===yE.NOT_LOADED||this.settingsLoadState===yE.LOADING}openDialog(){this.dialog.open(GY,{width:"400px"})}}WY.ɵfac=function t(e){return new(e||WY)(Sm(oW))},WY.ɵcmp=to({type:WY,selectors:[["settings-button-component"]],inputs:{settingsLoadState:"settingsLoadState"},decls:2,vars:1,consts:[["mat-icon-button","",3,"disabled","click"],["svgIcon","settings_24px"]],template:function t(e,n){1&e&&(Rm(0,"button",0),Vm("click",(function t(){return n.openDialog()})),Tm(1,"mat-icon",1),Am()),2&e&&Dm("disabled",n.isButtonDisabled())},directives:[XH,DW],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(WY,[{type:My,args:[{selector:"settings-button-component",template:'\n    <button\n      mat-icon-button\n      [disabled]="isButtonDisabled()"\n      (click)="openDialog()"\n    >\n      <mat-icon svgIcon="settings_24px"></mat-icon>\n    </button>\n  '}]}],(function(){return[{type:oW}]}),{settingsLoadState:[{type:xy}]});class YY{constructor(t){this.store=t,this.settingsLoadState$=this.store.select(SN)}}YY.ɵfac=function t(e){return new(e||YY)(Sm(Iw))},YY.ɵcmp=to({type:YY,selectors:[["settings-button"]],decls:2,vars:3,consts:[[3,"settingsLoadState"]],template:function t(e,n){1&e&&(Tm(0,"settings-button-component",0),Ah(1,"async")),2&e&&Dm("settingsLoadState",Th(1,1,n.settingsLoadState$))},directives:[WY],pipes:[wM],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(YY,[{type:My,args:[{selector:"settings-button",template:'\n    <settings-button-component\n      [settingsLoadState]="settingsLoadState$ | async"\n    ></settings-button-component>\n  '}]}],(function(){return[{type:Iw}]}),null);class qY{}qY.ɵfac=function t(e){return new(e||qY)},qY.ɵcmp=to({type:qY,selectors:[["app-header"]],decls:10,vars:0,consts:[[1,"brand"],[1,"plugins"],["mat-icon-button","","href","https://github.com/tensorflow/tensorboard/blob/master/README.md","rel","noopener noreferrer","target","_blank","aria-label","Help",1,"readme"],["svgIcon","help_outline_24px"]],template:function t(e,n){1&e&&(Rm(0,"mat-toolbar"),Rm(1,"span",0),ku(2,"TensorBoard"),Am(),Tm(3,"plugin-selector",1),Tm(4,"tbdev-upload-button"),Tm(5,"app-header-dark-mode-toggle"),Tm(6,"app-header-reload"),Tm(7,"settings-button"),Rm(8,"a",2),Tm(9,"mat-icon",3),Am(),Am())},directives:[eB,VG,IW,cY,pY,YY,KH,DW],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], 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.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28122em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-form-field-autofill-control[_ngcontent-%COMP%]:-webkit-autofill + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28121em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.2812em) scale(0.75)}}.mat-form-field-appearance-fill[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:.25em 0 .75em 0}.mat-form-field-appearance-fill[_ngcontent-%COMP%]  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new(e||Sq)},Sq.ɵmod=ao({type:Sq}),Sq.ɵinj=vn({providers:[kq],imports:[[cE]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Sq,[{type:Ay,args:[{imports:[cE],providers:[kq]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Sq,{imports:[cE]});class Dq{}Dq.ɵfac=function t(e){return new(e||Dq)},Dq.ɵcmp=to({type:Dq,selectors:[["inactive-component"]],decls:54,vars:0,consts:[[1,"container"],[1,"title"],[1,"code"],[1,"arg"],[1,"exhibits-container"],[1,"exhibit"],[1,"screenshot"],["src","data:image/png;base64,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"],[1,"description"],["src","data:image/png;base64,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0!==o&&(i.graphExecutions=Object.assign(Object.assign({},t.graphExecutions),{focusIndex:o})),i}function xZ(t,e){return _Z(t,e)}const OZ=Kw(pZ),PZ=Zw(OZ,(t=>t.runs)),wZ=Zw(OZ,(t=>t.runsLoaded)),kZ=Zw(OZ,(t=>t.activeRunId)),SZ=Zw(OZ,(t=>t.lastDataPollOnsetTimeMs-t.lastNonEmptyPollDataTimeMs)),DZ=Zw(OZ,(t=>t.alerts)),EZ=Zw(DZ,(t=>t.alertsLoaded)),RZ=Zw(DZ,(t=>t.numAlerts)),AZ=Zw(DZ,(t=>t.focusType)),TZ=Zw(DZ,(t=>null===t.focusType?0:t.alertsBreakdown[t.focusType]||0)),NZ=Zw(DZ,(t=>null===t.focusType||void 0===t.alerts[t.focusType]?null:t.alerts[t.focusType])),zZ=Zw(DZ,(t=>t.alertsBreakdown)),IZ=Zw(OZ,(t=>t.executions)),HZ=Zw(IZ,(t=>t.numExecutionsLoaded)),FZ=Zw(IZ,(t=>t.executionDigestsLoaded)),LZ=Zw(IZ,(t=>t.executionDigestsLoaded.numExecutions)),BZ=Zw(IZ,(t=>t.scrollBeginIndex)),VZ=Zw(IZ,(t=>t.pageSize)),jZ=Zw(IZ,(t=>t.displayCount)),UZ=Zw(IZ,(t=>{const e=[];for(let n=t.scrollBeginIndex;n<t.scrollBeginIndex+t.displayCount;++n)e.push(n in 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a=e[t].graph_id,r=Math.max(0,t-200);for(let s=t-1;s>=r;--s)if(void 0!==e[s])for(let t=0;t<n.length&&(o[t]||e[s].graph_id!==a||e[s].op_name!==n[t].op_name||e[s].output_slot!==n[t].output_slot||(i.push(s),o[t]=!0,i.length!==n.length));++t);return i})),iX=Zw(tX,(t=>{const{focusedOp:e,ops:n}=t;if(null!==e&&void 0!==n[e.graphId]&&n[e.graphId].has(e.opName)){const t=n[e.graphId],{consumers:o}=t.get(e.opName);return o.map((e=>e.map((e=>{const n=Object.assign({},e);return t.has(e.op_name)&&(n.data=t.get(e.op_name)),n}))))}return null})),aX=Zw(OZ,(t=>{const e=t.executions.scrollBeginIndex,n=t.executions.scrollBeginIndex+t.executions.displayCount,o=new Array(n-e).fill(null),i=t.alerts.focusType;if(null===i)return o;const a=t.alerts.executionIndices[i];if(void 0===a)return o;for(let i=e;i<n;++i)a.includes(i)&&(o[i-e]=t.alerts.focusType);return o})),rX=Zw(OZ,(t=>t.executions)),sX=Zw(rX,(t=>t.focusIndex)),lX=Zw(rX,(t=>{if(null===t.focusIndex)return null;const{focusIndex:e,scrollBeginIndex:n,displayCount:o}=t;return e<n||e>=n+o?null:e-n})),cX=Zw(rX,(t=>t.executionData)),dX=Zw(OZ,(t=>t.graphs.loadingOps)),pX=Zw(OZ,(t=>t.stackFrames)),mX=Zw(rX,(t=>{const{focusIndex:e,executionData:n}=t;return null===e||void 0===n[e]?null:n[e]})),uX=Zw(OZ,sX,mX,eX,((t,e,n,o)=>{const{codeLocationFocusType:i}=t;return null===i?null:i===fZ.EXECUTION?null===e||null===n?null:{codeLocationType:fZ.EXECUTION,opType:n.op_type,executionIndex:e}:null===o?null:{codeLocationType:fZ.GRAPH_OP_CREATION,opType:o.op_type,opName:o.op_name}})),fX=Zw(OZ,gZ),gX=Zw(OZ,(t=>t.sourceCode)),hX=Zw(gX,(t=>t.sourceFileListLoaded));Zw(gX,(t=>t.sourceFileList));const bX=Zw(gX,(t=>{const{sourceFileList:e,focusLineSpec:n}=t;return null===n?-1:hZ(e,n)})),yX=Zw(gX,bX,((t,e)=>-1===e?null:t.fileContents[e]||null)),_X=Zw(OZ,(t=>t.sourceCode.focusLineSpec)),CX=Zw(OZ,(t=>t.stickToBottommostFrameInFocusedFile)),MX=function(t){return[t]};function vX(t,e){if(1&t){const t=Hm();Rm(0,"div",7),Vm("click",(function e(){const n=hi(t).$implicit;return Ym().onToggleFocusType.emit(n.type)})),Rm(1,"div",8),ku(2),Am(),Rm(3,"div",9),ku(4),Am(),Tm(5,"div"),Am()}if(2&t){const t=e.$implicit,n=Ym();Dm("ngClass",Mh(4,MX,t.type===n.focusType?"focus":"")),rc(2),Su(t.displayName),rc(2),Eu(" ",t.displaySymbol,": ",t.count," ")}}class xX{constructor(){this.numAlerts=0,this.alertsBreakdown=[],this.focusType=null,this.onToggleFocusType=new Lh}}xX.ɵfac=function t(e){return new(e||xX)},xX.ɵcmp=to({type:xX,selectors:[["alerts-component"]],inputs:{numAlerts:"numAlerts",alertsBreakdown:"alertsBreakdown",focusType:"focusType"},outputs:{onToggleFocusType:"onToggleFocusType"},decls:10,vars:5,consts:[[1,"alerts-container"],[1,"debugging-title"],[1,"num-alerts-container"],[1,"num-alerts-label"],[1,"num-alerts-value",3,"ngClass"],[1,"alerts-breakdown-container"],["class","alerts-breakdown-type",3,"ngClass","click",4,"ngFor","ngForOf"],[1,"alerts-breakdown-type",3,"ngClass","click"],[1,"alert-type-name"],[1,"alert-type-count"]],template:function t(e,n){1&e&&(Rm(0,"div",0),Rm(1,"div",1),ku(2,"Debugging"),Am(),Rm(3,"div",2),Rm(4,"div",3),ku(5,"Alerts"),Am(),Rm(6,"div",4),ku(7),Am(),Am(),Rm(8,"div",5),Qp(9,vX,6,6,"div",6),Am(),Am()),2&e&&(rc(6),Dm("ngClass",Mh(3,MX,n.numAlerts>0?"non-zero":"")),rc(1),Du(" ",n.numAlerts," "),rc(2),Dm("ngForOf",n.alertsBreakdown))},directives:[aM,lM],styles:[".alerts-breakdown-container[_ngcontent-%COMP%] {\n  font-size: 13px;\n  padding: 10px 10px 10px;\n  position: relative;\n}\n\n.alerts-breakdown-type[_ngcontent-%COMP%] {\n  border-radius: 0 10px 10px 0;\n  cursor: pointer;\n  display: flex;\n  padding: 6px 0 6px 50px;\n  vertical-align: middle;\n}\n\n.alerts-breakdown-type.focus[_ngcontent-%COMP%] {\n  background-color: #ffeee0;\n}\n\n.alerts-container[_ngcontent-%COMP%] {\n  font-family: 'Roboto', Arial, Helvetica, sans-serif;\n}\n\n.alert-type-count[_ngcontent-%COMP%] {\n  \n  background-color: #e52592;\n  border-radius: 3px;\n  color: #fff;\n  display: inline-block;\n  padding: 3px;\n  position: absolute;\n  right: 20px;\n  vertical-align: middle;\n}\n\n.alert-type-name[_ngcontent-%COMP%] {\n  display: inline-block;\n  padding: 3px;\n  vertical-align: middle;\n}\n\n.debugging-title[_ngcontent-%COMP%] {\n  font-size: 18px;\n}\n\n.num-alerts-container[_ngcontent-%COMP%] {\n  font-weight: bold;\n  padding: 10px 10px 10px 30px;\n  position: relative;\n}\n\n.num-alerts-label[_ngcontent-%COMP%] {\n  display: inline-block;\n  font-size: 13px;\n}\n\n.num-alerts-value[_ngcontent-%COMP%] {\n  border-radius: 12px;\n  display: inline-block;\n  font-size: 13px;\n  font-weight: normal;\n  line-height: 24px;\n  position: absolute;\n  right: 20px;\n  text-align: center;\n  vertical-align: middle;\n  width: 24px;\n}\n\n.num-alerts-value.non-zero[_ngcontent-%COMP%] {\n  background-color: #ffb780;\n  font-weight: bold;\n}"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(xX,[{type:My,args:[{selector:"alerts-component",templateUrl:"./alerts_component.ng.html",styleUrls:["./alerts_component.css"]}]}],null,{numAlerts:[{type:xy}],alertsBreakdown:[{type:xy}],focusType:[{type:xy}],onToggleFocusType:[{type:Oy}]});const OX={[uZ.FUNCTION_RECOMPILE_ALERT]:{displayName:"Function recompiles",displaySymbol:"C"},[uZ.INF_NAN_ALERT]:{displayName:"NaN/∞",displaySymbol:"∞"},[uZ.TENSOR_SHAPE_ALERT]:{displayName:"Tensor shape",displaySymbol:"■"}};class PX{constructor(t){this.store=t,this.numAlerts$=this.store.pipe(Fw(RZ)),this.alertsBreakdown$=this.store.pipe(Fw(Zw(zZ,(t=>{const e=Object.keys(t);return e.sort(),e.map((e=>Object.assign(Object.assign({type:e},OX[e]),{count:t[e]})))})))),this.focusType$=this.store.pipe(Fw(AZ))}onToggleFocusType(t){this.store.dispatch(Bq({alertType:t}))}}PX.ɵfac=function t(e){return new(e||PX)(Sm(Iw))},PX.ɵcmp=to({type:PX,selectors:[["tf-debugger-v2-alerts"]],decls:4,vars:9,consts:[[3,"numAlerts","alertsBreakdown","focusType","onToggleFocusType"]],template:function t(e,n){1&e&&(Rm(0,"alerts-component",0),Vm("onToggleFocusType",(function t(e){return n.onToggleFocusType(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Am()),2&e&&Dm("numAlerts",Th(1,3,n.numAlerts$))("alertsBreakdown",Th(2,5,n.alertsBreakdown$))("focusType",Th(3,7,n.focusType$))},directives:[xX],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(PX,[{type:My,args:[{selector:"tf-debugger-v2-alerts",template:'\n    <alerts-component\n      [numAlerts]="numAlerts$ | async"\n      [alertsBreakdown]="alertsBreakdown$ | async"\n      [focusType]="focusType$ | async"\n      (onToggleFocusType)="onToggleFocusType($event)"\n    >\n    </alerts-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);const wX=["sliderWrapper"],kX=Nz({passive:!1}),SX={provide:IV,useExisting:qe((()=>RX)),multi:!0};class DX{}const EX=$I(JI(KI(class{constructor(t){this._elementRef=t}}),"accent"));class RX extends EX{constructor(t,e,n,o,i,a,r,s){super(t),this._focusMonitor=e,this._changeDetectorRef=n,this._dir=o,this._ngZone=a,this._animationMode=s,this._invert=!1,this._max=100,this._min=0,this._step=1,this._thumbLabel=!1,this._tickInterval=0,this._value=null,this._vertical=!1,this.change=new Lh,this.input=new Lh,this.valueChange=new Lh,this.onTouched=()=>{},this._percent=0,this._isSliding=null,this._isActive=!1,this._tickIntervalPercent=0,this._sliderDimensions=null,this._controlValueAccessorChangeFn=()=>{},this._dirChangeSubscription=m.EMPTY,this._pointerDown=t=>{this.disabled||this._isSliding||!AX(t)&&0!==t.button||this._ngZone.run((()=>{this._touchId=AX(t)?(function e(t,n){for(let e=0;e<t.touches.length;e++){const o=t.touches[e].target;if(n===o||n.contains(o))return t.touches[e].identifier}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t,this._elementRef.nativeElement):void 0;const n=TX(t,this._touchId);if(n){const e=this.value;this._isSliding="pointer",this._lastPointerEvent=t,t.preventDefault(),this._focusHostElement(),this._onMouseenter(),this._bindGlobalEvents(t),this._focusHostElement(),this._updateValueFromPosition(n),this._valueOnSlideStart=e,e!=this.value&&this._emitInputEvent()}}))},this._pointerMove=t=>{if("pointer"===this._isSliding){const e=TX(t,this._touchId);if(e){t.preventDefault();const n=this.value;this._lastPointerEvent=t,this._updateValueFromPosition(e),n!=this.value&&this._emitInputEvent()}}},this._pointerUp=t=>{"pointer"===this._isSliding&&(AX(t)&&"number"==typeof this._touchId&&!NX(t.changedTouches,this._touchId)||(t.preventDefault(),this._removeGlobalEvents(),this._isSliding=null,this._touchId=void 0,this._valueOnSlideStart==this.value||this.disabled||this._emitChangeEvent(),this._valueOnSlideStart=this._lastPointerEvent=null))},this._windowBlur=()=>{this._lastPointerEvent&&this._pointerUp(this._lastPointerEvent)},this._document=r,this.tabIndex=parseInt(i)||0,a.runOutsideAngular((()=>{const e=t.nativeElement;e.addEventListener("mousedown",this._pointerDown,kX),e.addEventListener("touchstart",this._pointerDown,kX)}))}get invert(){return this._invert}set invert(t){this._invert=yz(t)}get max(){return this._max}set max(t){this._max=_z(t,this._max),this._percent=this._calculatePercentage(this._value),this._changeDetectorRef.markForCheck()}get min(){return this._min}set min(t){this._min=_z(t,this._min),this._percent=this._calculatePercentage(this._value),this._changeDetectorRef.markForCheck()}get step(){return this._step}set step(t){this._step=_z(t,this._step),this._step%1!=0&&(this._roundToDecimal=this._step.toString().split(".").pop().length),this._changeDetectorRef.markForCheck()}get thumbLabel(){return this._thumbLabel}set thumbLabel(t){this._thumbLabel=yz(t)}get tickInterval(){return this._tickInterval}set tickInterval(t){this._tickInterval="auto"===t?"auto":"number"==typeof t||"string"==typeof t?_z(t,this._tickInterval):0}get value(){return null===this._value&&(this.value=this._min),this._value}set value(t){if(t!==this._value){let e=_z(t,0);this._roundToDecimal&&e!==this.min&&e!==this.max&&(e=parseFloat(e.toFixed(this._roundToDecimal))),this._value=e,this._percent=this._calculatePercentage(this._value),this._changeDetectorRef.markForCheck()}}get vertical(){return this._vertical}set vertical(t){this._vertical=yz(t)}get displayValue(){return this.displayWith?this.displayWith(this.value):this._roundToDecimal&&this.value&&this.value%1!=0?this.value.toFixed(this._roundToDecimal):this.value||0}focus(t){this._focusHostElement(t)}blur(){this._blurHostElement()}get percent(){return this._clamp(this._percent)}_shouldInvertAxis(){return this.vertical?!this.invert:this.invert}_isMinValue(){return 0===this.percent}_getThumbGap(){return this.disabled?7:this._isMinValue()&&!this.thumbLabel?this._isActive?10:7:0}_getTrackBackgroundStyles(){const t=this.vertical?`1, ${1-this.percent}, 1`:1-this.percent+", 1, 1";return{transform:`translate${this.vertical?"Y":"X"}(${this._shouldInvertMouseCoords()?"-":""}${this._getThumbGap()}px) scale3d(${t})`}}_getTrackFillStyles(){const t=this.percent,e=this.vertical?`1, ${t}, 1`:`${t}, 1, 1`;return{transform:`translate${this.vertical?"Y":"X"}(${this._shouldInvertMouseCoords()?"":"-"}${this._getThumbGap()}px) scale3d(${e})`,display:0===t?"none":""}}_getTicksContainerStyles(){return{transform:`translate${this.vertical?"Y":"X"}(${this.vertical||"rtl"!=this._getDirection()?"-":""}${this._tickIntervalPercent/2*100}%)`}}_getTicksStyles(){let t=100*this._tickIntervalPercent,e={backgroundSize:this.vertical?`2px ${t}%`:`${t}% 2px`,transform:`translateZ(0) translate${this.vertical?"Y":"X"}(${this.vertical||"rtl"!=this._getDirection()?"":"-"}${t/2}%)${this.vertical||"rtl"!=this._getDirection()?"":" rotate(180deg)"}`};if(this._isMinValue()&&this._getThumbGap()){const t=this._shouldInvertAxis();let n;n=this.vertical?t?"Bottom":"Top":t?"Right":"Left",e[`padding${n}`]=`${this._getThumbGap()}px`}return e}_getThumbContainerStyles(){const t=this._shouldInvertAxis();return{transform:`translate${this.vertical?"Y":"X"}(-${100*(("rtl"!=this._getDirection()||this.vertical?t:!t)?this.percent:1-this.percent)}%)`}}_shouldInvertMouseCoords(){const t=this._shouldInvertAxis();return"rtl"!=this._getDirection()||this.vertical?t:!t}_getDirection(){return this._dir&&"rtl"==this._dir.value?"rtl":"ltr"}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0).subscribe((t=>{this._isActive=!!t&&"keyboard"!==t,this._changeDetectorRef.detectChanges()})),this._dir&&(this._dirChangeSubscription=this._dir.change.subscribe((()=>{this._changeDetectorRef.markForCheck()})))}ngOnDestroy(){const t=this._elementRef.nativeElement;t.removeEventListener("mousedown",this._pointerDown,kX),t.removeEventListener("touchstart",this._pointerDown,kX),this._lastPointerEvent=null,this._removeGlobalEvents(),this._focusMonitor.stopMonitoring(this._elementRef),this._dirChangeSubscription.unsubscribe()}_onMouseenter(){this.disabled||(this._sliderDimensions=this._getSliderDimensions(),this._updateTickIntervalPercent())}_onFocus(){this._sliderDimensions=this._getSliderDimensions(),this._updateTickIntervalPercent()}_onBlur(){this.onTouched()}_onKeydown(t){if(this.disabled||bz(t)||this._isSliding&&"keyboard"!==this._isSliding)return;const e=this.value;switch(t.keyCode){case 33:this._increment(10);break;case 34:this._increment(-10);break;case 35:this.value=this.max;break;case 36:this.value=this.min;break;case 37:this._increment("rtl"==this._getDirection()?1:-1);break;case gz:this._increment(1);break;case 39:this._increment("rtl"==this._getDirection()?-1:1);break;case hz:this._increment(-1);break;default:return}e!=this.value&&(this._emitInputEvent(),this._emitChangeEvent()),this._isSliding="keyboard",t.preventDefault()}_onKeyup(){"keyboard"===this._isSliding&&(this._isSliding=null)}_getWindow(){return this._document.defaultView||window}_bindGlobalEvents(t){const e=this._document,n=AX(t),o=n?"touchend":"mouseup";e.addEventListener(n?"touchmove":"mousemove",this._pointerMove,kX),e.addEventListener(o,this._pointerUp,kX),n&&e.addEventListener("touchcancel",this._pointerUp,kX);const i=this._getWindow();void 0!==i&&i&&i.addEventListener("blur",this._windowBlur)}_removeGlobalEvents(){const t=this._document;t.removeEventListener("mousemove",this._pointerMove,kX),t.removeEventListener("mouseup",this._pointerUp,kX),t.removeEventListener("touchmove",this._pointerMove,kX),t.removeEventListener("touchend",this._pointerUp,kX),t.removeEventListener("touchcancel",this._pointerUp,kX);const e=this._getWindow();void 0!==e&&e&&e.removeEventListener("blur",this._windowBlur)}_increment(t){this.value=this._clamp((this.value||0)+this.step*t,this.min,this.max)}_updateValueFromPosition(t){if(!this._sliderDimensions)return;let e=this._clamp(((this.vertical?t.y:t.x)-(this.vertical?this._sliderDimensions.top:this._sliderDimensions.left))/(this.vertical?this._sliderDimensions.height:this._sliderDimensions.width));if(this._shouldInvertMouseCoords()&&(e=1-e),0===e)this.value=this.min;else if(1===e)this.value=this.max;else{const t=this._calculateValue(e),n=Math.round((t-this.min)/this.step)*this.step+this.min;this.value=this._clamp(n,this.min,this.max)}}_emitChangeEvent(){this._controlValueAccessorChangeFn(this.value),this.valueChange.emit(this.value),this.change.emit(this._createChangeEvent())}_emitInputEvent(){this.input.emit(this._createChangeEvent())}_updateTickIntervalPercent(){if(this.tickInterval&&this._sliderDimensions)if("auto"==this.tickInterval){let t=this.vertical?this._sliderDimensions.height:this._sliderDimensions.width,e=Math.ceil(30/(t*this.step/(this.max-this.min)));this._tickIntervalPercent=e*this.step/t}else this._tickIntervalPercent=this.tickInterval*this.step/(this.max-this.min)}_createChangeEvent(t=this.value){let e=new DX;return e.source=this,e.value=t,e}_calculatePercentage(t){return((t||0)-this.min)/(this.max-this.min)}_calculateValue(t){return this.min+t*(this.max-this.min)}_clamp(t,e=0,n=1){return Math.max(e,Math.min(t,n))}_getSliderDimensions(){return this._sliderWrapper?this._sliderWrapper.nativeElement.getBoundingClientRect():null}_focusHostElement(t){this._elementRef.nativeElement.focus(t)}_blurHostElement(){this._elementRef.nativeElement.blur()}writeValue(t){this.value=t}registerOnChange(t){this._controlValueAccessorChangeFn=t}registerOnTouched(t){this.onTouched=t}setDisabledState(t){this.disabled=t}}function AX(t){return"t"===t.type[0]}function TX(t,e){let n;return n=AX(t)?"number"==typeof e?NX(t.touches,e)||NX(t.changedTouches,e):t.touches[0]||t.changedTouches[0]:t,n?{x:n.clientX,y:n.clientY}:void 0}function NX(t,e){for(let n=0;n<t.length;n++)if(t[n].identifier===e)return t[n]}RX.ɵfac=function t(e){return new(e||RX)(Sm(hg),Sm(SI),Sm(Ug),Sm(HI,8),Na("tabindex"),Sm(a_),Sm(Z_),Sm(VP,8))},RX.ɵcmp=to({type:RX,selectors:[["mat-slider"]],viewQuery:function t(e,n){if(1&e&&Qh(wX,5),2&e){let t;Jh(t=tb())&&(n._sliderWrapper=t.first)}},hostAttrs:["role","slider",1,"mat-slider","mat-focus-indicator"],hostVars:29,hostBindings:function t(e,n){1&e&&Vm("focus",(function t(){return n._onFocus()}))("blur",(function t(){return n._onBlur()}))("keydown",(function t(e){return n._onKeydown(e)}))("keyup",(function t(){return n._onKeyup()}))("mouseenter",(function t(){return n._onMouseenter()}))("selectstart",(function t(e){return e.preventDefault()})),2&e&&(Tu("tabIndex",n.tabIndex),jp("aria-disabled",n.disabled)("aria-valuemax",n.max)("aria-valuemin",n.min)("aria-valuenow",n.value)("aria-valuetext",null==n.valueText?n.displayValue:n.valueText)("aria-orientation",n.vertical?"vertical":"horizontal"),pu("mat-slider-disabled",n.disabled)("mat-slider-has-ticks",n.tickInterval)("mat-slider-horizontal",!n.vertical)("mat-slider-axis-inverted",n._shouldInvertAxis())("mat-slider-invert-mouse-coords",n._shouldInvertMouseCoords())("mat-slider-sliding",n._isSliding)("mat-slider-thumb-label-showing",n.thumbLabel)("mat-slider-vertical",n.vertical)("mat-slider-min-value",n._isMinValue())("mat-slider-hide-last-tick",n.disabled||n._isMinValue()&&n._getThumbGap()&&n._shouldInvertAxis())("_mat-animation-noopable","NoopAnimations"===n._animationMode))},inputs:{disabled:"disabled",color:"color",tabIndex:"tabIndex",invert:"invert",max:"max",min:"min",step:"step",thumbLabel:"thumbLabel",tickInterval:"tickInterval",value:"value",vertical:"vertical",displayWith:"displayWith",valueText:"valueText"},outputs:{change:"change",input:"input",valueChange:"valueChange"},exportAs:["matSlider"],features:[pg([SX]),xp],decls:13,vars:6,consts:[[1,"mat-slider-wrapper"],["sliderWrapper",""],[1,"mat-slider-track-wrapper"],[1,"mat-slider-track-background",3,"ngStyle"],[1,"mat-slider-track-fill",3,"ngStyle"],[1,"mat-slider-ticks-container",3,"ngStyle"],[1,"mat-slider-ticks",3,"ngStyle"],[1,"mat-slider-thumb-container",3,"ngStyle"],[1,"mat-slider-focus-ring"],[1,"mat-slider-thumb"],[1,"mat-slider-thumb-label"],[1,"mat-slider-thumb-label-text"]],template:function 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CK{constructor(){this.activeRunId=null,this.loadingNumExecutions=!1,this.numExecutions=0,this.scrollBeginIndex=0,this.scrollBeginIndexUpperLimit=0,this.pageSize=0,this.displayCount=0,this.displayExecutionDigests=[],this.displayFocusedAlertTypes=[],this.focusedExecutionIndex=null,this.focusedExecutionDisplayIndex=null,this.focusedExecutionData=null,this.onNavigateLeft=new Lh,this.onNavigateRight=new Lh,this.onExecutionDigestClicked=new Lh,this.onSliderChange=new Lh}}CK.ɵfac=function t(e){return new(e||CK)},CK.ɵcmp=to({type:CK,selectors:[["timeline-component"]],inputs:{activeRunId:"activeRunId",loadingNumExecutions:"loadingNumExecutions",numExecutions:"numExecutions",scrollBeginIndex:"scrollBeginIndex",scrollBeginIndexUpperLimit:"scrollBeginIndexUpperLimit",pageSize:"pageSize",displayCount:"displayCount",displayExecutionDigests:"displayExecutionDigests",displayFocusedAlertTypes:"displayFocusedAlertTypes",focusedExecutionIndex:"focusedExecutionIndex",focusedExecutionDisplayIndex:"focusedExecutionDisplayIndex",focusedExecutionData:"focusedExecutionData"},outputs:{onNavigateLeft:"onNavigateLeft",onNavigateRight:"onNavigateRight",onExecutionDigestClicked:"onExecutionDigestClicked",onSliderChange:"onSliderChange"},decls:9,vars:4,consts:[[1,"timeline-title"],[1,"execution-count"],[1,"top-level-executions"],["class","navigation-section",4,"ngIf"],["class","execution-timeline",4,"ngIf"],[4,"ngIf"],[1,"navigation-section"],["mat-button","",1,"navigation-button-left",3,"click"],[1,"navigation-position-info"],["mat-button","",1,"navigation-button-right",3,"click"],["class","timeline-slider","step","1",3,"min","max","value","input",4,"ngIf"],["step","1",1,"timeline-slider",3,"min","max","value","input"],[1,"execution-timeline"],[3,"click",4,"ngFor","ngForOf"],[3,"click"],[1,"execution-digest",3,"ngClass","title"],[3,"focusedExecutionIndex"]],template:function 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vK{constructor(t){this.store=t,this.activeRunId$=this.store.pipe(Fw(kZ)),this.loadingNumExecutions$=this.store.pipe(Fw(Zw(HZ,(t=>t.state==yE.LOADING)))),this.scrollBeginIndex$=this.store.pipe(Fw(BZ)),this.scrollBeginIndexUpperLimit$=this.store.pipe(Fw(Zw(LZ,jZ,((t,e)=>Math.max(0,t-e))))),this.pageSize$=this.store.pipe(Fw(VZ)),this.displayCount$=this.store.pipe(Fw(jZ)),this.displayExecutionDigests$=this.store.pipe(Fw(Zw(UZ,(t=>t.map((t=>(function e(t,n=1){if(!t)return{op_type:"(N/A)",short_op_type:"..",is_graph:!1};const o=MK.filter((e=>t.op_type.startsWith(e)));if(o.length){const 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wK(t,e){if(1&t&&(Rm(0,"div",9),ku(1),Am()),2&t){const t=Ym();rc(1),Du(" ",t.opData.op_type," ")}}function kK(t,e){1&t&&(Rm(0,"span",10),ku(1," (Op info unavailable.) 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e(){hi(t);const n=Ym().$implicit,o=Ym(2);return o.onTensorNameClick.emit({index:n,graph_id:o.graphExecutionData[n].graph_id,op_name:o.graphExecutionData[n].op_name})})),ku(3),Am(),Rm(4,"div",15),ku(5),Am(),Am(),Qp(6,GK,1,4,"debug-tensor-value",16),Am()}if(2&t){const t=Ym().$implicit,e=Ym(2);rc(2),Km("title",e.getTensorName(t)),rc(1),Du(" ",e.getTensorName(t)," "),rc(2),Su(e.graphExecutionData[t].op_type),rc(1),Dm("ngIf",null!==e.graphExecutionData[t].debug_tensor_value)}}function YK(t,e){1&t&&(Rm(0,"div",18),ku(1," Loading... "),Am())}const qK=function(t){return{"input-of-focus":t}};function ZK(t,e){if(1&t&&(Rm(0,"div",5),Rm(1,"div",6),Rm(2,"div",7),Qp(3,jK,2,0,"div",8),ku(4),Am(),Qp(5,WK,7,4,"div",9),Qp(6,YK,2,0,"ng-template",10,11,ib),Am(),Am()),2&t){const t=e.$implicit,n=$p(7),o=Ym(2);rc(1),Dm("ngClass",Mh(5,qK,o.isInputOfFocus(t))),rc(2),Dm("ngIf",t===o.focusIndex),rc(1),Du(" ",t," "),rc(1),Dm("ngIf",o.graphExecutionData[t])("ngIfElse",n)}}function XK(t,e){if(1&t){const t=Hm();Rm(0,"cdk-virtual-scroll-viewport",3),Vm("scrolledIndexChange",(function e(n){return hi(t),Ym().onScrolledIndexChange.emit(n)})),Qp(1,ZK,8,7,"div",4),Am()}if(2&t){const t=Ym();rc(1),Dm("cdkVirtualForOf",t.graphExecutionIndices)}}class KK{constructor(){this.onScrolledIndexChange=new Lh,this.onTensorNameClick=new Lh,this.parseDebugTensorValue=HX,this.TEST_ONLY={getViewPort:()=>this.viewPort}}ngOnChanges(t){if(this.viewPort&&t.focusIndex&&null!==t.focusIndex.currentValue){const 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SJ{constructor(t){this.store=t,this.codeLocationType$=this.store.pipe(Fw(Zw(uX,(t=>null===t?null:t.codeLocationType)))),this.opType$=this.store.pipe(Fw(Zw(uX,(t=>null===t?null:t.opType)))),this.opName$=this.store.pipe(Fw(Zw(uX,(t=>null===t||t.codeLocationType!==fZ.GRAPH_OP_CREATION?null:t.opName)))),this.executionIndex$=this.store.pipe(Fw(Zw(uX,(t=>null===t||t.codeLocationType!==fZ.EXECUTION?null:t.executionIndex)))),this.stickToBottommostFrameInFocusedFile$=this.store.pipe(Fw(CX)),this.stackFramesForDisplay$=this.store.pipe(Fw(Zw(fX,_X,((t,e)=>{if(null===t)return null;const n=[];for(const o of t){const{host_name:t,file_path:i,lineno:a,function_name:r}=o,s=i.split("/"),l=null!==e&&t===e.host_name&&i===e.file_path;n.push({host_name:t,file_path:i,concise_file_path:s[s.length-1],lineno:a,function_name:r,belongsToFocusedFile:l,focused:l&&a===e.lineno})}return n}))))}onSourceLineClicked(t){const{host_name:e,file_path:n,lineno:o,function_name:i}=t;this.store.dispatch(sZ({stackFrame:{host_name:e,file_path:n,lineno:o,function_name:i}}))}}function DJ(t,e){1&t&&Tm(0,"tf-debugger-v2-inactive")}function EJ(t,e){1&t&&(Rm(0,"div",3),Tm(1,"tf-debugger-v2-alerts"),Rm(2,"div",4),Tm(3,"tf-debugger-v2-timeline"),Tm(4,"tf-debugger-v2-graph"),Am(),Tm(5,"tf-debugger-v2-graph-executions"),Am(),Rm(6,"div",5),Tm(7,"tf-debugger-v2-source-files"),Tm(8,"tf-debugger-v2-stack-trace"),Am())}SJ.ɵfac=function t(e){return new(e||SJ)(Sm(Iw))},SJ.ɵcmp=to({type:SJ,selectors:[["tf-debugger-v2-stack-trace"]],decls:7,vars:18,consts:[[3,"codeLocationType","opType","opName","executionIndex","stickToBottommostFrameInFocusedFile","stackFramesForDisplay","onSourceLineClicked"]],template:function t(e,n){1&e&&(Rm(0,"stack-trace-component",0),Vm("onSourceLineClicked",(function t(e){return n.onSourceLineClicked(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Am()),2&e&&Dm("codeLocationType",Th(1,6,n.codeLocationType$))("opType",Th(2,8,n.opType$))("opName",Th(3,10,n.opName$))("executionIndex",Th(4,12,n.executionIndex$))("stickToBottommostFrameInFocusedFile",Th(5,14,n.stickToBottommostFrameInFocusedFile$))("stackFramesForDisplay",Th(6,16,n.stackFramesForDisplay$))},directives:[kJ],pipes:[wM],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(SJ,[{type:My,args:[{selector:"tf-debugger-v2-stack-trace",template:'\n    <stack-trace-component\n      [codeLocationType]="codeLocationType$ | async"\n      [opType]="opType$ | async"\n      [opName]="opName$ | async"\n      [executionIndex]="executionIndex$ | async"\n      [stickToBottommostFrameInFocusedFile]="\n        stickToBottommostFrameInFocusedFile$ | async\n      "\n      [stackFramesForDisplay]="stackFramesForDisplay$ | async"\n      (onSourceLineClicked)="onSourceLineClicked($event)"\n    ></stack-trace-component>\n  '}]}],(function(){return[{type:Iw}]}),null);class RJ{constructor(){this.runs={},this.runIds=[],this.activeRunId=null}}RJ.ɵfac=function t(e){return new(e||RJ)},RJ.ɵcmp=to({type:RJ,selectors:[["debugger-component"]],inputs:{runs:"runs",runIds:"runIds",activeRunId:"activeRunId"},decls:4,vars:2,consts:[[1,"debugger-container"],[4,"ngIf","ngIfElse"],["dataAvailable",""],[1,"top-section"],[1,"top-center-section"],[1,"bottom-section"]],template:function t(e,n){if(1&e&&(Rm(0,"div",0),Qp(1,DJ,1,0,"tf-debugger-v2-inactive",1),Qp(2,EJ,9,0,"ng-template",null,2,ib),Am()),2&e){const t=$p(3);rc(1),Dm("ngIf",0===n.runIds.length)("ngIfElse",t)}},directives:[dM,Eq,PX,vK,VK,JK,pJ,SJ],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   .mat-chip-remove.mat-icon[_ngcontent-%COMP%]{font-size:18px}.mat-table[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-header-cell[_ngcontent-%COMP%]{font-size:12px;font-weight:500}.mat-cell[_ngcontent-%COMP%], .mat-footer-cell[_ngcontent-%COMP%]{font-size:14px}.mat-calendar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-calendar-body[_ngcontent-%COMP%]{font-size:13px}.mat-calendar-body-label[_ngcontent-%COMP%], .mat-calendar-period-button[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-calendar-table-header[_ngcontent-%COMP%]   th[_ngcontent-%COMP%]{font-size:11px;font-weight:400}.mat-dialog-title[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-expansion-panel-header[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", 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.mat-icon[_ngcontent-%COMP%]{height:1.125em;line-height:1.125}.mat-form-field-infix[_ngcontent-%COMP%]{padding:.5em 0;border-top:.84375em solid transparent}.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.34375em) scale(0.75);width:133.3333333333%}.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.34374em) 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.mat-form-field-label[_ngcontent-%COMP%]{top:1.28125em}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   .mat-form-field-underline[_ngcontent-%COMP%]{bottom:1.25em}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   .mat-form-field-subscript-wrapper[_ngcontent-%COMP%]{margin-top:.5416666667em;top:calc(100% - 1.6666666667em)}@media print{.mat-form-field-appearance-legacy.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28122em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-form-field-autofill-control[_ngcontent-%COMP%]:-webkit-autofill + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   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.mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59374em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:1em 0 1em 0}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{top:1.84375em;margin-top:-0.25em}.mat-form-field-appearance-outline.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59374em) scale(0.75);width:133.3333433333%}.mat-grid-tile-header[_ngcontent-%COMP%], .mat-grid-tile-footer[_ngcontent-%COMP%]{font-size:14px}.mat-grid-tile-header[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%], .mat-grid-tile-footer[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-grid-tile-header[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2), .mat-grid-tile-footer[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}input.mat-input-element[_ngcontent-%COMP%]{margin-top:-0.0625em}.mat-menu-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:400}.mat-paginator[_ngcontent-%COMP%], .mat-paginator-page-size[_ngcontent-%COMP%]   .mat-select-trigger[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px}.mat-radio-button[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", 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#555}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(RJ,[{type:My,args:[{selector:"debugger-component",templateUrl:"./debugger_component.ng.html",styleUrls:["./debugger_component.css"],changeDetection:zn.OnPush}]}],null,{runs:[{type:xy}],runIds:[{type:xy}],activeRunId:[{type:xy}]});class AJ{constructor(t){this.store=t,this.runs$=this.store.pipe(Fw(PZ)),this.runsIds$=this.store.pipe(Fw(Zw(PZ,(t=>Object.keys(t))))),this.activeRunId$=this.store.pipe(Fw(kZ))}ngOnInit(){this.store.dispatch(Rq())}ngOnDestroy(){this.store.dispatch(Aq())}}AJ.ɵfac=function t(e){return new(e||AJ)(Sm(Iw))},AJ.ɵcmp=to({type:AJ,selectors:[["tf-debugger-v2"]],decls:4,vars:9,consts:[[3,"runs","runIds","activeRunId"]],template:function t(e,n){1&e&&(Tm(0,"debugger-component",0),Ah(1,"async"),Ah(2,"async"),Ah(3,"async")),2&e&&Dm("runs",Th(1,3,n.runs$))("runIds",Th(2,5,n.runsIds$))("activeRunId",Th(3,7,n.activeRunId$))},directives:[RJ],pipes:[wM],styles:["[_nghost-%COMP%] {\n        display: block;\n        height: 100%;\n      }"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(AJ,[{type:My,args:[{selector:"tf-debugger-v2",template:'\n    <debugger-component\n      [runs]="runs$ | async"\n      [runIds]="runsIds$ | async"\n      [activeRunId]="activeRunId$ | async"\n    ></debugger-component>\n  ',styles:["\n      :host {\n        display: block;\n        height: 100%;\n      }\n    "]}]}],(function(){return[{type:Iw}]}),null);const TJ="debugger-v2";function NJ(t,e,n,o,i){if(n<=0||!Number.isInteger(n))throw new Error(`Invalid pageSize: ${n}`);if(e>o)throw new Error(`end index (${e}) exceeds total number of items (${o})`);if(e-t>n)throw new Error("begin-end span exceeds page size, which is not allowed");const a=[],r=Math.floor(t/n);(!(r in i)||i[r]<n&&r*n+i[r]<o)&&a.push(r);const s=Math.floor((e-1)/n);return s!==r&&(!(s in i)||s*n+i[s]<e&&e<o)&&a.push(s),a}class zJ{constructor(t,e,n){this.actions$=t,this.store=e,this.dataSource=n,this.loadData$=Mk((()=>{const t=this.loadDebuggerRuns(re(this.onDebuggerDataPoll(),this.onCoreReload())).pipe(Ee()),e=this.loadSourceFileList(t),n=this.createNumExecutionLoader(t),o=this.createNumAlertsAndBreakdownLoader(t),i=this.onAlertTypeFocused(),a=this.fetchExecutionDigestsForAlertTypeFocus(i),r=this.createInitialExecutionDetector(n).pipe(Ee()),s=this.createExecutionDigestLoader(re(this.onExecutionScroll(),this.createInitialExecutionDigest(r),a)),l=this.createExecutionDataAndStackFramesLoader(re(this.onExecutionDigestFocused(),r.pipe(Ve(this.store.select(kZ),this.store.select(cX)),It((([,t,e])=>({activeRunId:t,loadedExecutionData:e,focusIndex:0}))))));return re(o,s,l,this.createNumGraphExecutionLoader(t),e,this.onSourceFileFocused(),this.loadGraphExecutionPages(this.onGraphExecutionScroll()),this.loadGraphOpStackFrames(this.loadGraphOpInfo())).pipe(It((()=>({}))))}),{dispatch:!1})}onDebuggerDataPoll(){return this.actions$.pipe(Dk(Rq),ze((t=>(function e(t,n,o){return t.pipe((function i(t){return R((function(e,n){var o,i,a=!1,r=!1,s=!1,l=function(){return s&&r&&(n.complete(),!0)},c=function(){s=!1,o=e.subscribe(new T(n,void 0,(function(){s=!0,!l()&&(i||(i=new I,t(i).subscribe(new T(n,(function(){o?c():a=!0}),(function(){r=!0,l()})))),i).next()}))),a&&(o.unsubscribe(),o=null,a=!1,c())};c()}))})((t=>t.pipe(Ve(n),_e((([,t])=>ae(t)))))),Ie(o),It((()=>{})))})(Et(t),this.store.select(SZ).pipe(It((t=>(function e(t){return t>6e4?6e4:t>4e3?t:2e3})(t)))),this.actions$.pipe(Dk(Aq))))),Fe((()=>this.store.dispatch(Tq()))),It((()=>{})))}onCoreReload(){return re(this.actions$.pipe(Dk(vE,xE)),this.actions$.pipe(Dk(_E)).pipe(Ve(this.store.select(wZ)),ce((([,t])=>t.state===yE.NOT_LOADED||t.state===yE.FAILED&&null===t.lastLoadedTimeInMs)))).pipe(Ve(this.store.select(MR)),ce((([,t])=>t===TJ)),Fe((()=>this.store.dispatch(Tq()))),It((()=>{})))}loadDebuggerRuns(t){return t.pipe(Ve(this.store.select(wZ)),ce((([,{state:t}])=>t!==yE.LOADING)),Fe((()=>this.store.dispatch(Nq()))),Zt((()=>this.dataSource.fetchRuns().pipe(Fe((t=>{this.store.dispatch(zq({runs:t}))})),It((()=>{}))))))}createNumExecutionLoader(t){return t.pipe(Ve(this.store.select(PZ),this.store.select(HZ)),ce((([,t,e])=>Object.keys(t).length>0&&e.state!==yE.LOADING)),Fe((()=>this.store.dispatch(Vq()))),Zt((([,t])=>{const e=Object.keys(t)[0];return this.dataSource.fetchExecutionDigests(e,0,0).pipe(Fe((t=>{this.store.dispatch(jq({numExecutions:t.num_digests}))})),It((()=>{})))})))}createNumGraphExecutionLoader(t){return t.pipe(Ve(this.store.select(PZ),this.store.select(WZ)),ce((([,t,e])=>Object.keys(t).length>0&&e.state!==yE.LOADING)),Fe((()=>this.store.dispatch(Kq()))),Zt((([,t])=>{const e=Object.keys(t)[0];return this.dataSource.fetchGraphExecutionDigests(e,0,0).pipe(Fe((t=>{this.store.dispatch(Jq({numGraphExecutions:t.num_digests}))})),It((()=>{})))})))}createNumAlertsAndBreakdownLoader(t){return t.pipe(Ve(this.store.select(PZ),this.store.select(EZ)),ce((([,t,e])=>Object.keys(t).length>0&&e.state!==yE.LOADING)),Fe((()=>this.store.dispatch(Hq()))),Zt((([,t])=>{const e=Object.keys(t)[0];return this.dataSource.fetchAlerts(e,0,0).pipe(Fe((t=>{this.store.dispatch(Fq({numAlerts:t.num_alerts,alertsBreakdown:t.alerts_breakdown}))})),It((()=>{})))})))}createInitialExecutionDetector(t){return t.pipe(Ve(this.store.select(LZ),this.store.select(FZ)),ce((([,t,e])=>t>0&&0===Object.keys(e.pageLoadedSizes).length)),It((()=>{})))}createInitialExecutionDigest(t){return t.pipe(Ve(this.store.select(LZ),this.store.select(kZ),this.store.select(VZ)),ce((([,,t])=>null!==t)),It((([,t,e,n])=>({begin:0,end:Math.min(t,n),runId:e}))))}onExecutionScroll(){return this.actions$.pipe(Dk(Wq,Yq,qq),Ve(this.store.select(kZ),this.store.select(BZ),this.store.select(LZ),this.store.select(jZ),this.store.select(VZ)),ce((([t])=>null!==t)),It((([,t,e,n,o,i])=>({runId:t,begin:e,end:Math.min(n,e+o),pageSize:i}))),Ve(this.store.select(FZ)),It((([t,e])=>({props:t,loaded:e,missingPages:NJ(t.begin,t.end,t.pageSize,e.numExecutions,e.pageLoadedSizes)}))),ce((({missingPages:t})=>t.length>0)),It((({props:t,loaded:e,missingPages:n})=>{const{runId:o,pageSize:i}=t;return{begin:n[0]*i,end:Math.min(e.numExecutions,(n[n.length-1]+1)*i),runId:o}})))}createExecutionDigestLoader(t){return t.pipe(Ve(this.store.select(FZ)),ce((([{begin:t,end:e},n])=>e>t&&!(function o(t,e,n){if(e>=n)throw new Error(`Expected begin to be less than end, but got begin=${e}, end=${n}`);return-1!==t.findIndex((t=>t.begin>=e&&t.end<=n))})(n.loadingRanges,t,e))),Fe((([{begin:t,end:e}])=>{this.store.dispatch(Uq({begin:t,end:e}))})),Zt((([{runId:t,begin:e,end:n}])=>this.dataSource.fetchExecutionDigests(t,e,n).pipe(Fe((t=>{this.store.dispatch(Gq(t))})),It((()=>{}))))))}onExecutionDigestFocused(){return this.actions$.pipe(Dk(Zq),Ve(this.store.select(kZ),this.store.select(cX),this.store.select(BZ)),It((([t,e,n,o])=>({activeRunId:e,loadedExecutionData:n,focusIndex:o+t.displayIndex}))))}createExecutionDataAndStackFramesLoader(t){return t.pipe(ce((({activeRunId:t,loadedExecutionData:e,focusIndex:n})=>null!==t&&null!==n&&void 0===e[n])),Zt((({activeRunId:t,focusIndex:e})=>{const n=e,o=n+1;return this.dataSource.fetchExecutionData(t,n,o).pipe(Fe((t=>{this.store.dispatch(Xq(t))})),It((t=>({executionData:t,begin:n,end:o}))))})),It((({executionData:t})=>t.executions[0])),Ve(this.store.select(kZ),this.store.select(pX)),ce((([t,e,n])=>{if(null===e)return!1;for(const e of t.stack_frame_ids)if(void 0===n[e])return!0;return!1})),Zt((([t,e])=>{const n=t.stack_frame_ids;return this.dataSource.fetchStackFrames(e,n).pipe(Fe((t=>{const e={};for(let o=0;o<n.length;++o)e[n[o]]=t.stack_frames[o];this.store.dispatch(dZ({stackFrames:e}))})),It((()=>{})))})))}onGraphExecutionScroll(){return this.actions$.pipe(Dk(tZ),ge(100),Ve(this.store.select(kZ),this.store.select(YZ),this.store.select(qZ)),ce((([,t,e])=>null!==t&&e>0)),It((([,t,e,n])=>({runId:t,numGraphExecutions:e,scrollBeginIndex:n}))),Ve(this.store.select(XZ),this.store.select(ZZ),this.store.select(KZ),this.store.select(JZ)),It((([{runId:t,numGraphExecutions:e,scrollBeginIndex:n},o,i,a,r])=>{let s=NJ(n,Math.min(n+i,e),o,e,r);return s=s.filter((t=>-1===a.indexOf(t))),{runId:t,missingPages:s,pageSize:o,numGraphExecutions:e}})))}loadGraphExecutionPages(t){return t.pipe(ce((({missingPages:t})=>t.length>0)),Fe((({missingPages:t})=>{t.forEach((t=>{this.store.dispatch(Qq({pageIndex:t}))}))})),Zt((({runId:t,missingPages:e,pageSize:n,numGraphExecutions:o})=>{const i=e[0]*n,a=Math.min((e[e.length-1]+1)*n,o);return this.dataSource.fetchGraphExecutionData(t,i,a).pipe(Fe((t=>{this.store.dispatch($q(t))})),It((()=>{})))})))}loadGraphOpInfo(){return this.actions$.pipe(Dk(nZ,eZ),Ve(this.store.select(kZ),this.store.select(dX)),ce((([t,e,n])=>{const{graph_id:o,op_name:i}=t;return!(null===e||void 0!==n[o]&&n[o].has(i)&&(n[o].get(i)===yE.LOADING||n[o].get(i)===yE.LOADED))})),Fe((([{graph_id:t,op_name:e}])=>this.store.dispatch(oZ({graph_id:t,op_name:e})))),Zt((([t,e])=>{const{graph_id:n,op_name:o}=t;return this.dataSource.fetchGraphOpInfo(e,n,o).pipe(Fe((t=>this.store.dispatch(iZ({graphOpInfoResponse:t})))),It((t=>({runId:e,stackFrameIds:t.stack_frame_ids}))))})))}loadGraphOpStackFrames(t){return t.pipe(Ve(this.store.select(pX)),It((([{runId:t,stackFrameIds:e},n])=>({runId:t,missingStackFrameIds:e.filter((t=>void 0===n[t]))}))),ce((({runId:t,missingStackFrameIds:e})=>null!==t&&e.length>0)),Zt((({runId:t,missingStackFrameIds:e})=>this.dataSource.fetchStackFrames(t,e).pipe(Fe((t=>{const n={};for(let o=0;o<e.length;++o)n[e[o]]=t.stack_frames[o];this.store.dispatch(dZ({stackFrames:n}))})),It((()=>{}))))))}onAlertTypeFocused(){return this.actions$.pipe(Dk(Bq),Ve(this.store.select(kZ),this.store.select(AZ),this.store.select(TZ),this.store.select(NZ),this.store.select(EZ)),ce((([,t,e,n,o,i])=>null!==t&&null!==e&&n>0&&(null===o||Object.keys(o).length<n)&&i.state!==yE.LOADING)),Fe((()=>this.store.dispatch(Hq()))),Zt((([,t,e])=>this.dataSource.fetchAlerts(t,0,-1,e))),Fe((({num_alerts:t,alerts_breakdown:e,alert_type:n,begin:o,end:i,alerts:a})=>{this.store.dispatch(Lq({numAlerts:t,alertsBreakdown:e,alertType:n,begin:o,end:i,alerts:a}))})))}fetchExecutionDigestsForAlertTypeFocus(t){return t.pipe(Ve(this.store.select(VZ),this.store.select(jZ),this.store.select(LZ),this.store.select(FZ),this.store.select(kZ)),It((([t,e,n,o,i,a])=>{const r=t.alerts[0].execution_index,s=NJ(Math.max(0,r-Math.floor(n/2)),Math.min(r+Math.floor(n/2),o),e,o,i.pageLoadedSizes);return 0===s.length?{runId:a,begin:0,end:0}:{runId:a,begin:s[0]*e,end:Math.min(i.numExecutions,(s[s.length-1]+1)*e)}})))}loadSourceFileList(t){return t.pipe(Ve(this.store.select(kZ),this.store.select(hX)),ce((([,t,e])=>null!==t&&e.state!==yE.LOADING)),Fe((()=>this.store.dispatch(aZ()))),Zt((([,t])=>this.dataSource.fetchSourceFileList(t).pipe(Fe((t=>{const e=[];t.forEach((([t,n])=>{e.push({host_name:t,file_path:n})})),this.store.dispatch(rZ({sourceFiles:e}))})),It((()=>{}))))))}onSourceFileFocused(){return this.actions$.pipe(Dk(sZ),Ve(this.store.select(kZ),this.store.select(bX),this.store.select(yX)),It((([t,e,n,o])=>({runId:e,stackFrame:t.stackFrame,fileIndex:n,fileContent:o}))),ce((({runId:t,fileContent:e})=>null!==t&&null!==e&&e.loadState===yE.NOT_LOADED)),Fe((({stackFrame:t})=>this.store.dispatch(lZ({host_name:t.host_name,file_path:t.file_path})))),Zt((({fileIndex:t,runId:e})=>this.dataSource.fetchSourceFile(e,t).pipe(Fe((t=>{this.store.dispatch(cZ(t))})),It((()=>{}))))))}}zJ.ɵfac=function t(e){return new(e||zJ)(vr(Sk),vr(Iw),vr(kq))},zJ.ɵprov=Mn({token:zJ,factory:zJ.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(zJ,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:kq}]}),null);class IJ{}IJ.ɵfac=function t(e){return new(e||IJ)},IJ.ɵmod=ao({type:IJ}),IJ.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(IJ,[{type:Ay,args:[{declarations:[xX,PX],imports:[WM],exports:[PX]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(IJ,{declarations:[xX,PX],imports:[WM],exports:[PX]});class HJ{}HJ.ɵfac=function t(e){return new(e||HJ)},HJ.ɵmod=ao({type:HJ}),HJ.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(HJ,[{type:Ay,args:[{declarations:[BK,DK,VK],imports:[WM],exports:[VK]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(HJ,{declarations:[BK,DK,VK],imports:[WM],exports:[VK]});class FJ{}FJ.ɵfac=function t(e){return new(e||FJ)},FJ.ɵmod=ao({type:FJ}),FJ.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(FJ,[{type:Ay,args:[{declarations:[tK,iK,oK,eK,nK,aK],imports:[WM],exports:[aK]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(FJ,{declarations:[tK,iK,oK,eK,nK,aK],imports:[WM],exports:[aK]});class LJ{}LJ.ɵfac=function t(e){return new(e||LJ)},LJ.ɵmod=ao({type:LJ}),LJ.ɵinj=vn({imports:[[WM,FJ,_F]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(LJ,[{type:Ay,args:[{declarations:[KK,JK],imports:[WM,FJ,_F],exports:[JK]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(LJ,{declarations:[KK,JK],imports:[WM,FJ,_F],exports:[JK]});class BJ{}BJ.ɵfac=function t(e){return new(e||BJ)},BJ.ɵmod=ao({type:BJ}),BJ.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(BJ,[{type:Ay,args:[{declarations:[Dq,Eq],exports:[Eq]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(BJ,{declarations:[Dq,Eq],exports:[Eq]});class VJ{}VJ.ɵfac=function t(e){return new(e||VJ)},VJ.ɵmod=ao({type:VJ}),VJ.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(VJ,[{type:Ay,args:[{exports:[oJ],declarations:[oJ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(VJ,{declarations:[oJ],exports:[oJ]});const jJ=["codeViewerContainer"];class UJ{constructor(){this.firstText=null,this.secondText=null,this.renderSideBySide=!0,this.monaco=null,this.editor=null,this.RESIZE_DEBOUNCE_INTERVAL_MS=50}onResize(){this.editor&&this.editor.layout()}ngOnChanges(t){if(!this.monaco)return;const e=!this.editor;e&&(this.editor=this.monaco.editor.createDiffEditor(this.codeViewerContainer.nativeElement,{readOnly:!0,fontSize:10,minimap:{enabled:!0},renderSideBySide:this.renderSideBySide,theme:nJ(this.useDarkMode)})),(e||t.firstText||t.secondText)&&this.editor.setModel({original:this.monaco.editor.createModel(this.firstText||""),modified:this.monaco.editor.createModel(this.secondText||"")}),t.renderSideBySide&&this.editor.updateOptions({renderSideBySide:this.renderSideBySide}),t.useDarkMode&&this.monaco.editor.setTheme(nJ(this.useDarkMode))}}UJ.ɵfac=function t(e){return new(e||UJ)},UJ.ɵcmp=to({type:UJ,selectors:[["source-code-diff-component"]],viewQuery:function t(e,n){if(1&e&&Qh(jJ,7,hg),2&e){let t;Jh(t=tb())&&(n.codeViewerContainer=t.first)}},inputs:{firstText:"firstText",secondText:"secondText",renderSideBySide:"renderSideBySide",monaco:"monaco",useDarkMode:"useDarkMode"},features:[Bo],decls:2,vars:1,consts:[["detectResize","",1,"code-viewer-container",3,"resizeEventDebouncePeriodInMs","onResize"],["codeViewerContainer",""]],template:function t(e,n){1&e&&(Rm(0,"div",0,1),Vm("onResize",(function t(){return n.onResize()})),Am()),2&e&&Dm("resizeEventDebouncePeriodInMs",n.RESIZE_DEBOUNCE_INTERVAL_MS)},directives:[oJ],styles:[".code-viewer-container[_ngcontent-%COMP%] {\n        height: 100%;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(UJ,[{type:My,args:[{selector:"source-code-diff-component",template:'\n    <div\n      #codeViewerContainer\n      class="code-viewer-container"\n      detectResize\n      [resizeEventDebouncePeriodInMs]="RESIZE_DEBOUNCE_INTERVAL_MS"\n      (onResize)="onResize()"\n    ></div>\n  ',styles:["\n      .code-viewer-container {\n        height: 100%;\n      }\n    "],changeDetection:zn.OnPush}]}],null,{firstText:[{type:xy}],secondText:[{type:xy}],renderSideBySide:[{type:xy}],monaco:[{type:xy}],useDarkMode:[{type:xy}],codeViewerContainer:[{type:Za,args:["codeViewerContainer",{static:!0,read:hg}]}]});class GJ{constructor(){this.firstText=null,this.secondText=null,this.renderSideBySide=!0,this.useDarkMode=!1,this.monaco$=null}ngOnInit(){this.monaco$=Ct(eJ()).pipe(It((()=>window.monaco)))}}GJ.ɵfac=function t(e){return new(e||GJ)},GJ.ɵcmp=to({type:GJ,selectors:[["source-code-diff"]],inputs:{firstText:"firstText",secondText:"secondText",renderSideBySide:"renderSideBySide",useDarkMode:"useDarkMode"},decls:2,vars:7,consts:[[3,"firstText","secondText","renderSideBySide","monaco","useDarkMode"]],template:function t(e,n){1&e&&(Tm(0,"source-code-diff-component",0),Ah(1,"async")),2&e&&Dm("firstText",n.firstText)("secondText",n.secondText)("renderSideBySide",n.renderSideBySide)("monaco",Th(1,5,n.monaco$))("useDarkMode",n.useDarkMode)},directives:[UJ],pipes:[wM],styles:["source-code-diff-component[_ngcontent-%COMP%] {\n        display: block;\n        height: 100%;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(GJ,[{type:My,args:[{selector:"source-code-diff",template:'\n    <source-code-diff-component\n      [firstText]="firstText"\n      [secondText]="secondText"\n      [renderSideBySide]="renderSideBySide"\n      [monaco]="monaco$ | async"\n      [useDarkMode]="useDarkMode"\n    ></source-code-diff-component>\n  ',styles:["\n      source-code-diff-component {\n        display: block;\n        height: 100%;\n      }\n    "],changeDetection:zn.OnPush}]}],null,{firstText:[{type:xy}],secondText:[{type:xy}],renderSideBySide:[{type:xy}],useDarkMode:[{type:xy}]});class WJ{}WJ.ɵfac=function t(e){return new(e||WJ)},WJ.ɵmod=ao({type:WJ}),WJ.ɵinj=vn({imports:[[WM,VJ]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(WJ,[{type:Ay,args:[{declarations:[aJ,rJ,UJ,GJ],imports:[WM,VJ],exports:[rJ,GJ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(WJ,{declarations:[aJ,rJ,UJ,GJ],imports:[WM,VJ],exports:[rJ,GJ]});class YJ{}YJ.ɵfac=function t(e){return new(e||YJ)},YJ.ɵmod=ao({type:YJ}),YJ.ɵinj=vn({imports:[[WM,WJ]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(YJ,[{type:Ay,args:[{declarations:[dJ,pJ],imports:[WM,WJ],exports:[pJ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(YJ,{declarations:[dJ,pJ],imports:[WM,WJ],exports:[pJ]});class qJ{}qJ.ɵfac=function t(e){return new(e||qJ)},qJ.ɵmod=ao({type:qJ}),qJ.ɵinj=vn({imports:[[WM,WJ]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(qJ,[{type:Ay,args:[{declarations:[kJ,SJ],imports:[WM,WJ],exports:[SJ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(qJ,{declarations:[kJ,SJ],imports:[WM,WJ],exports:[SJ]});class ZJ{}ZJ.ɵfac=function t(e){return new(e||ZJ)},ZJ.ɵmod=ao({type:ZJ}),ZJ.ɵinj=vn({imports:[[WM,FJ]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ZJ,[{type:Ay,args:[{declarations:[pK,uK],imports:[WM,FJ],exports:[uK]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(ZJ,{declarations:[pK,uK],imports:[WM,FJ],exports:[uK]});class XJ{}XJ.ɵfac=function t(e){return new(e||XJ)},XJ.ɵmod=ao({type:XJ}),XJ.ɵinj=vn({imports:[[WM,ZJ,JH,zX]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(XJ,[{type:Ay,args:[{declarations:[CK,vK],imports:[WM,ZJ,JH,zX],exports:[vK]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(XJ,{declarations:[CK,vK],imports:[WM,ZJ,JH,zX],exports:[vK]});class KJ{}function JJ(t,e){const n={};for(const o of Object.keys(t))n[o]=e(t[o],o);return n}function QJ(t,e,n){const{plugin:o,tag:i,runId:a,sample:r}=e[t],s=nT(n,o,i,r);if(s){if(null!==a&&s.runToSeries.hasOwnProperty(a)){const t=s.runToSeries[a].length;return t>0?t-1:null}const t=Object.values(s.runToSeries).map((t=>t.length));if(t.length)return Math.max(...t)-1}return null}function $J(t,e,n,o){const i=Object.assign({},e);for(const a in t){if(!t.hasOwnProperty(a))continue;const r=QJ(a,t,n);if(null===r){e.hasOwnProperty(a)&&(i[a]=null);continue}const s=e.hasOwnProperty(a)?e[a]:null,l=QJ(a,t,o),c=null!==s&&s===l,d=null===s||c;(null!==s&&s>r||d)&&(i[a]=r)}return i}function tQ(t){const e=JJ(t.runToLoadState,(t=>t===yE.LOADING?yE.LOADING:yE.NOT_LOADED));return Object.assign(Object.assign({},t),{runToLoadState:e})}function eQ(t,e,n,o){return JSON.stringify([t,e,n||"",o])}KJ.ɵfac=function t(e){return new(e||KJ)},KJ.ɵmod=ao({type:KJ}),KJ.ɵinj=vn({imports:[[IJ,WM,ER,LJ,HJ,BJ,YJ,qJ,Sq,XJ,dk.forFeature(pZ,xZ),Wk.forFeature([zJ]),wq.forPlugin(TJ,AJ)]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(KJ,[{type:Ay,args:[{declarations:[RJ,AJ],imports:[IJ,WM,ER,LJ,HJ,BJ,YJ,qJ,Sq,XJ,dk.forFeature(pZ,xZ),Wk.forFeature([zJ]),wq.forPlugin(TJ,AJ)],exports:[AJ],entryComponents:[AJ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(KJ,{declarations:[RJ,AJ],imports:[IJ,WM,ER,LJ,HJ,BJ,YJ,qJ,Sq,XJ,ck,Gk,wq],exports:[AJ]});const{initialState:nQ,reducers:oQ}=HN({tagMetadataLoadState:{state:yE.NOT_LOADED,lastLoadedTimeInMs:null},tagMetadata:{scalars:{tagDescriptions:{},tagToRuns:{}},histograms:{tagDescriptions:{},tagToRuns:{}},images:{tagDescriptions:{},tagRunSampledInfo:{}}},cardList:[],cardToPinnedCopy:new Map,pinnedCardToOriginal:new Map,unresolvedImportedPinnedCards:[],cardMetadataMap:{},cardStepIndex:{},tagFilter:"",tagGroupExpanded:new Map,selectedTime:null,selectTimeEnabled:!1,useRangeSelectTime:!1,filteredPluginTypes:new Set,stepMinMax:{min:1/0,max:-1/0}},{isSettingsPaneOpen:!0,promoteTimeSeries:!0,timeSeriesData:{scalars:{},histograms:{},images:{}},settings:pT,settingOverrides:{},visibleCardMap:new Map},(t=>Object.assign(Object.assign({},t),{visibleCardMap:new Map}))),iQ=nQ,aQ=yk(nQ,bk(rS,((t,{routeKind:e,partialState:n})=>{if(e!==Zk.EXPERIMENT&&e!==Zk.COMPARE_EXPERIMENT)return t;const o=new Set;for(const e of t.pinnedCardToOriginal.keys()){const{plugin:n,tag:i,runId:a,sample:r}=t.cardMetadataMap[e];o.add(eQ(n,i,a,r))}const i=n,a=[];for(const e of[...t.unresolvedImportedPinnedCards,...i.metrics.pinnedCards]){const t=eQ(e.plugin,e.tag,e.runId,e.sample);o.has(t)||(o.add(t),a.push(e))}const r=sT(a,t.cardList,t.cardMetadataMap,t.cardToPinnedCopy,t.pinnedCardToOriginal,t.cardStepIndex),s=i.metrics.smoothing;let l=t.settingOverrides;if(Number.isFinite(s)&&null!==s){const e=Math.max(0,Math.min(.999,Number(s.toPrecision(3))));l=Object.assign(Object.assign({},t.settingOverrides),{scalarSmoothing:e})}const 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Object.keys(n[t])){const{maxSamplesPerStep:a}=n[t][i];for(let n=0;n<a;n++)e.push({plugin:o,tag:t,runId:i,sample:n,numSample:a})}}}else if(xA(o)){i=t[o].tagToRuns;for(const t of Object.keys(i))for(const n of i[t])e.push({plugin:o,tag:t,runId:n})}else{i=t[o].tagToRuns;for(const t of Object.keys(i))e.push({plugin:o,tag:t,runId:null})}}return e})(n),r=[];for(const e of i){const n=iT(e);t.cardMetadataMap.hasOwnProperty(n)||(o[n]=e,r.push(n))}const s=[...t.cardList,...r],l=sT(t.unresolvedImportedPinnedCards,r,o,t.cardToPinnedCopy,t.pinnedCardToOriginal,t.cardStepIndex);return Object.assign(Object.assign(Object.assign({},t),l),{tagMetadataLoadState:{state:yE.LOADED,lastLoadedTimeInMs:Date.now()},tagMetadata:n,cardList:s})})),bk(eR,((t,{tagFilter:e})=>Object.assign(Object.assign({},t),{tagFilter:e}))),bk(LE,((t,{sort:e})=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{tooltipSort:e})}))),bk(BE,(t=>{var e;const n=!(null!==(e=t.settingOverrides.ignoreOutliers)&&void 0!==e?e:t.settings.ignoreOutliers);return Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{ignoreOutliers:n})})})),bk(VE,((t,{xAxisType:e})=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{xAxisType:e})}))),bk(jE,((t,{smoothing:e})=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{scalarSmoothing:e})}))),bk(UE,(t=>{var e;const n=!(null!==(e=t.settingOverrides.scalarPartitionNonMonotonicX)&&void 0!==e?e:t.settings.scalarPartitionNonMonotonicX);return Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{scalarPartitionNonMonotonicX:n})})})),bk(GE,((t,{brightnessInMilli:e})=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{imageBrightnessInMilli:e})}))),bk(WE,((t,{contrastInMilli:e})=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{imageContrastInMilli:e})}))),bk(YE,(t=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{imageBrightnessInMilli:void 0})}))),bk(qE,(t=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{imageContrastInMilli:void 0})}))),bk(ZE,(t=>{var e;const n=!(null!==(e=t.settingOverrides.imageShowActualSize)&&void 0!==e?e:t.settings.imageShowActualSize);return Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{imageShowActualSize:n})})})),bk(XE,((t,{histogramMode:e})=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{histogramMode:e})}))),bk(KE,((t,{requests:e})=>{if(!e.length)return t;const n=Object.assign({},t.timeSeriesData);for(const o of e){const{plugin:e,tag:i,sample:a}=o;n[e]=oT(n,e,i,a);const r=nT(n,e,i,a),s=OA(o)?[o.runId]:rT(t.tagMetadata,e,i,a);r.runToLoadState=aT(yE.LOADING,s,r.runToLoadState)}return Object.assign(Object.assign({},t),{timeSeriesData:n})})),bk(JE,((t,{request:e})=>{const n=Object.assign({},t.timeSeriesData),{plugin:o,tag:i,sample:a}=e;n[o]=oT(n,o,i,a);const r=nT(n,o,i,a),s=OA(e)?[e.runId]:rT(t.tagMetadata,o,i,a);return r.runToLoadState=aT(yE.FAILED,s,r.runToLoadState),Object.assign(Object.assign({},t),{timeSeriesData:n})})),bk(QE,((t,{response:e})=>{const n=Object.assign({},t.stepMinMax),o=Object.assign({},t.timeSeriesData),{plugin:i,tag:a,runId:r,sample:s}=e;o[i]=oT(o,i,a,s);const l=nT(o,i,a,s);if(wA(e)){const e=r?[r]:rT(t.tagMetadata,i,a,s);l.runToLoadState=aT(yE.FAILED,e,l.runToLoadState)}else{const t=e.runToSeries;l.runToSeries=Object.assign({},l.runToSeries),l.runToLoadState=Object.assign({},l.runToLoadState);for(const e in t)if(t.hasOwnProperty(e)){l.runToSeries[e]=t[e],l.runToLoadState[e]=yE.LOADED;for(const o of t[e])n.min=Math.min(n.min,o.step),n.max=Math.max(n.max,o.step)}}return Object.assign(Object.assign({},t),{timeSeriesData:o,cardStepIndex:$J(t.cardMetadataMap,t.cardStepIndex,o,t.timeSeriesData),stepMinMax:n})})),bk(tR,((t,{cardId:e,stepIndex:n})=>{const o=QJ(e,t.cardMetadataMap,t.timeSeriesData);let i=n;return null===o?i=null:n>o&&(i=o),Object.assign(Object.assign({},t),{cardStepIndex:Object.assign(Object.assign({},t.cardStepIndex),{[e]:i})})})),bk(nR,((t,{tagGroup:e})=>{const n=new Map(t.tagGroupExpanded);return n.set(e,!n.get(e)),Object.assign(Object.assign({},t),{tagGroupExpanded:n})})),bk($E,((t,{enteredCards:e,exitedCards:n})=>{if(!e.length&&!n.length)return t;const o=new Map(t.visibleCardMap);return e.forEach((({elementId:t,cardId:e})=>{var n;const i=null!==(n=o.get(t))&&void 0!==n?n:null;if(null!==i&&i!==e)throw new Error("A DOM element cannot be reused for more than 1 unique card metadata");o.set(t,e)})),n.forEach((({elementId:t})=>{o.delete(t)})),Object.assign(Object.assign({},t),{visibleCardMap:o})})),bk(oR,((t,{cardId:e})=>{const n=t.pinnedCardToOriginal.has(e),o=!n&&!t.cardToPinnedCopy.has(e);if(o&&!cT(t))return t;let i=new Map(t.cardToPinnedCopy),a=new Map(t.pinnedCardToOriginal),r=Object.assign({},t.cardMetadataMap),s=Object.assign({},t.cardStepIndex);if(n){const n=t.pinnedCardToOriginal.get(e);i.delete(n),a.delete(e),delete r[e],delete s[e]}else if(o){const t=lT(e,i,a,s,r);i=t.cardToPinnedCopy,a=t.pinnedCardToOriginal,r=t.cardMetadataMap,s=t.cardStepIndex}else{const n=t.cardToPinnedCopy.get(e);i.delete(e),a.delete(n),delete r[n],delete s[n]}return Object.assign(Object.assign({},t),{cardMetadataMap:r,cardStepIndex:s,cardToPinnedCopy:i,pinnedCardToOriginal:a})})),bk(lR,(t=>Object.assign(Object.assign({},t),{selectTimeEnabled:!t.selectTimeEnabled}))),bk(rR,((t,e)=>{var n,o,i,a;const r=e.startStep;let s=null!==(a=null!==(n=e.endStep)&&void 0!==n?n:null===(i=null===(o=t.selectedTime)||void 0===o?void 0:o.end)||void 0===i?void 0:i.step)&&void 0!==a?a:t.stepMinMax.max;return r>s&&(s=r),Object.assign(Object.assign({},t),{selectTimeEnabled:!0,selectedTime:{start:{step:r},end:{step:s}}})})),bk(cR,(t=>Object.assign(Object.assign({},t),{useRangeSelectTime:!t.useRangeSelectTime}))),bk(sR,(t=>Object.assign(Object.assign({},t),{selectedTime:null}))),bk(iR,((t,{plugin:e})=>{let n=new Set(t.filteredPluginTypes);return n.has(e)?n.delete(e):n.add(e),Object.values(hA).every((t=>n.has(t)))&&(n=new Set),Object.assign(Object.assign({},t),{filteredPluginTypes:n})})),bk(aR,(t=>Object.assign(Object.assign({},t),{filteredPluginTypes:new Set}))),bk(dR,(t=>Object.assign(Object.assign({},t),{promoteTimeSeries:!1}))),bk(zE,(t=>Object.assign(Object.assign({},t),{isSettingsPaneOpen:!t.isSettingsPaneOpen}))),bk(NE,(t=>Object.assign(Object.assign({},t),{isSettingsPaneOpen:!1}))));function rQ(t,e){return BN(aQ,oQ)(t,e)}function sQ(t,e){return{tagDescriptions:t[e].tagDescriptions,tagToRuns:lQ(t[e].runTagInfo)}}function lQ(t){const e={};for(const n in t)for(const o of t[n])e[o]=[...e[o]||[],n];return e}const cQ=Zw(gT,yT,((t,e,n)=>e?Object.assign(Object.assign({},e),{loadState:t,id:n}):null)),dQ=JP("[Metrics Effects] Init");class pQ{constructor(t,e,n){this.actions$=t,this.store=e,this.dataSource=n,this.dashboardShownWithoutData$=this.actions$.pipe(Dk(dQ,_E,PE,dS),Ve(this.store.select(MR),this.store.select(uT)),ce((([,t,e])=>t===_A&&e.state===yE.NOT_LOADED))),this.reloadRequestedWhileShown$=this.actions$.pipe(Dk(xE,vE),Ve(this.store.select(MR)),ce((([,t])=>t===_A))),this.loadTagMetadata$=re(this.dashboardShownWithoutData$,this.reloadRequestedWhileShown$).pipe(Ve(this.store.select(uT),this.store.select(TS)),ce((([,t,e])=>t.state!==yE.LOADING&&null!==e)),Fe((()=>{this.store.dispatch(IE())})),ze((([,,t])=>this.dataSource.fetchTagMetadata(t).pipe(Fe((t=>{this.store.dispatch(HE({tagMetadata:t}))})),pe((()=>(this.store.dispatch(FE()),Et(null)))))))),this.visibleCardsWithoutDataChanged$=this.actions$.pipe(Dk($E),ze((()=>this.getVisibleCardFetchInfos().pipe(be(1)))),It((t=>t.filter((t=>t.loadState===yE.NOT_LOADED))))),this.visibleCardsReloaded$=this.reloadRequestedWhileShown$.pipe(ze((()=>this.getVisibleCardFetchInfos().pipe(be(1)))),It((t=>t.filter((t=>t.loadState!==yE.LOADING))))),this.loadTimeSeries$=re(this.visibleCardsWithoutDataChanged$,this.visibleCardsReloaded$).pipe(ce((t=>t.length>0)),Ve(this.store.select(TS).pipe(ce((t=>null!==t)))),Zt((([t,e])=>this.fetchTimeSeriesForCards(t,e)))),this.dataEffects$=Mk((()=>re(this.loadTagMetadata$,this.loadTimeSeries$)),{dispatch:!1})}ngrxOnInitEffects(){return dQ()}getVisibleCardFetchInfos(){return this.store.select(CT).pipe(ze((t=>t.size?$t([...t].map((t=>this.store.select(cQ,t).pipe(be(1))))):Et([]))),It((t=>t.filter(Boolean))))}fetchTimeSeries(t){return this.dataSource.fetchTimeSeries([t]).pipe(Fe((t=>{const e=t.filter(wA);e.length&&console.error("Time series response contained errors:",e),this.store.dispatch(QE({response:t[0]}))})),pe((()=>(this.store.dispatch(JE({request:t})),Et(null)))))}fetchTimeSeriesForCards(t,e){return Et(t.map((t=>{const{plugin:n,tag:o,runId:i,sample:a}=t;return xA(n)?{plugin:n,tag:o,sample:a,runId:i}:{plugin:n,tag:o,sample:a,experimentIds:e}}))).pipe(Fe((t=>{this.store.dispatch(KE({requests:t}))})),Zt((t=>re(...t.map((t=>this.fetchTimeSeries(t)))))))}}pQ.ɵfac=function t(e){return new(e||pQ)(vr(Sk),vr(Iw),vr(PA))},pQ.ɵprov=Mn({token:pQ,factory:pQ.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(pQ,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:PA}]}),null);const mQ=new Ga("Metrics Store Config"),uQ=new Ga("Metrics Initial Settings Config");function fQ(t){return t?{initialState:Object.assign(Object.assign({},iQ),{settings:t})}:{initialState:iQ}}var gQ;!(function(t){t[t.LEFT=1]="LEFT",t[t.RIGHT=2]="RIGHT",t[t.MIDDLE=4]="MIDDLE",t[t.FOURTH=8]="FOURTH",t[t.FIFTH=32]="FIFTH"})(gQ||(gQ={}));let hQ=0;function bQ(t,e){if(1&t){const t=Hm();Rm(0,"button",3),Vm("click",(function e(){return hi(t),Ym().expandSidebar()})),Tm(1,"mat-icon",4),Am()}}function yQ(t,e){if(1&t&&(Rm(0,"nav",5),Ah(1,"async"),Xm(2,1),Am()),2&t){const t=Ym();du("width",Th(1,4,t.width$),"%")("min-width",t.MINIMUM_SIDEBAR_WIDTH_IN_PX,"px")}}function _Q(t,e){if(1&t){const t=Hm();Rm(0,"div",6),Vm("mousedown",(function e(){return hi(t),Ym().resizeGrabbed()})),Tm(1,"mat-icon",7),Am()}}const CQ=[[["","main",""]],[["","sidebar",""]]];class MQ{constructor(t,e){this.store=t,this.width$=this.store.select(OR),this.ngUnsubscribe=new I,this.resizing=!1,this.MINIMUM_SIDEBAR_WIDTH_IN_PX=75,oe(e.nativeElement,"mousemove").pipe(Ie(this.ngUnsubscribe),ce((()=>this.resizing))).subscribe((t=>{if((t.buttons&gQ.LEFT)!==gQ.LEFT)return void(this.resizing=!1);t.preventDefault();const{width:n}=e.nativeElement.getBoundingClientRect();this.store.dispatch(TE({widthInPercent:t.clientX<=this.MINIMUM_SIDEBAR_WIDTH_IN_PX?0:t.clientX/n*100}))})),oe(e.nativeElement,"mouseup",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((()=>{this.resizing=!1}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}resizeGrabbed(){this.resizing=!0}expandSidebar(){this.store.dispatch(TE({widthInPercent:20}))}}var vQ;MQ.ɵfac=function t(e){return new(e||MQ)(Sm(Iw),Sm(hg))},MQ.ɵcmp=to({type:MQ,selectors:[["tb-dashboard-layout"]],ngContentSelectors:["[main]","[sidebar]"],decls:7,vars:9,consts:[["class","expand",3,"click",4,"ngIf"],["class","sidebar",3,"width","minWidth",4,"ngIf"],["class","resizer",3,"mousedown",4,"ngIf"],[1,"expand",3,"click"],["svgIcon","expand_more_24px"],[1,"sidebar"],[1,"resizer",3,"mousedown"],["svgIcon","drag_indicator_24px"]],template:function t(e,n){1&e&&(Zm(CQ),Qp(0,bQ,2,0,"button",0),Ah(1,"async"),Qp(2,yQ,3,6,"nav",1),Ah(3,"async"),Qp(4,_Q,2,0,"div",2),Ah(5,"async"),Xm(6)),2&e&&(Dm("ngIf",0===Th(1,3,n.width$)),rc(2),Dm("ngIf",Th(3,5,n.width$)>0),rc(2),Dm("ngIf",Th(5,7,n.width$)>0))},directives:[dM,DW],pipes:[wM],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   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sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action[_ngcontent-%COMP%]{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node[_ngcontent-%COMP%], .mat-nested-tree-node[_ngcontent-%COMP%]{font-weight:400;font-size:14px}.mat-ripple[_ngcontent-%COMP%]{overflow:hidden;position:relative}.mat-ripple[_ngcontent-%COMP%]:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded[_ngcontent-%COMP%]{overflow:visible}.mat-ripple-element[_ngcontent-%COMP%]{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms 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.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes 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Got: ${e.datasetType} vs. ${n.datasetType}`)}else o.set(t.tag,Object.assign({},t))}if(a.length)throw new Error(`Validation error:\n${a.join("\n")}`);return{hparams:[...e].map((([t,e])=>Object.assign(Object.assign({},e),{displayName:[...n.get(t)].join(" or ")}))),metrics:[...o].map((([t,e])=>Object.assign(Object.assign({},e),{displayName:[...i.get(t)].join(" or ")})))}})(...e.experimentIds.map((e=>{const n=t.specs[e];return n?{hparams:n.hparam.specs,metrics:n.metric.specs}:null})).filter(Boolean)))),DQ={tooltipState:nx("state",[rx("initial, void, hidden",ax({opacity:0,transform:"scale(0)"})),rx("visible",ax({transform:"scale(1)"})),lx("* => visible",ox("200ms cubic-bezier(0, 0, 0.2, 1)",sx([ax({opacity:0,transform:"scale(0)",offset:0}),ax({opacity:.5,transform:"scale(0.99)",offset:.5}),ax({opacity:1,transform:"scale(1)",offset:1})]))),lx("* => hidden",ox("100ms cubic-bezier(0, 0, 0.2, 1)",ax({opacity:0})))])},EQ=Nz({passive:!0});function RQ(t){return Error(`Tooltip position "${t}" is invalid.`)}const AQ=new Ga("mat-tooltip-scroll-strategy"),TQ={provide:AQ,deps:[pL],useFactory:function NQ(t){return()=>t.scrollStrategies.reposition({scrollThrottle:20})}},zQ=new Ga("mat-tooltip-default-options",{providedIn:"root",factory:function IQ(){return{showDelay:0,hideDelay:0,touchendHideDelay:1500}}});class HQ{constructor(t,e,n,o,i,a,r,s,l,c,d,p){this._overlay=t,this._elementRef=e,this._scrollDispatcher=n,this._viewContainerRef=o,this._ngZone=i,this._platform=a,this._ariaDescriber=r,this._focusMonitor=s,this._dir=c,this._defaultOptions=d,this._position="below",this._disabled=!1,this._viewInitialized=!1,this._pointerExitEventsInitialized=!1,this._viewportMargin=8,this._cssClassPrefix="mat",this.showDelay=this._defaultOptions.showDelay,this.hideDelay=this._defaultOptions.hideDelay,this.touchGestures="auto",this._message="",this._passiveListeners=[],this._destroyed=new I,this._handleKeydown=t=>{this._isTooltipVisible()&&t.keyCode===uz&&!bz(t)&&(t.preventDefault(),t.stopPropagation(),this._ngZone.run((()=>this.hide(0))))},this._scrollStrategy=l,this._document=p,d&&(d.position&&(this.position=d.position),d.touchGestures&&(this.touchGestures=d.touchGestures)),c.change.pipe(Ie(this._destroyed)).subscribe((()=>{this._overlayRef&&this._updatePosition(this._overlayRef)})),i.runOutsideAngular((()=>{e.nativeElement.addEventListener("keydown",this._handleKeydown)}))}get position(){return this._position}set position(t){var e;t!==this._position&&(this._position=t,this._overlayRef&&(this._updatePosition(this._overlayRef),null===(e=this._tooltipInstance)||void 0===e||e.show(0),this._overlayRef.updatePosition()))}get disabled(){return this._disabled}set disabled(t){this._disabled=yz(t),this._disabled?this.hide(0):this._setupPointerEnterEventsIfNeeded()}get message(){return this._message}set message(t){this._ariaDescriber.removeDescription(this._elementRef.nativeElement,this._message,"tooltip"),this._message=null!=t?String(t).trim():"",!this._message&&this._isTooltipVisible()?this.hide(0):(this._setupPointerEnterEventsIfNeeded(),this._updateTooltipMessage(),this._ngZone.runOutsideAngular((()=>{Promise.resolve().then((()=>{this._ariaDescriber.describe(this._elementRef.nativeElement,this.message,"tooltip")}))})))}get tooltipClass(){return this._tooltipClass}set tooltipClass(t){this._tooltipClass=t,this._tooltipInstance&&this._setTooltipClass(this._tooltipClass)}ngAfterViewInit(){this._viewInitialized=!0,this._setupPointerEnterEventsIfNeeded(),this._focusMonitor.monitor(this._elementRef).pipe(Ie(this._destroyed)).subscribe((t=>{t?"keyboard"===t&&this._ngZone.run((()=>this.show())):this._ngZone.run((()=>this.hide(0)))}))}ngOnDestroy(){const t=this._elementRef.nativeElement;clearTimeout(this._touchstartTimeout),this._overlayRef&&(this._overlayRef.dispose(),this._tooltipInstance=null),t.removeEventListener("keydown",this._handleKeydown),this._passiveListeners.forEach((([e,n])=>{t.removeEventListener(e,n,EQ)})),this._passiveListeners.length=0,this._destroyed.next(),this._destroyed.complete(),this._ariaDescriber.removeDescription(t,this.message,"tooltip"),this._focusMonitor.stopMonitoring(t)}show(t=this.showDelay){if(this.disabled||!this.message||this._isTooltipVisible()&&!this._tooltipInstance._showTimeoutId&&!this._tooltipInstance._hideTimeoutId)return;const e=this._createOverlay();this._detach(),this._portal=this._portal||new vF(this._tooltipComponent,this._viewContainerRef),this._tooltipInstance=e.attach(this._portal).instance,this._tooltipInstance.afterHidden().pipe(Ie(this._destroyed)).subscribe((()=>this._detach())),this._setTooltipClass(this._tooltipClass),this._updateTooltipMessage(),this._tooltipInstance.show(t)}hide(t=this.hideDelay){this._tooltipInstance&&this._tooltipInstance.hide(t)}toggle(){this._isTooltipVisible()?this.hide():this.show()}_isTooltipVisible(){return!!this._tooltipInstance&&this._tooltipInstance.isVisible()}_createOverlay(){if(this._overlayRef)return this._overlayRef;const t=this._scrollDispatcher.getAncestorScrollContainers(this._elementRef),e=this._overlay.position().flexibleConnectedTo(this._elementRef).withTransformOriginOn(`.${this._cssClassPrefix}-tooltip`).withFlexibleDimensions(!1).withViewportMargin(this._viewportMargin).withScrollableContainers(t);return e.positionChanges.pipe(Ie(this._destroyed)).subscribe((t=>{this._updateCurrentPositionClass(t.connectionPair),this._tooltipInstance&&t.scrollableViewProperties.isOverlayClipped&&this._tooltipInstance.isVisible()&&this._ngZone.run((()=>this.hide(0)))})),this._overlayRef=this._overlay.create({direction:this._dir,positionStrategy:e,panelClass:`${this._cssClassPrefix}-tooltip-panel`,scrollStrategy:this._scrollStrategy()}),this._updatePosition(this._overlayRef),this._overlayRef.detachments().pipe(Ie(this._destroyed)).subscribe((()=>this._detach())),this._overlayRef.outsidePointerEvents().pipe(Ie(this._destroyed)).subscribe((()=>{var t;return null===(t=this._tooltipInstance)||void 0===t?void 0:t._handleBodyInteraction()})),this._overlayRef}_detach(){this._overlayRef&&this._overlayRef.hasAttached()&&this._overlayRef.detach(),this._tooltipInstance=null}_updatePosition(t){const e=t.getConfig().positionStrategy,n=this._getOrigin(),o=this._getOverlayPosition();e.withPositions([this._addOffset(Object.assign(Object.assign({},n.main),o.main)),this._addOffset(Object.assign(Object.assign({},n.fallback),o.fallback))])}_addOffset(t){return t}_getOrigin(){const t=!this._dir||"ltr"==this._dir.value,e=this.position;let n;if("above"==e||"below"==e)n={originX:"center",originY:"above"==e?"top":"bottom"};else if("before"==e||"left"==e&&t||"right"==e&&!t)n={originX:"start",originY:"center"};else if("after"==e||"right"==e&&t||"left"==e&&!t)n={originX:"end",originY:"center"};else if("undefined"==typeof ngDevMode||ngDevMode)throw RQ(e);const{x:o,y:i}=this._invertPosition(n.originX,n.originY);return{main:n,fallback:{originX:o,originY:i}}}_getOverlayPosition(){const t=!this._dir||"ltr"==this._dir.value,e=this.position;let n;if("above"==e)n={overlayX:"center",overlayY:"bottom"};else if("below"==e)n={overlayX:"center",overlayY:"top"};else if("before"==e||"left"==e&&t||"right"==e&&!t)n={overlayX:"end",overlayY:"center"};else if("after"==e||"right"==e&&t||"left"==e&&!t)n={overlayX:"start",overlayY:"center"};else if("undefined"==typeof ngDevMode||ngDevMode)throw RQ(e);const{x:o,y:i}=this._invertPosition(n.overlayX,n.overlayY);return{main:n,fallback:{overlayX:o,overlayY:i}}}_updateTooltipMessage(){this._tooltipInstance&&(this._tooltipInstance.message=this.message,this._tooltipInstance._markForCheck(),this._ngZone.onMicrotaskEmpty.pipe(be(1),Ie(this._destroyed)).subscribe((()=>{this._tooltipInstance&&this._overlayRef.updatePosition()})))}_setTooltipClass(t){this._tooltipInstance&&(this._tooltipInstance.tooltipClass=t,this._tooltipInstance._markForCheck())}_invertPosition(t,e){return"above"===this.position||"below"===this.position?"top"===e?e="bottom":"bottom"===e&&(e="top"):"end"===t?t="start":"start"===t&&(t="end"),{x:t,y:e}}_updateCurrentPositionClass(t){const{overlayY:e,originX:n,originY:o}=t;let i;if(i="center"===e?this._dir&&"rtl"===this._dir.value?"end"===n?"left":"right":"start"===n?"left":"right":"bottom"===e&&"top"===o?"above":"below",i!==this._currentPosition){const t=this._overlayRef;if(t){const 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0,decorators:[{type:kr,args:[Z_]}]}],HQ.propDecorators={position:[{type:xy,args:["matTooltipPosition"]}],disabled:[{type:xy,args:["matTooltipDisabled"]}],showDelay:[{type:xy,args:["matTooltipShowDelay"]}],hideDelay:[{type:xy,args:["matTooltipHideDelay"]}],touchGestures:[{type:xy,args:["matTooltipTouchGestures"]}],message:[{type:xy,args:["matTooltip"]}],tooltipClass:[{type:xy,args:["matTooltipClass"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(HQ,[{type:Cy}],(function(){return[{type:pL},{type:hg},{type:pF},{type:eh},{type:a_},{type:wz},{type:Kz},{type:SI},{type:void 0},{type:HI},{type:void 0},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),{showDelay:[{type:xy,args:["matTooltipShowDelay"]}],hideDelay:[{type:xy,args:["matTooltipHideDelay"]}],touchGestures:[{type:xy,args:["matTooltipTouchGestures"]}],position:[{type:xy,args:["matTooltipPosition"]}],disabled:[{type:xy,args:["matTooltipDisabled"]}],message:[{type:xy,args:["matTooltip"]}],tooltipClass:[{type:xy,args:["matTooltipClass"]}]});class FQ extends HQ{constructor(t,e,n,o,i,a,r,s,l,c,d,p){super(t,e,n,o,i,a,r,s,l,c,d,p),this._tooltipComponent=BQ}}FQ.ɵfac=function t(e){return new(e||FQ)(Sm(pL),Sm(hg),Sm(pF),Sm(eh),Sm(a_),Sm(wz),Sm(Kz),Sm(SI),Sm(AQ),Sm(HI,8),Sm(zQ,8),Sm(Z_))},FQ.ɵdir=lo({type:FQ,selectors:[["","matTooltip",""]],hostAttrs:[1,"mat-tooltip-trigger"],exportAs:["matTooltip"],features:[xp]}),FQ.ctorParameters=()=>[{type:pL},{type:hg},{type:pF},{type:eh},{type:a_},{type:wz},{type:Kz},{type:SI},{type:void 0,decorators:[{type:kr,args:[AQ]}]},{type:HI,decorators:[{type:Sr}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[zQ]}]},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(FQ,[{type:Cy,args:[{selector:"[matTooltip]",exportAs:"matTooltip",host:{class:"mat-tooltip-trigger"}}]}],(function(){return[{type:pL},{type:hg},{type:pF},{type:eh},{type:a_},{type:wz},{type:Kz},{type:SI},{type:void 0,decorators:[{type:kr,args:[AQ]}]},{type:HI,decorators:[{type:Sr}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[zQ]}]},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);class LQ{constructor(t){this._changeDetectorRef=t,this._visibility="initial",this._closeOnInteraction=!1,this._onHide=new I}show(t){clearTimeout(this._hideTimeoutId),this._closeOnInteraction=!0,this._showTimeoutId=setTimeout((()=>{this._visibility="visible",this._showTimeoutId=void 0,this._onShow(),this._markForCheck()}),t)}hide(t){clearTimeout(this._showTimeoutId),this._hideTimeoutId=setTimeout((()=>{this._visibility="hidden",this._hideTimeoutId=void 0,this._markForCheck()}),t)}afterHidden(){return this._onHide}isVisible(){return"visible"===this._visibility}ngOnDestroy(){clearTimeout(this._showTimeoutId),clearTimeout(this._hideTimeoutId),this._onHide.complete()}_animationStart(){this._closeOnInteraction=!1}_animationDone(t){const e=t.toState;"hidden"!==e||this.isVisible()||this._onHide.next(),"visible"!==e&&"hidden"!==e||(this._closeOnInteraction=!0)}_handleBodyInteraction(){this._closeOnInteraction&&this.hide(0)}_markForCheck(){this._changeDetectorRef.markForCheck()}_onShow(){}}LQ.ɵfac=function t(e){return new(e||LQ)(Sm(Ug))},LQ.ɵdir=lo({type:LQ}),LQ.ctorParameters=()=>[{type:Ug}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(LQ,[{type:Cy}],(function(){return[{type:Ug}]}),null);class BQ extends LQ{constructor(t,e){super(t),this._breakpointObserver=e,this._isHandset=this._breakpointObserver.observe("(max-width: 599.98px) and (orientation: portrait), (max-width: 959.98px) and (orientation: landscape)")}}BQ.ɵfac=function t(e){return new(e||BQ)(Sm(Ug),Sm(PL))},BQ.ɵcmp=to({type:BQ,selectors:[["mat-tooltip-component"]],hostAttrs:["aria-hidden","true"],hostVars:2,hostBindings:function t(e,n){2&e&&du("zoom","visible"===n._visibility?1:null)},features:[xp],decls:3,vars:7,consts:[[1,"mat-tooltip",3,"ngClass"]],template:function t(e,n){if(1&e&&(Rm(0,"div",0),Vm("@state.start",(function t(){return n._animationStart()}))("@state.done",(function t(e){return n._animationDone(e)})),Ah(1,"async"),ku(2),Am()),2&e){let t;pu("mat-tooltip-handset",null==(t=Th(1,5,n._isHandset))?null:t.matches),Dm("ngClass",n.tooltipClass)("@state",n._visibility),rc(2),Su(n.message)}},directives:[aM],pipes:[wM],styles:[".mat-tooltip-panel{pointer-events:none !important}.mat-tooltip{color:#fff;border-radius:4px;margin:14px;max-width:250px;padding-left:8px;padding-right:8px;overflow:hidden;text-overflow:ellipsis}.cdk-high-contrast-active .mat-tooltip{outline:solid 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1px}.mat-tooltip-handset{margin:24px;padding-left:16px;padding-right:16px}\n"]}]}],(function(){return[{type:Ug},{type:PL}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class VQ{}function jQ(t,e){if(1&t&&(Rm(0,"mat-option",19),ku(1),Am()),2&t){const t=e.$implicit;Dm("value",t),rc(1),Du(" ",t," ")}}function UQ(t,e){if(1&t){const t=Hm();Rm(0,"mat-form-field",16),Rm(1,"mat-select",17),Vm("selectionChange",(function e(n){return hi(t),Ym(2)._changePageSize(n.value)})),Qp(2,jQ,2,2,"mat-option",18),Am(),Am()}if(2&t){const t=Ym(2);Dm("appearance",t._formFieldAppearance)("color",t.color),rc(1),Dm("value",t.pageSize)("disabled",t.disabled)("aria-label",t._intl.itemsPerPageLabel),rc(1),Dm("ngForOf",t._displayedPageSizeOptions)}}function GQ(t,e){if(1&t&&(Rm(0,"div",20),ku(1),Am()),2&t){const t=Ym(2);rc(1),Su(t.pageSize)}}function WQ(t,e){if(1&t&&(Rm(0,"div",12),Rm(1,"div",13),ku(2),Am(),Qp(3,UQ,3,6,"mat-form-field",14),Qp(4,GQ,2,1,"div",15),Am()),2&t){const t=Ym();rc(2),Du(" ",t._intl.itemsPerPageLabel," "),rc(1),Dm("ngIf",t._displayedPageSizeOptions.length>1),rc(1),Dm("ngIf",t._displayedPageSizeOptions.length<=1)}}function YQ(t,e){if(1&t){const t=Hm();Rm(0,"button",21),Vm("click",(function e(){return hi(t),Ym().firstPage()})),qi(),Rm(1,"svg",7),Tm(2,"path",22),Am(),Am()}if(2&t){const t=Ym();Dm("matTooltip",t._intl.firstPageLabel)("matTooltipDisabled",t._previousButtonsDisabled())("matTooltipPosition","above")("disabled",t._previousButtonsDisabled()),jp("aria-label",t._intl.firstPageLabel)}}function qQ(t,e){if(1&t){const t=Hm();qi(),Zi(),Rm(0,"button",23),Vm("click",(function e(){return hi(t),Ym().lastPage()})),qi(),Rm(1,"svg",7),Tm(2,"path",24),Am(),Am()}if(2&t){const t=Ym();Dm("matTooltip",t._intl.lastPageLabel)("matTooltipDisabled",t._nextButtonsDisabled())("matTooltipPosition","above")("disabled",t._nextButtonsDisabled()),jp("aria-label",t._intl.lastPageLabel)}}VQ.ɵfac=function t(e){return new(e||VQ)},VQ.ɵmod=ao({type:VQ}),VQ.ɵinj=vn({providers:[TQ],imports:[[NI,WM,yL,XI],XI,yF]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(VQ,[{type:Ay,args:[{imports:[NI,WM,yL,XI],exports:[FQ,BQ,XI,yF],declarations:[FQ,BQ],entryComponents:[BQ],providers:[TQ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(VQ,{declarations:function(){return[FQ,BQ]},imports:function(){return[NI,WM,yL,XI]},exports:function(){return[FQ,BQ,XI,yF]}});class ZQ{constructor(){this.changes=new I,this.itemsPerPageLabel="Items per page:",this.nextPageLabel="Next page",this.previousPageLabel="Previous page",this.firstPageLabel="First page",this.lastPageLabel="Last page",this.getRangeLabel=(t,e,n)=>{if(0==n||0==e)return`0 of ${n}`;const o=t*e;return`${o+1} – ${o<(n=Math.max(n,0))?Math.min(o+e,n):o+e} of ${n}`}}}ZQ.ɵfac=function t(e){return new(e||ZQ)},ZQ.ɵprov=Mn({factory:function t(){return new ZQ},token:ZQ,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ZQ,[{type:im,args:[{providedIn:"root"}]}],(function(){return[]}),null);const XQ={provide:ZQ,deps:[[new Sr,new Er,ZQ]],useFactory:function KQ(t){return t||new ZQ}},JQ=new Ga("MAT_PAGINATOR_DEFAULT_OPTIONS"),QQ=KI(eH(class{}));
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class $Q extends QQ{constructor(t,e,n){if(super(),this._intl=t,this._changeDetectorRef=e,this._pageIndex=0,this._length=0,this._pageSizeOptions=[],this._hidePageSize=!1,this._showFirstLastButtons=!1,this.page=new Lh,this._intlChanges=t.changes.subscribe((()=>this._changeDetectorRef.markForCheck())),n){const{pageSize:t,pageSizeOptions:e,hidePageSize:o,showFirstLastButtons:i}=n;null!=t&&(this._pageSize=t),null!=e&&(this._pageSizeOptions=e),null!=o&&(this._hidePageSize=o),null!=i&&(this._showFirstLastButtons=i)}}get pageIndex(){return this._pageIndex}set pageIndex(t){this._pageIndex=Math.max(_z(t),0),this._changeDetectorRef.markForCheck()}get length(){return this._length}set length(t){this._length=_z(t),this._changeDetectorRef.markForCheck()}get pageSize(){return this._pageSize}set pageSize(t){this._pageSize=Math.max(_z(t),0),this._updateDisplayedPageSizeOptions()}get pageSizeOptions(){return this._pageSizeOptions}set pageSizeOptions(t){this._pageSizeOptions=(t||[]).map((t=>_z(t))),this._updateDisplayedPageSizeOptions()}get hidePageSize(){return this._hidePageSize}set hidePageSize(t){this._hidePageSize=yz(t)}get showFirstLastButtons(){return this._showFirstLastButtons}set showFirstLastButtons(t){this._showFirstLastButtons=yz(t)}ngOnInit(){this._initialized=!0,this._updateDisplayedPageSizeOptions(),this._markInitialized()}ngOnDestroy(){this._intlChanges.unsubscribe()}nextPage(){if(!this.hasNextPage())return;const t=this.pageIndex;this.pageIndex++,this._emitPageEvent(t)}previousPage(){if(!this.hasPreviousPage())return;const t=this.pageIndex;this.pageIndex--,this._emitPageEvent(t)}firstPage(){if(!this.hasPreviousPage())return;const t=this.pageIndex;this.pageIndex=0,this._emitPageEvent(t)}lastPage(){if(!this.hasNextPage())return;const t=this.pageIndex;this.pageIndex=this.getNumberOfPages()-1,this._emitPageEvent(t)}hasPreviousPage(){return this.pageIndex>=1&&0!=this.pageSize}hasNextPage(){const t=this.getNumberOfPages()-1;return this.pageIndex<t&&0!=this.pageSize}getNumberOfPages(){return this.pageSize?Math.ceil(this.length/this.pageSize):0}_changePageSize(t){const e=this.pageIndex;this.pageIndex=Math.floor(this.pageIndex*this.pageSize/t)||0,this.pageSize=t,this._emitPageEvent(e)}_nextButtonsDisabled(){return this.disabled||!this.hasNextPage()}_previousButtonsDisabled(){return this.disabled||!this.hasPreviousPage()}_updateDisplayedPageSizeOptions(){this._initialized&&(this.pageSize||(this._pageSize=0!=this.pageSizeOptions.length?this.pageSizeOptions[0]:50),this._displayedPageSizeOptions=this.pageSizeOptions.slice(),-1===this._displayedPageSizeOptions.indexOf(this.pageSize)&&this._displayedPageSizeOptions.push(this.pageSize),this._displayedPageSizeOptions.sort(((t,e)=>t-e)),this._changeDetectorRef.markForCheck())}_emitPageEvent(t){this.page.emit({previousPageIndex:t,pageIndex:this.pageIndex,pageSize:this.pageSize,length:this.length})}}$Q.ɵfac=function t(e){return new(e||$Q)(Sm(ZQ),Sm(Ug),Sm(void 0))},$Q.ɵdir=lo({type:$Q,inputs:{pageIndex:"pageIndex",length:"length",pageSize:"pageSize",pageSizeOptions:"pageSizeOptions",hidePageSize:"hidePageSize",showFirstLastButtons:"showFirstLastButtons",color:"color"},outputs:{page:"page"},features:[xp]}),$Q.ctorParameters=()=>[{type:ZQ},{type:Ug},{type:void 0}],$Q.propDecorators={color:[{type:xy}],pageIndex:[{type:xy}],length:[{type:xy}],pageSize:[{type:xy}],pageSizeOptions:[{type:xy}],hidePageSize:[{type:xy}],showFirstLastButtons:[{type:xy}],page:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh($Q,[{type:Cy}],(function(){return[{type:ZQ},{type:Ug},{type:void 0}]}),{page:[{type:Oy}],pageIndex:[{type:xy}],length:[{type:xy}],pageSize:[{type:xy}],pageSizeOptions:[{type:xy}],hidePageSize:[{type:xy}],showFirstLastButtons:[{type:xy}],color:[{type:xy}]});class t$ extends $Q{constructor(t,e,n){super(t,e,n),n&&null!=n.formFieldAppearance&&(this._formFieldAppearance=n.formFieldAppearance)}}t$.ɵfac=function t(e){return new(e||t$)(Sm(ZQ),Sm(Ug),Sm(JQ,8))},t$.ɵcmp=to({type:t$,selectors:[["mat-paginator"]],hostAttrs:["role","group",1,"mat-paginator"],inputs:{disabled:"disabled"},exportAs:["matPaginator"],features:[xp],decls:14,vars:14,consts:[[1,"mat-paginator-outer-container"],[1,"mat-paginator-container"],["class","mat-paginator-page-size",4,"ngIf"],[1,"mat-paginator-range-actions"],[1,"mat-paginator-range-label"],["mat-icon-button","","type","button","class","mat-paginator-navigation-first",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click",4,"ngIf"],["mat-icon-button","","type","button",1,"mat-paginator-navigation-previous",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click"],["viewBox","0 0 24 24","focusable","false",1,"mat-paginator-icon"],["d","M15.41 7.41L14 6l-6 6 6 6 1.41-1.41L10.83 12z"],["mat-icon-button","","type","button",1,"mat-paginator-navigation-next",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click"],["d","M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z"],["mat-icon-button","","type","button","class","mat-paginator-navigation-last",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click",4,"ngIf"],[1,"mat-paginator-page-size"],[1,"mat-paginator-page-size-label"],["class","mat-paginator-page-size-select",3,"appearance","color",4,"ngIf"],["class","mat-paginator-page-size-value",4,"ngIf"],[1,"mat-paginator-page-size-select",3,"appearance","color"],[3,"value","disabled","aria-label","selectionChange"],[3,"value",4,"ngFor","ngForOf"],[3,"value"],[1,"mat-paginator-page-size-value"],["mat-icon-button","","type","button",1,"mat-paginator-navigation-first",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click"],["d","M18.41 16.59L13.82 12l4.59-4.59L17 6l-6 6 6 6zM6 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4px;width:56px}.mat-paginator-page-size-select.mat-form-field-appearance-outline{width:64px}.mat-paginator-page-size-select.mat-form-field-appearance-fill{width:64px}.mat-paginator-range-label{margin:0 32px 0 24px}.mat-paginator-range-actions{display:flex;align-items:center}.mat-paginator-icon{width:28px;fill:currentColor}[dir=rtl] .mat-paginator-icon{transform:rotate(180deg)}.cdk-high-contrast-active .mat-paginator-icon{fill:CanvasText}\n"],encapsulation:2,changeDetection:0}),t$.ctorParameters=()=>[{type:ZQ},{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[JQ]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(t$,[{type:My,args:[{selector:"mat-paginator",exportAs:"matPaginator",template:'<div class="mat-paginator-outer-container">\n  <div class="mat-paginator-container">\n    <div class="mat-paginator-page-size" *ngIf="!hidePageSize">\n      <div class="mat-paginator-page-size-label">\n        {{_intl.itemsPerPageLabel}}\n      </div>\n\n      <mat-form-field\n        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      (click)="firstPage()"\n              [attr.aria-label]="_intl.firstPageLabel"\n              [matTooltip]="_intl.firstPageLabel"\n              [matTooltipDisabled]="_previousButtonsDisabled()"\n              [matTooltipPosition]="\'above\'"\n              [disabled]="_previousButtonsDisabled()"\n              *ngIf="showFirstLastButtons">\n        <svg class="mat-paginator-icon" viewBox="0 0 24 24" focusable="false">\n          <path d="M18.41 16.59L13.82 12l4.59-4.59L17 6l-6 6 6 6zM6 6h2v12H6z"/>\n        </svg>\n      </button>\n      <button mat-icon-button type="button"\n              class="mat-paginator-navigation-previous"\n              (click)="previousPage()"\n              [attr.aria-label]="_intl.previousPageLabel"\n              [matTooltip]="_intl.previousPageLabel"\n              [matTooltipDisabled]="_previousButtonsDisabled()"\n              [matTooltipPosition]="\'above\'"\n              [disabled]="_previousButtonsDisabled()">\n        <svg class="mat-paginator-icon" viewBox="0 0 24 24" focusable="false">\n          <path d="M15.41 7.41L14 6l-6 6 6 6 1.41-1.41L10.83 12z"/>\n        </svg>\n      </button>\n      <button mat-icon-button type="button"\n              class="mat-paginator-navigation-next"\n              (click)="nextPage()"\n              [attr.aria-label]="_intl.nextPageLabel"\n              [matTooltip]="_intl.nextPageLabel"\n              [matTooltipDisabled]="_nextButtonsDisabled()"\n              [matTooltipPosition]="\'above\'"\n              [disabled]="_nextButtonsDisabled()">\n        <svg class="mat-paginator-icon" viewBox="0 0 24 24" focusable="false">\n          <path d="M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z"/>\n        </svg>\n      </button>\n      <button mat-icon-button type="button"\n              class="mat-paginator-navigation-last"\n              (click)="lastPage()"\n              [attr.aria-label]="_intl.lastPageLabel"\n              [matTooltip]="_intl.lastPageLabel"\n          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4px;width:56px}.mat-paginator-page-size-select.mat-form-field-appearance-outline{width:64px}.mat-paginator-page-size-select.mat-form-field-appearance-fill{width:64px}.mat-paginator-range-label{margin:0 32px 0 24px}.mat-paginator-range-actions{display:flex;align-items:center}.mat-paginator-icon{width:28px;fill:currentColor}[dir=rtl] .mat-paginator-icon{transform:rotate(180deg)}.cdk-high-contrast-active .mat-paginator-icon{fill:CanvasText}\n"]}]}],(function(){return[{type:ZQ},{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[JQ]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class e${}function n$(t,e){if(1&t){const t=Hm();Rm(0,"div",3),Vm("@arrowPosition.start",(function e(){return hi(t),Ym()._disableViewStateAnimation=!0}))("@arrowPosition.done",(function e(){return hi(t),Ym()._disableViewStateAnimation=!1})),Tm(1,"div",4),Rm(2,"div",5),Tm(3,"div",6),Tm(4,"div",7),Tm(5,"div",8),Am(),Am()}if(2&t){const t=Ym();Dm("@arrowOpacity",t._getArrowViewState())("@arrowPosition",t._getArrowViewState())("@allowChildren",t._getArrowDirectionState()),rc(2),Dm("@indicator",t._getArrowDirectionState()),rc(1),Dm("@leftPointer",t._getArrowDirectionState()),rc(1),Dm("@rightPointer",t._getArrowDirectionState())}}e$.ɵfac=function t(e){return new(e||e$)},e$.ɵmod=ao({type:e$}),e$.ɵinj=vn({providers:[XQ],imports:[[WM,JH,TG,VQ,XI]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(e$,[{type:Ay,args:[{imports:[WM,JH,TG,VQ,XI],exports:[t$],declarations:[t$],providers:[XQ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(e$,{declarations:function(){return[t$]},imports:function(){return[WM,JH,TG,VQ,XI]},exports:function(){return[t$]}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const o$=new Ga("MAT_SORT_DEFAULT_OPTIONS"),i$=eH(KI(class{}));class a$ extends i${constructor(t){super(),this._defaultOptions=t,this.sortables=new Map,this._stateChanges=new I,this.start="asc",this._direction="",this.sortChange=new Lh}get direction(){return this._direction}set direction(t){if(t&&"asc"!==t&&"desc"!==t&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(t){return Error(`${t} is not a valid sort direction ('asc' or 'desc').`)})(t);this._direction=t}get disableClear(){return this._disableClear}set disableClear(t){this._disableClear=yz(t)}register(t){if("undefined"==typeof ngDevMode||ngDevMode){if(!t.id)throw(function e(){return Error("MatSortHeader must be provided with a unique id.")})();if(this.sortables.has(t.id))throw(function n(t){return Error(`Cannot have two MatSortables with the same id (${t}).`)})(t.id)}this.sortables.set(t.id,t)}deregister(t){this.sortables.delete(t.id)}sort(t){this.active!=t.id?(this.active=t.id,this.direction=t.start?t.start:this.start):this.direction=this.getNextSortDirection(t),this.sortChange.emit({active:this.active,direction:this.direction})}getNextSortDirection(t){var e,n,o;if(!t)return"";const i=null!==(n=null!==(e=null==t?void 0:t.disableClear)&&void 0!==e?e:this.disableClear)&&void 0!==n?n:!!(null===(o=this._defaultOptions)||void 0===o?void 0:o.disableClear);let a=(function r(t,e){let n=["asc","desc"];return"desc"==t&&n.reverse(),e||n.push(""),n}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t.start||this.start,i),s=a.indexOf(this.direction)+1;return s>=a.length&&(s=0),a[s]}ngOnInit(){this._markInitialized()}ngOnChanges(){this._stateChanges.next()}ngOnDestroy(){this._stateChanges.complete()}}a$.ɵfac=function t(e){return new(e||a$)(Sm(o$,8))},a$.ɵdir=lo({type:a$,selectors:[["","matSort",""]],hostAttrs:[1,"mat-sort"],inputs:{disabled:["matSortDisabled","disabled"],start:["matSortStart","start"],direction:["matSortDirection","direction"],disableClear:["matSortDisableClear","disableClear"],active:["matSortActive","active"]},outputs:{sortChange:"matSortChange"},exportAs:["matSort"],features:[xp,Bo]}),a$.ctorParameters=()=>[{type:void 0,decorators:[{type:Sr},{type:kr,args:[o$]}]}],a$.propDecorators={active:[{type:xy,args:["matSortActive"]}],start:[{type:xy,args:["matSortStart"]}],direction:[{type:xy,args:["matSortDirection"]}],disableClear:[{type:xy,args:["matSortDisableClear"]}],sortChange:[{type:Oy,args:["matSortChange"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(a$,[{type:Cy,args:[{selector:"[matSort]",exportAs:"matSort",host:{class:"mat-sort"},inputs:["disabled: matSortDisabled"]}]}],(function(){return[{type:void 0,decorators:[{type:Sr},{type:kr,args:[o$]}]}]}),{start:[{type:xy,args:["matSortStart"]}],sortChange:[{type:Oy,args:["matSortChange"]}],direction:[{type:xy,args:["matSortDirection"]}],disableClear:[{type:xy,args:["matSortDisableClear"]}],active:[{type:xy,args:["matSortActive"]}]});const r$=WI.ENTERING+" "+GI.STANDARD_CURVE,s$={indicator:nx("indicator",[rx("active-asc, asc",ax({transform:"translateY(0px)"})),rx("active-desc, desc",ax({transform:"translateY(10px)"})),lx("active-asc <=> active-desc",ox(r$))]),leftPointer:nx("leftPointer",[rx("active-asc, asc",ax({transform:"rotate(-45deg)"})),rx("active-desc, desc",ax({transform:"rotate(45deg)"})),lx("active-asc <=> active-desc",ox(r$))]),rightPointer:nx("rightPointer",[rx("active-asc, asc",ax({transform:"rotate(45deg)"})),rx("active-desc, desc",ax({transform:"rotate(-45deg)"})),lx("active-asc <=> active-desc",ox(r$))]),arrowOpacity:nx("arrowOpacity",[rx("desc-to-active, asc-to-active, active",ax({opacity:1})),rx("desc-to-hint, asc-to-hint, hint",ax({opacity:.54})),rx("hint-to-desc, active-to-desc, desc, hint-to-asc, active-to-asc, asc, void",ax({opacity:0})),lx("* => asc, * => desc, * => active, * => hint, * => void",ox("0ms")),lx("* <=> *",ox(r$))]),arrowPosition:nx("arrowPosition",[lx("* => desc-to-hint, * => desc-to-active",ox(r$,sx([ax({transform:"translateY(-25%)"}),ax({transform:"translateY(0)"})]))),lx("* => hint-to-desc, * => active-to-desc",ox(r$,sx([ax({transform:"translateY(0)"}),ax({transform:"translateY(25%)"})]))),lx("* => asc-to-hint, * => asc-to-active",ox(r$,sx([ax({transform:"translateY(25%)"}),ax({transform:"translateY(0)"})]))),lx("* => hint-to-asc, * => active-to-asc",ox(r$,sx([ax({transform:"translateY(0)"}),ax({transform:"translateY(-25%)"})]))),rx("desc-to-hint, asc-to-hint, hint, desc-to-active, asc-to-active, active",ax({transform:"translateY(0)"})),rx("hint-to-desc, active-to-desc, desc",ax({transform:"translateY(-25%)"})),rx("hint-to-asc, active-to-asc, asc",ax({transform:"translateY(25%)"}))]),allowChildren:nx("allowChildren",[lx("* <=> *",[dx("@*",cx(),{optional:!0})])])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class l${constructor(){this.changes=new I}}l$.ɵfac=function t(e){return new(e||l$)},l$.ɵprov=Mn({factory:function t(){return new l$},token:l$,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(l$,[{type:im,args:[{providedIn:"root"}]}],(function(){return[]}),null);const c$={provide:l$,deps:[[new Sr,new Er,l$]],useFactory:function d$(t){return t||new l$}},p$=KI(class{});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class m$ extends p${constructor(t,e,n,o,i,a){if(super(),this._intl=t,this._changeDetectorRef=e,this._sort=n,this._columnDef=o,this._focusMonitor=i,this._elementRef=a,this._showIndicatorHint=!1,this._viewState={},this._arrowDirection="",this._disableViewStateAnimation=!1,this.arrowPosition="after",!n&&("undefined"==typeof ngDevMode||ngDevMode))throw(function r(){return Error("MatSortHeader must be placed within a parent element with the MatSort directive.")})();this._handleStateChanges()}get disableClear(){return this._disableClear}set disableClear(t){this._disableClear=yz(t)}ngOnInit(){!this.id&&this._columnDef&&(this.id=this._columnDef.name),this._updateArrowDirection(),this._setAnimationTransitionState({toState:this._isSorted()?"active":this._arrowDirection}),this._sort.register(this)}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0).subscribe((t=>{const e=!!t;e!==this._showIndicatorHint&&(this._setIndicatorHintVisible(e),this._changeDetectorRef.markForCheck())}))}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef),this._sort.deregister(this),this._rerenderSubscription.unsubscribe()}_setIndicatorHintVisible(t){this._isDisabled()&&t||(this._showIndicatorHint=t,this._isSorted()||(this._updateArrowDirection(),this._setAnimationTransitionState(this._showIndicatorHint?{fromState:this._arrowDirection,toState:"hint"}:{fromState:"hint",toState:this._arrowDirection})))}_setAnimationTransitionState(t){this._viewState=t||{},this._disableViewStateAnimation&&(this._viewState={toState:t.toState})}_toggleOnInteraction(){this._sort.sort(this),"hint"!==this._viewState.toState&&"active"!==this._viewState.toState||(this._disableViewStateAnimation=!0)}_handleClick(){this._isDisabled()||this._sort.sort(this)}_handleKeydown(t){this._isDisabled()||t.keyCode!==fz&&t.keyCode!==mz||(t.preventDefault(),this._toggleOnInteraction())}_isSorted(){return this._sort.active==this.id&&("asc"===this._sort.direction||"desc"===this._sort.direction)}_getArrowDirectionState(){return`${this._isSorted()?"active-":""}${this._arrowDirection}`}_getArrowViewState(){const t=this._viewState.fromState;return(t?`${t}-to-`:"")+this._viewState.toState}_updateArrowDirection(){this._arrowDirection=this._isSorted()?this._sort.direction:this.start||this._sort.start}_isDisabled(){return this._sort.disabled||this.disabled}_getAriaSortAttribute(){return this._isSorted()?"asc"==this._sort.direction?"ascending":"descending":"none"}_renderArrow(){return!this._isDisabled()||this._isSorted()}_handleStateChanges(){this._rerenderSubscription=re(this._sort.sortChange,this._sort._stateChanges,this._intl.changes).subscribe((()=>{this._isSorted()&&(this._updateArrowDirection(),"hint"!==this._viewState.toState&&"active"!==this._viewState.toState||(this._disableViewStateAnimation=!0),this._setAnimationTransitionState({fromState:this._arrowDirection,toState:"active"}),this._showIndicatorHint=!1),!this._isSorted()&&this._viewState&&"active"===this._viewState.toState&&(this._disableViewStateAnimation=!1,this._setAnimationTransitionState({fromState:"active",toState:this._arrowDirection})),this._changeDetectorRef.markForCheck()}))}}m$.ɵfac=function t(e){return new(e||m$)(Sm(l$),Sm(Ug),Sm(a$,8),Sm("MAT_SORT_HEADER_COLUMN_DEF",8),Sm(SI),Sm(hg))},m$.ɵcmp=to({type:m$,selectors:[["","mat-sort-header",""]],hostAttrs:[1,"mat-sort-header"],hostVars:3,hostBindings:function t(e,n){1&e&&Vm("click",(function t(){return n._handleClick()}))("keydown",(function t(e){return n._handleKeydown(e)}))("mouseenter",(function t(){return n._setIndicatorHintVisible(!0)}))("mouseleave",(function t(){return n._setIndicatorHintVisible(!1)})),2&e&&(jp("aria-sort",n._getAriaSortAttribute()),pu("mat-sort-header-disabled",n._isDisabled()))},inputs:{disabled:"disabled",arrowPosition:"arrowPosition",disableClear:"disableClear",id:["mat-sort-header","id"],start:"start"},exportAs:["matSortHeader"],features:[xp],attrs:["mat-sort-header",""],ngContentSelectors:["*"],decls:4,vars:6,consts:[["role","button",1,"mat-sort-header-container","mat-focus-indicator"],[1,"mat-sort-header-content"],["class","mat-sort-header-arrow",4,"ngIf"],[1,"mat-sort-header-arrow"],[1,"mat-sort-header-stem"],[1,"mat-sort-header-indicator"],[1,"mat-sort-header-pointer-left"],[1,"mat-sort-header-pointer-right"],[1,"mat-sort-header-pointer-middle"]],template:function t(e,n){1&e&&(Zm(),Rm(0,"div",0),Rm(1,"div",1),Xm(2),Am(),Qp(3,n$,6,6,"div",2),Am()),2&e&&(pu("mat-sort-header-sorted",n._isSorted())("mat-sort-header-position-before","before"==n.arrowPosition),jp("tabindex",n._isDisabled()?null:0),rc(3),Dm("ngIf",n._renderArrow()))},directives:[dM],styles:[".mat-sort-header-container{display:flex;cursor:pointer;align-items:center;letter-spacing:normal;outline:0}[mat-sort-header].cdk-keyboard-focused .mat-sort-header-container,[mat-sort-header].cdk-program-focused .mat-sort-header-container{border-bottom:solid 1px currentColor}.mat-sort-header-disabled .mat-sort-header-container{cursor:default}.mat-sort-header-content{text-align:center;display:flex;align-items:center}.mat-sort-header-position-before{flex-direction:row-reverse}.mat-sort-header-arrow{height:12px;width:12px;min-width:12px;position:relative;display:flex;opacity:0}.mat-sort-header-arrow,[dir=rtl] .mat-sort-header-position-before .mat-sort-header-arrow{margin:0 0 0 6px}.mat-sort-header-position-before .mat-sort-header-arrow,[dir=rtl] .mat-sort-header-arrow{margin:0 6px 0 0}.mat-sort-header-stem{background:currentColor;height:10px;width:2px;margin:auto;display:flex;align-items:center}.cdk-high-contrast-active .mat-sort-header-stem{width:0;border-left:solid 2px}.mat-sort-header-indicator{width:100%;height:2px;display:flex;align-items:center;position:absolute;top:0;left:0}.mat-sort-header-pointer-middle{margin:auto;height:2px;width:2px;background:currentColor;transform:rotate(45deg)}.cdk-high-contrast-active .mat-sort-header-pointer-middle{width:0;height:0;border-top:solid 2px;border-left:solid 2px}.mat-sort-header-pointer-left,.mat-sort-header-pointer-right{background:currentColor;width:6px;height:2px;position:absolute;top:0}.cdk-high-contrast-active .mat-sort-header-pointer-left,.cdk-high-contrast-active .mat-sort-header-pointer-right{width:0;height:0;border-left:solid 6px;border-top:solid 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class u${}u$.ɵfac=function t(e){return new(e||u$)},u$.ɵmod=ao({type:u$}),u$.ɵinj=vn({providers:[c$],imports:[[WM,XI]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(u$,[{type:Ay,args:[{imports:[WM,XI],exports:[a$,m$],declarations:[a$,m$],providers:[c$]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(u$,{declarations:function(){return[a$,m$]},imports:function(){return[WM,XI]},exports:function(){return[a$,m$]}});const f$=[[["caption"]],[["colgroup"],["col"]]];function g$(t,e){if(1&t&&(Rm(0,"th",3),ku(1),Am()),2&t){const t=Ym();du("text-align",t.justify),rc(1),Du(" ",t.headerText," ")}}function h$(t,e){if(1&t&&(Rm(0,"td",4),ku(1),Am()),2&t){const t=e.$implicit,n=Ym();du("text-align",n.justify),rc(1),Du(" ",n.dataAccessor(t,n.name)," ")}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function b$(t){return class extends t{constructor(...t){super(...t),this._sticky=!1,this._hasStickyChanged=!1}get sticky(){return this._sticky}set sticky(t){const e=this._sticky;this._sticky=yz(t),this._hasStickyChanged=e!==this._sticky}hasStickyChanged(){const t=this._hasStickyChanged;return this._hasStickyChanged=!1,t}resetStickyChanged(){this._hasStickyChanged=!1}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const y$=new Ga("CDK_TABLE"),_$=new Ga("text-column-options");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class C${constructor(t){this.template=t}}C$.ɵfac=function t(e){return new(e||C$)(Sm(Xg))},C$.ɵdir=lo({type:C$,selectors:[["","cdkCellDef",""]]}),C$.ctorParameters=()=>[{type:Xg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(C$,[{type:Cy,args:[{selector:"[cdkCellDef]"}]}],(function(){return[{type:Xg}]}),null);class M${constructor(t){this.template=t}}M$.ɵfac=function t(e){return new(e||M$)(Sm(Xg))},M$.ɵdir=lo({type:M$,selectors:[["","cdkHeaderCellDef",""]]}),M$.ctorParameters=()=>[{type:Xg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(M$,[{type:Cy,args:[{selector:"[cdkHeaderCellDef]"}]}],(function(){return[{type:Xg}]}),null);class v${constructor(t){this.template=t}}v$.ɵfac=function t(e){return new(e||v$)(Sm(Xg))},v$.ɵdir=lo({type:v$,selectors:[["","cdkFooterCellDef",""]]}),v$.ctorParameters=()=>[{type:Xg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(v$,[{type:Cy,args:[{selector:"[cdkFooterCellDef]"}]}],(function(){return[{type:Xg}]}),null);const x$=b$(class{});class O$ extends x${constructor(t){super(),this._table=t,this._stickyEnd=!1}get name(){return this._name}set name(t){this._setNameInput(t)}get stickyEnd(){return this._stickyEnd}set stickyEnd(t){const e=this._stickyEnd;this._stickyEnd=yz(t),this._hasStickyChanged=e!==this._stickyEnd}_updateColumnCssClassName(){this._columnCssClassName=[`cdk-column-${this.cssClassFriendlyName}`]}_setNameInput(t){t&&(this._name=t,this.cssClassFriendlyName=t.replace(/[^a-z0-9_-]/gi,"-"),this._updateColumnCssClassName())}}O$.ɵfac=function t(e){return new(e||O$)(Sm(y$,8))},O$.ɵdir=lo({type:O$,selectors:[["","cdkColumnDef",""]],contentQueries:function t(e,n,o){if(1&e&&($h(o,C$,5),$h(o,M$,5),$h(o,v$,5)),2&e){let t;Jh(t=tb())&&(n.cell=t.first),Jh(t=tb())&&(n.headerCell=t.first),Jh(t=tb())&&(n.footerCell=t.first)}},inputs:{sticky:"sticky",name:["cdkColumnDef","name"],stickyEnd:"stickyEnd"},features:[pg([{provide:"MAT_SORT_HEADER_COLUMN_DEF",useExisting:O$}]),xp]}),O$.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[y$]},{type:Sr}]}],O$.propDecorators={name:[{type:xy,args:["cdkColumnDef"]}],stickyEnd:[{type:xy,args:["stickyEnd"]}],cell:[{type:qa,args:[C$]}],headerCell:[{type:qa,args:[M$]}],footerCell:[{type:qa,args:[v$]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(O$,[{type:Cy,args:[{selector:"[cdkColumnDef]",inputs:["sticky"],providers:[{provide:"MAT_SORT_HEADER_COLUMN_DEF",useExisting:O$}]}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[y$]},{type:Sr}]}]}),{name:[{type:xy,args:["cdkColumnDef"]}],stickyEnd:[{type:xy,args:["stickyEnd"]}],cell:[{type:qa,args:[C$]}],headerCell:[{type:qa,args:[M$]}],footerCell:[{type:qa,args:[v$]}]});class P${constructor(t,e){const n=e.nativeElement.classList;for(const e of t._columnCssClassName)n.add(e)}}class w$ extends P${constructor(t,e){super(t,e)}}w$.ɵfac=function t(e){return new(e||w$)(Sm(O$),Sm(hg))},w$.ɵdir=lo({type:w$,selectors:[["cdk-header-cell"],["th","cdk-header-cell",""]],hostAttrs:["role","columnheader",1,"cdk-header-cell"],features:[xp]}),w$.ctorParameters=()=>[{type:O$},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(w$,[{type:Cy,args:[{selector:"cdk-header-cell, th[cdk-header-cell]",host:{class:"cdk-header-cell",role:"columnheader"}}]}],(function(){return[{type:O$},{type:hg}]}),null);class k$ extends P${constructor(t,e){var n;if(super(t,e),1===(null===(n=t._table)||void 0===n?void 0:n._elementRef.nativeElement.nodeType)){const n=t._table._elementRef.nativeElement.getAttribute("role");e.nativeElement.setAttribute("role","grid"===n||"treegrid"===n?"gridcell":"cell")}}}k$.ɵfac=function t(e){return new(e||k$)(Sm(O$),Sm(hg))},k$.ɵdir=lo({type:k$,selectors:[["cdk-footer-cell"],["td","cdk-footer-cell",""]],hostAttrs:[1,"cdk-footer-cell"],features:[xp]}),k$.ctorParameters=()=>[{type:O$},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(k$,[{type:Cy,args:[{selector:"cdk-footer-cell, td[cdk-footer-cell]",host:{class:"cdk-footer-cell"}}]}],(function(){return[{type:O$},{type:hg}]}),null);class S$ extends P${constructor(t,e){var n;if(super(t,e),1===(null===(n=t._table)||void 0===n?void 0:n._elementRef.nativeElement.nodeType)){const n=t._table._elementRef.nativeElement.getAttribute("role");e.nativeElement.setAttribute("role","grid"===n||"treegrid"===n?"gridcell":"cell")}}}S$.ɵfac=function t(e){return new(e||S$)(Sm(O$),Sm(hg))},S$.ɵdir=lo({type:S$,selectors:[["cdk-cell"],["td","cdk-cell",""]],hostAttrs:[1,"cdk-cell"],features:[xp]}),S$.ctorParameters=()=>[{type:O$},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(S$,[{type:Cy,args:[{selector:"cdk-cell, td[cdk-cell]",host:{class:"cdk-cell"}}]}],(function(){return[{type:O$},{type:hg}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class D${constructor(){this.tasks=[],this.endTasks=[]}}const E$=new Ga("_COALESCED_STYLE_SCHEDULER");class R${constructor(t){this._ngZone=t,this._currentSchedule=null,this._destroyed=new I}schedule(t){this._createScheduleIfNeeded(),this._currentSchedule.tasks.push(t)}scheduleEnd(t){this._createScheduleIfNeeded(),this._currentSchedule.endTasks.push(t)}ngOnDestroy(){this._destroyed.next(),this._destroyed.complete()}_createScheduleIfNeeded(){this._currentSchedule||(this._currentSchedule=new D$,this._getScheduleObservable().pipe(Ie(this._destroyed)).subscribe((()=>{for(;this._currentSchedule.tasks.length||this._currentSchedule.endTasks.length;){const t=this._currentSchedule;this._currentSchedule=new D$;for(const e of t.tasks)e();for(const e of t.endTasks)e()}this._currentSchedule=null})))}_getScheduleObservable(){return this._ngZone.isStable?Ct(Promise.resolve(void 0)):this._ngZone.onStable.pipe(be(1))}}R$.ɵfac=function t(e){return new(e||R$)(vr(a_))},R$.ɵprov=Mn({token:R$,factory:R$.ɵfac}),R$.ctorParameters=()=>[{type:a_}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(R$,[{type:im}],(function(){return[{type:a_}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const A$="<ng-container cdkCellOutlet></ng-container>";class T${constructor(t,e){this.template=t,this._differs=e}ngOnChanges(t){if(!this._columnsDiffer){const e=t.columns&&t.columns.currentValue||[];this._columnsDiffer=this._differs.find(e).create(),this._columnsDiffer.diff(e)}}getColumnsDiff(){return this._columnsDiffer.diff(this.columns)}extractCellTemplate(t){return this instanceof z$?t.headerCell.template:this instanceof H$?t.footerCell.template:t.cell.template}}T$.ɵfac=function t(e){return new(e||T$)(Sm(Xg),Sm(Hg))},T$.ɵdir=lo({type:T$,features:[Bo]}),T$.ctorParameters=()=>[{type:Xg},{type:Hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(T$,[{type:Cy}],(function(){return[{type:Xg},{type:Hg}]}),null);const N$=b$(class extends T${});class z$ extends N${constructor(t,e,n){super(t,e),this._table=n}ngOnChanges(t){super.ngOnChanges(t)}}z$.ɵfac=function t(e){return 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U${constructor(t){this.templateRef=t}}U$.ɵfac=function t(e){return new(e||U$)(Sm(Xg))},U$.ɵdir=lo({type:U$,selectors:[["ng-template","cdkNoDataRow",""]]}),U$.ctorParameters=()=>[{type:Xg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(U$,[{type:Cy,args:[{selector:"ng-template[cdkNoDataRow]"}]}],(function(){return[{type:Xg}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const G$=["top","bottom","left","right"];class W${constructor(t,e,n,o,i=!0,a=!0,r){this._isNativeHtmlTable=t,this._stickCellCss=e,this.direction=n,this._coalescedStyleScheduler=o,this._isBrowser=i,this._needsPositionStickyOnElement=a,this._positionListener=r,this._cachedCellWidths=[],this._borderCellCss={top:`${e}-border-elem-top`,bottom:`${e}-border-elem-bottom`,left:`${e}-border-elem-left`,right:`${e}-border-elem-right`}}clearStickyPositioning(t,e){const n=[];for(const e of t)if(e.nodeType===e.ELEMENT_NODE){n.push(e);for(let t=0;t<e.children.length;t++)n.push(e.children[t])}this._coalescedStyleScheduler.schedule((()=>{for(const t of n)this._removeStickyStyle(t,e)}))}updateStickyColumns(t,e,n,o=!0){if(!t.length||!this._isBrowser||!e.some((t=>t))&&!n.some((t=>t)))return void(this._positionListener&&(this._positionListener.stickyColumnsUpdated({sizes:[]}),this._positionListener.stickyEndColumnsUpdated({sizes:[]})));const i=t[0],a=i.children.length,r=this._getCellWidths(i,o),s=this._getStickyStartColumnPositions(r,e),l=this._getStickyEndColumnPositions(r,n),c=e.lastIndexOf(!0),d=n.indexOf(!0);this._coalescedStyleScheduler.schedule((()=>{const o="rtl"===this.direction,i=o?"right":"left",p=o?"left":"right";for(const o of t)for(let t=0;t<a;t++){const a=o.children[t];e[t]&&this._addStickyStyle(a,i,s[t],t===c),n[t]&&this._addStickyStyle(a,p,l[t],t===d)}this._positionListener&&(this._positionListener.stickyColumnsUpdated({sizes:-1===c?[]:r.slice(0,c+1).map(((t,n)=>e[n]?t:null))}),this._positionListener.stickyEndColumnsUpdated({sizes:-1===d?[]:r.slice(d).map(((t,e)=>n[e+d]?t:null)).reverse()}))}))}stickRows(t,e,n){if(!this._isBrowser)return;const o="bottom"===n?t.slice().reverse():t,i="bottom"===n?e.slice().reverse():e,a=[],r=[],s=[];for(let t=0,e=0;t<o.length;t++){if(!i[t])continue;a[t]=e;const n=o[t];s[t]=this._isNativeHtmlTable?Array.from(n.children):[n];const l=n.getBoundingClientRect().height;e+=l,r[t]=l}const l=i.lastIndexOf(!0);this._coalescedStyleScheduler.schedule((()=>{var t,e;for(let t=0;t<o.length;t++){if(!i[t])continue;const e=a[t],o=t===l;for(const i of s[t])this._addStickyStyle(i,n,e,o)}"top"===n?null===(t=this._positionListener)||void 0===t||t.stickyHeaderRowsUpdated({sizes:r,offsets:a,elements:s}):null===(e=this._positionListener)||void 0===e||e.stickyFooterRowsUpdated({sizes:r,offsets:a,elements:s})}))}updateStickyFooterContainer(t,e){if(!this._isNativeHtmlTable)return;const n=t.querySelector("tfoot");this._coalescedStyleScheduler.schedule((()=>{e.some((t=>!t))?this._removeStickyStyle(n,["bottom"]):this._addStickyStyle(n,"bottom",0,!1)}))}_removeStickyStyle(t,e){for(const n of e)t.style[n]="",t.classList.remove(this._borderCellCss[n]);G$.some((n=>-1===e.indexOf(n)&&t.style[n]))?t.style.zIndex=this._getCalculatedZIndex(t):(t.style.zIndex="",this._needsPositionStickyOnElement&&(t.style.position=""),t.classList.remove(this._stickCellCss))}_addStickyStyle(t,e,n,o){t.classList.add(this._stickCellCss),o&&t.classList.add(this._borderCellCss[e]),t.style[e]=`${n}px`,t.style.zIndex=this._getCalculatedZIndex(t),this._needsPositionStickyOnElement&&(t.style.cssText+="position: -webkit-sticky; position: sticky; ")}_getCalculatedZIndex(t){const e={top:100,bottom:10,left:1,right:1};let n=0;for(const o of G$)t.style[o]&&(n+=e[o]);return n?`${n}`:""}_getCellWidths(t,e=!0){if(!e&&this._cachedCellWidths.length)return this._cachedCellWidths;const n=[],o=t.children;for(let t=0;t<o.length;t++)n.push(o[t].getBoundingClientRect().width);return this._cachedCellWidths=n,n}_getStickyStartColumnPositions(t,e){const n=[];let o=0;for(let i=0;i<t.length;i++)e[i]&&(n[i]=o,o+=t[i]);return n}_getStickyEndColumnPositions(t,e){const n=[];let o=0;for(let i=t.length;i>0;i--)e[i]&&(n[i]=o,o+=t[i]);return n}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Y$(t){return Error(`Could not find column with id "${t}".`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const q$=new Ga("CDK_SPL");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Z${}Z$.ɵfac=function t(e){return new(e||Z$)},Z$.ɵdir=lo({type:Z$,selectors:[["cdk-table","recycleRows",""],["table","cdk-table","","recycleRows",""]],features:[pg([{provide:aF,useClass:nF}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Z$,[{type:Cy,args:[{selector:"cdk-table[recycleRows], table[cdk-table][recycleRows]",providers:[{provide:aF,useClass:nF}]}]}],null,null);class X${constructor(t,e){this.viewContainer=t,this.elementRef=e}}X$.ɵfac=function t(e){return new(e||X$)(Sm(eh),Sm(hg))},X$.ɵdir=lo({type:X$,selectors:[["","rowOutlet",""]]}),X$.ctorParameters=()=>[{type:eh},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(X$,[{type:Cy,args:[{selector:"[rowOutlet]"}]}],(function(){return[{type:eh},{type:hg}]}),null);class K${constructor(t,e){this.viewContainer=t,this.elementRef=e}}K$.ɵfac=function t(e){return new(e||K$)(Sm(eh),Sm(hg))},K$.ɵdir=lo({type:K$,selectors:[["","headerRowOutlet",""]]}),K$.ctorParameters=()=>[{type:eh},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(K$,[{type:Cy,args:[{selector:"[headerRowOutlet]"}]}],(function(){return[{type:eh},{type:hg}]}),null);class J${constructor(t,e){this.viewContainer=t,this.elementRef=e}}J$.ɵfac=function t(e){return new(e||J$)(Sm(eh),Sm(hg))},J$.ɵdir=lo({type:J$,selectors:[["","footerRowOutlet",""]]}),J$.ctorParameters=()=>[{type:eh},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(J$,[{type:Cy,args:[{selector:"[footerRowOutlet]"}]}],(function(){return[{type:eh},{type:hg}]}),null);class Q${constructor(t,e){this.viewContainer=t,this.elementRef=e}}Q$.ɵfac=function t(e){return new(e||Q$)(Sm(eh),Sm(hg))},Q$.ɵdir=lo({type:Q$,selectors:[["","noDataRowOutlet",""]]}),Q$.ctorParameters=()=>[{type:eh},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Q$,[{type:Cy,args:[{selector:"[noDataRowOutlet]"}]}],(function(){return[{type:eh},{type:hg}]}),null);const $$='\n  <ng-content select="caption"></ng-content>\n  <ng-content select="colgroup, col"></ng-content>\n  <ng-container headerRowOutlet></ng-container>\n  <ng-container rowOutlet></ng-container>\n  <ng-container noDataRowOutlet></ng-container>\n  <ng-container footerRowOutlet></ng-container>\n';class t0{constructor(t,e,n,o,i,a,r,s,l,c,d){this._differs=t,this._changeDetectorRef=e,this._elementRef=n,this._dir=i,this._platform=r,this._viewRepeater=s,this._coalescedStyleScheduler=l,this._viewportRuler=c,this._stickyPositioningListener=d,this._onDestroy=new I,this._columnDefsByName=new Map,this._customColumnDefs=new Set,this._customRowDefs=new Set,this._customHeaderRowDefs=new Set,this._customFooterRowDefs=new Set,this._headerRowDefChanged=!0,this._footerRowDefChanged=!0,this._stickyColumnStylesNeedReset=!0,this._forceRecalculateCellWidths=!0,this._cachedRenderRowsMap=new Map,this.stickyCssClass="cdk-table-sticky",this.needsPositionStickyOnElement=!0,this._isShowingNoDataRow=!1,this._multiTemplateDataRows=!1,this._fixedLayout=!1,this.contentChanged=new Lh,this.viewChange=new F({start:0,end:Number.MAX_VALUE}),o||this._elementRef.nativeElement.setAttribute("role","table"),this._document=a,this._isNativeHtmlTable="TABLE"===this._elementRef.nativeElement.nodeName}get trackBy(){return this._trackByFn}set trackBy(t){"undefined"!=typeof ngDevMode&&!ngDevMode||null==t||"function"==typeof t||console.warn(`trackBy must be a function, but received ${JSON.stringify(t)}.`),this._trackByFn=t}get dataSource(){return this._dataSource}set dataSource(t){this._dataSource!==t&&this._switchDataSource(t)}get multiTemplateDataRows(){return this._multiTemplateDataRows}set multiTemplateDataRows(t){this._multiTemplateDataRows=yz(t),this._rowOutlet&&this._rowOutlet.viewContainer.length&&(this._forceRenderDataRows(),this.updateStickyColumnStyles())}get fixedLayout(){return this._fixedLayout}set fixedLayout(t){this._fixedLayout=yz(t),this._forceRecalculateCellWidths=!0,this._stickyColumnStylesNeedReset=!0}ngOnInit(){this._setupStickyStyler(),this._isNativeHtmlTable&&this._applyNativeTableSections(),this._dataDiffer=this._differs.find([]).create(((t,e)=>this.trackBy?this.trackBy(e.dataIndex,e.data):e)),this._viewportRuler.change().pipe(Ie(this._onDestroy)).subscribe((()=>{this._forceRecalculateCellWidths=!0}))}ngAfterContentChecked(){if(this._cacheRowDefs(),this._cacheColumnDefs(),!this._headerRowDefs.length&&!this._footerRowDefs.length&&!this._rowDefs.length&&("undefined"==typeof ngDevMode||ngDevMode))throw(function t(){return Error("Missing definitions for header, footer, and row; cannot determine which columns should be rendered.")})();const e=this._renderUpdatedColumns()||this._headerRowDefChanged||this._footerRowDefChanged;this._stickyColumnStylesNeedReset=this._stickyColumnStylesNeedReset||e,this._forceRecalculateCellWidths=e,this._headerRowDefChanged&&(this._forceRenderHeaderRows(),this._headerRowDefChanged=!1),this._footerRowDefChanged&&(this._forceRenderFooterRows(),this._footerRowDefChanged=!1),this.dataSource&&this._rowDefs.length>0&&!this._renderChangeSubscription?this._observeRenderChanges():this._stickyColumnStylesNeedReset&&this.updateStickyColumnStyles(),this._checkStickyStates()}ngOnDestroy(){this._rowOutlet.viewContainer.clear(),this._noDataRowOutlet.viewContainer.clear(),this._headerRowOutlet.viewContainer.clear(),this._footerRowOutlet.viewContainer.clear(),this._cachedRenderRowsMap.clear(),this._onDestroy.next(),this._onDestroy.complete(),$H(this.dataSource)&&this.dataSource.disconnect(this)}renderRows(){this._renderRows=this._getAllRenderRows();const t=this._dataDiffer.diff(this._renderRows);if(!t)return this._updateNoDataRow(),void this.contentChanged.next();const e=this._rowOutlet.viewContainer;this._viewRepeater.applyChanges(t,e,((t,e,n)=>this._getEmbeddedViewArgs(t.item,n)),(t=>t.item.data),(t=>{1===t.operation&&t.context&&this._renderCellTemplateForItem(t.record.item.rowDef,t.context)})),this._updateRowIndexContext(),t.forEachIdentityChange((t=>{e.get(t.currentIndex).context.$implicit=t.item.data})),this._updateNoDataRow(),this.updateStickyColumnStyles(),this.contentChanged.next()}addColumnDef(t){this._customColumnDefs.add(t)}removeColumnDef(t){this._customColumnDefs.delete(t)}addRowDef(t){this._customRowDefs.add(t)}removeRowDef(t){this._customRowDefs.delete(t)}addHeaderRowDef(t){this._customHeaderRowDefs.add(t),this._headerRowDefChanged=!0}removeHeaderRowDef(t){this._customHeaderRowDefs.delete(t),this._headerRowDefChanged=!0}addFooterRowDef(t){this._customFooterRowDefs.add(t),this._footerRowDefChanged=!0}removeFooterRowDef(t){this._customFooterRowDefs.delete(t),this._footerRowDefChanged=!0}setNoDataRow(t){this._customNoDataRow=t}updateStickyHeaderRowStyles(){const t=this._getRenderedRows(this._headerRowOutlet),e=this._elementRef.nativeElement.querySelector("thead");e&&(e.style.display=t.length?"":"none");const n=this._headerRowDefs.map((t=>t.sticky));this._stickyStyler.clearStickyPositioning(t,["top"]),this._stickyStyler.stickRows(t,n,"top"),this._headerRowDefs.forEach((t=>t.resetStickyChanged()))}updateStickyFooterRowStyles(){const t=this._getRenderedRows(this._footerRowOutlet),e=this._elementRef.nativeElement.querySelector("tfoot");e&&(e.style.display=t.length?"":"none");const n=this._footerRowDefs.map((t=>t.sticky));this._stickyStyler.clearStickyPositioning(t,["bottom"]),this._stickyStyler.stickRows(t,n,"bottom"),this._stickyStyler.updateStickyFooterContainer(this._elementRef.nativeElement,n),this._footerRowDefs.forEach((t=>t.resetStickyChanged()))}updateStickyColumnStyles(){const t=this._getRenderedRows(this._headerRowOutlet),e=this._getRenderedRows(this._rowOutlet),n=this._getRenderedRows(this._footerRowOutlet);(this._isNativeHtmlTable&&!this._fixedLayout||this._stickyColumnStylesNeedReset)&&(this._stickyStyler.clearStickyPositioning([...t,...e,...n],["left","right"]),this._stickyColumnStylesNeedReset=!1),t.forEach(((t,e)=>{this._addStickyColumnStyles([t],this._headerRowDefs[e])})),this._rowDefs.forEach((t=>{const n=[];for(let o=0;o<e.length;o++)this._renderRows[o].rowDef===t&&n.push(e[o]);this._addStickyColumnStyles(n,t)})),n.forEach(((t,e)=>{this._addStickyColumnStyles([t],this._footerRowDefs[e])})),Array.from(this._columnDefsByName.values()).forEach((t=>t.resetStickyChanged()))}_getAllRenderRows(){const t=[],e=this._cachedRenderRowsMap;this._cachedRenderRowsMap=new Map;for(let n=0;n<this._data.length;n++){let o=this._data[n];const i=this._getRenderRowsForData(o,n,e.get(o));this._cachedRenderRowsMap.has(o)||this._cachedRenderRowsMap.set(o,new WeakMap);for(let e=0;e<i.length;e++){let n=i[e];const o=this._cachedRenderRowsMap.get(n.data);o.has(n.rowDef)?o.get(n.rowDef).push(n):o.set(n.rowDef,[n]),t.push(n)}}return t}_getRenderRowsForData(t,e,n){return this._getRowDefs(t,e).map((o=>{const i=n&&n.has(o)?n.get(o):[];if(i.length){const t=i.shift();return t.dataIndex=e,t}return{data:t,rowDef:o,dataIndex:e}}))}_cacheColumnDefs(){this._columnDefsByName.clear(),e0(this._getOwnDefs(this._contentColumnDefs),this._customColumnDefs).forEach((t=>{if(this._columnDefsByName.has(t.name)&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(t){return Error(`Duplicate column definition name provided: "${t}".`)})(t.name);this._columnDefsByName.set(t.name,t)}))}_cacheRowDefs(){this._headerRowDefs=e0(this._getOwnDefs(this._contentHeaderRowDefs),this._customHeaderRowDefs),this._footerRowDefs=e0(this._getOwnDefs(this._contentFooterRowDefs),this._customFooterRowDefs),this._rowDefs=e0(this._getOwnDefs(this._contentRowDefs),this._customRowDefs);const t=this._rowDefs.filter((t=>!t.when));if(!this.multiTemplateDataRows&&t.length>1&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("There can only be one default row without a when predicate function.")})();this._defaultRowDef=t[0]}_renderUpdatedColumns(){const t=(t,e)=>t||!!e.getColumnsDiff(),e=this._rowDefs.reduce(t,!1);e&&this._forceRenderDataRows();const n=this._headerRowDefs.reduce(t,!1);n&&this._forceRenderHeaderRows();const o=this._footerRowDefs.reduce(t,!1);return o&&this._forceRenderFooterRows(),e||n||o}_switchDataSource(t){this._data=[],$H(this.dataSource)&&this.dataSource.disconnect(this),this._renderChangeSubscription&&(this._renderChangeSubscription.unsubscribe(),this._renderChangeSubscription=null),t||(this._dataDiffer&&this._dataDiffer.diff([]),this._rowOutlet.viewContainer.clear()),this._dataSource=t}_observeRenderChanges(){if(!this.dataSource)return;let t;if($H(this.dataSource)?t=this.dataSource.connect(this):Nt(this.dataSource)?t=this.dataSource:Array.isArray(this.dataSource)&&(t=Et(this.dataSource)),void 0===t&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("Provided data source did not match an array, Observable, or DataSource")})();this._renderChangeSubscription=t.pipe(Ie(this._onDestroy)).subscribe((t=>{this._data=t||[],this.renderRows()}))}_forceRenderHeaderRows(){this._headerRowOutlet.viewContainer.length>0&&this._headerRowOutlet.viewContainer.clear(),this._headerRowDefs.forEach(((t,e)=>this._renderRow(this._headerRowOutlet,t,e))),this.updateStickyHeaderRowStyles()}_forceRenderFooterRows(){this._footerRowOutlet.viewContainer.length>0&&this._footerRowOutlet.viewContainer.clear(),this._footerRowDefs.forEach(((t,e)=>this._renderRow(this._footerRowOutlet,t,e))),this.updateStickyFooterRowStyles()}_addStickyColumnStyles(t,e){const n=Array.from(e.columns||[]).map((t=>{const e=this._columnDefsByName.get(t);if(!e&&("undefined"==typeof ngDevMode||ngDevMode))throw Y$(t);return e})),o=n.map((t=>t.sticky)),i=n.map((t=>t.stickyEnd));this._stickyStyler.updateStickyColumns(t,o,i,!this._fixedLayout||this._forceRecalculateCellWidths)}_getRenderedRows(t){const e=[];for(let n=0;n<t.viewContainer.length;n++){const o=t.viewContainer.get(n);e.push(o.rootNodes[0])}return e}_getRowDefs(t,e){if(1==this._rowDefs.length)return[this._rowDefs[0]];let n=[];if(this.multiTemplateDataRows)n=this._rowDefs.filter((n=>!n.when||n.when(e,t)));else{let o=this._rowDefs.find((n=>n.when&&n.when(e,t)))||this._defaultRowDef;o&&n.push(o)}if(!n.length&&("undefined"==typeof ngDevMode||ngDevMode))throw(function o(t){return Error(`Could not find a matching row definition for theprovided row data: ${JSON.stringify(t)}`)})(t);return n}_getEmbeddedViewArgs(t,e){return{templateRef:t.rowDef.template,context:{$implicit:t.data},index:e}}_renderRow(t,e,n,o={}){const i=t.viewContainer.createEmbeddedView(e.template,o,n);return this._renderCellTemplateForItem(e,o),i}_renderCellTemplateForItem(t,e){for(let n of this._getCellTemplates(t))L$.mostRecentCellOutlet&&L$.mostRecentCellOutlet._viewContainer.createEmbeddedView(n,e);this._changeDetectorRef.markForCheck()}_updateRowIndexContext(){const t=this._rowOutlet.viewContainer;for(let e=0,n=t.length;e<n;e++){const o=t.get(e).context;o.count=n,o.first=0===e,o.last=e===n-1,o.even=e%2==0,o.odd=!o.even,this.multiTemplateDataRows?(o.dataIndex=this._renderRows[e].dataIndex,o.renderIndex=e):o.index=this._renderRows[e].dataIndex}}_getCellTemplates(t){return t&&t.columns?Array.from(t.columns,(e=>{const n=this._columnDefsByName.get(e);if(!n&&("undefined"==typeof ngDevMode||ngDevMode))throw Y$(e);return t.extractCellTemplate(n)})):[]}_applyNativeTableSections(){const t=this._document.createDocumentFragment(),e=[{tag:"thead",outlets:[this._headerRowOutlet]},{tag:"tbody",outlets:[this._rowOutlet,this._noDataRowOutlet]},{tag:"tfoot",outlets:[this._footerRowOutlet]}];for(const n of e){const e=this._document.createElement(n.tag);e.setAttribute("role","rowgroup");for(const t of n.outlets)e.appendChild(t.elementRef.nativeElement);t.appendChild(e)}this._elementRef.nativeElement.appendChild(t)}_forceRenderDataRows(){this._dataDiffer.diff([]),this._rowOutlet.viewContainer.clear(),this.renderRows()}_checkStickyStates(){const t=(t,e)=>t||e.hasStickyChanged();this._headerRowDefs.reduce(t,!1)&&this.updateStickyHeaderRowStyles(),this._footerRowDefs.reduce(t,!1)&&this.updateStickyFooterRowStyles(),Array.from(this._columnDefsByName.values()).reduce(t,!1)&&(this._stickyColumnStylesNeedReset=!0,this.updateStickyColumnStyles())}_setupStickyStyler(){this._stickyStyler=new W$(this._isNativeHtmlTable,this.stickyCssClass,this._dir?this._dir.value:"ltr",this._coalescedStyleScheduler,this._platform.isBrowser,this.needsPositionStickyOnElement,this._stickyPositioningListener),(this._dir?this._dir.change:Et()).pipe(Ie(this._onDestroy)).subscribe((t=>{this._stickyStyler.direction=t,this.updateStickyColumnStyles()}))}_getOwnDefs(t){return t.filter((t=>!t._table||t._table===this))}_updateNoDataRow(){const t=this._customNoDataRow||this._noDataRow;if(t){const e=0===this._rowOutlet.viewContainer.length;if(e!==this._isShowingNoDataRow){const n=this._noDataRowOutlet.viewContainer;e?n.createEmbeddedView(t.templateRef):n.clear(),this._isShowingNoDataRow=e}}}}function e0(t,e){return t.concat(Array.from(e))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */t0.ɵfac=function t(e){return new(e||t0)(Sm(Hg),Sm(Ug),Sm(hg),Na("role"),Sm(HI,8),Sm(Z_),Sm(wz),Sm(aF),Sm(E$),Sm(uF),Sm(q$,12))},t0.ɵcmp=to({type:t0,selectors:[["cdk-table"],["table","cdk-table",""]],contentQueries:function t(e,n,o){if(1&e&&($h(o,U$,5),$h(o,O$,5),$h(o,F$,5),$h(o,z$,5),$h(o,H$,5)),2&e){let t;Jh(t=tb())&&(n._noDataRow=t.first),Jh(t=tb())&&(n._contentColumnDefs=t),Jh(t=tb())&&(n._contentRowDefs=t),Jh(t=tb())&&(n._contentHeaderRowDefs=t),Jh(t=tb())&&(n._contentFooterRowDefs=t)}},viewQuery:function t(e,n){if(1&e&&(Qh(X$,7),Qh(K$,7),Qh(J$,7),Qh(Q$,7)),2&e){let t;Jh(t=tb())&&(n._rowOutlet=t.first),Jh(t=tb())&&(n._headerRowOutlet=t.first),Jh(t=tb())&&(n._footerRowOutlet=t.first),Jh(t=tb())&&(n._noDataRowOutlet=t.first)}},hostAttrs:[1,"cdk-table"],hostVars:2,hostBindings:function t(e,n){2&e&&pu("cdk-table-fixed-layout",n.fixedLayout)},inputs:{trackBy:"trackBy",dataSource:"dataSource",multiTemplateDataRows:"multiTemplateDataRows",fixedLayout:"fixedLayout"},outputs:{contentChanged:"contentChanged"},exportAs:["cdkTable"],features:[pg([{provide:y$,useExisting:t0},{provide:aF,useClass:eF},{provide:E$,useClass:R$},{provide:q$,useValue:null}])],ngContentSelectors:["caption","colgroup, col"],decls:6,vars:0,consts:[["headerRowOutlet",""],["rowOutlet",""],["noDataRowOutlet",""],["footerRowOutlet",""]],template:function t(e,n){1&e&&(Zm(f$),Xm(0),Xm(1,1),Im(2,0),Im(3,1),Im(4,2),Im(5,3))},directives:[K$,X$,Q$,J$],styles:[".cdk-table-fixed-layout{table-layout:fixed}\n"],encapsulation:2}),t0.ctorParameters=()=>[{type:Hg},{type:Ug},{type:hg},{type:String,decorators:[{type:ja,args:["role"]}]},{type:HI,decorators:[{type:Sr}]},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz},{type:void 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const o0=[t0,F$,C$,L$,M$,v$,O$,S$,j$,w$,k$,B$,z$,V$,H$,X$,K$,J$,n0,U$,Z$,Q$];class i0{}i0.ɵfac=function t(e){return new(e||i0)},i0.ɵmod=ao({type:i0}),i0.ɵinj=vn({imports:[[_F]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(i0,[{type:Ay,args:[{exports:o0,declarations:o0,imports:[_F]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(i0,{declarations:function(){return[t0,F$,C$,L$,M$,v$,O$,S$,j$,w$,k$,B$,z$,V$,H$,X$,K$,J$,n0,U$,Z$,Q$]},imports:function(){return[_F]},exports:function(){return[t0,F$,C$,L$,M$,v$,O$,S$,j$,w$,k$,B$,z$,V$,H$,X$,K$,J$,n0,U$,Z$,Q$]}});const a0=[[["caption"]],[["colgroup"],["col"]]];function r0(t,e){if(1&t&&(Rm(0,"th",3),ku(1),Am()),2&t){const t=Ym();du("text-align",t.justify),rc(1),Du(" ",t.headerText," ")}}function s0(t,e){if(1&t&&(Rm(0,"td",4),ku(1),Am()),2&t){const t=e.$implicit,n=Ym();du("text-align",n.justify),rc(1),Du(" ",n.dataAccessor(t,n.name)," ")}}class l0{}l0.ɵfac=function t(e){return new(e||l0)},l0.ɵdir=lo({type:l0,selectors:[["mat-table","recycleRows",""],["table","mat-table","","recycleRows",""]],features:[pg([{provide:aF,useClass:nF}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(l0,[{type:Cy,args:[{selector:"mat-table[recycleRows], table[mat-table][recycleRows]",providers:[{provide:aF,useClass:nF}]}]}],null,null);class c0 extends t0{constructor(){super(...arguments),this.stickyCssClass="mat-table-sticky",this.needsPositionStickyOnElement=!1}}c0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(c0)))(n||c0)}})(),c0.ɵcmp=to({type:c0,selectors:[["mat-table"],["table","mat-table",""]],hostAttrs:[1,"mat-table"],hostVars:2,hostBindings:function t(e,n){2&e&&pu("mat-table-fixed-layout",n.fixedLayout)},exportAs:["matTable"],features:[pg([{provide:aF,useClass:eF},{provide:t0,useExisting:c0},{provide:y$,useExisting:c0},{provide:E$,useClass:R$},{provide:q$,useValue:null}]),xp],ngContentSelectors:["caption","colgroup, col"],decls:6,vars:0,consts:[["headerRowOutlet",""],["rowOutlet",""],["noDataRowOutlet",""],["footerRowOutlet",""]],template:function t(e,n){1&e&&(Zm(a0),Xm(0),Xm(1,1),Im(2,0),Im(3,1),Im(4,2),Im(5,3))},directives:[K$,X$,Q$,J$],styles:['mat-table{display:block}mat-header-row{min-height:56px}mat-row,mat-footer-row{min-height:48px}mat-row,mat-header-row,mat-footer-row{display:flex;border-width:0;border-bottom-width:1px;border-style:solid;align-items:center;box-sizing:border-box}mat-row::after,mat-header-row::after,mat-footer-row::after{display:inline-block;min-height:inherit;content:""}mat-cell:first-of-type,mat-header-cell:first-of-type,mat-footer-cell:first-of-type{padding-left:24px}[dir=rtl] mat-cell:first-of-type:not(:only-of-type),[dir=rtl] mat-header-cell:first-of-type:not(:only-of-type),[dir=rtl] mat-footer-cell:first-of-type:not(:only-of-type){padding-left:0;padding-right:24px}mat-cell:last-of-type,mat-header-cell:last-of-type,mat-footer-cell:last-of-type{padding-right:24px}[dir=rtl] mat-cell:last-of-type:not(:only-of-type),[dir=rtl] mat-header-cell:last-of-type:not(:only-of-type),[dir=rtl] mat-footer-cell:last-of-type:not(:only-of-type){padding-right:0;padding-left:24px}mat-cell,mat-header-cell,mat-footer-cell{flex:1;display:flex;align-items:center;overflow:hidden;word-wrap:break-word;min-height:inherit}table.mat-table{border-spacing:0}tr.mat-header-row{height:56px}tr.mat-row,tr.mat-footer-row{height:48px}th.mat-header-cell{text-align:left}[dir=rtl] th.mat-header-cell{text-align:right}th.mat-header-cell,td.mat-cell,td.mat-footer-cell{padding:0;border-bottom-width:1px;border-bottom-style:solid}th.mat-header-cell:first-of-type,td.mat-cell:first-of-type,td.mat-footer-cell:first-of-type{padding-left:24px}[dir=rtl] th.mat-header-cell:first-of-type:not(:only-of-type),[dir=rtl] 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table[mat-table]",exportAs:"matTable",template:$$,host:{class:"mat-table","[class.mat-table-fixed-layout]":"fixedLayout"},providers:[{provide:aF,useClass:eF},{provide:t0,useExisting:c0},{provide:y$,useExisting:c0},{provide:E$,useClass:R$},{provide:q$,useValue:null}],encapsulation:Hn.None,changeDetection:zn.Default,styles:['mat-table{display:block}mat-header-row{min-height:56px}mat-row,mat-footer-row{min-height:48px}mat-row,mat-header-row,mat-footer-row{display:flex;border-width:0;border-bottom-width:1px;border-style:solid;align-items:center;box-sizing:border-box}mat-row::after,mat-header-row::after,mat-footer-row::after{display:inline-block;min-height:inherit;content:""}mat-cell:first-of-type,mat-header-cell:first-of-type,mat-footer-cell:first-of-type{padding-left:24px}[dir=rtl] mat-cell:first-of-type:not(:only-of-type),[dir=rtl] mat-header-cell:first-of-type:not(:only-of-type),[dir=rtl] mat-footer-cell:first-of-type:not(:only-of-type){padding-left:0;padding-right:24px}mat-cell:last-of-type,mat-header-cell:last-of-type,mat-footer-cell:last-of-type{padding-right:24px}[dir=rtl] mat-cell:last-of-type:not(:only-of-type),[dir=rtl] mat-header-cell:last-of-type:not(:only-of-type),[dir=rtl] mat-footer-cell:last-of-type:not(:only-of-type){padding-right:0;padding-left:24px}mat-cell,mat-header-cell,mat-footer-cell{flex:1;display:flex;align-items:center;overflow:hidden;word-wrap:break-word;min-height:inherit}table.mat-table{border-spacing:0}tr.mat-header-row{height:56px}tr.mat-row,tr.mat-footer-row{height:48px}th.mat-header-cell{text-align:left}[dir=rtl] th.mat-header-cell{text-align:right}th.mat-header-cell,td.mat-cell,td.mat-footer-cell{padding:0;border-bottom-width:1px;border-bottom-style:solid}th.mat-header-cell:first-of-type,td.mat-cell:first-of-type,td.mat-footer-cell:first-of-type{padding-left:24px}[dir=rtl] th.mat-header-cell:first-of-type:not(:only-of-type),[dir=rtl] td.mat-cell:first-of-type:not(:only-of-type),[dir=rtl] td.mat-footer-cell:first-of-type:not(:only-of-type){padding-left:0;padding-right:24px}th.mat-header-cell:last-of-type,td.mat-cell:last-of-type,td.mat-footer-cell:last-of-type{padding-right:24px}[dir=rtl] th.mat-header-cell:last-of-type:not(:only-of-type),[dir=rtl] td.mat-cell:last-of-type:not(:only-of-type),[dir=rtl] td.mat-footer-cell:last-of-type:not(:only-of-type){padding-right:0;padding-left:24px}.mat-table-sticky{position:-webkit-sticky !important;position:sticky !important}.mat-table-fixed-layout{table-layout:fixed}\n']}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class d0 extends C${}d0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(d0)))(n||d0)}})(),d0.ɵdir=lo({type:d0,selectors:[["","matCellDef",""]],features:[pg([{provide:C$,useExisting:d0}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(d0,[{type:Cy,args:[{selector:"[matCellDef]",providers:[{provide:C$,useExisting:d0}]}]}],null,null);class p0 extends M${}p0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(p0)))(n||p0)}})(),p0.ɵdir=lo({type:p0,selectors:[["","matHeaderCellDef",""]],features:[pg([{provide:M$,useExisting:p0}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(p0,[{type:Cy,args:[{selector:"[matHeaderCellDef]",providers:[{provide:M$,useExisting:p0}]}]}],null,null);class m0 extends v${}m0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(m0)))(n||m0)}})(),m0.ɵdir=lo({type:m0,selectors:[["","matFooterCellDef",""]],features:[pg([{provide:v$,useExisting:m0}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(m0,[{type:Cy,args:[{selector:"[matFooterCellDef]",providers:[{provide:v$,useExisting:m0}]}]}],null,null);class u0 extends O${get name(){return this._name}set name(t){this._setNameInput(t)}_updateColumnCssClassName(){super._updateColumnCssClassName(),this._columnCssClassName.push(`mat-column-${this.cssClassFriendlyName}`)}}u0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(u0)))(n||u0)}})(),u0.ɵdir=lo({type:u0,selectors:[["","matColumnDef",""]],inputs:{sticky:"sticky",name:["matColumnDef","name"]},features:[pg([{provide:O$,useExisting:u0},{provide:"MAT_SORT_HEADER_COLUMN_DEF",useExisting:u0}]),xp]}),u0.propDecorators={name:[{type:xy,args:["matColumnDef"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(u0,[{type:Cy,args:[{selector:"[matColumnDef]",inputs:["sticky"],providers:[{provide:O$,useExisting:u0},{provide:"MAT_SORT_HEADER_COLUMN_DEF",useExisting:u0}]}]}],null,{name:[{type:xy,args:["matColumnDef"]}]});class f0 extends w${}f0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(f0)))(n||f0)}})(),f0.ɵdir=lo({type:f0,selectors:[["mat-header-cell"],["th","mat-header-cell",""]],hostAttrs:["role","columnheader",1,"mat-header-cell"],features:[xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(f0,[{type:Cy,args:[{selector:"mat-header-cell, th[mat-header-cell]",host:{class:"mat-header-cell",role:"columnheader"}}]}],null,null);class g0 extends k${}g0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(g0)))(n||g0)}})(),g0.ɵdir=lo({type:g0,selectors:[["mat-footer-cell"],["td","mat-footer-cell",""]],hostAttrs:["role","gridcell",1,"mat-footer-cell"],features:[xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(g0,[{type:Cy,args:[{selector:"mat-footer-cell, td[mat-footer-cell]",host:{class:"mat-footer-cell",role:"gridcell"}}]}],null,null);class h0 extends S${}h0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(h0)))(n||h0)}})(),h0.ɵdir=lo({type:h0,selectors:[["mat-cell"],["td","mat-cell",""]],hostAttrs:["role","gridcell",1,"mat-cell"],features:[xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(h0,[{type:Cy,args:[{selector:"mat-cell, td[mat-cell]",host:{class:"mat-cell",role:"gridcell"}}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class b0 extends z${}b0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(b0)))(n||b0)}})(),b0.ɵdir=lo({type:b0,selectors:[["","matHeaderRowDef",""]],inputs:{columns:["matHeaderRowDef","columns"],sticky:["matHeaderRowDefSticky","sticky"]},features:[pg([{provide:z$,useExisting:b0}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(b0,[{type:Cy,args:[{selector:"[matHeaderRowDef]",providers:[{provide:z$,useExisting:b0}],inputs:["columns: matHeaderRowDef","sticky: matHeaderRowDefSticky"]}]}],null,null);class y0 extends H${}y0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(y0)))(n||y0)}})(),y0.ɵdir=lo({type:y0,selectors:[["","matFooterRowDef",""]],inputs:{columns:["matFooterRowDef","columns"],sticky:["matFooterRowDefSticky","sticky"]},features:[pg([{provide:H$,useExisting:y0}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(y0,[{type:Cy,args:[{selector:"[matFooterRowDef]",providers:[{provide:H$,useExisting:y0}],inputs:["columns: matFooterRowDef","sticky: matFooterRowDefSticky"]}]}],null,null);class _0 extends F${}_0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(_0)))(n||_0)}})(),_0.ɵdir=lo({type:_0,selectors:[["","matRowDef",""]],inputs:{columns:["matRowDefColumns","columns"],when:["matRowDefWhen","when"]},features:[pg([{provide:F$,useExisting:_0}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(_0,[{type:Cy,args:[{selector:"[matRowDef]",providers:[{provide:F$,useExisting:_0}],inputs:["columns: matRowDefColumns","when: matRowDefWhen"]}]}],null,null);class C0 extends B${}C0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(C0)))(n||C0)}})(),C0.ɵcmp=to({type:C0,selectors:[["mat-header-row"],["tr","mat-header-row",""]],hostAttrs:["role","row",1,"mat-header-row"],exportAs:["matHeaderRow"],features:[pg([{provide:B$,useExisting:C0}]),xp],decls:1,vars:0,consts:[["cdkCellOutlet",""]],template:function t(e,n){1&e&&Im(0,0)},directives:[L$],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(C0,[{type:My,args:[{selector:"mat-header-row, tr[mat-header-row]",template:A$,host:{class:"mat-header-row",role:"row"},changeDetection:zn.Default,encapsulation:Hn.None,exportAs:"matHeaderRow",providers:[{provide:B$,useExisting:C0}]}]}],null,null);class M0 extends V${}M0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(M0)))(n||M0)}})(),M0.ɵcmp=to({type:M0,selectors:[["mat-footer-row"],["tr","mat-footer-row",""]],hostAttrs:["role","row",1,"mat-footer-row"],exportAs:["matFooterRow"],features:[pg([{provide:V$,useExisting:M0}]),xp],decls:1,vars:0,consts:[["cdkCellOutlet",""]],template:function t(e,n){1&e&&Im(0,0)},directives:[L$],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(M0,[{type:My,args:[{selector:"mat-footer-row, tr[mat-footer-row]",template:A$,host:{class:"mat-footer-row",role:"row"},changeDetection:zn.Default,encapsulation:Hn.None,exportAs:"matFooterRow",providers:[{provide:V$,useExisting:M0}]}]}],null,null);class v0 extends j${}v0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(v0)))(n||v0)}})(),v0.ɵcmp=to({type:v0,selectors:[["mat-row"],["tr","mat-row",""]],hostAttrs:["role","row",1,"mat-row"],exportAs:["matRow"],features:[pg([{provide:j$,useExisting:v0}]),xp],decls:1,vars:0,consts:[["cdkCellOutlet",""]],template:function t(e,n){1&e&&Im(0,0)},directives:[L$],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(v0,[{type:My,args:[{selector:"mat-row, tr[mat-row]",template:A$,host:{class:"mat-row",role:"row"},changeDetection:zn.Default,encapsulation:Hn.None,exportAs:"matRow",providers:[{provide:j$,useExisting:v0}]}]}],null,null);class x0 extends U${}x0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(x0)))(n||x0)}})(),x0.ɵdir=lo({type:x0,selectors:[["ng-template","matNoDataRow",""]],features:[pg([{provide:U$,useExisting:x0}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(x0,[{type:Cy,args:[{selector:"ng-template[matNoDataRow]",providers:[{provide:U$,useExisting:x0}]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class O0 extends n0{}O0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(O0)))(n||O0)}})(),O0.ɵcmp=to({type:O0,selectors:[["mat-text-column"]],features:[xp],decls:3,vars:0,consts:[["matColumnDef",""],["mat-header-cell","",3,"text-align",4,"matHeaderCellDef"],["mat-cell","",3,"text-align",4,"matCellDef"],["mat-header-cell",""],["mat-cell",""]],template:function t(e,n){1&e&&(Nm(0,0),Qp(1,r0,2,3,"th",1),Qp(2,s0,2,3,"td",2),zm())},directives:[u0,p0,d0,f0,h0],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(O0,[{type:My,args:[{selector:"mat-text-column",template:'\n    <ng-container matColumnDef>\n      <th mat-header-cell *matHeaderCellDef [style.text-align]="justify">\n        {{headerText}}\n      </th>\n      <td mat-cell *matCellDef="let data" [style.text-align]="justify">\n        {{dataAccessor(data, name)}}\n      </td>\n    </ng-container>\n  ',encapsulation:Hn.None,changeDetection:zn.Default}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const P0=[c0,l0,p0,b0,u0,d0,_0,m0,y0,f0,h0,g0,C0,v0,M0,x0,O0];class w0{}w0.ɵfac=function t(e){return new(e||w0)},w0.ɵmod=ao({type:w0}),w0.ɵinj=vn({imports:[[i0,XI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(w0,[{type:Ay,args:[{imports:[i0,XI],exports:[XI,P0],declarations:P0}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(w0,{declarations:function(){return[c0,l0,p0,b0,u0,d0,_0,m0,y0,f0,h0,g0,C0,v0,M0,x0,O0]},imports:function(){return[i0,XI]},exports:function(){return[XI,c0,l0,p0,b0,u0,d0,_0,m0,y0,f0,h0,g0,C0,v0,M0,x0,O0]}});class k0 extends QH{constructor(t=[]){super(),this._renderData=new F([]),this._filter=new F(""),this._internalPageChanges=new I,this._renderChangesSubscription=null,this.sortingDataAccessor=(t,e)=>{const n=t[e];if(Cz(n)){const t=Number(n);return t<9007199254740991?t:n}return n},this.sortData=(t,e)=>{const n=e.active,o=e.direction;return n&&""!=o?t.sort(((t,e)=>{let i=this.sortingDataAccessor(t,n),a=this.sortingDataAccessor(e,n);const r=typeof i,s=typeof a;r!==s&&("number"===r&&(i+=""),"number"===s&&(a+=""));let l=0;return null!=i&&null!=a?i>a?l=1:i<a&&(l=-1):null!=i?l=1:null!=a&&(l=-1),l*("asc"==o?1:-1)})):t},this.filterPredicate=(t,e)=>{const n=Object.keys(t).reduce(((e,n)=>e+t[n]+"◬"),"").toLowerCase(),o=e.trim().toLowerCase();return-1!=n.indexOf(o)},this._data=new F(t),this._updateChangeSubscription()}get data(){return this._data.value}set data(t){this._data.next(t),this._renderChangesSubscription||this._filterData(t)}get filter(){return this._filter.value}set filter(t){this._filter.next(t),this._renderChangesSubscription||this._filterData(this.data)}get sort(){return this._sort}set sort(t){this._sort=t,this._updateChangeSubscription()}get paginator(){return this._paginator}set paginator(t){this._paginator=t,this._updateChangeSubscription()}_updateChangeSubscription(){var t;const e=this._sort?re(this._sort.sortChange,this._sort.initialized):Et(null),n=this._paginator?re(this._paginator.page,this._internalPageChanges,this._paginator.initialized):Et(null),o=Wt([this._data,this._filter]).pipe(It((([t])=>this._filterData(t)))),i=Wt([o,e]).pipe(It((([t])=>this._orderData(t)))),a=Wt([i,n]).pipe(It((([t])=>this._pageData(t))));null===(t=this._renderChangesSubscription)||void 0===t||t.unsubscribe(),this._renderChangesSubscription=a.subscribe((t=>this._renderData.next(t)))}_filterData(t){return this.filteredData=null==this.filter||""===this.filter?t:t.filter((t=>this.filterPredicate(t,this.filter))),this.paginator&&this._updatePaginator(this.filteredData.length),this.filteredData}_orderData(t){return this.sort?this.sortData(t.slice(),this.sort):t}_pageData(t){if(!this.paginator)return t;const e=this.paginator.pageIndex*this.paginator.pageSize;return t.slice(e,e+this.paginator.pageSize)}_updatePaginator(t){Promise.resolve().then((()=>{const e=this.paginator;if(e&&(e.length=t,e.pageIndex>0)){const t=Math.ceil(e.length/e.pageSize)-1||0,n=Math.min(e.pageIndex,t);n!==e.pageIndex&&(e.pageIndex=n,this._internalPageChanges.next())}}))}connect(){return this._renderChangesSubscription||this._updateChangeSubscription(),this._renderData}disconnect(){var t;null===(t=this._renderChangesSubscription)||void 0===t||t.unsubscribe(),this._renderChangesSubscription=null}}class S0 extends k0{}const D0=["panel"];function E0(t,e){if(1&t&&(Rm(0,"div",0,1),Xm(2),Am()),2&t){const t=e.id,n=Ym();Dm("id",n.id)("ngClass",n._classList),jp("aria-label",n.ariaLabel||null)("aria-labelledby",n._getPanelAriaLabelledby(t))}}let R0=0;class A0{constructor(t,e){this.source=t,this.option=e}}const T0=QI(class{}),N0=new Ga("mat-autocomplete-default-options",{providedIn:"root",factory:function z0(){return{autoActiveFirstOption:!1}}});class I0 extends T0{constructor(t,e,n,o){super(),this._changeDetectorRef=t,this._elementRef=e,this._activeOptionChanges=m.EMPTY,this.showPanel=!1,this._isOpen=!1,this.displayWith=null,this.optionSelected=new Lh,this.opened=new Lh,this.closed=new Lh,this.optionActivated=new Lh,this._classList={},this.id="mat-autocomplete-"+R0++,this.inertGroups=(null==o?void 0:o.SAFARI)||!1,this._autoActiveFirstOption=!!n.autoActiveFirstOption}get isOpen(){return this._isOpen&&this.showPanel}get autoActiveFirstOption(){return this._autoActiveFirstOption}set autoActiveFirstOption(t){this._autoActiveFirstOption=yz(t)}set classList(t){this._classList=t&&t.length?(function e(t,n=/\s+/){const o=[];if(null!=t){const e=Array.isArray(t)?t:`${t}`.split(n);for(const t of e){const e=`${t}`.trim();e&&o.push(e)}}return o})(t).reduce(((t,e)=>(t[e]=!0,t)),{}):{},this._setVisibilityClasses(this._classList),this._elementRef.nativeElement.className=""}ngAfterContentInit(){this._keyManager=new tI(this.options).withWrap(),this._activeOptionChanges=this._keyManager.change.subscribe((t=>{this.optionActivated.emit({source:this,option:this.options.toArray()[t]||null})})),this._setVisibility()}ngOnDestroy(){this._activeOptionChanges.unsubscribe()}_setScrollTop(t){this.panel&&(this.panel.nativeElement.scrollTop=t)}_getScrollTop(){return this.panel?this.panel.nativeElement.scrollTop:0}_setVisibility(){this.showPanel=!!this.options.length,this._setVisibilityClasses(this._classList),this._changeDetectorRef.markForCheck()}_emitSelectEvent(t){const e=new A0(this,t);this.optionSelected.emit(e)}_getPanelAriaLabelledby(t){return this.ariaLabel?null:this.ariaLabelledby?(t?t+" ":"")+this.ariaLabelledby:t}_setVisibilityClasses(t){t[this._visibleClass]=this.showPanel,t[this._hiddenClass]=!this.showPanel}}I0.ɵfac=function t(e){return new(e||I0)(Sm(Ug),Sm(hg),Sm(N0),Sm(wz))},I0.ɵdir=lo({type:I0,viewQuery:function t(e,n){if(1&e&&(Qh(Xg,7),Qh(D0,5)),2&e){let t;Jh(t=tb())&&(n.template=t.first),Jh(t=tb())&&(n.panel=t.first)}},inputs:{displayWith:"displayWith",autoActiveFirstOption:"autoActiveFirstOption",classList:["class","classList"],ariaLabel:["aria-label","ariaLabel"],ariaLabelledby:["aria-labelledby","ariaLabelledby"],panelWidth:"panelWidth"},outputs:{optionSelected:"optionSelected",opened:"opened",closed:"closed",optionActivated:"optionActivated"},features:[xp]}),I0.ctorParameters=()=>[{type:Ug},{type:hg},{type:void 0,decorators:[{type:kr,args:[N0]}]},{type:wz}],I0.propDecorators={template:[{type:Za,args:[Xg,{static:!0}]}],panel:[{type:Za,args:["panel"]}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],displayWith:[{type:xy}],autoActiveFirstOption:[{type:xy}],panelWidth:[{type:xy}],optionSelected:[{type:Oy}],opened:[{type:Oy}],closed:[{type:Oy}],optionActivated:[{type:Oy}],classList:[{type:xy,args:["class"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(I0,[{type:Cy}],(function(){return[{type:Ug},{type:hg},{type:void 0,decorators:[{type:kr,args:[N0]}]},{type:wz}]}),{displayWith:[{type:xy}],optionSelected:[{type:Oy}],opened:[{type:Oy}],closed:[{type:Oy}],optionActivated:[{type:Oy}],autoActiveFirstOption:[{type:xy}],classList:[{type:xy,args:["class"]}],template:[{type:Za,args:[Xg,{static:!0}]}],panel:[{type:Za,args:["panel"]}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],panelWidth:[{type:xy}]});class H0 extends I0{constructor(){super(...arguments),this._visibleClass="mat-autocomplete-visible",this._hiddenClass="mat-autocomplete-hidden"}}H0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(H0)))(n||H0)}})(),H0.ɵcmp=to({type:H0,selectors:[["mat-autocomplete"]],contentQueries:function t(e,n,o){if(1&e&&($h(o,zH,5),$h(o,BH,5)),2&e){let t;Jh(t=tb())&&(n.optionGroups=t),Jh(t=tb())&&(n.options=t)}},hostAttrs:[1,"mat-autocomplete"],inputs:{disableRipple:"disableRipple"},exportAs:["matAutocomplete"],features:[pg([{provide:RH,useExisting:H0}]),xp],ngContentSelectors:["*"],decls:1,vars:0,consts:[["role","listbox",1,"mat-autocomplete-panel",3,"id","ngClass"],["panel",""]],template:function t(e,n){1&e&&(Zm(),Qp(0,E0,3,4,"ng-template"))},directives:[aM],styles:[".mat-autocomplete-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;visibility:hidden;max-width:none;max-height:256px;position:relative;width:100%;border-bottom-left-radius:4px;border-bottom-right-radius:4px}.mat-autocomplete-panel.mat-autocomplete-visible{visibility:visible}.mat-autocomplete-panel.mat-autocomplete-hidden{visibility:hidden}.mat-autocomplete-panel-above .mat-autocomplete-panel{border-radius:0;border-top-left-radius:4px;border-top-right-radius:4px}.mat-autocomplete-panel .mat-divider-horizontal{margin-top:-1px}.cdk-high-contrast-active .mat-autocomplete-panel{outline:solid 1px}mat-autocomplete{display:none}\n"],encapsulation:2,changeDetection:0}),H0.propDecorators={optionGroups:[{type:Ya,args:[zH,{descendants:!0}]}],options:[{type:Ya,args:[BH,{descendants:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(H0,[{type:My,args:[{selector:"mat-autocomplete",template:'<ng-template let-formFieldId="id">\n  <div class="mat-autocomplete-panel"\n       role="listbox"\n       [id]="id"\n       [attr.aria-label]="ariaLabel || null"\n       [attr.aria-labelledby]="_getPanelAriaLabelledby(formFieldId)"\n       [ngClass]="_classList"\n       #panel>\n    <ng-content></ng-content>\n  </div>\n</ng-template>\n',encapsulation:Hn.None,changeDetection:zn.OnPush,exportAs:"matAutocomplete",inputs:["disableRipple"],host:{class:"mat-autocomplete"},providers:[{provide:RH,useExisting:H0}],styles:[".mat-autocomplete-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;visibility:hidden;max-width:none;max-height:256px;position:relative;width:100%;border-bottom-left-radius:4px;border-bottom-right-radius:4px}.mat-autocomplete-panel.mat-autocomplete-visible{visibility:visible}.mat-autocomplete-panel.mat-autocomplete-hidden{visibility:hidden}.mat-autocomplete-panel-above .mat-autocomplete-panel{border-radius:0;border-top-left-radius:4px;border-top-right-radius:4px}.mat-autocomplete-panel .mat-divider-horizontal{margin-top:-1px}.cdk-high-contrast-active .mat-autocomplete-panel{outline:solid 1px}mat-autocomplete{display:none}\n"]}]}],null,{optionGroups:[{type:Ya,args:[zH,{descendants:!0}]}],options:[{type:Ya,args:[BH,{descendants:!0}]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class F0{constructor(t){this.elementRef=t}}F0.ɵfac=function t(e){return new(e||F0)(Sm(hg))},F0.ɵdir=lo({type:F0}),F0.ctorParameters=()=>[{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(F0,[{type:Cy}],(function(){return[{type:hg}]}),null);class L0 extends F0{}L0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(L0)))(n||L0)}})(),L0.ɵdir=lo({type:L0,selectors:[["","matAutocompleteOrigin",""]],exportAs:["matAutocompleteOrigin"],features:[xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(L0,[{type:Cy,args:[{selector:"[matAutocompleteOrigin]",exportAs:"matAutocompleteOrigin"}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const B0=new Ga("mat-autocomplete-scroll-strategy"),V0={provide:B0,deps:[pL],useFactory:function j0(t){return()=>t.scrollStrategies.reposition()}},U0={provide:IV,useExisting:qe((()=>W0)),multi:!0};class G0{constructor(t,e,n,o,i,a,r,s,l,c,d){this._element=t,this._overlay=e,this._viewContainerRef=n,this._zone=o,this._changeDetectorRef=i,this._dir=r,this._formField=s,this._document=l,this._viewportRuler=c,this._defaults=d,this._componentDestroyed=!1,this._autocompleteDisabled=!1,this._manuallyFloatingLabel=!1,this._viewportSubscription=m.EMPTY,this._canOpenOnNextFocus=!0,this._closeKeyEventStream=new I,this._windowBlurHandler=()=>{this._canOpenOnNextFocus=this._document.activeElement!==this._element.nativeElement||this.panelOpen},this._onChange=()=>{},this._onTouched=()=>{},this.position="auto",this.autocompleteAttribute="off",this._overlayAttached=!1,this.optionSelections=Qt((()=>this.autocomplete&&this.autocomplete.options?re(...this.autocomplete.options.map((t=>t.onSelectionChange))):this._zone.onStable.pipe(be(1),ze((()=>this.optionSelections))))),this._scrollStrategy=a}get autocompleteDisabled(){return this._autocompleteDisabled}set autocompleteDisabled(t){this._autocompleteDisabled=yz(t)}ngAfterViewInit(){const t=this._getWindow();void 0!==t&&this._zone.runOutsideAngular((()=>t.addEventListener("blur",this._windowBlurHandler)))}ngOnChanges(t){t.position&&this._positionStrategy&&(this._setStrategyPositions(this._positionStrategy),this.panelOpen&&this._overlayRef.updatePosition())}ngOnDestroy(){const t=this._getWindow();void 0!==t&&t.removeEventListener("blur",this._windowBlurHandler),this._viewportSubscription.unsubscribe(),this._componentDestroyed=!0,this._destroyPanel(),this._closeKeyEventStream.complete()}get panelOpen(){return this._overlayAttached&&this.autocomplete.showPanel}openPanel(){this._attachOverlay(),this._floatLabel()}closePanel(){this._resetLabel(),this._overlayAttached&&(this.panelOpen&&this.autocomplete.closed.emit(),this.autocomplete._isOpen=this._overlayAttached=!1,this._overlayRef&&this._overlayRef.hasAttached()&&(this._overlayRef.detach(),this._closingActionsSubscription.unsubscribe()),this._componentDestroyed||this._changeDetectorRef.detectChanges())}updatePosition(){this._overlayAttached&&this._overlayRef.updatePosition()}get panelClosingActions(){return re(this.optionSelections,this.autocomplete._keyManager.tabOut.pipe(ce((()=>this._overlayAttached))),this._closeKeyEventStream,this._getOutsideClickStream(),this._overlayRef?this._overlayRef.detachments().pipe(ce((()=>this._overlayAttached))):Et()).pipe(It((t=>t instanceof FH?t:null)))}get activeOption(){return this.autocomplete&&this.autocomplete._keyManager?this.autocomplete._keyManager.activeItem:null}_getOutsideClickStream(){return re(oe(this._document,"click"),oe(this._document,"auxclick"),oe(this._document,"touchend")).pipe(ce((t=>{const e=Lz(t),n=this._formField?this._formField._elementRef.nativeElement:null,o=this.connectedTo?this.connectedTo.elementRef.nativeElement:null;return this._overlayAttached&&e!==this._element.nativeElement&&(!n||!n.contains(e))&&(!o||!o.contains(e))&&!!this._overlayRef&&!this._overlayRef.overlayElement.contains(e)})))}writeValue(t){Promise.resolve(null).then((()=>this._setTriggerValue(t)))}registerOnChange(t){this._onChange=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this._element.nativeElement.disabled=t}_handleKeydown(t){const e=t.keyCode;if(e!==uz||bz(t)||t.preventDefault(),this.activeOption&&e===mz&&this.panelOpen)this.activeOption._selectViaInteraction(),this._resetActiveItem(),t.preventDefault();else if(this.autocomplete){const n=this.autocomplete._keyManager.activeItem,o=e===gz||e===hz;this.panelOpen||9===e?this.autocomplete._keyManager.onKeydown(t):o&&this._canOpen()&&this.openPanel(),(o||this.autocomplete._keyManager.activeItem!==n)&&this._scrollToOption(this.autocomplete._keyManager.activeItemIndex||0)}}_handleInput(t){let e=t.target,n=e.value;"number"===e.type&&(n=""==n?null:parseFloat(n)),this._previousValue!==n&&(this._previousValue=n,this._onChange(n),this._canOpen()&&this._document.activeElement===t.target&&this.openPanel())}_handleFocus(){this._canOpenOnNextFocus?this._canOpen()&&(this._previousValue=this._element.nativeElement.value,this._attachOverlay(),this._floatLabel(!0)):this._canOpenOnNextFocus=!0}_floatLabel(t=!1){this._formField&&"auto"===this._formField.floatLabel&&(t?this._formField._animateAndLockLabel():this._formField.floatLabel="always",this._manuallyFloatingLabel=!0)}_resetLabel(){this._manuallyFloatingLabel&&(this._formField.floatLabel="auto",this._manuallyFloatingLabel=!1)}_subscribeToClosingActions(){return re(this._zone.onStable.pipe(be(1)),this.autocomplete.options.changes.pipe(Fe((()=>this._positionStrategy.reapplyLastPosition())),Ce(0))).pipe(ze((()=>{const t=this.panelOpen;return this._resetActiveItem(),this.autocomplete._setVisibility(),this.panelOpen&&(this._overlayRef.updatePosition(),t!==this.panelOpen&&this.autocomplete.opened.emit()),this.panelClosingActions})),be(1)).subscribe((t=>this._setValueAndClose(t)))}_destroyPanel(){this._overlayRef&&(this.closePanel(),this._overlayRef.dispose(),this._overlayRef=null)}_setTriggerValue(t){const e=this.autocomplete&&this.autocomplete.displayWith?this.autocomplete.displayWith(t):t,n=null!=e?e:"";this._formField?this._formField._control.value=n:this._element.nativeElement.value=n,this._previousValue=n}_setValueAndClose(t){t&&t.source&&(this._clearPreviousSelectedOption(t.source),this._setTriggerValue(t.source.value),this._onChange(t.source.value),this._element.nativeElement.focus(),this.autocomplete._emitSelectEvent(t.source)),this.closePanel()}_clearPreviousSelectedOption(t){this.autocomplete.options.forEach((e=>{e!==t&&e.selected&&e.deselect()}))}_attachOverlay(){var t;if(!this.autocomplete&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("Attempting to open an undefined instance of `mat-autocomplete`. Make sure that the id passed to the `matAutocomplete` is correct and that you're attempting to open it after the ngAfterContentInit hook.")})();let n=this._overlayRef;n?(this._positionStrategy.setOrigin(this._getConnectedElement()),n.updateSize({width:this._getPanelWidth()})):(this._portal=new xF(this.autocomplete.template,this._viewContainerRef,{id:null===(t=this._formField)||void 0===t?void 0:t.getLabelId()}),n=this._overlay.create(this._getOverlayConfig()),this._overlayRef=n,n.keydownEvents().subscribe((t=>{(t.keyCode===uz&&!bz(t)||t.keyCode===gz&&bz(t,"altKey"))&&(this._resetActiveItem(),this._closeKeyEventStream.next(),t.stopPropagation(),t.preventDefault())})),this._viewportSubscription=this._viewportRuler.change().subscribe((()=>{this.panelOpen&&n&&n.updateSize({width:this._getPanelWidth()})}))),n&&!n.hasAttached()&&(n.attach(this._portal),this._closingActionsSubscription=this._subscribeToClosingActions());const o=this.panelOpen;this.autocomplete._setVisibility(),this.autocomplete._isOpen=this._overlayAttached=!0,this.panelOpen&&o!==this.panelOpen&&this.autocomplete.opened.emit()}_getOverlayConfig(){var t;return new VF({positionStrategy:this._getOverlayPosition(),scrollStrategy:this._scrollStrategy(),width:this._getPanelWidth(),direction:this._dir,panelClass:null===(t=this._defaults)||void 0===t?void 0:t.overlayPanelClass})}_getOverlayPosition(){const t=this._overlay.position().flexibleConnectedTo(this._getConnectedElement()).withFlexibleDimensions(!1).withPush(!1);return this._setStrategyPositions(t),this._positionStrategy=t,t}_setStrategyPositions(t){const e=[{originX:"start",originY:"bottom",overlayX:"start",overlayY:"top"},{originX:"end",originY:"bottom",overlayX:"end",overlayY:"top"}],n=this._aboveClass,o=[{originX:"start",originY:"top",overlayX:"start",overlayY:"bottom",panelClass:n},{originX:"end",originY:"top",overlayX:"end",overlayY:"bottom",panelClass:n}];let i;i="above"===this.position?o:"below"===this.position?e:[...e,...o],t.withPositions(i)}_getConnectedElement(){return this.connectedTo?this.connectedTo.elementRef:this._formField?this._formField.getConnectedOverlayOrigin():this._element}_getPanelWidth(){return this.autocomplete.panelWidth||this._getHostWidth()}_getHostWidth(){return this._getConnectedElement().nativeElement.getBoundingClientRect().width}_resetActiveItem(){const t=this.autocomplete;t.autoActiveFirstOption?t._keyManager.setFirstItemActive():t._keyManager.setActiveItem(-1)}_canOpen(){const t=this._element.nativeElement;return!t.readOnly&&!t.disabled&&!this._autocompleteDisabled}_getWindow(){var t;return(null===(t=this._document)||void 0===t?void 0:t.defaultView)||window}_scrollToOption(t){const e=this.autocomplete,n=VH(t,e.options,e.optionGroups);if(0===t&&1===n)e._setScrollTop(0);else if(e.panel){const n=e.options.toArray()[t];if(n){const t=n._getHostElement(),o=jH(t.offsetTop,t.offsetHeight,e._getScrollTop(),e.panel.nativeElement.offsetHeight);e._setScrollTop(o)}}}}G0.ɵfac=function t(e){return new(e||G0)(Sm(hg),Sm(pL),Sm(eh),Sm(a_),Sm(Ug),Sm(B0),Sm(HI,8),Sm(RV,9),Sm(Z_,8),Sm(uF),Sm(N0,8))},G0.ɵdir=lo({type:G0,inputs:{position:["matAutocompletePosition","position"],autocompleteAttribute:["autocomplete","autocompleteAttribute"],autocompleteDisabled:["matAutocompleteDisabled","autocompleteDisabled"],autocomplete:["matAutocomplete","autocomplete"],connectedTo:["matAutocompleteConnectedTo","connectedTo"]},features:[Bo]}),G0.ctorParameters=()=>[{type:hg},{type:pL},{type:eh},{type:a_},{type:Ug},{type:void 0,decorators:[{type:kr,args:[B0]}]},{type:HI,decorators:[{type:Sr}]},{type:AV,decorators:[{type:Sr},{type:kr,args:[RV]},{type:Rr}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:uF},{type:void 0,decorators:[{type:Sr},{type:kr,args:[N0]}]}],G0.propDecorators={autocomplete:[{type:xy,args:["matAutocomplete"]}],position:[{type:xy,args:["matAutocompletePosition"]}],connectedTo:[{type:xy,args:["matAutocompleteConnectedTo"]}],autocompleteAttribute:[{type:xy,args:["autocomplete"]}],autocompleteDisabled:[{type:xy,args:["matAutocompleteDisabled"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(G0,[{type:Cy}],(function(){return[{type:hg},{type:pL},{type:eh},{type:a_},{type:Ug},{type:void 0,decorators:[{type:kr,args:[B0]}]},{type:HI,decorators:[{type:Sr}]},{type:AV,decorators:[{type:Sr},{type:kr,args:[RV]},{type:Rr}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:uF},{type:void 0,decorators:[{type:Sr},{type:kr,args:[N0]}]}]}),{position:[{type:xy,args:["matAutocompletePosition"]}],autocompleteAttribute:[{type:xy,args:["autocomplete"]}],autocompleteDisabled:[{type:xy,args:["matAutocompleteDisabled"]}],autocomplete:[{type:xy,args:["matAutocomplete"]}],connectedTo:[{type:xy,args:["matAutocompleteConnectedTo"]}]});class W0 extends G0{constructor(){super(...arguments),this._aboveClass="mat-autocomplete-panel-above"}}W0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(W0)))(n||W0)}})(),W0.ɵdir=lo({type:W0,selectors:[["input","matAutocomplete",""],["textarea","matAutocomplete",""]],hostAttrs:[1,"mat-autocomplete-trigger"],hostVars:7,hostBindings:function t(e,n){1&e&&Vm("focusin",(function t(){return n._handleFocus()}))("blur",(function t(){return n._onTouched()}))("input",(function t(e){return n._handleInput(e)}))("keydown",(function t(e){return n._handleKeydown(e)})),2&e&&jp("autocomplete",n.autocompleteAttribute)("role",n.autocompleteDisabled?null:"combobox")("aria-autocomplete",n.autocompleteDisabled?null:"list")("aria-activedescendant",n.panelOpen&&n.activeOption?n.activeOption.id:null)("aria-expanded",n.autocompleteDisabled?null:n.panelOpen.toString())("aria-owns",n.autocompleteDisabled||!n.panelOpen||null==n.autocomplete?null:n.autocomplete.id)("aria-haspopup",!n.autocompleteDisabled)},exportAs:["matAutocompleteTrigger"],features:[pg([U0]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(W0,[{type:Cy,args:[{selector:"input[matAutocomplete], textarea[matAutocomplete]",host:{class:"mat-autocomplete-trigger","[attr.autocomplete]":"autocompleteAttribute","[attr.role]":'autocompleteDisabled ? null : "combobox"',"[attr.aria-autocomplete]":'autocompleteDisabled ? null : "list"',"[attr.aria-activedescendant]":"(panelOpen && activeOption) ? activeOption.id : null","[attr.aria-expanded]":"autocompleteDisabled ? null : panelOpen.toString()","[attr.aria-owns]":"(autocompleteDisabled || !panelOpen) ? null : autocomplete?.id","[attr.aria-haspopup]":"!autocompleteDisabled","(focusin)":"_handleFocus()","(blur)":"_onTouched()","(input)":"_handleInput($event)","(keydown)":"_handleKeydown($event)"},exportAs:"matAutocompleteTrigger",providers:[U0]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Y0{}Y0.ɵfac=function t(e){return new(e||Y0)},Y0.ɵmod=ao({type:Y0}),Y0.ɵinj=vn({providers:[V0],imports:[[yL,UH,XI,WM],yF,UH,XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Y0,[{type:Ay,args:[{imports:[yL,UH,XI,WM],exports:[H0,W0,L0,yF,UH,XI],declarations:[H0,W0,L0],providers:[V0]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Y0,{declarations:function(){return[H0,W0,L0]},imports:function(){return[yL,UH,XI,WM]},exports:function(){return[H0,W0,L0,yF,UH,XI]}});class q0{constructor(){this.value="",this.placeholder=""}onInputKeyUp(t){"Enter"===t.key&&this.autocompleteTrigger.closePanel()}}function Z0(t,e){if(1&t&&(qi(),Tm(0,"circle",3)),2&t){const t=Ym();du("animation-name","mat-progress-spinner-stroke-rotate-"+t._spinnerAnimationLabel)("stroke-dashoffset",t._getStrokeDashOffset(),"px")("stroke-dasharray",t._getStrokeCircumference(),"px")("stroke-width",t._getCircleStrokeWidth(),"%"),jp("r",t._getCircleRadius())}}function X0(t,e){if(1&t&&(qi(),Tm(0,"circle",3)),2&t){const t=Ym();du("stroke-dashoffset",t._getStrokeDashOffset(),"px")("stroke-dasharray",t._getStrokeCircumference(),"px")("stroke-width",t._getCircleStrokeWidth(),"%"),jp("r",t._getCircleRadius())}}function K0(t,e){if(1&t&&(qi(),Tm(0,"circle",3)),2&t){const t=Ym();du("animation-name","mat-progress-spinner-stroke-rotate-"+t._spinnerAnimationLabel)("stroke-dashoffset",t._getStrokeDashOffset(),"px")("stroke-dasharray",t._getStrokeCircumference(),"px")("stroke-width",t._getCircleStrokeWidth(),"%"),jp("r",t._getCircleRadius())}}function J0(t,e){if(1&t&&(qi(),Tm(0,"circle",3)),2&t){const t=Ym();du("stroke-dashoffset",t._getStrokeDashOffset(),"px")("stroke-dasharray",t._getStrokeCircumference(),"px")("stroke-width",t._getCircleStrokeWidth(),"%"),jp("r",t._getCircleRadius())}}q0.ɵfac=function t(e){return new(e||q0)},q0.ɵcmp=to({type:q0,selectors:[["tb-filter-input"]],viewQuery:function t(e,n){if(1&e&&Qh(W0,5),2&e){let t;Jh(t=tb())&&(n.autocompleteTrigger=t.first)}},inputs:{value:"value",matAutocomplete:"matAutocomplete",placeholder:"placeholder"},decls:2,vars:4,consts:[["svgIcon","search_24px"],["type","text","autocomplete","off",3,"placeholder","matAutocomplete","matAutocompleteDisabled","value","keyup"]],template:function t(e,n){1&e&&(Tm(0,"mat-icon",0),Rm(1,"input",1),Vm("keyup",(function t(e){return n.onInputKeyUp(e)})),Am()),2&e&&(rc(1),Dm("placeholder",n.placeholder)("matAutocomplete",n.matAutocomplete)("matAutocompleteDisabled",!n.matAutocomplete)("value",n.value))},directives:[DW,W0],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   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 .mat-form-field-label[_ngcontent-%COMP%]{top:1.09375em;margin-top:-0.5em}.mat-form-field-appearance-fill.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59374em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:1em 0 1em 0}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   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h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 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.mat-ripple-element[_ngcontent-%COMP%]{display:none}.cdk-visually-hidden[_ngcontent-%COMP%]{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container[_ngcontent-%COMP%], .cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{display:flex;font-size:13px}mat-icon[_ngcontent-%COMP%]{color:#616161;flex:none;margin-right:5px}body.dark-mode[_nghost-%COMP%]   mat-icon[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   mat-icon[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}input[_ngcontent-%COMP%]{background-color:inherit;caret-color:currentColor;color:currentColor;font:inherit;border:none;outline:none;padding:0;flex-grow:1}']}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(q0,[{type:My,args:[{selector:"tb-filter-input",template:'\n    <mat-icon svgIcon="search_24px"></mat-icon>\n\n    \x3c!-- Note: to allow falsy \'matAutocomplete\' values, we need \'matAutocompleteDisabled\'\n    to prevent runtime errors. --\x3e\n    <input\n      type="text"\n      autocomplete="off"\n      [placeholder]="placeholder"\n      [matAutocomplete]="matAutocomplete"\n      [matAutocompleteDisabled]="!matAutocomplete"\n      [value]="value"\n      (keyup)="onInputKeyUp($event)"\n    />\n  ',styleUrls:["filter_input_component.css"]}]}],null,{value:[{type:xy}],matAutocomplete:[{type:xy}],placeholder:[{type:xy}],autocompleteTrigger:[{type:Za,args:[W0]}]});const Q0=".mat-progress-spinner{display:block;position:relative;overflow:hidden}.mat-progress-spinner svg{position:absolute;transform:rotate(-90deg);top:0;left:0;transform-origin:center;overflow:visible}.mat-progress-spinner circle{fill:transparent;transform-origin:center;transition:stroke-dashoffset 225ms linear}._mat-animation-noopable.mat-progress-spinner circle{transition:none;animation:none}.cdk-high-contrast-active .mat-progress-spinner circle{stroke:currentColor;stroke:CanvasText}.mat-progress-spinner.mat-progress-spinner-indeterminate-animation[mode=indeterminate] svg{animation:mat-progress-spinner-linear-rotate 2000ms linear infinite}._mat-animation-noopable.mat-progress-spinner.mat-progress-spinner-indeterminate-animation[mode=indeterminate] svg{transition:none;animation:none}.mat-progress-spinner.mat-progress-spinner-indeterminate-animation[mode=indeterminate] circle{transition-property:stroke;animation-duration:4000ms;animation-timing-function:cubic-bezier(0.35, 0, 0.25, 1);animation-iteration-count:infinite}._mat-animation-noopable.mat-progress-spinner.mat-progress-spinner-indeterminate-animation[mode=indeterminate] circle{transition:none;animation:none}.mat-progress-spinner.mat-progress-spinner-indeterminate-fallback-animation[mode=indeterminate] svg{animation:mat-progress-spinner-stroke-rotate-fallback 10000ms cubic-bezier(0.87, 0.03, 0.33, 1) infinite}._mat-animation-noopable.mat-progress-spinner.mat-progress-spinner-indeterminate-fallback-animation[mode=indeterminate] svg{transition:none;animation:none}.mat-progress-spinner.mat-progress-spinner-indeterminate-fallback-animation[mode=indeterminate] circle{transition-property:stroke}._mat-animation-noopable.mat-progress-spinner.mat-progress-spinner-indeterminate-fallback-animation[mode=indeterminate] circle{transition:none;animation:none}@keyframes mat-progress-spinner-linear-rotate{0%{transform:rotate(0deg)}100%{transform:rotate(360deg)}}@keyframes mat-progress-spinner-stroke-rotate-100{0%{stroke-dashoffset:268.606171575px;transform:rotate(0)}12.5%{stroke-dashoffset:56.5486677px;transform:rotate(0)}12.5001%{stroke-dashoffset:56.5486677px;transform:rotateX(180deg) rotate(72.5deg)}25%{stroke-dashoffset:268.606171575px;transform:rotateX(180deg) rotate(72.5deg)}25.0001%{stroke-dashoffset:268.606171575px;transform:rotate(270deg)}37.5%{stroke-dashoffset:56.5486677px;transform:rotate(270deg)}37.5001%{stroke-dashoffset:56.5486677px;transform:rotateX(180deg) rotate(161.5deg)}50%{stroke-dashoffset:268.606171575px;transform:rotateX(180deg) rotate(161.5deg)}50.0001%{stroke-dashoffset:268.606171575px;transform:rotate(180deg)}62.5%{stroke-dashoffset:56.5486677px;transform:rotate(180deg)}62.5001%{stroke-dashoffset:56.5486677px;transform:rotateX(180deg) rotate(251.5deg)}75%{stroke-dashoffset:268.606171575px;transform:rotateX(180deg) rotate(251.5deg)}75.0001%{stroke-dashoffset:268.606171575px;transform:rotate(90deg)}87.5%{stroke-dashoffset:56.5486677px;transform:rotate(90deg)}87.5001%{stroke-dashoffset:56.5486677px;transform:rotateX(180deg) rotate(341.5deg)}100%{stroke-dashoffset:268.606171575px;transform:rotateX(180deg) rotate(341.5deg)}}@keyframes mat-progress-spinner-stroke-rotate-fallback{0%{transform:rotate(0deg)}25%{transform:rotate(1170deg)}50%{transform:rotate(2340deg)}75%{transform:rotate(3510deg)}100%{transform:rotate(4680deg)}}\n",$0=JI(class{constructor(t){this._elementRef=t}},"primary"),t1=new Ga("mat-progress-spinner-default-options",{providedIn:"root",factory:function e1(){return{diameter:100}}});class n1 extends $0{constructor(t,e,n,o,i){super(t),this._document=n,this._diameter=100,this._value=0,this._fallbackAnimation=!1,this.mode="determinate";const a=n1._diameters;this._spinnerAnimationLabel=this._getSpinnerAnimationLabel(),a.has(n.head)||a.set(n.head,new Set([100])),this._fallbackAnimation=e.EDGE||e.TRIDENT,this._noopAnimations="NoopAnimations"===o&&!!i&&!i._forceAnimations,i&&(i.diameter&&(this.diameter=i.diameter),i.strokeWidth&&(this.strokeWidth=i.strokeWidth))}get diameter(){return this._diameter}set diameter(t){this._diameter=_z(t),this._spinnerAnimationLabel=this._getSpinnerAnimationLabel(),!this._fallbackAnimation&&this._styleRoot&&this._attachStyleNode()}get strokeWidth(){return this._strokeWidth||this.diameter/10}set strokeWidth(t){this._strokeWidth=_z(t)}get value(){return"determinate"===this.mode?this._value:0}set value(t){this._value=Math.max(0,Math.min(100,_z(t)))}ngOnInit(){const t=this._elementRef.nativeElement;this._styleRoot=Hz(t)||this._document.head,this._attachStyleNode(),t.classList.add(`mat-progress-spinner-indeterminate${this._fallbackAnimation?"-fallback":""}-animation`)}_getCircleRadius(){return(this.diameter-10)/2}_getViewBox(){const t=2*this._getCircleRadius()+this.strokeWidth;return`0 0 ${t} ${t}`}_getStrokeCircumference(){return 2*Math.PI*this._getCircleRadius()}_getStrokeDashOffset(){return"determinate"===this.mode?this._getStrokeCircumference()*(100-this._value)/100:this._fallbackAnimation&&"indeterminate"===this.mode?.2*this._getStrokeCircumference():null}_getCircleStrokeWidth(){return this.strokeWidth/this.diameter*100}_attachStyleNode(){const t=this._styleRoot,e=this._diameter,n=n1._diameters;let o=n.get(t);if(!o||!o.has(e)){const i=this._document.createElement("style");i.setAttribute("mat-spinner-animation",this._spinnerAnimationLabel),i.textContent=this._getAnimationText(),t.appendChild(i),o||(o=new Set,n.set(t,o)),o.add(e)}}_getAnimationText(){const t=this._getStrokeCircumference();return"\n @keyframes mat-progress-spinner-stroke-rotate-DIAMETER {\n    0%      { stroke-dashoffset: START_VALUE;  transform: rotate(0); }\n    12.5%   { stroke-dashoffset: END_VALUE;    transform: rotate(0); }\n    12.5001%  { stroke-dashoffset: END_VALUE;    transform: rotateX(180deg) rotate(72.5deg); }\n    25%     { stroke-dashoffset: START_VALUE;  transform: rotateX(180deg) rotate(72.5deg); }\n\n    25.0001%   { stroke-dashoffset: START_VALUE;  transform: rotate(270deg); }\n    37.5%   { stroke-dashoffset: END_VALUE;    transform: rotate(270deg); }\n    37.5001%  { stroke-dashoffset: END_VALUE;    transform: rotateX(180deg) rotate(161.5deg); }\n    50%     { stroke-dashoffset: START_VALUE;  transform: rotateX(180deg) rotate(161.5deg); }\n\n    50.0001%  { stroke-dashoffset: START_VALUE;  transform: rotate(180deg); }\n    62.5%   { stroke-dashoffset: END_VALUE;    transform: rotate(180deg); }\n    62.5001%  { stroke-dashoffset: END_VALUE;    transform: rotateX(180deg) rotate(251.5deg); }\n    75%     { stroke-dashoffset: START_VALUE;  transform: rotateX(180deg) rotate(251.5deg); }\n\n    75.0001%  { stroke-dashoffset: START_VALUE;  transform: rotate(90deg); }\n    87.5%   { stroke-dashoffset: END_VALUE;    transform: rotate(90deg); }\n    87.5001%  { stroke-dashoffset: END_VALUE;    transform: rotateX(180deg) rotate(341.5deg); }\n    100%    { stroke-dashoffset: START_VALUE;  transform: rotateX(180deg) rotate(341.5deg); }\n  }\n".replace(/START_VALUE/g,""+.95*t).replace(/END_VALUE/g,""+.2*t).replace(/DIAMETER/g,`${this._spinnerAnimationLabel}`)}_getSpinnerAnimationLabel(){return this.diameter.toString().replace(".","_")}}n1.ɵfac=function t(e){return new(e||n1)(Sm(hg),Sm(wz),Sm(Z_,8),Sm(VP,8),Sm(t1))},n1.ɵcmp=to({type:n1,selectors:[["mat-progress-spinner"]],hostAttrs:["role","progressbar","tabindex","-1",1,"mat-progress-spinner"],hostVars:10,hostBindings:function 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class i1{}i1.ɵfac=function t(e){return new(e||i1)},i1.ɵmod=ao({type:i1}),i1.ɵinj=vn({imports:[[XI,WM],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(i1,[{type:Ay,args:[{imports:[XI,WM],exports:[n1,o1,XI],declarations:[n1,o1]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(i1,{declarations:function(){return[n1,o1]},imports:function(){return[XI,WM]},exports:function(){return[n1,o1,XI]}});const a1=["regexStringInput"];function r1(t,e){if(1&t&&(Nm(0),Rm(1,"li",22),ku(2),Am(),zm()),2&t){const t=e.$implicit;rc(1),Dm("title",t.name),rc(1),Su(t.name)}}function s1(t,e){if(1&t&&(Rm(0,"li",23),Rm(1,"em"),ku(2),Ah(3,"number"),Am(),Am()),2&t){const t=Ym().$implicit;rc(2),Du("and ",Th(3,1,t.runs.length-5)," more")}}function l1(t,e){1&t&&(Rm(0,"li",24),Rm(1,"em"),ku(2,"No runs are in the group"),Am(),Am())}const c1=function(t){return{borderColor:t}},d1=function(t){return{backgroundColor:t}};function p1(t,e){if(1&t&&(Rm(0,"ul",16),Rm(1,"li"),Rm(2,"label"),Tm(3,"span",17),Rm(4,"code",18),ku(5),Am(),Am(),Rm(6,"ul"),Qp(7,r1,3,2,"ng-container",19),Ah(8,"slice"),Qp(9,s1,4,3,"li",20),Qp(10,l1,3,0,"li",21),Am(),Am(),Am()),2&t){const t=e.$implicit;Dm("ngStyle",Mh(11,c1,t.color)),rc(3),Dm("ngStyle",Mh(13,d1,t.color)),rc(1),Dm("title",t.groupId),rc(1),Su(t.groupId),rc(2),Dm("ngForOf",zh(8,7,t.runs,0,5)),rc(2),Dm("ngIf",t.runs.length>5),rc(1),Dm("ngIf",0===t.runs.length)}}function m1(t,e){if(1&t&&(Rm(0,"div",14),Qp(1,p1,11,15,"ul",15),Am()),2&t){const t=Ym(2);rc(1),Dm("ngForOf",t.colorRunPairList)}}function u1(t,e){if(1&t&&(Rm(0,"div",25),ku(1," There are no runs matching the regex, "),Rm(2,"code"),ku(3),Am(),ku(4,". 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 [_nghost-%COMP%]   .grouping-preview[_ngcontent-%COMP%]{border:1px solid #555}.match-container[_ngcontent-%COMP%]{align-items:flex-start;display:grid;flex-wrap:wrap;gap:10px;grid-template-columns:repeat(2, minmax(50%, 1fr))}.color-swatch[_ngcontent-%COMP%]{border-radius:50%;box-shadow:0 0 2px #000;display:inline-block;height:15px;width:15px}ul[_ngcontent-%COMP%]{list-style-type:none;padding:0}mat-form-field[_ngcontent-%COMP%]{width:100%}.group[_ngcontent-%COMP%]{border:1px solid #ebebeb;border-radius:3px;margin:0;padding:10px}body.dark-mode[_nghost-%COMP%]   .group[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group[_ngcontent-%COMP%]{border:1px solid #555}.group[_ngcontent-%COMP%]   label[_ngcontent-%COMP%]{border-bottom:1px solid #ebebeb;align-items:center;display:grid;gap:10px;grid-template-columns:max-content auto;padding:5px 0}body.dark-mode[_nghost-%COMP%]   .group[_ngcontent-%COMP%]   label[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group[_ngcontent-%COMP%]   label[_ngcontent-%COMP%]{border-bottom:1px solid #555}.group[_ngcontent-%COMP%]   label[_ngcontent-%COMP%]   .group-id[_ngcontent-%COMP%]{font-size:.95em;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.group[_ngcontent-%COMP%]   ul[_ngcontent-%COMP%]{font-size:.9em}.group[_ngcontent-%COMP%]   ul[_ngcontent-%COMP%]   li[_ngcontent-%COMP%]{overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.group[_ngcontent-%COMP%]   .more[_ngcontent-%COMP%], .group[_ngcontent-%COMP%]   .no-match[_ngcontent-%COMP%]{color:#616161;margin-top:5px}body.dark-mode[_nghost-%COMP%]   .group[_ngcontent-%COMP%]   .more[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group[_ngcontent-%COMP%]   .more[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}body.dark-mode[_nghost-%COMP%]   .group[_ngcontent-%COMP%]   .no-match[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group[_ngcontent-%COMP%]   .no-match[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(g1,[{type:My,args:[{selector:"regex-edit-dialog-component",templateUrl:"regex_edit_dialog.ng.html",styleUrls:["regex_edit_dialog_component.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:XG},{type:hg}]}),{regexString:[{type:xy}],colorRunPairList:[{type:xy}],onSave:[{type:Oy}],regexInputOnChange:[{type:Oy}],regexStringInput:[{type:Za,args:["regexStringInput",{static:!0}]}]});class h1{constructor(t,e,n){this.store=t,this.dialogRef=e,this.tentativeRegexString$=new I,this.groupByRegexString$=Qt((()=>re(this.store.select(MN).pipe(be(1)),this.tentativeRegexString$))).pipe(Ne("")),this.colorRunPairList$=Qt((()=>this.groupByRegexString$.pipe(ge(500),ce((t=>{try{const e=new RegExp(t);return Boolean(e)}catch(t){return!1}})),fe(this.allRuns$,this.runIdToEid$,this.store.select(AN),this.store.select(JD)),It((([t,e,n,o,i])=>{const a=nN({key:tN.REGEX,regexString:t},e,n),r=new Map,s=[];for(const[t,e]of Object.entries(a.matches)){let n=r.get(t);if(!n){const e=o.colors[r.size%o.colors.length];n=i?e.darkHex:e.lightHex,r.set(t,n)}s.push({groupId:t,color:n,runs:e})}return s}))))).pipe(Ne([])),this.experimentIds=n.experimentIds,this.runIdToEid$=Wt(this.experimentIds.map((t=>this.store.select(cN,{experimentId:t}).pipe(It((e=>({experimentId:t,runIds:e}))))))).pipe(It((t=>{const e={};for(const{runIds:n,experimentId:o}of t)for(const t of n)e[t]=o;return e}))),this.allRuns$=Wt(this.experimentIds.map((t=>this.store.select(lN,{experimentId:t})))).pipe(It((t=>t.flat())))}onRegexInputOnChange(t){this.tentativeRegexString$.next(t)}onSave(t){this.store.dispatch(GR({experimentIds:this.experimentIds,groupBy:{key:tN.REGEX,regexString:t}}))}}function b1(t,e){1&t&&Tm(0,"mat-icon",11)}function y1(t,e){if(1&t){const t=Hm();Rm(0,"button",10),Vm("click",(function e(){hi(t);const n=Ym();return n.onGroupByChange.emit({key:n.GroupByKey.EXPERIMENT})})),Rm(1,"span"),Qp(2,b1,1,0,"mat-icon",7),Am(),Rm(3,"label"),ku(4,"Experiment"),Am(),Am()}if(2&t){const t=Ym();jp("aria-checked",t.selectedGroupBy.key===t.GroupByKey.EXPERIMENT),rc(2),Dm("ngIf",t.selectedGroupBy.key===t.GroupByKey.EXPERIMENT)}}function _1(t,e){1&t&&Tm(0,"mat-icon",11)}function C1(t,e){1&t&&Tm(0,"mat-icon",11)}function M1(t,e){if(1&t){const t=Hm();Rm(0,"button",12),Vm("click",(function e(){return hi(t),Ym().onGroupByRegexClick()})),Rm(1,"span"),Qp(2,C1,1,0,"mat-icon",7),Am(),Rm(3,"label"),ku(4,"Regex"),Am(),Am()}if(2&t){const t=Ym();jp("aria-checked",t.selectedGroupBy.key===t.GroupByKey.REGEX),rc(2),Dm("ngIf",t.selectedGroupBy.key===t.GroupByKey.REGEX)}}function v1(t,e){if(1&t&&(Rm(0,"label"),ku(1),Am()),2&t){const t=Ym(2);rc(1),Su(t.regexString)}}function x1(t,e){1&t&&(Rm(0,"label",17),ku(1,"(none set)"),Am())}function O1(t,e){if(1&t){const t=Hm();Rm(0,"button",13),Vm("click",(function e(){return hi(t),Ym().onRegexStringEdit()})),Rm(1,"span"),Tm(2,"mat-icon",14),Am(),Qp(3,v1,2,1,"label",15),Qp(4,x1,2,0,"label",16),Am()}if(2&t){const t=Ym();rc(3),Dm("ngIf",t.regexString),rc(1),Dm("ngIf",!t.regexString)}}h1.ɵfac=function t(e){return new(e||h1)(Sm(Iw),Sm(XG),Sm(JG))},h1.ɵcmp=to({type:h1,selectors:[["regex-edit-dialog"]],decls:3,vars:6,consts:[[3,"regexString","colorRunPairList","onSave","regexInputOnChange"]],template:function t(e,n){1&e&&(Rm(0,"regex-edit-dialog-component",0),Vm("onSave",(function t(e){return n.onSave(e)}))("regexInputOnChange",(function t(e){return n.onRegexInputOnChange(e)})),Ah(1,"async"),Ah(2,"async"),Am()),2&e&&Dm("regexString",Th(1,2,n.groupByRegexString$))("colorRunPairList",Th(2,4,n.colorRunPairList$))},directives:[g1],pipes:[wM],styles:["[_nghost-%COMP%], regex-edit-dialog-component[_ngcontent-%COMP%] {\n        display: block;\n        height: 100%;\n        width: 100%;\n      }"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(h1,[{type:My,args:[{selector:"regex-edit-dialog",template:'<regex-edit-dialog-component\n    [regexString]="groupByRegexString$ | async"\n    [colorRunPairList]="colorRunPairList$ | async"\n    (onSave)="onSave($event)"\n    (regexInputOnChange)="onRegexInputOnChange($event)"\n  ></regex-edit-dialog-component>',styles:["\n      :host,\n      regex-edit-dialog-component {\n        display: block;\n        height: 100%;\n        width: 100%;\n      }\n    "]}]}],(function(){return[{type:Iw},{type:XG},{type:void 0,decorators:[{type:kr,args:[JG]}]}]}),null);class P1{constructor(t){this.dialog=t,this.GroupByKey=tN,this.onGroupByChange=new Lh}onRegexStringEdit(){this.dialog.open(h1,{maxHeight:"95vh",maxWidth:"80vw",data:{experimentIds:this.experimentIds}})}onGroupByRegexClick(){this.regexString?this.onGroupByChange.emit({key:tN.REGEX,regexString:this.regexString}):this.onRegexStringEdit()}}P1.ɵfac=function t(e){return new(e||P1)(Sm(oW))},P1.ɵcmp=to({type:P1,selectors:[["runs-group-menu-button-component"]],inputs:{showExperimentsGroupBy:"showExperimentsGroupBy",experimentIds:"experimentIds",regexString:"regexString",selectedGroupBy:"selectedGroupBy",showGroupByRegex:"showGroupByRegex"},outputs:{onGroupByChange:"onGroupByChange"},decls:14,vars:6,consts:[["mat-icon-button","","title","Color runs by...",3,"matMenuTriggerFor"],["svgIcon","palette_24px"],[1,"run-table-color-group-by"],["groupByMenu","matMenu"],[1,"label"],["mat-menu-item","","role","menuitemradio","data-value","experiment",3,"click",4,"ngIf"],["mat-menu-item","","role","menuitemradio","data-value","run",3,"click"],["svgIcon","done_24px",4,"ngIf"],["mat-menu-item","","role","menuitemradio","data-value","regex",3,"click",4,"ngIf"],["mat-menu-item","","role","menuitem","data-value","regex-edit","class","display-regex-string",3,"click",4,"ngIf"],["mat-menu-item","","role","menuitemradio","data-value","experiment",3,"click"],["svgIcon","done_24px"],["mat-menu-item","","role","menuitemradio","data-value","regex",3,"click"],["mat-menu-item","","role","menuitem","data-value","regex-edit",1,"display-regex-string",3,"click"],["svgIcon","edit_24px"],[4,"ngIf"],["class","none-set-string",4,"ngIf"],[1,"none-set-string"]],template:function t(e,n){1&e&&(Rm(0,"button",0),Tm(1,"mat-icon",1),Am(),Rm(2,"mat-menu",2,3),Rm(4,"div",4),ku(5,"Color runs by"),Am(),Qp(6,y1,5,2,"button",5),Rm(7,"button",6),Vm("click",(function t(){return n.onGroupByChange.emit({key:n.GroupByKey.RUN})})),Rm(8,"span"),Qp(9,_1,1,0,"mat-icon",7),Am(),Rm(10,"label"),ku(11,"Run"),Am(),Am(),Qp(12,M1,5,2,"button",8),Qp(13,O1,5,2,"button",9),Am()),2&e&&(Dm("matMenuTriggerFor",$p(3)),rc(6),Dm("ngIf",n.showExperimentsGroupBy),rc(1),jp("aria-checked",n.selectedGroupBy.key===n.GroupByKey.RUN),rc(2),Dm("ngIf",n.selectedGroupBy.key===n.GroupByKey.RUN),rc(3),Dm("ngIf",n.showGroupByRegex),rc(1),Dm("ngIf",n.showGroupByRegex))},directives:[XH,eY,DW,KW,dM,WW],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   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scale(0.75)}}.mat-form-field-appearance-fill[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:.25em 0 .75em 0}.mat-form-field-appearance-fill[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{top:1.09375em;margin-top:-0.5em}.mat-form-field-appearance-fill.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59374em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:1em 0 1em 0}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{top:1.84375em;margin-top:-0.25em}.mat-form-field-appearance-outline.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59374em) 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sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   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.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}  .run-table-color-group-by{font-size:16px}  .run-table-color-group-by .label{color:#616161;font-size:.9em;margin:10px 0;padding:0 16px;pointer-events:none}  .run-table-color-group-by button{display:grid;gap:2px 10px;grid-template-columns:20px auto}  .run-table-color-group-by mat-icon{height:20px;width:20px}  .run-table-color-group-by .display-regex-string{padding-left:40px}  .run-table-color-group-by .display-regex-string .none-set-string{color:#616161}body.dark-mode[_nghost-%COMP%]     .run-table-color-group-by .display-regex-string .none-set-string, body.dark-mode   [_nghost-%COMP%]     .run-table-color-group-by .display-regex-string .none-set-string{color:rgba(255,255,255,.7)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(P1,[{type:My,args:[{selector:"runs-group-menu-button-component",templateUrl:"runs_group_menu_button_component.ng.html",styleUrls:["runs_group_menu_button_component.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:oW}]}),{showExperimentsGroupBy:[{type:xy}],experimentIds:[{type:xy}],regexString:[{type:xy}],selectedGroupBy:[{type:xy}],showGroupByRegex:[{type:xy}],onGroupByChange:[{type:Oy}]});class w1{constructor(t){this.store=t,this.showGroupByRegex$=this.store.select(nE),this.showExperimentsGroupBy$=this.store.select(ES).pipe(It((t=>t.has(Zk.COMPARE_EXPERIMENT)))),this.selectedGroupBy$=this.store.select(fN),this.groupByRegexString$=this.store.select(MN)}onGroupByChange(t){this.store.dispatch(GR({experimentIds:this.experimentIds,groupBy:t}))}}w1.ɵfac=function t(e){return new(e||w1)(Sm(Iw))},w1.ɵcmp=to({type:w1,selectors:[["runs-group-menu-button"]],inputs:{experimentIds:"experimentIds"},decls:5,vars:13,consts:[[3,"regexString","selectedGroupBy","showGroupByRegex","showExperimentsGroupBy","experimentIds","onGroupByChange"]],template:function t(e,n){1&e&&(Rm(0,"runs-group-menu-button-component",0),Vm("onGroupByChange",(function t(e){return n.onGroupByChange(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Am()),2&e&&Dm("regexString",Th(1,5,n.groupByRegexString$))("selectedGroupBy",Th(2,7,n.selectedGroupBy$))("showGroupByRegex",Th(3,9,n.showGroupByRegex$))("showExperimentsGroupBy",Th(4,11,n.showExperimentsGroupBy$))("experimentIds",n.experimentIds)},directives:[P1],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(w1,[{type:My,args:[{selector:"runs-group-menu-button",template:'\n    <runs-group-menu-button-component\n      [regexString]="groupByRegexString$ | async"\n      [selectedGroupBy]="selectedGroupBy$ | async"\n      [showGroupByRegex]="showGroupByRegex$ | async"\n      [showExperimentsGroupBy]="showExperimentsGroupBy$ | async"\n      [experimentIds]="experimentIds"\n      (onGroupByChange)="onGroupByChange($event)"\n    ></runs-group-menu-button-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{experimentIds:[{type:xy}]});const k1=["container"];var S1;!(function(t){t[t.NONE=0]="NONE",t[t.LEFT=1]="LEFT",t[t.RIGHT=2]="RIGHT"})(S1||(S1={}));class D1{constructor(t){this.changeDetector=t,this.tickCount=20,this.value=new Lh,this.Position=S1,this.activeThumb=S1.NONE,this.offsetXFromOriginOfActiveThumb=0,this.ngUnsubscribe=new I}getThumbPosition(t){const e=this.getClippedValue(t),n=this.max-this.min;return n<=0?"50%":(e-this.min)/n*100+"%"}getTrackWidth(){const t=this.max-this.min;return t<=0?"0%":(this.getClippedValue(this.upperValue)-this.getClippedValue(this.lowerValue))/t*100+"%"}getClippedValue(t){return Math.min(Math.max(t,this.min),this.max)}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}ngOnInit(){oe(document,"mousemove",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{this.handleMouseMove(t)})),oe(document,"mouseup",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{this.handleMouseOut(t)}))}handleMouseDown(t,e){this.activeThumb=e,this.offsetXFromOriginOfActiveThumb=6-t.offsetX}calculateValueFromMouseEvent(t){const{left:e,right:n}=this.container.nativeElement.getBoundingClientRect(),o=t.clientX-e+this.offsetXFromOriginOfActiveThumb;let i;if(null!==this.tickCount&&this.tickCount>0){const t=(n-e)/this.tickCount;i=Math.round(o/t)*t/(n-e)}else i=o/(n-e);const a=this.getClippedValue(this.min+(this.max-this.min)*i);return Number(a.toFixed(10))}handleMouseMove(t){if(this.activeThumb===S1.NONE)return;const e=this.calculateValueFromMouseEvent(t);let n=[this.lowerValue,this.upperValue];this.activeThumb===S1.LEFT?(e>this.upperValue&&(this.activeThumb=S1.RIGHT),n=[e,this.upperValue]):(e<this.lowerValue&&(this.activeThumb=S1.LEFT),n=[this.lowerValue,e]),this.maybeNotifyNextValue(n),this.changeDetector.markForCheck()}maybeNotifyNextValue(t){const[e,n]=t.sort(((t,e)=>t-e));this.lowerValue===e&&this.upperValue===n||this.value.emit({lowerValue:e,upperValue:n})}handleMouseOut(t){this.activeThumb!==S1.NONE&&(this.activeThumb=S1.NONE,this.changeDetector.markForCheck())}handleInputChange(t,e){const n=this.getClippedValue(Number(t.target.value));if(isNaN(n))return;let o=[this.lowerValue,this.upperValue];o=e===S1.LEFT?[n,this.upperValue]:[this.lowerValue,n],this.maybeNotifyNextValue(o)}isThumbActive(t){return this.activeThumb===t}}D1.ɵfac=function t(e){return new(e||D1)(Sm(Ug))},D1.ɵcmp=to({type:D1,selectors:[["tb-range-input"]],viewQuery:function t(e,n){if(1&e&&Qh(k1,7,hg),2&e){let t;Jh(t=tb())&&(n.container=t.first)}},inputs:{min:"min",max:"max",lowerValue:"lowerValue",upperValue:"upperValue",tickCount:"tickCount"},outputs:{value:"value"},decls:8,vars:14,consts:[["type","number",1,"lower-input",3,"value","change"],["type","number",1,"upper-input",3,"value","change"],[1,"container"],["container",""],[1,"slider-track"],[1,"slider-track-fill"],[1,"thumb",3,"mousedown"]],template:function t(e,n){1&e&&(Rm(0,"input",0),Vm("change",(function t(e){return n.handleInputChange(e,n.Position.LEFT)})),Am(),Rm(1,"input",1),Vm("change",(function t(e){return n.handleInputChange(e,n.Position.RIGHT)})),Am(),Rm(2,"span",2,3),Tm(4,"span",4),Tm(5,"span",5),Rm(6,"span",6),Vm("mousedown",(function t(e){return n.handleMouseDown(e,n.Position.LEFT)})),Am(),Rm(7,"span",6),Vm("mousedown",(function t(e){return n.handleMouseDown(e,n.Position.RIGHT)})),Am(),Am()),2&e&&(Dm("value",n.lowerValue),rc(1),Dm("value",n.upperValue),rc(4),du("left",n.getThumbPosition(n.lowerValue))("width",n.getTrackWidth()),rc(1),du("left",n.getThumbPosition(n.lowerValue)),pu("active",n.isThumbActive(n.Position.LEFT)),rc(1),du("left",n.getThumbPosition(n.upperValue)),pu("active",n.isThumbActive(n.Position.RIGHT)))},styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], 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g3{constructor(){this.active=null}setActive(t){this.active&&this.active!==t&&"inline"!==this.active.cpDialogDisplay&&this.active.closeDialog(),this.active=t}hsva2hsla(t){const e=t.h,n=t.s,o=t.v,i=t.a;if(0===o)return new c3(e,0,0,i);if(0===n&&1===o)return new c3(e,1,1,i);{const t=o*(2-n)/2;return new c3(e,o*n/(1-Math.abs(2*t-1)),t,i)}}hsla2hsva(t){const e=Math.min(t.h,1),n=Math.min(t.s,1),o=Math.min(t.l,1),i=Math.min(t.a,1);if(0===o)return new l3(e,0,0,i);{const t=o+n*(1-Math.abs(2*o-1))/2;return new l3(e,2*(t-o)/t,t,i)}}hsvaToRgba(t){let e,n,o;const i=t.h,a=t.s,r=t.v,s=t.a,l=Math.floor(6*i),c=6*i-l,d=r*(1-a),p=r*(1-c*a),m=r*(1-(1-c)*a);switch(l%6){case 0:e=r,n=m,o=d;break;case 1:e=p,n=r,o=d;break;case 2:e=d,n=r,o=m;break;case 3:e=d,n=p,o=r;break;case 4:e=m,n=d,o=r;break;case 5:e=r,n=d,o=p;break;default:e=0,n=0,o=0}return new s3(e,n,o,s)}cmykToRgb(t){return new s3((1-t.c)*(1-t.k),(1-t.m)*(1-t.k),(1-t.y)*(1-t.k),t.a)}rgbaToCmyk(t){const e=1-Math.max(t.r,t.g,t.b);return 1===e?new d3(0,0,0,1,t.a):new d3((1-t.r-e)/(1-e),(1-t.g-e)/(1-e),(1-t.b-e)/(1-e),e,t.a)}rgbaToHsva(t){let e,n;const o=Math.min(t.r,1),i=Math.min(t.g,1),a=Math.min(t.b,1),r=Math.min(t.a,1),s=Math.max(o,i,a),l=Math.min(o,i,a),c=s,d=s-l;if(n=0===s?0:d/s,s===l)e=0;else{switch(s){case o:e=(i-a)/d+(i<a?6:0);break;case i:e=(a-o)/d+2;break;case a:e=(o-i)/d+4;break;default:e=0}e/=6}return new l3(e,n,c,r)}rgbaToHex(t,e){let n="#"+(1<<24|t.r<<16|t.g<<8|t.b).toString(16).substr(1);return e&&(n+=(256|Math.round(255*t.a)).toString(16).substr(1)),n}normalizeCMYK(t){return new d3(t.c/100,t.m/100,t.y/100,t.k/100,t.a)}denormalizeCMYK(t){return new d3(Math.floor(100*t.c),Math.floor(100*t.m),Math.floor(100*t.y),Math.floor(100*t.k),t.a)}denormalizeRGBA(t){return new s3(Math.round(255*t.r),Math.round(255*t.g),Math.round(255*t.b),t.a)}stringToHsva(t="",e=!1){let n=null;t=(t||"").toLowerCase();const o=[{re:/(rgb)a?\(\s*(\d{1,3})\s*,\s*(\d{1,3})\s*%?,\s*(\d{1,3})\s*%?(?:,\s*(\d+(?:\.\d+)?)\s*)?\)/,parse:function(t){return new s3(parseInt(t[2],10)/255,parseInt(t[3],10)/255,parseInt(t[4],10)/255,isNaN(parseFloat(t[5]))?1:parseFloat(t[5]))}},{re:/(hsl)a?\(\s*(\d{1,3})\s*,\s*(\d{1,3})%\s*,\s*(\d{1,3})%\s*(?:,\s*(\d+(?:\.\d+)?)\s*)?\)/,parse:function(t){return new c3(parseInt(t[2],10)/360,parseInt(t[3],10)/100,parseInt(t[4],10)/100,isNaN(parseFloat(t[5]))?1:parseFloat(t[5]))}}];o.push(e?{re:/#([a-fA-F0-9]{2})([a-fA-F0-9]{2})([a-fA-F0-9]{2})([a-fA-F0-9]{2})?$/,parse:function(t){return new s3(parseInt(t[1],16)/255,parseInt(t[2],16)/255,parseInt(t[3],16)/255,parseInt(t[4]||"FF",16)/255)}}:{re:/#([a-fA-F0-9]{2})([a-fA-F0-9]{2})([a-fA-F0-9]{2})$/,parse:function(t){return new s3(parseInt(t[1],16)/255,parseInt(t[2],16)/255,parseInt(t[3],16)/255,1)}}),o.push({re:/#([a-fA-F0-9])([a-fA-F0-9])([a-fA-F0-9])$/,parse:function(t){return new s3(parseInt(t[1]+t[1],16)/255,parseInt(t[2]+t[2],16)/255,parseInt(t[3]+t[3],16)/255,1)}});for(const e in o)if(o.hasOwnProperty(e)){const i=o[e],a=i.re.exec(t),r=a&&i.parse(a);if(r)return r instanceof s3?n=this.rgbaToHsva(r):r instanceof c3&&(n=this.hsla2hsva(r)),n}return n}outputFormat(t,e,n){switch("auto"===e&&(e=t.a<1?"rgba":"hex"),e){case"hsla":const e=this.hsva2hsla(t),o=new c3(Math.round(360*e.h),Math.round(100*e.s),Math.round(100*e.l),Math.round(100*e.a)/100);return t.a<1||"always"===n?"hsla("+o.h+","+o.s+"%,"+o.l+"%,"+o.a+")":"hsl("+o.h+","+o.s+"%,"+o.l+"%)";case"rgba":const i=this.denormalizeRGBA(this.hsvaToRgba(t));return t.a<1||"always"===n?"rgba("+i.r+","+i.g+","+i.b+","+Math.round(100*i.a)/100+")":"rgb("+i.r+","+i.g+","+i.b+")";default:const a="always"===n||"forced"===n;return this.rgbaToHex(this.denormalizeRGBA(this.hsvaToRgba(t)),a)}}}g3.ɵfac=function t(e){return new(e||g3)},g3.ɵprov=Mn({token:g3,factory:g3.ɵfac}),g3.ctorParameters=()=>[],("undefined"==typeof ngDevMode||ngDevMode)&&hh(g3,[{type:im}],(function(){return[]}),null);class h3{constructor(t,e,n){this.elRef=t,this.cdRef=e,this.service=n,this.isIE10=!1,this.dialogArrowSize=10,this.dialogArrowOffset=15,this.dialogInputFields=[r3.HEX,r3.RGBA,r3.HSLA,r3.CMYK],this.useRootViewContainer=!1}handleEsc(t){this.show&&"popup"===this.cpDialogDisplay&&this.onCancelColor(t)}handleEnter(t){this.show&&"popup"===this.cpDialogDisplay&&this.onAcceptColor(t)}ngOnInit(){this.slider=new u3(0,0,0,0),this.sliderDimMax=new f3(this.hueSlider.nativeElement.offsetWidth||140,this.cpWidth,130,this.alphaSlider.nativeElement.offsetWidth||140),this.format=this.cpCmykEnabled?r3.CMYK:"rgba"===this.cpOutputFormat?r3.RGBA:"hsla"===this.cpOutputFormat?r3.HSLA:r3.HEX,this.listenerMouseDown=t=>{this.onMouseDown(t)},this.listenerResize=()=>{this.onResize()},this.openDialog(this.initialColor,!1)}ngOnDestroy(){this.closeDialog()}ngAfterViewInit(){230===this.cpWidth&&"inline"!==this.cpDialogDisplay||(this.sliderDimMax=new f3(this.hueSlider.nativeElement.offsetWidth||140,this.cpWidth,130,this.alphaSlider.nativeElement.offsetWidth||140),this.updateColorPicker(!1),this.cdRef.detectChanges())}openDialog(t,e=!0){this.service.setActive(this),this.width||(this.cpWidth=this.directiveElementRef.nativeElement.offsetWidth),this.height||(this.height=320),this.setInitialColor(t),this.setColorFromString(t,e),this.openColorPicker()}closeDialog(){this.closeColorPicker()}setupDialog(t,e,n,o,i,a,r,s,l,c,d,p,m,u,f,g,h,b,y,_,C,M,v,x,O,P,w,k,S,D,E,R,A,T,N,z){this.setInitialColor(n),this.setColorMode(s),this.isIE10=10===(function I(){let t="";"undefined"!=typeof navigator&&(t=navigator.userAgent.toLowerCase());const e=t.indexOf("msie ");return e>0&&parseInt(t.substring(e+5,t.indexOf(".",e)),10)})(),this.directiveInstance=t,this.directiveElementRef=e,this.cpDisableInput=p,this.cpCmykEnabled=l,this.cpAlphaChannel=c,this.cpOutputFormat=d,this.cpDialogDisplay=a,this.cpIgnoredElements=m,this.cpSaveClickOutside=u,this.cpCloseClickOutside=f,this.useRootViewContainer=g,this.width=this.cpWidth=parseInt(o,10),this.height=this.cpHeight=parseInt(i,10),this.cpPosition=h,this.cpPositionOffset=parseInt(b,10),this.cpOKButton=P,this.cpOKButtonText=k,this.cpOKButtonClass=w,this.cpCancelButton=S,this.cpCancelButtonText=E,this.cpCancelButtonClass=D,this.fallbackColor=r||"#fff",this.setPresetConfig(_,C),this.cpPresetColorsClass=M,this.cpMaxPresetColorsLength=v,this.cpPresetEmptyMessage=x,this.cpPresetEmptyMessageClass=O,this.cpAddColorButton=R,this.cpAddColorButtonText=T,this.cpAddColorButtonClass=A,this.cpRemoveColorButtonClass=N,this.cpTriggerElement=z,y||(this.dialogArrowOffset=0),"inline"===a&&(this.dialogArrowSize=0,this.dialogArrowOffset=0),"hex"===d&&"always"!==c&&"forced"!==c&&(this.cpAlphaChannel="disabled")}setColorMode(t){switch(t.toString().toUpperCase()){case"1":case"C":case"COLOR":this.cpColorMode=1;break;case"2":case"G":case"GRAYSCALE":this.cpColorMode=2;break;case"3":case"P":case"PRESETS":this.cpColorMode=3;break;default:this.cpColorMode=1}}setInitialColor(t){this.initialColor=t}setPresetConfig(t,e){this.cpPresetLabel=t,this.cpPresetColors=e}setColorFromString(t,e=!0,n=!0){let o;"always"===this.cpAlphaChannel||"forced"===this.cpAlphaChannel?(o=this.service.stringToHsva(t,!0),o||this.hsva||(o=this.service.stringToHsva(t,!1))):o=this.service.stringToHsva(t,!1),o||this.hsva||(o=this.service.stringToHsva(this.fallbackColor,!1)),o&&(this.hsva=o,this.sliderH=this.hsva.h,"hex"===this.cpOutputFormat&&"disabled"===this.cpAlphaChannel&&(this.hsva.a=1),this.updateColorPicker(e,n))}onResize(){"fixed"===this.position?this.setDialogPosition():"inline"!==this.cpDialogDisplay&&this.closeColorPicker()}onDragEnd(t){this.directiveInstance.sliderDragEnd({slider:t,color:this.outputColor})}onDragStart(t){this.directiveInstance.sliderDragStart({slider:t,color:this.outputColor})}onMouseDown(t){!this.show||this.isIE10||"popup"!==this.cpDialogDisplay||t.target===this.directiveElementRef.nativeElement||this.isDescendant(this.elRef.nativeElement,t.target)||this.isDescendant(this.directiveElementRef.nativeElement,t.target)||0!==this.cpIgnoredElements.filter((e=>e===t.target)).length||(this.cpSaveClickOutside?this.directiveInstance.colorSelected(this.outputColor):(this.hsva=null,this.setColorFromString(this.initialColor,!1),this.cpCmykEnabled&&this.directiveInstance.cmykChanged(this.cmykColor),this.directiveInstance.colorChanged(this.initialColor),this.directiveInstance.colorCanceled()),this.cpCloseClickOutside&&this.closeColorPicker())}onAcceptColor(t){t.stopPropagation(),this.outputColor&&this.directiveInstance.colorSelected(this.outputColor),"popup"===this.cpDialogDisplay&&this.closeColorPicker()}onCancelColor(t){this.hsva=null,t.stopPropagation(),this.directiveInstance.colorCanceled(),this.setColorFromString(this.initialColor,!0),"popup"===this.cpDialogDisplay&&(this.cpCmykEnabled&&this.directiveInstance.cmykChanged(this.cmykColor),this.directiveInstance.colorChanged(this.initialColor,!0),this.closeColorPicker())}onFormatToggle(t){const e=this.dialogInputFields.length-(this.cpCmykEnabled?0:1),n=((this.dialogInputFields.indexOf(this.format)+t)%e+e)%e;this.format=this.dialogInputFields[n]}onColorChange(t){this.hsva.s=t.s/t.rgX,this.hsva.v=t.v/t.rgY,this.updateColorPicker(),this.directiveInstance.sliderChanged({slider:"lightness",value:this.hsva.v,color:this.outputColor}),this.directiveInstance.sliderChanged({slider:"saturation",value:this.hsva.s,color:this.outputColor})}onHueChange(t){this.hsva.h=t.v/t.rgX,this.sliderH=this.hsva.h,this.updateColorPicker(),this.directiveInstance.sliderChanged({slider:"hue",value:this.hsva.h,color:this.outputColor})}onValueChange(t){this.hsva.v=t.v/t.rgX,this.updateColorPicker(),this.directiveInstance.sliderChanged({slider:"value",value:this.hsva.v,color:this.outputColor})}onAlphaChange(t){this.hsva.a=t.v/t.rgX,this.updateColorPicker(),this.directiveInstance.sliderChanged({slider:"alpha",value:this.hsva.a,color:this.outputColor})}onHexInput(t){if(null===t)this.updateColorPicker();else{t&&"#"!==t[0]&&(t="#"+t);let e=/^#([a-f0-9]{3}|[a-f0-9]{6})$/gi;"always"===this.cpAlphaChannel&&(e=/^#([a-f0-9]{3}|[a-f0-9]{6}|[a-f0-9]{8})$/gi);const n=e.test(t);n&&(t.length<5&&(t="#"+t.substring(1).split("").map((t=>t+t)).join("")),"forced"===this.cpAlphaChannel&&(t+=Math.round(255*this.hsva.a).toString(16)),this.setColorFromString(t,!0,!1)),this.directiveInstance.inputChanged({input:"hex",valid:n,value:t,color:this.outputColor})}}onRedInput(t){const e=this.service.hsvaToRgba(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.r=t.v/t.rg,this.hsva=this.service.rgbaToHsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"red",valid:n,value:e.r,color:this.outputColor})}onBlueInput(t){const e=this.service.hsvaToRgba(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.b=t.v/t.rg,this.hsva=this.service.rgbaToHsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"blue",valid:n,value:e.b,color:this.outputColor})}onGreenInput(t){const e=this.service.hsvaToRgba(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.g=t.v/t.rg,this.hsva=this.service.rgbaToHsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"green",valid:n,value:e.g,color:this.outputColor})}onHueInput(t){const e=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;e&&(this.hsva.h=t.v/t.rg,this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"hue",valid:e,value:this.hsva.h,color:this.outputColor})}onValueInput(t){const e=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;e&&(this.hsva.v=t.v/t.rg,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"value",valid:e,value:this.hsva.v,color:this.outputColor})}onAlphaInput(t){const e=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;e&&(this.hsva.a=t.v/t.rg,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"alpha",valid:e,value:this.hsva.a,color:this.outputColor})}onLightnessInput(t){const e=this.service.hsva2hsla(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.l=t.v/t.rg,this.hsva=this.service.hsla2hsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"lightness",valid:n,value:e.l,color:this.outputColor})}onSaturationInput(t){const e=this.service.hsva2hsla(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.s=t.v/t.rg,this.hsva=this.service.hsla2hsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"saturation",valid:n,value:e.s,color:this.outputColor})}onCyanInput(t){!isNaN(t.v)&&t.v>=0&&t.v<=t.rg&&(this.cmyk.c=t.v,this.updateColorPicker(!1,!0,!0)),this.directiveInstance.inputChanged({input:"cyan",valid:!0,value:this.cmyk.c,color:this.outputColor})}onMagentaInput(t){!isNaN(t.v)&&t.v>=0&&t.v<=t.rg&&(this.cmyk.m=t.v,this.updateColorPicker(!1,!0,!0)),this.directiveInstance.inputChanged({input:"magenta",valid:!0,value:this.cmyk.m,color:this.outputColor})}onYellowInput(t){!isNaN(t.v)&&t.v>=0&&t.v<=t.rg&&(this.cmyk.y=t.v,this.updateColorPicker(!1,!0,!0)),this.directiveInstance.inputChanged({input:"yellow",valid:!0,value:this.cmyk.y,color:this.outputColor})}onBlackInput(t){!isNaN(t.v)&&t.v>=0&&t.v<=t.rg&&(this.cmyk.k=t.v,this.updateColorPicker(!1,!0,!0)),this.directiveInstance.inputChanged({input:"black",valid:!0,value:this.cmyk.k,color:this.outputColor})}onAddPresetColor(t,e){t.stopPropagation(),this.cpPresetColors.filter((t=>t===e)).length||(this.cpPresetColors=this.cpPresetColors.concat(e),this.directiveInstance.presetColorsChanged(this.cpPresetColors))}onRemovePresetColor(t,e){t.stopPropagation(),this.cpPresetColors=this.cpPresetColors.filter((t=>t!==e)),this.directiveInstance.presetColorsChanged(this.cpPresetColors)}openColorPicker(){this.show||(this.show=!0,this.hidden=!0,setTimeout((()=>{this.hidden=!1,this.setDialogPosition(),this.cdRef.detectChanges()}),0),this.directiveInstance.stateChanged(!0),this.isIE10||(document.addEventListener("mousedown",this.listenerMouseDown),document.addEventListener("touchstart",this.listenerMouseDown)),window.addEventListener("resize",this.listenerResize))}closeColorPicker(){this.show&&(this.show=!1,this.directiveInstance.stateChanged(!1),this.isIE10||(document.removeEventListener("mousedown",this.listenerMouseDown),document.removeEventListener("touchstart",this.listenerMouseDown)),window.removeEventListener("resize",this.listenerResize),this.cdRef.destroyed||this.cdRef.detectChanges())}updateColorPicker(t=!0,e=!0,n=!1){if(this.sliderDimMax){let o,i,a;2===this.cpColorMode&&(this.hsva.s=0);const r=this.outputColor;if(i=this.service.hsva2hsla(this.hsva),this.cpCmykEnabled?(n?(a=this.service.cmykToRgb(this.service.normalizeCMYK(this.cmyk)),this.hsva=this.service.rgbaToHsva(a)):(a=this.service.hsvaToRgba(this.hsva),this.cmyk=this.service.denormalizeCMYK(this.service.rgbaToCmyk(a))),a=this.service.denormalizeRGBA(a),this.sliderH=this.hsva.h):a=this.service.denormalizeRGBA(this.service.hsvaToRgba(this.hsva)),o=this.service.denormalizeRGBA(this.service.hsvaToRgba(new l3(this.sliderH||this.hsva.h,1,1,1))),e&&(this.hslaText=new c3(Math.round(360*i.h),Math.round(100*i.s),Math.round(100*i.l),Math.round(100*i.a)/100),this.rgbaText=new s3(a.r,a.g,a.b,Math.round(100*a.a)/100),this.cpCmykEnabled&&(this.cmykText=new d3(this.cmyk.c,this.cmyk.m,this.cmyk.y,this.cmyk.k,Math.round(100*this.cmyk.a)/100)),this.hexText=this.service.rgbaToHex(a,"always"===this.cpAlphaChannel),this.hexAlpha=this.rgbaText.a),"auto"===this.cpOutputFormat&&this.format!==r3.RGBA&&this.format!==r3.CMYK&&this.hsva.a<1&&(this.format=this.hsva.a<1?r3.RGBA:r3.HEX),this.hueSliderColor="rgb("+o.r+","+o.g+","+o.b+")",this.alphaSliderColor="rgb("+a.r+","+a.g+","+a.b+")",this.outputColor=this.service.outputFormat(this.hsva,this.cpOutputFormat,this.cpAlphaChannel),this.selectedColor=this.service.outputFormat(this.hsva,"rgba",null),this.format!==r3.CMYK)this.cmykColor="";else if("always"===this.cpAlphaChannel||"enabled"===this.cpAlphaChannel||"forced"===this.cpAlphaChannel){const t=Math.round(100*this.cmyk.a)/100;this.cmykColor=`cmyka(${this.cmyk.c},${this.cmyk.m},${this.cmyk.y},${this.cmyk.k},${t})`}else this.cmykColor=`cmyk(${this.cmyk.c},${this.cmyk.m},${this.cmyk.y},${this.cmyk.k})`;this.slider=new u3((this.sliderH||this.hsva.h)*this.sliderDimMax.h-8,this.hsva.s*this.sliderDimMax.s-8,(1-this.hsva.v)*this.sliderDimMax.v-8,this.hsva.a*this.sliderDimMax.a-8),t&&r!==this.outputColor&&(this.cpCmykEnabled&&this.directiveInstance.cmykChanged(this.cmykColor),this.directiveInstance.colorChanged(this.outputColor))}}setDialogPosition(){if("inline"===this.cpDialogDisplay)this.position="relative";else{let t,e="static",n="",o=null,i=null,a=this.directiveElementRef.nativeElement.parentNode;const r=this.dialogElement.nativeElement.offsetHeight;for(;null!==a&&"HTML"!==a.tagName;){if(t=window.getComputedStyle(a),e=t.getPropertyValue("position"),n=t.getPropertyValue("transform"),"static"!==e&&null===o&&(o=a),n&&"none"!==n&&null===i&&(i=a),"fixed"===e){o=i;break}a=a.parentNode}const s=this.createDialogBox(this.directiveElementRef.nativeElement,"fixed"!==e);if(this.useRootViewContainer||"fixed"===e&&(!o||o instanceof HTMLUnknownElement))this.top=s.top,this.left=s.left;else{null===o&&(o=a);const t=this.createDialogBox(o,"fixed"!==e);this.top=s.top-t.top,this.left=s.left-t.left}"fixed"===e&&(this.position="fixed");let l=this.cpPosition;"auto"===this.cpPosition&&(l=(function c(t,e){let n="right",o="bottom";const{height:i,width:a}=t,{top:r,left:s}=e,l=r+e.height,c=s+e.width,d=r-i<0,p=l+i>(window.innerHeight||document.documentElement.clientHeight),m=s-a<0,u=c+a>(window.innerWidth||document.documentElement.clientWidth);return p&&(o="top"),d&&(o="bottom"),m&&(n="right"),u&&(n="left"),d&&p&&m&&u?["left","right","top","bottom"].reduce(((e,n)=>t[e]>t[n]?e:n)):m&&u?d?"bottom":p||r>l?"top":"bottom":d&&p?m?"right":u||s>c?"left":"right":`${o}-${n}`})(this.dialogElement.nativeElement.getBoundingClientRect(),this.cpTriggerElement.nativeElement.getBoundingClientRect())),"top"===l?(this.arrowTop=r-1,this.top-=r+this.dialogArrowSize,this.left+=this.cpPositionOffset/100*s.width-this.dialogArrowOffset):"bottom"===l?(this.top+=s.height+this.dialogArrowSize,this.left+=this.cpPositionOffset/100*s.width-this.dialogArrowOffset):"top-left"===l||"left-top"===l?(this.top-=r-s.height+s.height*this.cpPositionOffset/100,this.left-=this.cpWidth+this.dialogArrowSize-2-this.dialogArrowOffset):"top-right"===l||"right-top"===l?(this.top-=r-s.height+s.height*this.cpPositionOffset/100,this.left+=s.width+this.dialogArrowSize-2-this.dialogArrowOffset):"left"===l||"bottom-left"===l||"left-bottom"===l?(this.top+=s.height*this.cpPositionOffset/100-this.dialogArrowOffset,this.left-=this.cpWidth+this.dialogArrowSize-2):(this.top+=s.height*this.cpPositionOffset/100-this.dialogArrowOffset,this.left+=s.width+this.dialogArrowSize-2),this.cpUsePosition=l}}isDescendant(t,e){let 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");background-size:100% 100%;border:none;cursor:pointer;direction:ltr;height:130px;touch-action:manipulation;width:100%}.color-picker .cp-add-color-button-class{background:transparent;border:0;cursor:pointer;display:inline;margin:3px -3px;padding:0;position:absolute}.color-picker .cp-add-color-button-class:hover{text-decoration:underline}.color-picker .cp-add-color-button-class:disabled{color:#999;cursor:not-allowed}.color-picker .cp-add-color-button-class:disabled:hover{text-decoration:none}.color-picker .cp-remove-color-button-class{background:#fff;border-radius:50%;box-shadow:1px 1px 5px #333;cursor:pointer;display:block;height:10px;position:absolute;right:-5px;text-align:center;top:-5px;width:10px}.color-picker .cp-remove-color-button-class:before{bottom:3.5px;content:"x";display:inline-block;font-size:10px;position:relative}'],encapsulation:2}),h3.ctorParameters=()=>[{type:hg},{type:Ug},{type:g3}],h3.propDecorators={dialogElement:[{type:Za,args:["dialogPopup",{static:!0}]}],hueSlider:[{type:Za,args:["hueSlider",{static:!0}]}],alphaSlider:[{type:Za,args:["alphaSlider",{static:!0}]}],handleEsc:[{type:wy,args:["document:keyup.esc",["$event"]]}],handleEnter:[{type:wy,args:["document:keyup.enter",["$event"]]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(h3,[{type:My,args:[{selector:"color-picker",template:'<div #dialogPopup class="color-picker" [class.open]="show" [style.display]="!show ? \'none\' : \'block\'" [style.visibility]="hidden ? \'hidden\' : \'visible\'" [style.top.px]="top" [style.left.px]="left" [style.position]="position" [style.height.px]="cpHeight" [style.width.px]="cpWidth" (click)="$event.stopPropagation()">\n  <div *ngIf="cpDialogDisplay==\'popup\'" class="arrow arrow-{{cpUsePosition}}" [style.top.px]="arrowTop"></div>\n\n  <div *ngIf="(cpColorMode || 1) === 1" class="saturation-lightness" [slider] [rgX]="1" [rgY]="1" [style.background-color]="hueSliderColor" (newValue)="onColorChange($event)" (dragStart)="onDragStart(\'saturation-lightness\')" (dragEnd)="onDragEnd(\'saturation-lightness\')">\n    <div class="cursor" [style.top.px]="slider?.v" [style.left.px]="slider?.s"></div>\n  </div>\n\n  <div class="hue-alpha box">\n    <div class="left">\n      <div class="selected-color-background"></div>\n\n      <div class="selected-color" [style.background-color]="selectedColor"></div>\n\n      <button *ngIf="cpAddColorButton" type="button" class="{{cpAddColorButtonClass}}" [disabled]="cpPresetColors && cpPresetColors.length >= cpMaxPresetColorsLength" (click)="onAddPresetColor($event, selectedColor)">\n        {{cpAddColorButtonText}}\n      </button>\n    </div>\n\n    <div class="right">\n      <div *ngIf="cpAlphaChannel===\'disabled\'" style="height: 16px;"></div>\n\n      <div #hueSlider class="hue" [slider] [rgX]="1" [style.display]="(cpColorMode || 1) === 1 ? \'block\' : \'none\'" (newValue)="onHueChange($event)" (dragStart)="onDragStart(\'hue\')" (dragEnd)="onDragEnd(\'hue\')">\n        <div class="cursor" [style.left.px]="slider?.h"></div>\n      </div>\n\n      <div #valueSlider class="value" [slider] [rgX]="1" [style.display]="(cpColorMode || 1) === 2 ? \'block\': \'none\'" (newValue)="onValueChange($event)" (dragStart)="onDragStart(\'value\')" (dragEnd)="onDragEnd(\'value\')">\n        <div class="cursor" [style.right.px]="slider?.v"></div>\n      </div>\n\n      <div #alphaSlider class="alpha" [slider] [rgX]="1" [style.display]="cpAlphaChannel === \'disabled\' ? \'none\' : \'block\'" [style.background-color]="alphaSliderColor" (newValue)="onAlphaChange($event)" (dragStart)="onDragStart(\'alpha\')" (dragEnd)="onDragEnd(\'alpha\')">\n        <div class="cursor" [style.left.px]="slider?.a"></div>\n      </div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1" class="cmyk-text" [style.display]="format !== 3 ? \'none\' : \'block\'">\n    <div class="box">\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="cmykText?.c" (keyup.enter)="onAcceptColor($event)" (newValue)="onCyanInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="cmykText?.m" (keyup.enter)="onAcceptColor($event)" (newValue)="onMagentaInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="cmykText?.y" (keyup.enter)="onAcceptColor($event)" (newValue)="onYellowInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="cmykText?.k" (keyup.enter)="onAcceptColor($event)" (newValue)="onBlackInput($event)" />\n      <input *ngIf="cpAlphaChannel!==\'disabled\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1" [text] [rg]="1" [value]="cmykText?.a" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)" />\n    </div>\n\n     <div class="box">\n      <div>C</div><div>M</div><div>Y</div><div>K</div><div *ngIf="cpAlphaChannel!==\'disabled\'" >A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1 " class="hsla-text" [style.display]="format !== 2 ? \'none\' : \'block\'">\n    <div class="box">\n      <input type="number" pattern="[0-9]*" min="0" max="360" [text] [rg]="360" [value]="hslaText?.h" (keyup.enter)="onAcceptColor($event)" (newValue)="onHueInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="hslaText?.s" (keyup.enter)="onAcceptColor($event)" (newValue)="onSaturationInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="hslaText?.l" (keyup.enter)="onAcceptColor($event)" (newValue)="onLightnessInput($event)" />\n      <input *ngIf="cpAlphaChannel!==\'disabled\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1" [text] [rg]="1" [value]="hslaText?.a" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)" />\n    </div>\n\n    <div class="box">\n      <div>H</div><div>S</div><div>L</div><div *ngIf="cpAlphaChannel!==\'disabled\'">A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1 " [style.display]="format !== 1 ? \'none\' : \'block\'" class="rgba-text">\n    <div class="box">\n      <input type="number" pattern="[0-9]*" min="0" max="255" [text] [rg]="255" [value]="rgbaText?.r" (keyup.enter)="onAcceptColor($event)" (newValue)="onRedInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="255" [text] [rg]="255" [value]="rgbaText?.g" (keyup.enter)="onAcceptColor($event)" (newValue)="onGreenInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="255" [text] [rg]="255" [value]="rgbaText?.b" (keyup.enter)="onAcceptColor($event)" (newValue)="onBlueInput($event)" />\n      <input *ngIf="cpAlphaChannel!==\'disabled\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1" [text] [rg]="1" [value]="rgbaText?.a" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)" />\n    </div>\n\n    <div class="box">\n      <div>R</div><div>G</div><div>B</div><div *ngIf="cpAlphaChannel!==\'disabled\'" >A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1" class="hex-text" [class.hex-alpha]="cpAlphaChannel===\'forced\'"\n    [style.display]="format !== 0 ? \'none\' : \'block\'">\n    <div class="box">\n      <input [text] [value]="hexText" (blur)="onHexInput(null)" (keyup.enter)="onAcceptColor($event)" (newValue)="onHexInput($event)"/>\n      <input *ngIf="cpAlphaChannel===\'forced\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1" [text] [rg]="1" [value]="hexAlpha" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)"/>\n    </div>\n\n    <div class="box">\n      <div>Hex</div>\n      <div *ngIf="cpAlphaChannel===\'forced\'">A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 2" class="value-text">\n    <div class="box">\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="hslaText?.l" (keyup.enter)="onAcceptColor($event)" (newValue)="onValueInput($event)" />\n      <input *ngIf="cpAlphaChannel!==\'disabled\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1"  [text] [rg]="1" [value]="hslaText?.a" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)" />\n    </div>\n\n    <div class="box">\n      <div>V</div><div>A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1" class="type-policy">\n    <span class="type-policy-arrow" (click)="onFormatToggle(-1)"></span>\n    <span class="type-policy-arrow" (click)="onFormatToggle(1)"></span>\n  </div>\n\n  <div *ngIf="cpPresetColors?.length || cpAddColorButton" class="preset-area">\n    <hr>\n\n    <div class="preset-label">{{cpPresetLabel}}</div>\n\n    <div *ngIf="cpPresetColors?.length" class="{{cpPresetColorsClass}}">\n      <div *ngFor="let color of cpPresetColors" class="preset-color" [style.backgroundColor]="color" (click)="setColorFromString(color)">\n        <span *ngIf="cpAddColorButton" class="{{cpRemoveColorButtonClass}}" (click)="onRemovePresetColor($event, color)"></span>\n      </div>\n    </div>\n\n    <div *ngIf="!cpPresetColors?.length && cpAddColorButton" class="{{cpPresetEmptyMessageClass}}">{{cpPresetEmptyMessage}}</div>\n  </div>\n\n  <div *ngIf="cpOKButton || cpCancelButton" class="button-area">\n    <button *ngIf="cpCancelButton" type="button" class="{{cpCancelButtonClass}}" (click)="onCancelColor($event)">{{cpCancelButtonText}}</button>\n\n    <button *ngIf="cpOKButton" type="button" class="{{cpOKButtonClass}}" (click)="onAcceptColor($event)">{{cpOKButtonText}}</button>\n  </div>\n</div>\n',encapsulation:Hn.None,styles:['.color-picker{-moz-user-select:none;-ms-user-select:none;-webkit-user-select:none;background-color:#fff;border:1px solid #777;cursor:default;height:auto;position:absolute;user-select:none;width:230px;z-index:1000}.color-picker *{box-sizing:border-box;font-size:11px;margin:0}.color-picker input{color:#000;font-size:13px;height:26px;min-width:0;text-align:center;width:0}.color-picker input:-moz-submit-invalid,.color-picker input:-moz-ui-invalid,.color-picker input:invalid{box-shadow:none}.color-picker input::-webkit-inner-spin-button,.color-picker input::-webkit-outer-spin-button{-webkit-appearance:none;margin:0}.color-picker .arrow{border-style:solid;height:0;position:absolute;width:0;z-index:999999}.color-picker .arrow.arrow-top{border-color:#777 transparent transparent;border-width:10px 5px;left:8px}.color-picker .arrow.arrow-bottom{border-color:transparent transparent #777;border-width:10px 5px;left:8px;top:-20px}.color-picker .arrow.arrow-left-top,.color-picker .arrow.arrow-top-left{border-color:transparent transparent transparent #777;border-width:5px 10px;bottom:8px;right:-21px}.color-picker .arrow.arrow-right-top,.color-picker .arrow.arrow-top-right{border-color:transparent #777 transparent transparent;border-width:5px 10px;bottom:8px;left:-20px}.color-picker .arrow.arrow-bottom-left,.color-picker .arrow.arrow-left,.color-picker .arrow.arrow-left-bottom{border-color:transparent transparent transparent #777;border-width:5px 10px;right:-21px;top:8px}.color-picker .arrow.arrow-bottom-right,.color-picker .arrow.arrow-right,.color-picker .arrow.arrow-right-bottom{border-color:transparent #777 transparent transparent;border-width:5px 10px;left:-20px;top:8px}.color-picker .cursor{border:2px solid #222;border-radius:50%;cursor:default;height:16px;position:relative;width:16px}.color-picker .box{display:flex;padding:4px 8px}.color-picker .left{padding:16px 8px;position:relative}.color-picker .right{flex:1 1 auto;padding:12px 8px}.color-picker .button-area{padding:0 16px 16px;text-align:right}.color-picker .button-area button{margin-left:8px}.color-picker .preset-area{padding:4px 15px}.color-picker .preset-area .preset-label{color:#555;font-size:11px;overflow:hidden;padding:4px;text-align:left;text-overflow:ellipsis;white-space:nowrap;width:100%}.color-picker .preset-area .preset-color{border:1px solid #a9a9a9;border-radius:25%;cursor:pointer;display:inline-block;height:18px;margin:4px 6px 8px;position:relative;width:18px}.color-picker .preset-area .preset-empty-message{font-style:italic;margin-bottom:8px;margin-top:4px;min-height:18px;text-align:center}.color-picker .hex-text{font-size:11px;padding:4px 8px;width:100%}.color-picker .hex-text .box{padding:0 24px 8px 8px}.color-picker .hex-text .box div{clear:left;color:#555;flex:1 1 auto;float:left;text-align:center}.color-picker .hex-text .box input{border:1px solid #a9a9a9;flex:1 1 auto;padding:1px}.color-picker .hex-alpha .box div:first-child,.color-picker .hex-alpha .box input:first-child{flex-grow:3;margin-right:8px}.color-picker .cmyk-text,.color-picker .hsla-text,.color-picker .rgba-text,.color-picker .value-text{font-size:11px;padding:4px 8px;width:100%}.color-picker .cmyk-text .box,.color-picker .hsla-text .box,.color-picker .rgba-text .box{padding:0 24px 8px 8px}.color-picker .value-text .box{padding:0 8px 8px}.color-picker .cmyk-text .box div,.color-picker .hsla-text .box div,.color-picker .rgba-text .box div,.color-picker .value-text .box div{color:#555;flex:1 1 auto;margin-right:8px;text-align:center}.color-picker .cmyk-text .box div:last-child,.color-picker .hsla-text .box div:last-child,.color-picker .rgba-text .box div:last-child,.color-picker .value-text .box div:last-child{margin-right:0}.color-picker .cmyk-text .box input,.color-picker .hsla-text .box input,.color-picker .rgba-text .box input,.color-picker .value-text .box input{border:1px solid #a9a9a9;flex:1;float:left;margin:0 8px 0 0;padding:1px}.color-picker .cmyk-text .box input:last-child,.color-picker .hsla-text .box input:last-child,.color-picker .rgba-text .box input:last-child,.color-picker .value-text .box input:last-child{margin-right:0}.color-picker .hue-alpha{align-items:center;margin-bottom:3px}.color-picker .hue{background-image:url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJYAAAAQCAYAAAD06IYnAAAABmJLR0QA/wD/AP+gvaeTAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH4AIWDwkUFWbCCAAAAFxJREFUaN7t0kEKg0AQAME2x83/n2qu5qCgD1iDhCoYdpnbQC9bbY1qVO/jvc6k3ad91s7/7F1/csgPrujuQ17BDYSFsBAWwgJhISyEBcJCWAgLhIWwEBYIi2f7Ar/1TCgFH2X9AAAAAElFTkSuQmCC");direction:ltr}.color-picker .hue,.color-picker .value{background-size:100% 100%;border:none;cursor:pointer;height:16px;margin-bottom:16px;width:100%}.color-picker .value{background-image:url("data:image/png;base64,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");direction:rtl}.color-picker .alpha{background-image:url("data:image/png;base64,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");background-size:100% 100%;border:none;cursor:pointer;direction:ltr;height:16px;width:100%}.color-picker .type-policy{background-image:url("data:image/png;base64,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");background-position:50%;background-repeat:no-repeat;background-size:8px 16px;height:24px;position:absolute;right:12px;top:218px;width:16px}.color-picker .type-policy .type-policy-arrow{display:block;height:50%;width:100%}.color-picker .selected-color{border:1px solid #a9a9a9;border-radius:50%;height:40px;left:8px;position:absolute;top:16px;width:40px}.color-picker .selected-color-background{background-image:url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACgAAAAoCAYAAACM/rhtAAAAh0lEQVRYR+2W0QlAMQgD60zdfwOdqa8TmI/wQMr5K0I5bZLIzLOa2nt37VVVbd+dDx5obgCC3KBLwJ2ff4PnVidkf+ucIhw80HQaCLo3DMH3CRK3iFsmAWVl6hPNDwt8EvNE5q+YuEXcMgkonVM6SdyCoEvAnZ8v1Hjx817MilmxSUB5rdLJDycZgUAZUch/AAAAAElFTkSuQmCC");border-radius:50%;height:40px;width:40px}.color-picker 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");background-size:100% 100%;border:none;cursor:pointer;direction:ltr;height:130px;touch-action:manipulation;width:100%}.color-picker .cp-add-color-button-class{background:transparent;border:0;cursor:pointer;display:inline;margin:3px -3px;padding:0;position:absolute}.color-picker .cp-add-color-button-class:hover{text-decoration:underline}.color-picker .cp-add-color-button-class:disabled{color:#999;cursor:not-allowed}.color-picker .cp-add-color-button-class:disabled:hover{text-decoration:none}.color-picker .cp-remove-color-button-class{background:#fff;border-radius:50%;box-shadow:1px 1px 5px #333;cursor:pointer;display:block;height:10px;position:absolute;right:-5px;text-align:center;top:-5px;width:10px}.color-picker .cp-remove-color-button-class:before{bottom:3.5px;content:"x";display:inline-block;font-size:10px;position:relative}']}]}],(function(){return[{type:hg},{type:Ug},{type:g3}]}),{handleEsc:[{type:wy,args:["document:keyup.esc",["$event"]]}],handleEnter:[{type:wy,args:["document:keyup.enter",["$event"]]}],dialogElement:[{type:Za,args:["dialogPopup",{static:!0}]}],hueSlider:[{type:Za,args:["hueSlider",{static:!0}]}],alphaSlider:[{type:Za,args:["alphaSlider",{static:!0}]}]});class b3{constructor(t,e,n,o,i,a){this.injector=t,this.cfr=e,this.appRef=n,this.vcRef=o,this.elRef=i,this._service=a,this.dialogCreated=!1,this.ignoreChanges=!1,this.viewAttachedToAppRef=!1,this.cpWidth="230px",this.cpHeight="auto",this.cpToggle=!1,this.cpDisabled=!1,this.cpIgnoredElements=[],this.cpFallbackColor="",this.cpColorMode="color",this.cpCmykEnabled=!1,this.cpOutputFormat="auto",this.cpAlphaChannel="enabled",this.cpDisableInput=!1,this.cpDialogDisplay="popup",this.cpSaveClickOutside=!0,this.cpCloseClickOutside=!0,this.cpUseRootViewContainer=!1,this.cpPosition="auto",this.cpPositionOffset="0%",this.cpPositionRelativeToArrow=!1,this.cpOKButton=!1,this.cpOKButtonText="OK",this.cpOKButtonClass="cp-ok-button-class",this.cpCancelButton=!1,this.cpCancelButtonText="Cancel",this.cpCancelButtonClass="cp-cancel-button-class",this.cpPresetLabel="Preset colors",this.cpPresetColorsClass="cp-preset-colors-class",this.cpMaxPresetColorsLength=6,this.cpPresetEmptyMessage="No colors added",this.cpPresetEmptyMessageClass="preset-empty-message",this.cpAddColorButton=!1,this.cpAddColorButtonText="Add color",this.cpAddColorButtonClass="cp-add-color-button-class",this.cpRemoveColorButtonClass="cp-remove-color-button-class",this.cpInputChange=new Lh(!0),this.cpToggleChange=new Lh(!0),this.cpSliderChange=new Lh(!0),this.cpSliderDragEnd=new Lh(!0),this.cpSliderDragStart=new Lh(!0),this.colorPickerOpen=new Lh(!0),this.colorPickerClose=new Lh(!0),this.colorPickerCancel=new Lh(!0),this.colorPickerSelect=new Lh(!0),this.colorPickerChange=new Lh(!1),this.cpCmykColorChange=new Lh(!0),this.cpPresetColorsChange=new Lh(!0)}handleClick(){this.inputFocus()}handleFocus(){this.inputFocus()}handleInput(t){this.inputChange(t)}ngOnDestroy(){null!=this.cmpRef&&(this.viewAttachedToAppRef&&this.appRef.detachView(this.cmpRef.hostView),this.cmpRef.destroy(),this.cmpRef=null,this.dialog=null)}ngOnChanges(t){t.cpToggle&&!this.cpDisabled&&(t.cpToggle.currentValue?this.openDialog():t.cpToggle.currentValue||this.closeDialog()),t.colorPicker&&(this.dialog&&!this.ignoreChanges&&("inline"===this.cpDialogDisplay&&this.dialog.setInitialColor(t.colorPicker.currentValue),this.dialog.setColorFromString(t.colorPicker.currentValue,!1),this.cpUseRootViewContainer&&"inline"!==this.cpDialogDisplay&&this.cmpRef.changeDetectorRef.detectChanges()),this.ignoreChanges=!1),(t.cpPresetLabel||t.cpPresetColors)&&this.dialog&&this.dialog.setPresetConfig(this.cpPresetLabel,this.cpPresetColors)}openDialog(){if(this.dialogCreated)this.dialog&&this.dialog.openDialog(this.colorPicker);else{let t=this.vcRef;if(this.dialogCreated=!0,this.viewAttachedToAppRef=!1,this.cpUseRootViewContainer&&"inline"!==this.cpDialogDisplay){const e=this.injector.get(this.appRef.componentTypes[0],rp.NULL);e!==rp.NULL?(t=e.vcRef||e.viewContainerRef||this.vcRef,t===this.vcRef&&console.warn("You are using cpUseRootViewContainer, but the root component is not exposing viewContainerRef!Please expose it by adding 'public vcRef: ViewContainerRef' to the constructor.")):this.viewAttachedToAppRef=!0}const e=this.cfr.resolveComponentFactory(h3);if(this.viewAttachedToAppRef)this.cmpRef=e.create(this.injector),this.appRef.attachView(this.cmpRef.hostView),document.body.appendChild(this.cmpRef.hostView.rootNodes[0]);else{const n=wm.fromResolvedProviders([],t.parentInjector);this.cmpRef=t.createComponent(e,0,n,[])}this.cmpRef.instance.setupDialog(this,this.elRef,this.colorPicker,this.cpWidth,this.cpHeight,this.cpDialogDisplay,this.cpFallbackColor,this.cpColorMode,this.cpCmykEnabled,this.cpAlphaChannel,this.cpOutputFormat,this.cpDisableInput,this.cpIgnoredElements,this.cpSaveClickOutside,this.cpCloseClickOutside,this.cpUseRootViewContainer,this.cpPosition,this.cpPositionOffset,this.cpPositionRelativeToArrow,this.cpPresetLabel,this.cpPresetColors,this.cpPresetColorsClass,this.cpMaxPresetColorsLength,this.cpPresetEmptyMessage,this.cpPresetEmptyMessageClass,this.cpOKButton,this.cpOKButtonClass,this.cpOKButtonText,this.cpCancelButton,this.cpCancelButtonClass,this.cpCancelButtonText,this.cpAddColorButton,this.cpAddColorButtonClass,this.cpAddColorButtonText,this.cpRemoveColorButtonClass,this.elRef),this.dialog=this.cmpRef.instance,this.vcRef!==t&&this.cmpRef.changeDetectorRef.detectChanges()}}closeDialog(){this.dialog&&"popup"===this.cpDialogDisplay&&this.dialog.closeDialog()}cmykChanged(t){this.cpCmykColorChange.emit(t)}stateChanged(t){this.cpToggleChange.emit(t),t?this.colorPickerOpen.emit(this.colorPicker):this.colorPickerClose.emit(this.colorPicker)}colorChanged(t,e=!0){this.ignoreChanges=e,this.colorPickerChange.emit(t)}colorSelected(t){this.colorPickerSelect.emit(t)}colorCanceled(){this.colorPickerCancel.emit()}inputFocus(){const t=this.elRef.nativeElement,e=this.cpIgnoredElements.filter((e=>e===t));this.cpDisabled||e.length||("undefined"!=typeof document&&t===document.activeElement?this.openDialog():this.dialog&&this.dialog.show?this.closeDialog():this.openDialog())}inputChange(t){this.dialog?this.dialog.setColorFromString(t.target.value,!0):(this.colorPicker=t.target.value,this.colorPickerChange.emit(this.colorPicker))}inputChanged(t){this.cpInputChange.emit(t)}sliderChanged(t){this.cpSliderChange.emit(t)}sliderDragEnd(t){this.cpSliderDragEnd.emit(t)}sliderDragStart(t){this.cpSliderDragStart.emit(t)}presetColorsChanged(t){this.cpPresetColorsChange.emit(t)}}b3.ɵfac=function t(e){return new(e||b3)(Sm(rp),Sm(ug),Sm(O_),Sm(eh),Sm(hg),Sm(g3))},b3.ɵdir=lo({type:b3,selectors:[["","colorPicker",""]],hostBindings:function t(e,n){1&e&&Vm("click",(function t(){return n.handleClick()}))("focus",(function t(){return n.handleFocus()}))("input",(function t(e){return n.handleInput(e)}))},inputs:{cpWidth:"cpWidth",cpHeight:"cpHeight",cpToggle:"cpToggle",cpDisabled:"cpDisabled",cpIgnoredElements:"cpIgnoredElements",cpFallbackColor:"cpFallbackColor",cpColorMode:"cpColorMode",cpCmykEnabled:"cpCmykEnabled",cpOutputFormat:"cpOutputFormat",cpAlphaChannel:"cpAlphaChannel",cpDisableInput:"cpDisableInput",cpDialogDisplay:"cpDialogDisplay",cpSaveClickOutside:"cpSaveClickOutside",cpCloseClickOutside:"cpCloseClickOutside",cpUseRootViewContainer:"cpUseRootViewContainer",cpPosition:"cpPosition",cpPositionOffset:"cpPositionOffset",cpPositionRelativeToArrow:"cpPositionRelativeToArrow",cpOKButton:"cpOKButton",cpOKButtonText:"cpOKButtonText",cpOKButtonClass:"cpOKButtonClass",cpCancelButton:"cpCancelButton",cpCancelButtonText:"cpCancelButtonText",cpCancelButtonClass:"cpCancelButtonClass",cpPresetLabel:"cpPresetLabel",cpPresetColorsClass:"cpPresetColorsClass",cpMaxPresetColorsLength:"cpMaxPresetColorsLength",cpPresetEmptyMessage:"cpPresetEmptyMessage",cpPresetEmptyMessageClass:"cpPresetEmptyMessageClass",cpAddColorButton:"cpAddColorButton",cpAddColorButtonText:"cpAddColorButtonText",cpAddColorButtonClass:"cpAddColorButtonClass",cpRemoveColorButtonClass:"cpRemoveColorButtonClass",colorPicker:"colorPicker",cpPresetColors:"cpPresetColors"},outputs:{cpInputChange:"cpInputChange",cpToggleChange:"cpToggleChange",cpSliderChange:"cpSliderChange",cpSliderDragEnd:"cpSliderDragEnd",cpSliderDragStart:"cpSliderDragStart",colorPickerOpen:"colorPickerOpen",colorPickerClose:"colorPickerClose",colorPickerCancel:"colorPickerCancel",colorPickerSelect:"colorPickerSelect",colorPickerChange:"colorPickerChange",cpCmykColorChange:"cpCmykColorChange",cpPresetColorsChange:"cpPresetColorsChange"},exportAs:["ngxColorPicker"],features:[Bo]}),b3.ctorParameters=()=>[{type:rp},{type:ug},{type:O_},{type:eh},{type:hg},{type:g3}],b3.propDecorators={colorPicker:[{type:xy}],cpWidth:[{type:xy}],cpHeight:[{type:xy}],cpToggle:[{type:xy}],cpDisabled:[{type:xy}],cpIgnoredElements:[{type:xy}],cpFallbackColor:[{type:xy}],cpColorMode:[{type:xy}],cpCmykEnabled:[{type:xy}],cpOutputFormat:[{type:xy}],cpAlphaChannel:[{type:xy}],cpDisableInput:[{type:xy}],cpDialogDisplay:[{type:xy}],cpSaveClickOutside:[{type:xy}],cpCloseClickOutside:[{type:xy}],cpUseRootViewContainer:[{type:xy}],cpPosition:[{type:xy}],cpPositionOffset:[{type:xy}],cpPositionRelativeToArrow:[{type:xy}],cpOKButton:[{type:xy}],cpOKButtonText:[{type:xy}],cpOKButtonClass:[{type:xy}],cpCancelButton:[{type:xy}],cpCancelButtonText:[{type:xy}],cpCancelButtonClass:[{type:xy}],cpPresetLabel:[{type:xy}],cpPresetColors:[{type:xy}],cpPresetColorsClass:[{type:xy}],cpMaxPresetColorsLength:[{type:xy}],cpPresetEmptyMessage:[{type:xy}],cpPresetEmptyMessageClass:[{type:xy}],cpAddColorButton:[{type:xy}],cpAddColorButtonText:[{type:xy}],cpAddColorButtonClass:[{type:xy}],cpRemoveColorButtonClass:[{type:xy}],cpInputChange:[{type:Oy}],cpToggleChange:[{type:Oy}],cpSliderChange:[{type:Oy}],cpSliderDragEnd:[{type:Oy}],cpSliderDragStart:[{type:Oy}],colorPickerOpen:[{type:Oy}],colorPickerClose:[{type:Oy}],colorPickerCancel:[{type:Oy}],colorPickerSelect:[{type:Oy}],colorPickerChange:[{type:Oy}],cpCmykColorChange:[{type:Oy}],cpPresetColorsChange:[{type:Oy}],handleClick:[{type:wy,args:["click"]}],handleFocus:[{type:wy,args:["focus"]}],handleInput:[{type:wy,args:["input",["$event"]]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(b3,[{type:Cy,args:[{selector:"[colorPicker]",exportAs:"ngxColorPicker"}]}],(function(){return[{type:rp},{type:ug},{type:O_},{type:eh},{type:hg},{type:g3}]}),{cpWidth:[{type:xy}],cpHeight:[{type:xy}],cpToggle:[{type:xy}],cpDisabled:[{type:xy}],cpIgnoredElements:[{type:xy}],cpFallbackColor:[{type:xy}],cpColorMode:[{type:xy}],cpCmykEnabled:[{type:xy}],cpOutputFormat:[{type:xy}],cpAlphaChannel:[{type:xy}],cpDisableInput:[{type:xy}],cpDialogDisplay:[{type:xy}],cpSaveClickOutside:[{type:xy}],cpCloseClickOutside:[{type:xy}],cpUseRootViewContainer:[{type:xy}],cpPosition:[{type:xy}],cpPositionOffset:[{type:xy}],cpPositionRelativeToArrow:[{type:xy}],cpOKButton:[{type:xy}],cpOKButtonText:[{type:xy}],cpOKButtonClass:[{type:xy}],cpCancelButton:[{type:xy}],cpCancelButtonText:[{type:xy}],cpCancelButtonClass:[{type:xy}],cpPresetLabel:[{type:xy}],cpPresetColorsClass:[{type:xy}],cpMaxPresetColorsLength:[{type:xy}],cpPresetEmptyMessage:[{type:xy}],cpPresetEmptyMessageClass:[{type:xy}],cpAddColorButton:[{type:xy}],cpAddColorButtonText:[{type:xy}],cpAddColorButtonClass:[{type:xy}],cpRemoveColorButtonClass:[{type:xy}],cpInputChange:[{type:Oy}],cpToggleChange:[{type:Oy}],cpSliderChange:[{type:Oy}],cpSliderDragEnd:[{type:Oy}],cpSliderDragStart:[{type:Oy}],colorPickerOpen:[{type:Oy}],colorPickerClose:[{type:Oy}],colorPickerCancel:[{type:Oy}],colorPickerSelect:[{type:Oy}],colorPickerChange:[{type:Oy}],cpCmykColorChange:[{type:Oy}],cpPresetColorsChange:[{type:Oy}],handleClick:[{type:wy,args:["click"]}],handleFocus:[{type:wy,args:["focus"]}],handleInput:[{type:wy,args:["input",["$event"]]}],colorPicker:[{type:xy}],cpPresetColors:[{type:xy}]});class y3{}y3.ɵfac=function t(e){return new(e||y3)},y3.ɵmod=ao({type:y3}),y3.ɵinj=vn({providers:[g3],imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(y3,[{type:Ay,args:[{imports:[WM],exports:[b3],providers:[g3],declarations:[h3,b3,p3,m3],entryComponents:[h3]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(y3,{declarations:function(){return[h3,b3,p3,m3]},imports:function(){return[WM]},exports:function(){return[b3]}});const _3=["filter"];function C3(t,e){1&t&&Im(0)}function M3(t,e){1&t&&Im(0)}const v3=function(t){return{item:t}};function x3(t,e){if(1&t&&(Nm(0),Qp(1,M3,1,0,"ng-container",12),zm()),2&t){const t=e.$implicit;Ym();const n=$p(14);rc(1),Dm("ngTemplateOutlet",n)("ngTemplateOutletContext",Mh(2,v3,t))}}function O3(t,e){1&t&&(Rm(0,"div",13),Tm(1,"mat-spinner",14),Am())}function P3(t,e){1&t&&(Rm(0,"div",15),ku(1,"No Runs"),Am())}function w3(t,e){if(1&t&&(Rm(0,"div",15),Rm(1,"span"),ku(2,'No runs match "'),Rm(3,"code"),ku(4),Am(),ku(5,'"'),Am(),Am()),2&t){const t=Ym();rc(4),Su(t.regexFilter)}}const k3=function(){return[5,10,20]};function S3(t,e){if(1&t){const t=Hm();Rm(0,"mat-paginator",16),Vm("page",(function e(n){return hi(t),Ym().onPaginationChange.emit(n)})),Am()}if(2&t){const t=Ym();Dm("pageSizeOptions",Ch(4,k3))("pageIndex",t.paginationOption.pageIndex)("pageSize",t.paginationOption.pageSize)("length",t.filteredItemsLength)}}function D3(t,e){if(1&t){const t=Hm();Rm(0,"mat-checkbox",26),Vm("change",(function e(){return hi(t),Ym(3).handlePageToggle()})),Am()}if(2&t){const t=Ym(3);Dm("checked",t.allPageItemsSelected())("indeterminate",!t.allPageItemsSelected()&&t.somePageItemsSelected())}}const E3=function(t){return{type:t}};function R3(t,e){if(1&t&&(Rm(0,"span",27),ku(1,"Experiment"),Am()),2&t){const t=Ym(3);Dm("mat-sort-header",Mh(1,E3,t.SortType.EXPERIMENT_NAME))}}function A3(t,e){if(1&t&&(Rm(0,"span",27),ku(1,"Run"),Am()),2&t){const t=Ym(3);Dm("mat-sort-header",Mh(1,E3,t.SortType.RUN_NAME))}}function T3(t,e){1&t&&Tm(0,"runs-group-menu-button",29),2&t&&Dm("experimentIds",Ym(4).experimentIds)}function N3(t,e){if(1&t&&(Rm(0,"span"),Qp(1,T3,1,1,"runs-group-menu-button",28),Am()),2&t){const t=Ym(3);rc(1),Dm("ngIf",t.showGroupControl)}}const z3=function(t){return["column",t]};function I3(t,e){if(1&t&&(Rm(0,"span",21),Nm(1,22),Qp(2,D3,1,2,"mat-checkbox",23),Qp(3,R3,2,3,"span",24),Qp(4,A3,2,3,"span",24),Qp(5,N3,2,1,"span",25),zm(),Am()),2&t){const t=e.$implicit,n=Ym(2);Dm("ngClass",Mh(6,z3,"tb-column-"+t)),rc(1),Dm("ngSwitch",t),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.CHECKBOX),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.EXPERIMENT_NAME),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.RUN_NAME),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.RUN_COLOR)}}function H3(t,e){if(1&t){const t=Hm();Nm(0),Rm(1,"div",38),Vm("click",(function t(e){return e.stopPropagation()})),Rm(2,"tb-range-input",39),Vm("value",(function e(n){hi(t);const o=Ym(2).$implicit;return Ym(2).handleHparamIntervalChanged(o,n)})),Am(),Am(),zm()}if(2&t){const t=Ym(2).$implicit;rc(2),Dm("min",t.filter.minValue)("max",t.filter.maxValue)("lowerValue",t.filter.filterLowerValue)("upperValue",t.filter.filterUpperValue)}}function F3(t,e){if(1&t){const t=Hm();Rm(0,"div",41),Vm("click",(function t(e){return e.stopPropagation()})),Rm(1,"mat-checkbox",37),Vm("change",(function e(){const n=hi(t).$implicit,o=Ym(3).$implicit;return Ym(2).handleHparamDiscreteChanged(o,n)})),Rm(2,"span"),ku(3),Am(),Am(),Am()}if(2&t){const t=e.$implicit,n=Ym(3).$implicit;rc(1),Dm("checked",n.filter.filterValues.includes(t)),rc(2),Su(t)}}function L3(t,e){if(1&t&&(Nm(0),Qp(1,F3,4,2,"div",40),zm()),2&t){const t=Ym(2).$implicit;rc(1),Dm("ngForOf",t.filter.possibleValues)}}function B3(t,e){if(1&t){const t=Hm();Nm(0),Rm(1,"button",33),Vm("click",(function t(e){return e.stopPropagation()})),Tm(2,"mat-icon",34),Am(),Rm(3,"mat-menu",null,35),Rm(5,"div",36),Vm("click",(function t(e){return e.stopPropagation()})),Rm(6,"mat-checkbox",37),Vm("change",(function e(){hi(t);const n=Ym().$implicit;return Ym(2).handleHparamIncludeUndefinedToggled(n)})),Rm(7,"span"),ku(8,"(show empty value)"),Am(),Am(),Am(),Qp(9,H3,3,4,"ng-container",32),Qp(10,L3,2,1,"ng-container",32),Am(),zm()}if(2&t){const t=$p(4),e=Ym().$implicit,n=Ym(2);rc(1),Dm("matMenuTriggerFor",t),jp("aria-label","Filter hparam "+(e.displayName||e.name)),rc(5),Dm("checked",e.filter.includeUndefined),rc(3),Dm("ngIf",e.filter.type===n.DomainType.INTERVAL),rc(1),Dm("ngIf",e.filter.type===n.DomainType.DISCRETE)}}const V3=function(t,e){return{type:t,name:e}};function j3(t,e){if(1&t&&(Rm(0,"span",30),Rm(1,"span",31),ku(2),Am(),Qp(3,B3,11,5,"ng-container",32),Am()),2&t){const t=e.$implicit,n=Ym(2);Dm("mat-sort-header",vh(3,V3,n.SortType.HPARAM,t.name)),rc(2),Su(t.displayName||t.name),rc(1),Dm("ngIf",t.filter)}}function U3(t,e){if(1&t){const t=Hm();Nm(0),Rm(1,"button",33),Vm("click",(function t(e){return e.stopPropagation()})),Tm(2,"mat-icon",34),Am(),Rm(3,"mat-menu",null,35),Rm(5,"div",36),Vm("click",(function t(e){return e.stopPropagation()})),Rm(6,"mat-checkbox",37),Vm("change",(function e(){hi(t);const n=Ym().$implicit;return Ym(2).handleMetricIncludeUndefinedChanged(n)})),Rm(7,"span"),ku(8,"(show empty value)"),Am(),Am(),Am(),Rm(9,"div",38),Vm("click",(function t(e){return e.stopPropagation()})),Rm(10,"tb-range-input",39),Vm("value",(function e(n){hi(t);const o=Ym().$implicit;return Ym(2).handleMetricFilterChanged(o,n)})),Am(),Am(),Am(),zm()}if(2&t){const t=$p(4),e=Ym().$implicit;rc(1),Dm("matMenuTriggerFor",t),jp("aria-label","Filter metric "+(e.displayName||e.tag)),rc(5),Dm("checked",e.filter.includeUndefined),rc(4),Dm("min",e.filter.minValue)("max",e.filter.maxValue)("lowerValue",e.filter.filterLowerValue)("upperValue",e.filter.filterUpperValue)}}const G3=function(t,e){return{type:t,tag:e}};function W3(t,e){if(1&t&&(Rm(0,"span",30),Rm(1,"span",31),ku(2),Am(),Qp(3,U3,11,7,"ng-container",32),Am()),2&t){const t=e.$implicit,n=Ym(2);Dm("mat-sort-header",vh(3,G3,n.SortType.METRIC,t.tag)),rc(2),Su(t.displayName||t.tag),rc(1),Dm("ngIf",t.filter)}}function Y3(t,e){if(1&t){const t=Hm();Rm(0,"div",17),Rm(1,"div",18),Vm("matSortChange",(function e(n){return hi(t),Ym().handleSortChange(n)})),Qp(2,I3,6,8,"span",19),Qp(3,j3,4,6,"span",20),Qp(4,W3,4,6,"span",20),Am(),Am()}if(2&t){const t=Ym();rc(1),Dm("matSortActive",t.sortOption.column),rc(1),Dm("ngForOf",t.columns),rc(1),Dm("ngForOf",t.hparamColumns)("ngForTrackBy",t.trackByHparamColumn),rc(1),Dm("ngForOf",t.metricColumns)("ngForTrackBy",t.trackByMetricColumn)}}function q3(t,e){if(1&t){const t=Hm();Rm(0,"span"),Rm(1,"mat-checkbox",37),Vm("change",(function e(){hi(t);const n=Ym(2).item;return Ym().onSelectionToggle.emit(n)})),Am(),Am()}if(2&t){const t=Ym(2).item;rc(1),Dm("checked",t.selected)}}function Z3(t,e){if(1&t&&(Rm(0,"span",31),ku(1),Am()),2&t){const t=Ym(2).item;jp("title",t.experimentName),rc(1),Su(t.experimentAlias)}}function X3(t,e){if(1&t&&(Rm(0,"span",31),ku(1),Am()),2&t){const t=Ym(2).item;rc(1),Su(t.run.name)}}const K3=function(t){return{"run-color-swatch":!0,"no-color":t}};function J3(t,e){if(1&t){const t=Hm();Rm(0,"span"),Rm(1,"button",47),Vm("colorPickerChange",(function e(n){hi(t);const o=Ym(2).item;return Ym().onRunColorChange.emit({runId:o.run.id,newColor:n})})),Am(),Am()}if(2&t){const t=Ym(2).item;rc(1),du("background",t.runColor),Dm("ngClass",Mh(8,K3,!t.runColor))("colorPicker",t.runColor)("cpDialogDisplay","popup")("cpPositionOffset",-20)("cpUseRootViewContainer",!0)("cpOutputFormat","hex")}}function Q3(t,e){if(1&t&&(Rm(0,"span",45),Nm(1,22),Qp(2,q3,2,1,"span",25),Qp(3,Z3,2,2,"span",46),Qp(4,X3,2,1,"span",46),Qp(5,J3,2,10,"span",25),zm(),Am()),2&t){const t=e.$implicit,n=Ym(2);Dm("ngClass",Mh(6,z3,"tb-column-"+t)),rc(1),Dm("ngSwitch",t),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.CHECKBOX),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.EXPERIMENT_NAME),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.RUN_NAME),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.RUN_COLOR)}}function $3(t,e){if(1&t&&(Rm(0,"span",48),ku(1),Am()),2&t){const t=e.$implicit,n=Ym().item;rc(1),Su(n.hparams.get(t.name))}}function t2(t,e){if(1&t&&(Rm(0,"span",48),ku(1),Am()),2&t){const t=e.$implicit,n=Ym().item;rc(1),Su(n.metrics.get(t.tag))}}function e2(t,e){if(1&t&&(Rm(0,"div",42),Qp(1,Q3,6,8,"span",43),Qp(2,$3,2,1,"span",44),Qp(3,t2,2,1,"span",44),Am()),2&t){const t=e.item,n=Ym();jp("data-id",t.run.id),rc(1),Dm("ngForOf",n.columns),rc(1),Dm("ngForOf",n.hparamColumns),rc(1),Dm("ngForOf",n.metricColumns)}}class n2 extends ZQ{constructor(){super(...arguments),this.itemsPerPageLabel="Show runs:"}}n2.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(n2)))(n||n2)}})(),n2.ɵprov=Mn({token:n2,factory:n2.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(n2,[{type:im}],null,null);class o2{constructor(){this.dataSource=new S0,this.DomainType=ZR,this.RunsTableColumn=vQ,this.SortType=$T,this.onRegexFilterChange=new Lh,this.onSelectionToggle=new Lh,this.onPageSelectionToggle=new Lh,this.onPaginationChange=new Lh,this.onSortChange=new Lh,this.onRunColorChange=new Lh,this.onHparamDiscreteFilterChanged=new Lh,this.onHparamIntervalFilterChanged=new Lh,this.onMetricFilterChanged=new Lh}ngOnChanges(){this.dataSource.data=this.pageItems}getHparamColumnId(t){return`h:${t.name}`}getMetricColumnId(t){return`m:${t.tag}`}getColumnIds(){return[...this.columns,...this.hparamColumns.map(this.getHparamColumnId),...this.metricColumns.map(this.getMetricColumnId)]}allPageItemsSelected(){return Boolean(this.pageItems.length)&&this.pageItems.every((t=>t.selected))}somePageItemsSelected(){return this.pageItems.some((t=>t.selected))}handlePageToggle(){this.onPageSelectionToggle.emit({items:this.pageItems})}handleSortChange(t){let e;switch(t.direction){case"asc":e=FN.ASC;break;case"desc":e=FN.DESC;break;default:e=FN.UNSET}this.onSortChange.emit({key:t.active,direction:e})}onFilterKeyUp(t){this.onRegexFilterChange.emit(t.target.value)}tableTrackBy(t,e){return e.run.id}handleHparamIncludeUndefinedToggled(t){const{name:e,filter:n}=t;if(!n)throw new RangeError("Invariant error: require filter to exist for it to change");n.type===ZR.DISCRETE?this.onHparamDiscreteFilterChanged.emit({hparamName:e,includeUndefined:!n.includeUndefined,filterValues:n.filterValues}):this.onHparamIntervalFilterChanged.emit({name:e,includeUndefined:!n.includeUndefined,filterLowerValue:n.filterLowerValue,filterUpperValue:n.filterUpperValue})}handleHparamIntervalChanged(t,e){const{name:n,filter:o}=t;if(!o)throw new RangeError("Invariant error: require filter to exist for it to change");this.onHparamIntervalFilterChanged.emit({name:n,includeUndefined:o.includeUndefined,filterLowerValue:e.lowerValue,filterUpperValue:e.upperValue})}handleHparamDiscreteChanged(t,e){const{name:n,filter:o}=t;if(!o)throw new RangeError("Invariant error: require filter to exist for it to change");if(o.type!==ZR.DISCRETE)throw new RangeError(`Invariant error: expected discrete domain for ${n}`);const i=new Set([...o.filterValues]);i.has(e)?i.delete(e):i.add(e),this.onHparamDiscreteFilterChanged.emit({hparamName:n,includeUndefined:o.includeUndefined,filterValues:[...i]})}handleMetricIncludeUndefinedChanged(t){if(!t.filter)throw new RangeError("Invariant error: require filter to exist for it to change");this.onMetricFilterChanged.emit({name:t.tag,includeUndefined:!t.filter.includeUndefined,filterLowerValue:t.filter.filterLowerValue,filterUpperValue:t.filter.filterUpperValue})}handleMetricFilterChanged(t,e){if(!t.filter)throw new RangeError("Invariant error: require filter to exist for it to change");this.onMetricFilterChanged.emit({name:t.tag,includeUndefined:t.filter.includeUndefined,filterLowerValue:e.lowerValue,filterUpperValue:e.upperValue})}trackByHparamColumn(t){return t.name}trackByMetricColumn(t){return t.tag}}o2.ɵfac=function t(e){return new(e||o2)},o2.ɵcmp=to({type:o2,selectors:[["runs-table-component"]],viewQuery:function t(e,n){if(1&e&&(Qh(_3,7,hg),Qh(t$,7),Qh(a$,7)),2&e){let t;Jh(t=tb())&&(n.filter=t.first),Jh(t=tb())&&(n.paginator=t.first),Jh(t=tb())&&(n.sort=t.first)}},hostVars:2,hostBindings:function t(e,n){2&e&&pu("flex-layout",n.useFlexibleLayout)},inputs:{experimentIds:"experimentIds",showExperimentName:"showExperimentName",columns:"columns",hparamColumns:"hparamColumns",metricColumns:"metricColumns",allItemsLength:"allItemsLength",filteredItemsLength:"filteredItemsLength",useFlexibleLayout:"useFlexibleLayout",usePagination:"usePagination",showGroupControl:"showGroupControl",pageItems:"pageItems",loading:"loading",numSelectedItems:"numSelectedItems",sortOption:"sortOption",paginationOption:"paginationOption",regexFilter:"regexFilter"},outputs:{onRegexFilterChange:"onRegexFilterChange",onSelectionToggle:"onSelectionToggle",onPageSelectionToggle:"onPageSelectionToggle",onPaginationChange:"onPaginationChange",onSortChange:"onSortChange",onRunColorChange:"onRunColorChange",onHparamDiscreteFilterChanged:"onHparamDiscreteFilterChanged",onHparamIntervalFilterChanged:"onHparamIntervalFilterChanged",onMetricFilterChanged:"onMetricFilterChanged"},features:[pg([{provide:ZQ,useClass:n2}]),Bo],decls:15,vars:7,consts:[[1,"filter-row"],["placeholder","Filter runs 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t(e,n){if(1&e&&(Rm(0,"div",0),Rm(1,"tb-filter-input",1),Vm("keyup",(function t(e){return n.onFilterKeyUp(e)})),Am(),Am(),Rm(2,"div",2),Rm(3,"div",3),Qp(4,C3,1,0,"ng-container",4),Rm(5,"div",5),Qp(6,x3,2,4,"ng-container",6),Am(),Am(),Qp(7,O3,2,0,"div",7),Qp(8,P3,2,0,"div",8),Qp(9,w3,6,1,"div",8),Am(),Qp(10,S3,1,5,"mat-paginator",9),Qp(11,Y3,5,6,"ng-template",null,10,ib),Qp(13,e2,4,4,"ng-template",null,11,ib)),2&e){const t=$p(12);rc(4),Dm("ngTemplateOutlet",t),rc(2),Dm("ngForOf",n.pageItems)("ngForTrackBy",n.tableTrackBy),rc(1),Dm("ngIf",n.loading),rc(1),Dm("ngIf",!n.loading&&0===n.allItemsLength),rc(1),Dm("ngIf",!n.loading&&n.allItemsLength>0&&0===n.filteredItemsLength),rc(1),Dm("ngIf",n.usePagination)}},directives:[q0,MM,lM,dM,o1,t$,a$,aM,fM,gM,OY,m$,w1,XH,eY,DW,KW,WW,D1,b3],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   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ngDevMode||ngDevMode)&&hh(o2,[{type:My,args:[{selector:"runs-table-component",templateUrl:"runs_table_component.ng.html",host:{"[class.flex-layout]":"useFlexibleLayout"},styleUrls:["runs_table_component.css"],changeDetection:zn.OnPush,providers:[{provide:ZQ,useClass:n2}]}]}],null,{experimentIds:[{type:xy}],showExperimentName:[{type:xy}],columns:[{type:xy}],hparamColumns:[{type:xy}],metricColumns:[{type:xy}],allItemsLength:[{type:xy}],filteredItemsLength:[{type:xy}],useFlexibleLayout:[{type:xy}],usePagination:[{type:xy}],showGroupControl:[{type:xy}],pageItems:[{type:xy}],loading:[{type:xy}],numSelectedItems:[{type:xy}],sortOption:[{type:xy}],paginationOption:[{type:xy}],regexFilter:[{type:xy}],onRegexFilterChange:[{type:Oy}],onSelectionToggle:[{type:Oy}],onPageSelectionToggle:[{type:Oy}],onPaginationChange:[{type:Oy}],onSortChange:[{type:Oy}],onRunColorChange:[{type:Oy}],onHparamDiscreteFilterChanged:[{type:Oy}],onHparamIntervalFilterChanged:[{type:Oy}],onMetricFilterChanged:[{type:Oy}],filter:[{type:Za,args:["filter",{static:!0,read:hg}]}],paginator:[{type:Za,args:[t$,{static:!0}]}],sort:[{type:Za,args:[a$,{static:!0}]}]});const i2=Zw(pN,(t=>t.state===yE.LOADING));function a2(t,e){switch(e.type){case $T.EXPERIMENT_NAME:return[t.experimentAlias,t.run.name,t.run.id];case $T.RUN_NAME:return[t.run.name,t.experimentAlias,t.run.id];case $T.HPARAM:return[t.hparams.get(e.name),t.run.name,t.experimentAlias,t.run.id];case $T.METRIC:return[t.metrics.get(e.tag),t.run.name,t.experimentAlias,t.run.id];default:throw new Error(`Not yet implemented: ${e}`)}}function r2(t,e){return void 0===e?t.includeUndefined:t.type===ZR.DISCRETE?t.filterValues.includes(e):t.type===ZR.INTERVAL&&t.filterLowerValue<=e&&e<=t.filterUpperValue}class s2{constructor(t){this.store=t,this.loading$=null,this.hparamColumns$=Et([]),this.metricColumns$=Et([]),this.useFlexibleLayout=!1,this.usePagination=!1,this.columns=[vQ.RUN_NAME],this.showHparamsAndMetrics=!1,this.sortOption$=this.store.select(yN),this.paginationOption$=this.store.select(bN),this.regexFilter$=this.store.select(gN),this.ngUnsubscribe=new I}isExperimentNameVisible(){return this.columns.some((t=>t===vQ.EXPERIMENT_NAME))}ngOnInit(){const t=Wt(this.experimentIds.map((t=>this.getRunTableItemsForExperiment(t)))).pipe(It((t=>[].concat(...t))));this.allUnsortedRunTableItems$=t.pipe(Ie(this.ngUnsubscribe),Ae(1)),this.allItemsLength$=this.allUnsortedRunTableItems$.pipe(It((t=>t.length)));const e=this.getFilteredItems$(this.allUnsortedRunTableItems$).pipe(Ie(this.ngUnsubscribe),Ae(1));this.filteredItemsLength$=e.pipe(It((t=>t.length))),this.pageItems$=this.sortedAndSlicedItems$(e),this.numSelectedItems$=this.allUnsortedRunTableItems$.pipe(It((t=>t.reduce(((t,e)=>t+Number(e.selected)),0))));const n=this.experimentIds.map((t=>this.store.select(i2,{experimentId:t})));if(this.loading$=Wt(n).pipe(It((t=>t.some((t=>t))))),this.showHparamsAndMetrics){const t=this.store.select(SQ,{experimentIds:this.experimentIds});this.hparamColumns$=Wt([this.store.select(PQ,this.experimentIds),t]).pipe(It((([t,{hparams:e}])=>e.map((({name:e,displayName:n})=>{const o=t.get(e);if(!o)throw new RangeError(`Invariant error: a filter for ${e} must exist when the hparam exists`);return{displayName:n,name:e,filter:o}}))))),this.metricColumns$=Wt([this.store.select(kQ,this.experimentIds),t]).pipe(It((([t,{metrics:e}])=>e.map((({tag:e,displayName:n})=>{const o=t.get(e);if(!o)throw new RangeError(`Invariant error: a filter for ${e} must exist when the metric exists`);return{displayName:n,tag:e,filter:o}})))))}this.columns.includes(vQ.CHECKBOX)&&this.store.select(NS).pipe(Ie(this.ngUnsubscribe),ze((()=>t.pipe(ce((t=>t.length>500)),be(1))))).subscribe((()=>{this.store.dispatch(lz({localizedMessage:"The number of runs exceeds 500. New runs are unselected for performance reasons."}))})),this.showGroupControl$=this.store.select(eE).pipe(It((t=>t&&this.columns.includes(vQ.RUN_COLOR)))),this.store.dispatch(UR({experimentIds:this.experimentIds}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}getFilteredItems$(t){return Wt([t,this.store.select(gN)]).pipe(It((([t,e])=>{if(!e)return t;const n=this.columns.includes(vQ.EXPERIMENT_NAME);return t.filter((t=>TN({runName:t.run.name,experimentAlias:t.experimentAlias,experimentName:t.experimentName},e,n)))})),ze((t=>this.showHparamsAndMetrics?Wt(this.store.select(PQ,this.experimentIds),this.store.select(kQ,this.experimentIds)).pipe(It((([e,n])=>t.filter((({hparams:t,metrics:o})=>[...e.entries()].every((([e,n])=>r2(n,t.get(e))))&&[...n.entries()].every((([t,e])=>r2(e,o.get(t))))))))):Et(t))))}sortedAndSlicedItems$(t){const e=Wt([t,this.store.select(yN)]).pipe(It((([t,e])=>(function n(t,e){const n=e.key,o=[...t];return null===n||e.direction===FN.UNSET||o.sort(((t,o)=>{const i=a2(t,n),a=a2(o,n);if(i.length!==a.length)throw new Error(`Invariant error: a given sort should result in same number of items: ${e}`);for(let t=0;t<i.length;t++){const n=i[t],o=a[t];if(n!==o){if(void 0===n||void 0===o)return void 0===o?-1:1;if(typeof n!=typeof o)throw new Error(`Cannot compare values of different types: ${typeof n} vs. ${typeof o}`);return n<o==(e.direction===FN.ASC)?-1:1}}return 0})),o})(t,e))));return Wt([e,this.store.select(bN)]).pipe(It((([t,e])=>{if(!this.usePagination)return t.slice();const{pageSize:n,pageIndex:o}=e;return t.slice(o*n,(o+1)*n)})),Ne([]))}getRunTableItemsForExperiment(t){return Wt([this.store.select(lN,{experimentId:t}),this.store.select(uA,{experimentId:t}),this.store.select(NN),this.store.select(zN),this.store.select(zS)]).pipe(It((([e,n,o,i,a])=>e.map((e=>{const r=new Map;(e.hparams||[]).forEach((t=>{r.set(t.name,t.value)}));const s=new Map;return(e.metrics||[]).forEach((t=>{s.set(t.tag,t.value)})),{run:e,experimentName:(null==n?void 0:n.name)||"",experimentAlias:a[t],selected:Boolean(o&&o.get(e.id)),runColor:i[e.id],hparams:r,metrics:s}})))))}onRunSelectionToggle(t){this.store.dispatch(HR({experimentIds:this.experimentIds,runId:t.run.id}))}onPageSelectionToggle(t){const{items:e}=t,n=e.map((({run:t})=>t.id));this.store.dispatch(FR({experimentIds:this.experimentIds,runIds:n}))}onPaginationChange(t){if(!this.usePagination)throw new Error("Pagination events cannot be dispatched when pagination is disabled");const{pageIndex:e,pageSize:n}=t;this.store.dispatch(LR({pageIndex:e,pageSize:n}))}onSortChange(t){this.store.dispatch(BR(t))}onRegexFilterChange(t){this.store.dispatch(VR({regexString:t}))}onRunColorChange({runId:t,newColor:e}){this.store.dispatch(jR({runId:t,newColor:e}))}onHparamDiscreteFilterChanged(t){const{hparamName:e,filterValues:n,includeUndefined:o}=t;this.store.dispatch(KR({experimentIds:this.experimentIds,hparamName:e,filterValues:n,includeUndefined:o}))}onHparamIntervalFilterChanged(t){const{name:e,filterLowerValue:n,filterUpperValue:o,includeUndefined:i}=t;this.store.dispatch(JR({experimentIds:this.experimentIds,hparamName:e,filterLowerValue:n,filterUpperValue:o,includeUndefined:i}))}onMetricFilterChanged(t){const{name:e,includeUndefined:n,filterLowerValue:o,filterUpperValue:i}=t;this.store.dispatch(QR({experimentIds:this.experimentIds,metricTag:e,includeUndefined:n,filterLowerValue:o,filterUpperValue:i}))}}s2.ɵfac=function t(e){return new(e||s2)(Sm(Iw))},s2.ɵcmp=to({type:s2,selectors:[["runs-table"]],hostVars:2,hostBindings:function t(e,n){2&e&&pu("flex-layout",n.useFlexibleLayout)},inputs:{useFlexibleLayout:"useFlexibleLayout",usePagination:"usePagination",columns:"columns",experimentIds:"experimentIds",showHparamsAndMetrics:"showHparamsAndMetrics"},decls:12,vars:38,consts:[[3,"experimentIds","useFlexibleLayout","numSelectedItems","columns","hparamColumns","metricColumns","showExperimentName","pageItems","filteredItemsLength","allItemsLength","loading","paginationOption","regexFilter","showGroupControl","sortOption","usePagination","onSelectionToggle","onPageSelectionToggle","onPaginationChange","onRegexFilterChange","onSortChange","onRunColorChange","onHparamIntervalFilterChanged","onHparamDiscreteFilterChanged","onMetricFilterChanged"]],template:function t(e,n){1&e&&(Rm(0,"runs-table-component",0),Vm("onSelectionToggle",(function t(e){return n.onRunSelectionToggle(e)}))("onPageSelectionToggle",(function t(e){return n.onPageSelectionToggle(e)}))("onPaginationChange",(function t(e){return n.onPaginationChange(e)}))("onRegexFilterChange",(function t(e){return n.onRegexFilterChange(e)}))("onSortChange",(function t(e){return n.onSortChange(e)}))("onRunColorChange",(function t(e){return n.onRunColorChange(e)}))("onHparamIntervalFilterChanged",(function t(e){return n.onHparamIntervalFilterChanged(e)}))("onHparamDiscreteFilterChanged",(function t(e){return n.onHparamDiscreteFilterChanged(e)}))("onMetricFilterChanged",(function t(e){return n.onMetricFilterChanged(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Ah(7,"async"),Ah(8,"async"),Ah(9,"async"),Ah(10,"async"),Ah(11,"async"),Am()),2&e&&Dm("experimentIds",n.experimentIds)("useFlexibleLayout",n.useFlexibleLayout)("numSelectedItems",Th(1,16,n.numSelectedItems$))("columns",n.columns)("hparamColumns",Th(2,18,n.hparamColumns$))("metricColumns",Th(3,20,n.metricColumns$))("showExperimentName",n.isExperimentNameVisible())("pageItems",Th(4,22,n.pageItems$))("filteredItemsLength",Th(5,24,n.filteredItemsLength$))("allItemsLength",Th(6,26,n.allItemsLength$))("loading",Th(7,28,n.loading$))("paginationOption",Th(8,30,n.paginationOption$))("regexFilter",Th(9,32,n.regexFilter$))("showGroupControl",Th(10,34,n.showGroupControl$))("sortOption",Th(11,36,n.sortOption$))("usePagination",n.usePagination)},directives:[o2],pipes:[wM],styles:[".flex-layout[_nghost-%COMP%] {\n        display: flex;\n      }\n\n      .flex-layout[_nghost-%COMP%]    > runs-table-component[_ngcontent-%COMP%] {\n        width: 100%;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(s2,[{type:My,args:[{selector:"runs-table",template:'\n    <runs-table-component\n      [experimentIds]="experimentIds"\n      [useFlexibleLayout]="useFlexibleLayout"\n      [numSelectedItems]="numSelectedItems$ | async"\n      [columns]="columns"\n      [hparamColumns]="hparamColumns$ | async"\n      [metricColumns]="metricColumns$ | async"\n      [showExperimentName]="isExperimentNameVisible()"\n      [pageItems]="pageItems$ | async"\n      [filteredItemsLength]="filteredItemsLength$ | async"\n      [allItemsLength]="allItemsLength$ | async"\n      [loading]="loading$ | async"\n      [paginationOption]="paginationOption$ | async"\n      [regexFilter]="regexFilter$ | async"\n      [showGroupControl]="showGroupControl$ | async"\n      [sortOption]="sortOption$ | async"\n      [usePagination]="usePagination"\n      (onSelectionToggle)="onRunSelectionToggle($event)"\n      (onPageSelectionToggle)="onPageSelectionToggle($event)"\n      (onPaginationChange)="onPaginationChange($event)"\n      (onRegexFilterChange)="onRegexFilterChange($event)"\n      (onSortChange)="onSortChange($event)"\n      (onRunColorChange)="onRunColorChange($event)"\n      (onHparamIntervalFilterChanged)="onHparamIntervalFilterChanged($event)"\n      (onHparamDiscreteFilterChanged)="onHparamDiscreteFilterChanged($event)"\n      (onMetricFilterChanged)="onMetricFilterChanged($event)"\n    ></runs-table-component>\n  ',host:{"[class.flex-layout]":"useFlexibleLayout"},styles:["\n      :host.flex-layout {\n        display: flex;\n      }\n\n      :host.flex-layout > runs-table-component {\n        width: 100%;\n      }\n    "],changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{useFlexibleLayout:[{type:xy}],usePagination:[{type:xy}],columns:[{type:xy}],experimentIds:[{type:xy}],showHparamsAndMetrics:[{type:xy}]});class l2{}l2.ɵfac=function t(e){return new(e||l2)},l2.ɵcmp=to({type:l2,selectors:[["runs-selector-component"]],inputs:{experimentIds:"experimentIds",showHparamsAndMetrics:"showHparamsAndMetrics",columns:"columns"},decls:1,vars:4,consts:[[3,"useFlexibleLayout","columns","experimentIds","showHparamsAndMetrics"]],template:function t(e,n){1&e&&Tm(0,"runs-table",0),2&e&&Dm("useFlexibleLayout",!0)("columns",n.columns)("experimentIds",n.experimentIds)("showHparamsAndMetrics",n.showHparamsAndMetrics)},directives:[s2],styles:["runs-table[_ngcontent-%COMP%] {\n        height: 100%;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(l2,[{type:My,args:[{selector:"runs-selector-component",template:'\n    <runs-table\n      [useFlexibleLayout]="true"\n      [columns]="columns"\n      [experimentIds]="experimentIds"\n      [showHparamsAndMetrics]="showHparamsAndMetrics"\n    ></runs-table>\n  ',styles:["\n      runs-table {\n        height: 100%;\n      }\n    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sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   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.mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action[_ngcontent-%COMP%]{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node[_ngcontent-%COMP%], .mat-nested-tree-node[_ngcontent-%COMP%]{font-weight:400;font-size:14px}.mat-ripple[_ngcontent-%COMP%]{overflow:hidden;position:relative}.mat-ripple[_ngcontent-%COMP%]:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded[_ngcontent-%COMP%]{overflow:visible}.mat-ripple-element[_ngcontent-%COMP%]{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms cubic-bezier(0, 0, 0.2, 1);transform:scale(0)}.cdk-high-contrast-active[_ngcontent-%COMP%]   .mat-ripple-element[_ngcontent-%COMP%]{display:none}.cdk-visually-hidden[_ngcontent-%COMP%]{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container[_ngcontent-%COMP%], .cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}.tag-filter[_ngcontent-%COMP%]{display:flex;position:relative}tb-filter-input[_ngcontent-%COMP%]{flex-grow:1}[_nghost-%COMP%]{color:#212121}body.dark-mode   [_nghost-%COMP%]{color:#fff}[_nghost-%COMP%]:not(.valid){color:#c62828}[_nghost-%COMP%]:not(.valid)   .error-icon[_ngcontent-%COMP%]{color:#c62828;position:absolute;right:0}  .tag-options .option,   .tag-options .and-more{-webkit-box-orient:vertical;-webkit-line-clamp:3;display:-webkit-box;font-size:14px;line-height:1.4;padding:8px 16px}  .tag-options .and-more{color:#616161}body.dark-mode[_nghost-%COMP%]     .tag-options .and-more, body.dark-mode   [_nghost-%COMP%]     .tag-options .and-more{color:rgba(255,255,255,.7)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(v2,[{type:My,args:[{selector:"metrics-tag-filter-component",templateUrl:"filter_input_component.ng.html",styleUrls:["filter_input_component.css"],changeDetection:zn.OnPush}]}],null,{regexFilterValue:[{type:xy}],isRegexFilterValid:[{type:Py,args:["class.valid"]},{type:xy}],completions:[{type:xy}],onRegexFilterValueChange:[{type:Oy}]});class x2{constructor(t){this.store=t,this.tagFilter$=this.store.select(BT),this.isTagFilterRegexValid$=this.tagFilter$.pipe(It((t=>{try{return new RegExp(t),!0}catch(t){return!1}}))),this.completions$=this.store.select(MT).pipe(fe(this.store.select(qT)),It((([t,e])=>t.filter((({plugin:t})=>!e.size||e.has(t))).map((({tag:t})=>t)))),It((t=>[...new Set(t)])),It((t=>t.sort(g2))),fe(this.store.select(BT)),It((([t,e])=>{try{return[t,new RegExp(e,"i")]}catch(e){return[t,null]}})),ce((([,t])=>null!==t)),It((([t,e])=>t.filter((t=>e.test(t))))))}onTagFilterChange(t){this.store.dispatch(eR({tagFilter:t}))}}x2.ɵfac=function t(e){return new(e||x2)(Sm(Iw))},x2.ɵcmp=to({type:x2,selectors:[["metrics-tag-filter"]],decls:4,vars:9,consts:[[3,"regexFilterValue","isRegexFilterValid","completions","onRegexFilterValueChange"]],template:function t(e,n){1&e&&(Rm(0,"metrics-tag-filter-component",0),Vm("onRegexFilterValueChange",(function t(e){return n.onTagFilterChange(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Am()),2&e&&Dm("regexFilterValue",Th(1,3,n.tagFilter$))("isRegexFilterValid",Th(2,5,n.isTagFilterRegexValid$))("completions",Th(3,7,n.completions$))},directives:[v2],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(x2,[{type:My,args:[{selector:"metrics-tag-filter",template:'\n    <metrics-tag-filter-component\n      [regexFilterValue]="tagFilter$ | async"\n      [isRegexFilterValid]="isTagFilterRegexValid$ | async"\n      [completions]="completions$ | async"\n      (onRegexFilterValueChange)="onTagFilterChange($event)"\n    ></metrics-tag-filter-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);const O2=["button"],P2=new Ga("MAT_BUTTON_TOGGLE_DEFAULT_OPTIONS"),w2=new Ga("MatButtonToggleGroup"),k2={provide:IV,useExisting:qe((()=>E2)),multi:!0};let S2=0;class D2{constructor(t,e){this.source=t,this.value=e}}class E2{constructor(t,e){this._changeDetector=t,this._vertical=!1,this._multiple=!1,this._disabled=!1,this._controlValueAccessorChangeFn=()=>{},this._onTouched=()=>{},this._name="mat-button-toggle-group-"+S2++,this.valueChange=new Lh,this.change=new Lh,this.appearance=e&&e.appearance?e.appearance:"standard"}get name(){return this._name}set name(t){this._name=t,this._buttonToggles&&this._buttonToggles.forEach((t=>{t.name=this._name,t._markForCheck()}))}get vertical(){return this._vertical}set vertical(t){this._vertical=yz(t)}get value(){const t=this._selectionModel?this._selectionModel.selected:[];return this.multiple?t.map((t=>t.value)):t[0]?t[0].value:void 0}set value(t){this._setSelectionByValue(t),this.valueChange.emit(this.value)}get selected(){const t=this._selectionModel?this._selectionModel.selected:[];return this.multiple?t:t[0]||null}get multiple(){return this._multiple}set multiple(t){this._multiple=yz(t)}get disabled(){return this._disabled}set disabled(t){this._disabled=yz(t),this._buttonToggles&&this._buttonToggles.forEach((t=>t._markForCheck()))}ngOnInit(){this._selectionModel=new oF(this.multiple,void 0,!1)}ngAfterContentInit(){this._selectionModel.select(...this._buttonToggles.filter((t=>t.checked)))}writeValue(t){this.value=t,this._changeDetector.markForCheck()}registerOnChange(t){this._controlValueAccessorChangeFn=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t}_emitChangeEvent(){const t=this.selected,e=Array.isArray(t)?t[t.length-1]:t,n=new D2(e,this.value);this._controlValueAccessorChangeFn(n.value),this.change.emit(n)}_syncButtonToggle(t,e,n=!1,o=!1){this.multiple||!this.selected||t.checked||(this.selected.checked=!1),this._selectionModel?e?this._selectionModel.select(t):this._selectionModel.deselect(t):o=!0,o?Promise.resolve().then((()=>this._updateModelValue(n))):this._updateModelValue(n)}_isSelected(t){return this._selectionModel&&this._selectionModel.isSelected(t)}_isPrechecked(t){return void 0!==this._rawValue&&(this.multiple&&Array.isArray(this._rawValue)?this._rawValue.some((e=>null!=t.value&&e===t.value)):t.value===this._rawValue)}_setSelectionByValue(t){if(this._rawValue=t,this._buttonToggles)if(this.multiple&&t){if(!Array.isArray(t)&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Value must be an array in multiple-selection mode.");this._clearSelection(),t.forEach((t=>this._selectValue(t)))}else this._clearSelection(),this._selectValue(t)}_clearSelection(){this._selectionModel.clear(),this._buttonToggles.forEach((t=>t.checked=!1))}_selectValue(t){const e=this._buttonToggles.find((e=>null!=e.value&&e.value===t));e&&(e.checked=!0,this._selectionModel.select(e))}_updateModelValue(t){t&&this._emitChangeEvent(),this.valueChange.emit(this.value)}}E2.ɵfac=function t(e){return new(e||E2)(Sm(Ug),Sm(P2,8))},E2.ɵdir=lo({type:E2,selectors:[["mat-button-toggle-group"]],contentQueries:function t(e,n,o){if(1&e&&$h(o,A2,5),2&e){let t;Jh(t=tb())&&(n._buttonToggles=t)}},hostAttrs:["role","group",1,"mat-button-toggle-group"],hostVars:5,hostBindings:function t(e,n){2&e&&(jp("aria-disabled",n.disabled),pu("mat-button-toggle-vertical",n.vertical)("mat-button-toggle-group-appearance-standard","standard"===n.appearance))},inputs:{appearance:"appearance",name:"name",vertical:"vertical",value:"value",multiple:"multiple",disabled:"disabled"},outputs:{valueChange:"valueChange",change:"change"},exportAs:["matButtonToggleGroup"],features:[pg([k2,{provide:w2,useExisting:E2}])]}),E2.ctorParameters=()=>[{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[P2]}]}],E2.propDecorators={_buttonToggles:[{type:Ya,args:[qe((()=>A2)),{descendants:!0}]}],appearance:[{type:xy}],name:[{type:xy}],vertical:[{type:xy}],value:[{type:xy}],valueChange:[{type:Oy}],multiple:[{type:xy}],disabled:[{type:xy}],change:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(E2,[{type:Cy,args:[{selector:"mat-button-toggle-group",providers:[k2,{provide:w2,useExisting:E2}],host:{role:"group",class:"mat-button-toggle-group","[attr.aria-disabled]":"disabled","[class.mat-button-toggle-vertical]":"vertical","[class.mat-button-toggle-group-appearance-standard]":'appearance === "standard"'},exportAs:"matButtonToggleGroup"}]}],(function(){return[{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[P2]}]}]}),{valueChange:[{type:Oy}],change:[{type:Oy}],appearance:[{type:xy}],name:[{type:xy}],vertical:[{type:xy}],value:[{type:xy}],multiple:[{type:xy}],disabled:[{type:xy}],_buttonToggles:[{type:Ya,args:[qe((()=>A2)),{descendants:!0}]}]});const R2=QI(class{});class A2 extends R2{constructor(t,e,n,o,i,a){super(),this._changeDetectorRef=e,this._elementRef=n,this._focusMonitor=o,this._isSingleSelector=!1,this._checked=!1,this.ariaLabelledby=null,this._disabled=!1,this.change=new Lh;const r=Number(i);this.tabIndex=r||0===r?r:null,this.buttonToggleGroup=t,this.appearance=a&&a.appearance?a.appearance:"standard"}get buttonId(){return`${this.id}-button`}get appearance(){return this.buttonToggleGroup?this.buttonToggleGroup.appearance:this._appearance}set appearance(t){this._appearance=t}get checked(){return this.buttonToggleGroup?this.buttonToggleGroup._isSelected(this):this._checked}set checked(t){const e=yz(t);e!==this._checked&&(this._checked=e,this.buttonToggleGroup&&this.buttonToggleGroup._syncButtonToggle(this,this._checked),this._changeDetectorRef.markForCheck())}get disabled(){return this._disabled||this.buttonToggleGroup&&this.buttonToggleGroup.disabled}set disabled(t){this._disabled=yz(t)}ngOnInit(){const t=this.buttonToggleGroup;this._isSingleSelector=t&&!t.multiple,this.id=this.id||"mat-button-toggle-"+S2++,this._isSingleSelector&&(this.name=t.name),t&&(t._isPrechecked(this)?this.checked=!0:t._isSelected(this)!==this._checked&&t._syncButtonToggle(this,this._checked))}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0)}ngOnDestroy(){const t=this.buttonToggleGroup;this._focusMonitor.stopMonitoring(this._elementRef),t&&t._isSelected(this)&&t._syncButtonToggle(this,!1,!1,!0)}focus(t){this._buttonElement.nativeElement.focus(t)}_onButtonClick(){const t=!!this._isSingleSelector||!this._checked;t!==this._checked&&(this._checked=t,this.buttonToggleGroup&&(this.buttonToggleGroup._syncButtonToggle(this,this._checked,!0),this.buttonToggleGroup._onTouched())),this.change.emit(new D2(this,this.value))}_markForCheck(){this._changeDetectorRef.markForCheck()}}A2.ɵfac=function t(e){return new(e||A2)(Sm(w2,8),Sm(Ug),Sm(hg),Sm(SI),Na("tabindex"),Sm(P2,8))},A2.ɵcmp=to({type:A2,selectors:[["mat-button-toggle"]],viewQuery:function t(e,n){if(1&e&&Qh(O2,5),2&e){let t;Jh(t=tb())&&(n._buttonElement=t.first)}},hostAttrs:["role","presentation",1,"mat-button-toggle"],hostVars:12,hostBindings:function t(e,n){1&e&&Vm("focus",(function t(){return n.focus()})),2&e&&(jp("aria-label",null)("aria-labelledby",null)("id",n.id)("name",null),pu("mat-button-toggle-standalone",!n.buttonToggleGroup)("mat-button-toggle-checked",n.checked)("mat-button-toggle-disabled",n.disabled)("mat-button-toggle-appearance-standard","standard"===n.appearance))},inputs:{disableRipple:"disableRipple",ariaLabelledby:["aria-labelledby","ariaLabelledby"],tabIndex:"tabIndex",appearance:"appearance",checked:"checked",disabled:"disabled",id:"id",name:"name",ariaLabel:["aria-label","ariaLabel"],value:"value"},outputs:{change:"change"},exportAs:["matButtonToggle"],features:[xp],ngContentSelectors:["*"],decls:6,vars:9,consts:[["type","button",1,"mat-button-toggle-button","mat-focus-indicator",3,"id","disabled","click"],["button",""],[1,"mat-button-toggle-label-content"],[1,"mat-button-toggle-focus-overlay"],["matRipple","",1,"mat-button-toggle-ripple",3,"matRippleTrigger","matRippleDisabled"]],template:function t(e,n){if(1&e&&(Zm(),Rm(0,"button",0,1),Vm("click",(function t(){return n._onButtonClick()})),Rm(2,"span",2),Xm(3),Am(),Am(),Tm(4,"span",3),Tm(5,"span",4)),2&e){const t=$p(1);Dm("id",n.buttonId)("disabled",n.disabled||null),jp("tabindex",n.disabled?-1:n.tabIndex)("aria-pressed",n.checked)("name",n.name||null)("aria-label",n.ariaLabel)("aria-labelledby",n.ariaLabelledby),rc(5),Dm("matRippleTrigger",t)("matRippleDisabled",n.disableRipple||n.disabled)}},directives:[kH],styles:[".mat-button-toggle-standalone,.mat-button-toggle-group{position:relative;display:inline-flex;flex-direction:row;white-space:nowrap;overflow:hidden;border-radius:2px;-webkit-tap-highlight-color:transparent}.cdk-high-contrast-active .mat-button-toggle-standalone,.cdk-high-contrast-active .mat-button-toggle-group{outline:solid 1px}.mat-button-toggle-standalone.mat-button-toggle-appearance-standard,.mat-button-toggle-group-appearance-standard{border-radius:4px}.cdk-high-contrast-active 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this.disabled">\n</span>\n',encapsulation:Hn.None,exportAs:"matButtonToggle",changeDetection:zn.OnPush,inputs:["disableRipple"],host:{"[class.mat-button-toggle-standalone]":"!buttonToggleGroup","[class.mat-button-toggle-checked]":"checked","[class.mat-button-toggle-disabled]":"disabled","[class.mat-button-toggle-appearance-standard]":'appearance === "standard"',class:"mat-button-toggle","[attr.aria-label]":"null","[attr.aria-labelledby]":"null","[attr.id]":"id","[attr.name]":"null","(focus)":"focus()",role:"presentation"},styles:[".mat-button-toggle-standalone,.mat-button-toggle-group{position:relative;display:inline-flex;flex-direction:row;white-space:nowrap;overflow:hidden;border-radius:2px;-webkit-tap-highlight-color:transparent}.cdk-high-contrast-active .mat-button-toggle-standalone,.cdk-high-contrast-active .mat-button-toggle-group{outline:solid 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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ngDevMode||ngDevMode)&&hh(z2,[{type:My,args:[{selector:"tb-truncated-path",template:'\n    <span *ngIf="firstTextPart().length > 0" class="first-text-part">{{\n      firstTextPart()\n    }}</span>\n    <span class="second-text-part">{{ secondTextPart() }}</span>\n  ',styleUrls:["truncated_path_component.css"]}]}],null,{value:[{type:xy}]});class F2{}F2.ɵfac=function t(e){return new(e||F2)},F2.ɵcmp=to({type:F2,selectors:[["card-run-name-component"]],inputs:{name:"name"},decls:1,vars:1,template:function t(e,n){1&e&&ku(0),2&e&&Su(n.name)},styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, 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0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{display:flex;flex-direction:column;box-sizing:border-box;height:100%;overflow:auto;padding:16px;padding-top:4px}.actual-size[_nghost-%COMP%]{height:auto}.heading[_ngcontent-%COMP%]{align-items:center;font-size:14px;margin-bottom:4px;position:relative}.line[_ngcontent-%COMP%]{align-items:center;display:grid;grid-template-columns:1fr max-content}.metadata[_ngcontent-%COMP%]{display:flex;flex-wrap:wrap;gap:5px;justify-content:flex-end;max-width:175px;text-align:end}.tag[_ngcontent-%COMP%]{overflow:hidden}.pin-button[_ngcontent-%COMP%]   mat-icon[_ngcontent-%COMP%]{height:18px}.run[_ngcontent-%COMP%]{align-self:baseline;display:flex;overflow:hidden;white-space:nowrap}.run[_ngcontent-%COMP%]   .dot[_ngcontent-%COMP%]{flex:none;display:inline-block;width:13px;height:13px;border-radius:50%;margin-right:4px}.run[_ngcontent-%COMP%]   .run-text[_ngcontent-%COMP%]{overflow:hidden;text-overflow:ellipsis;max-width:120px}.run[_ngcontent-%COMP%], .sample[_ngcontent-%COMP%], .step[_ngcontent-%COMP%]{color:#616161;font-size:13px}body.dark-mode[_nghost-%COMP%]   .run[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .run[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}body.dark-mode[_nghost-%COMP%]   .sample[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .sample[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}body.dark-mode[_nghost-%COMP%]   .step[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .step[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.controls[_ngcontent-%COMP%]{color:#616161;white-space:nowrap;justify-self:flex-end;flex-shrink:0;margin-right:-12px}body.dark-mode[_nghost-%COMP%]   .controls[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .controls[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.img-container[_ngcontent-%COMP%]{flex:1 1 0;overflow-y:auto;position:relative}.img-container[_ngcontent-%COMP%]   img[_ngcontent-%COMP%]{image-rendering:-moz-crisp-edges;image-rendering:pixelated}.actual-size[_nghost-%COMP%]   .img-container[_ngcontent-%COMP%]{overflow:auto;flex:none}[_nghost-%COMP%]:not(.actual-size)   img[_ngcontent-%COMP%]{position:absolute;max-height:100%;max-width:100%;width:auto;height:100%;object-fit:contain}.slider-row[_ngcontent-%COMP%]{display:flex;align-items:center;height:24px}.step-slider[_ngcontent-%COMP%]{flex:1}[_nghost-%COMP%]     .mat-slider-min-value .mat-slider-thumb{background-color:#f57c00}.empty-message[_ngcontent-%COMP%]{margin-top:1em;font-size:13px}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Z2,[{type:My,args:[{selector:"image-card-component",templateUrl:"image_card_component.ng.html",styleUrls:["image_card_component.css"],host:{"[class.actual-size]":"showActualSize"},changeDetection:zn.OnPush}]}],null,{loadState:[{type:xy}],title:[{type:xy}],tag:[{type:xy}],runId:[{type:xy}],sample:[{type:xy}],numSample:[{type:xy}],imageUrl:[{type:xy}],stepIndex:[{type:xy}],stepValues:[{type:xy}],brightnessInMilli:[{type:xy}],contrastInMilli:[{type:xy}],showActualSize:[{type:xy}],runColorScale:[{type:xy}],allowToggleActualSize:[{type:xy}],isPinned:[{type:xy}],onActualSizeToggle:[{type:Oy}],stepIndexChange:[{type:Oy}],onPinClicked:[{type:Oy}]});class X2{constructor(t,e){this.store=t,this.dataSource=e,this.fullWidthChanged=new Lh,this.pinStateChanged=new Lh,this.brightnessInMilli$=this.store.select(HT),this.contrastInMilli$=this.store.select(FT),this.actualSizeGlobalSetting$=this.store.select(LT),this.showActualSize=!1,this.actualSizeUiToggled=!1,this.actualSizeUiToggleSubject=new F(this.actualSizeUiToggled),this.ngUnsubscribe=new I}onStepIndexChanged(t){this.store.dispatch(tR({cardId:this.cardId,stepIndex:t}))}isImageCardMetadata(t){const{plugin:e}=t;return e===hA.IMAGES}onActualSizeToggle(){this.actualSizeUiToggled=!this.actualSizeUiToggled,this.actualSizeUiToggleSubject.next(this.actualSizeUiToggled)}ngOnInit(){Wt([this.actualSizeGlobalSetting$,this.actualSizeUiToggleSubject]).pipe(Ie(this.ngUnsubscribe),Fe((([t,e])=>{this.showActualSize=t||e,this.fullWidthChanged.emit(this.showActualSize)}))).subscribe((()=>{}));const t=this.store.select(yT,this.cardId).pipe(Ie(this.ngUnsubscribe),ce((t=>!!t&&this.isImageCardMetadata(t))),It((t=>t)),Ae(1)),e=Wt([t,this.store.select(hT,this.cardId)]).pipe(Ie(this.ngUnsubscribe),It((([t,e])=>{const n=t.runId;return e&&e.hasOwnProperty(n)?e[n]:[]})),Me(((t,e)=>t.length===e.length&&0===t.length||t===e)),Ae(1));this.stepIndex$=this.store.select(vT,this.cardId),this.loadState$=this.store.select(gT,this.cardId);const n=Wt([e,this.stepIndex$]).pipe(It((([t,e])=>null!==e&&t[e]?t[e]:null)));this.tag$=t.pipe(It((t=>t.tag))),this.title$=this.tag$.pipe(It((t=>f2(t,this.groupName)))),this.runId$=t.pipe(It((t=>t.runId))),this.sample$=t.pipe(It((t=>t.sample))),this.numSample$=t.pipe(It((t=>t.numSample))),this.imageUrl$=n.pipe(It((t=>t?this.dataSource.imageUrl(t.imageId):null))),this.stepValues$=e.pipe(It((t=>t.map((t=>t.step))))),this.isPinned$=this.store.select(wT,this.cardId)}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}}var K2,J2,Q2;function $2(t,e){1&t&&(Rm(0,"span"),ku(1,"scalar"),Am())}function t5(t,e){1&t&&(Rm(0,"span"),ku(1,"histogram"),Am())}function e5(t,e){1&t&&(Rm(0,"span"),ku(1,"unknown"),Am())}function n5(t,e){if(1&t&&(Nm(0,13),Qp(1,$2,2,0,"span",14),Qp(2,t5,2,0,"span",14),Qp(3,e5,2,0,"span",15),zm()),2&t){const t=Ym(2);Dm("ngSwitch",t.cardMetadata.plugin),rc(1),Dm("ngSwitchCase",t.PluginType.SCALARS),rc(1),Dm("ngSwitchCase",t.PluginType.HISTOGRAMS)}}function o5(t,e){1&t&&Im(0)}function i5(t,e){if(1&t&&(Rm(0,"option",16),ku(1),Am()),2&t){const t=e.$implicit;Dm("value",t.id),rc(1),Su(t.name)}}function a5(t,e){if(1&t){const t=Hm();Nm(0),Rm(1,"h2"),Qp(2,n5,4,3,"ng-template",null,2,ib),Rm(4,"span"),ku(5,"Download "),Am(),Qp(6,o5,1,0,"ng-container",3),Rm(7,"span"),ku(8," data for "),Am(),Rm(9,"code",4),ku(10),Am(),Am(),Rm(11,"mat-dialog-content"),Rm(12,"mat-form-field",5),Rm(13,"mat-label"),ku(14,"Select a run to download a data for a series"),Am(),Rm(15,"select",6),Vm("change",(function e(n){return hi(t),Ym().runSelected.emit(n.target.value)})),Rm(16,"option",7),ku(17,"-"),Am(),Qp(18,i5,2,2,"option",8),Am(),Am(),Rm(19,"div",9),Rm(20,"span"),ku(21,"Download as…"),Am(),ku(22," "),Rm(23,"a",10),ku(24,"JSON"),Am(),Rm(25,"a",10),ku(26,"CSV"),Am(),Am(),Am(),Rm(27,"mat-dialog-actions",11),Rm(28,"button",12),ku(29,"Close"),Am(),Am(),zm()}if(2&t){const t=$p(3),e=Ym();rc(6),Dm("ngTemplateOutlet",t),rc(3),Dm("title",e.cardMetadata.tag),rc(1),Su(e.cardMetadata.tag),rc(5),Dm("value",e.selectedRunId||""),rc(1),Dm("value",""),rc(2),Dm("ngForOf",e.runs),rc(5),Dm("disabled",!e.downloadUrlJson)("download",e.getDownloadName("json")),jp("href",e.downloadUrlJson,Ts),rc(2),Dm("disabled",!e.downloadUrlCsv)("download",e.getDownloadName("csv")),jp("href",e.downloadUrlCsv,Ts)}}function r5(t,e){1&t&&ku(0,"Loading...")}X2.ɵfac=function t(e){return new(e||X2)(Sm(Iw),Sm(PA))},X2.ɵcmp=to({type:X2,selectors:[["image-card"]],inputs:{cardId:"cardId",groupName:"groupName",runColorScale:"runColorScale"},outputs:{fullWidthChanged:"fullWidthChanged",pinStateChanged:"pinStateChanged"},decls:14,vars:41,consts:[[3,"loadState","title","tag","runId","sample","numSample","imageUrl","stepIndex","stepValues","brightnessInMilli","contrastInMilli","runColorScale","showActualSize","allowToggleActualSize","isPinned","stepIndexChange","onActualSizeToggle","onPinClicked"]],template:function t(e,n){1&e&&(Rm(0,"image-card-component",0),Vm("stepIndexChange",(function t(e){return n.onStepIndexChanged(e)}))("onActualSizeToggle",(function t(){return n.onActualSizeToggle()}))("onPinClicked",(function t(e){return n.pinStateChanged.emit(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Ah(7,"async"),Ah(8,"async"),Ah(9,"async"),Ah(10,"async"),Ah(11,"async"),Ah(12,"async"),Ah(13,"async"),Am()),2&e&&Dm("loadState",Th(1,15,n.loadState$))("title",Th(2,17,n.title$))("tag",Th(3,19,n.tag$))("runId",Th(4,21,n.runId$))("sample",Th(5,23,n.sample$))("numSample",Th(6,25,n.numSample$))("imageUrl",Th(7,27,n.imageUrl$))("stepIndex",Th(8,29,n.stepIndex$))("stepValues",Th(9,31,n.stepValues$))("brightnessInMilli",Th(10,33,n.brightnessInMilli$))("contrastInMilli",Th(11,35,n.contrastInMilli$))("runColorScale",n.runColorScale)("showActualSize",n.showActualSize)("allowToggleActualSize",!1===Th(12,37,n.actualSizeGlobalSetting$))("isPinned",Th(13,39,n.isPinned$))},directives:[Z2],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(X2,[{type:My,args:[{selector:"image-card",template:'\n    <image-card-component\n      [loadState]="loadState$ | async"\n      [title]="title$ | async"\n      [tag]="tag$ | async"\n      [runId]="runId$ | async"\n      [sample]="sample$ | async"\n      [numSample]="numSample$ | async"\n      [imageUrl]="imageUrl$ | async"\n      [stepIndex]="stepIndex$ | async"\n      [stepValues]="stepValues$ | async"\n      (stepIndexChange)="onStepIndexChanged($event)"\n      [brightnessInMilli]="brightnessInMilli$ | async"\n      [contrastInMilli]="contrastInMilli$ | async"\n      [runColorScale]="runColorScale"\n      [showActualSize]="showActualSize"\n      [allowToggleActualSize]="(actualSizeGlobalSetting$ | async) === false"\n      [isPinned]="isPinned$ | async"\n      (onActualSizeToggle)="onActualSizeToggle()"\n      (onPinClicked)="pinStateChanged.emit($event)"\n    ></image-card-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw},{type:PA}]}),{cardId:[{type:xy}],groupName:[{type:xy}],runColorScale:[{type:xy}],fullWidthChanged:[{type:Oy}],pinStateChanged:[{type:Oy}]}),(function(t){t[t.SVG=0]="SVG",t[t.WEBGL=1]="WEBGL"})(K2||(K2={})),(function(t){t[t.LINEAR=0]="LINEAR",t[t.LOG10=1]="LOG10",t[t.TIME=2]="TIME"})(J2||(J2={}));class s5{constructor(){this.runSelected=new Lh,this.PluginType=hA}getDownloadName(t){const e=this.runs.find((t=>t.id===this.selectedRunId));return e?`${e.name}.${t}`:""}}s5.ɵfac=function t(e){return new(e||s5)},s5.ɵcmp=to({type:s5,selectors:[["data_download_dialog_component"]],inputs:{cardMetadata:"cardMetadata",runs:"runs",selectedRunId:"selectedRunId",downloadUrlCsv:"downloadUrlCsv",downloadUrlJson:"downloadUrlJson"},outputs:{runSelected:"runSelected"},decls:3,vars:2,consts:[[4,"ngIf","ngIfElse"],["noCardMetadata",""],["dataName",""],[4,"ngTemplateOutlet"],[1,"tag-name",3,"title"],["appearance","fill",1,"run-selector"],["matNativeControl","","name","run","cdkFocusInitial","","required","",3,"value","change"],["selected","",3,"value"],[3,"value",4,"ngFor","ngForOf"],[1,"download-controls"],["mat-stroked-button","",3,"disabled","download"],["align","end"],["mat-button","","mat-dialog-close",""],[3,"ngSwitch"],[4,"ngSwitchCase"],[4,"NgSwitchDefault"],[3,"value"]],template:function t(e,n){if(1&e&&(Qp(0,a5,30,12,"ng-container",0),Qp(1,r5,1,0,"ng-template",null,1,ib)),2&e){const t=$p(2);Dm("ngIf",n.cardMetadata)("ngIfElse",t)}},directives:[dM,MM,sW,AV,vV,LY,IU,BU,lM,KH,lW,XH,aW,fM,gM],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, 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.mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], 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/**
     * @license
     * Copyright 2010-2021 Three.js Authors
     * SPDX-License-Identifier: MIT
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this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,o=e.elements,i=this.elements,a=n[0],r=n[3],s=n[6],l=n[1],c=n[4],d=n[7],p=n[2],m=n[5],u=n[8],f=o[0],g=o[3],h=o[6],b=o[1],y=o[4],_=o[7],C=o[2],M=o[5],v=o[8];return i[0]=a*f+r*b+s*C,i[3]=a*g+r*y+s*M,i[6]=a*h+r*_+s*v,i[1]=l*f+c*b+d*C,i[4]=l*g+c*y+d*M,i[7]=l*h+c*_+d*v,i[2]=p*f+m*b+u*C,i[5]=p*g+m*y+u*M,i[8]=p*h+m*_+u*v,this}multiplyScalar(t){const e=this.elements;return 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this.set(n*s,n*l,-n*(s*a+l*r)+a+t,-o*l,o*s,-o*(-l*a+s*r)+r+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),o=this.elements,i=o[0],a=o[3],r=o[6],s=o[1],l=o[4],c=o[7];return o[0]=e*i+n*s,o[3]=e*a+n*l,o[6]=e*r+n*c,o[1]=-n*i+e*s,o[4]=-n*a+e*l,o[7]=-n*r+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}let tat;$it.prototype.isMatrix3=!0;class eat{static getDataURL(t){if(/^data:/i.test(t.src))return t.src;if("undefined"==typeof HTMLCanvasElement)return t.src;let e;if(t instanceof HTMLCanvasElement)e=t;else{void 0===tat&&(tat=document.createElementNS("http://www.w3.org/1999/xhtml","canvas")),tat.width=t.width,tat.height=t.height;const n=tat.getContext("2d");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=tat}return e.width>2048||e.height>2048?(console.warn("THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons",t),e.toDataURL("image/jpeg",.6)):e.toDataURL("image/png")}}let nat=0;class oat extends Uit{constructor(t=oat.DEFAULT_IMAGE,e=oat.DEFAULT_MAPPING,n=1001,o=1001,i=1006,a=1008,r=1023,s=1009,l=1,c=3e3){super(),Object.defineProperty(this,"id",{value:nat++}),this.uuid=qit(),this.name="",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=n,this.wrapT=o,this.magFilter=i,this.minFilter=a,this.anisotropy=l,this.format=r,this.internalFormat=null,this.type=s,this.offset=new Qit(0,0),this.repeat=new Qit(1,1),this.center=new Qit(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new $it,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.encoding=c,this.version=0,this.onUpdate=null,this.isRenderTargetTexture=!1}updateMatrix(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}clone(){return(new this.constructor).copy(this)}copy(t){return this.name=t.name,this.image=t.image,this.mipmaps=t.mipmaps.slice(0),this.mapping=t.mapping,this.wrapS=t.wrapS,this.wrapT=t.wrapT,this.magFilter=t.magFilter,this.minFilter=t.minFilter,this.anisotropy=t.anisotropy,this.format=t.format,this.internalFormat=t.internalFormat,this.type=t.type,this.offset.copy(t.offset),this.repeat.copy(t.repeat),this.center.copy(t.center),this.rotation=t.rotation,this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrix.copy(t.matrix),this.generateMipmaps=t.generateMipmaps,this.premultiplyAlpha=t.premultiplyAlpha,this.flipY=t.flipY,this.unpackAlignment=t.unpackAlignment,this.encoding=t.encoding,this}toJSON(t){const e=void 0===t||"string"==typeof t;if(!e&&void 0!==t.textures[this.uuid])return t.textures[this.uuid];const n={metadata:{version:4.5,type:"Texture",generator:"Texture.toJSON"},uuid:this.uuid,name:this.name,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){const o=this.image;if(void 0===o.uuid&&(o.uuid=qit()),!e&&void 0===t.images[o.uuid]){let e;if(Array.isArray(o)){e=[];for(let t=0,n=o.length;t<n;t++)e.push(iat(o[t].isDataTexture?o[t].image:o[t]))}else e=iat(o);t.images[o.uuid]={uuid:o.uuid,url:e}}n.image=o.uuid}return e||(t.textures[this.uuid]=n),n}dispose(){this.dispatchEvent({type:"dispose"})}transformUv(t){if(300!==this.mapping)return t;if(t.applyMatrix3(this.matrix),t.x<0||t.x>1)switch(this.wrapS){case uit:t.x=t.x-Math.floor(t.x);break;case fit:t.x=t.x<0?0:1;break;case git:t.x=1===Math.abs(Math.floor(t.x)%2)?Math.ceil(t.x)-t.x:t.x-Math.floor(t.x)}if(t.y<0||t.y>1)switch(this.wrapT){case uit:t.y=t.y-Math.floor(t.y);break;case fit:t.y=t.y<0?0:1;break;case git:t.y=1===Math.abs(Math.floor(t.y)%2)?Math.ceil(t.y)-t.y:t.y-Math.floor(t.y)}return this.flipY&&(t.y=1-t.y),t}set needsUpdate(t){!0===t&&this.version++}}function iat(t){return"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof ImageBitmap&&t instanceof ImageBitmap?eat.getDataURL(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn("THREE.Texture: Unable to serialize Texture."),{})}oat.DEFAULT_IMAGE=void 0,oat.DEFAULT_MAPPING=300,oat.prototype.isTexture=!0;class aat{constructor(t=0,e=0,n=0,o=1){this.x=t,this.y=e,this.z=n,this.w=o}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,o){return this.x=t,this.y=e,this.z=n,this.w=o,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector4: .add() now only accepts one argument. 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Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,o=this.z,i=this.w,a=t.elements;return this.x=a[0]*e+a[4]*n+a[8]*o+a[12]*i,this.y=a[1]*e+a[5]*n+a[9]*o+a[13]*i,this.z=a[2]*e+a[6]*n+a[10]*o+a[14]*i,this.w=a[3]*e+a[7]*n+a[11]*o+a[15]*i,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,o,i;const a=.01,r=.1,s=t.elements,l=s[0],c=s[4],d=s[8],p=s[1],m=s[5],u=s[9],f=s[2],g=s[6],h=s[10];if(Math.abs(c-p)<a&&Math.abs(d-f)<a&&Math.abs(u-g)<a){if(Math.abs(c+p)<r&&Math.abs(d+f)<r&&Math.abs(u+g)<r&&Math.abs(l+m+h-3)<r)return this.set(1,0,0,0),this;e=Math.PI;const t=(l+1)/2,s=(m+1)/2,b=(h+1)/2,y=(c+p)/4,_=(d+f)/4,C=(u+g)/4;return t>s&&t>b?t<a?(n=0,o=.707106781,i=.707106781):(n=Math.sqrt(t),o=y/n,i=_/n):s>b?s<a?(n=.707106781,o=0,i=.707106781):(o=Math.sqrt(s),n=y/o,i=C/o):b<a?(n=.707106781,o=.707106781,i=0):(i=Math.sqrt(b),n=_/i,o=C/i),this.set(n,o,i,e),this}let b=Math.sqrt((g-u)*(g-u)+(d-f)*(d-f)+(p-c)*(p-c));return Math.abs(b)<.001&&(b=1),this.x=(g-u)/b,this.y=(d-f)/b,this.z=(p-c)/b,this.w=Math.acos((l+m+h-1)/2),this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this.w=Math.min(this.w,t.w),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this.w=Math.max(this.w,t.w),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this.w=Math.max(t.w,Math.min(e.w,this.w)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this.w=Math.max(t,Math.min(e,this.w)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this.w=Math.floor(this.w),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this.w=Math.ceil(this.w),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this.w=Math.round(this.w),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this.w=this.w<0?Math.ceil(this.w):Math.floor(this.w),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this.w=-this.w,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z+this.w*t.w}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)+Math.abs(this.w)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this.w+=(t.w-this.w)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this.w=t.w+(e.w-t.w)*n,this}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z&&t.w===this.w}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this.w=t[e+3],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t[e+3]=this.w,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector4: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this.w=t.getW(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this.w=Math.random(),this}}aat.prototype.isVector4=!0;class rat extends Uit{constructor(t,e,n={}){super(),this.width=t,this.height=e,this.depth=1,this.scissor=new aat(0,0,t,e),this.scissorTest=!1,this.viewport=new aat(0,0,t,e),this.texture=new oat(void 0,n.mapping,n.wrapS,n.wrapT,n.magFilter,n.minFilter,n.format,n.type,n.anisotropy,n.encoding),this.texture.isRenderTargetTexture=!0,this.texture.image={width:t,height:e,depth:1},this.texture.generateMipmaps=void 0!==n.generateMipmaps&&n.generateMipmaps,this.texture.minFilter=void 0!==n.minFilter?n.minFilter:bit,this.depthBuffer=void 0===n.depthBuffer||n.depthBuffer,this.stencilBuffer=void 0!==n.stencilBuffer&&n.stencilBuffer,this.depthTexture=void 0!==n.depthTexture?n.depthTexture:null}setTexture(t){t.image={width:this.width,height:this.height,depth:this.depth},this.texture=t}setSize(t,e,n=1){this.width===t&&this.height===e&&this.depth===n||(this.width=t,this.height=e,this.depth=n,this.texture.image.width=t,this.texture.image.height=e,this.texture.image.depth=n,this.dispose()),this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e)}clone(){return(new this.constructor).copy(this)}copy(t){return this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.copy(t.viewport),this.texture=t.texture.clone(),this.texture.image={...this.texture.image},this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this}dispose(){this.dispatchEvent({type:"dispose"})}}rat.prototype.isWebGLRenderTarget=!0,class extends rat{constructor(t,e,n){super(t,e);const o=this.texture;this.texture=[];for(let t=0;t<n;t++)this.texture[t]=o.clone()}setSize(t,e,n=1){if(this.width!==t||this.height!==e||this.depth!==n){this.width=t,this.height=e,this.depth=n;for(let o=0,i=this.texture.length;o<i;o++)this.texture[o].image.width=t,this.texture[o].image.height=e,this.texture[o].image.depth=n;this.dispose()}return this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e),this}copy(t){this.dispose(),this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.set(0,0,this.width,this.height),this.scissor.set(0,0,this.width,this.height),this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this.texture.length=0;for(let e=0,n=t.texture.length;e<n;e++)this.texture[e]=t.texture[e].clone();return this}}.prototype.isWebGLMultipleRenderTargets=!0;class sat extends rat{constructor(t,e,n){super(t,e,n),this.samples=4}copy(t){return super.copy.call(this,t),this.samples=t.samples,this}}sat.prototype.isWebGLMultisampleRenderTarget=!0;class lat{constructor(t=0,e=0,n=0,o=1){this._x=t,this._y=e,this._z=n,this._w=o}static slerp(t,e,n,o){return console.warn("THREE.Quaternion: Static .slerp() has been deprecated. Use qm.slerpQuaternions( qa, qb, t ) instead."),n.slerpQuaternions(t,e,o)}static slerpFlat(t,e,n,o,i,a,r){let s=n[o+0],l=n[o+1],c=n[o+2],d=n[o+3];const p=i[a+0],m=i[a+1],u=i[a+2],f=i[a+3];if(0===r)return t[e+0]=s,t[e+1]=l,t[e+2]=c,void(t[e+3]=d);if(1===r)return t[e+0]=p,t[e+1]=m,t[e+2]=u,void(t[e+3]=f);if(d!==f||s!==p||l!==m||c!==u){let t=1-r;const e=s*p+l*m+c*u+d*f,n=e>=0?1:-1,o=1-e*e;if(o>Number.EPSILON){const i=Math.sqrt(o),a=Math.atan2(i,e*n);t=Math.sin(t*a)/i,r=Math.sin(r*a)/i}const i=r*n;if(s=s*t+p*i,l=l*t+m*i,c=c*t+u*i,d=d*t+f*i,t===1-r){const t=1/Math.sqrt(s*s+l*l+c*c+d*d);s*=t,l*=t,c*=t,d*=t}}t[e]=s,t[e+1]=l,t[e+2]=c,t[e+3]=d}static multiplyQuaternionsFlat(t,e,n,o,i,a){const r=n[o],s=n[o+1],l=n[o+2],c=n[o+3],d=i[a],p=i[a+1],m=i[a+2],u=i[a+3];return t[e]=r*u+c*d+s*m-l*p,t[e+1]=s*u+c*p+l*d-r*m,t[e+2]=l*u+c*m+r*p-s*d,t[e+3]=c*u-r*d-s*p-l*m,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,o){return this._x=t,this._y=e,this._z=n,this._w=o,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error("THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.");const n=t._x,o=t._y,i=t._z,a=t._order,r=Math.cos,s=Math.sin,l=r(n/2),c=r(o/2),d=r(i/2),p=s(n/2),m=s(o/2),u=s(i/2);switch(a){case"XYZ":this._x=p*c*d+l*m*u,this._y=l*m*d-p*c*u,this._z=l*c*u+p*m*d,this._w=l*c*d-p*m*u;break;case"YXZ":this._x=p*c*d+l*m*u,this._y=l*m*d-p*c*u,this._z=l*c*u-p*m*d,this._w=l*c*d+p*m*u;break;case"ZXY":this._x=p*c*d-l*m*u,this._y=l*m*d+p*c*u,this._z=l*c*u+p*m*d,this._w=l*c*d-p*m*u;break;case"ZYX":this._x=p*c*d-l*m*u,this._y=l*m*d+p*c*u,this._z=l*c*u-p*m*d,this._w=l*c*d+p*m*u;break;case"YZX":this._x=p*c*d+l*m*u,this._y=l*m*d+p*c*u,this._z=l*c*u-p*m*d,this._w=l*c*d-p*m*u;break;case"XZY":this._x=p*c*d-l*m*u,this._y=l*m*d-p*c*u,this._z=l*c*u+p*m*d,this._w=l*c*d+p*m*u;break;default:console.warn("THREE.Quaternion: .setFromEuler() encountered an unknown order: "+a)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,o=Math.sin(n);return this._x=t.x*o,this._y=t.y*o,this._z=t.z*o,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],o=e[4],i=e[8],a=e[1],r=e[5],s=e[9],l=e[2],c=e[6],d=e[10],p=n+r+d;if(p>0){const t=.5/Math.sqrt(p+1);this._w=.25/t,this._x=(c-s)*t,this._y=(i-l)*t,this._z=(a-o)*t}else if(n>r&&n>d){const t=2*Math.sqrt(1+n-r-d);this._w=(c-s)/t,this._x=.25*t,this._y=(o+a)/t,this._z=(i+l)/t}else if(r>d){const t=2*Math.sqrt(1+r-n-d);this._w=(i-l)/t,this._x=(o+a)/t,this._y=.25*t,this._z=(s+c)/t}else{const t=2*Math.sqrt(1+d-n-r);this._w=(a-o)/t,this._x=(i+l)/t,this._y=(s+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return n<Number.EPSILON?(n=0,Math.abs(t.x)>Math.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(Zit(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const o=Math.min(1,e/n);return this.slerp(t,o),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn("THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead."),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,o=t._y,i=t._z,a=t._w,r=e._x,s=e._y,l=e._z,c=e._w;return this._x=n*c+a*r+o*l-i*s,this._y=o*c+a*s+i*r-n*l,this._z=i*c+a*l+n*s-o*r,this._w=a*c-n*r-o*s-i*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,o=this._y,i=this._z,a=this._w;let r=a*t._w+n*t._x+o*t._y+i*t._z;if(r<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,r=-r):this.copy(t),r>=1)return this._w=a,this._x=n,this._y=o,this._z=i,this;const s=1-r*r;if(s<=Number.EPSILON){const t=1-e;return this._w=t*a+e*this._w,this._x=t*n+e*this._x,this._y=t*o+e*this._y,this._z=t*i+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(s),c=Math.atan2(l,r),d=Math.sin((1-e)*c)/l,p=Math.sin(e*c)/l;return this._w=a*d+this._w*p,this._x=n*d+this._x*p,this._y=o*d+this._y*p,this._z=i*d+this._z*p,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}lat.prototype.isQuaternion=!0;class cat{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}set(t,e,n){return void 0===n&&(n=this.z),this.x=t,this.y=e,this.z=n,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this}sub(t,e){return void 0!==e?(console.warn("THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiply(t,e){return void 0!==e?(console.warn("THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead."),this.multiplyVectors(t,e)):(this.x*=t.x,this.y*=t.y,this.z*=t.z,this)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}applyEuler(t){return t&&t.isEuler||console.error("THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order."),this.applyQuaternion(pat.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(pat.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,o=this.z,i=t.elements;return this.x=i[0]*e+i[3]*n+i[6]*o,this.y=i[1]*e+i[4]*n+i[7]*o,this.z=i[2]*e+i[5]*n+i[8]*o,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,o=this.z,i=t.elements,a=1/(i[3]*e+i[7]*n+i[11]*o+i[15]);return this.x=(i[0]*e+i[4]*n+i[8]*o+i[12])*a,this.y=(i[1]*e+i[5]*n+i[9]*o+i[13])*a,this.z=(i[2]*e+i[6]*n+i[10]*o+i[14])*a,this}applyQuaternion(t){const e=this.x,n=this.y,o=this.z,i=t.x,a=t.y,r=t.z,s=t.w,l=s*e+a*o-r*n,c=s*n+r*e-i*o,d=s*o+i*n-a*e,p=-i*e-a*n-r*o;return this.x=l*s+p*-i+c*-r-d*-a,this.y=c*s+p*-a+d*-i-l*-r,this.z=d*s+p*-r+l*-a-c*-i,this}project(t){return this.applyMatrix4(t.matrixWorldInverse).applyMatrix4(t.projectionMatrix)}unproject(t){return this.applyMatrix4(t.projectionMatrixInverse).applyMatrix4(t.matrixWorld)}transformDirection(t){const e=this.x,n=this.y,o=this.z,i=t.elements;return this.x=i[0]*e+i[4]*n+i[8]*o,this.y=i[1]*e+i[5]*n+i[9]*o,this.z=i[2]*e+i[6]*n+i[10]*o,this.normalize()}divide(t){return this.x/=t.x,this.y/=t.y,this.z/=t.z,this}divideScalar(t){return this.multiplyScalar(1/t)}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this}cross(t,e){return void 0!==e?(console.warn("THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead."),this.crossVectors(t,e)):this.crossVectors(this,t)}crossVectors(t,e){const n=t.x,o=t.y,i=t.z,a=e.x,r=e.y,s=e.z;return this.x=o*s-i*r,this.y=i*a-n*s,this.z=n*r-o*a,this}projectOnVector(t){const e=t.lengthSq();if(0===e)return this.set(0,0,0);const n=t.dot(this)/e;return this.copy(t).multiplyScalar(n)}projectOnPlane(t){return dat.copy(this).projectOnVector(t),this.sub(dat)}reflect(t){return this.sub(dat.copy(t).multiplyScalar(2*this.dot(t)))}angleTo(t){const e=Math.sqrt(this.lengthSq()*t.lengthSq());if(0===e)return Math.PI/2;const n=this.dot(t)/e;return Math.acos(Zit(n,-1,1))}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y,o=this.z-t.z;return e*e+n*n+o*o}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)+Math.abs(this.z-t.z)}setFromSpherical(t){return this.setFromSphericalCoords(t.radius,t.phi,t.theta)}setFromSphericalCoords(t,e,n){const o=Math.sin(e)*t;return this.x=o*Math.sin(n),this.y=Math.cos(e)*t,this.z=o*Math.cos(n),this}setFromCylindrical(t){return this.setFromCylindricalCoords(t.radius,t.theta,t.y)}setFromCylindricalCoords(t,e,n){return this.x=t*Math.sin(e),this.y=n,this.z=t*Math.cos(e),this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}setFromMatrixScale(t){const e=this.setFromMatrixColumn(t,0).length(),n=this.setFromMatrixColumn(t,1).length(),o=this.setFromMatrixColumn(t,2).length();return this.x=e,this.y=n,this.z=o,this}setFromMatrixColumn(t,e){return this.fromArray(t.elements,4*e)}setFromMatrix3Column(t,e){return this.fromArray(t.elements,3*e)}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector3: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}}cat.prototype.isVector3=!0;const dat=new cat,pat=new lat;class mat{constructor(t=new cat(1/0,1/0,1/0),e=new cat(-1/0,-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromArray(t){let e=1/0,n=1/0,o=1/0,i=-1/0,a=-1/0,r=-1/0;for(let s=0,l=t.length;s<l;s+=3){const l=t[s],c=t[s+1],d=t[s+2];l<e&&(e=l),c<n&&(n=c),d<o&&(o=d),l>i&&(i=l),c>a&&(a=c),d>r&&(r=d)}return this.min.set(e,n,o),this.max.set(i,a,r),this}setFromBufferAttribute(t){let e=1/0,n=1/0,o=1/0,i=-1/0,a=-1/0,r=-1/0;for(let s=0,l=t.count;s<l;s++){const l=t.getX(s),c=t.getY(s),d=t.getZ(s);l<e&&(e=l),c<n&&(n=c),d<o&&(o=d),l>i&&(i=l),c>a&&(a=c),d>r&&(r=d)}return this.min.set(e,n,o),this.max.set(i,a,r),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;e<n;e++)this.expandByPoint(t[e]);return this}setFromCenterAndSize(t,e){const n=fat.copy(e).multiplyScalar(.5);return this.min.copy(t).sub(n),this.max.copy(t).add(n),this}setFromObject(t){return this.makeEmpty(),this.expandByObject(t)}clone(){return(new this.constructor).copy(this)}copy(t){return this.min.copy(t.min),this.max.copy(t.max),this}makeEmpty(){return this.min.x=this.min.y=this.min.z=1/0,this.max.x=this.max.y=this.max.z=-1/0,this}isEmpty(){return this.max.x<this.min.x||this.max.y<this.min.y||this.max.z<this.min.z}getCenter(t){return this.isEmpty()?t.set(0,0,0):t.addVectors(this.min,this.max).multiplyScalar(.5)}getSize(t){return this.isEmpty()?t.set(0,0,0):t.subVectors(this.max,this.min)}expandByPoint(t){return this.min.min(t),this.max.max(t),this}expandByVector(t){return this.min.sub(t),this.max.add(t),this}expandByScalar(t){return this.min.addScalar(-t),this.max.addScalar(t),this}expandByObject(t){t.updateWorldMatrix(!1,!1);const e=t.geometry;void 0!==e&&(null===e.boundingBox&&e.computeBoundingBox(),gat.copy(e.boundingBox),gat.applyMatrix4(t.matrixWorld),this.union(gat));const n=t.children;for(let t=0,e=n.length;t<e;t++)this.expandByObject(n[t]);return this}containsPoint(t){return!(t.x<this.min.x||t.x>this.max.x||t.y<this.min.y||t.y>this.max.y||t.z<this.min.z||t.z>this.max.z)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y&&this.min.z<=t.min.z&&t.max.z<=this.max.z}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y),(t.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(t){return!(t.max.x<this.min.x||t.min.x>this.max.x||t.max.y<this.min.y||t.min.y>this.max.y||t.max.z<this.min.z||t.min.z>this.max.z)}intersectsSphere(t){return this.clampPoint(t.center,fat),fat.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,n;return t.normal.x>0?(e=t.normal.x*this.min.x,n=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,n=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,n+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,n+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,n+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,n+=t.normal.z*this.min.z),e<=-t.constant&&n>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(vat),xat.subVectors(this.max,vat),hat.subVectors(t.a,vat),bat.subVectors(t.b,vat),yat.subVectors(t.c,vat),_at.subVectors(bat,hat),Cat.subVectors(yat,bat),Mat.subVectors(hat,yat);let e=[0,-_at.z,_at.y,0,-Cat.z,Cat.y,0,-Mat.z,Mat.y,_at.z,0,-_at.x,Cat.z,0,-Cat.x,Mat.z,0,-Mat.x,-_at.y,_at.x,0,-Cat.y,Cat.x,0,-Mat.y,Mat.x,0];return!!wat(e,hat,bat,yat,xat)&&(e=[1,0,0,0,1,0,0,0,1],!!wat(e,hat,bat,yat,xat)&&(Oat.crossVectors(_at,Cat),e=[Oat.x,Oat.y,Oat.z],wat(e,hat,bat,yat,xat)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return fat.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(fat).length(),t}intersect(t){return this.min.max(t.min),this.max.min(t.max),this.isEmpty()&&this.makeEmpty(),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}applyMatrix4(t){return this.isEmpty()||(uat[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),uat[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),uat[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),uat[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),uat[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),uat[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),uat[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),uat[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(uat)),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}mat.prototype.isBox3=!0;const uat=[new cat,new cat,new cat,new cat,new cat,new cat,new cat,new cat],fat=new cat,gat=new mat,hat=new cat,bat=new cat,yat=new cat,_at=new cat,Cat=new cat,Mat=new cat,vat=new cat,xat=new cat,Oat=new cat,Pat=new cat;function wat(t,e,n,o,i){for(let a=0,r=t.length-3;a<=r;a+=3){Pat.fromArray(t,a);const r=i.x*Math.abs(Pat.x)+i.y*Math.abs(Pat.y)+i.z*Math.abs(Pat.z),s=e.dot(Pat),l=n.dot(Pat),c=o.dot(Pat);if(Math.max(-Math.max(s,l,c),Math.min(s,l,c))>r)return!1}return!0}const kat=new mat,Sat=new cat,Dat=new cat,Eat=new cat;class Rat{constructor(t=new cat,e=-1){this.center=t,this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}setFromPoints(t,e){const n=this.center;void 0!==e?n.copy(e):kat.setFromPoints(t).getCenter(n);let o=0;for(let e=0,i=t.length;e<i;e++)o=Math.max(o,n.distanceToSquared(t[e]));return this.radius=Math.sqrt(o),this}copy(t){return this.center.copy(t.center),this.radius=t.radius,this}isEmpty(){return this.radius<0}makeEmpty(){return this.center.set(0,0,0),this.radius=-1,this}containsPoint(t){return t.distanceToSquared(this.center)<=this.radius*this.radius}distanceToPoint(t){return t.distanceTo(this.center)-this.radius}intersectsSphere(t){const e=this.radius+t.radius;return t.center.distanceToSquared(this.center)<=e*e}intersectsBox(t){return t.intersectsSphere(this)}intersectsPlane(t){return Math.abs(t.distanceToPoint(this.center))<=this.radius}clampPoint(t,e){const n=this.center.distanceToSquared(t);return e.copy(t),n>this.radius*this.radius&&(e.sub(this.center).normalize(),e.multiplyScalar(this.radius).add(this.center)),e}getBoundingBox(t){return this.isEmpty()?(t.makeEmpty(),t):(t.set(this.center,this.center),t.expandByScalar(this.radius),t)}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}expandByPoint(t){Eat.subVectors(t,this.center);const e=Eat.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(Eat.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return Dat.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(Sat.copy(t.center).add(Dat)),this.expandByPoint(Sat.copy(t.center).sub(Dat)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const Aat=new cat,Tat=new cat,Nat=new cat,zat=new cat,Iat=new cat,Hat=new cat,Fat=new cat;class Lat{constructor(t=new cat,e=new cat(0,0,-1)){this.origin=t,this.direction=e}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return e.copy(this.direction).multiplyScalar(t).add(this.origin)}lookAt(t){return this.direction.copy(t).sub(this.origin).normalize(),this}recast(t){return this.origin.copy(this.at(t,Aat)),this}closestPointToPoint(t,e){e.subVectors(t,this.origin);const n=e.dot(this.direction);return n<0?e.copy(this.origin):e.copy(this.direction).multiplyScalar(n).add(this.origin)}distanceToPoint(t){return Math.sqrt(this.distanceSqToPoint(t))}distanceSqToPoint(t){const e=Aat.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(Aat.copy(this.direction).multiplyScalar(e).add(this.origin),Aat.distanceToSquared(t))}distanceSqToSegment(t,e,n,o){Tat.copy(t).add(e).multiplyScalar(.5),Nat.copy(e).sub(t).normalize(),zat.copy(this.origin).sub(Tat);const i=.5*t.distanceTo(e),a=-this.direction.dot(Nat),r=zat.dot(this.direction),s=-zat.dot(Nat),l=zat.lengthSq(),c=Math.abs(1-a*a);let d,p,m,u;if(c>0)if(d=a*s-r,p=a*r-s,u=i*c,d>=0)if(p>=-u)if(p<=u){const t=1/c;d*=t,p*=t,m=d*(d+a*p+2*r)+p*(a*d+p+2*s)+l}else p=i,d=Math.max(0,-(a*p+r)),m=-d*d+p*(p+2*s)+l;else p=-i,d=Math.max(0,-(a*p+r)),m=-d*d+p*(p+2*s)+l;else p<=-u?(d=Math.max(0,-(-a*i+r)),p=d>0?-i:Math.min(Math.max(-i,-s),i),m=-d*d+p*(p+2*s)+l):p<=u?(d=0,p=Math.min(Math.max(-i,-s),i),m=p*(p+2*s)+l):(d=Math.max(0,-(a*i+r)),p=d>0?i:Math.min(Math.max(-i,-s),i),m=-d*d+p*(p+2*s)+l);else p=a>0?-i:i,d=Math.max(0,-(a*p+r)),m=-d*d+p*(p+2*s)+l;return n&&n.copy(this.direction).multiplyScalar(d).add(this.origin),o&&o.copy(Nat).multiplyScalar(p).add(Tat),m}intersectSphere(t,e){Aat.subVectors(t.center,this.origin);const n=Aat.dot(this.direction),o=Aat.dot(Aat)-n*n,i=t.radius*t.radius;if(o>i)return null;const a=Math.sqrt(i-o),r=n-a,s=n+a;return r<0&&s<0?null:this.at(r<0?s:r,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);return 0===e||t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,o,i,a,r,s;const l=1/this.direction.x,c=1/this.direction.y,d=1/this.direction.z,p=this.origin;return l>=0?(n=(t.min.x-p.x)*l,o=(t.max.x-p.x)*l):(n=(t.max.x-p.x)*l,o=(t.min.x-p.x)*l),c>=0?(i=(t.min.y-p.y)*c,a=(t.max.y-p.y)*c):(i=(t.max.y-p.y)*c,a=(t.min.y-p.y)*c),n>a||i>o?null:((i>n||n!=n)&&(n=i),(a<o||o!=o)&&(o=a),d>=0?(r=(t.min.z-p.z)*d,s=(t.max.z-p.z)*d):(r=(t.max.z-p.z)*d,s=(t.min.z-p.z)*d),n>s||r>o?null:((r>n||n!=n)&&(n=r),(s<o||o!=o)&&(o=s),o<0?null:this.at(n>=0?n:o,e)))}intersectsBox(t){return null!==this.intersectBox(t,Aat)}intersectTriangle(t,e,n,o,i){Iat.subVectors(e,t),Hat.subVectors(n,t),Fat.crossVectors(Iat,Hat);let a,r=this.direction.dot(Fat);if(r>0){if(o)return null;a=1}else{if(!(r<0))return null;a=-1,r=-r}zat.subVectors(this.origin,t);const s=a*this.direction.dot(Hat.crossVectors(zat,Hat));if(s<0)return null;const l=a*this.direction.dot(Iat.cross(zat));if(l<0)return null;if(s+l>r)return null;const c=-a*zat.dot(Fat);return c<0?null:this.at(c/r,i)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}class Bat{constructor(){this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],arguments.length>0&&console.error("THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.")}set(t,e,n,o,i,a,r,s,l,c,d,p,m,u,f,g){const h=this.elements;return h[0]=t,h[4]=e,h[8]=n,h[12]=o,h[1]=i,h[5]=a,h[9]=r,h[13]=s,h[2]=l,h[6]=c,h[10]=d,h[14]=p,h[3]=m,h[7]=u,h[11]=f,h[15]=g,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new Bat).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return this.set(e[0],e[3],e[6],0,e[1],e[4],e[7],0,e[2],e[5],e[8],0,0,0,0,1),this}extractBasis(t,e,n){return t.setFromMatrixColumn(this,0),e.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(t,e,n){return this.set(t.x,e.x,n.x,0,t.y,e.y,n.y,0,t.z,e.z,n.z,0,0,0,0,1),this}extractRotation(t){const e=this.elements,n=t.elements,o=1/Vat.setFromMatrixColumn(t,0).length(),i=1/Vat.setFromMatrixColumn(t,1).length(),a=1/Vat.setFromMatrixColumn(t,2).length();return e[0]=n[0]*o,e[1]=n[1]*o,e[2]=n[2]*o,e[3]=0,e[4]=n[4]*i,e[5]=n[5]*i,e[6]=n[6]*i,e[7]=0,e[8]=n[8]*a,e[9]=n[9]*a,e[10]=n[10]*a,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error("THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.");const e=this.elements,n=t.x,o=t.y,i=t.z,a=Math.cos(n),r=Math.sin(n),s=Math.cos(o),l=Math.sin(o),c=Math.cos(i),d=Math.sin(i);if("XYZ"===t.order){const t=a*c,n=a*d,o=r*c,i=r*d;e[0]=s*c,e[4]=-s*d,e[8]=l,e[1]=n+o*l,e[5]=t-i*l,e[9]=-r*s,e[2]=i-t*l,e[6]=o+n*l,e[10]=a*s}else if("YXZ"===t.order){const t=s*c,n=s*d,o=l*c,i=l*d;e[0]=t+i*r,e[4]=o*r-n,e[8]=a*l,e[1]=a*d,e[5]=a*c,e[9]=-r,e[2]=n*r-o,e[6]=i+t*r,e[10]=a*s}else if("ZXY"===t.order){const t=s*c,n=s*d,o=l*c,i=l*d;e[0]=t-i*r,e[4]=-a*d,e[8]=o+n*r,e[1]=n+o*r,e[5]=a*c,e[9]=i-t*r,e[2]=-a*l,e[6]=r,e[10]=a*s}else if("ZYX"===t.order){const t=a*c,n=a*d,o=r*c,i=r*d;e[0]=s*c,e[4]=o*l-n,e[8]=t*l+i,e[1]=s*d,e[5]=i*l+t,e[9]=n*l-o,e[2]=-l,e[6]=r*s,e[10]=a*s}else if("YZX"===t.order){const t=a*s,n=a*l,o=r*s,i=r*l;e[0]=s*c,e[4]=i-t*d,e[8]=o*d+n,e[1]=d,e[5]=a*c,e[9]=-r*c,e[2]=-l*c,e[6]=n*d+o,e[10]=t-i*d}else if("XZY"===t.order){const t=a*s,n=a*l,o=r*s,i=r*l;e[0]=s*c,e[4]=-d,e[8]=l*c,e[1]=t*d+i,e[5]=a*c,e[9]=n*d-o,e[2]=o*d-n,e[6]=r*c,e[10]=i*d+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(Uat,t,Gat)}lookAt(t,e,n){const o=this.elements;return qat.subVectors(t,e),0===qat.lengthSq()&&(qat.z=1),qat.normalize(),Wat.crossVectors(n,qat),0===Wat.lengthSq()&&(1===Math.abs(n.z)?qat.x+=1e-4:qat.z+=1e-4,qat.normalize(),Wat.crossVectors(n,qat)),Wat.normalize(),Yat.crossVectors(qat,Wat),o[0]=Wat.x,o[4]=Yat.x,o[8]=qat.x,o[1]=Wat.y,o[5]=Yat.y,o[9]=qat.y,o[2]=Wat.z,o[6]=Yat.z,o[10]=qat.z,this}multiply(t,e){return void 0!==e?(console.warn("THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead."),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,o=e.elements,i=this.elements,a=n[0],r=n[4],s=n[8],l=n[12],c=n[1],d=n[5],p=n[9],m=n[13],u=n[2],f=n[6],g=n[10],h=n[14],b=n[3],y=n[7],_=n[11],C=n[15],M=o[0],v=o[4],x=o[8],O=o[12],P=o[1],w=o[5],k=o[9],S=o[13],D=o[2],E=o[6],R=o[10],A=o[14],T=o[3],N=o[7],z=o[11],I=o[15];return i[0]=a*M+r*P+s*D+l*T,i[4]=a*v+r*w+s*E+l*N,i[8]=a*x+r*k+s*R+l*z,i[12]=a*O+r*S+s*A+l*I,i[1]=c*M+d*P+p*D+m*T,i[5]=c*v+d*w+p*E+m*N,i[9]=c*x+d*k+p*R+m*z,i[13]=c*O+d*S+p*A+m*I,i[2]=u*M+f*P+g*D+h*T,i[6]=u*v+f*w+g*E+h*N,i[10]=u*x+f*k+g*R+h*z,i[14]=u*O+f*S+g*A+h*I,i[3]=b*M+y*P+_*D+C*T,i[7]=b*v+y*w+_*E+C*N,i[11]=b*x+y*k+_*R+C*z,i[15]=b*O+y*S+_*A+C*I,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],o=t[8],i=t[12],a=t[1],r=t[5],s=t[9],l=t[13],c=t[2],d=t[6],p=t[10],m=t[14];return t[3]*(+i*s*d-o*l*d-i*r*p+n*l*p+o*r*m-n*s*m)+t[7]*(+e*s*m-e*l*p+i*a*p-o*a*m+o*l*c-i*s*c)+t[11]*(+e*l*d-e*r*m-i*a*d+n*a*m+i*r*c-n*l*c)+t[15]*(-o*r*c-e*s*d+e*r*p+o*a*d-n*a*p+n*s*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const o=this.elements;return t.isVector3?(o[12]=t.x,o[13]=t.y,o[14]=t.z):(o[12]=t,o[13]=e,o[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],o=t[2],i=t[3],a=t[4],r=t[5],s=t[6],l=t[7],c=t[8],d=t[9],p=t[10],m=t[11],u=t[12],f=t[13],g=t[14],h=t[15],b=d*g*l-f*p*l+f*s*m-r*g*m-d*s*h+r*p*h,y=u*p*l-c*g*l-u*s*m+a*g*m+c*s*h-a*p*h,_=c*f*l-u*d*l+u*r*m-a*f*m-c*r*h+a*d*h,C=u*d*s-c*f*s-u*r*p+a*f*p+c*r*g-a*d*g,M=e*b+n*y+o*_+i*C;if(0===M)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const v=1/M;return t[0]=b*v,t[1]=(f*p*i-d*g*i-f*o*m+n*g*m+d*o*h-n*p*h)*v,t[2]=(r*g*i-f*s*i+f*o*l-n*g*l-r*o*h+n*s*h)*v,t[3]=(d*s*i-r*p*i-d*o*l+n*p*l+r*o*m-n*s*m)*v,t[4]=y*v,t[5]=(c*g*i-u*p*i+u*o*m-e*g*m-c*o*h+e*p*h)*v,t[6]=(u*s*i-a*g*i-u*o*l+e*g*l+a*o*h-e*s*h)*v,t[7]=(a*p*i-c*s*i+c*o*l-e*p*l-a*o*m+e*s*m)*v,t[8]=_*v,t[9]=(u*d*i-c*f*i-u*n*m+e*f*m+c*n*h-e*d*h)*v,t[10]=(a*f*i-u*r*i+u*n*l-e*f*l-a*n*h+e*r*h)*v,t[11]=(c*r*i-a*d*i-c*n*l+e*d*l+a*n*m-e*r*m)*v,t[12]=C*v,t[13]=(c*f*o-u*d*o+u*n*p-e*f*p-c*n*g+e*d*g)*v,t[14]=(u*r*o-a*f*o-u*n*s+e*f*s+a*n*g-e*r*g)*v,t[15]=(a*d*o-c*r*o+c*n*s-e*d*s-a*n*p+e*r*p)*v,this}scale(t){const e=this.elements,n=t.x,o=t.y,i=t.z;return e[0]*=n,e[4]*=o,e[8]*=i,e[1]*=n,e[5]*=o,e[9]*=i,e[2]*=n,e[6]*=o,e[10]*=i,e[3]*=n,e[7]*=o,e[11]*=i,this}getMaxScaleOnAxis(){const t=this.elements;return Math.sqrt(Math.max(t[0]*t[0]+t[1]*t[1]+t[2]*t[2],t[4]*t[4]+t[5]*t[5]+t[6]*t[6],t[8]*t[8]+t[9]*t[9]+t[10]*t[10]))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),o=Math.sin(e),i=1-n,a=t.x,r=t.y,s=t.z,l=i*a,c=i*r;return this.set(l*a+n,l*r-o*s,l*s+o*r,0,l*r+o*s,c*r+n,c*s-o*a,0,l*s-o*r,c*s+o*a,i*s*s+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,o,i,a){return this.set(1,n,i,0,t,1,a,0,e,o,1,0,0,0,0,1),this}compose(t,e,n){const o=this.elements,i=e._x,a=e._y,r=e._z,s=e._w,l=i+i,c=a+a,d=r+r,p=i*l,m=i*c,u=i*d,f=a*c,g=a*d,h=r*d,b=s*l,y=s*c,_=s*d,C=n.x,M=n.y,v=n.z;return o[0]=(1-(f+h))*C,o[1]=(m+_)*C,o[2]=(u-y)*C,o[3]=0,o[4]=(m-_)*M,o[5]=(1-(p+h))*M,o[6]=(g+b)*M,o[7]=0,o[8]=(u+y)*v,o[9]=(g-b)*v,o[10]=(1-(p+f))*v,o[11]=0,o[12]=t.x,o[13]=t.y,o[14]=t.z,o[15]=1,this}decompose(t,e,n){const o=this.elements;let i=Vat.set(o[0],o[1],o[2]).length();const a=Vat.set(o[4],o[5],o[6]).length(),r=Vat.set(o[8],o[9],o[10]).length();this.determinant()<0&&(i=-i),t.x=o[12],t.y=o[13],t.z=o[14],jat.copy(this);const s=1/i,l=1/a,c=1/r;return jat.elements[0]*=s,jat.elements[1]*=s,jat.elements[2]*=s,jat.elements[4]*=l,jat.elements[5]*=l,jat.elements[6]*=l,jat.elements[8]*=c,jat.elements[9]*=c,jat.elements[10]*=c,e.setFromRotationMatrix(jat),n.x=i,n.y=a,n.z=r,this}makePerspective(t,e,n,o,i,a){void 0===a&&console.warn("THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.");const r=this.elements,s=2*i/(n-o),l=(e+t)/(e-t),c=(n+o)/(n-o),d=-(a+i)/(a-i),p=-2*a*i/(a-i);return r[0]=2*i/(e-t),r[4]=0,r[8]=l,r[12]=0,r[1]=0,r[5]=s,r[9]=c,r[13]=0,r[2]=0,r[6]=0,r[10]=d,r[14]=p,r[3]=0,r[7]=0,r[11]=-1,r[15]=0,this}makeOrthographic(t,e,n,o,i,a){const r=this.elements,s=1/(e-t),l=1/(n-o),c=1/(a-i),d=(e+t)*s,p=(n+o)*l,m=(a+i)*c;return r[0]=2*s,r[4]=0,r[8]=0,r[12]=-d,r[1]=0,r[5]=2*l,r[9]=0,r[13]=-p,r[2]=0,r[6]=0,r[10]=-2*c,r[14]=-m,r[3]=0,r[7]=0,r[11]=0,r[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}Bat.prototype.isMatrix4=!0;const Vat=new cat,jat=new Bat,Uat=new cat(0,0,0),Gat=new cat(1,1,1),Wat=new cat,Yat=new cat,qat=new cat,Zat=new Bat,Xat=new lat;class Kat{constructor(t=0,e=0,n=0,o=Kat.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=o}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,o=this._order){return this._x=t,this._y=e,this._z=n,this._order=o,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const o=t.elements,i=o[0],a=o[4],r=o[8],s=o[1],l=o[5],c=o[9],d=o[2],p=o[6],m=o[10];switch(e){case"XYZ":this._y=Math.asin(Zit(r,-1,1)),Math.abs(r)<.9999999?(this._x=Math.atan2(-c,m),this._z=Math.atan2(-a,i)):(this._x=Math.atan2(p,l),this._z=0);break;case"YXZ":this._x=Math.asin(-Zit(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(r,m),this._z=Math.atan2(s,l)):(this._y=Math.atan2(-d,i),this._z=0);break;case"ZXY":this._x=Math.asin(Zit(p,-1,1)),Math.abs(p)<.9999999?(this._y=Math.atan2(-d,m),this._z=Math.atan2(-a,l)):(this._y=0,this._z=Math.atan2(s,i));break;case"ZYX":this._y=Math.asin(-Zit(d,-1,1)),Math.abs(d)<.9999999?(this._x=Math.atan2(p,m),this._z=Math.atan2(s,i)):(this._x=0,this._z=Math.atan2(-a,l));break;case"YZX":this._z=Math.asin(Zit(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-d,i)):(this._x=0,this._y=Math.atan2(r,m));break;case"XZY":this._z=Math.asin(-Zit(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(p,l),this._y=Math.atan2(r,i)):(this._x=Math.atan2(-c,m),this._y=0);break;default:console.warn("THREE.Euler: .setFromRotationMatrix() encountered an unknown order: "+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return Zat.makeRotationFromQuaternion(t),this.setFromRotationMatrix(Zat,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return Xat.setFromEuler(this),this.setFromQuaternion(Xat,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new cat(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}Kat.prototype.isEuler=!0,Kat.DefaultOrder="XYZ",Kat.RotationOrders=["XYZ","YZX","ZXY","XZY","YXZ","ZYX"];class Jat{constructor(){this.mask=1}set(t){this.mask=1<<t|0}enable(t){this.mask|=1<<t|0}enableAll(){this.mask=-1}toggle(t){this.mask^=1<<t|0}disable(t){this.mask&=~(1<<t|0)}disableAll(){this.mask=0}test(t){return 0!=(this.mask&t.mask)}}let Qat=0;const $at=new cat,trt=new lat,ert=new Bat,nrt=new cat,ort=new cat,irt=new cat,art=new lat,rrt=new cat(1,0,0),srt=new cat(0,1,0),lrt=new cat(0,0,1),crt={type:"added"},drt={type:"removed"};class prt extends Uit{constructor(){super(),Object.defineProperty(this,"id",{value:Qat++}),this.uuid=qit(),this.name="",this.type="Object3D",this.parent=null,this.children=[],this.up=prt.DefaultUp.clone();const t=new cat,e=new Kat,n=new lat,o=new cat(1,1,1);e._onChange((function i(){n.setFromEuler(e,!1)})),n._onChange((function a(){e.setFromQuaternion(n,void 0,!1)})),Object.defineProperties(this,{position:{configurable:!0,enumerable:!0,value:t},rotation:{configurable:!0,enumerable:!0,value:e},quaternion:{configurable:!0,enumerable:!0,value:n},scale:{configurable:!0,enumerable:!0,value:o},modelViewMatrix:{value:new Bat},normalMatrix:{value:new $it}}),this.matrix=new Bat,this.matrixWorld=new Bat,this.matrixAutoUpdate=prt.DefaultMatrixAutoUpdate,this.matrixWorldNeedsUpdate=!1,this.layers=new Jat,this.visible=!0,this.castShadow=!1,this.receiveShadow=!1,this.frustumCulled=!0,this.renderOrder=0,this.animations=[],this.userData={}}onBeforeRender(){}onAfterRender(){}applyMatrix4(t){this.matrixAutoUpdate&&this.updateMatrix(),this.matrix.premultiply(t),this.matrix.decompose(this.position,this.quaternion,this.scale)}applyQuaternion(t){return this.quaternion.premultiply(t),this}setRotationFromAxisAngle(t,e){this.quaternion.setFromAxisAngle(t,e)}setRotationFromEuler(t){this.quaternion.setFromEuler(t,!0)}setRotationFromMatrix(t){this.quaternion.setFromRotationMatrix(t)}setRotationFromQuaternion(t){this.quaternion.copy(t)}rotateOnAxis(t,e){return trt.setFromAxisAngle(t,e),this.quaternion.multiply(trt),this}rotateOnWorldAxis(t,e){return trt.setFromAxisAngle(t,e),this.quaternion.premultiply(trt),this}rotateX(t){return this.rotateOnAxis(rrt,t)}rotateY(t){return this.rotateOnAxis(srt,t)}rotateZ(t){return this.rotateOnAxis(lrt,t)}translateOnAxis(t,e){return $at.copy(t).applyQuaternion(this.quaternion),this.position.add($at.multiplyScalar(e)),this}translateX(t){return this.translateOnAxis(rrt,t)}translateY(t){return this.translateOnAxis(srt,t)}translateZ(t){return this.translateOnAxis(lrt,t)}localToWorld(t){return t.applyMatrix4(this.matrixWorld)}worldToLocal(t){return t.applyMatrix4(ert.copy(this.matrixWorld).invert())}lookAt(t,e,n){t.isVector3?nrt.copy(t):nrt.set(t,e,n);const o=this.parent;this.updateWorldMatrix(!0,!1),ort.setFromMatrixPosition(this.matrixWorld),this.isCamera||this.isLight?ert.lookAt(ort,nrt,this.up):ert.lookAt(nrt,ort,this.up),this.quaternion.setFromRotationMatrix(ert),o&&(ert.extractRotation(o.matrixWorld),trt.setFromRotationMatrix(ert),this.quaternion.premultiply(trt.invert()))}add(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.add(arguments[t]);return this}return t===this?(console.error("THREE.Object3D.add: object can't be added as a child of itself.",t),this):(t&&t.isObject3D?(null!==t.parent&&t.parent.remove(t),t.parent=this,this.children.push(t),t.dispatchEvent(crt)):console.error("THREE.Object3D.add: object not an instance of THREE.Object3D.",t),this)}remove(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.remove(arguments[t]);return this}const e=this.children.indexOf(t);return-1!==e&&(t.parent=null,this.children.splice(e,1),t.dispatchEvent(drt)),this}removeFromParent(){const t=this.parent;return null!==t&&t.remove(this),this}clear(){for(let t=0;t<this.children.length;t++){const e=this.children[t];e.parent=null,e.dispatchEvent(drt)}return this.children.length=0,this}attach(t){return this.updateWorldMatrix(!0,!1),ert.copy(this.matrixWorld).invert(),null!==t.parent&&(t.parent.updateWorldMatrix(!0,!1),ert.multiply(t.parent.matrixWorld)),t.applyMatrix4(ert),this.add(t),t.updateWorldMatrix(!1,!0),this}getObjectById(t){return this.getObjectByProperty("id",t)}getObjectByName(t){return this.getObjectByProperty("name",t)}getObjectByProperty(t,e){if(this[t]===e)return this;for(let n=0,o=this.children.length;n<o;n++){const o=this.children[n].getObjectByProperty(t,e);if(void 0!==o)return o}}getWorldPosition(t){return this.updateWorldMatrix(!0,!1),t.setFromMatrixPosition(this.matrixWorld)}getWorldQuaternion(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(ort,t,irt),t}getWorldScale(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(ort,art,t),t}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(e[8],e[9],e[10]).normalize()}raycast(){}traverse(t){t(this);const e=this.children;for(let n=0,o=e.length;n<o;n++)e[n].traverse(t)}traverseVisible(t){if(!1===this.visible)return;t(this);const e=this.children;for(let n=0,o=e.length;n<o;n++)e[n].traverseVisible(t)}traverseAncestors(t){const e=this.parent;null!==e&&(t(e),e.traverseAncestors(t))}updateMatrix(){this.matrix.compose(this.position,this.quaternion,this.scale),this.matrixWorldNeedsUpdate=!0}updateMatrixWorld(t){this.matrixAutoUpdate&&this.updateMatrix(),(this.matrixWorldNeedsUpdate||t)&&(null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),this.matrixWorldNeedsUpdate=!1,t=!0);const e=this.children;for(let n=0,o=e.length;n<o;n++)e[n].updateMatrixWorld(t)}updateWorldMatrix(t,e){const n=this.parent;if(!0===t&&null!==n&&n.updateWorldMatrix(!0,!1),this.matrixAutoUpdate&&this.updateMatrix(),null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),!0===e){const t=this.children;for(let e=0,n=t.length;e<n;e++)t[e].updateWorldMatrix(!1,!0)}}toJSON(t){const e=void 0===t||"string"==typeof t,n={};e&&(t={geometries:{},materials:{},textures:{},images:{},shapes:{},skeletons:{},animations:{}},n.metadata={version:4.5,type:"Object",generator:"Object3D.toJSON"});const o={};function i(e,n){return void 0===e[n.uuid]&&(e[n.uuid]=n.toJSON(t)),n.uuid}if(o.uuid=this.uuid,o.type=this.type,""!==this.name&&(o.name=this.name),!0===this.castShadow&&(o.castShadow=!0),!0===this.receiveShadow&&(o.receiveShadow=!0),!1===this.visible&&(o.visible=!1),!1===this.frustumCulled&&(o.frustumCulled=!1),0!==this.renderOrder&&(o.renderOrder=this.renderOrder),"{}"!==JSON.stringify(this.userData)&&(o.userData=this.userData),o.layers=this.layers.mask,o.matrix=this.matrix.toArray(),!1===this.matrixAutoUpdate&&(o.matrixAutoUpdate=!1),this.isInstancedMesh&&(o.type="InstancedMesh",o.count=this.count,o.instanceMatrix=this.instanceMatrix.toJSON(),null!==this.instanceColor&&(o.instanceColor=this.instanceColor.toJSON())),this.isScene)this.background&&(this.background.isColor?o.background=this.background.toJSON():this.background.isTexture&&(o.background=this.background.toJSON(t).uuid)),this.environment&&this.environment.isTexture&&(o.environment=this.environment.toJSON(t).uuid);else if(this.isMesh||this.isLine||this.isPoints){o.geometry=i(t.geometries,this.geometry);const e=this.geometry.parameters;if(void 0!==e&&void 0!==e.shapes){const n=e.shapes;if(Array.isArray(n))for(let e=0,o=n.length;e<o;e++)i(t.shapes,n[e]);else i(t.shapes,n)}}if(this.isSkinnedMesh&&(o.bindMode=this.bindMode,o.bindMatrix=this.bindMatrix.toArray(),void 0!==this.skeleton&&(i(t.skeletons,this.skeleton),o.skeleton=this.skeleton.uuid)),void 0!==this.material)if(Array.isArray(this.material)){const e=[];for(let n=0,o=this.material.length;n<o;n++)e.push(i(t.materials,this.material[n]));o.material=e}else o.material=i(t.materials,this.material);if(this.children.length>0){o.children=[];for(let e=0;e<this.children.length;e++)o.children.push(this.children[e].toJSON(t).object)}if(this.animations.length>0){o.animations=[];for(let e=0;e<this.animations.length;e++)o.animations.push(i(t.animations,this.animations[e]))}if(e){const e=a(t.geometries),o=a(t.materials),i=a(t.textures),r=a(t.images),s=a(t.shapes),l=a(t.skeletons),c=a(t.animations);e.length>0&&(n.geometries=e),o.length>0&&(n.materials=o),i.length>0&&(n.textures=i),r.length>0&&(n.images=r),s.length>0&&(n.shapes=s),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=o,n;function a(t){const e=[];for(const n in t){const o=t[n];delete o.metadata,e.push(o)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e<t.children.length;e++)this.add(t.children[e].clone());return this}}prt.DefaultUp=new cat(0,1,0),prt.DefaultMatrixAutoUpdate=!0,prt.prototype.isObject3D=!0;const mrt=new cat,urt=new cat,frt=new cat,grt=new cat,hrt=new cat,brt=new cat,yrt=new cat,_rt=new cat,Crt=new cat,Mrt=new cat;class vrt{constructor(t=new cat,e=new cat,n=new cat){this.a=t,this.b=e,this.c=n}static getNormal(t,e,n,o){o.subVectors(n,e),mrt.subVectors(t,e),o.cross(mrt);const i=o.lengthSq();return i>0?o.multiplyScalar(1/Math.sqrt(i)):o.set(0,0,0)}static getBarycoord(t,e,n,o,i){mrt.subVectors(o,e),urt.subVectors(n,e),frt.subVectors(t,e);const a=mrt.dot(mrt),r=mrt.dot(urt),s=mrt.dot(frt),l=urt.dot(urt),c=urt.dot(frt),d=a*l-r*r;if(0===d)return i.set(-2,-1,-1);const p=1/d,m=(l*s-r*c)*p,u=(a*c-r*s)*p;return i.set(1-m-u,u,m)}static containsPoint(t,e,n,o){return this.getBarycoord(t,e,n,o,grt),grt.x>=0&&grt.y>=0&&grt.x+grt.y<=1}static getUV(t,e,n,o,i,a,r,s){return this.getBarycoord(t,e,n,o,grt),s.set(0,0),s.addScaledVector(i,grt.x),s.addScaledVector(a,grt.y),s.addScaledVector(r,grt.z),s}static isFrontFacing(t,e,n,o){return mrt.subVectors(n,e),urt.subVectors(t,e),mrt.cross(urt).dot(o)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,o){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[o]),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return mrt.subVectors(this.c,this.b),urt.subVectors(this.a,this.b),.5*mrt.cross(urt).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return vrt.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return vrt.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,o,i){return vrt.getUV(t,this.a,this.b,this.c,e,n,o,i)}containsPoint(t){return vrt.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return vrt.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,o=this.b,i=this.c;let a,r;hrt.subVectors(o,n),brt.subVectors(i,n),_rt.subVectors(t,n);const s=hrt.dot(_rt),l=brt.dot(_rt);if(s<=0&&l<=0)return e.copy(n);Crt.subVectors(t,o);const c=hrt.dot(Crt),d=brt.dot(Crt);if(c>=0&&d<=c)return e.copy(o);const p=s*d-c*l;if(p<=0&&s>=0&&c<=0)return a=s/(s-c),e.copy(n).addScaledVector(hrt,a);Mrt.subVectors(t,i);const m=hrt.dot(Mrt),u=brt.dot(Mrt);if(u>=0&&m<=u)return e.copy(i);const f=m*l-s*u;if(f<=0&&l>=0&&u<=0)return r=l/(l-u),e.copy(n).addScaledVector(brt,r);const g=c*u-m*d;if(g<=0&&d-c>=0&&m-u>=0)return yrt.subVectors(i,o),r=(d-c)/(d-c+(m-u)),e.copy(o).addScaledVector(yrt,r);const h=1/(g+f+p);return a=f*h,r=p*h,e.copy(n).addScaledVector(hrt,a).addScaledVector(brt,r)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let xrt=0;class Ort extends Uit{constructor(){super(),Object.defineProperty(this,"id",{value:xrt++}),this.uuid=qit(),this.name="",this.type="Material",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=cit,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=3,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=519,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=Lit,this.stencilZFail=Lit,this.stencilZPass=Lit,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaTest=0,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0}onBuild(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn("THREE.Material: '"+e+"' parameter is undefined.");continue}if("shading"===e){console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead."),this.flatShading=1===n;continue}const o=this[e];void 0!==o?o&&o.isColor?o.set(n):o&&o.isVector3&&n&&n.isVector3?o.copy(n):this[e]=n:console.warn("THREE."+this.type+": '"+e+"' is not a property of this material.")}}toJSON(t){const e=void 0===t||"string"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:"Material",generator:"Material.toJSON"}};function o(t){const e=[];for(const n in t){const o=t[n];delete o.metadata,e.push(o)}return e}if(n.uuid=this.uuid,n.type=this.type,""!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),this.sheen&&this.sheen.isColor&&(n.sheen=this.sheen.getHex()),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),1!==this.blending&&(n.blending=this.blending),0!==this.side&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),"round"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),"round"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),"{}"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=o(t.textures),i=o(t.images);e.length>0&&(n.textures=e),i.length>0&&(n.images=i)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let o=0;o!==t;++o)n[o]=e[o].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:"dispose"})}set needsUpdate(t){!0===t&&this.version++}}Ort.prototype.isMaterial=!0;const Prt={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},wrt={h:0,s:0,l:0},krt={h:0,s:0,l:0};function Srt(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function Drt(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function Ert(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class Rrt{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):"number"==typeof t?this.setHex(t):"string"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=(function o(t,e){return(t%e+e)%e})(t,1),e=Zit(e,0,1),n=Zit(n,0,1),0===e)this.r=this.g=this.b=n;else{const o=n<=.5?n*(1+e):n+e-n*e,i=2*n-o;this.r=Srt(i,o,t+1/3),this.g=Srt(i,o,t),this.b=Srt(i,o,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn("THREE.Color: Alpha component of "+t+" will be ignored.")}let n;if(n=/^((?:rgb|hsl)a?)\(([^\)]*)\)/.exec(t)){let t;const o=n[2];switch(n[1]){case"rgb":case"rgba":if(t=/^\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(o))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\s*(\d+)\%\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(o))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case"hsl":case"hsla":if(t=/^\s*(\d*\.?\d+)\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(o)){const n=parseFloat(t[1])/360,o=parseInt(t[2],10)/100,i=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,o,i)}}}else if(n=/^\#([A-Fa-f\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=Prt[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn("THREE.Color: Unknown color "+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=Drt(t.r),this.g=Drt(t.g),this.b=Drt(t.b),this}copyLinearToSRGB(t){return this.r=Ert(t.r),this.g=Ert(t.g),this.b=Ert(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return("000000"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,o=this.b,i=Math.max(e,n,o),a=Math.min(e,n,o);let r,s;const l=(a+i)/2;if(a===i)r=0,s=0;else{const t=i-a;switch(s=l<=.5?t/(i+a):t/(2-i-a),i){case e:r=(n-o)/t+(n<o?6:0);break;case n:r=(o-e)/t+2;break;case o:r=(e-n)/t+4}r/=6}return t.h=r,t.s=s,t.l=l,t}getStyle(){return"rgb("+(255*this.r|0)+","+(255*this.g|0)+","+(255*this.b|0)+")"}offsetHSL(t,e,n){return this.getHSL(wrt),wrt.h+=t,wrt.s+=e,wrt.l+=n,this.setHSL(wrt.h,wrt.s,wrt.l),this}add(t){return this.r+=t.r,this.g+=t.g,this.b+=t.b,this}addColors(t,e){return this.r=t.r+e.r,this.g=t.g+e.g,this.b=t.b+e.b,this}addScalar(t){return this.r+=t,this.g+=t,this.b+=t,this}sub(t){return this.r=Math.max(0,this.r-t.r),this.g=Math.max(0,this.g-t.g),this.b=Math.max(0,this.b-t.b),this}multiply(t){return this.r*=t.r,this.g*=t.g,this.b*=t.b,this}multiplyScalar(t){return this.r*=t,this.g*=t,this.b*=t,this}lerp(t,e){return this.r+=(t.r-this.r)*e,this.g+=(t.g-this.g)*e,this.b+=(t.b-this.b)*e,this}lerpColors(t,e,n){return this.r=t.r+(e.r-t.r)*n,this.g=t.g+(e.g-t.g)*n,this.b=t.b+(e.b-t.b)*n,this}lerpHSL(t,e){this.getHSL(wrt),t.getHSL(krt);const n=Xit(wrt.h,krt.h,e),o=Xit(wrt.s,krt.s,e),i=Xit(wrt.l,krt.l,e);return this.setHSL(n,o,i),this}equals(t){return t.r===this.r&&t.g===this.g&&t.b===this.b}fromArray(t,e=0){return this.r=t[e],this.g=t[e+1],this.b=t[e+2],this}toArray(t=[],e=0){return t[e]=this.r,t[e+1]=this.g,t[e+2]=this.b,t}fromBufferAttribute(t,e){return this.r=t.getX(e),this.g=t.getY(e),this.b=t.getZ(e),!0===t.normalized&&(this.r/=255,this.g/=255,this.b/=255),this}toJSON(){return this.getHex()}}Rrt.NAMES=Prt,Rrt.prototype.isColor=!0,Rrt.prototype.r=1,Rrt.prototype.g=1,Rrt.prototype.b=1;class Art extends Ort{constructor(t){super(),this.type="MeshBasicMaterial",this.color=new Rrt(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}Art.prototype.isMeshBasicMaterial=!0;const Trt=new cat,Nrt=new Qit;class zrt{constructor(t,e,n){if(Array.isArray(t))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");this.name="",this.array=t,this.itemSize=e,this.count=void 0!==t?t.length/e:0,this.normalized=!0===n,this.usage=Bit,this.updateRange={offset:0,count:-1},this.version=0}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.name=t.name,this.array=new t.array.constructor(t.array),this.itemSize=t.itemSize,this.count=t.count,this.normalized=t.normalized,this.usage=t.usage,this}copyAt(t,e,n){t*=this.itemSize,n*=e.itemSize;for(let o=0,i=this.itemSize;o<i;o++)this.array[t+o]=e.array[n+o];return this}copyArray(t){return this.array.set(t),this}copyColorsArray(t){const e=this.array;let n=0;for(let o=0,i=t.length;o<i;o++){let i=t[o];void 0===i&&(console.warn("THREE.BufferAttribute.copyColorsArray(): color is undefined",o),i=new Rrt),e[n++]=i.r,e[n++]=i.g,e[n++]=i.b}return this}copyVector2sArray(t){const e=this.array;let n=0;for(let o=0,i=t.length;o<i;o++){let i=t[o];void 0===i&&(console.warn("THREE.BufferAttribute.copyVector2sArray(): vector is undefined",o),i=new Qit),e[n++]=i.x,e[n++]=i.y}return this}copyVector3sArray(t){const e=this.array;let n=0;for(let o=0,i=t.length;o<i;o++){let i=t[o];void 0===i&&(console.warn("THREE.BufferAttribute.copyVector3sArray(): vector is undefined",o),i=new cat),e[n++]=i.x,e[n++]=i.y,e[n++]=i.z}return this}copyVector4sArray(t){const e=this.array;let n=0;for(let o=0,i=t.length;o<i;o++){let i=t[o];void 0===i&&(console.warn("THREE.BufferAttribute.copyVector4sArray(): vector is undefined",o),i=new aat),e[n++]=i.x,e[n++]=i.y,e[n++]=i.z,e[n++]=i.w}return this}applyMatrix3(t){if(2===this.itemSize)for(let e=0,n=this.count;e<n;e++)Nrt.fromBufferAttribute(this,e),Nrt.applyMatrix3(t),this.setXY(e,Nrt.x,Nrt.y);else if(3===this.itemSize)for(let e=0,n=this.count;e<n;e++)Trt.fromBufferAttribute(this,e),Trt.applyMatrix3(t),this.setXYZ(e,Trt.x,Trt.y,Trt.z);return this}applyMatrix4(t){for(let e=0,n=this.count;e<n;e++)Trt.x=this.getX(e),Trt.y=this.getY(e),Trt.z=this.getZ(e),Trt.applyMatrix4(t),this.setXYZ(e,Trt.x,Trt.y,Trt.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)Trt.x=this.getX(e),Trt.y=this.getY(e),Trt.z=this.getZ(e),Trt.applyNormalMatrix(t),this.setXYZ(e,Trt.x,Trt.y,Trt.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)Trt.x=this.getX(e),Trt.y=this.getY(e),Trt.z=this.getZ(e),Trt.transformDirection(t),this.setXYZ(e,Trt.x,Trt.y,Trt.z);return this}set(t,e=0){return this.array.set(t,e),this}getX(t){return this.array[t*this.itemSize]}setX(t,e){return this.array[t*this.itemSize]=e,this}getY(t){return this.array[t*this.itemSize+1]}setY(t,e){return this.array[t*this.itemSize+1]=e,this}getZ(t){return this.array[t*this.itemSize+2]}setZ(t,e){return this.array[t*this.itemSize+2]=e,this}getW(t){return this.array[t*this.itemSize+3]}setW(t,e){return this.array[t*this.itemSize+3]=e,this}setXY(t,e,n){return this.array[0+(t*=this.itemSize)]=e,this.array[t+1]=n,this}setXYZ(t,e,n,o){return this.array[0+(t*=this.itemSize)]=e,this.array[t+1]=n,this.array[t+2]=o,this}setXYZW(t,e,n,o,i){return this.array[0+(t*=this.itemSize)]=e,this.array[t+1]=n,this.array[t+2]=o,this.array[t+3]=i,this}onUpload(t){return this.onUploadCallback=t,this}clone(){return new this.constructor(this.array,this.itemSize).copy(this)}toJSON(){const t={itemSize:this.itemSize,type:this.array.constructor.name,array:Array.prototype.slice.call(this.array),normalized:this.normalized};return""!==this.name&&(t.name=this.name),this.usage!==Bit&&(t.usage=this.usage),0===this.updateRange.offset&&-1===this.updateRange.count||(t.updateRange=this.updateRange),t}}zrt.prototype.isBufferAttribute=!0;class Irt extends zrt{constructor(t,e,n){super(new Uint16Array(t),e,n)}}class Hrt extends zrt{constructor(t,e,n){super(new Uint32Array(t),e,n)}}(class extends zrt{constructor(t,e,n){super(new Uint16Array(t),e,n)}}).prototype.isFloat16BufferAttribute=!0;class Frt extends zrt{constructor(t,e,n){super(new Float32Array(t),e,n)}}function Lrt(t){if(0===t.length)return-1/0;let e=t[0];for(let n=1,o=t.length;n<o;++n)t[n]>e&&(e=t[n]);return e}let Brt=0;const Vrt=new Bat,jrt=new prt,Urt=new cat,Grt=new mat,Wrt=new mat,Yrt=new cat;class qrt extends Uit{constructor(){super(),Object.defineProperty(this,"id",{value:Brt++}),this.uuid=qit(),this.name="",this.type="BufferGeometry",this.index=null,this.attributes={},this.morphAttributes={},this.morphTargetsRelative=!1,this.groups=[],this.boundingBox=null,this.boundingSphere=null,this.drawRange={start:0,count:1/0},this.userData={}}getIndex(){return this.index}setIndex(t){return this.index=Array.isArray(t)?new(Lrt(t)>65535?Hrt:Irt)(t,1):t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new $it).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const o=this.attributes.tangent;return void 0!==o&&(o.transformDirection(t),o.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return Vrt.makeRotationFromQuaternion(t),this.applyMatrix4(Vrt),this}rotateX(t){return Vrt.makeRotationX(t),this.applyMatrix4(Vrt),this}rotateY(t){return Vrt.makeRotationY(t),this.applyMatrix4(Vrt),this}rotateZ(t){return Vrt.makeRotationZ(t),this.applyMatrix4(Vrt),this}translate(t,e,n){return Vrt.makeTranslation(t,e,n),this.applyMatrix4(Vrt),this}scale(t,e,n){return Vrt.makeScale(t,e,n),this.applyMatrix4(Vrt),this}lookAt(t){return jrt.lookAt(t),jrt.updateMatrix(),this.applyMatrix4(jrt.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(Urt).negate(),this.translate(Urt.x,Urt.y,Urt.z),this}setFromPoints(t){const e=[];for(let n=0,o=t.length;n<o;n++){const o=t[n];e.push(o.x,o.y,o.z||0)}return this.setAttribute("position",new Frt(e,3)),this}computeBoundingBox(){null===this.boundingBox&&(this.boundingBox=new mat);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingBox.set(new cat(-1/0,-1/0,-1/0),new cat(1/0,1/0,1/0));if(void 0!==t){if(this.boundingBox.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++)Grt.setFromBufferAttribute(e[t]),this.morphTargetsRelative?(Yrt.addVectors(this.boundingBox.min,Grt.min),this.boundingBox.expandByPoint(Yrt),Yrt.addVectors(this.boundingBox.max,Grt.max),this.boundingBox.expandByPoint(Yrt)):(this.boundingBox.expandByPoint(Grt.min),this.boundingBox.expandByPoint(Grt.max))}else this.boundingBox.makeEmpty();(isNaN(this.boundingBox.min.x)||isNaN(this.boundingBox.min.y)||isNaN(this.boundingBox.min.z))&&console.error('THREE.BufferGeometry.computeBoundingBox(): Computed min/max have NaN values. The "position" attribute is likely to have NaN values.',this)}computeBoundingSphere(){null===this.boundingSphere&&(this.boundingSphere=new Rat);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingSphere(): GLBufferAttribute requires a manual bounding sphere. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingSphere.set(new cat,1/0);if(t){const n=this.boundingSphere.center;if(Grt.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++)Wrt.setFromBufferAttribute(e[t]),this.morphTargetsRelative?(Yrt.addVectors(Grt.min,Wrt.min),Grt.expandByPoint(Yrt),Yrt.addVectors(Grt.max,Wrt.max),Grt.expandByPoint(Yrt)):(Grt.expandByPoint(Wrt.min),Grt.expandByPoint(Wrt.max));Grt.getCenter(n);let o=0;for(let e=0,i=t.count;e<i;e++)Yrt.fromBufferAttribute(t,e),o=Math.max(o,n.distanceToSquared(Yrt));if(e)for(let i=0,a=e.length;i<a;i++){const a=e[i],r=this.morphTargetsRelative;for(let e=0,i=a.count;e<i;e++)Yrt.fromBufferAttribute(a,e),r&&(Urt.fromBufferAttribute(t,e),Yrt.add(Urt)),o=Math.max(o,n.distanceToSquared(Yrt))}this.boundingSphere.radius=Math.sqrt(o),isNaN(this.boundingSphere.radius)&&console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The "position" attribute is likely to have NaN values.',this)}}computeFaceNormals(){}computeTangents(){const t=this.index,e=this.attributes;if(null===t||void 0===e.position||void 0===e.normal||void 0===e.uv)return void console.error("THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)");const n=t.array,o=e.position.array,i=e.normal.array,a=e.uv.array,r=o.length/3;void 0===e.tangent&&this.setAttribute("tangent",new zrt(new Float32Array(4*r),4));const s=e.tangent.array,l=[],c=[];for(let t=0;t<r;t++)l[t]=new cat,c[t]=new cat;const d=new cat,p=new cat,m=new cat,u=new Qit,f=new Qit,g=new Qit,h=new cat,b=new cat;function y(t,e,n){d.fromArray(o,3*t),p.fromArray(o,3*e),m.fromArray(o,3*n),u.fromArray(a,2*t),f.fromArray(a,2*e),g.fromArray(a,2*n),p.sub(d),m.sub(d),f.sub(u),g.sub(u);const i=1/(f.x*g.y-g.x*f.y);isFinite(i)&&(h.copy(p).multiplyScalar(g.y).addScaledVector(m,-f.y).multiplyScalar(i),b.copy(m).multiplyScalar(f.x).addScaledVector(p,-g.x).multiplyScalar(i),l[t].add(h),l[e].add(h),l[n].add(h),c[t].add(b),c[e].add(b),c[n].add(b))}let _=this.groups;0===_.length&&(_=[{start:0,count:n.length}]);for(let t=0,e=_.length;t<e;++t){const e=_[t],o=e.start;for(let t=o,i=o+e.count;t<i;t+=3)y(n[t+0],n[t+1],n[t+2])}const C=new cat,M=new cat,v=new cat,x=new cat;function O(t){v.fromArray(i,3*t),x.copy(v);const e=l[t];C.copy(e),C.sub(v.multiplyScalar(v.dot(e))).normalize(),M.crossVectors(x,e);const n=M.dot(c[t])<0?-1:1;s[4*t]=C.x,s[4*t+1]=C.y,s[4*t+2]=C.z,s[4*t+3]=n}for(let t=0,e=_.length;t<e;++t){const e=_[t],o=e.start;for(let t=o,i=o+e.count;t<i;t+=3)O(n[t+0]),O(n[t+1]),O(n[t+2])}}computeVertexNormals(){const t=this.index,e=this.getAttribute("position");if(void 0!==e){let n=this.getAttribute("normal");if(void 0===n)n=new zrt(new Float32Array(3*e.count),3),this.setAttribute("normal",n);else for(let t=0,e=n.count;t<e;t++)n.setXYZ(t,0,0,0);const o=new cat,i=new cat,a=new cat,r=new cat,s=new cat,l=new cat,c=new cat,d=new cat;if(t)for(let p=0,m=t.count;p<m;p+=3){const m=t.getX(p+0),u=t.getX(p+1),f=t.getX(p+2);o.fromBufferAttribute(e,m),i.fromBufferAttribute(e,u),a.fromBufferAttribute(e,f),c.subVectors(a,i),d.subVectors(o,i),c.cross(d),r.fromBufferAttribute(n,m),s.fromBufferAttribute(n,u),l.fromBufferAttribute(n,f),r.add(c),s.add(c),l.add(c),n.setXYZ(m,r.x,r.y,r.z),n.setXYZ(u,s.x,s.y,s.z),n.setXYZ(f,l.x,l.y,l.z)}else for(let t=0,r=e.count;t<r;t+=3)o.fromBufferAttribute(e,t+0),i.fromBufferAttribute(e,t+1),a.fromBufferAttribute(e,t+2),c.subVectors(a,i),d.subVectors(o,i),c.cross(d),n.setXYZ(t+0,c.x,c.y,c.z),n.setXYZ(t+1,c.x,c.y,c.z),n.setXYZ(t+2,c.x,c.y,c.z);this.normalizeNormals(),n.needsUpdate=!0}}merge(t,e){if(!t||!t.isBufferGeometry)return void console.error("THREE.BufferGeometry.merge(): geometry not an instance of THREE.BufferGeometry.",t);void 0===e&&(e=0,console.warn("THREE.BufferGeometry.merge(): Overwriting original geometry, starting at offset=0. Use BufferGeometryUtils.mergeBufferGeometries() for lossless merge."));const n=this.attributes;for(const o in n){if(void 0===t.attributes[o])continue;const i=n[o].array,a=t.attributes[o],r=a.array,s=a.itemSize*e,l=Math.min(r.length,i.length-s);for(let t=0,e=s;t<l;t++,e++)i[e]=r[t]}return this}normalizeNormals(){const t=this.attributes.normal;for(let e=0,n=t.count;e<n;e++)Yrt.fromBufferAttribute(t,e),Yrt.normalize(),t.setXYZ(e,Yrt.x,Yrt.y,Yrt.z)}toNonIndexed(){function t(t,e){const n=t.array,o=t.itemSize,i=t.normalized,a=new n.constructor(e.length*o);let r=0,s=0;for(let i=0,l=e.length;i<l;i++){r=t.isInterleavedBufferAttribute?e[i]*t.data.stride+t.offset:e[i]*o;for(let t=0;t<o;t++)a[s++]=n[r++]}return new zrt(a,o,i)}if(null===this.index)return console.warn("THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed."),this;const e=new qrt,n=this.index.array,o=this.attributes;for(const i in o){const a=t(o[i],n);e.setAttribute(i,a)}const i=this.morphAttributes;for(const o in i){const a=[],r=i[o];for(let e=0,o=r.length;e<o;e++){const o=t(r[e],n);a.push(o)}e.morphAttributes[o]=a}e.morphTargetsRelative=this.morphTargetsRelative;const a=this.groups;for(let t=0,n=a.length;t<n;t++){const n=a[t];e.addGroup(n.start,n.count,n.materialIndex)}return e}toJSON(){const t={metadata:{version:4.5,type:"BufferGeometry",generator:"BufferGeometry.toJSON"}};if(t.uuid=this.uuid,t.type=this.type,""!==this.name&&(t.name=this.name),Object.keys(this.userData).length>0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n)t.data.attributes[e]=n[e].toJSON(t.data);const o={};let i=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],a=[];for(let e=0,o=n.length;e<o;e++)a.push(n[e].toJSON(t.data));a.length>0&&(o[e]=a,i=!0)}i&&(t.data.morphAttributes=o,t.data.morphTargetsRelative=this.morphTargetsRelative);const a=this.groups;a.length>0&&(t.data.groups=JSON.parse(JSON.stringify(a)));const r=this.boundingSphere;return null!==r&&(t.data.boundingSphere={center:r.center.toArray(),radius:r.radius}),t}clone(){return(new qrt).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const o=t.attributes;for(const t in o)this.setAttribute(t,o[t].clone(e));const i=t.morphAttributes;for(const t in i){const n=[],o=i[t];for(let t=0,i=o.length;t<i;t++)n.push(o[t].clone(e));this.morphAttributes[t]=n}this.morphTargetsRelative=t.morphTargetsRelative;const a=t.groups;for(let t=0,e=a.length;t<e;t++){const e=a[t];this.addGroup(e.start,e.count,e.materialIndex)}const r=t.boundingBox;null!==r&&(this.boundingBox=r.clone());const s=t.boundingSphere;return null!==s&&(this.boundingSphere=s.clone()),this.drawRange.start=t.drawRange.start,this.drawRange.count=t.drawRange.count,this.userData=t.userData,this}dispose(){this.dispatchEvent({type:"dispose"})}}qrt.prototype.isBufferGeometry=!0;const Zrt=new Bat,Xrt=new Lat,Krt=new Rat,Jrt=new cat,Qrt=new cat,$rt=new cat,tst=new cat,est=new cat,nst=new cat,ost=new cat,ist=new cat,ast=new cat,rst=new Qit,sst=new Qit,lst=new Qit,cst=new cat,dst=new cat;class pst extends prt{constructor(t=new qrt,e=new Art){super(),this.type="Mesh",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),void 0!==t.morphTargetInfluences&&(this.morphTargetInfluences=t.morphTargetInfluences.slice()),void 0!==t.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},t.morphTargetDictionary)),this.material=t.material,this.geometry=t.geometry,this}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}}raycast(t,e){const n=this.geometry,o=this.material,i=this.matrixWorld;if(void 0===o)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),Krt.copy(n.boundingSphere),Krt.applyMatrix4(i),!1===t.ray.intersectsSphere(Krt))return;if(Zrt.copy(i).invert(),Xrt.copy(t.ray).applyMatrix4(Zrt),null!==n.boundingBox&&!1===Xrt.intersectsBox(n.boundingBox))return;let a;if(n.isBufferGeometry){const i=n.index,r=n.attributes.position,s=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,d=n.attributes.uv2,p=n.groups,m=n.drawRange;if(null!==i)if(Array.isArray(o))for(let n=0,u=p.length;n<u;n++){const u=p[n],f=o[u.materialIndex];for(let n=Math.max(u.start,m.start),o=Math.min(u.start+u.count,m.start+m.count);n<o;n+=3){const o=i.getX(n),p=i.getX(n+1),m=i.getX(n+2);a=mst(this,f,t,Xrt,r,s,l,c,d,o,p,m),a&&(a.faceIndex=Math.floor(n/3),a.face.materialIndex=u.materialIndex,e.push(a))}}else for(let n=Math.max(0,m.start),p=Math.min(i.count,m.start+m.count);n<p;n+=3){const p=i.getX(n),m=i.getX(n+1),u=i.getX(n+2);a=mst(this,o,t,Xrt,r,s,l,c,d,p,m,u),a&&(a.faceIndex=Math.floor(n/3),e.push(a))}else if(void 0!==r)if(Array.isArray(o))for(let n=0,i=p.length;n<i;n++){const i=p[n],u=o[i.materialIndex];for(let n=Math.max(i.start,m.start),o=Math.min(i.start+i.count,m.start+m.count);n<o;n+=3)a=mst(this,u,t,Xrt,r,s,l,c,d,n,n+1,n+2),a&&(a.faceIndex=Math.floor(n/3),a.face.materialIndex=i.materialIndex,e.push(a))}else for(let n=Math.max(0,m.start),i=Math.min(r.count,m.start+m.count);n<i;n+=3)a=mst(this,o,t,Xrt,r,s,l,c,d,n,n+1,n+2),a&&(a.faceIndex=Math.floor(n/3),e.push(a))}else n.isGeometry&&console.error("THREE.Mesh.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}}function mst(t,e,n,o,i,a,r,s,l,c,d,p){Jrt.fromBufferAttribute(i,c),Qrt.fromBufferAttribute(i,d),$rt.fromBufferAttribute(i,p);const m=t.morphTargetInfluences;if(a&&m){ost.set(0,0,0),ist.set(0,0,0),ast.set(0,0,0);for(let t=0,e=a.length;t<e;t++){const e=m[t],n=a[t];0!==e&&(tst.fromBufferAttribute(n,c),est.fromBufferAttribute(n,d),nst.fromBufferAttribute(n,p),r?(ost.addScaledVector(tst,e),ist.addScaledVector(est,e),ast.addScaledVector(nst,e)):(ost.addScaledVector(tst.sub(Jrt),e),ist.addScaledVector(est.sub(Qrt),e),ast.addScaledVector(nst.sub($rt),e)))}Jrt.add(ost),Qrt.add(ist),$rt.add(ast)}t.isSkinnedMesh&&(t.boneTransform(c,Jrt),t.boneTransform(d,Qrt),t.boneTransform(p,$rt));const u=(function f(t,e,n,o,i,a,r,s){let l;if(l=1===e.side?o.intersectTriangle(r,a,i,!0,s):o.intersectTriangle(i,a,r,2!==e.side,s),null===l)return null;dst.copy(s),dst.applyMatrix4(t.matrixWorld);const c=n.ray.origin.distanceTo(dst);return c<n.near||c>n.far?null:{distance:c,point:dst.clone(),object:t}})(t,e,n,o,Jrt,Qrt,$rt,cst);if(u){s&&(rst.fromBufferAttribute(s,c),sst.fromBufferAttribute(s,d),lst.fromBufferAttribute(s,p),u.uv=vrt.getUV(cst,Jrt,Qrt,$rt,rst,sst,lst,new Qit)),l&&(rst.fromBufferAttribute(l,c),sst.fromBufferAttribute(l,d),lst.fromBufferAttribute(l,p),u.uv2=vrt.getUV(cst,Jrt,Qrt,$rt,rst,sst,lst,new Qit));const t={a:c,b:d,c:p,normal:new cat,materialIndex:0};vrt.getNormal(Jrt,Qrt,$rt,t.normal),u.face=t}return u}pst.prototype.isMesh=!0;class ust extends qrt{constructor(t=1,e=1,n=1,o=1,i=1,a=1){super(),this.type="BoxGeometry",this.parameters={width:t,height:e,depth:n,widthSegments:o,heightSegments:i,depthSegments:a};const r=this;o=Math.floor(o),i=Math.floor(i),a=Math.floor(a);const s=[],l=[],c=[],d=[];let p=0,m=0;function u(t,e,n,o,i,a,u,f,g,h,b){const y=a/g,_=u/h,C=a/2,M=u/2,v=f/2,x=g+1,O=h+1;let P=0,w=0;const k=new cat;for(let a=0;a<O;a++){const r=a*_-M;for(let s=0;s<x;s++)k[t]=(s*y-C)*o,k[e]=r*i,k[n]=v,l.push(k.x,k.y,k.z),k[t]=0,k[e]=0,k[n]=f>0?1:-1,c.push(k.x,k.y,k.z),d.push(s/g),d.push(1-a/h),P+=1}for(let t=0;t<h;t++)for(let e=0;e<g;e++){const n=p+e+x*(t+1),o=p+(e+1)+x*(t+1),i=p+(e+1)+x*t;s.push(p+e+x*t,n,i),s.push(n,o,i),w+=6}r.addGroup(m,w,b),m+=w,p+=P}u("z","y","x",-1,-1,n,e,t,a,i,0),u("z","y","x",1,-1,n,e,-t,a,i,1),u("x","z","y",1,1,t,n,e,o,a,2),u("x","z","y",1,-1,t,n,-e,o,a,3),u("x","y","z",1,-1,t,e,n,o,i,4),u("x","y","z",-1,-1,t,e,-n,o,i,5),this.setIndex(s),this.setAttribute("position",new Frt(l,3)),this.setAttribute("normal",new Frt(c,3)),this.setAttribute("uv",new Frt(d,2))}static fromJSON(t){return new ust(t.width,t.height,t.depth,t.widthSegments,t.heightSegments,t.depthSegments)}}function fst(t){const e={};for(const n in t){e[n]={};for(const o in t[n]){const i=t[n][o];e[n][o]=i&&(i.isColor||i.isMatrix3||i.isMatrix4||i.isVector2||i.isVector3||i.isVector4||i.isTexture||i.isQuaternion)?i.clone():Array.isArray(i)?i.slice():i}}return e}function gst(t){const e={};for(let n=0;n<t.length;n++){const o=fst(t[n]);for(const t in o)e[t]=o[t]}return e}const hst={clone:fst,merge:gst};class bst extends Ort{constructor(t){super(),this.type="ShaderMaterial",this.defines={},this.uniforms={},this.vertexShader="void main() {\n\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n}",this.fragmentShader="void main() {\n\tgl_FragColor = vec4( 1.0, 0.0, 0.0, 1.0 );\n}",this.linewidth=1,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.lights=!1,this.clipping=!1,this.extensions={derivatives:!1,fragDepth:!1,drawBuffers:!1,shaderTextureLOD:!1},this.defaultAttributeValues={color:[1,1,1],uv:[0,0],uv2:[0,0]},this.index0AttributeName=void 0,this.uniformsNeedUpdate=!1,this.glslVersion=null,void 0!==t&&(void 0!==t.attributes&&console.error("THREE.ShaderMaterial: attributes should now be defined in THREE.BufferGeometry instead."),this.setValues(t))}copy(t){return super.copy(t),this.fragmentShader=t.fragmentShader,this.vertexShader=t.vertexShader,this.uniforms=fst(t.uniforms),this.defines=Object.assign({},t.defines),this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.lights=t.lights,this.clipping=t.clipping,this.extensions=Object.assign({},t.extensions),this.glslVersion=t.glslVersion,this}toJSON(t){const e=super.toJSON(t);e.glslVersion=this.glslVersion,e.uniforms={};for(const n in this.uniforms){const o=this.uniforms[n].value;e.uniforms[n]=o&&o.isTexture?{type:"t",value:o.toJSON(t).uuid}:o&&o.isColor?{type:"c",value:o.getHex()}:o&&o.isVector2?{type:"v2",value:o.toArray()}:o&&o.isVector3?{type:"v3",value:o.toArray()}:o&&o.isVector4?{type:"v4",value:o.toArray()}:o&&o.isMatrix3?{type:"m3",value:o.toArray()}:o&&o.isMatrix4?{type:"m4",value:o.toArray()}:{value:o}}Object.keys(this.defines).length>0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}bst.prototype.isShaderMaterial=!0;class yst extends prt{constructor(){super(),this.type="Camera",this.matrixWorldInverse=new Bat,this.projectionMatrix=new Bat,this.projectionMatrixInverse=new Bat}copy(t,e){return super.copy(t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()}updateMatrixWorld(t){super.updateMatrixWorld(t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(t,e){super.updateWorldMatrix(t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return(new this.constructor).copy(this)}}yst.prototype.isCamera=!0;class _st extends yst{constructor(t=50,e=1,n=.1,o=2e3){super(),this.type="PerspectiveCamera",this.fov=t,this.zoom=1,this.near=n,this.far=o,this.focus=10,this.aspect=e,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.fov=t.fov,this.zoom=t.zoom,this.near=t.near,this.far=t.far,this.focus=t.focus,this.aspect=t.aspect,this.view=null===t.view?null:Object.assign({},t.view),this.filmGauge=t.filmGauge,this.filmOffset=t.filmOffset,this}setFocalLength(t){const e=.5*this.getFilmHeight()/t;this.fov=2*Yit*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*Wit*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*Yit*Math.atan(Math.tan(.5*Wit*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}setViewOffset(t,e,n,o,i,a){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=o,this.view.width=i,this.view.height=a,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*Wit*this.fov)/this.zoom,n=2*e,o=this.aspect*n,i=-.5*o;const a=this.view;if(null!==this.view&&this.view.enabled){const t=a.fullWidth,r=a.fullHeight;i+=a.offsetX*o/t,e-=a.offsetY*n/r,o*=a.width/t,n*=a.height/r}const r=this.filmOffset;0!==r&&(i+=t*r/this.getFilmWidth()),this.projectionMatrix.makePerspective(i,i+o,e,e-n,t,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.fov=this.fov,e.object.zoom=this.zoom,e.object.near=this.near,e.object.far=this.far,e.object.focus=this.focus,e.object.aspect=this.aspect,null!==this.view&&(e.object.view=Object.assign({},this.view)),e.object.filmGauge=this.filmGauge,e.object.filmOffset=this.filmOffset,e}}_st.prototype.isPerspectiveCamera=!0;const Cst=90;class Mst extends prt{constructor(t,e,n){if(super(),this.type="CubeCamera",!0!==n.isWebGLCubeRenderTarget)return void console.error("THREE.CubeCamera: The constructor now expects an instance of WebGLCubeRenderTarget as third parameter.");this.renderTarget=n;const o=new _st(Cst,1,t,e);o.layers=this.layers,o.up.set(0,-1,0),o.lookAt(new cat(1,0,0)),this.add(o);const i=new _st(Cst,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new cat(-1,0,0)),this.add(i);const a=new _st(Cst,1,t,e);a.layers=this.layers,a.up.set(0,0,1),a.lookAt(new cat(0,1,0)),this.add(a);const r=new _st(Cst,1,t,e);r.layers=this.layers,r.up.set(0,0,-1),r.lookAt(new cat(0,-1,0)),this.add(r);const s=new _st(Cst,1,t,e);s.layers=this.layers,s.up.set(0,-1,0),s.lookAt(new cat(0,0,1)),this.add(s);const l=new _st(Cst,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new cat(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[o,i,a,r,s,l]=this.children,c=t.xr.enabled,d=t.getRenderTarget();t.xr.enabled=!1;const p=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,o),t.setRenderTarget(n,1),t.render(e,i),t.setRenderTarget(n,2),t.render(e,a),t.setRenderTarget(n,3),t.render(e,r),t.setRenderTarget(n,4),t.render(e,s),n.texture.generateMipmaps=p,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(d),t.xr.enabled=c}}class vst extends oat{constructor(t,e,n,o,i,a,r,s,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:dit,n,o,i,a,r=void 0!==r?r:Pit,s,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}vst.prototype.isCubeTexture=!0;class xst extends rat{constructor(t,e,n){Number.isInteger(e)&&(console.warn("THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )"),e=n),super(t,t,e),this.texture=new vst(void 0,(e=e||{}).mapping,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.format,e.type,e.anisotropy,e.encoding),this.texture.isRenderTargetTexture=!0,this.texture.generateMipmaps=void 0!==e.generateMipmaps&&e.generateMipmaps,this.texture.minFilter=void 0!==e.minFilter?e.minFilter:bit,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=wit,this.texture.encoding=e.encoding,this.texture.generateMipmaps=e.generateMipmaps,this.texture.minFilter=e.minFilter,this.texture.magFilter=e.magFilter;const n=new ust(5,5,5),o=new bst({name:"CubemapFromEquirect",uniforms:fst({tEquirect:{value:null}}),vertexShader:"\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\n\t\t\t\t\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n\n\t\t\t\t}\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvWorldDirection = transformDirection( position, modelMatrix );\n\n\t\t\t\t\t#include <begin_vertex>\n\t\t\t\t\t#include <project_vertex>\n\n\t\t\t\t}\n\t\t\t",fragmentShader:"\n\n\t\t\t\tuniform sampler2D tEquirect;\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\t#include <common>\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvec3 direction = normalize( vWorldDirection );\n\n\t\t\t\t\tvec2 sampleUV = equirectUv( direction );\n\n\t\t\t\t\tgl_FragColor = texture2D( tEquirect, sampleUV );\n\n\t\t\t\t}\n\t\t\t",side:1,blending:0});o.uniforms.tEquirect.value=e;const i=new pst(n,o),a=e.minFilter;return e.minFilter===yit&&(e.minFilter=bit),new Mst(1,10,this).update(t,i),e.minFilter=a,i.geometry.dispose(),i.material.dispose(),this}clear(t,e,n,o){const i=t.getRenderTarget();for(let i=0;i<6;i++)t.setRenderTarget(this,i),t.clear(e,n,o);t.setRenderTarget(i)}}xst.prototype.isWebGLCubeRenderTarget=!0;const Ost=new cat,Pst=new cat,wst=new $it;class kst{constructor(t=new cat(1,0,0),e=0){this.normal=t,this.constant=e}set(t,e){return this.normal.copy(t),this.constant=e,this}setComponents(t,e,n,o){return this.normal.set(t,e,n),this.constant=o,this}setFromNormalAndCoplanarPoint(t,e){return this.normal.copy(t),this.constant=-e.dot(this.normal),this}setFromCoplanarPoints(t,e,n){const o=Ost.subVectors(n,e).cross(Pst.subVectors(t,e)).normalize();return this.setFromNormalAndCoplanarPoint(o,t),this}copy(t){return this.normal.copy(t.normal),this.constant=t.constant,this}normalize(){const t=1/this.normal.length();return this.normal.multiplyScalar(t),this.constant*=t,this}negate(){return this.constant*=-1,this.normal.negate(),this}distanceToPoint(t){return this.normal.dot(t)+this.constant}distanceToSphere(t){return this.distanceToPoint(t.center)-t.radius}projectPoint(t,e){return e.copy(this.normal).multiplyScalar(-this.distanceToPoint(t)).add(t)}intersectLine(t,e){const n=t.delta(Ost),o=this.normal.dot(n);if(0===o)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const i=-(t.start.dot(this.normal)+this.constant)/o;return i<0||i>1?null:e.copy(n).multiplyScalar(i).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||wst.getNormalMatrix(t),o=this.coplanarPoint(Ost).applyMatrix4(t),i=this.normal.applyMatrix3(n).normalize();return this.constant=-o.dot(i),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}kst.prototype.isPlane=!0;const Sst=new Rat,Dst=new cat;class Est{constructor(t=new kst,e=new kst,n=new kst,o=new kst,i=new kst,a=new kst){this.planes=[t,e,n,o,i,a]}set(t,e,n,o,i,a){const r=this.planes;return r[0].copy(t),r[1].copy(e),r[2].copy(n),r[3].copy(o),r[4].copy(i),r[5].copy(a),this}copy(t){const e=this.planes;for(let n=0;n<6;n++)e[n].copy(t.planes[n]);return this}setFromProjectionMatrix(t){const e=this.planes,n=t.elements,o=n[0],i=n[1],a=n[2],r=n[3],s=n[4],l=n[5],c=n[6],d=n[7],p=n[8],m=n[9],u=n[10],f=n[11],g=n[12],h=n[13],b=n[14],y=n[15];return e[0].setComponents(r-o,d-s,f-p,y-g).normalize(),e[1].setComponents(r+o,d+s,f+p,y+g).normalize(),e[2].setComponents(r+i,d+l,f+m,y+h).normalize(),e[3].setComponents(r-i,d-l,f-m,y-h).normalize(),e[4].setComponents(r-a,d-c,f-u,y-b).normalize(),e[5].setComponents(r+a,d+c,f+u,y+b).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),Sst.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(Sst)}intersectsSprite(t){return Sst.center.set(0,0,0),Sst.radius=.7071067811865476,Sst.applyMatrix4(t.matrixWorld),this.intersectsSphere(Sst)}intersectsSphere(t){const e=this.planes,n=t.center,o=-t.radius;for(let t=0;t<6;t++)if(e[t].distanceToPoint(n)<o)return!1;return!0}intersectsBox(t){const e=this.planes;for(let n=0;n<6;n++){const o=e[n];if(Dst.x=o.normal.x>0?t.max.x:t.min.x,Dst.y=o.normal.y>0?t.max.y:t.min.y,Dst.z=o.normal.z>0?t.max.z:t.min.z,o.distanceToPoint(Dst)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let n=0;n<6;n++)if(e[n].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}function Rst(){let t=null,e=!1,n=null,o=null;function i(e,a){n(e,a),o=t.requestAnimationFrame(i)}return{start:function(){!0!==e&&null!==n&&(o=t.requestAnimationFrame(i),e=!0)},stop:function(){t.cancelAnimationFrame(o),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function Ast(t,e){const n=e.isWebGL2,o=new WeakMap;return{get:function i(t){return t.isInterleavedBufferAttribute&&(t=t.data),o.get(t)},remove:function a(e){e.isInterleavedBufferAttribute&&(e=e.data);const n=o.get(e);n&&(t.deleteBuffer(n.buffer),o.delete(e))},update:function r(e,i){if(e.isGLBufferAttribute){const t=o.get(e);return void((!t||t.version<e.version)&&o.set(e,{buffer:e.buffer,type:e.type,bytesPerElement:e.elementSize,version:e.version}))}e.isInterleavedBufferAttribute&&(e=e.data);const a=o.get(e);void 0===a?o.set(e,(function r(e,o){const i=e.array,a=e.usage,r=t.createBuffer();t.bindBuffer(o,r),t.bufferData(o,i,a),e.onUploadCallback();let s=5126;return i instanceof Float32Array?s=5126:i instanceof Float64Array?console.warn("THREE.WebGLAttributes: Unsupported data buffer format: Float64Array."):i instanceof Uint16Array?e.isFloat16BufferAttribute?n?s=5131:console.warn("THREE.WebGLAttributes: Usage of Float16BufferAttribute requires WebGL2."):s=5123:i instanceof Int16Array?s=5122:i instanceof Uint32Array?s=5125:i instanceof Int32Array?s=5124:i instanceof Int8Array?s=5120:(i instanceof Uint8Array||i instanceof Uint8ClampedArray)&&(s=5121),{buffer:r,type:s,bytesPerElement:i.BYTES_PER_ELEMENT,version:e.version}})(e,i)):a.version<e.version&&((function s(e,o,i){const a=o.array,r=o.updateRange;t.bindBuffer(i,e),-1===r.count?t.bufferSubData(i,0,a):(n?t.bufferSubData(i,r.offset*a.BYTES_PER_ELEMENT,a,r.offset,r.count):t.bufferSubData(i,r.offset*a.BYTES_PER_ELEMENT,a.subarray(r.offset,r.offset+r.count)),r.count=-1)})(a.buffer,e,i),a.version=e.version)}}}class Tst extends qrt{constructor(t=1,e=1,n=1,o=1){super(),this.type="PlaneGeometry",this.parameters={width:t,height:e,widthSegments:n,heightSegments:o};const i=t/2,a=e/2,r=Math.floor(n),s=Math.floor(o),l=r+1,c=s+1,d=t/r,p=e/s,m=[],u=[],f=[],g=[];for(let t=0;t<c;t++){const e=t*p-a;for(let n=0;n<l;n++)u.push(n*d-i,-e,0),f.push(0,0,1),g.push(n/r),g.push(1-t/s)}for(let t=0;t<s;t++)for(let e=0;e<r;e++){const n=e+l*(t+1),o=e+1+l*(t+1),i=e+1+l*t;m.push(e+l*t,n,i),m.push(n,o,i)}this.setIndex(m),this.setAttribute("position",new Frt(u,3)),this.setAttribute("normal",new Frt(f,3)),this.setAttribute("uv",new Frt(g,2))}static fromJSON(t){return new Tst(t.width,t.height,t.widthSegments,t.heightSegments)}}const Nst={alphamap_fragment:"#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\n#endif",alphamap_pars_fragment:"#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",alphatest_fragment:"#ifdef ALPHATEST\n\tif ( diffuseColor.a < ALPHATEST ) discard;\n#endif",aomap_fragment:"#ifdef USE_AOMAP\n\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\n\treflectedLight.indirectDiffuse *= ambientOcclusion;\n\t#if defined( USE_ENVMAP ) && defined( STANDARD )\n\t\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.specularRoughness );\n\t#endif\n#endif",aomap_pars_fragment:"#ifdef USE_AOMAP\n\tuniform sampler2D aoMap;\n\tuniform float aoMapIntensity;\n#endif",begin_vertex:"vec3 transformed = vec3( position );",beginnormal_vertex:"vec3 objectNormal = vec3( normal );\n#ifdef USE_TANGENT\n\tvec3 objectTangent = vec3( tangent.xyz );\n#endif",bsdfs:"vec2 integrateSpecularBRDF( const in float dotNV, const in float roughness ) {\n\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\n\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\n\tvec4 r = roughness * c0 + c1;\n\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\n\treturn vec2( -1.04, 1.04 ) * a004 + r.zw;\n}\nfloat punctualLightIntensityToIrradianceFactor( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\n#if defined ( PHYSICALLY_CORRECT_LIGHTS )\n\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\n\tif( cutoffDistance > 0.0 ) {\n\t\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\n\t}\n\treturn distanceFalloff;\n#else\n\tif( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\n\t\treturn pow( saturate( -lightDistance / cutoffDistance + 1.0 ), decayExponent );\n\t}\n\treturn 1.0;\n#endif\n}\nvec3 BRDF_Diffuse_Lambert( const in vec3 diffuseColor ) {\n\treturn RECIPROCAL_PI * diffuseColor;\n}\nvec3 F_Schlick( const in vec3 f0, const in vec3 f90, const in float dotVH ) {\n\tfloat fresnel = exp2( ( -5.55473 * dotVH - 6.98316 ) * dotVH );\n\treturn ( f90 - f0 ) * fresnel + f0;\n}\nvec3 F_Schlick_RoughnessDependent( const in vec3 F0, const in float dotNV, const in float roughness ) {\n\tfloat fresnel = exp2( ( -5.55473 * dotNV - 6.98316 ) * dotNV );\n\tvec3 Fr = max( vec3( 1.0 - roughness ), F0 ) - F0;\n\treturn Fr * fresnel + F0;\n}\nfloat G_GGX_Smith( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 = pow2( alpha );\n\tfloat gl = dotNL + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\tfloat gv = dotNV + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\treturn 1.0 / ( gl * gv );\n}\nfloat G_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 = pow2( alpha );\n\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\treturn 0.5 / max( gv + gl, EPSILON );\n}\nfloat D_GGX( const in float alpha, const in float dotNH ) {\n\tfloat a2 = pow2( alpha );\n\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;\n\treturn RECIPROCAL_PI * a2 / pow2( denom );\n}\nvec3 BRDF_Specular_GGX( const in IncidentLight incidentLight, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in vec3 f90, const in float roughness ) {\n\tfloat alpha = pow2( roughness );\n\tvec3 halfDir = normalize( incidentLight.direction + viewDir );\n\tfloat dotNL = saturate( dot( normal, incidentLight.direction ) );\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F = F_Schlick( f0, f90, dotLH );\n\tfloat G = G_GGX_SmithCorrelated( alpha, dotNL, dotNV );\n\tfloat D = D_GGX( alpha, dotNH );\n\treturn F * ( G * D );\n}\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\n\tconst float LUT_SIZE = 64.0;\n\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\n\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\n\tfloat dotNV = saturate( dot( N, V ) );\n\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\n\tuv = uv * LUT_SCALE + LUT_BIAS;\n\treturn uv;\n}\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\n\tfloat l = length( f );\n\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\n}\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\n\tfloat x = dot( v1, v2 );\n\tfloat y = abs( x );\n\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\n\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\n\tfloat v = a / b;\n\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\n\treturn cross( v1, v2 ) * theta_sintheta;\n}\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\n\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\n\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\n\tvec3 lightNormal = cross( v1, v2 );\n\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\n\tvec3 T1, T2;\n\tT1 = normalize( V - N * dot( V, N ) );\n\tT2 = - cross( N, T1 );\n\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\n\tvec3 coords[ 4 ];\n\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\n\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\n\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\n\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\n\tcoords[ 0 ] = normalize( coords[ 0 ] );\n\tcoords[ 1 ] = normalize( coords[ 1 ] );\n\tcoords[ 2 ] = normalize( coords[ 2 ] );\n\tcoords[ 3 ] = normalize( coords[ 3 ] );\n\tvec3 vectorFormFactor = vec3( 0.0 );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\n\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\n\treturn vec3( result );\n}\nvec3 BRDF_Specular_GGX_Environment( const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float roughness ) {\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\n\treturn specularColor * brdf.x + brdf.y;\n}\nvoid BRDF_Specular_Multiscattering_Environment( const in GeometricContext geometry, const in vec3 specularColor, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\n\tvec3 F = F_Schlick_RoughnessDependent( specularColor, dotNV, roughness );\n\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\n\tvec3 FssEss = F * brdf.x + brdf.y;\n\tfloat Ess = brdf.x + brdf.y;\n\tfloat Ems = 1.0 - Ess;\n\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619;\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\n\tsingleScatter += FssEss;\n\tmultiScatter += Fms * Ems;\n}\nfloat G_BlinnPhong_Implicit( ) {\n\treturn 0.25;\n}\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\n\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\n}\nvec3 BRDF_Specular_BlinnPhong( const in IncidentLight incidentLight, const in GeometricContext geometry, const in vec3 specularColor, const in float shininess ) {\n\tvec3 halfDir = normalize( incidentLight.direction + geometry.viewDir );\n\tfloat dotNH = saturate( dot( geometry.normal, halfDir ) );\n\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F = F_Schlick( specularColor, vec3( 1.0 ), dotLH );\n\tfloat G = G_BlinnPhong_Implicit( );\n\tfloat D = D_BlinnPhong( shininess, dotNH );\n\treturn F * ( G * D );\n}\nfloat GGXRoughnessToBlinnExponent( const in float ggxRoughness ) {\n\treturn ( 2.0 / pow2( ggxRoughness + 0.0001 ) - 2.0 );\n}\nfloat BlinnExponentToGGXRoughness( const in float blinnExponent ) {\n\treturn sqrt( 2.0 / ( blinnExponent + 2.0 ) );\n}\n#if defined( USE_SHEEN )\nfloat D_Charlie(float roughness, float NoH) {\n\tfloat invAlpha = 1.0 / roughness;\n\tfloat cos2h = NoH * NoH;\n\tfloat sin2h = max(1.0 - cos2h, 0.0078125);\treturn (2.0 + invAlpha) * pow(sin2h, invAlpha * 0.5) / (2.0 * PI);\n}\nfloat V_Neubelt(float NoV, float NoL) {\n\treturn saturate(1.0 / (4.0 * (NoL + NoV - NoL * NoV)));\n}\nvec3 BRDF_Specular_Sheen( const in float roughness, const in vec3 L, const in GeometricContext geometry, vec3 specularColor ) {\n\tvec3 N = geometry.normal;\n\tvec3 V = geometry.viewDir;\n\tvec3 H = normalize( V + L );\n\tfloat dotNH = saturate( dot( N, H ) );\n\treturn specularColor * D_Charlie( roughness, dotNH ) * V_Neubelt( dot(N, V), dot(N, L) );\n}\n#endif",bumpmap_pars_fragment:"#ifdef USE_BUMPMAP\n\tuniform sampler2D bumpMap;\n\tuniform float bumpScale;\n\tvec2 dHdxy_fwd() {\n\t\tvec2 dSTdx = dFdx( vUv );\n\t\tvec2 dSTdy = dFdy( vUv );\n\t\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\n\t\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\n\t\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\n\t\treturn vec2( dBx, dBy );\n\t}\n\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\n\t\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\n\t\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\n\t\tvec3 vN = surf_norm;\n\t\tvec3 R1 = cross( vSigmaY, vN );\n\t\tvec3 R2 = cross( vN, vSigmaX );\n\t\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\n\t\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\n\t\treturn normalize( abs( fDet ) * surf_norm - vGrad );\n\t}\n#endif",clipping_planes_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvec4 plane;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\n\t\tplane = clippingPlanes[ i ];\n\t\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\n\t}\n\t#pragma unroll_loop_end\n\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\n\t\tbool clipped = true;\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\n\t\t\tplane = clippingPlanes[ i ];\n\t\t\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t\tif ( clipped ) discard;\n\t#endif\n#endif",clipping_planes_pars_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\n#endif",clipping_planes_pars_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n#endif",clipping_planes_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvClipPosition = - mvPosition.xyz;\n#endif",color_fragment:"#if defined( USE_COLOR_ALPHA )\n\tdiffuseColor *= vColor;\n#elif defined( USE_COLOR )\n\tdiffuseColor.rgb *= vColor;\n#endif",color_pars_fragment:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR )\n\tvarying vec3 vColor;\n#endif",color_pars_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvarying vec3 vColor;\n#endif",color_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvColor = vec4( 1.0 );\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvColor = vec3( 1.0 );\n#endif\n#ifdef USE_COLOR\n\tvColor *= color;\n#endif\n#ifdef USE_INSTANCING_COLOR\n\tvColor.xyz *= instanceColor.xyz;\n#endif",common:"#define PI 3.141592653589793\n#define PI2 6.283185307179586\n#define PI_HALF 1.5707963267948966\n#define RECIPROCAL_PI 0.3183098861837907\n#define RECIPROCAL_PI2 0.15915494309189535\n#define EPSILON 1e-6\n#ifndef saturate\n#define saturate(a) clamp( a, 0.0, 1.0 )\n#endif\n#define whiteComplement(a) ( 1.0 - saturate( a ) )\nfloat pow2( const in float x ) { return x*x; }\nfloat pow3( const in float x ) { return x*x*x; }\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\nhighp float rand( const in vec2 uv ) {\n\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\n\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\n\treturn fract(sin(sn) * c);\n}\n#ifdef HIGH_PRECISION\n\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\n#else\n\tfloat max3( vec3 v ) { return max( max( v.x, v.y ), v.z ); }\n\tfloat precisionSafeLength( vec3 v ) {\n\t\tfloat maxComponent = max3( abs( v ) );\n\t\treturn length( v / maxComponent ) * maxComponent;\n\t}\n#endif\nstruct IncidentLight {\n\tvec3 color;\n\tvec3 direction;\n\tbool visible;\n};\nstruct ReflectedLight {\n\tvec3 directDiffuse;\n\tvec3 directSpecular;\n\tvec3 indirectDiffuse;\n\tvec3 indirectSpecular;\n};\nstruct GeometricContext {\n\tvec3 position;\n\tvec3 normal;\n\tvec3 viewDir;\n#ifdef CLEARCOAT\n\tvec3 clearcoatNormal;\n#endif\n};\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n}\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\n}\nvec3 projectOnPlane(in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\tfloat distance = dot( planeNormal, point - pointOnPlane );\n\treturn - distance * planeNormal + point;\n}\nfloat sideOfPlane( in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn sign( dot( point - pointOnPlane, planeNormal ) );\n}\nvec3 linePlaneIntersect( in vec3 pointOnLine, in vec3 lineDirection, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn lineDirection * ( dot( planeNormal, pointOnPlane - pointOnLine ) / dot( planeNormal, lineDirection ) ) + pointOnLine;\n}\nmat3 transposeMat3( const in mat3 m ) {\n\tmat3 tmp;\n\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\n\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\n\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\n\treturn tmp;\n}\nfloat linearToRelativeLuminance( const in vec3 color ) {\n\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\n\treturn dot( weights, color.rgb );\n}\nbool isPerspectiveMatrix( mat4 m ) {\n\treturn m[ 2 ][ 3 ] == - 1.0;\n}\nvec2 equirectUv( in vec3 dir ) {\n\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\n\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\treturn vec2( u, v );\n}",cube_uv_reflection_fragment:"#ifdef ENVMAP_TYPE_CUBE_UV\n\t#define cubeUV_maxMipLevel 8.0\n\t#define cubeUV_minMipLevel 4.0\n\t#define cubeUV_maxTileSize 256.0\n\t#define cubeUV_minTileSize 16.0\n\tfloat getFace( vec3 direction ) {\n\t\tvec3 absDirection = abs( direction );\n\t\tfloat face = - 1.0;\n\t\tif ( absDirection.x > absDirection.z ) {\n\t\t\tif ( absDirection.x > absDirection.y )\n\t\t\t\tface = direction.x > 0.0 ? 0.0 : 3.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t} else {\n\t\t\tif ( absDirection.z > absDirection.y )\n\t\t\t\tface = direction.z > 0.0 ? 2.0 : 5.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t}\n\t\treturn face;\n\t}\n\tvec2 getUV( vec3 direction, float face ) {\n\t\tvec2 uv;\n\t\tif ( face == 0.0 ) {\n\t\t\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 1.0 ) {\n\t\t\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\n\t\t} else if ( face == 2.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\n\t\t} else if ( face == 3.0 ) {\n\t\t\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 4.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\n\t\t} else {\n\t\t\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\n\t\t}\n\t\treturn 0.5 * ( uv + 1.0 );\n\t}\n\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\n\t\tfloat face = getFace( direction );\n\t\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\n\t\tmipInt = max( mipInt, cubeUV_minMipLevel );\n\t\tfloat faceSize = exp2( mipInt );\n\t\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\n\t\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\n\t\tvec2 f = fract( uv );\n\t\tuv += 0.5 - f;\n\t\tif ( face > 2.0 ) {\n\t\t\tuv.y += faceSize;\n\t\t\tface -= 3.0;\n\t\t}\n\t\tuv.x += face * faceSize;\n\t\tif ( mipInt < cubeUV_maxMipLevel ) {\n\t\t\tuv.y += 2.0 * cubeUV_maxTileSize;\n\t\t}\n\t\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\n\t\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\n\t\tuv *= texelSize;\n\t\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.x += texelSize;\n\t\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.y += texelSize;\n\t\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.x -= texelSize;\n\t\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tvec3 tm = mix( tl, tr, f.x );\n\t\tvec3 bm = mix( bl, br, f.x );\n\t\treturn mix( tm, bm, f.y );\n\t}\n\t#define r0 1.0\n\t#define v0 0.339\n\t#define m0 - 2.0\n\t#define r1 0.8\n\t#define v1 0.276\n\t#define m1 - 1.0\n\t#define r4 0.4\n\t#define v4 0.046\n\t#define m4 2.0\n\t#define r5 0.305\n\t#define v5 0.016\n\t#define m5 3.0\n\t#define r6 0.21\n\t#define v6 0.0038\n\t#define m6 4.0\n\tfloat roughnessToMip( float roughness ) {\n\t\tfloat mip = 0.0;\n\t\tif ( roughness >= r1 ) {\n\t\t\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\n\t\t} else if ( roughness >= r4 ) {\n\t\t\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\n\t\t} else if ( roughness >= r5 ) {\n\t\t\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\n\t\t} else if ( roughness >= r6 ) {\n\t\t\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\n\t\t} else {\n\t\t\tmip = - 2.0 * log2( 1.16 * roughness );\t\t}\n\t\treturn mip;\n\t}\n\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\n\t\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\n\t\tfloat mipF = fract( mip );\n\t\tfloat mipInt = floor( mip );\n\t\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\n\t\tif ( mipF == 0.0 ) {\n\t\t\treturn vec4( color0, 1.0 );\n\t\t} else {\n\t\t\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\n\t\t\treturn vec4( mix( color0, color1, mipF ), 1.0 );\n\t\t}\n\t}\n#endif",defaultnormal_vertex:"vec3 transformedNormal = objectNormal;\n#ifdef USE_INSTANCING\n\tmat3 m = mat3( instanceMatrix );\n\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\n\ttransformedNormal = m * transformedNormal;\n#endif\ntransformedNormal = normalMatrix * transformedNormal;\n#ifdef FLIP_SIDED\n\ttransformedNormal = - transformedNormal;\n#endif\n#ifdef USE_TANGENT\n\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#ifdef FLIP_SIDED\n\t\ttransformedTangent = - transformedTangent;\n\t#endif\n#endif",displacementmap_pars_vertex:"#ifdef USE_DISPLACEMENTMAP\n\tuniform sampler2D displacementMap;\n\tuniform float displacementScale;\n\tuniform float displacementBias;\n#endif",displacementmap_vertex:"#ifdef USE_DISPLACEMENTMAP\n\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\n#endif",emissivemap_fragment:"#ifdef USE_EMISSIVEMAP\n\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\n\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\n\ttotalEmissiveRadiance *= emissiveColor.rgb;\n#endif",emissivemap_pars_fragment:"#ifdef USE_EMISSIVEMAP\n\tuniform sampler2D emissiveMap;\n#endif",encodings_fragment:"gl_FragColor = linearToOutputTexel( gl_FragColor );",encodings_pars_fragment:"\nvec4 LinearToLinear( in vec4 value ) {\n\treturn value;\n}\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\n}\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\n}\nvec4 sRGBToLinear( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\n}\nvec4 LinearTosRGB( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\n}\nvec4 RGBEToLinear( in vec4 value ) {\n\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\n}\nvec4 LinearToRGBE( in vec4 value ) {\n\tfloat maxComponent = max( max( value.r, value.g ), value.b );\n\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\n\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\n}\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\n}\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\n\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\n\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\n\tM = ceil( M * 255.0 ) / 255.0;\n\treturn vec4( value.rgb / ( M * maxRange ), M );\n}\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\n}\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\n\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\n\tfloat D = max( maxRange / maxRGB, 1.0 );\n\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\n\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\n}\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\nvec4 LinearToLogLuv( in vec4 value ) {\n\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\n\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\n\tvec4 vResult;\n\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\n\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\n\tvResult.w = fract( Le );\n\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\n\treturn vResult;\n}\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\nvec4 LogLuvToLinear( in vec4 value ) {\n\tfloat Le = value.z * 255.0 + value.w;\n\tvec3 Xp_Y_XYZp;\n\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\n\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\n\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\n\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\n\treturn vec4( max( vRGB, 0.0 ), 1.0 );\n}",envmap_fragment:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvec3 cameraToFrag;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\n\t\t#endif\n\t#else\n\t\tvec3 reflectVec = vReflect;\n\t#endif\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\n\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\n\t#else\n\t\tvec4 envColor = vec4( 0.0 );\n\t#endif\n\t#ifndef ENVMAP_TYPE_CUBE_UV\n\t\tenvColor = envMapTexelToLinear( envColor );\n\t#endif\n\t#ifdef ENVMAP_BLENDING_MULTIPLY\n\t\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_MIX )\n\t\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_ADD )\n\t\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\n\t#endif\n#endif",envmap_common_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float envMapIntensity;\n\tuniform float flipEnvMap;\n\tuniform int maxMipLevel;\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tuniform samplerCube envMap;\n\t#else\n\t\tuniform sampler2D envMap;\n\t#endif\n\t\n#endif",envmap_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float reflectivity;\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\tvarying vec3 vWorldPosition;\n\t\tuniform float refractionRatio;\n\t#else\n\t\tvarying vec3 vReflect;\n\t#endif\n#endif",envmap_pars_vertex:"#ifdef USE_ENVMAP\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\t\n\t\tvarying vec3 vWorldPosition;\n\t#else\n\t\tvarying vec3 vReflect;\n\t\tuniform float refractionRatio;\n\t#endif\n#endif",envmap_physical_pars_fragment:"#if defined( USE_ENVMAP )\n\t#ifdef ENVMAP_MODE_REFRACTION\n\t\tuniform float refractionRatio;\n\t#endif\n\tvec3 getLightProbeIndirectIrradiance( const in GeometricContext geometry, const in int maxMIPLevel ) {\n\t\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryVec = vec3( flipEnvMap * worldNormal.x, worldNormal.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor = textureCubeLodEXT( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor = textureCube( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\n\t\t#else\n\t\t\tvec4 envMapColor = vec4( 0.0 );\n\t\t#endif\n\t\treturn PI * envMapColor.rgb * envMapIntensity;\n\t}\n\tfloat getSpecularMIPLevel( const in float roughness, const in int maxMIPLevel ) {\n\t\tfloat maxMIPLevelScalar = float( maxMIPLevel );\n\t\tfloat sigma = PI * roughness * roughness / ( 1.0 + roughness );\n\t\tfloat desiredMIPLevel = maxMIPLevelScalar + log2( sigma );\n\t\treturn clamp( desiredMIPLevel, 0.0, maxMIPLevelScalar );\n\t}\n\tvec3 getLightProbeIndirectRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness, const in int maxMIPLevel ) {\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( -viewDir, normal );\n\t\t\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( -viewDir, normal, refractionRatio );\n\t\t#endif\n\t\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\n\t\tfloat specularMIPLevel = getSpecularMIPLevel( roughness, maxMIPLevel );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryReflectVec = vec3( flipEnvMap * reflectVec.x, reflectVec.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor = textureCubeLodEXT( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor = textureCube( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\n\t\t#endif\n\t\treturn envMapColor.rgb * envMapIntensity;\n\t}\n#endif",envmap_vertex:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvWorldPosition = worldPosition.xyz;\n\t#else\n\t\tvec3 cameraToVertex;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvReflect = reflect( cameraToVertex, worldNormal );\n\t\t#else\n\t\t\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\n\t\t#endif\n\t#endif\n#endif",fog_vertex:"#ifdef USE_FOG\n\tfogDepth = - mvPosition.z;\n#endif",fog_pars_vertex:"#ifdef USE_FOG\n\tvarying float fogDepth;\n#endif",fog_fragment:"#ifdef USE_FOG\n\t#ifdef FOG_EXP2\n\t\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * fogDepth * fogDepth );\n\t#else\n\t\tfloat fogFactor = smoothstep( fogNear, fogFar, fogDepth );\n\t#endif\n\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\n#endif",fog_pars_fragment:"#ifdef USE_FOG\n\tuniform vec3 fogColor;\n\tvarying float fogDepth;\n\t#ifdef FOG_EXP2\n\t\tuniform float fogDensity;\n\t#else\n\t\tuniform float fogNear;\n\t\tuniform float fogFar;\n\t#endif\n#endif",gradientmap_pars_fragment:"#ifdef USE_GRADIENTMAP\n\tuniform sampler2D gradientMap;\n#endif\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\n\tfloat dotNL = dot( normal, lightDirection );\n\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\n\t#ifdef USE_GRADIENTMAP\n\t\treturn texture2D( gradientMap, coord ).rgb;\n\t#else\n\t\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\n\t#endif\n}",lightmap_fragment:"#ifdef USE_LIGHTMAP\n\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\treflectedLight.indirectDiffuse += PI * lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n#endif",lightmap_pars_fragment:"#ifdef USE_LIGHTMAP\n\tuniform sampler2D lightMap;\n\tuniform float lightMapIntensity;\n#endif",lights_lambert_vertex:"vec3 diffuse = vec3( 1.0 );\nGeometricContext geometry;\ngeometry.position = mvPosition.xyz;\ngeometry.normal = normalize( transformedNormal );\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\nGeometricContext backGeometry;\nbackGeometry.position = geometry.position;\nbackGeometry.normal = -geometry.normal;\nbackGeometry.viewDir = geometry.viewDir;\nvLightFront = vec3( 0.0 );\nvIndirectFront = vec3( 0.0 );\n#ifdef DOUBLE_SIDED\n\tvLightBack = vec3( 0.0 );\n\tvIndirectBack = vec3( 0.0 );\n#endif\nIncidentLight directLight;\nfloat dotNL;\nvec3 directLightColor_Diffuse;\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry );\n#ifdef DOUBLE_SIDED\n\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\n\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry );\n#endif\n#if NUM_POINT_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tgetPointDirectLightIrradiance( pointLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tgetSpotDirectLightIrradiance( spotLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_DIR_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tgetDirectionalDirectLightIrradiance( directionalLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry );\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif",lights_pars_begin:"uniform bool receiveShadow;\nuniform vec3 ambientLightColor;\nuniform vec3 lightProbe[ 9 ];\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\n\tfloat x = normal.x, y = normal.y, z = normal.z;\n\tvec3 result = shCoefficients[ 0 ] * 0.886227;\n\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\n\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\n\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\n\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\n\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\n\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\n\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\n\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\n\treturn result;\n}\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in GeometricContext geometry ) {\n\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\n\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\n\treturn irradiance;\n}\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\n\tvec3 irradiance = ambientLightColor;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treturn irradiance;\n}\n#if NUM_DIR_LIGHTS > 0\n\tstruct DirectionalLight {\n\t\tvec3 direction;\n\t\tvec3 color;\n\t};\n\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n\tvoid getDirectionalDirectLightIrradiance( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tdirectLight.color = directionalLight.color;\n\t\tdirectLight.direction = directionalLight.direction;\n\t\tdirectLight.visible = true;\n\t}\n#endif\n#if NUM_POINT_LIGHTS > 0\n\tstruct PointLight {\n\t\tvec3 position;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t};\n\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n\tvoid getPointDirectLightIrradiance( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tvec3 lVector = pointLight.position - geometry.position;\n\t\tdirectLight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tdirectLight.color = pointLight.color;\n\t\tdirectLight.color *= punctualLightIntensityToIrradianceFactor( lightDistance, pointLight.distance, pointLight.decay );\n\t\tdirectLight.visible = ( directLight.color != vec3( 0.0 ) );\n\t}\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\tstruct SpotLight {\n\t\tvec3 position;\n\t\tvec3 direction;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t\tfloat coneCos;\n\t\tfloat penumbraCos;\n\t};\n\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\n\tvoid getSpotDirectLightIrradiance( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tvec3 lVector = spotLight.position - geometry.position;\n\t\tdirectLight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tfloat angleCos = dot( directLight.direction, spotLight.direction );\n\t\tif ( angleCos > spotLight.coneCos ) {\n\t\t\tfloat spotEffect = smoothstep( spotLight.coneCos, spotLight.penumbraCos, angleCos );\n\t\t\tdirectLight.color = spotLight.color;\n\t\t\tdirectLight.color *= spotEffect * punctualLightIntensityToIrradianceFactor( lightDistance, spotLight.distance, spotLight.decay );\n\t\t\tdirectLight.visible = true;\n\t\t} else {\n\t\t\tdirectLight.color = vec3( 0.0 );\n\t\t\tdirectLight.visible = false;\n\t\t}\n\t}\n#endif\n#if NUM_RECT_AREA_LIGHTS > 0\n\tstruct RectAreaLight {\n\t\tvec3 color;\n\t\tvec3 position;\n\t\tvec3 halfWidth;\n\t\tvec3 halfHeight;\n\t};\n\tuniform sampler2D ltc_1;\tuniform sampler2D ltc_2;\n\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\tstruct HemisphereLight {\n\t\tvec3 direction;\n\t\tvec3 skyColor;\n\t\tvec3 groundColor;\n\t};\n\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\n\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in GeometricContext geometry ) {\n\t\tfloat dotNL = dot( geometry.normal, hemiLight.direction );\n\t\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\n\t\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tirradiance *= PI;\n\t\t#endif\n\t\treturn irradiance;\n\t}\n#endif",lights_toon_fragment:"ToonMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;",lights_toon_pars_fragment:"varying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\nstruct ToonMaterial {\n\tvec3 diffuseColor;\n};\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_Toon\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Toon\n#define Material_LightProbeLOD( material )\t(0)",lights_phong_fragment:"BlinnPhongMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;\nmaterial.specularColor = specular;\nmaterial.specularShininess = shininess;\nmaterial.specularStrength = specularStrength;",lights_phong_pars_fragment:"varying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\nstruct BlinnPhongMaterial {\n\tvec3 diffuseColor;\n\tvec3 specularColor;\n\tfloat specularShininess;\n\tfloat specularStrength;\n};\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n\treflectedLight.directSpecular += irradiance * BRDF_Specular_BlinnPhong( directLight, geometry, material.specularColor, material.specularShininess ) * material.specularStrength;\n}\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_BlinnPhong\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_BlinnPhong\n#define Material_LightProbeLOD( material )\t(0)",lights_physical_fragment:"PhysicalMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\nmaterial.specularRoughness = max( roughnessFactor, 0.0525 );material.specularRoughness += geometryRoughness;\nmaterial.specularRoughness = min( material.specularRoughness, 1.0 );\n#ifdef REFLECTIVITY\n\t#ifdef SPECULAR\n\t\tvec3 specularIntensityFactor = vec3( specularIntensity );\n\t\tvec3 specularTintFactor = specularTint;\n\t\t#ifdef USE_SPECULARINTENSITYMAP\n\t\t\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\n\t\t#endif\n\t\t#ifdef USE_SPECULARTINTMAP\n\t\t\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\n\t\t#endif\n\t\tmaterial.specularColorF90 = mix( specularIntensityFactor, vec3( 1.0 ), metalnessFactor );\n\t#else\n\t\tvec3 specularIntensityFactor = vec3( 1.0 );\n\t\tvec3 specularTintFactor = vec3( 1.0 );\n\t\tmaterial.specularColorF90 = vec3( 1.0 );\n\t#endif\n\tmaterial.specularColor = mix( min( vec3( MAXIMUM_SPECULAR_COEFFICIENT * pow2( reflectivity ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\n#else\n\tmaterial.specularColor = mix( vec3( DEFAULT_SPECULAR_COEFFICIENT ), diffuseColor.rgb, metalnessFactor );\n\tmaterial.specularColorF90 = vec3( 1.0 );\n#endif\n#ifdef CLEARCOAT\n\tmaterial.clearcoat = clearcoat;\n\tmaterial.clearcoatRoughness = clearcoatRoughness;\n\t#ifdef USE_CLEARCOATMAP\n\t\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\n\t#endif\n\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\t\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\n\t#endif\n\tmaterial.clearcoat = saturate( material.clearcoat );\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\n\tmaterial.clearcoatRoughness += geometryRoughness;\n\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\n#endif\n#ifdef USE_SHEEN\n\tmaterial.sheenColor = sheen;\n#endif",lights_physical_pars_fragment:"struct PhysicalMaterial {\n\tvec3 diffuseColor;\n\tfloat specularRoughness;\n\tvec3 specularColor;\n\tvec3 specularColorF90;\n#ifdef CLEARCOAT\n\tfloat clearcoat;\n\tfloat clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tvec3 sheenColor;\n#endif\n};\n#define MAXIMUM_SPECULAR_COEFFICIENT 0.16\n#define DEFAULT_SPECULAR_COEFFICIENT 0.04\nfloat clearcoatDHRApprox( const in float roughness, const in float dotNL ) {\n\treturn DEFAULT_SPECULAR_COEFFICIENT + ( 1.0 - DEFAULT_SPECULAR_COEFFICIENT ) * ( pow( 1.0 - dotNL, 5.0 ) * pow( 1.0 - roughness, 2.0 ) );\n}\n#if NUM_RECT_AREA_LIGHTS > 0\n\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\t\tvec3 normal = geometry.normal;\n\t\tvec3 viewDir = geometry.viewDir;\n\t\tvec3 position = geometry.position;\n\t\tvec3 lightPos = rectAreaLight.position;\n\t\tvec3 halfWidth = rectAreaLight.halfWidth;\n\t\tvec3 halfHeight = rectAreaLight.halfHeight;\n\t\tvec3 lightColor = rectAreaLight.color;\n\t\tfloat roughness = material.specularRoughness;\n\t\tvec3 rectCoords[ 4 ];\n\t\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\t\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\n\t\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\n\t\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\n\t\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\n\t\tvec4 t1 = texture2D( ltc_1, uv );\n\t\tvec4 t2 = texture2D( ltc_2, uv );\n\t\tmat3 mInv = mat3(\n\t\t\tvec3( t1.x, 0, t1.y ),\n\t\t\tvec3(    0, 1,    0 ),\n\t\t\tvec3( t1.z, 0, t1.w )\n\t\t);\n\t\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\n\t\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\n\t\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\n\t}\n#endif\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNL = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\n\t\tvec3 ccIrradiance = ccDotNL * directLight.color;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tccIrradiance *= PI;\n\t\t#endif\n\t\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t\treflectedLight.directSpecular += ccIrradiance * material.clearcoat * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), vec3( 1.0 ), material.clearcoatRoughness );\n\t#else\n\t\tfloat clearcoatDHR = 0.0;\n\t#endif\n\t#ifdef USE_SHEEN\n\t\treflectedLight.directSpecular += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_Sheen(\n\t\t\tmaterial.specularRoughness,\n\t\t\tdirectLight.direction,\n\t\t\tgeometry,\n\t\t\tmaterial.sheenColor\n\t\t);\n\t#else\n\t\treflectedLight.directSpecular += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.normal, material.specularColor, material.specularColorF90, material.specularRoughness);\n\t#endif\n\treflectedLight.directDiffuse += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNV = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular += clearcoatRadiance * material.clearcoat * BRDF_Specular_GGX_Environment( geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearcoatRoughness );\n\t\tfloat ccDotNL = ccDotNV;\n\t\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t#else\n\t\tfloat clearcoatDHR = 0.0;\n\t#endif\n\tfloat clearcoatInv = 1.0 - clearcoatDHR;\n\tvec3 singleScattering = vec3( 0.0 );\n\tvec3 multiScattering = vec3( 0.0 );\n\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\n\tBRDF_Specular_Multiscattering_Environment( geometry, material.specularColor, material.specularRoughness, singleScattering, multiScattering );\n\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\n\treflectedLight.indirectSpecular += clearcoatInv * radiance * singleScattering;\n\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\n\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\n}\n#define RE_Direct\t\t\t\tRE_Direct_Physical\n#define RE_Direct_RectArea\t\tRE_Direct_RectArea_Physical\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Physical\n#define RE_IndirectSpecular\t\tRE_IndirectSpecular_Physical\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\n\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\n}",lights_fragment_begin:"\nGeometricContext geometry;\ngeometry.position = - vViewPosition;\ngeometry.normal = normal;\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\n#ifdef CLEARCOAT\n\tgeometry.clearcoatNormal = clearcoatNormal;\n#endif\nIncidentLight directLight;\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\n\tPointLight pointLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tpointLight = pointLights[ i ];\n\t\tgetPointDirectLightIrradiance( pointLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\n\t\tpointLightShadow = pointLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\n\tSpotLight spotLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tspotLight = spotLights[ i ];\n\t\tgetSpotDirectLightIrradiance( spotLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\tspotLightShadow = spotLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\n\tDirectionalLight directionalLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tdirectionalLight = directionalLights[ i ];\n\t\tgetDirectionalDirectLightIrradiance( directionalLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\n\t\tdirectionalLightShadow = directionalLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\n\tRectAreaLight rectAreaLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\n\t\trectAreaLight = rectAreaLights[ i ];\n\t\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if defined( RE_IndirectDiffuse )\n\tvec3 iblIrradiance = vec3( 0.0 );\n\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\n\tirradiance += getLightProbeIrradiance( lightProbe, geometry );\n\t#if ( NUM_HEMI_LIGHTS > 0 )\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\t\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n#endif\n#if defined( RE_IndirectSpecular )\n\tvec3 radiance = vec3( 0.0 );\n\tvec3 clearcoatRadiance = vec3( 0.0 );\n#endif",lights_fragment_maps:"#if defined( RE_IndirectDiffuse )\n\t#ifdef USE_LIGHTMAP\n\t\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\t\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tlightMapIrradiance *= PI;\n\t\t#endif\n\t\tirradiance += lightMapIrradiance;\n\t#endif\n\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\n\t\tiblIrradiance += getLightProbeIndirectIrradiance( geometry, maxMipLevel );\n\t#endif\n#endif\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\n\tradiance += getLightProbeIndirectRadiance( geometry.viewDir, geometry.normal, material.specularRoughness, maxMipLevel );\n\t#ifdef CLEARCOAT\n\t\tclearcoatRadiance += getLightProbeIndirectRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness, maxMipLevel );\n\t#endif\n#endif",lights_fragment_end:"#if defined( RE_IndirectDiffuse )\n\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\n#endif\n#if defined( RE_IndirectSpecular )\n\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\n#endif",logdepthbuf_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\n#endif",logdepthbuf_pars_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tuniform float logDepthBufFC;\n\tvarying float vFragDepth;\n\tvarying float vIsPerspective;\n#endif",logdepthbuf_pars_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvarying float vFragDepth;\n\t\tvarying float vIsPerspective;\n\t#else\n\t\tuniform float logDepthBufFC;\n\t#endif\n#endif",logdepthbuf_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvFragDepth = 1.0 + gl_Position.w;\n\t\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\n\t#else\n\t\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\n\t\t\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\n\t\t\tgl_Position.z *= gl_Position.w;\n\t\t}\n\t#endif\n#endif",map_fragment:"#ifdef USE_MAP\n\tvec4 texelColor = texture2D( map, vUv );\n\ttexelColor = mapTexelToLinear( texelColor );\n\tdiffuseColor *= texelColor;\n#endif",map_pars_fragment:"#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif",map_particle_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\n#endif\n#ifdef USE_MAP\n\tvec4 mapTexel = texture2D( map, uv );\n\tdiffuseColor *= mapTexelToLinear( mapTexel );\n#endif\n#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\n#endif",map_particle_pars_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tuniform mat3 uvTransform;\n#endif\n#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif\n#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",metalnessmap_fragment:"float metalnessFactor = metalness;\n#ifdef USE_METALNESSMAP\n\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\n\tmetalnessFactor *= texelMetalness.b;\n#endif",metalnessmap_pars_fragment:"#ifdef USE_METALNESSMAP\n\tuniform sampler2D metalnessMap;\n#endif",morphnormal_vertex:"#ifdef USE_MORPHNORMALS\n\tobjectNormal *= morphTargetBaseInfluence;\n\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\n\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\n\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\n\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\n#endif",morphtarget_pars_vertex:"#ifdef USE_MORPHTARGETS\n\tuniform float morphTargetBaseInfluence;\n\t#ifndef USE_MORPHNORMALS\n\t\tuniform float morphTargetInfluences[ 8 ];\n\t#else\n\t\tuniform float morphTargetInfluences[ 4 ];\n\t#endif\n#endif",morphtarget_vertex:"#ifdef USE_MORPHTARGETS\n\ttransformed *= morphTargetBaseInfluence;\n\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\n\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\n\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\n\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\n\t#ifndef USE_MORPHNORMALS\n\t\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\n\t\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\n\t\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\n\t\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\n\t#endif\n#endif",normal_fragment_begin:"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\n#ifdef FLAT_SHADED\n\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\n\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\n\tvec3 normal = normalize( cross( fdx, fdy ) );\n#else\n\tvec3 normal = normalize( vNormal );\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\t#ifdef USE_TANGENT\n\t\tvec3 tangent = normalize( vTangent );\n\t\tvec3 bitangent = normalize( vBitangent );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\ttangent = tangent * faceDirection;\n\t\t\tbitangent = bitangent * faceDirection;\n\t\t#endif\n\t\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\n\t\t\tmat3 vTBN = mat3( tangent, bitangent, normal );\n\t\t#endif\n\t#endif\n#endif\nvec3 geometryNormal = normal;",normal_fragment_maps:"#ifdef OBJECTSPACE_NORMALMAP\n\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t#ifdef FLIP_SIDED\n\t\tnormal = - normal;\n\t#endif\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\tnormal = normalize( normalMatrix * normal );\n#elif defined( TANGENTSPACE_NORMALMAP )\n\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\tmapN.xy *= normalScale;\n\t#ifdef USE_TANGENT\n\t\tnormal = normalize( vTBN * mapN );\n\t#else\n\t\tnormal = perturbNormal2Arb( -vViewPosition, normal, mapN, faceDirection );\n\t#endif\n#elif defined( USE_BUMPMAP )\n\tnormal = perturbNormalArb( -vViewPosition, normal, dHdxy_fwd(), faceDirection );\n#endif",normalmap_pars_fragment:"#ifdef USE_NORMALMAP\n\tuniform sampler2D normalMap;\n\tuniform vec2 normalScale;\n#endif\n#ifdef OBJECTSPACE_NORMALMAP\n\tuniform mat3 normalMatrix;\n#endif\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\n\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\n\t\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\n\t\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\n\t\tvec2 st0 = dFdx( vUv.st );\n\t\tvec2 st1 = dFdy( vUv.st );\n\t\tvec3 N = surf_norm;\n\t\tvec3 q1perp = cross( q1, N );\n\t\tvec3 q0perp = cross( N, q0 );\n\t\tvec3 T = q1perp * st0.x + q0perp * st1.x;\n\t\tvec3 B = q1perp * st0.y + q0perp * st1.y;\n\t\tfloat det = max( dot( T, T ), dot( B, B ) );\n\t\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\n\t\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\n\t}\n#endif",clearcoat_normal_fragment_begin:"#ifdef CLEARCOAT\n\tvec3 clearcoatNormal = geometryNormal;\n#endif",clearcoat_normal_fragment_maps:"#ifdef USE_CLEARCOAT_NORMALMAP\n\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\n\tclearcoatMapN.xy *= clearcoatNormalScale;\n\t#ifdef USE_TANGENT\n\t\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\n\t#else\n\t\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\n\t#endif\n#endif",clearcoat_pars_fragment:"#ifdef USE_CLEARCOATMAP\n\tuniform sampler2D clearcoatMap;\n#endif\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\tuniform sampler2D clearcoatRoughnessMap;\n#endif\n#ifdef USE_CLEARCOAT_NORMALMAP\n\tuniform sampler2D clearcoatNormalMap;\n\tuniform vec2 clearcoatNormalScale;\n#endif",packing:"vec3 packNormalToRGB( const in vec3 normal ) {\n\treturn normalize( normal ) * 0.5 + 0.5;\n}\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\n\treturn 2.0 * rgb.xyz - 1.0;\n}\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\nconst float ShiftRight8 = 1. / 256.;\nvec4 packDepthToRGBA( const in float v ) {\n\tvec4 r = vec4( fract( v * PackFactors ), v );\n\tr.yzw -= r.xyz * ShiftRight8;\treturn r * PackUpscale;\n}\nfloat unpackRGBAToDepth( const in vec4 v ) {\n\treturn dot( v, UnpackFactors );\n}\nvec4 pack2HalfToRGBA( vec2 v ) {\n\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ));\n\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w);\n}\nvec2 unpackRGBATo2Half( vec4 v ) {\n\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\n}\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn ( viewZ + near ) / ( near - far );\n}\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\n\treturn linearClipZ * ( near - far ) - near;\n}\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn (( near + viewZ ) * far ) / (( far - near ) * viewZ );\n}\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\n\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\n}",premultiplied_alpha_fragment:"#ifdef PREMULTIPLIED_ALPHA\n\tgl_FragColor.rgb *= gl_FragColor.a;\n#endif",project_vertex:"vec4 mvPosition = vec4( transformed, 1.0 );\n#ifdef USE_INSTANCING\n\tmvPosition = instanceMatrix * mvPosition;\n#endif\nmvPosition = modelViewMatrix * mvPosition;\ngl_Position = projectionMatrix * mvPosition;",dithering_fragment:"#ifdef DITHERING\n\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\n#endif",dithering_pars_fragment:"#ifdef DITHERING\n\tvec3 dithering( vec3 color ) {\n\t\tfloat grid_position = rand( gl_FragCoord.xy );\n\t\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\n\t\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\n\t\treturn color + dither_shift_RGB;\n\t}\n#endif",roughnessmap_fragment:"float roughnessFactor = roughness;\n#ifdef USE_ROUGHNESSMAP\n\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\n\troughnessFactor *= texelRoughness.g;\n#endif",roughnessmap_pars_fragment:"#ifdef USE_ROUGHNESSMAP\n\tuniform sampler2D roughnessMap;\n#endif",shadowmap_pars_fragment:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\n\t\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\n\t}\n\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\n\t\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\n\t}\n\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\n\t\tfloat occlusion = 1.0;\n\t\tvec2 distribution = texture2DDistribution( shadow, uv );\n\t\tfloat hard_shadow = step( compare , distribution.x );\n\t\tif (hard_shadow != 1.0 ) {\n\t\t\tfloat distance = compare - distribution.x ;\n\t\t\tfloat variance = max( 0.00000, distribution.y * distribution.y );\n\t\t\tfloat softness_probability = variance / (variance + distance * distance );\t\t\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\t\t\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\n\t\t}\n\t\treturn occlusion;\n\t}\n\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\n\t\tfloat shadow = 1.0;\n\t\tshadowCoord.xyz /= shadowCoord.w;\n\t\tshadowCoord.z += shadowBias;\n\t\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\n\t\tbool inFrustum = all( inFrustumVec );\n\t\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\n\t\tbool frustumTest = all( frustumTestVec );\n\t\tif ( frustumTest ) {\n\t\t#if defined( SHADOWMAP_TYPE_PCF )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx0 = - texelSize.x * shadowRadius;\n\t\t\tfloat dy0 = - texelSize.y * shadowRadius;\n\t\t\tfloat dx1 = + texelSize.x * shadowRadius;\n\t\t\tfloat dy1 = + texelSize.y * shadowRadius;\n\t\t\tfloat dx2 = dx0 / 2.0;\n\t\t\tfloat dy2 = dy0 / 2.0;\n\t\t\tfloat dx3 = dx1 / 2.0;\n\t\t\tfloat dy3 = dy1 / 2.0;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\n\t\t\t) * ( 1.0 / 17.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx = texelSize.x;\n\t\t\tfloat dy = texelSize.y;\n\t\t\tvec2 uv = shadowCoord.xy;\n\t\t\tvec2 f = fract( uv * shadowMapSize + 0.5 );\n\t\t\tuv -= f * texelSize;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t f.y )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_VSM )\n\t\t\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#else\n\t\t\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#endif\n\t\t}\n\t\treturn shadow;\n\t}\n\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\n\t\tvec3 absV = abs( v );\n\t\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\n\t\tabsV *= scaleToCube;\n\t\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\n\t\tvec2 planar = v.xy;\n\t\tfloat almostATexel = 1.5 * texelSizeY;\n\t\tfloat almostOne = 1.0 - almostATexel;\n\t\tif ( absV.z >= almostOne ) {\n\t\t\tif ( v.z > 0.0 )\n\t\t\t\tplanar.x = 4.0 - v.x;\n\t\t} else if ( absV.x >= almostOne ) {\n\t\t\tfloat signX = sign( v.x );\n\t\t\tplanar.x = v.z * signX + 2.0 * signX;\n\t\t} else if ( absV.y >= almostOne ) {\n\t\t\tfloat signY = sign( v.y );\n\t\t\tplanar.x = v.x + 2.0 * signY + 2.0;\n\t\t\tplanar.y = v.z * signY - 2.0;\n\t\t}\n\t\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\n\t}\n\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\n\t\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\n\t\tvec3 lightToPosition = shadowCoord.xyz;\n\t\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\t\tdp += shadowBias;\n\t\tvec3 bd3D = normalize( lightToPosition );\n\t\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\n\t\t\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\n\t\t\treturn (\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#else\n\t\t\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\n\t\t#endif\n\t}\n#endif",shadowmap_pars_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n#endif",shadowmap_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\n\t\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\tvec4 shadowWorldPosition;\n\t#endif\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n#endif",shadowmask_pars_fragment:"float getShadowMask() {\n\tfloat shadow = 1.0;\n\t#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tdirectionalLight = directionalLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tspotLight = spotLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tpointLight = pointLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#endif\n\treturn shadow;\n}",skinbase_vertex:"#ifdef USE_SKINNING\n\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\n\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\n\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\n\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\n#endif",skinning_pars_vertex:"#ifdef USE_SKINNING\n\tuniform mat4 bindMatrix;\n\tuniform mat4 bindMatrixInverse;\n\t#ifdef BONE_TEXTURE\n\t\tuniform highp sampler2D boneTexture;\n\t\tuniform int boneTextureSize;\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tfloat j = i * 4.0;\n\t\t\tfloat x = mod( j, float( boneTextureSize ) );\n\t\t\tfloat y = floor( j / float( boneTextureSize ) );\n\t\t\tfloat dx = 1.0 / float( boneTextureSize );\n\t\t\tfloat dy = 1.0 / float( boneTextureSize );\n\t\t\ty = dy * ( y + 0.5 );\n\t\t\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\n\t\t\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\n\t\t\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\n\t\t\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\n\t\t\tmat4 bone = mat4( v1, v2, v3, v4 );\n\t\t\treturn bone;\n\t\t}\n\t#else\n\t\tuniform mat4 boneMatrices[ MAX_BONES ];\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tmat4 bone = boneMatrices[ int(i) ];\n\t\t\treturn bone;\n\t\t}\n\t#endif\n#endif",skinning_vertex:"#ifdef USE_SKINNING\n\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\n\tvec4 skinned = vec4( 0.0 );\n\tskinned += boneMatX * skinVertex * skinWeight.x;\n\tskinned += boneMatY * skinVertex * skinWeight.y;\n\tskinned += boneMatZ * skinVertex * skinWeight.z;\n\tskinned += boneMatW * skinVertex * skinWeight.w;\n\ttransformed = ( bindMatrixInverse * skinned ).xyz;\n#endif",skinnormal_vertex:"#ifdef USE_SKINNING\n\tmat4 skinMatrix = mat4( 0.0 );\n\tskinMatrix += skinWeight.x * boneMatX;\n\tskinMatrix += skinWeight.y * boneMatY;\n\tskinMatrix += skinWeight.z * boneMatZ;\n\tskinMatrix += skinWeight.w * boneMatW;\n\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\n\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\n\t#ifdef USE_TANGENT\n\t\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#endif\n#endif",specularmap_fragment:"float specularStrength;\n#ifdef USE_SPECULARMAP\n\tvec4 texelSpecular = texture2D( specularMap, vUv );\n\tspecularStrength = texelSpecular.r;\n#else\n\tspecularStrength = 1.0;\n#endif",specularmap_pars_fragment:"#ifdef USE_SPECULARMAP\n\tuniform sampler2D specularMap;\n#endif",tonemapping_fragment:"#if defined( TONE_MAPPING )\n\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\n#endif",tonemapping_pars_fragment:"#ifndef saturate\n#define saturate(a) clamp( a, 0.0, 1.0 )\n#endif\nuniform float toneMappingExposure;\nvec3 LinearToneMapping( vec3 color ) {\n\treturn toneMappingExposure * color;\n}\nvec3 ReinhardToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\treturn saturate( color / ( vec3( 1.0 ) + color ) );\n}\nvec3 OptimizedCineonToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\tcolor = max( vec3( 0.0 ), color - 0.004 );\n\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\n}\nvec3 RRTAndODTFit( vec3 v ) {\n\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\n\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\n\treturn a / b;\n}\nvec3 ACESFilmicToneMapping( vec3 color ) {\n\tconst mat3 ACESInputMat = mat3(\n\t\tvec3( 0.59719, 0.07600, 0.02840 ),\t\tvec3( 0.35458, 0.90834, 0.13383 ),\n\t\tvec3( 0.04823, 0.01566, 0.83777 )\n\t);\n\tconst mat3 ACESOutputMat = mat3(\n\t\tvec3(  1.60475, -0.10208, -0.00327 ),\t\tvec3( -0.53108,  1.10813, -0.07276 ),\n\t\tvec3( -0.07367, -0.00605,  1.07602 )\n\t);\n\tcolor *= toneMappingExposure / 0.6;\n\tcolor = ACESInputMat * color;\n\tcolor = RRTAndODTFit( color );\n\tcolor = ACESOutputMat * color;\n\treturn saturate( color );\n}\nvec3 CustomToneMapping( vec3 color ) { return color; }",transmission_fragment:"#ifdef USE_TRANSMISSION\n\tfloat transmissionFactor = transmission;\n\tfloat thicknessFactor = thickness;\n\t#ifdef USE_TRANSMISSIONMAP\n\t\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\n\t#endif\n\tvec3 pos = vWorldPosition.xyz / vWorldPosition.w;\n\tvec3 v = normalize( cameraPosition - pos );\n\tvec3 n = inverseTransformDirection( normal, viewMatrix );\n\tfloat ior = ( 1.0 + 0.4 * reflectivity ) / ( 1.0 - 0.4 * reflectivity );\n\tvec3 transmission = transmissionFactor * getIBLVolumeRefraction(\n\t\tn, v, roughnessFactor, material.diffuseColor, material.specularColor,\n\t\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\n\t\tattenuationTint, attenuationDistance );\n\ttotalDiffuse = mix( totalDiffuse, transmission, transmissionFactor );\n#endif",transmission_pars_fragment:"#ifdef USE_TRANSMISSION\n\t#ifdef USE_TRANSMISSIONMAP\n\t\tuniform sampler2D transmissionMap;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tuniform sampler2D thicknessMap;\n\t#endif\n\tuniform vec2 transmissionSamplerSize;\n\tuniform sampler2D transmissionSamplerMap;\n\tuniform mat4 modelMatrix;\n\tuniform mat4 projectionMatrix;\n\tvarying vec4 vWorldPosition;\n\tvec3 getVolumeTransmissionRay(vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix) {\n\t\tvec3 refractionVector = refract(-v, normalize(n), 1.0 / ior);\n\t\tvec3 modelScale;\n\t\tmodelScale.x = length(vec3(modelMatrix[0].xyz));\n\t\tmodelScale.y = length(vec3(modelMatrix[1].xyz));\n\t\tmodelScale.z = length(vec3(modelMatrix[2].xyz));\n\t\treturn normalize(refractionVector) * thickness * modelScale;\n\t}\n\tfloat applyIorToRoughness(float roughness, float ior) {\n\t\treturn roughness * clamp(ior * 2.0 - 2.0, 0.0, 1.0);\n\t}\n\tvec3 getTransmissionSample(vec2 fragCoord, float roughness, float ior) {\n\t\tfloat framebufferLod = log2(transmissionSamplerSize.x) * applyIorToRoughness(roughness, ior);\n\t\treturn texture2DLodEXT(transmissionSamplerMap, fragCoord.xy, framebufferLod).rgb;\n\t}\n\tvec3 applyVolumeAttenuation(vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance) {\n\t\tif (attenuationDistance == 0.0) {\n\t\t\treturn radiance;\n\t\t} else {\n\t\t\tvec3 attenuationCoefficient = -log(attenuationColor) / attenuationDistance;\n\t\t\tvec3 transmittance = exp(-attenuationCoefficient * transmissionDistance);\t\t\treturn transmittance * radiance;\n\t\t}\n\t}\n\tvec3 getIBLVolumeRefraction(vec3 n, vec3 v, float perceptualRoughness, vec3 baseColor, vec3 specularColor,\n\t\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\n\t\tvec3 attenuationColor, float attenuationDistance) {\n\t\tvec3 transmissionRay = getVolumeTransmissionRay(n, v, thickness, ior, modelMatrix);\n\t\tvec3 refractedRayExit = position + transmissionRay;\n\t\tvec4 ndcPos = projMatrix * viewMatrix * vec4(refractedRayExit, 1.0);\n\t\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\n\t\trefractionCoords += 1.0;\n\t\trefractionCoords /= 2.0;\n\t\tvec3 transmittedLight = getTransmissionSample(refractionCoords, perceptualRoughness, ior);\n\t\tvec3 attenuatedColor = applyVolumeAttenuation(transmittedLight, length(transmissionRay), attenuationColor, attenuationDistance);\n\t\treturn (1.0 - specularColor) * attenuatedColor * baseColor;\n\t}\n#endif",uv_pars_fragment:"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\n\tvarying vec2 vUv;\n#endif",uv_pars_vertex:"#ifdef USE_UV\n\t#ifdef UVS_VERTEX_ONLY\n\t\tvec2 vUv;\n\t#else\n\t\tvarying vec2 vUv;\n\t#endif\n\tuniform mat3 uvTransform;\n#endif",uv_vertex:"#ifdef USE_UV\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n#endif",uv2_pars_fragment:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvarying vec2 vUv2;\n#endif",uv2_pars_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tattribute vec2 uv2;\n\tvarying vec2 vUv2;\n\tuniform mat3 uv2Transform;\n#endif",uv2_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\n#endif",worldpos_vertex:"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\n\tvec4 worldPosition = vec4( transformed, 1.0 );\n\t#ifdef USE_INSTANCING\n\t\tworldPosition = instanceMatrix * worldPosition;\n\t#endif\n\tworldPosition = modelMatrix * worldPosition;\n#endif",background_frag:"uniform sampler2D t2D;\nvarying vec2 vUv;\nvoid main() {\n\tvec4 texColor = texture2D( t2D, vUv );\n\tgl_FragColor = mapTexelToLinear( texColor );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",background_vert:"varying vec2 vUv;\nuniform mat3 uvTransform;\nvoid main() {\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n\tgl_Position = vec4( position.xy, 1.0, 1.0 );\n}",cube_frag:"#include <envmap_common_pars_fragment>\nuniform float opacity;\nvarying vec3 vWorldDirection;\n#include <cube_uv_reflection_fragment>\nvoid main() {\n\tvec3 vReflect = vWorldDirection;\n\t#include <envmap_fragment>\n\tgl_FragColor = envColor;\n\tgl_FragColor.a *= opacity;\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",cube_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\tgl_Position.z = gl_Position.w;\n}",depth_frag:"#if DEPTH_PACKING == 3200\n\tuniform float opacity;\n#endif\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#if DEPTH_PACKING == 3200\n\t\tdiffuseColor.a = opacity;\n\t#endif\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <logdepthbuf_fragment>\n\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\n\t#if DEPTH_PACKING == 3200\n\t\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\n\t#elif DEPTH_PACKING == 3201\n\t\tgl_FragColor = packDepthToRGBA( fragCoordZ );\n\t#endif\n}",depth_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvHighPrecisionZW = gl_Position.zw;\n}",distanceRGBA_frag:"#define DISTANCE\nuniform vec3 referencePosition;\nuniform float nearDistance;\nuniform float farDistance;\nvarying vec3 vWorldPosition;\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main () {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\tfloat dist = length( vWorldPosition - referencePosition );\n\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\n\tdist = saturate( dist );\n\tgl_FragColor = packDepthToRGBA( dist );\n}",distanceRGBA_vert:"#define DISTANCE\nvarying vec3 vWorldPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <clipping_planes_vertex>\n\tvWorldPosition = worldPosition.xyz;\n}",equirect_frag:"uniform sampler2D tEquirect;\nvarying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvec3 direction = normalize( vWorldDirection );\n\tvec2 sampleUV = equirectUv( direction );\n\tvec4 texColor = texture2D( tEquirect, sampleUV );\n\tgl_FragColor = mapTexelToLinear( texColor );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",equirect_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n}",linedashed_frag:"uniform vec3 diffuse;\nuniform float opacity;\nuniform float dashSize;\nuniform float totalSize;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\n\t\tdiscard;\n\t}\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <color_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",linedashed_vert:"uniform float scale;\nattribute float lineDistance;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\tvLineDistance = scale * lineDistance;\n\t#include <color_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n}",meshbasic_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <fog_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\t#ifdef USE_LIGHTMAP\n\t\n\t\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\t\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n\t#else\n\t\treflectedLight.indirectDiffuse += vec3( 1.0 );\n\t#endif\n\t#include <aomap_fragment>\n\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\n\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshbasic_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinbase_vertex>\n\t\t#include <skinnormal_vertex>\n\t\t#include <defaultnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <fog_vertex>\n}",meshlambert_frag:"uniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <fog_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <shadowmask_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <emissivemap_fragment>\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\n\t#else\n\t\treflectedLight.indirectDiffuse += vIndirectFront;\n\t#endif\n\t#include <lightmap_fragment>\n\treflectedLight.indirectDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb );\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\n\t#else\n\t\treflectedLight.directDiffuse = vLightFront;\n\t#endif\n\treflectedLight.directDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb ) * getShadowMask();\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshlambert_vert:"#define LAMBERT\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <envmap_pars_vertex>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <lights_lambert_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshmatcap_frag:"#define MATCAP\nuniform vec3 diffuse;\nuniform float opacity;\nuniform sampler2D matcap;\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tvec3 viewDir = normalize( vViewPosition );\n\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\n\tvec3 y = cross( viewDir, x );\n\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\n\t#ifdef USE_MATCAP\n\t\tvec4 matcapColor = texture2D( matcap, uv );\n\t\tmatcapColor = matcapTexelToLinear( matcapColor );\n\t#else\n\t\tvec4 matcapColor = vec4( 1.0 );\n\t#endif\n\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshmatcap_vert:"#define MATCAP\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <color_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#ifndef FLAT_SHADED\n\t\tvNormal = normalize( transformedNormal );\n\t\t#ifdef USE_TANGENT\n\t\t\tvTangent = normalize( transformedTangent );\n\t\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t\t#endif\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n\tvViewPosition = - mvPosition.xyz;\n}",meshtoon_frag:"#define TOON\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <gradientmap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <lights_toon_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_toon_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshtoon_vert:"#define TOON\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshphong_frag:"#define PHONG\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform vec3 specular;\nuniform float shininess;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <lights_phong_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_phong_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshphong_vert:"#define PHONG\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshphysical_frag:"#define STANDARD\n#ifdef PHYSICAL\n\t#define REFLECTIVITY\n\t#define CLEARCOAT\n\t#define SPECULAR\n#endif\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float roughness;\nuniform float metalness;\nuniform float opacity;\n#ifdef USE_TRANSMISSION\n\tuniform float transmission;\n\tuniform float thickness;\n\tuniform float attenuationDistance;\n\tuniform vec3 attenuationTint;\n#endif\n#ifdef REFLECTIVITY\n\tuniform float reflectivity;\n#endif\n#ifdef SPECULAR\n\tuniform float specularIntensity;\n\tuniform vec3 specularTint;\n\t#ifdef USE_SPECULARINTENSITYMAP\n\t\tuniform sampler2D specularIntensityMap;\n\t#endif\n\t#ifdef USE_SPECULARTINTMAP\n\t\tuniform sampler2D specularTintMap;\n\t#endif\n#endif\n#ifdef CLEARCOAT\n\tuniform float clearcoat;\n\tuniform float clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tuniform vec3 sheen;\n#endif\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <bsdfs>\n#include <transmission_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_physical_pars_fragment>\n#include <fog_pars_fragment>\n#include <lights_pars_begin>\n#include <lights_physical_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <clearcoat_pars_fragment>\n#include <roughnessmap_pars_fragment>\n#include <metalnessmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <roughnessmap_fragment>\n\t#include <metalnessmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <clearcoat_normal_fragment_begin>\n\t#include <clearcoat_normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_physical_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\n\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\n\t#include <transmission_fragment>\n\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshphysical_vert:"#define STANDARD\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#ifdef USE_TRANSMISSION\n\tvarying vec4 vWorldPosition;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n#ifdef USE_TRANSMISSION\n\tvWorldPosition = worldPosition;\n#endif\n}",normal_frag:"#define NORMAL\nuniform float opacity;\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <packing>\n#include <uv_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\t#include <logdepthbuf_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\n}",normal_vert:"#define NORMAL\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvViewPosition = - mvPosition.xyz;\n#endif\n}",points_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include <common>\n#include <color_pars_fragment>\n#include <map_particle_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_particle_fragment>\n\t#include <color_fragment>\n\t#include <alphatest_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",points_vert:"uniform float size;\nuniform float scale;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <color_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\tgl_PointSize = size;\n\t#ifdef USE_SIZEATTENUATION\n\t\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\n\t\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\n\t#endif\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <fog_vertex>\n}",shadow_frag:"uniform vec3 color;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <shadowmap_pars_fragment>\n#include <shadowmask_pars_fragment>\nvoid main() {\n\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n}",shadow_vert:"#include <common>\n#include <fog_pars_vertex>\n#include <shadowmap_pars_vertex>\nvoid main() {\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",sprite_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include <common>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n}",sprite_vert:"uniform float rotation;\nuniform vec2 center;\n#include <common>\n#include <uv_pars_vertex>\n#include <fog_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\n\tvec2 scale;\n\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\n\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\n\t#ifndef USE_SIZEATTENUATION\n\t\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\n\t\tif ( isPerspective ) scale *= - mvPosition.z;\n\t#endif\n\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\n\tvec2 rotatedPosition;\n\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\n\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\n\tmvPosition.xy += rotatedPosition;\n\tgl_Position = projectionMatrix * mvPosition;\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n}"},zst={common:{diffuse:{value:new Rrt(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new $it},uv2Transform:{value:new $it},alphaMap:{value:null}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new Qit(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new Rrt(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new Rrt(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},uvTransform:{value:new $it}},sprite:{diffuse:{value:new Rrt(16777215)},opacity:{value:1},center:{value:new Qit(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},uvTransform:{value:new $it}}},Ist={basic:{uniforms:gst([zst.common,zst.specularmap,zst.envmap,zst.aomap,zst.lightmap,zst.fog]),vertexShader:Nst.meshbasic_vert,fragmentShader:Nst.meshbasic_frag},lambert:{uniforms:gst([zst.common,zst.specularmap,zst.envmap,zst.aomap,zst.lightmap,zst.emissivemap,zst.fog,zst.lights,{emissive:{value:new Rrt(0)}}]),vertexShader:Nst.meshlambert_vert,fragmentShader:Nst.meshlambert_frag},phong:{uniforms:gst([zst.common,zst.specularmap,zst.envmap,zst.aomap,zst.lightmap,zst.emissivemap,zst.bumpmap,zst.normalmap,zst.displacementmap,zst.fog,zst.lights,{emissive:{value:new Rrt(0)},specular:{value:new Rrt(1118481)},shininess:{value:30}}]),vertexShader:Nst.meshphong_vert,fragmentShader:Nst.meshphong_frag},standard:{uniforms:gst([zst.common,zst.envmap,zst.aomap,zst.lightmap,zst.emissivemap,zst.bumpmap,zst.normalmap,zst.displacementmap,zst.roughnessmap,zst.metalnessmap,zst.fog,zst.lights,{emissive:{value:new Rrt(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:Nst.meshphysical_vert,fragmentShader:Nst.meshphysical_frag},toon:{uniforms:gst([zst.common,zst.aomap,zst.lightmap,zst.emissivemap,zst.bumpmap,zst.normalmap,zst.displacementmap,zst.gradientmap,zst.fog,zst.lights,{emissive:{value:new Rrt(0)}}]),vertexShader:Nst.meshtoon_vert,fragmentShader:Nst.meshtoon_frag},matcap:{uniforms:gst([zst.common,zst.bumpmap,zst.normalmap,zst.displacementmap,zst.fog,{matcap:{value:null}}]),vertexShader:Nst.meshmatcap_vert,fragmentShader:Nst.meshmatcap_frag},points:{uniforms:gst([zst.points,zst.fog]),vertexShader:Nst.points_vert,fragmentShader:Nst.points_frag},dashed:{uniforms:gst([zst.common,zst.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:Nst.linedashed_vert,fragmentShader:Nst.linedashed_frag},depth:{uniforms:gst([zst.common,zst.displacementmap]),vertexShader:Nst.depth_vert,fragmentShader:Nst.depth_frag},normal:{uniforms:gst([zst.common,zst.bumpmap,zst.normalmap,zst.displacementmap,{opacity:{value:1}}]),vertexShader:Nst.normal_vert,fragmentShader:Nst.normal_frag},sprite:{uniforms:gst([zst.sprite,zst.fog]),vertexShader:Nst.sprite_vert,fragmentShader:Nst.sprite_frag},background:{uniforms:{uvTransform:{value:new $it},t2D:{value:null}},vertexShader:Nst.background_vert,fragmentShader:Nst.background_frag},cube:{uniforms:gst([zst.envmap,{opacity:{value:1}}]),vertexShader:Nst.cube_vert,fragmentShader:Nst.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:Nst.equirect_vert,fragmentShader:Nst.equirect_frag},distanceRGBA:{uniforms:gst([zst.common,zst.displacementmap,{referencePosition:{value:new cat},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:Nst.distanceRGBA_vert,fragmentShader:Nst.distanceRGBA_frag},shadow:{uniforms:gst([zst.lights,zst.fog,{color:{value:new Rrt(0)},opacity:{value:1}}]),vertexShader:Nst.shadow_vert,fragmentShader:Nst.shadow_frag}};function Hst(t,e,n,o,i){const a=new Rrt(0);let r,s,l=0,c=null,d=0,p=null;function m(t,e){n.buffers.color.setClear(t.r,t.g,t.b,e,i)}return{getClearColor:function(){return a},setClearColor:function(t,e=1){a.set(t),l=e,m(a,l)},getClearAlpha:function(){return l},setClearAlpha:function(t){l=t,m(a,l)},render:function u(n,i){let u=!1,f=!0===i.isScene?i.background:null;f&&f.isTexture&&(f=e.get(f));const g=t.xr,h=g.getSession&&g.getSession();h&&"additive"===h.environmentBlendMode&&(f=null),null===f?m(a,l):f&&f.isColor&&(m(f,1),u=!0),(t.autoClear||u)&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),f&&(f.isCubeTexture||f.mapping===mit)?(void 0===s&&(s=new pst(new ust(1,1,1),new bst({name:"BackgroundCubeMaterial",uniforms:fst(Ist.cube.uniforms),vertexShader:Ist.cube.vertexShader,fragmentShader:Ist.cube.fragmentShader,side:1,depthTest:!1,depthWrite:!1,fog:!1})),s.geometry.deleteAttribute("normal"),s.geometry.deleteAttribute("uv"),s.onBeforeRender=function(t,e,n){this.matrixWorld.copyPosition(n.matrixWorld)},Object.defineProperty(s.material,"envMap",{get:function(){return this.uniforms.envMap.value}}),o.update(s)),s.material.uniforms.envMap.value=f,s.material.uniforms.flipEnvMap.value=f.isCubeTexture&&!1===f.isRenderTargetTexture?-1:1,c===f&&d===f.version&&p===t.toneMapping||(s.material.needsUpdate=!0,c=f,d=f.version,p=t.toneMapping),n.unshift(s,s.geometry,s.material,0,0,null)):f&&f.isTexture&&(void 0===r&&(r=new pst(new Tst(2,2),new bst({name:"BackgroundMaterial",uniforms:fst(Ist.background.uniforms),vertexShader:Ist.background.vertexShader,fragmentShader:Ist.background.fragmentShader,side:0,depthTest:!1,depthWrite:!1,fog:!1})),r.geometry.deleteAttribute("normal"),Object.defineProperty(r.material,"map",{get:function(){return this.uniforms.t2D.value}}),o.update(r)),r.material.uniforms.t2D.value=f,!0===f.matrixAutoUpdate&&f.updateMatrix(),r.material.uniforms.uvTransform.value.copy(f.matrix),c===f&&d===f.version&&p===t.toneMapping||(r.material.needsUpdate=!0,c=f,d=f.version,p=t.toneMapping),n.unshift(r,r.geometry,r.material,0,0,null))}}}function Fst(t,e,n,o){const i=t.getParameter(34921),a=o.isWebGL2?null:e.get("OES_vertex_array_object"),r=o.isWebGL2||null!==a,s={},l=m(null);let c=l;function d(e){return o.isWebGL2?t.bindVertexArray(e):a.bindVertexArrayOES(e)}function p(e){return o.isWebGL2?t.deleteVertexArray(e):a.deleteVertexArrayOES(e)}function m(t){const e=[],n=[],o=[];for(let t=0;t<i;t++)e[t]=0,n[t]=0,o[t]=0;return{geometry:null,program:null,wireframe:!1,newAttributes:e,enabledAttributes:n,attributeDivisors:o,object:t,attributes:{},index:null}}function u(){const t=c.newAttributes;for(let e=0,n=t.length;e<n;e++)t[e]=0}function f(t){g(t,0)}function g(n,i){const a=c.enabledAttributes,r=c.attributeDivisors;c.newAttributes[n]=1,0===a[n]&&(t.enableVertexAttribArray(n),a[n]=1),r[n]!==i&&((o.isWebGL2?t:e.get("ANGLE_instanced_arrays"))[o.isWebGL2?"vertexAttribDivisor":"vertexAttribDivisorANGLE"](n,i),r[n]=i)}function h(){const e=c.newAttributes,n=c.enabledAttributes;for(let o=0,i=n.length;o<i;o++)n[o]!==e[o]&&(t.disableVertexAttribArray(o),n[o]=0)}function b(e,n,i,a,r,s){!0!==o.isWebGL2||5124!==i&&5125!==i?t.vertexAttribPointer(e,n,i,a,r,s):t.vertexAttribIPointer(e,n,i,r,s)}function y(){_(),c!==l&&(c=l,d(c.object))}function _(){l.geometry=null,l.program=null,l.wireframe=!1}return{setup:function C(i,l,p,y,_){let C=!1;if(r){const e=(function M(e,n,i){const r=!0===i.wireframe;let l=s[e.id];void 0===l&&(l={},s[e.id]=l);let c=l[n.id];void 0===c&&(c={},l[n.id]=c);let d=c[r];return void 0===d&&(d=m((function p(){return o.isWebGL2?t.createVertexArray():a.createVertexArrayOES()})()),c[r]=d),d})(y,p,l);c!==e&&(c=e,d(c.object)),C=(function v(t,e){const n=c.attributes,o=t.attributes;let i=0;for(const t in o){const e=n[t],a=o[t];if(void 0===e)return!0;if(e.attribute!==a)return!0;if(e.data!==a.data)return!0;i++}return c.attributesNum!==i||c.index!==e})(y,_),C&&(function x(t,e){const n={},o=t.attributes;let i=0;for(const t in o){const e=o[t],a={};a.attribute=e,e.data&&(a.data=e.data),n[t]=a,i++}c.attributes=n,c.attributesNum=i,c.index=e})(y,_)}else{const t=!0===l.wireframe;c.geometry===y.id&&c.program===p.id&&c.wireframe===t||(c.geometry=y.id,c.program=p.id,c.wireframe=t,C=!0)}!0===i.isInstancedMesh&&(C=!0),null!==_&&n.update(_,34963),C&&((function O(i,a,r,s){if(!1===o.isWebGL2&&(i.isInstancedMesh||s.isInstancedBufferGeometry)&&null===e.get("ANGLE_instanced_arrays"))return;u();const l=s.attributes,c=r.getAttributes(),d=a.defaultAttributeValues;for(const e in c){const o=c[e];if(o>=0){const a=l[e];if(void 0!==a){const e=a.normalized,i=a.itemSize,r=n.get(a);if(void 0===r)continue;const l=r.buffer,c=r.type,d=r.bytesPerElement;if(a.isInterleavedBufferAttribute){const n=a.data,r=n.stride,p=a.offset;n&&n.isInstancedInterleavedBuffer?(g(o,n.meshPerAttribute),void 0===s._maxInstanceCount&&(s._maxInstanceCount=n.meshPerAttribute*n.count)):f(o),t.bindBuffer(34962,l),b(o,i,c,e,r*d,p*d)}else a.isInstancedBufferAttribute?(g(o,a.meshPerAttribute),void 0===s._maxInstanceCount&&(s._maxInstanceCount=a.meshPerAttribute*a.count)):f(o),t.bindBuffer(34962,l),b(o,i,c,e,0,0)}else if("instanceMatrix"===e){const e=n.get(i.instanceMatrix);if(void 0===e)continue;const a=e.buffer,r=e.type;g(o+0,1),g(o+1,1),g(o+2,1),g(o+3,1),t.bindBuffer(34962,a),t.vertexAttribPointer(o+0,4,r,!1,64,0),t.vertexAttribPointer(o+1,4,r,!1,64,16),t.vertexAttribPointer(o+2,4,r,!1,64,32),t.vertexAttribPointer(o+3,4,r,!1,64,48)}else if("instanceColor"===e){const e=n.get(i.instanceColor);if(void 0===e)continue;const a=e.buffer,r=e.type;g(o,1),t.bindBuffer(34962,a),t.vertexAttribPointer(o,3,r,!1,12,0)}else if(void 0!==d){const n=d[e];if(void 0!==n)switch(n.length){case 2:t.vertexAttrib2fv(o,n);break;case 3:t.vertexAttrib3fv(o,n);break;case 4:t.vertexAttrib4fv(o,n);break;default:t.vertexAttrib1fv(o,n)}}}}h()})(i,l,p,y),null!==_&&t.bindBuffer(34963,n.get(_).buffer))},reset:y,resetDefaultState:_,dispose:function M(){y();for(const t in s){const e=s[t];for(const t in e){const n=e[t];for(const t in n)p(n[t].object),delete n[t];delete e[t]}delete s[t]}},releaseStatesOfGeometry:function v(t){if(void 0===s[t.id])return;const e=s[t.id];for(const t in e){const n=e[t];for(const t in n)p(n[t].object),delete n[t];delete e[t]}delete s[t.id]},releaseStatesOfProgram:function x(t){for(const e in s){const n=s[e];if(void 0===n[t.id])continue;const o=n[t.id];for(const t in o)p(o[t].object),delete o[t];delete n[t.id]}},initAttributes:u,enableAttribute:f,disableUnusedAttributes:h}}function Lst(t,e,n,o){const i=o.isWebGL2;let a;this.setMode=function r(t){a=t},this.render=function s(e,o){t.drawArrays(a,e,o),n.update(o,a,1)},this.renderInstances=function l(o,r,s){if(0===s)return;let l,c;if(i)l=t,c="drawArraysInstanced";else if(l=e.get("ANGLE_instanced_arrays"),c="drawArraysInstancedANGLE",null===l)return void console.error("THREE.WebGLBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.");l[c](a,o,r,s),n.update(r,a,s)}}function Bst(t,e,n){let o;function i(e){if("highp"===e){if(t.getShaderPrecisionFormat(35633,36338).precision>0&&t.getShaderPrecisionFormat(35632,36338).precision>0)return"highp";e="mediump"}return"mediump"===e&&t.getShaderPrecisionFormat(35633,36337).precision>0&&t.getShaderPrecisionFormat(35632,36337).precision>0?"mediump":"lowp"}const a="undefined"!=typeof WebGL2RenderingContext&&t instanceof WebGL2RenderingContext||"undefined"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let r=void 0!==n.precision?n.precision:"highp";const s=i(r);s!==r&&(console.warn("THREE.WebGLRenderer:",r,"not supported, using",s,"instead."),r=s);const l=a||e.has("WEBGL_draw_buffers"),c=!0===n.logarithmicDepthBuffer,d=t.getParameter(34930),p=t.getParameter(35660),m=t.getParameter(3379),u=t.getParameter(34076),f=t.getParameter(34921),g=t.getParameter(36347),h=t.getParameter(36348),b=t.getParameter(36349),y=p>0,_=a||e.has("OES_texture_float");return{isWebGL2:a,drawBuffers:l,getMaxAnisotropy:function C(){if(void 0!==o)return o;if(!0===e.has("EXT_texture_filter_anisotropic")){const n=e.get("EXT_texture_filter_anisotropic");o=t.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else o=0;return o},getMaxPrecision:i,precision:r,logarithmicDepthBuffer:c,maxTextures:d,maxVertexTextures:p,maxTextureSize:m,maxCubemapSize:u,maxAttributes:f,maxVertexUniforms:g,maxVaryings:h,maxFragmentUniforms:b,vertexTextures:y,floatFragmentTextures:_,floatVertexTextures:y&&_,maxSamples:a?t.getParameter(36183):0}}function Vst(t){const e=this;let n=null,o=0,i=!1,a=!1;const r=new kst,s=new $it,l={value:null,needsUpdate:!1};function c(){l.value!==n&&(l.value=n,l.needsUpdate=o>0),e.numPlanes=o,e.numIntersection=0}function d(t,n,o,i){const a=null!==t?t.length:0;let c=null;if(0!==a){if(c=l.value,!0!==i||null===c){const e=o+4*a,i=n.matrixWorldInverse;s.getNormalMatrix(i),(null===c||c.length<e)&&(c=new Float32Array(e));for(let e=0,n=o;e!==a;++e,n+=4)r.copy(t[e]).applyMatrix4(i,s),r.normal.toArray(c,n),c[n+3]=r.constant}l.value=c,l.needsUpdate=!0}return e.numPlanes=a,e.numIntersection=0,c}this.uniform=l,this.numPlanes=0,this.numIntersection=0,this.init=function(t,e,a){const r=0!==t.length||e||0!==o||i;return i=e,n=d(t,a,0),o=t.length,r},this.beginShadows=function(){a=!0,d(null)},this.endShadows=function(){a=!1,c()},this.setState=function(e,r,s){const p=e.clippingPlanes,m=e.clipIntersection,u=e.clipShadows,f=t.get(e);if(!i||null===p||0===p.length||a&&!u)a?d(null):c();else{const t=a?0:o,e=4*t;let i=f.clippingState||null;l.value=i,i=d(p,r,e,s);for(let t=0;t!==e;++t)i[t]=n[t];f.clippingState=i,this.numIntersection=m?this.numPlanes:0,this.numPlanes+=t}}}function jst(t){let e=new WeakMap;function n(t,e){return 303===e?t.mapping=dit:304===e&&(t.mapping=pit),t}function o(t){const n=t.target;n.removeEventListener("dispose",o);const i=e.get(n);void 0!==i&&(e.delete(n),i.dispose())}return{get:function i(a){if(a&&a.isTexture&&!1===a.isRenderTargetTexture){const i=a.mapping;if(303===i||304===i){if(e.has(a))return n(e.get(a).texture,a.mapping);{const i=a.image;if(i&&i.height>0){const r=t.getRenderTarget(),s=new xst(i.height/2);return s.fromEquirectangularTexture(t,a),e.set(a,s),t.setRenderTarget(r),a.addEventListener("dispose",o),n(s.texture,a.mapping)}return null}}}return a},dispose:function a(){e=new WeakMap}}}Ist.physical={uniforms:gst([Ist.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new Qit(1,1)},clearcoatNormalMap:{value:null},sheen:{value:new Rrt(0)},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new Qit},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new Rrt(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new Rrt(1,1,1)},specularTintMap:{value:null}}]),vertexShader:Nst.meshphysical_vert,fragmentShader:Nst.meshphysical_frag};class Ust extends yst{constructor(t=-1,e=1,n=1,o=-1,i=.1,a=2e3){super(),this.type="OrthographicCamera",this.zoom=1,this.view=null,this.left=t,this.right=e,this.top=n,this.bottom=o,this.near=i,this.far=a,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.left=t.left,this.right=t.right,this.top=t.top,this.bottom=t.bottom,this.near=t.near,this.far=t.far,this.zoom=t.zoom,this.view=null===t.view?null:Object.assign({},t.view),this}setViewOffset(t,e,n,o,i,a){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=o,this.view.width=i,this.view.height=a,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=(this.right-this.left)/(2*this.zoom),e=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,o=(this.top+this.bottom)/2;let i=n-t,a=n+t,r=o+e,s=o-e;if(null!==this.view&&this.view.enabled){const t=(this.right-this.left)/this.view.fullWidth/this.zoom,e=(this.top-this.bottom)/this.view.fullHeight/this.zoom;i+=t*this.view.offsetX,a=i+t*this.view.width,r-=e*this.view.offsetY,s=r-e*this.view.height}this.projectionMatrix.makeOrthographic(i,a,r,s,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.zoom=this.zoom,e.object.left=this.left,e.object.right=this.right,e.object.top=this.top,e.object.bottom=this.bottom,e.object.near=this.near,e.object.far=this.far,null!==this.view&&(e.object.view=Object.assign({},this.view)),e}}Ust.prototype.isOrthographicCamera=!0;class Gst extends bst{constructor(t){super(t),this.type="RawShaderMaterial"}}Gst.prototype.isRawShaderMaterial=!0;const Wst=Math.pow(2,8),Yst=[.125,.215,.35,.446,.526,.582],qst=5+Yst.length,Zst={[zit]:0,[Iit]:1,[Fit]:2,3004:3,3005:4,3006:5,[Hit]:6},Xst=new Art({side:1,depthWrite:!1,depthTest:!1}),Kst=new pst(new ust,Xst),Jst=new Ust,{_lodPlanes:Qst,_sizeLods:$st,_sigmas:tlt}=clt(),elt=new Rrt;let nlt=null;const olt=(1+Math.sqrt(5))/2,ilt=1/olt,alt=[new cat(1,1,1),new cat(-1,1,1),new cat(1,1,-1),new cat(-1,1,-1),new cat(0,olt,ilt),new cat(0,olt,-ilt),new cat(ilt,0,olt),new cat(-ilt,0,olt),new cat(olt,ilt,0),new cat(-olt,ilt,0)];function rlt(t){const e=Math.max(t.r,t.g,t.b),n=Math.min(Math.max(Math.ceil(Math.log2(e)),-128),127);return t.multiplyScalar(Math.pow(2,-n)),(n+128)/255}class slt{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=(function e(t){const e=new Float32Array(t),n=new cat(0,1,0);return new Gst({name:"SphericalGaussianBlur",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:Zst[3e3]},outputEncoding:{value:Zst[3e3]}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute vec3 position;\n\t\tattribute vec2 uv;\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\t\t\tuniform int samples;\n\t\t\tuniform float weights[ n ];\n\t\t\tuniform bool latitudinal;\n\t\t\tuniform float dTheta;\n\t\t\tuniform float mipInt;\n\t\t\tuniform vec3 poleAxis;\n\n\t\t\t\n\n\t\tuniform int inputEncoding;\n\t\tuniform int outputEncoding;\n\n\t\t#include <encodings_pars_fragment>\n\n\t\tvec4 inputTexelToLinear( vec4 value ) {\n\n\t\t\tif ( inputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( inputEncoding == 1 ) {\n\n\t\t\t\treturn sRGBToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 2 ) {\n\n\t\t\t\treturn RGBEToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 3 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 7.0 );\n\n\t\t\t} else if ( inputEncoding == 4 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 16.0 );\n\n\t\t\t} else if ( inputEncoding == 5 ) {\n\n\t\t\t\treturn RGBDToLinear( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn GammaToLinear( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 linearToOutputTexel( vec4 value ) {\n\n\t\t\tif ( outputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( outputEncoding == 1 ) {\n\n\t\t\t\treturn LinearTosRGB( value );\n\n\t\t\t} else if ( outputEncoding == 2 ) {\n\n\t\t\t\treturn LinearToRGBE( value );\n\n\t\t\t} else if ( outputEncoding == 3 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 7.0 );\n\n\t\t\t} else if ( outputEncoding == 4 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 16.0 );\n\n\t\t\t} else if ( outputEncoding == 5 ) {\n\n\t\t\t\treturn LinearToRGBD( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn LinearToGamma( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 envMapTexelToLinear( vec4 color ) {\n\n\t\t\treturn inputTexelToLinear( color );\n\n\t\t}\n\t\n\n\t\t\t#define ENVMAP_TYPE_CUBE_UV\n\t\t\t#include <cube_uv_reflection_fragment>\n\n\t\t\tvec3 getSample( float theta, vec3 axis ) {\n\n\t\t\t\tfloat cosTheta = cos( theta );\n\t\t\t\t// Rodrigues' axis-angle rotation\n\t\t\t\tvec3 sampleDirection = vOutputDirection * cosTheta\n\t\t\t\t\t+ cross( axis, vOutputDirection ) * sin( theta )\n\t\t\t\t\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\n\n\t\t\t\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\n\n\t\t\t}\n\n\t\t\tvoid main() {\n\n\t\t\t\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\n\n\t\t\t\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\n\n\t\t\t\t\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\n\n\t\t\t\t}\n\n\t\t\t\taxis = normalize( axis );\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t\t\t\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\n\n\t\t\t\tfor ( int i = 1; i < n; i++ ) {\n\n\t\t\t\t\tif ( i >= samples ) {\n\n\t\t\t\t\t\tbreak;\n\n\t\t\t\t\t}\n\n\t\t\t\t\tfloat theta = dTheta * float( i );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\n\n\t\t\t\t}\n\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t",blending:0,depthTest:!1,depthWrite:!1})})(20),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,o=100){nlt=this._renderer.getRenderTarget();const i=this._allocateTargets();return this._sceneToCubeUV(t,n,o,i),e>0&&this._blur(i,0,0,e),this._applyPMREM(i),this._cleanup(i),i}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=ult(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=mlt(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t<Qst.length;t++)Qst[t].dispose()}_cleanup(t){this._pingPongRenderTarget.dispose(),this._renderer.setRenderTarget(nlt),t.scissorTest=!1,plt(t,0,0,t.width,t.height)}_fromTexture(t){nlt=this._renderer.getRenderTarget();const e=this._allocateTargets(t);return this._textureToCubeUV(t,e),this._applyPMREM(e),this._cleanup(e),e}_allocateTargets(t){const e={magFilter:hit,minFilter:hit,generateMipmaps:!1,type:_it,format:1023,encoding:llt(t)?t.encoding:Fit,depthBuffer:!1},n=dlt(e);return n.depthBuffer=!t,this._pingPongRenderTarget=dlt(e),n}_compileMaterial(t){const e=new pst(Qst[0],t);this._renderer.compile(e,Jst)}_sceneToCubeUV(t,e,n,o){const i=new _st(90,1,e,n),a=[1,-1,1,1,1,1],r=[1,1,1,-1,-1,-1],s=this._renderer,l=s.autoClear,c=s.outputEncoding,d=s.toneMapping;s.getClearColor(elt),s.toneMapping=0,s.outputEncoding=zit,s.autoClear=!1;let p=!1;const m=t.background;if(m){if(m.isColor){Xst.color.copy(m).convertSRGBToLinear(),t.background=null;const e=rlt(Xst.color);Xst.opacity=e,p=!0}}else{Xst.color.copy(elt).convertSRGBToLinear();const t=rlt(Xst.color);Xst.opacity=t,p=!0}for(let e=0;e<6;e++){const n=e%3;0==n?(i.up.set(0,a[e],0),i.lookAt(r[e],0,0)):1==n?(i.up.set(0,0,a[e]),i.lookAt(0,r[e],0)):(i.up.set(0,a[e],0),i.lookAt(0,0,r[e])),plt(o,n*Wst,e>2?Wst:0,Wst,Wst),s.setRenderTarget(o),p&&s.render(Kst,i),s.render(t,i)}s.toneMapping=d,s.outputEncoding=c,s.autoClear=l}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=ult()):null==this._equirectShader&&(this._equirectShader=mlt());const o=t.isCubeTexture?this._cubemapShader:this._equirectShader,i=new pst(Qst[0],o),a=o.uniforms;a.envMap.value=t,t.isCubeTexture||a.texelSize.value.set(1/t.image.width,1/t.image.height),a.inputEncoding.value=Zst[t.encoding],a.outputEncoding.value=Zst[e.texture.encoding],plt(e,0,0,3*Wst,2*Wst),n.setRenderTarget(e),n.render(i,Jst)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;e<qst;e++){const n=Math.sqrt(tlt[e]*tlt[e]-tlt[e-1]*tlt[e-1]);this._blur(t,e-1,e,n,alt[(e-1)%alt.length])}e.autoClear=n}_blur(t,e,n,o,i){const a=this._pingPongRenderTarget;this._halfBlur(t,a,e,n,o,"latitudinal",i),this._halfBlur(a,t,n,n,o,"longitudinal",i)}_halfBlur(t,e,n,o,i,a,r){const s=this._renderer,l=this._blurMaterial;"latitudinal"!==a&&"longitudinal"!==a&&console.error("blur direction must be either latitudinal or longitudinal!");const c=new pst(Qst[o],l),d=l.uniforms,p=$st[n]-1,m=isFinite(i)?Math.PI/(2*p):2*Math.PI/39,u=i/m,f=isFinite(i)?1+Math.floor(3*u):20;f>20&&console.warn(`sigmaRadians, ${i}, is too large and will clip, as it requested ${f} samples when the maximum is set to 20`);const g=[];let h=0;for(let t=0;t<20;++t){const e=t/u,n=Math.exp(-e*e/2);g.push(n),0==t?h+=n:t<f&&(h+=2*n)}for(let t=0;t<g.length;t++)g[t]=g[t]/h;d.envMap.value=t.texture,d.samples.value=f,d.weights.value=g,d.latitudinal.value="latitudinal"===a,r&&(d.poleAxis.value=r),d.dTheta.value=m,d.mipInt.value=8-n,d.inputEncoding.value=Zst[t.texture.encoding],d.outputEncoding.value=Zst[t.texture.encoding];const b=$st[o];plt(e,3*Math.max(0,Wst-2*b),(0===o?0:2*Wst)+2*b*(o>4?o-8+4:0),3*b,2*b),s.setRenderTarget(e),s.render(c,Jst)}}function llt(t){return void 0!==t&&t.type===_it&&(t.encoding===zit||t.encoding===Iit||t.encoding===Hit)}function clt(){const t=[],e=[],n=[];let o=8;for(let i=0;i<qst;i++){const a=Math.pow(2,o);e.push(a);let r=1/a;i>4?r=Yst[i-8+4-1]:0==i&&(r=0),n.push(r);const s=1/(a-1),l=-s/2,c=1+s/2,d=[l,l,c,l,c,c,l,l,c,c,l,c],p=6,m=6,u=3,f=2,g=1,h=new Float32Array(u*m*p),b=new Float32Array(f*m*p),y=new Float32Array(g*m*p);for(let t=0;t<p;t++){const e=t%3*2/3-1,n=t>2?0:-1;h.set([e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0],u*m*t),b.set(d,f*m*t),y.set([t,t,t,t,t,t],g*m*t)}const _=new qrt;_.setAttribute("position",new zrt(h,u)),_.setAttribute("uv",new zrt(b,f)),_.setAttribute("faceIndex",new zrt(y,g)),t.push(_),o>4&&o--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function dlt(t){const e=new rat(3*Wst,3*Wst,t);return e.texture.mapping=mit,e.texture.name="PMREM.cubeUv",e.scissorTest=!0,e}function plt(t,e,n,o,i){t.viewport.set(e,n,o,i),t.scissor.set(e,n,o,i)}function mlt(){const t=new Qit(1,1);return new Gst({name:"EquirectangularToCubeUV",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:Zst[3e3]},outputEncoding:{value:Zst[3e3]}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute vec3 position;\n\t\tattribute vec2 uv;\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\t\t\tuniform vec2 texelSize;\n\n\t\t\t\n\n\t\tuniform int inputEncoding;\n\t\tuniform int outputEncoding;\n\n\t\t#include <encodings_pars_fragment>\n\n\t\tvec4 inputTexelToLinear( vec4 value ) {\n\n\t\t\tif ( inputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( inputEncoding == 1 ) {\n\n\t\t\t\treturn sRGBToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 2 ) {\n\n\t\t\t\treturn RGBEToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 3 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 7.0 );\n\n\t\t\t} else if ( inputEncoding == 4 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 16.0 );\n\n\t\t\t} else if ( inputEncoding == 5 ) {\n\n\t\t\t\treturn RGBDToLinear( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn GammaToLinear( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 linearToOutputTexel( vec4 value ) {\n\n\t\t\tif ( outputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( outputEncoding == 1 ) {\n\n\t\t\t\treturn LinearTosRGB( value );\n\n\t\t\t} else if ( outputEncoding == 2 ) {\n\n\t\t\t\treturn LinearToRGBE( value );\n\n\t\t\t} else if ( outputEncoding == 3 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 7.0 );\n\n\t\t\t} else if ( outputEncoding == 4 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 16.0 );\n\n\t\t\t} else if ( outputEncoding == 5 ) {\n\n\t\t\t\treturn LinearToRGBD( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn LinearToGamma( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 envMapTexelToLinear( vec4 color ) {\n\n\t\t\treturn inputTexelToLinear( color );\n\n\t\t}\n\t\n\n\t\t\t#include <common>\n\n\t\t\tvoid main() {\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\n\t\t\t\tvec3 outputDirection = normalize( vOutputDirection );\n\t\t\t\tvec2 uv = equirectUv( outputDirection );\n\n\t\t\t\tvec2 f = fract( uv / texelSize - 0.5 );\n\t\t\t\tuv -= f * texelSize;\n\t\t\t\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.x += texelSize.x;\n\t\t\t\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.y += texelSize.y;\n\t\t\t\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.x -= texelSize.x;\n\t\t\t\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\n\t\t\t\tvec3 tm = mix( tl, tr, f.x );\n\t\t\t\tvec3 bm = mix( bl, br, f.x );\n\t\t\t\tgl_FragColor.rgb = mix( tm, bm, f.y );\n\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t",blending:0,depthTest:!1,depthWrite:!1})}function ult(){return new Gst({name:"CubemapToCubeUV",uniforms:{envMap:{value:null},inputEncoding:{value:Zst[3e3]},outputEncoding:{value:Zst[3e3]}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute vec3 position;\n\t\tattribute vec2 uv;\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform samplerCube envMap;\n\n\t\t\t\n\n\t\tuniform int inputEncoding;\n\t\tuniform int outputEncoding;\n\n\t\t#include <encodings_pars_fragment>\n\n\t\tvec4 inputTexelToLinear( vec4 value ) {\n\n\t\t\tif ( inputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( inputEncoding == 1 ) {\n\n\t\t\t\treturn sRGBToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 2 ) {\n\n\t\t\t\treturn RGBEToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 3 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 7.0 );\n\n\t\t\t} else if ( inputEncoding == 4 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 16.0 );\n\n\t\t\t} else if ( inputEncoding == 5 ) {\n\n\t\t\t\treturn RGBDToLinear( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn GammaToLinear( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 linearToOutputTexel( vec4 value ) {\n\n\t\t\tif ( outputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( outputEncoding == 1 ) {\n\n\t\t\t\treturn LinearTosRGB( value );\n\n\t\t\t} else if ( outputEncoding == 2 ) {\n\n\t\t\t\treturn LinearToRGBE( value );\n\n\t\t\t} else if ( outputEncoding == 3 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 7.0 );\n\n\t\t\t} else if ( outputEncoding == 4 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 16.0 );\n\n\t\t\t} else if ( outputEncoding == 5 ) {\n\n\t\t\t\treturn LinearToRGBD( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn LinearToGamma( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 envMapTexelToLinear( vec4 color ) {\n\n\t\t\treturn inputTexelToLinear( color );\n\n\t\t}\n\t\n\n\t\t\tvoid main() {\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t\t\t\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t",blending:0,depthTest:!1,depthWrite:!1})}function flt(t){let e=new WeakMap,n=null;function o(t){const n=t.target;n.removeEventListener("dispose",o);const i=e.get(n);void 0!==i&&(i.delete(n),i.dispose())}return{get:function i(a){if(a&&a.isTexture&&!1===a.isRenderTargetTexture){const i=a.mapping,r=303===i||304===i,s=i===dit||i===pit;if(r||s){if(e.has(a))return e.get(a).texture;{const i=a.image;if(r&&i&&i.height>0||s&&i&&(function r(t){let e=0;for(let n=0;n<6;n++)void 0!==t[n]&&e++;return 6===e})(i)){const i=t.getRenderTarget();null===n&&(n=new slt(t));const s=r?n.fromEquirectangular(a):n.fromCubemap(a);return e.set(a,s),t.setRenderTarget(i),a.addEventListener("dispose",o),s.texture}return null}}}return a},dispose:function a(){e=new WeakMap,null!==n&&(n.dispose(),n=null)}}}function glt(t){const e={};function n(n){if(void 0!==e[n])return e[n];let o;switch(n){case"WEBGL_depth_texture":o=t.getExtension("WEBGL_depth_texture")||t.getExtension("MOZ_WEBGL_depth_texture")||t.getExtension("WEBKIT_WEBGL_depth_texture");break;case"EXT_texture_filter_anisotropic":o=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic");break;case"WEBGL_compressed_texture_s3tc":o=t.getExtension("WEBGL_compressed_texture_s3tc")||t.getExtension("MOZ_WEBGL_compressed_texture_s3tc")||t.getExtension("WEBKIT_WEBGL_compressed_texture_s3tc");break;case"WEBGL_compressed_texture_pvrtc":o=t.getExtension("WEBGL_compressed_texture_pvrtc")||t.getExtension("WEBKIT_WEBGL_compressed_texture_pvrtc");break;default:o=t.getExtension(n)}return e[n]=o,o}return{has:function(t){return null!==n(t)},init:function(t){t.isWebGL2?n("EXT_color_buffer_float"):(n("WEBGL_depth_texture"),n("OES_texture_float"),n("OES_texture_half_float"),n("OES_texture_half_float_linear"),n("OES_standard_derivatives"),n("OES_element_index_uint"),n("OES_vertex_array_object"),n("ANGLE_instanced_arrays")),n("OES_texture_float_linear"),n("EXT_color_buffer_half_float")},get:function(t){const e=n(t);return null===e&&console.warn("THREE.WebGLRenderer: "+t+" extension not supported."),e}}}function hlt(t,e,n,o){const i={},a=new WeakMap;function r(t){const s=t.target;null!==s.index&&e.remove(s.index);for(const t in s.attributes)e.remove(s.attributes[t]);s.removeEventListener("dispose",r),delete i[s.id];const l=a.get(s);l&&(e.remove(l),a.delete(s)),o.releaseStatesOfGeometry(s),!0===s.isInstancedBufferGeometry&&delete s._maxInstanceCount,n.memory.geometries--}function s(t){const n=[],o=t.index,i=t.attributes.position;let r=0;if(null!==o){const t=o.array;r=o.version;for(let e=0,o=t.length;e<o;e+=3){const o=t[e+0],i=t[e+1],a=t[e+2];n.push(o,i,i,a,a,o)}}else{r=i.version;for(let t=0,e=i.array.length/3-1;t<e;t+=3){const e=t+0,o=t+1,i=t+2;n.push(e,o,o,i,i,e)}}const s=new(Lrt(n)>65535?Hrt:Irt)(n,1);s.version=r;const l=a.get(t);l&&e.remove(l),a.set(t,s)}return{get:function l(t,e){return!0===i[e.id]||(e.addEventListener("dispose",r),i[e.id]=!0,n.memory.geometries++),e},update:function c(t){const n=t.attributes;for(const t in n)e.update(n[t],34962);const o=t.morphAttributes;for(const t in o){const n=o[t];for(let t=0,o=n.length;t<o;t++)e.update(n[t],34962)}},getWireframeAttribute:function d(t){const e=a.get(t);if(e){const n=t.index;null!==n&&e.version<n.version&&s(t)}else s(t);return a.get(t)}}}function blt(t,e,n,o){const i=o.isWebGL2;let a,r,s;this.setMode=function l(t){a=t},this.setIndex=function c(t){r=t.type,s=t.bytesPerElement},this.render=function d(e,o){t.drawElements(a,o,r,e*s),n.update(o,a,1)},this.renderInstances=function p(o,l,c){if(0===c)return;let d,p;if(i)d=t,p="drawElementsInstanced";else if(d=e.get("ANGLE_instanced_arrays"),p="drawElementsInstancedANGLE",null===d)return void console.error("THREE.WebGLIndexedBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.");d[p](a,l,r,o*s,c),n.update(l,a,c)}}function ylt(t){const e={frame:0,calls:0,triangles:0,points:0,lines:0};return{memory:{geometries:0,textures:0},render:e,programs:null,autoReset:!0,reset:function n(){e.frame++,e.calls=0,e.triangles=0,e.points=0,e.lines=0},update:function o(t,n,i){switch(e.calls++,n){case 4:e.triangles+=i*(t/3);break;case 1:e.lines+=i*(t/2);break;case 3:e.lines+=i*(t-1);break;case 2:e.lines+=i*t;break;case 0:e.points+=i*t;break;default:console.error("THREE.WebGLInfo: Unknown draw mode:",n)}}}}function _lt(t,e){return t[0]-e[0]}function Clt(t,e){return Math.abs(e[1])-Math.abs(t[1])}function Mlt(t){const e={},n=new Float32Array(8),o=[];for(let t=0;t<8;t++)o[t]=[t,0];return{update:function i(a,r,s,l){const c=a.morphTargetInfluences,d=void 0===c?0:c.length;let p=e[r.id];if(void 0===p||p.length!==d){p=[];for(let t=0;t<d;t++)p[t]=[t,0];e[r.id]=p}for(let t=0;t<d;t++){const e=p[t];e[0]=t,e[1]=c[t]}p.sort(Clt);for(let t=0;t<8;t++)t<d&&p[t][1]?(o[t][0]=p[t][0],o[t][1]=p[t][1]):(o[t][0]=Number.MAX_SAFE_INTEGER,o[t][1]=0);o.sort(_lt);const m=r.morphAttributes.position,u=r.morphAttributes.normal;let f=0;for(let t=0;t<8;t++){const e=o[t],i=e[0],a=e[1];i!==Number.MAX_SAFE_INTEGER&&a?(m&&r.getAttribute("morphTarget"+t)!==m[i]&&r.setAttribute("morphTarget"+t,m[i]),u&&r.getAttribute("morphNormal"+t)!==u[i]&&r.setAttribute("morphNormal"+t,u[i]),n[t]=a,f+=a):(m&&!0===r.hasAttribute("morphTarget"+t)&&r.deleteAttribute("morphTarget"+t),u&&!0===r.hasAttribute("morphNormal"+t)&&r.deleteAttribute("morphNormal"+t),n[t]=0)}const g=r.morphTargetsRelative?1:1-f;l.getUniforms().setValue(t,"morphTargetBaseInfluence",g),l.getUniforms().setValue(t,"morphTargetInfluences",n)}}}function vlt(t,e,n,o){let i=new WeakMap;function a(t){const e=t.target;e.removeEventListener("dispose",a),n.remove(e.instanceMatrix),null!==e.instanceColor&&n.remove(e.instanceColor)}return{update:function r(t){const r=o.render.frame,s=e.get(t,t.geometry);return i.get(s)!==r&&(e.update(s),i.set(s,r)),t.isInstancedMesh&&(!1===t.hasEventListener("dispose",a)&&t.addEventListener("dispose",a),n.update(t.instanceMatrix,34962),null!==t.instanceColor&&n.update(t.instanceColor,34962)),s},dispose:function s(){i=new WeakMap}}}class xlt extends oat{constructor(t=null,e=1,n=1,o=1){super(null),this.image={data:t,width:e,height:n,depth:o},this.magFilter=hit,this.minFilter=hit,this.wrapR=fit,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}xlt.prototype.isDataTexture2DArray=!0;class Olt extends oat{constructor(t=null,e=1,n=1,o=1){super(null),this.image={data:t,width:e,height:n,depth:o},this.magFilter=hit,this.minFilter=hit,this.wrapR=fit,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}Olt.prototype.isDataTexture3D=!0;const Plt=new oat,wlt=new xlt,klt=new Olt,Slt=new vst,Dlt=[],Elt=[],Rlt=new Float32Array(16),Alt=new Float32Array(9),Tlt=new Float32Array(4);function Nlt(t,e,n){const o=t[0];if(o<=0||o>0)return t;const i=e*n;let a=Dlt[i];if(void 0===a&&(a=new Float32Array(i),Dlt[i]=a),0!==e){o.toArray(a,0);for(let o=1,i=0;o!==e;++o)i+=n,t[o].toArray(a,i)}return a}function zlt(t,e){if(t.length!==e.length)return!1;for(let n=0,o=t.length;n<o;n++)if(t[n]!==e[n])return!1;return!0}function Ilt(t,e){for(let n=0,o=e.length;n<o;n++)t[n]=e[n]}function Hlt(t,e){let n=Elt[e];void 0===n&&(n=new Int32Array(e),Elt[e]=n);for(let o=0;o!==e;++o)n[o]=t.allocateTextureUnit();return n}function Flt(t,e){const n=this.cache;n[0]!==e&&(t.uniform1f(this.addr,e),n[0]=e)}function Llt(t,e){const n=this.cache;if(void 0!==e.x)n[0]===e.x&&n[1]===e.y||(t.uniform2f(this.addr,e.x,e.y),n[0]=e.x,n[1]=e.y);else{if(zlt(n,e))return;t.uniform2fv(this.addr,e),Ilt(n,e)}}function Blt(t,e){const n=this.cache;if(void 0!==e.x)n[0]===e.x&&n[1]===e.y&&n[2]===e.z||(t.uniform3f(this.addr,e.x,e.y,e.z),n[0]=e.x,n[1]=e.y,n[2]=e.z);else if(void 0!==e.r)n[0]===e.r&&n[1]===e.g&&n[2]===e.b||(t.uniform3f(this.addr,e.r,e.g,e.b),n[0]=e.r,n[1]=e.g,n[2]=e.b);else{if(zlt(n,e))return;t.uniform3fv(this.addr,e),Ilt(n,e)}}function Vlt(t,e){const n=this.cache;if(void 0!==e.x)n[0]===e.x&&n[1]===e.y&&n[2]===e.z&&n[3]===e.w||(t.uniform4f(this.addr,e.x,e.y,e.z,e.w),n[0]=e.x,n[1]=e.y,n[2]=e.z,n[3]=e.w);else{if(zlt(n,e))return;t.uniform4fv(this.addr,e),Ilt(n,e)}}function jlt(t,e){const n=this.cache,o=e.elements;if(void 0===o){if(zlt(n,e))return;t.uniformMatrix2fv(this.addr,!1,e),Ilt(n,e)}else{if(zlt(n,o))return;Tlt.set(o),t.uniformMatrix2fv(this.addr,!1,Tlt),Ilt(n,o)}}function Ult(t,e){const n=this.cache,o=e.elements;if(void 0===o){if(zlt(n,e))return;t.uniformMatrix3fv(this.addr,!1,e),Ilt(n,e)}else{if(zlt(n,o))return;Alt.set(o),t.uniformMatrix3fv(this.addr,!1,Alt),Ilt(n,o)}}function Glt(t,e){const n=this.cache,o=e.elements;if(void 0===o){if(zlt(n,e))return;t.uniformMatrix4fv(this.addr,!1,e),Ilt(n,e)}else{if(zlt(n,o))return;Rlt.set(o),t.uniformMatrix4fv(this.addr,!1,Rlt),Ilt(n,o)}}function Wlt(t,e){const n=this.cache;n[0]!==e&&(t.uniform1i(this.addr,e),n[0]=e)}function Ylt(t,e){const n=this.cache;zlt(n,e)||(t.uniform2iv(this.addr,e),Ilt(n,e))}function qlt(t,e){const n=this.cache;zlt(n,e)||(t.uniform3iv(this.addr,e),Ilt(n,e))}function Zlt(t,e){const n=this.cache;zlt(n,e)||(t.uniform4iv(this.addr,e),Ilt(n,e))}function Xlt(t,e){const n=this.cache;n[0]!==e&&(t.uniform1ui(this.addr,e),n[0]=e)}function Klt(t,e){const n=this.cache;zlt(n,e)||(t.uniform2uiv(this.addr,e),Ilt(n,e))}function Jlt(t,e){const n=this.cache;zlt(n,e)||(t.uniform3uiv(this.addr,e),Ilt(n,e))}function Qlt(t,e){const n=this.cache;zlt(n,e)||(t.uniform4uiv(this.addr,e),Ilt(n,e))}function $lt(t,e,n){const o=this.cache,i=n.allocateTextureUnit();o[0]!==i&&(t.uniform1i(this.addr,i),o[0]=i),n.safeSetTexture2D(e||Plt,i)}function tct(t,e,n){const o=this.cache,i=n.allocateTextureUnit();o[0]!==i&&(t.uniform1i(this.addr,i),o[0]=i),n.setTexture3D(e||klt,i)}function ect(t,e,n){const o=this.cache,i=n.allocateTextureUnit();o[0]!==i&&(t.uniform1i(this.addr,i),o[0]=i),n.safeSetTextureCube(e||Slt,i)}function nct(t,e,n){const o=this.cache,i=n.allocateTextureUnit();o[0]!==i&&(t.uniform1i(this.addr,i),o[0]=i),n.setTexture2DArray(e||wlt,i)}function oct(t,e){t.uniform1fv(this.addr,e)}function ict(t,e){const n=Nlt(e,this.size,2);t.uniform2fv(this.addr,n)}function act(t,e){const n=Nlt(e,this.size,3);t.uniform3fv(this.addr,n)}function rct(t,e){const n=Nlt(e,this.size,4);t.uniform4fv(this.addr,n)}function sct(t,e){const n=Nlt(e,this.size,4);t.uniformMatrix2fv(this.addr,!1,n)}function lct(t,e){const n=Nlt(e,this.size,9);t.uniformMatrix3fv(this.addr,!1,n)}function cct(t,e){const n=Nlt(e,this.size,16);t.uniformMatrix4fv(this.addr,!1,n)}function dct(t,e){t.uniform1iv(this.addr,e)}function pct(t,e){t.uniform2iv(this.addr,e)}function mct(t,e){t.uniform3iv(this.addr,e)}function uct(t,e){t.uniform4iv(this.addr,e)}function fct(t,e){t.uniform1uiv(this.addr,e)}function gct(t,e){t.uniform2uiv(this.addr,e)}function hct(t,e){t.uniform3uiv(this.addr,e)}function bct(t,e){t.uniform4uiv(this.addr,e)}function yct(t,e,n){const o=e.length,i=Hlt(n,o);t.uniform1iv(this.addr,i);for(let t=0;t!==o;++t)n.safeSetTexture2D(e[t]||Plt,i[t])}function _ct(t,e,n){const o=e.length,i=Hlt(n,o);t.uniform1iv(this.addr,i);for(let t=0;t!==o;++t)n.safeSetTextureCube(e[t]||Slt,i[t])}function Cct(t,e,n){this.id=t,this.addr=n,this.cache=[],this.setValue=(function o(t){switch(t){case 5126:return Flt;case 35664:return Llt;case 35665:return Blt;case 35666:return Vlt;case 35674:return jlt;case 35675:return Ult;case 35676:return Glt;case 5124:case 35670:return Wlt;case 35667:case 35671:return Ylt;case 35668:case 35672:return qlt;case 35669:case 35673:return Zlt;case 5125:return Xlt;case 36294:return Klt;case 36295:return Jlt;case 36296:return Qlt;case 35678:case 36198:case 36298:case 36306:case 35682:return $lt;case 35679:case 36299:case 36307:return tct;case 35680:case 36300:case 36308:case 36293:return ect;case 36289:case 36303:case 36311:case 36292:return nct}})(e.type)}function Mct(t,e,n){this.id=t,this.addr=n,this.cache=[],this.size=e.size,this.setValue=(function o(t){switch(t){case 5126:return oct;case 35664:return ict;case 35665:return act;case 35666:return rct;case 35674:return sct;case 35675:return lct;case 35676:return cct;case 5124:case 35670:return dct;case 35667:case 35671:return pct;case 35668:case 35672:return mct;case 35669:case 35673:return uct;case 5125:return fct;case 36294:return gct;case 36295:return hct;case 36296:return bct;case 35678:case 36198:case 36298:case 36306:case 35682:return yct;case 35680:case 36300:case 36308:case 36293:return _ct}})(e.type)}function vct(t){this.id=t,this.seq=[],this.map={}}Mct.prototype.updateCache=function(t){const e=this.cache;t instanceof Float32Array&&e.length!==t.length&&(this.cache=new Float32Array(t.length)),Ilt(e,t)},vct.prototype.setValue=function(t,e,n){const o=this.seq;for(let i=0,a=o.length;i!==a;++i){const a=o[i];a.setValue(t,e[a.id],n)}};const xct=/(\w+)(\])?(\[|\.)?/g;function Oct(t,e){t.seq.push(e),t.map[e.id]=e}function Pct(t,e,n){const o=t.name,i=o.length;for(xct.lastIndex=0;;){const a=xct.exec(o),r=xct.lastIndex;let s=a[1];const l=a[3];if("]"===a[2]&&(s|=0),void 0===l||"["===l&&r+2===i){Oct(n,void 0===l?new Cct(s,t,e):new Mct(s,t,e));break}{let t=n.map[s];void 0===t&&(t=new vct(s),Oct(n,t)),n=t}}}function wct(t,e){this.seq=[],this.map={};const n=t.getProgramParameter(e,35718);for(let o=0;o<n;++o){const n=t.getActiveUniform(e,o);Pct(n,t.getUniformLocation(e,n.name),this)}}function kct(t,e,n){const o=t.createShader(e);return t.shaderSource(o,n),t.compileShader(o),o}wct.prototype.setValue=function(t,e,n,o){const i=this.map[e];void 0!==i&&i.setValue(t,n,o)},wct.prototype.setOptional=function(t,e,n){const o=e[n];void 0!==o&&this.setValue(t,n,o)},wct.upload=function(t,e,n,o){for(let i=0,a=e.length;i!==a;++i){const a=e[i],r=n[a.id];!1!==r.needsUpdate&&a.setValue(t,r.value,o)}},wct.seqWithValue=function(t,e){const n=[];for(let o=0,i=t.length;o!==i;++o){const i=t[o];i.id in e&&n.push(i)}return n};let Sct=0;function Dct(t){switch(t){case zit:return["Linear","( value )"];case Iit:return["sRGB","( value )"];case Fit:return["RGBE","( value )"];case 3004:return["RGBM","( value, 7.0 )"];case 3005:return["RGBM","( value, 16.0 )"];case 3006:return["RGBD","( value, 256.0 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0!==a.depthPacking&&a.depthPacking,index0AttributeName:a.index0AttributeName,extensionDerivatives:a.extensions&&a.extensions.derivatives,extensionFragDepth:a.extensions&&a.extensions.fragDepth,extensionDrawBuffers:a.extensions&&a.extensions.drawBuffers,extensionShaderTextureLOD:a.extensions&&a.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||o.has("EXT_frag_depth"),rendererExtensionDrawBuffers:l||o.has("WEBGL_draw_buffers"),rendererExtensionShaderTextureLod:l||o.has("EXT_shader_texture_lod"),customProgramCacheKey:a.customProgramCacheKey()}},getProgramCacheKey:function y(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t<g.length;t++)n.push(e[g[t]]);n.push(t.outputEncoding),n.push(t.gammaFactor)}return n.push(e.customProgramCacheKey),n.join()},getUniforms:function _(t){const e=f[t.type];let n;return n=e?hst.clone(Ist[e].uniforms):t.uniforms,n},acquireProgram:function C(e,n){let o;for(let t=0,e=s.length;t<e;t++){const e=s[t];if(e.cacheKey===n){o=e,++o.usedTimes;break}}return void 0===o&&(o=new Yct(t,n,e,a),s.push(o)),o},releaseProgram:function M(t){if(0==--t.usedTimes){const e=s.indexOf(t);s[e]=s[s.length-1],s.pop(),t.destroy()}},programs:s}}function Zct(){let t=new WeakMap;return{get:function e(n){let o=t.get(n);return void 0===o&&(o={},t.set(n,o)),o},remove:function n(e){t.delete(e)},update:function o(e,n,i){t.get(e)[n]=i},dispose:function i(){t=new WeakMap}}}function Xct(t,e){return t.groupOrder!==e.groupOrder?t.groupOrder-e.groupOrder:t.renderOrder!==e.renderOrder?t.renderOrder-e.renderOrder:t.program!==e.program?t.program.id-e.program.id:t.material.id!==e.material.id?t.material.id-e.material.id:t.z!==e.z?t.z-e.z:t.id-e.id}function Kct(t,e){return t.groupOrder!==e.groupOrder?t.groupOrder-e.groupOrder:t.renderOrder!==e.renderOrder?t.renderOrder-e.renderOrder:t.z!==e.z?e.z-t.z:t.id-e.id}function Jct(t){const e=[];let n=0;const o=[],i=[],a=[],r={id:-1};function s(o,i,a,s,l,c){let d=e[n];const p=t.get(a);return void 0===d?(d={id:o.id,object:o,geometry:i,material:a,program:p.program||r,groupOrder:s,renderOrder:o.renderOrder,z:l,group:c},e[n]=d):(d.id=o.id,d.object=o,d.geometry=i,d.material=a,d.program=p.program||r,d.groupOrder=s,d.renderOrder=o.renderOrder,d.z=l,d.group=c),n++,d}return{opaque:o,transmissive:i,transparent:a,init:function l(){n=0,o.length=0,i.length=0,a.length=0},push:function c(t,e,n,r,l,d){const p=s(t,e,n,r,l,d);n.transmission>0?i.push(p):!0===n.transparent?a.push(p):o.push(p)},unshift:function d(t,e,n,r,l,c){const d=s(t,e,n,r,l,c);n.transmission>0?i.unshift(d):!0===n.transparent?a.unshift(d):o.unshift(d)},finish:function p(){for(let t=n,o=e.length;t<o;t++){const n=e[t];if(null===n.id)break;n.id=null,n.object=null,n.geometry=null,n.material=null,n.program=null,n.group=null}},sort:function m(t,e){o.length>1&&o.sort(t||Xct),i.length>1&&i.sort(e||Kct),a.length>1&&a.sort(e||Kct)}}}function Qct(t){let e=new WeakMap;return{get:function n(o,i){let a;return!1===e.has(o)?(a=new Jct(t),e.set(o,[a])):i>=e.get(o).length?(a=new Jct(t),e.get(o).push(a)):a=e.get(o)[i],a},dispose:function o(){e=new WeakMap}}}function $ct(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case"DirectionalLight":n={direction:new cat,color:new Rrt};break;case"SpotLight":n={position:new cat,direction:new cat,color:new Rrt,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case"PointLight":n={position:new cat,color:new Rrt,distance:0,decay:0};break;case"HemisphereLight":n={direction:new cat,skyColor:new Rrt,groundColor:new Rrt};break;case"RectAreaLight":n={color:new Rrt,position:new cat,halfWidth:new cat,halfHeight:new cat}}return t[e.id]=n,n}}}let tdt=0;function edt(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function ndt(t,e){const n=new $ct,o=(function i(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case"DirectionalLight":case"SpotLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new Qit};break;case"PointLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new Qit,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}})(),a={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)a.probe.push(new cat);const r=new cat,s=new Bat,l=new Bat;return{setup:function c(i){let r=0,s=0,l=0;for(let t=0;t<9;t++)a.probe[t].set(0,0,0);let c=0,d=0,p=0,m=0,u=0,f=0,g=0,h=0;i.sort(edt);for(let t=0,e=i.length;t<e;t++){const e=i[t],b=e.color,y=e.intensity,_=e.distance,C=e.shadow&&e.shadow.map?e.shadow.map.texture:null;if(e.isAmbientLight)r+=b.r*y,s+=b.g*y,l+=b.b*y;else if(e.isLightProbe)for(let t=0;t<9;t++)a.probe[t].addScaledVector(e.sh.coefficients[t],y);else if(e.isDirectionalLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity),e.castShadow){const t=e.shadow,n=o.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,a.directionalShadow[c]=n,a.directionalShadowMap[c]=C,a.directionalShadowMatrix[c]=e.shadow.matrix,f++}a.directional[c]=t,c++}else if(e.isSpotLight){const t=n.get(e);if(t.position.setFromMatrixPosition(e.matrixWorld),t.color.copy(b).multiplyScalar(y),t.distance=_,t.coneCos=Math.cos(e.angle),t.penumbraCos=Math.cos(e.angle*(1-e.penumbra)),t.decay=e.decay,e.castShadow){const t=e.shadow,n=o.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,a.spotShadow[p]=n,a.spotShadowMap[p]=C,a.spotShadowMatrix[p]=e.shadow.matrix,h++}a.spot[p]=t,p++}else if(e.isRectAreaLight){const t=n.get(e);t.color.copy(b).multiplyScalar(y),t.halfWidth.set(.5*e.width,0,0),t.halfHeight.set(0,.5*e.height,0),a.rectArea[m]=t,m++}else if(e.isPointLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity),t.distance=e.distance,t.decay=e.decay,e.castShadow){const t=e.shadow,n=o.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,n.shadowCameraNear=t.camera.near,n.shadowCameraFar=t.camera.far,a.pointShadow[d]=n,a.pointShadowMap[d]=C,a.pointShadowMatrix[d]=e.shadow.matrix,g++}a.point[d]=t,d++}else if(e.isHemisphereLight){const t=n.get(e);t.skyColor.copy(e.color).multiplyScalar(y),t.groundColor.copy(e.groundColor).multiplyScalar(y),a.hemi[u]=t,u++}}m>0&&(e.isWebGL2||!0===t.has("OES_texture_float_linear")?(a.rectAreaLTC1=zst.LTC_FLOAT_1,a.rectAreaLTC2=zst.LTC_FLOAT_2):!0===t.has("OES_texture_half_float_linear")?(a.rectAreaLTC1=zst.LTC_HALF_1,a.rectAreaLTC2=zst.LTC_HALF_2):console.error("THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.")),a.ambient[0]=r,a.ambient[1]=s,a.ambient[2]=l;const b=a.hash;b.directionalLength===c&&b.pointLength===d&&b.spotLength===p&&b.rectAreaLength===m&&b.hemiLength===u&&b.numDirectionalShadows===f&&b.numPointShadows===g&&b.numSpotShadows===h||(a.directional.length=c,a.spot.length=p,a.rectArea.length=m,a.point.length=d,a.hemi.length=u,a.directionalShadow.length=f,a.directionalShadowMap.length=f,a.pointShadow.length=g,a.pointShadowMap.length=g,a.spotShadow.length=h,a.spotShadowMap.length=h,a.directionalShadowMatrix.length=f,a.pointShadowMatrix.length=g,a.spotShadowMatrix.length=h,b.directionalLength=c,b.pointLength=d,b.spotLength=p,b.rectAreaLength=m,b.hemiLength=u,b.numDirectionalShadows=f,b.numPointShadows=g,b.numSpotShadows=h,a.version=tdt++)},setupView:function d(t,e){let n=0,o=0,i=0,c=0,d=0;const p=e.matrixWorldInverse;for(let e=0,m=t.length;e<m;e++){const m=t[e];if(m.isDirectionalLight){const t=a.directional[n];t.direction.setFromMatrixPosition(m.matrixWorld),r.setFromMatrixPosition(m.target.matrixWorld),t.direction.sub(r),t.direction.transformDirection(p),n++}else if(m.isSpotLight){const t=a.spot[i];t.position.setFromMatrixPosition(m.matrixWorld),t.position.applyMatrix4(p),t.direction.setFromMatrixPosition(m.matrixWorld),r.setFromMatrixPosition(m.target.matrixWorld),t.direction.sub(r),t.direction.transformDirection(p),i++}else if(m.isRectAreaLight){const t=a.rectArea[c];t.position.setFromMatrixPosition(m.matrixWorld),t.position.applyMatrix4(p),l.identity(),s.copy(m.matrixWorld),s.premultiply(p),l.extractRotation(s),t.halfWidth.set(.5*m.width,0,0),t.halfHeight.set(0,.5*m.height,0),t.halfWidth.applyMatrix4(l),t.halfHeight.applyMatrix4(l),c++}else if(m.isPointLight){const t=a.point[o];t.position.setFromMatrixPosition(m.matrixWorld),t.position.applyMatrix4(p),o++}else if(m.isHemisphereLight){const t=a.hemi[d];t.direction.setFromMatrixPosition(m.matrixWorld),t.direction.transformDirection(p),t.direction.normalize(),d++}}},state:a}}function odt(t,e){const n=new ndt(t,e),o=[],i=[];return{init:function a(){o.length=0,i.length=0},state:{lightsArray:o,shadowsArray:i,lights:n},setupLights:function r(){n.setup(o)},setupLightsView:function s(t){n.setupView(o,t)},pushLight:function l(t){o.push(t)},pushShadow:function c(t){i.push(t)}}}function idt(t,e){let n=new WeakMap;return{get:function o(i,a=0){let r;return!1===n.has(i)?(r=new odt(t,e),n.set(i,[r])):a>=n.get(i).length?(r=new odt(t,e),n.get(i).push(r)):r=n.get(i)[a],r},dispose:function i(){n=new WeakMap}}}class adt extends Ort{constructor(t){super(),this.type="MeshDepthMaterial",this.depthPacking=3200,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}adt.prototype.isMeshDepthMaterial=!0;class rdt extends Ort{constructor(t){super(),this.type="MeshDistanceMaterial",this.referencePosition=new cat,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}function sdt(t,e,n){let o=new Est;const i=new Qit,a=new Qit,r=new aat,s=new adt({depthPacking:3201}),l=new rdt,c={},d=n.maxTextureSize,p={0:1,1:0,2:2},m=new bst({defines:{SAMPLE_RATE:2/8,HALF_SAMPLE_RATE:1/8},uniforms:{shadow_pass:{value:null},resolution:{value:new Qit},radius:{value:4}},vertexShader:"void main() {\n\tgl_Position = vec4( position, 1.0 );\n}",fragmentShader:"uniform sampler2D shadow_pass;\nuniform vec2 resolution;\nuniform float radius;\n#include <packing>\nvoid main() {\n\tfloat mean = 0.0;\n\tfloat squared_mean = 0.0;\n\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\n\tfor ( float i = -1.0; i < 1.0 ; i += SAMPLE_RATE) {\n\t\t#ifdef HORIZONTAL_PASS\n\t\t\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( i, 0.0 ) * radius ) / resolution ) );\n\t\t\tmean += distribution.x;\n\t\t\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\n\t\t#else\n\t\t\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, i ) * radius ) / resolution ) );\n\t\t\tmean += depth;\n\t\t\tsquared_mean += depth * depth;\n\t\t#endif\n\t}\n\tmean = mean * HALF_SAMPLE_RATE;\n\tsquared_mean = squared_mean * HALF_SAMPLE_RATE;\n\tfloat std_dev = sqrt( squared_mean - mean * mean );\n\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\n}"}),u=m.clone();u.defines.HORIZONTAL_PASS=1;const f=new qrt;f.setAttribute("position",new zrt(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const g=new pst(f,m),h=this;function b(n,o){const i=e.update(g);m.uniforms.shadow_pass.value=n.map.texture,m.uniforms.resolution.value=n.mapSize,m.uniforms.radius.value=n.radius,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(o,null,i,m,g,null),u.uniforms.shadow_pass.value=n.mapPass.texture,u.uniforms.resolution.value=n.mapSize,u.uniforms.radius.value=n.radius,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(o,null,i,u,g,null)}function y(e,n,o,i,a,r,d){let m=null;const u=!0===i.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(m=void 0!==u?u:!0===i.isPointLight?l:s,t.localClippingEnabled&&!0===o.clipShadows&&0!==o.clippingPlanes.length){const t=m.uuid,e=o.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let i=n[e];void 0===i&&(i=m.clone(),n[e]=i),m=i}return m.visible=o.visible,m.wireframe=o.wireframe,m.side=3===d?null!==o.shadowSide?o.shadowSide:o.side:null!==o.shadowSide?o.shadowSide:p[o.side],m.clipShadows=o.clipShadows,m.clippingPlanes=o.clippingPlanes,m.clipIntersection=o.clipIntersection,m.wireframeLinewidth=o.wireframeLinewidth,m.linewidth=o.linewidth,!0===i.isPointLight&&!0===m.isMeshDistanceMaterial&&(m.referencePosition.setFromMatrixPosition(i.matrixWorld),m.nearDistance=a,m.farDistance=r),m}function _(n,i,a,r,s){if(!1===n.visible)return;if(n.layers.test(i.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&3===s)&&(!n.frustumCulled||o.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(a.matrixWorldInverse,n.matrixWorld);const o=e.update(n),i=n.material;if(Array.isArray(i)){const e=o.groups;for(let l=0,c=e.length;l<c;l++){const c=e[l],d=i[c.materialIndex];if(d&&d.visible){const e=y(n,0,d,r,a.near,a.far,s);t.renderBufferDirect(a,null,o,e,n,c)}}}else if(i.visible){const e=y(n,0,i,r,a.near,a.far,s);t.renderBufferDirect(a,null,o,e,n,null)}}const l=n.children;for(let t=0,e=l.length;t<e;t++)_(l[t],i,a,r,s)}this.enabled=!1,this.autoUpdate=!0,this.needsUpdate=!1,this.type=1,this.render=function(e,n,s){if(!1===h.enabled)return;if(!1===h.autoUpdate&&!1===h.needsUpdate)return;if(0===e.length)return;const l=t.getRenderTarget(),c=t.getActiveCubeFace(),p=t.getActiveMipmapLevel(),m=t.state;m.setBlending(0),m.buffers.color.setClear(1,1,1,1),m.buffers.depth.setTest(!0),m.setScissorTest(!1);for(let l=0,c=e.length;l<c;l++){const c=e[l],p=c.shadow;if(void 0===p){console.warn("THREE.WebGLShadowMap:",c,"has no shadow.");continue}if(!1===p.autoUpdate&&!1===p.needsUpdate)continue;i.copy(p.mapSize);const u=p.getFrameExtents();if(i.multiply(u),a.copy(p.mapSize),(i.x>d||i.y>d)&&(i.x>d&&(a.x=Math.floor(d/u.x),i.x=a.x*u.x,p.mapSize.x=a.x),i.y>d&&(a.y=Math.floor(d/u.y),i.y=a.y*u.y,p.mapSize.y=a.y)),null===p.map&&!p.isPointLightShadow&&3===this.type){const t={minFilter:bit,magFilter:bit,format:wit};p.map=new rat(i.x,i.y,t),p.map.texture.name=c.name+".shadowMap",p.mapPass=new rat(i.x,i.y,t),p.camera.updateProjectionMatrix()}null===p.map&&(p.map=new 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n.texImage2D(3553,0,M,u,g,p),e.__maxMipLevel=0;y(o,m)&&_(r,o,p.width,p.height),e.__version=o.version,o.onUpdate&&o.onUpdate(o)}function A(e,i,r,s,l){const c=a.convert(r.format),d=a.convert(r.type),p=C(r.internalFormat,c,d);32879===l||35866===l?n.texImage3D(l,0,p,i.width,i.height,i.depth,0,c,d,null):n.texImage2D(l,0,p,i.width,i.height,0,c,d,null),n.bindFramebuffer(36160,e),t.framebufferTexture2D(36160,s,l,o.get(r).__webglTexture,0),n.bindFramebuffer(36160,null)}function T(e,n,o){if(t.bindRenderbuffer(36161,e),n.depthBuffer&&!n.stencilBuffer){let i=33189;if(o){const e=n.depthTexture;e&&e.isDepthTexture&&(e.type===vit?i=36012:e.type===Mit&&(i=33190));const o=N(n);t.renderbufferStorageMultisample(36161,o,i,n.width,n.height)}else t.renderbufferStorage(36161,i,n.width,n.height);t.framebufferRenderbuffer(36160,36096,36161,e)}else if(n.depthBuffer&&n.stencilBuffer){if(o){const e=N(n);t.renderbufferStorageMultisample(36161,e,35056,n.width,n.height)}else t.renderbufferStorage(36161,34041,n.width,n.height);t.framebufferRenderbuffer(36160,33306,36161,e)}else{const e=!0===n.isWebGLMultipleRenderTargets?n.texture[0]:n.texture,i=a.convert(e.format),r=a.convert(e.type),s=C(e.internalFormat,i,r);if(o){const e=N(n);t.renderbufferStorageMultisample(36161,e,s,n.width,n.height)}else t.renderbufferStorage(36161,s,n.width,n.height)}t.bindRenderbuffer(36161,null)}function N(t){return s&&t.isWebGLMultisampleRenderTarget?Math.min(p,t.samples):0}let z=!1,I=!1;this.allocateTextureUnit=function H(){const t=O;return t>=l&&console.warn("THREE.WebGLTextures: Trying to use "+t+" texture units while this GPU supports only "+l),O+=1,t},this.resetTextureUnits=function F(){O=0},this.setTexture2D=P,this.setTexture2DArray=function L(t,e){const i=o.get(t);t.version>0&&i.__version!==t.version?R(i,t,e):(n.activeTexture(33984+e),n.bindTexture(35866,i.__webglTexture))},this.setTexture3D=function B(t,e){const i=o.get(t);t.version>0&&i.__version!==t.version?R(i,t,e):(n.activeTexture(33984+e),n.bindTexture(32879,i.__webglTexture))},this.setTextureCube=w,this.setupRenderTarget=function V(e){const l=e.texture,c=o.get(e),d=o.get(l);e.addEventListener("dispose",x),!0!==e.isWebGLMultipleRenderTargets&&(d.__webglTexture=t.createTexture(),d.__version=l.version,r.memory.textures++);const p=!0===e.isWebGLCubeRenderTarget,m=!0===e.isWebGLMultipleRenderTargets,u=!0===e.isWebGLMultisampleRenderTarget,f=l.isDataTexture3D||l.isDataTexture2DArray,g=b(e)||s;if(!s||l.format!==Pit||l.type!==vit&&l.type!==xit||(l.format=wit,console.warn("THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.")),p){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),m)if(i.drawBuffers){const n=e.texture;for(let e=0,i=n.length;e<i;e++){const i=o.get(n[e]);void 0===i.__webglTexture&&(i.__webglTexture=t.createTexture(),r.memory.textures++)}}else console.warn("THREE.WebGLRenderer: WebGLMultipleRenderTargets can only be used with WebGL2 or WEBGL_draw_buffers extension.");else if(u)if(s){c.__webglMultisampledFramebuffer=t.createFramebuffer(),c.__webglColorRenderbuffer=t.createRenderbuffer(),t.bindRenderbuffer(36161,c.__webglColorRenderbuffer);const o=a.convert(l.format),i=a.convert(l.type),r=C(l.internalFormat,o,i),s=N(e);t.renderbufferStorageMultisample(36161,s,r,e.width,e.height),n.bindFramebuffer(36160,c.__webglMultisampledFramebuffer),t.framebufferRenderbuffer(36160,36064,36161,c.__webglColorRenderbuffer),t.bindRenderbuffer(36161,null),e.depthBuffer&&(c.__webglDepthRenderbuffer=t.createRenderbuffer(),T(c.__webglDepthRenderbuffer,e,!0)),n.bindFramebuffer(36160,null)}else console.warn("THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.");if(p){n.bindTexture(34067,d.__webglTexture),D(34067,l,g);for(let t=0;t<6;t++)A(c.__webglFramebuffer[t],e,l,36064,34069+t);y(l,g)&&_(34067,l,e.width,e.height),n.bindTexture(34067,null)}else if(m){const t=e.texture;for(let i=0,a=t.length;i<a;i++){const a=t[i],r=o.get(a);n.bindTexture(3553,r.__webglTexture),D(3553,a,g),A(c.__webglFramebuffer,e,a,36064+i,3553),y(a,g)&&_(3553,a,e.width,e.height)}n.bindTexture(3553,null)}else{let t=3553;f&&(s?t=l.isDataTexture3D?32879:35866:console.warn("THREE.DataTexture3D and THREE.DataTexture2DArray only supported with WebGL2.")),n.bindTexture(t,d.__webglTexture),D(t,l,g),A(c.__webglFramebuffer,e,l,36064,t),y(l,g)&&_(t,l,e.width,e.height,e.depth),n.bindTexture(t,null)}e.depthBuffer&&(function h(e){const i=o.get(e),a=!0===e.isWebGLCubeRenderTarget;if(e.depthTexture){if(a)throw new Error("target.depthTexture not supported in Cube render targets");!(function r(e,i){if(i&&i.isWebGLCubeRenderTarget)throw new Error("Depth Texture with cube render targets is not supported");if(n.bindFramebuffer(36160,e),!i.depthTexture||!i.depthTexture.isDepthTexture)throw new Error("renderTarget.depthTexture must be an instance of THREE.DepthTexture");o.get(i.depthTexture).__webglTexture&&i.depthTexture.image.width===i.width&&i.depthTexture.image.height===i.height||(i.depthTexture.image.width=i.width,i.depthTexture.image.height=i.height,i.depthTexture.needsUpdate=!0),P(i.depthTexture,0);const a=o.get(i.depthTexture).__webglTexture;if(i.depthTexture.format===kit)t.framebufferTexture2D(36160,36096,3553,a,0);else{if(i.depthTexture.format!==Sit)throw new Error("Unknown depthTexture format");t.framebufferTexture2D(36160,33306,3553,a,0)}})(i.__webglFramebuffer,e)}else if(a){i.__webglDepthbuffer=[];for(let o=0;o<6;o++)n.bindFramebuffer(36160,i.__webglFramebuffer[o]),i.__webglDepthbuffer[o]=t.createRenderbuffer(),T(i.__webglDepthbuffer[o],e,!1)}else n.bindFramebuffer(36160,i.__webglFramebuffer),i.__webglDepthbuffer=t.createRenderbuffer(),T(i.__webglDepthbuffer,e,!1);n.bindFramebuffer(36160,null)})(e)},this.updateRenderTargetMipmap=function j(t){const e=b(t)||s,i=!0===t.isWebGLMultipleRenderTargets?t.texture:[t.texture];for(let a=0,r=i.length;a<r;a++){const r=i[a];if(y(r,e)){const e=t.isWebGLCubeRenderTarget?34067:3553,i=o.get(r).__webglTexture;n.bindTexture(e,i),_(e,r,t.width,t.height),n.bindTexture(e,null)}}},this.updateMultisampleRenderTarget=function U(e){if(e.isWebGLMultisampleRenderTarget)if(s){const i=e.width,a=e.height;let r=16384;e.depthBuffer&&(r|=256),e.stencilBuffer&&(r|=1024);const s=o.get(e);n.bindFramebuffer(36008,s.__webglMultisampledFramebuffer),n.bindFramebuffer(36009,s.__webglFramebuffer),t.blitFramebuffer(0,0,i,a,0,0,i,a,r,9728),n.bindFramebuffer(36008,null),n.bindFramebuffer(36009,s.__webglMultisampledFramebuffer)}else console.warn("THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.")},this.safeSetTexture2D=function G(t,e){t&&t.isWebGLRenderTarget&&(!1===z&&(console.warn("THREE.WebGLTextures.safeSetTexture2D: don't use render targets as textures. Use their .texture property instead."),z=!0),t=t.texture),P(t,e)},this.safeSetTextureCube=function W(t,e){t&&t.isWebGLCubeRenderTarget&&(!1===I&&(console.warn("THREE.WebGLTextures.safeSetTextureCube: don't use cube render targets as textures. Use their .texture property instead."),I=!0),t=t.texture),w(t,e)}}function ddt(t,e,n){const o=n.isWebGL2;return{convert:function i(t){let n;if(t===_it)return 5121;if(1017===t)return 32819;if(1018===t)return 32820;if(1019===t)return 33635;if(1010===t)return 5120;if(1011===t)return 5122;if(t===Cit)return 5123;if(1013===t)return 5124;if(t===Mit)return 5125;if(t===vit)return 5126;if(t===xit)return o?5131:(n=e.get("OES_texture_half_float"),null!==n?n.HALF_FLOAT_OES:null);if(1021===t)return 6406;if(t===Pit)return 6407;if(t===wit)return 6408;if(1024===t)return 6409;if(1025===t)return 6410;if(t===kit)return 6402;if(t===Sit)return 34041;if(1028===t)return 6403;if(1029===t)return 36244;if(1030===t)return 33319;if(1031===t)return 33320;if(1032===t)return 36248;if(1033===t)return 36249;if(33776===t||33777===t||33778===t||33779===t){if(n=e.get("WEBGL_compressed_texture_s3tc"),null===n)return null;if(33776===t)return n.COMPRESSED_RGB_S3TC_DXT1_EXT;if(33777===t)return n.COMPRESSED_RGBA_S3TC_DXT1_EXT;if(33778===t)return n.COMPRESSED_RGBA_S3TC_DXT3_EXT;if(33779===t)return n.COMPRESSED_RGBA_S3TC_DXT5_EXT}if(35840===t||35841===t||35842===t||35843===t){if(n=e.get("WEBGL_compressed_texture_pvrtc"),null===n)return null;if(35840===t)return n.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(35841===t)return n.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(35842===t)return n.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(35843===t)return n.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}if(36196===t)return n=e.get("WEBGL_compressed_texture_etc1"),null!==n?n.COMPRESSED_RGB_ETC1_WEBGL:null;if((37492===t||37496===t)&&(n=e.get("WEBGL_compressed_texture_etc"),null!==n)){if(37492===t)return n.COMPRESSED_RGB8_ETC2;if(37496===t)return n.COMPRESSED_RGBA8_ETC2_EAC}return 37808===t||37809===t||37810===t||37811===t||37812===t||37813===t||37814===t||37815===t||37816===t||37817===t||37818===t||37819===t||37820===t||37821===t||37840===t||37841===t||37842===t||37843===t||37844===t||37845===t||37846===t||37847===t||37848===t||37849===t||37850===t||37851===t||37852===t||37853===t?(n=e.get("WEBGL_compressed_texture_astc"),null!==n?t:null):36492===t?(n=e.get("EXT_texture_compression_bptc"),null!==n?t:null):t===Oit?o?34042:(n=e.get("WEBGL_depth_texture"),null!==n?n.UNSIGNED_INT_24_8_WEBGL:null):void 0}}}rdt.prototype.isMeshDistanceMaterial=!0;class pdt extends _st{constructor(t=[]){super(),this.cameras=t}}pdt.prototype.isArrayCamera=!0;class mdt extends prt{constructor(){super(),this.type="Group"}}mdt.prototype.isGroup=!0;const udt={type:"move"};class fdt{constructor(){this._targetRay=null,this._grip=null,this._hand=null}getHandSpace(){return null===this._hand&&(this._hand=new mdt,this._hand.matrixAutoUpdate=!1,this._hand.visible=!1,this._hand.joints={},this._hand.inputState={pinching:!1}),this._hand}getTargetRaySpace(){return null===this._targetRay&&(this._targetRay=new mdt,this._targetRay.matrixAutoUpdate=!1,this._targetRay.visible=!1,this._targetRay.hasLinearVelocity=!1,this._targetRay.linearVelocity=new cat,this._targetRay.hasAngularVelocity=!1,this._targetRay.angularVelocity=new cat),this._targetRay}getGripSpace(){return null===this._grip&&(this._grip=new mdt,this._grip.matrixAutoUpdate=!1,this._grip.visible=!1,this._grip.hasLinearVelocity=!1,this._grip.linearVelocity=new cat,this._grip.hasAngularVelocity=!1,this._grip.angularVelocity=new cat),this._grip}dispatchEvent(t){return null!==this._targetRay&&this._targetRay.dispatchEvent(t),null!==this._grip&&this._grip.dispatchEvent(t),null!==this._hand&&this._hand.dispatchEvent(t),this}disconnect(t){return this.dispatchEvent({type:"disconnected",data:t}),null!==this._targetRay&&(this._targetRay.visible=!1),null!==this._grip&&(this._grip.visible=!1),null!==this._hand&&(this._hand.visible=!1),this}update(t,e,n){let o=null,i=null,a=null;const r=this._targetRay,s=this._grip,l=this._hand;if(t&&"visible-blurred"!==e.session.visibilityState)if(null!==r&&(o=e.getPose(t.targetRaySpace,n),null!==o&&(r.matrix.fromArray(o.transform.matrix),r.matrix.decompose(r.position,r.rotation,r.scale),o.linearVelocity?(r.hasLinearVelocity=!0,r.linearVelocity.copy(o.linearVelocity)):r.hasLinearVelocity=!1,o.angularVelocity?(r.hasAngularVelocity=!0,r.angularVelocity.copy(o.angularVelocity)):r.hasAngularVelocity=!1,this.dispatchEvent(udt))),l&&t.hand){a=!0;for(const o of t.hand.values()){const t=e.getJointPose(o,n);if(void 0===l.joints[o.jointName]){const t=new mdt;t.matrixAutoUpdate=!1,t.visible=!1,l.joints[o.jointName]=t,l.add(t)}const i=l.joints[o.jointName];null!==t&&(i.matrix.fromArray(t.transform.matrix),i.matrix.decompose(i.position,i.rotation,i.scale),i.jointRadius=t.radius),i.visible=null!==t}const o=l.joints["index-finger-tip"].position.distanceTo(l.joints["thumb-tip"].position),i=.02,r=.005;l.inputState.pinching&&o>i+r?(l.inputState.pinching=!1,this.dispatchEvent({type:"pinchend",handedness:t.handedness,target:this})):!l.inputState.pinching&&o<=i-r&&(l.inputState.pinching=!0,this.dispatchEvent({type:"pinchstart",handedness:t.handedness,target:this}))}else null!==s&&t.gripSpace&&(i=e.getPose(t.gripSpace,n),null!==i&&(s.matrix.fromArray(i.transform.matrix),s.matrix.decompose(s.position,s.rotation,s.scale),i.linearVelocity?(s.hasLinearVelocity=!0,s.linearVelocity.copy(i.linearVelocity)):s.hasLinearVelocity=!1,i.angularVelocity?(s.hasAngularVelocity=!0,s.angularVelocity.copy(i.angularVelocity)):s.hasAngularVelocity=!1));return null!==r&&(r.visible=null!==o),null!==s&&(s.visible=null!==i),null!==l&&(l.visible=null!==a),this}}class gdt extends Uit{constructor(t,e){super();const n=this,o=t.state;let i=null,a=1,r=null,s="local-floor",l=null,c=null,d=null,p=null,m=null;const u=[],f=new Map,g=new _st;g.layers.enable(1),g.viewport=new aat;const h=new _st;h.layers.enable(2),h.viewport=new aat;const b=[g,h],y=new pdt;y.layers.enable(1),y.layers.enable(2);let _=null,C=null;function M(t){const e=f.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function v(){f.forEach((function(t,e){t.disconnect(e)})),f.clear(),_=null,C=null,o.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),S.stop(),n.isPresenting=!1,n.dispatchEvent({type:"sessionend"})}function x(t){const e=i.inputSources;for(let t=0;t<u.length;t++)f.set(e[t],u[t]);for(let e=0;e<t.removed.length;e++){const n=t.removed[e],o=f.get(n);o&&(o.dispatchEvent({type:"disconnected",data:n}),f.delete(n))}for(let e=0;e<t.added.length;e++){const n=t.added[e],o=f.get(n);o&&o.dispatchEvent({type:"connected",data:n})}}this.cameraAutoUpdate=!0,this.enabled=!1,this.isPresenting=!1,this.getController=function(t){let e=u[t];return void 0===e&&(e=new fdt,u[t]=e),e.getTargetRaySpace()},this.getControllerGrip=function(t){let e=u[t];return void 0===e&&(e=new fdt,u[t]=e),e.getGripSpace()},this.getHand=function(t){let e=u[t];return void 0===e&&(e=new fdt,u[t]=e),e.getHandSpace()},this.setFramebufferScaleFactor=function(t){a=t,!0===n.isPresenting&&console.warn("THREE.WebXRManager: Cannot change framebuffer scale while presenting.")},this.setReferenceSpaceType=function(t){s=t,!0===n.isPresenting&&console.warn("THREE.WebXRManager: Cannot change reference space type while presenting.")},this.getReferenceSpace=function(){return r},this.getSession=function(){return i},this.setSession=async function(t){if(i=t,null!==i){i.addEventListener("select",M),i.addEventListener("selectstart",M),i.addEventListener("selectend",M),i.addEventListener("squeeze",M),i.addEventListener("squeezestart",M),i.addEventListener("squeezeend",M),i.addEventListener("end",v),i.addEventListener("inputsourceschange",x);const t=e.getContextAttributes();if(!0!==t.xrCompatible&&await e.makeXRCompatible(),void 0===i.renderState.layers)m=new XRWebGLLayer(i,e,{antialias:t.antialias,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:a}),i.updateRenderState({baseLayer:m});else{let n=0;if(t.antialias)m=new XRWebGLLayer(i,e,{antialias:!0,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:a}),i.updateRenderState({layers:[m]});else{t.depth&&(n=t.stencil?34041:6402);const o={colorFormat:t.alpha?6408:6407,depthFormat:n,scaleFactor:a};c=new XRWebGLBinding(i,e),p=c.createProjectionLayer(o),d=e.createFramebuffer(),i.updateRenderState({layers:[p]})}}r=await i.requestReferenceSpace(s),S.setContext(i),S.start(),n.isPresenting=!0,n.dispatchEvent({type:"sessionstart"})}};const O=new cat,P=new cat;function w(t,e){null===e?t.matrixWorld.copy(t.matrix):t.matrixWorld.multiplyMatrices(e.matrixWorld,t.matrix),t.matrixWorldInverse.copy(t.matrixWorld).invert()}this.updateCamera=function(t){if(null===i)return;y.near=h.near=g.near=t.near,y.far=h.far=g.far=t.far,_===y.near&&C===y.far||(i.updateRenderState({depthNear:y.near,depthFar:y.far}),_=y.near,C=y.far);const e=t.parent,n=y.cameras;w(y,e);for(let t=0;t<n.length;t++)w(n[t],e);y.matrixWorld.decompose(y.position,y.quaternion,y.scale),t.position.copy(y.position),t.quaternion.copy(y.quaternion),t.scale.copy(y.scale),t.matrix.copy(y.matrix),t.matrixWorld.copy(y.matrixWorld);const o=t.children;for(let t=0,e=o.length;t<e;t++)o[t].updateMatrixWorld(!0);2===n.length?(function a(t,e,n){O.setFromMatrixPosition(e.matrixWorld),P.setFromMatrixPosition(n.matrixWorld);const o=O.distanceTo(P),i=e.projectionMatrix.elements,a=n.projectionMatrix.elements,r=i[14]/(i[10]-1),s=i[14]/(i[10]+1),l=(i[9]+1)/i[5],c=(i[9]-1)/i[5],d=(i[8]-1)/i[0],p=(a[8]+1)/a[0],m=r*d,u=r*p,f=o/(-d+p),g=f*-d;e.matrixWorld.decompose(t.position,t.quaternion,t.scale),t.translateX(g),t.translateZ(f),t.matrixWorld.compose(t.position,t.quaternion,t.scale),t.matrixWorldInverse.copy(t.matrixWorld).invert();const h=r+f,b=s+f;t.projectionMatrix.makePerspective(m-g,u+(o-g),l*s/b*h,c*s/b*h,h,b)})(y,g,h):y.projectionMatrix.copy(g.projectionMatrix)},this.getCamera=function(){return y},this.getFoveation=function(){return null!==p?p.fixedFoveation:null!==m?m.fixedFoveation:void 0},this.setFoveation=function(t){null!==p&&(p.fixedFoveation=t),null!==m&&void 0!==m.fixedFoveation&&(m.fixedFoveation=t)};let k=null;const S=new Rst;S.setAnimationLoop((function D(t,n){if(l=n.getViewerPose(r),null!==l){const t=l.views;null!==m&&o.bindXRFramebuffer(m.framebuffer);let n=!1;t.length!==y.cameras.length&&(y.cameras.length=0,n=!0);for(let i=0;i<t.length;i++){const a=t[i];let r=null;if(null!==m)r=m.getViewport(a);else{const t=c.getViewSubImage(p,a);o.bindXRFramebuffer(d),void 0!==t.depthStencilTexture&&e.framebufferTexture2D(36160,36096,3553,t.depthStencilTexture,0),e.framebufferTexture2D(36160,36064,3553,t.colorTexture,0),r=t.viewport}const s=b[i];s.matrix.fromArray(a.transform.matrix),s.projectionMatrix.fromArray(a.projectionMatrix),s.viewport.set(r.x,r.y,r.width,r.height),0===i&&y.matrix.copy(s.matrix),!0===n&&y.cameras.push(s)}}const a=i.inputSources;for(let t=0;t<u.length;t++)u[t].update(a[t],n,r);k&&k(t,n)})),this.setAnimationLoop=function(t){k=t},this.dispose=function(){}}}function hdt(t){function e(e,n){e.opacity.value=n.opacity,n.color&&e.diffuse.value.copy(n.color),n.emissive&&e.emissive.value.copy(n.emissive).multiplyScalar(n.emissiveIntensity),n.map&&(e.map.value=n.map),n.alphaMap&&(e.alphaMap.value=n.alphaMap),n.specularMap&&(e.specularMap.value=n.specularMap);const o=t.get(n).envMap;if(o){e.envMap.value=o,e.flipEnvMap.value=o.isCubeTexture&&!1===o.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.refractionRatio.value=n.refractionRatio;const i=t.get(o).__maxMipLevel;void 0!==i&&(e.maxMipLevel.value=i)}let i,a;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?i=n.map:n.specularMap?i=n.specularMap:n.displacementMap?i=n.displacementMap:n.normalMap?i=n.normalMap:n.bumpMap?i=n.bumpMap:n.roughnessMap?i=n.roughnessMap:n.metalnessMap?i=n.metalnessMap:n.alphaMap?i=n.alphaMap:n.emissiveMap?i=n.emissiveMap:n.clearcoatMap?i=n.clearcoatMap:n.clearcoatNormalMap?i=n.clearcoatNormalMap:n.clearcoatRoughnessMap?i=n.clearcoatRoughnessMap:n.specularIntensityMap?i=n.specularIntensityMap:n.specularTintMap&&(i=n.specularTintMap),void 0!==i&&(i.isWebGLRenderTarget&&(i=i.texture),!0===i.matrixAutoUpdate&&i.updateMatrix(),e.uvTransform.value.copy(i.matrix)),n.aoMap?a=n.aoMap:n.lightMap&&(a=n.lightMap),void 0!==a&&(a.isWebGLRenderTarget&&(a=a.texture),!0===a.matrixAutoUpdate&&a.updateMatrix(),e.uv2Transform.value.copy(a.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,1===n.side&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),1===n.side&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias),t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function o(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function i(t,o,a,r,s){o.isMeshBasicMaterial?e(t,o):o.isMeshLambertMaterial?(e(t,o),(function l(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)})(t,o)):o.isMeshToonMaterial?(e(t,o),(function c(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap),e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1)),e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate()),e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,o)):o.isMeshPhongMaterial?(e(t,o),(function d(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap),e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1)),e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate()),e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,o)):o.isMeshStandardMaterial?(e(t,o),o.isMeshPhysicalMaterial?(function p(t,e,o){n(t,e),t.reflectivity.value=e.reflectivity,t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.sheen&&t.sheen.value.copy(e.sheen),e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,1===e.side&&t.clearcoatNormalScale.value.negate()),t.transmission.value=e.transmission,e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),e.transmission>0&&(t.transmissionSamplerMap.value=o.texture,t.transmissionSamplerSize.value.set(o.width,o.height)),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint),t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap),e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)})(t,o,s):n(t,o)):o.isMeshMatcapMaterial?(e(t,o),(function m(t,e){e.matcap&&(t.matcap.value=e.matcap),e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1)),e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate()),e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,o)):o.isMeshDepthMaterial?(e(t,o),(function u(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,o)):o.isMeshDistanceMaterial?(e(t,o),(function f(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias),t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance})(t,o)):o.isMeshNormalMaterial?(e(t,o),(function g(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1)),e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate()),e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,o)):o.isLineBasicMaterial?((function h(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity})(t,o),o.isLineDashedMaterial&&(function b(t,e){t.dashSize.value=e.dashSize,t.totalSize.value=e.dashSize+e.gapSize,t.scale.value=e.scale})(t,o)):o.isPointsMaterial?(function y(t,e,n,o){let i;t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.size.value=e.size*n,t.scale.value=.5*o,e.map&&(t.map.value=e.map),e.alphaMap&&(t.alphaMap.value=e.alphaMap),e.map?i=e.map:e.alphaMap&&(i=e.alphaMap),void 0!==i&&(!0===i.matrixAutoUpdate&&i.updateMatrix(),t.uvTransform.value.copy(i.matrix))})(t,o,a,r):o.isSpriteMaterial?(function _(t,e){let n;t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map),e.alphaMap&&(t.alphaMap.value=e.alphaMap),e.map?n=e.map:e.alphaMap&&(n=e.alphaMap),void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))})(t,o):o.isShadowMaterial?(t.color.value.copy(o.color),t.opacity.value=o.opacity):o.isShaderMaterial&&(o.uniformsNeedUpdate=!1)}}}function bdt(t={}){const e=void 0!==t.canvas?t.canvas:(function n(){const t=document.createElementNS("http://www.w3.org/1999/xhtml","canvas");return t.style.display="block",t})(),o=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,a=void 0===t.depth||t.depth,r=void 0===t.stencil||t.stencil,s=void 0!==t.antialias&&t.antialias,l=void 0===t.premultipliedAlpha||t.premultipliedAlpha,c=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,d=void 0!==t.powerPreference?t.powerPreference:"default",p=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let m=null,u=null;const f=[],g=[];this.domElement=e,this.debug={checkShaderErrors:!0},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this.gammaFactor=2,this.outputEncoding=zit,this.physicallyCorrectLights=!1,this.toneMapping=0,this.toneMappingExposure=1;const h=this;let b=!1,y=0,_=0,C=null,M=-1,v=null;const x=new aat,O=new aat;let P=null,w=e.width,k=e.height,S=1,D=null,E=null;const R=new aat(0,0,w,k),A=new aat(0,0,w,k);let T=!1;const N=[],z=new Est;let I=!1,H=!1,F=null;const L=new Bat,B=new cat,V={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0};function j(){return null===C?S:1}let U,G,W,Y,q,Z,X,K,J,Q,$,tt,et,nt,ot,it,at,rt,st,lt,ct,dt,pt,mt=o;function ut(t,n){for(let o=0;o<t.length;o++){const i=e.getContext(t[o],n);if(null!==i)return i}return null}try{const t={alpha:i,depth:a,stencil:r,antialias:s,premultipliedAlpha:l,preserveDrawingBuffer:c,powerPreference:d,failIfMajorPerformanceCaveat:p};if(e.addEventListener("webglcontextlost",ht,!1),e.addEventListener("webglcontextrestored",bt,!1),null===mt){const e=["webgl2","webgl","experimental-webgl"];if(!0===h.isWebGL1Renderer&&e.shift(),mt=ut(e,t),null===mt)throw ut(e)?new Error("Error creating WebGL context with your selected attributes."):new Error("Error creating WebGL context.")}void 0===mt.getShaderPrecisionFormat&&(mt.getShaderPrecisionFormat=function(){return{rangeMin:1,rangeMax:1,precision:1}})}catch(t){throw console.error("THREE.WebGLRenderer: "+t.message),t}function ft(){U=new glt(mt),G=new Bst(mt,U,t),U.init(G),dt=new ddt(mt,U,G),W=new ldt(mt,U,G),N[0]=1029,Y=new ylt(mt),q=new Zct,Z=new cdt(mt,U,W,q,G,dt,Y),X=new jst(h),K=new flt(h),J=new Ast(mt,G),pt=new Fst(mt,U,J,G),Q=new hlt(mt,J,Y,pt),$=new vlt(mt,Q,J,Y),st=new Mlt(mt),it=new Vst(q),tt=new qct(h,X,K,U,G,pt,it),et=new hdt(q),nt=new Qct(q),ot=new idt(U,G),rt=new Hst(h,X,W,$,l),at=new sdt(h,$,G),lt=new Lst(mt,U,Y,G),ct=new blt(mt,U,Y,G),Y.programs=tt.programs,h.capabilities=G,h.extensions=U,h.properties=q,h.renderLists=nt,h.shadowMap=at,h.state=W,h.info=Y}ft();const gt=new gdt(h,mt);function ht(t){t.preventDefault(),console.log("THREE.WebGLRenderer: Context Lost."),b=!0}function bt(){console.log("THREE.WebGLRenderer: Context Restored."),b=!1;const t=Y.autoReset,e=at.enabled,n=at.autoUpdate,o=at.needsUpdate,i=at.type;ft(),Y.autoReset=t,at.enabled=e,at.autoUpdate=n,at.needsUpdate=o,at.type=i}function yt(t){const e=t.target;e.removeEventListener("dispose",yt),(function n(t){(function e(t){const e=q.get(t).programs;void 0!==e&&e.forEach((function(t){tt.releaseProgram(t)}))})(t),q.remove(t)})(e)}this.xr=gt,this.getContext=function(){return mt},this.getContextAttributes=function(){return mt.getContextAttributes()},this.forceContextLoss=function(){const t=U.get("WEBGL_lose_context");t&&t.loseContext()},this.forceContextRestore=function(){const t=U.get("WEBGL_lose_context");t&&t.restoreContext()},this.getPixelRatio=function(){return S},this.setPixelRatio=function(t){void 0!==t&&(S=t,this.setSize(w,k,!1))},this.getSize=function(t){return t.set(w,k)},this.setSize=function(t,n,o){gt.isPresenting?console.warn("THREE.WebGLRenderer: Can't change size while VR device is presenting."):(w=t,k=n,e.width=Math.floor(t*S),e.height=Math.floor(n*S),!1!==o&&(e.style.width=t+"px",e.style.height=n+"px"),this.setViewport(0,0,t,n))},this.getDrawingBufferSize=function(t){return t.set(w*S,k*S).floor()},this.setDrawingBufferSize=function(t,n,o){w=t,k=n,S=o,e.width=Math.floor(t*o),e.height=Math.floor(n*o),this.setViewport(0,0,t,n)},this.getCurrentViewport=function(t){return t.copy(x)},this.getViewport=function(t){return t.copy(R)},this.setViewport=function(t,e,n,o){t.isVector4?R.set(t.x,t.y,t.z,t.w):R.set(t,e,n,o),W.viewport(x.copy(R).multiplyScalar(S).floor())},this.getScissor=function(t){return t.copy(A)},this.setScissor=function(t,e,n,o){t.isVector4?A.set(t.x,t.y,t.z,t.w):A.set(t,e,n,o),W.scissor(O.copy(A).multiplyScalar(S).floor())},this.getScissorTest=function(){return T},this.setScissorTest=function(t){W.setScissorTest(T=t)},this.setOpaqueSort=function(t){D=t},this.setTransparentSort=function(t){E=t},this.getClearColor=function(t){return t.copy(rt.getClearColor())},this.setClearColor=function(){rt.setClearColor.apply(rt,arguments)},this.getClearAlpha=function(){return 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o=e.getAttributes();t.hasPositions&&(mt.bindBuffer(34962,n.position),mt.bufferData(34962,t.positionArray,35048),pt.enableAttribute(o.position),mt.vertexAttribPointer(o.position,3,5126,!1,0,0)),t.hasNormals&&(mt.bindBuffer(34962,n.normal),mt.bufferData(34962,t.normalArray,35048),pt.enableAttribute(o.normal),mt.vertexAttribPointer(o.normal,3,5126,!1,0,0)),t.hasUvs&&(mt.bindBuffer(34962,n.uv),mt.bufferData(34962,t.uvArray,35048),pt.enableAttribute(o.uv),mt.vertexAttribPointer(o.uv,2,5126,!1,0,0)),t.hasColors&&(mt.bindBuffer(34962,n.color),mt.bufferData(34962,t.colorArray,35048),pt.enableAttribute(o.color),mt.vertexAttribPointer(o.color,3,5126,!1,0,0)),pt.disableUnusedAttributes(),mt.drawArrays(4,0,t.count),t.count=0},this.renderBufferDirect=function(t,e,n,o,i,a){null===e&&(e=V);const r=i.isMesh&&i.matrixWorld.determinant()<0,s=St(t,e,o,i);W.setMaterial(o,r);let l=n.index;const c=n.attributes.position;if(null===l){if(void 0===c||0===c.count)return}else if(0===l.count)return;let 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if((t.isMesh||t.isLine||t.isPoints)&&(t.isSkinnedMesh&&t.skeleton.frame!==Y.render.frame&&(t.skeleton.update(),t.skeleton.frame=Y.render.frame),!t.frustumCulled||z.intersectsObject(t))){o&&B.setFromMatrixPosition(t.matrixWorld).applyMatrix4(L);const e=$.update(t),i=t.material;if(Array.isArray(i)){const o=e.groups;for(let a=0,r=o.length;a<r;a++){const r=o[a],s=i[r.materialIndex];s&&s.visible&&m.push(t,e,s,n,B.z,r)}}else i.visible&&m.push(t,e,i,n,B.z,null)}const i=t.children;for(let t=0,a=i.length;t<a;t++)xt(i[t],e,n,o)}function Ot(t,e,n){const o=!0===e.isScene?e.overrideMaterial:null;if(n.isArrayCamera){const i=n.cameras;for(let n=0,a=i.length;n<a;n++){const a=i[n];W.viewport(x.copy(a.viewport)),u.setupLightsView(a);for(let n=0,i=t.length;n<i;n++){const i=t[n],r=i.object,s=i.geometry,l=null===o?i.material:o,c=i.group;r.layers.test(a.layers)&&Pt(r,e,a,s,l,c)}}}else for(let i=0,a=t.length;i<a;i++){const a=t[i];Pt(a.object,e,n,a.geometry,null===o?a.material:o,a.group)}}function Pt(t,e,n,o,i,a){if(t.onBeforeRender(h,e,n,o,i,a),t.modelViewMatrix.multiplyMatrices(n.matrixWorldInverse,t.matrixWorld),t.normalMatrix.getNormalMatrix(t.modelViewMatrix),t.isImmediateRenderObject){const o=St(n,e,i,t);W.setMaterial(i),pt.reset(),(function r(t,e){t.render((function(t){h.renderBufferImmediate(t,e)}))})(t,o)}else!0===i.transparent&&2===i.side?(i.side=1,i.needsUpdate=!0,h.renderBufferDirect(n,e,o,i,t,a),i.side=0,i.needsUpdate=!0,h.renderBufferDirect(n,e,o,i,t,a),i.side=2):h.renderBufferDirect(n,e,o,i,t,a);t.onAfterRender(h,e,n,o,i,a)}function wt(t,e,n){!0!==e.isScene&&(e=V);const o=q.get(t),i=u.state.lights,a=i.state.version,r=tt.getParameters(t,i.state,u.state.shadowsArray,e,n),s=tt.getProgramCacheKey(r);let l=o.programs;o.environment=t.isMeshStandardMaterial?e.environment:null,o.fog=e.fog,o.envMap=(t.isMeshStandardMaterial?K:X).get(t.envMap||o.environment),void 0===l&&(t.addEventListener("dispose",yt),l=new Map,o.programs=l);let c=l.get(s);if(void 0!==c){if(o.currentProgram===c&&o.lightsStateVersion===a)return kt(t,r),c}else r.uniforms=tt.getUniforms(t),t.onBuild(r,h),t.onBeforeCompile(r,h),c=tt.acquireProgram(r,s),l.set(s,c),o.uniforms=r.uniforms;const d=o.uniforms;(t.isShaderMaterial||t.isRawShaderMaterial)&&!0!==t.clipping||(d.clippingPlanes=it.uniform),kt(t,r),o.needsLights=(function p(t){return t.isMeshLambertMaterial||t.isMeshToonMaterial||t.isMeshPhongMaterial||t.isMeshStandardMaterial||t.isShadowMaterial||t.isShaderMaterial&&!0===t.lights})(t),o.lightsStateVersion=a,o.needsLights&&(d.ambientLightColor.value=i.state.ambient,d.lightProbe.value=i.state.probe,d.directionalLights.value=i.state.directional,d.directionalLightShadows.value=i.state.directionalShadow,d.spotLights.value=i.state.spot,d.spotLightShadows.value=i.state.spotShadow,d.rectAreaLights.value=i.state.rectArea,d.ltc_1.value=i.state.rectAreaLTC1,d.ltc_2.value=i.state.rectAreaLTC2,d.pointLights.value=i.state.point,d.pointLightShadows.value=i.state.pointShadow,d.hemisphereLights.value=i.state.hemi,d.directionalShadowMap.value=i.state.directionalShadowMap,d.directionalShadowMatrix.value=i.state.directionalShadowMatrix,d.spotShadowMap.value=i.state.spotShadowMap,d.spotShadowMatrix.value=i.state.spotShadowMatrix,d.pointShadowMap.value=i.state.pointShadowMap,d.pointShadowMatrix.value=i.state.pointShadowMatrix);const m=c.getUniforms(),f=wct.seqWithValue(m.seq,d);return o.currentProgram=c,o.uniformsList=f,c}function kt(t,e){const n=q.get(t);n.outputEncoding=e.outputEncoding,n.instancing=e.instancing,n.skinning=e.skinning,n.morphTargets=e.morphTargets,n.morphNormals=e.morphNormals,n.numClippingPlanes=e.numClippingPlanes,n.numIntersection=e.numClipIntersection,n.vertexAlphas=e.vertexAlphas,n.vertexTangents=e.vertexTangents}function St(t,e,n,o){!0!==e.isScene&&(e=V),Z.resetTextureUnits();const i=e.fog,a=null===C?h.outputEncoding:C.texture.encoding,r=(n.isMeshStandardMaterial?K:X).get(n.envMap||(n.isMeshStandardMaterial?e.environment:null)),s=!0===n.vertexColors&&!!o.geometry&&!!o.geometry.attributes.color&&4===o.geometry.attributes.color.itemSize,l=!!o.geometry&&!!o.geometry.attributes.tangent,c=!!o.geometry&&!!o.geometry.morphAttributes.position,d=!!o.geometry&&!!o.geometry.morphAttributes.normal,p=q.get(n),m=u.state.lights;!0!==I||!0!==H&&t===v||it.setState(n,t,t===v&&n.id===M);let f=!1;n.version===p.__version?p.needsLights&&p.lightsStateVersion!==m.state.version||p.outputEncoding!==a||o.isInstancedMesh&&!1===p.instancing?f=!0:o.isInstancedMesh||!0!==p.instancing?o.isSkinnedMesh&&!1===p.skinning?f=!0:o.isSkinnedMesh||!0!==p.skinning?p.envMap!==r||n.fog&&p.fog!==i?f=!0:void 0===p.numClippingPlanes||p.numClippingPlanes===it.numPlanes&&p.numIntersection===it.numIntersection?(p.vertexAlphas!==s||p.vertexTangents!==l||p.morphTargets!==c||p.morphNormals!==d)&&(f=!0):f=!0:f=!0:f=!0:(f=!0,p.__version=n.version);let g=p.currentProgram;!0===f&&(g=wt(n,e,o));let b=!1,y=!1,_=!1;const x=g.getUniforms(),O=p.uniforms;if(W.useProgram(g.program)&&(b=!0,y=!0,_=!0),n.id!==M&&(M=n.id,y=!0),b||v!==t){if(x.setValue(mt,"projectionMatrix",t.projectionMatrix),G.logarithmicDepthBuffer&&x.setValue(mt,"logDepthBufFC",2/(Math.log(t.far+1)/Math.LN2)),v!==t&&(v=t,y=!0,_=!0),n.isShaderMaterial||n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshStandardMaterial||n.envMap){const e=x.map.cameraPosition;void 0!==e&&e.setValue(mt,B.setFromMatrixPosition(t.matrixWorld))}(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial)&&x.setValue(mt,"isOrthographic",!0===t.isOrthographicCamera),(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial||n.isShadowMaterial||o.isSkinnedMesh)&&x.setValue(mt,"viewMatrix",t.matrixWorldInverse)}if(o.isSkinnedMesh){x.setOptional(mt,o,"bindMatrix"),x.setOptional(mt,o,"bindMatrixInverse");const t=o.skeleton;t&&(G.floatVertexTextures?(null===t.boneTexture&&t.computeBoneTexture(),x.setValue(mt,"boneTexture",t.boneTexture,Z),x.setValue(mt,"boneTextureSize",t.boneTextureSize)):x.setOptional(mt,t,"boneMatrices"))}return(y||p.receiveShadow!==o.receiveShadow)&&(p.receiveShadow=o.receiveShadow,x.setValue(mt,"receiveShadow",o.receiveShadow)),y&&(x.setValue(mt,"toneMappingExposure",h.toneMappingExposure),p.needsLights&&(function 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window&&vt.setContext(window),this.setAnimationLoop=function(t){_t=t,gt.setAnimationLoop(t),null===t?vt.stop():vt.start()},gt.addEventListener("sessionstart",Ct),gt.addEventListener("sessionend",Mt),this.render=function(t,e){if(void 0!==e&&!0!==e.isCamera)return void console.error("THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera.");if(!0===b)return;!0===t.autoUpdate&&t.updateMatrixWorld(),null===e.parent&&e.updateMatrixWorld(),!0===gt.enabled&&!0===gt.isPresenting&&(!0===gt.cameraAutoUpdate&&gt.updateCamera(e),e=gt.getCamera()),!0===t.isScene&&t.onBeforeRender(h,t,e,C),u=ot.get(t,g.length),u.init(),g.push(u),L.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),z.setFromProjectionMatrix(L),H=this.localClippingEnabled,I=it.init(this.clippingPlanes,H,e),m=nt.get(t,f.length),m.init(),f.push(m),xt(t,e,0,h.sortObjects),m.finish(),!0===h.sortObjects&&m.sort(D,E),!0===I&&it.beginShadows(),at.render(u.state.shadowsArray,t,e),u.setupLights(),u.setupLightsView(e),!0===I&&it.endShadows(),!0===this.info.autoReset&&this.info.reset(),rt.render(m,t);const n=m.opaque,o=m.transmissive,i=m.transparent;n.length>0&&Ot(n,t,e),o.length>0&&(function a(t,e,n,o){null===F&&(F=new(!0===s&&!0===G.isWebGL2?sat:rat)(1024,1024,{generateMipmaps:!0,type:null!==dt.convert(xit)?xit:_it,minFilter:yit,magFilter:hit,wrapS:fit,wrapT:fit}));const i=h.getRenderTarget();h.setRenderTarget(F),h.clear();const a=h.toneMapping;h.toneMapping=0,Ot(t,n,o),h.toneMapping=a,Z.updateMultisampleRenderTarget(F),Z.updateRenderTargetMipmap(F),h.setRenderTarget(i),Ot(e,n,o)})(n,o,t,e),i.length>0&&Ot(i,t,e),null!==C&&(Z.updateMultisampleRenderTarget(C),Z.updateRenderTargetMipmap(C)),!0===t.isScene&&t.onAfterRender(h,t,e),W.buffers.depth.setTest(!0),W.buffers.depth.setMask(!0),W.buffers.color.setMask(!0),W.setPolygonOffset(!1),pt.resetDefaultState(),M=-1,v=null,g.pop(),u=g.length>0?g[g.length-1]:null,f.pop(),m=f.length>0?f[f.length-1]:null},this.getActiveCubeFace=function(){return y},this.getActiveMipmapLevel=function(){return _},this.getRenderTarget=function(){return C},this.setRenderTarget=function(t,e=0,n=0){C=t,y=e,_=n,t&&void 0===q.get(t).__webglFramebuffer&&Z.setupRenderTarget(t);let o=null,i=!1,a=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(a=!0);const r=q.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(o=r[e],i=!0):o=t.isWebGLMultisampleRenderTarget?q.get(t).__webglMultisampledFramebuffer:r,x.copy(t.viewport),O.copy(t.scissor),P=t.scissorTest}else x.copy(R).multiplyScalar(S).floor(),O.copy(A).multiplyScalar(S).floor(),P=T;if(W.bindFramebuffer(36160,o)&&G.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(N.length!==n.length||36064!==N[0]){for(let t=0,e=n.length;t<e;t++)N[t]=36064+t;N.length=n.length,e=!0}}else 1===N.length&&36064===N[0]||(N[0]=36064,N.length=1,e=!0);else 1===N.length&&1029===N[0]||(N[0]=1029,N.length=1,e=!0);e&&(G.isWebGL2?mt.drawBuffers(N):U.get("WEBGL_draw_buffers").drawBuffersWEBGL(N))}if(W.viewport(x),W.scissor(O),W.setScissorTest(P),i){const o=q.get(t.texture);mt.framebufferTexture2D(36160,36064,34069+e,o.__webglTexture,n)}else if(a){const o=q.get(t.texture);mt.framebufferTextureLayer(36160,36064,o.__webglTexture,n||0,e||0)}},this.readRenderTargetPixels=function(t,e,n,o,i,a,r){if(!t||!t.isWebGLRenderTarget)return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.");let s=q.get(t).__webglFramebuffer;if(t.isWebGLCubeRenderTarget&&void 0!==r&&(s=s[r]),s){W.bindFramebuffer(36160,s);try{const r=t.texture,s=r.format,l=r.type;if(s!==wit&&dt.convert(s)!==mt.getParameter(35739))return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format.");const c=l===xit&&(U.has("EXT_color_buffer_half_float")||G.isWebGL2&&U.has("EXT_color_buffer_float"));if(!(l===_it||dt.convert(l)===mt.getParameter(35738)||l===vit&&(G.isWebGL2||U.has("OES_texture_float")||U.has("WEBGL_color_buffer_float"))||c))return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.");36053===mt.checkFramebufferStatus(36160)?e>=0&&e<=t.width-o&&n>=0&&n<=t.height-i&&mt.readPixels(e,n,o,i,dt.convert(s),dt.convert(l),a):console.error("THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.")}finally{const t=null!==C?q.get(C).__webglFramebuffer:null;W.bindFramebuffer(36160,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const o=Math.pow(2,-n),i=Math.floor(e.image.width*o),a=Math.floor(e.image.height*o);let r=dt.convert(e.format);G.isWebGL2&&(6407===r&&(r=32849),6408===r&&(r=32856)),Z.setTexture2D(e,0),mt.copyTexImage2D(3553,n,r,t.x,t.y,i,a,0),W.unbindTexture()},this.copyTextureToTexture=function(t,e,n,o=0){const i=e.image.width,a=e.image.height,r=dt.convert(n.format),s=dt.convert(n.type);Z.setTexture2D(n,0),mt.pixelStorei(37440,n.flipY),mt.pixelStorei(37441,n.premultiplyAlpha),mt.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?mt.texSubImage2D(3553,o,t.x,t.y,i,a,r,s,e.image.data):e.isCompressedTexture?mt.compressedTexSubImage2D(3553,o,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,r,e.mipmaps[0].data):mt.texSubImage2D(3553,o,t.x,t.y,r,s,e.image),0===o&&n.generateMipmaps&&mt.generateMipmap(3553),W.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,o,i=0){if(h.isWebGL1Renderer)return void console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.");const a=t.max.x-t.min.x+1,r=t.max.y-t.min.y+1,s=t.max.z-t.min.z+1,l=dt.convert(o.format),c=dt.convert(o.type);let d;if(o.isDataTexture3D)Z.setTexture3D(o,0),d=32879;else{if(!o.isDataTexture2DArray)return void console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and 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super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}ydt.prototype.isScene=!0;class _dt{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=Bit,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=qit()}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.array=new t.array.constructor(t.array),this.count=t.count,this.stride=t.stride,this.usage=t.usage,this}copyAt(t,e,n){t*=this.stride,n*=e.stride;for(let o=0,i=this.stride;o<i;o++)this.array[t+o]=e.array[n+o];return this}set(t,e=0){return this.array.set(t,e),this}clone(t){void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=qit()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=this.array.slice(0).buffer);const e=new this.array.constructor(t.arrayBuffers[this.array.buffer._uuid]),n=new this.constructor(e,this.stride);return n.setUsage(this.usage),n}onUpload(t){return this.onUploadCallback=t,this}toJSON(t){return void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=qit()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=Array.prototype.slice.call(new Uint32Array(this.array.buffer))),{uuid:this.uuid,buffer:this.array.buffer._uuid,type:this.array.constructor.name,stride:this.stride}}}_dt.prototype.isInterleavedBuffer=!0;const Cdt=new cat;class Mdt{constructor(t,e,n,o=!1){this.name="",this.data=t,this.itemSize=e,this.offset=n,this.normalized=!0===o}get count(){return this.data.count}get array(){return this.data.array}set needsUpdate(t){this.data.needsUpdate=t}applyMatrix4(t){for(let e=0,n=this.data.count;e<n;e++)Cdt.x=this.getX(e),Cdt.y=this.getY(e),Cdt.z=this.getZ(e),Cdt.applyMatrix4(t),this.setXYZ(e,Cdt.x,Cdt.y,Cdt.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)Cdt.x=this.getX(e),Cdt.y=this.getY(e),Cdt.z=this.getZ(e),Cdt.applyNormalMatrix(t),this.setXYZ(e,Cdt.x,Cdt.y,Cdt.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)Cdt.x=this.getX(e),Cdt.y=this.getY(e),Cdt.z=this.getZ(e),Cdt.transformDirection(t),this.setXYZ(e,Cdt.x,Cdt.y,Cdt.z);return this}setX(t,e){return this.data.array[t*this.data.stride+this.offset]=e,this}setY(t,e){return this.data.array[t*this.data.stride+this.offset+1]=e,this}setZ(t,e){return this.data.array[t*this.data.stride+this.offset+2]=e,this}setW(t,e){return this.data.array[t*this.data.stride+this.offset+3]=e,this}getX(t){return this.data.array[t*this.data.stride+this.offset]}getY(t){return this.data.array[t*this.data.stride+this.offset+1]}getZ(t){return this.data.array[t*this.data.stride+this.offset+2]}getW(t){return this.data.array[t*this.data.stride+this.offset+3]}setXY(t,e,n){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this}setXYZ(t,e,n,o){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=o,this}setXYZW(t,e,n,o,i){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=o,this.data.array[t+3]=i,this}clone(t){if(void 0===t){console.log("THREE.InterleavedBufferAttribute.clone(): Cloning an interlaved buffer attribute will deinterleave buffer data.");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return new zrt(new this.array.constructor(t),this.itemSize,this.normalized)}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.clone(t)),new Mdt(t.interleavedBuffers[this.data.uuid],this.itemSize,this.offset,this.normalized)}toJSON(t){if(void 0===t){console.log("THREE.InterleavedBufferAttribute.toJSON(): Serializing an interlaved buffer attribute will deinterleave buffer data.");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return{itemSize:this.itemSize,type:this.array.constructor.name,array:t,normalized:this.normalized}}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.toJSON(t)),{isInterleavedBufferAttribute:!0,itemSize:this.itemSize,data:this.data.uuid,offset:this.offset,normalized:this.normalized}}}Mdt.prototype.isInterleavedBufferAttribute=!0;class vdt extends Ort{constructor(t){super(),this.type="SpriteMaterial",this.color=new Rrt(16777215),this.map=null,this.alphaMap=null,this.rotation=0,this.sizeAttenuation=!0,this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.rotation=t.rotation,this.sizeAttenuation=t.sizeAttenuation,this}}let xdt;vdt.prototype.isSpriteMaterial=!0;const Odt=new cat,Pdt=new cat,wdt=new cat,kdt=new Qit,Sdt=new Qit,Ddt=new Bat,Edt=new cat,Rdt=new cat,Adt=new cat,Tdt=new Qit,Ndt=new Qit,zdt=new Qit;function Idt(t,e,n,o,i,a){kdt.subVectors(t,n).addScalar(.5).multiply(o),void 0!==i?(Sdt.x=a*kdt.x-i*kdt.y,Sdt.y=i*kdt.x+a*kdt.y):Sdt.copy(kdt),t.copy(e),t.x+=Sdt.x,t.y+=Sdt.y,t.applyMatrix4(Ddt)}(class extends prt{constructor(t){if(super(),this.type="Sprite",void 0===xdt){xdt=new qrt;const t=new Float32Array([-.5,-.5,0,0,0,.5,-.5,0,1,0,.5,.5,0,1,1,-.5,.5,0,0,1]),e=new _dt(t,5);xdt.setIndex([0,1,2,0,2,3]),xdt.setAttribute("position",new Mdt(e,3,0,!1)),xdt.setAttribute("uv",new Mdt(e,2,3,!1))}this.geometry=xdt,this.material=void 0!==t?t:new vdt,this.center=new Qit(.5,.5)}raycast(t,e){null===t.camera&&console.error('THREE.Sprite: "Raycaster.camera" needs to be set in order to raycast against sprites.'),Pdt.setFromMatrixScale(this.matrixWorld),Ddt.copy(t.camera.matrixWorld),this.modelViewMatrix.multiplyMatrices(t.camera.matrixWorldInverse,this.matrixWorld),wdt.setFromMatrixPosition(this.modelViewMatrix),t.camera.isPerspectiveCamera&&!1===this.material.sizeAttenuation&&Pdt.multiplyScalar(-wdt.z);const n=this.material.rotation;let o,i;0!==n&&(i=Math.cos(n),o=Math.sin(n));const a=this.center;Idt(Edt.set(-.5,-.5,0),wdt,a,Pdt,o,i),Idt(Rdt.set(.5,-.5,0),wdt,a,Pdt,o,i),Idt(Adt.set(.5,.5,0),wdt,a,Pdt,o,i),Tdt.set(0,0),Ndt.set(1,0),zdt.set(1,1);let r=t.ray.intersectTriangle(Edt,Rdt,Adt,!1,Odt);if(null===r&&(Idt(Rdt.set(-.5,.5,0),wdt,a,Pdt,o,i),Ndt.set(0,1),r=t.ray.intersectTriangle(Edt,Adt,Rdt,!1,Odt),null===r))return;const s=t.ray.origin.distanceTo(Odt);s<t.near||s>t.far||e.push({distance:s,point:Odt.clone(),uv:vrt.getUV(Odt,Edt,Rdt,Adt,Tdt,Ndt,zdt,new Qit),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}).prototype.isSprite=!0;const Hdt=new cat,Fdt=new aat,Ldt=new aat,Bdt=new cat,Vdt=new Bat;class jdt extends pst{constructor(t,e){super(t,e),this.type="SkinnedMesh",this.bindMode="attached",this.bindMatrix=new Bat,this.bindMatrixInverse=new Bat}copy(t){return super.copy(t),this.bindMode=t.bindMode,this.bindMatrix.copy(t.bindMatrix),this.bindMatrixInverse.copy(t.bindMatrixInverse),this.skeleton=t.skeleton,this}bind(t,e){this.skeleton=t,void 0===e&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),e=this.matrixWorld),this.bindMatrix.copy(e),this.bindMatrixInverse.copy(e).invert()}pose(){this.skeleton.pose()}normalizeSkinWeights(){const t=new aat,e=this.geometry.attributes.skinWeight;for(let n=0,o=e.count;n<o;n++){t.x=e.getX(n),t.y=e.getY(n),t.z=e.getZ(n),t.w=e.getW(n);const o=1/t.manhattanLength();o!==1/0?t.multiplyScalar(o):t.set(1,0,0,0),e.setXYZW(n,t.x,t.y,t.z,t.w)}}updateMatrixWorld(t){super.updateMatrixWorld(t),"attached"===this.bindMode?this.bindMatrixInverse.copy(this.matrixWorld).invert():"detached"===this.bindMode?this.bindMatrixInverse.copy(this.bindMatrix).invert():console.warn("THREE.SkinnedMesh: Unrecognized bindMode: "+this.bindMode)}boneTransform(t,e){const n=this.skeleton,o=this.geometry;Fdt.fromBufferAttribute(o.attributes.skinIndex,t),Ldt.fromBufferAttribute(o.attributes.skinWeight,t),Hdt.fromBufferAttribute(o.attributes.position,t).applyMatrix4(this.bindMatrix),e.set(0,0,0);for(let t=0;t<4;t++){const o=Ldt.getComponent(t);if(0!==o){const i=Fdt.getComponent(t);Vdt.multiplyMatrices(n.bones[i].matrixWorld,n.boneInverses[i]),e.addScaledVector(Bdt.copy(Hdt).applyMatrix4(Vdt),o)}}return e.applyMatrix4(this.bindMatrixInverse)}}jdt.prototype.isSkinnedMesh=!0,class extends prt{constructor(){super(),this.type="Bone"}}.prototype.isBone=!0,class extends oat{constructor(t=null,e=1,n=1,o,i,a,r,s,l=1003,c=1003,d,p){super(null,a,r,s,l,c,o,i,d,p),this.image={data:t,width:e,height:n},this.magFilter=l,this.minFilter=c,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}.prototype.isDataTexture=!0;const Udt=new Bat,Gdt=new Bat,Wdt=[],Ydt=new pst;(class extends pst{constructor(t,e,n){super(t,e),this.instanceMatrix=new zrt(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}copy(t){return super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,o=this.count;if(Ydt.geometry=this.geometry,Ydt.material=this.material,void 0!==Ydt.material)for(let i=0;i<o;i++){this.getMatrixAt(i,Udt),Gdt.multiplyMatrices(n,Udt),Ydt.matrixWorld=Gdt,Ydt.raycast(t,Wdt);for(let t=0,n=Wdt.length;t<n;t++){const n=Wdt[t];n.instanceId=i,n.object=this,e.push(n)}Wdt.length=0}}setColorAt(t,e){null===this.instanceColor&&(this.instanceColor=new zrt(new Float32Array(3*this.instanceMatrix.count),3)),e.toArray(this.instanceColor.array,3*t)}setMatrixAt(t,e){e.toArray(this.instanceMatrix.array,16*t)}updateMorphTargets(){}dispose(){this.dispatchEvent({type:"dispose"})}}).prototype.isInstancedMesh=!0;class qdt extends Ort{constructor(t){super(),this.type="LineBasicMaterial",this.color=new Rrt(16777215),this.linewidth=1,this.linecap="round",this.linejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.linewidth=t.linewidth,this.linecap=t.linecap,this.linejoin=t.linejoin,this}}qdt.prototype.isLineBasicMaterial=!0;const Zdt=new cat,Xdt=new cat,Kdt=new Bat,Jdt=new Lat,Qdt=new Rat;class $dt extends prt{constructor(t=new qrt,e=new qdt){super(),this.type="Line",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[0];for(let t=1,o=e.count;t<o;t++)Zdt.fromBufferAttribute(e,t-1),Xdt.fromBufferAttribute(e,t),n[t]=n[t-1],n[t]+=Zdt.distanceTo(Xdt);t.setAttribute("lineDistance",new Frt(n,1))}else console.warn("THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else t.isGeometry&&console.error("THREE.Line.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");return this}raycast(t,e){const n=this.geometry,o=this.matrixWorld,i=t.params.Line.threshold,a=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),Qdt.copy(n.boundingSphere),Qdt.applyMatrix4(o),Qdt.radius+=i,!1===t.ray.intersectsSphere(Qdt))return;Kdt.copy(o).invert(),Jdt.copy(t.ray).applyMatrix4(Kdt);const r=i/((this.scale.x+this.scale.y+this.scale.z)/3),s=r*r,l=new cat,c=new cat,d=new cat,p=new cat,m=this.isLineSegments?2:1;if(n.isBufferGeometry){const o=n.index,i=n.attributes.position;if(null!==o)for(let n=Math.max(0,a.start),r=Math.min(o.count,a.start+a.count)-1;n<r;n+=m){const a=o.getX(n),r=o.getX(n+1);if(l.fromBufferAttribute(i,a),c.fromBufferAttribute(i,r),Jdt.distanceSqToSegment(l,c,p,d)>s)continue;p.applyMatrix4(this.matrixWorld);const m=t.ray.origin.distanceTo(p);m<t.near||m>t.far||e.push({distance:m,point:d.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}else for(let n=Math.max(0,a.start),o=Math.min(i.count,a.start+a.count)-1;n<o;n+=m){if(l.fromBufferAttribute(i,n),c.fromBufferAttribute(i,n+1),Jdt.distanceSqToSegment(l,c,p,d)>s)continue;p.applyMatrix4(this.matrixWorld);const o=t.ray.origin.distanceTo(p);o<t.near||o>t.far||e.push({distance:o,point:d.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else n.isGeometry&&console.error("THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.")}}}$dt.prototype.isLine=!0;const tpt=new cat,ept=new cat;class npt extends $dt{constructor(t,e){super(t,e),this.type="LineSegments"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,o=e.count;t<o;t+=2)tpt.fromBufferAttribute(e,t),ept.fromBufferAttribute(e,t+1),n[t]=0===t?0:n[t-1],n[t+1]=n[t]+tpt.distanceTo(ept);t.setAttribute("lineDistance",new Frt(n,1))}else console.warn("THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else t.isGeometry&&console.error("THREE.LineSegments.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");return this}}npt.prototype.isLineSegments=!0,class extends $dt{constructor(t,e){super(t,e),this.type="LineLoop"}}.prototype.isLineLoop=!0;class opt extends Ort{constructor(t){super(),this.type="PointsMaterial",this.color=new Rrt(16777215),this.map=null,this.alphaMap=null,this.size=1,this.sizeAttenuation=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.size=t.size,this.sizeAttenuation=t.sizeAttenuation,this}}opt.prototype.isPointsMaterial=!0;const ipt=new Bat,apt=new Lat,rpt=new Rat,spt=new cat;function lpt(t,e,n,o,i,a,r){const s=apt.distanceSqToPoint(t);if(s<n){const n=new cat;apt.closestPointToPoint(t,n),n.applyMatrix4(o);const l=i.ray.origin.distanceTo(n);if(l<i.near||l>i.far)return;a.push({distance:l,distanceToRay:Math.sqrt(s),point:n,index:e,face:null,object:r})}}(class extends prt{constructor(t=new qrt,e=new opt){super(),this.type="Points",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}raycast(t,e){const n=this.geometry,o=this.matrixWorld,i=t.params.Points.threshold,a=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),rpt.copy(n.boundingSphere),rpt.applyMatrix4(o),rpt.radius+=i,!1===t.ray.intersectsSphere(rpt))return;ipt.copy(o).invert(),apt.copy(t.ray).applyMatrix4(ipt);const r=i/((this.scale.x+this.scale.y+this.scale.z)/3),s=r*r;if(n.isBufferGeometry){const i=n.index,r=n.attributes.position;if(null!==i)for(let n=Math.max(0,a.start),l=Math.min(i.count,a.start+a.count);n<l;n++){const a=i.getX(n);spt.fromBufferAttribute(r,a),lpt(spt,a,s,o,t,e,this)}else for(let n=Math.max(0,a.start),i=Math.min(r.count,a.start+a.count);n<i;n++)spt.fromBufferAttribute(r,n),lpt(spt,n,s,o,t,e,this)}else console.error("THREE.Points.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.")}}}).prototype.isPoints=!0,class extends oat{constructor(t,e,n,o,i,a,r,s,l){super(t,e,n,o,i,a,r,s,l),this.format=void 0!==r?r:Pit,this.minFilter=void 0!==a?a:bit,this.magFilter=void 0!==i?i:bit,this.generateMipmaps=!1;const c=this;"requestVideoFrameCallback"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;0=="requestVideoFrameCallback"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}.prototype.isVideoTexture=!0,class extends oat{constructor(t,e,n,o,i,a,r,s,l,c,d,p){super(null,a,r,s,l,c,o,i,d,p),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}.prototype.isCompressedTexture=!0,class extends oat{constructor(t,e,n,o,i,a,r,s,l){super(t,e,n,o,i,a,r,s,l),this.needsUpdate=!0}}.prototype.isCanvasTexture=!0,class extends oat{constructor(t,e,n,o,i,a,r,s,l,c){if((c=void 0!==c?c:kit)!==kit&&c!==Sit)throw new Error("DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat");void 0===n&&c===kit&&(n=Cit),void 0===n&&c===Sit&&(n=Oit),super(null,o,i,a,r,s,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==r?r:hit,this.minFilter=void 0!==s?s:hit,this.flipY=!1,this.generateMipmaps=!1}}.prototype.isDepthTexture=!0;class cpt extends qrt{constructor(t=1,e=8,n=0,o=2*Math.PI){super(),this.type="CircleGeometry",this.parameters={radius:t,segments:e,thetaStart:n,thetaLength:o},e=Math.max(3,e);const i=[],a=[],r=[],s=[],l=new cat,c=new Qit;a.push(0,0,0),r.push(0,0,1),s.push(.5,.5);for(let i=0,d=3;i<=e;i++,d+=3){const p=n+i/e*o;l.x=t*Math.cos(p),l.y=t*Math.sin(p),a.push(l.x,l.y,l.z),r.push(0,0,1),c.x=(a[d]/t+1)/2,c.y=(a[d+1]/t+1)/2,s.push(c.x,c.y)}for(let t=1;t<=e;t++)i.push(t,t+1,0);this.setIndex(i),this.setAttribute("position",new Frt(a,3)),this.setAttribute("normal",new Frt(r,3)),this.setAttribute("uv",new Frt(s,2))}static fromJSON(t){return new cpt(t.radius,t.segments,t.thetaStart,t.thetaLength)}}new cat,new cat,new cat,new vrt;class dpt{constructor(){this.type="Curve",this.arcLengthDivisions=200}getPoint(){return console.warn("THREE.Curve: .getPoint() not implemented."),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,o=this.getPoint(0),i=0;e.push(0);for(let a=1;a<=t;a++)n=this.getPoint(a/t),i+=n.distanceTo(o),e.push(i),o=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let o=0;const i=n.length;let a;a=e||t*n[i-1];let r,s=0,l=i-1;for(;s<=l;)if(o=Math.floor(s+(l-s)/2),r=n[o]-a,r<0)s=o+1;else{if(!(r>0)){l=o;break}l=o-1}if(o=l,n[o]===a)return o/(i-1);const c=n[o];return(o+(a-c)/(n[o+1]-c))/(i-1)}getTangent(t,e){const n=1e-4;let o=t-n,i=t+n;o<0&&(o=0),i>1&&(i=1);const a=this.getPoint(o),r=this.getPoint(i),s=e||(a.isVector2?new Qit:new cat);return s.copy(r).sub(a).normalize(),s}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new cat,o=[],i=[],a=[],r=new cat,s=new Bat;for(let e=0;e<=t;e++)o[e]=this.getTangentAt(e/t,new cat),o[e].normalize();i[0]=new cat,a[0]=new cat;let l=Number.MAX_VALUE;const c=Math.abs(o[0].x),d=Math.abs(o[0].y),p=Math.abs(o[0].z);c<=l&&(l=c,n.set(1,0,0)),d<=l&&(l=d,n.set(0,1,0)),p<=l&&n.set(0,0,1),r.crossVectors(o[0],n).normalize(),i[0].crossVectors(o[0],r),a[0].crossVectors(o[0],i[0]);for(let e=1;e<=t;e++){if(i[e]=i[e-1].clone(),a[e]=a[e-1].clone(),r.crossVectors(o[e-1],o[e]),r.length()>Number.EPSILON){r.normalize();const t=Math.acos(Zit(o[e-1].dot(o[e]),-1,1));i[e].applyMatrix4(s.makeRotationAxis(r,t))}a[e].crossVectors(o[e],i[e])}if(!0===e){let e=Math.acos(Zit(i[0].dot(i[t]),-1,1));e/=t,o[0].dot(r.crossVectors(i[0],i[t]))>0&&(e=-e);for(let n=1;n<=t;n++)i[n].applyMatrix4(s.makeRotationAxis(o[n],e*n)),a[n].crossVectors(o[n],i[n])}return{tangents:o,normals:i,binormals:a}}clone(){return(new this.constructor).copy(this)}copy(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}toJSON(){const t={metadata:{version:4.5,type:"Curve",generator:"Curve.toJSON"}};return t.arcLengthDivisions=this.arcLengthDivisions,t.type=this.type,t}fromJSON(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}}class ppt extends dpt{constructor(t=0,e=0,n=1,o=1,i=0,a=2*Math.PI,r=!1,s=0){super(),this.type="EllipseCurve",this.aX=t,this.aY=e,this.xRadius=n,this.yRadius=o,this.aStartAngle=i,this.aEndAngle=a,this.aClockwise=r,this.aRotation=s}getPoint(t,e){const n=e||new Qit,o=2*Math.PI;let i=this.aEndAngle-this.aStartAngle;const a=Math.abs(i)<Number.EPSILON;for(;i<0;)i+=o;for(;i>o;)i-=o;i<Number.EPSILON&&(i=a?0:o),!0!==this.aClockwise||a||(i===o?i=-o:i-=o);const r=this.aStartAngle+t*i;let s=this.aX+this.xRadius*Math.cos(r),l=this.aY+this.yRadius*Math.sin(r);if(0!==this.aRotation){const t=Math.cos(this.aRotation),e=Math.sin(this.aRotation),n=s-this.aX,o=l-this.aY;s=n*t-o*e+this.aX,l=n*e+o*t+this.aY}return n.set(s,l)}copy(t){return super.copy(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}toJSON(){const t=super.toJSON();return t.aX=this.aX,t.aY=this.aY,t.xRadius=this.xRadius,t.yRadius=this.yRadius,t.aStartAngle=this.aStartAngle,t.aEndAngle=this.aEndAngle,t.aClockwise=this.aClockwise,t.aRotation=this.aRotation,t}fromJSON(t){return super.fromJSON(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}}ppt.prototype.isEllipseCurve=!0;class mpt extends ppt{constructor(t,e,n,o,i,a){super(t,e,n,n,o,i,a),this.type="ArcCurve"}}function upt(){let t=0,e=0,n=0,o=0;function i(i,a,r,s){t=i,e=r,n=-3*i+3*a-2*r-s,o=2*i-2*a+r+s}return{initCatmullRom:function(t,e,n,o,a){i(e,n,a*(n-t),a*(o-e))},initNonuniformCatmullRom:function(t,e,n,o,a,r,s){let l=(e-t)/a-(n-t)/(a+r)+(n-e)/r,c=(n-e)/r-(o-e)/(r+s)+(o-n)/s;l*=r,c*=r,i(e,n,l,c)},calc:function(i){const a=i*i;return t+e*i+n*a+o*(a*i)}}}mpt.prototype.isArcCurve=!0;const fpt=new cat,gpt=new upt,hpt=new upt,bpt=new upt;class ypt extends dpt{constructor(t=[],e=!1,n="centripetal",o=.5){super(),this.type="CatmullRomCurve3",this.points=t,this.closed=e,this.curveType=n,this.tension=o}getPoint(t,e=new cat){const n=e,o=this.points,i=o.length,a=(i-(this.closed?0:1))*t;let r,s,l=Math.floor(a),c=a-l;this.closed?l+=l>0?0:(Math.floor(Math.abs(l)/i)+1)*i:0===c&&l===i-1&&(l=i-2,c=1),this.closed||l>0?r=o[(l-1)%i]:(fpt.subVectors(o[0],o[1]).add(o[0]),r=fpt);const d=o[l%i],p=o[(l+1)%i];if(this.closed||l+2<i?s=o[(l+2)%i]:(fpt.subVectors(o[i-1],o[i-2]).add(o[i-1]),s=fpt),"centripetal"===this.curveType||"chordal"===this.curveType){const t="chordal"===this.curveType?.5:.25;let e=Math.pow(r.distanceToSquared(d),t),n=Math.pow(d.distanceToSquared(p),t),o=Math.pow(p.distanceToSquared(s),t);n<1e-4&&(n=1),e<1e-4&&(e=n),o<1e-4&&(o=n),gpt.initNonuniformCatmullRom(r.x,d.x,p.x,s.x,e,n,o),hpt.initNonuniformCatmullRom(r.y,d.y,p.y,s.y,e,n,o),bpt.initNonuniformCatmullRom(r.z,d.z,p.z,s.z,e,n,o)}else"catmullrom"===this.curveType&&(gpt.initCatmullRom(r.x,d.x,p.x,s.x,this.tension),hpt.initCatmullRom(r.y,d.y,p.y,s.y,this.tension),bpt.initCatmullRom(r.z,d.z,p.z,s.z,this.tension));return n.set(gpt.calc(c),hpt.calc(c),bpt.calc(c)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++)this.points.push(t.points[e].clone());return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++)t.points.push(this.points[e].toArray());return t.closed=this.closed,t.curveType=this.curveType,t.tension=this.tension,t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new cat).fromArray(n))}return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}}function _pt(t,e,n,o,i){const a=.5*(o-e),r=.5*(i-n),s=t*t;return(2*n-2*o+a+r)*(t*s)+(-3*n+3*o-2*a-r)*s+a*t+n}function Cpt(t,e,n,o){return(function i(t,e){const n=1-t;return n*n*e})(t,e)+(function a(t,e){return 2*(1-t)*t*e})(t,n)+(function r(t,e){return t*t*e})(t,o)}function Mpt(t,e,n,o,i){return(function a(t,e){const n=1-t;return n*n*n*e})(t,e)+(function r(t,e){const n=1-t;return 3*n*n*t*e})(t,n)+(function s(t,e){return 3*(1-t)*t*t*e})(t,o)+(function l(t,e){return t*t*t*e})(t,i)}ypt.prototype.isCatmullRomCurve3=!0;class vpt extends dpt{constructor(t=new Qit,e=new Qit,n=new Qit,o=new Qit){super(),this.type="CubicBezierCurve",this.v0=t,this.v1=e,this.v2=n,this.v3=o}getPoint(t,e=new Qit){const n=e,o=this.v0,i=this.v1,a=this.v2,r=this.v3;return n.set(Mpt(t,o.x,i.x,a.x,r.x),Mpt(t,o.y,i.y,a.y,r.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}vpt.prototype.isCubicBezierCurve=!0;class xpt extends dpt{constructor(t=new cat,e=new cat,n=new cat,o=new cat){super(),this.type="CubicBezierCurve3",this.v0=t,this.v1=e,this.v2=n,this.v3=o}getPoint(t,e=new cat){const n=e,o=this.v0,i=this.v1,a=this.v2,r=this.v3;return n.set(Mpt(t,o.x,i.x,a.x,r.x),Mpt(t,o.y,i.y,a.y,r.y),Mpt(t,o.z,i.z,a.z,r.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}xpt.prototype.isCubicBezierCurve3=!0;class Opt extends dpt{constructor(t=new Qit,e=new Qit){super(),this.type="LineCurve",this.v1=t,this.v2=e}getPoint(t,e=new Qit){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}getTangent(t,e){const n=e||new Qit;return n.copy(this.v2).sub(this.v1).normalize(),n}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}Opt.prototype.isLineCurve=!0;class Ppt extends dpt{constructor(t=new Qit,e=new Qit,n=new Qit){super(),this.type="QuadraticBezierCurve",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new Qit){const n=e,o=this.v0,i=this.v1,a=this.v2;return n.set(Cpt(t,o.x,i.x,a.x),Cpt(t,o.y,i.y,a.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}Ppt.prototype.isQuadraticBezierCurve=!0;class wpt extends dpt{constructor(t=new cat,e=new cat,n=new cat){super(),this.type="QuadraticBezierCurve3",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new cat){const n=e,o=this.v0,i=this.v1,a=this.v2;return n.set(Cpt(t,o.x,i.x,a.x),Cpt(t,o.y,i.y,a.y),Cpt(t,o.z,i.z,a.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}wpt.prototype.isQuadraticBezierCurve3=!0;class kpt extends dpt{constructor(t=[]){super(),this.type="SplineCurve",this.points=t}getPoint(t,e=new Qit){const n=e,o=this.points,i=(o.length-1)*t,a=Math.floor(i),r=i-a,s=o[0===a?a:a-1],l=o[a],c=o[a>o.length-2?o.length-1:a+1],d=o[a>o.length-3?o.length-1:a+2];return n.set(_pt(r,s.x,l.x,c.x,d.x),_pt(r,s.y,l.y,c.y,d.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++)this.points.push(t.points[e].clone());return this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++)t.points.push(this.points[e].toArray());return t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new Qit).fromArray(n))}return this}}kpt.prototype.isSplineCurve=!0;var Spt=Object.freeze({__proto__:null,ArcCurve:mpt,CatmullRomCurve3:ypt,CubicBezierCurve:vpt,CubicBezierCurve3:xpt,EllipseCurve:ppt,LineCurve:Opt,LineCurve3:class extends dpt{constructor(t=new cat,e=new cat){super(),this.type="LineCurve3",this.isLineCurve3=!0,this.v1=t,this.v2=e}getPoint(t,e=new cat){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}},QuadraticBezierCurve:Ppt,QuadraticBezierCurve3:wpt,SplineCurve:kpt});function Dpt(t,e,n,o,i){let a,r;if(i===(function s(t,e,n,o){let i=0;for(let a=e,r=n-o;a<n;a+=o)i+=(t[r]-t[a])*(t[a+1]+t[r+1]),r=a;return i})(t,e,n,o)>0)for(a=e;a<n;a+=o)r=Kpt(a,t[a],t[a+1],r);else for(a=n-o;a>=e;a-=o)r=Kpt(a,t[a],t[a+1],r);return r&&Gpt(r,r.next)&&(Jpt(r),r=r.next),r}function Ept(t,e){if(!t)return t;e||(e=t);let n,o=t;do{if(n=!1,o.steiner||!Gpt(o,o.next)&&0!==Upt(o.prev,o,o.next))o=o.next;else{if(Jpt(o),o=e=o.prev,o===o.next)break;n=!0}}while(n||o!==e);return e}function Rpt(t,e,n,o,i,a,r){if(!t)return;!r&&a&&(function s(t,e,n,o){let i=t;do{null===i.z&&(i.z=Lpt(i.x,i.y,e,n,o)),i.prevZ=i.prev,i.nextZ=i.next,i=i.next}while(i!==t);i.prevZ.nextZ=null,i.prevZ=null,(function a(t){let e,n,o,i,a,r,s,l,c=1;do{for(n=t,t=null,a=null,r=0;n;){for(r++,o=n,s=0,e=0;e<c&&(s++,o=o.nextZ,o);e++);for(l=c;s>0||l>0&&o;)0!==s&&(0===l||!o||n.z<=o.z)?(i=n,n=n.nextZ,s--):(i=o,o=o.nextZ,l--),a?a.nextZ=i:t=i,i.prevZ=a,a=i;n=o}a.nextZ=null,c*=2}while(r>1)})(i)})(t,o,i,a);let l,c,d=t;for(;t.prev!==t.next;)if(l=t.prev,c=t.next,a?Tpt(t,o,i,a):Apt(t))e.push(l.i/n),e.push(t.i/n),e.push(c.i/n),Jpt(t),t=c.next,d=c.next;else if((t=c)===d){r?1===r?Rpt(t=Npt(Ept(t),e,n),e,n,o,i,a,2):2===r&&zpt(t,e,n,o,i,a):Rpt(Ept(t),e,n,o,i,a,1);break}}function Apt(t){const e=t.prev,n=t,o=t.next;if(Upt(e,n,o)>=0)return!1;let i=t.next.next;for(;i!==t.prev;){if(Vpt(e.x,e.y,n.x,n.y,o.x,o.y,i.x,i.y)&&Upt(i.prev,i,i.next)>=0)return!1;i=i.next}return!0}function Tpt(t,e,n,o){const i=t.prev,a=t,r=t.next;if(Upt(i,a,r)>=0)return!1;const s=i.x>a.x?i.x>r.x?i.x:r.x:a.x>r.x?a.x:r.x,l=i.y>a.y?i.y>r.y?i.y:r.y:a.y>r.y?a.y:r.y,c=Lpt(i.x<a.x?i.x<r.x?i.x:r.x:a.x<r.x?a.x:r.x,i.y<a.y?i.y<r.y?i.y:r.y:a.y<r.y?a.y:r.y,e,n,o),d=Lpt(s,l,e,n,o);let p=t.prevZ,m=t.nextZ;for(;p&&p.z>=c&&m&&m.z<=d;){if(p!==t.prev&&p!==t.next&&Vpt(i.x,i.y,a.x,a.y,r.x,r.y,p.x,p.y)&&Upt(p.prev,p,p.next)>=0)return!1;if(p=p.prevZ,m!==t.prev&&m!==t.next&&Vpt(i.x,i.y,a.x,a.y,r.x,r.y,m.x,m.y)&&Upt(m.prev,m,m.next)>=0)return!1;m=m.nextZ}for(;p&&p.z>=c;){if(p!==t.prev&&p!==t.next&&Vpt(i.x,i.y,a.x,a.y,r.x,r.y,p.x,p.y)&&Upt(p.prev,p,p.next)>=0)return!1;p=p.prevZ}for(;m&&m.z<=d;){if(m!==t.prev&&m!==t.next&&Vpt(i.x,i.y,a.x,a.y,r.x,r.y,m.x,m.y)&&Upt(m.prev,m,m.next)>=0)return!1;m=m.nextZ}return!0}function Npt(t,e,n){let o=t;do{const i=o.prev,a=o.next.next;!Gpt(i,a)&&Wpt(i,o,o.next,a)&&Zpt(i,a)&&Zpt(a,i)&&(e.push(i.i/n),e.push(o.i/n),e.push(a.i/n),Jpt(o),Jpt(o.next),o=t=a),o=o.next}while(o!==t);return Ept(o)}function zpt(t,e,n,o,i,a){let r=t;do{let t=r.next.next;for(;t!==r.prev;){if(r.i!==t.i&&jpt(r,t)){let s=Xpt(r,t);return r=Ept(r,r.next),s=Ept(s,s.next),Rpt(r,e,n,o,i,a),void Rpt(s,e,n,o,i,a)}t=t.next}r=r.next}while(r!==t)}function Ipt(t,e){return t.x-e.x}function Hpt(t,e){if(e=(function n(t,e){let n=e;const o=t.x,i=t.y;let a,r=-1/0;do{if(i<=n.y&&i>=n.next.y&&n.next.y!==n.y){const t=n.x+(i-n.y)*(n.next.x-n.x)/(n.next.y-n.y);if(t<=o&&t>r){if(r=t,t===o){if(i===n.y)return n;if(i===n.next.y)return n.next}a=n.x<n.next.x?n:n.next}}n=n.next}while(n!==e);if(!a)return null;if(o===r)return a;const s=a,l=a.x,c=a.y;let d,p=1/0;n=a;do{o>=n.x&&n.x>=l&&o!==n.x&&Vpt(i<c?o:r,i,l,c,i<c?r:o,i,n.x,n.y)&&(d=Math.abs(i-n.y)/(o-n.x),Zpt(n,t)&&(d<p||d===p&&(n.x>a.x||n.x===a.x&&Fpt(a,n)))&&(a=n,p=d)),n=n.next}while(n!==s);return a})(t,e)){const n=Xpt(e,t);Ept(e,e.next),Ept(n,n.next)}}function Fpt(t,e){return Upt(t.prev,t,e.prev)<0&&Upt(e.next,t,t.next)<0}function Lpt(t,e,n,o,i){return(t=1431655765&((t=858993459&((t=252645135&((t=16711935&((t=32767*(t-n)*i)|t<<8))|t<<4))|t<<2))|t<<1))|(e=1431655765&((e=858993459&((e=252645135&((e=16711935&((e=32767*(e-o)*i)|e<<8))|e<<4))|e<<2))|e<<1))<<1}function Bpt(t){let e=t,n=t;do{(e.x<n.x||e.x===n.x&&e.y<n.y)&&(n=e),e=e.next}while(e!==t);return n}function Vpt(t,e,n,o,i,a,r,s){return(i-r)*(e-s)-(t-r)*(a-s)>=0&&(t-r)*(o-s)-(n-r)*(e-s)>=0&&(n-r)*(a-s)-(i-r)*(o-s)>=0}function jpt(t,e){return t.next.i!==e.i&&t.prev.i!==e.i&&!(function n(t,e){let n=t;do{if(n.i!==t.i&&n.next.i!==t.i&&n.i!==e.i&&n.next.i!==e.i&&Wpt(n,n.next,t,e))return!0;n=n.next}while(n!==t);return!1})(t,e)&&(Zpt(t,e)&&Zpt(e,t)&&(function o(t,e){let n=t,o=!1;const i=(t.x+e.x)/2,a=(t.y+e.y)/2;do{n.y>a!=n.next.y>a&&n.next.y!==n.y&&i<(n.next.x-n.x)*(a-n.y)/(n.next.y-n.y)+n.x&&(o=!o),n=n.next}while(n!==t);return o})(t,e)&&(Upt(t.prev,t,e.prev)||Upt(t,e.prev,e))||Gpt(t,e)&&Upt(t.prev,t,t.next)>0&&Upt(e.prev,e,e.next)>0)}function Upt(t,e,n){return(e.y-t.y)*(n.x-e.x)-(e.x-t.x)*(n.y-e.y)}function Gpt(t,e){return t.x===e.x&&t.y===e.y}function Wpt(t,e,n,o){const i=qpt(Upt(t,e,n)),a=qpt(Upt(t,e,o)),r=qpt(Upt(n,o,t)),s=qpt(Upt(n,o,e));return i!==a&&r!==s||!(0!==i||!Ypt(t,n,e))||!(0!==a||!Ypt(t,o,e))||!(0!==r||!Ypt(n,t,o))||!(0!==s||!Ypt(n,e,o))}function Ypt(t,e,n){return e.x<=Math.max(t.x,n.x)&&e.x>=Math.min(t.x,n.x)&&e.y<=Math.max(t.y,n.y)&&e.y>=Math.min(t.y,n.y)}function qpt(t){return t>0?1:t<0?-1:0}function Zpt(t,e){return Upt(t.prev,t,t.next)<0?Upt(t,e,t.next)>=0&&Upt(t,t.prev,e)>=0:Upt(t,e,t.prev)<0||Upt(t,t.next,e)<0}function Xpt(t,e){const n=new Qpt(t.i,t.x,t.y),o=new Qpt(e.i,e.x,e.y),i=t.next,a=e.prev;return t.next=e,e.prev=t,n.next=i,i.prev=n,o.next=n,n.prev=o,a.next=o,o.prev=a,o}function Kpt(t,e,n,o){const i=new Qpt(t,e,n);return o?(i.next=o.next,i.prev=o,o.next.prev=i,o.next=i):(i.prev=i,i.next=i),i}function Jpt(t){t.next.prev=t.prev,t.prev.next=t.next,t.prevZ&&(t.prevZ.nextZ=t.nextZ),t.nextZ&&(t.nextZ.prevZ=t.prevZ)}function Qpt(t,e,n){this.i=t,this.x=e,this.y=n,this.prev=null,this.next=null,this.z=null,this.prevZ=null,this.nextZ=null,this.steiner=!1}class $pt{static area(t){const e=t.length;let n=0;for(let o=e-1,i=0;i<e;o=i++)n+=t[o].x*t[i].y-t[i].x*t[o].y;return.5*n}static isClockWise(t){return $pt.area(t)<0}static triangulateShape(t,e){const n=[],o=[],i=[];tmt(t),emt(n,t);let a=t.length;e.forEach(tmt);for(let t=0;t<e.length;t++)o.push(a),a+=e[t].length,emt(n,e[t]);const r=(function(t,e,n=2){const o=e&&e.length,i=o?e[0]*n:t.length;let a=Dpt(t,0,i,n,!0);const r=[];if(!a||a.next===a.prev)return r;let s,l,c,d,p,m,u;if(o&&(a=(function f(t,e,n,o){const i=[];let a,r,s,l,c;for(a=0,r=e.length;a<r;a++)s=e[a]*o,l=a<r-1?e[a+1]*o:t.length,c=Dpt(t,s,l,o,!1),c===c.next&&(c.steiner=!0),i.push(Bpt(c));for(i.sort(Ipt),a=0;a<i.length;a++)Hpt(i[a],n),n=Ept(n,n.next);return n})(t,e,a,n)),t.length>80*n){s=c=t[0],l=d=t[1];for(let e=n;e<i;e+=n)p=t[e],m=t[e+1],p<s&&(s=p),m<l&&(l=m),p>c&&(c=p),m>d&&(d=m);u=Math.max(c-s,d-l),u=0!==u?1/u:0}return Rpt(a,r,n,s,l,u),r})(n,o);for(let t=0;t<r.length;t+=3)i.push(r.slice(t,t+3));return i}}function tmt(t){const e=t.length;e>2&&t[e-1].equals(t[0])&&t.pop()}function emt(t,e){for(let n=0;n<e.length;n++)t.push(e[n].x),t.push(e[n].y)}class nmt extends qrt{constructor(t,e){super(),this.type="ExtrudeGeometry",this.parameters={shapes:t,options:e},t=Array.isArray(t)?t:[t];const n=this,o=[],i=[];for(let e=0,n=t.length;e<n;e++)a(t[e]);function a(t){const a=[],r=void 0!==e.curveSegments?e.curveSegments:12,s=void 0!==e.steps?e.steps:1;let l=void 0!==e.depth?e.depth:100,c=void 0===e.bevelEnabled||e.bevelEnabled,d=void 0!==e.bevelThickness?e.bevelThickness:6,p=void 0!==e.bevelSize?e.bevelSize:d-2,m=void 0!==e.bevelOffset?e.bevelOffset:0,u=void 0!==e.bevelSegments?e.bevelSegments:3;const f=e.extrudePath,g=void 0!==e.UVGenerator?e.UVGenerator:omt;void 0!==e.amount&&(console.warn("THREE.ExtrudeBufferGeometry: amount has been renamed to depth."),l=e.amount);let h,b,y,_,C,M=!1;f&&(h=f.getSpacedPoints(s),M=!0,c=!1,b=f.computeFrenetFrames(s,!1),y=new cat,_=new cat,C=new cat),c||(u=0,d=0,p=0,m=0);const v=t.extractPoints(r);let x=v.shape;const O=v.holes;if(!$pt.isClockWise(x)){x=x.reverse();for(let t=0,e=O.length;t<e;t++){const e=O[t];$pt.isClockWise(e)&&(O[t]=e.reverse())}}const P=$pt.triangulateShape(x,O),w=x;for(let t=0,e=O.length;t<e;t++)x=x.concat(O[t]);function k(t,e,n){return e||console.error("THREE.ExtrudeGeometry: vec does not exist"),e.clone().multiplyScalar(n).add(t)}const S=x.length,D=P.length;function E(t,e,n){let o,i,a;const r=t.x-e.x,s=t.y-e.y,l=n.x-t.x,c=n.y-t.y,d=r*r+s*s;if(Math.abs(r*c-s*l)>Number.EPSILON){const p=Math.sqrt(d),m=Math.sqrt(l*l+c*c),u=e.x-s/p,f=e.y+r/p,g=((n.x-c/m-u)*c-(n.y+l/m-f)*l)/(r*c-s*l);o=u+r*g-t.x,i=f+s*g-t.y;const h=o*o+i*i;if(h<=2)return new Qit(o,i);a=Math.sqrt(h/2)}else{let t=!1;r>Number.EPSILON?l>Number.EPSILON&&(t=!0):r<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(s)===Math.sign(c)&&(t=!0),t?(o=-s,i=r,a=Math.sqrt(d)):(o=r,i=s,a=Math.sqrt(d/2))}return new Qit(o/a,i/a)}const R=[];for(let t=0,e=w.length,n=e-1,o=t+1;t<e;t++,n++,o++)n===e&&(n=0),o===e&&(o=0),R[t]=E(w[t],w[n],w[o]);const A=[];let T,N=R.concat();for(let t=0,e=O.length;t<e;t++){const e=O[t];T=[];for(let t=0,n=e.length,o=n-1,i=t+1;t<n;t++,o++,i++)o===n&&(o=0),i===n&&(i=0),T[t]=E(e[t],e[o],e[i]);A.push(T),N=N.concat(T)}for(let t=0;t<u;t++){const e=t/u,n=d*Math.cos(e*Math.PI/2),o=p*Math.sin(e*Math.PI/2)+m;for(let t=0,e=w.length;t<e;t++){const e=k(w[t],R[t],o);H(e.x,e.y,-n)}for(let t=0,e=O.length;t<e;t++){const e=O[t];T=A[t];for(let t=0,i=e.length;t<i;t++){const i=k(e[t],T[t],o);H(i.x,i.y,-n)}}}const z=p+m;for(let t=0;t<S;t++){const e=c?k(x[t],N[t],z):x[t];M?(_.copy(b.normals[0]).multiplyScalar(e.x),y.copy(b.binormals[0]).multiplyScalar(e.y),C.copy(h[0]).add(_).add(y),H(C.x,C.y,C.z)):H(e.x,e.y,0)}for(let t=1;t<=s;t++)for(let e=0;e<S;e++){const n=c?k(x[e],N[e],z):x[e];M?(_.copy(b.normals[t]).multiplyScalar(n.x),y.copy(b.binormals[t]).multiplyScalar(n.y),C.copy(h[t]).add(_).add(y),H(C.x,C.y,C.z)):H(n.x,n.y,l/s*t)}for(let t=u-1;t>=0;t--){const e=t/u,n=d*Math.cos(e*Math.PI/2),o=p*Math.sin(e*Math.PI/2)+m;for(let t=0,e=w.length;t<e;t++){const e=k(w[t],R[t],o);H(e.x,e.y,l+n)}for(let t=0,e=O.length;t<e;t++){const e=O[t];T=A[t];for(let t=0,i=e.length;t<i;t++){const i=k(e[t],T[t],o);M?H(i.x,i.y+h[s-1].y,h[s-1].x+n):H(i.x,i.y,l+n)}}}function I(t,e){let n=t.length;for(;--n>=0;){const o=n;let i=n-1;i<0&&(i=t.length-1);for(let t=0,n=s+2*u;t<n;t++){const n=S*t,a=S*(t+1);L(e+o+n,e+i+n,e+i+a,e+o+a)}}}function H(t,e,n){a.push(t),a.push(e),a.push(n)}function F(t,e,i){B(t),B(e),B(i);const a=o.length/3,r=g.generateTopUV(n,o,a-3,a-2,a-1);V(r[0]),V(r[1]),V(r[2])}function L(t,e,i,a){B(t),B(e),B(a),B(e),B(i),B(a);const r=o.length/3,s=g.generateSideWallUV(n,o,r-6,r-3,r-2,r-1);V(s[0]),V(s[1]),V(s[3]),V(s[1]),V(s[2]),V(s[3])}function B(t){o.push(a[3*t+0]),o.push(a[3*t+1]),o.push(a[3*t+2])}function V(t){i.push(t.x),i.push(t.y)}!(function j(){const t=o.length/3;if(c){let t=0,e=S*t;for(let t=0;t<D;t++){const n=P[t];F(n[2]+e,n[1]+e,n[0]+e)}t=s+2*u,e=S*t;for(let t=0;t<D;t++){const n=P[t];F(n[0]+e,n[1]+e,n[2]+e)}}else{for(let t=0;t<D;t++){const e=P[t];F(e[2],e[1],e[0])}for(let t=0;t<D;t++){const e=P[t];F(e[0]+S*s,e[1]+S*s,e[2]+S*s)}}n.addGroup(t,o.length/3-t,0)})(),(function U(){const t=o.length/3;let e=0;I(w,e),e+=w.length;for(let t=0,n=O.length;t<n;t++){const n=O[t];I(n,e),e+=n.length}n.addGroup(t,o.length/3-t,1)})()}this.setAttribute("position",new Frt(o,3)),this.setAttribute("uv",new Frt(i,2)),this.computeVertexNormals()}toJSON(){const t=super.toJSON();return(function e(t,n,o){if(o.shapes=[],Array.isArray(t))for(let e=0,n=t.length;e<n;e++)o.shapes.push(t[e].uuid);else o.shapes.push(t.uuid);return void 0!==n.extrudePath&&(o.options.extrudePath=n.extrudePath.toJSON()),o})(this.parameters.shapes,this.parameters.options,t)}static fromJSON(t,e){const n=[];for(let o=0,i=t.shapes.length;o<i;o++)n.push(e[t.shapes[o]]);const o=t.options.extrudePath;return void 0!==o&&(t.options.extrudePath=(new Spt[o.type]).fromJSON(o)),new nmt(n,t.options)}}const omt={generateTopUV:function(t,e,n,o,i){const a=e[3*o],r=e[3*o+1],s=e[3*i],l=e[3*i+1];return[new Qit(e[3*n],e[3*n+1]),new Qit(a,r),new Qit(s,l)]},generateSideWallUV:function(t,e,n,o,i,a){const r=e[3*n],s=e[3*n+1],l=e[3*n+2],c=e[3*o],d=e[3*o+1],p=e[3*o+2],m=e[3*i],u=e[3*i+1],f=e[3*i+2],g=e[3*a],h=e[3*a+1],b=e[3*a+2];return Math.abs(s-d)<Math.abs(r-c)?[new Qit(r,1-l),new Qit(c,1-p),new Qit(m,1-f),new Qit(g,1-b)]:[new Qit(s,1-l),new Qit(d,1-p),new Qit(u,1-f),new Qit(h,1-b)]}};class imt extends qrt{constructor(t,e=12){super(),this.type="ShapeGeometry",this.parameters={shapes:t,curveSegments:e};const n=[],o=[],i=[],a=[];let r=0,s=0;if(!1===Array.isArray(t))l(t);else for(let e=0;e<t.length;e++)l(t[e]),this.addGroup(r,s,e),r+=s,s=0;function l(t){const r=o.length/3,l=t.extractPoints(e);let c=l.shape;const d=l.holes;!1===$pt.isClockWise(c)&&(c=c.reverse());for(let t=0,e=d.length;t<e;t++){const e=d[t];!0===$pt.isClockWise(e)&&(d[t]=e.reverse())}const p=$pt.triangulateShape(c,d);for(let t=0,e=d.length;t<e;t++)c=c.concat(d[t]);for(let t=0,e=c.length;t<e;t++){const e=c[t];o.push(e.x,e.y,0),i.push(0,0,1),a.push(e.x,e.y)}for(let t=0,e=p.length;t<e;t++){const e=p[t];n.push(e[0]+r,e[1]+r,e[2]+r),s+=3}}this.setIndex(n),this.setAttribute("position",new Frt(o,3)),this.setAttribute("normal",new Frt(i,3)),this.setAttribute("uv",new Frt(a,2))}toJSON(){const t=super.toJSON();return(function e(t,n){if(n.shapes=[],Array.isArray(t))for(let e=0,o=t.length;e<o;e++)n.shapes.push(t[e].uuid);else n.shapes.push(t.uuid);return n})(this.parameters.shapes,t)}static fromJSON(t,e){const n=[];for(let o=0,i=t.shapes.length;o<i;o++)n.push(e[t.shapes[o]]);return new imt(n,t.curveSegments)}}(class extends Ort{constructor(t){super(),this.type="ShadowMaterial",this.color=new Rrt(0),this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this}}).prototype.isShadowMaterial=!0;class amt extends Ort{constructor(t){super(),this.defines={STANDARD:""},this.type="MeshStandardMaterial",this.color=new Rrt(16777215),this.roughness=1,this.metalness=0,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Rrt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new Qit(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.roughnessMap=null,this.metalnessMap=null,this.alphaMap=null,this.envMap=null,this.envMapIntensity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:""},this.color.copy(t.color),this.roughness=t.roughness,this.metalness=t.metalness,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.roughnessMap=t.roughnessMap,this.metalnessMap=t.metalnessMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.envMapIntensity=t.envMapIntensity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}amt.prototype.isMeshStandardMaterial=!0,class extends amt{constructor(t){super(),this.defines={STANDARD:"",PHYSICAL:""},this.type="MeshPhysicalMaterial",this.clearcoat=0,this.clearcoatMap=null,this.clearcoatRoughness=0,this.clearcoatRoughnessMap=null,this.clearcoatNormalScale=new Qit(1,1),this.clearcoatNormalMap=null,this.reflectivity=.5,Object.defineProperty(this,"ior",{get:function(){return(1+.4*this.reflectivity)/(1-.4*this.reflectivity)},set:function(t){this.reflectivity=Zit(2.5*(t-1)/(t+1),0,1)}}),this.sheen=null,this.transmission=0,this.transmissionMap=null,this.thickness=.01,this.thicknessMap=null,this.attenuationDistance=0,this.attenuationTint=new Rrt(1,1,1),this.specularIntensity=1,this.specularIntensityMap=null,this.specularTint=new Rrt(1,1,1),this.specularTintMap=null,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:"",PHYSICAL:""},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.reflectivity=t.reflectivity,this.sheen=t.sheen?(this.sheen||new Rrt).copy(t.sheen):null,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}.prototype.isMeshPhysicalMaterial=!0,class extends Ort{constructor(t){super(),this.type="MeshPhongMaterial",this.color=new Rrt(16777215),this.specular=new Rrt(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Rrt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new Qit(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.specular.copy(t.specular),this.shininess=t.shininess,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}.prototype.isMeshPhongMaterial=!0,class extends Ort{constructor(t){super(),this.defines={TOON:""},this.type="MeshToonMaterial",this.color=new Rrt(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Rrt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new Qit(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.gradientMap=t.gradientMap,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}.prototype.isMeshToonMaterial=!0,class extends Ort{constructor(t){super(),this.type="MeshNormalMaterial",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new Qit(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.flatShading=t.flatShading,this}}.prototype.isMeshNormalMaterial=!0,class extends Ort{constructor(t){super(),this.type="MeshLambertMaterial",this.color=new Rrt(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Rrt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}.prototype.isMeshLambertMaterial=!0,class extends Ort{constructor(t){super(),this.defines={MATCAP:""},this.type="MeshMatcapMaterial",this.color=new Rrt(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new Qit(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={MATCAP:""},this.color.copy(t.color),this.matcap=t.matcap,this.map=t.map,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.flatShading=t.flatShading,this}}.prototype.isMeshMatcapMaterial=!0,class extends qdt{constructor(t){super(),this.type="LineDashedMaterial",this.scale=1,this.dashSize=3,this.gapSize=1,this.setValues(t)}copy(t){return super.copy(t),this.scale=t.scale,this.dashSize=t.dashSize,this.gapSize=t.gapSize,this}}.prototype.isLineDashedMaterial=!0;const rmt={arraySlice:function(t,e,n){return rmt.isTypedArray(t)?new t.constructor(t.subarray(e,void 0!==n?n:t.length)):t.slice(e,n)},convertArray:function(t,e,n){return!t||!n&&t.constructor===e?t:"number"==typeof e.BYTES_PER_ELEMENT?new e(t):Array.prototype.slice.call(t)},isTypedArray:function(t){return ArrayBuffer.isView(t)&&!(t instanceof DataView)},getKeyframeOrder:function(t){const e=t.length,n=new Array(e);for(let t=0;t!==e;++t)n[t]=t;return n.sort((function o(e,n){return t[e]-t[n]})),n},sortedArray:function(t,e,n){const o=t.length,i=new t.constructor(o);for(let a=0,r=0;r!==o;++a){const o=n[a]*e;for(let n=0;n!==e;++n)i[r++]=t[o+n]}return i},flattenJSON:function(t,e,n,o){let i=1,a=t[0];for(;void 0!==a&&void 0===a[o];)a=t[i++];if(void 0===a)return;let r=a[o];if(void 0!==r)if(Array.isArray(r))do{r=a[o],void 0!==r&&(e.push(a.time),n.push.apply(n,r)),a=t[i++]}while(void 0!==a);else if(void 0!==r.toArray)do{r=a[o],void 0!==r&&(e.push(a.time),r.toArray(n,n.length)),a=t[i++]}while(void 0!==a);else do{r=a[o],void 0!==r&&(e.push(a.time),n.push(r)),a=t[i++]}while(void 0!==a)},subclip:function(t,e,n,o,i=30){const a=t.clone();a.name=e;const r=[];for(let t=0;t<a.tracks.length;++t){const e=a.tracks[t],s=e.getValueSize(),l=[],c=[];for(let t=0;t<e.times.length;++t){const a=e.times[t]*i;if(!(a<n||a>=o)){l.push(e.times[t]);for(let n=0;n<s;++n)c.push(e.values[t*s+n])}}0!==l.length&&(e.times=rmt.convertArray(l,e.times.constructor),e.values=rmt.convertArray(c,e.values.constructor),r.push(e))}a.tracks=r;let s=1/0;for(let t=0;t<a.tracks.length;++t)s>a.tracks[t].times[0]&&(s=a.tracks[t].times[0]);for(let t=0;t<a.tracks.length;++t)a.tracks[t].shift(-1*s);return a.resetDuration(),a},makeClipAdditive:function(t,e=0,n=t,o=30){o<=0&&(o=30);const i=n.tracks.length,a=e/o;for(let e=0;e<i;++e){const o=n.tracks[e],i=o.ValueTypeName;if("bool"===i||"string"===i)continue;const r=t.tracks.find((function(t){return t.name===o.name&&t.ValueTypeName===i}));if(void 0===r)continue;let s=0;const l=o.getValueSize();o.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(s=l/3);let c=0;const d=r.getValueSize();r.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(c=d/3);const p=o.times.length-1;let m;if(a<=o.times[0])m=rmt.arraySlice(o.values,s,l-s);else if(a>=o.times[p]){const t=p*l+s;m=rmt.arraySlice(o.values,t,t+l-s)}else{const t=o.createInterpolant(),e=s,n=l-s;t.evaluate(a),m=rmt.arraySlice(t.resultBuffer,e,n)}"quaternion"===i&&(new lat).fromArray(m).normalize().conjugate().toArray(m);const u=r.times.length;for(let t=0;t<u;++t){const e=t*d+c;if("quaternion"===i)lat.multiplyQuaternionsFlat(r.values,e,m,0,r.values,e);else{const t=d-2*c;for(let n=0;n<t;++n)r.values[e+n]-=m[n]}}}return t.blendMode=2501,t}};class smt{constructor(t,e,n,o){this.parameterPositions=t,this._cachedIndex=0,this.resultBuffer=void 0!==o?o:new e.constructor(n),this.sampleValues=e,this.valueSize=n,this.settings=null,this.DefaultSettings_={}}evaluate(t){const e=this.parameterPositions;let n=this._cachedIndex,o=e[n],i=e[n-1];t:{e:{let a;n:{o:if(!(t<o)){for(let a=n+2;;){if(void 0===o){if(t<i)break o;return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,t,i)}if(n===a)break;if(i=o,o=e[++n],t<o)break e}a=e.length;break n}if(t>=i)break t;{const r=e[1];t<r&&(n=2,i=r);for(let a=n-2;;){if(void 0===i)return this._cachedIndex=0,this.beforeStart_(0,t,o);if(n===a)break;if(o=i,i=e[--n-1],t>=i)break e}a=n,n=0}}for(;n<a;){const o=n+a>>>1;t<e[o]?a=o:n=o+1}if(o=e[n],i=e[n-1],void 0===i)return this._cachedIndex=0,this.beforeStart_(0,t,o);if(void 0===o)return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,i,t)}this._cachedIndex=n,this.intervalChanged_(n,i,o)}return this.interpolate_(n,i,t,o)}getSettings_(){return this.settings||this.DefaultSettings_}copySampleValue_(t){const e=this.resultBuffer,n=this.sampleValues,o=this.valueSize,i=t*o;for(let t=0;t!==o;++t)e[t]=n[i+t];return e}interpolate_(){throw new Error("call to abstract method")}intervalChanged_(){}}smt.prototype.beforeStart_=smt.prototype.copySampleValue_,smt.prototype.afterEnd_=smt.prototype.copySampleValue_;class lmt extends smt{constructor(t,e,n,o){super(t,e,n,o),this._weightPrev=-0,this._offsetPrev=-0,this._weightNext=-0,this._offsetNext=-0,this.DefaultSettings_={endingStart:Ait,endingEnd:Ait}}intervalChanged_(t,e,n){const o=this.parameterPositions;let i=t-2,a=t+1,r=o[i],s=o[a];if(void 0===r)switch(this.getSettings_().endingStart){case Tit:i=t,r=2*e-n;break;case Nit:i=o.length-2,r=e+o[i]-o[i+1];break;default:i=t,r=n}if(void 0===s)switch(this.getSettings_().endingEnd){case Tit:a=t,s=2*n-e;break;case Nit:a=1,s=n+o[1]-o[0];break;default:a=t-1,s=e}const l=.5*(n-e),c=this.valueSize;this._weightPrev=l/(e-r),this._weightNext=l/(s-n),this._offsetPrev=i*c,this._offsetNext=a*c}interpolate_(t,e,n,o){const i=this.resultBuffer,a=this.sampleValues,r=this.valueSize,s=t*r,l=s-r,c=this._offsetPrev,d=this._offsetNext,p=this._weightPrev,m=this._weightNext,u=(n-e)/(o-e),f=u*u,g=f*u,h=-p*g+2*p*f-p*u,b=(1+p)*g+(-1.5-2*p)*f+(-.5+p)*u+1,y=(-1-m)*g+(1.5+m)*f+.5*u,_=m*g-m*f;for(let t=0;t!==r;++t)i[t]=h*a[c+t]+b*a[l+t]+y*a[s+t]+_*a[d+t];return i}}class cmt extends smt{constructor(t,e,n,o){super(t,e,n,o)}interpolate_(t,e,n,o){const i=this.resultBuffer,a=this.sampleValues,r=this.valueSize,s=t*r,l=s-r,c=(n-e)/(o-e),d=1-c;for(let t=0;t!==r;++t)i[t]=a[l+t]*d+a[s+t]*c;return i}}class dmt extends smt{constructor(t,e,n,o){super(t,e,n,o)}interpolate_(t){return this.copySampleValue_(t-1)}}class pmt{constructor(t,e,n,o){if(void 0===t)throw new Error("THREE.KeyframeTrack: track name is undefined");if(void 0===e||0===e.length)throw new Error("THREE.KeyframeTrack: no keyframes in track named "+t);this.name=t,this.times=rmt.convertArray(e,this.TimeBufferType),this.values=rmt.convertArray(n,this.ValueBufferType),this.setInterpolation(o||this.DefaultInterpolation)}static toJSON(t){const e=t.constructor;let n;if(e.toJSON!==this.toJSON)n=e.toJSON(t);else{n={name:t.name,times:rmt.convertArray(t.times,Array),values:rmt.convertArray(t.values,Array)};const e=t.getInterpolation();e!==t.DefaultInterpolation&&(n.interpolation=e)}return n.type=t.ValueTypeName,n}InterpolantFactoryMethodDiscrete(t){return new dmt(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodLinear(t){return new cmt(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodSmooth(t){return new lmt(this.times,this.values,this.getValueSize(),t)}setInterpolation(t){let e;switch(t){case Dit:e=this.InterpolantFactoryMethodDiscrete;break;case Eit:e=this.InterpolantFactoryMethodLinear;break;case Rit:e=this.InterpolantFactoryMethodSmooth}if(void 0===e){const e="unsupported interpolation for "+this.ValueTypeName+" keyframe track named "+this.name;if(void 0===this.createInterpolant){if(t===this.DefaultInterpolation)throw new Error(e);this.setInterpolation(this.DefaultInterpolation)}return console.warn("THREE.KeyframeTrack:",e),this}return this.createInterpolant=e,this}getInterpolation(){switch(this.createInterpolant){case this.InterpolantFactoryMethodDiscrete:return Dit;case this.InterpolantFactoryMethodLinear:return Eit;case this.InterpolantFactoryMethodSmooth:return Rit}}getValueSize(){return this.values.length/this.times.length}shift(t){if(0!==t){const e=this.times;for(let n=0,o=e.length;n!==o;++n)e[n]+=t}return this}scale(t){if(1!==t){const e=this.times;for(let n=0,o=e.length;n!==o;++n)e[n]*=t}return this}trim(t,e){const n=this.times,o=n.length;let i=0,a=o-1;for(;i!==o&&n[i]<t;)++i;for(;-1!==a&&n[a]>e;)--a;if(++a,0!==i||a!==o){i>=a&&(a=Math.max(a,1),i=a-1);const t=this.getValueSize();this.times=rmt.arraySlice(n,i,a),this.values=rmt.arraySlice(this.values,i*t,a*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error("THREE.KeyframeTrack: Invalid value size in track.",this),t=!1);const n=this.times,o=this.values,i=n.length;0===i&&(console.error("THREE.KeyframeTrack: Track is empty.",this),t=!1);let a=null;for(let e=0;e!==i;e++){const o=n[e];if("number"==typeof o&&isNaN(o)){console.error("THREE.KeyframeTrack: Time is not a valid number.",this,e,o),t=!1;break}if(null!==a&&a>o){console.error("THREE.KeyframeTrack: Out of order keys.",this,e,o,a),t=!1;break}a=o}if(void 0!==o&&rmt.isTypedArray(o))for(let e=0,n=o.length;e!==n;++e){const n=o[e];if(isNaN(n)){console.error("THREE.KeyframeTrack: Value is not a valid number.",this,e,n),t=!1;break}}return t}optimize(){const t=rmt.arraySlice(this.times),e=rmt.arraySlice(this.values),n=this.getValueSize(),o=this.getInterpolation()===Rit,i=t.length-1;let a=1;for(let r=1;r<i;++r){let i=!1;const s=t[r];if(s!==t[r+1]&&(1!==r||s!==t[0]))if(o)i=!0;else{const t=r*n,o=t-n,a=t+n;for(let r=0;r!==n;++r){const n=e[t+r];if(n!==e[o+r]||n!==e[a+r]){i=!0;break}}}if(i){if(r!==a){t[a]=t[r];const o=r*n,i=a*n;for(let t=0;t!==n;++t)e[i+t]=e[o+t]}++a}}if(i>0){t[a]=t[i];for(let t=i*n,o=a*n,r=0;r!==n;++r)e[o+r]=e[t+r];++a}return a!==t.length?(this.times=rmt.arraySlice(t,0,a),this.values=rmt.arraySlice(e,0,a*n)):(this.times=t,this.values=e),this}clone(){const t=rmt.arraySlice(this.times,0),e=rmt.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}pmt.prototype.TimeBufferType=Float32Array,pmt.prototype.ValueBufferType=Float32Array,pmt.prototype.DefaultInterpolation=Eit;class mmt extends pmt{}mmt.prototype.ValueTypeName="bool",mmt.prototype.ValueBufferType=Array,mmt.prototype.DefaultInterpolation=Dit,mmt.prototype.InterpolantFactoryMethodLinear=void 0,mmt.prototype.InterpolantFactoryMethodSmooth=void 0;class umt extends pmt{}umt.prototype.ValueTypeName="color";class fmt extends pmt{}fmt.prototype.ValueTypeName="number";class gmt extends smt{constructor(t,e,n,o){super(t,e,n,o)}interpolate_(t,e,n,o){const i=this.resultBuffer,a=this.sampleValues,r=this.valueSize,s=(n-e)/(o-e);let l=t*r;for(let t=l+r;l!==t;l+=4)lat.slerpFlat(i,0,a,l-r,a,l,s);return i}}class hmt extends pmt{InterpolantFactoryMethodLinear(t){return new gmt(this.times,this.values,this.getValueSize(),t)}}hmt.prototype.ValueTypeName="quaternion",hmt.prototype.DefaultInterpolation=Eit,hmt.prototype.InterpolantFactoryMethodSmooth=void 0;class bmt extends pmt{}bmt.prototype.ValueTypeName="string",bmt.prototype.ValueBufferType=Array,bmt.prototype.DefaultInterpolation=Dit,bmt.prototype.InterpolantFactoryMethodLinear=void 0,bmt.prototype.InterpolantFactoryMethodSmooth=void 0;class ymt extends pmt{}ymt.prototype.ValueTypeName="vector";class _mt{constructor(t,e=-1,n,o=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=o,this.uuid=qit(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,o=1/(t.fps||1);for(let t=0,i=n.length;t!==i;++t)e.push(Cmt(n[t]).scale(o));const i=new this(t.name,t.duration,e,t.blendMode);return i.uuid=t.uuid,i}static toJSON(t){const e=[],n=t.tracks,o={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,o=n.length;t!==o;++t)e.push(pmt.toJSON(n[t]));return o}static CreateFromMorphTargetSequence(t,e,n,o){const i=e.length,a=[];for(let t=0;t<i;t++){let r=[],s=[];r.push((t+i-1)%i,t,(t+1)%i),s.push(0,1,0);const l=rmt.getKeyframeOrder(r);r=rmt.sortedArray(r,1,l),s=rmt.sortedArray(s,1,l),o||0!==r[0]||(r.push(i),s.push(s[0])),a.push(new fmt(".morphTargetInfluences["+e[t].name+"]",r,s).scale(1/n))}return new this(t,-1,a)}static findByName(t,e){let n=t;if(!Array.isArray(t)){const e=t;n=e.geometry&&e.geometry.animations||e.animations}for(let t=0;t<n.length;t++)if(n[t].name===e)return n[t];return null}static CreateClipsFromMorphTargetSequences(t,e,n){const o={},i=/^([\w-]*?)([\d]+)$/;for(let e=0,n=t.length;e<n;e++){const n=t[e],a=n.name.match(i);if(a&&a.length>1){const t=a[1];let e=o[t];e||(o[t]=e=[]),e.push(n)}}const a=[];for(const t in o)a.push(this.CreateFromMorphTargetSequence(t,o[t],e,n));return a}static parseAnimation(t,e){if(!t)return console.error("THREE.AnimationClip: No animation in JSONLoader data."),null;const n=function(t,e,n,o,i){if(0!==n.length){const a=[],r=[];rmt.flattenJSON(n,a,r,o),0!==a.length&&i.push(new t(e,a,r))}},o=[],i=t.name||"default",a=t.fps||30,r=t.blendMode;let s=t.length||-1;const l=t.hierarchy||[];for(let t=0;t<l.length;t++){const i=l[t].keys;if(i&&0!==i.length)if(i[0].morphTargets){const t={};let e;for(e=0;e<i.length;e++)if(i[e].morphTargets)for(let n=0;n<i[e].morphTargets.length;n++)t[i[e].morphTargets[n]]=-1;for(const n in t){const t=[],a=[];for(let o=0;o!==i[e].morphTargets.length;++o){const o=i[e];t.push(o.time),a.push(o.morphTarget===n?1:0)}o.push(new fmt(".morphTargetInfluence["+n+"]",t,a))}s=t.length*(a||1)}else{const a=".bones["+e[t].name+"]";n(ymt,a+".position",i,"pos",o),n(hmt,a+".quaternion",i,"rot",o),n(ymt,a+".scale",i,"scl",o)}}return 0===o.length?null:new this(i,s,o,r)}resetDuration(){let t=0;for(let e=0,n=this.tracks.length;e!==n;++e){const n=this.tracks[e];t=Math.max(t,n.times[n.times.length-1])}return this.duration=t,this}trim(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].trim(0,this.duration);return this}validate(){let t=!0;for(let e=0;e<this.tracks.length;e++)t=t&&this.tracks[e].validate();return t}optimize(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].optimize();return this}clone(){const t=[];for(let e=0;e<this.tracks.length;e++)t.push(this.tracks[e].clone());return new this.constructor(this.name,this.duration,t,this.blendMode)}toJSON(){return this.constructor.toJSON(this)}}function Cmt(t){if(void 0===t.type)throw new Error("THREE.KeyframeTrack: track type undefined, can not parse");const e=(function n(t){switch(t.toLowerCase()){case"scalar":case"double":case"float":case"number":case"integer":return fmt;case"vector":case"vector2":case"vector3":case"vector4":return ymt;case"color":return umt;case"quaternion":return hmt;case"bool":case"boolean":return mmt;case"string":return bmt}throw new Error("THREE.KeyframeTrack: Unsupported typeName: "+t)})(t.type);if(void 0===t.times){const e=[],n=[];rmt.flattenJSON(t.keys,e,n,"value"),t.times=e,t.values=n}return void 0!==e.parse?e.parse(t):new e(t.name,t.times,t.values,t.interpolation)}const Mmt={enabled:!1,files:{},add:function(t,e){!1!==this.enabled&&(this.files[t]=e)},get:function(t){if(!1!==this.enabled)return this.files[t]},remove:function(t){delete this.files[t]},clear:function(){this.files={}}},vmt=new class{constructor(t,e,n){const o=this;let i,a=!1,r=0,s=0;const l=[];this.onStart=void 0,this.onLoad=t,this.onProgress=e,this.onError=n,this.itemStart=function(t){s++,!1===a&&void 0!==o.onStart&&o.onStart(t,r,s),a=!0},this.itemEnd=function(t){r++,void 0!==o.onProgress&&o.onProgress(t,r,s),r===s&&(a=!1,void 0!==o.onLoad&&o.onLoad())},this.itemError=function(t){void 0!==o.onError&&o.onError(t)},this.resolveURL=function(t){return i?i(t):t},this.setURLModifier=function(t){return i=t,this},this.addHandler=function(t,e){return l.push(t,e),this},this.removeHandler=function(t){const e=l.indexOf(t);return-1!==e&&l.splice(e,2),this},this.getHandler=function(t){for(let e=0,n=l.length;e<n;e+=2){const n=l[e],o=l[e+1];if(n.global&&(n.lastIndex=0),n.test(t))return o}return null}}};class xmt{constructor(t){this.manager=void 0!==t?t:vmt,this.crossOrigin="anonymous",this.withCredentials=!1,this.path="",this.resourcePath="",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(o,i){n.load(t,o,e,i)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}const Omt={};class Pmt extends xmt{constructor(t){super(t)}load(t,e,n,o){void 0===t&&(t=""),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const i=this,a=Mmt.get(t);if(void 0!==a)return i.manager.itemStart(t),setTimeout((function(){e&&e(a),i.manager.itemEnd(t)}),0),a;if(void 0!==Omt[t])return void Omt[t].push({onLoad:e,onProgress:n,onError:o});const r=t.match(/^data:(.*?)(;base64)?,(.*)$/);let s;if(r){const n=r[1],a=!!r[2];let s=r[3];s=decodeURIComponent(s),a&&(s=atob(s));try{let o;const a=(this.responseType||"").toLowerCase();switch(a){case"arraybuffer":case"blob":const t=new Uint8Array(s.length);for(let e=0;e<s.length;e++)t[e]=s.charCodeAt(e);o="blob"===a?new Blob([t.buffer],{type:n}):t.buffer;break;case"document":const e=new DOMParser;o=e.parseFromString(s,n);break;case"json":o=JSON.parse(s);break;default:o=s}setTimeout((function(){e&&e(o),i.manager.itemEnd(t)}),0)}catch(e){setTimeout((function(){o&&o(e),i.manager.itemError(t),i.manager.itemEnd(t)}),0)}}else{Omt[t]=[],Omt[t].push({onLoad:e,onProgress:n,onError:o}),s=new XMLHttpRequest,s.open("GET",t,!0),s.addEventListener("load",(function(e){const n=this.response,o=Omt[t];if(delete Omt[t],200===this.status||0===this.status){0===this.status&&console.warn("THREE.FileLoader: HTTP Status 0 received."),Mmt.add(t,n);for(let t=0,e=o.length;t<e;t++){const e=o[t];e.onLoad&&e.onLoad(n)}i.manager.itemEnd(t)}else{for(let t=0,n=o.length;t<n;t++){const n=o[t];n.onError&&n.onError(e)}i.manager.itemError(t),i.manager.itemEnd(t)}}),!1),s.addEventListener("progress",(function(e){const n=Omt[t];for(let t=0,o=n.length;t<o;t++){const o=n[t];o.onProgress&&o.onProgress(e)}}),!1),s.addEventListener("error",(function(e){const n=Omt[t];delete Omt[t];for(let t=0,o=n.length;t<o;t++){const o=n[t];o.onError&&o.onError(e)}i.manager.itemError(t),i.manager.itemEnd(t)}),!1),s.addEventListener("abort",(function(e){const n=Omt[t];delete Omt[t];for(let t=0,o=n.length;t<o;t++){const o=n[t];o.onError&&o.onError(e)}i.manager.itemError(t),i.manager.itemEnd(t)}),!1),void 0!==this.responseType&&(s.responseType=this.responseType),void 0!==this.withCredentials&&(s.withCredentials=this.withCredentials),s.overrideMimeType&&s.overrideMimeType(void 0!==this.mimeType?this.mimeType:"text/plain");for(const t in this.requestHeader)s.setRequestHeader(t,this.requestHeader[t]);s.send(null)}return i.manager.itemStart(t),s}setResponseType(t){return this.responseType=t,this}setMimeType(t){return this.mimeType=t,this}}class wmt extends xmt{constructor(t){super(t)}load(t,e,n,o){void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const i=this,a=Mmt.get(t);if(void 0!==a)return i.manager.itemStart(t),setTimeout((function(){e&&e(a),i.manager.itemEnd(t)}),0),a;const r=document.createElementNS("http://www.w3.org/1999/xhtml","img");function s(){r.removeEventListener("load",s,!1),r.removeEventListener("error",l,!1),Mmt.add(t,this),e&&e(this),i.manager.itemEnd(t)}function l(e){r.removeEventListener("load",s,!1),r.removeEventListener("error",l,!1),o&&o(e),i.manager.itemError(t),i.manager.itemEnd(t)}return r.addEventListener("load",s,!1),r.addEventListener("error",l,!1),"data:"!==t.substr(0,5)&&void 0!==this.crossOrigin&&(r.crossOrigin=this.crossOrigin),i.manager.itemStart(t),r.src=t,r}}class kmt extends xmt{constructor(t){super(t)}load(t,e,n,o){const i=new vst,a=new wmt(this.manager);a.setCrossOrigin(this.crossOrigin),a.setPath(this.path);let r=0;function s(n){a.load(t[n],(function(t){i.images[n]=t,r++,6===r&&(i.needsUpdate=!0,e&&e(i))}),void 0,o)}for(let e=0;e<t.length;++e)s(e);return i}}class Smt extends xmt{constructor(t){super(t)}load(t,e,n,o){const i=new oat,a=new wmt(this.manager);return a.setCrossOrigin(this.crossOrigin),a.setPath(this.path),a.load(t,(function(n){i.image=n;const o=t.search(/\.jpe?g($|\?)/i)>0||0===t.search(/^data\:image\/jpeg/);i.format=o?Pit:wit,i.needsUpdate=!0,void 0!==e&&e(i)}),n,o),i}}class Dmt extends dpt{constructor(){super(),this.type="CurvePath",this.curves=[],this.autoClose=!1}add(t){this.curves.push(t)}closePath(){const t=this.curves[0].getPoint(0),e=this.curves[this.curves.length-1].getPoint(1);t.equals(e)||this.curves.push(new Opt(e,t))}getPoint(t){const e=t*this.getLength(),n=this.getCurveLengths();let o=0;for(;o<n.length;){if(n[o]>=e){const t=n[o]-e,i=this.curves[o],a=i.getLength();return i.getPointAt(0===a?0:1-t/a)}o++}return null}getLength(){const t=this.getCurveLengths();return t[t.length-1]}updateArcLengths(){this.needsUpdate=!0,this.cacheLengths=null,this.getCurveLengths()}getCurveLengths(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;const t=[];let e=0;for(let n=0,o=this.curves.length;n<o;n++)e+=this.curves[n].getLength(),t.push(e);return this.cacheLengths=t,t}getSpacedPoints(t=40){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return this.autoClose&&e.push(e[0]),e}getPoints(t=12){const e=[];let n;for(let o=0,i=this.curves;o<i.length;o++){const a=i[o],r=a.getPoints(a&&a.isEllipseCurve?2*t:a&&(a.isLineCurve||a.isLineCurve3)?1:a&&a.isSplineCurve?t*a.points.length:t);for(let t=0;t<r.length;t++){const o=r[t];n&&n.equals(o)||(e.push(o),n=o)}}return this.autoClose&&e.length>1&&!e[e.length-1].equals(e[0])&&e.push(e[0]),e}copy(t){super.copy(t),this.curves=[];for(let e=0,n=t.curves.length;e<n;e++)this.curves.push(t.curves[e].clone());return this.autoClose=t.autoClose,this}toJSON(){const t=super.toJSON();t.autoClose=this.autoClose,t.curves=[];for(let e=0,n=this.curves.length;e<n;e++)t.curves.push(this.curves[e].toJSON());return t}fromJSON(t){super.fromJSON(t),this.autoClose=t.autoClose,this.curves=[];for(let e=0,n=t.curves.length;e<n;e++){const n=t.curves[e];this.curves.push((new Spt[n.type]).fromJSON(n))}return this}}class Emt extends Dmt{constructor(t){super(),this.type="Path",this.currentPoint=new Qit,t&&this.setFromPoints(t)}setFromPoints(t){this.moveTo(t[0].x,t[0].y);for(let e=1,n=t.length;e<n;e++)this.lineTo(t[e].x,t[e].y);return this}moveTo(t,e){return this.currentPoint.set(t,e),this}lineTo(t,e){const n=new Opt(this.currentPoint.clone(),new Qit(t,e));return this.curves.push(n),this.currentPoint.set(t,e),this}quadraticCurveTo(t,e,n,o){const i=new Ppt(this.currentPoint.clone(),new Qit(t,e),new Qit(n,o));return this.curves.push(i),this.currentPoint.set(n,o),this}bezierCurveTo(t,e,n,o,i,a){const r=new vpt(this.currentPoint.clone(),new Qit(t,e),new Qit(n,o),new Qit(i,a));return this.curves.push(r),this.currentPoint.set(i,a),this}splineThru(t){const e=[this.currentPoint.clone()].concat(t),n=new kpt(e);return this.curves.push(n),this.currentPoint.copy(t[t.length-1]),this}arc(t,e,n,o,i,a){return this.absarc(t+this.currentPoint.x,e+this.currentPoint.y,n,o,i,a),this}absarc(t,e,n,o,i,a){return this.absellipse(t,e,n,n,o,i,a),this}ellipse(t,e,n,o,i,a,r,s){return this.absellipse(t+this.currentPoint.x,e+this.currentPoint.y,n,o,i,a,r,s),this}absellipse(t,e,n,o,i,a,r,s){const l=new ppt(t,e,n,o,i,a,r,s);if(this.curves.length>0){const t=l.getPoint(0);t.equals(this.currentPoint)||this.lineTo(t.x,t.y)}this.curves.push(l);const c=l.getPoint(1);return this.currentPoint.copy(c),this}copy(t){return super.copy(t),this.currentPoint.copy(t.currentPoint),this}toJSON(){const t=super.toJSON();return t.currentPoint=this.currentPoint.toArray(),t}fromJSON(t){return super.fromJSON(t),this.currentPoint.fromArray(t.currentPoint),this}}class Rmt extends Emt{constructor(t){super(t),this.uuid=qit(),this.type="Shape",this.holes=[]}getPointsHoles(t){const e=[];for(let n=0,o=this.holes.length;n<o;n++)e[n]=this.holes[n].getPoints(t);return e}extractPoints(t){return{shape:this.getPoints(t),holes:this.getPointsHoles(t)}}copy(t){super.copy(t),this.holes=[];for(let e=0,n=t.holes.length;e<n;e++)this.holes.push(t.holes[e].clone());return this}toJSON(){const t=super.toJSON();t.uuid=this.uuid,t.holes=[];for(let e=0,n=this.holes.length;e<n;e++)t.holes.push(this.holes[e].toJSON());return t}fromJSON(t){super.fromJSON(t),this.uuid=t.uuid,this.holes=[];for(let e=0,n=t.holes.length;e<n;e++){const n=t.holes[e];this.holes.push((new Emt).fromJSON(n))}return this}}class Amt extends prt{constructor(t,e=1){super(),this.type="Light",this.color=new Rrt(t),this.intensity=e}dispose(){}copy(t){return super.copy(t),this.color.copy(t.color),this.intensity=t.intensity,this}toJSON(t){const e=super.toJSON(t);return e.object.color=this.color.getHex(),e.object.intensity=this.intensity,void 0!==this.groundColor&&(e.object.groundColor=this.groundColor.getHex()),void 0!==this.distance&&(e.object.distance=this.distance),void 0!==this.angle&&(e.object.angle=this.angle),void 0!==this.decay&&(e.object.decay=this.decay),void 0!==this.penumbra&&(e.object.penumbra=this.penumbra),void 0!==this.shadow&&(e.object.shadow=this.shadow.toJSON()),e}}Amt.prototype.isLight=!0,class extends Amt{constructor(t,e,n){super(t,n),this.type="HemisphereLight",this.position.copy(prt.DefaultUp),this.updateMatrix(),this.groundColor=new Rrt(e)}copy(t){return Amt.prototype.copy.call(this,t),this.groundColor.copy(t.groundColor),this}}.prototype.isHemisphereLight=!0;const Tmt=new Bat,Nmt=new cat,zmt=new cat;class Imt{constructor(t){this.camera=t,this.bias=0,this.normalBias=0,this.radius=1,this.mapSize=new Qit(512,512),this.map=null,this.mapPass=null,this.matrix=new Bat,this.autoUpdate=!0,this.needsUpdate=!1,this._frustum=new Est,this._frameExtents=new Qit(1,1),this._viewportCount=1,this._viewports=[new aat(0,0,1,1)]}getViewportCount(){return this._viewportCount}getFrustum(){return this._frustum}updateMatrices(t){const e=this.camera,n=this.matrix;Nmt.setFromMatrixPosition(t.matrixWorld),e.position.copy(Nmt),zmt.setFromMatrixPosition(t.target.matrixWorld),e.lookAt(zmt),e.updateMatrixWorld(),Tmt.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),this._frustum.setFromProjectionMatrix(Tmt),n.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),n.multiply(e.projectionMatrix),n.multiply(e.matrixWorldInverse)}getViewport(t){return this._viewports[t]}getFrameExtents(){return this._frameExtents}dispose(){this.map&&this.map.dispose(),this.mapPass&&this.mapPass.dispose()}copy(t){return this.camera=t.camera.clone(),this.bias=t.bias,this.radius=t.radius,this.mapSize.copy(t.mapSize),this}clone(){return(new this.constructor).copy(this)}toJSON(){const t={};return 0!==this.bias&&(t.bias=this.bias),0!==this.normalBias&&(t.normalBias=this.normalBias),1!==this.radius&&(t.radius=this.radius),512===this.mapSize.x&&512===this.mapSize.y||(t.mapSize=this.mapSize.toArray()),t.camera=this.camera.toJSON(!1).object,delete t.camera.matrix,t}}class Hmt extends Imt{constructor(){super(new _st(50,1,.5,500)),this.focus=1}updateMatrices(t){const e=this.camera,n=2*Yit*t.angle*this.focus,o=this.mapSize.width/this.mapSize.height,i=t.distance||e.far;n===e.fov&&o===e.aspect&&i===e.far||(e.fov=n,e.aspect=o,e.far=i,e.updateProjectionMatrix()),super.updateMatrices(t)}copy(t){return super.copy(t),this.focus=t.focus,this}}Hmt.prototype.isSpotLightShadow=!0,class extends Amt{constructor(t,e,n=0,o=Math.PI/3,i=0,a=1){super(t,e),this.type="SpotLight",this.position.copy(prt.DefaultUp),this.updateMatrix(),this.target=new prt,this.distance=n,this.angle=o,this.penumbra=i,this.decay=a,this.shadow=new Hmt}get power(){return this.intensity*Math.PI}set power(t){this.intensity=t/Math.PI}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.angle=t.angle,this.penumbra=t.penumbra,this.decay=t.decay,this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}.prototype.isSpotLight=!0;const Fmt=new Bat,Lmt=new cat,Bmt=new cat;class Vmt extends Imt{constructor(){super(new _st(90,1,.5,500)),this._frameExtents=new Qit(4,2),this._viewportCount=6,this._viewports=[new aat(2,1,1,1),new aat(0,1,1,1),new aat(3,1,1,1),new aat(1,1,1,1),new aat(3,0,1,1),new aat(1,0,1,1)],this._cubeDirections=[new cat(1,0,0),new cat(-1,0,0),new cat(0,0,1),new cat(0,0,-1),new cat(0,1,0),new cat(0,-1,0)],this._cubeUps=[new cat(0,1,0),new cat(0,1,0),new cat(0,1,0),new cat(0,1,0),new cat(0,0,1),new cat(0,0,-1)]}updateMatrices(t,e=0){const n=this.camera,o=this.matrix,i=t.distance||n.far;i!==n.far&&(n.far=i,n.updateProjectionMatrix()),Lmt.setFromMatrixPosition(t.matrixWorld),n.position.copy(Lmt),Bmt.copy(n.position),Bmt.add(this._cubeDirections[e]),n.up.copy(this._cubeUps[e]),n.lookAt(Bmt),n.updateMatrixWorld(),o.makeTranslation(-Lmt.x,-Lmt.y,-Lmt.z),Fmt.multiplyMatrices(n.projectionMatrix,n.matrixWorldInverse),this._frustum.setFromProjectionMatrix(Fmt)}}Vmt.prototype.isPointLightShadow=!0,class extends Amt{constructor(t,e,n=0,o=1){super(t,e),this.type="PointLight",this.distance=n,this.decay=o,this.shadow=new Vmt}get power(){return 4*this.intensity*Math.PI}set power(t){this.intensity=t/(4*Math.PI)}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.decay=t.decay,this.shadow=t.shadow.clone(),this}}.prototype.isPointLight=!0;class jmt extends Imt{constructor(){super(new Ust(-5,5,5,-5,.5,500))}}jmt.prototype.isDirectionalLightShadow=!0,class extends Amt{constructor(t,e){super(t,e),this.type="DirectionalLight",this.position.copy(prt.DefaultUp),this.updateMatrix(),this.target=new prt,this.shadow=new jmt}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}.prototype.isDirectionalLight=!0,class extends Amt{constructor(t,e){super(t,e),this.type="AmbientLight"}}.prototype.isAmbientLight=!0,class extends Amt{constructor(t,e,n=10,o=10){super(t,e),this.type="RectAreaLight",this.width=n,this.height=o}copy(t){return super.copy(t),this.width=t.width,this.height=t.height,this}toJSON(t){const e=super.toJSON(t);return e.object.width=this.width,e.object.height=this.height,e}}.prototype.isRectAreaLight=!0;class Umt{constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new cat)}set(t){for(let e=0;e<9;e++)this.coefficients[e].copy(t[e]);return this}zero(){for(let t=0;t<9;t++)this.coefficients[t].set(0,0,0);return this}getAt(t,e){const n=t.x,o=t.y,i=t.z,a=this.coefficients;return e.copy(a[0]).multiplyScalar(.282095),e.addScaledVector(a[1],.488603*o),e.addScaledVector(a[2],.488603*i),e.addScaledVector(a[3],.488603*n),e.addScaledVector(a[4],n*o*1.092548),e.addScaledVector(a[5],o*i*1.092548),e.addScaledVector(a[6],.315392*(3*i*i-1)),e.addScaledVector(a[7],n*i*1.092548),e.addScaledVector(a[8],.546274*(n*n-o*o)),e}getIrradianceAt(t,e){const n=t.x,o=t.y,i=t.z,a=this.coefficients;return e.copy(a[0]).multiplyScalar(.886227),e.addScaledVector(a[1],1.023328*o),e.addScaledVector(a[2],1.023328*i),e.addScaledVector(a[3],1.023328*n),e.addScaledVector(a[4],.858086*n*o),e.addScaledVector(a[5],.858086*o*i),e.addScaledVector(a[6],.743125*i*i-.247708),e.addScaledVector(a[7],.858086*n*i),e.addScaledVector(a[8],.429043*(n*n-o*o)),e}add(t){for(let e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let o=0;o<9;o++)n[o].fromArray(t,e+3*o);return this}toArray(t=[],e=0){const n=this.coefficients;for(let o=0;o<9;o++)n[o].toArray(t,e+3*o);return t}static getBasisAt(t,e){const n=t.x,o=t.y,i=t.z;e[0]=.282095,e[1]=.488603*o,e[2]=.488603*i,e[3]=.488603*n,e[4]=1.092548*n*o,e[5]=1.092548*o*i,e[6]=.315392*(3*i*i-1),e[7]=1.092548*n*i,e[8]=.546274*(n*n-o*o)}}Umt.prototype.isSphericalHarmonics3=!0;class Gmt extends Amt{constructor(t=new Umt,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}let Wmt;Gmt.prototype.isLightProbe=!0,class extends qrt{constructor(){super(),this.type="InstancedBufferGeometry",this.instanceCount=1/0}copy(t){return super.copy(t),this.instanceCount=t.instanceCount,this}clone(){return(new this.constructor).copy(this)}toJSON(){const t=super.toJSON(this);return t.instanceCount=this.instanceCount,t.isInstancedBufferGeometry=!0,t}}.prototype.isInstancedBufferGeometry=!0,class extends zrt{constructor(t,e,n,o=1){"number"==typeof n&&(o=n,n=!1,console.error("THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument.")),super(t,e,n),this.meshPerAttribute=o}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}toJSON(){const t=super.toJSON();return t.meshPerAttribute=this.meshPerAttribute,t.isInstancedBufferAttribute=!0,t}}.prototype.isInstancedBufferAttribute=!0,class extends xmt{constructor(t){super(t),"undefined"==typeof createImageBitmap&&console.warn("THREE.ImageBitmapLoader: createImageBitmap() not supported."),"undefined"==typeof fetch&&console.warn("THREE.ImageBitmapLoader: fetch() not supported."),this.options={premultiplyAlpha:"none"}}setOptions(t){return this.options=t,this}load(t,e,n,o){void 0===t&&(t=""),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const i=this,a=Mmt.get(t);if(void 0!==a)return i.manager.itemStart(t),setTimeout((function(){e&&e(a),i.manager.itemEnd(t)}),0),a;const r={};r.credentials="anonymous"===this.crossOrigin?"same-origin":"include",r.headers=this.requestHeader,fetch(t,r).then((function(t){return t.blob()})).then((function(t){return createImageBitmap(t,Object.assign(i.options,{colorSpaceConversion:"none"}))})).then((function(n){Mmt.add(t,n),e&&e(n),i.manager.itemEnd(t)})).catch((function(e){o&&o(e),i.manager.itemError(t),i.manager.itemEnd(t)})),i.manager.itemStart(t)}}.prototype.isImageBitmapLoader=!0;class Ymt extends xmt{constructor(t){super(t)}load(t,e,n,o){const i=this,a=new Pmt(this.manager);a.setResponseType("arraybuffer"),a.setPath(this.path),a.setRequestHeader(this.requestHeader),a.setWithCredentials(this.withCredentials),a.load(t,(function(n){try{const t=n.slice(0);(void 0===Wmt&&(Wmt=new(window.AudioContext||window.webkitAudioContext)),Wmt).decodeAudioData(t,(function(t){e(t)}))}catch(e){o?o(e):console.error(e),i.manager.itemError(t)}}),n,o)}}(class extends Gmt{constructor(t,e,n=1){super(void 0,n);const o=(new Rrt).set(t),i=(new Rrt).set(e),a=new cat(o.r,o.g,o.b),r=new cat(i.r,i.g,i.b),s=Math.sqrt(Math.PI),l=s*Math.sqrt(.75);this.sh.coefficients[0].copy(a).add(r).multiplyScalar(s),this.sh.coefficients[1].copy(a).sub(r).multiplyScalar(l)}}).prototype.isHemisphereLightProbe=!0,class extends Gmt{constructor(t,e=1){super(void 0,e);const n=(new Rrt).set(t);this.sh.coefficients[0].set(n.r,n.g,n.b).multiplyScalar(2*Math.sqrt(Math.PI))}}.prototype.isAmbientLightProbe=!0;class qmt{constructor(t,e,n){let o,i,a;switch(this.binding=t,this.valueSize=n,e){case"quaternion":o=this._slerp,i=this._slerpAdditive,a=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case"string":case"bool":o=this._select,i=this._select,a=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:o=this._lerp,i=this._lerpAdditive,a=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=o,this._mixBufferRegionAdditive=i,this._setIdentity=a,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(t,e){const n=this.buffer,o=this.valueSize,i=t*o+o;let a=this.cumulativeWeight;if(0===a){for(let t=0;t!==o;++t)n[i+t]=n[t];a=e}else a+=e,this._mixBufferRegion(n,i,0,e/a,o);this.cumulativeWeight=a}accumulateAdditive(t){const e=this.buffer,n=this.valueSize,o=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(e,o,0,t,n),this.cumulativeWeightAdditive+=t}apply(t){const e=this.valueSize,n=this.buffer,o=t*e+e,i=this.cumulativeWeight,a=this.cumulativeWeightAdditive,r=this.binding;this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,i<1&&this._mixBufferRegion(n,o,e*this._origIndex,1-i,e),a>0&&this._mixBufferRegionAdditive(n,o,this._addIndex*e,1,e);for(let t=e,i=e+e;t!==i;++t)if(n[t]!==n[t+e]){r.setValue(n,o);break}}saveOriginalState(){const t=this.buffer,e=this.valueSize,n=e*this._origIndex;this.binding.getValue(t,n);for(let o=e,i=n;o!==i;++o)t[o]=t[n+o%e];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){this.binding.setValue(this.buffer,3*this.valueSize)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n<e;n++)this.buffer[n]=0}_setAdditiveIdentityQuaternion(){this._setAdditiveIdentityNumeric(),this.buffer[this._addIndex*this.valueSize+3]=1}_setAdditiveIdentityOther(){const t=this._origIndex*this.valueSize,e=this._addIndex*this.valueSize;for(let n=0;n<this.valueSize;n++)this.buffer[e+n]=this.buffer[t+n]}_select(t,e,n,o,i){if(o>=.5)for(let o=0;o!==i;++o)t[e+o]=t[n+o]}_slerp(t,e,n,o){lat.slerpFlat(t,e,t,e,t,n,o)}_slerpAdditive(t,e,n,o,i){const a=this._workIndex*i;lat.multiplyQuaternionsFlat(t,a,t,e,t,n),lat.slerpFlat(t,e,t,e,t,a,o)}_lerp(t,e,n,o,i){const a=1-o;for(let r=0;r!==i;++r){const i=e+r;t[i]=t[i]*a+t[n+r]*o}}_lerpAdditive(t,e,n,o,i){for(let a=0;a!==i;++a){const i=e+a;t[i]=t[i]+t[n+a]*o}}}const Zmt=new RegExp("[\\[\\]\\.:\\/]","g"),Xmt="[^\\[\\]\\.:\\/]",Kmt="[^"+"\\[\\]\\.:\\/".replace("\\.","")+"]",Jmt=/((?:WC+[\/:])*)/.source.replace("WC",Xmt),Qmt=/(WCOD+)?/.source.replace("WCOD",Kmt),$mt=/(?:\.(WC+)(?:\[(.+)\])?)?/.source.replace("WC",Xmt),tut=/\.(WC+)(?:\[(.+)\])?/.source.replace("WC",Xmt),eut=new RegExp("^"+Jmt+Qmt+$mt+tut+"$"),nut=["material","materials","bones"];class out{constructor(t,e,n){this.path=e,this.parsedPath=n||out.parseTrackName(e),this.node=out.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new out.Composite(t,e,n):new out(t,e,n)}static sanitizeNodeName(t){return t.replace(/\s/g,"_").replace(Zmt,"")}static parseTrackName(t){const e=eut.exec(t);if(!e)throw new Error("PropertyBinding: Cannot parse trackName: "+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},o=n.nodeName&&n.nodeName.lastIndexOf(".");if(void 0!==o&&-1!==o){const t=n.nodeName.substring(o+1);-1!==nut.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,o),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error("PropertyBinding: can not parse propertyName from trackName: "+t);return n}static findNode(t,e){if(!e||""===e||"."===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let o=0;o<t.length;o++){const i=t[o];if(i.name===e||i.uuid===e)return i;const a=n(i.children);if(a)return a}return null},o=n(t.children);if(o)return o}return null}_getValue_unavailable(){}_setValue_unavailable(){}_getValue_direct(t,e){t[e]=this.node[this.propertyName]}_getValue_array(t,e){const n=this.resolvedProperty;for(let o=0,i=n.length;o!==i;++o)t[e++]=n[o]}_getValue_arrayElement(t,e){t[e]=this.resolvedProperty[this.propertyIndex]}_getValue_toArray(t,e){this.resolvedProperty.toArray(t,e)}_setValue_direct(t,e){this.targetObject[this.propertyName]=t[e]}_setValue_direct_setNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.needsUpdate=!0}_setValue_direct_setMatrixWorldNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_array(t,e){const n=this.resolvedProperty;for(let o=0,i=n.length;o!==i;++o)n[o]=t[e++]}_setValue_array_setNeedsUpdate(t,e){const n=this.resolvedProperty;for(let o=0,i=n.length;o!==i;++o)n[o]=t[e++];this.targetObject.needsUpdate=!0}_setValue_array_setMatrixWorldNeedsUpdate(t,e){const n=this.resolvedProperty;for(let o=0,i=n.length;o!==i;++o)n[o]=t[e++];this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_arrayElement(t,e){this.resolvedProperty[this.propertyIndex]=t[e]}_setValue_arrayElement_setNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.needsUpdate=!0}_setValue_arrayElement_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_fromArray(t,e){this.resolvedProperty.fromArray(t,e)}_setValue_fromArray_setNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.needsUpdate=!0}_setValue_fromArray_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.matrixWorldNeedsUpdate=!0}_getValue_unbound(t,e){this.bind(),this.getValue(t,e)}_setValue_unbound(t,e){this.bind(),this.setValue(t,e)}bind(){let t=this.node;const e=this.parsedPath,n=e.objectName,o=e.propertyName;let i=e.propertyIndex;if(t||(t=out.findNode(this.rootNode,e.nodeName)||this.rootNode,this.node=t),this.getValue=this._getValue_unavailable,this.setValue=this._setValue_unavailable,!t)return void console.error("THREE.PropertyBinding: Trying to update node for track: "+this.path+" but it wasn't found.");if(n){let o=e.objectIndex;switch(n){case"materials":if(!t.material)return void console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.",this);if(!t.material.materials)return void console.error("THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.",this);t=t.material.materials;break;case"bones":if(!t.skeleton)return void console.error("THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.",this);t=t.skeleton.bones;for(let e=0;e<t.length;e++)if(t[e].name===o){o=e;break}break;default:if(void 0===t[n])return void console.error("THREE.PropertyBinding: Can not bind to objectName of node undefined.",this);t=t[n]}if(void 0!==o){if(void 0===t[o])return void console.error("THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.",this,t);t=t[o]}}const a=t[o];if(void 0===a)return void console.error("THREE.PropertyBinding: Trying to update property for track: "+e.nodeName+"."+o+" but it wasn't found.",t);let r=this.Versioning.None;this.targetObject=t,void 0!==t.needsUpdate?r=this.Versioning.NeedsUpdate:void 0!==t.matrixWorldNeedsUpdate&&(r=this.Versioning.MatrixWorldNeedsUpdate);let s=this.BindingType.Direct;if(void 0!==i){if("morphTargetInfluences"===o){if(!t.geometry)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.",this);if(!t.geometry.isBufferGeometry)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences on THREE.Geometry. Use THREE.BufferGeometry instead.",this);if(!t.geometry.morphAttributes)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.",this);void 0!==t.morphTargetDictionary[i]&&(i=t.morphTargetDictionary[i])}s=this.BindingType.ArrayElement,this.resolvedProperty=a,this.propertyIndex=i}else void 0!==a.fromArray&&void 0!==a.toArray?(s=this.BindingType.HasFromToArray,this.resolvedProperty=a):Array.isArray(a)?(s=this.BindingType.EntireArray,this.resolvedProperty=a):this.propertyName=o;this.getValue=this.GetterByBindingType[s],this.setValue=this.SetterByBindingTypeAndVersioning[s][r]}unbind(){this.node=null,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}}out.Composite=class{constructor(t,e,n){const o=n||out.parseTrackName(e);this._targetGroup=t,this._bindings=t.subscribe_(e,o)}getValue(t,e){this.bind();const n=this._bindings[this._targetGroup.nCachedObjects_];void 0!==n&&n.getValue(t,e)}setValue(t,e){const n=this._bindings;for(let o=this._targetGroup.nCachedObjects_,i=n.length;o!==i;++o)n[o].setValue(t,e)}bind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].bind()}unbind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].unbind()}},out.prototype.BindingType={Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3},out.prototype.Versioning={None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2},out.prototype.GetterByBindingType=[out.prototype._getValue_direct,out.prototype._getValue_array,out.prototype._getValue_arrayElement,out.prototype._getValue_toArray],out.prototype.SetterByBindingTypeAndVersioning=[[out.prototype._setValue_direct,out.prototype._setValue_direct_setNeedsUpdate,out.prototype._setValue_direct_setMatrixWorldNeedsUpdate],[out.prototype._setValue_array,out.prototype._setValue_array_setNeedsUpdate,out.prototype._setValue_array_setMatrixWorldNeedsUpdate],[out.prototype._setValue_arrayElement,out.prototype._setValue_arrayElement_setNeedsUpdate,out.prototype._setValue_arrayElement_setMatrixWorldNeedsUpdate],[out.prototype._setValue_fromArray,out.prototype._setValue_fromArray_setNeedsUpdate,out.prototype._setValue_fromArray_setMatrixWorldNeedsUpdate]];class iut{constructor(t,e,n=null,o=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=o;const i=e.tracks,a=i.length,r=new Array(a),s={endingStart:Ait,endingEnd:Ait};for(let t=0;t!==a;++t){const e=i[t].createInterpolant(null);r[t]=e,e.settings=s}this._interpolantSettings=s,this._interpolants=r,this._propertyBindings=new Array(a),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=2201,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(t){return this._startTime=t,this}setLoop(t,e){return this.loop=t,this.repetitions=e,this}setEffectiveWeight(t){return this.weight=t,this._effectiveWeight=this.enabled?t:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(t){return this._scheduleFading(t,0,1)}fadeOut(t){return this._scheduleFading(t,1,0)}crossFadeFrom(t,e,n){if(t.fadeOut(e),this.fadeIn(e),n){const n=this._clip.duration,o=t._clip.duration,i=n/o;t.warp(1,o/n,e),this.warp(i,1,e)}return this}crossFadeTo(t,e,n){return t.crossFadeFrom(this,e,n)}stopFading(){const t=this._weightInterpolant;return null!==t&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}setEffectiveTimeScale(t){return this.timeScale=t,this._effectiveTimeScale=this.paused?0:t,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(t){return this.timeScale=this._clip.duration/t,this.stopWarping()}syncWith(t){return this.time=t.time,this.timeScale=t.timeScale,this.stopWarping()}halt(t){return this.warp(this._effectiveTimeScale,0,t)}warp(t,e,n){const o=this._mixer,i=o.time,a=this.timeScale;let r=this._timeScaleInterpolant;null===r&&(r=o._lendControlInterpolant(),this._timeScaleInterpolant=r);const s=r.parameterPositions,l=r.sampleValues;return s[0]=i,s[1]=i+n,l[0]=t/a,l[1]=e/a,this}stopWarping(){const t=this._timeScaleInterpolant;return null!==t&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(t,e,n,o){if(!this.enabled)return void this._updateWeight(t);const i=this._startTime;if(null!==i){const o=(t-i)*n;if(o<0||0===n)return;this._startTime=null,e=n*o}e*=this._updateTimeScale(t);const a=this._updateTime(e),r=this._updateWeight(t);if(r>0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case 2501:for(let n=0,o=t.length;n!==o;++n)t[n].evaluate(a),e[n].accumulateAdditive(r);break;case 2500:default:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(a),e[n].accumulate(o,r)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const o=n.evaluate(t)[0];e*=o,t>n.parameterPositions[1]&&(this.stopFading(),0===o&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;null!==n&&(e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e))}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let o=this.time+t,i=this._loopCount;const a=2202===n;if(0===t)return-1===i?o:a&&1==(1&i)?e-o:o;if(2200===n){-1===i&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(o>=e)o=e;else{if(!(o<0)){this.time=o;break t}o=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=o,this._mixer.dispatchEvent({type:"finished",action:this,direction:t<0?-1:1})}}else{if(-1===i&&(t>=0?(i=0,this._setEndings(!0,0===this.repetitions,a)):this._setEndings(0===this.repetitions,!0,a)),o>=e||o<0){const n=Math.floor(o/e);o-=e*n,i+=Math.abs(n);const r=this.repetitions-i;if(r<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,o=t>0?e:0,this.time=o,this._mixer.dispatchEvent({type:"finished",action:this,direction:t>0?1:-1});else{if(1===r){const e=t<0;this._setEndings(e,!e,a)}else this._setEndings(!1,!1,a);this._loopCount=i,this.time=o,this._mixer.dispatchEvent({type:"loop",action:this,loopDelta:n})}}else this.time=o;if(a&&1==(1&i))return e-o}return o}_setEndings(t,e,n){const o=this._interpolantSettings;n?(o.endingStart=Tit,o.endingEnd=Tit):(o.endingStart=t?this.zeroSlopeAtStart?Tit:Ait:Nit,o.endingEnd=e?this.zeroSlopeAtEnd?Tit:Ait:Nit)}_scheduleFading(t,e,n){const o=this._mixer,i=o.time;let a=this._weightInterpolant;null===a&&(a=o._lendControlInterpolant(),this._weightInterpolant=a);const r=a.parameterPositions,s=a.sampleValues;return r[0]=i,s[0]=e,r[1]=i+t,s[1]=n,this}}(class extends Uit{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,o=t._clip.tracks,i=o.length,a=t._propertyBindings,r=t._interpolants,s=n.uuid,l=this._bindingsByRootAndName;let c=l[s];void 0===c&&(c={},l[s]=c);for(let t=0;t!==i;++t){const i=o[t],l=i.name;let d=c[l];if(void 0!==d)a[t]=d;else{if(d=a[t],void 0!==d){null===d._cacheIndex&&(++d.referenceCount,this._addInactiveBinding(d,s,l));continue}d=new qmt(out.create(n,l,e&&e._propertyBindings[t].binding.parsedPath),i.ValueTypeName,i.getValueSize()),++d.referenceCount,this._addInactiveBinding(d,s,l),a[t]=d}r[t].resultBuffer=d.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,o=this._actionsByClip[n];this._bindAction(t,o&&o.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e<this._nActiveActions}_addInactiveAction(t,e,n){const o=this._actions,i=this._actionsByClip;let a=i[e];if(void 0===a)a={knownActions:[t],actionByRoot:{}},t._byClipCacheIndex=0,i[e]=a;else{const e=a.knownActions;t._byClipCacheIndex=e.length,e.push(t)}t._cacheIndex=o.length,o.push(t),a.actionByRoot[n]=t}_removeInactiveAction(t){const e=this._actions,n=e[e.length-1],o=t._cacheIndex;n._cacheIndex=o,e[o]=n,e.pop(),t._cacheIndex=null;const i=t._clip.uuid,a=this._actionsByClip,r=a[i],s=r.knownActions,l=s[s.length-1],c=t._byClipCacheIndex;l._byClipCacheIndex=c,s[c]=l,s.pop(),t._byClipCacheIndex=null,delete r.actionByRoot[(t._localRoot||this._root).uuid],0===s.length&&delete a[i],this._removeInactiveBindingsForAction(t)}_removeInactiveBindingsForAction(t){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.referenceCount&&this._removeInactiveBinding(n)}}_lendAction(t){const e=this._actions,n=t._cacheIndex,o=this._nActiveActions++,i=e[o];t._cacheIndex=o,e[o]=t,i._cacheIndex=n,e[n]=i}_takeBackAction(t){const e=this._actions,n=t._cacheIndex,o=--this._nActiveActions,i=e[o];t._cacheIndex=o,e[o]=t,i._cacheIndex=n,e[n]=i}_addInactiveBinding(t,e,n){const o=this._bindingsByRootAndName,i=this._bindings;let a=o[e];void 0===a&&(a={},o[e]=a),a[n]=t,t._cacheIndex=i.length,i.push(t)}_removeInactiveBinding(t){const e=this._bindings,n=t.binding,o=n.rootNode.uuid,i=n.path,a=this._bindingsByRootAndName,r=a[o],s=e[e.length-1],l=t._cacheIndex;s._cacheIndex=l,e[l]=s,e.pop(),delete r[i],0===Object.keys(r).length&&delete a[o]}_lendBinding(t){const e=this._bindings,n=t._cacheIndex,o=this._nActiveBindings++,i=e[o];t._cacheIndex=o,e[o]=t,i._cacheIndex=n,e[n]=i}_takeBackBinding(t){const e=this._bindings,n=t._cacheIndex,o=--this._nActiveBindings,i=e[o];t._cacheIndex=o,e[o]=t,i._cacheIndex=n,e[n]=i}_lendControlInterpolant(){const t=this._controlInterpolants,e=this._nActiveControlInterpolants++;let n=t[e];return void 0===n&&(n=new cmt(new Float32Array(2),new Float32Array(2),1,this._controlInterpolantsResultBuffer),n.__cacheIndex=e,t[e]=n),n}_takeBackControlInterpolant(t){const e=this._controlInterpolants,n=t.__cacheIndex,o=--this._nActiveControlInterpolants,i=e[o];t.__cacheIndex=o,e[o]=t,i.__cacheIndex=n,e[n]=i}clipAction(t,e,n){const o=e||this._root,i=o.uuid;let a="string"==typeof t?_mt.findByName(o,t):t;const r=null!==a?a.uuid:t,s=this._actionsByClip[r];let l=null;if(void 0===n&&(n=null!==a?a.blendMode:2500),void 0!==s){const t=s.actionByRoot[i];if(void 0!==t&&t.blendMode===n)return t;l=s.knownActions[0],null===a&&(a=l._clip)}if(null===a)return null;const c=new iut(this,a,e,n);return this._bindAction(c,l),this._addInactiveAction(c,r,i),c}existingAction(t,e){const n=e||this._root,o=n.uuid,i="string"==typeof t?_mt.findByName(n,t):t,a=this._actionsByClip[i?i.uuid:t];return void 0!==a&&a.actionByRoot[o]||null}stopAllAction(){const t=this._actions;for(let e=this._nActiveActions-1;e>=0;--e)t[e].stop();return this}update(t){const e=this._actions,n=this._nActiveActions,o=this.time+=t*=this.timeScale,i=Math.sign(t),a=this._accuIndex^=1;for(let r=0;r!==n;++r)e[r]._update(o,t,i,a);const r=this._bindings,s=this._nActiveBindings;for(let t=0;t!==s;++t)r[t].apply(a);return this}setTime(t){this.time=0;for(let t=0;t<this._actions.length;t++)this._actions[t].time=0;return this.update(t)}getRoot(){return this._root}uncacheClip(t){const e=this._actions,n=t.uuid,o=this._actionsByClip,i=o[n];if(void 0!==i){const t=i.knownActions;for(let n=0,o=t.length;n!==o;++n){const o=t[n];this._deactivateAction(o);const i=o._cacheIndex,a=e[e.length-1];o._cacheIndex=null,o._byClipCacheIndex=null,a._cacheIndex=i,e[i]=a,e.pop(),this._removeInactiveBindingsForAction(o)}delete o[n]}}uncacheRoot(t){const e=t.uuid,n=this._actionsByClip;for(const t in n){const o=n[t].actionByRoot[e];void 0!==o&&(this._deactivateAction(o),this._removeInactiveAction(o))}const o=this._bindingsByRootAndName[e];if(void 0!==o)for(const t in o){const e=o[t];e.restoreOriginalState(),this._removeInactiveBinding(e)}}uncacheAction(t,e){const n=this.existingAction(t,e);null!==n&&(this._deactivateAction(n),this._removeInactiveAction(n))}}).prototype._controlInterpolantsResultBuffer=new Float32Array(1),class extends _dt{constructor(t,e,n=1){super(t,e),this.meshPerAttribute=n}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}clone(t){const e=super.clone(t);return e.meshPerAttribute=this.meshPerAttribute,e}toJSON(t){const e=super.toJSON(t);return e.isInstancedInterleavedBuffer=!0,e.meshPerAttribute=this.meshPerAttribute,e}}.prototype.isInstancedInterleavedBuffer=!0,class extends prt{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}.prototype.isImmediateRenderObject=!0;const aut=new cat,rut=new Bat,sut=new Bat;function lut(t){const e=[];t&&t.isBone&&e.push(t);for(let n=0;n<t.children.length;n++)e.push.apply(e,lut(t.children[n]));return e}const cut=new Float32Array(1);function dut(t,e,n){if(1===n)return new Rrt(e);const o=d7(e);if(!o)throw new Error(`d3 failed to recognize the color: ${e}`);return new Rrt(d8(o,t)(1-n))}var put,mut,uut,fut;function gut(t,e){const n=e.length/2;let o=t.attributes.position;o&&o.count===3*n||(o=new zrt(new Float32Array(3*n),3),t.setAttribute("position",o));const i=o.array;for(let t=0;t<n;t++)i[3*t]=e[2*t],i[3*t+1]=e[2*t+1];o.needsUpdate=!0,t.setDrawRange(0,3*n),t.computeBoundingSphere()}function hut(t,e,n){const o=Math.max(e.length/2-1,0),i=2*o*3,a=3*i;let r=t.attributes.position;r&&r.count===i||(r=new zrt(new Float32Array(a),3),t.setAttribute("position",r));const s=r.array;for(let t=0;t<o;t++){const[o,i,a,r]=[e[2*t],e[2*t+1],e[2*t+2],e[2*t+3]],l=new Qit(o,i),c=new Qit(a,r),d=new Qit(a-o,r-i),p=new Qit(-d.y,d.x).setLength(n/2),m=l.clone().add(p),u=l.clone().sub(p),f=c.clone().add(p),g=c.clone().sub(p),h=[m.x,m.y,0,u.x,u.y,0,f.x,f.y,0,f.x,f.y,0,u.x,u.y,0,g.x,g.y,0];s.set(h,t*h.length)}r.needsUpdate=!0,t.setDrawRange(0,a),t.computeBoundingSphere()}function but(t,e,n,o){const{visible:i,color:a,opacity:r}=o;if(Array.isArray(e.material))throw new Error("Invariant error: only expect one material on an object");const s=e.material;if(s.visible!==i&&(s.visible=i,s.needsUpdate=!0),!i)return!1;const l=dut(t,a,null!=r?r:1),c=n(e.geometry);return e.geometry!==c&&(e.geometry=c),s.color.equals(l)||(s.color.set(l),s.needsUpdate=!0),!0}new Int32Array(cut.buffer),dpt.create=function(t,e){return console.log("THREE.Curve.create() has been deprecated"),t.prototype=Object.create(dpt.prototype),t.prototype.constructor=t,t.prototype.getPoint=e,t},Emt.prototype.fromPoints=function(t){return console.warn("THREE.Path: .fromPoints() has been renamed to .setFromPoints()."),this.setFromPoints(t)},class extends npt{constructor(t=10,e=10,n=4473924,o=8947848){n=new Rrt(n),o=new Rrt(o);const i=e/2,a=t/e,r=t/2,s=[],l=[];for(let t=0,c=0,d=-r;t<=e;t++,d+=a){s.push(-r,0,d,r,0,d),s.push(d,0,-r,d,0,r);const e=t===i?n:o;e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3}const c=new qrt;c.setAttribute("position",new Frt(s,3)),c.setAttribute("color",new Frt(l,3)),super(c,new qdt({vertexColors:!0,toneMapped:!1})),this.type="GridHelper"}}.prototype.setColors=function(){console.error("THREE.GridHelper: setColors() has been deprecated, pass them in the constructor instead.")},class extends npt{constructor(t){const e=lut(t),n=new qrt,o=[],i=[],a=new Rrt(0,0,1),r=new Rrt(0,1,0);for(let t=0;t<e.length;t++){const n=e[t];n.parent&&n.parent.isBone&&(o.push(0,0,0),o.push(0,0,0),i.push(a.r,a.g,a.b),i.push(r.r,r.g,r.b))}n.setAttribute("position",new Frt(o,3)),n.setAttribute("color",new Frt(i,3)),super(n,new qdt({vertexColors:!0,depthTest:!1,depthWrite:!1,toneMapped:!1,transparent:!0})),this.type="SkeletonHelper",this.isSkeletonHelper=!0,this.root=t,this.bones=e,this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1}updateMatrixWorld(t){const e=this.bones,n=this.geometry,o=n.getAttribute("position");sut.copy(this.root.matrixWorld).invert();for(let t=0,n=0;t<e.length;t++){const i=e[t];i.parent&&i.parent.isBone&&(rut.multiplyMatrices(sut,i.matrixWorld),aut.setFromMatrixPosition(rut),o.setXYZ(n,aut.x,aut.y,aut.z),rut.multiplyMatrices(sut,i.parent.matrixWorld),aut.setFromMatrixPosition(rut),o.setXYZ(n+1,aut.x,aut.y,aut.z),n+=2)}n.getAttribute("position").needsUpdate=!0,super.updateMatrixWorld(t)}}.prototype.update=function(){console.error("THREE.SkeletonHelper: update() no longer needs to be called.")},xmt.prototype.extractUrlBase=function(t){return console.warn("THREE.Loader: .extractUrlBase() has been deprecated. Use THREE.LoaderUtils.extractUrlBase() instead."),class{static decodeText(t){if("undefined"!=typeof TextDecoder)return(new TextDecoder).decode(t);let e="";for(let n=0,o=t.length;n<o;n++)e+=String.fromCharCode(t[n]);try{return decodeURIComponent(escape(e))}catch(t){return e}}static extractUrlBase(t){const e=t.lastIndexOf("/");return-1===e?"./":t.substr(0,e+1)}}.extractUrlBase(t)},xmt.Handlers={add:function(){console.error("THREE.Loader: Handlers.add() has been removed. Use LoadingManager.addHandler() instead.")},get:function(){console.error("THREE.Loader: Handlers.get() has been removed. Use LoadingManager.getHandler() instead.")}},mat.prototype.center=function(t){return console.warn("THREE.Box3: .center() has been renamed to .getCenter()."),this.getCenter(t)},mat.prototype.empty=function(){return console.warn("THREE.Box3: .empty() has been renamed to .isEmpty()."),this.isEmpty()},mat.prototype.isIntersectionBox=function(t){return console.warn("THREE.Box3: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},mat.prototype.isIntersectionSphere=function(t){return console.warn("THREE.Box3: .isIntersectionSphere() has been renamed to .intersectsSphere()."),this.intersectsSphere(t)},mat.prototype.size=function(t){return console.warn("THREE.Box3: .size() has been renamed to .getSize()."),this.getSize(t)},Rat.prototype.empty=function(){return console.warn("THREE.Sphere: .empty() has been renamed to .isEmpty()."),this.isEmpty()},Est.prototype.setFromMatrix=function(t){return console.warn("THREE.Frustum: .setFromMatrix() has been renamed to .setFromProjectionMatrix()."),this.setFromProjectionMatrix(t)},$it.prototype.flattenToArrayOffset=function(t,e){return console.warn("THREE.Matrix3: .flattenToArrayOffset() has been deprecated. Use .toArray() instead."),this.toArray(t,e)},$it.prototype.multiplyVector3=function(t){return console.warn("THREE.Matrix3: .multiplyVector3() has been removed. Use vector.applyMatrix3( matrix ) instead."),t.applyMatrix3(this)},$it.prototype.multiplyVector3Array=function(){console.error("THREE.Matrix3: .multiplyVector3Array() has been removed.")},$it.prototype.applyToBufferAttribute=function(t){return console.warn("THREE.Matrix3: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix3( matrix ) instead."),t.applyMatrix3(this)},$it.prototype.applyToVector3Array=function(){console.error("THREE.Matrix3: .applyToVector3Array() has been removed.")},$it.prototype.getInverse=function(t){return console.warn("THREE.Matrix3: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead."),this.copy(t).invert()},Bat.prototype.extractPosition=function(t){return console.warn("THREE.Matrix4: .extractPosition() has been renamed to .copyPosition()."),this.copyPosition(t)},Bat.prototype.flattenToArrayOffset=function(t,e){return console.warn("THREE.Matrix4: .flattenToArrayOffset() has been deprecated. Use .toArray() instead."),this.toArray(t,e)},Bat.prototype.getPosition=function(){return console.warn("THREE.Matrix4: .getPosition() has been removed. Use Vector3.setFromMatrixPosition( matrix ) instead."),(new cat).setFromMatrixColumn(this,3)},Bat.prototype.setRotationFromQuaternion=function(t){return console.warn("THREE.Matrix4: .setRotationFromQuaternion() has been renamed to .makeRotationFromQuaternion()."),this.makeRotationFromQuaternion(t)},Bat.prototype.multiplyToArray=function(){console.warn("THREE.Matrix4: .multiplyToArray() has been removed.")},Bat.prototype.multiplyVector3=function(t){return console.warn("THREE.Matrix4: .multiplyVector3() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},Bat.prototype.multiplyVector4=function(t){return console.warn("THREE.Matrix4: .multiplyVector4() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},Bat.prototype.multiplyVector3Array=function(){console.error("THREE.Matrix4: .multiplyVector3Array() has been removed.")},Bat.prototype.rotateAxis=function(t){console.warn("THREE.Matrix4: .rotateAxis() has been removed. Use Vector3.transformDirection( matrix ) instead."),t.transformDirection(this)},Bat.prototype.crossVector=function(t){return console.warn("THREE.Matrix4: .crossVector() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},Bat.prototype.translate=function(){console.error("THREE.Matrix4: .translate() has been removed.")},Bat.prototype.rotateX=function(){console.error("THREE.Matrix4: .rotateX() has been removed.")},Bat.prototype.rotateY=function(){console.error("THREE.Matrix4: .rotateY() has been removed.")},Bat.prototype.rotateZ=function(){console.error("THREE.Matrix4: .rotateZ() has been removed.")},Bat.prototype.rotateByAxis=function(){console.error("THREE.Matrix4: .rotateByAxis() has been removed.")},Bat.prototype.applyToBufferAttribute=function(t){return console.warn("THREE.Matrix4: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},Bat.prototype.applyToVector3Array=function(){console.error("THREE.Matrix4: .applyToVector3Array() has been removed.")},Bat.prototype.makeFrustum=function(t,e,n,o,i,a){return console.warn("THREE.Matrix4: .makeFrustum() has been removed. Use .makePerspective( left, right, top, bottom, near, far ) instead."),this.makePerspective(t,e,o,n,i,a)},Bat.prototype.getInverse=function(t){return console.warn("THREE.Matrix4: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead."),this.copy(t).invert()},kst.prototype.isIntersectionLine=function(t){return console.warn("THREE.Plane: .isIntersectionLine() has been renamed to .intersectsLine()."),this.intersectsLine(t)},lat.prototype.multiplyVector3=function(t){return console.warn("THREE.Quaternion: .multiplyVector3() has been removed. Use is now vector.applyQuaternion( quaternion ) instead."),t.applyQuaternion(this)},lat.prototype.inverse=function(){return console.warn("THREE.Quaternion: .inverse() has been renamed to invert()."),this.invert()},Lat.prototype.isIntersectionBox=function(t){return console.warn("THREE.Ray: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},Lat.prototype.isIntersectionPlane=function(t){return console.warn("THREE.Ray: .isIntersectionPlane() has been renamed to .intersectsPlane()."),this.intersectsPlane(t)},Lat.prototype.isIntersectionSphere=function(t){return console.warn("THREE.Ray: .isIntersectionSphere() has been renamed to .intersectsSphere()."),this.intersectsSphere(t)},vrt.prototype.area=function(){return console.warn("THREE.Triangle: .area() has been renamed to .getArea()."),this.getArea()},vrt.prototype.barycoordFromPoint=function(t,e){return console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord()."),this.getBarycoord(t,e)},vrt.prototype.midpoint=function(t){return console.warn("THREE.Triangle: .midpoint() has been renamed to .getMidpoint()."),this.getMidpoint(t)},vrt.prototypenormal=function(t){return console.warn("THREE.Triangle: .normal() has been renamed to .getNormal()."),this.getNormal(t)},vrt.prototype.plane=function(t){return console.warn("THREE.Triangle: .plane() has been renamed to .getPlane()."),this.getPlane(t)},vrt.barycoordFromPoint=function(t,e,n,o,i){return console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord()."),vrt.getBarycoord(t,e,n,o,i)},vrt.normal=function(t,e,n,o){return console.warn("THREE.Triangle: .normal() has been renamed to .getNormal()."),vrt.getNormal(t,e,n,o)},Rmt.prototype.extractAllPoints=function(t){return console.warn("THREE.Shape: .extractAllPoints() has been removed. Use .extractPoints() instead."),this.extractPoints(t)},Rmt.prototype.extrude=function(t){return console.warn("THREE.Shape: .extrude() has been removed. Use ExtrudeGeometry() instead."),new nmt(this,t)},Rmt.prototype.makeGeometry=function(t){return console.warn("THREE.Shape: .makeGeometry() has been removed. Use ShapeGeometry() instead."),new imt(this,t)},Qit.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector2: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},Qit.prototype.distanceToManhattan=function(t){return console.warn("THREE.Vector2: .distanceToManhattan() has been renamed to .manhattanDistanceTo()."),this.manhattanDistanceTo(t)},Qit.prototype.lengthManhattan=function(){return console.warn("THREE.Vector2: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},cat.prototype.setEulerFromRotationMatrix=function(){console.error("THREE.Vector3: .setEulerFromRotationMatrix() has been removed. Use Euler.setFromRotationMatrix() instead.")},cat.prototype.setEulerFromQuaternion=function(){console.error("THREE.Vector3: .setEulerFromQuaternion() has been removed. Use Euler.setFromQuaternion() instead.")},cat.prototype.getPositionFromMatrix=function(t){return console.warn("THREE.Vector3: .getPositionFromMatrix() has been renamed to .setFromMatrixPosition()."),this.setFromMatrixPosition(t)},cat.prototype.getScaleFromMatrix=function(t){return console.warn("THREE.Vector3: .getScaleFromMatrix() has been renamed to .setFromMatrixScale()."),this.setFromMatrixScale(t)},cat.prototype.getColumnFromMatrix=function(t,e){return console.warn("THREE.Vector3: .getColumnFromMatrix() has been renamed to .setFromMatrixColumn()."),this.setFromMatrixColumn(e,t)},cat.prototype.applyProjection=function(t){return console.warn("THREE.Vector3: .applyProjection() has been removed. Use .applyMatrix4( m ) instead."),this.applyMatrix4(t)},cat.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector3: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},cat.prototype.distanceToManhattan=function(t){return console.warn("THREE.Vector3: .distanceToManhattan() has been renamed to .manhattanDistanceTo()."),this.manhattanDistanceTo(t)},cat.prototype.lengthManhattan=function(){return console.warn("THREE.Vector3: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},aat.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector4: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},aat.prototype.lengthManhattan=function(){return console.warn("THREE.Vector4: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},prt.prototype.getChildByName=function(t){return console.warn("THREE.Object3D: .getChildByName() has been renamed to .getObjectByName()."),this.getObjectByName(t)},prt.prototype.renderDepth=function(){console.warn("THREE.Object3D: .renderDepth has been removed. Use .renderOrder, instead.")},prt.prototype.translate=function(t,e){return console.warn("THREE.Object3D: .translate() has been removed. Use .translateOnAxis( axis, distance ) instead."),this.translateOnAxis(e,t)},prt.prototype.getWorldRotation=function(){console.error("THREE.Object3D: .getWorldRotation() has been removed. Use THREE.Object3D.getWorldQuaternion( target ) instead.")},prt.prototype.applyMatrix=function(t){return console.warn("THREE.Object3D: .applyMatrix() has been renamed to .applyMatrix4()."),this.applyMatrix4(t)},Object.defineProperties(prt.prototype,{eulerOrder:{get:function(){return console.warn("THREE.Object3D: .eulerOrder is now .rotation.order."),this.rotation.order},set:function(t){console.warn("THREE.Object3D: .eulerOrder is now .rotation.order."),this.rotation.order=t}},useQuaternion:{get:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")},set:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")}}}),pst.prototype.setDrawMode=function(){console.error("THREE.Mesh: .setDrawMode() has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.")},Object.defineProperties(pst.prototype,{drawMode:{get:function(){return console.error("THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode."),0},set:function(){console.error("THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.")}}}),jdt.prototype.initBones=function(){console.error("THREE.SkinnedMesh: initBones() has been removed.")},_st.prototype.setLens=function(t,e){console.warn("THREE.PerspectiveCamera.setLens is deprecated. Use .setFocalLength and .filmGauge for a photographic setup."),void 0!==e&&(this.filmGauge=e),this.setFocalLength(t)},Object.defineProperties(Amt.prototype,{onlyShadow:{set:function(){console.warn("THREE.Light: .onlyShadow has been removed.")}},shadowCameraFov:{set:function(t){console.warn("THREE.Light: .shadowCameraFov is now .shadow.camera.fov."),this.shadow.camera.fov=t}},shadowCameraLeft:{set:function(t){console.warn("THREE.Light: .shadowCameraLeft is now .shadow.camera.left."),this.shadow.camera.left=t}},shadowCameraRight:{set:function(t){console.warn("THREE.Light: .shadowCameraRight is now .shadow.camera.right."),this.shadow.camera.right=t}},shadowCameraTop:{set:function(t){console.warn("THREE.Light: .shadowCameraTop is now .shadow.camera.top."),this.shadow.camera.top=t}},shadowCameraBottom:{set:function(t){console.warn("THREE.Light: .shadowCameraBottom is now .shadow.camera.bottom."),this.shadow.camera.bottom=t}},shadowCameraNear:{set:function(t){console.warn("THREE.Light: .shadowCameraNear is now .shadow.camera.near."),this.shadow.camera.near=t}},shadowCameraFar:{set:function(t){console.warn("THREE.Light: .shadowCameraFar is now .shadow.camera.far."),this.shadow.camera.far=t}},shadowCameraVisible:{set:function(){console.warn("THREE.Light: .shadowCameraVisible has been removed. Use new THREE.CameraHelper( light.shadow.camera ) instead.")}},shadowBias:{set:function(t){console.warn("THREE.Light: .shadowBias is now .shadow.bias."),this.shadow.bias=t}},shadowDarkness:{set:function(){console.warn("THREE.Light: .shadowDarkness has been removed.")}},shadowMapWidth:{set:function(t){console.warn("THREE.Light: .shadowMapWidth is now .shadow.mapSize.width."),this.shadow.mapSize.width=t}},shadowMapHeight:{set:function(t){console.warn("THREE.Light: .shadowMapHeight is now .shadow.mapSize.height."),this.shadow.mapSize.height=t}}}),Object.defineProperties(zrt.prototype,{length:{get:function(){return console.warn("THREE.BufferAttribute: .length has been deprecated. Use .count instead."),this.array.length}},dynamic:{get:function(){return console.warn("THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead."),this.usage===Vit},set:function(){console.warn("THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead."),this.setUsage(Vit)}}}),zrt.prototype.setDynamic=function(t){return console.warn("THREE.BufferAttribute: .setDynamic() has been deprecated. Use .setUsage() instead."),this.setUsage(!0===t?Vit:Bit),this},zrt.prototype.copyIndicesArray=function(){console.error("THREE.BufferAttribute: .copyIndicesArray() has been removed.")},zrt.prototype.setArray=function(){console.error("THREE.BufferAttribute: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers")},qrt.prototype.addIndex=function(t){console.warn("THREE.BufferGeometry: .addIndex() has been renamed to .setIndex()."),this.setIndex(t)},qrt.prototype.addAttribute=function(t,e){return console.warn("THREE.BufferGeometry: .addAttribute() has been renamed to .setAttribute()."),e&&e.isBufferAttribute||e&&e.isInterleavedBufferAttribute?"index"===t?(console.warn("THREE.BufferGeometry.addAttribute: Use .setIndex() for index attribute."),this.setIndex(e),this):this.setAttribute(t,e):(console.warn("THREE.BufferGeometry: .addAttribute() now expects ( name, attribute )."),this.setAttribute(t,new zrt(arguments[1],arguments[2])))},qrt.prototype.addDrawCall=function(t,e,n){void 0!==n&&console.warn("THREE.BufferGeometry: .addDrawCall() no longer supports indexOffset."),console.warn("THREE.BufferGeometry: .addDrawCall() is now .addGroup()."),this.addGroup(t,e)},qrt.prototype.clearDrawCalls=function(){console.warn("THREE.BufferGeometry: .clearDrawCalls() is now .clearGroups()."),this.clearGroups()},qrt.prototype.computeOffsets=function(){console.warn("THREE.BufferGeometry: .computeOffsets() has been removed.")},qrt.prototype.removeAttribute=function(t){return console.warn("THREE.BufferGeometry: .removeAttribute() has been renamed to .deleteAttribute()."),this.deleteAttribute(t)},qrt.prototype.applyMatrix=function(t){return console.warn("THREE.BufferGeometry: .applyMatrix() has been renamed to .applyMatrix4()."),this.applyMatrix4(t)},Object.defineProperties(qrt.prototype,{drawcalls:{get:function(){return console.error("THREE.BufferGeometry: .drawcalls has been renamed to .groups."),this.groups}},offsets:{get:function(){return console.warn("THREE.BufferGeometry: .offsets has been renamed to .groups."),this.groups}}}),_dt.prototype.setDynamic=function(t){return console.warn("THREE.InterleavedBuffer: .setDynamic() has been deprecated. Use .setUsage() instead."),this.setUsage(!0===t?Vit:Bit),this},_dt.prototype.setArray=function(){console.error("THREE.InterleavedBuffer: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers")},nmt.prototype.getArrays=function(){console.error("THREE.ExtrudeGeometry: .getArrays() has been removed.")},nmt.prototype.addShapeList=function(){console.error("THREE.ExtrudeGeometry: .addShapeList() has been removed.")},nmt.prototype.addShape=function(){console.error("THREE.ExtrudeGeometry: .addShape() has been removed.")},ydt.prototype.dispose=function(){console.error("THREE.Scene: .dispose() has been removed.")},Object.defineProperties(Ort.prototype,{wrapAround:{get:function(){console.warn("THREE.Material: .wrapAround has been removed.")},set:function(){console.warn("THREE.Material: .wrapAround has been removed.")}},overdraw:{get:function(){console.warn("THREE.Material: .overdraw has been removed.")},set:function(){console.warn("THREE.Material: .overdraw has been removed.")}},wrapRGB:{get:function(){return console.warn("THREE.Material: .wrapRGB has been removed."),new Rrt}},shading:{get:function(){console.error("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead.")},set:function(t){console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead."),this.flatShading=1===t}},stencilMask:{get:function(){return console.warn("THREE."+this.type+": .stencilMask has been removed. Use .stencilFuncMask instead."),this.stencilFuncMask},set:function(t){console.warn("THREE."+this.type+": .stencilMask has been removed. Use .stencilFuncMask instead."),this.stencilFuncMask=t}},vertexTangents:{get:function(){console.warn("THREE."+this.type+": .vertexTangents has been removed.")},set:function(){console.warn("THREE."+this.type+": .vertexTangents has been removed.")}}}),Object.defineProperties(bst.prototype,{derivatives:{get:function(){return console.warn("THREE.ShaderMaterial: .derivatives has been moved to .extensions.derivatives."),this.extensions.derivatives},set:function(t){console.warn("THREE. ShaderMaterial: .derivatives has been moved to .extensions.derivatives."),this.extensions.derivatives=t}}}),bdt.prototype.clearTarget=function(t,e,n,o){console.warn("THREE.WebGLRenderer: .clearTarget() has been deprecated. Use .setRenderTarget() and .clear() instead."),this.setRenderTarget(t),this.clear(e,n,o)},bdt.prototype.animate=function(t){console.warn("THREE.WebGLRenderer: .animate() is now .setAnimationLoop()."),this.setAnimationLoop(t)},bdt.prototype.getCurrentRenderTarget=function(){return console.warn("THREE.WebGLRenderer: .getCurrentRenderTarget() is now .getRenderTarget()."),this.getRenderTarget()},bdt.prototype.getMaxAnisotropy=function(){return console.warn("THREE.WebGLRenderer: .getMaxAnisotropy() is now .capabilities.getMaxAnisotropy()."),this.capabilities.getMaxAnisotropy()},bdt.prototype.getPrecision=function(){return console.warn("THREE.WebGLRenderer: .getPrecision() is now .capabilities.precision."),this.capabilities.precision},bdt.prototype.resetGLState=function(){return console.warn("THREE.WebGLRenderer: .resetGLState() is now .state.reset()."),this.state.reset()},bdt.prototype.supportsFloatTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsFloatTextures() is now .extensions.get( 'OES_texture_float' )."),this.extensions.get("OES_texture_float")},bdt.prototype.supportsHalfFloatTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsHalfFloatTextures() is now .extensions.get( 'OES_texture_half_float' )."),this.extensions.get("OES_texture_half_float")},bdt.prototype.supportsStandardDerivatives=function(){return console.warn("THREE.WebGLRenderer: .supportsStandardDerivatives() is now .extensions.get( 'OES_standard_derivatives' )."),this.extensions.get("OES_standard_derivatives")},bdt.prototype.supportsCompressedTextureS3TC=function(){return console.warn("THREE.WebGLRenderer: .supportsCompressedTextureS3TC() is now .extensions.get( 'WEBGL_compressed_texture_s3tc' )."),this.extensions.get("WEBGL_compressed_texture_s3tc")},bdt.prototype.supportsCompressedTexturePVRTC=function(){return console.warn("THREE.WebGLRenderer: .supportsCompressedTexturePVRTC() is now .extensions.get( 'WEBGL_compressed_texture_pvrtc' )."),this.extensions.get("WEBGL_compressed_texture_pvrtc")},bdt.prototype.supportsBlendMinMax=function(){return console.warn("THREE.WebGLRenderer: .supportsBlendMinMax() is now .extensions.get( 'EXT_blend_minmax' )."),this.extensions.get("EXT_blend_minmax")},bdt.prototype.supportsVertexTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsVertexTextures() is now .capabilities.vertexTextures."),this.capabilities.vertexTextures},bdt.prototype.supportsInstancedArrays=function(){return console.warn("THREE.WebGLRenderer: .supportsInstancedArrays() is now .extensions.get( 'ANGLE_instanced_arrays' )."),this.extensions.get("ANGLE_instanced_arrays")},bdt.prototype.enableScissorTest=function(t){console.warn("THREE.WebGLRenderer: .enableScissorTest() is now .setScissorTest()."),this.setScissorTest(t)},bdt.prototype.initMaterial=function(){console.warn("THREE.WebGLRenderer: .initMaterial() has been removed.")},bdt.prototype.addPrePlugin=function(){console.warn("THREE.WebGLRenderer: .addPrePlugin() has been removed.")},bdt.prototype.addPostPlugin=function(){console.warn("THREE.WebGLRenderer: .addPostPlugin() has been removed.")},bdt.prototype.updateShadowMap=function(){console.warn("THREE.WebGLRenderer: .updateShadowMap() has been removed.")},bdt.prototype.setFaceCulling=function(){console.warn("THREE.WebGLRenderer: .setFaceCulling() has been removed.")},bdt.prototype.allocTextureUnit=function(){console.warn("THREE.WebGLRenderer: .allocTextureUnit() has been removed.")},bdt.prototype.setTexture=function(){console.warn("THREE.WebGLRenderer: .setTexture() has been removed.")},bdt.prototype.setTexture2D=function(){console.warn("THREE.WebGLRenderer: .setTexture2D() has been removed.")},bdt.prototype.setTextureCube=function(){console.warn("THREE.WebGLRenderer: .setTextureCube() has been removed.")},bdt.prototype.getActiveMipMapLevel=function(){return console.warn("THREE.WebGLRenderer: .getActiveMipMapLevel() is now .getActiveMipmapLevel()."),this.getActiveMipmapLevel()},Object.defineProperties(bdt.prototype,{shadowMapEnabled:{get:function(){return this.shadowMap.enabled},set:function(t){console.warn("THREE.WebGLRenderer: .shadowMapEnabled is now .shadowMap.enabled."),this.shadowMap.enabled=t}},shadowMapType:{get:function(){return this.shadowMap.type},set:function(t){console.warn("THREE.WebGLRenderer: .shadowMapType is now .shadowMap.type."),this.shadowMap.type=t}},shadowMapCullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")}},context:{get:function(){return console.warn("THREE.WebGLRenderer: .context has been removed. Use .getContext() instead."),this.getContext()}},vr:{get:function(){return console.warn("THREE.WebGLRenderer: .vr has been renamed to .xr"),this.xr}},gammaInput:{get:function(){return console.warn("THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead."),!1},set:function(){console.warn("THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead.")}},gammaOutput:{get:function(){return console.warn("THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead."),!1},set:function(t){console.warn("THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead."),this.outputEncoding=!0===t?Iit:zit}},toneMappingWhitePoint:{get:function(){return console.warn("THREE.WebGLRenderer: .toneMappingWhitePoint has been removed."),1},set:function(){console.warn("THREE.WebGLRenderer: .toneMappingWhitePoint has been removed.")}}}),Object.defineProperties(sdt.prototype,{cullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")}},renderReverseSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")}},renderSingleSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")}}}),Object.defineProperties(rat.prototype,{wrapS:{get:function(){return console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS."),this.texture.wrapS},set:function(t){console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS."),this.texture.wrapS=t}},wrapT:{get:function(){return console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT."),this.texture.wrapT},set:function(t){console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT."),this.texture.wrapT=t}},magFilter:{get:function(){return console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter."),this.texture.magFilter},set:function(t){console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter."),this.texture.magFilter=t}},minFilter:{get:function(){return console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter."),this.texture.minFilter},set:function(t){console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter."),this.texture.minFilter=t}},anisotropy:{get:function(){return console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy."),this.texture.anisotropy},set:function(t){console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy."),this.texture.anisotropy=t}},offset:{get:function(){return console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset."),this.texture.offset},set:function(t){console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset."),this.texture.offset=t}},repeat:{get:function(){return console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat."),this.texture.repeat},set:function(t){console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat."),this.texture.repeat=t}},format:{get:function(){return console.warn("THREE.WebGLRenderTarget: .format is now .texture.format."),this.texture.format},set:function(t){console.warn("THREE.WebGLRenderTarget: .format is now .texture.format."),this.texture.format=t}},type:{get:function(){return console.warn("THREE.WebGLRenderTarget: .type is now .texture.type."),this.texture.type},set:function(t){console.warn("THREE.WebGLRenderTarget: .type is now .texture.type."),this.texture.type=t}},generateMipmaps:{get:function(){return console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps."),this.texture.generateMipmaps},set:function(t){console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps."),this.texture.generateMipmaps=t}}}),class extends prt{constructor(t){super(),this.type="Audio",this.listener=t,this.context=t.context,this.gain=this.context.createGain(),this.gain.connect(t.getInput()),this.autoplay=!1,this.buffer=null,this.detune=0,this.loop=!1,this.loopStart=0,this.loopEnd=0,this.offset=0,this.duration=void 0,this.playbackRate=1,this.isPlaying=!1,this.hasPlaybackControl=!0,this.source=null,this.sourceType="empty",this._startedAt=0,this._progress=0,this._connected=!1,this.filters=[]}getOutput(){return this.gain}setNodeSource(t){return this.hasPlaybackControl=!1,this.sourceType="audioNode",this.source=t,this.connect(),this}setMediaElementSource(t){return this.hasPlaybackControl=!1,this.sourceType="mediaNode",this.source=this.context.createMediaElementSource(t),this.connect(),this}setMediaStreamSource(t){return this.hasPlaybackControl=!1,this.sourceType="mediaStreamNode",this.source=this.context.createMediaStreamSource(t),this.connect(),this}setBuffer(t){return this.buffer=t,this.sourceType="buffer",this.autoplay&&this.play(),this}play(t=0){if(!0===this.isPlaying)return void console.warn("THREE.Audio: Audio is already playing.");if(!1===this.hasPlaybackControl)return void console.warn("THREE.Audio: this Audio has no playback control.");this._startedAt=this.context.currentTime+t;const e=this.context.createBufferSource();return e.buffer=this.buffer,e.loop=this.loop,e.loopStart=this.loopStart,e.loopEnd=this.loopEnd,e.onended=this.onEnded.bind(this),e.start(this._startedAt,this._progress+this.offset,this.duration),this.isPlaying=!0,this.source=e,this.setDetune(this.detune),this.setPlaybackRate(this.playbackRate),this.connect()}pause(){if(!1!==this.hasPlaybackControl)return!0===this.isPlaying&&(this._progress+=Math.max(this.context.currentTime-this._startedAt,0)*this.playbackRate,!0===this.loop&&(this._progress=this._progress%(this.duration||this.buffer.duration)),this.source.stop(),this.source.onended=null,this.isPlaying=!1),this;console.warn("THREE.Audio: this Audio has no playback control.")}stop(){if(!1!==this.hasPlaybackControl)return this._progress=0,this.source.stop(),this.source.onended=null,this.isPlaying=!1,this;console.warn("THREE.Audio: this Audio has no playback control.")}connect(){if(this.filters.length>0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].connect(this.filters[t]);this.filters[this.filters.length-1].connect(this.getOutput())}else this.source.connect(this.getOutput());return this._connected=!0,this}disconnect(){if(this.filters.length>0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].disconnect(this.filters[t]);this.filters[this.filters.length-1].disconnect(this.getOutput())}else this.source.disconnect(this.getOutput());return this._connected=!1,this}getFilters(){return this.filters}setFilters(t){return t||(t=[]),!0===this._connected?(this.disconnect(),this.filters=t.slice(),this.connect()):this.filters=t.slice(),this}setDetune(t){if(this.detune=t,void 0!==this.source.detune)return!0===this.isPlaying&&this.source.detune.setTargetAtTime(this.detune,this.context.currentTime,.01),this}getDetune(){return this.detune}getFilter(){return this.getFilters()[0]}setFilter(t){return this.setFilters(t?[t]:[])}setPlaybackRate(t){if(!1!==this.hasPlaybackControl)return this.playbackRate=t,!0===this.isPlaying&&this.source.playbackRate.setTargetAtTime(this.playbackRate,this.context.currentTime,.01),this;console.warn("THREE.Audio: this Audio has no playback control.")}getPlaybackRate(){return this.playbackRate}onEnded(){this.isPlaying=!1}getLoop(){return!1===this.hasPlaybackControl?(console.warn("THREE.Audio: this Audio has no playback control."),!1):this.loop}setLoop(t){if(!1!==this.hasPlaybackControl)return this.loop=t,!0===this.isPlaying&&(this.source.loop=this.loop),this;console.warn("THREE.Audio: this Audio has no playback control.")}setLoopStart(t){return this.loopStart=t,this}setLoopEnd(t){return this.loopEnd=t,this}getVolume(){return this.gain.gain.value}setVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}}.prototype.load=function(t){console.warn("THREE.Audio: .load has been deprecated. Use THREE.AudioLoader instead.");const e=this;return(new Ymt).load(t,(function(t){e.setBuffer(t)})),this},Mst.prototype.updateCubeMap=function(t,e){return console.warn("THREE.CubeCamera: .updateCubeMap() is now .update()."),this.update(t,e)},Mst.prototype.clear=function(t,e,n,o){return console.warn("THREE.CubeCamera: .clear() is now .renderTarget.clear()."),this.renderTarget.clear(t,e,n,o)},eat.crossOrigin=void 0,eat.loadTexture=function(t,e,n,o){console.warn("THREE.ImageUtils.loadTexture has been deprecated. Use THREE.TextureLoader() instead.");const i=new Smt;i.setCrossOrigin(this.crossOrigin);const a=i.load(t,n,void 0,o);return e&&(a.mapping=e),a},eat.loadTextureCube=function(t,e,n,o){console.warn("THREE.ImageUtils.loadTextureCube has been deprecated. Use THREE.CubeTextureLoader() instead.");const i=new kmt;i.setCrossOrigin(this.crossOrigin);const a=i.load(t,n,void 0,o);return e&&(a.mapping=e),a},eat.loadCompressedTexture=function(){console.error("THREE.ImageUtils.loadCompressedTexture has been removed. Use THREE.DDSLoader instead.")},eat.loadCompressedTextureCube=function(){console.error("THREE.ImageUtils.loadCompressedTextureCube has been removed. Use THREE.DDSLoader instead.")},"undefined"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("register",{detail:{revision:"131"}})),"undefined"!=typeof window&&(window.__THREE__?console.warn("WARNING: Multiple instances of Three.js being imported."):window.__THREE__="131"),(function(t){t[t.CIRCLE=0]="CIRCLE",t[t.LINE=1]="LINE",t[t.TRIANGLE=2]="TRIANGLE",t[t.TRAPEZOID=3]="TRAPEZOID"})(put||(put={}));class yut{constructor(t,e,n,o){this.coordinator=e,this.scene=new ydt,this.backgroundColor="#fff",iit()&&t instanceof OffscreenCanvas&&(t.style=t.style||{}),o&&t.addEventListener("webglcontextlost",o),this.renderer=new bdt({canvas:t,context:t.getContext("webgl2",{antialias:!0,precision:"highp",alpha:!0})}),this.renderer.setPixelRatio(n)}onResize(t){this.renderer.setSize(t.width,t.height)}destroyObject(t){const e=t.obj3d;if(this.scene.remove(e),e instanceof pst){e.geometry.dispose();const t=Array.isArray(e.material)?e.material:[e.material];for(const e of t)e.dispose()}}setUseDarkMode(t){this.backgroundColor=t?"#303030":"#fff"}createOrUpdateLineObject(t,e,n){var o;if(!t&&!n.visible)return null;const{visible:i,width:a}=n;if(!t){const t=dut(this.backgroundColor,n.color,null!==(o=n.opacity)&&void 0!==o?o:1),r=new qrt,s=new qdt({color:t}),l=new pst(r,s);return s.visible=i,hut(r,e,a),this.scene.add(l),{type:put.LINE,data:e,obj3d:l,width:a}}const{data:r,obj3d:s,width:l}=t;return but(this.backgroundColor,s,(t=>(a===l&&r&&ait(r,e)||hut(t,e,a),t)),n)?{type:put.LINE,data:e,obj3d:s,width:a}:t}createMesh(t,e){if(!e.visible)return null;const{visible:n,color:o,opacity:i}=e,a=dut(this.backgroundColor,o,null!=i?i:1),r=new Art({color:a,visible:n});return new pst(t,r)}createOrUpdateTriangleObject(t,e,n){const{size:o}=n,i=o*Math.sqrt(3)/2,a=new Float32Array([e.x-o/2,e.y-i/3,e.x+o/2,e.y-i/3,e.x,e.y+2*i/3]);if(!t){const t=new qrt;gut(t,a);const o=this.createMesh(t,n);return null===o?null:(this.scene.add(o),{type:put.TRIANGLE,data:e,obj3d:o})}return but(this.backgroundColor,t.obj3d,(t=>(gut(t,a),t)),n)?{type:put.TRIANGLE,data:e,obj3d:t.obj3d}:t}createOrUpdateCircleObject(t,e,n){const{radius:o}=n,i=new cpt(n.radius);if(!t){const t=this.createMesh(i,n);return null===t?null:(t.position.set(e.x,e.y,0),this.scene.add(t),{type:put.CIRCLE,data:{loc:e,radius:o},obj3d:t})}return but(this.backgroundColor,t.obj3d,(()=>i),n)?(t.obj3d.position.set(e.x,e.y,0),{type:put.CIRCLE,data:{loc:e,radius:o},obj3d:t.obj3d}):t}createOrUpdateTrapezoidObject(t,e,n,o){if(e.y!==n.y)throw new RangeError("Input error: start.y != end.y.");const{altitude:i}=o,a=2/Math.sqrt(3)*i,r=new Rmt([new Qit(e.x-a/2,e.y-i/2),new Qit(e.x,e.y+i/2),new Qit(n.x,n.y+i/2),new Qit(n.x+a/2,n.y-i/2)]);r.autoClose=!0;const s=new imt(r);if(!t){const t=this.createMesh(s,o);return null===t?null:(this.scene.add(t),{type:put.TRAPEZOID,data:[e,n],obj3d:t})}return but(this.backgroundColor,t.obj3d,(()=>s),o)?{type:put.TRAPEZOID,data:[e,n],obj3d:t.obj3d}:t}flush(){this.renderer.render(this.scene,this.coordinator.getCamera())}}class _ut{constructor(t,e){this.renderCache=t,this.renderer=e}setLine(t,e,n){const o=this.renderer.createOrUpdateLineObject(this.renderCache.getFromPreviousFrame(t),e,n);o&&this.renderCache.setToCurrentFrame(t,o)}setTriangle(t,e,n){const o=this.renderer.createOrUpdateTriangleObject(this.renderCache.getFromPreviousFrame(t),e,n);o&&this.renderCache.setToCurrentFrame(t,o)}setCircle(t,e,n){const o=this.renderer.createOrUpdateCircleObject(this.renderCache.getFromPreviousFrame(t),e,n);o&&this.renderCache.setToCurrentFrame(t,o)}setTrapezoid(t,e,n,o){const i=this.renderer.createOrUpdateTrapezoidObject(this.renderCache.getFromPreviousFrame(t),e,n,o);i&&this.renderCache.setToCurrentFrame(t,i)}}class Cut{constructor(){this.prevFrameCache=new Map,this.currFrameCache=new Map}getFromPreviousFrame(t){const e=this.prevFrameCache.get(t);return null!=e?e:null}setToCurrentFrame(t,e){this.currFrameCache.set(t,e)}finalizeFrameAndGetRemoved(){const t=[];for(const[e,n]of this.prevFrameCache.entries())this.currFrameCache.has(e)||t.push(n);return this.prevFrameCache=this.currFrameCache,this.currFrameCache=new Map,t}}!(function(t){t[t.NUMBER=0]="NUMBER",t[t.NAN=1]="NAN"})(mut||(mut={}));class Mut extends class{constructor(t){this.rawSeriesData=[],this.series=[],this.paintDirty=!0,this.renderCache=new Cut,this.coordinateIdentifier=null,this.layout={x:0,width:1,y:0,height:1},this.getMetadataMapImpl=t.getMetadataMap,this.coordinator=t.coordinator,this.renderer=t.renderer,this.paintBrush=new _ut(this.renderCache,this.renderer)}setLayoutRect(t){this.layout.x===t.x&&this.layout.width===t.width&&this.layout.y===t.y&&this.layout.height===t.height||(this.paintDirty=!0),this.layout=t}getLayoutRect(){return this.layout}getMetadataMap(){return this.getMetadataMapImpl()}markAsPaintDirty(){this.paintDirty=!0}render(){if(this.transformCoordinatesIfStale(),this.paintDirty){this.redraw();for(const t of this.renderCache.finalizeFrameAndGetRemoved())this.renderer.destroyObject(t);this.paintDirty=!1}}isCoordinateUpdated(){return this.coordinator.getUpdateIdentifier()!==this.coordinateIdentifier}clearCoordinateIdentifier(){this.coordinateIdentifier=null}setData(t){this.clearCoordinateIdentifier(),this.rawSeriesData=t}transformCoordinatesIfStale(){if(!this.isCoordinateUpdated())return;const t=this.getLayoutRect();this.series=new Array(this.rawSeriesData.length);for(let e=0;e<this.rawSeriesData.length;e++){const n=this.rawSeriesData[e];this.series[e]={id:n.id,polyline:new Float32Array(2*n.points.length)};for(let o=0;o<n.points.length;o++){const[i,a]=this.coordinator.transformDataToUiCoord(t,[n.points[o].x,n.points[o].y]);this.series[e].polyline[2*o]=i,this.series[e].polyline[2*o+1]=a}}this.coordinateIdentifier=this.coordinator.getUpdateIdentifier(),this.markAsPaintDirty()}}{recordPartition(t,e,n){return t?{type:mut.NUMBER,polyline:e}:{type:mut.NAN,polyline:e.map(((t,e)=>isNaN(t)?e%2==0?n.x:n.y:t))}}partitionPolyline(t){const e=[];let n=0,o=!1;const i=this.coordinator.transformDataToUiCoord(this.getLayoutRect(),[0,0]),a={x:i[0],y:i[1]};let r=null;for(let i=0;i<t.length;i+=2){const a=t[i],s=t[i+1],l=isNaN(a)||isNaN(s);l!==o&&n!==i&&(e.push(this.recordPartition(!o,t.slice(n,i),null===r?{x:a,y:s}:r)),n=i),l||(r={x:a,y:s}),o=l}return n!==t.length-1&&e.push(this.recordPartition(!o,t.slice(n,t.length),null!=r?r:a)),e}redraw(){var t,e,n;for(const o of this.series){const i=this.getMetadataMap()[o.id];if(!i)continue;if(o.polyline.length%2!=0)throw new Error(`Cannot have odd length-ed polyline: ${o.polyline.length}`);const a=this.partitionPolyline(o.polyline);for(const[r,{type:s,polyline:l}]of a.entries())if(s===mut.NUMBER)2===l.length?this.paintBrush.setCircle(JSON.stringify(["circle",o.id,r]),{x:l[0],y:l[1]},{color:i.color,visible:i.visible,opacity:null!==(t=i.opacity)&&void 0!==t?t:1,radius:4}):this.paintBrush.setLine(JSON.stringify(["line",o.id,r]),l,{color:i.color,visible:i.visible,opacity:null!==(e=i.opacity)&&void 0!==e?e:1,width:2});else if(!i.aux)for(let t=0;t<l.length;t+=2)this.paintBrush.setTriangle(JSON.stringify(["NaN",o.id,l[t],l[t+1]]),{x:l[t],y:l[t+1]},{color:i.color,visible:i.visible,opacity:null!==(n=i.opacity)&&void 0!==n?n:1,size:12})}}}class vut extends rit{constructor(){super(...arguments),this.camera=new Ust(0,1e3,1e3,0,0,100)}isYAxisPointedDown(){return!1}setDomContainerRect(t){super.setDomContainerRect(t),this.camera.left=t.x,this.camera.right=t.x+t.width,this.camera.top=t.y+t.height,this.camera.bottom=t.y,this.camera.updateProjectionMatrix()}getCamera(){return this.camera}}class xut{constructor(t){switch(this.metadataMap={},this.shouldRepaint=!1,this.callbacks=t.callbacks,t.type){case K2.SVG:this.coordinator=new rit,this.renderer=new lit(t.container);break;case K2.WEBGL:{const e=new vut;this.coordinator=e,this.renderer=new yut(t.container,e,t.devicePixelRatio,t.callbacks.onContextLost);break}}this.renderer.setUseDarkMode(t.useDarkMode),this.seriesLineView=new Mut({renderer:this.renderer,coordinator:this.coordinator,getMetadataMap:()=>this.metadataMap}),this.resize(t.domDimension)}dispose(){}setXScaleType(t){this.coordinator.setXScale($ot(t)),this.scheduleRepaint()}setYScaleType(t){this.coordinator.setYScale($ot(t)),this.scheduleRepaint()}resize(t){this.coordinator.setDomContainerRect(Object.assign({x:0,y:0},t)),this.renderer.onResize(Object.assign({x:0,y:0},t)),this.seriesLineView.setLayoutRect(Object.assign(Object.assign({},t),{x:0,y:0})),this.scheduleRepaint()}setMetadata(t){let e=!1;Object.entries(t).forEach((([t,n])=>{const o=this.metadataMap[t];o&&n.color===o.color&&n.visible===o.visible&&n.opacity===o.opacity||(e=!0),this.metadataMap[t]=n})),e&&this.seriesLineView.markAsPaintDirty(),this.scheduleRepaint()}setViewBox(t){this.coordinator.setViewBoxRect({x:t.x[0],width:t.x[1]-t.x[0],y:t.y[0],height:t.y[1]-t.y[0]}),this.scheduleRepaint()}setData(t){this.seriesLineView.setData(t),this.scheduleRepaint()}setUseDarkMode(t){this.renderer.setUseDarkMode(t),this.scheduleRepaint()}scheduleRepaint(){this.shouldRepaint||(this.shouldRepaint=!0,self.requestAnimationFrame((()=>{this.repaint(),this.shouldRepaint=!1})))}repaint(){this.seriesLineView.render(),this.renderer.flush(),this.callbacks.onDrawEnd()}}function Out(t){if(t.includes("/"))throw new RangeError("Worker factory only allows file name and no resource path.");return new Worker(t)}!(function(t){t[t.SERIES_DATA_UPDATED=0]="SERIES_DATA_UPDATED",t[t.SERIES_METADATA_CHANGED=1]="SERIES_METADATA_CHANGED",t[t.SCALE_UPDATED=2]="SCALE_UPDATED",t[t.VIEW_BOX_UPDATED=3]="VIEW_BOX_UPDATED",t[t.INIT=4]="INIT",t[t.DOM_RESIZED=5]="DOM_RESIZED",t[t.DARK_MODE_UPDATED=6]="DARK_MODE_UPDATED"})(uut||(uut={})),(function(t){t[t.ON_REDRAW_END=0]="ON_REDRAW_END",t[t.ON_CONTEXT_LOST=1]="ON_CONTEXT_LOST"})(fut||(fut={}));class Put{constructor(t){if(this.callbacks=t.callbacks,t.type!==K2.WEBGL)throw new RangeError(`Cannot use non WEBGL renderer for the offscreen line chart. Received ${K2[t.type]} `);const e=new MessageChannel;e.port1.onmessage=t=>{this.onMessageFromWorker(t.data)},this.txMessagePort=e.port1;const n=t.container.transferControlToOffscreen();this.workerInstance=Put.workerPool.getNext();const o={type:uut.INIT,canvas:n,devicePixelRatio:window.devicePixelRatio,dim:t.domDimension,rendererType:t.type,useDarkMode:t.useDarkMode};this.workerInstance.postMessage(o,[n,e.port2])}dispose(){this.workerInstance.free(),this.txMessagePort.close()}setXScaleType(t){this.sendMessage({type:uut.SCALE_UPDATED,axis:"x",scaleType:t})}setYScaleType(t){this.sendMessage({type:uut.SCALE_UPDATED,axis:"y",scaleType:t})}resize(t){this.sendMessage({type:uut.DOM_RESIZED,dim:t})}setMetadata(t){this.sendMessage({type:uut.SERIES_METADATA_CHANGED,metadata:t})}setViewBox(t){this.sendMessage({type:uut.VIEW_BOX_UPDATED,extent:t})}setData(t){const e=(function n(t){const e=t.reduce(((t,e)=>t+e.points.length),0);let n=0;const o=new Float64Array(2*e),i=[];for(const e of t){i.push({id:e.id,length:e.points.length});for(let t=0;t<e.points.length;t++)o[n++]=e.points[t].x,o[n++]=e.points[t].y}return{idsAndLengths:i,flattenedSeries:o.buffer}})(t);this.sendMessage({type:uut.SERIES_DATA_UPDATED,compactDataSeries:e},[e.flattenedSeries])}setUseDarkMode(t){this.sendMessage({type:uut.DARK_MODE_UPDATED,useDarkMode:t})}sendMessage(t,e){e?this.txMessagePort.postMessage(t,e):this.txMessagePort.postMessage(t)}onMessageFromWorker(t){switch(t.type){case fut.ON_REDRAW_END:this.callbacks.onDrawEnd();break;case fut.ON_CONTEXT_LOST:this.callbacks.onContextLost()}}}function wut(t,e){return"x"===e?[0,t.width]:[t.height,0]}function kut(t,e){const n=Math.floor(t/50);return Math.min(n,e)}function Sut(t,e){if(1&t&&(qi(),Tm(0,"line",2)),2&t){const t=e.$implicit,n=Ym();pu("zero",0===t),jp("x1",n.getDomX(t))("x2",n.getDomX(t))("y2",n.domDim.height)}}function Dut(t,e){if(1&t&&(qi(),Tm(0,"line",3)),2&t){const t=e.$implicit,n=Ym();pu("zero",0===t),jp("y1",n.getDomY(t))("x2",n.domDim.width)("y2",n.getDomY(t))}}Put.workerPool=new class{constructor(t,e=10,n=Out){this.workerResourcePath=t,this.maxPoolSize=e,this.workerFactory=n,this.workers=[]}getNext(){let t;if(this.workers.every((({activeCount:t})=>t>0))&&this.workers.length<this.maxPoolSize){const e=this.workerFactory(this.workerResourcePath);t={activeCount:0,postMessage:(t,n)=>{e.postMessage(t,n)},free:()=>{t.activeCount=Math.max(t.activeCount-1,0)}},this.workers.push(t)}else{const e=this.workers.map((({activeCount:t})=>t)),n=e.indexOf(Math.min(...e));t=this.workers[n]}return t.activeCount++,t}}("chart_worker.js?_file_hash=25338065");class Eut{getDomX(t){return this.xScale.forward(this.viewExtent.x,wut(this.domDim,"x"),t)}getDomY(t){return this.yScale.forward(this.viewExtent.y,wut(this.domDim,"y"),t)}getXTicks(){return this.xScale.ticks(this.viewExtent.x,kut(this.domDim.width,this.xGridCount))}getYTicks(){return this.yScale.ticks(this.viewExtent.y,kut(this.domDim.height,this.yGridCount))}}function Rut(t,e,n,o,i,a){let r;switch(t.deltaMode){case WheelEvent.DOM_DELTA_PIXEL:r=1;break;case WheelEvent.DOM_DELTA_LINE:r=8;break;case WheelEvent.DOM_DELTA_PAGE:r=20;break;default:r=1,console.warn(`Unknown WheelEvent deltaMode: ${t.deltaMode}.`)}const s=t.deltaY*r,l=s<0?Math.max(s*o,-.95):s*o,{width:c,height:d}=n,p=[i.reverse(e.x,[0,c],-t.offsetX*l),i.reverse(e.x,[0,c],c+(c-t.offsetX)*l)],m=[a.reverse(e.y,[d,0],-t.offsetY*l),a.reverse(e.y,[d,0],d+(d-t.offsetY)*l)];return{x:p[1]<p[0]?[p[1],p[0]]:p,y:m[1]<m[0]?[m[1],m[0]]:m}}Eut.ɵfac=function t(e){return new(e||Eut)},Eut.ɵcmp=to({type:Eut,selectors:[["line-chart-grid-view"]],inputs:{viewExtent:"viewExtent",xScale:"xScale",xGridCount:"xGridCount",yScale:"yScale",yGridCount:"yGridCount",domDim:"domDim"},decls:3,vars:2,consts:[["y1","0",3,"zero",4,"ngFor","ngForOf"],["x1","0",3,"zero",4,"ngFor","ngForOf"],["y1","0"],["x1","0"]],template:function t(e,n){1&e&&(qi(),Rm(0,"svg"),Qp(1,Sut,1,5,"line",0),Qp(2,Dut,1,5,"line",1),Am()),2&e&&(rc(1),Dm("ngForOf",n.getXTicks()),rc(1),Dm("ngForOf",n.getYTicks()))},directives:[lM],styles:["[_nghost-%COMP%] {\n        display: flex;\n        overflow: hidden;\n      }\n\n      svg[_ngcontent-%COMP%] {\n        height: 100%;\n        width: 100%;\n      }\n\n      line[_ngcontent-%COMP%] {\n        stroke: #ccc;\n        stroke-width: 1px;\n      }\n\n      .zero[_ngcontent-%COMP%] {\n        stroke: #aaa;\n        stroke-width: 1.5px;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Eut,[{type:My,args:[{selector:"line-chart-grid-view",template:'<svg>\n    <line\n      *ngFor="let tick of getXTicks()"\n      [class.zero]="tick === 0"\n      [attr.x1]="getDomX(tick)"\n      y1="0"\n      [attr.x2]="getDomX(tick)"\n      [attr.y2]="domDim.height"\n    ></line>\n    <line\n      *ngFor="let tick of getYTicks()"\n      [class.zero]="tick === 0"\n      x1="0"\n      [attr.y1]="getDomY(tick)"\n      [attr.x2]="domDim.width"\n      [attr.y2]="getDomY(tick)"\n    ></line>\n  </svg>',styles:["\n      :host {\n        display: flex;\n        overflow: hidden;\n      }\n\n      svg {\n        height: 100%;\n        width: 100%;\n      }\n\n      line {\n        stroke: #ccc;\n        stroke-width: 1px;\n      }\n\n      .zero {\n        stroke: #aaa;\n        stroke-width: 1.5px;\n      }\n    "],changeDetection:zn.OnPush}]}],null,{viewExtent:[{type:xy}],xScale:[{type:xy}],xGridCount:[{type:xy}],yScale:[{type:xy}],yGridCount:[{type:xy}],domDim:[{type:xy}]});const Aut=["dots"];function Tut(t,e){if(1&t&&(qi(),Tm(0,"circle",12)),2&t){const t=Ym().$implicit,e=Ym(2);jp("cx",e.getDomX(t.point.x))("cy",e.getDomY(t.point.y))("fill",t.metadata.color)}}function Nut(t,e){if(1&t&&(qi(),Nm(0),Qp(1,Tut,1,3,"circle",11),zm()),2&t){const t=e.$implicit,n=Ym(2);rc(1),Dm("ngIf",n.shouldRenderTooltipPoint(t.point))}}function zut(t,e){if(1&t&&(qi(),Nm(0),Qp(1,Nut,2,1,"ng-container",10),zm()),2&t){const t=Ym();rc(1),Dm("ngForOf",t.cursoredData)("ngForTrackBy",t.trackBySeriesName)}}function Iut(t,e){if(1&t&&(qi(),Tm(0,"rect",13)),2&t){const t=Ym();jp("x",t.zoomBoxInUiCoordinate.x)("width",t.zoomBoxInUiCoordinate.width)("y",t.zoomBoxInUiCoordinate.y)("height",t.zoomBoxInUiCoordinate.height)}}const Hut=function(t,e){return{data:t,cursorLocationInDataCoord:e}};function Fut(t,e){if(1&t&&(Rm(0,"div",14),Im(1,15),Am()),2&t){const t=Ym(),e=$p(11);rc(1),Dm("ngTemplateOutlet",t.tooltipTemplate?t.tooltipTemplate:e)("ngTemplateOutletContext",vh(2,Hut,t.cursoredData,t.cursorLocationInDataCoord))}}function Lut(t,e){if(1&t&&(Nm(0),Rm(1,"tr",17),Rm(2,"td",18),Tm(3,"span"),Am(),Rm(4,"td",19),ku(5),Am(),Rm(6,"td"),ku(7),Am(),Rm(8,"td"),ku(9),Am(),Am(),zm()),2&t){const t=e.$implicit;rc(3),du("background-color",t.metadata.color),rc(2),Su(t.metadata.displayName),rc(2),Su(t.point.y),rc(2),Su(t.point.x)}}function But(t,e){if(1&t&&(Rm(0,"table"),Rm(1,"thead"),Rm(2,"tr"),Tm(3,"th",16),Rm(4,"th"),ku(5,"Name"),Am(),Rm(6,"th"),ku(7,"Y"),Am(),Rm(8,"th"),ku(9,"X"),Am(),Am(),Am(),Rm(10,"tbody"),Qp(11,Lut,10,5,"ng-container",10),Am(),Am()),2&t){const t=e.data,n=Ym();rc(11),Dm("ngForOf",t)("ngForTrackBy",n.trackBySeriesName)}}var Vut;function jut(t){return t.scrollStrategies.reposition()}!(function(t){t[t.NONE=0]="NONE",t[t.DRAG_ZOOMING=1]="DRAG_ZOOMING",t[t.SCROLL_ZOOMING=2]="SCROLL_ZOOMING",t[t.PANNING=3]="PANNING"})(Vut||(Vut={}));class Uut{constructor(t,e){this.changeDetector=t,this.scrollStrategy=e,this.onViewExtentChange=new Lh,this.onViewExtentReset=new Lh,this.InteractionState=Vut,this.state=Vut.NONE,this.specialKeyPressed=!1,this.zoomBoxInUiCoordinate={x:0,width:0,height:0,y:0},this.tooltipPositions=[{offsetY:5,originX:"start",overlayX:"start",originY:"bottom",overlayY:"top"},{offsetY:5,originX:"end",overlayX:"end",originY:"bottom",overlayY:"top"},{offsetY:-15,originX:"start",overlayX:"start",originY:"top",overlayY:"bottom"},{offsetY:-15,originX:"end",overlayX:"end",originY:"top",overlayY:"bottom"},{offsetX:5,originX:"end",overlayX:"start",originY:"top",overlayY:"top"},{offsetX:-5,originX:"start",overlayX:"end",originY:"top",overlayY:"top"}],this.cursorLocationInDataCoord=null,this.cursoredData=[],this.tooltipDisplayAttached=!1,this.showZoomInstruction=!1,this.dragStartCoord=null,this.isCursorInside=!1,this.ngUnsubscribe=new I}ngAfterViewInit(){oe(this.dotsContainer.nativeElement,"dblclick",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((()=>{this.onViewExtentReset.emit(),this.state=Vut.NONE,this.changeDetector.markForCheck()})),oe(window,"keydown",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{const e=this.shouldPan(t);e!==this.specialKeyPressed&&(this.specialKeyPressed=e,this.changeDetector.markForCheck())})),oe(window,"keyup",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{const e=this.shouldPan(t);e!==this.specialKeyPressed&&(this.specialKeyPressed=e,this.changeDetector.markForCheck())})),oe(this.dotsContainer.nativeElement,"mousedown",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{const e=this.state,n=this.shouldPan(t)?Vut.PANNING:Vut.DRAG_ZOOMING;e===Vut.NONE&&n===Vut.DRAG_ZOOMING&&(this.dragStartCoord={x:t.offsetX,y:t.offsetY},this.zoomBoxInUiCoordinate={x:t.offsetX,width:0,y:t.offsetY,height:0}),e!==n&&(this.state=n,this.changeDetector.markForCheck())})),oe(this.dotsContainer.nativeElement,"mouseup",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{const e=(t.buttons&gQ.LEFT)===gQ.LEFT;this.dragStartCoord=null;const n=this.zoomBoxInUiCoordinate;if(!e&&this.state===Vut.DRAG_ZOOMING&&n.width>0&&n.height>0){const t=this.getDataX(n.x),e=this.getDataX(n.x+n.width),o=this.getDataY(n.y+n.height),i=this.getDataY(n.y);this.onViewExtentChange.emit({dataExtent:{x:[t,e],y:[o,i]}})}this.state!==Vut.NONE&&(this.state=Vut.NONE,this.changeDetector.markForCheck())})),oe(this.dotsContainer.nativeElement,"mouseenter",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{this.isCursorInside=!0,this.updateTooltip(t),this.changeDetector.markForCheck()})),oe(this.dotsContainer.nativeElement,"mouseleave",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{this.dragStartCoord=null,this.isCursorInside=!1,this.updateTooltip(t),this.state=Vut.NONE,this.changeDetector.markForCheck()})),oe(this.dotsContainer.nativeElement,"mousemove",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{switch(this.state){case Vut.SCROLL_ZOOMING:this.state=Vut.NONE,this.updateTooltip(t),this.changeDetector.markForCheck();break;case Vut.NONE:this.updateTooltip(t),this.changeDetector.markForCheck();break;case Vut.PANNING:{const e=-t.movementX,n=-t.movementY,{width:o,height:i}=this.domDim,a=this.getDataX(e),r=this.getDataX(o+e),s=this.getDataY(i+n),l=this.getDataY(n);this.onViewExtentChange.emit({dataExtent:{x:[a,r],y:[s,l]}});break}case Vut.DRAG_ZOOMING:{if(!this.dragStartCoord)break;const e=[this.dragStartCoord.x,t.offsetX],n=[this.dragStartCoord.y,t.offsetY];this.zoomBoxInUiCoordinate={x:Math.min(...e),width:Math.max(...e)-Math.min(...e),y:Math.min(...n),height:Math.max(...n)-Math.min(...n)}}this.changeDetector.markForCheck()}})),oe(this.dotsContainer.nativeElement,"wheel",{passive:!1}).pipe(Ie(this.ngUnsubscribe),ze((t=>{const e=!t.ctrlKey&&!t.shiftKey&&t.altKey;return this.showZoomInstruction=!e,this.changeDetector.markForCheck(),e?(t.preventDefault(),Et(t)):ae(3e3).pipe(Fe((()=>{this.showZoomInstruction=!1,this.changeDetector.markForCheck()})),It((()=>null)))})),ce((t=>Boolean(t)))).subscribe((t=>{this.onViewExtentChange.emit({dataExtent:Rut(t,this.viewExtent,this.domDim,.01,this.xScale,this.yScale)}),this.state!==Vut.SCROLL_ZOOMING&&(this.state=Vut.SCROLL_ZOOMING,this.changeDetector.markForCheck())}))}ngOnChanges(){this.updateCursoredDataAndTooltipVisibility()}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}shouldPan(t){const e=t.shiftKey||t.altKey;if(t instanceof KeyboardEvent)return e;const n=(t.buttons&gQ.LEFT)===gQ.LEFT,o=(t.buttons&gQ.MIDDLE)===gQ.MIDDLE;return!(!n&&!o)&&(o&&!n||e)}trackBySeriesName(t,e){return e.id}getDomX(t){return this.xScale.forward(this.viewExtent.x,wut(this.domDim,"x"),t)}getDataX(t){return this.xScale.reverse(this.viewExtent.x,wut(this.domDim,"x"),t)}getDomY(t){return this.yScale.forward(this.viewExtent.y,wut(this.domDim,"y"),t)}getDataY(t){return this.yScale.reverse(this.viewExtent.y,wut(this.domDim,"y"),t)}shouldRenderTooltipPoint(t){return null!==t&&!isNaN(t.x)&&!isNaN(t.y)}updateTooltip(t){this.cursorLocationInDataCoord={x:this.getDataX(t.offsetX),y:this.getDataY(t.offsetY)},this.updateCursoredDataAndTooltipVisibility()}onTooltipDisplayDetached(){this.tooltipDisplayAttached=!1}updateCursoredDataAndTooltipVisibility(){const t=this.cursorLocationInDataCoord;if(null===t)return this.cursoredData=[],void(this.tooltipDisplayAttached=!1);this.cursoredData=this.isCursorInside?this.seriesData.map((t=>({seriesDatum:t,metadata:this.seriesMetadataMap[t.id]}))).filter((({metadata:t})=>t&&t.visible&&!Boolean(t.aux))).map((({seriesDatum:e,metadata:n})=>{const o=(function i(t,e){const n=Math.min(p5(t.map((({x:t})=>t)),e),t.length-1),o=Math.max(0,n-1);return Math.abs(t[o].x-e)-Math.abs(t[n].x-e)<=0?o:n})(e.points,t.x);return{id:e.id,closestPointIndex:o,point:e.points[o],metadata:n}})).filter((t=>t)):[],this.tooltipDisplayAttached=Boolean(this.cursoredData.length)}}function Gut(t,e,n,o){return{major:[],minor:t.ticks(o,n).map((t=>({value:t,tickFormattedString:e.formatTick(t)})))}}Uut.ɵfac=function t(e){return new(e||Uut)(Sm(Ug),Sm(LF))},Uut.ɵcmp=to({type:Uut,selectors:[["line-chart-interactive-view"]],viewQuery:function t(e,n){if(1&e&&(Qh(Aut,7,hg),Qh(gL,5)),2&e){let t;Jh(t=tb())&&(n.dotsContainer=t.first),Jh(t=tb())&&(n.overlay=t.first)}},hostVars:2,hostBindings:function t(e,n){2&e&&pu("show-zoom-instruction",n.showZoomInstruction)},inputs:{seriesData:"seriesData",seriesMetadataMap:"seriesMetadataMap",viewExtent:"viewExtent",xScale:"xScale",yScale:"yScale",domDim:"domDim",tooltipOriginEl:"tooltipOriginEl",tooltipTemplate:"tooltipTemplate"},outputs:{onViewExtentChange:"onViewExtentChange",onViewExtentReset:"onViewExtentReset"},features:[pg([{provide:LF,useFactory:jut,deps:[pL]}]),Bo],decls:12,vars:15,consts:[[1,"dots"],["dots",""],[4,"ngIf"],["class","zoom-box",4,"ngIf"],[1,"zoom-instruction"],[1,"instruction-content"],["cdkOverlayOrigin","",1,"tooltip-origin"],["tooltipOrigin","cdkOverlayOrigin"],["cdkConnectedOverlay","",3,"cdkConnectedOverlayOrigin","cdkConnectedOverlayOpen","cdkConnectedOverlayPositions","cdkConnectedOverlayScrollStrategy","cdkConnectedOverlayLockPosition","cdkConnectedOverlayFlexibleDimensions","cdkConnectedOverlayGrowAfterOpen","detach"],["defaultTooltip",""],[4,"ngFor","ngForOf","ngForTrackBy"],["r","4",4,"ngIf"],["r","4"],[1,"zoom-box"],[1,"tooltip-container"],[3,"ngTemplateOutlet","ngTemplateOutletContext"],[1,"circle-header"],[1,"tooltip-row"],[1,"tooltip-row-circle"],[1,"name"]],template:function t(e,n){1&e&&(qi(),Rm(0,"svg",0,1),Qp(2,zut,2,2,"ng-container",2),Qp(3,Iut,1,4,"rect",3),Am(),Zi(),Rm(4,"div",4),Rm(5,"span",5),ku(6,"Alt + Scroll to Zoom"),Am(),Am(),Tm(7,"div",6,7),Qp(9,Fut,2,5,"ng-template",8),Vm("detach",(function t(){return n.onTooltipDisplayDetached()})),Qp(10,But,12,2,"ng-template",null,9,ib)),2&e&&(pu("pannable",n.specialKeyPressed)("draggable",n.state===n.InteractionState.NONE||n.state===n.InteractionState.DRAG_ZOOMING)("panning",n.state===n.InteractionState.PANNING),rc(2),Dm("ngIf",n.state===n.InteractionState.NONE),rc(1),Dm("ngIf",n.state===n.InteractionState.DRAG_ZOOMING),rc(6),Dm("cdkConnectedOverlayOrigin",n.tooltipOriginEl)("cdkConnectedOverlayOpen",n.tooltipDisplayAttached&&n.state===n.InteractionState.NONE)("cdkConnectedOverlayPositions",n.tooltipPositions)("cdkConnectedOverlayScrollStrategy",n.scrollStrategy)("cdkConnectedOverlayLockPosition",!1)("cdkConnectedOverlayFlexibleDimensions",!0)("cdkConnectedOverlayGrowAfterOpen",!0))},directives:[dM,fL,gL,lM,MM],styles:["[_nghost-%COMP%]{display:flex;position:relative;user-select:none}.dots[_ngcontent-%COMP%]{height:100%;width:100%}.dots.draggable[_ngcontent-%COMP%]{cursor:crosshair}.dots.pannable[_ngcontent-%COMP%]{cursor:grab}.dots.panning[_ngcontent-%COMP%]{cursor:grabbing}.tooltip-row-circle[_ngcontent-%COMP%]{align-items:center;display:inline-flex;height:12px;width:12px}.tooltip-row-circle[_ngcontent-%COMP%] > span[_ngcontent-%COMP%]{border-radius:50%;border:1px solid rgba(255,255,255,.6);display:inline-block;height:10px;width:10px}.tooltip-origin[_ngcontent-%COMP%]{bottom:0;left:0;position:absolute;right:0}.tooltip-container[_ngcontent-%COMP%]{background:rgba(0,0,0,.85);border-radius:4px;color:#fff;contain:paint style layout;font-size:.9em;overflow:auto;padding:5px;pointer-events:none;width:100%}th[_ngcontent-%COMP%], td[_ngcontent-%COMP%]{padding:2px 5px;text-align:left}th[_ngcontent-%COMP%]{font-weight:500;padding-bottom:5px}.zoom-box[_ngcontent-%COMP%]{fill-opacity:.03;fill:#000;stroke:#ccc}.zoom-instruction[_ngcontent-%COMP%]{align-items:center;display:flex;justify-content:center;left:0;opacity:0;pointer-events:none;position:absolute;right:0;top:10px;transition:opacity .5s;z-index:1}.instruction-content[_ngcontent-%COMP%]{background:rgba(0,0,0,.6);border-radius:5px;color:#fff;padding:5px 10px;user-select:none}.show-zoom-instruction[_nghost-%COMP%]   .zoom-instruction[_ngcontent-%COMP%]{opacity:1}"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Uut,[{type:My,args:[{selector:"line-chart-interactive-view",templateUrl:"./line_chart_interactive_view.ng.html",styleUrls:["./line_chart_interactive_view.css"],changeDetection:zn.OnPush,providers:[{provide:LF,useFactory:jut,deps:[pL]}]}]}],(function(){return[{type:Ug},{type:LF}]}),{dotsContainer:[{type:Za,args:["dots",{static:!0,read:hg}]}],overlay:[{type:Za,args:[gL]}],seriesData:[{type:xy}],seriesMetadataMap:[{type:xy}],viewExtent:[{type:xy}],xScale:[{type:xy}],yScale:[{type:xy}],domDim:[{type:xy}],tooltipOriginEl:[{type:xy}],tooltipTemplate:[{type:xy}],onViewExtentChange:[{type:Oy}],onViewExtentReset:[{type:Oy}],showZoomInstruction:[{type:Py,args:["class.show-zoom-instruction"]}]});const Wut=document.createElement("canvas").getContext("2d");function Yut(t,e){if(1&t&&(qi(),Rm(0,"g",17),Rm(1,"text"),ku(2),Am(),Rm(3,"title"),ku(4),Am(),Am()),2&t){const t=e.$implicit,n=Ym();rc(1),du("font",n.axisFont),jp("x",n.textXPosition(t.value))("y",n.textYPosition(t.value)),rc(1),Du(" ",t.tickFormattedString," "),rc(2),Su(n.getFormatter().formatLong(t.value))}}function qut(t,e){if(1&t&&(Rm(0,"span",20),Rm(1,"span"),ku(2),Am(),Am()),2&t){const t=e.$implicit,n=e.index,o=e.last,i=Ym(2);du("left",i.getMajorXPosition(t),"px")("width",i.getMajorWidthString(t,o,i.majorTicks[n+1]))("bottom",i.getMajorYPosition(t),"px")("height",i.getMajorHeightString(t,o,i.majorTicks[n+1]))("font",i.axisFont),pu("major-label",!0)("last",o),Dm("title",i.getFormatter().formatLong(t.start)),rc(2),Su(t.tickFormattedString)}}function Zut(t,e){if(1&t&&(Rm(0,"div",18),Qp(1,qut,3,16,"span",19),Am()),2&t){const t=Ym();rc(1),Dm("ngForOf",t.majorTicks)("ngForTrackBy",t.trackByMajorTick)}}class Xut{constructor(){this.onViewExtentChange=new Lh,this.editMenuOpened=!1,this.majorTicks=[],this.minorTicks=[]}ngOnChanges(){let t=null;const e=kut("x"===this.axis?this.domDim.width:this.domDim.height,this.gridCount);t=this.scale instanceof tit?(function n(t,e,o,i){const[a,r]=i,s=Math.abs(r-a);if(s>.001)return Gut(t,e,o,i);const l=t.ticks([a,r],o),c=t.ticks([a,r],2),d=[];let p=(function m(t){const e=t.toExponential().split("e-",2);return 2===e.length?Number(e[1])-1:0})(s);s<1&&c.every((t=>{const e=Math.abs(t);return e>=0&&e<1}))&&(p+=1);const u=new Map;for(const t of c){const[n,o=""]=String(t).split(".",2),i=Number(n+"."+o.slice(0,p));u.set(i,{start:i,tickFormattedString:0===i?"—":e.formatReadable(i)})}const f=10*Math.pow(10,-p);for(const t of l)for(const n of[...u.keys()].reverse()){const o=t-n;if(o>=0&&o<f){if(0===n)d.push({value:t,tickFormattedString:e.formatTick(t)});else{const e=String(t).slice(String(n).length);d.push({value:t,tickFormattedString:`…${e||"0"}`})}break}}return{major:Array.from(u.values()),minor:d}})(this.scale,this.getFormatter(),e,this.axisExtent):this.scale instanceof nit?(function o(t,e,n,i){const[a,r]=i;let s=t.ticks(i,2);if(r-a>=864e5||s.length>2)return Gut(t,e,n,i);const l=t.ticks(i,n);return{major:s.map((t=>({start:t,tickFormattedString:e.formatShort(t)}))),minor:l.map((t=>({value:t,tickFormattedString:e.formatTick(t)})))}})(this.scale,this.getFormatter(),e,this.axisExtent):Gut(this.scale,this.getFormatter(),e,this.axisExtent),this.majorTicks=t.major,this.minorTicks=(function i(t,e,n,o,a=5){if(!t.length||!Wut)return t;const r="x"===n?1:-1;let s=null;return t.filter((t=>{const i=e(t);Wut.font=o;const l=Wut.measureText(t.tickFormattedString),c="x"===n?l.width:l.actualBoundingBoxAscent-l.actualBoundingBoxDescent;return null===s?!(i+r*c<0||(s=i+r*c,0)):!(r*(s+r*a-i)>0||(s=i+r*c,0))}))})(t.minor,(t=>this.getDomPos(t.value)),this.axis,"11px Roboto, sans-serif")}getFormatter(){var t;return null!==(t=this.customFormatter)&&void 0!==t?t:this.scale.defaultFormatter}trackByMinorTick(t){return t.value}trackByMajorTick(t){return t.start}getDomPos(t){return 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.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{display:flex;flex-direction:column;box-sizing:border-box;height:100%;overflow:auto;padding:16px;padding-top:4px}.heading[_ngcontent-%COMP%]{align-items:center;display:flex;font-size:14px;justify-content:space-between;margin-bottom:4px;position:relative}.heading[_ngcontent-%COMP%]   .name[_ngcontent-%COMP%]{align-items:center;display:grid;gap:5px;grid-template-columns:auto auto}.heading[_ngcontent-%COMP%]   vis-selected-time-clipped[_ngcontent-%COMP%]{font-size:1.2em;line-height:0}.tag[_ngcontent-%COMP%]{overflow:hidden}.pin-button[_ngcontent-%COMP%]   mat-icon[_ngcontent-%COMP%]{height:18px}.controls[_ngcontent-%COMP%]{color:#616161;white-space:nowrap;flex-shrink:0;margin-right:-12px}body.dark-mode[_nghost-%COMP%]   .controls[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .controls[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.chart-container[_ngcontent-%COMP%]{position:relative;flex:1}.chart-container[_ngcontent-%COMP%]   mat-spinner[_ngcontent-%COMP%]{position:absolute;right:11px;top:11px}.chart-container[_ngcontent-%COMP%]   line-chart[_ngcontent-%COMP%]{display:block;height:100%}.tooltip[_ngcontent-%COMP%]{border-spacing:4px;font-size:13px}.tooltip[_ngcontent-%COMP%]   th[_ngcontent-%COMP%]{text-align:left}.tooltip[_ngcontent-%COMP%]   .tooltip-row[_ngcontent-%COMP%]{white-space:nowrap}.tooltip[_ngcontent-%COMP%]   .tooltip-row-circle[_ngcontent-%COMP%]{align-items:center;display:inline-flex;height:12px;width:12px}.tooltip[_ngcontent-%COMP%]   .tooltip-row-circle[_ngcontent-%COMP%] > span[_ngcontent-%COMP%]{border-radius:50%;border:1px solid rgba(255,255,255,.4);display:inline-block;height:10px;width:10px}.tooltip[_ngcontent-%COMP%]   .closest[_ngcontent-%COMP%]   .tooltip-row-circle[_ngcontent-%COMP%] > span[_ngcontent-%COMP%]{border-color:#fff;box-shadow:inset 0 0 0 1px #fff}.out-of-selected-time[_ngcontent-%COMP%]{border:0 dashed currentColor;height:100%;position:absolute}.out-of-selected-time.start[_ngcontent-%COMP%]{border-right-width:2px;margin-left:-1px}.out-of-selected-time.start.range[_ngcontent-%COMP%]{left:0}.out-of-selected-time.end[_ngcontent-%COMP%]{border-left-width:2px;margin-right:-1px;right:0}.out-of-selected-time.range[_ngcontent-%COMP%]{background-color:rgba(255,255,255,.5)}body.dark-mode[_nghost-%COMP%]   .out-of-selected-time.range[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .out-of-selected-time.range[_ngcontent-%COMP%]{background-color:rgba(0,0,0,.4)}.linked-time-fob-container[_ngcontent-%COMP%]{display:inline-block;left:0;position:absolute;top:0}linked-time-fob[_ngcontent-%COMP%]{transform:translateX(-50%)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Tft,[{type:My,args:[{selector:"scalar-card-component",templateUrl:"scalar_card_component.ng.html",styleUrls:["scalar_card_component.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:hg},{type:oW}]}),{cardId:[{type:xy}],chartMetadataMap:[{type:xy}],DataDownloadComponent:[{type:xy}],dataSeries:[{type:xy}],ignoreOutliers:[{type:xy}],isCardVisible:[{type:xy}],isPinned:[{type:xy}],loadState:[{type:xy}],showFullSize:[{type:xy}],smoothingEnabled:[{type:xy}],tag:[{type:xy}],title:[{type:xy}],tooltipSort:[{type:xy}],xAxisType:[{type:xy}],xScaleType:[{type:xy}],useDarkMode:[{type:xy}],selectedTime:[{type:xy}],onFullSizeToggle:[{type:Oy}],onPinClicked:[{type:Oy}],lineChart:[{type:Za,args:[fft]}]});class Nft{constructor(t,e){this.ref=t,this.cdkScrollable=e,this.onVisibilityChange=new Lh,this.ngUnsubscribe$=new I,this.onEvent$=new I}ngOnInit(){const t=new IntersectionObserver((t=>{this.onEvent$.next(t)}),{root:this.cdkScrollable?this.cdkScrollable.getElementRef().nativeElement:null,rootMargin:this.intersectionObserverMargin});t.observe(this.ref.nativeElement),this.ngUnsubscribe$.subscribe((()=>{t.unobserve(this.ref.nativeElement)})),this.onEvent$.pipe(Ie(this.ngUnsubscribe$)).subscribe((t=>{const e=t.slice(-1)[0];this.onVisibilityChange.emit({visible:e.isIntersecting})}))}ngOnDestroy(){this.ngUnsubscribe$.next(),this.ngUnsubscribe$.complete()}waitForEventForTestOnly(){return new Promise((t=>this.onEvent$.pipe(be(1)).subscribe((()=>{t()}))))}}function zft(t,e){return t.length===e.length&&t.every(((t,n)=>{const o=e[n],i=t.points,a=o.points;return t.runId===o.runId&&i.length===a.length&&i.every(((t,e)=>{const n=a[e];return t.x===n.x&&t.y===n.y}))}))}Nft.ɵfac=function t(e){return new(e||Nft)(Sm(hg),Sm(mF,8))},Nft.ɵdir=lo({type:Nft,selectors:[["","observeIntersection",""]],inputs:{intersectionObserverMargin:"intersectionObserverMargin"},outputs:{onVisibilityChange:"onVisibilityChange"}}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Nft,[{type:Cy,args:[{selector:"[observeIntersection]"}]}],(function(){return[{type:hg},{type:mF,decorators:[{type:Sr}]}]}),{intersectionObserverMargin:[{type:xy}],onVisibilityChange:[{type:Oy}]});class Ift{constructor(t){this.store=t,this.DataDownloadComponent=l5,this.fullWidthChanged=new Lh,this.fullHeightChanged=new Lh,this.pinStateChanged=new Lh,this.isVisible=!1,this.useDarkMode$=this.store.select(JD),this.ignoreOutliers$=this.store.select(AT),this.tooltipSort$=this.store.select(RT),this.xAxisType$=this.store.select(TT),this.xScaleType$=this.store.select(TT).pipe(It((t=>{switch(t){case yA.STEP:case yA.RELATIVE:return J2.LINEAR;case yA.WALL_TIME:return J2.TIME;default:throw new Error(`Invalid xAxisType for line chart. ${t}`)}}))),this.scalarSmoothing$=this.store.select(zT),this.smoothingEnabled$=this.store.select(zT).pipe(It((t=>t>0))),this.showFullSize=!1,this.ngUnsubscribe=new I}onVisibilityChange({visible:t}){this.isVisible=t}isScalarCardMetadata(t){const{plugin:e}=t;return e===hA.SCALARS}onFullSizeToggle(){this.showFullSize=!this.showFullSize,this.fullWidthChanged.emit(this.showFullSize),this.fullHeightChanged.emit(this.showFullSize)}ngOnInit(){const t=this.store.select(yT,this.cardId).pipe(ce((t=>!!t&&this.isScalarCardMetadata(t))),It((t=>t)));function e(t){return JSON.stringify(["smoothed",t])}const n=this.store.select(hT,this.cardId).pipe(Ie(this.ngUnsubscribe),ce((t=>Boolean(t))),It((t=>t)),Ae(1)).pipe(fe(this.store.select(TT)),It((([t,e])=>Object.keys(t).map((n=>({runId:n,points:this.stepSeriesToLineSeries(t[n],e)}))))),Me(zft)).pipe(fe(this.store.select(IT)),Ie(this.ngUnsubscribe),It((([t,e])=>e?(function n(t){var e;const n=[];for(const o of t){const t=[];let i=Number.isFinite(null===(e=o.points[0])||void 0===e?void 0:e.x)?o.points[0].x:-1/0,a=[];for(const e of o.points)Number.isFinite(e.x)?(e.x<i&&(t.push({seriesId:JSON.stringify([o.runId,t.length]),runId:o.runId,points:a}),a=[]),a.push(e),i=e.x):a.push(e);t.push({seriesId:JSON.stringify([o.runId,t.length]),runId:o.runId,points:a});for(let e=0;e<t.length;e++)n.push(Object.assign(Object.assign({},t[e]),{partitionIndex:e,partitionSize:t.length}))}return n})(t):t.map((t=>Object.assign(Object.assign({},t),{seriesId:t.runId,partitionIndex:0,partitionSize:1}))))),It((t=>t.map((t=>{var e;const n=null===(e=t.points[0])||void 0===e?void 0:e.wallTime;return Object.assign(Object.assign({},t),{points:t.points.map((t=>Object.assign(Object.assign({},t),{relativeTimeInMs:t.wallTime-n})))})})))),fe(this.store.select(TT)),It((([t,e])=>t.map((t=>Object.assign(Object.assign({},t),{points:t.points.map((t=>{let n;switch(e){case yA.RELATIVE:n=t.relativeTimeInMs;break;case yA.WALL_TIME:n=t.wallTime;break;case yA.STEP:default:n=t.step}return Object.assign(Object.assign({},t),{x:n})}))}))))),Ae(1));this.dataSeries$=n.pipe(fe(this.store.select(zT)),ze((([t,n])=>{const o=t.map((({seriesId:t,points:e})=>({id:t,points:e})));return n<=0?Et(o):Ct((function i(t,e){var n;return gA(this,void 0,void 0,(function*(){Number.isFinite(e)||(e=0),e=Math.max(0,Math.min(e,1));const o=[];for(const i of t){const t=null===(n=i.points[0])||void 0===n?void 0:n.y;if(i.points.every((e=>e.y==t))){o.push(i);continue}let a=i.points.length>0?0:NaN,r=0;const s=i.points.map((t=>{const n=t.y;if(Number.isFinite(n)){a=a*e+(1-e)*n,r++;const o=1===e?1:1-Math.pow(e,r);return{x:t.x,y:a/o}}return{x:t.x,y:n}}));o.push({id:i.id,points:s})}return o}))})(o,n)).pipe(It((t=>{const n=o.map(((n,o)=>({id:e(n.id),points:t[o].points.map((({y:t},e)=>Object.assign(Object.assign({},n.points[e]),{y:t})))})));return[...o,...n]})))})),Ne([])),this.selectedTime$=Wt([n,this.store.select(YT),this.store.select(TT)]).pipe(It((([t,e,n])=>{if(n!==yA.STEP||!e)return null;let o=1/0,i=-1/0;for(const{points:e}of t)for(const t of e)o=o>t.x?t.x:o,i=i<t.x?t.x:i;return H2(e,o,i)}))),this.chartMetadataMap$=n.pipe(ze((t=>Wt(t.map((t=>this.getRunDisplayName(t.runId).pipe(It((e=>Object.assign(Object.assign({},t),{displayName:e}))))))))),fe(this.store.select(NN),this.store.select(zN),this.store.select(zT)),ge(0),It((([t,n,o,i])=>{var a;const r={},s=i>0;for(const e of t){const{seriesId:t,runId:i,displayName:s,partitionIndex:l,partitionSize:c}=e;r[t]={type:Q2.ORIGINAL,id:t,displayName:c>1?`${s}: ${l}`:s,visible:Boolean(n&&n.get(i)),color:null!==(a=o[i])&&void 0!==a?a:"#fff",aux:!1,opacity:1}}if(!s)return r;for(const[t,n]of Object.entries(r)){const o=e(t);r[o]=Object.assign(Object.assign({},n),{id:o,type:Q2.DERIVED,aux:!1,originalSeriesId:t}),n.aux=!0,n.opacity=.25}return r})),Ne({})),this.loadState$=this.store.select(gT,this.cardId),this.tag$=t.pipe(It((t=>t.tag))),this.title$=this.tag$.pipe(It((t=>f2(t,this.groupName)))),this.isPinned$=this.store.select(wT,this.cardId)}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}getRunDisplayName(t){return Wt([this.store.select(rN,{runId:t}),this.store.select(zS),this.store.select(sN,{runId:t})]).pipe(It((([e,n,o])=>I2(t,o,e?n[e]:null))))}stepSeriesToLineSeries(t,e){const n=e===yA.STEP;return t.map((t=>{const e=1e3*t.wallTime;return Object.assign(Object.assign({},t),{x:n?t.step:e,y:t.value,wallTime:e,relativeTimeInMs:0})}))}}function Hft(t,e,n){const o=[],{left:i,right:a}=e,r=(a-i)/n;let s=0,l=0;for(let e=0;e<n;e++){const a=i+e*r,c=a+r,d=e===n-1;let p=l;for(l=0;s<t.length;){const e=t[s],n=Fft(e,a,c,!d);if(p+=n.curr,l+=n.next,e.x+e.dx>c)break;s++}o.push({x:a,dx:r,y:p})}return o}function Fft(t,e,n,o){const i=t.x,a=t.x+t.dx;if(i>n||a<e)return{curr:0,next:0};if(0===t.dx)return o&&a===n?{curr:.5*t.y,next:.5*t.y}:{curr:t.y,next:0};const r=Math.min(a,n)-Math.max(i,e);return{curr:t.y*r/t.dx,next:0}}Ift.ɵfac=function t(e){return new(e||Ift)(Sm(Iw))},Ift.ɵcmp=to({type:Ift,selectors:[["scalar-card"]],inputs:{DataDownloadComponent:"DataDownloadComponent",cardId:"cardId",groupName:"groupName"},outputs:{fullWidthChanged:"fullWidthChanged",fullHeightChanged:"fullHeightChanged",pinStateChanged:"pinStateChanged"},decls:14,vars:43,consts:[["observeIntersection","",3,"cardId","chartMetadataMap","DataDownloadComponent","dataSeries","ignoreOutliers","isCardVisible","isPinned","loadState","showFullSize","smoothingEnabled","tag","title","tooltipSort","xAxisType","xScaleType","useDarkMode","selectedTime","onFullSizeToggle","onPinClicked","onVisibilityChange"]],template:function t(e,n){1&e&&(Rm(0,"scalar-card-component",0),Vm("onFullSizeToggle",(function t(){return n.onFullSizeToggle()}))("onPinClicked",(function t(e){return n.pinStateChanged.emit(e)}))("onVisibilityChange",(function t(e){return n.onVisibilityChange(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Ah(7,"async"),Ah(8,"async"),Ah(9,"async"),Ah(10,"async"),Ah(11,"async"),Ah(12,"async"),Ah(13,"async"),Am()),2&e&&Dm("cardId",n.cardId)("chartMetadataMap",Th(1,17,n.chartMetadataMap$))("DataDownloadComponent",n.DataDownloadComponent)("dataSeries",Th(2,19,n.dataSeries$))("ignoreOutliers",Th(3,21,n.ignoreOutliers$))("isCardVisible",n.isVisible)("isPinned",Th(4,23,n.isPinned$))("loadState",Th(5,25,n.loadState$))("showFullSize",n.showFullSize)("smoothingEnabled",Th(6,27,n.smoothingEnabled$))("tag",Th(7,29,n.tag$))("title",Th(8,31,n.title$))("tooltipSort",Th(9,33,n.tooltipSort$))("xAxisType",Th(10,35,n.xAxisType$))("xScaleType",Th(11,37,n.xScaleType$))("useDarkMode",Th(12,39,n.useDarkMode$))("selectedTime",Th(13,41,n.selectedTime$))},directives:[Tft,Nft],pipes:[wM],styles:["[_nghost-%COMP%] {\n        display: block;\n        height: 100%;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Ift,[{type:My,args:[{selector:"scalar-card",template:'\n    <scalar-card-component\n      [cardId]="cardId"\n      [chartMetadataMap]="chartMetadataMap$ | async"\n      [DataDownloadComponent]="DataDownloadComponent"\n      [dataSeries]="dataSeries$ | async"\n      [ignoreOutliers]="ignoreOutliers$ | async"\n      [isCardVisible]="isVisible"\n      [isPinned]="isPinned$ | async"\n      [loadState]="loadState$ | async"\n      [showFullSize]="showFullSize"\n      [smoothingEnabled]="smoothingEnabled$ | async"\n      [tag]="tag$ | async"\n      [title]="title$ | async"\n      [tooltipSort]="tooltipSort$ | async"\n      [xAxisType]="xAxisType$ | async"\n      [xScaleType]="xScaleType$ | async"\n      [useDarkMode]="useDarkMode$ | async"\n      [selectedTime]="selectedTime$ | async"\n      (onFullSizeToggle)="onFullSizeToggle()"\n      (onPinClicked)="pinStateChanged.emit($event)"\n      observeIntersection\n      (onVisibilityChange)="onVisibilityChange($event)"\n    ></scalar-card-component>\n  ',styles:["\n      :host {\n        display: block;\n        height: 100%;\n      }\n    "],changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{DataDownloadComponent:[{type:xy}],cardId:[{type:xy}],groupName:[{type:xy}],fullWidthChanged:[{type:Oy}],fullHeightChanged:[{type:Oy}],pinStateChanged:[{type:Oy}]});const Lft=["main"],Bft=["xAxis"],Vft=["yAxis"],jft=["content"],Uft=["histograms"];function Gft(t,e){if(1&t&&(qi(),Rm(0,"g"),Rm(1,"text"),ku(2),Am(),Am()),2&t){const t=Ym();du("transform",t.getCssTranslatePx(t.tooltipData.xAxis.position,9)),rc(2),Su(t.tooltipData.xAxis.label)}}function Wft(t,e){if(1&t&&(qi(),Rm(0,"g"),Rm(1,"text"),ku(2),Am(),Am()),2&t){const t=Ym();du("transform",t.getGroupTransform(t.tooltipData.closestDatum)),rc(1),jp("y",t.tooltipData.yAxis.position),rc(1),Du(" ",t.tooltipData.yAxis.label," ")}}function Yft(t,e){if(1&t&&(Rm(0,"div",16),Tm(1,"linked-time-fob",17),Am()),2&t){const t=Ym(2);du("transform",t.getCssTranslatePx(0,t.scales.temporalScale(t.linkedTime.endStep))),rc(1),Dm("step",t.linkedTime.endStep)}}function qft(t,e){if(1&t&&(qi(),Zi(),Nm(0),Rm(1,"div",16),Tm(2,"linked-time-fob",17),Am(),Qp(3,Yft,2,3,"div",18),zm()),2&t){const t=Ym();rc(1),du("transform",t.getCssTranslatePx(0,t.scales.temporalScale(t.linkedTime.startStep))),rc(1),Dm("step",t.linkedTime.startStep),rc(1),Dm("ngIf",t.linkedTime.endStep)}}function Zft(t,e){if(1&t&&(qi(),Rm(0,"g"),Tm(1,"line",19),Am()),2&t){const t=e.$implicit;du("transform",Ym().getCssTranslatePx(0,t))}}function Xft(t,e){1&t&&(qi(),Tm(0,"line",22))}function Kft(t,e){if(1&t&&(qi(),Tm(0,"circle",23)),2&t){const t=Ym().$implicit,e=Ym();du("transform",e.getCssTranslatePx(e.getUiCoordFromBinForContent(e.getClosestBinFromBinCoordinate(t,e.tooltipData.xPositionInBinCoord)).x,e.getUiCoordFromBinForContent(e.getClosestBinFromBinCoordinate(t,e.tooltipData.xPositionInBinCoord)).y))}}function Jft(t,e){if(1&t&&(qi(),Rm(0,"g"),Qp(1,Xft,1,0,"line",20),Tm(2,"path"),Qp(3,Kft,1,2,"circle",21),Am()),2&t){const t=e.$implicit,n=Ym();du("transform",n.getGroupTransform(t))("color",n.getHistogramFill(t)),pu("histogram",!0)("no-color",!n.isDatumInLinkedTimeRange(t)),rc(1),Dm("ngIf",n.mode===n.HistogramMode.OFFSET),rc(1),jp("d",n.getHistogramPath(t)),rc(1),Dm("ngIf",n.tooltipData)}}function Qft(t,e){if(1&t&&(qi(),Tm(0,"circle",23)),2&t){const t=Ym(2);jp("cx",t.getUiCoordFromBinForContent(t.tooltipData.closestBin).x)("cy",t.getUiCoordFromBinForContent(t.tooltipData.closestBin).y)}}function $ft(t,e){if(1&t&&(qi(),Rm(0,"g",4),Rm(1,"g"),Tm(2,"path"),Qp(3,Qft,1,2,"circle",24),Am(),Rm(4,"g",25),Rm(5,"text",26),ku(6),Am(),Am(),Am()),2&t){const t=Ym();rc(1),du("transform",t.getGroupTransform(t.tooltipData.closestDatum)),rc(1),jp("d",t.getHistogramPath(t.tooltipData.closestDatum)),rc(1),Dm("ngIf",t.tooltipData.closestBin),rc(1),du("transform",t.getCssTranslatePx(t.tooltipData.value.position.x,t.tooltipData.value.position.y)),rc(2),Su(t.tooltipData.value.label)}}class tgt{constructor(t){this.changeDetector=t,this.mode=pE.OFFSET,this.timeProperty=dE.STEP,this.linkedTime=null,this.HistogramMode=pE,this.TimeProperty=dE,this.tooltipData=null,this.ngUnsubscribe=new I,this.layout={histogramHeight:0,contentClientRect:{height:0,width:0}},this.scales=null,this.formatters={binNumber:Ett(".3~s"),count:Ett(".3n"),wallTime:Wet("%m/%d %X"),step:Ett(".0f"),relative:t=>Ett(".1r")(t/36e5)+"h"},this.domVisible=!1}ngOnChanges(){this.updateChartIfVisible()}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}ngAfterViewInit(){oe(this.main.nativeElement,"mousemove",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>this.onMouseMove(t)))}getCssTranslatePx(t,e){return`translate(${t}px, ${e}px)`}getClosestBinFromBinCoordinate(t,e){if(!t.bins.length)return{x:0,dx:0,y:0};const n=t.bins[0],o=t.bins.slice(-1)[0];return e<n.x?n:e>=o.x+o.dx?o:t.bins.find((t=>t.x<=e&&e<t.x+t.dx))}getUiCoordFromBinForContent(t){return this.scales?{x:this.scales.binScale(ogt(t)),y:this.scales.countScale(t.y)}:{x:0,y:0}}getHistogramPath(t){if(!this.scales||!t.bins.length)return"";const e=this.scales.binScale,n=this.scales.countScale,o=t.bins[0],i=t.bins.slice(-1)[0],a=[`M${e(ogt(o))},${n(0)}`];for(const o of t.bins)a.push(`L${e(ogt(o))},${n(o.y)}`);return a.push(`L${e(ogt(i))},${n(0)}`),a.join("")}trackByWallTime(t){return t.wallTime}getGroupTransform(t){return this.scales&&this.mode!==pE.OVERLAY?this.getCssTranslatePx(0,this.scales.temporalScale(this.getTimeValue(t))):""}isLinkedTimeEnabled(t){return Boolean(this.mode===pE.OFFSET&&this.timeProperty===dE.STEP&&this.scales&&t)}isDatumInLinkedTimeRange(t){return!this.isLinkedTimeEnabled(this.linkedTime)||(null===this.linkedTime.endStep?this.linkedTime.startStep===t.step:this.linkedTime.startStep<=t.step&&this.linkedTime.endStep>=t.step)}getHistogramFill(t){return this.scales?this.scales.d3ColorScale(this.getTimeValue(t)):""}getGridTickYLocs(){if(!this.scales||this.mode===pE.OFFSET)return[];const t=this.scales.countScale;return t.ticks().map((e=>t(e)))}onResize(){this.updateClientRects(),this.updateChartIfVisible()}onVisibilityChange({visible:t}){this.domVisible=t,t&&(this.updateClientRects(),this.updateChartIfVisible())}getTimeValue(t){switch(this.timeProperty){case dE.WALL_TIME:return t.wallTime;case dE.STEP:return t.step;case dE.RELATIVE:return t.wallTime-this.data[0].wallTime}}updateClientRects(){this.content&&(this.layout.contentClientRect=this.content.nativeElement.getBoundingClientRect(),this.layout.histogramHeight=this.layout.contentClientRect.height/2.5)}updateChartIfVisible(){this.domVisible&&(this.scales=this.computeScales(this.data),this.renderXAxis(),this.renderYAxis(),this.changeDetector.detectChanges())}computeScales(t){const{width:e,height:n}=this.layout.contentClientRect,{min:o,max:i}=ngt(t,(t=>(function e(t,n){return t.reduce(((t,e)=>Math.min(t,n(e))),1/0)})(t.bins,(t=>t.x))),(t=>egt(t.bins,(({x:t,dx:e})=>t+e)))),a=egt(t,(t=>egt(t.bins,(({y:t})=>t)))),r=eet().domain([o,i]).nice(),s=this.mode!==pE.OVERLAY&&this.timeProperty==dE.WALL_TIME?fot():eet(),l=t.map((t=>this.getTimeValue(t))),{min:c,max:d}=ngt(l,(t=>t)),p=[c,d];s.domain(p);const m=eet();m.domain([0,a]);const u=S7(this.color||"#000"),f=eet();return f.domain(p),r.range([0,e]),f.range([u.brighter(),u.darker()]),f.interpolate(m8),this.mode===pE.OVERLAY?(s.range([n,n]),m.range([n,0])):(s.range([n-(this.mode===pE.OFFSET?n-this.layout.histogramHeight:0),n]),m.range([0,-this.layout.histogramHeight])),{binScale:r,d3ColorScale:f,countScale:m,temporalScale:s}}renderXAxis(){if(!this.scales)return;const{width:t}=this.layout.contentClientRect,e=z5(this.scales.binScale).ticks(Math.max(2,t/20));e.tickFormat(this.formatters.binNumber),e(J4(this.xAxis.nativeElement))}getYAxisFormatter(){if(this.mode===pE.OVERLAY)return this.formatters.count;switch(this.timeProperty){case dE.WALL_TIME:return this.formatters.wallTime;case dE.STEP:return this.formatters.step;case dE.RELATIVE:return this.formatters.relative;default:throw RangeError(`Y axis formatter for ${this.timeProperty} must be implemented`)}}renderYAxis(){if(!this.scales)return;const t=this.mode===pE.OVERLAY?this.scales.countScale:this.scales.temporalScale,{height:e}=this.layout.contentClientRect,n=N5(t).ticks(Math.max(2,e/15));n.tickFormat(this.getYAxisFormatter()),n(J4(this.yAxis.nativeElement))}findClosestDatumIndex(t){let e=t.target,n=e;for(;e&&e!==this.histograms.nativeElement;)n=e,e=e.parentElement;return e?Array.from(e.children).indexOf(n):-1}onMouseMoveForTestOnly(t){return this.onMouseMove(t)}onMouseMove(t){if(!this.scales)return;const e=t.offsetX,n=t.offsetY,o=this.findClosestDatumIndex(t);if(o<0)return;const i=this.scales.binScale.invert(e),a=this.data[o],r=this.getClosestBinFromBinCoordinate(a,i);this.tooltipData={value:{position:{x:e,y:n},label:this.mode===pE.OFFSET?this.formatters.count(r.y):`Step: ${this.formatters.step(a.step)}`},xAxis:{position:this.getUiCoordFromBinForContent(r).x,label:this.formatters.binNumber(ogt(r))},yAxis:{position:this.scales.countScale(this.mode===pE.OFFSET?0:r.y),label:this.mode===pE.OFFSET?this.getYAxisFormatter()(this.getTimeValue(a)):this.formatters.binNumber(r.y)},xPositionInBinCoord:i,closestDatum:a,closestBin:r},this.changeDetector.detectChanges()}}function egt(t,e){return t.reduce(((t,n)=>Math.max(t,e(n))),-1/0)}function ngt(t,e,n){n||(n=e);let o=1/0,i=-1/0;for(const a of t)o=Math.min(o,e(a)),i=Math.max(i,n(a));return{min:o,max:i}}function ogt(t){return t.x+.5*t.dx}function igt(t,e){1&t&&Tm(0,"vis-selected-time-clipped")}function agt(t,e){1&t&&(Rm(0,"span",14),Tm(1,"mat-spinner",15),Am())}tgt.ɵfac=function t(e){return new(e||tgt)(Sm(Ug))},tgt.ɵcmp=to({type:tgt,selectors:[["tb-histogram"]],viewQuery:function t(e,n){if(1&e&&(Qh(Lft,5),Qh(Bft,5),Qh(Vft,5),Qh(jft,5),Qh(Uft,5)),2&e){let 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auto;font-size:14px;margin-bottom:4px}.tag[_ngcontent-%COMP%]{align-items:center;display:flex;gap:5px;grid-area:tag;overflow:hidden}.tag[_ngcontent-%COMP%]   vis-selected-time-clipped[_ngcontent-%COMP%]{line-height:0}.pin-button[_ngcontent-%COMP%]   mat-icon[_ngcontent-%COMP%]{height:18px}.run[_ngcontent-%COMP%]{grid-area:run;display:flex;white-space:nowrap;font-size:13px}.run[_ngcontent-%COMP%]   .dot[_ngcontent-%COMP%]{flex:none;display:inline-block;width:13px;height:13px;border-radius:50%;margin-right:4px}.run[_ngcontent-%COMP%]   .run-text[_ngcontent-%COMP%]{overflow:hidden;text-overflow:ellipsis;max-width:120px}.controls[_ngcontent-%COMP%]{color:#616161;white-space:nowrap;grid-area:controls;justify-self:flex-end;flex-shrink:0;margin-right:-12px}body.dark-mode[_nghost-%COMP%]   .controls[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .controls[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.spinner[_ngcontent-%COMP%]{display:flex;grid-area:spinner;height:100%;justify-content:center;position:relative}mat-spinner[_ngcontent-%COMP%]{top:0;right:0;position:absolute}tb-histogram[_ngcontent-%COMP%]{flex-grow:1;overflow:hidden}.empty-message[_ngcontent-%COMP%]{margin-top:1em;font-size:13px}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(pgt,[{type:My,args:[{selector:"histogram-card-component",templateUrl:"histogram_card_component.ng.html",styleUrls:["histogram_card_component.css"],changeDetection:zn.OnPush}]}],null,{loadState:[{type:xy}],title:[{type:xy}],tag:[{type:xy}],runId:[{type:xy}],data:[{type:xy}],mode:[{type:xy}],xAxisType:[{type:xy}],runColorScale:[{type:xy}],showFullSize:[{type:xy}],isPinned:[{type:xy}],selectedTime:[{type:xy}],onFullSizeToggle:[{type:Oy}],onPinClicked:[{type:Oy}]});class mgt{constructor(t){this.store=t,this.fullWidthChanged=new Lh,this.fullHeightChanged=new Lh,this.pinStateChanged=new Lh,this.mode$=this.store.select(NT),this.xAxisType$=this.store.select(TT),this.showFullSize=!1}isHistogramCardMetadata(t){const{plugin:e}=t;return e===hA.HISTOGRAMS}onFullSizeToggle(){this.showFullSize=!this.showFullSize,this.fullWidthChanged.emit(this.showFullSize),this.fullHeightChanged.emit(this.showFullSize)}ngOnInit(){const t=this.store.select(yT,this.cardId).pipe(ce((t=>!!t&&this.isHistogramCardMetadata(t))),It((t=>t))),e=Wt([t,this.store.select(hT,this.cardId)]);this.data$=e.pipe(It((([t,e])=>{const n=t.runId;return e&&e.hasOwnProperty(n)?(function o(t,e=30){if(!t.length||e<1)return[];const n=(function o(t){let e=null,n=null;for(const{bins:o}of t){if(!o.length)continue;const t=o[o.length-1],i=o[0].x,a=t.x+t.dx;(null===e||i<e)&&(e=i),(null===n||a>n)&&(n=a)}return null===e||null===n?null:{left:e,right:n}})(t);return n&&n.left===n.right&&(n.right=1.1*n.right+1,n.left=n.left/1.1-1),t.map((t=>({step:t.step,wallTime:t.wallTime,bins:n?Hft(t.bins,n,e):[]})))})(e[n].map((t=>{const{wallTime:e,step:n}=t;return{wallTime:e,step:n,bins:t.bins.map((t=>({x:t.min,dx:t.max-t.min,y:t.count})))}}))):[]}))),this.selectedTime$=this.store.select(YT).pipe(fe(this.data$),It((([t,e])=>{if(!t)return null;let n=1/0,o=-1/0;for(const t of e)n=Math.min(t.step,n),o=Math.max(t.step,o);return H2(t,n,o)}))),this.loadState$=this.store.select(gT,this.cardId),this.tag$=t.pipe(It((t=>t.tag))),this.title$=this.tag$.pipe(It((t=>f2(t,this.groupName)))),this.runId$=t.pipe(It((t=>t.runId))),this.isPinned$=this.store.select(wT,this.cardId)}}function ugt(t,e){if(1&t){const t=Hm();Rm(0,"image-card",6),Vm("fullWidthChanged",(function e(n){return hi(t),Ym(2).onFullWidthChanged(n)}))("pinStateChanged",(function e(){return hi(t),Ym(2).onPinStateChanged()})),Am()}if(2&t){const t=Ym(2);Dm("cardId",t.cardId)("groupName",t.groupName)("runColorScale",t.runColorScale)}}function fgt(t,e){if(1&t){const t=Hm();Rm(0,"scalar-card",7),Vm("fullWidthChanged",(function e(n){return hi(t),Ym(2).onFullWidthChanged(n)}))("fullHeightChanged",(function e(n){return hi(t),Ym(2).onFullHeightChanged(n)}))("pinStateChanged",(function e(){return hi(t),Ym(2).onPinStateChanged()})),Am()}if(2&t){const t=Ym(2);Dm("cardId",t.cardId)("groupName",t.groupName)}}function ggt(t,e){if(1&t){const t=Hm();Rm(0,"histogram-card",8),Vm("fullWidthChanged",(function e(n){return hi(t),Ym(2).onFullWidthChanged(n)}))("fullHeightChanged",(function e(n){return hi(t),Ym(2).onFullHeightChanged(n)}))("pinStateChanged",(function e(){return hi(t),Ym(2).onPinStateChanged()})),Am()}if(2&t){const t=Ym(2);Dm("cardId",t.cardId)("groupName",t.groupName)("runColorScale",t.runColorScale)}}function hgt(t,e){if(1&t&&(Rm(0,"div"),ku(1),Am()),2&t){const t=Ym(2);rc(1),Du("Placeholder error for: ",t.cardId,"")}}function bgt(t,e){if(1&t&&(Nm(0,1),Qp(1,ugt,1,3,"image-card",2),Qp(2,fgt,1,2,"scalar-card",3),Qp(3,ggt,1,3,"histogram-card",4),Qp(4,hgt,2,1,"div",5),zm()),2&t){const t=Ym();Dm("ngSwitch",t.pluginType),rc(1),Dm("ngSwitchCase",t.PluginType.IMAGES),rc(1),Dm("ngSwitchCase",t.PluginType.SCALARS),rc(1),Dm("ngSwitchCase",t.PluginType.HISTOGRAMS)}}mgt.ɵfac=function t(e){return new(e||mgt)(Sm(Iw))},mgt.ɵcmp=to({type:mgt,selectors:[["histogram-card"]],inputs:{cardId:"cardId",groupName:"groupName",runColorScale:"runColorScale"},outputs:{fullWidthChanged:"fullWidthChanged",fullHeightChanged:"fullHeightChanged",pinStateChanged:"pinStateChanged"},decls:10,vars:29,consts:[[3,"loadState","title","tag","runId","data","mode","xAxisType","runColorScale","showFullSize","isPinned","selectedTime","onFullSizeToggle","onPinClicked"]],template:function t(e,n){1&e&&(Rm(0,"histogram-card-component",0),Vm("onFullSizeToggle",(function t(){return n.onFullSizeToggle()}))("onPinClicked",(function t(e){return n.pinStateChanged.emit(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Ah(7,"async"),Ah(8,"async"),Ah(9,"async"),Am()),2&e&&Dm("loadState",Th(1,11,n.loadState$))("title",Th(2,13,n.title$))("tag",Th(3,15,n.tag$))("runId",Th(4,17,n.runId$))("data",Th(5,19,n.data$))("mode",Th(6,21,n.mode$))("xAxisType",Th(7,23,n.xAxisType$))("runColorScale",n.runColorScale)("showFullSize",n.showFullSize)("isPinned",Th(8,25,n.isPinned$))("selectedTime",Th(9,27,n.selectedTime$))},directives:[pgt],pipes:[wM],styles:["[_nghost-%COMP%] {\n        display: block;\n        height: 100%;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(mgt,[{type:My,args:[{selector:"histogram-card",template:'\n    <histogram-card-component\n      [loadState]="loadState$ | async"\n      [title]="title$ | async"\n      [tag]="tag$ | async"\n      [runId]="runId$ | async"\n      [data]="data$ | async"\n      [mode]="mode$ | async"\n      [xAxisType]="xAxisType$ | async"\n      [runColorScale]="runColorScale"\n      [showFullSize]="showFullSize"\n      [isPinned]="isPinned$ | async"\n      [selectedTime]="selectedTime$ | async"\n      (onFullSizeToggle)="onFullSizeToggle()"\n      (onPinClicked)="pinStateChanged.emit($event)"\n    ></histogram-card-component>\n  ',styles:["\n      :host {\n        display: block;\n        height: 100%;\n      }\n    "],changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{cardId:[{type:xy}],groupName:[{type:xy}],runColorScale:[{type:xy}],fullWidthChanged:[{type:Oy}],fullHeightChanged:[{type:Oy}],pinStateChanged:[{type:Oy}]});class ygt{constructor(){this.PluginType=hA,this.fullWidthChanged=new Lh,this.fullHeightChanged=new Lh,this.pinStateChanged=new Lh}onFullWidthChanged(t){this.fullWidthChanged.emit(t)}onFullHeightChanged(t){this.fullHeightChanged.emit(t)}onPinStateChanged(){this.pinStateChanged.emit()}}ygt.ɵfac=function t(e){return new(e||ygt)},ygt.ɵcmp=to({type:ygt,selectors:[["card-view-component"]],inputs:{isEverVisible:"isEverVisible",cardId:"cardId",groupName:"groupName",pluginType:"pluginType",runColorScale:"runColorScale"},outputs:{fullWidthChanged:"fullWidthChanged",fullHeightChanged:"fullHeightChanged",pinStateChanged:"pinStateChanged"},decls:1,vars:1,consts:[[3,"ngSwitch",4,"ngIf"],[3,"ngSwitch"],[3,"cardId","groupName","runColorScale","fullWidthChanged","pinStateChanged",4,"ngSwitchCase"],[3,"cardId","groupName","fullWidthChanged","fullHeightChanged","pinStateChanged",4,"ngSwitchCase"],[3,"cardId","groupName","runColorScale","fullWidthChanged","fullHeightChanged","pinStateChanged",4,"ngSwitchCase"],[4,"ngSwitchDefault"],[3,"cardId","groupName","runColorScale","fullWidthChanged","pinStateChanged"],[3,"cardId","groupName","fullWidthChanged","fullHeightChanged","pinStateChanged"],[3,"cardId","groupName","runColorScale","fullWidthChanged","fullHeightChanged","pinStateChanged"]],template:function t(e,n){1&e&&Qp(0,bgt,5,4,"ng-container",0),2&e&&Dm("ngIf",n.isEverVisible)},directives:[dM,fM,gM,hM,X2,Ift,mgt],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ygt,[{type:My,args:[{selector:"card-view-component",templateUrl:"card_view_component.ng.html",changeDetection:zn.OnPush}]}],null,{isEverVisible:[{type:xy}],cardId:[{type:xy}],groupName:[{type:xy}],pluginType:[{type:xy}],runColorScale:[{type:xy}],fullWidthChanged:[{type:Oy}],fullHeightChanged:[{type:Oy}],pinStateChanged:[{type:Oy}]});class _gt{constructor(t){this.store=t,this.isEverVisible=!1,this.showFullWidth=!1,this.showFullHeight=!1,this.runColorScale$=this.store.select(zN).pipe(Be(350,void 0,{leading:!0,trailing:!0}),It((t=>e=>t.hasOwnProperty(e)?t[e]:"#fff")))}onVisibilityChange({visible:t}){this.isEverVisible=this.isEverVisible||t}onFullWidthChanged(t){this.showFullWidth=t}onFullHeightChanged(t){this.showFullHeight=t}onPinStateChanged(){this.store.select(wT,this.cardId).pipe(be(1),Ve(this.store.select(ST))).subscribe((([t,e])=>{this.store.dispatch(oR({cardId:this.cardId,canCreateNewPins:e,wasPinned:t}))}))}}function Cgt(t,e){1&t&&Im(0)}function Mgt(t,e){if(1&t&&Tm(0,"card-view",4),2&t){const t=e.$implicit,n=Ym();Dm("cardId",t.cardId)("groupName",n.groupName)("pluginType",t.plugin)("cardObserver",n.cardObserver)("cardLazyLoader",t.cardId)}}function vgt(t,e){1&t&&Im(0)}function xgt(t,e){if(1&t){const t=Hm();Rm(0,"button",13),Vm("click",(function e(n){hi(t);const o=Ym(3);return o.handlePageChange(o.pageIndex-1,n.target)})),ku(1," Previous "),Am()}2&t&&Dm("disabled",0===Ym(3).pageIndex)}function Ogt(t,e){1&t&&Tm(0,"mat-icon",17)}function Pgt(t,e){1&t&&Tm(0,"mat-icon",18)}function wgt(t,e){if(1&t){const t=Hm();Rm(0,"button",14),Vm("click",(function e(){return hi(t),Ym(3).groupExpansionToggled.emit()})),Qp(1,Ogt,1,0,"mat-icon",15),Qp(2,Pgt,1,0,"ng-template",null,16,ib),Am()}if(2&t){const t=$p(3),e=Ym(3);rc(1),Dm("ngIf",e.isGroupExpanded)("ngIfElse",t)}}function kgt(t,e){if(1&t){const t=Hm();Rm(0,"span",21),Rm(1,"input",22),Vm("input",(function e(n){return hi(t),Ym(4).onPaginationInputChange(n)}))("change",(function e(n){return hi(t),Ym(4).onPaginationInputChange(n)})),Am(),ku(2),Am()}if(2&t){const t=Ym(4);rc(1),Dm("value",t.pageIndex+1)("max",t.numPages),rc(1),Du(" of ",t.numPages,"")}}function Sgt(t,e){if(1&t){const t=Hm();Rm(0,"span"),Qp(1,kgt,3,3,"span",19),Rm(2,"button",20),Vm("click",(function e(n){hi(t);const o=Ym(3);return o.handlePageChange(o.pageIndex+1,n.target)})),ku(3," Next "),Am(),Am()}if(2&t){const t=Ym(2).isBottomControl,e=Ym();rc(1),Dm("ngIf",e.showPaginationInput(t)),rc(1),Dm("disabled",e.pageIndex+1>=e.numPages)}}function Dgt(t,e){if(1&t&&(Rm(0,"div",6),Rm(1,"span",7),Qp(2,xgt,2,1,"button",8),Am(),Rm(3,"span",9),Qp(4,wgt,4,2,"button",10),Am(),Rm(5,"span",11),Qp(6,Sgt,4,2,"span",12),Am(),Am()),2&t){const t=Ym().isBottomControl,e=Ym();rc(2),Dm("ngIf",e.showPaginationControls),rc(2),Dm("ngIf",e.showExpand(t)),rc(2),Dm("ngIf",e.showPaginationControls)}}function Egt(t,e){if(1&t&&Qp(0,Dgt,7,3,"div",5),2&t){const t=e.isBottomControl,n=Ym();Dm("ngIf",n.showPaginationControls||n.showExpand(t))}}_gt.ɵfac=function t(e){return new(e||_gt)(Sm(Iw))},_gt.ɵcmp=to({type:_gt,selectors:[["card-view"]],hostVars:4,hostBindings:function t(e,n){2&e&&pu("full-width",n.showFullWidth)("full-height",n.showFullHeight)},inputs:{cardId:"cardId",groupName:"groupName",pluginType:"pluginType"},decls:2,vars:7,consts:[["observeIntersection","","intersectionObserverMargin","200px 200px 200px 200px",3,"isEverVisible","cardId","groupName","pluginType","runColorScale","fullWidthChanged","fullHeightChanged","pinStateChanged","onVisibilityChange"]],template:function t(e,n){1&e&&(Rm(0,"card-view-component",0),Vm("fullWidthChanged",(function t(e){return n.onFullWidthChanged(e)}))("fullHeightChanged",(function t(e){return n.onFullHeightChanged(e)}))("pinStateChanged",(function t(){return n.onPinStateChanged()}))("onVisibilityChange",(function t(e){return n.onVisibilityChange(e)})),Ah(1,"async"),Am()),2&e&&Dm("isEverVisible",n.isEverVisible)("cardId",n.cardId)("groupName",n.groupName)("pluginType",n.pluginType)("runColorScale",Th(1,5,n.runColorScale$))},directives:[ygt,Nft],pipes:[wM],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   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.mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   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print{.mat-form-field-appearance-legacy.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28122em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-form-field-autofill-control[_ngcontent-%COMP%]:-webkit-autofill + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28121em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.2812em) 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sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action[_ngcontent-%COMP%]{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node[_ngcontent-%COMP%], .mat-nested-tree-node[_ngcontent-%COMP%]{font-weight:400;font-size:14px}.mat-ripple[_ngcontent-%COMP%]{overflow:hidden;position:relative}.mat-ripple[_ngcontent-%COMP%]:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded[_ngcontent-%COMP%]{overflow:visible}.mat-ripple-element[_ngcontent-%COMP%]{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms cubic-bezier(0, 0, 0.2, 1);transform:scale(0)}.cdk-high-contrast-active[_ngcontent-%COMP%]   .mat-ripple-element[_ngcontent-%COMP%]{display:none}.cdk-visually-hidden[_ngcontent-%COMP%]{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container[_ngcontent-%COMP%], .cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{background-color:#fff}body.dark-mode   [_nghost-%COMP%]{background-color:#303030}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(_gt,[{type:My,args:[{selector:"card-view",template:'\n    <card-view-component\n      [isEverVisible]="isEverVisible"\n      [cardId]="cardId"\n      [groupName]="groupName"\n      [pluginType]="pluginType"\n      [runColorScale]="runColorScale$ | async"\n      (fullWidthChanged)="onFullWidthChanged($event)"\n      (fullHeightChanged)="onFullHeightChanged($event)"\n      (pinStateChanged)="onPinStateChanged()"\n      observeIntersection\n      intersectionObserverMargin="200px 200px 200px 200px"\n      (onVisibilityChange)="onVisibilityChange($event)"\n    >\n    </card-view-component>\n  ',styleUrls:["card_view_container.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{cardId:[{type:xy}],groupName:[{type:xy}],pluginType:[{type:xy}],showFullWidth:[{type:Py,args:["class.full-width"]}],showFullHeight:[{type:Py,args:["class.full-height"]}]});const Rgt=function(){return{isBottomControl:!1}},Agt=function(){return{isBottomControl:!0}};class Tgt{constructor(t){this.cdkScrollable=t,this.PluginType=hA,this.pageIndexChanged=new Lh,this.groupExpansionToggled=new Lh}showExpand(t){return!!t&&this.isGroupExpandable}showPaginationInput(t){return t}handlePageChange(t,e){const n=e.getBoundingClientRect().top;setTimeout((()=>{this.scrollToKeepTargetPosition(e,n)}),0),this.pageIndexChanged.emit(t)}scrollToKeepTargetPosition(t,e){var n;const o=null===(n=this.cdkScrollable)||void 0===n?void 0:n.getElementRef().nativeElement;o&&o.scrollTo(0,t.getBoundingClientRect().top-e+o.scrollTop)}trackByCards(t,e){return e.cardId}onPaginationInputChange(t){const e=t.target;if("input"===t.type&&""===e.value)return;const n=Number(e.value)-1,o=Math.min(Math.max(0,n),this.numPages-1);e.value!==String(o+1)&&(e.value=String(o+1)),this.handlePageChange(o,e)}}Tgt.ɵfac=function t(e){return new(e||Tgt)(Sm(mF,8))},Tgt.ɵcmp=to({type:Tgt,selectors:[["metrics-card-grid-component"]],inputs:{isGroupExpandable:"isGroupExpandable",isGroupExpanded:"isGroupExpanded",groupName:"groupName",pageIndex:"pageIndex",numPages:"numPages",cardIdsWithMetadata:"cardIdsWithMetadata",cardObserver:"cardObserver",showPaginationControls:"showPaginationControls"},outputs:{pageIndexChanged:"pageIndexChanged",groupExpansionToggled:"groupExpansionToggled"},decls:6,vars:8,consts:function(){let t,e,n;return t="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Previous page"):$localize`:A button that sets a group to the previous page.␟575e782fd27f2ee70a034a775efe9ad162472250␟3629960544875360046:Previous page`,e="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Expand group"):$localize`:A button that allows user to expand a tag group.␟ffaa11471b878a6dffe2e68c6f37064a9e074853␟5386054325274779258:Expand group`,n="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Next page"):$localize`:A button that sets a group to the next page.␟ce3cefb1cd0099aa5003dda16ec9eb21fd8ba789␟3337301694210287595:Next page`,[[4,"ngTemplateOutlet","ngTemplateOutletContext"],[1,"card-grid"],[3,"cardId","groupName","pluginType","cardObserver","cardLazyLoader",4,"ngFor","ngForOf","ngForTrackBy"],["groupControls",""],[3,"cardId","groupName","pluginType","cardObserver","cardLazyLoader"],["class","group-controls",4,"ngIf"],[1,"group-controls"],[1,"prev-container"],["class","prev 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paint;grid-column-start:1;grid-column-end:-1}card-view.full-height[_ngcontent-%COMP%]{min-height:480px}.group-controls[_ngcontent-%COMP%]{color:#616161;display:grid;align-items:center;grid-template-columns:1fr 1fr 1fr;gap:16px;padding:0 16px}body.dark-mode[_nghost-%COMP%]   .group-controls[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group-controls[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.group-controls[_ngcontent-%COMP%]:first-of-type{padding-top:16px}.group-controls[_ngcontent-%COMP%]:last-of-type{padding-bottom:16px}.prev-container[_ngcontent-%COMP%]{justify-self:flex-start}.expand-container[_ngcontent-%COMP%]{justify-self:center}.input-and-next-container[_ngcontent-%COMP%]{justify-self:flex-end}.pagination-input[_ngcontent-%COMP%]{margin-right:16px}.pagination-input[_ngcontent-%COMP%]   input[_ngcontent-%COMP%]{background:transparent;border:1px solid currentColor;color:inherit;font:inherit}.expand-group-button[_ngcontent-%COMP%], .pagination-button[_ngcontent-%COMP%]{color:#616161;background-color:#fff}body.dark-mode[_nghost-%COMP%]   .expand-group-button[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .expand-group-button[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}body.dark-mode[_nghost-%COMP%]   .pagination-button[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .pagination-button[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.expand-group-button[_ngcontent-%COMP%]:disabled, .pagination-button[_ngcontent-%COMP%]:disabled{color:#757575}body.dark-mode[_nghost-%COMP%]   .expand-group-button[_ngcontent-%COMP%]:disabled, body.dark-mode   [_nghost-%COMP%]   .expand-group-button[_ngcontent-%COMP%]:disabled{color:#616161}body.dark-mode[_nghost-%COMP%]   .pagination-button[_ngcontent-%COMP%]:disabled, body.dark-mode   [_nghost-%COMP%]   .pagination-button[_ngcontent-%COMP%]:disabled{color:#616161}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Tgt,[{type:My,args:[{selector:"metrics-card-grid-component",templateUrl:"./card_grid_component.ng.html",styleUrls:["./card_grid_component.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:mF,decorators:[{type:Sr}]}]}),{isGroupExpandable:[{type:xy}],isGroupExpanded:[{type:xy}],groupName:[{type:xy}],pageIndex:[{type:xy}],numPages:[{type:xy}],cardIdsWithMetadata:[{type:xy}],cardObserver:[{type:xy}],showPaginationControls:[{type:xy}],pageIndexChanged:[{type:Oy}],groupExpansionToggled:[{type:Oy}]});class Ngt{constructor(t){this.store=t,this.groupName=null,this.groupName$=new F(null),this.pageIndex$=new F(0),this.items$=new F([]),this.ngUnsubscribe=new I,this.numPages$=Wt([this.items$,this.store.select(RN)]).pipe(It((([t,e])=>Math.ceil(t.length/e)))),this.isGroupExpanded$=this.groupName$.pipe(ze((t=>null!==t?this.store.select(VT,t):Et(!0)))),this.showPaginationControls$=Wt([this.numPages$,this.store.select(RN),this.isGroupExpanded$]).pipe(It((([t,e,n])=>!(t<=1)&&(e<=3||n)))),this.isGroupExpandable$=Wt([this.items$,this.store.select(RN)]).pipe(It((([t,e])=>!(null===this.groupName||e<=3||t.length<=3)))),this.normalizedPageIndex$=Wt([this.pageIndex$,this.numPages$]).pipe(Ie(this.ngUnsubscribe),Fe((([t,e])=>{0!==e&&(t>=e?this.pageIndex$.next(e-1):t<0&&this.pageIndex$.next(0))})),It((([t,e])=>Math.min(Math.max(t,0),e-1))),Ae(1)),this.pagedItems$=Wt([this.items$,this.store.select(RN),this.normalizedPageIndex$,this.isGroupExpanded$]).pipe(It((([t,e,n,o])=>{const i=e*n,a=e*n+Math.min(o?e:3,e);return t.slice(i,a)})))}ngOnChanges(t){t.cardIdsWithMetadata&&this.items$.next(this.cardIdsWithMetadata),t.groupName&&this.groupName$.next(this.groupName)}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}onPageIndexChanged(t){this.pageIndex$.next(t)}onGroupExpansionToggled(){if(null===this.groupName)throw new RangeError("Invariant error: expansion cannot be toggled when groupName is null");this.store.dispatch(nR({tagGroup:this.groupName}))}}function zgt(t,e){if(1&t&&(Rm(0,"span",8),ku(1),Am()),2&t){const t=Ym();rc(1),Du("",t.cardIdsWithMetadata.length," cards")}}function Igt(t,e){1&t&&(Rm(0,"span",9),ku(1,"New card pinned"),Am()),2&t&&jp("data-id",e.$implicit)}function Hgt(t,e){if(1&t&&Tm(0,"metrics-card-grid",10),2&t){const t=Ym();Dm("cardIdsWithMetadata",t.cardIdsWithMetadata)("cardObserver",t.cardObserver)}}function Fgt(t,e){1&t&&(Rm(0,"div",11),ku(1,"Pin cards for a quick view and comparison"),Am())}Ngt.ɵfac=function t(e){return new(e||Ngt)(Sm(Iw))},Ngt.ɵcmp=to({type:Ngt,selectors:[["metrics-card-grid"]],inputs:{groupName:"groupName",cardIdsWithMetadata:"cardIdsWithMetadata",cardObserver:"cardObserver"},features:[Bo],decls:7,vars:20,consts:[[3,"isGroupExpandable","isGroupExpanded","groupName","pageIndex","numPages","showPaginationControls","cardIdsWithMetadata","cardObserver","pageIndexChanged","groupExpansionToggled"]],template:function t(e,n){1&e&&(Rm(0,"metrics-card-grid-component",0),Vm("pageIndexChanged",(function t(e){return n.onPageIndexChanged(e)}))("groupExpansionToggled",(function t(){return n.onGroupExpansionToggled()})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Am()),2&e&&Dm("isGroupExpandable",Th(1,8,n.isGroupExpandable$))("isGroupExpanded",Th(2,10,n.isGroupExpanded$))("groupName",n.groupName)("pageIndex",Th(3,12,n.normalizedPageIndex$))("numPages",Th(4,14,n.numPages$))("showPaginationControls",Th(5,16,n.showPaginationControls$))("cardIdsWithMetadata",Th(6,18,n.pagedItems$))("cardObserver",n.cardObserver)},directives:[Tgt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Ngt,[{type:My,args:[{selector:"metrics-card-grid",template:'\n    <metrics-card-grid-component\n      [isGroupExpandable]="isGroupExpandable$ | async"\n      [isGroupExpanded]="isGroupExpanded$ | async"\n      [groupName]="groupName"\n      [pageIndex]="normalizedPageIndex$ | async"\n      [numPages]="numPages$ | async"\n      [showPaginationControls]="showPaginationControls$ | async"\n      [cardIdsWithMetadata]="pagedItems$ | async"\n      [cardObserver]="cardObserver"\n      (pageIndexChanged)="onPageIndexChanged($event)"\n      (groupExpansionToggled)="onGroupExpansionToggled()"\n    >\n    </metrics-card-grid-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{groupName:[{type:xy}],cardIdsWithMetadata:[{type:xy}],cardObserver:[{type:xy}]});class Lgt{}Lgt.ɵfac=function t(e){return new(e||Lgt)},Lgt.ɵcmp=to({type:Lgt,selectors:[["metrics-pinned-view-component"]],inputs:{cardObserver:"cardObserver",cardIdsWithMetadata:"cardIdsWithMetadata",newCardPinnedIds:"newCardPinnedIds"},decls:10,vars:4,consts:[[1,"group-toolbar"],["svgIcon","keep_24px"],[1,"group-text"],["aria-role","heading","aria-level","3",1,"group-title"],["class","group-card-count",4,"ngIf"],["class","new-card-pinned",4,"ngFor","ngForOf"],[3,"cardIdsWithMetadata","cardObserver",4,"ngIf","ngIfElse"],["emptyPinnedView",""],[1,"group-card-count"],[1,"new-card-pinned"],[3,"cardIdsWithMetadata","cardObserver"],[1,"empty-message"]],template:function t(e,n){if(1&e&&(Rm(0,"div",0),Tm(1,"mat-icon",1),Rm(2,"span",2),Rm(3,"span",3),ku(4,"Pinned"),Am(),Qp(5,zgt,2,1,"span",4),Qp(6,Igt,2,1,"span",5),Am(),Am(),Qp(7,Hgt,1,2,"metrics-card-grid",6),Qp(8,Fgt,2,0,"ng-template",null,7,ib)),2&e){const t=$p(9);rc(5),Dm("ngIf",n.cardIdsWithMetadata.length>1),rc(1),Dm("ngForOf",n.newCardPinnedIds),rc(1),Dm("ngIf",n.cardIdsWithMetadata.length)("ngIfElse",t)}},directives:[DW,dM,lM,Ngt],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   .mat-chip-remove.mat-icon[_ngcontent-%COMP%]{font-size:18px}.mat-table[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-header-cell[_ngcontent-%COMP%]{font-size:12px;font-weight:500}.mat-cell[_ngcontent-%COMP%], .mat-footer-cell[_ngcontent-%COMP%]{font-size:14px}.mat-calendar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-calendar-body[_ngcontent-%COMP%]{font-size:13px}.mat-calendar-body-label[_ngcontent-%COMP%], .mat-calendar-period-button[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-calendar-table-header[_ngcontent-%COMP%]   th[_ngcontent-%COMP%]{font-size:11px;font-weight:400}.mat-dialog-title[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-expansion-panel-header[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:15px;font-weight:400}.mat-expansion-panel-content[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-form-field[_ngcontent-%COMP%]{font-size:inherit;font-weight:400;line-height:1.125;font-family:Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-form-field-wrapper[_ngcontent-%COMP%]{padding-bottom:1.34375em}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%]{font-size:150%;line-height:1.125}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]{height:1.5em;width:1.5em}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%]{height:1.125em;line-height:1.125}.mat-form-field-infix[_ngcontent-%COMP%]{padding:.5em 0;border-top:.84375em solid transparent}.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.34375em) scale(0.75);width:133.3333333333%}.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + 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sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%] 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.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{background-color:#fff;border-bottom:1px solid #ebebeb;align-items:center;background-color:#fff;display:flex;flex:none;height:42px;margin-bottom:-1px;padding:0 16px;position:sticky;top:0;z-index:1;box-shadow:0px 2px 4px 0px rgba(0,0,0,.15)}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{background-color:#303030}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{border-bottom:1px solid #555}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{box-shadow:0px 2px 4px 0px rgba(255,255,255,.15)}mat-icon[_ngcontent-%COMP%]{color:#616161;flex:none;margin-right:5px}body.dark-mode[_nghost-%COMP%]   mat-icon[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   mat-icon[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.group-text[_ngcontent-%COMP%]{display:flex;align-items:baseline;gap:6px}.group-title[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.group-card-count[_ngcontent-%COMP%]{font-size:12px;font-weight:400;color:#616161}body.dark-mode[_nghost-%COMP%]   .group-card-count[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group-card-count[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.empty-message[_ngcontent-%COMP%]{color:#616161;font-size:13px;font-style:italic;padding:16px;text-align:center}body.dark-mode[_nghost-%COMP%]   .empty-message[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .empty-message[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.new-card-pinned[_ngcontent-%COMP%]{animation:pinned-view-fade-out 3s linear;background:#f44336;border-radius:5px;color:#fff;display:inline-block;font-size:13px;opacity:0;padding:3px 5px}@keyframes pinned-view-fade-out{from{opacity:1}66%{opacity:.99}to{opacity:0}}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Lgt,[{type:My,args:[{selector:"metrics-pinned-view-component",template:'\n    <div class="group-toolbar">\n      <mat-icon svgIcon="keep_24px"></mat-icon>\n      <span class="group-text">\n        <span class="group-title" aria-role="heading" aria-level="3"\n          >Pinned</span\n        >\n        <span *ngIf="cardIdsWithMetadata.length > 1" class="group-card-count"\n          >{{ cardIdsWithMetadata.length }} cards</span\n        >\n        <span\n          *ngFor="let id of newCardPinnedIds"\n          [attr.data-id]="id"\n          class="new-card-pinned"\n          >New card pinned</span\n        >\n      </span>\n    </div>\n    <metrics-card-grid\n      *ngIf="cardIdsWithMetadata.length; else emptyPinnedView"\n      [cardIdsWithMetadata]="cardIdsWithMetadata"\n      [cardObserver]="cardObserver"\n    ></metrics-card-grid>\n    <ng-template #emptyPinnedView>\n      <div class="empty-message">Pin cards for a quick view and comparison</div>\n    </ng-template>\n  ',styleUrls:["pinned_view_component.css"],changeDetection:zn.OnPush}]}],null,{cardObserver:[{type:xy}],cardIdsWithMetadata:[{type:xy}],newCardPinnedIds:[{type:xy}]});class Bgt{constructor(t){this.store=t,this.cardIdsWithMetadata$=this.store.select(PT).pipe(Ne([])),this.newCardPinnedIds$=this.store.select(PT).pipe(Te(1),It((t=>t.map((t=>t.cardId)))),De(),It((([t,e])=>{const n=new Set(t),o=new Set(e);for(const t of o)if(!n.has(t))return Date.now();return null})),Ne(null),De(),It((([t,e])=>null===t&&null===e?null:null===e?[t]:[e])),ce((t=>null!==t)),It((t=>[t[0]])))}}Bgt.ɵfac=function t(e){return new(e||Bgt)(Sm(Iw))},Bgt.ɵcmp=to({type:Bgt,selectors:[["metrics-pinned-view"]],inputs:{cardObserver:"cardObserver"},decls:3,vars:7,consts:[[3,"cardIdsWithMetadata","newCardPinnedIds","cardObserver"]],template:function t(e,n){1&e&&(Tm(0,"metrics-pinned-view-component",0),Ah(1,"async"),Ah(2,"async")),2&e&&Dm("cardIdsWithMetadata",Th(1,3,n.cardIdsWithMetadata$))("newCardPinnedIds",Th(2,5,n.newCardPinnedIds$))("cardObserver",n.cardObserver)},directives:[Lgt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Bgt,[{type:My,args:[{selector:"metrics-pinned-view",template:'\n    <metrics-pinned-view-component\n      [cardIdsWithMetadata]="cardIdsWithMetadata$ | async"\n      [newCardPinnedIds]="newCardPinnedIds$ | async"\n      [cardObserver]="cardObserver"\n    ></metrics-pinned-view-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{cardObserver:[{type:xy}]});const Vgt=Zw(MT,NN,((t,e)=>t.filter((t=>!xA(t.plugin)||Boolean(e&&e.get(t.runId)))))),jgt=Zw(Vgt,(t=>t.sort(((t,e)=>g2(t.tag,e.tag)))));function Ugt(t,e){if(1&t&&(Rm(0,"span",6),ku(1),Ah(2,"number"),Am()),2&t){const t=Ym().$implicit;rc(1),Du("",Th(2,1,t.items.length)," cards")}}function Ggt(t,e){if(1&t&&(Rm(0,"div",1),Rm(1,"div",2),Rm(2,"span"),Rm(3,"span",3),ku(4),Am(),Qp(5,Ugt,3,3,"span",4),Am(),Am(),Tm(6,"metrics-card-grid",5),Am()),2&t){const t=e.$implicit,n=Ym();rc(3),Km("title",t.groupName),rc(1),Su(t.groupName),rc(1),Dm("ngIf",t.items.length>1),rc(1),Dm("cardIdsWithMetadata",t.items)("cardObserver",n.cardObserver)("groupName",t.groupName)}}class Wgt{constructor(){this.PluginType=hA}trackByGroup(t,e){return e.groupName}}Wgt.ɵfac=function t(e){return new(e||Wgt)},Wgt.ɵcmp=to({type:Wgt,selectors:[["metrics-card-groups-component"]],inputs:{cardGroups:"cardGroups",cardObserver:"cardObserver"},decls:1,vars:2,consts:[["class","card-group",4,"ngFor","ngForOf","ngForTrackBy"],[1,"card-group"],[1,"group-toolbar"],["aria-role","heading","aria-level","3",1,"group-title",3,"title"],["class","group-card-count",4,"ngIf"],[3,"cardIdsWithMetadata","cardObserver","groupName"],[1,"group-card-count"]],template:function t(e,n){1&e&&Qp(0,Ggt,7,6,"div",0),2&e&&Dm("ngForOf",n.cardGroups)("ngForTrackBy",n.trackByGroup)},directives:[lM,dM,Ngt],pipes:[FM],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   .mat-chip-remove.mat-icon[_ngcontent-%COMP%]{font-size:18px}.mat-table[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", 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!important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{background-color:#fff;border-bottom:1px solid #ebebeb;align-items:center;background-color:#fff;display:flex;flex:none;height:42px;margin-bottom:-1px;padding:0 16px;position:sticky;top:0;z-index:1;box-shadow:0px 2px 4px 0px rgba(0,0,0,.15)}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{background-color:#303030}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{border-bottom:1px solid #555}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{box-shadow:0px 2px 4px 0px rgba(255,255,255,.15)}[_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{border-top:1px solid #ebebeb;top:-1px}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{border-top:1px solid #555}.card-group[_ngcontent-%COMP%]:first-of-type   .group-toolbar[_ngcontent-%COMP%]{border-top:none}.group-title[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.group-card-count[_ngcontent-%COMP%]{font-size:12px;font-weight:400;color:#616161;margin-left:6px}body.dark-mode[_nghost-%COMP%]   .group-card-count[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group-card-count[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Wgt,[{type:My,args:[{selector:"metrics-card-groups-component",template:'\n    <div\n      *ngFor="let group of cardGroups; trackBy: trackByGroup"\n      class="card-group"\n    >\n      <div class="group-toolbar">\n        <span>\n          <span\n            class="group-title"\n            aria-role="heading"\n            aria-level="3"\n            title="{{ group.groupName }}"\n            >{{ group.groupName }}</span\n          >\n          <span *ngIf="group.items.length > 1" class="group-card-count"\n            >{{ group.items.length | number }} cards</span\n          >\n        </span>\n      </div>\n      <metrics-card-grid\n        [cardIdsWithMetadata]="group.items"\n        [cardObserver]="cardObserver"\n        [groupName]="group.groupName"\n      ></metrics-card-grid>\n    </div>\n  ',styleUrls:["card_groups_component.css"],changeDetection:zn.OnPush}]}],null,{cardGroups:[{type:xy}],cardObserver:[{type:xy}]});class Ygt{constructor(t){this.store=t,this.cardGroups$=this.store.select(jgt).pipe(fe(this.store.select(qT)),It((([t,e])=>e.size?t.filter((t=>e.has(t.plugin))):t)),It((t=>(function e(t){const e=new Map,n=t.slice().sort(((t,e)=>g2(t.tag,e.tag)));for(const t of n){const n=t.tag.split("/",1)[0];e.has(n)||e.set(n,{groupName:n,items:[]}),e.get(n).items.push(t)}return[...e.values()]})(t))))}}function qgt(t,e){if(1&t&&(Rm(0,"span"),ku(1),Am()),2&t){const t=Ym();rc(1),Du(" and ",t.getPluginTypeFilterString(t.pluginTypes)," visualization filter")}}Ygt.ɵfac=function t(e){return new(e||Ygt)(Sm(Iw))},Ygt.ɵcmp=to({type:Ygt,selectors:[["metrics-card-groups"]],inputs:{cardObserver:"cardObserver"},decls:2,vars:4,consts:[[3,"cardGroups","cardObserver"]],template:function t(e,n){1&e&&(Tm(0,"metrics-card-groups-component",0),Ah(1,"async")),2&e&&Dm("cardGroups",Th(1,2,n.cardGroups$))("cardObserver",n.cardObserver)},directives:[Wgt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Ygt,[{type:My,args:[{selector:"metrics-card-groups",template:'\n    <metrics-card-groups-component\n      [cardGroups]="cardGroups$ | async"\n      [cardObserver]="cardObserver"\n    ></metrics-card-groups-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{cardObserver:[{type:xy}]});class Zgt{constructor(){this.PluginType=hA,this.listFormatter=new Intl.ListFormat(void 0,{style:"long",type:"disjunction"})}getPluginTypeFilterString(t){const e=[...t].map((t=>{switch(t){case hA.SCALARS:return"scalar";case hA.IMAGES:return"image";case hA.HISTOGRAMS:return"histogram";default:throw new RangeError(`Please implement human readable name for plugin type: ${t}`)}}));return this.listFormatter.format(e)}}Zgt.ɵfac=function t(e){return new(e||Zgt)},Zgt.ɵcmp=to({type:Zgt,selectors:[["metrics-empty-tag-match-component"]],inputs:{pluginTypes:"pluginTypes",tagFilterRegex:"tagFilterRegex",tagCounts:"tagCounts"},decls:6,vars:5,consts:[[4,"ngIf"]],template:function t(e,n){1&e&&(ku(0,"No matches for tag filter "),Rm(1,"code"),ku(2),Am(),Qp(3,qgt,2,1,"span",0),ku(4),Ah(5,"number")),2&e&&(rc(2),Du("/",n.tagFilterRegex,"/"),rc(1),Dm("ngIf",n.pluginTypes.size),rc(1),Du(" out of ",Th(5,3,n.tagCounts)," tags."))},directives:[dM],pipes:[FM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Zgt,[{type:My,args:[{selector:"metrics-empty-tag-match-component",template:'No matches for tag filter <code>/{{ tagFilterRegex }}/</code\n    ><span *ngIf="pluginTypes.size">\n      and {{ getPluginTypeFilterString(pluginTypes) }} visualization\n      filter</span\n    >\n    out of {{ tagCounts | number }} tags.',changeDetection:zn.OnPush}]}],null,{pluginTypes:[{type:xy}],tagFilterRegex:[{type:xy}],tagCounts:[{type:xy}]});class Xgt{constructor(t){this.store=t,this.pluginTypes$=this.store.select(qT),this.tagFilterRegex$=this.store.select(BT),this.tagCounts$=this.store.select(jgt).pipe(It((t=>new Set(t.map((({tag:t})=>t))).size)))}}function Kgt(t,e){if(1&t&&(Rm(0,"span",6),ku(1),Ah(2,"number"),Am()),2&t){const t=Ym();rc(1),Du("",Th(2,1,t.cardIdsWithMetadata.length)," cards")}}function Jgt(t,e){1&t&&Tm(0,"metrics-empty-tag-match",7)}Xgt.ɵfac=function t(e){return new(e||Xgt)(Sm(Iw))},Xgt.ɵcmp=to({type:Xgt,selectors:[["metrics-empty-tag-match"]],decls:4,vars:9,consts:[[3,"pluginTypes","tagFilterRegex","tagCounts"]],template:function t(e,n){1&e&&(Tm(0,"metrics-empty-tag-match-component",0),Ah(1,"async"),Ah(2,"async"),Ah(3,"async")),2&e&&Dm("pluginTypes",Th(1,3,n.pluginTypes$))("tagFilterRegex",Th(2,5,n.tagFilterRegex$))("tagCounts",Th(3,7,n.tagCounts$))},directives:[Zgt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Xgt,[{type:My,args:[{selector:"metrics-empty-tag-match",template:'\n    <metrics-empty-tag-match-component\n      [pluginTypes]="pluginTypes$ | async"\n      [tagFilterRegex]="tagFilterRegex$ | async"\n      [tagCounts]="tagCounts$ | async"\n    ></metrics-empty-tag-match-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);class Qgt{}Qgt.ɵfac=function t(e){return new(e||Qgt)},Qgt.ɵcmp=to({type:Qgt,selectors:[["metrics-filtered-view-component"]],inputs:{isEmptyMatch:"isEmptyMatch",cardObserver:"cardObserver",cardIdsWithMetadata:"cardIdsWithMetadata"},decls:7,vars:4,consts:[[1,"group-toolbar"],[1,"group-text"],["aria-role","heading","aria-level","3",1,"group-title"],["class","group-card-count",4,"ngIf"],["class","warn",4,"ngIf"],[3,"cardIdsWithMetadata","cardObserver"],[1,"group-card-count"],[1,"warn"]],template:function t(e,n){1&e&&(Rm(0,"div",0),Rm(1,"span",1),Rm(2,"span",2),ku(3,"Tags matching filter"),Am(),Qp(4,Kgt,3,3,"span",3),Am(),Am(),Qp(5,Jgt,1,0,"metrics-empty-tag-match",4),Tm(6,"metrics-card-grid",5)),2&e&&(rc(4),Dm("ngIf",n.cardIdsWithMetadata.length>1),rc(1),Dm("ngIf",n.isEmptyMatch),rc(1),Dm("cardIdsWithMetadata",n.cardIdsWithMetadata)("cardObserver",n.cardObserver))},directives:[dM,Ngt,Xgt],pipes:[FM],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   .mat-chip-remove.mat-icon[_ngcontent-%COMP%]{font-size:18px}.mat-table[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-header-cell[_ngcontent-%COMP%]{font-size:12px;font-weight:500}.mat-cell[_ngcontent-%COMP%], .mat-footer-cell[_ngcontent-%COMP%]{font-size:14px}.mat-calendar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-calendar-body[_ngcontent-%COMP%]{font-size:13px}.mat-calendar-body-label[_ngcontent-%COMP%], .mat-calendar-period-button[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-calendar-table-header[_ngcontent-%COMP%]   th[_ngcontent-%COMP%]{font-size:11px;font-weight:400}.mat-dialog-title[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-expansion-panel-header[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:15px;font-weight:400}.mat-expansion-panel-content[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-form-field[_ngcontent-%COMP%]{font-size:inherit;font-weight:400;line-height:1.125;font-family:Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-form-field-wrapper[_ngcontent-%COMP%]{padding-bottom:1.34375em}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%]{font-size:150%;line-height:1.125}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]{height:1.5em;width:1.5em}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%]{height:1.125em;line-height:1.125}.mat-form-field-infix[_ngcontent-%COMP%]{padding:.5em 0;border-top:.84375em solid transparent}.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.34375em) scale(0.75);width:133.3333333333%}.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.34374em) scale(0.75);width:133.3333433333%}.mat-form-field-label-wrapper[_ngcontent-%COMP%]{top:-0.84375em;padding-top:.84375em}.mat-form-field-label[_ngcontent-%COMP%]{top:1.34375em}.mat-form-field-underline[_ngcontent-%COMP%]{bottom:1.34375em}.mat-form-field-subscript-wrapper[_ngcontent-%COMP%]{font-size:75%;margin-top:.6666666667em;top:calc(100% - 1.7916666667em)}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   .mat-form-field-wrapper[_ngcontent-%COMP%]{padding-bottom:1.25em}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:.4375em 0}.mat-form-field-appearance-legacy.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28125em) scale(0.75) perspective(100px) translateZ(0.001px);-ms-transform:translateY(-1.28125em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-form-field-autofill-control[_ngcontent-%COMP%]:-webkit-autofill + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28125em) scale(0.75) perspective(100px) translateZ(0.00101px);-ms-transform:translateY(-1.28124em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28125em) scale(0.75) perspective(100px) translateZ(0.00102px);-ms-transform:translateY(-1.28123em) scale(0.75);width:133.3333533333%}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{top:1.28125em}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   .mat-form-field-underline[_ngcontent-%COMP%]{bottom:1.25em}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   .mat-form-field-subscript-wrapper[_ngcontent-%COMP%]{margin-top:.5416666667em;top:calc(100% - 1.6666666667em)}@media print{.mat-form-field-appearance-legacy.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28122em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-form-field-autofill-control[_ngcontent-%COMP%]:-webkit-autofill + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28121em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.2812em) scale(0.75)}}.mat-form-field-appearance-fill[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:.25em 0 .75em 0}.mat-form-field-appearance-fill[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{top:1.09375em;margin-top:-0.5em}.mat-form-field-appearance-fill.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59374em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:1em 0 1em 0}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{top:1.84375em;margin-top:-0.25em}.mat-form-field-appearance-outline.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59374em) scale(0.75);width:133.3333433333%}.mat-grid-tile-header[_ngcontent-%COMP%], .mat-grid-tile-footer[_ngcontent-%COMP%]{font-size:14px}.mat-grid-tile-header[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%], .mat-grid-tile-footer[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-grid-tile-header[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2), .mat-grid-tile-footer[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}input.mat-input-element[_ngcontent-%COMP%]{margin-top:-0.0625em}.mat-menu-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:400}.mat-paginator[_ngcontent-%COMP%], .mat-paginator-page-size[_ngcontent-%COMP%]   .mat-select-trigger[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px}.mat-radio-button[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   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.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}mat-select[_ngcontent-%COMP%]{border:1px solid #8e98a3;border-radius:3px;box-sizing:border-box;padding:6px}mat-select[_ngcontent-%COMP%]:focus{outline-color:-webkit-focus-ring-color;outline-style:auto}  .mat-select-panel{max-width:70vw}  mat-option.mat-option{height:auto}  .mat-option-text{white-space:normal;word-break:break-all}']}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(eht,[{type:My,args:[{selector:"tb-dropdown",template:'\n    <mat-select\n      [value]="value"\n      (selectionChange)="selectionChange.emit($event.value)"\n    >\n      <mat-option\n        *ngFor="let option of options"\n        [value]="option.value"\n        [disabled]="option.disabled"\n      >\n        {{ option.displayText }}\n      </mat-option>\n    </mat-select>\n  ',styleUrls:["dropdown_component.css"]}]}],null,{value:[{type:xy}],options:[{type:xy}],selectionChange:[{type:Oy}]});class rht{constructor(t){this.locale=t,this.selectTimeEnableToggled=new Lh,this.useRangeSelectTimeToggled=new Lh,this.selectTimeChanged=new Lh,this.TooltipSortDropdownOptions=[{value:bA.DEFAULT,displayText:"Default"},{value:bA.ASCENDING,displayText:"Ascending"},{value:bA.DESCENDING,displayText:"Descending"},{value:bA.NEAREST,displayText:"Nearest"}],this.tooltipSortChanged=new Lh,this.ignoreOutliersChanged=new Lh,this.XAxisType=yA,this.XAxisTypeDropdownOptions=[{value:yA.STEP,displayText:"Step"},{value:yA.RELATIVE,displayText:"Relative"},{value:yA.WALL_TIME,displayText:"Wall"}],this.xAxisTypeChanged=new Lh,this.HistogramModeDropdownOptions=[{value:pE.OFFSET,displayText:"Offset"},{value:pE.OVERLAY,displayText:"Overlay"}],this.histogramModeChanged=new Lh,this.MAX_SMOOTHING_VALUE=.999,this.MAX_SMOOTHING_SLIDER_VALUE=.99,this.scalarSmoothingControlChanged$=new Lh,this.scalarSmoothingChanged=this.scalarSmoothingControlChanged$.pipe(de(250)),this.scalarPartitionXToggled=new Lh,this.imageBrightnessSliderChanged$=new Lh,this.imageBrightnessInMilliChanged=this.imageBrightnessSliderChanged$.pipe(de(250)),this.imageBrightnessReset=new Lh,this.imageContrastSliderChanged$=new Lh,this.imageContrastInMilliChanged=this.imageContrastSliderChanged$.pipe(de(250)),this.imageContrastReset=new Lh,this.imageShowActualSizeChanged=new Lh}onScalarSmoothingInput(t){const e=t.target;if(!e.value)return;const n=Math.min(Math.max(0,parseFloat(e.value)),.999);n!==parseFloat(e.value)&&(e.value=String(n)),this.scalarSmoothingControlChanged$.emit(n)}formatMilliToZeroth(t){return QC(t/1e3,this.locale||"en-US","1.0-2")}onStepStartChanged(t){this.selectTimeChanged.emit({start:{step:t},end:null})}onStepRangeChanged({lowerValue:t,upperValue:e}){this.selectTimeChanged.emit({start:{step:t},end:{step:e}})}}rht.ɵfac=function t(e){return new(e||rht)(Sm(Wy))},rht.ɵcmp=to({type:rht,selectors:[["metrics-dashboard-settings-component"]],inputs:{isLinkedTimeFeatureEnabled:"isLinkedTimeFeatureEnabled",selectTimeEnabled:"selectTimeEnabled",useRangeSelectTime:"useRangeSelectTime",selectedTime:"selectedTime",stepMinMax:"stepMinMax",isImageSupportEnabled:"isImageSupportEnabled",tooltipSort:"tooltipSort",ignoreOutliers:"ignoreOutliers",xAxisType:"xAxisType",histogramMode:"histogramMode",scalarSmoothing:"scalarSmoothing",scalarPartitionX:"scalarPartitionX",imageBrightnessInMilli:"imageBrightnessInMilli",imageContrastInMilli:"imageContrastInMilli",imageShowActualSize:"imageShowActualSize"},outputs:{selectTimeEnableToggled:"selectTimeEnableToggled",useRangeSelectTimeToggled:"useRangeSelectTimeToggled",selectTimeChanged:"selectTimeChanged",tooltipSortChanged:"tooltipSortChanged",ignoreOutliersChanged:"ignoreOutliersChanged",xAxisTypeChanged:"xAxisTypeChanged",histogramModeChanged:"histogramModeChanged",scalarSmoothingChanged:"scalarSmoothingChanged",scalarPartitionXToggled:"scalarPartitionXToggled",imageBrightnessInMilliChanged:"imageBrightnessInMilliChanged",imageBrightnessReset:"imageBrightnessReset",imageContrastInMilliChanged:"imageContrastInMilliChanged",imageContrastReset:"imageContrastReset",imageShowActualSizeChanged:"imageShowActualSizeChanged"},decls:36,vars:17,consts:function(){let t,e;return t="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Reset brightness"):$localize`:A button to reset the image brightness setting␟c482b3a47ea0975fa8be01afb3fbec9b76628bd7␟1189161857240378395:Reset brightness`,e="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Reset contrast"):$localize`:A button to reset the image contrast setting␟ed712a8b927041be15252b29eb521ebb1374bad8␟5370703342923611955:Reset contrast`,[[1,"general"],[1,"section-title"],[1,"control-row","x-axis-type"],["id","x-axis-type-label",1,"control-name"],[3,"value","options","selectionChange"],["class","control-row linked-time",4,"ngIf"],[1,"scalars"],[1,"control-row","scalars-smoothing"],["id","scalars-smoothing-label",1,"control-name"],[1,"slider-row"],["aria-labelledby","scalars-smoothing-label","color","primary",3,"max","min","step","value","thumbLabel","input"],["aria-labelledby","scalars-smoothing-label","type","number","min","0","step","0.001",1,"slider-input",3,"max","value","input"],[1,"control-row","tooltip-sort"],[1,"control-name"],[1,"control-row","scalars-ignore-outliers"],[3,"checked","change"],[1,"control-row","scalars-partition-x"],["svgIcon","help_outline_24px","title","Non-monotonic steps can occur when reusing a logdir with multiple summary writers and overlapping steps. Line charts, without this option enabled, can appear zig zagged. This is common when restarting from a checkpoint.\n\nWhen enabled, a non-monotonic time series composed of N monotonic pieces will be shown as N monotonic lines.",1,"info"],[1,"Histograms"],[1,"control-row","histogram-mode"],["class","image",4,"ngIf"],[1,"control-row","linked-time"],[1,"controls"],[1,"step-selector"],["color","primary",3,"disabled","min","max","step","value","thumbLabel","input",4,"ngIf","ngIfElse"],["range",""],["color","primary",3,"disabled","min","max","step","value","thumbLabel","input"],[3,"min","max","lowerValue","upperValue","value"],[1,"image"],[1,"control-row","image-brightness"],["id","image-brightness-label",1,"control-name"],["aria-labelledby","image-brightness-label","color","primary",3,"max","min","step","value","thumbLabel","displayWith","input"],["mat-icon-button","","aria-label",t,"title","Reset brightness",1,"reset-button",3,"click"],["svgIcon","settings_backup_restore_24px"],[1,"control-row","image-contrast"],["id","image-constrast-label",1,"control-name"],["aria-labelledby","image-constrast-label","color","primary",3,"max","min","step","value","thumbLabel","displayWith","input"],["mat-icon-button","","aria-label",e,"title","Reset contrast",1,"reset-button",3,"click"],[1,"control-row","image-show-actual-size"]]},template:function t(e,n){1&e&&(Rm(0,"section",0),Rm(1,"h3",1),ku(2,"General"),Am(),Rm(3,"div",2),Rm(4,"div",3),ku(5,"Horizontal Axis"),Am(),Rm(6,"tb-dropdown",4),Vm("selectionChange",(function t(e){return n.xAxisTypeChanged.emit(e)})),Am(),Am(),Qp(7,iht,14,4,"div",5),Am(),Rm(8,"section",6),Rm(9,"h3",1),ku(10,"Scalars"),Am(),Rm(11,"div",7),Rm(12,"div",8),ku(13,"Smoothing"),Am(),Rm(14,"div",9),Rm(15,"mat-slider",10),Vm("input",(function t(e){return n.scalarSmoothingControlChanged$.emit(e.value)})),Am(),Rm(16,"input",11),Vm("input",(function t(e){return n.onScalarSmoothingInput(e)})),Am(),Am(),Am(),Rm(17,"div",12),Rm(18,"div",13),ku(19,"Tooltip sorting method"),Am(),Rm(20,"tb-dropdown",4),Vm("selectionChange",(function t(e){return n.tooltipSortChanged.emit(e)})),Am(),Am(),Rm(21,"div",14),Rm(22,"mat-checkbox",15),Vm("change",(function t(e){return n.ignoreOutliersChanged.emit(e.checked)})),ku(23,"Ignore outliers in chart scaling"),Am(),Am(),Rm(24,"div",16),Rm(25,"mat-checkbox",15),Vm("change",(function t(){return n.scalarPartitionXToggled.emit()})),ku(26,"Partition non-monotonic X axis"),Am(),Tm(27,"mat-icon",17),Am(),Am(),Rm(28,"section",18),Rm(29,"h3",1),ku(30,"Histograms"),Am(),Rm(31,"div",19),Rm(32,"div",13),ku(33,"Mode"),Am(),Rm(34,"tb-dropdown",4),Vm("selectionChange",(function t(e){return 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.section-title[_ngcontent-%COMP%]{color:#fff}section[_ngcontent-%COMP%]   .control-row[_ngcontent-%COMP%]:not(:last-child){margin-bottom:12px}.control-name[_ngcontent-%COMP%]{margin-bottom:8px}.slider-row[_ngcontent-%COMP%]{display:flex;align-items:center;height:28px}.slider-row[_ngcontent-%COMP%]   .reset-button[_ngcontent-%COMP%]{margin-left:6px}.slider-row[_ngcontent-%COMP%]   .slider-input[_ngcontent-%COMP%]{background-color:inherit;border:1px solid #8e98a3;border-radius:2px;box-sizing:border-box;color:inherit;height:100%;margin-left:12px;padding:0 4px}body.dark-mode[_nghost-%COMP%]   .slider-row[_ngcontent-%COMP%]   .slider-input[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .slider-row[_ngcontent-%COMP%]   .slider-input[_ngcontent-%COMP%]{border-color:#425066}.scalars-smoothing[_ngcontent-%COMP%]   .slider-input[_ngcontent-%COMP%]{flex:none;width:5em}.scalars-partition-x[_ngcontent-%COMP%]{align-items:center;display:flex}.scalars-partition-x[_ngcontent-%COMP%]   .info[_ngcontent-%COMP%]{height:15px;margin-left:5px;width:15px}mat-slider[_ngcontent-%COMP%]{flex:1;margin-left:-8px;margin-right:-8px}tb-dropdown[_ngcontent-%COMP%]{display:block}.linked-time[_ngcontent-%COMP%]   .step-selector[_ngcontent-%COMP%]{padding:0 10px}.linked-time[_ngcontent-%COMP%]   mat-slider[_ngcontent-%COMP%], .linked-time[_ngcontent-%COMP%]   tb-range-input[_ngcontent-%COMP%]{width:100%}.linked-time[_ngcontent-%COMP%]   .controls[_ngcontent-%COMP%]{padding:5px}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(rht,[{type:My,args:[{selector:"metrics-dashboard-settings-component",templateUrl:"settings_view_component.ng.html",changeDetection:zn.OnPush,styleUrls:["settings_view_component.css"]}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Wy]}]}]}),{isLinkedTimeFeatureEnabled:[{type:xy}],selectTimeEnabled:[{type:xy}],useRangeSelectTime:[{type:xy}],selectedTime:[{type:xy}],stepMinMax:[{type:xy}],selectTimeEnableToggled:[{type:Oy}],useRangeSelectTimeToggled:[{type:Oy}],selectTimeChanged:[{type:Oy}],isImageSupportEnabled:[{type:xy}],tooltipSort:[{type:xy}],tooltipSortChanged:[{type:Oy}],ignoreOutliers:[{type:xy}],ignoreOutliersChanged:[{type:Oy}],xAxisType:[{type:xy}],xAxisTypeChanged:[{type:Oy}],histogramMode:[{type:xy}],histogramModeChanged:[{type:Oy}],scalarSmoothing:[{type:xy}],scalarSmoothingChanged:[{type:Oy}],scalarPartitionX:[{type:xy}],scalarPartitionXToggled:[{type:Oy}],imageBrightnessInMilli:[{type:xy}],imageBrightnessInMilliChanged:[{type:Oy}],imageBrightnessReset:[{type:Oy}],imageContrastInMilli:[{type:xy}],imageContrastInMilliChanged:[{type:Oy}],imageContrastReset:[{type:Oy}],imageShowActualSize:[{type:xy}],imageShowActualSizeChanged:[{type:Oy}]});class sht{constructor(t){this.store=t,this.isLinkedTimeFeatureEnabled$=this.store.select(iE),this.selectTimeEnabled$=this.store.select(jT),this.useRangeSelectTime$=this.store.select(UT),this.selectedTime$=this.store.select(WT),this.stepMinMax$=this.store.select(GT),this.isImageSupportEnabled$=this.store.select(qD).pipe(ce(Boolean),be(1),Ve(this.store.select(oE)),It((([,t])=>t))),this.tooltipSort$=this.store.select(RT),this.ignoreOutliers$=this.store.select(AT),this.xAxisType$=this.store.select(TT),this.histogramMode$=this.store.select(NT),this.scalarSmoothing$=this.store.select(zT),this.scalarPartitionX$=this.store.select(IT),this.imageBrightnessInMilli$=this.store.select(HT),this.imageContrastInMilli$=this.store.select(FT),this.imageShowActualSize$=this.store.select(LT)}onTooltipSortChanged(t){this.store.dispatch(LE({sort:t}))}onIgnoreOutliersChanged(){this.store.dispatch(BE())}onXAxisTypeChanged(t){this.store.dispatch(VE({xAxisType:t}))}onHistogramModeChanged(t){this.store.dispatch(XE({histogramMode:t}))}onScalarSmoothingChanged(t){this.store.dispatch(jE({smoothing:t}))}onScalarPartitionXToggled(){this.store.dispatch(UE())}onImageBrightnessInMilliChanged(t){this.store.dispatch(GE({brightnessInMilli:t}))}onImageBrightnessReset(){this.store.dispatch(YE())}onImageContrastReset(){this.store.dispatch(qE())}onImageContrastInMilliChanged(t){this.store.dispatch(WE({contrastInMilli:t}))}onImageShowActualSizeChanged(){this.store.dispatch(ZE())}onSelectTimeEnableToggled(){this.store.dispatch(lR())}onUseRangeSelectTimeToggled(){this.store.dispatch(cR())}onSelectTimeChanged(t){var e;this.store.dispatch(rR({startStep:t.start.step,endStep:null===(e=t.end)||void 0===e?void 0:e.step}))}}sht.ɵfac=function t(e){return new(e||sht)(Sm(Iw))},sht.ɵcmp=to({type:sht,selectors:[["metrics-dashboard-settings"]],decls:16,vars:45,consts:[[3,"isImageSupportEnabled","tooltipSort","ignoreOutliers","xAxisType","histogramMode","scalarSmoothing","scalarPartitionX","imageBrightnessInMilli","imageContrastInMilli","imageShowActualSize","isLinkedTimeFeatureEnabled","selectTimeEnabled","selectedTime","useRangeSelectTime","stepMinMax","tooltipSortChanged","ignoreOutliersChanged","xAxisTypeChanged","histogramModeChanged","scalarSmoothingChanged","scalarPartitionXToggled","imageBrightnessInMilliChanged","imageBrightnessReset","imageContrastInMilliChanged","imageContrastReset","imageShowActualSizeChanged","selectTimeEnableToggled","useRangeSelectTimeToggled","selectTimeChanged"]],template:function t(e,n){1&e&&(Rm(0,"metrics-dashboard-settings-component",0),Vm("tooltipSortChanged",(function t(e){return n.onTooltipSortChanged(e)}))("ignoreOutliersChanged",(function t(){return n.onIgnoreOutliersChanged()}))("xAxisTypeChanged",(function t(e){return n.onXAxisTypeChanged(e)}))("histogramModeChanged",(function t(e){return n.onHistogramModeChanged(e)}))("scalarSmoothingChanged",(function t(e){return n.onScalarSmoothingChanged(e)}))("scalarPartitionXToggled",(function t(){return n.onScalarPartitionXToggled()}))("imageBrightnessInMilliChanged",(function t(e){return n.onImageBrightnessInMilliChanged(e)}))("imageBrightnessReset",(function t(){return n.onImageBrightnessReset()}))("imageContrastInMilliChanged",(function t(e){return n.onImageContrastInMilliChanged(e)}))("imageContrastReset",(function t(){return n.onImageContrastReset()}))("imageShowActualSizeChanged",(function t(){return n.onImageShowActualSizeChanged()}))("selectTimeEnableToggled",(function t(){return n.onSelectTimeEnableToggled()}))("useRangeSelectTimeToggled",(function t(){return n.onUseRangeSelectTimeToggled()}))("selectTimeChanged",(function t(e){return n.onSelectTimeChanged(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Ah(7,"async"),Ah(8,"async"),Ah(9,"async"),Ah(10,"async"),Ah(11,"async"),Ah(12,"async"),Ah(13,"async"),Ah(14,"async"),Ah(15,"async"),Am()),2&e&&Dm("isImageSupportEnabled",Th(1,15,n.isImageSupportEnabled$))("tooltipSort",Th(2,17,n.tooltipSort$))("ignoreOutliers",Th(3,19,n.ignoreOutliers$))("xAxisType",Th(4,21,n.xAxisType$))("histogramMode",Th(5,23,n.histogramMode$))("scalarSmoothing",Th(6,25,n.scalarSmoothing$))("scalarPartitionX",Th(7,27,n.scalarPartitionX$))("imageBrightnessInMilli",Th(8,29,n.imageBrightnessInMilli$))("imageContrastInMilli",Th(9,31,n.imageContrastInMilli$))("imageShowActualSize",Th(10,33,n.imageShowActualSize$))("isLinkedTimeFeatureEnabled",Th(11,35,n.isLinkedTimeFeatureEnabled$))("selectTimeEnabled",Th(12,37,n.selectTimeEnabled$))("selectedTime",Th(13,39,n.selectedTime$))("useRangeSelectTime",Th(14,41,n.useRangeSelectTime$))("stepMinMax",Th(15,43,n.stepMinMax$))},directives:[rht],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(sht,[{type:My,args:[{selector:"metrics-dashboard-settings",template:'\n    <metrics-dashboard-settings-component\n      [isImageSupportEnabled]="isImageSupportEnabled$ | async"\n      [tooltipSort]="tooltipSort$ | async"\n      (tooltipSortChanged)="onTooltipSortChanged($event)"\n      [ignoreOutliers]="ignoreOutliers$ | async"\n      (ignoreOutliersChanged)="onIgnoreOutliersChanged()"\n      [xAxisType]="xAxisType$ | async"\n      (xAxisTypeChanged)="onXAxisTypeChanged($event)"\n      [histogramMode]="histogramMode$ | async"\n      (histogramModeChanged)="onHistogramModeChanged($event)"\n      [scalarSmoothing]="scalarSmoothing$ | async"\n      (scalarSmoothingChanged)="onScalarSmoothingChanged($event)"\n      [scalarPartitionX]="scalarPartitionX$ | async"\n      (scalarPartitionXToggled)="onScalarPartitionXToggled()"\n      [imageBrightnessInMilli]="imageBrightnessInMilli$ | async"\n      (imageBrightnessInMilliChanged)="onImageBrightnessInMilliChanged($event)"\n      (imageBrightnessReset)="onImageBrightnessReset()"\n      [imageContrastInMilli]="imageContrastInMilli$ | async"\n      (imageContrastInMilliChanged)="onImageContrastInMilliChanged($event)"\n      (imageContrastReset)="onImageContrastReset()"\n      [imageShowActualSize]="imageShowActualSize$ | async"\n      (imageShowActualSizeChanged)="onImageShowActualSizeChanged()"\n      [isLinkedTimeFeatureEnabled]="isLinkedTimeFeatureEnabled$ | async"\n      [selectTimeEnabled]="selectTimeEnabled$ | async"\n      [selectedTime]="selectedTime$ | async"\n      [useRangeSelectTime]="useRangeSelectTime$ | async"\n      [stepMinMax]="stepMinMax$ | async"\n      (selectTimeEnableToggled)="onSelectTimeEnableToggled()"\n      (useRangeSelectTimeToggled)="onUseRangeSelectTimeToggled()"\n      (selectTimeChanged)="onSelectTimeChanged($event)"\n    >\n    </metrics-dashboard-settings-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);class lht{}function cht(t,e){1&t&&Tm(0,"metrics-filtered-view",9),2&t&&Dm("cardObserver",Ym().cardObserver)}function dht(t,e){1&t&&(Rm(0,"div",12),Tm(1,"mat-spinner",13),Am())}function pht(t,e){if(1&t){const t=Hm();Rm(0,"div",14),Rm(1,"div",15),Rm(2,"h2",16),ku(3,"Settings"),Am(),Rm(4,"button",17),Vm("click",(function e(){return hi(t),Ym().onCloseSidepaneButtonClicked.emit()})),Tm(5,"mat-icon",18),Am(),Am(),Tm(6,"metrics-dashboard-right-pane"),Am()}}lht.ɵfac=function t(e){return new(e||lht)},lht.ɵcmp=to({type:lht,selectors:[["metrics-dashboard-right-pane"]],decls:1,vars:0,template:function t(e,n){1&e&&Tm(0,"metrics-dashboard-settings")},directives:[sht],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lht,[{type:My,args:[{selector:"metrics-dashboard-right-pane",template:"<metrics-dashboard-settings></metrics-dashboard-settings>",changeDetection:zn.OnPush}]}],null,null);const mht=function(t){return{checked:t,"settings-button":!0}};class uht{constructor(t){this.host=t,this.onSettingsButtonClicked=new Lh,this.onCloseSidepaneButtonClicked=new Lh,this.onPluginTypeToggled=new Lh,this.onPluginTypeAllToggled=new Lh,this.PluginType=hA,this.cardObserver=new p2(this.host.nativeElement,"600px 0px 600px 0px")}}uht.ɵfac=function t(e){return new(e||uht)(Sm(hg))},uht.ɵcmp=to({type:uht,selectors:[["metrics-main-view-component"]],inputs:{showFilteredView:"showFilteredView",isSidepaneOpen:"isSidepaneOpen",filteredPluginTypes:"filteredPluginTypes",initialTagsLoading:"initialTagsLoading"},outputs:{onSettingsButtonClicked:"onSettingsButtonClicked",onCloseSidepaneButtonClicked:"onCloseSidepaneButtonClicked",onPluginTypeToggled:"onPluginTypeToggled",onPluginTypeAllToggled:"onPluginTypeAllToggled"},decls:22,vars:21,consts:function(){let t,e;return t="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Toggle settings side pane"):$localize`:Label on a toolbar button to toggle the settings side pane.␟d3516db6bbe6860a55beab66e4969dac625b8d72␟7659285445580838925:Toggle settings side pane`,e="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Close side pane"):$localize`:Label on a button to close the settings side pane.␟04521dc0b6a65cf5c382944c9a8b4b844a3e9598␟8156766997747165871:Close side pane`,[[1,"toolbar"],["multiple","","appearance","standard",1,"filter-view"],[3,"checked","click"],[1,"right-items"],["mat-stroked-button","","aria-label",t,3,"ngClass","click"],["svgIcon","settings_24px"],[1,"split-content"],["cdkScrollable",""],[3,"cardObserver",4,"ngIf"],[3,"cardObserver"],["class","loading-container",4,"ngIf"],["class","sidebar",4,"ngIf"],[1,"loading-container"],["diameter","36"],[1,"sidebar"],[1,"header"],[1,"title"],["mat-icon-button","","aria-label",e,3,"click"],["svgIcon","close_24px"]]},template:function t(e,n){1&e&&(Rm(0,"div",0),Tm(1,"metrics-tag-filter"),Rm(2,"mat-button-toggle-group",1),Rm(3,"mat-button-toggle",2),Vm("click",(function t(){return n.onPluginTypeAllToggled.emit()})),ku(4," All "),Am(),Rm(5,"mat-button-toggle",2),Vm("click",(function t(){return n.onPluginTypeToggled.emit(n.PluginType.SCALARS)})),ku(6," Scalars "),Am(),Rm(7,"mat-button-toggle",2),Vm("click",(function t(){return n.onPluginTypeToggled.emit(n.PluginType.IMAGES)})),ku(8," Image "),Am(),Rm(9,"mat-button-toggle",2),Vm("click",(function t(){return n.onPluginTypeToggled.emit(n.PluginType.HISTOGRAMS)})),ku(10," Histogram "),Am(),Am(),Rm(11,"div",3),Rm(12,"button",4),Vm("click",(function t(){return n.onSettingsButtonClicked.emit()})),Tm(13,"mat-icon",5),ku(14," Settings "),Am(),Am(),Am(),Rm(15,"div",6),Rm(16,"div",7),Qp(17,cht,1,1,"metrics-filtered-view",8),Tm(18,"metrics-pinned-view",9),Qp(19,dht,2,0,"div",10),Tm(20,"metrics-card-groups",9),Am(),Qp(21,pht,7,0,"div",11),Am()),2&e&&(rc(3),Dm("checked",0===n.filteredPluginTypes.size),rc(2),Dm("checked",n.filteredPluginTypes.has(n.PluginType.SCALARS)),rc(2),Dm("checked",n.filteredPluginTypes.has(n.PluginType.IMAGES)),rc(2),Dm("checked",n.filteredPluginTypes.has(n.PluginType.HISTOGRAMS)),rc(3),Dm("ngClass",Mh(19,mht,n.isSidepaneOpen)),jp("aria-pressed",n.isSidepaneOpen),rc(4),pu("main",!0)("filter-view",n.showFilteredView),rc(1),Dm("ngIf",n.showFilteredView),rc(1),du("display",n.showFilteredView?"none":""),Dm("cardObserver",n.cardObserver),rc(1),Dm("ngIf",n.initialTagsLoading),rc(1),du("display",n.showFilteredView?"none":""),Dm("cardObserver",n.cardObserver),rc(1),Dm("ngIf",n.isSidepaneOpen))},directives:[x2,E2,A2,XH,aM,DW,mF,dM,Bgt,Ygt,$gt,o1,lht],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   .mat-chip-remove.mat-icon[_ngcontent-%COMP%]{font-size:18px}.mat-table[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-header-cell[_ngcontent-%COMP%]{font-size:12px;font-weight:500}.mat-cell[_ngcontent-%COMP%], .mat-footer-cell[_ngcontent-%COMP%]{font-size:14px}.mat-calendar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-calendar-body[_ngcontent-%COMP%]{font-size:13px}.mat-calendar-body-label[_ngcontent-%COMP%], .mat-calendar-period-button[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-calendar-table-header[_ngcontent-%COMP%]   th[_ngcontent-%COMP%]{font-size:11px;font-weight:400}.mat-dialog-title[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", 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#ebebeb;margin-left:16px;padding-left:16px}body.dark-mode[_nghost-%COMP%]   .toolbar[_ngcontent-%COMP%]   .right-items[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .toolbar[_ngcontent-%COMP%]   .right-items[_ngcontent-%COMP%]{border-left:1px solid #555}.filter-view[_ngcontent-%COMP%]{flex:none;margin-right:5px}.filter-view[_ngcontent-%COMP%]   mat-button-toggle[_ngcontent-%COMP%]{height:25px;font-size:12px}.filter-view[_ngcontent-%COMP%]   mat-button-toggle[_ngcontent-%COMP%]     .mat-button-toggle-label-content{line-height:25px}.split-content[_ngcontent-%COMP%]{display:flex;overflow-y:auto;flex:1}.main[_ngcontent-%COMP%], .sidebar[_ngcontent-%COMP%]{contain:strict;overflow-x:hidden;overflow-y:auto;will-change:transform,scroll-position}.main[_ngcontent-%COMP%]{background-color:#f5f6f7;flex:1 1;display:flex;flex-direction:column}body.dark-mode[_nghost-%COMP%]   .main[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   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.settings-button.checked[_ngcontent-%COMP%]{background-color:#e0e0e0;border-color:#e0e0e0}body.dark-mode   [_nghost-%COMP%]   .settings-button.checked[_ngcontent-%COMP%]{background-color:#212121}[_nghost-%COMP%]   .settings-button[_ngcontent-%COMP%]     .mat-button-wrapper{display:inline-flex;align-items:center}[_nghost-%COMP%]   .settings-button[_ngcontent-%COMP%]   mat-icon[_ngcontent-%COMP%]{margin-right:4px}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(uht,[{type:My,args:[{selector:"metrics-main-view-component",templateUrl:"main_view_component.ng.html",styleUrls:["main_view_component.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:hg}]}),{showFilteredView:[{type:xy}],isSidepaneOpen:[{type:xy}],filteredPluginTypes:[{type:xy}],initialTagsLoading:[{type:xy}],onSettingsButtonClicked:[{type:Oy}],onCloseSidepaneButtonClicked:[{type:Oy}],onPluginTypeToggled:[{type:Oy}],onPluginTypeAllToggled:[{type:Oy}]});class fht{constructor(t){this.store=t,this.isSidepaneOpen$=this.store.select(XT),this.initialTagsLoading$=this.store.select(uT).pipe(He((t=>null===t.lastLoadedTimeInMs),!0),It((t=>t.state===yE.LOADING&&null===t.lastLoadedTimeInMs))),this.showFilteredView$=this.store.select(BT).pipe(It((t=>t.length>0))),this.filteredPluginTypes$=this.store.select(qT)}onSettingsButtonClicked(){this.store.dispatch(zE())}onCloseSidepaneButtonClicked(){this.store.dispatch(NE())}onPluginVisibilityToggled(t){this.store.dispatch(iR({plugin:t}))}onShowAllPlugins(){this.store.dispatch(aR())}}fht.ɵfac=function t(e){return new(e||fht)(Sm(Iw))},fht.ɵcmp=to({type:fht,selectors:[["metrics-main-view"]],decls:5,vars:12,consts:[[3,"showFilteredView","isSidepaneOpen","initialTagsLoading","filteredPluginTypes","onSettingsButtonClicked","onCloseSidepaneButtonClicked","onPluginTypeToggled","onPluginTypeAllToggled"]],template:function t(e,n){1&e&&(Rm(0,"metrics-main-view-component",0),Vm("onSettingsButtonClicked",(function t(){return n.onSettingsButtonClicked()}))("onCloseSidepaneButtonClicked",(function t(){return n.onCloseSidepaneButtonClicked()}))("onPluginTypeToggled",(function t(e){return n.onPluginVisibilityToggled(e)}))("onPluginTypeAllToggled",(function t(){return n.onShowAllPlugins()})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Am()),2&e&&Dm("showFilteredView",Th(1,4,n.showFilteredView$))("isSidepaneOpen",Th(2,6,n.isSidepaneOpen$))("initialTagsLoading",Th(3,8,n.initialTagsLoading$))("filteredPluginTypes",Th(4,10,n.filteredPluginTypes$))},directives:[uht],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(fht,[{type:My,args:[{selector:"metrics-main-view",template:'\n    <metrics-main-view-component\n      [showFilteredView]="showFilteredView$ | async"\n      [isSidepaneOpen]="isSidepaneOpen$ | async"\n      [initialTagsLoading]="initialTagsLoading$ | async"\n      [filteredPluginTypes]="filteredPluginTypes$ | async"\n      (onSettingsButtonClicked)="onSettingsButtonClicked()"\n      (onCloseSidepaneButtonClicked)="onCloseSidepaneButtonClicked()"\n      (onPluginTypeToggled)="onPluginVisibilityToggled($event)"\n      (onPluginTypeAllToggled)="onShowAllPlugins()"\n    ></metrics-main-view-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);const ght=new Ga("[Metrics] METRICS_Promo Message Component");function hht(t,e){1&t&&(Nm(0),Xm(1),zm())}class bht{constructor(t,e){this.viewContainerRef=t,this.componentFactoryResolver=e}ngOnInit(){if(this.customizableComponent){const t=this.componentFactoryResolver.resolveComponentFactory(this.customizableComponent.constructor);this.viewContainerRef.createComponent(t)}}}bht.ɵfac=function t(e){return new(e||bht)(Sm(eh),Sm(ug))},bht.ɵcmp=to({type:bht,selectors:[["tb-customization"]],inputs:{customizableComponent:"customizableComponent"},ngContentSelectors:["*"],decls:1,vars:1,consts:[[4,"ngIf"]],template:function t(e,n){1&e&&(Zm(),Qp(0,hht,2,0,"ng-container",0)),2&e&&Dm("ngIf",!n.customizableComponent)},directives:[dM],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(bht,[{type:My,args:[{selector:"tb-customization",template:'\n    <ng-container *ngIf="!customizableComponent">\n      <ng-content></ng-content>\n    </ng-container>\n  '}]}],(function(){return[{type:eh},{type:ug}]}),{customizableComponent:[{type:xy}]});class yht{constructor(t){this.customPromoMessage=t,this.onDismiss=new Lh,this.onGoToScalars=new Lh}}yht.ɵfac=function t(e){return new(e||yht)(Sm(ght,8))},yht.ɵcmp=to({type:yht,selectors:[["metrics-promo-notice-component"]],outputs:{onDismiss:"onDismiss",onGoToScalars:"onGoToScalars"},decls:7,vars:1,consts:[[1,"message",3,"customizableComponent"],[1,"go-to-scalars",3,"click"],[1,"dismiss",3,"click"],["inline","","svgIcon","close_24px"]],template:function t(e,n){1&e&&(Rm(0,"tb-customization",0),ku(1," Welcome to new default experience of TensorBoard. 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1}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Mht,[{type:My,args:[{selector:"metrics-dashboard",template:'\n    <metrics-promo-notice\n      *ngIf="isButterBarEnabled$ | async"\n      class="notice"\n    ></metrics-promo-notice>\n    <tb-dashboard-layout>\n      <runs-selector sidebar></runs-selector>\n      <metrics-main-view main></metrics-main-view>\n    </tb-dashboard-layout>\n  ',styleUrls:["metrics_container.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);class vht{}vht.ɵfac=function t(e){return new(e||vht)},vht.ɵmod=ao({type:vht}),vht.ɵinj=vn({imports:[[WM,EW,JH]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(vht,[{type:Ay,args:[{declarations:[MQ],exports:[MQ],imports:[WM,EW,JH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(vht,{declarations:[MQ],imports:[WM,EW,JH],exports:[MQ]});class xht{}xht.ɵfac=function t(e){return new(e||xht)},xht.ɵmod=ao({type:xht}),xht.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(xht,[{type:Ay,args:[{imports:[WM],declarations:[bht],exports:[bht]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(xht,{declarations:[bht],imports:[WM],exports:[bht]});class Oht{}Oht.ɵfac=function t(e){return new(e||Oht)},Oht.ɵmod=ao({type:Oht}),Oht.ɵinj=vn({imports:[[WM,Y0,EW]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Oht,[{type:Ay,args:[{declarations:[q0],exports:[q0],imports:[WM,Y0,EW]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Oht,{declarations:[q0],imports:[WM,Y0,EW],exports:[q0]});class Pht{}Pht.ɵfac=function t(e){return new(e||Pht)},Pht.ɵmod=ao({type:Pht}),Pht.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Pht,[{type:Ay,args:[{imports:[WM],exports:[D1],declarations:[D1]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Pht,{declarations:[D1],imports:[WM],exports:[D1]});class wht{}wht.ɵfac=function t(e){return 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ngDevMode||ngDevMode)&&hh(Sht,[{type:Ay,args:[{exports:[Nft],declarations:[Nft]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Sht,{declarations:[Nft],exports:[Nft]});class Dht{}Dht.ɵfac=function t(e){return new(e||Dht)},Dht.ɵmod=ao({type:Dht}),Dht.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Dht,[{type:Ay,args:[{declarations:[hft],exports:[hft],imports:[WM]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Dht,{declarations:[hft],imports:[WM],exports:[hft]});class Eht{}Eht.ɵfac=function t(e){return new(e||Eht)},Eht.ɵmod=ao({type:Eht}),Eht.ɵinj=vn({imports:[[WM,VJ,Sht,Dht]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Eht,[{type:Ay,args:[{declarations:[tgt],exports:[tgt],imports:[WM,VJ,Sht,Dht]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Eht,{declarations:[tgt],imports:[WM,VJ,Sht,Dht],exports:[tgt]});class Rht{}Rht.ɵfac=function t(e){return new(e||Rht)},Rht.ɵmod=ao({type:Rht}),Rht.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Rht,[{type:Ay,args:[{declarations:[z2],exports:[z2],imports:[WM]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Rht,{declarations:[z2],imports:[WM],exports:[z2]});class Aht{}Aht.ɵfac=function t(e){return new(e||Aht)},Aht.ɵmod=ao({type:Aht}),Aht.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Aht,[{type:Ay,args:[{declarations:[L2,F2],exports:[L2],imports:[WM]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Aht,{declarations:[L2,F2],imports:[WM],exports:[L2]});class Tht{}Tht.ɵfac=function t(e){return new(e||Tht)},Tht.ɵmod=ao({type:Tht}),Tht.ɵinj=vn({imports:[[WM,EW]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Tht,[{type:Ay,args:[{declarations:[gft],exports:[gft],imports:[WM,EW]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Tht,{declarations:[gft],imports:[WM,EW],exports:[gft]});class Nht{}Nht.ɵfac=function t(e){return new(e||Nht)},Nht.ɵmod=ao({type:Nht}),Nht.ɵinj=vn({imports:[[WM,Eht,JH,EW,i1,Aht,Rht,Tht]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Nht,[{type:Ay,args:[{declarations:[mgt,pgt],exports:[mgt],imports:[WM,Eht,JH,EW,i1,Aht,Rht,Tht]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Nht,{declarations:[mgt,pgt],imports:[WM,Eht,JH,EW,i1,Aht,Rht,Tht],exports:[mgt]});class zht{}zht.ɵfac=function t(e){return new(e||zht)},zht.ɵmod=ao({type:zht}),zht.ɵinj=vn({imports:[[WM,JH,EW,i1,zX,Aht,Rht]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(zht,[{type:Ay,args:[{declarations:[X2,Z2],exports:[X2],imports:[WM,JH,EW,i1,zX,Aht,Rht]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(zht,{declarations:[X2,Z2],imports:[WM,JH,EW,i1,zX,Aht,Rht],exports:[X2]});class Iht{}Iht.ɵfac=function t(e){return new(e||Iht)},Iht.ɵmod=ao({type:Iht}),Iht.ɵinj=vn({imports:[[WM,yL,JH,EW,BY,oY]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Iht,[{type:Ay,args:[{declarations:[Xut,Uut,Eut],exports:[Xut,Uut,Eut],imports:[WM,yL,JH,EW,BY,oY]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Iht,{declarations:[Xut,Uut,Eut],imports:[WM,yL,JH,EW,BY,oY],exports:[Xut,Uut,Eut]});class Hht{}Hht.ɵfac=function t(e){return new(e||Hht)},Hht.ɵmod=ao({type:Hht}),Hht.ɵinj=vn({imports:[[WM,yL,Iht,VJ]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Hht,[{type:Ay,args:[{declarations:[fft],exports:[fft],imports:[WM,yL,Iht,VJ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Hht,{declarations:[fft],imports:[WM,yL,Iht,VJ],exports:[fft]});class Fht{}Fht.ɵfac=function t(e){return new(e||Fht)},Fht.ɵmod=ao({type:Fht}),Fht.ɵinj=vn({imports:[[WM,cG,JH,dW,BY,TG,eT]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Fht,[{type:Ay,args:[{declarations:[l5,s5],exports:[l5],imports:[WM,cG,JH,dW,BY,TG,eT],entryComponents:[l5]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Fht,{declarations:[l5,s5],imports:[WM,cG,JH,dW,BY,TG,eT],exports:[l5]});class Lht{}Lht.ɵfac=function t(e){return new(e||Lht)},Lht.ɵmod=ao({type:Lht}),Lht.ɵinj=vn({imports:[[WM,Fht,Sht,Hht,Dht,JH,EW,oY,i1,VJ,Rht,Tht]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Lht,[{type:Ay,args:[{declarations:[Ift,Tft],exports:[Ift],imports:[WM,Fht,Sht,Hht,Dht,JH,EW,oY,i1,VJ,Rht,Tht]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Lht,{declarations:[Ift,Tft],imports:[WM,Fht,Sht,Hht,Dht,JH,EW,oY,i1,VJ,Rht,Tht],exports:[Ift]});class Bht{}Bht.ɵfac=function t(e){return new(e||Bht)},Bht.ɵmod=ao({type:Bht}),Bht.ɵinj=vn({imports:[[WM,zht,Lht,Nht,Sht]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Bht,[{type:Ay,args:[{declarations:[m2,ygt,_gt],exports:[m2,_gt],imports:[WM,zht,Lht,Nht,Sht]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Bht,{declarations:[m2,ygt,_gt],imports:[WM,zht,Lht,Nht,Sht],exports:[m2,_gt]});class Vht{}Vht.ɵfac=function t(e){return new(e||Vht)},Vht.ɵmod=ao({type:Vht}),Vht.ɵinj=vn({imports:[[WM,TG]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Vht,[{type:Ay,args:[{declarations:[eht],exports:[eht],imports:[WM,TG]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Vht,{declarations:[eht],imports:[WM,TG],exports:[eht]});class jht{}jht.ɵfac=function t(e){return new(e||jht)},jht.ɵmod=ao({type:jht}),jht.ɵinj=vn({imports:[[WM,Vht,JH,T2,SY,EW,TG,zX,tT,Pht]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(jht,[{type:Ay,args:[{declarations:[lht,rht,sht],exports:[lht],imports:[WM,Vht,JH,T2,SY,EW,TG,zX,tT,Pht]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(jht,{declarations:[lht,rht,sht],imports:[WM,Vht,JH,T2,SY,EW,TG,zX,tT,Pht],exports:[lht]});class Uht{}Uht.ɵfac=function t(e){return new(e||Uht)},Uht.ɵmod=ao({type:Uht}),Uht.ɵinj=vn({imports:[[Bht,WM,Oht,Y0,JH,T2,EW,BY,i1,jht,_F]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Uht,[{type:Ay,args:[{declarations:[Tgt,Ngt,Wgt,Ygt,Zgt,Xgt,Qgt,$gt,uht,fht,v2,x2,Lgt,Bgt],exports:[fht],imports:[Bht,WM,Oht,Y0,JH,T2,EW,BY,i1,jht,_F]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Uht,{declarations:[Tgt,Ngt,Wgt,Ygt,Zgt,Xgt,Qgt,$gt,uht,fht,v2,x2,Lgt,Bgt],imports:[Bht,WM,Oht,Y0,JH,T2,EW,BY,i1,jht,_F],exports:[fht]});class Ght{}function Wht(){return[{actionCreator:oR,alertFromAction:t=>{const{wasPinned:e,canCreateNewPins:n}=t;return e||n?null:{localizedMessage:"Max pin limit exceeded. 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0!==n&&(o=n-1,i=n+1),Object.assign(Object.assign({},t),{metricArithmetic:[...t.metricArithmetic.slice(0,o),...t.metricArithmetic.slice(i)],metricFilters:a})})),bk(Bbt,((t,{metric:e,max:n,min:o,includeNaN:i})=>t.metricFilters[e]?Object.assign(Object.assign({},t),{metricFilters:Object.assign(Object.assign({},t.metricFilters),{[e]:{max:n,min:o,includeNaN:i}})}):t)),bk(Vbt,((t,{metric:e})=>{const n={metric:e,order:nbt.DESCENDING};return t.sort.metric===e&&t.sort.order===nbt.DESCENDING&&(n.order=nbt.ASCENDNG),Object.assign(Object.assign({},t),{sort:n})})),bk(jbt,((t,{annotation:e})=>{const n={metric:e,order:nbt.SIMILAR};return t.sort.metric===e&&t.sort.order===nbt.SIMILAR&&(n.order=nbt.DISSIMILAR),Object.assign(Object.assign({},t),{sort:n})})),bk(Ubt,(t=>Object.assign(Object.assign({},t),{pcExpanded:!t.pcExpanded}))),bk(Gbt,(t=>Object.assign(Object.assign({},t),{annotationsExpanded:!t.annotationsExpanded}))),bk(Wbt,(t=>Object.assign(Object.assign({},t),{sidebarExpanded:!t.sidebarExpanded}))),bk(Ybt,(t=>Object.assign(Object.assign({},t),{showCounts:!t.showCounts}))),bk(qbt,(t=>Object.assign(Object.assign({},t),{showHiddenAnnotations:!t.showHiddenAnnotations}))),bk(Zbt,((t,{metric:e})=>{let n=ebt.EMBEDDINGS,o=e;return 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t(e,n){2&e&&pu("valid",n.isRegexFilterValid)},inputs:{completions:"completions",regexFilterValue:"regexFilterValue",isRegexFilterValid:"isRegexFilterValid"},outputs:{onRegexFilterValueChange:"onRegexFilterValueChange",onAddFilter:"onAddFilter"},decls:7,vars:4,consts:[["svgIcon","search_24px"],["matInput","","autocomplete","off","placeholder","Add Metric Filter",3,"value","matAutocomplete","input"],["matInput",""],["svgIcon","error_24px","class","error-icon","matTooltip","Invalid regex filter. 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!important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}mat-icon[_ngcontent-%COMP%]{flex:none;margin-right:5px}[_nghost-%COMP%]{display:flex;padding:0 10px;position:relative;font-size:.9em}[_nghost-%COMP%]:not(.valid){color:#c62828}[_nghost-%COMP%]:not(.valid)   input[_ngcontent-%COMP%]{caret-color:currentColor}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(syt,[{type:My,args:[{selector:"metric-search-component",templateUrl:"./metric_search_component.ng.html",styleUrls:["./metric_search_component.css"],changeDetection:zn.OnPush}]}],null,{completions:[{type:xy}],regexFilterValue:[{type:xy}],isRegexFilterValid:[{type:Py,args:["class.valid"]},{type:xy}],onRegexFilterValueChange:[{type:Oy}],onAddFilter:[{type:Oy}]});class lyt{constructor(t){this.store=t,this.metricsRegex$=this.store.select(ubt),this.activeRuns$=this.store.pipe(Fw(NN)).pipe(It((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.metricsForActiveRuns$=Wt(this.activeRuns$,this.store.select(sbt)).pipe(It((([t,e])=>{const n=new Set;for(const o of t)if(e[o])for(const t of e[o])n.add(t);return[...n]}))),this.isMetricsFilterValid$=this.metricsRegex$.pipe(It((t=>{try{return Boolean(new RegExp(t))}catch(t){return!1}}))),this.metricFilterKeys$=this.store.pipe(Fw(gbt)).pipe(It((t=>Object.keys(t)))),this.completions$=Wt(this.metricsForActiveRuns$,this.metricsRegex$,this.metricFilterKeys$).pipe(It((([t,e,n])=>{const o=t.filter((t=>!n.includes(t)));try{const t=new RegExp(e,"i");return o.filter((e=>t.test(e))).sort()}catch(t){return[]}})))}onFilterChange(t){this.store.dispatch(Hbt({regex:t}))}onAddFilter(t){this.store.dispatch(Fbt({metric:t})),this.store.dispatch(Hbt({regex:""}))}}function cyt(t,e,n){const o=[[e,...n]];if(!n.length||!t.length)return"data:text/csv;charset=utf-8,"+o.map((t=>t.join(","))).join("\n");const i=n.map((t=>nyt(t)));for(const[n,a]of t){const t=a.filter((t=>t.run===e));if(t.length){const e=[n];for(const n of i){const o=t.find((t=>t.metric===n));e.push(void 0===o?"null":`${o.nPMIValue}`)}o.push(e)}}return"data:text/csv;charset=utf-8,"+o.map((t=>t.join(","))).join("\n")}lyt.ɵfac=function t(e){return new(e||lyt)(Sm(Iw))},lyt.ɵcmp=to({type:lyt,selectors:[["npmi-metric-search"]],decls:4,vars:9,consts:[[3,"regexFilterValue","completions","isRegexFilterValid","onRegexFilterValueChange","onAddFilter"]],template:function t(e,n){1&e&&(Rm(0,"metric-search-component",0),Vm("onRegexFilterValueChange",(function t(e){return n.onFilterChange(e)}))("onAddFilter",(function t(e){return n.onAddFilter(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Am()),2&e&&Dm("regexFilterValue",Th(1,3,n.metricsRegex$))("completions",Th(2,5,n.completions$))("isRegexFilterValid",Th(3,7,n.isMetricsFilterValid$))},directives:[syt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lyt,[{type:My,args:[{selector:"npmi-metric-search",template:'\n    <metric-search-component\n      [regexFilterValue]="metricsRegex$ | async"\n      [completions]="completions$ | async"\n      [isRegexFilterValid]="isMetricsFilterValid$ | async"\n      (onRegexFilterValueChange)="onFilterChange($event)"\n      (onAddFilter)="onAddFilter($event)"\n    ></metric-search-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);const dyt=function(t){return{"active-button":t}};class pyt{downloadResults(){for(const t of this.runs){const e=cyt(this.flaggedData,t,this.metrics),n=document.createElement("a");n.setAttribute("href",e),n.setAttribute("download",`report_${t}.csv`),n.click()}}}pyt.ɵfac=function t(e){return new(e||pyt)},pyt.ɵcmp=to({type:pyt,selectors:[["results-download-component"]],inputs:{numFlaggedAnnotations:"numFlaggedAnnotations",runs:"runs",flaggedData:"flaggedData",metrics:"metrics"},decls:4,vars:5,consts:[["mat-stroked-button","","title","Export CSV reports of all flagged annotations. Will generate one CSV per active run.",3,"disabled","ngClass","click"],[1,"button-contents"],["svgIcon","get_app_24px"]],template:function t(e,n){1&e&&(Rm(0,"button",0),Vm("click",(function t(){return n.downloadResults()})),Rm(1,"span",1),Tm(2,"mat-icon",2),ku(3),Am(),Am()),2&e&&(Dm("disabled",0===n.numFlaggedAnnotations)("ngClass",Mh(3,dyt,n.numFlaggedAnnotations>0)),rc(3),Du(" Flagged Rows (",n.numFlaggedAnnotations,") "))},directives:[XH,aM,DW],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 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ngDevMode||ngDevMode)&&hh(pyt,[{type:My,args:[{selector:"results-download-component",templateUrl:"./results_download_component.ng.html",styleUrls:["./results_download_component.css"],changeDetection:zn.OnPush}]}],null,{numFlaggedAnnotations:[{type:xy}],runs:[{type:xy}],flaggedData:[{type:xy}],metrics:[{type:xy}]});class myt{constructor(t){this.store=t,this.flaggedAnnotations$=this.store.select(dbt),this.numFlaggedAnnotations$=this.flaggedAnnotations$.pipe(It((t=>t.length))),this.activeRuns$=this.store.select(NN).pipe(It((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.flaggedData$=Wt([this.store.select(rbt),this.flaggedAnnotations$]).pipe(It((([t,e])=>{const n=new Set(e);return Object.entries(t).filter((t=>n.has(t[0])))}))),this.metrics$=Wt([this.store.select(sbt),this.activeRuns$,this.store.select(gbt)]).pipe(It((([t,e,n])=>{let o=Object.keys(n);for(const n of e)t[n]&&(o=o.concat(t[n].filter((t=>eyt(t)))));return o=[...new Set(o)],o})))}}myt.ɵfac=function t(e){return new(e||myt)(Sm(Iw))},myt.ɵcmp=to({type:myt,selectors:[["npmi-results-download"]],decls:5,vars:12,consts:[[3,"numFlaggedAnnotations","runs","flaggedData","metrics"]],template:function t(e,n){1&e&&(Tm(0,"results-download-component",0),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async")),2&e&&Dm("numFlaggedAnnotations",Th(1,4,n.numFlaggedAnnotations$))("runs",Th(2,6,n.activeRuns$))("flaggedData",Th(3,8,n.flaggedData$))("metrics",Th(4,10,n.metrics$))},directives:[pyt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(myt,[{type:My,args:[{selector:"npmi-results-download",template:'\n    <results-download-component\n      [numFlaggedAnnotations]="numFlaggedAnnotations$ | async"\n      [runs]="activeRuns$ | async"\n      [flaggedData]="flaggedData$ | async"\n      [metrics]="metrics$ | async"\n    ></results-download-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);const uyt=new Ga("MatChipRemove"),fyt=new Ga("MatChipAvatar"),gyt=new Ga("MatChipTrailingIcon"),hyt=$I(JI(QI(class{constructor(t){this._elementRef=t}}),"primary"),-1);class byt{}byt.ɵfac=function t(e){return new(e||byt)},byt.ɵdir=lo({type:byt,selectors:[["mat-chip-avatar"],["","matChipAvatar",""]],hostAttrs:[1,"mat-chip-avatar"],features:[pg([{provide:fyt,useExisting:byt}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(byt,[{type:Cy,args:[{selector:"mat-chip-avatar, [matChipAvatar]",host:{class:"mat-chip-avatar"},providers:[{provide:fyt,useExisting:byt}]}]}],null,null);class yyt{}yyt.ɵfac=function t(e){return new(e||yyt)},yyt.ɵdir=lo({type:yyt,selectors:[["mat-chip-trailing-icon"],["","matChipTrailingIcon",""]],hostAttrs:[1,"mat-chip-trailing-icon"],features:[pg([{provide:gyt,useExisting:yyt}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(yyt,[{type:Cy,args:[{selector:"mat-chip-trailing-icon, [matChipTrailingIcon]",host:{class:"mat-chip-trailing-icon"},providers:[{provide:gyt,useExisting:yyt}]}]}],null,null);class _yt extends hyt{constructor(t,e,n,o,i,a,r,s){super(t),this._ngZone=e,this._changeDetectorRef=i,this._hasFocus=!1,this.chipListSelectable=!0,this._chipListMultiple=!1,this._chipListDisabled=!1,this._selected=!1,this._selectable=!0,this._disabled=!1,this._removable=!0,this._onFocus=new I,this._onBlur=new I,this.selectionChange=new Lh,this.destroyed=new Lh,this.removed=new Lh,this._addHostClassName(),this._chipRippleTarget=a.createElement("div"),this._chipRippleTarget.classList.add("mat-chip-ripple"),this._elementRef.nativeElement.appendChild(this._chipRippleTarget),this._chipRipple=new PH(this,e,this._chipRippleTarget,n),this._chipRipple.setupTriggerEvents(t),this.rippleConfig=o||{},this._animationsDisabled="NoopAnimations"===r,this.tabIndex=null!=s&&parseInt(s)||-1}get rippleDisabled(){return this.disabled||this.disableRipple||this._animationsDisabled||!!this.rippleConfig.disabled}get selected(){return this._selected}set selected(t){const e=yz(t);e!==this._selected&&(this._selected=e,this._dispatchSelectionChange())}get value(){return void 0!==this._value?this._value:this._elementRef.nativeElement.textContent}set value(t){this._value=t}get selectable(){return this._selectable&&this.chipListSelectable}set selectable(t){this._selectable=yz(t)}get disabled(){return this._chipListDisabled||this._disabled}set disabled(t){this._disabled=yz(t)}get removable(){return this._removable}set removable(t){this._removable=yz(t)}get ariaSelected(){return this.selectable&&(this._chipListMultiple||this.selected)?this.selected.toString():null}_addHostClassName(){const t="mat-basic-chip",e=this._elementRef.nativeElement;e.hasAttribute(t)||e.tagName.toLowerCase()===t?e.classList.add(t):e.classList.add("mat-standard-chip")}ngOnDestroy(){this.destroyed.emit({chip:this}),this._chipRipple._removeTriggerEvents()}select(){this._selected||(this._selected=!0,this._dispatchSelectionChange(),this._changeDetectorRef.markForCheck())}deselect(){this._selected&&(this._selected=!1,this._dispatchSelectionChange(),this._changeDetectorRef.markForCheck())}selectViaInteraction(){this._selected||(this._selected=!0,this._dispatchSelectionChange(!0),this._changeDetectorRef.markForCheck())}toggleSelected(t=!1){return this._selected=!this.selected,this._dispatchSelectionChange(t),this._changeDetectorRef.markForCheck(),this.selected}focus(){this._hasFocus||(this._elementRef.nativeElement.focus(),this._onFocus.next({chip:this})),this._hasFocus=!0}remove(){this.removable&&this.removed.emit({chip:this})}_handleClick(t){this.disabled?t.preventDefault():t.stopPropagation()}_handleKeydown(t){if(!this.disabled)switch(t.keyCode){case 46:case 8:this.remove(),t.preventDefault();break;case fz:this.selectable&&this.toggleSelected(!0),t.preventDefault()}}_blur(){this._ngZone.onStable.pipe(be(1)).subscribe((()=>{this._ngZone.run((()=>{this._hasFocus=!1,this._onBlur.next({chip:this})}))}))}_dispatchSelectionChange(t=!1){this.selectionChange.emit({source:this,isUserInput:t,selected:this._selected})}}_yt.ɵfac=function t(e){return new(e||_yt)(Sm(hg),Sm(a_),Sm(wz),Sm(wH,8),Sm(Ug),Sm(Z_),Sm(VP,8),Na("tabindex"))},_yt.ɵdir=lo({type:_yt,selectors:[["mat-basic-chip"],["","mat-basic-chip",""],["mat-chip"],["","mat-chip",""]],contentQueries:function t(e,n,o){if(1&e&&($h(o,fyt,5),$h(o,gyt,5),$h(o,uyt,5)),2&e){let t;Jh(t=tb())&&(n.avatar=t.first),Jh(t=tb())&&(n.trailingIcon=t.first),Jh(t=tb())&&(n.removeIcon=t.first)}},hostAttrs:["role","option",1,"mat-chip","mat-focus-indicator"],hostVars:14,hostBindings:function t(e,n){1&e&&Vm("click",(function t(e){return n._handleClick(e)}))("keydown",(function t(e){return n._handleKeydown(e)}))("focus",(function t(){return n.focus()}))("blur",(function t(){return n._blur()})),2&e&&(jp("tabindex",n.disabled?null:n.tabIndex)("disabled",n.disabled||null)("aria-disabled",n.disabled.toString())("aria-selected",n.ariaSelected),pu("mat-chip-selected",n.selected)("mat-chip-with-avatar",n.avatar)("mat-chip-with-trailing-icon",n.trailingIcon||n.removeIcon)("mat-chip-disabled",n.disabled)("_mat-animation-noopable",n._animationsDisabled))},inputs:{color:"color",disableRipple:"disableRipple",tabIndex:"tabIndex",selected:"selected",value:"value",selectable:"selectable",disabled:"disabled",removable:"removable"},outputs:{selectionChange:"selectionChange",destroyed:"destroyed",removed:"removed"},exportAs:["matChip"],features:[xp]}),_yt.ctorParameters=()=>[{type:hg},{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:Ug},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]}],_yt.propDecorators={avatar:[{type:qa,args:[fyt]}],trailingIcon:[{type:qa,args:[gyt]}],removeIcon:[{type:qa,args:[uyt]}],selected:[{type:xy}],value:[{type:xy}],selectable:[{type:xy}],disabled:[{type:xy}],removable:[{type:xy}],selectionChange:[{type:Oy}],destroyed:[{type:Oy}],removed:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(_yt,[{type:Cy,args:[{selector:"mat-basic-chip, [mat-basic-chip], mat-chip, [mat-chip]",inputs:["color","disableRipple","tabIndex"],exportAs:"matChip",host:{class:"mat-chip mat-focus-indicator","[attr.tabindex]":"disabled ? null : tabIndex",role:"option","[class.mat-chip-selected]":"selected","[class.mat-chip-with-avatar]":"avatar","[class.mat-chip-with-trailing-icon]":"trailingIcon || removeIcon","[class.mat-chip-disabled]":"disabled","[class._mat-animation-noopable]":"_animationsDisabled","[attr.disabled]":"disabled || null","[attr.aria-disabled]":"disabled.toString()","[attr.aria-selected]":"ariaSelected","(click)":"_handleClick($event)","(keydown)":"_handleKeydown($event)","(focus)":"focus()","(blur)":"_blur()"}}]}],(function(){return[{type:hg},{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:Ug},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]}]}),{selectionChange:[{type:Oy}],destroyed:[{type:Oy}],removed:[{type:Oy}],selected:[{type:xy}],value:[{type:xy}],selectable:[{type:xy}],disabled:[{type:xy}],removable:[{type:xy}],avatar:[{type:qa,args:[fyt]}],trailingIcon:[{type:qa,args:[gyt]}],removeIcon:[{type:qa,args:[uyt]}]});class Cyt{constructor(t,e){this._parentChip=t,"BUTTON"===e.nativeElement.nodeName&&e.nativeElement.setAttribute("type","button")}_handleClick(t){const e=this._parentChip;e.removable&&!e.disabled&&e.remove(),t.stopPropagation()}}Cyt.ɵfac=function t(e){return new(e||Cyt)(Sm(_yt),Sm(hg))},Cyt.ɵdir=lo({type:Cyt,selectors:[["","matChipRemove",""]],hostAttrs:[1,"mat-chip-remove","mat-chip-trailing-icon"],hostBindings:function t(e,n){1&e&&Vm("click",(function t(e){return n._handleClick(e)}))},features:[pg([{provide:uyt,useExisting:Cyt}])]}),Cyt.ctorParameters=()=>[{type:_yt},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Cyt,[{type:Cy,args:[{selector:"[matChipRemove]",host:{class:"mat-chip-remove mat-chip-trailing-icon","(click)":"_handleClick($event)"},providers:[{provide:uyt,useExisting:Cyt}]}]}],(function(){return[{type:_yt},{type:hg}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Myt=new Ga("mat-chips-default-options"),vyt=tH(class{constructor(t,e,n,o){this._defaultErrorStateMatcher=t,this._parentForm=e,this._parentFormGroup=n,this.ngControl=o}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let xyt=0;class Oyt{constructor(t,e){this.source=t,this.value=e}}class Pyt extends vyt{constructor(t,e,n,o,i,a,r){super(a,o,i,r),this._elementRef=t,this._changeDetectorRef=e,this._dir=n,this.controlType="mat-chip-list",this._lastDestroyedChipIndex=null,this._destroyed=new I,this._uid="mat-chip-list-"+xyt++,this._tabIndex=0,this._userTabIndex=null,this._onTouched=()=>{},this._onChange=()=>{},this._multiple=!1,this._compareWith=(t,e)=>t===e,this._required=!1,this._disabled=!1,this.ariaOrientation="horizontal",this._selectable=!0,this.change=new Lh,this.valueChange=new Lh,this.ngControl&&(this.ngControl.valueAccessor=this)}get selected(){return this.multiple?this._selectionModel.selected:this._selectionModel.selected[0]}get role(){return this.empty?null:"listbox"}get multiple(){return this._multiple}set multiple(t){this._multiple=yz(t),this._syncChipsState()}get compareWith(){return this._compareWith}set compareWith(t){this._compareWith=t,this._selectionModel&&this._initializeSelection()}get value(){return this._value}set value(t){this.writeValue(t),this._value=t}get id(){return this._chipInput?this._chipInput.id:this._uid}get required(){return this._required}set required(t){this._required=yz(t),this.stateChanges.next()}get placeholder(){return this._chipInput?this._chipInput.placeholder:this._placeholder}set placeholder(t){this._placeholder=t,this.stateChanges.next()}get focused(){return this._chipInput&&this._chipInput.focused||this._hasFocusedChip()}get empty(){return(!this._chipInput||this._chipInput.empty)&&(!this.chips||0===this.chips.length)}get shouldLabelFloat(){return!this.empty||this.focused}get disabled(){return this.ngControl?!!this.ngControl.disabled:this._disabled}set disabled(t){this._disabled=yz(t),this._syncChipsState()}get selectable(){return this._selectable}set selectable(t){this._selectable=yz(t),this.chips&&this.chips.forEach((t=>t.chipListSelectable=this._selectable))}set tabIndex(t){this._userTabIndex=t,this._tabIndex=t}get chipSelectionChanges(){return re(...this.chips.map((t=>t.selectionChange)))}get chipFocusChanges(){return re(...this.chips.map((t=>t._onFocus)))}get chipBlurChanges(){return re(...this.chips.map((t=>t._onBlur)))}get chipRemoveChanges(){return re(...this.chips.map((t=>t.destroyed)))}ngAfterContentInit(){this._keyManager=new eI(this.chips).withWrap().withVerticalOrientation().withHomeAndEnd().withHorizontalOrientation(this._dir?this._dir.value:"ltr"),this._dir&&this._dir.change.pipe(Ie(this._destroyed)).subscribe((t=>this._keyManager.withHorizontalOrientation(t))),this._keyManager.tabOut.pipe(Ie(this._destroyed)).subscribe((()=>{this._allowFocusEscape()})),this.chips.changes.pipe(Ne(null),Ie(this._destroyed)).subscribe((()=>{this.disabled&&Promise.resolve().then((()=>{this._syncChipsState()})),this._resetChips(),this._initializeSelection(),this._updateTabIndex(),this._updateFocusForDestroyedChips(),this.stateChanges.next()}))}ngOnInit(){this._selectionModel=new oF(this.multiple,void 0,!1),this.stateChanges.next()}ngDoCheck(){this.ngControl&&(this.updateErrorState(),this.ngControl.disabled!==this._disabled&&(this.disabled=!!this.ngControl.disabled))}ngOnDestroy(){this._destroyed.next(),this._destroyed.complete(),this.stateChanges.complete(),this._dropSubscriptions()}registerInput(t){this._chipInput=t,this._elementRef.nativeElement.setAttribute("data-mat-chip-input",t.id)}setDescribedByIds(t){this._ariaDescribedby=t.join(" ")}writeValue(t){this.chips&&this._setSelectionByValue(t,!1)}registerOnChange(t){this._onChange=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t,this.stateChanges.next()}onContainerClick(t){this._originatesFromChip(t)||this.focus()}focus(t){this.disabled||this._chipInput&&this._chipInput.focused||(this.chips.length>0?(this._keyManager.setFirstItemActive(),this.stateChanges.next()):(this._focusInput(t),this.stateChanges.next()))}_focusInput(t){this._chipInput&&this._chipInput.focus(t)}_keydown(t){const e=t.target;e&&e.classList.contains("mat-chip")&&(this._keyManager.onKeydown(t),this.stateChanges.next())}_updateTabIndex(){this._tabIndex=this._userTabIndex||(0===this.chips.length?-1:0)}_updateFocusForDestroyedChips(){if(null!=this._lastDestroyedChipIndex)if(this.chips.length){const t=Math.min(this._lastDestroyedChipIndex,this.chips.length-1);this._keyManager.setActiveItem(t)}else this.focus();this._lastDestroyedChipIndex=null}_isValidIndex(t){return t>=0&&t<this.chips.length}_setSelectionByValue(t,e=!0){if(this._clearSelection(),this.chips.forEach((t=>t.deselect())),Array.isArray(t))t.forEach((t=>this._selectValue(t,e))),this._sortValues();else{const n=this._selectValue(t,e);n&&e&&this._keyManager.setActiveItem(n)}}_selectValue(t,e=!0){const n=this.chips.find((e=>null!=e.value&&this._compareWith(e.value,t)));return n&&(e?n.selectViaInteraction():n.select(),this._selectionModel.select(n)),n}_initializeSelection(){Promise.resolve().then((()=>{(this.ngControl||this._value)&&(this._setSelectionByValue(this.ngControl?this.ngControl.value:this._value,!1),this.stateChanges.next())}))}_clearSelection(t){this._selectionModel.clear(),this.chips.forEach((e=>{e!==t&&e.deselect()})),this.stateChanges.next()}_sortValues(){this._multiple&&(this._selectionModel.clear(),this.chips.forEach((t=>{t.selected&&this._selectionModel.select(t)})),this.stateChanges.next())}_propagateChanges(t){let e=null;e=Array.isArray(this.selected)?this.selected.map((t=>t.value)):this.selected?this.selected.value:t,this._value=e,this.change.emit(new Oyt(this,e)),this.valueChange.emit(e),this._onChange(e),this._changeDetectorRef.markForCheck()}_blur(){this._hasFocusedChip()||this._keyManager.setActiveItem(-1),this.disabled||(this._chipInput?setTimeout((()=>{this.focused||this._markAsTouched()})):this._markAsTouched())}_markAsTouched(){this._onTouched(),this._changeDetectorRef.markForCheck(),this.stateChanges.next()}_allowFocusEscape(){-1!==this._tabIndex&&(this._tabIndex=-1,setTimeout((()=>{this._tabIndex=this._userTabIndex||0,this._changeDetectorRef.markForCheck()})))}_resetChips(){this._dropSubscriptions(),this._listenToChipsFocus(),this._listenToChipsSelection(),this._listenToChipsRemoved()}_dropSubscriptions(){this._chipFocusSubscription&&(this._chipFocusSubscription.unsubscribe(),this._chipFocusSubscription=null),this._chipBlurSubscription&&(this._chipBlurSubscription.unsubscribe(),this._chipBlurSubscription=null),this._chipSelectionSubscription&&(this._chipSelectionSubscription.unsubscribe(),this._chipSelectionSubscription=null),this._chipRemoveSubscription&&(this._chipRemoveSubscription.unsubscribe(),this._chipRemoveSubscription=null)}_listenToChipsSelection(){this._chipSelectionSubscription=this.chipSelectionChanges.subscribe((t=>{t.source.selected?this._selectionModel.select(t.source):this._selectionModel.deselect(t.source),this.multiple||this.chips.forEach((t=>{!this._selectionModel.isSelected(t)&&t.selected&&t.deselect()})),t.isUserInput&&this._propagateChanges()}))}_listenToChipsFocus(){this._chipFocusSubscription=this.chipFocusChanges.subscribe((t=>{let e=this.chips.toArray().indexOf(t.chip);this._isValidIndex(e)&&this._keyManager.updateActiveItem(e),this.stateChanges.next()})),this._chipBlurSubscription=this.chipBlurChanges.subscribe((()=>{this._blur(),this.stateChanges.next()}))}_listenToChipsRemoved(){this._chipRemoveSubscription=this.chipRemoveChanges.subscribe((t=>{const e=t.chip,n=this.chips.toArray().indexOf(t.chip);this._isValidIndex(n)&&e._hasFocus&&(this._lastDestroyedChipIndex=n)}))}_originatesFromChip(t){let e=t.target;for(;e&&e!==this._elementRef.nativeElement;){if(e.classList.contains("mat-chip"))return!0;e=e.parentElement}return!1}_hasFocusedChip(){return this.chips&&this.chips.some((t=>t._hasFocus))}_syncChipsState(){this.chips&&this.chips.forEach((t=>{t._chipListDisabled=this._disabled,t._chipListMultiple=this.multiple}))}}Pyt.ɵfac=function t(e){return new(e||Pyt)(Sm(hg),Sm(Ug),Sm(HI,8),Sm(iU,8),Sm(PU,8),Sm(bH),Sm(Mj,10))},Pyt.ɵcmp=to({type:Pyt,selectors:[["mat-chip-list"]],contentQueries:function t(e,n,o){if(1&e&&$h(o,_yt,5),2&e){let t;Jh(t=tb())&&(n.chips=t)}},hostAttrs:[1,"mat-chip-list"],hostVars:15,hostBindings:function t(e,n){1&e&&Vm("focus",(function t(){return n.focus()}))("blur",(function t(){return n._blur()}))("keydown",(function t(e){return n._keydown(e)})),2&e&&(Tu("id",n._uid),jp("tabindex",n.disabled?null:n._tabIndex)("aria-describedby",n._ariaDescribedby||null)("aria-required",n.role?n.required:null)("aria-disabled",n.disabled.toString())("aria-invalid",n.errorState)("aria-multiselectable",n.multiple)("role",n.role)("aria-orientation",n.ariaOrientation),pu("mat-chip-list-disabled",n.disabled)("mat-chip-list-invalid",n.errorState)("mat-chip-list-required",n.required))},inputs:{ariaOrientation:["aria-orientation","ariaOrientation"],multiple:"multiple",compareWith:"compareWith",value:"value",required:"required",placeholder:"placeholder",disabled:"disabled",selectable:"selectable",tabIndex:"tabIndex",errorStateMatcher:"errorStateMatcher"},outputs:{change:"change",valueChange:"valueChange"},exportAs:["matChipList"],features:[pg([{provide:bV,useExisting:Pyt}]),xp],ngContentSelectors:["*"],decls:2,vars:0,consts:[[1,"mat-chip-list-wrapper"]],template:function t(e,n){1&e&&(Zm(),Rm(0,"div",0),Xm(1),Am())},styles:['.mat-chip{position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;transform:translateZ(0);border:none;-webkit-appearance:none;-moz-appearance:none}.mat-standard-chip{transition:box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);display:inline-flex;padding:7px 12px;border-radius:16px;align-items:center;cursor:default;min-height:32px;height:1px}._mat-animation-noopable.mat-standard-chip{transition:none;animation:none}.mat-standard-chip .mat-chip-remove.mat-icon{width:18px;height:18px}.mat-standard-chip::after{top:0;left:0;right:0;bottom:0;position:absolute;border-radius:inherit;opacity:0;content:"";pointer-events:none;transition:opacity 200ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-standard-chip:hover::after{opacity:.12}.mat-standard-chip:focus{outline:none}.mat-standard-chip:focus::after{opacity:.16}.cdk-high-contrast-active .mat-standard-chip{outline:solid 1px}.cdk-high-contrast-active .mat-standard-chip:focus{outline:dotted 2px}.mat-standard-chip.mat-chip-disabled::after{opacity:0}.mat-standard-chip.mat-chip-disabled .mat-chip-remove,.mat-standard-chip.mat-chip-disabled .mat-chip-trailing-icon{cursor:default}.mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar,.mat-standard-chip.mat-chip-with-avatar{padding-top:0;padding-bottom:0}.mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar{padding-right:8px;padding-left:0}[dir=rtl] .mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar{padding-left:8px;padding-right:0}.mat-standard-chip.mat-chip-with-trailing-icon{padding-top:7px;padding-bottom:7px;padding-right:8px;padding-left:12px}[dir=rtl] .mat-standard-chip.mat-chip-with-trailing-icon{padding-left:8px;padding-right:12px}.mat-standard-chip.mat-chip-with-avatar{padding-left:0;padding-right:12px}[dir=rtl] .mat-standard-chip.mat-chip-with-avatar{padding-right:0;padding-left:12px}.mat-standard-chip .mat-chip-avatar{width:24px;height:24px;margin-right:8px;margin-left:4px}[dir=rtl] .mat-standard-chip .mat-chip-avatar{margin-left:8px;margin-right:4px}.mat-standard-chip .mat-chip-remove,.mat-standard-chip .mat-chip-trailing-icon{width:18px;height:18px;cursor:pointer}.mat-standard-chip .mat-chip-remove,.mat-standard-chip .mat-chip-trailing-icon{margin-left:8px;margin-right:0}[dir=rtl] .mat-standard-chip .mat-chip-remove,[dir=rtl] .mat-standard-chip .mat-chip-trailing-icon{margin-right:8px;margin-left:0}.mat-chip-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none;border-radius:inherit;overflow:hidden}.mat-chip-list-wrapper{display:flex;flex-direction:row;flex-wrap:wrap;align-items:center;margin:-4px}.mat-chip-list-wrapper input.mat-input-element,.mat-chip-list-wrapper .mat-standard-chip{margin:4px}.mat-chip-list-stacked .mat-chip-list-wrapper{flex-direction:column;align-items:flex-start}.mat-chip-list-stacked .mat-chip-list-wrapper .mat-standard-chip{width:100%}.mat-chip-avatar{border-radius:50%;justify-content:center;align-items:center;display:flex;overflow:hidden;object-fit:cover}input.mat-chip-input{width:150px;margin:4px;flex:1 0 150px}\n'],encapsulation:2,changeDetection:0}),Pyt.ctorParameters=()=>[{type:hg},{type:Ug},{type:HI,decorators:[{type:Sr}]},{type:iU,decorators:[{type:Sr}]},{type:PU,decorators:[{type:Sr}]},{type:bH},{type:Mj,decorators:[{type:Sr},{type:Dr}]}],Pyt.propDecorators={errorStateMatcher:[{type:xy}],multiple:[{type:xy}],compareWith:[{type:xy}],value:[{type:xy}],required:[{type:xy}],placeholder:[{type:xy}],disabled:[{type:xy}],ariaOrientation:[{type:xy,args:["aria-orientation"]}],selectable:[{type:xy}],tabIndex:[{type:xy}],change:[{type:Oy}],valueChange:[{type:Oy}],chips:[{type:Ya,args:[_yt,{descendants:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(Pyt,[{type:My,args:[{selector:"mat-chip-list",template:'<div class="mat-chip-list-wrapper"><ng-content></ng-content></div>',exportAs:"matChipList",host:{"[attr.tabindex]":"disabled ? null : _tabIndex","[attr.aria-describedby]":"_ariaDescribedby || null","[attr.aria-required]":"role ? required : null","[attr.aria-disabled]":"disabled.toString()","[attr.aria-invalid]":"errorState","[attr.aria-multiselectable]":"multiple","[attr.role]":"role","[class.mat-chip-list-disabled]":"disabled","[class.mat-chip-list-invalid]":"errorState","[class.mat-chip-list-required]":"required","[attr.aria-orientation]":"ariaOrientation",class:"mat-chip-list","(focus)":"focus()","(blur)":"_blur()","(keydown)":"_keydown($event)","[id]":"_uid"},providers:[{provide:bV,useExisting:Pyt}],encapsulation:Hn.None,changeDetection:zn.OnPush,styles:['.mat-chip{position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;transform:translateZ(0);border:none;-webkit-appearance:none;-moz-appearance:none}.mat-standard-chip{transition:box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);display:inline-flex;padding:7px 12px;border-radius:16px;align-items:center;cursor:default;min-height:32px;height:1px}._mat-animation-noopable.mat-standard-chip{transition:none;animation:none}.mat-standard-chip .mat-chip-remove.mat-icon{width:18px;height:18px}.mat-standard-chip::after{top:0;left:0;right:0;bottom:0;position:absolute;border-radius:inherit;opacity:0;content:"";pointer-events:none;transition:opacity 200ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-standard-chip:hover::after{opacity:.12}.mat-standard-chip:focus{outline:none}.mat-standard-chip:focus::after{opacity:.16}.cdk-high-contrast-active .mat-standard-chip{outline:solid 1px}.cdk-high-contrast-active .mat-standard-chip:focus{outline:dotted 2px}.mat-standard-chip.mat-chip-disabled::after{opacity:0}.mat-standard-chip.mat-chip-disabled .mat-chip-remove,.mat-standard-chip.mat-chip-disabled .mat-chip-trailing-icon{cursor:default}.mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar,.mat-standard-chip.mat-chip-with-avatar{padding-top:0;padding-bottom:0}.mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar{padding-right:8px;padding-left:0}[dir=rtl] .mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar{padding-left:8px;padding-right:0}.mat-standard-chip.mat-chip-with-trailing-icon{padding-top:7px;padding-bottom:7px;padding-right:8px;padding-left:12px}[dir=rtl] .mat-standard-chip.mat-chip-with-trailing-icon{padding-left:8px;padding-right:12px}.mat-standard-chip.mat-chip-with-avatar{padding-left:0;padding-right:12px}[dir=rtl] .mat-standard-chip.mat-chip-with-avatar{padding-right:0;padding-left:12px}.mat-standard-chip .mat-chip-avatar{width:24px;height:24px;margin-right:8px;margin-left:4px}[dir=rtl] .mat-standard-chip .mat-chip-avatar{margin-left:8px;margin-right:4px}.mat-standard-chip .mat-chip-remove,.mat-standard-chip .mat-chip-trailing-icon{width:18px;height:18px;cursor:pointer}.mat-standard-chip .mat-chip-remove,.mat-standard-chip .mat-chip-trailing-icon{margin-left:8px;margin-right:0}[dir=rtl] .mat-standard-chip .mat-chip-remove,[dir=rtl] .mat-standard-chip .mat-chip-trailing-icon{margin-right:8px;margin-left:0}.mat-chip-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none;border-radius:inherit;overflow:hidden}.mat-chip-list-wrapper{display:flex;flex-direction:row;flex-wrap:wrap;align-items:center;margin:-4px}.mat-chip-list-wrapper input.mat-input-element,.mat-chip-list-wrapper .mat-standard-chip{margin:4px}.mat-chip-list-stacked .mat-chip-list-wrapper{flex-direction:column;align-items:flex-start}.mat-chip-list-stacked .mat-chip-list-wrapper .mat-standard-chip{width:100%}.mat-chip-avatar{border-radius:50%;justify-content:center;align-items:center;display:flex;overflow:hidden;object-fit:cover}input.mat-chip-input{width:150px;margin:4px;flex:1 0 150px}\n']}]}],(function(){return[{type:hg},{type:Ug},{type:HI,decorators:[{type:Sr}]},{type:iU,decorators:[{type:Sr}]},{type:PU,decorators:[{type:Sr}]},{type:bH},{type:Mj,decorators:[{type:Sr},{type:Dr}]}]}),{ariaOrientation:[{type:xy,args:["aria-orientation"]}],change:[{type:Oy}],valueChange:[{type:Oy}],multiple:[{type:xy}],compareWith:[{type:xy}],value:[{type:xy}],required:[{type:xy}],placeholder:[{type:xy}],disabled:[{type:xy}],selectable:[{type:xy}],tabIndex:[{type:xy}],errorStateMatcher:[{type:xy}],chips:[{type:Ya,args:[_yt,{descendants:!0}]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let wyt=0;class kyt{constructor(t,e){this._elementRef=t,this._defaultOptions=e,this.focused=!1,this._addOnBlur=!1,this.separatorKeyCodes=this._defaultOptions.separatorKeyCodes,this.chipEnd=new Lh,this.placeholder="",this.id="mat-chip-list-input-"+wyt++,this._disabled=!1,this.inputElement=this._elementRef.nativeElement}set chipList(t){t&&(this._chipList=t,this._chipList.registerInput(this))}get addOnBlur(){return this._addOnBlur}set addOnBlur(t){this._addOnBlur=yz(t)}get disabled(){return this._disabled||this._chipList&&this._chipList.disabled}set disabled(t){this._disabled=yz(t)}get empty(){return!this.inputElement.value}ngOnChanges(){this._chipList.stateChanges.next()}ngOnDestroy(){this.chipEnd.complete()}ngAfterContentInit(){this._focusLastChipOnBackspace=this.empty}_keydown(t){if(t){if(9!==t.keyCode||bz(t,"shiftKey")||this._chipList._allowFocusEscape(),8===t.keyCode&&this._focusLastChipOnBackspace)return this._chipList._keyManager.setLastItemActive(),void t.preventDefault();this._focusLastChipOnBackspace=!1}this._emitChipEnd(t)}_keyup(t){!this._focusLastChipOnBackspace&&8===t.keyCode&&this.empty&&(this._focusLastChipOnBackspace=!0,t.preventDefault())}_blur(){this.addOnBlur&&this._emitChipEnd(),this.focused=!1,this._chipList.focused||this._chipList._blur(),this._chipList.stateChanges.next()}_focus(){this.focused=!0,this._focusLastChipOnBackspace=this.empty,this._chipList.stateChanges.next()}_emitChipEnd(t){!this.inputElement.value&&t&&this._chipList._keydown(t),t&&!this._isSeparatorKey(t)||(this.chipEnd.emit({input:this.inputElement,value:this.inputElement.value,chipInput:this}),null==t||t.preventDefault())}_onInput(){this._chipList.stateChanges.next()}focus(t){this.inputElement.focus(t)}clear(){this.inputElement.value="",this._focusLastChipOnBackspace=!0}_isSeparatorKey(t){return!bz(t)&&new Set(this.separatorKeyCodes).has(t.keyCode)}}kyt.ɵfac=function t(e){return new(e||kyt)(Sm(hg),Sm(Myt))},kyt.ɵdir=lo({type:kyt,selectors:[["input","matChipInputFor",""]],hostAttrs:[1,"mat-chip-input","mat-input-element"],hostVars:5,hostBindings:function t(e,n){1&e&&Vm("keydown",(function t(e){return n._keydown(e)}))("keyup",(function t(e){return n._keyup(e)}))("blur",(function t(){return n._blur()}))("focus",(function t(){return n._focus()}))("input",(function t(){return n._onInput()})),2&e&&(Tu("id",n.id),jp("disabled",n.disabled||null)("placeholder",n.placeholder||null)("aria-invalid",n._chipList&&n._chipList.ngControl?n._chipList.ngControl.invalid:null)("aria-required",n._chipList&&n._chipList.required||null))},inputs:{separatorKeyCodes:["matChipInputSeparatorKeyCodes","separatorKeyCodes"],placeholder:"placeholder",id:"id",chipList:["matChipInputFor","chipList"],addOnBlur:["matChipInputAddOnBlur","addOnBlur"],disabled:"disabled"},outputs:{chipEnd:"matChipInputTokenEnd"},exportAs:["matChipInput","matChipInputFor"],features:[Bo]}),kyt.ctorParameters=()=>[{type:hg},{type:void 0,decorators:[{type:kr,args:[Myt]}]}],kyt.propDecorators={chipList:[{type:xy,args:["matChipInputFor"]}],addOnBlur:[{type:xy,args:["matChipInputAddOnBlur"]}],separatorKeyCodes:[{type:xy,args:["matChipInputSeparatorKeyCodes"]}],chipEnd:[{type:Oy,args:["matChipInputTokenEnd"]}],placeholder:[{type:xy}],id:[{type:xy}],disabled:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(kyt,[{type:Cy,args:[{selector:"input[matChipInputFor]",exportAs:"matChipInput, matChipInputFor",host:{class:"mat-chip-input mat-input-element","(keydown)":"_keydown($event)","(keyup)":"_keyup($event)","(blur)":"_blur()","(focus)":"_focus()","(input)":"_onInput()","[id]":"id","[attr.disabled]":"disabled || null","[attr.placeholder]":"placeholder || null","[attr.aria-invalid]":"_chipList && _chipList.ngControl ? _chipList.ngControl.invalid : null","[attr.aria-required]":"_chipList && _chipList.required || null"}}]}],(function(){return[{type:hg},{type:void 0,decorators:[{type:kr,args:[Myt]}]}]}),{separatorKeyCodes:[{type:xy,args:["matChipInputSeparatorKeyCodes"]}],chipEnd:[{type:Oy,args:["matChipInputTokenEnd"]}],placeholder:[{type:xy}],id:[{type:xy}],chipList:[{type:xy,args:["matChipInputFor"]}],addOnBlur:[{type:xy,args:["matChipInputAddOnBlur"]}],disabled:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Syt=[Pyt,_yt,kyt,Cyt,byt,yyt],Dyt={separatorKeyCodes:[mz]};class Eyt{}Eyt.ɵfac=function t(e){return new(e||Eyt)},Eyt.ɵmod=ao({type:Eyt}),Eyt.ɵinj=vn({providers:[bH,{provide:Myt,useValue:Dyt}],imports:[[XI]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Eyt,[{type:Ay,args:[{imports:[XI],exports:Syt,declarations:Syt,providers:[bH,{provide:Myt,useValue:Dyt}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Eyt,{declarations:function(){return[Pyt,_yt,kyt,Cyt,byt,yyt]},imports:function(){return[XI]},exports:function(){return[Pyt,_yt,kyt,Cyt,byt,yyt]}});const Ryt=function(t,e){return{"embedding-selected":t,"embedding-unselected":e}};function Ayt(t,e){if(1&t){const t=Hm();Rm(0,"mat-icon",5),Vm("click",(function e(){hi(t);const n=Ym();return n.onSelect.emit(n.metric)})),Am()}if(2&t){const t=Ym();Dm("ngClass",vh(1,Ryt,t.embeddingsMetric===t.metric,t.embeddingsMetric!==t.metric))}}const Tyt=function(t){return{width:t}},Nyt=function(t){return{"value-invalid":t}};class zyt{constructor(){this.onRemove=new Lh,this.onSelect=new Lh,this.onFilterChange=new Lh,this.focusMin=!1,this.focusMax=!1,this.ngUnsubscribe=new I}ngOnInit(){this.minFormControl=new $j(this.filterValues.min,[qV.required,qV.min(-1),qV.max(1),this.minValueValidator.bind(this)]),this.maxFormControl=new $j(this.filterValues.max,[qV.required,qV.min(-1),qV.max(1),this.maxValueValidator.bind(this)]),this.minFormControl.valueChanges.pipe(Ie(this.ngUnsubscribe)).subscribe((()=>{this.minFormControl.valid&&this.maxFormControl.valid&&this.onFilterChange.emit({min:parseFloat(this.minFormControl.value),max:parseFloat(this.maxFormControl.value)})})),this.maxFormControl.valueChanges.pipe(Ie(this.ngUnsubscribe)).subscribe((()=>{this.minFormControl.valid&&this.maxFormControl.valid&&this.onFilterChange.emit({min:parseFloat(this.minFormControl.value),max:parseFloat(this.maxFormControl.value)})}))}ngOnChanges(t){this.minFormControl&&this.maxFormControl&&(this.minFormControl.setValue(this.filterValues.min,{emitEvent:!1}),this.maxFormControl.setValue(this.filterValues.max,{emitEvent:!1}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}minValueValidator(t){return this.maxFormControl&&"NaN"!==t.value?isNaN(parseFloat(t.value))?{value:"the string you entered is neither NaN nor a number"}:parseFloat(t.value)>parseFloat(this.maxFormControl.value)?{value:"the number you entered is larger than the max value"}:null:null}maxValueValidator(t){return this.minFormControl?"NaN"===this.minFormControl.value&&"NaN"===t.value?null:isNaN(parseFloat(t.value))?{value:"the string you entered is neither NaN nor a number"}:t.value<this.minFormControl.value?{value:"the number you entered is smaller than the min value"}:null:null}getErrorDescription(t){if(t){const e=Object.keys(t)[0];return"required"===e?"you did not enter anything":"min"===e?"the number must be at least -1.0":"max"===e?"the number is bigger than 1.0":t[e]}return""}}zyt.ɵfac=function t(e){return new(e||zyt)},zyt.ɵcmp=to({type:zyt,selectors:[["metric-arithmetic-element-component"]],inputs:{metric:"metric",filterValues:"filterValues",hasEmbeddingsData:"hasEmbeddingsData",embeddingsMetric:"embeddingsMetric"},outputs:{onRemove:"onRemove",onSelect:"onSelect",onFilterChange:"onFilterChange"},features:[Bo],decls:10,vars:22,consts:[[1,"filter-chip",3,"removed"],["class","embeddings-button","svgIcon","group_work_24px",3,"ngClass","click",4,"ngIf"],[1,"metric-arithmetic-element-range",3,"keydown"],["matInput","",1,"input-field",3,"value","matTooltip","matTooltipDisabled","ngStyle","ngClass","formControl","focus","focusout"],["matChipRemove","","svgIcon","cancel_24px"],["svgIcon","group_work_24px",1,"embeddings-button",3,"ngClass","click"]],template:function t(e,n){1&e&&(Rm(0,"mat-chip",0),Vm("removed",(function t(){return n.onRemove.emit(n.metric)})),Qp(1,Ayt,1,4,"mat-icon",1),ku(2),Rm(3,"div",2),Vm("keydown",(function t(e){return e.stopPropagation()})),ku(4," [ "),Rm(5,"input",3),Vm("focus",(function t(){return n.focusMin=!0}))("focusout",(function t(){return n.focusMin=!1})),Am(),ku(6," ; "),Rm(7,"input",3),Vm("focus",(function t(){return n.focusMax=!0}))("focusout",(function t(){return n.focusMax=!1})),Am(),ku(8," ] "),Am(),Tm(9,"mat-icon",4),Am()),2&e&&(rc(1),Dm("ngIf",n.hasEmbeddingsData),rc(1),Du(" ",n.metric," "),rc(3),Dm("value",n.filterValues.min)("matTooltip",n.getErrorDescription(n.minFormControl.errors))("matTooltipDisabled",!n.minFormControl.invalid)("ngStyle",Mh(14,Tyt,n.focusMin?"100px":n.minFormControl.value.toString().length+"ch"))("ngClass",Mh(16,Nyt,!n.minFormControl.valid))("formControl",n.minFormControl),rc(2),Dm("value",n.filterValues.max)("matTooltip",n.getErrorDescription(n.maxFormControl.errors))("matTooltipDisabled",!n.maxFormControl.invalid)("ngStyle",Mh(18,Tyt,n.focusMax?"100px":n.maxFormControl.value.toString().length+"ch"))("ngClass",Mh(20,Nyt,!n.maxFormControl.valid))("formControl",n.maxFormControl))},directives:[_yt,dM,VV,FQ,CM,aM,xj,xU,DW,Cyt],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   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1s}.input-field[_ngcontent-%COMP%]:focus{background-color:rgba(0,0,0,.12);border:none;outline:none}.value-invalid[_ngcontent-%COMP%]{color:#f44336}.embedding-selected[_ngcontent-%COMP%]{color:#f57c00;opacity:1}.embedding-unselected[_ngcontent-%COMP%]{opacity:.4}.embeddings-button[_ngcontent-%COMP%]{width:18px;height:18px;margin-right:8px;cursor:pointer}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(zyt,[{type:My,args:[{selector:"metric-arithmetic-element-component",templateUrl:"./metric_arithmetic_element_component.ng.html",styleUrls:["./metric_arithmetic_element_component.css"],changeDetection:zn.OnPush}]}],null,{metric:[{type:xy}],filterValues:[{type:xy}],hasEmbeddingsData:[{type:xy}],embeddingsMetric:[{type:xy}],onRemove:[{type:Oy}],onSelect:[{type:Oy}],onFilterChange:[{type:Oy}]});class Iyt{constructor(t){this.store=t,this.filterValues$=this.store.pipe(Fw(gbt)).pipe(It((t=>{const e=t[this.metric];return e?{min:e.includeNaN?"NaN":this.roundToThreeDecimalPoints(e.min),max:e.max<e.min?"NaN":this.roundToThreeDecimalPoints(e.max)}:{min:-1,max:1}}))),this.hasEmbeddingsData$=this.store.pipe(Fw(lbt)).pipe(It((t=>void 0!==t))),this.embeddingsMetric$=this.store.pipe(Fw(Obt))}remove(t){this.store.dispatch(Lbt({metric:t}))}select(t){this.store.dispatch(Zbt({metric:t}))}filterChange(t){const e=isNaN(t.min)?-1:t.min,n=isNaN(t.max)?-2:t.max,o=isNaN(t.min);this.store.dispatch(Bbt({metric:this.metric,max:n,min:e,includeNaN:o}))}roundToThreeDecimalPoints(t){return Math.round(1e3*(t+Number.EPSILON))/1e3}}Iyt.ɵfac=function t(e){return new(e||Iyt)(Sm(Iw))},Iyt.ɵcmp=to({type:Iyt,selectors:[["npmi-metric-arithmetic-element"]],inputs:{metric:"metric"},decls:4,vars:10,consts:[[3,"metric","filterValues","hasEmbeddingsData","embeddingsMetric","onRemove","onSelect","onFilterChange"]],template:function t(e,n){1&e&&(Rm(0,"metric-arithmetic-element-component",0),Vm("onRemove",(function t(e){return n.remove(e)}))("onSelect",(function t(e){return n.select(e)}))("onFilterChange",(function t(e){return n.filterChange(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Am()),2&e&&Dm("metric",n.metric)("filterValues",Th(1,4,n.filterValues$))("hasEmbeddingsData",Th(2,6,n.hasEmbeddingsData$))("embeddingsMetric",Th(3,8,n.embeddingsMetric$))},directives:[zyt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Iyt,[{type:My,args:[{selector:"npmi-metric-arithmetic-element",template:'\n    <metric-arithmetic-element-component\n      [metric]="metric"\n      [filterValues]="filterValues$ | async"\n      [hasEmbeddingsData]="hasEmbeddingsData$ | async"\n      [embeddingsMetric]="embeddingsMetric$ | async"\n      (onRemove)="remove($event)"\n      (onSelect)="select($event)"\n      (onFilterChange)="filterChange($event)"\n    ></metric-arithmetic-element-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{metric:[{type:xy}]});class Hyt{constructor(){this.Operator=$ht}}function Fyt(t,e){1&t&&Tm(0,"npmi-metric-arithmetic-element",4),2&t&&Dm("metric",Ym().$implicit.metric)}function Lyt(t,e){1&t&&Tm(0,"npmi-metric-arithmetic-operator",5),2&t&&Dm("operator",Ym().$implicit.operator)}function Byt(t,e){if(1&t&&(Rm(0,"div"),Qp(1,Fyt,1,1,"npmi-metric-arithmetic-element",2),Qp(2,Lyt,1,1,"npmi-metric-arithmetic-operator",3),Am()),2&t){const t=e.$implicit,n=Ym();rc(1),Dm("ngIf",t.kind===n.ArithmeticKind.METRIC),rc(1),Dm("ngIf",t.kind===n.ArithmeticKind.OPERATOR)}}Hyt.ɵfac=function t(e){return new(e||Hyt)},Hyt.ɵcmp=to({type:Hyt,selectors:[["npmi-metric-arithmetic-operator"]],inputs:{operator:"operator"},decls:2,vars:1,template:function t(e,n){1&e&&(Rm(0,"mat-chip"),ku(1),Am()),2&e&&(rc(1),Du(" ",n.operator===n.Operator.AND?"&":""," "))},directives:[_yt],encapsulation:2,changeDetection:0}),("undefined"==typeof 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t=J4(this),e=att(this).selection;e?(t.selectAll(".selection").style("display",null).attr("x",e[0][0]).attr("y",e[0][1]).attr("width",e[1][0]-e[0][0]).attr("height",e[1][1]-e[0][1]),t.selectAll(".handle").style("display",null).attr("x",(function(t){return"e"===t.type[t.type.length-1]?e[1][0]-l/2:e[0][0]-l/2})).attr("y",(function(t){return"s"===t.type[0]?e[1][1]-l/2:e[0][1]-l/2})).attr("width",(function(t){return"n"===t.type||"s"===t.type?e[1][0]-e[0][0]+l:l})).attr("height",(function(t){return"e"===t.type||"w"===t.type?e[1][1]-e[0][1]+l:l}))):t.selectAll(".selection,.handle").style("display","none").attr("x",null).attr("y",null).attr("width",null).attr("height",null)}function p(t,e,n){var o=t.__brush.emitter;return!o||n&&o.clean?new m(t,e,n):o}function m(t,e,n){this.that=t,this.args=e,this.state=t.__brush,this.active=0,this.clean=n}function u(){if((!n||L4.touches)&&i.apply(this,arguments)){var 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qyt{constructor(t){this.store=t,this.activeRuns$=this.store.pipe(Fw(NN)).pipe(It((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.visibleAnnotations$=Wt([this.store.select(rbt),this.store.select(pbt),this.store.select(Mbt)]).pipe(It((([t,e,n])=>Gyt(t,e,n)))),this.chartWidth$=this.store.pipe(Fw(xbt)).pipe(It((t=>Math.max(150,t)))),this.runColorScale$=this.store.select(zN).pipe(It((t=>e=>{if(!t.hasOwnProperty(e))throw new Error(`[Color scale] unknown runId: ${e}.`);return t[e]})))}ngOnInit(){this.chartData$=Wt([this.visibleAnnotations$,this.activeRuns$]).pipe(It((([t,e])=>(function n(t,e,o){const i={},a={},r=new Set(e),s=nyt(o),l={max:-1,min:1};Object.values(t).forEach((t=>{t.forEach((t=>{const e=t.run;if(r.has(e)&&t.metric===s)if(null===t.nPMIValue)a[e]?a[e].push(null):a[e]=[null];else{const n=t.nPMIValue;l.max=l.max<n?n:l.max,l.min=l.min>n?n:l.min,i[t.run]?i[e].push(n):i[e]=[n]}}))}));const c={},d=O5().domain([l.min,l.max]).value((t=>t)),p=O5().domain([-1/0,1/0]).thresholds(0).value((t=>t));for(const t of r)if(c[t]=d(i[t]),a[t]){const e=p(a[t]);c[t].unshift(e[0])}return{violinData:c,extremes:l}})(t,e,this.metricName))))}removeMetric(){this.store.dispatch(Lbt({metric:this.metricName}))}updateFilter(t){this.store.dispatch(Bbt(Object.assign({metric:this.metricName},t)))}}function Zyt(t,e){if(1&t&&Tm(0,"npmi-violin-filter",8),2&t){const t=e.$implicit;Dm("metricName",t[0])("filter",t[1])}}function Xyt(t,e){1&t&&(Rm(0,"div",9),Rm(1,"span",10),ku(2," You can add more filters at the top. "),Am(),Am())}qyt.ɵfac=function t(e){return new(e||qyt)(Sm(Iw))},qyt.ɵcmp=to({type:qyt,selectors:[["npmi-violin-filter"]],inputs:{metricName:"metricName",filter:"filter"},decls:4,vars:11,consts:[[3,"metricName","filter","chartData","width","colorScale","onRemove","onUpdateFilter"]],template:function t(e,n){1&e&&(Rm(0,"violin-filter-component",0),Vm("onRemove",(function t(){return n.removeMetric()}))("onUpdateFilter",(function t(e){return n.updateFilter(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Am()),2&e&&Dm("metricName",n.metricName)("filter",n.filter)("chartData",Th(1,5,n.chartData$))("width",Th(2,7,n.chartWidth$))("colorScale",Th(3,9,n.runColorScale$))},directives:[Yyt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(qyt,[{type:My,args:[{selector:"npmi-violin-filter",template:'\n    <violin-filter-component\n      [metricName]="metricName"\n      [filter]="filter"\n      [chartData]="chartData$ | async"\n      [width]="chartWidth$ | async"\n      [colorScale]="runColorScale$ | async"\n      (onRemove)="removeMetric()"\n      (onUpdateFilter)="updateFilter($event)"\n    ></violin-filter-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{metricName:[{type:xy}],filter:[{type:xy}]});class Kyt{constructor(){this.toggleSidebarExpanded=new Lh}}Kyt.ɵfac=function t(e){return new(e||Kyt)},Kyt.ɵcmp=to({type:Kyt,selectors:[["violin-filters-component"]],inputs:{sidebarExpanded:"sidebarExpanded",metricFilters:"metricFilters"},outputs:{toggleSidebarExpanded:"toggleSidebarExpanded"},decls:9,vars:2,consts:function(){let t;return t="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Expand/Hide Sidebar"):$localize`:Label for a button that expands/hides the sidebar.␟48c29903ce881ab61088f8d49d827203716aaed4␟4658602991970260215:Expand/Hide 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.mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", 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.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes 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16px}.filters-hint-text[_ngcontent-%COMP%]{color:rgba(0,0,0,.38)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Kyt,[{type:My,args:[{selector:"violin-filters-component",templateUrl:"./violin_filters_component.ng.html",styleUrls:["./violin_filters_component.css"],changeDetection:zn.OnPush}]}],null,{sidebarExpanded:[{type:xy}],metricFilters:[{type:xy}],toggleSidebarExpanded:[{type:Oy}]});class Jyt{constructor(t){this.store=t,this.sidebarExpanded$=this.store.select(_bt),this.metricFilters$=this.store.select(gbt).pipe(It((t=>Object.entries(t))))}onToggleSidebarExpanded(){this.store.dispatch(Wbt())}}function Qyt(t,e,n){return t.length!=e.length?n:e.map(((e,n)=>e-t[n])).map((t=>Math.pow(t,2))).reduce(((t,e)=>t+e),0)}Jyt.ɵfac=function t(e){return new(e||Jyt)(Sm(Iw))},Jyt.ɵcmp=to({type:Jyt,selectors:[["npmi-violin-filters"]],decls:3,vars:6,consts:[[3,"sidebarExpanded","metricFilters","toggleSidebarExpanded"]],template:function t(e,n){1&e&&(Rm(0,"violin-filters-component",0),Vm("toggleSidebarExpanded",(function t(){return n.onToggleSidebarExpanded()})),Ah(1,"async"),Ah(2,"async"),Am()),2&e&&Dm("sidebarExpanded",Th(1,2,n.sidebarExpanded$))("metricFilters",Th(2,4,n.metricFilters$))},directives:[Kyt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Jyt,[{type:My,args:[{selector:"npmi-violin-filters",template:'\n    <violin-filters-component\n      [sidebarExpanded]="sidebarExpanded$ | async"\n      [metricFilters]="metricFilters$ | async"\n      (toggleSidebarExpanded)="onToggleSidebarExpanded()"\n    ></violin-filters-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);const $yt=["thumbContainer"],t_t=["toggleBar"],e_t=["input"],n_t=function(t){return{enterDuration:t}},o_t=new Ga("mat-slide-toggle-default-options",{providedIn:"root",factory:()=>({disableToggleValue:!1})});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let i_t=0;const a_t={provide:IV,useExisting:qe((()=>l_t)),multi:!0};class r_t{constructor(t,e){this.source=t,this.checked=e}}const s_t=$I(JI(QI(KI(class{constructor(t){this._elementRef=t}}))));class l_t extends s_t{constructor(t,e,n,o,i,a){super(t),this._focusMonitor=e,this._changeDetectorRef=n,this.defaults=i,this._onChange=t=>{},this._onTouched=()=>{},this._uniqueId="mat-slide-toggle-"+ ++i_t,this._required=!1,this._checked=!1,this.name=null,this.id=this._uniqueId,this.labelPosition="after",this.ariaLabel=null,this.ariaLabelledby=null,this.change=new Lh,this.toggleChange=new Lh,this.tabIndex=parseInt(o)||0,this.color=this.defaultColor=i.color||"accent",this._noopAnimations="NoopAnimations"===a}get required(){return this._required}set required(t){this._required=yz(t)}get checked(){return this._checked}set checked(t){this._checked=yz(t),this._changeDetectorRef.markForCheck()}get inputId(){return`${this.id||this._uniqueId}-input`}ngAfterContentInit(){this._focusMonitor.monitor(this._elementRef,!0).subscribe((t=>{"keyboard"===t||"program"===t?this._inputElement.nativeElement.focus():t||Promise.resolve().then((()=>this._onTouched()))}))}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef)}_onChangeEvent(t){t.stopPropagation(),this.toggleChange.emit(),this.defaults.disableToggleValue?this._inputElement.nativeElement.checked=this.checked:(this.checked=this._inputElement.nativeElement.checked,this._emitChangeEvent())}_onInputClick(t){t.stopPropagation()}writeValue(t){this.checked=!!t}registerOnChange(t){this._onChange=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t,this._changeDetectorRef.markForCheck()}focus(t,e){e?this._focusMonitor.focusVia(this._inputElement,e,t):this._inputElement.nativeElement.focus(t)}toggle(){this.checked=!this.checked,this._onChange(this.checked)}_emitChangeEvent(){this._onChange(this.checked),this.change.emit(new r_t(this,this.checked))}_onLabelTextChange(){this._changeDetectorRef.detectChanges()}}l_t.ɵfac=function t(e){return new(e||l_t)(Sm(hg),Sm(SI),Sm(Ug),Na("tabindex"),Sm(o_t),Sm(VP,8))},l_t.ɵcmp=to({type:l_t,selectors:[["mat-slide-toggle"]],viewQuery:function t(e,n){if(1&e&&(Qh($yt,5),Qh(t_t,5),Qh(e_t,5)),2&e){let t;Jh(t=tb())&&(n._thumbEl=t.first),Jh(t=tb())&&(n._thumbBarEl=t.first),Jh(t=tb())&&(n._inputElement=t.first)}},hostAttrs:[1,"mat-slide-toggle"],hostVars:12,hostBindings:function t(e,n){2&e&&(Tu("id",n.id),jp("tabindex",n.disabled?null:-1)("aria-label",null)("aria-labelledby",null),pu("mat-checked",n.checked)("mat-disabled",n.disabled)("mat-slide-toggle-label-before","before"==n.labelPosition)("_mat-animation-noopable",n._noopAnimations))},inputs:{disabled:"disabled",disableRipple:"disableRipple",color:"color",tabIndex:"tabIndex",name:"name",id:"id",labelPosition:"labelPosition",ariaLabel:["aria-label","ariaLabel"],ariaLabelledby:["aria-labelledby","ariaLabelledby"],required:"required",checked:"checked",ariaDescribedby:["aria-describedby","ariaDescribedby"]},outputs:{change:"change",toggleChange:"toggleChange"},exportAs:["matSlideToggle"],features:[pg([a_t]),xp],ngContentSelectors:["*"],decls:16,vars:20,consts:[[1,"mat-slide-toggle-label"],["label",""],[1,"mat-slide-toggle-bar"],["toggleBar",""],["type","checkbox","role","switch",1,"mat-slide-toggle-input","cdk-visually-hidden",3,"id","required","tabIndex","checked","disabled","change","click"],["input",""],[1,"mat-slide-toggle-thumb-container"],["thumbContainer",""],[1,"mat-slide-toggle-thumb"],["mat-ripple","",1,"mat-slide-toggle-ripple","mat-focus-indicator",3,"matRippleTrigger","matRippleDisabled","matRippleCentered","matRippleRadius","matRippleAnimation"],[1,"mat-ripple-element","mat-slide-toggle-persistent-ripple"],[1,"mat-slide-toggle-content",3,"cdkObserveContent"],["labelContent",""],[2,"display","none"]],template:function t(e,n){if(1&e&&(Zm(),Rm(0,"label",0,1),Rm(2,"div",2,3),Rm(4,"input",4,5),Vm("change",(function t(e){return n._onChangeEvent(e)}))("click",(function t(e){return n._onInputClick(e)})),Am(),Rm(6,"div",6,7),Tm(8,"div",8),Rm(9,"div",9),Tm(10,"div",10),Am(),Am(),Am(),Rm(11,"span",11,12),Vm("cdkObserveContent",(function t(){return n._onLabelTextChange()})),Rm(13,"span",13),ku(14," "),Am(),Xm(15),Am(),Am()),2&e){const t=$p(1),e=$p(12);jp("for",n.inputId),rc(2),pu("mat-slide-toggle-bar-no-side-margin",!e.textContent||!e.textContent.trim()),rc(2),Dm("id",n.inputId)("required",n.required)("tabIndex",n.tabIndex)("checked",n.checked)("disabled",n.disabled),jp("name",n.name)("aria-checked",n.checked.toString())("aria-label",n.ariaLabel)("aria-labelledby",n.ariaLabelledby)("aria-describedby",n.ariaDescribedby),rc(5),Dm("matRippleTrigger",t)("matRippleDisabled",n.disableRipple||n.disabled)("matRippleCentered",!0)("matRippleRadius",20)("matRippleAnimation",Mh(18,n_t,n._noopAnimations?0:150))}},directives:[kH,jz],styles:[".mat-slide-toggle{display:inline-block;height:24px;max-width:100%;line-height:24px;white-space:nowrap;outline:none;-webkit-tap-highlight-color:transparent}.mat-slide-toggle.mat-checked .mat-slide-toggle-thumb-container{transform:translate3d(16px, 0, 0)}[dir=rtl] .mat-slide-toggle.mat-checked .mat-slide-toggle-thumb-container{transform:translate3d(-16px, 0, 0)}.mat-slide-toggle.mat-disabled{opacity:.38}.mat-slide-toggle.mat-disabled .mat-slide-toggle-label,.mat-slide-toggle.mat-disabled .mat-slide-toggle-thumb-container{cursor:default}.mat-slide-toggle-label{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;display:flex;flex:1;flex-direction:row;align-items:center;height:inherit;cursor:pointer}.mat-slide-toggle-content{white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.mat-slide-toggle-label-before .mat-slide-toggle-label{order:1}.mat-slide-toggle-label-before .mat-slide-toggle-bar{order:2}[dir=rtl] .mat-slide-toggle-label-before .mat-slide-toggle-bar,.mat-slide-toggle-bar{margin-right:8px;margin-left:0}[dir=rtl] .mat-slide-toggle-bar,.mat-slide-toggle-label-before .mat-slide-toggle-bar{margin-left:8px;margin-right:0}.mat-slide-toggle-bar-no-side-margin{margin-left:0;margin-right:0}.mat-slide-toggle-thumb-container{position:absolute;z-index:1;width:20px;height:20px;top:-3px;left:0;transform:translate3d(0, 0, 0);transition:all 80ms linear;transition-property:transform}._mat-animation-noopable .mat-slide-toggle-thumb-container{transition:none}[dir=rtl] .mat-slide-toggle-thumb-container{left:auto;right:0}.mat-slide-toggle-thumb{height:20px;width:20px;border-radius:50%}.mat-slide-toggle-bar{position:relative;width:36px;height:14px;flex-shrink:0;border-radius:8px}.mat-slide-toggle-input{bottom:0;left:10px}[dir=rtl] .mat-slide-toggle-input{left:auto;right:10px}.mat-slide-toggle-bar,.mat-slide-toggle-thumb{transition:all 80ms linear;transition-property:background-color;transition-delay:50ms}._mat-animation-noopable .mat-slide-toggle-bar,._mat-animation-noopable .mat-slide-toggle-thumb{transition:none}.mat-slide-toggle .mat-slide-toggle-ripple{position:absolute;top:calc(50% - 20px);left:calc(50% - 20px);height:40px;width:40px;z-index:1;pointer-events:none}.mat-slide-toggle .mat-slide-toggle-ripple .mat-ripple-element:not(.mat-slide-toggle-persistent-ripple){opacity:.12}.mat-slide-toggle-persistent-ripple{width:100%;height:100%;transform:none}.mat-slide-toggle-bar:hover .mat-slide-toggle-persistent-ripple{opacity:.04}.mat-slide-toggle:not(.mat-disabled).cdk-keyboard-focused .mat-slide-toggle-persistent-ripple{opacity:.12}.mat-slide-toggle-persistent-ripple,.mat-slide-toggle.mat-disabled .mat-slide-toggle-bar:hover .mat-slide-toggle-persistent-ripple{opacity:0}@media(hover: none){.mat-slide-toggle-bar:hover .mat-slide-toggle-persistent-ripple{display:none}}.cdk-high-contrast-active .mat-slide-toggle-thumb,.cdk-high-contrast-active .mat-slide-toggle-bar{border:1px solid}.cdk-high-contrast-active .mat-slide-toggle.cdk-keyboard-focused .mat-slide-toggle-bar{outline:2px dotted;outline-offset:5px}\n"],encapsulation:2,changeDetection:0}),l_t.ctorParameters=()=>[{type:hg},{type:SI},{type:Ug},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:void 0,decorators:[{type:kr,args:[o_t]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],l_t.propDecorators={_thumbEl:[{type:Za,args:["thumbContainer"]}],_thumbBarEl:[{type:Za,args:["toggleBar"]}],name:[{type:xy}],id:[{type:xy}],labelPosition:[{type:xy}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],ariaDescribedby:[{type:xy,args:["aria-describedby"]}],required:[{type:xy}],checked:[{type:xy}],change:[{type:Oy}],toggleChange:[{type:Oy}],_inputElement:[{type:Za,args:["input"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(l_t,[{type:My,args:[{selector:"mat-slide-toggle",exportAs:"matSlideToggle",host:{class:"mat-slide-toggle","[id]":"id","[attr.tabindex]":"disabled ? null : -1","[attr.aria-label]":"null","[attr.aria-labelledby]":"null","[class.mat-checked]":"checked","[class.mat-disabled]":"disabled","[class.mat-slide-toggle-label-before]":'labelPosition == "before"',"[class._mat-animation-noopable]":"_noopAnimations"},template:'<label [attr.for]="inputId" class="mat-slide-toggle-label" #label>\n  <div #toggleBar class="mat-slide-toggle-bar"\n       [class.mat-slide-toggle-bar-no-side-margin]="!labelContent.textContent || !labelContent.textContent.trim()">\n\n    <input #input class="mat-slide-toggle-input cdk-visually-hidden" type="checkbox"\n           role="switch"\n           [id]="inputId"\n           [required]="required"\n           [tabIndex]="tabIndex"\n           [checked]="checked"\n           [disabled]="disabled"\n           [attr.name]="name"\n           [attr.aria-checked]="checked.toString()"\n           [attr.aria-label]="ariaLabel"\n           [attr.aria-labelledby]="ariaLabelledby"\n           [attr.aria-describedby]="ariaDescribedby"\n           (change)="_onChangeEvent($event)"\n           (click)="_onInputClick($event)">\n\n    <div class="mat-slide-toggle-thumb-container" #thumbContainer>\n      <div class="mat-slide-toggle-thumb"></div>\n      <div class="mat-slide-toggle-ripple mat-focus-indicator" mat-ripple\n           [matRippleTrigger]="label"\n           [matRippleDisabled]="disableRipple || disabled"\n           [matRippleCentered]="true"\n           [matRippleRadius]="20"\n           [matRippleAnimation]="{enterDuration: _noopAnimations ? 0 : 150}">\n\n        <div class="mat-ripple-element mat-slide-toggle-persistent-ripple"></div>\n      </div>\n    </div>\n\n  </div>\n\n  <span class="mat-slide-toggle-content" #labelContent (cdkObserveContent)="_onLabelTextChange()">\n    \x3c!-- Add an invisible span so JAWS can read the label --\x3e\n    <span style="display:none">&nbsp;</span>\n    <ng-content></ng-content>\n  </span>\n</label>\n',providers:[a_t],inputs:["disabled","disableRipple","color","tabIndex"],encapsulation:Hn.None,changeDetection:zn.OnPush,styles:[".mat-slide-toggle{display:inline-block;height:24px;max-width:100%;line-height:24px;white-space:nowrap;outline:none;-webkit-tap-highlight-color:transparent}.mat-slide-toggle.mat-checked .mat-slide-toggle-thumb-container{transform:translate3d(16px, 0, 0)}[dir=rtl] .mat-slide-toggle.mat-checked .mat-slide-toggle-thumb-container{transform:translate3d(-16px, 0, 0)}.mat-slide-toggle.mat-disabled{opacity:.38}.mat-slide-toggle.mat-disabled .mat-slide-toggle-label,.mat-slide-toggle.mat-disabled .mat-slide-toggle-thumb-container{cursor:default}.mat-slide-toggle-label{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;display:flex;flex:1;flex-direction:row;align-items:center;height:inherit;cursor:pointer}.mat-slide-toggle-content{white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.mat-slide-toggle-label-before .mat-slide-toggle-label{order:1}.mat-slide-toggle-label-before .mat-slide-toggle-bar{order:2}[dir=rtl] .mat-slide-toggle-label-before .mat-slide-toggle-bar,.mat-slide-toggle-bar{margin-right:8px;margin-left:0}[dir=rtl] .mat-slide-toggle-bar,.mat-slide-toggle-label-before .mat-slide-toggle-bar{margin-left:8px;margin-right:0}.mat-slide-toggle-bar-no-side-margin{margin-left:0;margin-right:0}.mat-slide-toggle-thumb-container{position:absolute;z-index:1;width:20px;height:20px;top:-3px;left:0;transform:translate3d(0, 0, 0);transition:all 80ms linear;transition-property:transform}._mat-animation-noopable .mat-slide-toggle-thumb-container{transition:none}[dir=rtl] .mat-slide-toggle-thumb-container{left:auto;right:0}.mat-slide-toggle-thumb{height:20px;width:20px;border-radius:50%}.mat-slide-toggle-bar{position:relative;width:36px;height:14px;flex-shrink:0;border-radius:8px}.mat-slide-toggle-input{bottom:0;left:10px}[dir=rtl] 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const c_t={provide:GV,useExisting:qe((()=>d_t)),multi:!0};class d_t extends KU{}d_t.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(d_t)))(n||d_t)}})(),d_t.ɵdir=lo({type:d_t,selectors:[["mat-slide-toggle","required","","formControlName",""],["mat-slide-toggle","required","","formControl",""],["mat-slide-toggle","required","","ngModel",""]],features:[pg([c_t]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(d_t,[{type:Cy,args:[{selector:"mat-slide-toggle[required][formControlName],\n             mat-slide-toggle[required][formControl], mat-slide-toggle[required][ngModel]",providers:[c_t]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class p_t{}p_t.ɵfac=function t(e){return new(e||p_t)},p_t.ɵmod=ao({type:p_t}),p_t.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(p_t,[{type:Ay,args:[{exports:[d_t],declarations:[d_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(p_t,{declarations:[d_t],exports:[d_t]});class m_t{}function u_t(t,e){1&t&&Tm(0,"mat-icon",3)}m_t.ɵfac=function t(e){return new(e||m_t)},m_t.ɵmod=ao({type:m_t}),m_t.ɵinj=vn({imports:[[p_t,SH,XI,Uz],p_t,XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(m_t,[{type:Ay,args:[{imports:[p_t,SH,XI,Uz],exports:[p_t,l_t,XI],declarations:[l_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(m_t,{declarations:function(){return[l_t]},imports:function(){return[p_t,SH,XI,Uz]},exports:function(){return[p_t,l_t,XI]}});class f_t{constructor(){this.onRegexFilterValueChange=new Lh}}f_t.ɵfac=function t(e){return new(e||f_t)},f_t.ɵcmp=to({type:f_t,selectors:[["npmi-annotations-search-component"]],hostVars:2,hostBindings:function t(e,n){2&e&&pu("valid",n.isRegexFilterValid)},inputs:{regexFilterValue:"regexFilterValue",isRegexFilterValid:"isRegexFilterValid"},outputs:{onRegexFilterValueChange:"onRegexFilterValueChange"},decls:3,vars:2,consts:[["svgIcon","search_24px"],["autocomplete","off","placeholder","Filter Annotations",3,"value","input"],["svgIcon","error_24px","class","error-icon","matTooltip","Invalid regex filter. The result may be stale.",4,"ngIf"],["svgIcon","error_24px","matTooltip","Invalid regex filter. 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ngDevMode||ngDevMode)&&hh(f_t,[{type:My,args:[{selector:"npmi-annotations-search-component",templateUrl:"./annotations_search_component.ng.html",styleUrls:["./annotations_search_component.css"],changeDetection:zn.OnPush}]}],null,{regexFilterValue:[{type:xy}],onRegexFilterValueChange:[{type:Oy}],isRegexFilterValid:[{type:Py,args:["class.valid"]},{type:xy}]});class g_t{constructor(t){this.store=t,this.annotationsFilter$=this.store.select(mbt),this.isAnnotationsFilterValid$=this.annotationsFilter$.pipe(It((t=>{try{return new RegExp(t),!0}catch(t){return!1}})))}filterChange(t){this.store.dispatch(Ibt({regex:t}))}}function h_t(t,e){if(1&t){const t=Hm();Nm(0),Rm(1,"button",5),Vm("click",(function e(){hi(t);const n=Ym();return n.onFlagAnnotations.emit(n.selectedAnnotations)})),Tm(2,"mat-icon",6),Am(),Rm(3,"button",7),Vm("click",(function e(){hi(t);const n=Ym();return n.onHideAnnotations.emit(n.selectedAnnotations)})),Tm(4,"mat-icon",8),Am(),zm()}if(2&t){const t=Ym();rc(1),Dm("disabled",0===t.selectedAnnotations.length),rc(2),Dm("disabled",0===t.selectedAnnotations.length)}}function b_t(t,e){if(1&t){const t=Hm();Nm(0),Rm(1,"mat-slide-toggle",9),Vm("change",(function e(){return hi(t),Ym().onToggleShowCounts.emit()})),ku(2," Sample Count "),Am(),Rm(3,"mat-slide-toggle",10),Vm("change",(function e(){return hi(t),Ym().onToggleShowHidden.emit()})),ku(4," Show Hidden "),Am(),Tm(5,"npmi-annotations-search"),zm()}if(2&t){const t=Ym();rc(1),Dm("checked",t.showCounts),rc(2),Dm("checked",t.showHidden)}}g_t.ɵfac=function t(e){return new(e||g_t)(Sm(Iw))},g_t.ɵcmp=to({type:g_t,selectors:[["npmi-annotations-search"]],decls:3,vars:6,consts:[[3,"regexFilterValue","isRegexFilterValid","onRegexFilterValueChange"]],template:function t(e,n){1&e&&(Rm(0,"npmi-annotations-search-component",0),Vm("onRegexFilterValueChange",(function t(e){return 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new(e||y_t)},y_t.ɵcmp=to({type:y_t,selectors:[["npmi-annotations-list-toolbar-component"]],inputs:{numAnnotations:"numAnnotations",expanded:"expanded",selectedAnnotations:"selectedAnnotations",annotationsExpanded:"annotationsExpanded",showCounts:"showCounts",showHidden:"showHidden"},outputs:{onFlagAnnotations:"onFlagAnnotations",onHideAnnotations:"onHideAnnotations",onToggleExpanded:"onToggleExpanded",onToggleShowCounts:"onToggleShowCounts",onToggleShowHidden:"onToggleShowHidden"},decls:7,vars:4,consts:function(){let t,e,n;return t="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Hides/Shows the Annotations List"):$localize`:Label for a button that hides/shows the annotations list.␟b3603ba33e5308dd8c5e805e508b2f7233df89d4␟7336374413056342492:Hides/Shows the Annotations List`,e="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Flag Selected Annotations"):$localize`:Label for a button that flags selected 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ngDevMode||ngDevMode)&&hh(y_t,[{type:My,args:[{selector:"npmi-annotations-list-toolbar-component",templateUrl:"./annotations_list_toolbar_component.ng.html",styleUrls:["./annotations_list_toolbar_component.css"],changeDetection:zn.OnPush}]}],null,{numAnnotations:[{type:xy}],expanded:[{type:xy}],selectedAnnotations:[{type:xy}],annotationsExpanded:[{type:xy}],showCounts:[{type:xy}],showHidden:[{type:xy}],onFlagAnnotations:[{type:Oy}],onHideAnnotations:[{type:Oy}],onToggleExpanded:[{type:Oy}],onToggleShowCounts:[{type:Oy}],onToggleShowHidden:[{type:Oy}]});class __t{constructor(t){this.store=t,this.selectedAnnotations$=this.store.select(cbt),this.annotationsExpanded$=this.store.select(ybt),this.showCounts$=this.store.select(Cbt),this.showHidden$=this.store.select(Mbt),this.annotationsFilter$=this.store.select(mbt),this.isAnnotationsFilterValid$=this.annotationsFilter$.pipe(It((t=>{try{return Boolean(new RegExp(t))}catch(t){return!1}})))}filterChange(t){this.store.dispatch(Ibt({regex:t}))}flagAnnotations(t){this.store.dispatch(Nbt({annotations:t}))}hideAnnotations(t){this.store.dispatch(zbt({annotations:t}))}toggleExpanded(){this.store.dispatch(Gbt())}toggleShowCounts(){this.store.dispatch(Ybt())}toggleShowHidden(){this.store.dispatch(qbt())}}__t.ɵfac=function t(e){return new(e||__t)(Sm(Iw))},__t.ɵcmp=to({type:__t,selectors:[["npmi-annotations-list-toolbar"]],inputs:{numAnnotations:"numAnnotations",expanded:"expanded"},decls:5,vars:14,consts:[[3,"numAnnotations","expanded","selectedAnnotations","annotationsExpanded","showCounts","showHidden","onFlagAnnotations","onHideAnnotations","onToggleExpanded","onToggleShowCounts","onToggleShowHidden"]],template:function t(e,n){1&e&&(Rm(0,"npmi-annotations-list-toolbar-component",0),Vm("onFlagAnnotations",(function t(e){return n.flagAnnotations(e)}))("onHideAnnotations",(function t(e){return n.hideAnnotations(e)}))("onToggleExpanded",(function t(){return n.toggleExpanded()}))("onToggleShowCounts",(function t(){return n.toggleShowCounts()}))("onToggleShowHidden",(function t(){return n.toggleShowHidden()})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Am()),2&e&&Dm("numAnnotations",n.numAnnotations)("expanded",n.expanded)("selectedAnnotations",Th(1,6,n.selectedAnnotations$))("annotationsExpanded",Th(2,8,n.annotationsExpanded$))("showCounts",Th(3,10,n.showCounts$))("showHidden",Th(4,12,n.showHidden$))},directives:[y_t],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(__t,[{type:My,args:[{selector:"npmi-annotations-list-toolbar",template:'\n    <npmi-annotations-list-toolbar-component\n      [numAnnotations]="numAnnotations"\n      [expanded]="expanded"\n      [selectedAnnotations]="selectedAnnotations$ | async"\n      [annotationsExpanded]="annotationsExpanded$ | async"\n      [showCounts]="showCounts$ | async"\n      [showHidden]="showHidden$ | async"\n      (onFlagAnnotations)="flagAnnotations($event)"\n      (onHideAnnotations)="hideAnnotations($event)"\n      (onToggleExpanded)="toggleExpanded()"\n      (onToggleShowCounts)="toggleShowCounts()"\n      (onToggleShowHidden)="toggleShowHidden()"\n    ></npmi-annotations-list-toolbar-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{numAnnotations:[{type:xy}],expanded:[{type:xy}]});const C_t=["glyph"];class M_t{ngAfterViewInit(){this.svg=J4(this.glyphSVG.nativeElement),this.mainContainer=this.svg.append("g"),this.draw()}draw(){"circle"==this.shape?this.mainContainer.append("circle").attr("fill",this.color).attr("stroke","black").attr("cx",5).attr("cy",5).attr("r",5):"bar"==this.shape?this.mainContainer.append("rect").attr("fill",this.color).attr("x",0).attr("y",0).attr("width",10).attr("height",10):"runIndicator"==this.shape&&this.mainContainer.append("g").append("path").attr("fill",this.color).attr("stroke","black").attr("d","M 2 0 L 10 0 L 7 5 L 10 10 L 2 10 Z")}}M_t.ɵfac=function t(e){return new(e||M_t)},M_t.ɵcmp=to({type:M_t,selectors:[["npmi-legend-element"]],viewQuery:function t(e,n){if(1&e&&Qh(C_t,7,hg),2&e){let t;Jh(t=tb())&&(n.glyphSVG=t.first)}},inputs:{text:"text",color:"color",shape:"shape"},decls:4,vars:1,consts:[[1,"glyph"],["glyph",""],[1,"legend-element-title"]],template:function t(e,n){1&e&&(qi(),Tm(0,"svg",0,1),Zi(),Rm(2,"div",2),ku(3),Am()),2&e&&(rc(3),Su(n.text))},styles:["[_nghost-%COMP%]{align-items:center;display:flex;padding-right:10px}.legend-element-title[_ngcontent-%COMP%]{font-size:.8em;padding-left:5px}.glyph[_ngcontent-%COMP%]{width:10px;height:10px}"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(M_t,[{type:My,args:[{selector:"npmi-legend-element",templateUrl:"./legend_element_component.ng.html",styleUrls:["./legend_element_component.css"],changeDetection:zn.OnPush}]}],null,{text:[{type:xy}],color:[{type:xy}],shape:[{type:xy}],glyphSVG:[{type:Za,args:["glyph",{static:!0,read:hg}]}]});class v_t{}function x_t(t,e){if(1&t&&Tm(0,"mat-icon",8),2&t){const t=Ym(2);Dm("svgIcon",t.sort.order===t.SortOrder.DESCENDING?"arrow_downward_24px":"arrow_upward_24px")("ngClass",t.sort.order===t.SortOrder.DESCENDING?"down-icon":"up-icon")}}function O_t(t,e){if(1&t){const t=Hm();Rm(0,"div",4),Rm(1,"div",5),Rm(2,"div",6),Vm("click",(function e(){const n=hi(t).$implicit;return Ym().onChangeSort.emit(n)})),ku(3),Qp(4,x_t,1,2,"mat-icon",7),Am(),Am(),Am()}if(2&t){const t=e.$implicit,n=Ym();rc(3),Du(" ",n.stripMetric(t)," "),rc(1),Dm("ngIf",t===n.sort.metric)}}v_t.ɵfac=function t(e){return new(e||v_t)},v_t.ɵcmp=to({type:v_t,selectors:[["npmi-annotations-list-legend"]],decls:4,vars:0,consts:[["text","run indicator","color","rgb(0,0,0)","shape","runIndicator"],["text","positive correlation","color","rgb(109, 174, 213)","shape","bar"],["text","negative correlation","color","rgb(249, 105, 76)","shape","bar"],["text","sample count","color","rgb(151, 151, 151)","shape","circle"]],template:function t(e,n){1&e&&(Tm(0,"npmi-legend-element",0),Tm(1,"npmi-legend-element",1),Tm(2,"npmi-legend-element",2),Tm(3,"npmi-legend-element",3))},directives:[M_t],styles:["[_nghost-%COMP%]{display:flex;padding:0 16px}"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(v_t,[{type:My,args:[{selector:"npmi-annotations-list-legend",templateUrl:"./legend_component.ng.html",styleUrls:["./legend_component.css"]}]}],null,null);class P_t{constructor(){this.onChangeSort=new Lh,this.onAllAnnotationsToggled=new Lh,this.SortOrder=nbt}stripMetric(t){return nyt(t)}}P_t.ɵfac=function t(e){return new(e||P_t)},P_t.ɵcmp=to({type:P_t,selectors:[["npmi-annotations-list-header-component"]],inputs:{numAnnotations:"numAnnotations",selectedAnnotations:"selectedAnnotations",activeMetrics:"activeMetrics",sort:"sort"},outputs:{onChangeSort:"onChangeSort",onAllAnnotationsToggled:"onAllAnnotationsToggled"},decls:4,vars:2,consts:[[1,"toggle-all-container"],[3,"checked","change"],[1,"annotations-header-containers"],["class","header-column",4,"ngFor","ngForOf"],[1,"header-column"],[1,"header-container"],["tabindex","0","role","button","title","Change the sort by clicking any of the metrics.",1,"header-clickable",3,"click"],["class","sort-icon",3,"svgIcon","ngClass",4,"ngIf"],[1,"sort-icon",3,"svgIcon","ngClass"]],template:function t(e,n){1&e&&(Rm(0,"div",0),Rm(1,"mat-checkbox",1),Vm("change",(function t(e){return 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ngDevMode||ngDevMode)&&hh(P_t,[{type:My,args:[{selector:"npmi-annotations-list-header-component",templateUrl:"./header_component.ng.html",styleUrls:["./header_component.css"],changeDetection:zn.OnPush}]}],null,{numAnnotations:[{type:xy}],selectedAnnotations:[{type:xy}],activeMetrics:[{type:xy}],sort:[{type:xy}],onChangeSort:[{type:Oy}],onAllAnnotationsToggled:[{type:Oy}]});class w_t{constructor(t){this.store=t,this.selectedAnnotations$=this.store.select(cbt),this.annotationSort$=this.store.select(hbt)}changeSort(t){this.store.dispatch(Vbt({metric:t}))}allAnnotationsToggled(t){this.store.dispatch(Abt(t?{annotations:Object.keys(this.annotations)}:{annotations:[]}))}}w_t.ɵfac=function t(e){return new(e||w_t)(Sm(Iw))},w_t.ɵcmp=to({type:w_t,selectors:[["npmi-annotations-list-header"]],inputs:{numAnnotations:"numAnnotations",annotations:"annotations",activeMetrics:"activeMetrics"},decls:3,vars:8,consts:[[3,"numAnnotations","selectedAnnotations","sort","activeMetrics","onChangeSort","onAllAnnotationsToggled"]],template:function t(e,n){1&e&&(Rm(0,"npmi-annotations-list-header-component",0),Vm("onChangeSort",(function t(e){return n.changeSort(e)}))("onAllAnnotationsToggled",(function t(e){return n.allAnnotationsToggled(e)})),Ah(1,"async"),Ah(2,"async"),Am()),2&e&&Dm("numAnnotations",n.numAnnotations)("selectedAnnotations",Th(1,4,n.selectedAnnotations$))("sort",Th(2,6,n.annotationSort$))("activeMetrics",n.activeMetrics)},directives:[P_t],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(w_t,[{type:My,args:[{selector:"npmi-annotations-list-header",template:'\n    <npmi-annotations-list-header-component\n      [numAnnotations]="numAnnotations"\n      [selectedAnnotations]="selectedAnnotations$ | async"\n      [sort]="annotationSort$ | async"\n      [activeMetrics]="activeMetrics"\n      (onChangeSort)="changeSort($event)"\n      (onAllAnnotationsToggled)="allAnnotationsToggled($event)"\n    ></npmi-annotations-list-header-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{numAnnotations:[{type:xy}],annotations:[{type:xy}],activeMetrics:[{type:xy}]});const k_t=["chart"],S_t=["hintClip"];function D_t(t,e){1&t&&Tm(0,"mat-icon",12)}function E_t(t,e){1&t&&Tm(0,"mat-icon",13)}function R_t(t,e){if(1&t&&Tm(0,"mat-icon",14),2&t){const t=Ym();Dm("svgIcon",t.sort.order===t.SortOrder.SIMILAR?"arrow_downward_24px":"arrow_upward_24px")("ngClass",t.sort.order===t.SortOrder.SIMILAR?"down-icon":"up-icon")}}class A_t{constructor(){this.selected=!1,this.onShowSimilarAnnotations=new Lh,this.SortOrder=nbt,this.width=10,this.chartWidth=10,this.chartHeight=10,this.maxDotRadius=10,this.countDotOffset=70,this.countTextPadding=2,this.margin={top:0,right:0,bottom:0,left:100},this.strokeColor="#fff",this.textClass="default-text",this.runs=[]}onResize(t){this.redraw()}ngAfterViewInit(){this.svg=J4(this.annotationContainer.nativeElement).select("svg"),this.xScale=Utt().padding(0),this.yScale=Utt().padding(0),this.sizeScale=eet().domain([0,1]),this.countSizeScale=eet().range([2,this.maxDotRadius]),this.mainContainer=this.svg.append("g").attr("transform",`translate(${this.margin.left}, ${this.margin.top})`),this.barsGroup=this.mainContainer.append("g"),this.countDotsGroup=this.mainContainer.append("g"),this.textsGroup=this.mainContainer.append("g"),this.countTextsGroup=this.mainContainer.append("g"),this.runHintGroup=this.svg.append("g"),this.redraw()}ngOnChanges(t){this.svg&&this.redraw()}redraw(){this.selected=this.selectedAnnotations.includes(this.annotation),this.updateDimensions(),this.setTextClass(),this.updateAxes(),this.draw()}updateDimensions(){const t=new Set;this.data.forEach((e=>{t.add(e.run)})),this.runs=[...t],this.svg.style("height",this.numActiveRuns*this.runHeight+"px"),this.chartHeight=this.runs.length*this.runHeight-this.margin.top-this.margin.bottom,this.width=this.annotationContainer.nativeElement.clientWidth||10,this.chartWidth=this.width-this.margin.left-this.margin.right}setTextClass(){this.textClass="default-text",this.flaggedAnnotations.includes(this.annotation)?this.textClass="flag-text":this.hiddenAnnotations.includes(this.annotation)&&(this.textClass="hidden-text")}updateAxes(){this.xScale.rangeRound([0,this.chartWidth-this.chartWidth/this.activeMetrics.length]).domain(this.activeMetrics.map((t=>nyt(t)))),this.yScale.rangeRound([0,this.chartHeight-this.runHeight]).domain(this.runs),this.sizeScale.range([0,this.chartWidth/this.activeMetrics.length]),this.countSizeScale.domain([0,this.maxCount])}draw(){this.drawRunIndicators(),this.drawRunHintTexts(),this.drawBars(),this.drawTexts(),this.showCounts?(this.drawCountDots(),this.drawCountTexts()):(this.countDotsGroup.selectAll(".count-dot").remove(),this.countTextsGroup.selectAll(".count-background-text").remove(),this.countTextsGroup.selectAll(".count-text").remove())}drawRunIndicators(){J4(this.clipPathElement.nativeElement).select("rect").attr("width",this.margin.left-30).attr("height",this.chartHeight);const t=this.runHintGroup.selectAll(".hint").data(this.runs),e=t.enter().append("g").attr("class","hint");e.append("path").attr("d","M 0 0 L 15 0 L 10 10 L 15 20 L 0 20 Z"),e.merge(t).attr("transform",function(t){return`translate(10, ${this.yScale(t)+5})`}.bind(this)).attr("fill",function(t){return this.colorScale(t)}.bind(this)),t.exit().remove()}drawRunHintTexts(){const t=this.runHintGroup.selectAll(".hint-text").data(this.runs);t.enter().append("text").attr("x",25).attr("font-size","10px").attr("alignment-baseline","middle").attr("clip-path","url(#hint-clip)").merge(t).attr("y",function(t){return this.yScale(t)+15}.bind(this)).attr("class",`hint-text ${this.textClass}`).text((t=>{var e;return(null===(e=this.runIdToRuns.get(t))||void 0===e?void 0:e.name)||""})),t.exit().remove()}drawBars(){const t=this.barsGroup.selectAll(".bar").data(this.data);t.enter().append("rect").attr("class","bar").attr("height",20).merge(t).attr("fill",(t=>null===t.nPMIValue?"":t.nPMIValue>=0?bot(t.nPMIValue):_ot(-1*t.nPMIValue))).attr("x",function(t){return this.xScale(t.metric)}.bind(this)).attr("y",function(t){return this.yScale(t.run)+5}.bind(this)).attr("width",function(t){return null===t.nPMIValue?0:this.sizeScale(Math.abs(t.nPMIValue))}.bind(this)),t.exit().remove()}drawCountDots(){const t=this.countDotsGroup.selectAll(".count-dot").data(this.data);t.enter().append("circle").attr("class","count-dot").attr("stroke","black").merge(t).attr("fill",function(t){return null===t.countValue?"":yot(t.countValue/this.maxCount)}.bind(this)).attr("cx",function(t){return this.xScale(t.metric)+this.countDotOffset}.bind(this)).attr("cy",function(t){return this.yScale(t.run)+this.runHeight/2}.bind(this)).attr("r",function(t){return null===t.countValue?0:this.countSizeScale(t.countValue)}.bind(this)),t.exit().remove()}drawTexts(){const t=this.textsGroup.selectAll(".npmi-background-text").data(this.data);t.enter().append("text").attr("class","npmi-background-text").attr("stroke-width",3).attr("stroke-linejoin","round").attr("stroke",this.strokeColor).attr("font-size","13px").attr("alignment-baseline","middle").merge(t).attr("x",function(t){return this.xScale(t.metric)+5}.bind(this)).attr("y",function(t){return this.yScale(t.run)+this.runHeight/2}.bind(this)).text((t=>null===t.nPMIValue?"null":Math.round(1e3*(t.nPMIValue+Number.EPSILON))/1e3)),t.exit().remove();const e=this.textsGroup.selectAll(".npmi-text").data(this.data);e.enter().append("text").attr("class","npmi-text").attr("font-size","13px").attr("alignment-baseline","middle").merge(e).attr("x",function(t){return this.xScale(t.metric)+5}.bind(this)).attr("y",function(t){return this.yScale(t.run)+this.runHeight/2}.bind(this)).text((t=>null===t.nPMIValue?"null":Math.round(1e3*(t.nPMIValue+Number.EPSILON))/1e3)),e.exit().remove()}drawCountTexts(){const t=this.countTextsGroup.selectAll(".count-background-text").data(this.data);t.enter().append("text").attr("class","count-background-text").attr("stroke-width",3).attr("stroke-linejoin","round").attr("stroke",this.strokeColor).attr("font-size","10px").attr("alignment-baseline","middle").merge(t).attr("x",function(t){return this.xScale(t.metric)+this.countDotOffset+this.countTextPadding+this.maxDotRadius}.bind(this)).attr("y",function(t){return this.yScale(t.run)+this.runHeight/2}.bind(this)).text((t=>null===t.countValue?"":Intl.NumberFormat().format(t.countValue))),t.exit().remove();const e=this.countTextsGroup.selectAll(".count-text").data(this.data);e.enter().append("text").attr("class","count-text").attr("font-size","10px").attr("alignment-baseline","middle").merge(e).attr("x",function(t){return this.xScale(t.metric)+this.countDotOffset+this.countTextPadding+this.maxDotRadius}.bind(this)).attr("y",function(t){return this.yScale(t.run)+this.runHeight/2}.bind(this)).text((t=>null===t.countValue?"":Intl.NumberFormat().format(t.countValue))),e.exit().remove()}similaritySort(t){this.hasEmbedding&&(t.stopPropagation(),this.onShowSimilarAnnotations.emit())}}A_t.ɵfac=function t(e){return new(e||A_t)},A_t.ɵcmp=to({type:A_t,selectors:[["annotation-component"]],viewQuery:function t(e,n){if(1&e&&(Qh(k_t,7,hg),Qh(S_t,7,hg)),2&e){let t;Jh(t=tb())&&(n.annotationContainer=t.first),Jh(t=tb())&&(n.clipPathElement=t.first)}},hostVars:2,hostBindings:function t(e,n){1&e&&Vm("resize",(function t(){return n.onResize()}),!1,ol),2&e&&pu("selected-row",n.selected)},inputs:{data:"data",maxCount:"maxCount",selectedAnnotations:"selectedAnnotations",flaggedAnnotations:"flaggedAnnotations",hiddenAnnotations:"hiddenAnnotations",activeMetrics:"activeMetrics",numActiveRuns:"numActiveRuns",showCounts:"showCounts",annotation:"annotation",runHeight:"runHeight",hasEmbedding:"hasEmbedding",sort:"sort",sidebarWidth:"sidebarWidth",colorScale:"colorScale",runIdToRuns:"runIdToRuns"},outputs:{onShowSimilarAnnotations:"onShowSimilarAnnotations"},features:[Bo],decls:14,vars:10,consts:[[1,"annotation-title"],[1,"annotation-checkbox",3,"checked","click"],[1,"annotation-button",3,"ngClass","click"],["class","flagged-icon","svgIcon","flag_24px",4,"ngIf"],["class","hidden-icon","svgIcon","visibility_off_24px",4,"ngIf"],["class","annotation-icon",3,"svgIcon","ngClass",4,"ngIf"],[1,"chart-div"],["chart",""],[1,"chart-svg"],["id","hint-clip"],["hintClip",""],["x","0","y","0"],["svgIcon","flag_24px",1,"flagged-icon"],["svgIcon","visibility_off_24px",1,"hidden-icon"],[1,"annotation-icon",3,"svgIcon","ngClass"]],template:function t(e,n){1&e&&(Rm(0,"div",0),Rm(1,"mat-checkbox",1),Vm("click",(function t(e){return e.preventDefault()})),Am(),Rm(2,"button",2),Vm("click",(function t(e){return n.similaritySort(e)})),ku(3),Am(),Qp(4,D_t,1,0,"mat-icon",3),Qp(5,E_t,1,0,"mat-icon",4),Qp(6,R_t,1,2,"mat-icon",5),Am(),Rm(7,"div",6,7),qi(),Rm(9,"svg",8),Rm(10,"defs"),Rm(11,"clipPath",9,10),Tm(13,"rect",11),Am(),Am(),Am(),Am()),2&e&&(pu("flagged-annotation",n.flaggedAnnotations.includes(n.annotation))("hidden-annotation",n.hiddenAnnotations.includes(n.annotation)&&!n.flaggedAnnotations.includes(n.annotation)),rc(1),Dm("checked",n.selectedAnnotations.includes(n.annotation)),rc(1),Dm("ngClass",n.hasEmbedding?"clickable-annotation":""),rc(1),Du(" ",n.annotation," 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ngDevMode||ngDevMode)&&hh(A_t,[{type:My,args:[{selector:"annotation-component",templateUrl:"./annotation_component.ng.html",styleUrls:["./annotation_component.css"],changeDetection:zn.OnPush,encapsulation:Hn.None}]}],null,{data:[{type:xy}],maxCount:[{type:xy}],selectedAnnotations:[{type:xy}],flaggedAnnotations:[{type:xy}],hiddenAnnotations:[{type:xy}],activeMetrics:[{type:xy}],numActiveRuns:[{type:xy}],showCounts:[{type:xy}],annotation:[{type:xy}],runHeight:[{type:xy}],hasEmbedding:[{type:xy}],sort:[{type:xy}],sidebarWidth:[{type:xy}],colorScale:[{type:xy}],runIdToRuns:[{type:xy}],annotationContainer:[{type:Za,args:["chart",{static:!0,read:hg}]}],clipPathElement:[{type:Za,args:["hintClip",{static:!0,read:hg}]}],selected:[{type:Py,args:["class.selected-row"]}],onResize:[{type:wy,args:["window:resize"]}],onShowSimilarAnnotations:[{type:Oy}]});class T_t{constructor(t){this.store=t,this.sort$=this.store.select(hbt),this.flaggedAnnotations$=this.store.select(dbt),this.hiddenAnnotations$=this.store.select(pbt),this.selectedAnnotations$=this.store.select(cbt),this.showCounts$=this.store.select(Cbt),this.sidebarWidth$=this.store.select(xbt),this.runColorScale$=this.store.select(zN).pipe(It((t=>e=>{if(!t.hasOwnProperty(e))throw new Error(`[Color scale] unknown runId: ${e}.`);return t[e]}))),this.runIdToRuns$=this.store.select(dN)}showSimilarAnnotations(){this.store.dispatch(jbt({annotation:this.annotation}))}}function N_t(t,e){if(1&t){const t=Hm();Rm(0,"npmi-annotation",5),Vm("click",(function e(n){const o=hi(t).$implicit;return Ym(2).rowClicked(n,o)})),Am()}if(2&t){const t=e.$implicit,n=Ym(2);Dm("data",n.annotations[t])("activeMetrics",n.activeMetrics)("numActiveRuns",n.numActiveRuns)("maxCount",n.maxCount)("annotation",t)("runHeight",n.runHeight)("hasEmbedding",n.embeddingData&&void 0!==n.embeddingData[t])}}function z_t(t,e){if(1&t&&(Nm(0),Tm(1,"npmi-annotations-list-legend"),Tm(2,"npmi-annotations-list-header",2),Rm(3,"cdk-virtual-scroll-viewport",3),Qp(4,N_t,1,7,"npmi-annotation",4),Am(),zm()),2&t){const t=Ym();rc(2),Dm("annotations",t.annotations)("numAnnotations",t.numAnnotations)("activeMetrics",t.activeMetrics),rc(1),Km("itemSize",t.numActiveRuns*t.runHeight+25),rc(1),Dm("cdkVirtualForOf",t.sortedAnnotations)}}T_t.ɵfac=function t(e){return new(e||T_t)(Sm(Iw))},T_t.ɵcmp=to({type:T_t,selectors:[["npmi-annotation"]],inputs:{data:"data",maxCount:"maxCount",activeMetrics:"activeMetrics",numActiveRuns:"numActiveRuns",annotation:"annotation",runHeight:"runHeight",hasEmbedding:"hasEmbedding"},decls:9,vars:31,consts:[[3,"data","maxCount","activeMetrics","numActiveRuns","annotation","runHeight","hasEmbedding","sort","selectedAnnotations","flaggedAnnotations","hiddenAnnotations","showCounts","sidebarWidth","colorScale","runIdToRuns","onShowSimilarAnnotations"]],template:function 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.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{background-color:#fff;border:1px solid #ebebeb;display:flex;flex-direction:column;height:calc(100% - 2px);width:calc(100% - 2px)}.annotation-rows[_ngcontent-%COMP%]{display:flex;flex-direction:column;flex:1 1;overflow-y:auto}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(I_t,[{type:My,args:[{selector:"annotations-list-component",templateUrl:"./annotations_list_component.ng.html",styleUrls:["./annotations_list_component.css"],changeDetection:zn.OnPush}]}],null,{annotations:[{type:xy}],embeddingData:[{type:xy}],annotationsExpanded:[{type:xy}],numAnnotations:[{type:xy}],annotationSort:[{type:xy}],activeMetrics:[{type:xy}],numActiveRuns:[{type:xy}],sortedAnnotations:[{type:xy}],selectedAnnotations:[{type:xy}],maxCount:[{type:xy}],onRowClick:[{type:Oy}]});class H_t{constructor(t){this.store=t,this.annotationsExpanded$=this.store.pipe(Fw(ybt)),this.activeRuns$=this.store.pipe(Fw(NN)).pipe(It((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.embeddingData$=this.store.pipe(Fw(lbt)),this.numActiveRuns$=this.activeRuns$.pipe(It((t=>t.length))),this.activeMetrics$=Wt([this.store.select(sbt),this.activeRuns$,this.store.select(gbt)]).pipe(It((([t,e,n])=>{let o=[];for(const n of e)t[n]&&(o=o.concat(t[n].filter((t=>eyt(t)))));return o=[...new Set([...Object.keys(n),...o])],o}))),this.visibleAnnotations$=Wt([this.store.select(rbt),this.store.select(pbt),this.store.select(Mbt)]).pipe(It((([t,e,n])=>Gyt(t,e,n)))),this.filteredAnnotations$=Wt([this.visibleAnnotations$,this.store.select(fbt),this.store.select(gbt),this.activeRuns$,this.activeMetrics$,this.store.select(mbt)]).pipe(It((([t,e,n,o,i,a])=>(function r(t,e,n,o,i,a){const r={},s=new Set(e),l=new Set(i.map((t=>nyt(t)))),c=new RegExp(a,"i");return Object.entries(t).forEach((t=>{if(!c.test(t[0]))return;let e=t[1];e=e.filter((t=>s.has(t.run)&&l.has(t.metric))),(function i(t,e,n){return t.every((t=>{if(t.kind===tbt.OPERATOR)return!0;const o=e[t.metric];return void 0===o||n.some((e=>e.metric===nyt(t.metric)&&(null===e.nPMIValue?o.includeNaN:e.nPMIValue<=o.max&&e.nPMIValue>=o.min)))}))})(n,o,e)&&0!==e.length&&(r[t[0]]=e)})),r})(t,o,e,n,i,a)))).pipe(Ee()),this.numAnnotations$=this.filteredAnnotations$.pipe(It((t=>Object.keys(t).length))),this.sortedAnnotations$=Wt([this.filteredAnnotations$,this.store.pipe(Fw(hbt)),this.embeddingData$]).pipe(It((([t,e,n])=>(function o(t,e,n){const o=Object.keys(t),i=e.order===nbt.DISSIMILAR||e.order===nbt.SIMILAR;return""===e.metric||(void 0===n||void 0===n.points[e.metric])&&i?o:(function s(t,e,n){return t.sort(n?(t,n)=>e[t]-e[n]:(t,n)=>e[n]-e[t])})(o,i?(function a(t,e,n){const o={};let i=Number.POSITIVE_INFINITY,a=Number.NEGATIVE_INFINITY;n.order===nbt.SIMILAR&&(i=Number.NEGATIVE_INFINITY,a=Number.POSITIVE_INFINITY);for(const r of t)o[r]=r===n.metric?i:void 0===e.points[r]?a:e.points[r].vector?Qyt(e.points[n.metric].vector,e.points[r].vector,a):a;return o})(o,n,e):(function r(t,e,n){const o=nyt(n.metric),i={};if(n.order===nbt.DESCENDING)for(const n of t)i[n]=Math.max(...e[n].filter((t=>t.metric===o)).map((t=>null===t.nPMIValue?-1/0:t.nPMIValue)));else for(const n of t)i[n]=Math.min(...e[n].filter((t=>t.metric===o)).map((t=>null===t.nPMIValue?1/0:t.nPMIValue)));return i})(o,t,e),e.order===nbt.ASCENDNG||e.order===nbt.SIMILAR)})(t,e,n)))),this.selectedAnnotations$=this.store.pipe(Fw(cbt)),this.maxCount$=this.filteredAnnotations$.pipe(It((t=>{let e=0;return Object.values(t).forEach((t=>{t.forEach((t=>{t.countValue&&(e=Math.max(e,t.countValue))}))})),e})))}rowClicked(t){this.store.dispatch(Rbt({annotations:t}))}}H_t.ɵfac=function t(e){return new(e||H_t)(Sm(Iw))},H_t.ɵcmp=to({type:H_t,selectors:[["npmi-annotations-list"]],decls:10,vars:27,consts:[[3,"annotations","embeddingData","annotationsExpanded","numAnnotations","activeMetrics","numActiveRuns","sortedAnnotations","selectedAnnotations","maxCount","onRowClick"]],template:function t(e,n){1&e&&(Rm(0,"annotations-list-component",0),Vm("onRowClick",(function t(e){return n.rowClicked(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Ah(7,"async"),Ah(8,"async"),Ah(9,"async"),Am()),2&e&&Dm("annotations",Th(1,9,n.filteredAnnotations$))("embeddingData",Th(2,11,n.embeddingData$))("annotationsExpanded",Th(3,13,n.annotationsExpanded$))("numAnnotations",Th(4,15,n.numAnnotations$))("activeMetrics",Th(5,17,n.activeMetrics$))("numActiveRuns",Th(6,19,n.numActiveRuns$))("sortedAnnotations",Th(7,21,n.sortedAnnotations$))("selectedAnnotations",Th(8,23,n.selectedAnnotations$))("maxCount",Th(9,25,n.maxCount$))},directives:[I_t],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(H_t,[{type:My,args:[{selector:"npmi-annotations-list",template:'\n    <annotations-list-component\n      [annotations]="filteredAnnotations$ | async"\n      [embeddingData]="embeddingData$ | async"\n      [annotationsExpanded]="annotationsExpanded$ | async"\n      [numAnnotations]="numAnnotations$ | async"\n      [activeMetrics]="activeMetrics$ | async"\n      [numActiveRuns]="numActiveRuns$ | async"\n      [sortedAnnotations]="sortedAnnotations$ | async"\n      [selectedAnnotations]="selectedAnnotations$ | async"\n      [maxCount]="maxCount$ | async"\n      (onRowClick)="rowClicked($event)"\n    ></annotations-list-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);const F_t=["chart"];class L_t{constructor(){this.width=0,this.chartWidth=0,this.height=300,this.margin={top:20,right:40,bottom:20,left:40},this.chartHeight=this.height-this.margin.top-this.margin.bottom}onResize(t){this.redraw()}ngAfterViewInit(){this.svg=J4(this.svgElement.nativeElement),this.mainContainer=this.svg.append("g").attr("transform",`translate(${this.margin.left}, ${this.margin.top})`),this.coordinatesGroup=this.mainContainer.append("g"),this.labelsGroup=this.mainContainer.append("g"),this.axisGroup=this.mainContainer.append("g"),this.xScale=Utt().padding(.1),this.yScale=eet().range([this.chartHeight,0]),this.yAxis=N5(this.yScale),this.redraw()}ngOnChanges(t){this.svg&&this.redraw()}redraw(){this.updateDimensions(),this.updateAxes(),this.draw()}updateDimensions(){this.width=this.svgElement.nativeElement.clientWidth||10,this.chartWidth=this.width-this.margin.left-this.margin.right}updateAxes(){this.xScale.rangeRound([0,this.chartWidth]).domain(this.activeMetrics),this.yScale.domain([this.coordinateData.extremes.min,this.coordinateData.extremes.max])}draw(){this.drawAxes(),this.drawAxisLabels(),this.drawCoordinates(),this.drawLabels()}drawAxes(){const t=this.axisGroup.selectAll(".axis-y").data(this.activeMetrics);t.enter().append("g").attr("class","axis-y").merge(t).attr("transform",function(t){return`translate(${this.xScale(t)}, 0)`}.bind(this)).call(this.yAxis),t.exit().remove()}drawAxisLabels(){const t=this.axisGroup.selectAll(".axis-bg-text").data(this.activeMetrics);t.enter().append("text").attr("class","axis-bg-text").attr("font-size","13px").attr("stroke-width",2).attr("stroke-linejoin","round").attr("stroke","white").merge(t).text((t=>t)).attr("transform",function(t){return`translate(${this.xScale(t)-5}, ${this.yScale(this.coordinateData.extremes.min)}) rotate(-90)`}.bind(this)),t.exit().remove();const e=this.axisGroup.selectAll(".axis-text").data(this.activeMetrics);e.enter().append("text").attr("font-size","13px").attr("class","axis-text").merge(e).text((t=>t)).attr("transform",function(t){return`translate(${this.xScale(t)-5}, ${this.yScale(this.coordinateData.extremes.min)}) rotate(-90)`}.bind(this)),e.exit().remove()}drawCoordinates(){const t=this.coordinatesGroup.selectAll(".coord").data(this.coordinateData.coordinates);t.enter().append("path").attr("class","coord").attr("fill","none").merge(t).attr("d",this.path.bind(this)).attr("stroke",function(t){return this.colorScale(t.runId)}.bind(this)),t.exit().remove();const e=this.coordinatesGroup.selectAll(".hiddenCoord").data(this.coordinateData.coordinates);e.enter().append("path").attr("class","hiddenCoord").attr("stroke-width","10px").attr("fill","none").attr("stroke","rgba(0, 0, 0, 0.0)").on("mouseover",this.handleCoordinateMouseOver.bind(this)).on("mouseout",this.handleCoordinateMouseOut.bind(this)).merge(e).attr("d",this.path.bind(this)),e.exit().remove()}path(t){return t.values.sort(((t,e)=>this.activeMetrics.indexOf(t.metric)-this.activeMetrics.indexOf(e.metric))),Pot()(t.values.map(function(t){let e=this.yScale(t.nPMIValue);return[this.xScale(t.metric),e]}.bind(this)))}handleCoordinateMouseOver(t,e){this.labelsGroup.selectAll(".coordinate-label").filter((function(e){return!(e.annotation===t.annotation)})).style("opacity",.1),this.coordinatesGroup.selectAll(".coord").filter((function(e){return!(e.annotation===t.annotation)})).style("opacity",.1)}handleCoordinateMouseOut(){this.labelsGroup.selectAll(".coordinate-label").style("opacity",1),this.coordinatesGroup.selectAll(".coord").style("opacity",1)}drawLabels(){const t=30/this.xScale.step(),e=this.coordinateData.coordinates.length<30?this.coordinateData.coordinates:[],n=this.labelsGroup.selectAll(".coordinate-label").data(e);n.enter().append("text").attr("class","coordinate-label").attr("font-size","10px").merge(n).text((function(t){return t.annotation})).attr("x",this.xScale(this.activeMetrics[0])+30).attr("y",function(e){const n=this.yScale(e.values[0].nPMIValue?e.values[0].nPMIValue:0),o=this.yScale(e.values[1].nPMIValue?e.values[1].nPMIValue:0);return(1-t)*n+t*o}.bind(this)),n.exit().remove()}}L_t.ɵfac=function t(e){return new(e||L_t)},L_t.ɵcmp=to({type:L_t,selectors:[["parallel-coordinates-component"]],viewQuery:function t(e,n){if(1&e&&Qh(F_t,7,hg),2&e){let t;Jh(t=tb())&&(n.svgElement=t.first)}},hostBindings:function t(e,n){1&e&&Vm("resize",(function t(e){return n.onResize(e)}),!1,ol)},inputs:{activeMetrics:"activeMetrics",coordinateData:"coordinateData",sidebarWidth:"sidebarWidth",colorScale:"colorScale"},features:[Bo],decls:2,vars:0,consts:[[1,"pc-chart"],["chart",""]],template:function t(e,n){1&e&&(qi(),Tm(0,"svg",0,1))},styles:[".pc-chart[_ngcontent-%COMP%]{height:300px;width:100%}"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(L_t,[{type:My,args:[{selector:"parallel-coordinates-component",templateUrl:"./parallel_coordinates_component.ng.html",styleUrls:["./parallel_coordinates_component.css"],changeDetection:zn.OnPush}]}],null,{activeMetrics:[{type:xy}],coordinateData:[{type:xy}],sidebarWidth:[{type:xy}],colorScale:[{type:xy}],svgElement:[{type:Za,args:["chart",{static:!0,read:hg}]}],onResize:[{type:wy,args:["window:resize",["$event"]]}]});class B_t{constructor(t){this.store=t,this.activeRuns$=this.store.pipe(Fw(NN)).pipe(It((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.activeMetrics$=Wt(this.store.select(sbt),this.activeRuns$,this.store.select(gbt)).pipe(It((([t,e,n])=>{let o=[];for(const n of e)t[n]&&(o=o.concat(t[n].filter((t=>eyt(t)))));return o=[...new Set([...Object.keys(n),...o])],o.map((t=>nyt(t)))}))),this.coordinateData$=Wt([this.store.select(rbt),this.store.select(cbt),this.activeRuns$,this.activeMetrics$]).pipe(It((([t,e,n,o])=>(function i(t,e,n,o){const i=[],a={max:-1,min:1},r=new Set(n),s=new Set(o);return 0===r.size||0===s.size||0===Object.keys(t).length?{coordinates:[],extremes:{min:-1,max:1}}:(e.forEach((e=>{const n={};t[e].forEach((t=>{r.has(t.run)&&s.has(t.metric)&&(n[t.run]?n[t.run].push(t):n[t.run]=[t],null!==t.nPMIValue?(a.max=Math.max(a.max,t.nPMIValue),a.min=Math.min(a.min,t.nPMIValue)):(a.max=Math.max(a.max,0),a.min=Math.min(a.min,0)))}));for(const t of Object.keys(n))i.push({annotation:e,runId:t,values:n[t]})})),a.max<a.min&&(a.max=1,a.min=-1),{coordinates:i,extremes:a})})(t,e,n,o)))),this.sidebarWidth$=this.store.select(xbt),this.runColorScale$=this.store.select(zN).pipe(It((t=>e=>{if(!t.hasOwnProperty(e))throw new Error(`[Color scale] unknown runId: ${e}.`);return t[e]})))}}function V_t(t,e){1&t&&Tm(0,"mat-icon",9)}function j_t(t,e){1&t&&Tm(0,"mat-icon",10)}function U_t(t,e){1&t&&Tm(0,"npmi-parallel-coordinates")}B_t.ɵfac=function t(e){return new(e||B_t)(Sm(Iw))},B_t.ɵcmp=to({type:B_t,selectors:[["npmi-parallel-coordinates"]],decls:5,vars:12,consts:[[3,"activeMetrics","coordinateData","sidebarWidth","colorScale"]],template:function t(e,n){1&e&&(Tm(0,"parallel-coordinates-component",0),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async")),2&e&&Dm("activeMetrics",Th(1,4,n.activeMetrics$))("coordinateData",Th(2,6,n.coordinateData$))("sidebarWidth",Th(3,8,n.sidebarWidth$))("colorScale",Th(4,10,n.runColorScale$))},directives:[L_t],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(B_t,[{type:My,args:[{selector:"npmi-parallel-coordinates",template:'\n    <parallel-coordinates-component\n      [activeMetrics]="activeMetrics$ | async"\n      [coordinateData]="coordinateData$ | async"\n      [sidebarWidth]="sidebarWidth$ | async"\n      [colorScale]="runColorScale$ | async"\n    ></parallel-coordinates-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);class G_t{constructor(){this.onClearSelectedAnnotations=new Lh,this.onToggleExpanded=new Lh}}G_t.ɵfac=function t(e){return new(e||G_t)},G_t.ɵcmp=to({type:G_t,selectors:[["selected-annotations-component"]],inputs:{pcExpanded:"pcExpanded",selectedAnnotations:"selectedAnnotations"},outputs:{onClearSelectedAnnotations:"onClearSelectedAnnotations",onToggleExpanded:"onToggleExpanded"},decls:11,vars:4,consts:function(){let t,e;return t="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Clear Annotation Selection"):$localize`:Label for a button that clears the annotation selection.␟2b52289f2c4b7f5b182ed23c9154722ecd46a2d4␟2494660520341308830:Clear Annotation Selection`,e="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Expand/Hide Selected Annotations"):$localize`:Label for a button that expands or hides selected annotations.␟8f4ad305c19e3655f3189e3e264e83fb76f7bb95␟5670797784070952095:Expand/Hide Selected Annotations`,[[1,"pc-container"],[1,"pc-toolbar"],[1,"pc-title"],["mat-icon-button","","aria-label",t,"title","Deselects all selected annotations.",1,"clear-button",3,"disabled","click"],["svgIcon","clear_24px"],["mat-icon-button","","aria-label",e,1,"expand-button",3,"click"],["svgIcon","expand_less_24px","class","expand-less-icon",4,"ngIf","ngIfElse"],["notExpanded",""],[4,"ngIf"],["svgIcon","expand_less_24px",1,"expand-less-icon"],["svgIcon","expand_more_24px",1,"expand-icon"]]},template:function t(e,n){if(1&e&&(Rm(0,"div",0),Rm(1,"div",1),Rm(2,"h3",2),ku(3,"Selected Annotations"),Am(),Rm(4,"button",3),Vm("click",(function t(){return n.onClearSelectedAnnotations.emit()})),Tm(5,"mat-icon",4),Am(),Rm(6,"button",5),Vm("click",(function t(){return n.onToggleExpanded.emit()})),Qp(7,V_t,1,0,"mat-icon",6),Qp(8,j_t,1,0,"ng-template",null,7,ib),Am(),Am(),Qp(10,U_t,1,0,"npmi-parallel-coordinates",8),Am()),2&e){const t=$p(9);rc(4),Dm("disabled",0===n.selectedAnnotations.length),rc(3),Dm("ngIf",n.pcExpanded)("ngIfElse",t),rc(3),Dm("ngIf",n.pcExpanded)}},directives:[XH,DW,dM,B_t],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   .mat-chip-remove.mat-icon[_ngcontent-%COMP%]{font-size:18px}.mat-table[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-header-cell[_ngcontent-%COMP%]{font-size:12px;font-weight:500}.mat-cell[_ngcontent-%COMP%], .mat-footer-cell[_ngcontent-%COMP%]{font-size:14px}.mat-calendar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-calendar-body[_ngcontent-%COMP%]{font-size:13px}.mat-calendar-body-label[_ngcontent-%COMP%], .mat-calendar-period-button[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-calendar-table-header[_ngcontent-%COMP%]   th[_ngcontent-%COMP%]{font-size:11px;font-weight:400}.mat-dialog-title[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-expansion-panel-header[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:15px;font-weight:400}.mat-expansion-panel-content[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-form-field[_ngcontent-%COMP%]{font-size:inherit;font-weight:400;line-height:1.125;font-family:Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-form-field-wrapper[_ngcontent-%COMP%]{padding-bottom:1.34375em}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   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Array.isArray(e)||(e=Array.from(e)),e}function JMt(t,e,n,o,i){if(5!==arguments.length)throw new RangeError("expected 4 arguments");if($Mt("startRow",e),$Mt("endRow",n),$Mt("startColumn",o),$Mt("endColumn",i),e>n||o>i||e<0||e>=t.rows||n<0||n>=t.rows||o<0||o>=t.columns||i<0||i>=t.columns)throw new RangeError("Submatrix indices are out of range")}function QMt(t,e=0){let n=[];for(let o=0;o<t;o++)n.push(e);return n}function $Mt(t,e){if("number"!=typeof e)throw new TypeError(`${t} must be a number`)}function tvt(t){if(t.isEmpty())throw new Error("Empty matrix has no elements to index")}class evt{static from1DArray(t,e,n){if(t*e!==n.length)throw new RangeError("data length does not match given dimensions");let o=new ivt(t,e);for(let i=0;i<t;i++)for(let t=0;t<e;t++)o.set(i,t,n[i*e+t]);return o}static rowVector(t){let e=new ivt(1,t.length);for(let n=0;n<t.length;n++)e.set(0,n,t[n]);return e}static columnVector(t){let e=new ivt(t.length,1);for(let n=0;n<t.length;n++)e.set(n,0,t[n]);return 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i=Math.min(o,e,n),a=this.zeros(e,n);for(let e=0;e<i;e++)a.set(e,e,t[e]);return a}static min(t,e){t=this.checkMatrix(t),e=this.checkMatrix(e);let n=t.rows,o=t.columns,i=new ivt(n,o);for(let a=0;a<n;a++)for(let n=0;n<o;n++)i.set(a,n,Math.min(t.get(a,n),e.get(a,n)));return i}static max(t,e){t=this.checkMatrix(t),e=this.checkMatrix(e);let n=t.rows,o=t.columns,i=new this(n,o);for(let a=0;a<n;a++)for(let n=0;n<o;n++)i.set(a,n,Math.max(t.get(a,n),e.get(a,n)));return i}static checkMatrix(t){return evt.isMatrix(t)?t:new ivt(t)}static isMatrix(t){return null!=t&&"Matrix"===t.klass}get size(){return this.rows*this.columns}apply(t){if("function"!=typeof t)throw new TypeError("callback must be a function");for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)t.call(this,e,n);return this}to1DArray(){let t=[];for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)t.push(this.get(e,n));return t}to2DArray(){let t=[];for(let e=0;e<this.rows;e++){t.push([]);for(let n=0;n<this.columns;n++)t[e].push(this.get(e,n))}return t}toJSON(){return this.to2DArray()}isRowVector(){return 1===this.rows}isColumnVector(){return 1===this.columns}isVector(){return 1===this.rows||1===this.columns}isSquare(){return this.rows===this.columns}isEmpty(){return 0===this.rows||0===this.columns}isSymmetric(){if(this.isSquare()){for(let t=0;t<this.rows;t++)for(let e=0;e<=t;e++)if(this.get(t,e)!==this.get(e,t))return!1;return!0}return!1}isEchelonForm(){let t=0,e=0,n=-1,o=!0,i=!1;for(;t<this.rows&&o;){for(e=0,i=!1;e<this.columns&&!1===i;)0===this.get(t,e)?e++:1===this.get(t,e)&&e>n?(i=!0,n=e):(o=!1,i=!0);t++}return o}isReducedEchelonForm(){let t=0,e=0,n=-1,o=!0,i=!1;for(;t<this.rows&&o;){for(e=0,i=!1;e<this.columns&&!1===i;)0===this.get(t,e)?e++:1===this.get(t,e)&&e>n?(i=!0,n=e):(o=!1,i=!0);for(let n=e+1;n<this.rows;n++)0!==this.get(t,n)&&(o=!1);t++}return o}echelonForm(){let t=this.clone(),e=0,n=0;for(;e<t.rows&&n<t.columns;){let o=e;for(let i=e;i<t.rows;i++)t.get(i,n)>t.get(o,n)&&(o=i);if(0===t.get(o,n))n++;else{t.swapRows(e,o);let i=t.get(e,n);for(let o=n;o<t.columns;o++)t.set(e,o,t.get(e,o)/i);for(let o=e+1;o<t.rows;o++){let i=t.get(o,n)/t.get(e,n);t.set(o,n,0);for(let a=n+1;a<t.columns;a++)t.set(o,a,t.get(o,a)-t.get(e,a)*i)}e++,n++}}return t}reducedEchelonForm(){let t=this.echelonForm(),e=t.columns,n=t.rows,o=n-1;for(;o>=0;)if(0===t.maxRow(o))o--;else{let i=0,a=!1;for(;i<n&&!1===a;)1===t.get(o,i)?a=!0:i++;for(let n=0;n<o;n++){let a=t.get(n,i);for(let r=i;r<e;r++){let e=t.get(n,r)-a*t.get(o,r);t.set(n,r,e)}}o--}return t}set(){throw new Error("set method is unimplemented")}get(){throw new Error("get method is unimplemented")}repeat(t={}){if("object"!=typeof t)throw new TypeError("options must be an object");const{rows:e=1,columns:n=1}=t;if(!Number.isInteger(e)||e<=0)throw new TypeError("rows must be a positive integer");if(!Number.isInteger(n)||n<=0)throw new TypeError("columns must be a positive integer");let o=new ivt(this.rows*e,this.columns*n);for(let t=0;t<e;t++)for(let e=0;e<n;e++)o.setSubMatrix(this,this.rows*t,this.columns*e);return o}fill(t){for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,t);return this}neg(){return this.mulS(-1)}getRow(t){WMt(this,t);let e=[];for(let n=0;n<this.columns;n++)e.push(this.get(t,n));return e}getRowVector(t){return ivt.rowVector(this.getRow(t))}setRow(t,e){WMt(this,t),e=qMt(this,e);for(let n=0;n<this.columns;n++)this.set(t,n,e[n]);return this}swapRows(t,e){WMt(this,t),WMt(this,e);for(let n=0;n<this.columns;n++){let o=this.get(t,n);this.set(t,n,this.get(e,n)),this.set(e,n,o)}return this}getColumn(t){YMt(this,t);let e=[];for(let n=0;n<this.rows;n++)e.push(this.get(n,t));return e}getColumnVector(t){return ivt.columnVector(this.getColumn(t))}setColumn(t,e){YMt(this,t),e=ZMt(this,e);for(let n=0;n<this.rows;n++)this.set(n,t,e[n]);return this}swapColumns(t,e){YMt(this,t),YMt(this,e);for(let n=0;n<this.rows;n++){let o=this.get(n,t);this.set(n,t,this.get(n,e)),this.set(n,e,o)}return this}addRowVector(t){t=qMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)+t[n]);return this}subRowVector(t){t=qMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)-t[n]);return this}mulRowVector(t){t=qMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)*t[n]);return this}divRowVector(t){t=qMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)/t[n]);return this}addColumnVector(t){t=ZMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)+t[e]);return this}subColumnVector(t){t=ZMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)-t[e]);return this}mulColumnVector(t){t=ZMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)*t[e]);return this}divColumnVector(t){t=ZMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)/t[e]);return this}mulRow(t,e){WMt(this,t);for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)*e);return this}mulColumn(t,e){YMt(this,t);for(let n=0;n<this.rows;n++)this.set(n,t,this.get(n,t)*e);return this}max(){if(this.isEmpty())return NaN;let t=this.get(0,0);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.get(e,n)>t&&(t=this.get(e,n));return t}maxIndex(){tvt(this);let t=this.get(0,0),e=[0,0];for(let n=0;n<this.rows;n++)for(let o=0;o<this.columns;o++)this.get(n,o)>t&&(t=this.get(n,o),e[0]=n,e[1]=o);return e}min(){if(this.isEmpty())return NaN;let t=this.get(0,0);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.get(e,n)<t&&(t=this.get(e,n));return t}minIndex(){tvt(this);let t=this.get(0,0),e=[0,0];for(let n=0;n<this.rows;n++)for(let o=0;o<this.columns;o++)this.get(n,o)<t&&(t=this.get(n,o),e[0]=n,e[1]=o);return e}maxRow(t){if(WMt(this,t),this.isEmpty())return NaN;let e=this.get(t,0);for(let n=1;n<this.columns;n++)this.get(t,n)>e&&(e=this.get(t,n));return e}maxRowIndex(t){WMt(this,t),tvt(this);let e=this.get(t,0),n=[t,0];for(let o=1;o<this.columns;o++)this.get(t,o)>e&&(e=this.get(t,o),n[1]=o);return n}minRow(t){if(WMt(this,t),this.isEmpty())return NaN;let e=this.get(t,0);for(let n=1;n<this.columns;n++)this.get(t,n)<e&&(e=this.get(t,n));return e}minRowIndex(t){WMt(this,t),tvt(this);let e=this.get(t,0),n=[t,0];for(let o=1;o<this.columns;o++)this.get(t,o)<e&&(e=this.get(t,o),n[1]=o);return n}maxColumn(t){if(YMt(this,t),this.isEmpty())return NaN;let e=this.get(0,t);for(let n=1;n<this.rows;n++)this.get(n,t)>e&&(e=this.get(n,t));return e}maxColumnIndex(t){YMt(this,t),tvt(this);let e=this.get(0,t),n=[0,t];for(let o=1;o<this.rows;o++)this.get(o,t)>e&&(e=this.get(o,t),n[0]=o);return n}minColumn(t){if(YMt(this,t),this.isEmpty())return NaN;let e=this.get(0,t);for(let n=1;n<this.rows;n++)this.get(n,t)<e&&(e=this.get(n,t));return e}minColumnIndex(t){YMt(this,t),tvt(this);let e=this.get(0,t),n=[0,t];for(let o=1;o<this.rows;o++)this.get(o,t)<e&&(e=this.get(o,t),n[0]=o);return n}diag(){let t=Math.min(this.rows,this.columns),e=[];for(let n=0;n<t;n++)e.push(this.get(n,n));return e}norm(t="frobenius"){let e=0;if("max"===t)return this.max();if("frobenius"===t){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)e+=this.get(t,n)*this.get(t,n);return Math.sqrt(e)}throw new RangeError(`unknown norm type: ${t}`)}cumulativeSum(){let t=0;for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)t+=this.get(e,n),this.set(e,n,t);return this}dot(t){evt.isMatrix(t)&&(t=t.to1DArray());let e=this.to1DArray();if(e.length!==t.length)throw new RangeError("vectors do not have the same size");let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n}mmul(t){t=ivt.checkMatrix(t);let e=this.rows,n=this.columns,o=t.columns,i=new ivt(e,o),a=new Float64Array(n);for(let r=0;r<o;r++){for(let e=0;e<n;e++)a[e]=t.get(e,r);for(let t=0;t<e;t++){let e=0;for(let o=0;o<n;o++)e+=this.get(t,o)*a[o];i.set(t,r,e)}}return i}strassen2x2(t){t=ivt.checkMatrix(t);let e=new ivt(2,2);const n=this.get(0,0),o=t.get(0,0),i=this.get(0,1),a=t.get(0,1),r=this.get(1,0),s=t.get(1,0),l=this.get(1,1),c=t.get(1,1),d=(n+l)*(o+c),p=(r+l)*o,m=n*(a-c),u=l*(s-o),f=(n+i)*c,g=m+f,h=p+u,b=d-p+m+(r-n)*(o+a);return e.set(0,0,d+u-f+(i-l)*(s+c)),e.set(0,1,g),e.set(1,0,h),e.set(1,1,b),e}strassen3x3(t){t=ivt.checkMatrix(t);let e=new ivt(3,3);const n=this.get(0,0),o=this.get(0,1),i=this.get(0,2),a=this.get(1,0),r=this.get(1,1),s=this.get(1,2),l=this.get(2,0),c=this.get(2,1),d=this.get(2,2),p=t.get(0,0),m=t.get(0,1),u=t.get(0,2),f=t.get(1,0),g=t.get(1,1),h=t.get(1,2),b=t.get(2,0),y=t.get(2,1),_=t.get(2,2),C=(n-a)*(-m+g),M=(-n+a+r)*(p-m+g),v=(a+r)*(-p+m),x=n*p,O=(-n+l+c)*(p-u+h),P=(-n+l)*(u-h),w=(l+c)*(-p+u),k=(-i+c+d)*(g+b-y),S=(i-d)*(g-y),D=i*b,E=(c+d)*(-b+y),R=(-i+r+s)*(h+b-_),A=(i-s)*(h-_),T=(r+s)*(-b+_),N=(n+o+i-a-r-c-d)*g+M+v+x+k+D+E,z=x+O+w+(n+o+i-r-s-l-c)*h+D+R+T,I=C+r*(-p+m+f-g-h-b+_)+M+x+D+R+A,H=C+M+v+x+s*y,F=D+R+A+T+a*u,L=x+O+P+c*(-p+u+f-g-h-b+y)+k+S+D,B=k+S+D+E+l*m,V=x+O+P+w+d*_;return e.set(0,0,x+D+o*f),e.set(0,1,N),e.set(0,2,z),e.set(1,0,I),e.set(1,1,H),e.set(1,2,F),e.set(2,0,L),e.set(2,1,B),e.set(2,2,V),e}mmulStrassen(t){t=ivt.checkMatrix(t);let e=this.clone(),n=e.rows,o=e.columns,i=t.rows,a=t.columns;function r(t,e,n){if(t.rows===e&&t.columns===n)return t;{let o=evt.zeros(e,n);return o=o.setSubMatrix(t,0,0),o}}o!==i&&console.warn(`Multiplying ${n} x ${o} and ${i} x ${a} matrix: dimensions do not match.`);let s=Math.max(n,i),l=Math.max(o,a);return e=r(e,s,l),(function t(e,n,o,i){if(o<=512||i<=512)return e.mmul(n);o%2==1&&i%2==1?(e=r(e,o+1,i+1),n=r(n,o+1,i+1)):o%2==1?(e=r(e,o+1,i),n=r(n,o+1,i)):i%2==1&&(e=r(e,o,i+1),n=r(n,o,i+1));let a=parseInt(e.rows/2,10),s=parseInt(e.columns/2,10),l=e.subMatrix(0,a-1,0,s-1),c=n.subMatrix(0,a-1,0,s-1),d=e.subMatrix(0,a-1,s,e.columns-1),p=n.subMatrix(0,a-1,s,n.columns-1),m=e.subMatrix(a,e.rows-1,0,s-1),u=n.subMatrix(a,n.rows-1,0,s-1),f=e.subMatrix(a,e.rows-1,s,e.columns-1),g=n.subMatrix(a,n.rows-1,s,n.columns-1),h=t(evt.add(l,f),evt.add(c,g),a,s),b=t(evt.add(m,f),c,a,s),y=t(l,evt.sub(p,g),a,s),_=t(f,evt.sub(u,c),a,s),C=t(evt.add(l,d),g,a,s),M=t(evt.sub(m,l),evt.add(c,p),a,s),v=t(evt.sub(d,f),evt.add(u,g),a,s),x=evt.add(h,_);x.sub(C),x.add(v);let O=evt.add(y,C),P=evt.add(b,_),w=evt.sub(h,b);w.add(y),w.add(M);let k=evt.zeros(2*x.rows,2*x.columns);return k=k.setSubMatrix(x,0,0),k=k.setSubMatrix(O,x.rows,0),k=k.setSubMatrix(P,0,x.columns),k=k.setSubMatrix(w,x.rows,x.columns),k.subMatrix(0,o-1,0,i-1)})(e,t=r(t,s,l),s,l)}scaleRows(t={}){if("object"!=typeof t)throw new TypeError("options must be an object");const{min:e=0,max:n=1}=t;if(!Number.isFinite(e))throw new TypeError("min must be a number");if(!Number.isFinite(n))throw new TypeError("max must be a number");if(e>=n)throw new RangeError("min must be smaller than max");let o=new ivt(this.rows,this.columns);for(let t=0;t<this.rows;t++){const i=this.getRow(t);i.length>0&&BMt(i,{min:e,max:n,output:i}),o.setRow(t,i)}return o}scaleColumns(t={}){if("object"!=typeof t)throw new TypeError("options must be an object");const{min:e=0,max:n=1}=t;if(!Number.isFinite(e))throw new TypeError("min must be a number");if(!Number.isFinite(n))throw new TypeError("max must be a number");if(e>=n)throw new RangeError("min must be smaller than max");let o=new ivt(this.rows,this.columns);for(let t=0;t<this.columns;t++){const i=this.getColumn(t);i.length&&BMt(i,{min:e,max:n,output:i}),o.setColumn(t,i)}return o}flipRows(){const t=Math.ceil(this.columns/2);for(let e=0;e<this.rows;e++)for(let n=0;n<t;n++){let t=this.get(e,n),o=this.get(e,this.columns-1-n);this.set(e,n,o),this.set(e,this.columns-1-n,t)}return this}flipColumns(){const t=Math.ceil(this.rows/2);for(let e=0;e<this.columns;e++)for(let n=0;n<t;n++){let t=this.get(n,e),o=this.get(this.rows-1-n,e);this.set(n,e,o),this.set(this.rows-1-n,e,t)}return this}kroneckerProduct(t){t=ivt.checkMatrix(t);let e=this.rows,n=this.columns,o=t.rows,i=t.columns,a=new ivt(e*o,n*i);for(let r=0;r<e;r++)for(let e=0;e<n;e++)for(let n=0;n<o;n++)for(let s=0;s<i;s++)a.set(o*r+n,i*e+s,this.get(r,e)*t.get(n,s));return a}kroneckerSum(t){if(t=ivt.checkMatrix(t),!this.isSquare()||!t.isSquare())throw new Error("Kronecker Sum needs two Square Matrices");let e=this.rows,n=t.rows,o=this.kroneckerProduct(ivt.eye(n,n)),i=ivt.eye(e,e).kroneckerProduct(t);return o.add(i)}transpose(){let t=new ivt(this.columns,this.rows);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)t.set(n,e,this.get(e,n));return t}sortRows(t=nvt){for(let e=0;e<this.rows;e++)this.setRow(e,this.getRow(e).sort(t));return this}sortColumns(t=nvt){for(let e=0;e<this.columns;e++)this.setColumn(e,this.getColumn(e).sort(t));return this}subMatrix(t,e,n,o){JMt(this,t,e,n,o);let i=new ivt(e-t+1,o-n+1);for(let a=t;a<=e;a++)for(let e=n;e<=o;e++)i.set(a-t,e-n,this.get(a,e));return i}subMatrixRow(t,e,n){if(void 0===e&&(e=0),void 0===n&&(n=this.columns-1),e>n||e<0||e>=this.columns||n<0||n>=this.columns)throw new RangeError("Argument out of range");let o=new ivt(t.length,n-e+1);for(let i=0;i<t.length;i++)for(let a=e;a<=n;a++){if(t[i]<0||t[i]>=this.rows)throw new RangeError(`Row index out of range: ${t[i]}`);o.set(i,a-e,this.get(t[i],a))}return o}subMatrixColumn(t,e,n){if(void 0===e&&(e=0),void 0===n&&(n=this.rows-1),e>n||e<0||e>=this.rows||n<0||n>=this.rows)throw new RangeError("Argument out of range");let o=new ivt(n-e+1,t.length);for(let i=0;i<t.length;i++)for(let a=e;a<=n;a++){if(t[i]<0||t[i]>=this.columns)throw new RangeError(`Column index out of range: ${t[i]}`);o.set(a-e,i,this.get(a,t[i]))}return o}setSubMatrix(t,e,n){if((t=ivt.checkMatrix(t)).isEmpty())return this;JMt(this,e,e+t.rows-1,n,n+t.columns-1);for(let o=0;o<t.rows;o++)for(let i=0;i<t.columns;i++)this.set(e+o,n+i,t.get(o,i));return this}selection(t,e){let n=(function o(t,e,n){return{row:XMt(t,e),column:KMt(t,n)}})(this,t,e),i=new ivt(t.length,e.length);for(let t=0;t<n.row.length;t++){let e=n.row[t];for(let o=0;o<n.column.length;o++)i.set(t,o,this.get(e,n.column[o]))}return i}trace(){let t=Math.min(this.rows,this.columns),e=0;for(let n=0;n<t;n++)e+=this.get(n,n);return e}clone(){let t=new ivt(this.rows,this.columns);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)t.set(e,n,this.get(e,n));return t}sum(t){switch(t){case"row":return(function e(t){let e=QMt(t.rows);for(let n=0;n<t.rows;++n)for(let o=0;o<t.columns;++o)e[n]+=t.get(n,o);return e})(this);case"column":return(function n(t){let e=QMt(t.columns);for(let n=0;n<t.rows;++n)for(let o=0;o<t.columns;++o)e[o]+=t.get(n,o);return e})(this);case void 0:return(function o(t){let e=0;for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)e+=t.get(n,o);return e})(this);default:throw new Error(`invalid option: ${t}`)}}product(t){switch(t){case"row":return(function e(t){let e=QMt(t.rows,1);for(let n=0;n<t.rows;++n)for(let o=0;o<t.columns;++o)e[n]*=t.get(n,o);return e})(this);case"column":return(function n(t){let e=QMt(t.columns,1);for(let n=0;n<t.rows;++n)for(let o=0;o<t.columns;++o)e[o]*=t.get(n,o);return e})(this);case void 0:return(function o(t){let e=1;for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)e*=t.get(n,o);return e})(this);default:throw new Error(`invalid option: ${t}`)}}mean(t){const e=this.sum(t);switch(t){case"row":for(let t=0;t<this.rows;t++)e[t]/=this.columns;return e;case"column":for(let t=0;t<this.columns;t++)e[t]/=this.rows;return e;case void 0:return e/this.size;default:throw new Error(`invalid option: ${t}`)}}variance(t,e={}){if("object"==typeof t&&(e=t,t=void 0),"object"!=typeof e)throw new TypeError("options must be an object");const{unbiased:n=!0,mean:o=this.mean(t)}=e;if("boolean"!=typeof n)throw new TypeError("unbiased must be a boolean");switch(t){case"row":if(!Array.isArray(o))throw new TypeError("mean must be an array");return(function e(t,n,o){const i=t.rows,a=t.columns,r=[];for(let e=0;e<i;e++){let i=0,s=0,l=0;for(let n=0;n<a;n++)l=t.get(e,n)-o[e],i+=l,s+=l*l;r.push(n?(s-i*i/a)/(a-1):(s-i*i/a)/a)}return r})(this,n,o);case"column":if(!Array.isArray(o))throw new TypeError("mean must be an array");return(function i(t,e,n){const o=t.rows,i=t.columns,a=[];for(let r=0;r<i;r++){let i=0,s=0,l=0;for(let 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n=0;n<t.columns;n++){let o=0;for(let e=0;e<t.rows;e++)o+=Math.pow(t.get(e,n),2)/(t.rows-1);e.push(Math.sqrt(o))}return e})(this);else if(!Array.isArray(n))throw new TypeError("scale must be an array");return(function a(t,e){for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)t.set(n,o,t.get(n,o)/e[o])})(this,n),this;case void 0:if(void 0===n)n=(function r(t){const e=t.size-1;let n=0;for(let o=0;o<t.columns;o++)for(let i=0;i<t.rows;i++)n+=Math.pow(t.get(i,o),2)/e;return Math.sqrt(n)})(this);else if("number"!=typeof n)throw new TypeError("scale must be a number");return(function s(t,e){for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)t.set(n,o,t.get(n,o)/e)})(this,n),this;default:throw new Error(`invalid option: ${t}`)}}toString(t){return UMt(this,t)}}function nvt(t,e){return t-e}evt.prototype.klass="Matrix","undefined"!=typeof Symbol&&(evt.prototype[Symbol.for("nodejs.util.inspect.custom")]=function ovt(){return UMt(this)}),evt.random=evt.rand,evt.randomInt=evt.randInt,evt.diagonal=evt.diag,evt.prototype.diagonal=evt.prototype.diag,evt.identity=evt.eye,evt.prototype.negate=evt.prototype.neg,evt.prototype.tensorProduct=evt.prototype.kroneckerProduct;class ivt extends evt{constructor(t,e){if(super(),ivt.isMatrix(t))return t.clone();if(Number.isInteger(t)&&t>=0){if(this.data=[],!(Number.isInteger(e)&&e>=0))throw new TypeError("nColumns must be a positive integer");for(let n=0;n<t;n++)this.data.push(new Float64Array(e))}else{if(!Array.isArray(t))throw new TypeError("First argument must be a positive number or an array");{const n=t;if("number"!=typeof(e=(t=n.length)?n[0].length:0))throw new TypeError("Data must be a 2D array with at least one element");this.data=[];for(let o=0;o<t;o++){if(n[o].length!==e)throw new RangeError("Inconsistent array dimensions");this.data.push(Float64Array.from(n[o]))}}}this.rows=t,this.columns=e}set(t,e,n){return this.data[t][e]=n,this}get(t,e){return 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this},t.prototype.addM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)+n.get(t,e));return this},t.add=function t(n,o){return new e(n).add(o)},t.prototype.sub=function t(e){return"number"==typeof e?this.subS(e):this.subM(e)},t.prototype.subS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)-e);return this},t.prototype.subM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)-n.get(t,e));return this},t.sub=function t(n,o){return new 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new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)|n.get(t,e));return this},t.or=function t(n,o){return new e(n).or(o)},t.prototype.xor=function t(e){return"number"==typeof e?this.xorS(e):this.xorM(e)},t.prototype.xorS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)^e);return this},t.prototype.xorM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)^n.get(t,e));return this},t.xor=function t(n,o){return new e(n).xor(o)},t.prototype.leftShift=function t(e){return"number"==typeof e?this.leftShiftS(e):this.leftShiftM(e)},t.prototype.leftShiftS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)<<e);return this},t.prototype.leftShiftM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)<<n.get(t,e));return this},t.leftShift=function t(n,o){return new e(n).leftShift(o)},t.prototype.signPropagatingRightShift=function t(e){return"number"==typeof e?this.signPropagatingRightShiftS(e):this.signPropagatingRightShiftM(e)},t.prototype.signPropagatingRightShiftS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)>>e);return this},t.prototype.signPropagatingRightShiftM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)>>n.get(t,e));return this},t.signPropagatingRightShift=function t(n,o){return new e(n).signPropagatingRightShift(o)},t.prototype.rightShift=function t(e){return"number"==typeof e?this.rightShiftS(e):this.rightShiftM(e)},t.prototype.rightShiftS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)>>>e);return this},t.prototype.rightShiftM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)>>>n.get(t,e));return this},t.rightShift=function t(n,o){return new e(n).rightShift(o)},t.prototype.zeroFillRightShift=t.prototype.rightShift,t.prototype.zeroFillRightShiftS=t.prototype.rightShiftS,t.prototype.zeroFillRightShiftM=t.prototype.rightShiftM,t.zeroFillRightShift=t.rightShift,t.prototype.not=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,~this.get(t,e));return this},t.not=function t(n){return new 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t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.atan(this.get(t,e)));return this},t.atan=function t(n){return new e(n).atan()},t.prototype.atanh=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.atanh(this.get(t,e)));return this},t.atanh=function t(n){return new e(n).atanh()},t.prototype.cbrt=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.cbrt(this.get(t,e)));return this},t.cbrt=function t(n){return new e(n).cbrt()},t.prototype.ceil=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.ceil(this.get(t,e)));return this},t.ceil=function t(n){return new e(n).ceil()},t.prototype.clz32=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.clz32(this.get(t,e)));return this},t.clz32=function t(n){return new e(n).clz32()},t.prototype.cos=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.cos(this.get(t,e)));return this},t.cos=function t(n){return new e(n).cos()},t.prototype.cosh=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.cosh(this.get(t,e)));return this},t.cosh=function t(n){return new e(n).cosh()},t.prototype.exp=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.exp(this.get(t,e)));return this},t.exp=function t(n){return new e(n).exp()},t.prototype.expm1=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.expm1(this.get(t,e)));return this},t.expm1=function t(n){return new e(n).expm1()},t.prototype.floor=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.floor(this.get(t,e)));return this},t.floor=function t(n){return new e(n).floor()},t.prototype.fround=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.fround(this.get(t,e)));return this},t.fround=function t(n){return new e(n).fround()},t.prototype.log=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.log(this.get(t,e)));return this},t.log=function t(n){return new e(n).log()},t.prototype.log1p=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.log1p(this.get(t,e)));return this},t.log1p=function t(n){return new e(n).log1p()},t.prototype.log10=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.log10(this.get(t,e)));return this},t.log10=function t(n){return new e(n).log10()},t.prototype.log2=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.log2(this.get(t,e)));return this},t.log2=function t(n){return new e(n).log2()},t.prototype.round=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.round(this.get(t,e)));return this},t.round=function t(n){return new e(n).round()},t.prototype.sign=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sign(this.get(t,e)));return this},t.sign=function t(n){return new e(n).sign()},t.prototype.sin=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sin(this.get(t,e)));return this},t.sin=function t(n){return new e(n).sin()},t.prototype.sinh=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sinh(this.get(t,e)));return this},t.sinh=function t(n){return new e(n).sinh()},t.prototype.sqrt=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sqrt(this.get(t,e)));return this},t.sqrt=function t(n){return new e(n).sqrt()},t.prototype.tan=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.tan(this.get(t,e)));return this},t.tan=function t(n){return new e(n).tan()},t.prototype.tanh=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.tanh(this.get(t,e)));return this},t.tanh=function t(n){return new e(n).tanh()},t.prototype.trunc=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.trunc(this.get(t,e)));return this},t.trunc=function t(n){return new e(n).trunc()},t.pow=function t(n,o){return new e(n).pow(o)},t.prototype.pow=function t(e){return"number"==typeof e?this.powS(e):this.powM(e)},t.prototype.powS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,Math.pow(this.get(t,n),e));return this},t.prototype.powM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.pow(this.get(t,e),n.get(t,e)));return this}})(evt,ivt);class rvt extends evt{constructor(t){super(),this.data=t,this.rows=t.length,this.columns=t[0].length}set(t,e,n){return this.data[t][e]=n,this}get(t,e){return this.data[t][e]}}class svt{constructor(t){let e,n,o,i,a,r,s,l,c,d=(t=rvt.checkMatrix(t)).clone(),p=d.rows,m=d.columns,u=new Float64Array(p),f=1;for(e=0;e<p;e++)u[e]=e;for(l=new Float64Array(p),n=0;n<m;n++){for(e=0;e<p;e++)l[e]=d.get(e,n);for(e=0;e<p;e++){for(c=Math.min(e,n),a=0,o=0;o<c;o++)a+=d.get(e,o)*l[o];l[e]-=a,d.set(e,n,l[e])}for(i=n,e=n+1;e<p;e++)Math.abs(l[e])>Math.abs(l[i])&&(i=e);if(i!==n){for(o=0;o<m;o++)r=d.get(i,o),d.set(i,o,d.get(n,o)),d.set(n,o,r);s=u[i],u[i]=u[n],u[n]=s,f=-f}if(n<p&&0!==d.get(n,n))for(e=n+1;e<p;e++)d.set(e,n,d.get(e,n)/d.get(n,n))}this.LU=d,this.pivotVector=u,this.pivotSign=f}isSingular(){let t=this.LU,e=t.columns;for(let n=0;n<e;n++)if(0===t.get(n,n))return!0;return!1}solve(t){t=ivt.checkMatrix(t);let e=this.LU;if(e.rows!==t.rows)throw new Error("Invalid matrix dimensions");if(this.isSingular())throw new Error("LU matrix is singular");let n,o,i,a=t.columns,r=t.subMatrixRow(this.pivotVector,0,a-1),s=e.columns;for(i=0;i<s;i++)for(n=i+1;n<s;n++)for(o=0;o<a;o++)r.set(n,o,r.get(n,o)-r.get(i,o)*e.get(n,i));for(i=s-1;i>=0;i--){for(o=0;o<a;o++)r.set(i,o,r.get(i,o)/e.get(i,i));for(n=0;n<i;n++)for(o=0;o<a;o++)r.set(n,o,r.get(n,o)-r.get(i,o)*e.get(n,i))}return r}get determinant(){let t=this.LU;if(!t.isSquare())throw new Error("Matrix must be square");let e=this.pivotSign,n=t.columns;for(let o=0;o<n;o++)e*=t.get(o,o);return e}get lowerTriangularMatrix(){let t=this.LU,e=t.rows,n=t.columns,o=new ivt(e,n);for(let i=0;i<e;i++)for(let e=0;e<n;e++)o.set(i,e,i>e?t.get(i,e):i===e?1:0);return o}get upperTriangularMatrix(){let t=this.LU,e=t.rows,n=t.columns,o=new ivt(e,n);for(let i=0;i<e;i++)for(let e=0;e<n;e++)o.set(i,e,i<=e?t.get(i,e):0);return o}get pivotPermutationVector(){return Array.from(this.pivotVector)}}function lvt(t,e){let n=0;return Math.abs(t)>Math.abs(e)?(n=e/t,Math.abs(t)*Math.sqrt(1+n*n)):0!==e?(n=t/e,Math.abs(e)*Math.sqrt(1+n*n)):0}class cvt{constructor(t){let e,n,o,i,a=(t=rvt.checkMatrix(t)).clone(),r=t.rows,s=t.columns,l=new Float64Array(s);for(o=0;o<s;o++){let t=0;for(e=o;e<r;e++)t=lvt(t,a.get(e,o));if(0!==t){for(a.get(o,o)<0&&(t=-t),e=o;e<r;e++)a.set(e,o,a.get(e,o)/t);for(a.set(o,o,a.get(o,o)+1),n=o+1;n<s;n++){for(i=0,e=o;e<r;e++)i+=a.get(e,o)*a.get(e,n);for(i=-i/a.get(o,o),e=o;e<r;e++)a.set(e,n,a.get(e,n)+i*a.get(e,o))}}l[o]=-t}this.QR=a,this.Rdiag=l}solve(t){t=ivt.checkMatrix(t);let e=this.QR,n=e.rows;if(t.rows!==n)throw new Error("Matrix row dimensions must agree");if(!this.isFullRank())throw new Error("Matrix is rank deficient");let o,i,a,r,s=t.columns,l=t.clone(),c=e.columns;for(a=0;a<c;a++)for(i=0;i<s;i++){for(r=0,o=a;o<n;o++)r+=e.get(o,a)*l.get(o,i);for(r=-r/e.get(a,a),o=a;o<n;o++)l.set(o,i,l.get(o,i)+r*e.get(o,a))}for(a=c-1;a>=0;a--){for(i=0;i<s;i++)l.set(a,i,l.get(a,i)/this.Rdiag[a]);for(o=0;o<a;o++)for(i=0;i<s;i++)l.set(o,i,l.get(o,i)-l.get(a,i)*e.get(o,a))}return l.subMatrix(0,c-1,0,s-1)}isFullRank(){let t=this.QR.columns;for(let e=0;e<t;e++)if(0===this.Rdiag[e])return!1;return!0}get upperTriangularMatrix(){let t,e,n=this.QR,o=n.columns,i=new ivt(o,o);for(t=0;t<o;t++)for(e=0;e<o;e++)i.set(t,e,t<e?n.get(t,e):t===e?this.Rdiag[t]:0);return i}get orthogonalMatrix(){let t,e,n,o,i=this.QR,a=i.rows,r=i.columns,s=new ivt(a,r);for(n=r-1;n>=0;n--){for(t=0;t<a;t++)s.set(t,n,0);for(s.set(n,n,1),e=n;e<r;e++)if(0!==i.get(n,n)){for(o=0,t=n;t<a;t++)o+=i.get(t,n)*s.get(t,e);for(o=-o/i.get(n,n),t=n;t<a;t++)s.set(t,e,s.get(t,e)+o*i.get(t,n))}}return s}}class dvt{constructor(t,e={}){if((t=rvt.checkMatrix(t)).isEmpty())throw new Error("Matrix must be non-empty");let n=t.rows,o=t.columns;const{computeLeftSingularVectors:i=!0,computeRightSingularVectors:a=!0,autoTranspose:r=!1}=e;let s,l=Boolean(i),c=Boolean(a),d=!1;if(n<o)if(r){s=t.transpose(),n=s.rows,o=s.columns,d=!0;let e=l;l=c,c=e}else s=t.clone(),console.warn("Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose");else s=t.clone();let p=Math.min(n,o),m=Math.min(n+1,o),u=new Float64Array(m),f=new ivt(n,p),g=new ivt(o,o),h=new Float64Array(o),b=new Float64Array(n),y=new Float64Array(m);for(let t=0;t<m;t++)y[t]=t;let _=Math.min(n-1,o),C=Math.max(0,Math.min(o-2,n)),M=Math.max(_,C);for(let t=0;t<M;t++){if(t<_){u[t]=0;for(let e=t;e<n;e++)u[t]=lvt(u[t],s.get(e,t));if(0!==u[t]){s.get(t,t)<0&&(u[t]=-u[t]);for(let e=t;e<n;e++)s.set(e,t,s.get(e,t)/u[t]);s.set(t,t,s.get(t,t)+1)}u[t]=-u[t]}for(let e=t+1;e<o;e++){if(t<_&&0!==u[t]){let o=0;for(let i=t;i<n;i++)o+=s.get(i,t)*s.get(i,e);o=-o/s.get(t,t);for(let i=t;i<n;i++)s.set(i,e,s.get(i,e)+o*s.get(i,t))}h[e]=s.get(t,e)}if(l&&t<_)for(let e=t;e<n;e++)f.set(e,t,s.get(e,t));if(t<C){h[t]=0;for(let e=t+1;e<o;e++)h[t]=lvt(h[t],h[e]);if(0!==h[t]){h[t+1]<0&&(h[t]=0-h[t]);for(let e=t+1;e<o;e++)h[e]/=h[t];h[t+1]+=1}if(h[t]=-h[t],t+1<n&&0!==h[t]){for(let e=t+1;e<n;e++)b[e]=0;for(let e=t+1;e<n;e++)for(let n=t+1;n<o;n++)b[e]+=h[n]*s.get(e,n);for(let e=t+1;e<o;e++){let o=-h[e]/h[t+1];for(let i=t+1;i<n;i++)s.set(i,e,s.get(i,e)+o*b[i])}}if(c)for(let e=t+1;e<o;e++)g.set(e,t,h[e])}}let v=Math.min(o,n+1);if(_<o&&(u[_]=s.get(_,_)),n<v&&(u[v-1]=0),C+1<v&&(h[C]=s.get(C,v-1)),h[v-1]=0,l){for(let t=_;t<p;t++){for(let e=0;e<n;e++)f.set(e,t,0);f.set(t,t,1)}for(let t=_-1;t>=0;t--)if(0!==u[t]){for(let e=t+1;e<p;e++){let o=0;for(let i=t;i<n;i++)o+=f.get(i,t)*f.get(i,e);o=-o/f.get(t,t);for(let i=t;i<n;i++)f.set(i,e,f.get(i,e)+o*f.get(i,t))}for(let e=t;e<n;e++)f.set(e,t,-f.get(e,t));f.set(t,t,1+f.get(t,t));for(let e=0;e<t-1;e++)f.set(e,t,0)}else{for(let e=0;e<n;e++)f.set(e,t,0);f.set(t,t,1)}}if(c)for(let t=o-1;t>=0;t--){if(t<C&&0!==h[t])for(let e=t+1;e<o;e++){let n=0;for(let i=t+1;i<o;i++)n+=g.get(i,t)*g.get(i,e);n=-n/g.get(t+1,t);for(let i=t+1;i<o;i++)g.set(i,e,g.get(i,e)+n*g.get(i,t))}for(let e=0;e<o;e++)g.set(e,t,0);g.set(t,t,1)}let x=v-1,O=Number.EPSILON;for(;v>0;){let t,e;for(t=v-2;t>=-1&&-1!==t;t--){const e=Number.MIN_VALUE+O*Math.abs(u[t]+Math.abs(u[t+1]));if(Math.abs(h[t])<=e||Number.isNaN(h[t])){h[t]=0;break}}if(t===v-2)e=4;else{let n;for(n=v-1;n>=t&&n!==t;n--){let e=(n!==v?Math.abs(h[n]):0)+(n!==t+1?Math.abs(h[n-1]):0);if(Math.abs(u[n])<=O*e){u[n]=0;break}}n===t?e=3:n===v-1?e=1:(e=2,t=n)}switch(t++,e){case 1:{let e=h[v-2];h[v-2]=0;for(let n=v-2;n>=t;n--){let i=lvt(u[n],e),a=u[n]/i,r=e/i;if(u[n]=i,n!==t&&(e=-r*h[n-1],h[n-1]=a*h[n-1]),c)for(let t=0;t<o;t++)i=a*g.get(t,n)+r*g.get(t,v-1),g.set(t,v-1,-r*g.get(t,n)+a*g.get(t,v-1)),g.set(t,n,i)}break}case 2:{let e=h[t-1];h[t-1]=0;for(let o=t;o<v;o++){let i=lvt(u[o],e),a=u[o]/i,r=e/i;if(u[o]=i,e=-r*h[o],h[o]=a*h[o],l)for(let e=0;e<n;e++)i=a*f.get(e,o)+r*f.get(e,t-1),f.set(e,t-1,-r*f.get(e,o)+a*f.get(e,t-1)),f.set(e,o,i)}break}case 3:{const e=Math.max(Math.abs(u[v-1]),Math.abs(u[v-2]),Math.abs(h[v-2]),Math.abs(u[t]),Math.abs(h[t])),i=u[v-1]/e,a=u[v-2]/e,r=h[v-2]/e,s=u[t]/e,d=h[t]/e,p=((a+i)*(a-i)+r*r)/2,m=i*r*(i*r);let b=0;0===p&&0===m||(b=p<0?0-Math.sqrt(p*p+m):Math.sqrt(p*p+m),b=m/(p+b));let y=(s+i)*(s-i)+b,_=s*d;for(let e=t;e<v-1;e++){let i=lvt(y,_);0===i&&(i=Number.MIN_VALUE);let a=y/i,r=_/i;if(e!==t&&(h[e-1]=i),y=a*u[e]+r*h[e],h[e]=a*h[e]-r*u[e],_=r*u[e+1],u[e+1]=a*u[e+1],c)for(let t=0;t<o;t++)i=a*g.get(t,e)+r*g.get(t,e+1),g.set(t,e+1,-r*g.get(t,e)+a*g.get(t,e+1)),g.set(t,e,i);if(i=lvt(y,_),0===i&&(i=Number.MIN_VALUE),a=y/i,r=_/i,u[e]=i,y=a*h[e]+r*u[e+1],u[e+1]=-r*h[e]+a*u[e+1],_=r*h[e+1],h[e+1]=a*h[e+1],l&&e<n-1)for(let t=0;t<n;t++)i=a*f.get(t,e)+r*f.get(t,e+1),f.set(t,e+1,-r*f.get(t,e)+a*f.get(t,e+1)),f.set(t,e,i)}h[v-2]=y;break}case 4:if(u[t]<=0&&(u[t]=u[t]<0?-u[t]:0,c))for(let e=0;e<=x;e++)g.set(e,t,-g.get(e,t));for(;t<x&&!(u[t]>=u[t+1]);){let e=u[t];if(u[t]=u[t+1],u[t+1]=e,c&&t<o-1)for(let n=0;n<o;n++)e=g.get(n,t+1),g.set(n,t+1,g.get(n,t)),g.set(n,t,e);if(l&&t<n-1)for(let o=0;o<n;o++)e=f.get(o,t+1),f.set(o,t+1,f.get(o,t)),f.set(o,t,e);t++}v--}}if(d){let t=g;g=f,f=t}this.m=n,this.n=o,this.s=u,this.U=f,this.V=g}solve(t){let e=t,n=this.threshold,o=this.s.length,i=ivt.zeros(o,o);for(let t=0;t<o;t++)Math.abs(this.s[t])<=n?i.set(t,t,0):i.set(t,t,1/this.s[t]);let a=this.U,r=this.rightSingularVectors,s=r.mmul(i),l=r.rows,c=a.rows,d=ivt.zeros(l,c);for(let t=0;t<l;t++)for(let e=0;e<c;e++){let n=0;for(let i=0;i<o;i++)n+=s.get(t,i)*a.get(e,i);d.set(t,e,n)}return d.mmul(e)}solveForDiagonal(t){return this.solve(ivt.diag(t))}inverse(){let t=this.V,e=this.threshold,n=t.rows,o=t.columns,i=new ivt(n,this.s.length);for(let a=0;a<n;a++)for(let n=0;n<o;n++)Math.abs(this.s[n])>e&&i.set(a,n,t.get(a,n)/this.s[n]);let a=this.U,r=a.rows,s=a.columns,l=new ivt(n,r);for(let t=0;t<n;t++)for(let e=0;e<r;e++){let n=0;for(let o=0;o<s;o++)n+=i.get(t,o)*a.get(e,o);l.set(t,e,n)}return l}get condition(){return this.s[0]/this.s[Math.min(this.m,this.n)-1]}get norm2(){return this.s[0]}get rank(){let t=Math.max(this.m,this.n)*this.s[0]*Number.EPSILON,e=0,n=this.s;for(let o=0,i=n.length;o<i;o++)n[o]>t&&e++;return e}get diagonal(){return Array.from(this.s)}get threshold(){return Number.EPSILON/2*Math.max(this.m,this.n)*this.s[0]}get leftSingularVectors(){return this.U}get rightSingularVectors(){return this.V}get diagonalMatrix(){return ivt.diag(this.s)}}function pvt(t,e,n,o,i){let a=ivt.eye(e.length,e.length,n*o*o);const r=i(e);let s=new Float64Array(t.x.length);for(let e=0;e<t.x.length;e++)s[e]=r(t.x[e]);let l=(function c(t,e,n,o,i){const a=n.length,r=t.x.length;let s=new Array(a);for(let l=0;l<a;l++){s[l]=new Array(r);let a=n.slice();a[l]+=o;let c=i(a);for(let n=0;n<r;n++)s[l][n]=e[n]-c(t.x[n])}return new ivt(s)})(t,s,e,o,i),d=(function p(t,e){const n=t.x.length;let o=new Array(n);for(let i=0;i<n;i++)o[i]=[t.y[i]-e[i]];return new ivt(o)})(t,s),m=(function u(t,e=!1){return t=rvt.checkMatrix(t),e?new dvt(t).inverse():(function n(t,e,o=!1){return t=rvt.checkMatrix(t),e=rvt.checkMatrix(e),o?new dvt(t).solve(e):t.isSquare()?new svt(t).solve(e):new cvt(t).solve(e)})(t,ivt.eye(t.rows))})(a.add(l.mmul(l.transpose())));return(e=(e=new ivt([e])).sub(m.mmul(l).mmul(d).mul(o).transpose())).to1DArray()}var mvt=$v(Object.freeze({__proto__:null,default:function uvt(t,e,n={}){let{maxIterations:o=100,gradientDifference:i=.1,damping:a=0,errorTolerance:r=.01,minValues:s,maxValues:l,initialValues:c}=n;if(a<=0)throw new Error("The damping option must be a positive number");if(!t.x||!t.y)throw new Error("The data parameter must have x and y elements");if(!NMt(t.x)||t.x.length<2||!NMt(t.y)||t.y.length<2)throw new Error("The data parameter elements must be an array with more than 2 points");if(t.x.length!==t.y.length)throw new Error("The data parameter elements must have the same size");let d=c||new Array(e.length).fill(1),p=d.length;if(l=l||new 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s=[],l=[],c=[],d=r.getAll();for(a=0;a<d.length;a++){var p=d[a];p.value&&(s.push(p.value),c.push(p.row),l.push(p.col))}return{head:l,tail:c,epochsPerSample:this.makeEpochsPerSample(s,e)}},t.prototype.makeEpochsPerSample=function(t,e){var n=Ovt.filled(t.length,-1),o=Ovt.max(t),i=t.map((function(t){return t/o*e}));return i.forEach((function(t,o){t>0&&(n[o]=e/i[o])})),n},t.prototype.assignOptimizationStateParameters=function(t){Object.assign(this.optimizationState,t)},t.prototype.prepareForOptimizationLoop=function(){var t=this,e=t.repulsionStrength,n=t.learningRate,o=t.negativeSampleRate,i=this.optimizationState,a=i.epochsPerSample,r=i.headEmbedding,s=r[0].length,l=r.length===i.tailEmbedding.length,c=a.map((function(t){return t/o})),d=bvt(c),p=bvt(a);this.assignOptimizationStateParameters({epochOfNextSample:p,epochOfNextNegativeSample:d,epochsPerNegativeSample:c,moveOther:l,initialAlpha:n,alpha:n,gamma:e,dim:s})},t.prototype.initializeOptimization=function(){var 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D=Ovt.tauRandInt(y,this.random),E=a[D],R=Tvt(M,E),A=0;if(R>0)A=2*u*g,A/=(.001+R)*(f*Math.pow(R,g)+1);else if(C===D)continue;for(P=0;P<h;P++)w=4,A>0&&(w=Avt(A*(M[P]-E[P]),4)),M[P]+=w*m}l[_]+=k*c[_]}return e.alpha=p*(1-t/b),e.currentEpoch+=1,i},t.prototype.optimizeLayoutAsync=function(t){var e=this;return void 0===t&&(t=function(){return!0}),new Promise((function(n,o){var i=function(){return fvt(e,void 0,void 0,(function(){var e,a,r,s,l;return gvt(this,(function(c){try{if(a=(e=this.optimizationState).nEpochs,this.embedding=this.optimizeLayoutStep(e.currentEpoch),s=!1===t(r=this.optimizationState.currentEpoch),l=r===a,s||l)return[2,n(l)];setTimeout((function(){return i()}),0)}catch(t){o(t)}return[2]}))}))};setTimeout((function(){return i()}),0)}))},t.prototype.optimizeLayout=function(t){void 0===t&&(t=function(){return!0});for(var e=!1,n=[];!e;){var o=this.optimizationState,i=o.nEpochs;n=this.optimizeLayoutStep(o.currentEpoch);var 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n=0,o=0;o<t.length;o++)n+=Math.pow(t[o]-e[o],2);return n}function Nvt(t,e){var n=Ovt.linear(0,3*t,300).map((function(t){return t<e?1:t})),o=Ovt.zeros(n.length).map((function(o,i){return n[i]>=e?Math.exp(-(n[i]-e)/t):o})),i=Pvt.default({x:n,y:o},(function(t){var e=hvt(t,2),n=e[0],o=e[1];return function(t){return 1/(1+n*Math.pow(t,2*o))}}),{damping:1.5,initialValues:[.5,.5],gradientDifference:.1,maxIterations:100,errorTolerance:.01}).parameterValues,a=hvt(i,2);return{a:a[0],b:a[1]}}function zvt(t,e,n,o){return void 0===n&&(n=1),void 0===o&&(o=5),t.map((function(t,i,a){return-1===e[i]||-1===e[a]?t*Math.exp(-n):e[i]!==e[a]?t*Math.exp(-o):t}))}function Ivt(t){t=Mvt.normalize(t,"max");var e=Mvt.transpose(t),n=Mvt.pairwiseMultiply(e,t);return t=Mvt.add(t,Mvt.subtract(e,n)),Mvt.eliminateZeros(t)}function Hvt(t,e,n){for(var o=Ovt.zeros(t.length).map((function(t){return Ovt.zeros(n[0].length)})),i=0;i<t.length;i++)for(var a=0;a<t[0].length;a++)for(var r=0;r<n[0].length;r++)o[i][r]+=e[i][a]*n[t[i][a]][r];return o}function Fvt(t){let e,n,o=t.length;for(;o;)n=Math.floor(Math.random()*o--),e=t[o],t[o]=t[n],t[n]=e;return t}function Lvt(t,e){return`${e}/${t}`}uCt.findABParams=Nvt,uCt.fastIntersection=zvt,uCt.resetLocalConnectivity=Ivt,uCt.initTransform=Hvt,Object.defineProperty(mCt,"__esModule",{value:!0}),mCt.UMAP=uCt.UMAP;class Bvt{constructor(t){this.http=t,this.httpPathPrefix="data/plugin/npmi"}fetchData(t){return $t(this.fetchAnnotations(t),this.fetchMetrics(t),this.fetchValues(t),this.fetchEmbeddings(t)).pipe(It((([t,e,n,o])=>{const i={},a={};let r,s=0;for(const r of Object.keys(t))for(const l in t[r]){const c=t[r][l];Object.keys(o).length&&!a[c]&&o[r][l]&&o[r][l].some((t=>0!==t))&&(a[c]={vector:o[r][l],index:s,name:c},s+=1);const d=new Map;for(const t in e[r]){const o=e[r][t],i=nyt(o);let a=d.get(i);a||(a={nPMIValue:null,countValue:null,annotation:c,metric:i,run:r},d.set(i,a)),$bt(o)?a.countValue=n[r][l][t]:tyt(o)&&(a.nPMIValue=n[r][l][t])}i[c]=[...i[c]?i[c]:[],...d.values()]}return Object.keys(a).length&&(r=(function l(t){const e=Object.keys(t);return{points:t,pointKeys:e,shuffledDataIndices:Fvt((n=e.length,[...new Array(n)].map(((t,e)=>e)))),hasUmapRun:!1};var n})(a)),{annotationData:i,metrics:e,embeddingDataSet:r}})),pe((t=>t instanceof xD&&400<=t.status&&t.status<500?Et({annotationData:{},metrics:{},embeddingDataSet:void 0}):Rt(t))))}fetchAnnotations(t){return $t(t.map((t=>this.http.get(`/experiment/${t}/${this.httpPathPrefix}/annotations`).pipe(It((e=>(function n(t,e){return Object.fromEntries(Object.entries(t).map((([t,n])=>[Lvt(t,e),n])))})(e,t))))))).pipe(It((t=>{let e={};for(const n of t)e=Object.assign(Object.assign({},e),n);return e})))}fetchMetrics(t){return $t(t.map((t=>this.http.get(`/experiment/${t}/${this.httpPathPrefix}/metrics`).pipe(It((e=>(function n(t,e){return Object.fromEntries(Object.entries(t).map((([t,n])=>[Lvt(t,e),n])))})(e,t))))))).pipe(It((t=>{let e={};for(const n of t)e=Object.assign(Object.assign({},e),n);return e})))}fetchValues(t){return $t(t.map((t=>this.http.get(`/experiment/${t}/${this.httpPathPrefix}/values`).pipe(It((e=>(function n(t,e){return Object.fromEntries(Object.entries(t).map((([t,n])=>[Lvt(t,e),n])))})(e,t))))))).pipe(It((t=>{let e={};for(const n of t)e=Object.assign(Object.assign({},e),n);return e})))}fetchEmbeddings(t){return $t(t.map((t=>this.http.get(`/experiment/${t}/${this.httpPathPrefix}/embeddings`).pipe(It((e=>(function n(t,e){return Object.fromEntries(Object.entries(t).map((([t,n])=>[Lvt(t,e),n])))})(e,t))))))).pipe(It((t=>{let e={};for(const n of t)e=Object.assign(Object.assign({},e),n);return e})))}}Bvt.ɵfac=function t(e){return new(e||Bvt)(vr(lE))},Bvt.ɵprov=Mn({token:Bvt,factory:Bvt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Bvt,[{type:im}],(function(){return[{type:lE}]}),null);class Vvt{}Vvt.ɵfac=function t(e){return new(e||Vvt)},Vvt.ɵmod=ao({type:Vvt}),Vvt.ɵinj=vn({providers:[Bvt],imports:[[cE]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Vvt,[{type:Ay,args:[{imports:[cE],providers:[Bvt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Vvt,{imports:[cE]});class jvt{constructor(t,e,n){this.actions$=t,this.store=e,this.dataSource=n,this.loadData$=Mk((()=>re(this.loadPluginData()).pipe(It((()=>({}))))),{dispatch:!1})}loadPluginData(){return this.actions$.pipe(Dk(kbt),Ve(this.store.select(abt),this.store.select(TS)),ce((([,t,e])=>t!==yE.LOADING&&null!==e)),Fe((()=>this.store.dispatch(Sbt()))),Zt((([,,t])=>this.dataSource.fetchData(t).pipe(Fe((t=>{this.store.dispatch(Dbt(t))})),It((()=>{})),pe((()=>(this.store.dispatch(Ebt()),rt)))))))}}jvt.ɵfac=function t(e){return new(e||jvt)(vr(Sk),vr(Iw),vr(Bvt))},jvt.ɵprov=Mn({token:jvt,factory:jvt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(jvt,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:Bvt}]}),null);class Uvt{}Uvt.ɵfac=function t(e){return new(e||Uvt)},Uvt.ɵmod=ao({type:Uvt}),Uvt.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Uvt,[{type:Ay,args:[{declarations:[obt],exports:[obt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Uvt,{declarations:[obt],exports:[obt]});class Gvt{}Gvt.ɵfac=function t(e){return new(e||Gvt)},Gvt.ɵmod=ao({type:Gvt}),Gvt.ɵinj=vn({imports:[[WM,EW,BY,Y0]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Gvt,[{type:Ay,args:[{declarations:[syt,lyt],imports:[WM,EW,BY,Y0],exports:[lyt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Gvt,{declarations:[syt,lyt],imports:[WM,EW,BY,Y0],exports:[lyt]});class Wvt{}Wvt.ɵfac=function t(e){return new(e||Wvt)},Wvt.ɵmod=ao({type:Wvt}),Wvt.ɵinj=vn({imports:[[WM,cG,dG,EW,Eyt,VQ]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Wvt,[{type:Ay,args:[{declarations:[zyt,Iyt],imports:[WM,cG,dG,EW,Eyt,VQ],exports:[Iyt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Wvt,{declarations:[zyt,Iyt],imports:[WM,cG,dG,EW,Eyt,VQ],exports:[Iyt]});class Yvt{}Yvt.ɵfac=function t(e){return new(e||Yvt)},Yvt.ɵmod=ao({type:Yvt}),Yvt.ɵinj=vn({imports:[[WM,Eyt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Yvt,[{type:Ay,args:[{declarations:[Hyt],imports:[WM,Eyt],exports:[Hyt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Yvt,{declarations:[Hyt],imports:[WM,Eyt],exports:[Hyt]});class qvt{}qvt.ɵfac=function t(e){return new(e||qvt)},qvt.ɵmod=ao({type:qvt}),qvt.ɵinj=vn({imports:[[WM,Eyt,Wvt,Yvt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(qvt,[{type:Ay,args:[{declarations:[Vyt,jyt],imports:[WM,Eyt,Wvt,Yvt],exports:[jyt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(qvt,{declarations:[Vyt,jyt],imports:[WM,Eyt,Wvt,Yvt],exports:[jyt]});class Zvt{}Zvt.ɵfac=function t(e){return new(e||Zvt)},Zvt.ɵmod=ao({type:Zvt}),Zvt.ɵinj=vn({imports:[[WM,EW,JH]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Zvt,[{type:Ay,args:[{declarations:[pyt,myt],imports:[WM,EW,JH],exports:[myt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Zvt,{declarations:[pyt,myt],imports:[WM,EW,JH],exports:[myt]});class Xvt{}Xvt.ɵfac=function t(e){return new(e||Xvt)},Xvt.ɵmod=ao({type:Xvt}),Xvt.ɵinj=vn({imports:[[WM,Gvt,qvt,Zvt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Xvt,[{type:Ay,args:[{declarations:[Uyt],imports:[WM,Gvt,qvt,Zvt],exports:[Uyt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Xvt,{declarations:[Uyt],imports:[WM,Gvt,qvt,Zvt],exports:[Uyt]});class Kvt{}Kvt.ɵfac=function t(e){return new(e||Kvt)},Kvt.ɵmod=ao({type:Kvt}),Kvt.ɵinj=vn({imports:[[WM,cG,JH,EW]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Kvt,[{type:Ay,args:[{declarations:[Yyt,qyt],imports:[WM,cG,JH,EW],exports:[qyt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Kvt,{declarations:[Yyt,qyt],imports:[WM,cG,JH,EW],exports:[qyt]});class Jvt{}Jvt.ɵfac=function t(e){return new(e||Jvt)},Jvt.ɵmod=ao({type:Jvt}),Jvt.ɵinj=vn({imports:[[WM,cG,EW,JH,Kvt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Jvt,[{type:Ay,args:[{declarations:[Kyt,Jyt],imports:[WM,cG,EW,JH,Kvt],exports:[Jyt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Jvt,{declarations:[Kyt,Jyt],imports:[WM,cG,EW,JH,Kvt],exports:[Jyt]});class Qvt{}Qvt.ɵfac=function t(e){return new(e||Qvt)},Qvt.ɵmod=ao({type:Qvt}),Qvt.ɵinj=vn({imports:[[WM,cG,EW,BY]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Qvt,[{type:Ay,args:[{declarations:[f_t,g_t],imports:[WM,cG,EW,BY],exports:[g_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Qvt,{declarations:[f_t,g_t],imports:[WM,cG,EW,BY],exports:[g_t]});class $vt{}$vt.ɵfac=function t(e){return new(e||$vt)},$vt.ɵmod=ao({type:$vt}),$vt.ɵinj=vn({imports:[[WM,cG,EW,JH,m_t,Qvt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh($vt,[{type:Ay,args:[{declarations:[y_t,__t],imports:[WM,cG,EW,JH,m_t,Qvt],exports:[__t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro($vt,{declarations:[y_t,__t],imports:[WM,cG,EW,JH,m_t,Qvt],exports:[__t]});class txt{}txt.ɵfac=function t(e){return new(e||txt)},txt.ɵmod=ao({type:txt}),txt.ɵinj=vn({imports:[[WM,cG,SY,EW]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(txt,[{type:Ay,args:[{declarations:[P_t,w_t],imports:[WM,cG,SY,EW],exports:[w_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(txt,{declarations:[P_t,w_t],imports:[WM,cG,SY,EW],exports:[w_t]});class ext{}ext.ɵfac=function t(e){return new(e||ext)},ext.ɵmod=ao({type:ext}),ext.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ext,[{type:Ay,args:[{declarations:[M_t],imports:[WM],exports:[M_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(ext,{declarations:[M_t],imports:[WM],exports:[M_t]});class nxt{}nxt.ɵfac=function t(e){return new(e||nxt)},nxt.ɵmod=ao({type:nxt}),nxt.ɵinj=vn({imports:[[ext]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(nxt,[{type:Ay,args:[{declarations:[v_t],imports:[ext],exports:[v_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(nxt,{declarations:[v_t],imports:[ext],exports:[v_t]});class oxt{}oxt.ɵfac=function t(e){return new(e||oxt)},oxt.ɵmod=ao({type:oxt}),oxt.ɵinj=vn({imports:[[WM,cG,SY,EW,JH]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(oxt,[{type:Ay,args:[{declarations:[T_t,A_t],imports:[WM,cG,SY,EW,JH],exports:[T_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(oxt,{declarations:[T_t,A_t],imports:[WM,cG,SY,EW,JH],exports:[T_t]});class ixt{}ixt.ɵfac=function t(e){return new(e||ixt)},ixt.ɵmod=ao({type:ixt}),ixt.ɵinj=vn({imports:[[WM,$vt,txt,nxt,_F,oxt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ixt,[{type:Ay,args:[{declarations:[I_t,H_t],imports:[WM,$vt,txt,nxt,_F,oxt],exports:[H_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(ixt,{declarations:[I_t,H_t],imports:[WM,$vt,txt,nxt,_F,oxt],exports:[H_t]});class axt{}axt.ɵfac=function t(e){return new(e||axt)},axt.ɵmod=ao({type:axt}),axt.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(axt,[{type:Ay,args:[{declarations:[L_t,B_t],imports:[WM],exports:[B_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(axt,{declarations:[L_t,B_t],imports:[WM],exports:[B_t]});class rxt{}rxt.ɵfac=function t(e){return new(e||rxt)},rxt.ɵmod=ao({type:rxt}),rxt.ɵinj=vn({imports:[[WM,EW,JH,axt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(rxt,[{type:Ay,args:[{declarations:[G_t,W_t],imports:[WM,EW,JH,axt],exports:[W_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(rxt,{declarations:[G_t,W_t],imports:[WM,EW,JH,axt],exports:[W_t]});class sxt{}sxt.ɵfac=function t(e){return new(e||sxt)},sxt.ɵmod=ao({type:sxt}),sxt.ɵinj=vn({imports:[[WM,cG,SY,EW,kht,Xvt,JH,Jvt,ixt,rxt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(sxt,[{type:Ay,args:[{declarations:[J_t,Q_t],imports:[WM,cG,SY,EW,kht,Xvt,JH,Jvt,ixt,rxt],exports:[Q_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(sxt,{declarations:[J_t,Q_t],imports:[WM,cG,SY,EW,kht,Xvt,JH,Jvt,ixt,rxt],exports:[Q_t]});class lxt{}lxt.ɵfac=function t(e){return new(e||lxt)},lxt.ɵmod=ao({type:lxt}),lxt.ɵinj=vn({imports:[[WM,cG,SY,EW,kht,Xvt,JH,ixt,rxt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lxt,[{type:Ay,args:[{declarations:[iCt,aCt],imports:[WM,cG,SY,EW,kht,Xvt,JH,ixt,rxt],exports:[aCt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(lxt,{declarations:[iCt,aCt],imports:[WM,cG,SY,EW,kht,Xvt,JH,ixt,rxt],exports:[aCt]});class cxt{}cxt.ɵfac=function t(e){return new(e||cxt)},cxt.ɵmod=ao({type:cxt}),cxt.ɵinj=vn({imports:[[WM,Uvt,sxt,lxt,Vvt,dk.forFeature(Qht,iyt),Wk.forFeature([jvt]),wq.forPlugin("npmi",pCt)]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(cxt,[{type:Ay,args:[{declarations:[dCt,pCt],imports:[WM,Uvt,sxt,lxt,Vvt,dk.forFeature(Qht,iyt),Wk.forFeature([jvt]),wq.forPlugin("npmi",pCt)],exports:[pCt],entryComponents:[pCt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(cxt,{declarations:[dCt,pCt],imports:[WM,Uvt,sxt,lxt,Vvt,ck,Gk,wq],exports:[pCt]});class dxt{}dxt.ɵfac=function t(e){return new(e||dxt)},dxt.ɵcmp=to({type:dxt,selectors:[["text-dashboard"]],decls:1,vars:0,template:function t(e,n){1&e&&ku(0," This is the text dashboard ")},encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(dxt,[{type:My,args:[{selector:"text-dashboard",template:" This is the text dashboard ",changeDetection:zn.OnPush}]}],null,null);class pxt{}pxt.ɵfac=function t(e){return new(e||pxt)},pxt.ɵmod=ao({type:pxt}),pxt.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(pxt,[{type:Ay,args:[{declarations:[dxt],exports:[dxt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(pxt,{declarations:[dxt],exports:[dxt]});class mxt{}mxt.ɵfac=function t(e){return new(e||mxt)},mxt.ɵprov=Mn({token:mxt,factory:mxt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(mxt,[{type:im}],null,null);class uxt{constructor(t){this.http=t,this.httpPathPrefix="data/plugin/text_v2"}fetchRunToTag(){return this.http.get(this.httpPathPrefix+"/tags").pipe(It((t=>{const e=new Map;return Object.entries(t).forEach((([t,n])=>{e.set(t,n)})),e})))}fetchTextData(t,e){const n=new URLSearchParams({run:t,tag:e});return this.http.get(this.httpPathPrefix+`/text?${n.toString()}`).pipe(It((t=>t.map((t=>({originalShape:t.original_shape,step:t.step,stringArray:t.string_array,wallTimeInMs:1e3*t.wall_time,truncated:t.truncated}))))))}}uxt.ɵfac=function t(e){return new(e||uxt)(vr(lE))},uxt.ɵprov=Mn({token:uxt,factory:uxt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(uxt,[{type:im}],(function(){return[{type:lE}]}),null);class fxt{}fxt.ɵfac=function t(e){return new(e||fxt)},fxt.ɵmod=ao({type:fxt}),fxt.ɵinj=vn({providers:[uxt,{provide:mxt,useExisting:uxt}],imports:[[cE]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(fxt,[{type:Ay,args:[{imports:[cE],providers:[uxt,{provide:mxt,useExisting:uxt}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(fxt,{imports:[cE]});const gxt=JP("[Text] Text Plugin Loaded"),hxt=JP("[Text] Runs To Tag Loaded",{_as:"props",_p:void 0}),bxt=JP("[Text] Tag Group Visibility Changed",{_as:"props",_p:void 0}),yxt=JP("[Text] Text Data Loaded Loaded",{_as:"props",_p:void 0}),_xt="text",Cxt=Kw(_xt);Zw(Cxt,(t=>t.runToTags));const Mxt=Zw(Cxt,(t=>{const e=new Set,n=new Set;for(const o of t.visibleRunTags.values())for(const t of o){const o=JSON.stringify(t);e.has(o)||(e.add(o),n.add(t))}return[...n]})),vxt=Zw(Cxt,((t,e)=>{const n=t.data.get(e.run);return n&&n.get(e.tag)||null}));class xxt{constructor(t,e,n){this.actions$=t,this.store=e,this.dataSource=n,this.loadRunToTags$=Mk((()=>this.actions$.pipe(Dk(gxt),ze((()=>this.dataSource.fetchRunToTag().pipe(Fe((t=>{this.store.dispatch(hxt({runToTags:t}))})),It((()=>{}))))))),{dispatch:!1}),this.loadData$=Mk((()=>re(this.actions$.pipe(Dk(bxt),ze((({visibleTextCards:t})=>$t(t.map((({run:t,tag:e})=>this.store.select(vxt,{run:t,tag:e}).pipe((function n(t,e){var n=arguments.length>=2;return function(o){return o.pipe(t?ce((function(e,n){return t(e,n,o)})):w,Se(1),n?he(e):xe((function(){return new 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.mat-form-field-label[_ngcontent-%COMP%]{top:1.84375em;margin-top:-0.25em}.mat-form-field-appearance-outline.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59374em) scale(0.75);width:133.3333433333%}.mat-grid-tile-header[_ngcontent-%COMP%], .mat-grid-tile-footer[_ngcontent-%COMP%]{font-size:14px}.mat-grid-tile-header[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%], .mat-grid-tile-footer[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-grid-tile-header[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2), .mat-grid-tile-footer[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}input.mat-input-element[_ngcontent-%COMP%]{margin-top:-0.0625em}.mat-menu-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:400}.mat-paginator[_ngcontent-%COMP%], .mat-paginator-page-size[_ngcontent-%COMP%]   .mat-select-trigger[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px}.mat-radio-button[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", 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h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action[_ngcontent-%COMP%]{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node[_ngcontent-%COMP%], .mat-nested-tree-node[_ngcontent-%COMP%]{font-weight:400;font-size:14px}.mat-ripple[_ngcontent-%COMP%]{overflow:hidden;position:relative}.mat-ripple[_ngcontent-%COMP%]:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded[_ngcontent-%COMP%]{overflow:visible}.mat-ripple-element[_ngcontent-%COMP%]{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms cubic-bezier(0, 0, 0.2, 1);transform:scale(0)}.cdk-high-contrast-active[_ngcontent-%COMP%]   .mat-ripple-element[_ngcontent-%COMP%]{display:none}.cdk-visually-hidden[_ngcontent-%COMP%]{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container[_ngcontent-%COMP%], .cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{background-color:#fff;color:#212121;display:block;position:relative}body.dark-mode   [_nghost-%COMP%]   .plugins.is-first-party-plugin[_ngcontent-%COMP%]{background-color:#303030;color:#fff}.plugins[_ngcontent-%COMP%]{height:100%;position:relative}.warning[_ngcontent-%COMP%]{background:#fff;bottom:0;left:0;position:absolute;right:0;top:0}body.dark-mode[_nghost-%COMP%]   .warning[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .warning[_ngcontent-%COMP%]{background:#303030}.warning-message[_ngcontent-%COMP%]{color:#212121;margin:80px auto 0;max-width:540px}body.dark-mode[_nghost-%COMP%]   .warning-message[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .warning-message[_ngcontent-%COMP%]{color:#fff}.last-reload-time[_ngcontent-%COMP%]{font-style:italic}.plugins[_ngcontent-%COMP%]     iframe{border:0;display:block;height:100%;width:100%}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Uxt,[{type:My,args:[{selector:"plugins-component",templateUrl:"./plugins_component.ng.html",styleUrls:["plugins_component.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:ug},{type:wq},{type:sz,decorators:[{type:Sr}]}]}),{pluginsContainer:[{type:Za,args:["pluginContainer",{static:!0,read:hg}]}],ngPluginContainer:[{type:Za,args:["ngPluginContainer",{static:!0,read:eh}]}],activePluginId:[{type:xy}],activeKnownPlugin:[{type:xy}],pluginLoadState:[{type:xy}],dataLocation:[{type:xy}],isFeatureFlagsLoaded:[{type:xy}],settingsLoadState:[{type:xy}],featureFlags:[{type:xy}],lastUpdated:[{type:xy}],environmentFailureNotFoundTemplate:[{type:xy}],environmentFailureUnknownTemplate:[{type:xy}]});const Gxt=Zw(vR,MR,((t,e)=>e&&t[e]?Object.assign({id:e},t[e]):null));class Wxt{constructor(t){this.store=t,this.activeKnownPlugin$=this.store.select(Gxt),this.activePluginId$=this.store.select(MR),this.pluginLoadState$=Wt(this.activeKnownPlugin$,this.activePluginId$,this.store.select(bR)).pipe(It((([t,e,n])=>null!==n.failureCode?n.failureCode===mE.NOT_FOUND?jxt.ENVIRONMENT_FAILURE_NOT_FOUND:jxt.ENVIRONMENT_FAILURE_UNKNOWN:null!==t?jxt.LOADED:null===n.lastLoadedTimeInMs&&n.state===yE.LOADING?jxt.LOADING:e?jxt.UNKNOWN_PLUGIN_ID:jxt.NO_ENABLED_PLUGINS))),this.lastLoadedTimeInMs$=this.store.select(CR),this.dataLocation$=this.store.select(xR).pipe(It((t=>t.data_location))),this.isFeatureFlagsLoaded$=this.store.select(qD),this.featureFlags$=this.store.select(ZD),this.settingsLoadState$=this.store.select(SN)}}Wxt.ɵfac=function t(e){return new(e||Wxt)(Sm(Iw))},Wxt.ɵcmp=to({type:Wxt,selectors:[["plugins"]],inputs:{environmentFailureNotFoundTemplate:"environmentFailureNotFoundTemplate",environmentFailureUnknownTemplate:"environmentFailureUnknownTemplate"},decls:9,vars:26,consts:[[3,"activeKnownPlugin","activePluginId","dataLocation","lastUpdated","pluginLoadState","isFeatureFlagsLoaded","settingsLoadState","featureFlags","environmentFailureNotFoundTemplate","environmentFailureUnknownTemplate"]],template:function t(e,n){1&e&&(Tm(0,"plugins-component",0),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Ah(7,"async"),Ah(8,"async")),2&e&&Dm("activeKnownPlugin",Th(1,10,n.activeKnownPlugin$))("activePluginId",Th(2,12,n.activePluginId$))("dataLocation",Th(3,14,n.dataLocation$))("lastUpdated",Th(4,16,n.lastLoadedTimeInMs$))("pluginLoadState",Th(5,18,n.pluginLoadState$))("isFeatureFlagsLoaded",Th(6,20,n.isFeatureFlagsLoaded$))("settingsLoadState",Th(7,22,n.settingsLoadState$))("featureFlags",Th(8,24,n.featureFlags$))("environmentFailureNotFoundTemplate",n.environmentFailureNotFoundTemplate)("environmentFailureUnknownTemplate",n.environmentFailureUnknownTemplate)},styles:["plugins-component[_ngcontent-%COMP%] { height: 100%; }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Wxt,[{type:My,args:[{selector:"plugins",template:'\n    <plugins-component\n      [activeKnownPlugin]="activeKnownPlugin$ | async"\n      [activePluginId]="activePluginId$ | async"\n      [dataLocation]="dataLocation$ | async"\n      [lastUpdated]="lastLoadedTimeInMs$ | async"\n      [pluginLoadState]="pluginLoadState$ | async"\n      [isFeatureFlagsLoaded]="isFeatureFlagsLoaded$ | async"\n      [settingsLoadState]="settingsLoadState$ | async"\n      [featureFlags]="featureFlags$ | async"\n      [environmentFailureNotFoundTemplate]="environmentFailureNotFoundTemplate"\n      [environmentFailureUnknownTemplate]="environmentFailureUnknownTemplate"\n    ></plugins-component>\n  ',styles:["plugins-component { height: 100%; }"],changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{environmentFailureNotFoundTemplate:[{type:xy}],environmentFailureUnknownTemplate:[{type:xy}]});class Yxt{}Yxt.ɵfac=function t(e){return new(e||Yxt)},Yxt.ɵmod=ao({type:Yxt}),Yxt.ɵinj=vn({imports:[[ER,WM,wq]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Yxt,[{type:Ay,args:[{declarations:[Wxt,Uxt],exports:[Wxt],imports:[ER,WM,wq]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Yxt,{declarations:[Wxt,Uxt],imports:[ER,WM,wq],exports:[Wxt]}),eo(Wxt,[Uxt],[wM]);class qxt{constructor(t,e){this.store=t,this.document=e,this.onVisibilityChange=this.onVisibilityChangeImpl.bind(this),this.reloadEnabled$=this.store.pipe(Fw(DN)),this.reloadPeriodInMs$=this.store.pipe(Fw(EN)),this.reloadTimerId=null,this.missedAutoReload=!1}ngOnInit(){this.document.addEventListener("visibilitychange",this.onVisibilityChange),Wt(this.reloadEnabled$.pipe(Me()),this.reloadPeriodInMs$.pipe(Me())).subscribe((([t,e])=>{this.cancelLoad(),t&&this.load(e)}))}onVisibilityChangeImpl(){"visible"===this.document.visibilityState&&this.missedAutoReload&&(this.missedAutoReload=!1,this.store.dispatch(xE()))}load(t){this.reloadTimerId=setTimeout((()=>{"visible"===this.document.visibilityState?this.store.dispatch(xE()):this.missedAutoReload=!0,this.load(t)}),t)}cancelLoad(){null!==this.reloadTimerId&&clearTimeout(this.reloadTimerId),this.reloadTimerId=null}ngOnDestroy(){this.cancelLoad(),this.document.removeEventListener("visibilitychange",this.onVisibilityChange)}}qxt.ɵfac=function t(e){return new(e||qxt)(Sm(Iw),Sm(Z_))},qxt.ɵcmp=to({type:qxt,selectors:[["reloader"]],decls:0,vars:0,template:function t(e,n){},encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(qxt,[{type:My,args:[{selector:"reloader",template:"",changeDetection:zn.OnPush}]}],(function(){return[{type:Iw},{type:Document,decorators:[{type:kr,args:[Z_]}]}]}),null);class Zxt{}Zxt.ɵfac=function t(e){return new(e||Zxt)},Zxt.ɵcmp=to({type:Zxt,selectors:[["tensorboard-wrapper-component"]],decls:2,vars:0,consts:[[1,"plugins"]],template:function t(e,n){1&e&&(Tm(0,"plugins",0),Tm(1,"reloader"))},directives:[Wxt,qxt],styles:["[_nghost-%COMP%] {\n        display: flex;\n        flex-direction: column;\n        height: 100%;\n      }\n\n      .plugins[_ngcontent-%COMP%] {\n        flex: 1 1;\n        overflow: auto;\n        position: relative;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Zxt,[{type:My,args:[{selector:"tensorboard-wrapper-component",template:'\n    <plugins class="plugins"></plugins>\n    <reloader></reloader>\n  ',styles:["\n      :host {\n        display: flex;\n        flex-direction: column;\n        height: 100%;\n      }\n\n      .plugins {\n        flex: 1 1;\n        overflow: auto;\n        position: relative;\n      }\n    "],changeDetection:zn.OnPush}]}],null,null);class Xxt{}Xxt.ɵfac=function t(e){return new(e||Xxt)},Xxt.ɵprov=Mn({token:Xxt,factory:Xxt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Xxt,[{type:im}],null,null);const Kxt="smoothing",Jxt="runColorGroup",Qxt="tagFilter",$xt="regex:";let tOt=class extends Xxt{getMetricsPinnedCards(t){return Wt([t.select(PT),t.select(kT)]).pipe(It((([t,e])=>{if(!t.length&&!e.length)return[];const n=[...t.map((({plugin:t,tag:e,sample:n,runId:o})=>{const i={plugin:t,tag:e};return xA(t)&&(i.runId=o),MA(t)&&(i.sample=n),i})),...e];return[{key:"pinnedCards",value:JSON.stringify(n)}]})))}getFeatureFlagStates(t){return Wt([t.select($D),t.select(XD)]).pipe(It((([t,e])=>{const n=t.map((t=>({key:NA,value:t})));return"boolean"==typeof e.enabledColorGroup&&n.push({key:IA,value:String(e.enabledColorGroup)}),"boolean"==typeof e.enabledColorGroupByRegex&&n.push({key:HA,value:String(e.enabledColorGroupByRegex)}),n})))}serializeStateToQueryParams(t){return Wt([this.getMetricsPinnedCards(t),t.select(BT).pipe(It((t=>t?[{key:Qxt,value:t}]:[]))),this.getFeatureFlagStates(t),t.select(ET).pipe(It((t=>Number.isFinite(t.scalarSmoothing)?[{key:Kxt,value:String(t.scalarSmoothing)}]:[]))),t.select(uN).pipe(It((t=>{if(!t)return[];let e;switch(t.key){case tN.EXPERIMENT:e="experiment";break;case tN.RUN:e="run";break;case tN.REGEX:e=`regex:${t.regexString}`;break;default:throw new RangeError("Serialization not implemented")}return[{key:Jxt,value:e}]})))]).pipe(It((t=>t.flat())))}deserializeQueryParams(t){let e=null,n=null,o=null,i=null;for(const{key:a,value:r}of t)switch(a){case"pinnedCards":e=eOt(r);break;case Kxt:n=Number(r);break;case Jxt:switch(r){case"experiment":i={key:tN.EXPERIMENT};break;case"run":i={key:tN.RUN}}if(r.startsWith($xt)){const t=r.slice($xt.length);i={key:tN.REGEX,regexString:t}}break;case Qxt:o=r}return{metrics:{pinnedCards:e||[],smoothing:n,tagFilter:o},runs:{groupBy:i}}}};function eOt(t){let e;try{e=JSON.parse(t)}catch(t){return null}if(!Array.isArray(e))return null;const n=[];for(const t of e){const e="string"==typeof t.runId,i="number"==typeof t.sample,a="string"==typeof t.tag,r=e||void 0===t.runId,s=i||void 0===t.sample;if(!("string"==typeof t.plugin&&a&&r&&s))continue;if((o=t.plugin)!==hA.SCALARS&&o!==hA.HISTOGRAMS&&o!==hA.IMAGES)continue;if(!t.tag)continue;if(xA(t.plugin)){if(!t.runId)continue}else if(t.runId)continue;if(i){if(!MA(t.plugin))continue;if(!Number.isInteger(t.sample)||t.sample<0)continue}const l={plugin:t.plugin,tag:t.tag};e&&(l.runId=t.runId),i&&(l.sample=t.sample),n.push(l)}var o;return n}function nOt(){return[{routeKind:Zk.EXPERIMENT,path:"/",ngComponent:Zxt,defaultRoute:!0,deepLinkProvider:new tOt}]}function oOt(t){return(e,n)=>{const o=t(e,n);return console.groupCollapsed(n.type),console.log("prev state",e),console.log("action",n),console.log("next state",o),console.groupEnd(),o}}function iOt(){return y_()?oOt:t=>(e,n)=>t(e,n)}tOt=(function aOt(t,e,n,o){var i,a=arguments.length,r=a<3?e:null===o?o=Object.getOwnPropertyDescriptor(e,n):o;if("object"==typeof Reflect&&"function"==typeof Reflect.decorate)r=Reflect.decorate(t,e,n,o);else for(var s=t.length-1;s>=0;s--)(i=t[s])&&(r=(a<3?i(r):a>3?i(e,n,r):i(e,n))||r);return a>3&&r&&Object.defineProperty(e,n,r),r})([im()],tOt);const rOt=new Ga("Root reducers token",{factory:()=>({})});class sOt{}sOt.ɵfac=function 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ngJitMode||ngJitMode)&&ro(dOt,{declarations:[aq],imports:[tT,Uv,ZP,BS,sq,PS,JL,KL,lq,cOt,ER,gq,dq,vq,aA,xq,pq,rD,sz,Yxt,KN,Cq,sOt,kxt]}),"loading"!==document.readyState?Fv().bootstrapModule(dOt):window.addEventListener("DOMContentLoaded",(()=>{Fv().bootstrapModule(dOt)})),(function pOt(){if(b_)throw new Error("Cannot enable prod mode after platform setup.");(void 0===typeof ngDevMode||ngDevMode)&&(jn.ngDevMode=!1),h_=!1}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)()})();
",
+              "headers": [
+                [
+                  "content-type",
+                  "application/javascript; charset=utf-8"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            }
+          }
+        },
+        "id": "PW2NAam_7irv",
+        "outputId": "38b8995a-c139-441c-e393-56c11f214655"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "Reusing TensorBoard on port 6006 (pid 639), started 0:12:12 ago. (Use '!kill 639' to kill it.)"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/javascript": "\n        (async () => {\n            const url = new URL(await google.colab.kernel.proxyPort(6006, {'cache': true}));\n            url.searchParams.set('tensorboardColab', 'true');\n            const iframe = document.createElement('iframe');\n            iframe.src = url;\n            iframe.setAttribute('width', '100%');\n            iframe.setAttribute('height', '800');\n            iframe.setAttribute('frameborder', 0);\n            document.body.appendChild(iframe);\n        })();\n    ",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# load tensorboard in colab\n",
+        "%load_ext tensorboard\n",
+        "\n",
+        "# see curves in tensorboard\n",
+        "%tensorboard --logdir ./tutorial_exps"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MfQ-yspZLuuI"
+      },
+      "source": [
+        "## Test the Trained Detector\n",
+        "\n",
+        "After finetuning the detector, let's visualize the prediction results!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 854
+        },
+        "id": "_MuZurfGLq0p",
+        "outputId": "5df4dc5f-5b90-46c3-9aeb-a4d0ac13564a"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/content/mmdetection/mmdet/datasets/utils.py:69: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n",
+            "  'data pipeline in your config file.', UserWarning)\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 2048.01x1360.01 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "img = mmcv.imread('balloon/train/7178882742_f090f3ce56_k.jpg')\n",
+        "\n",
+        "model.cfg = cfg\n",
+        "result = inference_detector(model, img)\n",
+        "show_result_pyplot(model, img, result)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6rzruCwFgPXm"
+      },
+      "source": [
+        "## What to Do Next?\n",
+        "\n",
+        "So far, we have learnt how to test and train Mask R-CNN. To further explore the segmentation task, you could do several other things as shown below:\n",
+        "\n",
+        "- Try cascade methods, e.g., [Cascade Mask R-CNN](https://github.com/open-mmlab/mmdetection/tree/master/configs/cascade_rcnn) and [HTC](https://github.com/open-mmlab/mmdetection/tree/master/configs/htc) in [MMDetection model zoo](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md). They are powerful detectors that are ranked high in many benchmarks, e.g., COCO dataset.\n",
+        "- Try single-stage methods, e.g., [K-Net](https://github.com/ZwwWayne/K-Net) and [Dense-RepPoints](https://github.com/justimyhxu/Dense-RepPoints). These two algorithms are based on MMDetection. Box-free instance segmentation is a new trend in the instance segmentation community.\n",
+        "- Try semantic segmentation. Semantic segmentation is also a popular task with wide applications. You can explore [MMSegmentation](https://github.com/open-mmlab/mmsegmentation/); we also provide a [colab tutorial](https://github.com/open-mmlab/mmsegmentation/blob/master/demo/MMSegmentation_Tutorial.ipynb) for semantic segmentation using MMSegmentation.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [],
+      "name": "tutorial_03_image_segmentation_final.ipynb",
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/demo/MMDet_Tutorial.ipynb b/demo/MMDet_Tutorial.ipynb
new file mode 100644
index 0000000..c6c19c2
--- /dev/null
+++ b/demo/MMDet_Tutorial.ipynb
@@ -0,0 +1,2067 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aGYwt_UjIrqp"
+      },
+      "source": [
+        "# Object Detection\n",
+        "\n",
+        "In this tutorial, you will learn:\n",
+        "- the basic structure of Faster R-CNN.\n",
+        "- to perform inference with a MMDetection detector.\n",
+        "- to train a new detector with a new dataset.\n",
+        "\n",
+        "Let's start!\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "tJxJHruNLb7Y"
+      },
+      "source": [
+        "## Install MMDetection"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Wi4LPmsR66sy",
+        "outputId": "a5005e9d-afb9-4d06-d51c-2c3fa19687b8"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "nvcc: NVIDIA (R) Cuda compiler driver\n",
+            "Copyright (c) 2005-2020 NVIDIA Corporation\n",
+            "Built on Mon_Oct_12_20:09:46_PDT_2020\n",
+            "Cuda compilation tools, release 11.1, V11.1.105\n",
+            "Build cuda_11.1.TC455_06.29190527_0\n",
+            "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
+            "Copyright (C) 2017 Free Software Foundation, Inc.\n",
+            "This is free software; see the source for copying conditions.  There is NO\n",
+            "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Check nvcc version\n",
+        "!nvcc -V\n",
+        "# Check GCC version\n",
+        "!gcc --version"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "gkGnB9WyHSXB",
+        "outputId": "6af7be0b-a75f-4e52-b54b-8d92212f7722"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Looking in links: https://download.pytorch.org/whl/torch_stable.html\n",
+            "Collecting torch==1.9.0+cu111\n",
+            "  Downloading https://download.pytorch.org/whl/cu111/torch-1.9.0%2Bcu111-cp37-cp37m-linux_x86_64.whl (2041.3 MB)\n",
+            "\u001b[K     |█████████████                   | 834.1 MB 1.5 MB/s eta 0:13:16tcmalloc: large alloc 1147494400 bytes == 0x55a4587ba000 @  0x7f26db5db615 0x55a41edd03bc 0x55a41eeb118a 0x55a41edd31cd 0x55a41eec5b3d 0x55a41ee47458 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee472c0 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43cd4 0x55a41eec6986 0x55a41ee43350 0x55a41eec6986 0x55a41ee43350 0x55a41eec6986 0x55a41ee43350 0x55a41edd4f19 0x55a41ee18a79 0x55a41edd3b32 0x55a41ee471dd 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43cd4 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee42eae 0x55a41edd49da 0x55a41ee43108 0x55a41ee4202f\n",
+            "\u001b[K     |████████████████▌               | 1055.7 MB 1.4 MB/s eta 0:11:52tcmalloc: large alloc 1434370048 bytes == 0x55a49ce10000 @  0x7f26db5db615 0x55a41edd03bc 0x55a41eeb118a 0x55a41edd31cd 0x55a41eec5b3d 0x55a41ee47458 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee472c0 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43cd4 0x55a41eec6986 0x55a41ee43350 0x55a41eec6986 0x55a41ee43350 0x55a41eec6986 0x55a41ee43350 0x55a41edd4f19 0x55a41ee18a79 0x55a41edd3b32 0x55a41ee471dd 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43cd4 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee42eae 0x55a41edd49da 0x55a41ee43108 0x55a41ee4202f\n",
+            "\u001b[K     |█████████████████████           | 1336.2 MB 1.3 MB/s eta 0:09:01tcmalloc: large alloc 1792966656 bytes == 0x55a421c42000 @  0x7f26db5db615 0x55a41edd03bc 0x55a41eeb118a 0x55a41edd31cd 0x55a41eec5b3d 0x55a41ee47458 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee472c0 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43cd4 0x55a41eec6986 0x55a41ee43350 0x55a41eec6986 0x55a41ee43350 0x55a41eec6986 0x55a41ee43350 0x55a41edd4f19 0x55a41ee18a79 0x55a41edd3b32 0x55a41ee471dd 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43cd4 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee42eae 0x55a41edd49da 0x55a41ee43108 0x55a41ee4202f\n",
+            "\u001b[K     |██████████████████████████▌     | 1691.1 MB 1.3 MB/s eta 0:04:36tcmalloc: large alloc 2241208320 bytes == 0x55a48ca2a000 @  0x7f26db5db615 0x55a41edd03bc 0x55a41eeb118a 0x55a41edd31cd 0x55a41eec5b3d 0x55a41ee47458 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee472c0 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43cd4 0x55a41eec6986 0x55a41ee43350 0x55a41eec6986 0x55a41ee43350 0x55a41eec6986 0x55a41ee43350 0x55a41edd4f19 0x55a41ee18a79 0x55a41edd3b32 0x55a41ee471dd 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43cd4 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee42eae 0x55a41edd49da 0x55a41ee43108 0x55a41ee4202f\n",
+            "\u001b[K     |████████████████████████████████| 2041.3 MB 1.1 MB/s eta 0:00:01tcmalloc: large alloc 2041348096 bytes == 0x55a51238c000 @  0x7f26db5da1e7 0x55a41ee065d7 0x55a41edd03bc 0x55a41eeb118a 0x55a41edd31cd 0x55a41eec5b3d 0x55a41ee47458 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43108 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43108 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43108 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43108 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43108 0x55a41edd49da 0x55a41ee43108 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43cd4 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43cd4 0x55a41ee4202f\n",
+            "tcmalloc: large alloc 2551685120 bytes == 0x55a600300000 @  0x7f26db5db615 0x55a41edd03bc 0x55a41eeb118a 0x55a41edd31cd 0x55a41eec5b3d 0x55a41ee47458 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43108 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43108 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43108 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43108 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43108 0x55a41edd49da 0x55a41ee43108 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43cd4 0x55a41ee4202f 0x55a41edd4aba 0x55a41ee43cd4 0x55a41ee4202f 0x55a41edd5151\n",
+            "\u001b[K     |████████████████████████████████| 2041.3 MB 7.2 kB/s \n",
+            "\u001b[?25hCollecting torchvision==0.10.0+cu111\n",
+            "  Downloading https://download.pytorch.org/whl/cu111/torchvision-0.10.0%2Bcu111-cp37-cp37m-linux_x86_64.whl (23.2 MB)\n",
+            "\u001b[K     |████████████████████████████████| 23.2 MB 13.8 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from torch==1.9.0+cu111) (3.10.0.2)\n",
+            "Requirement already satisfied: pillow>=5.3.0 in /usr/local/lib/python3.7/dist-packages (from torchvision==0.10.0+cu111) (7.1.2)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from torchvision==0.10.0+cu111) (1.19.5)\n",
+            "Installing collected packages: torch, torchvision\n",
+            "  Attempting uninstall: torch\n",
+            "    Found existing installation: torch 1.10.0+cu111\n",
+            "    Uninstalling torch-1.10.0+cu111:\n",
+            "      Successfully uninstalled torch-1.10.0+cu111\n",
+            "  Attempting uninstall: torchvision\n",
+            "    Found existing installation: torchvision 0.11.1+cu111\n",
+            "    Uninstalling torchvision-0.11.1+cu111:\n",
+            "      Successfully uninstalled torchvision-0.11.1+cu111\n",
+            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "torchtext 0.11.0 requires torch==1.10.0, but you have torch 1.9.0+cu111 which is incompatible.\n",
+            "torchaudio 0.10.0+cu111 requires torch==1.10.0, but you have torch 1.9.0+cu111 which is incompatible.\u001b[0m\n",
+            "Successfully installed torch-1.9.0+cu111 torchvision-0.10.0+cu111\n",
+            "Looking in links: https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n",
+            "Collecting mmcv-full\n",
+            "  Downloading https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/mmcv_full-1.4.4-cp37-cp37m-manylinux1_x86_64.whl (67.3 MB)\n",
+            "\u001b[K     |████████████████████████████████| 67.3 MB 1.3 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (21.3)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (1.19.5)\n",
+            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (3.13)\n",
+            "Collecting addict\n",
+            "  Downloading addict-2.4.0-py3-none-any.whl (3.8 kB)\n",
+            "Requirement already satisfied: opencv-python>=3 in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (4.1.2.30)\n",
+            "Collecting yapf\n",
+            "  Downloading yapf-0.32.0-py2.py3-none-any.whl (190 kB)\n",
+            "\u001b[K     |████████████████████████████████| 190 kB 5.1 MB/s \n",
+            "\u001b[?25hRequirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from mmcv-full) (7.1.2)\n",
+            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->mmcv-full) (3.0.7)\n",
+            "Installing collected packages: yapf, addict, mmcv-full\n",
+            "Successfully installed addict-2.4.0 mmcv-full-1.4.4 yapf-0.32.0\n",
+            "Cloning into 'mmdetection'...\n",
+            "remote: Enumerating objects: 22983, done.\u001b[K\n",
+            "remote: Counting objects: 100% (25/25), done.\u001b[K\n",
+            "remote: Compressing objects: 100% (23/23), done.\u001b[K\n",
+            "remote: Total 22983 (delta 4), reused 17 (delta 2), pack-reused 22958\u001b[K\n",
+            "Receiving objects: 100% (22983/22983), 25.79 MiB | 34.48 MiB/s, done.\n",
+            "Resolving deltas: 100% (16102/16102), done.\n",
+            "/content/mmdetection\n",
+            "Obtaining file:///content/mmdetection\n",
+            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mmdet==2.21.0) (3.2.2)\n",
+            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mmdet==2.21.0) (1.19.5)\n",
+            "Requirement already satisfied: pycocotools in /usr/local/lib/python3.7/dist-packages (from mmdet==2.21.0) (2.0.4)\n",
+            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from mmdet==2.21.0) (1.15.0)\n",
+            "Collecting terminaltables\n",
+            "  Downloading terminaltables-3.1.10-py2.py3-none-any.whl (15 kB)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet==2.21.0) (1.3.2)\n",
+            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet==2.21.0) (3.0.7)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet==2.21.0) (0.11.0)\n",
+            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mmdet==2.21.0) (2.8.2)\n",
+            "Installing collected packages: terminaltables, mmdet\n",
+            "  Running setup.py develop for mmdet\n",
+            "Successfully installed mmdet-2.21.0 terminaltables-3.1.10\n"
+          ]
+        }
+      ],
+      "source": [
+        "# install dependencies: (use cu111 because colab has CUDA 11.1)\n",
+        "!pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 -f https://download.pytorch.org/whl/torch_stable.html\n",
+        "\n",
+        "# install mmcv-full thus we could use CUDA operators\n",
+        "!pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html\n",
+        "\n",
+        "# Install mmdetection\n",
+        "!rm -rf mmdetection\n",
+        "!git clone https://github.com/open-mmlab/mmdetection.git\n",
+        "%cd mmdetection\n",
+        "\n",
+        "!pip install -e ."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "_YeUiqAoCaoV",
+        "outputId": "7f894255-c0a0-4ca7-9083-2cf0e2c0646e"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/plain": [
+              "{'CUDA available': True,\n",
+              " 'CUDA_HOME': '/usr/local/cuda',\n",
+              " 'GCC': 'gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0',\n",
+              " 'GPU 0': 'Tesla T4',\n",
+              " 'MMCV': '1.4.4',\n",
+              " 'MMCV CUDA Compiler': '11.1',\n",
+              " 'MMCV Compiler': 'GCC 7.3',\n",
+              " 'NVCC': 'Build cuda_11.1.TC455_06.29190527_0',\n",
+              " 'OpenCV': '4.1.2',\n",
+              " 'PyTorch': '1.9.0+cu111',\n",
+              " 'PyTorch compiling details': 'PyTorch built with:\\n  - GCC 7.3\\n  - C++ Version: 201402\\n  - Intel(R) Math Kernel Library Version 2020.0.0 Product Build 20191122 for Intel(R) 64 architecture applications\\n  - Intel(R) MKL-DNN v2.1.2 (Git Hash 98be7e8afa711dc9b66c8ff3504129cb82013cdb)\\n  - OpenMP 201511 (a.k.a. OpenMP 4.5)\\n  - NNPACK is enabled\\n  - CPU capability usage: AVX2\\n  - CUDA Runtime 11.1\\n  - NVCC architecture flags: -gencode;arch=compute_37,code=sm_37;-gencode;arch=compute_50,code=sm_50;-gencode;arch=compute_60,code=sm_60;-gencode;arch=compute_70,code=sm_70;-gencode;arch=compute_75,code=sm_75;-gencode;arch=compute_80,code=sm_80;-gencode;arch=compute_86,code=sm_86\\n  - CuDNN 8.0.5\\n  - Magma 2.5.2\\n  - Build settings: BLAS_INFO=mkl, BUILD_TYPE=Release, CUDA_VERSION=11.1, CUDNN_VERSION=8.0.5, CXX_COMPILER=/opt/rh/devtoolset-7/root/usr/bin/c++, CXX_FLAGS= -Wno-deprecated -fvisibility-inlines-hidden -DUSE_PTHREADPOOL -fopenmp -DNDEBUG -DUSE_KINETO -DUSE_FBGEMM -DUSE_QNNPACK -DUSE_PYTORCH_QNNPACK -DUSE_XNNPACK -DSYMBOLICATE_MOBILE_DEBUG_HANDLE -O2 -fPIC -Wno-narrowing -Wall -Wextra -Werror=return-type -Wno-missing-field-initializers -Wno-type-limits -Wno-array-bounds -Wno-unknown-pragmas -Wno-sign-compare -Wno-unused-parameter -Wno-unused-variable -Wno-unused-function -Wno-unused-result -Wno-unused-local-typedefs -Wno-strict-overflow -Wno-strict-aliasing -Wno-error=deprecated-declarations -Wno-stringop-overflow -Wno-psabi -Wno-error=pedantic -Wno-error=redundant-decls -Wno-error=old-style-cast -fdiagnostics-color=always -faligned-new -Wno-unused-but-set-variable -Wno-maybe-uninitialized -fno-math-errno -fno-trapping-math -Werror=format -Wno-stringop-overflow, LAPACK_INFO=mkl, PERF_WITH_AVX=1, PERF_WITH_AVX2=1, PERF_WITH_AVX512=1, TORCH_VERSION=1.9.0, USE_CUDA=ON, USE_CUDNN=ON, USE_EXCEPTION_PTR=1, USE_GFLAGS=OFF, USE_GLOG=OFF, USE_MKL=ON, USE_MKLDNN=ON, USE_MPI=OFF, USE_NCCL=ON, USE_NNPACK=ON, USE_OPENMP=ON, \\n',\n",
+              " 'Python': '3.7.12 (default, Jan 15 2022, 18:48:18) [GCC 7.5.0]',\n",
+              " 'TorchVision': '0.10.0+cu111',\n",
+              " 'sys.platform': 'linux'}"
+            ]
+          },
+          "execution_count": 3,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "from mmcv import collect_env\n",
+        "collect_env()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "6hD0mmMixT0p",
+        "outputId": "ac4aaaeb-6b18-4500-c95c-6f781cda76fc"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "1.9.0+cu111 True\n",
+            "2.21.0\n",
+            "11.1\n",
+            "GCC 7.3\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Check Pytorch installation\n",
+        "import torch, torchvision\n",
+        "print(torch.__version__, torch.cuda.is_available())\n",
+        "\n",
+        "# Check MMDetection installation\n",
+        "import mmdet\n",
+        "print(mmdet.__version__)\n",
+        "\n",
+        "# Check mmcv installation\n",
+        "from mmcv.ops import get_compiling_cuda_version, get_compiler_version\n",
+        "print(get_compiling_cuda_version())\n",
+        "print(get_compiler_version())"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gi9zw03oM4CH"
+      },
+      "source": [
+        "## Perform Inference with An MMDet detector\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "s99mDGBG1S1z"
+      },
+      "source": [
+        "### A two-stage detector\n",
+        "\n",
+        "In this tutorial, we use Faster R-CNN, a simple two-stage detector as an example.\n",
+        "\n",
+        "The high-level architecture of Faster R-CNN is shown in the following picture. More details can be found in the [paper](https://arxiv.org/abs/1506.01497).\n",
+        "\n",
+        "![faster rcnn](https://pic1.zhimg.com/80/v2-c0172be282021a1029f7b72b51079ffe_1440w.jpg)\n",
+        "\n",
+        "![mmdet](https://pic2.zhimg.com/v2-e49ebcf931b5cf424ed311338f9ff35d_b.jpg)\n",
+        "\n",
+        "Briefly, it uses a convolutional neural network (CNN) as backbone to extract features from an image. Then, it uses a region proposal network (RPN) to predict proposals, i.e., potential objects. After that, it uses a feature extractor to crop features for the region of interests (RoI), and uses a RoI Head to perform classification and bounding box prediction.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "j4doHX4exvS1",
+        "outputId": "b42719be-cb70-47a1-867a-56649a794c44"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "--2022-02-08 11:29:13--  https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth\n",
+            "Resolving download.openmmlab.com (download.openmmlab.com)... 47.252.96.28\n",
+            "Connecting to download.openmmlab.com (download.openmmlab.com)|47.252.96.28|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 167291982 (160M) [application/octet-stream]\n",
+            "Saving to: ‘checkpoints/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth’\n",
+            "\n",
+            "checkpoints/faster_ 100%[===================>] 159.54M  7.92MB/s    in 22s     \n",
+            "\n",
+            "2022-02-08 11:29:37 (7.28 MB/s) - ‘checkpoints/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth’ saved [167291982/167291982]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# We download the pre-trained checkpoints for inference and finetuning.\n",
+        "!mkdir checkpoints\n",
+        "!wget -c https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth \\\n",
+        "      -O checkpoints/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8M5KUnX7Np3h",
+        "outputId": "a061bced-262e-404f-94c5-6400a75078b3"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "load checkpoint from local path: checkpoints/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "FasterRCNN(\n",
+              "  (backbone): ResNet(\n",
+              "    (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n",
+              "    (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "    (relu): ReLU(inplace=True)\n",
+              "    (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n",
+              "    (layer1): ResLayer(\n",
+              "      (0): Bottleneck(\n",
+              "        (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "        (downsample): Sequential(\n",
+              "          (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "          (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        )\n",
+              "      )\n",
+              "      (1): Bottleneck(\n",
+              "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (2): Bottleneck(\n",
+              "        (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "    )\n",
+              "    (layer2): ResLayer(\n",
+              "      (0): Bottleneck(\n",
+              "        (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "        (downsample): Sequential(\n",
+              "          (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "          (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        )\n",
+              "      )\n",
+              "      (1): Bottleneck(\n",
+              "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (2): Bottleneck(\n",
+              "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (3): Bottleneck(\n",
+              "        (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "    )\n",
+              "    (layer3): ResLayer(\n",
+              "      (0): Bottleneck(\n",
+              "        (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "        (downsample): Sequential(\n",
+              "          (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "          (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        )\n",
+              "      )\n",
+              "      (1): Bottleneck(\n",
+              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (2): Bottleneck(\n",
+              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (3): Bottleneck(\n",
+              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (4): Bottleneck(\n",
+              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (5): Bottleneck(\n",
+              "        (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "    )\n",
+              "    (layer4): ResLayer(\n",
+              "      (0): Bottleneck(\n",
+              "        (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "        (downsample): Sequential(\n",
+              "          (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
+              "          (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        )\n",
+              "      )\n",
+              "      (1): Bottleneck(\n",
+              "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "      (2): Bottleneck(\n",
+              "        (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n",
+              "        (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
+              "        (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
+              "        (relu): ReLU(inplace=True)\n",
+              "      )\n",
+              "    )\n",
+              "  )\n",
+              "  init_cfg={'type': 'Pretrained', 'checkpoint': 'open-mmlab://detectron2/resnet50_caffe'}\n",
+              "  (neck): FPN(\n",
+              "    (lateral_convs): ModuleList(\n",
+              "      (0): ConvModule(\n",
+              "        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
+              "      )\n",
+              "      (1): ConvModule(\n",
+              "        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
+              "      )\n",
+              "      (2): ConvModule(\n",
+              "        (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
+              "      )\n",
+              "      (3): ConvModule(\n",
+              "        (conv): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
+              "      )\n",
+              "    )\n",
+              "    (fpn_convs): ModuleList(\n",
+              "      (0): ConvModule(\n",
+              "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "      )\n",
+              "      (1): ConvModule(\n",
+              "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "      )\n",
+              "      (2): ConvModule(\n",
+              "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "      )\n",
+              "      (3): ConvModule(\n",
+              "        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "      )\n",
+              "    )\n",
+              "  )\n",
+              "  init_cfg={'type': 'Xavier', 'layer': 'Conv2d', 'distribution': 'uniform'}\n",
+              "  (rpn_head): RPNHead(\n",
+              "    (loss_cls): CrossEntropyLoss()\n",
+              "    (loss_bbox): L1Loss()\n",
+              "    (rpn_conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
+              "    (rpn_cls): Conv2d(256, 3, kernel_size=(1, 1), stride=(1, 1))\n",
+              "    (rpn_reg): Conv2d(256, 12, kernel_size=(1, 1), stride=(1, 1))\n",
+              "  )\n",
+              "  init_cfg={'type': 'Normal', 'layer': 'Conv2d', 'std': 0.01}\n",
+              "  (roi_head): StandardRoIHead(\n",
+              "    (bbox_roi_extractor): SingleRoIExtractor(\n",
+              "      (roi_layers): ModuleList(\n",
+              "        (0): RoIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
+              "        (1): RoIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
+              "        (2): RoIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
+              "        (3): RoIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, pool_mode=avg, aligned=True, use_torchvision=False)\n",
+              "      )\n",
+              "    )\n",
+              "    (bbox_head): Shared2FCBBoxHead(\n",
+              "      (loss_cls): CrossEntropyLoss()\n",
+              "      (loss_bbox): L1Loss()\n",
+              "      (fc_cls): Linear(in_features=1024, out_features=81, bias=True)\n",
+              "      (fc_reg): Linear(in_features=1024, out_features=320, bias=True)\n",
+              "      (shared_convs): ModuleList()\n",
+              "      (shared_fcs): ModuleList(\n",
+              "        (0): Linear(in_features=12544, out_features=1024, bias=True)\n",
+              "        (1): Linear(in_features=1024, out_features=1024, bias=True)\n",
+              "      )\n",
+              "      (cls_convs): ModuleList()\n",
+              "      (cls_fcs): ModuleList()\n",
+              "      (reg_convs): ModuleList()\n",
+              "      (reg_fcs): ModuleList()\n",
+              "      (relu): ReLU(inplace=True)\n",
+              "    )\n",
+              "    init_cfg=[{'type': 'Normal', 'std': 0.01, 'override': {'name': 'fc_cls'}}, {'type': 'Normal', 'std': 0.001, 'override': {'name': 'fc_reg'}}, {'type': 'Xavier', 'distribution': 'uniform', 'override': [{'name': 'shared_fcs'}, {'name': 'cls_fcs'}, {'name': 'reg_fcs'}]}]\n",
+              "  )\n",
+              ")"
+            ]
+          },
+          "execution_count": 7,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "import mmcv\n",
+        "from mmcv.runner import load_checkpoint\n",
+        "\n",
+        "from mmdet.apis import inference_detector, show_result_pyplot\n",
+        "from mmdet.models import build_detector\n",
+        "\n",
+        "# Choose to use a config and initialize the detector\n",
+        "config = 'configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco.py'\n",
+        "# Setup a checkpoint file to load\n",
+        "checkpoint = 'checkpoints/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth'\n",
+        "\n",
+        "# Set the device to be used for evaluation\n",
+        "device='cuda:0'\n",
+        "\n",
+        "# Load the config\n",
+        "config = mmcv.Config.fromfile(config)\n",
+        "# Set pretrained to be None since we do not need pretrained model here\n",
+        "config.model.pretrained = None\n",
+        "\n",
+        "# Initialize the detector\n",
+        "model = build_detector(config.model)\n",
+        "\n",
+        "# Load checkpoint\n",
+        "checkpoint = load_checkpoint(model, checkpoint, map_location=device)\n",
+        "\n",
+        "# Set the classes of models for inference\n",
+        "model.CLASSES = checkpoint['meta']['CLASSES']\n",
+        "\n",
+        "# We need to set the model's cfg for inference\n",
+        "model.cfg = config\n",
+        "\n",
+        "# Convert the model to GPU\n",
+        "model.to(device)\n",
+        "# Convert the model into evaluation mode\n",
+        "model.eval()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fLgFRMtP91ue"
+      },
+      "source": [
+        "From the printed model, we will find that the model does consist of the components that we described earlier. It uses ResNet as its CNN backbone, and has a RPN head and RoI Head. In addition, the model has a neural network module, named neck, directly after the CNN backbone. It is a [feature pyramid network (FPN)](https://arxiv.org/abs/1612.03144) for enhancing the multi-scale features.\n",
+        "\n",
+        "\n",
+        "### Inference the detector\n",
+        "\n",
+        "Since the model is successfully created and loaded, let's see how good it is. We use the high-level API `inference_detector` implemented in the MMDetection. This API is created to ease the inference process. The details of the codes can be found [here](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/apis/inference.py#L15)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Wi6DRpsQPEmV",
+        "outputId": "a0e1e23c-d78c-4381-9f4c-5603c81a0f87"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/content/mmdetection/mmdet/datasets/utils.py:69: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n",
+            "  'data pipeline in your config file.', UserWarning)\n",
+            "/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at  /pytorch/c10/core/TensorImpl.h:1156.)\n",
+            "  return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Use the detector to do inference\n",
+        "img = 'demo/demo.jpg'\n",
+        "result = inference_detector(model, img)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 474
+        },
+        "id": "UsJU5D-QPX8L",
+        "outputId": "b70aceab-d264-4f5e-cdbe-5db1389eeb29"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 640.01x427.01 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Let's plot the result\n",
+        "show_result_pyplot(model, img, result, score_thr=0.3)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7GrWIJywLV-V"
+      },
+      "source": [
+        "## Train A Detector on A Customized Dataset\n",
+        "\n",
+        "To train a new detector, there are usually three things to do:\n",
+        "1. Support a new dataset\n",
+        "2. Modify the config\n",
+        "3. Train a new detector\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "E73y5Lru-wBx"
+      },
+      "source": [
+        "### Support a new dataset\n",
+        "\n",
+        "There are three ways to support a new dataset in MMDetection: \n",
+        "  1. Reorganize the dataset into a COCO format.\n",
+        "  2. Reorganize the dataset into a middle format.\n",
+        "  3. Implement a new dataset.\n",
+        "\n",
+        "We recommend the first two methods, as they are usually easier than the third one.\n",
+        "\n",
+        "In this tutorial, we give an example that converts the data into the formats of existing datasets, e.g. COCO, VOC, etc. Other methods and more advanced usages can be found in the [doc](https://mmdetection.readthedocs.io/en/latest/tutorials/customize_dataset.html#).\n",
+        "\n",
+        "First, let's download a tiny dataset obtained from [KITTI](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d). We select the first 75 images and their annotations from the 3D object detection dataset (it is the same dataset as the 2D object detection dataset but with 3D annotations). We convert the original images from PNG to JPEG format with 80% quality to reduce the size of the dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "rHnw5Q_nARXq",
+        "outputId": "089f8810-be3a-4627-e3d7-945b3c5cf29e"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "--2022-02-08 11:33:06--  https://download.openmmlab.com/mmdetection/data/kitti_tiny.zip\n",
+            "Resolving download.openmmlab.com (download.openmmlab.com)... 47.245.16.66\n",
+            "Connecting to download.openmmlab.com (download.openmmlab.com)|47.245.16.66|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 6918271 (6.6M) [application/zip]\n",
+            "Saving to: ‘kitti_tiny.zip’\n",
+            "\n",
+            "kitti_tiny.zip      100%[===================>]   6.60M  4.69MB/s    in 1.4s    \n",
+            "\n",
+            "2022-02-08 11:33:09 (4.69 MB/s) - ‘kitti_tiny.zip’ saved [6918271/6918271]\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "# download, decompress the data\n",
+        "!wget https://download.openmmlab.com/mmdetection/data/kitti_tiny.zip\n",
+        "!unzip kitti_tiny.zip > /dev/null"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 11,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Wuwxw1oZRtVZ",
+        "outputId": "c1cb0332-a381-4685-c692-ea7a6279d65d"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Reading package lists...\n",
+            "Building dependency tree...\n",
+            "Reading state information...\n",
+            "The following packages were automatically installed and are no longer required:\n",
+            "  cuda-command-line-tools-10-0 cuda-command-line-tools-10-1\n",
+            "  cuda-command-line-tools-11-0 cuda-compiler-10-0 cuda-compiler-10-1\n",
+            "  cuda-compiler-11-0 cuda-cuobjdump-10-0 cuda-cuobjdump-10-1\n",
+            "  cuda-cuobjdump-11-0 cuda-cupti-10-0 cuda-cupti-10-1 cuda-cupti-11-0\n",
+            "  cuda-cupti-dev-11-0 cuda-documentation-10-0 cuda-documentation-10-1\n",
+            "  cuda-documentation-11-0 cuda-documentation-11-1 cuda-gdb-10-0 cuda-gdb-10-1\n",
+            "  cuda-gdb-11-0 cuda-gpu-library-advisor-10-0 cuda-gpu-library-advisor-10-1\n",
+            "  cuda-libraries-10-0 cuda-libraries-10-1 cuda-libraries-11-0\n",
+            "  cuda-memcheck-10-0 cuda-memcheck-10-1 cuda-memcheck-11-0 cuda-nsight-10-0\n",
+            "  cuda-nsight-10-1 cuda-nsight-11-0 cuda-nsight-11-1 cuda-nsight-compute-10-0\n",
+            "  cuda-nsight-compute-10-1 cuda-nsight-compute-11-0 cuda-nsight-compute-11-1\n",
+            "  cuda-nsight-systems-10-1 cuda-nsight-systems-11-0 cuda-nsight-systems-11-1\n",
+            "  cuda-nvcc-10-0 cuda-nvcc-10-1 cuda-nvcc-11-0 cuda-nvdisasm-10-0\n",
+            "  cuda-nvdisasm-10-1 cuda-nvdisasm-11-0 cuda-nvml-dev-10-0 cuda-nvml-dev-10-1\n",
+            "  cuda-nvml-dev-11-0 cuda-nvprof-10-0 cuda-nvprof-10-1 cuda-nvprof-11-0\n",
+            "  cuda-nvprune-10-0 cuda-nvprune-10-1 cuda-nvprune-11-0 cuda-nvtx-10-0\n",
+            "  cuda-nvtx-10-1 cuda-nvtx-11-0 cuda-nvvp-10-0 cuda-nvvp-10-1 cuda-nvvp-11-0\n",
+            "  cuda-nvvp-11-1 cuda-samples-10-0 cuda-samples-10-1 cuda-samples-11-0\n",
+            "  cuda-samples-11-1 cuda-sanitizer-11-0 cuda-sanitizer-api-10-1\n",
+            "  cuda-toolkit-10-0 cuda-toolkit-10-1 cuda-toolkit-11-0 cuda-toolkit-11-1\n",
+            "  cuda-tools-10-0 cuda-tools-10-1 cuda-tools-11-0 cuda-tools-11-1\n",
+            "  cuda-visual-tools-10-0 cuda-visual-tools-10-1 cuda-visual-tools-11-0\n",
+            "  cuda-visual-tools-11-1 default-jre dkms freeglut3 freeglut3-dev\n",
+            "  keyboard-configuration libargon2-0 libcap2 libcryptsetup12\n",
+            "  libdevmapper1.02.1 libfontenc1 libidn11 libip4tc0 libjansson4\n",
+            "  libnvidia-cfg1-510 libnvidia-common-460 libnvidia-common-510\n",
+            "  libnvidia-extra-510 libnvidia-fbc1-510 libnvidia-gl-510 libpam-systemd\n",
+            "  libpolkit-agent-1-0 libpolkit-backend-1-0 libpolkit-gobject-1-0 libxfont2\n",
+            "  libxi-dev libxkbfile1 libxmu-dev libxmu-headers libxnvctrl0 libxtst6\n",
+            "  nsight-compute-2020.2.1 nsight-compute-2022.1.0 nsight-systems-2020.3.2\n",
+            "  nsight-systems-2020.3.4 nsight-systems-2021.5.2 nvidia-dkms-510\n",
+            "  nvidia-kernel-common-510 nvidia-kernel-source-510 nvidia-modprobe\n",
+            "  nvidia-settings openjdk-11-jre policykit-1 policykit-1-gnome python3-xkit\n",
+            "  screen-resolution-extra systemd systemd-sysv udev x11-xkb-utils\n",
+            "  xserver-common xserver-xorg-core-hwe-18.04 xserver-xorg-video-nvidia-510\n",
+            "Use 'apt autoremove' to remove them.\n",
+            "The following NEW packages will be installed:\n",
+            "  tree\n",
+            "0 upgraded, 1 newly installed, 0 to remove and 39 not upgraded.\n",
+            "Need to get 40.7 kB of archives.\n",
+            "After this operation, 105 kB of additional disk space will be used.\n",
+            "Get:1 http://archive.ubuntu.com/ubuntu bionic/universe amd64 tree amd64 1.7.0-5 [40.7 kB]\n",
+            "Fetched 40.7 kB in 0s (146 kB/s)\n",
+            "Selecting previously unselected package tree.\n",
+            "(Reading database ... 155113 files and directories currently installed.)\n",
+            "Preparing to unpack .../tree_1.7.0-5_amd64.deb ...\n",
+            "Unpacking tree (1.7.0-5) ...\n",
+            "Setting up tree (1.7.0-5) ...\n",
+            "Processing triggers for man-db (2.8.3-2ubuntu0.1) ...\n",
+            "kitti_tiny\n",
+            "├── training\n",
+            "│   ├── image_2\n",
+            "│   │   ├── 000000.jpeg\n",
+            "│   │   ├── 000001.jpeg\n",
+            "│   │   ├── 000002.jpeg\n",
+            "│   │   ├── 000003.jpeg\n",
+            "│   │   ├── 000004.jpeg\n",
+            "│   │   ├── 000005.jpeg\n",
+            "│   │   ├── 000006.jpeg\n",
+            "│   │   ├── 000007.jpeg\n",
+            "│   │   ├── 000008.jpeg\n",
+            "│   │   ├── 000009.jpeg\n",
+            "│   │   ├── 000010.jpeg\n",
+            "│   │   ├── 000011.jpeg\n",
+            "│   │   ├── 000012.jpeg\n",
+            "│   │   ├── 000013.jpeg\n",
+            "│   │   ├── 000014.jpeg\n",
+            "│   │   ├── 000015.jpeg\n",
+            "│   │   ├── 000016.jpeg\n",
+            "│   │   ├── 000017.jpeg\n",
+            "│   │   ├── 000018.jpeg\n",
+            "│   │   ├── 000019.jpeg\n",
+            "│   │   ├── 000020.jpeg\n",
+            "│   │   ├── 000021.jpeg\n",
+            "│   │   ├── 000022.jpeg\n",
+            "│   │   ├── 000023.jpeg\n",
+            "│   │   ├── 000024.jpeg\n",
+            "│   │   ├── 000025.jpeg\n",
+            "│   │   ├── 000026.jpeg\n",
+            "│   │   ├── 000027.jpeg\n",
+            "│   │   ├── 000028.jpeg\n",
+            "│   │   ├── 000029.jpeg\n",
+            "│   │   ├── 000030.jpeg\n",
+            "│   │   ├── 000031.jpeg\n",
+            "│   │   ├── 000032.jpeg\n",
+            "│   │   ├── 000033.jpeg\n",
+            "│   │   ├── 000034.jpeg\n",
+            "│   │   ├── 000035.jpeg\n",
+            "│   │   ├── 000036.jpeg\n",
+            "│   │   ├── 000037.jpeg\n",
+            "│   │   ├── 000038.jpeg\n",
+            "│   │   ├── 000039.jpeg\n",
+            "│   │   ├── 000040.jpeg\n",
+            "│   │   ├── 000041.jpeg\n",
+            "│   │   ├── 000042.jpeg\n",
+            "│   │   ├── 000043.jpeg\n",
+            "│   │   ├── 000044.jpeg\n",
+            "│   │   ├── 000045.jpeg\n",
+            "│   │   ├── 000046.jpeg\n",
+            "│   │   ├── 000047.jpeg\n",
+            "│   │   ├── 000048.jpeg\n",
+            "│   │   ├── 000049.jpeg\n",
+            "│   │   ├── 000050.jpeg\n",
+            "│   │   ├── 000051.jpeg\n",
+            "│   │   ├── 000052.jpeg\n",
+            "│   │   ├── 000053.jpeg\n",
+            "│   │   ├── 000054.jpeg\n",
+            "│   │   ├── 000055.jpeg\n",
+            "│   │   ├── 000056.jpeg\n",
+            "│   │   ├── 000057.jpeg\n",
+            "│   │   ├── 000058.jpeg\n",
+            "│   │   ├── 000059.jpeg\n",
+            "│   │   ├── 000060.jpeg\n",
+            "│   │   ├── 000061.jpeg\n",
+            "│   │   ├── 000062.jpeg\n",
+            "│   │   ├── 000063.jpeg\n",
+            "│   │   ├── 000064.jpeg\n",
+            "│   │   ├── 000065.jpeg\n",
+            "│   │   ├── 000066.jpeg\n",
+            "│   │   ├── 000067.jpeg\n",
+            "│   │   ├── 000068.jpeg\n",
+            "│   │   ├── 000069.jpeg\n",
+            "│   │   ├── 000070.jpeg\n",
+            "│   │   ├── 000071.jpeg\n",
+            "│   │   ├── 000072.jpeg\n",
+            "│   │   ├── 000073.jpeg\n",
+            "│   │   └── 000074.jpeg\n",
+            "│   └── label_2\n",
+            "│       ├── 000000.txt\n",
+            "│       ├── 000001.txt\n",
+            "│       ├── 000002.txt\n",
+            "│       ├── 000003.txt\n",
+            "│       ├── 000004.txt\n",
+            "│       ├── 000005.txt\n",
+            "│       ├── 000006.txt\n",
+            "│       ├── 000007.txt\n",
+            "│       ├── 000008.txt\n",
+            "│       ├── 000009.txt\n",
+            "│       ├── 000010.txt\n",
+            "│       ├── 000011.txt\n",
+            "│       ├── 000012.txt\n",
+            "│       ├── 000013.txt\n",
+            "│       ├── 000014.txt\n",
+            "│       ├── 000015.txt\n",
+            "│       ├── 000016.txt\n",
+            "│       ├── 000017.txt\n",
+            "│       ├── 000018.txt\n",
+            "│       ├── 000019.txt\n",
+            "│       ├── 000020.txt\n",
+            "│       ├── 000021.txt\n",
+            "│       ├── 000022.txt\n",
+            "│       ├── 000023.txt\n",
+            "│       ├── 000024.txt\n",
+            "│       ├── 000025.txt\n",
+            "│       ├── 000026.txt\n",
+            "│       ├── 000027.txt\n",
+            "│       ├── 000028.txt\n",
+            "│       ├── 000029.txt\n",
+            "│       ├── 000030.txt\n",
+            "│       ├── 000031.txt\n",
+            "│       ├── 000032.txt\n",
+            "│       ├── 000033.txt\n",
+            "│       ├── 000034.txt\n",
+            "│       ├── 000035.txt\n",
+            "│       ├── 000036.txt\n",
+            "│       ├── 000037.txt\n",
+            "│       ├── 000038.txt\n",
+            "│       ├── 000039.txt\n",
+            "│       ├── 000040.txt\n",
+            "│       ├── 000041.txt\n",
+            "│       ├── 000042.txt\n",
+            "│       ├── 000043.txt\n",
+            "│       ├── 000044.txt\n",
+            "│       ├── 000045.txt\n",
+            "│       ├── 000046.txt\n",
+            "│       ├── 000047.txt\n",
+            "│       ├── 000048.txt\n",
+            "│       ├── 000049.txt\n",
+            "│       ├── 000050.txt\n",
+            "│       ├── 000051.txt\n",
+            "│       ├── 000052.txt\n",
+            "│       ├── 000053.txt\n",
+            "│       ├── 000054.txt\n",
+            "│       ├── 000055.txt\n",
+            "│       ├── 000056.txt\n",
+            "│       ├── 000057.txt\n",
+            "│       ├── 000058.txt\n",
+            "│       ├── 000059.txt\n",
+            "│       ├── 000060.txt\n",
+            "│       ├── 000061.txt\n",
+            "│       ├── 000062.txt\n",
+            "│       ├── 000063.txt\n",
+            "│       ├── 000064.txt\n",
+            "│       ├── 000065.txt\n",
+            "│       ├── 000066.txt\n",
+            "│       ├── 000067.txt\n",
+            "│       ├── 000068.txt\n",
+            "│       ├── 000069.txt\n",
+            "│       ├── 000070.txt\n",
+            "│       ├── 000071.txt\n",
+            "│       ├── 000072.txt\n",
+            "│       ├── 000073.txt\n",
+            "│       └── 000074.txt\n",
+            "├── train.txt\n",
+            "└── val.txt\n",
+            "\n",
+            "3 directories, 152 files\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Check the directory structure of the tiny data\n",
+        "\n",
+        "# Install tree first\n",
+        "!apt-get -q install tree\n",
+        "!tree kitti_tiny"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 304
+        },
+        "id": "YnQQqzOWzE91",
+        "outputId": "baf6a89b-dbb2-4212-9e34-7055a9e2574c"
+      },
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1080x720 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Let's take a look at the dataset image\n",
+        "import mmcv\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "img = mmcv.imread('kitti_tiny/training/image_2/000073.jpeg')\n",
+        "plt.figure(figsize=(15, 10))\n",
+        "plt.imshow(mmcv.bgr2rgb(img))\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PMZvtSIl71qi"
+      },
+      "source": [
+        "After downloading the data, we need to implement a function to convert the KITTI annotation format into the middle format. In this tutorial, we choose to convert them in **`load_annotations`** function in a newly implemented **`KittiTinyDataset`**.\n",
+        "\n",
+        "Let's take a look at the annotation txt file.\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "n7rwalnPd6e1",
+        "outputId": "54bfbfa4-463b-45a0-f77c-a80557c1bd69"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Pedestrian 0.00 0 -0.20 712.40 143.00 810.73 307.92 1.89 0.48 1.20 1.84 1.47 8.41 0.01\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Check the label of a single image\n",
+        "!cat kitti_tiny/training/label_2/000000.txt"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QA1pFg-FeO3l"
+      },
+      "source": [
+        "According to the KITTI's documentation, the first column indicates the class of the object, and the 5th to 8th columns indicate the bboxes. We need to read annotations of each image and convert them into middle format that MMDetection can accept, as follows:\n",
+        "\n",
+        "```python\n",
+        "[\n",
+        "    {\n",
+        "        'filename': 'a.jpg',\n",
+        "        'width': 1280,\n",
+        "        'height': 720,\n",
+        "        'ann': {\n",
+        "            'bboxes': <np.ndarray> (n, 4) in (x1, y1, x2, y2) order,\n",
+        "            'labels': <np.ndarray> (n, ),\n",
+        "            'bboxes_ignore': <np.ndarray> (k, 4), (optional field)\n",
+        "            'labels_ignore': <np.ndarray> (k, 4) (optional field)\n",
+        "        }\n",
+        "    },\n",
+        "    ...\n",
+        "]\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "GdSaB2ad0EdX"
+      },
+      "outputs": [],
+      "source": [
+        "import copy\n",
+        "import os.path as osp\n",
+        "\n",
+        "import mmcv\n",
+        "import numpy as np\n",
+        "\n",
+        "from mmdet.datasets.builder import DATASETS\n",
+        "from mmdet.datasets.custom import CustomDataset\n",
+        "\n",
+        "@DATASETS.register_module()\n",
+        "class KittiTinyDataset(CustomDataset):\n",
+        "\n",
+        "    CLASSES = ('Car', 'Pedestrian', 'Cyclist')\n",
+        "\n",
+        "    def load_annotations(self, ann_file):\n",
+        "        cat2label = {k: i for i, k in enumerate(self.CLASSES)}\n",
+        "        # load image list from file\n",
+        "        image_list = mmcv.list_from_file(self.ann_file)\n",
+        "    \n",
+        "        data_infos = []\n",
+        "        # convert annotations to middle format\n",
+        "        for image_id in image_list:\n",
+        "            filename = f'{self.img_prefix}/{image_id}.jpeg'\n",
+        "            image = mmcv.imread(filename)\n",
+        "            height, width = image.shape[:2]\n",
+        "    \n",
+        "            data_info = dict(filename=f'{image_id}.jpeg', width=width, height=height)\n",
+        "    \n",
+        "            # load annotations\n",
+        "            label_prefix = self.img_prefix.replace('image_2', 'label_2')\n",
+        "            lines = mmcv.list_from_file(osp.join(label_prefix, f'{image_id}.txt'))\n",
+        "    \n",
+        "            content = [line.strip().split(' ') for line in lines]\n",
+        "            bbox_names = [x[0] for x in content]\n",
+        "            bboxes = [[float(info) for info in x[4:8]] for x in content]\n",
+        "    \n",
+        "            gt_bboxes = []\n",
+        "            gt_labels = []\n",
+        "            gt_bboxes_ignore = []\n",
+        "            gt_labels_ignore = []\n",
+        "    \n",
+        "            # filter 'DontCare'\n",
+        "            for bbox_name, bbox in zip(bbox_names, bboxes):\n",
+        "                if bbox_name in cat2label:\n",
+        "                    gt_labels.append(cat2label[bbox_name])\n",
+        "                    gt_bboxes.append(bbox)\n",
+        "                else:\n",
+        "                    gt_labels_ignore.append(-1)\n",
+        "                    gt_bboxes_ignore.append(bbox)\n",
+        "\n",
+        "            data_anno = dict(\n",
+        "                bboxes=np.array(gt_bboxes, dtype=np.float32).reshape(-1, 4),\n",
+        "                labels=np.array(gt_labels, dtype=np.long),\n",
+        "                bboxes_ignore=np.array(gt_bboxes_ignore,\n",
+        "                                       dtype=np.float32).reshape(-1, 4),\n",
+        "                labels_ignore=np.array(gt_labels_ignore, dtype=np.long))\n",
+        "\n",
+        "            data_info.update(ann=data_anno)\n",
+        "            data_infos.append(data_info)\n",
+        "\n",
+        "        return data_infos"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PwqJOpBe-bMj"
+      },
+      "source": [
+        "### Modify the config\n",
+        "\n",
+        "In the next step, we need to modify the config for the training.\n",
+        "To accelerate the process, we finetune a detector using a pre-trained detector."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "id": "hamZrlnH-YDD"
+      },
+      "outputs": [],
+      "source": [
+        "from mmcv import Config\n",
+        "cfg = Config.fromfile('./configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py')"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HntziLGq-92Z"
+      },
+      "source": [
+        "Given a config that trains a Faster R-CNN on COCO dataset, we need to modify some values to use it for training Faster R-CNN on KITTI dataset. We modify the config of datasets, learning rate schedules, and runtime settings."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 17,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pUbwD8uV0PR8",
+        "outputId": "76e68abb-6b42-488f-cbca-6da39e094943"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Config:\n",
+            "model = dict(\n",
+            "    type='FasterRCNN',\n",
+            "    backbone=dict(\n",
+            "        type='ResNet',\n",
+            "        depth=50,\n",
+            "        num_stages=4,\n",
+            "        out_indices=(0, 1, 2, 3),\n",
+            "        frozen_stages=1,\n",
+            "        norm_cfg=dict(type='BN', requires_grad=False),\n",
+            "        norm_eval=True,\n",
+            "        style='caffe',\n",
+            "        init_cfg=dict(\n",
+            "            type='Pretrained',\n",
+            "            checkpoint='open-mmlab://detectron2/resnet50_caffe')),\n",
+            "    neck=dict(\n",
+            "        type='FPN',\n",
+            "        in_channels=[256, 512, 1024, 2048],\n",
+            "        out_channels=256,\n",
+            "        num_outs=5),\n",
+            "    rpn_head=dict(\n",
+            "        type='RPNHead',\n",
+            "        in_channels=256,\n",
+            "        feat_channels=256,\n",
+            "        anchor_generator=dict(\n",
+            "            type='AnchorGenerator',\n",
+            "            scales=[8],\n",
+            "            ratios=[0.5, 1.0, 2.0],\n",
+            "            strides=[4, 8, 16, 32, 64]),\n",
+            "        bbox_coder=dict(\n",
+            "            type='DeltaXYWHBBoxCoder',\n",
+            "            target_means=[0.0, 0.0, 0.0, 0.0],\n",
+            "            target_stds=[1.0, 1.0, 1.0, 1.0]),\n",
+            "        loss_cls=dict(\n",
+            "            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),\n",
+            "        loss_bbox=dict(type='L1Loss', loss_weight=1.0)),\n",
+            "    roi_head=dict(\n",
+            "        type='StandardRoIHead',\n",
+            "        bbox_roi_extractor=dict(\n",
+            "            type='SingleRoIExtractor',\n",
+            "            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),\n",
+            "            out_channels=256,\n",
+            "            featmap_strides=[4, 8, 16, 32]),\n",
+            "        bbox_head=dict(\n",
+            "            type='Shared2FCBBoxHead',\n",
+            "            in_channels=256,\n",
+            "            fc_out_channels=1024,\n",
+            "            roi_feat_size=7,\n",
+            "            num_classes=3,\n",
+            "            bbox_coder=dict(\n",
+            "                type='DeltaXYWHBBoxCoder',\n",
+            "                target_means=[0.0, 0.0, 0.0, 0.0],\n",
+            "                target_stds=[0.1, 0.1, 0.2, 0.2]),\n",
+            "            reg_class_agnostic=False,\n",
+            "            loss_cls=dict(\n",
+            "                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),\n",
+            "            loss_bbox=dict(type='L1Loss', loss_weight=1.0))),\n",
+            "    train_cfg=dict(\n",
+            "        rpn=dict(\n",
+            "            assigner=dict(\n",
+            "                type='MaxIoUAssigner',\n",
+            "                pos_iou_thr=0.7,\n",
+            "                neg_iou_thr=0.3,\n",
+            "                min_pos_iou=0.3,\n",
+            "                match_low_quality=True,\n",
+            "                ignore_iof_thr=-1),\n",
+            "            sampler=dict(\n",
+            "                type='RandomSampler',\n",
+            "                num=256,\n",
+            "                pos_fraction=0.5,\n",
+            "                neg_pos_ub=-1,\n",
+            "                add_gt_as_proposals=False),\n",
+            "            allowed_border=-1,\n",
+            "            pos_weight=-1,\n",
+            "            debug=False),\n",
+            "        rpn_proposal=dict(\n",
+            "            nms_pre=2000,\n",
+            "            max_per_img=1000,\n",
+            "            nms=dict(type='nms', iou_threshold=0.7),\n",
+            "            min_bbox_size=0),\n",
+            "        rcnn=dict(\n",
+            "            assigner=dict(\n",
+            "                type='MaxIoUAssigner',\n",
+            "                pos_iou_thr=0.5,\n",
+            "                neg_iou_thr=0.5,\n",
+            "                min_pos_iou=0.5,\n",
+            "                match_low_quality=False,\n",
+            "                ignore_iof_thr=-1),\n",
+            "            sampler=dict(\n",
+            "                type='RandomSampler',\n",
+            "                num=512,\n",
+            "                pos_fraction=0.25,\n",
+            "                neg_pos_ub=-1,\n",
+            "                add_gt_as_proposals=True),\n",
+            "            pos_weight=-1,\n",
+            "            debug=False)),\n",
+            "    test_cfg=dict(\n",
+            "        rpn=dict(\n",
+            "            nms_pre=1000,\n",
+            "            max_per_img=1000,\n",
+            "            nms=dict(type='nms', iou_threshold=0.7),\n",
+            "            min_bbox_size=0),\n",
+            "        rcnn=dict(\n",
+            "            score_thr=0.05,\n",
+            "            nms=dict(type='nms', iou_threshold=0.5),\n",
+            "            max_per_img=100)))\n",
+            "dataset_type = 'KittiTinyDataset'\n",
+            "data_root = 'kitti_tiny/'\n",
+            "img_norm_cfg = dict(\n",
+            "    mean=[103.53, 116.28, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)\n",
+            "train_pipeline = [\n",
+            "    dict(type='LoadImageFromFile'),\n",
+            "    dict(type='LoadAnnotations', with_bbox=True),\n",
+            "    dict(\n",
+            "        type='Resize',\n",
+            "        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),\n",
+            "                   (1333, 768), (1333, 800)],\n",
+            "        multiscale_mode='value',\n",
+            "        keep_ratio=True),\n",
+            "    dict(type='RandomFlip', flip_ratio=0.5),\n",
+            "    dict(\n",
+            "        type='Normalize',\n",
+            "        mean=[103.53, 116.28, 123.675],\n",
+            "        std=[1.0, 1.0, 1.0],\n",
+            "        to_rgb=False),\n",
+            "    dict(type='Pad', size_divisor=32),\n",
+            "    dict(type='DefaultFormatBundle'),\n",
+            "    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n",
+            "]\n",
+            "test_pipeline = [\n",
+            "    dict(type='LoadImageFromFile'),\n",
+            "    dict(\n",
+            "        type='MultiScaleFlipAug',\n",
+            "        img_scale=(1333, 800),\n",
+            "        flip=False,\n",
+            "        transforms=[\n",
+            "            dict(type='Resize', keep_ratio=True),\n",
+            "            dict(type='RandomFlip'),\n",
+            "            dict(\n",
+            "                type='Normalize',\n",
+            "                mean=[103.53, 116.28, 123.675],\n",
+            "                std=[1.0, 1.0, 1.0],\n",
+            "                to_rgb=False),\n",
+            "            dict(type='Pad', size_divisor=32),\n",
+            "            dict(type='ImageToTensor', keys=['img']),\n",
+            "            dict(type='Collect', keys=['img'])\n",
+            "        ])\n",
+            "]\n",
+            "data = dict(\n",
+            "    samples_per_gpu=2,\n",
+            "    workers_per_gpu=2,\n",
+            "    train=dict(\n",
+            "        type='KittiTinyDataset',\n",
+            "        ann_file='train.txt',\n",
+            "        img_prefix='training/image_2',\n",
+            "        pipeline=[\n",
+            "            dict(type='LoadImageFromFile'),\n",
+            "            dict(type='LoadAnnotations', with_bbox=True),\n",
+            "            dict(\n",
+            "                type='Resize',\n",
+            "                img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),\n",
+            "                           (1333, 768), (1333, 800)],\n",
+            "                multiscale_mode='value',\n",
+            "                keep_ratio=True),\n",
+            "            dict(type='RandomFlip', flip_ratio=0.5),\n",
+            "            dict(\n",
+            "                type='Normalize',\n",
+            "                mean=[103.53, 116.28, 123.675],\n",
+            "                std=[1.0, 1.0, 1.0],\n",
+            "                to_rgb=False),\n",
+            "            dict(type='Pad', size_divisor=32),\n",
+            "            dict(type='DefaultFormatBundle'),\n",
+            "            dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])\n",
+            "        ],\n",
+            "        data_root='kitti_tiny/'),\n",
+            "    val=dict(\n",
+            "        type='KittiTinyDataset',\n",
+            "        ann_file='val.txt',\n",
+            "        img_prefix='training/image_2',\n",
+            "        pipeline=[\n",
+            "            dict(type='LoadImageFromFile'),\n",
+            "            dict(\n",
+            "                type='MultiScaleFlipAug',\n",
+            "                img_scale=(1333, 800),\n",
+            "                flip=False,\n",
+            "                transforms=[\n",
+            "                    dict(type='Resize', keep_ratio=True),\n",
+            "                    dict(type='RandomFlip'),\n",
+            "                    dict(\n",
+            "                        type='Normalize',\n",
+            "                        mean=[103.53, 116.28, 123.675],\n",
+            "                        std=[1.0, 1.0, 1.0],\n",
+            "                        to_rgb=False),\n",
+            "                    dict(type='Pad', size_divisor=32),\n",
+            "                    dict(type='ImageToTensor', keys=['img']),\n",
+            "                    dict(type='Collect', keys=['img'])\n",
+            "                ])\n",
+            "        ],\n",
+            "        data_root='kitti_tiny/'),\n",
+            "    test=dict(\n",
+            "        type='KittiTinyDataset',\n",
+            "        ann_file='train.txt',\n",
+            "        img_prefix='training/image_2',\n",
+            "        pipeline=[\n",
+            "            dict(type='LoadImageFromFile'),\n",
+            "            dict(\n",
+            "                type='MultiScaleFlipAug',\n",
+            "                img_scale=(1333, 800),\n",
+            "                flip=False,\n",
+            "                transforms=[\n",
+            "                    dict(type='Resize', keep_ratio=True),\n",
+            "                    dict(type='RandomFlip'),\n",
+            "                    dict(\n",
+            "                        type='Normalize',\n",
+            "                        mean=[103.53, 116.28, 123.675],\n",
+            "                        std=[1.0, 1.0, 1.0],\n",
+            "                        to_rgb=False),\n",
+            "                    dict(type='Pad', size_divisor=32),\n",
+            "                    dict(type='ImageToTensor', keys=['img']),\n",
+            "                    dict(type='Collect', keys=['img'])\n",
+            "                ])\n",
+            "        ],\n",
+            "        data_root='kitti_tiny/'))\n",
+            "evaluation = dict(interval=12, metric='mAP')\n",
+            "optimizer = dict(type='SGD', lr=0.0025, momentum=0.9, weight_decay=0.0001)\n",
+            "optimizer_config = dict(grad_clip=None)\n",
+            "lr_config = dict(\n",
+            "    policy='step',\n",
+            "    warmup=None,\n",
+            "    warmup_iters=500,\n",
+            "    warmup_ratio=0.001,\n",
+            "    step=[8, 11])\n",
+            "runner = dict(type='EpochBasedRunner', max_epochs=12)\n",
+            "checkpoint_config = dict(interval=12)\n",
+            "log_config = dict(\n",
+            "    interval=10,\n",
+            "    hooks=[dict(type='TextLoggerHook'),\n",
+            "           dict(type='TensorboardLoggerHook')])\n",
+            "custom_hooks = [dict(type='NumClassCheckHook')]\n",
+            "dist_params = dict(backend='nccl')\n",
+            "log_level = 'INFO'\n",
+            "load_from = 'checkpoints/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth'\n",
+            "resume_from = None\n",
+            "workflow = [('train', 1)]\n",
+            "opencv_num_threads = 0\n",
+            "mp_start_method = 'fork'\n",
+            "work_dir = './tutorial_exps'\n",
+            "seed = 0\n",
+            "gpu_ids = range(0, 1)\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "from mmdet.apis import set_random_seed\n",
+        "\n",
+        "# Modify dataset type and path\n",
+        "cfg.dataset_type = 'KittiTinyDataset'\n",
+        "cfg.data_root = 'kitti_tiny/'\n",
+        "\n",
+        "cfg.data.test.type = 'KittiTinyDataset'\n",
+        "cfg.data.test.data_root = 'kitti_tiny/'\n",
+        "cfg.data.test.ann_file = 'train.txt'\n",
+        "cfg.data.test.img_prefix = 'training/image_2'\n",
+        "\n",
+        "cfg.data.train.type = 'KittiTinyDataset'\n",
+        "cfg.data.train.data_root = 'kitti_tiny/'\n",
+        "cfg.data.train.ann_file = 'train.txt'\n",
+        "cfg.data.train.img_prefix = 'training/image_2'\n",
+        "\n",
+        "cfg.data.val.type = 'KittiTinyDataset'\n",
+        "cfg.data.val.data_root = 'kitti_tiny/'\n",
+        "cfg.data.val.ann_file = 'val.txt'\n",
+        "cfg.data.val.img_prefix = 'training/image_2'\n",
+        "\n",
+        "# modify num classes of the model in box head\n",
+        "cfg.model.roi_head.bbox_head.num_classes = 3\n",
+        "# If we need to finetune a model based on a pre-trained detector, we need to\n",
+        "# use load_from to set the path of checkpoints.\n",
+        "cfg.load_from = 'checkpoints/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth'\n",
+        "\n",
+        "# Set up working dir to save files and logs.\n",
+        "cfg.work_dir = './tutorial_exps'\n",
+        "\n",
+        "# The original learning rate (LR) is set for 8-GPU training.\n",
+        "# We divide it by 8 since we only use one GPU.\n",
+        "cfg.optimizer.lr = 0.02 / 8\n",
+        "cfg.lr_config.warmup = None\n",
+        "cfg.log_config.interval = 10\n",
+        "\n",
+        "# Change the evaluation metric since we use customized dataset.\n",
+        "cfg.evaluation.metric = 'mAP'\n",
+        "# We can set the evaluation interval to reduce the evaluation times\n",
+        "cfg.evaluation.interval = 12\n",
+        "# We can set the checkpoint saving interval to reduce the storage cost\n",
+        "cfg.checkpoint_config.interval = 12\n",
+        "\n",
+        "# Set seed thus the results are more reproducible\n",
+        "cfg.seed = 0\n",
+        "set_random_seed(0, deterministic=False)\n",
+        "cfg.device = 'cuda'\n",
+        "cfg.gpu_ids = range(1)\n",
+        "\n",
+        "# We can also use tensorboard to log the training process\n",
+        "cfg.log_config.hooks = [\n",
+        "    dict(type='TextLoggerHook'),\n",
+        "    dict(type='TensorboardLoggerHook')]\n",
+        "\n",
+        "\n",
+        "# We can initialize the logger for training and have a look\n",
+        "# at the final config used for training\n",
+        "print(f'Config:\\n{cfg.pretty_text}')\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "111W_oZV_3wa"
+      },
+      "source": [
+        "### Train a new detector\n",
+        "\n",
+        "Finally, lets initialize the dataset and detector, then train a new detector! We use the high-level API `train_detector` implemented by MMDetection. This is also used in our training scripts. For details of the implementation, please see [here](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/apis/train.py)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "7WBWHu010PN3",
+        "outputId": "a7646284-f909-46d6-a360-22160daeb1cc"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/content/mmdetection/mmdet/datasets/custom.py:180: UserWarning: CustomDataset does not support filtering empty gt images.\n",
+            "  'CustomDataset does not support filtering empty gt images.')\n",
+            "2022-02-08 11:38:22,273 - mmdet - INFO - load checkpoint from local path: checkpoints/faster_rcnn_r50_caffe_fpn_mstrain_3x_coco_20210526_095054-1f77628b.pth\n",
+            "2022-02-08 11:38:22,406 - mmdet - WARNING - The model and loaded state dict do not match exactly\n",
+            "\n",
+            "size mismatch for roi_head.bbox_head.fc_cls.weight: copying a param with shape torch.Size([81, 1024]) from checkpoint, the shape in current model is torch.Size([4, 1024]).\n",
+            "size mismatch for roi_head.bbox_head.fc_cls.bias: copying a param with shape torch.Size([81]) from checkpoint, the shape in current model is torch.Size([4]).\n",
+            "size mismatch for roi_head.bbox_head.fc_reg.weight: copying a param with shape torch.Size([320, 1024]) from checkpoint, the shape in current model is torch.Size([12, 1024]).\n",
+            "size mismatch for roi_head.bbox_head.fc_reg.bias: copying a param with shape torch.Size([320]) from checkpoint, the shape in current model is torch.Size([12]).\n",
+            "2022-02-08 11:38:22,410 - mmdet - INFO - Start running, host: root@503df4019aac, work_dir: /content/mmdetection/tutorial_exps\n",
+            "2022-02-08 11:38:22,412 - mmdet - INFO - Hooks will be executed in the following order:\n",
+            "before_run:\n",
+            "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
+            "(NORMAL      ) CheckpointHook                     \n",
+            "(LOW         ) EvalHook                           \n",
+            "(VERY_LOW    ) TextLoggerHook                     \n",
+            "(VERY_LOW    ) TensorboardLoggerHook              \n",
+            " -------------------- \n",
+            "before_train_epoch:\n",
+            "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
+            "(NORMAL      ) NumClassCheckHook                  \n",
+            "(LOW         ) IterTimerHook                      \n",
+            "(LOW         ) EvalHook                           \n",
+            "(VERY_LOW    ) TextLoggerHook                     \n",
+            "(VERY_LOW    ) TensorboardLoggerHook              \n",
+            " -------------------- \n",
+            "before_train_iter:\n",
+            "(VERY_HIGH   ) StepLrUpdaterHook                  \n",
+            "(LOW         ) IterTimerHook                      \n",
+            "(LOW         ) EvalHook                           \n",
+            " -------------------- \n",
+            "after_train_iter:\n",
+            "(ABOVE_NORMAL) OptimizerHook                      \n",
+            "(NORMAL      ) CheckpointHook                     \n",
+            "(LOW         ) IterTimerHook                      \n",
+            "(LOW         ) EvalHook                           \n",
+            "(VERY_LOW    ) TextLoggerHook                     \n",
+            "(VERY_LOW    ) TensorboardLoggerHook              \n",
+            " -------------------- \n",
+            "after_train_epoch:\n",
+            "(NORMAL      ) CheckpointHook                     \n",
+            "(LOW         ) EvalHook                           \n",
+            "(VERY_LOW    ) TextLoggerHook                     \n",
+            "(VERY_LOW    ) TensorboardLoggerHook              \n",
+            " -------------------- \n",
+            "before_val_epoch:\n",
+            "(NORMAL      ) NumClassCheckHook                  \n",
+            "(LOW         ) IterTimerHook                      \n",
+            "(VERY_LOW    ) TextLoggerHook                     \n",
+            "(VERY_LOW    ) TensorboardLoggerHook              \n",
+            " -------------------- \n",
+            "before_val_iter:\n",
+            "(LOW         ) IterTimerHook                      \n",
+            " -------------------- \n",
+            "after_val_iter:\n",
+            "(LOW         ) IterTimerHook                      \n",
+            " -------------------- \n",
+            "after_val_epoch:\n",
+            "(VERY_LOW    ) TextLoggerHook                     \n",
+            "(VERY_LOW    ) TensorboardLoggerHook              \n",
+            " -------------------- \n",
+            "after_run:\n",
+            "(VERY_LOW    ) TextLoggerHook                     \n",
+            "(VERY_LOW    ) TensorboardLoggerHook              \n",
+            " -------------------- \n",
+            "2022-02-08 11:38:22,414 - mmdet - INFO - workflow: [('train', 1)], max: 12 epochs\n",
+            "2022-02-08 11:38:22,417 - mmdet - INFO - Checkpoints will be saved to /content/mmdetection/tutorial_exps by HardDiskBackend.\n",
+            "2022-02-08 11:38:35,245 - mmdet - INFO - Epoch [1][10/25]\tlr: 2.500e-03, eta: 0:03:51, time: 0.799, data_time: 0.231, memory: 2455, loss_rpn_cls: 0.0254, loss_rpn_bbox: 0.0173, loss_cls: 0.5374, acc: 81.6309, loss_bbox: 0.3946, loss: 0.9746\n",
+            "2022-02-08 11:38:38,778 - mmdet - INFO - Epoch [1][20/25]\tlr: 2.500e-03, eta: 0:02:41, time: 0.353, data_time: 0.024, memory: 2455, loss_rpn_cls: 0.0158, loss_rpn_bbox: 0.0119, loss_cls: 0.1778, acc: 93.3789, loss_bbox: 0.3290, loss: 0.5344\n",
+            "2022-02-08 11:38:46,422 - mmdet - INFO - Epoch [2][10/25]\tlr: 2.500e-03, eta: 0:02:10, time: 0.576, data_time: 0.230, memory: 2456, loss_rpn_cls: 0.0203, loss_rpn_bbox: 0.0139, loss_cls: 0.1573, acc: 94.4824, loss_bbox: 0.2689, loss: 0.4603\n",
+            "2022-02-08 11:38:50,015 - mmdet - INFO - Epoch [2][20/25]\tlr: 2.500e-03, eta: 0:01:58, time: 0.360, data_time: 0.023, memory: 2456, loss_rpn_cls: 0.0127, loss_rpn_bbox: 0.0127, loss_cls: 0.1446, acc: 94.6777, loss_bbox: 0.2154, loss: 0.3854\n",
+            "2022-02-08 11:38:57,686 - mmdet - INFO - Epoch [3][10/25]\tlr: 2.500e-03, eta: 0:01:46, time: 0.575, data_time: 0.226, memory: 2456, loss_rpn_cls: 0.0064, loss_rpn_bbox: 0.0104, loss_cls: 0.0943, acc: 96.5039, loss_bbox: 0.1586, loss: 0.2697\n",
+            "2022-02-08 11:39:01,390 - mmdet - INFO - Epoch [3][20/25]\tlr: 2.500e-03, eta: 0:01:39, time: 0.370, data_time: 0.024, memory: 2456, loss_rpn_cls: 0.0072, loss_rpn_bbox: 0.0132, loss_cls: 0.1439, acc: 94.6191, loss_bbox: 0.2597, loss: 0.4242\n",
+            "2022-02-08 11:39:09,266 - mmdet - INFO - Epoch [4][10/25]\tlr: 2.500e-03, eta: 0:01:31, time: 0.590, data_time: 0.228, memory: 2456, loss_rpn_cls: 0.0057, loss_rpn_bbox: 0.0134, loss_cls: 0.1181, acc: 95.4199, loss_bbox: 0.2243, loss: 0.3616\n",
+            "2022-02-08 11:39:13,065 - mmdet - INFO - Epoch [4][20/25]\tlr: 2.500e-03, eta: 0:01:26, time: 0.379, data_time: 0.024, memory: 2456, loss_rpn_cls: 0.0050, loss_rpn_bbox: 0.0117, loss_cls: 0.1196, acc: 95.4004, loss_bbox: 0.2120, loss: 0.3484\n",
+            "2022-02-08 11:39:20,854 - mmdet - INFO - Epoch [5][10/25]\tlr: 2.500e-03, eta: 0:01:19, time: 0.582, data_time: 0.228, memory: 2456, loss_rpn_cls: 0.0028, loss_rpn_bbox: 0.0091, loss_cls: 0.1021, acc: 96.1719, loss_bbox: 0.2075, loss: 0.3216\n",
+            "2022-02-08 11:39:24,557 - mmdet - INFO - Epoch [5][20/25]\tlr: 2.500e-03, eta: 0:01:14, time: 0.369, data_time: 0.023, memory: 2456, loss_rpn_cls: 0.0030, loss_rpn_bbox: 0.0106, loss_cls: 0.0942, acc: 96.6309, loss_bbox: 0.1926, loss: 0.3003\n",
+            "2022-02-08 11:39:32,255 - mmdet - INFO - Epoch [6][10/25]\tlr: 2.500e-03, eta: 0:01:07, time: 0.576, data_time: 0.226, memory: 2456, loss_rpn_cls: 0.0025, loss_rpn_bbox: 0.0081, loss_cls: 0.0787, acc: 97.2363, loss_bbox: 0.1827, loss: 0.2721\n",
+            "2022-02-08 11:39:35,900 - mmdet - INFO - Epoch [6][20/25]\tlr: 2.500e-03, eta: 0:01:02, time: 0.364, data_time: 0.023, memory: 2456, loss_rpn_cls: 0.0035, loss_rpn_bbox: 0.0100, loss_cls: 0.0901, acc: 96.5332, loss_bbox: 0.1857, loss: 0.2893\n",
+            "2022-02-08 11:39:43,555 - mmdet - INFO - Epoch [7][10/25]\tlr: 2.500e-03, eta: 0:00:56, time: 0.576, data_time: 0.228, memory: 2456, loss_rpn_cls: 0.0023, loss_rpn_bbox: 0.0093, loss_cls: 0.0877, acc: 96.7383, loss_bbox: 0.1736, loss: 0.2730\n",
+            "2022-02-08 11:39:47,186 - mmdet - INFO - Epoch [7][20/25]\tlr: 2.500e-03, eta: 0:00:52, time: 0.362, data_time: 0.024, memory: 2456, loss_rpn_cls: 0.0040, loss_rpn_bbox: 0.0112, loss_cls: 0.0889, acc: 96.6699, loss_bbox: 0.1800, loss: 0.2840\n",
+            "2022-02-08 11:39:54,874 - mmdet - INFO - Epoch [8][10/25]\tlr: 2.500e-03, eta: 0:00:46, time: 0.575, data_time: 0.227, memory: 2456, loss_rpn_cls: 0.0020, loss_rpn_bbox: 0.0094, loss_cls: 0.0748, acc: 97.0801, loss_bbox: 0.1381, loss: 0.2243\n",
+            "2022-02-08 11:39:58,511 - mmdet - INFO - Epoch [8][20/25]\tlr: 2.500e-03, eta: 0:00:41, time: 0.364, data_time: 0.025, memory: 2456, loss_rpn_cls: 0.0031, loss_rpn_bbox: 0.0081, loss_cls: 0.0743, acc: 97.0801, loss_bbox: 0.1635, loss: 0.2489\n",
+            "2022-02-08 11:40:06,228 - mmdet - INFO - Epoch [9][10/25]\tlr: 2.500e-04, eta: 0:00:35, time: 0.577, data_time: 0.227, memory: 2456, loss_rpn_cls: 0.0024, loss_rpn_bbox: 0.0085, loss_cls: 0.0649, acc: 97.5781, loss_bbox: 0.1307, loss: 0.2065\n",
+            "2022-02-08 11:40:09,873 - mmdet - INFO - Epoch [9][20/25]\tlr: 2.500e-04, eta: 0:00:31, time: 0.365, data_time: 0.025, memory: 2456, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0066, loss_cls: 0.0530, acc: 97.9199, loss_bbox: 0.1090, loss: 0.1695\n",
+            "2022-02-08 11:40:17,597 - mmdet - INFO - Epoch [10][10/25]\tlr: 2.500e-04, eta: 0:00:25, time: 0.579, data_time: 0.227, memory: 2456, loss_rpn_cls: 0.0041, loss_rpn_bbox: 0.0084, loss_cls: 0.0676, acc: 97.3633, loss_bbox: 0.1367, loss: 0.2168\n",
+            "2022-02-08 11:40:21,269 - mmdet - INFO - Epoch [10][20/25]\tlr: 2.500e-04, eta: 0:00:21, time: 0.367, data_time: 0.025, memory: 2456, loss_rpn_cls: 0.0008, loss_rpn_bbox: 0.0055, loss_cls: 0.0593, acc: 97.7246, loss_bbox: 0.1277, loss: 0.1934\n",
+            "2022-02-08 11:40:29,010 - mmdet - INFO - Epoch [11][10/25]\tlr: 2.500e-04, eta: 0:00:15, time: 0.579, data_time: 0.228, memory: 2456, loss_rpn_cls: 0.0007, loss_rpn_bbox: 0.0072, loss_cls: 0.0618, acc: 97.5977, loss_bbox: 0.1196, loss: 0.1892\n",
+            "2022-02-08 11:40:32,714 - mmdet - INFO - Epoch [11][20/25]\tlr: 2.500e-04, eta: 0:00:11, time: 0.370, data_time: 0.024, memory: 2456, loss_rpn_cls: 0.0011, loss_rpn_bbox: 0.0074, loss_cls: 0.0552, acc: 97.9297, loss_bbox: 0.1246, loss: 0.1883\n",
+            "2022-02-08 11:40:40,497 - mmdet - INFO - Epoch [12][10/25]\tlr: 2.500e-05, eta: 0:00:05, time: 0.583, data_time: 0.227, memory: 2456, loss_rpn_cls: 0.0010, loss_rpn_bbox: 0.0060, loss_cls: 0.0563, acc: 97.7637, loss_bbox: 0.1237, loss: 0.1871\n",
+            "2022-02-08 11:40:44,191 - mmdet - INFO - Epoch [12][20/25]\tlr: 2.500e-05, eta: 0:00:01, time: 0.369, data_time: 0.024, memory: 2456, loss_rpn_cls: 0.0013, loss_rpn_bbox: 0.0049, loss_cls: 0.0487, acc: 98.0273, loss_bbox: 0.0890, loss: 0.1439\n",
+            "2022-02-08 11:40:45,980 - mmdet - INFO - Saving checkpoint at 12 epochs\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 25/25, 9.7 task/s, elapsed: 3s, ETA:     0s\n",
+            "---------------iou_thr: 0.5---------------\n"
+          ]
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "2022-02-08 11:40:51,306 - mmdet - INFO - \n",
+            "+------------+-----+------+--------+-------+\n",
+            "| class      | gts | dets | recall | ap    |\n",
+            "+------------+-----+------+--------+-------+\n",
+            "| Car        | 62  | 129  | 0.968  | 0.869 |\n",
+            "| Pedestrian | 13  | 38   | 0.846  | 0.752 |\n",
+            "| Cyclist    | 7   | 51   | 0.571  | 0.123 |\n",
+            "+------------+-----+------+--------+-------+\n",
+            "| mAP        |     |      |        | 0.581 |\n",
+            "+------------+-----+------+--------+-------+\n",
+            "2022-02-08 11:40:51,309 - mmdet - INFO - Epoch(val) [12][25]\tAP50: 0.5810, mAP: 0.5813\n"
+          ]
+        }
+      ],
+      "source": [
+        "from mmdet.datasets import build_dataset\n",
+        "from mmdet.models import build_detector\n",
+        "from mmdet.apis import train_detector\n",
+        "\n",
+        "\n",
+        "# Build dataset\n",
+        "datasets = [build_dataset(cfg.data.train)]\n",
+        "\n",
+        "# Build the detector\n",
+        "model = build_detector(cfg.model)\n",
+        "# Add an attribute for visualization convenience\n",
+        "model.CLASSES = datasets[0].CLASSES\n",
+        "\n",
+        "# Create work_dir\n",
+        "mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))\n",
+        "train_detector(model, datasets, cfg, distributed=False, validate=True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_vYQF5K2NqqI"
+      },
+      "source": [
+        "### Understand the log\n",
+        "From the log, we can have a basic understanding on the training process and know how well the detector is trained.\n",
+        "\n",
+        "First, since the dataset we are using is small, we loaded a pre-trained Faster R-CNN model and fine-tune it for detection. \n",
+        "The original Faster R-CNN is trained on COCO dataset that contains 80 classes but KITTI Tiny dataset only have 3 classes. Therefore, the last FC layers of the pre-trained Faster R-CNN for classification and regression have different weight shape and are not used.\n",
+        "\n",
+        "Second, after training, the detector is evaluated by the default VOC-style evaluation. The results show that the detector achieves 58.1 mAP on the val dataset, not bad!\n",
+        "\n",
+        "We can also check the tensorboard to see the curves."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 821,
+          "resources": {
+            "https://localhost:6006/?tensorboardColab=true": {
+              "data": "<!doctype html><meta name="tb-relative-root" content="./"><!doctype html><!--
@license
Copyright 2019 The TensorFlow Authors. All Rights Reserved.

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
--><html><head><meta charset="utf-8">
<title>TensorBoard</title>
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xnLgWr/rygoBdZnUxmJA5tOQQaGr6ctWyoAZwJeyVW84F122UY3w3h4/F4XWIoBZJr2Js9iHF4B7iKIZ7mIUW3gDpZEsmgFCaghZwidAhbtM/mjJmCy2EHoTX9VmVmLxKEQvZETv5Hi9i8cRfg0cHoSG0J5vKk2s1mmMRhjlPrMPgtIlC4bQBfTDYhrCdGAvv+vzGr2H/emiDgl+PSS0hgDA4nqUs4D+aZcRvDl86zKpIiziYoSFEL2rinU5RRRzHV3hWQ8JZZcpiTTxDkK9dwV6VlLq+DTtqgs5kUU8Cawmima4kxEU8RJKC4TDDJBlQ6hNobp9LPvSArzouZjsmGMTyqteFqhNHKoLWY7wR+BUL8vOBnoHQ/R2HnDi2r4ZtJ6d8d0QOpk+OoURWkErn/AuU7jKTX6x6US5msSCoD/4YQ7lAyzOkhre8KzIhVxEAX+G6IVlA+jtjAdeQ9yHZWcLX75UnUJfncIoncJKCvkQeBgoAw5CaNYqBropT+bzHHCzH1p3rcyTUt6mgO9INS97UtoXHEO0Y72GEfJxq2fitJwBKOcijEL5EdC/l4drH7q4BVzOHikzEH4E7syUEemZ4zUK+b5MZ6PHagxZIKMWQiexv8YYqzHaUD4kMcAbw55nhUZqBT92U5fM51Mk9LvPHqeA040ZootrQ+hkBmoZV2uMNgp5n8Rq6EjcxskIS7XS3SqqNHI/yq9c1eMVe24t1lLCeVLNv7OgxuAT7i5uL+ciLN5BWE7CBJnsoDuAOM2ucymTIOCHbmdzKPfyZS6SCu9usjEEg9srtfrh5ZyMcLlO5YeussxnI8oszzRkisUSurhSJoYjzNuQGcFP3Qk3azX7usrTlyXAH/0RlDIK1MsMysQmHrAWg0cEbwg4hE6a3GQQmzgWEyGw/5W7UK6RWuyA6jf4RBgMATBBp/E9NxlkHq8AS3zSszvaES6TOlYEULfBZ8JiCCHOcrVdBvH1oRZ42x9JPbIZYZTMZE0W6zRkkbAYAoTD2cxcV1lstjghw9ngYyxGSC2/y1J9hgAIjyESTNbpnO4mg8zjQeAXPulJ8g7CqTIz8IG8wWfCZgiLOLer7XKTSyeTgX/5I4n1FHC61LLBp/INISJshgAYwmZq3WRwDkSb6YOW5yjiTJnJ39MtQBs4yktBBn8JoyEAqrSaE1zlKOYW4CkPNTzGXpwtNfwj3QL0RqYjVHqoyeAzYTVEIXHu0KtTDw0RmzgFXIM3axO/Ai6Q6XyabgHawGzn1AhDhAirIQCOYQBVbjLIHF5F0oiP2pG7gUvEZls6mVURbaAFcdftM4SDMBsCoFanuzyDdAs28D9p1SY0ic04selMJ7uupoB53A7E0qrfEDhhN0Qf4A69JPWjSaSZrU5EbOoIilIpNtVuBSbR1RTwZ+4CxqdbhiF4wm4IUE7i60x2k0Xm8RDCz1P8eBcwQWZzk3txCdSmmD+zBmFMumUYwkH4DQEgNGg1R7rKE6cc2LSHT31OYrxwZ7rS1KaEQtqAH6VbhiE8RMMQUIJym5sjbGQeH8Juu0D/wmKEzOb+dEXpAvo5ZnAVmGgIL1ExBMB3qeanrnI0cBvwZA/vJI6IsXt8LyXUZgDbWAecnW4ZhvARJUOAskBn8NVUPy6JTTwTSHSNkiSOiLF5KW0ZNgdSwH8DJ6dbhiGcRMsQsDdd3Oomg8xlA7DQ+evrxPmO2LyVrgC1OZgCHiGCp2sb9kzUDAFwgVZzhaschcxFuZsCTpeGFO6d6AWdy2FYPAEmPilXCfUparuhVWtY5wyc94iz6jwukwrVZjBx1kHqXTZD9IhiCwGwP8ribFWmcxiGxWMYM+Q8UTUEKKV6Axf5Xo3N8cR5HDjU77oMwRNdQyRYpjb7+VW4zuZEhIeBA/yqwxAuom6Ig9mWfsjF7tDZnInyCPhnOEP4iLohQLhKqznPyyK1nvOJ8yBZvgHTEDzRNwSAsFyrvHl41eYHKPcDfb0ozxAtcsMQ8DUKuTHTQrSOy4Bfkgg7N+QhuWIIgGt1Bmekm1ltrka4l+iuzRg8IJcMYaHcplPcd3W0jknAreTW92FIg1x7AAZT4u4AYq1jOsJSNAQ3WhsCJ9cMAcpUreXElD46i3owJ2PkMG+g/NJNBneG6OJxJPRnmxYQ5w61e7/iS0F0Fq0oddkUZsgam4BqCjlORrs7ftTVAFKW8Tfg+zqFEcRpAZcnYmSP4bRzA1C/8xtqY9HJCpSfBKDL4C9x4D6KmCql6R0wl1aXSZpZx1aOQykn6PveekOo0VqGd39JL6GATu5EjBlyDuFRLI6TKxibrhkggzGErKBDFtNKJ0cAi0mcXhEmiolzj9qJVlBtihnKaoQrgxZm8JS3EErlCs6Ry3kl08IyHlTLMj6SVmIIw4GHMi3PY46jg3KdTB86WQP+R8cassZmoJ5OhssV3l1g49kilLSwHjhPY1yCMB843KuyM0KYzT5cBJwStBSDJyhwL51UyTg+8Lpwz6ddpZU1DGAIQjnwidflp0FfjBlyhWdRTpOxjPXDDODTOoTYtEszrRQxDFgB5tpaQ0a8h3IlYzhJruRpPyvydWFO5rNRmpkInAT8wc+6DDnJVqCJvgyVq1gpgvpdYVYC2aSZ5xS+QwUXAwuAw7JRryHSrEWZLFfx12xWmrXQDQGVRazhM76JUE/C/QbDzryAcoZcxSgZl10zQACxTLKCLbIQmwIGA6vA/2bQEAk+Asrpx7dlHE8EJSKw4D6Zz7tyE2OBUxBz3W0e0wEspp0jZBytUhrsAm/g0a6ykGfox2nOCnJKB48ZcoZ1wLEynphMDEcIUOCGgMSFibKAlShHOOOLz/eYyRBlNmBxoYznXBnPG0GL6U4oDJFEFrJZ5mNTwFGId8vxhtDwMcpk/s5RMo7fBC2mJ0K5f1gaeQso1WrOdsLMh+8pjyH8yITMD4Lwm1C1EDsjjTxKCccjTIT0Q3oNhlQJtSEAxKZTGlmBMASlCWgPWpMhdwm9IZJII5tkPtXEORrC2f80RJ/IGCKJzGeDNHIhcC6Ea4bCEH0iZ4gk0sg6PuJYJ8w8FHPYhugTWUOAs411Hq3AEc4FKmHbxmqIGJE2RBK5kY/kRmLEORF4PGg9huiSE4ZIIo28KPM4E/gB8HbQegzRI6cMkUQaaGML30KpBj4NWo8hOuSkIQCkma0yjyYKGYqabayG1MhZQyQRm43SwESUkxCeClpPxPgv4tsvvc8Lct4QSaSB55jN6QilwDtB6wk5bwLnyyT+Uybn11gsbwwBzjbW2ayhgGEkzn3dFrSmkPExSjn/ZLhM4rdBiwmCvDJEErHZIrOxsRgErCILpzmEnE5gBUUMkcm0ik1n0IKCIi8NkURs3pXZjKWLs4CXgtYTCMojxDlOJjNRJvLPoOUETV4bIonM4fcIJ+TZNta/IJRKGSMkxmtBiwkLxhAOYhMXm5XAkZDT21g/Q6kHhstksytxZ4whdkJsPhMbmwKGQ049MHFgFcqREsOWspw1fEaEcgtpGJBa/gKUaj3nAM1EexvrMygxKeeZoIWEHdNC7AGZxSMox0Mkt7G+C1xJjFOMGVLDGCIFxKZT6lhBnKEkbksK+7TkFqCeOIOlPDuHBOcKxhAuEJuPpY4YBQxHeTBoPT2gwBq6+KZMwZYKc36uW8wYIg2khjeBC3QOI0i0GMMClgTC80BMpphrBzLBtBAZILWs48scE/BtrO8jTOTffNuYIXOMITJEJtIhtbRicQSS1W2s7cBiihgqU1ghtglv9wJjCI+QGj6SGmLAt8H349zXYjFMphKTslDc45czmDGEx8gMXgDO0LmMwqLV4+LfRDhbpvKYx+UaDP6jNiXayBlB6zCkzv8DQd7QrMbLR1AAAAAASUVORK5CYII=">
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">

<style>
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/uYECMKoHcO9x1wdmbyHIm3-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/sTdaA6j0Psb920Vjv-mrzH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/_VYFx-s824kXq_Ul2BHqYH-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/tnj4SB6DNbdaQnsM8CFqBX-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/oMMgfZMQthOryQo9n22dcuvvDin1pK8aKteLpeZ5c0A.woff2) format('woff2');
  unicode-range: U+0000-00FF, U+0131, U+0152-0153, U+02C6, U+02DA, U+02DC, U+2000-206F, U+2074, U+20AC, U+2212, U+2215;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/Ks_cVxiCiwUWVsFWFA3Bjn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0100-024F, U+1E00-1EFF, U+20A0-20AB, U+20AD-20CF, U+2C60-2C7F, U+A720-A7FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 400;
  src: local('Roboto'), local('Roboto-Regular'), url(/font-roboto/NJ4vxlgWwWbEsv18dAhqnn-_kf6ByYO6CLYdB4HQE-Y.woff2) format('woff2');
  unicode-range: U+0102-0103, U+1EA0-1EF9, U+20AB;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/isZ-wbCXNKAbnjo6_TwHToX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0400-045F, U+0490-0491, U+04B0-04B1, U+2116;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/77FXFjRbGzN4aCrSFhlh3oX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0460-052F, U+20B4, U+2DE0-2DFF, U+A640-A69F;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/jSN2CGVDbcVyCnfJfjSdfIX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+0370-03FF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
  src: local('Roboto Bold'), local('Roboto-Bold'), url(/font-roboto/UX6i4JxQDm3fVTc1CPuwqoX0hVgzZQUfRDuZrPvH3D8.woff2) format('woff2');
  unicode-range: U+1F00-1FFF;
}
@font-face {
  font-family: 'Roboto';
  font-style: normal;
  font-weight: 700;
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@font-face {
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}
</style>



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    color: var(--primary-text-color);

    /* Legacy mechanism to avoid issues with subpixel anti-aliasing on macOS.
     *
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     * would have no effect for recent versions of macOS, it still is needed to avoid the bolding.
     *
     * [1]: http://www.lighterra.com/articles/macosxtextaabug/
     * [2]: https://bugs.chromium.org/p/chromium/issues/detail?id=858861
     * [3]: https://developer.mozilla.org/en-US/docs/Web/CSS/font-smooth
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+                [
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+                  "application/json"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/data/plugin/scalars/scalars?run=tf_logs&tag=train%2Floss_rpn_cls": {
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+              "headers": [
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+                  "application/json"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            },
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+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/data/plugins_listing": {
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+                [
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+                  "application/json"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/data/runs": {
+              "data": "WyJ0Zl9sb2dzIl0=",
+              "headers": [
+                [
+                  "content-type",
+                  "application/json"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/font-roboto/Hgo13k-tfSpn0qi1SFdUfZBw1xU1rKptJj_0jans920.woff2": {
+              "data": 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",
+              "headers": [
+                [
+                  "content-type",
+                  "font/woff2"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/font-roboto/RxZJdnzeo3R5zSexge8UUZBw1xU1rKptJj_0jans920.woff2": {
+              "data": 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",
+              "headers": [
+                [
+                  "content-type",
+                  "font/woff2"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/font-roboto/d-6IYplOFocCacKzxwXSOJBw1xU1rKptJj_0jans920.woff2": {
+              "data": 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",
+              "headers": [
+                [
+                  "content-type",
+                  "font/woff2"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/font-roboto/oMMgfZMQthOryQo9n22dcuvvDin1pK8aKteLpeZ5c0A.woff2": {
+              "data": 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0UTVEIniStK2KkQNSWnadTS44MC0ZcShqn/tNbjSpzhJPWqmVtdIahqMy4JIurfISwFqdDknlN/ZkJWaJOfPmu0hCIViLEiCXeDo1fx31S8zs39RXB2funyM/h/4qJvzk1FE1AlLSBoRckNzDUC2Vie0A3dVydpP2+nlQlXWLmsM6kpxf2qNAnzxXE9I8zbaSdNBbUmBp9qN4v7SdxKEPSQw7OPKYqmGDHU1G25UrXOzQT8UESQ8gg7ZI7KplJNU1l8Ei8eqUd9TGu/Hq7MDh3qFLtVYpmTYB1RVu8jGGn+HIKixbp7pl08klbTB60qEpikY9sjLo9EaglvU96Bz3EvfEcLRjtkf+1rba2RopIJY4orU7uObY3Y9SlGnOR8UZRdWYYRWrEpkS6FgkfdvN4lxqew4RQe5mP6W+wnEkhvXYbXxIZd2qOGV+DxkBE9TT6D3p7Zq2VzJUAseOmluQLiocOOkAKDVWaXR015dYYhhgYrCcD6ml8QGDF/GOWXf4pN3DFIOvh2olo92vyup7stXa0MvaZcUB90LXMpfiqT0HdGwvKlR7KYetkJMa2zJ/eArTc+pPLwuQVoRD7jxII8nWDhElbFkfTa6MUhEyZ1g5yvyhBBlM5pzxUDZLT/37WD/EMrpTwTbt4IhlG7ZotGO0+OI4aZ8Go5MwWJcz21FJpnoSIFY/ccFOPVCPGOEsSkbF/PQIj5jGOrVbVnfwRTqNq6nNasQEQSYH7oLXLiW31UgXpk8J44vqKc0GKe+BUadt3wUGAkJHB7zFtQeGQxccdSieReslSPWf21E+PxPTi3gAyczhLgUOemFaBT28Nb0szb2eFfH0QV74/FTUWTUSxGVUnmvtGSxEF+eUJztVfeYpr5KlNEbYbqQ/w8UBwJAH0HamgKFli1D0/cDgt1aKRsyIiBkajPOqYfpIH2OmzjUXl2DjQBcnYNPzP23nDgeJTLHZjvA3F6eZS+qL+hBHp2CFSjh9r5hEHI5LWf8yYeu+fiXuj2dEpxTy4BSscCAS7mcaFpIjOC3TN15a8XSiiv1d5nw1J3XvVVdSdCXBLyTQ7X6UP791na9WoVPtyKsOfHdGN4V1cnOIkpCaGROZGQLYjJzIqtrI6Io6olJUVVV4RG1VQhxWw8BWVUnfSk9FQ88uQ89O+1zrjciqGmx1dYKSoDTFEzCaH0DKetZ6qpr6NipKhta6Pz/vyl71JjjIgm2Mo3dThbeljyxJqar/UU1W/8NoQ8MMt3tOGvIR8uHF1YdJ9djPRUuCiuyQgv0MsjKrmh7gbl1DWxUY4ZuThDq3YelD8tGWvyGgLy1vY6imbeEGLDOpHO7hsJgJURqr/sUfdMIUeQNS3akvcKdANyLxpG6ttwWvR5SKf+OeDogqLHnnfDSKVObzyPWhfkSuc08aOBScttLk84hUfq/nmw8c5a+vGV3LuuaOM8ilM73oei1b0HiQOciMcqb1WLAxXFpjymrKekl9Wf4AqqVC9FkHJbMIdHx1e6qd9XNg5NrO3gaSJT+J0MR/vZGQBOTgYj7stSKQKXNVXJDY31kSRgv+kF24ebC3cBUl1sW4FzlLSg6fQRYu7hOXE+KiplBr8IY64U2sYRs6DULU6B833EN1i/QIcale8TkFiZTnqB9vXuxNN79muxhd9Q6O2QgmRCCZ0QQ4jJKJnpTGv+v9oztv+iwCmx2v+PFGND0YOBw/gIUOqldmXGhMUkLkg/zYNCQBblntjGSPaFO1s10v9n8avPBleHDEAF69UZnZrVe/kugkTRzewS6vfEa97f0yw/q4iTWYFOobntaUn1ZEjgpQEKYF4W/NhTyv0Yd/jpcJkcNPVxmmV74yjU6gZf4ppenXaNNMkCyKPbn0FNpK3wWEJHdmZqV0gZyeMm+7ch9v5jGOnDmQHhB2JdRa3fDkj3rk9+T/0vjk3+XHvmFPPPktb7jXunTtve7t2X9N52URmJf7Oyv7Z1pSZER6MpvtCdeYGG/3KOJiemfEJYDQapiYQomOZSg1yG6ZmOa28yKIOPdT7qhorWaMNs35ggvr6G+bbL/MCjzP6ah2FK4FBKZTczOTe4A20vPvgNDZoDTV5MABkCq/LlcqJyNXIgcCLfEZqKciVoFpdFmDesA2F/EViFPTiYjRMXd1dPV1vB/kbEJBSleHOp5MjjQ3lda6OCtr5fRIdt1PHpfS7wyfXh9tfyRcLaIbr62anLqFf8lc4Y96mu5478FlTWmhS/jNdM+EJUwrPupOtsbzm0OtfvTfGH6QXPh6JozbrrScJ8y+FHD8bb/cUsVwggG/0BH0/sMvm6XGSpkVD6YkrWxDSTe7c+a4a9+DZXxyDkByItiaGDTEM0iu4ZkhD7Yc//CjBmoBQ54/eN/fc9+rwOayDzexK2zg39L2rRpGqZi8+EBartu5PKFZIS7+CS6kU6AePnpik+v3e5Sop2jiixQhuoqm7aMozvXeY41V+Vld4yx/kg4Gj7aU1KWcbDU7WvOfnqsuEoFnZqNFInyV/QiTCN5nZPE7+yH+3MbPICCNKvWjWSgsBAw5vWhq2g8k+yJtInsAQ44pGvpsA+o93SqOOAdyC3tVlhAy8kBF0JFz/cg210yPCDdP7xAzQPhtibvbpfSGstK9AlbQCoPe0sXS0v49hEVCL/h99hL428m9tBO/9ybefFp41hNCTIjwS8uEGDhR0dy1v7/a8ZixbckIvvJrm+BY0FLoZabrhRNHyKcHKnr6S5R8k83m9Hp++unpHLzlzRK4Kve1aWvrXsVreu2rvX03aKXVzpKkaavJDXQ1abeQV46TqbL0dQ1N9DUkmZmnCHqM41HHOAfqm/OfSS4K9QUz6i84iIkjPTGOf2D+8nwat/5Dv8AorzwlLqI8PxllC1r42qlofn899IsIf8EvSmxaRteT6WtFppluQF5yPm0H5ff4GW+Z9FRNX92p9/Tqd9zSLDHteYSAniPjaOrOBVJNenBsCSkb+ECP+P3O8mhOA355PmvXK+xFQG9UdVZuclGwu1FWoJPdy0Drcvdm9umtLDQhcfhBb1RfTnlOadBspjeo+OCnAYPvBgx+qug+qFaSF+EVYN6xnP1wEkFWlp++LWp8WFre0Fj0DuL/n6Bpnjw7SK47206egNWRk90XG/Don2Vs6T8vNHH1M59b7+U8Qm7CD/ByunG6sXvzNnHWWfcvykeYXlt9MTG7sG530/7m2uuFlxMrYDsd9PbtGZvUMz1npmGdh1sFsoXnUwTWI9iKfiQBd/t2N2CJ3TkBQNsVAKhoMpkPe40M8E5YoCEH5vQGfmL1womKR09WGi7PPrg0Ww9iYdkZ2RlwuuJtMcK7BBcPd0eHRbeWvGxLqAXqTpuelRfB0ybaRol6j5A1YOD7aIqKLv+cyWvA+jXRIN+IR2kxVXfYwMsK5FBsP9eu9rxyT8EF1wEWKFLu8fHLl9BdNE9SX8V1atZO69a2Rae/AqkIm3Q/WqMmGlOf6gEbtwUaPjhT5PVtLjaj4+Mb2sYmWofkGMekvJGec49yn4M576+9691P3cKFKYCFOpyXW+zQ432hn13YruyxGLJW4SftnAZi9gSosc+zc3/mKQ38O58cPrVzBK2+gC5uGYi3jtUUlPUwiB3He414ZJexpXEx0ybG5sFWfT2p3v0h5UmdzDJd5raIsZWDNWGLtFUPFrz/di73Drkl4PxquSd/fvrZZ8lGXN/CzTetITYrO97M7UHP010ByufOzPHYpntF73Xz6mKzJ4BDpzMwa9AAFfGC8hK8HdzdjWMteNXRFMACBfQo4pRhrmuuLiHGQS7OwNEm3nnrpJRAxg9haRk5nUd4rqtuIqKZvwlQvnr5B2uUXmrtq2WzUtE/4/MG9JI3MrBXiNP4+SaMrN1rFFFvsPC1xfjPUEa8HQkh9rGB9Paj1MH9rQ65PE6KOLC3iWNig/KfOSOIf67GfMXB/xbjSAusAv+Zivx/LyekzbMK/KXg2qreJCZVzg+31MwnxlW/AdlVth8r13oHPYLpKIB5W4vz80h43r/Ki+1Cd+ZPEWt0MjDa6fXRBZNgznu4PjDY4xZG61IHmLcUGnc/6sA2/98uWmrBVHSlfireILkspnAaLnllZAl0yOQBRbwNFWycUi36BPjPnrnroX1MokJHw0srermzqWv9SQZVLczV2s7TQEneUZ5Dql5P298gdeshJWLYxKPCkkJjGu9CcAzVhWM+MtgR0kixrIiMLHdQljK7YnBIZnzxIGkQC4Jhzs6hE41tz2Cgh/Lo+7NpxUJ2ijgZPv5e6b/95JiCeNKxKPEnoFBy6JYEhb1NPIvVS5gVLZzFXiHexaRzmaX/ciZ8f/ghfs6K04Vn9lfGr4zXLjxWnLNwwMzSf2XopPCKLdM8jwJjFru5w92zthddj52/PgJiWQHZJkReneNqucKR4OdKFmhZfzz8ZTp1+vM40vFwuUWQlzAud2JkrcqwymhjFIV/JQvyTJmAfZoJ+N8jMli6+a02nbZ7Oy9OjMvxEsgCzcvQNflhOnX6y8jjtpWHAqDxha6IOhen/EC4Jl3l3GUNHSfzEqSk81WCWsLtppJfvWrmXhZQW18D0xx9jJlqUfd4qETYpvvTGjZhQgafkY7vUHZbzg6fnD83/fwUmXuVspTJ2T2FyWx/XN5I63GlfZcnZbwABbhCdX+BCMQCRztfWViCQw9/PXj2dGGquds5NMjdMTw3BIe+XQgKHz2KIifGisKjY8Nj4hPt80dG3cvSssJj0zIL/foHivwzsiOJWbmuZS8G7IqSSZGx4qigIMJUf8Bqf6DHZNCww4C/h2e/370LcLb1uFDWjNsYJkShUiFYQa5XTjpYulI6hJZG+RykMR2lHn1G/U4FpdSmJxRdBX6lWNsa9NC6x7ObirfiPV5RkIjWrAJipbelUZLDuFT7jW430zx3aruBLP+dWNsq+uHFbfTKK0pIdV5CcgnBWvGmZowMl2aNkoelWBhXndFR1ROaeFVbxTsimlGyJ++R1dl1bBUN410Aq2cyj4Fgrmu8HNb/FBQ/6+U2lBQVPp7scCM/ncwvqS/PTLMx1zUxd44L8vR3jjM3NdWx+x/ZUHweXynDVll8vqGu8Cy+qrSEvaLoHKF2hGqkOhSmMkQ1GhlsNVAdHxpUHW03AMeeZr27emF+4UH27lV+DS7CscGKGiJCJQz29eFx0bnJMZJWx7VMAq8YXKAme2iDobSQOUis+FFx0aW3wWgO0UEYcqC8klL2moKzDfUF3Ozpp1v3V+2kexy5NREhQQ9kAgQgV0ofw5o6ReFmsSCgmx4UKCEGsqIq6pK28SC4wVlCKgHc+R6HjHwnh7Q8R6e0TAfHzMz/R0RV7oiJq6iK3lTVcFFVgYvp+i7ZLkXVReHZ4YXVHBxf5HPkoS7PwOXKcRc7RYG/knfv3BUrJFSl43JD3RycnHQ0mlJIwJpxX1dVSZL/2s4dMVXBS4XWZYHBcfn+DjcVjWWPZVwkOdUGE6MqQ5yLrmlIyN9RlhDyzEcMgX0Ilhl4M+GIsHgAlN3P9x6WyuamilfjCKYxdelayjMUwLyFGJzuJu7f7Azd/00mwJAhbe73yGi98ZCQDKdbc06kWRV7kLBOmvpCN67+ssKdiYb3eYVGr2+FWXvjgbbCpuhRX17+o54q1WJqr/CT3soyjK2lp66OOcFOw8bSU+eehStiCXerwfFiqIVPZw+9NYerF8HVNtjDhIKULL/oHJh51tpY/NDFQ1Y9rUcyfuzXrqbD6OMBXsyWwi8tV29iyJ05reyzGtnKR/C9emz+z7jmwVBZRVNJwzdQWUN5mq+koaJpVdXq8iy6yqyPuGeZl0zafNLtoDNDavVsevD9iqSi/0QqwislY/PVKvGcPNHOrWoRVqaAFlPHzQgeHU/j2wGzsc9NTgyZ92j6Owff3tA+2s01vBgVMGTKY54jycVZ/kHFWbEAz1AMxkd3qW0DLcUhD0YUhVTNH5H0YtWKR5o77rVdMbmcxxJzRcTZw0xbWMXicZp+3D2j+FFdc3snw7e8oLHMgYltldFbMUuqftSQXbjauLCk5+wa6OCY4HqvE0/61PbIq0lbQFtao1HfcXILdrzmsE2NTzMz/O++vx1b6H/bI0u5UOXIwbo3kuCqo+5oKoIW9sqoY40/cSYqOg2EFI/8pM01K/L++5oUE/suzH8pJt19ezQ8vNzf80ZfYuKbdGXr7ohI4w7+W0XvRR7GPw5TtsFGMh1ljFc2TS19Wp1Y1R8WkrPSBXKThptdmxZiMZHcSEhC/WAVfhwwcr7SgX+X40luZpeU04PtXvblyhQF/Vsipjjo8ahkBNuMvMzoEu2zdNNvX1KOr35j19ah76bT5CCXUPWWAnKE0fTiaUsooh6RfbKHelRxKnikgFYzB6HsX2YCM9SJNMSz/ssqC65lv/Nj4Uuu0I9mQUnmpKcdU7ILs0tj7zoyKjbVtwuWfCX+/94Sk+OeeUvayNj1EzzGdv7teY9ia8sHYh77OauEECO/xb50l7aRsh7yiJmnvfG/C9dZ/Dohrnh2tKNkLj62dEZufNYms+xIHPOJEGIm8Nu+p8/NrM2ozWX8xuIBPD5hBkHeJqaBPg2BAV2BAap6N+TkBfjlZa3lFAQEZRVBaGPeJTDa9FKrpVTDqUtFAkFYZ/8Es3NeFmJnz1y5JX8NWDfZHZVMQ1FpxC7v+5bDQJfB6aBkGo4ipXRGgI3lECSecFA2DkWl+lJs7ayfQ+JVByVjIiqHQqlwsZ6cv50bQmqFs+U44MppDNzcTU08Xcx4DVxdzc3c3AzhNC+SmMQXyc4XlQTMXu2zkGVMRpKQZChztf9XC2d/bUXwp8PQfa0Tld52Z9mL/U77/zx1HsPQEtwS1xLSQoc5xfXFKYaLvdSFx62CPIJ59CAkLC4MpFsS5LPlYWTu2FLT9tHnTzzqJLnygwU8/RycnM0XSAv1Ko2WUQhjE8Su9mzSeT5JKQc+HL7Q0N3ZbHdZEjBzlLlngRRjHMHL0cV8kbT46iml1KdRxEuEMJrPJpQ6HKgfYi0ferUX0wUKZdmNSQn59VCcXJ7XmJCUXY8UFGB74qPRra19CVHoNsCNDMfvAs3LzhHOtYW1fhg7f0xp0M7SWQZwjxBIXYwyO7JVVStzSjUsmRgQXJwSE1AVgrF9mM76elklVQVpdTVVlTrguZFrzMYI54dKhxF8Y1L8vItyoxKqKrOFflz46uUOuVPolSssPMdp696WUtcIvnMXKp40pVaZpg1GBBC/j6+vaLz42KnYn7Uv2klOxfL31Tv42mf5hISqKoiDwupi0rdVxYRF7oqF4k2b8OHmOv/1LY/vTvvDtz33k7HzTYwLDSgrDo/Lr0Cl7/5RIvvag6xIq8BC80t3JHU0NCUl7urIy6qow/BDX2R/8eCH/sKLD/7dvqB0/Tw9bdbyF/8BbA7/B8ACfv5RXi4s/FISFuuq3TPy/xld+aEw/7/MKFgNXAXhBB65quZ3mSNchXEMzjWjNmffEVy77YdFatbn0cri6o3s6doSBL/VEMh8d5R8AGqv7OXj7Ca4f4GMTaYvg+n0kAfMWAUnGXGbq5WubLETFC6/WIYgF1AL8uFFVasHdK2u0/W6QTfiIbHA5gvygmuF4A/RnOkT3Way54spBkO81cmv3dOk0ApZXJgOiMn5By5dK/gTcmD6RLyZLGPEyQCumQpztelDxEyeZUacZg8r6CL+WCHfqB23tFDzijCT7c2n2ArJAFtxcrcVQzi4EqKJ6RNUM9nAiJOvVkoJxLqiODYDRAJrtvBsknxFNpJyCU2M1VykPk3yDYNVohi3tQoHgVZp6OZCZDnjOkFDskq4nijcegh9PuvE5JKvQ50g+jd1bdeqZ1sdixjTOjCv6diLvkJ1nJdYR4410sawNoJ+zUjOShINpFgzFZyEmXM6HtqB8mpjAeduDMj/r/PZkuf+D7hl/haureDS0u+otzR8X3bZVuu5csq/2umBcedopEW8gHvF349HyjOhk4pHKvnj+bNyaIF8NLGTbM6MmQDumb/1qAmimZ+PBplWJmXh6LqSU9bIxFQDfVc/Ooi7qETTFD9xKpmamfyj8OaZZEJ1OAXGegvRs0KesdMf8OOk8lvar+fGgFrQByzkYE7awz8Q118VeWAeDX/e4QBmRp+xoE2+m7GMlofvvD/oaG9B4fka2H8Se6HrFqJLTH+wgH5KKNfdqN0JIDb2+w0mUSZMl/ar/dZgRnxZN8SfnmUeAnHhOHQfyB3h9xrb8lTF+40BiaA+G/tMsBstPAFki2qMQUS/5tcNCQScSy8XOQVfxRiEALnJ2ldCUowtWCR/bx1fBoCnf+puD8A7of3bP+v8X7X5l3kBy6EAAiwuW1oFsPzgUpZe2gHEfVPfVyJbQ+Pfi3eaa0BJkhD7gF+HchvzHPsXu7Bjn49F5zN8RXCq42hEmtMpgjdPSik1+KfGC9Rhhjki5BGFOh7P2jY+iolml5qNctvK6etiatjdxQ1O45ZT/UftEOE3bqvtRDIu1wZeAkdbRF7T2U5EBo1LadSY/qpN8LQwvoPx7dm69oGa7qbVQwQGv8Rx1KnFcVKDBsR6FEuP0M8z0krZRXo2dGVIpgrQZACxNubaIRKbn2lHsmgpZjlM0jyXZTtIjE+SYmfuBjMu9EnBAg0J7SSxU5jouEQW2Q64r7UQXTsi1rKzExqu1A+X6jlOatkw28nEF02OfXiKPKIS2pJjVzEZbbh3ISyxJ723GZchu0mFSaVLr173282DSdX1rOok5z8RfYdVfiCXH0hnhKAmyqsd8skpPf7wplGBVT7cT4/7Y57eJEJrEuNezyksAufPrcCIunfeVFXdJ2HqpdFkoZ1KqYlMukBm/Ja/KOWwUGBVA2qzUUANCRoyyqbbUiKXJc7FH9nO5zSHfEcye4+oFvHDWEbe54KHMapyEvmBaIey6DQ17eZMJ5Ccps8yPphAoIU0opigjziaqITS2h0IHAZOJg0ztFwK+wTcwrzSCwDrvIzxyBPmHfMMp0JDJH6JwCfgTFgBHAL+5BPz43dD42VNaMz8iDuhIZIjIvBGKEtHgB+QcNKFyZXG6iChNoVd1bfMfWhI0wE4A1wvQzAyX4bCzEmN3qWmgWUYfPyXYZ1S1F1muqjkufMQgMCRPQfeuAniJ9At5KbEXeAZLuxWVeHGxnWjrMG7WLQ1mFfOaLe47BD4atnWdUN2Z13y3lDgbc+HC6sgr7+IwIsjc7PHX98u5CYJ6wVv6hZ2bQUWvb15EHej0/q5rrPikXjbcGDnOhsJmr8hO7LRvBu4V/sNalTIU6RBh6Jrts38i4GTzfts3QMAAAA=",
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+                  "content-type",
+                  "image/svg+xml; charset=utf-8"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            },
+            "https://localhost:6006/index.js?_file_hash=29a7d03a": {
+              "data": "var CLOSURE_NO_DEPS = true;
window.polymerSkipLoadingFontRoboto = true;
/** vim: et:ts=4:sw=4:sts=4
 * @license RequireJS 2.3.6 Copyright jQuery Foundation and other contributors.
 * Released under MIT license, https://github.com/requirejs/requirejs/blob/master/LICENSE
 */
//Not using strict: uneven strict support in browsers, #392, and causes
//problems with requirejs.exec()/transpiler plugins that may not be strict.
/*jslint regexp: true, nomen: true, sloppy: true */
/*global window, navigator, document, importScripts, setTimeout, opera */

var requirejs, require, define;
(function (global, setTimeout) {
    var req, s, head, baseElement, dataMain, src,
        interactiveScript, currentlyAddingScript, mainScript, subPath,
        version = '2.3.6',
        commentRegExp = /\/\*[\s\S]*?\*\/|([^:"'=]|^)\/\/.*$/mg,
        cjsRequireRegExp = /[^.]\s*require\s*\(\s*["']([^'"\s]+)["']\s*\)/g,
        jsSuffixRegExp = /\.js$/,
        currDirRegExp = /^\.\//,
        op = Object.prototype,
        ostring = op.toString,
        hasOwn = op.hasOwnProperty,
        isBrowser = !!(typeof window !== 'undefined' && typeof navigator !== 'undefined' && window.document),
        isWebWorker = !isBrowser && typeof importScripts !== 'undefined',
        //PS3 indicates loaded and complete, but need to wait for complete
        //specifically. Sequence is 'loading', 'loaded', execution,
        // then 'complete'. The UA check is unfortunate, but not sure how
        //to feature test w/o causing perf issues.
        readyRegExp = isBrowser && navigator.platform === 'PLAYSTATION 3' ?
                      /^complete$/ : /^(complete|loaded)$/,
        defContextName = '_',
        //Oh the tragedy, detecting opera. See the usage of isOpera for reason.
        isOpera = typeof opera !== 'undefined' && opera.toString() === '[object Opera]',
        contexts = {},
        cfg = {},
        globalDefQueue = [],
        useInteractive = false;

    //Could match something like ')//comment', do not lose the prefix to comment.
    function commentReplace(match, singlePrefix) {
        return singlePrefix || '';
    }

    function isFunction(it) {
        return ostring.call(it) === '[object Function]';
    }

    function isArray(it) {
        return ostring.call(it) === '[object Array]';
    }

    /**
     * Helper function for iterating over an array. If the func returns
     * a true value, it will break out of the loop.
     */
    function each(ary, func) {
        if (ary) {
            var i;
            for (i = 0; i < ary.length; i += 1) {
                if (ary[i] && func(ary[i], i, ary)) {
                    break;
                }
            }
        }
    }

    /**
     * Helper function for iterating over an array backwards. If the func
     * returns a true value, it will break out of the loop.
     */
    function eachReverse(ary, func) {
        if (ary) {
            var i;
            for (i = ary.length - 1; i > -1; i -= 1) {
                if (ary[i] && func(ary[i], i, ary)) {
                    break;
                }
            }
        }
    }

    function hasProp(obj, prop) {
        return hasOwn.call(obj, prop);
    }

    function getOwn(obj, prop) {
        return hasProp(obj, prop) && obj[prop];
    }

    /**
     * Cycles over properties in an object and calls a function for each
     * property value. If the function returns a truthy value, then the
     * iteration is stopped.
     */
    function eachProp(obj, func) {
        var prop;
        for (prop in obj) {
            if (hasProp(obj, prop)) {
                if (func(obj[prop], prop)) {
                    break;
                }
            }
        }
    }

    /**
     * Simple function to mix in properties from source into target,
     * but only if target does not already have a property of the same name.
     */
    function mixin(target, source, force, deepStringMixin) {
        if (source) {
            eachProp(source, function (value, prop) {
                if (force || !hasProp(target, prop)) {
                    if (deepStringMixin && typeof value === 'object' && value &&
                        !isArray(value) && !isFunction(value) &&
                        !(value instanceof RegExp)) {

                        if (!target[prop]) {
                            target[prop] = {};
                        }
                        mixin(target[prop], value, force, deepStringMixin);
                    } else {
                        target[prop] = value;
                    }
                }
            });
        }
        return target;
    }

    //Similar to Function.prototype.bind, but the 'this' object is specified
    //first, since it is easier to read/figure out what 'this' will be.
    function bind(obj, fn) {
        return function () {
            return fn.apply(obj, arguments);
        };
    }

    function scripts() {
        return document.getElementsByTagName('script');
    }

    function defaultOnError(err) {
        throw err;
    }

    //Allow getting a global that is expressed in
    //dot notation, like 'a.b.c'.
    function getGlobal(value) {
        if (!value) {
            return value;
        }
        var g = global;
        each(value.split('.'), function (part) {
            g = g[part];
        });
        return g;
    }

    /**
     * Constructs an error with a pointer to an URL with more information.
     * @param {String} id the error ID that maps to an ID on a web page.
     * @param {String} message human readable error.
     * @param {Error} [err] the original error, if there is one.
     *
     * @returns {Error}
     */
    function makeError(id, msg, err, requireModules) {
        var e = new Error(msg + '\nhttps://requirejs.org/docs/errors.html#' + id);
        e.requireType = id;
        e.requireModules = requireModules;
        if (err) {
            e.originalError = err;
        }
        return e;
    }

    if (typeof define !== 'undefined') {
        //If a define is already in play via another AMD loader,
        //do not overwrite.
        return;
    }

    if (typeof requirejs !== 'undefined') {
        if (isFunction(requirejs)) {
            //Do not overwrite an existing requirejs instance.
            return;
        }
        cfg = requirejs;
        requirejs = undefined;
    }

    //Allow for a require config object
    if (typeof require !== 'undefined' && !isFunction(require)) {
        //assume it is a config object.
        cfg = require;
        require = undefined;
    }

    function newContext(contextName) {
        var inCheckLoaded, Module, context, handlers,
            checkLoadedTimeoutId,
            config = {
                //Defaults. Do not set a default for map
                //config to speed up normalize(), which
                //will run faster if there is no default.
                waitSeconds: 7,
                baseUrl: './',
                paths: {},
                bundles: {},
                pkgs: {},
                shim: {},
                config: {}
            },
            registry = {},
            //registry of just enabled modules, to speed
            //cycle breaking code when lots of modules
            //are registered, but not activated.
            enabledRegistry = {},
            undefEvents = {},
            defQueue = [],
            defined = {},
            urlFetched = {},
            bundlesMap = {},
            requireCounter = 1,
            unnormalizedCounter = 1;

        /**
         * Trims the . and .. from an array of path segments.
         * It will keep a leading path segment if a .. will become
         * the first path segment, to help with module name lookups,
         * which act like paths, but can be remapped. But the end result,
         * all paths that use this function should look normalized.
         * NOTE: this method MODIFIES the input array.
         * @param {Array} ary the array of path segments.
         */
        function trimDots(ary) {
            var i, part;
            for (i = 0; i < ary.length; i++) {
                part = ary[i];
                if (part === '.') {
                    ary.splice(i, 1);
                    i -= 1;
                } else if (part === '..') {
                    // If at the start, or previous value is still ..,
                    // keep them so that when converted to a path it may
                    // still work when converted to a path, even though
                    // as an ID it is less than ideal. In larger point
                    // releases, may be better to just kick out an error.
                    if (i === 0 || (i === 1 && ary[2] === '..') || ary[i - 1] === '..') {
                        continue;
                    } else if (i > 0) {
                        ary.splice(i - 1, 2);
                        i -= 2;
                    }
                }
            }
        }

        /**
         * Given a relative module name, like ./something, normalize it to
         * a real name that can be mapped to a path.
         * @param {String} name the relative name
         * @param {String} baseName a real name that the name arg is relative
         * to.
         * @param {Boolean} applyMap apply the map config to the value. Should
         * only be done if this normalization is for a dependency ID.
         * @returns {String} normalized name
         */
        function normalize(name, baseName, applyMap) {
            var pkgMain, mapValue, nameParts, i, j, nameSegment, lastIndex,
                foundMap, foundI, foundStarMap, starI, normalizedBaseParts,
                baseParts = (baseName && baseName.split('/')),
                map = config.map,
                starMap = map && map['*'];

            //Adjust any relative paths.
            if (name) {
                name = name.split('/');
                lastIndex = name.length - 1;

                // If wanting node ID compatibility, strip .js from end
                // of IDs. Have to do this here, and not in nameToUrl
                // because node allows either .js or non .js to map
                // to same file.
                if (config.nodeIdCompat && jsSuffixRegExp.test(name[lastIndex])) {
                    name[lastIndex] = name[lastIndex].replace(jsSuffixRegExp, '');
                }

                // Starts with a '.' so need the baseName
                if (name[0].charAt(0) === '.' && baseParts) {
                    //Convert baseName to array, and lop off the last part,
                    //so that . matches that 'directory' and not name of the baseName's
                    //module. For instance, baseName of 'one/two/three', maps to
                    //'one/two/three.js', but we want the directory, 'one/two' for
                    //this normalization.
                    normalizedBaseParts = baseParts.slice(0, baseParts.length - 1);
                    name = normalizedBaseParts.concat(name);
                }

                trimDots(name);
                name = name.join('/');
            }

            //Apply map config if available.
            if (applyMap && map && (baseParts || starMap)) {
                nameParts = name.split('/');

                outerLoop: for (i = nameParts.length; i > 0; i -= 1) {
                    nameSegment = nameParts.slice(0, i).join('/');

                    if (baseParts) {
                        //Find the longest baseName segment match in the config.
                        //So, do joins on the biggest to smallest lengths of baseParts.
                        for (j = baseParts.length; j > 0; j -= 1) {
                            mapValue = getOwn(map, baseParts.slice(0, j).join('/'));

                            //baseName segment has config, find if it has one for
                            //this name.
                            if (mapValue) {
                                mapValue = getOwn(mapValue, nameSegment);
                                if (mapValue) {
                                    //Match, update name to the new value.
                                    foundMap = mapValue;
                                    foundI = i;
                                    break outerLoop;
                                }
                            }
                        }
                    }

                    //Check for a star map match, but just hold on to it,
                    //if there is a shorter segment match later in a matching
                    //config, then favor over this star map.
                    if (!foundStarMap && starMap && getOwn(starMap, nameSegment)) {
                        foundStarMap = getOwn(starMap, nameSegment);
                        starI = i;
                    }
                }

                if (!foundMap && foundStarMap) {
                    foundMap = foundStarMap;
                    foundI = starI;
                }

                if (foundMap) {
                    nameParts.splice(0, foundI, foundMap);
                    name = nameParts.join('/');
                }
            }

            // If the name points to a package's name, use
            // the package main instead.
            pkgMain = getOwn(config.pkgs, name);

            return pkgMain ? pkgMain : name;
        }

        function removeScript(name) {
            if (isBrowser) {
                each(scripts(), function (scriptNode) {
                    if (scriptNode.getAttribute('data-requiremodule') === name &&
                            scriptNode.getAttribute('data-requirecontext') === context.contextName) {
                        scriptNode.parentNode.removeChild(scriptNode);
                        return true;
                    }
                });
            }
        }

        function hasPathFallback(id) {
            var pathConfig = getOwn(config.paths, id);
            if (pathConfig && isArray(pathConfig) && pathConfig.length > 1) {
                //Pop off the first array value, since it failed, and
                //retry
                pathConfig.shift();
                context.require.undef(id);

                //Custom require that does not do map translation, since
                //ID is "absolute", already mapped/resolved.
                context.makeRequire(null, {
                    skipMap: true
                })([id]);

                return true;
            }
        }

        //Turns a plugin!resource to [plugin, resource]
        //with the plugin being undefined if the name
        //did not have a plugin prefix.
        function splitPrefix(name) {
            var prefix,
                index = name ? name.indexOf('!') : -1;
            if (index > -1) {
                prefix = name.substring(0, index);
                name = name.substring(index + 1, name.length);
            }
            return [prefix, name];
        }

        /**
         * Creates a module mapping that includes plugin prefix, module
         * name, and path. If parentModuleMap is provided it will
         * also normalize the name via require.normalize()
         *
         * @param {String} name the module name
         * @param {String} [parentModuleMap] parent module map
         * for the module name, used to resolve relative names.
         * @param {Boolean} isNormalized: is the ID already normalized.
         * This is true if this call is done for a define() module ID.
         * @param {Boolean} applyMap: apply the map config to the ID.
         * Should only be true if this map is for a dependency.
         *
         * @returns {Object}
         */
        function makeModuleMap(name, parentModuleMap, isNormalized, applyMap) {
            var url, pluginModule, suffix, nameParts,
                prefix = null,
                parentName = parentModuleMap ? parentModuleMap.name : null,
                originalName = name,
                isDefine = true,
                normalizedName = '';

            //If no name, then it means it is a require call, generate an
            //internal name.
            if (!name) {
                isDefine = false;
                name = '_@r' + (requireCounter += 1);
            }

            nameParts = splitPrefix(name);
            prefix = nameParts[0];
            name = nameParts[1];

            if (prefix) {
                prefix = normalize(prefix, parentName, applyMap);
                pluginModule = getOwn(defined, prefix);
            }

            //Account for relative paths if there is a base name.
            if (name) {
                if (prefix) {
                    if (isNormalized) {
                        normalizedName = name;
                    } else if (pluginModule && pluginModule.normalize) {
                        //Plugin is loaded, use its normalize method.
                        normalizedName = pluginModule.normalize(name, function (name) {
                            return normalize(name, parentName, applyMap);
                        });
                    } else {
                        // If nested plugin references, then do not try to
                        // normalize, as it will not normalize correctly. This
                        // places a restriction on resourceIds, and the longer
                        // term solution is not to normalize until plugins are
                        // loaded and all normalizations to allow for async
                        // loading of a loader plugin. But for now, fixes the
                        // common uses. Details in #1131
                        normalizedName = name.indexOf('!') === -1 ?
                                         normalize(name, parentName, applyMap) :
                                         name;
                    }
                } else {
                    //A regular module.
                    normalizedName = normalize(name, parentName, applyMap);

                    //Normalized name may be a plugin ID due to map config
                    //application in normalize. The map config values must
                    //already be normalized, so do not need to redo that part.
                    nameParts = splitPrefix(normalizedName);
                    prefix = nameParts[0];
                    normalizedName = nameParts[1];
                    isNormalized = true;

                    url = context.nameToUrl(normalizedName);
                }
            }

            //If the id is a plugin id that cannot be determined if it needs
            //normalization, stamp it with a unique ID so two matching relative
            //ids that may conflict can be separate.
            suffix = prefix && !pluginModule && !isNormalized ?
                     '_unnormalized' + (unnormalizedCounter += 1) :
                     '';

            return {
                prefix: prefix,
                name: normalizedName,
                parentMap: parentModuleMap,
                unnormalized: !!suffix,
                url: url,
                originalName: originalName,
                isDefine: isDefine,
                id: (prefix ?
                        prefix + '!' + normalizedName :
                        normalizedName) + suffix
            };
        }

        function getModule(depMap) {
            var id = depMap.id,
                mod = getOwn(registry, id);

            if (!mod) {
                mod = registry[id] = new context.Module(depMap);
            }

            return mod;
        }

        function on(depMap, name, fn) {
            var id = depMap.id,
                mod = getOwn(registry, id);

            if (hasProp(defined, id) &&
                    (!mod || mod.defineEmitComplete)) {
                if (name === 'defined') {
                    fn(defined[id]);
                }
            } else {
                mod = getModule(depMap);
                if (mod.error && name === 'error') {
                    fn(mod.error);
                } else {
                    mod.on(name, fn);
                }
            }
        }

        function onError(err, errback) {
            var ids = err.requireModules,
                notified = false;

            if (errback) {
                errback(err);
            } else {
                each(ids, function (id) {
                    var mod = getOwn(registry, id);
                    if (mod) {
                        //Set error on module, so it skips timeout checks.
                        mod.error = err;
                        if (mod.events.error) {
                            notified = true;
                            mod.emit('error', err);
                        }
                    }
                });

                if (!notified) {
                    req.onError(err);
                }
            }
        }

        /**
         * Internal method to transfer globalQueue items to this context's
         * defQueue.
         */
        function takeGlobalQueue() {
            //Push all the globalDefQueue items into the context's defQueue
            if (globalDefQueue.length) {
                each(globalDefQueue, function(queueItem) {
                    var id = queueItem[0];
                    if (typeof id === 'string') {
                        context.defQueueMap[id] = true;
                    }
                    defQueue.push(queueItem);
                });
                globalDefQueue = [];
            }
        }

        handlers = {
            'require': function (mod) {
                if (mod.require) {
                    return mod.require;
                } else {
                    return (mod.require = context.makeRequire(mod.map));
                }
            },
            'exports': function (mod) {
                mod.usingExports = true;
                if (mod.map.isDefine) {
                    if (mod.exports) {
                        return (defined[mod.map.id] = mod.exports);
                    } else {
                        return (mod.exports = defined[mod.map.id] = {});
                    }
                }
            },
            'module': function (mod) {
                if (mod.module) {
                    return mod.module;
                } else {
                    return (mod.module = {
                        id: mod.map.id,
                        uri: mod.map.url,
                        config: function () {
                            return getOwn(config.config, mod.map.id) || {};
                        },
                        exports: mod.exports || (mod.exports = {})
                    });
                }
            }
        };

        function cleanRegistry(id) {
            //Clean up machinery used for waiting modules.
            delete registry[id];
            delete enabledRegistry[id];
        }

        function breakCycle(mod, traced, processed) {
            var id = mod.map.id;

            if (mod.error) {
                mod.emit('error', mod.error);
            } else {
                traced[id] = true;
                each(mod.depMaps, function (depMap, i) {
                    var depId = depMap.id,
                        dep = getOwn(registry, depId);

                    //Only force things that have not completed
                    //being defined, so still in the registry,
                    //and only if it has not been matched up
                    //in the module already.
                    if (dep && !mod.depMatched[i] && !processed[depId]) {
                        if (getOwn(traced, depId)) {
                            mod.defineDep(i, defined[depId]);
                            mod.check(); //pass false?
                        } else {
                            breakCycle(dep, traced, processed);
                        }
                    }
                });
                processed[id] = true;
            }
        }

        function checkLoaded() {
            var err, usingPathFallback,
                waitInterval = config.waitSeconds * 1000,
                //It is possible to disable the wait interval by using waitSeconds of 0.
                expired = waitInterval && (context.startTime + waitInterval) < new Date().getTime(),
                noLoads = [],
                reqCalls = [],
                stillLoading = false,
                needCycleCheck = true;

            //Do not bother if this call was a result of a cycle break.
            if (inCheckLoaded) {
                return;
            }

            inCheckLoaded = true;

            //Figure out the state of all the modules.
            eachProp(enabledRegistry, function (mod) {
                var map = mod.map,
                    modId = map.id;

                //Skip things that are not enabled or in error state.
                if (!mod.enabled) {
                    return;
                }

                if (!map.isDefine) {
                    reqCalls.push(mod);
                }

                if (!mod.error) {
                    //If the module should be executed, and it has not
                    //been inited and time is up, remember it.
                    if (!mod.inited && expired) {
                        if (hasPathFallback(modId)) {
                            usingPathFallback = true;
                            stillLoading = true;
                        } else {
                            noLoads.push(modId);
                            removeScript(modId);
                        }
                    } else if (!mod.inited && mod.fetched && map.isDefine) {
                        stillLoading = true;
                        if (!map.prefix) {
                            //No reason to keep looking for unfinished
                            //loading. If the only stillLoading is a
                            //plugin resource though, keep going,
                            //because it may be that a plugin resource
                            //is waiting on a non-plugin cycle.
                            return (needCycleCheck = false);
                        }
                    }
                }
            });

            if (expired && noLoads.length) {
                //If wait time expired, throw error of unloaded modules.
                err = makeError('timeout', 'Load timeout for modules: ' + noLoads, null, noLoads);
                err.contextName = context.contextName;
                return onError(err);
            }

            //Not expired, check for a cycle.
            if (needCycleCheck) {
                each(reqCalls, function (mod) {
                    breakCycle(mod, {}, {});
                });
            }

            //If still waiting on loads, and the waiting load is something
            //other than a plugin resource, or there are still outstanding
            //scripts, then just try back later.
            if ((!expired || usingPathFallback) && stillLoading) {
                //Something is still waiting to load. Wait for it, but only
                //if a timeout is not already in effect.
                if ((isBrowser || isWebWorker) && !checkLoadedTimeoutId) {
                    checkLoadedTimeoutId = setTimeout(function () {
                        checkLoadedTimeoutId = 0;
                        checkLoaded();
                    }, 50);
                }
            }

            inCheckLoaded = false;
        }

        Module = function (map) {
            this.events = getOwn(undefEvents, map.id) || {};
            this.map = map;
            this.shim = getOwn(config.shim, map.id);
            this.depExports = [];
            this.depMaps = [];
            this.depMatched = [];
            this.pluginMaps = {};
            this.depCount = 0;

            /* this.exports this.factory
               this.depMaps = [],
               this.enabled, this.fetched
            */
        };

        Module.prototype = {
            init: function (depMaps, factory, errback, options) {
                options = options || {};

                //Do not do more inits if already done. Can happen if there
                //are multiple define calls for the same module. That is not
                //a normal, common case, but it is also not unexpected.
                if (this.inited) {
                    return;
                }

                this.factory = factory;

                if (errback) {
                    //Register for errors on this module.
                    this.on('error', errback);
                } else if (this.events.error) {
                    //If no errback already, but there are error listeners
                    //on this module, set up an errback to pass to the deps.
                    errback = bind(this, function (err) {
                        this.emit('error', err);
                    });
                }

                //Do a copy of the dependency array, so that
                //source inputs are not modified. For example
                //"shim" deps are passed in here directly, and
                //doing a direct modification of the depMaps array
                //would affect that config.
                this.depMaps = depMaps && depMaps.slice(0);

                this.errback = errback;

                //Indicate this module has be initialized
                this.inited = true;

                this.ignore = options.ignore;

                //Could have option to init this module in enabled mode,
                //or could have been previously marked as enabled. However,
                //the dependencies are not known until init is called. So
                //if enabled previously, now trigger dependencies as enabled.
                if (options.enabled || this.enabled) {
                    //Enable this module and dependencies.
                    //Will call this.check()
                    this.enable();
                } else {
                    this.check();
                }
            },

            defineDep: function (i, depExports) {
                //Because of cycles, defined callback for a given
                //export can be called more than once.
                if (!this.depMatched[i]) {
                    this.depMatched[i] = true;
                    this.depCount -= 1;
                    this.depExports[i] = depExports;
                }
            },

            fetch: function () {
                if (this.fetched) {
                    return;
                }
                this.fetched = true;

                context.startTime = (new Date()).getTime();

                var map = this.map;

                //If the manager is for a plugin managed resource,
                //ask the plugin to load it now.
                if (this.shim) {
                    context.makeRequire(this.map, {
                        enableBuildCallback: true
                    })(this.shim.deps || [], bind(this, function () {
                        return map.prefix ? this.callPlugin() : this.load();
                    }));
                } else {
                    //Regular dependency.
                    return map.prefix ? this.callPlugin() : this.load();
                }
            },

            load: function () {
                var url = this.map.url;

                //Regular dependency.
                if (!urlFetched[url]) {
                    urlFetched[url] = true;
                    context.load(this.map.id, url);
                }
            },

            /**
             * Checks if the module is ready to define itself, and if so,
             * define it.
             */
            check: function () {
                if (!this.enabled || this.enabling) {
                    return;
                }

                var err, cjsModule,
                    id = this.map.id,
                    depExports = this.depExports,
                    exports = this.exports,
                    factory = this.factory;

                if (!this.inited) {
                    // Only fetch if not already in the defQueue.
                    if (!hasProp(context.defQueueMap, id)) {
                        this.fetch();
                    }
                } else if (this.error) {
                    this.emit('error', this.error);
                } else if (!this.defining) {
                    //The factory could trigger another require call
                    //that would result in checking this module to
                    //define itself again. If already in the process
                    //of doing that, skip this work.
                    this.defining = true;

                    if (this.depCount < 1 && !this.defined) {
                        if (isFunction(factory)) {
                            //If there is an error listener, favor passing
                            //to that instead of throwing an error. However,
                            //only do it for define()'d  modules. require
                            //errbacks should not be called for failures in
                            //their callbacks (#699). However if a global
                            //onError is set, use that.
                            if ((this.events.error && this.map.isDefine) ||
                                req.onError !== defaultOnError) {
                                try {
                                    exports = context.execCb(id, factory, depExports, exports);
                                } catch (e) {
                                    err = e;
                                }
                            } else {
                                exports = context.execCb(id, factory, depExports, exports);
                            }

                            // Favor return value over exports. If node/cjs in play,
                            // then will not have a return value anyway. Favor
                            // module.exports assignment over exports object.
                            if (this.map.isDefine && exports === undefined) {
                                cjsModule = this.module;
                                if (cjsModule) {
                                    exports = cjsModule.exports;
                                } else if (this.usingExports) {
                                    //exports already set the defined value.
                                    exports = this.exports;
                                }
                            }

                            if (err) {
                                err.requireMap = this.map;
                                err.requireModules = this.map.isDefine ? [this.map.id] : null;
                                err.requireType = this.map.isDefine ? 'define' : 'require';
                                return onError((this.error = err));
                            }

                        } else {
                            //Just a literal value
                            exports = factory;
                        }

                        this.exports = exports;

                        if (this.map.isDefine && !this.ignore) {
                            defined[id] = exports;

                            if (req.onResourceLoad) {
                                var resLoadMaps = [];
                                each(this.depMaps, function (depMap) {
                                    resLoadMaps.push(depMap.normalizedMap || depMap);
                                });
                                req.onResourceLoad(context, this.map, resLoadMaps);
                            }
                        }

                        //Clean up
                        cleanRegistry(id);

                        this.defined = true;
                    }

                    //Finished the define stage. Allow calling check again
                    //to allow define notifications below in the case of a
                    //cycle.
                    this.defining = false;

                    if (this.defined && !this.defineEmitted) {
                        this.defineEmitted = true;
                        this.emit('defined', this.exports);
                        this.defineEmitComplete = true;
                    }

                }
            },

            callPlugin: function () {
                var map = this.map,
                    id = map.id,
                    //Map already normalized the prefix.
                    pluginMap = makeModuleMap(map.prefix);

                //Mark this as a dependency for this plugin, so it
                //can be traced for cycles.
                this.depMaps.push(pluginMap);

                on(pluginMap, 'defined', bind(this, function (plugin) {
                    var load, normalizedMap, normalizedMod,
                        bundleId = getOwn(bundlesMap, this.map.id),
                        name = this.map.name,
                        parentName = this.map.parentMap ? this.map.parentMap.name : null,
                        localRequire = context.makeRequire(map.parentMap, {
                            enableBuildCallback: true
                        });

                    //If current map is not normalized, wait for that
                    //normalized name to load instead of continuing.
                    if (this.map.unnormalized) {
                        //Normalize the ID if the plugin allows it.
                        if (plugin.normalize) {
                            name = plugin.normalize(name, function (name) {
                                return normalize(name, parentName, true);
                            }) || '';
                        }

                        //prefix and name should already be normalized, no need
                        //for applying map config again either.
                        normalizedMap = makeModuleMap(map.prefix + '!' + name,
                                                      this.map.parentMap,
                                                      true);
                        on(normalizedMap,
                            'defined', bind(this, function (value) {
                                this.map.normalizedMap = normalizedMap;
                                this.init([], function () { return value; }, null, {
                                    enabled: true,
                                    ignore: true
                                });
                            }));

                        normalizedMod = getOwn(registry, normalizedMap.id);
                        if (normalizedMod) {
                            //Mark this as a dependency for this plugin, so it
                            //can be traced for cycles.
                            this.depMaps.push(normalizedMap);

                            if (this.events.error) {
                                normalizedMod.on('error', bind(this, function (err) {
                                    this.emit('error', err);
                                }));
                            }
                            normalizedMod.enable();
                        }

                        return;
                    }

                    //If a paths config, then just load that file instead to
                    //resolve the plugin, as it is built into that paths layer.
                    if (bundleId) {
                        this.map.url = context.nameToUrl(bundleId);
                        this.load();
                        return;
                    }

                    load = bind(this, function (value) {
                        this.init([], function () { return value; }, null, {
                            enabled: true
                        });
                    });

                    load.error = bind(this, function (err) {
                        this.inited = true;
                        this.error = err;
                        err.requireModules = [id];

                        //Remove temp unnormalized modules for this module,
                        //since they will never be resolved otherwise now.
                        eachProp(registry, function (mod) {
                            if (mod.map.id.indexOf(id + '_unnormalized') === 0) {
                                cleanRegistry(mod.map.id);
                            }
                        });

                        onError(err);
                    });

                    //Allow plugins to load other code without having to know the
                    //context or how to 'complete' the load.
                    load.fromText = bind(this, function (text, textAlt) {
                        /*jslint evil: true */
                        var moduleName = map.name,
                            moduleMap = makeModuleMap(moduleName),
                            hasInteractive = useInteractive;

                        //As of 2.1.0, support just passing the text, to reinforce
                        //fromText only being called once per resource. Still
                        //support old style of passing moduleName but discard
                        //that moduleName in favor of the internal ref.
                        if (textAlt) {
                            text = textAlt;
                        }

                        //Turn off interactive script matching for IE for any define
                        //calls in the text, then turn it back on at the end.
                        if (hasInteractive) {
                            useInteractive = false;
                        }

                        //Prime the system by creating a module instance for
                        //it.
                        getModule(moduleMap);

                        //Transfer any config to this other module.
                        if (hasProp(config.config, id)) {
                            config.config[moduleName] = config.config[id];
                        }

                        try {
                            req.exec(text);
                        } catch (e) {
                            return onError(makeError('fromtexteval',
                                             'fromText eval for ' + id +
                                            ' failed: ' + e,
                                             e,
                                             [id]));
                        }

                        if (hasInteractive) {
                            useInteractive = true;
                        }

                        //Mark this as a dependency for the plugin
                        //resource
                        this.depMaps.push(moduleMap);

                        //Support anonymous modules.
                        context.completeLoad(moduleName);

                        //Bind the value of that module to the value for this
                        //resource ID.
                        localRequire([moduleName], load);
                    });

                    //Use parentName here since the plugin's name is not reliable,
                    //could be some weird string with no path that actually wants to
                    //reference the parentName's path.
                    plugin.load(map.name, localRequire, load, config);
                }));

                context.enable(pluginMap, this);
                this.pluginMaps[pluginMap.id] = pluginMap;
            },

            enable: function () {
                enabledRegistry[this.map.id] = this;
                this.enabled = true;

                //Set flag mentioning that the module is enabling,
                //so that immediate calls to the defined callbacks
                //for dependencies do not trigger inadvertent load
                //with the depCount still being zero.
                this.enabling = true;

                //Enable each dependency
                each(this.depMaps, bind(this, function (depMap, i) {
                    var id, mod, handler;

                    if (typeof depMap === 'string') {
                        //Dependency needs to be converted to a depMap
                        //and wired up to this module.
                        depMap = makeModuleMap(depMap,
                                               (this.map.isDefine ? this.map : this.map.parentMap),
                                               false,
                                               !this.skipMap);
                        this.depMaps[i] = depMap;

                        handler = getOwn(handlers, depMap.id);

                        if (handler) {
                            this.depExports[i] = handler(this);
                            return;
                        }

                        this.depCount += 1;

                        on(depMap, 'defined', bind(this, function (depExports) {
                            if (this.undefed) {
                                return;
                            }
                            this.defineDep(i, depExports);
                            this.check();
                        }));

                        if (this.errback) {
                            on(depMap, 'error', bind(this, this.errback));
                        } else if (this.events.error) {
                            // No direct errback on this module, but something
                            // else is listening for errors, so be sure to
                            // propagate the error correctly.
                            on(depMap, 'error', bind(this, function(err) {
                                this.emit('error', err);
                            }));
                        }
                    }

                    id = depMap.id;
                    mod = registry[id];

                    //Skip special modules like 'require', 'exports', 'module'
                    //Also, don't call enable if it is already enabled,
                    //important in circular dependency cases.
                    if (!hasProp(handlers, id) && mod && !mod.enabled) {
                        context.enable(depMap, this);
                    }
                }));

                //Enable each plugin that is used in
                //a dependency
                eachProp(this.pluginMaps, bind(this, function (pluginMap) {
                    var mod = getOwn(registry, pluginMap.id);
                    if (mod && !mod.enabled) {
                        context.enable(pluginMap, this);
                    }
                }));

                this.enabling = false;

                this.check();
            },

            on: function (name, cb) {
                var cbs = this.events[name];
                if (!cbs) {
                    cbs = this.events[name] = [];
                }
                cbs.push(cb);
            },

            emit: function (name, evt) {
                each(this.events[name], function (cb) {
                    cb(evt);
                });
                if (name === 'error') {
                    //Now that the error handler was triggered, remove
                    //the listeners, since this broken Module instance
                    //can stay around for a while in the registry.
                    delete this.events[name];
                }
            }
        };

        function callGetModule(args) {
            //Skip modules already defined.
            if (!hasProp(defined, args[0])) {
                getModule(makeModuleMap(args[0], null, true)).init(args[1], args[2]);
            }
        }

        function removeListener(node, func, name, ieName) {
            //Favor detachEvent because of IE9
            //issue, see attachEvent/addEventListener comment elsewhere
            //in this file.
            if (node.detachEvent && !isOpera) {
                //Probably IE. If not it will throw an error, which will be
                //useful to know.
                if (ieName) {
                    node.detachEvent(ieName, func);
                }
            } else {
                node.removeEventListener(name, func, false);
            }
        }

        /**
         * Given an event from a script node, get the requirejs info from it,
         * and then removes the event listeners on the node.
         * @param {Event} evt
         * @returns {Object}
         */
        function getScriptData(evt) {
            //Using currentTarget instead of target for Firefox 2.0's sake. Not
            //all old browsers will be supported, but this one was easy enough
            //to support and still makes sense.
            var node = evt.currentTarget || evt.srcElement;

            //Remove the listeners once here.
            removeListener(node, context.onScriptLoad, 'load', 'onreadystatechange');
            removeListener(node, context.onScriptError, 'error');

            return {
                node: node,
                id: node && node.getAttribute('data-requiremodule')
            };
        }

        function intakeDefines() {
            var args;

            //Any defined modules in the global queue, intake them now.
            takeGlobalQueue();

            //Make sure any remaining defQueue items get properly processed.
            while (defQueue.length) {
                args = defQueue.shift();
                if (args[0] === null) {
                    return onError(makeError('mismatch', 'Mismatched anonymous define() module: ' +
                        args[args.length - 1]));
                } else {
                    //args are id, deps, factory. Should be normalized by the
                    //define() function.
                    callGetModule(args);
                }
            }
            context.defQueueMap = {};
        }

        context = {
            config: config,
            contextName: contextName,
            registry: registry,
            defined: defined,
            urlFetched: urlFetched,
            defQueue: defQueue,
            defQueueMap: {},
            Module: Module,
            makeModuleMap: makeModuleMap,
            nextTick: req.nextTick,
            onError: onError,

            /**
             * Set a configuration for the context.
             * @param {Object} cfg config object to integrate.
             */
            configure: function (cfg) {
                //Make sure the baseUrl ends in a slash.
                if (cfg.baseUrl) {
                    if (cfg.baseUrl.charAt(cfg.baseUrl.length - 1) !== '/') {
                        cfg.baseUrl += '/';
                    }
                }

                // Convert old style urlArgs string to a function.
                if (typeof cfg.urlArgs === 'string') {
                    var urlArgs = cfg.urlArgs;
                    cfg.urlArgs = function(id, url) {
                        return (url.indexOf('?') === -1 ? '?' : '&') + urlArgs;
                    };
                }

                //Save off the paths since they require special processing,
                //they are additive.
                var shim = config.shim,
                    objs = {
                        paths: true,
                        bundles: true,
                        config: true,
                        map: true
                    };

                eachProp(cfg, function (value, prop) {
                    if (objs[prop]) {
                        if (!config[prop]) {
                            config[prop] = {};
                        }
                        mixin(config[prop], value, true, true);
                    } else {
                        config[prop] = value;
                    }
                });

                //Reverse map the bundles
                if (cfg.bundles) {
                    eachProp(cfg.bundles, function (value, prop) {
                        each(value, function (v) {
                            if (v !== prop) {
                                bundlesMap[v] = prop;
                            }
                        });
                    });
                }

                //Merge shim
                if (cfg.shim) {
                    eachProp(cfg.shim, function (value, id) {
                        //Normalize the structure
                        if (isArray(value)) {
                            value = {
                                deps: value
                            };
                        }
                        if ((value.exports || value.init) && !value.exportsFn) {
                            value.exportsFn = context.makeShimExports(value);
                        }
                        shim[id] = value;
                    });
                    config.shim = shim;
                }

                //Adjust packages if necessary.
                if (cfg.packages) {
                    each(cfg.packages, function (pkgObj) {
                        var location, name;

                        pkgObj = typeof pkgObj === 'string' ? {name: pkgObj} : pkgObj;

                        name = pkgObj.name;
                        location = pkgObj.location;
                        if (location) {
                            config.paths[name] = pkgObj.location;
                        }

                        //Save pointer to main module ID for pkg name.
                        //Remove leading dot in main, so main paths are normalized,
                        //and remove any trailing .js, since different package
                        //envs have different conventions: some use a module name,
                        //some use a file name.
                        config.pkgs[name] = pkgObj.name + '/' + (pkgObj.main || 'main')
                                     .replace(currDirRegExp, '')
                                     .replace(jsSuffixRegExp, '');
                    });
                }

                //If there are any "waiting to execute" modules in the registry,
                //update the maps for them, since their info, like URLs to load,
                //may have changed.
                eachProp(registry, function (mod, id) {
                    //If module already has init called, since it is too
                    //late to modify them, and ignore unnormalized ones
                    //since they are transient.
                    if (!mod.inited && !mod.map.unnormalized) {
                        mod.map = makeModuleMap(id, null, true);
                    }
                });

                //If a deps array or a config callback is specified, then call
                //require with those args. This is useful when require is defined as a
                //config object before require.js is loaded.
                if (cfg.deps || cfg.callback) {
                    context.require(cfg.deps || [], cfg.callback);
                }
            },

            makeShimExports: function (value) {
                function fn() {
                    var ret;
                    if (value.init) {
                        ret = value.init.apply(global, arguments);
                    }
                    return ret || (value.exports && getGlobal(value.exports));
                }
                return fn;
            },

            makeRequire: function (relMap, options) {
                options = options || {};

                function localRequire(deps, callback, errback) {
                    var id, map, requireMod;

                    if (options.enableBuildCallback && callback && isFunction(callback)) {
                        callback.__requireJsBuild = true;
                    }

                    if (typeof deps === 'string') {
                        if (isFunction(callback)) {
                            //Invalid call
                            return onError(makeError('requireargs', 'Invalid require call'), errback);
                        }

                        //If require|exports|module are requested, get the
                        //value for them from the special handlers. Caveat:
                        //this only works while module is being defined.
                        if (relMap && hasProp(handlers, deps)) {
                            return handlers[deps](registry[relMap.id]);
                        }

                        //Synchronous access to one module. If require.get is
                        //available (as in the Node adapter), prefer that.
                        if (req.get) {
                            return req.get(context, deps, relMap, localRequire);
                        }

                        //Normalize module name, if it contains . or ..
                        map = makeModuleMap(deps, relMap, false, true);
                        id = map.id;

                        if (!hasProp(defined, id)) {
                            return onError(makeError('notloaded', 'Module name "' +
                                        id +
                                        '" has not been loaded yet for context: ' +
                                        contextName +
                                        (relMap ? '' : '. Use require([])')));
                        }
                        return defined[id];
                    }

                    //Grab defines waiting in the global queue.
                    intakeDefines();

                    //Mark all the dependencies as needing to be loaded.
                    context.nextTick(function () {
                        //Some defines could have been added since the
                        //require call, collect them.
                        intakeDefines();

                        requireMod = getModule(makeModuleMap(null, relMap));

                        //Store if map config should be applied to this require
                        //call for dependencies.
                        requireMod.skipMap = options.skipMap;

                        requireMod.init(deps, callback, errback, {
                            enabled: true
                        });

                        checkLoaded();
                    });

                    return localRequire;
                }

                mixin(localRequire, {
                    isBrowser: isBrowser,

                    /**
                     * Converts a module name + .extension into an URL path.
                     * *Requires* the use of a module name. It does not support using
                     * plain URLs like nameToUrl.
                     */
                    toUrl: function (moduleNamePlusExt) {
                        var ext,
                            index = moduleNamePlusExt.lastIndexOf('.'),
                            segment = moduleNamePlusExt.split('/')[0],
                            isRelative = segment === '.' || segment === '..';

                        //Have a file extension alias, and it is not the
                        //dots from a relative path.
                        if (index !== -1 && (!isRelative || index > 1)) {
                            ext = moduleNamePlusExt.substring(index, moduleNamePlusExt.length);
                            moduleNamePlusExt = moduleNamePlusExt.substring(0, index);
                        }

                        return context.nameToUrl(normalize(moduleNamePlusExt,
                                                relMap && relMap.id, true), ext,  true);
                    },

                    defined: function (id) {
                        return hasProp(defined, makeModuleMap(id, relMap, false, true).id);
                    },

                    specified: function (id) {
                        id = makeModuleMap(id, relMap, false, true).id;
                        return hasProp(defined, id) || hasProp(registry, id);
                    }
                });

                //Only allow undef on top level require calls
                if (!relMap) {
                    localRequire.undef = function (id) {
                        //Bind any waiting define() calls to this context,
                        //fix for #408
                        takeGlobalQueue();

                        var map = makeModuleMap(id, relMap, true),
                            mod = getOwn(registry, id);

                        mod.undefed = true;
                        removeScript(id);

                        delete defined[id];
                        delete urlFetched[map.url];
                        delete undefEvents[id];

                        //Clean queued defines too. Go backwards
                        //in array so that the splices do not
                        //mess up the iteration.
                        eachReverse(defQueue, function(args, i) {
                            if (args[0] === id) {
                                defQueue.splice(i, 1);
                            }
                        });
                        delete context.defQueueMap[id];

                        if (mod) {
                            //Hold on to listeners in case the
                            //module will be attempted to be reloaded
                            //using a different config.
                            if (mod.events.defined) {
                                undefEvents[id] = mod.events;
                            }

                            cleanRegistry(id);
                        }
                    };
                }

                return localRequire;
            },

            /**
             * Called to enable a module if it is still in the registry
             * awaiting enablement. A second arg, parent, the parent module,
             * is passed in for context, when this method is overridden by
             * the optimizer. Not shown here to keep code compact.
             */
            enable: function (depMap) {
                var mod = getOwn(registry, depMap.id);
                if (mod) {
                    getModule(depMap).enable();
                }
            },

            /**
             * Internal method used by environment adapters to complete a load event.
             * A load event could be a script load or just a load pass from a synchronous
             * load call.
             * @param {String} moduleName the name of the module to potentially complete.
             */
            completeLoad: function (moduleName) {
                var found, args, mod,
                    shim = getOwn(config.shim, moduleName) || {},
                    shExports = shim.exports;

                takeGlobalQueue();

                while (defQueue.length) {
                    args = defQueue.shift();
                    if (args[0] === null) {
                        args[0] = moduleName;
                        //If already found an anonymous module and bound it
                        //to this name, then this is some other anon module
                        //waiting for its completeLoad to fire.
                        if (found) {
                            break;
                        }
                        found = true;
                    } else if (args[0] === moduleName) {
                        //Found matching define call for this script!
                        found = true;
                    }

                    callGetModule(args);
                }
                context.defQueueMap = {};

                //Do this after the cycle of callGetModule in case the result
                //of those calls/init calls changes the registry.
                mod = getOwn(registry, moduleName);

                if (!found && !hasProp(defined, moduleName) && mod && !mod.inited) {
                    if (config.enforceDefine && (!shExports || !getGlobal(shExports))) {
                        if (hasPathFallback(moduleName)) {
                            return;
                        } else {
                            return onError(makeError('nodefine',
                                             'No define call for ' + moduleName,
                                             null,
                                             [moduleName]));
                        }
                    } else {
                        //A script that does not call define(), so just simulate
                        //the call for it.
                        callGetModule([moduleName, (shim.deps || []), shim.exportsFn]);
                    }
                }

                checkLoaded();
            },

            /**
             * Converts a module name to a file path. Supports cases where
             * moduleName may actually be just an URL.
             * Note that it **does not** call normalize on the moduleName,
             * it is assumed to have already been normalized. This is an
             * internal API, not a public one. Use toUrl for the public API.
             */
            nameToUrl: function (moduleName, ext, skipExt) {
                var paths, syms, i, parentModule, url,
                    parentPath, bundleId,
                    pkgMain = getOwn(config.pkgs, moduleName);

                if (pkgMain) {
                    moduleName = pkgMain;
                }

                bundleId = getOwn(bundlesMap, moduleName);

                if (bundleId) {
                    return context.nameToUrl(bundleId, ext, skipExt);
                }

                //If a colon is in the URL, it indicates a protocol is used and it is just
                //an URL to a file, or if it starts with a slash, contains a query arg (i.e. ?)
                //or ends with .js, then assume the user meant to use an url and not a module id.
                //The slash is important for protocol-less URLs as well as full paths.
                if (req.jsExtRegExp.test(moduleName)) {
                    //Just a plain path, not module name lookup, so just return it.
                    //Add extension if it is included. This is a bit wonky, only non-.js things pass
                    //an extension, this method probably needs to be reworked.
                    url = moduleName + (ext || '');
                } else {
                    //A module that needs to be converted to a path.
                    paths = config.paths;

                    syms = moduleName.split('/');
                    //For each module name segment, see if there is a path
                    //registered for it. Start with most specific name
                    //and work up from it.
                    for (i = syms.length; i > 0; i -= 1) {
                        parentModule = syms.slice(0, i).join('/');

                        parentPath = getOwn(paths, parentModule);
                        if (parentPath) {
                            //If an array, it means there are a few choices,
                            //Choose the one that is desired
                            if (isArray(parentPath)) {
                                parentPath = parentPath[0];
                            }
                            syms.splice(0, i, parentPath);
                            break;
                        }
                    }

                    //Join the path parts together, then figure out if baseUrl is needed.
                    url = syms.join('/');
                    url += (ext || (/^data\:|^blob\:|\?/.test(url) || skipExt ? '' : '.js'));
                    url = (url.charAt(0) === '/' || url.match(/^[\w\+\.\-]+:/) ? '' : config.baseUrl) + url;
                }

                return config.urlArgs && !/^blob\:/.test(url) ?
                       url + config.urlArgs(moduleName, url) : url;
            },

            //Delegates to req.load. Broken out as a separate function to
            //allow overriding in the optimizer.
            load: function (id, url) {
                req.load(context, id, url);
            },

            /**
             * Executes a module callback function. Broken out as a separate function
             * solely to allow the build system to sequence the files in the built
             * layer in the right sequence.
             *
             * @private
             */
            execCb: function (name, callback, args, exports) {
                return callback.apply(exports, args);
            },

            /**
             * callback for script loads, used to check status of loading.
             *
             * @param {Event} evt the event from the browser for the script
             * that was loaded.
             */
            onScriptLoad: function (evt) {
                //Using currentTarget instead of target for Firefox 2.0's sake. Not
                //all old browsers will be supported, but this one was easy enough
                //to support and still makes sense.
                if (evt.type === 'load' ||
                        (readyRegExp.test((evt.currentTarget || evt.srcElement).readyState))) {
                    //Reset interactive script so a script node is not held onto for
                    //to long.
                    interactiveScript = null;

                    //Pull out the name of the module and the context.
                    var data = getScriptData(evt);
                    context.completeLoad(data.id);
                }
            },

            /**
             * Callback for script errors.
             */
            onScriptError: function (evt) {
                var data = getScriptData(evt);
                if (!hasPathFallback(data.id)) {
                    var parents = [];
                    eachProp(registry, function(value, key) {
                        if (key.indexOf('_@r') !== 0) {
                            each(value.depMaps, function(depMap) {
                                if (depMap.id === data.id) {
                                    parents.push(key);
                                    return true;
                                }
                            });
                        }
                    });
                    return onError(makeError('scripterror', 'Script error for "' + data.id +
                                             (parents.length ?
                                             '", needed by: ' + parents.join(', ') :
                                             '"'), evt, [data.id]));
                }
            }
        };

        context.require = context.makeRequire();
        return context;
    }

    /**
     * Main entry point.
     *
     * If the only argument to require is a string, then the module that
     * is represented by that string is fetched for the appropriate context.
     *
     * If the first argument is an array, then it will be treated as an array
     * of dependency string names to fetch. An optional function callback can
     * be specified to execute when all of those dependencies are available.
     *
     * Make a local req variable to help Caja compliance (it assumes things
     * on a require that are not standardized), and to give a short
     * name for minification/local scope use.
     */
    req = requirejs = function (deps, callback, errback, optional) {

        //Find the right context, use default
        var context, config,
            contextName = defContextName;

        // Determine if have config object in the call.
        if (!isArray(deps) && typeof deps !== 'string') {
            // deps is a config object
            config = deps;
            if (isArray(callback)) {
                // Adjust args if there are dependencies
                deps = callback;
                callback = errback;
                errback = optional;
            } else {
                deps = [];
            }
        }

        if (config && config.context) {
            contextName = config.context;
        }

        context = getOwn(contexts, contextName);
        if (!context) {
            context = contexts[contextName] = req.s.newContext(contextName);
        }

        if (config) {
            context.configure(config);
        }

        return context.require(deps, callback, errback);
    };

    /**
     * Support require.config() to make it easier to cooperate with other
     * AMD loaders on globally agreed names.
     */
    req.config = function (config) {
        return req(config);
    };

    /**
     * Execute something after the current tick
     * of the event loop. Override for other envs
     * that have a better solution than setTimeout.
     * @param  {Function} fn function to execute later.
     */
    req.nextTick = typeof setTimeout !== 'undefined' ? function (fn) {
        setTimeout(fn, 4);
    } : function (fn) { fn(); };

    /**
     * Export require as a global, but only if it does not already exist.
     */
    if (!require) {
        require = req;
    }

    req.version = version;

    //Used to filter out dependencies that are already paths.
    req.jsExtRegExp = /^\/|:|\?|\.js$/;
    req.isBrowser = isBrowser;
    s = req.s = {
        contexts: contexts,
        newContext: newContext
    };

    //Create default context.
    req({});

    //Exports some context-sensitive methods on global require.
    each([
        'toUrl',
        'undef',
        'defined',
        'specified'
    ], function (prop) {
        //Reference from contexts instead of early binding to default context,
        //so that during builds, the latest instance of the default context
        //with its config gets used.
        req[prop] = function () {
            var ctx = contexts[defContextName];
            return ctx.require[prop].apply(ctx, arguments);
        };
    });

    if (isBrowser) {
        head = s.head = document.getElementsByTagName('head')[0];
        //If BASE tag is in play, using appendChild is a problem for IE6.
        //When that browser dies, this can be removed. Details in this jQuery bug:
        //http://dev.jquery.com/ticket/2709
        baseElement = document.getElementsByTagName('base')[0];
        if (baseElement) {
            head = s.head = baseElement.parentNode;
        }
    }

    /**
     * Any errors that require explicitly generates will be passed to this
     * function. Intercept/override it if you want custom error handling.
     * @param {Error} err the error object.
     */
    req.onError = defaultOnError;

    /**
     * Creates the node for the load command. Only used in browser envs.
     */
    req.createNode = function (config, moduleName, url) {
        var node = config.xhtml ?
                document.createElementNS('http://www.w3.org/1999/xhtml', 'html:script') :
                document.createElement('script');
        node.type = config.scriptType || 'text/javascript';
        node.charset = 'utf-8';
        node.async = true;
        return node;
    };

    /**
     * Does the request to load a module for the browser case.
     * Make this a separate function to allow other environments
     * to override it.
     *
     * @param {Object} context the require context to find state.
     * @param {String} moduleName the name of the module.
     * @param {Object} url the URL to the module.
     */
    req.load = function (context, moduleName, url) {
        var config = (context && context.config) || {},
            node;
        if (isBrowser) {
            //In the browser so use a script tag
            node = req.createNode(config, moduleName, url);

            node.setAttribute('data-requirecontext', context.contextName);
            node.setAttribute('data-requiremodule', moduleName);

            //Set up load listener. Test attachEvent first because IE9 has
            //a subtle issue in its addEventListener and script onload firings
            //that do not match the behavior of all other browsers with
            //addEventListener support, which fire the onload event for a
            //script right after the script execution. See:
            //https://connect.microsoft.com/IE/feedback/details/648057/script-onload-event-is-not-fired-immediately-after-script-execution
            //UNFORTUNATELY Opera implements attachEvent but does not follow the script
            //script execution mode.
            if (node.attachEvent &&
                    //Check if node.attachEvent is artificially added by custom script or
                    //natively supported by browser
                    //read https://github.com/requirejs/requirejs/issues/187
                    //if we can NOT find [native code] then it must NOT natively supported.
                    //in IE8, node.attachEvent does not have toString()
                    //Note the test for "[native code" with no closing brace, see:
                    //https://github.com/requirejs/requirejs/issues/273
                    !(node.attachEvent.toString && node.attachEvent.toString().indexOf('[native code') < 0) &&
                    !isOpera) {
                //Probably IE. IE (at least 6-8) do not fire
                //script onload right after executing the script, so
                //we cannot tie the anonymous define call to a name.
                //However, IE reports the script as being in 'interactive'
                //readyState at the time of the define call.
                useInteractive = true;

                node.attachEvent('onreadystatechange', context.onScriptLoad);
                //It would be great to add an error handler here to catch
                //404s in IE9+. However, onreadystatechange will fire before
                //the error handler, so that does not help. If addEventListener
                //is used, then IE will fire error before load, but we cannot
                //use that pathway given the connect.microsoft.com issue
                //mentioned above about not doing the 'script execute,
                //then fire the script load event listener before execute
                //next script' that other browsers do.
                //Best hope: IE10 fixes the issues,
                //and then destroys all installs of IE 6-9.
                //node.attachEvent('onerror', context.onScriptError);
            } else {
                node.addEventListener('load', context.onScriptLoad, false);
                node.addEventListener('error', context.onScriptError, false);
            }
            node.src = url;

            //Calling onNodeCreated after all properties on the node have been
            //set, but before it is placed in the DOM.
            if (config.onNodeCreated) {
                config.onNodeCreated(node, config, moduleName, url);
            }

            //For some cache cases in IE 6-8, the script executes before the end
            //of the appendChild execution, so to tie an anonymous define
            //call to the module name (which is stored on the node), hold on
            //to a reference to this node, but clear after the DOM insertion.
            currentlyAddingScript = node;
            if (baseElement) {
                head.insertBefore(node, baseElement);
            } else {
                head.appendChild(node);
            }
            currentlyAddingScript = null;

            return node;
        } else if (isWebWorker) {
            try {
                //In a web worker, use importScripts. This is not a very
                //efficient use of importScripts, importScripts will block until
                //its script is downloaded and evaluated. However, if web workers
                //are in play, the expectation is that a build has been done so
                //that only one script needs to be loaded anyway. This may need
                //to be reevaluated if other use cases become common.

                // Post a task to the event loop to work around a bug in WebKit
                // where the worker gets garbage-collected after calling
                // importScripts(): https://webkit.org/b/153317
                setTimeout(function() {}, 0);
                importScripts(url);

                //Account for anonymous modules
                context.completeLoad(moduleName);
            } catch (e) {
                context.onError(makeError('importscripts',
                                'importScripts failed for ' +
                                    moduleName + ' at ' + url,
                                e,
                                [moduleName]));
            }
        }
    };

    function getInteractiveScript() {
        if (interactiveScript && interactiveScript.readyState === 'interactive') {
            return interactiveScript;
        }

        eachReverse(scripts(), function (script) {
            if (script.readyState === 'interactive') {
                return (interactiveScript = script);
            }
        });
        return interactiveScript;
    }

    //Look for a data-main script attribute, which could also adjust the baseUrl.
    if (isBrowser && !cfg.skipDataMain) {
        //Figure out baseUrl. Get it from the script tag with require.js in it.
        eachReverse(scripts(), function (script) {
            //Set the 'head' where we can append children by
            //using the script's parent.
            if (!head) {
                head = script.parentNode;
            }

            //Look for a data-main attribute to set main script for the page
            //to load. If it is there, the path to data main becomes the
            //baseUrl, if it is not already set.
            dataMain = script.getAttribute('data-main');
            if (dataMain) {
                //Preserve dataMain in case it is a path (i.e. contains '?')
                mainScript = dataMain;

                //Set final baseUrl if there is not already an explicit one,
                //but only do so if the data-main value is not a loader plugin
                //module ID.
                if (!cfg.baseUrl && mainScript.indexOf('!') === -1) {
                    //Pull off the directory of data-main for use as the
                    //baseUrl.
                    src = mainScript.split('/');
                    mainScript = src.pop();
                    subPath = src.length ? src.join('/')  + '/' : './';

                    cfg.baseUrl = subPath;
                }

                //Strip off any trailing .js since mainScript is now
                //like a module name.
                mainScript = mainScript.replace(jsSuffixRegExp, '');

                //If mainScript is still a path, fall back to dataMain
                if (req.jsExtRegExp.test(mainScript)) {
                    mainScript = dataMain;
                }

                //Put the data-main script in the files to load.
                cfg.deps = cfg.deps ? cfg.deps.concat(mainScript) : [mainScript];

                return true;
            }
        });
    }

    /**
     * The function that handles definitions of modules. Differs from
     * require() in that a string for the module should be the first argument,
     * and the function to execute after dependencies are loaded should
     * return a value to define the module corresponding to the first argument's
     * name.
     */
    define = function (name, deps, callback) {
        var node, context;

        //Allow for anonymous modules
        if (typeof name !== 'string') {
            //Adjust args appropriately
            callback = deps;
            deps = name;
            name = null;
        }

        //This module may not have dependencies
        if (!isArray(deps)) {
            callback = deps;
            deps = null;
        }

        //If no name, and callback is a function, then figure out if it a
        //CommonJS thing with dependencies.
        if (!deps && isFunction(callback)) {
            deps = [];
            //Remove comments from the callback string,
            //look for require calls, and pull them into the dependencies,
            //but only if there are function args.
            if (callback.length) {
                callback
                    .toString()
                    .replace(commentRegExp, commentReplace)
                    .replace(cjsRequireRegExp, function (match, dep) {
                        deps.push(dep);
                    });

                //May be a CommonJS thing even without require calls, but still
                //could use exports, and module. Avoid doing exports and module
                //work though if it just needs require.
                //REQUIRES the function to expect the CommonJS variables in the
                //order listed below.
                deps = (callback.length === 1 ? ['require'] : ['require', 'exports', 'module']).concat(deps);
            }
        }

        //If in IE 6-8 and hit an anonymous define() call, do the interactive
        //work.
        if (useInteractive) {
            node = currentlyAddingScript || getInteractiveScript();
            if (node) {
                if (!name) {
                    name = node.getAttribute('data-requiremodule');
                }
                context = contexts[node.getAttribute('data-requirecontext')];
            }
        }

        //Always save off evaluating the def call until the script onload handler.
        //This allows multiple modules to be in a file without prematurely
        //tracing dependencies, and allows for anonymous module support,
        //where the module name is not known until the script onload event
        //occurs. If no context, use the global queue, and get it processed
        //in the onscript load callback.
        if (context) {
            context.defQueue.push([name, deps, callback]);
            context.defQueueMap[name] = true;
        } else {
            globalDefQueue.push([name, deps, callback]);
        }
    };

    define.amd = {
        jQuery: true
    };

    /**
     * Executes the text. Normally just uses eval, but can be modified
     * to use a better, environment-specific call. Only used for transpiling
     * loader plugins, not for plain JS modules.
     * @param {String} text the text to execute/evaluate.
     */
    req.exec = function (text) {
        /*jslint evil: true */
        return eval(text);
    };

    //Set up with config info.
    req(cfg);
}(this, (typeof setTimeout === 'undefined' ? undefined : setTimeout)));

// Copyright 2014 Google Inc. All rights reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
//     You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
//     See the License for the specific language governing permissions and
// limitations under the License.

!function(){var a={},b={},c={};!function(a,b){function c(a){if("number"==typeof a)return a;var b={};for(var c in a)b[c]=a[c];return b}function d(){this._delay=0,this._endDelay=0,this._fill="none",this._iterationStart=0,this._iterations=1,this._duration=0,this._playbackRate=1,this._direction="normal",this._easing="linear",this._easingFunction=x}function e(){return a.isDeprecated("Invalid timing inputs","2016-03-02","TypeError exceptions will be thrown instead.",!0)}function f(b,c,e){var f=new d;return c&&(f.fill="both",f.duration="auto"),"number"!=typeof b||isNaN(b)?void 0!==b&&Object.getOwnPropertyNames(b).forEach(function(c){if("auto"!=b[c]){if(("number"==typeof f[c]||"duration"==c)&&("number"!=typeof b[c]||isNaN(b[c])))return;if("fill"==c&&-1==v.indexOf(b[c]))return;if("direction"==c&&-1==w.indexOf(b[c]))return;if("playbackRate"==c&&1!==b[c]&&a.isDeprecated("AnimationEffectTiming.playbackRate","2014-11-28","Use Animation.playbackRate 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e<0?e=a.length-Math.floor(-e):e&&(e=Math.floor(e)),r=2===arguments.length?a.splice(e):a.splice(e,n,...i),(i.length||r.length)&&ue(this,a,o.path,e,i.length,r),r}shift(t){let e={path:""},n=nt(this,t,e),i=Boolean(n.length),r=n.shift();return i&&ue(this,n,e.path,0,0,[r]),r}unshift(t,...e){let n={path:""},i=nt(this,t,n),r=i.unshift(...e);return e.length&&ue(this,i,n.path,0,e.length,[]),r}notifyPath(t,e){let n;if(1==arguments.length){let i={path:""};e=nt(this,t,i),n=i.path}else n=Array.isArray(t)?tt(t):t;this._setPendingPropertyOrPath(n,e,!0,!0)&&this._invalidateProperties()}_createReadOnlyProperty(t,e){this._addPropertyEffect(t,zt.READ_ONLY),e&&(this["_set"+(function n(t){return t[0].toUpperCase()+t.substring(1)})(t)]=function(e){this._setProperty(t,e)})}_createPropertyObserver(t,e,n){let i={property:t,method:e,dynamicFn:Boolean(n)};this._addPropertyEffect(t,zt.OBSERVE,{fn:Ut,info:i,trigger:{name:t}}),n&&this._addPropertyEffect(e,zt.OBSERVE,{fn:Ut,info:i,trigger:{name:e}})}_createMethodObserver(t,e){let n=ae(t);if(!n)throw new Error("Malformed observer expression '"+t+"'");ee(this,n,zt.OBSERVE,ne,null,e)}_createNotifyingProperty(t){this._addPropertyEffect(t,zt.NOTIFY,{fn:Wt,info:{eventName:lt(t)+"-changed",property:t}})}_createReflectedProperty(t){let e=this.constructor.attributeNameForProperty(t);"-"===e[0]?console.warn("Property "+t+" cannot be reflected to attribute "+e+' because "-" is not a valid starting attribute name. Use a lowercase first letter for the property instead.'):this._addPropertyEffect(t,zt.REFLECT,{fn:qt,info:{attrName:e}})}_createComputedProperty(t,e,n){let i=ae(e);if(!i)throw new Error("Malformed computed expression '"+e+"'");const r=ee(this,i,zt.COMPUTE,$t,t,n);Bt(this,"__computeInfo")[t]=r}_marshalArgs(t,e,n){const i=this.__data,r=[];for(let o=0,a=t.length;o<a;o++){let{name:a,structured:s,wildcard:l,value:c,literal:u}=t[o];if(!u)if(l){const t=Z(a,e),r=le(i,n,t?e:a);c={path:t?e:a,value:r,base:t?nt(i,a):r}}else c=s?le(i,n,a):i[a];if(E&&!this._overrideLegacyUndefined&&void 0===c&&t.length>1)return Ot;r[o]=c}return r}static addPropertyEffect(t,e,n){this.prototype._addPropertyEffect(t,e,n)}static createPropertyObserver(t,e,n){this.prototype._createPropertyObserver(t,e,n)}static createMethodObserver(t,e){this.prototype._createMethodObserver(t,e)}static createNotifyingProperty(t){this.prototype._createNotifyingProperty(t)}static createReadOnlyProperty(t,e){this.prototype._createReadOnlyProperty(t,e)}static createReflectedProperty(t){this.prototype._createReflectedProperty(t)}static createComputedProperty(t,e,n){this.prototype._createComputedProperty(t,e,n)}static bindTemplate(t){return this.prototype._bindTemplate(t)}_bindTemplate(t,e){let n=this.constructor._parseTemplate(t),i=this.__preBoundTemplateInfo==n;if(!i)for(let t in n.propertyEffects)this._createPropertyAccessor(t);if(e)if(n=Object.create(n),n.wasPreBound=i,this.__templateInfo){const e=t._parentTemplateInfo||this.__templateInfo,i=e.lastChild;n.parent=e,e.lastChild=n,n.previousSibling=i,i?i.nextSibling=n:e.firstChild=n}else this.__templateInfo=n;else this.__preBoundTemplateInfo=n;return n}static _addTemplatePropertyEffect(t,e,n){(t.hostProps=t.hostProps||{})[e]=!0;let i=t.propertyEffects=t.propertyEffects||{};(i[e]=i[e]||[]).push(n)}_stampTemplate(t,e){e=e||this._bindTemplate(t,!0),de.push(this);let n=super._stampTemplate(t,e);if(de.pop(),e.nodeList=n.nodeList,!e.wasPreBound){let t=e.childNodes=[];for(let e=n.firstChild;e;e=e.nextSibling)t.push(e)}return n.templateInfo=e,(function i(t,e){let{nodeList:n,nodeInfoList:i}=e;if(i.length)for(let e=0;e<i.length;e++){let r=n[e],o=i[e].bindings;if(o)for(let e=0;e<o.length;e++){let n=o[e];Qt(r,n),te(r,t,n)}r.__dataHost=t}})(this,e),this.__dataClientsReady&&(this._runEffectsForTemplate(e,this.__data,null,!1),this._flushClients()),n}_removeBoundDom(t){const e=t.templateInfo,{previousSibling:n,nextSibling:i,parent:r}=e;n?n.nextSibling=i:r&&(r.firstChild=i),i?i.previousSibling=n:r&&(r.lastChild=n),e.nextSibling=e.previousSibling=null;let o=e.childNodes;for(let t=0;t<o.length;t++){let e=o[t];Y(Y(e).parentNode).removeChild(e)}}static _parseTemplateNode(t,n,i){let r=e._parseTemplateNode.call(this,t,n,i);if(t.nodeType===Node.TEXT_NODE){let e=this._parseBindings(t.textContent,n);e&&(t.textContent=oe(e)||" ",Kt(this,n,i,"text","textContent",e),r=!0)}return r}static _parseTemplateNodeAttribute(t,n,i,r,o){let a=this._parseBindings(o,n);if(a){let e=r,o="property";Dt.test(r)?o="attribute":"$"==r[r.length-1]&&(r=r.slice(0,-1),o="attribute");let s=oe(a);return s&&"attribute"==o&&("class"==r&&t.hasAttribute("class")&&(s+=" "+t.getAttribute(r)),t.setAttribute(r,s)),"attribute"==o&&"disable-upgrade$"==e&&t.setAttribute(r,""),"input"===t.localName&&"value"===e&&t.setAttribute(e,""),t.removeAttribute(e),"property"===o&&(r=st(r)),Kt(this,n,i,o,r,a,s),!0}return e._parseTemplateNodeAttribute.call(this,t,n,i,r,o)}static _parseTemplateNestedTemplate(t,n,i){let r=e._parseTemplateNestedTemplate.call(this,t,n,i);const o=t.parentNode,a=i.templateInfo,s="dom-if"===o.localName;C&&(s||"dom-repeat"===o.localName)&&(o.removeChild(t),(i=i.parentInfo).templateInfo=a,i.noted=!0,r=!1);let l=a.hostProps;if(A&&s)l&&(n.hostProps=Object.assign(n.hostProps||{},l),C||(i.parentInfo.noted=!0));else{let t="{";for(let e in l)Kt(this,n,i,"property","_host_"+e,[{mode:t,source:e,dependencies:[e],hostProp:!0}])}return r}static _parseBindings(t,e){let n,i=[],r=0;for(;null!==(n=re.exec(t));){n.index>r&&i.push({literal:t.slice(r,n.index)});let o=n[1][0],a=Boolean(n[2]),s=n[3].trim(),l=!1,c="",u=-1;"{"==o&&(u=s.indexOf("::"))>0&&(c=s.substring(u+2),s=s.substring(0,u),l=!0);let h=ae(s),d=[];if(h){let{args:t,methodName:n}=h;for(let e=0;e<t.length;e++){let n=t[e];n.literal||d.push(n)}let i=e.dynamicFns;(i&&i[n]||h.static)&&(d.push(n),h.dynamicFn=!0)}else d.push(s);i.push({source:s,mode:o,negate:a,customEvent:l,signature:h,dependencies:d,event:c}),r=re.lastIndex}if(r&&r<t.length){let e=t.substring(r);e&&i.push({literal:e})}return i.length?i:null}static _evaluateBinding(t,e,n,i,r,o){let a;return a=e.signature?ne(t,n,i,0,e.signature):n!=e.source?nt(t,e.source):o&&X(n)?nt(t,n):t.__data[n],e.negate&&(a=!a),a}}})),de=[],pe=I((t=>{const e=xt(t);function n(t){const e=Object.getPrototypeOf(t);return e.prototype instanceof r?e:null}function i(t){if(!t.hasOwnProperty(JSCompiler_renameProperty("__ownProperties",t))){let e=null;if(t.hasOwnProperty(JSCompiler_renameProperty("properties",t))){const n=t.properties;n&&(e=(
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
function e(t){const e={};for(let n in t){const i=t[n];e[n]="function"==typeof i?{type:i}:i}return e})(n))}t.__ownProperties=e}return t.__ownProperties}class r extends e{static get observedAttributes(){if(!this.hasOwnProperty(JSCompiler_renameProperty("__observedAttributes",this))){const t=this._properties;this.__observedAttributes=t?Object.keys(t).map((t=>this.prototype._addPropertyToAttributeMap(t))):[]}return this.__observedAttributes}static finalize(){if(!this.hasOwnProperty(JSCompiler_renameProperty("__finalized",this))){const t=n(this);t&&t.finalize(),this.__finalized=!0,this._finalizeClass()}}static _finalizeClass(){const t=i(this);t&&this.createProperties(t)}static get _properties(){if(!this.hasOwnProperty(JSCompiler_renameProperty("__properties",this))){const t=n(this);this.__properties=Object.assign({},t&&t._properties,i(this))}return this.__properties}static typeForProperty(t){const e=this._properties[t];return e&&e.type}_initializeProperties(){this.constructor.finalize(),super._initializeProperties()}connectedCallback(){super.connectedCallback&&super.connectedCallback(),this._enableProperties()}disconnectedCallback(){super.disconnectedCallback&&super.disconnectedCallback()}}return r})),fe=window.ShadyCSS&&window.ShadyCSS.cssBuild,me=I((t=>{const e=pe(he(t));function n(t,e,n,i){n.computed&&(n.readOnly=!0),n.computed&&(t._hasReadOnlyEffect(e)?console.warn(`Cannot redefine computed property '${e}'.`):t._createComputedProperty(e,n.computed,i)),n.readOnly&&!t._hasReadOnlyEffect(e)?t._createReadOnlyProperty(e,!n.computed):!1===n.readOnly&&t._hasReadOnlyEffect(e)&&console.warn(`Cannot make readOnly property '${e}' non-readOnly.`),n.reflectToAttribute&&!t._hasReflectEffect(e)?t._createReflectedProperty(e):!1===n.reflectToAttribute&&t._hasReflectEffect(e)&&console.warn(`Cannot make reflected property '${e}' non-reflected.`),n.notify&&!t._hasNotifyEffect(e)?t._createNotifyingProperty(e):!1===n.notify&&t._hasNotifyEffect(e)&&console.warn(`Cannot make notify property '${e}' non-notify.`),n.observer&&t._createPropertyObserver(e,n.observer,i[n.observer]),t._addPropertyToAttributeMap(e)}return class extends e{static get polymerElementVersion(){return"3.4.1"}static _finalizeClass(){e._finalizeClass.call(this);const t=(function n(t){return t.hasOwnProperty(JSCompiler_renameProperty("__ownObservers",t))||(t.__ownObservers=t.hasOwnProperty(JSCompiler_renameProperty("observers",t))?t.observers:null),t.__ownObservers})(this);t&&this.createObservers(t,this._properties),this._prepareTemplate()}static _prepareTemplate(){let t=this.template;t&&("string"==typeof t?(console.error("template getter must return HTMLTemplateElement"),t=null):w||(t=t.cloneNode(!0))),this.prototype._template=t}static createProperties(t){for(let e in t)n(this.prototype,e,t[e],t)}static createObservers(t,e){const n=this.prototype;for(let i=0;i<t.length;i++)n._createMethodObserver(t[i],e)}static get template(){if(!this.hasOwnProperty(JSCompiler_renameProperty("_template",this))){const t=this.prototype.hasOwnProperty(JSCompiler_renameProperty("_template",this.prototype))?this.prototype._template:void 0;this._template=void 0!==t?t:this.hasOwnProperty(JSCompiler_renameProperty("is",this))&&(function e(t){let e=null;if(t&&(!b||x)&&(e=B.import(t,"template"),b&&!e))throw new Error(`strictTemplatePolicy: expecting dom-module or null template for ${t}`);return e})(this.is)||Object.getPrototypeOf(this.prototype).constructor.template}return this._template}static set template(t){this._template=t}static get importPath(){if(!this.hasOwnProperty(JSCompiler_renameProperty("_importPath",this))){const t=this.importMeta;if(t)this._importPath=f(t.url);else{const t=B.import(this.is);this._importPath=t&&t.assetpath||Object.getPrototypeOf(this.prototype).constructor.importPath}}return this._importPath}constructor(){super()}_initializeProperties(){this.constructor.finalize(),this.constructor._finalizeTemplate(this.localName),super._initializeProperties(),this.rootPath=_,this.importPath=this.constructor.importPath;let t=(function e(t){if(!t.hasOwnProperty(JSCompiler_renameProperty("__propertyDefaults",t))){t.__propertyDefaults=null;let e=t._properties;for(let n in e){let i=e[n];"value"in i&&(t.__propertyDefaults=t.__propertyDefaults||{},t.__propertyDefaults[n]=i)}}return t.__propertyDefaults})(this.constructor);if(t)for(let e in t){let n=t[e];if(this._canApplyPropertyDefault(e)){let t="function"==typeof n.value?n.value.call(this):n.value;this._hasAccessor(e)?this._setPendingProperty(e,t,!0):this[e]=t}}}_canApplyPropertyDefault(t){return!this.hasOwnProperty(t)}static _processStyleText(t,e){return p(t,e)}static _finalizeTemplate(t){const e=this.prototype._template;if(e&&!e.__polymerFinalized){e.__polymerFinalized=!0;const n=this.importPath;(function i(t,e,n,r){if(!fe){const i=e.content.querySelectorAll("style"),o=G(e),a=(function s(t){let e=F(t);return e?W(e):[]})(n),l=e.content.firstElementChild;for(let n=0;n<a.length;n++){let i=a[n];i.textContent=t._processStyleText(i.textContent,r),e.content.insertBefore(i,l)}let c=0;for(let e=0;e<o.length;e++){let n=o[e],a=i[c];a!==n?(n=n.cloneNode(!0),a.parentNode.insertBefore(n,a)):c++,n.textContent=t._processStyleText(n.textContent,r)}}if(window.ShadyCSS&&window.ShadyCSS.prepareTemplate(e,n),P&&fe&&g){const n=e.content.querySelectorAll("style");if(n){let e="";Array.from(n).forEach((t=>{e+=t.textContent,t.parentNode.removeChild(t)})),t._styleSheet=new CSSStyleSheet,t._styleSheet.replaceSync(e)}}})(this,e,t,n?d(n):""),this.prototype._bindTemplate(e)}}connectedCallback(){window.ShadyCSS&&this._template&&window.ShadyCSS.styleElement(this),super.connectedCallback()}ready(){this._template&&(this.root=this._stampTemplate(this._template),this.$=this.root.$),super.ready()}_readyClients(){this._template&&(this.root=this._attachDom(this.root)),super._readyClients()}_attachDom(t){const e=Y(this);if(e.attachShadow)return t?(e.shadowRoot||(e.attachShadow({mode:"open",shadyUpgradeFragment:t}),e.shadowRoot.appendChild(t),this.constructor._styleSheet&&(e.shadowRoot.adoptedStyleSheets=[this.constructor._styleSheet])),M&&window.ShadyDOM&&window.ShadyDOM.flushInitial(e.shadowRoot),e.shadowRoot):null;throw new Error("ShadowDOM not available. PolymerElement can create dom as children instead of in ShadowDOM by setting `this.root = this;` before `ready`.")}updateStyles(t){window.ShadyCSS&&window.ShadyCSS.styleSubtree(this,t)}resolveUrl(t,e){return!e&&this.importPath&&(e=d(this.importPath)),d(t,e)}static _parseTemplateContent(t,n,i){return n.dynamicFns=n.dynamicFns||this._properties,e._parseTemplateContent.call(this,t,n,i)}static _addTemplatePropertyEffect(t,n,i){return!S||n in this._properties||i.info.part.signature&&i.info.part.signature.static||i.info.part.hostProp||t.nestedTemplate||console.warn(`Property '${n}' used in template but not declared in 'properties'; attribute will not be observed.`),e._addTemplatePropertyEffect.call(this,t,n,i)}}}));
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
class ge{constructor(t){this.value=t.toString()}toString(){return this.value}}const _e=function t(e,...n){const i=document.createElement("template");return i.innerHTML=n.reduce(((t,n,i)=>t+(function r(t){if(t instanceof HTMLTemplateElement)return t.innerHTML;if(t instanceof ge)return(function e(t){if(t instanceof ge)return t.value;throw new Error(`non-literal value passed to Polymer's htmlLiteral function: ${t}`)})(t);throw new Error(`non-template value passed to Polymer's html function: ${t}`)})(n)+e[i+1]),e[0]),i},ye=me(HTMLElement);
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */var ve="undefined"!=typeof globalThis?globalThis:"undefined"!=typeof window?window:"undefined"!=typeof global?global:"undefined"!=typeof self?self:{};function be(t){if(t.__esModule)return t;var e=Object.defineProperty({},"__esModule",{value:!0});return Object.keys(t).forEach((function(n){var i=Object.getOwnPropertyDescriptor(t,n);Object.defineProperty(e,n,i.get?i:{enumerable:!0,get:function(){return t[n]}})})),e}var xe,we,Se={exports:{}};
/**
     * @license
     * Lodash <https://lodash.com/>
     * Copyright OpenJS Foundation and other contributors <https://openjsf.org/>
     * Released under MIT license <https://lodash.com/license>
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     * Copyright Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors
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/**
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/**
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
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/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
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/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
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/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
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/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
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/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */function Pi(){document.body.removeAttribute("unresolved")}
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
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    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
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    */
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
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    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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/**
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    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
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    */
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observers(){return["__itemsChanged(items.*)"]}constructor(){super(),this.__instances=[],this.__renderDebouncer=null,this.__itemsIdxToInstIdx={},this.__chunkCount=null,this.__renderStartTime=null,this.__itemsArrayChanged=!1,this.__shouldMeasureChunk=!1,this.__shouldContinueChunking=!1,this.__chunkingId=0,this.__sortFn=null,this.__filterFn=null,this.__observePaths=null,this.__ctor=null,this.__isDetached=!0,this.template=null}disconnectedCallback(){super.disconnectedCallback(),this.__isDetached=!0;for(let t=0;t<this.__instances.length;t++)this.__detachInstance(t)}connectedCallback(){if(super.connectedCallback(),Qr()||(this.style.display="none"),this.__isDetached){this.__isDetached=!1;let t=Y(Y(this).parentNode);for(let e=0;e<this.__instances.length;e++)this.__attachInstance(e,t)}}__ensureTemplatized(){if(!this.__ctor){const t=this;let e=this.template=t._templateInfo?t:this.querySelector("template");if(!e){let t=new MutationObserver((()=>{if(!this.querySelector("template"))throw new Error("dom-repeat requires a <template> child");t.disconnect(),this.__render()}));return t.observe(this,{childList:!0}),!1}let n={};n[this.as]=!0,n[this.indexAs]=!0,n[this.itemsIndexAs]=!0,this.__ctor=$r(e,this,{mutableData:this.mutableData,parentModel:!0,instanceProps:n,forwardHostProp:function(t,e){let n=this.__instances;for(let i,r=0;r<n.length&&(i=n[r]);r++)i.forwardHostProp(t,e)},notifyInstanceProp:function(t,e,n){if(Q(this.as,e)){let i=t[this.itemsIndexAs];e==this.as&&(this.items[i]=n);let r=J(this.as,`${JSCompiler_renameProperty("items",this)}.${i}`,e);this.notifyPath(r,n)}}})}return!0}__getMethodHost(){return this.__dataHost._methodHost||this.__dataHost}__functionFromPropertyValue(t){if("string"==typeof t){let e=t,n=this.__getMethodHost();return function(){return n[e].apply(n,arguments)}}return t}__sortChanged(t){this.__sortFn=this.__functionFromPropertyValue(t),this.items&&this.__debounceRender(this.__render)}__filterChanged(t){this.__filterFn=this.__functionFromPropertyValue(t),this.items&&this.__debounceRender(this.__render)}__computeFrameTime(t){return Math.ceil(1e3/t)}__observeChanged(){this.__observePaths=this.observe&&this.observe.replace(".*",".").split(" ")}__handleObservedPaths(t){if(this.__sortFn||this.__filterFn)if(t){if(this.__observePaths){let e=this.__observePaths;for(let n=0;n<e.length;n++)0===t.indexOf(e[n])&&this.__debounceRender(this.__render,this.delay)}}else this.__debounceRender(this.__render,this.delay)}__itemsChanged(t){this.items&&!Array.isArray(this.items)&&console.warn("dom-repeat expected array for `items`, found",this.items),this.__handleItemPath(t.path,t.value)||("items"===t.path&&(this.__itemsArrayChanged=!0),this.__debounceRender(this.__render))}__debounceRender(t,e=0){this.__renderDebouncer=Tn.debounce(this.__renderDebouncer,e>0?gt.after(e):vt,t.bind(this)),An(this.__renderDebouncer)}render(){this.__debounceRender(this.__render),Di()}__render(){if(!this.__ensureTemplatized())return;let t=this.items||[];const e=this.__sortAndFilterItems(t),n=this.__calculateLimit(e.length);this.__updateInstances(t,n,e),this.initialCount&&(this.__shouldMeasureChunk||this.__shouldContinueChunking)&&(cancelAnimationFrame(this.__chunkingId),this.__chunkingId=requestAnimationFrame((()=>this.__continueChunking()))),this._setRenderedItemCount(this.__instances.length),k&&!this.notifyDomChange||this.dispatchEvent(new CustomEvent("dom-change",{bubbles:!0,composed:!0}))}__sortAndFilterItems(t){let e=new Array(t.length);for(let n=0;n<t.length;n++)e[n]=n;return this.__filterFn&&(e=e.filter(((e,n,i)=>this.__filterFn(t[e],n,i)))),this.__sortFn&&e.sort(((e,n)=>this.__sortFn(t[e],t[n]))),e}__calculateLimit(t){let e=t;const n=this.__instances.length;if(this.initialCount){let i;!this.__chunkCount||this.__itemsArrayChanged&&!this.reuseChunkedInstances?(e=Math.min(t,this.initialCount),i=Math.max(e-n,0),this.__chunkCount=i||1):(i=Math.min(Math.max(t-n,0),this.__chunkCount),e=Math.min(n+i,t)),this.__shouldMeasureChunk=i===this.__chunkCount,this.__shouldContinueChunking=e<t,this.__renderStartTime=performance.now()}return this.__itemsArrayChanged=!1,e}__continueChunking(){if(this.__shouldMeasureChunk){const t=performance.now()-this.__renderStartTime;this.__chunkCount=Math.round(this.__chunkCount*(this._targetFrameTime/t))||1}this.__shouldContinueChunking&&this.__debounceRender(this.__render)}__updateInstances(t,e,n){const i=this.__itemsIdxToInstIdx={};let r;for(r=0;r<e;r++){let e=this.__instances[r],o=n[r],a=t[o];i[o]=r,e?(e._setPendingProperty(this.as,a),e._setPendingProperty(this.indexAs,r),e._setPendingProperty(this.itemsIndexAs,o),e._flushProperties()):this.__insertInstance(a,r,o)}for(let t=this.__instances.length-1;t>=r;t--)this.__detachAndRemoveInstance(t)}__detachInstance(t){let e=this.__instances[t];const n=Y(e.root);for(let t=0;t<e.children.length;t++)n.appendChild(e.children[t]);return e}__attachInstance(t,e){e.insertBefore(this.__instances[t].root,this)}__detachAndRemoveInstance(t){this.__detachInstance(t),this.__instances.splice(t,1)}__stampInstance(t,e,n){let i={};return i[this.as]=t,i[this.indexAs]=e,i[this.itemsIndexAs]=n,new this.__ctor(i)}__insertInstance(t,e,n){const i=this.__stampInstance(t,e,n);let r=this.__instances[e+1],o=r?r.children[0]:this;return Y(Y(this).parentNode).insertBefore(i.root,o),this.__instances[e]=i,i}_showHideChildren(t){for(let e=0;e<this.__instances.length;e++)this.__instances[e]._showHideChildren(t)}__handleItemPath(t,e){let n=t.slice(6),i=n.indexOf("."),r=i<0?n:n.substring(0,i);if(r==parseInt(r,10)){let t=i<0?"":n.substring(i+1);this.__handleObservedPaths(t);let o=this.__instances[this.__itemsIdxToInstIdx[r]];return o&&(o._setPendingPropertyOrPath(this.as+(t?"."+t:""),e,!1,!0),o._flushProperties()),!0}}itemForElement(t){let e=this.modelForElement(t);return e&&e[this.as]}indexForElement(t){let e=this.modelForElement(t);return e&&e[this.indexAs]}modelForElement(t){return Kr(this.template,t)}}customElements.define(no.is,no);
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
class io extends ye{static get is(){return"dom-if"}static get template(){return null}static get properties(){return{if:{type:Boolean,observer:"__debounceRender"},restamp:{type:Boolean,observer:"__debounceRender"},notifyDomChange:{type:Boolean}}}constructor(){super(),this.__renderDebouncer=null,this._lastIf=!1,this.__hideTemplateChildren__=!1}__debounceRender(){this.__renderDebouncer=Tn.debounce(this.__renderDebouncer,vt,(()=>this.__render())),An(this.__renderDebouncer)}disconnectedCallback(){super.disconnectedCallback();const t=Y(this).parentNode;t&&(t.nodeType!=Node.DOCUMENT_FRAGMENT_NODE||Y(t).host)||this.__teardownInstance()}connectedCallback(){super.connectedCallback(),Qr()||(this.style.display="none"),this.if&&this.__debounceRender()}__ensureTemplate(){if(!this.__template){const t=this;let e=t._templateInfo?t:Y(t).querySelector("template");if(!e){let t=new MutationObserver((()=>{if(!Y(this).querySelector("template"))throw new Error("dom-if requires a <template> child");t.disconnect(),this.__render()}));return t.observe(this,{childList:!0}),!1}this.__template=e}return!0}__ensureInstance(){let t=Y(this).parentNode;if(this.__hasInstance()){let e=this.__getInstanceNodes();if(e&&e.length&&Y(this).previousSibling!==e[e.length-1])for(let n,i=0;i<e.length&&(n=e[i]);i++)Y(t).insertBefore(n,this)}else{if(!t)return!1;if(!this.__ensureTemplate())return!1;this.__createAndInsertInstance(t)}return!0}render(){Di()}__render(){if(this.if){if(!this.__ensureInstance())return}else this.restamp&&this.__teardownInstance();this._showHideChildren(),k&&!this.notifyDomChange||this.if==this._lastIf||(this.dispatchEvent(new CustomEvent("dom-change",{bubbles:!0,composed:!0})),this._lastIf=this.if)}__hasInstance(){}__getInstanceNodes(){}__createAndInsertInstance(t){}__teardownInstance(){}_showHideChildren(){}}const ro=A?class extends io{constructor(){super(),this.__instance=null,this.__syncInfo=null}__hasInstance(){return Boolean(this.__instance)}__getInstanceNodes(){return this.__instance.templateInfo.childNodes}__createAndInsertInstance(t){const e=this.__dataHost||this;if(b&&!this.__dataHost)throw new Error("strictTemplatePolicy: template owner not trusted");const n=e._bindTemplate(this.__template,!0);n.runEffects=(t,e,n)=>{let i=this.__syncInfo;if(this.if)i&&(this.__syncInfo=null,this._showHideChildren(),e=Object.assign(i.changedProps,e)),t(e,n);else if(this.__instance)if(i||(i=this.__syncInfo={runEffects:t,changedProps:{}}),n)for(const t in e){const e=$(t);i.changedProps[e]=this.__dataHost[e]}else Object.assign(i.changedProps,e)},this.__instance=e._stampTemplate(this.__template,n),Y(t).insertBefore(this.__instance,this)}__syncHostProperties(){const t=this.__syncInfo;t&&(this.__syncInfo=null,t.runEffects(t.changedProps,!1))}__teardownInstance(){this.__instance&&((this.__dataHost||this)._removeBoundDom(this.__instance),this.__instance=null,this.__syncInfo=null)}_showHideChildren(){const t=this.__hideTemplateChildren__||!this.if;this.__instance&&Boolean(this.__instance.__hidden)!==t&&(this.__instance.__hidden=t,jr(t,this.__instance.templateInfo.childNodes)),t||this.__syncHostProperties()}}:class extends io{constructor(){super(),this.__ctor=null,this.__instance=null,this.__invalidProps=null}__hasInstance(){return Boolean(this.__instance)}__getInstanceNodes(){return this.__instance.children}__createAndInsertInstance(t){this.__ctor||(this.__ctor=$r(this.__template,this,{mutableData:!0,forwardHostProp:function(t,e){this.__instance&&(this.if?this.__instance.forwardHostProp(t,e):(this.__invalidProps=this.__invalidProps||Object.create(null),this.__invalidProps[$(t)]=!0))}})),this.__instance=new this.__ctor,Y(t).insertBefore(this.__instance.root,this)}__teardownInstance(){if(this.__instance){let t=this.__instance.children;if(t&&t.length){let e=Y(t[0]).parentNode;if(e){e=Y(e);for(let n,i=0;i<t.length&&(n=t[i]);i++)e.removeChild(n)}}this.__invalidProps=null,this.__instance=null}}__syncHostProperties(){let t=this.__invalidProps;if(t){this.__invalidProps=null;for(let e in t)this.__instance._setPendingProperty(e,this.__dataHost[e]);this.__instance._flushProperties()}}_showHideChildren(){const t=this.__hideTemplateChildren__||!this.if;this.__instance&&Boolean(this.__instance.__hidden)!==t&&(this.__instance.__hidden=t,this.__instance._showHideChildren(t)),t||this.__syncHostProperties()}};customElements.define(ro.is,ro);
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
let oo=I((t=>{let e=me(t);return class extends e{static get properties(){return{items:{type:Array},multi:{type:Boolean,value:!1},selected:{type:Object,notify:!0},selectedItem:{type:Object,notify:!0},toggle:{type:Boolean,value:!1}}}static get observers(){return["__updateSelection(multi, items.*)"]}constructor(){super(),this.__lastItems=null,this.__lastMulti=null,this.__selectedMap=null}__updateSelection(t,e){let n=e.path;if(n==JSCompiler_renameProperty("items",this)){let n=e.base||[],i=this.__lastItems;if(t!==this.__lastMulti&&this.clearSelection(),i){let t=Ii(n,i);this.__applySplices(t)}this.__lastItems=n,this.__lastMulti=t}else if(e.path==`${JSCompiler_renameProperty("items",this)}.splices`)this.__applySplices(e.value.indexSplices);else{let t=n.slice(`${JSCompiler_renameProperty("items",this)}.`.length),e=parseInt(t,10);t.indexOf(".")<0&&t==e&&this.__deselectChangedIdx(e)}}__applySplices(t){let e=this.__selectedMap;for(let n=0;n<t.length;n++){let i=t[n];e.forEach(((t,n)=>{t<i.index||e.set(n,t>=i.index+i.removed.length?t+i.addedCount-i.removed.length:-1)}));for(let t=0;t<i.addedCount;t++){let n=i.index+t;e.has(this.items[n])&&e.set(this.items[n],n)}}this.__updateLinks();let n=0;e.forEach(((t,i)=>{t<0?(this.multi?this.splice(JSCompiler_renameProperty("selected",this),n,1):this.selected=this.selectedItem=null,e.delete(i)):n++}))}__updateLinks(){if(this.__dataLinkedPaths={},this.multi){let t=0;this.__selectedMap.forEach((e=>{e>=0&&this.linkPaths(`${JSCompiler_renameProperty("items",this)}.${e}`,`${JSCompiler_renameProperty("selected",this)}.${t++}`)}))}else this.__selectedMap.forEach((t=>{this.linkPaths(JSCompiler_renameProperty("selected",this),`${JSCompiler_renameProperty("items",this)}.${t}`),this.linkPaths(JSCompiler_renameProperty("selectedItem",this),`${JSCompiler_renameProperty("items",this)}.${t}`)}))}clearSelection(){this.__dataLinkedPaths={},this.__selectedMap=new Map,this.selected=this.multi?[]:null,this.selectedItem=null}isSelected(t){return this.__selectedMap.has(t)}isIndexSelected(t){return this.isSelected(this.items[t])}__deselectChangedIdx(t){let e=this.__selectedIndexForItemIndex(t);if(e>=0){let t=0;this.__selectedMap.forEach(((n,i)=>{e==t++&&this.deselect(i)}))}}__selectedIndexForItemIndex(t){let e=this.__dataLinkedPaths[`${JSCompiler_renameProperty("items",this)}.${t}`];if(e)return parseInt(e.slice(`${JSCompiler_renameProperty("selected",this)}.`.length),10)}deselect(t){let e=this.__selectedMap.get(t);if(e>=0){let n;this.__selectedMap.delete(t),this.multi&&(n=this.__selectedIndexForItemIndex(e)),this.__updateLinks(),this.multi?this.splice(JSCompiler_renameProperty("selected",this),n,1):this.selected=this.selectedItem=null}}deselectIndex(t){this.deselect(this.items[t])}select(t){this.selectIndex(this.items.indexOf(t))}selectIndex(t){let e=this.items[t];this.isSelected(e)?this.toggle&&this.deselectIndex(t):(this.multi||this.__selectedMap.clear(),this.__selectedMap.set(e,t),this.__updateLinks(),this.multi?this.push(JSCompiler_renameProperty("selected",this),e):this.selected=this.selectedItem=e)}}}))(ye);class ao extends oo{static get is(){return"array-selector"}static get template(){return null}}customElements.define(ao.is,ao);
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const so=new Sn;window.ShadyCSS||(window.ShadyCSS={prepareTemplate(t,e,n){},prepareTemplateDom(t,e){},prepareTemplateStyles(t,e,n){},styleSubtree(t,e){so.processStyles(),Qe(t,e)},styleElement(t){so.processStyles()},styleDocument(t){so.processStyles(),Qe(document.body,t)},getComputedStyleValue:(t,e)=>tn(t,e),flushCustomStyles(){},nativeCss:ke,nativeShadow:Me,cssBuild:Te,disableRuntime:Ae}),window.ShadyCSS.CustomStyleInterface=so;
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const lo="include",co=window.ShadyCSS.CustomStyleInterface;class uo extends HTMLElement{constructor(){super(),this._style=null,co.addCustomStyle(this)}getStyle(){if(this._style)return this._style;const t=this.querySelector("style");if(!t)return null;this._style=t;const e=t.getAttribute(lo);return e&&(t.removeAttribute(lo),t.textContent=(function n(t){let e=t.trim().split(/\s+/),n="";for(let t=0;t<e.length;t++)n+=q(e[t]);return n})(e)+t.textContent),this.ownerDocument!==window.document&&window.document.head.appendChild(this),this._style}}
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
let ho;window.customElements.define("custom-style",uo),ho=zr._mutablePropertyChange;const po={properties:{mutableData:Boolean},_shouldPropertyChange(t,e,n){return ho(this,t,e,n,this.mutableData)}},fo=er(HTMLElement).prototype;
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var mo=new Set;const go={properties:{_parentResizable:{type:Object,observer:"_parentResizableChanged"},_notifyingDescendant:{type:Boolean,value:!1}},listeners:{"iron-request-resize-notifications":"_onIronRequestResizeNotifications"},created:function(){this._interestedResizables=[],this._boundNotifyResize=this.notifyResize.bind(this),this._boundOnDescendantIronResize=this._onDescendantIronResize.bind(this)},attached:function(){this._requestResizeNotifications()},detached:function(){this._parentResizable?this._parentResizable.stopResizeNotificationsFor(this):(mo.delete(this),window.removeEventListener("resize",this._boundNotifyResize)),this._parentResizable=null},notifyResize:function(){this.isAttached&&(this._interestedResizables.forEach((function(t){this.resizerShouldNotify(t)&&this._notifyDescendant(t)}),this),this._fireResize())},assignParentResizable:function(t){this._parentResizable&&this._parentResizable.stopResizeNotificationsFor(this),this._parentResizable=t,t&&-1===t._interestedResizables.indexOf(this)&&(t._interestedResizables.push(this),t._subscribeIronResize(this))},stopResizeNotificationsFor:function(t){var e=this._interestedResizables.indexOf(t);e>-1&&(this._interestedResizables.splice(e,1),this._unsubscribeIronResize(t))},_subscribeIronResize:function(t){t.addEventListener("iron-resize",this._boundOnDescendantIronResize)},_unsubscribeIronResize:function(t){t.removeEventListener("iron-resize",this._boundOnDescendantIronResize)},resizerShouldNotify:function(t){return!0},_onDescendantIronResize:function(t){this._notifyingDescendant?t.stopPropagation():m||this._fireResize()},_fireResize:function(){this.fire("iron-resize",null,{node:this,bubbles:!1})},_onIronRequestResizeNotifications:function(t){var e=Yi(t).rootTarget;e!==this&&(e.assignParentResizable(this),this._notifyDescendant(e),t.stopPropagation())},_parentResizableChanged:function(t){t&&window.removeEventListener("resize",this._boundNotifyResize)},_notifyDescendant:function(t){this.isAttached&&(this._notifyingDescendant=!0,t.notifyResize(),this._notifyingDescendant=!1)},_requestResizeNotifications:function(){if(this.isAttached)if("loading"===document.readyState){var t=this._requestResizeNotifications.bind(this);document.addEventListener("readystatechange",(function e(){document.removeEventListener("readystatechange",e),t()}))}else this._findParent(),this._parentResizable?this._parentResizable._interestedResizables.forEach((function(t){t!==this&&t._findParent()}),this):(mo.forEach((function(t){t!==this&&t._findParent()}),this),window.addEventListener("resize",this._boundNotifyResize),this.notifyResize())},_findParent:function(){this.assignParentResizable(null),this.fire("iron-request-resize-notifications",null,{node:this,bubbles:!0,cancelable:!0}),this._parentResizable?mo.delete(this):mo.add(this)}};var _o=Object.freeze({__proto__:null,IronResizableBehavior:go});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */Rr({_template:_e`
    <style>
      :host {
        display: block;
        transition-duration: var(--iron-collapse-transition-duration, 300ms);
        /* Safari 10 needs this property prefixed to correctly apply the custom property */
        -webkit-transition-duration: var(--iron-collapse-transition-duration, 300ms);
        overflow: visible;
      }

      :host(.iron-collapse-closed) {
        display: none;
      }

      :host(:not(.iron-collapse-opened)) {
        overflow: hidden;
      }
    </style>

    <slot></slot>
`,is:"iron-collapse",behaviors:[go],properties:{horizontal:{type:Boolean,value:!1,observer:"_horizontalChanged"},opened:{type:Boolean,value:!1,notify:!0,observer:"_openedChanged"},transitioning:{type:Boolean,notify:!0,readOnly:!0},noAnimation:{type:Boolean},_desiredSize:{type:String,value:""}},get dimension(){return this.horizontal?"width":"height"},get _dimensionMax(){return this.horizontal?"maxWidth":"maxHeight"},get _dimensionMaxCss(){return this.horizontal?"max-width":"max-height"},hostAttributes:{role:"group","aria-hidden":"true"},listeners:{transitionend:"_onTransitionEnd"},toggle:function(){this.opened=!this.opened},show:function(){this.opened=!0},hide:function(){this.opened=!1},updateSize:function(t,e){t="auto"===t?"":t;var n=e&&!this.noAnimation&&this.isAttached&&this._desiredSize!==t;if(this._desiredSize=t,this._updateTransition(!1),n){var i=this._calcSize();""===t&&(this.style[this._dimensionMax]="",t=this._calcSize()),this.style[this._dimensionMax]=i,this.scrollTop=this.scrollTop,this._updateTransition(!0),n=t!==i}this.style[this._dimensionMax]=t,n||this._transitionEnd()},enableTransition:function(t){fo._warn("`enableTransition()` is deprecated, use `noAnimation` instead."),this.noAnimation=!t},_updateTransition:function(t){this.style.transitionDuration=t&&!this.noAnimation?"":"0s"},_horizontalChanged:function(){this.style.transitionProperty=this._dimensionMaxCss,this.style["maxWidth"===this._dimensionMax?"maxHeight":"maxWidth"]="",this.updateSize(this.opened?"auto":"0px",!1)},_openedChanged:function(){this.setAttribute("aria-hidden",!this.opened),this._setTransitioning(!0),this.toggleClass("iron-collapse-closed",!1),this.toggleClass("iron-collapse-opened",!1),this.updateSize(this.opened?"auto":"0px",!0),this.opened&&this.focus()},_transitionEnd:function(){this.style[this._dimensionMax]=this._desiredSize,this.toggleClass("iron-collapse-closed",!this.opened),this.toggleClass("iron-collapse-opened",this.opened),this._updateTransition(!1),this.notifyResize(),this._setTransitioning(!1)},_onTransitionEnd:function(t){Yi(t).rootTarget===this&&this._transitionEnd()},_calcSize:function(){return this.getBoundingClientRect()[this.dimension]+"px"}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const yo=_e`
/* Most common used flex styles*/
<dom-module id="iron-flex">
  <template>
    <style>
      .layout.horizontal,
      .layout.vertical {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      }

      .layout.inline {
        display: -ms-inline-flexbox;
        display: -webkit-inline-flex;
        display: inline-flex;
      }

      .layout.horizontal {
        -ms-flex-direction: row;
        -webkit-flex-direction: row;
        flex-direction: row;
      }

      .layout.vertical {
        -ms-flex-direction: column;
        -webkit-flex-direction: column;
        flex-direction: column;
      }

      .layout.wrap {
        -ms-flex-wrap: wrap;
        -webkit-flex-wrap: wrap;
        flex-wrap: wrap;
      }

      .layout.no-wrap {
        -ms-flex-wrap: nowrap;
        -webkit-flex-wrap: nowrap;
        flex-wrap: nowrap;
      }

      .layout.center,
      .layout.center-center {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      }

      .layout.center-justified,
      .layout.center-center {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      }

      .flex {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      }

      .flex-auto {
        -ms-flex: 1 1 auto;
        -webkit-flex: 1 1 auto;
        flex: 1 1 auto;
      }

      .flex-none {
        -ms-flex: none;
        -webkit-flex: none;
        flex: none;
      }
    </style>
  </template>
</dom-module>
/* Basic flexbox reverse styles */
<dom-module id="iron-flex-reverse">
  <template>
    <style>
      .layout.horizontal-reverse,
      .layout.vertical-reverse {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      }

      .layout.horizontal-reverse {
        -ms-flex-direction: row-reverse;
        -webkit-flex-direction: row-reverse;
        flex-direction: row-reverse;
      }

      .layout.vertical-reverse {
        -ms-flex-direction: column-reverse;
        -webkit-flex-direction: column-reverse;
        flex-direction: column-reverse;
      }

      .layout.wrap-reverse {
        -ms-flex-wrap: wrap-reverse;
        -webkit-flex-wrap: wrap-reverse;
        flex-wrap: wrap-reverse;
      }
    </style>
  </template>
</dom-module>
/* Flexbox alignment */
<dom-module id="iron-flex-alignment">
  <template>
    <style>
      /**
       * Alignment in cross axis.
       */
      .layout.start {
        -ms-flex-align: start;
        -webkit-align-items: flex-start;
        align-items: flex-start;
      }

      .layout.center,
      .layout.center-center {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      }

      .layout.end {
        -ms-flex-align: end;
        -webkit-align-items: flex-end;
        align-items: flex-end;
      }

      .layout.baseline {
        -ms-flex-align: baseline;
        -webkit-align-items: baseline;
        align-items: baseline;
      }

      /**
       * Alignment in main axis.
       */
      .layout.start-justified {
        -ms-flex-pack: start;
        -webkit-justify-content: flex-start;
        justify-content: flex-start;
      }

      .layout.center-justified,
      .layout.center-center {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      }

      .layout.end-justified {
        -ms-flex-pack: end;
        -webkit-justify-content: flex-end;
        justify-content: flex-end;
      }

      .layout.around-justified {
        -ms-flex-pack: distribute;
        -webkit-justify-content: space-around;
        justify-content: space-around;
      }

      .layout.justified {
        -ms-flex-pack: justify;
        -webkit-justify-content: space-between;
        justify-content: space-between;
      }

      /**
       * Self alignment.
       */
      .self-start {
        -ms-align-self: flex-start;
        -webkit-align-self: flex-start;
        align-self: flex-start;
      }

      .self-center {
        -ms-align-self: center;
        -webkit-align-self: center;
        align-self: center;
      }

      .self-end {
        -ms-align-self: flex-end;
        -webkit-align-self: flex-end;
        align-self: flex-end;
      }

      .self-stretch {
        -ms-align-self: stretch;
        -webkit-align-self: stretch;
        align-self: stretch;
      }

      .self-baseline {
        -ms-align-self: baseline;
        -webkit-align-self: baseline;
        align-self: baseline;
      }

      /**
       * multi-line alignment in main axis.
       */
      .layout.start-aligned {
        -ms-flex-line-pack: start;  /* IE10 */
        -ms-align-content: flex-start;
        -webkit-align-content: flex-start;
        align-content: flex-start;
      }

      .layout.end-aligned {
        -ms-flex-line-pack: end;  /* IE10 */
        -ms-align-content: flex-end;
        -webkit-align-content: flex-end;
        align-content: flex-end;
      }

      .layout.center-aligned {
        -ms-flex-line-pack: center;  /* IE10 */
        -ms-align-content: center;
        -webkit-align-content: center;
        align-content: center;
      }

      .layout.between-aligned {
        -ms-flex-line-pack: justify;  /* IE10 */
        -ms-align-content: space-between;
        -webkit-align-content: space-between;
        align-content: space-between;
      }

      .layout.around-aligned {
        -ms-flex-line-pack: distribute;  /* IE10 */
        -ms-align-content: space-around;
        -webkit-align-content: space-around;
        align-content: space-around;
      }
    </style>
  </template>
</dom-module>
/* Non-flexbox positioning helper styles */
<dom-module id="iron-flex-factors">
  <template>
    <style>
      .flex,
      .flex-1 {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      }

      .flex-2 {
        -ms-flex: 2;
        -webkit-flex: 2;
        flex: 2;
      }

      .flex-3 {
        -ms-flex: 3;
        -webkit-flex: 3;
        flex: 3;
      }

      .flex-4 {
        -ms-flex: 4;
        -webkit-flex: 4;
        flex: 4;
      }

      .flex-5 {
        -ms-flex: 5;
        -webkit-flex: 5;
        flex: 5;
      }

      .flex-6 {
        -ms-flex: 6;
        -webkit-flex: 6;
        flex: 6;
      }

      .flex-7 {
        -ms-flex: 7;
        -webkit-flex: 7;
        flex: 7;
      }

      .flex-8 {
        -ms-flex: 8;
        -webkit-flex: 8;
        flex: 8;
      }

      .flex-9 {
        -ms-flex: 9;
        -webkit-flex: 9;
        flex: 9;
      }

      .flex-10 {
        -ms-flex: 10;
        -webkit-flex: 10;
        flex: 10;
      }

      .flex-11 {
        -ms-flex: 11;
        -webkit-flex: 11;
        flex: 11;
      }

      .flex-12 {
        -ms-flex: 12;
        -webkit-flex: 12;
        flex: 12;
      }
    </style>
  </template>
</dom-module>
<dom-module id="iron-positioning">
  <template>
    <style>
      .block {
        display: block;
      }

      [hidden] {
        display: none !important;
      }

      .invisible {
        visibility: hidden !important;
      }

      .relative {
        position: relative;
      }

      .fit {
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      }

      body.fullbleed {
        margin: 0;
        height: 100vh;
      }

      .scroll {
        -webkit-overflow-scrolling: touch;
        overflow: auto;
      }

      /* fixed position */
      .fixed-bottom,
      .fixed-left,
      .fixed-right,
      .fixed-top {
        position: fixed;
      }

      .fixed-top {
        top: 0;
        left: 0;
        right: 0;
      }

      .fixed-right {
        top: 0;
        right: 0;
        bottom: 0;
      }

      .fixed-bottom {
        right: 0;
        bottom: 0;
        left: 0;
      }

      .fixed-left {
        top: 0;
        bottom: 0;
        left: 0;
      }
    </style>
  </template>
</dom-module>
`;yo.setAttribute("style","display: none;"),document.head.appendChild(yo.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const vo=_e`
<custom-style>
  <style is="custom-style">
    [hidden] {
      display: none !important;
    }
  </style>
</custom-style>
<custom-style>
  <style is="custom-style">
    html {

      --layout: {
        display: -ms-flexbox;
        display: -webkit-flex;
        display: flex;
      };

      --layout-inline: {
        display: -ms-inline-flexbox;
        display: -webkit-inline-flex;
        display: inline-flex;
      };

      --layout-horizontal: {
        @apply --layout;

        -ms-flex-direction: row;
        -webkit-flex-direction: row;
        flex-direction: row;
      };

      --layout-horizontal-reverse: {
        @apply --layout;

        -ms-flex-direction: row-reverse;
        -webkit-flex-direction: row-reverse;
        flex-direction: row-reverse;
      };

      --layout-vertical: {
        @apply --layout;

        -ms-flex-direction: column;
        -webkit-flex-direction: column;
        flex-direction: column;
      };

      --layout-vertical-reverse: {
        @apply --layout;

        -ms-flex-direction: column-reverse;
        -webkit-flex-direction: column-reverse;
        flex-direction: column-reverse;
      };

      --layout-wrap: {
        -ms-flex-wrap: wrap;
        -webkit-flex-wrap: wrap;
        flex-wrap: wrap;
      };

      --layout-wrap-reverse: {
        -ms-flex-wrap: wrap-reverse;
        -webkit-flex-wrap: wrap-reverse;
        flex-wrap: wrap-reverse;
      };

      --layout-flex-auto: {
        -ms-flex: 1 1 auto;
        -webkit-flex: 1 1 auto;
        flex: 1 1 auto;
      };

      --layout-flex-none: {
        -ms-flex: none;
        -webkit-flex: none;
        flex: none;
      };

      --layout-flex: {
        -ms-flex: 1 1 0.000000001px;
        -webkit-flex: 1;
        flex: 1;
        -webkit-flex-basis: 0.000000001px;
        flex-basis: 0.000000001px;
      };

      --layout-flex-2: {
        -ms-flex: 2;
        -webkit-flex: 2;
        flex: 2;
      };

      --layout-flex-3: {
        -ms-flex: 3;
        -webkit-flex: 3;
        flex: 3;
      };

      --layout-flex-4: {
        -ms-flex: 4;
        -webkit-flex: 4;
        flex: 4;
      };

      --layout-flex-5: {
        -ms-flex: 5;
        -webkit-flex: 5;
        flex: 5;
      };

      --layout-flex-6: {
        -ms-flex: 6;
        -webkit-flex: 6;
        flex: 6;
      };

      --layout-flex-7: {
        -ms-flex: 7;
        -webkit-flex: 7;
        flex: 7;
      };

      --layout-flex-8: {
        -ms-flex: 8;
        -webkit-flex: 8;
        flex: 8;
      };

      --layout-flex-9: {
        -ms-flex: 9;
        -webkit-flex: 9;
        flex: 9;
      };

      --layout-flex-10: {
        -ms-flex: 10;
        -webkit-flex: 10;
        flex: 10;
      };

      --layout-flex-11: {
        -ms-flex: 11;
        -webkit-flex: 11;
        flex: 11;
      };

      --layout-flex-12: {
        -ms-flex: 12;
        -webkit-flex: 12;
        flex: 12;
      };

      /* alignment in cross axis */

      --layout-start: {
        -ms-flex-align: start;
        -webkit-align-items: flex-start;
        align-items: flex-start;
      };

      --layout-center: {
        -ms-flex-align: center;
        -webkit-align-items: center;
        align-items: center;
      };

      --layout-end: {
        -ms-flex-align: end;
        -webkit-align-items: flex-end;
        align-items: flex-end;
      };

      --layout-baseline: {
        -ms-flex-align: baseline;
        -webkit-align-items: baseline;
        align-items: baseline;
      };

      /* alignment in main axis */

      --layout-start-justified: {
        -ms-flex-pack: start;
        -webkit-justify-content: flex-start;
        justify-content: flex-start;
      };

      --layout-center-justified: {
        -ms-flex-pack: center;
        -webkit-justify-content: center;
        justify-content: center;
      };

      --layout-end-justified: {
        -ms-flex-pack: end;
        -webkit-justify-content: flex-end;
        justify-content: flex-end;
      };

      --layout-around-justified: {
        -ms-flex-pack: distribute;
        -webkit-justify-content: space-around;
        justify-content: space-around;
      };

      --layout-justified: {
        -ms-flex-pack: justify;
        -webkit-justify-content: space-between;
        justify-content: space-between;
      };

      --layout-center-center: {
        @apply --layout-center;
        @apply --layout-center-justified;
      };

      /* self alignment */

      --layout-self-start: {
        -ms-align-self: flex-start;
        -webkit-align-self: flex-start;
        align-self: flex-start;
      };

      --layout-self-center: {
        -ms-align-self: center;
        -webkit-align-self: center;
        align-self: center;
      };

      --layout-self-end: {
        -ms-align-self: flex-end;
        -webkit-align-self: flex-end;
        align-self: flex-end;
      };

      --layout-self-stretch: {
        -ms-align-self: stretch;
        -webkit-align-self: stretch;
        align-self: stretch;
      };

      --layout-self-baseline: {
        -ms-align-self: baseline;
        -webkit-align-self: baseline;
        align-self: baseline;
      };

      /* multi-line alignment in main axis */

      --layout-start-aligned: {
        -ms-flex-line-pack: start;  /* IE10 */
        -ms-align-content: flex-start;
        -webkit-align-content: flex-start;
        align-content: flex-start;
      };

      --layout-end-aligned: {
        -ms-flex-line-pack: end;  /* IE10 */
        -ms-align-content: flex-end;
        -webkit-align-content: flex-end;
        align-content: flex-end;
      };

      --layout-center-aligned: {
        -ms-flex-line-pack: center;  /* IE10 */
        -ms-align-content: center;
        -webkit-align-content: center;
        align-content: center;
      };

      --layout-between-aligned: {
        -ms-flex-line-pack: justify;  /* IE10 */
        -ms-align-content: space-between;
        -webkit-align-content: space-between;
        align-content: space-between;
      };

      --layout-around-aligned: {
        -ms-flex-line-pack: distribute;  /* IE10 */
        -ms-align-content: space-around;
        -webkit-align-content: space-around;
        align-content: space-around;
      };

      /*******************************
                Other Layout
      *******************************/

      --layout-block: {
        display: block;
      };

      --layout-invisible: {
        visibility: hidden !important;
      };

      --layout-relative: {
        position: relative;
      };

      --layout-fit: {
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      };

      --layout-scroll: {
        -webkit-overflow-scrolling: touch;
        overflow: auto;
      };

      --layout-fullbleed: {
        margin: 0;
        height: 100vh;
      };

      /* fixed position */

      --layout-fixed-top: {
        position: fixed;
        top: 0;
        left: 0;
        right: 0;
      };

      --layout-fixed-right: {
        position: fixed;
        top: 0;
        right: 0;
        bottom: 0;
      };

      --layout-fixed-bottom: {
        position: fixed;
        right: 0;
        bottom: 0;
        left: 0;
      };

      --layout-fixed-left: {
        position: fixed;
        top: 0;
        bottom: 0;
        left: 0;
      };

    }
  </style>
</custom-style>`;vo.setAttribute("style","display: none;"),document.head.appendChild(vo.content);var bo=document.createElement("style");bo.textContent="[hidden] { display: none !important; }",document.head.appendChild(bo);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
class xo{constructor(t){xo[" "](t),this.type=t&&t.type||"default",this.key=t&&t.key,t&&"value"in t&&(this.value=t.value)}get value(){var t=this.type,e=this.key;if(t&&e)return xo.types[t]&&xo.types[t][e]}set value(t){var e=this.type,n=this.key;e&&n&&(e=xo.types[e]=xo.types[e]||{},null==t?delete e[n]:e[n]=t)}get list(){if(this.type){var t=xo.types[this.type];return t?Object.keys(t).map((function(t){return wo[this.type][t]}),this):[]}}byKey(t){return this.key=t,this.value}}xo[" "]=function(){},xo.types={};var wo=xo.types;Rr({is:"iron-meta",properties:{type:{type:String,value:"default"},key:{type:String},value:{type:String,notify:!0},self:{type:Boolean,observer:"_selfChanged"},__meta:{type:Boolean,computed:"__computeMeta(type, key, value)"}},hostAttributes:{hidden:!0},__computeMeta:function(t,e,n){var i=new xo({type:t,key:e});return void 0!==n&&n!==i.value?i.value=n:this.value!==i.value&&(this.value=i.value),i},get list(){return this.__meta&&this.__meta.list},_selfChanged:function(t){t&&(this.value=this)},byKey:function(t){return new xo({type:this.type,key:t}).value}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        @apply --layout-inline;
        @apply --layout-center-center;
        position: relative;

        vertical-align: middle;

        fill: var(--iron-icon-fill-color, currentcolor);
        stroke: var(--iron-icon-stroke-color, none);

        width: var(--iron-icon-width, 24px);
        height: var(--iron-icon-height, 24px);
        @apply --iron-icon;
      }

      :host([hidden]) {
        display: none;
      }
    </style>
`,is:"iron-icon",properties:{icon:{type:String},theme:{type:String},src:{type:String},_meta:{value:fo.create("iron-meta",{type:"iconset"})}},observers:["_updateIcon(_meta, isAttached)","_updateIcon(theme, isAttached)","_srcChanged(src, isAttached)","_iconChanged(icon, isAttached)"],_DEFAULT_ICONSET:"icons",_iconChanged:function(t){var e=(t||"").split(":");this._iconName=e.pop(),this._iconsetName=e.pop()||this._DEFAULT_ICONSET,this._updateIcon()},_srcChanged:function(t){this._updateIcon()},_usesIconset:function(){return this.icon||!this.src},_updateIcon:function(){this._usesIconset()?(this._img&&this._img.parentNode&&Yi(this.root).removeChild(this._img),""===this._iconName?this._iconset&&this._iconset.removeIcon(this):this._iconsetName&&this._meta&&(this._iconset=this._meta.byKey(this._iconsetName),this._iconset?(this._iconset.applyIcon(this,this._iconName,this.theme),this.unlisten(window,"iron-iconset-added","_updateIcon")):this.listen(window,"iron-iconset-added","_updateIcon"))):(this._iconset&&this._iconset.removeIcon(this),this._img||(this._img=document.createElement("img"),this._img.style.width="100%",this._img.style.height="100%",this._img.draggable=!1),this._img.src=this.src,Yi(this.root).appendChild(this._img))}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({is:"iron-iconset-svg",properties:{name:{type:String,observer:"_nameChanged"},size:{type:Number,value:24},rtlMirroring:{type:Boolean,value:!1},useGlobalRtlAttribute:{type:Boolean,value:!1}},created:function(){this._meta=new xo({type:"iconset",key:null,value:null})},attached:function(){this.style.display="none"},getIconNames:function(){return this._icons=this._createIconMap(),Object.keys(this._icons).map((function(t){return this.name+":"+t}),this)},applyIcon:function(t,e){this.removeIcon(t);var n=this._cloneIcon(e,this.rtlMirroring&&this._targetIsRTL(t));if(n){var i=Yi(t.root||t);return i.insertBefore(n,i.childNodes[0]),t._svgIcon=n}return null},removeIcon:function(t){t._svgIcon&&(Yi(t.root||t).removeChild(t._svgIcon),t._svgIcon=null)},_targetIsRTL:function(t){if(null==this.__targetIsRTL)if(this.useGlobalRtlAttribute){var e=document.body&&document.body.hasAttribute("dir")?document.body:document.documentElement;this.__targetIsRTL="rtl"===e.getAttribute("dir")}else t&&t.nodeType!==Node.ELEMENT_NODE&&(t=t.host),this.__targetIsRTL=t&&"rtl"===window.getComputedStyle(t).direction;return this.__targetIsRTL},_nameChanged:function(){this._meta.value=null,this._meta.key=this.name,this._meta.value=this,this.async((function(){this.fire("iron-iconset-added",this,{node:window})}))},_createIconMap:function(){var t=Object.create(null);return Yi(this).querySelectorAll("[id]").forEach((function(e){t[e.id]=e})),t},_cloneIcon:function(t,e){return this._icons=this._icons||this._createIconMap(),this._prepareSvgClone(this._icons[t],this.size,e)},_prepareSvgClone:function(t,e,n){if(t){var i=t.cloneNode(!0),r=document.createElementNS("http://www.w3.org/2000/svg","svg"),o=i.getAttribute("viewBox")||"0 0 "+e+" "+e,a="pointer-events: none; display: block; width: 100%; height: 100%;";return n&&i.hasAttribute("mirror-in-rtl")&&(a+="-webkit-transform:scale(-1,1);transform:scale(-1,1);transform-origin:center;"),r.setAttribute("viewBox",o),r.setAttribute("preserveAspectRatio","xMidYMid meet"),r.setAttribute("focusable","false"),r.style.cssText=a,r.appendChild(i).removeAttribute("id"),r}return null}});
/**
    @license
    Copyright (c) 2014 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const So=_e`<iron-iconset-svg name="image" size="24">
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/**
    @license
    Copyright (c) 2014 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
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/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var Eo={"U+0008":"backspace","U+0009":"tab","U+001B":"esc","U+0020":"space","U+007F":"del"},To={8:"backspace",9:"tab",13:"enter",27:"esc",33:"pageup",34:"pagedown",35:"end",36:"home",32:"space",37:"left",38:"up",39:"right",40:"down",46:"del",106:"*"},Co={shift:"shiftKey",ctrl:"ctrlKey",alt:"altKey",meta:"metaKey"},Ao=/[a-z0-9*]/,ko=/U\+/,Lo=/^arrow/,Po=/^space(bar)?/,No=/^escape$/;function Io(t,e){var n="";if(t){var i=t.toLowerCase();" "===i||Po.test(i)?n="space":No.test(i)?n="esc":1==i.length?e&&!Ao.test(i)||(n=i):n=Lo.test(i)?i.replace("arrow",""):"multiply"==i?"*":i}return n}function Ro(t,e){return(function n(t,e){return t.key?Io(t.key,e):t.detail&&t.detail.key?Io(t.detail.key,e):(function n(t){var e="";return t&&(t in Eo?e=Eo[t]:ko.test(t)?(t=parseInt(t.replace("U+","0x"),16),e=String.fromCharCode(t).toLowerCase()):e=t.toLowerCase()),e})(t.keyIdentifier)||(function i(t){var e="";return 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/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var Bo=navigator.userAgent.match(/iP(?:hone|ad;(?: U;)? CPU) OS (\d+)/),Ho=Bo&&Bo[1]>=8,Fo="-10000px",Vo=-100;Rr({_template:_e`
    <style>
      :host {
        display: block;
      }

      @media only screen and (-webkit-max-device-pixel-ratio: 1) {
        :host {
          will-change: transform;
        }
      }

      #items {
        @apply --iron-list-items-container;
        position: relative;
      }

      :host(:not([grid])) #items > ::slotted(*) {
        width: 100%;
      }

      #items > ::slotted(*) {
        box-sizing: border-box;
        margin: 0;
        position: absolute;
        top: 0;
        will-change: transform;
      }
    </style>

    <array-selector id="selector" items="{{items}}" selected="{{selectedItems}}" selected-item="{{selectedItem}}"></array-selector>

    <div id="items">
      <slot></slot>
    </div>
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scrollOffset)"],behaviors:[Zr,go,Do,po],_ratio:.5,_scrollerPaddingTop:0,_scrollPosition:0,_physicalSize:0,_physicalAverage:0,_physicalAverageCount:0,_physicalTop:0,_virtualCount:0,_estScrollHeight:0,_scrollHeight:0,_viewportHeight:0,_viewportWidth:0,_physicalItems:null,_physicalSizes:null,_firstVisibleIndexVal:null,_lastVisibleIndexVal:null,_maxPages:2,_focusedItem:null,_focusedVirtualIndex:-1,_focusedPhysicalIndex:-1,_offscreenFocusedItem:null,_focusBackfillItem:null,_itemsPerRow:1,_itemWidth:0,_rowHeight:0,_templateCost:0,_parentModel:!0,get _physicalBottom(){return this._physicalTop+this._physicalSize},get _scrollBottom(){return this._scrollPosition+this._viewportHeight},get _virtualEnd(){return this._virtualStart+this._physicalCount-1},get _hiddenContentSize(){return(this.grid?this._physicalRows*this._rowHeight:this._physicalSize)-this._viewportHeight},get _itemsParent(){return Yi(Yi(this._userTemplate).parentNode)},get _maxScrollTop(){return this._estScrollHeight-this._viewportHeight+this._scrollOffset},get _maxVirtualStart(){var t=this._convertIndexToCompleteRow(this._virtualCount);return Math.max(0,t-this._physicalCount)},set _virtualStart(t){t=this._clamp(t,0,this._maxVirtualStart),this.grid&&(t-=t%this._itemsPerRow),this._virtualStartVal=t},get _virtualStart(){return this._virtualStartVal||0},set _physicalStart(t){(t%=this._physicalCount)<0&&(t=this._physicalCount+t),this.grid&&(t-=t%this._itemsPerRow),this._physicalStartVal=t},get _physicalStart(){return this._physicalStartVal||0},get _physicalEnd(){return(this._physicalStart+this._physicalCount-1)%this._physicalCount},set _physicalCount(t){this._physicalCountVal=t},get _physicalCount(){return this._physicalCountVal||0},get _optPhysicalSize(){return 0===this._viewportHeight?1/0:this._viewportHeight*this._maxPages},get _isVisible(){return Boolean(this.offsetWidth||this.offsetHeight)},get firstVisibleIndex(){var t=this._firstVisibleIndexVal;if(null==t){var e=this._physicalTop+this._scrollOffset;t=this._iterateItems((function(t,n){return(e+=this._getPhysicalSizeIncrement(t))>this._scrollPosition?this.grid?n-n%this._itemsPerRow:n:this.grid&&this._virtualCount-1===n?n-n%this._itemsPerRow:void 0}))||0,this._firstVisibleIndexVal=t}return t},get lastVisibleIndex(){var t=this._lastVisibleIndexVal;if(null==t){if(this.grid)t=Math.min(this._virtualCount,this.firstVisibleIndex+this._estRowsInView*this._itemsPerRow-1);else{var e=this._physicalTop+this._scrollOffset;this._iterateItems((function(n,i){e<this._scrollBottom&&(t=i),e+=this._getPhysicalSizeIncrement(n)}))}this._lastVisibleIndexVal=t}return t},get _defaultScrollTarget(){return this},get _virtualRowCount(){return Math.ceil(this._virtualCount/this._itemsPerRow)},get _estRowsInView(){return Math.ceil(this._viewportHeight/this._rowHeight)},get _physicalRows(){return Math.ceil(this._physicalCount/this._itemsPerRow)},get _scrollOffset(){return this._scrollerPaddingTop+this.scrollOffset},ready:function(){this.addEventListener("focus",this._didFocus.bind(this),!0)},attached:function(){this._debounce("_render",this._render,_t),this.listen(this,"iron-resize","_resizeHandler"),this.listen(this,"keydown","_keydownHandler")},detached:function(){this.unlisten(this,"iron-resize","_resizeHandler"),this.unlisten(this,"keydown","_keydownHandler")},_setOverflow:function(t){this.style.webkitOverflowScrolling=t===this?"touch":"",this.style.overflowY=t===this?"auto":"",this._lastVisibleIndexVal=null,this._firstVisibleIndexVal=null,this._debounce("_render",this._render,_t)},updateViewportBoundaries:function(){var t=window.getComputedStyle(this);this._scrollerPaddingTop=this.scrollTarget===this?0:parseInt(t["padding-top"],10),this._isRTL=Boolean("rtl"===t.direction),this._viewportWidth=this.$.items.offsetWidth,this._viewportHeight=this._scrollTargetHeight,this.grid&&this._updateGridMetrics()},_scrollHandler:function(){var t=Math.max(0,Math.min(this._maxScrollTop,this._scrollTop)),e=t-this._scrollPosition,n=e>=0;if(this._scrollPosition=t,this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,Math.abs(e)>this._physicalSize&&this._physicalSize>0){e-=this._scrollOffset;var i=Math.round(e/this._physicalAverage)*this._itemsPerRow;this._virtualStart=this._virtualStart+i,this._physicalStart=this._physicalStart+i,this._physicalTop=Math.min(Math.floor(this._virtualStart/this._itemsPerRow)*this._physicalAverage,this._scrollPosition),this._update()}else if(this._physicalCount>0){var r=this._getReusables(n);n?(this._physicalTop=r.physicalTop,this._virtualStart=this._virtualStart+r.indexes.length,this._physicalStart=this._physicalStart+r.indexes.length):(this._virtualStart=this._virtualStart-r.indexes.length,this._physicalStart=this._physicalStart-r.indexes.length),this._update(r.indexes,n?null:r.indexes),this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,0),vt)}},_getReusables:function(t){var e,n,i,r=[],o=this._hiddenContentSize*this._ratio,a=this._virtualStart,s=this._virtualEnd,l=this._physicalCount,c=this._physicalTop+this._scrollOffset,u=this._scrollPosition,h=this._scrollBottom;for(t?(e=this._physicalStart,n=u-c):(e=this._physicalEnd,n=this._physicalBottom+this._scrollOffset-h);n-=i=this._getPhysicalSizeIncrement(e),!(r.length>=l||n<=o);)if(t){if(s+r.length+1>=this._virtualCount)break;if(c+i>=u-this._scrollOffset)break;r.push(e),c+=i,e=(e+1)%l}else{if(a-r.length<=0)break;if(c+this._physicalSize-i<=h)break;r.push(e),c-=i,e=0===e?l-1:e-1}return{indexes:r,physicalTop:c-this._scrollOffset}},_update:function(t,e){if(!(t&&0===t.length||0===this._physicalCount)){if(this._manageFocus(),this._assignModels(t),this._updateMetrics(t),e)for(;e.length;){var n=e.pop();this._physicalTop-=this._getPhysicalSizeIncrement(n)}this._positionItems(),this._updateScrollerSize()}},_createPool:function(t){var e,n;this._ensureTemplatized();var i=new Array(t);for(e=0;e<t;e++)n=this.stamp(null),i[e]=n.root.querySelector("*"),this._itemsParent.appendChild(n.root);return i},_isClientFull:function(){return 0!=this._scrollBottom&&this._physicalBottom-1>=this._scrollBottom&&this._physicalTop<=this._scrollPosition},_increasePoolIfNeeded:function(t){var e=this._clamp(this._physicalCount+t,3,this._virtualCount-this._virtualStart);if(e=this._convertIndexToCompleteRow(e),this.grid){var n=e%this._itemsPerRow;n&&e-n<=this._physicalCount&&(e+=this._itemsPerRow),e-=n}var i=e-this._physicalCount,r=Math.round(.5*this._physicalCount);if(!(i<0)){if(i>0){var o=window.performance.now();[].push.apply(this._physicalItems,this._createPool(i));for(var a=0;a<i;a++)this._physicalSizes.push(0);this._physicalCount=this._physicalCount+i,this._physicalStart>this._physicalEnd&&this._isIndexRendered(this._focusedVirtualIndex)&&this._getPhysicalIndex(this._focusedVirtualIndex)<this._physicalEnd&&(this._physicalStart=this._physicalStart+i),this._update(),this._templateCost=(window.performance.now()-o)/i,r=Math.round(.5*this._physicalCount)}this._virtualEnd>=this._virtualCount-1||0===r||(this._isClientFull()?this._physicalSize<this._optPhysicalSize&&this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,this._clamp(Math.round(50/this._templateCost),1,r)),yt):this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,r),vt))}},_render:function(){if(this.isAttached&&this._isVisible)if(0!==this._physicalCount){var t=this._getReusables(!0);this._physicalTop=t.physicalTop,this._virtualStart=this._virtualStart+t.indexes.length,this._physicalStart=this._physicalStart+t.indexes.length,this._update(t.indexes),this._update(),this._increasePoolIfNeeded(0)}else this._virtualCount>0&&(this.updateViewportBoundaries(),this._increasePoolIfNeeded(3))},_ensureTemplatized:function(){if(!this.ctor){this._userTemplate=this.queryEffectiveChildren("template"),this._userTemplate||console.warn("iron-list requires a template to be provided in light-dom");var t={__key__:!0};t[this.as]=!0,t[this.indexAs]=!0,t[this.selectedAs]=!0,t.tabIndex=!0,this._instanceProps=t,this.templatize(this._userTemplate,this.mutableData)}},_gridChanged:function(t,e){void 0!==e&&(this.notifyResize(),Di(),t&&this._updateGridMetrics())},_itemsChanged:function(t){if("items"===t.path)this._virtualStart=0,this._physicalTop=0,this._virtualCount=this.items?this.items.length:0,this._physicalIndexForKey={},this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,this._physicalCount=this._physicalCount||0,this._physicalItems=this._physicalItems||[],this._physicalSizes=this._physicalSizes||[],this._physicalStart=0,this._scrollTop>this._scrollOffset&&this._resetScrollPosition(0),this._removeFocusedItem(),this._debounce("_render",this._render,_t);else if("items.splices"===t.path){if(this._adjustVirtualIndex(t.value.indexSplices),this._virtualCount=this.items?this.items.length:0,t.value.indexSplices.some((function(t){return t.addedCount>0||t.removed.length>0}))){var e=this._getActiveElement();this.contains(e)&&e.blur()}var n=t.value.indexSplices.some((function(t){return t.index+t.addedCount>=this._virtualStart&&t.index<=this._virtualEnd}),this);this._isClientFull()&&!n||this._debounce("_render",this._render,_t)}else"items.length"!==t.path&&this._forwardItemPath(t.path,t.value)},_forwardItemPath:function(t,e){var n,i,r,o=(t=t.slice(6)).indexOf(".");-1===o&&(o=t.length);var a=this.modelForElement(this._offscreenFocusedItem),s=parseInt(t.substring(0,o),10);(n=this._isIndexRendered(s))?(i=this._getPhysicalIndex(s),r=this.modelForElement(this._physicalItems[i])):a&&(r=a),r&&r[this.indexAs]===s&&(t=t.substring(o+1),r._setPendingPropertyOrPath(t=this.as+(t?"."+t:""),e,!1,!0),r._flushProperties&&r._flushProperties(),n&&(this._updateMetrics([i]),this._positionItems(),this._updateScrollerSize()))},_adjustVirtualIndex:function(t){t.forEach((function(t){if(t.removed.forEach(this._removeItem,this),t.index<this._virtualStart){var e=Math.max(t.addedCount-t.removed.length,t.index-this._virtualStart);this._virtualStart=this._virtualStart+e,this._focusedVirtualIndex>=0&&(this._focusedVirtualIndex=this._focusedVirtualIndex+e)}}),this)},_removeItem:function(t){this.$.selector.deselect(t),this._focusedItem&&this.modelForElement(this._focusedItem)[this.as]===t&&this._removeFocusedItem()},_iterateItems:function(t,e){var n,i,r,o;if(2===arguments.length&&e){for(o=0;o<e.length;o++)if(i=this._computeVidx(n=e[o]),null!=(r=t.call(this,n,i)))return r}else{for(n=this._physicalStart,i=this._virtualStart;n<this._physicalCount;n++,i++)if(null!=(r=t.call(this,n,i)))return r;for(n=0;n<this._physicalStart;n++,i++)if(null!=(r=t.call(this,n,i)))return r}},_computeVidx:function(t){return t>=this._physicalStart?this._virtualStart+(t-this._physicalStart):this._virtualStart+(this._physicalCount-this._physicalStart)+t},_assignModels:function(t){this._iterateItems((function(t,e){var n=this._physicalItems[t],i=this.items&&this.items[e];if(null!=i){var r=this.modelForElement(n);r.__key__=null,this._forwardProperty(r,this.as,i),this._forwardProperty(r,this.selectedAs,this.$.selector.isSelected(i)),this._forwardProperty(r,this.indexAs,e),this._forwardProperty(r,"tabIndex",this._focusedVirtualIndex===e?0:-1),this._physicalIndexForKey[r.__key__]=t,r._flushProperties&&r._flushProperties(!0),n.removeAttribute("hidden")}else n.setAttribute("hidden","")}),t)},_updateMetrics:function(t){Di();var e=0,n=0,i=this._physicalAverageCount,r=this._physicalAverage;this._iterateItems((function(t,i){n+=this._physicalSizes[t],this._physicalSizes[t]=this._physicalItems[t].offsetHeight,e+=this._physicalSizes[t],this._physicalAverageCount+=this._physicalSizes[t]?1:0}),t),this.grid?(this._updateGridMetrics(),this._physicalSize=Math.ceil(this._physicalCount/this._itemsPerRow)*this._rowHeight):(n=1===this._itemsPerRow?n:Math.ceil(this._physicalCount/this._itemsPerRow)*this._rowHeight,this._physicalSize=this._physicalSize+e-n,this._itemsPerRow=1),this._physicalAverageCount!==i&&(this._physicalAverage=Math.round((r*i+e)/this._physicalAverageCount))},_updateGridMetrics:function(){this._itemWidth=this._physicalCount>0?this._physicalItems[0].getBoundingClientRect().width:200,this._rowHeight=this._physicalCount>0?this._physicalItems[0].offsetHeight:200,this._itemsPerRow=this._itemWidth?Math.floor(this._viewportWidth/this._itemWidth):this._itemsPerRow},_positionItems:function(){this._adjustScrollPosition();var t=this._physicalTop;if(this.grid){var e=(this._viewportWidth-this._itemsPerRow*this._itemWidth)/2;this._iterateItems((function(n,i){var r=Math.floor(i%this._itemsPerRow*this._itemWidth+e);this._isRTL&&(r*=-1),this.translate3d(r+"px",t+"px",0,this._physicalItems[n]),this._shouldRenderNextRow(i)&&(t+=this._rowHeight)}))}else{const e=[];this._iterateItems((function(n,i){const r=this._physicalItems[n];this.translate3d(0,t+"px",0,r),t+=this._physicalSizes[n];const o=r.id;o&&e.push(o)})),e.length&&this.setAttribute("aria-owns",e.join(" "))}},_getPhysicalSizeIncrement:function(t){return this.grid?this._computeVidx(t)%this._itemsPerRow!=this._itemsPerRow-1?0:this._rowHeight:this._physicalSizes[t]},_shouldRenderNextRow:function(t){return t%this._itemsPerRow==this._itemsPerRow-1},_adjustScrollPosition:function(){var t=0===this._virtualStart?this._physicalTop:Math.min(this._scrollPosition+this._physicalTop,0);if(0!==t){this._physicalTop=this._physicalTop-t;var e=this._scrollPosition;!Ho&&e>0&&this._resetScrollPosition(e-t)}},_resetScrollPosition:function(t){this.scrollTarget&&t>=0&&(this._scrollTop=t,this._scrollPosition=this._scrollTop)},_updateScrollerSize:function(t){this._estScrollHeight=this.grid?this._virtualRowCount*this._rowHeight:this._physicalBottom+Math.max(this._virtualCount-this._physicalCount-this._virtualStart,0)*this._physicalAverage,((t=(t=(t=t||0===this._scrollHeight)||this._scrollPosition>=this._estScrollHeight-this._physicalSize)||this.grid&&this.$.items.style.height<this._estScrollHeight)||Math.abs(this._estScrollHeight-this._scrollHeight)>=this._viewportHeight)&&(this.$.items.style.height=this._estScrollHeight+"px",this._scrollHeight=this._estScrollHeight)},scrollToItem:function(t){return this.scrollToIndex(this.items.indexOf(t))},scrollToIndex:function(t){if(!("number"!=typeof t||t<0||t>this.items.length-1)&&(Di(),0!==this._physicalCount)){t=this._clamp(t,0,this._virtualCount-1),(!this._isIndexRendered(t)||t>=this._maxVirtualStart)&&(this._virtualStart=this.grid?t-2*this._itemsPerRow:t-1),this._manageFocus(),this._assignModels(),this._updateMetrics(),this._physicalTop=Math.floor(this._virtualStart/this._itemsPerRow)*this._physicalAverage;for(var e=this._physicalStart,n=this._virtualStart,i=0,r=this._hiddenContentSize;n<t&&i<=r;)i+=this._getPhysicalSizeIncrement(e),e=(e+1)%this._physicalCount,n++;this._updateScrollerSize(!0),this._positionItems(),this._resetScrollPosition(this._physicalTop+this._scrollOffset+i),this._increasePoolIfNeeded(0),this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null}},_resetAverage:function(){this._physicalAverage=0,this._physicalAverageCount=0},_resizeHandler:function(){this._debounce("_render",(function(){this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,this._isVisible?(this.updateViewportBoundaries(),this.toggleScrollListener(!0),this._resetAverage(),this._render()):this.toggleScrollListener(!1)}),_t)},selectItem:function(t){return this.selectIndex(this.items.indexOf(t))},selectIndex:function(t){if(!(t<0||t>=this._virtualCount)){if(!this.multiSelection&&this.selectedItem&&this.clearSelection(),this._isIndexRendered(t)){var e=this.modelForElement(this._physicalItems[this._getPhysicalIndex(t)]);e&&(e[this.selectedAs]=!0),this.updateSizeForIndex(t)}this.$.selector.selectIndex(t)}},deselectItem:function(t){return this.deselectIndex(this.items.indexOf(t))},deselectIndex:function(t){t<0||t>=this._virtualCount||(this._isIndexRendered(t)&&(this.modelForElement(this._physicalItems[this._getPhysicalIndex(t)])[this.selectedAs]=!1,this.updateSizeForIndex(t)),this.$.selector.deselectIndex(t))},toggleSelectionForItem:function(t){return this.toggleSelectionForIndex(this.items.indexOf(t))},toggleSelectionForIndex:function(t){(this.$.selector.isIndexSelected?this.$.selector.isIndexSelected(t):this.$.selector.isSelected(this.items[t]))?this.deselectIndex(t):this.selectIndex(t)},clearSelection:function(){this._iterateItems((function(t,e){this.modelForElement(this._physicalItems[t])[this.selectedAs]=!1})),this.$.selector.clearSelection()},_selectionEnabledChanged:function(t){(t?this.listen:this.unlisten).call(this,this,"tap","_selectionHandler")},_selectionHandler:function(t){var e=this.modelForElement(t.target);if(e){var n,i,r=Yi(t).path[0],o=this._getActiveElement(),a=this._physicalItems[this._getPhysicalIndex(e[this.indexAs])];"input"!==r.localName&&"button"!==r.localName&&"select"!==r.localName&&(n=e.tabIndex,e.tabIndex=Vo,i=o?o.tabIndex:-1,e.tabIndex=n,o&&a!==o&&a.contains(o)&&i!==Vo||this.toggleSelectionForItem(e[this.as]))}},_multiSelectionChanged:function(t){this.clearSelection(),this.$.selector.multi=t},updateSizeForItem:function(t){return this.updateSizeForIndex(this.items.indexOf(t))},updateSizeForIndex:function(t){return this._isIndexRendered(t)?(this._updateMetrics([this._getPhysicalIndex(t)]),this._positionItems(),null):null},_manageFocus:function(){var t=this._focusedVirtualIndex;t>=0&&t<this._virtualCount?this._isIndexRendered(t)?this._restoreFocusedItem():this._createFocusBackfillItem():this._virtualCount>0&&this._physicalCount>0&&(this._focusedPhysicalIndex=this._physicalStart,this._focusedVirtualIndex=this._virtualStart,this._focusedItem=this._physicalItems[this._physicalStart])},_convertIndexToCompleteRow:function(t){return this._itemsPerRow=this._itemsPerRow||1,this.grid?Math.ceil(t/this._itemsPerRow)*this._itemsPerRow:t},_isIndexRendered:function(t){return t>=this._virtualStart&&t<=this._virtualEnd},_isIndexVisible:function(t){return t>=this.firstVisibleIndex&&t<=this.lastVisibleIndex},_getPhysicalIndex:function(t){return(this._physicalStart+(t-this._virtualStart))%this._physicalCount},focusItem:function(t){this._focusPhysicalItem(t)},_focusPhysicalItem:function(t){if(!(t<0||t>=this._virtualCount)){this._restoreFocusedItem(),this._isIndexRendered(t)||this.scrollToIndex(t);var e,n=this._physicalItems[this._getPhysicalIndex(t)],i=this.modelForElement(n);i.tabIndex=Vo,n.tabIndex===Vo&&(e=n),e||(e=Yi(n).querySelector('[tabindex="-100"]')),i.tabIndex=0,this._focusedVirtualIndex=t,e&&e.focus()}},_removeFocusedItem:function(){this._offscreenFocusedItem&&this._itemsParent.removeChild(this._offscreenFocusedItem),this._offscreenFocusedItem=null,this._focusBackfillItem=null,this._focusedItem=null,this._focusedVirtualIndex=-1,this._focusedPhysicalIndex=-1},_createFocusBackfillItem:function(){var t=this._focusedPhysicalIndex;if(!(this._offscreenFocusedItem||this._focusedVirtualIndex<0)){if(!this._focusBackfillItem){var e=this.stamp(null);this._focusBackfillItem=e.root.querySelector("*"),this._itemsParent.appendChild(e.root)}this._offscreenFocusedItem=this._physicalItems[t],this.modelForElement(this._offscreenFocusedItem).tabIndex=0,this._physicalItems[t]=this._focusBackfillItem,this._focusedPhysicalIndex=t,this.translate3d(0,Fo,0,this._offscreenFocusedItem)}},_restoreFocusedItem:function(){if(this._offscreenFocusedItem&&!(this._focusedVirtualIndex<0)){this._assignModels();var t=this._focusedPhysicalIndex=this._getPhysicalIndex(this._focusedVirtualIndex),e=this._physicalItems[t];if(e){var n=this.modelForElement(e),i=this.modelForElement(this._offscreenFocusedItem);n[this.as]===i[this.as]?(this._focusBackfillItem=e,n.tabIndex=-1,this._physicalItems[t]=this._offscreenFocusedItem,this.translate3d(0,Fo,0,this._focusBackfillItem)):(this._removeFocusedItem(),this._focusBackfillItem=null),this._offscreenFocusedItem=null}}},_didFocus:function(t){var e=this.modelForElement(t.target),n=this.modelForElement(this._focusedItem),i=null!==this._offscreenFocusedItem,r=this._focusedVirtualIndex;e&&(n===e?this._isIndexVisible(r)||this.scrollToIndex(r):(this._restoreFocusedItem(),n&&(n.tabIndex=-1),e.tabIndex=0,this._focusedVirtualIndex=r=e[this.indexAs],this._focusedPhysicalIndex=this._getPhysicalIndex(r),this._focusedItem=this._physicalItems[this._focusedPhysicalIndex],i&&!this._offscreenFocusedItem&&this._update()))},_keydownHandler:function(t){switch(t.keyCode){case 40:this._focusedVirtualIndex<this._virtualCount-1&&t.preventDefault(),this._focusPhysicalItem(this._focusedVirtualIndex+(this.grid?this._itemsPerRow:1));break;case 39:this.grid&&this._focusPhysicalItem(this._focusedVirtualIndex+(this._isRTL?-1:1));break;case 38:this._focusedVirtualIndex>0&&t.preventDefault(),this._focusPhysicalItem(this._focusedVirtualIndex-(this.grid?this._itemsPerRow:1));break;case 37:this.grid&&this._focusPhysicalItem(this._focusedVirtualIndex+(this._isRTL?1:-1));break;case 13:this._focusPhysicalItem(this._focusedVirtualIndex),this.selectionEnabled&&this._selectionHandler(t)}},_clamp:function(t,e,n){return Math.min(n,Math.max(e,t))},_debounce:function(t,e,n){this._debouncers=this._debouncers||{},this._debouncers[t]=Tn.debounce(this._debouncers[t],n,e.bind(this)),An(this._debouncers[t])},_forwardProperty:function(t,e,n){t._setPendingProperty(e,n)},_forwardHostPropV2:function(t,e){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach((function(n){n&&this.modelForElement(n).forwardHostProp(t,e)}),this)},_notifyInstancePropV2:function(t,e,n){if(Q(this.as,e)){var i=t[this.indexAs];e==this.as&&(this.items[i]=n),this.notifyPath(J(this.as,"items."+i,e),n)}},_getStampedChildren:function(){return this._physicalItems},_forwardInstancePath:function(t,e,n){0===e.indexOf(this.as+".")&&this.notifyPath("items."+t.__key__+"."+e.slice(this.as.length+1),n)},_forwardParentPath:function(t,e){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach((function(n){n&&this.modelForElement(n).notifyPath(t,e)}),this)},_forwardParentProp:function(t,e){(this._physicalItems||[]).concat([this._offscreenFocusedItem,this._focusBackfillItem]).forEach((function(n){n&&(this.modelForElement(n)[t]=e)}),this)},_getActiveElement:function(){var t=this._itemsParent.node.domHost;return Yi(t?t.root:document).activeElement}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
class Uo{constructor(t){this.selection=[],this.selectCallback=t}get(){return this.multi?this.selection.slice():this.selection[0]}clear(t){this.selection.slice().forEach((function(e){(!t||t.indexOf(e)<0)&&this.setItemSelected(e,!1)}),this)}isSelected(t){return this.selection.indexOf(t)>=0}setItemSelected(t,e){if(null!=t&&e!==this.isSelected(t)){if(e)this.selection.push(t);else{var n=this.selection.indexOf(t);n>=0&&this.selection.splice(n,1)}this.selectCallback&&this.selectCallback(t,e)}}select(t){this.multi?this.toggle(t):this.get()!==t&&(this.setItemSelected(this.get(),!1),this.setItemSelected(t,!0))}toggle(t){this.setItemSelected(t,!this.isSelected(t))}}
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */const jo={properties:{attrForSelected:{type:String,value:null},selected:{type:String,notify:!0},selectedItem:{type:Object,readOnly:!0,notify:!0},activateEvent:{type:String,value:"tap",observer:"_activateEventChanged"},selectable:String,selectedClass:{type:String,value:"iron-selected"},selectedAttribute:{type:String,value:null},fallbackSelection:{type:String,value:null},items:{type:Array,readOnly:!0,notify:!0,value:function(){return[]}},_excludedLocalNames:{type:Object,value:function(){return{template:1,"dom-bind":1,"dom-if":1,"dom-repeat":1}}}},observers:["_updateAttrForSelected(attrForSelected)","_updateSelected(selected)","_checkFallback(fallbackSelection)"],created:function(){this._bindFilterItem=this._filterItem.bind(this),this._selection=new Uo(this._applySelection.bind(this))},attached:function(){this._observer=this._observeItems(this),this._addListener(this.activateEvent)},detached:function(){this._observer&&Yi(this).unobserveNodes(this._observer),this._removeListener(this.activateEvent)},indexOf:function(t){return this.items?this.items.indexOf(t):-1},select:function(t){this.selected=t},selectPrevious:function(){var t=this.items.length,e=t-1;void 0!==this.selected&&(e=(Number(this._valueToIndex(this.selected))-1+t)%t),this.selected=this._indexToValue(e)},selectNext:function(){var t=0;void 0!==this.selected&&(t=(Number(this._valueToIndex(this.selected))+1)%this.items.length),this.selected=this._indexToValue(t)},selectIndex:function(t){this.select(this._indexToValue(t))},forceSynchronousItemUpdate:function(){this._observer&&"function"==typeof this._observer.flush?this._observer.flush():this._updateItems()},get _shouldUpdateSelection(){return null!=this.selected},_checkFallback:function(){this._updateSelected()},_addListener:function(t){this.listen(this,t,"_activateHandler")},_removeListener:function(t){this.unlisten(this,t,"_activateHandler")},_activateEventChanged:function(t,e){this._removeListener(e),this._addListener(t)},_updateItems:function(){var t=Yi(this).queryDistributedElements(this.selectable||"*");t=Array.prototype.filter.call(t,this._bindFilterItem),this._setItems(t)},_updateAttrForSelected:function(){this.selectedItem&&(this.selected=this._valueForItem(this.selectedItem))},_updateSelected:function(){this._selectSelected(this.selected)},_selectSelected:function(t){if(this.items){var e=this._valueToItem(this.selected);e?this._selection.select(e):this._selection.clear(),this.fallbackSelection&&this.items.length&&void 0===this._selection.get()&&(this.selected=this.fallbackSelection)}},_filterItem:function(t){return!this._excludedLocalNames[t.localName]},_valueToItem:function(t){return null==t?null:this.items[this._valueToIndex(t)]},_valueToIndex:function(t){if(!this.attrForSelected)return Number(t);for(var e,n=0;e=this.items[n];n++)if(this._valueForItem(e)==t)return n},_indexToValue:function(t){if(!this.attrForSelected)return t;var e=this.items[t];return e?this._valueForItem(e):void 0},_valueForItem:function(t){if(!t)return null;if(!this.attrForSelected){var e=this.indexOf(t);return-1===e?null:e}var n=t[st(this.attrForSelected)];return null!=n?n:t.getAttribute(this.attrForSelected)},_applySelection:function(t,e){this.selectedClass&&this.toggleClass(this.selectedClass,e,t),this.selectedAttribute&&this.toggleAttribute(this.selectedAttribute,e,t),this._selectionChange(),this.fire("iron-"+(e?"select":"deselect"),{item:t})},_selectionChange:function(){this._setSelectedItem(this._selection.get())},_observeItems:function(t){return Yi(t).observeNodes((function(t){this._updateItems(),this._updateSelected(),this.fire("iron-items-changed",t,{bubbles:!1,cancelable:!1})}))},_activateHandler:function(t){for(var e=t.target,n=this.items;e&&e!=this;){var i=n.indexOf(e);if(i>=0){var r=this._indexToValue(i);return void this._itemActivate(r,e)}e=e.parentNode}},_itemActivate:function(t,e){this.fire("iron-activate",{selected:t,item:e},{cancelable:!0}).defaultPrevented||this.select(t)}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */Rr({_template:_e`
    <style>
      :host {
        display: block;
      }

      :host > ::slotted(:not(slot):not(.iron-selected)) {
        display: none !important;
      }
    </style>

    <slot></slot>
`,is:"iron-pages",behaviors:[go,jo],properties:{activateEvent:{type:String,value:null}},observers:["_selectedPageChanged(selected)"],_selectedPageChanged:function(t,e){this.async(this.notifyResize)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Go=_e`
<custom-style>
  <style is="custom-style">
    html {

      --shadow-transition: {
        transition: box-shadow 0.28s cubic-bezier(0.4, 0, 0.2, 1);
      };

      --shadow-none: {
        box-shadow: none;
      };

      /* from http://codepen.io/shyndman/pen/c5394ddf2e8b2a5c9185904b57421cdb */

      --shadow-elevation-2dp: {
        box-shadow: 0 2px 2px 0 rgba(0, 0, 0, 0.14),
                    0 1px 5px 0 rgba(0, 0, 0, 0.12),
                    0 3px 1px -2px rgba(0, 0, 0, 0.2);
      };

      --shadow-elevation-3dp: {
        box-shadow: 0 3px 4px 0 rgba(0, 0, 0, 0.14),
                    0 1px 8px 0 rgba(0, 0, 0, 0.12),
                    0 3px 3px -2px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-4dp: {
        box-shadow: 0 4px 5px 0 rgba(0, 0, 0, 0.14),
                    0 1px 10px 0 rgba(0, 0, 0, 0.12),
                    0 2px 4px -1px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-6dp: {
        box-shadow: 0 6px 10px 0 rgba(0, 0, 0, 0.14),
                    0 1px 18px 0 rgba(0, 0, 0, 0.12),
                    0 3px 5px -1px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-8dp: {
        box-shadow: 0 8px 10px 1px rgba(0, 0, 0, 0.14),
                    0 3px 14px 2px rgba(0, 0, 0, 0.12),
                    0 5px 5px -3px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-12dp: {
        box-shadow: 0 12px 16px 1px rgba(0, 0, 0, 0.14),
                    0 4px 22px 3px rgba(0, 0, 0, 0.12),
                    0 6px 7px -4px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-16dp: {
        box-shadow: 0 16px 24px 2px rgba(0, 0, 0, 0.14),
                    0  6px 30px 5px rgba(0, 0, 0, 0.12),
                    0  8px 10px -5px rgba(0, 0, 0, 0.4);
      };

      --shadow-elevation-24dp: {
        box-shadow: 0 24px 38px 3px rgba(0, 0, 0, 0.14),
                    0 9px 46px 8px rgba(0, 0, 0, 0.12),
                    0 11px 15px -7px rgba(0, 0, 0, 0.4);
      };
    }
  </style>
</custom-style>`;Go.setAttribute("style","display: none;"),document.head.appendChild(Go.content);
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Wo=_e`
<dom-module id="paper-material-styles">
  <template>
    <style>
      html {
        --paper-material: {
          display: block;
          position: relative;
        };
        --paper-material-elevation-1: {
          @apply --shadow-elevation-2dp;
        };
        --paper-material-elevation-2: {
          @apply --shadow-elevation-4dp;
        };
        --paper-material-elevation-3: {
          @apply --shadow-elevation-6dp;
        };
        --paper-material-elevation-4: {
          @apply --shadow-elevation-8dp;
        };
        --paper-material-elevation-5: {
          @apply --shadow-elevation-16dp;
        };
      }
      .paper-material {
        @apply --paper-material;
      }
      .paper-material[elevation="1"] {
        @apply --paper-material-elevation-1;
      }
      .paper-material[elevation="2"] {
        @apply --paper-material-elevation-2;
      }
      .paper-material[elevation="3"] {
        @apply --paper-material-elevation-3;
      }
      .paper-material[elevation="4"] {
        @apply --paper-material-elevation-4;
      }
      .paper-material[elevation="5"] {
        @apply --paper-material-elevation-5;
      }

      /* Duplicate the styles because of https://github.com/webcomponents/shadycss/issues/193 */
      :host {
        --paper-material: {
          display: block;
          position: relative;
        };
        --paper-material-elevation-1: {
          @apply --shadow-elevation-2dp;
        };
        --paper-material-elevation-2: {
          @apply --shadow-elevation-4dp;
        };
        --paper-material-elevation-3: {
          @apply --shadow-elevation-6dp;
        };
        --paper-material-elevation-4: {
          @apply --shadow-elevation-8dp;
        };
        --paper-material-elevation-5: {
          @apply --shadow-elevation-16dp;
        };
      }
      :host(.paper-material) {
        @apply --paper-material;
      }
      :host(.paper-material[elevation="1"]) {
        @apply --paper-material-elevation-1;
      }
      :host(.paper-material[elevation="2"]) {
        @apply --paper-material-elevation-2;
      }
      :host(.paper-material[elevation="3"]) {
        @apply --paper-material-elevation-3;
      }
      :host(.paper-material[elevation="4"]) {
        @apply --paper-material-elevation-4;
      }
      :host(.paper-material[elevation="5"]) {
        @apply --paper-material-elevation-5;
      }
    </style>
  </template>
</dom-module>`;Wo.setAttribute("style","display: none;"),document.head.appendChild(Wo.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const qo={properties:{focused:{type:Boolean,value:!1,notify:!0,readOnly:!0,reflectToAttribute:!0},disabled:{type:Boolean,value:!1,notify:!0,observer:"_disabledChanged",reflectToAttribute:!0},_oldTabIndex:{type:String},_boundFocusBlurHandler:{type:Function,value:function(){return this._focusBlurHandler.bind(this)}}},observers:["_changedControlState(focused, disabled)"],ready:function(){this.addEventListener("focus",this._boundFocusBlurHandler,!0),this.addEventListener("blur",this._boundFocusBlurHandler,!0)},_focusBlurHandler:function(t){this._setFocused("focus"===t.type)},_disabledChanged:function(t,e){this.setAttribute("aria-disabled",t?"true":"false"),this.style.pointerEvents=t?"none":"",t?(this._oldTabIndex=this.getAttribute("tabindex"),this._setFocused(!1),this.tabIndex=-1,this.blur()):void 0!==this._oldTabIndex&&(null===this._oldTabIndex?this.removeAttribute("tabindex"):this.setAttribute("tabindex",this._oldTabIndex))},_changedControlState:function(){this._controlStateChanged&&this._controlStateChanged()}},Yo={properties:{pressed:{type:Boolean,readOnly:!0,value:!1,reflectToAttribute:!0,observer:"_pressedChanged"},toggles:{type:Boolean,value:!1,reflectToAttribute:!0},active:{type:Boolean,value:!1,notify:!0,reflectToAttribute:!0},pointerDown:{type:Boolean,readOnly:!0,value:!1},receivedFocusFromKeyboard:{type:Boolean,readOnly:!0},ariaActiveAttribute:{type:String,value:"aria-pressed",observer:"_ariaActiveAttributeChanged"}},listeners:{down:"_downHandler",up:"_upHandler",tap:"_tapHandler"},observers:["_focusChanged(focused)","_activeChanged(active, ariaActiveAttribute)"],keyBindings:{"enter:keydown":"_asyncClick","space:keydown":"_spaceKeyDownHandler","space:keyup":"_spaceKeyUpHandler"},_mouseEventRe:/^mouse/,_tapHandler:function(){this.toggles?this._userActivate(!this.active):this.active=!1},_focusChanged:function(t){this._detectKeyboardFocus(t),t||this._setPressed(!1)},_detectKeyboardFocus:function(t){this._setReceivedFocusFromKeyboard(!this.pointerDown&&t)},_userActivate:function(t){this.active!==t&&(this.active=t,this.fire("change"))},_downHandler:function(t){this._setPointerDown(!0),this._setPressed(!0),this._setReceivedFocusFromKeyboard(!1)},_upHandler:function(){this._setPointerDown(!1),this._setPressed(!1)},_spaceKeyDownHandler:function(t){var e=t.detail.keyboardEvent,n=Yi(e).localTarget;this.isLightDescendant(n)||(e.preventDefault(),e.stopImmediatePropagation(),this._setPressed(!0))},_spaceKeyUpHandler:function(t){var e=Yi(t.detail.keyboardEvent).localTarget;this.isLightDescendant(e)||(this.pressed&&this._asyncClick(),this._setPressed(!1))},_asyncClick:function(){this.async((function(){this.click()}),1)},_pressedChanged:function(t){this._changedButtonState()},_ariaActiveAttributeChanged:function(t,e){e&&e!=t&&this.hasAttribute(e)&&this.removeAttribute(e)},_activeChanged:function(t,e){this.toggles?this.setAttribute(this.ariaActiveAttribute,t?"true":"false"):this.removeAttribute(this.ariaActiveAttribute),this._changedButtonState()},_controlStateChanged:function(){this.disabled?this._setPressed(!1):this._changedButtonState()},_changedButtonState:function(){this._buttonStateChanged&&this._buttonStateChanged()}},Xo=[zo,Yo];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2014 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var $o={distance:function(t,e,n,i){var r=t-n,o=e-i;return Math.sqrt(r*r+o*o)},now:window.performance&&window.performance.now?window.performance.now.bind(window.performance):Date.now};function Ko(t){this.element=t,this.width=this.boundingRect.width,this.height=this.boundingRect.height,this.size=Math.max(this.width,this.height)}function Zo(t){this.element=t,this.color=window.getComputedStyle(t).color,this.wave=document.createElement("div"),this.waveContainer=document.createElement("div"),this.wave.style.backgroundColor=this.color,this.wave.classList.add("wave"),this.waveContainer.classList.add("wave-container"),Yi(this.waveContainer).appendChild(this.wave),this.resetInteractionState()}Ko.prototype={get boundingRect(){return this.element.getBoundingClientRect()},furthestCornerDistanceFrom:function(t,e){var n=$o.distance(t,e,0,0),i=$o.distance(t,e,this.width,0),r=$o.distance(t,e,0,this.height),o=$o.distance(t,e,this.width,this.height);return Math.max(n,i,r,o)}},Zo.MAX_RADIUS=300,Zo.prototype={get recenters(){return this.element.recenters},get center(){return this.element.center},get mouseDownElapsed(){var t;return this.mouseDownStart?(t=$o.now()-this.mouseDownStart,this.mouseUpStart&&(t-=this.mouseUpElapsed),t):0},get mouseUpElapsed(){return this.mouseUpStart?$o.now()-this.mouseUpStart:0},get mouseDownElapsedSeconds(){return this.mouseDownElapsed/1e3},get mouseUpElapsedSeconds(){return this.mouseUpElapsed/1e3},get mouseInteractionSeconds(){return this.mouseDownElapsedSeconds+this.mouseUpElapsedSeconds},get initialOpacity(){return this.element.initialOpacity},get opacityDecayVelocity(){return this.element.opacityDecayVelocity},get radius(){var t=1.1*Math.min(Math.sqrt(this.containerMetrics.width*this.containerMetrics.width+this.containerMetrics.height*this.containerMetrics.height),Zo.MAX_RADIUS)+5,e=t*(1-Math.pow(80,-this.mouseInteractionSeconds/(1.1-t/Zo.MAX_RADIUS*.2)));return Math.abs(e)},get opacity(){return this.mouseUpStart?Math.max(0,this.initialOpacity-this.mouseUpElapsedSeconds*this.opacityDecayVelocity):this.initialOpacity},get outerOpacity(){return Math.max(0,Math.min(.3*this.mouseUpElapsedSeconds,this.opacity))},get isOpacityFullyDecayed(){return this.opacity<.01&&this.radius>=Math.min(this.maxRadius,Zo.MAX_RADIUS)},get isRestingAtMaxRadius(){return this.opacity>=this.initialOpacity&&this.radius>=Math.min(this.maxRadius,Zo.MAX_RADIUS)},get isAnimationComplete(){return this.mouseUpStart?this.isOpacityFullyDecayed:this.isRestingAtMaxRadius},get translationFraction(){return Math.min(1,this.radius/this.containerMetrics.size*2/Math.sqrt(2))},get xNow(){return this.xEnd?this.xStart+this.translationFraction*(this.xEnd-this.xStart):this.xStart},get yNow(){return this.yEnd?this.yStart+this.translationFraction*(this.yEnd-this.yStart):this.yStart},get isMouseDown(){return this.mouseDownStart&&!this.mouseUpStart},resetInteractionState:function(){this.maxRadius=0,this.mouseDownStart=0,this.mouseUpStart=0,this.xStart=0,this.yStart=0,this.xEnd=0,this.yEnd=0,this.slideDistance=0,this.containerMetrics=new Ko(this.element)},draw:function(){var t,e,n;this.wave.style.opacity=this.opacity,t=this.radius/(this.containerMetrics.size/2),this.waveContainer.style.webkitTransform="translate("+(e=this.xNow-this.containerMetrics.width/2)+"px, "+(n=this.yNow-this.containerMetrics.height/2)+"px)",this.waveContainer.style.transform="translate3d("+e+"px, "+n+"px, 0)",this.wave.style.webkitTransform="scale("+t+","+t+")",this.wave.style.transform="scale3d("+t+","+t+",1)"},downAction:function(t){var e=this.containerMetrics.width/2,n=this.containerMetrics.height/2;this.resetInteractionState(),this.mouseDownStart=$o.now(),this.center?(this.xStart=e,this.yStart=n,this.slideDistance=$o.distance(this.xStart,this.yStart,this.xEnd,this.yEnd)):(this.xStart=t?t.detail.x-this.containerMetrics.boundingRect.left:this.containerMetrics.width/2,this.yStart=t?t.detail.y-this.containerMetrics.boundingRect.top:this.containerMetrics.height/2),this.recenters&&(this.xEnd=e,this.yEnd=n,this.slideDistance=$o.distance(this.xStart,this.yStart,this.xEnd,this.yEnd)),this.maxRadius=this.containerMetrics.furthestCornerDistanceFrom(this.xStart,this.yStart),this.waveContainer.style.top=(this.containerMetrics.height-this.containerMetrics.size)/2+"px",this.waveContainer.style.left=(this.containerMetrics.width-this.containerMetrics.size)/2+"px",this.waveContainer.style.width=this.containerMetrics.size+"px",this.waveContainer.style.height=this.containerMetrics.size+"px"},upAction:function(t){this.isMouseDown&&(this.mouseUpStart=$o.now())},remove:function(){Yi(Yi(this.waveContainer).parentNode).removeChild(this.waveContainer)}},Rr({_template:_e`
    <style>
      :host {
        display: block;
        position: absolute;
        border-radius: inherit;
        overflow: hidden;
        top: 0;
        left: 0;
        right: 0;
        bottom: 0;

        /* See PolymerElements/paper-behaviors/issues/34. On non-Chrome browsers,
         * creating a node (with a position:absolute) in the middle of an event
         * handler "interrupts" that event handler (which happens when the
         * ripple is created on demand) */
        pointer-events: none;
      }

      :host([animating]) {
        /* This resolves a rendering issue in Chrome (as of 40) where the
           ripple is not properly clipped by its parent (which may have
           rounded corners). See: http://jsbin.com/temexa/4

           Note: We only apply this style conditionally. Otherwise, the browser
           will create a new compositing layer for every ripple element on the
           page, and that would be bad. */
        -webkit-transform: translate(0, 0);
        transform: translate3d(0, 0, 0);
      }

      #background,
      #waves,
      .wave-container,
      .wave {
        pointer-events: none;
        position: absolute;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
      }

      #background,
      .wave {
        opacity: 0;
      }

      #waves,
      .wave {
        overflow: hidden;
      }

      .wave-container,
      .wave {
        border-radius: 50%;
      }

      :host(.circle) #background,
      :host(.circle) #waves {
        border-radius: 50%;
      }

      :host(.circle) .wave-container {
        overflow: hidden;
      }
    </style>

    <div id="background"></div>
    <div id="waves"></div>
`,is:"paper-ripple",behaviors:[zo],properties:{initialOpacity:{type:Number,value:.25},opacityDecayVelocity:{type:Number,value:.8},recenters:{type:Boolean,value:!1},center:{type:Boolean,value:!1},ripples:{type:Array,value:function(){return[]}},animating:{type:Boolean,readOnly:!0,reflectToAttribute:!0,value:!1},holdDown:{type:Boolean,value:!1,observer:"_holdDownChanged"},noink:{type:Boolean,value:!1},_animating:{type:Boolean},_boundAnimate:{type:Function,value:function(){return this.animate.bind(this)}}},get target(){return this.keyEventTarget},keyBindings:{"enter:keydown":"_onEnterKeydown","space:keydown":"_onSpaceKeydown","space:keyup":"_onSpaceKeyup"},attached:function(){this.keyEventTarget=11==Yi(this).parentNode.nodeType?Yi(this).getOwnerRoot().host:Yi(this).parentNode;var t=this.keyEventTarget;this.listen(t,"up","uiUpAction"),this.listen(t,"down","uiDownAction")},detached:function(){this.unlisten(this.keyEventTarget,"up","uiUpAction"),this.unlisten(this.keyEventTarget,"down","uiDownAction"),this.keyEventTarget=null},get shouldKeepAnimating(){for(var t=0;t<this.ripples.length;++t)if(!this.ripples[t].isAnimationComplete)return!0;return!1},simulatedRipple:function(){this.downAction(null),this.async((function(){this.upAction()}),1)},uiDownAction:function(t){this.noink||this.downAction(t)},downAction:function(t){this.holdDown&&this.ripples.length>0||(this.addRipple().downAction(t),this._animating||(this._animating=!0,this.animate()))},uiUpAction:function(t){this.noink||this.upAction(t)},upAction:function(t){this.holdDown||(this.ripples.forEach((function(e){e.upAction(t)})),this._animating=!0,this.animate())},onAnimationComplete:function(){this._animating=!1,this.$.background.style.backgroundColor="",this.fire("transitionend")},addRipple:function(){var t=new Zo(this);return Yi(this.$.waves).appendChild(t.waveContainer),this.$.background.style.backgroundColor=t.color,this.ripples.push(t),this._setAnimating(!0),t},removeRipple:function(t){var e=this.ripples.indexOf(t);e<0||(this.ripples.splice(e,1),t.remove(),this.ripples.length||this._setAnimating(!1))},animate:function(){if(this._animating){var t,e;for(t=0;t<this.ripples.length;++t)(e=this.ripples[t]).draw(),this.$.background.style.opacity=e.outerOpacity,e.isOpacityFullyDecayed&&!e.isRestingAtMaxRadius&&this.removeRipple(e);this.shouldKeepAnimating||0!==this.ripples.length?window.requestAnimationFrame(this._boundAnimate):this.onAnimationComplete()}},animateRipple:function(){return this.animate()},_onEnterKeydown:function(){this.uiDownAction(),this.async(this.uiUpAction,1)},_onSpaceKeydown:function(){this.uiDownAction()},_onSpaceKeyup:function(){this.uiUpAction()},_holdDownChanged:function(t,e){void 0!==e&&(t?this.downAction():this.upAction())}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Jo={properties:{noink:{type:Boolean,observer:"_noinkChanged"},_rippleContainer:{type:Object}},_buttonStateChanged:function(){this.focused&&this.ensureRipple()},_downHandler:function(t){Yo._downHandler.call(this,t),this.pressed&&this.ensureRipple(t)},ensureRipple:function(t){if(!this.hasRipple()){this._ripple=this._createRipple(),this._ripple.noink=this.noink;var e=this._rippleContainer||this.root;if(e&&Yi(e).appendChild(this._ripple),t){var n=Yi(this._rippleContainer||this),i=Yi(t).rootTarget;n.deepContains(i)&&this._ripple.uiDownAction(t)}}},getRipple:function(){return this.ensureRipple(),this._ripple},hasRipple:function(){return Boolean(this._ripple)},_createRipple:function(){return document.createElement("paper-ripple")},_noinkChanged:function(t){this.hasRipple()&&(this._ripple.noink=t)}},Qo={properties:{elevation:{type:Number,reflectToAttribute:!0,readOnly:!0}},observers:["_calculateElevation(focused, disabled, active, pressed, receivedFocusFromKeyboard)","_computeKeyboardClass(receivedFocusFromKeyboard)"],hostAttributes:{role:"button",tabindex:"0",animated:!0},_calculateElevation:function(){var t=1;this.disabled?t=0:this.active||this.pressed?t=4:this.receivedFocusFromKeyboard&&(t=3),this._setElevation(t)},_computeKeyboardClass:function(t){this.toggleClass("keyboard-focus",t)},_spaceKeyDownHandler:function(t){Yo._spaceKeyDownHandler.call(this,t),this.hasRipple()&&this.getRipple().ripples.length<1&&this._ripple.uiDownAction()},_spaceKeyUpHandler:function(t){Yo._spaceKeyUpHandler.call(this,t),this.hasRipple()&&this._ripple.uiUpAction()}},ta=[Xo,qo,Jo,Qo],ea=_e`
  <style include="paper-material-styles">
    /* Need to specify the same specificity as the styles imported from paper-material. */
    :host {
      @apply --layout-inline;
      @apply --layout-center-center;
      position: relative;
      box-sizing: border-box;
      min-width: 5.14em;
      margin: 0 0.29em;
      background: transparent;
      -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
      -webkit-tap-highlight-color: transparent;
      font: inherit;
      text-transform: uppercase;
      outline-width: 0;
      border-radius: 3px;
      -moz-user-select: none;
      -ms-user-select: none;
      -webkit-user-select: none;
      user-select: none;
      cursor: pointer;
      z-index: 0;
      padding: 0.7em 0.57em;

      @apply --paper-font-common-base;
      @apply --paper-button;
    }

    :host([elevation="1"]) {
      @apply --paper-material-elevation-1;
    }

    :host([elevation="2"]) {
      @apply --paper-material-elevation-2;
    }

    :host([elevation="3"]) {
      @apply --paper-material-elevation-3;
    }

    :host([elevation="4"]) {
      @apply --paper-material-elevation-4;
    }

    :host([elevation="5"]) {
      @apply --paper-material-elevation-5;
    }

    :host([hidden]) {
      display: none !important;
    }

    :host([raised].keyboard-focus) {
      font-weight: bold;
      @apply --paper-button-raised-keyboard-focus;
    }

    :host(:not([raised]).keyboard-focus) {
      font-weight: bold;
      @apply --paper-button-flat-keyboard-focus;
    }

    :host([disabled]) {
      background: none;
      color: #a8a8a8;
      cursor: auto;
      pointer-events: none;

      @apply --paper-button-disabled;
    }

    :host([disabled][raised]) {
      background: #eaeaea;
    }


    :host([animated]) {
      @apply --shadow-transition;
    }

    paper-ripple {
      color: var(--paper-button-ink-color);
    }
  </style>

  <slot></slot>`;
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */ea.setAttribute("strip-whitespace",""),Rr({_template:ea,is:"paper-button",behaviors:[ta],properties:{raised:{type:Boolean,reflectToAttribute:!0,value:!1,observer:"_calculateElevation"}},_calculateElevation:function(){this.raised?Qo._calculateElevation.apply(this):this._setElevation(0)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const na=_e`
<custom-style>
  <style is="custom-style">
    html {

      /* Material Design color palette for Google products */

      --google-red-100: #f4c7c3;
      --google-red-300: #e67c73;
      --google-red-500: #db4437;
      --google-red-700: #c53929;

      --google-blue-100: #c6dafc;
      --google-blue-300: #7baaf7;
      --google-blue-500: #4285f4;
      --google-blue-700: #3367d6;

      --google-green-100: #b7e1cd;
      --google-green-300: #57bb8a;
      --google-green-500: #0f9d58;
      --google-green-700: #0b8043;

      --google-yellow-100: #fce8b2;
      --google-yellow-300: #f7cb4d;
      --google-yellow-500: #f4b400;
      --google-yellow-700: #f09300;

      --google-grey-100: #f5f5f5;
      --google-grey-300: #e0e0e0;
      --google-grey-500: #9e9e9e;
      --google-grey-700: #616161;

      /* Material Design color palette from online spec document */

      --paper-red-50: #ffebee;
      --paper-red-100: #ffcdd2;
      --paper-red-200: #ef9a9a;
      --paper-red-300: #e57373;
      --paper-red-400: #ef5350;
      --paper-red-500: #f44336;
      --paper-red-600: #e53935;
      --paper-red-700: #d32f2f;
      --paper-red-800: #c62828;
      --paper-red-900: #b71c1c;
      --paper-red-a100: #ff8a80;
      --paper-red-a200: #ff5252;
      --paper-red-a400: #ff1744;
      --paper-red-a700: #d50000;

      --paper-pink-50: #fce4ec;
      --paper-pink-100: #f8bbd0;
      --paper-pink-200: #f48fb1;
      --paper-pink-300: #f06292;
      --paper-pink-400: #ec407a;
      --paper-pink-500: #e91e63;
      --paper-pink-600: #d81b60;
      --paper-pink-700: #c2185b;
      --paper-pink-800: #ad1457;
      --paper-pink-900: #880e4f;
      --paper-pink-a100: #ff80ab;
      --paper-pink-a200: #ff4081;
      --paper-pink-a400: #f50057;
      --paper-pink-a700: #c51162;

      --paper-purple-50: #f3e5f5;
      --paper-purple-100: #e1bee7;
      --paper-purple-200: #ce93d8;
      --paper-purple-300: #ba68c8;
      --paper-purple-400: #ab47bc;
      --paper-purple-500: #9c27b0;
      --paper-purple-600: #8e24aa;
      --paper-purple-700: #7b1fa2;
      --paper-purple-800: #6a1b9a;
      --paper-purple-900: #4a148c;
      --paper-purple-a100: #ea80fc;
      --paper-purple-a200: #e040fb;
      --paper-purple-a400: #d500f9;
      --paper-purple-a700: #aa00ff;

      --paper-deep-purple-50: #ede7f6;
      --paper-deep-purple-100: #d1c4e9;
      --paper-deep-purple-200: #b39ddb;
      --paper-deep-purple-300: #9575cd;
      --paper-deep-purple-400: #7e57c2;
      --paper-deep-purple-500: #673ab7;
      --paper-deep-purple-600: #5e35b1;
      --paper-deep-purple-700: #512da8;
      --paper-deep-purple-800: #4527a0;
      --paper-deep-purple-900: #311b92;
      --paper-deep-purple-a100: #b388ff;
      --paper-deep-purple-a200: #7c4dff;
      --paper-deep-purple-a400: #651fff;
      --paper-deep-purple-a700: #6200ea;

      --paper-indigo-50: #e8eaf6;
      --paper-indigo-100: #c5cae9;
      --paper-indigo-200: #9fa8da;
      --paper-indigo-300: #7986cb;
      --paper-indigo-400: #5c6bc0;
      --paper-indigo-500: #3f51b5;
      --paper-indigo-600: #3949ab;
      --paper-indigo-700: #303f9f;
      --paper-indigo-800: #283593;
      --paper-indigo-900: #1a237e;
      --paper-indigo-a100: #8c9eff;
      --paper-indigo-a200: #536dfe;
      --paper-indigo-a400: #3d5afe;
      --paper-indigo-a700: #304ffe;

      --paper-blue-50: #e3f2fd;
      --paper-blue-100: #bbdefb;
      --paper-blue-200: #90caf9;
      --paper-blue-300: #64b5f6;
      --paper-blue-400: #42a5f5;
      --paper-blue-500: #2196f3;
      --paper-blue-600: #1e88e5;
      --paper-blue-700: #1976d2;
      --paper-blue-800: #1565c0;
      --paper-blue-900: #0d47a1;
      --paper-blue-a100: #82b1ff;
      --paper-blue-a200: #448aff;
      --paper-blue-a400: #2979ff;
      --paper-blue-a700: #2962ff;

      --paper-light-blue-50: #e1f5fe;
      --paper-light-blue-100: #b3e5fc;
      --paper-light-blue-200: #81d4fa;
      --paper-light-blue-300: #4fc3f7;
      --paper-light-blue-400: #29b6f6;
      --paper-light-blue-500: #03a9f4;
      --paper-light-blue-600: #039be5;
      --paper-light-blue-700: #0288d1;
      --paper-light-blue-800: #0277bd;
      --paper-light-blue-900: #01579b;
      --paper-light-blue-a100: #80d8ff;
      --paper-light-blue-a200: #40c4ff;
      --paper-light-blue-a400: #00b0ff;
      --paper-light-blue-a700: #0091ea;

      --paper-cyan-50: #e0f7fa;
      --paper-cyan-100: #b2ebf2;
      --paper-cyan-200: #80deea;
      --paper-cyan-300: #4dd0e1;
      --paper-cyan-400: #26c6da;
      --paper-cyan-500: #00bcd4;
      --paper-cyan-600: #00acc1;
      --paper-cyan-700: #0097a7;
      --paper-cyan-800: #00838f;
      --paper-cyan-900: #006064;
      --paper-cyan-a100: #84ffff;
      --paper-cyan-a200: #18ffff;
      --paper-cyan-a400: #00e5ff;
      --paper-cyan-a700: #00b8d4;

      --paper-teal-50: #e0f2f1;
      --paper-teal-100: #b2dfdb;
      --paper-teal-200: #80cbc4;
      --paper-teal-300: #4db6ac;
      --paper-teal-400: #26a69a;
      --paper-teal-500: #009688;
      --paper-teal-600: #00897b;
      --paper-teal-700: #00796b;
      --paper-teal-800: #00695c;
      --paper-teal-900: #004d40;
      --paper-teal-a100: #a7ffeb;
      --paper-teal-a200: #64ffda;
      --paper-teal-a400: #1de9b6;
      --paper-teal-a700: #00bfa5;

      --paper-green-50: #e8f5e9;
      --paper-green-100: #c8e6c9;
      --paper-green-200: #a5d6a7;
      --paper-green-300: #81c784;
      --paper-green-400: #66bb6a;
      --paper-green-500: #4caf50;
      --paper-green-600: #43a047;
      --paper-green-700: #388e3c;
      --paper-green-800: #2e7d32;
      --paper-green-900: #1b5e20;
      --paper-green-a100: #b9f6ca;
      --paper-green-a200: #69f0ae;
      --paper-green-a400: #00e676;
      --paper-green-a700: #00c853;

      --paper-light-green-50: #f1f8e9;
      --paper-light-green-100: #dcedc8;
      --paper-light-green-200: #c5e1a5;
      --paper-light-green-300: #aed581;
      --paper-light-green-400: #9ccc65;
      --paper-light-green-500: #8bc34a;
      --paper-light-green-600: #7cb342;
      --paper-light-green-700: #689f38;
      --paper-light-green-800: #558b2f;
      --paper-light-green-900: #33691e;
      --paper-light-green-a100: #ccff90;
      --paper-light-green-a200: #b2ff59;
      --paper-light-green-a400: #76ff03;
      --paper-light-green-a700: #64dd17;

      --paper-lime-50: #f9fbe7;
      --paper-lime-100: #f0f4c3;
      --paper-lime-200: #e6ee9c;
      --paper-lime-300: #dce775;
      --paper-lime-400: #d4e157;
      --paper-lime-500: #cddc39;
      --paper-lime-600: #c0ca33;
      --paper-lime-700: #afb42b;
      --paper-lime-800: #9e9d24;
      --paper-lime-900: #827717;
      --paper-lime-a100: #f4ff81;
      --paper-lime-a200: #eeff41;
      --paper-lime-a400: #c6ff00;
      --paper-lime-a700: #aeea00;

      --paper-yellow-50: #fffde7;
      --paper-yellow-100: #fff9c4;
      --paper-yellow-200: #fff59d;
      --paper-yellow-300: #fff176;
      --paper-yellow-400: #ffee58;
      --paper-yellow-500: #ffeb3b;
      --paper-yellow-600: #fdd835;
      --paper-yellow-700: #fbc02d;
      --paper-yellow-800: #f9a825;
      --paper-yellow-900: #f57f17;
      --paper-yellow-a100: #ffff8d;
      --paper-yellow-a200: #ffff00;
      --paper-yellow-a400: #ffea00;
      --paper-yellow-a700: #ffd600;

      --paper-amber-50: #fff8e1;
      --paper-amber-100: #ffecb3;
      --paper-amber-200: #ffe082;
      --paper-amber-300: #ffd54f;
      --paper-amber-400: #ffca28;
      --paper-amber-500: #ffc107;
      --paper-amber-600: #ffb300;
      --paper-amber-700: #ffa000;
      --paper-amber-800: #ff8f00;
      --paper-amber-900: #ff6f00;
      --paper-amber-a100: #ffe57f;
      --paper-amber-a200: #ffd740;
      --paper-amber-a400: #ffc400;
      --paper-amber-a700: #ffab00;

      --paper-orange-50: #fff3e0;
      --paper-orange-100: #ffe0b2;
      --paper-orange-200: #ffcc80;
      --paper-orange-300: #ffb74d;
      --paper-orange-400: #ffa726;
      --paper-orange-500: #ff9800;
      --paper-orange-600: #fb8c00;
      --paper-orange-700: #f57c00;
      --paper-orange-800: #ef6c00;
      --paper-orange-900: #e65100;
      --paper-orange-a100: #ffd180;
      --paper-orange-a200: #ffab40;
      --paper-orange-a400: #ff9100;
      --paper-orange-a700: #ff6500;

      --paper-deep-orange-50: #fbe9e7;
      --paper-deep-orange-100: #ffccbc;
      --paper-deep-orange-200: #ffab91;
      --paper-deep-orange-300: #ff8a65;
      --paper-deep-orange-400: #ff7043;
      --paper-deep-orange-500: #ff5722;
      --paper-deep-orange-600: #f4511e;
      --paper-deep-orange-700: #e64a19;
      --paper-deep-orange-800: #d84315;
      --paper-deep-orange-900: #bf360c;
      --paper-deep-orange-a100: #ff9e80;
      --paper-deep-orange-a200: #ff6e40;
      --paper-deep-orange-a400: #ff3d00;
      --paper-deep-orange-a700: #dd2c00;

      --paper-brown-50: #efebe9;
      --paper-brown-100: #d7ccc8;
      --paper-brown-200: #bcaaa4;
      --paper-brown-300: #a1887f;
      --paper-brown-400: #8d6e63;
      --paper-brown-500: #795548;
      --paper-brown-600: #6d4c41;
      --paper-brown-700: #5d4037;
      --paper-brown-800: #4e342e;
      --paper-brown-900: #3e2723;

      --paper-grey-50: #fafafa;
      --paper-grey-100: #f5f5f5;
      --paper-grey-200: #eeeeee;
      --paper-grey-300: #e0e0e0;
      --paper-grey-400: #bdbdbd;
      --paper-grey-500: #9e9e9e;
      --paper-grey-600: #757575;
      --paper-grey-700: #616161;
      --paper-grey-800: #424242;
      --paper-grey-900: #212121;

      --paper-blue-grey-50: #eceff1;
      --paper-blue-grey-100: #cfd8dc;
      --paper-blue-grey-200: #b0bec5;
      --paper-blue-grey-300: #90a4ae;
      --paper-blue-grey-400: #78909c;
      --paper-blue-grey-500: #607d8b;
      --paper-blue-grey-600: #546e7a;
      --paper-blue-grey-700: #455a64;
      --paper-blue-grey-800: #37474f;
      --paper-blue-grey-900: #263238;

      /* opacity for dark text on a light background */
      --dark-divider-opacity: 0.12;
      --dark-disabled-opacity: 0.38; /* or hint text or icon */
      --dark-secondary-opacity: 0.54;
      --dark-primary-opacity: 0.87;

      /* opacity for light text on a dark background */
      --light-divider-opacity: 0.12;
      --light-disabled-opacity: 0.3; /* or hint text or icon */
      --light-secondary-opacity: 0.7;
      --light-primary-opacity: 1.0;

    }

  </style>
</custom-style>
`;na.setAttribute("style","display: none;"),document.head.appendChild(na.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ia=_e`
<custom-style>
  <style is="custom-style">
    html {
      /*
       * You can use these generic variables in your elements for easy theming.
       * For example, if all your elements use \`--primary-text-color\` as its main
       * color, then switching from a light to a dark theme is just a matter of
       * changing the value of \`--primary-text-color\` in your application.
       */
      --primary-text-color: var(--light-theme-text-color);
      --primary-background-color: var(--light-theme-background-color);
      --secondary-text-color: var(--light-theme-secondary-color);
      --disabled-text-color: var(--light-theme-disabled-color);
      --divider-color: var(--light-theme-divider-color);
      --error-color: var(--paper-deep-orange-a700);

      /*
       * Primary and accent colors. Also see color.js for more colors.
       */
      --primary-color: var(--paper-indigo-500);
      --light-primary-color: var(--paper-indigo-100);
      --dark-primary-color: var(--paper-indigo-700);

      --accent-color: var(--paper-pink-a200);
      --light-accent-color: var(--paper-pink-a100);
      --dark-accent-color: var(--paper-pink-a400);


      /*
       * Material Design Light background theme
       */
      --light-theme-background-color: #ffffff;
      --light-theme-base-color: #000000;
      --light-theme-text-color: var(--paper-grey-900);
      --light-theme-secondary-color: #737373;  /* for secondary text and icons */
      --light-theme-disabled-color: #9b9b9b;  /* disabled/hint text */
      --light-theme-divider-color: #dbdbdb;

      /*
       * Material Design Dark background theme
       */
      --dark-theme-background-color: var(--paper-grey-900);
      --dark-theme-base-color: #ffffff;
      --dark-theme-text-color: #ffffff;
      --dark-theme-secondary-color: #bcbcbc;  /* for secondary text and icons */
      --dark-theme-disabled-color: #646464;  /* disabled/hint text */
      --dark-theme-divider-color: #3c3c3c;

      /*
       * Deprecated values because of their confusing names.
       */
      --text-primary-color: var(--dark-theme-text-color);
      --default-primary-color: var(--primary-color);
    }
  </style>
</custom-style>`;ia.setAttribute("style","display: none;"),document.head.appendChild(ia.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ra={properties:{name:{type:String},value:{notify:!0,type:String},required:{type:Boolean,value:!1}},attached:function(){},detached:function(){}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */let oa=null;const aa={properties:{validator:{type:String},invalid:{notify:!0,reflectToAttribute:!0,type:Boolean,value:!1,observer:"_invalidChanged"}},registered:function(){oa=new xo({type:"validator"})},_invalidChanged:function(){this.invalid?this.setAttribute("aria-invalid","true"):this.removeAttribute("aria-invalid")},get _validator(){return oa&&oa.byKey(this.validator)},hasValidator:function(){return null!=this._validator},validate:function(t){return this.invalid=void 0===t&&void 0!==this.value?!this._getValidity(this.value):!this._getValidity(t),!this.invalid},_getValidity:function(t){return!this.hasValidator()||this._validator.validate(t)}},sa={properties:{checked:{type:Boolean,value:!1,reflectToAttribute:!0,notify:!0,observer:"_checkedChanged"},toggles:{type:Boolean,value:!0,reflectToAttribute:!0},value:{type:String,value:"on",observer:"_valueChanged"}},observers:["_requiredChanged(required)"],created:function(){this._hasIronCheckedElementBehavior=!0},_getValidity:function(t){return this.disabled||!this.required||this.checked},_requiredChanged:function(){this.required?this.setAttribute("aria-required","true"):this.removeAttribute("aria-required")},_checkedChanged:function(){this.active=this.checked,this.fire("iron-change")},_valueChanged:function(){null==this.value&&(this.value="on")}},la={observers:["_focusedChanged(receivedFocusFromKeyboard)"],_focusedChanged:function(t){t&&this.ensureRipple(),this.hasRipple()&&(this._ripple.holdDown=t)},_createRipple:function(){var t=Jo._createRipple();return t.id="ink",t.setAttribute("center",""),t.classList.add("circle"),t}},ca=[Xo,qo,Jo,la],ua=[ca,[ra,aa,sa],{_checkedChanged:function(){sa._checkedChanged.call(this),this.hasRipple()&&(this.checked?this._ripple.setAttribute("checked",""):this._ripple.removeAttribute("checked"))},_buttonStateChanged:function(){Jo._buttonStateChanged.call(this),this.disabled||this.isAttached&&(this.checked=this.active)}}],ha=_e`<style>
  :host {
    display: inline-block;
    white-space: nowrap;
    cursor: pointer;
    --calculated-paper-checkbox-size: var(--paper-checkbox-size, 18px);
    /* -1px is a sentinel for the default and is replaced in \`attached\`. */
    --calculated-paper-checkbox-ink-size: var(--paper-checkbox-ink-size, -1px);
    @apply --paper-font-common-base;
    line-height: 0;
    -webkit-tap-highlight-color: transparent;
  }

  :host([hidden]) {
    display: none !important;
  }

  :host(:focus) {
    outline: none;
  }

  .hidden {
    display: none;
  }

  #checkboxContainer {
    display: inline-block;
    position: relative;
    width: var(--calculated-paper-checkbox-size);
    height: var(--calculated-paper-checkbox-size);
    min-width: var(--calculated-paper-checkbox-size);
    margin: var(--paper-checkbox-margin, initial);
    vertical-align: var(--paper-checkbox-vertical-align, middle);
    background-color: var(--paper-checkbox-unchecked-background-color, transparent);
  }

  #ink {
    position: absolute;

    /* Center the ripple in the checkbox by negative offsetting it by
     * (inkWidth - rippleWidth) / 2 */
    top: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
    left: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
    width: var(--calculated-paper-checkbox-ink-size);
    height: var(--calculated-paper-checkbox-ink-size);
    color: var(--paper-checkbox-unchecked-ink-color, var(--primary-text-color));
    opacity: 0.6;
    pointer-events: none;
  }

  #ink:dir(rtl) {
    right: calc(0px - (var(--calculated-paper-checkbox-ink-size) - var(--calculated-paper-checkbox-size)) / 2);
    left: auto;
  }

  #ink[checked] {
    color: var(--paper-checkbox-checked-ink-color, var(--primary-color));
  }

  #checkbox {
    position: relative;
    box-sizing: border-box;
    height: 100%;
    border: solid 2px;
    border-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
    border-radius: 2px;
    pointer-events: none;
    -webkit-transition: background-color 140ms, border-color 140ms;
    transition: background-color 140ms, border-color 140ms;

    -webkit-transition-duration: var(--paper-checkbox-animation-duration, 140ms);
    transition-duration: var(--paper-checkbox-animation-duration, 140ms);
  }

  /* checkbox checked animations */
  #checkbox.checked #checkmark {
    -webkit-animation: checkmark-expand 140ms ease-out forwards;
    animation: checkmark-expand 140ms ease-out forwards;

    -webkit-animation-duration: var(--paper-checkbox-animation-duration, 140ms);
    animation-duration: var(--paper-checkbox-animation-duration, 140ms);
  }

  @-webkit-keyframes checkmark-expand {
    0% {
      -webkit-transform: scale(0, 0) rotate(45deg);
    }
    100% {
      -webkit-transform: scale(1, 1) rotate(45deg);
    }
  }

  @keyframes checkmark-expand {
    0% {
      transform: scale(0, 0) rotate(45deg);
    }
    100% {
      transform: scale(1, 1) rotate(45deg);
    }
  }

  #checkbox.checked {
    background-color: var(--paper-checkbox-checked-color, var(--primary-color));
    border-color: var(--paper-checkbox-checked-color, var(--primary-color));
  }

  #checkmark {
    position: absolute;
    width: 36%;
    height: 70%;
    border-style: solid;
    border-top: none;
    border-left: none;
    border-right-width: calc(2/15 * var(--calculated-paper-checkbox-size));
    border-bottom-width: calc(2/15 * var(--calculated-paper-checkbox-size));
    border-color: var(--paper-checkbox-checkmark-color, white);
    -webkit-transform-origin: 97% 86%;
    transform-origin: 97% 86%;
    box-sizing: content-box; /* protect against page-level box-sizing */
  }

  #checkmark:dir(rtl) {
    -webkit-transform-origin: 50% 14%;
    transform-origin: 50% 14%;
  }

  /* label */
  #checkboxLabel {
    position: relative;
    display: inline-block;
    vertical-align: middle;
    padding-left: var(--paper-checkbox-label-spacing, 8px);
    white-space: normal;
    line-height: normal;
    color: var(--paper-checkbox-label-color, var(--primary-text-color));
    @apply --paper-checkbox-label;
  }

  :host([checked]) #checkboxLabel {
    color: var(--paper-checkbox-label-checked-color, var(--paper-checkbox-label-color, var(--primary-text-color)));
    @apply --paper-checkbox-label-checked;
  }

  #checkboxLabel:dir(rtl) {
    padding-right: var(--paper-checkbox-label-spacing, 8px);
    padding-left: 0;
  }

  #checkboxLabel[hidden] {
    display: none;
  }

  /* disabled state */

  :host([disabled]) #checkbox {
    opacity: 0.5;
    border-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
  }

  :host([disabled][checked]) #checkbox {
    background-color: var(--paper-checkbox-unchecked-color, var(--primary-text-color));
    opacity: 0.5;
  }

  :host([disabled]) #checkboxLabel  {
    opacity: 0.65;
  }

  /* invalid state */
  #checkbox.invalid:not(.checked) {
    border-color: var(--paper-checkbox-error-color, var(--error-color));
  }
</style>

<div id="checkboxContainer">
  <div id="checkbox" class$="[[_computeCheckboxClass(checked, invalid)]]">
    <div id="checkmark" class$="[[_computeCheckmarkClass(checked)]]"></div>
  </div>
</div>

<div id="checkboxLabel"><slot></slot></div>`;
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
let da;ha.setAttribute("strip-whitespace",""),Rr({_template:ha,is:"paper-checkbox",behaviors:[ua],hostAttributes:{role:"checkbox","aria-checked":!1,tabindex:0},properties:{ariaActiveAttribute:{type:String,value:"aria-checked"}},attached:function(){Li(this,(function(){if("-1px"===this.getComputedStyleValue("--calculated-paper-checkbox-ink-size").trim()){var t=this.getComputedStyleValue("--calculated-paper-checkbox-size").trim(),e="px",n=t.match(/[A-Za-z]+$/);null!==n&&(e=n[0]);var i=parseFloat(t),r=8/3*i;"px"===e&&(r=Math.floor(r))%2!=i%2&&r++,this.updateStyles({"--paper-checkbox-ink-size":r+e})}}))},_computeCheckboxClass:function(t,e){var n="";return t&&(n+="checked "),e&&(n+="invalid"),n},_computeCheckmarkClass:function(t){return t?"":"hidden"},_createRipple:function(){return this._rippleContainer=this.$.checkboxContainer,la._createRipple.call(this)}});const pa={properties:{sizingTarget:{type:Object,value:function(){return this}},fitInto:{type:Object,value:window},noOverlap:{type:Boolean},positionTarget:{type:Element},horizontalAlign:{type:String},verticalAlign:{type:String},dynamicAlign:{type:Boolean},horizontalOffset:{type:Number,value:0,notify:!0},verticalOffset:{type:Number,value:0,notify:!0},autoFitOnAttach:{type:Boolean,value:!1},expandSizingTargetForScrollbars:{type:Boolean,value:!1},_fitInfo:{type:Object}},get _fitWidth(){return this.fitInto===window?this.fitInto.innerWidth:this.fitInto.getBoundingClientRect().width},get _fitHeight(){return this.fitInto===window?this.fitInto.innerHeight:this.fitInto.getBoundingClientRect().height},get _fitLeft(){return this.fitInto===window?0:this.fitInto.getBoundingClientRect().left},get _fitTop(){return this.fitInto===window?0:this.fitInto.getBoundingClientRect().top},get _defaultPositionTarget(){var t=Yi(this).parentNode;return t&&t.nodeType===Node.DOCUMENT_FRAGMENT_NODE&&(t=t.host),t},get _localeHorizontalAlign(){if(this._isRTL){if("right"===this.horizontalAlign)return"left";if("left"===this.horizontalAlign)return"right"}return this.horizontalAlign},get __shouldPosition(){return(this.horizontalAlign||this.verticalAlign)&&this.positionTarget},get _isRTL(){return void 0===this._memoizedIsRTL&&(this._memoizedIsRTL="rtl"==window.getComputedStyle(this).direction),this._memoizedIsRTL},attached:function(){this.positionTarget=this.positionTarget||this._defaultPositionTarget,this.autoFitOnAttach&&("none"===window.getComputedStyle(this).display?setTimeout(function(){this.fit()}.bind(this)):(window.ShadyDOM&&ShadyDOM.flush(),this.fit()))},detached:function(){this.__deferredFit&&(clearTimeout(this.__deferredFit),this.__deferredFit=null)},fit:function(){this.position(),this.constrain(),this.center()},_discoverInfo:function(){if(!this._fitInfo){var t=window.getComputedStyle(this),e=window.getComputedStyle(this.sizingTarget);this._fitInfo={inlineStyle:{top:this.style.top||"",left:this.style.left||"",position:this.style.position||""},sizerInlineStyle:{maxWidth:this.sizingTarget.style.maxWidth||"",maxHeight:this.sizingTarget.style.maxHeight||"",boxSizing:this.sizingTarget.style.boxSizing||""},positionedBy:{vertically:"auto"!==t.top?"top":"auto"!==t.bottom?"bottom":null,horizontally:"auto"!==t.left?"left":"auto"!==t.right?"right":null},sizedBy:{height:"none"!==e.maxHeight,width:"none"!==e.maxWidth,minWidth:parseInt(e.minWidth,10)||0,minHeight:parseInt(e.minHeight,10)||0},margin:{top:parseInt(t.marginTop,10)||0,right:parseInt(t.marginRight,10)||0,bottom:parseInt(t.marginBottom,10)||0,left:parseInt(t.marginLeft,10)||0}}}},resetFit:function(){var t=this._fitInfo||{};for(var e in t.sizerInlineStyle)this.sizingTarget.style[e]=t.sizerInlineStyle[e];for(var e in t.inlineStyle)this.style[e]=t.inlineStyle[e];this._fitInfo=null},refit:function(){var t=this.sizingTarget.scrollLeft,e=this.sizingTarget.scrollTop;this.resetFit(),this.fit(),this.sizingTarget.scrollLeft=t,this.sizingTarget.scrollTop=e},position:function(){if(!this.__shouldPosition)return;this._discoverInfo(),window.ShadyDOM&&window.ShadyDOM.flush(),this.style.position="fixed",this.sizingTarget.style.boxSizing="border-box",this.style.left="0px",this.style.top="0px";var t=this.getBoundingClientRect(),e=this.__getNormalizedRect(this.positionTarget),n=this.__getNormalizedRect(this.fitInto);let i,r,o,a;this.expandSizingTargetForScrollbars&&(i=this.sizingTarget.offsetWidth,r=this.sizingTarget.offsetHeight,o=this.sizingTarget.clientWidth,a=this.sizingTarget.clientHeight);var s=this._fitInfo.margin,l=this.__getPosition(this._localeHorizontalAlign,this.verticalAlign,{width:t.width+s.left+s.right,height:t.height+s.top+s.bottom},t,e,n),c=l.left+s.left,u=l.top+s.top,h=Math.min(n.right-s.right,c+t.width),d=Math.min(n.bottom-s.bottom,u+t.height);c=Math.max(n.left+s.left,Math.min(c,h-this._fitInfo.sizedBy.minWidth)),u=Math.max(n.top+s.top,Math.min(u,d-this._fitInfo.sizedBy.minHeight));const p=Math.max(h-c,this._fitInfo.sizedBy.minWidth),f=Math.max(d-u,this._fitInfo.sizedBy.minHeight);this.sizingTarget.style.maxWidth=p+"px",this.sizingTarget.style.maxHeight=f+"px";const m=c-t.left,g=u-t.top;if(this.style.left=`${m}px`,this.style.top=`${g}px`,this.expandSizingTargetForScrollbars){const t=this.sizingTarget.offsetHeight,e=t-this.sizingTarget.clientHeight-(r-a);if(e>0){const i=Math.min(n.height-s.top-s.bottom,f+e);this.sizingTarget.style.maxHeight=`${i}px`;const r=this.sizingTarget.offsetHeight,o=r-t;let a;"top"===l.verticalAlign?a=g:"middle"===l.verticalAlign?a=g-o/2:"bottom"===l.verticalAlign&&(a=g-o),a=Math.max(n.top+s.top,Math.min(a,n.bottom-s.bottom-r)),this.style.top=`${a}px`}const c=this.sizingTarget.offsetWidth,u=c-this.sizingTarget.clientWidth-(i-o);if(u>0){const t=(()=>{if(void 0!==da)return da;const t=document.createElement("div");Object.assign(t.style,{overflow:"auto",position:"fixed",left:"0px",top:"0px",maxWidth:"100px",maxHeight:"100px"});const e=document.createElement("div");return e.style.width="200px",e.style.height="200px",t.appendChild(e),document.body.appendChild(t),da=Math.abs(t.offsetWidth-100)>1?t.offsetWidth-t.clientWidth:0,document.body.removeChild(t),da})(),e=Math.min(n.width-s.left-s.right,p+u-t);this.sizingTarget.style.maxWidth=`${e}px`;const i=this.sizingTarget.offsetWidth+t,r=i-c;let o;"left"===l.horizontalAlign?o=m:"center"===l.horizontalAlign?o=m-r/2:"right"===l.horizontalAlign&&(o=m-r),o=Math.max(n.left+s.left,Math.min(o,n.right-s.right-i)),this.style.left=`${o}px`}}},constrain:function(){if(!this.__shouldPosition){this._discoverInfo();var t=this._fitInfo;t.positionedBy.vertically||(this.style.position="fixed",this.style.top="0px"),t.positionedBy.horizontally||(this.style.position="fixed",this.style.left="0px"),this.sizingTarget.style.boxSizing="border-box";var e=this.getBoundingClientRect();t.sizedBy.height||this.__sizeDimension(e,t.positionedBy.vertically,"top","bottom","Height"),t.sizedBy.width||this.__sizeDimension(e,t.positionedBy.horizontally,"left","right","Width")}},_sizeDimension:function(t,e,n,i,r){this.__sizeDimension(t,e,n,i,r)},__sizeDimension:function(t,e,n,i,r){var o=this._fitInfo,a=this.__getNormalizedRect(this.fitInto),s="Width"===r?a.width:a.height,l=e===i,c="offset"+r;this.sizingTarget.style["max"+r]=s-o.margin[l?n:i]-(l?s-t[i]:t[n])-(this[c]-this.sizingTarget[c])+"px"},center:function(){if(!this.__shouldPosition){this._discoverInfo();var t=this._fitInfo.positionedBy;if(!t.vertically||!t.horizontally){this.style.position="fixed",t.vertically||(this.style.top="0px"),t.horizontally||(this.style.left="0px");var e=this.getBoundingClientRect(),n=this.__getNormalizedRect(this.fitInto);t.vertically||(this.style.top=n.top-e.top+(n.height-e.height)/2+"px"),t.horizontally||(this.style.left=n.left-e.left+(n.width-e.width)/2+"px")}}},__getNormalizedRect:function(t){return t===document.documentElement||t===window?{top:0,left:0,width:window.innerWidth,height:window.innerHeight,right:window.innerWidth,bottom:window.innerHeight}:t.getBoundingClientRect()},__getOffscreenArea:function(t,e,n){var i=Math.min(0,t.top)+Math.min(0,n.bottom-(t.top+e.height)),r=Math.min(0,t.left)+Math.min(0,n.right-(t.left+e.width));return Math.abs(i)*e.width+Math.abs(r)*e.height},__getPosition:function(t,e,n,i,r,o){var a,s=[{verticalAlign:"top",horizontalAlign:"left",top:r.top+this.verticalOffset,left:r.left+this.horizontalOffset},{verticalAlign:"top",horizontalAlign:"right",top:r.top+this.verticalOffset,left:r.right-n.width-this.horizontalOffset},{verticalAlign:"bottom",horizontalAlign:"left",top:r.bottom-n.height-this.verticalOffset,left:r.left+this.horizontalOffset},{verticalAlign:"bottom",horizontalAlign:"right",top:r.bottom-n.height-this.verticalOffset,left:r.right-n.width-this.horizontalOffset}];if(this.noOverlap){for(var l=0,c=s.length;l<c;l++){var u={};for(var h in s[l])u[h]=s[l][h];s.push(u)}s[0].top=s[1].top+=r.height,s[2].top=s[3].top-=r.height,s[4].left=s[6].left+=r.width,s[5].left=s[7].left-=r.width}for(e="auto"===e?null:e,(t="auto"===t?null:t)&&"center"!==t||(s.push({verticalAlign:"top",horizontalAlign:"center",top:r.top+this.verticalOffset+(this.noOverlap?r.height:0),left:r.left-i.width/2+r.width/2+this.horizontalOffset}),s.push({verticalAlign:"bottom",horizontalAlign:"center",top:r.bottom-n.height-this.verticalOffset-(this.noOverlap?r.height:0),left:r.left-i.width/2+r.width/2+this.horizontalOffset})),e&&"middle"!==e||(s.push({verticalAlign:"middle",horizontalAlign:"left",top:r.top-i.height/2+r.height/2+this.verticalOffset,left:r.left+this.horizontalOffset+(this.noOverlap?r.width:0)}),s.push({verticalAlign:"middle",horizontalAlign:"right",top:r.top-i.height/2+r.height/2+this.verticalOffset,left:r.right-n.width-this.horizontalOffset-(this.noOverlap?r.width:0)})),"middle"===e&&"center"===t&&s.push({verticalAlign:"middle",horizontalAlign:"center",top:r.top-i.height/2+r.height/2+this.verticalOffset,left:r.left-i.width/2+r.width/2+this.horizontalOffset}),l=0;l<s.length;l++){var d=s[l],p=d.verticalAlign===e,f=d.horizontalAlign===t;if(!this.dynamicAlign&&!this.noOverlap&&p&&f){a=d;break}var m=(!e||p)&&(!t||f);if(this.dynamicAlign||m){if(d.offscreenArea=this.__getOffscreenArea(d,n,o),0===d.offscreenArea&&m){a=d;break}var g=d.offscreenArea-(a=a||d).offscreenArea;(g<0||0===g&&(p||f))&&(a=d)}}return a}};
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var fa=Element.prototype,ma=fa.matches||fa.matchesSelector||fa.mozMatchesSelector||fa.msMatchesSelector||fa.oMatchesSelector||fa.webkitMatchesSelector;const ga=new class{getTabbableNodes(t){var e=[];return this._collectTabbableNodes(t,e)?this._sortByTabIndex(e):e}isFocusable(t){return ma.call(t,"input, select, textarea, button, object")?ma.call(t,":not([disabled])"):ma.call(t,"a[href], area[href], iframe, [tabindex], [contentEditable]")}isTabbable(t){return this.isFocusable(t)&&ma.call(t,':not([tabindex="-1"])')&&this._isVisible(t)}_normalizedTabIndex(t){if(this.isFocusable(t)){var e=t.getAttribute("tabindex")||0;return Number(e)}return-1}_collectTabbableNodes(t,e){if(t.nodeType!==Node.ELEMENT_NODE)return!1;var n=t;if(!this._isVisible(n))return!1;var i,r=this._normalizedTabIndex(n),o=r>0;r>=0&&e.push(n),i="content"===n.localName||"slot"===n.localName?Yi(n).getDistributedNodes():Yi(n.root||n).children;for(var a=0;a<i.length;a++)o=this._collectTabbableNodes(i[a],e)||o;return o}_isVisible(t){var e=t.style;return"hidden"!==e.visibility&&"none"!==e.display&&"hidden"!==(e=window.getComputedStyle(t)).visibility&&"none"!==e.display}_sortByTabIndex(t){var e=t.length;if(e<2)return t;var n=Math.ceil(e/2),i=this._sortByTabIndex(t.slice(0,n)),r=this._sortByTabIndex(t.slice(n));return this._mergeSortByTabIndex(i,r)}_mergeSortByTabIndex(t,e){for(var n=[];t.length>0&&e.length>0;)this._hasLowerTabOrder(t[0],e[0])?n.push(e.shift()):n.push(t.shift());return n.concat(t,e)}_hasLowerTabOrder(t,e){var n=Math.max(t.tabIndex,0),i=Math.max(e.tabIndex,0);return 0===n||0===i?i>n:n>i}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */Rr({_template:_e`
    <style>
      :host {
        position: fixed;
        top: 0;
        left: 0;
        width: 100%;
        height: 100%;
        background-color: var(--iron-overlay-backdrop-background-color, #000);
        opacity: 0;
        transition: opacity 0.2s;
        pointer-events: none;
        @apply --iron-overlay-backdrop;
      }

      :host(.opened) {
        opacity: var(--iron-overlay-backdrop-opacity, 0.6);
        pointer-events: auto;
        @apply --iron-overlay-backdrop-opened;
      }
    </style>

    <slot></slot>
`,is:"iron-overlay-backdrop",properties:{opened:{reflectToAttribute:!0,type:Boolean,value:!1,observer:"_openedChanged"}},listeners:{transitionend:"_onTransitionend"},created:function(){this.__openedRaf=null},attached:function(){this.opened&&this._openedChanged(this.opened)},prepare:function(){this.opened&&!this.parentNode&&Yi(document.body).appendChild(this)},open:function(){this.opened=!0},close:function(){this.opened=!1},complete:function(){this.opened||this.parentNode!==document.body||Yi(this.parentNode).removeChild(this)},_onTransitionend:function(t){t&&t.target===this&&this.complete()},_openedChanged:function(t){if(t)this.prepare();else{var e=window.getComputedStyle(this);"0s"!==e.transitionDuration&&0!=e.opacity||this.complete()}this.isAttached&&(this.__openedRaf&&(window.cancelAnimationFrame(this.__openedRaf),this.__openedRaf=null),this.scrollTop=this.scrollTop,this.__openedRaf=window.requestAnimationFrame(function(){this.__openedRaf=null,this.toggleClass("opened",this.opened)}.bind(this)))}});const _a=new
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
class{constructor(){this._overlays=[],this._minimumZ=101,this._backdropElement=null,ii(document.documentElement,"tap",(function(){})),document.addEventListener("tap",this._onCaptureClick.bind(this),!0),document.addEventListener("focus",this._onCaptureFocus.bind(this),!0),document.addEventListener("keydown",this._onCaptureKeyDown.bind(this),!0)}get backdropElement(){return this._backdropElement||(this._backdropElement=document.createElement("iron-overlay-backdrop")),this._backdropElement}get deepActiveElement(){var t=document.activeElement;for(t&&t instanceof Element!=0||(t=document.body);t.root&&Yi(t.root).activeElement;)t=Yi(t.root).activeElement;return t}_bringOverlayAtIndexToFront(t){var e=this._overlays[t];if(e){var n=this._overlays.length-1,i=this._overlays[n];if(i&&this._shouldBeBehindOverlay(e,i)&&n--,!(t>=n)){var r=Math.max(this.currentOverlayZ(),this._minimumZ);for(this._getZ(e)<=r&&this._applyOverlayZ(e,r);t<n;)this._overlays[t]=this._overlays[t+1],t++;this._overlays[n]=e}}}addOrRemoveOverlay(t){t.opened?this.addOverlay(t):this.removeOverlay(t)}addOverlay(t){var e=this._overlays.indexOf(t);if(e>=0)return this._bringOverlayAtIndexToFront(e),void this.trackBackdrop();var n=this._overlays.length,i=this._overlays[n-1],r=Math.max(this._getZ(i),this._minimumZ),o=this._getZ(t);i&&this._shouldBeBehindOverlay(t,i)&&(this._applyOverlayZ(i,r),n--,r=Math.max(this._getZ(this._overlays[n-1]),this._minimumZ)),o<=r&&this._applyOverlayZ(t,r),this._overlays.splice(n,0,t),this.trackBackdrop()}removeOverlay(t){var e=this._overlays.indexOf(t);-1!==e&&(this._overlays.splice(e,1),this.trackBackdrop())}currentOverlay(){return this._overlays[this._overlays.length-1]}currentOverlayZ(){return this._getZ(this.currentOverlay())}ensureMinimumZ(t){this._minimumZ=Math.max(this._minimumZ,t)}focusOverlay(){var t=this.currentOverlay();t&&t._applyFocus()}trackBackdrop(){var t=this._overlayWithBackdrop();(t||this._backdropElement)&&(this.backdropElement.style.zIndex=this._getZ(t)-1,this.backdropElement.opened=!!t,this.backdropElement.prepare())}getBackdrops(){for(var t=[],e=0;e<this._overlays.length;e++)this._overlays[e].withBackdrop&&t.push(this._overlays[e]);return t}backdropZ(){return this._getZ(this._overlayWithBackdrop())-1}_overlayWithBackdrop(){for(var t=this._overlays.length-1;t>=0;t--)if(this._overlays[t].withBackdrop)return this._overlays[t]}_getZ(t){var e=this._minimumZ;if(t){var n=Number(t.style.zIndex||window.getComputedStyle(t).zIndex);n==n&&(e=n)}return e}_setZ(t,e){t.style.zIndex=e}_applyOverlayZ(t,e){this._setZ(t,e+2)}_overlayInPath(t){t=t||[];for(var e=0;e<t.length;e++)if(t[e]._manager===this)return t[e]}_onCaptureClick(t){var e=this._overlays.length-1;if(-1!==e)for(var n,i=Yi(t).path;(n=this._overlays[e])&&this._overlayInPath(i)!==n&&(n._onCaptureClick(t),n.allowClickThrough);)e--}_onCaptureFocus(t){var e=this.currentOverlay();e&&e._onCaptureFocus(t)}_onCaptureKeyDown(t){var e=this.currentOverlay();e&&(zo.keyboardEventMatchesKeys(t,"esc")?e._onCaptureEsc(t):zo.keyboardEventMatchesKeys(t,"tab")&&e._onCaptureTab(t))}_shouldBeBehindOverlay(t,e){return!t.alwaysOnTop&&e.alwaysOnTop}};
/**
    @license
    Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */var ya,va,ba={pageX:0,pageY:0},xa=null,wa=[],Sa=["wheel","mousewheel","DOMMouseScroll","touchstart","touchmove"];function Ma(t){Ea.indexOf(t)>=0||(0===Ea.length&&(function e(){ya=ya||Ta.bind(void 0);for(var t=0,e=Sa.length;t<e;t++)document.addEventListener(Sa[t],ya,{capture:!0,passive:!1})})(),Ea.push(t),va=Ea[Ea.length-1])}const Ea=[];function Ta(t){if(t.cancelable&&(function e(t){var e=Yi(t).rootTarget;if("touchmove"!==t.type&&xa!==e&&(xa=e,wa=(function n(t){for(var e=[],n=t.indexOf(va),i=0;i<=n;i++)if(t[i].nodeType===Node.ELEMENT_NODE){var r=t[i],o=r.style;"scroll"!==o.overflow&&"auto"!==o.overflow&&(o=window.getComputedStyle(r)),"scroll"!==o.overflow&&"auto"!==o.overflow||e.push(r)}return e})(Yi(t).path)),!wa.length)return!0;if("touchstart"===t.type)return!1;var i=(function r(t){var e={deltaX:t.deltaX,deltaY:t.deltaY};if("deltaX"in t);else if("wheelDeltaX"in t&&"wheelDeltaY"in t)e.deltaX=-t.wheelDeltaX,e.deltaY=-t.wheelDeltaY;else if("wheelDelta"in t)e.deltaX=0,e.deltaY=-t.wheelDelta;else if("axis"in t)e.deltaX=1===t.axis?t.detail:0,e.deltaY=2===t.axis?t.detail:0;else if(t.targetTouches){var n=t.targetTouches[0];e.deltaX=ba.pageX-n.pageX,e.deltaY=ba.pageY-n.pageY}return e}
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */)(t);return!(function o(t,e,n){if(e||n)for(var i=Math.abs(n)>=Math.abs(e),r=0;r<t.length;r++){var o=t[r];if(i?n<0?o.scrollTop>0:o.scrollTop<o.scrollHeight-o.clientHeight:e<0?o.scrollLeft>0:o.scrollLeft<o.scrollWidth-o.clientWidth)return o}})(wa,i.deltaX,i.deltaY)})(t)&&t.preventDefault(),t.targetTouches){var n=t.targetTouches[0];ba.pageX=n.pageX,ba.pageY=n.pageY}}const Ca={properties:{opened:{observer:"_openedChanged",type:Boolean,value:!1,notify:!0},canceled:{observer:"_canceledChanged",readOnly:!0,type:Boolean,value:!1},withBackdrop:{observer:"_withBackdropChanged",type:Boolean},noAutoFocus:{type:Boolean,value:!1},noCancelOnEscKey:{type:Boolean,value:!1},noCancelOnOutsideClick:{type:Boolean,value:!1},closingReason:{type:Object},restoreFocusOnClose:{type:Boolean,value:!1},allowClickThrough:{type:Boolean},alwaysOnTop:{type:Boolean},scrollAction:{type:String},_manager:{type:Object,value:_a},_focusedChild:{type:Object}},listeners:{"iron-resize":"_onIronResize"},observers:["__updateScrollObservers(isAttached, opened, scrollAction)"],get backdropElement(){return this._manager.backdropElement},get _focusNode(){return this._focusedChild||Yi(this).querySelector("[autofocus]")||this},get _focusableNodes(){return ga.getTabbableNodes(this)},ready:function(){this.__isAnimating=!1,this.__shouldRemoveTabIndex=!1,this.__firstFocusableNode=this.__lastFocusableNode=null,this.__rafs={},this.__restoreFocusNode=null,this.__scrollTop=this.__scrollLeft=null,this.__onCaptureScroll=this.__onCaptureScroll.bind(this),this.__rootNodes=null,this._ensureSetup()},attached:function(){this.opened&&this._openedChanged(this.opened),this._observer=Yi(this).observeNodes(this._onNodesChange)},detached:function(){for(var t in this._observer&&Yi(this).unobserveNodes(this._observer),this._observer=null,this.__rafs)null!==this.__rafs[t]&&cancelAnimationFrame(this.__rafs[t]);this.__rafs={},this._manager.removeOverlay(this),this.__isAnimating&&(this.opened?this._finishRenderOpened():(this._applyFocus(),this._finishRenderClosed()))},toggle:function(){this._setCanceled(!1),this.opened=!this.opened},open:function(){this._setCanceled(!1),this.opened=!0},close:function(){this._setCanceled(!1),this.opened=!1},cancel:function(t){this.fire("iron-overlay-canceled",t,{cancelable:!0}).defaultPrevented||(this._setCanceled(!0),this.opened=!1)},invalidateTabbables:function(){this.__firstFocusableNode=this.__lastFocusableNode=null},_ensureSetup:function(){this._overlaySetup||(this._overlaySetup=!0,this.style.outline="none",this.style.display="none")},_openedChanged:function(t){t?this.removeAttribute("aria-hidden"):this.setAttribute("aria-hidden","true"),this.isAttached&&(this.__isAnimating=!0,this.__deraf("__openedChanged",this.__openedChanged))},_canceledChanged:function(){this.closingReason=this.closingReason||{},this.closingReason.canceled=this.canceled},_withBackdropChanged:function(){this.withBackdrop&&!this.hasAttribute("tabindex")?(this.setAttribute("tabindex","-1"),this.__shouldRemoveTabIndex=!0):this.__shouldRemoveTabIndex&&(this.removeAttribute("tabindex"),this.__shouldRemoveTabIndex=!1),this.opened&&this.isAttached&&this._manager.trackBackdrop()},_prepareRenderOpened:function(){this.__restoreFocusNode=this._manager.deepActiveElement,this._preparePositioning(),this.refit(),this._finishPositioning(),this.noAutoFocus&&document.activeElement===this._focusNode&&(this._focusNode.blur(),this.__restoreFocusNode.focus())},_renderOpened:function(){this._finishRenderOpened()},_renderClosed:function(){this._finishRenderClosed()},_finishRenderOpened:function(){this.notifyResize(),this.__isAnimating=!1,this.fire("iron-overlay-opened")},_finishRenderClosed:function(){this.style.display="none",this.style.zIndex="",this.notifyResize(),this.__isAnimating=!1,this.fire("iron-overlay-closed",this.closingReason)},_preparePositioning:function(){this.style.transition=this.style.webkitTransition="none",this.style.transform=this.style.webkitTransform="none",this.style.display=""},_finishPositioning:function(){this.style.display="none",this.scrollTop=this.scrollTop,this.style.transition=this.style.webkitTransition="",this.style.transform=this.style.webkitTransform="",this.style.display="",this.scrollTop=this.scrollTop},_applyFocus:function(){if(this.opened)this.noAutoFocus||this._focusNode.focus();else{if(this.restoreFocusOnClose&&this.__restoreFocusNode){var t=this._manager.deepActiveElement;(t===document.body||Aa(this,t))&&this.__restoreFocusNode.focus()}this.__restoreFocusNode=null,this._focusNode.blur(),this._focusedChild=null}},_onCaptureClick:function(t){this.noCancelOnOutsideClick||this.cancel(t)},_onCaptureFocus:function(t){if(this.withBackdrop){var e=Yi(t).path;-1===e.indexOf(this)?(t.stopPropagation(),this._applyFocus()):this._focusedChild=e[0]}},_onCaptureEsc:function(t){this.noCancelOnEscKey||this.cancel(t)},_onCaptureTab:function(t){if(this.withBackdrop){this.__ensureFirstLastFocusables();var e=t.shiftKey,n=e?this.__firstFocusableNode:this.__lastFocusableNode,i=e?this.__lastFocusableNode:this.__firstFocusableNode,r=!1;if(n===i)r=!0;else{var o=this._manager.deepActiveElement;r=o===n||o===this}r&&(t.preventDefault(),this._focusedChild=i,this._applyFocus())}},_onIronResize:function(){this.opened&&!this.__isAnimating&&this.__deraf("refit",this.refit)},_onNodesChange:function(){this.opened&&!this.__isAnimating&&(this.invalidateTabbables(),this.notifyResize())},__ensureFirstLastFocusables:function(){var t=this._focusableNodes;this.__firstFocusableNode=t[0],this.__lastFocusableNode=t[t.length-1]},__openedChanged:function(){this.opened?(this._prepareRenderOpened(),this._manager.addOverlay(this),this._applyFocus(),this._renderOpened()):(this._manager.removeOverlay(this),this._applyFocus(),this._renderClosed())},__deraf:function(t,e){var n=this.__rafs;null!==n[t]&&cancelAnimationFrame(n[t]),n[t]=requestAnimationFrame(function i(){n[t]=null,e.call(this)}.bind(this))},__updateScrollObservers:function(t,e,n){t&&e&&this.__isValidScrollAction(n)?("lock"===n&&(this.__saveScrollPosition(),Ma(this)),this.__addScrollListeners()):((function i(t){var e=Ea.indexOf(t);-1!==e&&(Ea.splice(e,1),va=Ea[Ea.length-1],0===Ea.length&&(function n(){for(var t=0,e=Sa.length;t<e;t++)document.removeEventListener(Sa[t],ya,{capture:!0,passive:!1})})())})(this),this.__removeScrollListeners())},__addScrollListeners:function(){if(!this.__rootNodes){if(this.__rootNodes=[],m)for(var t=this;t;)t.nodeType===Node.DOCUMENT_FRAGMENT_NODE&&t.host&&this.__rootNodes.push(t),t=t.host||t.assignedSlot||t.parentNode;this.__rootNodes.push(document)}this.__rootNodes.forEach((function(t){t.addEventListener("scroll",this.__onCaptureScroll,{capture:!0,passive:!0})}),this)},__removeScrollListeners:function(){this.__rootNodes&&this.__rootNodes.forEach((function(t){t.removeEventListener("scroll",this.__onCaptureScroll,{capture:!0,passive:!0})}),this),this.isAttached||(this.__rootNodes=null)},__isValidScrollAction:function(t){return"lock"===t||"refit"===t||"cancel"===t},__onCaptureScroll:function(t){if(!(this.__isAnimating||Yi(t).path.indexOf(this)>=0))switch(this.scrollAction){case"lock":this.__restoreScrollPosition();break;case"refit":this.__deraf("refit",this.refit);break;case"cancel":this.cancel(t)}},__saveScrollPosition:function(){document.scrollingElement?(this.__scrollTop=document.scrollingElement.scrollTop,this.__scrollLeft=document.scrollingElement.scrollLeft):(this.__scrollTop=Math.max(document.documentElement.scrollTop,document.body.scrollTop),this.__scrollLeft=Math.max(document.documentElement.scrollLeft,document.body.scrollLeft))},__restoreScrollPosition:function(){document.scrollingElement?(document.scrollingElement.scrollTop=this.__scrollTop,document.scrollingElement.scrollLeft=this.__scrollLeft):(document.documentElement.scrollTop=document.body.scrollTop=this.__scrollTop,document.documentElement.scrollLeft=document.body.scrollLeft=this.__scrollLeft)}},Aa=(t,e)=>{for(let i=e;i;i=(n=i).assignedSlot||n.parentNode||n.host)if(i===t)return!0;var n;return!1},ka=[pa,go,Ca],La={hostAttributes:{role:"dialog",tabindex:"-1"},properties:{modal:{type:Boolean,value:!1},__readied:{type:Boolean,value:!1}},observers:["_modalChanged(modal, __readied)"],listeners:{tap:"_onDialogClick"},ready:function(){this.__prevNoCancelOnOutsideClick=this.noCancelOnOutsideClick,this.__prevNoCancelOnEscKey=this.noCancelOnEscKey,this.__prevWithBackdrop=this.withBackdrop,this.__readied=!0},_modalChanged:function(t,e){e&&(t?(this.__prevNoCancelOnOutsideClick=this.noCancelOnOutsideClick,this.__prevNoCancelOnEscKey=this.noCancelOnEscKey,this.__prevWithBackdrop=this.withBackdrop,this.noCancelOnOutsideClick=!0,this.noCancelOnEscKey=!0,this.withBackdrop=!0):(this.noCancelOnOutsideClick=this.noCancelOnOutsideClick&&this.__prevNoCancelOnOutsideClick,this.noCancelOnEscKey=this.noCancelOnEscKey&&this.__prevNoCancelOnEscKey,this.withBackdrop=this.withBackdrop&&this.__prevWithBackdrop))},_updateClosingReasonConfirmed:function(t){this.closingReason=this.closingReason||{},this.closingReason.confirmed=t},_onDialogClick:function(t){for(var e=Yi(t).path,n=0,i=e.indexOf(this);n<i;n++){var r=e[n];if(r.hasAttribute&&(r.hasAttribute("dialog-dismiss")||r.hasAttribute("dialog-confirm"))){this._updateClosingReasonConfirmed(r.hasAttribute("dialog-confirm")),this.close(),t.stopPropagation();break}}}},Pa=[ka,La];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
if(
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>

      :host {
        display: block;
        @apply --layout-relative;
      }

      :host(.is-scrolled:not(:first-child))::before {
        content: '';
        position: absolute;
        top: 0;
        left: 0;
        right: 0;
        height: 1px;
        background: var(--divider-color);
      }

      :host(.can-scroll:not(.scrolled-to-bottom):not(:last-child))::after {
        content: '';
        position: absolute;
        bottom: 0;
        left: 0;
        right: 0;
        height: 1px;
        background: var(--divider-color);
      }

      .scrollable {
        padding: 0 24px;

        @apply --layout-scroll;
        @apply --paper-dialog-scrollable;
      }

      .fit {
        @apply --layout-fit;
      }
    </style>

    <div id="scrollable" class="scrollable" on-scroll="updateScrollState">
      <slot></slot>
    </div>
`,is:"paper-dialog-scrollable",properties:{dialogElement:{type:Object}},get scrollTarget(){return this.$.scrollable},ready:function(){this._ensureTarget(),this.classList.add("no-padding")},attached:function(){this._ensureTarget(),requestAnimationFrame(this.updateScrollState.bind(this))},updateScrollState:function(){this.toggleClass("is-scrolled",this.scrollTarget.scrollTop>0),this.toggleClass("can-scroll",this.scrollTarget.offsetHeight<this.scrollTarget.scrollHeight),this.toggleClass("scrolled-to-bottom",this.scrollTarget.scrollTop+this.scrollTarget.offsetHeight>=this.scrollTarget.scrollHeight)},_ensureTarget:function(){this.dialogElement=this.dialogElement||this.parentElement,this.dialogElement&&this.dialogElement.behaviors&&this.dialogElement.behaviors.indexOf(La)>=0?(this.dialogElement.sizingTarget=this.scrollTarget,this.scrollTarget.classList.remove("fit")):this.dialogElement&&this.scrollTarget.classList.add("fit")}}),!window.polymerSkipLoadingFontRoboto){const t=document.createElement("link");t.rel="stylesheet",t.type="text/css",t.crossOrigin="anonymous",t.href="https://fonts.googleapis.com/css?family=Roboto+Mono:400,700|Roboto:400,300,300italic,400italic,500,500italic,700,700italic",document.head.appendChild(t)}
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */const Na=_e`<custom-style>
  <style is="custom-style">
    html {

      /* Shared Styles */
      --paper-font-common-base: {
        font-family: 'Roboto', 'Noto', sans-serif;
        -webkit-font-smoothing: antialiased;
      };

      --paper-font-common-code: {
        font-family: 'Roboto Mono', 'Consolas', 'Menlo', monospace;
        -webkit-font-smoothing: antialiased;
      };

      --paper-font-common-expensive-kerning: {
        text-rendering: optimizeLegibility;
      };

      --paper-font-common-nowrap: {
        white-space: nowrap;
        overflow: hidden;
        text-overflow: ellipsis;
      };

      /* Material Font Styles */

      --paper-font-display4: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 112px;
        font-weight: 300;
        letter-spacing: -.044em;
        line-height: 120px;
      };

      --paper-font-display3: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 56px;
        font-weight: 400;
        letter-spacing: -.026em;
        line-height: 60px;
      };

      --paper-font-display2: {
        @apply --paper-font-common-base;

        font-size: 45px;
        font-weight: 400;
        letter-spacing: -.018em;
        line-height: 48px;
      };

      --paper-font-display1: {
        @apply --paper-font-common-base;

        font-size: 34px;
        font-weight: 400;
        letter-spacing: -.01em;
        line-height: 40px;
      };

      --paper-font-headline: {
        @apply --paper-font-common-base;

        font-size: 24px;
        font-weight: 400;
        letter-spacing: -.012em;
        line-height: 32px;
      };

      --paper-font-title: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 20px;
        font-weight: 500;
        line-height: 28px;
      };

      --paper-font-subhead: {
        @apply --paper-font-common-base;

        font-size: 16px;
        font-weight: 400;
        line-height: 24px;
      };

      --paper-font-body2: {
        @apply --paper-font-common-base;

        font-size: 14px;
        font-weight: 500;
        line-height: 24px;
      };

      --paper-font-body1: {
        @apply --paper-font-common-base;

        font-size: 14px;
        font-weight: 400;
        line-height: 20px;
      };

      --paper-font-caption: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 12px;
        font-weight: 400;
        letter-spacing: 0.011em;
        line-height: 20px;
      };

      --paper-font-menu: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 13px;
        font-weight: 500;
        line-height: 24px;
      };

      --paper-font-button: {
        @apply --paper-font-common-base;
        @apply --paper-font-common-nowrap;

        font-size: 14px;
        font-weight: 500;
        letter-spacing: 0.018em;
        line-height: 24px;
        text-transform: uppercase;
      };

      --paper-font-code2: {
        @apply --paper-font-common-code;

        font-size: 14px;
        font-weight: 700;
        line-height: 20px;
      };

      --paper-font-code1: {
        @apply --paper-font-common-code;

        font-size: 14px;
        font-weight: 500;
        line-height: 20px;
      };

    }

  </style>
</custom-style>`;Na.setAttribute("style","display: none;"),document.head.appendChild(Na.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ia=document.createElement("template");Ia.setAttribute("style","display: none;"),Ia.innerHTML='<dom-module id="paper-dialog-shared-styles">\n  <template>\n    <style>\n      :host {\n        display: block;\n        margin: 24px 40px;\n\n        background: var(--paper-dialog-background-color, var(--primary-background-color));\n        color: var(--paper-dialog-color, var(--primary-text-color));\n\n        @apply --paper-font-body1;\n        @apply --shadow-elevation-16dp;\n        @apply --paper-dialog;\n      }\n\n      :host > ::slotted(*) {\n        margin-top: 20px;\n        padding: 0 24px;\n      }\n\n      :host > ::slotted(.no-padding) {\n        padding: 0;\n      }\n\n      \n      :host > ::slotted(*:first-child) {\n        margin-top: 24px;\n      }\n\n      :host > ::slotted(*:last-child) {\n        margin-bottom: 24px;\n      }\n\n      /* In 1.x, this selector was `:host > ::content h2`. In 2.x <slot> allows\n      to select direct children only, which increases the weight of this\n      selector, so we have to re-define first-child/last-child margins below. */\n      :host > ::slotted(h2) {\n        position: relative;\n        margin: 0;\n\n        @apply --paper-font-title;\n        @apply --paper-dialog-title;\n      }\n\n      /* Apply mixin again, in case it sets margin-top. */\n      :host > ::slotted(h2:first-child) {\n        margin-top: 24px;\n        @apply --paper-dialog-title;\n      }\n\n      /* Apply mixin again, in case it sets margin-bottom. */\n      :host > ::slotted(h2:last-child) {\n        margin-bottom: 24px;\n        @apply --paper-dialog-title;\n      }\n\n      :host > ::slotted(.paper-dialog-buttons),\n      :host > ::slotted(.buttons) {\n        position: relative;\n        padding: 8px 8px 8px 24px;\n        margin: 0;\n\n        color: var(--paper-dialog-button-color, var(--primary-color));\n\n        @apply --layout-horizontal;\n        @apply --layout-end-justified;\n      }\n    </style>\n  </template>\n</dom-module>',document.head.appendChild(Ia.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ra=[{properties:{animationConfig:{type:Object},entryAnimation:{observer:"_entryAnimationChanged",type:String},exitAnimation:{observer:"_exitAnimationChanged",type:String}},_entryAnimationChanged:function(){this.animationConfig=this.animationConfig||{},this.animationConfig.entry=[{name:this.entryAnimation,node:this}]},_exitAnimationChanged:function(){this.animationConfig=this.animationConfig||{},this.animationConfig.exit=[{name:this.exitAnimation,node:this}]},_copyProperties:function(t,e){for(var n in e)t[n]=e[n]},_cloneConfig:function(t){var e={isClone:!0};return this._copyProperties(e,t),e},_getAnimationConfigRecursive:function(t,e,n){var i;if(this.animationConfig)if(this.animationConfig.value&&"function"==typeof this.animationConfig.value)this._warn(this._logf("playAnimation","Please put 'animationConfig' inside of your components 'properties' object instead of outside of it."));else if(i=t?this.animationConfig[t]:this.animationConfig,Array.isArray(i)||(i=[i]),i)for(var r,o=0;r=i[o];o++)if(r.animatable)r.animatable._getAnimationConfigRecursive(r.type||t,e,n);else if(r.id){var a=e[r.id];a?(a.isClone||(e[r.id]=this._cloneConfig(a),a=e[r.id]),this._copyProperties(a,r)):e[r.id]=r}else n.push(r)},getAnimationConfig:function(t){var e={},n=[];for(var i in this._getAnimationConfigRecursive(t,e,n),e)n.push(e[i]);return n}},{_configureAnimations:function(t){var e=[],n=[];if(t.length>0)for(let e,i=0;e=t[i];i++){let t=document.createElement(e.name);if(t.isNeonAnimation){let i=null;t.configure||(t.configure=function(t){return null}),i=t.configure(e),n.push({result:i,config:e,neonAnimation:t})}else console.warn(this.is+":",e.name,"not found!")}for(var i=0;i<n.length;i++){let t=n[i].result,r=n[i].config,o=n[i].neonAnimation;try{"function"!=typeof t.cancel&&(t=document.timeline.play(t))}catch(e){t=null,console.warn("Couldnt play","(",r.name,").",e)}t&&e.push({neonAnimation:o,config:r,animation:t})}return e},_shouldComplete:function(t){for(var e=!0,n=0;n<t.length;n++)if("finished"!=t[n].animation.playState){e=!1;break}return e},_complete:function(t){for(var e=0;e<t.length;e++)t[e].neonAnimation.complete(t[e].config);for(e=0;e<t.length;e++)t[e].animation.cancel()},playAnimation:function(t,e){var n=this.getAnimationConfig(t);if(n){this._active=this._active||{},this._active[t]&&(this._complete(this._active[t]),delete this._active[t]);var i=this._configureAnimations(n);if(0!=i.length){this._active[t]=i;for(var r=0;r<i.length;r++)i[r].animation.onfinish=function(){this._shouldComplete(i)&&(this._complete(i),delete this._active[t],this.fire("neon-animation-finish",e,{bubbles:!1}))}.bind(this)}else this.fire("neon-animation-finish",e,{bubbles:!1})}},cancelAnimation:function(){for(var t in this._active){var e=this._active[t];for(var n in e)e[n].animation.cancel()}this._active={}}}];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style include="paper-dialog-shared-styles"></style>
    <slot></slot>
`,is:"paper-dialog",behaviors:[Pa,Ra],listeners:{"neon-animation-finish":"_onNeonAnimationFinish"},_renderOpened:function(){this.cancelAnimation(),this.playAnimation("entry")},_renderClosed:function(){this.cancelAnimation(),this.playAnimation("exit")},_onNeonAnimationFinish:function(){this.opened?this._finishRenderOpened():this._finishRenderClosed()}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Oa=Rr({_template:_e`
    <style>
      :host {
        display: inline-block;
        position: fixed;
        clip: rect(0px,0px,0px,0px);
      }
    </style>
    <div aria-live$="[[mode]]">[[_text]]</div>
`,is:"iron-a11y-announcer",properties:{mode:{type:String,value:"polite"},timeout:{type:Number,value:150},_text:{type:String,value:""}},created:function(){Oa.instance||(Oa.instance=this),document.addEventListener("iron-announce",this._onIronAnnounce.bind(this))},announce:function(t){this._text="",this.async((function(){this._text=t}),this.timeout)},_onIronAnnounce:function(t){t.detail&&t.detail.text&&this.announce(t.detail.text)}});Oa.instance=null,Oa.requestAvailability=function(){Oa.instance||(Oa.instance=document.createElement("iron-a11y-announcer")),document.body?document.body.appendChild(Oa.instance):document.addEventListener("load",(function(){document.body.appendChild(Oa.instance)}))},
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        display: inline-block;
      }
    </style>
    <slot id="content"></slot>
`,is:"iron-input",behaviors:[aa],properties:{bindValue:{type:String,value:""},value:{type:String,computed:"_computeValue(bindValue)"},allowedPattern:{type:String},autoValidate:{type:Boolean,value:!1},_inputElement:Object},observers:["_bindValueChanged(bindValue, _inputElement)"],listeners:{input:"_onInput",keypress:"_onKeypress"},created:function(){Oa.requestAvailability(),this._previousValidInput="",this._patternAlreadyChecked=!1},attached:function(){this._observer=Yi(this).observeNodes(function(t){this._initSlottedInput()}.bind(this))},detached:function(){this._observer&&(Yi(this).unobserveNodes(this._observer),this._observer=null)},get inputElement(){return this._inputElement},_initSlottedInput:function(){this._inputElement=this.getEffectiveChildren()[0],this.inputElement&&this.inputElement.value&&(this.bindValue=this.inputElement.value),this.fire("iron-input-ready")},get _patternRegExp(){var t;if(this.allowedPattern)t=new RegExp(this.allowedPattern);else switch(this.inputElement.type){case"number":t=/[0-9.,e-]/}return t},_bindValueChanged:function(t,e){e&&(void 0===t?e.value=null:t!==e.value&&(this.inputElement.value=t),this.autoValidate&&this.validate(),this.fire("bind-value-changed",{value:t}))},_onInput:function(){this.allowedPattern&&!this._patternAlreadyChecked&&(this._checkPatternValidity()||(this._announceInvalidCharacter("Invalid string of characters not entered."),this.inputElement.value=this._previousValidInput)),this.bindValue=this._previousValidInput=this.inputElement.value,this._patternAlreadyChecked=!1},_isPrintable:function(t){return!(8==t.keyCode||9==t.keyCode||13==t.keyCode||27==t.keyCode||0==t.charCode&&(19==t.keyCode||20==t.keyCode||45==t.keyCode||46==t.keyCode||144==t.keyCode||145==t.keyCode||t.keyCode>32&&t.keyCode<41||t.keyCode>111&&t.keyCode<124))},_onKeypress:function(t){if(this.allowedPattern||"number"===this.inputElement.type){var e=this._patternRegExp;if(e&&!(t.metaKey||t.ctrlKey||t.altKey)){this._patternAlreadyChecked=!0;var n=String.fromCharCode(t.charCode);this._isPrintable(t)&&!e.test(n)&&(t.preventDefault(),this._announceInvalidCharacter("Invalid character "+n+" not entered."))}}},_checkPatternValidity:function(){var t=this._patternRegExp;if(!t)return!0;for(var e=0;e<this.inputElement.value.length;e++)if(!t.test(this.inputElement.value[e]))return!1;return!0},validate:function(){if(!this.inputElement)return this.invalid=!1,!0;var t=this.inputElement.checkValidity();return t&&(this.required&&""===this.bindValue?t=!1:this.hasValidator()&&(t=aa.validate.call(this,this.bindValue))),this.invalid=!t,this.fire("iron-input-validate"),t},_announceInvalidCharacter:function(t){this.fire("iron-announce",{text:t})},_computeValue:function(t){return t}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const za={attached:function(){this.fire("addon-attached")},update:function(t){}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */Rr({_template:_e`
    <style>
      :host {
        display: inline-block;
        float: right;

        @apply --paper-font-caption;
        @apply --paper-input-char-counter;
      }

      :host([hidden]) {
        display: none !important;
      }

      :host(:dir(rtl)) {
        float: left;
      }
    </style>

    <span>[[_charCounterStr]]</span>
`,is:"paper-input-char-counter",behaviors:[za],properties:{_charCounterStr:{type:String,value:"0"}},update:function(t){if(t.inputElement){t.value=t.value||"";var e=t.value.toString().length.toString();t.inputElement.hasAttribute("maxlength")&&(e+="/"+t.inputElement.getAttribute("maxlength")),this._charCounterStr=e}}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Da=_e`
<custom-style>
  <style is="custom-style">
    html {
      --paper-input-container-shared-input-style: {
        position: relative; /* to make a stacking context */
        outline: none;
        box-shadow: none;
        padding: 0;
        margin: 0;
        width: 100%;
        max-width: 100%;
        background: transparent;
        border: none;
        color: var(--paper-input-container-input-color, var(--primary-text-color));
        -webkit-appearance: none;
        text-align: inherit;
        vertical-align: var(--paper-input-container-input-align, bottom);

        @apply --paper-font-subhead;
      };
    }
  </style>
</custom-style>
`;Da.setAttribute("style","display: none;"),document.head.appendChild(Da.content),Rr({_template:_e`
    <style>
      :host {
        display: block;
        padding: 8px 0;
        @apply --paper-input-container;
      }

      :host([inline]) {
        display: inline-block;
      }

      :host([disabled]) {
        pointer-events: none;
        opacity: 0.33;

        @apply --paper-input-container-disabled;
      }

      :host([hidden]) {
        display: none !important;
      }

      [hidden] {
        display: none !important;
      }

      .floated-label-placeholder {
        @apply --paper-font-caption;
      }

      .underline {
        height: 2px;
        position: relative;
      }

      .focused-line {
        @apply --layout-fit;
        border-bottom: 2px solid var(--paper-input-container-focus-color, var(--primary-color));

        -webkit-transform-origin: center center;
        transform-origin: center center;
        -webkit-transform: scale3d(0,1,1);
        transform: scale3d(0,1,1);

        @apply --paper-input-container-underline-focus;
      }

      .underline.is-highlighted .focused-line {
        -webkit-transform: none;
        transform: none;
        -webkit-transition: -webkit-transform 0.25s;
        transition: transform 0.25s;

        @apply --paper-transition-easing;
      }

      .underline.is-invalid .focused-line {
        border-color: var(--paper-input-container-invalid-color, var(--error-color));
        -webkit-transform: none;
        transform: none;
        -webkit-transition: -webkit-transform 0.25s;
        transition: transform 0.25s;

        @apply --paper-transition-easing;
      }

      .unfocused-line {
        @apply --layout-fit;
        border-bottom: 1px solid var(--paper-input-container-color, var(--secondary-text-color));
        @apply --paper-input-container-underline;
      }

      :host([disabled]) .unfocused-line {
        border-bottom: 1px dashed;
        border-color: var(--paper-input-container-color, var(--secondary-text-color));
        @apply --paper-input-container-underline-disabled;
      }

      .input-wrapper {
        @apply --layout-horizontal;
        @apply --layout-center;
        position: relative;
      }

      .input-content {
        @apply --layout-flex-auto;
        @apply --layout-relative;
        max-width: 100%;
      }

      .input-content ::slotted(label),
      .input-content ::slotted(.paper-input-label) {
        position: absolute;
        top: 0;
        left: 0;
        width: 100%;
        font: inherit;
        color: var(--paper-input-container-color, var(--secondary-text-color));
        -webkit-transition: -webkit-transform 0.25s, width 0.25s;
        transition: transform 0.25s, width 0.25s;
        -webkit-transform-origin: left top;
        transform-origin: left top;
        /* Fix for safari not focusing 0-height date/time inputs with -webkit-apperance: none; */
        min-height: 1px;

        @apply --paper-font-common-nowrap;
        @apply --paper-font-subhead;
        @apply --paper-input-container-label;
        @apply --paper-transition-easing;
      }


      .input-content ::slotted(label):before,
      .input-content ::slotted(.paper-input-label):before {
        @apply --paper-input-container-label-before;
      }

      .input-content ::slotted(label):after,
      .input-content ::slotted(.paper-input-label):after {
        @apply --paper-input-container-label-after;
      }

      .input-content.label-is-floating ::slotted(label),
      .input-content.label-is-floating ::slotted(.paper-input-label) {
        -webkit-transform: translateY(-75%) scale(0.75);
        transform: translateY(-75%) scale(0.75);

        /* Since we scale to 75/100 of the size, we actually have 100/75 of the
        original space now available */
        width: 133%;

        @apply --paper-input-container-label-floating;
      }

      :host(:dir(rtl)) .input-content.label-is-floating ::slotted(label),
      :host(:dir(rtl)) .input-content.label-is-floating ::slotted(.paper-input-label) {
        right: 0;
        left: auto;
        -webkit-transform-origin: right top;
        transform-origin: right top;
      }

      .input-content.label-is-highlighted ::slotted(label),
      .input-content.label-is-highlighted ::slotted(.paper-input-label) {
        color: var(--paper-input-container-focus-color, var(--primary-color));

        @apply --paper-input-container-label-focus;
      }

      .input-content.is-invalid ::slotted(label),
      .input-content.is-invalid ::slotted(.paper-input-label) {
        color: var(--paper-input-container-invalid-color, var(--error-color));
      }

      .input-content.label-is-hidden ::slotted(label),
      .input-content.label-is-hidden ::slotted(.paper-input-label) {
        visibility: hidden;
      }

      .input-content ::slotted(input),
      .input-content ::slotted(iron-input),
      .input-content ::slotted(textarea),
      .input-content ::slotted(iron-autogrow-textarea),
      .input-content ::slotted(.paper-input-input) {
        @apply --paper-input-container-shared-input-style;
        /* The apply shim doesn't apply the nested color custom property,
          so we have to re-apply it here. */
        color: var(--paper-input-container-input-color, var(--primary-text-color));
        @apply --paper-input-container-input;
      }

      .input-content ::slotted(input)::-webkit-outer-spin-button,
      .input-content ::slotted(input)::-webkit-inner-spin-button {
        @apply --paper-input-container-input-webkit-spinner;
      }

      .input-content.focused ::slotted(input),
      .input-content.focused ::slotted(iron-input),
      .input-content.focused ::slotted(textarea),
      .input-content.focused ::slotted(iron-autogrow-textarea),
      .input-content.focused ::slotted(.paper-input-input) {
        @apply --paper-input-container-input-focus;
      }

      .input-content.is-invalid ::slotted(input),
      .input-content.is-invalid ::slotted(iron-input),
      .input-content.is-invalid ::slotted(textarea),
      .input-content.is-invalid ::slotted(iron-autogrow-textarea),
      .input-content.is-invalid ::slotted(.paper-input-input) {
        @apply --paper-input-container-input-invalid;
      }

      .prefix ::slotted(*) {
        display: inline-block;
        @apply --paper-font-subhead;
        @apply --layout-flex-none;
        @apply --paper-input-prefix;
      }

      .suffix ::slotted(*) {
        display: inline-block;
        @apply --paper-font-subhead;
        @apply --layout-flex-none;

        @apply --paper-input-suffix;
      }

      /* Firefox sets a min-width on the input, which can cause layout issues */
      .input-content ::slotted(input) {
        min-width: 0;
      }

      .input-content ::slotted(textarea) {
        resize: none;
      }

      .add-on-content {
        position: relative;
      }

      .add-on-content.is-invalid ::slotted(*) {
        color: var(--paper-input-container-invalid-color, var(--error-color));
      }

      .add-on-content.is-highlighted ::slotted(*) {
        color: var(--paper-input-container-focus-color, var(--primary-color));
      }
    </style>

    <div class="floated-label-placeholder" aria-hidden="true" hidden="[[noLabelFloat]]">&nbsp;</div>

    <div class="input-wrapper">
      <span class="prefix"><slot name="prefix"></slot></span>

      <div class$="[[_computeInputContentClass(noLabelFloat,alwaysFloatLabel,focused,invalid,_inputHasContent)]]" id="labelAndInputContainer">
        <slot name="label"></slot>
        <slot name="input"></slot>
      </div>

      <span class="suffix"><slot name="suffix"></slot></span>
    </div>

    <div class$="[[_computeUnderlineClass(focused,invalid)]]">
      <div class="unfocused-line"></div>
      <div class="focused-line"></div>
    </div>

    <div class$="[[_computeAddOnContentClass(focused,invalid)]]">
      <slot name="add-on"></slot>
    </div>
`,is:"paper-input-container",properties:{noLabelFloat:{type:Boolean,value:!1},alwaysFloatLabel:{type:Boolean,value:!1},attrForValue:{type:String,value:"bind-value"},autoValidate:{type:Boolean,value:!1},invalid:{observer:"_invalidChanged",type:Boolean,value:!1},focused:{readOnly:!0,type:Boolean,value:!1,notify:!0},_addons:{type:Array},_inputHasContent:{type:Boolean,value:!1},_inputSelector:{type:String,value:"input,iron-input,textarea,.paper-input-input"},_boundOnFocus:{type:Function,value:function(){return this._onFocus.bind(this)}},_boundOnBlur:{type:Function,value:function(){return this._onBlur.bind(this)}},_boundOnInput:{type:Function,value:function(){return this._onInput.bind(this)}},_boundValueChanged:{type:Function,value:function(){return this._onValueChanged.bind(this)}}},listeners:{"addon-attached":"_onAddonAttached","iron-input-validate":"_onIronInputValidate"},get _valueChangedEvent(){return this.attrForValue+"-changed"},get _propertyForValue(){return st(this.attrForValue)},get _inputElement(){return Yi(this).querySelector(this._inputSelector)},get _inputElementValue(){return this._inputElement[this._propertyForValue]||this._inputElement.value},ready:function(){this.__isFirstValueUpdate=!0,this._addons||(this._addons=[]),this.addEventListener("focus",this._boundOnFocus,!0),this.addEventListener("blur",this._boundOnBlur,!0)},attached:function(){this.attrForValue?this._inputElement.addEventListener(this._valueChangedEvent,this._boundValueChanged):this.addEventListener("input",this._onInput),this._inputElementValue&&""!=this._inputElementValue?this._handleValueAndAutoValidate(this._inputElement):this._handleValue(this._inputElement)},_onAddonAttached:function(t){this._addons||(this._addons=[]);var e=t.target;-1===this._addons.indexOf(e)&&(this._addons.push(e),this.isAttached&&this._handleValue(this._inputElement))},_onFocus:function(){this._setFocused(!0)},_onBlur:function(){this._setFocused(!1),this._handleValueAndAutoValidate(this._inputElement)},_onInput:function(t){this._handleValueAndAutoValidate(t.target)},_onValueChanged:function(t){var e=t.target;this.__isFirstValueUpdate&&(this.__isFirstValueUpdate=!1,void 0===e.value||""===e.value)||this._handleValueAndAutoValidate(t.target)},_handleValue:function(t){var e=this._inputElementValue;this._inputHasContent=!(!e&&0!==e&&("number"!==t.type||t.checkValidity())),this.updateAddons({inputElement:t,value:e,invalid:this.invalid})},_handleValueAndAutoValidate:function(t){var e;this.autoValidate&&t&&(e=t.validate?t.validate(this._inputElementValue):t.checkValidity(),this.invalid=!e),this._handleValue(t)},_onIronInputValidate:function(t){this.invalid=this._inputElement.invalid},_invalidChanged:function(){this._addons&&this.updateAddons({invalid:this.invalid})},updateAddons:function(t){for(var e,n=0;e=this._addons[n];n++)e.update(t)},_computeInputContentClass:function(t,e,n,i,r){var o="input-content";if(t)r&&(o+=" label-is-hidden"),i&&(o+=" is-invalid");else{var a=this.querySelector("label");e||r?(o+=" label-is-floating",this.$.labelAndInputContainer.style.position="static",i?o+=" is-invalid":n&&(o+=" label-is-highlighted")):(a&&(this.$.labelAndInputContainer.style.position="relative"),i&&(o+=" is-invalid"))}return n&&(o+=" focused"),o},_computeUnderlineClass:function(t,e){var n="underline";return e?n+=" is-invalid":t&&(n+=" is-highlighted"),n},_computeAddOnContentClass:function(t,e){var n="add-on-content";return e?n+=" is-invalid":t&&(n+=" is-highlighted"),n}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        display: inline-block;
        visibility: hidden;

        color: var(--paper-input-container-invalid-color, var(--error-color));

        @apply --paper-font-caption;
        @apply --paper-input-error;
        position: absolute;
        left:0;
        right:0;
      }

      :host([invalid]) {
        visibility: visible;
      }

      #a11yWrapper {
        visibility: hidden;
      }

      :host([invalid]) #a11yWrapper {
        visibility: visible;
      }
    </style>

    <!--
    If the paper-input-error element is directly referenced by an
    \`aria-describedby\` attribute, such as when used as a paper-input add-on,
    then applying \`visibility: hidden;\` to the paper-input-error element itself
    does not hide the error.

    For more information, see:
    https://www.w3.org/TR/accname-1.1/#mapping_additional_nd_description
    -->
    <div id="a11yWrapper">
      <slot></slot>
    </div>
`,is:"paper-input-error",behaviors:[za],properties:{invalid:{readOnly:!0,reflectToAttribute:!0,type:Boolean}},update:function(t){this._setInvalid(t.invalid)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ba={NextLabelID:1,NextAddonID:1,NextInputID:1},Ha={properties:{label:{type:String},value:{notify:!0,type:String},disabled:{type:Boolean,value:!1},invalid:{type:Boolean,value:!1,notify:!0},allowedPattern:{type:String},type:{type:String},list:{type:String},pattern:{type:String},required:{type:Boolean,value:!1},errorMessage:{type:String},charCounter:{type:Boolean,value:!1},noLabelFloat:{type:Boolean,value:!1},alwaysFloatLabel:{type:Boolean,value:!1},autoValidate:{type:Boolean,value:!1},validator:{type:String},autocomplete:{type:String,value:"off"},autofocus:{type:Boolean,observer:"_autofocusChanged"},inputmode:{type:String},minlength:{type:Number},maxlength:{type:Number},min:{type:String},max:{type:String},step:{type:String},name:{type:String},placeholder:{type:String,value:""},readonly:{type:Boolean,value:!1},size:{type:Number},autocapitalize:{type:String,value:"none"},autocorrect:{type:String,value:"off"},autosave:{type:String},results:{type:Number},accept:{type:String},multiple:{type:Boolean},_ariaDescribedBy:{type:String,value:""},_ariaLabelledBy:{type:String,value:""},_inputId:{type:String,value:""}},listeners:{"addon-attached":"_onAddonAttached"},keyBindings:{"shift+tab:keydown":"_onShiftTabDown"},hostAttributes:{tabindex:0},get inputElement(){return this.$||(this.$={}),this.$.input||(this._generateInputId(),this.$.input=this.$$("#"+this._inputId)),this.$.input},get _focusableElement(){return this.inputElement},created:function(){this._typesThatHaveText=["date","datetime","datetime-local","month","time","week","file"]},attached:function(){this._updateAriaLabelledBy(),!ye&&this.inputElement&&-1!==this._typesThatHaveText.indexOf(this.inputElement.type)&&(this.alwaysFloatLabel=!0)},_appendStringWithSpace:function(t,e){return t?t+" "+e:e},_onAddonAttached:function(t){var e=Yi(t).rootTarget;if(e.id)this._ariaDescribedBy=this._appendStringWithSpace(this._ariaDescribedBy,e.id);else{var n="paper-input-add-on-"+Ba.NextAddonID++;e.id=n,this._ariaDescribedBy=this._appendStringWithSpace(this._ariaDescribedBy,n)}},validate:function(){return this.inputElement.validate()},_focusBlurHandler:function(t){qo._focusBlurHandler.call(this,t),this.focused&&!this._shiftTabPressed&&this._focusableElement&&this._focusableElement.focus()},_onShiftTabDown:function(t){var e=this.getAttribute("tabindex");this._shiftTabPressed=!0,this.setAttribute("tabindex","-1"),this.async((function(){this.setAttribute("tabindex",e),this._shiftTabPressed=!1}),1)},_handleAutoValidate:function(){this.autoValidate&&this.validate()},updateValueAndPreserveCaret:function(t){try{var e=this.inputElement.selectionStart;this.value=t,this.inputElement.selectionStart=e,this.inputElement.selectionEnd=e}catch(e){this.value=t}},_computeAlwaysFloatLabel:function(t,e){return e||t},_updateAriaLabelledBy:function(){var t,e=Yi(this.root).querySelector("label");e?(e.id?t=e.id:(t="paper-input-label-"+Ba.NextLabelID++,e.id=t),this._ariaLabelledBy=t):this._ariaLabelledBy=""},_generateInputId:function(){this._inputId&&""!==this._inputId||(this._inputId="input-"+Ba.NextInputID++)},_onChange:function(t){this.shadowRoot&&this.fire(t.type,{sourceEvent:t},{node:this,bubbles:t.bubbles,cancelable:t.cancelable})},_autofocusChanged:function(){if(this.autofocus&&this._focusableElement){var t=document.activeElement;t instanceof HTMLElement&&t!==document.body&&t!==document.documentElement||this._focusableElement.focus()}}},Fa=[qo,zo,Ha];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({is:"paper-input",_template:_e`
    <style>
      :host {
        display: block;
      }

      :host([focused]) {
        outline: none;
      }

      :host([hidden]) {
        display: none !important;
      }

      input {
        /* Firefox sets a min-width on the input, which can cause layout issues */
        min-width: 0;
      }

      /* In 1.x, the <input> is distributed to paper-input-container, which styles it.
      In 2.x the <iron-input> is distributed to paper-input-container, which styles
      it, but in order for this to work correctly, we need to reset some
      of the native input's properties to inherit (from the iron-input) */
      iron-input > input {
        @apply --paper-input-container-shared-input-style;
        font-family: inherit;
        font-weight: inherit;
        font-size: inherit;
        letter-spacing: inherit;
        word-spacing: inherit;
        line-height: inherit;
        text-shadow: inherit;
        color: inherit;
        cursor: inherit;
      }

      input:disabled {
        @apply --paper-input-container-input-disabled;
      }

      input::-webkit-outer-spin-button,
      input::-webkit-inner-spin-button {
        @apply --paper-input-container-input-webkit-spinner;
      }

      input::-webkit-clear-button {
        @apply --paper-input-container-input-webkit-clear;
      }

      input::-webkit-calendar-picker-indicator {
        @apply --paper-input-container-input-webkit-calendar-picker-indicator;
      }

      input::-webkit-input-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input:-moz-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input::-moz-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      input::-ms-clear {
        @apply --paper-input-container-ms-clear;
      }

      input::-ms-reveal {
        @apply --paper-input-container-ms-reveal;
      }

      input:-ms-input-placeholder {
        color: var(--paper-input-container-color, var(--secondary-text-color));
      }

      label {
        pointer-events: none;
      }
    </style>

    <paper-input-container id="container" no-label-float="[[noLabelFloat]]" always-float-label="[[_computeAlwaysFloatLabel(alwaysFloatLabel,placeholder)]]" auto-validate$="[[autoValidate]]" disabled$="[[disabled]]" invalid="[[invalid]]">

      <slot name="prefix" slot="prefix"></slot>

      <label hidden$="[[!label]]" aria-hidden="true" for$="[[_inputId]]" slot="label">[[label]]</label>

      <!-- Need to bind maxlength so that the paper-input-char-counter works correctly -->
      <iron-input bind-value="{{value}}" slot="input" class="input-element" id$="[[_inputId]]" maxlength$="[[maxlength]]" allowed-pattern="[[allowedPattern]]" invalid="{{invalid}}" validator="[[validator]]">
        <input aria-labelledby$="[[_ariaLabelledBy]]" aria-describedby$="[[_ariaDescribedBy]]" disabled$="[[disabled]]" title$="[[title]]" type$="[[type]]" pattern$="[[pattern]]" required$="[[required]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" inputmode$="[[inputmode]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]" min$="[[min]]" max$="[[max]]" step$="[[step]]" name$="[[name]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" list$="[[list]]" size$="[[size]]" autocapitalize$="[[autocapitalize]]" autocorrect$="[[autocorrect]]" on-change="_onChange" tabindex$="[[tabIndex]]" autosave$="[[autosave]]" results$="[[results]]" accept$="[[accept]]" multiple$="[[multiple]]" role$="[[inputRole]]" aria-haspopup$="[[inputAriaHaspopup]]">
      </iron-input>

      <slot name="suffix" slot="suffix"></slot>

      <template is="dom-if" if="[[errorMessage]]">
        <paper-input-error aria-live="assertive" slot="add-on">[[errorMessage]]</paper-input-error>
      </template>

      <template is="dom-if" if="[[charCounter]]">
        <paper-input-char-counter slot="add-on"></paper-input-char-counter>
      </template>

    </paper-input-container>
  `,behaviors:[Fa,ra],properties:{value:{type:String},inputRole:{type:String,value:void 0},inputAriaHaspopup:{type:String,value:void 0}},get _focusableElement(){return this.inputElement._inputElement},listeners:{"iron-input-ready":"_onIronInputReady"},_onIronInputReady:function(){this.$.nativeInput||(this.$.nativeInput=this.$$("input")),this.inputElement&&-1!==this._typesThatHaveText.indexOf(this.$.nativeInput.type)&&(this.alwaysFloatLabel=!0),this.inputElement.bindValue&&this.$.container._handleValueAndAutoValidate(this.inputElement)}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        position: fixed;
      }

      #contentWrapper ::slotted(*) {
        overflow: auto;
      }

      #contentWrapper.animating ::slotted(*) {
        overflow: hidden;
        pointer-events: none;
      }
    </style>

    <div id="contentWrapper">
      <slot id="content" name="dropdown-content"></slot>
    </div>
`,is:"iron-dropdown",behaviors:[qo,zo,ka,Ra],properties:{horizontalAlign:{type:String,value:"left",reflectToAttribute:!0},verticalAlign:{type:String,value:"top",reflectToAttribute:!0},openAnimationConfig:{type:Object},closeAnimationConfig:{type:Object},focusTarget:{type:Object},noAnimations:{type:Boolean,value:!1},allowOutsideScroll:{type:Boolean,value:!1,observer:"_allowOutsideScrollChanged"}},listeners:{"neon-animation-finish":"_onNeonAnimationFinish"},observers:["_updateOverlayPosition(positionTarget, verticalAlign, horizontalAlign, verticalOffset, horizontalOffset)"],get containedElement(){for(var t=Yi(this.$.content).getDistributedNodes(),e=0,n=t.length;e<n;e++)if(t[e].nodeType===Node.ELEMENT_NODE)return t[e]},ready:function(){this.scrollAction||(this.scrollAction=this.allowOutsideScroll?"refit":"lock"),this._readied=!0},attached:function(){this.sizingTarget&&this.sizingTarget!==this||(this.sizingTarget=this.containedElement||this)},detached:function(){this.cancelAnimation()},_openedChanged:function(){this.opened&&this.disabled?this.cancel():(this.cancelAnimation(),this._updateAnimationConfig(),Ca._openedChanged.apply(this,arguments))},_renderOpened:function(){!this.noAnimations&&this.animationConfig.open?(this.$.contentWrapper.classList.add("animating"),this.playAnimation("open")):Ca._renderOpened.apply(this,arguments)},_renderClosed:function(){!this.noAnimations&&this.animationConfig.close?(this.$.contentWrapper.classList.add("animating"),this.playAnimation("close")):Ca._renderClosed.apply(this,arguments)},_onNeonAnimationFinish:function(){this.$.contentWrapper.classList.remove("animating"),this.opened?this._finishRenderOpened():this._finishRenderClosed()},_updateAnimationConfig:function(){for(var t=this.containedElement,e=[].concat(this.openAnimationConfig||[]).concat(this.closeAnimationConfig||[]),n=0;n<e.length;n++)e[n].node=t;this.animationConfig={open:this.openAnimationConfig,close:this.closeAnimationConfig}},_updateOverlayPosition:function(){this.isAttached&&this.notifyResize()},_allowOutsideScrollChanged:function(t){this._readied&&(t?this.scrollAction&&"lock"!==this.scrollAction||(this.scrollAction="refit"):this.scrollAction="lock")},_applyFocus:function(){var t=this.focusTarget||this.containedElement;t&&this.opened&&!this.noAutoFocus?t.focus():Ca._applyFocus.apply(this,arguments)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Va={properties:{animationTiming:{type:Object,value:function(){return{duration:500,easing:"cubic-bezier(0.4, 0, 0.2, 1)",fill:"both"}}}},isNeonAnimation:!0,created:function(){document.body.animate||console.warn("No web animations detected. This element will not function without a web animations polyfill.")},timingFromConfig:function(t){if(t.timing)for(var e in t.timing)this.animationTiming[e]=t.timing[e];return this.animationTiming},setPrefixedProperty:function(t,e,n){for(var i,r={transform:["webkitTransform"],transformOrigin:["mozTransformOrigin","webkitTransformOrigin"]}[e],o=0;i=r[o];o++)t.style[i]=n;t.style[e]=n},complete:function(t){}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */Rr({is:"fade-in-animation",behaviors:[Va],configure:function(t){return this._effect=new KeyframeEffect(t.node,[{opacity:"0"},{opacity:"1"}],this.timingFromConfig(t)),this._effect}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({is:"fade-out-animation",behaviors:[Va],configure:function(t){return this._effect=new KeyframeEffect(t.node,[{opacity:"1"},{opacity:"0"}],this.timingFromConfig(t)),this._effect}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({is:"paper-menu-grow-height-animation",_template:null,behaviors:[Va],configure:function(t){var e=t.node,n=e.getBoundingClientRect().height;return this._effect=new KeyframeEffect(e,[{height:n/2+"px"},{height:n+"px"}],this.timingFromConfig(t)),this._effect}}),Rr({is:"paper-menu-grow-width-animation",_template:null,behaviors:[Va],configure:function(t){var e=t.node,n=e.getBoundingClientRect().width;return this._effect=new KeyframeEffect(e,[{width:n/2+"px"},{width:n+"px"}],this.timingFromConfig(t)),this._effect}}),Rr({is:"paper-menu-shrink-width-animation",_template:null,behaviors:[Va],configure:function(t){var e=t.node,n=e.getBoundingClientRect().width;return this._effect=new KeyframeEffect(e,[{width:n+"px"},{width:n-n/20+"px"}],this.timingFromConfig(t)),this._effect}}),Rr({is:"paper-menu-shrink-height-animation",_template:null,behaviors:[Va],configure:function(t){var e=t.node,n=e.getBoundingClientRect().height;return this.setPrefixedProperty(e,"transformOrigin","0 0"),this._effect=new KeyframeEffect(e,[{height:n+"px",transform:"translateY(0)"},{height:n/2+"px",transform:"translateY(-20px)"}],this.timingFromConfig(t)),this._effect}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var Ua={ANIMATION_CUBIC_BEZIER:"cubic-bezier(.3,.95,.5,1)",MAX_ANIMATION_TIME_MS:400};const ja=Rr({_template:_e`
    <style>
      :host {
        display: inline-block;
        position: relative;
        padding: 8px;
        outline: none;

        @apply --paper-menu-button;
      }

      :host([disabled]) {
        cursor: auto;
        color: var(--disabled-text-color);

        @apply --paper-menu-button-disabled;
      }

      iron-dropdown {
        @apply --paper-menu-button-dropdown;
      }

      .dropdown-content {
        @apply --shadow-elevation-2dp;

        position: relative;
        border-radius: 2px;
        background-color: var(--paper-menu-button-dropdown-background, var(--primary-background-color));

        @apply --paper-menu-button-content;
      }

      :host([vertical-align="top"]) .dropdown-content {
        margin-bottom: 20px;
        margin-top: -10px;
        top: 10px;
      }

      :host([vertical-align="bottom"]) .dropdown-content {
        bottom: 10px;
        margin-bottom: -10px;
        margin-top: 20px;
      }

      #trigger {
        cursor: pointer;
      }
    </style>

    <div id="trigger" on-tap="toggle">
      <slot name="dropdown-trigger"></slot>
    </div>

    <iron-dropdown id="dropdown" opened="{{opened}}" horizontal-align="[[horizontalAlign]]" vertical-align="[[verticalAlign]]" dynamic-align="[[dynamicAlign]]" horizontal-offset="[[horizontalOffset]]" vertical-offset="[[verticalOffset]]" no-overlap="[[noOverlap]]" open-animation-config="[[openAnimationConfig]]" close-animation-config="[[closeAnimationConfig]]" no-animations="[[noAnimations]]" focus-target="[[_dropdownContent]]" allow-outside-scroll="[[allowOutsideScroll]]" restore-focus-on-close="[[restoreFocusOnClose]]" on-iron-overlay-canceled="__onIronOverlayCanceled" expand-sizing-target-for-scrollbars="[[expandSizingTargetForScrollbars]]">
      <div slot="dropdown-content" class="dropdown-content">
        <slot id="content" name="dropdown-content"></slot>
      </div>
    </iron-dropdown>
`,is:"paper-menu-button",behaviors:[zo,qo],properties:{opened:{type:Boolean,value:!1,notify:!0,observer:"_openedChanged"},horizontalAlign:{type:String,value:"left",reflectToAttribute:!0},verticalAlign:{type:String,value:"top",reflectToAttribute:!0},dynamicAlign:{type:Boolean},horizontalOffset:{type:Number,value:0,notify:!0},verticalOffset:{type:Number,value:0,notify:!0},noOverlap:{type:Boolean},noAnimations:{type:Boolean,value:!1},ignoreSelect:{type:Boolean,value:!1},closeOnActivate:{type:Boolean,value:!1},openAnimationConfig:{type:Object,value:function(){return[{name:"fade-in-animation",timing:{delay:100,duration:200}},{name:"paper-menu-grow-width-animation",timing:{delay:100,duration:150,easing:Ua.ANIMATION_CUBIC_BEZIER}},{name:"paper-menu-grow-height-animation",timing:{delay:100,duration:275,easing:Ua.ANIMATION_CUBIC_BEZIER}}]}},closeAnimationConfig:{type:Object,value:function(){return[{name:"fade-out-animation",timing:{duration:150}},{name:"paper-menu-shrink-width-animation",timing:{delay:100,duration:50,easing:Ua.ANIMATION_CUBIC_BEZIER}},{name:"paper-menu-shrink-height-animation",timing:{duration:200,easing:"ease-in"}}]}},allowOutsideScroll:{type:Boolean,value:!1},restoreFocusOnClose:{type:Boolean,value:!0},expandSizingTargetForScrollbars:{type:Boolean,value:!1},_dropdownContent:{type:Object}},hostAttributes:{role:"group","aria-haspopup":"true"},listeners:{"iron-activate":"_onIronActivate","iron-select":"_onIronSelect"},get contentElement(){for(var t=Yi(this.$.content).getDistributedNodes(),e=0,n=t.length;e<n;e++)if(t[e].nodeType===Node.ELEMENT_NODE)return t[e]},toggle:function(){this.opened?this.close():this.open()},open:function(){this.disabled||this.$.dropdown.open()},close:function(){this.$.dropdown.close()},_onIronSelect:function(t){this.ignoreSelect||this.close()},_onIronActivate:function(t){this.closeOnActivate&&this.close()},_openedChanged:function(t,e){t?(this._dropdownContent=this.contentElement,this.fire("paper-dropdown-open")):null!=e&&this.fire("paper-dropdown-close")},_disabledChanged:function(t){qo._disabledChanged.apply(this,arguments),t&&this.opened&&this.close()},__onIronOverlayCanceled:function(t){var e=this.$.trigger;Yi(t.detail).path.indexOf(e)>-1&&t.preventDefault()}});Object.keys(Ua).forEach((function(t){ja[t]=Ua[t]}));
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ga=document.createElement("template");Ga.setAttribute("style","display: none;"),Ga.innerHTML='<iron-iconset-svg name="paper-dropdown-menu" size="24">\n<svg><defs>\n<g id="arrow-drop-down"><path d="M7 10l5 5 5-5z"></path></g>\n</defs></svg>\n</iron-iconset-svg>',document.head.appendChild(Ga.content);
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Wa=document.createElement("template");Wa.setAttribute("style","display: none;"),Wa.innerHTML='<dom-module id="paper-dropdown-menu-shared-styles">\n  <template>\n    <style>\n      :host {\n        display: inline-block;\n        position: relative;\n        text-align: left;\n\n        /* NOTE(cdata): Both values are needed, since some phones require the\n         * value to be `transparent`.\n         */\n        -webkit-tap-highlight-color: rgba(0,0,0,0);\n        -webkit-tap-highlight-color: transparent;\n\n        --paper-input-container-input: {\n          overflow: hidden;\n          white-space: nowrap;\n          text-overflow: ellipsis;\n          max-width: 100%;\n          box-sizing: border-box;\n          cursor: pointer;\n        };\n\n        @apply --paper-dropdown-menu;\n      }\n\n      /* paper-dropdown-menu and paper-dropdown-menu-light both delegate focus\n       * to other internal elements which manage focus styling. */\n      :host(:focus) {\n        outline: none;\n      }\n\n      :host(:dir(rtl)) {\n        text-align: right;\n\n        @apply(--paper-dropdown-menu);\n      }\n\n      :host([disabled]) {\n        @apply --paper-dropdown-menu-disabled;\n      }\n\n      :host([noink]) paper-ripple {\n        display: none;\n      }\n\n      :host([no-label-float]) paper-ripple {\n        top: 8px;\n      }\n\n      paper-ripple {\n        top: 12px;\n        left: 0px;\n        bottom: 8px;\n        right: 0px;\n\n        @apply --paper-dropdown-menu-ripple;\n      }\n\n      paper-menu-button {\n        display: block;\n        padding: 0;\n\n        @apply --paper-dropdown-menu-button;\n      }\n\n      paper-input {\n        @apply --paper-dropdown-menu-input;\n      }\n\n      iron-icon {\n        color: var(--disabled-text-color);\n\n        @apply --paper-dropdown-menu-icon;\n      }\n    </style>\n  </template>\n</dom-module>',document.head.appendChild(Wa.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const qa=er(HTMLElement);Rr({_template:_e`
    <style include="paper-dropdown-menu-shared-styles"></style>

    <paper-menu-button id="menuButton" vertical-align="[[verticalAlign]]" horizontal-align="[[horizontalAlign]]" dynamic-align="[[dynamicAlign]]" vertical-offset="[[_computeMenuVerticalOffset(noLabelFloat, verticalOffset)]]" disabled="[[disabled]]" no-animations="[[noAnimations]]" on-iron-select="_onIronSelect" on-iron-deselect="_onIronDeselect" opened="{{opened}}" close-on-activate allow-outside-scroll="[[allowOutsideScroll]]" restore-focus-on-close="[[restoreFocusOnClose]]" expand-sizing-target-for-scrollbars="[[expandSizingTargetForScrollbars]]">
      <!-- support hybrid mode: user might be using paper-menu-button 1.x which distributes via <content> -->
      <div class="dropdown-trigger" slot="dropdown-trigger">
        <paper-ripple></paper-ripple>
        <!-- paper-input has type="text" for a11y, do not remove -->
        <paper-input id="input" type="text" invalid="[[invalid]]" readonly disabled="[[disabled]]" value="[[value]]" placeholder="[[placeholder]]" error-message="[[errorMessage]]" always-float-label="[[alwaysFloatLabel]]" no-label-float="[[noLabelFloat]]" label="[[label]]" input-role="button" input-aria-haspopup="listbox" autocomplete="off">
          <!-- support hybrid mode: user might be using paper-input 1.x which distributes via <content> -->
          <iron-icon icon="paper-dropdown-menu:arrow-drop-down" suffix slot="suffix"></iron-icon>
        </paper-input>
      </div>
      <slot id="content" name="dropdown-content" slot="dropdown-content"></slot>
    </paper-menu-button>
`,is:"paper-dropdown-menu",behaviors:[Xo,qo,ra,aa],properties:{selectedItemLabel:{type:String,notify:!0,readOnly:!0},selectedItem:{type:Object,notify:!0,readOnly:!0},value:{type:String,notify:!0},label:{type:String},placeholder:{type:String},errorMessage:{type:String},opened:{type:Boolean,notify:!0,value:!1,observer:"_openedChanged"},allowOutsideScroll:{type:Boolean,value:!1},noLabelFloat:{type:Boolean,value:!1,reflectToAttribute:!0},alwaysFloatLabel:{type:Boolean,value:!1},noAnimations:{type:Boolean,value:!1},horizontalAlign:{type:String,value:"right"},verticalAlign:{type:String,value:"top"},verticalOffset:Number,dynamicAlign:{type:Boolean},restoreFocusOnClose:{type:Boolean,value:!0},expandSizingTargetForScrollbars:{type:Boolean,value:!1}},listeners:{tap:"_onTap"},keyBindings:{"up down":"open",esc:"close"},observers:["_selectedItemChanged(selectedItem)"],_attachDom(t){const e=Y(this);return e.attachShadow({mode:"open",delegatesFocus:!0,shadyUpgradeFragment:t}),e.shadowRoot.appendChild(t),qa.prototype._attachDom.call(this,t)},focus(){this.$.input._focusableElement.focus()},attached:function(){var t=this.contentElement;t&&t.selectedItem&&this._setSelectedItem(t.selectedItem)},get contentElement(){for(var t=Yi(this.$.content).getDistributedNodes(),e=0,n=t.length;e<n;e++)if(t[e].nodeType===Node.ELEMENT_NODE)return t[e]},open:function(){this.$.menuButton.open()},close:function(){this.$.menuButton.close()},_onIronSelect:function(t){this._setSelectedItem(t.detail.item)},_onIronDeselect:function(t){this._setSelectedItem(null)},_onTap:function(t){di(t)===this&&this.open()},_selectedItemChanged:function(t){var e;e=t?t.label||t.getAttribute("label")||t.textContent.trim():"",this.value=e,this._setSelectedItemLabel(e)},_computeMenuVerticalOffset:function(t,e){return e||(t?-4:8)},_getValidity:function(t){return this.disabled||!this.required||this.required&&!!this.value},_openedChanged:function(){var t=this.contentElement;t&&t.setAttribute("aria-expanded",this.opened?"true":"false")}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var Ya={outerScroll:{scroll:!0},shadowMode:{standard:2,waterfall:1,"waterfall-tall":1},tallMode:{"waterfall-tall":!0}};Rr({_template:_e`
    <style>
      :host {
        @apply --layout-vertical;
        position: relative;
        height: 100%;
        @apply --paper-header-panel;
      }

      #mainContainer {
        @apply --layout-flex;
        position: relative;
        overflow-y: auto;
        overflow-x: hidden;
        -webkit-overflow-scrolling: touch;
      }

      #mainPanel {
        @apply --layout-vertical;
        @apply --layout-flex;
        position: relative;
        min-height: 0;
        @apply --paper-header-panel-body;
      }

      #mainContainer {
        @apply --paper-header-panel-container;
      }

      /*
       * mode: scroll
       */
      :host([mode=scroll]) #mainContainer {
        @apply --paper-header-panel-scroll-container;
        overflow: visible;
      }

      :host([mode=scroll]) {
        overflow-y: auto;
        overflow-x: hidden;
        -webkit-overflow-scrolling: touch;
      }

      /*
       * mode: cover
       */
      :host([mode=cover]) #mainContainer {
        @apply --paper-header-panel-cover-container;
        position: absolute;
        top: 0;
        right: 0;
        bottom: 0;
        left: 0;
      }

      :host([mode=cover]) #mainPanel {
        position: static;
      }

      /*
       * mode: standard
       */
      :host([mode=standard]) #mainContainer {
        @apply --paper-header-panel-standard-container;
      }

      /*
       * mode: seamed
       */
      :host([mode=seamed]) #mainContainer {
        @apply --paper-header-panel-seamed-container;
      }


      /*
       * mode: waterfall
       */
      :host([mode=waterfall]) #mainContainer {
        @apply --paper-header-panel-waterfall-container;
      }

      /*
       * mode: waterfall-tall
       */
      :host([mode=waterfall-tall]) #mainContainer {
        @apply --paper-header-panel-waterfall-tall-container;
      }

      #dropShadow {
        transition: opacity 0.5s;
        height: 6px;
        box-shadow: inset 0px 5px 6px -3px rgba(0, 0, 0, 0.4);
        @apply --paper-header-panel-shadow;
        position: absolute;
        top: 0;
        left: 0;
        right: 0;
        opacity: 0;
        pointer-events: none;
      }

      #dropShadow.has-shadow {
        opacity: 1;
      }

      #mainContainer > ::slotted(.fit) {
        @apply --layout-fit;
      }

    </style>

    <slot id="headerSlot" name="header"></slot>

    <div id="mainPanel">
      <div id="mainContainer" class\$="[[_computeMainContainerClass(mode)]]">
        <slot></slot>
      </div>
      <div id="dropShadow"></div>
    </div>
`,is:"paper-header-panel",properties:{mode:{type:String,value:"standard",observer:"_modeChanged",reflectToAttribute:!0},shadow:{type:Boolean,value:!1},tallClass:{type:String,value:"tall"},atTop:{type:Boolean,value:!0,notify:!0,readOnly:!0,reflectToAttribute:!0}},observers:["_computeDropShadowHidden(atTop, mode, shadow)"],attached:function(){this._addListener(),this._keepScrollingState()},detached:function(){this._removeListener()},ready:function(){this.scrollHandler=this._scroll.bind(this),console.warn(this.is,"is deprecated. Please use app-layout instead!")},get header(){return Yi(this.$.headerSlot).getDistributedNodes()[0]},get scroller(){return this._getScrollerForMode(this.mode)},get visibleShadow(){return this.$.dropShadow.classList.contains("has-shadow")},_computeDropShadowHidden:function(t,e,n){var i=Ya.shadowMode[e];this.toggleClass("has-shadow",!!this.shadow||2===i||1===i&&!t,this.$.dropShadow)},_computeMainContainerClass:function(t){var e={};return e.flex="cover"!==t,Object.keys(e).filter((function(t){return e[t]})).join(" ")},_addListener:function(){this.scroller.addEventListener("scroll",this.scrollHandler)},_removeListener:function(){this.scroller.removeEventListener("scroll",this.scrollHandler)},_modeChanged:function(t,e){var n=Ya,i=this.header;i&&(n.tallMode[e]&&!n.tallMode[t]?(i.classList.remove(this.tallClass),this.async((function(){i.classList.remove("animate")}),200)):this.toggleClass("animate",n.tallMode[t],i)),this._keepScrollingState()},_keepScrollingState:function(){var t=this.scroller,e=this.header;this._setAtTop(0===t.scrollTop),e&&this.tallClass&&Ya.tallMode[this.mode]&&this.toggleClass(this.tallClass,this.atTop||e.classList.contains(this.tallClass)&&t.scrollHeight<this.offsetHeight,e)},_scroll:function(){this._keepScrollingState(),this.fire("content-scroll",{target:this.scroller},{bubbles:!1})},_getScrollerForMode:function(t){return Ya.outerScroll[t]?this:this.$.mainContainer}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({is:"paper-icon-button",_template:_e`
    <style>
      :host {
        display: inline-block;
        position: relative;
        padding: 8px;
        outline: none;
        -webkit-user-select: none;
        -moz-user-select: none;
        -ms-user-select: none;
        user-select: none;
        cursor: pointer;
        z-index: 0;
        line-height: 1;

        width: 40px;
        height: 40px;

        /*
          NOTE: Both values are needed, since some phones require the value to
          be \`transparent\`.
        */
        -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
        -webkit-tap-highlight-color: transparent;

        /* Because of polymer/2558, this style has lower specificity than * */
        box-sizing: border-box !important;

        @apply --paper-icon-button;
      }

      :host #ink {
        color: var(--paper-icon-button-ink-color, var(--primary-text-color));
        opacity: 0.6;
      }

      :host([disabled]) {
        color: var(--paper-icon-button-disabled-text, var(--disabled-text-color));
        pointer-events: none;
        cursor: auto;

        @apply --paper-icon-button-disabled;
      }

      :host([hidden]) {
        display: none !important;
      }

      :host(:hover) {
        @apply --paper-icon-button-hover;
      }

      iron-icon {
        --iron-icon-width: 100%;
        --iron-icon-height: 100%;
      }
    </style>

    <iron-icon id="icon" src="[[src]]" icon="[[icon]]"
               alt$="[[alt]]"></iron-icon>
  `,hostAttributes:{role:"button",tabindex:"0"},behaviors:[ca],registered:function(){this._template.setAttribute("strip-whitespace","")},properties:{src:{type:String},icon:{type:String},alt:{type:String,observer:"_altChanged"}},_altChanged:function(t,e){var n=this.getAttribute("aria-label");n&&e!=n||this.setAttribute("aria-label",t)}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        display: inline-block;
        position: relative;
        width: 400px;
        border: 1px solid;
        padding: 2px;
        -moz-appearance: textarea;
        -webkit-appearance: textarea;
        overflow: hidden;
      }

      .mirror-text {
        visibility: hidden;
        word-wrap: break-word;
        @apply --iron-autogrow-textarea;
      }

      .fit {
        @apply --layout-fit;
      }

      textarea {
        position: relative;
        outline: none;
        border: none;
        resize: none;
        background: inherit;
        color: inherit;
        /* see comments in template */
        width: 100%;
        height: 100%;
        font-size: inherit;
        font-family: inherit;
        line-height: inherit;
        text-align: inherit;
        @apply --iron-autogrow-textarea;
      }

      textarea::-webkit-input-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea:-moz-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea::-moz-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }

      textarea:-ms-input-placeholder {
        @apply --iron-autogrow-textarea-placeholder;
      }
    </style>

    <!-- the mirror sizes the input/textarea so it grows with typing -->
    <!-- use &#160; instead &nbsp; of to allow this element to be used in XHTML -->
    <div id="mirror" class="mirror-text" aria-hidden="true">&nbsp;</div>

    <!-- size the input/textarea with a div, because the textarea has intrinsic size in ff -->
    <div class="textarea-container fit">
      <textarea id="textarea" name$="[[name]]" aria-label$="[[label]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" autocapitalize$="[[autocapitalize]]" inputmode$="[[inputmode]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" required$="[[required]]" disabled$="[[disabled]]" rows$="[[rows]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]"></textarea>
    </div>
`,is:"iron-autogrow-textarea",behaviors:[aa,qo],properties:{value:{observer:"_valueChanged",type:String,notify:!0},bindValue:{observer:"_bindValueChanged",type:String,notify:!0},rows:{type:Number,value:1,observer:"_updateCached"},maxRows:{type:Number,value:0,observer:"_updateCached"},autocomplete:{type:String,value:"off"},autofocus:{type:Boolean,value:!1},autocapitalize:{type:String,value:"none"},inputmode:{type:String},placeholder:{type:String},readonly:{type:String},required:{type:Boolean},minlength:{type:Number},maxlength:{type:Number},label:{type:String}},listeners:{input:"_onInput"},get textarea(){return this.$.textarea},get selectionStart(){return this.$.textarea.selectionStart},get selectionEnd(){return this.$.textarea.selectionEnd},set selectionStart(t){this.$.textarea.selectionStart=t},set selectionEnd(t){this.$.textarea.selectionEnd=t},attached:function(){navigator.userAgent.match(/iP(?:[oa]d|hone)/)&&!navigator.userAgent.match(/OS 1[3456789]/)&&(this.$.textarea.style.marginLeft="-3px")},validate:function(){var t=this.$.textarea.validity.valid;return t&&(this.required&&""===this.value?t=!1:this.hasValidator()&&(t=aa.validate.call(this,this.value))),this.invalid=!t,this.fire("iron-input-validate"),t},_bindValueChanged:function(t){this.value=t},_valueChanged:function(t){var e=this.textarea;e&&(e.value!==t&&(e.value=t||0===t?t:""),this.bindValue=t,this.$.mirror.innerHTML=this._valueForMirror(),this.fire("bind-value-changed",{value:this.bindValue}))},_onInput:function(t){var e=Yi(t).path;this.value=e?e[0].value:t.target.value},_constrain:function(t){var e;for(t=t||[""],e=this.maxRows>0&&t.length>this.maxRows?t.slice(0,this.maxRows):t.slice(0);this.rows>0&&e.length<this.rows;)e.push("");return e.join("<br/>")+"&#160;"},_valueForMirror:function(){var t=this.textarea;if(t)return this.tokens=t&&t.value?t.value.replace(/&/gm,"&amp;").replace(/"/gm,"&quot;").replace(/'/gm,"&#39;").replace(/</gm,"&lt;").replace(/>/gm,"&gt;").split("\n"):[""],this._constrain(this.tokens)},_updateCached:function(){this.$.mirror.innerHTML=this._constrain(this.tokens)}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        display: block;
      }

      :host([hidden]) {
        display: none !important;
      }

      label {
        pointer-events: none;
      }
    </style>

    <paper-input-container no-label-float$="[[noLabelFloat]]" always-float-label="[[_computeAlwaysFloatLabel(alwaysFloatLabel,placeholder)]]" auto-validate$="[[autoValidate]]" disabled$="[[disabled]]" invalid="[[invalid]]">

      <label hidden$="[[!label]]" aria-hidden="true" for$="[[_inputId]]" slot="label">[[label]]</label>

      <iron-autogrow-textarea class="paper-input-input" slot="input" id$="[[_inputId]]" aria-labelledby$="[[_ariaLabelledBy]]" aria-describedby$="[[_ariaDescribedBy]]" bind-value="{{value}}" invalid="{{invalid}}" validator$="[[validator]]" disabled$="[[disabled]]" autocomplete$="[[autocomplete]]" autofocus$="[[autofocus]]" inputmode$="[[inputmode]]" name$="[[name]]" placeholder$="[[placeholder]]" readonly$="[[readonly]]" required$="[[required]]" minlength$="[[minlength]]" maxlength$="[[maxlength]]" autocapitalize$="[[autocapitalize]]" rows$="[[rows]]" max-rows$="[[maxRows]]" on-change="_onChange"></iron-autogrow-textarea>

      <template is="dom-if" if="[[errorMessage]]">
        <paper-input-error aria-live="assertive" slot="add-on">[[errorMessage]]</paper-input-error>
      </template>

      <template is="dom-if" if="[[charCounter]]">
        <paper-input-char-counter slot="add-on"></paper-input-char-counter>
      </template>

    </paper-input-container>
`,is:"paper-textarea",behaviors:[Fa,ra],properties:{_ariaLabelledBy:{observer:"_ariaLabelledByChanged",type:String},_ariaDescribedBy:{observer:"_ariaDescribedByChanged",type:String},value:{type:String},rows:{type:Number,value:1},maxRows:{type:Number,value:0}},get selectionStart(){return this.$.input.textarea.selectionStart},set selectionStart(t){this.$.input.textarea.selectionStart=t},get selectionEnd(){return this.$.input.textarea.selectionEnd},set selectionEnd(t){this.$.input.textarea.selectionEnd=t},_ariaLabelledByChanged:function(t){this._focusableElement.setAttribute("aria-labelledby",t)},_ariaDescribedByChanged:function(t){this._focusableElement.setAttribute("aria-describedby",t)},get _focusableElement(){return this.inputElement.textarea}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Xa=document.createElement("template");Xa.setAttribute("style","display: none;"),Xa.innerHTML="<dom-module id=\"paper-item-shared-styles\">\n  <template>\n    <style>\n      :host, .paper-item {\n        display: block;\n        position: relative;\n        min-height: var(--paper-item-min-height, 48px);\n        padding: 0px 16px;\n      }\n\n      .paper-item {\n        @apply --paper-font-subhead;\n        border:none;\n        outline: none;\n        background: white;\n        width: 100%;\n        text-align: left;\n      }\n\n      :host([hidden]), .paper-item[hidden] {\n        display: none !important;\n      }\n\n      :host(.iron-selected), .paper-item.iron-selected {\n        font-weight: var(--paper-item-selected-weight, bold);\n\n        @apply --paper-item-selected;\n      }\n\n      :host([disabled]), .paper-item[disabled] {\n        color: var(--paper-item-disabled-color, var(--disabled-text-color));\n\n        @apply --paper-item-disabled;\n      }\n\n      :host(:focus), .paper-item:focus {\n        position: relative;\n        outline: 0;\n\n        @apply --paper-item-focused;\n      }\n\n      :host(:focus):before, .paper-item:focus:before {\n        @apply --layout-fit;\n\n        background: currentColor;\n        content: '';\n        opacity: var(--dark-divider-opacity);\n        pointer-events: none;\n\n        @apply --paper-item-focused-before;\n      }\n    </style>\n  </template>\n</dom-module>",document.head.appendChild(Xa.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const $a=[Xo,qo,{hostAttributes:{role:"option",tabindex:"0"}}];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style include="paper-item-shared-styles">
      :host {
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-font-subhead;

        @apply --paper-item;
      }
    </style>
    <slot></slot>
`,is:"paper-item",behaviors:[$a]}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        overflow: hidden; /* needed for text-overflow: ellipsis to work on ff */
        @apply --layout-vertical;
        @apply --layout-center-justified;
        @apply --layout-flex;
      }

      :host([two-line]) {
        min-height: var(--paper-item-body-two-line-min-height, 72px);
      }

      :host([three-line]) {
        min-height: var(--paper-item-body-three-line-min-height, 88px);
      }

      :host > ::slotted(*) {
        overflow: hidden;
        text-overflow: ellipsis;
        white-space: nowrap;
      }

      :host > ::slotted([secondary]) {
        @apply --paper-font-body1;

        color: var(--paper-item-body-secondary-color, var(--secondary-text-color));

        @apply --paper-item-body-secondary;
      }
    </style>

    <slot></slot>
`,is:"paper-item-body"}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style include="paper-item-shared-styles"></style>
    <style>
      :host {
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-font-subhead;

        @apply --paper-item;
        @apply --paper-icon-item;
      }

      .content-icon {
        @apply --layout-horizontal;
        @apply --layout-center;

        width: var(--paper-item-icon-width, 56px);
        @apply --paper-item-icon;
      }
    </style>

    <div id="contentIcon" class="content-icon">
      <slot name="item-icon"></slot>
    </div>
    <slot></slot>
`,is:"paper-icon-item",behaviors:[$a]});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Ka={properties:{multi:{type:Boolean,value:!1,observer:"multiChanged"},selectedValues:{type:Array,notify:!0,value:function(){return[]}},selectedItems:{type:Array,readOnly:!0,notify:!0,value:function(){return[]}}},observers:["_updateSelected(selectedValues.splices)"],select:function(t){this.multi?this._toggleSelected(t):this.selected=t},multiChanged:function(t){this._selection.multi=t,this._updateSelected()},get _shouldUpdateSelection(){return null!=this.selected||null!=this.selectedValues&&this.selectedValues.length},_updateAttrForSelected:function(){this.multi?this.selectedItems&&this.selectedItems.length>0&&(this.selectedValues=this.selectedItems.map((function(t){return this._indexToValue(this.indexOf(t))}),this).filter((function(t){return null!=t}),this)):jo._updateAttrForSelected.apply(this)},_updateSelected:function(){this.multi?this._selectMulti(this.selectedValues):this._selectSelected(this.selected)},_selectMulti:function(t){var e=(this._valuesToItems(t=t||[])||[]).filter((function(t){return null!=t}));this._selection.clear(e);for(var n=0;n<e.length;n++)this._selection.setItemSelected(e[n],!0);this.fallbackSelection&&!this._selection.get().length&&this._valueToItem(this.fallbackSelection)&&this.select(this.fallbackSelection)},_selectionChange:function(){var t=this._selection.get();this.multi?(this._setSelectedItems(t),this._setSelectedItem(t.length?t[0]:null)):null!=t?(this._setSelectedItems([t]),this._setSelectedItem(t)):(this._setSelectedItems([]),this._setSelectedItem(null))},_toggleSelected:function(t){var e=this.selectedValues.indexOf(t);e<0?this.push("selectedValues",t):this.splice("selectedValues",e,1)},_valuesToItems:function(t){return null==t?null:t.map((function(t){return this._valueToItem(t)}),this)}},Za={properties:{focusedItem:{observer:"_focusedItemChanged",readOnly:!0,type:Object},attrForItemTitle:{type:String},disabled:{type:Boolean,value:!1,observer:"_disabledChanged"}},_MODIFIER_KEYS:["Alt","AltGraph","CapsLock","Control","Fn","FnLock","Hyper","Meta","NumLock","OS","ScrollLock","Shift","Super","Symbol","SymbolLock"],_SEARCH_RESET_TIMEOUT_MS:1e3,_previousTabIndex:0,hostAttributes:{role:"menu"},observers:["_updateMultiselectable(multi)"],listeners:{focus:"_onFocus",keydown:"_onKeydown","iron-items-changed":"_onIronItemsChanged"},keyBindings:{up:"_onUpKey",down:"_onDownKey",esc:"_onEscKey","shift+tab:keydown":"_onShiftTabDown"},attached:function(){this._resetTabindices()},select:function(t){this._defaultFocusAsync&&(this.cancelAsync(this._defaultFocusAsync),this._defaultFocusAsync=null);var e=this._valueToItem(t);e&&e.hasAttribute("disabled")||(this._setFocusedItem(e),Ka.select.apply(this,arguments))},_resetTabindices:function(){var t=this.multi?this.selectedItems&&this.selectedItems[0]:this.selectedItem;this.items.forEach((function(e){e.setAttribute("tabindex",e===t?"0":"-1"),e.setAttribute("aria-selected",this._selection.isSelected(e))}),this)},_updateMultiselectable:function(t){t?this.setAttribute("aria-multiselectable","true"):this.removeAttribute("aria-multiselectable")},_focusWithKeyboardEvent:function(t){if(-1===this._MODIFIER_KEYS.indexOf(t.key)){this.cancelDebouncer("_clearSearchText");for(var e,n=this._searchText||"",i=(n+=(t.key&&1==t.key.length?t.key:String.fromCharCode(t.keyCode)).toLocaleLowerCase()).length,r=0;e=this.items[r];r++)if(!e.hasAttribute("disabled")){var o=this.attrForItemTitle||"textContent",a=(e[o]||e.getAttribute(o)||"").trim();if(!(a.length<i)&&a.slice(0,i).toLocaleLowerCase()==n){this._setFocusedItem(e);break}}this._searchText=n,this.debounce("_clearSearchText",this._clearSearchText,this._SEARCH_RESET_TIMEOUT_MS)}},_clearSearchText:function(){this._searchText=""},_focusPrevious:function(){for(var t=this.items.length,e=Number(this.indexOf(this.focusedItem)),n=1;n<t+1;n++){var i=this.items[(e-n+t)%t];if(!i.hasAttribute("disabled")){var r=Yi(i).getOwnerRoot()||document;if(this._setFocusedItem(i),Yi(r).activeElement==i)return}}},_focusNext:function(){for(var t=this.items.length,e=Number(this.indexOf(this.focusedItem)),n=1;n<t+1;n++){var i=this.items[(e+n)%t];if(!i.hasAttribute("disabled")){var r=Yi(i).getOwnerRoot()||document;if(this._setFocusedItem(i),Yi(r).activeElement==i)return}}},_applySelection:function(t,e){t.setAttribute("aria-selected",e?"true":"false"),jo._applySelection.apply(this,arguments)},_focusedItemChanged:function(t,e){e&&e.setAttribute("tabindex","-1"),!t||t.hasAttribute("disabled")||this.disabled||(t.setAttribute("tabindex","0"),t.focus())},_onIronItemsChanged:function(t){t.detail.addedNodes.length&&this._resetTabindices()},_onShiftTabDown:function(t){var e=this.getAttribute("tabindex");Za._shiftTabPressed=!0,this._setFocusedItem(null),this.setAttribute("tabindex","-1"),this.async((function(){this.setAttribute("tabindex",e),Za._shiftTabPressed=!1}),1)},_onFocus:function(t){if(!Za._shiftTabPressed){var e=Yi(t).rootTarget;(e===this||void 0===e.tabIndex||this.isLightDescendant(e))&&(this._defaultFocusAsync=this.async((function(){var t=this.multi?this.selectedItems&&this.selectedItems[0]:this.selectedItem;this._setFocusedItem(null),t?this._setFocusedItem(t):this.items[0]&&this._focusNext()})))}},_onUpKey:function(t){this._focusPrevious(),t.detail.keyboardEvent.preventDefault()},_onDownKey:function(t){this._focusNext(),t.detail.keyboardEvent.preventDefault()},_onEscKey:function(t){var e=this.focusedItem;e&&e.blur()},_onKeydown:function(t){this.keyboardEventMatchesKeys(t,"up down esc")||this._focusWithKeyboardEvent(t),t.stopPropagation()},_activateHandler:function(t){jo._activateHandler.call(this,t),t.stopPropagation()},_disabledChanged:function(t){t?(this._previousTabIndex=this.hasAttribute("tabindex")?this.tabIndex:0,this.removeAttribute("tabindex")):this.hasAttribute("tabindex")||this.setAttribute("tabindex",this._previousTabIndex)},_shiftTabPressed:!1},Ja=[[jo,Ka],zo,Za];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        display: block;
        padding: 8px 0;

        background: var(--paper-listbox-background-color, var(--primary-background-color));
        color: var(--paper-listbox-color, var(--primary-text-color));

        @apply --paper-listbox;
      }
    </style>

    <slot></slot>
`,is:"paper-listbox",behaviors:[Ja],hostAttributes:{role:"listbox"}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const Qa=_e`
<dom-module id="paper-material-shared-styles">
  <template>
    <style>
      :host {
        display: block;
        position: relative;
      }

      :host([elevation="1"]) {
        @apply --shadow-elevation-2dp;
      }

      :host([elevation="2"]) {
        @apply --shadow-elevation-4dp;
      }

      :host([elevation="3"]) {
        @apply --shadow-elevation-6dp;
      }

      :host([elevation="4"]) {
        @apply --shadow-elevation-8dp;
      }

      :host([elevation="5"]) {
        @apply --shadow-elevation-16dp;
      }
    </style>
  </template>
</dom-module>
`;Qa.setAttribute("style","display: none;"),document.body.appendChild(Qa.content),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style include="paper-material-shared-styles"></style>
    <style>
      :host([animated]) {
        @apply --shadow-transition;
      }
      :host {
        @apply --paper-material;
      }
    </style>

    <slot></slot>
`,is:"paper-material",properties:{elevation:{type:Number,reflectToAttribute:!0,value:1},animated:{type:Boolean,reflectToAttribute:!0,value:!1}}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ts={properties:{value:{type:Number,value:0,notify:!0,reflectToAttribute:!0},min:{type:Number,value:0,notify:!0},max:{type:Number,value:100,notify:!0},step:{type:Number,value:1,notify:!0},ratio:{type:Number,value:0,readOnly:!0,notify:!0}},observers:["_update(value, min, max, step)"],_calcRatio:function(t){return(this._clampValue(t)-this.min)/(this.max-this.min)},_clampValue:function(t){return Math.min(this.max,Math.max(this.min,this._calcStep(t)))},_calcStep:function(t){if(t=parseFloat(t),!this.step)return t;var e=Math.round((t-this.min)/this.step);return this.step<1?e/(1/this.step)+this.min:e*this.step+this.min},_validateValue:function(){var t=this._clampValue(this.value);return this.value=this.oldValue=isNaN(t)?this.oldValue:t,this.value!==t},_update:function(){this._validateValue(),this._setRatio(100*this._calcRatio(this.value))}};
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */Rr({_template:_e`
    <style>
      :host {
        display: block;
        width: 200px;
        position: relative;
        overflow: hidden;
      }

      :host([hidden]), [hidden] {
        display: none !important;
      }

      #progressContainer {
        @apply --paper-progress-container;
        position: relative;
      }

      #progressContainer,
      /* the stripe for the indeterminate animation*/
      .indeterminate::after {
        height: var(--paper-progress-height, 4px);
      }

      #primaryProgress,
      #secondaryProgress,
      .indeterminate::after {
        @apply --layout-fit;
      }

      #progressContainer,
      .indeterminate::after {
        background: var(--paper-progress-container-color, var(--google-grey-300));
      }

      :host(.transiting) #primaryProgress,
      :host(.transiting) #secondaryProgress {
        -webkit-transition-property: -webkit-transform;
        transition-property: transform;

        /* Duration */
        -webkit-transition-duration: var(--paper-progress-transition-duration, 0.08s);
        transition-duration: var(--paper-progress-transition-duration, 0.08s);

        /* Timing function */
        -webkit-transition-timing-function: var(--paper-progress-transition-timing-function, ease);
        transition-timing-function: var(--paper-progress-transition-timing-function, ease);

        /* Delay */
        -webkit-transition-delay: var(--paper-progress-transition-delay, 0s);
        transition-delay: var(--paper-progress-transition-delay, 0s);
      }

      #primaryProgress,
      #secondaryProgress {
        @apply --layout-fit;
        -webkit-transform-origin: left center;
        transform-origin: left center;
        -webkit-transform: scaleX(0);
        transform: scaleX(0);
        will-change: transform;
      }

      #primaryProgress {
        background: var(--paper-progress-active-color, var(--google-green-500));
      }

      #secondaryProgress {
        background: var(--paper-progress-secondary-color, var(--google-green-100));
      }

      :host([disabled]) #primaryProgress {
        background: var(--paper-progress-disabled-active-color, var(--google-grey-500));
      }

      :host([disabled]) #secondaryProgress {
        background: var(--paper-progress-disabled-secondary-color, var(--google-grey-300));
      }

      :host(:not([disabled])) #primaryProgress.indeterminate {
        -webkit-transform-origin: right center;
        transform-origin: right center;
        -webkit-animation: indeterminate-bar var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
        animation: indeterminate-bar var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
      }

      :host(:not([disabled])) #primaryProgress.indeterminate::after {
        content: "";
        -webkit-transform-origin: center center;
        transform-origin: center center;

        -webkit-animation: indeterminate-splitter var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
        animation: indeterminate-splitter var(--paper-progress-indeterminate-cycle-duration, 2s) linear infinite;
      }

      @-webkit-keyframes indeterminate-bar {
        0% {
          -webkit-transform: scaleX(1) translateX(-100%);
        }
        50% {
          -webkit-transform: scaleX(1) translateX(0%);
        }
        75% {
          -webkit-transform: scaleX(1) translateX(0%);
          -webkit-animation-timing-function: cubic-bezier(.28,.62,.37,.91);
        }
        100% {
          -webkit-transform: scaleX(0) translateX(0%);
        }
      }

      @-webkit-keyframes indeterminate-splitter {
        0% {
          -webkit-transform: scaleX(.75) translateX(-125%);
        }
        30% {
          -webkit-transform: scaleX(.75) translateX(-125%);
          -webkit-animation-timing-function: cubic-bezier(.42,0,.6,.8);
        }
        90% {
          -webkit-transform: scaleX(.75) translateX(125%);
        }
        100% {
          -webkit-transform: scaleX(.75) translateX(125%);
        }
      }

      @keyframes indeterminate-bar {
        0% {
          transform: scaleX(1) translateX(-100%);
        }
        50% {
          transform: scaleX(1) translateX(0%);
        }
        75% {
          transform: scaleX(1) translateX(0%);
          animation-timing-function: cubic-bezier(.28,.62,.37,.91);
        }
        100% {
          transform: scaleX(0) translateX(0%);
        }
      }

      @keyframes indeterminate-splitter {
        0% {
          transform: scaleX(.75) translateX(-125%);
        }
        30% {
          transform: scaleX(.75) translateX(-125%);
          animation-timing-function: cubic-bezier(.42,0,.6,.8);
        }
        90% {
          transform: scaleX(.75) translateX(125%);
        }
        100% {
          transform: scaleX(.75) translateX(125%);
        }
      }
    </style>

    <div id="progressContainer">
      <div id="secondaryProgress" hidden\$="[[_hideSecondaryProgress(secondaryRatio)]]"></div>
      <div id="primaryProgress"></div>
    </div>
`,is:"paper-progress",behaviors:[ts],properties:{secondaryProgress:{type:Number,value:0},secondaryRatio:{type:Number,value:0,readOnly:!0},indeterminate:{type:Boolean,value:!1,observer:"_toggleIndeterminate"},disabled:{type:Boolean,value:!1,reflectToAttribute:!0,observer:"_disabledChanged"}},observers:["_progressChanged(secondaryProgress, value, min, max, indeterminate)"],hostAttributes:{role:"progressbar"},_toggleIndeterminate:function(t){this.toggleClass("indeterminate",t,this.$.primaryProgress)},_transformProgress:function(t,e){t.style.transform=t.style.webkitTransform="scaleX("+e/100+")"},_mainRatioChanged:function(t){this._transformProgress(this.$.primaryProgress,t)},_progressChanged:function(t,e,n,i,r){t=this._clampValue(t),e=this._clampValue(e);var o=100*this._calcRatio(t),a=100*this._calcRatio(e);this._setSecondaryRatio(o),this._transformProgress(this.$.secondaryProgress,o),this._transformProgress(this.$.primaryProgress,a),this.secondaryProgress=t,r?this.removeAttribute("aria-valuenow"):this.setAttribute("aria-valuenow",e),this.setAttribute("aria-valuemin",n),this.setAttribute("aria-valuemax",i)},_disabledChanged:function(t){this.setAttribute("aria-disabled",t?"true":"false")},_hideSecondaryProgress:function(t){return 0===t}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const es=_e`
<style>
  :host {
    display: inline-block;
    line-height: 0;
    white-space: nowrap;
    cursor: pointer;
    @apply --paper-font-common-base;
    --calculated-paper-radio-button-size: var(--paper-radio-button-size, 16px);
    /* -1px is a sentinel for the default and is replace in \`attached\`. */
    --calculated-paper-radio-button-ink-size: var(--paper-radio-button-ink-size, -1px);
  }

  :host(:focus) {
    outline: none;
  }

  #radioContainer {
    @apply --layout-inline;
    @apply --layout-center-center;
    position: relative;
    width: var(--calculated-paper-radio-button-size);
    height: var(--calculated-paper-radio-button-size);
    vertical-align: middle;

    @apply --paper-radio-button-radio-container;
  }

  #ink {
    position: absolute;
    top: 50%;
    left: 50%;
    right: auto;
    width: var(--calculated-paper-radio-button-ink-size);
    height: var(--calculated-paper-radio-button-ink-size);
    color: var(--paper-radio-button-unchecked-ink-color, var(--primary-text-color));
    opacity: 0.6;
    pointer-events: none;
    -webkit-transform: translate(-50%, -50%);
    transform: translate(-50%, -50%);
  }

  #ink[checked] {
    color: var(--paper-radio-button-checked-ink-color, var(--primary-color));
  }

  #offRadio, #onRadio {
    position: absolute;
    box-sizing: border-box;
    top: 0;
    left: 0;
    width: 100%;
    height: 100%;
    border-radius: 50%;
  }

  #offRadio {
    border: 2px solid var(--paper-radio-button-unchecked-color, var(--primary-text-color));
    background-color: var(--paper-radio-button-unchecked-background-color, transparent);
    transition: border-color 0.28s;
  }

  #onRadio {
    background-color: var(--paper-radio-button-checked-color, var(--primary-color));
    -webkit-transform: scale(0);
    transform: scale(0);
    transition: -webkit-transform ease 0.28s;
    transition: transform ease 0.28s;
    will-change: transform;
  }

  :host([checked]) #offRadio {
    border-color: var(--paper-radio-button-checked-color, var(--primary-color));
  }

  :host([checked]) #onRadio {
    -webkit-transform: scale(0.5);
    transform: scale(0.5);
  }

  #radioLabel {
    line-height: normal;
    position: relative;
    display: inline-block;
    vertical-align: middle;
    margin-left: var(--paper-radio-button-label-spacing, 10px);
    white-space: normal;
    color: var(--paper-radio-button-label-color, var(--primary-text-color));

    @apply --paper-radio-button-label;
  }

  :host([checked]) #radioLabel {
    @apply --paper-radio-button-label-checked;
  }

  #radioLabel:dir(rtl) {
    margin-left: 0;
    margin-right: var(--paper-radio-button-label-spacing, 10px);
  }

  #radioLabel[hidden] {
    display: none;
  }

  /* disabled state */

  :host([disabled]) #offRadio {
    border-color: var(--paper-radio-button-unchecked-color, var(--primary-text-color));
    opacity: 0.5;
  }

  :host([disabled][checked]) #onRadio {
    background-color: var(--paper-radio-button-unchecked-color, var(--primary-text-color));
    opacity: 0.5;
  }

  :host([disabled]) #radioLabel {
    /* slightly darker than the button, so that it's readable */
    opacity: 0.65;
  }
</style>

<div id="radioContainer">
  <div id="offRadio"></div>
  <div id="onRadio"></div>
</div>

<div id="radioLabel"><slot></slot></div>`;es.setAttribute("strip-whitespace",""),Rr({_template:es,is:"paper-radio-button",behaviors:[ua],hostAttributes:{role:"radio","aria-checked":!1,tabindex:0},properties:{ariaActiveAttribute:{type:String,value:"aria-checked"}},ready:function(){this._rippleContainer=this.$.radioContainer},attached:function(){Li(this,(function(){if("-1px"===this.getComputedStyleValue("--calculated-paper-radio-button-ink-size").trim()){var t=parseFloat(this.getComputedStyleValue("--calculated-paper-radio-button-size").trim()),e=Math.floor(3*t);e%2!=t%2&&e++,this.updateStyles({"--paper-radio-button-ink-size":e+"px"})}}))}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ns={hostAttributes:{role:"menubar"},keyBindings:{left:"_onLeftKey",right:"_onRightKey"},_onUpKey:function(t){this.focusedItem.click(),t.detail.keyboardEvent.preventDefault()},_onDownKey:function(t){this.focusedItem.click(),t.detail.keyboardEvent.preventDefault()},get _isRTL(){return"rtl"===window.getComputedStyle(this).direction},_onLeftKey:function(t){this._isRTL?this._focusNext():this._focusPrevious(),t.detail.keyboardEvent.preventDefault()},_onRightKey:function(t){this._isRTL?this._focusPrevious():this._focusNext(),t.detail.keyboardEvent.preventDefault()},_onKeydown:function(t){this.keyboardEventMatchesKeys(t,"up down left right esc")||this._focusWithKeyboardEvent(t)}},is=[Ja,ns];
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        display: inline-block;
      }

      :host ::slotted(*) {
        padding: var(--paper-radio-group-item-padding, 12px);
      }
    </style>

    <slot></slot>
`,is:"paper-radio-group",behaviors:[is],hostAttributes:{role:"radiogroup"},properties:{attrForSelected:{type:String,value:"name"},selectedAttribute:{type:String,value:"checked"},selectable:{type:String,value:"paper-radio-button"},allowEmptySelection:{type:Boolean,value:!1}},select:function(t){var e=this._valueToItem(t);if(!e||!e.hasAttribute("disabled")){if(this.selected){var n=this._valueToItem(this.selected);if(this.selected==t){if(!this.allowEmptySelection)return void(n&&(n.checked=!0));t=""}n&&(n.checked=!1)}jo.select.apply(this,[t]),this.fire("paper-radio-group-changed")}},_activateFocusedItem:function(){this._itemActivate(this._valueForItem(this.focusedItem),this.focusedItem)},_onUpKey:function(t){this._focusPrevious(),t.preventDefault(),this._activateFocusedItem()},_onDownKey:function(t){this._focusNext(),t.preventDefault(),this._activateFocusedItem()},_onLeftKey:function(t){ns._onLeftKey.apply(this,arguments),this._activateFocusedItem()},_onRightKey:function(t){ns._onRightKey.apply(this,arguments),this._activateFocusedItem()}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const rs=_e`
  <style>
    :host {
      @apply --layout;
      @apply --layout-justified;
      @apply --layout-center;
      width: 200px;
      cursor: default;
      -webkit-user-select: none;
      -moz-user-select: none;
      -ms-user-select: none;
      user-select: none;
      -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
      --paper-progress-active-color: var(--paper-slider-active-color, var(--google-blue-700));
      --paper-progress-secondary-color: var(--paper-slider-secondary-color, var(--google-blue-300));
      --paper-progress-disabled-active-color: var(--paper-slider-disabled-active-color, var(--paper-grey-400));
      --paper-progress-disabled-secondary-color: var(--paper-slider-disabled-secondary-color, var(--paper-grey-400));
      --calculated-paper-slider-height: var(--paper-slider-height, 2px);
    }

    /* focus shows the ripple */
    :host(:focus) {
      outline: none;
    }

    /**
      * NOTE(keanulee): Though :host-context is not universally supported, some pages
      * still rely on paper-slider being flipped when dir="rtl" is set on body. For full
      * compatibility, dir="rtl" must be explicitly set on paper-slider.
      */
    :dir(rtl) #sliderContainer {
      -webkit-transform: scaleX(-1);
      transform: scaleX(-1);
    }

    /**
      * NOTE(keanulee): This is separate from the rule above because :host-context may
      * not be recognized.
      */
    :host([dir="rtl"]) #sliderContainer {
      -webkit-transform: scaleX(-1);
      transform: scaleX(-1);
    }

    /**
      * NOTE(keanulee): Needed to override the :host-context rule (where supported)
      * to support LTR sliders in RTL pages.
      */
    :host([dir="ltr"]) #sliderContainer {
      -webkit-transform: scaleX(1);
      transform: scaleX(1);
    }

    #sliderContainer {
      position: relative;
      width: 100%;
      height: calc(30px + var(--calculated-paper-slider-height));
      margin-left: calc(15px + var(--calculated-paper-slider-height)/2);
      margin-right: calc(15px + var(--calculated-paper-slider-height)/2);
    }

    #sliderContainer:focus {
      outline: 0;
    }

    #sliderContainer.editable {
      margin-top: 12px;
      margin-bottom: 12px;
    }

    .bar-container {
      position: absolute;
      top: 0;
      bottom: 0;
      left: 0;
      right: 0;
      overflow: hidden;
    }

    .ring > .bar-container {
      left: calc(5px + var(--calculated-paper-slider-height)/2);
      transition: left 0.18s ease;
    }

    .ring.expand.dragging > .bar-container {
      transition: none;
    }

    .ring.expand:not(.pin) > .bar-container {
      left: calc(8px + var(--calculated-paper-slider-height)/2);
    }

    #sliderBar {
      padding: 15px 0;
      width: 100%;
      background-color: var(--paper-slider-bar-color, transparent);
      --paper-progress-container-color: var(--paper-slider-container-color, var(--paper-grey-400));
      --paper-progress-height: var(--calculated-paper-slider-height);
    }

    .slider-markers {
      position: absolute;
      /* slider-knob is 30px + the slider-height so that the markers should start at a offset of 15px*/
      top: 15px;
      height: var(--calculated-paper-slider-height);
      left: 0;
      right: -1px;
      box-sizing: border-box;
      pointer-events: none;
      @apply --layout-horizontal;
    }

    .slider-marker {
      @apply --layout-flex;
    }
    .slider-markers::after,
    .slider-marker::after {
      content: "";
      display: block;
      margin-left: -1px;
      width: 2px;
      height: var(--calculated-paper-slider-height);
      border-radius: 50%;
      background-color: var(--paper-slider-markers-color, #000);
    }

    .slider-knob {
      position: absolute;
      left: 0;
      top: 0;
      margin-left: calc(-15px - var(--calculated-paper-slider-height)/2);
      width: calc(30px + var(--calculated-paper-slider-height));
      height: calc(30px + var(--calculated-paper-slider-height));
    }

    .transiting > .slider-knob {
      transition: left 0.08s ease;
    }

    .slider-knob:focus {
      outline: none;
    }

    .slider-knob.dragging {
      transition: none;
    }

    .snaps > .slider-knob.dragging {
      transition: -webkit-transform 0.08s ease;
      transition: transform 0.08s ease;
    }

    .slider-knob-inner {
      margin: 10px;
      width: calc(100% - 20px);
      height: calc(100% - 20px);
      background-color: var(--paper-slider-knob-color, var(--google-blue-700));
      border: 2px solid var(--paper-slider-knob-color, var(--google-blue-700));
      border-radius: 50%;

      -moz-box-sizing: border-box;
      box-sizing: border-box;

      transition-property: -webkit-transform, background-color, border;
      transition-property: transform, background-color, border;
      transition-duration: 0.18s;
      transition-timing-function: ease;
    }

    .expand:not(.pin) > .slider-knob > .slider-knob-inner {
      -webkit-transform: scale(1.5);
      transform: scale(1.5);
    }

    .ring > .slider-knob > .slider-knob-inner {
      background-color: var(--paper-slider-knob-start-color, transparent);
      border: 2px solid var(--paper-slider-knob-start-border-color, var(--paper-grey-400));
    }

    .slider-knob-inner::before {
      background-color: var(--paper-slider-pin-color, var(--google-blue-700));
    }

    .pin > .slider-knob > .slider-knob-inner::before {
      content: "";
      position: absolute;
      top: 0;
      left: 50%;
      margin-left: -13px;
      width: 26px;
      height: 26px;
      border-radius: 50% 50% 50% 0;

      -webkit-transform: rotate(-45deg) scale(0) translate(0);
      transform: rotate(-45deg) scale(0) translate(0);
    }

    .slider-knob-inner::before,
    .slider-knob-inner::after {
      transition: -webkit-transform .18s ease, background-color .18s ease;
      transition: transform .18s ease, background-color .18s ease;
    }

    .pin.ring > .slider-knob > .slider-knob-inner::before {
      background-color: var(--paper-slider-pin-start-color, var(--paper-grey-400));
    }

    .pin.expand > .slider-knob > .slider-knob-inner::before {
      -webkit-transform: rotate(-45deg) scale(1) translate(17px, -17px);
      transform: rotate(-45deg) scale(1) translate(17px, -17px);
    }

    .pin > .slider-knob > .slider-knob-inner::after {
      content: attr(value);
      position: absolute;
      top: 0;
      left: 50%;
      margin-left: -16px;
      width: 32px;
      height: 26px;
      text-align: center;
      color: var(--paper-slider-font-color, #fff);
      font-size: 10px;

      -webkit-transform: scale(0) translate(0);
      transform: scale(0) translate(0);
    }

    .pin.expand > .slider-knob > .slider-knob-inner::after {
      -webkit-transform: scale(1) translate(0, -17px);
      transform: scale(1) translate(0, -17px);
    }

    /* paper-input */
    .slider-input {
      width: 50px;
      overflow: hidden;
      --paper-input-container-input: {
        text-align: center;
        @apply --paper-slider-input-container-input;
      };
      @apply --paper-slider-input;
    }

    /* disabled state */
    #sliderContainer.disabled {
      pointer-events: none;
    }

    .disabled > .slider-knob > .slider-knob-inner {
      background-color: var(--paper-slider-disabled-knob-color, var(--paper-grey-400));
      border: 2px solid var(--paper-slider-disabled-knob-color, var(--paper-grey-400));
      -webkit-transform: scale3d(0.75, 0.75, 1);
      transform: scale3d(0.75, 0.75, 1);
    }

    .disabled.ring > .slider-knob > .slider-knob-inner {
      background-color: var(--paper-slider-knob-start-color, transparent);
      border: 2px solid var(--paper-slider-knob-start-border-color, var(--paper-grey-400));
    }

    paper-ripple {
      color: var(--paper-slider-knob-color, var(--google-blue-700));
    }
  </style>

  <div id="sliderContainer" class\$="[[_getClassNames(disabled, pin, snaps, immediateValue, min, expand, dragging, transiting, editable)]]">
    <div class="bar-container">
      <paper-progress disabled\$="[[disabled]]" id="sliderBar" aria-hidden="true" min="[[min]]" max="[[max]]" step="[[step]]" value="[[immediateValue]]" secondary-progress="[[secondaryProgress]]" on-down="_bardown" on-up="_resetKnob" on-track="_bartrack" on-tap="_barclick">
      </paper-progress>
    </div>

    <template is="dom-if" if="[[snaps]]">
      <div class="slider-markers">
        <template is="dom-repeat" items="[[markers]]">
          <div class="slider-marker"></div>
        </template>
      </div>
    </template>

    <div id="sliderKnob" class="slider-knob" on-down="_knobdown" on-up="_resetKnob" on-track="_onTrack" on-transitionend="_knobTransitionEnd">
        <div class="slider-knob-inner" value\$="[[immediateValue]]"></div>
    </div>
  </div>

  <template is="dom-if" if="[[editable]]">
    <paper-input id="input" type="number" step="[[step]]" min="[[min]]" max="[[max]]" class="slider-input" disabled\$="[[disabled]]" value="[[immediateValue]]" on-change="_changeValue" on-keydown="_inputKeyDown" no-label-float>
    </paper-input>
  </template>
`;rs.setAttribute("strip-whitespace",""),Rr({_template:rs,is:"paper-slider",behaviors:[zo,ra,ca,ts],properties:{value:{type:Number,value:0},snaps:{type:Boolean,value:!1,notify:!0},pin:{type:Boolean,value:!1,notify:!0},secondaryProgress:{type:Number,value:0,notify:!0,observer:"_secondaryProgressChanged"},editable:{type:Boolean,value:!1},immediateValue:{type:Number,value:0,readOnly:!0,notify:!0},maxMarkers:{type:Number,value:0,notify:!0},expand:{type:Boolean,value:!1,readOnly:!0},ignoreBarTouch:{type:Boolean,value:!1},dragging:{type:Boolean,value:!1,readOnly:!0,notify:!0},transiting:{type:Boolean,value:!1,readOnly:!0},markers:{type:Array,readOnly:!0,value:function(){return[]}}},observers:["_updateKnob(value, min, max, snaps, step)","_valueChanged(value)","_immediateValueChanged(immediateValue)","_updateMarkers(maxMarkers, min, max, snaps)"],hostAttributes:{role:"slider",tabindex:0},keyBindings:{left:"_leftKey",right:"_rightKey","down pagedown home":"_decrementKey","up pageup end":"_incrementKey"},ready:function(){this.ignoreBarTouch&&oi(this.$.sliderBar,"auto")},increment:function(){this.value=this._clampValue(this.value+this.step)},decrement:function(){this.value=this._clampValue(this.value-this.step)},_updateKnob:function(t,e,n,i,r){this.setAttribute("aria-valuemin",e),this.setAttribute("aria-valuemax",n),this.setAttribute("aria-valuenow",t),this._positionKnob(100*this._calcRatio(t))},_valueChanged:function(){this.fire("value-change",{composed:!0})},_immediateValueChanged:function(){this.dragging?this.fire("immediate-value-change",{composed:!0}):this.value=this.immediateValue},_secondaryProgressChanged:function(){this.secondaryProgress=this._clampValue(this.secondaryProgress)},_expandKnob:function(){this._setExpand(!0)},_resetKnob:function(){this.cancelDebouncer("expandKnob"),this._setExpand(!1)},_positionKnob:function(t){this._setImmediateValue(this._calcStep(this._calcKnobPosition(t))),this._setRatio(100*this._calcRatio(this.immediateValue)),this.$.sliderKnob.style.left=this.ratio+"%",this.dragging&&(this._knobstartx=this.ratio*this._w/100,this.translate3d(0,0,0,this.$.sliderKnob))},_calcKnobPosition:function(t){return(this.max-this.min)*t/100+this.min},_onTrack:function(t){switch(t.stopPropagation(),t.detail.state){case"start":this._trackStart(t);break;case"track":this._trackX(t);break;case"end":this._trackEnd()}},_trackStart:function(t){this._setTransiting(!1),this._w=this.$.sliderBar.offsetWidth,this._x=this.ratio*this._w/100,this._startx=this._x,this._knobstartx=this._startx,this._minx=-this._startx,this._maxx=this._w-this._startx,this.$.sliderKnob.classList.add("dragging"),this._setDragging(!0)},_trackX:function(t){this.dragging||this._trackStart(t);var e=Math.min(this._maxx,Math.max(this._minx,t.detail.dx*(this._isRTL?-1:1)));this._x=this._startx+e;var n=this._calcStep(this._calcKnobPosition(this._x/this._w*100));this._setImmediateValue(n);var i=this._calcRatio(this.immediateValue)*this._w-this._knobstartx;this.translate3d(i+"px",0,0,this.$.sliderKnob)},_trackEnd:function(){var t=this.$.sliderKnob.style;this.$.sliderKnob.classList.remove("dragging"),this._setDragging(!1),this._resetKnob(),this.value=this.immediateValue,t.transform=t.webkitTransform="",this.fire("change",{composed:!0})},_knobdown:function(t){this._expandKnob(),t.preventDefault(),this.focus()},_bartrack:function(t){this._allowBarEvent(t)&&this._onTrack(t)},_barclick:function(t){this._w=this.$.sliderBar.offsetWidth;var e=this.$.sliderBar.getBoundingClientRect(),n=(t.detail.x-e.left)/this._w*100;this._isRTL&&(n=100-n);var i=this.ratio;this._setTransiting(!0),this._positionKnob(n),i===this.ratio&&this._setTransiting(!1),this.async((function(){this.fire("change",{composed:!0})})),t.preventDefault(),this.focus()},_bardown:function(t){this._allowBarEvent(t)&&(this.debounce("expandKnob",this._expandKnob,60),this._barclick(t))},_knobTransitionEnd:function(t){t.target===this.$.sliderKnob&&this._setTransiting(!1)},_updateMarkers:function(t,e,n,i){i||this._setMarkers([]);var r=Math.round((n-e)/this.step);r>t&&(r=t),(r<0||!isFinite(r))&&(r=0),this._setMarkers(new Array(r))},_mergeClasses:function(t){return Object.keys(t).filter((function(e){return t[e]})).join(" ")},_getClassNames:function(){return this._mergeClasses({disabled:this.disabled,pin:this.pin,snaps:this.snaps,ring:this.immediateValue<=this.min,expand:this.expand,dragging:this.dragging,transiting:this.transiting,editable:this.editable})},_allowBarEvent:function(t){return!this.ignoreBarTouch||t.detail.sourceEvent instanceof MouseEvent},get _isRTL(){return void 0===this.__isRTL&&(this.__isRTL="rtl"===window.getComputedStyle(this).direction),this.__isRTL},_leftKey:function(t){this._isRTL?this._incrementKey(t):this._decrementKey(t)},_rightKey:function(t){this._isRTL?this._decrementKey(t):this._incrementKey(t)},_incrementKey:function(t){this.disabled||("end"===t.detail.key?this.value=this.max:this.increment(),this.fire("change"),t.preventDefault())},_decrementKey:function(t){this.disabled||("home"===t.detail.key?this.value=this.min:this.decrement(),this.fire("change"),t.preventDefault())},_changeValue:function(t){this.value=t.target.value,this.fire("change",{composed:!0})},_inputKeyDown:function(t){t.stopPropagation()},_createRipple:function(){return this._rippleContainer=this.$.sliderKnob,la._createRipple.call(this)},_focusedChanged:function(t){t&&this.ensureRipple(),this.hasRipple()&&(this._ripple.style.display=t?"":"none",this._ripple.holdDown=t)}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const os=document.createElement("template");os.setAttribute("style","display: none;"),os.innerHTML="<dom-module id=\"paper-spinner-styles\">\n  <template>\n    <style>\n      /*\n      /**************************/\n      /* STYLES FOR THE SPINNER */\n      /**************************/\n\n      /*\n       * Constants:\n       *      ARCSIZE     = 270 degrees (amount of circle the arc takes up)\n       *      ARCTIME     = 1333ms (time it takes to expand and contract arc)\n       *      ARCSTARTROT = 216 degrees (how much the start location of the arc\n       *                                should rotate each time, 216 gives us a\n       *                                5 pointed star shape (it's 360/5 * 3).\n       *                                For a 7 pointed star, we might do\n       *                                360/7 * 3 = 154.286)\n       *      SHRINK_TIME = 400ms\n       */\n\n      :host {\n        display: inline-block;\n        position: relative;\n        width: 28px;\n        height: 28px;\n\n        /* 360 * ARCTIME / (ARCSTARTROT + (360-ARCSIZE)) */\n        --paper-spinner-container-rotation-duration: 1568ms;\n\n        /* ARCTIME */\n        --paper-spinner-expand-contract-duration: 1333ms;\n\n        /* 4 * ARCTIME */\n        --paper-spinner-full-cycle-duration: 5332ms;\n\n        /* SHRINK_TIME */\n        --paper-spinner-cooldown-duration: 400ms;\n      }\n\n      #spinnerContainer {\n        width: 100%;\n        height: 100%;\n\n        /* The spinner does not have any contents that would have to be\n         * flipped if the direction changes. Always use ltr so that the\n         * style works out correctly in both cases. */\n        direction: ltr;\n      }\n\n      #spinnerContainer.active {\n        -webkit-animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite;\n        animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite;\n      }\n\n      @-webkit-keyframes container-rotate {\n        to { -webkit-transform: rotate(360deg) }\n      }\n\n      @keyframes container-rotate {\n        to { transform: rotate(360deg) }\n      }\n\n      .spinner-layer {\n        position: absolute;\n        width: 100%;\n        height: 100%;\n        opacity: 0;\n        white-space: nowrap;\n        color: var(--paper-spinner-color, var(--google-blue-500));\n      }\n\n      .layer-1 {\n        color: var(--paper-spinner-layer-1-color, var(--google-blue-500));\n      }\n\n      .layer-2 {\n        color: var(--paper-spinner-layer-2-color, var(--google-red-500));\n      }\n\n      .layer-3 {\n        color: var(--paper-spinner-layer-3-color, var(--google-yellow-500));\n      }\n\n      .layer-4 {\n        color: var(--paper-spinner-layer-4-color, var(--google-green-500));\n      }\n\n      /**\n       * IMPORTANT NOTE ABOUT CSS ANIMATION PROPERTIES (keanulee):\n       *\n       * iOS Safari (tested on iOS 8.1) does not handle animation-delay very well - it doesn't\n       * guarantee that the animation will start _exactly_ after that value. So we avoid using\n       * animation-delay and instead set custom keyframes for each color (as layer-2undant as it\n       * seems).\n       */\n      .active .spinner-layer {\n        -webkit-animation-name: fill-unfill-rotate;\n        -webkit-animation-duration: var(--paper-spinner-full-cycle-duration);\n        -webkit-animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);\n        -webkit-animation-iteration-count: infinite;\n        animation-name: fill-unfill-rotate;\n        animation-duration: var(--paper-spinner-full-cycle-duration);\n        animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);\n        animation-iteration-count: infinite;\n        opacity: 1;\n      }\n\n      .active .spinner-layer.layer-1 {\n        -webkit-animation-name: fill-unfill-rotate, layer-1-fade-in-out;\n        animation-name: fill-unfill-rotate, layer-1-fade-in-out;\n      }\n\n      .active .spinner-layer.layer-2 {\n        -webkit-animation-name: fill-unfill-rotate, layer-2-fade-in-out;\n        animation-name: fill-unfill-rotate, layer-2-fade-in-out;\n      }\n\n      .active .spinner-layer.layer-3 {\n        -webkit-animation-name: fill-unfill-rotate, layer-3-fade-in-out;\n        animation-name: fill-unfill-rotate, layer-3-fade-in-out;\n      }\n\n      .active .spinner-layer.layer-4 {\n        -webkit-animation-name: fill-unfill-rotate, layer-4-fade-in-out;\n        animation-name: fill-unfill-rotate, layer-4-fade-in-out;\n      }\n\n      @-webkit-keyframes fill-unfill-rotate {\n        12.5% { -webkit-transform: rotate(135deg) } /* 0.5 * ARCSIZE */\n        25%   { -webkit-transform: rotate(270deg) } /* 1   * ARCSIZE */\n        37.5% { -webkit-transform: rotate(405deg) } /* 1.5 * ARCSIZE */\n        50%   { -webkit-transform: rotate(540deg) } /* 2   * ARCSIZE */\n        62.5% { -webkit-transform: rotate(675deg) } /* 2.5 * ARCSIZE */\n        75%   { -webkit-transform: rotate(810deg) } /* 3   * ARCSIZE */\n        87.5% { -webkit-transform: rotate(945deg) } /* 3.5 * ARCSIZE */\n        to    { -webkit-transform: rotate(1080deg) } /* 4   * ARCSIZE */\n      }\n\n      @keyframes fill-unfill-rotate {\n        12.5% { transform: rotate(135deg) } /* 0.5 * ARCSIZE */\n        25%   { transform: rotate(270deg) } /* 1   * ARCSIZE */\n        37.5% { transform: rotate(405deg) } /* 1.5 * ARCSIZE */\n        50%   { transform: rotate(540deg) } /* 2   * ARCSIZE */\n        62.5% { transform: rotate(675deg) } /* 2.5 * ARCSIZE */\n        75%   { transform: rotate(810deg) } /* 3   * ARCSIZE */\n        87.5% { transform: rotate(945deg) } /* 3.5 * ARCSIZE */\n        to    { transform: rotate(1080deg) } /* 4   * ARCSIZE */\n      }\n\n      @-webkit-keyframes layer-1-fade-in-out {\n        0% { opacity: 1 }\n        25% { opacity: 1 }\n        26% { opacity: 0 }\n        89% { opacity: 0 }\n        90% { opacity: 1 }\n        to { opacity: 1 }\n      }\n\n      @keyframes layer-1-fade-in-out {\n        0% { opacity: 1 }\n        25% { opacity: 1 }\n        26% { opacity: 0 }\n        89% { opacity: 0 }\n        90% { opacity: 1 }\n        to { opacity: 1 }\n      }\n\n      @-webkit-keyframes layer-2-fade-in-out {\n        0% { opacity: 0 }\n        15% { opacity: 0 }\n        25% { opacity: 1 }\n        50% { opacity: 1 }\n        51% { opacity: 0 }\n        to { opacity: 0 }\n      }\n\n      @keyframes layer-2-fade-in-out {\n        0% { opacity: 0 }\n        15% { opacity: 0 }\n        25% { opacity: 1 }\n        50% { opacity: 1 }\n        51% { opacity: 0 }\n        to { opacity: 0 }\n      }\n\n      @-webkit-keyframes layer-3-fade-in-out {\n        0% { opacity: 0 }\n        40% { opacity: 0 }\n        50% { opacity: 1 }\n        75% { opacity: 1 }\n        76% { opacity: 0 }\n        to { opacity: 0 }\n      }\n\n      @keyframes layer-3-fade-in-out {\n        0% { opacity: 0 }\n        40% { opacity: 0 }\n        50% { opacity: 1 }\n        75% { opacity: 1 }\n        76% { opacity: 0 }\n        to { opacity: 0 }\n      }\n\n      @-webkit-keyframes layer-4-fade-in-out {\n        0% { opacity: 0 }\n        65% { opacity: 0 }\n        75% { opacity: 1 }\n        90% { opacity: 1 }\n        to { opacity: 0 }\n      }\n\n      @keyframes layer-4-fade-in-out {\n        0% { opacity: 0 }\n        65% { opacity: 0 }\n        75% { opacity: 1 }\n        90% { opacity: 1 }\n        to { opacity: 0 }\n      }\n\n      .circle-clipper {\n        display: inline-block;\n        position: relative;\n        width: 50%;\n        height: 100%;\n        overflow: hidden;\n      }\n\n      /**\n       * Patch the gap that appear between the two adjacent div.circle-clipper while the\n       * spinner is rotating (appears on Chrome 50, Safari 9.1.1, and Edge).\n       */\n      .spinner-layer::after {\n        content: '';\n        left: 45%;\n        width: 10%;\n        border-top-style: solid;\n      }\n\n      .spinner-layer::after,\n      .circle-clipper .circle {\n        box-sizing: border-box;\n        position: absolute;\n        top: 0;\n        border-width: var(--paper-spinner-stroke-width, 3px);\n        border-radius: 50%;\n      }\n\n      .circle-clipper .circle {\n        bottom: 0;\n        width: 200%;\n        border-style: solid;\n        border-bottom-color: transparent !important;\n      }\n\n      .circle-clipper.left .circle {\n        left: 0;\n        border-right-color: transparent !important;\n        -webkit-transform: rotate(129deg);\n        transform: rotate(129deg);\n      }\n\n      .circle-clipper.right .circle {\n        left: -100%;\n        border-left-color: transparent !important;\n        -webkit-transform: rotate(-129deg);\n        transform: rotate(-129deg);\n      }\n\n      .active .gap-patch::after,\n      .active .circle-clipper .circle {\n        -webkit-animation-duration: var(--paper-spinner-expand-contract-duration);\n        -webkit-animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);\n        -webkit-animation-iteration-count: infinite;\n        animation-duration: var(--paper-spinner-expand-contract-duration);\n        animation-timing-function: cubic-bezier(0.4, 0.0, 0.2, 1);\n        animation-iteration-count: infinite;\n      }\n\n      .active .circle-clipper.left .circle {\n        -webkit-animation-name: left-spin;\n        animation-name: left-spin;\n      }\n\n      .active .circle-clipper.right .circle {\n        -webkit-animation-name: right-spin;\n        animation-name: right-spin;\n      }\n\n      @-webkit-keyframes left-spin {\n        0% { -webkit-transform: rotate(130deg) }\n        50% { -webkit-transform: rotate(-5deg) }\n        to { -webkit-transform: rotate(130deg) }\n      }\n\n      @keyframes left-spin {\n        0% { transform: rotate(130deg) }\n        50% { transform: rotate(-5deg) }\n        to { transform: rotate(130deg) }\n      }\n\n      @-webkit-keyframes right-spin {\n        0% { -webkit-transform: rotate(-130deg) }\n        50% { -webkit-transform: rotate(5deg) }\n        to { -webkit-transform: rotate(-130deg) }\n      }\n\n      @keyframes right-spin {\n        0% { transform: rotate(-130deg) }\n        50% { transform: rotate(5deg) }\n        to { transform: rotate(-130deg) }\n      }\n\n      #spinnerContainer.cooldown {\n        -webkit-animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite, fade-out var(--paper-spinner-cooldown-duration) cubic-bezier(0.4, 0.0, 0.2, 1);\n        animation: container-rotate var(--paper-spinner-container-rotation-duration) linear infinite, fade-out var(--paper-spinner-cooldown-duration) cubic-bezier(0.4, 0.0, 0.2, 1);\n      }\n\n      @-webkit-keyframes fade-out {\n        0% { opacity: 1 }\n        to { opacity: 0 }\n      }\n\n      @keyframes fade-out {\n        0% { opacity: 1 }\n        to { opacity: 0 }\n      }\n    </style>\n  </template>\n</dom-module>",document.head.appendChild(os.content);
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const as={properties:{active:{type:Boolean,value:!1,reflectToAttribute:!0,observer:"__activeChanged"},alt:{type:String,value:"loading",observer:"__altChanged"},__coolingDown:{type:Boolean,value:!1}},__computeContainerClasses:function(t,e){return[t||e?"active":"",e?"cooldown":""].join(" ")},__activeChanged:function(t,e){this.__setAriaHidden(!t),this.__coolingDown=!t&&e},__altChanged:function(t){"loading"===t?this.alt=this.getAttribute("aria-label")||t:(this.__setAriaHidden(""===t),this.setAttribute("aria-label",t))},__setAriaHidden:function(t){var e="aria-hidden";t?this.setAttribute(e,"true"):this.removeAttribute(e)},__reset:function(){this.active=!1,this.__coolingDown=!1}},ss=_e`
  <style include="paper-spinner-styles"></style>

  <div id="spinnerContainer" class-name="[[__computeContainerClasses(active, __coolingDown)]]" on-animationend="__reset" on-webkit-animation-end="__reset">
    <div class="spinner-layer">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>
  </div>
`;
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */ss.setAttribute("strip-whitespace",""),Rr({_template:ss,is:"paper-spinner-lite",behaviors:[as]});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const ls=_e`
  <style include="paper-spinner-styles"></style>

  <div id="spinnerContainer" class-name="[[__computeContainerClasses(active, __coolingDown)]]" on-animationend="__reset" on-webkit-animation-end="__reset">
    <div class="spinner-layer layer-1">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>

    <div class="spinner-layer layer-2">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>

    <div class="spinner-layer layer-3">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>

    <div class="spinner-layer layer-4">
      <div class="circle-clipper left">
        <div class="circle"></div>
      </div>
      <div class="circle-clipper right">
        <div class="circle"></div>
      </div>
    </div>
  </div>
`;ls.setAttribute("strip-whitespace",""),Rr({_template:ls,is:"paper-spinner",behaviors:[as]});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const cs=_e`<iron-iconset-svg name="paper-tabs" size="24">
<svg><defs>
<g id="chevron-left"><path d="M15.41 7.41L14 6l-6 6 6 6 1.41-1.41L10.83 12z"></path></g>
<g id="chevron-right"><path d="M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z"></path></g>
</defs></svg>
</iron-iconset-svg>`;document.head.appendChild(cs.content),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        @apply --layout-inline;
        @apply --layout-center;
        @apply --layout-center-justified;
        @apply --layout-flex-auto;

        position: relative;
        padding: 0 12px;
        overflow: hidden;
        cursor: pointer;
        vertical-align: middle;

        @apply --paper-font-common-base;
        @apply --paper-tab;
      }

      :host(:focus) {
        outline: none;
      }

      :host([link]) {
        padding: 0;
      }

      .tab-content {
        height: 100%;
        transform: translateZ(0);
          -webkit-transform: translateZ(0);
        transition: opacity 0.1s cubic-bezier(0.4, 0.0, 1, 1);
        @apply --layout-horizontal;
        @apply --layout-center-center;
        @apply --layout-flex-auto;
        @apply --paper-tab-content;
      }

      :host(:not(.iron-selected)) > .tab-content {
        opacity: 0.8;

        @apply --paper-tab-content-unselected;
      }

      :host(:focus) .tab-content {
        opacity: 1;
        font-weight: 700;

        @apply --paper-tab-content-focused;
      }

      paper-ripple {
        color: var(--paper-tab-ink, var(--paper-yellow-a100));
      }

      .tab-content > ::slotted(a) {
        @apply --layout-flex-auto;

        height: 100%;
      }
    </style>

    <div class="tab-content">
      <slot></slot>
    </div>
`,is:"paper-tab",behaviors:[qo,Xo,Jo],properties:{link:{type:Boolean,value:!1,reflectToAttribute:!0}},hostAttributes:{role:"tab"},listeners:{down:"_updateNoink",tap:"_onTap"},attached:function(){this._updateNoink()},get _parentNoink(){var t=Yi(this).parentNode;return!!t&&!!t.noink},_updateNoink:function(){this.noink=!!this.noink||!!this._parentNoink},_onTap:function(t){if(this.link){var e=this.queryEffectiveChildren("a");if(!e)return;if(t.target===e)return;e.click()}}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        @apply --layout;
        @apply --layout-center;

        height: 48px;
        font-size: 14px;
        font-weight: 500;
        overflow: hidden;
        -moz-user-select: none;
        -ms-user-select: none;
        -webkit-user-select: none;
        user-select: none;

        /* NOTE: Both values are needed, since some phones require the value to be \`transparent\`. */
        -webkit-tap-highlight-color: rgba(0, 0, 0, 0);
        -webkit-tap-highlight-color: transparent;

        @apply --paper-tabs;
      }

      :host(:dir(rtl)) {
        @apply --layout-horizontal-reverse;
      }

      #tabsContainer {
        position: relative;
        height: 100%;
        white-space: nowrap;
        overflow: hidden;
        @apply --layout-flex-auto;
        @apply --paper-tabs-container;
      }

      #tabsContent {
        height: 100%;
        -moz-flex-basis: auto;
        -ms-flex-basis: auto;
        flex-basis: auto;
        @apply --paper-tabs-content;
      }

      #tabsContent.scrollable {
        position: absolute;
        white-space: nowrap;
      }

      #tabsContent:not(.scrollable),
      #tabsContent.scrollable.fit-container {
        @apply --layout-horizontal;
      }

      #tabsContent.scrollable.fit-container {
        min-width: 100%;
      }

      #tabsContent.scrollable.fit-container > ::slotted(*) {
        /* IE - prevent tabs from compressing when they should scroll. */
        -ms-flex: 1 0 auto;
        -webkit-flex: 1 0 auto;
        flex: 1 0 auto;
      }

      .hidden {
        display: none;
      }

      .not-visible {
        opacity: 0;
        cursor: default;
      }

      paper-icon-button {
        width: 48px;
        height: 48px;
        padding: 12px;
        margin: 0 4px;
      }

      #selectionBar {
        position: absolute;
        height: 0;
        bottom: 0;
        left: 0;
        right: 0;
        border-bottom: 2px solid var(--paper-tabs-selection-bar-color, var(--paper-yellow-a100));
          -webkit-transform: scale(0);
        transform: scale(0);
          -webkit-transform-origin: left center;
        transform-origin: left center;
          transition: -webkit-transform;
        transition: transform;

        @apply --paper-tabs-selection-bar;
      }

      #selectionBar.align-bottom {
        top: 0;
        bottom: auto;
      }

      #selectionBar.expand {
        transition-duration: 0.15s;
        transition-timing-function: cubic-bezier(0.4, 0.0, 1, 1);
      }

      #selectionBar.contract {
        transition-duration: 0.18s;
        transition-timing-function: cubic-bezier(0.0, 0.0, 0.2, 1);
      }

      #tabsContent > ::slotted(:not(#selectionBar)) {
        height: 100%;
      }
    </style>

    <paper-icon-button icon="paper-tabs:chevron-left" class$="[[_computeScrollButtonClass(_leftHidden, scrollable, hideScrollButtons)]]" on-up="_onScrollButtonUp" on-down="_onLeftScrollButtonDown" tabindex="-1"></paper-icon-button>

    <div id="tabsContainer" on-track="_scroll" on-down="_down">
      <div id="tabsContent" class$="[[_computeTabsContentClass(scrollable, fitContainer)]]">
        <div id="selectionBar" class$="[[_computeSelectionBarClass(noBar, alignBottom)]]" on-transitionend="_onBarTransitionEnd"></div>
        <slot></slot>
      </div>
    </div>

    <paper-icon-button icon="paper-tabs:chevron-right" class$="[[_computeScrollButtonClass(_rightHidden, scrollable, hideScrollButtons)]]" on-up="_onScrollButtonUp" on-down="_onRightScrollButtonDown" tabindex="-1"></paper-icon-button>
`,is:"paper-tabs",behaviors:[go,is],properties:{noink:{type:Boolean,value:!1,observer:"_noinkChanged"},noBar:{type:Boolean,value:!1},noSlide:{type:Boolean,value:!1},scrollable:{type:Boolean,value:!1},fitContainer:{type:Boolean,value:!1},disableDrag:{type:Boolean,value:!1},hideScrollButtons:{type:Boolean,value:!1},alignBottom:{type:Boolean,value:!1},selectable:{type:String,value:"paper-tab"},autoselect:{type:Boolean,value:!1},autoselectDelay:{type:Number,value:0},_step:{type:Number,value:10},_holdDelay:{type:Number,value:1},_leftHidden:{type:Boolean,value:!1},_rightHidden:{type:Boolean,value:!1},_previousTab:{type:Object}},hostAttributes:{role:"tablist"},listeners:{"iron-resize":"_onTabSizingChanged","iron-items-changed":"_onTabSizingChanged","iron-select":"_onIronSelect","iron-deselect":"_onIronDeselect"},keyBindings:{"left:keyup right:keyup":"_onArrowKeyup"},created:function(){this._holdJob=null,this._pendingActivationItem=void 0,this._pendingActivationTimeout=void 0,this._bindDelayedActivationHandler=this._delayedActivationHandler.bind(this),this.addEventListener("blur",this._onBlurCapture.bind(this),!0)},ready:function(){this.setScrollDirection("y",this.$.tabsContainer)},detached:function(){this._cancelPendingActivation()},_noinkChanged:function(t){Yi(this).querySelectorAll("paper-tab").forEach(t?this._setNoinkAttribute:this._removeNoinkAttribute)},_setNoinkAttribute:function(t){t.setAttribute("noink","")},_removeNoinkAttribute:function(t){t.removeAttribute("noink")},_computeScrollButtonClass:function(t,e,n){return!e||n?"hidden":t?"not-visible":""},_computeTabsContentClass:function(t,e){return t?"scrollable"+(e?" fit-container":""):" fit-container"},_computeSelectionBarClass:function(t,e){return t?"hidden":e?"align-bottom":""},_onTabSizingChanged:function(){this.debounce("_onTabSizingChanged",(function(){this._scroll(),this._tabChanged(this.selectedItem)}),10)},_onIronSelect:function(t){this._tabChanged(t.detail.item,this._previousTab),this._previousTab=t.detail.item,this.cancelDebouncer("tab-changed")},_onIronDeselect:function(t){this.debounce("tab-changed",(function(){this._tabChanged(null,this._previousTab),this._previousTab=null}),1)},_activateHandler:function(){this._cancelPendingActivation(),Za._activateHandler.apply(this,arguments)},_scheduleActivation:function(t,e){this._pendingActivationItem=t,this._pendingActivationTimeout=this.async(this._bindDelayedActivationHandler,e)},_delayedActivationHandler:function(){var t=this._pendingActivationItem;this._pendingActivationItem=void 0,this._pendingActivationTimeout=void 0,t.fire(this.activateEvent,null,{bubbles:!0,cancelable:!0})},_cancelPendingActivation:function(){void 0!==this._pendingActivationTimeout&&(this.cancelAsync(this._pendingActivationTimeout),this._pendingActivationItem=void 0,this._pendingActivationTimeout=void 0)},_onArrowKeyup:function(t){this.autoselect&&this._scheduleActivation(this.focusedItem,this.autoselectDelay)},_onBlurCapture:function(t){t.target===this._pendingActivationItem&&this._cancelPendingActivation()},get _tabContainerScrollSize(){return Math.max(0,this.$.tabsContainer.scrollWidth-this.$.tabsContainer.offsetWidth)},_scroll:function(t,e){this.scrollable&&this._affectScroll(e&&-e.ddx||0)},_down:function(t){this.async((function(){this._defaultFocusAsync&&(this.cancelAsync(this._defaultFocusAsync),this._defaultFocusAsync=null)}),1)},_affectScroll:function(t){this.$.tabsContainer.scrollLeft+=t;var e=this.$.tabsContainer.scrollLeft;this._leftHidden=0===e,this._rightHidden=e===this._tabContainerScrollSize},_onLeftScrollButtonDown:function(){this._scrollToLeft(),this._holdJob=setInterval(this._scrollToLeft.bind(this),this._holdDelay)},_onRightScrollButtonDown:function(){this._scrollToRight(),this._holdJob=setInterval(this._scrollToRight.bind(this),this._holdDelay)},_onScrollButtonUp:function(){clearInterval(this._holdJob),this._holdJob=null},_scrollToLeft:function(){this._affectScroll(-this._step)},_scrollToRight:function(){this._affectScroll(this._step)},_tabChanged:function(t,e){if(!t)return this.$.selectionBar.classList.remove("expand"),this.$.selectionBar.classList.remove("contract"),void this._positionBar(0,0);var n=this.$.tabsContent.getBoundingClientRect(),i=n.width,r=t.getBoundingClientRect(),o=r.left-n.left;if(this._pos={width:this._calcPercent(r.width,i),left:this._calcPercent(o,i)},this.noSlide||null==e)return this.$.selectionBar.classList.remove("expand"),this.$.selectionBar.classList.remove("contract"),void this._positionBar(this._pos.width,this._pos.left);var a=e.getBoundingClientRect(),s=this.items.indexOf(e),l=this.items.indexOf(t);this.$.selectionBar.classList.add("expand");var c=s<l;this._isRTL&&(c=!c),c?this._positionBar(this._calcPercent(r.left+r.width-a.left,i)-5,this._left):this._positionBar(this._calcPercent(a.left+a.width-r.left,i)-5,this._calcPercent(o,i)+5),this.scrollable&&this._scrollToSelectedIfNeeded(r.width,o)},_scrollToSelectedIfNeeded:function(t,e){var n=e-this.$.tabsContainer.scrollLeft;(n<0||(n+=t-this.$.tabsContainer.offsetWidth)>0)&&(this.$.tabsContainer.scrollLeft+=n)},_calcPercent:function(t,e){return 100*t/e},_positionBar:function(t,e){e=e||0,this._width=t=t||0,this._left=e,this.transform("translateX("+e+"%) scaleX("+t/100+")",this.$.selectionBar)},_onBarTransitionEnd:function(t){var e=this.$.selectionBar.classList;e.contains("expand")?(e.remove("expand"),e.add("contract"),this._positionBar(this._pos.width,this._pos.left)):e.contains("contract")&&e.remove("contract")}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
var us=null;Rr({_template:_e`
    <style>
      :host {
        display: block;
        position: fixed;
        background-color: var(--paper-toast-background-color, #323232);
        color: var(--paper-toast-color, #f1f1f1);
        min-height: 48px;
        min-width: 288px;
        padding: 16px 24px;
        box-sizing: border-box;
        box-shadow: 0 2px 5px 0 rgba(0, 0, 0, 0.26);
        border-radius: 2px;
        margin: 12px;
        font-size: 14px;
        cursor: default;
        -webkit-transition: -webkit-transform 0.3s, opacity 0.3s;
        transition: transform 0.3s, opacity 0.3s;
        opacity: 0;
        -webkit-transform: translateY(100px);
        transform: translateY(100px);
        @apply --paper-font-common-base;
      }

      :host(.capsule) {
        border-radius: 24px;
      }

      :host(.fit-bottom) {
        width: 100%;
        min-width: 0;
        border-radius: 0;
        margin: 0;
      }

      :host(.paper-toast-open) {
        opacity: 1;
        -webkit-transform: translateY(0px);
        transform: translateY(0px);
      }
    </style>

    <span id="label">{{text}}</span>
    <slot></slot>
`,is:"paper-toast",behaviors:[ka],properties:{fitInto:{type:Object,value:window,observer:"_onFitIntoChanged"},horizontalAlign:{type:String,value:"left"},verticalAlign:{type:String,value:"bottom"},duration:{type:Number,value:3e3},text:{type:String,value:""},noCancelOnOutsideClick:{type:Boolean,value:!0},noAutoFocus:{type:Boolean,value:!0}},listeners:{transitionend:"__onTransitionEnd"},get visible(){return fo._warn("`visible` is deprecated, use `opened` instead"),this.opened},get _canAutoClose(){return this.duration>0&&this.duration!==1/0},created:function(){this._autoClose=null,Oa.requestAvailability()},show:function(t){for(var e in"string"==typeof t&&(t={text:t}),t)0===e.indexOf("_")?fo._warn('The property "'+e+'" is private and was not set.'):e in this?this[e]=t[e]:fo._warn('The property "'+e+'" is not valid.');this.open()},hide:function(){this.close()},__onTransitionEnd:function(t){t&&t.target===this&&"opacity"===t.propertyName&&(this.opened?this._finishRenderOpened():this._finishRenderClosed())},_openedChanged:function(){null!==this._autoClose&&(this.cancelAsync(this._autoClose),this._autoClose=null),this.opened?(us&&us!==this&&us.close(),us=this,this.fire("iron-announce",{text:this.text}),this._canAutoClose&&(this._autoClose=this.async(this.close,this.duration))):us===this&&(us=null),Ca._openedChanged.apply(this,arguments)},_renderOpened:function(){this.classList.add("paper-toast-open")},_renderClosed:function(){this.classList.remove("paper-toast-open")},_onFitIntoChanged:function(t){this.positionTarget=t}});
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const hs=_e`

    <style>
      :host {
        display: inline-block;
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-font-common-base;
      }

      :host([disabled]) {
        pointer-events: none;
      }

      :host(:focus) {
        outline:none;
      }

      .toggle-bar {
        position: absolute;
        height: 100%;
        width: 100%;
        border-radius: 8px;
        pointer-events: none;
        opacity: 0.4;
        transition: background-color linear .08s;
        background-color: var(--paper-toggle-button-unchecked-bar-color, #000000);

        @apply --paper-toggle-button-unchecked-bar;
      }

      .toggle-button {
        position: absolute;
        top: -3px;
        left: 0;
        height: 20px;
        width: 20px;
        border-radius: 50%;
        box-shadow: 0 1px 5px 0 rgba(0, 0, 0, 0.6);
        transition: -webkit-transform linear .08s, background-color linear .08s;
        transition: transform linear .08s, background-color linear .08s;
        will-change: transform;
        background-color: var(--paper-toggle-button-unchecked-button-color, var(--paper-grey-50));

        @apply --paper-toggle-button-unchecked-button;
      }

      .toggle-button.dragging {
        -webkit-transition: none;
        transition: none;
      }

      :host([checked]:not([disabled])) .toggle-bar {
        opacity: 0.5;
        background-color: var(--paper-toggle-button-checked-bar-color, var(--primary-color));

        @apply --paper-toggle-button-checked-bar;
      }

      :host([disabled]) .toggle-bar {
        background-color: #000;
        opacity: 0.12;
      }

      :host([checked]) .toggle-button {
        -webkit-transform: translate(16px, 0);
        transform: translate(16px, 0);
      }

      :host([checked]:not([disabled])) .toggle-button {
        background-color: var(--paper-toggle-button-checked-button-color, var(--primary-color));

        @apply --paper-toggle-button-checked-button;
      }

      :host([disabled]) .toggle-button {
        background-color: #bdbdbd;
        opacity: 1;
      }

      .toggle-ink {
        position: absolute;
        top: -14px;
        left: -14px;
        right: auto;
        bottom: auto;
        width: 48px;
        height: 48px;
        opacity: 0.5;
        pointer-events: none;
        color: var(--paper-toggle-button-unchecked-ink-color, var(--primary-text-color));

        @apply --paper-toggle-button-unchecked-ink;
      }

      :host([checked]) .toggle-ink {
        color: var(--paper-toggle-button-checked-ink-color, var(--primary-color));

        @apply --paper-toggle-button-checked-ink;
      }

      .toggle-container {
        display: inline-block;
        position: relative;
        width: 36px;
        height: 14px;
        /* The toggle button has an absolute position of -3px; The extra 1px
        /* accounts for the toggle button shadow box. */
        margin: 4px 1px;
      }

      .toggle-label {
        position: relative;
        display: inline-block;
        vertical-align: middle;
        padding-left: var(--paper-toggle-button-label-spacing, 8px);
        pointer-events: none;
        color: var(--paper-toggle-button-label-color, var(--primary-text-color));
      }

      /* invalid state */
      :host([invalid]) .toggle-bar {
        background-color: var(--paper-toggle-button-invalid-bar-color, var(--error-color));
      }

      :host([invalid]) .toggle-button {
        background-color: var(--paper-toggle-button-invalid-button-color, var(--error-color));
      }

      :host([invalid]) .toggle-ink {
        color: var(--paper-toggle-button-invalid-ink-color, var(--error-color));
      }
    </style>

    <div class="toggle-container">
      <div id="toggleBar" class="toggle-bar"></div>
      <div id="toggleButton" class="toggle-button"></div>
    </div>

    <div class="toggle-label"><slot></slot></div>

  `;hs.setAttribute("strip-whitespace",""),Rr({_template:hs,is:"paper-toggle-button",behaviors:[ua],hostAttributes:{role:"button","aria-pressed":"false",tabindex:0},properties:{},listeners:{track:"_ontrack"},attached:function(){Li(this,(function(){oi(this,"pan-y")}))},_ontrack:function(t){var e=t.detail;"start"===e.state?this._trackStart(e):"track"===e.state?this._trackMove(e):"end"===e.state&&this._trackEnd(e)},_trackStart:function(t){this._width=this.$.toggleBar.offsetWidth/2,this._trackChecked=this.checked,this.$.toggleButton.classList.add("dragging")},_trackMove:function(t){var e=t.dx;this._x=Math.min(this._width,Math.max(0,this._trackChecked?this._width+e:e)),this.translate3d(this._x+"px",0,0,this.$.toggleButton),this._userActivate(this._x>this._width/2)},_trackEnd:function(t){this.$.toggleButton.classList.remove("dragging"),this.transform("",this.$.toggleButton)},_createRipple:function(){this._rippleContainer=this.$.toggleButton;var t=Jo._createRipple();return t.id="ink",t.setAttribute("recenters",""),t.classList.add("circle","toggle-ink"),t}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        --calculated-paper-toolbar-height: var(--paper-toolbar-height, 64px);
        --calculated-paper-toolbar-sm-height: var(--paper-toolbar-sm-height, 56px);
        display: block;
        position: relative;
        box-sizing: border-box;
        -moz-box-sizing: border-box;
        height: var(--calculated-paper-toolbar-height);
        background: var(--paper-toolbar-background, var(--primary-color));
        color: var(--paper-toolbar-color, var(--dark-theme-text-color));
        @apply --paper-toolbar;
      }

      :host(.animate) {
        transition: var(--paper-toolbar-transition, height 0.18s ease-in);
      }

      :host(.medium-tall) {
        height: calc(var(--calculated-paper-toolbar-height) * 2);
        @apply --paper-toolbar-medium;
      }

      :host(.tall) {
        height: calc(var(--calculated-paper-toolbar-height) * 3);
        @apply --paper-toolbar-tall;
      }

      .toolbar-tools {
        position: relative;
        height: var(--calculated-paper-toolbar-height);
        padding: 0 16px;
        pointer-events: none;
        @apply --layout-horizontal;
        @apply --layout-center;
        @apply --paper-toolbar-content;
      }

      /*
       * TODO: Where should media query breakpoints live so they can be shared between elements?
       */

      @media (max-width: 600px) {
        :host {
          height: var(--calculated-paper-toolbar-sm-height);
        }

        :host(.medium-tall) {
          height: calc(var(--calculated-paper-toolbar-sm-height) * 2);
        }

        :host(.tall) {
          height: calc(var(--calculated-paper-toolbar-sm-height) * 3);
        }

        .toolbar-tools {
          height: var(--calculated-paper-toolbar-sm-height);
        }
      }

      #topBar {
        position: relative;
      }

      /* middle bar */
      #middleBar {
        position: absolute;
        top: 0;
        right: 0;
        left: 0;
      }

      :host(.tall) #middleBar,
      :host(.medium-tall) #middleBar {
        -webkit-transform: translateY(100%);
        transform: translateY(100%);
      }

      /* bottom bar */
      #bottomBar {
        position: absolute;
        right: 0;
        bottom: 0;
        left: 0;
      }

      /*
       * make elements (e.g. buttons) respond to mouse/touch events
       *
       * \`.toolbar-tools\` disables touch events so multiple toolbars can stack and not
       * absorb events. All children must have pointer events re-enabled to work as
       * expected.
       */
      .toolbar-tools > ::slotted(*:not([disabled])) {
        pointer-events: auto;
      }

      .toolbar-tools > ::slotted(.title) {
        @apply --paper-font-common-base;
        white-space: nowrap;
        overflow: hidden;
        text-overflow: ellipsis;
        font-size: 20px;
        font-weight: 400;
        line-height: 1;
        pointer-events: none;
        @apply --layout-flex;
      }

      .toolbar-tools > ::slotted(.title) {
        margin-left: 56px;
      }

      .toolbar-tools > ::slotted(paper-icon-button + .title) {
        margin-left: 0;
      }

      /**
       * The --paper-toolbar-title mixin is applied here instead of above to
       * fix the issue with margin-left being ignored due to css ordering.
       */
      .toolbar-tools > ::slotted(.title) {
        @apply --paper-toolbar-title;
      }

      .toolbar-tools > ::slotted(paper-icon-button[icon=menu]) {
        margin-right: 24px;
      }

      .toolbar-tools > ::slotted(.fit) {
        position: absolute;
        top: auto;
        right: 0;
        bottom: 0;
        left: 0;
        width: auto;
        margin: 0;
      }

      /* TODO(noms): Until we have a better solution for classes that don't use
       * /deep/ create our own.
       */
      .start-justified {
        @apply --layout-start-justified;
      }

      .center-justified {
        @apply --layout-center-justified;
      }

      .end-justified {
        @apply --layout-end-justified;
      }

      .around-justified {
        @apply --layout-around-justified;
      }

      .justified {
        @apply --layout-justified;
      }
    </style>

    <div id="topBar" class\$="toolbar-tools [[_computeBarExtraClasses(justify)]]">
      <slot name="top"></slot>
    </div>

    <div id="middleBar" class\$="toolbar-tools [[_computeBarExtraClasses(middleJustify)]]">
      <slot name="middle"></slot>
    </div>

    <div id="bottomBar" class\$="toolbar-tools [[_computeBarExtraClasses(bottomJustify)]]">
      <slot name="bottom"></slot>
    </div>
`,is:"paper-toolbar",hostAttributes:{role:"toolbar"},properties:{bottomJustify:{type:String,value:""},justify:{type:String,value:""},middleJustify:{type:String,value:""}},ready:function(){console.warn(this.is,"is deprecated. Please use app-layout instead!")},attached:function(){this._observer=this._observe(this),this._updateAriaLabelledBy()},detached:function(){this._observer&&this._observer.disconnect()},_observe:function(t){var e=new MutationObserver(function(){this._updateAriaLabelledBy()}.bind(this));return e.observe(t,{childList:!0,subtree:!0}),e},_updateAriaLabelledBy:function(){Di();for(var t,e=[],n=Array.prototype.slice.call(Yi(this.root).querySelectorAll("slot")).concat(Array.prototype.slice.call(Yi(this.root).querySelectorAll("content"))),i=0;t=n[i];i++)for(var r,o=Yi(t).getDistributedNodes(),a=0;r=o[a];a++)if(r.classList&&r.classList.contains("title"))if(r.id)e.push(r.id);else{var s="paper-toolbar-label-"+Math.floor(1e4*Math.random());r.id=s,e.push(s)}e.length>0&&this.setAttribute("aria-labelledby",e.join(" "))},_computeBarExtraClasses:function(t){return t?t+("justified"===t?"":"-justified"):""}}),
/**
    @license
    Copyright (c) 2015 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
Rr({_template:_e`
    <style>
      :host {
        display: block;
        position: absolute;
        outline: none;
        z-index: 1002;
        -moz-user-select: none;
        -ms-user-select: none;
        -webkit-user-select: none;
        user-select: none;
        cursor: default;
      }

      #tooltip {
        display: block;
        outline: none;
        @apply --paper-font-common-base;
        font-size: 10px;
        line-height: 1;
        background-color: var(--paper-tooltip-background, #616161);
        color: var(--paper-tooltip-text-color, white);
        padding: 8px;
        border-radius: 2px;
        @apply --paper-tooltip;
      }

      @keyframes keyFrameScaleUp {
        0% {
          transform: scale(0.0);
        }
        100% {
          transform: scale(1.0);
        }
      }

      @keyframes keyFrameScaleDown {
        0% {
          transform: scale(1.0);
        }
        100% {
          transform: scale(0.0);
        }
      }

      @keyframes keyFrameFadeInOpacity {
        0% {
          opacity: 0;
        }
        100% {
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
      }

      @keyframes keyFrameFadeOutOpacity {
        0% {
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
        100% {
          opacity: 0;
        }
      }

      @keyframes keyFrameSlideDownIn {
        0% {
          transform: translateY(-2000px);
          opacity: 0;
        }
        10% {
          opacity: 0.2;
        }
        100% {
          transform: translateY(0);
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
      }

      @keyframes keyFrameSlideDownOut {
        0% {
          transform: translateY(0);
          opacity: var(--paper-tooltip-opacity, 0.9);
        }
        10% {
          opacity: 0.2;
        }
        100% {
          transform: translateY(-2000px);
          opacity: 0;
        }
      }

      .fade-in-animation {
        opacity: 0;
        animation-delay: var(--paper-tooltip-delay-in, 500ms);
        animation-name: keyFrameFadeInOpacity;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-in, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .fade-out-animation {
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 0ms);
        animation-name: keyFrameFadeOutOpacity;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .scale-up-animation {
        transform: scale(0);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-in, 500ms);
        animation-name: keyFrameScaleUp;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-in, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .scale-down-animation {
        transform: scale(1);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameScaleDown;
        animation-iteration-count: 1;
        animation-timing-function: ease-in;
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .slide-down-animation {
        transform: translateY(-2000px);
        opacity: 0;
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameSlideDownIn;
        animation-iteration-count: 1;
        animation-timing-function: cubic-bezier(0.0, 0.0, 0.2, 1);
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .slide-down-animation-out {
        transform: translateY(0);
        opacity: var(--paper-tooltip-opacity, 0.9);
        animation-delay: var(--paper-tooltip-delay-out, 500ms);
        animation-name: keyFrameSlideDownOut;
        animation-iteration-count: 1;
        animation-timing-function: cubic-bezier(0.4, 0.0, 1, 1);
        animation-duration: var(--paper-tooltip-duration-out, 500ms);
        animation-fill-mode: forwards;
        @apply --paper-tooltip-animation;
      }

      .cancel-animation {
        animation-delay: -30s !important;
      }

      /* Thanks IE 10. */

      .hidden {
        display: none !important;
      }
    </style>

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e=(this.category.items||[]).length-1;t>e&&(t=e),t<0&&(t=0),this.set("_activeIndex",t)}_clampActiveIndex(){this._setActiveIndex(this._activeIndex)}_performPreviousPage(){this._setActiveIndex(this._activeIndex-this._limit)}_performNextPage(){this._setActiveIndex(this._activeIndex+this._limit)}_computePageInputValue(t,e,n){return t?e:n.toString()}_handlePageInputEvent(t){this.set("_pageInputRawValue",t.target.value);const e=Number(t.target.value||NaN);if(isNaN(e))return;const n=Math.max(1,Math.min(e,this._pageCount))-1;this._setActiveIndex(this._limit*n)}_handlePageChangeEvent(){this.set("_pageInputRawValue",this._currentPage.toString())}_handlePageFocusEvent(){this.set("_pageInputRawValue",this._pageInputValue),this.set("_pageInputFocused",!0)}_handlePageBlurEvent(){this.set("_pageInputFocused",!1)}_updatePageInputValue(t){const e=this.shadowRoot.querySelector("#page-input input");e&&(e.value=t)}_updateInputWidth(){this.updateStyles({"--tf-category-paginated-view-page-input-width":this._inputWidth})}};pl.template=_e`
    <template is="dom-if" if="[[_paneRendered]]" id="ifRendered">
      <button class="heading" on-tap="_togglePane" open-button$="[[opened]]">
        <span class="name">
          <template is="dom-if" if="[[_isSearchResults]]">
            <template is="dom-if" if="[[_isCompositeSearch(category)]]">
              <span>Tags matching multiple experiments</span>
              <template is="dom-if" if="[[_isInvalidSearchResults]]">
                <span
                  >&nbsp;<strong>(malformed regular expression)</strong></span
                >
              </template>
            </template>
            <template is="dom-if" if="[[!_isCompositeSearch(category)]]">
              <span class="light">Tags matching /</span>
              <span class="category-name" title$="[[category.name]]"
                >[[category.name]]</span
              >
              <span class="light">/</span>
              <template is="dom-if" if="[[_isUniversalSearchQuery]]">
                <span> (all tags)</span>
              </template>
              <template is="dom-if" if="[[_isInvalidSearchResults]]">
                <span> <strong>(malformed regular expression)</strong></span>
              </template>
            </template>
          </template>
          <template is="dom-if" if="[[!_isSearchResults]]">
            <span class="category-name" title$="[[category.name]]"
              >[[category.name]]</span
            >
          </template>
        </span>
        <span class="count">
          <template is="dom-if" if="[[_hasMultiple]]">
            <span>[[_count]]</span>
          </template>
          <iron-icon icon="expand-more" class="expand-arrow"></iron-icon>
        </span>
      </button>
      <!-- TODO(stephanwlee): investigate further. For some reason,
        transitionend that the iron-collapse relies on sometimes does not
        trigger when rendering a chart with a spinner. A toy example cannot
        reproduce this bug. -->
      <iron-collapse opened="[[opened]]" no-animation="">
        <div class="content">
          <span id="top-of-container"></span>
          <template is="dom-if" if="[[_multiplePagesExist]]">
            <div class="big-page-buttons" style="margin-bottom: 10px;">
              <paper-button
                on-tap="_performPreviousPage"
                disabled$="[[!_hasPreviousPage]]"
                >Previous page</paper-button
              >
              <paper-button
                on-tap="_performNextPage"
                disabled$="[[!_hasNextPage]]"
                >Next page</paper-button
              >
            </div>
          </template>

          <div id="items">
            <slot name="items"></slot>
          </div>
          <template is="dom-if" if="[[_multiplePagesExist]]">
            <div id="controls-container">
              <div style="display: inline-block; padding: 0 5px">
                Page
                <paper-input
                  id="page-input"
                  type="number"
                  no-label-float=""
                  min="1"
                  max="[[_pageCount]]"
                  value="[[_pageInputValue]]"
                  on-input="_handlePageInputEvent"
                  on-change="_handlePageChangeEvent"
                  on-focus="_handlePageFocusEvent"
                  on-blur="_handlePageBlurEvent"
                ></paper-input>
                of [[_pageCount]]
              </div>
            </div>

            <div class="big-page-buttons" style="margin-top: 10px;">
              <paper-button
                on-tap="_performPreviousPage"
                disabled$="[[!_hasPreviousPage]]"
                >Previous page</paper-button
              >
              <paper-button
                on-tap="_performNextPage"
                disabled$="[[!_hasNextPage]]"
                >Next page</paper-button
              >
            </div>
          </template>
        </div>
      </iron-collapse>
    </template>
    <style>
      :host {
        display: block;
        margin: 0 5px 1px 10px;
      }

      :host(:first-of-type) {
        margin-top: 10px;
      }

      :host(:last-of-type) {
        margin-bottom: 20px;
      }

      .heading {
        background-color: var(--primary-background-color);
        border: none;
        color: inherit;
        cursor: pointer;
        width: 100%;
        font-size: 15px;
        line-height: 1;
        box-shadow: 0 1px 5px var(--tb-raised-button-shadow-color);
        padding: 10px 15px;
        display: flex;
        align-items: center;
        justify-content: space-between;
      }

      .heading::-moz-focus-inner {
        padding: 10px 15px;
      }

      [open-button] {
        border-bottom-left-radius: 0 !important;
        border-bottom-right-radius: 0 !important;
      }

      [open-button] .expand-arrow {
        transform: rotateZ(180deg);
      }

      .name {
        display: inline-flex;
        overflow: hidden;
      }

      .light {
        color: var(--paper-grey-500);
      }

      .category-name {
        white-space: pre;
        overflow: hidden;
        text-overflow: ellipsis;
        padding: 2px 0;
      }

      .count {
        margin: 0 5px;
        font-size: 12px;
        color: var(--paper-grey-500);
        display: flex;
        align-items: center;
        flex: none;
      }

      .heading::-moz-focus-inner {
        padding: 10px 15px;
      }

      .content {
        display: flex;
        flex-direction: column;
        background-color: var(--primary-background-color);
        border-bottom-left-radius: 2px;
        border-bottom-right-radius: 2px;
        border-top: none;
        border: 1px solid #dedede;
        padding: 15px;
      }

      .light {
        color: var(--paper-grey-500);
      }

      #controls-container {
        justify-content: center;
        display: flex;
        flex-direction: row;
        flex-grow: 0;
        flex-shrink: 0;
        width: 100%;
      }

      #controls-container paper-button {
        display: inline-block;
      }

      .big-page-buttons {
        display: flex;
      }

      .big-page-buttons paper-button {
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
        display: inline-block;
        flex-basis: 0;
        flex-grow: 1;
        flex-shrink: 1;
        font-size: 13px;
      }

      .big-page-buttons paper-button[disabled] {
        background: none;
      }

      slot {
        display: flex;
        flex-direction: row;
        flex-wrap: wrap;
      }

      ::slotted([slot='items']) {
        /* Tooltip for descriptions and others break with more strict ones. */
        contain: style;
      }

      #page-input {
        display: inline-block;
        width: var(--tf-category-paginated-view-page-input-width, 100%);
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],pl.prototype,"category",void 0),t([o({type:Boolean}),e("design:type",Boolean)],pl.prototype,"initialOpened",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],pl.prototype,"opened",void 0),t([o({type:Boolean}),e("design:type",Boolean)],pl.prototype,"disablePagination",void 0),t([o({type:Number,computed:"_computeCount(category.items.*)"}),e("design:type",Number)],pl.prototype,"_count",void 0),t([o({type:Boolean,computed:"_computePaneRendered(category)",observer:"_onPaneRenderedChanged"}),e("design:type",Boolean)],pl.prototype,"_paneRendered",void 0),t([o({type:Boolean,computed:"_computeIsSearchResults(category.metadata.type)"}),e("design:type",Boolean)],pl.prototype,"_isSearchResults",void 0),t([o({type:Boolean,computed:"_computeIsInvalidSearchResults(category.metadata)"}),e("design:type",Boolean)],pl.prototype,"_isInvalidSearchResults",void 0),t([o({type:Boolean,computed:"_computeIsUniversalSearchQuery(category.metadata)"}),e("design:type",Boolean)],pl.prototype,"_isUniversalSearchQuery",void 0),t([o({type:Object,observer:"_getCategoryItemKeyChanged"}),e("design:type",Object)],pl.prototype,"getCategoryItemKey",void 0),t([o({type:Number,observer:"_limitChanged"}),e("design:type",Number)],pl.prototype,"_limit",void 0),t([o({type:Number}),e("design:type",Number)],pl.prototype,"_activeIndex",void 0),t([o({type:Number,computed:"_computePageCount(category.items.*, _limit)"}),e("design:type",Number)],pl.prototype,"_pageCount",void 0),t([o({type:String,computed:"_computeInputWidth(_pageCount)",observer:"_updateInputWidth"}),e("design:type",String)],pl.prototype,"_inputWidth",void 0),t([o({type:String,computed:"_computePageInputValue(_pageInputFocused, _pageInputRawValue, _currentPage)",observer:"_updatePageInputValue"}),e("design:type",String)],pl.prototype,"_pageInputValue",void 0),t([o({type:String}),e("design:type",String)],pl.prototype,"_pageInputRawValue",void 0),t([o({type:Boolean}),e("design:type",Boolean)],pl.prototype,"_pageInputFocused",void 0),t([s("_count"),e("design:type",Boolean),e("design:paramtypes",[])],pl.prototype,"_hasMultiple",null),t([a("opened"),e("design:type",Function),e("design:paramtypes",[Boolean]),e("design:returntype",void 0)],pl.prototype,"_changeContentActive",null),t([s("opened","_paneRendered"),e("design:type",Boolean),e("design:paramtypes",[])],pl.prototype,"_itemsRendered",null),t([a("_itemsRendered","category.items.*","_limit","_activeIndex","_pageCount","disablePagination"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],pl.prototype,"_updateRenderedItems",null),t([s("_limit","_activeIndex"),e("design:type",Number),e("design:paramtypes",[])],pl.prototype,"_currentPage",null),t([s("_pageCount","disablePagination"),e("design:type",Boolean),e("design:paramtypes",[])],pl.prototype,"_multiplePagesExist",null),t([s("_currentPage"),e("design:type",Boolean),e("design:paramtypes",[])],pl.prototype,"_hasPreviousPage",null),t([s("_currentPage","_pageCount"),e("design:type",Boolean),e("design:paramtypes",[])],pl.prototype,"_hasNextPage",null),t([a("category.items.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],pl.prototype,"_clampActiveIndex",null),pl=t([i("tf-category-paginated-view")],pl);class fl{constructor(t){this.listener=t}}class ml{constructor(){this.requestManager=new dr(1),this._listeners=new Set,this.initialized=!1}refresh(){return this.load().then((()=>{this.initialized=!0}))}addListener(t){const e=new fl(t);return this._listeners.add(e),e}removeListenerByKey(t){this._listeners.delete(t)}emitChange(){this._listeners.forEach((t=>{try{t.listener()}catch(t){}}))}}class gl extends ml{load(){const t=_r().environment();return this.requestManager.request(t).then((t=>{const e={dataLocation:t.data_location,windowTitle:t.window_title};void 0!==t.experiment_name&&(e.experimentName=t.experiment_name),void 0!==t.experiment_description&&(e.experimentDescription=t.experiment_description),void 0!==t.creation_time&&(e.creationTime=t.creation_time),Se.exports.isEqual(this.environment,e)||(this.environment=e,this.emitChange())}))}getDataLocation(){return this.environment?this.environment.dataLocation:""}getWindowTitle(){return this.environment?this.environment.windowTitle:""}getExperimentName(){return this.environment?this.environment.experimentName:""}getExperimentDescription(){return this.environment?this.environment.experimentDescription:""}getCreationTime(){return this.environment?this.environment.creationTime:null}}const _l=new gl;class yl extends ml{constructor(){super(...arguments),this._runs=[]}load(){const t=_r().runs();return this.requestManager.request(t).then((t=>{Se.exports.isEqual(this._runs,t)||(this._runs=t,this.emitChange())}))}getRuns(){return this._runs.slice()}}const vl=new yl;function bl(t,e){return t<e?-1:t>e?1:t>=e?0:NaN}function xl(t){return 1===t.length&&(t=(function e(t){return function(e,n){return bl(t(e),n)}})(t)),{left:function(e,n,i,r){for(null==i&&(i=0),null==r&&(r=e.length);i<r;){var o=i+r>>>1;t(e[o],n)<0?i=o+1:r=o}return i},right:function(e,n,i,r){for(null==i&&(i=0),null==r&&(r=e.length);i<r;){var o=i+r>>>1;t(e[o],n)>0?r=o:i=o+1}return i}}}var wl=xl(bl),Sl=wl.right,Ml=wl.left;function El(t,e){return[t,e]}function Tl(t,e,n){var i,r,o,a,s=t.length,l=e.length,c=new Array(s*l);for(null==n&&(n=El),i=o=0;i<s;++i)for(a=t[i],r=0;r<l;++r,++o)c[o]=n(a,e[r]);return c}function Cl(t){return null===t?NaN:+t}function Al(t,e){var 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2:this._point=3,this._x4=t,this._y4=e,this._context.moveTo((this._x0+4*this._x1+t)/6,(this._y0+4*this._y1+e)/6);break;default:YL(this,t,e)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=e}},ZL.prototype={areaStart:function(){this._line=0},areaEnd:function(){this._line=NaN},lineStart:function(){this._x0=this._x1=this._y0=this._y1=NaN,this._point=0},lineEnd:function(){(this._line||0!==this._line&&3===this._point)&&this._context.closePath(),this._line=1-this._line},point:function(t,e){switch(t=+t,e=+e,this._point){case 0:this._point=1;break;case 1:this._point=2;break;case 2:this._point=3;var n=(this._x0+4*this._x1+t)/6,i=(this._y0+4*this._y1+e)/6;this._line?this._context.lineTo(n,i):this._context.moveTo(n,i);break;case 3:this._point=4;default:YL(this,t,e)}this._x0=this._x1,this._x1=t,this._y0=this._y1,this._y1=e}},JL.prototype={lineStart:function(){this._x=[],this._y=[],this._basis.lineStart()},lineEnd:function(){var t=this._x,e=this._y,n=t.length-1;if(n>0)for(var 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var(--paper-green-500);\n      --paper-checkbox-checked-ink-color: var(--paper-green-500);\n      --paper-checkbox-unchecked-color: var(--paper-green-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-green-900);\n    }\n    [color-class='purple'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-purple-500);\n      --paper-checkbox-checked-ink-color: var(--paper-purple-500);\n      --paper-checkbox-unchecked-color: var(--paper-purple-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-purple-900);\n    }\n    [color-class='teal'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-teal-500);\n      --paper-checkbox-checked-ink-color: var(--paper-teal-500);\n      --paper-checkbox-unchecked-color: var(--paper-teal-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-teal-900);\n    }\n    [color-class='pink'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-pink-500);\n      --paper-checkbox-checked-ink-color: var(--paper-pink-500);\n      --paper-checkbox-unchecked-color: var(--paper-pink-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-pink-900);\n    }\n    [color-class='orange'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-orange-500);\n      --paper-checkbox-checked-ink-color: var(--paper-orange-500);\n      --paper-checkbox-unchecked-color: var(--paper-orange-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-orange-900);\n    }\n    [color-class='brown'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-brown-500);\n      --paper-checkbox-checked-ink-color: var(--paper-brown-500);\n      --paper-checkbox-unchecked-color: var(--paper-brown-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-brown-900);\n    }\n    [color-class='indigo'] paper-checkbox {\n      --paper-checkbox-checked-color: var(--paper-indigo-500);\n      --paper-checkbox-checked-ink-color: var(--paper-indigo-500);\n      --paper-checkbox-unchecked-color: var(--paper-indigo-900);\n      --paper-checkbox-unchecked-ink-color: var(--paper-indigo-900);\n    }\n  "});let WR=class extends(er(ye)){constructor(){super(...arguments),this.names=[],this.coloring={getColor:()=>""},this.regex="",this.selectionState={},this.maxNamesToEnableByDefault=40,this._debouncedRegexChange=this._debouncedRegexChangeImpl()}_debouncedRegexChangeImpl(){var t=Se.exports.debounce((t=>{this.regex=t}),150,{leading:!1});return function(){var e=this.$$("#names-regex").value;""==e?this.async((()=>{this.regex=e}),30):t(e)}}get _regex(){var t=this.regex;try{return new RegExp(t)}catch(t){return null}}_setIsolatorIcon(){var t=this.selectionState,e=Se.exports.filter(Se.exports.values(t)).length;Array.prototype.slice.call(this.root.querySelectorAll(".isolator")).forEach((function(n){n.icon=1===e&&t[n.name]?"radio-button-checked":"radio-button-unchecked"}))}computeNamesMatchingRegex(t,e){const n=this._regex;return n?this.names.filter((t=>n.test(t))):this.names}computeOutSelected(t,e){var n=this.selectionState,i=this.namesMatchingRegex.length<=this.maxNamesToEnableByDefault;return this.namesMatchingRegex.filter((t=>null==n[t]?i:n[t]))}synchronizeColors(t){this._setIsolatorIcon(),this.root.querySelectorAll("paper-checkbox").forEach((t=>{const e=this.coloring.getColor(t.name);t.updateStyles({"--paper-checkbox-checked-color":e,"--paper-checkbox-checked-ink-color":e,"--paper-checkbox-unchecked-color":e,"--paper-checkbox-unchecked-ink-color":e})})),this.root.querySelectorAll(".isolator").forEach((t=>{const e=this.coloring.getColor(t.name);t.style.color=e})),window.requestAnimationFrame((()=>{this.updateStyles()}))}_isolateName(t){var e=t.target.name,n={};this.names.forEach((function(t){n[t]=t==e})),this.selectionState=n}_checkboxChange(t){var e=t.target;const n=Se.exports.clone(this.selectionState);n[e.name]=e.checked,this.selectionState=n}_isChecked(t,e){return-1!=this.outSelected.indexOf(t)}toggleAll(){const t=this.namesMatchingRegex.some((t=>this.outSelected.includes(t))),e={};this.names.forEach((n=>{e[n]=!t})),this.selectionState=e}};WR.template=_e`
    <style include="scrollbar-style"></style>
    <style include="run-color-style"></style>

    <paper-input
      id="names-regex"
      no-label-float=""
      label="Write a regex to filter runs"
      value="[[regex]]"
      on-bind-value-changed="_debouncedRegexChange"
    ></paper-input>
    <div id="outer-container" class="scrollbar">
      <template
        is="dom-repeat"
        items="[[namesMatchingRegex]]"
        on-dom-change="synchronizeColors"
      >
        <div class="name-row">
          <div
            class="icon-container checkbox-container vertical-align-container"
          >
            <paper-checkbox
              class="checkbox vertical-align-center"
              id$="checkbox-[[item]]"
              name="[[item]]"
              checked$="[[_isChecked(item, selectionState.*)]]"
              on-change="_checkboxChange"
            ></paper-checkbox>
          </div>
          <div
            class="icon-container isolator-container vertical-align-container"
          >
            <paper-icon-button
              icon="radio-button-unchecked"
              class="isolator vertical-align-center"
              on-tap="_isolateName"
              name="[[item]]"
            ></paper-icon-button>
          </div>
          <div class="item-label-container">
            <span>[[item]]</span>
          </div>
        </div>
      </template>
    </div>
    <style>
      paper-input {
        --paper-input-container-focus-color: var(--tb-orange-strong);
        --paper-input-container-input: {
          font-size: 14px;
        }
        --paper-input-container-label: {
          font-size: 14px;
        }
      }
      :host {
        display: flex;
        flex-direction: column;
        height: 100%;
        overflow: hidden;
      }
      #outer-container {
        contain: content;
        flex-grow: 1;
        flex-shrink: 1;
        overflow-x: hidden;
        overflow-y: auto;
        width: 100%;
        will-change: transform;
        word-wrap: break-word;
      }
      .name-row {
        contain: content;
        padding-top: 5px;
        padding-bottom: 5px;
        display: flex;
        flex-direction: row;
        font-size: 13px;
        word-break: break-all; /* makes wrapping of hyperparam strings better */
      }
      .icon-container {
        flex-grow: 0;
        flex-shrink: 0;
        padding-left: 2px;
      }
      .checkbox {
        padding-left: 2px;
        width: 18px;
        height: 18px;
      }
      .isolator {
        width: 18px;
        height: 18px;
        padding: 0px;
      }
      .isolator-container {
        padding-left: 6px;
        padding-right: 3px;
      }
      .checkbox-container {
        padding-left: 2px;
      }
      .item-label-container {
        padding-left: 5px;
        flex-grow: 1;
        flex-shrink: 1;
        width: 0px; /* hack to get the flex-grow to work properly */
      }
      .tooltip-value-container {
        display: flex;
        justify-content: center;
        flex-grow: 0;
        flex-shrink: 0;
        text-align: right;
        padding-left: 2px;
      }
      .vertical-align-container {
        display: flex;
        justify-content: center;
      }
      .vertical-align-container .vertical-align-center {
        align-self: center;
      }
      .vertical-align-container .vertical-align-top {
        align-self: start;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],WR.prototype,"names",void 0),t([o({type:Object}),e("design:type",Object)],WR.prototype,"coloring",void 0),t([o({type:String,notify:!0}),e("design:type",String)],WR.prototype,"regex",void 0),t([o({type:Array,computed:"computeNamesMatchingRegex(names.*, _regex)"}),e("design:type",Array)],WR.prototype,"namesMatchingRegex",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],WR.prototype,"selectionState",void 0),t([o({type:Array,notify:!0,computed:"computeOutSelected(namesMatchingRegex.*, selectionState.*)"}),e("design:type",Array)],WR.prototype,"outSelected",void 0),t([o({type:Number}),e("design:type",Number)],WR.prototype,"maxNamesToEnableByDefault",void 0),t([o({type:Object}),e("design:type",Object)],WR.prototype,"_debouncedRegexChange",void 0),t([s("regex"),e("design:type",RegExp),e("design:paramtypes",[])],WR.prototype,"_regex",null),t([a("selectionState","names"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],WR.prototype,"_setIsolatorIcon",null),WR=t([i("tf-multi-checkbox")],WR);let qR=class extends ye{get _parts(){var t=this.value,e=this.delimiterPattern;const n=[];for(;;){const i=new RegExp(e,"g");if(i.test(t),0===i.lastIndex){n.push(t);break}n.push(t.slice(0,i.lastIndex)),t=t.slice(i.lastIndex)}return n}};qR.template=_e`
    <!--
      This ugly formatting is required to prevent spaces from slipping
      into the HTML.
    -->
    <template is="dom-repeat" items="[[_parts]]" as="part"
      >[[part]]<wbr
    /></template>
  `,t([o({type:String}),e("design:type",String)],qR.prototype,"value",void 0),t([o({type:String}),e("design:type",String)],qR.prototype,"delimiterPattern",void 0),t([s("value","delimiterPattern"),e("design:type",Array),e("design:paramtypes",[])],qR.prototype,"_parts",null),qR=t([i("tf-wbr-string")],qR);let YR=class extends(er(ye)){constructor(){super(...arguments),this.runSelectionState=$s("runSelectionState",{defaultValue:{}}).call(this),this.regexInput=Rs("regexInput",{defaultValue:""}).call(this),this._dataLocationClipLength=250,this._dataLocationDelimiterPattern="[/=_,-]",this.coloring={getColor:GR},this._storeRunSelectionState=Ks("runSelectionState",{defaultValue:{}}),this._regexObserver=Os("regexInput",{defaultValue:""})}attached(){this._runStoreListener=vl.addListener((()=>{this.set("runs",vl.getRuns())})),this.set("runs",vl.getRuns()),this._envStoreListener=_l.addListener((()=>{this.set("dataLocation",_l.getDataLocation())})),this.set("dataLocation",_l.getDataLocation())}detached(){vl.removeListenerByKey(this._runStoreListener),_l.removeListenerByKey(this._envStoreListener)}_toggleAll(){this.$.multiCheckbox.toggleAll()}get _clippedDataLocation(){var t=this.dataLocation,e=this._dataLocationClipLength;if(void 0!==t)return t.length>e?t.substring(0,e):t}_openDataLocationDialog(t){t.preventDefault(),this.$$("#data-location-dialog").open()}_shouldShowExpandDataLocationButton(t,e){return t&&t.length>e}};YR.template=_e`
    <paper-dialog with-backdrop="" id="data-location-dialog">
      <h2>Data Location</h2>
      <tf-wbr-string
        value="[[dataLocation]]"
        delimiter-pattern="[[_dataLocationDelimiterPattern]]"
      >
      </tf-wbr-string
    ></paper-dialog>
    <div id="top-text">
      <h3 id="tooltip-help" class="tooltip-container">Runs</h3>
    </div>
    <tf-multi-checkbox
      id="multiCheckbox"
      names="[[runs]]"
      selection-state="{{runSelectionState}}"
      out-selected="{{selectedRuns}}"
      regex="{{regexInput}}"
      coloring="[[coloring]]"
    ></tf-multi-checkbox>
    <paper-button class="x-button" id="toggle-all" on-tap="_toggleAll">
      Toggle All Runs
    </paper-button>
    <template is="dom-if" if="[[dataLocation]]">
      <div id="data-location">
        <tf-wbr-string
          value="[[_clippedDataLocation]]"
          delimiter-pattern="[[_dataLocationDelimiterPattern]]"
        ></tf-wbr-string
        ><!--
          We use HTML comments to remove spaces before the ellipsis.
        --><template
          is="dom-if"
          if="[[_shouldShowExpandDataLocationButton(dataLocation, _dataLocationClipLength)]]"
          ><!--
          --><a href="" on-click="_openDataLocationDialog">…</a>
        </template>
      </div>
    </template>
    <style>
      :host {
        box-sizing: border-box;
        display: flex;
        flex-direction: column;
        padding-bottom: 10px;
      }
      #top-text {
        color: var(--tb-secondary-text-color);
        width: 100%;
        flex-grow: 0;
        flex-shrink: 0;
        padding-right: 16px;
        box-sizing: border-box;
      }
      tf-wbr-string {
        overflow-wrap: break-word;
      }
      tf-multi-checkbox {
        display: flex;
        flex-grow: 1;
        flex-shrink: 1;
        overflow: hidden;
      }
      .x-button {
        font-size: 13px;
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
      }
      #tooltip-help {
        color: var(--tb-secondary-text-color);
        margin: 0;
        font-weight: normal;
        font-size: 14px;
        margin-bottom: 5px;
      }
      paper-button {
        margin-left: 0;
      }
      #data-location {
        color: var(--tb-ui-dark-accent);
        font-size: 13px;
        margin: 5px 0 0 0;
        max-width: 288px;
      }
    </style>
  `,t([o({type:Object,observer:"_storeRunSelectionState"}),e("design:type",Object)],YR.prototype,"runSelectionState",void 0),t([o({type:String,observer:"_regexObserver"}),e("design:type",String)],YR.prototype,"regexInput",void 0),t([o({type:Array,notify:!0}),e("design:type",Array)],YR.prototype,"selectedRuns",void 0),t([o({type:Array}),e("design:type",Array)],YR.prototype,"runs",void 0),t([o({type:String,notify:!0}),e("design:type",String)],YR.prototype,"dataLocation",void 0),t([o({type:Number}),e("design:type",Number)],YR.prototype,"_dataLocationClipLength",void 0),t([o({type:String}),e("design:type",String)],YR.prototype,"_dataLocationDelimiterPattern",void 0),t([o({type:Object}),e("design:type",Object)],YR.prototype,"coloring",void 0),t([s("dataLocation","_dataLocationClipLength"),e("design:type",String),e("design:paramtypes",[])],YR.prototype,"_clippedDataLocation",null),YR=t([i("tf-runs-selector")],YR);class XR{constructor(){this.cancellationCount=0}cancellable(t){const e=this.cancellationCount;return n=>t({value:n,cancelled:this.cancellationCount!==e})}cancelAll(){this.cancellationCount++}}let $R=class extends(er(ye)){constructor(){super(...arguments),this.html=""}get sanitizedHtml(){return this.html}attached(){window.requestAnimationFrame((()=>{this.scopeSubtree(this.$.markdown,!0)}))}};function KR(t){return t?t.toString().replace(/GMT-\d+ \(([^)]+)\)/,"$1"):""}$R.template=_e`
    <div id="markdown" inner-h-t-m-l="[[sanitizedHtml]]"></div>
    <style>
      /*
       * Reduce topmost and bottommost margins from 16px to 0.3em (renders
       * at about 4.8px) to keep the layout compact. This improves the
       * appearance when there is only one line of text; standard Markdown
       * renderers will still include a \`<p>\` element.
       *
       * By targeting only the top-level, extremal elements, we preserve any
       * actual paragraph breaks and only change the padding against the
       * component edges.
       */
      #markdown > p:first-child {
        margin-top: 0.3em;
      }
      #markdown > p:last-child {
        margin-bottom: 0.3em;
      }

      /* Pleasant styles for Markdown tables. */
      #markdown table {
        border-collapse: collapse;
      }
      #markdown table th {
        font-weight: 600;
      }
      #markdown table th,
      #markdown table td {
        padding: 6px 13px;
        border: 1px solid var(--tb-ui-border, #dfe2e5);
      }
      #markdown table tr {
        background-color: inherit;
        border-top: 1px solid var(--tb-ui-border, #c6cbd1);
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],$R.prototype,"html",void 0),t([s("html"),e("design:type",Object),e("design:paramtypes",[])],$R.prototype,"sanitizedHtml",null),$R=t([i("tf-markdown-view")],$R),el({moduleName:"tf-card-heading-style",styleContent:"\n    figcaption {\n      width: 100%;\n    }\n\n    /** Horizontal line of labels. */\n    .heading-row {\n      margin-top: -4px;\n      display: flex;\n      flex-direction: row;\n      flex-wrap: wrap;\n    }\n\n    /** Piece of text in the figure caption. */\n    .heading-label {\n      flex-grow: 1;\n      margin-top: 4px;\n      max-width: 100%;\n      word-wrap: break-word;\n    }\n\n    /** Makes label show on the right. */\n    .heading-right {\n      flex-grow: 0;\n    }\n  "});let ZR=class extends ye{constructor(){super(...arguments),this.displayName=null,this.tag=null,this.run=null,this.description=null,this.color=null}_updateHeadingStyle(){this.updateStyles({"--tf-card-heading-background-color":this._runBackground,"--tf-card-heading-color":this._runColor})}_computeRunBackground(t){return t||"none"}_computeRunColor(t){return(function e(t){const e=(function n(t){if(!t)return null;let e=t.match(/^#([0-9a-f]{1,2})([0-9a-f]{1,2})([0-9a-f]{1,2})$/);if(!e)return null;if(4==t.length)for(var n=1;n<=3;n++)e[n]=e[n]+e[n];return[parseInt(e[1],16),parseInt(e[2],16),parseInt(e[3],16)]})(t);return e?Math.round((299*e[0]+587*e[1]+114*e[2])/1e3)>125?"inherit":"#eee":"inherit"})(t)}get _nameLabel(){return this.displayName||this.tag||""}get _tagLabel(){var t=this.tag;return t&&t!==this.displayName?t:""}_toggleDescriptionDialog(t){const e=this.$.descriptionDialog;e.positionTarget=t.target,e.toggle()}};ZR.template=_e`
    <div class="container">
      <figcaption class="content">
        <div class="heading-row">
          <template is="dom-if" if="[[_nameLabel]]">
            <div itemprop="name" class="heading-label name">[[_nameLabel]]</div>
          </template>
          <template is="dom-if" if="[[run]]">
            <!-- Extra wrapping span needed to avoid flexbox blockification. -->
            <!-- (see flexbox spec, section 4 "Flex Items") -->
            <span>
              <span
                itemprop="run"
                id="heading-run"
                class="heading-label heading-right run"
                >[[run]]</span
              >
            </span>
          </template>
        </div>
        <template is="dom-if" if="[[_tagLabel]]">
          <div class="heading-row">
            <div class="heading-label">
              tag: <span itemprop="tag">[[_tagLabel]]</span>
            </div>
          </div>
        </template>
        <slot></slot>
      </figcaption>
      <template is="dom-if" if="[[description]]">
        <paper-icon-button
          icon="info"
          on-tap="_toggleDescriptionDialog"
          title="Show summary description"
        ></paper-icon-button>
      </template>
      <paper-dialog
        id="descriptionDialog"
        no-overlap=""
        horizontal-align="auto"
        vertical-align="auto"
      >
        <paper-dialog-scrollable>
          <tf-markdown-view html="[[description]]"></tf-markdown-view>
        </paper-dialog-scrollable>
      </paper-dialog>
    </div>
    <style include="tf-card-heading-style">
      .container {
        display: flex;
      }
      .content {
        font-size: 12px;
        flex-grow: 1;
      }
      .name {
        font-size: 14px;
      }
      .run {
        font-size: 11px;
        width: auto;
        border-radius: 3px;
        font-weight: bold;
        padding: 1px 4px 2px;
      }
      paper-icon-button {
        flex-grow: 0;
      }
      paper-dialog-scrollable {
        max-width: 640px;
      }
      #heading-run {
        background: var(--tf-card-heading-background-color);
        color: var(--tf-card-heading-color);
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],ZR.prototype,"displayName",void 0),t([o({type:String}),e("design:type",String)],ZR.prototype,"tag",void 0),t([o({type:String}),e("design:type",String)],ZR.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],ZR.prototype,"description",void 0),t([o({type:String}),e("design:type",String)],ZR.prototype,"color",void 0),t([o({type:String,computed:"_computeRunBackground(color)",readOnly:!0,observer:"_updateHeadingStyle"}),e("design:type",String)],ZR.prototype,"_runBackground",void 0),t([o({type:String,computed:"_computeRunColor(color)",readOnly:!0,observer:"_updateHeadingStyle"}),e("design:type",String)],ZR.prototype,"_runColor",void 0),t([s("displayName","tag"),e("design:type",String),e("design:paramtypes",[])],ZR.prototype,"_nameLabel",null),t([s("displayName","tag"),e("design:type",String),e("design:paramtypes",[])],ZR.prototype,"_tagLabel",null),ZR=t([i("tf-card-heading")],ZR);let JR=class extends(er(ye)){constructor(){super(...arguments),this._metadataCanceller=new XR,this._steps=[],this._attached=!1}get _runColor(){return GR(this.run)}get _hasAtLeastOneStep(){var t=this._steps;return!!t&&t.length>0}get _hasMultipleSteps(){var t=this._steps;return!!t&&t.length>1}get _maxStepIndex(){return this._steps.length-1}get _currentDatum(){return this._steps[this._stepIndex]}get _sampleText(){return`${this.sample+1}`}get _hasMultipleSamples(){return this.totalSamples>1}attached(){this._attached=!0,this.reload()}_reloadOnRunTagChange(){this.reload()}reload(){if(!this._attached)return;this._metadataCanceller.cancelAll();const t=_r().pluginRoute("audio","/audio",new URLSearchParams({tag:this.tag,run:this.run,sample:String(this.sample)})),e=this._metadataCanceller.cancellable((t=>{if(t.cancelled)return;const e=t.value.map(this._createStepDatum.bind(this));this.set("_steps",e),this.set("_stepIndex",e.length-1)}));this.requestManager.request(t).then(e)}_createStepDatum(t){const e=new URLSearchParams(t.query);e.append("ts",String(t.wall_time));const n=_r().pluginRoute("audio","/individualAudio",e);return{wall_time:KR(new Date(1e3*t.wall_time)),step:t.step,label:t.label,contentType:t.contentType,url:n}}};JR.template=_e`
    <tf-card-heading
      tag="[[tag]]"
      run="[[run]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
      color="[[_runColor]]"
    >
      <template is="dom-if" if="[[_hasMultipleSamples]]">
        <div class="heading-row">
          <div class="heading-label">
            sample: [[_sampleText]] of [[totalSamples]]
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep]]">
        <div class="heading-row">
          <div class="heading-label">
            step <strong>[[_currentDatum.step]]</strong>
          </div>
          <template is="dom-if" if="[[_currentDatum.wall_time]]">
            <div class="heading-label heading-right">
              [[_currentDatum.wall_time]]
            </div>
          </template>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps]]">
        <div class="heading-row">
          <paper-slider
            id="steps"
            immediate-value="{{_stepIndex}}"
            max="[[_maxStepIndex]]"
            max-markers="[[_maxStepIndex]]"
            snaps=""
            step="1"
            value="{{_stepIndex}}"
          ></paper-slider>
        </div>
      </template>
    </tf-card-heading>
    <template is="dom-if" if="[[_hasAtLeastOneStep]]">
      <audio
        controls=""
        src$="[[_currentDatum.url]]"
        type$="[[_currentDatum.contentType]]"
      ></audio>
      <tf-markdown-view html="[[_currentDatum.label]]"></tf-markdown-view>
    </template>
    <div id="main-audio-container"></div>

    <style include="tf-card-heading-style">
      :host {
        display: block;
        width: 350px;
        height: auto;
        position: relative;
        --step-slider-knob-color: #424242;
        margin-right: 15px;
        margin-bottom: 15px;
      }

      #steps {
        height: 15px;
        margin: 0 0 0 -15px;
        width: 100%;
        box-sizing: border-box;
        padding: 0 5px; /* so the slider knob doesn't butt out */
        margin-top: 5px;
        --paper-slider-active-color: var(--step-slider-knob-color);
        --paper-slider-knob-color: var(--step-slider-knob-color);
        --paper-slider-pin-color: var(--step-slider-knob-color);
        --paper-slider-knob-start-color: var(--step-slider-knob-color);
        --paper-slider-knob-start-border-color: var(--step-slider-knob-color);
        --paper-slider-pin-start-color: var(--step-slider-knob-color);
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],JR.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],JR.prototype,"tag",void 0),t([o({type:Number}),e("design:type",Number)],JR.prototype,"sample",void 0),t([o({type:Number}),e("design:type",Number)],JR.prototype,"totalSamples",void 0),t([o({type:Object}),e("design:type",Object)],JR.prototype,"tagMetadata",void 0),t([o({type:Object}),e("design:type",dr)],JR.prototype,"requestManager",void 0),t([o({type:Object}),e("design:type",XR)],JR.prototype,"_metadataCanceller",void 0),t([o({type:Array}),e("design:type",Array)],JR.prototype,"_steps",void 0),t([o({type:Number}),e("design:type",Number)],JR.prototype,"_stepIndex",void 0),t([s("run"),e("design:type",String),e("design:paramtypes",[])],JR.prototype,"_runColor",null),t([s("_steps"),e("design:type",Boolean),e("design:paramtypes",[])],JR.prototype,"_hasAtLeastOneStep",null),t([s("_steps"),e("design:type",Boolean),e("design:paramtypes",[])],JR.prototype,"_hasMultipleSteps",null),t([s("_steps"),e("design:type",Number),e("design:paramtypes",[])],JR.prototype,"_maxStepIndex",null),t([s("_steps","_stepIndex"),e("design:type",Object),e("design:paramtypes",[])],JR.prototype,"_currentDatum",null),t([s("sample"),e("design:type",String),e("design:paramtypes",[])],JR.prototype,"_sampleText",null),t([s("totalSamples"),e("design:type",Boolean),e("design:paramtypes",[])],JR.prototype,"_hasMultipleSamples",null),t([a("run","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],JR.prototype,"_reloadOnRunTagChange",null),JR=t([i("tf-audio-loader")],JR);let QR=class extends(er(ye)){constructor(){super(...arguments),this.reloadOnReady=!0,this._tagFilter="",this._requestManager=new dr}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){this._fetchTags().then((()=>{this._reloadAudio()}))}_fetchTags(){const t=_r().pluginRoute("audio","/tags");return this._requestManager.request(t).then((t=>{if(Se.exports.isEqual(t,this._runToTagInfo))return;const e=ar(Se.exports.mapValues(t,(t=>Object.keys(t))));this.set("_dataNotFound",0===e.length),this.set("_runToTagInfo",t)}))}_reloadAudio(){this.root.querySelectorAll("tf-audio-loader").forEach((t=>{t.reload()}))}_shouldOpen(t){return t<=2}get _categories(){var t=this._runToTagInfo,e=this._selectedRuns,n=this._tagFilter;function i(e){const n=t[e.run][e.tag].samples;return Se.exports.range(n).map((t=>Object.assign({},e,{sample:t,totalSamples:n})))}return Mr(Se.exports.mapValues(t,(t=>Object.keys(t))),e,n).map((t=>Object.assign({},t,{items:[].concat.apply([],t.items.map(i))})))}_tagMetadata(t,e,n){return t[e][n]}};QR.template=_e`
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="sidebar-section runs-selector">
          <tf-runs-selector
            id="runs-selector"
            selected-runs="{{_selectedRuns}}"
          ></tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No audio data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>You haven’t written any audio data to your event files.</li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
            >
              <template>
                <tf-audio-loader
                  active="[[active]]"
                  run="[[item.run]]"
                  tag="[[item.tag]]"
                  sample="[[item.sample]]"
                  total-samples="[[item.totalSamples]]"
                  tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]"
                  request-manager="[[_requestManager]]"
                ></tf-audio-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>
    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
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/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     *
     * @fileoverview manually add d3-selection-multi to d3 default bundle. Most of this code is
     * copied from d3-selection-multi@1.0.0.
     * See https://github.com/d3/d3-selection-multi/issues/11 for why we have to do this
     */
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/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Smt,"__esModule",{value:!0}),Smt.makeEnum=function Mmt(t){return t.reduce((function(t,e){return t[e]=e,t}),{})},
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
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/**
     * Copyright 2014-present Palantir Technologies
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/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
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agt={},sgt={};Object.defineProperty(sgt,"__esModule",{value:!0});var lgt=(function(){function t(t){this.ruler=null!=t.createRuler?t.createRuler():t}return t.prototype.measure=function(e){return void 0===e&&(e=t.HEIGHT_TEXT),this.ruler(e)},t})();lgt.HEIGHT_TEXT="bdpql",sgt.AbstractMeasurer=lgt;var cgt={},ugt={},hgt={},dgt=ve&&ve.__extends||(function(){var t=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(t,e){t.__proto__=e}||function(t,e){for(var n in e)e.hasOwnProperty(n)&&(t[n]=e[n])};return function(e,n){function i(){this.constructor=e}t(e,n),e.prototype=null===n?Object.create(n):(i.prototype=n.prototype,new i)}})();Object.defineProperty(hgt,"__esModule",{value:!0});var pgt=sgt,fgt=(function(t){function e(e,n){void 0===n&&(n=!1);var i=t.call(this,e)||this;return i.useGuards=n,i}return dgt(e,t),e.prototype._addGuards=function(t){return pgt.AbstractMeasurer.HEIGHT_TEXT+t+pgt.AbstractMeasurer.HEIGHT_TEXT},e.prototype._measureLine=function(e,n){void 0===n&&(n=!1);var 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     * Copyright 2014-present Palantir Technologies
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     * Copyright 2014-present Palantir Technologies
     * @license MIT
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     * Copyright 2014-present Palantir Technologies
     * @license MIT
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     * Copyright 2014-present Palantir Technologies
     * @license MIT
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     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Bgt,"__esModule",{value:!0});var lyt=Edt,cyt=Hgt,uyt=Fgt,hyt=Se.exports,dyt=bmt,pyt=Smt;Bgt.XAlignment=pyt.makeEnum(["left","center","right"]),Bgt.YAlignment=pyt.makeEnum(["top","center","bottom"]);var fyt=(function(){function t(){this._overflowHidden=!1,this._origin={x:0,y:0},this._xAlignment="left",this._yAlignment="top",this._isSetup=!1,this._isAnchored=!1,this._cssClasses=new uyt.Set,this._destroyed=!1,this._onAnchorCallbacks=new uyt.CallbackSet,this._onDetachCallbacks=new uyt.CallbackSet,this._cssClasses.add("component")}return t.prototype.anchor=function(t){if(t=dyt.coerceExternalD3(t),this._destroyed)throw new Error("Can't reuse destroy()-ed Components!");return this.isRoot()&&(this._rootElement=t,this._rootElement.classed("plottable",!0)),null!=this._element?t.node().appendChild(this._element.node()):(this._element=t.append("div"),this._setup()),this._isAnchored=!0,this._onAnchorCallbacks.callCallbacks(this),this},t.prototype.onAnchor=function(t){return this._isAnchored&&t(this),this._onAnchorCallbacks.add(t),this},t.prototype.offAnchor=function(t){return this._onAnchorCallbacks.delete(t),this},t.prototype._setup=function(){var t=this;this._isSetup||(this._cssClasses.forEach((function(e){t._element.classed(e,!0)})),this._cssClasses=new uyt.Set,this._backgroundContainer=this._element.append("svg").classed("background-container",!0),this._content=this._element.append("svg").classed("content",!0),this._foregroundContainer=this._element.append("svg").classed("foreground-container",!0),this._content.classed(this._overflowHidden?"component-overflow-hidden":"component-overflow-visible",!0),this._isSetup=!0)},t.prototype.requestedSpace=function(t,e){return{minWidth:0,minHeight:0}},t.prototype.computeLayout=function(e,n,i){if(null==e||null==n||null==i){if(null==this._element)throw new Error("anchor() must be called before computeLayout()");if(null==this._rootElement)throw new Error("null arguments cannot be passed to computeLayout() on a non-root, unanchored node");e={x:0,y:0};var r=this._rootElement.node();n=uyt.DOM.elementWidth(r),i=uyt.DOM.elementHeight(r)}var o=this._sizeFromOffer(n,i),a=o.height,s=o.width;return this.setBounds(s,a,e.x+(n-s)*t._xAlignToProportion[this._xAlignment],e.y+(i-a)*t._yAlignToProportion[this._yAlignment]),this},t.prototype.setBounds=function(t,e,n,i){return void 0===n&&(n=0),void 0===i&&(i=0),this._width=t,this._height=e,this._origin={x:n,y:i},null!=this._element&&this._element.styles({left:n+"px",height:e+"px",top:i+"px",width:t+"px"}),null!=this._resizeHandler&&this._resizeHandler({width:t,height:e}),this},t.prototype._sizeFromOffer=function(t,e){var n=this.requestedSpace(t,e);return{width:this.fixedWidth()?Math.min(t,n.minWidth):t,height:this.fixedHeight()?Math.min(e,n.minHeight):e}},t.prototype.render=function(){return this._isAnchored&&this._isSetup&&this.width()>=0&&this.height()>=0&&cyt.registerToRender(this),this},t.prototype.renderLowPriority=function(){return this.render()},t.prototype._scheduleComputeLayout=function(){this._isAnchored&&this._isSetup&&cyt.registerToComputeLayoutAndRender(this)},t.prototype.onResize=function(t){return this._resizeHandler=t,this},t.prototype.renderImmediately=function(){return this},t.prototype.redraw=function(){return this._isAnchored&&this._isSetup&&(this.isRoot()?this._scheduleComputeLayout():this.parent().redraw()),this},t.prototype.invalidateCache=function(){},t.prototype.renderTo=function(t){if(this.detach(),null!=t){var e=void 0;if(!(e="string"==typeof t||hyt.isElement(t)?lyt.select(t):dyt.coerceExternalD3(t)).node()||null==e.node().nodeName)throw new Error("Plottable requires a valid Element to renderTo");if("svg"===e.node().nodeName)throw new Error("Plottable 3.x and later can only renderTo an HTML component; pass a div instead!");this.anchor(e)}if(null==this._element)throw new Error("If a Component has never been rendered before, then renderTo must be given a node to render to, or a d3.Selection, or a selector string");return cyt.registerToComputeLayoutAndRender(this),cyt.flush(),this},t.prototype.xAlignment=function(e){if(null==e)return this._xAlignment;if(e=e.toLowerCase(),null==t._xAlignToProportion[e])throw new Error("Unsupported alignment: "+e);return this._xAlignment=e,this.redraw(),this},t.prototype.yAlignment=function(e){if(null==e)return this._yAlignment;if(e=e.toLowerCase(),null==t._yAlignToProportion[e])throw new Error("Unsupported alignment: "+e);return this._yAlignment=e,this.redraw(),this},t.prototype.hasClass=function(t){return null!=t&&(null==this._element?this._cssClasses.has(t):this._element.classed(t))},t.prototype.addClass=function(t){return null==t||(null==this._element?this._cssClasses.add(t):this._element.classed(t,!0)),this},t.prototype.removeClass=function(t){return null==t||(null==this._element?this._cssClasses.delete(t):this._element.classed(t,!1)),this},t.prototype.fixedWidth=function(){return!1},t.prototype.fixedHeight=function(){return!1},t.prototype.detach=function(){return this.parent(null),this._isAnchored&&this._element.remove(),this._isAnchored=!1,this._onDetachCallbacks.callCallbacks(this),this},t.prototype.onDetach=function(t){return this._onDetachCallbacks.add(t),this},t.prototype.offDetach=function(t){return this._onDetachCallbacks.delete(t),this},t.prototype.parent=function(t){if(void 0===t)return this._parent;if(null!==t&&!t.has(this))throw new Error("Passed invalid parent");return this._parent=t,this},t.prototype.bounds=function(){var t=this.origin();return{topLeft:t,bottomRight:{x:t.x+this.width(),y:t.y+this.height()}}},t.prototype.destroy=function(){this._destroyed=!0,this.detach()},t.prototype.width=function(){return this._width},t.prototype.height=function(){return this._height},t.prototype.origin=function(){return{x:this._origin.x,y:this._origin.y}},t.prototype.originToRoot=function(){for(var t=this.origin(),e=this.parent();null!=e;){var n=e.origin();t.x+=n.x,t.y+=n.y,e=e.parent()}return t},t.prototype.root=function(){for(var t=this;!t.isRoot();)t=t.parent();return t},t.prototype.isRoot=function(){return null==this.parent()},t.prototype.foreground=function(){return this._foregroundContainer},t.prototype.content=function(){return this._content},t.prototype.element=function(){return this._element},t.prototype.rootElement=function(){return this.root()._rootElement},t.prototype.background=function(){return this._backgroundContainer},t._xAlignToProportion={left:0,center:.5,right:1},t._yAlignToProportion={top:0,center:.5,bottom:1},t})();Bgt.Component=fyt;var myt={},gyt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(gyt,"__esModule",{value:!0});var _yt=Edt;function yyt(t){return void 0===t&&(t=3),vyt(t),function(e){return e.toFixed(t)}}function vyt(t){if(t<0||t>20)throw new RangeError("Formatter precision must be between 0 and 20");if(t!==Math.floor(t))throw new RangeError("Formatter precision must be an integer")}
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */gyt.currency=function byt(t,e,n){void 0===t&&(t=2),void 0===e&&(e="$"),void 0===n&&(n=!0);var i=yyt(t);return function(t){var r=i(Math.abs(t));return""!==r&&(n?r=e+r:r+=e,t<0&&(r="-"+r)),r}},gyt.fixed=yyt,gyt.general=function xyt(t){return void 0===t&&(t=3),vyt(t),function(e){if("number"==typeof e){var n=Math.pow(10,t);return String(Math.round(e*n)/n)}return String(e)}},gyt.identity=function wyt(){return function(t){return String(t)}},gyt.percentage=function Syt(t){void 0===t&&(t=0);var e=yyt(t);return function(t){var n=100*t,i=t.toString(),r=Math.pow(10,i.length-(i.indexOf(".")+1));return n=parseInt((n*r).toString(),10)/r,e(n)+"%"}},gyt.siSuffix=function Myt(t){return void 0===t&&(t=3),vyt(t),function(e){return _yt.format("."+t+"s")(e)}},gyt.shortScale=function Eyt(t){void 0===t&&(t=3),vyt(t);var e="KMBTQ",n=_yt.format("."+t+"e"),i=_yt.format("."+t+"f"),r=Math.pow(10,3*(e.length+1)),o=Math.pow(10,-t);return function(t){var a=Math.abs(t);if((a<o||a>=r)&&0!==a)return n(t);for(var s=-1;a>=Math.pow(1e3,s+2)&&s<e.length-1;)s++;var l="";return l=-1===s?i(t):i(t/Math.pow(1e3,s+1))+e[s],(t>0&&"1000"===l.substr(0,4)||t<0&&"-1000"===l.substr(0,5))&&(s<e.length-1?(s++,l=i(t/Math.pow(1e3,s+1))+e[s]):l=n(t)),l}},gyt.multiTime=function Tyt(){var t=[{specifier:".%L",predicate:function(t){return 0!==t.getMilliseconds()}},{specifier:":%S",predicate:function(t){return 0!==t.getSeconds()}},{specifier:"%I:%M",predicate:function(t){return 0!==t.getMinutes()}},{specifier:"%I %p",predicate:function(t){return 0!==t.getHours()}},{specifier:"%a %d",predicate:function(t){return 0!==t.getDay()&&1!==t.getDate()}},{specifier:"%b %d",predicate:function(t){return 1!==t.getDate()}},{specifier:"%b",predicate:function(t){return 0!==t.getMonth()}}];return function(e){var n=t.filter((function(t){return t.predicate(e)}));return _yt.timeFormat(n.length>0?n[0].specifier:"%Y")(e)}},gyt.time=function Cyt(t,e){return void 0===e&&(e=!1),e?_yt.utcFormat(t):_yt.timeFormat(t)},Object.defineProperty(myt,"__esModule",{value:!0});var Ayt=uO,kyt=Edt,Lyt=Imt,Pyt=Bgt,Nyt=gyt,Iyt=Fgt;myt.AxisOrientation=Smt.makeEnum(["bottom","left","right","top"]);var Ryt=(function(t){function e(e,n){var i=t.call(this)||this;if(i._endTickLength=5,i._innerTickLength=5,i._tickLabelPadding=10,i._margin=15,i._showEndTickLabels=!1,i._annotationsEnabled=!1,i._annotationTierCount=1,null==e||null==n)throw new Error("Axis requires a scale and orientation");return i._scale=e,i.orientation(n),i._setDefaultAlignment(),i.addClass("axis"),i.isHorizontal()?i.addClass("x-axis"):i.addClass("y-axis"),i.formatter(Nyt.identity()),i._rescaleCallback=function(t){return i._rescale()},i._scale.onUpdate(i._rescaleCallback),i._annotatedTicks=[],i._annotationFormatter=Nyt.identity(),i}return Ayt.__extends(e,t),e.prototype.destroy=function(){t.prototype.destroy.call(this),this._scale.offUpdate(this._rescaleCallback)},e.prototype.tickLabelDataOnElement=function(t){if(null!=t){for(var n;null!=t&&t.classList&&void 0===n;)t.classList.contains(e.TICK_LABEL_CLASS)?n=t:t=t.parentNode;return void 0===t?void 0:kyt.select(t).datum()}},e.prototype._computeWidth=function(){return this._maxLabelTickLength()},e.prototype._computeHeight=function(){return this._maxLabelTickLength()},e.prototype.requestedSpace=function(t,n){var i=0,r=0;return this.isHorizontal()?(r=this._computeHeight()+this._margin,this.annotationsEnabled()&&(r+=(this._annotationMeasurer.measure().height+2*e._ANNOTATION_LABEL_PADDING)*this.annotationTierCount())):(i=this._computeWidth()+this._margin,this.annotationsEnabled()&&(i+=(this._annotationMeasurer.measure().height+2*e._ANNOTATION_LABEL_PADDING)*this.annotationTierCount())),{minWidth:i,minHeight:r}},e.prototype.fixedHeight=function(){return this.isHorizontal()},e.prototype.fixedWidth=function(){return!this.isHorizontal()},e.prototype._rescale=function(){this.render()},e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),this.isHorizontal()?this._scale.range([0,this.width()]):this._scale.range([this.height(),0]),this},e.prototype._sizeFromOffer=function(t,e){var n=this.requestedSpace(t,e);return this.isHorizontal()?{width:t,height:n.minHeight}:{height:e,width:n.minWidth}},e.prototype._setup=function(){t.prototype._setup.call(this),this._tickMarkContainer=this.content().append("g").classed(e.TICK_MARK_CLASS+"-container",!0),this._tickLabelContainer=this.content().append("g").classed(e.TICK_LABEL_CLASS+"-container",!0),this._baseline=this.content().append("line").classed("baseline",!0),this._annotationContainer=this.content().append("g").classed("annotation-container",!0),this._annotationContainer.append("g").classed("annotation-line-container",!0),this._annotationContainer.append("g").classed("annotation-circle-container",!0),this._annotationContainer.append("g").classed("annotation-rect-container",!0);var n=this._annotationContainer.append("g").classed("annotation-label-container",!0),i=new Lyt.SvgContext(n.node());this._annotationMeasurer=new Lyt.CacheMeasurer(i),this._annotationWriter=new Lyt.Writer(this._annotationMeasurer,i)},e.prototype._getTickValues=function(){return[]},e.prototype.renderImmediately=function(){var t=this._getTickValues(),n=this._tickMarkContainer.selectAll("."+e.TICK_MARK_CLASS).data(t),i=n.enter().append("line").classed(e.TICK_MARK_CLASS,!0).merge(n);return i.attrs(this._generateTickMarkAttrHash()),kyt.select(i.nodes()[0]).classed(e.END_TICK_MARK_CLASS,!0).attrs(this._generateTickMarkAttrHash(!0)),kyt.select(i.nodes()[t.length-1]).classed(e.END_TICK_MARK_CLASS,!0).attrs(this._generateTickMarkAttrHash(!0)),n.exit().remove(),this._baseline.attrs(this._generateBaselineAttrHash()),this.annotationsEnabled()?this._drawAnnotations():this._removeAnnotations(),this},e.prototype.annotatedTicks=function(t){return null==t?this._annotatedTicks:(this._annotatedTicks=t,this.render(),this)},e.prototype.annotationFormatter=function(t){return null==t?this._annotationFormatter:(this._annotationFormatter=t,this.render(),this)},e.prototype.annotationsEnabled=function(t){return null==t?this._annotationsEnabled:(this._annotationsEnabled=t,this.redraw(),this)},e.prototype.annotationTierCount=function(t){if(null==t)return this._annotationTierCount;if(t<0)throw new Error("annotationTierCount cannot be negative");return this._annotationTierCount=t,this.redraw(),this},e.prototype._drawAnnotations=function(){var t=this,n=e._ANNOTATION_LABEL_PADDING,i=new Iyt.Map,r=this._annotatedTicksToRender();r.forEach((function(e){var r=t._annotationMeasurer.measure(t.annotationFormatter()(e));i.set(e,{width:r.width+2*n,height:r.height+2*n})}));var o=this._annotationMeasurer.measure().height+2*n,a=this._annotationToTier(i),s=new Iyt.Set,l=this.isHorizontal()?this.height():this.width(),c=this._coreSize(),u=Math.min(this.annotationTierCount(),Math.floor((l-c)/o));a.forEach((function(t,e){(-1===t||t>=u)&&s.add(e)}));var h,d=function(t,e,n){var i=t.selectAll("."+n).data(r),o=i.enter().append(e).classed(n,!0).merge(i);return i.exit().remove(),o},p=function(e){switch(t.orientation()){case"bottom":case"right":return a.get(e)*o+c;case"top":case"left":return l-c-a.get(e)*o}},f=function(e){return t._scale.scale(e)},m=function(t){return s.has(t)?"hidden":"visible"};switch(this.orientation()){case"bottom":case"right":h=0;break;case"top":h=this.height();break;case"left":h=this.width()}var g=this.isHorizontal();d(this._annotationContainer.select(".annotation-line-container"),"line",e.ANNOTATION_LINE_CLASS).attrs({x1:g?f:h,x2:g?f:p,y1:g?h:f,y2:g?p:f,visibility:m}),d(this._annotationContainer.select(".annotation-circle-container"),"circle",e.ANNOTATION_CIRCLE_CLASS).attrs({cx:g?f:h,cy:g?h:f,r:3});var _=function(e){switch(t.orientation()){case"bottom":case"right":return p(e);case"top":case"left":return p(e)-i.get(e).height}};d(this._annotationContainer.select(".annotation-rect-container"),"rect",e.ANNOTATION_RECT_CLASS).attrs({x:g?f:_,y:g?_:f,width:g?function(t){return i.get(t).width}:function(t){return i.get(t).height},height:g?function(t){return i.get(t).height}:function(t){return i.get(t).width},visibility:m});var y=this._annotationWriter,v=this.annotationFormatter(),b=d(this._annotationContainer.select(".annotation-label-container"),"g",e.ANNOTATION_LABEL_CLASS);b.selectAll(".text-container").remove(),b.attrs({transform:function(t){return"translate("+(g?f(t):_(t))+","+(g?_(t):f(t))+")"},visibility:m}).each((function(t){y.write(v(t),g?i.get(t).width:i.get(t).height,g?i.get(t).height:i.get(t).width,{xAlign:"center",yAlign:"center",textRotation:g?0:90},kyt.select(this).node())}))},e.prototype._annotatedTicksToRender=function(){var t=this,e=this._scale.range();return Iyt.Array.uniq(this.annotatedTicks().filter((function(n){return null!=n&&Iyt.Math.inRange(t._scale.scale(n),e[0],e[1])})))},e.prototype._coreSize=function(){var t=this.isHorizontal()?this.height():this.width(),e=this.isHorizontal()?this._computeHeight():this._computeWidth();return Math.min(e,t)},e.prototype._annotationTierHeight=function(){return this._annotationMeasurer.measure().height+2*e._ANNOTATION_LABEL_PADDING},e.prototype._annotationToTier=function(t){var e=this,n=[[]],i=new Iyt.Map,r=this.isHorizontal()?this.width():this.height();return this._annotatedTicksToRender().forEach((function(o){var a=e._scale.scale(o),s=t.get(o).width;if(a<0||a+s>r)i.set(o,-1);else{for(var l=0;n[l].some((function(n){var i=e._scale.scale(n),r=t.get(n).width;return a+s>=i&&a<=i+r}));)l++,n.length===l&&n.push([]);n[l].push(o),i.set(o,l)}})),i},e.prototype._removeAnnotations=function(){this._annotationContainer.selectAll(".annotation-line").remove(),this._annotationContainer.selectAll(".annotation-circle").remove(),this._annotationContainer.selectAll(".annotation-rect").remove(),this._annotationContainer.selectAll(".annotation-label").remove()},e.prototype._generateBaselineAttrHash=function(){var t={x1:0,y1:0,x2:0,y2:0};switch(this._orientation){case"bottom":t.x2=this.width();break;case"top":t.x2=this.width(),t.y1=this.height(),t.y2=this.height();break;case"left":t.x1=this.width(),t.x2=this.width(),t.y2=this.height();break;case"right":t.y2=this.height()}return t},e.prototype._generateTickMarkAttrHash=function(t){var e=this;void 0===t&&(t=!1);var n={x1:0,y1:0,x2:0,y2:0},i=function(t){return e._scale.scale(t)};this.isHorizontal()?(n.x1=i,n.x2=i):(n.y1=i,n.y2=i);var r=t?this._endTickLength:this._innerTickLength;switch(this._orientation){case"bottom":n.y2=r;break;case"top":n.y1=this.height(),n.y2=this.height()-r;break;case"left":n.x1=this.width(),n.x2=this.width()-r;break;case"right":n.x2=r}return n},e.prototype._setDefaultAlignment=function(){switch(this._orientation){case"bottom":this.yAlignment("top");break;case"top":this.yAlignment("bottom");break;case"left":this.xAlignment("right");break;case"right":this.xAlignment("left")}},e.prototype.isHorizontal=function(){return"top"===this._orientation||"bottom"===this._orientation},e.prototype.getScale=function(){return this._scale},e.prototype.formatter=function(t){return null==t?this._formatter:(this._formatter=t,this.redraw(),this)},e.prototype.innerTickLength=function(t){if(null==t)return this._innerTickLength;if(t<0)throw new Error("inner tick length must be positive");return this._innerTickLength=t,this.redraw(),this},e.prototype.endTickLength=function(t){if(null==t)return this._endTickLength;if(t<0)throw new Error("end tick length must be positive");return this._endTickLength=t,this.redraw(),this},e.prototype._maxLabelTickLength=function(){return this.showEndTickLabels()?Math.max(this.innerTickLength(),this.endTickLength()):this.innerTickLength()},e.prototype.tickLabelPadding=function(t){if(null==t)return this._tickLabelPadding;if(t<0)throw new Error("tick label padding must be positive");return this._tickLabelPadding=t,this.redraw(),this},e.prototype.margin=function(t){if(null==t)return this._margin;if(t<0)throw new Error("margin size must be positive");return this._margin=t,this.redraw(),this},e.prototype.orientation=function(t){if(null==t)return this._orientation;var e=t.toLowerCase();if("top"!==e&&"bottom"!==e&&"left"!==e&&"right"!==e)throw new Error("unsupported orientation");return this._orientation=e,this.redraw(),this},e.prototype.showEndTickLabels=function(t){return null==t?this._showEndTickLabels:(this._showEndTickLabels=t,this.render(),this)},e.prototype._showAllTickMarks=function(){this._tickMarkContainer.selectAll("."+e.TICK_MARK_CLASS).each((function(){kyt.select(this).style("visibility","inherit")}))},e.prototype._showAllTickLabels=function(){this._tickLabelContainer.selectAll("."+e.TICK_LABEL_CLASS).each((function(){kyt.select(this).style("visibility","inherit")}))},e.prototype._hideOverflowingTickLabels=function(){var t=this.element().node().getBoundingClientRect(),n=this._tickLabelContainer.selectAll("."+e.TICK_LABEL_CLASS);n.empty()||n.each((function(e,n){Iyt.DOM.clientRectInside(this.getBoundingClientRect(),t)||kyt.select(this).style("visibility","hidden")}))},e.prototype._hideTickMarksWithoutLabel=function(){var t=this._tickMarkContainer.selectAll("."+e.TICK_MARK_CLASS),n=this._tickLabelContainer.selectAll("."+e.TICK_LABEL_CLASS).filter((function(t,e){var n=kyt.select(this).style("visibility");return"inherit"===n||"visible"===n})).data();t.each((function(t,e){-1===n.indexOf(t)&&kyt.select(this).style("visibility","hidden")}))},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._annotationMeasurer.reset()},e.END_TICK_MARK_CLASS="end-tick-mark",e.TICK_MARK_CLASS="tick-mark",e.TICK_LABEL_CLASS="tick-label",e.ANNOTATION_LINE_CLASS="annotation-line",e.ANNOTATION_RECT_CLASS="annotation-rect",e.ANNOTATION_CIRCLE_CLASS="annotation-circle",e.ANNOTATION_LABEL_CLASS="annotation-label",e._ANNOTATION_LABEL_PADDING=4,e})(Pyt.Component);myt.Axis=Ryt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Nmt,"__esModule",{value:!0});var Oyt=uO,zyt=Edt,Dyt=Imt,Byt=Bgt,Hyt=Fgt,Fyt=myt,Vyt=(function(t){function e(e,n){void 0===n&&(n="bottom");var i=t.call(this,e,n)||this;return i._tickLabelAngle=0,i._tickLabelShearAngle=0,i.addClass("category-axis"),i}return Oyt.__extends(e,t),Object.defineProperty(e.prototype,"_wrapper",{get:function(){var t=new Dyt.Wrapper;return null!=this._tickLabelMaxLines&&t.maxLines(this._tickLabelMaxLines),t},enumerable:!0,configurable:!0}),Object.defineProperty(e.prototype,"_writer",{get:function(){return new Dyt.Writer(this._measurer,this._typesetterContext,this._wrapper)},enumerable:!0,configurable:!0}),e.prototype._setup=function(){t.prototype._setup.call(this),this._typesetterContext=new Dyt.SvgContext(this._tickLabelContainer.node()),this._measurer=new Dyt.CacheMeasurer(this._typesetterContext)},e.prototype._rescale=function(){return this.redraw()},e.prototype.requestedSpace=function(t,e){var n=this.isHorizontal()?0:this._tickSpaceRequired()+this.margin(),i=this.isHorizontal()?this._tickSpaceRequired()+this.margin():0;if(0===this._scale.domain().length)return{minWidth:0,minHeight:0};if(this.annotationsEnabled()){var r=this._annotationTierHeight()*this.annotationTierCount();this.isHorizontal()?i+=r:n+=r}var o=this._measureTickLabels(t,e);return{minWidth:o.usedWidth+n,minHeight:o.usedHeight+i}},e.prototype._coreSize=function(){var t=this.isHorizontal()?this.height():this.width(),e=this.isHorizontal()?this.requestedSpace(this.width(),this.height()).minHeight:this.requestedSpace(this.width(),this.height()).minWidth,n=this.margin()+this._annotationTierHeight();return Math.min(e-n,t)},e.prototype._getTickValues=function(){return this.getDownsampleInfo().domain},e.prototype._sizeFromOffer=function(t,e){return Byt.Component.prototype._sizeFromOffer.call(this,t,e)},e.prototype.getDownsampleInfo=function(t,n){void 0===t&&(t=this._scale),void 0===n&&(n=t.invertRange());var i=0===this._tickLabelAngle?1:1/Math.cos(this._tickLabelShearAngle/180*Math.PI),r=Math.ceil(e._MINIMUM_WIDTH_PER_LABEL_PX*i/t.stepWidth());return{domain:n.filter((function(t,e){return e%r==0})),stepWidth:r*t.stepWidth()}},e.prototype.tickLabelAngle=function(t){if(null==t)return this._tickLabelAngle;if(0!==t&&90!==t&&-90!==t)throw new Error("Angle "+t+" not supported; only 0, 90, and -90 are valid values");return this._tickLabelAngle=t,this.redraw(),this},e.prototype.tickLabelShearAngle=function(t){if(null==t)return this._tickLabelShearAngle;if(t<-80||t>80)throw new Error("Angle "+t+" not supported; Must be between [-80, 80]");return this._tickLabelShearAngle=t,this.redraw(),this},e.prototype.tickLabelMaxWidth=function(t){return 0===arguments.length?this._tickLabelMaxWidth:(this._tickLabelMaxWidth=t,this.redraw(),this)},e.prototype.tickLabelMaxLines=function(t){return 0===arguments.length?this._tickLabelMaxLines:(this._tickLabelMaxLines=t,this.redraw(),this)},e.prototype._tickSpaceRequired=function(){return this._maxLabelTickLength()+this.tickLabelPadding()},e.prototype._drawTicks=function(t,e){var n,i,r=this;switch(this.tickLabelAngle()){case 0:n={left:"right",right:"left",top:"center",bottom:"center"},i={left:"center",right:"center",top:"bottom",bottom:"top"};break;case 90:n={left:"center",right:"center",top:"right",bottom:"left"},i={left:"top",right:"bottom",top:"center",bottom:"center"};break;case-90:n={left:"center",right:"center",top:"left",bottom:"right"},i={left:"bottom",right:"top",top:"center",bottom:"center"}}e.each((function(e){var o=zyt.select(this),a=r.isHorizontal()?t:r.width()-r._tickSpaceRequired(),s=r.isHorizontal()?r.height()-r._tickSpaceRequired():t,l={xAlign:n[r.orientation()],yAlign:i[r.orientation()],textRotation:r.tickLabelAngle(),textShear:r.tickLabelShearAngle()};if(null!=r._tickLabelMaxWidth){if("left"===r.orientation()&&a>r._tickLabelMaxWidth){var c=a-r._tickLabelMaxWidth,u=o.attr("transform")+" translate("+c+", 0)";o.attr("transform",u)}a=Math.min(a,r._tickLabelMaxWidth)}r._writer.write(r.formatter()(e),a,s,l,o.node())}))},e.prototype._measureTickLabels=function(t,e){var n=this,i=this._scale.cloneWithoutProviders().range([0,this.isHorizontal()?t:e]),r=this.getDownsampleInfo(i),o=r.domain,a=r.stepWidth,s=t-this._tickSpaceRequired();this.isHorizontal()&&(s=a,0!==this._tickLabelAngle&&(s=e-this._tickSpaceRequired()),s=Math.max(s,0));var l=a;this.isHorizontal()&&(l=e-this._tickSpaceRequired(),0!==this._tickLabelAngle&&(l=t-this._tickSpaceRequired()),l=Math.max(l,0)),null!=this._tickLabelMaxWidth&&(s=Math.min(s,this._tickLabelMaxWidth));var c,u=o.map((function(t){return n._wrapper.wrap(n.formatter()(t),n._measurer,s,l)})),h=this.isHorizontal()&&0===this._tickLabelAngle?zyt.sum:Hyt.Math.max,d=this.isHorizontal()&&0===this._tickLabelAngle?Hyt.Math.max:zyt.sum,p=h(u,(function(t){return n._measurer.measure(t.wrappedText).width}),0),f=d(u,(function(t){return n._measurer.measure(t.wrappedText).height}),0);return 0!==this._tickLabelAngle&&(p=(c=[f,p])[0],f=c[1]),{usedWidth:p,usedHeight:f}},e.prototype.renderImmediately=function(){var e=this;t.prototype.renderImmediately.call(this);var n=this._scale,i=this.getDownsampleInfo(n),r=i.domain,o=i.stepWidth,a=o;this.isHorizontal()&&null!=this._tickLabelMaxWidth&&(a=Math.min(a,this._tickLabelMaxWidth));var s=this._tickLabelContainer.selectAll("."+Fyt.Axis.TICK_LABEL_CLASS).data(r),l=s.enter().append("g").classed(Fyt.Axis.TICK_LABEL_CLASS,!0).merge(s);s.exit().remove(),l.attr("transform",(function(t,i){var r=n.scale(t)-a/2;return"translate("+(e.isHorizontal()?r:0)+","+(e.isHorizontal()?0:r)+")"})),l.text(""),this._drawTicks(o,l);var c="right"===this.orientation()?this._tickSpaceRequired():0,u="bottom"===this.orientation()?this._tickSpaceRequired():0;return this._tickLabelContainer.attr("transform","translate("+c+","+u+")"),this._showAllTickMarks(),this._showAllTickLabels(),this._hideTickMarksWithoutLabel(),this},e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),this.isHorizontal()||this._scale.range([0,this.height()]),this},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e._MINIMUM_WIDTH_PER_LABEL_PX=15,e})(Fyt.Axis);Nmt.Category=Vyt;var Uyt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Uyt,"__esModule",{value:!0});var jyt=uO,Gyt=Edt,Wyt=Imt,qyt=gyt,Yyt=Fgt,Xyt=myt,$yt=(function(t){function e(e,n){var i=t.call(this,e,n)||this;return i._tickLabelPositioning="center",i._usesTextWidthApproximation=!1,i.formatter(qyt.general()),i}return jyt.__extends(e,t),e.prototype._setup=function(){t.prototype._setup.call(this);var e=new Wyt.SvgContext(this._tickLabelContainer.node(),Xyt.Axis.TICK_LABEL_CLASS);this._measurer=new Wyt.CacheMeasurer(e),this._wrapper=(new Wyt.Wrapper).maxLines(1)},e.prototype._computeWidth=function(){var t=this._usesTextWidthApproximation?this._computeApproximateTextWidth():this._computeExactTextWidth();return"center"===this._tickLabelPositioning?this._maxLabelTickLength()+this.tickLabelPadding()+t:Math.max(this._maxLabelTickLength(),this.tickLabelPadding()+t)},e.prototype._computeExactTextWidth=function(){var t=this,e=this._getTickValues().map((function(e){var n=t.formatter()(e);return t._measurer.measure(n).width}));return Yyt.Math.max(e,0)},e.prototype._computeApproximateTextWidth=function(){var t=this,e=this._getTickValues(),n=this._measurer.measure("M").width,i=e.map((function(e){return t.formatter()(e).length*n}));return Yyt.Math.max(i,0)},e.prototype._computeHeight=function(){var t=this._measurer.measure().height;return"center"===this._tickLabelPositioning?this._maxLabelTickLength()+this.tickLabelPadding()+t:Math.max(this._maxLabelTickLength(),this.tickLabelPadding()+t)},e.prototype._getTickValues=function(){var t=this._scale,e=t.domain(),n=e[0]<=e[1]?e[0]:e[1],i=e[0]>=e[1]?e[0]:e[1];return t.ticks().filter((function(t){return t>=n&&t<=i}))},e.prototype._rescale=function(){if(this._isSetup){if(!this.isHorizontal()){var t=this._computeWidth();if(t>this.width()||t<this.width()-this.margin())return void this.redraw()}this.render()}},e.prototype.renderImmediately=function(){var e=this;t.prototype.renderImmediately.call(this);var n={x:0,y:0,dx:"0em",dy:"0.3em"},i=this._maxLabelTickLength(),r=this.tickLabelPadding(),o="middle",a=0,s=0,l=0,c=0;if(this.isHorizontal())switch(this._tickLabelPositioning){case"left":o="end",a=-r,c=r;break;case"center":c=i+r;break;case"right":o="start",a=r,c=r}else switch(this._tickLabelPositioning){case"top":n.dy="-0.3em",l=r,s=-r;break;case"center":l=i+r;break;case"bottom":n.dy="1em",l=r,s=r}var u=this._generateTickMarkAttrHash();switch(this.orientation()){case"bottom":n.x=u.x1,n.dy="0.95em",s=u.y1+c;break;case"top":n.x=u.x1,n.dy="-.25em",s=u.y1-c;break;case"left":o="end",a=u.x1-l,n.y=u.y1;break;case"right":o="start",a=u.x1+l,n.y=u.y1}var h=this._getTickValues(),d=this._tickLabelContainer.selectAll("."+Xyt.Axis.TICK_LABEL_CLASS).data(h);return d.exit().remove(),d.enter().append("text").classed(Xyt.Axis.TICK_LABEL_CLASS,!0).merge(d).style("text-anchor",o).style("visibility","inherit").attrs(n).text((function(t){return e.formatter()(t)})),this._tickLabelContainer.attr("transform","translate("+a+", "+s+")"),this._showAllTickMarks(),this.showEndTickLabels()||this._hideEndTickLabels(),this._hideOverflowingTickLabels(),this._hideOverlappingTickLabels(),"center"!==this._tickLabelPositioning&&this._hideTickMarksWithoutLabel(),this},e.prototype.tickLabelPosition=function(t){if(null==t)return this._tickLabelPositioning;var e=t.toLowerCase();if(this.isHorizontal()){if("left"!==e&&"center"!==e&&"right"!==e)throw new Error(e+" is not a valid tick label position for a horizontal NumericAxis")}else if("top"!==e&&"center"!==e&&"bottom"!==e)throw new Error(e+" is not a valid tick label position for a vertical NumericAxis");return this._tickLabelPositioning=e,this.redraw(),this},e.prototype.usesTextWidthApproximation=function(t){return null==t?this._usesTextWidthApproximation:(this._usesTextWidthApproximation=t,this)},e.prototype._hideEndTickLabels=function(){var t=this.element().node().getBoundingClientRect(),e=this._tickLabelContainer.selectAll("."+Xyt.Axis.TICK_LABEL_CLASS);if(0!==e.size()){var n=e.nodes()[0];Yyt.DOM.clientRectInside(n.getBoundingClientRect(),t)||Gyt.select(n).style("visibility","hidden");var i=e.nodes()[e.size()-1];Yyt.DOM.clientRectInside(i.getBoundingClientRect(),t)||Gyt.select(i).style("visibility","hidden")}},e.prototype._hideOverlappingTickLabels=function(){for(var t=this._tickLabelContainer.selectAll("."+Xyt.Axis.TICK_LABEL_CLASS).filter((function(t,e){var n=Gyt.select(this).style("visibility");return"inherit"===n||"visible"===n})),e=t.nodes().map((function(t){return t.getBoundingClientRect()})),n=1;!this._hasOverlapWithInterval(n,e)&&n<e.length;)n+=1;t.each((function(t,e){var i=Gyt.select(this);e%n!=0&&i.style("visibility","hidden")}))},e.prototype._hasOverlapWithInterval=function(t,e){for(var n="center"===this._tickLabelPositioning?this.tickLabelPadding():3*this.tickLabelPadding(),i=e.map((function(t){return Yyt.DOM.expandRect(t,n)})),r=0;r<i.length-t;r+=t)if(Yyt.DOM.clientRectsOverlap(i[r],i[r+t]))return!1;return!0},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e})(Xyt.Axis);Uyt.Numeric=$yt;var Kyt={},Zyt={},Jyt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Jyt,"__esModule",{value:!0});var Qyt=Fgt;Jyt.intervalTickGenerator=function tvt(t){if(t<=0)throw new Error("interval must be positive number");return function(e){var n=e.domain(),i=Math.min(n[0],n[1]),r=Math.max(n[0],n[1]),o=Math.ceil(i/t)*t,a=Math.floor((r-o)/t)+1,s=i%t==0?[]:[i],l=Qyt.Math.range(0,a).map((function(e){return o+e*t})),c=r%t==0?[]:[r];return s.concat(l).concat(c)}},Jyt.integerTickGenerator=function evt(){return function(t){var e=t.defaultTicks();return e.filter((function(t,n){return t%1==0||0===n||n===e.length-1}))}};var nvt={},ivt={};function rvt(t,e,n){return n-(n-t)*e}function ovt(t,e,n){return(t*e-n)/(e-1)}function avt(t,e,n,i){var r=e>1,o=r?i:n;if(null==o)return e;var a=t.getTransformationDomain(),s=Math.abs(a[1]-a[0]);return(r?Math.min:Math.max)(e,o/s)}function svt(t,e,n,i,r){if(e<=1)return{centerPoint:n,zoomAmount:e};if(null==i&&null==r)return{centerPoint:n,zoomAmount:e};var o=lvt(t),a=(function s(t){var e=t.getTransformationDomain();return e[1]<e[0]})(t);i=null==i?a?1/0:-1/0:i,r=null==r?a?-1/0:1/0:r;var l=t.getTransformationDomain(),c=l[0],u=l[1],h=t.scaleTransformation(r),d=t.scaleTransformation(u),p=rvt(d,e,n),f=t.scaleTransformation(i),m=t.scaleTransformation(c),g=rvt(m,e,n),_=Math.abs(h-f);if(Math.abs(p-g)>_){var y=(h-f)/(d-m);return 1!==y?{centerPoint:ovt(d,y,h),zoomAmount:y}:{centerPoint:n,zoomAmount:y}}return p>h!=o?{centerPoint:ovt(d,e,h),zoomAmount:e}:g<f!=o?{centerPoint:ovt(m,e,f),zoomAmount:e}:{centerPoint:n,zoomAmount:e}}function lvt(t){var e=t.range();return e[1]<e[0]}
/**
     * Copyright 2017-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(ivt,"__esModule",{value:!0}),ivt.zoomOut=rvt,ivt.constrainedZoom=function cvt(t,e,n,i,r,o,a){return svt(t,e=avt(t,e,i,r),n,o,a)},ivt.constrainZoomExtents=avt,ivt.constrainZoomValues=svt,ivt.constrainedTranslation=function uvt(t,e,n,i){var r,o=t.getTransformationDomain(),a=o[0],s=o[1],l=lvt(t);if(e>0!==l){if(null!=(r=i)){var c=t.scaleTransformation(s),u=t.scaleTransformation(r);e=(l?Math.max:Math.min)(c+e,u)-c}}else if(null!=(r=n)){var h=t.scaleTransformation(a),d=t.scaleTransformation(r);e=(l?Math.min:Math.max)(h+e,d)-h}return e};var hvt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(hvt,"__esModule",{value:!0});var dvt=Fgt,pvt=(function(){function t(){this._autoDomainAutomatically=!0,this._domainModificationInProgress=!1,this._updateId=0,this._callbacks=new dvt.CallbackSet,this._includedValuesProviders=new dvt.Set}return t.prototype.extentOfValues=function(t){return[]},t.prototype._getAllIncludedValues=function(t){var e=this;void 0===t&&(t=!1);var n=[];return this._includedValuesProviders.forEach((function(i){var r=i(e,t);n=n.concat(r)})),n},t.prototype._getExtent=function(){return[]},t.prototype.onUpdate=function(t){return this._callbacks.add(t),this},t.prototype.offUpdate=function(t){return this._callbacks.delete(t),this},t.prototype._dispatchUpdate=function(){this._updateId++,this._callbacks.callCallbacks(this)},t.prototype.autoDomain=function(){return this._autoDomainAutomatically=!0,this._setDomain(this._getExtent()),this},t.prototype.autoDomainIfAutomaticMode=function(){this._autoDomainAutomatically&&this.autoDomain()},t.prototype.scale=function(t){throw new Error("Subclasses should override scale")},t.prototype.ticks=function(){return this.domain()},t.prototype.domain=function(t){return null==t?this._getDomain():(this._autoDomainAutomatically=!1,this._setDomain(t),this)},t.prototype._getDomain=function(){throw new Error("Subclasses should override _getDomain")},t.prototype._setDomain=function(t){this._domainModificationInProgress||(this._domainModificationInProgress=!0,this._backingScaleDomain(t),this._dispatchUpdate(),this._domainModificationInProgress=!1)},t.prototype._backingScaleDomain=function(t){throw new Error("Subclasses should override _backingDomain")},t.prototype.range=function(t){return null==t?this._getRange():(this._setRange(t),this)},t.prototype._getRange=function(){throw new Error("Subclasses should override _getRange")},t.prototype._setRange=function(t){throw new Error("Subclasses should override _setRange")},t.prototype.addIncludedValuesProvider=function(t){return this._includedValuesProviders.add(t),this.autoDomainIfAutomaticMode(),this},t.prototype.removeIncludedValuesProvider=function(t){return this._includedValuesProviders.delete(t),this.autoDomainIfAutomaticMode(),this},t.prototype.updateId=function(){return this._updateId},t})();hvt.Scale=pvt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(nvt,"__esModule",{value:!0});var fvt=uO,mvt=Edt,gvt=ivt,_vt=Fgt,yvt=[0,1],vvt=(function(t){function e(){var n=t.call(this)||this;return n._range=[0,1],n._d3Scale=mvt.scaleBand(),n._d3Scale.range(yvt),n._d3TransformationScale=mvt.scaleLinear(),n._d3TransformationScale.domain(yvt),n._innerPadding=e._convertToPlottableInnerPadding(.3),n._outerPadding=e._convertToPlottableOuterPadding(.5,.3),n}return fvt.__extends(e,t),e.prototype.cloneWithoutProviders=function(){var t=(new e).domain(this.domain()).range(this.range()).innerPadding(this.innerPadding()).outerPadding(this.outerPadding());return t._d3TransformationScale.domain(this._d3TransformationScale.domain()),t},e.prototype.extentOfValues=function(t){return _vt.Array.uniq(t)},e.prototype._getExtent=function(){return _vt.Array.uniq(this._getAllIncludedValues())},e.prototype.domain=function(e){return t.prototype.domain.call(this,e)},e.prototype.invertRange=function(t){var e=this;void 0===t&&(t=this.range());var n=this._d3Scale.bandwidth(),i=this.invertedTransformation(t[0]),r=this.invertedTransformation(t[1]),o=this._d3Scale.domain(),a=o.map((function(t){return e._d3Scale(t)+n/2})),s=mvt.bisect(a,i),l=mvt.bisect(a,r);return o.slice(s,l)},e.prototype.range=function(e){return t.prototype.range.call(this,e)},e._convertToPlottableInnerPadding=function(t){return 1/(1-t)-1},e._convertToPlottableOuterPadding=function(t,e){return t/(1-e)},e.prototype._setBands=function(){var t=1-1/(1+this.innerPadding()),e=this.outerPadding()/(1+this.innerPadding());this._d3Scale.paddingInner(t),this._d3Scale.paddingOuter(e)},e.prototype.rangeBand=function(){return this._rescaleBand(this._d3Scale.bandwidth())},e.prototype.stepWidth=function(){return this._rescaleBand(this._d3Scale.bandwidth()*(1+this.innerPadding()))},e.prototype.ticks=function(){return this.domain()},e.prototype.innerPadding=function(t){return null==t?this._innerPadding:(this._innerPadding=t,this.range(this.range()),this._dispatchUpdate(),this)},e.prototype.outerPadding=function(t){return null==t?this._outerPadding:(this._outerPadding=t,this.range(this.range()),this._dispatchUpdate(),this)},e.prototype.scale=function(t){var e=this._d3Scale(t)+this._d3Scale.bandwidth()/2;return this._d3TransformationScale(e)},e.prototype.zoom=function(t,e){var n=this;this._d3TransformationScale.domain(this._d3TransformationScale.range().map((function(i){return n._d3TransformationScale.invert(gvt.zoomOut(i,t,e))}))),this._dispatchUpdate()},e.prototype.pan=function(t){var e=this;this._d3TransformationScale.domain(this._d3TransformationScale.range().map((function(n){return e._d3TransformationScale.invert(n+t)}))),this._dispatchUpdate()},e.prototype.scaleTransformation=function(t){return this._d3TransformationScale(t)},e.prototype.invertedTransformation=function(t){return this._d3TransformationScale.invert(t)},e.prototype.getTransformationExtent=function(){return yvt},e.prototype.getTransformationDomain=function(){return this._d3TransformationScale.domain()},e.prototype.setTransformationDomain=function(t){this._d3TransformationScale.domain(t),this._dispatchUpdate()},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this._setBands(),this)},e.prototype._getRange=function(){return this._range},e.prototype._setRange=function(t){this._range=t,this._d3TransformationScale.range(t),this._setBands()},e.prototype._rescaleBand=function(t){return Math.abs(this._d3TransformationScale(t)-this._d3TransformationScale(0))},e})(hvt.Scale);nvt.Category=vvt;var bvt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(bvt,"__esModule",{value:!0});var xvt=uO,wvt=Edt,Svt=Fgt,Mvt=hvt,Evt=(function(){function t(){this.count=0,this.tracker={}}return t.prototype.getIndex=function(t){if(null!=this.tracker[t])return this.tracker[t];var e=this.count;return this.tracker[t]=e,this.count+=1,e},t.prototype.clear=function(){this.count=0,this.tracker={}},t})(),Tvt=(function(t){function e(n){var i,r=t.call(this)||this;switch(r._rangeLength=1,r._tracker=new Evt,n){case null:case void 0:null==e._plottableColorCache&&(e._plottableColorCache=e._getPlottableColors()),i=wvt.scaleOrdinal().range(e._plottableColorCache);break;case"Category10":case"category10":case"10":i=wvt.scaleOrdinal(wvt.schemeCategory10);break;case"Category20":case"category20":case"20":i=wvt.scaleOrdinal(wvt.schemeCategory20);break;case"Category20b":case"category20b":case"20b":i=wvt.scaleOrdinal(wvt.schemeCategory20b);break;case"Category20c":case"category20c":case"20c":i=wvt.scaleOrdinal(wvt.schemeCategory20c);break;default:throw new Error("Unsupported ColorScale type")}return r._d3Scale=i,r._rangeLength=r._d3Scale.range().length,r}return xvt.__extends(e,t),e.prototype.extentOfValues=function(t){return Svt.Array.uniq(t)},e.prototype._getExtent=function(){return Svt.Array.uniq(this._getAllIncludedValues())},e.invalidateColorCache=function(){e._plottableColorCache=null},e._getPlottableColors=function(){for(var t=[],e=wvt.select("body").append("plottable-color-tester"),n=Svt.Color.colorTest(e,""),i=0,r=Svt.Color.colorTest(e,"plottable-colors-0");null!=r&&i<this._MAXIMUM_COLORS_FROM_CSS&&(r!==n||r!==t[t.length-1]);)t.push(r),i++,r=Svt.Color.colorTest(e,"plottable-colors-"+i);return e.remove(),t},e.prototype.scale=function(t){var n=this._d3Scale(t),i=this._tracker.getIndex(t),r=Math.floor(i/this._rangeLength);if(0===r)return n;var o=Math.log(r*e._LOOP_LIGHTEN_FACTOR+1);return Svt.Color.lightenColor(n,o)},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this._tracker.clear(),this)},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t),this._rangeLength=t.length},e._LOOP_LIGHTEN_FACTOR=1.6,e._MAXIMUM_COLORS_FROM_CSS=256,e})(Mvt.Scale);bvt.Color=Tvt;var Cvt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Cvt,"__esModule",{value:!0});var Avt=uO,kvt=Edt,Lvt=Fgt,Pvt=(function(t){function e(n){void 0===n&&(n="linear");var i=t.call(this)||this;switch(n){case"linear":i._colorScale=kvt.scaleLinear();break;case"log":i._colorScale=kvt.scaleLog();break;case"sqrt":i._colorScale=kvt.scaleSqrt();break;case"pow":i._colorScale=kvt.scalePow()}if(null==i._colorScale)throw new Error("unknown QuantitativeScale scale type "+n);return i.range(e.REDS),i}return Avt.__extends(e,t),e.prototype.extentOfValues=function(t){var e=kvt.extent(t);return null==e[0]||null==e[1]?[]:e},e.prototype._d3InterpolatedScale=function(){return this._colorScale.range([0,1]).interpolate(this._interpolateColors())},e.prototype._interpolateColors=function(){var t=this._colorRange;if(t.length<2)throw new Error("Color scale arrays must have at least two elements.");return function(e,n){return function(e){var n=(e=Math.max(0,Math.min(1,e)))*(t.length-1),i=Math.floor(n),r=Math.ceil(n),o=n-i;return kvt.interpolateLab(t[i],t[r])(o)}}},e.prototype._resetScale=function(){this._d3Scale=this._d3InterpolatedScale(),this.autoDomainIfAutomaticMode(),this._dispatchUpdate()},e.prototype.autoDomain=function(){var t=this._getAllIncludedValues();return t.length>0&&this._setDomain([Lvt.Math.min(t,0),Lvt.Math.max(t,0)]),this},e.prototype.scale=function(t){return this._d3Scale(t)},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._getRange=function(){return this._colorRange},e.prototype._setRange=function(t){this._colorRange=t,this._resetScale()},e.REDS=["#FFFFFF","#FFF6E1","#FEF4C0","#FED976","#FEB24C","#FD8D3C","#FC4E2A","#E31A1C","#B10026"],e.BLUES=["#FFFFFF","#CCFFFF","#A5FFFD","#85F7FB","#6ED3EF","#55A7E0","#417FD0","#2545D3","#0B02E1"],e.POSNEG=["#0B02E1","#2545D3","#417FD0","#55A7E0","#6ED3EF","#85F7FB","#A5FFFD","#CCFFFF","#FFFFFF","#FFF6E1","#FEF4C0","#FED976","#FEB24C","#FD8D3C","#FC4E2A","#E31A1C","#B10026"],e})(hvt.Scale);Cvt.InterpolatedColor=Pvt;var Nvt={},Ivt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Ivt,"__esModule",{value:!0});var Rvt=uO,Ovt=Edt,zvt=ivt,Dvt=Fgt,Bvt=(function(t){function e(){var e=t.call(this)||this;return e._tickGenerator=function(t){return t.defaultTicks()},e._padProportion=.05,e._snappingDomainEnabled=!0,e._paddingExceptionsProviders=new Dvt.Set,e}return Rvt.__extends(e,t),e.prototype.autoDomain=function(){return this._domainMin=null,this._domainMax=null,t.prototype.autoDomain.call(this),this},e.prototype.autoDomainIfAutomaticMode=function(){if(null==this._domainMin||null==this._domainMax){var e=this._getExtent();if(null!=this._domainMin){var n=e[1];return this._domainMin>=n&&(n=this._expandSingleValueDomain([this._domainMin,this._domainMin])[1]),void this._setDomain([this._domainMin,n])}if(null!=this._domainMax){var i=e[0];return this._domainMax<=i&&(i=this._expandSingleValueDomain([this._domainMax,this._domainMax])[0]),void this._setDomain([i,this._domainMax])}t.prototype.autoDomainIfAutomaticMode.call(this)}else this._setDomain([this._domainMin,this._domainMax])},e.prototype._getUnboundedExtent=function(t){void 0===t&&(t=!1);var e=this._getAllIncludedValues(t),n=this._defaultExtent();if(0!==e.length){var i=[Dvt.Math.min(e,n[0]),Dvt.Math.max(e,n[1])];n=this._padDomain(i)}return n},e.prototype._getExtent=function(){var t=this._getUnboundedExtent();return null!=this._domainMin&&(t[0]=this._domainMin),null!=this._domainMax&&(t[1]=this._domainMax),t},e.prototype.addPaddingExceptionsProvider=function(t){return this._paddingExceptionsProviders.add(t),this.autoDomainIfAutomaticMode(),this},e.prototype.removePaddingExceptionsProvider=function(t){return this._paddingExceptionsProviders.delete(t),this.autoDomainIfAutomaticMode(),this},e.prototype.padProportion=function(t){if(null==t)return this._padProportion;if(t<0)throw new Error("padProportion must be non-negative");return this._padProportion=t,this.autoDomainIfAutomaticMode(),this},e.prototype._padDomain=function(t){var e=this;if(t[0].valueOf()===t[1].valueOf())return this._expandSingleValueDomain(t);if(0===this._padProportion)return t;var n=this._padProportion/2,i=t[0],r=t[1],o=!1,a=!1;this._paddingExceptionsProviders.forEach((function(t){t(e).forEach((function(t){t.valueOf()===i.valueOf()&&(o=!0),t.valueOf()===r.valueOf()&&(a=!0)}))}));var s=this._backingScaleDomain();this._backingScaleDomain(t);var l=o?i:this.invert(this.scale(i)-(this.scale(r)-this.scale(i))*n),c=a?r:this.invert(this.scale(r)+(this.scale(r)-this.scale(i))*n);return this._backingScaleDomain(s),this._snappingDomainEnabled?this._niceDomain([l,c]):[l,c]},e.prototype.snappingDomainEnabled=function(t){return null==t?this._snappingDomainEnabled:(this._snappingDomainEnabled=t,this.autoDomainIfAutomaticMode(),this)},e.prototype._expandSingleValueDomain=function(t){return t},e.prototype.invert=function(t){throw new Error("Subclasses should override invert")},e.prototype.domain=function(e){return null!=e&&(this._domainMin=e[0],this._domainMax=e[1]),t.prototype.domain.call(this,e)},e.prototype.domainMin=function(t){return null==t?this.domain()[0]:(this._domainMin=t,this.autoDomainIfAutomaticMode(),this)},e.prototype.domainMax=function(t){return null==t?this.domain()[1]:(this._domainMax=t,this.autoDomainIfAutomaticMode(),this)},e.prototype.extentOfValues=function(t){var e=Ovt.extent(t.filter((function(t){return Dvt.Math.isValidNumber(+t)})));return null==e[0]||null==e[1]?[]:e},e.prototype.zoom=function(t,e){var n=this;this.domain(this.range().map((function(i){return n.invert(zvt.zoomOut(i,t,e))})))},e.prototype.pan=function(t){var e=this;this.domain(this.range().map((function(n){return e.invert(n+t)})))},e.prototype.scaleTransformation=function(t){throw new Error("Subclasses should override scaleTransformation")},e.prototype.invertedTransformation=function(t){throw new Error("Subclasses should override invertedTransformation")},e.prototype.getTransformationExtent=function(){throw new Error("Subclasses should override getTransformationExtent")},e.prototype.getTransformationDomain=function(){throw new Error("Subclasses should override getTransformationDomain")},e.prototype.setTransformationDomain=function(t){throw new Error("Subclasses should override setTransformationDomain")},e.prototype._setDomain=function(e){var n=function(t){return Dvt.Math.isNaN(t)||t===1/0||t===-1/0};n(e[0])||n(e[1])?Dvt.Window.warn("Warning: QuantitativeScales cannot take NaN or Infinity as a domain value. Ignoring."):t.prototype._setDomain.call(this,e)},e.prototype.defaultTicks=function(){throw new Error("Subclasses should override _getDefaultTicks")},e.prototype.ticks=function(){return this._tickGenerator(this)},e.prototype._niceDomain=function(t,e){throw new Error("Subclasses should override _niceDomain")},e.prototype._defaultExtent=function(){throw new Error("Subclasses should override _defaultExtent")},e.prototype.tickGenerator=function(t){return null==t?this._tickGenerator:(this._tickGenerator=t,this)},e._DEFAULT_NUM_TICKS=10,e})(hvt.Scale);Ivt.QuantitativeScale=Bvt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Nvt,"__esModule",{value:!0});var Hvt=uO,Fvt=Edt,Vvt=(function(t){function e(){var e=t.call(this)||this;return e._d3Scale=Fvt.scaleLinear(),e}return Hvt.__extends(e,t),e.prototype._defaultExtent=function(){return[0,1]},e.prototype._expandSingleValueDomain=function(t){return t[0]===t[1]?[t[0]-1,t[1]+1]:t},e.prototype.scale=function(t){return this._d3Scale(t)},e.prototype.scaleTransformation=function(t){return this.scale(t)},e.prototype.invertedTransformation=function(t){return this.invert(t)},e.prototype.getTransformationExtent=function(){return this._getUnboundedExtent(!0)},e.prototype.getTransformationDomain=function(){return this.domain()},e.prototype.setTransformationDomain=function(t){this.domain(t)},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t)},e.prototype.invert=function(t){return this._d3Scale.invert(t)},e.prototype.defaultTicks=function(){return this._d3Scale.ticks(e._DEFAULT_NUM_TICKS)},e.prototype._niceDomain=function(t,e){return this._d3Scale.copy().domain(t).nice(e).domain()},e})(Ivt.QuantitativeScale);Nvt.Linear=Vvt;var Uvt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Uvt,"__esModule",{value:!0});var jvt=uO,Gvt=Edt,Wvt=(function(t){function e(e){void 0===e&&(e=10);var n=t.call(this)||this;return n._d3Scale=Gvt.scaleLog().base(e),n._setDomain(n._defaultExtent()),n}return jvt.__extends(e,t),e.prototype._defaultExtent=function(){return[1,this._d3Scale.base()]},e.prototype._expandSingleValueDomain=function(t){return t[0]===t[1]?[t[0]/this._d3Scale.base(),t[1]*this._d3Scale.base()]:t},e.prototype.scale=function(t){return this._d3Scale(t)},e.prototype.scaleTransformation=function(t){return this.scale(t)},e.prototype.invertedTransformation=function(t){return this.invert(t)},e.prototype.getTransformationExtent=function(){return this._getUnboundedExtent(!0)},e.prototype.getTransformationDomain=function(){return this.domain()},e.prototype.setTransformationDomain=function(t){this.domain(t)},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t)},e.prototype.invert=function(t){return this._d3Scale.invert(t)},e.prototype.defaultTicks=function(){return this._d3Scale.ticks(e._DEFAULT_NUM_TICKS)},e.prototype._niceDomain=function(t,e){return this._d3Scale.copy().domain(t).nice().domain()},e})(Ivt.QuantitativeScale);Uvt.Log=Wvt;var qvt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(qvt,"__esModule",{value:!0});var Yvt=uO,Xvt=Edt,$vt=Fgt,Kvt=Zyt,Zvt=(function(t){function e(e){void 0===e&&(e=10);var n=t.call(this)||this;if(n._logTickGenerator=function(t){var e=function(t,e,n){return[t,e,n].sort((function(t,e){return t-e}))[1]},i=$vt.Math.min(n._untransformedDomain,0),r=$vt.Math.max(n._untransformedDomain,0),o=i,a=e(i,r,-n._pivot),s=e(i,r,n._pivot),l=r,c=n._logTicks(-a,-o).map((function(t){return-t})).reverse(),u=n._logTicks(s,l),h=Math.max(i,-n._pivot),d=Math.min(r,n._pivot),p=Xvt.scaleLinear().domain([h,d]).ticks(n._howManyTicks(h,d)),f=c.concat(p).concat(u);return f.length<=1&&(f=Xvt.scaleLinear().domain([i,r]).ticks(Kvt.ModifiedLog._DEFAULT_NUM_TICKS)),f},n._d3Scale=Xvt.scaleLinear(),n._base=e,n._pivot=n._base,n._setDomain(n._defaultExtent()),n.tickGenerator(n._logTickGenerator),e<=1)throw new Error("ModifiedLogScale: The base must be > 1");return n}return Yvt.__extends(e,t),e.prototype._adjustedLog=function(t){var e=t<0?-1:1;return(t*=e)<this._pivot&&(t+=(this._pivot-t)/this._pivot),(t=Math.log(t)/Math.log(this._base))*e},e.prototype._invertedAdjustedLog=function(t){var e=t<0?-1:1;return t*=e,(t=Math.pow(this._base,t))<this._pivot&&(t=this._pivot*(t-1)/(this._pivot-1)),t*e},e.prototype.scale=function(t){return this._d3Scale(this._adjustedLog(t))},e.prototype.invert=function(t){return this._invertedAdjustedLog(this._d3Scale.invert(t))},e.prototype.scaleTransformation=function(t){return this.scale(t)},e.prototype.invertedTransformation=function(t){return this.invert(t)},e.prototype.getTransformationExtent=function(){return this._getUnboundedExtent(!0)},e.prototype.getTransformationDomain=function(){return this.domain()},e.prototype.setTransformationDomain=function(t){this.domain(t)},e.prototype._getDomain=function(){return this._untransformedDomain},e.prototype._setDomain=function(e){this._untransformedDomain=e;var n=[this._adjustedLog(e[0]),this._adjustedLog(e[1])];t.prototype._setDomain.call(this,n)},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._logTicks=function(t,e){var n=this,i=this._howManyTicks(t,e);if(0===i)return[];var r=Math.floor(Math.log(t)/Math.log(this._base)),o=Math.ceil(Math.log(e)/Math.log(this._base)),a=Xvt.range(o,r,-Math.ceil((o-r)/i)),s=Xvt.range(this._base,1,-(this._base-1)).map(Math.floor),l=$vt.Array.uniq(s),c=a.map((function(t){return l.map((function(e){return Math.pow(n._base,t-1)*e}))}));return $vt.Array.flatten(c).filter((function(n){return t<=n&&n<=e})).sort((function(t,e){return t-e}))},e.prototype._howManyTicks=function(t,e){var n=this._adjustedLog($vt.Math.min(this._untransformedDomain,0)),i=this._adjustedLog($vt.Math.max(this._untransformedDomain,0)),r=this._adjustedLog(t),o=this._adjustedLog(e);return Math.ceil((o-r)/(i-n)*Kvt.ModifiedLog._DEFAULT_NUM_TICKS)},e.prototype._niceDomain=function(t,e){return t},e.prototype._defaultExtent=function(){return[0,this._base]},e.prototype._expandSingleValueDomain=function(t){if(t[0]===t[1]){var e=t[0];return e>0?[e/this._base,e*this._base]:0===e?[-this._base,this._base]:[e*this._base,e/this._base]}return t},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t)},e.prototype.defaultTicks=function(){return this._d3Scale.ticks(Kvt.ModifiedLog._DEFAULT_NUM_TICKS)},e})(Ivt.QuantitativeScale);qvt.ModifiedLog=Zvt;var Jvt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Jvt,"__esModule",{value:!0});var Qvt=uO,tbt=Edt,ebt=Kyt,nbt=(function(t){function e(){var e=t.call(this)||this;return e._d3Scale=tbt.scaleTime(),e.autoDomain(),e}return Qvt.__extends(e,t),e.prototype.tickInterval=function(t,n,i){void 0===n&&(n=1),void 0===i&&(i=!1);var r=tbt.scaleTime(),o=e.timeIntervalToD3Time(t,i).every(n);return r.domain(this.domain()),r.range(this.range()),r.ticks(o)},e.prototype._setDomain=function(e){if(e[1]<e[0])throw new Error("Scale.Time domain values must be in chronological order");return t.prototype._setDomain.call(this,e)},e.prototype._defaultExtent=function(){return[new Date("1970-01-01"),new Date("1970-01-02")]},e.prototype._expandSingleValueDomain=function(t){var e=t[0].getTime(),n=t[1].getTime();if(e===n){var i=new Date(e);i.setDate(i.getDate()-1);var r=new Date(n);return r.setDate(r.getDate()+1),[i,r]}return t},e.prototype.scale=function(t){return this._d3Scale(t)},e.prototype.scaleTransformation=function(t){return this.scale(new Date(t))},e.prototype.invertedTransformation=function(t){return this.invert(t).getTime()},e.prototype.getTransformationExtent=function(){var t=this._getUnboundedExtent(!0);return[t[0].valueOf(),t[1].valueOf()]},e.prototype.getTransformationDomain=function(){var t=this.domain();return[t[0].valueOf(),t[1].valueOf()]},e.prototype.setTransformationDomain=function(t){var e=t[1];this.domain([new Date(t[0]),new Date(e)])},e.prototype._getDomain=function(){return this._backingScaleDomain()},e.prototype._backingScaleDomain=function(t){return null==t?this._d3Scale.domain():(this._d3Scale.domain(t),this)},e.prototype._getRange=function(){return this._d3Scale.range()},e.prototype._setRange=function(t){this._d3Scale.range(t)},e.prototype.invert=function(t){return this._d3Scale.invert(t)},e.prototype.defaultTicks=function(){return this._d3Scale.ticks(e._DEFAULT_NUM_TICKS)},e.prototype._niceDomain=function(t){return this._d3Scale.copy().domain(t).nice().domain()},e.timeIntervalToD3Time=function(t,e){switch(t){case ebt.TimeInterval.second:return e?tbt.utcSecond:tbt.timeSecond;case ebt.TimeInterval.minute:return e?tbt.utcMinute:tbt.timeMinute;case ebt.TimeInterval.hour:return e?tbt.utcHour:tbt.timeHour;case ebt.TimeInterval.day:return e?tbt.utcDay:tbt.timeDay;case ebt.TimeInterval.week:return e?tbt.utcWeek:tbt.timeWeek;case ebt.TimeInterval.month:return e?tbt.utcMonth:tbt.timeMonth;case ebt.TimeInterval.year:return e?tbt.utcYear:tbt.timeYear;default:throw Error("TimeInterval specified does not exist: "+t)}},e})(Ivt.QuantitativeScale);Jvt.Time=nbt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;t.TickGenerators=Jyt,e.__exportStar(nvt,t),e.__exportStar(bvt,t),e.__exportStar(Cvt,t),e.__exportStar(Nvt,t),e.__exportStar(Uvt,t),e.__exportStar(qvt,t),e.__exportStar(Jvt,t);var n=nvt,i=Ivt;t.isTransformable=function r(t){return t instanceof i.QuantitativeScale||t instanceof n.Category}})(Zyt),(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO,n=Edt,i=Imt,r=gyt,o=Zyt,a=Fgt,s=Smt,l=myt;t.TimeInterval=s.makeEnum(["second","minute","hour","day","week","month","year"]),t.TimeAxisOrientation=s.makeEnum(["top","bottom"]),t.TierLabelPosition=s.makeEnum(["between","center"]);var c,u=(function(s){function u(t,e,n){var i=s.call(this,t,e)||this;return i._maxTimeIntervalPrecision=null,i._tierLabelPositions=[],i._useUTC=n,i.addClass("time-axis"),i.tickLabelPadding(5),i.axisConfigurations(u._DEFAULT_TIME_AXIS_CONFIGURATIONS(i._useUTC)),i.annotationFormatter(r.time("%a %b %d, %Y",i._useUTC)),i}return e.__extends(u,s),u.prototype.tierLabelPositions=function(t){if(null==t)return this._tierLabelPositions;if(!t.every((function(t){return"between"===t.toLowerCase()||"center"===t.toLowerCase()})))throw new Error("Unsupported position for tier labels");return this._tierLabelPositions=t,this.redraw(),this},u.prototype.maxTimeIntervalPrecision=function(t){return null==t?this._maxTimeIntervalPrecision:(this._maxTimeIntervalPrecision=t,this.redraw(),this)},u.prototype.currentAxisConfiguration=function(){return this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex]},u.prototype.axisConfigurations=function(t){if(null==t)return this._possibleTimeAxisConfigurations;this._possibleTimeAxisConfigurations=t,this._numTiers=a.Math.max(this._possibleTimeAxisConfigurations.map((function(t){return t.length})),0),this._isAnchored&&this._setupDomElements();for(var e=this.tierLabelPositions(),n=[],i=0;i<this._numTiers;i++)n.push(e[i]||"between");return this.tierLabelPositions(n),this.redraw(),this},u.prototype._getMostPreciseConfigurationIndex=function(){var t=this,e=this._possibleTimeAxisConfigurations.length;return this._possibleTimeAxisConfigurations.forEach((function(n,i){i<e&&n.every((function(e){return t._checkTimeAxisTierConfiguration(e)}))&&(e=i)})),e===this._possibleTimeAxisConfigurations.length&&(a.Window.warn("zoomed out too far: could not find suitable interval to display labels"),--e),e},u.prototype.orientation=function(t){if(t&&("right"===t.toLowerCase()||"left"===t.toLowerCase()))throw new Error(t+" is not a supported orientation for TimeAxis - only horizontal orientations are supported");return s.prototype.orientation.call(this,t)},u.prototype._computeHeight=function(){var t=this._measurer.measure().height;this._tierHeights=[];for(var e=0;e<this._numTiers;e++)this._tierHeights.push(t+this.tickLabelPadding()+("between"===this._tierLabelPositions[e]?0:this._maxLabelTickLength()));return n.sum(this._tierHeights)},u.prototype._getIntervalLength=function(t){var e=this._scale.domain()[0],n=o.Time.timeIntervalToD3Time(t.interval,this._useUTC).offset(e,t.step);return n>this._scale.domain()[1]?this.width():Math.abs(this._scale.scale(n)-this._scale.scale(e))},u.prototype._maxWidthForInterval=function(t){return this._measurer.measure(t.formatter(u._LONG_DATE)).width},u.prototype._checkTimeAxisTierConfiguration=function(t){if(null!=this._maxTimeIntervalPrecision){var e=u._SORTED_TIME_INTERVAL_INDEX[this._maxTimeIntervalPrecision],n=u._SORTED_TIME_INTERVAL_INDEX[t.interval];if(null!=e&&null!=n&&n<e)return!1}var i=this._maxWidthForInterval(t)+2*this.tickLabelPadding();return Math.min(this._getIntervalLength(t),this.width())>=i},u.prototype._sizeFromOffer=function(t,e){var n=s.prototype._sizeFromOffer.call(this,t,e),i=this._tierHeights.reduce((function(t,e,i,r){return t+e>n.height?t:t+e})),r=this.margin()+(this.annotationsEnabled()?this.annotationTierCount()*this._annotationTierHeight():0);return n.height=Math.min(n.height,i+r),n},u.prototype._setup=function(){s.prototype._setup.call(this),this._setupDomElements()},u.prototype._setupDomElements=function(){this.content().selectAll("."+u.TIME_AXIS_TIER_CLASS).remove(),this._tierLabelContainers=[],this._tierMarkContainers=[],this._tierBaselines=[],this._tickLabelContainer.remove(),this._baseline.remove();for(var t=0;t<this._numTiers;++t){var e=this.content().append("g").classed(u.TIME_AXIS_TIER_CLASS,!0);this._tierLabelContainers.push(e.append("g").classed(l.Axis.TICK_LABEL_CLASS+"-container",!0)),this._tierMarkContainers.push(e.append("g").classed(l.Axis.TICK_MARK_CLASS+"-container",!0)),this._tierBaselines.push(e.append("line").classed("baseline",!0))}var n=new i.SvgContext(this._tierLabelContainers[0].node());this._measurer=new i.CacheMeasurer(n)},u.prototype._getTickIntervalValues=function(t){return this._scale.tickInterval(t.interval,t.step,this._useUTC)},u.prototype._getTickValues=function(){var t=this;return this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex].reduce((function(e,n){return e.concat(t._getTickIntervalValues(n))}),[])},u.prototype._cleanTiers=function(){for(var t=0;t<this._tierLabelContainers.length;t++)this._tierLabelContainers[t].selectAll("."+l.Axis.TICK_LABEL_CLASS).remove(),this._tierMarkContainers[t].selectAll("."+l.Axis.TICK_MARK_CLASS).remove(),this._tierBaselines[t].style("visibility","hidden")},u.prototype._getTickValuesForConfiguration=function(t){var e=this._scale.tickInterval(t.interval,t.step,this._useUTC),n=this._scale.domain(),i=e.map((function(t){return t.valueOf()}));return-1===i.indexOf(n[0].valueOf())&&e.unshift(n[0]),-1===i.indexOf(n[1].valueOf())&&e.push(n[1]),e},u.prototype._renderTierLabels=function(t,e,i){var r=this,o=this._getTickValuesForConfiguration(e),a=[];"between"===this._tierLabelPositions[i]&&1===e.step?o.map((function(t,e){e+1>=o.length||a.push(new Date((o[e+1].valueOf()-o[e].valueOf())/2+o[e].valueOf()))})):a=o;var s=t.selectAll("."+l.Axis.TICK_LABEL_CLASS).data(a,(function(t){return String(t.valueOf())})),c=s.enter().append("g").classed(l.Axis.TICK_LABEL_CLASS,!0);c.append("text");var u,h="center"===this._tierLabelPositions[i]||1===e.step?0:this.tickLabelPadding();u="bottom"===this.orientation()?n.sum(this._tierHeights.slice(0,i+1))-this.tickLabelPadding():"center"===this._tierLabelPositions[i]?this.height()-n.sum(this._tierHeights.slice(0,i))-this.tickLabelPadding()-this._maxLabelTickLength():this.height()-n.sum(this._tierHeights.slice(0,i))-this.tickLabelPadding();var d=s.merge(c),p=d.selectAll("text");p.size()>0&&p.attr("transform","translate("+h+","+u+")"),s.exit().remove(),d.attr("transform",(function(t){return"translate("+r._scale.scale(t)+",0)"}));var f="center"===this._tierLabelPositions[i]||1===e.step?"middle":"start";d.selectAll("text").text(e.formatter).style("text-anchor",f)},u.prototype._renderTickMarks=function(t,e){var i=this._tierMarkContainers[e].selectAll("."+l.Axis.TICK_MARK_CLASS).data(t),r=i.enter().append("line").classed(l.Axis.TICK_MARK_CLASS,!0).merge(i),o=this._generateTickMarkAttrHash(),a=this._tierHeights.slice(0,e).reduce((function(t,e){return t+e}),0);"bottom"===this.orientation()?(o.y1=a,o.y2=a+("center"===this._tierLabelPositions[e]?this.innerTickLength():this._tierHeights[e])):(o.y1=this.height()-a,o.y2=this.height()-(a+("center"===this._tierLabelPositions[e]?this.innerTickLength():this._tierHeights[e]))),r.attrs(o),"bottom"===this.orientation()?(o.y1=a,o.y2=a+("center"===this._tierLabelPositions[e]?this.endTickLength():this._tierHeights[e])):(o.y1=this.height()-a,o.y2=this.height()-(a+("center"===this._tierLabelPositions[e]?this.endTickLength():this._tierHeights[e]))),n.select(r.nodes()[0]).attrs(o),n.select(r.nodes()[r.size()-1]).attrs(o),n.select(r.nodes()[0]).classed(l.Axis.END_TICK_MARK_CLASS,!0),n.select(r.nodes()[r.size()-1]).classed(l.Axis.END_TICK_MARK_CLASS,!0),i.exit().remove()},u.prototype._renderLabellessTickMarks=function(t){var e=this._tickMarkContainer.selectAll("."+l.Axis.TICK_MARK_CLASS).data(t),n=e.enter().append("line").classed(l.Axis.TICK_MARK_CLASS,!0).merge(e),i=this._generateTickMarkAttrHash();i.y2="bottom"===this.orientation()?this.tickLabelPadding():this.height()-this.tickLabelPadding(),n.attrs(i),e.exit().remove()},u.prototype._generateLabellessTicks=function(){return this._mostPreciseConfigIndex<1?[]:this._getTickIntervalValues(this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex-1][0])},u.prototype.renderImmediately=function(){var t=this;this._mostPreciseConfigIndex=this._getMostPreciseConfigurationIndex();var e=this._possibleTimeAxisConfigurations[this._mostPreciseConfigIndex];this._cleanTiers(),e.forEach((function(e,n){return t._renderTierLabels(t._tierLabelContainers[n],e,n)}));for(var n=e.map((function(e,n){return t._getTickValuesForConfiguration(e)})),i=0,r=0;r<Math.max(e.length,1);++r){var o=this._generateBaselineAttrHash();o.y1+="bottom"===this.orientation()?i:-i,o.y2=o.y1,this._tierBaselines[r].attrs(o).style("visibility","inherit"),i+=this._tierHeights[r]}var a=[],s=this._scale.domain(),l=this._scale.scale(s[1])-this._scale.scale(s[0]);for(1.5*this._getIntervalLength(e[0])>=l&&(a=this._generateLabellessTicks()),this._renderLabellessTickMarks(a),this._hideOverflowingTiers(),r=0;r<e.length;++r)this._renderTickMarks(n[r],r),this._hideOverlappingAndCutOffLabels(r);return this.annotationsEnabled()?this._drawAnnotations():this._removeAnnotations(),this},u.prototype._hideOverflowingTiers=function(){var t=this,e=this.height(),n=0;this.content().selectAll("."+u.TIME_AXIS_TIER_CLASS).attr("visibility",(function(i,r){return(n+=t._tierHeights[r])<=e?"inherit":"hidden"}))},u.prototype._hideOverlappingAndCutOffLabels=function(t){var e,i=this,r=this.element().node().getBoundingClientRect(),o=this._tierMarkContainers[t].selectAll("."+l.Axis.TICK_MARK_CLASS).filter((function(t,e){var i=n.select(this).style("visibility");return"visible"===i||"inherit"===i})).nodes().map((function(t){return t.getBoundingClientRect()}));this._tierLabelContainers[t].selectAll("."+l.Axis.TICK_LABEL_CLASS).filter((function(t,e){var i=n.select(this).style("visibility");return"visible"===i||"inherit"===i})).each((function(t,s){var l,c=this.getBoundingClientRect(),u=n.select(this),h=o[s],d=o[s+1],p=null!=e&&a.DOM.clientRectsOverlap(c,e),f=null!=h&&a.DOM.clientRectsOverlap(c,h),m=null!=d&&a.DOM.clientRectsOverlap(c,d);l=c,!(Math.floor(r.left)<=Math.ceil(l.left)&&Math.floor(r.top)<=Math.ceil(l.top)&&Math.floor(l.right)<=Math.ceil(r.left+i.width())&&Math.floor(l.bottom)<=Math.ceil(r.top+i.height()))||p||f||m?u.style("visibility","hidden"):(e=c,u.style("visibility","inherit"))}))},u.prototype.invalidateCache=function(){s.prototype.invalidateCache.call(this),this._measurer.reset()},u.TIME_AXIS_TIER_CLASS="time-axis-tier",u._SORTED_TIME_INTERVAL_INDEX=((c={})[t.TimeInterval.second]=0,c[t.TimeInterval.minute]=1,c[t.TimeInterval.hour]=2,c[t.TimeInterval.day]=3,c[t.TimeInterval.week]=4,c[t.TimeInterval.month]=5,c[t.TimeInterval.year]=6,c),u._DEFAULT_TIME_AXIS_CONFIGURATIONS=function(e){var n=function(t){return r.time(t,e)};return[[{interval:t.TimeInterval.second,step:1,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.second,step:5,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.second,step:10,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.second,step:15,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.second,step:30,formatter:n("%I:%M:%S %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:1,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:5,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:10,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:15,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.minute,step:30,formatter:n("%I:%M %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.hour,step:1,formatter:n("%I %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.hour,step:3,formatter:n("%I %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.hour,step:6,formatter:n("%I %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.hour,step:12,formatter:n("%I %p")},{interval:t.TimeInterval.day,step:1,formatter:n("%B %e, %Y")}],[{interval:t.TimeInterval.day,step:1,formatter:n("%a %e")},{interval:t.TimeInterval.month,step:1,formatter:n("%B %Y")}],[{interval:t.TimeInterval.day,step:1,formatter:n("%e")},{interval:t.TimeInterval.month,step:1,formatter:n("%B %Y")}],[{interval:t.TimeInterval.month,step:1,formatter:n("%B")},{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.month,step:1,formatter:n("%b")},{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.month,step:3,formatter:n("%b")},{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.month,step:6,formatter:n("%b")},{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:1,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:1,formatter:n("%y")}],[{interval:t.TimeInterval.year,step:5,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:25,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:50,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:100,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:200,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:500,formatter:n("%Y")}],[{interval:t.TimeInterval.year,step:1e3,formatter:n("%Y")}]]},u._LONG_DATE=new Date(9999,8,29,12,59,9999),u})(l.Axis);t.Time=u})(Kyt),(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(Nmt,t),e.__exportStar(Uyt,t),e.__exportStar(Kyt,t)})(Pmt);var ibt={},rbt={},obt={},abt={},sbt={},lbt={},cbt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(cbt,"__esModule",{value:!0});var ubt=Fgt,hbt=(function(){function t(){this._eventToProcessingFunction={},this._eventTarget=document,this._eventNameToCallbackSet={},this._connected=!1}return t.prototype._hasNoCallbacks=function(){for(var t=Object.keys(this._eventNameToCallbackSet),e=0;e<t.length;e++)if(0!==this._eventNameToCallbackSet[t[e]].size)return!1;return!0},t.prototype._connect=function(){var t=this;this._connected||(Object.keys(this._eventToProcessingFunction).forEach((function(e){t._eventTarget.addEventListener(e,t._eventToProcessingFunction[e],"wheel"===e?{passive:!1}:void 0)})),this._connected=!0)},t.prototype._disconnect=function(){var t=this;this._connected&&this._hasNoCallbacks()&&(Object.keys(this._eventToProcessingFunction).forEach((function(e){t._eventTarget.removeEventListener(e,t._eventToProcessingFunction[e])})),this._connected=!1)},t.prototype._addCallbackForEvent=function(t,e){null==this._eventNameToCallbackSet[t]&&(this._eventNameToCallbackSet[t]=new ubt.CallbackSet),this._eventNameToCallbackSet[t].add(e),this._connect()},t.prototype._removeCallbackForEvent=function(t,e){null!=this._eventNameToCallbackSet[t]&&this._eventNameToCallbackSet[t].delete(e),this._disconnect()},t.prototype._callCallbacksForEvent=function(t){for(var e=[],n=1;n<arguments.length;n++)e[n-1]=arguments[n];var i=this._eventNameToCallbackSet[t];null!=i&&i.callCallbacks.apply(i,e)},t})();cbt.Dispatcher=hbt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(lbt,"__esModule",{value:!0});var dbt=uO,pbt=(function(t){function e(){var n=t.call(this)||this;return n._eventToProcessingFunction[e._KEYDOWN_EVENT_NAME]=function(t){return n._processKeydown(t)},n._eventToProcessingFunction[e._KEYUP_EVENT_NAME]=function(t){return n._processKeyup(t)},n}return dbt.__extends(e,t),e.getDispatcher=function(){var t=document[e._DISPATCHER_KEY];return null==t&&(t=new e,document[e._DISPATCHER_KEY]=t),t},e.prototype._processKeydown=function(t){this._callCallbacksForEvent(e._KEYDOWN_EVENT_NAME,t.keyCode,t)},e.prototype._processKeyup=function(t){this._callCallbacksForEvent(e._KEYUP_EVENT_NAME,t.keyCode,t)},e.prototype.onKeyDown=function(t){return this._addCallbackForEvent(e._KEYDOWN_EVENT_NAME,t),this},e.prototype.offKeyDown=function(t){return this._removeCallbackForEvent(e._KEYDOWN_EVENT_NAME,t),this},e.prototype.onKeyUp=function(t){return this._addCallbackForEvent(e._KEYUP_EVENT_NAME,t),this},e.prototype.offKeyUp=function(t){return this._removeCallbackForEvent(e._KEYUP_EVENT_NAME,t),this},e._DISPATCHER_KEY="__Plottable_Dispatcher_Key",e._KEYDOWN_EVENT_NAME="keydown",e._KEYUP_EVENT_NAME="keyup",e})(cbt.Dispatcher);lbt.Key=pbt;var fbt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(fbt,"__esModule",{value:!0});var mbt=uO,gbt=Fgt,_bt=(function(t){function e(n){var i=t.call(this)||this;i._lastMousePosition={x:-1,y:-1},i._translator=gbt.getTranslator(n);var r=function(t){return i._measureAndDispatch(n,t,e._MOUSEMOVE_EVENT_NAME,"page")};return i._eventToProcessingFunction[e._MOUSEOVER_EVENT_NAME]=r,i._eventToProcessingFunction[e._MOUSEMOVE_EVENT_NAME]=r,i._eventToProcessingFunction[e._MOUSEOUT_EVENT_NAME]=r,i._eventToProcessingFunction[e._MOUSEDOWN_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._MOUSEDOWN_EVENT_NAME)},i._eventToProcessingFunction[e._MOUSEUP_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._MOUSEUP_EVENT_NAME,"page")},i._eventToProcessingFunction[e._WHEEL_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._WHEEL_EVENT_NAME)},i._eventToProcessingFunction[e._DBLCLICK_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._DBLCLICK_EVENT_NAME)},i}return mbt.__extends(e,t),e.getDispatcher=function(t){var n=t.root().rootElement(),i=n[e._DISPATCHER_KEY];return null==i&&(i=new e(t),n[e._DISPATCHER_KEY]=i),i},e.prototype.onMouseMove=function(t){return this._addCallbackForEvent(e._MOUSEMOVE_EVENT_NAME,t),this},e.prototype.offMouseMove=function(t){return this._removeCallbackForEvent(e._MOUSEMOVE_EVENT_NAME,t),this},e.prototype.onMouseDown=function(t){return this._addCallbackForEvent(e._MOUSEDOWN_EVENT_NAME,t),this},e.prototype.offMouseDown=function(t){return this._removeCallbackForEvent(e._MOUSEDOWN_EVENT_NAME,t),this},e.prototype.onMouseUp=function(t){return this._addCallbackForEvent(e._MOUSEUP_EVENT_NAME,t),this},e.prototype.offMouseUp=function(t){return this._removeCallbackForEvent(e._MOUSEUP_EVENT_NAME,t),this},e.prototype.onWheel=function(t){return this._addCallbackForEvent(e._WHEEL_EVENT_NAME,t),this},e.prototype.offWheel=function(t){return this._removeCallbackForEvent(e._WHEEL_EVENT_NAME,t),this},e.prototype.onDblClick=function(t){return this._addCallbackForEvent(e._DBLCLICK_EVENT_NAME,t),this},e.prototype.offDblClick=function(t){return this._removeCallbackForEvent(e._DBLCLICK_EVENT_NAME,t),this},e.prototype._measureAndDispatch=function(t,e,n,i){if(void 0===i&&(i="element"),"page"!==i&&"element"!==i)throw new Error("Invalid scope '"+i+"', must be 'element' or 'page'");if("page"===i||this.eventInside(t,e)){var r=this._translator.computePosition(e.clientX,e.clientY);this._lastMousePosition=r,this._callCallbacksForEvent(n,this.lastMousePosition(),e)}},e.prototype.eventInside=function(t,e){return gbt.Translator.isEventInside(t,e)},e.prototype.lastMousePosition=function(){return this._lastMousePosition},e._DISPATCHER_KEY="__Plottable_Dispatcher_Mouse",e._MOUSEOVER_EVENT_NAME="mouseover",e._MOUSEMOVE_EVENT_NAME="mousemove",e._MOUSEOUT_EVENT_NAME="mouseout",e._MOUSEDOWN_EVENT_NAME="mousedown",e._MOUSEUP_EVENT_NAME="mouseup",e._WHEEL_EVENT_NAME="wheel",e._DBLCLICK_EVENT_NAME="dblclick",e})(cbt.Dispatcher);fbt.Mouse=_bt;var ybt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(ybt,"__esModule",{value:!0});var vbt=uO,bbt=Fgt,xbt=(function(t){function e(n){var i=t.call(this)||this;return i._translator=bbt.getTranslator(n),i._eventToProcessingFunction[e._TOUCHSTART_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._TOUCHSTART_EVENT_NAME,"page")},i._eventToProcessingFunction[e._TOUCHMOVE_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._TOUCHMOVE_EVENT_NAME,"page")},i._eventToProcessingFunction[e._TOUCHEND_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._TOUCHEND_EVENT_NAME,"page")},i._eventToProcessingFunction[e._TOUCHCANCEL_EVENT_NAME]=function(t){return i._measureAndDispatch(n,t,e._TOUCHCANCEL_EVENT_NAME,"page")},i}return vbt.__extends(e,t),e.getDispatcher=function(t){var n=t.root().rootElement(),i=n[e._DISPATCHER_KEY];return null==i&&(i=new e(t),n[e._DISPATCHER_KEY]=i),i},e.prototype.onTouchStart=function(t){return this._addCallbackForEvent(e._TOUCHSTART_EVENT_NAME,t),this},e.prototype.offTouchStart=function(t){return this._removeCallbackForEvent(e._TOUCHSTART_EVENT_NAME,t),this},e.prototype.onTouchMove=function(t){return this._addCallbackForEvent(e._TOUCHMOVE_EVENT_NAME,t),this},e.prototype.offTouchMove=function(t){return this._removeCallbackForEvent(e._TOUCHMOVE_EVENT_NAME,t),this},e.prototype.onTouchEnd=function(t){return this._addCallbackForEvent(e._TOUCHEND_EVENT_NAME,t),this},e.prototype.offTouchEnd=function(t){return this._removeCallbackForEvent(e._TOUCHEND_EVENT_NAME,t),this},e.prototype.onTouchCancel=function(t){return this._addCallbackForEvent(e._TOUCHCANCEL_EVENT_NAME,t),this},e.prototype.offTouchCancel=function(t){return this._removeCallbackForEvent(e._TOUCHCANCEL_EVENT_NAME,t),this},e.prototype._measureAndDispatch=function(t,e,n,i){if(void 0===i&&(i="element"),"page"!==i&&"element"!==i)throw new Error("Invalid scope '"+i+"', must be 'element' or 'page'");if("element"!==i||this.eventInside(t,e)){for(var r=e.changedTouches,o={},a=[],s=0;s<r.length;s++){var l=r[s],c=l.identifier,u=this._translator.computePosition(l.clientX,l.clientY);null!=u&&(o[c]=u,a.push(c))}a.length>0&&this._callCallbacksForEvent(n,a,o,e)}},e.prototype.eventInside=function(t,e){return bbt.Translator.isEventInside(t,e)},e._DISPATCHER_KEY="__Plottable_Dispatcher_Touch",e._TOUCHSTART_EVENT_NAME="touchstart",e._TOUCHMOVE_EVENT_NAME="touchmove",e._TOUCHEND_EVENT_NAME="touchend",e._TOUCHCANCEL_EVENT_NAME="touchcancel",e})(cbt.Dispatcher);ybt.Touch=xbt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(lbt,t),e.__exportStar(fbt,t),e.__exportStar(ybt,t)})(sbt);var wbt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(wbt,"__esModule",{value:!0});var Sbt=(function(){function t(){var t=this;this._anchorCallback=function(e){return t._anchor(e)},this._enabled=!0}return t.prototype.attachTo=function(t){return this._disconnect(),this._componentAttachedTo=t,this._connect(),this},t.prototype.detachFrom=function(t){return this.detach()},t.prototype.detach=function(){return this._disconnect(),this._componentAttachedTo=null,this},t.prototype.enabled=function(t){return null==t?this._enabled:(this._enabled=t,this._enabled?this._connect():this._disconnect(),this)},t.prototype._anchor=function(t){this._isAnchored=!0},t.prototype._unanchor=function(){this._isAnchored=!1},t.prototype._translateToComponentSpace=function(t){var e=this._componentAttachedTo.originToRoot();return{x:t.x-e.x,y:t.y-e.y}},t.prototype._isInsideComponent=function(t){return 0<=t.x&&0<=t.y&&t.x<=this._componentAttachedTo.width()&&t.y<=this._componentAttachedTo.height()},t.prototype._connect=function(){this.enabled()&&null!=this._componentAttachedTo&&!this._isAnchored&&this._componentAttachedTo.onAnchor(this._anchorCallback)},t.prototype._disconnect=function(){this._isAnchored&&this._unanchor(),null!=this._componentAttachedTo&&this._componentAttachedTo.offAnchor(this._anchorCallback)},t})();wbt.Interaction=Sbt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(abt,"__esModule",{value:!0});var Mbt=uO,Ebt=sbt,Tbt=Fgt,Cbt=(function(t){function e(){var e=null!==t&&t.apply(this,arguments)||this;return e._clickedDown=!1,e._doubleClicking=!1,e._onClickCallbacks=new Tbt.CallbackSet,e._onDoubleClickCallbacks=new Tbt.CallbackSet,e._mouseDownCallback=function(t,n){return e._handleClickDown(t,n)},e._mouseUpCallback=function(t,n){return e._handleClickUp(t,n)},e._dblClickCallback=function(t,n){return e._handleDblClick(t,n)},e._touchStartCallback=function(t,n,i){return e._handleClickDown(n[t[0]],i)},e._touchEndCallback=function(t,n,i){return e._handleClickUp(n[t[0]],i)},e._touchCancelCallback=function(t,n){return e._clickedDown=!1},e}return Mbt.__extends(e,t),e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._mouseDispatcher=Ebt.Mouse.getDispatcher(e),this._mouseDispatcher.onMouseDown(this._mouseDownCallback),this._mouseDispatcher.onMouseUp(this._mouseUpCallback),this._mouseDispatcher.onDblClick(this._dblClickCallback),this._touchDispatcher=Ebt.Touch.getDispatcher(e),this._touchDispatcher.onTouchStart(this._touchStartCallback),this._touchDispatcher.onTouchEnd(this._touchEndCallback),this._touchDispatcher.onTouchCancel(this._touchCancelCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._mouseDispatcher.offMouseDown(this._mouseDownCallback),this._mouseDispatcher.offMouseUp(this._mouseUpCallback),this._mouseDispatcher.offDblClick(this._dblClickCallback),this._mouseDispatcher=null,this._touchDispatcher.offTouchStart(this._touchStartCallback),this._touchDispatcher.offTouchEnd(this._touchEndCallback),this._touchDispatcher.offTouchCancel(this._touchCancelCallback),this._touchDispatcher=null},e.prototype._handleClickDown=function(t,e){var n=this._translateToComponentSpace(t);this._isInsideComponent(n)&&(this._clickedDown=!0,this._clickedPoint=n)},e.prototype._handleClickUp=function(t,n){var i=this,r=this._translateToComponentSpace(t);this._clickedDown&&e._pointsEqual(r,this._clickedPoint)&&setTimeout((function(){i._doubleClicking||i._onClickCallbacks.callCallbacks(r,n)}),0),this._clickedDown=!1},e.prototype._handleDblClick=function(t,e){var n=this,i=this._translateToComponentSpace(t);this._doubleClicking=!0,this._onDoubleClickCallbacks.callCallbacks(i,e),setTimeout((function(){return n._doubleClicking=!1}),0)},e._pointsEqual=function(t,e){return t.x===e.x&&t.y===e.y},e.prototype.onClick=function(t){return this._onClickCallbacks.add(t),this},e.prototype.offClick=function(t){return this._onClickCallbacks.delete(t),this},e.prototype.onDoubleClick=function(t){return this._onDoubleClickCallbacks.add(t),this},e.prototype.offDoubleClick=function(t){return this._onDoubleClickCallbacks.delete(t),this},e})(wbt.Interaction);abt.Click=Cbt;var Abt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Abt,"__esModule",{value:!0});var kbt=uO,Lbt=sbt,Pbt=Fgt,Nbt=(function(t){function e(n){var i=t.call(this)||this;return i._dragging=!1,i._constrainedToComponent=!0,i._mouseFilter=e._DEFAULT_MOUSE_FILTER,i._dragStartCallbacks=new Pbt.CallbackSet,i._dragCallbacks=new Pbt.CallbackSet,i._dragEndCallbacks=new Pbt.CallbackSet,i._mouseDownCallback=function(t,e){return i._startDrag(t,e)},i._mouseMoveCallback=function(t,e){return i._doDrag(t,e)},i._mouseUpCallback=function(t,e){return i._endDrag(t,e)},i._touchStartCallback=function(t,e,n){return i._startDrag(e[t[0]],n)},i._touchMoveCallback=function(t,e,n){return i._doDrag(e[t[0]],n)},i._touchEndCallback=function(t,e,n){return i._endDrag(e[t[0]],n)},i._mouseButton=void 0!==n?n:0,i}return kbt.__extends(e,t),e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._mouseDispatcher=Lbt.Mouse.getDispatcher(this._componentAttachedTo),this._mouseDispatcher.onMouseDown(this._mouseDownCallback),this._mouseDispatcher.onMouseMove(this._mouseMoveCallback),this._mouseDispatcher.onMouseUp(this._mouseUpCallback),this._touchDispatcher=Lbt.Touch.getDispatcher(this._componentAttachedTo),this._touchDispatcher.onTouchStart(this._touchStartCallback),this._touchDispatcher.onTouchMove(this._touchMoveCallback),this._touchDispatcher.onTouchEnd(this._touchEndCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._mouseDispatcher.offMouseDown(this._mouseDownCallback),this._mouseDispatcher.offMouseMove(this._mouseMoveCallback),this._mouseDispatcher.offMouseUp(this._mouseUpCallback),this._mouseDispatcher=null,this._touchDispatcher.offTouchStart(this._touchStartCallback),this._touchDispatcher.offTouchMove(this._touchMoveCallback),this._touchDispatcher.offTouchEnd(this._touchEndCallback),this._touchDispatcher=null},e.prototype._translateAndConstrain=function(t){var e=this._translateToComponentSpace(t);return this._constrainedToComponent?{x:Pbt.Math.clamp(e.x,0,this._componentAttachedTo.width()),y:Pbt.Math.clamp(e.y,0,this._componentAttachedTo.height())}:e},e.prototype._startDrag=function(t,e){if(!(e instanceof MouseEvent)||this._mouseFilter(e)){var n=this._translateToComponentSpace(t);this._isInsideComponent(n)&&(e.preventDefault(),this._dragging=!0,this._dragOrigin=n,this._dragStartCallbacks.callCallbacks(this._dragOrigin))}},e.prototype._doDrag=function(t,e){this._dragging&&this._dragCallbacks.callCallbacks(this._dragOrigin,this._translateAndConstrain(t))},e.prototype._endDrag=function(t,e){e instanceof MouseEvent&&e.button!==this._mouseButton||this._dragging&&(this._dragging=!1,this._dragEndCallbacks.callCallbacks(this._dragOrigin,this._translateAndConstrain(t)))},e.prototype.constrainedToComponent=function(t){return null==t?this._constrainedToComponent:(this._constrainedToComponent=t,this)},e.prototype.mouseFilter=function(t){return 0===arguments.length?this._mouseFilter:(this._mouseFilter=t,this)},e.prototype.onDragStart=function(t){return this._dragStartCallbacks.add(t),this},e.prototype.offDragStart=function(t){return this._dragStartCallbacks.delete(t),this},e.prototype.onDrag=function(t){return this._dragCallbacks.add(t),this},e.prototype.offDrag=function(t){return this._dragCallbacks.delete(t),this},e.prototype.onDragEnd=function(t){return this._dragEndCallbacks.add(t),this},e.prototype.offDragEnd=function(t){return this._dragEndCallbacks.delete(t),this},e._DEFAULT_MOUSE_FILTER=function(t){return 0===t.button},e})(wbt.Interaction);Abt.Drag=Nbt;var Ibt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Ibt,"__esModule",{value:!0});var Rbt=uO,Obt=sbt,zbt=Fgt,Dbt=(function(t){function e(){var e=null!==t&&t.apply(this,arguments)||this;return e._keyPressCallbacks={},e._keyReleaseCallbacks={},e._mouseMoveCallback=function(t){return!1},e._downedKeys=new zbt.Set,e._keyDownCallback=function(t,n){return e._handleKeyDownEvent(t,n)},e._keyUpCallback=function(t){return e._handleKeyUpEvent(t)},e}return Rbt.__extends(e,t),e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._positionDispatcher=Obt.Mouse.getDispatcher(this._componentAttachedTo),this._positionDispatcher.onMouseMove(this._mouseMoveCallback),this._keyDispatcher=Obt.Key.getDispatcher(),this._keyDispatcher.onKeyDown(this._keyDownCallback),this._keyDispatcher.onKeyUp(this._keyUpCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._positionDispatcher.offMouseMove(this._mouseMoveCallback),this._positionDispatcher=null,this._keyDispatcher.offKeyDown(this._keyDownCallback),this._keyDispatcher.offKeyUp(this._keyUpCallback),this._keyDispatcher=null},e.prototype._handleKeyDownEvent=function(t,e){var n=this._translateToComponentSpace(this._positionDispatcher.lastMousePosition());this._isInsideComponent(n)&&!e.repeat&&(this._keyPressCallbacks[t]&&this._keyPressCallbacks[t].callCallbacks(t),this._downedKeys.add(t))},e.prototype._handleKeyUpEvent=function(t){this._downedKeys.has(t)&&this._keyReleaseCallbacks[t]&&this._keyReleaseCallbacks[t].callCallbacks(t),this._downedKeys.delete(t)},e.prototype.onKeyPress=function(t,e){return this._keyPressCallbacks[t]||(this._keyPressCallbacks[t]=new zbt.CallbackSet),this._keyPressCallbacks[t].add(e),this},e.prototype.offKeyPress=function(t,e){return this._keyPressCallbacks[t].delete(e),0===this._keyPressCallbacks[t].size&&delete this._keyPressCallbacks[t],this},e.prototype.onKeyRelease=function(t,e){return this._keyReleaseCallbacks[t]||(this._keyReleaseCallbacks[t]=new zbt.CallbackSet),this._keyReleaseCallbacks[t].add(e),this},e.prototype.offKeyRelease=function(t,e){return this._keyReleaseCallbacks[t].delete(e),0===this._keyReleaseCallbacks[t].size&&delete this._keyReleaseCallbacks[t],this},e})(wbt.Interaction);Ibt.Key=Dbt;var Bbt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Bbt,"__esModule",{value:!0});var Hbt=uO,Fbt=Edt,Vbt=sbt,Ubt=Zyt,jbt=Fgt,Gbt=obt,Wbt=ivt,qbt=(function(t){function e(e,n){var i=t.call(this)||this;return i._wheelFilter=function(t){return!0},i._wheelCallback=function(t,e){return i._handleWheelEvent(t,e)},i._touchStartCallback=function(t,e,n){return i._handleTouchStart(t,e,n)},i._touchMoveCallback=function(t,e,n){return i._handlePinch(t,e,n)},i._touchEndCallback=function(t,e,n){return i._handleTouchEnd(t,e,n)},i._touchCancelCallback=function(t,e,n){return i._handleTouchEnd(t,e,n)},i._panEndCallbacks=new jbt.CallbackSet,i._zoomEndCallbacks=new jbt.CallbackSet,i._panZoomUpdateCallbacks=new jbt.CallbackSet,i._xScales=new jbt.Set,i._yScales=new jbt.Set,i._dragInteraction=new Gbt.Drag,i._setupDragInteraction(),i._touchIds=Fbt.map(),i._minDomainExtents=new jbt.Map,i._maxDomainExtents=new jbt.Map,i._minDomainValues=new jbt.Map,i._maxDomainValues=new jbt.Map,null!=e&&i.addXScale(e),null!=n&&i.addYScale(n),i}return Hbt.__extends(e,t),e.prototype.dragInteraction=function(){return this._dragInteraction},e.prototype.wheelFilter=function(t){return 0===arguments.length?this._wheelFilter:(this._wheelFilter=t,this)},e.prototype.pan=function(t){var e=this;this.xScales().forEach((function(n){n.pan(e._constrainedTranslation(n,t.x))})),this.yScales().forEach((function(n){n.pan(e._constrainedTranslation(n,t.y))})),this._panZoomUpdateCallbacks.callCallbacks()},e.prototype.zoom=function(t,e,n){var i,r,o=this;return void 0===n&&(n=!0),null!=e&&(i=e.x,r=e.y,n&&(this.xScales().forEach((function(e){var n=o._constrainedZoom(e,t,i);i=n.centerPoint,t=n.zoomAmount})),this.yScales().forEach((function(e){var n=o._constrainedZoom(e,t,r);r=n.centerPoint,t=n.zoomAmount})))),this.xScales().forEach((function(e){var n=e.range();e.zoom(t,null==i?(n[1]+n[0])/2:i)})),this.yScales().forEach((function(e){var n=e.range();e.zoom(t,null==r?(n[1]+n[0])/2:r)})),this._panZoomUpdateCallbacks.callCallbacks(),{zoomAmount:t,centerValue:{centerX:i,centerY:r}}},e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._dragInteraction.attachTo(e),this._mouseDispatcher=Vbt.Mouse.getDispatcher(this._componentAttachedTo),this._mouseDispatcher.onWheel(this._wheelCallback),this._touchDispatcher=Vbt.Touch.getDispatcher(this._componentAttachedTo),this._touchDispatcher.onTouchStart(this._touchStartCallback),this._touchDispatcher.onTouchMove(this._touchMoveCallback),this._touchDispatcher.onTouchEnd(this._touchEndCallback),this._touchDispatcher.onTouchCancel(this._touchCancelCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._mouseDispatcher.offWheel(this._wheelCallback),this._mouseDispatcher=null,this._touchDispatcher.offTouchStart(this._touchStartCallback),this._touchDispatcher.offTouchMove(this._touchMoveCallback),this._touchDispatcher.offTouchEnd(this._touchEndCallback),this._touchDispatcher.offTouchCancel(this._touchCancelCallback),this._touchDispatcher=null,this._dragInteraction.detach()},e.prototype._handleTouchStart=function(t,e,n){for(var i=0;i<t.length&&this._touchIds.size()<2;i++){var r=t[i];this._touchIds.set(r.toString(),this._translateToComponentSpace(e[r]))}},e.prototype._handlePinch=function(t,n,i){var r=this;if(!(this._touchIds.size()<2)){var o=this._touchIds.values();if(this._isInsideComponent(this._translateToComponentSpace(o[0]))&&this._isInsideComponent(this._translateToComponentSpace(o[1]))){var a=e._pointDistance(o[0],o[1]);if(0!==a){t.forEach((function(t){r._touchIds.has(t.toString())&&r._touchIds.set(t.toString(),r._translateToComponentSpace(n[t]))}));var s=this._touchIds.values(),l=e._pointDistance(s[0],s[1]);if(0!==l){var c=a/l,u=s.map((function(t,e){return{x:(t.x-o[e].x)/c,y:(t.y-o[e].y)/c}})),h=e.centerPoint(o[0],o[1]),d=this.zoom(c,h),p=d.centerValue,f=d.zoomAmount,m=p.centerX,g=p.centerY,_=o.map((function(t,e){return{x:u[e].x*f+t.x,y:u[e].y*f+t.y}}));this.pan({x:m-(_[0].x+_[1].x)/2,y:g-(_[0].y+_[1].y)/2})}}}}},e.centerPoint=function(t,e){var n=Math.min(t.x,e.x),i=Math.max(t.x,e.x),r=Math.min(t.y,e.y);return{x:(n+i)/2,y:(Math.max(t.y,e.y)+r)/2}},e._pointDistance=function(t,e){var n=Math.min(t.x,e.x),i=Math.max(t.x,e.x),r=Math.min(t.y,e.y),o=Math.max(t.y,e.y);return Math.sqrt(Math.pow(i-n,2)+Math.pow(o-r,2))},e.prototype._handleTouchEnd=function(t,e,n){var i=this;t.forEach((function(t){i._touchIds.remove(t.toString())})),this._touchIds.size()>0&&this._zoomEndCallbacks.callCallbacks()},e.prototype._handleWheelEvent=function(t,n){if(this._wheelFilter(n)){var i=this._translateToComponentSpace(t);if(this._isInsideComponent(i)){n.preventDefault();var r=Math.pow(2,(0!==n.deltaY?n.deltaY:n.deltaX)*(n.deltaMode?e._PIXELS_PER_LINE:1)*.002);this.zoom(r,i),this._zoomEndCallbacks.callCallbacks()}}},e.prototype._constrainedZoom=function(t,e,n){return Wbt.constrainedZoom(t,e,n,this.minDomainExtent(t),this.maxDomainExtent(t),this.minDomainValue(t),this.maxDomainValue(t))},e.prototype._constrainedTranslation=function(t,e){return Wbt.constrainedTranslation(t,e,this.minDomainValue(t),this.maxDomainValue(t))},e.prototype._setupDragInteraction=function(){var t,e=this;this._dragInteraction.constrainedToComponent(!1),this._dragInteraction.onDragStart((function(){return t=null})),this._dragInteraction.onDrag((function(n,i){e._touchIds.size()>=2||(e.pan({x:(null==t?n.x:t.x)-i.x,y:(null==t?n.y:t.y)-i.y}),t=i)})),this._dragInteraction.onDragEnd((function(){return e._panEndCallbacks.callCallbacks()}))},e.prototype._nonLinearScaleWithExtents=function(t){return!(null==this.minDomainExtent(t)||null==this.maxDomainExtent(t)||t instanceof Ubt.Linear||t instanceof Ubt.Time)},e.prototype.xScales=function(t){var e=this;if(null==t){var n=[];return this._xScales.forEach((function(t){n.push(t)})),n}return this._xScales=new jbt.Set,t.forEach((function(t){e.addXScale(t)})),this},e.prototype.yScales=function(t){var e=this;if(null==t){var n=[];return this._yScales.forEach((function(t){n.push(t)})),n}return this._yScales=new jbt.Set,t.forEach((function(t){e.addYScale(t)})),this},e.prototype.addXScale=function(t){return this._xScales.add(t),this},e.prototype.removeXScale=function(t){return this._xScales.delete(t),this._minDomainExtents.delete(t),this._maxDomainExtents.delete(t),this._minDomainValues.delete(t),this._maxDomainValues.delete(t),this},e.prototype.addYScale=function(t){return this._yScales.add(t),this},e.prototype.removeYScale=function(t){return this._yScales.delete(t),this._minDomainExtents.delete(t),this._maxDomainExtents.delete(t),this._minDomainValues.delete(t),this._maxDomainValues.delete(t),this},e.prototype.minDomainExtent=function(t,e){if(null==e)return this._minDomainExtents.get(t);if(e.valueOf()<0)throw new Error("extent must be non-negative");var n=this.maxDomainExtent(t);if(null!=n&&n.valueOf()<e.valueOf())throw new Error("minDomainExtent must be smaller than maxDomainExtent for the same Scale");return this._nonLinearScaleWithExtents(t)&&jbt.Window.warn("Panning and zooming with extents on a nonlinear scale may have unintended behavior."),this._minDomainExtents.set(t,e),this},e.prototype.maxDomainExtent=function(t,e){if(null==e)return this._maxDomainExtents.get(t);if(e.valueOf()<=0)throw new Error("extent must be positive");var n=this.minDomainExtent(t);if(null!=n&&e.valueOf()<n.valueOf())throw new Error("maxDomainExtent must be larger than minDomainExtent for the same Scale");return this._nonLinearScaleWithExtents(t)&&jbt.Window.warn("Panning and zooming with extents on a nonlinear scale may have unintended behavior."),this._maxDomainExtents.set(t,e),this},e.prototype.minDomainValue=function(t,e){return null==e?this._minDomainValues.get(t):(this._minDomainValues.set(t,e),this)},e.prototype.maxDomainValue=function(t,e){return null==e?this._maxDomainValues.get(t):(this._maxDomainValues.set(t,e),this)},e.prototype.setMinMaxDomainValuesTo=function(t){this._minDomainValues.delete(t),this._maxDomainValues.delete(t);var e=t.getTransformationDomain(),n=e[1];return this.minDomainValue(t,e[0]),this.maxDomainValue(t,n),this},e.prototype.onPanEnd=function(t){return this._panEndCallbacks.add(t),this},e.prototype.offPanEnd=function(t){return this._panEndCallbacks.delete(t),this},e.prototype.onZoomEnd=function(t){return this._zoomEndCallbacks.add(t),this},e.prototype.offZoomEnd=function(t){return this._zoomEndCallbacks.delete(t),this},e.prototype.onPanZoomUpdate=function(t){return this._panZoomUpdateCallbacks.add(t),this},e.prototype.offPanZoomUpdate=function(t){return this._panZoomUpdateCallbacks.delete(t),this},e._PIXELS_PER_LINE=120,e})(wbt.Interaction);Bbt.PanZoom=qbt;var Ybt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Ybt,"__esModule",{value:!0});var Xbt=uO,$bt=sbt,Kbt=Fgt,Zbt=(function(t){function e(){var e=null!==t&&t.apply(this,arguments)||this;return e._overComponent=!1,e._pointerEnterCallbacks=new Kbt.CallbackSet,e._pointerMoveCallbacks=new Kbt.CallbackSet,e._pointerExitCallbacks=new Kbt.CallbackSet,e._mouseMoveCallback=function(t,n){return e._handleMouseEvent(t,n)},e._touchStartCallback=function(t,n,i){return e._handleTouchEvent(n[t[0]],i)},e}return Xbt.__extends(e,t),e.prototype._anchor=function(e){t.prototype._anchor.call(this,e),this._mouseDispatcher=$bt.Mouse.getDispatcher(this._componentAttachedTo),this._mouseDispatcher.onMouseMove(this._mouseMoveCallback),this._touchDispatcher=$bt.Touch.getDispatcher(this._componentAttachedTo),this._touchDispatcher.onTouchStart(this._touchStartCallback)},e.prototype._unanchor=function(){t.prototype._unanchor.call(this),this._mouseDispatcher.offMouseMove(this._mouseMoveCallback),this._mouseDispatcher=null,this._touchDispatcher.offTouchStart(this._touchStartCallback),this._touchDispatcher=null},e.prototype._handleMouseEvent=function(t,e){var n=this._mouseDispatcher.eventInside(this._componentAttachedTo,e);this._handlePointerEvent(t,n)},e.prototype._handleTouchEvent=function(t,e){var n=this._touchDispatcher.eventInside(this._componentAttachedTo,e);this._handlePointerEvent(t,n)},e.prototype._handlePointerEvent=function(t,e){var n=this._translateToComponentSpace(t),i=this._isInsideComponent(n);i&&e?(this._overComponent||this._pointerEnterCallbacks.callCallbacks(n),this._pointerMoveCallbacks.callCallbacks(n)):this._overComponent&&this._pointerExitCallbacks.callCallbacks(n),this._overComponent=i&&e},e.prototype.onPointerEnter=function(t){return this._pointerEnterCallbacks.add(t),this},e.prototype.offPointerEnter=function(t){return this._pointerEnterCallbacks.delete(t),this},e.prototype.onPointerMove=function(t){return this._pointerMoveCallbacks.add(t),this},e.prototype.offPointerMove=function(t){return this._pointerMoveCallbacks.delete(t),this},e.prototype.onPointerExit=function(t){return this._pointerExitCallbacks.add(t),this},e.prototype.offPointerExit=function(t){return this._pointerExitCallbacks.delete(t),this},e})(wbt.Interaction);Ybt.Pointer=Zbt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(abt,t),e.__exportStar(Abt,t),e.__exportStar(Ibt,t),e.__exportStar(Bbt,t),e.__exportStar(Ybt,t),t.zoomOut=ivt.zoomOut})(obt);var Jbt={};!(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e,n=uO,i=Fgt,r=Bgt;!(function(t){t[t.VALUE=0]="VALUE",t[t.PIXEL=1]="PIXEL"})(e=t.PropertyMode||(t.PropertyMode={}));var o=(function(t){function r(){var n=t.call(this)||this;return n._boxVisible=!1,n._boxBounds={topLeft:{x:0,y:0},bottomRight:{x:0,y:0}},n._xBoundsMode=e.PIXEL,n._yBoundsMode=e.PIXEL,n.addClass("selection-box-layer"),n._adjustBoundsCallback=function(){n.render()},n._overflowHidden=!0,n._xExtent=[void 0,void 0],n._yExtent=[void 0,void 0],n}return n.__extends(r,t),r.prototype._setup=function(){t.prototype._setup.call(this),this._box=this.content().append("g").classed("selection-box",!0).remove(),this._boxArea=this._box.append("rect").classed("selection-area",!0)},r.prototype._sizeFromOffer=function(t,e){return{width:t,height:e}},r.prototype.bounds=function(t){return null==t?this._getBounds():(this._setBounds(t),this._xBoundsMode=e.PIXEL,this._yBoundsMode=e.PIXEL,this.render(),this)},r.prototype._setBounds=function(t){var e={x:Math.min(t.topLeft.x,t.bottomRight.x),y:Math.min(t.topLeft.y,t.bottomRight.y)},n={x:Math.max(t.topLeft.x,t.bottomRight.x),y:Math.max(t.topLeft.y,t.bottomRight.y)};this._boxBounds={topLeft:e,bottomRight:n}},r.prototype._getBounds=function(){return{topLeft:{x:this._xBoundsMode===e.PIXEL?this._boxBounds.topLeft.x:null==this._xScale?0:Math.min(this.xScale().scale(this.xExtent()[0]),this.xScale().scale(this.xExtent()[1])),y:this._yBoundsMode===e.PIXEL?this._boxBounds.topLeft.y:null==this._yScale?0:Math.min(this.yScale().scale(this.yExtent()[0]),this.yScale().scale(this.yExtent()[1]))},bottomRight:{x:this._xBoundsMode===e.PIXEL?this._boxBounds.bottomRight.x:null==this._xScale?0:Math.max(this.xScale().scale(this.xExtent()[0]),this.xScale().scale(this.xExtent()[1])),y:this._yBoundsMode===e.PIXEL?this._boxBounds.bottomRight.y:null==this._yScale?0:Math.max(this.yScale().scale(this.yExtent()[0]),this.yScale().scale(this.yExtent()[1]))}}},r.prototype.renderImmediately=function(){if(t.prototype.renderImmediately.call(this),this._boxVisible){var e=this.bounds(),n=e.topLeft.y,r=e.bottomRight.y,o=e.topLeft.x,a=e.bottomRight.x;if(!(i.Math.isValidNumber(n)&&i.Math.isValidNumber(r)&&i.Math.isValidNumber(o)&&i.Math.isValidNumber(a)))throw new Error("bounds have not been properly set");this._boxArea.attrs({x:o,y:n,width:a-o,height:r-n}),this.content().node().appendChild(this._box.node())}else this._box.remove();return this},r.prototype.boxVisible=function(t){return null==t?this._boxVisible:(this._boxVisible=t,this.render(),this)},r.prototype.fixedWidth=function(){return!0},r.prototype.fixedHeight=function(){return!0},r.prototype.xScale=function(t){return null==t?this._xScale:(null!=this._xScale&&this._xScale.offUpdate(this._adjustBoundsCallback),this._xScale=t,this._xBoundsMode=e.VALUE,this._xScale.onUpdate(this._adjustBoundsCallback),this.render(),this)},r.prototype.yScale=function(t){return null==t?this._yScale:(null!=this._yScale&&this._yScale.offUpdate(this._adjustBoundsCallback),this._yScale=t,this._yBoundsMode=e.VALUE,this._yScale.onUpdate(this._adjustBoundsCallback),this.render(),this)},r.prototype.xExtent=function(t){return null==t?this._getXExtent():(this._setXExtent(t),this._xBoundsMode=e.VALUE,this.render(),this)},r.prototype._getXExtent=function(){return this._xBoundsMode===e.VALUE?this._xExtent:null==this._xScale?[void 0,void 0]:[this._xScale.invert(this._boxBounds.topLeft.x),this._xScale.invert(this._boxBounds.bottomRight.x)]},r.prototype._setXExtent=function(t){this._xExtent=t},r.prototype.yExtent=function(t){return null==t?this._getYExtent():(this._setYExtent(t),this._yBoundsMode=e.VALUE,this.render(),this)},r.prototype._getYExtent=function(){return this._yBoundsMode===e.VALUE?this._yExtent:null==this._yScale?[void 0,void 0]:[this._yScale.invert(this._boxBounds.topLeft.y),this._yScale.invert(this._boxBounds.bottomRight.y)]},r.prototype._setYExtent=function(t){this._yExtent=t},r.prototype.destroy=function(){t.prototype.destroy.call(this),null!=this._xScale&&this.xScale().offUpdate(this._adjustBoundsCallback),null!=this._yScale&&this.yScale().offUpdate(this._adjustBoundsCallback)},r})(r.Component);t.SelectionBoxLayer=o})(Jbt),
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(rbt,"__esModule",{value:!0});var Qbt=uO,txt=obt,ext=Fgt,nxt=bmt,ixt=ibt,rxt=(function(t){function e(){var e=t.call(this)||this;return e._detectionRadius=3,e._resizable=!1,e._movable=!1,e._hasCorners=!0,e.addClass("drag-box-layer"),e._dragInteraction=new txt.Drag,e._setUpCallbacks(),e._dragInteraction.attachTo(e),e._dragStartCallbacks=new ext.CallbackSet,e._dragCallbacks=new ext.CallbackSet,e._dragEndCallbacks=new ext.CallbackSet,e}return Qbt.__extends(e,t),e.prototype._setUpCallbacks=function(){var t,e,n,i,r=this,o=0,a=function(a){t=r._getResizingEdges(a);var s=r.bounds(),l=s.topLeft.x<=a.x&&a.x<=s.bottomRight.x&&s.topLeft.y<=a.y&&a.y<=s.bottomRight.y;r.boxVisible()&&(t.top||t.bottom||t.left||t.right)?o=1:r.boxVisible()&&r.movable()&&l?o=2:(o=0,r._setBounds({topLeft:a,bottomRight:a}),r._xBoundsMode===ixt.PropertyMode.VALUE&&null!=r.xScale()&&r._setXExtent([r.xScale().invert(a.x),r.xScale().invert(a.x)]),r._yBoundsMode===ixt.PropertyMode.VALUE&&null!=r.yScale()&&r._setYExtent([r.yScale().invert(a.y),r.yScale().invert(a.y)]),r.render()),r.boxVisible(!0),s=r.bounds(),e={x:s.topLeft.x,y:s.topLeft.y},n={x:s.bottomRight.x,y:s.bottomRight.y},i=a,r._dragStartCallbacks.callCallbacks(s)},s=function(a,s){switch(o){case 0:n.x=s.x,n.y=s.y;break;case 1:t.bottom?n.y=s.y:t.top&&(e.y=s.y),t.right?n.x=s.x:t.left&&(e.x=s.x);break;case 2:var l=s.x-i.x,c=s.y-i.y;e.x+=l,e.y+=c,n.x+=l,n.y+=c,i=s}r._setBounds({topLeft:e,bottomRight:n}),r._xBoundsMode===ixt.PropertyMode.VALUE&&null!=r.xScale()&&r._setXExtent([r.xScale().invert(e.x),r.xScale().invert(n.x)]),r._yBoundsMode===ixt.PropertyMode.VALUE&&null!=r.yScale()&&r._setYExtent([r.yScale().invert(e.y),r.yScale().invert(n.y)]),r.render(),r._dragCallbacks.callCallbacks(r.bounds())},l=function(t,e){0===o&&t.x===e.x&&t.y===e.y&&r.boxVisible(!1),r._dragEndCallbacks.callCallbacks(r.bounds())};this._dragInteraction.onDragStart(a),this._dragInteraction.onDrag(s),this._dragInteraction.onDragEnd(l),this._disconnectInteraction=function(){r._dragInteraction.offDragStart(a),r._dragInteraction.offDrag(s),r._dragInteraction.offDragEnd(l),r._dragInteraction.detach()}},e.prototype._setup=function(){var e=this;t.prototype._setup.call(this);var n=function(){return e._box.append("line").styles({opacity:0,stroke:"pink","pointer-events":"visibleStroke"})};if(this._detectionEdgeT=n().classed("drag-edge-tb",!0),this._detectionEdgeB=n().classed("drag-edge-tb",!0),this._detectionEdgeL=n().classed("drag-edge-lr",!0),this._detectionEdgeR=n().classed("drag-edge-lr",!0),this._hasCorners){var i=function(){return e._box.append("circle").styles({opacity:0,fill:"pink","pointer-events":"visibleFill"})};this._detectionCornerTL=i().classed("drag-corner-tl",!0),this._detectionCornerTR=i().classed("drag-corner-tr",!0),this._detectionCornerBL=i().classed("drag-corner-bl",!0),this._detectionCornerBR=i().classed("drag-corner-br",!0)}},e.prototype._getResizingEdges=function(t){var e={top:!1,bottom:!1,left:!1,right:!1};if(!this.resizable())return e;var n=this.bounds(),i=n.topLeft.y,r=n.bottomRight.y,o=n.topLeft.x,a=n.bottomRight.x,s=this._detectionRadius;return o-s<=t.x&&t.x<=a+s&&(e.top=i-s<=t.y&&t.y<=i+s,e.bottom=r-s<=t.y&&t.y<=r+s),i-s<=t.y&&t.y<=r+s&&(e.left=o-s<=t.x&&t.x<=o+s,e.right=a-s<=t.x&&t.x<=a+s),e},e.prototype.renderImmediately=function(){if(t.prototype.renderImmediately.call(this),this.boxVisible()){var e=this.bounds(),n=e.topLeft.y,i=e.bottomRight.y,r=e.topLeft.x,o=e.bottomRight.x;this._detectionEdgeT.attrs({x1:r,y1:n,x2:o,y2:n,"stroke-width":2*this._detectionRadius}),this._detectionEdgeB.attrs({x1:r,y1:i,x2:o,y2:i,"stroke-width":2*this._detectionRadius}),this._detectionEdgeL.attrs({x1:r,y1:n,x2:r,y2:i,"stroke-width":2*this._detectionRadius}),this._detectionEdgeR.attrs({x1:o,y1:n,x2:o,y2:i,"stroke-width":2*this._detectionRadius}),this._hasCorners&&(this._detectionCornerTL.attrs({cx:r,cy:n,r:this._detectionRadius}),this._detectionCornerTR.attrs({cx:o,cy:n,r:this._detectionRadius}),this._detectionCornerBL.attrs({cx:r,cy:i,r:this._detectionRadius}),this._detectionCornerBR.attrs({cx:o,cy:i,r:this._detectionRadius}))}return this},e.prototype.detectionRadius=function(t){if(null==t)return this._detectionRadius;if(t<0)throw new Error("detection radius cannot be negative.");return this._detectionRadius=t,this.render(),this},e.prototype.resizable=function(t){return null==t?this._resizable:(this._resizable=t,this._setResizableClasses(t),this)},e.prototype._setResizableClasses=function(t){t&&this.enabled()?(this.addClass("x-resizable"),this.addClass("y-resizable")):(this.removeClass("x-resizable"),this.removeClass("y-resizable"))},e.prototype.movable=function(t){return null==t?this._movable:(this._movable=t,this._setMovableClass(),this)},e.prototype._setMovableClass=function(){this.movable()&&this.enabled()?this.addClass("movable"):this.removeClass("movable")},e.prototype.onDragStart=function(t){return this._dragStartCallbacks.add(t),this},e.prototype.offDragStart=function(t){return this._dragStartCallbacks.delete(t),this},e.prototype.onDrag=function(t){return this._dragCallbacks.add(t),this},e.prototype.offDrag=function(t){return this._dragCallbacks.delete(t),this},e.prototype.onDragEnd=function(t){return this._dragEndCallbacks.add(t),this},e.prototype.offDragEnd=function(t){return this._dragEndCallbacks.delete(t),this},e.prototype.dragInteraction=function(){return this._dragInteraction},e.prototype.enabled=function(t){return null==t?this._dragInteraction.enabled():(this._dragInteraction.enabled(t),this._setResizableClasses(this.resizable()),this._setMovableClass(),this)},e.prototype.destroy=function(){var e=this;t.prototype.destroy.call(this),this._dragStartCallbacks.forEach((function(t){return e._dragCallbacks.delete(t)})),this._dragCallbacks.forEach((function(t){return e._dragCallbacks.delete(t)})),this._dragEndCallbacks.forEach((function(t){return e._dragEndCallbacks.delete(t)})),this._disconnectInteraction()},e.prototype.detach=function(){return this._resetState(),this._dragInteraction.detach(),t.prototype.detach.call(this),this},e.prototype.anchor=function(e){return e=nxt.coerceExternalD3(e),this._dragInteraction.attachTo(this),t.prototype.anchor.call(this,e),this},e.prototype._resetState=function(){this.bounds({topLeft:{x:0,y:0},bottomRight:{x:0,y:0}})},e})(Jbt.SelectionBoxLayer);rbt.DragBoxLayer=rxt;var oxt={},axt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(axt,"__esModule",{value:!0});var sxt,lxt=uO,cxt=Fgt,uxt=Bgt;!(function(t){t[t.VALUE=0]="VALUE",t[t.PIXEL=1]="PIXEL"})(sxt||(sxt={}));var hxt=(function(t){function e(n){var i=t.call(this)||this;if(i._mode=sxt.VALUE,n!==e.ORIENTATION_VERTICAL&&n!==e.ORIENTATION_HORIZONTAL)throw new Error(n+" is not a valid orientation for GuideLineLayer");return i._orientation=n,i._overflowHidden=!0,i.addClass("guide-line-layer"),i._isVertical()?i.addClass("vertical"):i.addClass("horizontal"),i._scaleUpdateCallback=function(){i._syncPixelPositionAndValue(),i.render()},i}return lxt.__extends(e,t),e.prototype._setup=function(){t.prototype._setup.call(this),this._guideLine=this.content().append("line").classed("guide-line",!0)},e.prototype._sizeFromOffer=function(t,e){return{width:t,height:e}},e.prototype._isVertical=function(){return this._orientation===e.ORIENTATION_VERTICAL},e.prototype.fixedWidth=function(){return!0},e.prototype.fixedHeight=function(){return!0},e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),null!=this.scale()&&(this._isVertical()?this.scale().range([0,this.width()]):this.scale().range([this.height(),0])),this},e.prototype.renderImmediately=function(){return t.prototype.renderImmediately.call(this),this._syncPixelPositionAndValue(),this._guideLine.attrs({x1:this._isVertical()?this.pixelPosition():0,y1:this._isVertical()?0:this.pixelPosition(),x2:this._isVertical()?this.pixelPosition():this.width(),y2:this._isVertical()?this.height():this.pixelPosition()}),this},e.prototype._syncPixelPositionAndValue=function(){null!=this.scale()&&(this._mode===sxt.VALUE&&null!=this.value()?this._pixelPosition=this.scale().scale(this.value()):this._mode===sxt.PIXEL&&null!=this.pixelPosition()&&(this._value=this.scale().invert(this.pixelPosition())))},e.prototype._setPixelPositionWithoutChangingMode=function(t){this._pixelPosition=t,null!=this.scale()&&(this._value=this.scale().invert(this.pixelPosition())),this.render()},e.prototype.scale=function(t){if(null==t)return this._scale;var e=this._scale;return null!=e&&e.offUpdate(this._scaleUpdateCallback),this._scale=t,this._scale.onUpdate(this._scaleUpdateCallback),this._syncPixelPositionAndValue(),this.redraw(),this},e.prototype.value=function(t){return null==t?this._value:(this._value=t,this._mode=sxt.VALUE,this._syncPixelPositionAndValue(),this.render(),this)},e.prototype.pixelPosition=function(t){if(null==t)return this._pixelPosition;if(!cxt.Math.isValidNumber(t))throw new Error("pixelPosition must be a finite number");return this._pixelPosition=t,this._mode=sxt.PIXEL,this._syncPixelPositionAndValue(),this.render(),this},e.prototype.destroy=function(){t.prototype.destroy.call(this),null!=this.scale()&&this.scale().offUpdate(this._scaleUpdateCallback)},e.ORIENTATION_VERTICAL="vertical",e.ORIENTATION_HORIZONTAL="horizontal",e})(uxt.Component);axt.GuideLineLayer=hxt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(oxt,"__esModule",{value:!0});var dxt=uO,pxt=obt,fxt=Fgt,mxt=(function(t){function e(e){var n=t.call(this,e)||this;n._detectionRadius=3,n._enabled=!0,n.addClass("drag-line-layer"),n.addClass("enabled"),n._dragInteraction=new pxt.Drag,n._dragInteraction.attachTo(n);var i=!1,r=function(t){(function(t){return n._isVertical()&&n.pixelPosition()-n.detectionRadius()<=t.x&&t.x<=n.pixelPosition()+n.detectionRadius()||!n._isVertical()&&n.pixelPosition()-n.detectionRadius()<=t.y&&t.y<=n.pixelPosition()+n.detectionRadius()})(t)&&(i=!0,n._dragStartCallbacks.callCallbacks(n))};n._dragInteraction.onDragStart(r);var o=function(t,e){i&&(n._setPixelPositionWithoutChangingMode(n._isVertical()?e.x:e.y),n._dragCallbacks.callCallbacks(n))};n._dragInteraction.onDrag(o);var a=function(t,e){i&&(i=!1,n._dragEndCallbacks.callCallbacks(n))};return n._dragInteraction.onDragEnd(a),n._disconnectInteraction=function(){n._dragInteraction.offDragStart(r),n._dragInteraction.offDrag(o),n._dragInteraction.offDragEnd(a),n._dragInteraction.detach()},n._dragStartCallbacks=new fxt.CallbackSet,n._dragCallbacks=new fxt.CallbackSet,n._dragEndCallbacks=new fxt.CallbackSet,n}return dxt.__extends(e,t),e.prototype._setup=function(){t.prototype._setup.call(this),this._detectionEdge=this.content().append("line").styles({opacity:0,stroke:"pink","pointer-events":"visibleStroke"}).classed("drag-edge",!0)},e.prototype.renderImmediately=function(){return t.prototype.renderImmediately.call(this),this._detectionEdge.attrs({x1:this._isVertical()?this.pixelPosition():0,y1:this._isVertical()?0:this.pixelPosition(),x2:this._isVertical()?this.pixelPosition():this.width(),y2:this._isVertical()?this.height():this.pixelPosition(),"stroke-width":2*this._detectionRadius}),this},e.prototype.detectionRadius=function(t){if(null==t)return this._detectionRadius;if(t<0)throw new Error("detection radius cannot be negative.");return this._detectionRadius=t,this.render(),this},e.prototype.enabled=function(t){return null==t?this._enabled:(this._enabled=t,t?this.addClass("enabled"):this.removeClass("enabled"),this._dragInteraction.enabled(t),this)},e.prototype.onDragStart=function(t){return this._dragStartCallbacks.add(t),this},e.prototype.offDragStart=function(t){return this._dragStartCallbacks.delete(t),this},e.prototype.onDrag=function(t){return this._dragCallbacks.add(t),this},e.prototype.offDrag=function(t){return this._dragCallbacks.delete(t),this},e.prototype.onDragEnd=function(t){return this._dragEndCallbacks.add(t),this},e.prototype.offDragEnd=function(t){return this._dragEndCallbacks.delete(t),this},e.prototype.destroy=function(){var e=this;t.prototype.destroy.call(this),this._dragStartCallbacks.forEach((function(t){return e._dragStartCallbacks.delete(t)})),this._dragCallbacks.forEach((function(t){return e._dragCallbacks.delete(t)})),this._dragEndCallbacks.forEach((function(t){return e._dragEndCallbacks.delete(t)})),this._disconnectInteraction()},e})(axt.GuideLineLayer);oxt.DragLineLayer=mxt;var gxt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(gxt,"__esModule",{value:!0});var _xt=uO;function yxt(t,e,n){var i={};if(void 0!==n)for(var r=0;r<n.length;r++)i[n[r]]=n[r-1];return function(n){var r,o=t.scale(n);if(!e)return o;var a=void 0===i[n]?void 0:t.scale(i[n]);return void 0!==a&&(r=a+(o-a)/2),r}}var vxt=(function(t){function e(e,n){var i=t.call(this)||this;return i.addClass("gridlines"),i._xScale=e,i._yScale=n,i._renderCallback=function(t){return i.render()},i._xScale&&i._xScale.onUpdate(i._renderCallback),i._yScale&&i._yScale.onUpdate(i._renderCallback),i}return _xt.__extends(e,t),e.prototype.betweenX=function(t){return void 0===t?this._betweenX:(t!==this._betweenX&&(this._betweenX=t,this.render()),this)},e.prototype.betweenY=function(t){return void 0===t?this._betweenY:(t!==this._betweenY&&(this._betweenY=t,this.render()),this)},e.prototype.destroy=function(){return t.prototype.destroy.call(this),this._xScale&&this._xScale.offUpdate(this._renderCallback),this._yScale&&this._yScale.offUpdate(this._renderCallback),this},e.prototype._setup=function(){t.prototype._setup.call(this),this._xLinesContainer=this.content().append("g").classed("x-gridlines",!0),this._yLinesContainer=this.content().append("g").classed("y-gridlines",!0)},e.prototype.renderImmediately=function(){return t.prototype.renderImmediately.call(this),this._redrawXLines(),this._redrawYLines(),this},e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),null!=this._xScale&&this._xScale.range([0,this.width()]),null!=this._yScale&&this._yScale.range([this.height(),0]),this},e.prototype._redrawXLines=function(){if(this._xScale){var t=this.betweenX(),e=this._xScale.ticks().slice(t?1:0),n=this._xLinesContainer.selectAll("line").data(e);n.enter().append("line").merge(n).attr("x1",yxt(this._xScale,t,this._xScale.ticks())).attr("y1",0).attr("x2",yxt(this._xScale,t,this._xScale.ticks())).attr("y2",this.height()).classed("betweenline",t).classed("zeroline",(function(t){return 0===t})),n.exit().remove()}},e.prototype._redrawYLines=function(){if(this._yScale){var t=this.betweenY(),e=this._yScale.ticks().slice(t?1:0),n=this._yLinesContainer.selectAll("line").data(e);n.enter().append("line").merge(n).attr("x1",0).attr("y1",yxt(this._yScale,t,this._yScale.ticks())).attr("x2",this.width()).attr("y2",yxt(this._yScale,t,this._yScale.ticks())).classed("betweenline",t).classed("zeroline",(function(t){return 0===t})),n.exit().remove()}},e})(Bgt.Component);gxt.Gridlines=vxt;var bxt={},xxt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(xxt,"__esModule",{value:!0});var wxt=uO,Sxt=bmt,Mxt=(function(t){function e(){var e=t.call(this)||this;return e._detachCallback=function(t){return e.remove(t)},e}return wxt.__extends(e,t),e.prototype.anchor=function(e){var n=this;return e=Sxt.coerceExternalD3(e),t.prototype.anchor.call(this,e),this._forEach((function(t){return t.anchor(n.element())})),this},e.prototype.render=function(){return this._forEach((function(t){return t.render()})),this},e.prototype.has=function(t){throw new Error("has() is not implemented on ComponentContainer")},e.prototype._adoptAndAnchor=function(t){t.parent(this),t.onDetach(this._detachCallback),this._isAnchored&&t.anchor(this.element())},e.prototype.remove=function(t){return this.has(t)&&(t.offDetach(this._detachCallback),this._remove(t),t.detach(),this.redraw()),this},e.prototype._remove=function(t){return!1},e.prototype._forEach=function(t){throw new Error("_forEach() is not implemented on ComponentContainer")},e.prototype.destroy=function(){t.prototype.destroy.call(this),this._forEach((function(t){return t.destroy()}))},e.prototype.invalidateCache=function(){this._forEach((function(t){return t.invalidateCache()}))},e})(Bgt.Component);xxt.ComponentContainer=Mxt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(bxt,"__esModule",{value:!0});var Ext=uO,Txt=Fgt,Cxt=(function(t){function e(e){void 0===e&&(e=[]);var n=t.call(this)||this;return n._components=[],n.addClass("component-group"),e.forEach((function(t){return n.append(t)})),n}return Ext.__extends(e,t),e.prototype._forEach=function(t){this.components().forEach(t)},e.prototype.has=function(t){return this._components.indexOf(t)>=0},e.prototype.requestedSpace=function(t,e){var n=this._components.map((function(n){return n.requestedSpace(t,e)}));return{minWidth:Txt.Math.max(n,(function(t){return t.minWidth}),0),minHeight:Txt.Math.max(n,(function(t){return t.minHeight}),0)}},e.prototype.computeLayout=function(e,n,i){var r=this;return t.prototype.computeLayout.call(this,e,n,i),this._forEach((function(t){t.computeLayout({x:0,y:0},r.width(),r.height())})),this},e.prototype._sizeFromOffer=function(t,e){return{width:t,height:e}},e.prototype.fixedWidth=function(){return this._components.every((function(t){return t.fixedWidth()}))},e.prototype.fixedHeight=function(){return this._components.every((function(t){return t.fixedHeight()}))},e.prototype.components=function(){return this._components.slice()},e.prototype.append=function(t){return null==t||this.has(t)||(t.detach(),this._components.push(t),this._adoptAndAnchor(t),this.redraw()),this},e.prototype._remove=function(t){var e=this._components.indexOf(t);return e>=0&&(this._components.splice(e,1),!0)},e})(xxt.ComponentContainer);bxt.Group=Cxt;var Axt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Axt,"__esModule",{value:!0});var kxt=uO,Lxt=Imt,Pxt=L_t,Nxt=gyt,Ixt=Fgt,Rxt=(function(t){function e(e){var n=t.call(this)||this;if(n._textPadding=5,null==e)throw new Error("InterpolatedColorLegend requires a interpolatedColorScale");return n._scale=e,n._redrawCallback=function(t){return n.redraw()},n._scale.onUpdate(n._redrawCallback),n._formatter=Nxt.general(),n._orientation="horizontal",n._expands=!1,n.addClass("legend"),n.addClass("interpolated-color-legend"),n}return kxt.__extends(e,t),e.prototype.destroy=function(){t.prototype.destroy.call(this),this._scale.offUpdate(this._redrawCallback)},e.prototype.formatter=function(t){return void 0===t?this._formatter:(this._formatter=t,this.redraw(),this)},e.prototype.expands=function(t){return null==t?this._expands:(this._expands=t,this.redraw(),this)},e._ensureOrientation=function(t){if("horizontal"===(t=t.toLowerCase())||"left"===t||"right"===t)return t;throw new Error('"'+t+'" is not a valid orientation for InterpolatedColorLegend')},e.prototype.orientation=function(t){return null==t?this._orientation:(this._orientation=e._ensureOrientation(t),this.redraw(),this)},e.prototype.fixedWidth=function(){return!this.expands()||this._isVertical()},e.prototype.fixedHeight=function(){return!this.expands()||!this._isVertical()},e.prototype._generateTicks=function(t){void 0===t&&(t=e._DEFAULT_NUM_SWATCHES);var n=this._scale.domain();if(1===t)return[n[0]];for(var i=(n[1]-n[0])/(t-1),r=[],o=0;o<t;o++)r.push(n[0]+i*o);return r},e.prototype._setup=function(){t.prototype._setup.call(this),this._swatchContainer=this.content().append("g").classed("swatch-container",!0),this._swatchBoundingBox=this.content().append("rect").classed("swatch-bounding-box",!0),this._lowerLabel=this.content().append("g").classed(e.LEGEND_LABEL_CLASS,!0),this._upperLabel=this.content().append("g").classed(e.LEGEND_LABEL_CLASS,!0);var n=new Lxt.SvgContext(this.content().node());this._measurer=new Lxt.Measurer(n),this._wrapper=new Lxt.Wrapper,this._writer=new Lxt.Writer(this._measurer,n,this._wrapper)},e.prototype.requestedSpace=function(t,n){var i,r,o=this,a=this._measurer.measure().height,s=a,l=this._scale.domain().map((function(t){return o._measurer.measure(o._formatter(t)).width})),c=e._DEFAULT_NUM_SWATCHES;if(this._isVertical()){var u=Ixt.Math.max(l,0);r=s+a+this._textPadding+u+this._textPadding,i=c*a}else i=s+a+s,r=this._textPadding+l[0]+c*a+l[1]+this._textPadding;return{minWidth:r,minHeight:i}},e.prototype._isVertical=function(){return"horizontal"!==this._orientation},e.prototype.renderImmediately=function(){var e=this;t.prototype.renderImmediately.call(this);var n,i,r,o,a,s,l=this._scale.domain(),c=this._formatter(l[0]),u=this._measurer.measure(c).width,h=this._formatter(l[1]),d=this._measurer.measure(h).width,p=this._measurer.measure().height,f=this._textPadding,m={x:0,y:0},g={x:0,y:0},_={xAlign:"center",yAlign:"center",textRotation:0},y={xAlign:"center",yAlign:"center",textRotation:0},v={x:0,y:0,width:0,height:0};if(this._isVertical()){s=Math.floor(this.height());var b=Math.max(u,d);a=(this.width()-b-2*this._textPadding)/2,n=Math.max(this.width()-a-2*f-b,0),i=1,o=function(t,n){return e.height()-(n+1)},y.yAlign="top",m.y=0,_.yAlign="bottom",g.y=0,"left"===this._orientation?(r=function(t,e){return f+b+f},y.xAlign="right",m.x=-(a+n+f),_.xAlign="right",g.x=-(a+n+f)):(r=function(t,e){return a},y.xAlign="left",m.x=a+n+f,_.xAlign="left",g.x=a+n+f),v.width=n,v.height=s*i}else a=Math.max(f,(this.height()-p)/2),s=Math.max(Math.floor(this.width()-4*f-u-d),0),n=1,i=Math.max(this.height()-2*a,0),r=function(t,e){return Math.floor(u+2*f)+e},o=function(t,e){return a},y.xAlign="right",m.x=-f,_.xAlign="left",g.x=f,v.y=a,v.width=s*n,v.height=i;v.x=r(null,0),this._upperLabel.text(""),this._writer.write(h,this.width(),this.height(),y,this._upperLabel.node()),this._upperLabel.attr("transform","translate("+m.x+", "+m.y+")"),this._lowerLabel.text(""),this._writer.write(c,this.width(),this.height(),_,this._lowerLabel.node()),this._lowerLabel.attr("transform","translate("+g.x+", "+g.y+")"),this._swatchBoundingBox.attrs(v);var x=this._generateTicks(s),w=this._swatchContainer.selectAll("rect.swatch").data(x),S=w.enter().append("rect").classed("swatch",!0),M=w.merge(S);return w.exit().remove(),M.attrs({fill:function(t,n){return e._scale.scale(t)},width:n,height:i,x:r,y:o,"shape-rendering":"crispEdges"}),Pxt.ADD_TITLE_ELEMENTS&&S.append("title").text((function(t){return e._formatter(t)})),this},e._DEFAULT_NUM_SWATCHES=11,e.LEGEND_LABEL_CLASS="legend-label",e})(Bgt.Component);Axt.InterpolatedColorLegend=Rxt;var Oxt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Oxt,"__esModule",{value:!0});var zxt=uO,Dxt=Imt,Bxt=(function(t){function e(e,n){void 0===e&&(e=""),void 0===n&&(n=0);var i=t.call(this)||this;return i.addClass("label"),i.text(e),i.angle(n),i.xAlignment("center").yAlignment("center"),i._padding=0,i}return zxt.__extends(e,t),e.prototype.requestedSpace=function(t,e){var n=this._measurer.measure(this._text);return{minWidth:(0===this.angle()?n.width:n.height)+2*this.padding(),minHeight:(0===this.angle()?n.height:n.width)+2*this.padding()}},e.prototype._setup=function(){t.prototype._setup.call(this),this._textContainer=this.content().append("g");var e=new Dxt.SvgContext(this._textContainer.node());this._measurer=new Dxt.CacheMeasurer(e),this._wrapper=new Dxt.Wrapper,this._writer=new Dxt.Writer(this._measurer,e,this._wrapper),this.text(this._text)},e.prototype.text=function(t){if(null==t)return this._text;if("string"!=typeof t)throw new Error("Label.text() only takes strings as input");return this._text=t,this.redraw(),this},e.prototype.angle=function(t){if(null==t)return this._angle;if((t%=360)>180?t-=360:t<-180&&(t+=360),-90!==t&&0!==t&&90!==t)throw new Error(t+" is not a valid angle for Label");return this._angle=t,this.redraw(),this},e.prototype.padding=function(t){if(null==t)return this._padding;if((t=+t)<0)throw new Error(t+" is not a valid padding value. Cannot be less than 0.");return this._padding=t,this.redraw(),this},e.prototype.fixedWidth=function(){return!0},e.prototype.fixedHeight=function(){return!0},e.prototype.renderImmediately=function(){t.prototype.renderImmediately.call(this),this._textContainer.selectAll("g").remove();var e=this._measurer.measure(this._text),n=Math.max(Math.min((this.height()-e.height)/2,this.padding()),0),i=Math.max(Math.min((this.width()-e.width)/2,this.padding()),0);this._textContainer.attr("transform","translate("+i+","+n+")");var r=this.width()-2*i,o=this.height()-2*n,a={xAlign:this.xAlignment(),yAlign:this.yAlignment(),textRotation:this.angle()};return this._writer.write(this._text,r,o,a),this},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e})(Bgt.Component);Oxt.Label=Bxt;var Hxt=(function(t){function e(n,i){var r=t.call(this,n,i)||this;return r.addClass(e.TITLE_LABEL_CLASS),r}return zxt.__extends(e,t),e.TITLE_LABEL_CLASS="title-label",e})(Bxt);Oxt.TitleLabel=Hxt;var Fxt=(function(t){function e(n,i){var r=t.call(this,n,i)||this;return r.addClass(e.AXIS_LABEL_CLASS),r}return zxt.__extends(e,t),e.AXIS_LABEL_CLASS="axis-label",e})(Bxt);Oxt.AxisLabel=Fxt;var Vxt={},Uxt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Uxt,"__esModule",{value:!0});var jxt=Edt;Uxt.circle=function Gxt(){return function(t){return jxt.symbol().type(jxt.symbolCircle).size(Math.PI*Math.pow(t/2,2))}},Uxt.square=function Wxt(){return function(t){return jxt.symbol().type(jxt.symbolSquare).size(Math.pow(t,2))}},Uxt.cross=function qxt(){return function(t){return jxt.symbol().type(jxt.symbolCross).size(5/9*Math.pow(t,2))}},Uxt.diamond=function Yxt(){return function(t){return jxt.symbol().type(jxt.symbolDiamond).size(Math.tan(Math.PI/6)*Math.pow(t,2)/2)}},Uxt.triangle=function Xxt(){return function(t){return jxt.symbol().type(jxt.symbolTriangle).size(Math.sqrt(3)*Math.pow(t/2,2))}},Uxt.star=function $xt(){return function(t){return jxt.symbol().type(jxt.symbolStar).size(.8908130915292852*Math.pow(t/2,2))}};var Kxt=3*(1/Math.sqrt(12)/2+1);Uxt.wye=function Zxt(){return function(t){return jxt.symbol().type(jxt.symbolWye).size(Kxt*Math.pow(t/2.4,2))}},
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(Vxt,"__esModule",{value:!0});var Jxt=uO,Qxt=Edt,twt=Imt,ewt=L_t,nwt=gyt,iwt=Uxt,rwt=Fgt,owt=Bgt,awt=(function(){function t(t,e,n){void 0===t&&(t=[]),void 0===e&&(e=0),void 0===n&&(n=1/0),this.columns=t,this.bottomPadding=e,this.maxWidth=n}return t.prototype.addColumn=function(t){var e=t.width,n=this.getWidthAvailable();t.width=Math.min(n,e),this.columns.push(t)},t.prototype.getBounds=function(t){for(var e=this.columns[t],n=0,i=0;i<t;i++)n+=this.columns[i].width;return{topLeft:{x:n,y:0},bottomRight:{x:n+e.width,y:e.height}}},t.prototype.getHeight=function(){return rwt.Math.max(this.columns.map((function(t){return t.height})),0)+this.bottomPadding},t.prototype.getWidth=function(){return Math.min(this.columns.reduce((function(t,e){return t+e.width}),0),this.maxWidth)},t.prototype.getWidthAvailable=function(){var t=this.getWidth();return Math.max(this.maxWidth-t,0)},t})(),swt=(function(){function t(t,e,n,i){void 0===t&&(t=1/0),void 0===e&&(e=1/0),void 0===n&&(n=0),void 0===i&&(i=[]),this.maxWidth=t,this.maxHeight=e,this.padding=n,this.rows=i}return t.prototype.addRow=function(t){t.maxWidth=this.maxWidth-2*this.padding,this.rows.push(t)},t.prototype.getColumnBounds=function(t,e){var n=this.getRowBounds(t),i=this.rows[t].getBounds(e);return i.topLeft.x+=n.topLeft.x,i.bottomRight.x+=n.topLeft.x,i.topLeft.y+=n.topLeft.y,i.bottomRight.y+=n.topLeft.y,i},t.prototype.getRowBounds=function(t){for(var e=this.padding,n=this.padding,i=0;i<t;i++)n+=this.rows[i].getHeight();return{topLeft:{x:e,y:n},bottomRight:{x:e+this.rows[t].getWidth(),y:n+this.rows[t].getHeight()}}},t.prototype.getHeight=function(){return Math.min(this.rows.reduce((function(t,e){return t+e.getHeight()}),0)+2*this.padding,this.maxHeight)},t.prototype.getWidth=function(){return Math.min(rwt.Math.max(this.rows.map((function(t){return t.getWidth()})),0)+2*this.padding,this.maxWidth)},t})(),lwt=(function(t){function e(e){var n=t.call(this)||this;if(n._padding=5,n._rowBottomPadding=3,n.addClass("legend"),n.maxEntriesPerRow(1),null==e)throw new Error("Legend requires a colorScale");return n._colorScale=e,n._redrawCallback=function(t){return n.redraw()},n._colorScale.onUpdate(n._redrawCallback),n._formatter=nwt.identity(),n.maxLinesPerEntry(1),n.xAlignment("right").yAlignment("top"),n.comparator((function(t,e){var i=n._colorScale.domain().slice().map((function(t){return n._formatter(t)}));return i.indexOf(t)-i.indexOf(e)})),n._symbolFactoryAccessor=function(){return iwt.circle()},n._symbolOpacityAccessor=function(){return 1},n}return Jxt.__extends(e,t),e.prototype._setup=function(){t.prototype._setup.call(this);var n=this.content().append("g").classed(e.LEGEND_ROW_CLASS,!0);n.append("g").classed(e.LEGEND_ENTRY_CLASS,!0).append("text");var i=new twt.SvgContext(n.node(),null,ewt.ADD_TITLE_ELEMENTS);this._measurer=new twt.CacheMeasurer(i),this._wrapper=(new twt.Wrapper).maxLines(this.maxLinesPerEntry()),this._writer=new twt.Writer(this._measurer,i,this._wrapper)},e.prototype.formatter=function(t){return null==t?this._formatter:(this._formatter=t,this.redraw(),this)},e.prototype.maxEntriesPerRow=function(t){return null==t?this._maxEntriesPerRow:(this._maxEntriesPerRow=t,this.redraw(),this)},e.prototype.maxLinesPerEntry=function(t){return null==t?this._maxLinesPerEntry:(this._maxLinesPerEntry=t,this.redraw(),this)},e.prototype.maxWidth=function(t){return null==t?this._maxWidth:(this._maxWidth=t,this.redraw(),this)},e.prototype.comparator=function(t){return null==t?this._comparator:(this._comparator=t,this.redraw(),this)},e.prototype.colorScale=function(t){return null!=t?(this._colorScale.offUpdate(this._redrawCallback),this._colorScale=t,this._colorScale.onUpdate(this._redrawCallback),this.redraw(),this):this._colorScale},e.prototype.destroy=function(){t.prototype.destroy.call(this),this._colorScale.offUpdate(this._redrawCallback)},e.prototype._buildLegendTable=function(t,e){var n=this,i=this._measurer.measure().height,r=new swt(t,e,this._padding),o=this._colorScale.domain().slice().sort((function(t,e){return n._comparator(n._formatter(t),n._formatter(e))})),a=new awt;return r.addRow(a),a.bottomPadding=this._rowBottomPadding,o.forEach((function(t,e){a.columns.length/2===n.maxEntriesPerRow()&&((a=new awt).bottomPadding=n._rowBottomPadding,r.addRow(a));var o=a.getWidthAvailable(),s=n._formatter(t),l=n._measurer.measure(s).width;o-i-l<0&&a.columns.length>1&&((a=new awt).bottomPadding=n._rowBottomPadding,r.addRow(a)),a.addColumn({width:i,height:i,data:{name:t,type:"symbol"}}),o=a.getWidthAvailable();var c=Math.min(o,l);n._wrapper.maxLines(n.maxLinesPerEntry());var u=n._wrapper.wrap(s,n._measurer,c).noLines;a.addColumn({width:c,height:u*i,data:{name:t,type:"text"}})})),r},e.prototype.requestedSpace=function(t,e){var n=this._buildLegendTable(rwt.Math.min([this.maxWidth(),t],t),e);return{minHeight:n.getHeight(),minWidth:n.getWidth()}},e.prototype.entitiesAt=function(t){var n=this;if(!this._isSetup)return[];var i=this._buildLegendTable(this.width(),this.height());return i.rows.reduce((function(r,o,a){if(0!==r.length)return r;var s=i.getRowBounds(a);return rwt.Math.within(t,s)?o.columns.reduce((function(r,o,s){var l=i.getColumnBounds(a,s);if(rwt.Math.within(t,l)){var c=n.content().selectAll("."+e.LEGEND_ROW_CLASS).nodes()[a],u=Qxt.select(c).selectAll("."+e.LEGEND_ENTRY_CLASS).nodes()[Math.floor(s/2)],h=Qxt.select(u).select("."+e.LEGEND_SYMBOL_CLASS),d=rwt.DOM.getTranslateValues(Qxt.select(c)),p=rwt.DOM.getTranslateValues(h);return[{bounds:rwt.DOM.elementBBox(Qxt.select(c)),datum:o.data.name,position:{x:d[0]+p[0],y:d[1]+p[1]},selection:Qxt.select(u),component:n}]}return r}),r):r}),[])},e.prototype.renderImmediately=function(){t.prototype.renderImmediately.call(this);var n=this._buildLegendTable(this.width(),this.height());this.content().selectAll("*").remove();var i=this.content().selectAll("g."+e.LEGEND_ROW_CLASS).data(n.rows),r=i.enter().append("g").classed(e.LEGEND_ROW_CLASS,!0).merge(i);i.exit().remove(),r.attr("transform",(function(t,e){var i=n.getRowBounds(e);return"translate("+i.topLeft.x+", "+i.topLeft.y+")"}));var o=this;return r.each((function(t,i){for(var r=[],a=0;a<t.columns.length;a+=2)r.push([t.columns[a],t.columns[a+1]]);var s=Qxt.select(this).selectAll("g."+e.LEGEND_ENTRY_CLASS).data(r),l=s.enter().append("g").classed(e.LEGEND_ENTRY_CLASS,!0).merge(s);l.append("path").attr("d",(function(t,e){var n=t[0];return o.symbol()(n.data.name,i)(.6*n.height)(null)})).attr("transform",(function(t,e){var r=t[0],o=n.rows[i].columns.indexOf(r);return"translate("+(n.getColumnBounds(i,o).topLeft.x+r.width/2)+", "+r.height/2+")"})).attr("fill",(function(t){return o._colorScale.scale(t[0].data.name)})).attr("opacity",(function(t,e){return o.symbolOpacity()(t[0].data.name,i)})).classed(e.LEGEND_SYMBOL_CLASS,!0),l.append("g").classed("text-container",!0).attr("transform",(function(t,e){var r=n.rows[i].columns.indexOf(t[1]);return"translate("+n.getColumnBounds(i,r).topLeft.x+", 0)"})).each((function(t,e,n){var i=Qxt.select(this),r=t[1];o._writer.write(o._formatter(r.data.name),r.width,o.height(),{xAlign:"left",yAlign:"top",textRotation:0},i.node())})),s.exit().remove()})),this},e.prototype.symbol=function(t){return null==t?this._symbolFactoryAccessor:(this._symbolFactoryAccessor=t,this.render(),this)},e.prototype.symbolOpacity=function(t){return null==t?this._symbolOpacityAccessor:(this._symbolOpacityAccessor="number"==typeof t?function(){return t}:t,this.render(),this)},e.prototype.fixedWidth=function(){return!0},e.prototype.fixedHeight=function(){return!0},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e.LEGEND_ROW_CLASS="legend-row",e.LEGEND_ENTRY_CLASS="legend-entry",e.LEGEND_SYMBOL_CLASS="legend-symbol",e})(owt.Component);Vxt.Legend=lwt;var cwt={},uwt={},hwt={};!(function(t){var e;
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0}),(e=t.Animator||(t.Animator={})).MAIN="main",e.RESET="reset"})(hwt);var dwt={},pwt={},fwt={};function mwt(t){return!0===(function t(e){return null!=e&&"object"==typeof e&&!1===Array.isArray(e)})(t)&&"[object Object]"===Object.prototype.toString.call(t)}var gwt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(gwt,"__esModule",{value:!0});var _wt=Fgt,ywt=0,vwt=(function(){function t(t,e){void 0===t&&(t=[]),void 0===e&&(e={}),this._updateId=ywt++,this._data=t,this._metadata=e,this._callbacks=new _wt.CallbackSet}return t.prototype.onUpdate=function(t){return this._callbacks.add(t),this},t.prototype.offUpdate=function(t){return this._callbacks.delete(t),this},t.prototype.data=function(t){return null==t?this._data:(this._data=t,this._dispatchUpdate(),this)},t.prototype.metadata=function(t){return null==t?this._metadata:(this._metadata=t,this._dispatchUpdate(),this)},t.prototype.updateId=function(){return this._updateId},t.prototype._dispatchUpdate=function(){this._updateId=ywt++,this._callbacks.callCallbacks(this)},t})();gwt.Dataset=vwt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     * @fileoverview Implements the Signature API to help in comparing when two
     * Plottable objects have "changed".
     *
     * Memoization in Plottable is complicated by mutable scales and datasets. We cannot simply
     * reference compare two e.g. scales since it may have internally mutated. To resolve this,
     * we write a recursive Signature interface that holds an immutable snapshot of whatever
     * state the scale/data was in at the time. Then on memoized function invocation we sign the
     * new inputs and compare the signatures to decide if we should recompute.
     *
     * We must hand-write a signature for each custom class we wish to support.
     */
Object.defineProperty(fwt,"__esModule",{value:!0});var bwt=uO,xwt=gwt,wwt=hvt;function Swt(t){return t instanceof kwt?t:t instanceof Date?Twt(t.valueOf()):t instanceof wwt.Scale?Mwt(t):t instanceof xwt.Dataset?Ewt(t):(function t(e){var n,i;return!1!==mwt(e)&&"function"==typeof(n=e.constructor)&&!1!==mwt(i=n.prototype)&&!1!==i.hasOwnProperty("isPrototypeOf")})(t)?Awt(t):Array.isArray(t)?Cwt(t):Twt(t)}function Mwt(t){return Awt({domain:t.domain(),range:t.range(),updateId:t.updateId(),ref:Twt(t)})}function Ewt(t){return Awt({ref:Twt(t),updateId:t.updateId()})}function Twt(t){return new Pwt(t)}function Cwt(t){return new Lwt(t.map((function(t){return Swt(t)})))}function Awt(t){var e={};for(var n in t)t.hasOwnProperty(n)&&(e[n]=Swt(t[n]));return new Nwt(e)}fwt.sign=Swt,fwt.signScale=Mwt,fwt.signDataset=Ewt,fwt.signRef=Twt,fwt.signArray=Cwt,fwt.signObj=Awt;var kwt=(function(){function t(){}return t.prototype.isDifferent=function(t){return!(t instanceof this.constructor)||this.isSignatureDifferent(t)},t})();fwt.Signature=kwt;var Lwt=(function(t){function e(e){var n=t.call(this)||this;return n.array=e,n}return bwt.__extends(e,t),e.prototype.isSignatureDifferent=function(t){if(t.array.length!==this.array.length)return!0;for(var e=0;e<this.array.length;e++)if(this.array[e].isDifferent(t.array[e]))return!0;return!1},e})(kwt);fwt.ArraySignature=Lwt;var Pwt=(function(t){function e(e){var n=t.call(this)||this;return n.ref=e,n}return bwt.__extends(e,t),e.prototype.isSignatureDifferent=function(t){return this.ref!==t.ref},e})(kwt);fwt.ReferenceSignature=Pwt;var Nwt=(function(t){function e(e){var n=t.call(this)||this;return n.obj=e,n}return bwt.__extends(e,t),e.prototype.isSignatureDifferent=function(t){var e=Object.keys(this.obj),n=Object.keys(t.obj);if(e.length!==n.length)return!0;for(var i=0,r=e;i<r.length;i++){var o=r[i];if(!t.obj.hasOwnProperty(o))return!0;if(this.obj[o].isDifferent(t.obj[o]))return!0}return!1},e})(kwt);fwt.ObjectSignature=Nwt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     * @fileoverview Implements a function memoizer using the Signature API.
     */
Object.defineProperty(pwt,"__esModule",{value:!0});var Iwt=fwt;pwt.memoize=function Rwt(t){var e,n=void 0,i=!1,r=!1,o=function(){for(var o=[],a=0;a<arguments.length;a++)o[a]=arguments[a];if(i)return e;var s=Iwt.signArray(o);return void 0===n||n.isDifferent(s)?(r&&console.warn("cache miss! computing"),n=s,e=t.apply(this,o)):r&&console.warn("cache hit!"),e};return o.doLocked=function(t){if(i)throw new Error("Locking an already locked memoize function!");i=!0;var e=t.apply(this);return i=!1,e},o.logPerformance=function(t){return void 0===t&&(t=!0),r=t,this},o};var Owt={};Object.defineProperty(Owt,"__esModule",{value:!0});var zwt=Se.exports,Dwt=(function(){function t(){this.map=Object.create(null),this.exists=Object.create(null)}return t.prototype.delete=function(t){return delete this.map[t],delete this.exists[t],!0},t.prototype.get=function(t){return this.map[t]},t.prototype.has=function(t){return!!this.exists[t]},t.prototype.set=function(t,e){return this.map[t]=e,this.exists[t]=!0,this},t})(),Bwt=(function(){function t(){this.map=new Dwt}return t.prototype.get=function(t){return this.map.get(t[0]).get(t[1])},t.prototype.has=function(t){return this.map.has(t[0])&&this.map.get(t[0]).has(t[1])},t.prototype.set=function(t,e){return this.map.has(t[0])||this.map.set(t[0],new Dwt),this.map.get(t[0]).set(t[1],e),this},t.prototype.delete=function(t){return this.map.has(t[0])&&this.map.get(t[0]).delete(t[1]),!0},t.prototype.clear=function(){this.map=new Dwt},t.resolver=function(t,e,n){return[n.updateId(),e]},t})();function Hwt(t){var e=zwt.memoize(t,Bwt.resolver);return e.cache=new Bwt,e}Owt.memoizeProjector=Hwt,Owt.memoizeProjectors=function Fwt(t){return Object.keys(t).forEach((function(e){t[e]=Hwt(t[e])})),t};var Vwt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     * @fileoverview Implements a convenient thunk function to handle the common case
     * of creating a memoized function that takes its inputs from mutable class properties.
     */Object.defineProperty(Vwt,"__esModule",{value:!0});var Uwt=dwt;Vwt.memThunk=function jwt(){for(var t=[],e=0;e<arguments.length;e++)t[e]=arguments[e];var n=t.slice(0,-1),i=t[t.length-1],r=Uwt.memoize(i),o=function(){var t=this,e=n.map((function(e){return e.apply(t)}));return r.apply(void 0,e)};return o},(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(pwt,t),e.__exportStar(Owt,t),e.__exportStar(Vwt,t),t.sign=fwt.sign})(dwt);var Gwt={};!(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=Edt,n=(function(){function t(t,e){this._context=t,this._drawStep=e}return t.prototype.getDrawStep=function(){return this._drawStep},t.prototype.draw=function(t,e){var n=e[e.length-1].attrToAppliedProjector;this._context.save(),this._drawStep(this._context,t,n),this._context.restore()},t.prototype.getVisualPrimitives=function(){return[]},t.prototype.getVisualPrimitiveAtIndex=function(t){return null},t.prototype.remove=function(){},t})();function i(t,e,n,i){for(var r={},o=0,a=e;o<a.length;o++){var s=a[o];t.hasOwnProperty(s)&&(r[s]=t[s](n,i))}return r}function r(t){return null!=t["stroke-width"]?parseFloat(t["stroke-width"]):1}function o(t){var e=t["stroke-dasharray"];if(null!=e)try{return e.split(/[ ,]+/).map((function(t){return parseInt(t,10)}))}catch(t){return console.error("getStrokeDashArray failed with: "+t),[]}return[]}function a(t,n){if(n.stroke){t.lineWidth=r(n);var i=e.color(n.stroke),a=o(n);t.setLineDash(a),i.opacity*=(function s(t){var e=null!=t.opacity?parseFloat(t.opacity):1;return(null!=t["stroke-opacity"]?parseFloat(t["stroke-opacity"]):1)*e})(n),t.strokeStyle=i.toString(),t.stroke()}if(n.fill){var l=e.color(n.fill);l.opacity*=(function c(t){var e=null!=t.opacity?parseFloat(t.opacity):1;return(null!=t["fill-opacity"]?parseFloat(t["fill-opacity"]):1)*e})(n),t.fillStyle=l.toString(),t.fill()}}t.CanvasDrawer=n,t.ContextStyleAttrs=["fill-opacity","fill","opacity","stroke-opacity","stroke-width","stroke","stroke-dasharray"],t.resolveAttributesSubsetWithStyles=function s(e,n,r,o){return i(e,t.ContextStyleAttrs.concat(n),r,o)},t.resolveAttributes=i,t.getStrokeWidth=r,t.getStrokeDashArray=o,t.renderArea=function l(t,e,n,i){t.save(),t.beginPath(),e.context(t),e(n),t.lineJoin="round",a(t,i),t.restore()},t.renderLine=function c(t,e,n,i){t.save(),t.beginPath(),e.context(t),e(n),t.lineJoin="round",a(t,i),t.restore()},t.renderPathWithStyle=a})(Gwt);var Wwt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Wwt,"__esModule",{value:!0});var qwt=(function(){function t(t,e){this._svgDrawerFactory=t,this._canvasDrawerFactory=e}return t.prototype.useSVG=function(t){null!=this._currentDrawer&&this._currentDrawer.remove();var e=this._svgDrawerFactory();e.attachTo(t),this._currentDrawer=e},t.prototype.useCanvas=function(t){null!=this._currentDrawer&&this._currentDrawer.remove(),this._currentDrawer=this._canvasDrawerFactory(t.node().getContext("2d"))},t.prototype.getDrawer=function(){return this._currentDrawer},t.prototype.remove=function(){null!=this._currentDrawer&&this._currentDrawer.remove()},t.prototype.draw=function(t,e){this._currentDrawer.draw(t,e)},t.prototype.getVisualPrimitives=function(){return this._currentDrawer.getVisualPrimitives()},t.prototype.getVisualPrimitiveAtIndex=function(t){return this._currentDrawer.getVisualPrimitiveAtIndex(t)},t})();Wwt.ProxyDrawer=qwt;var Ywt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Ywt,"__esModule",{value:!0});var Xwt=Edt,$wt=Fgt,Kwt=(function(){function t(t,e){this._root=Xwt.select(document.createElementNS("http://www.w3.org/2000/svg","g")),this._className=e,this._svgElementName=t}return t.prototype.draw=function(t,e){var n=this;this._createAndDestroyDOMElements(t);for(var i=0,r=e.length,o=function(r){var o=e[r];$wt.Window.setTimeout((function(){return n._drawStep(o)}),i),i+=o.animator.totalTime(t.length)},a=0;a<r;a++)o(a)},t.prototype.getVisualPrimitives=function(){return null==this._cachedVisualPrimitivesNodes&&(this._cachedVisualPrimitivesNodes=this._selection.nodes()),this._cachedVisualPrimitivesNodes},t.prototype.getVisualPrimitiveAtIndex=function(t){return null==this._cachedVisualPrimitivesNodeMap?null:this._cachedVisualPrimitivesNodeMap.get(t)},t.prototype.remove=function(){this._root.remove()},t.prototype.attachTo=function(t){t.node().appendChild(this._root.node())},t.prototype.getRoot=function(){return this._root},t.prototype.selector=function(){return this._svgElementName},t.prototype._applyDefaultAttributes=function(t){},t.prototype._createAndDestroyDOMElements=function(t){var e=t.map((function(t,e){return null!=t?{d:t,i:e}:null})).filter((function(t){return null!=t})),n=this._root.selectAll(this.selector()).data(e);this._selection=n.enter().append(this._svgElementName).merge(n),n.exit().remove();var i=new $wt.Map;this._selection.each((function(t){i.set(t.i,this)})),this._cachedVisualPrimitivesNodeMap=i,this._cachedVisualPrimitivesNodes=null,this._selection.data(this._selection.data().map((function(t){return t.d}))),null!=this._className&&this._selection.classed(this._className,!0),this._applyDefaultAttributes(this._selection)},t.prototype._drawStep=function(t){var e=this;["fill","stroke"].forEach((function(n){null!=t.attrToAppliedProjector[n]&&e._selection.attr(n,t.attrToAppliedProjector[n])})),t.animator.animate(this._selection,t.attrToAppliedProjector),null!=this._className&&this._selection.classed(this._className,!0)},t})();Ywt.SVGDrawer=Kwt;var Zwt={};
/**
     * Copyright 2017-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(Zwt,"__esModule",{value:!0});var Jwt=(function(){function t(){var t=this;this.scale=0,this.translate=0,this.cachedDomain=[null,null],this.lastSeenDomain=[null,null],this.updateDomain=function(e){t.lastSeenDomain=e.getTransformationDomain();var n=e.scaleTransformation(t.cachedDomain[1])-e.scaleTransformation(t.cachedDomain[0]),i=e.scaleTransformation(t.lastSeenDomain[1])-e.scaleTransformation(t.lastSeenDomain[0]);t.scale=n/i||1,t.translate=e.scaleTransformation(t.cachedDomain[0])-e.scaleTransformation(t.lastSeenDomain[0])*t.scale||0}}return t.prototype.reset=function(){this.scale=1,this.translate=0,this.cachedDomain=this.lastSeenDomain},t.prototype.setDomain=function(t){this.cachedDomain=t.getTransformationDomain()},t})(),Qwt=(function(){function t(e,n){var i=this;this.renderCallback=e,this.applyTransformCallback=n,this.domainTransformX=new Jwt,this.domainTransformY=new Jwt,this.renderDeferred=function(){i.applyTransform(),clearTimeout(i.timeoutToken),i.timeoutToken=setTimeout((function(){i.renderCallback()}),t.DEFERRED_RENDERING_DELAY)}}return t.prototype.setDomains=function(t,e){t&&this.domainTransformX.setDomain(t),e&&this.domainTransformY.setDomain(e),this.renderDeferred()},t.prototype.updateDomains=function(t,e){t&&this.domainTransformX.updateDomain(t),e&&this.domainTransformY.updateDomain(e),this.renderDeferred()},t.prototype.resetTransforms=function(){this.domainTransformX.reset(),this.domainTransformY.reset(),this.applyTransform()},t.prototype.applyTransform=function(){this.applyTransformCallback(this.domainTransformX.translate,this.domainTransformY.translate,this.domainTransformX.scale,this.domainTransformY.scale)},t.DEFERRED_RENDERING_DELAY=200,t})();Zwt.DeferredRenderer=Qwt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(uwt,"__esModule",{value:!0});var tSt=uO,eSt=Edt,nSt=_mt,iSt=Fgt,rSt=hwt,oSt=dwt,aSt=Bgt,sSt=Gwt,lSt=Wwt,cSt=Ywt,uSt=bmt,hSt=Zwt;uwt.Renderer=Smt.makeEnum(["svg","canvas"]);var dSt=(function(t){function e(){var n=t.call(this)||this;n._dataChanged=!1,n._attrExtents={},n._animate=!1,n._animators={},n._propertyExtents={},n._resetEntityStore=function(){n._cachedEntityStore=void 0},n._overflowHidden=!0,n.addClass("plot"),n._datasetToDrawer=new iSt.Map,n._attrBindings=eSt.map(),n._includedValuesProvider=function(t,e){return n._includedValuesForScale(t,e)},n._renderCallback=function(){return n.render()},n._onDatasetUpdateCallback=function(){return n._onDatasetUpdate()},n._propertyBindings=eSt.map();var i=(new nSt.Easing).maxTotalDuration(e._ANIMATION_MAX_DURATION);return n.animator(rSt.Animator.MAIN,i),n.animator(rSt.Animator.RESET,new nSt.Null),n._deferredResetEntityStore=iSt.Window.debounce(hSt.DeferredRenderer.DEFERRED_RENDERING_DELAY,n._resetEntityStore),n}return 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t=this.element().select(".plot-canvas-container");t.empty()&&(t=this.element().append("div").classed("plot-canvas-container",!0)).node().appendChild(this._canvas.node())},e.prototype.setBounds=function(e,n,i,r){if(t.prototype.setBounds.call(this,e,n,i,r),this._updateExtents(),null!=this._canvas){if(this._bufferCanvas&&!this._bufferCanvasValid){this._bufferCanvas.attr("width",this._canvas.attr("width")),this._bufferCanvas.attr("height",this._canvas.attr("height"));var o=this._bufferCanvas.node().getContext("2d");if(o){var a=this._canvas.node();a.width>0&&a.height>0?o.canvas.width>0&&o.canvas.height>0&&o.drawImage(a,0,0):console.warn("Failed to fill buffer canvas with with 0x0 canvas")}this._bufferCanvasValid=!0}var s=null!=window.devicePixelRatio?window.devicePixelRatio:1;this._canvas.attr("width",e*s),this._canvas.attr("height",n*s);var l=this._canvas.node().getContext("2d");if(l&&(l.setTransform(s,0,0,s,0,0),this._bufferCanvas)){var c=this._bufferCanvas.node();c.width>0&&c.height>0?l.canvas.width>0&&l.canvas.height>0&&l.drawImage(c,0,0,e,n):console.warn("Failed to fill canvas with 0x0 buffer canvas")}}return this},e.prototype.destroy=function(){var e=this;t.prototype.destroy.call(this),this._scales().forEach((function(t){return t.offUpdate(e._renderCallback)})),this.datasets([])},e.prototype._createNodesForDataset=function(t){var e=this._datasetToDrawer.get(t);return"svg"===this.renderer()?e.useSVG(this._renderArea):e.useCanvas(this._canvas),e},e.prototype._createDrawer=function(t){return new lSt.ProxyDrawer((function(){return new cSt.SVGDrawer("path","")}),(function(t){return new sSt.CanvasDrawer(t,(function(){}))}))},e.prototype._getAnimator=function(t){return this._animateOnNextRender()&&this._animators[t]||new nSt.Null},e.prototype._onDatasetUpdate=function(){this._updateExtents(),this._dataChanged=!0,this._resetEntityStore(),this.renderLowPriority()},e.prototype.attr=function(t,e,n){return null==e?this._attrBindings.get(t):(this._bindAttr(t,e,n),this.render(),this)},e.prototype._bindProperty=function(t,e,n,i){var r=this._propertyBindings.get(t),o=null!=r?r.scale:null;this._propertyBindings.set(t,{accessor:"function"==typeof e?e:function(){return e},scale:n,postScale:i}),null!=o&&this._uninstallScaleForKey(o,t),null!=n&&this._installScaleForKey(n,t),this._clearAttrToProjectorCache()},e.prototype._bindAttr=function(t,e,n){var i=this._attrBindings.get(t),r=null!=i?i.scale:null;this._attrBindings.set(t,{accessor:"function"==typeof e?e:function(){return e},scale:n}),null!=r&&this._uninstallScaleForKey(r,t),null!=n&&this._installScaleForKey(n,t),this._clearAttrToProjectorCache()},e.prototype._clearAttrToProjectorCache=function(){delete this._cachedAttrToProjector},e.prototype._getAttrToProjector=function(){if(null==this._cachedAttrToProjector){var t=this._generateAttrToProjector();e.OPTIMIZE_MEMOIZE_PROJECTORS&&(t=oSt.memoizeProjectors(t)),this._cachedAttrToProjector=t}return iSt.assign({},this._cachedAttrToProjector)},e.prototype._generateAttrToProjector=function(){var t={};this._attrBindings.each((function(n,i){t[i]=e._scaledAccessor(n)}));var n=this._propertyProjectors();return Object.keys(n).forEach((function(e){null==t[e]&&(t[e]=n[e])})),t},e.prototype.renderImmediately=function(){return t.prototype.renderImmediately.call(this),this._isAnchored&&(this._paint(),this._dataChanged=!1),this},e.prototype.renderLowPriority=function(){return this._renderCallback(),this},e.prototype.animated=function(t){return null==t?this._animate:(this._animate=t,this)},e.prototype.detach=function(){return t.prototype.detach.call(this),this._updateExtents(),this},e.prototype._scales=function(){var t=[];return this._attrBindings.each((function(e,n){var i=e.scale;null!=i&&-1===t.indexOf(i)&&t.push(i)})),this._propertyBindings.each((function(e,n){var i=e.scale;null!=i&&-1===t.indexOf(i)&&t.push(i)})),t},e.prototype._updateExtents=function(){var t=this;this._resetEntityStore(),this._scales().forEach((function(e){return e.addIncludedValuesProvider(t._includedValuesProvider)}))},e.prototype._filterForProperty=function(t){return null},e.prototype.getExtentsForAttr=function(t){var e=this;if(null==this._attrExtents[t]){var n=oSt.memThunk((function(){return e.datasets()}),(function(){return e._attrBindings.get(t)}),(function(t,e){return null==e||null==e.accessor?null:t.map((function(t){return pSt(t,e,null)}))}));this._attrExtents[t]=n}return this._attrExtents[t]()},e.prototype.getExtentsForProperty=function(t){var e=this;if(null==this._propertyExtents[t]){var n=oSt.memThunk((function(){return e.datasets()}),(function(){return e._propertyBindings.get(t)}),(function(){return e._filterForProperty(t)}),(function(t,e,n){return null==e||null==e.accessor?null:t.map((function(t){return pSt(t,e,n)}))}));this._propertyExtents[t]=n}return this._propertyExtents[t]()},e.prototype._includedValuesForScale=function(t,e){var n=this;if(!this._isAnchored&&!e)return[];var i=[];return this._attrBindings.each((function(e,r){if(e.scale===t){var o=n.getExtentsForAttr(r);null!=o&&(i=i.concat(eSt.merge(o)))}})),this._propertyBindings.each((function(e,r){if(e.scale===t){var o=n.getExtentsForProperty(r);null!=o&&(i=i.concat(eSt.merge(o)))}})),i},e.prototype.animator=function(t,e){return void 0===e?this._animators[t]:(this._animators[t]=e,this)},e.prototype.renderer=function(t){var e=this;return void 0===t?null==this._canvas?"svg":"canvas":(null==this._canvas&&"canvas"===t?(this._canvas=eSt.select(document.createElement("canvas")).classed("plot-canvas",!0),this._bufferCanvas=eSt.select(document.createElement("canvas")),null!=this.element()&&this._appendCanvasNode(),this._datasetToDrawer.forEach((function(t){t.useCanvas(e._canvas)})),this.render()):null!=this._canvas&&"svg"==t&&(this._canvas.remove(),this._canvas=null,this._bufferCanvas=null,this._datasetToDrawer.forEach((function(t){t.useSVG(e._renderArea)})),this.render()),this)},e.prototype.addDataset=function(t){return this._addDataset(t),this._onDatasetUpdate(),this},e.prototype._addDataset=function(t){this._removeDataset(t);var e=this._createDrawer(t);return this._datasetToDrawer.set(t,e),this._isSetup&&this._createNodesForDataset(t),t.onUpdate(this._onDatasetUpdateCallback),this},e.prototype.removeDataset=function(t){return this._removeDataset(t),this._onDatasetUpdate(),this},e.prototype._removeDataset=function(t){return-1===this.datasets().indexOf(t)||(this._removeDatasetNodes(t),t.offUpdate(this._onDatasetUpdateCallback),this._datasetToDrawer.delete(t)),this},e.prototype._removeDatasetNodes=function(t){this._datasetToDrawer.get(t).remove()},e.prototype.datasets=function(t){var e=this,n=[];return this._datasetToDrawer.forEach((function(t,e){return n.push(e)})),null==t?n:(n.forEach((function(t){return e._removeDataset(t)})),t.forEach((function(t){return e._addDataset(t)})),this._onDatasetUpdate(),this)},e.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new nSt.Null}]},e.prototype._additionalPaint=function(t){},e.prototype._buildLightweightPlotEntities=function(t){var e=this,n=[];return t.forEach((function(t,i){for(var r=e._datasetToDrawer.get(t),o=0,a=t.data(),s=a.length,l=function(s){var 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e.getTotalDrawTime(i.get(t),n)})),s=iSt.Math.max(a,0);this._additionalPaint(s)},e.prototype.selections=function(t){var e=this;if(void 0===t&&(t=this.datasets()),"canvas"===this.renderer())return eSt.selectAll();var n=[];return t.forEach((function(t){var i=e._datasetToDrawer.get(t);if(null!=i){var r=i.getVisualPrimitives();n.push.apply(n,r)}})),eSt.selectAll(n)},e.prototype.entities=function(t){var e=this;return this._getEntityStore(t).entities().map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype.filterEntities=function(t){var e=this;return this._getEntityStore().entities().filter(t).map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype._getEntityStore=function(t){var e,n=this,i=function(t){return n._entityBounds(t)};return void 0!==t?((e=new iSt.EntityStore).addAll(this._buildLightweightPlotEntities(t),i,this._localOriginBounds()),e):(void 0===this._cachedEntityStore&&((e=new iSt.EntityStore).addAll(this._buildLightweightPlotEntities(this.datasets()),i,this._localOriginBounds()),this._cachedEntityStore=e),this._cachedEntityStore)},e.prototype._localOriginBounds=function(){return{topLeft:{x:0,y:0},bottomRight:{x:this.width(),y:this.height()}}},e.prototype._entityBounds=function(t){var e=this._pixelPoint(t.datum,t.index,t.dataset);return{x:e.x,y:e.y,width:0,height:0}},e.prototype._lightweightPlotEntityToPlotEntity=function(t){return{bounds:this._entityBounds(t),component:t.component,dataset:t.dataset,datasetIndex:t.datasetIndex,datum:t.datum,index:t.index,position:t.position,selection:eSt.select(t.drawer.getVisualPrimitiveAtIndex(t.validDatumIndex))}},e.prototype.entitiesAt=function(t){throw new Error("plots must implement entitiesAt")},e.prototype.entityNearest=function(t){var e=this._getEntityStore().entityNearest(t);return void 0===e?void 0:this._lightweightPlotEntityToPlotEntity(e)},e.prototype.entitiesIn=function(t,e){return this.entitiesInBounds(null==e?{x:t.topLeft.x,y:t.topLeft.y,width:t.bottomRight.x-t.topLeft.x,height:t.bottomRight.y-t.topLeft.y}:{x:t.min,y:e.min,width:t.max-t.min,height:e.max-e.min})},e.prototype.entitiesInBounds=function(t){var e=this,n=this._getEntityStore().entitiesInBounds(t);if(n)return n.map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype.entitiesInXBounds=function(t){var e=this,n=this._getEntityStore().entitiesInXBounds(t);if(n)return n.map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype.entitiesInYBounds=function(t){var e=this,n=this._getEntityStore().entitiesInYBounds(t);if(n)return n.map((function(t){return e._lightweightPlotEntityToPlotEntity(t)}))},e.prototype._uninstallScaleForKey=function(t,e){t.offUpdate(this._renderCallback),t.offUpdate(this._deferredResetEntityStore),t.removeIncludedValuesProvider(this._includedValuesProvider)},e.prototype._installScaleForKey=function(t,e){t.onUpdate(this._renderCallback),t.onUpdate(this._deferredResetEntityStore),t.addIncludedValuesProvider(this._includedValuesProvider)},e.prototype._propertyProjectors=function(){return{}},e._scaledAccessor=function(t){var e=t.scale,n=t.accessor,i=t.postScale,r=null==e?n:function(t,i,r){return e.scale(n(t,i,r))};return null==i?r:function(t,e,n){return i(r(t,e,n),t,e,n)}},e.prototype._pixelPoint=function(t,e,n){return{x:0,y:0}},e.prototype._animateOnNextRender=function(){return this._animate&&this._dataChanged},e.OPTIMIZE_MEMOIZE_PROJECTORS=!1,e._ANIMATION_MAX_DURATION=600,e})(aSt.Component);function pSt(t,e,n){var i=e.accessor,r=e.scale;if(null==r)return[];var o=t.data();null!=n&&(o=o.filter((function(e,i){return 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/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */uwt.Plot=dSt,Object.defineProperty(cwt,"__esModule",{value:!0});var fSt=uO,mSt=uwt,gSt=Fgt,_St=(function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return fSt.__extends(e,t),e.prototype.entityNearest=function(t){var e,n=1/0;return this.components().forEach((function(i){var r=i.entityNearest(t);if(null!=r){var o=gSt.Math.distanceSquared(r.position,t);o<=n&&(n=o,e=r)}})),e},e.prototype.append=function(e){if(null!=e&&!(e instanceof mSt.Plot))throw new Error("Plot Group only accepts plots");return t.prototype.append.call(this,e),this},e})(bxt.Group);cwt.PlotGroup=_St;var ySt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(ySt,"__esModule",{value:!0});var vSt=uO,bSt=Edt,xSt=Fgt,wSt=(function(t){function e(e){void 0===e&&(e=[]);var n=t.call(this)||this;return n._rowPadding=0,n._columnPadding=0,n._rows=[],n._rowWeights=[],n._columnWeights=[],n._nRows=0,n._nCols=0,n._calculatedLayout=null,n.addClass("table"),e.forEach((function(t,e){t.forEach((function(t,i){null!=t&&n.add(t,e,i)}))})),n}return vSt.__extends(e,t),e.prototype._forEach=function(t){for(var e=0;e<this._nRows;e++)for(var n=0;n<this._nCols;n++)null!=this._rows[e][n]&&t(this._rows[e][n])},e.prototype.has=function(t){for(var e=0;e<this._nRows;e++)for(var n=0;n<this._nCols;n++)if(this._rows[e][n]===t)return!0;return!1},e.prototype.componentAt=function(t,e){return t<0||t>=this._nRows||e<0||e>=this._nCols?null:this._rows[t][e]},e.prototype.add=function(t,e,n){if(null==t)throw Error("Cannot add null to a table cell");if(!this.has(t)){if(null!=(this._rows[e]&&this._rows[e][n]))throw new Error("cell is occupied");t.detach(),this._nRows=Math.max(e+1,this._nRows),this._nCols=Math.max(n+1,this._nCols),this._padTableToSize(this._nRows,this._nCols),this._rows[e][n]=t,this._adoptAndAnchor(t),this.redraw()}return this},e.prototype._remove=function(t){for(var e=0;e<this._nRows;e++)for(var n=0;n<this._nCols;n++)if(this._rows[e][n]===t)return this._rows[e][n]=null,!0;return!1},e.prototype._iterateLayout=function(t,n,i){void 0===i&&(i=!1);for(var r,o,a,s,l,c=this._rows,u=bSt.transpose(this._rows),h=t-this._columnPadding*(this._nCols-1),d=n-this._rowPadding*(this._nRows-1),p=e._calcComponentWeights(this._rowWeights,c,(function(t){return null==t||t.fixedHeight()})),f=e._calcComponentWeights(this._columnWeights,u,(function(t){return null==t||t.fixedWidth()})),m=f.map((function(t){return 0===t?.5:t})),g=p.map((function(t){return 0===t?.5:t})),_=e._calcProportionalSpace(m,h),y=e._calcProportionalSpace(g,d),v=xSt.Array.createFilledArray(0,this._nCols),b=xSt.Array.createFilledArray(0,this._nRows),x=0;;){var w=xSt.Array.add(b,y),S=xSt.Array.add(v,_);v=(a=this._determineGuarantees(S,w,i)).guaranteedWidths,b=a.guaranteedHeights,s=a.wantsWidthArr.some((function(t){return t})),l=a.wantsHeightArr.some((function(t){return t}));var M=r,E=o;r=h-bSt.sum(a.guaranteedWidths),o=d-bSt.sum(a.guaranteedHeights);var T=void 0;s?(T=a.wantsWidthArr.map((function(t){return t?.1:0})),T=xSt.Array.add(T,f)):T=f;var C=void 0;if(l?(C=a.wantsHeightArr.map((function(t){return t?.1:0})),C=xSt.Array.add(C,p)):C=p,_=e._calcProportionalSpace(T,r),y=e._calcProportionalSpace(C,o),x++,!(r>0&&r!==M||o>0&&o!==E))break;if(x>5)break}return r=h-bSt.sum(a.guaranteedWidths),o=d-bSt.sum(a.guaranteedHeights),{colProportionalSpace:_=e._calcProportionalSpace(f,r),rowProportionalSpace:y=e._calcProportionalSpace(p,o),guaranteedWidths:a.guaranteedWidths,guaranteedHeights:a.guaranteedHeights,wantsWidth:s,wantsHeight:l}},e.prototype._determineGuarantees=function(t,e,n){void 0===n&&(n=!1);var i=xSt.Array.createFilledArray(0,this._nCols),r=xSt.Array.createFilledArray(0,this._nRows),o=xSt.Array.createFilledArray(!1,this._nCols),a=xSt.Array.createFilledArray(!1,this._nRows);return this._rows.forEach((function(s,l){s.forEach((function(s,c){var u;u=null!=s?s.requestedSpace(t[c],e[l]):{minWidth:0,minHeight:0};var h=n?Math.min(u.minWidth,t[c]):u.minWidth;i[c]=Math.max(i[c],h);var d=n?Math.min(u.minHeight,e[l]):u.minHeight;r[l]=Math.max(r[l],d),o[c]=o[c]||u.minWidth>t[c],a[l]=a[l]||u.minHeight>e[l]}))})),{guaranteedWidths:i,guaranteedHeights:r,wantsWidthArr:o,wantsHeightArr:a}},e.prototype.requestedSpace=function(t,e){return this._calculatedLayout=this._iterateLayout(t,e),{minWidth:bSt.sum(this._calculatedLayout.guaranteedWidths),minHeight:bSt.sum(this._calculatedLayout.guaranteedHeights)}},e.prototype.computeLayout=function(e,n,i){var r=this;t.prototype.computeLayout.call(this,e,n,i);var o=bSt.sum(this._calculatedLayout.guaranteedWidths),a=bSt.sum(this._calculatedLayout.guaranteedHeights),s=this._calculatedLayout;(o>this.width()||a>this.height())&&(s=this._iterateLayout(this.width(),this.height(),!0));var l=0,c=xSt.Array.add(s.rowProportionalSpace,s.guaranteedHeights),u=xSt.Array.add(s.colProportionalSpace,s.guaranteedWidths);return this._rows.forEach((function(t,e){var n=0;t.forEach((function(t,i){null!=t&&t.computeLayout({x:n,y:l},u[i],c[e]),n+=u[i]+r._columnPadding})),l+=c[e]+r._rowPadding})),this},e.prototype.rowPadding=function(t){if(null==t)return this._rowPadding;if(!xSt.Math.isValidNumber(t)||t<0)throw Error("rowPadding must be a non-negative finite value");return this._rowPadding=t,this.redraw(),this},e.prototype.columnPadding=function(t){if(null==t)return this._columnPadding;if(!xSt.Math.isValidNumber(t)||t<0)throw Error("columnPadding must be a non-negative finite value");return this._columnPadding=t,this.redraw(),this},e.prototype.rowWeight=function(t,e){if(null==e)return this._rowWeights[t];if(!xSt.Math.isValidNumber(e)||e<0)throw Error("rowWeight must be a non-negative finite value");return this._rowWeights[t]=e,this.redraw(),this},e.prototype.columnWeight=function(t,e){if(null==e)return this._columnWeights[t];if(!xSt.Math.isValidNumber(e)||e<0)throw Error("columnWeight must be a non-negative finite value");return this._columnWeights[t]=e,this.redraw(),this},e.prototype.fixedWidth=function(){var t=bSt.transpose(this._rows);return e._fixedSpace(t,(function(t){return null==t||t.fixedWidth()}))},e.prototype.fixedHeight=function(){return e._fixedSpace(this._rows,(function(t){return null==t||t.fixedHeight()}))},e.prototype._padTableToSize=function(t,e){for(var n=0;n<t;n++){void 0===this._rows[n]&&(this._rows[n]=[],this._rowWeights[n]=null);for(var i=0;i<e;i++)void 0===this._rows[n][i]&&(this._rows[n][i]=null)}for(i=0;i<e;i++)void 0===this._columnWeights[i]&&(this._columnWeights[i]=null)},e._calcComponentWeights=function(t,e,n){return t.map((function(t,i){return null!=t?t:e[i].map(n).reduce((function(t,e){return t&&e}),!0)?0:1}))},e._calcProportionalSpace=function(t,e){var n=bSt.sum(t);return 0===n?xSt.Array.createFilledArray(0,t.length):t.map((function(t){return e*t/n}))},e._fixedSpace=function(t,e){var n=function(t){return t.reduce((function(t,e){return t&&e}),!0)};return n(t.map((function(t){return n(t.map(e))})))},e})(xxt.ComponentContainer);ySt.Table=wSt;var SSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(SSt,"__esModule",{value:!0});var MSt=uO,ESt=(function(t){function e(){var e=null!==t&&t.apply(this,arguments)||this;return e._maxLines=2,e}return MSt.__extends(e,t),e.prototype.requestedSpace=function(t,e){this._wrapper.maxLines(this._maxLines);var n=0===this.angle()?t:e;0===n&&(n=1/0);var i=this._wrapper.wrap(this._text,this._measurer,n),r=this._measurer.measure(i.wrappedText);return{minWidth:(0===this.angle()?r.width:r.height)+2*this.padding(),minHeight:(0===this.angle()?r.height:r.width)+2*this.padding()}},e.prototype.maxLines=function(t){return 0===arguments.length?this._maxLines:(this._maxLines=t,this.redraw(),this)},e})(Oxt.Label);SSt.WrappedLabel=ESt;var TSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(TSt,"__esModule",{value:!0});var CSt=uO,ASt=(function(t){function e(){var e=t.call(this)||this;return e.addClass("x-drag-box-layer"),e._hasCorners=!1,e}return CSt.__extends(e,t),e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),this._setBounds(this.bounds()),this},e.prototype._setBounds=function(e){t.prototype._setBounds.call(this,{topLeft:{x:e.topLeft.x,y:0},bottomRight:{x:e.bottomRight.x,y:this.height()}})},e.prototype._setResizableClasses=function(t){t&&this.enabled()?this.addClass("x-resizable"):this.removeClass("x-resizable")},e.prototype.yScale=function(e){if(null==e)return t.prototype.yScale.call(this);throw new Error("yScales cannot be set on an XDragBoxLayer")},e.prototype.yExtent=function(e){if(null==e)return t.prototype.yExtent.call(this);throw new Error("XDragBoxLayer has no yExtent")},e})(rbt.DragBoxLayer);TSt.XDragBoxLayer=ASt;var kSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(kSt,"__esModule",{value:!0});var LSt=uO,PSt=(function(t){function e(){var e=t.call(this)||this;return e.addClass("y-drag-box-layer"),e._hasCorners=!1,e}return LSt.__extends(e,t),e.prototype.computeLayout=function(e,n,i){return t.prototype.computeLayout.call(this,e,n,i),this._setBounds(this.bounds()),this},e.prototype._setBounds=function(e){t.prototype._setBounds.call(this,{topLeft:{x:0,y:e.topLeft.y},bottomRight:{x:this.width(),y:e.bottomRight.y}})},e.prototype._setResizableClasses=function(t){t&&this.enabled()?this.addClass("y-resizable"):this.removeClass("y-resizable")},e.prototype.xScale=function(e){if(null==e)return t.prototype.xScale.call(this);throw new Error("xScales cannot be set on an YDragBoxLayer")},e.prototype.xExtent=function(e){if(null==e)return t.prototype.xExtent.call(this);throw new Error("YDragBoxLayer has no xExtent")},e})(rbt.DragBoxLayer);kSt.YDragBoxLayer=PSt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(rbt,t),e.__exportStar(oxt,t),e.__exportStar(gxt,t),e.__exportStar(bxt,t),e.__exportStar(axt,t),e.__exportStar(Axt,t),e.__exportStar(Oxt,t),e.__exportStar(Vxt,t),e.__exportStar(cwt,t),e.__exportStar(Jbt,t),e.__exportStar(ySt,t),e.__exportStar(SSt,t),e.__exportStar(TSt,t),e.__exportStar(kSt,t)})(ibt);var NSt={},ISt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(ISt,"__esModule",{value:!0});var RSt=uO,OSt=(function(t){function e(){return t.call(this,"path","arc fill")||this}return RSt.__extends(e,t),e.prototype._applyDefaultAttributes=function(t){t.style("stroke","none")},e})(Ywt.SVGDrawer);ISt.ArcSVGDrawer=OSt;var zSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(zSt,"__esModule",{value:!0});var DSt=uO,BSt=(function(t){function e(){return t.call(this,"path","arc outline")||this}return DSt.__extends(e,t),e.prototype._applyDefaultAttributes=function(t){t.style("fill","none")},e})(Ywt.SVGDrawer);zSt.ArcOutlineSVGDrawer=BSt;var HSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(HSt,"__esModule",{value:!0});var FSt=uO,VSt=Gwt,USt=(function(t){function e(){return t.call(this,"path","area")||this}return FSt.__extends(e,t),e.prototype._applyDefaultAttributes=function(t){t.style("stroke","none")},e.prototype.getVisualPrimitiveAtIndex=function(e){return t.prototype.getVisualPrimitiveAtIndex.call(this,0)},e})(Ywt.SVGDrawer);HSt.AreaSVGDrawer=USt;var jSt=["opacity","fill","fill-opacity"],GSt=["opacity","stroke","stroke-width"];HSt.makeAreaCanvasDrawStep=function WSt(t,e){return function(n,i,r){var o=VSt.resolveAttributes(r,jSt,i[0],0);VSt.renderArea(n,t(),i[0],o);var a=VSt.resolveAttributes(r,GSt,i[0],0);VSt.renderLine(n,e(),i[0],a)}};var qSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(qSt,"__esModule",{value:!0});var YSt=uO,XSt=Gwt,$St=(function(t){function e(){return t.call(this,"path","line")||this}return YSt.__extends(e,t),e.prototype._applyDefaultAttributes=function(t){t.style("fill","none")},e.prototype.getVisualPrimitiveAtIndex=function(e){return t.prototype.getVisualPrimitiveAtIndex.call(this,0)},e})(Ywt.SVGDrawer);qSt.LineSVGDrawer=$St;var KSt=["opacity","stroke-opacity","stroke-width","stroke","stroke-dasharray"];qSt.makeLineCanvasDrawStep=function ZSt(t){return function(e,n,i){var r=XSt.resolveAttributes(i,KSt,n[0],0);XSt.renderLine(e,t(),n[0],r)}};var JSt={};!(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO,n=Gwt,i=(function(t){function n(e){void 0===e&&(e="");var n=t.call(this,"rect","")||this;return n._rootClassName=e,n._root.classed(n._rootClassName,!0),n}return e.__extends(n,t),n})(Ywt.SVGDrawer);t.RectangleSVGDrawer=i;var r=n.ContextStyleAttrs.concat(["x","y","width","height"]);t.RectangleCanvasDrawStep=function(t,e,i){t.save();for(var o=e.length,a=0;a<o;a++){var s=e[a];if(null!=s){var l=n.resolveAttributes(i,r,s,a);t.beginPath(),t.rect(l.x,l.y,l.width,l.height),n.renderPathWithStyle(t,l)}}t.restore()};var o=(function(n){function i(e){return n.call(this,e,t.RectangleCanvasDrawStep)||this}return e.__extends(i,n),i})(n.CanvasDrawer);t.RectangleCanvasDrawer=o})(JSt);var QSt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(QSt,"__esModule",{value:!0});var tMt=uO,eMt=(function(t){function e(){return t.call(this,"line","")||this}return tMt.__extends(e,t),e})(Ywt.SVGDrawer);QSt.SegmentSVGDrawer=eMt;var nMt={},iMt={};
/**
     * Copyright 2017-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(iMt,"__esModule",{value:!0});var rMt=(function(){function t(e,n,i){void 0===i&&(i=window.devicePixelRatio),this.screenWidth=e,this.screenHeight=n,this.devicePixelRatio=i,this.pixelWidth=e*i,this.pixelHeight=n*i,this.canvas=document.createElement("canvas"),this.ctx=this.canvas.getContext("2d"),t.sizePixels(this.ctx,e,n,i)}return t.sizePixels=function(t,e,n,i){var r=t.canvas;r.width=e*i,r.height=n*i,r.style.width=e+"px",r.style.height=n+"px",t.setTransform(1,0,0,1,0,0),t.scale(i,i)},t.prototype.blit=function(t,e,n){void 0===e&&(e=0),void 0===n&&(n=0),t.drawImage(this.canvas,e,n,this.screenWidth,this.screenHeight)},t.prototype.blitCenter=function(t,e,n){void 0===e&&(e=0),void 0===n&&(n=0),this.blit(t,Math.floor(e-this.screenWidth/2),Math.floor(n-this.screenHeight/2))},t.prototype.resize=function(e,n,i){void 0===i&&(i=!1);var r=this.devicePixelRatio;return this.screenWidth=e,this.screenHeight=n,this.pixelWidth=e*r,this.pixelHeight=n*r,t.sizePixels(this.ctx,e,n,r),i&&this.ctx.translate(e/2,e/2),this},t.prototype.clear=function(t){var e=this,n=e.pixelWidth,i=e.pixelHeight,r=e.ctx;return r.save(),r.setTransform(1,0,0,1,0,0),null==t?r.clearRect(0,0,n,i):(r.fillStyle=t,r.fillRect(0,0,n,i)),r.restore(),this},t.prototype.getImageData=function(){return this.ctx.getImageData(0,0,this.pixelWidth,this.pixelHeight)},t})();iMt.CanvasBuffer=rMt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(nMt,"__esModule",{value:!0});var oMt=uO,aMt=Gwt,sMt=iMt,lMt=(function(t){function e(){return t.call(this,"path","symbol")||this}return oMt.__extends(e,t),e})(Ywt.SVGDrawer);nMt.SymbolSVGDrawer=lMt;var cMt=aMt.ContextStyleAttrs.concat(["x","y"]);function uMt(t,e,n,i,r){return n+r>=0&&n-r<=t&&i+r>=0&&i-r<=e}function hMt(t,e,n){if(null==t)return!1;for(var i=0;i<n.length;i++){var r=n[i];if(t[r]!=e[r])return!1}return!0}nMt.makeSymbolCanvasDrawStep=function dMt(t,e,n,i){var r=this;return function(o,a,s){for(var l=o.canvas,c=l.clientWidth,u=l.clientHeight,h=void 0===i?new sMt.CanvasBuffer(0,0):i,d=e(),p=n(),f=null,m=null,g=null,_=0;_<a.length;_++){var y=a[_];if(null!=y){var v=aMt.resolveAttributes(s,cMt,y,_),b=p(y,_,t);if(uMt(c,u,v.x,v.y,b)){var x=hMt(f,v,aMt.ContextStyleAttrs),w=d(y,_,r._dataset);if(x&&g==b&&m==w);else{var S=b+aMt.getStrokeWidth(v)+1;(S>h.screenWidth||S>h.screenHeight)&&h.resize(S,S,!0),h.clear();var M=h.ctx;M.beginPath(),w(b).context(M)(null),M.closePath(),aMt.renderPathWithStyle(M,v),m=w,g=b,f=v}h.blitCenter(o,v.x,v.y)}}}}},(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(ISt,t),e.__exportStar(zSt,t),e.__exportStar(HSt,t),e.__exportStar(Gwt,t),e.__exportStar(Wwt,t),e.__exportStar(qSt,t),e.__exportStar(JSt,t),e.__exportStar(QSt,t),e.__exportStar(Ywt,t),e.__exportStar(nMt,t)})(NSt);var pMt={},fMt={},mMt={},gMt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(gMt,"__esModule",{value:!0});var _Mt=uO,yMt=Zyt,vMt=Fgt,bMt=Zwt,xMt=uwt,wMt=(function(t){function e(){var e=t.call(this)||this;return e._autoAdjustXScaleDomain=!1,e._autoAdjustYScaleDomain=!1,e._deferredRendering=!1,e._applyDeferredRenderingTransform=function(t,n,i,r){e._isAnchored&&(null!=e._renderArea&&e._renderArea.attr("transform","translate("+t+", "+n+") scale("+i+", "+r+")"),null!=e._canvas&&e._canvas.style("transform","translate("+t+"px, "+n+"px) scale("+i+", "+r+")"))},e.addClass("xy-plot"),e._adjustYDomainOnChangeFromXCallback=function(t){return e._adjustYDomainOnChangeFromX()},e._adjustXDomainOnChangeFromYCallback=function(t){return e._adjustXDomainOnChangeFromY()},e._renderCallback=function(){if(e.deferredRendering()){var t=e.x()&&e.x().scale,n=e.y()&&e.y().scale;e._deferredRenderer.updateDomains(t,n)}else e.render()},e._deferredRenderer=new bMt.DeferredRenderer((function(){return e.render()}),e._applyDeferredRenderingTransform),e}return _Mt.__extends(e,t),e.prototype.render=function(){return this.deferredRendering()&&this._deferredRenderer.resetTransforms(),t.prototype.render.call(this)},e.prototype.deferredRendering=function(t){if(null==t)return this._deferredRendering;if(t){var e=this.x()&&this.x().scale,n=this.y()&&this.y().scale;this._deferredRenderer.setDomains(e,n)}return this._deferredRendering=t,this},e.prototype.x=function(t,n,i){if(null==t)return this._propertyBindings.get(e._X_KEY);this._bindProperty(e._X_KEY,t,n,i);var r=this.width();return null!=n&&null!=r&&n.range([0,r]),this._autoAdjustYScaleDomain&&this._updateYExtentsAndAutodomain(),this.render(),this},e.prototype.y=function(t,n,i){if(null==t)return this._propertyBindings.get(e._Y_KEY);this._bindProperty(e._Y_KEY,t,n,i);var r=this.height();return null!=n&&null!=r&&n.range(n instanceof yMt.Category?[0,r]:[r,0]),this._autoAdjustXScaleDomain&&this._updateXExtentsAndAutodomain(),this.render(),this},e.prototype._filterForProperty=function(t){return"x"===t&&this._autoAdjustXScaleDomain?this._makeFilterByProperty("y"):"y"!==t&&"y0"!==t||!this._autoAdjustYScaleDomain?null:this._makeFilterByProperty("x")},e.prototype._makeFilterByProperty=function(t){var e=this._propertyBindings.get(t);if(null!=e){var n=e.accessor,i=e.scale;if(null!=i)return function(t,e,r){var o=i.range();return vMt.Math.inRange(i.scale(n(t,e,r)),o[0],o[1])}}return null},e.prototype._uninstallScaleForKey=function(n,i){t.prototype._uninstallScaleForKey.call(this,n,i),n.offUpdate(i===e._X_KEY?this._adjustYDomainOnChangeFromXCallback:this._adjustXDomainOnChangeFromYCallback)},e.prototype._installScaleForKey=function(n,i){t.prototype._installScaleForKey.call(this,n,i),n.onUpdate(i===e._X_KEY?this._adjustYDomainOnChangeFromXCallback:this._adjustXDomainOnChangeFromYCallback)},e.prototype.destroy=function(){return t.prototype.destroy.call(this),this.x().scale&&this.x().scale.offUpdate(this._adjustYDomainOnChangeFromXCallback),this.y().scale&&this.y().scale.offUpdate(this._adjustXDomainOnChangeFromYCallback),this},e.prototype.autorangeMode=function(t){if(null==t)return this._autoAdjustXScaleDomain?"x":this._autoAdjustYScaleDomain?"y":"none";switch(t){case"x":this._autoAdjustXScaleDomain=!0,this._autoAdjustYScaleDomain=!1,this._adjustXDomainOnChangeFromY();break;case"y":this._autoAdjustXScaleDomain=!1,this._autoAdjustYScaleDomain=!0,this._adjustYDomainOnChangeFromX();break;case"none":this._autoAdjustXScaleDomain=!1,this._autoAdjustYScaleDomain=!1;break;default:throw new Error("Invalid scale name '"+t+"', must be 'x', 'y' or 'none'")}return this},e.prototype.computeLayout=function(e,n,i){t.prototype.computeLayout.call(this,e,n,i);var r=this.x(),o=r&&r.scale;null!=o&&o.range([0,this.width()]);var a=this.y(),s=a&&a.scale;return null!=s&&s.range(s instanceof yMt.Category?[0,this.height()]:[this.height(),0]),this},e.prototype._updateXExtentsAndAutodomain=function(){var t=this.x().scale;null!=t&&t.autoDomain()},e.prototype._updateYExtentsAndAutodomain=function(){var t=this.y().scale;null!=t&&t.autoDomain()},e.prototype.showAllData=function(){return this._updateXExtentsAndAutodomain(),this._updateYExtentsAndAutodomain(),this},e.prototype._adjustYDomainOnChangeFromX=function(){this._projectorsReady()&&this._autoAdjustYScaleDomain&&this._updateYExtentsAndAutodomain()},e.prototype._adjustXDomainOnChangeFromY=function(){this._projectorsReady()&&this._autoAdjustXScaleDomain&&this._updateXExtentsAndAutodomain()},e.prototype._projectorsReady=function(){var t=this.x(),e=this.y();return null!=t&&null!=t.accessor&&null!=e&&null!=e.accessor},e.prototype._pixelPoint=function(t,e,n){var i=xMt.Plot._scaledAccessor(this.x()),r=xMt.Plot._scaledAccessor(this.y());return{x:i(t,e,n),y:r(t,e,n)}},e.prototype._getDataToDraw=function(){var e=this,n=t.prototype._getDataToDraw.call(this),i=this.attr("defined");return this.datasets().forEach((function(t){n.set(t,n.get(t).filter((function(n,r){return(function(t,n,r){var o=xMt.Plot._scaledAccessor(e.x())(t,n,r),a=xMt.Plot._scaledAccessor(e.y())(t,n,r);return(!i||!1!==i.accessor(t,n,r))&&vMt.Math.isValidNumber(o)&&vMt.Math.isValidNumber(a)})(n,r,t)})))})),n},e._X_KEY="x",e._Y_KEY="y",e})(xMt.Plot);gMt.XYPlot=wMt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(mMt,"__esModule",{value:!0});var SMt=uO,MMt=Edt,EMt=_mt,TMt=NSt,CMt=Wwt,AMt=qSt,kMt=Zyt,LMt=Ivt,PMt=Fgt,NMt=pMt,IMt=uwt,RMt=gMt,OMt={linear:MMt.curveLinear,linearClosed:MMt.curveLinearClosed,step:MMt.curveStep,stepBefore:MMt.curveStepBefore,stepAfter:MMt.curveStepAfter,basis:MMt.curveBasis,basisOpen:MMt.curveBasisOpen,basisClosed:MMt.curveBasisClosed,bundle:MMt.curveBundle,cardinal:MMt.curveCardinal,cardinalOpen:MMt.curveCardinalOpen,cardinalClosed:MMt.curveCardinalClosed,monotone:MMt.curveMonotoneX};mMt.CurveName=Smt.makeEnum(["linear","linearClosed","step","stepBefore","stepAfter","basis","basisOpen","basisClosed","bundle","cardinal","cardinalOpen","cardinalClosed","monotone"]);var zMt=(function(t){function e(){var e=t.call(this)||this;e._curve="linear",e._autorangeSmooth=!1,e._croppedRenderingEnabled=!0,e._collapseDenseVerticalLinesEnabled=!1,e._downsamplingEnabled=!1,e.addClass("line-plot");var n=new EMt.Easing;return n.stepDuration(IMt.Plot._ANIMATION_MAX_DURATION),n.easingMode("expInOut"),n.maxTotalDuration(IMt.Plot._ANIMATION_MAX_DURATION),e.animator(NMt.Animator.MAIN,n),e.attr("stroke",(new kMt.Color).range()[0]),e.attr("stroke-width","2px"),e}return SMt.__extends(e,t),e.prototype.x=function(e,n,i){return null==e?t.prototype.x.call(this):(t.prototype.x.call(this,e,n,i),this._setScaleSnapping(),this)},e.prototype.y=function(e,n,i){return null==e?t.prototype.y.call(this):(t.prototype.y.call(this,e,n,i),this._setScaleSnapping(),this)},e.prototype.autorangeMode=function(e){return null==e?t.prototype.autorangeMode.call(this):(t.prototype.autorangeMode.call(this,e),this._setScaleSnapping(),this)},e.prototype.autorangeSmooth=function(t){return null==t?this._autorangeSmooth:(this._autorangeSmooth=t,this._setScaleSnapping(),this)},e.prototype._setScaleSnapping=function(){"x"===this.autorangeMode()&&this.x()&&this.x().scale&&this.x().scale instanceof LMt.QuantitativeScale&&this.x().scale.snappingDomainEnabled(!this.autorangeSmooth()),"y"===this.autorangeMode()&&this.y()&&this.y().scale&&this.y().scale instanceof LMt.QuantitativeScale&&this.y().scale.snappingDomainEnabled(!this.autorangeSmooth())},e.prototype.curve=function(t){return null==t?this._curve:(this._curve=t,this.render(),this)},e.prototype.downsamplingEnabled=function(t){return null==t?this._downsamplingEnabled:(this._downsamplingEnabled=t,this)},e.prototype.croppedRenderingEnabled=function(t){return null==t?this._croppedRenderingEnabled:(this._croppedRenderingEnabled=t,this.render(),this)},e.prototype.collapseDenseLinesEnabled=function(t){return null==t?this._collapseDenseVerticalLinesEnabled:(this._collapseDenseVerticalLinesEnabled=t,this.render(),this)},e.prototype._createDrawer=function(t){var e=this;return new CMt.ProxyDrawer((function(){return new AMt.LineSVGDrawer}),(function(n){return new TMt.CanvasDrawer(n,AMt.makeLineCanvasDrawStep((function(){return e._d3LineFactory(t)})))}))},e.prototype.getExtentsForProperty=function(e){var n=t.prototype.getExtentsForProperty.call(this,e);if(!this._autorangeSmooth)return n;if(this.autorangeMode()!==e)return n;if("x"!==this.autorangeMode()&&"y"!==this.autorangeMode())return n;var i,r=this._getEdgeIntersectionPoints();return i="y"===this.autorangeMode()?r.left.concat(r.right).map((function(t){return t.y})):r.top.concat(r.bottom).map((function(t){return t.x})),n.map((function(t){return MMt.extent(MMt.merge([t,i]))}))},e.prototype._getEdgeIntersectionPoints=function(){var t=this;if(!(this.y().scale instanceof LMt.QuantitativeScale&&this.x().scale instanceof LMt.QuantitativeScale))return{left:[],right:[],top:[],bottom:[]};var e=this.y().scale,n=this.x().scale,i={left:[],right:[],top:[],bottom:[]},r=n.scale(n.domain()[0]),o=n.scale(n.domain()[1]),a=e.scale(e.domain()[0]),s=e.scale(e.domain()[1]);return this.datasets().forEach((function(l){for(var c,u,h,d,p=l.data(),f=1;f<p.length;f++)c=h||n.scale(t.x().accessor(p[f-1],f-1,l)),u=d||e.scale(t.y().accessor(p[f-1],f-1,l)),h=n.scale(t.x().accessor(p[f],f,l)),d=e.scale(t.y().accessor(p[f],f,l)),c<r==r<=h&&i.left.push({x:r,y:e.invert(u+(r-c)*(d-u)/(h-c))}),c<o==o<=h&&i.right.push({x:o,y:e.invert(u+(o-c)*(d-u)/(h-c))}),u<s==s<=d&&i.top.push({x:n.invert(c+(s-u)*(h-c)/(d-u)),y:s}),u<a==a<=d&&i.bottom.push({x:n.invert(c+(a-u)*(h-c)/(d-u)),y:a})})),i},e.prototype._getResetYFunction=function(){var t=this.y().scale.domain(),e=Math.max(t[0],t[1]),n=Math.min(t[0],t[1]),i=e<0&&e||n>0&&n||0,r=this.y().scale.scale(i);return function(t,e,n){return r}},e.prototype._generateDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._getAttrToProjector();e.d=this._constructLineProjector(IMt.Plot._scaledAccessor(this.x()),this._getResetYFunction()),t.push({attrToProjector:e,animator:this._getAnimator(NMt.Animator.RESET)})}return t.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(NMt.Animator.MAIN)}),t},e.prototype._generateAttrToProjector=function(){var e=t.prototype._generateAttrToProjector.call(this);return Object.keys(e).forEach((function(t){if("d"!==t){var n=e[t];e[t]=function(t,e,i){return t.length>0?n(t[0],e,i):null}}})),e},e.prototype.entitiesAt=function(t){var e=this.entityNearestByXThenY(t);return null!=e?[e]:[]},e.prototype.entityNearestByXThenY=function(t){for(var e,n=1/0,i=1/0,r=this.bounds(),o=this.entities(),a=o.length,s=0;s<a;s++){var l=o[s];if(PMt.Math.within(l.position,r)){var c=Math.abs(t.x-l.position.x),u=Math.abs(t.y-l.position.y);(c<n||c===n&&u<i)&&(e=l,n=c,i=u)}}return e},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this);return e.d=this._constructLineProjector(IMt.Plot._scaledAccessor(this.x()),IMt.Plot._scaledAccessor(this.y())),e},e.prototype._constructLineProjector=function(t,e){var n=this;return function(i,r,o){return n._d3LineFactory(o,t,e)(i)}},e.prototype._d3LineFactory=function(t,e,n){return void 0===e&&(e=IMt.Plot._scaledAccessor(this.x())),void 0===n&&(n=IMt.Plot._scaledAccessor(this.y())),MMt.line().x((function(n,i){return e(n,i,t)})).y((function(e,i){return n(e,i,t)})).curve(this._getCurveFactory()).defined((function(i,r){return(function(t,i,r){var o=e(t,i,r),a=n(t,i,r);return PMt.Math.isValidNumber(o)&&PMt.Math.isValidNumber(a)})(i,r,t)}))},e.prototype._getCurveFactory=function(){var t=this.curve();if("string"==typeof t){var e=OMt[t];return null==e?OMt.linear:e}return t},e.prototype._getDataToDraw=function(){var t=this,e=new PMt.Map;return this.datasets().forEach((function(n){var i=n.data();if(t._croppedRenderingEnabled||t._downsamplingEnabled){for(var r=[],o=i.length,a=0;a<o;a++)r[a]=a;t._croppedRenderingEnabled&&(r=t._filterCroppedRendering(n,r)),t._downsamplingEnabled&&(r=t._filterDownsampling(n,r)),t._collapseDenseVerticalLinesEnabled&&(r=t._filterDenseLines(n,r));var s=[],l=r.length;for(a=0;a<l;a++)s[a]=i[r[a]];e.set(n,[s])}else e.set(n,[i])})),e},e.prototype._filterCroppedRendering=function(t,e){for(var n=this,i=IMt.Plot._scaledAccessor(this.x()),r=IMt.Plot._scaledAccessor(this.y()),o=t.data(),a=[],s=function(t,e){return PMt.Math.inRange(t,0,n.width())&&PMt.Math.inRange(e,0,n.height())},l=0;l<e.length;l++){var c=s(i(o[e[l]],e[l],t),r(o[e[l]],e[l],t));if(!c&&null!=e[l-1]&&null!=o[e[l-1]]){var u=i(o[e[l-1]],e[l-1],t),h=r(o[e[l-1]],e[l-1],t);c=c||s(u,h)}if(!c&&null!=e[l+1]&&null!=o[e[l+1]]){var d=i(o[e[l+1]],e[l+1],t),p=r(o[e[l+1]],e[l+1],t);c=c||s(d,p)}c&&a.push(e[l])}return a},e.prototype._filterDownsampling=function(t,e){if(0===e.length)return[];for(var n=t.data(),i=IMt.Plot._scaledAccessor(this.x()),r=IMt.Plot._scaledAccessor(this.y()),o=[e[0]],a=function(o,a){var s=i(n[e[o]],e[o],t),l=r(n[e[o]],e[o],t),c=i(n[e[o+1]],e[o+1],t),u=r(n[e[o+1]],e[o+1],t);if(a===1/0)return Math.floor(s)===Math.floor(c);var h=l+(c-s)*a;return Math.floor(u)===Math.floor(h)},s=0;s<e.length-1;){for(var l=e[s],c=i(n[e[s]],e[s],t),u=r(n[e[s]],e[s],t),h=i(n[e[s+1]],e[s+1],t),d=r(n[e[s+1]],e[s+1],t),p=Math.floor(c)===Math.floor(h)?1/0:(d-u)/(h-c),f=e[s],m=p===1/0?u:c,g=f,_=m,y=!0;s<e.length-1&&(y||a(s,p));){s++,y=!1;var v=p===1/0?r(n[e[s]],e[s],t):i(n[e[s]],e[s],t);v>_&&(_=v,g=e[s]),v<m&&(m=v,f=e[s])}var b=e[s];f!==l&&o.push(f),g!==f&&g!==l&&o.push(g),b!==l&&b!==f&&b!==g&&o.push(b)}return o},e.prototype._filterDenseLines=function(t,e){if(0===e.length)return[];var n=t.data(),i=IMt.Plot._scaledAccessor(this.x()),r=IMt.Plot._scaledAccessor(this.y());return this._bucketByX(t,e,(function(e){return i(n[e],e,t)}),(function(e){return r(n[e],e,t)}))},e.prototype._bucketByX=function(t,e,n,i){for(var r=[],o=t.data(),a=null,s=e.length,l=0;l<=s;++l){var c=e[l];if(null!=o[c]){var u=Math.floor(n(c)),h=i(c);null==a?a=new PMt.Bucket(c,u,h):a.isInBucket(u)?a.addToBucket(h,c):(r.push.apply(r,a.getUniqueIndices()),a=new PMt.Bucket(c,u,h))}}return null!=a&&r.push.apply(r,a.getUniqueIndices()),r},e})(RMt.XYPlot);mMt.Line=zMt,
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(fMt,"__esModule",{value:!0});var DMt=uO,BMt=Edt,HMt=Zyt,FMt=Fgt,VMt=NSt,UMt=HSt,jMt=Wwt,GMt=qSt,WMt=pMt,qMt=uwt,YMt=(function(t){function e(){var e=t.call(this)||this;return e.addClass("area-plot"),e.y0(0),e.attr("fill-opacity",.25),e.attr("fill",(new HMt.Color).range()[0]),e._lineDrawers=new FMt.Map,e}return DMt.__extends(e,t),e.prototype.y=function(n,i){if(null==n)return t.prototype.y.call(this);if(null==i?t.prototype.y.call(this,n):t.prototype.y.call(this,n,i),null!=i){var r=this.y0().accessor;null!=r&&this._bindProperty(e._Y0_KEY,r,i),this._updateYScale()}return this},e.prototype.y0=function(t){if(null==t)return this._propertyBindings.get(e._Y0_KEY);var n=this.y();return this._bindProperty(e._Y0_KEY,t,n&&n.scale),this._updateYScale(),this.render(),this},e.prototype._onDatasetUpdate=function(){t.prototype._onDatasetUpdate.call(this),this._updateYScale()},e.prototype._addDataset=function(e){var n=this;return this._lineDrawers.set(e,new VMt.ProxyDrawer((function(){return new GMt.LineSVGDrawer}),(function(t){return new VMt.CanvasDrawer(t,GMt.makeLineCanvasDrawStep((function(){var t=qMt.Plot._scaledAccessor(n.x()),i=qMt.Plot._scaledAccessor(n.y());return n._d3LineFactory(e,t,i)})))}))),t.prototype._addDataset.call(this,e),this},e.prototype._createNodesForDataset=function(e){t.prototype._createNodesForDataset.call(this,e);var n=this._lineDrawers.get(e);return"svg"===this.renderer()?n.useSVG(this._renderArea):n.useCanvas(this._canvas),n},e.prototype._removeDatasetNodes=function(e){t.prototype._removeDatasetNodes.call(this,e),this._lineDrawers.get(e).remove()},e.prototype._additionalPaint=function(){var t=this,e=this._generateLineDrawSteps(),n=this._getDataToDraw();this.datasets().forEach((function(i){var r=qMt.Plot.applyDrawSteps(e,i);t._lineDrawers.get(i).draw(n.get(i),r)}))},e.prototype._generateLineDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._generateLineAttrToProjector();e.d=this._constructLineProjector(qMt.Plot._scaledAccessor(this.x()),this._getResetYFunction()),t.push({attrToProjector:e,animator:this._getAnimator(WMt.Animator.RESET)})}return t.push({attrToProjector:this._generateLineAttrToProjector(),animator:this._getAnimator(WMt.Animator.MAIN)}),t},e.prototype._generateLineAttrToProjector=function(){var t=this._getAttrToProjector();return t.d=this._constructLineProjector(qMt.Plot._scaledAccessor(this.x()),qMt.Plot._scaledAccessor(this.y())),t},e.prototype._createDrawer=function(t){var e=this;return new jMt.ProxyDrawer((function(){return new UMt.AreaSVGDrawer}),(function(n){return new VMt.CanvasDrawer(n,UMt.makeAreaCanvasDrawStep((function(){var n=e._coordinateProjectors(),i=n[0],r=n[1],o=n[2],a=e._createDefinedProjector(i,r);return e._createAreaGenerator(i,r,o,a,t)}),(function(){var n=e._coordinateProjectors(),i=n[0],r=n[1],o=e._createDefinedProjector(i,r);return e._createTopLineGenerator(i,r,o,t)})))}))},e.prototype._generateDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._getAttrToProjector();e.d=this._constructAreaProjector(qMt.Plot._scaledAccessor(this.x()),this._getResetYFunction(),qMt.Plot._scaledAccessor(this.y0())),t.push({attrToProjector:e,animator:this._getAnimator(WMt.Animator.RESET)})}return t.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(WMt.Animator.MAIN)}),t},e.prototype._updateYScale=function(){var t=this.getExtentsForProperty("y0"),e=FMt.Array.flatten(t),n=FMt.Array.uniq(e),i=1===n.length?n[0]:null,r=this.y(),o=r&&r.scale;null!=o&&(null!=this._constantBaselineValueProvider&&(o.removePaddingExceptionsProvider(this._constantBaselineValueProvider),this._constantBaselineValueProvider=null),null!=i&&(this._constantBaselineValueProvider=function(){return[i]},o.addPaddingExceptionsProvider(this._constantBaselineValueProvider)))},e.prototype._getResetYFunction=function(){return qMt.Plot._scaledAccessor(this.y0())},e.prototype._coordinateProjectors=function(){return[qMt.Plot._scaledAccessor(this.x()),qMt.Plot._scaledAccessor(this.y()),qMt.Plot._scaledAccessor(this.y0())]},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this),n=this._coordinateProjectors();return e.d=this._constructAreaProjector(n[0],n[1],n[2]),e},e.prototype.selections=function(e){var n=this;if(void 0===e&&(e=this.datasets()),"canvas"===this.renderer())return BMt.selectAll();var i=t.prototype.selections.call(this,e).nodes();return e.map((function(t){return n._lineDrawers.get(t)})).filter((function(t){return null!=t})).forEach((function(t){return i.push.apply(i,t.getVisualPrimitives())})),BMt.selectAll(i)},e.prototype._constructAreaProjector=function(t,e,n){var i=this,r=this._createDefinedProjector(qMt.Plot._scaledAccessor(this.x()),qMt.Plot._scaledAccessor(this.y()));return function(o,a,s){return i._createAreaGenerator(t,e,n,r,s)(o)}},e.prototype._createDefinedProjector=function(t,e){return function(n,i,r){var o=t(n,i,r),a=e(n,i,r);return FMt.Math.isValidNumber(o)&&FMt.Math.isValidNumber(a)}},e.prototype._createAreaGenerator=function(t,e,n,i,r){var o=this._getCurveFactory();return BMt.area().x((function(e,n){return t(e,n,r)})).y1((function(t,n){return e(t,n,r)})).y0((function(t,e){return n(t,e,r)})).curve(o).defined((function(t,e){return i(t,e,r)}))},e.prototype._createTopLineGenerator=function(t,e,n,i){var r=this._getCurveFactory();return BMt.line().x((function(e,n){return t(e,n,i)})).y((function(t,n){return e(t,n,i)})).curve(r).defined((function(t,e){return n(t,e,i)}))},e._Y0_KEY="y0",e})(mMt.Line);fMt.Area=YMt;var XMt={};!(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO,n=Edt,i=Imt,r=_mt,o=gyt,a=NSt,s=Wwt,l=JSt,c=dwt,u=Zyt,h=Ivt,d=Fgt,p=Smt,f=pMt,m=uwt,g=gMt;t.BarOrientation=p.makeEnum(["vertical","horizontal"]),t.LabelsPosition=p.makeEnum(["start","middle","end","outside"]),t.BarAlignment=p.makeEnum(["start","middle","end"]);var _=(function(p){function g(e){void 0===e&&(e="vertical");var n=p.call(this)||this;if(n._labelFormatter=o.identity(),n._labelsEnabled=!1,n._labelsPosition=t.LabelsPosition.end,n._hideBarsIfAnyAreTooWide=!0,n._barAlignment="middle",n._computeBarPixelThickness=c.memoize(y),n._fixedBarPixelThickness=!0,n.addClass("bar-plot"),"vertical"!==e&&"horizontal"!==e)throw new Error(e+" is not a valid orientation for Plots.Bar");return n._isVertical="vertical"===e,n.animator("baseline",new r.Null),n.attr("fill",(new u.Color).range()[0]),n.attr(g._BAR_THICKNESS_KEY,(function(){return n._barPixelThickness()})),n._labelConfig=new d.Map,n._baselineValueProvider=function(){return[n.baselineValue()]},n}return e.__extends(g,p),g.prototype.computeLayout=function(t,e,n){return p.prototype.computeLayout.call(this,t,e,n),this._updateExtents(),this},g.prototype.x=function(t,e){return null==t?p.prototype.x.call(this):(null==e?p.prototype.x.call(this,t):p.prototype.x.call(this,t,e),this._updateThicknessAttr(),this._updateLengthScale(),this)},g.prototype.y=function(t,e){return null==t?p.prototype.y.call(this):(null==e?p.prototype.y.call(this,t):p.prototype.y.call(this,t,e),this._updateLengthScale(),this)},g.prototype.length=function(){return this._isVertical?this.y():this.x()},g.prototype.position=function(){return this._isVertical?this.x():this.y()},g.prototype.barEnd=function(t){if(null==t)return this._propertyBindings.get(g._BAR_END_KEY);var e=this.position();return this._bindProperty(g._BAR_END_KEY,t,e&&e.scale),this._updateThicknessAttr(),this._updateLengthScale(),this.render(),this},g.prototype.barAlignment=function(t){return null==t?this._barAlignment:(this._barAlignment=t,this._clearAttrToProjectorCache(),this.render(),this)},g.prototype.orientation=function(){return this._isVertical?"vertical":"horizontal"},g.prototype._createDrawer=function(){return new s.ProxyDrawer((function(){return new l.RectangleSVGDrawer(g._BAR_AREA_CLASS)}),(function(t){return new a.RectangleCanvasDrawer(t)}))},g.prototype._setup=function(){p.prototype._setup.call(this),this._baseline=this._renderArea.append("line").classed("baseline",!0)},g.prototype.baselineValue=function(t){if(null==t){if(null!=this._baselineValue)return this._baselineValue;if(!this._projectorsReady())return 0;var e=this.length().scale;return e&&e instanceof u.Time?new Date(0):0}return this._baselineValue=t,this._updateLengthScale(),this._clearAttrToProjectorCache(),this.render(),this},g.prototype.addDataset=function(t){return p.prototype.addDataset.call(this,t),this},g.prototype._addDataset=function(t){return p.prototype._addDataset.call(this,t),this},g.prototype.removeDataset=function(t){return p.prototype.removeDataset.call(this,t),this},g.prototype._removeDataset=function(t){return p.prototype._removeDataset.call(this,t),this},g.prototype.datasets=function(t){return null==t?p.prototype.datasets.call(this):(p.prototype.datasets.call(this,t),this)},g.prototype.labelsEnabled=function(t,e){return null==t?this._labelsEnabled:(this._labelsEnabled=t,null!=e&&(this._labelsPosition=e),this._clearAttrToProjectorCache(),this.render(),this)},g.prototype.labelFormatter=function(t){return null==t?this._labelFormatter:(this._labelFormatter=t,this._clearAttrToProjectorCache(),this.render(),this)},g.prototype._createNodesForDataset=function(t){var e=p.prototype._createNodesForDataset.call(this,t),n=this._renderArea.append("g").classed(g._LABEL_AREA_CLASS,!0),r=new i.SvgContext(n.node()),o=new i.CacheMeasurer(r),a=new i.Writer(o,r);return this._labelConfig.set(t,{labelArea:n,measurer:o,writer:a}),e},g.prototype._removeDatasetNodes=function(t){p.prototype._removeDatasetNodes.call(this,t);var e=this._labelConfig.get(t);null!=e&&(e.labelArea.remove(),this._labelConfig.delete(t))},g.prototype.entityNearest=function(t){var e=this,n=function(){var n=e._isVertical?e._getEntityStore().entityNearestX(t):e._getEntityStore().entityNearestY(t);return void 0===n?void 0:e._lightweightPlotEntityToPlotEntity(n)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype.entitiesAt=function(t){var e=this,n=function(){return e._entitiesIntersecting(t.x,t.y)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype.entitiesInBounds=function(t){var e=this,n=function(){return p.prototype.entitiesInBounds.call(e,t)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype.entitiesInXBounds=function(t){var e=this,n=function(){return p.prototype.entitiesInXBounds.call(e,t)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype.entitiesInYBounds=function(t){var e=this,n=function(){return p.prototype.entitiesInYBounds.call(e,t)};return this._fixedBarPixelThickness?this._computeBarPixelThickness.doLocked(n):n()},g.prototype._entitiesIntersecting=function(t,e){for(var n=[],i=this._getEntityStore().entities(),r=i.length,o=0;o<r;o++){var a=i[o];d.DOM.intersectsBBox(t,e,this._entityBounds(a))&&n.push(this._lightweightPlotEntityToPlotEntity(a))}return n},g.prototype._updateLengthScale=function(){if(this._projectorsReady()){var t=this.length().scale;t instanceof h.QuantitativeScale&&(t.addPaddingExceptionsProvider(this._baselineValueProvider),t.addIncludedValuesProvider(this._baselineValueProvider))}},g.prototype.renderImmediately=function(){var t=this;return this._barPixelThickness(),this._computeBarPixelThickness.doLocked((function(){return p.prototype.renderImmediately.call(t)}))},g.prototype._additionalPaint=function(t){var e=this,n=this.length().scale.scale(this.baselineValue()),i={x1:this._isVertical?0:n,y1:this._isVertical?n:0,x2:this._isVertical?this.width():n,y2:this._isVertical?n:this.height()};this._getAnimator("baseline").animate(this._baseline,i),this.datasets().forEach((function(t){return e._labelConfig.get(t).labelArea.selectAll("g").remove()})),this._labelsEnabled&&d.Window.setTimeout((function(){return e._drawLabels()}),t)},g.prototype.getExtentsForProperty=function(t){var e,i=this,r=p.prototype.getExtentsForProperty.call(this,t);if("x"===t&&this._isVertical)e=this.x();else{if("y"!==t||this._isVertical)return r;e=this.y()}if(!(e&&e.scale&&e.scale instanceof h.QuantitativeScale))return r;var o=e.scale,a=this._barPixelThickness();return r.map((function(t){return n.extent([o.invert(i._getPositionAttr(o.scale(t[0]),a)),o.invert(i._getPositionAttr(o.scale(t[0]),a)+a),o.invert(i._getPositionAttr(o.scale(t[1]),a)),o.invert(i._getPositionAttr(o.scale(t[1]),a)+a)])}))},g.prototype._getPositionAttr=function(t,e){switch(this._isVertical||(t-=e,e*=-1),this._barAlignment){case"start":return t;case"end":return t-e;case"middle":default:return t-e/2}},g.prototype._drawLabels=function(){var t=this,e=this._getDataToDraw(),n=this._getAttrToProjector(),i=this.datasets().some((function(i){return e.get(i).some((function(e,r){return null!=e&&t._drawLabel(e,r,i,n)}))}));this._hideBarsIfAnyAreTooWide&&i&&this.datasets().forEach((function(e){return t._labelConfig.get(e).labelArea.selectAll("g").remove()}))},g.prototype._drawLabel=function(t,e,n,i){var r=this._labelConfig.get(n),o=r.labelArea,a=r.measurer,s=r.writer,l=(0,this.length().accessor)(t,e,n),c=this.length().scale,u=null!=c?c.scale(l):l,h=null!=c?c.scale(this.baselineValue()):this.baselineValue(),d={x:i.x(t,e,n),y:i.y(t,e,n)},p={width:i.width(t,e,n),height:i.height(t,e,n)},f=this._labelFormatter(l,t,e,n),m=a.measure(f),g=this._shouldShowLabelOnBar(d,p,m),_=this._calculateLabelProperties(d,p,m,g,this._isVertical?u<=h:u<h),y=_.containerDimensions,v=_.labelContainerOrigin,b=_.labelOrigin,x=_.alignment,w=i.fill(t,e,n),S=this._createLabelContainer(o,v,b,m,g,w);return s.write(f,y.width,y.height,{xAlign:x.x,yAlign:x.y},S.node()),this._isVertical?p.width<m.width:p.height<m.height},g.prototype._shouldShowLabelOnBar=function(e,n,i){if(this._labelsPosition===t.LabelsPosition.outside)return!1;var r=this._isVertical?e.y:e.x,o=this._isVertical?n.height:n.width,a=this._isVertical?this.height():this.width(),s=r+o,l=o;return s>a?l=a-r:r<0&&(l=s),(this._isVertical?i.height:i.width)+g._LABEL_MARGIN_INSIDE_BAR<=l},g.prototype._calculateLabelProperties=function(e,n,i,r,o){var a=this,s=this._isVertical?e.y:e.x,l=this._isVertical?n.height:n.width,c=this._isVertical?i.height:i.width,u="center",h=l,d=s,p=s,f=function(t){switch(t){case"topLeft":return u=a._isVertical?"top":"left",d+=g._LABEL_MARGIN_INSIDE_BAR,void(p+=g._LABEL_MARGIN_INSIDE_BAR);case"center":return void(p+=(l+c)/2);case"bottomRight":return u=a._isVertical?"bottom":"right",d-=g._LABEL_MARGIN_INSIDE_BAR,void(p+=h-g._LABEL_MARGIN_INSIDE_BAR-c)}};if(r)switch(this._labelsPosition){case t.LabelsPosition.start:f(o?"bottomRight":"topLeft");break;case t.LabelsPosition.middle:f("center");break;case t.LabelsPosition.end:f(o?"topLeft":"bottomRight")}else o?(u=this._isVertical?"top":"left",h=l+g._LABEL_MARGIN_INSIDE_BAR+c,d-=g._LABEL_MARGIN_INSIDE_BAR+c,p-=g._LABEL_MARGIN_INSIDE_BAR+c):(u=this._isVertical?"bottom":"right",h=l+g._LABEL_MARGIN_INSIDE_BAR+c,p+=l+g._LABEL_MARGIN_INSIDE_BAR);return{containerDimensions:{width:this._isVertical?n.width:h,height:this._isVertical?h:n.height},labelContainerOrigin:{x:this._isVertical?e.x:d,y:this._isVertical?d:e.y},labelOrigin:{x:this._isVertical?e.x+n.width/2-i.width/2:p,y:this._isVertical?p:e.y+n.height/2-i.height/2},alignment:{x:this._isVertical?"center":u,y:this._isVertical?u:"center"}}},g.prototype._createLabelContainer=function(t,e,n,i,r,o){var a=t.append("g").attr("transform","translate("+e.x+", "+e.y+")");if(r){a.classed("on-bar-label",!0);var s=1.6*d.Color.contrast("white",o)<d.Color.contrast("black",o);a.classed(s?"dark-label":"light-label",!0)}else a.classed("off-bar-label",!0);return a},g.prototype._generateDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._getAttrToProjector(),n=this.length().scale.scale(this.baselineValue()),i=this._isVertical?"height":"width";e[this._isVertical?"y":"x"]=function(){return n},e[i]=function(){return 0},t.push({attrToProjector:e,animator:this._getAnimator(f.Animator.RESET)})}return t.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(f.Animator.MAIN)}),t},g.prototype._generateAttrToProjector=function(){var t=this,e=p.prototype._generateAttrToProjector.call(this),n=this.length().scale.scale(this.baselineValue()),i=this._isVertical?"y":"x",r=this._isVertical?"x":"y",o=m.Plot._scaledAccessor(this.position()),a=m.Plot._scaledAccessor(this.length()),s=function(t,e,i){return Math.abs(n-a(t,e,i))},l=e[g._BAR_THICKNESS_KEY],c=e.gap,u=null==c?l:function(t,e,n){var i=l(t,e,n);return i<g._BAR_GAPLESS_THRESHOLD_PX?i:i-c(t,e,n)};return e.width=this._isVertical?u:s,e.height=this._isVertical?s:u,e[i]=function(t,e,i){var r=a(t,e,i);return r>n?n:r},e[r]=function(e,n,i){return t._getPositionAttr(o(e,n,i),l(e,n,i))},e},g.prototype._updateThicknessAttr=function(){var t=this,e=this.position(),n=this.barEnd();null!=e&&null!=n?(this._fixedBarPixelThickness=!1,this.attr(g._BAR_THICKNESS_KEY,(function(t,i,r){var o=e.accessor(t,i,r),a=n.accessor(t,i,r);return o=e.scale?e.scale.scale(o):o,a=n.scale?n.scale.scale(a):a,Math.abs(a-o)}))):(this._fixedBarPixelThickness=!0,this.attr(g._BAR_THICKNESS_KEY,(function(){return t._barPixelThickness()})))},g.prototype._barPixelThickness=function(){return this._fixedBarPixelThickness&&this._projectorsReady()?this._computeBarPixelThickness(this.position(),this.datasets(),this._isVertical?this.width():this.height()):0},g.prototype.entities=function(t){return void 0===t&&(t=this.datasets()),this._projectorsReady()?p.prototype.entities.call(this,t):[]},g.prototype._entityBounds=function(t){return this._pixelBounds(t.datum,t.index,t.dataset)},g.prototype._pixelBounds=function(t,e,n){var i=this._getAttrToProjector();return{x:i.x(t,e,n),y:i.y(t,e,n),width:i.width(t,e,n),height:i.height(t,e,n)}},g.prototype._pixelPoint=function(t,e,n){var i=this._pixelBounds(t,e,n),r=m.Plot._scaledAccessor(this._isVertical?this.y():this.x())(t,e,n),o=(this._isVertical?this.y().scale:this.x().scale).scale(this.baselineValue());return this._pixelPointBar(r,o,i)},g.prototype._pixelPointBar=function(t,e,n){var i,r;return this._isVertical?(i=n.x+n.width/2,r=t<=e?n.y:n.y+n.height):(i=t>=e?n.x+n.width:n.x,r=n.y+n.height/2),{x:i,y:r}},g.prototype._uninstallScaleForKey=function(t,e){p.prototype._uninstallScaleForKey.call(this,t,e)},g.prototype._getDataToDraw=function(){var t=this,e=new d.Map,n=this._getAttrToProjector(),i=this.width(),r=this.height();return this.datasets().forEach((function(o){var a=o.data().map((function(e,a){return t._isDatumOnScreen(n,i,r,e,a,o)?e:null}));e.set(o,a)})),e},g.prototype._isDatumOnScreen=function(t,e,n,i,r,o){var a=t.x(i,r,o),s=t.y(i,r,o),l=t.width(i,r,o),c=t.height(i,r,o);return!!(d.Math.isValidNumber(a)&&d.Math.isValidNumber(s)&&d.Math.isValidNumber(l)&&d.Math.isValidNumber(c))&&d.Math.boundsIntersects(a,s,l,c,0,0,e,n)},g.prototype.invalidateCache=function(){var t=this;p.prototype.invalidateCache.call(this),this.datasets().forEach((function(e){return t._labelConfig.get(e).measurer.reset()}))},g._BAR_THICKNESS_RATIO=.95,g._BAR_GAPLESS_THRESHOLD_PX=3,g._SINGLE_BAR_DIMENSION_RATIO=.4,g._BAR_AREA_CLASS="bar-area",g._BAR_END_KEY="barEnd",g._BAR_THICKNESS_KEY="width",g._LABEL_AREA_CLASS="bar-label-text-area",g._LABEL_MARGIN_INSIDE_BAR=10,g})(g.XYPlot);function y(t,e,i){var r,o=t.scale;if(o instanceof u.Category)r=o.rangeBand();else{var a=t.accessor,s=n.set(d.Array.flatten(e.map((function(t){return t.data().map((function(e,n){return a(e,n,t)})).filter((function(t){return null!=t})).map((function(t){return t.valueOf()}))})))).values().map((function(t){return+t}));s.sort((function(t,e){return t-e}));var l=s.map((function(t){return o.scale(t)})),c=n.pairs(l);r=d.Math.min(c,(function(t,e){return Math.abs(t[1]-t[0])}),i*_._SINGLE_BAR_DIMENSION_RATIO),r*=_._BAR_THICKNESS_RATIO}return r}t.Bar=_})(XMt);var $Mt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty($Mt,"__esModule",{value:!0});var KMt=uO,ZMt=Zyt,JMt=Fgt,QMt=XMt,tEt=uwt,eEt=(function(t){function e(e){void 0===e&&(e="vertical");var n=t.call(this,e)||this;return n._clusterOffsets=new JMt.Map,n}return KMt.__extends(e,t),e.prototype._generateAttrToProjector=function(){var e=this,n=t.prototype._generateAttrToProjector.call(this),i=this._makeInnerScale(),r=function(t,e){return i.rangeBand()};n.width=this._isVertical?r:n.width,n.height=this._isVertical?n.height:r;var o=n.x,a=n.y;return n.x=this._isVertical?function(t,n,i){return o(t,n,i)+e._clusterOffsets.get(i)}:function(t,e,n){return o(t,e,n)},n.y=this._isVertical?function(t,e,n){return a(t,e,n)}:function(t,n,i){return a(t,n,i)+e._clusterOffsets.get(i)},n},e.prototype._updateClusterPosition=function(){var t=this,e=this._makeInnerScale();this.datasets().forEach((function(n,i){return t._clusterOffsets.set(n,e.scale(String(i))-e.rangeBand()/2)}))},e.prototype._makeInnerScale=function(){var t=new ZMt.Category;t.domain(this.datasets().map((function(t,e){return String(e)})));var e=tEt.Plot._scaledAccessor(this.attr(QMt.Bar._BAR_THICKNESS_KEY));return t.range([0,e(null,0,null)]),t},e.prototype._getDataToDraw=function(){return this._updateClusterPosition(),t.prototype._getDataToDraw.call(this)},e})(QMt.Bar);$Mt.ClusteredBar=eEt;var nEt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(nEt,"__esModule",{value:!0});var iEt=uO,rEt=Edt,oEt=Imt,aEt=_mt,sEt=gyt,lEt=Zyt,cEt=Fgt,uEt=ISt,hEt=zSt,dEt=Wwt,pEt=k_t,fEt=uwt,mEt=(function(t){function e(){var e=t.call(this)||this;return e._startAngle=0,e._endAngle=2*Math.PI,e._labelFormatter=sEt.identity(),e._labelsEnabled=!1,e.innerRadius(0),e.outerRadius((function(){var t=e._pieCenter();return Math.min(Math.max(e.width()-t.x,t.x),Math.max(e.height()-t.y,t.y))})),e.addClass("pie-plot"),e.attr("fill",(function(t,e){return String(e)}),new lEt.Color),e._strokeDrawers=new cEt.Map,e}return iEt.__extends(e,t),e.prototype._setup=function(){var e=this;t.prototype._setup.call(this),this._strokeDrawers.forEach((function(t){return t.attachTo(e._renderArea)}))},e.prototype.computeLayout=function(e,n,i){t.prototype.computeLayout.call(this,e,n,i);var r=this._pieCenter();this._renderArea.attr("transform","translate("+r.x+","+r.y+")");var o=Math.min(Math.max(this.width()-r.x,r.x),Math.max(this.height()-r.y,r.y));return null!=this.innerRadius().scale&&this.innerRadius().scale.range([0,o]),null!=this.outerRadius().scale&&this.outerRadius().scale.range([0,o]),this},e.prototype.addDataset=function(e){return t.prototype.addDataset.call(this,e),this},e.prototype._addDataset=function(e){if(1===this.datasets().length)return cEt.Window.warn("Only one dataset is supported in Pie plots"),this;this._updatePieAngles(),t.prototype._addDataset.call(this,e);var n=new hEt.ArcOutlineSVGDrawer;return this._isSetup&&n.attachTo(this._renderArea),this._strokeDrawers.set(e,n),this},e.prototype.removeDataset=function(e){return t.prototype.removeDataset.call(this,e),this},e.prototype._removeDatasetNodes=function(e){t.prototype._removeDatasetNodes.call(this,e),this._strokeDrawers.get(e).remove()},e.prototype._removeDataset=function(e){return t.prototype._removeDataset.call(this,e),this._strokeDrawers.delete(e),this._startAngles=[],this._endAngles=[],this},e.prototype.selections=function(e){var n=this;void 0===e&&(e=this.datasets());var i=t.prototype.selections.call(this,e).nodes();return e.forEach((function(t){var e=n._strokeDrawers.get(t);null!=e&&i.push.apply(i,e.getVisualPrimitives())})),rEt.selectAll(i)},e.prototype._onDatasetUpdate=function(){t.prototype._onDatasetUpdate.call(this),this._updatePieAngles(),this.render()},e.prototype._createDrawer=function(){return new dEt.ProxyDrawer((function(){return new uEt.ArcSVGDrawer}),(function(){return pEt.warn("canvas renderer is not supported on Pie Plot!"),null}))},e.prototype.entities=function(e){var n=this;return void 0===e&&(e=this.datasets()),t.prototype.entities.call(this,e).map((function(t){t.position.x+=n.width()/2,t.position.y+=n.height()/2;var e=rEt.select(n._strokeDrawers.get(t.dataset).getVisualPrimitiveAtIndex(t.index)),i=t;return i.strokeSelection=e,i}))},e.prototype.sectorValue=function(t,n){return null==t?this._propertyBindings.get(e._SECTOR_VALUE_KEY):(this._bindProperty(e._SECTOR_VALUE_KEY,t,n),this._updatePieAngles(),this.render(),this)},e.prototype.innerRadius=function(t,n){return null==t?this._propertyBindings.get(e._INNER_RADIUS_KEY):(this._bindProperty(e._INNER_RADIUS_KEY,t,n),this.render(),this)},e.prototype.outerRadius=function(t,n){return null==t?this._propertyBindings.get(e._OUTER_RADIUS_KEY):(this._bindProperty(e._OUTER_RADIUS_KEY,t,n),this.render(),this)},e.prototype.startAngle=function(t){return null==t?this._startAngle:(this._startAngle=t,this._updatePieAngles(),this.render(),this)},e.prototype.endAngle=function(t){return null==t?this._endAngle:(this._endAngle=t,this._updatePieAngles(),this.render(),this)},e.prototype.labelsEnabled=function(t){return null==t?this._labelsEnabled:(this._labelsEnabled=t,this.render(),this)},e.prototype.labelFormatter=function(t){return null==t?this._labelFormatter:(this._labelFormatter=t,this.render(),this)},e.prototype.entitiesAt=function(t){var e=this.width()/2,n=this.height()/2,i=this._sliceIndexForPoint({x:t.x-e,y:t.y-n});return null==i?[]:[this.entities()[i]]},e.prototype._propertyProjectors=function(){var e=this,n=t.prototype._propertyProjectors.call(this),i=fEt.Plot._scaledAccessor(this.innerRadius()),r=fEt.Plot._scaledAccessor(this.outerRadius());return n.d=function(t,n,o){return rEt.arc().innerRadius(i(t,n,o)).outerRadius(r(t,n,o)).startAngle(e._startAngles[n]).endAngle(e._endAngles[n])(t,n)},n},e.prototype._updatePieAngles=function(){if(null!=this.sectorValue()&&0!==this.datasets().length){var t=fEt.Plot._scaledAccessor(this.sectorValue()),e=this.datasets()[0],n=this._getDataToDraw().get(e),i=rEt.pie().sort(null).startAngle(this._startAngle).endAngle(this._endAngle).value((function(n,i){return t(n,i,e)}))(n);this._startAngles=i.map((function(t){return t.startAngle})),this._endAngles=i.map((function(t){return t.endAngle}))}},e.prototype._pieCenter=function(){var t,e,n,i,r=this._startAngle<this._endAngle?this._startAngle:this._endAngle,o=this._startAngle<this._endAngle?this._endAngle:this._startAngle,a=Math.sin(r),s=Math.cos(r),l=Math.sin(o),c=Math.cos(o);return a>=0&&l>=0?s>=0&&c>=0?(t=s,e=0,i=0,n=l):s<0&&c<0?(t=0,e=-c,i=0,n=a):s>=0&&c<0?(t=s,e=-c,i=0,n=a):s<0&&c>=0&&(t=1,e=1,i=1,n=Math.max(a,l)):a>=0&&l<0?s>=0&&c>=0?(t=Math.max(s,c),e=1,i=1,n=1):s<0&&c<0?(t=0,e=1,i=-l,n=a):s>=0&&c<0?(t=s,e=1,i=-l,n=1):s<0&&c>=0&&(t=c,e=1,i=1,n=a):a<0&&l>=0?s>=0&&c>=0?(t=1,e=0,i=-a,n=l):s<0&&c<0?(t=1,e=Math.max(-s,-c),i=1,n=1):s>=0&&c<0?(t=1,e=-c,i=-a,n=1):s<0&&c>=0&&(t=1,e=-s,i=1,n=l):a<0&&l<0&&(s>=0&&c>=0?(t=c,e=0,i=-a,n=0):s<0&&c<0?(t=0,e=-s,i=-l,n=0):s>=0&&c<0?(t=1,e=1,i=Math.max(s,-c),n=1):s<0&&c>=0&&(t=c,e=-s,i=1,n=0)),{x:i+n==0?0:i/(i+n)*this.width(),y:t+e==0?0:t/(t+e)*this.height()}},e.prototype._getDataToDraw=function(){var n=t.prototype._getDataToDraw.call(this);if(0===this.datasets().length)return n;var i=fEt.Plot._scaledAccessor(this.sectorValue()),r=this.datasets()[0],o=n.get(r).filter((function(t,n){return e._isValidData(i(t,n,r))}));return n.set(r,o),n},e._isValidData=function(t){return cEt.Math.isValidNumber(t)&&t>=0},e.prototype._pixelPoint=function(t,n,i){var r=fEt.Plot._scaledAccessor(this.sectorValue());if(!e._isValidData(r(t,n,i)))return{x:NaN,y:NaN};var o=(fEt.Plot._scaledAccessor(this.innerRadius())(t,n,i)+fEt.Plot._scaledAccessor(this.outerRadius())(t,n,i))/2,a=rEt.pie().sort(null).value((function(t,n){var o=r(t,n,i);return e._isValidData(o)?o:0})).startAngle(this._startAngle).endAngle(this._endAngle)(i.data()),s=(a[n].startAngle+a[n].endAngle)/2;return{x:o*Math.sin(s),y:-o*Math.cos(s)}},e.prototype._additionalPaint=function(t){var e=this;this._renderArea.select(".label-area").remove(),this._labelsEnabled&&cEt.Window.setTimeout((function(){return e._drawLabels()}),t);var n=this._generateStrokeDrawSteps(),i=this._getDataToDraw();this.datasets().forEach((function(t){var r=fEt.Plot.applyDrawSteps(n,t);e._strokeDrawers.get(t).draw(i.get(t),r)}))},e.prototype._generateStrokeDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new aEt.Null}]},e.prototype._sliceIndexForPoint=function(t){var e,n=Math.sqrt(Math.pow(t.x,2)+Math.pow(t.y,2)),i=Math.acos(-t.y/n);t.x<0&&(i=2*Math.PI-i);for(var r=0;r<this._startAngles.length;r++)if(this._startAngles[r]<i&&this._endAngles[r]>i){e=r;break}if(void 0!==e){var o=this.datasets()[0],a=o.data()[e],s=this.innerRadius().accessor(a,e,o),l=this.outerRadius().accessor(a,e,o);if(n>s&&n<l)return e}return null},e.prototype._drawLabels=function(){for(var t=this,e=this._getAttrToProjector(),n=this._renderArea.append("g").classed("label-area",!0),i=new oEt.SvgContext(n.node()),r=new oEt.CacheMeasurer(i),o=new oEt.Writer(r,i),a=this.datasets()[0],s=this._getDataToDraw().get(a),l=s.length,c=function(i){var l=s[i],c=u.sectorValue().accessor(l,i,a);if(!cEt.Math.isValidNumber(c))return"continue";c=u._labelFormatter(c,l,i,a);var h=r.measure(c),d=(u._endAngles[i]+u._startAngles[i])/2,p=u.outerRadius().accessor(l,i,a);u.outerRadius().scale&&(p=u.outerRadius().scale.scale(p));var f=u.innerRadius().accessor(l,i,a);u.innerRadius().scale&&(f=u.innerRadius().scale.scale(f));var m=(p+f)/2,g=Math.sin(d)*m-h.width/2,_=-Math.cos(d)*m-h.height/2,y=[{x:g,y:_},{x:g,y:_+h.height},{x:g+h.width,y:_},{x:g+h.width,y:_+h.height}],v=y.every((function(e){return Math.abs(e.x)<=t.width()/2&&Math.abs(e.y)<=t.height()/2}));v&&(v=y.map((function(e){return t._sliceIndexForPoint(e)})).every((function(t){return t===i})));var b=e.fill(l,i,a),x=1.6*cEt.Color.contrast("white",b)<cEt.Color.contrast("black",b),w=n.append("g").attr("transform","translate("+g+","+_+")");w.classed(x?"dark-label":"light-label",!0),w.style("visibility",v?"inherit":"hidden"),o.write(c,h.width,h.height,{xAlign:"center",yAlign:"center"},w.node())},u=this,h=0;h<l;h++)c(h)},e._INNER_RADIUS_KEY="inner-radius",e._OUTER_RADIUS_KEY="outer-radius",e._SECTOR_VALUE_KEY="sector-value",e})(fEt.Plot);nEt.Pie=mEt;var gEt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(gEt,"__esModule",{value:!0});var _Et=uO,yEt=Edt,vEt=Imt,bEt=_mt,xEt=NSt,wEt=Wwt,SEt=JSt,MEt=Zyt,EEt=Fgt,TEt=uwt,CEt=(function(t){function e(){var e=t.call(this)||this;return e._labelsEnabled=!1,e._label=null,e.animator("rectangles",new bEt.Null),e.addClass("rectangle-plot"),e.attr("fill",(new MEt.Color).range()[0]),e}return _Et.__extends(e,t),e.prototype._createDrawer=function(){return new wEt.ProxyDrawer((function(){return new SEt.RectangleSVGDrawer}),(function(t){return new xEt.RectangleCanvasDrawer(t)}))},e.prototype._generateAttrToProjector=function(){var n=this,i=t.prototype._generateAttrToProjector.call(this),r=TEt.Plot._scaledAccessor(this.x()),o=i[e._X2_KEY],a=TEt.Plot._scaledAccessor(this.y()),s=i[e._Y2_KEY],l=this.x().scale,c=this.y().scale;return null!=o?(i.width=function(t,e,n){return Math.abs(o(t,e,n)-r(t,e,n))},i.x=function(t,e,n){return Math.min(o(t,e,n),r(t,e,n))}):(i.width=function(t,e,i){return n._rectangleWidth(l)},i.x=function(t,e,n){return r(t,e,n)-.5*i.width(t,e,n)}),null!=s?(i.height=function(t,e,n){return Math.abs(s(t,e,n)-a(t,e,n))},i.y=function(t,e,n){return Math.max(s(t,e,n),a(t,e,n))-i.height(t,e,n)}):(i.height=function(t,e,i){return n._rectangleWidth(c)},i.y=function(t,e,n){return a(t,e,n)-.5*i.height(t,e,n)}),delete i[e._X2_KEY],delete i[e._Y2_KEY],i},e.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:this._getAnimator("rectangles")}]},e.prototype._filterForProperty=function(e){return t.prototype._filterForProperty.call(this,"x2"===e?"x":"y2"===e?"y":e)},e.prototype.x=function(n,i,r){if(null==n)return t.prototype.x.call(this);if(null==i?t.prototype.x.call(this,n):t.prototype.x.call(this,n,i,r),null!=i){var o=this.x2(),a=o&&o.accessor;null!=a&&this._bindProperty(e._X2_KEY,a,i,o.postScale)}return i instanceof MEt.Category&&i.innerPadding(0).outerPadding(0),this},e.prototype.x2=function(t,n){if(null==t)return this._propertyBindings.get(e._X2_KEY);var i=this.x();return this._bindProperty(e._X2_KEY,t,i&&i.scale,n),this.render(),this},e.prototype.y=function(n,i,r){if(null==n)return t.prototype.y.call(this);if(null==i?t.prototype.y.call(this,n):t.prototype.y.call(this,n,i,r),null!=i){var o=this.y2(),a=o&&o.accessor;null!=a&&this._bindProperty(e._Y2_KEY,a,i,o.postScale)}return i instanceof MEt.Category&&i.innerPadding(0).outerPadding(0),this},e.prototype.y2=function(t,n){if(null==t)return this._propertyBindings.get(e._Y2_KEY);var i=this.y();return this._bindProperty(e._Y2_KEY,t,i&&i.scale,n),this.render(),this},e.prototype.entitiesAt=function(t){var e=this._getAttrToProjector();return this.entities().filter((function(n){var i=n.datum,r=n.index,o=n.dataset,a=e.x(i,r,o),s=e.y(i,r,o),l=e.width(i,r,o),c=e.height(i,r,o);return a<=t.x&&t.x<=a+l&&s<=t.y&&t.y<=s+c}))},e.prototype._entityBounds=function(t){return this._entityBBox(t.datum,t.index,t.dataset,this._getAttrToProjector())},e.prototype._entityBBox=function(t,e,n,i){return{x:i.x(t,e,n),y:i.y(t,e,n),width:i.width(t,e,n),height:i.height(t,e,n)}},e.prototype.label=function(t){return null==t?this._label:(this._label=t,this.render(),this)},e.prototype.labelsEnabled=function(t){return null==t?this._labelsEnabled:(this._labelsEnabled=t,this.render(),this)},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this);return null!=this.x2()&&(e.x2=TEt.Plot._scaledAccessor(this.x2())),null!=this.y2()&&(e.y2=TEt.Plot._scaledAccessor(this.y2())),e},e.prototype._pixelPoint=function(t,e,n){var i=this._getAttrToProjector(),r=i.x(t,e,n),o=i.y(t,e,n);return{x:r+i.width(t,e,n)/2,y:o+i.height(t,e,n)/2}},e.prototype._rectangleWidth=function(t){if(t instanceof MEt.Category)return t.rangeBand();var e=t===this.x().scale?this.x().accessor:this.y().accessor,n=yEt.set(EEt.Array.flatten(this.datasets().map((function(t){return t.data().map((function(n,i){return e(n,i,t).valueOf()}))})))).values().map((function(t){return+t})),i=EEt.Math.min(n,0),r=EEt.Math.max(n,0),o=t.scale(i);return(t.scale(r)-o)/Math.abs(r-i)},e.prototype._getDataToDraw=function(){var t=new EEt.Map,e=this._getAttrToProjector();return this.datasets().forEach((function(n){var i=n.data().map((function(t,i){return EEt.Math.isValidNumber(e.x(t,i,n))&&EEt.Math.isValidNumber(e.y(t,i,n))&&EEt.Math.isValidNumber(e.width(t,i,n))&&EEt.Math.isValidNumber(e.height(t,i,n))?t:null}));t.set(n,i)})),t},e.prototype._additionalPaint=function(t){var e=this;this._renderArea.selectAll(".label-area").remove(),this._labelsEnabled&&null!=this.label()&&EEt.Window.setTimeout((function(){return e._drawLabels()}),t)},e.prototype._drawLabels=function(){var t=this,e=this._getDataToDraw();this.datasets().forEach((function(n,i){return t._drawLabel(e,n,i)}))},e.prototype._drawLabel=function(t,e,n){for(var i=this._getAttrToProjector(),r=this._renderArea.append("g").classed("label-area",!0),o=new vEt.SvgContext(r.node()),a=new vEt.CacheMeasurer(o),s=new vEt.Writer(a,o),l=this.x().scale.range(),c=this.y().scale.range(),u=Math.min.apply(null,l),h=Math.max.apply(null,l),d=Math.min.apply(null,c),p=Math.max.apply(null,c),f=t.get(e),m=f.length,g=0;g<m;g++){var _=f[g];if(null!=_){var y=""+this.label()(_,g,e),v=a.measure(y),b=i.x(_,g,e),x=i.y(_,g,e),w=i.width(_,g,e),S=i.height(_,g,e);if(v.height<=S&&v.width<=w){var M={min:b+=(w-v.width)/2,max:b+v.width},E={min:x+=(S-v.height)/2,max:x+v.height};if(M.min<u||M.max>h||E.min<d||E.max>p)continue;if(this._overlayLabel(M,E,g,n,t))continue;var T=i.fill(_,g,e),C=1.6*EEt.Color.contrast("white",T)<EEt.Color.contrast("black",T),A=r.append("g").attr("transform","translate("+b+","+x+")");A.classed(C?"dark-label":"light-label",!0),s.write(y,v.width,v.height,{xAlign:"center",yAlign:"center"},A.node())}}}},e.prototype._overlayLabel=function(t,e,n,i,r){for(var o=this._getAttrToProjector(),a=this.datasets(),s=i;s<a.length;s++)for(var l=a[s],c=r.get(l),u=c.length,h=s===i?n+1:0;h<u;h++)if(EEt.DOM.intersectsBBox(t,e,this._entityBBox(c[h],h,l,o)))return!0;return!1},e._X2_KEY="x2",e._Y2_KEY="y2",e})(gMt.XYPlot);gEt.Rectangle=CEt;var AEt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(AEt,"__esModule",{value:!0});var kEt=uO,LEt=Imt,PEt=gyt,NEt=Uxt,IEt=Wwt,REt=nMt,OEt=_mt,zEt=NSt,DEt=Zyt,BEt=Fgt,HEt=pMt,FEt=uwt,VEt=(function(t){function e(){var e=t.call(this)||this;e._labelFormatter=PEt.identity(),e._labelsEnabled=!1,e.addClass("scatter-plot");var n=new OEt.Easing;n.startDelay(5),n.stepDuration(250),n.maxTotalDuration(FEt.Plot._ANIMATION_MAX_DURATION),e.animator(HEt.Animator.MAIN,n),e.attr("opacity",.6),e.attr("fill",(new DEt.Color).range()[0]),e.size(6);var i=NEt.circle();return e.symbol((function(){return i})),e._labelConfig=new BEt.Map,e}return kEt.__extends(e,t),e.prototype._buildLightweightPlotEntities=function(e){var n=this;return t.prototype._buildLightweightPlotEntities.call(this,e).map((function(t){var e=FEt.Plot._scaledAccessor(n.size())(t.datum,t.index,t.dataset);return t.diameter=e,t}))},e.prototype._createDrawer=function(t){var e=this;return new IEt.ProxyDrawer((function(){return new REt.SymbolSVGDrawer}),(function(n){return new zEt.CanvasDrawer(n,REt.makeSymbolCanvasDrawStep(t,(function(){return FEt.Plot._scaledAccessor(e.symbol())}),(function(){return FEt.Plot._scaledAccessor(e.size())})))}))},e.prototype.size=function(t,n){return null==t?this._propertyBindings.get(e._SIZE_KEY):(this._bindProperty(e._SIZE_KEY,t,n),this.render(),this)},e.prototype.symbol=function(t){return null==t?this._propertyBindings.get(e._SYMBOL_KEY):(this._propertyBindings.set(e._SYMBOL_KEY,{accessor:t}),this.render(),this)},e.prototype._generateDrawSteps=function(){var t=[];if(this._animateOnNextRender()){var e=this._getAttrToProjector(),n=FEt.Plot._scaledAccessor(this.symbol());e.d=function(t,e,i){return n(t,e,i)(0)(null)},t.push({attrToProjector:e,animator:this._getAnimator(HEt.Animator.RESET)})}return t.push({attrToProjector:this._getAttrToProjector(),animator:this._getAnimator(HEt.Animator.MAIN)}),t},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this),n=FEt.Plot._scaledAccessor(this.x()),i=FEt.Plot._scaledAccessor(this.y());return e.x=n,e.y=i,e.transform=function(t,e,r){return"translate("+n(t,e,r)+","+i(t,e,r)+")"},e.d=this._constructSymbolGenerator(),e},e.prototype._constructSymbolGenerator=function(){var t=FEt.Plot._scaledAccessor(this.symbol()),e=FEt.Plot._scaledAccessor(this.size());return function(n,i,r){return t(n,i,r)(e(n,i,r))(null)}},e.prototype._entityBounds=function(t){return{x:t.position.x-t.diameter/2,y:t.position.y-t.diameter/2,width:t.diameter,height:t.diameter}},e.prototype._entityVisibleOnPlot=function(t,e){var n={min:e.topLeft.x,max:e.bottomRight.x},i={min:e.topLeft.y,max:e.bottomRight.y},r=this._entityBounds(t);return BEt.DOM.intersectsBBox(n,i,r)},e.prototype.entitiesAt=function(t){var e=FEt.Plot._scaledAccessor(this.x()),n=FEt.Plot._scaledAccessor(this.y()),i=FEt.Plot._scaledAccessor(this.size());return this.entities().filter((function(r){var o=r.datum,a=r.index,s=r.dataset,l=e(o,a,s),c=n(o,a,s),u=i(o,a,s);return l-u/2<=t.x&&t.x<=l+u/2&&c-u/2<=t.y&&t.y<=c+u/2}))},e.prototype.labelsEnabled=function(t){return null==t?this._labelsEnabled:(this._labelsEnabled=t,this._clearAttrToProjectorCache(),this.render(),this)},e.prototype._createNodesForDataset=function(n){var i=t.prototype._createNodesForDataset.call(this,n),r=this._renderArea.append("g").classed(e._LABEL_AREA_CLASS,!0),o=new LEt.SvgContext(r.node()),a=new LEt.CacheMeasurer(o),s=new LEt.Writer(a,o);return this._labelConfig.set(n,{labelArea:r,measurer:a,writer:s}),i},e.prototype._removeDatasetNodes=function(e){t.prototype._removeDatasetNodes.call(this,e);var n=this._labelConfig.get(e);null!=n&&(n.labelArea.remove(),this._labelConfig.delete(e))},e.prototype._additionalPaint=function(t){var e=this;this.datasets().forEach((function(t){return e._labelConfig.get(t).labelArea.selectAll("g").remove()})),this._labelsEnabled&&BEt.Window.setTimeout((function(){return e._drawLabels()}),t)},e.prototype._drawLabels=function(){var t=this,e=this._getDataToDraw(),n=this._getAttrToProjector();this.datasets().forEach((function(i){for(var r=e.get(i),o=r.length,a=0;a<o;a++){var s=r[a];null!=s&&t._drawLabel(s,a,i,n)}}))},e.prototype._drawLabel=function(t,e,n,i){if(null!=t.label){var r=this._labelConfig.get(n),o=r.labelArea,a=r.measurer,s=r.writer,l={x:i.x(t,e,n),y:i.y(t,e,n)},c=FEt.Plot._scaledAccessor(this.size())(t,e,n),u=this._labelFormatter(t.label,t,e,n),h=a.measure(u),d=this._calculateLabelProperties(l,c,h),p=d.containerDimensions,f=d.alignment,m=this._createLabelContainer(o,d.labelContainerOrigin,d.labelOrigin,h);s.write(u,p.width,p.height,{xAlign:f.x,yAlign:f.y},m.node())}},e.prototype._calculateLabelProperties=function(t,n,i){return{containerDimensions:{width:i.width,height:i.height},labelContainerOrigin:{x:t.x-i.width/2,y:t.y-i.height/2+(n<i.height?n/2+e._LABEL_MARGIN_FROM_BUBBLE:0)},labelOrigin:{x:t.x,y:t.y},alignment:{x:"center",y:"center"}}},e.prototype._createLabelContainer=function(t,e,n,i){var r=t.append("g").attr("transform","translate("+e.x+", "+e.y+")");return r.classed("on-bar-label",!0),r},e._SIZE_KEY="size",e._SYMBOL_KEY="symbol",e._LABEL_AREA_CLASS="scatter-label-text-area",e._LABEL_MARGIN_FROM_BUBBLE=15,e})(gMt.XYPlot);AEt.Scatter=VEt;var UEt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(UEt,"__esModule",{value:!0});var jEt=uO,GEt=_mt,WEt=Wwt,qEt=QSt,YEt=Zyt,XEt=k_t,$Et=uwt,KEt=(function(t){function e(){var e=t.call(this)||this;return e.addClass("segment-plot"),e.attr("stroke",(new YEt.Color).range()[0]),e.attr("stroke-width","2px"),e}return jEt.__extends(e,t),e.prototype._createDrawer=function(){return new WEt.ProxyDrawer((function(){return new qEt.SegmentSVGDrawer}),(function(){return XEt.warn("canvas renderer is not supported on Segment Plot!"),null}))},e.prototype._generateDrawSteps=function(){return[{attrToProjector:this._getAttrToProjector(),animator:new GEt.Null}]},e.prototype._filterForProperty=function(e){return t.prototype._filterForProperty.call(this,"x2"===e?"x":"y2"===e?"y":e)},e.prototype.x=function(n,i){if(null==n)return t.prototype.x.call(this);if(null==i)t.prototype.x.call(this,n);else{t.prototype.x.call(this,n,i);var r=this.x2(),o=r&&r.accessor;null!=o&&this._bindProperty(e._X2_KEY,o,i)}return this},e.prototype.x2=function(t){if(null==t)return this._propertyBindings.get(e._X2_KEY);var n=this.x();return this._bindProperty(e._X2_KEY,t,n&&n.scale),this.render(),this},e.prototype.y=function(n,i){if(null==n)return t.prototype.y.call(this);if(null==i)t.prototype.y.call(this,n);else{t.prototype.y.call(this,n,i);var r=this.y2(),o=r&&r.accessor;null!=o&&this._bindProperty(e._Y2_KEY,o,i)}return this},e.prototype.y2=function(t){if(null==t)return this._propertyBindings.get(e._Y2_KEY);var n=this.y();return this._bindProperty(e._Y2_KEY,t,n&&n.scale),this.render(),this},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this);return e.x1=$Et.Plot._scaledAccessor(this.x()),e.x2=null==this.x2()?$Et.Plot._scaledAccessor(this.x()):$Et.Plot._scaledAccessor(this.x2()),e.y1=$Et.Plot._scaledAccessor(this.y()),e.y2=null==this.y2()?$Et.Plot._scaledAccessor(this.y()):$Et.Plot._scaledAccessor(this.y2()),e},e.prototype.entitiesAt=function(t){var e=this.entityNearest(t);return null!=e?[e]:[]},e.prototype.entitiesIn=function(t,e){var n,i;return null==e?(n={min:t.topLeft.x,max:t.bottomRight.x},i={min:t.topLeft.y,max:t.bottomRight.y}):(n=t,i=e),this._entitiesIntersecting(n,i)},e.prototype._entitiesIntersecting=function(t,e){for(var n=[],i=this._getAttrToProjector(),r=this.entities(),o=r.length,a=0;a<o;a++){var s=r[a];this._lineIntersectsBox(s,t,e,i)&&n.push(s)}return n},e.prototype._lineIntersectsBox=function(t,e,n,i){var r=this,o=i.x1(t.datum,t.index,t.dataset),a=i.x2(t.datum,t.index,t.dataset),s=i.y1(t.datum,t.index,t.dataset),l=i.y2(t.datum,t.index,t.dataset);if(e.min<=o&&o<=e.max&&n.min<=s&&s<=n.max||e.min<=a&&a<=e.max&&n.min<=l&&l<=n.max)return!0;var c={x:o,y:s},u={x:a,y:l},h=[{x:e.min,y:n.min},{x:e.min,y:n.max},{x:e.max,y:n.max},{x:e.max,y:n.min}];return h.filter((function(t,e){return 0!==e&&r._lineIntersectsSegment(c,u,t,h[e-1])&&r._lineIntersectsSegment(t,h[e-1],c,u)})).length>0},e.prototype._lineIntersectsSegment=function(t,e,n,i){var r=function(t,e,n){return(e.x-t.x)*(n.y-e.y)-(e.y-t.y)*(n.x-e.x)};return r(t,e,n)*r(t,e,i)<0},e._X2_KEY="x2",e._Y2_KEY="y2",e})(gMt.XYPlot);UEt.Segment=KEt;var ZEt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(ZEt,"__esModule",{value:!0});var JEt=uO,QEt=Edt,tTt=_mt,eTt=dwt,nTt=Fgt,iTt=uwt,rTt=(function(t){function e(){var e=t.call(this)||this;return e._stackingResult=eTt.memThunk((function(){return e.datasets()}),(function(){return e.x().accessor}),(function(){return e.y().accessor}),(function(){return e._stackingOrder}),(function(t,e,n,i){return nTt.Stacking.stack(t,e,n,i)})),e._stackedExtent=eTt.memThunk(e._stackingResult,(function(){return e.x().accessor}),(function(){return e._filterForProperty("y")}),(function(t,e,n){return nTt.Stacking.stackedExtent(t,e,n)})),e._baselineValue=0,e._stackingOrder="bottomup",e.addClass("stacked-area-plot"),e._baselineValueProvider=function(){return[e._baselineValue]},e.croppedRenderingEnabled(!1),e}return JEt.__extends(e,t),e.prototype.croppedRenderingEnabled=function(e){return null==e?t.prototype.croppedRenderingEnabled.call(this):e?(nTt.Window.warn("Warning: Stacked Area Plot does not support cropped rendering."),this):t.prototype.croppedRenderingEnabled.call(this,e)},e.prototype._getAnimator=function(t){return new tTt.Null},e.prototype._setup=function(){t.prototype._setup.call(this),this._baseline=this._renderArea.append("line").classed("baseline",!0)},e.prototype.x=function(e,n){return null==e?t.prototype.x.call(this):(null==n?t.prototype.x.call(this,e):t.prototype.x.call(this,e,n),this._checkSameDomain(),this)},e.prototype.y=function(e,n){return null==e?t.prototype.y.call(this):(null==n?t.prototype.y.call(this,e):t.prototype.y.call(this,e,n),this._checkSameDomain(),this)},e.prototype.yOffset=function(t,e){var n=this._stackingResult();if(null!=n){var i=n.get(t);if(null!=i){var r=i.get(String(e));if(null!=r)return r.offset}}},e.prototype.stackingOrder=function(t){return null==t?this._stackingOrder:(this._stackingOrder=t,this._onDatasetUpdate(),this)},e.prototype.downsamplingEnabled=function(e){return null==e?t.prototype.downsamplingEnabled.call(this):(nTt.Window.warn("Warning: Stacked Area Plot does not support downsampling"),this)},e.prototype._additionalPaint=function(){var t=this.y().scale.scale(this._baselineValue),e={x1:0,y1:t,x2:this.width(),y2:t};this._getAnimator("baseline").animate(this._baseline,e)},e.prototype._updateYScale=function(){var t=this.y(),e=t&&t.scale;null!=e&&(e.addPaddingExceptionsProvider(this._baselineValueProvider),e.addIncludedValuesProvider(this._baselineValueProvider))},e.prototype._onDatasetUpdate=function(){return this._checkSameDomain(),t.prototype._onDatasetUpdate.call(this),this},e.prototype.getExtentsForProperty=function(e){return"y"===e?[this._stackedExtent()]:t.prototype.getExtentsForProperty.call(this,e)},e.prototype._checkSameDomain=function(){if(this._projectorsReady()){var t=this.datasets(),n=this.x().accessor,i=t.map((function(t){return QEt.set(t.data().map((function(e,i){return nTt.Stacking.normalizeKey(n(e,i,t))}))).values()})),r=e._domainKeys(t,n);i.some((function(t){return t.length!==r.length}))&&nTt.Window.warn("the domains across the datasets are not the same. Plot may produce unintended behavior.")}},e._domainKeys=function(t,e){var n=QEt.set();return t.forEach((function(t){for(var i=t.data(),r=i.length,o=0;o<r;o++)n.add(e(i[o],o,t))})),n.values()},e.prototype._coordinateProjectors=function(){var t=this,e=iTt.Plot._scaledAccessor(this.x()),n=this.y().accessor,i=this.x().accessor,r=function(t,e,n){return nTt.Stacking.normalizeKey(i(t,e,n))},o=this._stackingResult();return[e,function(e,i,a){var s=+n(e,i,a),l=o.get(a).get(r(e,i,a)).offset;return t.y().scale.scale(s+l)},function(e,n,i){var a=o.get(i).get(r(e,n,i)).offset;return t.y().scale.scale(a)}]},e.prototype._propertyProjectors=function(){var e=t.prototype._propertyProjectors.call(this),n=this._coordinateProjectors();return e.d=this._constructAreaProjector(n[0],n[1],n[2]),e},e.prototype._pixelPoint=function(e,n,i){var r=t.prototype._pixelPoint.call(this,e,n,i),o=this.x().accessor(e,n,i),a=this.y().accessor(e,n,i),s=this.y().scale.scale(+a+this._stackingResult().get(i).get(nTt.Stacking.normalizeKey(o)).offset);return{x:r.x,y:s}},e})(fMt.Area);ZEt.StackedArea=rTt;var oTt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(oTt,"__esModule",{value:!0});var aTt=uO,sTt=Imt,lTt=gyt,cTt=dwt,uTt=Fgt,hTt=XMt,dTt=uwt,pTt=(function(t){function e(e){void 0===e&&(e="vertical");var n=t.call(this,e)||this;return n._extremaFormatter=lTt.identity(),n._stackingResult=cTt.memThunk((function(){return n.datasets()}),(function(){return n.position().accessor}),(function(){return n.length().accessor}),(function(){return n._stackingOrder}),(function(t,e,n,i){return uTt.Stacking.stack(t,e,n,i)})),n._stackedExtent=cTt.memThunk(n._stackingResult,(function(){return n.position().accessor}),(function(){return n._filterForProperty(n._isVertical?"y":"x")}),(function(t,e,n){return uTt.Stacking.stackedExtent(t,e,n)})),n.addClass("stacked-bar-plot"),n._stackingOrder="bottomup",n}return aTt.__extends(e,t),e.prototype.stackingOrder=function(t){return null==t?this._stackingOrder:(this._stackingOrder=t,this._onDatasetUpdate(),this)},e.prototype.extremaFormatter=function(t){return 0===arguments.length?this._extremaFormatter:(this._extremaFormatter=t,this.render(),this)},e.prototype._setup=function(){t.prototype._setup.call(this),this._labelArea=this._renderArea.append("g").classed(hTt.Bar._LABEL_AREA_CLASS,!0);var e=new sTt.SvgContext(this._labelArea.node());this._measurer=new sTt.CacheMeasurer(e),this._writer=new sTt.Writer(this._measurer,e)},e.prototype._drawLabels=function(){var n=this;t.prototype._drawLabels.call(this),this._labelArea.selectAll("g").remove();var i=+this.baselineValue(),r=this.position().scale,o=this.length().scale,a=uTt.Stacking.stackedExtents(this._stackingResult()),s=a.minimumExtents,l=[],c=function(t,e){var a=n._generateAttrToProjector(),s=n.width(),c=n.height();t.forEach((function(t){if(t.extent!==i){var u=n.extremaFormatter()(t.extent),h=n._measurer.measure(u),d=t.stackedDatum,p=d.originalDatum,f=d.originalIndex,m=d.originalDataset;if(!n._isDatumOnScreen(a,s,c,p,f,m))return;var g=dTt.Plot._scaledAccessor(n.attr(hTt.Bar._BAR_THICKNESS_KEY))(p,f,m),_=o.scale(t.extent),y=n._getPositionAttr(r.scale(t.axisValue),g)+g/2,v=e(n._isVertical?{x:y,y:_}:{x:_,y:y},h,g),b=(function(t,e,i){var r=e.topLeft,o=r.x,a=r.y,s=e.bottomRight.x-e.topLeft.x,l=e.bottomRight.y-e.topLeft.y,c=n._isVertical?s>i:l>i;if(!c){var u=n._labelArea.append("g").attr("transform","translate("+o+", "+a+")");u.classed("stacked-bar-label",!0),n._writer.write(t,s,l,{xAlign:"center",yAlign:"center"},u.node())}return c})(u,{topLeft:v,bottomRight:{x:v.x+h.width,y:v.y+h.height}},g);l.push(b)}}))};c(a.maximumExtents,(function(t,i,r){var o=n._isVertical?i.width:i.height;return{x:n._isVertical?t.x-o/2:t.x+e._EXTREMA_LABEL_MARGIN_FROM_BAR,y:n._isVertical?t.y-(n._isVertical?i.height:i.width):t.y-o/2}})),c(s,(function(t,i,r){var o=n._isVertical?i.width:i.height;return{x:n._isVertical?t.x-o/2:t.x-(n._isVertical?i.height:i.width),y:n._isVertical?t.y+e._EXTREMA_LABEL_MARGIN_FROM_BAR:t.y-o/2}})),l.some((function(t){return t}))&&this._labelArea.selectAll("g").remove()},e.prototype._generateAttrToProjector=function(){var e=this,n=t.prototype._generateAttrToProjector.call(this),i=this._isVertical?"y":"x",r=this.length().scale,o=this.length().accessor,a=this.position().accessor,s=function(t,e,n){return uTt.Stacking.normalizeKey(a(t,e,n))},l=this._stackingResult(),c=function(t,e,n){return r.scale(l.get(n).get(s(t,e,n)).offset)},u=function(t,e,n){return r.scale(+o(t,e,n)+l.get(n).get(s(t,e,n)).offset)},h=function(t,e,n){return Math.abs(u(t,e,n)-c(t,e,n))};n[this._isVertical?"height":"width"]=h;var d=function(t,e,n){return+o(t,e,n)<0?c(t,e,n):u(t,e,n)};return n[i]=function(t,n,i){return e._isVertical?d(t,n,i):d(t,n,i)-h(t,n,i)},n},e.prototype.getExtentsForProperty=function(e){return e===(this._isVertical?"y":"x")?[this._stackedExtent()]:t.prototype.getExtentsForProperty.call(this,e)},e.prototype.invalidateCache=function(){t.prototype.invalidateCache.call(this),this._measurer.reset()},e._EXTREMA_LABEL_MARGIN_FROM_BAR=5,e})(hTt.Bar);oTt.StackedBar=pTt;var fTt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(fTt,"__esModule",{value:!0});var mTt=uO,gTt=Fgt,_Tt=uwt,yTt=(function(t){function e(){var e=t.call(this)||this;return e._connectorsEnabled=!1,e.addClass("waterfall-plot"),e}return mTt.__extends(e,t),e.prototype.connectorsEnabled=function(t){return null==t?this._connectorsEnabled:(this._connectorsEnabled=t,this)},e.prototype.total=function(t){return null==t?this._propertyBindings.get(e._TOTAL_KEY):(this._bindProperty(e._TOTAL_KEY,t,null),this)},e.prototype._additionalPaint=function(t){var e=this;this._connectorArea.selectAll("line").remove(),this._connectorsEnabled&&gTt.Window.setTimeout((function(){return e._drawConnectors()}),t)},e.prototype._createNodesForDataset=function(n){var i=t.prototype._createNodesForDataset.call(this,n);return this._connectorArea=this._renderArea.append("g").classed(e._CONNECTOR_AREA_CLASS,!0),i},e.prototype.getExtentsForProperty=function(e){return"y"===e?[this._extent]:t.prototype.getExtentsForProperty.call(this,e)},e.prototype._generateAttrToProjector=function(){var n=this,i=t.prototype._generateAttrToProjector.call(this),r=this.y().scale,o=_Tt.Plot._scaledAccessor(this.total());return null==this.attr("y")&&(i.y=function(t,e,i){var a=n.y().accessor(t,e,i);if(o(t,e,i))return Math.min(r.scale(a),r.scale(0));var s=n._subtotals[e];if(0===e)return r.scale(a<0?s-a:s);var l=n._subtotals[e-1];return r.scale(s>l?s:l)}),null==this.attr("height")&&(i.height=function(t,e,i){var a=o(t,e,i),s=n.y().accessor(t,e,i);if(a)return Math.abs(r.scale(s)-r.scale(0));var l=n._subtotals[e];if(0===e)return Math.abs(r.scale(l)-r.scale(l-s));var c=n._subtotals[e-1];return Math.abs(r.scale(l)-r.scale(c))}),i.class=function(t,i,r){var a="";return null!=n.attr("class")&&(a=n.attr("class").accessor(t,i,r)+" "),o(t,i,r)?a+e._BAR_TOTAL_CLASS:a+(n.y().accessor(t,i,r)>0?e._BAR_GROWTH_CLASS:e._BAR_DECLINE_CLASS)},i},e.prototype._onDatasetUpdate=function(){return this._updateSubtotals(),t.prototype._onDatasetUpdate.call(this),this},e.prototype._calculateSubtotalsAndExtent=function(t){for(var e=Number.MAX_VALUE,n=Number.MIN_VALUE,i=0,r=!1,o=t.data(),a=o.length,s=0;s<a;s++){var l=o[s],c=this.y().accessor(l,s,t),u=this.total().accessor(l,s,t);if(u&&0!==s||(i+=c),this._subtotals.push(i),i<e&&(e=i),i>n&&(n=i),u&&(c<e&&(e=c),c>n&&(n=c)),!r&&u){for(var h=c-i,d=0;d<this._subtotals.length;d++)this._subtotals[d]+=h;r=!0,i+=h,e+=h,n+=h}}this._extent=[e,n]},e.prototype._drawConnectors=function(){for(var t=this._getAttrToProjector(),n=this.datasets()[0],i=1;i<n.data().length;i++){var r=i-1,o=n.data()[i],a=n.data()[r],s=t.x(a,r,n),l=t.x(o,i,n)+t.width(o,i,n),c=t.y(o,i,n);(this._subtotals[i]>0&&this._subtotals[i]>this._subtotals[r]||this._subtotals[i]<0&&this._subtotals[i]>=this._subtotals[r])&&(c=t.y(o,i,n)+t.height(o,i,n)),this._connectorArea.append("line").classed(e._CONNECTOR_CLASS,!0).attr("x1",s).attr("x2",l).attr("y1",c).attr("y2",c)}},e.prototype._updateSubtotals=function(){var t=this.datasets();if(t.length>0){var e=t[t.length-1];this._subtotals=new Array,this._calculateSubtotalsAndExtent(e)}},e._BAR_DECLINE_CLASS="waterfall-decline",e._BAR_GROWTH_CLASS="waterfall-growth",e._BAR_TOTAL_CLASS="waterfall-total",e._CONNECTOR_CLASS="connector",e._CONNECTOR_AREA_CLASS="connector-area",e._TOTAL_KEY="total",e})(XMt.Bar);fTt.Waterfall=yTt,(function(t){
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */
Object.defineProperty(t,"__esModule",{value:!0});var e=uO;e.__exportStar(fMt,t),e.__exportStar(XMt,t),e.__exportStar(hwt,t),e.__exportStar($Mt,t),e.__exportStar(mMt,t),e.__exportStar(nEt,t),e.__exportStar(gEt,t),e.__exportStar(AEt,t),e.__exportStar(UEt,t),e.__exportStar(ZEt,t),e.__exportStar(oTt,t),e.__exportStar(fTt,t)})(pMt);var vTt={};
/**
     * Copyright 2014-present Palantir Technologies
     * @license MIT
     */Object.defineProperty(vTt,"__esModule",{value:!0}),vTt.version="3.9.0",(function(t){Object.defineProperty(t,"__esModule",{value:!0});var e=uO;t.Animators=_mt,t.Axes=Pmt,t.Components=ibt,t.Configs=L_t,t.Formatters=gyt,t.RenderController=Hgt,t.RenderPolicies=nyt,t.SymbolFactories=Uxt,t.Dispatchers=sbt,t.Drawers=NSt,t.Interactions=obt,t.Plots=pMt,t.Scales=Zyt,t.Utils=Fgt,e.__exportStar(myt,t),t.TimeInterval=Kyt.TimeInterval,e.__exportStar(Bgt,t),e.__exportStar(xxt,t),e.__exportStar(gwt,t),t.version=vTt.version,e.__exportStar(cbt,t),e.__exportStar(Wwt,t),e.__exportStar(wbt,t),e.__exportStar(Ibt,t),e.__exportStar(gMt,t),e.__exportStar(uwt,t),e.__exportStar(Ivt,t),e.__exportStar(hvt,t)})(rO);const bTt=[{character:"◼",method:rO.SymbolFactories.square},{character:"◆",method:rO.SymbolFactories.diamond},{character:"▲",method:rO.SymbolFactories.triangle},{character:"★",method:rO.SymbolFactories.star},{character:"✚",method:rO.SymbolFactories.cross}];var xTt;function wTt(t){return e=>{let n,i=Math.abs(e);return i<1e-15&&(i=0),n=my(i>=1e4||i>0&&i<.01?"."+t+"~e":"."+t+"~g"),n(e)}}!(function(t){t.STEP="step",t.RELATIVE="relative",t.WALL_TIME="wall_time"})(xTt||(xTt={}));const STt=my(".4~s");function MTt(){let t=new rO.Scales.Linear;t.tickGenerator(rO.Scales.TickGenerators.integerTickGenerator());let e=new rO.Axes.Numeric(t,"bottom");return e.formatter(STt),{scale:t,axis:e,accessor:t=>t.step}}let ETt=rO.Formatters.time("%a %b %e, %H:%M:%S"),TTt=(t,e,n)=>{if(null!=t.relative)return t.relative;let i=n.data();return(+t.wall_time-(i.length>0?+i[0].wall_time:0))/36e5},CTt=t=>{let e="",n=Math.floor(t/24);t-=24*n,n&&(e+=n+"d ");let i=Math.floor(t);t-=i,t*=60,(i||n)&&(e+=i+"h ");let r=Math.floor(t);return t-=r,t*=60,(r||i||n)&&(e+=r+"m "),e+Math.floor(t)+"s"};function ATt(t){switch(t){case xTt.STEP:return MTt();case xTt.WALL_TIME:return(function e(){let t=new rO.Scales.Time;return{scale:t,axis:new rO.Axes.Time(t,"bottom"),accessor:t=>t.wall_time}})();case xTt.RELATIVE:return(function n(){let t=new rO.Scales.Linear;return{scale:t,axis:new rO.Axes.Numeric(t,"bottom"),accessor:TTt}})();default:throw new Error("invalid xType: "+t)}}var kTt;function LTt(t){return class extends t{constructor(){super(...arguments),this.loadKey="",this.dataToLoad=[],this.getDataLoadName=t=>String(t),this.dataLoading=!1,this.dataLoadedAtLeastOnce=!1,this._isConnected=!1,this._dataLoadState=new Map,this._canceller=new XR,this._loadDataAsync=null,this._loadData=Se.exports.throttle(this._loadDataImpl,100,{leading:!0,trailing:!0})}connectedCallback(){super.connectedCallback(),this._isConnected=!0}disconnectedCallback(){super.disconnectedCallback(),this._isConnected=!1}static get properties(){return{active:{type:Boolean,observer:"_loadDataIfActive"},_isConnected:{type:Boolean},loadKey:{type:String},dataToLoad:{type:Array},getDataLoadName:{type:Object},loadDataCallback:{type:Object},requestData:{type:Object}}}static get observers(){return["_dataToLoadChanged(_isConnected, dataToLoad.*)"]}onLoadFinish(){}reload(){this._dataLoadState.clear(),this._loadData()}reset(){null!=this._loadDataAsync&&(clearTimeout(this._loadDataAsync),this._loadDataAsync=null),this._canceller&&this._canceller.cancelAll(),this._dataLoadState&&this._dataLoadState.clear(),this._isConnected&&this._loadData()}_dataToLoadChanged(){this._isConnected&&this._loadData()}detached(){null!=this._loadDataAsync&&(clearTimeout(this._loadDataAsync),this._loadDataAsync=null)}_loadDataIfActive(){this.active&&this._loadData()}_loadDataImpl(){this.active&&(null!==this._loadDataAsync&&clearTimeout(this._loadDataAsync),this._loadDataAsync=setTimeout(this._canceller.cancellable((t=>{if(t.cancelled)return;this.dataLoading=!0;const e=this.dataToLoad.filter((t=>{const e=this.getDataLoadName(t);return!this._dataLoadState.has(e)}));for(const t of e){const e=this.getDataLoadName(t);this._dataLoadState.set(e,kTt.LOADING)}const n=this._canceller.cancellable((t=>{if(t.cancelled)return;const{item:e,data:n}=t.value,i=this.getDataLoadName(e);this._dataLoadState.set(i,kTt.LOADED),this.loadDataCallback(this,e,n)})),i=this._canceller.cancellable((t=>{if(!t.cancelled){const t=new Set(e.map((t=>this.getDataLoadName(t))));this.dataToLoad.some((e=>t.has(this.getDataLoadName(e))))&&this.onLoadFinish(),this._loadDataAsync=null,this.dataLoadedAtLeastOnce=!0}Array.from(this._dataLoadState.values()).includes(kTt.LOADING)||(this.dataLoading=!1)}));this.requestData(e,n,(()=>i(void 0)))}))))}}}!(function(t){t[t.LOADING=0]="LOADING",t[t.LOADED=1]="LOADED"})(kTt||(kTt={})),el({moduleName:"plottable-style",styleContent:"\n    \n.plottable-colors-0 {\n  background-color: #5279c7; /* INDIGO */\n}\n\n.plottable-colors-1 {\n  background-color: #fd373e; /* CORAL_RED */\n}\n\n.plottable-colors-2 {\n  background-color: #63c261; /* FERN */\n}\n\n.plottable-colors-3 {\n  background-color: #fad419; /* BRIGHT_SUN */\n}\n\n.plottable-colors-4 {\n  background-color: #2c2b6f; /* JACARTA */\n}\n\n.plottable-colors-5 {\n  background-color: #ff7939; /* BURNING_ORANGE */\n}\n\n.plottable-colors-6 {\n  background-color: #db2e65; /* CERISE_RED */\n}\n\n.plottable-colors-7 {\n  background-color: #99ce50; /* CONIFER */\n}\n\n.plottable-colors-8 {\n  background-color: #962565; /* ROYAL_HEATH */\n}\n\n.plottable-colors-9 {\n  background-color: #06cccc; /* ROBINS_EGG_BLUE */\n}\n\n/**\n * User-supplied renderTo element.\n */\n.plottable {\n  display: block; /* must be block elements for width/height calculations to work in Firefox. */\n  pointer-events: visibleFill;\n  position: relative;\n  /**\n   * Pre 3.0, users could set the dimension of the root element in two ways: either using CSS\n   * (inline or through a stylesheet), or using the SVG width/height attributes. By default, we\n   * set the SVG width/height attributes to 100%.\n   *\n   * Post 3.0 the root element is always a normal div and the only way to set the dimensions is\n   * to use CSS. To replicate the \"100%-by-default\" behavior, we apply width/height 100%.\n   */\n  width: 100%;\n  height: 100%;\n}\n\n/**\n * The _element that roots each Component's DOM.\n */\n.plottable .component {\n  /* Allow components to be positioned with explicit left/top/width/height styles */\n  position: absolute;\n}\n\n.plottable .background-container,\n.plottable .content,\n.plottable .foreground-container {\n  position: absolute;\n  width: 100%;\n  height: 100%;\n}\n\n/**\n * Don't allow svg elements above the content to steal events\n */\n.plottable .foreground-container {\n  pointer-events: none;\n}\n\n.plottable .component-overflow-hidden {\n  overflow: hidden;\n}\n\n.plottable .component-overflow-visible {\n  overflow: visible;\n}\n\n.plottable .plot-canvas-container {\n  width: 100%;\n  height: 100%;\n  overflow: hidden;\n}\n\n.plottable .plot-canvas {\n  width: 100%;\n  height: 100%;\n  /**\n   * Play well with deferred rendering.\n   */\n  transform-origin: 0px 0px 0px;\n}\n\n.plottable text {\n  text-rendering: geometricPrecision;\n}\n\n.plottable .label text {\n  fill: #32313F;\n}\n\n.plottable .bar-label-text-area text,\n.plottable .scatter-label-text-area text {\n  font-size: 12px;\n}\n\n.plottable .label-area text {\n  fill: #32313F;\n  font-size: 14px;\n}\n\n.plottable .light-label text {\n  fill: white;\n}\n\n.plottable .dark-label text {\n  fill: #32313F;\n}\n\n.plottable .off-bar-label text {\n  fill: #32313F;\n}\n\n.plottable .stacked-bar-label text {\n  fill: #32313F;\n  font-style: normal;\n}\n\n.plottable .stacked-bar-plot .off-bar-label {\n  /* HACKHACK #2795: correct off-bar label logic to be implemented on StackedBar */\n  visibility: hidden !important;\n}\n\n.plottable .axis-label text {\n  font-size: 10px;\n  font-weight: bold;\n  letter-spacing: 1px;\n  line-height: normal;\n  text-transform: uppercase;\n}\n\n.plottable .title-label text {\n  font-size: 20px;\n  font-weight: bold;\n}\n\n.plottable .axis line.baseline {\n  stroke: #CCC;\n  stroke-width: 1px;\n}\n\n.plottable .axis line.tick-mark {\n  stroke: #CCC;\n  stroke-width: 1px;\n}\n\n.plottable .axis text {\n  fill: #32313F;\n  font-size: 12px;\n  font-weight: 200;\n  line-height: normal;\n}\n\n.plottable .axis .annotation-circle {\n  fill: white;\n  stroke-width: 1px;\n  stroke: #CCC;\n}\n\n.plottable .axis .annotation-line {\n  stroke: #CCC;\n  stroke-width: 1px;\n}\n\n.plottable .axis .annotation-rect {\n  stroke: #CCC;\n  stroke-width: 1px;\n  fill: white;\n}\n\n.plottable .bar-plot .baseline {\n  stroke: #999;\n}\n\n.plottable .gridlines line {\n  stroke: #3C3C3C; /* hackhack: gridlines should be solid; see #820 */\n  opacity: 0.25;\n  stroke-width: 1px;\n}\n\n.plottable .selection-box-layer .selection-area {\n  fill: black;\n  fill-opacity: 0.03;\n  stroke: #CCC;\n}\n/* DragBoxLayer */\n.plottable .drag-box-layer.x-resizable .drag-edge-lr {\n  cursor: ew-resize;\n}\n.plottable .drag-box-layer.y-resizable .drag-edge-tb {\n  cursor: ns-resize;\n}\n\n.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-tl {\n  cursor: nwse-resize;\n}\n.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-tr {\n  cursor: nesw-resize;\n}\n.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-bl {\n  cursor: nesw-resize;\n}\n.plottable .drag-box-layer.x-resizable.y-resizable .drag-corner-br {\n  cursor: nwse-resize;\n}\n\n.plottable .drag-box-layer.movable .selection-area {\n  cursor: move; /* IE fallback */\n  cursor: -moz-grab;\n  cursor: -webkit-grab;\n  cursor: grab;\n}\n\n.plottable .drag-box-layer.movable .selection-area:active {\n  cursor: -moz-grabbing;\n  cursor: -webkit-grabbing;\n  cursor: grabbing;\n}\n/* /DragBoxLayer */\n\n.plottable .guide-line-layer line.guide-line {\n  stroke: #CCC;\n  stroke-width: 1px;\n}\n\n.plottable .drag-line-layer.enabled.vertical line.drag-edge {\n  cursor: ew-resize;\n}\n\n.plottable .drag-line-layer.enabled.horizontal line.drag-edge {\n  cursor: ns-resize;\n}\n\n.plottable .legend text {\n  fill: #32313F;\n  font-size: 12px;\n  font-weight: bold;\n  line-height: normal;\n}\n\n.plottable .interpolated-color-legend rect.swatch-bounding-box {\n  fill: none;\n  stroke: #CCC;\n  stroke-width: 1px;\n  pointer-events: none;\n}\n\n.plottable .waterfall-plot line.connector {\n  stroke: #CCC;\n  stroke-width: 1px;\n}\n\n.plottable .pie-plot .arc.outline {\n  stroke-linejoin: round;\n}\n\n"});const PTt=[1,0,0,1,0,0];class NTt extends rO.Utils.Translator{computePosition(t,e){const n={x:t,y:e},i=(function r(t){const e=(function n(t){const e=[];for(;t&&t instanceof HTMLElement;)if(e.push(t),t.assignedSlot)t=t.assignedSlot;else if(t.parentElement)t=t.parentElement;else{const e=t.parentNode;t=e instanceof DocumentFragment?e.host:e!==t?e:null}return e})(t);let i=PTt,r=null;for(const t of e){const e=rO.Utils.DOM.getElementTransform(t);if(null!=e){const n=t.clientWidth/2,r=t.clientHeight/2;i=rO.Utils.Math.multiplyTranslate(i,[n,r]),i=rO.Utils.Math.multiplyMatrix(i,rO.Utils.Math.invertMatrix(e)),i=rO.Utils.Math.multiplyTranslate(i,[-n,-r])}let n=t.scrollLeft,o=t.scrollTop;null!==r&&t!==r||(n-=t.offsetLeft+t.clientLeft,o-=t.offsetTop+t.clientTop,r=t.offsetParent),i=rO.Utils.Math.multiplyTranslate(i,[n,o])}return i})(this._rootElement);return null==i?n:rO.Utils.Math.applyTransform(i,n)}}class ITt extends rO.Dispatchers.Mouse{constructor(t){super(t),this._eventTarget=t.root().rootElement().node(),this._translator=new NTt(t.root().rootElement().node())}static getDispatcher(t){const e=t.root().rootElement();let n=e[ITt._DISPATCHER_KEY];return n||(n=new ITt(t),e[ITt._DISPATCHER_KEY]=n),n}}class RTt extends rO.Dispatchers.Touch{constructor(t){super(t),this._eventTarget=t.root().rootElement().node(),this._translator=new NTt(t.root().rootElement().node())}static getDispatcher(t){const e=t.root().rootElement();let n=e[RTt._DISPATCHER_KEY];return n||(n=new RTt(t),e[RTt._DISPATCHER_KEY]=n),n}}rO.Interaction.prototype._isInsideComponent=function(t){return 0<=t.x&&0<=t.y&&t.x<this._componentAttachedTo.width()&&t.y<this._componentAttachedTo.height()};class OTt extends rO.Interactions.Pointer{_anchor(t){const e=this;e._isAnchored=!0,e._mouseDispatcher=ITt.getDispatcher(e._componentAttachedTo),e._mouseDispatcher.onMouseMove(e._mouseMoveCallback),e._touchDispatcher=RTt.getDispatcher(e._componentAttachedTo),e._touchDispatcher.onTouchStart(e._touchStartCallback)}}var zTt;!(function(t){t.AUTO="auto",t.BOTTOM="bottom",t.RIGHT="right"})(zTt||(zTt={}));const DTt={boxShadow:"0 1px 4px rgba(0, 0, 0, .3)",opacity:0,position:"fixed",willChange:"transform",zIndex:5};let BTt=class extends(er(ye)){constructor(){super(...arguments),this.position=zTt.AUTO,this.minDistFromEdge=15,this._styleCache=null,this._raf=null,this._tunnel=null}ready(){this._styleCache=null,this._raf=null,this._tunnel=null}attached(){this._tunnel=this._createTunnel(),this._hideOnBlur=()=>{document.hidden&&this.hide()},window.addEventListener("visibilitychange",this._hideOnBlur)}detached(){this.hide(),this._removeTunnel(this._tunnel),this._tunnel=null,window.removeEventListener("visibilitychange",this._hideOnBlur)}content(){return this._tunnel.shadowRoot}hide(){window.cancelAnimationFrame(this._raf),this._styleCache=null,this._tunnel.style.opacity=0}updateAndPosition(t){window.cancelAnimationFrame(this._raf),this._raf=window.requestAnimationFrame((()=>{this.isAttached&&this._repositionImpl(t)}))}_repositionImpl(t){const e=this._tunnel,n=t.getBoundingClientRect(),i=e.getBoundingClientRect(),r=window.innerHeight,o=document.body.clientWidth,a=n.top,s=a+n.height,l=i.height+20;let c=null,u=Math.max(this.minDistFromEdge,n.left),h=null,d=a;this.position==zTt.RIGHT?u=n.right:(d=s+20,o<u+i.width+this.minDistFromEdge&&(u=null,h=this.minDistFromEdge)),this.position==zTt.AUTO&&n.top-l>0&&r<n.top+n.height+l&&(d=null,c=r-a+20);const p={contain:"content",opacity:1,left:u?`${u}px`:null,right:h?`${h}px`:null,top:d?`${d}px`:null,bottom:c?`${c}px`:null};Se.exports.isEqual(this._styleCache,p)||(Object.assign(e.style,p),this._styleCache=p)}_createTunnel(){if(!this.contentComponentName)throw new RangeError("Require `contentComponentName` to be a name of a Polymer component");const t=document.createElement(this.contentComponentName);return Object.assign(t.style,DTt),document.body.appendChild(t),t}_removeTunnel(t){document.body.removeChild(t)}};t([o({type:String}),e("design:type",String)],BTt.prototype,"contentComponentName",void 0),t([o({type:String}),e("design:type",String)],BTt.prototype,"position",void 0),t([o({type:Number}),e("design:type",Number)],BTt.prototype,"minDistFromEdge",void 0),BTt=t([i("vz-chart-tooltip")],BTt);const HTt=my(".2~e"),FTt=my(".4~r"),VTt=my(",~");function UTt(t){if(0===t)return"0";const e=Math.abs(t);return e>=1e4||e<.001?HTt(t):FTt(t)}const jTt={formatTick:UTt,formatShort:UTt,formatReadable(t){const e=Math.abs(t);return e>=1e4||e<.001?HTt(t):VTt(t)},formatLong:VTt};my("0.3~s"),my(",.3~f"),my(".4~");const GTt=cA().tickFormat();let WTt;const qTt={formatTick:t=>GTt(new Date(t)),formatShort:t=>new Date(t).toLocaleString(WTt,{year:"numeric",month:"short",day:"numeric",hour:"numeric",minute:"numeric",second:"numeric"}),formatReadable:t=>new Date(t).toLocaleString(WTt,{year:"numeric",month:"short",day:"numeric",hour:"numeric",minute:"numeric",second:"numeric",timeZoneName:"short"}),formatLong:t=>new Date(t).toLocaleString(WTt,{year:"numeric",month:"long",day:"numeric",hour:"numeric",minute:"numeric",second:"numeric",timeZoneName:"short",fractionalSecondDigits:3})};var YTt;!(function(t){t[t.LINEAR=0]="LINEAR",t[t.LOG10=1]="LOG10",t[t.TIME=2]="TIME"})(YTt||(YTt={}));class XTt{constructor(){this.defaultFormatter=jTt}transform(t,e,n){const[i,r]=t,o=r-i,[a,s]=e;return 0===o?a:(s-a)/o*(n-i)+a}forward(t,e,n){return this.transform(t,e,n)}reverse(t,e,n){return this.transform(e,t,n)}niceDomain(t){let[e,n]=t;if(n<e)throw new Error("Unexpected input: min is larger than max");if(n===e)return 0===e?[-1,1]:e<0?[2*e,0]:[0,2*e];const i=VM(),r=.05*(n-e+Number.EPSILON),[o,a]=i.domain([e-r,n+r]).nice().domain();return[o,a]}ticks(t,e){return VM().domain(t).ticks(e)}isSafeNumber(t){return Number.isFinite(t)}}class $Tt{constructor(){this.defaultFormatter=jTt}transform(t){return Math.log10(t>0?t:Number.MIN_VALUE)}untransform(t){return Math.exp(t/Math.LOG10E)}forward(t,e,n){if(n<=0)return e[0];const[i,r]=t,[o,a]=e,s=this.transform(i),l=this.transform(r)-s,c=a-o;return n=this.transform(n),c/(l+Number.EPSILON)*(n-s)+o}reverse(t,e,n){const[i,r]=t,[o,a]=e,s=this.transform(i),l=this.transform(r);return this.untransform((l-s)/(a-o+Number.EPSILON)*(n-o)+s)}niceDomain(t){const[e,n]=t;if(e>n)throw new Error("Unexpected input: min is larger than max");const i=Math.max(e,Number.MIN_VALUE),r=Math.max(n,Number.MIN_VALUE);return n<=0?[Number.MIN_VALUE,1]:[Math.max(Number.MIN_VALUE,.5*i),2*r]}ticks(t,e){const n=t[0]<=0?Number.MIN_VALUE:t[0],i=t[1]<=0?Number.MIN_VALUE:t[1],r=KM().domain([n,i]).ticks(e);return r.length?r:t}isSafeNumber(t){return Number.isFinite(t)&&t>0}}class KTt{constructor(){this.scale=cA(),this.defaultFormatter=qTt}forward(t,e,n){return this.scale.domain(t).range(e)(n)}reverse(t,e,n){return this.scale.domain(t).range(e).invert(n).getTime()}niceDomain(t){const[e,n]=this.scale.domain(t).nice().domain();return[e.getTime(),n.getTime()]}ticks(t,e){return this.scale.domain(t).ticks(e).map((t=>t.getTime()))}isSafeNumber(t){return Number.isFinite(t)}}class ZTt extends rO.Scales.Linear{constructor(){super(),this._ignoreOutlier=!1,this.padProportion(.2)}setValueProviderForDomain(t){return this._valueProviderForDomain=t,this}_niceDomain(t,e){const[n,i]=t;return(function r(t){switch(t){case YTt.LINEAR:return new XTt;case YTt.LOG10:return new $Tt;case YTt.TIME:return new KTt;default:throw new RangeError(`ScaleType ${t} not supported.`)}})(YTt.LINEAR).niceDomain([n,i])}_getUnboundedExtent(t){const e=this._getAllIncludedValues(t);let n=this._defaultExtent();if(0!==e.length){const t=[rO.Utils.Math.min(e,n[0]),rO.Utils.Math.max(e,n[1])];n=this._niceDomain(t)}return n}_getAllIncludedValues(t=!1){const e=this._valueProviderForDomain?this._valueProviderForDomain():[];return this.extentOfValues(e)}extentOfValues(t){const e=t.filter((t=>rO.Utils.Math.isValidNumber(t)));let n=e;if(this.ignoreOutlier()){const t=e.sort(((t,e)=>t-e)),i=Gl(t,.05),r=Gl(t,.95);n=e.filter((t=>t>=i&&t<=r))}const i=Ll(n);return null==i[0]||null==i[1]?[]:i}ignoreOutlier(t){return"boolean"==typeof t?(this._ignoreOutlier=t,this):this._ignoreOutlier}}class JTt extends rO.QuantitativeScale{constructor(){super(...arguments),this._ignoreOutlier=!1}setValueProviderForDomain(t){return this._valueProviderForDomain=t,this}ignoreOutlier(t){return"boolean"==typeof t?(this._ignoreOutlier=t,this):this._ignoreOutlier}_getAllIncludedValues(t=!1){const e=this._valueProviderForDomain?this._valueProviderForDomain():[];return this.extentOfValues(e)}}const QTt=Math.pow(2,-1074);function tCt(t){return Math.log10(t)}function eCt(t){return Math.pow(10,t)}class nCt extends JTt{constructor(){super(),this._d3LogScale=KM(),this.padProportion(.2)}scale(t){return t<=0?NaN:this._d3LogScale(t)}invert(t){return this._d3LogScale.invert(t)}scaleTransformation(t){return this.scale(t)}invertedTransformation(t){return this.invert(t)}getTransformationDomain(){return this.domain()}setTransformationDomain(t){this.domain(t)}getTransformationExtent(){return this._getUnboundedExtent(!0)}_getDomain(){return this._untransformedDomain}_setDomain(t){this._untransformedDomain=t;const[e,n]=t;super._setDomain([Math.max(QTt,e),n])}_niceDomain(t,e){const[n,i]=t,r=Math.max(tCt(QTt),tCt(n)),o=tCt(i),a=o-r,s=a?a*this.padProportion():1;return[eCt(Math.max(tCt(QTt),r-s)),eCt(o+s)]}_getUnboundedExtent(t){const e=this._getAllIncludedValues(t);let n=this._defaultExtent();if(0!==e.length){const t=[rO.Utils.Math.min(e,n[0]),rO.Utils.Math.max(e,n[1])];n=this._niceDomain(t)}return n}_getAllIncludedValues(t=!1){return super._getAllIncludedValues().map((t=>t>0?t:QTt))}_defaultExtent(){return[1,10]}_backingScaleDomain(t){return null==t?this._d3LogScale.domain():(this._d3LogScale.domain(t),this)}_getRange(){return this._d3LogScale.range()}_setRange(t){this._d3LogScale.range(t)}defaultTicks(){return this._d3LogScale.ticks(1)}ticks(){return this._d3LogScale.ticks()}extentOfValues(t){const e=t.filter((t=>rO.Utils.Math.isValidNumber(t)&&t>0));let n=e;if(this.ignoreOutlier()){const t=e.map(tCt).sort(((t,e)=>t-e)),i=Gl(t,.05),r=Gl(t,.95);n=t.filter((t=>t>=i&&t<=r)).map(eCt)}const i=Ll(n);return null==i[0]||null==i[1]?[]:i}}class iCt extends rO.Components.SelectionBoxLayer{constructor(t,e,n){super(),this.easeFn=Tf,this._animationTime=750,this.xScale(t),this.yScale(e),this._dragInteraction=new rO.Interactions.Drag,this._doubleClickInteraction=new rO.Interactions.Click,this.setupCallbacks(),this.unzoomMethod=n,this.onDetach((()=>{this._doubleClickInteraction.detachFrom(this),this._dragInteraction.detachFrom(this)})),this.onAnchor((()=>{this._doubleClickInteraction.attachTo(this),this._dragInteraction.attachTo(this)}))}interactionStart(t){this.onStart=t}interactionEnd(t){this.onEnd=t}dragInteraction(){return this._dragInteraction}setupCallbacks(){let t=!1;this._dragInteraction.onDragStart((t=>{this.bounds({topLeft:t,bottomRight:t}),this.onStart()})),this._dragInteraction.onDrag(((e,n)=>{this.bounds({topLeft:e,bottomRight:n}),this.boxVisible(!0),t=!0})),this._dragInteraction.onDragEnd(((e,n)=>{this.boxVisible(!1),this.bounds({topLeft:e,bottomRight:n}),t?this.zoom():this.onEnd(),t=!1})),this._doubleClickInteraction.onDoubleClick(this.unzoom.bind(this))}animationTime(t){if(null==t)return this._animationTime;if(t<0)throw new Error("animationTime cannot be negative");return this._animationTime=t,this}ease(t){if("function"!=typeof t)throw new Error("ease function must be a function");return 0===t(0)&&1===t(1)||rO.Utils.Window.warn("Easing function does not maintain invariant f(0)==0 && f(1)==1. Bad behavior may result."),this.easeFn=t,this}zoom(){let t=this.xExtent()[0].valueOf(),e=this.xExtent()[1].valueOf(),n=this.yExtent()[1].valueOf(),i=this.yExtent()[0].valueOf();t!==e&&n!==i&&this.interpolateZoom(t,e,n,i)}unzoom(){let t=this.xScale();t._domainMin=null,t._domainMax=null;let e=t._getExtent();this.xScale().domain(e),this.unzoomMethod()}isZooming(t){this._dragInteraction.enabled(!t),this._doubleClickInteraction.enabled(!t)}interpolateZoom(t,e,n,i){let r=this.xScale().domain()[0].valueOf(),o=this.xScale().domain()[1].valueOf(),a=this.yScale().domain()[0].valueOf(),s=this.yScale().domain()[1].valueOf(),l=this.easeFn,c=(t,e,n)=>Bd(t,e)(l(n));this.isZooming(!0);let u=Date.now(),h=()=>{let l=Date.now(),d=0===this._animationTime?1:Math.min(1,(l-u)/this._animationTime),p=c(r,t,d),f=c(o,e,d),m=c(a,n,d),g=c(s,i,d);this.xScale().domain([p,f]),this.yScale().domain([m,g]),d<1?rO.Utils.DOM.requestAnimationFramePolyfill(h):(this.onEnd(),this.isZooming(!1))};h()}}var rCt,oCt,aCt,sCt;!(function(t){t[t.NONE=0]="NONE",t[t.DRAG_ZOOMING=1]="DRAG_ZOOMING",t[t.PANNING=2]="PANNING"})(rCt||(rCt={}));class lCt extends rO.Components.Group{constructor(t,e,n){super(),this.state=rCt.NONE,this.panStartCallback=new rO.Utils.CallbackSet,this.panEndCallback=new rO.Utils.CallbackSet,this.panZoom=new rO.Interactions.PanZoom(t,e),this.panZoom.dragInteraction().mouseFilter((t=>lCt.isPanKey(t)&&0===t.button)),this.panZoom.wheelFilter(this.canScrollZoom),this.dragZoomLayer=new iCt(t,e,n),this.dragZoomLayer.dragInteraction().mouseFilter((t=>!lCt.isPanKey(t)&&0===t.button)),this.append(this.dragZoomLayer);const i=this.onWheel.bind(this);this.onAnchor((()=>{this._mouseDispatcher=rO.Dispatchers.Mouse.getDispatcher(this),this._mouseDispatcher.onWheel(i),this.panZoom.attachTo(this)})),this.onDetach((()=>{this.panZoom.detachFrom(this),this._mouseDispatcher&&(this._mouseDispatcher.offWheel(i),this._mouseDispatcher=null)})),this.panZoom.dragInteraction().onDragStart((()=>{this.state==rCt.NONE&&this.setState(rCt.PANNING)})),this.panZoom.dragInteraction().onDragEnd((()=>{this.state==rCt.PANNING&&this.setState(rCt.NONE)})),this.dragZoomLayer.dragInteraction().onDragStart((()=>{this.state==rCt.NONE&&this.setState(rCt.DRAG_ZOOMING)})),this.dragZoomLayer.dragInteraction().onDragEnd((()=>{this.state==rCt.DRAG_ZOOMING&&this.setState(rCt.NONE)}))}onWheel(t,e){if(this.canScrollZoom(e))return;const n=this.element();if(!n.select(".help").empty())return;const i=n.append("div").classed("help",!0);i.append("span").text("Alt + Scroll to Zoom"),i.on("animationend",(()=>{i.remove()}))}static isPanKey(t){return Boolean(t.altKey)||Boolean(t.shiftKey)}canScrollZoom(t){return t.altKey}setState(t){if(this.state==t)return;const e=this.state;this.state=t,this.root().removeClass(this.stateClassName(e)),this.root().addClass(this.stateClassName(t)),e==rCt.PANNING&&this.panEndCallback.callCallbacks(),t==rCt.PANNING&&this.panStartCallback.callCallbacks()}stateClassName(t){switch(t){case rCt.PANNING:return"panning";case rCt.DRAG_ZOOMING:return"drag-zooming";case rCt.NONE:default:return""}}onPanStart(t){this.panStartCallback.add(t)}onPanEnd(t){this.panEndCallback.add(t)}onScrollZoom(t){this.panZoom.onZoomEnd(t)}onDragZoomStart(t){this.dragZoomLayer.interactionStart(t)}onDragZoomEnd(t){this.dragZoomLayer.interactionEnd(t)}}!(function(t){t[t.TEXT=0]="TEXT",t[t.DOM=1]="DOM"})(oCt||(oCt={})),(function(t){t.LOG="log",t.LINEAR="linear"})(aCt||(aCt={}));class cCt{constructor(t,e,n,i,r,o,a,s,l,c,u){this.dirtyDatasets=new Set,this.seriesNames=[],this.name2datasets={},this.colorScale=i,this.tooltip=r,this.datasets=[],this._ignoreYOutliers=!1,this.lastPointsDataset=new rO.Dataset,this.nanDataset=new rO.Dataset,this.yValueAccessor=e,this.symbolFunction=c,this._defaultXRange=s,this._defaultYRange=l,this.tooltipColumns=o,this.buildChart(t,e,n,a,u)}buildChart(t,e,n,i,r){this.destroy();const o=t();this.xAccessor=o.accessor,this.xScale=o.scale,this.xAxis=o.axis,this.xAxis.margin(1).tickLabelPadding(3),r&&this.xAxis.formatter(r),this.yScale=cCt.getYScaleFromType(n),this.yScale.setValueProviderForDomain((()=>this.getValuesForYAxisDomainCompute())),this.yAxis=new rO.Axes.Numeric(this.yScale,"left");let a=wTt(3);this.yAxis.margin(0).tickLabelPadding(5).formatter(a),this.yAxis.usesTextWidthApproximation(!0),this.fillArea=i;const s=new lCt(this.xScale,this.yScale,(()=>this.resetDomain()));this.tooltipInteraction=this.createTooltipInteraction(s),this.tooltipPointsComponent=new rO.Component;const l=this.buildPlot(this.xScale,this.yScale,i);this.gridlines=new rO.Components.Gridlines(this.xScale,this.yScale);let c=null;n!==aCt.LOG&&(c=new rO.Components.GuideLineLayer("horizontal"),c.scale(this.yScale).value(0));let u=new rO.Components.GuideLineLayer("vertical");u.scale(this.xScale).value(0),this.center=new rO.Components.Group([this.gridlines,c,u,l,this.tooltipPointsComponent,s]),this.center.addClass("main"),this.outer=new rO.Components.Table([[this.yAxis,this.center],[null,this.xAxis]])}buildPlot(t,e,n){n&&(this.marginAreaPlot=new rO.Plots.Area,this.marginAreaPlot.x(this.xAccessor,t),this.marginAreaPlot.y(n.higherAccessor,e),this.marginAreaPlot.y0(n.lowerAccessor),this.marginAreaPlot.attr("fill",((t,e,n)=>this.colorScale.scale(n.metadata().name))),this.marginAreaPlot.attr("fill-opacity",.3),this.marginAreaPlot.attr("stroke-width",0)),this.smoothedAccessor=t=>t.smoothed;let i=new rO.Plots.Line;i.x(this.xAccessor,t),i.y(this.yValueAccessor,e),i.attr("stroke",((t,e,n)=>this.colorScale.scale(n.metadata().name))),this.linePlot=i,this.setupTooltips(i);let r=new rO.Plots.Line;if(r.x(this.xAccessor,t),r.y(this.smoothedAccessor,e),r.attr("stroke",((t,e,n)=>this.colorScale.scale(n.metadata().name))),this.smoothLinePlot=r,this.symbolFunction){const n=new rO.Plots.Scatter;n.x(this.xAccessor,t),n.y(this.yValueAccessor,e),n.attr("fill",((t,e,n)=>this.colorScale.scale(n.metadata().name))),n.attr("opacity",1),n.size(8),n.symbol(((t,e,n)=>this.symbolFunction(n.metadata().name))),this.markersScatterPlot=n}let o=new rO.Plots.Scatter;o.x(this.xAccessor,t),o.y(this.yValueAccessor,e),o.attr("fill",(t=>this.colorScale.scale(t.name))),o.attr("opacity",1),o.size(8),o.datasets([this.lastPointsDataset]),this.scatterPlot=o;let a=new rO.Plots.Scatter;a.x(this.xAccessor,t),a.y((t=>t.displayY),e),a.attr("fill",(t=>this.colorScale.scale(t.name))),a.attr("opacity",1),a.size(12),a.datasets([this.nanDataset]),a.symbol(rO.SymbolFactories.triangle),this.nanDisplay=a;const s=[a,o,r,i];return this.marginAreaPlot&&s.push(this.marginAreaPlot),this.markersScatterPlot&&s.push(this.markersScatterPlot),new rO.Components.Group(s)}ignoreYOutliers(t){t!==this._ignoreYOutliers&&(this._ignoreYOutliers=t,this.updateSpecialDatasets(),this.yScale.ignoreOutlier(t),this.resetYDomain())}getValuesForYAxisDomainCompute(){const t=this.getAccessorsForComputingYRange();return Se.exports.flattenDeep(this.datasets.map((e=>t.map((t=>e.data().map((n=>t(n,-1,e)))))))).filter(isFinite)}updateSpecialDatasets(){const t=this.getYAxisAccessor();let e=this.datasets.map((e=>{let n=null,i=e.data().filter((n=>!isNaN(t(n,-1,e))));return i.length>0&&(n=i[i.length-1],n.name=e.metadata().name,n.relative=TTt(n,0,e)),n})).filter((t=>null!=t));this.lastPointsDataset.data(e),this.markersScatterPlot&&this.markersScatterPlot.datasets(this.datasets.map(this.createSampledDatasetForMarkers));let n=Se.exports.flatten(this.datasets.map((e=>{let n=null,i=e.data(),r=0;for(;r<i.length&&null==n;)isNaN(t(i[r],-1,e))||(n=t(i[r],-1,e)),r++;null==n&&(n=0);let o=[];for(r=0;r<i.length;r++)isNaN(t(i[r],-1,e))?(i[r].name=e.metadata().name,i[r].displayY=n,i[r].relative=TTt(i[r],0,e),o.push(i[r])):n=t(i[r],-1,e);return o})));this.nanDataset.data(n)}resetDomain(){this.resetXDomain(),this.resetYDomain()}resetXDomain(){let t;if(null!=this._defaultXRange)t=this._defaultXRange;else{const e=this.xScale;e._domainMin=null,e._domainMax=null,t=e._getExtent()}this.xScale.domain(t)}resetYDomain(){null!=this._defaultYRange?this.yScale.domain(this._defaultYRange):(this.yScale.autoDomain(),this.yScale.domain(this.yScale.domain()))}getAccessorsForComputingYRange(){const t=[this.getYAxisAccessor()];return this.fillArea&&t.push(this.fillArea.lowerAccessor,this.fillArea.higherAccessor),t}getYAxisAccessor(){return this.smoothingEnabled?this.smoothedAccessor:this.yValueAccessor}createTooltipInteraction(t){const e=new OTt,n=()=>{e.enabled(!1),this.hideTooltips()},i=()=>e.enabled(!0);return t.onPanStart(n),t.onDragZoomStart(n),t.onPanEnd(i),t.onDragZoomEnd(i),t.onScrollZoom((()=>this.updateTooltipContent(this._lastMousePosition))),e.onPointerMove((t=>{this._lastMousePosition=t,this.updateTooltipContent(t)})),e.onPointerExit((()=>this.hideTooltips())),e}updateTooltipContent(t){this.linePlot&&(window.cancelAnimationFrame(this._tooltipUpdateAnimationFrame),this._tooltipUpdateAnimationFrame=window.requestAnimationFrame((()=>{let e={x:t.x,y:t.y,datum:null,dataset:null},n=this.gridlines.content().node().getBBox(),i=this.linePlot.datasets().map((t=>this.findClosestPoint(e,t))).filter(Boolean),r=rO.Utils.DOM.intersectsBBox,o=i.filter((t=>r(t.x,t.y,n)||isNaN(this.yValueAccessor(t.datum,0,t.dataset)))),a=o.filter((t=>!isNaN(this.yValueAccessor(t.datum,0,t.dataset))));if(0!==i.length){this.scatterPlot.attr("display","none");const t=this.tooltipPointsComponent.content().selectAll(".point").data(a,(t=>t.dataset.metadata().name));t.enter().append("circle").classed("point",!0),t.attr("r",4).attr("cx",(t=>t.x)).attr("cy",(t=>t.y)).style("stroke","none").attr("fill",(t=>this.colorScale.scale(t.dataset.metadata().name))),t.exit().remove(),this.drawTooltips(o,e,this.tooltipColumns)}else this.hideTooltips()})))}hideTooltips(){window.cancelAnimationFrame(this._tooltipUpdateAnimationFrame),this.tooltip.hide(),this.scatterPlot.attr("display","block"),this.tooltipPointsComponent.content().selectAll(".point").remove()}setupTooltips(t){t.onDetach((()=>{this.tooltipInteraction.detachFrom(t),this.tooltipInteraction.enabled(!1)})),t.onAnchor((()=>{this.tooltipInteraction.attachTo(t),this.tooltipInteraction.enabled(!0)}))}drawTooltips(t,e,n){if(!t.length)return void this.tooltip.hide();const{colorScale:i}=this;n=[{title:"",static:!1,evalType:oCt.DOM,evaluate(t){return Su(this).select("span").style("background-color",(()=>i.scale(t.dataset.metadata().name))),""},enter(t){Su(this).append("span").classed("swatch",!0).style("background-color",(()=>i.scale(t.dataset.metadata().name)))}},...n];const r=t=>Math.pow(t.x-e.x,2)+Math.pow(t.y-e.y,2),o=Se.exports.min(t.map(r)),a=this.smoothingEnabled?this.smoothedAccessor:this.yValueAccessor;t="ascending"===this.tooltipSortingMethod?Se.exports.sortBy(t,(t=>a(t.datum,-1,t.dataset))):"descending"===this.tooltipSortingMethod?Se.exports.sortBy(t,(t=>a(t.datum,-1,t.dataset))).reverse():"nearest"===this.tooltipSortingMethod?Se.exports.sortBy(t,r):t.slice(0).reverse();const s=this,l=Su(this.tooltip.content()).select("table"),c=l.select("thead").selectAll("th").data(n,((t,e,n)=>t.title));c.enter().append("th").text((t=>t.title)).nodes(),c.exit().remove();const u=l.select("tbody").selectAll("tr").data(t,((t,e,n)=>t.dataset.metadata().name));u.classed("distant",(t=>{let n=t.dataset.data()[0],i=Se.exports.last(t.dataset.data()),r=this.xScale.scale(this.xAccessor(n,0,t.dataset)),o=this.xScale.scale(this.xAccessor(i,0,t.dataset)),a=this.smoothingEnabled?t.datum.smoothed:this.yValueAccessor(t.datum,0,t.dataset);return e.x<r||e.x>o||isNaN(a)})).classed("closest",(t=>r(t)===o)).each((function(t){s.drawTooltipRow(this,n,t)})).order(),u.exit().remove(),u.enter().append("tr").each((function(t){s.drawTooltipRow(this,n,t)})).nodes(),this.tooltip.updateAndPosition(this.targetSVG.node())}drawTooltipRow(t,e,n){const i=this,r=Su(t).selectAll("td").data(e);r.each((function(t){t.static||i.drawTooltipColumn.call(i,this,t,n)})),r.exit().remove(),r.enter().append("td").each((function(t){"enter"in t&&t.enter&&t.enter.call(this,n),i.drawTooltipColumn.call(i,this,t,n)}))}drawTooltipColumn(t,e,n){const{smoothingEnabled:i}=this;"evalType"in e&&e.evalType==oCt.DOM?e.evaluate.call(t,n,{smoothingEnabled:i}):Su(t).text(e.evaluate.call(t,n,{smoothingEnabled:i}))}findClosestPoint(t,e){const n=e.data().map(((t,n)=>this.xScale.scale(this.xAccessor(t,n,e))));let i=Se.exports.sortedIndex(n,t.x);if(0==n.length)return null;i===n.length?i-=1:0!==i&&(i=Math.abs(n[i-1]-t.x)<Math.abs(n[i]-t.x)?i-1:i);const r=e.data()[i],o=this.smoothingEnabled?this.smoothedAccessor(r,i,e):this.yValueAccessor(r,i,e);return{x:n[i],y:this.yScale.scale(o),datum:r,dataset:e}}resmoothDataset(t){let e=t.data();const n=this.smoothingWeight;let i=e.length>0?0:NaN,r=0;const o=e.map(((e,n)=>this.yValueAccessor(e,n,t))),a=o.every((t=>t==o[0]));e.forEach(((t,e)=>{const s=o[e];if(a||!Number.isFinite(s))t.smoothed=s;else{i=i*n+(1-n)*s,r++;let e=1;1!==n&&(e=1-Math.pow(n,r)),t.smoothed=i/e}}))}getDataset(t){return void 0===this.name2datasets[t]&&(this.name2datasets[t]=new rO.Dataset([],{name:t,meta:null})),this.name2datasets[t]}static getYScaleFromType(t){if(t===aCt.LOG)return new nCt;if(t===aCt.LINEAR)return new ZTt;throw new Error("Unrecognized yScale type "+t)}setVisibleSeries(t){this.disableChanges(),(t=t.sort()).reverse(),this.seriesNames=t}disableChanges(){this.dirtyDatasets.size||(this.linePlot.datasets([]),this.smoothLinePlot&&this.smoothLinePlot.datasets([]),this.marginAreaPlot&&this.marginAreaPlot.datasets([]))}commitChanges(){this.datasets=this.seriesNames.map((t=>this.getDataset(t))),[...this.dirtyDatasets].forEach((t=>{this.smoothingEnabled&&this.resmoothDataset(this.getDataset(t))})),this.updateSpecialDatasets(),this.linePlot.datasets(this.datasets),this.smoothingEnabled&&this.smoothLinePlot.datasets(this.datasets),this.marginAreaPlot&&this.marginAreaPlot.datasets(this.datasets),this.measureBBoxAndMaybeInvalidateLayoutInRaf(),this.dirtyDatasets.clear()}createSampledDatasetForMarkers(t){const e=t.data();if(e.length<=20)return t;const n=Math.ceil(e.length/20),i=new Array(Math.floor(e.length/n));for(let t=0,r=0;t<i.length;t++,r+=n)i[t]=e[r];return new rO.Dataset(i,t.metadata())}setSeriesData(t,e){this.disableChanges(),this.getDataset(t).data(e),this.dirtyDatasets.add(t)}setSeriesMetadata(t,e){this.disableChanges(),this.getDataset(t).metadata(Object.assign(Object.assign({},this.getDataset(t).metadata()),{meta:e})),this.dirtyDatasets.add(t)}smoothingUpdate(t){this.smoothingWeight=t,this.datasets.forEach((t=>this.resmoothDataset(t))),this.smoothingEnabled||(this.linePlot.addClass("ghost"),this.scatterPlot.y(this.smoothedAccessor,this.yScale),this.smoothingEnabled=!0,this.smoothLinePlot.datasets(this.datasets)),this.markersScatterPlot&&this.markersScatterPlot.y(this.getYAxisAccessor(),this.yScale),this.updateSpecialDatasets()}smoothingDisable(){this.smoothingEnabled&&(this.linePlot.removeClass("ghost"),this.scatterPlot.y(this.yValueAccessor,this.yScale),this.smoothLinePlot.datasets([]),this.smoothingEnabled=!1,this.updateSpecialDatasets()),this.markersScatterPlot&&this.markersScatterPlot.y(this.getYAxisAccessor(),this.yScale)}setColorScale(t){this.colorScale=t}setTooltipColumns(t){this.tooltipColumns=t}setTooltipSortingMethod(t){this.tooltipSortingMethod=t}renderTo(t){this.targetSVG=t,this.outer.renderTo(t),null!=this._defaultXRange&&this.resetXDomain(),null!=this._defaultYRange&&this.resetYDomain(),this.measureBBoxAndMaybeInvalidateLayoutInRaf()}redraw(){window.cancelAnimationFrame(this._redrawRaf),this._redrawRaf=window.requestAnimationFrame((()=>{this.measureBBoxAndMaybeInvalidateLayout(),this.outer.redraw()}))}measureBBoxAndMaybeInvalidateLayoutInRaf(){window.cancelAnimationFrame(this._invalidateLayoutRaf),this._invalidateLayoutRaf=window.requestAnimationFrame((()=>{this.measureBBoxAndMaybeInvalidateLayout()}))}measureBBoxAndMaybeInvalidateLayout(){if(this._lastDrawBBox){const{width:t}=this._lastDrawBBox,{width:e}=this.targetSVG.node().getBoundingClientRect();0==t&&t<e&&this.outer.invalidateCache()}this._lastDrawBBox=this.targetSVG.node().getBoundingClientRect()}destroy(){window.cancelAnimationFrame(this._redrawRaf),window.cancelAnimationFrame(this._invalidateLayoutRaf),this.outer&&this.outer.destroy()}onAnchor(t){this.outer&&this.outer.onAnchor(t)}isDataFitToDomain(){return t(this.xAxis.getScale())&&t(this.yAxis.getScale());function t(t){const e=t.getTransformationDomain(),n=t.getTransformationExtent();return n[0]===e[0]&&n[1]===e[1]}}}!(function(t){t.GROUP="G",t.DIV="DIV",t.SVG="SVG",t.TEXT="TEXT"})(sCt||(sCt={}));class uCt extends class{constructor(t){this.uniqueId=0,this.root=t}exportAsString(){const t=this.convert(this.root);if(!t)return"";const e=this.createRootSvg();return e.appendChild(t),e.outerHTML}createUniqueId(t){return`${t}_${this.uniqueId++}`}getSize(){return this.root.getBoundingClientRect()}createRootSvg(){const t=document.createElement("svg"),e=this.getSize();return t.setAttributeNS("svg","viewBox",`0 0 ${e.width} ${e.height}`),t.setAttribute("xmlns","http://www.w3.org/2000/svg"),t}convert(t){let e=null;const n=t.nodeName.toUpperCase();if(t.nodeType!=Node.ELEMENT_NODE||n!=sCt.DIV&&n!=sCt.SVG)e=t.cloneNode();else{e=document.createElement(sCt.GROUP);const n=window.getComputedStyle(t),i=parseInt(n.left,10),r=parseInt(n.top,10);if(i||r){const t=this.createUniqueId("clip");e.setAttribute("transform",`translate(${i}, ${r})`),e.setAttribute("clip-path",`url(#${t})`);const o=parseInt(n.width,10),a=parseInt(n.height,10),s=document.createElement("rect");s.setAttribute("width",String(o)),s.setAttribute("height",String(a));const l=document.createElementNS("svg","clipPath");l.id=t,l.appendChild(s),e.appendChild(l)}}return Array.from(t.childNodes).map((t=>this.convert(t))).filter(Boolean).forEach((t=>e.appendChild(t))),e.nodeName.toUpperCase()==sCt.GROUP&&!e.hasChildNodes()||this.shouldOmitNode(t)?null:this.stripClass(this.transferStyle(t,e))}stripClass(t){return t.nodeType==Node.ELEMENT_NODE&&t.removeAttribute("class"),t}transferStyle(t,e){if(e.nodeType!=Node.ELEMENT_NODE)return e;const n=e,i=e.nodeName.toUpperCase(),r=window.getComputedStyle(t);return i==sCt.TEXT&&Object.assign(n.style,{fontFamily:r.fontFamily,fontSize:r.fontSize,fontWeight:r.fontWeight}),i!=sCt.GROUP&&(n.setAttribute("fill",r.fill),n.setAttribute("stroke",r.stroke),n.setAttribute("stroke-width",r.strokeWidth)),"1"!=r.opacity&&n.setAttribute("opacity",r.opacity),e}shouldOmitNode(t){return!1}}{shouldOmitNode(t){return t.nodeType==Node.ELEMENT_NODE&&t.classList.contains("scatter-plot")}}el({moduleName:"vz-pan-zoom-style",styleContent:"\n    .help {\n      align-items: center;\n      animation-delay: 1s;\n      animation-duration: 1s;\n      animation-name: fade-out;\n      background: rgba(30, 30, 30, 0.6);\n      bottom: 0;\n      color: #fff;\n      display: flex;\n      justify-content: center;\n      left: 0;\n      opacity: 1;\n      padding: 20px;\n      pointer-events: none;\n      position: absolute;\n      right: 0;\n      top: 0;\n    }\n\n    .help > span {\n      white-space: normal;\n    }\n\n    @keyframes fade-out {\n      0% {\n        opacity: 1;\n      }\n\n      100% {\n        opacity: 0;\n      }\n    }\n  "});const hCt=wTt(4),dCt=t=>isNaN(t)?"NaN":hCt(t),pCt=[{title:"Name",evaluate:t=>t.dataset.metadata().name},{title:"Smoothed",evaluate(t,e){const{smoothingEnabled:n}=e;return dCt(n?t.datum.smoothed:t.datum.scalar)}},{title:"Value",evaluate:t=>dCt(t.datum.scalar)},{title:"Step",evaluate:t=>STt(t.datum.step)},{title:"Time",evaluate:t=>ETt(t.datum.wall_time)},{title:"Relative",evaluate:t=>CTt(TTt(t.datum,0,t.dataset))}];let fCt=class extends(er(ye)){constructor(){super(...arguments),this.colorScale=(new rO.Scales.Color).range(gA.slice(0)),this.smoothingEnabled=!1,this.smoothingWeight=.6,this.xType=null,this.xComponentsCreationMethod=null,this.yValueAccessor=t=>t.scalar,this.tooltipColumns=pCt,this.yScaleType=aCt.LINEAR,this.ignoreYOutliers=!1,this.tooltipSortingMethod="default",this.tooltipPosition=zTt.BOTTOM,this._visibleSeriesCache=[],this._seriesDataCache={},this._seriesMetadataCache={},this._makeChartAsyncCallbackId=null}ready(){super.ready(),this.scopeSubtree(this.$.chartdiv,!0)}attached(){const t={capture:!0,passive:!0};this._listen(this,"mousedown",this._onMouseDown.bind(this),t),this._listen(this,"mouseup",this._onMouseUp.bind(this),t),this._listen(window,"keydown",this._onKeyDown.bind(this),t),this._listen(window,"keyup",this._onKeyUp.bind(this),t)}detached(){this.cancelAsync(this._makeChartAsyncCallbackId),this._chart&&(this._chart.destroy(),this._chart=void 0),this._listeners&&(this._listeners.forEach((({node:t,eventName:e,func:n,option:i})=>{t.removeEventListener(e,n,i)})),this._listeners.clear())}_listen(t,e,n,i={}){this._listeners||(this._listeners=new Set),this._listeners.add({node:t,eventName:e,func:n,option:i}),t.addEventListener(e,n,i)}_onKeyDown(t){this.toggleClass("pankey",lCt.isPanKey(t))}_onKeyUp(t){this.toggleClass("pankey",lCt.isPanKey(t))}_onMouseDown(t){this.toggleClass("mousedown",!0)}_onMouseUp(t){this.toggleClass("mousedown",!1)}isDataFitToDomain(){return!this._chart||this._chart.isDataFitToDomain()}setVisibleSeries(t){Se.exports.isEqual(this._visibleSeriesCache,t)||(this._visibleSeriesCache=t)}setSeriesData(t,e){this._seriesDataCache[t]=e,this._chart&&this._chart.setSeriesData(t,e)}setSeriesMetadata(t,e){this._seriesMetadataCache[t]=e,this._chart&&this._chart.setSeriesMetadata(t,e)}commitChanges(){this._chart&&this._chart.commitChanges()}resetDomain(){this._chart&&this._chart.resetDomain()}redraw(){this._chart&&this._chart.redraw()}_makeChart(){null!==this._makeChartAsyncCallbackId&&(this.cancelAsync(this._makeChartAsyncCallbackId),this._makeChartAsyncCallbackId=null),this._makeChartAsyncCallbackId=this.async((function(){this._makeChartAsyncCallbackId=null;let t=this.xComponentsCreationMethod;if(this.xType||t?this.xType&&(t=()=>ATt(this.xType)):t=MTt,t&&this.yValueAccessor&&this.tooltipColumns){var e=new cCt(t,this.yValueAccessor,this.yScaleType,this.colorScale,this.$.tooltip,this.tooltipColumns,this.fillArea,this.defaultXRange,this.defaultYRange,this.symbolFunction,this.xAxisFormatter),n=Su(this.$.chartdiv);e.renderTo(n),this._chart&&this._chart.destroy(),this._chart=e,this._chart.onAnchor((()=>this.fire("chart-attached")))}}),350)}_reloadFromCache(){this._chart&&(this._visibleSeriesCache.forEach((t=>{this._chart.setSeriesData(t,this._seriesDataCache[t]||[])})),this._visibleSeriesCache.filter((t=>this._seriesMetadataCache[t])).forEach((t=>{this._chart.setSeriesMetadata(t,this._seriesMetadataCache[t])})),this._chart.setVisibleSeries(this._visibleSeriesCache),this._chart.commitChanges())}_smoothingChanged(){this._chart&&(this.smoothingEnabled?this._chart.smoothingUpdate(this.smoothingWeight):this._chart.smoothingDisable())}_outliersChanged(){this._chart&&this._chart.ignoreYOutliers(this.ignoreYOutliers)}_colorScaleChanged(){this._chart&&(this._chart.setColorScale(this.colorScale),this._chart.redraw())}_tooltipColumnsChanged(){this._chart&&this._chart.setTooltipColumns(this.tooltipColumns)}_tooltipSortingMethodChanged(){this._chart&&this._chart.setTooltipSortingMethod(this.tooltipSortingMethod)}getExporter(){return new uCt(this.$.chartdiv)}};fCt.template=_e`
    <div id="chartdiv"></div>
    <vz-chart-tooltip
      id="tooltip"
      position="[[tooltipPosition]]"
      content-component-name="vz-line-chart-tooltip"
    ></vz-chart-tooltip>
    <style include="plottable-style"></style>
    <style include="vz-pan-zoom-style"></style>
    <style>
      :host {
        -moz-user-select: none;
        -webkit-user-select: none;
        display: flex;
        flex-direction: column;
        flex-grow: 1;
        flex-shrink: 1;
        outline: none;
        position: relative;
        white-space: nowrap;
      }
      div {
        -webkit-user-select: none;
        -moz-user-select: none;
        flex-grow: 1;
        flex-shrink: 1;
      }

      #chartdiv .main {
        contain: strict;
        cursor: crosshair;
      }

      :host(.pankey) #chartdiv :not(.drag-zooming) .main {
        cursor: -webkit-grab;
        cursor: grab;
      }

      :host(.mousedown) #chartdiv .panning .main {
        cursor: -webkit-grabbing;
        cursor: grabbing;
      }

      #chartdiv {
        contain: strict;
      }

      #chartdiv line.guide-line {
        stroke: #999;
        stroke-width: 1.5px;
      }
      #chartdiv:hover .main {
        will-change: transform;
      }

      .ghost {
        opacity: 0.2;
        stroke-width: 1px;
      }

      .plottable .axis text {
        fill: currentColor;
      }

      .plottable .gridlines line {
        stroke: var(--tb-secondary-text-color);
      }
    </style>
  `,t([o({type:Object}),e("design:type",rO.Scales.Color)],fCt.prototype,"colorScale",void 0),t([o({type:Object}),e("design:type",Function)],fCt.prototype,"symbolFunction",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],fCt.prototype,"smoothingEnabled",void 0),t([o({type:Number}),e("design:type",Number)],fCt.prototype,"smoothingWeight",void 0),t([o({type:String}),e("design:type",String)],fCt.prototype,"xType",void 0),t([o({type:Object}),e("design:type",Function)],fCt.prototype,"xComponentsCreationMethod",void 0),t([o({type:Object}),e("design:type",Function)],fCt.prototype,"xAxisFormatter",void 0),t([o({type:Object}),e("design:type",Function)],fCt.prototype,"yValueAccessor",void 0),t([o({type:Array}),e("design:type",Array)],fCt.prototype,"tooltipColumns",void 0),t([o({type:Object}),e("design:type",Object)],fCt.prototype,"fillArea",void 0),t([o({type:Array}),e("design:type",Array)],fCt.prototype,"defaultXRange",void 0),t([o({type:Array}),e("design:type",Array)],fCt.prototype,"defaultYRange",void 0),t([o({type:String}),e("design:type",String)],fCt.prototype,"yScaleType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],fCt.prototype,"ignoreYOutliers",void 0),t([o({type:String}),e("design:type",String)],fCt.prototype,"tooltipSortingMethod",void 0),t([o({type:String}),e("design:type",String)],fCt.prototype,"tooltipPosition",void 0),t([o({type:Object}),e("design:type",Object)],fCt.prototype,"_chart",void 0),t([o({type:Array}),e("design:type",Array)],fCt.prototype,"_visibleSeriesCache",void 0),t([o({type:Object}),e("design:type",Object)],fCt.prototype,"_seriesDataCache",void 0),t([o({type:Object}),e("design:type",Object)],fCt.prototype,"_seriesMetadataCache",void 0),t([o({type:Number}),e("design:type",Number)],fCt.prototype,"_makeChartAsyncCallbackId",void 0),t([a("xComponentsCreationMethod","xType","yValueAccessor","yScaleType","isAttached"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fCt.prototype,"_makeChart",null),t([a("_chart","_visibleSeriesCache"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fCt.prototype,"_reloadFromCache",null),t([a("smoothingEnabled","smoothingWeight","_chart"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fCt.prototype,"_smoothingChanged",null),t([a("ignoreYOutliers","_chart"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fCt.prototype,"_outliersChanged",null),t([a("colorScale"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fCt.prototype,"_colorScaleChanged",null),t([a("tooltipColumns"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fCt.prototype,"_tooltipColumnsChanged",null),t([a("tooltipSortingMethod","_chart"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],fCt.prototype,"_tooltipSortingMethodChanged",null),fCt=t([i("vz-line-chart2")],fCt);let mCt=class extends ye{};mCt.template=_e`
    <div class="content">
      <table>
        <thead></thead>
        <tbody></tbody>
      </table>
    </div>
    <style>
      :host {
        pointer-events: none;
      }

      .content {
        background: rgba(0, 0, 0, 0.8);
        border-radius: 4px;
        color: #fff;
        overflow: hidden;
        pointer-events: none;
      }

      table {
        font-size: 13px;
        line-height: 1.4em;
        margin-top: 10px;
        padding: 8px;
      }

      thead {
        font-size: 14px;
      }

      tbody {
        font-size: 13px;
        line-height: 21px;
        white-space: nowrap;
      }

      td {
        padding: 0 5px;
      }

      .swatch {
        border-radius: 50%;
        display: block;
        height: 18px;
        width: 18px;
      }

      .closest .swatch {
        box-shadow: inset 0 0 0 2px #fff;
      }

      th {
        padding: 0 5px;
        text-align: left;
      }

      .distant td:not(.swatch) {
        opacity: 0.8;
      }

      .ghost {
        opacity: 0.2;
        stroke-width: 1px;
      }
    </style>
  `,mCt=t([i("vz-line-chart-tooltip")],mCt);const gCt=[],_Ct=Se.exports.throttle((function t(){if(0==gCt.length)return;const e=gCt.shift();e&&e.active&&(e.redraw(),e._maybeRenderedInBadState=!1),window.cancelAnimationFrame(0),window.requestAnimationFrame(t)}),100);let yCt=class extends(LTt(er(ye))){constructor(){super(...arguments),this._redrawRaf=null,this.active=!1,this.logScaleActive=!1,this.colorScale={scale:GR},this._resetDomainOnNextLoad=!0,this._maybeRenderedInBadState=!1}onLoadFinish(){this.commitChanges(),this.dataToLoad.length>0&&this._resetDomainOnNextLoad&&(this._resetDomainOnNextLoad=!1,this.getChart().resetDomain()),this.redraw()}disconnectedCallback(){super.disconnectedCallback(),null!==this._redrawRaf&&cancelAnimationFrame(this._redrawRaf)}exportAsSvgString(){return this.getChart().getExporter().exportAsString()}getChart(){return this.$.chart}resetDomain(){this.getChart().resetDomain()}setSeriesData(t,e){this.getChart().setSeriesData(t,e)}setSeriesMetadata(t,e){this.getChart().setSeriesMetadata(t,e)}commitChanges(){this.getChart().commitChanges()}redraw(){null!==this._redrawRaf&&cancelAnimationFrame(this._redrawRaf),this._redrawRaf=window.requestAnimationFrame((()=>{this.active?this.getChart().redraw():this._maybeRenderedInBadState=!0}))}_loadKeyChanged(){this.reset(),this._resetDomainOnNextLoad=!0}_dataSeriesChanged(){this.getChart().setVisibleSeries(this.dataSeries)}_logScaleChanged(t){this.getChart().yScaleType=t?aCt.LOG:aCt.LINEAR,this.redraw()}_fixBadStateWhenActive(){this.active&&this._maybeRenderedInBadState&&(gCt.push(this),_Ct())}_onChartAttached(){this.active||(this._maybeRenderedInBadState=!0)}};yCt.template=_e`
    <div id="chart-and-spinner-container">
      <vz-line-chart2
        id="chart"
        data-loading$="[[dataLoading]]"
        data-loaded-once$="[[dataLoadedAtLeastOnce]]"
        color-scale="[[colorScale]]"
        default-x-range="[[defaultXRange]]"
        default-y-range="[[defaultYRange]]"
        fill-area="[[fillArea]]"
        ignore-y-outliers="[[ignoreYOutliers]]"
        on-chart-attached="_onChartAttached"
        smoothing-enabled="[[smoothingEnabled]]"
        smoothing-weight="[[smoothingWeight]]"
        symbol-function="[[symbolFunction]]"
        tooltip-columns="[[tooltipColumns]]"
        tooltip-position="[[tooltipPosition]]"
        tooltip-sorting-method="[[tooltipSortingMethod]]"
        x-components-creation-method="[[xComponentsCreationMethod]]"
        x-type="[[xType]]"
        y-value-accessor="[[yValueAccessor]]"
      ></vz-line-chart2>
      <template is="dom-if" if="[[dataLoading]]">
        <div id="loading-spinner-container">
          <paper-spinner-lite active=""></paper-spinner-lite>
        </div>
      </template>
    </div>
    <style>
      :host {
        height: 100%;
        width: 100%;
        display: flex;
        flex-direction: column;
      }

      :host([_maybe-rendered-in-bad-state]) vz-line-chart {
        visibility: hidden;
      }

      #chart-and-spinner-container {
        display: flex;
        flex-grow: 1;
        position: relative;
      }

      #loading-spinner-container {
        align-items: center;
        bottom: 0;
        display: flex;
        display: flex;
        justify-content: center;
        left: 0;
        pointer-events: none;
        position: absolute;
        right: 0;
        top: 0;
      }

      vz-line-chart2 {
        -webkit-user-select: none;
        -moz-user-select: none;
      }

      vz-line-chart2[data-loading] {
        opacity: 0.3;
      }
    </style>
  `,t([o({type:Boolean,observer:"_fixBadStateWhenActive"}),e("design:type",Boolean)],yCt.prototype,"active",void 0),t([o({type:Array}),e("design:type",Array)],yCt.prototype,"dataSeries",void 0),t([o({type:Object}),e("design:type",dr)],yCt.prototype,"requestManager",void 0),t([o({type:Boolean,observer:"_logScaleChanged"}),e("design:type",Boolean)],yCt.prototype,"logScaleActive",void 0),t([o({type:Object}),e("design:type",Object)],yCt.prototype,"xComponentsCreationMethod",void 0),t([o({type:String}),e("design:type",String)],yCt.prototype,"xType",void 0),t([o({type:Object}),e("design:type",Function)],yCt.prototype,"yValueAccessor",void 0),t([o({type:Object}),e("design:type",Object)],yCt.prototype,"fillArea",void 0),t([o({type:Boolean}),e("design:type",Boolean)],yCt.prototype,"smoothingEnabled",void 0),t([o({type:Number}),e("design:type",Number)],yCt.prototype,"smoothingWeight",void 0),t([o({type:Array}),e("design:type",Array)],yCt.prototype,"tooltipColumns",void 0),t([o({type:String}),e("design:type",Object)],yCt.prototype,"tooltipSortingMethod",void 0),t([o({type:String}),e("design:type",String)],yCt.prototype,"tooltipPosition",void 0),t([o({type:Boolean}),e("design:type",Boolean)],yCt.prototype,"ignoreYOutliers",void 0),t([o({type:Array}),e("design:type",Array)],yCt.prototype,"defaultXRange",void 0),t([o({type:Array}),e("design:type",Array)],yCt.prototype,"defaultYRange",void 0),t([o({type:Object}),e("design:type",Function)],yCt.prototype,"symbolFunction",void 0),t([o({type:Object}),e("design:type",Object)],yCt.prototype,"colorScale",void 0),t([o({type:Boolean}),e("design:type",Boolean)],yCt.prototype,"_resetDomainOnNextLoad",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],yCt.prototype,"_maybeRenderedInBadState",void 0),t([a("loadKey"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],yCt.prototype,"_loadKeyChanged",null),t([a("dataSeries.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],yCt.prototype,"_dataSeriesChanged",null),yCt=t([i("tf-line-chart-data-loader")],yCt),el({moduleName:"tf-custom-scalar-card-style",styleContent:"\n    :host {\n      margin: 5px 10px;\n      display: inline-block;\n      width: 330px;\n      vertical-align: text-top;\n    }\n\n    :host([_expanded]) {\n      width: 100%;\n    }\n\n    :host([_expanded]) #tf-line-chart-data-loader-container {\n      height: 400px;\n    }\n\n    h1 {\n      font-size: 19px;\n      font-weight: normal;\n    }\n\n    #tf-line-chart-data-loader-container {\n      height: 200px;\n      width: 100%;\n    }\n\n    #buttons {\n      display: flex;\n      flex-direction: row;\n    }\n\n    paper-icon-button {\n      color: #2196f3;\n      border-radius: 100%;\n      width: 32px;\n      height: 32px;\n      padding: 4px;\n    }\n\n    paper-icon-button[selected] {\n      background: var(--tb-ui-light-accent);\n    }\n\n    .download-links {\n      display: flex;\n      height: 32px;\n    }\n\n    .download-links a {\n      font-size: 10px;\n      align-self: center;\n      margin: 2px;\n    }\n\n    .download-links paper-dropdown-menu {\n      width: 100px;\n      --paper-input-container-label: {\n        font-size: 10px;\n      }\n      --paper-input-container-input: {\n        font-size: 10px;\n      }\n    }\n  "});class vCt{constructor(t,e,n,i,r){this.run=t,this.tag=e,this.name=n,this.scalarData=i,this.symbol=r}getName(){return this.name}setData(t){this.scalarData=t}getData(){return this.scalarData}getRun(){return this.run}getTag(){return this.tag}getSymbol(){return this.symbol}}function bCt(t,e){return`${e} (${t})`}class xCt{constructor(t){this.runBasedColorScale=t}scale(t){return this.runBasedColorScale.scale(this.parseRunName(t))}parseRunName(t){const e=t.match(/\((.*)\)$/);return e?e[1]:""}}let wCt=class extends(er(ye)){constructor(){super(...arguments),this.active=!0,this._colorScale=new xCt({scale:GR}),this._nameToDataSeries={},this._expanded=!1,this._requestData=(t,e,n)=>{const i=_r().pluginRoute("custom_scalars","/scalars");Promise.all(t.map((t=>{const n=iO(i,{tag:this._tagFilter,run:t});return this.requestManager.request(n).then((n=>{e({item:t,data:n})}))}))).finally((()=>{n()}))},this._runToNextAvailableSymbolIndex={},this._matchesListOpened=!1,this._fillArea={lowerAccessor:t=>t.lower,higherAccessor:t=>t.upper},this._tooltipColumns=(()=>{const t=wTt(4),e=e=>isNaN(e)?"NaN":t(e);return[{title:"Name",evaluate:t=>t.dataset.metadata().name},{title:"Value",evaluate:t=>e(t.datum.scalar)},{title:"Lower Margin",evaluate:t=>e(t.datum.lower)},{title:"Upper Margin",evaluate:t=>e(t.datum.upper)},{title:"Step",evaluate:t=>STt(t.datum.step)},{title:"Time",evaluate:t=>ETt(t.datum.wall_time)},{title:"Relative",evaluate:t=>CTt(TTt(t.datum,0,t.dataset))}]})(),this._missingTags=[],this._missingTagsCollapsibleOpened=!1}reload(){this.$.loader.reload()}redraw(){this.$.loader.redraw()}_toggleExpanded(t){this.set("_expanded",!this._expanded),this.redraw()}_toggleLogScale(){this.set("_logScaleActive",!this._logScaleActive)}_resetDomain(){const t=this.$.loader;t&&t.resetDomain()}_csvUrl(t,e){return e?iO(this._downloadDataUrl(t,e),{format:"csv"}):""}_jsonUrl(t,e){return e?iO(this._downloadDataUrl(t,e),{format:"json"}):""}_downloadDataUrl(t,e){const n=t[e],i={tag:n.getTag(),run:n.getRun()};return iO(_r().pluginRoute("custom_scalars","/download_data"),i)}_createProcessDataFunction(t){return(e,n,i)=>{if(!i.regex_valid)return void this.set("_tagFilterInvalid",!0);const r=Se.exports.clone(this._nameToDataSeries),o=[];Se.exports.forEach(t,(t=>{let e=!1;const a=i.tag_to_events[t.value],s=i.tag_to_events[t.lower],l=i.tag_to_events[t.upper];if(Se.exports.isUndefined(a)&&(o.push(t.value),e=!0),Se.exports.isUndefined(s)&&(o.push(t.lower),e=!0),Se.exports.isUndefined(l)&&(o.push(t.upper),e=!0),e)return;const c=t=>t[1],u=this._findStepMismatch(t,a.map(c),s.map(c),l.map(c));if(u)return void this.set("_stepsMismatch",u);const h=t=>t[2],d=a.map(((t,e)=>({wall_time:new Date(1e3*t[0]),step:c(t),scalar:h(t),lower:h(s[e]),upper:h(l[e])}))),p=bCt(n,t.value),f=r[p];if(f)f.setData(d);else{const e=this._createNewDataSeries(n,t.value,p,d);r[p]=e}})),this.set("_nameToDataSeries",r);const a=Se.exports.findIndex(this._missingTags,(t=>t.run===n));if(o.length&&3!=o.length){const t={run:n,tags:o};a>=0?this.splice("_missingTags",a,1,t):this.push("_missingTags",t)}else a>=0&&this.splice("_missingTags",a,1)}}_findStepMismatch(t,e,n,i){return Se.exports.isEqual(n,e)&&Se.exports.isEqual(i,e)?null:{seriesObject:t,valueSteps:e,lowerSteps:n,upperSteps:i}}_createNewDataSeries(t,e,n,i){this._runToNextAvailableSymbolIndex[t]|=0;const r=new vCt(t,e,n,i,bTt[this._runToNextAvailableSymbolIndex[t]]);return this._runToNextAvailableSymbolIndex[t]=(this._runToNextAvailableSymbolIndex[t]+1)%bTt.length,r}_updateChart(){Se.exports.forOwn(this._nameToDataSeries,(t=>{this.$.loader.setSeriesData(t.getName(),t.getData())})),this.$.loader.commitChanges()}get _seriesNames(){const t=new Set(this.runs);return Object.entries(this._nameToDataSeries).filter((([e,n])=>t.has(n.run))).map((([t])=>t))}_determineColor(t,e){return t.scale(e)}_refreshDataSeries(){this.set("_nameToDataSeries",{})}_createSymbolFunction(){return t=>this._nameToDataSeries[t].getSymbol().method()}_determineSymbol(t,e){return t[e].getSymbol().character}get _tagFilter(){return Se.exports.flatten(this.marginChartSeries.map((t=>[t.value,t.lower,t.upper]))).map((t=>"("+this._escapeRegexCharacters(t)+")")).join("|")}_escapeRegexCharacters(t){return t.replace(/[.*+?^${}()|[\]\\]/g,"\\$&")}_getToggleCollapsibleIcon(t){return t?"expand-less":"expand-more"}_toggleMatchesOpen(){this.set("_matchesListOpened",!this._matchesListOpened)}get _titleDisplayString(){return this.title||"untitled"}_separateWithCommas(t){return t.join(", ")}_toggleMissingTagsCollapsibleOpen(){this.set("_missingTagsCollapsibleOpened",!this._missingTagsCollapsibleOpened)}_matchListEntryColorUpdated(){const t=this.$$("#match-list-repeat");t&&this.root.querySelectorAll(".match-list-entry").forEach((e=>{const n=t.itemForElement(e);e.style.color=this._determineColor(this._colorScale,n)}))}};wCt.template=_e`
    <tf-card-heading display-name="[[_titleDisplayString]]"></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader
        id="loader"
        active="[[active]]"
        color-scale="[[_colorScale]]"
        data-series="[[_seriesNames]]"
        fill-area="[[_fillArea]]"
        ignore-y-outliers="[[ignoreYOutliers]]"
        load-key="[[_tagFilter]]"
        data-to-load="[[runs]]"
        request-data="[[_requestData]]"
        log-scale-active="[[_logScaleActive]]"
        load-data-callback="[[_createProcessDataFunction(marginChartSeries)]]"
        request-manager="[[requestManager]]"
        symbol-function="[[_createSymbolFunction()]]"
        tooltip-columns="[[_tooltipColumns]]"
        tooltip-sorting-method="[[tooltipSortingMethod]]"
        x-type="[[xType]]"
      >
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
      <paper-icon-button
        selected$="[[_logScaleActive]]"
        icon="line-weight"
        on-tap="_toggleLogScale"
        title="Toggle y-axis log scale"
      ></paper-icon-button>
      <paper-icon-button
        icon="settings-overscan"
        on-tap="_resetDomain"
        title="Fit domain to data"
      ></paper-icon-button>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <paper-dropdown-menu
            no-label-float="true"
            label="series to download"
            selected-item-label="{{_dataSeriesNameToDownload}}"
          >
            <paper-listbox class="dropdown-content" slot="dropdown-content">
              <template
                is="dom-repeat"
                items="[[_seriesNames]]"
                as="dataSeriesName"
              >
                <paper-item no-label-float="true"
                  >[[dataSeriesName]]</paper-item
                >
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
          <a
            download="[[_dataSeriesNameToDownload]].csv"
            href="[[_csvUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]"
            >CSV</a
          >
          <a
            download="[[_dataSeriesNameToDownload]].json"
            href="[[_jsonUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]"
            >JSON</a
          >
        </div>
      </template>
    </div>

    <!-- here -->
    <template is="dom-if" if="[[_missingTags.length]]">
      <div class="collapsible-list-title">
        <paper-icon-button
          icon="[[_getToggleCollapsibleIcon(_missingTagsCollapsibleOpened)]]"
          on-click="_toggleMissingTagsCollapsibleOpen"
          class="toggle-collapsible-button"
        >
        </paper-icon-button>
        <span class="collapsible-title-text">
          <iron-icon icon="icons:error"></iron-icon> Missing Tags
        </span>
      </div>
      <iron-collapse opened="[[_missingTagsCollapsibleOpened]]">
        <div class="error-content">
          <iron-icon class="error-icon" icon="icons:error"></iron-icon>
          <template is="dom-repeat" items="[[_missingTags]]" as="missingEntry">
            <div class="missing-tags-for-run-container">
              Run "[[missingEntry.run]]" lacks data for tags
              <ul>
                <template
                  is="dom-repeat"
                  items="[[missingEntry.tags]]"
                  as="tag"
                >
                  <li>[[tag]]</li>
                </template>
              </ul>
            </div>
          </template>
        </div>
      </iron-collapse>
    </template>

    <template is="dom-if" if="[[_tagFilterInvalid]]">
      <div class="error-content">
        <iron-icon class="error-icon" icon="icons:error"></iron-icon>
        This regular expresion is invalid:<br />
        <span class="invalid-regex">[[_tagFilter]]</span>
      </div>
    </template>

    <template is="dom-if" if="[[_stepsMismatch]]">
      <div class="error-content">
        <iron-icon class="error-icon" icon="icons:error"></iron-icon>
        The steps for value, lower, and upper tags do not match:
        <ul>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.value]]</span>:
            [[_separateWithCommas(_stepsMismatch.valueSteps)]]
          </li>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.lower]]</span>:
            [[_separateWithCommas(_stepsMismatch.lowerSteps)]]
          </li>
          <li>
            <span class="tag-name">[[_stepsMismatch.seriesObject.upper]]</span>:
            [[_separateWithCommas(_stepsMismatch.upperSteps)]]
          </li>
        </ul>
      </div>
    </template>

    <div id="matches-container">
      <div class="collapsible-list-title">
        <template is="dom-if" if="[[_seriesNames.length]]">
          <paper-icon-button
            icon="[[_getToggleCollapsibleIcon(_matchesListOpened)]]"
            on-click="_toggleMatchesOpen"
            class="toggle-matches-button"
          >
          </paper-icon-button>
        </template>

        <span class="collapsible-title-text">
          Matches ([[_seriesNames.length]])
        </span>
      </div>
      <template is="dom-if" if="[[_seriesNames.length]]">
        <iron-collapse opened="[[_matchesListOpened]]">
          <div id="matches-list">
            <template
              is="dom-repeat"
              items="[[_seriesNames]]"
              as="seriesName"
              id="match-list-repeat"
              on-dom-change="_matchListEntryColorUpdated"
            >
              <div class="match-list-entry">
                <span class="match-entry-symbol">
                  [[_determineSymbol(_nameToDataSeries, seriesName)]]
                </span>
                [[seriesName]]
              </div>
            </template>
          </div>
        </iron-collapse>
      </template>
    </div>

    <style include="tf-custom-scalar-card-style"></style>
    <style>
      .error-content {
        background: #f00;
        border-radius: 5px;
        color: #fff;
        margin: 10px 0 0 0;
        padding: 10px;
      }

      .error-icon {
        display: block;
        fill: #fff;
        margin: 0 auto 5px auto;
      }

      .invalid-regex {
        font-weight: bold;
      }

      .error-content ul {
        margin: 1px 0 0 0;
        padding: 0 0 0 19px;
      }

      .tag-name {
        font-weight: bold;
      }

      .collapsible-list-title {
        margin: 10px 0 5px 0;
      }

      .collapsible-title-text {
        vertical-align: middle;
      }

      #matches-list {
        max-height: 200px;
        overflow-y: auto;
      }

      .match-list-entry {
        margin: 0 0 5px 0;
      }

      .match-entry-symbol {
        font-family: arial, sans-serif;
        display: inline-block;
        width: 10px;
      }

      .missing-tags-for-run-container {
        margin: 8px 0 0 0;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],wCt.prototype,"runs",void 0),t([o({type:String}),e("design:type",String)],wCt.prototype,"xType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wCt.prototype,"active",void 0),t([o({type:String}),e("design:type",String)],wCt.prototype,"title",void 0),t([o({type:Array}),e("design:type",Array)],wCt.prototype,"marginChartSeries",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wCt.prototype,"ignoreYOutliers",void 0),t([o({type:Object}),e("design:type",dr)],wCt.prototype,"requestManager",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wCt.prototype,"showDownloadLinks",void 0),t([o({type:Object}),e("design:type",Object)],wCt.prototype,"tagMetadata",void 0),t([o({type:String}),e("design:type",String)],wCt.prototype,"tooltipSortingMethod",void 0),t([o({type:Object}),e("design:type",Object)],wCt.prototype,"_colorScale",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wCt.prototype,"_tagFilterInvalid",void 0),t([o({type:Object}),e("design:type",Object)],wCt.prototype,"_nameToDataSeries",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],wCt.prototype,"_expanded",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wCt.prototype,"_logScaleActive",void 0),t([o({type:Object}),e("design:type",Function)],wCt.prototype,"_requestData",void 0),t([o({type:Object}),e("design:type",Object)],wCt.prototype,"_runToNextAvailableSymbolIndex",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wCt.prototype,"_matchesListOpened",void 0),t([o({type:Object}),e("design:type",Object)],wCt.prototype,"_fillArea",void 0),t([o({type:Array}),e("design:type",Array)],wCt.prototype,"_tooltipColumns",void 0),t([o({type:Array}),e("design:type",Array)],wCt.prototype,"_missingTags",void 0),t([o({type:Boolean}),e("design:type",Boolean)],wCt.prototype,"_missingTagsCollapsibleOpened",void 0),t([o({type:Object}),e("design:type",Object)],wCt.prototype,"_stepsMismatch",void 0),t([a("_nameToDataSeries"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],wCt.prototype,"_updateChart",null),t([s("_nameToDataSeries","runs"),e("design:type",Object),e("design:paramtypes",[])],wCt.prototype,"_seriesNames",null),t([a("_tagFilter"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],wCt.prototype,"_refreshDataSeries",null),t([s("marginChartSeries"),e("design:type",String),e("design:paramtypes",[])],wCt.prototype,"_tagFilter",null),t([s("title"),e("design:type",String),e("design:paramtypes",[])],wCt.prototype,"_titleDisplayString",null),wCt=t([i("tf-custom-scalar-margin-chart-card")],wCt);var SCt=Object.freeze({__proto__:null,TYPES:[],getRunsNamed:function MCt(t){return Se.exports.keys(t).sort(nr)},getTags:ar,filterTags:function ECt(t,e){let n=[];return e.forEach((e=>n=n.concat(t[e]))),Se.exports.uniq(n).sort(nr)},ListenKey:fl,BaseStore:ml,Canceller:XR,EnvironmentStore:gl,environmentStore:_l,ExperimentsStore:FR,experimentsStore:VR,RequestCancellationError:sr,InvalidRequestOptionsError:lr,RequestNetworkError:cr,get HttpMethodType(){return ur},RequestOptions:hr,RequestManager:dr,createRouter:gr,getRouter:_r,setRouter:function TCt(t){if(null==t)throw new Error("Router required, but got: "+t);mr=t},createSearchParam:vr,RunsStore:yl,runsStore:vl,addParams:iO});let CCt=class extends(er(ye)){constructor(){super(...arguments),this.active=!0,this._colorScale=new xCt({scale:GR}),this._nameToDataSeries={},this._expanded=!1,this._requestData=(t,e,n)=>{const i=_r().pluginRoute("custom_scalars","/scalars");Promise.all(t.map((t=>{const n=iO(i,{tag:this._tagFilter,run:t});return this.requestManager.request(n).then((n=>{e({item:t,data:n})}))}))).finally((()=>{n()}))},this._runToNextAvailableSymbolIndex={},this._matchesListOpened=!1}reload(){this.$.loader.reload()}redraw(){this.$.loader.redraw()}_toggleExpanded(t){this.set("_expanded",!this._expanded),this.redraw()}_toggleLogScale(){this.set("_logScaleActive",!this._logScaleActive)}_resetDomain(){const t=this.$.loader;t&&t.resetDomain()}_csvUrl(t,e){return e?iO(this._downloadDataUrl(t,e),{format:"csv"}):""}_jsonUrl(t,e){return e?iO(this._downloadDataUrl(t,e),{format:"json"}):""}_downloadDataUrl(t,e){const n=t[e],i={tag:n.getTag(),run:n.getRun()};return iO(_r().pluginRoute("custom_scalars","/download_data"),i)}_createProcessDataFunction(){return(t,e,n)=>{if(n.regex_valid){const t=Se.exports.clone(this._nameToDataSeries);Se.exports.forOwn(n.tag_to_events,((n,i)=>{const r=n.map((t=>({wall_time:new Date(1e3*t[0]),step:t[1],scalar:t[2]}))),o=bCt(e,i),a=t[o];if(a)a.setData(r);else{Se.exports.isUndefined(this._runToNextAvailableSymbolIndex[e])&&(this._runToNextAvailableSymbolIndex[e]=0);const n=new vCt(e,i,o,r,bTt[this._runToNextAvailableSymbolIndex[e]]);t[o]=n,this._runToNextAvailableSymbolIndex[e]=(this._runToNextAvailableSymbolIndex[e]+1)%bTt.length}})),this.set("_nameToDataSeries",t)}}}_updateChart(){Object.entries(this._nameToDataSeries).forEach((([t,e])=>{this.$.loader.setSeriesData(t,e.getData())})),this.$.loader.commitChanges()}_computeSelectedRunsSet(t){const e={};return Se.exports.forEach(t,(t=>{e[t]=1})),e}get _seriesNames(){const t=new Set(this.runs);return Object.entries(this._nameToDataSeries).filter((([e,n])=>t.has(n.run))).map((([t])=>t))}_determineColor(t,e){return t.scale(e)}_refreshDataSeries(){this.set("_nameToDataSeries",{})}_createSymbolFunction(){return t=>this._nameToDataSeries[t].getSymbol().method()}_determineSymbol(t,e){return t[e].getSymbol().character}get _tagFilter(){var t=this.tagRegexes;return 1===t.length?t[0]:t.map((t=>"("+t+")")).join("|")}_getToggleMatchesIcon(t){return t?"expand-less":"expand-more"}_toggleMatchesOpen(){this.set("_matchesListOpened",!this._matchesListOpened)}get _titleDisplayString(){return this.title||"untitled"}_matchListEntryColorUpdated(t){const e=this.$$("#match-list-repeat");e&&this.root.querySelectorAll(".match-list-entry").forEach((t=>{const n=e.itemForElement(t);t.style.color=this._determineColor(this._colorScale,n)}))}};CCt.template=_e`
    <tf-card-heading display-name="[[_titleDisplayString]]"></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader
        id="loader"
        active="[[active]]"
        color-scale="[[_colorScale]]"
        data-series="[[_seriesNames]]"
        ignore-y-outliers="[[ignoreYOutliers]]"
        load-key="[[_tagFilter]]"
        data-to-load="[[runs]]"
        request-data="[[_requestData]]"
        log-scale-active="[[_logScaleActive]]"
        load-data-callback="[[_createProcessDataFunction()]]"
        request-manager="[[requestManager]]"
        smoothing-enabled="[[smoothingEnabled]]"
        smoothing-weight="[[smoothingWeight]]"
        symbol-function="[[_createSymbolFunction()]]"
        tooltip-sorting-method="[[tooltipSortingMethod]]"
        x-type="[[xType]]"
      >
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
      <paper-icon-button
        selected$="[[_logScaleActive]]"
        icon="line-weight"
        on-tap="_toggleLogScale"
        title="Toggle y-axis log scale"
      ></paper-icon-button>
      <paper-icon-button
        icon="settings-overscan"
        on-tap="_resetDomain"
        title="Fit domain to data"
      ></paper-icon-button>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <paper-dropdown-menu
            no-label-float="true"
            label="series to download"
            selected-item-label="{{_dataSeriesNameToDownload}}"
          >
            <paper-listbox class="dropdown-content" slot="dropdown-content">
              <template
                is="dom-repeat"
                items="[[_seriesNames]]"
                as="dataSeriesName"
              >
                <paper-item no-label-float="true"
                  >[[dataSeriesName]]</paper-item
                >
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
          <a
            download="[[_dataSeriesNameToDownload]].csv"
            href="[[_csvUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]"
            >CSV</a
          >
          <a
            download="[[_dataSeriesNameToDownload]].json"
            href="[[_jsonUrl(_nameToDataSeries, _dataSeriesNameToDownload)]]"
            >JSON</a
          >
        </div>
      </template>
    </div>
    <div id="matches-container">
      <div id="matches-list-title">
        <template is="dom-if" if="[[_seriesNames.length]]">
          <paper-icon-button
            icon="[[_getToggleMatchesIcon(_matchesListOpened)]]"
            on-click="_toggleMatchesOpen"
            class="toggle-matches-button"
          >
          </paper-icon-button>
        </template>

        <span class="matches-text"> Matches ([[_seriesNames.length]]) </span>
      </div>
      <template is="dom-if" if="[[_seriesNames.length]]">
        <iron-collapse opened="[[_matchesListOpened]]">
          <div id="matches-list">
            <template
              is="dom-repeat"
              items="[[_seriesNames]]"
              as="seriesName"
              id="match-list-repeat"
              on-dom-change="_matchListEntryColorUpdated"
            >
              <div class="match-list-entry">
                <span class="match-entry-symbol">
                  [[_determineSymbol(_nameToDataSeries, seriesName)]]
                </span>
                [[seriesName]]
              </div>
            </template>
          </div>
        </iron-collapse>
      </template>
    </div>

    <style include="tf-custom-scalar-card-style"></style>
    <style>
      #matches-list-title {
        margin: 10px 0 5px 0;
      }

      #matches-list {
        max-height: 200px;
        overflow-y: auto;
      }

      .match-list-entry {
        margin: 0 0 5px 0;
      }

      .match-entry-symbol {
        font-family: arial, sans-serif;
        display: inline-block;
        width: 10px;
      }

      .matches-text {
        vertical-align: middle;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],CCt.prototype,"runs",void 0),t([o({type:String}),e("design:type",String)],CCt.prototype,"xType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CCt.prototype,"active",void 0),t([o({type:String}),e("design:type",String)],CCt.prototype,"title",void 0),t([o({type:Array}),e("design:type",Array)],CCt.prototype,"tagRegexes",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CCt.prototype,"ignoreYOutliers",void 0),t([o({type:Object}),e("design:type",dr)],CCt.prototype,"requestManager",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CCt.prototype,"showDownloadLinks",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CCt.prototype,"smoothingEnabled",void 0),t([o({type:Number}),e("design:type",Number)],CCt.prototype,"smoothingWeight",void 0),t([o({type:Object}),e("design:type",Object)],CCt.prototype,"tagMetadata",void 0),t([o({type:String}),e("design:type",String)],CCt.prototype,"tooltipSortingMethod",void 0),t([o({type:Object}),e("design:type",xCt)],CCt.prototype,"_colorScale",void 0),t([o({type:Object}),e("design:type",Object)],CCt.prototype,"_nameToDataSeries",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],CCt.prototype,"_expanded",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CCt.prototype,"_logScaleActive",void 0),t([o({type:Object}),e("design:type",Function)],CCt.prototype,"_requestData",void 0),t([o({type:Object}),e("design:type",Object)],CCt.prototype,"_runToNextAvailableSymbolIndex",void 0),t([o({type:Boolean}),e("design:type",Boolean)],CCt.prototype,"_matchesListOpened",void 0),t([a("_nameToDataSeries"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],CCt.prototype,"_updateChart",null),t([s("_nameToDataSeries","runs"),e("design:type",Object),e("design:paramtypes",[])],CCt.prototype,"_seriesNames",null),t([a("_tagFilter"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],CCt.prototype,"_refreshDataSeries",null),t([s("tagRegexes"),e("design:type",String),e("design:paramtypes",[])],CCt.prototype,"_tagFilter",null),t([s("title"),e("design:type",String),e("design:paramtypes",[])],CCt.prototype,"_titleDisplayString",null),CCt=t([i("tf-custom-scalar-multi-line-chart-card")],CCt);let ACt=class extends ye{constructor(){super(...arguments),this._requestManager=new dr(50),this._canceller=new XR,this._showDownloadLinks=Hs("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}).call(this),this._smoothingWeight=Gs("_smoothingWeight",{defaultValue:.6}).call(this),this._ignoreYOutliers=Hs("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0}).call(this),this._xType="step",this._active=!0,this.reloadOnReady=!0,this._showDownloadLinksObserver=Fs("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}),this._smoothingWeightObserver=Ws("_smoothingWeight",{defaultValue:.6}),this._ignoreYOutliersObserver=Fs("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0})}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){const t=_r().pluginsListing(),e=this._canceller.cancellable((t=>{t.cancelled||(this.set("_dataNotFound",!t.value.custom_scalars),this._dataNotFound||this._retrieveLayoutAndData())}));this._requestManager.request(t).then(e)}_reloadCharts(){this.root.querySelectorAll("tf-custom-scalar-margin-chart-card, tf-custom-scalar-multi-line-chart-card").forEach((t=>{t.reload()}))}_retrieveLayoutAndData(){const t=_r().pluginRoute("custom_scalars","/layout"),e=this._canceller.cancellable((t=>{t.cancelled||(this.set("_layout",t.value),this._dataNotFound||this._reloadCharts())}));this._requestManager.request(t).then(e)}get _smoothingEnabled(){return this._smoothingWeight>0}get _categories(){var t=this._layout;if(!t.category)return[];let e=!1;return this._openedCategories||(e=!0,this._openedCategories={}),t.category.map((t=>(e&&!t.closed&&(this._openedCategories[t.title]=!0),{name:t.title,items:t.chart,metadata:{type:br.PREFIX_GROUP,opened:!!this._openedCategories[t.title]}})))}_categoryOpenedToggled(t){const e=t.target;e.opened?this._openedCategories[e.category.name]=!0:delete this._openedCategories[e.category.name]}};ACt.template=_e`
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="settings">
          <div class="sidebar-section">
            <div class="line-item">
              <paper-checkbox checked="{{_showDownloadLinks}}"
                >Show data download links</paper-checkbox
              >
            </div>
            <div class="line-item">
              <paper-checkbox checked="{{_ignoreYOutliers}}"
                >Ignore outliers in chart scaling</paper-checkbox
              >
            </div>
            <div id="tooltip-sorting">
              <div id="tooltip-sorting-label">Tooltip sorting method:</div>
              <paper-dropdown-menu
                no-label-float=""
                selected-item-label="{{_tooltipSortingMethod}}"
              >
                <paper-listbox
                  class="dropdown-content"
                  selected="0"
                  slot="dropdown-content"
                >
                  <paper-item>default</paper-item>
                  <paper-item>descending</paper-item>
                  <paper-item>ascending</paper-item>
                  <paper-item>nearest</paper-item>
                </paper-listbox>
              </paper-dropdown-menu>
            </div>
          </div>
          <div class="sidebar-section">
            <tf-smoothing-input
              weight="{{_smoothingWeight}}"
              step="0.001"
              min="0"
              max="1"
            ></tf-smoothing-input>
          </div>
          <div class="sidebar-section">
            <tf-option-selector
              id="x-type-selector"
              name="Horizontal Axis"
              selected-id="{{_xType}}"
            >
              <paper-button id="step">step</paper-button
              ><!--
            --><paper-button id="relative">relative</paper-button
              ><!--
            --><paper-button id="wall_time">wall</paper-button>
            </tf-option-selector>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center" id="categories-container">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>The custom scalars dashboard is inactive.</h3>
            <p>Probable causes:</p>
            <ol>
              <li>You haven't laid out the dashboard.</li>
              <li>You haven’t written any scalar data to your event files.</li>
            </ol>

            <p>
              To lay out the dashboard, pass a <code>Layout</code> protobuffer
              to the <code>set_layout</code> method. For example,
            </p>
            <pre>
from tensorboard import summary
from tensorboard.plugins.custom_scalar import layout_pb2
...
# This action does not have to be performed at every step, so the action is not
# taken care of by an op in the graph. We only need to specify the layout once
# (instead of per step).
layout_summary = summary_lib.custom_scalar_pb(layout_pb2.Layout(
  category=[
    layout_pb2.Category(
      title='losses',
      chart=[
          layout_pb2.Chart(
              title='losses',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'loss.*'],
              )),
          layout_pb2.Chart(
              title='baz',
              margin=layout_pb2.MarginChartContent(
                series=[
                  layout_pb2.MarginChartContent.Series(
                    value='loss/baz/scalar_summary',
                    lower='baz_lower/baz/scalar_summary',
                    upper='baz_upper/baz/scalar_summary'),
                ],
              )),
      ]),
    layout_pb2.Category(
      title='trig functions',
      chart=[
          layout_pb2.Chart(
              title='wave trig functions',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'trigFunctions/cosine', r'trigFunctions/sine'],
              )),
          # The range of tangent is different. Let's give it its own chart.
          layout_pb2.Chart(
              title='tan',
              multiline=layout_pb2.MultilineChartContent(
                tag=[r'trigFunctions/tangent'],
              )),
      ],
      # This category we care less about. Let's make it initially closed.
      closed=True),
  ]))
writer.add_summary(layout_summary)
</pre
            >
            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              as="chart"
              category="[[category]]"
              disable-pagination
              initial-opened="[[category.metadata.opened]]"
            >
              <template>
                <template is="dom-if" if="[[chart.multiline]]">
                  <tf-custom-scalar-multi-line-chart-card
                    active="[[active]]"
                    request-manager="[[_requestManager]]"
                    runs="[[_selectedRuns]]"
                    title="[[chart.title]]"
                    x-type="[[_xType]]"
                    smoothing-enabled="[[_smoothingEnabled]]"
                    smoothing-weight="[[_smoothingWeight]]"
                    tooltip-sorting-method="[[tooltipSortingMethod]]"
                    ignore-y-outliers="[[_ignoreYOutliers]]"
                    show-download-links="[[_showDownloadLinks]]"
                    tag-regexes="[[chart.multiline.tag]]"
                  ></tf-custom-scalar-multi-line-chart-card>
                </template>
                <template is="dom-if" if="[[chart.margin]]">
                  <tf-custom-scalar-margin-chart-card
                    active="[[active]]"
                    request-manager="[[_requestManager]]"
                    runs="[[_selectedRuns]]"
                    title="[[chart.title]]"
                    x-type="[[_xType]]"
                    tooltip-sorting-method="[[tooltipSortingMethod]]"
                    ignore-y-outliers="[[_ignoreYOutliers]]"
                    show-download-links="[[_showDownloadLinks]]"
                    margin-chart-series="[[chart.margin.series]]"
                  ></tf-custom-scalar-margin-chart-card>
                </template>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      #tooltip-sorting {
        align-items: center;
        display: flex;
        font-size: 14px;
        margin-top: 15px;
      }
      #tooltip-sorting paper-dropdown-menu {
        margin-left: 10px;
        --paper-input-container-focus-color: var(--tb-orange-strong);
        width: 105px;
      }
      .line-item {
        display: block;
        padding-top: 5px;
      }
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  `,t([o({type:Object}),e("design:type",dr)],ACt.prototype,"_requestManager",void 0),t([o({type:Object}),e("design:type",XR)],ACt.prototype,"_canceller",void 0),t([o({type:Array}),e("design:type",Array)],ACt.prototype,"_selectedRuns",void 0),t([o({type:Boolean,notify:!0,observer:"_showDownloadLinksObserver"}),e("design:type",Boolean)],ACt.prototype,"_showDownloadLinks",void 0),t([o({type:Number,notify:!0,observer:"_smoothingWeightObserver"}),e("design:type",Number)],ACt.prototype,"_smoothingWeight",void 0),t([o({type:Boolean,observer:"_ignoreYOutliersObserver"}),e("design:type",Boolean)],ACt.prototype,"_ignoreYOutliers",void 0),t([o({type:String}),e("design:type",String)],ACt.prototype,"_xType",void 0),t([o({type:Object}),e("design:type",Object)],ACt.prototype,"_layout",void 0),t([o({type:Boolean}),e("design:type",Boolean)],ACt.prototype,"_dataNotFound",void 0),t([o({type:Object}),e("design:type",Object)],ACt.prototype,"_openedCategories",void 0),t([o({type:Boolean}),e("design:type",Boolean)],ACt.prototype,"_active",void 0),t([o({type:Boolean}),e("design:type",Boolean)],ACt.prototype,"reloadOnReady",void 0),t([s("_smoothingWeight"),e("design:type",Boolean),e("design:paramtypes",[])],ACt.prototype,"_smoothingEnabled",null),t([s("_layout"),e("design:type",Array),e("design:paramtypes",[])],ACt.prototype,"_categories",null),ACt=t([i("tf-custom-scalar-dashboard")],ACt);class kCt{constructor(t,e){this.run2datasets={},this.colorScale=e,this.buildChart(t)}getDataset(t){return void 0===this.run2datasets[t]&&(this.run2datasets[t]=new rO.Dataset([],{run:t})),this.run2datasets[t]}buildChart(t){this.outer&&this.outer.destroy();let e=ATt(t);this.xAccessor=e.accessor,this.xScale=e.scale,this.xAxis=e.axis,this.xAxis.margin(0),this.xAxis.tickLabelPadding(3),this.yScale=new rO.Scales.Linear,this.yAxis=new rO.Axes.Numeric(this.yScale,"left");let n=wTt(3);this.yAxis.margin(0).tickLabelPadding(5).formatter(n),this.yAxis.usesTextWidthApproximation(!0);let i=this.buildPlot(this.xAccessor,this.xScale,this.yScale);this.gridlines=new rO.Components.Gridlines(this.xScale,this.yScale),this.center=new rO.Components.Group([this.gridlines,i]),this.outer=new rO.Components.Table([[this.yAxis,this.center],[null,this.xAxis]])}buildPlot(t,e,n){let i=[0,228,1587,3085,5e3,6915,8413,9772,1e4],r=Se.exports.range(i.length-1).map((t=>(i[t+1]-i[t])/2500)),o=i.map(((t,e)=>t=>t[e][1])),a=o[4],s=Se.exports.range(o.length-1).map((i=>{let a=new rO.Plots.Area;a.x(t,e);let s=i>4?o[i]:o[i+1];return a.y(i>4?o[i+1]:o[i],n),a.y0(s),a.attr("fill",((t,e,n)=>this.colorScale.scale(n.metadata().run))),a.attr("stroke",((t,e,n)=>this.colorScale.scale(n.metadata().run))),a.attr("stroke-weight",((t,e,n)=>"0.5px")),a.attr("stroke-opacity",(()=>r[i])),a.attr("fill-opacity",(()=>r[i])),a})),l=new rO.Plots.Line;return l.x(t,e),l.y(a,n),l.attr("stroke",((t,e,n)=>this.colorScale.scale(n.run))),this.plots=s,new rO.Components.Group(s)}setVisibleSeries(t){this.runs=t;let e=t.map((t=>this.getDataset(t)));this.plots.forEach((t=>t.datasets(e)))}setSeriesData(t,e){this.getDataset(t).data(e)}renderTo(t){this.targetSVG=t,this.outer.renderTo(t)}redraw(){this.outer.redraw()}destroy(){this.outer.destroy()}}let LCt=class extends(er(ye)){constructor(){super(...arguments),this.colorScale=(new rO.Scales.Color).range(gA.slice()),this.xType="step",this._visibleSeriesCache=[],this._seriesDataCache={},this._makeChartAsyncCallbackId=null}setVisibleSeries(t){this._visibleSeriesCache=t,this._chart&&(this._chart.setVisibleSeries(t),this.redraw())}setSeriesData(t,e){this._seriesDataCache[t]=e,this._chart&&this._chart.setSeriesData(t,e)}redraw(){this._chart.redraw()}_makeChart(){var t=this.xType,e=this.colorScale,n=this._attached;null===this._makeChartAsyncCallbackId&&this.cancelAsync(this._makeChartAsyncCallbackId),this._makeChartAsyncCallbackId=this.async((function(){if(this._makeChartAsyncCallbackId=null,n){this._chart&&this._chart.destroy();var i=new kCt(t,e),r=Su(this.$.chartdiv);i.renderTo(r),this._chart=i}}),350)}_reloadFromCache(){this._chart&&(this._chart.setVisibleSeries(this._visibleSeriesCache),this._visibleSeriesCache.forEach(function(t){this._chart.setSeriesData(t,this._seriesDataCache[t]||[])}.bind(this)))}attached(){this._attached=!0}detached(){this._attached=!1}};LCt.template=_e`
    <style include="plottable-style"></style>
    <div id="chartdiv"></div>
    <style>
      :host {
        -webkit-user-select: none;
        -moz-user-select: none;
        display: flex;
        flex-direction: column;
        flex-grow: 1;
        flex-shrink: 1;
        position: relative;
      }
      #chartdiv {
        -webkit-user-select: none;
        -moz-user-select: none;
        flex-grow: 1;
        flex-shrink: 1;
      }
      .plottable .axis text {
        fill: currentColor;
      }
    </style>
  `,t([o({type:Object}),e("design:type",rO.Scales.Color)],LCt.prototype,"colorScale",void 0),t([o({type:String}),e("design:type",String)],LCt.prototype,"xType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],LCt.prototype,"_attached",void 0),t([o({type:Object}),e("design:type",kCt)],LCt.prototype,"_chart",void 0),t([o({type:Array}),e("design:type",Array)],LCt.prototype,"_visibleSeriesCache",void 0),t([o({type:Object}),e("design:type",Object)],LCt.prototype,"_seriesDataCache",void 0),t([o({type:Number}),e("design:type",Number)],LCt.prototype,"_makeChartAsyncCallbackId",void 0),t([a("xType","colorScale","_attached"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],LCt.prototype,"_makeChart",null),t([a("_chart"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],LCt.prototype,"_reloadFromCache",null),LCt=t([i("vz-distribution-chart")],LCt);let PCt=class extends(LTt(er(ye))){constructor(){super(...arguments),this.getDataLoadName=({run:t})=>t,this.requestData=(t,e,n)=>{const i=_r().pluginRoute("distributions","/distributions");Promise.all(t.map((t=>{const n=iO(i,{tag:t.tag,run:t.run});return this.requestManager.request(n).then((n=>{e({item:t,data:n})}))}))).finally((()=>{n()}))},this.loadDataCallback=(t,e,n)=>{const i=n.map((t=>{const[e,n,i]=t;return i.wall_time=new Date(1e3*e),i.step=n,i})),r=this.getDataLoadName(e);this.$.chart.setSeriesData(r,i),this.$.chart.setVisibleSeries([r])},this._colorScale={scale:GR},this._expanded=!1,this._canceller=new XR}_reloadOnRunTagChange(){this.reload()}_updateDataToLoad(){this.dataToLoad=[{run:this.run,tag:this.tag}]}get _runColor(){return this._colorScale.scale(this.run)}redraw(){this.$.chart.redraw()}_toggleExpanded(t){this.set("_expanded",!this._expanded),this.redraw()}};PCt.template=_e`
    <tf-card-heading
      tag="[[tag]]"
      run="[[run]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
      color="[[_runColor]]"
    ></tf-card-heading>
    <!--
      The main distribution that we render. Data is set directly with
      \`setSeriesData\`, not with a bound property.
    -->
    <vz-distribution-chart
      id="chart"
      x-type="[[xType]]"
      color-scale="[[_colorScale]]"
    ></vz-distribution-chart>
    <div style="display: flex; flex-direction: row;">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
    </div>
    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 330px;
        height: 235px;
        margin-right: 10px;
        margin-bottom: 15px;
      }
      :host([_expanded]) {
        width: 700px;
        height: 500px;
      }

      vz-histogram-timeseries {
        -moz-user-select: none;
        -webkit-user-select: none;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }
      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      tf-card-heading {
        margin-bottom: 10px;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],PCt.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],PCt.prototype,"tag",void 0),t([o({type:Object}),e("design:type",Object)],PCt.prototype,"tagMetadata",void 0),t([o({type:String}),e("design:type",String)],PCt.prototype,"xType",void 0),t([o({type:Object}),e("design:type",Object)],PCt.prototype,"getDataLoadName",void 0),t([o({type:Object}),e("design:type",Object)],PCt.prototype,"loadDataCallback",void 0),t([o({type:Object}),e("design:type",Object)],PCt.prototype,"_colorScale",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],PCt.prototype,"_expanded",void 0),t([o({type:Object}),e("design:type",dr)],PCt.prototype,"requestManager",void 0),t([o({type:Object}),e("design:type",XR)],PCt.prototype,"_canceller",void 0),t([a("run","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],PCt.prototype,"_reloadOnRunTagChange",null),t([a("run","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],PCt.prototype,"_updateDataToLoad",null),t([s("run"),e("design:type",String),e("design:paramtypes",[])],PCt.prototype,"_runColor",null),PCt=t([i("tf-distribution-loader")],PCt);let NCt=class extends(er(ye)){constructor(){super(...arguments),this.reloadOnReady=!0,this._xType="step",this._requestManager=new dr}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){this._fetchTags().then((()=>{this._reloadDistributions()}))}_fetchTags(){const t=_r().pluginRoute("distributions","/tags");return this._requestManager.request(t).then((t=>{if(Se.exports.isEqual(t,this._runToTagInfo))return;const e=Se.exports.mapValues(t,(t=>Object.keys(t))),n=ar(e);this.set("_dataNotFound",0===n.length),this.set("_runToTag",e),this.set("_runToTagInfo",t),this.async((()=>{this.set("_categoriesDomReady",!0)}))}))}_reloadDistributions(){this.root.querySelectorAll("tf-distribution-loader").forEach((t=>{t.reload()}))}_shouldOpen(t){return t<=2}get _categories(){return Mr(this._runToTag,this._selectedRuns,this._tagFilter)}_tagMetadata(t,e,n){return t[e][n]}};NCt.template=_e`
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="settings">
          <div class="sidebar-section">
            <tf-option-selector
              id="xTypeSelector"
              name="Horizontal axis"
              selected-id="{{_xType}}"
            >
              <paper-button id="step">step</paper-button>
              <paper-button id="relative">relative</paper-button>
              <paper-button id="wall_time">wall</paper-button>
            </tf-option-selector>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>

      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No distribution data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any histogram data to your event files.
                (Histograms and distributions both use the histogram summary
                operation.)
              </li>

              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
            >
              <template>
                <tf-distribution-loader
                  active="[[active]]"
                  run="[[item.run]]"
                  tag="[[item.tag]]"
                  tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]"
                  x-type="[[_xType]]"
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e=this._sucs[t];if(e)return eBt.keys(e)},rBt.prototype.neighbors=function(t){var e=this.predecessors(t);if(e)return eBt.union(e,this.successors(t))},rBt.prototype.isLeaf=function(t){return 0===(this.isDirected()?this.successors(t):this.neighbors(t)).length},rBt.prototype.filterNodes=function(t){var e=new this.constructor({directed:this._isDirected,multigraph:this._isMultigraph,compound:this._isCompound});e.setGraph(this.graph());var n=this;eBt.each(this._nodes,(function(n,i){t(i)&&e.setNode(i,n)})),eBt.each(this._edgeObjs,(function(t){e.hasNode(t.v)&&e.hasNode(t.w)&&e.setEdge(t,n.edge(t))}));var i={};function r(t){var o=n.parent(t);return void 0===o||e.hasNode(o)?(i[t]=o,o):o in i?i[o]:r(o)}return this._isCompound&&eBt.each(e.nodes(),(function(t){e.setParent(t,r(t))})),e},rBt.prototype.setDefaultEdgeLabel=function(t){return eBt.isFunction(t)||(t=eBt.constant(t)),this._defaultEdgeLabelFn=t,this},rBt.prototype.edgeCount=function(){return 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null})(t.attr),this.compatible=!1,this.type=jGt.OP,this.isGroupNode=!1,this.cardinality=1,this.inEmbeddings=[],this.outEmbeddings=[],this.parentNode=null,this.include=GGt.UNSPECIFIED,this.owningSeries=null}}function $Gt(t,e={}){return new KGt(t,e)}class KGt{constructor(t,e={}){this.name=t,this.type=jGt.META,this.depth=1,this.isGroupNode=!0,this.cardinality=0,this.metagraph=oWt(t,UGt.META,e),this.bridgegraph=null,this.opHistogram={},this.deviceHistogram={},this.xlaClusterHistogram={},this.compatibilityHistogram={compatible:0,incompatible:0},this.templateId=null,this.parentNode=null,this.hasNonControlEdges=!1,this.include=GGt.UNSPECIFIED,this.associatedFunction=""}getFirstChild(){return this.metagraph.node(this.metagraph.nodes()[0])}getRootOp(){let t=this.name.split("/");return this.metagraph.node(this.name+"/("+t[t.length-1]+")")}leaves(){let t,e=[],n=[this];for(;n.length;){let i=n.shift();i.isGroupNode?(t=i.metagraph,Se.exports.each(t.nodes(),(e=>n.push(t.node(e))))):e.push(i.name)}return e}}function ZGt(t,e){return new JGt(t,e)}class JGt{constructor(t,e){this.v=t,this.w=e,this.baseEdgeList=[],this.inbound=null,this.numRegularEdges=0,this.numControlEdges=0,this.numRefEdges=0,this.totalSize=0}addBaseEdge(t,e){this.baseEdgeList.push(t),t.isControlDependency?this.numControlEdges+=1:this.numRegularEdges+=1,t.isReferenceEdge&&(this.numRefEdges+=1),this.totalSize+=JGt.computeSizeOfEdge(t,e),e.maxMetaEdgeSize=Math.max(e.maxMetaEdgeSize,this.totalSize)}static computeSizeOfEdge(t,e){let n=e.node(t.v);if(!n.outputShapes)return 1;e.hasShapeInfo=!0;const i=Object.keys(n.outputShapes).map((t=>n.outputShapes[t])).map((t=>null==t?1:t.reduce(((t,e)=>(-1===e&&(e=1),t*e)),1)));return Se.exports.sum(i)}}function QGt(t,e,n,i,r,o){return new eWt(t,e,n,i,r,o)}function tWt(t,e,n,i,r){return(n?n+"/":"")+t+(void 0!==i&&void 0!==r?"["+i+"-"+r+"]":"#")+e}class eWt{constructor(t,e,n,i,r,o){this.name=r||tWt(t,e,n),this.type=jGt.SERIES,this.hasLoop=!1,this.prefix=t,this.suffix=e,this.clusterId=i,this.ids=[],this.parent=n,this.isGroupNode=!0,this.cardinality=0,this.metagraph=oWt(r,UGt.SERIES,o),this.bridgegraph=null,this.parentNode=null,this.deviceHistogram={},this.xlaClusterHistogram={},this.compatibilityHistogram={compatible:0,incompatible:0},this.hasNonControlEdges=!1,this.include=GGt.UNSPECIFIED}}const nWt=/^([^:]+):((\w+:|)\d+)$/;function iWt(t,e,n,i,r,o){e!==n.name&&t.edges.push({v:e,w:n.name,outputTensorKey:i.outputTensorKey,isControlDependency:i.isControlDependency,isReferenceEdge:!0===r.refEdges[n.op+" "+o]})}const rWt={enableEmbedding:!0,inEmbeddingTypes:["Const"],outEmbeddingTypes:["^[a-zA-Z]+Summary$"],refEdges:{"Assign 0":!0,"AssignAdd 0":!0,"AssignSub 0":!0,"assign 0":!0,"assign_add 0":!0,"assign_sub 0":!0,"count_up_to 0":!0,"ScatterAdd 0":!0,"ScatterSub 0":!0,"ScatterUpdate 0":!0,"scatter_add 0":!0,"scatter_sub 0":!0,"scatter_update 0":!0}};function oWt(t,e,n={}){const i=new fGt.graphlib.Graph(n);return i.setGraph({name:t,rankdir:n.rankdir||"BT",type:e}),i}function aWt(t){return function(e){for(let n=0;n<t.length;n++){let i=new RegExp(t[n]);if("string"==typeof e.op&&e.op.match(i))return!0}return!1}}function sWt(t){let e=t.split(HGt);return t+HGt+"("+e[e.length-1]+")"}function lWt(t){let e=t.nodes().map((function(e){return t.neighbors(e).length}));return e.sort(),e}function cWt(t,e){let n=[],i=t.indexOf(HGt);for(;i>=0;)n.push(t.substring(0,i)),i=t.indexOf(HGt,i+1);if(e){let i=e[t];i&&n.push(i)}return n.push(t),n}function uWt(t){return t===GGt.EXCLUDE?"Add to main graph":"Remove from main graph"}let hWt="#0f9d58",dWt="#db4437",pWt={DEFAULT_FILL:"#d9d9d9",DEFAULT_STROKE:"#a6a6a6",SATURATION:.6,LIGHTNESS:.85,EXPANDED_COLOR:"#f0f0f0",HUES:[220,100,180,40,20,340,260,300,140,60],STRUCTURE_PALETTE(t,e){let n=pWt.HUES,i=n[t%n.length],r=Math.sin(i*Math.PI/360);return $h(i,.01*(e?30:90-60*r),.01*(e?95:80)).toString()},DEVICE_PALETTE:t=>pWt.STRUCTURE_PALETTE(t),XLA_CLUSTER_PALETTE:t=>pWt.STRUCTURE_PALETTE(t),UNKNOWN:"#eee",GRADIENT_OUTLINE:"#888"};const fWt=["NoOp"],mWt=[],gWt=["#fff5f0","#fb6a4a"],_Wt=new RegExp("^(?:__function_library__)?(\\w+)_[a-z0-9]{8}(?:_\\d+)?$");class yWt{constructor(t,e,n){this.hierarchy=t,this.displayingStats=e,this.autoExtractNodes=n,this.index={},this.renderedOpNames=[],this.computeScales(),this.hasSubhierarchy={},this.root=new TWt(t.root,t.graphOptions),this.index[t.root.name]=this.root,this.renderedOpNames.push(t.root.name),this.buildSubhierarchy(t.root.name),this.root.expanded=!0,this.traceInputs=!1}computeScales(){this.deviceColorMap=EM().domain(this.hierarchy.devices).range(Se.exports.map(zl(this.hierarchy.devices.length),pWt.DEVICE_PALETTE)),this.xlaClusterColorMap=EM().domain(this.hierarchy.xlaClusters).range(Se.exports.map(zl(this.hierarchy.xlaClusters.length),pWt.XLA_CLUSTER_PALETTE));let t=this.hierarchy.root.metagraph,e=Wl(t.nodes(),((e,n)=>{let i=t.node(e);if(null!=i.stats)return i.stats.totalBytes}));this.memoryUsageScale=VM().domain([0,e]).range(gWt);let n=Wl(t.nodes(),((e,n)=>{let i=t.node(e);if(null!=i.stats)return i.stats.getTotalMicros()}));this.computeTimeScale=VM().domain([0,n]).range(gWt),this.edgeWidthSizedBasedScale=this.hierarchy.hasShapeInfo?EGt:VM().domain([1,this.hierarchy.maxMetaEdgeSize]).range([MGt,12])}getRenderNodeByName(t){return this.index[t]}getNodeByName(t){return this.hierarchy.node(t)}colorHistogram(t,e){if(Object.keys(t).length>0){const n=Se.exports.sum(Object.keys(t).map((e=>t[e])));return Object.keys(t).map((i=>({color:e(i),proportion:t[i]/n})))}return null}getOrCreateRenderNodeByName(t){if(!t)return null;if(t in this.index)return this.index[t];let e=this.hierarchy.node(t);if(!e)return null;let n=e.isGroupNode?new TWt(e,this.hierarchy.graphOptions):new wWt(e);this.index[t]=n,this.renderedOpNames.push(t),e.stats&&(n.memoryColor=this.memoryUsageScale(e.stats.totalBytes),n.computeTimeColor=this.computeTimeScale(e.stats.getTotalMicros())),n.isFadedOut=this.displayingStats&&!OGt(e.stats);var i=null,r=null,o=null;if(e.isGroupNode){i=e.deviceHistogram,r=e.xlaClusterHistogram;let t=e.compatibilityHistogram.compatible,n=e.compatibilityHistogram.incompatible;0==t&&0==n||(o=t/(t+n))}else{let t=n.node.device;t&&(i={[t]:1});let e=n.node.xlaCluster;e&&(r={[e]:1}),n.node.type===jGt.OP&&(o=n.node.compatible?1:0)}return i&&(n.deviceColors=this.colorHistogram(i,this.deviceColorMap)),r&&(n.xlaClusterColors=this.colorHistogram(r,this.xlaClusterColorMap)),null!=o&&(n.compatibilityColors=[{color:hWt,proportion:o},{color:dWt,proportion:1-o}]),this.index[t]}getNearestVisibleAncestor(t){let e=cWt(t),n=0,i=null,r=t;for(;n<e.length&&(r=e[n],i=this.getRenderNodeByName(r),i.expanded);n++);if(n==e.length-2){let t=e[n+1];if(i.inAnnotations.nodeNames[t])return t;if(i.outAnnotations.nodeNames[t])return t}return r}setDepth(t){CWt(this.root,+t)}isNodeAuxiliary(t){let e=this.getRenderNodeByName(t.node.parentNode.name),n=Se.exports.find(e.isolatedInExtract,(e=>e.node.name===t.node.name));return!!n||(n=Se.exports.find(e.isolatedOutExtract,(e=>e.node.name===t.node.name)),!!n)}getNamesOfRenderedOps(){return this.renderedOpNames}cloneAndAddFunctionOpNode(t,e,n,i){const r=n.name.replace(e,i);let o=t.metagraph.node(r);if(o)return o;o=new XGt({name:r,input:[],device:n.device,op:n.op,attr:Se.exports.cloneDeep(n.attr)}),o.cardinality=n.cardinality,o.include=n.include,o.outputShapes=Se.exports.cloneDeep(n.outputShapes),o.xlaCluster=n.xlaCluster,o.functionInputIndex=n.functionInputIndex,o.functionOutputIndex=n.functionOutputIndex,o.inputs=n.inputs.map((t=>{const n=Se.exports.clone(t);return n.name=t.name.replace(e,i),n})),o.parentNode=t,t.metagraph.setNode(o.name,o),this.hierarchy.setNode(o.name,o);const a=n=>this.cloneAndAddFunctionOpNode(t,e,n,i);return o.inEmbeddings=n.inEmbeddings.map(a),o.outEmbeddings=n.outEmbeddings.map(a),o}cloneFunctionLibraryMetanode(t,e,n,i,r){const o={},a=this.cloneFunctionLibraryMetanodeHelper(t,e,n,i,r,o);return Se.exports.isEmpty(o)||this.patchEdgesFromFunctionOutputs(e,o),a}cloneFunctionLibraryMetanodeHelper(t,e,n,i,r,o){const a=$Gt(n.name.replace(i,r));return a.depth=n.depth,a.cardinality=n.cardinality,a.templateId=n.templateId,a.opHistogram=Se.exports.clone(n.opHistogram),a.deviceHistogram=Se.exports.clone(n.deviceHistogram),a.xlaClusterHistogram=Se.exports.clone(n.xlaClusterHistogram),a.hasNonControlEdges=n.hasNonControlEdges,a.include=n.include,a.nodeAttributes=Se.exports.clone(n.nodeAttributes),a.associatedFunction=n.associatedFunction,Se.exports.each(n.metagraph.nodes(),(s=>{const l=n.metagraph.node(s);switch(l.type){case jGt.META:const n=this.cloneFunctionLibraryMetanodeHelper(t,e,l,i,r,o);n.parentNode=a,a.metagraph.setNode(n.name,n),this.hierarchy.setNode(n.name,n);break;case jGt.OP:const s=this.cloneAndAddFunctionOpNode(a,i,l,r);Se.exports.isNumber(s.functionInputIndex)&&this.patchEdgesIntoFunctionInputs(e,s),Se.exports.isNumber(s.functionOutputIndex)&&(o[s.functionOutputIndex]=s);break;default:console.warn(l.name+" is oddly neither a metanode nor an opnode.")}})),this.cloneLibraryMetanodeEdges(n,a,i,r),a}cloneLibraryMetanodeEdges(t,e,n,i){Se.exports.each(t.metagraph.edges(),(r=>{const o=t.metagraph.edge(r),a=o.v.replace(n,i),s=o.w.replace(n,i),l=new JGt(a,s);l.inbound=o.inbound,l.numRegularEdges=o.numRegularEdges,l.numControlEdges=o.numControlEdges,l.numRefEdges=o.numRefEdges,l.totalSize=o.totalSize,o.baseEdgeList&&(l.baseEdgeList=o.baseEdgeList.map((t=>{const e=Se.exports.clone(t);return e.v=t.v.replace(n,i),e.w=t.w.replace(n,i),e}))),e.metagraph.node(s)?e.metagraph.setEdge(a,s,l):e.metagraph.setEdge(s,a,l)}))}patchEdgesIntoFunctionInputs(t,e){let n=Math.min(e.functionInputIndex,t.inputs.length-1),i=Se.exports.clone(t.inputs[n]);for(;i.isControlDependency;)n++,i=t.inputs[n];e.inputs.push(i);const r=this.hierarchy.getPredecessors(t.name);let o,a=0;Se.exports.each(r.regular,(t=>{if(a+=t.numRegularEdges,a>n)return o=t,!1})),Se.exports.each(o.baseEdgeList,(n=>{n.w===t.name&&(n.w=e.name),n.v===t.name&&(n.v=e.name)}))}patchEdgesFromFunctionOutputs(t,e){const n=this.hierarchy.getSuccessors(t.name);Se.exports.each(n.regular,(n=>{Se.exports.each(n.baseEdgeList,(n=>{const i=this.hierarchy.node(n.w);Se.exports.each(i.inputs,(i=>{i.name===t.name&&(i.name=e[i.outputTensorKey].name,i.outputTensorKey=n.outputTensorKey)}))})),Se.exports.each(n.baseEdgeList,(t=>{t.v=e[t.outputTensorKey].name,t.outputTensorKey="0"}))}))}buildSubhierarchy(t){if(t in this.hasSubhierarchy)return;this.hasSubhierarchy[t]=!0;let e=this.index[t];if(e.node.type!==jGt.META&&e.node.type!==jGt.SERIES)return;let n=e,i=n.node.metagraph,r=n.coreGraph;const o=[],a=[];Se.exports.isEmpty(this.hierarchy.libraryFunctions)||(Se.exports.each(i.nodes(),(t=>{const e=i.node(t),n=this.hierarchy.libraryFunctions[e.op];if(!n)return;if(0===t.indexOf(VGt))return;const r=this.cloneFunctionLibraryMetanode(i,e,n.node,n.node.name,e.name);o.push(e),a.push(r)})),Se.exports.each(a,((t,e)=>{const n=o[e];t.parentNode=n.parentNode,i.setNode(n.name,t),this.hierarchy.setNode(n.name,t)}))),Se.exports.each(i.nodes(),(t=>{let e=this.getOrCreateRenderNodeByName(t),n=e.node;r.setNode(t,e),n.isGroupNode||(Se.exports.each(n.inEmbeddings,(t=>{let n=new SWt(null),i=new wWt(t);MWt(e,t,i,n,bWt.CONSTANT),this.index[t.name]=i})),Se.exports.each(n.outEmbeddings,(t=>{let n=new SWt(null),i=new wWt(t);EWt(e,t,i,n,bWt.SUMMARY),this.index[t.name]=i})))})),Se.exports.each(i.edges(),(t=>{let e=i.edge(t),n=new SWt(e);n.isFadedOut=this.index[t.v].isFadedOut||this.index[t.w].isFadedOut,r.setEdge(t.v,t.w,n)})),n.node.type===jGt.META&&(function s(t,e){(function n(t){let e=t.coreGraph;Se.exports.each(e.nodes(),(n=>{e.node(n).node.include!==GGt.EXCLUDE||n.startsWith(VGt)||(t.coreGraph.outEdges(n).length>t.coreGraph.inEdges(n).length?kWt(t,n):LWt(t,n))}))})(t),fWt.length&&(function i(t){let e=t.coreGraph;Se.exports.each(e.nodes(),(n=>{let i=e.node(n);i.node.include===GGt.UNSPECIFIED&&PWt(i.node,fWt)&&kWt(t,n)}))})(t),mWt.length&&(function r(t){let e=t.coreGraph;Se.exports.each(e.nodes(),(n=>{let i=e.node(n);i.node.include===GGt.UNSPECIFIED&&PWt(i.node,mWt)&&LWt(t,n)}))})(t),e&&(function o(t){let e=t.coreGraph,n={},i={},r=0;if(Se.exports.each(e.nodes(),(t=>{if(e.node(t).node.include!==GGt.UNSPECIFIED)return;let o=Se.exports.reduce(e.predecessors(t),((n,i)=>n+(e.edge(i,t).metaedge.numRegularEdges?1:0)),0);0===o&&e.predecessors(t).length>0&&(o=e.predecessors(t).length);let a=Se.exports.reduce(e.successors(t),((n,i)=>n+(e.edge(t,i).metaedge.numRegularEdges?1:0)),0);0===a&&e.successors(t).length>0&&(a=e.successors(t).length),n[t]=o,i[t]=a,r++})),r<15)return;let o=Math.round(.75*r),a=Math.round(.25*r),s=Object.keys(n).sort(((t,e)=>n[t]-n[e])),l=n[s[o]],c=l+l-n[s[a]];c=Math.max(c,4);for(let e=r-1;n[s[e]]>c;e--)LWt(t,s[e]);let u=Object.keys(i).sort(((t,e)=>i[t]-i[e])),h=i[u[o]],d=h+4*(h-i[u[a]]);d=Math.max(d,4);for(let n=r-1;i[u[n]]>d;n--){let i=e.node(u[n]);i&&!i.isInExtract&&kWt(t,u[n])}})(t),(function a(t){let e=t.coreGraph,n={};Se.exports.each(e.edges(),(t=>{e.edge(t).metaedge.numRegularEdges||((n[t.v]=n[t.v]||[]).push(t),(n[t.w]=n[t.w]||[]).push(t))})),Se.exports.each(n,((t,n)=>{t.length>4&&Se.exports.each(t,(t=>AWt(e,t.v,t.w)))}))})(t);let s=t.coreGraph;Se.exports.each(s.nodes(),(e=>{let n=s.node(e),i=s.neighbors(e).length;if(n.node.include===GGt.UNSPECIFIED&&0===i){let i=n.outAnnotations.list.length>0,r=n.inAnnotations.list.length>0;n.isInExtract?(t.isolatedInExtract.push(n),n.node.include=GGt.EXCLUDE,s.removeNode(e)):n.isOutExtract?(t.isolatedOutExtract.push(n),n.node.include=GGt.EXCLUDE,s.removeNode(e)):i&&!r?(n.isInExtract=!0,t.isolatedInExtract.push(n),n.node.include=GGt.EXCLUDE,s.removeNode(e)):r&&!i&&(n.isOutExtract=!0,t.isolatedOutExtract.push(n),n.node.include=GGt.EXCLUDE,s.removeNode(e))}}))})(n,this.autoExtractNodes),Se.exports.isEmpty(this.hierarchy.libraryFunctions)||this.buildSubhierarchiesForNeededFunctions(i),t===FGt&&Se.exports.forOwn(this.hierarchy.libraryFunctions,((t,e)=>{const i=t.node,o=this.getOrCreateRenderNodeByName(i.name);n.libraryFunctionsExtract.push(o),o.node.include=GGt.EXCLUDE,r.removeNode(i.name)}));let l=n.node.parentNode;if(!l)return;let c=this.index[l.name],u=(t,...e)=>e.concat([t?"IN":"OUT"]).join("~~"),h=this.hierarchy.getBridgegraph(t),d={in:{},out:{},control:{}};Se.exports.each(h.edges(),(t=>{let e=!!i.node(t.w),n=e?t.v:t.w;h.edge(t).numRegularEdges?e?d.out[n]=(d.out[n]||0)+1:d.in[n]=(d.in[n]||0)+1:d.control[n]=(d.control[n]||0)+1}));let p=this.hierarchy.getNodeMap();Se.exports.each(h.edges(),(n=>{let o=h.edge(n),a=!!i.node(n.w),[s,f]=a?[n.w,n.v]:[n.v,n.w],m=this.index[s],g=this.index[f],_=g?g.node:p[f],y=!o.numRegularEdges&&d.control[f]>4,[,v]=a?[e.inAnnotations,m.inAnnotations]:[e.outAnnotations,m.outAnnotations],b=null,x=!1;if(!((a?d.out:d.in)[f]>4)&&!y&&m.isInCore()){let e=e=>c.coreGraph.edge(a?{v:e,w:t}:{v:t,w:e});b=e(f),b||(b=e(u(a,f,l.name))),x=!!b}let w=!1;if(b&&!o.numRegularEdges){let t=b,e=c.node;for(;t.adjoiningMetaedge;)t=t.adjoiningMetaedge,e=e.parentNode;let n=this.hierarchy.getTopologicalOrdering(e.name),i=t.metaedge;w=n[i.v]>n[i.w]}if(x=x&&!w,!x)return void v.push(new vWt(_,g,new SWt(o),bWt.SHORTCUT,a));let S=u(a,t),M=u(a,f,t),E=r.node(M);if(!E){let t=r.node(S);t||(t=new wWt({name:S,type:jGt.BRIDGE,isGroupNode:!1,cardinality:0,parentNode:null,stats:null,include:GGt.UNSPECIFIED,inbound:a,nodeAttributes:{}}),this.index[S]=t,r.setNode(S,t)),E=new wWt({name:M,type:jGt.BRIDGE,isGroupNode:!1,cardinality:1,parentNode:null,stats:null,include:GGt.UNSPECIFIED,inbound:a,nodeAttributes:{}}),this.index[M]=E,r.setNode(M,E),r.setParent(M,S),t.node.cardinality++}let T=new SWt(o);T.adjoiningMetaedge=b,a?r.setEdge(M,s,T):r.setEdge(s,M,T)})),Se.exports.each([!0,!1],(e=>{let n=u(e,t),i=r.node(n);i&&Se.exports.each(r.nodes(),(o=>{if(r.node(o).node.type===jGt.BRIDGE)return;if(!(e?!r.predecessors(o).length:!r.successors(o).length))return;let a=u(e,t,"STRUCTURAL_TARGET"),s=r.node(a);s||(s=new wWt({name:a,type:jGt.BRIDGE,isGroupNode:!1,cardinality:1,parentNode:null,stats:null,include:GGt.UNSPECIFIED,inbound:e,nodeAttributes:{}}),s.structural=!0,this.index[a]=s,r.setNode(a,s),i.node.cardinality++,r.setParent(a,n));let l=new SWt(null);l.structural=!0,l.weight--,e?r.setEdge(a,o,l):r.setEdge(o,a,l)}))}))}buildSubhierarchiesForNeededFunctions(t){Se.exports.each(t.edges(),(e=>{let n=t.edge(e),i=new SWt(n);Se.exports.forEach(i.metaedge.baseEdgeList,(t=>{const e=t.v.split(HGt);for(let t=e.length;t>=0;t--){const n=e.slice(0,t),i=this.hierarchy.node(n.join(HGt));if(i){if(i.type===jGt.OP&&this.hierarchy.libraryFunctions[i.op])for(let t=1;t<n.length;t++){const e=n.slice(0,t).join(HGt);e&&this.buildSubhierarchy(e)}break}}}))}))}}class vWt{constructor(t,e,n,i,r){this.node=t,this.renderNodeInfo=e,this.renderMetaedgeInfo=n,this.annotationType=i,this.dx=0,this.dy=0,this.width=0,this.height=0,n&&n.metaedge&&(this.v=n.metaedge.v,this.w=n.metaedge.w),this.isIn=r,this.points=[]}}var bWt;!(function(t){t[t.SHORTCUT=0]="SHORTCUT",t[t.CONSTANT=1]="CONSTANT",t[t.SUMMARY=2]="SUMMARY",t[t.ELLIPSIS=3]="ELLIPSIS"})(bWt||(bWt={}));class xWt{constructor(){this.list=[],this.nodeNames={}}push(t){if(t.node.name in this.nodeNames)return;if(this.nodeNames[t.node.name]=!0,this.list.length<5)return void this.list.push(t);let e=this.list[this.list.length-1];if(e.annotationType===bWt.ELLIPSIS){let t=e.node;return void t.setNumMoreNodes(++t.numMoreNodes)}let n=new YGt(1);this.list.push(new vWt(n,new wWt(n),null,bWt.ELLIPSIS,t.isIn))}}class wWt{constructor(t){if(this.node=t,this.expanded=!1,this.inAnnotations=new xWt,this.outAnnotations=new xWt,this.x=0,this.y=0,this.width=0,this.height=0,this.inboxWidth=0,this.outboxWidth=0,this.excluded=!1,this.structural=!1,this.labelOffset=0,this.radius=0,this.labelHeight=0,this.paddingTop=0,this.paddingLeft=0,this.paddingRight=0,this.paddingBottom=0,this.isInExtract=!1,this.isOutExtract=!1,this.coreBox={width:0,height:0},this.isFadedOut=!1,this.displayName=t.name.substring(t.name.lastIndexOf(HGt)+1),t.type===jGt.META&&t.associatedFunction){const t=this.displayName.match(_Wt);t?this.displayName=t[1]:Se.exports.startsWith(this.displayName,VGt)&&(this.displayName=this.displayName.substring(VGt.length))}}isInCore(){return!this.isInExtract&&!this.isOutExtract&&!this.isLibraryFunction}}class SWt{constructor(t){this.metaedge=t,this.adjoiningMetaedge=null,this.structural=!1,this.weight=1,this.isFadedOut=!1}}function MWt(t,e,n,i,r){let o=new vWt(e,n,i,r,!0);t.inAnnotations.push(o)}function EWt(t,e,n,i,r){let o=new vWt(e,n,i,r,!1);t.outAnnotations.push(o)}class TWt extends wWt{constructor(t,e){super(t);let n=t.metagraph.graph();this.coreGraph=oWt(n.name,UGt.CORE,e),this.inExtractBox={width:0,height:0},this.outExtractBox={width:0,height:0},this.libraryFunctionsBox={width:0,height:0},this.isolatedInExtract=[],this.isolatedOutExtract=[],this.libraryFunctionsExtract=[]}}function CWt(t,e){t.coreGraph&&(function n(t,e){Se.exports.each(t.nodes(),(n=>{let i=t.node(n);if(i.expanded=e>1,e>0)switch(i.node.type){case jGt.META:case jGt.SERIES:CWt(i,e-1)}}))})(t.coreGraph,e)}function AWt(t,e,n){let i=t.node(e),r=t.node(n),o=t.edge(e,n);(i.node.include!==GGt.INCLUDE&&r.node.include!==GGt.INCLUDE||i.node.include===GGt.EXCLUDE||r.node.include===GGt.EXCLUDE)&&(EWt(i,r.node,r,o,bWt.SHORTCUT),MWt(r,i.node,i,o,bWt.SHORTCUT),t.removeEdge(e,n))}function kWt(t,e,n){let i=t.coreGraph,r=i.node(e);r.isOutExtract=!0,Se.exports.each(i.predecessors(e),((t,n)=>{AWt(i,t,e)})),Se.exports.each(i.successors(e),((t,n)=>{AWt(i,e,t)})),0===i.neighbors(e).length&&(r.node.include=GGt.EXCLUDE,t.isolatedOutExtract.push(r),i.removeNode(e))}function LWt(t,e,n){let i=t.coreGraph,r=i.node(e);r.isInExtract=!0,Se.exports.each(i.successors(e),((t,n)=>{AWt(i,e,t)})),Se.exports.each(i.predecessors(e),((t,n)=>{AWt(i,t,e)})),0===i.neighbors(e).length&&(r.node.include=GGt.EXCLUDE,t.isolatedInExtract.push(r),i.removeNode(e))}function PWt(t,e){if(t.type===jGt.OP){for(let n=0;n<e.length;n++)if(t.op===e[n])return!0}else if(t.type===jGt.META){let n=t.getRootOp();if(n)for(let t=0;t<e.length;t++)if(n.op===e[t])return!0}return!1}const NWt={animation:{duration:250},graph:{meta:{nodeSep:5,rankSep:25,edgeSep:5},series:{nodeSep:5,rankSep:25,edgeSep:5},padding:{paddingTop:40,paddingLeft:20}},subscene:{meta:{paddingTop:10,paddingBottom:10,paddingLeft:10,paddingRight:10,labelHeight:20,extractXOffset:15,extractYOffset:20},series:{paddingTop:10,paddingBottom:10,paddingLeft:10,paddingRight:10,labelHeight:10}},nodeSize:{meta:{radius:5,width:60,maxLabelWidth:52,height:VM().domain([1,200]).range([15,60]).clamp(!0),expandButtonRadius:3},op:{width:15,height:6,radius:3,labelOffset:-8,maxLabelWidth:30},series:{expanded:{radius:10,labelOffset:0},vertical:{width:16,height:13,labelOffset:-13},horizontal:{width:24,height:8,radius:10,labelOffset:-10}},bridge:{width:20,height:20,radius:2,labelOffset:0}},shortcutSize:{op:{width:10,height:4},meta:{width:12,height:4,radius:1},series:{width:14,height:4}},annotations:{inboxWidth:50,outboxWidth:50,xOffset:10,yOffset:3,labelOffset:2,maxLabelWidth:40},constant:{size:{width:4,height:4}},series:{maxStackCount:3,parallelStackOffsetRatio:.2,towerStackOffsetRatio:.5},minimap:{size:150}},IWt=140;function RWt(t){t.node.isGroupNode&&(function e(t){let e=t.coreGraph.nodes().map((e=>t.coreGraph.node(e))).concat(t.isolatedInExtract,t.isolatedOutExtract,t.libraryFunctionsExtract);Se.exports.each(e,(t=>{switch(t.node.type){case jGt.OP:Se.exports.extend(t,NWt.nodeSize.op);break;case jGt.BRIDGE:Se.exports.extend(t,NWt.nodeSize.bridge);break;case jGt.META:t.expanded?RWt(t):(Se.exports.extend(t,NWt.nodeSize.meta),t.height=NWt.nodeSize.meta.height(t.node.cardinality));break;case jGt.SERIES:t.expanded?(Se.exports.extend(t,NWt.nodeSize.series.expanded),RWt(t)):Se.exports.extend(t,t.node.hasNonControlEdges?NWt.nodeSize.series.vertical:NWt.nodeSize.series.horizontal);break;default:throw Error("Unrecognized node type: "+t.node.type)}t.expanded||(function e(t){t.inboxWidth=t.inAnnotations.list.length>0?NWt.annotations.inboxWidth:0,t.outboxWidth=t.outAnnotations.list.length>0?NWt.annotations.outboxWidth:0,t.coreBox.width=t.width,t.coreBox.height=t.height,t.width=Math.max(t.coreBox.width+t.inboxWidth+t.outboxWidth,3*t.displayName.length)})(t),(function n(t){if(t.expanded)return;let e=t.inAnnotations.list,n=t.outAnnotations.list;Se.exports.each(e,(t=>zWt(t))),Se.exports.each(n,(t=>zWt(t)));let i=NWt.annotations,r=Se.exports.reduce(e,((e,n,r)=>{let o=r>0?i.yOffset:0;return n.dx=-(t.coreBox.width+n.width)/2-i.xOffset,n.dy=e+o+n.height/2,e+o+n.height}),0);Se.exports.each(e,(t=>{t.dy-=r/2,t.labelOffset=i.labelOffset}));let o=Se.exports.reduce(n,((e,n,r)=>{let o=r>0?i.yOffset:0;return n.dx=(t.coreBox.width+n.width)/2+i.xOffset,n.dy=e+o+n.height/2,e+o+n.height}),0);Se.exports.each(n,(t=>{t.dy-=o/2,t.labelOffset=i.labelOffset}));let a=Math.min(t.height/2-t.radius,r/2);a=a<0?0:a;let s=VM().domain([0,e.length-1]).range([-a,a]);Se.exports.each(e,((n,i)=>{n.points=[{dx:n.dx+n.width/2,dy:n.dy},{dx:-t.coreBox.width/2,dy:e.length>1?s(i):0}]}));let l=Math.min(t.height/2-t.radius,o/2);l=l<0?0:l;let c=VM().domain([0,n.length-1]).range([-l,l]);Se.exports.each(n,((e,i)=>{e.points=[{dx:t.coreBox.width/2,dy:n.length>1?c(i):0},{dx:e.dx-e.width/2,dy:e.dy}]})),t.height=Math.max(t.height,r,o)})(t)}))})(t),t.node.type===jGt.META?(function n(t){let e=NWt.subscene.meta;Se.exports.extend(t,e),Se.exports.extend(t.coreBox,OWt(t.coreGraph,NWt.graph.meta));let n=t.isolatedInExtract.length?Se.exports.maxBy(t.isolatedInExtract,(t=>t.width)).width:null;t.inExtractBox.width=null!=n?n:0,t.inExtractBox.height=Se.exports.reduce(t.isolatedInExtract,((t,n,i)=>{let r=i>0?e.extractYOffset:0;return n.x=0,n.y=t+r+n.height/2,t+r+n.height}),0);let i=t.isolatedOutExtract.length?Se.exports.maxBy(t.isolatedOutExtract,(t=>t.width)).width:null;t.outExtractBox.width=null!=i?i:0,t.outExtractBox.height=Se.exports.reduce(t.isolatedOutExtract,((t,n,i)=>{let r=i>0?e.extractYOffset:0;return n.x=0,n.y=t+r+n.height/2,t+r+n.height}),0);let r=t.libraryFunctionsExtract.length?Se.exports.maxBy(t.libraryFunctionsExtract,(t=>t.width)).width:null;t.libraryFunctionsBox.width=null!=r?r:0,t.libraryFunctionsBox.height=Se.exports.reduce(t.libraryFunctionsExtract,((t,n,i)=>{let r=i>0?e.extractYOffset:0;return n.x=0,n.y=t+r+n.height/2,t+r+n.height}),0);let o=0;t.isolatedInExtract.length>0&&o++,t.isolatedOutExtract.length>0&&o++,t.libraryFunctionsExtract.length>0&&o++,t.coreGraph.nodeCount()>0&&o++;let a=o<=1?0:o*NWt.subscene.meta.extractXOffset;const s=Math.max(IWt,t.inExtractBox.width+t.outExtractBox.width);t.coreBox.width+=s+a+t.libraryFunctionsBox.width+a,t.coreBox.height=e.labelHeight+Math.max(t.inExtractBox.height,t.coreBox.height,t.libraryFunctionsBox.height,t.outExtractBox.height),t.width=t.coreBox.width+e.paddingLeft+e.paddingRight,t.height=t.paddingTop+t.coreBox.height+t.paddingBottom})(t):t.node.type===jGt.SERIES&&(function i(t){let e=t.coreGraph,n=NWt.subscene.series;Se.exports.extend(t,n),Se.exports.extend(t.coreBox,OWt(t.coreGraph,NWt.graph.series)),Se.exports.each(e.nodes(),(t=>{e.node(t).excluded=!1})),t.width=t.coreBox.width+n.paddingLeft+n.paddingRight,t.height=t.coreBox.height+n.paddingTop+n.paddingBottom})(t)}function OWt(t,e){Se.exports.extend(t.graph(),{nodesep:e.nodeSep,ranksep:e.rankSep,edgesep:e.edgeSep});let n=[];if(Se.exports.each(t.nodes(),(e=>{t.node(e).node.type===jGt.BRIDGE||n.push(e)})),!n.length)return{width:0,height:0};fGt.layout(t);let i=1/0,r=1/0,o=-1/0,a=-1/0;return Se.exports.each(n,(e=>{let n=t.node(e),s=.5*n.width,l=n.x-s,c=n.x+s;i=l<i?l:i,o=c>o?c:o;let u=.5*n.height,h=n.y-u,d=n.y+u;r=h<r?h:r,a=d>a?d:a})),Se.exports.each(t.edges(),(e=>{let n=t.edge(e);if(n.structural)return;let s=t.node(n.metaedge.v),l=t.node(n.metaedge.w);if(3===n.points.length&&(function c(t){let e=BWt(t[0],t[1]);for(let n=1;n<t.length-1;n++){let i=BWt(t[n],t[n+1]);if(Math.abs(i-e)>1)return!1;e=i}return!0})(n.points)){if(null!=s){let t=s.expanded?s.x:DWt(s);n.points[0].x=t}if(null!=l){let t=l.expanded?l.x:DWt(l);n.points[2].x=t}n.points=[n.points[0],n.points[1]]}null!=l&&(n.points[n.points.length-1]=HWt(n.points[n.points.length-2],l)),null!=s&&(n.points[0]=HWt(n.points[1],s)),Se.exports.each(n.points,(t=>{i=t.x<i?t.x:i,o=t.x>o?t.x:o,r=t.y<r?t.y:r,a=t.y>a?t.y:a}))})),Se.exports.each(t.nodes(),(e=>{let n=t.node(e);n.x-=i,n.y-=r})),Se.exports.each(t.edges(),(e=>{Se.exports.each(t.edge(e).points,(t=>{t.x-=i,t.y-=r}))})),{width:o-i,height:a-r}}function zWt(t){switch(t.annotationType){case bWt.CONSTANT:Se.exports.extend(t,NWt.constant.size);break;case bWt.SHORTCUT:if(t.node.type===jGt.OP)Se.exports.extend(t,NWt.shortcutSize.op);else if(t.node.type===jGt.META)Se.exports.extend(t,NWt.shortcutSize.meta);else{if(t.node.type!==jGt.SERIES)throw Error("Invalid node type: "+t.node.type);Se.exports.extend(t,NWt.shortcutSize.series)}break;case bWt.SUMMARY:Se.exports.extend(t,NWt.constant.size)}}function DWt(t){return t.expanded?t.x:t.x-t.width/2+(t.inAnnotations.list.length?t.inboxWidth:0)+t.coreBox.width/2}function BWt(t,e){return 180*Math.atan((e.y-t.y)/(e.x-t.x))/Math.PI}function HWt(t,e){let n,i,r=e.expanded?e.x:DWt(e),o=e.y,a=t.x-r,s=t.y-o,l=e.expanded?e.width:e.coreBox.width,c=e.expanded?e.height:e.coreBox.height;return Math.abs(s)*l/2>Math.abs(a)*c/2?(s<0&&(c=-c),n=0===s?0:c/2*a/s,i=c/2):(a<0&&(l=-l),n=l/2,i=0===a?0:l/2*s/a),{x:r+n,y:o+i}}const FWt=xGt,VWt=vGt;let UWt=[{background_color:"#CC2F2C",label:"NaN"},{background_color:"#FF8D00",label:"-∞"},{background_color:"#EAEAEA",label:"-"},{background_color:"#A5A5A5",label:"0"},{background_color:"#262626",label:"+"},{background_color:"#003ED4",label:"+∞"}];function jWt(t,e,n){null!=t.attr("transform")&&(t=t.transition("position")),t.attr("transform","translate("+e+","+n+")")}function GWt(t,e,n,i,r){t.transition().attr("x",e-i/2).attr("y",n-r/2).attr("width",i).attr("height",r)}function WWt(t,e,n,i,r){t.transition().attr("cx",e).attr("cy",n).attr("rx",i/2).attr("ry",r/2)}function qWt(t,e){return e?t.toFixed(0):Math.abs(t)>=1?t.toFixed(1):t.toExponential(1)}let YWt=class extends(er(ye)){constructor(){super(...arguments),this.specificHealthPillStep=0,this.healthPillEntries=UWt}ready(){super.ready();var t=document.getElementById("mainContainer"),e=document.querySelector("tf-dashboard-layout .scrollbar");t&&e&&(t.style.overflow="hidden",e.style.overflow="hidden")}_healthPillsAvailable(t,e){return t&&e}_computeTensorCountString(t,e){return t?t[e].toFixed(0):""}get healthPillValuesForSelectedNode(){var t=this.selectedNode;if(this.areHealthPillsLoading)return null;if(!t)return null;const e=this.nodeNamesToHealthPills[t];if(!e)return null;const n=e[this.allStepsModeEnabled?0:this.healthPillStepIndex];return n?n.value.slice(2,8):null}get _currentStepDisplayValue(){var t=this.nodeNamesToHealthPills,e=this.healthPillStepIndex,n=this.areHealthPillsLoading;if(this.allStepsModeEnabled)return this.specificHealthPillStep.toFixed(0);if(n)return 0;for(let n in t)return t[n][e].step.toFixed(0);return 0}get _biggestStepEverSeen(){var t=this.nodeNamesToHealthPills;for(let n in t){var e=t[n];return Math.max(this._biggestStepEverSeen,e[e.length-1].step)}return this._biggestStepEverSeen||0}get _maxStepIndex(){var t=this.nodeNamesToHealthPills;for(let e in t)return t[e].length-1;return 0}_hasDebuggerNumericAlerts(t){return t&&t.length}_updateAlertsList(){var t=this.debuggerNumericAlerts,e=this.$$("#numeric-alerts-body");if(e){e.innerText="";for(var n=0;n<t.length;n++){var i=t[n],r=document.createElement("tr"),o=document.createElement("td");o.innerText=(f=i.first_timestamp,(m=+new Date-+new Date(f/1e3))<3e4?"just now":m<6e4?Math.floor(m/1e3)+" seconds ago":m<12e4?"a minute ago":m<36e5?Math.floor(m/6e4)+" minutes ago":1==Math.floor(m/36e5)?"an hour ago":m<864e5?Math.floor(m/36e5)+" hours ago":m<1728e5?"yesterday":Math.floor(m/864e5)+" days ago"),o.classList.add("first-offense-td"),r.appendChild(o);var a=document.createElement("td");a.classList.add("tensor-device-td");var s=document.createElement("div");s.classList.add("tensor-section-within-table"),s.innerText=i.tensor_name,this._addOpExpansionListener(s,i.tensor_name),a.appendChild(s);var l=document.createElement("div");l.classList.add("device-section-within-table"),l.innerText="("+i.device_name+")",a.appendChild(l),r.appendChild(a);var c=document.createElement("div");c.classList.add("mini-health-pill");var u=document.createElement("td");if(u.classList.add("mini-health-pill-td"),u.appendChild(c),r.appendChild(u),i.neg_inf_event_count){var h=document.createElement("div");h.classList.add("negative-inf-mini-health-pill-section"),h.innerText=i.neg_inf_event_count,h.setAttribute("title",i.neg_inf_event_count+" events with -∞"),c.appendChild(h)}if(i.pos_inf_event_count){var d=document.createElement("div");d.classList.add("positive-inf-mini-health-pill-section"),d.innerText=i.pos_inf_event_count,d.setAttribute("title",i.pos_inf_event_count+" events with +∞"),c.appendChild(d)}if(i.nan_event_count){var p=document.createElement("div");p.classList.add("nan-mini-health-pill-section"),p.innerText=i.nan_event_count,p.setAttribute("title",i.nan_event_count+" events with NaN"),c.appendChild(p)}Yi(e).appendChild(r)}var f,m}}_addOpExpansionListener(t,e){t.addEventListener("click",(()=>{var t,n=(function i(t,e,n){const i=n.split("/"),r=i[i.length-1].match(/(.*):\w+/);2===r.length&&(i[i.length-1]=r[1]);let o=i[0],a=e.getRenderNodeByName(o);for(let n=1;n<i.length&&a.node.type!==jGt.OP;n++)e.buildSubhierarchy(o),a.expanded=!0,t.setNodeExpanded(a),o+="/"+i[n],a=e.getRenderNodeByName(o);return a.node.name})(document.getElementById("scene"),this.renderHierarchy,e),r=document.querySelector("tf-graph-info#graph-info");r&&(t=r.scrollHeight-r.scrollTop);var o=this.selectedNode;this.set("selectedNode",n);var a=()=>{r.scrollTop=r.scrollHeight-t};r&&(o?a():window.setTimeout(a,20))}))}};var XWt;YWt.template=_e`
    <style>
      :host {
        font-size: 12px;
        margin: 0;
        padding: 0;
        display: block;
      }

      h2 {
        padding: 0;
        text-align: center;
        margin: 0;
      }

      .health-pill-legend {
        padding: 15px;
      }

      .health-pill-legend h2 {
        text-align: left;
      }

      .health-pill-entry {
        margin: 10px 10px 10px 0;
      }

      .health-pill-entry .color-preview {
        width: 26px;
        height: 26px;
        border-radius: 3px;
        display: inline-block;
        margin: 0 10px 0 0;
      }

      .health-pill-entry .color-label,
      .health-pill-entry .tensor-count {
        color: #777;
        display: inline-block;
        height: 26px;
        font-size: 22px;
        line-height: 26px;
        vertical-align: top;
      }

      .health-pill-entry .tensor-count {
        float: right;
      }

      #health-pill-step-slider {
        width: 100%;
        margin: 0 0 0 -15px;
        /* 31 comes from adding a padding of 15px from both sides of the paper-slider, subtracting
   * 1px so that the slider width aligns with the image (the last slider marker takes up 1px),
   * and adding 2px to account for a border of 1px on both sides of the image. 30 - 1 + 2.
   * Apparently, the paper-slider lacks a mixin for those padding values. */
        width: calc(100% + 31px);
      }

      #health-pills-loading-spinner {
        width: 20px;
        height: 20px;
        vertical-align: top;
      }

      #health-pill-step-number-input {
        text-align: center;
        vertical-align: top;
      }

      #numeric-alerts-table-container {
        max-height: 400px;
        overflow-x: hidden;
        overflow-y: auto;
      }

      #numeric-alerts-table {
        text-align: left;
      }

      #numeric-alerts-table td {
        vertical-align: top;
      }

      #numeric-alerts-table .first-offense-td {
        display: inline-block;
      }

      .first-offense-td {
        width: 80px;
      }

      .tensor-device-td {
        max-width: 140px;
        word-wrap: break-word;
      }

      .tensor-section-within-table {
        color: #266236;
        cursor: pointer;
        opacity: 0.8;
        text-decoration: underline;
      }

      .tensor-section-within-table:hover {
        opacity: 1;
      }

      .device-section-within-table {
        color: #666;
      }

      .mini-health-pill {
        width: 130px;
      }

      .mini-health-pill > div {
        height: 100%;
        width: 60px;
        border-radius: 3px;
      }

      #event-counts-th {
        padding: 0 0 0 10px;
      }

      .negative-inf-mini-health-pill-section {
        background: rgb(255, 141, 0);
        width: 20px;
      }

      .positive-inf-mini-health-pill-section {
        background: rgb(0, 62, 212);
        width: 20px;
      }

      .nan-mini-health-pill-section {
        background: rgb(204, 47, 44);
        width: 20px;
      }

      .negative-inf-mini-health-pill-section,
      .positive-inf-mini-health-pill-section,
      .nan-mini-health-pill-section {
        color: #fff;
        display: inline-block;
        height: 100%;
        line-height: 20px;
        margin: 0 0 0 10px;
        text-align: center;
      }

      .no-numeric-alerts-notification {
        margin: 0;
      }
    </style>
    <paper-material elevation="1" class="card health-pill-legend">
      <div class="title">
        Enable all (not just sampled) steps. Requires slow disk read.
      </div>
      <paper-toggle-button
        id="enableAllStepsModeToggle"
        checked="{{allStepsModeEnabled}}"
      >
      </paper-toggle-button>
      <h2>
        Step of Health Pills:
        <template is="dom-if" if="[[allStepsModeEnabled]]">
          <input
            type="number"
            id="health-pill-step-number-input"
            min="0"
            max="[[_biggestStepEverSeen]]"
            value="{{specificHealthPillStep::input}}"
          />
        </template>
        <template is="dom-if" if="[[!allStepsModeEnabled]]">
          [[_currentStepDisplayValue]]
        </template>
        <paper-spinner-lite
          active
          hidden$="[[!areHealthPillsLoading]]"
          id="health-pills-loading-spinner"
        ></paper-spinner-lite>
      </h2>
      <template is="dom-if" if="[[allStepsModeEnabled]]">
        <paper-slider
          id="health-pill-step-slider"
          immediate-value="{{specificHealthPillStep}}"
          max="[[_biggestStepEverSeen]]"
          snaps
          step="1"
          value="{{specificHealthPillStep}}"
        ></paper-slider>
      </template>
      <template is="dom-if" if="[[!allStepsModeEnabled]]">
        <template is="dom-if" if="[[_maxStepIndex]]">
          <paper-slider
            id="health-pill-step-slider"
            immediate-value="{{healthPillStepIndex}}"
            max="[[_maxStepIndex]]"
            snaps
            step="1"
            value="{{healthPillStepIndex}}"
          ></paper-slider>
        </template>
      </template>
      <h2>
        Health Pill
        <template is="dom-if" if="[[healthPillValuesForSelectedNode]]">
          Counts for Selected Node
        </template>
        <template is="dom-if" if="[[!healthPillValuesForSelectedNode]]">
          Legend
        </template>
      </h2>
      <template is="dom-repeat" items="[[healthPillEntries]]">
        <div class="health-pill-entry">
          <div
            class="color-preview"
            style="background:[[item.background_color]]"
          ></div>
          <div class="color-label">[[item.label]]</div>
          <div class="tensor-count">
            [[_computeTensorCountString(healthPillValuesForSelectedNode,
            index)]]
          </div>
        </div>
      </template>
      <div hidden$="[[!_hasDebuggerNumericAlerts(debuggerNumericAlerts)]]">
        <h2 id="numeric-alerts-header">Numeric Alerts</h2>
        <p>Alerts are sorted from top to bottom by increasing timestamp.</p>
        <div id="numeric-alerts-table-container">
          <table id="numeric-alerts-table">
            <thead>
              <tr>
                <th>First Offense</th>
                <th>Tensor (Device)</th>
                <th id="event-counts-th">Event Counts</th>
              </tr>
            </thead>
            <tbody id="numeric-alerts-body"></tbody>
          </table>
        </div>
      </div>
      <template
        is="dom-if"
        if="[[!_hasDebuggerNumericAlerts(debuggerNumericAlerts)]]"
      >
        <p class="no-numeric-alerts-notification">
          No numeric alerts so far. That is likely good. Alerts indicate the
          presence of NaN or (+/-) Infinity values, which may be concerning.
        </p>
      </template>
    </paper-material>
  `,t([o({type:Object}),e("design:type",yWt)],YWt.prototype,"renderHierarchy",void 0),t([o({type:Array,notify:!0}),e("design:type",Object)],YWt.prototype,"debuggerNumericAlerts",void 0),t([o({type:Object}),e("design:type",Object)],YWt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Number,notify:!0}),e("design:type",Object)],YWt.prototype,"healthPillStepIndex",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],YWt.prototype,"specificHealthPillStep",void 0),t([o({type:String,notify:!0}),e("design:type",Object)],YWt.prototype,"selectedNode",void 0),t([o({type:String,notify:!0}),e("design:type",Object)],YWt.prototype,"highlightedNode",void 0),t([o({type:Number,notify:!0}),e("design:type",Object)],YWt.prototype,"selectedNodeInclude",void 0),t([o({type:Boolean}),e("design:type",Object)],YWt.prototype,"areHealthPillsLoading",void 0),t([o({type:Array}),e("design:type",Array)],YWt.prototype,"healthPillEntries",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Object)],YWt.prototype,"allStepsModeEnabled",void 0),t([s("nodeNamesToHealthPills","healthPillStepIndex","selectedNode","allStepsModeEnabled","areHealthPillsLoading"),e("design:type",Array),e("design:paramtypes",[])],YWt.prototype,"healthPillValuesForSelectedNode",null),t([s("nodeNamesToHealthPills","healthPillStepIndex","allStepsModeEnabled","specificHealthPillStep","areHealthPillsLoading"),e("design:type",Object),e("design:paramtypes",[])],YWt.prototype,"_currentStepDisplayValue",null),t([s("nodeNamesToHealthPills"),e("design:type",Number),e("design:paramtypes",[])],YWt.prototype,"_biggestStepEverSeen",null),t([s("nodeNamesToHealthPills"),e("design:type",Number),e("design:paramtypes",[])],YWt.prototype,"_maxStepIndex",null),t([a("debuggerNumericAlerts"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],YWt.prototype,"_updateAlertsList",null),YWt=t([i("tf-graph-debugger-data-card")],YWt),(function(t){t.CONST="CONST",t.META="META",t.OP="OP",t.SERIES="SERIES",t.SUMMARY="SUMMARY"})(XWt||(XWt={}));let $Wt=class extends(er(il(ye))){constructor(){super(...arguments),this.vertical=!1,this.fillOverride=null,this.strokeOverride=null,this.height=20,this.faded=!1}getSvgDefinableElement(){return this.$.svgDefs}get _fill(){var t=this.fillOverride;if(null!=t)return t;switch(this.type){case XWt.META:return pWt.DEFAULT_FILL;case XWt.SERIES:return"white";default:return"#ffffff"}}get _stroke(){var t=this.strokeOverride;if(null!=t)return t;switch(this.type){case XWt.META:return pWt.DEFAULT_STROKE;case XWt.SERIES:default:return"#b2b2b2"}}_isType(t,e){return t===e}_fadedClass(t,e){return t?"faded-"+e:""}};$Wt.template=_e`
    <style>
      :host {
        font-size: 0;
      }

      :host(.dark-mode) svg {
        filter: invert(1);
      }

      .faded-rect {
        fill: url(#rectHatch);
      }

      .faded-ellipse {
        fill: url(#ellipseHatch);
      }

      .faded-rect,
      .faded-ellipse,
      .faded-series {
        stroke: var(--tb-graph-faded) !important;
      }
      #rectHatch line,
      #ellipseHatch line {
        color: #e0d4b3 !important;
        fill: white;
        stroke: #e0d4b3 !important;
      }
    </style>
    <!-- SVG for definitions -->
    <svg height="0" width="0" id="svgDefs">
      <defs>
        <!-- Hatch patterns for faded out nodes. -->
        <pattern
          id="rectHatch"
          patternTransform="rotate(45 0 0)"
          width="5"
          height="5"
          patternUnits="userSpaceOnUse"
        >
          <line x1="0" y1="0" x2="0" y2="5" style="stroke-width: 1"></line>
        </pattern>
        <pattern
          id="ellipseHatch"
          patternTransform="rotate(45 0 0)"
          width="2"
          height="2"
          patternUnits="userSpaceOnUse"
        >
          <line x1="0" y1="0" x2="0" y2="2" style="stroke-width: 1"></line>
        </pattern>
        <!-- Template for an Op node ellipse. -->
        <ellipse
          id="op-node-stamp"
          rx="7.5"
          ry="3"
          stroke="inherit"
          fill="inherit"
        ></ellipse>
        <!-- Template for an Op node annotation ellipse (smaller). -->
        <ellipse
          id="op-node-annotation-stamp"
          rx="5"
          ry="2"
          stroke="inherit"
          fill="inherit"
        ></ellipse>
        <!-- Vertically stacked series of Op nodes when unexpanded. -->
        <g id="op-series-vertical-stamp">
          <use xlink:href="#op-node-stamp" x="8" y="9"></use>
          <use xlink:href="#op-node-stamp" x="8" y="6"></use>
          <use xlink:href="#op-node-stamp" x="8" y="3"></use>
        </g>
        <g id="op-series-horizontal-stamp">
          <use xlink:href="#op-node-stamp" x="16" y="4"></use>
          <use xlink:href="#op-node-stamp" x="12" y="4"></use>
          <use xlink:href="#op-node-stamp" x="8" y="4"></use>
        </g>
        <g
          id="summary-icon"
          fill="#848484"
          height="12"
          viewBox="0 0 24 24"
          width="12"
        >
          <path
            d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z"
          ></path>
        </g>
      </defs>
    </svg>
    <template is="dom-if" if="[[_isType(type, 'CONST')]]">
      <svg
        height$="[[height]]"
        preserveAspectRatio="xMinYMid meet"
        viewBox="0 0 10 10"
      >
        <circle
          cx="5"
          cy="5"
          r="3"
          fill$="[[_fill]]"
          stroke$="[[_stroke]]"
        ></circle>
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'SUMMARY')]]">
      <svg
        width$="[[height]]"
        height$="[[height]]"
        viewBox="0 0 24 24"
        fill="#848484"
      >
        <path
          d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z"
        ></path>
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'OP')]]">
      <svg
        height$="[[height]]"
        preserveAspectRatio="xMinYMid meet"
        viewBox="0 0 16 8"
      >
        <use
          xmlns:xlink="http://www.w3.org/1999/xlink"
          xlink:href="#op-node-stamp"
          fill$="[[_fill]]"
          stroke$="[[_stroke]]"
          class$="{{_fadedClass(faded, 'ellipse')}}"
          x="8"
          y="4"
        ></use>
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'META')]]">
      <svg
        height$="[[height]]"
        preserveAspectRatio="xMinYMid meet"
        viewBox="0 0 37 16"
      >
        <rect
          x="1"
          y="1"
          fill$="[[_fill]]"
          stroke$="[[_stroke]]"
          class$="{{_fadedClass(faded, 'rect')}}"
          stroke-width="2px"
          height="14"
          width="35"
          rx="5"
          ry="5"
        ></rect>
      </svg>
    </template>
    <template is="dom-if" if="[[_isType(type, 'SERIES')]]">
      <template is="dom-if" if="[[vertical]]">
        <svg
          height$="[[height]]"
          preserveAspectRatio="xMinYMid meet"
          viewBox="0 0 16 15"
        >
          <use
            xmlns:xlink="http://www.w3.org/1999/xlink"
            xlink:href="#op-series-vertical-stamp"
            fill$="[[_fill]]"
            stroke$="[[_stroke]]"
            class$="{{_fadedClass(faded, 'series')}}"
            x="0"
            y="2"
          ></use>
        </svg>
      </template>
      <template is="dom-if" if="[[!vertical]]">
        <svg
          height$="[[height]]"
          preserveAspectRatio="xMinYMid meet"
          viewBox="0 0 24 10"
        >
          <use
            xmlns:xlink="http://www.w3.org/1999/xlink"
            xlink:href="#op-series-horizontal-stamp"
            fill$="[[_fill]]"
            stroke$="[[_stroke]]"
            class$="{{_fadedClass(faded, 'series')}}"
            x="0"
            y="1"
          ></use>
        </svg>
      </template>
    </template>
  `,t([o({type:String}),e("design:type",String)],$Wt.prototype,"type",void 0),t([o({type:Boolean}),e("design:type",Boolean)],$Wt.prototype,"vertical",void 0),t([o({type:String}),e("design:type",String)],$Wt.prototype,"fillOverride",void 0),t([o({type:String}),e("design:type",String)],$Wt.prototype,"strokeOverride",void 0),t([o({type:Number}),e("design:type",Number)],$Wt.prototype,"height",void 0),t([o({type:Boolean}),e("design:type",Boolean)],$Wt.prototype,"faded",void 0),t([s("type","fillOverride"),e("design:type",String),e("design:paramtypes",[])],$Wt.prototype,"_fill",null),t([s("type","strokeOverride"),e("design:type",String),e("design:paramtypes",[])],$Wt.prototype,"_stroke",null),$Wt=t([i("tf-graph-icon")],$Wt);var KWt=Object.freeze({__proto__:null,get GraphIconType(){return XWt}});function ZWt(t,e){const n=t.getContextMenu(),i=Su(t.getContextMenu());return function(r,o){let a=uu;const s=(function l(t){let e=0,n=0,i=t;for(;i&&i.offsetLeft>=0&&i.offsetTop>=0;)e+=i.offsetLeft-i.scrollLeft,n+=i.offsetTop-i.scrollTop,i=i.offsetParent;return{left:e,top:n}})(t);function c(t){t&&t.composedPath().includes(n)||(i.style("display","none"),document.body.removeEventListener("mousedown",c,{capture:!0}))}i.style("display","block").style("left",a.clientX-s.left+1+"px").style("top",a.clientY-s.top+1+"px"),a.preventDefault(),a.stopPropagation(),document.body.addEventListener("mousedown",c,{capture:!0}),i.html(""),i.append("ul").selectAll("li").data(e).enter().append("li").on("click",((t,e)=>{t.action(this,r,o),c()})).html((function(t){return t.title(r)}))}}let JWt=aE().domain([MGt,12]).range(["small","medium","large","xlarge"]);function QWt(t){return t.v+"--"+t.w}function tqt(t,e){let n=e.getNodeByName(t.v);if(null==n.outputShapes||Se.exports.isEmpty(n.outputShapes))return null;let i=n.outputShapes[t.outputTensorKey];return null==i?null:0===i.length?"scalar":i.map((t=>-1===t?"?":t)).join("×")}function eqt(t,e){return e.edgeLabelFunction?e.edgeLabelFunction(t,e):t.baseEdgeList.length>1?t.baseEdgeList.length+" tensors":tqt(t.baseEdgeList[0],e)}function nqt(t,e,n){const i=document.createElementNS(bGt,"path");for(let r=1;r<t.length;r++)if(i.setAttribute("d",n(t.slice(0,r))),i.getTotalLength()>e)return r-1;return t.length-1}function iqt(t,e,n){let i=uL().x((t=>t.x)).y((t=>t.y)),r=Su(document.createElementNS("http://www.w3.org/2000/svg","path")).attr("d",i(t)),o=+e.attr("markerWidth"),a=e.attr("viewBox").split(" ").map(Number),s=a[2]-a[0],l=+e.attr("refX"),c=r.node();if(n){const e=o*(1-l/s),n=c.getPointAtLength(e),r=nqt(t,e,i);return t[r-1]={x:n.x,y:n.y},t.slice(r-1)}{const e=1-l/s,n=c.getTotalLength()-o*e,r=c.getPointAtLength(n),a=nqt(t,n,i);return t[a]={x:r.x,y:r.y},t.slice(0,a+1)}}function rqt(t,e,n,i){i=i||vGt.Edge.LINE,e.label&&e.label.structural&&(i+=" "+vGt.Edge.STRUCTURAL),e.label&&e.label.metaedge&&e.label.metaedge.numRefEdges&&(i+=" "+vGt.Edge.REFERENCE_EDGE),n.handleEdgeSelected&&(i+=" "+vGt.Edge.SELECTABLE);let r,o="path_"+QWt(e);if(n.renderHierarchy.edgeWidthFunction)r=n.renderHierarchy.edgeWidthFunction(e,i);else{let t=1;null!=e.label&&null!=e.label.metaedge&&(t=e.label.metaedge.totalSize),r=n.renderHierarchy.edgeWidthSizedBasedScale(t)}let a=t.append("path").attr("id",o).attr("class",i).style("stroke-width",r+"px");if(e.label&&e.label.metaedge)if(e.label.metaedge.numRefEdges){const t=`reference-arrowhead-${JWt(r)}`;a.style("marker-start",`url(#${t})`),e.label.startMarkerId=t}else{const t=`dataflow-arrowhead-${JWt(r)}`;a.style("marker-end",`url(#${t})`),e.label.endMarkerId=t}if(null==e.label||null==e.label.metaedge)return;let s=eqt(e.label.metaedge,n.renderHierarchy);null!=s&&t.append("text").append("textPath").attr("xlink:href","#"+o).attr("startOffset","50%").attr("text-anchor","middle").attr("dominant-baseline","central").text(s)}let oqt=uL().curve($L).x((t=>t.x)).y((t=>t.y));var aqt;function sqt(t,e,n){let i=wGt(t,"g",vGt.Node.CONTAINER).selectAll((function(){return this.childNodes})).data(e,(t=>t.node.name+":"+t.node.type));return i.enter().append("g").attr("data-name",(t=>t.node.name)).each((function(t){let e=Su(this);n.addNodeGroup(t.node.name,e)})).merge(i).attr("class",(t=>vGt.Node.GROUP+" "+yqt(t))).each((function(t){let e=Su(this);Aqt(wGt(e,"g",vGt.Annotation.INBOX),t.inAnnotations,t,n),Aqt(wGt(e,"g",vGt.Annotation.OUTBOX),t.outAnnotations,t,n);let i=_qt(e,t,vGt.Node.SHAPE);t.node.isGroupNode&&(function r(t,e,n){let i=wGt(t,"g",vGt.Node.BUTTON_CONTAINER);wGt(i,"circle",vGt.Node.BUTTON_CIRCLE),wGt(i,"path",vGt.Node.EXPAND_BUTTON).attr("d","M0,-2.2 V2.2 M-2.2,0 H2.2"),wGt(i,"path",vGt.Node.COLLAPSE_BUTTON).attr("d","M-2.2,0 H2.2"),i.on("click",(t=>{uu.stopPropagation(),n.fire("node-toggle-expand",{name:t.node.name})})),(function r(t,e){let n=DWt(e)+(e.expanded?e.width:e.coreBox.width)/2-6,i=e.y-(e.expanded?e.height:e.coreBox.height)/2+6;e.node.type!==jGt.SERIES||e.expanded||(n+=10,i-=2);let r="translate("+n+","+i+")";t.selectAll("path").transition().attr("transform",r),t.select("circle").transition().attr({cx:n,cy:i,r:NWt.nodeSize.meta.expandButtonRadius})})(i,e)})(i,t,n),cqt(i,t,n),(function o(t,e,n){if(e.node.isGroupNode){if(e.expanded)return Lqt(t,e,n,vGt.Subscene.GROUP);FWt(t,"g",vGt.Subscene.GROUP).remove()}})(e,t,n),cqt((function a(t,e,n){let i=e.displayName,r=e.node.type===jGt.META&&!e.expanded,o=wGt(t,"text",vGt.Node.LABEL),a=o.node();a.parentNode.appendChild(a),o.attr("dy",".35em").attr("text-anchor","middle");let s=8;switch(e.node.type){case jGt.META:s=e.expanded?9:8;break;case jGt.OP:s=6}if(r){i.length>n.maxMetanodeLabelLength&&(i=i.substr(0,n.maxMetanodeLabelLength-2)+"…");let t=(function l(t){return mqt||(mqt=VM().domain([t.maxMetanodeLabelLengthLargeFont,t.maxMetanodeLabelLength]).range([t.maxMetanodeLabelLengthFontSize,t.minMetanodeLabelLengthFontSize]).clamp(!0)),mqt})(n);o.attr("font-size",t(i.length)+"px"),s=t(i.length)}return fqt(o.text(i),e.node.type,s,e),o})(e,t,n),t,n,t.node.type===jGt.META),wqt(e,t,n),(function s(t,e){let n=FWt(t,"g",vGt.Node.SHAPE),i=DWt(e);switch(e.node.type){case jGt.OP:{const r=e.node;Se.exports.isNumber(r.functionInputIndex)||Se.exports.isNumber(r.functionOutputIndex)?(function r(t,e,n,i,o){const a=o/2,s=i/2,l=[[e,n-a],[e+s,n+a],[e-s,n+a]];t.transition().attr("points",l.map((t=>t.join(","))).join(" "))})(FWt(n,"polygon"),e.x,e.y,e.coreBox.width,e.coreBox.height):WWt(FWt(n,"ellipse"),i,e.y,e.coreBox.width,e.coreBox.height),gqt(t,i,e.y,e.labelOffset);break}case jGt.META:{let r=n.selectAll("rect");e.expanded?(GWt(r,e.x,e.y,e.width,e.height),lqt(t,e),gqt(t,i,e.y,-e.height/2+e.labelHeight/2)):(GWt(r,i,e.y,e.coreBox.width,e.coreBox.height),gqt(t,i,e.y,0));break}case jGt.SERIES:{let r=FWt(n,"use");e.expanded?(GWt(r,e.x,e.y,e.width,e.height),lqt(t,e),gqt(t,i,e.y,-e.height/2+e.labelHeight/2)):(GWt(r,i,e.y,e.coreBox.width,e.coreBox.height),gqt(t,i,e.y,e.labelOffset));break}case jGt.BRIDGE:GWt(FWt(n,"rect"),e.x,e.y,e.width,e.height);break;default:throw Error("Unrecognized node type: "+e.node.type)}})(e,t)})),i.exit().each((function(t){n.removeNodeGroup(t.node.name);let e=Su(this);t.inAnnotations.list.length>0&&e.select("."+vGt.Annotation.INBOX).selectAll("."+vGt.Annotation.GROUP).each((e=>{n.removeAnnotationGroup(e,t)})),t.outAnnotations.list.length>0&&e.select("."+vGt.Annotation.OUTBOX).selectAll("."+vGt.Annotation.GROUP).each((e=>{n.removeAnnotationGroup(e,t)}))})).remove(),i}function lqt(t,e){let n=e.x-e.width/2+e.paddingLeft,i=e.y-e.height/2+e.paddingTop;jWt(FWt(t,"g",vGt.Subscene.GROUP),n,i)}function cqt(t,e,n,i){if(i)return void t.attr("pointer-events","none");let r=ZWt(n,uqt(e.node,n));t.on("dblclick",(t=>{n.fire("node-toggle-expand",{name:t.node.name})})).on("mouseover",(t=>{n.isNodeExpanded(t)||n.fire("node-highlight",{name:t.node.name})})).on("mouseout",(t=>{n.isNodeExpanded(t)||n.fire("node-unhighlight",{name:t.node.name})})).on("click",(t=>{uu.stopPropagation(),n.fire("node-select",{name:t.node.name})})).on("contextmenu",((t,e)=>{n.fire("node-select",{name:t.node.name}),r.call(t,e)}))}function uqt(t,e){let n=[{title:e=>uWt(t.include),action:(n,i,r)=>{e.fire("node-toggle-extract",{name:t.name})}}];return e.nodeContextMenuItems&&(n=n.concat(e.nodeContextMenuItems)),hqt(t)&&n.push({title:e=>pqt(t),action:(n,i,r)=>{e.fire("node-toggle-seriesgroup",{name:dqt(t)})}}),n}function hqt(t){return null!==dqt(t)}function dqt(t){return t?t.type===jGt.SERIES?t.name:t.type===jGt.OP?t.owningSeries:null:null}function pqt(t){return(function n(t){return t===WGt.GROUP?"Ungroup this series of nodes":"Group this series of nodes"})(null!==(function e(t){let e=null;return t?(t.type===jGt.SERIES?e=t:t.parentNode&&t.parentNode.type===jGt.SERIES&&(e=t.parentNode),e):null})(t)?WGt.GROUP:WGt.UNGROUP)}function fqt(t,e,n,i){let r=t.node(),o=r.textContent,a=null;switch(e){case jGt.META:i&&!i.expanded&&(a=NWt.nodeSize.meta.maxLabelWidth);break;case jGt.OP:a=NWt.nodeSize.op.maxLabelWidth;break;case-1:a=NWt.annotations.maxLabelWidth}if(null!==a)return r.textContent=(function s(t,e,n){if(!t)return"";if(BGt(t,e)<=n)return t;let i=0,r=t.length;for(;i<r;){const o=i+Math.round((r-i)/2);BGt(t.slice(0,o)+"…",e)<=n?i=o:r=o-1}return 0===i?t[0]:t.slice(0,i)+"…"})(r.textContent,n,a),t.append("title").text(o)}!(function(t){t.NONE="none",t.COMPUTE_TIME="compute_time",t.DEVICE="device",t.MEMORY="memory",t.OP_COMPATIBILITY="op_compatibility",t.STRUCTURE="structure",t.XLA_CLUSTER="xla_cluster"})(aqt||(aqt={}));let mqt=null;function gqt(t,e,n,i){FWt(t,"text",vGt.Node.LABEL).transition().attr("x",e).attr("y",n+i)}function _qt(t,e,n){let i=wGt(t,"g",n);switch(e.node.type){case jGt.OP:const t=e.node;if(Se.exports.isNumber(t.functionInputIndex)||Se.exports.isNumber(t.functionOutputIndex)){wGt(i,"polygon",vGt.Node.COLOR_TARGET);break}wGt(i,"ellipse",vGt.Node.COLOR_TARGET);break;case jGt.SERIES:let n="annotation",r=e;r.coreGraph&&(n=r.node.hasNonControlEdges?"vertical":"horizontal");let o=[vGt.Node.COLOR_TARGET];r.isFadedOut&&o.push("faded-ellipse"),wGt(i,"use",o).attr("xlink:href","#op-series-"+n+"-stamp"),wGt(i,"rect",vGt.Node.COLOR_TARGET).attr("rx",e.radius).attr("ry",e.radius);break;case jGt.BRIDGE:case jGt.META:wGt(i,"rect",vGt.Node.COLOR_TARGET).attr("rx",e.radius).attr("ry",e.radius);break;default:throw Error("Unrecognized node type: "+e.node.type)}return i}function yqt(t){switch(t.node.type){case jGt.OP:return vGt.OPNODE;case jGt.META:return vGt.METANODE;case jGt.SERIES:return vGt.SERIESNODE;case jGt.BRIDGE:return vGt.BRIDGENODE;case jGt.ELLIPSIS:return vGt.ELLIPSISNODE}throw Error("Unrecognized node type: "+t.node.type)}function vqt(t,e,n){let i=(function r(t){return t.replace(/([:.\[\],/\\\(\)])/g,"\\$1")})(t);if(!n)return`url(#${i})`;let o=Su(n),a=o.select("defs#_graph-gradients");a.empty()&&(a=o.append("defs").attr("id","_graph-gradients"));let s=a.select("linearGradient#"+i);if(s.empty()){s=a.append("linearGradient").attr("id",t),s.selectAll("*").remove();let n=0;Se.exports.each(e,(t=>{let e=t.color;s.append("stop").attr("offset",n).attr("stop-color",e),s.append("stop").attr("offset",n+t.proportion).attr("stop-color",e),n+=t.proportion}))}return`url(#${i})`}function bqt(t){Su(t).select("defs#_graph-gradients").remove()}function xqt(t,e,n,i,r){let o=pWt;switch(t=t||(()=>0),e){case aqt.NONE:case aqt.STRUCTURE:if(n.node.type===jGt.META){let r=n.node.templateId;return e===aqt.STRUCTURE&&null!==r?o.STRUCTURE_PALETTE(t(r),i):o.UNKNOWN}return n.node.type===jGt.SERIES?i?o.EXPANDED_COLOR:"white":n.node.type===jGt.BRIDGE?n.structural?"#f0e":n.node.inbound?"#0ef":"#fe0":Se.exports.isNumber(n.node.functionInputIndex)?"#795548":Se.exports.isNumber(n.node.functionOutputIndex)?"#009688":"white";case aqt.DEVICE:return null==n.deviceColors?o.UNKNOWN:i?o.EXPANDED_COLOR:vqt("device-"+n.node.name,n.deviceColors,r);case aqt.XLA_CLUSTER:return null==n.xlaClusterColors?o.UNKNOWN:i?o.EXPANDED_COLOR:vqt("xla-"+n.node.name,n.xlaClusterColors,r);case aqt.COMPUTE_TIME:return i?o.EXPANDED_COLOR:n.computeTimeColor||o.UNKNOWN;case aqt.MEMORY:return i?o.EXPANDED_COLOR:n.memoryColor||o.UNKNOWN;case aqt.OP_COMPATIBILITY:return null==n.compatibilityColors?o.UNKNOWN:i?o.EXPANDED_COLOR:vqt("op-compat-"+n.node.name,n.compatibilityColors,r);default:throw new Error("Unknown case to color nodes by")}}function wqt(t,e,n,i){i=i||vGt.Node.SHAPE;const r=n.isNodeHighlighted(e.node.name),o=n.isNodeSelected(e.node.name),a=e.isInExtract||e.isOutExtract||e.isLibraryFunction,s=e.expanded&&i!==vGt.Annotation.NODE,l=e.isFadedOut;t.classed("highlighted",r),t.classed("selected",o),t.classed("extract",a),t.classed("expanded",s),t.classed("faded",l);const c=t.select("."+i+" ."+vGt.Node.COLOR_TARGET),u=xqt(n.templateIndex,n.colorBy,e,s,n.getGraphSvgRoot());c.style("fill",u),c.style("stroke",o?null:Sqt(u))}function Sqt(t){return"url"===t.substring(0,3)?pWt.GRADIENT_OUTLINE:Uh(t).darker().toString()}function Mqt(t,e,n,i){const r=Su(t);if(r.selectAll(".input-highlight").classed("input-highlight",!1),r.selectAll(".non-input").classed("non-input",!1),r.selectAll(".input-parent").classed("input-parent",!1),r.selectAll(".input-child").classed("input-child",!1),r.selectAll(".input-edge-highlight").classed("input-edge-highlight",!1),r.selectAll(".non-input-edge-highlight").classed("non-input-edge-highlight",!1),r.selectAll(".input-highlight-selected").classed("input-highlight-selected",!1),!e||!i||!n)return;let o=Eqt(n,e),a={};Se.exports.each(o,(function(n){a=Tqt(t,e,n,a)}));let s=Object.keys(a),l=(function c(t,e){let n={};return Se.exports.each(e,(function(e){let i=t.getNodeByName(e),r=Cqt(t,i);n[r.name]=r})),n})(e,s);!(function u(t,e){Se.exports.forOwn(e,(function(e){let n=e;for(;n.name!==FGt;){const e=Su(t).select(`.node[data-name="${n.name}"]`);!e.nodes().length||e.classed("input-highlight")||e.classed("selected")||e.classed("op")||e.classed("input-parent",!0),n=n.parentNode}}))})(t,l),r.selectAll("g.node:not(.selected):not(.input-highlight):not(.input-parent):not(.input-children)").classed("non-input",!0).each((function(t){r.selectAll(`[data-name="${t.node.name}"]`).classed("non-input",!0)})),r.selectAll("g.edge:not(.input-edge-highlight)").classed("non-input-edge-highlight",!0)}function Eqt(t,e){let n=[],i=e.getNodeByName(t);if(i instanceof XGt)return[i].concat(i.inEmbeddings);let r=i.metagraph.nodes();return Se.exports.each(r,(function(t){n=n.concat(Eqt(t,e))})),n}function Tqt(t,e,n,i){if(i[n.name])return i;i[n.name]=!0;let r=n.inputs,o=Cqt(e,n);Su(t).select(`.node[data-name="${o.name}"]`).classed("input-highlight",!0);let a={};Se.exports.each(r,(function(t){let n=e.getNodeByName(t.name);if(void 0===n)return;if(n instanceof KGt){let t=sWt(n.name);n=e.getNodeByName(t)}let i=Cqt(e,n),r=a[i.name];r?r.opNodes.push(n):a[i.name]={visibleParent:i,opNodes:[n]}}));let s={},l=[o];s[o.name]={traced:!1,index:0,connectionEndpoints:[]};let c=o;for(let t=1;c.name!==FGt;t++)c=c.parentNode,s[c.name]={traced:!1,index:t,connectionEndpoints:[]},l[t]=c;return Se.exports.forOwn(a,(function(n,r){let a=n.visibleParent;Se.exports.each(n.opNodes,(function(n){i=Tqt(t,e,n,i)})),a.name!==o.name&&(function c(t,e,n,i){let r=e,o=e,a=[];for(;!n[r.name];)o.name!==r.name&&a.push([o,r]),o=r,r=r.parentNode;let s=n[r.name].index,l=i[Math.max(s-1,0)].name,c=l,u=o.name,h=o.name;const d=Su(t);d.selectAll(`[data-edge="${h}--${l}"]`).classed("input-edge-highlight",!0),Se.exports.each(a,(function(t){d.selectAll(`[data-edge="${t[0].name}--${c}~~${t[1].name}~~OUT"]`).classed("input-edge-highlight",!0)}));for(let t=1;t<s;t++)d.selectAll(`[data-edge="${u}~~${i[t].name}~~IN--${i[t-1].name}"]`).classed("input-edge-highlight",!0)})(t,a,s,l)})),i}function Cqt(t,e){let n=!1,i=e;for(;!n;)if(i=(e=i).parentNode,void 0===i)n=!0;else{let e=t.getRenderNodeByName(i.name);e&&(e.expanded||i instanceof XGt)&&(n=!0)}return e}function Aqt(t,e,n,i){let r=t.selectAll((function(){return this.childNodes})).data(e.list,(t=>t.node.name));return r.enter().append("g").attr("data-name",(t=>t.node.name)).each((function(t){let e=Su(this);i.addAnnotationGroup(t,n,e);let r=vGt.Annotation.EDGE,o=t.renderMetaedgeInfo&&t.renderMetaedgeInfo.metaedge;o&&!o.numRegularEdges&&(r+=" "+vGt.Annotation.CONTROL_EDGE),o&&o.numRefEdges&&(r+=" "+vGt.Edge.REF_LINE),rqt(e,t,i,r),t.annotationType!==bWt.ELLIPSIS?((function a(t,e){let n=e.node.name.split("/");kqt(t,n[n.length-1],e,null)})(e,t),(function s(t,e){e.annotationType===bWt.SUMMARY?wGt(t,"use").attr("class","summary").attr("xlink:href","#summary-icon").attr("cursor","pointer"):wGt(_qt(t,e,vGt.Annotation.NODE),"title").text(e.node.name)})(e,t)):kqt(e,t.node.name,t,vGt.Annotation.ELLIPSIS)})).merge(r).attr("class",(t=>vGt.Annotation.GROUP+" "+(function e(t){return(bWt[t]||"").toLowerCase()||null})(t.annotationType)+" "+yqt(t))).each((function(t){let e=Su(this);!(function r(t,e,n,i){let r=DWt(e);n.renderNodeInfo&&n.annotationType!==bWt.ELLIPSIS&&wqt(t,n.renderNodeInfo,i,vGt.Annotation.NODE),n.annotationType===bWt.SUMMARY&&(n.width+=10),t.select("text."+vGt.Annotation.LABEL).transition().attr("x",r+n.dx+(n.isIn?-1:1)*(n.width/2+n.labelOffset)).attr("y",e.y+n.dy),t.select("use.summary").transition().attr("x",r+n.dx-3).attr("y",e.y+n.dy-6),WWt(t.select("."+vGt.Annotation.NODE+" ellipse"),r+n.dx,e.y+n.dy,n.width,n.height),GWt(t.select("."+vGt.Annotation.NODE+" rect"),r+n.dx,e.y+n.dy,n.width,n.height),GWt(t.select("."+vGt.Annotation.NODE+" use"),r+n.dx,e.y+n.dy,n.width,n.height),t.select("path."+vGt.Annotation.EDGE).transition().attr("d",(t=>{let n=t.points.map((t=>({x:t.dx+r,y:t.dy+e.y})));return oqt(n)}))})(e,n,t,i),t.annotationType!==bWt.ELLIPSIS&&(function o(t,e,n,i){t.on("mouseover",(t=>{i.fire("annotation-highlight",{name:t.node.name,hostName:e.node.name})})).on("mouseout",(t=>{i.fire("annotation-unhighlight",{name:t.node.name,hostName:e.node.name})})).on("click",(t=>{uu.stopPropagation(),i.fire("annotation-select",{name:t.node.name,hostName:e.node.name})})),n.annotationType!==bWt.SUMMARY&&n.annotationType!==bWt.CONSTANT&&t.on("contextmenu",ZWt(i,uqt(n.node,i)))})(e,n,t,i)})),r.exit().each((function(t){i.removeAnnotationGroup(t,n)})).remove(),r}function kqt(t,e,n,i){let r=vGt.Annotation.LABEL;return i&&(r+=" "+i),fqt(t.append("text").attr("class",r).attr("dy",".35em").attr("text-anchor",n.isIn?"end":"start").text(e),-1,5)}function Lqt(t,e,n,i){let r=xGt(t,"g",i=i||vGt.Scene.GROUP).empty(),o=wGt(t,"g",i),a=wGt(o,"g",vGt.Scene.CORE),s=Se.exports.reduce(e.coreGraph.nodes(),((t,n)=>{let i=e.coreGraph.node(n);return i.excluded||t.push(i),t}),[]);return e.node.type===jGt.SERIES&&s.reverse(),(function l(t,e,n){const i=n;let r=[];r=Se.exports.reduce(e.edges(),((t,n)=>{let i=e.edge(n);return t.push({v:n.v,w:n.w,label:i}),t}),r);let o=wGt(t,"g",vGt.Edge.CONTAINER).selectAll((function(){return this.childNodes})).data(r,QWt);o.enter().append("g").attr("class",vGt.Edge.GROUP).attr("data-edge",QWt).each((function(t){let e=Su(this);t.label.edgeGroup=e,i._edgeGroupIndex[QWt(t)]=e,i.handleEdgeSelected&&e.on("click",(t=>{uu.stopPropagation(),i.fire("edge-select",{edgeData:t,edgeGroup:e})})),rqt(e,t,i)})).merge(o).each((function(){!(function t(e,n){Su(n).select("path."+vGt.Edge.LINE).transition().attrTween("d",(function(t,n,i){return(function r(t,e,n,i,o){let a=n.label,s=a.adjoiningMetaedge,l=a.points;const{shadowRoot:c}=t;if(n.label.startMarkerId&&(l=iqt(l,Su(c.querySelector("#"+n.label.startMarkerId)),!0)),n.label.endMarkerId&&(l=iqt(l,Su(c.querySelector("#"+n.label.endMarkerId)),!1)),!s)return jd(o,oqt(l));let u=s.edgeGroup.node().firstChild,h=a.metaedge.inbound;return function(t){let n=u.getPointAtLength(h?u.getTotalLength():0).matrixTransform(u.getCTM()).matrixTransform(e.getCTM().inverse()),i=h?0:l.length-1;return l[i].x=n.x,l[i].y=n.y,oqt(l)}})(e,this,t,0,i)}))})(n,this)})).each((function(t){!(function e(t,n,i){t.classed("faded",n.label.isFadedOut);let r=n.label.metaedge;t.select("path."+vGt.Edge.LINE).classed("control-dep",r&&!r.numRegularEdges)})(Su(this),t)})),o.exit().each((t=>{delete i._edgeGroupIndex[QWt(t)]})).remove()})(a,e.coreGraph,n),sqt(a,s,n),e.isolatedInExtract.length>0?sqt(wGt(o,"g",vGt.Scene.INEXTRACT),e.isolatedInExtract,n):xGt(o,"g",vGt.Scene.INEXTRACT).remove(),e.isolatedOutExtract.length>0?sqt(wGt(o,"g",vGt.Scene.OUTEXTRACT),e.isolatedOutExtract,n):xGt(o,"g",vGt.Scene.OUTEXTRACT).remove(),e.libraryFunctionsExtract.length>0?sqt(wGt(o,"g",vGt.Scene.FUNCTION_LIBRARY),e.libraryFunctionsExtract,n):xGt(o,"g",vGt.Scene.FUNCTION_LIBRARY).remove(),(function c(t,e){let n=e.node.type===jGt.SERIES?0:NWt.subscene.meta.labelHeight;jWt(FWt(t,"g",VWt.Scene.CORE),0,n);let i=e.isolatedInExtract.length>0,r=e.isolatedOutExtract.length>0,o=e.libraryFunctionsExtract.length>0,a=NWt.subscene.meta.extractXOffset,s=0;if(i&&(s+=e.outExtractBox.width),r&&(s+=e.outExtractBox.width),i){let i=e.coreBox.width;i=s<IWt?i-IWt+e.inExtractBox.width/2:i-e.inExtractBox.width/2-e.outExtractBox.width-(r?a:0),i=i-e.libraryFunctionsBox.width-(o?a:0),jWt(FWt(t,"g",VWt.Scene.INEXTRACT),i,n)}if(r){let i=e.coreBox.width;s<IWt?i=i-IWt+e.outExtractBox.width/2:i-=e.outExtractBox.width/2,i=i-e.libraryFunctionsBox.width-(o?a:0),jWt(FWt(t,"g",VWt.Scene.OUTEXTRACT),i,n)}if(o){let i=e.coreBox.width-e.libraryFunctionsBox.width/2;jWt(FWt(t,"g",VWt.Scene.FUNCTION_LIBRARY),i,n)}})(o,e),r&&o.attr("opacity",0).transition().attr("opacity",1),o}let Pqt=class extends(er(ye)){constructor(){super(...arguments),this.node=null,this.renderInfo=null,this.colorBy=aqt.STRUCTURE,this.templateIndex=null,this.type=null,this.vertical=!1,this.const=!1,this.summary=!1,this.fill=null,this.height=20}_computeFillOverride(t,e,n,i,r){return t&&e&&i?xqt(i,n,e,!1):r}_getStrokeOverride(t){return t?Sqt(t):null}_getType(t,e,n,i){const{GraphIconType:r}=KWt;if(t)switch(t.type){case jGt.OP:{const i=t.op;return"string"!=typeof i?r.OP:"Const"===i||n?r.CONST:i.endsWith("Summary")||e?r.SUMMARY:r.OP}case jGt.META:return r.META;case jGt.SERIES:return r.SERIES}return i}_isVertical(t,e){return t?t.hasNonControlEdges:!!e}_getFaded(t){return t&&t.isFadedOut}_onFillOverrideChanged(t,e){const{node:n,renderInfo:i,colorBy:r,templateIndex:o}=this;t!==e&&bqt(this.$.icon.getSvgDefinableElement()),n&&i&&o&&xqt(o,r,i,!1,this.$.icon.getSvgDefinableElement())}};Pqt.template=_e`
    <style>
      tf-graph-icon {
        --tb-graph-faded: var(--tb-graph-faded);
      }
    </style>
    <tf-graph-icon
      id="icon"
      type="[[_getType(node, summary, const, type)]]"
      height="[[height]]"
      fill-override="[[_fillOverride]]"
      stroke-override="[[_getStrokeOverride(_fillOverride)]]"
      faded="[[_getFaded(renderInfo)]]"
      vertical="[[_isVertical(node, vertical)]]"
    ></tf-graph-icon>
  `,t([o({type:Object}),e("design:type",Object)],Pqt.prototype,"node",void 0),t([o({type:Object}),e("design:type",Object)],Pqt.prototype,"renderInfo",void 0),t([o({type:Object}),e("design:type",String)],Pqt.prototype,"colorBy",void 0),t([o({type:Object}),e("design:type",Function)],Pqt.prototype,"templateIndex",void 0),t([o({type:String}),e("design:type",String)],Pqt.prototype,"type",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Pqt.prototype,"vertical",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Pqt.prototype,"const",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Pqt.prototype,"summary",void 0),t([o({type:String}),e("design:type",String)],Pqt.prototype,"fill",void 0),t([o({type:Number}),e("design:type",Number)],Pqt.prototype,"height",void 0),t([o({type:String,computed:"_computeFillOverride(node, renderInfo, colorBy, templateIndex, fill)",observer:"_onFillOverrideChanged"}),e("design:type",String)],Pqt.prototype,"_fillOverride",void 0),Pqt=t([i("tf-node-icon")],Pqt);let Nqt=class extends(er(ye)){_itemTypeChanged(){"subnode"!==this.itemType?this.$["list-item"].classList.add("clickable"):this.$["list-item"].classList.remove("clickable")}_nodeListener(t){this.fire("node-list-item-"+t.type,{nodeName:this.name,type:this.itemType})}_fadedClass(t){return t&&t.isFadedOut?"faded":""}};function Iqt(t,e,n){return Se.exports.sortBy(t,[t=>e.node(t).op,t=>e.node(t).templateId,t=>e.neighbors(t).length,t=>e.predecessors(t).length,t=>e.successors(t).length,t=>t.substr(n.length)])}function Rqt(t,e){if(!(function n(t,e){let n=lWt(t),i=lWt(e);for(let t=0;t<n.length;t++)if(n[t]!==i[t])return!1;return!0})(t,e))return!1;let i=t.graph().name,r=e.graph().name,o={},a={},s=[];function l(t,e){let n=t.substr(i.length),l=e.substr(r.length);return o[n]^a[l]?(console.warn("different visit pattern","["+i+"]",n,"["+r+"]",l),!0):(o[n]||(o[n]=a[l]=!0,s.push({n1:t,n2:e})),!1)}let c=t.sources(),u=e.sources();if(c.length!==u.length)return console.log("different source length"),!1;c=Iqt(c,t,i),u=Iqt(u,e,r);for(let t=0;t<c.length;t++)if(l(c[t],u[t]))return!1;for(;s.length>0;){let n=s.pop();if(!Oqt(t.node(n.n1),e.node(n.n2)))return!1;let o=t.successors(n.n1),a=e.successors(n.n2);if(o.length!==a.length)return console.log("# of successors mismatch",o,a),!1;o=Iqt(o,t,i),a=Iqt(a,e,r);for(let t=0;t<o.length;t++)if(l(o[t],a[t]))return!1}return!0}function Oqt(t,e){if(t.type===jGt.META){let n=t,i=e;return n.templateId&&i.templateId&&n.templateId===i.templateId}if(t.type===jGt.OP&&e.type===jGt.OP)return t.op===e.op;if(t.type===jGt.SERIES&&e.type===jGt.SERIES){let n=t,i=e,r=n.metagraph.nodeCount();return r===i.metagraph.nodeCount()&&(0===r||n.metagraph.node(n.metagraph.nodes()[0]).op===i.metagraph.node(i.metagraph.nodes()[0]).op)}return!1}var zqt;Nqt.template=_e`
    <style>
      #list-item {
        width: 100%;
        color: var(--secondary-text-color);
        font-size: 11pt;
        font-weight: 400;
        position: relative;
        display: inline-block;
      }

      #list-item:hover {
        background-color: var(--google-yellow-100);
      }

      .clickable {
        cursor: pointer;
      }

      #list-item span {
        margin-left: 40px;
      }

      #list-item.excluded span {
        color: #999;
      }

      #list-item span.edge-label {
        float: right;
        font-size: 10px;
        margin-left: 3px;
        margin-right: 5px;
      }

      .node-icon {
        position: absolute;
        top: 1px;
        left: 2px;
      }

      .faded span {
        color: var(--tb-graph-faded);
      }
    </style>

    <div
      id="list-item"
      on-mouseover="_nodeListener"
      on-mouseout="_nodeListener"
      on-click="_nodeListener"
    >
      <div class$="{{_fadedClass(itemRenderInfo)}}">
        <tf-node-icon
          class="node-icon"
          height="12"
          color-by="[[colorBy]]"
          color-by-params="[[colorByParams]]"
          node="[[itemNode]]"
          render-info="[[itemRenderInfo]]"
          template-index="[[templateIndex]]"
        >
        </tf-node-icon>
        <span title$="[[name]]">[[name]]</span>
      </div>
    </div>
  `,t([o({type:Object}),e("design:type",Object)],Nqt.prototype,"cardNode",void 0),t([o({type:Object}),e("design:type",Object)],Nqt.prototype,"itemNode",void 0),t([o({type:String}),e("design:type",String)],Nqt.prototype,"edgeLabel",void 0),t([o({type:Object}),e("design:type",Object)],Nqt.prototype,"itemRenderInfo",void 0),t([o({type:String}),e("design:type",String)],Nqt.prototype,"name",void 0),t([o({type:String,observer:"_itemTypeChanged"}),e("design:type",String)],Nqt.prototype,"itemType",void 0),t([o({type:String}),e("design:type",String)],Nqt.prototype,"colorBy",void 0),t([o({type:Object}),e("design:type",Object)],Nqt.prototype,"colorByParams",void 0),t([o({type:Object}),e("design:type",Function)],Nqt.prototype,"templateIndex",void 0),Nqt=t([i("tf-graph-op-compat-list-item")],Nqt),(function(t){t[t.TEMPLATES_UPDATED=0]="TEMPLATES_UPDATED"})(zqt||(zqt={}));class Dqt extends class{constructor(){this.eventTypeToListeners=new Map}getListeners(t){return this.eventTypeToListeners.has(t)||this.eventTypeToListeners.set(t,[]),this.eventTypeToListeners.get(t)}addListener(t,e){this.getListeners(t).push(e)}removeListener(t,e){const n=this.getListeners(t).filter((t=>t!==e));this.eventTypeToListeners.set(t,n)}dispatchEvent(t,e){for(const n of this.getListeners(t))n(e)}}{constructor(t){super(),this.hasShapeInfo=!1,this.maxMetaEdgeSize=1,this.graphOptions={},this.templates=null,this.graphOptions.compound=!0,this.graphOptions.rankdir=t.rankDirection,this.root=$Gt(FGt,this.graphOptions),this.libraryFunctions={},this.seriesGroupMap=new Map(t.seriesMap),this.devices=null,this.xlaClusters=null,this.verifyTemplate=t.verifyTemplate,this.index={},this.index.__root__=this.root,this.orderings={}}getSeriesGroupType(t){var e;return null!==(e=this.seriesGroupMap.get(t))&&void 0!==e?e:WGt.GROUP}setSeriesGroupType(t,e){return this.seriesGroupMap.set(t,e)}buildSeriesGroupMapToggled(t){const e=this.getSeriesGroupType(t)===WGt.GROUP?WGt.UNGROUP:WGt.GROUP;return new Map([...this.seriesGroupMap,[t,e]])}getNodeMap(){return this.index}node(t){return this.index[t]}setNode(t,e){this.index[t]=e}getBridgegraph(t){let e=this.index[t];if(!e)throw Error("Could not find node in hierarchy: "+t);if(!("metagraph"in e))return null;if(e.bridgegraph)return e.bridgegraph;let n=e.bridgegraph=oWt("BRIDGEGRAPH",UGt.BRIDGE,this.graphOptions);if(!e.parentNode||!("metagraph"in e.parentNode))return n;let i=e.parentNode,r=i.metagraph,o=this.getBridgegraph(i.name);return Se.exports.each([r,o],(e=>{e.edges().filter((e=>e.v===t||e.w===t)).forEach((i=>{let r=i.w===t,o=e.edge(i);Se.exports.each(o.baseEdgeList,(e=>{let[o,a]=r?[e.w,i.v]:[e.v,i.w],s=this.getChildName(t,o),l={v:r?a:s,w:r?s:a},c=n.edge(l);c||(c=ZGt(l.v,l.w),c.inbound=r,n.setEdge(l.v,l.w,c)),c.addBaseEdge(e,this)}))}))})),n}getChildName(t,e){let n=this.index[e];for(;n;){if(n.parentNode&&n.parentNode.name===t)return n.name;n=n.parentNode}throw Error("Could not find immediate child for descendant: "+e)}getPredecessors(t){let e=this.index[t];if(!e)throw Error("Could not find node with name: "+t);let n=this.getOneWayEdges(e,!0);return e.isGroupNode||Se.exports.each(e.inEmbeddings,(i=>{Se.exports.each(e.inputs,(e=>{if(e.name===i.name){let r=new JGt(i.name,t);r.addBaseEdge({isControlDependency:e.isControlDependency,outputTensorKey:e.outputTensorKey,isReferenceEdge:!1,v:i.name,w:t},this),n.regular.push(r)}}))})),n}getSuccessors(t){let e=this.index[t];if(!e)throw Error("Could not find node with name: "+t);let n=this.getOneWayEdges(e,!1);return e.isGroupNode||Se.exports.each(e.outEmbeddings,(e=>{Se.exports.each(e.inputs,(i=>{if(i.name===t){let r=new JGt(t,e.name);r.addBaseEdge({isControlDependency:i.isControlDependency,outputTensorKey:i.outputTensorKey,isReferenceEdge:!1,v:t,w:e.name},this),n.regular.push(r)}}))})),n}getOneWayEdges(t,e){let n={control:[],regular:[]};if(!t.parentNode||!t.parentNode.isGroupNode)return n;let i=t.parentNode,r=i.metagraph,o=this.getBridgegraph(i.name);return Bqt(r,t,e,n),Bqt(o,t,e,n),n}getTopologicalOrdering(t){let e=this.index[t];if(!e)throw Error("Could not find node with name: "+t);if(!e.isGroupNode)return null;if(t in this.orderings)return this.orderings[t];let n={},i={},r=e.metagraph;Se.exports.each(r.edges(),(t=>{r.edge(t).numRegularEdges&&(t.v in n||(n[t.v]=[]),n[t.v].push(t.w),i[t.w]=!0)}));let o=Se.exports.difference(Se.exports.keys(n),Se.exports.keys(i)),a=this.orderings[t]={},s=0;for(;o.length;){let t=o.shift();a[t]=s++,Se.exports.each(n[t],(t=>o.push(t))),delete n[t]}return a}getTemplateIndex(){if(!this.templates)return null;let t=Lg(this.templates);if(!t.length)return null;let e=EM().domain(t).range(zl(0,t.length));return t=>e(t)}updateTemplates(){CGt("Finding similar subgraphs",(()=>{this.templates=(function t(e,n){let i=(function o(t,e){return Se.exports.reduce(t,(function(t,n){let i=n[0],r=[];return n[1].nodes.forEach((function(t){for(let n=0;n<r.length;n++)if(!e||Rqt(r[n].metanode.metagraph,t.metagraph))return t.templateId=r[n].metanode.templateId,void r[n].members.push(t.name);t.templateId=i+"["+r.length+"]",r.push({metanode:t,members:[t.name]})})),r.forEach((function(e){t[e.metanode.templateId]={level:n[1].level,nodes:e.members}})),t}),{})})((function r(t){const e=t.getNodeMap();let n=Object.keys(e).reduce(((t,n)=>{const i=e[n];if(i.type!==jGt.META)return t;let r=n.split("/").length-1,o=(function a(t){return Se.exports.map({depth:t.depth,"|V|":t.metagraph.nodes().length,"|E|":t.metagraph.edges().length},(function(t,e){return e+"="+t})).join(" ")+" [ops] "+Se.exports.map(t.opHistogram,(function(t,e){return e+"="+t})).join(",")})(i),s=t[o]||{nodes:[],level:r};return t[o]=s,s.nodes.push(i),s.level>r&&(s.level=r),t}),{});return Object.keys(n).map((t=>[t,n[t]])).filter((([t,e])=>{const{nodes:n}=e;if(n.length>1)return!0;const i=n[0];return i.type===jGt.META&&i.associatedFunction})).sort((([t,e])=>e.nodes[0].depth))})(e),n);return Object.keys(i).sort((t=>i[t].level)).reduce(((t,e)=>(t[e]=i[e],t)),{})})(this,this.verifyTemplate),this.dispatchEvent(zqt.TEMPLATES_UPDATED)}),_Gt.HIERARCHY_FIND_SIMILAR_SUBGRAPHS)}}function Bqt(t,e,n,i){let r=n?t.inEdges(e.name):t.outEdges(e.name);Se.exports.each(r,(e=>{let n=t.edge(e);(n.numRegularEdges?i.regular:i.control).push(n)}))}const Hqt={verifyTemplate:!0,seriesNodeMinSize:5,seriesMap:new Map,rankDirection:"BT",useGeneralizedSeriesPatterns:!1};function Fqt(t,e,n){const i=new Dqt(e),r={};return LGt("Adding nodes",30,(()=>{let e={},n={};Se.exports.each(t.nodes,((t,i)=>{t.device&&(e[t.device]=!0),t.xlaCluster&&(n[t.xlaCluster]=!0)})),i.devices=Se.exports.keys(e),i.xlaClusters=Se.exports.keys(n),(function r(t,e){const n={};Se.exports.each(e.nodes,((e,i)=>{let r=cWt(e.name),o=t.root;o.depth=Math.max(r.length,o.depth),n[e.op]||(n[e.op]=[]),n[e.op].push(e);for(let i=0;i<r.length&&(o.depth=Math.max(o.depth,r.length-i),o.cardinality+=e.cardinality,o.opHistogram[e.op]=(o.opHistogram[e.op]||0)+1,null!=e.device&&(o.deviceHistogram[e.device]=(o.deviceHistogram[e.device]||0)+1),null!=e.xlaCluster&&(o.xlaClusterHistogram[e.xlaCluster]=(o.xlaClusterHistogram[e.xlaCluster]||0)+1),e.compatible?o.compatibilityHistogram.compatible=(o.compatibilityHistogram.compatible||0)+1:o.compatibilityHistogram.incompatible=(o.compatibilityHistogram.incompatible||0)+1,Se.exports.each(e.inEmbeddings,(t=>{t.compatible?o.compatibilityHistogram.compatible=(o.compatibilityHistogram.compatible||0)+1:o.compatibilityHistogram.incompatible=(o.compatibilityHistogram.incompatible||0)+1})),Se.exports.each(e.outEmbeddings,(t=>{t.compatible?o.compatibilityHistogram.compatible=(o.compatibilityHistogram.compatible||0)+1:o.compatibilityHistogram.incompatible=(o.compatibilityHistogram.incompatible||0)+1})),i!==r.length-1);i++){let e=r[i],a=t.node(e);if(!a&&(a=$Gt(e,t.graphOptions),a.parentNode=o,t.setNode(e,a),o.metagraph.setNode(e,a),0===e.indexOf(VGt)&&o.name===FGt)){const i=e.substring(VGt.length);n[i]||(n[i]=[]),t.libraryFunctions[i]={node:a,usages:n[i]},a.associatedFunction=i}o=a}t.setNode(e.name,e),e.parentNode=o,o.metagraph.setNode(e.name,e),Se.exports.each(e.inEmbeddings,(function(n){t.setNode(n.name,n),n.parentNode=e})),Se.exports.each(e.outEmbeddings,(function(n){t.setNode(n.name,n),n.parentNode=e}))}))})(i,t)}),n,_Gt.HIERARCHY_ADD_NODES).then((()=>LGt("Detect series",30,(()=>{e.seriesNodeMinSize>0&&Vqt(i.root,i,r,e.seriesNodeMinSize,e.seriesMap,e.useGeneralizedSeriesPatterns)}),n,_Gt.HIERARCHY_DETECT_SERIES))).then((()=>LGt("Adding edges",40,(()=>{!(function e(t,n,i){let r=t.getNodeMap(),o=[],a=[],s=(t,e)=>{let n=0;for(;t;)e[n++]=t.name,t=t.parentNode;return n-1};Se.exports.each(n.edges,(e=>{let i=s(n.nodes[e.v],o),l=s(n.nodes[e.w],a);if(-1===i||-1===l)return;for(;o[i]===a[l];)if(i--,l--,i<0||l<0)throw Error("No difference found between ancestor paths.");let c=r[o[i+1]],u=o[i],h=a[l],d=c.metagraph.edge(u,h);d||(d=ZGt(u,h),c.metagraph.setEdge(u,h,d)),c.hasNonControlEdges||e.isControlDependency||(c.hasNonControlEdges=!0),d.addBaseEdge(e,t)}))})(i,t)}),n,_Gt.HIERARCHY_ADD_EDGES))).then((()=>i))}function Vqt(t,e,n,i,r,o){let a=t.metagraph;Se.exports.each(a.nodes(),(t=>{let s=a.node(t);s.type===jGt.META&&Vqt(s,e,n,i,r,o)}));let s=(function l(t){return Se.exports.reduce(t.nodes(),((e,n)=>{let i=t.node(n);if(i.type===jGt.META)return e;let r=i.op;return r&&(e[r]=e[r]||[],e[r].push(i.name)),e}),{})})(a),c=(o?jqt:Uqt)(s,a,e.graphOptions);Se.exports.each(c,(function(t,r){let o=t.metagraph.nodes();Se.exports.each(o,(t=>{let e=a.node(t);e.owningSeries||(e.owningSeries=r)})),o.length<i&&e.getSeriesGroupType(t.name)===WGt.GROUP&&e.setSeriesGroupType(t.name,WGt.UNGROUP),e.getSeriesGroupType(t.name)!==WGt.UNGROUP&&(e.setNode(r,t),a.setNode(r,t),Se.exports.each(o,(e=>{let i=a.node(e);t.metagraph.setNode(e,i),t.parentNode=i.parentNode,t.cardinality++,null!=i.device&&(t.deviceHistogram[i.device]=(t.deviceHistogram[i.device]||0)+1),null!=i.xlaCluster&&(t.xlaClusterHistogram[i.xlaCluster]=(t.xlaClusterHistogram[i.xlaCluster]||0)+1),i.compatible?t.compatibilityHistogram.compatible=(t.compatibilityHistogram.compatible||0)+1:t.compatibilityHistogram.incompatible=(t.compatibilityHistogram.incompatible||0)+1,Se.exports.each(i.inEmbeddings,(e=>{e.compatible?t.compatibilityHistogram.compatible=(t.compatibilityHistogram.compatible||0)+1:t.compatibilityHistogram.incompatible=(t.compatibilityHistogram.incompatible||0)+1})),Se.exports.each(i.outEmbeddings,(e=>{e.compatible?t.compatibilityHistogram.compatible=(t.compatibilityHistogram.compatible||0)+1:t.compatibilityHistogram.incompatible=(t.compatibilityHistogram.incompatible||0)+1})),i.parentNode=t,n[e]=r,a.removeNode(e)})))}))}function Uqt(t,e,n){let i={};return Se.exports.each(t,(function(t,r){if(t.length<=1)return;let o={};Se.exports.each(t,(function(t){const e="*"===t.charAt(t.length-1),i=t.split("/"),r=i[i.length-1],a=i.slice(0,i.length-1).join("/"),s=r.match(/^(\D*)(\d+)$/);let l,c,u="";s?(l=s[1],c=s[2]):(l=e?r.substr(0,r.length-1):r,c=0,u=e?"*":"");const h=tWt(l,u,a);o[h]=o[h]||[];const d=QGt(l,u,a,+c,t,n);o[h].push(d)})),Se.exports.each(o,(function(t,o){if(t.length<2)return;t.sort((function(t,e){return+t.clusterId-+e.clusterId}));let a=[t[0]];for(let o=1;o<t.length;o++){let s=t[o];s.clusterId!==a[a.length-1].clusterId+1?(Gqt(a,i,+r,e,n),a=[s]):a.push(s)}Gqt(a,i,+r,e,n)}))})),i}function jqt(t,e,n){let i={};return Se.exports.each(t,(function(t,r){if(t.length<=1)return;let o={},a={};Se.exports.each(t,(function(t){let e="*"===t.charAt(t.length-1),i=t.split("/"),r=i[i.length-1],s=i.slice(0,i.length-1).join("/");const l=/(\d+)/g;let c,u,h,d,p,f=0;for(;c=l.exec(r);)++f,u=r.slice(0,c.index),h=c[0],d=r.slice(c.index+c[0].length),p=tWt(u,d,s),o[p]=o[p],o[p]||(o[p]=QGt(u,d,s,+h,t,n)),o[p].ids.push(h),a[t]=a[t]||[],a[t].push([p,h]);f<1&&(u=e?r.substr(0,r.length-1):r,h=0,d=e?"*":"",p=tWt(u,d,s),o[p]=o[p],o[p]||(o[p]=QGt(u,d,s,+h,t,n)),o[p].ids.push(h),a[t]=a[t]||[],a[t].push([p,h]))}));var s={};Se.exports.each(a,(function(t,e){t.sort((function(t,e){return o[e[0]].ids.length-o[t[0]].ids.length}));var i=t[0][0],r=t[0][1];s[i]=s[i]||[];const a=e.split("/"),l=a.slice(0,a.length-1).join("/");var c=QGt(o[i].prefix,o[i].suffix,l,+r,e,n);s[i].push(c)})),Se.exports.each(s,(function(t,o){if(t.length<2)return;t.sort((function(t,e){return+t.clusterId-+e.clusterId}));let a=[t[0]];for(let o=1;o<t.length;o++){let s=t[o];s.clusterId!==a[a.length-1].clusterId+1?(Gqt(a,i,+r,e,n),a=[s]):a.push(s)}Gqt(a,i,+r,e,n)}))})),i}function Gqt(t,e,n,i,r){if(t.length>1){let o=tWt(t[0].prefix,t[0].suffix,t[0].parent,t[0].clusterId,t[t.length-1].clusterId),a=QGt(t[0].prefix,t[0].suffix,t[0].parent,n,o,r);Se.exports.each(t,(function(t){a.ids.push(t.clusterId),a.metagraph.setNode(t.name,i.node(t.name))})),e[o]=a}}let Wqt=class extends(er(il(ye))){constructor(){super(...arguments),this._expanded=!0,this._opCompatColor=hWt,this._opIncompatColor=dWt,this._templateIndex=null}_getNode(t,e){return e.node(t)}_getRenderInfo(t,e){return this.renderHierarchy.getOrCreateRenderNodeByName(t)}_toggleExpanded(){this._expanded=!this._expanded}_getToggleIcon(t){return t?"expand-less":"expand-more"}_resizeList(t){var e=document.querySelector(t);e&&e.fire("iron-resize")}get _incompatibleOpNodes(){const t=this.graphHierarchy;return t&&t.root?(this.async(this._resizeList.bind(this,"#incompatibleOpsList")),(function e(t){const e=[],n={};return Se.exports.each(t.root.leaves(),(i=>{let r=t.node(i);if(r.type==jGt.OP){let i=r;if(!i.compatible)if(i.owningSeries){if(t.getSeriesGroupType(i.owningSeries)===WGt.UNGROUP)e.push(i);else if(!n[i.owningSeries]){let r=t.node(i.owningSeries);r&&(n[i.owningSeries]=r,e.push(r))}}else e.push(i);Se.exports.each(i.inEmbeddings,(t=>{t.compatible||e.push(t)})),Se.exports.each(i.outEmbeddings,(t=>{t.compatible||e.push(t)}))}})),e})(t)):[]}get _opCompatScore(){var t=this.graphHierarchy;if(t&&t.root){var e=t.root,n=e.compatibilityHistogram.compatible,i=e.compatibilityHistogram.incompatible;return 0==n&&0==i?0:Math.floor(100*n/(n+i))/100}return 0}get _opCompatScoreLabel(){var t=this._opCompatScore;return my(".0%")(t)}get _totalIncompatOps(){var t=this.graphHierarchy;return t&&t.root?t.root.compatibilityHistogram.incompatible:0}_graphHierarchyChanged(){this._templateIndex=this.graphHierarchy.getTemplateIndex(),this.graphHierarchy.addListener(zqt.TEMPLATES_UPDATED,(()=>{this._templateIndex=this.graphHierarchy.getTemplateIndex()}))}};Wqt.template=_e`
    <style>
      :host {
        max-height: 500px;
      }

      .incompatible-ops-list {
        height: 350px;
        max-height: 400px;
        overflow-y: scroll;
        display: flex;
        flex-direction: column;
      }

      iron-list {
        flex: 1 1 auto;
      }

      paper-item {
        padding: 0;
        background: var(--secondary-background-color);
      }

      paper-item-body[two-line] {
        min-height: 0;
        padding: 8px 12px 4px;
      }

      .expandedInfo {
        padding: 8px 12px;
        font-weight: 500;
        font-size: 12pt;
        width: 100%;
      }

      .node-name {
        white-space: normal;
        word-wrap: break-word;
        font-size: 14pt;
        font-weight: 500;
      }

      .subtitle {
        color: var(--secondary-text-color);
        font-size: 12pt;
      }

      .toggle-button {
        float: right;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .non-control-list-item {
        padding-left: 10px;
      }

      div.op-compat-display {
        margin-top: 10px;
        display: inline-block;
      }

      /**
       * Sadly, because the whole body is inverted in color, legends also need
       * to be inverted.
       **/
      :host(.dark-mode) div.op-compat-display {
        filter: invert(1);
      }

      svg.op-compat {
        width: 250px;
        height: 25px;
        float: left;
      }

      div.op-compat-value {
        float: right;
        height: 100%;
        font-size: 14px;
        color: black;
        margin-left: 10px;
      }
    </style>

    <paper-item>
      <paper-item-body two-line>
        <div>
          <paper-icon-button
            icon="{{_getToggleIcon(_expanded)}}"
            on-click="_toggleExpanded"
            class="toggle-button"
          >
          </paper-icon-button>
          <div class="node-name" id="nodetitle">[[nodeTitle]]</div>
        </div>
        <div secondary>
          <div class="subtitle">
            <div class="op-compat-display">
              <svg
                class="op-compat"
                preserveAspectRatio="xMinYMid meet"
                viewBox="0 0 250 25"
              >
                <defs>
                  <linearGradient id="op-compat-fill">
                    <stop offset="0" stop-color$="[[_opCompatColor]]"></stop>
                    <stop
                      offset$="[[_opCompatScore]]"
                      stop-color$="[[_opCompatColor]]"
                    ></stop>
                    <stop
                      offset$="[[_opCompatScore]]"
                      stop-color$="[[_opIncompatColor]]"
                    ></stop>
                    <stop offset="1" stop-color$="[[_opIncompatColor ]]"></stop>
                  </linearGradient>
                </defs>
                <rect
                  height="25"
                  width="250"
                  rx="5"
                  ry="5"
                  style="fill: url('#op-compat-fill');"
                ></rect>
              </svg>
              <div class="op-compat-value">[[_opCompatScoreLabel]]</div>
            </div>
          </div>
        </div>
      </paper-item-body>
    </paper-item>

    <iron-collapse opened="{{_expanded}}">
      <template is="dom-if" if="{{_expanded}}" restamp="true">
        <div class="expandedInfo">
          Incompatible Operations: (<span>[[_totalIncompatOps]]</span>)
          <iron-list
            class="incompatible-ops-list"
            id="incompatibleOpsList"
            items="[[_incompatibleOpNodes]]"
          >
            <template>
              <tf-graph-op-compat-list-item
                class="non-control-list-item"
                item-node="[[item]]"
                item-render-info="[[_getRenderInfo(item.name, renderHierarchy)]]"
                name="[[item.name]]"
                template-index="[[_templateIndex]]"
                color-by="[[colorBy]]"
                item-type="incompatible-ops"
              >
              </tf-graph-op-compat-list-item>
            </template>
          </iron-list>
        </div>
      </template>
    </iron-collapse>
  `,t([o({type:Object}),e("design:type",Dqt)],Wqt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",yWt)],Wqt.prototype,"renderHierarchy",void 0),t([o({type:String}),e("design:type",String)],Wqt.prototype,"nodeTitle",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Wqt.prototype,"_expanded",void 0),t([o({type:String}),e("design:type",String)],Wqt.prototype,"_opCompatColor",void 0),t([o({type:String}),e("design:type",String)],Wqt.prototype,"_opIncompatColor",void 0),t([o({type:Object}),e("design:type",Function)],Wqt.prototype,"_templateIndex",void 0),t([s("graphHierarchy"),e("design:type",Array),e("design:paramtypes",[])],Wqt.prototype,"_incompatibleOpNodes",null),t([s("graphHierarchy"),e("design:type",Number),e("design:paramtypes",[])],Wqt.prototype,"_opCompatScore",null),t([s("_opCompatScore"),e("design:type",String),e("design:paramtypes",[])],Wqt.prototype,"_opCompatScoreLabel",null),t([s("graphHierarchy"),e("design:type",Number),e("design:paramtypes",[])],Wqt.prototype,"_totalIncompatOps",null),t([a("graphHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Wqt.prototype,"_graphHierarchyChanged",null),Wqt=t([i("tf-graph-op-compat-card")],Wqt);let qqt=class extends(er(il(ye))){_itemTypeChanged(){"subnode"!==this.itemType?this.$["list-item"].classList.add("clickable"):this.$["list-item"].classList.remove("clickable")}_nodeListener(t){this.fire("node-list-item-"+t.type,{cardNode:this.cardNode.name,nodeName:this.name,type:this.itemType})}_fadedClass(t){return t&&t.isFadedOut?"faded":""}};qqt.template=_e`
    <style>
      #list-item {
        width: 100%;
        color: var(--secondary-text-color);
        font-size: 11pt;
        font-weight: 400;
        position: relative;
        display: inline-block;
      }

      #list-item:hover {
        background-color: var(--google-yellow-100);
      }

      :host(.dark-mode) #list-item:hover {
        background-color: var(--paper-yellow-900);
        color: #fff;
      }

      .clickable {
        cursor: pointer;
      }

      #list-item span {
        margin-left: 40px;
      }

      #list-item.excluded span {
        color: #999;
      }

      #list-item span.edge-label {
        float: right;
        font-size: 10px;
        margin-left: 3px;
        margin-right: 5px;
      }

      .node-icon {
        position: absolute;
        top: 1px;
        left: 2px;
      }

      .faded span {
        color: var(--tb-graph-faded);
      }
    </style>
    <div
      id="list-item"
      on-mouseover="_nodeListener"
      on-mouseout="_nodeListener"
      on-click="_nodeListener"
    >
      <div class$="{{_fadedClass(itemRenderInfo)}}">
        <tf-node-icon
          class="node-icon"
          height="12"
          color-by="[[colorBy]]"
          color-by-params="[[colorByParams]]"
          node="[[itemNode]]"
          render-info="[[itemRenderInfo]]"
          template-index="[[templateIndex]]"
        ></tf-node-icon>
        <span title$="[[name]]">[[name]]</span>
        <span class="edge-label">[[edgeLabel]]</span>
      </div>
    </div>
  `,t([o({type:Object}),e("design:type",Object)],qqt.prototype,"cardNode",void 0),t([o({type:Object}),e("design:type",Object)],qqt.prototype,"itemNode",void 0),t([o({type:String}),e("design:type",String)],qqt.prototype,"edgeLabel",void 0),t([o({type:Object}),e("design:type",Object)],qqt.prototype,"itemRenderInfo",void 0),t([o({type:String}),e("design:type",String)],qqt.prototype,"name",void 0),t([o({type:String,observer:"_itemTypeChanged"}),e("design:type",String)],qqt.prototype,"itemType",void 0),t([o({type:String}),e("design:type",String)],qqt.prototype,"colorBy",void 0),t([o({type:Object}),e("design:type",Object)],qqt.prototype,"colorByParams",void 0),t([o({type:Object}),e("design:type",Object)],qqt.prototype,"templateIndex",void 0),qqt=t([i("tf-node-list-item")],qqt);let Yqt=class extends(er(ye)){constructor(){super(...arguments),this._expanded=!0,this._openedControlPred=!1,this._openedControlSucc=!1,this._templateIndex=null}expandNode(){this.fire("_node.expand",this.node)}_getNode(t,e){return e.node(t)}_getNodeStats(t,e){var n=this._getNode(t,e);return n?n.stats:null}_getTotalMicros(t){return t?t.getTotalMicros():0}get _hasDisplayableNodeStats(){return OGt(this._nodeStats)}get _nodeStatsFormattedBytes(){var t=this._nodeStats;if(t&&t.totalBytes)return RGt(t.totalBytes,NGt)}get _nodeStatsFormattedComputeTime(){var t=this._nodeStats;if(t&&t.getTotalMicros())return RGt(t.getTotalMicros(),IGt)}get _nodeStatsFormattedOutputSizes(){var t=this._nodeStats;if(t&&t.outputSize&&t.outputSize.length)return Se.exports.map(t.outputSize,(function(t){return 0===t.length?"scalar":"["+t.join(", ")+"]"}))}_getRenderInfo(t,e){return this.renderHierarchy.getOrCreateRenderNodeByName(t)}get _attributes(){var t=this._node;if(this.async(this._resizeList.bind(this,"#attributesList")),!t||!t.attr)return[];var e=[];return Se.exports.each(t.attr,(function(t){"_too_large_attrs"===t.key?e=e.concat(t.value.list.s.map((function(t){return{key:t,value:"Too large to show..."}}))):e.push({key:t.key,value:JSON.stringify(t.value)})})),e}get _device(){var t=this._node;return t?t.device:null}get _successors(){var t=this._node,e=this.graphHierarchy;return this._refreshNodeItemList("inputsList"),t?this._convertEdgeListToEdgeInfoList(e.getSuccessors(t.name),!1,t.isGroupNode):{regular:[],control:[]}}get _predecessors(){var t=this._node,e=this.graphHierarchy;return this._refreshNodeItemList("outputsList"),t?this._convertEdgeListToEdgeInfoList(e.getPredecessors(t.name),!0,t.isGroupNode):{regular:[],control:[]}}get _functionUsages(){var t=this._node,e=this.graphHierarchy;if(this._refreshNodeItemList("functionUsagesList"),!t||t.type!==jGt.META)return[];const n=e.libraryFunctions[t.associatedFunction];return n?n.usages:[]}_refreshNodeItemList(t){this.async(this._resizeList.bind(this,`#${t}`))}_convertEdgeListToEdgeInfoList(t,e,n){var i=t=>Se.exports.map(t.baseEdgeList,(t=>{var n=e?t.v:t.w;return{name:n,node:this._getNode(n,this.graphHierarchy),edgeLabel:tqt(t,this.renderHierarchy),renderInfo:this._getRenderInfo(n,this.renderHierarchy)}})),r=function(t){var r=[];return Se.exports.each(t,(t=>{var o=e?t.v:t.w;n&&1!=t.baseEdgeList.length?r.push({name:o,node:this._getNode(o,this.graphHierarchy),edgeLabel:eqt(t,this.renderHierarchy),renderInfo:this._getRenderInfo(o,this.renderHierarchy)}):r=r.concat(i(t))})),r}.bind(this);return{regular:r(t.regular),control:r(t.control)}}get _subnodes(){var t=this._node;return t&&t.metagraph?t.metagraph.nodes():null}get _totalPredecessors(){var t=this._predecessors;return t.regular.length+t.control.length}get _totalSuccessors(){var t=this._successors;return t.regular.length+t.control.length}_toggleControlPred(){this._openedControlPred=!this._openedControlPred}_toggleControlSucc(){this._openedControlSucc=!this._openedControlSucc}_toggleExpanded(){this._expanded=!this._expanded}_getToggleIcon(t){return t?"expand-less":"expand-more"}_resetState(){this._openedControlPred=!1,this._openedControlSucc=!1,this.set("_groupButtonText",pqt(this._node))}_resizeList(t){var e=document.querySelector(t);e&&e.fire("iron-resize")}_toggleInclude(){this.fire("node-toggle-inclusion",{name:this.graphNodeName})}_nodeIncludeStateChanged(t,e){this.set("_auxButtonText",uWt(t))}_toggleGroup(){var t=dqt(this._node);this.fire("node-toggle-seriesgroup",{name:t})}_isLibraryFunction(t){return t&&t.name.startsWith(VGt)}_isInSeries(t){return hqt(t)}_graphHierarchyChanged(){this._templateIndex=this.graphHierarchy.getTemplateIndex(),this.graphHierarchy.addListener(zqt.TEMPLATES_UPDATED,(()=>{this._templateIndex=this.graphHierarchy.getTemplateIndex()}))}};Yqt.template=_e`
    <style>
      .sub-list-group {
        font-weight: 500;
        font-size: 12pt;
        padding-bottom: 8px;
        width: 100%;
      }

      .sub-list {
        max-height: 300px;
        overflow-y: scroll;
      }

      .attr-left {
        float: left;
        width: 30%;
        word-wrap: break-word;
        color: var(--secondary-text-color);
        font-size: 11pt;
        font-weight: 400;
      }

      .attr-right {
        margin-left: 30%;
        word-wrap: break-word;
        color: var(--secondary-text-color);
        font-weight: 400;
      }

      .sub-list-table {
        display: table;
        width: 100%;
      }

      .sub-list-table-row {
        display: table-row;
      }

      .sub-list-table-row .sub-list-table-cell:last-child {
        text-align: right;
      }

      .sub-list-table-cell {
        color: var(--secondary-text-color);
        display: table-cell;
        font-size: 11pt;
        font-weight: 400;
        max-width: 200px;
        padding: 0 4px;
      }

      paper-item {
        padding: 0;
        background: var(--primary-background-color);
      }

      paper-item-body[two-line] {
        min-height: 0;
        padding: 8px 12px 4px;
      }

      .expandedInfo {
        padding: 8px 12px;
      }

      .controlDeps {
        padding: 0 0 0 8px;
      }

      .node-name {
        white-space: normal;
        word-wrap: break-word;
        font-size: 14pt;
        font-weight: 500;
      }

      .node-icon {
        float: right;
      }

      .subtitle {
        color: var(--secondary-text-color);
        font-size: 12pt;
      }

      .controlLine {
        font-size: 11pt;
        font-weight: 400;
      }

      .toggle-button {
        float: right;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .control-toggle-button {
        float: left;
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .toggle-include-group {
        padding-top: 4px;
      }

      .toggle-include {
        margin: 5px 6px;
        text-transform: none;
        padding: 4px 6px;
        font-size: 10pt;
        background-color: #fafafa;
        color: #666;
      }

      .toggle-include:hover {
        background-color: var(--google-yellow-100);
      }

      .non-control-list-item {
        padding-left: 10px;
      }
    </style>
    <paper-item>
      <paper-item-body two-line>
        <div>
          <paper-icon-button
            icon="{{_getToggleIcon(_expanded)}}"
            on-click="_toggleExpanded"
            class="toggle-button"
          >
          </paper-icon-button>
          <div class="node-name">
            <tf-wbr-string value="[[_node.name]]" delimiter-pattern="/">
            </tf-wbr-string>
          </div>
        </div>
        <div secondary>
          <tf-node-icon
            class="node-icon"
            node="[[_node]]"
            render-info="[[_getRenderInfo(graphNodeName, renderHierarchy)]]"
            color-by="[[colorBy]]"
            template-index="[[_templateIndex]]"
          ></tf-node-icon>
          <template is="dom-if" if="{{_node.op}}">
            <div class="subtitle">
              Operation:
              <span>[[_node.op]]</span>
            </div>
          </template>
          <template is="dom-if" if="{{_node.metagraph}}">
            <div class="subtitle">
              Subgraph:
              <span>[[_node.cardinality]]</span> nodes
            </div>
          </template>
        </div>
      </paper-item-body>
    </paper-item>
    <iron-collapse opened="{{_expanded}}">
      <template is="dom-if" if="{{_expanded}}" restamp="true">
        <div class="expandedInfo">
          <div class="sub-list-group attributes">
            Attributes (<span>[[_attributes.length]]</span>)
            <iron-list
              class="sub-list"
              id="attributesList"
              items="[[_attributes]]"
            >
              <template>
                <div>
                  <div class="attr-left">[[item.key]]</div>
                  <div class="attr-right">[[item.value]]</div>
                </div>
              </template>
            </iron-list>
          </div>

          <template is="dom-if" if="{{_device}}">
            <div class="sub-list-group device">
              <div class="attr-left">Device</div>
              <div class="attr-right">[[_device]]</div>
            </div>
          </template>

          <div class="sub-list-group predecessors">
            Inputs (<span>[[_totalPredecessors]]</span>)
            <iron-list
              class="sub-list"
              id="inputsList"
              items="[[_predecessors.regular]]"
            >
              <template>
                <tf-node-list-item
                  class="non-control-list-item"
                  card-node="[[_node]]"
                  item-node="[[item.node]]"
                  edge-label="[[item.edgeLabel]]"
                  item-render-info="[[item.renderInfo]]"
                  name="[[item.name]]"
                  item-type="predecessors"
                  color-by="[[colorBy]]"
                  template-index="[[_templateIndex]]"
                >
                </tf-node-list-item>
              </template>
            </iron-list>
            <template is="dom-if" if="[[_predecessors.control.length]]">
              <div class="controlDeps">
                <div class="controlLine">
                  <paper-icon-button
                    icon="{{_getToggleIcon(_openedControlPred)}}"
                    on-click="_toggleControlPred"
                    class="control-toggle-button"
                  >
                  </paper-icon-button>
                  Control dependencies
                </div>
                <iron-collapse opened="{{_openedControlPred}}" no-animation>
                  <template
                    is="dom-if"
                    if="{{_openedControlPred}}"
                    restamp="true"
                  >
                    <iron-list
                      class="sub-list"
                      items="[[_predecessors.control]]"
                    >
                      <template>
                        <tf-node-list-item
                          card-node="[[_node]]"
                          item-node="[[item.node]]"
                          item-render-info="[[item.renderInfo]]"
                          name="[[item.name]]"
                          item-type="predecessors"
                          color-by="[[colorBy]]"
                          template-index="[[_templateIndex]]"
                        >
                        </tf-node-list-item>
                      </template>
                    </iron-list>
                  </template>
                </iron-collapse>
              </div>
            </template>
          </div>

          <div class="sub-list-group successors">
            Outputs (<span>[[_totalSuccessors]]</span>)
            <iron-list
              class="sub-list"
              id="outputsList"
              items="[[_successors.regular]]"
            >
              <template>
                <tf-node-list-item
                  class="non-control-list-item"
                  card-node="[[_node]]"
                  item-node="[[item.node]]"
                  edge-label="[[item.edgeLabel]]"
                  item-render-info="[[item.renderInfo]]"
                  name="[[item.name]]"
                  item-type="successor"
                  color-by="[[colorBy]]"
                  template-index="[[_templateIndex]]"
                >
                </tf-node-list-item>
              </template>
            </iron-list>
            <template is="dom-if" if="[[_successors.control.length]]">
              <div class="controlDeps">
                <div class="controlLine">
                  <paper-icon-button
                    icon="{{_getToggleIcon(_openedControlSucc)}}"
                    on-click="_toggleControlSucc"
                    class="control-toggle-button"
                  >
                  </paper-icon-button>
                  Control dependencies
                </div>
                <iron-collapse opened="{{_openedControlSucc}}" no-animation>
                  <template
                    is="dom-if"
                    if="{{_openedControlSucc}}"
                    restamp="true"
                  >
                    <iron-list class="sub-list" items="[[_successors.control]]">
                      <template>
                        <tf-node-list-item
                          card-node="[[_node]]"
                          item-node="[[item.node]]"
                          item-render-info="[[item.renderInfo]]"
                          name="[[item.name]]"
                          item-type="successors"
                          color-by="[[colorBy]]"
                          template-index="[[_templateIndex]]"
                        >
                        </tf-node-list-item>
                      </template>
                    </iron-list>
                  </template>
                </iron-collapse>
              </div>
            </template>
          </div>
          <template is="dom-if" if="{{_hasDisplayableNodeStats}}">
            <div class="sub-list-group node-stats">
              Node Stats
              <div class="sub-list-table">
                <template is="dom-if" if="{{_nodeStats.totalBytes}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Memory</div>
                    <div class="sub-list-table-cell">
                      [[_nodeStatsFormattedBytes]]
                    </div>
                  </div>
                </template>
                <template is="dom-if" if="{{_getTotalMicros(_nodeStats)}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Compute Time</div>
                    <div class="sub-list-table-cell">
                      [[_nodeStatsFormattedComputeTime]]
                    </div>
                  </div>
                </template>
                <template is="dom-if" if="{{_nodeStats.outputSize}}">
                  <div class="sub-list-table-row">
                    <div class="sub-list-table-cell">Tensor Output Sizes</div>
                    <div class="sub-list-table-cell">
                      <template
                        is="dom-repeat"
                        items="{{_nodeStatsFormattedOutputSizes}}"
                      >
                        [[item]] <br />
                      </template>
                    </div>
                  </div>
                </template>
              </div>
            </div>
          </template>

          <template is="dom-if" if="[[_functionUsages.length]]">
            <div class="sub-list-group predecessors">
              Usages of the Function (<span>[[_functionUsages.length]]</span>)
              <iron-list
                class="sub-list"
                id="functionUsagesList"
                items="[[_functionUsages]]"
              >
                <template>
                  <tf-node-list-item
                    class="non-control-list-item"
                    card-node="[[_node]]"
                    item-node="[[item]]"
                    name="[[item.name]]"
                    item-type="functionUsages"
                    color-by="[[colorBy]]"
                    template-index="[[_templateIndex]]"
                  >
                  </tf-node-list-item>
                </template>
              </iron-list>
            </div>
          </template>

          <template is="dom-if" if="[[!_isLibraryFunction(_node)]]">
            <div class="toggle-include-group">
              <paper-button
                raised
                class="toggle-include"
                on-click="_toggleInclude"
              >
                <span>[[_auxButtonText]]</span>
              </paper-button>
            </div>
          </template>

          <template is="dom-if" if="{{_isInSeries(_node)}}">
            <div class="toggle-include-group">
              <paper-button
                raised
                class="toggle-include"
                on-click="_toggleGroup"
              >
                <span>[[_groupButtonText]]</span>
              </paper-button>
            </div>
          </template>
        </div>
      </template>
    </iron-collapse>
  `,t([o({type:String}),e("design:type",String)],Yqt.prototype,"graphNodeName",void 0),t([o({type:Object}),e("design:type",Dqt)],Yqt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",Object)],Yqt.prototype,"renderHierarchy",void 0),t([o({type:String}),e("design:type",String)],Yqt.prototype,"colorBy",void 0),t([o({type:Object,computed:"_getNode(graphNodeName, graphHierarchy)",observer:"_resetState"}),e("design:type",Object)],Yqt.prototype,"_node",void 0),t([o({type:Object,computed:"_getNodeStats(graphNodeName, graphHierarchy)",observer:"_resetState"}),e("design:type",Object)],Yqt.prototype,"_nodeStats",void 0),t([o({type:Number,observer:"_nodeIncludeStateChanged"}),e("design:type",Number)],Yqt.prototype,"nodeInclude",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Yqt.prototype,"_expanded",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Yqt.prototype,"_openedControlPred",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Yqt.prototype,"_openedControlSucc",void 0),t([o({type:String}),e("design:type",String)],Yqt.prototype,"_auxButtonText",void 0),t([o({type:String}),e("design:type",String)],Yqt.prototype,"_groupButtonText",void 0),t([o({type:Object}),e("design:type",Function)],Yqt.prototype,"_templateIndex",void 0),t([s("_nodeStats"),e("design:type",Boolean),e("design:paramtypes",[])],Yqt.prototype,"_hasDisplayableNodeStats",null),t([s("_nodeStats"),e("design:type",String),e("design:paramtypes",[])],Yqt.prototype,"_nodeStatsFormattedBytes",null),t([s("_nodeStats"),e("design:type",String),e("design:paramtypes",[])],Yqt.prototype,"_nodeStatsFormattedComputeTime",null),t([s("_nodeStats"),e("design:type",Array),e("design:paramtypes",[])],Yqt.prototype,"_nodeStatsFormattedOutputSizes",null),t([s("_node"),e("design:type",Array),e("design:paramtypes",[])],Yqt.prototype,"_attributes",null),t([s("_node"),e("design:type",String),e("design:paramtypes",[])],Yqt.prototype,"_device",null),t([s("_node","graphHierarchy"),e("design:type",Object),e("design:paramtypes",[])],Yqt.prototype,"_successors",null),t([s("_node","graphHierarchy"),e("design:type",Object),e("design:paramtypes",[])],Yqt.prototype,"_predecessors",null),t([s("_node","graphHierarchy"),e("design:type",Array),e("design:paramtypes",[])],Yqt.prototype,"_functionUsages",null),t([s("_node"),e("design:type",Array),e("design:paramtypes",[])],Yqt.prototype,"_subnodes",null),t([s("_predecessors"),e("design:type",Number),e("design:paramtypes",[])],Yqt.prototype,"_totalPredecessors",null),t([s("_successors"),e("design:type",Number),e("design:paramtypes",[])],Yqt.prototype,"_totalSuccessors",null),t([a("graphHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Yqt.prototype,"_graphHierarchyChanged",null),Yqt=t([i("tf-node-info")],Yqt);let Xqt=class extends(er(ye)){ready(){super.ready(),this.addEventListener("node-list-item-click",this._nodeListItemClicked.bind(this)),this.addEventListener("node-list-item-mouseover",this._nodeListItemMouseover.bind(this)),this.addEventListener("node-list-item-mouseout",this._nodeListItemMouseout.bind(this))}_nodeListItemClicked(t){this.selectedNode=t.detail.nodeName}_nodeListItemMouseover(t){this.highlightedNode=t.detail.nodeName}_nodeListItemMouseout(){this.highlightedNode=null}_healthPillsAvailable(t,e){return t&&e&&Object.keys(e).length>0}_equals(t,e){return t===e}};Xqt.template=_e`
    <style>
      :host {
        background: var(--secondary-background-color);
        font-size: 12px;
        margin: 0;
        padding: 0;
        display: block;
        max-height: 650px;
        overflow-x: hidden;
        overflow-y: auto;
      }

      h2 {
        padding: 0;
        text-align: center;
        margin: 0;
      }
    </style>
    <template is="dom-if" if="{{selectedNode}}">
      <paper-material elevation="1" class="card">
        <tf-node-info
          graph-hierarchy="[[graphHierarchy]]"
          render-hierarchy="[[renderHierarchy]]"
          flat-graph="[[graph]]"
          graph-node-name="[[selectedNode]]"
          node-include="[[selectedNodeInclude]]"
          highlighted-node="{{highlightedNode}}"
          color-by="[[colorBy]]"
        >
        </tf-node-info>
      </paper-material>
    </template>
    <template is="dom-if" if="[[_equals(colorBy, 'op_compatibility')]]">
      <tf-graph-op-compat-card
        graph-hierarchy="[[graphHierarchy]]"
        render-hierarchy="[[renderHierarchy]]"
        color-by="[[colorBy]]"
        node-title="[[compatNodeTitle]]"
      >
      </tf-graph-op-compat-card>
    </template>
    <template
      is="dom-if"
      if="[[_healthPillsAvailable(debuggerDataEnabled, nodeNamesToHealthPills)]]"
    >
      <tf-graph-debugger-data-card
        render-hierarchy="[[renderHierarchy]]"
        debugger-numeric-alerts="[[debuggerNumericAlerts]]"
        node-names-to-health-pills="[[nodeNamesToHealthPills]]"
        selected-node="{{selectedNode}}"
        highlighted-node="{{highlightedNode}}"
        are-health-pills-loading="[[areHealthPillsLoading]]"
        all-steps-mode-enabled="{{allStepsModeEnabled}}"
        specific-health-pill-step="{{specificHealthPillStep}}"
        health-pill-step-index="{{healthPillStepIndex}}"
      >
      </tf-graph-debugger-data-card>
    </template>
  `,t([o({type:String}),e("design:type",String)],Xqt.prototype,"title",void 0),t([o({type:Object}),e("design:type",Dqt)],Xqt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",qGt)],Xqt.prototype,"graph",void 0),t([o({type:Object}),e("design:type",yWt)],Xqt.prototype,"renderHierarchy",void 0),t([o({type:Object}),e("design:type",Object)],Xqt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],Xqt.prototype,"healthPillStepIndex",void 0),t([o({type:String}),e("design:type",String)],Xqt.prototype,"colorBy",void 0),t([o({type:String}),e("design:type",String)],Xqt.prototype,"compatNodeTitle",void 0),t([o({type:String,notify:!0}),e("design:type",String)],Xqt.prototype,"selectedNode",void 0),t([o({type:String,notify:!0}),e("design:type",String)],Xqt.prototype,"highlightedNode",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],Xqt.prototype,"selectedNodeInclude",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Xqt.prototype,"debuggerDataEnabled",void 0),Xqt=t([i("tf-graph-info")],Xqt);class $qt{constructor(t,e,n,i,r,o){this.svg=t,this.labelPadding=o,this.zoomG=e,this.mainZoom=n,this.maxWandH=r;let a=Su(i.shadowRoot),s=a.select("svg"),l=s.select("rect");this.viewpointCoord={x:0,y:0};let c=vh().subject(Object).on("drag",(t=>{this.viewpointCoord.x=uu.x,this.viewpointCoord.y=uu.y,this.updateViewpoint()}));l.datum(this.viewpointCoord).call(c),s.on("click",(()=>{if(uu.defaultPrevented)return;let t=Number(l.attr("width")),e=Number(l.attr("height")),n=ah(s.node());this.viewpointCoord.x=n[0]-t/2,this.viewpointCoord.y=n[1]-e/2,this.updateViewpoint()})),this.viewpoint=l.node(),this.minimapSvg=s.node(),this.minimap=i,this.canvas=a.select("canvas.first").node(),this.canvasBuffer=a.select("canvas.second").node(),this.downloadCanvas=a.select("canvas.download").node(),Su(this.downloadCanvas).style("display","none"),this.update()}updateViewpoint(){Su(this.viewpoint).attr("x",this.viewpointCoord.x).attr("y",this.viewpointCoord.y);let t=-this.viewpointCoord.x*this.scaleMain/this.scaleMinimap,e=-this.viewpointCoord.y*this.scaleMain/this.scaleMinimap;Su(this.svg).call(this.mainZoom.transform,vN.translate(t,e).scale(this.scaleMain))}getImageBlob(){return new Promise((t=>{this.downloadCanvas.toBlob((e=>{t(e)}),"image/png")}))}update(){let t=null;try{if(t=this.zoomG.getBBox(),0===t.width)return}catch(t){return}let e=Su(this.svg),n="";const i=this.svg,r=(i.getRootNode?i.getRootNode():this.svg.parentNode).styleSheets;for(let t=0;t<r.length;t++)try{let e=r[t].cssRules||r[t].rules;if(null==e)continue;for(let t=0;t<e.length;t++)n+=e[t].cssText.replace(/ ?tf-[\w-]+ ?/g,"")+"\n"}catch(t){if("SecurityError"!==t.name)throw t}let o=e.append("style");o.text(n);let a=Su(this.zoomG),s=a.attr("transform");a.attr("transform",null),t.height+=t.y,t.width+=t.x,t.height+=2*this.labelPadding,t.width+=2*this.labelPadding,e.attr("width",t.width).attr("height",t.height),this.scaleMinimap=this.maxWandH/Math.max(t.width,t.height),this.minimapSize={width:t.width*this.scaleMinimap,height:t.height*this.scaleMinimap},Su(this.minimapSvg).attr(this.minimapSize),Su(this.canvasBuffer).attr(this.minimapSize);const l=Su(this.downloadCanvas);l.style("width",t.width),l.style("height",t.height),l.attr("width",3*t.width),l.attr("height",3*t.height),null!=this.translate&&null!=this.zoom&&requestAnimationFrame((()=>this.zoom()));let c=(new XMLSerializer).serializeToString(this.svg);o.remove(),e.attr("width",null).attr("height",null),a.attr("transform",s);let u=new Image;u.onload=()=>{let t=this.canvasBuffer.getContext("2d");t.clearRect(0,0,this.canvasBuffer.width,this.canvasBuffer.height),t.drawImage(u,0,0,this.minimapSize.width,this.minimapSize.height),requestAnimationFrame((()=>{Su(this.canvasBuffer).style("display",null),Su(this.canvas).style("display","none"),[this.canvas,this.canvasBuffer]=[this.canvasBuffer,this.canvas]}));let e=this.downloadCanvas.getContext("2d");e.clearRect(0,0,this.downloadCanvas.width,this.downloadCanvas.height),e.drawImage(u,0,0,this.downloadCanvas.width,this.downloadCanvas.height)},u.onerror=()=>{let t=new Blob([c],{type:"image/svg+xml;charset=utf-8"});u.src=URL.createObjectURL(t)},u.src="data:image/svg+xml;charset=utf-8,"+encodeURIComponent(c)}zoom(t){if(null==this.scaleMinimap)return;t&&(this.translate=[t.x,t.y],this.scaleMain=t.k);let e=this.svg.getBoundingClientRect(),n=Su(this.viewpoint);this.viewpointCoord.x=-this.translate[0]*this.scaleMinimap/this.scaleMain,this.viewpointCoord.y=-this.translate[1]*this.scaleMinimap/this.scaleMain;let i=e.width*this.scaleMinimap/this.scaleMain,r=e.height*this.scaleMinimap/this.scaleMain;n.attr("x",this.viewpointCoord.x).attr("y",this.viewpointCoord.y).attr("width",i).attr("height",r);let o=this.minimapSize.width,a=this.minimapSize.height,s=this.viewpointCoord.x,l=this.viewpointCoord.y;(Math.min(Math.max(0,s+i),o)-Math.min(Math.max(0,s),o))*(Math.min(Math.max(0,l+r),a)-Math.min(Math.max(0,l),a))/(o*a)<.8?this.minimap.classList.remove("hidden"):this.minimap.classList.add("hidden")}}let Kqt=class extends ye{init(t,e,n,i,r){return new $qt(t,e,n,this,i,r)}};Kqt.template=_e`
    <style>
      :host {
        background-color: white;
        transition: opacity 0.3s linear;
        pointer-events: auto;
      }

      :host(.hidden) {
        opacity: 0;
        pointer-events: none;
      }

      canvas {
        border: 1px solid #999;
      }

      rect {
        fill: white;
        stroke: #111111;
        stroke-width: 1px;
        fill-opacity: 0;
        filter: url(#minimapDropShadow);
        cursor: move;
      }

      svg {
        position: absolute;
      }
    </style>
    <svg>
      <defs>
        <filter
          id="minimapDropShadow"
          x="-20%"
          y="-20%"
          width="150%"
          height="150%"
        >
          <feOffset result="offOut" in="SourceGraphic" dx="1" dy="1"></feOffset>
          <feColorMatrix
            result="matrixOut"
            in="offOut"
            type="matrix"
            values="0.1 0 0 0 0 0 0.1 0 0 0 0 0 0.1 0 0 0 0 0 0.5 0"
          ></feColorMatrix>
          <feGaussianBlur
            result="blurOut"
            in="matrixOut"
            stdDeviation="2"
          ></feGaussianBlur>
          <feBlend in="SourceGraphic" in2="blurOut" mode="normal"></feBlend>
        </filter>
      </defs>
      <rect></rect>
    </svg>
    <canvas class="first"></canvas>
    <!-- Additional canvas to use as buffer to avoid flickering between updates -->
    <canvas class="second"></canvas>
    <canvas class="download"></canvas>
  `,Kqt=t([i("tf-graph-minimap")],Kqt);const Zqt=_e`
  <style>
    :host(.dark-mode) {
      filter: invert(1);
    }

    :host {
      display: flex;
      font-size: 20px;
      height: 100%;
      width: 100%;
    }

    #svg {
      flex: 1;
      font-family: Roboto, sans-serif;
      height: 100%;
      overflow: hidden;
      width: 100%;
    }

    #hidden {
      position: fixed;
      top: 0px;
      visibility: hidden;
    }

    text {
      user-select: none;
    }

    /* --- Node and annotation-node for Metanode --- */

    .meta > .nodeshape > rect,
    .meta > .annotation-node > rect {
      cursor: pointer;
      fill: hsl(0, 0%, 70%);
    }
    .node.meta.highlighted > .nodeshape > rect,
    .node.meta.highlighted > .annotation-node > rect {
      stroke-width: 2;
    }
    .annotation.meta.highlighted > .nodeshape > rect,
    .annotation.meta.highlighted > .annotation-node > rect {
      stroke-width: 1;
    }
    .meta.selected > .nodeshape > rect,
    .meta.selected > .annotation-node > rect {
      stroke: red;
      stroke-width: 2;
    }
    .node.meta.selected.expanded > .nodeshape > rect,
    .node.meta.selected.expanded > .annotation-node > rect {
      stroke: red;
      stroke-width: 3;
    }
    .annotation.meta.selected > .nodeshape > rect,
    .annotation.meta.selected > .annotation-node > rect {
      stroke: red;
      stroke-width: 2;
    }
    .node.meta.selected.expanded.highlighted > .nodeshape > rect,
    .node.meta.selected.expanded.highlighted > .annotation-node > rect {
      stroke: red;
      stroke-width: 4;
    }

    .faded,
    .faded rect,
    .faded ellipse,
    .faded path,
    .faded use,
    #rectHatch line,
    #ellipseHatch line {
      color: #e0d4b3 !important;
      fill: white;
      stroke: #e0d4b3 !important;
    }

    .faded path {
      stroke-width: 1px !important;
    }

    .faded rect {
      fill: url(#rectHatch) !important;
    }

    .faded ellipse,
    .faded use {
      fill: url(#ellipseHatch) !important;
    }

    .faded text {
      opacity: 0;
    }

    /* Rules used for input-tracing. */
    .input-highlight > * > rect,
    .input-highlight > * > ellipse,
    .input-highlight > * > use {
      fill: white;
      stroke: #ff9800 !important;
    }

    /*  - Faded non-input styling */
    .non-input > * > rect,
.non-input > * > ellipse,
.non-input > * > use,
/* For Const nodes. */
.non-input > * > .constant:not([class*="input-highlight"]) >
  .annotation-node > ellipse,
/* For styling of annotation nodes of non-input nodes. */
.non-input > g > .annotation > .annotation-node > rect {
      stroke: #e0d4b3 !important;
      stroke-width: inherit;
      stroke-dasharray: inherit;
    }

    .non-input path {
      visibility: hidden;
    }

    .non-input > .nodeshape > rect,
.non-input > .annotation-node > rect,
/* For styling of annotation nodes of non-input nodes. */
.non-input > g > .annotation > .annotation-node > rect {
      fill: url(#rectHatch) !important;
    }

    .non-input ellipse,
    .non-input use {
      fill: url(#ellipseHatch) !important;
    }

    .non-input > text {
      opacity: 0;
    }

    .non-input .annotation > .annotation-edge {
      marker-end: url(#annotation-arrowhead-faded);
    }

    .non-input .annotation > .annotation-edge.refline {
      marker-start: url(#ref-annotation-arrowhead-faded);
    }

    /* Input edges. */
    .input-edge-highlight > text {
      fill: black !important;
    }
    .input-highlight > .in-annotations > .annotation > .annotation-edge,
    .input-highlight-selected
      > .in-annotations
      > .annotation
      > .annotation-edge {
      stroke: #999 !important;
    }

    /* Non-input edges. */
    .non-input-edge-highlight,
.non-input > g > .annotation > path,
/* Annotation styles (label and edges respectively). */
.non-input > g >
.annotation:not(.input-highlight):not(.input-highlight-selected) >
.annotation-label
/*.annotation-edge*/ {
      visibility: hidden;
    }

    /* --- Op Node --- */

    .op > .nodeshape > .nodecolortarget,
    .op > .annotation-node > .nodecolortarget {
      cursor: pointer;
      fill: #fff;
      stroke: #ccc;
    }

    .op.selected > .nodeshape > .nodecolortarget,
    .op.selected > .annotation-node > .nodecolortarget {
      stroke: red;
      stroke-width: 2;
    }

    .op.highlighted > .nodeshape > .nodecolortarget,
    .op.highlighted > .annotation-node > .nodecolortarget {
      stroke-width: 2;
    }

    /* --- Series Node --- */

    /* By default, don't show the series background <rect>. */
    .series > .nodeshape > rect {
      fill: hsl(0, 0%, 70%);
      fill-opacity: 0;
      stroke-dasharray: 5, 5;
      stroke-opacity: 0;
      cursor: pointer;
    }

    /* Once expanded, show the series background <rect> and hide the <use>. */
    .series.expanded > .nodeshape > rect {
      fill-opacity: 0.15;
      stroke: hsl(0, 0%, 70%);
      stroke-opacity: 1;
    }
    .series.expanded > .nodeshape > use {
      visibility: hidden;
    }

    /**
 * TODO: Simplify this by applying a stable class name to all <g>
 * elements that currently have either the nodeshape or annotation-node classes.
 */
    .series > .nodeshape > use,
    .series > .annotation-node > use {
      stroke: #ccc;
    }
    .series.highlighted > .nodeshape > use,
    .series.highlighted > .annotation-node > use {
      stroke-width: 2;
    }
    .series.selected > .nodeshape > use,
    .series.selected > .annotation-node > use {
      stroke: red;
      stroke-width: 2;
    }

    .series.selected > .nodeshape > rect {
      stroke: red;
      stroke-width: 2;
    }

    .annotation.series.selected > .annotation-node > use {
      stroke: red;
      stroke-width: 2;
    }

    /* --- Bridge Node --- */
    .bridge > .nodeshape > rect {
      stroke: #f0f;
      opacity: 0.2;
      display: none;
    }

    /* --- Structural Elements --- */
    .edge > path.edgeline.structural {
      stroke: #f0f;
      opacity: 0.2;
      display: none;
    }

    /* Reference Edge */
    .edge > path.edgeline.referenceedge {
      stroke: #ffb74d;
      opacity: 1;
    }

    /* --- Series Nodes --- */

    /* Hide the rect for a series' annotation. */
    .series > .annotation-node > rect {
      display: none;
    }

    /* --- Node label --- */

    .node {
      /* Provide a hint to browsers to avoid using their static rasterization
      at initial scale, which looks very pixelated on Chromium when zoomed in.
      Note that we intentionally do *not* use 'will-change: transform' and
      'translateZ(0) here, which introduce blurriness on Firefox.
      See https://github.com/tensorflow/tensorboard/issues/4744 */
      transform: translateZ(1px);
    }

    .node > text.nodelabel {
      cursor: pointer;
      fill: #444;
    }

    .meta.expanded > text.nodelabel {
      font-size: 9px;
    }

    .series > text.nodelabel {
      font-size: 8px;
    }

    .op > text.nodelabel {
      font-size: 6px;
    }

    .bridge > text.nodelabel {
      display: none;
    }

    .node.meta.expanded > text.nodelabel {
      cursor: normal;
    }

    .annotation.meta.highlighted > text.annotation-label {
      fill: #50a3f7;
    }

    .annotation.meta.selected > text.annotation-label {
      fill: #4285f4;
    }

    /* --- Annotation --- */

    /* only applied for annotations that are not summary or constant.
(.summary, .constant gets overridden below) */
    .annotation > .annotation-node > * {
      stroke-width: 0.5;
      stroke-dasharray: 1, 1;
    }

    .annotation.summary > .annotation-node > *,
    .annotation.constant > .annotation-node > * {
      stroke-width: 1;
      stroke-dasharray: none;
    }

    .annotation > .annotation-edge {
      fill: none;
      stroke: #aaa;
      stroke-width: 0.5;
      marker-end: url(#annotation-arrowhead);
    }

    .faded .annotation > .annotation-edge {
      marker-end: url(#annotation-arrowhead-faded);
    }

    .annotation > .annotation-edge.refline {
      marker-start: url(#ref-annotation-arrowhead);
    }

    .faded .annotation > .annotation-edge.refline {
      marker-start: url(#ref-annotation-arrowhead-faded);
    }

    .annotation > .annotation-control-edge {
      stroke-dasharray: 1, 1;
    }

    #annotation-arrowhead {
      fill: #aaa;
    }

    #annotation-arrowhead-faded {
      fill: #e0d4b3;
    }

    #ref-annotation-arrowhead {
      fill: #aaa;
    }

    #ref-annotation-arrowhead-faded {
      fill: #e0d4b3;
    }

    .annotation > .annotation-label {
      font-size: 5px;
      cursor: pointer;
    }
    .annotation > .annotation-label.annotation-ellipsis {
      cursor: default;
    }

    /* Hide annotations on expanded meta nodes since they're redundant. */
    .expanded > .in-annotations,
    .expanded > .out-annotations {
      display: none;
    }

    /* --- Annotation: Constant --- */

    .constant > .annotation-node > ellipse {
      cursor: pointer;
      fill: white;
      stroke: #848484;
    }

    .constant.selected > .annotation-node > ellipse {
      fill: white;
      stroke: red;
    }

    .constant.highlighted > .annotation-node > ellipse {
      stroke-width: 1.5;
    }

    /* --- Annotation: Summary --- */

    .summary > .annotation-node > ellipse {
      cursor: pointer;
      fill: #db4437;
      stroke: #db4437;
    }

    .summary.selected > .annotation-node > ellipse {
      fill: #a52714;
      stroke: #a52714;
    }

    .summary.highlighted > .annotation-node > ellipse {
      stroke-width: 1.5;
    }

    /* --- Edge --- */

    .edge > path.edgeline {
      fill: none;
      stroke: #bbb;
      stroke-linecap: round;
      stroke-width: 0.75;
    }

    .edge .selectableedge {
      cursor: pointer;
    }

    .selectededge > path.edgeline {
      cursor: default;
      stroke: #f00;
    }

    .edge.selectededge text {
      fill: #000;
    }

    /* Labels showing tensor shapes on edges */
    .edge > text {
      font-size: 3.5px;
      fill: #666;
    }

    .dataflow-arrowhead {
      fill: #bbb;
    }

    .reference-arrowhead {
      fill: #ffb74d;
    }

    .selected-arrowhead {
      fill: #f00;
    }

    .edge .control-dep {
      stroke-dasharray: 2, 2;
    }

    /* --- Group node expand/collapse button --- */

    /* Hides expand/collapse buttons when a node isn't expanded or highlighted. Using
   incredibly small opacity so that the bounding box of the <g> parent still takes
   this container into account even when it isn't visible */
    .node:not(.highlighted):not(.expanded) > .nodeshape > .buttoncontainer {
      opacity: 0.01;
    }
    .node.highlighted > .nodeshape > .buttoncontainer {
      cursor: pointer;
    }
    .buttoncircle {
      fill: #e7811d;
    }
    .buttoncircle:hover {
      fill: #b96717;
    }
    .expandbutton,
    .collapsebutton {
      stroke: white;
    }
    /* Do not let the path elements in the button take pointer focus */
    .node > .nodeshape > .buttoncontainer > .expandbutton,
    .node > .nodeshape > .buttoncontainer > .collapsebutton {
      pointer-events: none;
    }
    /* Only show the expand button when a node is collapsed and only show the
   collapse button when a node is expanded. */
    .node.expanded > .nodeshape > .buttoncontainer > .expandbutton {
      display: none;
    }
    .node:not(.expanded) > .nodeshape > .buttoncontainer > .collapsebutton {
      display: none;
    }

    .health-pill-stats {
      font-size: 4px;
      text-anchor: middle;
    }

    .health-pill rect {
      filter: url(#health-pill-shadow);
      rx: 3;
      ry: 3;
    }

    .titleContainer {
      position: relative;
      top: 20px;
    }

    .title,
    .auxTitle,
    .functionLibraryTitle {
      position: absolute;
    }

    #minimap {
      position: absolute;
      right: 20px;
      bottom: 20px;
    }

    .context-menu {
      position: absolute;
      display: none;
      background-color: #e2e2e2;
      border-radius: 2px;
      font-size: 14px;
      min-width: 150px;
      border: 1px solid #d4d4d4;
    }

    .context-menu ul {
      list-style-type: none;
      margin: 0;
      padding: 0;
      cursor: default;
    }

    .context-menu ul li {
      padding: 4px 16px;
    }

    .context-menu ul li:hover {
      background-color: #f3913e;
      color: white;
    }
  </style>
  <div class="titleContainer">
    <div id="title" class="title">Main Graph</div>
    <div id="auxTitle" class="auxTitle">Auxiliary Nodes</div>
    <div id="functionLibraryTitle" class="functionLibraryTitle">Functions</div>
  </div>
  <svg id="svg">
    <defs>
      <!-- Arrow heads for reference edge paths of different predefined sizes per color. -->
      <path
        id="reference-arrowhead-path"
        d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0"
      ></path>
      <marker
        class="reference-arrowhead"
        id="reference-arrowhead-small"
        viewBox="0 0 10 10"
        markerWidth="5"
        markerHeight="5"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#reference-arrowhead-path"></use>
      </marker>
      <marker
        class="reference-arrowhead"
        id="reference-arrowhead-medium"
        viewBox="0 0 10 10"
        markerWidth="13"
        markerHeight="13"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#reference-arrowhead-path"></use>
      </marker>
      <marker
        class="reference-arrowhead"
        id="reference-arrowhead-large"
        viewBox="0 0 10 10"
        markerWidth="16"
        markerHeight="16"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#reference-arrowhead-path"></use>
      </marker>
      <marker
        class="reference-arrowhead"
        id="reference-arrowhead-xlarge"
        viewBox="0 0 10 10"
        markerWidth="20"
        markerHeight="20"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#reference-arrowhead-path"></use>
      </marker>

      <!-- Arrow heads for dataflow edge paths of different predefined sizes per color. -->
      <path
        id="dataflow-arrowhead-path"
        d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0"
      ></path>
      <marker
        class="dataflow-arrowhead"
        id="dataflow-arrowhead-small"
        viewBox="0 0 10 10"
        markerWidth="5"
        markerHeight="5"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#dataflow-arrowhead-path"></use>
      </marker>
      <marker
        class="dataflow-arrowhead"
        id="dataflow-arrowhead-medium"
        viewBox="0 0 10 10"
        markerWidth="13"
        markerHeight="13"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#dataflow-arrowhead-path"></use>
      </marker>
      <marker
        class="dataflow-arrowhead"
        id="dataflow-arrowhead-large"
        viewBox="0 0 10 10"
        markerWidth="16"
        markerHeight="16"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#dataflow-arrowhead-path"></use>
      </marker>
      <marker
        class="dataflow-arrowhead"
        id="dataflow-arrowhead-xlarge"
        viewBox="0 0 10 10"
        markerWidth="20"
        markerHeight="20"
        refX="2"
        refY="5"
        orient="auto-start-reverse"
        markerUnits="userSpaceOnUse"
      >
        <use xlink:href="#dataflow-arrowhead-path"></use>
      </marker>

      <!-- Arrow head for annotation edge paths. -->
      <marker
        id="annotation-arrowhead"
        markerWidth="5"
        markerHeight="5"
        refX="5"
        refY="2.5"
        orient="auto"
      >
        <path d="M 0,0 L 5,2.5 L 0,5 L 0,0"></path>
      </marker>
      <marker
        id="annotation-arrowhead-faded"
        markerWidth="5"
        markerHeight="5"
        refX="5"
        refY="2.5"
        orient="auto"
      >
        <path d="M 0,0 L 5,2.5 L 0,5 L 0,0"></path>
      </marker>
      <marker
        id="ref-annotation-arrowhead"
        markerWidth="5"
        markerHeight="5"
        refX="0"
        refY="2.5"
        orient="auto"
      >
        <path d="M 5,0 L 0,2.5 L 5,5 L 5,0"></path>
      </marker>
      <marker
        id="ref-annotation-arrowhead-faded"
        markerWidth="5"
        markerHeight="5"
        refX="0"
        refY="2.5"
        orient="auto"
      >
        <path d="M 5,0 L 0,2.5 L 5,5 L 5,0"></path>
      </marker>
      <!-- Template for an Op node ellipse. -->
      <ellipse
        id="op-node-stamp"
        rx="7.5"
        ry="3"
        stroke="inherit"
        fill="inherit"
      ></ellipse>
      <!-- Template for an Op node annotation ellipse (smaller). -->
      <ellipse
        id="op-node-annotation-stamp"
        rx="5"
        ry="2"
        stroke="inherit"
        fill="inherit"
      ></ellipse>
      <!-- Vertically stacked series of Op nodes when unexpanded. -->
      <g id="op-series-vertical-stamp">
        <use xlink:href="#op-node-stamp" x="8" y="9"></use>
        <use xlink:href="#op-node-stamp" x="8" y="6"></use>
        <use xlink:href="#op-node-stamp" x="8" y="3"></use>
      </g>
      <!-- Horizontally stacked series of Op nodes when unexpanded. -->
      <g id="op-series-horizontal-stamp">
        <use xlink:href="#op-node-stamp" x="16" y="4"></use>
        <use xlink:href="#op-node-stamp" x="12" y="4"></use>
        <use xlink:href="#op-node-stamp" x="8" y="4"></use>
      </g>
      <!-- Horizontally stacked series of Op nodes for annotation. -->
      <g id="op-series-annotation-stamp">
        <use xlink:href="#op-node-annotation-stamp" x="9" y="2"></use>
        <use xlink:href="#op-node-annotation-stamp" x="7" y="2"></use>
        <use xlink:href="#op-node-annotation-stamp" x="5" y="2"></use>
      </g>
      <svg
        id="summary-icon"
        fill="#848484"
        height="12"
        viewBox="0 0 24 24"
        width="12"
      >
        <path
          d="M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z"
        ></path>
      </svg>

      <!-- Hatch patterns for faded out nodes. -->
      <pattern
        id="rectHatch"
        patternTransform="rotate(45 0 0)"
        width="5"
        height="5"
        patternUnits="userSpaceOnUse"
      >
        <line x1="0" y1="0" x2="0" y2="5" style="stroke-width: 1"></line>
      </pattern>
      <pattern
        id="ellipseHatch"
        patternTransform="rotate(45 0 0)"
        width="2"
        height="2"
        patternUnits="userSpaceOnUse"
      >
        <line x1="0" y1="0" x2="0" y2="2" style="stroke-width: 1"></line>
      </pattern>

      <!-- A shadow for health pills. -->
      <filter
        id="health-pill-shadow"
        x="-40%"
        y="-40%"
        width="180%"
        height="180%"
      >
        <feGaussianBlur in="SourceAlpha" stdDeviation="0.8"></feGaussianBlur>
        <feOffset dx="0" dy="0" result="offsetblur"></feOffset>
        <feFlood flood-color="#000000"></feFlood>
        <feComposite in2="offsetblur" operator="in"></feComposite>
        <feMerge>
          <feMergeNode></feMergeNode>
          <feMergeNode in="SourceGraphic"></feMergeNode>
        </feMerge>
      </filter>
    </defs>
    <!-- Make a large rectangle that fills the svg space so that
  zoom events get captured on safari -->
    <rect fill="white" width="10000" height="10000"></rect>
    <g id="root"></g>
  </svg>
  <tf-graph-minimap id="minimap"></tf-graph-minimap>
  <div id="contextMenu" class="context-menu"></div>
`;let Jqt=class extends(er(il(ye))){constructor(){super(...arguments),this._zoomed=!1,this._zoomStartCoords=null,this._zoomTransform=null,this._maxZoomDistanceForClick=20,this._nodeGroupIndex={},this._annotationGroupIndex={},this._edgeGroupIndex={},this.maxMetanodeLabelLengthFontSize=9,this.minMetanodeLabelLengthFontSize=6,this.maxMetanodeLabelLengthLargeFont=11,this.maxMetanodeLabelLength=18}getNode(t){return this.renderHierarchy.getRenderNodeByName(t)}isNodeExpanded(t){return t.expanded}setNodeExpanded(t){this._build(this.renderHierarchy),this._updateLabels(!this._zoomed)}panToNode(t){(function e(t,n,i,r){const o=Su(n).select(`[data-name="${t}"]`).node();if(!o)return console.warn(`panToNode() failed for node name "${t}"`),!1;let a=o.getBBox(),s=o.getScreenCTM(),l=n.createSVGPoint(),c=n.createSVGPoint();l.x=a.x,l.y=a.y,c.x=a.x+a.width,c.y=a.y+a.height,l=l.matrixTransform(s),c=c.matrixTransform(s);let u=(t,e,n,i)=>!(t>n&&e<i),h=n.getBoundingClientRect();const d=h.top+h.height-150;if(u(l.x,c.x,h.left,h.left+h.width-320)||u(l.y,c.y,h.top,d)){let t=h.left+h.width/2-(l.x+c.x)/2,e=h.top+h.height/2-(l.y+c.y)/2;const i=bN(n);return Su(n).transition().duration(500).call(r.translateBy,t/i.k,e/i.k),!0}return!1})(t,this.$.svg,0,this._zoom)&&(this._zoomed=!0)}getGraphSvgRoot(){return this.$.svg}getContextMenu(){return this.$.contextMenu}_resetState(){this._nodeGroupIndex={},this._annotationGroupIndex={},this._edgeGroupIndex={},this._updateLabels(!1),Su(this.$.svg).select("#root").selectAll("*").remove(),bqt(this.$.svg)}_build(t){this.templateIndex=t.hierarchy.getTemplateIndex(),CGt("tf-graph-scene (layout):",function(){RWt(t.root)}.bind(this),_Gt.RENDER_SCENE_LAYOUT),CGt("tf-graph-scene (build scene):",function(){Lqt(Su(this.$.root),t.root,this),(function e(t,n){Su(t).on("click",(()=>{n.fire("graph-select")}))})(this.$.svg,this),this._updateInputTrace()}.bind(this),_Gt.RENDER_SCENE_BUILD_SCENE),setTimeout(function(){this._updateHealthPills(this.nodeNamesToHealthPills,this.healthPillStepIndex),this.minimap.update()}.bind(this),NWt.animation.duration)}ready(){super.ready(),this._zoom=kN().on("end",function(){this._zoomStartCoords&&(Math.sqrt(Math.pow(this._zoomStartCoords.x-this._zoomTransform.x,2)+Math.pow(this._zoomStartCoords.y-this._zoomTransform.y,2))<this._maxZoomDistanceForClick?this._fireEnableClick():setTimeout(this._fireEnableClick.bind(this),50)),this._zoomStartCoords=null}.bind(this)).on("zoom",function(){this._zoomTransform=uu.transform,this._zoomStartCoords||(this._zoomStartCoords=this._zoomTransform,this.fire("disable-click")),this._zoomed=!0,Su(this.$.root).attr("transform",uu.transform),this.minimap.zoom(uu.transform)}.bind(this)),Su(this.$.svg).call(this._zoom).on("dblclick.zoom",null),Su(window).on("resize",function(){this.minimap.zoom()}.bind(this)),this.minimap=this.$.minimap.init(this.$.svg,this.$.root,this._zoom,NWt.minimap.size,NWt.subscene.meta.labelHeight)}attached(){this.set("_isAttached",!0)}detached(){this.set("_isAttached",!1)}_renderHierarchyChanged(){var t=this.renderHierarchy;this._hasRenderHierarchyBeenFitOnce=!1,this._resetState(),this._build(t)}_animateAndFit(){!this._hasRenderHierarchyBeenFitOnce&&this._isAttached&&setTimeout(this.fit.bind(this),NWt.animation.duration)}_updateLabels(t){var e=this.$$(".title"),n=e.style,i=this.$$(".auxTitle"),r=i.style,o=this.$$(".functionLibraryTitle").style;const a=Su(this.$.svg);var s=a.select("."+VWt.Scene.GROUP+">."+VWt.Scene.CORE).node();if(t&&s&&this.progress&&100===this.progress.value){var l=a.select("."+VWt.Scene.GROUP+">."+VWt.Scene.INEXTRACT).node()||a.select("."+VWt.Scene.GROUP+">."+VWt.Scene.OUTEXTRACT).node(),c=s.getCTM().e,u=l?l.getCTM().e:null;n.display="inline",n.left=c+"px",null!==u&&u!==c?(r.display="inline",u=Math.max(c+e.getBoundingClientRect().width,u),r.left=u+"px"):r.display="none";let t=a.select("."+VWt.Scene.GROUP+">."+VWt.Scene.FUNCTION_LIBRARY).node(),h=t?t.getCTM().e:null;null!==h&&h!==u?(o.display="inline",h=Math.max(u+i.getBoundingClientRect().width,h),o.left=h+"px"):o.display="none"}else n.display="none",r.display="none",o.display="none"}nodeColorsChanged(){null!=this.renderHierarchy&&(this.templateIndex=this.renderHierarchy.hierarchy.getTemplateIndex(),Se.exports.each(this._nodeGroupIndex,((t,e)=>{this._updateNodeState(e)})),this.minimap.update())}fit(){this._hasRenderHierarchyBeenFitOnce=!0,(function t(e,n,i,r){let o=e.getBoundingClientRect(),a=null;try{if(a=n.getBBox(),0===a.width)return}catch(t){return}let s=.9*Math.min(o.width/a.width,o.height/a.height,2),l=NWt.graph;const c=vN.scale(s).translate(l.padding.paddingLeft,l.padding.paddingTop);Su(e).transition().duration(500).call(i.transform,c).on("end.fitted",(()=>{i.on("end.fitted",null),r()}))})(this.$.svg,this.$.root,this._zoom,function(){this._zoomed=!1}.bind(this))}getImageBlob(){return this.minimap.getImageBlob()}isNodeSelected(t){return t===this.selectedNode}isNodeHighlighted(t){return t===this.highlightedNode}addAnnotationGroup(t,e,n){var i=t.node.name;this._annotationGroupIndex[i]=this._annotationGroupIndex[i]||{},this._annotationGroupIndex[i][e.node.name]=n}getAnnotationGroupsIndex(t){return this._annotationGroupIndex[t]}removeAnnotationGroup(t,e){delete this._annotationGroupIndex[t.node.name][e.node.name]}addNodeGroup(t,e){this._nodeGroupIndex[t]=e}getNodeGroup(t){return this._nodeGroupIndex[t]}removeNodeGroup(t){delete this._nodeGroupIndex[t]}addEdgeGroup(t,e){this._edgeGroupIndex[t]=e}getEdgeGroup(t){return this._edgeGroupIndex[t]}_updateHealthPills(){!(function t(e,n,i){if(!n)return;let r=1;Su(e).selectAll("g.nodeshape").each((function(t){const e=n[t.node.name];!(function o(t,e,n,i,r=60,a=10,s=0,l){if(Su(t.parentNode).selectAll(".health-pill").remove(),!e)return;const c=e.value,u=c.slice(2,8),h=u[0],d=u[1],p=u[5];let f=c[1];const m={min:c[8],max:c[9],mean:c[10],stddev:Math.sqrt(c[11])};null==r&&(r=60),null==a&&(a=10),null==s&&(s=0),null!=n&&n.node.type===jGt.OP&&(r/=2,a/=2);let g=document.createElementNS(bGt,"g");g.classList.add("health-pill");let _=document.createElementNS(bGt,"defs");g.appendChild(_);let y=document.createElementNS(bGt,"linearGradient");const v="health-pill-gradient-"+i;y.setAttribute("id",v);let b=0,x="0%";for(let t=0;t<u.length;t++){if(!u[t])continue;b+=u[t];let e=document.createElementNS(bGt,"stop");e.setAttribute("offset",x),e.setAttribute("stop-color",UWt[t].background_color),y.appendChild(e);let n=document.createElementNS(bGt,"stop"),i=100*b/f+"%";n.setAttribute("offset",i),n.setAttribute("stop-color",UWt[t].background_color),y.appendChild(n),x=i}_.appendChild(y);let w=document.createElementNS(bGt,"rect");w.setAttribute("fill","url(#"+v+")"),w.setAttribute("width",String(r)),w.setAttribute("height",String(a)),w.setAttribute("y",String(s)),g.appendChild(w);let S=document.createElementNS(bGt,"title");S.textContent=(function M(t,e,n,i){let r="Device: "+t.device_name+"\n";r+="dtype: "+t.dtype+"\n";let o="(scalar)";t.shape.length>0&&(o="("+t.shape.join(",")+")"),r+="\nshape: "+o+"\n\n",r+="#(elements): "+e+"\n";const a=[];for(let t=0;t<n.length;t++)n[t]>0&&a.push("#("+UWt[t].label+"): "+n[t]);return r+=a.join(", ")+"\n\n",i.max>=i.min&&(r+="min: "+i.min+", max: "+i.max+"\n",r+="mean: "+i.mean+", stddev: "+i.stddev),r})(e,f,u,m),g.appendChild(S);let E=!1;if(null!=n){let t=n.y-a-n.height/2-2;if(n.labelOffset<0&&(t+=n.labelOffset),g.setAttribute("transform","translate("+(n.x-r/2)+", "+t+")"),u[2]||u[3]||u[4]){let t=n.node.attr;if(t&&t.length)for(let e=0;e<t.length;e++)if("T"===t[e].key){let n=t[e].value.type;E=n&&/^DT_(BOOL|INT|UINT)/.test(n);break}}}let T=document.createElementNS(bGt,"text");if(Number.isFinite(m.min)&&Number.isFinite(m.max)){const t=qWt(m.min,E),e=qWt(m.max,E);if(T.textContent=f>1?t+" ~ "+e:t,h>0||d>0||p>0){T.textContent+=" (";const t=[];h>0&&t.push(`NaN×${h}`),d>0&&t.push(`-∞×${d}`),p>0&&t.push(`+∞×${p}`),T.textContent+=t.join("; ")+")"}}else T.textContent="(No finite elements)";T.classList.add("health-pill-stats"),null==l&&(l=r/2),T.setAttribute("x",String(l)),T.setAttribute("y",String(s-2)),g.appendChild(T),Yi(t.parentNode).appendChild(g)})(this,e?e[i]:null,t,r++)}))})(this.$.svg,this.nodeNamesToHealthPills,this.healthPillStepIndex)}_updateNodeState(t){var e=this.getNode(t),n=this.getNodeGroup(t);n&&wqt(n,e,this),e.node.type===jGt.META&&e.node.associatedFunction&&!e.isLibraryFunction&&wqt(Su("."+VWt.Scene.GROUP+">."+VWt.Scene.FUNCTION_LIBRARY+' g[data-name="'+(VGt+e.node.associatedFunction)+'"]'),e,this);var i=this.getAnnotationGroupsIndex(t);Se.exports.each(i,((t,n)=>{wqt(t,e,this,VWt.Annotation.NODE)}))}_selectedNodeChanged(t,e){if(t!==e&&(e&&this._updateNodeState(e),t)){this.minimap.update();for(var n,i=this.renderHierarchy.hierarchy.node(t),r=[];null!=i.parentNode&&i.parentNode.name!=FGt;)r.push((i=i.parentNode).name);Se.exports.forEachRight(r,(t=>{this.renderHierarchy.buildSubhierarchy(t);var e=this.renderHierarchy.getRenderNodeByName(t);e.node.isGroupNode&&!e.expanded&&(e.expanded=!0,n||(n=e))})),n&&(this.setNodeExpanded(n),this._zoomed=!0),t&&this._updateNodeState(t),setTimeout((()=>{this.panToNode(t)}),NWt.animation.duration)}}_highlightedNodeChanged(t,e){t!==e&&(t&&this._updateNodeState(t),e&&this._updateNodeState(e))}_onZoomChanged(){this._updateLabels(!this._zoomed)}_fireEnableClick(){this.fire("enable-click")}_updateInputTrace(){Mqt(this.getGraphSvgRoot(),this.renderHierarchy,this.selectedNode,this.traceInputs)}};Jqt.template=Zqt,t([o({type:Object}),e("design:type",yWt)],Jqt.prototype,"renderHierarchy",void 0),t([o({type:String}),e("design:type",String)],Jqt.prototype,"name",void 0),t([o({type:String}),e("design:type",String)],Jqt.prototype,"colorBy",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Jqt.prototype,"traceInputs",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Jqt.prototype,"_hasRenderHierarchyBeenFitOnce",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Jqt.prototype,"_isAttached",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"_zoom",void 0),t([o({type:String,observer:"_highlightedNodeChanged"}),e("design:type",String)],Jqt.prototype,"highlightedNode",void 0),t([o({type:String,observer:"_selectedNodeChanged"}),e("design:type",String)],Jqt.prototype,"selectedNode",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"handleEdgeSelected",void 0),t([o({type:Boolean,observer:"_onZoomChanged"}),e("design:type",Boolean)],Jqt.prototype,"_zoomed",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"_zoomStartCoords",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"_zoomTransform",void 0),t([o({type:Number}),e("design:type",Number)],Jqt.prototype,"_maxZoomDistanceForClick",void 0),t([o({type:Object}),e("design:type",Function)],Jqt.prototype,"templateIndex",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"_nodeGroupIndex",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"_annotationGroupIndex",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"_edgeGroupIndex",void 0),t([o({type:Number}),e("design:type",Number)],Jqt.prototype,"maxMetanodeLabelLengthFontSize",void 0),t([o({type:Number}),e("design:type",Number)],Jqt.prototype,"minMetanodeLabelLengthFontSize",void 0),t([o({type:Number}),e("design:type",Number)],Jqt.prototype,"maxMetanodeLabelLengthLargeFont",void 0),t([o({type:Number}),e("design:type",Number)],Jqt.prototype,"maxMetanodeLabelLength",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"progress",void 0),t([o({type:Array}),e("design:type",Array)],Jqt.prototype,"nodeContextMenuItems",void 0),t([o({type:Object}),e("design:type",Object)],Jqt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Number}),e("design:type",Number)],Jqt.prototype,"healthPillStepIndex",void 0),t([a("renderHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Jqt.prototype,"_renderHierarchyChanged",null),t([a("_isAttached","renderHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Jqt.prototype,"_animateAndFit",null),t([a("colorBy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Jqt.prototype,"nodeColorsChanged",null),t([a("nodeNamesToHealthPills","healthPillStepIndex"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Jqt.prototype,"_updateHealthPills",null),t([a("traceInputs","selectedNode"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Jqt.prototype,"_updateInputTrace",null),Jqt=t([i("tf-graph-scene")],Jqt);let Qqt=class extends(er(ye)){constructor(){super(...arguments),this._renderDepth=1,this._allowGraphSelect=!0,this.edgeWidthFunction="",this.handleNodeSelected="",this.edgeLabelFunction="",this.handleEdgeSelected=""}panToNode(t){this.$$("tf-graph-scene").panToNode(t)}_autoExtractNodesChanged(){var t=this.graphHierarchy;if(t){for(const e of Object.values(t.getNodeMap()))e.include=GGt.UNSPECIFIED;this._buildRenderHierarchy(t)}}_buildNewRenderHierarchy(){var t=this.graphHierarchy;t&&this._buildRenderHierarchy(t)}_statsChanged(){var t=this.stats,e=this.devicesForStats;this.graphHierarchy&&(t&&e&&((function n(t,e,i){Se.exports.each(t.nodes,(t=>{t.stats=null})),Se.exports.each(e.dev_stats,(e=>{i&&!i[e.device]||Se.exports.each(e.node_stats,(n=>{let i=n.node_name in t.nodes?n.node_name:sWt(n.node_name);if(!(i in t.nodes))return;let r=0;n.memory&&Se.exports.each(n.memory,(t=>{t.total_bytes&&(t.total_bytes>0?r+=Number(t.total_bytes):console.log("ignoring negative memory allocation for "+i))}));let o=null;n.output&&(o=Se.exports.map(n.output,(t=>Se.exports.map(t.tensor_description.shape.dim,(t=>Number(t.size)))))),t.nodes[i].device=e.device,null==t.nodes[i].stats&&(t.nodes[i].stats=new SGt(o)),t.nodes[i].stats.addBytesAllocation(r),n.all_end_rel_micros&&(n.all_end_rel_micros>0?t.nodes[i].stats.addExecutionTime(n.all_start_micros,n.all_start_micros+n.all_end_rel_micros):console.log("ignoring negative runtime for "+i))}))}))})(this.basicGraph,t,e),(function i(t,e){let n={},i={};Se.exports.each(t.root.leaves(),(e=>{let r=t.node(e);null!=r.device&&(n[r.device]=!0),null!=r.xlaCluster&&(i[r.xlaCluster]=!0)})),t.devices=Se.exports.keys(n),t.xlaClusters=Se.exports.keys(i),Se.exports.each(t.getNodeMap(),((t,e)=>{t.isGroupNode&&(t.stats=new SGt(null),t.deviceHistogram={})})),Se.exports.each(t.root.leaves(),(e=>{let n=t.node(e),i=n;for(;null!=i.parentNode;){if(null!=n.device){let t=i.parentNode.deviceHistogram;t[n.device]=(t[n.device]||0)+1}if(null!=n.xlaCluster){let t=i.parentNode.xlaClusterHistogram;t[n.xlaCluster]=(t[n.xlaCluster]||0)+1}null!=n.stats&&i.parentNode.stats.combine(n.stats),i=i.parentNode}}))})(this.graphHierarchy)),this._buildRenderHierarchy(this.graphHierarchy))}ready(){super.ready(),this.addEventListener("graph-select",this._graphSelected.bind(this)),this.addEventListener("disable-click",this._disableClick.bind(this)),this.addEventListener("enable-click",this._enableClick.bind(this)),this.addEventListener("node-toggle-expand",this._nodeToggleExpand.bind(this)),this.addEventListener("node-select",this._nodeSelected.bind(this)),this.addEventListener("node-highlight",this._nodeHighlighted.bind(this)),this.addEventListener("node-unhighlight",this._nodeUnhighlighted.bind(this)),this.addEventListener("node-toggle-extract",this._nodeToggleExtract.bind(this)),this.addEventListener("node-toggle-seriesgroup",this._nodeToggleSeriesGroup.bind(this)),this.addEventListener("edge-select",this._edgeSelected.bind(this)),this.addEventListener("annotation-select",this._nodeSelected.bind(this)),this.addEventListener("annotation-highlight",this._nodeHighlighted.bind(this)),this.addEventListener("annotation-unhighlight",this._nodeUnhighlighted.bind(this))}_buildRenderHierarchy(t){if(t.root.type!==jGt.META)return;const e=this,n=CGt("new tf_graph_render.Hierarchy",(()=>{const n=new yWt(t,!!this.stats,this.autoExtractNodes);function i(t){return{minValue:t.domain()[0],maxValue:t.domain()[1],startColor:t.range()[0],endColor:t.range()[1]}}return n.edgeLabelFunction=this.edgeLabelFunction,n.edgeWidthFunction=this.edgeWidthFunction,e._setColorByParams({compute_time:i(n.computeTimeScale),memory:i(n.memoryUsageScale),device:Se.exports.map(n.deviceColorMap.domain(),(function(t){return{device:t,color:n.deviceColorMap(t)}})),xla_cluster:Se.exports.map(n.xlaClusterColorMap.domain(),(function(t){return{xla_cluster:t,color:n.xlaClusterColorMap(t)}}))}),n}),_Gt.RENDER_BUILD_HIERARCHY);e._setRenderHierarchy(n)}_getVisible(t){return t?this.renderHierarchy.getNearestVisibleAncestor(t):t}fit(){this.$.scene.fit()}getImageBlob(){return this.$.scene.getImageBlob()}_graphChanged(){this.graphHierarchy&&(this.graphHierarchy.addListener(zqt.TEMPLATES_UPDATED,(()=>{this.$.scene.nodeColorsChanged()})),this.fire("graph-select"))}_graphSelected(t){this._allowGraphSelect&&(this.set("selectedNode",null),this.set("selectedEdge",null)),this._allowGraphSelect=!0}_disableClick(t){this._allowGraphSelect=!1}_enableClick(t){this._allowGraphSelect=!0}_selectedNodeChanged(){this.handleNodeSelected&&this.handleNodeSelected(this.selectedNode)}_selectedEdgeChanged(){var t=this.selectedEdge;this._deselectPreviousEdge(),t&&(this._lastSelectedEdgeGroup.classed(VWt.Edge.SELECTED,!0),this._updateMarkerOfSelectedEdge(t)),this.handleEdgeSelected&&this.handleEdgeSelected(t)}_nodeSelected(t){this._allowGraphSelect&&this.set("selectedNode",t.detail.name),this._allowGraphSelect=!0}_edgeSelected(t){this._allowGraphSelect&&(this.set("_lastSelectedEdgeGroup",t.detail.edgeGroup),this.set("selectedEdge",t.detail.edgeData)),this._allowGraphSelect=!0}_nodeHighlighted(t){this.set("highlightedNode",t.detail.name)}_nodeUnhighlighted(t){this.set("highlightedNode",null)}_nodeToggleExpand(t){this._nodeSelected(t);var e=t.detail.name,n=this.renderHierarchy.getRenderNodeByName(e);n.node.type!==jGt.OP&&(this.renderHierarchy.buildSubhierarchy(e),n.expanded=!n.expanded,this.async((function(){this.$.scene.setNodeExpanded(n)}),75),TGt({actionId:_Gt.NODE_EXPANSION_TOGGLED,eventLabel:n.expanded?"expanded":"collapsed"}))}_nodeToggleExtract(t){this.nodeToggleExtract(t.detail.name)}nodeToggleExtract(t){const e=this.renderHierarchy.getRenderNodeByName(t);e.node.include=e.node.include==GGt.INCLUDE?GGt.EXCLUDE:e.node.include==GGt.EXCLUDE||this.renderHierarchy.isNodeAuxiliary(e)?GGt.INCLUDE:GGt.EXCLUDE,this._buildRenderHierarchy(this.graphHierarchy),TGt({actionId:_Gt.NODE_AUXILIARY_EXTRACTION_CHANGED,eventLabel:e.node.include===GGt.INCLUDE?"Auxiliary to Main":"Main to Auxiliary"})}_nodeToggleSeriesGroup(t){this.nodeToggleSeriesGroup(t.detail.name)}nodeToggleSeriesGroup(t){this.set("progress",{value:0,msg:""});var e=kGt(AGt(this),100,"Namespace hierarchy");const n=Object.assign(Object.assign({},this.hierarchyParams),{seriesMap:this.graphHierarchy.buildSeriesGroupMapToggled(t)});Fqt(this.basicGraph,n,e).then(function(t){this.set("graphHierarchy",t),this._buildRenderHierarchy(this.graphHierarchy)}.bind(this))}_deselectPreviousEdge(){Su("."+VWt.Edge.SELECTED).classed(VWt.Edge.SELECTED,!1).each(((t,e)=>{if(t.label){const e=Su(this).selectAll("path.edgeline");t.label.startMarkerId&&e.style("marker-start",`url(#${t.label.startMarkerId})`),t.label.endMarkerId&&e.style("marker-end",`url(#${t.label.endMarkerId})`)}}))}_updateMarkerOfSelectedEdge(t){if(t.label){const e=t.label.startMarkerId||t.label.endMarkerId;if(e){const n=e.replace("dataflow-","selected-");let i=this.$$("#"+n);if(!i){const t=this.$.scene.querySelector("#"+e);i=t.cloneNode(!0),i.setAttribute("id",n),i.classList.add("selected-arrowhead"),t.parentNode.appendChild(i)}const r=t.label.startMarkerId?"marker-start":"marker-end";this._lastSelectedEdgeGroup.selectAll("path.edgeline").style(r,`url(#${n})`)}}}not(t){return!t}};Qqt.template=_e`
    <style>
      .container {
        width: 100%;
        height: 100%;
        background: white;
        box-shadow: 0 1px 5px rgba(0, 0, 0, 0.2);
      }

      .vertical {
        width: 100%;
        height: 100%;
        @apply --layout-vertical;
      }

      .auto {
        @apply --layout-flex-auto;
        @apply --layout-vertical;
      }

      h2 {
        text-align: center;
      }

      paper-button {
        text-transform: none;
      }
    </style>
    <div class="container">
      <div class="vertical">
        <template is="dom-if" if="[[title]]">
          <h2>[[title]]</h2>
        </template>
        <tf-graph-scene
          id="scene"
          class="auto"
          render-hierarchy="[[renderHierarchy]]"
          highlighted-node="[[_getVisible(highlightedNode)]]"
          selected-node="{{selectedNode}}"
          selected-edge="{{selectedEdge}}"
          color-by="[[colorBy]]"
          progress="[[progress]]"
          node-context-menu-items="[[nodeContextMenuItems]]"
          node-names-to-health-pills="[[nodeNamesToHealthPills]]"
          health-pill-step-index="{{healthPillStepIndex}}"
          handle-edge-selected="[[handleEdgeSelected]]"
          trace-inputs="[[traceInputs]]"
        ></tf-graph-scene>
      </div>
    </div>
  `,t([o({type:Object,notify:!0,observer:"_graphChanged"}),e("design:type",Dqt)],Qqt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",qGt)],Qqt.prototype,"basicGraph",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"stats",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"devicesForStats",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"hierarchyParams",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],Qqt.prototype,"progress",void 0),t([o({type:String}),e("design:type",String)],Qqt.prototype,"title",void 0),t([o({type:String,notify:!0}),e("design:type",String)],Qqt.prototype,"selectedNode",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],Qqt.prototype,"selectedEdge",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"_lastSelectedEdgeGroup",void 0),t([o({type:String,notify:!0}),e("design:type",String)],Qqt.prototype,"highlightedNode",void 0),t([o({type:String}),e("design:type",String)],Qqt.prototype,"colorBy",void 0),t([o({type:Object,notify:!0,readOnly:!0}),e("design:type",Object)],Qqt.prototype,"colorByParams",void 0),t([o({type:Object,readOnly:!0,notify:!0}),e("design:type",yWt)],Qqt.prototype,"renderHierarchy",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Qqt.prototype,"traceInputs",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Qqt.prototype,"autoExtractNodes",void 0),t([o({type:Array}),e("design:type",Array)],Qqt.prototype,"nodeContextMenuItems",void 0),t([o({type:Number}),e("design:type",Number)],Qqt.prototype,"_renderDepth",void 0),t([o({type:Boolean}),e("design:type",Boolean)],Qqt.prototype,"_allowGraphSelect",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Number}),e("design:type",Number)],Qqt.prototype,"healthPillStepIndex",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"edgeWidthFunction",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"handleNodeSelected",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"edgeLabelFunction",void 0),t([o({type:Object}),e("design:type",Object)],Qqt.prototype,"handleEdgeSelected",void 0),t([a("autoExtractNodes"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Qqt.prototype,"_autoExtractNodesChanged",null),t([a("graphHierarchy","edgeWidthFunction","handleNodeSelected","edgeLabelFunction","handleEdgeSelected"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Qqt.prototype,"_buildNewRenderHierarchy",null),t([a("stats","devicesForStats"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Qqt.prototype,"_statsChanged",null),t([a("selectedNode"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Qqt.prototype,"_selectedNodeChanged",null),t([a("selectedEdge"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],Qqt.prototype,"_selectedEdgeChanged",null),Qqt=t([i("tf-graph")],Qqt);const tYt={MAX_NODE_COUNT:1e4,MAX_EDGE_COUNT:1e4};let eYt=class extends(er(ye)){constructor(){super(...arguments),this.hierarchyParams=Hqt,this.allStepsModeEnabled=!1,this.specificHealthPillStep=0,this.compatNodeTitle="TPU Compatibility"}fit(){this.$.graph.fit()}downloadAsImage(t){return n(this,void 0,void 0,(function*(){const e=yield this.$.graph.getImageBlob(),n=document.createElement("a");n.href=URL.createObjectURL(e),n.download=t,n.click(),URL.revokeObjectURL(n.href)}))}_isNotComplete(t){return t.value<100}_getContainerClass(t){var e="container";return t.error&&(e+=" error"),this._isNotComplete(t)&&(e+=" loading"),e}_onNodeInclusionToggled(t){this.$.graph.nodeToggleExtract(t.detail.name)}_onNodeSeriesGroupToggled(t){this.$.graph.nodeToggleSeriesGroup(t.detail.name)}_updateNodeInclude(){const t=this.renderHierarchy?this.renderHierarchy.getNodeByName(this.selectedNode):null;this._selectedNodeInclude=t?t.include:GGt.UNSPECIFIED}_slimGraphChanged(){if(!this.graph)return;const{MAX_NODE_COUNT:t,MAX_EDGE_COUNT:e}=tYt;Object.keys(this.graph.nodes).length>t&&this.graph.edges.length>e&&this.colorBy===aqt.STRUCTURE&&(this.colorBy=aqt.NONE)}_ensureTemplates(){this.graphHierarchy&&this.colorBy===aqt.STRUCTURE&&(this.graphHierarchy.getTemplateIndex()||this.graphHierarchy.updateTemplates())}};eYt.template=_e`
    <style>
      ::host {
        display: block;
      }

      /deep/ .close {
        position: absolute;
        cursor: pointer;
        left: 15px;
        bottom: 15px;
      }

      .container {
        width: 100%;
        height: 100%;
        opacity: 1;
      }

      .container.loading {
        cursor: progress;
        opacity: 0.1;
      }

      .container.loading.error {
        cursor: auto;
      }

      #info {
        position: absolute;
        right: 5px;
        top: 5px;
        padding: 0px;
        max-width: 380px;
        min-width: 320px;
        background-color: rgba(255, 255, 255, 0.9);
        @apply --shadow-elevation-2dp;
      }

      #main {
        width: 100%;
        height: 100%;
      }

      #progress-bar {
        display: flex;
        flex-direction: column;
        align-items: center;
        justify-content: center;
        width: 100%;
        position: absolute;
        top: 40px;
        left: 0;
        font-size: 13px;
      }

      #progress-msg {
        margin-bottom: 5px;
        white-space: pre-wrap;
        width: 400px;
      }

      paper-progress {
        width: 400px;
        --paper-progress-height: 6px;
        --paper-progress-active-color: #f3913e;
      }

      .context-menu {
        position: absolute;
        display: none;
        background-color: #e2e2e2;
        border-radius: 2px;
        font-size: 14px;
        min-width: 150px;
        border: 1px solid #d4d4d4;
      }

      /deep/ .context-menu ul {
        list-style-type: none;
        margin: 0;
        padding: 0;
        cursor: default;
      }

      /deep/ .context-menu ul li {
        padding: 4px 16px;
      }

      /deep/ .context-menu ul li:hover {
        background-color: #f3913e;
        color: white;
      }
    </style>
    <template is="dom-if" if="[[_isNotComplete(progress)]]">
      <div id="progress-bar">
        <div id="progress-msg">[[progress.msg]]</div>
        <paper-progress value="[[progress.value]]"></paper-progress>
      </div>
    </template>
    <div class$="[[_getContainerClass(progress)]]">
      <div id="main">
        <tf-graph
          id="graph"
          graph-hierarchy="{{graphHierarchy}}"
          basic-graph="[[graph]]"
          hierarchy-params="[[hierarchyParams]]"
          render-hierarchy="{{renderHierarchy}}"
          devices-for-stats="[[devicesForStats]]"
          stats="[[stats]]"
          selected-node="{{selectedNode}}"
          highlighted-node="{{_highlightedNode}}"
          color-by="[[colorBy]]"
          color-by-params="{{colorByParams}}"
          progress="{{progress}}"
          edge-label-function="[[edgeLabelFunction]]"
          edge-width-function="[[edgeWidthFunction]]"
          node-names-to-health-pills="[[nodeNamesToHealthPills]]"
          health-pill-step-index="[[healthPillStepIndex]]"
          handle-node-selected="[[handleNodeSelected]]"
          handle-edge-selected="[[handleEdgeSelected]]"
          trace-inputs="[[traceInputs]]"
          auto-extract-nodes="[[autoExtractNodes]]"
        ></tf-graph>
      </div>
      <div id="info">
        <tf-graph-info
          id="graph-info"
          title="selected"
          graph-hierarchy="[[graphHierarchy]]"
          render-hierarchy="[[renderHierarchy]]"
          graph="[[graph]]"
          selected-node="{{selectedNode}}"
          selected-node-include="{{_selectedNodeInclude}}"
          highlighted-node="{{_highlightedNode}}"
          color-by="[[colorBy]]"
          color-by-params="[[colorByParams]]"
          debugger-data-enabled="[[debuggerDataEnabled]]"
          are-health-pills-loading="[[areHealthPillsLoading]]"
          debugger-numeric-alerts="[[debuggerNumericAlerts]]"
          node-names-to-health-pills="[[nodeNamesToHealthPills]]"
          all-steps-mode-enabled="{{allStepsModeEnabled}}"
          specific-health-pill-step="{{specificHealthPillStep}}"
          health-pill-step-index="{{healthPillStepIndex}}"
          compat-node-title="[[compatNodeTitle]]"
          on-node-toggle-inclusion="_onNodeInclusionToggled"
          on-node-toggle-seriesgroup="_onNodeSeriesGroupToggled"
        ></tf-graph-info>
      </div>
    </div>
  `,t([o({type:Object}),e("design:type",Dqt)],eYt.prototype,"graphHierarchy",void 0),t([o({type:Object}),e("design:type",qGt)],eYt.prototype,"graph",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"hierarchyParams",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"stats",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"progress",void 0),t([o({type:Boolean}),e("design:type",Boolean)],eYt.prototype,"traceInputs",void 0),t([o({type:Boolean}),e("design:type",Boolean)],eYt.prototype,"autoExtractNodes",void 0),t([o({type:String,notify:!0}),e("design:type",String)],eYt.prototype,"colorBy",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],eYt.prototype,"colorByParams",void 0),t([o({type:Object,notify:!0}),e("design:type",yWt)],eYt.prototype,"renderHierarchy",void 0),t([o({type:Boolean}),e("design:type",Boolean)],eYt.prototype,"debuggerDataEnabled",void 0),t([o({type:Boolean}),e("design:type",Boolean)],eYt.prototype,"areHealthPillsLoading",void 0),t([o({type:Array,notify:!0}),e("design:type",Array)],eYt.prototype,"debuggerNumericAlerts",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"nodeNamesToHealthPills",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],eYt.prototype,"allStepsModeEnabled",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],eYt.prototype,"specificHealthPillStep",void 0),t([o({type:Number}),e("design:type",Number)],eYt.prototype,"healthPillStepIndex",void 0),t([o({type:String,notify:!0}),e("design:type",String)],eYt.prototype,"selectedNode",void 0),t([o({type:String}),e("design:type",String)],eYt.prototype,"compatNodeTitle",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"edgeWidthFunction",void 0),t([o({type:Number}),e("design:type",Number)],eYt.prototype,"_selectedNodeInclude",void 0),t([o({type:String}),e("design:type",String)],eYt.prototype,"_highlightedNode",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"handleNodeSelected",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"edgeLabelFunction",void 0),t([o({type:Object}),e("design:type",Object)],eYt.prototype,"handleEdgeSelected",void 0),t([a("selectedNode","renderHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],eYt.prototype,"_updateNodeInclude",null),t([a("graph"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],eYt.prototype,"_slimGraphChanged",null),t([a("colorBy","graphHierarchy"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],eYt.prototype,"_ensureTemplates",null),eYt=t([i("tf-graph-board")],eYt);let nYt=class extends(er(ye)){constructor(){super(...arguments),this._rawRegexInput="",this._previousRegexInput="",this._searchTimeoutDelay=150,this._maxRegexResults=42}get _regexInput(){return this._rawRegexInput.trim()}_regexInputChanged(){this._requestSearch()}_clearSearchResults(){this.set("_regexMatches",[])}_requestSearch(){this._searchPending||(this._regexInput!==this._previousRegexInput?(this._searchPending=!0,this._executeSearch(),this.async((()=>{this._searchPending=!1,this._requestSearch()}),this._searchTimeoutDelay)):this._searchPending=!1)}_executeSearch(){if(this._previousRegexInput=this._regexInput,!this._regexInput)return void this._clearSearchResults();try{var t=new RegExp(this._regexInput)}catch(t){return void this._clearSearchResults()}const e=[],n=this.renderHierarchy.hierarchy.getNodeMap();Se.exports.each(n,((n,i)=>{if(e.length>=this._maxRegexResults)return!1;t.test(i)&&e.push(i)})),this.set("_regexMatches",e)}_matchClicked(t){this.set("selectedNode",t.model.item),TGt({actionId:_Gt.NODE_SEARCH_RESULT_FOCUSED})}};nYt.template=_e`
    <div id="search-container">
      <paper-input
        id="runs-regex"
        label="Search nodes (regex)"
        value="{{_rawRegexInput}}"
      >
      </paper-input>
      <div id="search-results-anchor">
        <div id="search-results">
          <template is="dom-repeat" items="[[_regexMatches]]">
            <div id="search-match" on-click="_matchClicked">[[item]]</div>
          </template>
        </div>
      </div>
    </div>
    <style>
      #search-container {
        width: 100%;
        overflow: visible;
      }

      #runs-regex {
        width: 100%;
      }

      #search-results-anchor {
        position: relative;
      }

      #search-results {
        color: #fff;
        position: absolute;
        max-height: 200px;
        overflow-x: hidden;
        overflow-y: auto;
        text-align: right;
        max-width: 100%;
        box-sizing: border-box;
      }

      #search-match {
        background: var(--tb-orange-strong);
        padding: 3px;
        float: right;
        width: 100%;
        box-sizing: border-box;
        direction: rtl;
      }

      #search-match:hover {
        background: var(--tb-orange-weak);
        cursor: pointer;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],nYt.prototype,"renderHierarchy",void 0),t([o({type:String,notify:!0}),e("design:type",String)],nYt.prototype,"selectedNode",void 0),t([o({type:String}),e("design:type",String)],nYt.prototype,"_rawRegexInput",void 0),t([o({type:String}),e("design:type",String)],nYt.prototype,"_previousRegexInput",void 0),t([o({type:Number}),e("design:type",Number)],nYt.prototype,"_searchTimeoutDelay",void 0),t([o({type:Boolean}),e("design:type",Boolean)],nYt.prototype,"_searchPending",void 0),t([o({type:Number}),e("design:type",Number)],nYt.prototype,"_maxRegexResults",void 0),t([o({type:Array}),e("design:type",Array)],nYt.prototype,"_regexMatches",void 0),t([s("renderHierarchy","_rawRegexInput"),e("design:type",String),e("design:paramtypes",[])],nYt.prototype,"_regexInput",null),t([a("_regexInput"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],nYt.prototype,"_regexInputChanged",null),nYt=t([i("tf-graph-node-search")],nYt);const iYt=/device:([^:]+:[0-9]+)$/,rYt=[{regex:iYt}],oYt=[],aYt=new Set([aqt.COMPUTE_TIME,aqt.MEMORY]);let sYt=class extends(er(il(ye))){constructor(){super(...arguments),this.ColorBy=aqt,this.stats=null,this.devicesForStats=null,this.colorBy=aqt.STRUCTURE,this.datasets=[],this._selectedRunIndex=0,this.traceInputs=!1,this.autoExtractNodes=!0,this._selectedTagIndex=0,this._selectedGraphType=yGt.OP_GRAPH,this.showSessionRunsDropdown=!0,this.showUploadButton=!0,this._legendOpened=!0,this._downloadFilename="graph.png"}_onGraphTypeChangedByUserGesture(){TGt({actionId:_Gt.GRAPH_TYPE_CHANGED,eventLabel:this._selectedGraphType})}_onColorByChangedByUserGesture(){TGt({actionId:_Gt.NODE_COLOR_MODE_CHANGED,eventLabel:this.colorBy})}_onTraceInputsChangedByUserGesture(){TGt({actionId:_Gt.TRACE_INPUT_MODE_TOGGLED})}_xlaClustersProvided(t){return t&&t.hierarchy&&t.hierarchy.xlaClusters.length>0}_statsChanged(t){if(null!=t){var e={};Se.exports.each(t.dev_stats,(function(t){var n=Se.exports.some(rYt,(function(e){return e.regex.test(t.device)})),i=Se.exports.some(oYt,(function(e){return e.regex.test(t.device)}));n&&!i&&(e[t.device]=!0)})),this.set("devicesForStats",e)}}get _currentDevices(){var t=this.devicesForStats;const e=this.stats,n=(e?e.dev_stats:[]).map((t=>t.device)).filter((t=>rYt.some((e=>e.regex.test(t))))),i=zGt(n);if(1==i.length){const t=i[0].match(iYt);t&&(i[0]=t[1])}return n.map(((e,n)=>{let r=null;return oYt.forEach((t=>{t.regex.test(e)&&(r=t.msg)})),{device:e,suffix:i[n],used:t[e],ignoredMsg:r}}))}_deviceCheckboxClicked(t){const e=t.target,n=Object.assign({},this.devicesForStats),i=e.value;e.checked?n[i]=!0:delete n[i],this.set("devicesForStats",n)}_numTags(t,e){return this._getTags(t,e).length}_getTags(t,e){return t&&t[e]?t[e].tags:[]}_fit(){this.fire("fit-tap")}_isGradientColoring(t,e){return aYt.has(e)&&null!=t}_equals(t,e){return t===e}get _currentDeviceParams(){const t=this.colorByParams.device.filter((t=>rYt.some((e=>e.regex.test(t.device))))),e=zGt(t.map((t=>t.device)));if(1==e.length){var n=e[0].match(iYt);n&&(e[0]=n[1])}return t.map(((t,n)=>({device:e[n],color:t.color})))}get _currentXlaClusterParams(){return this.colorByParams.xla_cluster}get _currentGradientParams(){var t=this.colorByParams,e=this.colorBy;if(!this._isGradientColoring(this.stats,e))return;const n=t[e];let i=n.minValue,r=n.maxValue;return e===aqt.MEMORY?(i=RGt(i,NGt),r=RGt(r,NGt)):e===aqt.COMPUTE_TIME&&(i=RGt(i,IGt),r=RGt(r,IGt)),{minValue:i,maxValue:r,startColor:n.startColor,endColor:n.endColor}}download(){this.fire("download-image-requested",this._downloadFilename)}_updateFileInput(t){const e=t.target.files[0];if(!e)return;let n=e.name;const i=n.lastIndexOf(".");i>=0&&(n=n.substring(0,i));const r=n.lastIndexOf("/");r>=0&&(n=n.substring(r+1)),this._setDownloadFilename(n),this.set("selectedFile",t),TGt({actionId:_Gt.UPLOADED_GRAPH_FROM_FILESYSTEM})}_datasetsChanged(t,e){var n;null!=e&&(this._selectedRunIndex=0),this._setDownloadFilename(null===(n=this.datasets[this._selectedRunIndex])||void 0===n?void 0:n.name)}_computeSelection(t,e,n,i){return t[e]&&t[e].tags[n]?{run:t[e].name,tag:t[e].tags[n].tag,type:i}:null}_selectedRunIndexChanged(t){var e;this.datasets&&(this.colorBy=aqt.STRUCTURE,this._selectedTagIndex=0,this._selectedGraphType=this._getDefaultSelectionType(),this.traceInputs=!1,this._setDownloadFilename(null===(e=this.datasets[t])||void 0===e?void 0:e.name))}_selectedTagIndexChanged(){this._selectedGraphType=this._getDefaultSelectionType()}_getDefaultSelectionType(){const{datasets:t,_selectedRunIndex:e,_selectedTagIndex:n}=this;if(!t||!t[e]||!t[e].tags[n]||t[e].tags[n].opGraph)return yGt.OP_GRAPH;const i=t[e];return i.tags[n].profile?yGt.PROFILE:i.tags[n].conceptualGraph?yGt.CONCEPTUAL_GRAPH:yGt.OP_GRAPH}_getFile(){this.$$("#file").click()}_setDownloadFilename(t){this._downloadFilename=(t||"graph")+".png"}_statsNotNull(t){return null!==t}_toggleLegendOpen(){this.set("_legendOpened",!this._legendOpened)}_getToggleLegendIcon(t){return t?"expand-more":"expand-less"}_getSelectionOpGraphDisabled(t,e,n){return!t[e]||!t[e].tags[n]||!t[e].tags[n].opGraph}_getSelectionProfileDisabled(t,e,n){return!t[e]||!t[e].tags[n]||!t[e].tags[n].profile}_getSelectionConceptualGraphDisabled(t,e,n){return!t[e]||!t[e].tags[n]||!t[e].tags[n].conceptualGraph}};sYt.template=_e`
    <style>
      :host {
        color: #555;
        display: flex;
        flex-direction: column;
        font-size: 12px;
        width: 100%;
        --tb-graph-controls-title-color: #000;
        --tb-graph-controls-legend-text-color: #000;
        --tb-graph-controls-text-color: #555;
        --tb-graph-controls-title-font-size: 14px;
        --tb-graph-controls-subtitle-font-size: 14px;
        --paper-input-container-shared-input-style_-_font-size: 14px;
        --paper-font-subhead_-_font-size: 14px;
      }

      :host(.dark-mode) {
        --tb-graph-controls-title-color: #fff;
        --tb-graph-controls-legend-text-color: #f3f3f3;
        --tb-graph-controls-text-color: #eee;
      }

      paper-dropdown-menu {
        --paper-dropdown-menu-input: {
          padding: 0;
          color: gray;
        }
        --iron-icon-width: 15px;
        --iron-icon-height: 15px;
        --primary-text-color: gray;
        --paper-item-min-height: 30px;
      }

      paper-button[raised].keyboard-focus {
        font-weight: normal;
      }

      .run-dropdown {
        --paper-input-container: {
          padding: 5px 0 5px 5px;
        }
      }

      table {
        border-collapse: collapse;
        border-spacing: 0;
      }

      table tr {
        height: 20px;
      }

      table td {
        padding: 0;
        margin: 0;
      }

      .allcontrols {
        padding: 0 20px 20px;
        flex-grow: 1;
        overflow-y: auto;
      }

      .legend-holder {
        background: var(--secondary-background-color);
        box-sizing: border-box;
        color: var(--tb-graph-controls-text-color);
        width: 100%;
      }

      .legend-toolbar {
        appearance: none;
        background-color: inherit;
        border-top: 1px solid #ccc;
        border-bottom: 1px solid #ccc;
        border-right: none;
        border-left: none;
        cursor: pointer;
        color: var(--tb-graph-controls-legend-text-color);
        font: inherit;
        display: flex;
        align-items: center;
        justify-content: space-between;
        width: 100%;
      }

      .legend-toolbar,
      .legend-content {
        padding: 8px 20px;
      }

      .toggle-legend-button {
        max-height: 20px;
        max-width: 20px;
        padding: 0;
      }

      .toggle-legend-text {
        font-size: var(--tb-graph-controls-subtitle-font-size);
      }

      paper-radio-button {
        display: block;
        padding: 5px;
      }
      svg.icon,
      tf-graph-icon {
        width: 60px;
        height: 18px;
      }
      .domainValues {
        margin-bottom: 10px;
        width: 165px;
      }
      .domainStart {
        float: left;
      }
      .domainEnd {
        float: right;
      }
      .colorBox {
        width: 20px;
      }

      .image-icon {
        width: 24px;
        height: 24px;
      }

      .help-icon {
        height: 15px;
        margin: 0;
        padding: 0;
      }

      .gray {
        color: #666;
      }

      .title {
        font-size: var(--tb-graph-controls-title-font-size);
        margin: 8px 5px 8px 0;
        color: var(--tb-graph-controls-title-color);
      }
      .title small {
        font-weight: normal;
      }
      .deviceList,
      .xlaClusterList {
        max-height: 200px;
        overflow-y: auto;
      }

      #file {
        padding: 8px 0;
      }

      .color-legend-row {
        align-items: center;
        clear: both;
        display: flex;
        height: 20px;
        margin-top: 5px;
      }

      .color-legend-row .label,
      .color-legend-row svg,
      .color-legend-row tf-graph-icon {
        flex: 0 0 40px;
        margin-right: 20px;
      }

      .devices-checkbox input {
        text-align: left;
        vertical-align: middle;
      }

      .control-holder .icon-button {
        font-size: var(--tb-graph-controls-subtitle-font-size);
        margin: 0 -5px;
        padding: 5px;
        display: flex;
        justify-content: flex-start;
        color: var(--tb-graph-controls-text-color);
      }

      .button-text {
        padding-left: 20px;
        text-transform: none;
      }

      .upload-button {
        width: 165px;
        height: 25px;
        text-transform: none;
        margin-top: 4px;
      }

      .button-icon {
        width: 26px;
        height: 26px;
        color: var(--paper-orange-500);
      }

      .hidden-input {
        display: none;
      }

      .allcontrols .control-holder {
        clear: both;
        display: flex;
        justify-content: space-between;
      }

      .allcontrols .control-holder.control-options {
        padding: 0 0 15px 15px;
        flex-direction: column;
      }

      .allcontrols .control-holder paper-toggle-button {
        margin-bottom: 5px;
      }

      span.counter {
        font-size: var(--tb-graph-controls-subtitle-font-size);
        color: gray;
        margin-left: 4px;
      }

      .runs-row .title,
      .tags-row .title {
        display: flex;
        align-items: baseline;
      }

      .runs-row paper-item,
      .tags-row paper-item {
        --paper-item: {
          white-space: nowrap;
        }
      }

      table.control-holder {
        border: 0;
        border-collapse: collapse;
      }

      table.tf-graph-controls td.input-element-table-data {
        padding: 0 0 0 20px;
      }

      .spacer {
        flex-grow: 1;
      }

      .color-text {
        overflow: hidden;
      }

      .color-text.gradient-container {
        margin: 0 5px;
      }

      /** Override inline styles that suppress pointer events for disabled buttons. Otherwise, the */
      /*  tooltips do not appear. */
      paper-radio-group paper-radio-button {
        pointer-events: auto !important;
      }

      .legend-clarifier {
        color: #266236;
        cursor: help;
        display: inline-block;
        text-decoration: underline;
      }

      .legend-clarifier paper-tooltip {
        width: 150px;
      }

      /** Otherwise, polymer UI controls appear atop node search. */
      tf-graph-node-search {
        z-index: 1;
        width: 100%;
      }

      paper-dropdown-menu {
        flex-grow: 1;
      }
    </style>

    <div class="allcontrols">
      <div class="control-holder">
        <tf-graph-node-search
          selected-node="{{selectedNode}}"
          render-hierarchy="[[renderHierarchy]]"
        ></tf-graph-node-search>
      </div>
      <div class="control-holder">
        <paper-button class="icon-button" on-tap="_fit" alt="Fit to screen">
          <iron-icon icon="aspect-ratio" class="button-icon"></iron-icon>
          <span class="button-text">Fit to screen</span>
        </paper-button>
      </div>
      <div class="control-holder">
        <paper-button
          class="icon-button"
          on-click="download"
          alt="Download PNG"
        >
          <iron-icon icon="file-download" class="button-icon"></iron-icon>
          <span class="button-text">Download PNG</span>
        </paper-button>
      </div>
      <template is="dom-if" if="[[showUploadButton]]">
        <div class="control-holder">
          <paper-button
            class="icon-button"
            on-click="_getFile"
            alt="Upload file"
            title="Upload a pbtxt file to view a graph from the local filesystem"
          >
            <iron-icon icon="file-upload" class="button-icon"></iron-icon>
            <span class="button-text">Upload file</span>
          </paper-button>

          <div class="hidden-input">
            <input
              type="file"
              id="file"
              name="file"
              on-change="_updateFileInput"
              accept=".pbtxt"
            />
          </div>
        </div>
      </template>
      <div class="control-holder runs-row">
        <div class="title">
          Run <span class="counter">([[datasets.length]])</span>
        </div>
        <paper-dropdown-menu
          no-label-float
          no-animations
          noink
          horizontal-align="left"
          class="run-dropdown"
        >
          <paper-listbox
            class="dropdown-content"
            selected="{{_selectedRunIndex}}"
            slot="dropdown-content"
          >
            <template is="dom-repeat" items="[[datasets]]">
              <paper-item>[[item.name]]</paper-item>
            </template>
          </paper-listbox>
        </paper-dropdown-menu>
      </div>
      <template is="dom-if" if="[[showSessionRunsDropdown]]">
        <div class="control-holder tags-row">
          <div class="title">
            Tag
            <span class="counter"
              >([[_numTags(datasets, _selectedRunIndex)]])</span
            >
          </div>
          <paper-dropdown-menu
            no-label-float
            no-animations
            horizontal-align="left"
            noink
            class="run-dropdown"
          >
            <paper-listbox
              class="dropdown-content"
              selected="{{_selectedTagIndex}}"
              slot="dropdown-content"
            >
              <template
                is="dom-repeat"
                items="[[_getTags(datasets, _selectedRunIndex)]]"
              >
                <paper-item>[[item.displayName]]</paper-item>
              </template>
            </paper-listbox>
          </paper-dropdown-menu>
        </div>
      </template>
      <div class="title">Graph type</div>
      <div class="control-holder control-options">
        <paper-radio-group
          selected="{{_selectedGraphType}}"
          on-paper-radio-group-changed="_onGraphTypeChangedByUserGesture"
        >
          <!-- Note that the name has to match that of tf_graph_common.SelectionType. -->
          <paper-radio-button
            name="op_graph"
            disabled="[[_getSelectionOpGraphDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]"
            >Op graph</paper-radio-button
          >
          <paper-radio-button
            name="conceptual_graph"
            disabled="[[_getSelectionConceptualGraphDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]"
            >Conceptual graph</paper-radio-button
          >
          <paper-radio-button
            name="profile"
            disabled="[[_getSelectionProfileDisabled(datasets, _selectedRunIndex, _selectedTagIndex)]]"
            >Profile</paper-radio-button
          >
        </paper-radio-group>
      </div>
      <div class="title">Node options</div>
      <div class="control-holder control-options">
        <paper-toggle-button
          checked="{{traceInputs}}"
          on-change="_onTraceInputsChangedByUserGesture"
        >
          Trace inputs
        </paper-toggle-button>
        <paper-toggle-button checked="{{autoExtractNodes}}">
          Auto-extract high-degree nodes
        </paper-toggle-button>
      </div>
      <template is="dom-if" if="[[healthPillsFeatureEnabled]]">
        <div class="control-holder">
          <paper-toggle-button checked="{{healthPillsToggledOn}}"
            >Show health pills</paper-toggle-button
          >
        </div>
      </template>
      <div class="title">Color by</div>
      <div class="control-holder control-options">
        <paper-radio-group
          selected="{{colorBy}}"
          on-paper-radio-group-changed="_onColorByChangedByUserGesture"
        >
          <paper-radio-button name="[[ColorBy.NONE]]">None</paper-radio-button>

          <paper-radio-button name="[[ColorBy.STRUCTURE]]"
            >Structure</paper-radio-button
          >

          <paper-radio-button name="[[ColorBy.DEVICE]]"
            >Device</paper-radio-button
          >

          <paper-radio-button
            id="xla-cluster-radio-button"
            name="[[ColorBy.XLA_CLUSTER]]"
            disabled="[[!_xlaClustersProvided(renderHierarchy)]]"
          >
            XLA cluster
          </paper-radio-button>
          <paper-tooltip
            animation-delay="0"
            for="xla-cluster-radio-button"
            position="right"
            offset="0"
          >
            Coloring by XLA cluster is only enabled if at least 1 op specifies
            an XLA cluster.
          </paper-tooltip>

          <paper-radio-button
            id="compute-time-radio-button"
            name="[[ColorBy.COMPUTE_TIME]]"
            disabled="[[!stats]]"
          >
            Compute time
          </paper-radio-button>
          <paper-tooltip
            animation-delay="0"
            for="compute-time-radio-button"
            position="right"
            offset="0"
          >
            Coloring by compute time is only enabled if the RunMetadata proto is
            passed to the FileWriter when a specific session is run.
          </paper-tooltip>

          <paper-radio-button
            id="memory-radio-button"
            name="[[ColorBy.MEMORY]]"
            disabled="[[!stats]]"
          >
            Memory
          </paper-radio-button>
          <paper-tooltip
            animation-delay="0"
            for="memory-radio-button"
            position="right"
            offset="0"
          >
            Coloring by memory is only enabled if the RunMetadata proto is
            passed to the FileWriter when a specific session is run.
          </paper-tooltip>

          <paper-radio-button
            id="tpu-compatibility-radio-button"
            name="[[ColorBy.OP_COMPATIBILITY]]"
          >
            TPU compatibility
          </paper-radio-button>
          <paper-tooltip
            animation-delay="0"
            for="tpu-compatibility-radio-button"
            position="right"
            offset="0"
          >
            Coloring by whether an operation is compatible for the TPU device.
          </paper-tooltip>
        </paper-radio-group>
        <span class="spacer"></span>
      </div>
    </div>
    <div class="legend-holder">
      <button class="legend-toolbar" on-click="_toggleLegendOpen">
        <span class="toggle-legend-text">Legend</span>
        <iron-icon
          icon="[[_getToggleLegendIcon(_legendOpened)]]"
          class="toggle-legend-button"
        >
        </iron-icon>
      </button>
      <iron-collapse opened="[[_legendOpened]]" class="legend-content">
        <!-- Color-mode-specific legend items -->
        <div>
          <template is="dom-if" if="[[_isGradientColoring(stats, colorBy)]]">
            <svg width="140" height="20" class="color-text gradient-container">
              <defs>
                <linearGradient
                  id="linearGradient"
                  x1="0%"
                  y1="0%"
                  x2="100%"
                  y2="0%"
                >
                  <stop
                    class="start"
                    offset="0%"
                    stop-color$="[[_currentGradientParams.startColor]]"
                  ></stop>
                  <stop
                    class="end"
                    offset="100%"
                    stop-color$="[[_currentGradientParams.endColor]]"
                  ></stop>
                </linearGradient>
              </defs>
              <rect
                x="0"
                y="0"
                width="135"
                height="20"
                fill="url(#linearGradient)"
                stroke="black"
              ></rect>
            </svg>
            <div class="domainValues color-text">
              <div class="domainStart">[[_currentGradientParams.minValue]]</div>
              <div class="domainEnd">[[_currentGradientParams.maxValue]]</div>
            </div>
            <br style="clear: both" />
            <div>Devices included in stats:</div>
            <div class="deviceList">
              <template is="dom-repeat" items="[[_currentDevices]]">
                <div class="color-legend-row devices-checkbox">
                  <span
                    ><input
                      type="checkbox"
                      value$="[[item.device]]"
                      checked$="[[item.used]]"
                      on-click="_deviceCheckboxClicked"
                  /></span>
                  <span>[[item.suffix]]</span>
                  <template is="dom-if" if="[[item.ignoredMsg]]">
                    <paper-icon-button
                      icon="help"
                      class="help-icon"
                    ></paper-icon-button>
                    <paper-tooltip
                      position="right"
                      offset="0"
                      animation-delay="0"
                      >[[item.ignoredMsg]]</paper-tooltip
                    >
                  </template>
                </div>
              </template>
            </div>
          </template>
          <template is="dom-if" if="[[_equals(colorBy, 'structure')]]">
            <div class="color-text">
              <div class="color-legend-row">
                <span class="label"> colors </span>
                <span class="color-legend-value">same substructure</span>
              </div>
              <div class="color-legend-row">
                <tf-graph-icon
                  type="META"
                  height="16"
                  fill-override="#eee"
                  stroke-override="#a6a6a6"
                ></tf-graph-icon>
                <span class="color-legend-value">unique substructure</span>
              </div>
            </div>
          </template>
          <template is="dom-if" if="[[_equals(colorBy, 'device')]]">
            <div>
              <template is="dom-repeat" items="[[_currentDeviceParams]]">
                <div class="color-legend-row">
                  <tf-graph-icon
                    type="META"
                    height="16"
                    fill-override="[[item.color]]"
                    stroke-override="#a6a6a6"
                  ></tf-graph-icon>
                  <span class="color-legend-value">[[item.device]]</span>
                </div>
              </template>
              <div class="color-legend-row">
                <tf-graph-icon
                  type="META"
                  height="16"
                  fill-override="#eee"
                  stroke-override="#a6a6a6"
                ></tf-graph-icon>
                <span class="color-legend-value">unknown device</span>
              </div>
            </div>
          </template>
          <template is="dom-if" if="[[_equals(colorBy, 'xla_cluster')]]">
            <div>
              <template is="dom-repeat" items="[[_currentXlaClusterParams]]">
                <div class="color-legend-row">
                  <svg>
                    <use
                      xmlns:xlink="http://www.w3.org/1999/xlink"
                      xlink:href="#unfilled-rect"
                      x="0"
                      y="0"
                      style="fill:[[item.color]]"
                    ></use>
                  </svg>
                  <span class="color-legend-value">[[item.xla_cluster]]</span>
                </div>
              </template>
              <div class="color-legend-row">
                <svg>
                  <use
                    xmlns:xlink="http://www.w3.org/1999/xlink"
                    xlink:href="#grey-rect"
                    x="0"
                    y="0"
                  ></use>
                </svg>
                <span class="color-legend-value">unknown XLA cluster</span>
              </div>
            </div>
          </template>
          <template is="dom-if" if="[[_equals(colorBy, 'op_compatibility')]]">
            <div class="color-text">
              <div class="color-legend-row">
                <tf-graph-icon
                  type="OP"
                  height="16"
                  fill-override="#0f9d58"
                  stroke-override="#ccc"
                ></tf-graph-icon>
                <span class="color-legend-value">Valid Op</span>
              </div>
              <div class="color-legend-row">
                <tf-graph-icon
                  type="OP"
                  height="16"
                  fill-override="#db4437"
                  stroke-override="#ccc"
                ></tf-graph-icon>
                <span class="color-legend-value">Invalid Op</span>
              </div>
            </div>
          </template>
          <template is="dom-if" if="[[_statsNotNull(stats)]]">
            <div class="color-legend-row">
              <tf-graph-icon type="META" height="16" faded></tf-graph-icon>
              <span class="color-legend-value">unused substructure</span>
            </div>
          </template>
        </div>

        <!-- Common legend items -->
        <div>
          <table>
            <tbody>
              <tr>
                <td></td>
                <td>(* = expandable)</td>
              </tr>
              <tr>
                <td>
                  <tf-graph-icon
                    type="META"
                    height="16"
                    fill-override="#d9d9d9"
                    stroke-override="#ccc"
                  ></tf-graph-icon>
                </td>
                <td>
                  Namespace<span class="gray">*</span>
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Encapsulates a set of nodes. Namespace is hierarchical and
                      based on scope.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <tf-graph-icon type="OP" height="16"></tf-graph-icon>
                </td>
                <td>
                  OpNode
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Node that performs an operation. These nodes cannot
                      expand.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <tf-graph-icon type="SERIES" height="16"></tf-graph-icon>
                </td>
                <td>
                  Unconnected series<span class="gray">*</span>
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Sequence of numbered nodes that are not connected to each
                      other.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <tf-graph-icon
                    type="SERIES"
                    height="16"
                    vertical
                  ></tf-graph-icon>
                </td>
                <td>
                  Connected series<span class="gray">*</span>
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Sequence of numbered nodes that are connected to each
                      other.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <svg class="icon">
                    <circle
                      fill="white"
                      stroke="#848484"
                      cx="10"
                      cy="10"
                      r="5"
                    ></circle>
                  </svg>
                </td>
                <td>
                  Constant
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Node that outputs a constant value.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <tf-graph-icon type="SUMMARY" height="20"></tf-graph-icon>
                </td>
                <td>
                  Summary
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Node that collects data for visualization within
                      TensorBoard.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <svg
                    class="icon"
                    height="15px"
                    preserveAspectRatio="xMinYMid meet"
                    viewBox="0 0 15 15"
                  >
                    <defs>
                      <marker
                        id="dataflow-arrowhead-legend"
                        fill="#bbb"
                        markerWidth="10"
                        markerHeight="10"
                        refX="9"
                        refY="5"
                        orient="auto-start-reverse"
                      >
                        <path d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0"></path>
                      </marker>
                    </defs>
                    <path
                      marker-end="url(#dataflow-arrowhead-legend)"
                      stroke="#bbb"
                      d="M2 9 l 29 0"
                      stroke-linecap="round"
                    ></path>
                  </svg>
                </td>
                <td>
                  Dataflow edge
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Edge showing the data flow between operations. Edges flow
                      upwards unless arrowheads specify otherwise.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <svg
                    class="icon"
                    height="15px"
                    preserveAspectRatio="xMinYMid meet"
                    viewBox="0 0 15 15"
                  >
                    <path
                      stroke="#bbb"
                      d="M2 9 l 29 0"
                      stroke-linecap="round"
                      stroke-dasharray="2, 2"
                    ></path>
                  </svg>
                </td>
                <td>
                  Control dependency edge
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Edge showing the control dependency between operations.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
              <tr>
                <td>
                  <svg
                    class="icon"
                    height="15px"
                    preserveAspectRatio="xMinYMid meet"
                    viewBox="0 0 15 15"
                  >
                    <defs>
                      <marker
                        id="reference-arrowhead-legend"
                        fill="#FFB74D"
                        markerWidth="10"
                        markerHeight="10"
                        refX="9"
                        refY="5"
                        orient="auto-start-reverse"
                      >
                        <path d="M 0,0 L 10,5 L 0,10 C 3,7 3,3 0,0"></path>
                      </marker>
                    </defs>
                    <path
                      marker-end="url(#reference-arrowhead-legend)"
                      stroke="#FFB74D"
                      d="M2 9 l 29 0"
                      stroke-linecap="round"
                    ></path>
                  </svg>
                </td>
                <td>
                  Reference edge
                  <div class="legend-clarifier">
                    <span>?</span>
                    <paper-tooltip
                      animation-delay="0"
                      position="right"
                      offset="0"
                    >
                      Edge showing that the outgoing operation node can mutate
                      the incoming tensor.
                    </paper-tooltip>
                  </div>
                </td>
              </tr>
            </tbody>
          </table>
        </div>
      </iron-collapse>
    </div>
  `,t([o({type:Object,observer:"_statsChanged"}),e("design:type",Object)],sYt.prototype,"stats",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],sYt.prototype,"devicesForStats",void 0),t([o({type:String,notify:!0}),e("design:type",String)],sYt.prototype,"colorBy",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],sYt.prototype,"colorByParams",void 0),t([o({type:Array,observer:"_datasetsChanged"}),e("design:type",Object)],sYt.prototype,"datasets",void 0),t([o({type:Object}),e("design:type",yWt)],sYt.prototype,"renderHierarchy",void 0),t([o({type:Object,notify:!0,readOnly:!0,computed:"_computeSelection(datasets, _selectedRunIndex, _selectedTagIndex, _selectedGraphType)"}),e("design:type",Object)],sYt.prototype,"selection",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],sYt.prototype,"selectedFile",void 0),t([o({type:Number,observer:"_selectedRunIndexChanged"}),e("design:type",Number)],sYt.prototype,"_selectedRunIndex",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],sYt.prototype,"traceInputs",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],sYt.prototype,"autoExtractNodes",void 0),t([o({type:Number,observer:"_selectedTagIndexChanged"}),e("design:type",Number)],sYt.prototype,"_selectedTagIndex",void 0),t([o({type:String}),e("design:type",String)],sYt.prototype,"_selectedGraphType",void 0),t([o({type:String,notify:!0}),e("design:type",String)],sYt.prototype,"selectedNode",void 0),t([o({type:Boolean}),e("design:type",Boolean)],sYt.prototype,"showSessionRunsDropdown",void 0),t([o({type:Boolean}),e("design:type",Boolean)],sYt.prototype,"showUploadButton",void 0),t([o({type:Boolean}),e("design:type",Boolean)],sYt.prototype,"healthPillsFeatureEnabled",void 0),t([o({type:Boolean,notify:!0}),e("design:type",Boolean)],sYt.prototype,"healthPillsToggledOn",void 0),t([o({type:Boolean}),e("design:type",Boolean)],sYt.prototype,"_legendOpened",void 0),t([s("devicesForStats"),e("design:type",Array),e("design:paramtypes",[])],sYt.prototype,"_currentDevices",null),t([s("colorByParams"),e("design:type",Array),e("design:paramtypes",[])],sYt.prototype,"_currentDeviceParams",null),t([s("colorByParams"),e("design:type",Array),e("design:paramtypes",[])],sYt.prototype,"_currentXlaClusterParams",null),t([s("colorByParams","colorBy"),e("design:type",Object),e("design:paramtypes",[])],sYt.prototype,"_currentGradientParams",null),sYt=t([i("tf-graph-controls")],sYt);class lYt{isNotTpuOp(t){return-1!=t.toLowerCase().search("cpu:")||-1!=t.toLowerCase().search("gpu:")||-1==t.toLowerCase().search("tpu")}opValid(t){return 0==t.name.search(VGt)||!t.op||!(!t.device||!this.isNotTpuOp(t.device))||!(!t.device||-1==t.device.search("TPU_SYSTEM"))||Se.exports.includes(lYt.WHITELIST,t.op)}}function cYt(t){return new Promise(((e,n)=>{fetch(t).then((t=>{t.ok?t.arrayBuffer().then(e,n):t.text().then(n,n)}))}))}lYt.WHITELIST=["Abs","Acos","Acosh","Add","AddN","AddV2","AdjustContrastv2","AdjustHue","AdjustSaturation","All","AllToAll","Angle","Any","ApproximateEqual","ArgMax","ArgMin","Asin","Asinh","Assert","AssignAddVariableOp","AssignSubVariableOp","AssignVariableOp","Atan","Atan2","Atanh","AvgPool","AvgPool3D","AvgPool3DGrad","AvgPoolGrad","BatchMatMul","BatchMatMulV2","BatchToSpace","BatchToSpaceND","BesselI0e","BesselI1e","Betainc","BiasAdd","BiasAddGrad","BiasAddV1","Bitcast","BitwiseAnd","BitwiseOr","BitwiseXor","BroadcastArgs","BroadcastGradientArgs","BroadcastTo","Bucketize","Case","Cast","Ceil","CheckNumerics","Cholesky","ClipByValue","CollectivePermute","CollectiveReduceV2","Complex","ComplexAbs","Concat","ConcatOffset","ConcatV2","Conj","ConjugateTranspose","Const","ControlTrigger","Conv2D","Conv2DBackpropFilter","Conv2DBackpropInput","Conv3D","Conv3DBackpropFilterV2","Conv3DBackpropInputV2","Cos","Cosh","Cross","CrossReplicaSum","Cumprod","Cumsum","DataFormatDimMap","DataFormatVecPermute","DepthToSpace","DepthwiseConv2dNative","DepthwiseConv2dNativeBackpropFilter","DepthwiseConv2dNativeBackpropInput","Dequantize","DeviceIndex","Diag","DiagPart","Digamma","Div","DivNoNan","DynamicStitch","Einsum","Elu","EluGrad","Empty","EmptyTensorList","EnsureShape","Equal","Erf","Erfc","Erfinv","Exp","ExpandDims","Expm1","ExtractImagePatches","FFT","FFT2D","FFT3D","FakeParam","FakeQuantWithMinMaxArgs","FakeQuantWithMinMaxArgsGradient","FakeQuantWithMinMaxVars","FakeQuantWithMinMaxVarsGradient","Fill","Floor","FloorDiv","FloorMod","FusedBatchNorm","FusedBatchNormGrad","FusedBatchNormGradV2","FusedBatchNormGradV3","FusedBatchNormV2","FusedBatchNormV3","Gather","GatherNd","GatherV2","GetItem","Greater","GreaterEqual","HSVToRGB","IFFT","IFFT2D","IFFT3D","IRFFT","IRFFT2D","IRFFT3D","Identity","IdentityN","If","Igamma","IgammaGradA","Igammac","Imag","InTopKV2","InfeedDequeue","InfeedDequeueTuple","InplaceAdd","InplaceUpdate","Inv","Invert","InvertPermutation","IsFinite","IsInf","IsNan","KthOrderStatistic","L2Loss","LRN","LRNGrad","LeakyRelu","LeakyReluGrad","LeftShift","Less","LessEqual","Lgamma","LinSpace","ListDiff","Log","Log1p","LogSoftmax","LogicalAnd","LogicalNot","LogicalOr","LowerBound","MakeUnique","MatMul","MatrixBandPart","MatrixDiag","MatrixDiagPart","MatrixDiagPartV2","MatrixDiagPartV3","MatrixDiagV2","MatrixDiagV3","MatrixInverse","MatrixSetDiag","MatrixSetDiagV2","MatrixSetDiagV3","MatrixSolve","MatrixTriangularSolve","Max","MaxPool","MaxPool3D","MaxPool3DGrad","MaxPool3DGradGrad","MaxPoolGrad","MaxPoolGradGrad","MaxPoolGradGradV2","MaxPoolGradV2","MaxPoolV2","Maximum","Mean","Min","Minimum","MirrorPad","MirrorPadGrad","Mod","Mul","MulNoNan","Multinomial","Ndtri","Neg","NextAfter","NoOp","NonMaxSuppressionV4","NotEqual","OneHot","OnesLike","OutfeedEnqueue","OutfeedEnqueueTuple","Pack","Pad","PadV2","ParallelDynamicStitch","ParameterizedTruncatedNormal","PartitionedCall","PlaceholderWithDefault","Polygamma","PopulationCount","Pow","PreventGradient","Prod","Qr","QuantizeAndDequantizeV2","QuantizeAndDequantizeV3","RFFT","RFFT2D","RFFT3D","RGBToHSV","RandomGammaGrad","RandomShuffle","RandomStandardNormal","RandomUniform","RandomUniformInt","Range","Rank","ReadVariableOp","Real","RealDiv","Reciprocal","ReciprocalGrad","Relu","Relu6","Relu6Grad","ReluGrad","Reshape","ResizeBilinear","ResizeBilinearGrad","ResizeNearestNeighbor","ResizeNearestNeighborGrad","ResourceApplyAdaMax","ResourceApplyAdadelta","ResourceApplyAdagrad","ResourceApplyAdagradDA","ResourceApplyAdagradV2","ResourceApplyAdam","ResourceApplyAddSign","ResourceApplyCenteredRMSProp","ResourceApplyFtrl","ResourceApplyFtrlV2","ResourceApplyGradientDescent","ResourceApplyKerasMomentum","ResourceApplyMomentum","ResourceApplyPowerSign","ResourceApplyProximalAdagrad","ResourceApplyProximalGradientDescent","ResourceApplyRMSProp","ResourceGather","ResourceScatterAdd","ResourceScatterDiv","ResourceScatterMax","ResourceScatterMin","ResourceScatterMul","ResourceScatterNdAdd","ResourceScatterNdSub","ResourceScatterNdUpdate","ResourceScatterSub","ResourceScatterUpdate","ResourceStridedSliceAssign","Reverse","ReverseSequence","ReverseV2","RightShift","Rint","RngReadAndSkip","RngSkip","Roll","Round","Rsqrt","RsqrtGrad","ScatterNd","Select","SelectV2","SelfAdjointEigV2","Selu","SeluGrad","Shape","ShapeN","Sigmoid","SigmoidGrad","Sign","Sin","Sinh","Size","Slice","Snapshot","Softmax","SoftmaxCrossEntropyWithLogits","Softplus","SoftplusGrad","Softsign","SoftsignGrad","SpaceToBatch","SpaceToBatchND","SpaceToDepth","SparseMatMul","SparseSoftmaxCrossEntropyWithLogits","SparseToDense","Split","SplitV","Sqrt","SqrtGrad","Square","SquaredDifference","Squeeze","StackCloseV2","StackPopV2","StackPushV2","StackV2","StatefulPartitionedCall","StatefulStandardNormalV2","StatefulTruncatedNormal","StatefulUniform","StatefulUniformFullInt","StatefulUniformInt","StatelessCase","StatelessIf","State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this._requestManager.request(_r().pluginRoute("graphs","/info"))}_fetchHealthPills(t,e){const n={node_names:JSON.stringify(t),run:"__debugger_data__"};void 0!==e&&(n.step=e);const i=_r().pluginRoute("debugger","/health_pills");return this._requestManager.request(i,n)}_fetchDebuggerNumericsAlerts(){return this._requestManager.request(_r().pluginRoute("debugger","/numerics_alert_report"))}_graphUrl(t,e,n){return _r().pluginRoute("graphs","/graph",new URLSearchParams({run:t,limit_attr_size:e,large_attrs_key:n}))}_shouldRequestHealthPills(){return this._debuggerDataEnabled&&this.healthPillsToggledOn&&this._renderHierarchy&&this._datasetsState(this._datasetsFetched,this._datasets,"PRESENT")}_maybeInitializeDashboard(){!this._initialized&&this._isAttached&&(this.set("_compatibilityProvider",new lYt),this._initialized=!0,this._fetchDataset().then((t=>{const e=Object.keys(t);this._datasets=e.sort(nr).map((e=>{const n=t[e],i=Object.keys(n.tags).sort(nr).map((t=>n.tags[t])).map((({tag:t,conceptual_graph:e,op_graph:n,profile:i})=>({tag:t,displayName:t,conceptualGraph:e,opGraph:n,profile:i})));return{name:e,tags:n.run_graph?[{tag:null,displayName:"Default",conceptualGraph:!1,opGraph:!0,profile:!1},...i]:i}})),this._datasetsFetched=!0})))}_determineSelectedDataset(){var t=this._datasetsFetched,e=this._datasets,n=this.run;if(!n)return void this.set("_selectedDataset",0);const i=e.findIndex((t=>t.name===n));if(-1!==i)this.set("_selectedDataset",i);else if(t){const t=this.$$("#error-dialog");t.textContent=`No dataset named "${n}" could be found.`,t.open()}}_updateSelectedDatasetName(){var t=this._datasets,e=this._selectedDataset;this._datasetsFetched&&(t.length<=e||this.set("run",t[e].name))}_requestHealthPills(){this.set("_areHealthPillsLoading",!0);var 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          selection="[[_selection]]"
          selected-file="[[_selectedFile]]"
          out-graph-hierarchy="{{_graphHierarchy}}"
          out-graph="{{_graph}}"
          out-stats="{{_stats}}"
          progress="{{_progress}}"
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            <a href="https://www.tensorflow.org/tensorboard/graphs"
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            >.
          </p>

          <p>
            If you’re new to using TensorBoard, and want to find out how to add
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              >README</a
            >
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            <a
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    <div id="tooltip"><span></span></div>
    <svg id="svg">
      <g>
        <g class="axis x"></g>
        <g class="axis y"></g>
        <g class="axis y slice"></g>
        <g class="stage">
          <rect class="background"></rect>
        </g>
        <g class="x-axis-hover"></g>
        <g class="y-axis-hover"></g>
        <g class="y-slice-axis-hover"></g>
      </g>
    </svg>

    <style>
      :host {
        color: #aaa;
        display: flex;
        flex-direction: column;
        flex-grow: 1;
        flex-shrink: 1;
        position: relative;
        --vz-histogram-timeseries-hover-bg-color: #fff;
        --vz-histogram-timeseries-outline-color: #fff;
        --vz-histogram-timeseries-hover-outline-color: #000;
      }

      :host(.dark-mode) {
        --vz-histogram-timeseries-hover-bg-color: var(
          --primary-background-color
        );
        --vz-histogram-timeseries-outline-color: var(--paper-grey-600);
        --vz-histogram-timeseries-hover-outline-color: #fff;
      }

      svg {
        font-family: roboto, sans-serif;
        overflow: visible;
        display: block;
        width: 100%;
        flex-grow: 1;
        flex-shrink: 1;
      }

      text {
        fill: currentColor;
      }

      #tooltip {
        position: absolute;
        display: block;
        opacity: 0;
        font-weight: bold;
        font-size: 11px;
      }

      .background {
        fill-opacity: 0;
        fill: red;
      }

      .histogram {
        pointer-events: none;
      }

      .hover {
        font-size: 9px;
        dominant-baseline: middle;
        opacity: 0;
      }

      .hover circle {
        stroke: white;
        stroke-opacity: 0.5;
        stroke-width: 1px;
      }

      .hover text {
        fill: black;
        opacity: 0;
      }

      .hover.hover-closest circle {
        fill: var(--vz-histogram-timeseries-hover-outline-color) !important;
      }

      .hover.hover-closest text {
        opacity: 1;
      }

      .baseline {
        stroke: black;
        stroke-opacity: 0.1;
      }

      .outline {
        fill: none;
        stroke: var(--vz-histogram-timeseries-outline-color);
        stroke-opacity: 0.5;
      }

      .outline.outline-hover {
        stroke: var(--vz-histogram-timeseries-hover-outline-color) !important;
        stroke-opacity: 1;
      }

      .x-axis-hover,
      .y-axis-hover,
      .y-slice-axis-hover {
        pointer-events: none;
      }

      .x-axis-hover .label,
      .y-axis-hover .label,
      .y-slice-axis-hover .label {
        opacity: 0;
        font-weight: bold;
        font-size: 11px;
        text-anchor: end;
      }

      .x-axis-hover text {
        text-anchor: middle;
      }

      .y-axis-hover text,
      .y-slice-axis-hover text {
        text-anchor: start;
      }

      .x-axis-hover line,
      .y-axis-hover line,
      .y-slice-axis-hover line {
        stroke: currentColor;
      }

      .x-axis-hover rect,
      .y-axis-hover rect,
      .y-slice-axis-hover rect {
        fill: var(--vz-histogram-timeseries-hover-bg-color);
      }

      #tooltip,
      .x-axis-hover text,
      .y-axis-hover text,
      .y-slice-axis-hover text {
        color: var(--vz-histogram-timeseries-hover-outline-color);
      }

      .axis {
        font-size: 11px;
      }

      .axis path.domain {
        fill: none;
      }

      .axis .tick line {
        stroke: #ddd;
      }

      .axis.slice {
        opacity: 0;
      }

      .axis.slice .tick line {
        stroke-dasharray: 2;
      }

      .small .axis text {
        display: none;
      }
      .small .axis .tick:first-of-type text {
        display: block;
      }
      .small .axis .tick:last-of-type text {
        display: block;
      }
      .medium .axis text {
        display: none;
      }
      .medium .axis .tick:nth-child(2n + 1) text {
        display: block;
      }
      .large .axis text {
        display: none;
      }
      .large .axis .tick:nth-child(2n + 1) text {
        display: block;
      }
    </style>
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    <tf-card-heading
      tag="[[tag]]"
      run="[[run]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
      color="[[_runColor]]"
    ></tf-card-heading>
    <!--
      The main histogram that we render. Data is set directly with
      \`setSeriesData\`, not with a bound property.
    -->
    <vz-histogram-timeseries
      id="chart"
      time-property="[[timeProperty]]"
      mode="[[histogramMode]]"
      color-scale="[[_colorScaleFunction]]"
    ></vz-histogram-timeseries>
    <div style="display: flex; flex-direction: row;">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
    </div>
    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 330px;
        height: 235px;
        margin-right: 10px;
        margin-bottom: 15px;
      }
      :host([_expanded]) {
        width: 700px;
        height: 500px;
      }

      vz-histogram-timeseries {
        -moz-user-select: none;
        -webkit-user-select: none;
        will-change: transform;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }

      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      tf-card-heading {
        margin-bottom: 10px;
        width: 90%;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],vYt.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],vYt.prototype,"tag",void 0),t([o({type:Object}),e("design:type",Object)],vYt.prototype,"getDataLoadName",void 0),t([o({type:Object}),e("design:type",dr)],vYt.prototype,"requestManager",void 0),t([o({type:Object}),e("design:type",Object)],vYt.prototype,"loadDataCallback",void 0),t([o({type:Object}),e("design:type",Object)],vYt.prototype,"tagMetadata",void 0),t([o({type:String}),e("design:type",String)],vYt.prototype,"timeProperty",void 0),t([o({type:String}),e("design:type",String)],vYt.prototype,"histogramMode",void 0),t([o({type:Object}),e("design:type",Function)],vYt.prototype,"_colorScaleFunction",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],vYt.prototype,"_expanded",void 0),t([a("run","tag","requestManager"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],vYt.prototype,"_reloadOnRunTagRequestManagerChange",null),t([a("run","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],vYt.prototype,"_updateDataToLoad",null),t([s("run"),e("design:type",String),e("design:paramtypes",[])],vYt.prototype,"_runColor",null),vYt=t([i("tf-histogram-loader")],vYt);let bYt=class extends(er(ye)){constructor(){super(...arguments),this.reloadOnReady=!0,this._histogramMode="offset",this._timeProperty="step",this._restamp=!1,this._requestManager=new dr}_redrawCategoryPane(t,e){e&&t.target.querySelectorAll("tf-histogram-loader").forEach((t=>t.redraw()))}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){this._fetchTags().then((()=>{this._reloadHistograms()}))}_fetchTags(){const t=_r().pluginRoute("histograms","/tags");return this._requestManager.request(t).then((t=>{if(Se.exports.isEqual(t,this._runToTagInfo))return;const e=Se.exports.mapValues(t,(t=>Object.keys(t))),n=ar(e);this.set("_dataNotFound",0===n.length),this.set("_runToTag",e),this.set("_runToTagInfo",t),this.async((()=>{this.set("_categoriesDomReady",!0)}))}))}_reloadHistograms(){this.root.querySelectorAll("tf-histogram-loader").forEach((t=>{t.reload()}))}_shouldOpen(t){return t<=2}get _categories(){return Mr(this._runToTag,this._selectedRuns,this._tagFilter)}_tagMetadata(t,e,n){return t[e][n]}};function xYt(t){return""!==t.displayName&&void 0!==t.displayName?t.displayName:t.name}function wYt(t){if(""!==t.displayName&&void 0!==t.displayName)return t.displayName;let e=t.name.group,n=t.name.tag;return void 0===e&&(e=""),void 0===n&&(n=""),""===e?n:e+"."+n}function SYt(t,e){return e<t.hparamColumns.length?xYt(t.hparamColumns[e].hparamInfo):wYt(t.metricColumns[e-t.hparamColumns.length].metricInfo)}function MYt(t){return t.hparamColumns.length}function EYt(t){return t.metricColumns.length}function TYt(t){return MYt(t)+EYt(t)}function CYt(t,e){return t[e]}function AYt(t,e){return t.find((t=>Se.exports.isEqual(t.name,e)))}function kYt(t,e,n){return e.hparams[t.hparamColumns[n].hparamInfo.name]}function LYt(t,e,n){const i=AYt(e.metricValues,t.metricColumns[n].metricInfo.name);return void 0===i?void 0:i.value}function PYt(t,e,n){return n<t.hparamColumns.length?kYt(t,e,n):LYt(t,e,n-t.hparamColumns.length)}function NYt(t,e,n){return Ll(e,(e=>PYt(t,e,n)))}function IYt(t,e,n){let i;if(n<e.hparamInfos.length)i=t.hparamColumns.findIndex((t=>t.hparamInfo.name===e.hparamInfos[n].name));else{const r=e.metricInfos[n-e.hparamInfos.length].name;i=t.hparamColumns.length+t.metricColumns.findIndex((t=>t.metricInfo.name===r))}return console.assert(-1!==i),i}function RYt(t){return t.hparamInfos.length}function OYt(t){return t.metricInfos.length}function zYt(t,e,n){return Ll(e,(e=>FYt(t,e,n)))}function DYt(t,e){return t.find((t=>t.name===e))}function BYt(t,e,n){return e.hparams[t.hparamInfos[n].name]}function HYt(t,e,n){const i=AYt(e.metricValues,t.metricInfos[n].name);return void 0===i?void 0:i.value}function FYt(t,e,n){return n<t.hparamInfos.length?BYt(t,e,n):HYt(t,e,n-t.hparamInfos.length)}function VYt(t){return Se.exports.isNumber(t)?t.toPrecision(5):void 0===t?"":t.toString()}function UYt(t,e){return t*t+e*e}function jYt(t,e,n,i){return Math.sqrt(UYt(t-n,e-i))}function GYt(t,e,n,i,r,o){if(t<n&&e<i)return jYt(t,e,n,i);if(n<=t&&t<r&&e<i)return i-e;if(r<=t&&e<i)return jYt(t,e,r,i);if(t<n&&i<=e&&e<o)return n-t;if(n<=t&&t<r&&i<=e&&e<o)return 0;if(r<=t&&i<=e&&e<o)return t-r;if(t<n&&o<=e)return jYt(t,e,n,o);if(n<=t&&t<r&&o<=e)return e-o;if(r<=t&&o<=e)return jYt(t,e,r,o);throw"Point (x,y) must be in one of the regions defined above."}function WYt(t,e){return void 0===e?"translate("+t+")":"translate("+t+","+e+")"}function qYt(t,e,n){const i=t.get(e,t);Array.isArray(i)?t.splice.apply(t,[e,0,i.length].concat(n)):t.set(e,n)}function YYt(t){let e=0;for(let n=0;n<t.length;++n)e=31*e+t.charCodeAt(n)&4294967295;return e+Math.pow(2,31)}bYt.template=_e`
    <tf-dashboard-layout>
      <div slot="sidebar">
        <div class="settings">
          <div class="sidebar-section">
            <tf-option-selector
              id="histogramModeSelector"
              name="Histogram mode"
              selected-id="{{_histogramMode}}"
            >
              <paper-button id="overlay">overlay</paper-button>
              <paper-button id="offset">offset</paper-button>
            </tf-option-selector>
          </div>
          <div class="sidebar-section">
            <tf-option-selector
              id="timePropertySelector"
              name="Offset time axis"
              selected-id="{{_timeProperty}}"
            >
              <paper-button id="step">step</paper-button>
              <paper-button id="relative">relative</paper-button>
              <paper-button id="wall_time">wall</paper-button>
            </tf-option-selector>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No histogram data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any histogram data to your event files.
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
            >
              <template>
                <tf-histogram-loader
                  run="[[item.run]]"
                  tag="[[item.tag]]"
                  active="[[active]]"
                  tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]"
                  time-property="[[_timeProperty]]"
                  histogram-mode="[[_histogramMode]]"
                  request-manager="[[_requestManager]]"
                ></tf-histogram-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
    </style>
  `,t([o({type:Boolean}),e("design:type",Boolean)],bYt.prototype,"reloadOnReady",void 0),t([o({type:String}),e("design:type",String)],bYt.prototype,"_histogramMode",void 0),t([o({type:String}),e("design:type",String)],bYt.prototype,"_timeProperty",void 0),t([o({type:Array}),e("design:type",Array)],bYt.prototype,"_selectedRuns",void 0),t([o({type:Object}),e("design:type",Object)],bYt.prototype,"_runToTag",void 0),t([o({type:Object}),e("design:type",Object)],bYt.prototype,"_runToTagInfo",void 0),t([o({type:Boolean}),e("design:type",Boolean)],bYt.prototype,"_dataNotFound",void 0),t([o({type:String}),e("design:type",String)],bYt.prototype,"_tagFilter",void 0),t([o({type:Boolean}),e("design:type",Boolean)],bYt.prototype,"_restamp",void 0),t([o({type:Boolean}),e("design:type",Boolean)],bYt.prototype,"_categoriesDomReady",void 0),t([o({type:Object}),e("design:type",dr)],bYt.prototype,"_requestManager",void 0),t([s("_runToTag","_selectedRuns","_tagFilter","_categoriesDomReady"),e("design:type",Array),e("design:paramtypes",[])],bYt.prototype,"_categories",null),bYt=t([i("tf-histogram-dashboard")],bYt);var XYt=Object.freeze({__proto__:null,hparamName:xYt,metricName:wYt,schemaColumnName:SYt,numHParams:MYt,numMetrics:EYt,numColumns:TYt,hparamValueByName:CYt,metricValueByName:AYt,hparamValueByIndex:kYt,metricValueByIndex:LYt,columnValueByIndex:PYt,numericColumnExtent:NYt,getAbsoluteColumnIndex:IYt,schemaVisibleColumnName:function $Yt(t,e){return e<t.hparamInfos.length?xYt(t.hparamInfos[e]):wYt(t.metricInfos[e-t.hparamInfos.length])},numVisibleHParams:RYt,numVisibleMetrics:OYt,numVisibleColumns:function KYt(t){return RYt(t)+OYt(t)},visibleNumericColumnExtent:zYt,prettyPrintHParamValueByName:function ZYt(t,e){return VYt(CYt(t,e))},prettyPrintMetricValueByName:function JYt(t,e){return VYt(AYt(t,e))},sessionGroupWithName:DYt,hparamValueByVisibleIndex:BYt,metricValueByVisibleIndex:HYt,columnValueByVisibleIndex:FYt,prettyPrint:VYt,l2NormSquared:UYt,euclideanDist:jYt,pointToRectangleDist:GYt,translateStr:WYt,rotateStr:function QYt(t,e,n){let i="rotate("+t;return void 0!==e&&void 0!==n&&(i=i+","+e+","+n),i+=")",i},isNullOrUndefined:function tXt(t){return null==t},quadTreeVisitPointsInRect:function eXt(t,e,n,i,r,o){t.visit(((a,s,l,c,u)=>{if(void 0===a.length){do{const s=t.x()(a.data),l=t.y()(a.data);e<=s&&s<i&&n<=l&&l<r&&o(a.data)}while(a=a.next);return!0}return s>=i||c<=e||l>=r||u<=n}))},quadTreeVisitPointsInDisk:function nXt(t,e,n,i,r){t.visit(((o,a,s,l,c)=>{if(void 0===o.length){do{const a=t.x()(o.data),s=t.y()(o.data),l=jYt(e,n,a,s);l<=i&&r(o.data,l)}while(o=o.next);return!0}return GYt(e,n,a,s,l,c)>i}))},filterSet:function iXt(t,e){const n=new Set;return t.forEach((t=>{e(t)&&n.add(t)})),n},setArrayObservably:qYt,hashOfString:YYt});let rXt=class extends ye{constructor(){super(...arguments),this.orientation="horizontal"}};rXt.template=_e`
    <slot name="content"></slot>

    <style>
      :host {
        display: block;
      }

      :host slot {
        display: flex;
        height: 100%;
        width: 100%;
      }

      :host ::slotted(*) {
        flex: 0 0 auto;
      }

      :host([orientation='horizontal']) slot {
        flex-direction: row;
        overflow-x: auto;
      }

      :host([orientation='vertical']) slot {
        flex-direction: column;
        overflow-y: auto;
      }

      :host ::slotted(*:not(:last-child)) {
        border: 0 solid var(--divider-color, #ccc);
      }

      :host([orientation='vertical']) ::slotted(*:not(:last-child)) {
        border-bottom-width: 5px;
      }

      :host([orientation='horizontal']) ::slotted(*:not(:last-child)) {
        border-right-width: 5px;
      }
    </style>
  `,t([o({type:String,reflectToAttribute:!0}),e("design:type",String)],rXt.prototype,"orientation",void 0),rXt=t([i("hparams-split-layout")],rXt);let oXt=class extends(er(ye)){constructor(){super(...arguments),this.configuration={schema:{hparamColumns:[],metricColumns:[]},columnsVisibility:[],visibleSchema:{hparamInfos:[],metricInfos:[]}},this.sessionGroups=[],this.dataLoadedWithNonEmptyHparams=!1,this.dataLoadedWithEmptyHparams=!1,this._statuses=[{value:"STATUS_UNKNOWN",displayName:"Unknown",allowed:!0},{value:"STATUS_SUCCESS",displayName:"Success",allowed:!0},{value:"STATUS_FAILURE",displayName:"Failure",allowed:!0},{value:"STATUS_RUNNING",displayName:"Running",allowed:!0}],this._getExperimentResolved=new Promise((t=>{this._resolveGetExperiment=t})),this._listSessionGroupsCanceller=new XR,this._pageSizeInput={value:"100",invalid:!1},this._pageNumberInput={value:"1",invalid:!1},this._pageCountStr="?",this._hparamName=xYt,this._metricName=wYt,this._prettyPrint=VYt}reload(){this._queryServer()}_csvUrl(t,e){return this._downloadDataUrl(t,e,"csv")}_jsonUrl(t,e){return this._downloadDataUrl(t,e,"json")}_latexUrl(t,e){return this._downloadDataUrl(t,e,"latex")}_downloadDataUrl(t,e,n){return this.backend.getDownloadUrl(n,t,e.columnsVisibility)}_computeExperimentAndRelatedProps(){const t=XYt;t.isNullOrUndefined(this.backend)||t.isNullOrUndefined(this.experimentName)||this.backend.getExperiment({experimentName:this.experimentName}).then((t=>{Se.exports.isEqual(t,this._experiment)||(this.set("_experiment",t),this._computeHParams(),this._computeMetrics(),this._queryServer(),this._resolveGetExperiment())})).finally((()=>{this._computeDataFound()}))}_computeDataFound(){const t=Boolean(this._experiment&&this._experiment.hparamInfos&&this._experiment.hparamInfos.length>0&&this._experiment.metricInfos&&this._experiment.metricInfos.length>0);this.set("dataLoadedWithNonEmptyHparams",t),this.set("dataLoadedWithEmptyHparams",!t)}_computeHParams(){const t=[];this._experiment.hparamInfos.forEach(((e,n)=>{const i={info:e,displayed:n<5,filter:{}};i.info.hasOwnProperty("domainDiscrete")?(i.filter.domainDiscrete=[],i.info.domainDiscrete.forEach((t=>{i.filter.domainDiscrete.push({value:t,checked:!0})}))):"DATA_TYPE_BOOL"===i.info.type?i.filter.domainDiscrete=[{value:!1,checked:!0},{value:!0,checked:!0}]:"DATA_TYPE_FLOAT64"===i.info.type?i.filter.interval={min:{value:"",invalid:!1},max:{value:"",invalid:!1}}:"DATA_TYPE_STRING"===i.info.type?i.filter.regexp="":console.warn("unknown hparam.info.type: %s",i.info.type),t.push(i)})),this.set("_hparams",t)}_computeMetrics(){const t=[];this._experiment.metricInfos.forEach(((e,n)=>{t.push({info:e,filter:{interval:{min:{value:"",invalid:!1},max:{value:"",invalid:!1}}},displayed:n<5})})),this.set("_metrics",t)}_computeSchema(){return this._hparams&&this._metrics?{hparamColumns:this._hparams.map((t=>({hparamInfo:t.info}))),metricColumns:this._metrics.map((t=>({metricInfo:t.info})))}:{hparamColumns:[],metricColumns:[]}}_updateConfiguration(){this.debounce("_updateConfiguration",(()=>{this.configuration={schema:this._computeSchema(),columnsVisibility:this._computeColumnsVisibility(),visibleSchema:this._computeVisibleSchema()}}))}_computeColumnsVisibility(){return this._hparams&&this._metrics?this._hparams.map((t=>t.displayed)).concat(this._metrics.map((t=>t.displayed))):[]}_computeVisibleSchema(){return this._hparams&&this._metrics?{hparamInfos:this._hparams.filter((t=>t.displayed)).map((t=>t.info)),metricInfos:this._metrics.filter((t=>t.displayed)).map((t=>t.info))}:{hparamInfos:[],metricInfos:[]}}_queryServer(){this.debounce("queryServer",(()=>this._queryServerNoDebounce()),100)}_queryServerNoDebounce(){if(this._hparams&&this._metrics)return this._sendListSessionGroupsRequest().then(this._listSessionGroupsCanceller.cancellable((({value:t,cancelled:e})=>{e||(t.totalSize>=0?(this.set("_pageCountStr",String(Math.ceil(t.totalSize/+this._pageSizeInput.value))),this.set("_totalSessionGroupsCountStr",t.totalSize)):(this.set("_pageCountStr","?"),this.set("_totalSessionGroupsCountStr","Unknown")),qYt(this,"sessionGroups",t.sessionGroups))})))}_sendListSessionGroupsRequest(){const t=this._buildListSessionGroupsRequest();if(null!==t)return this.set("_sessionGroupsRequest",t),this._listSessionGroupsCanceller.cancelAll(),this.backend.listSessionGroups(t)}_buildListSessionGroupsRequest(){const t=this;let e=!0;function n(n){const i=t.get(n+".min.value");console.assert(void 0!==i);const r=""===i?"-Infinity":+i;t.set(n+".min.invalid",isNaN(r)),e=e&&!isNaN(r);const o=t.get(n+".max.value");console.assert(void 0!==o);const a=""===o?"Infinity":+o;return t.set(n+".max.invalid",isNaN(a)),e=e&&!isNaN(a),isNaN(r)||isNaN(a)?null:{minValue:r,maxValue:a}}function i(n){const i=t.get(n+".value");console.assert(void 0!==i);const r=+i,o=Number.isInteger(r)&&r>0;return t.set(n+".invalid",!o),e=e&&o,o?r:null}const r=this._statuses.filter((t=>t.allowed)).map((t=>t.value));let o=[];if(this._hparams.forEach(((t,e)=>{let i={hparam:t.info.name};if(t.filter.domainDiscrete)i.filterDiscrete=[],t.filter.domainDiscrete.forEach((t=>{t.checked&&i.filterDiscrete.push(t.value)}));else if(t.filter.interval)i.filterInterval=n("_hparams."+e+".filter.interval");else{if(!t.filter.regexp)return console.error("hparam.filter with no domainDiscrete, interval or regexp properties set: %s",t),null;i.filterRegexp=t.filter.regexp}o.push(i)})),this._metrics.forEach(((t,e)=>{let i={metric:t.info.name,filterInterval:n("_metrics."+e+".filter.interval")};o.push(i)})),void 0!==this._sortByIndex&&void 0!==this._sortDirection){if(!(this._sortByIndex in o))return console.error("No column in colParams with index sortByIndex: %s",this._sortByIndex),null;o[this._sortByIndex].order=0===this._sortDirection?"ORDER_ASC":"ORDER_DESC"}const a=i("_pageNumberInput"),s=i("_pageSizeInput");return e?{experimentName:this.experimentName,allowedStatuses:r,colParams:o,startIndex:s*(a-1),sliceSize:s}:null}_metricSortByIndex(t){return t+this._hparams.length}};oXt.template=_e`
    <hparams-split-layout orientation="vertical">
      <div slot="content" class="section hyperparameters">
        <div class="section-title">Hyperparameters</div>
        <template is="dom-repeat" items="{{_hparams}}" as="hparam">
          <div class="hparam">
            <paper-checkbox
              checked="{{hparam.displayed}}"
              class="hparam-checkbox"
            >
              [[_hparamName(hparam.info)]]
            </paper-checkbox>
            <!-- Precisely one of the templates below will be stamped.-->
            <!-- 1. A list of checkboxes -->
            <template is="dom-if" if="[[hparam.filter.domainDiscrete]]">
              <template
                is="dom-repeat"
                items="[[hparam.filter.domainDiscrete]]"
              >
                <paper-checkbox
                  checked="{{item.checked}}"
                  class="discrete-value-checkbox"
                  on-change="_queryServer"
                >
                  [[_prettyPrint(item.value)]]
                </paper-checkbox>
              </template>
            </template>
            <!-- 2. A numeric interval -->
            <template is="dom-if" if="[[hparam.filter.interval]]">
              <paper-input
                label="Min"
                value="{{hparam.filter.interval.min.value}}"
                allowed_pattern="[0-9.e\\-]"
                on-value-changed="_queryServer"
                error-message="Invalid input"
                invalid="[[hparam.filter.interval.min.invalid]]"
                placeholder="-infinity"
              >
              </paper-input>
              <paper-input
                label="Max"
                value="{{hparam.filter.interval.max.value}}"
                allowed_pattern="[0-9.e\\-]"
                on-value-changed="_queryServer"
                error-message="Invalid input"
                invalid="[[hparam.filter.interval.max.invalid]]"
                placeholder="+infinity"
              >
              </paper-input>
            </template>
            <!-- 3. A regexp -->
            <template is="dom-if" if="[[hparam.filter.regexp]]">
              <paper-input
                label="Regular expression"
                value="{{hparam.filter.regexp}}"
                on-value-changed="_queryServer"
              >
              </paper-input>
            </template>
          </div>
        </template>
      </div>
      <div slot="content" class="section metrics">
        <div class="section-title">Metrics</div>
        <template is="dom-repeat" items="{{_metrics}}" as="metric">
          <div class="metric">
            <!-- TODO(erez): Make it easier to handle a large number of
                  metrics:
                  1. Add an 'isolator' radio-button to select just one
                  metric and
                  hide all the rest
                  2. Add a 'toggle-all' button that will hide/unhide
                    all the
                  metrics.
                  Use similar logic/appearance to the run-selector of
                  scalars.-->
            <paper-checkbox
              checked="{{metric.displayed}}"
              class="metric-checkbox"
            >
              [[_metricName(metric.info)]]
            </paper-checkbox>
            <div class="inline-element">
              <paper-input
                label="Min"
                value="{{metric.filter.interval.min.value}}"
                allowed-pattern="[0-9.e\\-]"
                on-value-changed="_queryServer"
                error-message="Invalid input"
                invalid="{{metric.filter.interval.min.invalid}}"
                placeholder="-infinity"
              >
              </paper-input>
            </div>
            <div class="inline-element">
              <paper-input
                label="Max"
                allowed-pattern="[0-9.e\\-]"
                value="{{metric.filter.interval.max.value}}"
                on-value-changed="_queryServer"
                error-message="Invalid input"
                invalid="{{metric.filter.interval.max.invalid}}"
                placeholder="+infinity"
              >
              </paper-input>
            </div>
          </div>
        </template>
      </div>
      <div slot="content" class="section status">
        <div class="section-title">Status</div>
        <template is="dom-repeat" items="[[_statuses]]" as="status">
          <paper-checkbox checked="{{status.allowed}}" on-change="_queryServer">
            [[status.displayName]]
          </paper-checkbox>
        </template>
      </div>
      <div slot="content" class="section sorting">
        <div class="section-title">Sorting</div>
        <paper-dropdown-menu
          label="Sort by"
          on-selected-item-changed="_queryServer"
          horizontal-align="left"
        >
          <paper-listbox
            class="dropdown-content"
            slot="dropdown-content"
            selected="{{_sortByIndex}}"
            on-selected-item-changed="_queryServer"
          >
            <template is="dom-repeat" items="[[_hparams]]" as="hparam">
              <paper-item> [[_hparamName(hparam.info)]] </paper-item>
            </template>
            <template is="dom-repeat" items="[[_metrics]]" as="metric">
              <paper-item> [[_metricName(metric.info)]] </paper-item>
            </template>
          </paper-listbox>
        </paper-dropdown-menu>
        <paper-dropdown-menu
          label="Direction"
          on-selected-item-changed="_queryServer"
          horizontal-align="left"
        >
          <paper-listbox
            class="dropdown-content"
            slot="dropdown-content"
            selected="{{_sortDirection}}"
          >
            <paper-item>Ascending</paper-item>
            <paper-item>Descending</paper-item>
          </paper-listbox>
        </paper-dropdown-menu>
      </div>
      <div slot="content" class="section paging">
        <div class="section-title">Paging</div>
        <div>
          Number of matching session groups: [[_totalSessionGroupsCountStr]]
        </div>
        <div class="inline-element page-number-input">
          <paper-input
            label="Page #"
            value="{{_pageNumberInput.value}}"
            allowed-pattern="[0-9]"
            error-message="Invalid input"
            invalid="[[_pageNumberInput.invalid]]"
            on-value-changed="_queryServer"
          >
            <div slot="suffix" class="page-suffix">/ [[_pageCountStr]]</div>
          </paper-input>
        </div>
        <div class="inline-element page-size-input">
          <paper-input
            label="Max # of session groups per page:"
            value="{{_pageSizeInput.value}}"
            allowed-pattern="[0-9]"
            error-message="Invalid input"
            invalid="[[_pageSizeInput.invalid]]"
            on-value-changed="_queryServer"
          >
          </paper-input>
        </div>
      </div>
      <div slot="content" class="section download">
        <template is="dom-if" if="[[_sessionGroupsRequest]]">
          Download data as
          <span>
            <a
              id="csvLink"
              download="hparams_table.csv"
              href="[[_csvUrl(_sessionGroupsRequest, configuration)]]"
              >CSV</a
            >
            <a
              id="jsonLink"
              download="hparams_table.json"
              href="[[_jsonUrl(_sessionGroupsRequest, configuration)]]"
              >JSON</a
            >
            <a
              id="latexLink"
              download="hparams_table.tex"
              href="[[_latexUrl(_sessionGroupsRequest, configuration)]]"
              >LaTeX</a
            >
          </span>
        </template>
      </div>
    </hparams-split-layout>
    <style>
      .section {
        padding: 10px;
      }
      .section-title {
        display: block;
        font-weight: bold;
        text-decoration: underline;
        margin-bottom: 7px;
      }
      .discrete-value-checkbox,
      .metric-checkbox,
      .hparam-checkbox {
        display: block;
      }
      .discrete-value-checkbox {
        margin-left: 20px;
      }
      .hparam,
      .metric {
        display: block;
      }
      .inline-element {
        display: inline-block;
        width: 40%;
        margin-left: 10px;
      }
      .page-number-input {
        width: 20%;
      }
      .page-size-input {
        width: 60%;
      }
      vaadin-split-layout {
        height: 100%;
      }
      paper-listbox {
        max-height: 15em;
      }
      .page-suffix {
        white-space: nowrap;
      }
    </style>
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    <div class="control-panel">
      <!-- 'Color by' drop down menu -->
      <paper-dropdown-menu
        label="Color by"
        id="colorByDropDownMenu"
        horizontal-align="left"
      >
        <paper-listbox
          class="dropdown-content"
          slot="dropdown-content"
          selected="{{options.colorByColumnIndex}}"
          id="colorByListBox"
        >
          <template
            is="dom-repeat"
            items="[[options.columns]]"
            as="column"
            id="colorByColumnTemplate"
          >
            <paper-item disabled="[[!_isNumericColumn(column.index)]]">
              [[column.name]]
            </paper-item>
          </template>
        </paper-listbox>
      </paper-dropdown-menu>

      <!-- Columns scales -->
      <div class="columns-container">
        <!-- Scale options for each numeric feature -->
        <template is="dom-repeat" items="{{options.columns}}" as="column">
          <template is="dom-if" if="[[_isNumericColumn(column.index)]]">
            <div class="column">
              <div class="column-title">[[column.name]]</div>
              <div>
                <paper-radio-group
                  class="scale-radio-group"
                  selected="{{column.scale}}"
                >
                  <paper-radio-button name="LINEAR">
                    Linear
                  </paper-radio-button>
                  <!-- The id here is used to access this button in unit
                       tests.-->
                  <paper-radio-button
                    id="logScaleButton_[[column.name]]"
                    name="LOG"
                    disabled="[[!_allowLogScale(column, sessionGroups.*)]]"
                  >
                    Logarithmic
                  </paper-radio-button>
                  <paper-radio-button name="QUANTILE">
                    Quantile
                  </paper-radio-button>
                </paper-radio-group>
              </div>
            </div>
          </template>
        </template>
      </div>
    </div>

    <style>
      :host {
        display: block;
      }
      .control-panel {
        overflow: auto;
      }
      .column {
        flex-grow: 1;
        flex-shrink: 1;
        margin-right: 5px;
        border: solid 1px darkgray;
        padding: 3px;
      }
      .column-title {
        /* Fit every title in one line so the radio boxes align vertically. */
        white-space: nowrap;
        text-decoration: underline;
      }
      .columns-container {
        display: flex;
        flex-direction: row;
      }
      .scale-radio-group paper-radio-button {
        padding: 2px;
        display: block;
      }
      paper-listbox {
        max-height: 15em;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],aXt.prototype,"configuration",void 0),t([o({type:Array}),e("design:type",Array)],aXt.prototype,"sessionGroups",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],aXt.prototype,"options",void 0),t([a("configuration.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],aXt.prototype,"_configurationChanged",null),t([a("sessionGroups.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],aXt.prototype,"_unselectDisabledLogScales",null),aXt=t([i("tf-hparams-scale-and-color-controls")],aXt);
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
const sXt="undefined"!=typeof window&&null!=window.customElements&&void 0!==window.customElements.polyfillWrapFlushCallback,lXt=(t,e,n=null)=>{for(;e!==n;){const n=e.nextSibling;t.removeChild(e),e=n}},cXt=`{{lit-${String(Math.random()).slice(2)}}}`,uXt=`\x3c!--${cXt}--\x3e`,hXt=new RegExp(`${cXt}|${uXt}`),dXt="$lit$";class pXt{constructor(t,e){this.parts=[],this.element=e;const n=[],i=[],r=document.createTreeWalker(e.content,133,null,!1);let o=0,a=-1,s=0;const{strings:l,values:{length:c}}=t;for(;s<c;){const t=r.nextNode();if(null!==t){if(a++,1===t.nodeType){if(t.hasAttributes()){const e=t.attributes,{length:n}=e;let i=0;for(let t=0;t<n;t++)fXt(e[t].name,dXt)&&i++;for(;i-- >0;){const e=_Xt.exec(l[s])[2],n=e.toLowerCase()+dXt,i=t.getAttribute(n);t.removeAttribute(n);const r=i.split(hXt);this.parts.push({type:"attribute",index:a,name:e,strings:r}),s+=r.length-1}}"TEMPLATE"===t.tagName&&(i.push(t),r.currentNode=t.content)}else if(3===t.nodeType){const e=t.data;if(e.indexOf(cXt)>=0){const i=t.parentNode,r=e.split(hXt),o=r.length-1;for(let e=0;e<o;e++){let n,o=r[e];if(""===o)n=gXt();else{const t=_Xt.exec(o);null!==t&&fXt(t[2],dXt)&&(o=o.slice(0,t.index)+t[1]+t[2].slice(0,-dXt.length)+t[3]),n=document.createTextNode(o)}i.insertBefore(n,t),this.parts.push({type:"node",index:++a})}""===r[o]?(i.insertBefore(gXt(),t),n.push(t)):t.data=r[o],s+=o}}else if(8===t.nodeType)if(t.data===cXt){const e=t.parentNode;null!==t.previousSibling&&a!==o||(a++,e.insertBefore(gXt(),t)),o=a,this.parts.push({type:"node",index:a}),null===t.nextSibling?t.data="":(n.push(t),a--),s++}else{let e=-1;for(;-1!==(e=t.data.indexOf(cXt,e+1));)this.parts.push({type:"node",index:-1}),s++}}else r.currentNode=i.pop()}for(const t of n)t.parentNode.removeChild(t)}}const fXt=(t,e)=>{const n=t.length-e.length;return n>=0&&t.slice(n)===e},mXt=t=>-1!==t.index,gXt=()=>document.createComment(""),_Xt=/([ \x09\x0a\x0c\x0d])([^\0-\x1F\x7F-\x9F "'>=/]+)([ \x09\x0a\x0c\x0d]*=[ \x09\x0a\x0c\x0d]*(?:[^ \x09\x0a\x0c\x0d"'`<>=]*|"[^"]*|'[^']*))$/;function yXt(t,e){const{element:{content:n},parts:i}=t,r=document.createTreeWalker(n,133,null,!1);let o=bXt(i),a=i[o],s=-1,l=0;const c=[];let u=null;for(;r.nextNode();){s++;const t=r.currentNode;for(t.previousSibling===u&&(u=null),e.has(t)&&(c.push(t),null===u&&(u=t)),null!==u&&l++;void 0!==a&&a.index===s;)a.index=null!==u?-1:a.index-l,o=bXt(i,o),a=i[o]}c.forEach((t=>t.parentNode.removeChild(t)))}const vXt=t=>{let e=11===t.nodeType?0:1;const n=document.createTreeWalker(t,133,null,!1);for(;n.nextNode();)e++;return e},bXt=(t,e=-1)=>{for(let n=e+1;n<t.length;n++)if(mXt(t[n]))return n;return-1},xXt=new WeakMap,wXt={},SXt={};
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
class MXt{constructor(t,e,n){this.__parts=[],this.template=t,this.processor=e,this.options=n}update(t){let e=0;for(const n of this.__parts)void 0!==n&&n.setValue(t[e]),e++;for(const t of this.__parts)void 0!==t&&t.commit()}_clone(){const t=sXt?this.template.element.content.cloneNode(!0):document.importNode(this.template.element.content,!0),e=[],n=this.template.parts,i=document.createTreeWalker(t,133,null,!1);let r,o=0,a=0,s=i.nextNode();for(;o<n.length;)if(r=n[o],mXt(r)){for(;a<r.index;)a++,"TEMPLATE"===s.nodeName&&(e.push(s),i.currentNode=s.content),null===(s=i.nextNode())&&(i.currentNode=e.pop(),s=i.nextNode());if("node"===r.type){const t=this.processor.handleTextExpression(this.options);t.insertAfterNode(s.previousSibling),this.__parts.push(t)}else this.__parts.push(...this.processor.handleAttributeExpressions(s,r.name,r.strings,this.options));o++}else this.__parts.push(void 0),o++;return sXt&&(document.adoptNode(t),customElements.upgrade(t)),t}}
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */const EXt=window.trustedTypes&&trustedTypes.createPolicy("lit-html",{createHTML:t=>t}),TXt=` ${cXt} `;class CXt{constructor(t){this.value=void 0,this.__pendingValue=void 0,this.options=t}appendInto(t){this.startNode=t.appendChild(gXt()),this.endNode=t.appendChild(gXt())}insertAfterNode(t){this.startNode=t,this.endNode=t.nextSibling}appendIntoPart(t){t.__insert(this.startNode=gXt()),t.__insert(this.endNode=gXt())}insertAfterPart(t){t.__insert(this.startNode=gXt()),this.endNode=t.endNode,t.endNode=this.startNode}setValue(t){this.__pendingValue=t}commit(){if(null===this.startNode.parentNode)return;for(;"function"==typeof(t=this.__pendingValue)&&xXt.has(t);){const t=this.__pendingValue;this.__pendingValue=wXt,t(this)}var t;const e=this.__pendingValue;e!==wXt&&((t=>null===t||!("object"==typeof t||"function"==typeof t))(e)?e!==this.value&&this.__commitText(e):e instanceof class{constructor(t,e,n,i){this.strings=t,this.values=e,this.type=n,this.processor=i}getHTML(){const t=this.strings.length-1;let e="",n=!1;for(let i=0;i<t;i++){const t=this.strings[i],r=t.lastIndexOf("\x3c!--");n=(r>-1||n)&&-1===t.indexOf("--\x3e",r+1);const o=_Xt.exec(t);e+=null===o?t+(n?TXt:uXt):t.substr(0,o.index)+o[1]+o[2]+dXt+o[3]+cXt}return e+=this.strings[t],e}getTemplateElement(){const t=document.createElement("template");let e=this.getHTML();return void 0!==EXt&&(e=EXt.createHTML(e)),t.innerHTML=e,t}}
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */?this.__commitTemplateResult(e):e instanceof Node?this.__commitNode(e):(t=>Array.isArray(t)||!(!t||!t[Symbol.iterator]))(e)?this.__commitIterable(e):e===SXt?(this.value=SXt,this.clear()):this.__commitText(e))}__insert(t){this.endNode.parentNode.insertBefore(t,this.endNode)}__commitNode(t){this.value!==t&&(this.clear(),this.__insert(t),this.value=t)}__commitText(t){const e=this.startNode.nextSibling,n="string"==typeof(t=null==t?"":t)?t:String(t);e===this.endNode.previousSibling&&3===e.nodeType?e.data=n:this.__commitNode(document.createTextNode(n)),this.value=t}__commitTemplateResult(t){const e=this.options.templateFactory(t);if(this.value instanceof MXt&&this.value.template===e)this.value.update(t.values);else{const n=new MXt(e,t.processor,this.options),i=n._clone();n.update(t.values),this.__commitNode(i),this.value=n}}__commitIterable(t){Array.isArray(this.value)||(this.value=[],this.clear());const e=this.value;let n,i=0;for(const r of t)n=e[i],void 0===n&&(n=new CXt(this.options),e.push(n),0===i?n.appendIntoPart(this):n.insertAfterPart(e[i-1])),n.setValue(r),n.commit(),i++;i<e.length&&(e.length=i,this.clear(n&&n.endNode))}clear(t=this.startNode){lXt(this.startNode.parentNode,t.nextSibling,this.endNode)}}let AXt=!1;
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
function kXt(t){let e=LXt.get(t.type);void 0===e&&(e={stringsArray:new WeakMap,keyString:new Map},LXt.set(t.type,e));let n=e.stringsArray.get(t.strings);if(void 0!==n)return n;const i=t.strings.join(cXt);return n=e.keyString.get(i),void 0===n&&(n=new pXt(t,t.getTemplateElement()),e.keyString.set(i,n)),e.stringsArray.set(t.strings,n),n}(()=>{try{const t={get capture(){return AXt=!0,!1}};window.addEventListener("test",t,t),window.removeEventListener("test",t,t)}catch(t){}})();const LXt=new Map,PXt=new WeakMap;
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
"undefined"!=typeof window&&(window.litHtmlVersions||(window.litHtmlVersions=[])).push("1.4.1");
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */const NXt=(t,e)=>`${t}--${e}`;let IXt=!0;void 0===window.ShadyCSS?IXt=!1:void 0===window.ShadyCSS.prepareTemplateDom&&(console.warn("Incompatible ShadyCSS version detected. Please update to at least @webcomponents/webcomponentsjs@2.0.2 and @webcomponents/shadycss@1.3.1."),IXt=!1);const RXt=t=>e=>{const n=NXt(e.type,t);let i=LXt.get(n);void 0===i&&(i={stringsArray:new WeakMap,keyString:new Map},LXt.set(n,i));let r=i.stringsArray.get(e.strings);if(void 0!==r)return r;const o=e.strings.join(cXt);if(r=i.keyString.get(o),void 0===r){const n=e.getTemplateElement();IXt&&window.ShadyCSS.prepareTemplateDom(n,t),r=new pXt(e,n),i.keyString.set(o,r)}return i.stringsArray.set(e.strings,r),r},OXt=["html","svg"],zXt=new Set;window.JSCompiler_renameProperty=(t,e)=>t;const DXt={toAttribute(t,e){switch(e){case Boolean:return t?"":null;case Object:case Array:return null==t?t:JSON.stringify(t)}return t},fromAttribute(t,e){switch(e){case Boolean:return null!==t;case Number:return null===t?null:Number(t);case Object:case Array:return JSON.parse(t)}return t}},BXt=(t,e)=>e!==t&&(e==e||t==t),HXt={attribute:!0,type:String,converter:DXt,reflect:!1,hasChanged:BXt};class FXt extends HTMLElement{constructor(){super(),this.initialize()}static get observedAttributes(){this.finalize();const t=[];return this._classProperties.forEach(((e,n)=>{const i=this._attributeNameForProperty(n,e);void 0!==i&&(this._attributeToPropertyMap.set(i,n),t.push(i))})),t}static _ensureClassProperties(){if(!this.hasOwnProperty(JSCompiler_renameProperty("_classProperties",this))){this._classProperties=new Map;const t=Object.getPrototypeOf(this)._classProperties;void 0!==t&&t.forEach(((t,e)=>this._classProperties.set(e,t)))}}static createProperty(t,e=HXt){if(this._ensureClassProperties(),this._classProperties.set(t,e),e.noAccessor||this.prototype.hasOwnProperty(t))return;const n="symbol"==typeof t?Symbol():`__${t}`,i=this.getPropertyDescriptor(t,n,e);void 0!==i&&Object.defineProperty(this.prototype,t,i)}static getPropertyDescriptor(t,e,n){return{get(){return this[e]},set(i){const r=this[t];this[e]=i,this.requestUpdateInternal(t,r,n)},configurable:!0,enumerable:!0}}static getPropertyOptions(t){return this._classProperties&&this._classProperties.get(t)||HXt}static finalize(){const t=Object.getPrototypeOf(this);if(t.hasOwnProperty("finalized")||t.finalize(),this.finalized=!0,this._ensureClassProperties(),this._attributeToPropertyMap=new Map,this.hasOwnProperty(JSCompiler_renameProperty("properties",this))){const t=this.properties,e=[...Object.getOwnPropertyNames(t),..."function"==typeof Object.getOwnPropertySymbols?Object.getOwnPropertySymbols(t):[]];for(const n of e)this.createProperty(n,t[n])}}static _attributeNameForProperty(t,e){const n=e.attribute;return!1===n?void 0:"string"==typeof n?n:"string"==typeof t?t.toLowerCase():void 0}static _valueHasChanged(t,e,n=BXt){return n(t,e)}static _propertyValueFromAttribute(t,e){const n=e.converter||DXt,i="function"==typeof n?n:n.fromAttribute;return i?i(t,e.type):t}static _propertyValueToAttribute(t,e){if(void 0===e.reflect)return;const n=e.converter;return(n&&n.toAttribute||DXt.toAttribute)(t,e.type)}initialize(){this._updateState=0,this._updatePromise=new Promise((t=>this._enableUpdatingResolver=t)),this._changedProperties=new Map,this._saveInstanceProperties(),this.requestUpdateInternal()}_saveInstanceProperties(){this.constructor._classProperties.forEach(((t,e)=>{if(this.hasOwnProperty(e)){const t=this[e];delete this[e],this._instanceProperties||(this._instanceProperties=new Map),this._instanceProperties.set(e,t)}}))}_applyInstanceProperties(){this._instanceProperties.forEach(((t,e)=>this[e]=t)),this._instanceProperties=void 0}connectedCallback(){this.enableUpdating()}enableUpdating(){void 0!==this._enableUpdatingResolver&&(this._enableUpdatingResolver(),this._enableUpdatingResolver=void 0)}disconnectedCallback(){}attributeChangedCallback(t,e,n){e!==n&&this._attributeToProperty(t,n)}_propertyToAttribute(t,e,n=HXt){const i=this.constructor,r=i._attributeNameForProperty(t,n);if(void 0!==r){const t=i._propertyValueToAttribute(e,n);if(void 0===t)return;this._updateState=8|this._updateState,null==t?this.removeAttribute(r):this.setAttribute(r,t),this._updateState=-9&this._updateState}}_attributeToProperty(t,e){if(8&this._updateState)return;const n=this.constructor,i=n._attributeToPropertyMap.get(t);if(void 0!==i){const t=n.getPropertyOptions(i);this._updateState=16|this._updateState,this[i]=n._propertyValueFromAttribute(e,t),this._updateState=-17&this._updateState}}requestUpdateInternal(t,e,n){let i=!0;if(void 0!==t){const r=this.constructor;n=n||r.getPropertyOptions(t),r._valueHasChanged(this[t],e,n.hasChanged)?(this._changedProperties.has(t)||this._changedProperties.set(t,e),!0!==n.reflect||16&this._updateState||(void 0===this._reflectingProperties&&(this._reflectingProperties=new Map),this._reflectingProperties.set(t,n))):i=!1}!this._hasRequestedUpdate&&i&&(this._updatePromise=this._enqueueUpdate())}requestUpdate(t,e){return this.requestUpdateInternal(t,e),this.updateComplete}async _enqueueUpdate(){this._updateState=4|this._updateState;try{await this._updatePromise}catch(t){}const t=this.performUpdate();return null!=t&&await t,!this._hasRequestedUpdate}get _hasRequestedUpdate(){return 4&this._updateState}get hasUpdated(){return 1&this._updateState}performUpdate(){if(!this._hasRequestedUpdate)return;this._instanceProperties&&this._applyInstanceProperties();let t=!1;const e=this._changedProperties;try{t=this.shouldUpdate(e),t?this.update(e):this._markUpdated()}catch(e){throw t=!1,this._markUpdated(),e}t&&(1&this._updateState||(this._updateState=1|this._updateState,this.firstUpdated(e)),this.updated(e))}_markUpdated(){this._changedProperties=new Map,this._updateState=-5&this._updateState}get updateComplete(){return this._getUpdateComplete()}_getUpdateComplete(){return this.getUpdateComplete()}getUpdateComplete(){return this._updatePromise}shouldUpdate(t){return!0}update(t){void 0!==this._reflectingProperties&&this._reflectingProperties.size>0&&(this._reflectingProperties.forEach(((t,e)=>this._propertyToAttribute(e,this[e],t))),this._reflectingProperties=void 0),this._markUpdated()}updated(t){}firstUpdated(t){}}FXt.finalized=!0;
/**
    @license
    Copyright (c) 2019 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at
    http://polymer.github.io/LICENSE.txt The complete set of authors may be found at
    http://polymer.github.io/AUTHORS.txt The complete set of contributors may be
    found at http://polymer.github.io/CONTRIBUTORS.txt Code distributed by Google as
    part of the polymer project is also subject to an additional IP rights grant
    found at http://polymer.github.io/PATENTS.txt
    */
const VXt=window.ShadowRoot&&(void 0===window.ShadyCSS||window.ShadyCSS.nativeShadow)&&"adoptedStyleSheets"in Document.prototype&&"replace"in CSSStyleSheet.prototype,UXt=Symbol();class jXt{constructor(t,e){if(e!==UXt)throw new Error("CSSResult is not constructable. Use `unsafeCSS` or `css` instead.");this.cssText=t}get styleSheet(){return void 0===this._styleSheet&&(VXt?(this._styleSheet=new CSSStyleSheet,this._styleSheet.replaceSync(this.cssText)):this._styleSheet=null),this._styleSheet}toString(){return this.cssText}}const GXt=(t,...e)=>{const n=e.reduce(((e,n,i)=>e+(t=>{if(t instanceof jXt)return t.cssText;if("number"==typeof t)return t;throw new Error(`Value passed to 'css' function must be a 'css' function result: ${t}. Use 'unsafeCSS' to pass non-literal values, but\n            take care to ensure page security.`)})(n)+t[i+1]),t[0]);return new jXt(n,UXt)};
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */
(window.litElementVersions||(window.litElementVersions=[])).push("2.5.1");const WXt={};class qXt extends FXt{static getStyles(){return this.styles}static _getUniqueStyles(){if(this.hasOwnProperty(JSCompiler_renameProperty("_styles",this)))return;const t=this.getStyles();if(Array.isArray(t)){const e=(t,n)=>t.reduceRight(((t,n)=>Array.isArray(n)?e(n,t):(t.add(n),t)),n),n=e(t,new Set),i=[];n.forEach((t=>i.unshift(t))),this._styles=i}else this._styles=void 0===t?[]:[t];this._styles=this._styles.map((t=>t instanceof CSSStyleSheet&&!VXt?(t=>new jXt(String(t),UXt))(Array.prototype.slice.call(t.cssRules).reduce(((t,e)=>t+e.cssText),"")):t))}initialize(){super.initialize(),this.constructor._getUniqueStyles(),this.renderRoot=this.createRenderRoot(),window.ShadowRoot&&this.renderRoot instanceof window.ShadowRoot&&this.adoptStyles()}createRenderRoot(){return this.attachShadow(this.constructor.shadowRootOptions)}adoptStyles(){const t=this.constructor._styles;0!==t.length&&(void 0===window.ShadyCSS||window.ShadyCSS.nativeShadow?VXt?this.renderRoot.adoptedStyleSheets=t.map((t=>t instanceof CSSStyleSheet?t:t.styleSheet)):this._needsShimAdoptedStyleSheets=!0:window.ShadyCSS.ScopingShim.prepareAdoptedCssText(t.map((t=>t.cssText)),this.localName))}connectedCallback(){super.connectedCallback(),this.hasUpdated&&void 0!==window.ShadyCSS&&window.ShadyCSS.styleElement(this)}update(t){const e=this.render();super.update(t),e!==WXt&&this.constructor.render(e,this.renderRoot,{scopeName:this.localName,eventContext:this}),this._needsShimAdoptedStyleSheets&&(this._needsShimAdoptedStyleSheets=!1,this.constructor._styles.forEach((t=>{const e=document.createElement("style");e.textContent=t.cssText,this.renderRoot.appendChild(e)})))}render(){return WXt}}qXt.finalized=!0,qXt.render=(t,e,n)=>{if(!n||"object"!=typeof n||!n.scopeName)throw new Error("The `scopeName` option is required.");const i=n.scopeName,r=PXt.has(e),o=IXt&&11===e.nodeType&&!!e.host,a=o&&!zXt.has(i),s=a?document.createDocumentFragment():e;if(((t,e,n)=>{let i=PXt.get(e);void 0===i&&(lXt(e,e.firstChild),PXt.set(e,i=new CXt(Object.assign({templateFactory:kXt},n))),i.appendInto(e)),i.setValue(t),i.commit()})(t,s,Object.assign({templateFactory:RXt(i)},n)),a){const t=PXt.get(s);PXt.delete(s),((t,e,n)=>{zXt.add(t);const i=n?n.element:document.createElement("template"),r=e.querySelectorAll("style"),{length:o}=r;if(0===o)return void window.ShadyCSS.prepareTemplateStyles(i,t);const a=document.createElement("style");for(let t=0;t<o;t++){const e=r[t];e.parentNode.removeChild(e),a.textContent+=e.textContent}(t=>{OXt.forEach((e=>{const n=LXt.get(NXt(e,t));void 0!==n&&n.keyString.forEach((t=>{const{element:{content:e}}=t,n=new Set;Array.from(e.querySelectorAll("style")).forEach((t=>{n.add(t)})),yXt(t,n)}))}))})(t);const s=i.content;n?(function l(t,e,n=null){const{element:{content:i},parts:r}=t;if(null==n)return void i.appendChild(e);const o=document.createTreeWalker(i,133,null,!1);let a=bXt(r),s=0,l=-1;for(;o.nextNode();)for(l++,o.currentNode===n&&(s=vXt(e),n.parentNode.insertBefore(e,n));-1!==a&&r[a].index===l;){if(s>0){for(;-1!==a;)r[a].index+=s,a=bXt(r,a);return}a=bXt(r,a)}}
/**
     * @license
     * Copyright (c) 2017 The Polymer Project Authors. All rights reserved.
     * This code may only be used under the BSD style license found at
     * http://polymer.github.io/LICENSE.txt
     * The complete set of authors may be found at
     * http://polymer.github.io/AUTHORS.txt
     * The complete set of contributors may be found at
     * http://polymer.github.io/CONTRIBUTORS.txt
     * Code distributed by Google as part of the polymer project is also
     * subject to an additional IP rights grant found at
     * http://polymer.github.io/PATENTS.txt
     */)(n,a,s.firstChild):s.insertBefore(a,s.firstChild),window.ShadyCSS.prepareTemplateStyles(i,t);const c=s.querySelector("style");if(window.ShadyCSS.nativeShadow&&null!==c)e.insertBefore(c.cloneNode(!0),e.firstChild);else if(n){s.insertBefore(a,s.firstChild);const t=new Set;t.add(a),yXt(n,t)}})(i,s,t.value instanceof MXt?t.value.template:void 0),lXt(e,e.firstChild),e.appendChild(s),PXt.set(e,t)}!r&&o&&window.ShadyCSS.styleElement(e.host)},qXt.shadowRootOptions={mode:"open"};let YXt=0;const XXt={},$Xt=(t,e,n)=>{const i=n&&n.moduleId||"custom-style-module-"+YXt++;Array.isArray(e)||(e=e?[e]:[]),e.forEach((t=>{if(!(t instanceof jXt))throw new Error("An item in styles is not of type CSSResult. Use `unsafeCSS` or `css`.");if(!XXt[t]){const e=document.createElement("dom-module");e.innerHTML=`\n        <template>\n          <style>${t.toString()}</style>\n        </template>\n      `;const n="custom-style-module-"+YXt++;e.register(n),XXt[t]=n}}));const r=document.createElement("dom-module");if(t){const e=customElements.get(t);e&&Object.prototype.hasOwnProperty.call(e,"__finalized")&&console.warn(`The custom element definition for "${t}"\n      was finalized before a style module was registered.\n      Make sure to add component specific style modules before\n      importing the corresponding custom element.`),r.setAttribute("theme-for",t)}r.innerHTML=`\n    <template>\n      ${(n&&n.include||[]).map((t=>`<style include=${t}></style>`))}\n      ${e.map((t=>`<style include=${XXt[t]}></style>`))}\n    </template>\n  `,r.register(i)};
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
class KXt extends HTMLElement{static get version(){return"20.0.2"}}customElements.define("vaadin-lumo-styles",KXt);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const ZXt=GXt`
  :host {
    /* Base (background) */
    --lumo-base-color: #fff;

    /* Tint */
    --lumo-tint-5pct: hsla(0, 0%, 100%, 0.3);
    --lumo-tint-10pct: hsla(0, 0%, 100%, 0.37);
    --lumo-tint-20pct: hsla(0, 0%, 100%, 0.44);
    --lumo-tint-30pct: hsla(0, 0%, 100%, 0.5);
    --lumo-tint-40pct: hsla(0, 0%, 100%, 0.57);
    --lumo-tint-50pct: hsla(0, 0%, 100%, 0.64);
    --lumo-tint-60pct: hsla(0, 0%, 100%, 0.7);
    --lumo-tint-70pct: hsla(0, 0%, 100%, 0.77);
    --lumo-tint-80pct: hsla(0, 0%, 100%, 0.84);
    --lumo-tint-90pct: hsla(0, 0%, 100%, 0.9);
    --lumo-tint: #fff;

    /* Shade */
    --lumo-shade-5pct: hsla(214, 61%, 25%, 0.05);
    --lumo-shade-10pct: hsla(214, 57%, 24%, 0.1);
    --lumo-shade-20pct: hsla(214, 53%, 23%, 0.16);
    --lumo-shade-30pct: hsla(214, 50%, 22%, 0.26);
    --lumo-shade-40pct: hsla(214, 47%, 21%, 0.38);
    --lumo-shade-50pct: hsla(214, 45%, 20%, 0.5);
    --lumo-shade-60pct: hsla(214, 43%, 19%, 0.61);
    --lumo-shade-70pct: hsla(214, 42%, 18%, 0.72);
    --lumo-shade-80pct: hsla(214, 41%, 17%, 0.83);
    --lumo-shade-90pct: hsla(214, 40%, 16%, 0.94);
    --lumo-shade: hsl(214, 35%, 15%);

    /* Contrast */
    --lumo-contrast-5pct: var(--lumo-shade-5pct);
    --lumo-contrast-10pct: var(--lumo-shade-10pct);
    --lumo-contrast-20pct: var(--lumo-shade-20pct);
    --lumo-contrast-30pct: var(--lumo-shade-30pct);
    --lumo-contrast-40pct: var(--lumo-shade-40pct);
    --lumo-contrast-50pct: var(--lumo-shade-50pct);
    --lumo-contrast-60pct: var(--lumo-shade-60pct);
    --lumo-contrast-70pct: var(--lumo-shade-70pct);
    --lumo-contrast-80pct: var(--lumo-shade-80pct);
    --lumo-contrast-90pct: var(--lumo-shade-90pct);
    --lumo-contrast: var(--lumo-shade);

    /* Text */
    --lumo-header-text-color: var(--lumo-contrast);
    --lumo-body-text-color: var(--lumo-contrast-90pct);
    --lumo-secondary-text-color: var(--lumo-contrast-70pct);
    --lumo-tertiary-text-color: var(--lumo-contrast-50pct);
    --lumo-disabled-text-color: var(--lumo-contrast-30pct);

    /* Primary */
    --lumo-primary-color: hsl(214, 90%, 52%);
    --lumo-primary-color-50pct: hsla(214, 90%, 52%, 0.5);
    --lumo-primary-color-10pct: hsla(214, 90%, 52%, 0.1);
    --lumo-primary-text-color: var(--lumo-primary-color);
    --lumo-primary-contrast-color: #fff;

    /* Error */
    --lumo-error-color: hsl(3, 100%, 61%);
    --lumo-error-color-50pct: hsla(3, 100%, 60%, 0.5);
    --lumo-error-color-10pct: hsla(3, 100%, 60%, 0.1);
    --lumo-error-text-color: hsl(3, 92%, 53%);
    --lumo-error-contrast-color: #fff;

    /* Success */
    --lumo-success-color: hsl(145, 80%, 42%); /* hsl(144,82%,37%); */
    --lumo-success-color-50pct: hsla(145, 76%, 44%, 0.55);
    --lumo-success-color-10pct: hsla(145, 76%, 44%, 0.12);
    --lumo-success-text-color: hsl(145, 100%, 32%);
    --lumo-success-contrast-color: #fff;
  }
`,JXt=document.createElement("template");JXt.innerHTML=`<style>${ZXt.toString().replace(":host","html")}</style>`,document.head.appendChild(JXt.content),$Xt("",GXt`
  [theme~='dark'] {
    /* Base (background) */
    --lumo-base-color: hsl(214, 35%, 21%);

    /* Tint */
    --lumo-tint-5pct: hsla(214, 65%, 85%, 0.06);
    --lumo-tint-10pct: hsla(214, 60%, 80%, 0.14);
    --lumo-tint-20pct: hsla(214, 64%, 82%, 0.23);
    --lumo-tint-30pct: hsla(214, 69%, 84%, 0.32);
    --lumo-tint-40pct: hsla(214, 73%, 86%, 0.41);
    --lumo-tint-50pct: hsla(214, 78%, 88%, 0.5);
    --lumo-tint-60pct: hsla(214, 82%, 90%, 0.6);
    --lumo-tint-70pct: hsla(214, 87%, 92%, 0.7);
    --lumo-tint-80pct: hsla(214, 91%, 94%, 0.8);
    --lumo-tint-90pct: hsla(214, 96%, 96%, 0.9);
    --lumo-tint: hsl(214, 100%, 98%);

    /* Shade */
    --lumo-shade-5pct: hsla(214, 0%, 0%, 0.07);
    --lumo-shade-10pct: hsla(214, 4%, 2%, 0.15);
    --lumo-shade-20pct: hsla(214, 8%, 4%, 0.23);
    --lumo-shade-30pct: hsla(214, 12%, 6%, 0.32);
    --lumo-shade-40pct: hsla(214, 16%, 8%, 0.41);
    --lumo-shade-50pct: hsla(214, 20%, 10%, 0.5);
    --lumo-shade-60pct: hsla(214, 24%, 12%, 0.6);
    --lumo-shade-70pct: hsla(214, 28%, 13%, 0.7);
    --lumo-shade-80pct: hsla(214, 32%, 13%, 0.8);
    --lumo-shade-90pct: hsla(214, 33%, 13%, 0.9);
    --lumo-shade: hsl(214, 33%, 13%);

    /* Contrast */
    --lumo-contrast-5pct: var(--lumo-tint-5pct);
    --lumo-contrast-10pct: var(--lumo-tint-10pct);
    --lumo-contrast-20pct: var(--lumo-tint-20pct);
    --lumo-contrast-30pct: var(--lumo-tint-30pct);
    --lumo-contrast-40pct: var(--lumo-tint-40pct);
    --lumo-contrast-50pct: var(--lumo-tint-50pct);
    --lumo-contrast-60pct: var(--lumo-tint-60pct);
    --lumo-contrast-70pct: var(--lumo-tint-70pct);
    --lumo-contrast-80pct: var(--lumo-tint-80pct);
    --lumo-contrast-90pct: var(--lumo-tint-90pct);
    --lumo-contrast: var(--lumo-tint);

    /* Text */
    --lumo-header-text-color: var(--lumo-contrast);
    --lumo-body-text-color: var(--lumo-contrast-90pct);
    --lumo-secondary-text-color: var(--lumo-contrast-70pct);
    --lumo-tertiary-text-color: var(--lumo-contrast-50pct);
    --lumo-disabled-text-color: var(--lumo-contrast-30pct);

    /* Primary */
    --lumo-primary-color: hsl(214, 86%, 55%);
    --lumo-primary-color-50pct: hsla(214, 86%, 55%, 0.5);
    --lumo-primary-color-10pct: hsla(214, 90%, 63%, 0.1);
    --lumo-primary-text-color: hsl(214, 100%, 70%);
    --lumo-primary-contrast-color: #fff;

    /* Error */
    --lumo-error-color: hsl(3, 90%, 63%);
    --lumo-error-color-50pct: hsla(3, 90%, 63%, 0.5);
    --lumo-error-color-10pct: hsla(3, 90%, 63%, 0.1);
    --lumo-error-text-color: hsl(3, 100%, 67%);

    /* Success */
    --lumo-success-color: hsl(145, 65%, 42%);
    --lumo-success-color-50pct: hsla(145, 65%, 42%, 0.5);
    --lumo-success-color-10pct: hsla(145, 65%, 42%, 0.1);
    --lumo-success-text-color: hsl(145, 85%, 47%);
  }

  html {
    color: var(--lumo-body-text-color);
    background-color: var(--lumo-base-color);
  }

  [theme~='dark'] {
    color: var(--lumo-body-text-color);
    background-color: var(--lumo-base-color);
  }

  h1,
  h2,
  h3,
  h4,
  h5,
  h6 {
    color: var(--lumo-header-text-color);
  }

  a {
    color: var(--lumo-primary-text-color);
  }

  blockquote {
    color: var(--lumo-secondary-text-color);
  }

  code,
  pre {
    background-color: var(--lumo-contrast-10pct);
    border-radius: var(--lumo-border-radius-m);
  }
`,{moduleId:"lumo-color"}),$Xt("",GXt`
  :host {
    color: var(--lumo-body-text-color) !important;
    background-color: var(--lumo-base-color) !important;
  }
`,{moduleId:"lumo-color-legacy",include:["lumo-color"]});
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const QXt=document.createElement("template");QXt.innerHTML='\n  <style>\n    @font-face {\n      font-family: \'lumo-icons\';\n      src: url(data:application/font-woff;charset=utf-8;base64,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) format(\'woff\');\n      font-weight: normal;\n      font-style: normal;\n    }\n\n    html {\n      --lumo-icons-align-center: "\\ea01";\n      --lumo-icons-align-left: "\\ea02";\n      --lumo-icons-align-right: "\\ea03";\n      --lumo-icons-angle-down: "\\ea04";\n      --lumo-icons-angle-left: "\\ea05";\n      --lumo-icons-angle-right: "\\ea06";\n      --lumo-icons-angle-up: "\\ea07";\n      --lumo-icons-arrow-down: "\\ea08";\n      --lumo-icons-arrow-left: "\\ea09";\n      --lumo-icons-arrow-right: "\\ea0a";\n      --lumo-icons-arrow-up: "\\ea0b";\n      --lumo-icons-bar-chart: "\\ea0c";\n      --lumo-icons-bell: "\\ea0d";\n      --lumo-icons-calendar: "\\ea0e";\n      --lumo-icons-checkmark: "\\ea0f";\n      --lumo-icons-chevron-down: "\\ea10";\n      --lumo-icons-chevron-left: "\\ea11";\n      --lumo-icons-chevron-right: "\\ea12";\n      --lumo-icons-chevron-up: "\\ea13";\n      --lumo-icons-clock: "\\ea14";\n      --lumo-icons-cog: "\\ea15";\n      --lumo-icons-cross: "\\ea16";\n      --lumo-icons-download: "\\ea17";\n      --lumo-icons-dropdown: "\\ea18";\n      --lumo-icons-edit: "\\ea19";\n      --lumo-icons-error: "\\ea1a";\n      --lumo-icons-eye: "\\ea1b";\n      --lumo-icons-eye-disabled: "\\ea1c";\n      --lumo-icons-menu: "\\ea1d";\n      --lumo-icons-minus: "\\ea1e";\n      --lumo-icons-ordered-list: "\\ea1f";\n      --lumo-icons-phone: "\\ea20";\n      --lumo-icons-photo: "\\ea21";\n      --lumo-icons-play: "\\ea22";\n      --lumo-icons-plus: "\\ea23";\n      --lumo-icons-redo: "\\ea24";\n      --lumo-icons-reload: "\\ea25";\n      --lumo-icons-search: "\\ea26";\n      --lumo-icons-undo: "\\ea27";\n      --lumo-icons-unordered-list: "\\ea28";\n      --lumo-icons-upload: "\\ea29";\n      --lumo-icons-user: "\\ea2a";\n    }\n  </style>\n',document.head.appendChild(QXt.content);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const t$t=GXt`
  :host {
    --lumo-size-xs: 1.625rem;
    --lumo-size-s: 1.875rem;
    --lumo-size-m: 2.25rem;
    --lumo-size-l: 2.75rem;
    --lumo-size-xl: 3.5rem;

    /* Icons */
    --lumo-icon-size-s: 1.25em;
    --lumo-icon-size-m: 1.5em;
    --lumo-icon-size-l: 2.25em;
    /* For backwards compatibility */
    --lumo-icon-size: var(--lumo-icon-size-m);
  }
`,e$t=document.createElement("template");e$t.innerHTML=`<style>${t$t.toString().replace(":host","html")}</style>`,document.head.appendChild(e$t.content);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const n$t=GXt`
  :host {
    /* Square */
    --lumo-space-xs: 0.25rem;
    --lumo-space-s: 0.5rem;
    --lumo-space-m: 1rem;
    --lumo-space-l: 1.5rem;
    --lumo-space-xl: 2.5rem;

    /* Wide */
    --lumo-space-wide-xs: calc(var(--lumo-space-xs) / 2) var(--lumo-space-xs);
    --lumo-space-wide-s: calc(var(--lumo-space-s) / 2) var(--lumo-space-s);
    --lumo-space-wide-m: calc(var(--lumo-space-m) / 2) var(--lumo-space-m);
    --lumo-space-wide-l: calc(var(--lumo-space-l) / 2) var(--lumo-space-l);
    --lumo-space-wide-xl: calc(var(--lumo-space-xl) / 2) var(--lumo-space-xl);

    /* Tall */
    --lumo-space-tall-xs: var(--lumo-space-xs) calc(var(--lumo-space-xs) / 2);
    --lumo-space-tall-s: var(--lumo-space-s) calc(var(--lumo-space-s) / 2);
    --lumo-space-tall-m: var(--lumo-space-m) calc(var(--lumo-space-m) / 2);
    --lumo-space-tall-l: var(--lumo-space-l) calc(var(--lumo-space-l) / 2);
    --lumo-space-tall-xl: var(--lumo-space-xl) calc(var(--lumo-space-xl) / 2);
  }
`,i$t=document.createElement("template");i$t.innerHTML=`<style>${n$t.toString().replace(":host","html")}</style>`,document.head.appendChild(i$t.content);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const r$t=GXt`
  :host {
    /* Border radius */
    --lumo-border-radius-s: 0.25em; /* Checkbox, badge, date-picker year indicator, etc */
    --lumo-border-radius-m: var(--lumo-border-radius, 0.25em); /* Button, text field, menu overlay, etc */
    --lumo-border-radius-l: 0.5em; /* Dialog, notification, etc */
    --lumo-border-radius: 0.25em; /* Deprecated */

    /* Shadow */
    --lumo-box-shadow-xs: 0 1px 4px -1px var(--lumo-shade-50pct);
    --lumo-box-shadow-s: 0 2px 4px -1px var(--lumo-shade-20pct), 0 3px 12px -1px var(--lumo-shade-30pct);
    --lumo-box-shadow-m: 0 2px 6px -1px var(--lumo-shade-20pct), 0 8px 24px -4px var(--lumo-shade-40pct);
    --lumo-box-shadow-l: 0 3px 18px -2px var(--lumo-shade-20pct), 0 12px 48px -6px var(--lumo-shade-40pct);
    --lumo-box-shadow-xl: 0 4px 24px -3px var(--lumo-shade-20pct), 0 18px 64px -8px var(--lumo-shade-40pct);

    /* Clickable element cursor */
    --lumo-clickable-cursor: default;
  }
`,o$t=document.createElement("template");o$t.innerHTML=`<style>${r$t.toString().replace(":host","html")}</style>`,document.head.appendChild(o$t.content);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const a$t=GXt`
  :host {
    /* prettier-ignore */
    --lumo-font-family: -apple-system, BlinkMacSystemFont, 'Roboto', 'Segoe UI', Helvetica, Arial, sans-serif, 'Apple Color Emoji', 'Segoe UI Emoji', 'Segoe UI Symbol';

    /* Font sizes */
    --lumo-font-size-xxs: 0.75rem;
    --lumo-font-size-xs: 0.8125rem;
    --lumo-font-size-s: 0.875rem;
    --lumo-font-size-m: 1rem;
    --lumo-font-size-l: 1.125rem;
    --lumo-font-size-xl: 1.375rem;
    --lumo-font-size-xxl: 1.75rem;
    --lumo-font-size-xxxl: 2.5rem;

    /* Line heights */
    --lumo-line-height-xs: 1.25;
    --lumo-line-height-s: 1.375;
    --lumo-line-height-m: 1.625;
  }
`,s$t=document.createElement("template");s$t.innerHTML=`<style>${a$t.toString().replace(":host","html")}</style>`,document.head.appendChild(s$t.content),$Xt("",GXt`
  html {
    font-family: var(--lumo-font-family);
    font-size: var(--lumo-font-size, var(--lumo-font-size-m));
    line-height: var(--lumo-line-height-m);
    -webkit-text-size-adjust: 100%;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
  }

  /* Can’t combine with the above selector because that doesn’t work in browsers without native shadow dom */
  :host {
    font-family: var(--lumo-font-family);
    font-size: var(--lumo-font-size, var(--lumo-font-size-m));
    line-height: var(--lumo-line-height-m);
    -webkit-text-size-adjust: 100%;
    -webkit-font-smoothing: antialiased;
    -moz-osx-font-smoothing: grayscale;
  }

  small,
  [theme~='font-size-s'] {
    font-size: var(--lumo-font-size-s);
    line-height: var(--lumo-line-height-s);
  }

  [theme~='font-size-xs'] {
    font-size: var(--lumo-font-size-xs);
    line-height: var(--lumo-line-height-xs);
  }

  h1,
  h2,
  h3,
  h4,
  h5,
  h6 {
    font-weight: 600;
    line-height: var(--lumo-line-height-xs);
    margin-top: 1.25em;
  }

  h1 {
    font-size: var(--lumo-font-size-xxxl);
    margin-bottom: 0.75em;
  }

  h2 {
    font-size: var(--lumo-font-size-xxl);
    margin-bottom: 0.5em;
  }

  h3 {
    font-size: var(--lumo-font-size-xl);
    margin-bottom: 0.5em;
  }

  h4 {
    font-size: var(--lumo-font-size-l);
    margin-bottom: 0.5em;
  }

  h5 {
    font-size: var(--lumo-font-size-m);
    margin-bottom: 0.25em;
  }

  h6 {
    font-size: var(--lumo-font-size-xs);
    margin-bottom: 0;
    text-transform: uppercase;
    letter-spacing: 0.03em;
  }

  p,
  blockquote {
    margin-top: 0.5em;
    margin-bottom: 0.75em;
  }

  a {
    text-decoration: none;
  }

  a:hover {
    text-decoration: underline;
  }

  hr {
    display: block;
    align-self: stretch;
    height: 1px;
    border: 0;
    padding: 0;
    margin: var(--lumo-space-s) calc(var(--lumo-border-radius-m) / 2);
    background-color: var(--lumo-contrast-10pct);
  }

  blockquote {
    border-left: 2px solid var(--lumo-contrast-30pct);
  }

  b,
  strong {
    font-weight: 600;
  }

  /* RTL specific styles */

  blockquote[dir='rtl'] {
    border-left: none;
    border-right: 2px solid var(--lumo-contrast-30pct);
  }
`,{moduleId:"lumo-typography"}),$Xt("vaadin-checkbox",GXt`
    :host {
      -webkit-tap-highlight-color: transparent;
      -webkit-user-select: none;
      -moz-user-select: none;
      user-select: none;
      cursor: default;
      outline: none;
    }

    [part='label']:not([empty]) {
      margin: 0.1875em 0.875em 0.1875em 0.375em;
    }

    [part='checkbox'] {
      width: calc(1em + 2px);
      height: calc(1em + 2px);
      margin: 0.1875em;
      position: relative;
      border-radius: var(--lumo-border-radius-s);
      background-color: var(--lumo-contrast-20pct);
      transition: transform 0.2s cubic-bezier(0.12, 0.32, 0.54, 2), background-color 0.15s;
      pointer-events: none;
      line-height: 1.2;
    }

    :host([indeterminate]) [part='checkbox'],
    :host([checked]) [part='checkbox'] {
      background-color: var(--lumo-primary-color);
    }

    /* Needed to align the checkbox nicely on the baseline */
    [part='checkbox']::before {
      content: '\\2003';
    }

    /* Checkmark */
    [part='checkbox']::after {
      content: '';
      display: inline-block;
      width: 0;
      height: 0;
      border: 0 solid var(--lumo-primary-contrast-color);
      border-width: 0.1875em 0 0 0.1875em;
      box-sizing: border-box;
      transform-origin: 0 0;
      position: absolute;
      top: 0.8125em;
      left: 0.5em;
      transform: scale(0.55) rotate(-135deg);
      opacity: 0;
    }

    :host([checked]) [part='checkbox']::after {
      opacity: 1;
      width: 0.625em;
      height: 1.0625em;
    }

    /* Indeterminate checkmark */
    :host([indeterminate]) [part='checkbox']::after {
      transform: none;
      opacity: 1;
      top: 45%;
      height: 10%;
      left: 22%;
      right: 22%;
      width: auto;
      border: 0;
      background-color: var(--lumo-primary-contrast-color);
      transition: opacity 0.25s;
    }

    /* Focus ring */
    :host([focus-ring]) [part='checkbox'] {
      box-shadow: 0 0 0 3px var(--lumo-primary-color-50pct);
    }

    /* Disabled */
    :host([disabled]) {
      pointer-events: none;
      color: var(--lumo-disabled-text-color);
    }

    :host([disabled]) [part='label'] ::slotted(*) {
      color: inherit;
    }

    :host([disabled]) [part='checkbox'] {
      background-color: var(--lumo-contrast-10pct);
    }

    :host([disabled]) [part='checkbox']::after {
      border-color: var(--lumo-contrast-30pct);
    }

    :host([indeterminate][disabled]) [part='checkbox']::after {
      background-color: var(--lumo-contrast-30pct);
    }

    /* RTL specific styles */
    :host([dir='rtl']) [part='label']:not([empty]) {
      margin: 0.1875em 0.375em 0.1875em 0.875em;
    }

    /* Transition the checkmark if activated with the mouse (disabled for grid select-all this way) */
    :host(:hover) [part='checkbox']::after {
      transition: width 0.1s, height 0.25s;
    }

    /* Used for activation "halo" */
    [part='checkbox']::before {
      color: transparent;
      display: inline-block;
      width: 100%;
      height: 100%;
      border-radius: inherit;
      background-color: inherit;
      transform: scale(1.4);
      opacity: 0;
      transition: transform 0.1s, opacity 0.8s;
    }

    /* Hover */
    :host(:not([checked]):not([indeterminate]):not([disabled]):hover) [part='checkbox'] {
      background-color: var(--lumo-contrast-30pct);
    }

    /* Disable hover for touch devices */
    @media (pointer: coarse) {
      :host(:not([checked]):not([indeterminate]):not([disabled]):hover) [part='checkbox'] {
        background-color: var(--lumo-contrast-20pct);
      }
    }

    /* Active */
    :host([active]) [part='checkbox'] {
      transform: scale(0.9);
      transition-duration: 0.05s;
    }

    :host([active][checked]) [part='checkbox'] {
      transform: scale(1.1);
    }

    :host([active]:not([checked])) [part='checkbox']::before {
      transition-duration: 0.01s, 0.01s;
      transform: scale(0);
      opacity: 0.4;
    }
  `,{moduleId:"lumo-checkbox"});const l$t=t=>class extends t{static get properties(){return{theme:{type:String,readOnly:!0}}}attributeChangedCallback(t,e,n){super.attributeChangedCallback(t,e,n),"theme"===t&&this._setTheme(n)}},c$t=t=>class extends(l$t(t)){static finalize(){super.finalize();const t=this.prototype._template,e=Object.getPrototypeOf(this.prototype)._template;e&&Array.from(e.content.querySelectorAll("style[include]")).forEach((e=>{this._includeStyle(e.getAttribute("include"),t)})),this._includeMatchingThemes(t)}static _includeMatchingThemes(t){const e=B.prototype.modules;let n=!1;const i=this.is+"-default-theme";Object.keys(e).sort(((t,e)=>{const n=0===t.indexOf("vaadin-"),i=0===e.indexOf("vaadin-"),r=["lumo-","material-"],o=r.filter((e=>0===t.indexOf(e))).length>0,a=r.filter((t=>0===e.indexOf(t))).length>0;return n!==i?n?-1:1:o!==a?o?-1:1:0})).forEach((r=>{if(r!==i){const i=e[r].getAttribute("theme-for");i&&i.split(" ").forEach((e=>{new RegExp("^"+e.split("*").join(".*")+"$").test(this.is)&&(n=!0,this._includeStyle(r,t))}))}})),!n&&e[i]&&this._includeStyle(i,t)}static _includeStyle(t,e){if(e&&!e.content.querySelector(`style[include="${t}"]`)){const n=document.createElement("style");n.setAttribute("include",t),e.content.appendChild(n)}}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */;let u$t=!1;window.addEventListener("keydown",(()=>{u$t=!0}),{capture:!0}),window.addEventListener("mousedown",(()=>{u$t=!1}),{capture:!0});const h$t=t=>class extends((t=>class extends t{static get properties(){return{tabindex:{type:Number,value:0,reflectToAttribute:!0,observer:"_tabindexChanged"}}}})(t)){static get properties(){return{autofocus:{type:Boolean},_previousTabIndex:{type:Number},disabled:{type:Boolean,observer:"_disabledChanged",reflectToAttribute:!0},_isShiftTabbing:{type:Boolean}}}ready(){this.addEventListener("focusin",(t=>{t.composedPath()[0]===this?this.contains(t.relatedTarget)||this._focus():-1===t.composedPath().indexOf(this.focusElement)||this.disabled||this._setFocused(!0)})),this.addEventListener("focusout",(()=>this._setFocused(!1))),super.ready(),this.addEventListener("keydown",(t=>{!t.defaultPrevented&&9===t.keyCode&&t.shiftKey&&(this._isShiftTabbing=!0,HTMLElement.prototype.focus.apply(this),this._setFocused(!1),setTimeout((()=>this._isShiftTabbing=!1),0))})),this.autofocus&&!this.disabled&&window.requestAnimationFrame((()=>{this._focus(),this._setFocused(!0),this.setAttribute("focus-ring","")}))}disconnectedCallback(){super.disconnectedCallback(),this.hasAttribute("focused")&&this._setFocused(!1)}_setFocused(t){t?this.setAttribute("focused",""):this.removeAttribute("focused"),t&&u$t?this.setAttribute("focus-ring",""):this.removeAttribute("focus-ring")}get focusElement(){return window.console.warn(`Please implement the 'focusElement' property in <${this.localName}>`),this}_focus(){this.focusElement&&!this._isShiftTabbing&&(this.focusElement.focus(),this._setFocused(!0))}focus(){this.focusElement&&!this.disabled&&(this.focusElement.focus(),this._setFocused(!0))}blur(){this.focusElement&&(this.focusElement.blur(),this._setFocused(!1))}_disabledChanged(t){this.focusElement.disabled=t,t?(this.blur(),this._previousTabIndex=this.tabindex,this.tabindex=-1,this.setAttribute("aria-disabled","true")):(void 0!==this._previousTabIndex&&(this.tabindex=this._previousTabIndex),this.removeAttribute("aria-disabled"))}_tabindexChanged(t){void 0!==t&&(this.focusElement.tabIndex=t),this.disabled&&this.tabindex&&(-1!==this.tabindex&&(this._previousTabIndex=this.tabindex),this.tabindex=t=void 0)}click(){this.disabled||super.click()}},d$t=/\/\*\*\s+vaadin-dev-mode:start([\s\S]*)vaadin-dev-mode:end\s+\*\*\//i,p$t=window.Vaadin&&window.Vaadin.Flow&&window.Vaadin.Flow.clients;function f$t(t,e){if("function"!=typeof t)return;const n=d$t.exec(t.toString());if(n)try{t=new Function(n[1])}catch(t){console.log("vaadin-development-mode-detector: uncommentAndRun() failed",t)}return t(e)}function m$t(){}window.Vaadin=window.Vaadin||{},void 0===window.Vaadin.developmentMode&&(window.Vaadin.developmentMode=(function g$t(){try{return!!(function t(){return localStorage.getItem("vaadin.developmentmode.force")})()||!!(function e(){return["localhost","127.0.0.1"].indexOf(window.location.hostname)>=0})()&&(p$t?!(function n(){return!!(p$t&&Object.keys(p$t).map((t=>p$t[t])).filter((t=>t.productionMode)).length>0)})():!(function i(){return f$t((function t(){return!0}))})())}catch(t){return!1}})());
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
class _$t{static detectScrollType(){const t=document.createElement("div");t.textContent="ABCD",t.dir="rtl",t.style.fontSize="14px",t.style.width="4px",t.style.height="1px",t.style.position="absolute",t.style.top="-1000px",t.style.overflow="scroll",document.body.appendChild(t);let e="reverse";return t.scrollLeft>0?e="default":(t.scrollLeft=2,t.scrollLeft<2&&(e="negative")),document.body.removeChild(t),e}static getNormalizedScrollLeft(t,e,n){const{scrollLeft:i}=n;if("rtl"!==e||!t)return i;switch(t){case"negative":return n.scrollWidth-n.clientWidth+i;case"reverse":return n.scrollWidth-n.clientWidth-i}return i}static setNormalizedScrollLeft(t,e,n,i){if("rtl"===e&&t)switch(t){case"negative":n.scrollLeft=n.clientWidth-n.scrollWidth+i;break;case"reverse":n.scrollLeft=n.scrollWidth-n.clientWidth-i;break;default:n.scrollLeft=i}else n.scrollLeft=i}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */const y$t=[];let v$t;new MutationObserver((function(){const t=x$t();y$t.forEach((e=>{b$t(e,t)}))})).observe(document.documentElement,{attributes:!0,attributeFilter:["dir"]});const b$t=function(t,e,n=t.getAttribute("dir")){e?t.setAttribute("dir",e):null!=n&&t.removeAttribute("dir")},x$t=function(){return document.documentElement.getAttribute("dir")},w$t=t=>class extends t{static get properties(){return{dir:{type:String,value:"",reflectToAttribute:!0}}}static finalize(){super.finalize(),v$t||(v$t=_$t.detectScrollType())}connectedCallback(){super.connectedCallback(),this.hasAttribute("dir")||(this.__subscribe(),b$t(this,x$t(),null))}attributeChangedCallback(t,e,n){if(super.attributeChangedCallback(t,e,n),"dir"!==t)return;const i=x$t(),r=n===i&&-1===y$t.indexOf(this),o=!n&&e&&-1===y$t.indexOf(this),a=n!==i&&e===i;r||o?(this.__subscribe(),b$t(this,i,n)):a&&this.__subscribe(!1)}disconnectedCallback(){super.disconnectedCallback(),this.__subscribe(!1),this.removeAttribute("dir")}_valueToNodeAttribute(t,e,n){("dir"!==n||""!==e||t.hasAttribute("dir"))&&super._valueToNodeAttribute(t,e,n)}_attributeToProperty(t,e,n){"dir"!==t||e?super._attributeToProperty(t,e,n):this.dir=""}__subscribe(t=!0){t?-1===y$t.indexOf(this)&&y$t.push(this):y$t.indexOf(this)>-1&&y$t.splice(y$t.indexOf(this),1)}__getNormalizedScrollLeft(t){return _$t.getNormalizedScrollLeft(v$t,this.getAttribute("dir")||"ltr",t)}__setNormalizedScrollLeft(t,e){return _$t.setNormalizedScrollLeft(v$t,this.getAttribute("dir")||"ltr",t,e)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */;let S$t;window.Vaadin=window.Vaadin||{},window.Vaadin.registrations=window.Vaadin.registrations||[],window.Vaadin.developmentModeCallback=window.Vaadin.developmentModeCallback||{},window.Vaadin.developmentModeCallback["vaadin-usage-statistics"]=function(){var t;t=m$t,window.Vaadin.developmentMode&&f$t(t,void 0)};const M$t=new Set,E$t=t=>class extends(w$t(t)){static finalize(){super.finalize();const{is:t}=this;t&&!M$t.has(t)&&(window.Vaadin.registrations.push(this),M$t.add(t),window.Vaadin.developmentModeCallback&&(S$t=Tn.debounce(S$t,yt,(()=>{window.Vaadin.developmentModeCallback["vaadin-usage-statistics"]()})),An(S$t)))}constructor(){super(),null===document.doctype&&console.warn('Vaadin components require the "standards mode" declaration. Please add <!DOCTYPE html> to the HTML document.')}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */;class T$t extends(E$t(h$t(c$t(pi(ye))))){static get template(){return _e`
      <style>
        :host {
          display: inline-block;
        }

        :host([hidden]) {
          display: none !important;
        }

        label {
          display: inline-flex;
          align-items: baseline;
          outline: none;
        }

        [part='checkbox'] {
          position: relative;
          display: inline-block;
          flex: none;
        }

        input[type='checkbox'] {
          position: absolute;
          top: 0;
          left: 0;
          right: 0;
          width: 100%;
          height: 100%;
          opacity: 0;
          cursor: inherit;
          margin: 0;
        }

        :host([disabled]) {
          -webkit-tap-highlight-color: transparent;
        }
      </style>

      <label>
        <span part="checkbox">
          <input
            type="checkbox"
            checked="{{checked::change}}"
            disabled$="[[disabled]]"
            indeterminate="{{indeterminate::change}}"
            role="presentation"
            tabindex="-1"
          />
        </span>

        <span part="label">
          <slot></slot>
        </span>
      </label>
    `}static get is(){return"vaadin-checkbox"}static get version(){return"20.0.2"}static get properties(){return{checked:{type:Boolean,value:!1,notify:!0,observer:"_checkedChanged",reflectToAttribute:!0},indeterminate:{type:Boolean,notify:!0,observer:"_indeterminateChanged",reflectToAttribute:!0,value:!1},value:{type:String,value:"on"},_nativeCheckbox:{type:Object}}}constructor(){super()}get name(){return this.checked?this._storedName:""}set name(t){this._storedName=t}ready(){super.ready(),this.setAttribute("role","checkbox"),this._nativeCheckbox=this.shadowRoot.querySelector('input[type="checkbox"]'),this.addEventListener("click",this._handleClick.bind(this)),this._addActiveListeners();const t=this.getAttribute("name");t&&(this.name=t),this.shadowRoot.querySelector('[part~="label"]').querySelector("slot").addEventListener("slotchange",this._updateLabelAttribute.bind(this)),this._updateLabelAttribute()}_updateLabelAttribute(){const t=this.shadowRoot.querySelector('[part~="label"]'),e=t.firstElementChild.assignedNodes();this._isAssignedNodesEmpty(e)?t.setAttribute("empty",""):t.removeAttribute("empty")}_isAssignedNodesEmpty(t){return 0===t.length||1==t.length&&t[0].nodeType==Node.TEXT_NODE&&""===t[0].textContent.trim()}_checkedChanged(t){this.setAttribute("aria-checked",this.indeterminate?"mixed":Boolean(t))}_indeterminateChanged(t){this.setAttribute("aria-checked",t?"mixed":this.checked)}_addActiveListeners(){this._addEventListenerToNode(this,"down",(t=>{this.__interactionsAllowed(t)&&this.setAttribute("active","")})),this._addEventListenerToNode(this,"up",(()=>this.removeAttribute("active"))),this.addEventListener("keydown",(t=>{this.__interactionsAllowed(t)&&32===t.keyCode&&(t.preventDefault(),this.setAttribute("active",""))})),this.addEventListener("keyup",(t=>{this.__interactionsAllowed(t)&&32===t.keyCode&&(t.preventDefault(),this._toggleChecked(),this.removeAttribute("active"),this.indeterminate&&(this.indeterminate=!1))}))}get focusElement(){return this.shadowRoot.querySelector("input")}__interactionsAllowed(t){return!this.disabled&&"a"!==t.target.localName}_handleClick(t){this.__interactionsAllowed(t)&&(this.indeterminate?(this.indeterminate=!1,t.preventDefault(),this._toggleChecked()):t.composedPath()[0]!==this._nativeCheckbox&&(t.preventDefault(),this._toggleChecked()))}_toggleChecked(){this.checked=!this.checked,this.dispatchEvent(new CustomEvent("change",{composed:!1,bubbles:!0}))}}customElements.define(T$t.is,T$t),$Xt("vaadin-grid",GXt`
    :host {
      font-family: var(--lumo-font-family);
      font-size: var(--lumo-font-size-m);
      line-height: var(--lumo-line-height-s);
      color: var(--lumo-body-text-color);
      background-color: var(--lumo-base-color);
      box-sizing: border-box;
      -webkit-text-size-adjust: 100%;
      -webkit-tap-highlight-color: transparent;
      -webkit-font-smoothing: antialiased;
      -moz-osx-font-smoothing: grayscale;

      /* For internal use only */
      --_lumo-grid-border-color: var(--lumo-contrast-20pct);
      --_lumo-grid-secondary-border-color: var(--lumo-contrast-10pct);
      --_lumo-grid-border-width: 1px;
      --_lumo-grid-selected-row-color: var(--lumo-primary-color-10pct);
    }

    /* No (outer) border */

    :host(:not([theme~='no-border'])) {
      border: var(--_lumo-grid-border-width) solid var(--_lumo-grid-border-color);
    }

    /* Cell styles */

    [part~='cell'] {
      min-height: var(--lumo-size-m);
      background-color: var(--lumo-base-color);
    }

    [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      cursor: default;
      padding: var(--lumo-space-xs) var(--lumo-space-m);
    }

    /* Apply row borders by default and introduce the "no-row-borders" variant */
    :host(:not([theme~='no-row-borders'])) [part~='cell']:not([part~='details-cell']) {
      border-top: var(--_lumo-grid-border-width) solid var(--_lumo-grid-secondary-border-color);
    }

    /* Hide first body row top border */
    :host(:not([theme~='no-row-borders'])) [part='row'][first] [part~='cell']:not([part~='details-cell']) {
      border-top: 0;
      min-height: calc(var(--lumo-size-m) - var(--_lumo-grid-border-width));
    }

    /* Focus-ring */

    [part~='cell']:focus {
      outline: none;
    }

    :host([navigating]) [part~='cell']:focus::before {
      content: '';
      position: absolute;
      top: 0;
      right: 0;
      bottom: 0;
      left: 0;
      pointer-events: none;
      box-shadow: inset 0 0 0 2px var(--lumo-primary-color-50pct);
    }

    /* Drag and Drop styles */
    :host([dragover])::after {
      content: '';
      position: absolute;
      z-index: 100;
      top: 0;
      right: 0;
      bottom: 0;
      left: 0;
      pointer-events: none;
      box-shadow: inset 0 0 0 2px var(--lumo-primary-color-50pct);
    }

    [part~='row'][dragover] {
      z-index: 100 !important;
    }

    [part~='row'][dragover] [part~='cell'] {
      overflow: visible;
    }

    [part~='row'][dragover] [part~='cell']::after {
      content: '';
      position: absolute;
      top: 0;
      right: 0;
      bottom: 0;
      left: 0;
      height: calc(var(--_lumo-grid-border-width) + 2px);
      pointer-events: none;
      background: var(--lumo-primary-color-50pct);
    }

    :host([theme~='no-row-borders']) [dragover] [part~='cell']::after {
      height: 2px;
    }

    [part~='row'][dragover='below'] [part~='cell']::after {
      top: 100%;
      bottom: auto;
      margin-top: -1px;
    }

    [part~='row'][dragover='above'] [part~='cell']::after {
      top: auto;
      bottom: 100%;
      margin-bottom: -1px;
    }

    [part~='row'][details-opened][dragover='below'] [part~='cell']:not([part~='details-cell'])::after,
    [part~='row'][details-opened][dragover='above'] [part~='details-cell']::after {
      display: none;
    }

    [part~='row'][dragover][dragover='on-top'] [part~='cell']::after {
      height: 100%;
    }

    [part~='row'][dragstart] {
      /* Add bottom-space to the row so the drag number doesn't get clipped. Needed for IE/Edge */
      border-bottom: 100px solid transparent;
      z-index: 100 !important;
      opacity: 0.9;
    }

    [part~='row'][dragstart] [part~='cell'] {
      border: none !important;
      box-shadow: none !important;
    }

    [part~='row'][dragstart] [part~='cell'][last-column] {
      border-radius: 0 var(--lumo-border-radius-s) var(--lumo-border-radius-s) 0;
    }

    [part~='row'][dragstart] [part~='cell'][first-column] {
      border-radius: var(--lumo-border-radius-s) 0 0 var(--lumo-border-radius-s);
    }

    [ios] [part~='row'][dragstart] [part~='cell'] {
      background: var(--lumo-primary-color-50pct);
    }

    #scroller:not([ios]) [part~='row'][dragstart]:not([dragstart=''])::after {
      display: block;
      position: absolute;
      left: var(--_grid-drag-start-x);
      top: var(--_grid-drag-start-y);
      z-index: 100;
      content: attr(dragstart);
      align-items: center;
      justify-content: center;
      box-sizing: border-box;
      padding: calc(var(--lumo-space-xs) * 0.8);
      color: var(--lumo-error-contrast-color);
      background-color: var(--lumo-error-color);
      border-radius: var(--lumo-border-radius-m);
      font-family: var(--lumo-font-family);
      font-size: var(--lumo-font-size-xxs);
      line-height: 1;
      font-weight: 500;
      text-transform: initial;
      letter-spacing: initial;
      min-width: calc(var(--lumo-size-s) * 0.7);
      text-align: center;
    }

    /* Headers and footers */

    [part~='header-cell'] ::slotted(vaadin-grid-cell-content),
    [part~='footer-cell'] ::slotted(vaadin-grid-cell-content),
    [part~='reorder-ghost'] {
      font-size: var(--lumo-font-size-s);
      font-weight: 500;
    }

    [part~='footer-cell'] ::slotted(vaadin-grid-cell-content) {
      font-weight: 400;
    }

    [part='row']:only-child [part~='header-cell'] {
      min-height: var(--lumo-size-xl);
    }

    /* Header borders */

    /* Hide first header row top border */
    :host(:not([theme~='no-row-borders'])) [part='row']:first-child [part~='header-cell'] {
      border-top: 0;
    }

    [part='row']:last-child [part~='header-cell'] {
      border-bottom: var(--_lumo-grid-border-width) solid transparent;
    }

    :host(:not([theme~='no-row-borders'])) [part='row']:last-child [part~='header-cell'] {
      border-bottom-color: var(--_lumo-grid-secondary-border-color);
    }

    /* Overflow uses a stronger border color */
    :host([overflow~='top']) [part='row']:last-child [part~='header-cell'] {
      border-bottom-color: var(--_lumo-grid-border-color);
    }

    /* Footer borders */

    [part='row']:first-child [part~='footer-cell'] {
      border-top: var(--_lumo-grid-border-width) solid transparent;
    }

    :host(:not([theme~='no-row-borders'])) [part='row']:first-child [part~='footer-cell'] {
      border-top-color: var(--_lumo-grid-secondary-border-color);
    }

    /* Overflow uses a stronger border color */
    :host([overflow~='bottom']) [part='row']:first-child [part~='footer-cell'] {
      border-top-color: var(--_lumo-grid-border-color);
    }

    /* Column reordering */

    :host([reordering]) [part~='cell'] {
      background: linear-gradient(var(--lumo-shade-20pct), var(--lumo-shade-20pct)) var(--lumo-base-color);
    }

    :host([reordering]) [part~='cell'][reorder-status='allowed'] {
      background: var(--lumo-base-color);
    }

    :host([reordering]) [part~='cell'][reorder-status='dragging'] {
      background: linear-gradient(var(--lumo-contrast-5pct), var(--lumo-contrast-5pct)) var(--lumo-base-color);
    }

    [part~='reorder-ghost'] {
      opacity: 0.85;
      box-shadow: var(--lumo-box-shadow-s);
      /* TODO Use the same styles as for the cell element (reorder-ghost copies styles from the cell element) */
      padding: var(--lumo-space-s) var(--lumo-space-m) !important;
    }

    /* Column resizing */

    [part='resize-handle'] {
      width: 3px;
      background-color: var(--lumo-primary-color-50pct);
      opacity: 0;
      transition: opacity 0.2s;
    }

    :host(:not([reordering])) *:not([column-resizing]) [part~='cell']:hover [part='resize-handle'],
    [part='resize-handle']:active {
      opacity: 1;
      transition-delay: 0.15s;
    }

    /* Column borders */

    :host([theme~='column-borders']) [part~='cell']:not([last-column]):not([part~='details-cell']) {
      border-right: var(--_lumo-grid-border-width) solid var(--_lumo-grid-secondary-border-color);
    }

    /* Frozen columns */

    [last-frozen] {
      border-right: var(--_lumo-grid-border-width) solid transparent;
      overflow: hidden;
    }

    :host([overflow~='left']) [part~='cell'][last-frozen]:not([part~='details-cell']) {
      border-right-color: var(--_lumo-grid-border-color);
    }

    /* Row stripes */

    :host([theme~='row-stripes']) [part~='row']:not([odd]) [part~='body-cell'],
    :host([theme~='row-stripes']) [part~='row']:not([odd]) [part~='details-cell'] {
      background-image: linear-gradient(var(--lumo-contrast-5pct), var(--lumo-contrast-5pct));
      background-repeat: repeat-x;
    }

    /* Selected row */

    /* Raise the selected rows above unselected rows (so that box-shadow can cover unselected rows) */
    :host(:not([reordering])) [part~='row'][selected] {
      z-index: 1;
    }

    :host(:not([reordering])) [part~='row'][selected] [part~='body-cell']:not([part~='details-cell']) {
      background-image: linear-gradient(var(--_lumo-grid-selected-row-color), var(--_lumo-grid-selected-row-color));
      background-repeat: repeat;
    }

    /* Cover the border of an unselected row */
    :host(:not([theme~='no-row-borders'])) [part~='row'][selected] [part~='cell']:not([part~='details-cell']) {
      box-shadow: 0 var(--_lumo-grid-border-width) 0 0 var(--_lumo-grid-selected-row-color);
    }

    /* Compact */

    :host([theme~='compact']) [part='row']:only-child [part~='header-cell'] {
      min-height: var(--lumo-size-m);
    }

    :host([theme~='compact']) [part~='cell'] {
      min-height: var(--lumo-size-s);
    }

    :host([theme~='compact']) [part='row'][first] [part~='cell']:not([part~='details-cell']) {
      min-height: calc(var(--lumo-size-s) - var(--_lumo-grid-border-width));
    }

    :host([theme~='compact']) [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      padding: var(--lumo-space-xs) var(--lumo-space-s);
    }

    /* Wrap cell contents */

    :host([theme~='wrap-cell-content']) [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      white-space: normal;
    }

    /* RTL specific styles */

    :host([dir='rtl']) [part~='row'][dragstart] [part~='cell'][last-column] {
      border-radius: var(--lumo-border-radius-s) 0 0 var(--lumo-border-radius-s);
    }

    :host([dir='rtl']) [part~='row'][dragstart] [part~='cell'][first-column] {
      border-radius: 0 var(--lumo-border-radius-s) var(--lumo-border-radius-s) 0;
    }

    :host([dir='rtl'][theme~='column-borders']) [part~='cell']:not([last-column]):not([part~='details-cell']) {
      border-right: none;
      border-left: var(--_lumo-grid-border-width) solid var(--_lumo-grid-secondary-border-color);
    }

    :host([dir='rtl']) [last-frozen] {
      border-right: none;
      border-left: var(--_lumo-grid-border-width) solid transparent;
    }

    :host([dir='rtl'][overflow~='right']) [part~='cell'][last-frozen]:not([part~='details-cell']) {
      border-left-color: var(--_lumo-grid-border-color);
    }
  `,{moduleId:"lumo-grid"}),$Xt("vaadin-checkbox",GXt`
    :host(.vaadin-grid-select-all-checkbox) {
      font-size: var(--lumo-font-size-m);
    }
  `,{moduleId:"vaadin-grid-select-all-checkbox-lumo"});
/**
    @license
    Copyright (c) 2016 The Polymer Project Authors. All rights reserved.
    This code may only be used under the BSD style license found at http://polymer.github.io/LICENSE.txt
    The complete set of authors may be found at http://polymer.github.io/AUTHORS.txt
    The complete set of contributors may be found at http://polymer.github.io/CONTRIBUTORS.txt
    Code distributed by Google as part of the polymer project is also
    subject to an additional IP rights grant found at http://polymer.github.io/PATENTS.txt
    */
const C$t=navigator.userAgent.match(/iP(?:hone|ad;(?: U;)? CPU) OS (\d+)/),A$t=C$t&&C$t[1]>=8,k$t=Ir({behaviors:[go,Do],_ratio:.5,_scrollerPaddingTop:0,_scrollPosition:0,_physicalSize:0,_physicalAverage:0,_physicalAverageCount:0,_physicalTop:0,_virtualCount:0,_estScrollHeight:0,_scrollHeight:0,_viewportHeight:0,_viewportWidth:0,_physicalItems:null,_physicalSizes:null,_firstVisibleIndexVal:null,_lastVisibleIndexVal:null,_maxPages:2,_focusedVirtualIndex:-1,_templateCost:0,get _physicalBottom(){return this._physicalTop+this._physicalSize},get _scrollBottom(){return this._scrollPosition+this._viewportHeight},get _virtualEnd(){return this._virtualStart+this._physicalCount-1},get _hiddenContentSize(){return this._physicalSize-this._viewportHeight},get _maxScrollTop(){return this._estScrollHeight-this._viewportHeight+this._scrollOffset},get _maxVirtualStart(){return Math.max(0,this._virtualCount-this._physicalCount)},set _virtualStart(t){t=this._clamp(t,0,this._maxVirtualStart),this._virtualStartVal=t},get _virtualStart(){return this._virtualStartVal||0},set _physicalStart(t){(t%=this._physicalCount)<0&&(t=this._physicalCount+t),this._physicalStartVal=t},get _physicalStart(){return this._physicalStartVal||0},get _physicalEnd(){return(this._physicalStart+this._physicalCount-1)%this._physicalCount},set _physicalCount(t){this._physicalCountVal=t},get _physicalCount(){return this._physicalCountVal||0},get _optPhysicalSize(){return 0===this._viewportHeight?1/0:this._viewportHeight*this._maxPages},get _isVisible(){return Boolean(this.offsetWidth||this.offsetHeight)},get firstVisibleIndex(){let t=this._firstVisibleIndexVal;if(null==t){let e=this._physicalTop+this._scrollOffset;t=this._iterateItems((function(t,n){if(e+=this._physicalSizes[t],e>this._scrollPosition)return n}))||0,this._firstVisibleIndexVal=t}return t},get lastVisibleIndex(){let t=this._lastVisibleIndexVal;if(null==t){let e=this._physicalTop+this._scrollOffset;this._iterateItems((function(n,i){e<this._scrollBottom&&(t=i),e+=this._physicalSizes[n]})),this._lastVisibleIndexVal=t}return t},get _scrollOffset(){return this._scrollerPaddingTop},attached:function(){this._debounce("_render",this._render,_t),this.listen(this,"iron-resize","_resizeHandler")},detached:function(){this.unlisten(this,"iron-resize","_resizeHandler")},updateViewportBoundaries:function(){const t=window.getComputedStyle(this);this._scrollerPaddingTop=this.scrollTarget===this?0:parseInt(t["padding-top"],10),this._isRTL=Boolean("rtl"===t.direction),this._viewportWidth=this.$.items.offsetWidth,this._viewportHeight=this._scrollTargetHeight},_scrollHandler:function(){const t=Math.max(0,Math.min(this._maxScrollTop,this._scrollTop));let e=t-this._scrollPosition;const n=e>=0;if(this._scrollPosition=t,this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,Math.abs(e)>this._physicalSize&&this._physicalSize>0){e-=this._scrollOffset;const t=Math.round(e/this._physicalAverage);this._virtualStart=this._virtualStart+t,this._physicalStart=this._physicalStart+t,this._physicalTop=Math.floor(this._virtualStart)*this._physicalAverage,this._update()}else if(this._physicalCount>0){const{physicalTop:t,indexes:e}=this._getReusables(n);n?(this._physicalTop=t,this._virtualStart=this._virtualStart+e.length,this._physicalStart=this._physicalStart+e.length):(this._virtualStart=this._virtualStart-e.length,this._physicalStart=this._physicalStart-e.length),this._update(e,n?null:e),this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,0),vt)}},_getReusables:function(t){let e,n,i;const r=[],o=this._hiddenContentSize*this._ratio,a=this._virtualStart,s=this._virtualEnd,l=this._physicalCount;let c=this._physicalTop+this._scrollOffset;const u=this._scrollTop,h=this._scrollBottom;for(t?(e=this._physicalStart,n=u-c):(e=this._physicalEnd,n=this._physicalBottom+this._scrollOffset-h);i=this._physicalSizes[e],n-=i,!(r.length>=l||n<=o);)if(t){if(s+r.length+1>=this._virtualCount)break;if(c+i>=u-this._scrollOffset)break;r.push(e),c+=i,e=(e+1)%l}else{if(a-r.length<=0)break;if(c+this._physicalSize-i<=h)break;r.push(e),c-=i,e=0===e?l-1:e-1}return{indexes:r,physicalTop:c-this._scrollOffset}},_update:function(t,e){if(!(t&&0===t.length||0===this._physicalCount)){if(this._assignModels(t),this._updateMetrics(t),e)for(;e.length;){const t=e.pop();this._physicalTop-=this._physicalSizes[t]}this._positionItems(),this._updateScrollerSize()}},_isClientFull:function(){return 0!=this._scrollBottom&&this._physicalBottom-1>=this._scrollBottom&&this._physicalTop<=this._scrollPosition},_increasePoolIfNeeded:function(t){const e=this._clamp(this._physicalCount+t,3,this._virtualCount-this._virtualStart)-this._physicalCount;let n=Math.round(.5*this._physicalCount);if(!(e<0)){if(e>0){const t=window.performance.now();[].push.apply(this._physicalItems,this._createPool(e));for(let t=0;t<e;t++)this._physicalSizes.push(0);this._physicalCount=this._physicalCount+e,this._physicalStart>this._physicalEnd&&this._isIndexRendered(this._focusedVirtualIndex)&&this._getPhysicalIndex(this._focusedVirtualIndex)<this._physicalEnd&&(this._physicalStart=this._physicalStart+e),this._update(),this._templateCost=(window.performance.now()-t)/e,n=Math.round(.5*this._physicalCount)}this._virtualEnd>=this._virtualCount-1||0===n||(this._isClientFull()?this._physicalSize<this._optPhysicalSize&&this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,this._clamp(Math.round(50/this._templateCost),1,n)),yt):this._debounce("_increasePoolIfNeeded",this._increasePoolIfNeeded.bind(this,n),vt))}},_render:function(){if(this.isAttached&&this._isVisible)if(0!==this._physicalCount){const{physicalTop:t,indexes:e}=this._getReusables(!0);this._physicalTop=t,this._virtualStart=this._virtualStart+e.length,this._physicalStart=this._physicalStart+e.length,this._update(e),this._update(),this._increasePoolIfNeeded(0)}else this._virtualCount>0&&(this.updateViewportBoundaries(),this._increasePoolIfNeeded(3))},_itemsChanged:function(t){"items"===t.path&&(this._virtualStart=0,this._physicalTop=0,this._virtualCount=this.items?this.items.length:0,this._physicalIndexForKey={},this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,this._physicalCount=this._physicalCount||0,this._physicalItems=this._physicalItems||[],this._physicalSizes=this._physicalSizes||[],this._physicalStart=0,this._scrollTop>this._scrollOffset&&this._resetScrollPosition(0),this._debounce("_render",this._render,_t))},_iterateItems:function(t,e){let n,i,r,o;if(2===arguments.length&&e){for(o=0;o<e.length;o++)if(n=e[o],i=this._computeVidx(n),null!=(r=t.call(this,n,i)))return r}else{for(n=this._physicalStart,i=this._virtualStart;n<this._physicalCount;n++,i++)if(null!=(r=t.call(this,n,i)))return r;for(n=0;n<this._physicalStart;n++,i++)if(null!=(r=t.call(this,n,i)))return r}},_computeVidx:function(t){return t>=this._physicalStart?this._virtualStart+(t-this._physicalStart):this._virtualStart+(this._physicalCount-this._physicalStart)+t},_updateMetrics:function(t){if(!this._isVisible)return;Di();let e=0,n=0;const i=this._physicalAverageCount,r=this._physicalAverage;this._iterateItems((function(t){n+=this._physicalSizes[t],this._physicalSizes[t]=this._physicalItems[t].offsetHeight,e+=this._physicalSizes[t],this._physicalAverageCount+=this._physicalSizes[t]?1:0}),t),this._physicalSize=this._physicalSize+e-n,this._physicalAverageCount!==i&&(this._physicalAverage=Math.round((r*i+e)/this._physicalAverageCount))},_positionItems:function(){this._adjustScrollPosition();let t=this._physicalTop;this._iterateItems((function(e){this.translate3d(0,t+"px",0,this._physicalItems[e]),t+=this._physicalSizes[e]}))},_adjustScrollPosition:function(){const t=0===this._virtualStart?this._physicalTop:Math.min(this._scrollPosition+this._physicalTop,0);if(0!==t){this._physicalTop=this._physicalTop-t;const e=this._scrollTop;!A$t&&e>0&&this._resetScrollPosition(e-t)}},_resetScrollPosition:function(t){this.scrollTarget&&t>=0&&(this._scrollTop=t,this._scrollPosition=this._scrollTop)},_updateScrollerSize:function(t){this._estScrollHeight=this._physicalBottom+Math.max(this._virtualCount-this._physicalCount-this._virtualStart,0)*this._physicalAverage,((t=(t=t||0===this._scrollHeight)||this._scrollPosition>=this._estScrollHeight-this._physicalSize)||Math.abs(this._estScrollHeight-this._scrollHeight)>=this._viewportHeight)&&(this.$.items.style.height=this._estScrollHeight+"px",this._scrollHeight=this._estScrollHeight)},scrollToIndex:function(t){if("number"!=typeof t||t<0||t>this.items.length-1)return;if(Di(),0===this._physicalCount)return;t=this._clamp(t,0,this._virtualCount-1),(!this._isIndexRendered(t)||t>=this._maxVirtualStart)&&(this._virtualStart=t-1),this._assignModels(),this._updateMetrics(),this._physicalTop=Math.floor(this._virtualStart)*this._physicalAverage;let e=this._physicalStart,n=this._virtualStart,i=0;const r=this._hiddenContentSize;for(;n<t&&i<=r;)i+=this._physicalSizes[e],e=(e+1)%this._physicalCount,n++;this._updateScrollerSize(!0),this._positionItems(),this._resetScrollPosition(this._physicalTop+this._scrollOffset+i),this._increasePoolIfNeeded(0),this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null},_resetAverage:function(){this._physicalAverage=0,this._physicalAverageCount=0},_resizeHandler:function(){this._debounce("_render",(function(){this._firstVisibleIndexVal=null,this._lastVisibleIndexVal=null,this.updateViewportBoundaries(),this._isVisible?(this.toggleScrollListener(!0),this._resetAverage(),this._render()):this.toggleScrollListener(!1)}),_t)},_isIndexRendered:function(t){return t>=this._virtualStart&&t<=this._virtualEnd},_getPhysicalIndex:function(t){return(this._physicalStart+(t-this._virtualStart))%this._physicalCount},_clamp:function(t,e,n){return Math.min(n,Math.max(e,t))},_debounce:function(t,e,n){this._debouncers=this._debouncers||{},this._debouncers[t]=Tn.debounce(this._debouncers[t],n,e.bind(this)),An(this._debouncers[t])}});
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
class L$t extends k$t{static get properties(){return{size:{type:Number,notify:!0},_vidxOffset:{type:Number,value:0}}}static get observers(){return["_effectiveSizeChanged(_effectiveSize)"]}connectedCallback(){super.connectedCallback(),this._scrollHandler()}_updateScrollerItem(){}_afterScroll(){}_getRowTarget(){}_createScrollerRows(){}_canPopulate(){}scrollToIndex(t){this._warnPrivateAPIAccess("scrollToIndex"),this._scrollingToIndex=!0,t=Math.min(Math.max(t,0),this._effectiveSize-1),this.$.table.scrollTop=t/this._effectiveSize*(this.$.table.scrollHeight-this.$.table.offsetHeight),this._scrollHandler(),this._accessIronListAPI((()=>this._maxScrollTop))&&this._virtualCount<this._effectiveSize&&this._adjustVirtualIndexOffset(1e6),this._accessIronListAPI((()=>super.scrollToIndex(t-this._vidxOffset))),this._scrollHandler();const e=Array.from(this.$.items.children).filter((e=>e.index===t))[0];if(e){const t=e.getBoundingClientRect().top-this.$.header.getBoundingClientRect().bottom;Math.abs(t)>1&&(this.$.table.scrollTop+=t,this._scrollHandler())}this._scrollingToIndex=!1}_effectiveSizeChanged(t){let e,n=0;this._iterateItems(((t,i)=>{if(i===this._firstVisibleIndex){const i=this._physicalItems[t];e=i.index,n=i.getBoundingClientRect().top}})),this.items&&t<this.items.length&&(this._scrollTop=0),Array.isArray(this.items)||(this.items={length:Math.min(t,1e5)}),this._accessIronListAPI((()=>super._itemsChanged({path:"items"}))),this._virtualCount=Math.min(this.items.length,t)||0,0===this._scrollTop&&(this._accessIronListAPI((()=>this._scrollToIndex(Math.min(t-1,e)))),this._iterateItems((t=>{const i=this._physicalItems[t];if(i.index===e&&(this.$.table.scrollTop+=Math.round(i.getBoundingClientRect().top-n)),i.index===this._focusedItemIndex&&this._itemsFocusable&&this.$.items.contains(this.shadowRoot.activeElement)){const t=Array.from(this._itemsFocusable.parentElement.children).indexOf(this._itemsFocusable);i.children[t].focus()}}))),this._assignModels(),requestAnimationFrame((()=>this._update())),this.__updateFooterPositioning()}_positionItems(){let t;this._adjustScrollPosition(),isNaN(this._physicalTop)&&(t=!0,this._physicalTop=0);let e=this._physicalTop;this._iterateItems((t=>{this._physicalItems[t].style.transform=`translateY(${e}px)`,e+=this._physicalSizes[t]})),t&&this._scrollToIndex(0)}_increasePoolIfNeeded(t){0===t&&this._scrollingToIndex||!this._canPopulate()||!this._effectiveSize||(this._initialPoolCreated?this._optPhysicalSize!==1/0&&(this._debounceIncreasePool=Tn.debounce(this._debounceIncreasePool,_t,(()=>{this._updateMetrics();let t=Math.ceil((this._optPhysicalSize-this._physicalSize)/this._physicalAverage);this._physicalCount+t>this._effectiveSize&&(t=Math.max(0,this._effectiveSize-this._physicalCount)),this._physicalSize&&t>0&&this._optPhysicalSize!==1/0&&(super._increasePoolIfNeeded(t),this.__reorderChildNodes())}))):(this._initialPoolCreated=!0,super._increasePoolIfNeeded(25)))}__reorderChildNodes(){const t=Array.from(this.$.items.childNodes);t.reduce(((t,e,n,i)=>{if(0===n||i[n-1].index===e.index-1)return t}),!0)||t.sort(((t,e)=>t.index-e.index)).forEach((t=>this.$.items.appendChild(t)))}_createPool(t){const e=document.createDocumentFragment(),n=this._createScrollerRows(t);n.forEach((t=>e.appendChild(t))),this._getRowTarget().appendChild(e);const i=this.querySelector("[slot]");if(i){const t=i.getAttribute("slot");i.setAttribute("slot","foo-bar"),i.setAttribute("slot",t)}return Li(this,(()=>this.notifyResize())),n}_assignModels(t){this._iterateItems(((t,e)=>{const n=this._physicalItems[t];this._toggleAttribute("hidden",e>=this._effectiveSize,n),this._updateScrollerItem(n,e+(this._vidxOffset||0))}),t)}_scrollHandler(){const t=this.$.table.scrollTop-this._scrollPosition;this._accessIronListAPI(super._scrollHandler);const e=this._vidxOffset;this._accessIronListAPI((()=>this._maxScrollTop))&&this._virtualCount<this._effectiveSize?this._adjustVirtualIndexOffset(t):this._vidxOffset=0,this._vidxOffset!==e&&this._update(),this._afterScroll()}_adjustVirtualIndexOffset(t){if(Math.abs(t)>1e4){if(this._noScale)return void(this._noScale=!1);const t=this.$.table.scrollTop/(this.$.table.scrollHeight-this.$.table.offsetHeight);this._vidxOffset=Math.round(t*this._effectiveSize-t*this._virtualCount)}else{const t=this._vidxOffset||0,e=1e3,n=100;0===this._scrollTop?(this._vidxOffset=0,t!==this._vidxOffset&&super.scrollToIndex(0)):this.firstVisibleIndex<e&&this._vidxOffset>0&&(this._vidxOffset-=Math.min(this._vidxOffset,n),t!==this._vidxOffset&&super.scrollToIndex(this.firstVisibleIndex+(t-this._vidxOffset)),this._noScale=!0);const i=this._effectiveSize-this._virtualCount;this._scrollTop>=this._maxScrollTop&&this._maxScrollTop>0?(this._vidxOffset=i,t!==this._vidxOffset&&super.scrollToIndex(this._virtualCount)):this.firstVisibleIndex>this._virtualCount-e&&this._vidxOffset<i&&(this._vidxOffset+=Math.min(i-this._vidxOffset,n),t!==this._vidxOffset&&super.scrollToIndex(this.firstVisibleIndex-(this._vidxOffset-t)),this._noScale=!0)}}_accessIronListAPI(t){this._warnPrivateAPIAccessAsyncEnabled=!1;const e=t.apply(this);return this._debouncerWarnPrivateAPIAccess=Tn.debounce(this._debouncerWarnPrivateAPIAccess,_t,(()=>this._warnPrivateAPIAccessAsyncEnabled=!0)),e}_debounceRender(t,e){super._debounceRender((()=>this._accessIronListAPI(t)),e)}_warnPrivateAPIAccess(t){this._warnPrivateAPIAccessAsyncEnabled&&console.warn(`Accessing private API (${t})!`)}_render(){this._accessIronListAPI(super._render)}_itemsChanged(){}get _firstVisibleIndex(){return this._accessIronListAPI((()=>super.firstVisibleIndex))}get _lastVisibleIndex(){return this._accessIronListAPI((()=>super.lastVisibleIndex))}_scrollToIndex(t){this._accessIronListAPI((()=>this.scrollToIndex(t)))}get firstVisibleIndex(){return this._warnPrivateAPIAccess("firstVisibleIndex"),super.firstVisibleIndex}set firstVisibleIndex(t){this._warnPrivateAPIAccess("firstVisibleIndex"),super.firstVisibleIndex=t}get lastVisibleIndex(){return this._warnPrivateAPIAccess("lastVisibleIndex"),super.lastVisibleIndex}set lastVisibleIndex(t){this._warnPrivateAPIAccess("lastVisibleIndex"),super.lastVisibleIndex=t}updateViewportBoundaries(){this._warnPrivateAPIAccess("updateViewportBoundaries"),super.updateViewportBoundaries.apply(this,arguments)}_resizeHandler(){super._resizeHandler(),Di()}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */const P$t=t=>class extends t{static get observers(){return["_a11yUpdateGridSize(size, _columnTree, _columnTree.*)"]}_a11yGetHeaderRowCount(t){return t.filter((t=>t.some((t=>t._headerTemplate||t.headerRenderer||t.path||t.header)))).length}_a11yGetFooterRowCount(t){return t.filter((t=>t.some((t=>t._headerTemplate||t.headerRenderer)))).length}_a11yUpdateGridSize(t,e){if(void 0===t||void 0===e)return;const n=e[e.length-1];this.$.table.setAttribute("aria-rowcount",t+this._a11yGetHeaderRowCount(e)+this._a11yGetFooterRowCount(e)),this.$.table.setAttribute("aria-colcount",n&&n.length||0),this._a11yUpdateHeaderRows(),this._a11yUpdateFooterRows()}_a11yUpdateHeaderRows(){Array.from(this.$.header.children).forEach(((t,e)=>t.setAttribute("aria-rowindex",e+1)))}_a11yUpdateFooterRows(){Array.from(this.$.footer.children).forEach(((t,e)=>t.setAttribute("aria-rowindex",this._a11yGetHeaderRowCount(this._columnTree)+this.size+e+1)))}_a11yUpdateRowRowindex(t,e){t.setAttribute("aria-rowindex",e+this._a11yGetHeaderRowCount(this._columnTree)+1)}_a11yUpdateRowSelected(t,e){t.setAttribute("aria-selected",Boolean(e)),Array.from(t.children).forEach((t=>t.setAttribute("aria-selected",Boolean(e))))}_a11yUpdateRowLevel(t,e){t.setAttribute("aria-level",e+1)}_a11yUpdateRowDetailsOpened(t,e){Array.from(t.children).forEach((t=>{"boolean"==typeof e?t.setAttribute("aria-expanded",e):t.hasAttribute("aria-expanded")&&t.removeAttribute("aria-expanded")}))}_a11ySetRowDetailsCell(t,e){Array.from(t.children).forEach((t=>{t!==e&&t.setAttribute("aria-controls",e.id)}))}_a11yUpdateCellColspan(t,e){t.setAttribute("aria-colspan",Number(e))}_a11yUpdateSorters(){Array.from(this.querySelectorAll("vaadin-grid-sorter")).forEach((t=>{let e=t.parentNode;for(;e&&"vaadin-grid-cell-content"!==e.localName;)e=e.parentNode;e&&e.assignedSlot&&e.assignedSlot.parentNode.setAttribute("aria-sort",{asc:"ascending",desc:"descending"}[String(t.direction)]||"none")}))}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,N$t=t=>class extends t{static get properties(){return{activeItem:{type:Object,notify:!0,value:null}}}ready(){super.ready(),this.$.scroller.addEventListener("click",this._onClick.bind(this)),this.addEventListener("cell-activate",this._activateItem.bind(this))}_activateItem(t){const e=t.detail.model,n=e?e.item:null;n&&(this.activeItem=this._itemsEqual(this.activeItem,n)?null:n)}_onClick(t){if(t.defaultPrevented)return;const e=t.composedPath(),n=e[e.indexOf(this.$.table)-3];if(!n||n.getAttribute("part").indexOf("details-cell")>-1)return;const i=n._content,r=this.getRootNode().activeElement;i.contains(r)||this._isFocusable(t.target)||this.dispatchEvent(new CustomEvent("cell-activate",{detail:{model:this.__getRowModel(n.parentElement)}}))}_isFocusable(t){return I$t(t)}},I$t=t=>{if(!t.parentNode)return!1;const e=-1!==Array.from(t.parentNode.querySelectorAll("[tabindex], button, input, select, textarea, object, iframe, label, a[href], area[href]")).filter((t=>"cell body-cell"!==t.getAttribute("part"))).indexOf(t);return!t.disabled&&e},R$t=t=>class extends t{static get properties(){return{items:Array}}static get observers(){return["_itemsChanged(items, items.*, isAttached)"]}_itemsChanged(t,e,n){if(n){if(!Array.isArray(t))return null==t&&(this.size=0),void(this.dataProvider===this._arrayDataProvider&&(this.dataProvider=void 0));this.size=t.length,this.dataProvider=this.dataProvider||this._arrayDataProvider,this.clearCache(),this._ensureFirstPageLoaded()}}_arrayDataProvider(t,e){let n=(Array.isArray(this.items)?this.items:[]).slice(0);this._filters&&this._checkPaths(this._filters,"filtering",n)&&(n=this._filter(n)),this.size=n.length,t.sortOrders.length&&this._checkPaths(this._sorters,"sorting",n)&&(n=n.sort(this._multiSort.bind(this)));const i=t.page*t.pageSize;e(n.slice(i,i+t.pageSize),n.length)}_checkPaths(t,e,n){if(!n.length)return!1;let i=!0;for(let r in t){const o=t[r].path;if(!o||-1===o.indexOf("."))continue;const a=o.replace(/\.[^.]*$/,"");void 0===fo.get(a,n[0])&&(console.warn(`Path "${o}" used for ${e} does not exist in all of the items, ${e} is disabled.`),i=!1)}return i}_multiSort(t,e){return this._sorters.map((n=>"asc"===n.direction?this._compare(fo.get(n.path,t),fo.get(n.path,e)):"desc"===n.direction?this._compare(fo.get(n.path,e),fo.get(n.path,t)):0)).reduce(((t,e)=>t||e),0)}_normalizeEmptyValue(t){return[void 0,null].indexOf(t)>=0?"":isNaN(t)?t.toString():t}_compare(t,e){return(t=this._normalizeEmptyValue(t))<(e=this._normalizeEmptyValue(e))?-1:t>e?1:0}_filter(t){return t.filter((t=>0===this._filters.filter((e=>{const n=this._normalizeEmptyValue(fo.get(e.path,t)),i=this._normalizeEmptyValue(e.value).toString().toLowerCase();return-1===n.toString().toLowerCase().indexOf(i)})).length))}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,O$t=t=>class extends(pi(t)){ready(){super.ready();const t=this.$.scroller;ii(t,"track",this._onHeaderTrack.bind(this)),t.addEventListener("touchmove",(e=>t.hasAttribute("column-resizing")&&e.preventDefault())),t.addEventListener("contextmenu",(t=>"resize-handle"==t.target.getAttribute("part")&&t.preventDefault())),t.addEventListener("mousedown",(t=>"resize-handle"===t.target.getAttribute("part")&&t.preventDefault()))}_onHeaderTrack(t){const e=t.target;if("resize-handle"===e.getAttribute("part")){let n=e.parentElement._column;for(this._toggleAttribute("column-resizing",!0,this.$.scroller);"vaadin-grid-column-group"===n.localName;)n=Array.prototype.slice.call(n._childColumns,0).sort((function(t,e){return t._order-e._order})).filter((function(t){return!t.hidden})).pop();const i=Array.from(this.$.header.querySelectorAll('[part~="row"]:last-child [part~="cell"]')),r=i.filter((t=>t._column===n))[0];if(r.offsetWidth){const e=window.getComputedStyle(r),i=10+parseInt(e.paddingLeft)+parseInt(e.paddingRight)+parseInt(e.borderLeftWidth)+parseInt(e.borderRightWidth)+parseInt(e.marginLeft)+parseInt(e.marginRight),o=r.offsetWidth+(this.__isRTL?r.getBoundingClientRect().left-t.detail.x:t.detail.x-r.getBoundingClientRect().right);n.width=Math.max(i,o)+"px",n.flexGrow=0}i.sort((function(t,e){return t._column._order-e._column._order})).forEach((function(t,e,n){e<n.indexOf(r)&&(t._column.width=t.offsetWidth+"px",t._column.flexGrow=0)})),"end"===t.detail.state&&(this._toggleAttribute("column-resizing",!1,this.$.scroller),this.dispatchEvent(new CustomEvent("column-resize",{detail:{resizedColumn:n}}))),this._resizeHandler()}}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,z$t=class t{constructor(t,e,n){this.grid=t,this.parentCache=e,this.parentItem=n,this.itemCaches={},this.items={},this.effectiveSize=0,this.size=0,this.pendingRequests={}}isLoading(){return Boolean(Object.keys(this.pendingRequests).length||Object.keys(this.itemCaches).filter((t=>this.itemCaches[t].isLoading()))[0])}getItemForIndex(t){const{cache:e,scaledIndex:n}=this.getCacheAndIndex(t);return e.items[n]}updateSize(){this.effectiveSize=!this.parentItem||this.grid._isExpanded(this.parentItem)?this.size+Object.keys(this.itemCaches).reduce(((t,e)=>{const n=this.itemCaches[e];return n.updateSize(),t+n.effectiveSize}),0):0}ensureSubCacheForScaledIndex(e){if(!this.itemCaches[e]){const n=new t(this.grid,this,this.items[e]);this.itemCaches[e]=n,this.grid._loadPage(0,n)}}getCacheAndIndex(t){let e=t;const n=Object.keys(this.itemCaches);for(let t=0;t<n.length;t++){const i=Number(n[t]),r=this.itemCaches[i];if(e<=i)return{cache:this,scaledIndex:e};if(e<=i+r.effectiveSize)return r.getCacheAndIndex(e-i-1);e-=r.effectiveSize}return{cache:this,scaledIndex:e}}},D$t=t=>class extends t{static get properties(){return{pageSize:{type:Number,value:50,observer:"_pageSizeChanged"},dataProvider:{type:Object,notify:!0,observer:"_dataProviderChanged"},loading:{type:Boolean,notify:!0,readOnly:!0,reflectToAttribute:!0},_cache:{type:Object,value:function(){return new z$t(this)}},itemIdPath:{type:String,value:null},expandedItems:{type:Object,notify:!0,value:()=>[]}}}static get observers(){return["_sizeChanged(size)","_itemIdPathChanged(itemIdPath)","_expandedItemsChanged(expandedItems.*)"]}_sizeChanged(t){const e=t-this._cache.size;this._cache.size+=e,this._cache.effectiveSize+=e,this._effectiveSize=this._cache.effectiveSize,this._increasePoolIfNeeded(0),this._debounceIncreasePool&&this._debounceIncreasePool.flush()}_getItem(t,e){if(t>=this._effectiveSize)return;e.index=t;const{cache:n,scaledIndex:i}=this._cache.getCacheAndIndex(t),r=n.items[i];r?(this._toggleAttribute("loading",!1,e),this._updateItem(e,r),this._isExpanded(r)&&n.ensureSubCacheForScaledIndex(i)):(this._toggleAttribute("loading",!0,e),this._loadPage(this._getPageForIndex(i),n))}_expandedInstanceChangedCallback(t,e){void 0!==t.item&&(e?this.expandItem(t.item):this.collapseItem(t.item))}getItemId(t){return this.itemIdPath?this.get(this.itemIdPath,t):t}_isExpanded(t){return this.__expandedKeys.has(this.getItemId(t))}_expandedItemsChanged(){this.__cacheExpandedKeys(),this._cache.updateSize(),this._effectiveSize=this._cache.effectiveSize,this._assignModels()}_itemIdPathChanged(){this.__cacheExpandedKeys()}__cacheExpandedKeys(){this.expandedItems&&(this.__expandedKeys=new Set,this.expandedItems.forEach((t=>{this.__expandedKeys.add(this.getItemId(t))})))}expandItem(t){this._isExpanded(t)||(this.expandedItems=[...this.expandedItems,t])}collapseItem(t){this._isExpanded(t)&&(this.expandedItems=this.expandedItems.filter((e=>!this._itemsEqual(e,t))))}_getIndexLevel(t){let{cache:e}=this._cache.getCacheAndIndex(t),n=0;for(;e.parentCache;)e=e.parentCache,n++;return n}_canPopulate(){return Boolean(this._hasData&&this._columnTree)}_loadPage(t,e){if(!e.pendingRequests[t]&&this.dataProvider){this._setLoading(!0),e.pendingRequests[t]=!0;const n={page:t,pageSize:this.pageSize,sortOrders:this._mapSorters(),filters:this._mapFilters(),parentItem:e.parentItem};this._debounceIncreasePool&&this._debounceIncreasePool.flush(),this.dataProvider(n,((i,r)=>{void 0!==r?e.size=r:n.parentItem&&(e.size=i.length);const o=Array.from(this.$.items.children).map((t=>t._item));i.forEach(((n,i)=>{const r=t*this.pageSize+i;e.items[r]=n,this._isExpanded(n)&&o.indexOf(n)>-1&&e.ensureSubCacheForScaledIndex(r)})),this._hasData=!0,delete e.pendingRequests[t],this._debouncerApplyCachedData=Tn.debounce(this._debouncerApplyCachedData,gt.after(0),(()=>{this._setLoading(!1),this._cache.updateSize(),this._effectiveSize=this._cache.effectiveSize,Array.from(this.$.items.children).filter((t=>!t.hidden)).forEach((t=>{this._cache.getItemForIndex(t.index)&&this._getItem(t.index,t)})),this._increasePoolIfNeeded(0),this.__scrollToPendingIndex()})),this._cache.isLoading()||this._debouncerApplyCachedData.flush(),this.__itemsReceived()}))}}_getPageForIndex(t){return Math.floor(t/this.pageSize)}clearCache(){this._cache=new z$t(this),Array.from(this.$.items.children).forEach((t=>{Array.from(t.children).forEach((t=>{t._instance&&t._instance._setPendingProperty("item",{},!1)}))})),this._cache.size=this.size||0,this._cache.updateSize(),this._hasData=!1,this._assignModels(),this._effectiveSize&&this._initialPoolCreated||this._loadPage(0,this._cache)}_pageSizeChanged(t,e){void 0!==e&&t!==e&&this.clearCache()}_checkSize(){void 0===this.size&&0===this._effectiveSize&&console.warn("The <vaadin-grid> needs the total number of items in order to display rows. Set the total number of items to the `size` property, or provide the total number of items in the second argument of the `dataProvider`’s `callback` call.")}_dataProviderChanged(t,e){void 0!==e&&this.clearCache(),t&&this.items&&this.items.length&&this._scrollToIndex(this._firstVisibleIndex),this._ensureFirstPageLoaded(),this._debouncerCheckSize=Tn.debounce(this._debouncerCheckSize,gt.after(2e3),this._checkSize.bind(this)),this._scrollHandler()}_ensureFirstPageLoaded(){this._hasData||this._loadPage(0,this._cache)}_itemsEqual(t,e){return this.getItemId(t)===this.getItemId(e)}_getItemIndexInArray(t,e){let n=-1;return e.forEach(((e,i)=>{this._itemsEqual(e,t)&&(n=i)})),n}scrollToIndex(t){super.scrollToIndex(t),isNaN(t)||!this._cache.isLoading()&&this.clientHeight||(this.__pendingScrollToIndex=t)}__scrollToPendingIndex(){if(this.__pendingScrollToIndex&&this.$.items.children.length){const t=this.__pendingScrollToIndex;delete this.__pendingScrollToIndex,this._debounceIncreasePool&&this._debounceIncreasePool.flush(),this.scrollToIndex(t)}}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,B$t=t=>class extends t{ready(){super.ready(),this._addNodeObserver()}_hasColumnGroups(t){for(let e=0;e<t.length;e++)if("vaadin-grid-column-group"===t[e].localName)return!0;return!1}_getChildColumns(t){return zi.getFlattenedNodes(t).filter(this._isColumnElement)}_flattenColumnGroups(t){return t.map((t=>"vaadin-grid-column-group"===t.localName?this._getChildColumns(t):[t])).reduce(((t,e)=>t.concat(e)),[])}_getColumnTree(){const t=[];for(let e=zi.getFlattenedNodes(this).filter(this._isColumnElement);t.push(e),this._hasColumnGroups(e);)e=this._flattenColumnGroups(e);return t}_updateColumnTree(){const t=this._getColumnTree();this._arrayEquals(t,this._columnTree)||(this._columnTree=t)}_addNodeObserver(){this._observer=new zi(this,(t=>{const e=t.addedNodes.filter((t=>"template"===t.localName&&t.classList.contains("row-details")))[0];e&&this._rowDetailsTemplate!==e&&(this._rowDetailsTemplate=e);const n=t=>t.filter(this._isColumnElement).length>0;if(n(t.addedNodes)||n(t.removedNodes)){const e=t.removedNodes.flatMap((t=>t._allCells)),n=t=>e.filter((e=>e._content.contains(t))).length;this.__removeSorters(this._sorters.filter(n)),this.__removeFilters(this._filters.filter(n)),this._updateColumnTree()}this._debouncerCheckImports=Tn.debounce(this._debouncerCheckImports,gt.after(2e3),this._checkImports.bind(this)),this._ensureFirstPageLoaded()}))}_arrayEquals(t,e){if(!t||!e||t.length!=e.length)return!1;for(let n=0,i=t.length;n<i;n++)if(t[n]instanceof Array&&e[n]instanceof Array){if(!this._arrayEquals(t[n],e[n]))return!1}else if(t[n]!=e[n])return!1;return!0}_checkImports(){["vaadin-grid-column-group","vaadin-grid-filter","vaadin-grid-filter-column","vaadin-grid-tree-toggle","vaadin-grid-selection-column","vaadin-grid-sort-column","vaadin-grid-sorter"].forEach((t=>{const e=this.querySelector(t);!e||e instanceof ye||console.warn(`Make sure you have imported the required module for <${t}> element.`)}))}_updateFirstAndLastColumn(){Array.from(this.shadowRoot.querySelectorAll("tr")).forEach((t=>this._updateFirstAndLastColumnForRow(t)))}_updateFirstAndLastColumnForRow(t){Array.from(t.querySelectorAll('[part~="cell"]:not([part~="details-cell"])')).sort(((t,e)=>t._column._order-e._column._order)).forEach(((t,e,n)=>{this._toggleAttribute("first-column",0===e,t),this._toggleAttribute("last-column",e===n.length-1,t)}))}_isColumnElement(t){return t.nodeType===Node.ELEMENT_NODE&&/\bcolumn\b/.test(t.localName)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,H$t=t=>class extends t{getEventContext(t){const e={},n=t.composedPath(),i=n[n.indexOf(this.$.table)-3];return i?(e.section=["body","header","footer","details"].filter((t=>i.getAttribute("part").indexOf(t)>-1))[0],i._column&&(e.column=i._column),"body"!==e.section&&"details"!==e.section||Object.assign(e,this.__getRowModel(i.parentElement)),e):e}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,F$t=t=>class extends t{static get properties(){return{_filters:{type:Array,value:function(){return[]}}}}ready(){super.ready(),this.addEventListener("filter-changed",this._filterChanged.bind(this))}_filterChanged(t){t.stopPropagation(),this.__addFilter(t.target),this.__applyFilters()}__removeFilters(t){0!=t.length&&(this._filters=this._filters.filter((e=>t.indexOf(e)<0)),this.__applyFilters())}__addFilter(t){-1===this._filters.indexOf(t)&&this._filters.push(t)}__applyFilters(){this.dataProvider&&this.isAttached&&this.clearCache()}_mapFilters(){return this._filters.map((t=>({path:t.path,value:t.value})))}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */;class V$t extends ye{static get is(){return"vaadin-grid-templatizer"}static get properties(){return{dataHost:Object,template:Object,_templateInstances:{type:Array,value:function(){return[]}},_parentPathValues:{value:function(){return{}}},_grid:Object}}static get observers(){return["_templateInstancesChanged(_templateInstances.*, _parentPathValues.*)"]}constructor(){super(),this._instanceProps={detailsOpened:!0,index:!0,item:!0,selected:!0,expanded:!0,level:!0}}createInstance(){this._ensureTemplatized();const t=new this._TemplateClass({});return this.addInstance(t),t}addInstance(t){-1===this._templateInstances.indexOf(t)&&(this._templateInstances.push(t),requestAnimationFrame((()=>this.notifyPath("_templateInstances.*",this._templateInstances))))}removeInstance(t){const e=this._templateInstances.indexOf(t);this.splice("_templateInstances",e,1)}_ensureTemplatized(){this._TemplateClass||(this._TemplateClass=$r(this.template,this,{instanceProps:this._instanceProps,parentModel:!0,forwardHostProp:function(t,e){this._forwardParentProp(t,e),this._templateInstances&&this._templateInstances.forEach((n=>n.notifyPath(t,e)))},notifyInstanceProp:function(t,e,n){if("index"===e||"item"===e)return;const i=`__${e}__`;if(t[i]===n)return;t[i]=n;const r=Array.from(this._grid.$.items.children).filter((e=>this._grid._itemsEqual(e._item,t.item)))[0];r&&Array.from(r.children).forEach((t=>{t._instance&&(t._instance[i]=n,t._instance.notifyPath(e,n))}));const o="item.";if(Array.isArray(this._grid.items)&&0===e.indexOf(o)){const i=this._grid.items.indexOf(t.item),r=e.slice(o.length);this._grid.notifyPath(`items.${i}.${r}`,n)}const a=`_${e}InstanceChangedCallback`;this._grid&&this._grid[a]&&this._grid[a](t,n)}}))}_forwardParentProp(t,e){this._parentPathValues[t]=e,this._templateInstances.forEach((n=>n.notifyPath(t,e)))}_templateInstancesChanged(t){let e,n;if("_templateInstances"===t.path)e=0,n=this._templateInstances.length;else{if("_templateInstances.splices"!==t.path)return;e=t.value.index,n=t.value.addedCount}Object.keys(this._parentPathValues||{}).forEach((t=>{for(let i=e;i<e+n;i++)this._templateInstances[i].set(t,this._parentPathValues[t])}))}}customElements.define(V$t.is,V$t);
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const U$t=t=>class extends t{static get properties(){return{detailsOpenedItems:{type:Array,value:function(){return[]}},_rowDetailsTemplate:Object,rowDetailsRenderer:Function,_detailsCells:{type:Array}}}static get observers(){return["_detailsOpenedItemsChanged(detailsOpenedItems.*, _rowDetailsTemplate, rowDetailsRenderer)","_rowDetailsTemplateOrRendererChanged(_rowDetailsTemplate, rowDetailsRenderer)"]}_rowDetailsTemplateOrRendererChanged(t,e){if(t&&e)throw new Error("You should only use either a renderer or a template for row details");if(t||e){if(t&&!t.templatizer){const e=new V$t;e._grid=this,e.dataHost=this.dataHost,e.template=t,t.templatizer=e}this._columnTree&&Array.from(this.$.items.children).forEach((t=>{t.querySelector("[part~=details-cell]")||(this._updateRow(t,this._columnTree[this._columnTree.length-1]),this._a11yUpdateRowDetailsOpened(t,!1)),delete t.querySelector("[part~=details-cell]")._instance})),this.detailsOpenedItems.length&&(Array.from(this.$.items.children).forEach(this._toggleDetailsCell,this),this._update())}}_detailsOpenedItemsChanged(t){"detailsOpenedItems.length"!==t.path&&t.value&&Array.from(this.$.items.children).forEach((t=>{this._toggleDetailsCell(t,t._item),this._a11yUpdateRowDetailsOpened(t,this._isDetailsOpened(t._item)),this._toggleAttribute("details-opened",this._isDetailsOpened(t._item),t)}))}_configureDetailsCell(t){t.setAttribute("part","cell details-cell"),this._toggleAttribute("frozen",!0,t)}_toggleDetailsCell(t,e){const n=t.querySelector('[part~="details-cell"]');if(!n)return;const i=!this._isDetailsOpened(e),r=!!n.hidden!==i;(n._instance||n._renderer)&&n.hidden===i||(n.hidden=i,i?t.style.removeProperty("padding-bottom"):(this.rowDetailsRenderer?(n._renderer=this.rowDetailsRenderer,n._renderer.call(this,n._content,this,{index:t.index,item:e})):this._rowDetailsTemplate&&!n._instance&&(n._instance=this._rowDetailsTemplate.templatizer.createInstance(),n._content.innerHTML="",n._content.appendChild(n._instance.root),this._updateItem(t,e)),Di(),t.style.setProperty("padding-bottom",`${n.offsetHeight}px`),requestAnimationFrame((()=>this.notifyResize())))),r&&(this._updateMetrics(),this._positionItems())}_updateDetailsCellHeights(){Array.from(this.$.items.querySelectorAll('[part~="details-cell"]:not([hidden])')).forEach((t=>{t.parentElement.style.setProperty("padding-bottom",`${t.offsetHeight}px`)}))}_isDetailsOpened(t){return this.detailsOpenedItems&&-1!==this._getItemIndexInArray(t,this.detailsOpenedItems)}openItemDetails(t){this._isDetailsOpened(t)||(this.detailsOpenedItems=[...this.detailsOpenedItems,t])}closeItemDetails(t){this._isDetailsOpened(t)&&(this.detailsOpenedItems=this.detailsOpenedItems.filter((e=>!this._itemsEqual(e,t))))}_detailsOpenedInstanceChangedCallback(t,e){e?this.openItemDetails(t.item):this.closeItemDetails(t.item)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,j$t=t=>class extends t{static get properties(){return{_frozenCells:{type:Array,value:()=>[]},_rowWithFocusedElement:Element,_deltaYAcc:{type:Number,value:0},_useSticky:{type:Boolean,value:window.CSS&&window.CSS.supports&&(window.CSS.supports("position","sticky")||window.CSS.supports("position","-webkit-sticky"))}}}static get observers(){return["_scrollViewportHeightUpdated(_viewportHeight)"]}set _scrollTop(t){this.$.table.scrollTop=t}get _scrollTop(){return this.$.table.scrollTop}constructor(){super(),this._scrollLineHeight=this._getScrollLineHeight()}_getScrollLineHeight(){const t=document.createElement("div");t.style.fontSize="initial",t.style.display="none",document.body.appendChild(t);const e=window.getComputedStyle(t).fontSize;return document.body.removeChild(t),e?window.parseInt(e):void 0}_scrollViewportHeightUpdated(t){this._scrollPageHeight=t-this.$.header.clientHeight-this.$.footer.clientHeight-this._scrollLineHeight}ready(){super.ready(),this.$.outerscroller=document.createElement("div"),this.scrollTarget=this.$.table,this.addEventListener("wheel",this._onWheel),this.$.items.addEventListener("focusin",(t=>{const e=t.composedPath().indexOf(this.$.items);this._rowWithFocusedElement=t.composedPath()[e-1]})),this.$.items.addEventListener("focusout",(()=>this._rowWithFocusedElement=void 0)),this.scrollTarget.addEventListener("mousedown",(()=>this.__mouseDown=!0)),this.scrollTarget.addEventListener("mouseup",(()=>{this.__mouseDown=!1,this.__pendingReorder&&(this.__pendingReorder=!1,setTimeout((()=>this._reorderRows()),500))}))}scrollToIndex(t){this._accessIronListAPI((()=>super.scrollToIndex(t)))}_onWheel(t){if(t.ctrlKey||this._hasScrolledAncestor(t.target,t.deltaX,t.deltaY))return;const e=this.$.table;let n=t.deltaY;if(t.deltaMode===WheelEvent.DOM_DELTA_LINE?n*=this._scrollLineHeight:t.deltaMode===WheelEvent.DOM_DELTA_PAGE&&(n*=this._scrollPageHeight),this._wheelAnimationFrame)return this._deltaYAcc+=n,void t.preventDefault();n+=this._deltaYAcc,this._deltaYAcc=0,this._wheelAnimationFrame=!0,this._debouncerWheelAnimationFrame=Tn.debounce(this._debouncerWheelAnimationFrame,_t,(()=>this._wheelAnimationFrame=!1));const i=Math.abs(t.deltaX)+Math.abs(n);this._canScroll(e,t.deltaX,n)?(t.preventDefault(),e.scrollTop+=n,e.scrollLeft+=t.deltaX,this._scrollHandler(),this._hasResidualMomentum=!0,this._ignoreNewWheel=!0,this._debouncerIgnoreNewWheel=Tn.debounce(this._debouncerIgnoreNewWheel,gt.after(500),(()=>this._ignoreNewWheel=!1))):this._hasResidualMomentum&&i<=this._previousMomentum||this._ignoreNewWheel?t.preventDefault():i>this._previousMomentum&&(this._hasResidualMomentum=!1),this._previousMomentum=i}_hasScrolledAncestor(t,e,n){return"vaadin-grid-cell-content"!==t.localName&&(!(!this._canScroll(t,e,n)||-1===["auto","scroll"].indexOf(getComputedStyle(t).overflow))||(t!==this&&t.parentElement?this._hasScrolledAncestor(t.parentElement,e,n):void 0))}_canScroll(t,e,n){return n>0&&t.scrollTop<t.scrollHeight-t.offsetHeight||n<0&&t.scrollTop>0||e>0&&t.scrollLeft<t.scrollWidth-t.offsetWidth||e<0&&t.scrollLeft>0}_scheduleScrolling(){this._scrollingFrame||(this._scrollingFrame=requestAnimationFrame((()=>this._toggleAttribute("scrolling",!0,this.$.scroller)))),this._debounceScrolling=Tn.debounce(this._debounceScrolling,gt.after(500),(()=>{cancelAnimationFrame(this._scrollingFrame),delete this._scrollingFrame,this._toggleAttribute("scrolling",!1,this.$.scroller),this._reorderRows()}))}_afterScroll(){this._translateStationaryElements(),this.hasAttribute("reordering")||this._scheduleScrolling(),this._updateOverflow()}_updateOverflow(){let t="";const e=this.$.table;e.scrollTop<e.scrollHeight-e.clientHeight&&(t+=" bottom"),e.scrollTop>0&&(t+=" top"),e.scrollLeft<e.scrollWidth-e.clientWidth&&(t+=" right"),e.scrollLeft>0&&(t+=" left"),this._debounceOverflow=Tn.debounce(this._debounceOverflow,_t,(()=>{const e=t.trim();e.length>0&&this.getAttribute("overflow")!==e?this.setAttribute("overflow",e):0==e.length&&this.hasAttribute("overflow")&&this.removeAttribute("overflow")}))}_reorderRows(){if(this.__mouseDown)return void(this.__pendingReorder=!0);const t=this.$.items,e=t.querySelectorAll("tr");if(!e.length)return;const n=this._virtualStart+this._vidxOffset,i=this._rowWithFocusedElement||Array.from(e).filter((t=>!t.hidden))[0];if(!i)return;const r=i.index-n,o=Array.from(e).indexOf(i)-r;if(o>0)for(let n=0;n<o;n++)t.appendChild(e[n]);else if(o<0)for(let n=e.length+o;n<e.length;n++)t.insertBefore(e[n],e[0]);if(this._safari){const{transform:t}=this.$.header.style;this.$.header.style.transform="",setTimeout((()=>this.$.header.style.transform=t))}}_frozenCellsChanged(){this._debouncerCacheElements=Tn.debounce(this._debouncerCacheElements,vt,(()=>{Array.from(this.shadowRoot.querySelectorAll('[part~="cell"]')).forEach((function(t){t.style.transform=""})),this._frozenCells=Array.prototype.slice.call(this.$.table.querySelectorAll("[frozen]")),this._updateScrollerMeasurements(),this._translateStationaryElements()})),this._updateLastFrozen()}_updateScrollerMeasurements(){this._frozenCells.length>0&&this.__isRTL&&(this.__scrollerMetrics={scrollWidth:this.$.table.scrollWidth,clientWidth:this.$.table.clientWidth})}_updateLastFrozen(){if(!this._columnTree)return;const t=this._columnTree[this._columnTree.length-1].slice(0);t.sort(((t,e)=>t._order-e._order));const e=t.reduce(((t,e,n)=>(e._lastFrozen=!1,e.frozen&&!e.hidden?n:t)),void 0);void 0!==e&&(t[e]._lastFrozen=!0)}_translateStationaryElements(){const t=Math.max(0,this._scrollLeft),e=Math.max(0,this._scrollTop);let n=0,i=0,r=0;if(this._useSticky||(n=t,i=e,r=this.$.table.clientHeight-this.$.footer.offsetHeight-this.$.footer.offsetTop),this.$.header.style.transform=this._getTranslate(-t+n,i),this.$.footer.style.transform=this._getTranslate(-t+n,i+r),this.$.items.style.transform=this._getTranslate(-t+n,0),this._frozenCells.length>0){const t=this.__isRTL?this.__getNormalizedScrollLeft(this.$.table)+this.__scrollerMetrics.clientWidth-this.__scrollerMetrics.scrollWidth:this._scrollLeft,e=this._getTranslate(t,0);for(let t=0;t<this._frozenCells.length;t++)this._frozenCells[t].style.transform=e}}_getTranslate(t,e){return`translate(${t}px, ${e}px)`}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,G$t=t=>class extends t{static get properties(){return{selectedItems:{type:Object,notify:!0,value:()=>[]}}}static get observers(){return["_selectedItemsChanged(selectedItems.*)"]}_isSelected(t){return this.selectedItems&&this._getItemIndexInArray(t,this.selectedItems)>-1}selectItem(t){this._isSelected(t)||(this.selectedItems=[...this.selectedItems,t])}deselectItem(t){this._isSelected(t)&&(this.selectedItems=this.selectedItems.filter((e=>!this._itemsEqual(e,t))))}_toggleItem(t){-1===this._getItemIndexInArray(t,this.selectedItems)?this.selectItem(t):this.deselectItem(t)}_selectedItemsChanged(t){!this.$.items.children.length||"selectedItems"!==t.path&&"selectedItems.splices"!==t.path||Array.from(this.$.items.children).forEach((t=>{this._updateItem(t,t._item)}))}_selectedInstanceChangedCallback(t,e){e?this.selectItem(t.item):this.deselectItem(t.item)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,W$t=t=>class extends t{static get properties(){return{multiSort:{type:Boolean,value:!1},_sorters:{type:Array,value:function(){return[]}},_previousSorters:{type:Array,value:function(){return[]}}}}ready(){super.ready(),this.addEventListener("sorter-changed",this._onSorterChanged)}_onSorterChanged(t){const e=t.target;t.stopPropagation(),this.__updateSorter(e),this.__applySorters()}__removeSorters(t){0!=t.length&&(this._sorters=this._sorters.filter((e=>t.indexOf(e)<0)),this.multiSort&&this.__updateSortOrders(),this.__applySorters())}__updateSortOrders(){this._sorters.forEach(((t,e)=>t._order=this._sorters.length>1?e:null),this)}__updateSorter(t){if(t.direction||-1!==this._sorters.indexOf(t))if(t._order=null,this.multiSort)this._removeArrayItem(this._sorters,t),t.direction&&this._sorters.unshift(t),this.__updateSortOrders();else if(t.direction){const e=this._sorters.filter((e=>e!=t));this._sorters=[t],e.forEach((t=>{t._order=null,t.direction=null}))}}__applySorters(){this.dataProvider&&this.isAttached&&JSON.stringify(this._previousSorters)!==JSON.stringify(this._mapSorters())&&this.clearCache(),this._a11yUpdateSorters(),this._previousSorters=this._mapSorters()}_mapSorters(){return this._sorters.map((t=>({path:t.path,direction:t.direction})))}_removeArrayItem(t,e){const n=t.indexOf(e);n>-1&&t.splice(n,1)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,q$t=t=>class extends t{static get properties(){return{cellClassNameGenerator:Function}}static get observers(){return["__cellClassNameGeneratorChanged(cellClassNameGenerator)"]}__cellClassNameGeneratorChanged(){this.generateCellClassNames()}generateCellClassNames(){Array.from(this.$.items.children).filter((t=>!t.hidden)).forEach((t=>this._generateCellClassNames(t,this.__getRowModel(t))))}_generateCellClassNames(t,e){Array.from(t.children).forEach((t=>{if(t.__generatedClasses&&t.__generatedClasses.forEach((e=>t.classList.remove(e))),this.cellClassNameGenerator){const n=this.cellClassNameGenerator(t._column,e);t.__generatedClasses=n&&n.split(" ").filter((t=>t.length>0)),t.__generatedClasses&&t.__generatedClasses.forEach((e=>t.classList.add(e)))}}))}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,Y$t="between",X$t="on-top-or-between",$$t="above",K$t="below",Z$t="empty",J$t=t=>class extends t{static get properties(){return{dropMode:String,rowsDraggable:Boolean,dragFilter:Function,dropFilter:Function,__dndAutoScrollThreshold:{value:50}}}static get observers(){return["_dragDropAccessChanged(rowsDraggable, dropMode, dragFilter, dropFilter)"]}ready(){super.ready(),this.$.table.addEventListener("dragstart",this._onDragStart.bind(this)),this.$.table.addEventListener("dragend",this._onDragEnd.bind(this)),this.$.table.addEventListener("dragover",this._onDragOver.bind(this)),this.$.table.addEventListener("dragleave",this._onDragLeave.bind(this)),this.$.table.addEventListener("drop",this._onDrop.bind(this)),this.$.table.addEventListener("dragenter",(t=>{this.dropMode&&(t.preventDefault(),t.stopPropagation())}))}_onDragStart(t){if(this.rowsDraggable){let e=t.target;if("vaadin-grid-cell-content"===e.localName&&(e=e.assignedSlot.parentNode.parentNode),e.parentNode!==this.$.items)return;if(t.stopPropagation(),this._toggleAttribute("dragging-rows",!0,this),this._safari){const t=e.style.transform;e.style.top=/translateY\((.*)\)/.exec(t)[1],e.style.transform="none",requestAnimationFrame((()=>{e.style.top="",e.style.transform=t}))}const n=e.getBoundingClientRect();this._ios?t.dataTransfer.setDragImage(e):t.dataTransfer.setDragImage(e,t.clientX-n.left,t.clientY-n.top);let i=[e];this._isSelected(e._item)&&(i=this.__getViewportRows().filter((t=>this._isSelected(t._item))).filter((t=>!this.dragFilter||this.dragFilter(this.__getRowModel(t))))),t.dataTransfer.setData("text",this.__formatDefaultTransferData(i)),e.setAttribute("dragstart",i.length>1?i.length:""),this.updateStyles({"--_grid-drag-start-x":t.clientX-n.left+20+"px","--_grid-drag-start-y":t.clientY-n.top+10+"px"}),requestAnimationFrame((()=>{e.removeAttribute("dragstart"),this.updateStyles({"--_grid-drag-start-x":"","--_grid-drag-start-y":""})}));const r=new CustomEvent("grid-dragstart",{detail:{draggedItems:i.map((t=>t._item)),setDragData:(e,n)=>t.dataTransfer.setData(e,n),setDraggedItemsCount:t=>e.setAttribute("dragstart",t)}});r.originalEvent=t,this.dispatchEvent(r)}}_onDragEnd(t){this._toggleAttribute("dragging-rows",!1,this),t.stopPropagation();const e=new CustomEvent("grid-dragend");e.originalEvent=t,this.dispatchEvent(e)}_onDragLeave(t){t.stopPropagation(),this._clearDragStyles()}_onDragOver(t){if(this.dropMode){if(this._dropLocation=void 0,this._dragOverItem=void 0,this.__dndAutoScroll(t.clientY))return void this._clearDragStyles();let e=t.composedPath().filter((t=>"tr"===t.localName))[0];if(this._effectiveSize&&"on-grid"!==this.dropMode)if(e&&e.parentNode===this.$.items){const n=e.getBoundingClientRect();this._dropLocation="on-top",this.dropMode===Y$t?this._dropLocation=t.clientY-n.top<n.bottom-t.clientY?$$t:K$t:this.dropMode===X$t&&(t.clientY-n.top<n.height/3?this._dropLocation=$$t:t.clientY-n.top>n.height/3*2&&(this._dropLocation=K$t))}else{if(e)return;if(this.dropMode!==Y$t&&this.dropMode!==X$t)return;e=Array.from(this.$.items.children).filter((t=>!t.hidden)).pop(),this._dropLocation=K$t}else this._dropLocation=Z$t;if(e&&e.hasAttribute("drop-disabled"))return void(this._dropLocation=void 0);t.stopPropagation(),t.preventDefault(),this._dropLocation===Z$t?this._toggleAttribute("dragover",!0,this):e?(this._dragOverItem=e._item,e.getAttribute("dragover")!==this._dropLocation&&e.setAttribute("dragover",this._dropLocation)):this._clearDragStyles()}}__dndAutoScroll(t){if(this.__dndAutoScrolling)return!0;const e=this.$.header.getBoundingClientRect().bottom,n=this.$.footer.getBoundingClientRect().top,i=e-t+this.__dndAutoScrollThreshold,r=t-n+this.__dndAutoScrollThreshold;let o=0;if(r>0?o=2*r:i>0&&(o=2*-i),o){const t=this.$.table.scrollTop;if(this.$.table.scrollTop+=o,t!==this.$.table.scrollTop)return this.__dndAutoScrolling=!0,setTimeout((()=>this.__dndAutoScrolling=!1),20),this._scrollHandler(),!0}}__getViewportRows(){const t=this.$.header.getBoundingClientRect().bottom,e=this.$.footer.getBoundingClientRect().top;return Array.from(this.$.items.children).filter((n=>{const i=n.getBoundingClientRect();return i.bottom>t&&i.top<e}))}_clearDragStyles(){this.removeAttribute("dragover"),Array.from(this.$.items.children).forEach((t=>t.removeAttribute("dragover")))}_onDrop(t){if(this.dropMode){t.stopPropagation(),t.preventDefault();const e=t.dataTransfer.types&&Array.from(t.dataTransfer.types).map((e=>({type:e,data:t.dataTransfer.getData(e)})));this._clearDragStyles();const n=new CustomEvent("grid-drop",{bubbles:t.bubbles,cancelable:t.cancelable,detail:{dropTargetItem:this._dragOverItem,dropLocation:this._dropLocation,dragData:e}});n.originalEvent=t,this.dispatchEvent(n)}}__formatDefaultTransferData(t){return t.map((t=>Array.from(t.children).filter((t=>!t.hidden&&-1===t.getAttribute("part").indexOf("details-cell"))).sort(((t,e)=>t._column._order>e._column._order?1:-1)).map((t=>t._content.textContent.trim())).filter((t=>t)).join("\t"))).join("\n")}_dragDropAccessChanged(){this.filterDragAndDrop()}filterDragAndDrop(){Array.from(this.$.items.children).filter((t=>!t.hidden)).forEach((t=>{this._filterDragAndDrop(t,this.__getRowModel(t))}))}_filterDragAndDrop(t,e){const n=!this.rowsDraggable||this.dragFilter&&!this.dragFilter(e),i=!this.dropMode||this.dropFilter&&!this.dropFilter(e);Array.from(t.children).map((t=>t._content)).forEach((t=>{n?t.removeAttribute("draggable"):t.setAttribute("draggable",!0)})),this._toggleAttribute("drag-disabled",n,t),this._toggleAttribute("drop-disabled",i,t)}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,Q$t=t=>class extends t{static get properties(){return{_headerFocusable:{type:Object,observer:"_focusableChanged"},_itemsFocusable:{type:Object,observer:"_focusableChanged"},_footerFocusable:{type:Object,observer:"_focusableChanged"},_navigatingIsHidden:Boolean,_focusedItemIndex:{type:Number,value:0},_focusedColumnOrder:Number,interacting:{type:Boolean,value:!1,reflectToAttribute:!0,readOnly:!0,observer:"_interactingChanged"}}}ready(){super.ready(),this._ios||this._android||(this.addEventListener("keydown",this._onKeyDown),this.addEventListener("keyup",this._onKeyUp),this.addEventListener("focusin",this._onFocusIn),this.addEventListener("focusout",this._onFocusOut),this.$.table.addEventListener("focusin",this._onCellFocusIn.bind(this)),this.$.table.addEventListener("focusout",this._onCellFocusOut.bind(this)),this.addEventListener("mousedown",(()=>{this._toggleAttribute("navigating",!1,this),this._isMousedown=!0})),this.addEventListener("mouseup",(()=>this._isMousedown=!1)))}_focusableChanged(t,e){e&&e.setAttribute("tabindex","-1"),t&&this._updateGridSectionFocusTarget(t)}_interactingChanged(){this._updateGridSectionFocusTarget(this._headerFocusable),this._updateGridSectionFocusTarget(this._itemsFocusable),this._updateGridSectionFocusTarget(this._footerFocusable)}_onKeyDown(t){const e=t.key;let n;switch(e){case"ArrowUp":case"ArrowDown":case"ArrowLeft":case"ArrowRight":case"PageUp":case"PageDown":case"Home":case"End":n="Navigation";break;case"Enter":case"Escape":case"F2":n="Interaction";break;case"Tab":n="Tab";break;case" ":n="Space"}this._detectInteracting(t),this.interacting&&"Interaction"!==n&&(n=void 0),n&&this[`_on${n}KeyDown`](t,e)}_ensureScrolledToIndex(t){Array.from(this.$.items.children).filter((e=>e.index===t))[0]||this._scrollToIndex(t)}_onNavigationKeyDown(t,e){function n(t){return Array.prototype.indexOf.call(t.parentNode.children,t)}this._scrollHandler(),t.preventDefault();const i=this._lastVisibleIndex-this._firstVisibleIndex-1;let r=0,o=0;switch(e){case"ArrowRight":r=this.__isRTL?-1:1;break;case"ArrowLeft":r=this.__isRTL?1:-1;break;case"Home":r=-1/0,t.ctrlKey&&(o=-1/0);break;case"End":r=1/0,t.ctrlKey&&(o=1/0);break;case"ArrowDown":o=1;break;case"ArrowUp":o=-1;break;case"PageDown":o=i;break;case"PageUp":o=-i}const a=t.composedPath()[0],s=n(a),l=this._elementMatches(a,'[part~="details-cell"]'),c=a.parentNode,u=c.parentNode,h=(u===this.$.items?this._effectiveSize:u.children.length)-1,d=u===this.$.items?void 0!==this._focusedItemIndex?this._focusedItemIndex:c.index:n(c);let p=Math.max(0,Math.min(d+o,h)),f=!1;if(u===this.$.items){const t=c._item,e=this._cache.getItemForIndex(p);f=l?0===o:1===o&&this._isDetailsOpened(t)||-1===o&&p!==d&&this._isDetailsOpened(e),f!==l&&(1===o&&f||-1===o&&!f)&&(p=d)}if(u!==this.$.items)if(p>d)for(;p<h&&u.children[p].hidden;)p++;else if(p<d)for(;p>0&&u.children[p].hidden;)p--;void 0===this._focusedColumnOrder&&(this._focusedColumnOrder=l?0:this._getColumns(u,d).filter((t=>!t.hidden))[s]._order);const m=this._getColumns(u,p).filter((t=>!t.hidden)),g=m.map((t=>t._order)).sort(((t,e)=>t-e)),_=g.length-1,y=g.indexOf(g.slice(0).sort(((t,e)=>Math.abs(t-this._focusedColumnOrder)-Math.abs(e-this._focusedColumnOrder)))[0]),v=0===o&&l?y:Math.max(0,Math.min(y+r,_));v!==y&&(this._focusedColumnOrder=void 0),u===this.$.items&&this._ensureScrolledToIndex(p),this._toggleAttribute("navigating",!0,this);const b=m.reduce(((t,e,n)=>(t[e._order]=n,t)),{})[g[v]],x=u===this.$.items?Array.from(u.children).filter((t=>t.index===p))[0]:u.children[p];if(!x)return;const w=f?Array.from(x.children).filter((t=>this._elementMatches(t,'[part~="details-cell"]')))[0]:x.children[b];if(this._scrollHorizontallyToCell(w),u===this.$.items&&(this._focusedItemIndex=p),u===this.$.items){const t=w.getBoundingClientRect(),e=this.$.footer.getBoundingClientRect().top,n=this.$.header.getBoundingClientRect().bottom;t.bottom>e?(this.$.table.scrollTop+=t.bottom-e,this._scrollHandler()):t.top<n&&(this.$.table.scrollTop-=n-t.top,this._scrollHandler())}w.focus()}_onInteractionKeyDown(t,e){const n=t.composedPath()[0],i="input"===n.localName&&!/^(button|checkbox|color|file|image|radio|range|reset|submit)$/i.test(n.type);let r;switch(e){case"Enter":r=!this.interacting||!i;break;case"Escape":r=!1;break;case"F2":r=!this.interacting}const{cell:o}=this._getGridEventLocation(t);if(this.interacting!==r&&null!==o)if(r){const e=o._content.querySelector("[focus-target]")||o._content.firstElementChild;e&&(t.preventDefault(),e.focus(),this._setInteracting(!0),this._toggleAttribute("navigating",!1,this))}else t.preventDefault(),this._focusedColumnOrder=void 0,o.focus(),this._setInteracting(!1),this._toggleAttribute("navigating",!0,this)}_predictFocusStepTarget(t,e){const n=[this.$.table,this._headerFocusable,this._itemsFocusable,this._footerFocusable,this.$.focusexit];let i=n.indexOf(t);for(i+=e;i>=0&&i<=n.length-1&&(!n[i]||n[i].parentNode.hidden);)i+=e;return n[i]}_onTabKeyDown(t){const e=this._predictFocusStepTarget(t.composedPath()[0],t.shiftKey?-1:1);if(e===this.$.table)this.$.table.focus();else if(e===this.$.focusexit)this.$.focusexit.focus();else if(e===this._itemsFocusable){let n=e;const i=this._itemsFocusable.parentNode;if(this._ensureScrolledToIndex(this._focusedItemIndex),i.index!==this._focusedItemIndex){const t=Array.from(i.children).indexOf(this._itemsFocusable),e=Array.from(this.$.items.children).filter((t=>t.index===this._focusedItemIndex))[0];e&&(n=e.children[t])}t.preventDefault(),n.focus()}else t.preventDefault(),e.focus();this._toggleAttribute("navigating",!0,this)}_onSpaceKeyDown(t){t.preventDefault();const e=t.composedPath()[0];e._content&&e._content.firstElementChild||this.dispatchEvent(new CustomEvent("cell-activate",{detail:{model:this.__getRowModel(e.parentElement)}}))}_onKeyUp(t){if(!/^( |SpaceBar)$/.test(t.key))return;t.preventDefault();const e=t.composedPath()[0];if(e._content&&e._content.firstElementChild){const t=this.hasAttribute("navigating");e._content.firstElementChild.click(),this._toggleAttribute("navigating",t,this)}}_onFocusIn(t){this._isMousedown||this._toggleAttribute("navigating",!0,this);const e=t.composedPath()[0];e===this.$.table||e===this.$.focusexit?(this._predictFocusStepTarget(e,e===this.$.table?1:-1).focus(),this._setInteracting(!1)):this._detectInteracting(t)}_onFocusOut(t){this._toggleAttribute("navigating",!1,this),this._detectInteracting(t)}_onCellFocusIn(t){const{section:e,cell:n}=this._getGridEventLocation(t);this._detectInteracting(t),e&&n&&(this._activeRowGroup=e,this.$.header===e?this._headerFocusable=n:this.$.items===e?this._itemsFocusable=n:this.$.footer===e&&(this._footerFocusable=n),n._content.dispatchEvent(new CustomEvent("cell-focusin",{bubbles:!1})),n.dispatchEvent(new CustomEvent("cell-focus",{bubbles:!0,composed:!0}))),this._detectFocusedItemIndex(t)}_onCellFocusOut(t){3===t.composedPath().indexOf(this.$.table)&&t.composedPath()[0]._content.dispatchEvent(new CustomEvent("cell-focusout",{bubbles:!1}))}_detectInteracting(t){const e=t.composedPath().some((t=>"vaadin-grid-cell-content"===t.localName));this._setInteracting(e)}_detectFocusedItemIndex(t){const{section:e,row:n}=this._getGridEventLocation(t);e===this.$.items&&(this._focusedItemIndex=n.index)}_updateGridSectionFocusTarget(t){if(!t)return;const e=this._getGridSectionFromFocusTarget(t);t.tabIndex=this.interacting&&e===this._activeRowGroup?-1:0}_preventScrollerRotatingCellFocus(t,e){t.index===this._focusedItemIndex&&this.hasAttribute("navigating")&&this._activeRowGroup===this.$.items&&(this._navigatingIsHidden=!0,this._toggleAttribute("navigating",!1,this)),e===this._focusedItemIndex&&this._navigatingIsHidden&&(this._navigatingIsHidden=!1,this._toggleAttribute("navigating",!0,this))}_getColumns(t,e){let n=this._columnTree.length-1;return t===this.$.header?n=e:t===this.$.footer&&(n=this._columnTree.length-1-e),this._columnTree[n]}_resetKeyboardNavigation(){if(this.$.header.firstElementChild&&(this._headerFocusable=Array.from(this.$.header.firstElementChild.children).filter((t=>!t.hidden))[0]),this.$.items.firstElementChild){const t=this._iterateItems(((t,e)=>{if(this._firstVisibleIndex===e)return this.$.items.children[t]}));t&&(this._itemsFocusable=Array.from(t.children).filter((t=>!t.hidden))[0])}this.$.footer.firstElementChild&&(this._footerFocusable=Array.from(this.$.footer.firstElementChild.children).filter((t=>!t.hidden))[0])}_scrollHorizontallyToCell(t){if(t.hasAttribute("frozen")||this._elementMatches(t,'[part~="details-cell"]'))return;const e=t.getBoundingClientRect(),n=t.parentNode,i=Array.from(n.children).indexOf(t),r=this.$.table.getBoundingClientRect();let o=r.left,a=r.right;for(let t=i-1;t>=0;t--){const e=n.children[t];if(!e.hasAttribute("hidden")&&!this._elementMatches(e,'[part~="details-cell"]')&&e.hasAttribute("frozen")){o=e.getBoundingClientRect().right;break}}for(let t=i+1;t<n.children.length;t++){const e=n.children[t];if(!e.hasAttribute("hidden")&&!this._elementMatches(e,'[part~="details-cell"]')&&e.hasAttribute("frozen")){a=e.getBoundingClientRect().left;break}}e.left<o&&(this.$.table.scrollLeft+=Math.round(e.left-o)),e.right>a&&(this.$.table.scrollLeft+=Math.round(e.right-a))}_elementMatches(t,e){return t.matches?t.matches(e):-1!==Array.from(t.parentNode.querySelectorAll(e)).indexOf(t)}_getGridEventLocation(t){const e=t.composedPath(),n=e.indexOf(this.$.table);return{section:n>=1?e[n-1]:null,row:n>=2?e[n-2]:null,cell:n>=3?e[n-3]:null}}_getGridSectionFromFocusTarget(t){return t===this._headerFocusable?this.$.header:t===this._itemsFocusable?this.$.items:t===this._footerFocusable?this.$.footer:null}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,tKt=t=>class extends(pi(t)){static get properties(){return{columnReorderingAllowed:{type:Boolean,value:!1},_orderBaseScope:{type:Number,value:1e7}}}static get observers(){return["_updateOrders(_columnTree, _columnTree.*)"]}ready(){super.ready(),ii(this,"track",this._onTrackEvent),this._reorderGhost=this.shadowRoot.querySelector('[part="reorder-ghost"]'),this.addEventListener("touchstart",this._onTouchStart.bind(this)),this.addEventListener("touchmove",this._onTouchMove.bind(this)),this.addEventListener("touchend",this._onTouchEnd.bind(this)),this.addEventListener("contextmenu",this._onContextMenu.bind(this))}_onContextMenu(t){this.hasAttribute("reordering")&&t.preventDefault()}_onTouchStart(t){this._startTouchReorderTimeout=setTimeout((()=>{this._onTrackStart({detail:{x:t.touches[0].clientX,y:t.touches[0].clientY}})}),100)}_onTouchMove(t){this._draggedColumn&&t.preventDefault(),clearTimeout(this._startTouchReorderTimeout)}_onTouchEnd(){clearTimeout(this._startTouchReorderTimeout),this._onTrackEnd()}_onTrackEvent(t){if("start"===t.detail.state){const e=t.composedPath(),n=e[e.indexOf(this.$.header)-2];if(!n||!n._content)return;if(n._content.contains(this.getRootNode().activeElement))return;if(this.$.scroller.hasAttribute("column-resizing"))return;this._touchDevice||this._onTrackStart(t)}else"track"===t.detail.state?this._onTrack(t):"end"===t.detail.state&&this._onTrackEnd(t)}_onTrackStart(t){if(!this.columnReorderingAllowed)return;const e=t.composedPath&&t.composedPath();if(e&&e.filter((t=>t.hasAttribute&&t.hasAttribute("draggable")))[0])return;const n=this._cellFromPoint(t.detail.x,t.detail.y);if(n&&-1!==n.getAttribute("part").indexOf("header-cell")){for(this._toggleAttribute("reordering",!0,this),this._draggedColumn=n._column;1===this._draggedColumn.parentElement.childElementCount;)this._draggedColumn=this._draggedColumn.parentElement;this._setSiblingsReorderStatus(this._draggedColumn,"allowed"),this._draggedColumn._reorderStatus="dragging",this._updateGhost(n),this._reorderGhost.style.visibility="visible",this._updateGhostPosition(t.detail.x,this._touchDevice?t.detail.y-50:t.detail.y),this._autoScroller()}}_onTrack(t){if(!this._draggedColumn)return;const e=this._cellFromPoint(t.detail.x,t.detail.y);if(!e)return;const n=this._getTargetColumn(e,this._draggedColumn);this._isSwapAllowed(this._draggedColumn,n)&&this._isSwappableByPosition(n,t.detail.x)&&this._swapColumnOrders(this._draggedColumn,n),this._updateGhostPosition(t.detail.x,this._touchDevice?t.detail.y-50:t.detail.y),this._lastDragClientX=t.detail.x}_onTrackEnd(){this._draggedColumn&&(this._toggleAttribute("reordering",!1,this),this._draggedColumn._reorderStatus="",this._setSiblingsReorderStatus(this._draggedColumn,""),this._draggedColumn=null,this._lastDragClientX=null,this._reorderGhost.style.visibility="hidden",this.dispatchEvent(new CustomEvent("column-reorder",{detail:{columns:this._getColumnsInOrder()}})))}_getColumnsInOrder(){return this._columnTree.slice(0).pop().filter((t=>!t.hidden)).sort(((t,e)=>t._order-e._order))}_cellFromPoint(t,e){t=t||0,e=e||0,this._draggedColumn||this._toggleAttribute("no-content-pointer-events",!0,this.$.scroller);const n=this.shadowRoot.elementFromPoint(t,e);if(this._toggleAttribute("no-content-pointer-events",!1,this.$.scroller),n&&n._column)return n}_updateGhostPosition(t,e){const n=this._reorderGhost.getBoundingClientRect(),i=t-n.width/2,r=e-n.height/2,o=parseInt(this._reorderGhost._left||0),a=parseInt(this._reorderGhost._top||0);this._reorderGhost._left=o-(n.left-i),this._reorderGhost._top=a-(n.top-r),this._reorderGhost.style.transform=`translate(${this._reorderGhost._left}px, ${this._reorderGhost._top}px)`}_updateGhost(t){const e=this._reorderGhost;e.textContent=t._content.innerText;const n=window.getComputedStyle(t);return["boxSizing","display","width","height","background","alignItems","padding","border","flex-direction","overflow"].forEach((t=>e.style[t]=n[t])),e}_updateOrders(t,e){void 0!==t&&void 0!==e&&(t[0].forEach((t=>t._order=0)),(function n(t,e,i){let r=1;t.forEach((t=>{r%10==0&&r++,t._order=i+r*e,r++}))}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */)(t[0],this._orderBaseScope,0))}_setSiblingsReorderStatus(t,e){Array.from(t.parentNode.children).filter((e=>/column/.test(e.localName)&&this._isSwapAllowed(e,t))).forEach((t=>t._reorderStatus=e))}_autoScroller(){if(this._lastDragClientX){const t=this._lastDragClientX-this.getBoundingClientRect().right+50,e=this.getBoundingClientRect().left-this._lastDragClientX+50;t>0?this.$.table.scrollLeft+=t/10:e>0&&(this.$.table.scrollLeft-=e/10),this._scrollHandler()}this._draggedColumn&&this.async(this._autoScroller,10)}_isSwapAllowed(t,e){if(t&&e){const n=t.parentElement===e.parentElement,i=t.frozen===e.frozen;return t!==e&&n&&i}}_isSwappableByPosition(t,e){const n=Array.from(this.$.header.querySelectorAll('tr:not([hidden]) [part~="cell"]')).filter((e=>t.contains(e._column)))[0],i=this.$.header.querySelector("tr:not([hidden]) [reorder-status=dragging]").getBoundingClientRect(),r=n.getBoundingClientRect();return r.left>i.left?e>r.right-i.width:e<r.left+i.width}_swapColumnOrders(t,e){const n=t._order;t._order=e._order,e._order=n,this._updateLastFrozen(),this._updateFirstAndLastColumn()}_getTargetColumn(t,e){if(t&&e){let n=t._column;for(;n.parentElement!==e.parentElement&&n!==this;)n=n.parentElement;return n.parentElement===e.parentElement?n:t._column}}}
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */,eKt=t=>class extends t{static get properties(){return{resizable:{type:Boolean,value:function(){if("vaadin-grid-column-group"===this.localName)return;const t=this.parentNode;return t&&"vaadin-grid-column-group"===t.localName&&t.resizable||!1}},_headerTemplate:{type:Object},_footerTemplate:{type:Object},frozen:{type:Boolean,value:!1},hidden:{type:Boolean},header:{type:String},textAlign:{type:String},_lastFrozen:{type:Boolean,value:!1},_order:Number,_reorderStatus:Boolean,_emptyCells:Array,_headerCell:Object,_footerCell:Object,_grid:Object,headerRenderer:Function,footerRenderer:Function}}static get observers(){return["_widthChanged(width, _headerCell, _footerCell, _cells.*)","_frozenChanged(frozen, _headerCell, _footerCell, _cells.*)","_flexGrowChanged(flexGrow, _headerCell, _footerCell, _cells.*)","_pathOrHeaderChanged(path, header, _headerCell, _footerCell, _cells.*, renderer, headerRenderer, _bodyTemplate, _headerTemplate)","_textAlignChanged(textAlign, _cells.*, _headerCell, _footerCell)","_orderChanged(_order, _headerCell, _footerCell, _cells.*)","_lastFrozenChanged(_lastFrozen)","_setBodyTemplateOrRenderer(_bodyTemplate, renderer, _cells, _cells.*)","_setHeaderTemplateOrRenderer(_headerTemplate, headerRenderer, _headerCell)","_setFooterTemplateOrRenderer(_footerTemplate, footerRenderer, _footerCell)","_resizableChanged(resizable, _headerCell)","_reorderStatusChanged(_reorderStatus, _headerCell, _footerCell, _cells.*)","_hiddenChanged(hidden, _headerCell, _footerCell, _cells.*)"]}connectedCallback(){super.connectedCallback(),this._bodyTemplate&&(this._bodyTemplate.templatizer._grid=this._grid),this._headerTemplate&&(this._headerTemplate.templatizer._grid=this._grid),this._footerTemplate&&(this._footerTemplate.templatizer._grid=this._grid),this._templateObserver.flush(),this._bodyTemplate||this._templateObserver.callback(),requestAnimationFrame((()=>{this._allCells.forEach((t=>{t._content.parentNode||this._grid&&this._grid.appendChild(t._content)}))}))}disconnectedCallback(){super.disconnectedCallback(),requestAnimationFrame((()=>{this._findHostGrid()||this._allCells.forEach((t=>{t._content.parentNode&&t._content.parentNode.removeChild(t._content)}))})),this._gridValue=void 0}_findHostGrid(){let t=this;for(;t&&!/^vaadin.*grid(-pro)?$/.test(t.localName);)t=t.assignedSlot?t.assignedSlot.parentNode:t.parentNode;return t||void 0}get _grid(){return this._gridValue||(this._gridValue=this._findHostGrid()),this._gridValue}get _allCells(){return[].concat(this._cells||[]).concat(this._emptyCells||[]).concat(this._headerCell).concat(this._footerCell).filter((t=>t))}constructor(){super(),this._templateObserver=new zi(this,(()=>{this._headerTemplate=this._prepareHeaderTemplate(),this._footerTemplate=this._prepareFooterTemplate(),this._bodyTemplate=this._prepareBodyTemplate()}))}_prepareHeaderTemplate(){return this._prepareTemplatizer(this._findTemplate(!0)||null,{})}_prepareFooterTemplate(){return this._prepareTemplatizer(this._findTemplate(!1,!0)||null,{})}_prepareBodyTemplate(){return this._prepareTemplatizer(this._findTemplate()||null)}_prepareTemplatizer(t,e){if(t&&!t.templatizer){const n=new V$t;n._grid=this._grid,n.dataHost=this.dataHost,n._instanceProps=e||n._instanceProps,n.template=t,t.templatizer=n}return t}_renderHeaderAndFooter(){this.headerRenderer&&this._headerCell&&this.__runRenderer(this.headerRenderer,this._headerCell),this.footerRenderer&&this._footerCell&&this.__runRenderer(this.footerRenderer,this._footerCell)}__runRenderer(t,e,n){const i=[e._content,this];n&&n.item&&i.push(n),t.apply(this,i)}__setColumnTemplateOrRenderer(t,e,n){if(!this.hidden){if(t&&e)throw new Error("You should only use either a renderer or a template");n.forEach((n=>{const i=this._grid.__getRowModel(n.parentElement);if(e)n._renderer=e,(i.item||e===this.headerRenderer||e===this.footerRenderer)&&this.__runRenderer(e,n,i);else if(n._template!==t){n._template=t,n._content.innerHTML="",t.templatizer._grid=t.templatizer._grid||this._grid;const e=t.templatizer.createInstance();n._content.appendChild(e.root),n._instance=e,i.item&&n._instance.setProperties(i)}}))}}_setBodyTemplateOrRenderer(t,e,n){(t||e)&&n&&this.__setColumnTemplateOrRenderer(t,e,n)}_setHeaderTemplateOrRenderer(t,e,n){(t||e)&&n&&this.__setColumnTemplateOrRenderer(t,e,[n])}_setFooterTemplateOrRenderer(t,e,n){(t||e)&&n&&(this.__setColumnTemplateOrRenderer(t,e,[n]),this._grid.__updateHeaderFooterRowVisibility(n.parentElement))}_selectFirstTemplate(t=!1,e=!1){return zi.getFlattenedNodes(this).filter((n=>"template"===n.localName&&n.classList.contains("header")===t&&n.classList.contains("footer")===e))[0]}_findTemplate(t,e){const n=this._selectFirstTemplate(t,e);return n&&this.dataHost&&(n._rootDataHost=this.dataHost._rootDataHost||this.dataHost),n}_flexGrowChanged(t){this.parentElement&&this.parentElement._columnPropChanged&&this.parentElement._columnPropChanged("flexGrow"),this._allCells.forEach((e=>e.style.flexGrow=t))}_orderChanged(t){this._allCells.forEach((e=>e.style.order=t))}_widthChanged(t){this.parentElement&&this.parentElement._columnPropChanged&&this.parentElement._columnPropChanged("width"),this._allCells.forEach((e=>e.style.width=t)),this._grid&&this._grid.__forceReflow&&this._grid.__forceReflow()}_frozenChanged(t){this.parentElement&&this.parentElement._columnPropChanged&&this.parentElement._columnPropChanged("frozen",t),this._allCells.forEach((e=>this._toggleAttribute("frozen",t,e))),this._grid&&this._grid._frozenCellsChanged&&this._grid._frozenCellsChanged()}_lastFrozenChanged(t){this._allCells.forEach((e=>this._toggleAttribute("last-frozen",t,e))),this.parentElement&&this.parentElement._columnPropChanged&&(this.parentElement._lastFrozen=t)}_pathOrHeaderChanged(t,e,n,i,r,o,a,s,l){const c=void 0!==e;!a&&!l&&c&&n&&this.__setTextContent(n._content,e),t&&r.value&&(o||s||this.__setColumnTemplateOrRenderer(void 0,((e,n,{item:i})=>this.__setTextContent(e,this.get(t,i))),r.value),a||l||c||!n||null===e||this.__setTextContent(n._content,this._generateHeader(t))),n&&this._grid.__updateHeaderFooterRowVisibility(n.parentElement)}__setTextContent(t,e){t.textContent!==e&&(t.textContent=e)}_generateHeader(t){return t.substr(t.lastIndexOf(".")+1).replace(/([A-Z])/g,"-$1").toLowerCase().replace(/-/g," ").replace(/^./,(t=>t.toUpperCase()))}_toggleAttribute(t,e,n){n.hasAttribute(t)===!e&&(e?n.setAttribute(t,""):n.removeAttribute(t))}_reorderStatusChanged(t){this._allCells.forEach((e=>e.setAttribute("reorder-status",t)))}_resizableChanged(t,e){void 0!==t&&void 0!==e&&e&&[e].concat(this._emptyCells).forEach((e=>{if(e){const n=e.querySelector('[part~="resize-handle"]');if(n&&e.removeChild(n),t){const t=document.createElement("div");t.setAttribute("part","resize-handle"),e.appendChild(t)}}}))}_textAlignChanged(t){if(void 0===t)return;if(-1===["start","end","center"].indexOf(t))return void console.warn('textAlign can only be set as "start", "end" or "center"');let e;"ltr"===getComputedStyle(this._grid).direction?"start"===t?e="left":"end"===t&&(e="right"):"start"===t?e="right":"end"===t&&(e="left"),this._allCells.forEach((n=>{n._content.style.textAlign=t,getComputedStyle(n._content).textAlign!==t&&(n._content.style.textAlign=e)}))}_hiddenChanged(t){this.parentElement&&this.parentElement._columnPropChanged&&this.parentElement._columnPropChanged("hidden",t),!!t!=!!this._previousHidden&&this._grid&&(!0===t&&this._allCells.forEach((t=>{t._content.parentNode&&t._content.parentNode.removeChild(t._content)})),this._grid._debouncerHiddenChanged=Tn.debounce(this._grid._debouncerHiddenChanged,_t,(()=>{this._grid&&this._grid._renderColumnTree&&this._grid._renderColumnTree(this._grid._columnTree)})),this._grid._updateLastFrozen&&this._grid._updateLastFrozen(),this._grid.notifyResize&&this._grid.notifyResize(),this._grid._resetKeyboardNavigation&&this._grid._resetKeyboardNavigation()),this._previousHidden=t}};class nKt extends(eKt(w$t(ye))){static get is(){return"vaadin-grid-column"}static get properties(){return{width:{type:String,value:"100px"},flexGrow:{type:Number,value:1},renderer:Function,path:{type:String},autoWidth:{type:Boolean,value:!1},_bodyTemplate:{type:Object},_cells:Array}}}customElements.define(nKt.is,nKt),
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
$Xt("vaadin-grid",GXt`
    @keyframes vaadin-grid-appear {
      to {
        opacity: 1;
      }
    }

    :host {
      display: block;
      animation: 1ms vaadin-grid-appear;
      height: 400px;
      flex: 1 1 auto;
      align-self: stretch;
      position: relative;
    }

    :host([hidden]) {
      display: none !important;
    }

    #scroller {
      display: block;
      transform: translateY(0);
      width: auto;
      height: auto;
      position: absolute;
      top: 0;
      right: 0;
      bottom: 0;
      left: 0;
    }

    :host([height-by-rows]) {
      height: auto;
      align-self: flex-start;
      flex-grow: 0;
      width: 100%;
    }

    :host([height-by-rows]) #scroller {
      width: 100%;
      height: 100%;
      position: relative;
    }

    #table {
      display: flex;
      flex-direction: column;
      width: 100%;
      height: 100%;
      overflow: auto;
      position: relative;
      outline: none;
      /* Workaround for a Desktop Safari bug: new stacking context here prevents the scrollbar from getting hidden */
      z-index: 0;
    }

    #header,
    #footer {
      display: block;
      position: -webkit-sticky;
      position: sticky;
      left: 0;
      overflow: visible;
      width: 100%;
      z-index: 1;
    }

    #header {
      top: 0;
    }

    th {
      text-align: inherit;
    }

    /* Safari doesn't work with "inherit" */
    [safari] th {
      text-align: initial;
    }

    #footer {
      bottom: 0;
    }

    #items {
      flex-grow: 1;
      flex-shrink: 0;
      display: block;
      position: -webkit-sticky;
      position: sticky;
      width: 100%;
      left: 0;
      overflow: visible;
    }

    [part~='row'] {
      display: flex;
      width: 100%;
      box-sizing: border-box;
      margin: 0;
    }

    [part~='row'][loading] [part~='body-cell'] ::slotted(vaadin-grid-cell-content) {
      opacity: 0;
    }

    #items [part~='row'] {
      position: absolute;
    }

    #items [part~='row']:empty {
      height: 1em;
    }

    [part~='cell']:not([part~='details-cell']) {
      flex-shrink: 0;
      flex-grow: 1;
      box-sizing: border-box;
      display: flex;
      width: 100%;
      position: relative;
      align-items: center;
      padding: 0;
      white-space: nowrap;
    }

    [part~='details-cell'] {
      position: absolute;
      bottom: 0;
      width: 100%;
      box-sizing: border-box;
      padding: 0;
    }

    [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      display: block;
      width: 100%;
      box-sizing: border-box;
      overflow: hidden;
      text-overflow: ellipsis;
    }

    [hidden] {
      display: none !important;
    }

    [frozen] {
      z-index: 2;
      will-change: transform;
    }

    [no-scrollbars][safari] #table,
    [no-scrollbars][firefox] #table {
      overflow: hidden;
    }

    /* Reordering styles */
    :host([reordering]) [part~='cell'] ::slotted(vaadin-grid-cell-content),
    :host([reordering]) [part~='resize-handle'],
    #scroller[no-content-pointer-events] [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      pointer-events: none;
    }

    [part~='reorder-ghost'] {
      visibility: hidden;
      position: fixed;
      pointer-events: none;
      opacity: 0.5;

      /* Prevent overflowing the grid in Firefox */
      top: 0;
      left: 0;
    }

    :host([reordering]) {
      -moz-user-select: none;
      -webkit-user-select: none;
      user-select: none;
    }

    /* Resizing styles */
    [part~='resize-handle'] {
      position: absolute;
      top: 0;
      right: 0;
      height: 100%;
      cursor: col-resize;
      z-index: 1;
    }

    [part~='resize-handle']::before {
      position: absolute;
      content: '';
      height: 100%;
      width: 35px;
      transform: translateX(-50%);
    }

    [last-column] [part~='resize-handle']::before,
    [last-frozen] [part~='resize-handle']::before {
      width: 18px;
      transform: none;
      right: 0;
    }

    #scroller[column-resizing] {
      -ms-user-select: none;
      -moz-user-select: none;
      -webkit-user-select: none;
      user-select: none;
    }

    /* Sizer styles */
    #sizer {
      display: flex;
      position: absolute;
      visibility: hidden;
    }

    #sizer [part~='details-cell'] {
      display: none !important;
    }

    #sizer [part~='cell'][hidden] {
      display: none !important;
    }

    #sizer [part~='cell'] {
      display: block;
      flex-shrink: 0;
      line-height: 0;
      height: 0 !important;
      min-height: 0 !important;
      max-height: 0 !important;
      padding: 0 !important;
      border: none !important;
    }

    #sizer [part~='cell']::before {
      content: '-';
    }

    #sizer [part~='cell'] ::slotted(vaadin-grid-cell-content) {
      display: none !important;
    }

    /* RTL specific styles */

    :host([dir='rtl']) #items,
    :host([dir='rtl']) #header,
    :host([dir='rtl']) #footer {
      left: auto;
    }

    :host([dir='rtl']) [part~='reorder-ghost'] {
      left: auto;
      right: 0;
    }

    :host([dir='rtl']) [part~='resize-handle'] {
      left: 0;
      right: auto;
    }

    :host([dir='rtl']) [part~='resize-handle']::before {
      transform: translateX(50%);
    }

    :host([dir='rtl']) [last-column] [part~='resize-handle']::before,
    :host([dir='rtl']) [last-frozen] [part~='resize-handle']::before {
      left: 0;
      right: auto;
    }
  `,{moduleId:"vaadin-grid-styles"});
/**
     * @license
     * Copyright (c) 2021 Vaadin Ltd.
     * This program is available under Apache License Version 2.0, available at https://vaadin.com/license/
     */
const iKt=(()=>{try{return document.createEvent("TouchEvent"),!0}catch(t){return!1}})();class rKt extends(E$t(c$t(D$t(R$t(B$t(N$t(j$t(G$t(W$t(U$t(Q$t(P$t(F$t(tKt(O$t(H$t(J$t(q$t(L$t))))))))))))))))))){static get template(){return _e`
      <div
        id="scroller"
        safari$="[[_safari]]"
        ios$="[[_ios]]"
        loading$="[[loading]]"
        column-reordering-allowed$="[[columnReorderingAllowed]]"
      >
        <table id="table" role="grid" aria-multiselectable="true" tabindex="0">
          <caption id="sizer" part="row"></caption>
          <thead id="header" role="rowgroup"></thead>
          <tbody id="items" role="rowgroup"></tbody>
          <tfoot id="footer" role="rowgroup"></tfoot>
        </table>

        <div part="reorder-ghost"></div>
      </div>

      <div id="focusexit" tabindex="0"></div>
    `}static get is(){return"vaadin-grid"}static get version(){return"20.0.2"}static get observers(){return["_columnTreeChanged(_columnTree, _columnTree.*)"]}static get properties(){return{_safari:{type:Boolean,value:/^((?!chrome|android).)*safari/i.test(navigator.userAgent)},_ios:{type:Boolean,value:/iPad|iPhone|iPod/.test(navigator.userAgent)&&!window.MSStream||"MacIntel"===navigator.platform&&navigator.maxTouchPoints>1},_firefox:{type:Boolean,value:navigator.userAgent.toLowerCase().indexOf("firefox")>-1},_android:{type:Boolean,value:/android/i.test(navigator.userAgent)},_touchDevice:{type:Boolean,value:iKt},heightByRows:{type:Boolean,value:!1,reflectToAttribute:!0,observer:"_heightByRowsChanged"},_recalculateColumnWidthOnceLoadingFinished:{type:Boolean,value:!0}}}constructor(){super(),this.addEventListener("animationend",this._onAnimationEnd)}connectedCallback(){super.connectedCallback(),this.recalculateColumnWidths()}attributeChangedCallback(t,e,n){super.attributeChangedCallback(t,e,n),"dir"===t&&(this.__isRTL="rtl"===n,this._updateScrollerMeasurements())}__hasRowsWithClientHeight(){return!!Array.from(this.$.items.children).filter((t=>t.clientHeight)).length}__itemsReceived(){this._recalculateColumnWidthOnceLoadingFinished&&!this._cache.isLoading()&&this.__hasRowsWithClientHeight()&&(this._recalculateColumnWidthOnceLoadingFinished=!1,this.recalculateColumnWidths())}_recalculateColumnWidths(t){t.forEach((t=>{t.width="auto",t._origFlexGrow=t.flexGrow,t.flexGrow=0})),t.forEach((t=>{t._currentWidth=0,t._allCells.forEach((e=>{t._currentWidth=Math.max(t._currentWidth,e.offsetWidth+1)}))})),t.forEach((t=>{t.width=`${t._currentWidth}px`,t.flexGrow=t._origFlexGrow,t._currentWidth=void 0,t._origFlexGrow=void 0}))}recalculateColumnWidths(){if(this._columnTree)if(this._cache.isLoading())this._recalculateColumnWidthOnceLoadingFinished=!0;else{const t=this._getColumns().filter((t=>!t.hidden&&t.autoWidth));this._recalculateColumnWidths(t)}}_createScrollerRows(t){const e=[];for(let n=0;n<t;n++){const t=document.createElement("tr");t.setAttribute("part","row"),t.setAttribute("role","row"),this._columnTree&&this._updateRow(t,this._columnTree[this._columnTree.length-1],"body",!1,!0),e.push(t)}return this._columnTree&&this._columnTree[this._columnTree.length-1].forEach((t=>t.isConnected&&t.notifyPath&&t.notifyPath("_cells.*",t._cells))),(function n(t,e,i){Ei||Ai(),Ti.push([t,e,i])})(this,(()=>{this._updateFirstAndLastColumn(),this._resetKeyboardNavigation()})),e}_getRowTarget(){return this.$.items}_createCell(t){const e="vaadin-grid-cell-content-"+(this._contentIndex=this._contentIndex+1||0),n=document.createElement("vaadin-grid-cell-content");n.setAttribute("slot",e);const i=document.createElement(t);i.id=e.replace("-content-","-"),i.setAttribute("tabindex","-1"),i.setAttribute("role","td"===t?"gridcell":"columnheader");const r=document.createElement("slot");return r.setAttribute("name",e),i.appendChild(r),i._content=n,n.addEventListener("mousedown",(()=>{if(window.chrome){const t=()=>{n.contains(this.getRootNode().activeElement)||i.focus(),document.removeEventListener("mouseup",t,!0)};document.addEventListener("mouseup",t,!0)}else setTimeout((()=>{n.contains(this.getRootNode().activeElement)||i.focus()}))})),i}_updateRow(t,e,n,i,r){n=n||"body";const o=document.createDocumentFragment();Array.from(t.children).forEach((t=>t._vacant=!0)),t.innerHTML="","sizer"!==t.id&&(t.hidden=!0),e.filter((t=>!t.hidden)).forEach(((e,a,s)=>{let l;if("body"===n){if(e._cells=e._cells||[],l=e._cells.filter((t=>t._vacant))[0],l||(l=this._createCell("td"),e._cells.push(l)),l.setAttribute("part","cell body-cell"),t.appendChild(l),a===s.length-1&&(this._rowDetailsTemplate||this.rowDetailsRenderer)){this._detailsCells=this._detailsCells||[];const e=this._detailsCells.filter((t=>t._vacant))[0]||this._createCell("td");-1===this._detailsCells.indexOf(e)&&this._detailsCells.push(e),e._content.parentElement||o.appendChild(e._content),this._configureDetailsCell(e),t.appendChild(e),this._a11ySetRowDetailsCell(t,e),e._vacant=!1}e.notifyPath&&!r&&e.notifyPath("_cells.*",e._cells)}else{const r="header"===n?"th":"td";i||"vaadin-grid-column-group"===e.localName?(l=e[`_${n}Cell`]||this._createCell(r),l._column=e,t.appendChild(l),e[`_${n}Cell`]=l):(e._emptyCells=e._emptyCells||[],l=e._emptyCells.filter((t=>t._vacant))[0]||this._createCell(r),l._column=e,t.appendChild(l),-1===e._emptyCells.indexOf(l)&&e._emptyCells.push(l)),l.setAttribute("part",`cell ${n}-cell`),this.__updateHeaderFooterRowVisibility(t)}l._content.parentElement||o.appendChild(l._content),l._vacant=!1,l._column=e})),this.appendChild(o),this._frozenCellsChanged(),this._updateFirstAndLastColumnForRow(t)}__updateHeaderFooterRowVisibility(t){if(!t)return;const e=Array.from(t.children).filter((e=>{const n=e._column;if(n._emptyCells&&n._emptyCells.indexOf(e)>-1)return!1;if(t.parentElement===this.$.header){if(n.headerRenderer||n._headerTemplate)return!0;if(null===n.header)return!1;if(n.path||void 0!==n.header)return!0}else if(n.footerRenderer||n._footerTemplate)return!0}));t.hidden!==!e.length&&(t.hidden=!e.length,this.notifyResize())}_updateScrollerItem(t,e){this._preventScrollerRotatingCellFocus(t,e),this._columnTree&&(this._toggleAttribute("first",0===e,t),this._toggleAttribute("odd",e%2,t),this._a11yUpdateRowRowindex(t,e),this._getItem(e,t))}_columnTreeChanged(t){this._renderColumnTree(t),this.recalculateColumnWidths()}_renderColumnTree(t){for(Array.from(this.$.items.children).forEach((e=>this._updateRow(e,t[t.length-1],null,!1,!0)));this.$.header.children.length<t.length;){const t=document.createElement("tr");t.setAttribute("part","row"),t.setAttribute("role","row"),this.$.header.appendChild(t);const e=document.createElement("tr");e.setAttribute("part","row"),e.setAttribute("role","row"),this.$.footer.appendChild(e)}for(;this.$.header.children.length>t.length;)this.$.header.removeChild(this.$.header.firstElementChild),this.$.footer.removeChild(this.$.footer.firstElementChild);Array.from(this.$.header.children).forEach(((e,n)=>this._updateRow(e,t[n],"header",n===t.length-1))),Array.from(this.$.footer.children).forEach(((e,n)=>this._updateRow(e,t[t.length-1-n],"footer",0===n))),this._updateRow(this.$.sizer,t[t.length-1]),this._resizeHandler(),this._frozenCellsChanged(),this._updateFirstAndLastColumn(),this._resetKeyboardNavigation(),this._a11yUpdateHeaderRows(),this._a11yUpdateFooterRows(),this.__updateFooterPositioning()}__updateFooterPositioning(){this._firefox&&(this.$.items.style.paddingBottom=0,this.heightByRows||(this.$.items.style.paddingBottom=`${this.$.footer.offsetHeight}px`)),this._ios&&!window.CSS.supports("position","sticky")&&(this.$.table.style.height="",this.$.table.style.minHeight="100%",this.$.table.style.maxHeight="100%",setTimeout((()=>this.$.table.style.height=`${this.$.scroller.offsetHeight}px`)))}_updateItem(t,e){t._item=e;const n=this.__getRowModel(t);this._toggleAttribute("selected",n.selected,t),this._a11yUpdateRowSelected(t,n.selected),this._a11yUpdateRowLevel(t,n.level),this._toggleAttribute("expanded",n.expanded,t),this._toggleAttribute("details-opened",this._isDetailsOpened(e),t),(this._rowDetailsTemplate||this.rowDetailsRenderer)&&this._toggleDetailsCell(t,e),this._generateCellClassNames(t,n),this._filterDragAndDrop(t,n),Array.from(t.children).forEach((t=>{if(t._renderer){const e=t._column||this;t._renderer.call(e,t._content,e,n)}else t._instance&&(t._instance.__detailsOpened__=n.detailsOpened,t._instance.__selected__=n.selected,t._instance.__level__=n.level,t._instance.__expanded__=n.expanded,t._instance.setProperties(n))})),this._debouncerUpdateHeights=Tn.debounce(this._debouncerUpdateHeights,gt.after(1),(()=>{this._updateMetrics(),this._positionItems(),this._updateScrollerSize()}))}_resizeHandler(){this._updateDetailsCellHeights(),this._accessIronListAPI(super._resizeHandler,!0),this._updateScrollerMeasurements(),this.__updateFooterPositioning()}_onAnimationEnd(t){0===t.animationName.indexOf("vaadin-grid-appear")&&(this._render(),t.stopPropagation(),this.notifyResize(),this.__itemsReceived(),requestAnimationFrame((()=>{this.__scrollToPendingIndex(),this.$.table.style.webkitOverflowScrolling="touch"})))}_toggleAttribute(t,e,n){n.hasAttribute(t)===!e&&(e?n.setAttribute(t,""):n.removeAttribute(t))}__getRowModel(t){return{index:t.index,item:t._item,level:this._getIndexLevel(t.index),expanded:this._isExpanded(t._item),selected:this._isSelected(t._item),detailsOpened:!(!this._rowDetailsTemplate&&!this.rowDetailsRenderer)&&this._isDetailsOpened(t._item)}}render(){this._columnTree&&(this._columnTree.forEach((t=>{t.forEach((t=>t._renderHeaderAndFooter()))})),this._update())}notifyResize(){super.notifyResize()}_heightByRowsChanged(t,e){(t||e)&&this.notifyResize()}__forceReflow(){this._debouncerForceReflow=Tn.debounce(this._debouncerForceReflow,_t,(()=>{this.$.scroller.style.overflow="hidden",setTimeout((()=>this.$.scroller.style.overflow=""))}))}}customElements.define(rKt.is,rKt);let oKt=class extends ye{constructor(){super(...arguments),this._run=""}_csvUrl(t,e,n){return e?iO(n(t,e),{format:"csv"}):""}_jsonUrl(t,e,n){return e?n(t,e):""}_csvName(t,e){return e?`run-${e}-tag-${t}.csv`:""}_jsonName(t,e){return e?`run-${e}-tag-${t}.json`:""}};oKt.template=_e`
    <paper-dropdown-menu
      no-label-float="true"
      label="run to download"
      selected-item-label="{{_run}}"
    >
      <paper-listbox slot="dropdown-content">
        <template is="dom-repeat" items="[[runs]]">
          <paper-item no-label-float="true">[[item]]</paper-item>
        </template>
      </paper-listbox>
    </paper-dropdown-menu>
    <template is="dom-if" if="[[_run]]">
      <a download="[[_csvName(tag, _run)]]" href="[[_csvUrl(tag, _run, urlFn)]]"
        >CSV</a
      ><!--
      --><a
        download="[[_jsonName(tag, _run)]]"
        href="[[_jsonUrl(tag, _run, urlFn)]]"
        >JSON</a
      >
    </template>
    <style>
      :host {
        display: flex;
        align-items: center;
        height: 32px;
      }
      paper-dropdown-menu {
        width: 100px;
        --paper-input-container-label: {
          font-size: 10px;
        }
        --paper-input-container-input: {
          font-size: 10px;
        }
      }
      a {
        font-size: 10px;
        margin: 0 0.2em;
      }
      paper-input {
        font-size: 22px;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],oKt.prototype,"_run",void 0),t([o({type:Array}),e("design:type",Array)],oKt.prototype,"runs",void 0),t([o({type:String}),e("design:type",String)],oKt.prototype,"tag",void 0),t([o({type:Object}),e("design:type",Object)],oKt.prototype,"urlFn",void 0),oKt=t([i("tf-downloader")],oKt),new URLSearchParams(window.location.search);let aKt=class extends ye{constructor(){super(...arguments),this.colorScale=null,this._loadDataCallback=(t,e,n)=>{if(null==n)return void console.error("Failed to load data for:",e);const i=n.map((t=>({wall_time:new Date(1e3*t[0]),step:t[1],scalar:t[2]}))),r=this._getSeriesNameFromDatum(e);t.setSeriesMetadata(r,e),t.setSeriesData(r,i)},this.getDataLoadUrl=({tag:t,run:e})=>_r().pluginRoute("scalars","/scalars",new URLSearchParams({tag:t,run:e})),this._downloadUrlFn=(t,e)=>this.getDataLoadUrl({tag:t,run:e}),this.requestData=(t,e,n)=>this.inColab?this._requestDataGet(t,e,n):this._requestDataPost(t,e,n),this._requestDataGet=(t,e,n)=>{const i=_r().pluginRoute("scalars","/scalars");Promise.all(t.map((t=>{const n=iO(i,{tag:t.tag,run:t.run});return this.requestManager.request(n).then((n=>{e({item:t,data:n})}))}))).finally((()=>{n()}))},this._requestDataPost=(t,e,n)=>{var i;const r=_r().pluginRoute("scalars","/scalars_multirun"),o=new Map;for(const{tag:e,run:n}of t){let t=o.get(e);null==t&&o.set(e,t=[]),t.push(n)}const a=null!==(i=this.batchSize)&&void 0!==i?i:64,s=[];for(const[t,e]of o)for(let n=0;n<e.length;n+=a)s.push({tag:t,runs:e.slice(n,n+a)});Promise.all(s.map((({tag:t,runs:n})=>this.requestManager.request(r,{tag:t,runs:n}).then((i=>{for(const r of n){const n={tag:t,run:r};Object.prototype.hasOwnProperty.call(i,r)?e({item:n,data:i[r]}):e({item:n,data:null})}}))))).finally((()=>{n()}))},this._getDataLoadName=t=>this._getSeriesNameFromDatum(t),this._expanded=!1,this._tooltipColumns=(()=>{const t=pCt.slice(),e=t.findIndex((t=>"Name"==t.title));return t.splice(e,1,{title:"Name",evaluate:t=>{const e=t.dataset.metadata().meta;return this._getSeriesDisplayNameFromDatum(e)}}),t})()}_getChartDataLoader(){return this.shadowRoot.querySelector("tf-line-chart-data-loader")}reload(){this._getChartDataLoader().reload()}redraw(){this._getChartDataLoader().redraw()}_toggleExpanded(t){this.set("_expanded",!this._expanded),this.redraw()}_toggleLogScale(){this.set("_logScaleActive",!this._logScaleActive)}_resetDomain(){const t=this._getChartDataLoader();t&&t.resetDomain()}_updateDownloadLink(){const t=this._getChartDataLoader().exportAsSvgString();this.shadowRoot.querySelector("#svgLink").href=`data:image/svg+xml;base64,${btoa(t)}`}_runsFromData(t){return t.map((t=>t.run))}_getDataSeries(){return this.dataToLoad.map((t=>this._getSeriesNameFromDatum(t)))}_getSeriesNameFromDatum({run:t,experiment:e={name:"_default"}}){return JSON.stringify([e.name,t])}_getSeriesDisplayNameFromDatum(t){return t.run}_getColorScale(){return null!==this.colorScale?this.colorScale:{scale:t=>{const[,e]=JSON.parse(t);return GR(e)}}}};aKt.template=_e`
    <tf-card-heading
      tag="[[tag]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
    ></tf-card-heading>
    <div id="tf-line-chart-data-loader-container">
      <tf-line-chart-data-loader
        active="[[active]]"
        color-scale="[[_getColorScale(colorScale)]]"
        data-series="[[_getDataSeries(dataToLoad.*)]]"
        data-to-load="[[dataToLoad]]"
        get-data-load-name="[[_getDataLoadName]]"
        get-data-load-url="[[getDataLoadUrl]]"
        request-data="[[requestData]]"
        ignore-y-outliers="[[ignoreYOutliers]]"
        load-data-callback="[[_loadDataCallback]]"
        load-key="[[tag]]"
        log-scale-active="[[_logScaleActive]]"
        request-manager="[[requestManager]]"
        smoothing-enabled="[[smoothingEnabled]]"
        smoothing-weight="[[smoothingWeight]]"
        tag-metadata="[[tagMetadata]]"
        tooltip-columns="[[_tooltipColumns]]"
        tooltip-position="auto"
        tooltip-sorting-method="[[tooltipSortingMethod]]"
        x-type="[[xType]]"
      >
      </tf-line-chart-data-loader>
    </div>
    <div id="buttons">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
      <paper-icon-button
        selected$="[[_logScaleActive]]"
        icon="line-weight"
        on-tap="_toggleLogScale"
        title="Toggle y-axis log scale"
      ></paper-icon-button>
      <paper-icon-button
        icon="settings-overscan"
        on-tap="_resetDomain"
        title="Fit domain to data"
      ></paper-icon-button>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <paper-menu-button on-paper-dropdown-open="_updateDownloadLink">
          <paper-icon-button
            class="dropdown-trigger"
            slot="dropdown-trigger"
            icon="file-download"
          ></paper-icon-button>
          <paper-listbox class="dropdown-content" slot="dropdown-content">
            <paper-item>
              <a id="svgLink" download="[[tag]].svg">
                Download Current Chart as SVG
              </a>
            </paper-item>
          </paper-listbox>
        </paper-menu-button>
      </template>
      <span style="flex-grow: 1"></span>
      <template is="dom-if" if="[[showDownloadLinks]]">
        <div class="download-links">
          <tf-downloader
            runs="[[_runsFromData(dataToLoad)]]"
            tag="[[tag]]"
            url-fn="[[_downloadUrlFn]]"
          ></tf-downloader>
        </div>
      </template>
    </div>
    <style>
      :host {
        margin: 5px;
        display: block;
        width: 330px;
      }

      :host([_expanded]) {
        width: 100%;
      }

      :host([_expanded]) #tf-line-chart-data-loader-container {
        height: 400px;
      }

      #tf-line-chart-data-loader-container {
        height: 200px;
        width: 100%;
      }

      tf-card-heading {
        display: block;
        margin-bottom: 10px;
      }

      #buttons {
        display: flex;
        flex-direction: row;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }

      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }

      .download-links {
        display: flex;
        height: 32px;
      }

      .download-links a {
        align-self: center;
        font-size: 10px;
        margin: 2px;
      }

      .download-links paper-dropdown-menu {
        width: 100px;
        --paper-input-container-label: {
          font-size: 10px;
        }
        --paper-input-container-input: {
          font-size: 10px;
        }
      }

      paper-menu-button {
        padding: 0;
      }
      paper-item a {
        color: inherit;
        text-decoration: none;
        white-space: nowrap;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],aKt.prototype,"tag",void 0),t([o({type:Array}),e("design:type",Array)],aKt.prototype,"dataToLoad",void 0),t([o({type:String}),e("design:type",String)],aKt.prototype,"xType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],aKt.prototype,"active",void 0),t([o({type:Boolean}),e("design:type",Boolean)],aKt.prototype,"ignoreYOutliers",void 0),t([o({type:Object}),e("design:type",dr)],aKt.prototype,"requestManager",void 0),t([o({type:Boolean}),e("design:type",Boolean)],aKt.prototype,"showDownLinks",void 0),t([o({type:Boolean}),e("design:type",Boolean)],aKt.prototype,"smoothingEnabled",void 0),t([o({type:Number}),e("design:type",Number)],aKt.prototype,"smoothingWeight",void 0),t([o({type:Object}),e("design:type",Object)],aKt.prototype,"tagMetadata",void 0),t([o({type:Object}),e("design:type",Object)],aKt.prototype,"colorScale",void 0),t([o({type:String}),e("design:type",String)],aKt.prototype,"tooltipSortingMethod",void 0),t([o({type:Number}),e("design:type",Number)],aKt.prototype,"batchSize",void 0),t([o({type:Boolean}),e("design:type",Number)],aKt.prototype,"inColab",void 0),t([o({type:Object}),e("design:type",Object)],aKt.prototype,"_loadDataCallback",void 0),t([o({type:Object}),e("design:type",Function)],aKt.prototype,"getDataLoadUrl",void 0),t([o({type:Object}),e("design:type",Object)],aKt.prototype,"_downloadUrlFn",void 0),t([o({type:Object}),e("design:type",Function)],aKt.prototype,"requestData",void 0),t([o({type:Object}),e("design:type",Object)],aKt.prototype,"_getDataLoadName",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],aKt.prototype,"_expanded",void 0),t([o({type:Boolean}),e("design:type",Boolean)],aKt.prototype,"_logScaleActive",void 0),t([o({type:Array}),e("design:type",Array)],aKt.prototype,"_tooltipColumns",void 0),aKt=t([i("tf-scalar-card")],aKt);let sKt=class extends((function t(e,n){return(function i(t,e){return Nr({},er(e),t)})(e,n)})([_o],ye)){constructor(){super(...arguments),this.sessionGroup=null,this._xType=xTt.STEP,this._noMultiExperiments=!1,this._requestData=(t,e,n)=>{Promise.all(t.map((t=>this.backend.listMetricEvals({experimentName:this.experimentName,sessionName:t.run,metricName:t.tag}).then((n=>{e({item:t,data:n})}))))).finally((()=>{n()}))},this._colorScale={scale:t=>{const e=JSON.parse(t)[1],n=this._indexOfSession.get(e);return HR[(this._sessionGroupNameHash+n)%HR.length]}}}connectedCallback(){super.connectedCallback(),this.addEventListener("iron-resize",this.redraw.bind(this))}redraw(){this.shadowRoot.querySelectorAll("tf-scalar-card").forEach((t=>t.redraw()))}_sessionGroupChanged(){this.sessionGroup&&0!=Object.keys(this.sessionGroup).length?(this._indexOfSession=new Map(this.sessionGroup.sessions.map(((t,e)=>[t.name,e]))),this._sessionGroupNameHash=YYt(this.sessionGroup.name)):(this._indexOfSession=new Map,this._sessionGroupNameHash=0),this.shadowRoot.querySelectorAll("tf-scalar-card").forEach((t=>{const e=t,n=e.get("tag");e.set("tag",""),e.set("tag",n)}))}_haveMetrics(){return this.visibleSchema&&Array.isArray(this.visibleSchema.metricInfos)&&this.visibleSchema.metricInfos.length>0}_haveMetricsAndSessionGroup(){return this.sessionGroup&&this._haveMetrics()}_computeSeriesForSessionGroupMetric(t,e){return null===t||0==Object.keys(t).length||null===e?[]:t.sessions.filter((t=>void 0!==AYt(t.metricValues,e.name))).map((t=>({tag:e.name,run:t.name})))}_computeTagMetadata(t){return{displayName:wYt(t),description:t.description||""}}};sKt.template=_e`
    <template is="dom-if" if="[[!sessionGroup]]">
      <div>
        <h3>No session group selected</h3>
        <p>Please select a session group to see its metric-graphs here.</p>
      </div>
    </template>
    <template is="dom-if" if="[[!_haveMetrics(visibleSchema.*)]]">
      <div>
        <h3>No metrics are enabled</h3>
        <p>Please enable some metrics to see content here.</p>
      </div>
    </template>
    <div class="layout horizontal wrap session-group-details">
      <template
        is="dom-if"
        if="[[_haveMetricsAndSessionGroup(visibleSchema.*, sessionGroup)]]"
      >
        <template
          is="dom-repeat"
          items="[[visibleSchema.metricInfos]]"
          as="metricInfo"
        >
          <!-- Note that we do not provide a request-manager attribute since
               we provide a function in request-data for calling the backend
               to get the metrics data.
            -->
          <tf-scalar-card
            class="scalar-card"
            color-scale="[[_colorScale]]"
            data-to-load="[[_computeSeriesForSessionGroupMetric(sessionGroup, metricInfo)]]"
            tag="[[metricInfo.name.tag]]"
            tag-metadata="[[_computeTagMetadata(metricInfo)]]"
            x-type="[[_xType]]"
            multi-experiments="[[_noMultiExperiments]]"
            request-data="[[_requestData]]"
            active
          >
          </tf-scalar-card>
        </template>
      </template>
    </div>
    <!-- "iron-flex" is needed to use the layout classes in the div above -->
    <style include="iron-flex">
      :host {
        display: block;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],sKt.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],sKt.prototype,"experimentName",void 0),t([o({type:Object}),e("design:type",Object)],sKt.prototype,"visibleSchema",void 0),t([o({type:Object}),e("design:type",Object)],sKt.prototype,"sessionGroup",void 0),t([o({type:String}),e("design:type",String)],sKt.prototype,"_xType",void 0),t([o({type:Boolean}),e("design:type",Boolean)],sKt.prototype,"_noMultiExperiments",void 0),t([o({type:Object}),e("design:type",Object)],sKt.prototype,"_indexOfSession",void 0),t([o({type:Number}),e("design:type",Number)],sKt.prototype,"_sessionGroupNameHash",void 0),t([o({type:Object}),e("design:type",Function)],sKt.prototype,"_requestData",void 0),t([o({type:Object}),e("design:type",Object)],sKt.prototype,"_colorScale",void 0),t([a("sessionGroup.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],sKt.prototype,"_sessionGroupChanged",null),sKt=t([i("tf-hparams-session-group-details")],sKt);let lKt=class extends(er(il(ye))){constructor(){super(...arguments),this._hparamName=xYt,this._metricName=wYt}_visibleSchemaOrSessionGroupsChanged(){const t=this.$.sessionGroupsTable.get("detailsOpenedItems");this.$.sessionGroupsTable.set("detailsOpenedItems",[]),Di();const e=new Map;this.sessionGroups.forEach((t=>{e.set(t.name,t)})),this.$.sessionGroupsTable.set("detailsOpenedItems",t.map((t=>e.get(t.name))).filter(Boolean))}_sessionGroupHParam(t,e){return null!=t&&0!=Object.keys(t).length&&Object.prototype.hasOwnProperty.call(t.hparams,e)?VYt(t.hparams[e]):""}_sessionGroupMetric(t,e){if(null==t||0==Object.keys(t).length)return"";for(let n=0;n<t.metricValues.length;++n){let i=t.metricValues[n];if(i.name.group===e.group&&i.name.tag==e.tag)return VYt(i.value)}return""}_rowNumber(t){return t+1}};lKt.template=_e`
    <vaadin-grid
      class="session-group-table"
      id="sessionGroupsTable"
      column-reordering-allowed=""
      items="[[sessionGroups]]"
    >
      <vaadin-grid-column flex-grow="0" width="10em" resizable="">
        <template class="header">
          <div class="table-header table-cell">Trial ID</div>
        </template>
        <template>
          <div class="table-cell">[[item.name]]</div>
        </template>
      </vaadin-grid-column>
      <template is="dom-if" if="[[enableShowMetrics]]">
        <vaadin-grid-column flex-grow="0" autoWidth="" resizable="">
          <template class="header">
            <div class="table-header table-cell">Show Metrics</div>
          </template>
          <template>
            <paper-checkbox class="table-cell" checked="{{detailsOpened}}">
            </paper-checkbox>
          </template>
        </vaadin-grid-column>
      </template>
      <template
        is="dom-repeat"
        items="[[visibleSchema.hparamInfos]]"
        as="hparamInfo"
        index-as="hparamIndex"
      >
        <vaadin-grid-column flex-grow="2" width="10em" resizable="">
          <template class="header">
            <div class="table-header table-cell">
              [[_hparamName(hparamInfo)]]
            </div>
          </template>
          <template>
            <div class="table-cell">
              [[_sessionGroupHParam(item, hparamInfo.name)]]
            </div>
          </template>
        </vaadin-grid-column>
      </template>
      <template
        is="dom-repeat"
        items="{{visibleSchema.metricInfos}}"
        as="metricInfo"
        index-as="metricIndex"
      >
        <vaadin-grid-column flex-grow="2" width="10em" resizable="">
          <template class="header">
            <div class="table-header table-cell">
              [[_metricName(metricInfo)]]
            </div>
          </template>
          <template>
            <div class="table-cell">
              [[_sessionGroupMetric(item, metricInfo.name)]]
            </div>
          </template>
        </vaadin-grid-column>
      </template>
      <template class="row-details">
        <tf-hparams-session-group-details
          backend="[[backend]]"
          experiment-name="[[experimentName]]"
          session-group="[[item]]"
          visible-schema="[[visibleSchema]]"
          class="session-group-details"
        >
        </tf-hparams-session-group-details>
      </template>
    </vaadin-grid>

    <style>
      :host {
        display: inline;
      }

      :host(.dark-mode) {
        --lumo-base-color: #303030;
        --lumo-body-text-color: #fff;
      }

      :host(.dark-mode) vaadin-grid {
        --_lumo-grid-secondary-border-color: #505050;
      }

      .table-cell {
        white-space: nowrap;
        text-overflow: ellipsis;
        overflow: hidden;
      }
      .table-header {
        /* line-break overflowing column headers */
        white-space: normal;
        overflow-wrap: break-word;
      }
      .session-group-table {
        height: 100%;
      }
      .session-group-details {
        height: 360px;
        overflow-y: auto;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],lKt.prototype,"visibleSchema",void 0),t([o({type:Array}),e("design:type",Array)],lKt.prototype,"sessionGroups",void 0),t([o({type:Boolean}),e("design:type",Boolean)],lKt.prototype,"enableShowMetrics",void 0),t([o({type:Object}),e("design:type",Object)],lKt.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],lKt.prototype,"experimentName",void 0),t([a("visibleSchema.*","sessionGroups.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],lKt.prototype,"_visibleSchemaOrSessionGroupsChanged",null),lKt=t([i("tf-hparams-table-view")],lKt);let cKt=class extends ye{constructor(){super(...arguments),this.sessionGroup=null,this.visibleSchema=null}_propertiesArePopulated(t,e){return null!=t&&null!=e}_singletonSessionGroups(t){return null==t?[]:[t]}};function uKt(t,e,n){function i(){if(0===t.length)return[1,2];const[e,n]=Ll(t);return e!==n?[e,n]:e>0?[.5*e,1.5*e]:e<0?[1.5*e,.5*e]:[-1,1]}if("LINEAR"===n)return VM().domain(i()).range([e,0]);if("LOG"===n){const n=i();return n[0]<=0&&n[1]>=0?uKt(t,e,"LINEAR"):KM().domain(n).range([e,0])}if("QUANTILE"===n){const n=20,i=zl(n).map((t=>e-t*e/(n-1)));return 0===t.length&&(t=[1]),oE().domain(Se.exports.uniq(t)).range(i)}if("NON_NUMERIC"===n)return AM().domain(Se.exports.uniq(t.sort())).range([e,0]).padding(.1);throw RangeError("Unknown scale: "+n)}var hKt,dKt;cKt.template=_e`
    <!-- If sessionGroup or visibleSchema are not populated, do not display
         anything.
      -->
    <template
      is="dom-if"
      if="[[_propertiesArePopulated(visibleSchema, sessionGroup)]]"
    >
      <!-- Display one row without a "show-metrics" column -->
      <tf-hparams-table-view
        visible-schema="[[visibleSchema]]"
        session-groups="[[_singletonSessionGroups(sessionGroup)]]"
      >
      </tf-hparams-table-view>
    </template>
    <template
      is="dom-if"
      if="[[!_propertiesArePopulated(visibleSchema, sessionGroup)]]"
    >
      <div>Click or hover over a session group to display its values here.</div>
    </template>

    <style>
      :host {
        display: block;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],cKt.prototype,"sessionGroup",void 0),t([o({type:Object}),e("design:type",Object)],cKt.prototype,"visibleSchema",void 0),cKt=t([i("tf-hparams-session-group-values")],cKt),(function(t){t.LINEAR="LINEAR",t.LOG="LOG",t.QUANTILE="QUANTILE",t.NON_NUMERIC="NON_NUMERIC"})(hKt||(hKt={}));class pKt{isPassing(t){return!0}}class fKt{constructor(t,e,n,i){this._lower=t,this._upper=e,this._lowerOpen=n,this._upperOpen=i}isPassing(t){const e=t;return this._before(this._lower,e,!this._lowerOpen)&&this._before(e,this._upper,!this._upperOpen)}_before(t,e,n){return n?t<=e:t<e}}class mKt{constructor(t){this._domainSet=t}isPassing(t){return-1!==this._domainSet.findIndex((e=>e===t))}}class gKt{constructor(t,e,n,i){this._svgProps=t,this._schema=e,this._interactionManager=n,this._colIndex=i,this._isDisplayed=!1,this._yScale=null,this._scaleType=null,this.setBrushSelection(null)}colIndex(){return this._colIndex}yScale(){return this._yScale}scaleType(){return this._scaleType}brushSelection(){return this._brushSelection}isDisplayed(){return this._isDisplayed}setBrushSelection(t){this._brushSelection=t,this._brushFilter=this._buildBrushFilter(this.brushSelection(),this.scaleType(),this.yScale())}setDomainAndScale(t,e){this._scaleType=e,this._yScale=uKt(t.slice(),this._svgProps.height,this.scaleType()),this._brushFilter=this._buildBrushFilter(this.brushSelection(),this.scaleType(),this.yScale())}brushFilter(){return this._brushFilter}updateDOM(t){let e=ac(this.yScale());this.scaleType()===hKt.QUANTILE&&(e=e.tickValues(this.yScale().quantiles()).tickFormat(my("-.6g")));const n=Su(t);n.selectAll("g").remove(),n.append("g").classed("axis",!0).call(e).append("text").classed("axis-title",!0).style("cursor","move").style("text-anchor","middle").attr("y",-9).text((t=>SYt(this._schema,t))),n.call(vh().on("start",(()=>{t.setAttribute("is-dragging",""),this._interactionManager.onDragStart(this.colIndex())})).on("drag",(()=>this._interactionManager.onDrag(uu.x))).on("end",(()=>{this._interactionManager.onDragEnd(),t.removeAttribute("is-dragging")})));const i=Zm().extent([[-8,0],[8,this._svgProps.height+1]]).on("start",(()=>{yKt(uu)&&(t.setAttribute("is-brushing",""),this._interactionManager.onBrushChanged(this.colIndex(),uu.selection))})).on("brush",(()=>{yKt(uu)&&this._interactionManager.onBrushChanged(this.colIndex(),uu.selection)})).on("end",(()=>{yKt(uu)&&(this._interactionManager.onBrushChanged(this.colIndex(),uu.selection),t.removeAttribute("is-brushing"))})),r=Su(t).append("g").classed("brush",!0);r.call(i),i.move(r,this.brushSelection())}setDisplayed(t){this._isDisplayed=t}_buildBrushFilter(t,e,n){if(null===t)return new pKt;if(null===e)return console.error("Scale type is null, but brushSelection isn't: ",t),new pKt;switch(e){case hKt.LINEAR:case hKt.LOG:{const[e,i]=(function r(t,e,n){return[t.invert(e),t.invert(n)].sort(((t,e)=>t-e))})(n,t[0],t[1]);return new fKt(e,i,!1,!1)}case hKt.QUANTILE:{const[e,i]=(function r(t,e,n){const i=t.range(),r=i.filter((t=>e<=t&&t<=n)).map((e=>{const n=t.invertExtent(e);return e===i[i.length-1]?[n[0],n[1]+1]:n}));return 0==r.length?[0,0]:Ll(ql(r))})(n,t[0],t[1]);return new fKt(e,i,!1,!0)}case hKt.NON_NUMERIC:return new mKt((function e(t,n,i){return t.domain().filter((e=>{const r=t(e);return n<=r&&r<=i}))})(n,t[0],t[1]))}return console.error("Unknown scale type: ",e),new pKt}}class _Kt{constructor(t,e,n){this._svgProps=t,this._schema=e,this._axes=this._createAxes(n),this._stationaryAxesPositions=AM().range([1,this._svgProps.width-1]).padding(.5),this._draggedAxis=null,this._svgProps.svgG.selectAll("g.axis-parent").remove(),this._parentsSel=this._svgProps.svgG.selectAll(".axis-parent")}updateAxes(t,e){console.assert(!this.isAxisDragging());const n=new Set;t.columns.forEach((t=>{const i=t.absoluteIndex;let r=this._axes[i];r.setDisplayed(!0);const o=e.map((t=>PYt(this._schema,t,i)));r.setDomainAndScale(o,t.scale),n.add(i)})),this._axes.forEach((t=>{n.has(t.colIndex())||t.setDisplayed(!1)})),this._updateStationaryAxesPositions(n),this._parentsSel=this._parentsSel.data(Array.from(n),(t=>t)),this._parentsSel.exit().remove(),this._parentsSel=this._parentsSel.enter().append("g").classed("axis-parent",!0).merge(this._parentsSel);const i=this;this._parentsSel.call((t=>this._updateAxesPositionsInDOM(t))).each((function(t){i._axes[t].updateDOM(this)}))}mapVisibleAxes(t){return this._stationaryAxesPositions.domain().map((e=>t(this.getAxisPosition(e),this._axes[e])))}allVisibleAxesSatisfy(t){return this._stationaryAxesPositions.domain().every((e=>t(this.getAxisPosition(e),this._axes[e])))}getAxisForColIndex(t){return this._axes[t]}dragStart(t){console.assert(!this.isAxisDragging()),console.assert(this._axes[t].isDisplayed()),this._draggedAxis=this._axes[t],this._draggedAxisPosition=this._stationaryAxesPositions(t)}drag(t){t=Math.min(Math.max(t,0),this._svgProps.width),this._draggedAxisPosition=t;let e=this._stationaryAxesPositions.domain();e.sort(((t,e)=>this.getAxisPosition(t)-this.getAxisPosition(e))),this._stationaryAxesPositions.domain(e),this._updateAxesPositionsInDOM(this._parentsSel)}dragEnd(t){console.assert(this.isAxisDragging()),this._draggedAxisPosition=null,this._draggedAxis=null,this._updateAxesPositionsInDOM(this._parentsSel.transition().duration(t))}isAxisDragging(){return null!==this._draggedAxis}getAxisPosition(t){return null!==this._draggedAxis&&this._draggedAxis.colIndex()===t?this._draggedAxisPosition:this._stationaryAxesPositions(t)}_updateStationaryAxesPositions(t){const e=this._stationaryAxesPositions.domain().filter((e=>t.has(e))),n=Array.from(new Set([...e,...Array.from(t)]));this._stationaryAxesPositions.domain(n)}_updateAxesPositionsInDOM(t){t.attr("transform",(t=>WYt(this.getAxisPosition(t))))}_createAxes(t){return zl(TYt(this._schema)).map((e=>new gKt(this._svgProps,this._schema,t,e)))}}function yKt(t){return null!==t.sourceEvent}!(function(t){t[t.FOREGROUND=0]="FOREGROUND",t[t.BACKGROUND=1]="BACKGROUND"})(dKt||(dKt={}));class vKt{constructor(t){void 0===t&&(t=sh(null)),console.assert(t.size()<=1),this._sessionGroupSel=t}sessionGroup(){return 1===this._sessionGroupSel.size()?this._sessionGroupSel.datum():null}isNull(){return null===this.sessionGroup()}selection(){return this._sessionGroupSel}equalsTo(t){return this.isNull()?t.isNull():!t.isNull()&&t.sessionGroup().name==this.sessionGroup().name}}class bKt{constructor(t,e,n){this._svgProps=t,this._schema=e,this._axesCollection=n,this._sessionGroups=[],this._svgProps.svgG.selectAll("g.background").remove(),this._svgProps.svgG.selectAll("g.foreground").remove(),this._bgPathsSel=this._svgProps.svgG.append("g").classed("background",!0).selectAll("path"),this._fgPathsSel=this._svgProps.svgG.append("g").classed("foreground",!0).selectAll("path"),this._updateVisibleFgPathsSel(),this._peakedSessionGroupHandle=new vKt,this._selectedSessionGroupHandle=new vKt,this._d3line=uL().curve(sL)}getSessionGroupHandle(t){return null==t?new vKt:new vKt(this._fgPathsSel.filter((e=>e.name===t.name)))}hideBackgroundLines(){this._bgPathsSel.attr("visibility","hidden")}showBackgroundLines(){this._bgPathsSel.attr("visibility",null)}peakedSessionGroupHandle(){return this._peakedSessionGroupHandle}selectedSessionGroupHandle(){return this._selectedSessionGroupHandle}recomputeControlPoints(t,e=0){(t===dKt.FOREGROUND?this._fgPathsSel:this._bgPathsSel).transition().duration(e).attr("d",(t=>this._pathDAttribute(t))),t===dKt.FOREGROUND&&window.setTimeout((()=>{const t=this;this._fgPathsSel.each((function(e){t._setControlPointsProperty(this,e)}))}))}recomputeForegroundLinesVisibility(){this._fgPathsSel.classed("invisible-path",(t=>!this._axesCollection.allVisibleAxesSatisfy(((e,n)=>n.brushFilter().isPassing(PYt(this._schema,t,n.colIndex())))))),this._updateVisibleFgPathsSel()}setForegroundLinesColor(t,e,n){const i=this._createLineColorFunction(t,e,n);this._fgPathsSel.attr("stroke",i)}redraw(t,e,n,i){const r=this._peakedSessionGroupHandle.sessionGroup(),o=this._selectedSessionGroupHandle.sessionGroup();this._sessionGroups=t,this._fgPathsSel=this._recomputePathSelection(this._fgPathsSel),this._bgPathsSel=this._recomputePathSelection(this._bgPathsSel),this._peakedSessionGroupHandle=this.getSessionGroupHandle(r),this._selectedSessionGroupHandle=this.getSessionGroupHandle(o),this.recomputeControlPoints(dKt.FOREGROUND),this.recomputeControlPoints(dKt.BACKGROUND),this.recomputeForegroundLinesVisibility(),this.setForegroundLinesColor(e,n,i)}updatePeakedSessionGroup(t){this._peakedSessionGroupHandle.selection().classed("peaked-path",!1),this._peakedSessionGroupHandle=t,this._peakedSessionGroupHandle.selection().classed("peaked-path",!0)}clearPeakedSessionGroup(){this.updatePeakedSessionGroup(new vKt)}updateSelectedSessionGroup(t){this._selectedSessionGroupHandle.selection().classed("selected-path",!1),this._selectedSessionGroupHandle=t,this._selectedSessionGroupHandle.selection().classed("selected-path",!0)}findClosestSessionGroup(t,e){const n=this._axesCollection.mapVisibleAxes(((t,e)=>t)),i=(function r(t,e,n,i){if(e.length<2)return console.error("Less than two axes in parallel coordinates plot."),null;const r=n[0],o=n[1];if(r<=e[0]||r>=e[e.length-1])return null;const a=Se.exports.sortedIndex(e,r);console.assert(a>0),console.assert(a<e.length);const s=a-1;let l=null,c=null;return t.forEach((t=>{const e=(function n(t,e,i,a){const s=t-i,l=e-a,c=r-i,u=o-a,h=(s*c+l*u)/(s*s+l*l);return h<=0?UYt(c,u):h>=1?UYt(t-r,e-o):UYt(c-h*s,u-h*l)})(t.controlPoints[s][0],t.controlPoints[s][1],t.controlPoints[a][0],t.controlPoints[a][1]);e>i||(null===l||e<l)&&(l=e,c=t)})),c})(this._visibleFgPathsSel.nodes(),n,[t,e],100);return null===i?new vKt:new vKt(Su(i))}_createLineColorFunction(t,e,n){if(null===t)return()=>"red";const i=VM().domain(NYt(this._schema,this._sessionGroups,t)).range([e,n]).interpolate(cp);return e=>i(PYt(this._schema,e,t))}_recomputePathSelection(t){return(t=t.data(this._sessionGroups,(t=>t.name))).exit().remove(),t.enter().append("path").merge(t)}_setControlPointsProperty(t,e){t.controlPoints=this._computeControlPoints(e)}_computeControlPoints(t){return this._axesCollection.mapVisibleAxes(((e,n)=>[e,n.yScale()(PYt(this._schema,t,n.colIndex()))]))}_pathDAttribute(t){return this._d3line(this._computeControlPoints(t))}_updateVisibleFgPathsSel(){this._visibleFgPathsSel=this._fgPathsSel.filter(":not(.invisible-path)")}}class xKt{constructor(t,e){this.svg=Su(t);const n=100*e+10+10;this.svg.attr("viewBox",`0 0 ${n} 240`),this.svg.attr("preserveAspectRatio","xMidYMid"),this.svg.style("min-width",n+"px"),this.svg.style("min-height","240px"),this.width=n-10-10,this.height=200,this.svgG=this.svg.append("g").attr("transform",WYt(10,30))}}class wKt{constructor(t,e,n,i){this._svgProps=t,this._schema=e,this._peakedSessionGroupChangedCB=n,this._selectedSessionGroupChangedCB=i,this._axesCollection=new _Kt(t,e,this),this._linesCollection=new bKt(t,e,this._axesCollection),this._svgProps.svg.on("click",(()=>this.onClick())).on("mousemove mouseenter",(()=>{const[t,e]=ah(this._svgProps.svgG.node());this.onMouseMoved(t,e)})).on("mouseleave",(()=>this.onMouseLeave()))}onDragStart(t){this._axesCollection.dragStart(t),this._linesCollection.hideBackgroundLines()}onDrag(t){this._axesCollection.drag(t),this._linesCollection.recomputeControlPoints(dKt.FOREGROUND)}onDragEnd(){this._axesCollection.dragEnd(500),this._linesCollection.recomputeControlPoints(dKt.FOREGROUND,500),window.setTimeout((()=>{this._linesCollection.recomputeControlPoints(dKt.BACKGROUND),this._linesCollection.showBackgroundLines()}),500)}onBrushChanged(t,e){this._axesCollection.getAxisForColIndex(t).setBrushSelection(e),this._linesCollection.recomputeForegroundLinesVisibility()}onMouseMoved(t,e){this._linesCollection.updatePeakedSessionGroup(this._linesCollection.findClosestSessionGroup(t,e)),this._peakedSessionGroupChangedCB(this._linesCollection.peakedSessionGroupHandle().sessionGroup())}onMouseLeave(){this._linesCollection.peakedSessionGroupHandle().isNull()||(this._linesCollection.clearPeakedSessionGroup(),this._peakedSessionGroupChangedCB(null))}onClick(){this._linesCollection.peakedSessionGroupHandle().sessionGroup()===this._linesCollection.selectedSessionGroupHandle().sessionGroup()?this._linesCollection.updateSelectedSessionGroup(new vKt):this._linesCollection.updateSelectedSessionGroup(this._linesCollection.peakedSessionGroupHandle()),this._selectedSessionGroupChangedCB(this._linesCollection.selectedSessionGroupHandle().sessionGroup())}onOptionsOrSessionGroupsChanged(t,e){this._axesCollection.updateAxes(t,e);const n=this._linesCollection.peakedSessionGroupHandle(),i=this._linesCollection.selectedSessionGroupHandle();this._linesCollection.redraw(e,void 0!==t.colorByColumnIndex?t.columns[t.colorByColumnIndex].absoluteIndex:null,t.minColor,t.maxColor),n.equalsTo(this._linesCollection.peakedSessionGroupHandle())||this._peakedSessionGroupChangedCB(this._linesCollection.peakedSessionGroupHandle().sessionGroup()),i.equalsTo(this._linesCollection.selectedSessionGroupHandle())||this._selectedSessionGroupChangedCB(this._linesCollection.selectedSessionGroupHandle().sessionGroup())}schema(){return this._schema}}let SKt=class extends(er(il(ye))){constructor(){super(...arguments),this.selectedSessionGroup=null,this.closestSessionGroup=null,this.redrawCount=0}_optionsOrSessionGroupsChanged(){var t;if(!this.options)return;const{configuration:e}=null!==(t=this._prevOptions)&&void 0!==t?t:{},{configuration:n}=this.options;if(void 0===this._interactionManager||!Se.exports.isEqual(e.schema,n.schema)||!Se.exports.isEqual(e.columnsVisibility,n.columnsVisibility)){Su(this.$.svg).selectAll("*").remove();const t=new xKt(this.$.svg,n.columnsVisibility.filter(Boolean).length);this.scopeSubtree(this.$.svg,!0),this._interactionManager=new wKt(t,n.schema,(t=>this.closestSessionGroupChanged(t)),(t=>this.selectedSessionGroupChanged(t)))}this._computeValidSessionGroups(),this._interactionManager.onOptionsOrSessionGroupsChanged(this.options,this._validSessionGroups),this.redrawCount++,this._prevOptions=this.options}closestSessionGroupChanged(t){this.closestSessionGroup=t}selectedSessionGroupChanged(t){this.selectedSessionGroup=t}_computeValidSessionGroups(){const t=XYt;if(void 0===this.sessionGroups)return void(this._validSessionGroups=void 0);const e=this.options.configuration.schema;this._validSessionGroups=this.sessionGroups.filter((n=>{for(let i=0;i<t.numColumns(e);++i)if(this.options.configuration.columnsVisibility[i]&&void 0===t.columnValueByIndex(e,n,i))return!1;return!0}))}};SKt.template=_e`
    <div id="container">
      <svg id="svg"></svg>
    </div>
    <style>
      :host {
        display: block;
        --tf-hparams-parallel-coords-plot-axis-shadow: 0 1px 0 #fff,
          1px 0 0 #fff, 0 -1px 0 #fff, -1px 0 0 #fff;
      }
      :host(.dark-mode) {
        --tf-hparams-parallel-coords-plot-axis-shadow: 0 1px 0 #000,
          1px 0 0 #000, 0 -1px 0 #000, -1px 0 0 #000;
      }
      svg {
        font: 10px sans-serif;
      }

      .background path {
        fill: none;
        stroke: #ddd;
        shape-rendering: crispEdges;
      }

      .foreground path {
        fill: none;
        stroke-opacity: 0.7;
        stroke-width: 1;
      }

      /* Will be set on foreground paths that are not "contained" in the current
         axes brushes. If no brushes are set, no path will have this class. */
      .foreground .invisible-path {
        display: none;
      }

      /* Style for the path closest to the mouse pointer (typically will become
      the selected path when the user clicks). */
      .foreground .peaked-path {
        stroke-width: 3;
      }

      /* The currently selected path class. We use !important to override the
         inline style that sets the regular color of a path. */
      .foreground .selected-path {
        stroke-width: 3 !important;
        stroke: #0f0 !important;
      }

      #container {
        height: 100%;
        width: 100%;
      }

      svg {
        width: 100%;
        height: 100%;
      }

      .axis text {
        text-shadow: var(--tf-hparams-parallel-coords-plot-axis-shadow);
        fill: currentColor;
        cursor: move;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],SKt.prototype,"sessionGroups",void 0),t([o({type:Object}),e("design:type",Object)],SKt.prototype,"options",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],SKt.prototype,"selectedSessionGroup",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],SKt.prototype,"closestSessionGroup",void 0),t([o({type:Number}),e("design:type",Number)],SKt.prototype,"redrawCount",void 0),t([o({type:Array}),e("design:type",Array)],SKt.prototype,"_validSessionGroups",void 0),t([o({type:Object}),e("design:type",Object)],SKt.prototype,"_interactionManager",void 0),t([a("options.*","sessionGroups.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],SKt.prototype,"_optionsOrSessionGroupsChanged",null),SKt=t([i("tf-hparams-parallel-coords-plot")],SKt);let MKt=class extends ye{_closestOrSelected(t,e){return null!==t?t:e}};MKt.template=_e`
    <!-- Controls behavior of parallel coordinates plot
         outputs set options to the _options property.
      -->
    <hparams-split-layout orientation="vertical">
      <!-- The scale and color controls. -->
      <tf-hparams-scale-and-color-controls
        id="controls"
        slot="content"
        class="section"
        configuration="[[configuration]]"
        session-groups="[[sessionGroups]]"
        options="{{_options}}"
      >
      </tf-hparams-scale-and-color-controls>
      <!-- The actual parallel coordinates plot -->
      <tf-hparams-parallel-coords-plot
        id="plot"
        slot="content"
        class="section"
        session-groups="[[sessionGroups]]"
        selected-session-group="{{_selectedGroup}}"
        closest-session-group="{{_closestGroup}}"
        options="[[_options]]"
      >
      </tf-hparams-parallel-coords-plot>
      <tf-hparams-session-group-values
        id="values"
        slot="content"
        class="section"
        visible-schema="[[configuration.visibleSchema]]"
        session-group="[[_closestOrSelected(
                             _closestGroup, _selectedGroup)]]"
      >
      </tf-hparams-session-group-values>
      <tf-hparams-session-group-details
        id="details"
        slot="content"
        class="section"
        backend="[[backend]]"
        experiment-name="[[experimentName]]"
        session-group="[[_selectedGroup]]"
        visible-schema="[[configuration.visibleSchema]]"
      >
      </tf-hparams-session-group-details>
    </hparams-split-layout>

    <style>
      .section {
        padding: 10px;
      }
      #values {
        height: 115px;
      }
      #details {
        flex-grow: 1;
        max-height: fit-content;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],MKt.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],MKt.prototype,"experimentName",void 0),t([o({type:Object}),e("design:type",Object)],MKt.prototype,"configuration",void 0),t([o({type:Array}),e("design:type",Array)],MKt.prototype,"sessionGroups",void 0),MKt=t([i("tf-hparams-parallel-coords-view")],MKt);let EKt=class extends(er(ye)){constructor(){super(...arguments),this.selectedSessionGroup=null,this.closestSessionGroup=null,this._container=null,this._svg=null,this.width=0,this.height=0,this._brushedCellIndex=null,this._brushSelection=null}ready(){super.ready(),this._container=this.$.container,this._svg=Su(this.$.svg),this._redraw()}_sessionGroupsChanged(){null!==this.selectedSessionGroup&&(this.selectedSessionGroup=DYt(this.sessionGroups,this.selectedSessionGroup.name)||null),this._redraw()}_visibleSchemaChanged(){this._brushedCellIndex=null,this._brushSelection=null,this._redraw()}_redraw(){this.debounce("_redraw",(()=>{const t=XYt;this.width=Math.max(150*t.numVisibleColumns(this.visibleSchema),1200),this.height=Math.max(112.5*t.numVisibleMetrics(this.visibleSchema),480),this._container.style.width=this.width+"px",this._container.style.height=this.height+"px",this._svg.attr("width",this.width).attr("height",this.height),this._svg.selectAll("g").remove(),this._draw()}),100)}_draw(){const t=XYt,e=this;if(!this.sessionGroups||0==this.sessionGroups.length||!this.visibleSchema||0==this.visibleSchema.metricInfos.length)return;const n=zl(t.numVisibleColumns(e.visibleSchema)),i=zl(t.numVisibleMetrics(e.visibleSchema)),r=TM().domain(n).range([85,this.width-1-5]).paddingInner(.1),o=TM().domain(i).range([this.height-1-5-50,5]).paddingInner(.1),a=r.bandwidth(),s=o.bandwidth(),l=n.map((t=>e._cellScale(t,[0,a-1]))),c=i.map((n=>e._cellScale(n+t.numVisibleHParams(e.visibleSchema),[s-1,0]))),u=this._svg.selectAll(".x-axis").data(n).enter().append("g").classed("x-axis",!0).attr("transform",(e=>t.translateStr(r(e),0)));function h(t){return"x-axis-clip-path-"+t}function d(t){return"x-label-clip-path-"+t}u.append("clipPath").attr("id",h).append("rect").attr("x",-5).attr("y",0).attr("width",a+10).attr("height",e.height-25),u.append("clipPath").attr("id",d).append("rect").attr("x",0).attr("y",e.height-25).attr("width",a).attr("height",25),u.append("g").attr("clip-path",(t=>"url(#"+h(t)+")")).each((function(t){Su(this).call(g,oc(l[t]).tickSize(e.height-50),a,40,e.options.columns[t].scale)})),u.append("g").classed("x-axis-label",!0).attr("clip-path",(t=>"url(#"+d(t)+")")).append("text").attr("text-anchor","middle").attr("x",a/2).attr("y",e.height-1-12.5).text((n=>t.schemaVisibleColumnName(e.visibleSchema,n))).append("title").text((n=>t.schemaVisibleColumnName(e.visibleSchema,n)));const p=this._svg.selectAll(".y-axis").data(i).enter().append("g").classed("y-axis",!0).attr("transform",(n=>t.translateStr(e.width-1,o(n))));function f(t){return"y-axis-clip-path-"+t}function m(t){return"y-label-clip-path-"+t}function g(t,e,n,i,r){const o=Math.floor(n/i),a=e.scale();if("QUANTILE"===r){let t=a.quantiles();const n=Math.ceil(t.length/o);t=zl(0,t.length,n).map((e=>t[e])),e.tickValues(t).tickFormat(my("-.2g"))}"LINEAR"!==r&&"LOG"!==r||e.ticks(o),t.call(e),t.selectAll(".domain").remove(),t.selectAll(".tick line").attr("stroke","#ddd")}p.append("clipPath").attr("id",f).append("rect").attr("x",-(e.width-40-1)).attr("y",-5).attr("width",e.width-40).attr("height",s+10),p.append("clipPath").attr("id",m).append("rect").attr("x",-(e.width-1)).attr("y",0).attr("width",40).attr("height",s),p.append("g").attr("clip-path",(t=>"url(#"+f(t)+")")).each((function(n){Su(this).call(g,ac(c[n]).tickSize(e.width-80),s,20,e.options.columns[n+t.numVisibleHParams(e.visibleSchema)].scale)})),p.append("g").classed("y-axis-label",!0).attr("clip-path",(t=>"url(#"+m(t)+")")).append("text").attr("text-anchor","middle").attr("x",-(e.width-20-1)).attr("y",s/2).attr("transform",t.rotateStr(90,-(e.width-20-1),s/2)).text((n=>t.metricName(e.visibleSchema.metricInfos[n]))).append("title").text((n=>t.metricName(e.visibleSchema.metricInfos[n])));const _=this._svg.selectAll(".cell").data(Tl(n,i)).enter().append("g").classed("cell",!0).attr("transform",(([e,n])=>t.translateStr(r(e),o(n))));_.append("g").classed("frame",!0).append("rect").attr("x",-5).attr("y",-5).attr("width",a+10).attr("height",s+10).attr("stroke","#000").attr("fill","none").attr("shape-rendering","crispEdges");let y=null;void 0!==e.options.colorByColumnIndex&&(y=VM().domain(this._colExtent(this.options.colorByColumnIndex)).range([this.options.minColor,this.options.maxColor]).interpolate(cp));const v=void 0===e.options.colorByColumnIndex?()=>"red":({sessionGroup:t})=>y(this._colValue(t,e.options.colorByColumnIndex));function b(t,n){return l[n](e._colValue(t,n))}function x(t,n){return c[n](e._metricValue(t,n))}const[w,S,M]=(function E(t,r){const o=t.selectAll(".data-marker").data((([t,n])=>e.sessionGroups.filter((i=>void 0!==e._colValue(i,t)&&void 0!==e._metricValue(i,n))).map((e=>({col:t,metric:n,sessionGroup:e,x:b(e,t),y:x(e,n),sessionGroupMarkers:null}))))).enter().append("circle").classed("data-marker",!0).attr("cx",(({x:t})=>t)).attr("cy",(({y:t})=>t)).attr("r",2).attr("fill",r),a=new Map;e.sessionGroups.forEach((t=>{a.set(t,[])})),o.each((function(t){a.get(t.sessionGroup).push(this)})),o.each((t=>{const e=a.get(t.sessionGroup);t.sessionGroupMarkers=new Set(e)}));const s=n.map((t=>i.map((e=>o.filter((n=>n.col==t&&n.metric==e))))));return[o,s,a]})(_.append("g"),v),T=n.map((t=>i.map((e=>(function n(t,e){const n=[];return S[t][e].each((function(){n.push(this)})),N_().x((t=>Su(t).datum().x)).y((t=>Su(t).datum().y)).addAll(n)})(t,e)))));let C=null;N()&&(C=_.filter((t=>Se.exports.isEqual(t,e._brushedCellIndex))),console.assert(1==C.size(),C));let A=new Set(w.nodes());function k(){let n=new Set(w.nodes());(function i(){return!N()||e._brushSelection[0][0]===e._brushSelection[1][0]||e._brushSelection[0][1]===e._brushSelection[1][1]})()||(n=(function r(e,n){console.assert(null!==e),console.assert(null!==n);const[i,r]=e,o=new Set;return t.quadTreeVisitPointsInRect(T[i][r],n[0][0],n[0][1],n[1][0],n[1][1],(t=>{Su(t).datum().sessionGroupMarkers.forEach((t=>{o.add(t)}))})),o})(e._brushedCellIndex,e._brushSelection)),sh(Array.from(t.filterSet(n,(t=>!A.has(t))))).attr("fill",v),sh(Array.from(t.filterSet(A,(t=>!n.has(t))))).attr("fill","#ddd"),A=n}k();const L=Jm().extent([[-4,-4],[a-1+5-1,s-1+5-1]]).on("start",(function(){N()&&C.node()!=this&&L.move(C,null),P(this)})).on("brush",(function(){P(this)})).on("end",(function(){P(this)}));function P(t){const n=Km(t);!N()&&null===n||N()&&t===C.node()&&Se.exports.isEqual(n,e._brushSelection)||(e._brushSelection=n,null!==n?(C=Su(t),e._brushedCellIndex=C.datum()):(C=null,e._brushedCellIndex=null),k())}function N(){return null!==e._brushedCellIndex&&null!==e._brushSelection}_.call(L),N()&&L.move(C,e._brushSelection);let I=null,R=null;null!==this.selectedSessionGroup&&(R=sh(M.get(this.selectedSessionGroup)).classed("selected-marker",!0)),_.on("click",(function(){const t=I===R?null:I;if(t===R)return;null!==R&&R.classed("selected-marker",!1),R=t,null!==R&&R.classed("selected-marker",!0);const n=null===R?null:R.datum().sessionGroup;e.selectedSessionGroup=n})).on("mousemove mouseenter",(function([n,i]){const[r,o]=ah(this),a=(function s(e,n,i,r,o){let a=1/0,s=null;return t.quadTreeVisitPointsInDisk(T[e][n],i,r,o,((t,e)=>{if(A.has(t)&&e<a){const n=Su(t).datum();a=e,s=n.sessionGroup}})),null===s?null:sh(M.get(s))})(n,i,r,o,20);I!==a&&(null!==I&&I.classed("closest-marker",!1),I=a,null!==I?(I.classed("closest-marker",!0),e.closestSessionGroup=I.datum().sessionGroup):e.closestSessionGroup=null)})).on("mouseleave",(function([t,n]){null!==I&&(I.classed("closest-marker",!1),I=null,e.closestSessionGroup=null)})),this._svg.selectAll("*").classed("tf-hparams-scatter-plot-matrix-plot",!0)}_cellScale(t,e){const n=this._colExtent(t),i=VM().domain(n).range(e);if("LINEAR"===this.options.columns[t].scale)return i;if("LOG"===this.options.columns[t].scale)return n[0]<=0&&n[1]>=0?i:KM().domain(n).range(e);if("QUANTILE"===this.options.columns[t].scale){const n=(e[1]-e[0])/19,i=zl(20).map((t=>e[0]+n*t));return oE().domain(Se.exports.uniq(this.sessionGroups.map((e=>this._colValue(e,t))))).range(i)}if("NON_NUMERIC"===this.options.columns[t].scale)return AM().domain(Se.exports.uniq(this.sessionGroups.map((e=>this._colValue(e,t))).sort())).range(e).padding(.1);throw"Unknown scale for column: "+t+". options: "+this.options}_colValue(t,e){return FYt(this.visibleSchema,t,e)}_metricValue(t,e){return HYt(this.visibleSchema,t,e)}_colExtent(t){return zYt(this.visibleSchema,this.sessionGroups,t)}};EKt.template=_e`
    <div id="container">
      <svg id="svg"></svg>
    </div>

    <style>
      :host {
        display: block;
      }
      svg {
        font: 10px sans-serif;
      }

      text {
        fill: currentColor;
      }

      .frame rect {
        stroke: currentColor;
      }

      /* The closest data point marker to the mouse pointer. We use !important
         to override the inline style that sets the regular style of a marker.
      */
      .closest-marker {
        r: 6 !important;
      }

      /* The currently selected data point marker. We use !important to
         override the inline style that sets the regular style of a marker. */
      .selected-marker {
        r: 6 !important;
        fill: #0f0 !important;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],EKt.prototype,"visibleSchema",void 0),t([o({type:Array}),e("design:type",Array)],EKt.prototype,"sessionGroups",void 0),t([o({type:Object}),e("design:type",Object)],EKt.prototype,"options",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],EKt.prototype,"selectedSessionGroup",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],EKt.prototype,"closestSessionGroup",void 0),t([o({type:Object}),e("design:type",HTMLElement)],EKt.prototype,"_container",void 0),t([o({type:Object}),e("design:type",Object)],EKt.prototype,"_svg",void 0),t([o({type:Number}),e("design:type",Number)],EKt.prototype,"width",void 0),t([o({type:Number}),e("design:type",Number)],EKt.prototype,"height",void 0),t([o({type:Object}),e("design:type",Object)],EKt.prototype,"_brushedCellIndex",void 0),t([o({type:Object}),e("design:type",Object)],EKt.prototype,"_brushSelection",void 0),t([a("sessionGroups.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],EKt.prototype,"_sessionGroupsChanged",null),t([a("visibleSchema.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],EKt.prototype,"_visibleSchemaChanged",null),t([a("options.*"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],EKt.prototype,"_redraw",null),EKt=t([i("tf-hparams-scatter-plot-matrix-plot")],EKt);let TKt=class extends ye{_closestOrSelected(t,e){return null!==t?t:e}};TKt.template=_e`
    <hparams-split-layout orientation="vertical">
      <!-- Controls behavior of the scatter plot matrix
             outputs the configured options to the _options property. -->
      <tf-hparams-scale-and-color-controls
        slot="content"
        class="section"
        id="controls"
        configuration="[[configuration]]"
        session-groups="[[sessionGroups]]"
        options="{{_options}}"
      >
      </tf-hparams-scale-and-color-controls>
      <!-- The actual scatter plot matrix -->
      <tf-hparams-scatter-plot-matrix-plot
        slot="content"
        class="section"
        id="plot"
        visible-schema="[[configuration.visibleSchema]]"
        session-groups="[[sessionGroups]]"
        selected-session-group="{{_selectedGroup}}"
        closest-session-group="{{_closestGroup}}"
        options="[[_options]]"
      >
      </tf-hparams-scatter-plot-matrix-plot>
      <tf-hparams-session-group-values
        slot="content"
        class="section"
        id="values"
        visible-schema="[[configuration.visibleSchema]]"
        session-group="[[_closestOrSelected(
                                 _closestGroup, _selectedGroup)]]"
      >
      </tf-hparams-session-group-values>
      <!-- Shows session group details for the clicked marker. -->
      <tf-hparams-session-group-details
        slot="content"
        class="section"
        id="details"
        backend="[[backend]]"
        experiment-name="[[experimentName]]"
        session-group="[[_selectedGroup]]"
        visible-schema="[[configuration.visibleSchema]]"
      >
      </tf-hparams-session-group-details>
    </hparams-split-layout>
    <style>
      .section {
        padding: 10px;
      }
      #controls {
        flex-grow: 0;
        flex-shrink: 0;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      #plot {
        flex-grow: 1;
        flex-shrink: 1;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      #values {
        flex-grow: 0;
        flex-shrink: 0;
        flex-basis: auto;
        height: 115px;
        overflow-y: auto;
        max-height: fit-content;
      }
      #details {
        flex-grow: 0;
        flex-shrink: 1;
        flex-basis: auto;
        height: auto;
        overflow-y: auto;
        max-height: fit-content;
      }
      vaadin-split-layout {
        height: 100%;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],TKt.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],TKt.prototype,"experimentName",void 0),t([o({type:Object}),e("design:type",Object)],TKt.prototype,"configuration",void 0),t([o({type:Array}),e("design:type",Array)],TKt.prototype,"sessionGroups",void 0),TKt=t([i("tf-hparams-scatter-plot-matrix-view")],TKt);let CKt=class extends ye{constructor(){super(...arguments),this._selectedTab=0}};CKt.template=_e`
    <paper-header-panel>
      <paper-toolbar slot="header" class="tab-bar">
        <paper-tabs selected="{{_selectedTab}}" slot="top">
          <!-- view-id can be used by integration tests to locate a tab.
               It should be the name of the root element implementing the view
               without the 'tf-hparams-' prefix. -->
          <paper-tab view-id="table-view"> TABLE VIEW </paper-tab>
          <paper-tab view-id="parallel-coords-view">
            PARALLEL COORDINATES VIEW
          </paper-tab>
          <paper-tab view-id="scatter-plot-matrix-view">
            SCATTER PLOT MATRIX VIEW
          </paper-tab>
          <div class="help-and-feedback">
            <template is="dom-if" if="[[bugReportUrl]]">
              <a
                href$="[[bugReportUrl]]"
                target="_blank"
                rel="noopener noreferrer"
              >
                <paper-button
                  id="bug-report"
                  raised
                  title="Send a bug report or feature request"
                >
                  Bug Report / Feature Request
                </paper-button>
              </a>
            </template>
            <template is="dom-if" if="[[helpUrl]]">
              <a href$="[[helpUrl]]" target="_blank" rel="noopener noreferrer">
                <paper-icon-button
                  icon="help-outline"
                  title="View documentation"
                >
                </paper-icon-button>
              </a>
            </template>
          </div>
        </paper-tabs>
      </paper-toolbar>
      <iron-pages selected="[[_selectedTab]]" class="fit tab-view">
        <div id="0" class="tab">
          <tf-hparams-table-view
            backend="[[backend]]"
            experiment-name="[[experimentName]]"
            visible-schema="[[configuration.visibleSchema]]"
            session-groups="[[sessionGroups]]"
            enable-show-metrics
          >
          </tf-hparams-table-view>
        </div>
        <div id="1" class="tab">
          <tf-hparams-parallel-coords-view
            backend="[[backend]]"
            experiment-name="[[experimentName]]"
            configuration="[[configuration]]"
            session-groups="[[sessionGroups]]"
          >
          </tf-hparams-parallel-coords-view>
        </div>
        <div id="2" class="tab">
          <tf-hparams-scatter-plot-matrix-view
            backend="[[backend]]"
            experiment-name="[[experimentName]]"
            configuration="[[configuration]]"
            session-groups="[[sessionGroups]]"
          >
          </tf-hparams-scatter-plot-matrix-view>
        </div>
      </iron-pages>
    </paper-header-panel>

    <style>
      .tab-view {
        height: 100%;
      }
      .tab-bar {
        overflow-y: auto;
        color: white;
        background-color: var(
          --tb-toolbar-background-color,
          var(--tb-orange-strong)
        );
      }
      .tab {
        height: 100%;
      }
      paper-tabs {
        flex-grow: 1;
        width: 100%;
        height: 100%;
        --paper-tabs-selection-bar-color: white;
        --paper-tabs-content: {
          -webkit-font-smoothing: antialiased;
        }
      }
      tf-hparams-table-view {
        width: 100%;
        height: 100%;
      }
      .help-and-feedback {
        display: inline-flex; /* Ensure that icons stay aligned */
        justify-content: flex-end;
        align-items: center;
        text-align: right;
        color: white;
      }
      #bug-report {
        border: solid black;
        background: red;
        white-space: normal;
        word-break: break-words;
        font-size: 12px;
        max-width: 150px;
        text-align: left;
      }
      .help-and-feedback a {
        color: white;
        text-decoration: none;
      }
    </style>
  `,t([o({type:Object}),e("design:type",Object)],CKt.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],CKt.prototype,"helpUrl",void 0),t([o({type:String}),e("design:type",String)],CKt.prototype,"bugReportUrl",void 0),t([o({type:String}),e("design:type",String)],CKt.prototype,"experimentName",void 0),t([o({type:Object}),e("design:type",Object)],CKt.prototype,"configuration",void 0),t([o({type:Array}),e("design:type",Array)],CKt.prototype,"sessionGroups",void 0),t([o({type:Number}),e("design:type",Number)],CKt.prototype,"_selectedTab",void 0),CKt=t([i("tf-hparams-sessions-pane")],CKt);class AKt{constructor(t,e,n=!0){this._apiUrl=t,this._requestManager=e,this._useHttpGet=n}getExperiment(t){return this._sendRequest("experiment",t)}getDownloadUrl(t,e,n){return this._apiUrl+"/download_data?"+new URLSearchParams({format:t,columnsVisibility:JSON.stringify(n),request:JSON.stringify(e)})}listSessionGroups(t){return this._sendRequest("session_groups",t)}listMetricEvals(t){return this._sendRequest("metric_evals",t)}_sendRequest(t,e){if(this._useHttpGet){const n=encodeURIComponent(JSON.stringify(e));return this._requestManager.request(this._apiUrl+"/"+t+"?request="+n)}const n=new hr;return n.withCredentials=!0,n.methodType=ur.POST,n.contentType="text/plain",n.body=JSON.stringify(e),this._requestManager.requestWithOptions(this._apiUrl+"/"+t,n)}}let kKt=class extends(er(ye)){reload(){this.$["query-pane"].reload()}};kKt.template=_e`
    <hparams-split-layout>
      <div slot="content" class="sidebar">
        <tf-hparams-query-pane
          id="query-pane"
          backend="[[backend]]"
          experiment-name="[[experimentName]]"
          configuration="{{_configuration}}"
          session-groups="{{_sessionGroups}}"
          data-loaded-with-non-empty-hparams="{{_dataLoadedWithNonEmptyHparams}}"
          data-loaded-with-empty-hparams="{{_dataLoadedWithEmptyHparams}}"
        >
        </tf-hparams-query-pane>
      </div>
      <div slot="content" class="center">
        <template is="dom-if" if="[[_dataLoadedWithEmptyHparams]]">
          <div class="no-data-warning">
            <h3>No hparams data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>You haven’t written any hparams data to your event files.</li>
              <li>
                Event files are still being loaded (try reloading this page).
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>

        <template is="dom-if" if="[[_dataLoadedWithNonEmptyHparams]]">
          <tf-hparams-sessions-pane
            id="sessions-pane"
            backend="[[backend]]"
            help-url="[[helpUrl]]"
            bug-report-url="[[bugReportUrl]]"
            experiment-name="[[experimentName]]"
            configuration="[[_configuration]]"
            session-groups="[[_sessionGroups]]"
          >
          </tf-hparams-sessions-pane>
        </template>
      </div>
    </hparams-split-layout>
    <style>
      hparams-split-layout {
        width: 100%;
      }

      .sidebar {
        width: 20%;
        height: 100%;
        overflow: auto;
        flex-grow: 0;
        flex-shrink: 0;
        min-width: 10%;
      }

      .center {
        height: 100%;
        overflow-y: auto;
        flex-grow: 1;
        flex-shrink: 1;
        width: 80%;
      }

      :host {
        display: flex;
        flex-direction: row;
        height: 100%;
        width: 100%;
      }

      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }

      a {
        color: var(--tb-link);
      }

      a:visited {
        color: var(--tb-link-visited);
      }
    </style>
  `,t([o({type:Object}),e("design:type",AKt)],kKt.prototype,"backend",void 0),t([o({type:String}),e("design:type",String)],kKt.prototype,"experimentName",void 0),t([o({type:String}),e("design:type",String)],kKt.prototype,"helpUrl",void 0),t([o({type:String}),e("design:type",String)],kKt.prototype,"bugReportUrl",void 0),t([o({type:Object}),e("design:type",Object)],kKt.prototype,"_configuration",void 0),t([o({type:Array}),e("design:type",Array)],kKt.prototype,"_sessionGroups",void 0),t([o({type:Boolean}),e("design:type",Boolean)],kKt.prototype,"_dataLoadedWithNonEmptyHparams",void 0),t([o({type:Boolean}),e("design:type",Boolean)],kKt.prototype,"_dataLoadedWithEmptyHparams",void 0),kKt=t([i("tf-hparams-main")],kKt);const LKt="true"===new URLSearchParams(window.location.search).get("tensorboardColab");let PKt=class extends(er(ye)){constructor(){super(...arguments),this._backend=new AKt(_r().pluginRoute("hparams",""),new dr,LKt)}reload(){this.$["hparams-main"].reload()}};PKt.template=_e`
    <!-- TensorBoard does not specify an experimentName. Currently it only
         supports one experiment per invocation. -->
    <tf-hparams-main
      id="hparams-main"
      backend="[[_backend]]"
      experiment-name=""
    >
    </tf-hparams-main>
  `,t([o({type:Object}),e("design:type",Object)],PKt.prototype,"_backend",void 0),PKt=t([i("tf-hparams-dashboard")],PKt);let NKt=class extends(er(ye)){constructor(){super(...arguments),this.actualSize=!1,this.brightnessAdjustment=.5,this.contrastPercentage=0,this._metadataCanceller=new XR,this._imageCanceller=new XR,this._steps=[],this._isImageLoading=!1}get _runColor(){return GR(this.run)}get _hasAtLeastOneStep(){var t=this._steps;return!!t&&t.length>0}get _hasMultipleSteps(){var t=this._steps;return!!t&&t.length>1}get _currentStep(){return this._steps[this._stepIndex]||null}get _stepValue(){var t=this._currentStep;return t?t.step:0}get _currentWallTime(){var t=this._currentStep;return t?KR(t.wall_time):""}get _maxStepIndex(){return this._steps.length-1}get _sampleText(){return`${this.sample+1}`}get _hasMultipleSamples(){return this.ofSamples>1}_getAriaExpanded(){return this.actualSize?"true":"false"}attached(){this.reload()}reload(){if(!this.isAttached)return;this._metadataCanceller.cancelAll();const t=iO(_r().pluginRoute("images","/images"),{tag:this.tag,run:this.run,sample:this.sample}),e=this._metadataCanceller.cancellable((t=>{if(t.cancelled)return;const e=t.value.map(this._createStepDatum.bind(this));this.set("_steps",e),this.set("_stepIndex",e.length-1)}));this.requestManager.request(t).then(e)}_createStepDatum(t){let e=_r().pluginRoute("images","/individualImage");return e=iO(e,{ts:t.wall_time}),e+="&"+t.query,{wall_time:new Date(1e3*t.wall_time),step:t.step,url:e}}_updateImageUrl(){var t=this._currentStep,e=this.brightnessAdjustment,n=this.contrastPercentage;if(!t)return;const i=new Image;this._imageCanceller.cancelAll(),i.onload=i.onerror=this._imageCanceller.cancellable((t=>{if(t.cancelled)return;const e=this.$$("#main-image-container");e.textContent="",Yi(e).appendChild(i),this.set("_isImageLoading",!1)})).bind(this),i.style.filter=`contrast(${n}%) `,i.style.filter+=`brightness(${e})`,this.set("_isImageLoading",!0),i.src=t.url}_handleTap(t){this.set("actualSize",!this.actualSize)}_toLocaleString(t){return t.toLocaleString()}};NKt.template=_e`
    <tf-card-heading
      tag="[[tag]]"
      run="[[run]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
      color="[[_runColor]]"
    >
      <template is="dom-if" if="[[_hasMultipleSamples]]">
        <div>sample: [[_sampleText]] of [[ofSamples]]</div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep]]">
        <div class="heading-row">
          <div class="heading-label">
            step
            <span style="font-weight: bold"
              >[[_toLocaleString(_stepValue)]]</span
            >
          </div>
          <div class="heading-label heading-right datetime">
            <template is="dom-if" if="[[_currentWallTime]]">
              [[_currentWallTime]]
            </template>
          </div>
          <div class="label right">
            <paper-spinner-lite active hidden$="[[!_isImageLoading]]">
            </paper-spinner-lite>
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps]]">
        <div>
          <paper-slider
            id="steps"
            immediate-value="{{_stepIndex}}"
            max="[[_maxStepIndex]]"
            max-markers="[[_maxStepIndex]]"
            snaps
            step="1"
            value="{{_stepIndex}}"
          ></paper-slider>
        </div>
      </template>
    </tf-card-heading>

    <!-- Semantically a button but <img> inside a <button> disallows user to do
    an interesting operation like "Copy Image" in non-Chromium browsers. -->
    <a
      id="main-image-container"
      role="button"
      aria-label="Toggle actual size"
      aria-expanded$="[[_getAriaExpanded(actualSize)]]"
      on-tap="_handleTap"
    ></a>

    <style include="tf-card-heading-style">
      /** Make button a div. */
      button {
        width: 100%;
        display: block;
        background: none;
        border: 0;
        padding: 0;
      }

      /** Firefox: Get rid of dotted line inside button. */
      button::-moz-focus-inner {
        border: 0;
        padding: 0;
      }

      /** Firefox: Simulate Chrome's outer glow on button when focused. */
      button:-moz-focusring {
        outline: none;
        box-shadow: 0px 0px 1px 2px Highlight;
      }

      :host {
        display: block;
        width: 350px;
        height: auto;
        position: relative;
        margin: 0 15px 40px 0;
        overflow-x: auto;
      }

      /** When actual size shown is on, use the actual image width. */
      :host([actual-size]) {
        max-width: 100%;
        width: auto;
      }

      :host([actual-size]) #main-image-container {
        max-height: none;
        width: auto;
      }

      :host([actual-size]) #main-image-container img {
        width: auto;
      }

      paper-spinner-lite {
        width: 14px;
        height: 14px;
        vertical-align: text-bottom;
        --paper-spinner-color: var(--tb-orange-strong);
      }

      #steps {
        height: 15px;
        margin: 0 0 0 -15px;
        /*
         * 31 comes from adding a padding of 15px from both sides of the
         * paper-slider, subtracting 1px so that the slider width aligns
         * with the image (the last slider marker takes up 1px), and
         * adding 2px to account for a border of 1px on both sides of
         * the image. 30 - 1 + 2.
         */
        width: calc(100% + 31px);
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
      }

      #main-image-container {
        max-height: 1024px;
        overflow: auto;
      }

      #main-image-container img {
        cursor: pointer;
        display: block;
        image-rendering: -moz-crisp-edges;
        image-rendering: pixelated;
        width: 100%;
        height: auto;
      }

      paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }
      paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }
      [hidden] {
        display: none;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],NKt.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],NKt.prototype,"tag",void 0),t([o({type:Number}),e("design:type",Number)],NKt.prototype,"sample",void 0),t([o({type:Number}),e("design:type",Number)],NKt.prototype,"ofSamples",void 0),t([o({type:Object}),e("design:type",Object)],NKt.prototype,"tagMetadata",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],NKt.prototype,"actualSize",void 0),t([o({type:Number}),e("design:type",Number)],NKt.prototype,"brightnessAdjustment",void 0),t([o({type:Number}),e("design:type",Number)],NKt.prototype,"contrastPercentage",void 0),t([o({type:Object}),e("design:type",dr)],NKt.prototype,"requestManager",void 0),t([o({type:Object}),e("design:type",Object)],NKt.prototype,"_metadataCanceller",void 0),t([o({type:Object}),e("design:type",Object)],NKt.prototype,"_imageCanceller",void 0),t([o({type:Array,notify:!0}),e("design:type",Array)],NKt.prototype,"_steps",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],NKt.prototype,"_stepIndex",void 0),t([o({type:Boolean}),e("design:type",Boolean)],NKt.prototype,"_isImageLoading",void 0),t([s("run"),e("design:type",String),e("design:paramtypes",[])],NKt.prototype,"_runColor",null),t([s("_steps"),e("design:type",Boolean),e("design:paramtypes",[])],NKt.prototype,"_hasAtLeastOneStep",null),t([s("_steps"),e("design:type",Boolean),e("design:paramtypes",[])],NKt.prototype,"_hasMultipleSteps",null),t([s("_steps","_stepIndex"),e("design:type",Object),e("design:paramtypes",[])],NKt.prototype,"_currentStep",null),t([s("_currentStep"),e("design:type",Number),e("design:paramtypes",[])],NKt.prototype,"_stepValue",null),t([s("_currentStep"),e("design:type",String),e("design:paramtypes",[])],NKt.prototype,"_currentWallTime",null),t([s("_steps"),e("design:type",Number),e("design:paramtypes",[])],NKt.prototype,"_maxStepIndex",null),t([s("sample"),e("design:type",String),e("design:paramtypes",[])],NKt.prototype,"_sampleText",null),t([s("ofSamples"),e("design:type",Boolean),e("design:paramtypes",[])],NKt.prototype,"_hasMultipleSamples",null),t([a("run","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],NKt.prototype,"reload",null),t([a("_currentStep","brightnessAdjustment","contrastPercentage"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],NKt.prototype,"_updateImageUrl",null),NKt=t([i("tf-image-loader")],NKt);let IKt=class extends(er(ye)){constructor(){super(...arguments),this.reloadOnReady=!0,this._defaultBrightnessAdjustment=1,this._defaultContrastPercentage=100,this._brightnessAdjustment=1,this._contrastPercentage=100,this._requestManager=new dr}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){this._fetchTags().then((()=>{this._reloadImages()}))}_fetchTags(){const t=_r().pluginRoute("images","/tags");return this._requestManager.request(t).then((t=>{if(Se.exports.isEqual(t,this._runToTagInfo))return;const e=ar(Se.exports.mapValues(t,(t=>Object.keys(t))));this.set("_dataNotFound",0===e.length),this.set("_runToTagInfo",t),this.async((()=>{this.set("_categoriesDomReady",!0)}))}))}_reloadImages(){this.root.querySelectorAll("tf-image-loader").forEach((t=>{t.reload()}))}_shouldOpen(t){return t<=2}_resetBrightness(){this._brightnessAdjustment=this._defaultBrightnessAdjustment}_resetContrast(){this._contrastPercentage=this._defaultContrastPercentage}get _brightnessIsDefault(){return this._brightnessAdjustment===this._defaultBrightnessAdjustment}get _contrastIsDefault(){return this._contrastPercentage===this._defaultContrastPercentage}get _categories(){var t=this._runToTagInfo,e=this._selectedRuns,n=this._tagFilter;function i(e){const n=t[e.run][e.tag].samples;return Se.exports.range(n).map((t=>Object.assign({},e,{sample:t,ofSamples:n})))}return Mr(Se.exports.mapValues(t,(t=>Object.keys(t))),e,n).map((t=>Object.assign({},t,{items:[].concat.apply([],t.items.map(i))})))}_tagMetadata(t,e,n){return t[e][n]}};var RKt,OKt,zKt;IKt.template=_e`
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="settings">
          <div class="sidebar-section">
            <div class="line-item">
              <paper-checkbox checked="{{_actualSize}}"
                >Show actual image size</paper-checkbox
              >
            </div>
          </div>
          <div class="sidebar-section">
            <h3 class="tooltip-container">Brightness adjustment</h3>
            <div class="resettable-slider-container">
              <paper-slider
                min="0"
                max="2"
                snaps
                pin
                step="0.01"
                value="{{_brightnessAdjustment}}"
                immediate-value="{{_brightnessAdjustment}}"
              ></paper-slider>
              <paper-button
                class="x-button"
                on-tap="_resetBrightness"
                disabled="[[_brightnessIsDefault]]"
                >Reset</paper-button
              >
            </div>
          </div>
          <div class="sidebar-section">
            <h3 class="tooltip-container">Contrast adjustment</h3>
            <div class="resettable-slider-container">
              <paper-slider
                min="0"
                max="500"
                snaps
                pin
                step="1"
                value="{{_contrastPercentage}}"
                immediate-value="{{_contrastPercentage}}"
              ></paper-slider>
              <paper-button
                class="x-button"
                on-tap="_resetContrast"
                disabled="[[_contrastIsDefault]]"
                >Reset</paper-button
              >
            </div>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector
            id="runs-selector"
            selected-runs="{{_selectedRuns}}"
          ></tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No image data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>You haven’t written any image data to your event files.</li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
            >
              <template>
                <tf-image-loader
                  active="[[active]]"
                  run="[[item.run]]"
                  tag="[[item.tag]]"
                  sample="[[item.sample]]"
                  of-samples="[[item.ofSamples]]"
                  tag-metadata="[[_tagMetadata(_runToTagInfo, item.run, item.tag)]]"
                  request-manager="[[_requestManager]]"
                  actual-size="[[_actualSize]]"
                  brightness-adjustment="[[_brightnessAdjustment]]"
                  contrast-percentage="[[_contrastPercentage]]"
                ></tf-image-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>
    <style include="dashboard-style"></style>
    <style>
      .resettable-slider-container {
        display: flex;
      }
      .resettable-slider-container paper-slider {
        flex-grow: 1;
      }
      .resettable-slider-container paper-button {
        flex-grow: 0;
      }
      .resettable-slider-container paper-button[disabled] {
        background-color: unset;
      }
      .x-button {
        font-size: 13px;
        background-color: var(--tb-ui-light-accent);
        color: var(--tb-ui-dark-accent);
      }
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      paper-slider {
        --paper-slider-active-color: var(--tb-orange-strong);
        --paper-slider-knob-color: var(--tb-orange-strong);
        --paper-slider-knob-start-border-color: var(--tb-orange-strong);
        --paper-slider-knob-start-color: var(--tb-orange-strong);
        --paper-slider-markers-color: var(--tb-orange-strong);
        --paper-slider-pin-color: var(--tb-orange-strong);
        --paper-slider-pin-start-color: var(--tb-orange-strong);
      }
    </style>
  `,t([o({type:Boolean}),e("design:type",Boolean)],IKt.prototype,"reloadOnReady",void 0),t([o({type:Array}),e("design:type",Array)],IKt.prototype,"_selectedRuns",void 0),t([o({type:Object}),e("design:type",Object)],IKt.prototype,"_runToTagInfo",void 0),t([o({type:Boolean}),e("design:type",Boolean)],IKt.prototype,"_dataNotFound",void 0),t([o({type:Boolean}),e("design:type",Boolean)],IKt.prototype,"_actualSize",void 0),t([o({type:Number}),e("design:type",Number)],IKt.prototype,"_defaultBrightnessAdjustment",void 0),t([o({type:Number}),e("design:type",Number)],IKt.prototype,"_defaultContrastPercentage",void 0),t([o({type:Number}),e("design:type",Number)],IKt.prototype,"_brightnessAdjustment",void 0),t([o({type:Number}),e("design:type",Number)],IKt.prototype,"_contrastPercentage",void 0),t([o({type:String}),e("design:type",String)],IKt.prototype,"_tagFilter",void 0),t([o({type:Boolean}),e("design:type",Boolean)],IKt.prototype,"_categoriesDomReady",void 0),t([o({type:Object}),e("design:type",Object)],IKt.prototype,"_requestManager",void 0),t([s("_brightnessAdjustment"),e("design:type",Boolean),e("design:paramtypes",[])],IKt.prototype,"_brightnessIsDefault",null),t([s("_contrastPercentage"),e("design:type",Boolean),e("design:paramtypes",[])],IKt.prototype,"_contrastIsDefault",null),t([s("_runToTagInfo","_selectedRuns","_tagFilter","_categoriesDomReady"),e("design:type",Array),e("design:paramtypes",[])],IKt.prototype,"_categories",null),IKt=t([i("tf-image-dashboard")],IKt),(function(t){t[t.CANCELLED=1]="CANCELLED"})(RKt||(RKt={})),(function(t){t[t.VERTEX=1]="VERTEX",t[t.FACE=2]="FACE",t[t.COLOR=3]="COLOR"})(OKt||(OKt={})),(function(t){t.VERTEX="float32",t.FACE="int32",t.COLOR="uint8"})(zKt||(zKt={}));class DKt{constructor(t){this._canceller=new XR,this._requestManager=t}reload(t,e,n){return this._canceller.cancelAll(),this._fetchMetadata(t,e,n)}_fetchDataByStep(t,e,n,i,r,o){const a=_r().pluginRoute("mesh","/data",new URLSearchParams({tag:e,run:t,content_type:n,sample:String(i),step:String(r)})),s=function(t){let e=[];for(let n=0;n<t.length/3;n++){let i=[];for(let e=0;e<3;e++)i.push(t[3*n+e]);e.push(i)}return e},l=this._canceller.cancellable((t=>{if(t.cancelled)return Promise.reject({code:RKt.CANCELLED,message:"Response was invalidated."});let e=t.value;switch(n){case"VERTEX":o.vertices=s(new Float32Array(e));break;case"FACE":o.faces=s(new Int32Array(e));break;case"COLOR":o.colors=s(new Uint8Array(e))}return o}));return this._requestManager.fetch(a,{method:"GET",headers:{responseType:"arraybuffer",contentType:zKt[n]}}).then((t=>t.arrayBuffer())).then(l)}fetchData(t,e,n,i){let r=[],o=new Map;return Object.keys(OKt).forEach((a=>{t.components&1<<OKt[a]&&r.push(this._fetchDataByStep(e,n,a,i,t.step,o))})),Promise.all(r)}_fetchMetadata(t,e,n){this._canceller.cancelAll();const i=_r().pluginRoute("mesh","/meshes",new URLSearchParams({tag:e,run:t,sample:n})),r=this._canceller.cancellable((t=>t.cancelled?Promise.reject({code:RKt.CANCELLED,message:"Response was invalidated."}):t.value));return this._requestManager.fetch(i).then((t=>t.json())).then(r).then(this._processMetadata.bind(this))}_processMetadata(t){if(!t)return;const e=new Map;for(let n=0;n<t.length;n++){let i=t[n];e.has(i.step)||e.set(i.step,[]),e.get(i.step).push(i)}let n=[];return e.forEach((t=>{let e=this._createStepDatum(t[0]);n.push(e)})),n}_createStepDatum(t){return{wall_time:new Date(1e3*t.wall_time),step:t.step,config:t.config,content_type:t.content_type,components:t.components}}}
/**
     * @license
     * Copyright 2010-2021 Three.js Authors
     * SPDX-License-Identifier: MIT
     */const BKt="131",HKt={LEFT:0,MIDDLE:1,RIGHT:2,ROTATE:0,DOLLY:1,PAN:2},FKt={ROTATE:0,PAN:1,DOLLY_PAN:2,DOLLY_ROTATE:3},VKt=100,UKt=300,jKt=301,GKt=302,WKt=303,qKt=304,YKt=306,XKt=307,$Kt=1e3,KKt=1001,ZKt=1002,JKt=1003,QKt=1004,tZt=1005,eZt=1006,nZt=1007,iZt=1008,rZt=1009,oZt=1012,aZt=1014,sZt=1015,lZt=1016,cZt=1020,uZt=1022,hZt=1023,dZt=1026,pZt=1027,fZt=33776,mZt=33777,gZt=33778,_Zt=33779,yZt=35840,vZt=35841,bZt=35842,xZt=35843,wZt=37492,SZt=37496,MZt=2300,EZt=2301,TZt=2302,CZt=2400,AZt=2401,kZt=2402,LZt=2500,PZt=2501,NZt=3e3,IZt=3001,RZt=3007,OZt=3002,zZt=3004,DZt=3005,BZt=3006,HZt=7680,FZt=35044,VZt=35048,UZt="300 es";class jZt{addEventListener(t,e){void 0===this._listeners&&(this._listeners={});const n=this._listeners;void 0===n[t]&&(n[t]=[]),-1===n[t].indexOf(e)&&n[t].push(e)}hasEventListener(t,e){if(void 0===this._listeners)return!1;const n=this._listeners;return void 0!==n[t]&&-1!==n[t].indexOf(e)}removeEventListener(t,e){if(void 0===this._listeners)return;const n=this._listeners[t];if(void 0!==n){const t=n.indexOf(e);-1!==t&&n.splice(t,1)}}dispatchEvent(t){if(void 0===this._listeners)return;const e=this._listeners[t.type];if(void 0!==e){t.target=this;const n=e.slice(0);for(let e=0,i=n.length;e<i;e++)n[e].call(this,t);t.target=null}}}const GZt=[];for(let t=0;t<256;t++)GZt[t]=(t<16?"0":"")+t.toString(16);let WZt=1234567;const qZt=Math.PI/180,YZt=180/Math.PI;function XZt(){const t=4294967295*Math.random()|0,e=4294967295*Math.random()|0,n=4294967295*Math.random()|0,i=4294967295*Math.random()|0;return(GZt[255&t]+GZt[t>>8&255]+GZt[t>>16&255]+GZt[t>>24&255]+"-"+GZt[255&e]+GZt[e>>8&255]+"-"+GZt[e>>16&15|64]+GZt[e>>24&255]+"-"+GZt[63&n|128]+GZt[n>>8&255]+"-"+GZt[n>>16&255]+GZt[n>>24&255]+GZt[255&i]+GZt[i>>8&255]+GZt[i>>16&255]+GZt[i>>24&255]).toUpperCase()}function $Zt(t,e,n){return Math.max(e,Math.min(n,t))}function KZt(t,e){return(t%e+e)%e}function ZZt(t,e,n){return(1-n)*t+n*e}function JZt(t){return 0==(t&t-1)&&0!==t}function QZt(t){return Math.pow(2,Math.ceil(Math.log(t)/Math.LN2))}function tJt(t){return Math.pow(2,Math.floor(Math.log(t)/Math.LN2))}var eJt=Object.freeze({__proto__:null,DEG2RAD:qZt,RAD2DEG:YZt,generateUUID:XZt,clamp:$Zt,euclideanModulo:KZt,mapLinear:function nJt(t,e,n,i,r){return i+(t-e)*(r-i)/(n-e)},inverseLerp:function iJt(t,e,n){return t!==e?(n-t)/(e-t):0},lerp:ZZt,damp:function rJt(t,e,n,i){return ZZt(t,e,1-Math.exp(-n*i))},pingpong:function oJt(t,e=1){return e-Math.abs(KZt(t,2*e)-e)},smoothstep:function aJt(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*(3-2*t)},smootherstep:function sJt(t,e,n){return t<=e?0:t>=n?1:(t=(t-e)/(n-e))*t*t*(t*(6*t-15)+10)},randInt:function lJt(t,e){return t+Math.floor(Math.random()*(e-t+1))},randFloat:function cJt(t,e){return t+Math.random()*(e-t)},randFloatSpread:function uJt(t){return t*(.5-Math.random())},seededRandom:function hJt(t){return void 0!==t&&(WZt=t%2147483647),WZt=16807*WZt%2147483647,(WZt-1)/2147483646},degToRad:function dJt(t){return t*qZt},radToDeg:function pJt(t){return t*YZt},isPowerOfTwo:JZt,ceilPowerOfTwo:QZt,floorPowerOfTwo:tJt,setQuaternionFromProperEuler:function fJt(t,e,n,i,r){const o=Math.cos,a=Math.sin,s=o(n/2),l=a(n/2),c=o((e+i)/2),u=a((e+i)/2),h=o((e-i)/2),d=a((e-i)/2),p=o((i-e)/2),f=a((i-e)/2);switch(r){case"XYX":t.set(s*u,l*h,l*d,s*c);break;case"YZY":t.set(l*d,s*u,l*h,s*c);break;case"ZXZ":t.set(l*h,l*d,s*u,s*c);break;case"XZX":t.set(s*u,l*f,l*p,s*c);break;case"YXY":t.set(l*p,s*u,l*f,s*c);break;case"ZYZ":t.set(l*f,l*p,s*u,s*c);break;default:console.warn("THREE.MathUtils: .setQuaternionFromProperEuler() encountered an unknown order: "+r)}}});class mJt{constructor(t=0,e=0){this.x=t,this.y=e}get width(){return this.x}set width(t){this.x=t}get height(){return this.y}set height(t){this.y=t}set(t,e){return this.x=t,this.y=e,this}setScalar(t){return this.x=t,this.y=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y)}copy(t){return this.x=t.x,this.y=t.y,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector2: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this)}addScalar(t){return this.x+=t,this.y+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this}sub(t,e){return void 0!==e?(console.warn("THREE.Vector2: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this)}subScalar(t){return this.x-=t,this.y-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this}multiply(t){return this.x*=t.x,this.y*=t.y,this}multiplyScalar(t){return this.x*=t,this.y*=t,this}divide(t){return this.x/=t.x,this.y/=t.y,this}divideScalar(t){return this.multiplyScalar(1/t)}applyMatrix3(t){const e=this.x,n=this.y,i=t.elements;return this.x=i[0]*e+i[3]*n+i[6],this.y=i[1]*e+i[4]*n+i[7],this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this}negate(){return this.x=-this.x,this.y=-this.y,this}dot(t){return this.x*t.x+this.y*t.y}cross(t){return this.x*t.y-this.y*t.x}lengthSq(){return this.x*this.x+this.y*this.y}length(){return Math.sqrt(this.x*this.x+this.y*this.y)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)}normalize(){return this.divideScalar(this.length()||1)}angle(){return Math.atan2(-this.y,-this.x)+Math.PI}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y;return e*e+n*n}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this}equals(t){return t.x===this.x&&t.y===this.y}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector2: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this}rotateAround(t,e){const n=Math.cos(e),i=Math.sin(e),r=this.x-t.x,o=this.y-t.y;return this.x=r*n-o*i+t.x,this.y=r*i+o*n+t.y,this}random(){return this.x=Math.random(),this.y=Math.random(),this}}mJt.prototype.isVector2=!0;class gJt{constructor(){this.elements=[1,0,0,0,1,0,0,0,1],arguments.length>0&&console.error("THREE.Matrix3: the constructor no longer reads arguments. use .set() instead.")}set(t,e,n,i,r,o,a,s,l){const c=this.elements;return c[0]=t,c[1]=i,c[2]=a,c[3]=e,c[4]=r,c[5]=s,c[6]=n,c[7]=o,c[8]=l,this}identity(){return 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e[0]*=t,e[3]*=t,e[6]*=t,e[1]*=t,e[4]*=t,e[7]*=t,e[2]*=t,e[5]*=t,e[8]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],o=t[4],a=t[5],s=t[6],l=t[7],c=t[8];return e*o*c-e*a*l-n*r*c+n*a*s+i*r*l-i*o*s}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],o=t[4],a=t[5],s=t[6],l=t[7],c=t[8],u=c*o-a*l,h=a*s-c*r,d=l*r-o*s,p=e*u+n*h+i*d;if(0===p)return this.set(0,0,0,0,0,0,0,0,0);const f=1/p;return t[0]=u*f,t[1]=(i*l-c*n)*f,t[2]=(a*n-i*o)*f,t[3]=h*f,t[4]=(c*e-i*s)*f,t[5]=(i*r-a*e)*f,t[6]=d*f,t[7]=(n*s-l*e)*f,t[8]=(o*e-n*r)*f,this}transpose(){let t;const e=this.elements;return t=e[1],e[1]=e[3],e[3]=t,t=e[2],e[2]=e[6],e[6]=t,t=e[5],e[5]=e[7],e[7]=t,this}getNormalMatrix(t){return this.setFromMatrix4(t).invert().transpose()}transposeIntoArray(t){const e=this.elements;return t[0]=e[0],t[1]=e[3],t[2]=e[6],t[3]=e[1],t[4]=e[4],t[5]=e[7],t[6]=e[2],t[7]=e[5],t[8]=e[8],this}setUvTransform(t,e,n,i,r,o,a){const s=Math.cos(r),l=Math.sin(r);return 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t.src;if("undefined"==typeof HTMLCanvasElement)return t.src;let e;if(t instanceof HTMLCanvasElement)e=t;else{void 0===_Jt&&(_Jt=document.createElementNS("http://www.w3.org/1999/xhtml","canvas")),_Jt.width=t.width,_Jt.height=t.height;const n=_Jt.getContext("2d");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=_Jt}return e.width>2048||e.height>2048?(console.warn("THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons",t),e.toDataURL("image/jpeg",.6)):e.toDataURL("image/png")}}let vJt=0;class bJt extends jZt{constructor(t=bJt.DEFAULT_IMAGE,e=bJt.DEFAULT_MAPPING,n=1001,i=1001,r=1006,o=1008,a=1023,s=1009,l=1,c=3e3){super(),Object.defineProperty(this,"id",{value:vJt++}),this.uuid=XZt(),this.name="",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=n,this.wrapT=i,this.magFilter=r,this.minFilter=o,this.anisotropy=l,this.format=a,this.internalFormat=null,this.type=s,this.offset=new mJt(0,0),this.repeat=new mJt(1,1),this.center=new mJt(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new gJt,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.encoding=c,this.version=0,this.onUpdate=null,this.isRenderTargetTexture=!1}updateMatrix(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}clone(){return(new this.constructor).copy(this)}copy(t){return this.name=t.name,this.image=t.image,this.mipmaps=t.mipmaps.slice(0),this.mapping=t.mapping,this.wrapS=t.wrapS,this.wrapT=t.wrapT,this.magFilter=t.magFilter,this.minFilter=t.minFilter,this.anisotropy=t.anisotropy,this.format=t.format,this.internalFormat=t.internalFormat,this.type=t.type,this.offset.copy(t.offset),this.repeat.copy(t.repeat),this.center.copy(t.center),this.rotation=t.rotation,this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrix.copy(t.matrix),this.generateMipmaps=t.generateMipmaps,this.premultiplyAlpha=t.premultiplyAlpha,this.flipY=t.flipY,this.unpackAlignment=t.unpackAlignment,this.encoding=t.encoding,this}toJSON(t){const e=void 0===t||"string"==typeof t;if(!e&&void 0!==t.textures[this.uuid])return t.textures[this.uuid];const n={metadata:{version:4.5,type:"Texture",generator:"Texture.toJSON"},uuid:this.uuid,name:this.name,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){const i=this.image;if(void 0===i.uuid&&(i.uuid=XZt()),!e&&void 0===t.images[i.uuid]){let e;if(Array.isArray(i)){e=[];for(let t=0,n=i.length;t<n;t++)e.push(xJt(i[t].isDataTexture?i[t].image:i[t]))}else e=xJt(i);t.images[i.uuid]={uuid:i.uuid,url:e}}n.image=i.uuid}return e||(t.textures[this.uuid]=n),n}dispose(){this.dispatchEvent({type:"dispose"})}transformUv(t){if(this.mapping!==UKt)return t;if(t.applyMatrix3(this.matrix),t.x<0||t.x>1)switch(this.wrapS){case $Kt:t.x=t.x-Math.floor(t.x);break;case KKt:t.x=t.x<0?0:1;break;case ZKt:t.x=1===Math.abs(Math.floor(t.x)%2)?Math.ceil(t.x)-t.x:t.x-Math.floor(t.x)}if(t.y<0||t.y>1)switch(this.wrapT){case $Kt:t.y=t.y-Math.floor(t.y);break;case KKt:t.y=t.y<0?0:1;break;case ZKt:t.y=1===Math.abs(Math.floor(t.y)%2)?Math.ceil(t.y)-t.y:t.y-Math.floor(t.y)}return this.flipY&&(t.y=1-t.y),t}set needsUpdate(t){!0===t&&this.version++}}function xJt(t){return"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof ImageBitmap&&t instanceof ImageBitmap?yJt.getDataURL(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn("THREE.Texture: Unable to serialize Texture."),{})}bJt.DEFAULT_IMAGE=void 0,bJt.DEFAULT_MAPPING=UKt,bJt.prototype.isTexture=!0;class wJt{constructor(t=0,e=0,n=0,i=1){this.x=t,this.y=e,this.z=n,this.w=i}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,i){return this.x=t,this.y=e,this.z=n,this.w=i,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector4: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this.w+=t.w,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this.w+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this.w=t.w+e.w,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this.w+=t.w*e,this}sub(t,e){return void 0!==e?(console.warn("THREE.Vector4: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=this.w,o=t.elements;return this.x=o[0]*e+o[4]*n+o[8]*i+o[12]*r,this.y=o[1]*e+o[5]*n+o[9]*i+o[13]*r,this.z=o[2]*e+o[6]*n+o[10]*i+o[14]*r,this.w=o[3]*e+o[7]*n+o[11]*i+o[15]*r,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,i,r;const o=.01,a=.1,s=t.elements,l=s[0],c=s[4],u=s[8],h=s[1],d=s[5],p=s[9],f=s[2],m=s[6],g=s[10];if(Math.abs(c-h)<o&&Math.abs(u-f)<o&&Math.abs(p-m)<o){if(Math.abs(c+h)<a&&Math.abs(u+f)<a&&Math.abs(p+m)<a&&Math.abs(l+d+g-3)<a)return this.set(1,0,0,0),this;e=Math.PI;const t=(l+1)/2,s=(d+1)/2,_=(g+1)/2,y=(c+h)/4,v=(u+f)/4,b=(p+m)/4;return t>s&&t>_?t<o?(n=0,i=.707106781,r=.707106781):(n=Math.sqrt(t),i=y/n,r=v/n):s>_?s<o?(n=.707106781,i=0,r=.707106781):(i=Math.sqrt(s),n=y/i,r=b/i):_<o?(n=.707106781,i=.707106781,r=0):(r=Math.sqrt(_),n=v/r,i=b/r),this.set(n,i,r,e),this}let _=Math.sqrt((m-p)*(m-p)+(u-f)*(u-f)+(h-c)*(h-c));return Math.abs(_)<.001&&(_=1),this.x=(m-p)/_,this.y=(u-f)/_,this.z=(h-c)/_,this.w=Math.acos((l+d+g-1)/2),this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this.w=Math.min(this.w,t.w),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this.w=Math.max(this.w,t.w),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this.w=Math.max(t.w,Math.min(e.w,this.w)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this.w=Math.max(t,Math.min(e,this.w)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this.w=Math.floor(this.w),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this.w=Math.ceil(this.w),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this.w=Math.round(this.w),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this.w=this.w<0?Math.ceil(this.w):Math.floor(this.w),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this.w=-this.w,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z+this.w*t.w}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)+Math.abs(this.w)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this.w+=(t.w-this.w)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this.w=t.w+(e.w-t.w)*n,this}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z&&t.w===this.w}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this.w=t[e+3],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t[e+3]=this.w,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector4: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this.w=t.getW(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this.w=Math.random(),this}}wJt.prototype.isVector4=!0;class SJt extends jZt{constructor(t,e,n={}){super(),this.width=t,this.height=e,this.depth=1,this.scissor=new wJt(0,0,t,e),this.scissorTest=!1,this.viewport=new wJt(0,0,t,e),this.texture=new bJt(void 0,n.mapping,n.wrapS,n.wrapT,n.magFilter,n.minFilter,n.format,n.type,n.anisotropy,n.encoding),this.texture.isRenderTargetTexture=!0,this.texture.image={width:t,height:e,depth:1},this.texture.generateMipmaps=void 0!==n.generateMipmaps&&n.generateMipmaps,this.texture.minFilter=void 0!==n.minFilter?n.minFilter:eZt,this.depthBuffer=void 0===n.depthBuffer||n.depthBuffer,this.stencilBuffer=void 0!==n.stencilBuffer&&n.stencilBuffer,this.depthTexture=void 0!==n.depthTexture?n.depthTexture:null}setTexture(t){t.image={width:this.width,height:this.height,depth:this.depth},this.texture=t}setSize(t,e,n=1){this.width===t&&this.height===e&&this.depth===n||(this.width=t,this.height=e,this.depth=n,this.texture.image.width=t,this.texture.image.height=e,this.texture.image.depth=n,this.dispose()),this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e)}clone(){return(new this.constructor).copy(this)}copy(t){return this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.copy(t.viewport),this.texture=t.texture.clone(),this.texture.image={...this.texture.image},this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this}dispose(){this.dispatchEvent({type:"dispose"})}}SJt.prototype.isWebGLRenderTarget=!0;class MJt extends SJt{constructor(t,e,n){super(t,e);const i=this.texture;this.texture=[];for(let t=0;t<n;t++)this.texture[t]=i.clone()}setSize(t,e,n=1){if(this.width!==t||this.height!==e||this.depth!==n){this.width=t,this.height=e,this.depth=n;for(let i=0,r=this.texture.length;i<r;i++)this.texture[i].image.width=t,this.texture[i].image.height=e,this.texture[i].image.depth=n;this.dispose()}return this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e),this}copy(t){this.dispose(),this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.set(0,0,this.width,this.height),this.scissor.set(0,0,this.width,this.height),this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this.texture.length=0;for(let e=0,n=t.texture.length;e<n;e++)this.texture[e]=t.texture[e].clone();return this}}MJt.prototype.isWebGLMultipleRenderTargets=!0;class EJt extends SJt{constructor(t,e,n){super(t,e,n),this.samples=4}copy(t){return super.copy.call(this,t),this.samples=t.samples,this}}EJt.prototype.isWebGLMultisampleRenderTarget=!0;class TJt{constructor(t=0,e=0,n=0,i=1){this._x=t,this._y=e,this._z=n,this._w=i}static slerp(t,e,n,i){return console.warn("THREE.Quaternion: Static .slerp() has been deprecated. Use qm.slerpQuaternions( qa, qb, t ) instead."),n.slerpQuaternions(t,e,i)}static slerpFlat(t,e,n,i,r,o,a){let s=n[i+0],l=n[i+1],c=n[i+2],u=n[i+3];const h=r[o+0],d=r[o+1],p=r[o+2],f=r[o+3];if(0===a)return t[e+0]=s,t[e+1]=l,t[e+2]=c,void(t[e+3]=u);if(1===a)return t[e+0]=h,t[e+1]=d,t[e+2]=p,void(t[e+3]=f);if(u!==f||s!==h||l!==d||c!==p){let t=1-a;const e=s*h+l*d+c*p+u*f,n=e>=0?1:-1,i=1-e*e;if(i>Number.EPSILON){const r=Math.sqrt(i),o=Math.atan2(r,e*n);t=Math.sin(t*o)/r,a=Math.sin(a*o)/r}const r=a*n;if(s=s*t+h*r,l=l*t+d*r,c=c*t+p*r,u=u*t+f*r,t===1-a){const t=1/Math.sqrt(s*s+l*l+c*c+u*u);s*=t,l*=t,c*=t,u*=t}}t[e]=s,t[e+1]=l,t[e+2]=c,t[e+3]=u}static multiplyQuaternionsFlat(t,e,n,i,r,o){const a=n[i],s=n[i+1],l=n[i+2],c=n[i+3],u=r[o],h=r[o+1],d=r[o+2],p=r[o+3];return t[e]=a*p+c*u+s*d-l*h,t[e+1]=s*p+c*h+l*u-a*d,t[e+2]=l*p+c*d+a*h-s*u,t[e+3]=c*p-a*u-s*h-l*d,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,i){return this._x=t,this._y=e,this._z=n,this._w=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error("THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.");const n=t._x,i=t._y,r=t._z,o=t._order,a=Math.cos,s=Math.sin,l=a(n/2),c=a(i/2),u=a(r/2),h=s(n/2),d=s(i/2),p=s(r/2);switch(o){case"XYZ":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case"YXZ":this._x=h*c*u+l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case"ZXY":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u-h*d*p;break;case"ZYX":this._x=h*c*u-l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u+h*d*p;break;case"YZX":this._x=h*c*u+l*d*p,this._y=l*d*u+h*c*p,this._z=l*c*p-h*d*u,this._w=l*c*u-h*d*p;break;case"XZY":this._x=h*c*u-l*d*p,this._y=l*d*u-h*c*p,this._z=l*c*p+h*d*u,this._w=l*c*u+h*d*p;break;default:console.warn("THREE.Quaternion: .setFromEuler() encountered an unknown order: "+o)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,i=Math.sin(n);return this._x=t.x*i,this._y=t.y*i,this._z=t.z*i,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],i=e[4],r=e[8],o=e[1],a=e[5],s=e[9],l=e[2],c=e[6],u=e[10],h=n+a+u;if(h>0){const t=.5/Math.sqrt(h+1);this._w=.25/t,this._x=(c-s)*t,this._y=(r-l)*t,this._z=(o-i)*t}else if(n>a&&n>u){const t=2*Math.sqrt(1+n-a-u);this._w=(c-s)/t,this._x=.25*t,this._y=(i+o)/t,this._z=(r+l)/t}else if(a>u){const t=2*Math.sqrt(1+a-n-u);this._w=(r-l)/t,this._x=(i+o)/t,this._y=.25*t,this._z=(s+c)/t}else{const t=2*Math.sqrt(1+u-n-a);this._w=(o-i)/t,this._x=(r+l)/t,this._y=(s+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return n<Number.EPSILON?(n=0,Math.abs(t.x)>Math.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs($Zt(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const i=Math.min(1,e/n);return this.slerp(t,i),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn("THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead."),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,i=t._y,r=t._z,o=t._w,a=e._x,s=e._y,l=e._z,c=e._w;return this._x=n*c+o*a+i*l-r*s,this._y=i*c+o*s+r*a-n*l,this._z=r*c+o*l+n*s-i*a,this._w=o*c-n*a-i*s-r*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,i=this._y,r=this._z,o=this._w;let a=o*t._w+n*t._x+i*t._y+r*t._z;if(a<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,a=-a):this.copy(t),a>=1)return this._w=o,this._x=n,this._y=i,this._z=r,this;const s=1-a*a;if(s<=Number.EPSILON){const t=1-e;return this._w=t*o+e*this._w,this._x=t*n+e*this._x,this._y=t*i+e*this._y,this._z=t*r+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(s),c=Math.atan2(l,a),u=Math.sin((1-e)*c)/l,h=Math.sin(e*c)/l;return this._w=o*u+this._w*h,this._x=n*u+this._x*h,this._y=i*u+this._y*h,this._z=r*u+this._z*h,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}TJt.prototype.isQuaternion=!0;class CJt{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}set(t,e,n){return void 0===n&&(n=this.z),this.x=t,this.y=e,this.z=n,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this}sub(t,e){return void 0!==e?(console.warn("THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiply(t,e){return void 0!==e?(console.warn("THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead."),this.multiplyVectors(t,e)):(this.x*=t.x,this.y*=t.y,this.z*=t.z,this)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}applyEuler(t){return t&&t.isEuler||console.error("THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order."),this.applyQuaternion(kJt.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(kJt.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[3]*n+r[6]*i,this.y=r[1]*e+r[4]*n+r[7]*i,this.z=r[2]*e+r[5]*n+r[8]*i,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,i=this.z,r=t.elements,o=1/(r[3]*e+r[7]*n+r[11]*i+r[15]);return this.x=(r[0]*e+r[4]*n+r[8]*i+r[12])*o,this.y=(r[1]*e+r[5]*n+r[9]*i+r[13])*o,this.z=(r[2]*e+r[6]*n+r[10]*i+r[14])*o,this}applyQuaternion(t){const e=this.x,n=this.y,i=this.z,r=t.x,o=t.y,a=t.z,s=t.w,l=s*e+o*i-a*n,c=s*n+a*e-r*i,u=s*i+r*n-o*e,h=-r*e-o*n-a*i;return this.x=l*s+h*-r+c*-a-u*-o,this.y=c*s+h*-o+u*-r-l*-a,this.z=u*s+h*-a+l*-o-c*-r,this}project(t){return this.applyMatrix4(t.matrixWorldInverse).applyMatrix4(t.projectionMatrix)}unproject(t){return this.applyMatrix4(t.projectionMatrixInverse).applyMatrix4(t.matrixWorld)}transformDirection(t){const e=this.x,n=this.y,i=this.z,r=t.elements;return this.x=r[0]*e+r[4]*n+r[8]*i,this.y=r[1]*e+r[5]*n+r[9]*i,this.z=r[2]*e+r[6]*n+r[10]*i,this.normalize()}divide(t){return this.x/=t.x,this.y/=t.y,this.z/=t.z,this}divideScalar(t){return this.multiplyScalar(1/t)}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this}cross(t,e){return void 0!==e?(console.warn("THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead."),this.crossVectors(t,e)):this.crossVectors(this,t)}crossVectors(t,e){const n=t.x,i=t.y,r=t.z,o=e.x,a=e.y,s=e.z;return this.x=i*s-r*a,this.y=r*o-n*s,this.z=n*a-i*o,this}projectOnVector(t){const e=t.lengthSq();if(0===e)return this.set(0,0,0);const n=t.dot(this)/e;return this.copy(t).multiplyScalar(n)}projectOnPlane(t){return AJt.copy(this).projectOnVector(t),this.sub(AJt)}reflect(t){return this.sub(AJt.copy(t).multiplyScalar(2*this.dot(t)))}angleTo(t){const e=Math.sqrt(this.lengthSq()*t.lengthSq());if(0===e)return Math.PI/2;const n=this.dot(t)/e;return Math.acos($Zt(n,-1,1))}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y,i=this.z-t.z;return e*e+n*n+i*i}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)+Math.abs(this.z-t.z)}setFromSpherical(t){return this.setFromSphericalCoords(t.radius,t.phi,t.theta)}setFromSphericalCoords(t,e,n){const i=Math.sin(e)*t;return this.x=i*Math.sin(n),this.y=Math.cos(e)*t,this.z=i*Math.cos(n),this}setFromCylindrical(t){return this.setFromCylindricalCoords(t.radius,t.theta,t.y)}setFromCylindricalCoords(t,e,n){return this.x=t*Math.sin(e),this.y=n,this.z=t*Math.cos(e),this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}setFromMatrixScale(t){const e=this.setFromMatrixColumn(t,0).length(),n=this.setFromMatrixColumn(t,1).length(),i=this.setFromMatrixColumn(t,2).length();return this.x=e,this.y=n,this.z=i,this}setFromMatrixColumn(t,e){return this.fromArray(t.elements,4*e)}setFromMatrix3Column(t,e){return this.fromArray(t.elements,3*e)}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector3: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}}CJt.prototype.isVector3=!0;const AJt=new CJt,kJt=new TJt;class LJt{constructor(t=new CJt(1/0,1/0,1/0),e=new CJt(-1/0,-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromArray(t){let e=1/0,n=1/0,i=1/0,r=-1/0,o=-1/0,a=-1/0;for(let s=0,l=t.length;s<l;s+=3){const l=t[s],c=t[s+1],u=t[s+2];l<e&&(e=l),c<n&&(n=c),u<i&&(i=u),l>r&&(r=l),c>o&&(o=c),u>a&&(a=u)}return this.min.set(e,n,i),this.max.set(r,o,a),this}setFromBufferAttribute(t){let e=1/0,n=1/0,i=1/0,r=-1/0,o=-1/0,a=-1/0;for(let s=0,l=t.count;s<l;s++){const l=t.getX(s),c=t.getY(s),u=t.getZ(s);l<e&&(e=l),c<n&&(n=c),u<i&&(i=u),l>r&&(r=l),c>o&&(o=c),u>a&&(a=u)}return this.min.set(e,n,i),this.max.set(r,o,a),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;e<n;e++)this.expandByPoint(t[e]);return this}setFromCenterAndSize(t,e){const n=NJt.copy(e).multiplyScalar(.5);return this.min.copy(t).sub(n),this.max.copy(t).add(n),this}setFromObject(t){return this.makeEmpty(),this.expandByObject(t)}clone(){return(new this.constructor).copy(this)}copy(t){return this.min.copy(t.min),this.max.copy(t.max),this}makeEmpty(){return this.min.x=this.min.y=this.min.z=1/0,this.max.x=this.max.y=this.max.z=-1/0,this}isEmpty(){return this.max.x<this.min.x||this.max.y<this.min.y||this.max.z<this.min.z}getCenter(t){return this.isEmpty()?t.set(0,0,0):t.addVectors(this.min,this.max).multiplyScalar(.5)}getSize(t){return this.isEmpty()?t.set(0,0,0):t.subVectors(this.max,this.min)}expandByPoint(t){return this.min.min(t),this.max.max(t),this}expandByVector(t){return this.min.sub(t),this.max.add(t),this}expandByScalar(t){return this.min.addScalar(-t),this.max.addScalar(t),this}expandByObject(t){t.updateWorldMatrix(!1,!1);const e=t.geometry;void 0!==e&&(null===e.boundingBox&&e.computeBoundingBox(),IJt.copy(e.boundingBox),IJt.applyMatrix4(t.matrixWorld),this.union(IJt));const n=t.children;for(let t=0,e=n.length;t<e;t++)this.expandByObject(n[t]);return this}containsPoint(t){return!(t.x<this.min.x||t.x>this.max.x||t.y<this.min.y||t.y>this.max.y||t.z<this.min.z||t.z>this.max.z)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y&&this.min.z<=t.min.z&&t.max.z<=this.max.z}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y),(t.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(t){return!(t.max.x<this.min.x||t.min.x>this.max.x||t.max.y<this.min.y||t.min.y>this.max.y||t.max.z<this.min.z||t.min.z>this.max.z)}intersectsSphere(t){return this.clampPoint(t.center,NJt),NJt.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,n;return t.normal.x>0?(e=t.normal.x*this.min.x,n=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,n=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,n+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,n+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,n+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,n+=t.normal.z*this.min.z),e<=-t.constant&&n>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(FJt),VJt.subVectors(this.max,FJt),RJt.subVectors(t.a,FJt),OJt.subVectors(t.b,FJt),zJt.subVectors(t.c,FJt),DJt.subVectors(OJt,RJt),BJt.subVectors(zJt,OJt),HJt.subVectors(RJt,zJt);let e=[0,-DJt.z,DJt.y,0,-BJt.z,BJt.y,0,-HJt.z,HJt.y,DJt.z,0,-DJt.x,BJt.z,0,-BJt.x,HJt.z,0,-HJt.x,-DJt.y,DJt.x,0,-BJt.y,BJt.x,0,-HJt.y,HJt.x,0];return!!GJt(e,RJt,OJt,zJt,VJt)&&(e=[1,0,0,0,1,0,0,0,1],!!GJt(e,RJt,OJt,zJt,VJt)&&(UJt.crossVectors(DJt,BJt),e=[UJt.x,UJt.y,UJt.z],GJt(e,RJt,OJt,zJt,VJt)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return NJt.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(NJt).length(),t}intersect(t){return this.min.max(t.min),this.max.min(t.max),this.isEmpty()&&this.makeEmpty(),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}applyMatrix4(t){return this.isEmpty()||(PJt[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),PJt[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),PJt[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),PJt[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),PJt[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),PJt[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),PJt[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),PJt[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(PJt)),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}LJt.prototype.isBox3=!0;const PJt=[new CJt,new CJt,new CJt,new CJt,new CJt,new CJt,new CJt,new CJt],NJt=new CJt,IJt=new LJt,RJt=new CJt,OJt=new CJt,zJt=new CJt,DJt=new CJt,BJt=new CJt,HJt=new CJt,FJt=new CJt,VJt=new CJt,UJt=new CJt,jJt=new CJt;function GJt(t,e,n,i,r){for(let o=0,a=t.length-3;o<=a;o+=3){jJt.fromArray(t,o);const a=r.x*Math.abs(jJt.x)+r.y*Math.abs(jJt.y)+r.z*Math.abs(jJt.z),s=e.dot(jJt),l=n.dot(jJt),c=i.dot(jJt);if(Math.max(-Math.max(s,l,c),Math.min(s,l,c))>a)return!1}return!0}const WJt=new LJt,qJt=new CJt,YJt=new CJt,XJt=new CJt;class $Jt{constructor(t=new CJt,e=-1){this.center=t,this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}setFromPoints(t,e){const n=this.center;void 0!==e?n.copy(e):WJt.setFromPoints(t).getCenter(n);let i=0;for(let e=0,r=t.length;e<r;e++)i=Math.max(i,n.distanceToSquared(t[e]));return this.radius=Math.sqrt(i),this}copy(t){return this.center.copy(t.center),this.radius=t.radius,this}isEmpty(){return this.radius<0}makeEmpty(){return this.center.set(0,0,0),this.radius=-1,this}containsPoint(t){return t.distanceToSquared(this.center)<=this.radius*this.radius}distanceToPoint(t){return t.distanceTo(this.center)-this.radius}intersectsSphere(t){const e=this.radius+t.radius;return t.center.distanceToSquared(this.center)<=e*e}intersectsBox(t){return t.intersectsSphere(this)}intersectsPlane(t){return Math.abs(t.distanceToPoint(this.center))<=this.radius}clampPoint(t,e){const n=this.center.distanceToSquared(t);return e.copy(t),n>this.radius*this.radius&&(e.sub(this.center).normalize(),e.multiplyScalar(this.radius).add(this.center)),e}getBoundingBox(t){return this.isEmpty()?(t.makeEmpty(),t):(t.set(this.center,this.center),t.expandByScalar(this.radius),t)}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}expandByPoint(t){XJt.subVectors(t,this.center);const e=XJt.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(XJt.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return YJt.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(qJt.copy(t.center).add(YJt)),this.expandByPoint(qJt.copy(t.center).sub(YJt)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const KJt=new CJt,ZJt=new CJt,JJt=new CJt,QJt=new CJt,tQt=new CJt,eQt=new CJt,nQt=new CJt;class iQt{constructor(t=new CJt,e=new CJt(0,0,-1)){this.origin=t,this.direction=e}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return e.copy(this.direction).multiplyScalar(t).add(this.origin)}lookAt(t){return this.direction.copy(t).sub(this.origin).normalize(),this}recast(t){return this.origin.copy(this.at(t,KJt)),this}closestPointToPoint(t,e){e.subVectors(t,this.origin);const n=e.dot(this.direction);return n<0?e.copy(this.origin):e.copy(this.direction).multiplyScalar(n).add(this.origin)}distanceToPoint(t){return Math.sqrt(this.distanceSqToPoint(t))}distanceSqToPoint(t){const e=KJt.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(KJt.copy(this.direction).multiplyScalar(e).add(this.origin),KJt.distanceToSquared(t))}distanceSqToSegment(t,e,n,i){ZJt.copy(t).add(e).multiplyScalar(.5),JJt.copy(e).sub(t).normalize(),QJt.copy(this.origin).sub(ZJt);const r=.5*t.distanceTo(e),o=-this.direction.dot(JJt),a=QJt.dot(this.direction),s=-QJt.dot(JJt),l=QJt.lengthSq(),c=Math.abs(1-o*o);let u,h,d,p;if(c>0)if(u=o*s-a,h=o*a-s,p=r*c,u>=0)if(h>=-p)if(h<=p){const t=1/c;u*=t,h*=t,d=u*(u+o*h+2*a)+h*(o*u+h+2*s)+l}else h=r,u=Math.max(0,-(o*h+a)),d=-u*u+h*(h+2*s)+l;else h=-r,u=Math.max(0,-(o*h+a)),d=-u*u+h*(h+2*s)+l;else h<=-p?(u=Math.max(0,-(-o*r+a)),h=u>0?-r:Math.min(Math.max(-r,-s),r),d=-u*u+h*(h+2*s)+l):h<=p?(u=0,h=Math.min(Math.max(-r,-s),r),d=h*(h+2*s)+l):(u=Math.max(0,-(o*r+a)),h=u>0?r:Math.min(Math.max(-r,-s),r),d=-u*u+h*(h+2*s)+l);else h=o>0?-r:r,u=Math.max(0,-(o*h+a)),d=-u*u+h*(h+2*s)+l;return n&&n.copy(this.direction).multiplyScalar(u).add(this.origin),i&&i.copy(JJt).multiplyScalar(h).add(ZJt),d}intersectSphere(t,e){KJt.subVectors(t.center,this.origin);const n=KJt.dot(this.direction),i=KJt.dot(KJt)-n*n,r=t.radius*t.radius;if(i>r)return null;const o=Math.sqrt(r-i),a=n-o,s=n+o;return a<0&&s<0?null:this.at(a<0?s:a,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);return 0===e||t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,i,r,o,a,s;const l=1/this.direction.x,c=1/this.direction.y,u=1/this.direction.z,h=this.origin;return l>=0?(n=(t.min.x-h.x)*l,i=(t.max.x-h.x)*l):(n=(t.max.x-h.x)*l,i=(t.min.x-h.x)*l),c>=0?(r=(t.min.y-h.y)*c,o=(t.max.y-h.y)*c):(r=(t.max.y-h.y)*c,o=(t.min.y-h.y)*c),n>o||r>i?null:((r>n||n!=n)&&(n=r),(o<i||i!=i)&&(i=o),u>=0?(a=(t.min.z-h.z)*u,s=(t.max.z-h.z)*u):(a=(t.max.z-h.z)*u,s=(t.min.z-h.z)*u),n>s||a>i?null:((a>n||n!=n)&&(n=a),(s<i||i!=i)&&(i=s),i<0?null:this.at(n>=0?n:i,e)))}intersectsBox(t){return null!==this.intersectBox(t,KJt)}intersectTriangle(t,e,n,i,r){tQt.subVectors(e,t),eQt.subVectors(n,t),nQt.crossVectors(tQt,eQt);let o,a=this.direction.dot(nQt);if(a>0){if(i)return null;o=1}else{if(!(a<0))return null;o=-1,a=-a}QJt.subVectors(this.origin,t);const s=o*this.direction.dot(eQt.crossVectors(QJt,eQt));if(s<0)return null;const l=o*this.direction.dot(tQt.cross(QJt));if(l<0)return null;if(s+l>a)return null;const c=-o*QJt.dot(nQt);return c<0?null:this.at(c/a,r)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}class rQt{constructor(){this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],arguments.length>0&&console.error("THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.")}set(t,e,n,i,r,o,a,s,l,c,u,h,d,p,f,m){const g=this.elements;return g[0]=t,g[4]=e,g[8]=n,g[12]=i,g[1]=r,g[5]=o,g[9]=a,g[13]=s,g[2]=l,g[6]=c,g[10]=u,g[14]=h,g[3]=d,g[7]=p,g[11]=f,g[15]=m,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new rQt).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return this.set(e[0],e[3],e[6],0,e[1],e[4],e[7],0,e[2],e[5],e[8],0,0,0,0,1),this}extractBasis(t,e,n){return t.setFromMatrixColumn(this,0),e.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(t,e,n){return this.set(t.x,e.x,n.x,0,t.y,e.y,n.y,0,t.z,e.z,n.z,0,0,0,0,1),this}extractRotation(t){const e=this.elements,n=t.elements,i=1/oQt.setFromMatrixColumn(t,0).length(),r=1/oQt.setFromMatrixColumn(t,1).length(),o=1/oQt.setFromMatrixColumn(t,2).length();return e[0]=n[0]*i,e[1]=n[1]*i,e[2]=n[2]*i,e[3]=0,e[4]=n[4]*r,e[5]=n[5]*r,e[6]=n[6]*r,e[7]=0,e[8]=n[8]*o,e[9]=n[9]*o,e[10]=n[10]*o,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error("THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.");const e=this.elements,n=t.x,i=t.y,r=t.z,o=Math.cos(n),a=Math.sin(n),s=Math.cos(i),l=Math.sin(i),c=Math.cos(r),u=Math.sin(r);if("XYZ"===t.order){const t=o*c,n=o*u,i=a*c,r=a*u;e[0]=s*c,e[4]=-s*u,e[8]=l,e[1]=n+i*l,e[5]=t-r*l,e[9]=-a*s,e[2]=r-t*l,e[6]=i+n*l,e[10]=o*s}else if("YXZ"===t.order){const t=s*c,n=s*u,i=l*c,r=l*u;e[0]=t+r*a,e[4]=i*a-n,e[8]=o*l,e[1]=o*u,e[5]=o*c,e[9]=-a,e[2]=n*a-i,e[6]=r+t*a,e[10]=o*s}else if("ZXY"===t.order){const t=s*c,n=s*u,i=l*c,r=l*u;e[0]=t-r*a,e[4]=-o*u,e[8]=i+n*a,e[1]=n+i*a,e[5]=o*c,e[9]=r-t*a,e[2]=-o*l,e[6]=a,e[10]=o*s}else if("ZYX"===t.order){const t=o*c,n=o*u,i=a*c,r=a*u;e[0]=s*c,e[4]=i*l-n,e[8]=t*l+r,e[1]=s*u,e[5]=r*l+t,e[9]=n*l-i,e[2]=-l,e[6]=a*s,e[10]=o*s}else if("YZX"===t.order){const t=o*s,n=o*l,i=a*s,r=a*l;e[0]=s*c,e[4]=r-t*u,e[8]=i*u+n,e[1]=u,e[5]=o*c,e[9]=-a*c,e[2]=-l*c,e[6]=n*u+i,e[10]=t-r*u}else if("XZY"===t.order){const t=o*s,n=o*l,i=a*s,r=a*l;e[0]=s*c,e[4]=-u,e[8]=l*c,e[1]=t*u+r,e[5]=o*c,e[9]=n*u-i,e[2]=i*u-n,e[6]=a*c,e[10]=r*u+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(sQt,t,lQt)}lookAt(t,e,n){const i=this.elements;return hQt.subVectors(t,e),0===hQt.lengthSq()&&(hQt.z=1),hQt.normalize(),cQt.crossVectors(n,hQt),0===cQt.lengthSq()&&(1===Math.abs(n.z)?hQt.x+=1e-4:hQt.z+=1e-4,hQt.normalize(),cQt.crossVectors(n,hQt)),cQt.normalize(),uQt.crossVectors(hQt,cQt),i[0]=cQt.x,i[4]=uQt.x,i[8]=hQt.x,i[1]=cQt.y,i[5]=uQt.y,i[9]=hQt.y,i[2]=cQt.z,i[6]=uQt.z,i[10]=hQt.z,this}multiply(t,e){return void 0!==e?(console.warn("THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead."),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,i=e.elements,r=this.elements,o=n[0],a=n[4],s=n[8],l=n[12],c=n[1],u=n[5],h=n[9],d=n[13],p=n[2],f=n[6],m=n[10],g=n[14],_=n[3],y=n[7],v=n[11],b=n[15],x=i[0],w=i[4],S=i[8],M=i[12],E=i[1],T=i[5],C=i[9],A=i[13],k=i[2],L=i[6],P=i[10],N=i[14],I=i[3],R=i[7],O=i[11],z=i[15];return r[0]=o*x+a*E+s*k+l*I,r[4]=o*w+a*T+s*L+l*R,r[8]=o*S+a*C+s*P+l*O,r[12]=o*M+a*A+s*N+l*z,r[1]=c*x+u*E+h*k+d*I,r[5]=c*w+u*T+h*L+d*R,r[9]=c*S+u*C+h*P+d*O,r[13]=c*M+u*A+h*N+d*z,r[2]=p*x+f*E+m*k+g*I,r[6]=p*w+f*T+m*L+g*R,r[10]=p*S+f*C+m*P+g*O,r[14]=p*M+f*A+m*N+g*z,r[3]=_*x+y*E+v*k+b*I,r[7]=_*w+y*T+v*L+b*R,r[11]=_*S+y*C+v*P+b*O,r[15]=_*M+y*A+v*N+b*z,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],i=t[8],r=t[12],o=t[1],a=t[5],s=t[9],l=t[13],c=t[2],u=t[6],h=t[10],d=t[14];return t[3]*(+r*s*u-i*l*u-r*a*h+n*l*h+i*a*d-n*s*d)+t[7]*(+e*s*d-e*l*h+r*o*h-i*o*d+i*l*c-r*s*c)+t[11]*(+e*l*u-e*a*d-r*o*u+n*o*d+r*a*c-n*l*c)+t[15]*(-i*a*c-e*s*u+e*a*h+i*o*u-n*o*h+n*s*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const i=this.elements;return t.isVector3?(i[12]=t.x,i[13]=t.y,i[14]=t.z):(i[12]=t,i[13]=e,i[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],i=t[2],r=t[3],o=t[4],a=t[5],s=t[6],l=t[7],c=t[8],u=t[9],h=t[10],d=t[11],p=t[12],f=t[13],m=t[14],g=t[15],_=u*m*l-f*h*l+f*s*d-a*m*d-u*s*g+a*h*g,y=p*h*l-c*m*l-p*s*d+o*m*d+c*s*g-o*h*g,v=c*f*l-p*u*l+p*a*d-o*f*d-c*a*g+o*u*g,b=p*u*s-c*f*s-p*a*h+o*f*h+c*a*m-o*u*m,x=e*_+n*y+i*v+r*b;if(0===x)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const w=1/x;return t[0]=_*w,t[1]=(f*h*r-u*m*r-f*i*d+n*m*d+u*i*g-n*h*g)*w,t[2]=(a*m*r-f*s*r+f*i*l-n*m*l-a*i*g+n*s*g)*w,t[3]=(u*s*r-a*h*r-u*i*l+n*h*l+a*i*d-n*s*d)*w,t[4]=y*w,t[5]=(c*m*r-p*h*r+p*i*d-e*m*d-c*i*g+e*h*g)*w,t[6]=(p*s*r-o*m*r-p*i*l+e*m*l+o*i*g-e*s*g)*w,t[7]=(o*h*r-c*s*r+c*i*l-e*h*l-o*i*d+e*s*d)*w,t[8]=v*w,t[9]=(p*u*r-c*f*r-p*n*d+e*f*d+c*n*g-e*u*g)*w,t[10]=(o*f*r-p*a*r+p*n*l-e*f*l-o*n*g+e*a*g)*w,t[11]=(c*a*r-o*u*r-c*n*l+e*u*l+o*n*d-e*a*d)*w,t[12]=b*w,t[13]=(c*f*i-p*u*i+p*n*h-e*f*h-c*n*m+e*u*m)*w,t[14]=(p*a*i-o*f*i-p*n*s+e*f*s+o*n*m-e*a*m)*w,t[15]=(o*u*i-c*a*i+c*n*s-e*u*s-o*n*h+e*a*h)*w,this}scale(t){const e=this.elements,n=t.x,i=t.y,r=t.z;return e[0]*=n,e[4]*=i,e[8]*=r,e[1]*=n,e[5]*=i,e[9]*=r,e[2]*=n,e[6]*=i,e[10]*=r,e[3]*=n,e[7]*=i,e[11]*=r,this}getMaxScaleOnAxis(){const t=this.elements;return Math.sqrt(Math.max(t[0]*t[0]+t[1]*t[1]+t[2]*t[2],t[4]*t[4]+t[5]*t[5]+t[6]*t[6],t[8]*t[8]+t[9]*t[9]+t[10]*t[10]))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),i=Math.sin(e),r=1-n,o=t.x,a=t.y,s=t.z,l=r*o,c=r*a;return this.set(l*o+n,l*a-i*s,l*s+i*a,0,l*a+i*s,c*a+n,c*s-i*o,0,l*s-i*a,c*s+i*o,r*s*s+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,i,r,o){return this.set(1,n,r,0,t,1,o,0,e,i,1,0,0,0,0,1),this}compose(t,e,n){const i=this.elements,r=e._x,o=e._y,a=e._z,s=e._w,l=r+r,c=o+o,u=a+a,h=r*l,d=r*c,p=r*u,f=o*c,m=o*u,g=a*u,_=s*l,y=s*c,v=s*u,b=n.x,x=n.y,w=n.z;return i[0]=(1-(f+g))*b,i[1]=(d+v)*b,i[2]=(p-y)*b,i[3]=0,i[4]=(d-v)*x,i[5]=(1-(h+g))*x,i[6]=(m+_)*x,i[7]=0,i[8]=(p+y)*w,i[9]=(m-_)*w,i[10]=(1-(h+f))*w,i[11]=0,i[12]=t.x,i[13]=t.y,i[14]=t.z,i[15]=1,this}decompose(t,e,n){const i=this.elements;let r=oQt.set(i[0],i[1],i[2]).length();const o=oQt.set(i[4],i[5],i[6]).length(),a=oQt.set(i[8],i[9],i[10]).length();this.determinant()<0&&(r=-r),t.x=i[12],t.y=i[13],t.z=i[14],aQt.copy(this);const s=1/r,l=1/o,c=1/a;return aQt.elements[0]*=s,aQt.elements[1]*=s,aQt.elements[2]*=s,aQt.elements[4]*=l,aQt.elements[5]*=l,aQt.elements[6]*=l,aQt.elements[8]*=c,aQt.elements[9]*=c,aQt.elements[10]*=c,e.setFromRotationMatrix(aQt),n.x=r,n.y=o,n.z=a,this}makePerspective(t,e,n,i,r,o){void 0===o&&console.warn("THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.");const a=this.elements,s=2*r/(n-i),l=(e+t)/(e-t),c=(n+i)/(n-i),u=-(o+r)/(o-r),h=-2*o*r/(o-r);return a[0]=2*r/(e-t),a[4]=0,a[8]=l,a[12]=0,a[1]=0,a[5]=s,a[9]=c,a[13]=0,a[2]=0,a[6]=0,a[10]=u,a[14]=h,a[3]=0,a[7]=0,a[11]=-1,a[15]=0,this}makeOrthographic(t,e,n,i,r,o){const a=this.elements,s=1/(e-t),l=1/(n-i),c=1/(o-r),u=(e+t)*s,h=(n+i)*l,d=(o+r)*c;return a[0]=2*s,a[4]=0,a[8]=0,a[12]=-u,a[1]=0,a[5]=2*l,a[9]=0,a[13]=-h,a[2]=0,a[6]=0,a[10]=-2*c,a[14]=-d,a[3]=0,a[7]=0,a[11]=0,a[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}rQt.prototype.isMatrix4=!0;const oQt=new CJt,aQt=new rQt,sQt=new CJt(0,0,0),lQt=new CJt(1,1,1),cQt=new CJt,uQt=new CJt,hQt=new CJt,dQt=new rQt,pQt=new TJt;class fQt{constructor(t=0,e=0,n=0,i=fQt.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=i}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,i=this._order){return this._x=t,this._y=e,this._z=n,this._order=i,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const i=t.elements,r=i[0],o=i[4],a=i[8],s=i[1],l=i[5],c=i[9],u=i[2],h=i[6],d=i[10];switch(e){case"XYZ":this._y=Math.asin($Zt(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(-c,d),this._z=Math.atan2(-o,r)):(this._x=Math.atan2(h,l),this._z=0);break;case"YXZ":this._x=Math.asin(-$Zt(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(a,d),this._z=Math.atan2(s,l)):(this._y=Math.atan2(-u,r),this._z=0);break;case"ZXY":this._x=Math.asin($Zt(h,-1,1)),Math.abs(h)<.9999999?(this._y=Math.atan2(-u,d),this._z=Math.atan2(-o,l)):(this._y=0,this._z=Math.atan2(s,r));break;case"ZYX":this._y=Math.asin(-$Zt(u,-1,1)),Math.abs(u)<.9999999?(this._x=Math.atan2(h,d),this._z=Math.atan2(s,r)):(this._x=0,this._z=Math.atan2(-o,l));break;case"YZX":this._z=Math.asin($Zt(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-u,r)):(this._x=0,this._y=Math.atan2(a,d));break;case"XZY":this._z=Math.asin(-$Zt(o,-1,1)),Math.abs(o)<.9999999?(this._x=Math.atan2(h,l),this._y=Math.atan2(a,r)):(this._x=Math.atan2(-c,d),this._y=0);break;default:console.warn("THREE.Euler: .setFromRotationMatrix() encountered an unknown order: "+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return dQt.makeRotationFromQuaternion(t),this.setFromRotationMatrix(dQt,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return pQt.setFromEuler(this),this.setFromQuaternion(pQt,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new CJt(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}fQt.prototype.isEuler=!0,fQt.DefaultOrder="XYZ",fQt.RotationOrders=["XYZ","YZX","ZXY","XZY","YXZ","ZYX"];class mQt{constructor(){this.mask=1}set(t){this.mask=1<<t|0}enable(t){this.mask|=1<<t|0}enableAll(){this.mask=-1}toggle(t){this.mask^=1<<t|0}disable(t){this.mask&=~(1<<t|0)}disableAll(){this.mask=0}test(t){return 0!=(this.mask&t.mask)}}let gQt=0;const _Qt=new CJt,yQt=new TJt,vQt=new rQt,bQt=new CJt,xQt=new CJt,wQt=new CJt,SQt=new TJt,MQt=new CJt(1,0,0),EQt=new CJt(0,1,0),TQt=new CJt(0,0,1),CQt={type:"added"},AQt={type:"removed"};class kQt extends jZt{constructor(){super(),Object.defineProperty(this,"id",{value:gQt++}),this.uuid=XZt(),this.name="",this.type="Object3D",this.parent=null,this.children=[],this.up=kQt.DefaultUp.clone();const t=new CJt,e=new fQt,n=new TJt,i=new CJt(1,1,1);e._onChange((function r(){n.setFromEuler(e,!1)})),n._onChange((function o(){e.setFromQuaternion(n,void 0,!1)})),Object.defineProperties(this,{position:{configurable:!0,enumerable:!0,value:t},rotation:{configurable:!0,enumerable:!0,value:e},quaternion:{configurable:!0,enumerable:!0,value:n},scale:{configurable:!0,enumerable:!0,value:i},modelViewMatrix:{value:new rQt},normalMatrix:{value:new gJt}}),this.matrix=new rQt,this.matrixWorld=new rQt,this.matrixAutoUpdate=kQt.DefaultMatrixAutoUpdate,this.matrixWorldNeedsUpdate=!1,this.layers=new mQt,this.visible=!0,this.castShadow=!1,this.receiveShadow=!1,this.frustumCulled=!0,this.renderOrder=0,this.animations=[],this.userData={}}onBeforeRender(){}onAfterRender(){}applyMatrix4(t){this.matrixAutoUpdate&&this.updateMatrix(),this.matrix.premultiply(t),this.matrix.decompose(this.position,this.quaternion,this.scale)}applyQuaternion(t){return this.quaternion.premultiply(t),this}setRotationFromAxisAngle(t,e){this.quaternion.setFromAxisAngle(t,e)}setRotationFromEuler(t){this.quaternion.setFromEuler(t,!0)}setRotationFromMatrix(t){this.quaternion.setFromRotationMatrix(t)}setRotationFromQuaternion(t){this.quaternion.copy(t)}rotateOnAxis(t,e){return yQt.setFromAxisAngle(t,e),this.quaternion.multiply(yQt),this}rotateOnWorldAxis(t,e){return yQt.setFromAxisAngle(t,e),this.quaternion.premultiply(yQt),this}rotateX(t){return this.rotateOnAxis(MQt,t)}rotateY(t){return this.rotateOnAxis(EQt,t)}rotateZ(t){return this.rotateOnAxis(TQt,t)}translateOnAxis(t,e){return _Qt.copy(t).applyQuaternion(this.quaternion),this.position.add(_Qt.multiplyScalar(e)),this}translateX(t){return this.translateOnAxis(MQt,t)}translateY(t){return this.translateOnAxis(EQt,t)}translateZ(t){return this.translateOnAxis(TQt,t)}localToWorld(t){return t.applyMatrix4(this.matrixWorld)}worldToLocal(t){return t.applyMatrix4(vQt.copy(this.matrixWorld).invert())}lookAt(t,e,n){t.isVector3?bQt.copy(t):bQt.set(t,e,n);const i=this.parent;this.updateWorldMatrix(!0,!1),xQt.setFromMatrixPosition(this.matrixWorld),this.isCamera||this.isLight?vQt.lookAt(xQt,bQt,this.up):vQt.lookAt(bQt,xQt,this.up),this.quaternion.setFromRotationMatrix(vQt),i&&(vQt.extractRotation(i.matrixWorld),yQt.setFromRotationMatrix(vQt),this.quaternion.premultiply(yQt.invert()))}add(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.add(arguments[t]);return this}return t===this?(console.error("THREE.Object3D.add: object can't be added as a child of itself.",t),this):(t&&t.isObject3D?(null!==t.parent&&t.parent.remove(t),t.parent=this,this.children.push(t),t.dispatchEvent(CQt)):console.error("THREE.Object3D.add: object not an instance of THREE.Object3D.",t),this)}remove(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.remove(arguments[t]);return this}const e=this.children.indexOf(t);return-1!==e&&(t.parent=null,this.children.splice(e,1),t.dispatchEvent(AQt)),this}removeFromParent(){const t=this.parent;return null!==t&&t.remove(this),this}clear(){for(let t=0;t<this.children.length;t++){const e=this.children[t];e.parent=null,e.dispatchEvent(AQt)}return this.children.length=0,this}attach(t){return this.updateWorldMatrix(!0,!1),vQt.copy(this.matrixWorld).invert(),null!==t.parent&&(t.parent.updateWorldMatrix(!0,!1),vQt.multiply(t.parent.matrixWorld)),t.applyMatrix4(vQt),this.add(t),t.updateWorldMatrix(!1,!0),this}getObjectById(t){return this.getObjectByProperty("id",t)}getObjectByName(t){return this.getObjectByProperty("name",t)}getObjectByProperty(t,e){if(this[t]===e)return this;for(let n=0,i=this.children.length;n<i;n++){const i=this.children[n].getObjectByProperty(t,e);if(void 0!==i)return i}}getWorldPosition(t){return this.updateWorldMatrix(!0,!1),t.setFromMatrixPosition(this.matrixWorld)}getWorldQuaternion(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(xQt,t,wQt),t}getWorldScale(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(xQt,SQt,t),t}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(e[8],e[9],e[10]).normalize()}raycast(){}traverse(t){t(this);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].traverse(t)}traverseVisible(t){if(!1===this.visible)return;t(this);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].traverseVisible(t)}traverseAncestors(t){const e=this.parent;null!==e&&(t(e),e.traverseAncestors(t))}updateMatrix(){this.matrix.compose(this.position,this.quaternion,this.scale),this.matrixWorldNeedsUpdate=!0}updateMatrixWorld(t){this.matrixAutoUpdate&&this.updateMatrix(),(this.matrixWorldNeedsUpdate||t)&&(null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),this.matrixWorldNeedsUpdate=!1,t=!0);const e=this.children;for(let n=0,i=e.length;n<i;n++)e[n].updateMatrixWorld(t)}updateWorldMatrix(t,e){const n=this.parent;if(!0===t&&null!==n&&n.updateWorldMatrix(!0,!1),this.matrixAutoUpdate&&this.updateMatrix(),null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),!0===e){const t=this.children;for(let e=0,n=t.length;e<n;e++)t[e].updateWorldMatrix(!1,!0)}}toJSON(t){const e=void 0===t||"string"==typeof t,n={};e&&(t={geometries:{},materials:{},textures:{},images:{},shapes:{},skeletons:{},animations:{}},n.metadata={version:4.5,type:"Object",generator:"Object3D.toJSON"});const i={};function r(e,n){return void 0===e[n.uuid]&&(e[n.uuid]=n.toJSON(t)),n.uuid}if(i.uuid=this.uuid,i.type=this.type,""!==this.name&&(i.name=this.name),!0===this.castShadow&&(i.castShadow=!0),!0===this.receiveShadow&&(i.receiveShadow=!0),!1===this.visible&&(i.visible=!1),!1===this.frustumCulled&&(i.frustumCulled=!1),0!==this.renderOrder&&(i.renderOrder=this.renderOrder),"{}"!==JSON.stringify(this.userData)&&(i.userData=this.userData),i.layers=this.layers.mask,i.matrix=this.matrix.toArray(),!1===this.matrixAutoUpdate&&(i.matrixAutoUpdate=!1),this.isInstancedMesh&&(i.type="InstancedMesh",i.count=this.count,i.instanceMatrix=this.instanceMatrix.toJSON(),null!==this.instanceColor&&(i.instanceColor=this.instanceColor.toJSON())),this.isScene)this.background&&(this.background.isColor?i.background=this.background.toJSON():this.background.isTexture&&(i.background=this.background.toJSON(t).uuid)),this.environment&&this.environment.isTexture&&(i.environment=this.environment.toJSON(t).uuid);else if(this.isMesh||this.isLine||this.isPoints){i.geometry=r(t.geometries,this.geometry);const e=this.geometry.parameters;if(void 0!==e&&void 0!==e.shapes){const n=e.shapes;if(Array.isArray(n))for(let e=0,i=n.length;e<i;e++)r(t.shapes,n[e]);else r(t.shapes,n)}}if(this.isSkinnedMesh&&(i.bindMode=this.bindMode,i.bindMatrix=this.bindMatrix.toArray(),void 0!==this.skeleton&&(r(t.skeletons,this.skeleton),i.skeleton=this.skeleton.uuid)),void 0!==this.material)if(Array.isArray(this.material)){const e=[];for(let n=0,i=this.material.length;n<i;n++)e.push(r(t.materials,this.material[n]));i.material=e}else i.material=r(t.materials,this.material);if(this.children.length>0){i.children=[];for(let e=0;e<this.children.length;e++)i.children.push(this.children[e].toJSON(t).object)}if(this.animations.length>0){i.animations=[];for(let e=0;e<this.animations.length;e++)i.animations.push(r(t.animations,this.animations[e]))}if(e){const e=o(t.geometries),i=o(t.materials),r=o(t.textures),a=o(t.images),s=o(t.shapes),l=o(t.skeletons),c=o(t.animations);e.length>0&&(n.geometries=e),i.length>0&&(n.materials=i),r.length>0&&(n.textures=r),a.length>0&&(n.images=a),s.length>0&&(n.shapes=s),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=i,n;function o(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e<t.children.length;e++)this.add(t.children[e].clone());return this}}kQt.DefaultUp=new CJt(0,1,0),kQt.DefaultMatrixAutoUpdate=!0,kQt.prototype.isObject3D=!0;const LQt=new CJt,PQt=new CJt,NQt=new CJt,IQt=new CJt,RQt=new CJt,OQt=new CJt,zQt=new CJt,DQt=new CJt,BQt=new CJt,HQt=new CJt;class FQt{constructor(t=new CJt,e=new CJt,n=new CJt){this.a=t,this.b=e,this.c=n}static getNormal(t,e,n,i){i.subVectors(n,e),LQt.subVectors(t,e),i.cross(LQt);const r=i.lengthSq();return r>0?i.multiplyScalar(1/Math.sqrt(r)):i.set(0,0,0)}static getBarycoord(t,e,n,i,r){LQt.subVectors(i,e),PQt.subVectors(n,e),NQt.subVectors(t,e);const o=LQt.dot(LQt),a=LQt.dot(PQt),s=LQt.dot(NQt),l=PQt.dot(PQt),c=PQt.dot(NQt),u=o*l-a*a;if(0===u)return r.set(-2,-1,-1);const h=1/u,d=(l*s-a*c)*h,p=(o*c-a*s)*h;return r.set(1-d-p,p,d)}static containsPoint(t,e,n,i){return this.getBarycoord(t,e,n,i,IQt),IQt.x>=0&&IQt.y>=0&&IQt.x+IQt.y<=1}static getUV(t,e,n,i,r,o,a,s){return this.getBarycoord(t,e,n,i,IQt),s.set(0,0),s.addScaledVector(r,IQt.x),s.addScaledVector(o,IQt.y),s.addScaledVector(a,IQt.z),s}static isFrontFacing(t,e,n,i){return LQt.subVectors(n,e),PQt.subVectors(t,e),LQt.cross(PQt).dot(i)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,i){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[i]),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return LQt.subVectors(this.c,this.b),PQt.subVectors(this.a,this.b),.5*LQt.cross(PQt).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return FQt.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return FQt.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,i,r){return FQt.getUV(t,this.a,this.b,this.c,e,n,i,r)}containsPoint(t){return FQt.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return FQt.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,i=this.b,r=this.c;let o,a;RQt.subVectors(i,n),OQt.subVectors(r,n),DQt.subVectors(t,n);const s=RQt.dot(DQt),l=OQt.dot(DQt);if(s<=0&&l<=0)return e.copy(n);BQt.subVectors(t,i);const c=RQt.dot(BQt),u=OQt.dot(BQt);if(c>=0&&u<=c)return e.copy(i);const h=s*u-c*l;if(h<=0&&s>=0&&c<=0)return o=s/(s-c),e.copy(n).addScaledVector(RQt,o);HQt.subVectors(t,r);const d=RQt.dot(HQt),p=OQt.dot(HQt);if(p>=0&&d<=p)return e.copy(r);const f=d*l-s*p;if(f<=0&&l>=0&&p<=0)return a=l/(l-p),e.copy(n).addScaledVector(OQt,a);const m=c*p-d*u;if(m<=0&&u-c>=0&&d-p>=0)return zQt.subVectors(r,i),a=(u-c)/(u-c+(d-p)),e.copy(i).addScaledVector(zQt,a);const g=1/(m+f+h);return o=f*g,a=h*g,e.copy(n).addScaledVector(RQt,o).addScaledVector(OQt,a)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let VQt=0;class UQt extends jZt{constructor(){super(),Object.defineProperty(this,"id",{value:VQt++}),this.uuid=XZt(),this.name="",this.type="Material",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=VKt,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=3,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=519,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=HZt,this.stencilZFail=HZt,this.stencilZPass=HZt,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaTest=0,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0}onBuild(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn("THREE.Material: '"+e+"' parameter is undefined.");continue}if("shading"===e){console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead."),this.flatShading=1===n;continue}const i=this[e];void 0!==i?i&&i.isColor?i.set(n):i&&i.isVector3&&n&&n.isVector3?i.copy(n):this[e]=n:console.warn("THREE."+this.type+": '"+e+"' is not a property of this material.")}}toJSON(t){const e=void 0===t||"string"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:"Material",generator:"Material.toJSON"}};function i(t){const e=[];for(const n in t){const i=t[n];delete i.metadata,e.push(i)}return e}if(n.uuid=this.uuid,n.type=this.type,""!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),this.sheen&&this.sheen.isColor&&(n.sheen=this.sheen.getHex()),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),1!==this.blending&&(n.blending=this.blending),0!==this.side&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),"round"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),"round"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),"{}"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=i(t.textures),r=i(t.images);e.length>0&&(n.textures=e),r.length>0&&(n.images=r)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let i=0;i!==t;++i)n[i]=e[i].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:"dispose"})}set needsUpdate(t){!0===t&&this.version++}}UQt.prototype.isMaterial=!0;const jQt={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},GQt={h:0,s:0,l:0},WQt={h:0,s:0,l:0};function qQt(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function YQt(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function XQt(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class $Qt{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):"number"==typeof t?this.setHex(t):"string"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=KZt(t,1),e=$Zt(e,0,1),n=$Zt(n,0,1),0===e)this.r=this.g=this.b=n;else{const i=n<=.5?n*(1+e):n+e-n*e,r=2*n-i;this.r=qQt(r,i,t+1/3),this.g=qQt(r,i,t),this.b=qQt(r,i,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn("THREE.Color: Alpha component of "+t+" will be ignored.")}let n;if(n=/^((?:rgb|hsl)a?)\(([^\)]*)\)/.exec(t)){let t;const i=n[2];switch(n[1]){case"rgb":case"rgba":if(t=/^\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(i))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\s*(\d+)\%\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(i))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case"hsl":case"hsla":if(t=/^\s*(\d*\.?\d+)\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(i)){const n=parseFloat(t[1])/360,i=parseInt(t[2],10)/100,r=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,i,r)}}}else if(n=/^\#([A-Fa-f\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=jQt[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn("THREE.Color: Unknown color "+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=YQt(t.r),this.g=YQt(t.g),this.b=YQt(t.b),this}copyLinearToSRGB(t){return this.r=XQt(t.r),this.g=XQt(t.g),this.b=XQt(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return("000000"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,i=this.b,r=Math.max(e,n,i),o=Math.min(e,n,i);let a,s;const l=(o+r)/2;if(o===r)a=0,s=0;else{const t=r-o;switch(s=l<=.5?t/(r+o):t/(2-r-o),r){case e:a=(n-i)/t+(n<i?6:0);break;case n:a=(i-e)/t+2;break;case i:a=(e-n)/t+4}a/=6}return t.h=a,t.s=s,t.l=l,t}getStyle(){return"rgb("+(255*this.r|0)+","+(255*this.g|0)+","+(255*this.b|0)+")"}offsetHSL(t,e,n){return this.getHSL(GQt),GQt.h+=t,GQt.s+=e,GQt.l+=n,this.setHSL(GQt.h,GQt.s,GQt.l),this}add(t){return this.r+=t.r,this.g+=t.g,this.b+=t.b,this}addColors(t,e){return this.r=t.r+e.r,this.g=t.g+e.g,this.b=t.b+e.b,this}addScalar(t){return this.r+=t,this.g+=t,this.b+=t,this}sub(t){return this.r=Math.max(0,this.r-t.r),this.g=Math.max(0,this.g-t.g),this.b=Math.max(0,this.b-t.b),this}multiply(t){return this.r*=t.r,this.g*=t.g,this.b*=t.b,this}multiplyScalar(t){return this.r*=t,this.g*=t,this.b*=t,this}lerp(t,e){return this.r+=(t.r-this.r)*e,this.g+=(t.g-this.g)*e,this.b+=(t.b-this.b)*e,this}lerpColors(t,e,n){return this.r=t.r+(e.r-t.r)*n,this.g=t.g+(e.g-t.g)*n,this.b=t.b+(e.b-t.b)*n,this}lerpHSL(t,e){this.getHSL(GQt),t.getHSL(WQt);const n=ZZt(GQt.h,WQt.h,e),i=ZZt(GQt.s,WQt.s,e),r=ZZt(GQt.l,WQt.l,e);return this.setHSL(n,i,r),this}equals(t){return t.r===this.r&&t.g===this.g&&t.b===this.b}fromArray(t,e=0){return this.r=t[e],this.g=t[e+1],this.b=t[e+2],this}toArray(t=[],e=0){return t[e]=this.r,t[e+1]=this.g,t[e+2]=this.b,t}fromBufferAttribute(t,e){return this.r=t.getX(e),this.g=t.getY(e),this.b=t.getZ(e),!0===t.normalized&&(this.r/=255,this.g/=255,this.b/=255),this}toJSON(){return this.getHex()}}$Qt.NAMES=jQt,$Qt.prototype.isColor=!0,$Qt.prototype.r=1,$Qt.prototype.g=1,$Qt.prototype.b=1;class KQt extends UQt{constructor(t){super(),this.type="MeshBasicMaterial",this.color=new $Qt(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}KQt.prototype.isMeshBasicMaterial=!0;const ZQt=new CJt,JQt=new mJt;class QQt{constructor(t,e,n){if(Array.isArray(t))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");this.name="",this.array=t,this.itemSize=e,this.count=void 0!==t?t.length/e:0,this.normalized=!0===n,this.usage=FZt,this.updateRange={offset:0,count:-1},this.version=0}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.name=t.name,this.array=new t.array.constructor(t.array),this.itemSize=t.itemSize,this.count=t.count,this.normalized=t.normalized,this.usage=t.usage,this}copyAt(t,e,n){t*=this.itemSize,n*=e.itemSize;for(let i=0,r=this.itemSize;i<r;i++)this.array[t+i]=e.array[n+i];return this}copyArray(t){return this.array.set(t),this}copyColorsArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn("THREE.BufferAttribute.copyColorsArray(): color is undefined",i),r=new $Qt),e[n++]=r.r,e[n++]=r.g,e[n++]=r.b}return this}copyVector2sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn("THREE.BufferAttribute.copyVector2sArray(): vector is undefined",i),r=new mJt),e[n++]=r.x,e[n++]=r.y}return this}copyVector3sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn("THREE.BufferAttribute.copyVector3sArray(): vector is undefined",i),r=new CJt),e[n++]=r.x,e[n++]=r.y,e[n++]=r.z}return this}copyVector4sArray(t){const e=this.array;let n=0;for(let i=0,r=t.length;i<r;i++){let r=t[i];void 0===r&&(console.warn("THREE.BufferAttribute.copyVector4sArray(): vector is undefined",i),r=new wJt),e[n++]=r.x,e[n++]=r.y,e[n++]=r.z,e[n++]=r.w}return this}applyMatrix3(t){if(2===this.itemSize)for(let e=0,n=this.count;e<n;e++)JQt.fromBufferAttribute(this,e),JQt.applyMatrix3(t),this.setXY(e,JQt.x,JQt.y);else if(3===this.itemSize)for(let e=0,n=this.count;e<n;e++)ZQt.fromBufferAttribute(this,e),ZQt.applyMatrix3(t),this.setXYZ(e,ZQt.x,ZQt.y,ZQt.z);return this}applyMatrix4(t){for(let e=0,n=this.count;e<n;e++)ZQt.x=this.getX(e),ZQt.y=this.getY(e),ZQt.z=this.getZ(e),ZQt.applyMatrix4(t),this.setXYZ(e,ZQt.x,ZQt.y,ZQt.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)ZQt.x=this.getX(e),ZQt.y=this.getY(e),ZQt.z=this.getZ(e),ZQt.applyNormalMatrix(t),this.setXYZ(e,ZQt.x,ZQt.y,ZQt.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)ZQt.x=this.getX(e),ZQt.y=this.getY(e),ZQt.z=this.getZ(e),ZQt.transformDirection(t),this.setXYZ(e,ZQt.x,ZQt.y,ZQt.z);return this}set(t,e=0){return this.array.set(t,e),this}getX(t){return this.array[t*this.itemSize]}setX(t,e){return this.array[t*this.itemSize]=e,this}getY(t){return this.array[t*this.itemSize+1]}setY(t,e){return this.array[t*this.itemSize+1]=e,this}getZ(t){return this.array[t*this.itemSize+2]}setZ(t,e){return this.array[t*this.itemSize+2]=e,this}getW(t){return this.array[t*this.itemSize+3]}setW(t,e){return this.array[t*this.itemSize+3]=e,this}setXY(t,e,n){return this.array[0+(t*=this.itemSize)]=e,this.array[t+1]=n,this}setXYZ(t,e,n,i){return this.array[0+(t*=this.itemSize)]=e,this.array[t+1]=n,this.array[t+2]=i,this}setXYZW(t,e,n,i,r){return this.array[0+(t*=this.itemSize)]=e,this.array[t+1]=n,this.array[t+2]=i,this.array[t+3]=r,this}onUpload(t){return this.onUploadCallback=t,this}clone(){return new this.constructor(this.array,this.itemSize).copy(this)}toJSON(){const t={itemSize:this.itemSize,type:this.array.constructor.name,array:Array.prototype.slice.call(this.array),normalized:this.normalized};return""!==this.name&&(t.name=this.name),this.usage!==FZt&&(t.usage=this.usage),0===this.updateRange.offset&&-1===this.updateRange.count||(t.updateRange=this.updateRange),t}}QQt.prototype.isBufferAttribute=!0;class t1t extends QQt{constructor(t,e,n){super(new Int8Array(t),e,n)}}class e1t extends QQt{constructor(t,e,n){super(new Uint8Array(t),e,n)}}class n1t extends QQt{constructor(t,e,n){super(new Uint8ClampedArray(t),e,n)}}class i1t extends QQt{constructor(t,e,n){super(new Int16Array(t),e,n)}}class r1t extends QQt{constructor(t,e,n){super(new Uint16Array(t),e,n)}}class o1t extends QQt{constructor(t,e,n){super(new Int32Array(t),e,n)}}class a1t extends QQt{constructor(t,e,n){super(new Uint32Array(t),e,n)}}class s1t extends QQt{constructor(t,e,n){super(new Uint16Array(t),e,n)}}s1t.prototype.isFloat16BufferAttribute=!0;class l1t extends QQt{constructor(t,e,n){super(new Float32Array(t),e,n)}}class c1t extends QQt{constructor(t,e,n){super(new Float64Array(t),e,n)}}function u1t(t){if(0===t.length)return-1/0;let e=t[0];for(let n=1,i=t.length;n<i;++n)t[n]>e&&(e=t[n]);return e}const h1t={Int8Array:Int8Array,Uint8Array:Uint8Array,Uint8ClampedArray:Uint8ClampedArray,Int16Array:Int16Array,Uint16Array:Uint16Array,Int32Array:Int32Array,Uint32Array:Uint32Array,Float32Array:Float32Array,Float64Array:Float64Array};function d1t(t,e){return new h1t[t](e)}let p1t=0;const f1t=new rQt,m1t=new kQt,g1t=new CJt,_1t=new LJt,y1t=new LJt,v1t=new CJt;class b1t extends jZt{constructor(){super(),Object.defineProperty(this,"id",{value:p1t++}),this.uuid=XZt(),this.name="",this.type="BufferGeometry",this.index=null,this.attributes={},this.morphAttributes={},this.morphTargetsRelative=!1,this.groups=[],this.boundingBox=null,this.boundingSphere=null,this.drawRange={start:0,count:1/0},this.userData={}}getIndex(){return this.index}setIndex(t){return this.index=Array.isArray(t)?new(u1t(t)>65535?a1t:r1t)(t,1):t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new gJt).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const i=this.attributes.tangent;return void 0!==i&&(i.transformDirection(t),i.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return f1t.makeRotationFromQuaternion(t),this.applyMatrix4(f1t),this}rotateX(t){return f1t.makeRotationX(t),this.applyMatrix4(f1t),this}rotateY(t){return f1t.makeRotationY(t),this.applyMatrix4(f1t),this}rotateZ(t){return f1t.makeRotationZ(t),this.applyMatrix4(f1t),this}translate(t,e,n){return f1t.makeTranslation(t,e,n),this.applyMatrix4(f1t),this}scale(t,e,n){return f1t.makeScale(t,e,n),this.applyMatrix4(f1t),this}lookAt(t){return m1t.lookAt(t),m1t.updateMatrix(),this.applyMatrix4(m1t.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(g1t).negate(),this.translate(g1t.x,g1t.y,g1t.z),this}setFromPoints(t){const e=[];for(let n=0,i=t.length;n<i;n++){const i=t[n];e.push(i.x,i.y,i.z||0)}return this.setAttribute("position",new l1t(e,3)),this}computeBoundingBox(){null===this.boundingBox&&(this.boundingBox=new LJt);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingBox.set(new CJt(-1/0,-1/0,-1/0),new CJt(1/0,1/0,1/0));if(void 0!==t){if(this.boundingBox.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++)_1t.setFromBufferAttribute(e[t]),this.morphTargetsRelative?(v1t.addVectors(this.boundingBox.min,_1t.min),this.boundingBox.expandByPoint(v1t),v1t.addVectors(this.boundingBox.max,_1t.max),this.boundingBox.expandByPoint(v1t)):(this.boundingBox.expandByPoint(_1t.min),this.boundingBox.expandByPoint(_1t.max))}else this.boundingBox.makeEmpty();(isNaN(this.boundingBox.min.x)||isNaN(this.boundingBox.min.y)||isNaN(this.boundingBox.min.z))&&console.error('THREE.BufferGeometry.computeBoundingBox(): Computed min/max have NaN values. The "position" attribute is likely to have NaN values.',this)}computeBoundingSphere(){null===this.boundingSphere&&(this.boundingSphere=new $Jt);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingSphere(): GLBufferAttribute requires a manual bounding sphere. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingSphere.set(new CJt,1/0);if(t){const n=this.boundingSphere.center;if(_1t.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++)y1t.setFromBufferAttribute(e[t]),this.morphTargetsRelative?(v1t.addVectors(_1t.min,y1t.min),_1t.expandByPoint(v1t),v1t.addVectors(_1t.max,y1t.max),_1t.expandByPoint(v1t)):(_1t.expandByPoint(y1t.min),_1t.expandByPoint(y1t.max));_1t.getCenter(n);let i=0;for(let e=0,r=t.count;e<r;e++)v1t.fromBufferAttribute(t,e),i=Math.max(i,n.distanceToSquared(v1t));if(e)for(let r=0,o=e.length;r<o;r++){const o=e[r],a=this.morphTargetsRelative;for(let e=0,r=o.count;e<r;e++)v1t.fromBufferAttribute(o,e),a&&(g1t.fromBufferAttribute(t,e),v1t.add(g1t)),i=Math.max(i,n.distanceToSquared(v1t))}this.boundingSphere.radius=Math.sqrt(i),isNaN(this.boundingSphere.radius)&&console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The "position" attribute is likely to have NaN values.',this)}}computeFaceNormals(){}computeTangents(){const t=this.index,e=this.attributes;if(null===t||void 0===e.position||void 0===e.normal||void 0===e.uv)return void console.error("THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)");const n=t.array,i=e.position.array,r=e.normal.array,o=e.uv.array,a=i.length/3;void 0===e.tangent&&this.setAttribute("tangent",new QQt(new Float32Array(4*a),4));const s=e.tangent.array,l=[],c=[];for(let t=0;t<a;t++)l[t]=new CJt,c[t]=new CJt;const u=new CJt,h=new CJt,d=new CJt,p=new mJt,f=new mJt,m=new mJt,g=new CJt,_=new CJt;function y(t,e,n){u.fromArray(i,3*t),h.fromArray(i,3*e),d.fromArray(i,3*n),p.fromArray(o,2*t),f.fromArray(o,2*e),m.fromArray(o,2*n),h.sub(u),d.sub(u),f.sub(p),m.sub(p);const r=1/(f.x*m.y-m.x*f.y);isFinite(r)&&(g.copy(h).multiplyScalar(m.y).addScaledVector(d,-f.y).multiplyScalar(r),_.copy(d).multiplyScalar(f.x).addScaledVector(h,-m.x).multiplyScalar(r),l[t].add(g),l[e].add(g),l[n].add(g),c[t].add(_),c[e].add(_),c[n].add(_))}let v=this.groups;0===v.length&&(v=[{start:0,count:n.length}]);for(let t=0,e=v.length;t<e;++t){const e=v[t],i=e.start;for(let t=i,r=i+e.count;t<r;t+=3)y(n[t+0],n[t+1],n[t+2])}const b=new CJt,x=new CJt,w=new CJt,S=new CJt;function M(t){w.fromArray(r,3*t),S.copy(w);const e=l[t];b.copy(e),b.sub(w.multiplyScalar(w.dot(e))).normalize(),x.crossVectors(S,e);const n=x.dot(c[t])<0?-1:1;s[4*t]=b.x,s[4*t+1]=b.y,s[4*t+2]=b.z,s[4*t+3]=n}for(let t=0,e=v.length;t<e;++t){const e=v[t],i=e.start;for(let t=i,r=i+e.count;t<r;t+=3)M(n[t+0]),M(n[t+1]),M(n[t+2])}}computeVertexNormals(){const t=this.index,e=this.getAttribute("position");if(void 0!==e){let n=this.getAttribute("normal");if(void 0===n)n=new QQt(new Float32Array(3*e.count),3),this.setAttribute("normal",n);else for(let t=0,e=n.count;t<e;t++)n.setXYZ(t,0,0,0);const i=new CJt,r=new CJt,o=new CJt,a=new CJt,s=new CJt,l=new CJt,c=new CJt,u=new CJt;if(t)for(let h=0,d=t.count;h<d;h+=3){const d=t.getX(h+0),p=t.getX(h+1),f=t.getX(h+2);i.fromBufferAttribute(e,d),r.fromBufferAttribute(e,p),o.fromBufferAttribute(e,f),c.subVectors(o,r),u.subVectors(i,r),c.cross(u),a.fromBufferAttribute(n,d),s.fromBufferAttribute(n,p),l.fromBufferAttribute(n,f),a.add(c),s.add(c),l.add(c),n.setXYZ(d,a.x,a.y,a.z),n.setXYZ(p,s.x,s.y,s.z),n.setXYZ(f,l.x,l.y,l.z)}else for(let t=0,a=e.count;t<a;t+=3)i.fromBufferAttribute(e,t+0),r.fromBufferAttribute(e,t+1),o.fromBufferAttribute(e,t+2),c.subVectors(o,r),u.subVectors(i,r),c.cross(u),n.setXYZ(t+0,c.x,c.y,c.z),n.setXYZ(t+1,c.x,c.y,c.z),n.setXYZ(t+2,c.x,c.y,c.z);this.normalizeNormals(),n.needsUpdate=!0}}merge(t,e){if(!t||!t.isBufferGeometry)return void console.error("THREE.BufferGeometry.merge(): geometry not an instance of THREE.BufferGeometry.",t);void 0===e&&(e=0,console.warn("THREE.BufferGeometry.merge(): Overwriting original geometry, starting at offset=0. Use BufferGeometryUtils.mergeBufferGeometries() for lossless merge."));const n=this.attributes;for(const i in n){if(void 0===t.attributes[i])continue;const r=n[i].array,o=t.attributes[i],a=o.array,s=o.itemSize*e,l=Math.min(a.length,r.length-s);for(let t=0,e=s;t<l;t++,e++)r[e]=a[t]}return this}normalizeNormals(){const t=this.attributes.normal;for(let e=0,n=t.count;e<n;e++)v1t.fromBufferAttribute(t,e),v1t.normalize(),t.setXYZ(e,v1t.x,v1t.y,v1t.z)}toNonIndexed(){function t(t,e){const n=t.array,i=t.itemSize,r=t.normalized,o=new n.constructor(e.length*i);let a=0,s=0;for(let r=0,l=e.length;r<l;r++){a=t.isInterleavedBufferAttribute?e[r]*t.data.stride+t.offset:e[r]*i;for(let t=0;t<i;t++)o[s++]=n[a++]}return new QQt(o,i,r)}if(null===this.index)return console.warn("THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed."),this;const e=new b1t,n=this.index.array,i=this.attributes;for(const r in i){const o=t(i[r],n);e.setAttribute(r,o)}const r=this.morphAttributes;for(const i in r){const o=[],a=r[i];for(let e=0,i=a.length;e<i;e++){const i=t(a[e],n);o.push(i)}e.morphAttributes[i]=o}e.morphTargetsRelative=this.morphTargetsRelative;const o=this.groups;for(let t=0,n=o.length;t<n;t++){const n=o[t];e.addGroup(n.start,n.count,n.materialIndex)}return e}toJSON(){const t={metadata:{version:4.5,type:"BufferGeometry",generator:"BufferGeometry.toJSON"}};if(t.uuid=this.uuid,t.type=this.type,""!==this.name&&(t.name=this.name),Object.keys(this.userData).length>0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n)t.data.attributes[e]=n[e].toJSON(t.data);const i={};let r=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],o=[];for(let e=0,i=n.length;e<i;e++)o.push(n[e].toJSON(t.data));o.length>0&&(i[e]=o,r=!0)}r&&(t.data.morphAttributes=i,t.data.morphTargetsRelative=this.morphTargetsRelative);const o=this.groups;o.length>0&&(t.data.groups=JSON.parse(JSON.stringify(o)));const a=this.boundingSphere;return null!==a&&(t.data.boundingSphere={center:a.center.toArray(),radius:a.radius}),t}clone(){return(new b1t).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const i=t.attributes;for(const t in i)this.setAttribute(t,i[t].clone(e));const r=t.morphAttributes;for(const t in r){const n=[],i=r[t];for(let t=0,r=i.length;t<r;t++)n.push(i[t].clone(e));this.morphAttributes[t]=n}this.morphTargetsRelative=t.morphTargetsRelative;const o=t.groups;for(let t=0,e=o.length;t<e;t++){const e=o[t];this.addGroup(e.start,e.count,e.materialIndex)}const a=t.boundingBox;null!==a&&(this.boundingBox=a.clone());const s=t.boundingSphere;return null!==s&&(this.boundingSphere=s.clone()),this.drawRange.start=t.drawRange.start,this.drawRange.count=t.drawRange.count,this.userData=t.userData,this}dispose(){this.dispatchEvent({type:"dispose"})}}b1t.prototype.isBufferGeometry=!0;const x1t=new rQt,w1t=new iQt,S1t=new $Jt,M1t=new CJt,E1t=new CJt,T1t=new CJt,C1t=new CJt,A1t=new CJt,k1t=new CJt,L1t=new CJt,P1t=new CJt,N1t=new CJt,I1t=new mJt,R1t=new mJt,O1t=new mJt,z1t=new CJt,D1t=new CJt;class B1t extends kQt{constructor(t=new b1t,e=new KQt){super(),this.type="Mesh",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),void 0!==t.morphTargetInfluences&&(this.morphTargetInfluences=t.morphTargetInfluences.slice()),void 0!==t.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},t.morphTargetDictionary)),this.material=t.material,this.geometry=t.geometry,this}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}}raycast(t,e){const n=this.geometry,i=this.material,r=this.matrixWorld;if(void 0===i)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),S1t.copy(n.boundingSphere),S1t.applyMatrix4(r),!1===t.ray.intersectsSphere(S1t))return;if(x1t.copy(r).invert(),w1t.copy(t.ray).applyMatrix4(x1t),null!==n.boundingBox&&!1===w1t.intersectsBox(n.boundingBox))return;let o;if(n.isBufferGeometry){const r=n.index,a=n.attributes.position,s=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,u=n.attributes.uv2,h=n.groups,d=n.drawRange;if(null!==r)if(Array.isArray(i))for(let n=0,p=h.length;n<p;n++){const p=h[n],f=i[p.materialIndex];for(let n=Math.max(p.start,d.start),i=Math.min(p.start+p.count,d.start+d.count);n<i;n+=3){const i=r.getX(n),h=r.getX(n+1),d=r.getX(n+2);o=H1t(this,f,t,w1t,a,s,l,c,u,i,h,d),o&&(o.faceIndex=Math.floor(n/3),o.face.materialIndex=p.materialIndex,e.push(o))}}else for(let n=Math.max(0,d.start),h=Math.min(r.count,d.start+d.count);n<h;n+=3){const h=r.getX(n),d=r.getX(n+1),p=r.getX(n+2);o=H1t(this,i,t,w1t,a,s,l,c,u,h,d,p),o&&(o.faceIndex=Math.floor(n/3),e.push(o))}else if(void 0!==a)if(Array.isArray(i))for(let n=0,r=h.length;n<r;n++){const r=h[n],p=i[r.materialIndex];for(let n=Math.max(r.start,d.start),i=Math.min(r.start+r.count,d.start+d.count);n<i;n+=3)o=H1t(this,p,t,w1t,a,s,l,c,u,n,n+1,n+2),o&&(o.faceIndex=Math.floor(n/3),o.face.materialIndex=r.materialIndex,e.push(o))}else for(let n=Math.max(0,d.start),r=Math.min(a.count,d.start+d.count);n<r;n+=3)o=H1t(this,i,t,w1t,a,s,l,c,u,n,n+1,n+2),o&&(o.faceIndex=Math.floor(n/3),e.push(o))}else n.isGeometry&&console.error("THREE.Mesh.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}}function H1t(t,e,n,i,r,o,a,s,l,c,u,h){M1t.fromBufferAttribute(r,c),E1t.fromBufferAttribute(r,u),T1t.fromBufferAttribute(r,h);const d=t.morphTargetInfluences;if(o&&d){L1t.set(0,0,0),P1t.set(0,0,0),N1t.set(0,0,0);for(let t=0,e=o.length;t<e;t++){const e=d[t],n=o[t];0!==e&&(C1t.fromBufferAttribute(n,c),A1t.fromBufferAttribute(n,u),k1t.fromBufferAttribute(n,h),a?(L1t.addScaledVector(C1t,e),P1t.addScaledVector(A1t,e),N1t.addScaledVector(k1t,e)):(L1t.addScaledVector(C1t.sub(M1t),e),P1t.addScaledVector(A1t.sub(E1t),e),N1t.addScaledVector(k1t.sub(T1t),e)))}M1t.add(L1t),E1t.add(P1t),T1t.add(N1t)}t.isSkinnedMesh&&(t.boneTransform(c,M1t),t.boneTransform(u,E1t),t.boneTransform(h,T1t));const p=(function f(t,e,n,i,r,o,a,s){let l;if(l=1===e.side?i.intersectTriangle(a,o,r,!0,s):i.intersectTriangle(r,o,a,2!==e.side,s),null===l)return null;D1t.copy(s),D1t.applyMatrix4(t.matrixWorld);const c=n.ray.origin.distanceTo(D1t);return c<n.near||c>n.far?null:{distance:c,point:D1t.clone(),object:t}})(t,e,n,i,M1t,E1t,T1t,z1t);if(p){s&&(I1t.fromBufferAttribute(s,c),R1t.fromBufferAttribute(s,u),O1t.fromBufferAttribute(s,h),p.uv=FQt.getUV(z1t,M1t,E1t,T1t,I1t,R1t,O1t,new mJt)),l&&(I1t.fromBufferAttribute(l,c),R1t.fromBufferAttribute(l,u),O1t.fromBufferAttribute(l,h),p.uv2=FQt.getUV(z1t,M1t,E1t,T1t,I1t,R1t,O1t,new mJt));const t={a:c,b:u,c:h,normal:new CJt,materialIndex:0};FQt.getNormal(M1t,E1t,T1t,t.normal),p.face=t}return p}B1t.prototype.isMesh=!0;class F1t extends b1t{constructor(t=1,e=1,n=1,i=1,r=1,o=1){super(),this.type="BoxGeometry",this.parameters={width:t,height:e,depth:n,widthSegments:i,heightSegments:r,depthSegments:o};const a=this;i=Math.floor(i),r=Math.floor(r),o=Math.floor(o);const s=[],l=[],c=[],u=[];let h=0,d=0;function p(t,e,n,i,r,o,p,f,m,g,_){const y=o/m,v=p/g,b=o/2,x=p/2,w=f/2,S=m+1,M=g+1;let E=0,T=0;const C=new CJt;for(let o=0;o<M;o++){const a=o*v-x;for(let s=0;s<S;s++)C[t]=(s*y-b)*i,C[e]=a*r,C[n]=w,l.push(C.x,C.y,C.z),C[t]=0,C[e]=0,C[n]=f>0?1:-1,c.push(C.x,C.y,C.z),u.push(s/m),u.push(1-o/g),E+=1}for(let t=0;t<g;t++)for(let e=0;e<m;e++){const n=h+e+S*(t+1),i=h+(e+1)+S*(t+1),r=h+(e+1)+S*t;s.push(h+e+S*t,n,r),s.push(n,i,r),T+=6}a.addGroup(d,T,_),d+=T,h+=E}p("z","y","x",-1,-1,n,e,t,o,r,0),p("z","y","x",1,-1,n,e,-t,o,r,1),p("x","z","y",1,1,t,n,e,i,o,2),p("x","z","y",1,-1,t,n,-e,i,o,3),p("x","y","z",1,-1,t,e,n,i,r,4),p("x","y","z",-1,-1,t,e,-n,i,r,5),this.setIndex(s),this.setAttribute("position",new l1t(l,3)),this.setAttribute("normal",new l1t(c,3)),this.setAttribute("uv",new l1t(u,2))}static fromJSON(t){return new F1t(t.width,t.height,t.depth,t.widthSegments,t.heightSegments,t.depthSegments)}}function V1t(t){const e={};for(const n in t){e[n]={};for(const i in t[n]){const r=t[n][i];e[n][i]=r&&(r.isColor||r.isMatrix3||r.isMatrix4||r.isVector2||r.isVector3||r.isVector4||r.isTexture||r.isQuaternion)?r.clone():Array.isArray(r)?r.slice():r}}return e}function U1t(t){const e={};for(let n=0;n<t.length;n++){const i=V1t(t[n]);for(const t in i)e[t]=i[t]}return e}const j1t={clone:V1t,merge:U1t};class G1t extends UQt{constructor(t){super(),this.type="ShaderMaterial",this.defines={},this.uniforms={},this.vertexShader="void main() {\n\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n}",this.fragmentShader="void main() {\n\tgl_FragColor = vec4( 1.0, 0.0, 0.0, 1.0 );\n}",this.linewidth=1,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.lights=!1,this.clipping=!1,this.extensions={derivatives:!1,fragDepth:!1,drawBuffers:!1,shaderTextureLOD:!1},this.defaultAttributeValues={color:[1,1,1],uv:[0,0],uv2:[0,0]},this.index0AttributeName=void 0,this.uniformsNeedUpdate=!1,this.glslVersion=null,void 0!==t&&(void 0!==t.attributes&&console.error("THREE.ShaderMaterial: attributes should now be defined in THREE.BufferGeometry instead."),this.setValues(t))}copy(t){return super.copy(t),this.fragmentShader=t.fragmentShader,this.vertexShader=t.vertexShader,this.uniforms=V1t(t.uniforms),this.defines=Object.assign({},t.defines),this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.lights=t.lights,this.clipping=t.clipping,this.extensions=Object.assign({},t.extensions),this.glslVersion=t.glslVersion,this}toJSON(t){const e=super.toJSON(t);e.glslVersion=this.glslVersion,e.uniforms={};for(const n in this.uniforms){const i=this.uniforms[n].value;e.uniforms[n]=i&&i.isTexture?{type:"t",value:i.toJSON(t).uuid}:i&&i.isColor?{type:"c",value:i.getHex()}:i&&i.isVector2?{type:"v2",value:i.toArray()}:i&&i.isVector3?{type:"v3",value:i.toArray()}:i&&i.isVector4?{type:"v4",value:i.toArray()}:i&&i.isMatrix3?{type:"m3",value:i.toArray()}:i&&i.isMatrix4?{type:"m4",value:i.toArray()}:{value:i}}Object.keys(this.defines).length>0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}G1t.prototype.isShaderMaterial=!0;class W1t extends kQt{constructor(){super(),this.type="Camera",this.matrixWorldInverse=new rQt,this.projectionMatrix=new rQt,this.projectionMatrixInverse=new rQt}copy(t,e){return super.copy(t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()}updateMatrixWorld(t){super.updateMatrixWorld(t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(t,e){super.updateWorldMatrix(t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return(new this.constructor).copy(this)}}W1t.prototype.isCamera=!0;class q1t extends W1t{constructor(t=50,e=1,n=.1,i=2e3){super(),this.type="PerspectiveCamera",this.fov=t,this.zoom=1,this.near=n,this.far=i,this.focus=10,this.aspect=e,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.fov=t.fov,this.zoom=t.zoom,this.near=t.near,this.far=t.far,this.focus=t.focus,this.aspect=t.aspect,this.view=null===t.view?null:Object.assign({},t.view),this.filmGauge=t.filmGauge,this.filmOffset=t.filmOffset,this}setFocalLength(t){const e=.5*this.getFilmHeight()/t;this.fov=2*YZt*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*qZt*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*YZt*Math.atan(Math.tan(.5*qZt*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}setViewOffset(t,e,n,i,r,o){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=o,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*qZt*this.fov)/this.zoom,n=2*e,i=this.aspect*n,r=-.5*i;const o=this.view;if(null!==this.view&&this.view.enabled){const t=o.fullWidth,a=o.fullHeight;r+=o.offsetX*i/t,e-=o.offsetY*n/a,i*=o.width/t,n*=o.height/a}const a=this.filmOffset;0!==a&&(r+=t*a/this.getFilmWidth()),this.projectionMatrix.makePerspective(r,r+i,e,e-n,t,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.fov=this.fov,e.object.zoom=this.zoom,e.object.near=this.near,e.object.far=this.far,e.object.focus=this.focus,e.object.aspect=this.aspect,null!==this.view&&(e.object.view=Object.assign({},this.view)),e.object.filmGauge=this.filmGauge,e.object.filmOffset=this.filmOffset,e}}q1t.prototype.isPerspectiveCamera=!0;const Y1t=90;class X1t extends kQt{constructor(t,e,n){if(super(),this.type="CubeCamera",!0!==n.isWebGLCubeRenderTarget)return void console.error("THREE.CubeCamera: The constructor now expects an instance of WebGLCubeRenderTarget as third parameter.");this.renderTarget=n;const i=new q1t(Y1t,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new CJt(1,0,0)),this.add(i);const r=new q1t(Y1t,1,t,e);r.layers=this.layers,r.up.set(0,-1,0),r.lookAt(new CJt(-1,0,0)),this.add(r);const o=new q1t(Y1t,1,t,e);o.layers=this.layers,o.up.set(0,0,1),o.lookAt(new CJt(0,1,0)),this.add(o);const a=new q1t(Y1t,1,t,e);a.layers=this.layers,a.up.set(0,0,-1),a.lookAt(new CJt(0,-1,0)),this.add(a);const s=new q1t(Y1t,1,t,e);s.layers=this.layers,s.up.set(0,-1,0),s.lookAt(new CJt(0,0,1)),this.add(s);const l=new q1t(Y1t,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new CJt(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[i,r,o,a,s,l]=this.children,c=t.xr.enabled,u=t.getRenderTarget();t.xr.enabled=!1;const h=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,i),t.setRenderTarget(n,1),t.render(e,r),t.setRenderTarget(n,2),t.render(e,o),t.setRenderTarget(n,3),t.render(e,a),t.setRenderTarget(n,4),t.render(e,s),n.texture.generateMipmaps=h,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(u),t.xr.enabled=c}}class $1t extends bJt{constructor(t,e,n,i,r,o,a,s,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:jKt,n,i,r,o,a=void 0!==a?a:uZt,s,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}$1t.prototype.isCubeTexture=!0;class K1t extends SJt{constructor(t,e,n){Number.isInteger(e)&&(console.warn("THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )"),e=n),super(t,t,e),this.texture=new $1t(void 0,(e=e||{}).mapping,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.format,e.type,e.anisotropy,e.encoding),this.texture.isRenderTargetTexture=!0,this.texture.generateMipmaps=void 0!==e.generateMipmaps&&e.generateMipmaps,this.texture.minFilter=void 0!==e.minFilter?e.minFilter:eZt,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=hZt,this.texture.encoding=e.encoding,this.texture.generateMipmaps=e.generateMipmaps,this.texture.minFilter=e.minFilter,this.texture.magFilter=e.magFilter;const n=new F1t(5,5,5),i=new G1t({name:"CubemapFromEquirect",uniforms:V1t({tEquirect:{value:null}}),vertexShader:"\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\n\t\t\t\t\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n\n\t\t\t\t}\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvWorldDirection = transformDirection( position, modelMatrix );\n\n\t\t\t\t\t#include <begin_vertex>\n\t\t\t\t\t#include <project_vertex>\n\n\t\t\t\t}\n\t\t\t",fragmentShader:"\n\n\t\t\t\tuniform sampler2D tEquirect;\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\t#include <common>\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvec3 direction = normalize( vWorldDirection );\n\n\t\t\t\t\tvec2 sampleUV = equirectUv( direction );\n\n\t\t\t\t\tgl_FragColor = texture2D( tEquirect, sampleUV );\n\n\t\t\t\t}\n\t\t\t",side:1,blending:0});i.uniforms.tEquirect.value=e;const r=new B1t(n,i),o=e.minFilter;return e.minFilter===iZt&&(e.minFilter=eZt),new X1t(1,10,this).update(t,r),e.minFilter=o,r.geometry.dispose(),r.material.dispose(),this}clear(t,e,n,i){const r=t.getRenderTarget();for(let r=0;r<6;r++)t.setRenderTarget(this,r),t.clear(e,n,i);t.setRenderTarget(r)}}K1t.prototype.isWebGLCubeRenderTarget=!0;const Z1t=new CJt,J1t=new CJt,Q1t=new gJt;class t0t{constructor(t=new CJt(1,0,0),e=0){this.normal=t,this.constant=e}set(t,e){return this.normal.copy(t),this.constant=e,this}setComponents(t,e,n,i){return this.normal.set(t,e,n),this.constant=i,this}setFromNormalAndCoplanarPoint(t,e){return this.normal.copy(t),this.constant=-e.dot(this.normal),this}setFromCoplanarPoints(t,e,n){const i=Z1t.subVectors(n,e).cross(J1t.subVectors(t,e)).normalize();return this.setFromNormalAndCoplanarPoint(i,t),this}copy(t){return this.normal.copy(t.normal),this.constant=t.constant,this}normalize(){const t=1/this.normal.length();return this.normal.multiplyScalar(t),this.constant*=t,this}negate(){return this.constant*=-1,this.normal.negate(),this}distanceToPoint(t){return this.normal.dot(t)+this.constant}distanceToSphere(t){return this.distanceToPoint(t.center)-t.radius}projectPoint(t,e){return e.copy(this.normal).multiplyScalar(-this.distanceToPoint(t)).add(t)}intersectLine(t,e){const n=t.delta(Z1t),i=this.normal.dot(n);if(0===i)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const r=-(t.start.dot(this.normal)+this.constant)/i;return r<0||r>1?null:e.copy(n).multiplyScalar(r).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||Q1t.getNormalMatrix(t),i=this.coplanarPoint(Z1t).applyMatrix4(t),r=this.normal.applyMatrix3(n).normalize();return this.constant=-i.dot(r),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}t0t.prototype.isPlane=!0;const e0t=new $Jt,n0t=new CJt;class i0t{constructor(t=new t0t,e=new t0t,n=new t0t,i=new t0t,r=new t0t,o=new t0t){this.planes=[t,e,n,i,r,o]}set(t,e,n,i,r,o){const a=this.planes;return a[0].copy(t),a[1].copy(e),a[2].copy(n),a[3].copy(i),a[4].copy(r),a[5].copy(o),this}copy(t){const e=this.planes;for(let n=0;n<6;n++)e[n].copy(t.planes[n]);return this}setFromProjectionMatrix(t){const e=this.planes,n=t.elements,i=n[0],r=n[1],o=n[2],a=n[3],s=n[4],l=n[5],c=n[6],u=n[7],h=n[8],d=n[9],p=n[10],f=n[11],m=n[12],g=n[13],_=n[14],y=n[15];return e[0].setComponents(a-i,u-s,f-h,y-m).normalize(),e[1].setComponents(a+i,u+s,f+h,y+m).normalize(),e[2].setComponents(a+r,u+l,f+d,y+g).normalize(),e[3].setComponents(a-r,u-l,f-d,y-g).normalize(),e[4].setComponents(a-o,u-c,f-p,y-_).normalize(),e[5].setComponents(a+o,u+c,f+p,y+_).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),e0t.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(e0t)}intersectsSprite(t){return e0t.center.set(0,0,0),e0t.radius=.7071067811865476,e0t.applyMatrix4(t.matrixWorld),this.intersectsSphere(e0t)}intersectsSphere(t){const e=this.planes,n=t.center,i=-t.radius;for(let t=0;t<6;t++)if(e[t].distanceToPoint(n)<i)return!1;return!0}intersectsBox(t){const e=this.planes;for(let n=0;n<6;n++){const i=e[n];if(n0t.x=i.normal.x>0?t.max.x:t.min.x,n0t.y=i.normal.y>0?t.max.y:t.min.y,n0t.z=i.normal.z>0?t.max.z:t.min.z,i.distanceToPoint(n0t)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let n=0;n<6;n++)if(e[n].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}function r0t(){let t=null,e=!1,n=null,i=null;function r(e,o){n(e,o),i=t.requestAnimationFrame(r)}return{start:function(){!0!==e&&null!==n&&(i=t.requestAnimationFrame(r),e=!0)},stop:function(){t.cancelAnimationFrame(i),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function o0t(t,e){const n=e.isWebGL2,i=new WeakMap;return{get:function r(t){return t.isInterleavedBufferAttribute&&(t=t.data),i.get(t)},remove:function o(e){e.isInterleavedBufferAttribute&&(e=e.data);const n=i.get(e);n&&(t.deleteBuffer(n.buffer),i.delete(e))},update:function a(e,r){if(e.isGLBufferAttribute){const t=i.get(e);return void((!t||t.version<e.version)&&i.set(e,{buffer:e.buffer,type:e.type,bytesPerElement:e.elementSize,version:e.version}))}e.isInterleavedBufferAttribute&&(e=e.data);const o=i.get(e);void 0===o?i.set(e,(function a(e,i){const r=e.array,o=e.usage,a=t.createBuffer();t.bindBuffer(i,a),t.bufferData(i,r,o),e.onUploadCallback();let s=5126;return r instanceof Float32Array?s=5126:r instanceof Float64Array?console.warn("THREE.WebGLAttributes: Unsupported data buffer format: Float64Array."):r instanceof Uint16Array?e.isFloat16BufferAttribute?n?s=5131:console.warn("THREE.WebGLAttributes: Usage of Float16BufferAttribute requires WebGL2."):s=5123:r instanceof Int16Array?s=5122:r instanceof Uint32Array?s=5125:r instanceof Int32Array?s=5124:r instanceof Int8Array?s=5120:(r instanceof Uint8Array||r instanceof Uint8ClampedArray)&&(s=5121),{buffer:a,type:s,bytesPerElement:r.BYTES_PER_ELEMENT,version:e.version}})(e,r)):o.version<e.version&&((function s(e,i,r){const o=i.array,a=i.updateRange;t.bindBuffer(r,e),-1===a.count?t.bufferSubData(r,0,o):(n?t.bufferSubData(r,a.offset*o.BYTES_PER_ELEMENT,o,a.offset,a.count):t.bufferSubData(r,a.offset*o.BYTES_PER_ELEMENT,o.subarray(a.offset,a.offset+a.count)),a.count=-1)})(o.buffer,e,r),o.version=e.version)}}}class a0t extends b1t{constructor(t=1,e=1,n=1,i=1){super(),this.type="PlaneGeometry",this.parameters={width:t,height:e,widthSegments:n,heightSegments:i};const r=t/2,o=e/2,a=Math.floor(n),s=Math.floor(i),l=a+1,c=s+1,u=t/a,h=e/s,d=[],p=[],f=[],m=[];for(let t=0;t<c;t++){const e=t*h-o;for(let n=0;n<l;n++)p.push(n*u-r,-e,0),f.push(0,0,1),m.push(n/a),m.push(1-t/s)}for(let t=0;t<s;t++)for(let e=0;e<a;e++){const n=e+l*(t+1),i=e+1+l*(t+1),r=e+1+l*t;d.push(e+l*t,n,r),d.push(n,i,r)}this.setIndex(d),this.setAttribute("position",new l1t(p,3)),this.setAttribute("normal",new l1t(f,3)),this.setAttribute("uv",new l1t(m,2))}static fromJSON(t){return new a0t(t.width,t.height,t.widthSegments,t.heightSegments)}}const s0t={alphamap_fragment:"#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\n#endif",alphamap_pars_fragment:"#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",alphatest_fragment:"#ifdef ALPHATEST\n\tif ( diffuseColor.a < ALPHATEST ) discard;\n#endif",aomap_fragment:"#ifdef USE_AOMAP\n\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\n\treflectedLight.indirectDiffuse *= ambientOcclusion;\n\t#if defined( USE_ENVMAP ) && defined( STANDARD )\n\t\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.specularRoughness );\n\t#endif\n#endif",aomap_pars_fragment:"#ifdef USE_AOMAP\n\tuniform sampler2D aoMap;\n\tuniform float aoMapIntensity;\n#endif",begin_vertex:"vec3 transformed = vec3( position );",beginnormal_vertex:"vec3 objectNormal = vec3( normal );\n#ifdef USE_TANGENT\n\tvec3 objectTangent = vec3( tangent.xyz );\n#endif",bsdfs:"vec2 integrateSpecularBRDF( const in float dotNV, const in float roughness ) {\n\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\n\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\n\tvec4 r = roughness * c0 + c1;\n\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\n\treturn vec2( -1.04, 1.04 ) * a004 + r.zw;\n}\nfloat punctualLightIntensityToIrradianceFactor( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\n#if defined ( PHYSICALLY_CORRECT_LIGHTS )\n\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\n\tif( cutoffDistance > 0.0 ) {\n\t\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\n\t}\n\treturn distanceFalloff;\n#else\n\tif( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\n\t\treturn pow( saturate( -lightDistance / cutoffDistance + 1.0 ), decayExponent );\n\t}\n\treturn 1.0;\n#endif\n}\nvec3 BRDF_Diffuse_Lambert( const in vec3 diffuseColor ) {\n\treturn RECIPROCAL_PI * diffuseColor;\n}\nvec3 F_Schlick( const in vec3 f0, const in vec3 f90, const in float dotVH ) {\n\tfloat fresnel = exp2( ( -5.55473 * dotVH - 6.98316 ) * dotVH );\n\treturn ( f90 - f0 ) * fresnel + f0;\n}\nvec3 F_Schlick_RoughnessDependent( const in vec3 F0, const in float dotNV, const in float roughness ) {\n\tfloat fresnel = exp2( ( -5.55473 * dotNV - 6.98316 ) * dotNV );\n\tvec3 Fr = max( vec3( 1.0 - roughness ), F0 ) - F0;\n\treturn Fr * fresnel + F0;\n}\nfloat G_GGX_Smith( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 = pow2( alpha );\n\tfloat gl = dotNL + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\tfloat gv = dotNV + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\treturn 1.0 / ( gl * gv );\n}\nfloat G_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 = pow2( alpha );\n\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\treturn 0.5 / max( gv + gl, EPSILON );\n}\nfloat D_GGX( const in float alpha, const in float dotNH ) {\n\tfloat a2 = pow2( alpha );\n\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;\n\treturn RECIPROCAL_PI * a2 / pow2( denom );\n}\nvec3 BRDF_Specular_GGX( const in IncidentLight incidentLight, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in vec3 f90, const in float roughness ) {\n\tfloat alpha = pow2( roughness );\n\tvec3 halfDir = normalize( incidentLight.direction + viewDir );\n\tfloat dotNL = saturate( dot( normal, incidentLight.direction ) );\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F = F_Schlick( f0, f90, dotLH );\n\tfloat G = G_GGX_SmithCorrelated( alpha, dotNL, dotNV );\n\tfloat D = D_GGX( alpha, dotNH );\n\treturn F * ( G * D );\n}\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\n\tconst float LUT_SIZE = 64.0;\n\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\n\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\n\tfloat dotNV = saturate( dot( N, V ) );\n\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\n\tuv = uv * LUT_SCALE + LUT_BIAS;\n\treturn uv;\n}\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\n\tfloat l = length( f );\n\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\n}\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\n\tfloat x = dot( v1, v2 );\n\tfloat y = abs( x );\n\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\n\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\n\tfloat v = a / b;\n\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\n\treturn cross( v1, v2 ) * theta_sintheta;\n}\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\n\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\n\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\n\tvec3 lightNormal = cross( v1, v2 );\n\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\n\tvec3 T1, T2;\n\tT1 = normalize( V - N * dot( V, N ) );\n\tT2 = - cross( N, T1 );\n\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\n\tvec3 coords[ 4 ];\n\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\n\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\n\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\n\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\n\tcoords[ 0 ] = normalize( coords[ 0 ] );\n\tcoords[ 1 ] = normalize( coords[ 1 ] );\n\tcoords[ 2 ] = normalize( coords[ 2 ] );\n\tcoords[ 3 ] = normalize( coords[ 3 ] );\n\tvec3 vectorFormFactor = vec3( 0.0 );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\n\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\n\treturn vec3( result );\n}\nvec3 BRDF_Specular_GGX_Environment( const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float roughness ) {\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\n\treturn specularColor * brdf.x + brdf.y;\n}\nvoid BRDF_Specular_Multiscattering_Environment( const in GeometricContext geometry, const in vec3 specularColor, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\n\tvec3 F = F_Schlick_RoughnessDependent( specularColor, dotNV, roughness );\n\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\n\tvec3 FssEss = F * brdf.x + brdf.y;\n\tfloat Ess = brdf.x + brdf.y;\n\tfloat Ems = 1.0 - Ess;\n\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619;\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\n\tsingleScatter += FssEss;\n\tmultiScatter += Fms * Ems;\n}\nfloat G_BlinnPhong_Implicit( ) {\n\treturn 0.25;\n}\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\n\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\n}\nvec3 BRDF_Specular_BlinnPhong( const in IncidentLight incidentLight, const in GeometricContext geometry, const in vec3 specularColor, const in float shininess ) {\n\tvec3 halfDir = normalize( incidentLight.direction + geometry.viewDir );\n\tfloat dotNH = saturate( dot( geometry.normal, halfDir ) );\n\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F = F_Schlick( specularColor, vec3( 1.0 ), dotLH );\n\tfloat G = G_BlinnPhong_Implicit( );\n\tfloat D = D_BlinnPhong( shininess, dotNH );\n\treturn F * ( G * D );\n}\nfloat GGXRoughnessToBlinnExponent( const in float ggxRoughness ) {\n\treturn ( 2.0 / pow2( ggxRoughness + 0.0001 ) - 2.0 );\n}\nfloat BlinnExponentToGGXRoughness( const in float blinnExponent ) {\n\treturn sqrt( 2.0 / ( blinnExponent + 2.0 ) );\n}\n#if defined( USE_SHEEN )\nfloat D_Charlie(float roughness, float NoH) {\n\tfloat invAlpha = 1.0 / roughness;\n\tfloat cos2h = NoH * NoH;\n\tfloat sin2h = max(1.0 - cos2h, 0.0078125);\treturn (2.0 + invAlpha) * pow(sin2h, invAlpha * 0.5) / (2.0 * PI);\n}\nfloat V_Neubelt(float NoV, float NoL) {\n\treturn saturate(1.0 / (4.0 * (NoL + NoV - NoL * NoV)));\n}\nvec3 BRDF_Specular_Sheen( const in float roughness, const in vec3 L, const in GeometricContext geometry, vec3 specularColor ) {\n\tvec3 N = geometry.normal;\n\tvec3 V = geometry.viewDir;\n\tvec3 H = normalize( V + L );\n\tfloat dotNH = saturate( dot( N, H ) );\n\treturn specularColor * D_Charlie( roughness, dotNH ) * V_Neubelt( dot(N, V), dot(N, L) );\n}\n#endif",bumpmap_pars_fragment:"#ifdef USE_BUMPMAP\n\tuniform sampler2D bumpMap;\n\tuniform float bumpScale;\n\tvec2 dHdxy_fwd() {\n\t\tvec2 dSTdx = dFdx( vUv );\n\t\tvec2 dSTdy = dFdy( vUv );\n\t\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\n\t\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\n\t\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\n\t\treturn vec2( dBx, dBy );\n\t}\n\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\n\t\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\n\t\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\n\t\tvec3 vN = surf_norm;\n\t\tvec3 R1 = cross( vSigmaY, vN );\n\t\tvec3 R2 = cross( vN, vSigmaX );\n\t\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\n\t\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\n\t\treturn normalize( abs( fDet ) * surf_norm - vGrad );\n\t}\n#endif",clipping_planes_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvec4 plane;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\n\t\tplane = clippingPlanes[ i ];\n\t\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\n\t}\n\t#pragma unroll_loop_end\n\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\n\t\tbool clipped = true;\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\n\t\t\tplane = clippingPlanes[ i ];\n\t\t\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t\tif ( clipped ) discard;\n\t#endif\n#endif",clipping_planes_pars_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\n#endif",clipping_planes_pars_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n#endif",clipping_planes_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvClipPosition = - mvPosition.xyz;\n#endif",color_fragment:"#if defined( USE_COLOR_ALPHA )\n\tdiffuseColor *= vColor;\n#elif defined( USE_COLOR )\n\tdiffuseColor.rgb *= vColor;\n#endif",color_pars_fragment:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR )\n\tvarying vec3 vColor;\n#endif",color_pars_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvarying vec3 vColor;\n#endif",color_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvColor = vec4( 1.0 );\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvColor = vec3( 1.0 );\n#endif\n#ifdef USE_COLOR\n\tvColor *= color;\n#endif\n#ifdef USE_INSTANCING_COLOR\n\tvColor.xyz *= instanceColor.xyz;\n#endif",common:"#define PI 3.141592653589793\n#define PI2 6.283185307179586\n#define PI_HALF 1.5707963267948966\n#define RECIPROCAL_PI 0.3183098861837907\n#define RECIPROCAL_PI2 0.15915494309189535\n#define EPSILON 1e-6\n#ifndef saturate\n#define saturate(a) clamp( a, 0.0, 1.0 )\n#endif\n#define whiteComplement(a) ( 1.0 - saturate( a ) )\nfloat pow2( const in float x ) { return x*x; }\nfloat pow3( const in float x ) { return x*x*x; }\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\nhighp float rand( const in vec2 uv ) {\n\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\n\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\n\treturn fract(sin(sn) * c);\n}\n#ifdef HIGH_PRECISION\n\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\n#else\n\tfloat max3( vec3 v ) { return max( max( v.x, v.y ), v.z ); }\n\tfloat precisionSafeLength( vec3 v ) {\n\t\tfloat maxComponent = max3( abs( v ) );\n\t\treturn length( v / maxComponent ) * maxComponent;\n\t}\n#endif\nstruct IncidentLight {\n\tvec3 color;\n\tvec3 direction;\n\tbool visible;\n};\nstruct ReflectedLight {\n\tvec3 directDiffuse;\n\tvec3 directSpecular;\n\tvec3 indirectDiffuse;\n\tvec3 indirectSpecular;\n};\nstruct GeometricContext {\n\tvec3 position;\n\tvec3 normal;\n\tvec3 viewDir;\n#ifdef CLEARCOAT\n\tvec3 clearcoatNormal;\n#endif\n};\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n}\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\n}\nvec3 projectOnPlane(in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\tfloat distance = dot( planeNormal, point - pointOnPlane );\n\treturn - distance * planeNormal + point;\n}\nfloat sideOfPlane( in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn sign( dot( point - pointOnPlane, planeNormal ) );\n}\nvec3 linePlaneIntersect( in vec3 pointOnLine, in vec3 lineDirection, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn lineDirection * ( dot( planeNormal, pointOnPlane - pointOnLine ) / dot( planeNormal, lineDirection ) ) + pointOnLine;\n}\nmat3 transposeMat3( const in mat3 m ) {\n\tmat3 tmp;\n\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\n\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\n\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\n\treturn tmp;\n}\nfloat linearToRelativeLuminance( const in vec3 color ) {\n\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\n\treturn dot( weights, color.rgb );\n}\nbool isPerspectiveMatrix( mat4 m ) {\n\treturn m[ 2 ][ 3 ] == - 1.0;\n}\nvec2 equirectUv( in vec3 dir ) {\n\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\n\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\treturn vec2( u, v );\n}",cube_uv_reflection_fragment:"#ifdef ENVMAP_TYPE_CUBE_UV\n\t#define cubeUV_maxMipLevel 8.0\n\t#define cubeUV_minMipLevel 4.0\n\t#define cubeUV_maxTileSize 256.0\n\t#define cubeUV_minTileSize 16.0\n\tfloat getFace( vec3 direction ) {\n\t\tvec3 absDirection = abs( direction );\n\t\tfloat face = - 1.0;\n\t\tif ( absDirection.x > absDirection.z ) {\n\t\t\tif ( absDirection.x > absDirection.y )\n\t\t\t\tface = direction.x > 0.0 ? 0.0 : 3.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t} else {\n\t\t\tif ( absDirection.z > absDirection.y )\n\t\t\t\tface = direction.z > 0.0 ? 2.0 : 5.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t}\n\t\treturn face;\n\t}\n\tvec2 getUV( vec3 direction, float face ) {\n\t\tvec2 uv;\n\t\tif ( face == 0.0 ) {\n\t\t\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 1.0 ) {\n\t\t\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\n\t\t} else if ( face == 2.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\n\t\t} else if ( face == 3.0 ) {\n\t\t\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 4.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\n\t\t} else {\n\t\t\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\n\t\t}\n\t\treturn 0.5 * ( uv + 1.0 );\n\t}\n\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\n\t\tfloat face = getFace( direction );\n\t\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\n\t\tmipInt = max( mipInt, cubeUV_minMipLevel );\n\t\tfloat faceSize = exp2( mipInt );\n\t\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\n\t\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\n\t\tvec2 f = fract( uv );\n\t\tuv += 0.5 - f;\n\t\tif ( face > 2.0 ) {\n\t\t\tuv.y += faceSize;\n\t\t\tface -= 3.0;\n\t\t}\n\t\tuv.x += face * faceSize;\n\t\tif ( mipInt < cubeUV_maxMipLevel ) {\n\t\t\tuv.y += 2.0 * cubeUV_maxTileSize;\n\t\t}\n\t\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\n\t\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\n\t\tuv *= texelSize;\n\t\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.x += texelSize;\n\t\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.y += texelSize;\n\t\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.x -= texelSize;\n\t\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tvec3 tm = mix( tl, tr, f.x );\n\t\tvec3 bm = mix( bl, br, f.x );\n\t\treturn mix( tm, bm, f.y );\n\t}\n\t#define r0 1.0\n\t#define v0 0.339\n\t#define m0 - 2.0\n\t#define r1 0.8\n\t#define v1 0.276\n\t#define m1 - 1.0\n\t#define r4 0.4\n\t#define v4 0.046\n\t#define m4 2.0\n\t#define r5 0.305\n\t#define v5 0.016\n\t#define m5 3.0\n\t#define r6 0.21\n\t#define v6 0.0038\n\t#define m6 4.0\n\tfloat roughnessToMip( float roughness ) {\n\t\tfloat mip = 0.0;\n\t\tif ( roughness >= r1 ) {\n\t\t\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\n\t\t} else if ( roughness >= r4 ) {\n\t\t\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\n\t\t} else if ( roughness >= r5 ) {\n\t\t\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\n\t\t} else if ( roughness >= r6 ) {\n\t\t\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\n\t\t} else {\n\t\t\tmip = - 2.0 * log2( 1.16 * roughness );\t\t}\n\t\treturn mip;\n\t}\n\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\n\t\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\n\t\tfloat mipF = fract( mip );\n\t\tfloat mipInt = floor( mip );\n\t\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\n\t\tif ( mipF == 0.0 ) {\n\t\t\treturn vec4( color0, 1.0 );\n\t\t} else {\n\t\t\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\n\t\t\treturn vec4( mix( color0, color1, mipF ), 1.0 );\n\t\t}\n\t}\n#endif",defaultnormal_vertex:"vec3 transformedNormal = objectNormal;\n#ifdef USE_INSTANCING\n\tmat3 m = mat3( instanceMatrix );\n\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\n\ttransformedNormal = m * transformedNormal;\n#endif\ntransformedNormal = normalMatrix * transformedNormal;\n#ifdef FLIP_SIDED\n\ttransformedNormal = - transformedNormal;\n#endif\n#ifdef USE_TANGENT\n\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#ifdef FLIP_SIDED\n\t\ttransformedTangent = - transformedTangent;\n\t#endif\n#endif",displacementmap_pars_vertex:"#ifdef USE_DISPLACEMENTMAP\n\tuniform sampler2D displacementMap;\n\tuniform float displacementScale;\n\tuniform float displacementBias;\n#endif",displacementmap_vertex:"#ifdef USE_DISPLACEMENTMAP\n\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\n#endif",emissivemap_fragment:"#ifdef USE_EMISSIVEMAP\n\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\n\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\n\ttotalEmissiveRadiance *= emissiveColor.rgb;\n#endif",emissivemap_pars_fragment:"#ifdef USE_EMISSIVEMAP\n\tuniform sampler2D emissiveMap;\n#endif",encodings_fragment:"gl_FragColor = linearToOutputTexel( gl_FragColor );",encodings_pars_fragment:"\nvec4 LinearToLinear( in vec4 value ) {\n\treturn value;\n}\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\n}\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\n}\nvec4 sRGBToLinear( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\n}\nvec4 LinearTosRGB( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\n}\nvec4 RGBEToLinear( in vec4 value ) {\n\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\n}\nvec4 LinearToRGBE( in vec4 value ) {\n\tfloat maxComponent = max( max( value.r, value.g ), value.b );\n\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\n\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\n}\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\n}\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\n\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\n\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\n\tM = ceil( M * 255.0 ) / 255.0;\n\treturn vec4( value.rgb / ( M * maxRange ), M );\n}\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\n}\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\n\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\n\tfloat D = max( maxRange / maxRGB, 1.0 );\n\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\n\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\n}\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\nvec4 LinearToLogLuv( in vec4 value ) {\n\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\n\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\n\tvec4 vResult;\n\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\n\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\n\tvResult.w = fract( Le );\n\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\n\treturn vResult;\n}\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\nvec4 LogLuvToLinear( in vec4 value ) {\n\tfloat Le = value.z * 255.0 + value.w;\n\tvec3 Xp_Y_XYZp;\n\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\n\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\n\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\n\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\n\treturn vec4( max( vRGB, 0.0 ), 1.0 );\n}",envmap_fragment:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvec3 cameraToFrag;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\n\t\t#endif\n\t#else\n\t\tvec3 reflectVec = vReflect;\n\t#endif\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\n\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\n\t#else\n\t\tvec4 envColor = vec4( 0.0 );\n\t#endif\n\t#ifndef ENVMAP_TYPE_CUBE_UV\n\t\tenvColor = envMapTexelToLinear( envColor );\n\t#endif\n\t#ifdef ENVMAP_BLENDING_MULTIPLY\n\t\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_MIX )\n\t\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_ADD )\n\t\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\n\t#endif\n#endif",envmap_common_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float envMapIntensity;\n\tuniform float flipEnvMap;\n\tuniform int maxMipLevel;\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tuniform samplerCube envMap;\n\t#else\n\t\tuniform sampler2D envMap;\n\t#endif\n\t\n#endif",envmap_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float reflectivity;\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\tvarying vec3 vWorldPosition;\n\t\tuniform float refractionRatio;\n\t#else\n\t\tvarying vec3 vReflect;\n\t#endif\n#endif",envmap_pars_vertex:"#ifdef USE_ENVMAP\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\t\n\t\tvarying vec3 vWorldPosition;\n\t#else\n\t\tvarying vec3 vReflect;\n\t\tuniform float refractionRatio;\n\t#endif\n#endif",envmap_physical_pars_fragment:"#if defined( USE_ENVMAP )\n\t#ifdef ENVMAP_MODE_REFRACTION\n\t\tuniform float refractionRatio;\n\t#endif\n\tvec3 getLightProbeIndirectIrradiance( const in GeometricContext geometry, const in int maxMIPLevel ) {\n\t\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryVec = vec3( flipEnvMap * worldNormal.x, worldNormal.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor = textureCubeLodEXT( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor = textureCube( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\n\t\t#else\n\t\t\tvec4 envMapColor = vec4( 0.0 );\n\t\t#endif\n\t\treturn PI * envMapColor.rgb * envMapIntensity;\n\t}\n\tfloat getSpecularMIPLevel( const in float roughness, const in int maxMIPLevel ) {\n\t\tfloat maxMIPLevelScalar = float( maxMIPLevel );\n\t\tfloat sigma = PI * roughness * roughness / ( 1.0 + roughness );\n\t\tfloat desiredMIPLevel = maxMIPLevelScalar + log2( sigma );\n\t\treturn clamp( desiredMIPLevel, 0.0, maxMIPLevelScalar );\n\t}\n\tvec3 getLightProbeIndirectRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness, const in int maxMIPLevel ) {\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( -viewDir, normal );\n\t\t\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( -viewDir, normal, refractionRatio );\n\t\t#endif\n\t\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\n\t\tfloat specularMIPLevel = getSpecularMIPLevel( roughness, maxMIPLevel );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryReflectVec = vec3( flipEnvMap * reflectVec.x, reflectVec.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor = textureCubeLodEXT( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor = textureCube( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\n\t\t#endif\n\t\treturn envMapColor.rgb * envMapIntensity;\n\t}\n#endif",envmap_vertex:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvWorldPosition = worldPosition.xyz;\n\t#else\n\t\tvec3 cameraToVertex;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvReflect = reflect( cameraToVertex, worldNormal );\n\t\t#else\n\t\t\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\n\t\t#endif\n\t#endif\n#endif",fog_vertex:"#ifdef USE_FOG\n\tfogDepth = - mvPosition.z;\n#endif",fog_pars_vertex:"#ifdef USE_FOG\n\tvarying float fogDepth;\n#endif",fog_fragment:"#ifdef USE_FOG\n\t#ifdef FOG_EXP2\n\t\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * fogDepth * fogDepth );\n\t#else\n\t\tfloat fogFactor = smoothstep( fogNear, fogFar, fogDepth );\n\t#endif\n\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\n#endif",fog_pars_fragment:"#ifdef USE_FOG\n\tuniform vec3 fogColor;\n\tvarying float fogDepth;\n\t#ifdef FOG_EXP2\n\t\tuniform float fogDensity;\n\t#else\n\t\tuniform float fogNear;\n\t\tuniform float fogFar;\n\t#endif\n#endif",gradientmap_pars_fragment:"#ifdef USE_GRADIENTMAP\n\tuniform sampler2D gradientMap;\n#endif\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\n\tfloat dotNL = dot( normal, lightDirection );\n\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\n\t#ifdef USE_GRADIENTMAP\n\t\treturn texture2D( gradientMap, coord ).rgb;\n\t#else\n\t\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\n\t#endif\n}",lightmap_fragment:"#ifdef USE_LIGHTMAP\n\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\treflectedLight.indirectDiffuse += PI * lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n#endif",lightmap_pars_fragment:"#ifdef USE_LIGHTMAP\n\tuniform sampler2D lightMap;\n\tuniform float lightMapIntensity;\n#endif",lights_lambert_vertex:"vec3 diffuse = vec3( 1.0 );\nGeometricContext geometry;\ngeometry.position = mvPosition.xyz;\ngeometry.normal = normalize( transformedNormal );\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\nGeometricContext backGeometry;\nbackGeometry.position = geometry.position;\nbackGeometry.normal = -geometry.normal;\nbackGeometry.viewDir = geometry.viewDir;\nvLightFront = vec3( 0.0 );\nvIndirectFront = vec3( 0.0 );\n#ifdef DOUBLE_SIDED\n\tvLightBack = vec3( 0.0 );\n\tvIndirectBack = vec3( 0.0 );\n#endif\nIncidentLight directLight;\nfloat dotNL;\nvec3 directLightColor_Diffuse;\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry );\n#ifdef DOUBLE_SIDED\n\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\n\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry );\n#endif\n#if NUM_POINT_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tgetPointDirectLightIrradiance( pointLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tgetSpotDirectLightIrradiance( spotLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_DIR_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tgetDirectionalDirectLightIrradiance( directionalLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry );\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif",lights_pars_begin:"uniform bool receiveShadow;\nuniform vec3 ambientLightColor;\nuniform vec3 lightProbe[ 9 ];\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\n\tfloat x = normal.x, y = normal.y, z = normal.z;\n\tvec3 result = shCoefficients[ 0 ] * 0.886227;\n\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\n\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\n\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\n\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\n\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\n\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\n\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\n\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\n\treturn result;\n}\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in GeometricContext geometry ) {\n\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\n\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\n\treturn irradiance;\n}\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\n\tvec3 irradiance = ambientLightColor;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treturn irradiance;\n}\n#if NUM_DIR_LIGHTS > 0\n\tstruct DirectionalLight {\n\t\tvec3 direction;\n\t\tvec3 color;\n\t};\n\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n\tvoid getDirectionalDirectLightIrradiance( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tdirectLight.color = directionalLight.color;\n\t\tdirectLight.direction = directionalLight.direction;\n\t\tdirectLight.visible = true;\n\t}\n#endif\n#if NUM_POINT_LIGHTS > 0\n\tstruct PointLight {\n\t\tvec3 position;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t};\n\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n\tvoid getPointDirectLightIrradiance( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tvec3 lVector = pointLight.position - geometry.position;\n\t\tdirectLight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tdirectLight.color = pointLight.color;\n\t\tdirectLight.color *= punctualLightIntensityToIrradianceFactor( lightDistance, pointLight.distance, pointLight.decay );\n\t\tdirectLight.visible = ( directLight.color != vec3( 0.0 ) );\n\t}\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\tstruct SpotLight {\n\t\tvec3 position;\n\t\tvec3 direction;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t\tfloat coneCos;\n\t\tfloat penumbraCos;\n\t};\n\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\n\tvoid getSpotDirectLightIrradiance( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tvec3 lVector = spotLight.position - geometry.position;\n\t\tdirectLight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tfloat angleCos = dot( directLight.direction, spotLight.direction );\n\t\tif ( angleCos > spotLight.coneCos ) {\n\t\t\tfloat spotEffect = smoothstep( spotLight.coneCos, spotLight.penumbraCos, angleCos );\n\t\t\tdirectLight.color = spotLight.color;\n\t\t\tdirectLight.color *= spotEffect * punctualLightIntensityToIrradianceFactor( lightDistance, spotLight.distance, spotLight.decay );\n\t\t\tdirectLight.visible = true;\n\t\t} else {\n\t\t\tdirectLight.color = vec3( 0.0 );\n\t\t\tdirectLight.visible = false;\n\t\t}\n\t}\n#endif\n#if NUM_RECT_AREA_LIGHTS > 0\n\tstruct RectAreaLight {\n\t\tvec3 color;\n\t\tvec3 position;\n\t\tvec3 halfWidth;\n\t\tvec3 halfHeight;\n\t};\n\tuniform sampler2D ltc_1;\tuniform sampler2D ltc_2;\n\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\tstruct HemisphereLight {\n\t\tvec3 direction;\n\t\tvec3 skyColor;\n\t\tvec3 groundColor;\n\t};\n\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\n\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in GeometricContext geometry ) {\n\t\tfloat dotNL = dot( geometry.normal, hemiLight.direction );\n\t\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\n\t\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tirradiance *= PI;\n\t\t#endif\n\t\treturn irradiance;\n\t}\n#endif",lights_toon_fragment:"ToonMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;",lights_toon_pars_fragment:"varying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\nstruct ToonMaterial {\n\tvec3 diffuseColor;\n};\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_Toon\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Toon\n#define Material_LightProbeLOD( material )\t(0)",lights_phong_fragment:"BlinnPhongMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;\nmaterial.specularColor = specular;\nmaterial.specularShininess = shininess;\nmaterial.specularStrength = specularStrength;",lights_phong_pars_fragment:"varying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\nstruct BlinnPhongMaterial {\n\tvec3 diffuseColor;\n\tvec3 specularColor;\n\tfloat specularShininess;\n\tfloat specularStrength;\n};\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n\treflectedLight.directSpecular += irradiance * BRDF_Specular_BlinnPhong( directLight, geometry, material.specularColor, material.specularShininess ) * material.specularStrength;\n}\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_BlinnPhong\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_BlinnPhong\n#define Material_LightProbeLOD( material )\t(0)",lights_physical_fragment:"PhysicalMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\nmaterial.specularRoughness = max( roughnessFactor, 0.0525 );material.specularRoughness += geometryRoughness;\nmaterial.specularRoughness = min( material.specularRoughness, 1.0 );\n#ifdef REFLECTIVITY\n\t#ifdef SPECULAR\n\t\tvec3 specularIntensityFactor = vec3( specularIntensity );\n\t\tvec3 specularTintFactor = specularTint;\n\t\t#ifdef USE_SPECULARINTENSITYMAP\n\t\t\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\n\t\t#endif\n\t\t#ifdef USE_SPECULARTINTMAP\n\t\t\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\n\t\t#endif\n\t\tmaterial.specularColorF90 = mix( specularIntensityFactor, vec3( 1.0 ), metalnessFactor );\n\t#else\n\t\tvec3 specularIntensityFactor = vec3( 1.0 );\n\t\tvec3 specularTintFactor = vec3( 1.0 );\n\t\tmaterial.specularColorF90 = vec3( 1.0 );\n\t#endif\n\tmaterial.specularColor = mix( min( vec3( MAXIMUM_SPECULAR_COEFFICIENT * pow2( reflectivity ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\n#else\n\tmaterial.specularColor = mix( vec3( DEFAULT_SPECULAR_COEFFICIENT ), diffuseColor.rgb, metalnessFactor );\n\tmaterial.specularColorF90 = vec3( 1.0 );\n#endif\n#ifdef CLEARCOAT\n\tmaterial.clearcoat = clearcoat;\n\tmaterial.clearcoatRoughness = clearcoatRoughness;\n\t#ifdef USE_CLEARCOATMAP\n\t\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\n\t#endif\n\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\t\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\n\t#endif\n\tmaterial.clearcoat = saturate( material.clearcoat );\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\n\tmaterial.clearcoatRoughness += geometryRoughness;\n\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\n#endif\n#ifdef USE_SHEEN\n\tmaterial.sheenColor = sheen;\n#endif",lights_physical_pars_fragment:"struct PhysicalMaterial {\n\tvec3 diffuseColor;\n\tfloat specularRoughness;\n\tvec3 specularColor;\n\tvec3 specularColorF90;\n#ifdef CLEARCOAT\n\tfloat clearcoat;\n\tfloat clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tvec3 sheenColor;\n#endif\n};\n#define MAXIMUM_SPECULAR_COEFFICIENT 0.16\n#define DEFAULT_SPECULAR_COEFFICIENT 0.04\nfloat clearcoatDHRApprox( const in float roughness, const in float dotNL ) {\n\treturn DEFAULT_SPECULAR_COEFFICIENT + ( 1.0 - DEFAULT_SPECULAR_COEFFICIENT ) * ( pow( 1.0 - dotNL, 5.0 ) * pow( 1.0 - roughness, 2.0 ) );\n}\n#if NUM_RECT_AREA_LIGHTS > 0\n\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\t\tvec3 normal = geometry.normal;\n\t\tvec3 viewDir = geometry.viewDir;\n\t\tvec3 position = geometry.position;\n\t\tvec3 lightPos = rectAreaLight.position;\n\t\tvec3 halfWidth = rectAreaLight.halfWidth;\n\t\tvec3 halfHeight = rectAreaLight.halfHeight;\n\t\tvec3 lightColor = rectAreaLight.color;\n\t\tfloat roughness = material.specularRoughness;\n\t\tvec3 rectCoords[ 4 ];\n\t\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\t\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\n\t\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\n\t\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\n\t\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\n\t\tvec4 t1 = texture2D( ltc_1, uv );\n\t\tvec4 t2 = texture2D( ltc_2, uv );\n\t\tmat3 mInv = mat3(\n\t\t\tvec3( t1.x, 0, t1.y ),\n\t\t\tvec3(    0, 1,    0 ),\n\t\t\tvec3( t1.z, 0, t1.w )\n\t\t);\n\t\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\n\t\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\n\t\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\n\t}\n#endif\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNL = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\n\t\tvec3 ccIrradiance = ccDotNL * directLight.color;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tccIrradiance *= PI;\n\t\t#endif\n\t\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t\treflectedLight.directSpecular += ccIrradiance * material.clearcoat * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), vec3( 1.0 ), material.clearcoatRoughness );\n\t#else\n\t\tfloat clearcoatDHR = 0.0;\n\t#endif\n\t#ifdef USE_SHEEN\n\t\treflectedLight.directSpecular += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_Sheen(\n\t\t\tmaterial.specularRoughness,\n\t\t\tdirectLight.direction,\n\t\t\tgeometry,\n\t\t\tmaterial.sheenColor\n\t\t);\n\t#else\n\t\treflectedLight.directSpecular += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.normal, material.specularColor, material.specularColorF90, material.specularRoughness);\n\t#endif\n\treflectedLight.directDiffuse += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNV = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular += clearcoatRadiance * material.clearcoat * BRDF_Specular_GGX_Environment( geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearcoatRoughness );\n\t\tfloat ccDotNL = ccDotNV;\n\t\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t#else\n\t\tfloat clearcoatDHR = 0.0;\n\t#endif\n\tfloat clearcoatInv = 1.0 - clearcoatDHR;\n\tvec3 singleScattering = vec3( 0.0 );\n\tvec3 multiScattering = vec3( 0.0 );\n\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\n\tBRDF_Specular_Multiscattering_Environment( geometry, material.specularColor, material.specularRoughness, singleScattering, multiScattering );\n\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\n\treflectedLight.indirectSpecular += clearcoatInv * radiance * singleScattering;\n\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\n\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\n}\n#define RE_Direct\t\t\t\tRE_Direct_Physical\n#define RE_Direct_RectArea\t\tRE_Direct_RectArea_Physical\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Physical\n#define RE_IndirectSpecular\t\tRE_IndirectSpecular_Physical\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\n\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\n}",lights_fragment_begin:"\nGeometricContext geometry;\ngeometry.position = - vViewPosition;\ngeometry.normal = normal;\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\n#ifdef CLEARCOAT\n\tgeometry.clearcoatNormal = clearcoatNormal;\n#endif\nIncidentLight directLight;\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\n\tPointLight pointLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tpointLight = pointLights[ i ];\n\t\tgetPointDirectLightIrradiance( pointLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\n\t\tpointLightShadow = pointLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\n\tSpotLight spotLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tspotLight = spotLights[ i ];\n\t\tgetSpotDirectLightIrradiance( spotLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\tspotLightShadow = spotLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\n\tDirectionalLight directionalLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tdirectionalLight = directionalLights[ i ];\n\t\tgetDirectionalDirectLightIrradiance( directionalLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\n\t\tdirectionalLightShadow = directionalLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\n\tRectAreaLight rectAreaLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\n\t\trectAreaLight = rectAreaLights[ i ];\n\t\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if defined( RE_IndirectDiffuse )\n\tvec3 iblIrradiance = vec3( 0.0 );\n\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\n\tirradiance += getLightProbeIrradiance( lightProbe, geometry );\n\t#if ( NUM_HEMI_LIGHTS > 0 )\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\t\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n#endif\n#if defined( RE_IndirectSpecular )\n\tvec3 radiance = vec3( 0.0 );\n\tvec3 clearcoatRadiance = vec3( 0.0 );\n#endif",lights_fragment_maps:"#if defined( RE_IndirectDiffuse )\n\t#ifdef USE_LIGHTMAP\n\t\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\t\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tlightMapIrradiance *= PI;\n\t\t#endif\n\t\tirradiance += lightMapIrradiance;\n\t#endif\n\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\n\t\tiblIrradiance += getLightProbeIndirectIrradiance( geometry, maxMipLevel );\n\t#endif\n#endif\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\n\tradiance += getLightProbeIndirectRadiance( geometry.viewDir, geometry.normal, material.specularRoughness, maxMipLevel );\n\t#ifdef CLEARCOAT\n\t\tclearcoatRadiance += getLightProbeIndirectRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness, maxMipLevel );\n\t#endif\n#endif",lights_fragment_end:"#if defined( RE_IndirectDiffuse )\n\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\n#endif\n#if defined( RE_IndirectSpecular )\n\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\n#endif",logdepthbuf_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\n#endif",logdepthbuf_pars_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tuniform float logDepthBufFC;\n\tvarying float vFragDepth;\n\tvarying float vIsPerspective;\n#endif",logdepthbuf_pars_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvarying float vFragDepth;\n\t\tvarying float vIsPerspective;\n\t#else\n\t\tuniform float logDepthBufFC;\n\t#endif\n#endif",logdepthbuf_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvFragDepth = 1.0 + gl_Position.w;\n\t\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\n\t#else\n\t\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\n\t\t\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\n\t\t\tgl_Position.z *= gl_Position.w;\n\t\t}\n\t#endif\n#endif",map_fragment:"#ifdef USE_MAP\n\tvec4 texelColor = texture2D( map, vUv );\n\ttexelColor = mapTexelToLinear( texelColor );\n\tdiffuseColor *= texelColor;\n#endif",map_pars_fragment:"#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif",map_particle_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\n#endif\n#ifdef USE_MAP\n\tvec4 mapTexel = texture2D( map, uv );\n\tdiffuseColor *= mapTexelToLinear( mapTexel );\n#endif\n#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\n#endif",map_particle_pars_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tuniform mat3 uvTransform;\n#endif\n#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif\n#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",metalnessmap_fragment:"float metalnessFactor = metalness;\n#ifdef USE_METALNESSMAP\n\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\n\tmetalnessFactor *= texelMetalness.b;\n#endif",metalnessmap_pars_fragment:"#ifdef USE_METALNESSMAP\n\tuniform sampler2D metalnessMap;\n#endif",morphnormal_vertex:"#ifdef USE_MORPHNORMALS\n\tobjectNormal *= morphTargetBaseInfluence;\n\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\n\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\n\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\n\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\n#endif",morphtarget_pars_vertex:"#ifdef USE_MORPHTARGETS\n\tuniform float morphTargetBaseInfluence;\n\t#ifndef USE_MORPHNORMALS\n\t\tuniform float morphTargetInfluences[ 8 ];\n\t#else\n\t\tuniform float morphTargetInfluences[ 4 ];\n\t#endif\n#endif",morphtarget_vertex:"#ifdef USE_MORPHTARGETS\n\ttransformed *= morphTargetBaseInfluence;\n\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\n\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\n\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\n\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\n\t#ifndef USE_MORPHNORMALS\n\t\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\n\t\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\n\t\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\n\t\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\n\t#endif\n#endif",normal_fragment_begin:"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\n#ifdef FLAT_SHADED\n\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\n\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\n\tvec3 normal = normalize( cross( fdx, fdy ) );\n#else\n\tvec3 normal = normalize( vNormal );\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\t#ifdef USE_TANGENT\n\t\tvec3 tangent = normalize( vTangent );\n\t\tvec3 bitangent = normalize( vBitangent );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\ttangent = tangent * faceDirection;\n\t\t\tbitangent = bitangent * faceDirection;\n\t\t#endif\n\t\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\n\t\t\tmat3 vTBN = mat3( tangent, bitangent, normal );\n\t\t#endif\n\t#endif\n#endif\nvec3 geometryNormal = normal;",normal_fragment_maps:"#ifdef OBJECTSPACE_NORMALMAP\n\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t#ifdef FLIP_SIDED\n\t\tnormal = - normal;\n\t#endif\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\tnormal = normalize( normalMatrix * normal );\n#elif defined( TANGENTSPACE_NORMALMAP )\n\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\tmapN.xy *= normalScale;\n\t#ifdef USE_TANGENT\n\t\tnormal = normalize( vTBN * mapN );\n\t#else\n\t\tnormal = perturbNormal2Arb( -vViewPosition, normal, mapN, faceDirection );\n\t#endif\n#elif defined( USE_BUMPMAP )\n\tnormal = perturbNormalArb( -vViewPosition, normal, dHdxy_fwd(), faceDirection );\n#endif",normalmap_pars_fragment:"#ifdef USE_NORMALMAP\n\tuniform sampler2D normalMap;\n\tuniform vec2 normalScale;\n#endif\n#ifdef OBJECTSPACE_NORMALMAP\n\tuniform mat3 normalMatrix;\n#endif\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\n\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\n\t\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\n\t\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\n\t\tvec2 st0 = dFdx( vUv.st );\n\t\tvec2 st1 = dFdy( vUv.st );\n\t\tvec3 N = surf_norm;\n\t\tvec3 q1perp = cross( q1, N );\n\t\tvec3 q0perp = cross( N, q0 );\n\t\tvec3 T = q1perp * st0.x + q0perp * st1.x;\n\t\tvec3 B = q1perp * st0.y + q0perp * st1.y;\n\t\tfloat det = max( dot( T, T ), dot( B, B ) );\n\t\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\n\t\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\n\t}\n#endif",clearcoat_normal_fragment_begin:"#ifdef CLEARCOAT\n\tvec3 clearcoatNormal = geometryNormal;\n#endif",clearcoat_normal_fragment_maps:"#ifdef USE_CLEARCOAT_NORMALMAP\n\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\n\tclearcoatMapN.xy *= clearcoatNormalScale;\n\t#ifdef USE_TANGENT\n\t\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\n\t#else\n\t\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\n\t#endif\n#endif",clearcoat_pars_fragment:"#ifdef USE_CLEARCOATMAP\n\tuniform sampler2D clearcoatMap;\n#endif\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\tuniform sampler2D clearcoatRoughnessMap;\n#endif\n#ifdef USE_CLEARCOAT_NORMALMAP\n\tuniform sampler2D clearcoatNormalMap;\n\tuniform vec2 clearcoatNormalScale;\n#endif",packing:"vec3 packNormalToRGB( const in vec3 normal ) {\n\treturn normalize( normal ) * 0.5 + 0.5;\n}\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\n\treturn 2.0 * rgb.xyz - 1.0;\n}\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\nconst float ShiftRight8 = 1. / 256.;\nvec4 packDepthToRGBA( const in float v ) {\n\tvec4 r = vec4( fract( v * PackFactors ), v );\n\tr.yzw -= r.xyz * ShiftRight8;\treturn r * PackUpscale;\n}\nfloat unpackRGBAToDepth( const in vec4 v ) {\n\treturn dot( v, UnpackFactors );\n}\nvec4 pack2HalfToRGBA( vec2 v ) {\n\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ));\n\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w);\n}\nvec2 unpackRGBATo2Half( vec4 v ) {\n\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\n}\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn ( viewZ + near ) / ( near - far );\n}\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\n\treturn linearClipZ * ( near - far ) - near;\n}\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn (( near + viewZ ) * far ) / (( far - near ) * viewZ );\n}\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\n\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\n}",premultiplied_alpha_fragment:"#ifdef PREMULTIPLIED_ALPHA\n\tgl_FragColor.rgb *= gl_FragColor.a;\n#endif",project_vertex:"vec4 mvPosition = vec4( transformed, 1.0 );\n#ifdef USE_INSTANCING\n\tmvPosition = instanceMatrix * mvPosition;\n#endif\nmvPosition = modelViewMatrix * mvPosition;\ngl_Position = projectionMatrix * mvPosition;",dithering_fragment:"#ifdef DITHERING\n\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\n#endif",dithering_pars_fragment:"#ifdef DITHERING\n\tvec3 dithering( vec3 color ) {\n\t\tfloat grid_position = rand( gl_FragCoord.xy );\n\t\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\n\t\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\n\t\treturn color + dither_shift_RGB;\n\t}\n#endif",roughnessmap_fragment:"float roughnessFactor = roughness;\n#ifdef USE_ROUGHNESSMAP\n\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\n\troughnessFactor *= texelRoughness.g;\n#endif",roughnessmap_pars_fragment:"#ifdef USE_ROUGHNESSMAP\n\tuniform sampler2D roughnessMap;\n#endif",shadowmap_pars_fragment:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\n\t\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\n\t}\n\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\n\t\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\n\t}\n\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\n\t\tfloat occlusion = 1.0;\n\t\tvec2 distribution = texture2DDistribution( shadow, uv );\n\t\tfloat hard_shadow = step( compare , distribution.x );\n\t\tif (hard_shadow != 1.0 ) {\n\t\t\tfloat distance = compare - distribution.x ;\n\t\t\tfloat variance = max( 0.00000, distribution.y * distribution.y );\n\t\t\tfloat softness_probability = variance / (variance + distance * distance );\t\t\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\t\t\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\n\t\t}\n\t\treturn occlusion;\n\t}\n\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\n\t\tfloat shadow = 1.0;\n\t\tshadowCoord.xyz /= shadowCoord.w;\n\t\tshadowCoord.z += shadowBias;\n\t\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\n\t\tbool inFrustum = all( inFrustumVec );\n\t\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\n\t\tbool frustumTest = all( frustumTestVec );\n\t\tif ( frustumTest ) {\n\t\t#if defined( SHADOWMAP_TYPE_PCF )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx0 = - texelSize.x * shadowRadius;\n\t\t\tfloat dy0 = - texelSize.y * shadowRadius;\n\t\t\tfloat dx1 = + texelSize.x * shadowRadius;\n\t\t\tfloat dy1 = + texelSize.y * shadowRadius;\n\t\t\tfloat dx2 = dx0 / 2.0;\n\t\t\tfloat dy2 = dy0 / 2.0;\n\t\t\tfloat dx3 = dx1 / 2.0;\n\t\t\tfloat dy3 = dy1 / 2.0;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\n\t\t\t) * ( 1.0 / 17.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx = texelSize.x;\n\t\t\tfloat dy = texelSize.y;\n\t\t\tvec2 uv = shadowCoord.xy;\n\t\t\tvec2 f = fract( uv * shadowMapSize + 0.5 );\n\t\t\tuv -= f * texelSize;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t f.y )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_VSM )\n\t\t\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#else\n\t\t\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#endif\n\t\t}\n\t\treturn shadow;\n\t}\n\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\n\t\tvec3 absV = abs( v );\n\t\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\n\t\tabsV *= scaleToCube;\n\t\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\n\t\tvec2 planar = v.xy;\n\t\tfloat almostATexel = 1.5 * texelSizeY;\n\t\tfloat almostOne = 1.0 - almostATexel;\n\t\tif ( absV.z >= almostOne ) {\n\t\t\tif ( v.z > 0.0 )\n\t\t\t\tplanar.x = 4.0 - v.x;\n\t\t} else if ( absV.x >= almostOne ) {\n\t\t\tfloat signX = sign( v.x );\n\t\t\tplanar.x = v.z * signX + 2.0 * signX;\n\t\t} else if ( absV.y >= almostOne ) {\n\t\t\tfloat signY = sign( v.y );\n\t\t\tplanar.x = v.x + 2.0 * signY + 2.0;\n\t\t\tplanar.y = v.z * signY - 2.0;\n\t\t}\n\t\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\n\t}\n\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\n\t\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\n\t\tvec3 lightToPosition = shadowCoord.xyz;\n\t\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\t\tdp += shadowBias;\n\t\tvec3 bd3D = normalize( lightToPosition );\n\t\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\n\t\t\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\n\t\t\treturn (\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#else\n\t\t\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\n\t\t#endif\n\t}\n#endif",shadowmap_pars_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n#endif",shadowmap_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\n\t\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\tvec4 shadowWorldPosition;\n\t#endif\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n#endif",shadowmask_pars_fragment:"float getShadowMask() {\n\tfloat shadow = 1.0;\n\t#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tdirectionalLight = directionalLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tspotLight = spotLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tpointLight = pointLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#endif\n\treturn shadow;\n}",skinbase_vertex:"#ifdef USE_SKINNING\n\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\n\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\n\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\n\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\n#endif",skinning_pars_vertex:"#ifdef USE_SKINNING\n\tuniform mat4 bindMatrix;\n\tuniform mat4 bindMatrixInverse;\n\t#ifdef BONE_TEXTURE\n\t\tuniform highp sampler2D boneTexture;\n\t\tuniform int boneTextureSize;\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tfloat j = i * 4.0;\n\t\t\tfloat x = mod( j, float( boneTextureSize ) );\n\t\t\tfloat y = floor( j / float( boneTextureSize ) );\n\t\t\tfloat dx = 1.0 / float( boneTextureSize );\n\t\t\tfloat dy = 1.0 / float( boneTextureSize );\n\t\t\ty = dy * ( y + 0.5 );\n\t\t\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\n\t\t\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\n\t\t\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\n\t\t\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\n\t\t\tmat4 bone = mat4( v1, v2, v3, v4 );\n\t\t\treturn bone;\n\t\t}\n\t#else\n\t\tuniform mat4 boneMatrices[ MAX_BONES ];\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tmat4 bone = boneMatrices[ int(i) ];\n\t\t\treturn bone;\n\t\t}\n\t#endif\n#endif",skinning_vertex:"#ifdef USE_SKINNING\n\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\n\tvec4 skinned = vec4( 0.0 );\n\tskinned += boneMatX * skinVertex * skinWeight.x;\n\tskinned += boneMatY * skinVertex * skinWeight.y;\n\tskinned += boneMatZ * skinVertex * skinWeight.z;\n\tskinned += boneMatW * skinVertex * skinWeight.w;\n\ttransformed = ( bindMatrixInverse * skinned ).xyz;\n#endif",skinnormal_vertex:"#ifdef USE_SKINNING\n\tmat4 skinMatrix = mat4( 0.0 );\n\tskinMatrix += skinWeight.x * boneMatX;\n\tskinMatrix += skinWeight.y * boneMatY;\n\tskinMatrix += skinWeight.z * boneMatZ;\n\tskinMatrix += skinWeight.w * boneMatW;\n\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\n\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\n\t#ifdef USE_TANGENT\n\t\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#endif\n#endif",specularmap_fragment:"float specularStrength;\n#ifdef USE_SPECULARMAP\n\tvec4 texelSpecular = texture2D( specularMap, vUv );\n\tspecularStrength = texelSpecular.r;\n#else\n\tspecularStrength = 1.0;\n#endif",specularmap_pars_fragment:"#ifdef USE_SPECULARMAP\n\tuniform sampler2D specularMap;\n#endif",tonemapping_fragment:"#if defined( TONE_MAPPING )\n\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\n#endif",tonemapping_pars_fragment:"#ifndef saturate\n#define saturate(a) clamp( a, 0.0, 1.0 )\n#endif\nuniform float toneMappingExposure;\nvec3 LinearToneMapping( vec3 color ) {\n\treturn toneMappingExposure * color;\n}\nvec3 ReinhardToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\treturn saturate( color / ( vec3( 1.0 ) + color ) );\n}\nvec3 OptimizedCineonToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\tcolor = max( vec3( 0.0 ), color - 0.004 );\n\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\n}\nvec3 RRTAndODTFit( vec3 v ) {\n\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\n\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\n\treturn a / b;\n}\nvec3 ACESFilmicToneMapping( vec3 color ) {\n\tconst mat3 ACESInputMat = mat3(\n\t\tvec3( 0.59719, 0.07600, 0.02840 ),\t\tvec3( 0.35458, 0.90834, 0.13383 ),\n\t\tvec3( 0.04823, 0.01566, 0.83777 )\n\t);\n\tconst mat3 ACESOutputMat = mat3(\n\t\tvec3(  1.60475, -0.10208, -0.00327 ),\t\tvec3( -0.53108,  1.10813, -0.07276 ),\n\t\tvec3( -0.07367, -0.00605,  1.07602 )\n\t);\n\tcolor *= toneMappingExposure / 0.6;\n\tcolor = ACESInputMat * color;\n\tcolor = RRTAndODTFit( color );\n\tcolor = ACESOutputMat * color;\n\treturn saturate( color );\n}\nvec3 CustomToneMapping( vec3 color ) { return color; }",transmission_fragment:"#ifdef USE_TRANSMISSION\n\tfloat transmissionFactor = transmission;\n\tfloat thicknessFactor = thickness;\n\t#ifdef USE_TRANSMISSIONMAP\n\t\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\n\t#endif\n\tvec3 pos = vWorldPosition.xyz / vWorldPosition.w;\n\tvec3 v = normalize( cameraPosition - pos );\n\tvec3 n = inverseTransformDirection( normal, viewMatrix );\n\tfloat ior = ( 1.0 + 0.4 * reflectivity ) / ( 1.0 - 0.4 * reflectivity );\n\tvec3 transmission = transmissionFactor * getIBLVolumeRefraction(\n\t\tn, v, roughnessFactor, material.diffuseColor, material.specularColor,\n\t\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\n\t\tattenuationTint, attenuationDistance );\n\ttotalDiffuse = mix( totalDiffuse, transmission, transmissionFactor );\n#endif",transmission_pars_fragment:"#ifdef USE_TRANSMISSION\n\t#ifdef USE_TRANSMISSIONMAP\n\t\tuniform sampler2D transmissionMap;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tuniform sampler2D thicknessMap;\n\t#endif\n\tuniform vec2 transmissionSamplerSize;\n\tuniform sampler2D transmissionSamplerMap;\n\tuniform mat4 modelMatrix;\n\tuniform mat4 projectionMatrix;\n\tvarying vec4 vWorldPosition;\n\tvec3 getVolumeTransmissionRay(vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix) {\n\t\tvec3 refractionVector = refract(-v, normalize(n), 1.0 / ior);\n\t\tvec3 modelScale;\n\t\tmodelScale.x = length(vec3(modelMatrix[0].xyz));\n\t\tmodelScale.y = length(vec3(modelMatrix[1].xyz));\n\t\tmodelScale.z = length(vec3(modelMatrix[2].xyz));\n\t\treturn normalize(refractionVector) * thickness * modelScale;\n\t}\n\tfloat applyIorToRoughness(float roughness, float ior) {\n\t\treturn roughness * clamp(ior * 2.0 - 2.0, 0.0, 1.0);\n\t}\n\tvec3 getTransmissionSample(vec2 fragCoord, float roughness, float ior) {\n\t\tfloat framebufferLod = log2(transmissionSamplerSize.x) * applyIorToRoughness(roughness, ior);\n\t\treturn texture2DLodEXT(transmissionSamplerMap, fragCoord.xy, framebufferLod).rgb;\n\t}\n\tvec3 applyVolumeAttenuation(vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance) {\n\t\tif (attenuationDistance == 0.0) {\n\t\t\treturn radiance;\n\t\t} else {\n\t\t\tvec3 attenuationCoefficient = -log(attenuationColor) / attenuationDistance;\n\t\t\tvec3 transmittance = exp(-attenuationCoefficient * transmissionDistance);\t\t\treturn transmittance * radiance;\n\t\t}\n\t}\n\tvec3 getIBLVolumeRefraction(vec3 n, vec3 v, float perceptualRoughness, vec3 baseColor, vec3 specularColor,\n\t\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\n\t\tvec3 attenuationColor, float attenuationDistance) {\n\t\tvec3 transmissionRay = getVolumeTransmissionRay(n, v, thickness, ior, modelMatrix);\n\t\tvec3 refractedRayExit = position + transmissionRay;\n\t\tvec4 ndcPos = projMatrix * viewMatrix * vec4(refractedRayExit, 1.0);\n\t\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\n\t\trefractionCoords += 1.0;\n\t\trefractionCoords /= 2.0;\n\t\tvec3 transmittedLight = getTransmissionSample(refractionCoords, perceptualRoughness, ior);\n\t\tvec3 attenuatedColor = applyVolumeAttenuation(transmittedLight, length(transmissionRay), attenuationColor, attenuationDistance);\n\t\treturn (1.0 - specularColor) * attenuatedColor * baseColor;\n\t}\n#endif",uv_pars_fragment:"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\n\tvarying vec2 vUv;\n#endif",uv_pars_vertex:"#ifdef USE_UV\n\t#ifdef UVS_VERTEX_ONLY\n\t\tvec2 vUv;\n\t#else\n\t\tvarying vec2 vUv;\n\t#endif\n\tuniform mat3 uvTransform;\n#endif",uv_vertex:"#ifdef USE_UV\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n#endif",uv2_pars_fragment:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvarying vec2 vUv2;\n#endif",uv2_pars_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tattribute vec2 uv2;\n\tvarying vec2 vUv2;\n\tuniform mat3 uv2Transform;\n#endif",uv2_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\n#endif",worldpos_vertex:"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\n\tvec4 worldPosition = vec4( transformed, 1.0 );\n\t#ifdef USE_INSTANCING\n\t\tworldPosition = instanceMatrix * worldPosition;\n\t#endif\n\tworldPosition = modelMatrix * worldPosition;\n#endif",background_frag:"uniform sampler2D t2D;\nvarying vec2 vUv;\nvoid main() {\n\tvec4 texColor = texture2D( t2D, vUv );\n\tgl_FragColor = mapTexelToLinear( texColor );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",background_vert:"varying vec2 vUv;\nuniform mat3 uvTransform;\nvoid main() {\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n\tgl_Position = vec4( position.xy, 1.0, 1.0 );\n}",cube_frag:"#include <envmap_common_pars_fragment>\nuniform float opacity;\nvarying vec3 vWorldDirection;\n#include <cube_uv_reflection_fragment>\nvoid main() {\n\tvec3 vReflect = vWorldDirection;\n\t#include <envmap_fragment>\n\tgl_FragColor = envColor;\n\tgl_FragColor.a *= opacity;\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",cube_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\tgl_Position.z = gl_Position.w;\n}",depth_frag:"#if DEPTH_PACKING == 3200\n\tuniform float opacity;\n#endif\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#if DEPTH_PACKING == 3200\n\t\tdiffuseColor.a = opacity;\n\t#endif\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <logdepthbuf_fragment>\n\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\n\t#if DEPTH_PACKING == 3200\n\t\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\n\t#elif DEPTH_PACKING == 3201\n\t\tgl_FragColor = packDepthToRGBA( fragCoordZ );\n\t#endif\n}",depth_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvHighPrecisionZW = gl_Position.zw;\n}",distanceRGBA_frag:"#define DISTANCE\nuniform vec3 referencePosition;\nuniform float nearDistance;\nuniform float farDistance;\nvarying vec3 vWorldPosition;\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main () {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\tfloat dist = length( vWorldPosition - referencePosition );\n\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\n\tdist = saturate( dist );\n\tgl_FragColor = packDepthToRGBA( dist );\n}",distanceRGBA_vert:"#define DISTANCE\nvarying vec3 vWorldPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <clipping_planes_vertex>\n\tvWorldPosition = worldPosition.xyz;\n}",equirect_frag:"uniform sampler2D tEquirect;\nvarying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvec3 direction = normalize( vWorldDirection );\n\tvec2 sampleUV = equirectUv( direction );\n\tvec4 texColor = texture2D( tEquirect, sampleUV );\n\tgl_FragColor = mapTexelToLinear( texColor );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",equirect_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n}",linedashed_frag:"uniform vec3 diffuse;\nuniform float opacity;\nuniform float dashSize;\nuniform float totalSize;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\n\t\tdiscard;\n\t}\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <color_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",linedashed_vert:"uniform float scale;\nattribute float lineDistance;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\tvLineDistance = scale * lineDistance;\n\t#include <color_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n}",meshbasic_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <fog_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\t#ifdef USE_LIGHTMAP\n\t\n\t\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\t\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n\t#else\n\t\treflectedLight.indirectDiffuse += vec3( 1.0 );\n\t#endif\n\t#include <aomap_fragment>\n\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\n\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshbasic_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinbase_vertex>\n\t\t#include <skinnormal_vertex>\n\t\t#include <defaultnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <fog_vertex>\n}",meshlambert_frag:"uniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <fog_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <shadowmask_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <emissivemap_fragment>\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\n\t#else\n\t\treflectedLight.indirectDiffuse += vIndirectFront;\n\t#endif\n\t#include <lightmap_fragment>\n\treflectedLight.indirectDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb );\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\n\t#else\n\t\treflectedLight.directDiffuse = vLightFront;\n\t#endif\n\treflectedLight.directDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb ) * getShadowMask();\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshlambert_vert:"#define LAMBERT\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <envmap_pars_vertex>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <lights_lambert_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshmatcap_frag:"#define MATCAP\nuniform vec3 diffuse;\nuniform float opacity;\nuniform sampler2D matcap;\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tvec3 viewDir = normalize( vViewPosition );\n\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\n\tvec3 y = cross( viewDir, x );\n\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\n\t#ifdef USE_MATCAP\n\t\tvec4 matcapColor = texture2D( matcap, uv );\n\t\tmatcapColor = matcapTexelToLinear( matcapColor );\n\t#else\n\t\tvec4 matcapColor = vec4( 1.0 );\n\t#endif\n\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshmatcap_vert:"#define MATCAP\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <color_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#ifndef FLAT_SHADED\n\t\tvNormal = normalize( transformedNormal );\n\t\t#ifdef USE_TANGENT\n\t\t\tvTangent = normalize( transformedTangent );\n\t\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t\t#endif\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n\tvViewPosition = - mvPosition.xyz;\n}",meshtoon_frag:"#define TOON\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <gradientmap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <lights_toon_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_toon_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshtoon_vert:"#define TOON\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshphong_frag:"#define PHONG\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform vec3 specular;\nuniform float shininess;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <lights_phong_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_phong_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshphong_vert:"#define PHONG\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshphysical_frag:"#define STANDARD\n#ifdef PHYSICAL\n\t#define REFLECTIVITY\n\t#define CLEARCOAT\n\t#define SPECULAR\n#endif\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float roughness;\nuniform float metalness;\nuniform float opacity;\n#ifdef USE_TRANSMISSION\n\tuniform float transmission;\n\tuniform float thickness;\n\tuniform float attenuationDistance;\n\tuniform vec3 attenuationTint;\n#endif\n#ifdef REFLECTIVITY\n\tuniform float reflectivity;\n#endif\n#ifdef SPECULAR\n\tuniform float specularIntensity;\n\tuniform vec3 specularTint;\n\t#ifdef USE_SPECULARINTENSITYMAP\n\t\tuniform sampler2D specularIntensityMap;\n\t#endif\n\t#ifdef USE_SPECULARTINTMAP\n\t\tuniform sampler2D specularTintMap;\n\t#endif\n#endif\n#ifdef CLEARCOAT\n\tuniform float clearcoat;\n\tuniform float clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tuniform vec3 sheen;\n#endif\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <bsdfs>\n#include <transmission_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_physical_pars_fragment>\n#include <fog_pars_fragment>\n#include <lights_pars_begin>\n#include <lights_physical_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <clearcoat_pars_fragment>\n#include <roughnessmap_pars_fragment>\n#include <metalnessmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <roughnessmap_fragment>\n\t#include <metalnessmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <clearcoat_normal_fragment_begin>\n\t#include <clearcoat_normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_physical_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\n\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\n\t#include <transmission_fragment>\n\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshphysical_vert:"#define STANDARD\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#ifdef USE_TRANSMISSION\n\tvarying vec4 vWorldPosition;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n#ifdef USE_TRANSMISSION\n\tvWorldPosition = worldPosition;\n#endif\n}",normal_frag:"#define NORMAL\nuniform float opacity;\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <packing>\n#include <uv_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\t#include <logdepthbuf_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\n}",normal_vert:"#define NORMAL\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvViewPosition = - mvPosition.xyz;\n#endif\n}",points_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include <common>\n#include <color_pars_fragment>\n#include <map_particle_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_particle_fragment>\n\t#include <color_fragment>\n\t#include <alphatest_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",points_vert:"uniform float size;\nuniform float scale;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <color_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\tgl_PointSize = size;\n\t#ifdef USE_SIZEATTENUATION\n\t\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\n\t\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\n\t#endif\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <fog_vertex>\n}",shadow_frag:"uniform vec3 color;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <shadowmap_pars_fragment>\n#include <shadowmask_pars_fragment>\nvoid main() {\n\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n}",shadow_vert:"#include <common>\n#include <fog_pars_vertex>\n#include <shadowmap_pars_vertex>\nvoid main() {\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",sprite_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include <common>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n}",sprite_vert:"uniform float rotation;\nuniform vec2 center;\n#include <common>\n#include <uv_pars_vertex>\n#include <fog_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\n\tvec2 scale;\n\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\n\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\n\t#ifndef USE_SIZEATTENUATION\n\t\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\n\t\tif ( isPerspective ) scale *= - mvPosition.z;\n\t#endif\n\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\n\tvec2 rotatedPosition;\n\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\n\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\n\tmvPosition.xy += rotatedPosition;\n\tgl_Position = projectionMatrix * mvPosition;\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n}"},l0t={common:{diffuse:{value:new $Qt(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new gJt},uv2Transform:{value:new 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$Qt(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},uvTransform:{value:new gJt}},sprite:{diffuse:{value:new $Qt(16777215)},opacity:{value:1},center:{value:new mJt(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},uvTransform:{value:new gJt}}},c0t={basic:{uniforms:U1t([l0t.common,l0t.specularmap,l0t.envmap,l0t.aomap,l0t.lightmap,l0t.fog]),vertexShader:s0t.meshbasic_vert,fragmentShader:s0t.meshbasic_frag},lambert:{uniforms:U1t([l0t.common,l0t.specularmap,l0t.envmap,l0t.aomap,l0t.lightmap,l0t.emissivemap,l0t.fog,l0t.lights,{emissive:{value:new $Qt(0)}}]),vertexShader:s0t.meshlambert_vert,fragmentShader:s0t.meshlambert_frag},phong:{uniforms:U1t([l0t.common,l0t.specularmap,l0t.envmap,l0t.aomap,l0t.lightmap,l0t.emissivemap,l0t.bumpmap,l0t.normalmap,l0t.displacementmap,l0t.fog,l0t.lights,{emissive:{value:new $Qt(0)},specular:{value:new $Qt(1118481)},shininess:{value:30}}]),vertexShader:s0t.meshphong_vert,fragmentShader:s0t.meshphong_frag},standard:{uniforms:U1t([l0t.common,l0t.envmap,l0t.aomap,l0t.lightmap,l0t.emissivemap,l0t.bumpmap,l0t.normalmap,l0t.displacementmap,l0t.roughnessmap,l0t.metalnessmap,l0t.fog,l0t.lights,{emissive:{value:new $Qt(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:s0t.meshphysical_vert,fragmentShader:s0t.meshphysical_frag},toon:{uniforms:U1t([l0t.common,l0t.aomap,l0t.lightmap,l0t.emissivemap,l0t.bumpmap,l0t.normalmap,l0t.displacementmap,l0t.gradientmap,l0t.fog,l0t.lights,{emissive:{value:new 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h0t(t,e,n,i){const r=t.getParameter(34921),o=i.isWebGL2?null:e.get("OES_vertex_array_object"),a=i.isWebGL2||null!==o,s={},l=d(null);let c=l;function u(e){return i.isWebGL2?t.bindVertexArray(e):o.bindVertexArrayOES(e)}function h(e){return i.isWebGL2?t.deleteVertexArray(e):o.deleteVertexArrayOES(e)}function d(t){const e=[],n=[],i=[];for(let t=0;t<r;t++)e[t]=0,n[t]=0,i[t]=0;return{geometry:null,program:null,wireframe:!1,newAttributes:e,enabledAttributes:n,attributeDivisors:i,object:t,attributes:{},index:null}}function p(){const t=c.newAttributes;for(let e=0,n=t.length;e<n;e++)t[e]=0}function f(t){m(t,0)}function m(n,r){const o=c.enabledAttributes,a=c.attributeDivisors;c.newAttributes[n]=1,0===o[n]&&(t.enableVertexAttribArray(n),o[n]=1),a[n]!==r&&((i.isWebGL2?t:e.get("ANGLE_instanced_arrays"))[i.isWebGL2?"vertexAttribDivisor":"vertexAttribDivisorANGLE"](n,r),a[n]=r)}function g(){const e=c.newAttributes,n=c.enabledAttributes;for(let 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mJt(1,1)},clearcoatNormalMap:{value:null},sheen:{value:new $Qt(0)},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new mJt},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new $Qt(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new $Qt(1,1,1)},specularTintMap:{value:null}}]),vertexShader:s0t.meshphysical_vert,fragmentShader:s0t.meshphysical_frag};class g0t extends W1t{constructor(t=-1,e=1,n=1,i=-1,r=.1,o=2e3){super(),this.type="OrthographicCamera",this.zoom=1,this.view=null,this.left=t,this.right=e,this.top=n,this.bottom=i,this.near=r,this.far=o,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.left=t.left,this.right=t.right,this.top=t.top,this.bottom=t.bottom,this.near=t.near,this.far=t.far,this.zoom=t.zoom,this.view=null===t.view?null:Object.assign({},t.view),this}setViewOffset(t,e,n,i,r,o){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=i,this.view.width=r,this.view.height=o,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=(this.right-this.left)/(2*this.zoom),e=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,i=(this.top+this.bottom)/2;let r=n-t,o=n+t,a=i+e,s=i-e;if(null!==this.view&&this.view.enabled){const t=(this.right-this.left)/this.view.fullWidth/this.zoom,e=(this.top-this.bottom)/this.view.fullHeight/this.zoom;r+=t*this.view.offsetX,o=r+t*this.view.width,a-=e*this.view.offsetY,s=a-e*this.view.height}this.projectionMatrix.makeOrthographic(r,o,a,s,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.zoom=this.zoom,e.object.left=this.left,e.object.right=this.right,e.object.top=this.top,e.object.bottom=this.bottom,e.object.near=this.near,e.object.far=this.far,null!==this.view&&(e.object.view=Object.assign({},this.view)),e}}g0t.prototype.isOrthographicCamera=!0;class _0t extends G1t{constructor(t){super(t),this.type="RawShaderMaterial"}}_0t.prototype.isRawShaderMaterial=!0;const y0t=Math.pow(2,8),v0t=[.125,.215,.35,.446,.526,.582],b0t=5+v0t.length,x0t={[NZt]:0,[IZt]:1,[OZt]:2,[zZt]:3,[DZt]:4,[BZt]:5,[RZt]:6},w0t=new KQt({side:1,depthWrite:!1,depthTest:!1}),S0t=new B1t(new F1t,w0t),M0t=new g0t,{_lodPlanes:E0t,_sizeLods:T0t,_sigmas:C0t}=z0t(),A0t=new $Qt;let k0t=null;const L0t=(1+Math.sqrt(5))/2,P0t=1/L0t,N0t=[new CJt(1,1,1),new CJt(-1,1,1),new CJt(1,1,-1),new CJt(-1,1,-1),new CJt(0,L0t,P0t),new CJt(0,L0t,-P0t),new CJt(P0t,0,L0t),new CJt(-P0t,0,L0t),new CJt(L0t,P0t,0),new CJt(-L0t,P0t,0)];function I0t(t){const e=Math.max(t.r,t.g,t.b),n=Math.min(Math.max(Math.ceil(Math.log2(e)),-128),127);return t.multiplyScalar(Math.pow(2,-n)),(n+128)/255}class R0t{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=(function e(t){const e=new Float32Array(t),n=new CJt(0,1,0);return new _0t({name:"SphericalGaussianBlur",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:x0t[3e3]},outputEncoding:{value:x0t[3e3]}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute vec3 position;\n\t\tattribute vec2 uv;\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\t\t\tuniform int samples;\n\t\t\tuniform float weights[ n ];\n\t\t\tuniform bool latitudinal;\n\t\t\tuniform float dTheta;\n\t\t\tuniform float mipInt;\n\t\t\tuniform vec3 poleAxis;\n\n\t\t\t\n\n\t\tuniform int inputEncoding;\n\t\tuniform int outputEncoding;\n\n\t\t#include <encodings_pars_fragment>\n\n\t\tvec4 inputTexelToLinear( vec4 value ) {\n\n\t\t\tif ( inputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( inputEncoding == 1 ) {\n\n\t\t\t\treturn sRGBToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 2 ) {\n\n\t\t\t\treturn RGBEToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 3 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 7.0 );\n\n\t\t\t} else if ( inputEncoding == 4 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 16.0 );\n\n\t\t\t} else if ( inputEncoding == 5 ) {\n\n\t\t\t\treturn RGBDToLinear( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn GammaToLinear( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 linearToOutputTexel( vec4 value ) {\n\n\t\t\tif ( outputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( outputEncoding == 1 ) {\n\n\t\t\t\treturn LinearTosRGB( value );\n\n\t\t\t} else if ( outputEncoding == 2 ) {\n\n\t\t\t\treturn LinearToRGBE( value );\n\n\t\t\t} else if ( outputEncoding == 3 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 7.0 );\n\n\t\t\t} else if ( outputEncoding == 4 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 16.0 );\n\n\t\t\t} else if ( outputEncoding == 5 ) {\n\n\t\t\t\treturn LinearToRGBD( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn LinearToGamma( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 envMapTexelToLinear( vec4 color ) {\n\n\t\t\treturn inputTexelToLinear( color );\n\n\t\t}\n\t\n\n\t\t\t#define ENVMAP_TYPE_CUBE_UV\n\t\t\t#include <cube_uv_reflection_fragment>\n\n\t\t\tvec3 getSample( float theta, vec3 axis ) {\n\n\t\t\t\tfloat cosTheta = cos( theta );\n\t\t\t\t// Rodrigues' axis-angle rotation\n\t\t\t\tvec3 sampleDirection = vOutputDirection * cosTheta\n\t\t\t\t\t+ cross( axis, vOutputDirection ) * sin( theta )\n\t\t\t\t\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\n\n\t\t\t\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\n\n\t\t\t}\n\n\t\t\tvoid main() {\n\n\t\t\t\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\n\n\t\t\t\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\n\n\t\t\t\t\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\n\n\t\t\t\t}\n\n\t\t\t\taxis = normalize( axis );\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t\t\t\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\n\n\t\t\t\tfor ( int i = 1; i < n; i++ ) {\n\n\t\t\t\t\tif ( i >= samples ) {\n\n\t\t\t\t\t\tbreak;\n\n\t\t\t\t\t}\n\n\t\t\t\t\tfloat theta = dTheta * float( i );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\n\n\t\t\t\t}\n\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t",blending:0,depthTest:!1,depthWrite:!1})})(20),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,i=100){k0t=this._renderer.getRenderTarget();const r=this._allocateTargets();return this._sceneToCubeUV(t,n,i,r),e>0&&this._blur(r,0,0,e),this._applyPMREM(r),this._cleanup(r),r}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=F0t(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=H0t(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t<E0t.length;t++)E0t[t].dispose()}_cleanup(t){this._pingPongRenderTarget.dispose(),this._renderer.setRenderTarget(k0t),t.scissorTest=!1,B0t(t,0,0,t.width,t.height)}_fromTexture(t){k0t=this._renderer.getRenderTarget();const e=this._allocateTargets(t);return this._textureToCubeUV(t,e),this._applyPMREM(e),this._cleanup(e),e}_allocateTargets(t){const e={magFilter:JKt,minFilter:JKt,generateMipmaps:!1,type:rZt,format:1023,encoding:O0t(t)?t.encoding:OZt,depthBuffer:!1},n=D0t(e);return n.depthBuffer=!t,this._pingPongRenderTarget=D0t(e),n}_compileMaterial(t){const e=new B1t(E0t[0],t);this._renderer.compile(e,M0t)}_sceneToCubeUV(t,e,n,i){const r=new q1t(90,1,e,n),o=[1,-1,1,1,1,1],a=[1,1,1,-1,-1,-1],s=this._renderer,l=s.autoClear,c=s.outputEncoding,u=s.toneMapping;s.getClearColor(A0t),s.toneMapping=0,s.outputEncoding=NZt,s.autoClear=!1;let h=!1;const d=t.background;if(d){if(d.isColor){w0t.color.copy(d).convertSRGBToLinear(),t.background=null;const e=I0t(w0t.color);w0t.opacity=e,h=!0}}else{w0t.color.copy(A0t).convertSRGBToLinear();const t=I0t(w0t.color);w0t.opacity=t,h=!0}for(let e=0;e<6;e++){const n=e%3;0==n?(r.up.set(0,o[e],0),r.lookAt(a[e],0,0)):1==n?(r.up.set(0,0,o[e]),r.lookAt(0,a[e],0)):(r.up.set(0,o[e],0),r.lookAt(0,0,a[e])),B0t(i,n*y0t,e>2?y0t:0,y0t,y0t),s.setRenderTarget(i),h&&s.render(S0t,r),s.render(t,r)}s.toneMapping=u,s.outputEncoding=c,s.autoClear=l}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=F0t()):null==this._equirectShader&&(this._equirectShader=H0t());const i=t.isCubeTexture?this._cubemapShader:this._equirectShader,r=new B1t(E0t[0],i),o=i.uniforms;o.envMap.value=t,t.isCubeTexture||o.texelSize.value.set(1/t.image.width,1/t.image.height),o.inputEncoding.value=x0t[t.encoding],o.outputEncoding.value=x0t[e.texture.encoding],B0t(e,0,0,3*y0t,2*y0t),n.setRenderTarget(e),n.render(r,M0t)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;e<b0t;e++){const n=Math.sqrt(C0t[e]*C0t[e]-C0t[e-1]*C0t[e-1]);this._blur(t,e-1,e,n,N0t[(e-1)%N0t.length])}e.autoClear=n}_blur(t,e,n,i,r){const o=this._pingPongRenderTarget;this._halfBlur(t,o,e,n,i,"latitudinal",r),this._halfBlur(o,t,n,n,i,"longitudinal",r)}_halfBlur(t,e,n,i,r,o,a){const s=this._renderer,l=this._blurMaterial;"latitudinal"!==o&&"longitudinal"!==o&&console.error("blur direction must be either latitudinal or longitudinal!");const c=new B1t(E0t[i],l),u=l.uniforms,h=T0t[n]-1,d=isFinite(r)?Math.PI/(2*h):2*Math.PI/39,p=r/d,f=isFinite(r)?1+Math.floor(3*p):20;f>20&&console.warn(`sigmaRadians, ${r}, is too large and will clip, as it requested ${f} samples when the maximum is set to 20`);const m=[];let g=0;for(let t=0;t<20;++t){const e=t/p,n=Math.exp(-e*e/2);m.push(n),0==t?g+=n:t<f&&(g+=2*n)}for(let t=0;t<m.length;t++)m[t]=m[t]/g;u.envMap.value=t.texture,u.samples.value=f,u.weights.value=m,u.latitudinal.value="latitudinal"===o,a&&(u.poleAxis.value=a),u.dTheta.value=d,u.mipInt.value=8-n,u.inputEncoding.value=x0t[t.texture.encoding],u.outputEncoding.value=x0t[t.texture.encoding];const _=T0t[i];B0t(e,3*Math.max(0,y0t-2*_),(0===i?0:2*y0t)+2*_*(i>4?i-8+4:0),3*_,2*_),s.setRenderTarget(e),s.render(c,M0t)}}function O0t(t){return void 0!==t&&t.type===rZt&&(t.encoding===NZt||t.encoding===IZt||t.encoding===RZt)}function z0t(){const t=[],e=[],n=[];let i=8;for(let r=0;r<b0t;r++){const o=Math.pow(2,i);e.push(o);let a=1/o;r>4?a=v0t[r-8+4-1]:0==r&&(a=0),n.push(a);const s=1/(o-1),l=-s/2,c=1+s/2,u=[l,l,c,l,c,c,l,l,c,c,l,c],h=6,d=6,p=3,f=2,m=1,g=new Float32Array(p*d*h),_=new Float32Array(f*d*h),y=new Float32Array(m*d*h);for(let t=0;t<h;t++){const e=t%3*2/3-1,n=t>2?0:-1;g.set([e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0],p*d*t),_.set(u,f*d*t),y.set([t,t,t,t,t,t],m*d*t)}const v=new b1t;v.setAttribute("position",new QQt(g,p)),v.setAttribute("uv",new QQt(_,f)),v.setAttribute("faceIndex",new QQt(y,m)),t.push(v),i>4&&i--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function D0t(t){const e=new SJt(3*y0t,3*y0t,t);return e.texture.mapping=YKt,e.texture.name="PMREM.cubeUv",e.scissorTest=!0,e}function B0t(t,e,n,i,r){t.viewport.set(e,n,i,r),t.scissor.set(e,n,i,r)}function H0t(){const t=new mJt(1,1);return new _0t({name:"EquirectangularToCubeUV",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:x0t[3e3]},outputEncoding:{value:x0t[3e3]}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute vec3 position;\n\t\tattribute vec2 uv;\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\t\t\tuniform vec2 texelSize;\n\n\t\t\t\n\n\t\tuniform int inputEncoding;\n\t\tuniform int outputEncoding;\n\n\t\t#include <encodings_pars_fragment>\n\n\t\tvec4 inputTexelToLinear( vec4 value ) {\n\n\t\t\tif ( inputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( inputEncoding == 1 ) {\n\n\t\t\t\treturn sRGBToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 2 ) {\n\n\t\t\t\treturn RGBEToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 3 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 7.0 );\n\n\t\t\t} else if ( inputEncoding == 4 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 16.0 );\n\n\t\t\t} else if ( inputEncoding == 5 ) {\n\n\t\t\t\treturn RGBDToLinear( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn GammaToLinear( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 linearToOutputTexel( vec4 value ) {\n\n\t\t\tif ( outputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( outputEncoding == 1 ) {\n\n\t\t\t\treturn LinearTosRGB( value );\n\n\t\t\t} else if ( outputEncoding == 2 ) {\n\n\t\t\t\treturn LinearToRGBE( value );\n\n\t\t\t} else if ( outputEncoding == 3 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 7.0 );\n\n\t\t\t} else if ( outputEncoding == 4 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 16.0 );\n\n\t\t\t} else if ( outputEncoding == 5 ) {\n\n\t\t\t\treturn LinearToRGBD( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn LinearToGamma( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 envMapTexelToLinear( vec4 color ) {\n\n\t\t\treturn inputTexelToLinear( color );\n\n\t\t}\n\t\n\n\t\t\t#include <common>\n\n\t\t\tvoid main() {\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\n\t\t\t\tvec3 outputDirection = normalize( vOutputDirection );\n\t\t\t\tvec2 uv = equirectUv( outputDirection );\n\n\t\t\t\tvec2 f = fract( uv / texelSize - 0.5 );\n\t\t\t\tuv -= f * texelSize;\n\t\t\t\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.x += texelSize.x;\n\t\t\t\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.y += texelSize.y;\n\t\t\t\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.x -= texelSize.x;\n\t\t\t\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\n\t\t\t\tvec3 tm = mix( tl, tr, f.x );\n\t\t\t\tvec3 bm = mix( bl, br, f.x );\n\t\t\t\tgl_FragColor.rgb = mix( tm, bm, f.y );\n\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t",blending:0,depthTest:!1,depthWrite:!1})}function F0t(){return new _0t({name:"CubemapToCubeUV",uniforms:{envMap:{value:null},inputEncoding:{value:x0t[3e3]},outputEncoding:{value:x0t[3e3]}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute vec3 position;\n\t\tattribute vec2 uv;\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform samplerCube envMap;\n\n\t\t\t\n\n\t\tuniform int inputEncoding;\n\t\tuniform int outputEncoding;\n\n\t\t#include <encodings_pars_fragment>\n\n\t\tvec4 inputTexelToLinear( vec4 value ) {\n\n\t\t\tif ( inputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( inputEncoding == 1 ) {\n\n\t\t\t\treturn sRGBToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 2 ) {\n\n\t\t\t\treturn RGBEToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 3 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 7.0 );\n\n\t\t\t} else if ( inputEncoding == 4 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 16.0 );\n\n\t\t\t} else if ( inputEncoding == 5 ) {\n\n\t\t\t\treturn RGBDToLinear( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn GammaToLinear( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 linearToOutputTexel( vec4 value ) {\n\n\t\t\tif ( outputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( outputEncoding == 1 ) {\n\n\t\t\t\treturn LinearTosRGB( value );\n\n\t\t\t} else if ( outputEncoding == 2 ) {\n\n\t\t\t\treturn LinearToRGBE( value );\n\n\t\t\t} else if ( outputEncoding == 3 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 7.0 );\n\n\t\t\t} else if ( outputEncoding == 4 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 16.0 );\n\n\t\t\t} else if ( outputEncoding == 5 ) {\n\n\t\t\t\treturn LinearToRGBD( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn LinearToGamma( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 envMapTexelToLinear( vec4 color ) {\n\n\t\t\treturn inputTexelToLinear( color );\n\n\t\t}\n\t\n\n\t\t\tvoid main() {\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t\t\t\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t",blending:0,depthTest:!1,depthWrite:!1})}function V0t(t){let e=new WeakMap,n=null;function i(t){const n=t.target;n.removeEventListener("dispose",i);const r=e.get(n);void 0!==r&&(r.delete(n),r.dispose())}return{get:function r(o){if(o&&o.isTexture&&!1===o.isRenderTargetTexture){const r=o.mapping,a=r===WKt||r===qKt,s=r===jKt||r===GKt;if(a||s){if(e.has(o))return e.get(o).texture;{const r=o.image;if(a&&r&&r.height>0||s&&r&&(function a(t){let e=0;for(let n=0;n<6;n++)void 0!==t[n]&&e++;return 6===e})(r)){const r=t.getRenderTarget();null===n&&(n=new R0t(t));const s=a?n.fromEquirectangular(o):n.fromCubemap(o);return e.set(o,s),t.setRenderTarget(r),o.addEventListener("dispose",i),s.texture}return null}}}return o},dispose:function o(){e=new WeakMap,null!==n&&(n.dispose(),n=null)}}}function U0t(t){const e={};function n(n){if(void 0!==e[n])return e[n];let 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Use their .texture property instead."),e=t.texture.encoding):e=NZt,e}return{getParameters:function _(o,s,m,y,v){const b=y.fog,x=(o.isMeshStandardMaterial?n:e).get(o.envMap||(o.isMeshStandardMaterial?y.environment:null)),w=f[o.type],S=v.isSkinnedMesh?(function M(t){const e=t.skeleton.bones;if(u)return 1024;{const t=Math.floor((h-20)/4),n=Math.min(t,e.length);return n<e.length?(console.warn("THREE.WebGLRenderer: Skeleton has "+e.length+" bones. This GPU supports "+n+"."),0):n}})(v):0;let E,T;if(null!==o.precision&&(p=r.getMaxPrecision(o.precision),p!==o.precision&&console.warn("THREE.WebGLProgram.getParameters:",o.precision,"not supported, using",p,"instead.")),w){const t=c0t[w];E=t.vertexShader,T=t.fragmentShader}else E=o.vertexShader,T=o.fragmentShader;const C=t.getRenderTarget();return{isWebGL2:l,shaderID:w,shaderName:o.type,vertexShader:E,fragmentShader:T,defines:o.defines,isRawShaderMaterial:!0===o.isRawShaderMaterial,glslVersion:o.glslVersion,precision:p,instancing:!0===v.isInstancedMesh,instancingColor:!0===v.isInstancedMesh&&null!==v.instanceColor,supportsVertexTextures:d,outputEncoding:null!==C?g(C.texture):t.outputEncoding,map:!!o.map,mapEncoding:g(o.map),matcap:!!o.matcap,matcapEncoding:g(o.matcap),envMap:!!x,envMapMode:x&&x.mapping,envMapEncoding:g(x),envMapCubeUV:!!x&&(x.mapping===YKt||x.mapping===XKt),lightMap:!!o.lightMap,lightMapEncoding:g(o.lightMap),aoMap:!!o.aoMap,emissiveMap:!!o.emissiveMap,emissiveMapEncoding:g(o.emissiveMap),bumpMap:!!o.bumpMap,normalMap:!!o.normalMap,objectSpaceNormalMap:1===o.normalMapType,tangentSpaceNormalMap:0===o.normalMapType,clearcoatMap:!!o.clearcoatMap,clearcoatRoughnessMap:!!o.clearcoatRoughnessMap,clearcoatNormalMap:!!o.clearcoatNormalMap,displacementMap:!!o.displacementMap,roughnessMap:!!o.roughnessMap,metalnessMap:!!o.metalnessMap,specularMap:!!o.specularMap,specularIntensityMap:!!o.specularIntensityMap,specularTintMap:!!o.specularTintMap,specularTintMapEncoding:g(o.specularTintMap),alphaMap:!!o.alphaMap,gradientMap:!!o.gradientMap,sheen:!!o.sheen,transmission:!!o.transmission,transmissionMap:!!o.transmissionMap,thicknessMap:!!o.thicknessMap,combine:o.combine,vertexTangents:!!o.normalMap&&!!v.geometry&&!!v.geometry.attributes.tangent,vertexColors:o.vertexColors,vertexAlphas:!0===o.vertexColors&&!!v.geometry&&!!v.geometry.attributes.color&&4===v.geometry.attributes.color.itemSize,vertexUvs:!!(o.map||o.bumpMap||o.normalMap||o.specularMap||o.alphaMap||o.emissiveMap||o.roughnessMap||o.metalnessMap||o.clearcoatMap||o.clearcoatRoughnessMap||o.clearcoatNormalMap||o.displacementMap||o.transmissionMap||o.thicknessMap||o.specularIntensityMap||o.specularTintMap),uvsVertexOnly:!(o.map||o.bumpMap||o.normalMap||o.specularMap||o.alphaMap||o.emissiveMap||o.roughnessMap||o.metalnessMap||o.clearcoatNormalMap||o.transmission||o.transmissionMap||o.thicknessMap||o.specularIntensityMap||o.specularTintMap||!o.displacementMap),fog:!!b,useFog:o.fog,fogExp2:b&&b.isFogExp2,flatShading:!!o.flatShading,sizeAttenuation:o.sizeAttenuation,logarithmicDepthBuffer:c,skinning:!0===v.isSkinnedMesh&&S>0,maxBones:S,useVertexTexture:u,morphTargets:!!v.geometry&&!!v.geometry.morphAttributes.position,morphNormals:!!v.geometry&&!!v.geometry.morphAttributes.normal,numDirLights:s.directional.length,numPointLights:s.point.length,numSpotLights:s.spot.length,numRectAreaLights:s.rectArea.length,numHemiLights:s.hemi.length,numDirLightShadows:s.directionalShadowMap.length,numPointLightShadows:s.pointShadowMap.length,numSpotLightShadows:s.spotShadowMap.length,numClippingPlanes:a.numPlanes,numClipIntersection:a.numIntersection,dithering:o.dithering,shadowMapEnabled:t.shadowMap.enabled&&m.length>0,shadowMapType:t.shadowMap.type,toneMapping:o.toneMapped?t.toneMapping:0,physicallyCorrectLights:t.physicallyCorrectLights,premultipliedAlpha:o.premultipliedAlpha,alphaTest:o.alphaTest,doubleSided:2===o.side,flipSided:1===o.side,depthPacking:void 0!==o.depthPacking&&o.depthPacking,index0AttributeName:o.index0AttributeName,extensionDerivatives:o.extensions&&o.extensions.derivatives,extensionFragDepth:o.extensions&&o.extensions.fragDepth,extensionDrawBuffers:o.extensions&&o.extensions.drawBuffers,extensionShaderTextureLOD:o.extensions&&o.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||i.has("EXT_frag_depth"),rendererExtensionDrawBuffers:l||i.has("WEBGL_draw_buffers"),rendererExtensionShaderTextureLod:l||i.has("EXT_shader_texture_lod"),customProgramCacheKey:o.customProgramCacheKey()}},getProgramCacheKey:function y(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t<m.length;t++)n.push(e[m[t]]);n.push(t.outputEncoding),n.push(t.gammaFactor)}return n.push(e.customProgramCacheKey),n.join()},getUniforms:function v(t){const e=f[t.type];let n;return n=e?j1t.clone(c0t[e].uniforms):t.uniforms,n},acquireProgram:function b(e,n){let i;for(let t=0,e=s.length;t<e;t++){const e=s[t];if(e.cacheKey===n){i=e,++i.usedTimes;break}}return void 0===i&&(i=new v5t(t,n,e,o),s.push(i)),i},releaseProgram:function x(t){if(0==--t.usedTimes){const e=s.indexOf(t);s[e]=s[s.length-1],s.pop(),t.destroy()}},programs:s}}function x5t(){let t=new WeakMap;return{get:function e(n){let i=t.get(n);return void 0===i&&(i={},t.set(n,i)),i},remove:function n(e){t.delete(e)},update:function i(e,n,r){t.get(e)[n]=r},dispose:function r(){t=new WeakMap}}}function w5t(t,e){return t.groupOrder!==e.groupOrder?t.groupOrder-e.groupOrder:t.renderOrder!==e.renderOrder?t.renderOrder-e.renderOrder:t.program!==e.program?t.program.id-e.program.id:t.material.id!==e.material.id?t.material.id-e.material.id:t.z!==e.z?t.z-e.z:t.id-e.id}function S5t(t,e){return t.groupOrder!==e.groupOrder?t.groupOrder-e.groupOrder:t.renderOrder!==e.renderOrder?t.renderOrder-e.renderOrder:t.z!==e.z?e.z-t.z:t.id-e.id}function M5t(t){const e=[];let n=0;const i=[],r=[],o=[],a={id:-1};function s(i,r,o,s,l,c){let u=e[n];const h=t.get(o);return void 0===u?(u={id:i.id,object:i,geometry:r,material:o,program:h.program||a,groupOrder:s,renderOrder:i.renderOrder,z:l,group:c},e[n]=u):(u.id=i.id,u.object=i,u.geometry=r,u.material=o,u.program=h.program||a,u.groupOrder=s,u.renderOrder=i.renderOrder,u.z=l,u.group=c),n++,u}return{opaque:i,transmissive:r,transparent:o,init:function l(){n=0,i.length=0,r.length=0,o.length=0},push:function c(t,e,n,a,l,u){const h=s(t,e,n,a,l,u);n.transmission>0?r.push(h):!0===n.transparent?o.push(h):i.push(h)},unshift:function u(t,e,n,a,l,c){const u=s(t,e,n,a,l,c);n.transmission>0?r.unshift(u):!0===n.transparent?o.unshift(u):i.unshift(u)},finish:function h(){for(let t=n,i=e.length;t<i;t++){const n=e[t];if(null===n.id)break;n.id=null,n.object=null,n.geometry=null,n.material=null,n.program=null,n.group=null}},sort:function d(t,e){i.length>1&&i.sort(t||w5t),r.length>1&&r.sort(e||S5t),o.length>1&&o.sort(e||S5t)}}}function E5t(t){let e=new WeakMap;return{get:function n(i,r){let o;return!1===e.has(i)?(o=new M5t(t),e.set(i,[o])):r>=e.get(i).length?(o=new M5t(t),e.get(i).push(o)):o=e.get(i)[r],o},dispose:function i(){e=new WeakMap}}}function T5t(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case"DirectionalLight":n={direction:new CJt,color:new $Qt};break;case"SpotLight":n={position:new CJt,direction:new CJt,color:new $Qt,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case"PointLight":n={position:new CJt,color:new $Qt,distance:0,decay:0};break;case"HemisphereLight":n={direction:new CJt,skyColor:new $Qt,groundColor:new $Qt};break;case"RectAreaLight":n={color:new $Qt,position:new CJt,halfWidth:new CJt,halfHeight:new CJt}}return t[e.id]=n,n}}}let C5t=0;function A5t(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function k5t(t,e){const n=new T5t,i=(function r(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case"DirectionalLight":case"SpotLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new mJt};break;case"PointLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new mJt,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}})(),o={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)o.probe.push(new CJt);const a=new CJt,s=new rQt,l=new rQt;return{setup:function c(r){let a=0,s=0,l=0;for(let t=0;t<9;t++)o.probe[t].set(0,0,0);let c=0,u=0,h=0,d=0,p=0,f=0,m=0,g=0;r.sort(A5t);for(let t=0,e=r.length;t<e;t++){const e=r[t],_=e.color,y=e.intensity,v=e.distance,b=e.shadow&&e.shadow.map?e.shadow.map.texture:null;if(e.isAmbientLight)a+=_.r*y,s+=_.g*y,l+=_.b*y;else if(e.isLightProbe)for(let t=0;t<9;t++)o.probe[t].addScaledVector(e.sh.coefficients[t],y);else if(e.isDirectionalLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity),e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,o.directionalShadow[c]=n,o.directionalShadowMap[c]=b,o.directionalShadowMatrix[c]=e.shadow.matrix,f++}o.directional[c]=t,c++}else if(e.isSpotLight){const t=n.get(e);if(t.position.setFromMatrixPosition(e.matrixWorld),t.color.copy(_).multiplyScalar(y),t.distance=v,t.coneCos=Math.cos(e.angle),t.penumbraCos=Math.cos(e.angle*(1-e.penumbra)),t.decay=e.decay,e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,o.spotShadow[h]=n,o.spotShadowMap[h]=b,o.spotShadowMatrix[h]=e.shadow.matrix,g++}o.spot[h]=t,h++}else if(e.isRectAreaLight){const t=n.get(e);t.color.copy(_).multiplyScalar(y),t.halfWidth.set(.5*e.width,0,0),t.halfHeight.set(0,.5*e.height,0),o.rectArea[d]=t,d++}else if(e.isPointLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity),t.distance=e.distance,t.decay=e.decay,e.castShadow){const t=e.shadow,n=i.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,n.shadowCameraNear=t.camera.near,n.shadowCameraFar=t.camera.far,o.pointShadow[u]=n,o.pointShadowMap[u]=b,o.pointShadowMatrix[u]=e.shadow.matrix,m++}o.point[u]=t,u++}else if(e.isHemisphereLight){const t=n.get(e);t.skyColor.copy(e.color).multiplyScalar(y),t.groundColor.copy(e.groundColor).multiplyScalar(y),o.hemi[p]=t,p++}}d>0&&(e.isWebGL2||!0===t.has("OES_texture_float_linear")?(o.rectAreaLTC1=l0t.LTC_FLOAT_1,o.rectAreaLTC2=l0t.LTC_FLOAT_2):!0===t.has("OES_texture_half_float_linear")?(o.rectAreaLTC1=l0t.LTC_HALF_1,o.rectAreaLTC2=l0t.LTC_HALF_2):console.error("THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.")),o.ambient[0]=a,o.ambient[1]=s,o.ambient[2]=l;const _=o.hash;_.directionalLength===c&&_.pointLength===u&&_.spotLength===h&&_.rectAreaLength===d&&_.hemiLength===p&&_.numDirectionalShadows===f&&_.numPointShadows===m&&_.numSpotShadows===g||(o.directional.length=c,o.spot.length=h,o.rectArea.length=d,o.point.length=u,o.hemi.length=p,o.directionalShadow.length=f,o.directionalShadowMap.length=f,o.pointShadow.length=m,o.pointShadowMap.length=m,o.spotShadow.length=g,o.spotShadowMap.length=g,o.directionalShadowMatrix.length=f,o.pointShadowMatrix.length=m,o.spotShadowMatrix.length=g,_.directionalLength=c,_.pointLength=u,_.spotLength=h,_.rectAreaLength=d,_.hemiLength=p,_.numDirectionalShadows=f,_.numPointShadows=m,_.numSpotShadows=g,o.version=C5t++)},setupView:function u(t,e){let n=0,i=0,r=0,c=0,u=0;const h=e.matrixWorldInverse;for(let e=0,d=t.length;e<d;e++){const d=t[e];if(d.isDirectionalLight){const t=o.directional[n];t.direction.setFromMatrixPosition(d.matrixWorld),a.setFromMatrixPosition(d.target.matrixWorld),t.direction.sub(a),t.direction.transformDirection(h),n++}else if(d.isSpotLight){const t=o.spot[r];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),t.direction.setFromMatrixPosition(d.matrixWorld),a.setFromMatrixPosition(d.target.matrixWorld),t.direction.sub(a),t.direction.transformDirection(h),r++}else if(d.isRectAreaLight){const t=o.rectArea[c];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),l.identity(),s.copy(d.matrixWorld),s.premultiply(h),l.extractRotation(s),t.halfWidth.set(.5*d.width,0,0),t.halfHeight.set(0,.5*d.height,0),t.halfWidth.applyMatrix4(l),t.halfHeight.applyMatrix4(l),c++}else if(d.isPointLight){const t=o.point[i];t.position.setFromMatrixPosition(d.matrixWorld),t.position.applyMatrix4(h),i++}else if(d.isHemisphereLight){const t=o.hemi[u];t.direction.setFromMatrixPosition(d.matrixWorld),t.direction.transformDirection(h),t.direction.normalize(),u++}}},state:o}}function L5t(t,e){const n=new k5t(t,e),i=[],r=[];return{init:function o(){i.length=0,r.length=0},state:{lightsArray:i,shadowsArray:r,lights:n},setupLights:function a(){n.setup(i)},setupLightsView:function s(t){n.setupView(i,t)},pushLight:function l(t){i.push(t)},pushShadow:function c(t){r.push(t)}}}function P5t(t,e){let n=new WeakMap;return{get:function i(r,o=0){let a;return!1===n.has(r)?(a=new L5t(t,e),n.set(r,[a])):o>=n.get(r).length?(a=new L5t(t,e),n.get(r).push(a)):a=n.get(r)[o],a},dispose:function r(){n=new WeakMap}}}class N5t extends UQt{constructor(t){super(),this.type="MeshDepthMaterial",this.depthPacking=3200,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}N5t.prototype.isMeshDepthMaterial=!0;class I5t extends UQt{constructor(t){super(),this.type="MeshDistanceMaterial",this.referencePosition=new CJt,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}function R5t(t,e,n){let i=new i0t;const r=new mJt,o=new mJt,a=new wJt,s=new N5t({depthPacking:3201}),l=new I5t,c={},u=n.maxTextureSize,h={0:1,1:0,2:2},d=new G1t({defines:{SAMPLE_RATE:2/8,HALF_SAMPLE_RATE:1/8},uniforms:{shadow_pass:{value:null},resolution:{value:new mJt},radius:{value:4}},vertexShader:"void main() {\n\tgl_Position = vec4( position, 1.0 );\n}",fragmentShader:"uniform sampler2D shadow_pass;\nuniform vec2 resolution;\nuniform float radius;\n#include <packing>\nvoid main() {\n\tfloat mean = 0.0;\n\tfloat squared_mean = 0.0;\n\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\n\tfor ( float i = -1.0; i < 1.0 ; i += SAMPLE_RATE) {\n\t\t#ifdef HORIZONTAL_PASS\n\t\t\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( i, 0.0 ) * radius ) / resolution ) );\n\t\t\tmean += distribution.x;\n\t\t\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\n\t\t#else\n\t\t\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, i ) * radius ) / resolution ) );\n\t\t\tmean += depth;\n\t\t\tsquared_mean += depth * depth;\n\t\t#endif\n\t}\n\tmean = mean * HALF_SAMPLE_RATE;\n\tsquared_mean = squared_mean * HALF_SAMPLE_RATE;\n\tfloat std_dev = sqrt( squared_mean - mean * mean );\n\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\n}"}),p=d.clone();p.defines.HORIZONTAL_PASS=1;const f=new b1t;f.setAttribute("position",new QQt(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const m=new B1t(f,d),g=this;function _(n,i){const r=e.update(m);d.uniforms.shadow_pass.value=n.map.texture,d.uniforms.resolution.value=n.mapSize,d.uniforms.radius.value=n.radius,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(i,null,r,d,m,null),p.uniforms.shadow_pass.value=n.mapPass.texture,p.uniforms.resolution.value=n.mapSize,p.uniforms.radius.value=n.radius,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(i,null,r,p,m,null)}function y(e,n,i,r,o,a,u){let d=null;const p=!0===r.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(d=void 0!==p?p:!0===r.isPointLight?l:s,t.localClippingEnabled&&!0===i.clipShadows&&0!==i.clippingPlanes.length){const t=d.uuid,e=i.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let r=n[e];void 0===r&&(r=d.clone(),n[e]=r),d=r}return d.visible=i.visible,d.wireframe=i.wireframe,d.side=3===u?null!==i.shadowSide?i.shadowSide:i.side:null!==i.shadowSide?i.shadowSide:h[i.side],d.clipShadows=i.clipShadows,d.clippingPlanes=i.clippingPlanes,d.clipIntersection=i.clipIntersection,d.wireframeLinewidth=i.wireframeLinewidth,d.linewidth=i.linewidth,!0===r.isPointLight&&!0===d.isMeshDistanceMaterial&&(d.referencePosition.setFromMatrixPosition(r.matrixWorld),d.nearDistance=o,d.farDistance=a),d}function v(n,r,o,a,s){if(!1===n.visible)return;if(n.layers.test(r.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&3===s)&&(!n.frustumCulled||i.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(o.matrixWorldInverse,n.matrixWorld);const i=e.update(n),r=n.material;if(Array.isArray(r)){const e=i.groups;for(let l=0,c=e.length;l<c;l++){const c=e[l],u=r[c.materialIndex];if(u&&u.visible){const e=y(n,0,u,a,o.near,o.far,s);t.renderBufferDirect(o,null,i,e,n,c)}}}else if(r.visible){const e=y(n,0,r,a,o.near,o.far,s);t.renderBufferDirect(o,null,i,e,n,null)}}const l=n.children;for(let t=0,e=l.length;t<e;t++)v(l[t],r,o,a,s)}this.enabled=!1,this.autoUpdate=!0,this.needsUpdate=!1,this.type=1,this.render=function(e,n,s){if(!1===g.enabled)return;if(!1===g.autoUpdate&&!1===g.needsUpdate)return;if(0===e.length)return;const 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Texture.minFilter should be set to THREE.NearestFilter or THREE.LinearFilter.")),!0===e.has("EXT_texture_filter_anisotropic")){const a=e.get("EXT_texture_filter_anisotropic");if(o.type===sZt&&!1===e.has("OES_texture_float_linear"))return;if(!1===s&&o.type===lZt&&!1===e.has("OES_texture_half_float_linear"))return;(o.anisotropy>1||i.get(o).__currentAnisotropy)&&(t.texParameterf(n,a.TEXTURE_MAX_ANISOTROPY_EXT,Math.min(o.anisotropy,r.getMaxAnisotropy())),i.get(o).__currentAnisotropy=o.anisotropy)}}function L(e,n){void 0===e.__webglInit&&(e.__webglInit=!0,n.addEventListener("dispose",w),e.__webglTexture=t.createTexture(),a.memory.textures++)}function P(e,i,r){let a=3553;i.isDataTexture2DArray&&(a=35866),i.isDataTexture3D&&(a=32879),L(e,i),n.activeTexture(33984+r),n.bindTexture(a,e.__webglTexture),t.pixelStorei(37440,i.flipY),t.pixelStorei(37441,i.premultiplyAlpha),t.pixelStorei(3317,i.unpackAlignment),t.pixelStorei(37443,0);const l=(function c(t){return!s&&(t.wrapS!==KKt||t.wrapT!==KKt||t.minFilter!==JKt&&t.minFilter!==eZt)})(i)&&!1===_(i.image),h=g(i.image,l,!1,u),d=_(h)||s,p=o.convert(i.format);let f,m=o.convert(i.type),x=b(i.internalFormat,p,m);k(a,i,d);const w=i.mipmaps;if(i.isDepthTexture)x=6402,s?x=i.type===sZt?36012:i.type===aZt?33190:i.type===cZt?35056:33189:i.type===sZt&&console.error("WebGLRenderer: Floating point depth texture requires WebGL2."),i.format===dZt&&6402===x&&i.type!==oZt&&i.type!==aZt&&(console.warn("THREE.WebGLRenderer: Use UnsignedShortType or UnsignedIntType for DepthFormat DepthTexture."),i.type=oZt,m=o.convert(i.type)),i.format===pZt&&6402===x&&(x=34041,i.type!==cZt&&(console.warn("THREE.WebGLRenderer: Use UnsignedInt248Type for DepthStencilFormat DepthTexture."),i.type=cZt,m=o.convert(i.type))),n.texImage2D(3553,0,x,h.width,h.height,0,p,m,null);else if(i.isDataTexture)if(w.length>0&&d){for(let t=0,e=w.length;t<e;t++)f=w[t],n.texImage2D(3553,t,x,f.width,f.height,0,p,m,f.data);i.generateMipmaps=!1,e.__maxMipLevel=w.length-1}else n.texImage2D(3553,0,x,h.width,h.height,0,p,m,h.data),e.__maxMipLevel=0;else if(i.isCompressedTexture){for(let t=0,e=w.length;t<e;t++)f=w[t],i.format!==hZt&&i.format!==uZt?null!==p?n.compressedTexImage2D(3553,t,x,f.width,f.height,0,f.data):console.warn("THREE.WebGLRenderer: Attempt to load unsupported compressed texture format in .uploadTexture()"):n.texImage2D(3553,t,x,f.width,f.height,0,p,m,f.data);e.__maxMipLevel=w.length-1}else if(i.isDataTexture2DArray)n.texImage3D(35866,0,x,h.width,h.height,h.depth,0,p,m,h.data),e.__maxMipLevel=0;else if(i.isDataTexture3D)n.texImage3D(32879,0,x,h.width,h.height,h.depth,0,p,m,h.data),e.__maxMipLevel=0;else if(w.length>0&&d){for(let t=0,e=w.length;t<e;t++)f=w[t],n.texImage2D(3553,t,x,p,m,f);i.generateMipmaps=!1,e.__maxMipLevel=w.length-1}else n.texImage2D(3553,0,x,p,m,h),e.__maxMipLevel=0;y(i,d)&&v(a,i,h.width,h.height),e.__version=i.version,i.onUpdate&&i.onUpdate(i)}function N(e,r,a,s,l){const c=o.convert(a.format),u=o.convert(a.type),h=b(a.internalFormat,c,u);32879===l||35866===l?n.texImage3D(l,0,h,r.width,r.height,r.depth,0,c,u,null):n.texImage2D(l,0,h,r.width,r.height,0,c,u,null),n.bindFramebuffer(36160,e),t.framebufferTexture2D(36160,s,l,i.get(a).__webglTexture,0),n.bindFramebuffer(36160,null)}function I(e,n,i){if(t.bindRenderbuffer(36161,e),n.depthBuffer&&!n.stencilBuffer){let r=33189;if(i){const e=n.depthTexture;e&&e.isDepthTexture&&(e.type===sZt?r=36012:e.type===aZt&&(r=33190));const i=R(n);t.renderbufferStorageMultisample(36161,i,r,n.width,n.height)}else t.renderbufferStorage(36161,r,n.width,n.height);t.framebufferRenderbuffer(36160,36096,36161,e)}else if(n.depthBuffer&&n.stencilBuffer){if(i){const e=R(n);t.renderbufferStorageMultisample(36161,e,35056,n.width,n.height)}else t.renderbufferStorage(36161,34041,n.width,n.height);t.framebufferRenderbuffer(36160,33306,36161,e)}else{const e=!0===n.isWebGLMultipleRenderTargets?n.texture[0]:n.texture,r=o.convert(e.format),a=o.convert(e.type),s=b(e.internalFormat,r,a);if(i){const e=R(n);t.renderbufferStorageMultisample(36161,e,s,n.width,n.height)}else t.renderbufferStorage(36161,s,n.width,n.height)}t.bindRenderbuffer(36161,null)}function R(t){return s&&t.isWebGLMultisampleRenderTarget?Math.min(h,t.samples):0}let O=!1,z=!1;this.allocateTextureUnit=function D(){const t=M;return t>=l&&console.warn("THREE.WebGLTextures: Trying to use "+t+" texture units while this GPU supports only "+l),M+=1,t},this.resetTextureUnits=function B(){M=0},this.setTexture2D=E,this.setTexture2DArray=function H(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?P(r,t,e):(n.activeTexture(33984+e),n.bindTexture(35866,r.__webglTexture))},this.setTexture3D=function F(t,e){const r=i.get(t);t.version>0&&r.__version!==t.version?P(r,t,e):(n.activeTexture(33984+e),n.bindTexture(32879,r.__webglTexture))},this.setTextureCube=T,this.setupRenderTarget=function V(e){const l=e.texture,c=i.get(e),u=i.get(l);e.addEventListener("dispose",S),!0!==e.isWebGLMultipleRenderTargets&&(u.__webglTexture=t.createTexture(),u.__version=l.version,a.memory.textures++);const h=!0===e.isWebGLCubeRenderTarget,d=!0===e.isWebGLMultipleRenderTargets,p=!0===e.isWebGLMultisampleRenderTarget,f=l.isDataTexture3D||l.isDataTexture2DArray,m=_(e)||s;if(!s||l.format!==uZt||l.type!==sZt&&l.type!==lZt||(l.format=hZt,console.warn("THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.")),h){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),d)if(r.drawBuffers){const n=e.texture;for(let e=0,r=n.length;e<r;e++){const r=i.get(n[e]);void 0===r.__webglTexture&&(r.__webglTexture=t.createTexture(),a.memory.textures++)}}else console.warn("THREE.WebGLRenderer: WebGLMultipleRenderTargets can only be used with WebGL2 or WEBGL_draw_buffers extension.");else if(p)if(s){c.__webglMultisampledFramebuffer=t.createFramebuffer(),c.__webglColorRenderbuffer=t.createRenderbuffer(),t.bindRenderbuffer(36161,c.__webglColorRenderbuffer);const i=o.convert(l.format),r=o.convert(l.type),a=b(l.internalFormat,i,r),s=R(e);t.renderbufferStorageMultisample(36161,s,a,e.width,e.height),n.bindFramebuffer(36160,c.__webglMultisampledFramebuffer),t.framebufferRenderbuffer(36160,36064,36161,c.__webglColorRenderbuffer),t.bindRenderbuffer(36161,null),e.depthBuffer&&(c.__webglDepthRenderbuffer=t.createRenderbuffer(),I(c.__webglDepthRenderbuffer,e,!0)),n.bindFramebuffer(36160,null)}else console.warn("THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.");if(h){n.bindTexture(34067,u.__webglTexture),k(34067,l,m);for(let t=0;t<6;t++)N(c.__webglFramebuffer[t],e,l,36064,34069+t);y(l,m)&&v(34067,l,e.width,e.height),n.bindTexture(34067,null)}else if(d){const t=e.texture;for(let r=0,o=t.length;r<o;r++){const o=t[r],a=i.get(o);n.bindTexture(3553,a.__webglTexture),k(3553,o,m),N(c.__webglFramebuffer,e,o,36064+r,3553),y(o,m)&&v(3553,o,e.width,e.height)}n.bindTexture(3553,null)}else{let t=3553;f&&(s?t=l.isDataTexture3D?32879:35866:console.warn("THREE.DataTexture3D and THREE.DataTexture2DArray only supported with WebGL2.")),n.bindTexture(t,u.__webglTexture),k(t,l,m),N(c.__webglFramebuffer,e,l,36064,t),y(l,m)&&v(t,l,e.width,e.height,e.depth),n.bindTexture(t,null)}e.depthBuffer&&(function g(e){const r=i.get(e),o=!0===e.isWebGLCubeRenderTarget;if(e.depthTexture){if(o)throw new Error("target.depthTexture not supported in Cube render targets");!(function a(e,r){if(r&&r.isWebGLCubeRenderTarget)throw new Error("Depth Texture with cube render targets is not supported");if(n.bindFramebuffer(36160,e),!r.depthTexture||!r.depthTexture.isDepthTexture)throw new Error("renderTarget.depthTexture must be an instance of THREE.DepthTexture");i.get(r.depthTexture).__webglTexture&&r.depthTexture.image.width===r.width&&r.depthTexture.image.height===r.height||(r.depthTexture.image.width=r.width,r.depthTexture.image.height=r.height,r.depthTexture.needsUpdate=!0),E(r.depthTexture,0);const o=i.get(r.depthTexture).__webglTexture;if(r.depthTexture.format===dZt)t.framebufferTexture2D(36160,36096,3553,o,0);else{if(r.depthTexture.format!==pZt)throw new Error("Unknown depthTexture format");t.framebufferTexture2D(36160,33306,3553,o,0)}})(r.__webglFramebuffer,e)}else if(o){r.__webglDepthbuffer=[];for(let i=0;i<6;i++)n.bindFramebuffer(36160,r.__webglFramebuffer[i]),r.__webglDepthbuffer[i]=t.createRenderbuffer(),I(r.__webglDepthbuffer[i],e,!1)}else n.bindFramebuffer(36160,r.__webglFramebuffer),r.__webglDepthbuffer=t.createRenderbuffer(),I(r.__webglDepthbuffer,e,!1);n.bindFramebuffer(36160,null)})(e)},this.updateRenderTargetMipmap=function U(t){const e=_(t)||s,r=!0===t.isWebGLMultipleRenderTargets?t.texture:[t.texture];for(let o=0,a=r.length;o<a;o++){const a=r[o];if(y(a,e)){const e=t.isWebGLCubeRenderTarget?34067:3553,r=i.get(a).__webglTexture;n.bindTexture(e,r),v(e,a,t.width,t.height),n.bindTexture(e,null)}}},this.updateMultisampleRenderTarget=function j(e){if(e.isWebGLMultisampleRenderTarget)if(s){const r=e.width,o=e.height;let a=16384;e.depthBuffer&&(a|=256),e.stencilBuffer&&(a|=1024);const s=i.get(e);n.bindFramebuffer(36008,s.__webglMultisampledFramebuffer),n.bindFramebuffer(36009,s.__webglFramebuffer),t.blitFramebuffer(0,0,r,o,0,0,r,o,a,9728),n.bindFramebuffer(36008,null),n.bindFramebuffer(36009,s.__webglMultisampledFramebuffer)}else console.warn("THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.")},this.safeSetTexture2D=function G(t,e){t&&t.isWebGLRenderTarget&&(!1===O&&(console.warn("THREE.WebGLTextures.safeSetTexture2D: don't use render targets as textures. Use their .texture property instead."),O=!0),t=t.texture),E(t,e)},this.safeSetTextureCube=function W(t,e){t&&t.isWebGLCubeRenderTarget&&(!1===z&&(console.warn("THREE.WebGLTextures.safeSetTextureCube: don't use cube render targets as textures. Use their .texture property instead."),z=!0),t=t.texture),T(t,e)}}function D5t(t,e,n){const i=n.isWebGL2;return{convert:function r(t){let n;if(t===rZt)return 5121;if(1017===t)return 32819;if(1018===t)return 32820;if(1019===t)return 33635;if(1010===t)return 5120;if(1011===t)return 5122;if(t===oZt)return 5123;if(1013===t)return 5124;if(t===aZt)return 5125;if(t===sZt)return 5126;if(t===lZt)return i?5131:(n=e.get("OES_texture_half_float"),null!==n?n.HALF_FLOAT_OES:null);if(1021===t)return 6406;if(t===uZt)return 6407;if(t===hZt)return 6408;if(1024===t)return 6409;if(1025===t)return 6410;if(t===dZt)return 6402;if(t===pZt)return 34041;if(1028===t)return 6403;if(1029===t)return 36244;if(1030===t)return 33319;if(1031===t)return 33320;if(1032===t)return 36248;if(1033===t)return 36249;if(t===fZt||t===mZt||t===gZt||t===_Zt){if(n=e.get("WEBGL_compressed_texture_s3tc"),null===n)return null;if(t===fZt)return n.COMPRESSED_RGB_S3TC_DXT1_EXT;if(t===mZt)return n.COMPRESSED_RGBA_S3TC_DXT1_EXT;if(t===gZt)return n.COMPRESSED_RGBA_S3TC_DXT3_EXT;if(t===_Zt)return n.COMPRESSED_RGBA_S3TC_DXT5_EXT}if(t===yZt||t===vZt||t===bZt||t===xZt){if(n=e.get("WEBGL_compressed_texture_pvrtc"),null===n)return null;if(t===yZt)return n.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(t===vZt)return n.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(t===bZt)return n.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(t===xZt)return n.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}if(36196===t)return n=e.get("WEBGL_compressed_texture_etc1"),null!==n?n.COMPRESSED_RGB_ETC1_WEBGL:null;if((t===wZt||t===SZt)&&(n=e.get("WEBGL_compressed_texture_etc"),null!==n)){if(t===wZt)return n.COMPRESSED_RGB8_ETC2;if(t===SZt)return n.COMPRESSED_RGBA8_ETC2_EAC}return 37808===t||37809===t||37810===t||37811===t||37812===t||37813===t||37814===t||37815===t||37816===t||37817===t||37818===t||37819===t||37820===t||37821===t||37840===t||37841===t||37842===t||37843===t||37844===t||37845===t||37846===t||37847===t||37848===t||37849===t||37850===t||37851===t||37852===t||37853===t?(n=e.get("WEBGL_compressed_texture_astc"),null!==n?t:null):36492===t?(n=e.get("EXT_texture_compression_bptc"),null!==n?t:null):t===cZt?i?34042:(n=e.get("WEBGL_depth_texture"),null!==n?n.UNSIGNED_INT_24_8_WEBGL:null):void 0}}}I5t.prototype.isMeshDistanceMaterial=!0;class B5t extends q1t{constructor(t=[]){super(),this.cameras=t}}B5t.prototype.isArrayCamera=!0;class H5t extends kQt{constructor(){super(),this.type="Group"}}H5t.prototype.isGroup=!0;const F5t={type:"move"};class V5t{constructor(){this._targetRay=null,this._grip=null,this._hand=null}getHandSpace(){return null===this._hand&&(this._hand=new H5t,this._hand.matrixAutoUpdate=!1,this._hand.visible=!1,this._hand.joints={},this._hand.inputState={pinching:!1}),this._hand}getTargetRaySpace(){return null===this._targetRay&&(this._targetRay=new H5t,this._targetRay.matrixAutoUpdate=!1,this._targetRay.visible=!1,this._targetRay.hasLinearVelocity=!1,this._targetRay.linearVelocity=new CJt,this._targetRay.hasAngularVelocity=!1,this._targetRay.angularVelocity=new CJt),this._targetRay}getGripSpace(){return null===this._grip&&(this._grip=new H5t,this._grip.matrixAutoUpdate=!1,this._grip.visible=!1,this._grip.hasLinearVelocity=!1,this._grip.linearVelocity=new CJt,this._grip.hasAngularVelocity=!1,this._grip.angularVelocity=new CJt),this._grip}dispatchEvent(t){return null!==this._targetRay&&this._targetRay.dispatchEvent(t),null!==this._grip&&this._grip.dispatchEvent(t),null!==this._hand&&this._hand.dispatchEvent(t),this}disconnect(t){return this.dispatchEvent({type:"disconnected",data:t}),null!==this._targetRay&&(this._targetRay.visible=!1),null!==this._grip&&(this._grip.visible=!1),null!==this._hand&&(this._hand.visible=!1),this}update(t,e,n){let i=null,r=null,o=null;const a=this._targetRay,s=this._grip,l=this._hand;if(t&&"visible-blurred"!==e.session.visibilityState)if(null!==a&&(i=e.getPose(t.targetRaySpace,n),null!==i&&(a.matrix.fromArray(i.transform.matrix),a.matrix.decompose(a.position,a.rotation,a.scale),i.linearVelocity?(a.hasLinearVelocity=!0,a.linearVelocity.copy(i.linearVelocity)):a.hasLinearVelocity=!1,i.angularVelocity?(a.hasAngularVelocity=!0,a.angularVelocity.copy(i.angularVelocity)):a.hasAngularVelocity=!1,this.dispatchEvent(F5t))),l&&t.hand){o=!0;for(const i of t.hand.values()){const t=e.getJointPose(i,n);if(void 0===l.joints[i.jointName]){const t=new H5t;t.matrixAutoUpdate=!1,t.visible=!1,l.joints[i.jointName]=t,l.add(t)}const r=l.joints[i.jointName];null!==t&&(r.matrix.fromArray(t.transform.matrix),r.matrix.decompose(r.position,r.rotation,r.scale),r.jointRadius=t.radius),r.visible=null!==t}const i=l.joints["index-finger-tip"].position.distanceTo(l.joints["thumb-tip"].position),r=.02,a=.005;l.inputState.pinching&&i>r+a?(l.inputState.pinching=!1,this.dispatchEvent({type:"pinchend",handedness:t.handedness,target:this})):!l.inputState.pinching&&i<=r-a&&(l.inputState.pinching=!0,this.dispatchEvent({type:"pinchstart",handedness:t.handedness,target:this}))}else null!==s&&t.gripSpace&&(r=e.getPose(t.gripSpace,n),null!==r&&(s.matrix.fromArray(r.transform.matrix),s.matrix.decompose(s.position,s.rotation,s.scale),r.linearVelocity?(s.hasLinearVelocity=!0,s.linearVelocity.copy(r.linearVelocity)):s.hasLinearVelocity=!1,r.angularVelocity?(s.hasAngularVelocity=!0,s.angularVelocity.copy(r.angularVelocity)):s.hasAngularVelocity=!1));return null!==a&&(a.visible=null!==i),null!==s&&(s.visible=null!==r),null!==l&&(l.visible=null!==o),this}}class U5t extends jZt{constructor(t,e){super();const n=this,i=t.state;let r=null,o=1,a=null,s="local-floor",l=null,c=null,u=null,h=null,d=null;const p=[],f=new Map,m=new q1t;m.layers.enable(1),m.viewport=new wJt;const g=new q1t;g.layers.enable(2),g.viewport=new wJt;const _=[m,g],y=new B5t;y.layers.enable(1),y.layers.enable(2);let v=null,b=null;function x(t){const e=f.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function w(){f.forEach((function(t,e){t.disconnect(e)})),f.clear(),v=null,b=null,i.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),A.stop(),n.isPresenting=!1,n.dispatchEvent({type:"sessionend"})}function S(t){const e=r.inputSources;for(let t=0;t<p.length;t++)f.set(e[t],p[t]);for(let e=0;e<t.removed.length;e++){const n=t.removed[e],i=f.get(n);i&&(i.dispatchEvent({type:"disconnected",data:n}),f.delete(n))}for(let e=0;e<t.added.length;e++){const n=t.added[e],i=f.get(n);i&&i.dispatchEvent({type:"connected",data:n})}}this.cameraAutoUpdate=!0,this.enabled=!1,this.isPresenting=!1,this.getController=function(t){let e=p[t];return void 0===e&&(e=new V5t,p[t]=e),e.getTargetRaySpace()},this.getControllerGrip=function(t){let e=p[t];return void 0===e&&(e=new V5t,p[t]=e),e.getGripSpace()},this.getHand=function(t){let e=p[t];return void 0===e&&(e=new V5t,p[t]=e),e.getHandSpace()},this.setFramebufferScaleFactor=function(t){o=t,!0===n.isPresenting&&console.warn("THREE.WebXRManager: Cannot change framebuffer scale while presenting.")},this.setReferenceSpaceType=function(t){s=t,!0===n.isPresenting&&console.warn("THREE.WebXRManager: Cannot change reference space type while presenting.")},this.getReferenceSpace=function(){return a},this.getSession=function(){return r},this.setSession=async function(t){if(r=t,null!==r){r.addEventListener("select",x),r.addEventListener("selectstart",x),r.addEventListener("selectend",x),r.addEventListener("squeeze",x),r.addEventListener("squeezestart",x),r.addEventListener("squeezeend",x),r.addEventListener("end",w),r.addEventListener("inputsourceschange",S);const t=e.getContextAttributes();if(!0!==t.xrCompatible&&await e.makeXRCompatible(),void 0===r.renderState.layers)d=new XRWebGLLayer(r,e,{antialias:t.antialias,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:o}),r.updateRenderState({baseLayer:d});else{let n=0;if(t.antialias)d=new XRWebGLLayer(r,e,{antialias:!0,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:o}),r.updateRenderState({layers:[d]});else{t.depth&&(n=t.stencil?34041:6402);const i={colorFormat:t.alpha?6408:6407,depthFormat:n,scaleFactor:o};c=new XRWebGLBinding(r,e),h=c.createProjectionLayer(i),u=e.createFramebuffer(),r.updateRenderState({layers:[h]})}}a=await r.requestReferenceSpace(s),A.setContext(r),A.start(),n.isPresenting=!0,n.dispatchEvent({type:"sessionstart"})}};const M=new CJt,E=new CJt;function T(t,e){null===e?t.matrixWorld.copy(t.matrix):t.matrixWorld.multiplyMatrices(e.matrixWorld,t.matrix),t.matrixWorldInverse.copy(t.matrixWorld).invert()}this.updateCamera=function(t){if(null===r)return;y.near=g.near=m.near=t.near,y.far=g.far=m.far=t.far,v===y.near&&b===y.far||(r.updateRenderState({depthNear:y.near,depthFar:y.far}),v=y.near,b=y.far);const e=t.parent,n=y.cameras;T(y,e);for(let t=0;t<n.length;t++)T(n[t],e);y.matrixWorld.decompose(y.position,y.quaternion,y.scale),t.position.copy(y.position),t.quaternion.copy(y.quaternion),t.scale.copy(y.scale),t.matrix.copy(y.matrix),t.matrixWorld.copy(y.matrixWorld);const i=t.children;for(let t=0,e=i.length;t<e;t++)i[t].updateMatrixWorld(!0);2===n.length?(function o(t,e,n){M.setFromMatrixPosition(e.matrixWorld),E.setFromMatrixPosition(n.matrixWorld);const 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n=!1;t.length!==y.cameras.length&&(y.cameras.length=0,n=!0);for(let r=0;r<t.length;r++){const o=t[r];let a=null;if(null!==d)a=d.getViewport(o);else{const t=c.getViewSubImage(h,o);i.bindXRFramebuffer(u),void 0!==t.depthStencilTexture&&e.framebufferTexture2D(36160,36096,3553,t.depthStencilTexture,0),e.framebufferTexture2D(36160,36064,3553,t.colorTexture,0),a=t.viewport}const s=_[r];s.matrix.fromArray(o.transform.matrix),s.projectionMatrix.fromArray(o.projectionMatrix),s.viewport.set(a.x,a.y,a.width,a.height),0===r&&y.matrix.copy(s.matrix),!0===n&&y.cameras.push(s)}}const o=r.inputSources;for(let t=0;t<p.length;t++)p[t].update(o[t],n,a);C&&C(t,n)})),this.setAnimationLoop=function(t){C=t},this.dispose=function(){}}}function j5t(t){function e(e,n){e.opacity.value=n.opacity,n.color&&e.diffuse.value.copy(n.color),n.emissive&&e.emissive.value.copy(n.emissive).multiplyScalar(n.emissiveIntensity),n.map&&(e.map.value=n.map),n.alphaMap&&(e.alphaMap.value=n.alphaMap),n.specularMap&&(e.specularMap.value=n.specularMap);const i=t.get(n).envMap;if(i){e.envMap.value=i,e.flipEnvMap.value=i.isCubeTexture&&!1===i.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.refractionRatio.value=n.refractionRatio;const r=t.get(i).__maxMipLevel;void 0!==r&&(e.maxMipLevel.value=r)}let r,o;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?r=n.map:n.specularMap?r=n.specularMap:n.displacementMap?r=n.displacementMap:n.normalMap?r=n.normalMap:n.bumpMap?r=n.bumpMap:n.roughnessMap?r=n.roughnessMap:n.metalnessMap?r=n.metalnessMap:n.alphaMap?r=n.alphaMap:n.emissiveMap?r=n.emissiveMap:n.clearcoatMap?r=n.clearcoatMap:n.clearcoatNormalMap?r=n.clearcoatNormalMap:n.clearcoatRoughnessMap?r=n.clearcoatRoughnessMap:n.specularIntensityMap?r=n.specularIntensityMap:n.specularTintMap&&(r=n.specularTintMap),void 0!==r&&(r.isWebGLRenderTarget&&(r=r.texture),!0===r.matrixAutoUpdate&&r.updateMatrix(),e.uvTransform.value.copy(r.matrix)),n.aoMap?o=n.aoMap:n.lightMap&&(o=n.lightMap),void 0!==o&&(o.isWebGLRenderTarget&&(o=o.texture),!0===o.matrixAutoUpdate&&o.updateMatrix(),e.uv2Transform.value.copy(o.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,1===n.side&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),1===n.side&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias),t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function i(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function r(t,i,o,a,s){i.isMeshBasicMaterial?e(t,i):i.isMeshLambertMaterial?(e(t,i),(function l(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)})(t,i)):i.isMeshToonMaterial?(e(t,i),(function c(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap),e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1)),e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate()),e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,i)):i.isMeshPhongMaterial?(e(t,i),(function u(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap),e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1)),e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate()),e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,i)):i.isMeshStandardMaterial?(e(t,i),i.isMeshPhysicalMaterial?(function h(t,e,i){n(t,e),t.reflectivity.value=e.reflectivity,t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.sheen&&t.sheen.value.copy(e.sheen),e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,1===e.side&&t.clearcoatNormalScale.value.negate()),t.transmission.value=e.transmission,e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),e.transmission>0&&(t.transmissionSamplerMap.value=i.texture,t.transmissionSamplerSize.value.set(i.width,i.height)),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint),t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap),e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)})(t,i,s):n(t,i)):i.isMeshMatcapMaterial?(e(t,i),(function d(t,e){e.matcap&&(t.matcap.value=e.matcap),e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1)),e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate()),e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,i)):i.isMeshDepthMaterial?(e(t,i),(function p(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,i)):i.isMeshDistanceMaterial?(e(t,i),(function 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i=e.getAttributes();t.hasPositions&&(dt.bindBuffer(34962,n.position),dt.bufferData(34962,t.positionArray,35048),ht.enableAttribute(i.position),dt.vertexAttribPointer(i.position,3,5126,!1,0,0)),t.hasNormals&&(dt.bindBuffer(34962,n.normal),dt.bufferData(34962,t.normalArray,35048),ht.enableAttribute(i.normal),dt.vertexAttribPointer(i.normal,3,5126,!1,0,0)),t.hasUvs&&(dt.bindBuffer(34962,n.uv),dt.bufferData(34962,t.uvArray,35048),ht.enableAttribute(i.uv),dt.vertexAttribPointer(i.uv,2,5126,!1,0,0)),t.hasColors&&(dt.bindBuffer(34962,n.color),dt.bufferData(34962,t.colorArray,35048),ht.enableAttribute(i.color),dt.vertexAttribPointer(i.color,3,5126,!1,0,0)),ht.disableUnusedAttributes(),dt.drawArrays(4,0,t.count),t.count=0},this.renderBufferDirect=function(t,e,n,i,r,o){null===e&&(e=V);const a=r.isMesh&&r.matrixWorld.determinant()<0,s=At(t,e,i,r);W.setMaterial(i,a);let l=n.index;const c=n.attributes.position;if(null===l){if(void 0===c||0===c.count)return}else if(0===l.count)return;let 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if((t.isMesh||t.isLine||t.isPoints)&&(t.isSkinnedMesh&&t.skeleton.frame!==q.render.frame&&(t.skeleton.update(),t.skeleton.frame=q.render.frame),!t.frustumCulled||O.intersectsObject(t))){i&&F.setFromMatrixPosition(t.matrixWorld).applyMatrix4(H);const e=Q.update(t),r=t.material;if(Array.isArray(r)){const i=e.groups;for(let o=0,a=i.length;o<a;o++){const a=i[o],s=r[a.materialIndex];s&&s.visible&&d.push(t,e,s,n,F.z,a)}}else r.visible&&d.push(t,e,r,n,F.z,null)}const r=t.children;for(let t=0,o=r.length;t<o;t++)St(r[t],e,n,i)}function Mt(t,e,n){const i=!0===e.isScene?e.overrideMaterial:null;if(n.isArrayCamera){const r=n.cameras;for(let n=0,o=r.length;n<o;n++){const o=r[n];W.viewport(S.copy(o.viewport)),p.setupLightsView(o);for(let n=0,r=t.length;n<r;n++){const r=t[n],a=r.object,s=r.geometry,l=null===i?r.material:i,c=r.group;a.layers.test(o.layers)&&Et(a,e,o,s,l,c)}}}else for(let r=0,o=t.length;r<o;r++){const o=t[r];Et(o.object,e,n,o.geometry,null===i?o.material:i,o.group)}}function Et(t,e,n,i,r,o){if(t.onBeforeRender(g,e,n,i,r,o),t.modelViewMatrix.multiplyMatrices(n.matrixWorldInverse,t.matrixWorld),t.normalMatrix.getNormalMatrix(t.modelViewMatrix),t.isImmediateRenderObject){const i=At(n,e,r,t);W.setMaterial(r),ht.reset(),(function a(t,e){t.render((function(t){g.renderBufferImmediate(t,e)}))})(t,i)}else!0===r.transparent&&2===r.side?(r.side=1,r.needsUpdate=!0,g.renderBufferDirect(n,e,i,r,t,o),r.side=0,r.needsUpdate=!0,g.renderBufferDirect(n,e,i,r,t,o),r.side=2):g.renderBufferDirect(n,e,i,r,t,o);t.onAfterRender(g,e,n,i,r,o)}function Tt(t,e,n){!0!==e.isScene&&(e=V);const i=Y.get(t),r=p.state.lights,o=r.state.version,a=tt.getParameters(t,r.state,p.state.shadowsArray,e,n),s=tt.getProgramCacheKey(a);let l=i.programs;i.environment=t.isMeshStandardMaterial?e.environment:null,i.fog=e.fog,i.envMap=(t.isMeshStandardMaterial?K:$).get(t.envMap||i.environment),void 0===l&&(t.addEventListener("dispose",yt),l=new Map,i.programs=l);let c=l.get(s);if(void 0!==c){if(i.currentProgram===c&&i.lightsStateVersion===o)return Ct(t,a),c}else a.uniforms=tt.getUniforms(t),t.onBuild(a,g),t.onBeforeCompile(a,g),c=tt.acquireProgram(a,s),l.set(s,c),i.uniforms=a.uniforms;const u=i.uniforms;(t.isShaderMaterial||t.isRawShaderMaterial)&&!0!==t.clipping||(u.clippingPlanes=rt.uniform),Ct(t,a),i.needsLights=(function h(t){return t.isMeshLambertMaterial||t.isMeshToonMaterial||t.isMeshPhongMaterial||t.isMeshStandardMaterial||t.isShadowMaterial||t.isShaderMaterial&&!0===t.lights})(t),i.lightsStateVersion=o,i.needsLights&&(u.ambientLightColor.value=r.state.ambient,u.lightProbe.value=r.state.probe,u.directionalLights.value=r.state.directional,u.directionalLightShadows.value=r.state.directionalShadow,u.spotLights.value=r.state.spot,u.spotLightShadows.value=r.state.spotShadow,u.rectAreaLights.value=r.state.rectArea,u.ltc_1.value=r.state.rectAreaLTC1,u.ltc_2.value=r.state.rectAreaLTC2,u.pointLights.value=r.state.point,u.pointLightShadows.value=r.state.pointShadow,u.hemisphereLights.value=r.state.hemi,u.directionalShadowMap.value=r.state.directionalShadowMap,u.directionalShadowMatrix.value=r.state.directionalShadowMatrix,u.spotShadowMap.value=r.state.spotShadowMap,u.spotShadowMatrix.value=r.state.spotShadowMatrix,u.pointShadowMap.value=r.state.pointShadowMap,u.pointShadowMatrix.value=r.state.pointShadowMatrix);const d=c.getUniforms(),f=Q2t.seqWithValue(d.seq,u);return i.currentProgram=c,i.uniformsList=f,c}function Ct(t,e){const n=Y.get(t);n.outputEncoding=e.outputEncoding,n.instancing=e.instancing,n.skinning=e.skinning,n.morphTargets=e.morphTargets,n.morphNormals=e.morphNormals,n.numClippingPlanes=e.numClippingPlanes,n.numIntersection=e.numClipIntersection,n.vertexAlphas=e.vertexAlphas,n.vertexTangents=e.vertexTangents}function At(t,e,n,i){!0!==e.isScene&&(e=V),X.resetTextureUnits();const r=e.fog,o=null===b?g.outputEncoding:b.texture.encoding,a=(n.isMeshStandardMaterial?K:$).get(n.envMap||(n.isMeshStandardMaterial?e.environment:null)),s=!0===n.vertexColors&&!!i.geometry&&!!i.geometry.attributes.color&&4===i.geometry.attributes.color.itemSize,l=!!i.geometry&&!!i.geometry.attributes.tangent,c=!!i.geometry&&!!i.geometry.morphAttributes.position,u=!!i.geometry&&!!i.geometry.morphAttributes.normal,h=Y.get(n),d=p.state.lights;!0!==z||!0!==D&&t===w||rt.setState(n,t,t===w&&n.id===x);let f=!1;n.version===h.__version?h.needsLights&&h.lightsStateVersion!==d.state.version||h.outputEncoding!==o||i.isInstancedMesh&&!1===h.instancing?f=!0:i.isInstancedMesh||!0!==h.instancing?i.isSkinnedMesh&&!1===h.skinning?f=!0:i.isSkinnedMesh||!0!==h.skinning?h.envMap!==a||n.fog&&h.fog!==r?f=!0:void 0===h.numClippingPlanes||h.numClippingPlanes===rt.numPlanes&&h.numIntersection===rt.numIntersection?(h.vertexAlphas!==s||h.vertexTangents!==l||h.morphTargets!==c||h.morphNormals!==u)&&(f=!0):f=!0:f=!0:f=!0:(f=!0,h.__version=n.version);let m=h.currentProgram;!0===f&&(m=Tt(n,e,i));let _=!1,y=!1,v=!1;const S=m.getUniforms(),M=h.uniforms;if(W.useProgram(m.program)&&(_=!0,y=!0,v=!0),n.id!==x&&(x=n.id,y=!0),_||w!==t){if(S.setValue(dt,"projectionMatrix",t.projectionMatrix),G.logarithmicDepthBuffer&&S.setValue(dt,"logDepthBufFC",2/(Math.log(t.far+1)/Math.LN2)),w!==t&&(w=t,y=!0,v=!0),n.isShaderMaterial||n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshStandardMaterial||n.envMap){const e=S.map.cameraPosition;void 0!==e&&e.setValue(dt,F.setFromMatrixPosition(t.matrixWorld))}(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial)&&S.setValue(dt,"isOrthographic",!0===t.isOrthographicCamera),(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial||n.isShadowMaterial||i.isSkinnedMesh)&&S.setValue(dt,"viewMatrix",t.matrixWorldInverse)}if(i.isSkinnedMesh){S.setOptional(dt,i,"bindMatrix"),S.setOptional(dt,i,"bindMatrixInverse");const t=i.skeleton;t&&(G.floatVertexTextures?(null===t.boneTexture&&t.computeBoneTexture(),S.setValue(dt,"boneTexture",t.boneTexture,X),S.setValue(dt,"boneTextureSize",t.boneTextureSize)):S.setOptional(dt,t,"boneMatrices"))}return(y||h.receiveShadow!==i.receiveShadow)&&(h.receiveShadow=i.receiveShadow,S.setValue(dt,"receiveShadow",i.receiveShadow)),y&&(S.setValue(dt,"toneMappingExposure",g.toneMappingExposure),h.needsLights&&(function E(t,e){t.ambientLightColor.needsUpdate=e,t.lightProbe.needsUpdate=e,t.directionalLights.needsUpdate=e,t.directionalLightShadows.needsUpdate=e,t.pointLights.needsUpdate=e,t.pointLightShadows.needsUpdate=e,t.spotLights.needsUpdate=e,t.spotLightShadows.needsUpdate=e,t.rectAreaLights.needsUpdate=e,t.hemisphereLights.needsUpdate=e})(M,v),r&&n.fog&&et.refreshFogUniforms(M,r),et.refreshMaterialUniforms(M,n,A,C,B),Q2t.upload(dt,h.uniformsList,M,X)),n.isShaderMaterial&&!0===n.uniformsNeedUpdate&&(Q2t.upload(dt,h.uniformsList,M,X),n.uniformsNeedUpdate=!1),n.isSpriteMaterial&&S.setValue(dt,"center",i.center),S.setValue(dt,"modelViewMatrix",i.modelViewMatrix),S.setValue(dt,"normalMatrix",i.normalMatrix),S.setValue(dt,"modelMatrix",i.matrixWorld),m}wt.setAnimationLoop((function kt(t){vt&&vt(t)})),"undefined"!=typeof window&&wt.setContext(window),this.setAnimationLoop=function(t){vt=t,mt.setAnimationLoop(t),null===t?wt.stop():wt.start()},mt.addEventListener("sessionstart",bt),mt.addEventListener("sessionend",xt),this.render=function(t,e){if(void 0!==e&&!0!==e.isCamera)return void console.error("THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera.");if(!0===_)return;!0===t.autoUpdate&&t.updateMatrixWorld(),null===e.parent&&e.updateMatrixWorld(),!0===mt.enabled&&!0===mt.isPresenting&&(!0===mt.cameraAutoUpdate&&mt.updateCamera(e),e=mt.getCamera()),!0===t.isScene&&t.onBeforeRender(g,t,e,b),p=it.get(t,m.length),p.init(),m.push(p),H.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),O.setFromProjectionMatrix(H),D=this.localClippingEnabled,z=rt.init(this.clippingPlanes,D,e),d=nt.get(t,f.length),d.init(),f.push(d),St(t,e,0,g.sortObjects),d.finish(),!0===g.sortObjects&&d.sort(k,L),!0===z&&rt.beginShadows(),ot.render(p.state.shadowsArray,t,e),p.setupLights(),p.setupLightsView(e),!0===z&&rt.endShadows(),!0===this.info.autoReset&&this.info.reset(),at.render(d,t);const n=d.opaque,i=d.transmissive,r=d.transparent;n.length>0&&Mt(n,t,e),i.length>0&&(function o(t,e,n,i){null===B&&(B=new(!0===s&&!0===G.isWebGL2?EJt:SJt)(1024,1024,{generateMipmaps:!0,type:null!==ut.convert(lZt)?lZt:rZt,minFilter:iZt,magFilter:JKt,wrapS:KKt,wrapT:KKt}));const r=g.getRenderTarget();g.setRenderTarget(B),g.clear();const o=g.toneMapping;g.toneMapping=0,Mt(t,n,i),g.toneMapping=o,X.updateMultisampleRenderTarget(B),X.updateRenderTargetMipmap(B),g.setRenderTarget(r),Mt(e,n,i)})(n,i,t,e),r.length>0&&Mt(r,t,e),null!==b&&(X.updateMultisampleRenderTarget(b),X.updateRenderTargetMipmap(b)),!0===t.isScene&&t.onAfterRender(g,t,e),W.buffers.depth.setTest(!0),W.buffers.depth.setMask(!0),W.buffers.color.setMask(!0),W.setPolygonOffset(!1),ht.resetDefaultState(),x=-1,w=null,m.pop(),p=m.length>0?m[m.length-1]:null,f.pop(),d=f.length>0?f[f.length-1]:null},this.getActiveCubeFace=function(){return y},this.getActiveMipmapLevel=function(){return v},this.getRenderTarget=function(){return b},this.setRenderTarget=function(t,e=0,n=0){b=t,y=e,v=n,t&&void 0===Y.get(t).__webglFramebuffer&&X.setupRenderTarget(t);let i=null,r=!1,o=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(o=!0);const a=Y.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(i=a[e],r=!0):i=t.isWebGLMultisampleRenderTarget?Y.get(t).__webglMultisampledFramebuffer:a,S.copy(t.viewport),M.copy(t.scissor),E=t.scissorTest}else S.copy(P).multiplyScalar(A).floor(),M.copy(N).multiplyScalar(A).floor(),E=I;if(W.bindFramebuffer(36160,i)&&G.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(R.length!==n.length||36064!==R[0]){for(let t=0,e=n.length;t<e;t++)R[t]=36064+t;R.length=n.length,e=!0}}else 1===R.length&&36064===R[0]||(R[0]=36064,R.length=1,e=!0);else 1===R.length&&1029===R[0]||(R[0]=1029,R.length=1,e=!0);e&&(G.isWebGL2?dt.drawBuffers(R):j.get("WEBGL_draw_buffers").drawBuffersWEBGL(R))}if(W.viewport(S),W.scissor(M),W.setScissorTest(E),r){const i=Y.get(t.texture);dt.framebufferTexture2D(36160,36064,34069+e,i.__webglTexture,n)}else if(o){const i=Y.get(t.texture);dt.framebufferTextureLayer(36160,36064,i.__webglTexture,n||0,e||0)}},this.readRenderTargetPixels=function(t,e,n,i,r,o,a){if(!t||!t.isWebGLRenderTarget)return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.");let s=Y.get(t).__webglFramebuffer;if(t.isWebGLCubeRenderTarget&&void 0!==a&&(s=s[a]),s){W.bindFramebuffer(36160,s);try{const a=t.texture,s=a.format,l=a.type;if(s!==hZt&&ut.convert(s)!==dt.getParameter(35739))return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format.");const c=l===lZt&&(j.has("EXT_color_buffer_half_float")||G.isWebGL2&&j.has("EXT_color_buffer_float"));if(!(l===rZt||ut.convert(l)===dt.getParameter(35738)||l===sZt&&(G.isWebGL2||j.has("OES_texture_float")||j.has("WEBGL_color_buffer_float"))||c))return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.");36053===dt.checkFramebufferStatus(36160)?e>=0&&e<=t.width-i&&n>=0&&n<=t.height-r&&dt.readPixels(e,n,i,r,ut.convert(s),ut.convert(l),o):console.error("THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.")}finally{const t=null!==b?Y.get(b).__webglFramebuffer:null;W.bindFramebuffer(36160,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const i=Math.pow(2,-n),r=Math.floor(e.image.width*i),o=Math.floor(e.image.height*i);let a=ut.convert(e.format);G.isWebGL2&&(6407===a&&(a=32849),6408===a&&(a=32856)),X.setTexture2D(e,0),dt.copyTexImage2D(3553,n,a,t.x,t.y,r,o,0),W.unbindTexture()},this.copyTextureToTexture=function(t,e,n,i=0){const r=e.image.width,o=e.image.height,a=ut.convert(n.format),s=ut.convert(n.type);X.setTexture2D(n,0),dt.pixelStorei(37440,n.flipY),dt.pixelStorei(37441,n.premultiplyAlpha),dt.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?dt.texSubImage2D(3553,i,t.x,t.y,r,o,a,s,e.image.data):e.isCompressedTexture?dt.compressedTexSubImage2D(3553,i,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,a,e.mipmaps[0].data):dt.texSubImage2D(3553,i,t.x,t.y,a,s,e.image),0===i&&n.generateMipmaps&&dt.generateMipmap(3553),W.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,i,r=0){if(g.isWebGL1Renderer)return void console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.");const o=t.max.x-t.min.x+1,a=t.max.y-t.min.y+1,s=t.max.z-t.min.z+1,l=ut.convert(i.format),c=ut.convert(i.type);let u;if(i.isDataTexture3D)X.setTexture3D(i,0),u=32879;else{if(!i.isDataTexture2DArray)return void console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and THREE.DataTexture2DArray.");X.setTexture2DArray(i,0),u=35866}dt.pixelStorei(37440,i.flipY),dt.pixelStorei(37441,i.premultiplyAlpha),dt.pixelStorei(3317,i.unpackAlignment);const h=dt.getParameter(3314),d=dt.getParameter(32878),p=dt.getParameter(3316),f=dt.getParameter(3315),m=dt.getParameter(32877),_=n.isCompressedTexture?n.mipmaps[0]:n.image;dt.pixelStorei(3314,_.width),dt.pixelStorei(32878,_.height),dt.pixelStorei(3316,t.min.x),dt.pixelStorei(3315,t.min.y),dt.pixelStorei(32877,t.min.z),n.isDataTexture||n.isDataTexture3D?dt.texSubImage3D(u,r,e.x,e.y,e.z,o,a,s,l,c,_.data):n.isCompressedTexture?(console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: untested support for compressed srcTexture."),dt.compressedTexSubImage3D(u,r,e.x,e.y,e.z,o,a,s,l,_.data)):dt.texSubImage3D(u,r,e.x,e.y,e.z,o,a,s,l,c,_),dt.pixelStorei(3314,h),dt.pixelStorei(32878,d),dt.pixelStorei(3316,p),dt.pixelStorei(3315,f),dt.pixelStorei(32877,m),0===r&&i.generateMipmaps&&dt.generateMipmap(u),W.unbindTexture()},this.initTexture=function(t){X.setTexture2D(t,0),W.unbindTexture()},this.resetState=function(){y=0,v=0,b=null,W.reset(),ht.reset()},"undefined"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}class W5t extends G5t{}W5t.prototype.isWebGL1Renderer=!0;class q5t{constructor(t,e=25e-5){this.name="",this.color=new $Qt(t),this.density=e}clone(){return new q5t(this.color,this.density)}toJSON(){return{type:"FogExp2",color:this.color.getHex(),density:this.density}}}q5t.prototype.isFogExp2=!0;class Y5t{constructor(t,e=1,n=1e3){this.name="",this.color=new $Qt(t),this.near=e,this.far=n}clone(){return new Y5t(this.color,this.near,this.far)}toJSON(){return{type:"Fog",color:this.color.getHex(),near:this.near,far:this.far}}}Y5t.prototype.isFog=!0;class X5t extends kQt{constructor(){super(),this.type="Scene",this.background=null,this.environment=null,this.fog=null,this.overrideMaterial=null,this.autoUpdate=!0,"undefined"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("observe",{detail:this}))}copy(t,e){return super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}X5t.prototype.isScene=!0;class $5t{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=FZt,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=XZt()}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.array=new t.array.constructor(t.array),this.count=t.count,this.stride=t.stride,this.usage=t.usage,this}copyAt(t,e,n){t*=this.stride,n*=e.stride;for(let i=0,r=this.stride;i<r;i++)this.array[t+i]=e.array[n+i];return this}set(t,e=0){return this.array.set(t,e),this}clone(t){void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=XZt()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=this.array.slice(0).buffer);const e=new this.array.constructor(t.arrayBuffers[this.array.buffer._uuid]),n=new this.constructor(e,this.stride);return n.setUsage(this.usage),n}onUpload(t){return this.onUploadCallback=t,this}toJSON(t){return void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=XZt()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=Array.prototype.slice.call(new Uint32Array(this.array.buffer))),{uuid:this.uuid,buffer:this.array.buffer._uuid,type:this.array.constructor.name,stride:this.stride}}}$5t.prototype.isInterleavedBuffer=!0;const K5t=new CJt;class Z5t{constructor(t,e,n,i=!1){this.name="",this.data=t,this.itemSize=e,this.offset=n,this.normalized=!0===i}get count(){return this.data.count}get array(){return this.data.array}set needsUpdate(t){this.data.needsUpdate=t}applyMatrix4(t){for(let e=0,n=this.data.count;e<n;e++)K5t.x=this.getX(e),K5t.y=this.getY(e),K5t.z=this.getZ(e),K5t.applyMatrix4(t),this.setXYZ(e,K5t.x,K5t.y,K5t.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)K5t.x=this.getX(e),K5t.y=this.getY(e),K5t.z=this.getZ(e),K5t.applyNormalMatrix(t),this.setXYZ(e,K5t.x,K5t.y,K5t.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)K5t.x=this.getX(e),K5t.y=this.getY(e),K5t.z=this.getZ(e),K5t.transformDirection(t),this.setXYZ(e,K5t.x,K5t.y,K5t.z);return this}setX(t,e){return this.data.array[t*this.data.stride+this.offset]=e,this}setY(t,e){return this.data.array[t*this.data.stride+this.offset+1]=e,this}setZ(t,e){return this.data.array[t*this.data.stride+this.offset+2]=e,this}setW(t,e){return this.data.array[t*this.data.stride+this.offset+3]=e,this}getX(t){return this.data.array[t*this.data.stride+this.offset]}getY(t){return this.data.array[t*this.data.stride+this.offset+1]}getZ(t){return this.data.array[t*this.data.stride+this.offset+2]}getW(t){return this.data.array[t*this.data.stride+this.offset+3]}setXY(t,e,n){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this}setXYZ(t,e,n,i){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=i,this}setXYZW(t,e,n,i,r){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=i,this.data.array[t+3]=r,this}clone(t){if(void 0===t){console.log("THREE.InterleavedBufferAttribute.clone(): Cloning an interlaved buffer attribute will deinterleave buffer data.");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return new QQt(new this.array.constructor(t),this.itemSize,this.normalized)}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.clone(t)),new Z5t(t.interleavedBuffers[this.data.uuid],this.itemSize,this.offset,this.normalized)}toJSON(t){if(void 0===t){console.log("THREE.InterleavedBufferAttribute.toJSON(): Serializing an interlaved buffer attribute will deinterleave buffer data.");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return{itemSize:this.itemSize,type:this.array.constructor.name,array:t,normalized:this.normalized}}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.toJSON(t)),{isInterleavedBufferAttribute:!0,itemSize:this.itemSize,data:this.data.uuid,offset:this.offset,normalized:this.normalized}}}Z5t.prototype.isInterleavedBufferAttribute=!0;class J5t extends UQt{constructor(t){super(),this.type="SpriteMaterial",this.color=new $Qt(16777215),this.map=null,this.alphaMap=null,this.rotation=0,this.sizeAttenuation=!0,this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.rotation=t.rotation,this.sizeAttenuation=t.sizeAttenuation,this}}let Q5t;J5t.prototype.isSpriteMaterial=!0;const t3t=new CJt,e3t=new CJt,n3t=new CJt,i3t=new mJt,r3t=new mJt,o3t=new rQt,a3t=new CJt,s3t=new CJt,l3t=new CJt,c3t=new mJt,u3t=new mJt,h3t=new mJt;class d3t extends kQt{constructor(t){if(super(),this.type="Sprite",void 0===Q5t){Q5t=new b1t;const t=new Float32Array([-.5,-.5,0,0,0,.5,-.5,0,1,0,.5,.5,0,1,1,-.5,.5,0,0,1]),e=new $5t(t,5);Q5t.setIndex([0,1,2,0,2,3]),Q5t.setAttribute("position",new Z5t(e,3,0,!1)),Q5t.setAttribute("uv",new Z5t(e,2,3,!1))}this.geometry=Q5t,this.material=void 0!==t?t:new J5t,this.center=new mJt(.5,.5)}raycast(t,e){null===t.camera&&console.error('THREE.Sprite: "Raycaster.camera" needs to be set in order to raycast against sprites.'),e3t.setFromMatrixScale(this.matrixWorld),o3t.copy(t.camera.matrixWorld),this.modelViewMatrix.multiplyMatrices(t.camera.matrixWorldInverse,this.matrixWorld),n3t.setFromMatrixPosition(this.modelViewMatrix),t.camera.isPerspectiveCamera&&!1===this.material.sizeAttenuation&&e3t.multiplyScalar(-n3t.z);const n=this.material.rotation;let i,r;0!==n&&(r=Math.cos(n),i=Math.sin(n));const o=this.center;p3t(a3t.set(-.5,-.5,0),n3t,o,e3t,i,r),p3t(s3t.set(.5,-.5,0),n3t,o,e3t,i,r),p3t(l3t.set(.5,.5,0),n3t,o,e3t,i,r),c3t.set(0,0),u3t.set(1,0),h3t.set(1,1);let a=t.ray.intersectTriangle(a3t,s3t,l3t,!1,t3t);if(null===a&&(p3t(s3t.set(-.5,.5,0),n3t,o,e3t,i,r),u3t.set(0,1),a=t.ray.intersectTriangle(a3t,l3t,s3t,!1,t3t),null===a))return;const s=t.ray.origin.distanceTo(t3t);s<t.near||s>t.far||e.push({distance:s,point:t3t.clone(),uv:FQt.getUV(t3t,a3t,s3t,l3t,c3t,u3t,h3t,new mJt),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}function p3t(t,e,n,i,r,o){i3t.subVectors(t,n).addScalar(.5).multiply(i),void 0!==r?(r3t.x=o*i3t.x-r*i3t.y,r3t.y=r*i3t.x+o*i3t.y):r3t.copy(i3t),t.copy(e),t.x+=r3t.x,t.y+=r3t.y,t.applyMatrix4(o3t)}d3t.prototype.isSprite=!0;const f3t=new CJt,m3t=new CJt;class g3t extends kQt{constructor(){super(),this._currentLevel=0,this.type="LOD",Object.defineProperties(this,{levels:{enumerable:!0,value:[]},isLOD:{value:!0}}),this.autoUpdate=!0}copy(t){super.copy(t,!1);const e=t.levels;for(let t=0,n=e.length;t<n;t++){const n=e[t];this.addLevel(n.object.clone(),n.distance)}return this.autoUpdate=t.autoUpdate,this}addLevel(t,e=0){e=Math.abs(e);const n=this.levels;let i;for(i=0;i<n.length&&!(e<n[i].distance);i++);return n.splice(i,0,{distance:e,object:t}),this.add(t),this}getCurrentLevel(){return this._currentLevel}getObjectForDistance(t){const e=this.levels;if(e.length>0){let n,i;for(n=1,i=e.length;n<i&&!(t<e[n].distance);n++);return e[n-1].object}return null}raycast(t,e){if(this.levels.length>0){f3t.setFromMatrixPosition(this.matrixWorld);const n=t.ray.origin.distanceTo(f3t);this.getObjectForDistance(n).raycast(t,e)}}update(t){const e=this.levels;if(e.length>1){f3t.setFromMatrixPosition(t.matrixWorld),m3t.setFromMatrixPosition(this.matrixWorld);const n=f3t.distanceTo(m3t)/t.zoom;let i,r;for(e[0].object.visible=!0,i=1,r=e.length;i<r&&n>=e[i].distance;i++)e[i-1].object.visible=!1,e[i].object.visible=!0;for(this._currentLevel=i-1;i<r;i++)e[i].object.visible=!1}}toJSON(t){const e=super.toJSON(t);!1===this.autoUpdate&&(e.object.autoUpdate=!1),e.object.levels=[];const n=this.levels;for(let t=0,i=n.length;t<i;t++){const i=n[t];e.object.levels.push({object:i.object.uuid,distance:i.distance})}return e}}const _3t=new CJt,y3t=new wJt,v3t=new wJt,b3t=new CJt,x3t=new rQt;class w3t extends B1t{constructor(t,e){super(t,e),this.type="SkinnedMesh",this.bindMode="attached",this.bindMatrix=new rQt,this.bindMatrixInverse=new rQt}copy(t){return super.copy(t),this.bindMode=t.bindMode,this.bindMatrix.copy(t.bindMatrix),this.bindMatrixInverse.copy(t.bindMatrixInverse),this.skeleton=t.skeleton,this}bind(t,e){this.skeleton=t,void 0===e&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),e=this.matrixWorld),this.bindMatrix.copy(e),this.bindMatrixInverse.copy(e).invert()}pose(){this.skeleton.pose()}normalizeSkinWeights(){const t=new wJt,e=this.geometry.attributes.skinWeight;for(let n=0,i=e.count;n<i;n++){t.x=e.getX(n),t.y=e.getY(n),t.z=e.getZ(n),t.w=e.getW(n);const i=1/t.manhattanLength();i!==1/0?t.multiplyScalar(i):t.set(1,0,0,0),e.setXYZW(n,t.x,t.y,t.z,t.w)}}updateMatrixWorld(t){super.updateMatrixWorld(t),"attached"===this.bindMode?this.bindMatrixInverse.copy(this.matrixWorld).invert():"detached"===this.bindMode?this.bindMatrixInverse.copy(this.bindMatrix).invert():console.warn("THREE.SkinnedMesh: Unrecognized bindMode: "+this.bindMode)}boneTransform(t,e){const n=this.skeleton,i=this.geometry;y3t.fromBufferAttribute(i.attributes.skinIndex,t),v3t.fromBufferAttribute(i.attributes.skinWeight,t),_3t.fromBufferAttribute(i.attributes.position,t).applyMatrix4(this.bindMatrix),e.set(0,0,0);for(let t=0;t<4;t++){const i=v3t.getComponent(t);if(0!==i){const r=y3t.getComponent(t);x3t.multiplyMatrices(n.bones[r].matrixWorld,n.boneInverses[r]),e.addScaledVector(b3t.copy(_3t).applyMatrix4(x3t),i)}}return e.applyMatrix4(this.bindMatrixInverse)}}w3t.prototype.isSkinnedMesh=!0;class S3t extends kQt{constructor(){super(),this.type="Bone"}}S3t.prototype.isBone=!0;class M3t extends bJt{constructor(t=null,e=1,n=1,i,r,o,a,s,l=1003,c=1003,u,h){super(null,o,a,s,l,c,i,r,u,h),this.image={data:t,width:e,height:n},this.magFilter=l,this.minFilter=c,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}M3t.prototype.isDataTexture=!0;const E3t=new rQt,T3t=new rQt;class C3t{constructor(t=[],e=[]){this.uuid=XZt(),this.bones=t.slice(0),this.boneInverses=e,this.boneMatrices=null,this.boneTexture=null,this.boneTextureSize=0,this.frame=-1,this.init()}init(){const t=this.bones,e=this.boneInverses;if(this.boneMatrices=new Float32Array(16*t.length),0===e.length)this.calculateInverses();else if(t.length!==e.length){console.warn("THREE.Skeleton: Number of inverse bone matrices does not match amount of bones."),this.boneInverses=[];for(let t=0,e=this.bones.length;t<e;t++)this.boneInverses.push(new rQt)}}calculateInverses(){this.boneInverses.length=0;for(let t=0,e=this.bones.length;t<e;t++){const e=new rQt;this.bones[t]&&e.copy(this.bones[t].matrixWorld).invert(),this.boneInverses.push(e)}}pose(){for(let t=0,e=this.bones.length;t<e;t++){const e=this.bones[t];e&&e.matrixWorld.copy(this.boneInverses[t]).invert()}for(let t=0,e=this.bones.length;t<e;t++){const e=this.bones[t];e&&(e.parent&&e.parent.isBone?(e.matrix.copy(e.parent.matrixWorld).invert(),e.matrix.multiply(e.matrixWorld)):e.matrix.copy(e.matrixWorld),e.matrix.decompose(e.position,e.quaternion,e.scale))}}update(){const t=this.bones,e=this.boneInverses,n=this.boneMatrices,i=this.boneTexture;for(let i=0,r=t.length;i<r;i++)E3t.multiplyMatrices(t[i]?t[i].matrixWorld:T3t,e[i]),E3t.toArray(n,16*i);null!==i&&(i.needsUpdate=!0)}clone(){return new C3t(this.bones,this.boneInverses)}computeBoneTexture(){let t=Math.sqrt(4*this.bones.length);t=QZt(t),t=Math.max(t,4);const e=new Float32Array(t*t*4);e.set(this.boneMatrices);const n=new M3t(e,t,t,hZt,sZt);return this.boneMatrices=e,this.boneTexture=n,this.boneTextureSize=t,this}getBoneByName(t){for(let e=0,n=this.bones.length;e<n;e++){const n=this.bones[e];if(n.name===t)return n}}dispose(){null!==this.boneTexture&&(this.boneTexture.dispose(),this.boneTexture=null)}fromJSON(t,e){this.uuid=t.uuid;for(let n=0,i=t.bones.length;n<i;n++){const i=t.bones[n];let r=e[i];void 0===r&&(console.warn("THREE.Skeleton: No bone found with UUID:",i),r=new S3t),this.bones.push(r),this.boneInverses.push((new rQt).fromArray(t.boneInverses[n]))}return this.init(),this}toJSON(){const t={metadata:{version:4.5,type:"Skeleton",generator:"Skeleton.toJSON"},bones:[],boneInverses:[]};t.uuid=this.uuid;const e=this.bones,n=this.boneInverses;for(let i=0,r=e.length;i<r;i++)t.bones.push(e[i].uuid),t.boneInverses.push(n[i].toArray());return t}}const A3t=new rQt,k3t=new rQt,L3t=[],P3t=new B1t;class N3t extends B1t{constructor(t,e,n){super(t,e),this.instanceMatrix=new QQt(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}copy(t){return super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,i=this.count;if(P3t.geometry=this.geometry,P3t.material=this.material,void 0!==P3t.material)for(let r=0;r<i;r++){this.getMatrixAt(r,A3t),k3t.multiplyMatrices(n,A3t),P3t.matrixWorld=k3t,P3t.raycast(t,L3t);for(let t=0,n=L3t.length;t<n;t++){const n=L3t[t];n.instanceId=r,n.object=this,e.push(n)}L3t.length=0}}setColorAt(t,e){null===this.instanceColor&&(this.instanceColor=new QQt(new Float32Array(3*this.instanceMatrix.count),3)),e.toArray(this.instanceColor.array,3*t)}setMatrixAt(t,e){e.toArray(this.instanceMatrix.array,16*t)}updateMorphTargets(){}dispose(){this.dispatchEvent({type:"dispose"})}}N3t.prototype.isInstancedMesh=!0;class I3t extends UQt{constructor(t){super(),this.type="LineBasicMaterial",this.color=new $Qt(16777215),this.linewidth=1,this.linecap="round",this.linejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.linewidth=t.linewidth,this.linecap=t.linecap,this.linejoin=t.linejoin,this}}I3t.prototype.isLineBasicMaterial=!0;const R3t=new CJt,O3t=new CJt,z3t=new rQt,D3t=new iQt,B3t=new $Jt;class H3t extends kQt{constructor(t=new b1t,e=new I3t){super(),this.type="Line",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[0];for(let t=1,i=e.count;t<i;t++)R3t.fromBufferAttribute(e,t-1),O3t.fromBufferAttribute(e,t),n[t]=n[t-1],n[t]+=R3t.distanceTo(O3t);t.setAttribute("lineDistance",new l1t(n,1))}else console.warn("THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else t.isGeometry&&console.error("THREE.Line.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");return this}raycast(t,e){const n=this.geometry,i=this.matrixWorld,r=t.params.Line.threshold,o=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),B3t.copy(n.boundingSphere),B3t.applyMatrix4(i),B3t.radius+=r,!1===t.ray.intersectsSphere(B3t))return;z3t.copy(i).invert(),D3t.copy(t.ray).applyMatrix4(z3t);const a=r/((this.scale.x+this.scale.y+this.scale.z)/3),s=a*a,l=new CJt,c=new CJt,u=new CJt,h=new CJt,d=this.isLineSegments?2:1;if(n.isBufferGeometry){const i=n.index,r=n.attributes.position;if(null!==i)for(let n=Math.max(0,o.start),a=Math.min(i.count,o.start+o.count)-1;n<a;n+=d){const o=i.getX(n),a=i.getX(n+1);if(l.fromBufferAttribute(r,o),c.fromBufferAttribute(r,a),D3t.distanceSqToSegment(l,c,h,u)>s)continue;h.applyMatrix4(this.matrixWorld);const d=t.ray.origin.distanceTo(h);d<t.near||d>t.far||e.push({distance:d,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}else for(let n=Math.max(0,o.start),i=Math.min(r.count,o.start+o.count)-1;n<i;n+=d){if(l.fromBufferAttribute(r,n),c.fromBufferAttribute(r,n+1),D3t.distanceSqToSegment(l,c,h,u)>s)continue;h.applyMatrix4(this.matrixWorld);const i=t.ray.origin.distanceTo(h);i<t.near||i>t.far||e.push({distance:i,point:u.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else n.isGeometry&&console.error("THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.")}}}H3t.prototype.isLine=!0;const F3t=new CJt,V3t=new CJt;class U3t extends H3t{constructor(t,e){super(t,e),this.type="LineSegments"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,i=e.count;t<i;t+=2)F3t.fromBufferAttribute(e,t),V3t.fromBufferAttribute(e,t+1),n[t]=0===t?0:n[t-1],n[t+1]=n[t]+F3t.distanceTo(V3t);t.setAttribute("lineDistance",new l1t(n,1))}else console.warn("THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else t.isGeometry&&console.error("THREE.LineSegments.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");return this}}U3t.prototype.isLineSegments=!0;class j3t extends H3t{constructor(t,e){super(t,e),this.type="LineLoop"}}j3t.prototype.isLineLoop=!0;class G3t extends UQt{constructor(t){super(),this.type="PointsMaterial",this.color=new $Qt(16777215),this.map=null,this.alphaMap=null,this.size=1,this.sizeAttenuation=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.size=t.size,this.sizeAttenuation=t.sizeAttenuation,this}}G3t.prototype.isPointsMaterial=!0;const W3t=new rQt,q3t=new iQt,Y3t=new $Jt,X3t=new CJt;class $3t extends kQt{constructor(t=new b1t,e=new G3t){super(),this.type="Points",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}raycast(t,e){const n=this.geometry,i=this.matrixWorld,r=t.params.Points.threshold,o=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),Y3t.copy(n.boundingSphere),Y3t.applyMatrix4(i),Y3t.radius+=r,!1===t.ray.intersectsSphere(Y3t))return;W3t.copy(i).invert(),q3t.copy(t.ray).applyMatrix4(W3t);const a=r/((this.scale.x+this.scale.y+this.scale.z)/3),s=a*a;if(n.isBufferGeometry){const r=n.index,a=n.attributes.position;if(null!==r)for(let n=Math.max(0,o.start),l=Math.min(r.count,o.start+o.count);n<l;n++){const o=r.getX(n);X3t.fromBufferAttribute(a,o),K3t(X3t,o,s,i,t,e,this)}else for(let n=Math.max(0,o.start),r=Math.min(a.count,o.start+o.count);n<r;n++)X3t.fromBufferAttribute(a,n),K3t(X3t,n,s,i,t,e,this)}else console.error("THREE.Points.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.")}}}function K3t(t,e,n,i,r,o,a){const s=q3t.distanceSqToPoint(t);if(s<n){const n=new CJt;q3t.closestPointToPoint(t,n),n.applyMatrix4(i);const l=r.ray.origin.distanceTo(n);if(l<r.near||l>r.far)return;o.push({distance:l,distanceToRay:Math.sqrt(s),point:n,index:e,face:null,object:a})}}$3t.prototype.isPoints=!0;class Z3t extends bJt{constructor(t,e,n,i,r,o,a,s,l){super(t,e,n,i,r,o,a,s,l),this.format=void 0!==a?a:uZt,this.minFilter=void 0!==o?o:eZt,this.magFilter=void 0!==r?r:eZt,this.generateMipmaps=!1;const c=this;"requestVideoFrameCallback"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;0=="requestVideoFrameCallback"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}Z3t.prototype.isVideoTexture=!0;class J3t extends bJt{constructor(t,e,n,i,r,o,a,s,l,c,u,h){super(null,o,a,s,l,c,i,r,u,h),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}J3t.prototype.isCompressedTexture=!0;class Q3t extends bJt{constructor(t,e,n,i,r,o,a,s,l){super(t,e,n,i,r,o,a,s,l),this.needsUpdate=!0}}Q3t.prototype.isCanvasTexture=!0;class t4t extends bJt{constructor(t,e,n,i,r,o,a,s,l,c){if((c=void 0!==c?c:dZt)!==dZt&&c!==pZt)throw new Error("DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat");void 0===n&&c===dZt&&(n=oZt),void 0===n&&c===pZt&&(n=cZt),super(null,i,r,o,a,s,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==a?a:JKt,this.minFilter=void 0!==s?s:JKt,this.flipY=!1,this.generateMipmaps=!1}}t4t.prototype.isDepthTexture=!0;class e4t extends b1t{constructor(t=1,e=8,n=0,i=2*Math.PI){super(),this.type="CircleGeometry",this.parameters={radius:t,segments:e,thetaStart:n,thetaLength:i},e=Math.max(3,e);const r=[],o=[],a=[],s=[],l=new CJt,c=new mJt;o.push(0,0,0),a.push(0,0,1),s.push(.5,.5);for(let r=0,u=3;r<=e;r++,u+=3){const h=n+r/e*i;l.x=t*Math.cos(h),l.y=t*Math.sin(h),o.push(l.x,l.y,l.z),a.push(0,0,1),c.x=(o[u]/t+1)/2,c.y=(o[u+1]/t+1)/2,s.push(c.x,c.y)}for(let t=1;t<=e;t++)r.push(t,t+1,0);this.setIndex(r),this.setAttribute("position",new l1t(o,3)),this.setAttribute("normal",new l1t(a,3)),this.setAttribute("uv",new l1t(s,2))}static fromJSON(t){return new e4t(t.radius,t.segments,t.thetaStart,t.thetaLength)}}class n4t extends b1t{constructor(t=1,e=1,n=1,i=8,r=1,o=!1,a=0,s=2*Math.PI){super(),this.type="CylinderGeometry",this.parameters={radiusTop:t,radiusBottom:e,height:n,radialSegments:i,heightSegments:r,openEnded:o,thetaStart:a,thetaLength:s};const l=this;i=Math.floor(i),r=Math.floor(r);const c=[],u=[],h=[],d=[];let p=0;const f=[],m=n/2;let g=0;function _(n){const r=p,o=new mJt,f=new CJt;let _=0;const y=!0===n?t:e,v=!0===n?1:-1;for(let t=1;t<=i;t++)u.push(0,m*v,0),h.push(0,v,0),d.push(.5,.5),p++;const b=p;for(let t=0;t<=i;t++){const e=t/i*s+a,n=Math.cos(e),r=Math.sin(e);f.x=y*r,f.y=m*v,f.z=y*n,u.push(f.x,f.y,f.z),h.push(0,v,0),o.x=.5*n+.5,o.y=.5*r*v+.5,d.push(o.x,o.y),p++}for(let t=0;t<i;t++){const e=r+t,i=b+t;!0===n?c.push(i,i+1,e):c.push(i+1,i,e),_+=3}l.addGroup(g,_,!0===n?1:2),g+=_}!(function y(){const o=new CJt,_=new CJt;let y=0;const v=(e-t)/n;for(let l=0;l<=r;l++){const c=[],g=l/r,y=g*(e-t)+t;for(let t=0;t<=i;t++){const e=t/i,r=e*s+a,l=Math.sin(r),f=Math.cos(r);_.x=y*l,_.y=-g*n+m,_.z=y*f,u.push(_.x,_.y,_.z),o.set(l,v,f).normalize(),h.push(o.x,o.y,o.z),d.push(e,1-g),c.push(p++)}f.push(c)}for(let t=0;t<i;t++)for(let e=0;e<r;e++){const n=f[e+1][t],i=f[e+1][t+1],r=f[e][t+1];c.push(f[e][t],n,r),c.push(n,i,r),y+=6}l.addGroup(g,y,0),g+=y})(),!1===o&&(t>0&&_(!0),e>0&&_(!1)),this.setIndex(c),this.setAttribute("position",new l1t(u,3)),this.setAttribute("normal",new l1t(h,3)),this.setAttribute("uv",new l1t(d,2))}static fromJSON(t){return new n4t(t.radiusTop,t.radiusBottom,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class i4t extends n4t{constructor(t=1,e=1,n=8,i=1,r=!1,o=0,a=2*Math.PI){super(0,t,e,n,i,r,o,a),this.type="ConeGeometry",this.parameters={radius:t,height:e,radialSegments:n,heightSegments:i,openEnded:r,thetaStart:o,thetaLength:a}}static fromJSON(t){return new i4t(t.radius,t.height,t.radialSegments,t.heightSegments,t.openEnded,t.thetaStart,t.thetaLength)}}class r4t extends b1t{constructor(t,e,n=1,i=0){super(),this.type="PolyhedronGeometry",this.parameters={vertices:t,indices:e,radius:n,detail:i};const r=[],o=[];function a(t,e,n,i){const r=i+1,o=[];for(let i=0;i<=r;i++){o[i]=[];const a=t.clone().lerp(n,i/r),s=e.clone().lerp(n,i/r),l=r-i;for(let t=0;t<=l;t++)o[i][t]=0===t&&i===r?a:a.clone().lerp(s,t/l)}for(let t=0;t<r;t++)for(let e=0;e<2*(r-t)-1;e++){const n=Math.floor(e/2);e%2==0?(s(o[t][n+1]),s(o[t+1][n]),s(o[t][n])):(s(o[t][n+1]),s(o[t+1][n+1]),s(o[t+1][n]))}}function s(t){r.push(t.x,t.y,t.z)}function l(e,n){const i=3*e;n.x=t[i+0],n.y=t[i+1],n.z=t[i+2]}function c(t,e,n,i){i<0&&1===t.x&&(o[e]=t.x-1),0===n.x&&0===n.z&&(o[e]=i/2/Math.PI+.5)}function u(t){return Math.atan2(t.z,-t.x)}!(function h(t){const n=new CJt,i=new CJt,r=new CJt;for(let o=0;o<e.length;o+=3)l(e[o+0],n),l(e[o+1],i),l(e[o+2],r),a(n,i,r,t)})(i),(function d(t){const e=new CJt;for(let n=0;n<r.length;n+=3)e.x=r[n+0],e.y=r[n+1],e.z=r[n+2],e.normalize().multiplyScalar(t),r[n+0]=e.x,r[n+1]=e.y,r[n+2]=e.z})(n),(function p(){const t=new CJt;for(let n=0;n<r.length;n+=3){t.x=r[n+0],t.y=r[n+1],t.z=r[n+2];const i=u(t)/2/Math.PI+.5,a=(e=t,Math.atan2(-e.y,Math.sqrt(e.x*e.x+e.z*e.z))/Math.PI+.5);o.push(i,1-a)}var e;(function n(){const t=new CJt,e=new CJt,n=new CJt,i=new CJt,a=new mJt,s=new mJt,l=new mJt;for(let h=0,d=0;h<r.length;h+=9,d+=6){t.set(r[h+0],r[h+1],r[h+2]),e.set(r[h+3],r[h+4],r[h+5]),n.set(r[h+6],r[h+7],r[h+8]),a.set(o[d+0],o[d+1]),s.set(o[d+2],o[d+3]),l.set(o[d+4],o[d+5]),i.copy(t).add(e).add(n).divideScalar(3);const p=u(i);c(a,d+0,t,p),c(s,d+2,e,p),c(l,d+4,n,p)}})(),(function i(){for(let t=0;t<o.length;t+=6){const e=o[t+0],n=o[t+2],i=o[t+4],r=Math.max(e,n,i),a=Math.min(e,n,i);r>.9&&a<.1&&(e<.2&&(o[t+0]+=1),n<.2&&(o[t+2]+=1),i<.2&&(o[t+4]+=1))}})()})(),this.setAttribute("position",new l1t(r,3)),this.setAttribute("normal",new l1t(r.slice(),3)),this.setAttribute("uv",new l1t(o,2)),0===i?this.computeVertexNormals():this.normalizeNormals()}static fromJSON(t){return new r4t(t.vertices,t.indices,t.radius,t.details)}}class o4t extends r4t{constructor(t=1,e=0){const n=(1+Math.sqrt(5))/2,i=1/n;super([-1,-1,-1,-1,-1,1,-1,1,-1,-1,1,1,1,-1,-1,1,-1,1,1,1,-1,1,1,1,0,-i,-n,0,-i,n,0,i,-n,0,i,n,-i,-n,0,-i,n,0,i,-n,0,i,n,0,-n,0,-i,n,0,-i,-n,0,i,n,0,i],[3,11,7,3,7,15,3,15,13,7,19,17,7,17,6,7,6,15,17,4,8,17,8,10,17,10,6,8,0,16,8,16,2,8,2,10,0,12,1,0,1,18,0,18,16,6,10,2,6,2,13,6,13,15,2,16,18,2,18,3,2,3,13,18,1,9,18,9,11,18,11,3,4,14,12,4,12,0,4,0,8,11,9,5,11,5,19,11,19,7,19,5,14,19,14,4,19,4,17,1,12,14,1,14,5,1,5,9],t,e),this.type="DodecahedronGeometry",this.parameters={radius:t,detail:e}}static fromJSON(t){return new o4t(t.radius,t.detail)}}const a4t=new CJt,s4t=new CJt,l4t=new CJt,c4t=new FQt;class u4t extends b1t{constructor(t,e){if(super(),this.type="EdgesGeometry",this.parameters={thresholdAngle:e},e=void 0!==e?e:1,!0===t.isGeometry)return void console.error("THREE.EdgesGeometry no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");const n=Math.cos(qZt*e),i=t.getIndex(),r=t.getAttribute("position"),o=i?i.count:r.count,a=[0,0,0],s=["a","b","c"],l=new Array(3),c={},u=[];for(let t=0;t<o;t+=3){i?(a[0]=i.getX(t),a[1]=i.getX(t+1),a[2]=i.getX(t+2)):(a[0]=t,a[1]=t+1,a[2]=t+2);const{a:e,b:o,c:h}=c4t;if(e.fromBufferAttribute(r,a[0]),o.fromBufferAttribute(r,a[1]),h.fromBufferAttribute(r,a[2]),c4t.getNormal(l4t),l[0]=`${e.x},${e.y},${e.z}`,l[1]=`${o.x},${o.y},${o.z}`,l[2]=`${h.x},${h.y},${h.z}`,l[0]!==l[1]&&l[1]!==l[2]&&l[2]!==l[0])for(let t=0;t<3;t++){const e=(t+1)%3,i=l[t],r=l[e],o=c4t[s[t]],h=c4t[s[e]],d=`${i}_${r}`,p=`${r}_${i}`;p in c&&c[p]?(l4t.dot(c[p].normal)<=n&&(u.push(o.x,o.y,o.z),u.push(h.x,h.y,h.z)),c[p]=null):d in c||(c[d]={index0:a[t],index1:a[e],normal:l4t.clone()})}}for(const t in c)if(c[t]){const{index0:e,index1:n}=c[t];a4t.fromBufferAttribute(r,e),s4t.fromBufferAttribute(r,n),u.push(a4t.x,a4t.y,a4t.z),u.push(s4t.x,s4t.y,s4t.z)}this.setAttribute("position",new l1t(u,3))}}class h4t{constructor(){this.type="Curve",this.arcLengthDivisions=200}getPoint(){return console.warn("THREE.Curve: .getPoint() not implemented."),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,i=this.getPoint(0),r=0;e.push(0);for(let o=1;o<=t;o++)n=this.getPoint(o/t),r+=n.distanceTo(i),e.push(r),i=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let i=0;const r=n.length;let o;o=e||t*n[r-1];let a,s=0,l=r-1;for(;s<=l;)if(i=Math.floor(s+(l-s)/2),a=n[i]-o,a<0)s=i+1;else{if(!(a>0)){l=i;break}l=i-1}if(i=l,n[i]===o)return i/(r-1);const c=n[i];return(i+(o-c)/(n[i+1]-c))/(r-1)}getTangent(t,e){const n=1e-4;let i=t-n,r=t+n;i<0&&(i=0),r>1&&(r=1);const o=this.getPoint(i),a=this.getPoint(r),s=e||(o.isVector2?new mJt:new CJt);return s.copy(a).sub(o).normalize(),s}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new CJt,i=[],r=[],o=[],a=new CJt,s=new rQt;for(let e=0;e<=t;e++)i[e]=this.getTangentAt(e/t,new CJt),i[e].normalize();r[0]=new CJt,o[0]=new CJt;let l=Number.MAX_VALUE;const c=Math.abs(i[0].x),u=Math.abs(i[0].y),h=Math.abs(i[0].z);c<=l&&(l=c,n.set(1,0,0)),u<=l&&(l=u,n.set(0,1,0)),h<=l&&n.set(0,0,1),a.crossVectors(i[0],n).normalize(),r[0].crossVectors(i[0],a),o[0].crossVectors(i[0],r[0]);for(let e=1;e<=t;e++){if(r[e]=r[e-1].clone(),o[e]=o[e-1].clone(),a.crossVectors(i[e-1],i[e]),a.length()>Number.EPSILON){a.normalize();const t=Math.acos($Zt(i[e-1].dot(i[e]),-1,1));r[e].applyMatrix4(s.makeRotationAxis(a,t))}o[e].crossVectors(i[e],r[e])}if(!0===e){let e=Math.acos($Zt(r[0].dot(r[t]),-1,1));e/=t,i[0].dot(a.crossVectors(r[0],r[t]))>0&&(e=-e);for(let n=1;n<=t;n++)r[n].applyMatrix4(s.makeRotationAxis(i[n],e*n)),o[n].crossVectors(i[n],r[n])}return{tangents:i,normals:r,binormals:o}}clone(){return(new this.constructor).copy(this)}copy(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}toJSON(){const t={metadata:{version:4.5,type:"Curve",generator:"Curve.toJSON"}};return t.arcLengthDivisions=this.arcLengthDivisions,t.type=this.type,t}fromJSON(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}}class d4t extends h4t{constructor(t=0,e=0,n=1,i=1,r=0,o=2*Math.PI,a=!1,s=0){super(),this.type="EllipseCurve",this.aX=t,this.aY=e,this.xRadius=n,this.yRadius=i,this.aStartAngle=r,this.aEndAngle=o,this.aClockwise=a,this.aRotation=s}getPoint(t,e){const n=e||new mJt,i=2*Math.PI;let r=this.aEndAngle-this.aStartAngle;const o=Math.abs(r)<Number.EPSILON;for(;r<0;)r+=i;for(;r>i;)r-=i;r<Number.EPSILON&&(r=o?0:i),!0!==this.aClockwise||o||(r===i?r=-i:r-=i);const a=this.aStartAngle+t*r;let s=this.aX+this.xRadius*Math.cos(a),l=this.aY+this.yRadius*Math.sin(a);if(0!==this.aRotation){const t=Math.cos(this.aRotation),e=Math.sin(this.aRotation),n=s-this.aX,i=l-this.aY;s=n*t-i*e+this.aX,l=n*e+i*t+this.aY}return n.set(s,l)}copy(t){return super.copy(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}toJSON(){const t=super.toJSON();return t.aX=this.aX,t.aY=this.aY,t.xRadius=this.xRadius,t.yRadius=this.yRadius,t.aStartAngle=this.aStartAngle,t.aEndAngle=this.aEndAngle,t.aClockwise=this.aClockwise,t.aRotation=this.aRotation,t}fromJSON(t){return super.fromJSON(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}}d4t.prototype.isEllipseCurve=!0;class p4t extends d4t{constructor(t,e,n,i,r,o){super(t,e,n,n,i,r,o),this.type="ArcCurve"}}function f4t(){let t=0,e=0,n=0,i=0;function r(r,o,a,s){t=r,e=a,n=-3*r+3*o-2*a-s,i=2*r-2*o+a+s}return{initCatmullRom:function(t,e,n,i,o){r(e,n,o*(n-t),o*(i-e))},initNonuniformCatmullRom:function(t,e,n,i,o,a,s){let l=(e-t)/o-(n-t)/(o+a)+(n-e)/a,c=(n-e)/a-(i-e)/(a+s)+(i-n)/s;l*=a,c*=a,r(e,n,l,c)},calc:function(r){const o=r*r;return t+e*r+n*o+i*(o*r)}}}p4t.prototype.isArcCurve=!0;const m4t=new CJt,g4t=new f4t,_4t=new f4t,y4t=new f4t;class v4t extends h4t{constructor(t=[],e=!1,n="centripetal",i=.5){super(),this.type="CatmullRomCurve3",this.points=t,this.closed=e,this.curveType=n,this.tension=i}getPoint(t,e=new CJt){const n=e,i=this.points,r=i.length,o=(r-(this.closed?0:1))*t;let a,s,l=Math.floor(o),c=o-l;this.closed?l+=l>0?0:(Math.floor(Math.abs(l)/r)+1)*r:0===c&&l===r-1&&(l=r-2,c=1),this.closed||l>0?a=i[(l-1)%r]:(m4t.subVectors(i[0],i[1]).add(i[0]),a=m4t);const u=i[l%r],h=i[(l+1)%r];if(this.closed||l+2<r?s=i[(l+2)%r]:(m4t.subVectors(i[r-1],i[r-2]).add(i[r-1]),s=m4t),"centripetal"===this.curveType||"chordal"===this.curveType){const t="chordal"===this.curveType?.5:.25;let e=Math.pow(a.distanceToSquared(u),t),n=Math.pow(u.distanceToSquared(h),t),i=Math.pow(h.distanceToSquared(s),t);n<1e-4&&(n=1),e<1e-4&&(e=n),i<1e-4&&(i=n),g4t.initNonuniformCatmullRom(a.x,u.x,h.x,s.x,e,n,i),_4t.initNonuniformCatmullRom(a.y,u.y,h.y,s.y,e,n,i),y4t.initNonuniformCatmullRom(a.z,u.z,h.z,s.z,e,n,i)}else"catmullrom"===this.curveType&&(g4t.initCatmullRom(a.x,u.x,h.x,s.x,this.tension),_4t.initCatmullRom(a.y,u.y,h.y,s.y,this.tension),y4t.initCatmullRom(a.z,u.z,h.z,s.z,this.tension));return n.set(g4t.calc(c),_4t.calc(c),y4t.calc(c)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++)this.points.push(t.points[e].clone());return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++)t.points.push(this.points[e].toArray());return t.closed=this.closed,t.curveType=this.curveType,t.tension=this.tension,t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new CJt).fromArray(n))}return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}}function b4t(t,e,n,i,r){const o=.5*(i-e),a=.5*(r-n),s=t*t;return(2*n-2*i+o+a)*(t*s)+(-3*n+3*i-2*o-a)*s+o*t+n}function x4t(t,e,n,i){return(function r(t,e){const n=1-t;return n*n*e})(t,e)+(function o(t,e){return 2*(1-t)*t*e})(t,n)+(function a(t,e){return t*t*e})(t,i)}function w4t(t,e,n,i,r){return(function o(t,e){const n=1-t;return n*n*n*e})(t,e)+(function a(t,e){const n=1-t;return 3*n*n*t*e})(t,n)+(function s(t,e){return 3*(1-t)*t*t*e})(t,i)+(function l(t,e){return t*t*t*e})(t,r)}v4t.prototype.isCatmullRomCurve3=!0;class S4t extends h4t{constructor(t=new mJt,e=new mJt,n=new mJt,i=new mJt){super(),this.type="CubicBezierCurve",this.v0=t,this.v1=e,this.v2=n,this.v3=i}getPoint(t,e=new mJt){const n=e,i=this.v0,r=this.v1,o=this.v2,a=this.v3;return n.set(w4t(t,i.x,r.x,o.x,a.x),w4t(t,i.y,r.y,o.y,a.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}S4t.prototype.isCubicBezierCurve=!0;class M4t extends h4t{constructor(t=new CJt,e=new CJt,n=new CJt,i=new CJt){super(),this.type="CubicBezierCurve3",this.v0=t,this.v1=e,this.v2=n,this.v3=i}getPoint(t,e=new CJt){const n=e,i=this.v0,r=this.v1,o=this.v2,a=this.v3;return n.set(w4t(t,i.x,r.x,o.x,a.x),w4t(t,i.y,r.y,o.y,a.y),w4t(t,i.z,r.z,o.z,a.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}M4t.prototype.isCubicBezierCurve3=!0;class E4t extends h4t{constructor(t=new mJt,e=new mJt){super(),this.type="LineCurve",this.v1=t,this.v2=e}getPoint(t,e=new mJt){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}getTangent(t,e){const n=e||new mJt;return n.copy(this.v2).sub(this.v1).normalize(),n}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}E4t.prototype.isLineCurve=!0;class T4t extends h4t{constructor(t=new CJt,e=new CJt){super(),this.type="LineCurve3",this.isLineCurve3=!0,this.v1=t,this.v2=e}getPoint(t,e=new CJt){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}class C4t extends h4t{constructor(t=new mJt,e=new mJt,n=new mJt){super(),this.type="QuadraticBezierCurve",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new mJt){const n=e,i=this.v0,r=this.v1,o=this.v2;return n.set(x4t(t,i.x,r.x,o.x),x4t(t,i.y,r.y,o.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}C4t.prototype.isQuadraticBezierCurve=!0;class A4t extends h4t{constructor(t=new CJt,e=new CJt,n=new CJt){super(),this.type="QuadraticBezierCurve3",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new CJt){const n=e,i=this.v0,r=this.v1,o=this.v2;return n.set(x4t(t,i.x,r.x,o.x),x4t(t,i.y,r.y,o.y),x4t(t,i.z,r.z,o.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}A4t.prototype.isQuadraticBezierCurve3=!0;class k4t extends h4t{constructor(t=[]){super(),this.type="SplineCurve",this.points=t}getPoint(t,e=new mJt){const n=e,i=this.points,r=(i.length-1)*t,o=Math.floor(r),a=r-o,s=i[0===o?o:o-1],l=i[o],c=i[o>i.length-2?i.length-1:o+1],u=i[o>i.length-3?i.length-1:o+2];return n.set(b4t(a,s.x,l.x,c.x,u.x),b4t(a,s.y,l.y,c.y,u.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++)this.points.push(t.points[e].clone());return this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++)t.points.push(this.points[e].toArray());return t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new mJt).fromArray(n))}return this}}k4t.prototype.isSplineCurve=!0;var L4t=Object.freeze({__proto__:null,ArcCurve:p4t,CatmullRomCurve3:v4t,CubicBezierCurve:S4t,CubicBezierCurve3:M4t,EllipseCurve:d4t,LineCurve:E4t,LineCurve3:T4t,QuadraticBezierCurve:C4t,QuadraticBezierCurve3:A4t,SplineCurve:k4t});function P4t(t,e,n,i,r){let o,a;if(r===(function s(t,e,n,i){let r=0;for(let o=e,a=n-i;o<n;o+=i)r+=(t[a]-t[o])*(t[o+1]+t[a+1]),a=o;return r})(t,e,n,i)>0)for(o=e;o<n;o+=i)a=J4t(o,t[o],t[o+1],a);else for(o=n-i;o>=e;o-=i)a=J4t(o,t[o],t[o+1],a);return a&&q4t(a,a.next)&&(Q4t(a),a=a.next),a}function N4t(t,e){if(!t)return t;e||(e=t);let n,i=t;do{if(n=!1,i.steiner||!q4t(i,i.next)&&0!==W4t(i.prev,i,i.next))i=i.next;else{if(Q4t(i),i=e=i.prev,i===i.next)break;n=!0}}while(n||i!==e);return e}function I4t(t,e,n,i,r,o,a){if(!t)return;!a&&o&&(function s(t,e,n,i){let r=t;do{null===r.z&&(r.z=V4t(r.x,r.y,e,n,i)),r.prevZ=r.prev,r.nextZ=r.next,r=r.next}while(r!==t);r.prevZ.nextZ=null,r.prevZ=null,(function o(t){let e,n,i,r,o,a,s,l,c=1;do{for(n=t,t=null,o=null,a=0;n;){for(a++,i=n,s=0,e=0;e<c&&(s++,i=i.nextZ,i);e++);for(l=c;s>0||l>0&&i;)0!==s&&(0===l||!i||n.z<=i.z)?(r=n,n=n.nextZ,s--):(r=i,i=i.nextZ,l--),o?o.nextZ=r:t=r,r.prevZ=o,o=r;n=i}o.nextZ=null,c*=2}while(a>1)})(r)})(t,i,r,o);let l,c,u=t;for(;t.prev!==t.next;)if(l=t.prev,c=t.next,o?O4t(t,i,r,o):R4t(t))e.push(l.i/n),e.push(t.i/n),e.push(c.i/n),Q4t(t),t=c.next,u=c.next;else if((t=c)===u){a?1===a?I4t(t=z4t(N4t(t),e,n),e,n,i,r,o,2):2===a&&D4t(t,e,n,i,r,o):I4t(N4t(t),e,n,i,r,o,1);break}}function R4t(t){const e=t.prev,n=t,i=t.next;if(W4t(e,n,i)>=0)return!1;let r=t.next.next;for(;r!==t.prev;){if(j4t(e.x,e.y,n.x,n.y,i.x,i.y,r.x,r.y)&&W4t(r.prev,r,r.next)>=0)return!1;r=r.next}return!0}function O4t(t,e,n,i){const r=t.prev,o=t,a=t.next;if(W4t(r,o,a)>=0)return!1;const 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Q4t(t){t.next.prev=t.prev,t.prev.next=t.next,t.prevZ&&(t.prevZ.nextZ=t.nextZ),t.nextZ&&(t.nextZ.prevZ=t.prevZ)}function t6t(t,e,n){this.i=t,this.x=e,this.y=n,this.prev=null,this.next=null,this.z=null,this.prevZ=null,this.nextZ=null,this.steiner=!1}class e6t{static area(t){const e=t.length;let n=0;for(let i=e-1,r=0;r<e;i=r++)n+=t[i].x*t[r].y-t[r].x*t[i].y;return.5*n}static isClockWise(t){return e6t.area(t)<0}static triangulateShape(t,e){const n=[],i=[],r=[];n6t(t),i6t(n,t);let o=t.length;e.forEach(n6t);for(let t=0;t<e.length;t++)i.push(o),o+=e[t].length,i6t(n,e[t]);const a=(function(t,e,n=2){const i=e&&e.length,r=i?e[0]*n:t.length;let o=P4t(t,0,r,n,!0);const a=[];if(!o||o.next===o.prev)return a;let s,l,c,u,h,d,p;if(i&&(o=(function f(t,e,n,i){const r=[];let o,a,s,l,c;for(o=0,a=e.length;o<a;o++)s=e[o]*i,l=o<a-1?e[o+1]*i:t.length,c=P4t(t,s,l,i,!1),c===c.next&&(c.steiner=!0),r.push(U4t(c));for(r.sort(B4t),o=0;o<r.length;o++)H4t(r[o],n),n=N4t(n,n.next);return 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e(t,n,i){if(i.shapes=[],Array.isArray(t))for(let e=0,n=t.length;e<n;e++)i.shapes.push(t[e].uuid);else i.shapes.push(t.uuid);return void 0!==n.extrudePath&&(i.options.extrudePath=n.extrudePath.toJSON()),i})(this.parameters.shapes,this.parameters.options,t)}static fromJSON(t,e){const n=[];for(let i=0,r=t.shapes.length;i<r;i++)n.push(e[t.shapes[i]]);const i=t.options.extrudePath;return void 0!==i&&(t.options.extrudePath=(new L4t[i.type]).fromJSON(i)),new r6t(n,t.options)}}const o6t={generateTopUV:function(t,e,n,i,r){const o=e[3*i],a=e[3*i+1],s=e[3*r],l=e[3*r+1];return[new mJt(e[3*n],e[3*n+1]),new mJt(o,a),new mJt(s,l)]},generateSideWallUV:function(t,e,n,i,r,o){const a=e[3*n],s=e[3*n+1],l=e[3*n+2],c=e[3*i],u=e[3*i+1],h=e[3*i+2],d=e[3*r],p=e[3*r+1],f=e[3*r+2],m=e[3*o],g=e[3*o+1],_=e[3*o+2];return Math.abs(s-u)<Math.abs(a-c)?[new mJt(a,1-l),new mJt(c,1-h),new mJt(d,1-f),new mJt(m,1-_)]:[new mJt(s,1-l),new mJt(u,1-h),new mJt(p,1-f),new mJt(g,1-_)]}};class a6t extends r4t{constructor(t=1,e=0){const n=(1+Math.sqrt(5))/2;super([-1,n,0,1,n,0,-1,-n,0,1,-n,0,0,-1,n,0,1,n,0,-1,-n,0,1,-n,n,0,-1,n,0,1,-n,0,-1,-n,0,1],[0,11,5,0,5,1,0,1,7,0,7,10,0,10,11,1,5,9,5,11,4,11,10,2,10,7,6,7,1,8,3,9,4,3,4,2,3,2,6,3,6,8,3,8,9,4,9,5,2,4,11,6,2,10,8,6,7,9,8,1],t,e),this.type="IcosahedronGeometry",this.parameters={radius:t,detail:e}}static fromJSON(t){return new a6t(t.radius,t.detail)}}class s6t extends b1t{constructor(t,e=12,n=0,i=2*Math.PI){super(),this.type="LatheGeometry",this.parameters={points:t,segments:e,phiStart:n,phiLength:i},e=Math.floor(e),i=$Zt(i,0,2*Math.PI);const r=[],o=[],a=[],s=1/e,l=new CJt,c=new mJt;for(let r=0;r<=e;r++){const u=n+r*s*i,h=Math.sin(u),d=Math.cos(u);for(let n=0;n<=t.length-1;n++)l.x=t[n].x*h,l.y=t[n].y,l.z=t[n].x*d,o.push(l.x,l.y,l.z),c.x=r/e,c.y=n/(t.length-1),a.push(c.x,c.y)}for(let n=0;n<e;n++)for(let e=0;e<t.length-1;e++){const i=e+n*t.length,o=i+t.length,a=i+t.length+1,s=i+1;r.push(i,o,s),r.push(o,a,s)}if(this.setIndex(r),this.setAttribute("position",new l1t(o,3)),this.setAttribute("uv",new l1t(a,2)),this.computeVertexNormals(),i===2*Math.PI){const n=this.attributes.normal.array,i=new CJt,r=new CJt,o=new CJt,a=e*t.length*3;for(let e=0,s=0;e<t.length;e++,s+=3)i.x=n[s+0],i.y=n[s+1],i.z=n[s+2],r.x=n[a+s+0],r.y=n[a+s+1],r.z=n[a+s+2],o.addVectors(i,r).normalize(),n[s+0]=n[a+s+0]=o.x,n[s+1]=n[a+s+1]=o.y,n[s+2]=n[a+s+2]=o.z}}static fromJSON(t){return new s6t(t.points,t.segments,t.phiStart,t.phiLength)}}class l6t extends r4t{constructor(t=1,e=0){super([1,0,0,-1,0,0,0,1,0,0,-1,0,0,0,1,0,0,-1],[0,2,4,0,4,3,0,3,5,0,5,2,1,2,5,1,5,3,1,3,4,1,4,2],t,e),this.type="OctahedronGeometry",this.parameters={radius:t,detail:e}}static fromJSON(t){return new l6t(t.radius,t.detail)}}class c6t extends b1t{constructor(t,e,n){super(),this.type="ParametricGeometry",this.parameters={func:t,slices:e,stacks:n};const i=[],r=[],o=[],a=[],s=1e-5,l=new CJt,c=new CJt,u=new CJt,h=new CJt,d=new CJt;t.length<3&&console.error("THREE.ParametricGeometry: Function must now modify a Vector3 as third parameter.");const p=e+1;for(let i=0;i<=n;i++){const p=i/n;for(let n=0;n<=e;n++){const i=n/e;t(i,p,c),r.push(c.x,c.y,c.z),i-s>=0?(t(i-s,p,u),h.subVectors(c,u)):(t(i+s,p,u),h.subVectors(u,c)),p-s>=0?(t(i,p-s,u),d.subVectors(c,u)):(t(i,p+s,u),d.subVectors(u,c)),l.crossVectors(h,d).normalize(),o.push(l.x,l.y,l.z),a.push(i,p)}}for(let t=0;t<n;t++)for(let n=0;n<e;n++){const e=t*p+n+1,r=(t+1)*p+n+1,o=(t+1)*p+n;i.push(t*p+n,e,o),i.push(e,r,o)}this.setIndex(i),this.setAttribute("position",new l1t(r,3)),this.setAttribute("normal",new l1t(o,3)),this.setAttribute("uv",new l1t(a,2))}}class u6t extends b1t{constructor(t=.5,e=1,n=8,i=1,r=0,o=2*Math.PI){super(),this.type="RingGeometry",this.parameters={innerRadius:t,outerRadius:e,thetaSegments:n,phiSegments:i,thetaStart:r,thetaLength:o},n=Math.max(3,n);const a=[],s=[],l=[],c=[];let u=t;const h=(e-t)/(i=Math.max(1,i)),d=new CJt,p=new mJt;for(let t=0;t<=i;t++){for(let t=0;t<=n;t++){const i=r+t/n*o;d.x=u*Math.cos(i),d.y=u*Math.sin(i),s.push(d.x,d.y,d.z),l.push(0,0,1),p.x=(d.x/e+1)/2,p.y=(d.y/e+1)/2,c.push(p.x,p.y)}u+=h}for(let t=0;t<i;t++){const e=t*(n+1);for(let t=0;t<n;t++){const i=t+e,r=i+n+1,o=i+n+2,s=i+1;a.push(i,r,s),a.push(r,o,s)}}this.setIndex(a),this.setAttribute("position",new l1t(s,3)),this.setAttribute("normal",new l1t(l,3)),this.setAttribute("uv",new l1t(c,2))}static fromJSON(t){return new u6t(t.innerRadius,t.outerRadius,t.thetaSegments,t.phiSegments,t.thetaStart,t.thetaLength)}}class h6t extends b1t{constructor(t,e=12){super(),this.type="ShapeGeometry",this.parameters={shapes:t,curveSegments:e};const n=[],i=[],r=[],o=[];let a=0,s=0;if(!1===Array.isArray(t))l(t);else for(let e=0;e<t.length;e++)l(t[e]),this.addGroup(a,s,e),a+=s,s=0;function l(t){const a=i.length/3,l=t.extractPoints(e);let c=l.shape;const u=l.holes;!1===e6t.isClockWise(c)&&(c=c.reverse());for(let t=0,e=u.length;t<e;t++){const e=u[t];!0===e6t.isClockWise(e)&&(u[t]=e.reverse())}const h=e6t.triangulateShape(c,u);for(let t=0,e=u.length;t<e;t++)c=c.concat(u[t]);for(let t=0,e=c.length;t<e;t++){const e=c[t];i.push(e.x,e.y,0),r.push(0,0,1),o.push(e.x,e.y)}for(let t=0,e=h.length;t<e;t++){const e=h[t];n.push(e[0]+a,e[1]+a,e[2]+a),s+=3}}this.setIndex(n),this.setAttribute("position",new l1t(i,3)),this.setAttribute("normal",new l1t(r,3)),this.setAttribute("uv",new l1t(o,2))}toJSON(){const t=super.toJSON();return(function e(t,n){if(n.shapes=[],Array.isArray(t))for(let e=0,i=t.length;e<i;e++)n.shapes.push(t[e].uuid);else n.shapes.push(t.uuid);return n})(this.parameters.shapes,t)}static fromJSON(t,e){const n=[];for(let i=0,r=t.shapes.length;i<r;i++)n.push(e[t.shapes[i]]);return new h6t(n,t.curveSegments)}}class d6t extends b1t{constructor(t=1,e=32,n=16,i=0,r=2*Math.PI,o=0,a=Math.PI){super(),this.type="SphereGeometry",this.parameters={radius:t,widthSegments:e,heightSegments:n,phiStart:i,phiLength:r,thetaStart:o,thetaLength:a},e=Math.max(3,Math.floor(e)),n=Math.max(2,Math.floor(n));const s=Math.min(o+a,Math.PI);let l=0;const c=[],u=new CJt,h=new CJt,d=[],p=[],f=[],m=[];for(let d=0;d<=n;d++){const g=[],_=d/n;let y=0;0==d&&0==o?y=.5/e:d==n&&s==Math.PI&&(y=-.5/e);for(let n=0;n<=e;n++){const s=n/e;u.x=-t*Math.cos(i+s*r)*Math.sin(o+_*a),u.y=t*Math.cos(o+_*a),u.z=t*Math.sin(i+s*r)*Math.sin(o+_*a),p.push(u.x,u.y,u.z),h.copy(u).normalize(),f.push(h.x,h.y,h.z),m.push(s+y,1-_),g.push(l++)}c.push(g)}for(let t=0;t<n;t++)for(let i=0;i<e;i++){const e=c[t][i+1],r=c[t][i],a=c[t+1][i],l=c[t+1][i+1];(0!==t||o>0)&&d.push(e,r,l),(t!==n-1||s<Math.PI)&&d.push(r,a,l)}this.setIndex(d),this.setAttribute("position",new l1t(p,3)),this.setAttribute("normal",new l1t(f,3)),this.setAttribute("uv",new l1t(m,2))}static fromJSON(t){return new d6t(t.radius,t.widthSegments,t.heightSegments,t.phiStart,t.phiLength,t.thetaStart,t.thetaLength)}}class p6t extends r4t{constructor(t=1,e=0){super([1,1,1,-1,-1,1,-1,1,-1,1,-1,-1],[2,1,0,0,3,2,1,3,0,2,3,1],t,e),this.type="TetrahedronGeometry",this.parameters={radius:t,detail:e}}static fromJSON(t){return new p6t(t.radius,t.detail)}}class f6t extends r6t{constructor(t,e={}){const n=e.font;if(!n||!n.isFont)return console.error("THREE.TextGeometry: font parameter is not an instance of THREE.Font."),new b1t;const i=n.generateShapes(t,e.size);e.depth=void 0!==e.height?e.height:50,void 0===e.bevelThickness&&(e.bevelThickness=10),void 0===e.bevelSize&&(e.bevelSize=8),void 0===e.bevelEnabled&&(e.bevelEnabled=!1),super(i,e),this.type="TextGeometry"}}class m6t extends b1t{constructor(t=1,e=.4,n=8,i=6,r=2*Math.PI){super(),this.type="TorusGeometry",this.parameters={radius:t,tube:e,radialSegments:n,tubularSegments:i,arc:r},n=Math.floor(n),i=Math.floor(i);const o=[],a=[],s=[],l=[],c=new CJt,u=new CJt,h=new CJt;for(let o=0;o<=n;o++)for(let d=0;d<=i;d++){const p=d/i*r,f=o/n*Math.PI*2;u.x=(t+e*Math.cos(f))*Math.cos(p),u.y=(t+e*Math.cos(f))*Math.sin(p),u.z=e*Math.sin(f),a.push(u.x,u.y,u.z),c.x=t*Math.cos(p),c.y=t*Math.sin(p),h.subVectors(u,c).normalize(),s.push(h.x,h.y,h.z),l.push(d/i),l.push(o/n)}for(let t=1;t<=n;t++)for(let e=1;e<=i;e++){const n=(i+1)*(t-1)+e-1,r=(i+1)*(t-1)+e,a=(i+1)*t+e;o.push((i+1)*t+e-1,n,a),o.push(n,r,a)}this.setIndex(o),this.setAttribute("position",new l1t(a,3)),this.setAttribute("normal",new l1t(s,3)),this.setAttribute("uv",new l1t(l,2))}static fromJSON(t){return new m6t(t.radius,t.tube,t.radialSegments,t.tubularSegments,t.arc)}}class g6t extends b1t{constructor(t=1,e=.4,n=64,i=8,r=2,o=3){super(),this.type="TorusKnotGeometry",this.parameters={radius:t,tube:e,tubularSegments:n,radialSegments:i,p:r,q:o},n=Math.floor(n),i=Math.floor(i);const a=[],s=[],l=[],c=[],u=new CJt,h=new CJt,d=new CJt,p=new CJt,f=new CJt,m=new CJt,g=new CJt;for(let a=0;a<=n;++a){const y=a/n*r*Math.PI*2;_(y,r,o,t,d),_(y+.01,r,o,t,p),m.subVectors(p,d),g.addVectors(p,d),f.crossVectors(m,g),g.crossVectors(f,m),f.normalize(),g.normalize();for(let t=0;t<=i;++t){const r=t/i*Math.PI*2,o=-e*Math.cos(r),p=e*Math.sin(r);u.x=d.x+(o*g.x+p*f.x),u.y=d.y+(o*g.y+p*f.y),u.z=d.z+(o*g.z+p*f.z),s.push(u.x,u.y,u.z),h.subVectors(u,d).normalize(),l.push(h.x,h.y,h.z),c.push(a/n),c.push(t/i)}}for(let t=1;t<=n;t++)for(let e=1;e<=i;e++){const n=(i+1)*t+(e-1),r=(i+1)*t+e,o=(i+1)*(t-1)+e;a.push((i+1)*(t-1)+(e-1),n,o),a.push(n,r,o)}function _(t,e,n,i,r){const o=Math.cos(t),a=Math.sin(t),s=n/e*t,l=Math.cos(s);r.x=i*(2+l)*.5*o,r.y=i*(2+l)*a*.5,r.z=i*Math.sin(s)*.5}this.setIndex(a),this.setAttribute("position",new l1t(s,3)),this.setAttribute("normal",new l1t(l,3)),this.setAttribute("uv",new l1t(c,2))}static fromJSON(t){return new g6t(t.radius,t.tube,t.tubularSegments,t.radialSegments,t.p,t.q)}}class _6t extends b1t{constructor(t,e=64,n=1,i=8,r=!1){super(),this.type="TubeGeometry",this.parameters={path:t,tubularSegments:e,radius:n,radialSegments:i,closed:r};const o=t.computeFrenetFrames(e,r);this.tangents=o.tangents,this.normals=o.normals,this.binormals=o.binormals;const a=new CJt,s=new CJt,l=new mJt;let c=new CJt;const u=[],h=[],d=[],p=[];function f(r){c=t.getPointAt(r/e,c);const l=o.normals[r],d=o.binormals[r];for(let t=0;t<=i;t++){const e=t/i*Math.PI*2,r=Math.sin(e),o=-Math.cos(e);s.x=o*l.x+r*d.x,s.y=o*l.y+r*d.y,s.z=o*l.z+r*d.z,s.normalize(),h.push(s.x,s.y,s.z),a.x=c.x+n*s.x,a.y=c.y+n*s.y,a.z=c.z+n*s.z,u.push(a.x,a.y,a.z)}}!(function m(){for(let t=0;t<e;t++)f(t);f(!1===r?e:0),(function t(){for(let t=0;t<=e;t++)for(let n=0;n<=i;n++)l.x=t/e,l.y=n/i,d.push(l.x,l.y)})(),(function n(){for(let t=1;t<=e;t++)for(let e=1;e<=i;e++){const n=(i+1)*t+(e-1),r=(i+1)*t+e,o=(i+1)*(t-1)+e;p.push((i+1)*(t-1)+(e-1),n,o),p.push(n,r,o)}})()})(),this.setIndex(p),this.setAttribute("position",new l1t(u,3)),this.setAttribute("normal",new l1t(h,3)),this.setAttribute("uv",new l1t(d,2))}toJSON(){const t=super.toJSON();return t.path=this.parameters.path.toJSON(),t}static fromJSON(t){return new _6t((new L4t[t.path.type]).fromJSON(t.path),t.tubularSegments,t.radius,t.radialSegments,t.closed)}}class y6t extends b1t{constructor(t){if(super(),this.type="WireframeGeometry",!0===t.isGeometry)return void console.error("THREE.WireframeGeometry no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");const e=[],n=new Set,i=new CJt,r=new CJt;if(null!==t.index){const o=t.attributes.position,a=t.index;let s=t.groups;0===s.length&&(s=[{start:0,count:a.count,materialIndex:0}]);for(let t=0,l=s.length;t<l;++t){const l=s[t],c=l.start;for(let t=c,s=c+l.count;t<s;t+=3)for(let s=0;s<3;s++){const l=a.getX(t+s),c=a.getX(t+(s+1)%3);i.fromBufferAttribute(o,l),r.fromBufferAttribute(o,c),!0===v6t(i,r,n)&&(e.push(i.x,i.y,i.z),e.push(r.x,r.y,r.z))}}}else{const o=t.attributes.position;for(let t=0,a=o.count/3;t<a;t++)for(let a=0;a<3;a++){const s=3*t+(a+1)%3;i.fromBufferAttribute(o,3*t+a),r.fromBufferAttribute(o,s),!0===v6t(i,r,n)&&(e.push(i.x,i.y,i.z),e.push(r.x,r.y,r.z))}}this.setAttribute("position",new l1t(e,3))}}function v6t(t,e,n){const i=`${t.x},${t.y},${t.z}-${e.x},${e.y},${e.z}`,r=`${e.x},${e.y},${e.z}-${t.x},${t.y},${t.z}`;return!0!==n.has(i)&&!0!==n.has(r)&&(n.add(i,r),!0)}var b6t=Object.freeze({__proto__:null,BoxGeometry:F1t,BoxBufferGeometry:F1t,CircleGeometry:e4t,CircleBufferGeometry:e4t,ConeGeometry:i4t,ConeBufferGeometry:i4t,CylinderGeometry:n4t,CylinderBufferGeometry:n4t,DodecahedronGeometry:o4t,DodecahedronBufferGeometry:o4t,EdgesGeometry:u4t,ExtrudeGeometry:r6t,ExtrudeBufferGeometry:r6t,IcosahedronGeometry:a6t,IcosahedronBufferGeometry:a6t,LatheGeometry:s6t,LatheBufferGeometry:s6t,OctahedronGeometry:l6t,OctahedronBufferGeometry:l6t,ParametricGeometry:c6t,ParametricBufferGeometry:c6t,PlaneGeometry:a0t,PlaneBufferGeometry:a0t,PolyhedronGeometry:r4t,PolyhedronBufferGeometry:r4t,RingGeometry:u6t,RingBufferGeometry:u6t,ShapeGeometry:h6t,ShapeBufferGeometry:h6t,SphereGeometry:d6t,SphereBufferGeometry:d6t,TetrahedronGeometry:p6t,TetrahedronBufferGeometry:p6t,TextGeometry:f6t,TextBufferGeometry:f6t,TorusGeometry:m6t,TorusBufferGeometry:m6t,TorusKnotGeometry:g6t,TorusKnotBufferGeometry:g6t,TubeGeometry:_6t,TubeBufferGeometry:_6t,WireframeGeometry:y6t});class x6t extends UQt{constructor(t){super(),this.type="ShadowMaterial",this.color=new $Qt(0),this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this}}x6t.prototype.isShadowMaterial=!0;class w6t extends UQt{constructor(t){super(),this.defines={STANDARD:""},this.type="MeshStandardMaterial",this.color=new $Qt(16777215),this.roughness=1,this.metalness=0,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new $Qt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new mJt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.roughnessMap=null,this.metalnessMap=null,this.alphaMap=null,this.envMap=null,this.envMapIntensity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:""},this.color.copy(t.color),this.roughness=t.roughness,this.metalness=t.metalness,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.roughnessMap=t.roughnessMap,this.metalnessMap=t.metalnessMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.envMapIntensity=t.envMapIntensity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}w6t.prototype.isMeshStandardMaterial=!0;class S6t extends w6t{constructor(t){super(),this.defines={STANDARD:"",PHYSICAL:""},this.type="MeshPhysicalMaterial",this.clearcoat=0,this.clearcoatMap=null,this.clearcoatRoughness=0,this.clearcoatRoughnessMap=null,this.clearcoatNormalScale=new mJt(1,1),this.clearcoatNormalMap=null,this.reflectivity=.5,Object.defineProperty(this,"ior",{get:function(){return(1+.4*this.reflectivity)/(1-.4*this.reflectivity)},set:function(t){this.reflectivity=$Zt(2.5*(t-1)/(t+1),0,1)}}),this.sheen=null,this.transmission=0,this.transmissionMap=null,this.thickness=.01,this.thicknessMap=null,this.attenuationDistance=0,this.attenuationTint=new $Qt(1,1,1),this.specularIntensity=1,this.specularIntensityMap=null,this.specularTint=new $Qt(1,1,1),this.specularTintMap=null,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:"",PHYSICAL:""},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.reflectivity=t.reflectivity,this.sheen=t.sheen?(this.sheen||new $Qt).copy(t.sheen):null,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}S6t.prototype.isMeshPhysicalMaterial=!0;class M6t extends UQt{constructor(t){super(),this.type="MeshPhongMaterial",this.color=new $Qt(16777215),this.specular=new $Qt(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new $Qt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new mJt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.specular.copy(t.specular),this.shininess=t.shininess,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}M6t.prototype.isMeshPhongMaterial=!0;class E6t extends UQt{constructor(t){super(),this.defines={TOON:""},this.type="MeshToonMaterial",this.color=new $Qt(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new $Qt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new mJt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.gradientMap=t.gradientMap,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}E6t.prototype.isMeshToonMaterial=!0;class T6t extends UQt{constructor(t){super(),this.type="MeshNormalMaterial",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new mJt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.flatShading=t.flatShading,this}}T6t.prototype.isMeshNormalMaterial=!0;class C6t extends UQt{constructor(t){super(),this.type="MeshLambertMaterial",this.color=new $Qt(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new $Qt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}C6t.prototype.isMeshLambertMaterial=!0;class A6t extends UQt{constructor(t){super(),this.defines={MATCAP:""},this.type="MeshMatcapMaterial",this.color=new $Qt(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new mJt(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={MATCAP:""},this.color.copy(t.color),this.matcap=t.matcap,this.map=t.map,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.flatShading=t.flatShading,this}}A6t.prototype.isMeshMatcapMaterial=!0;class k6t extends I3t{constructor(t){super(),this.type="LineDashedMaterial",this.scale=1,this.dashSize=3,this.gapSize=1,this.setValues(t)}copy(t){return super.copy(t),this.scale=t.scale,this.dashSize=t.dashSize,this.gapSize=t.gapSize,this}}k6t.prototype.isLineDashedMaterial=!0;var L6t=Object.freeze({__proto__:null,ShadowMaterial:x6t,SpriteMaterial:J5t,RawShaderMaterial:_0t,ShaderMaterial:G1t,PointsMaterial:G3t,MeshPhysicalMaterial:S6t,MeshStandardMaterial:w6t,MeshPhongMaterial:M6t,MeshToonMaterial:E6t,MeshNormalMaterial:T6t,MeshLambertMaterial:C6t,MeshDepthMaterial:N5t,MeshDistanceMaterial:I5t,MeshBasicMaterial:KQt,MeshMatcapMaterial:A6t,LineDashedMaterial:k6t,LineBasicMaterial:I3t,Material:UQt});const P6t={arraySlice:function(t,e,n){return P6t.isTypedArray(t)?new t.constructor(t.subarray(e,void 0!==n?n:t.length)):t.slice(e,n)},convertArray:function(t,e,n){return!t||!n&&t.constructor===e?t:"number"==typeof e.BYTES_PER_ELEMENT?new e(t):Array.prototype.slice.call(t)},isTypedArray:function(t){return ArrayBuffer.isView(t)&&!(t instanceof DataView)},getKeyframeOrder:function(t){const e=t.length,n=new Array(e);for(let t=0;t!==e;++t)n[t]=t;return n.sort((function i(e,n){return t[e]-t[n]})),n},sortedArray:function(t,e,n){const i=t.length,r=new t.constructor(i);for(let o=0,a=0;a!==i;++o){const i=n[o]*e;for(let n=0;n!==e;++n)r[a++]=t[i+n]}return r},flattenJSON:function(t,e,n,i){let r=1,o=t[0];for(;void 0!==o&&void 0===o[i];)o=t[r++];if(void 0===o)return;let a=o[i];if(void 0!==a)if(Array.isArray(a))do{a=o[i],void 0!==a&&(e.push(o.time),n.push.apply(n,a)),o=t[r++]}while(void 0!==o);else if(void 0!==a.toArray)do{a=o[i],void 0!==a&&(e.push(o.time),a.toArray(n,n.length)),o=t[r++]}while(void 0!==o);else do{a=o[i],void 0!==a&&(e.push(o.time),n.push(a)),o=t[r++]}while(void 0!==o)},subclip:function(t,e,n,i,r=30){const o=t.clone();o.name=e;const a=[];for(let t=0;t<o.tracks.length;++t){const e=o.tracks[t],s=e.getValueSize(),l=[],c=[];for(let t=0;t<e.times.length;++t){const o=e.times[t]*r;if(!(o<n||o>=i)){l.push(e.times[t]);for(let n=0;n<s;++n)c.push(e.values[t*s+n])}}0!==l.length&&(e.times=P6t.convertArray(l,e.times.constructor),e.values=P6t.convertArray(c,e.values.constructor),a.push(e))}o.tracks=a;let s=1/0;for(let t=0;t<o.tracks.length;++t)s>o.tracks[t].times[0]&&(s=o.tracks[t].times[0]);for(let t=0;t<o.tracks.length;++t)o.tracks[t].shift(-1*s);return o.resetDuration(),o},makeClipAdditive:function(t,e=0,n=t,i=30){i<=0&&(i=30);const r=n.tracks.length,o=e/i;for(let e=0;e<r;++e){const i=n.tracks[e],r=i.ValueTypeName;if("bool"===r||"string"===r)continue;const a=t.tracks.find((function(t){return t.name===i.name&&t.ValueTypeName===r}));if(void 0===a)continue;let s=0;const l=i.getValueSize();i.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(s=l/3);let c=0;const u=a.getValueSize();a.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(c=u/3);const h=i.times.length-1;let d;if(o<=i.times[0])d=P6t.arraySlice(i.values,s,l-s);else if(o>=i.times[h]){const t=h*l+s;d=P6t.arraySlice(i.values,t,t+l-s)}else{const t=i.createInterpolant(),e=s,n=l-s;t.evaluate(o),d=P6t.arraySlice(t.resultBuffer,e,n)}"quaternion"===r&&(new TJt).fromArray(d).normalize().conjugate().toArray(d);const p=a.times.length;for(let t=0;t<p;++t){const e=t*u+c;if("quaternion"===r)TJt.multiplyQuaternionsFlat(a.values,e,d,0,a.values,e);else{const t=u-2*c;for(let n=0;n<t;++n)a.values[e+n]-=d[n]}}}return t.blendMode=PZt,t}};class N6t{constructor(t,e,n,i){this.parameterPositions=t,this._cachedIndex=0,this.resultBuffer=void 0!==i?i:new e.constructor(n),this.sampleValues=e,this.valueSize=n,this.settings=null,this.DefaultSettings_={}}evaluate(t){const e=this.parameterPositions;let n=this._cachedIndex,i=e[n],r=e[n-1];t:{e:{let o;n:{i:if(!(t<i)){for(let o=n+2;;){if(void 0===i){if(t<r)break i;return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,t,r)}if(n===o)break;if(r=i,i=e[++n],t<i)break e}o=e.length;break n}if(t>=r)break t;{const a=e[1];t<a&&(n=2,r=a);for(let o=n-2;;){if(void 0===r)return this._cachedIndex=0,this.beforeStart_(0,t,i);if(n===o)break;if(i=r,r=e[--n-1],t>=r)break e}o=n,n=0}}for(;n<o;){const i=n+o>>>1;t<e[i]?o=i:n=i+1}if(i=e[n],r=e[n-1],void 0===r)return this._cachedIndex=0,this.beforeStart_(0,t,i);if(void 0===i)return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,r,t)}this._cachedIndex=n,this.intervalChanged_(n,r,i)}return this.interpolate_(n,r,t,i)}getSettings_(){return this.settings||this.DefaultSettings_}copySampleValue_(t){const e=this.resultBuffer,n=this.sampleValues,i=this.valueSize,r=t*i;for(let t=0;t!==i;++t)e[t]=n[r+t];return e}interpolate_(){throw new Error("call to abstract method")}intervalChanged_(){}}N6t.prototype.beforeStart_=N6t.prototype.copySampleValue_,N6t.prototype.afterEnd_=N6t.prototype.copySampleValue_;class I6t extends N6t{constructor(t,e,n,i){super(t,e,n,i),this._weightPrev=-0,this._offsetPrev=-0,this._weightNext=-0,this._offsetNext=-0,this.DefaultSettings_={endingStart:CZt,endingEnd:CZt}}intervalChanged_(t,e,n){const i=this.parameterPositions;let r=t-2,o=t+1,a=i[r],s=i[o];if(void 0===a)switch(this.getSettings_().endingStart){case AZt:r=t,a=2*e-n;break;case kZt:r=i.length-2,a=e+i[r]-i[r+1];break;default:r=t,a=n}if(void 0===s)switch(this.getSettings_().endingEnd){case AZt:o=t,s=2*n-e;break;case kZt:o=1,s=n+i[1]-i[0];break;default:o=t-1,s=e}const l=.5*(n-e),c=this.valueSize;this._weightPrev=l/(e-a),this._weightNext=l/(s-n),this._offsetPrev=r*c,this._offsetNext=o*c}interpolate_(t,e,n,i){const r=this.resultBuffer,o=this.sampleValues,a=this.valueSize,s=t*a,l=s-a,c=this._offsetPrev,u=this._offsetNext,h=this._weightPrev,d=this._weightNext,p=(n-e)/(i-e),f=p*p,m=f*p,g=-h*m+2*h*f-h*p,_=(1+h)*m+(-1.5-2*h)*f+(-.5+h)*p+1,y=(-1-d)*m+(1.5+d)*f+.5*p,v=d*m-d*f;for(let t=0;t!==a;++t)r[t]=g*o[c+t]+_*o[l+t]+y*o[s+t]+v*o[u+t];return r}}class R6t extends N6t{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const r=this.resultBuffer,o=this.sampleValues,a=this.valueSize,s=t*a,l=s-a,c=(n-e)/(i-e),u=1-c;for(let t=0;t!==a;++t)r[t]=o[l+t]*u+o[s+t]*c;return r}}class O6t extends N6t{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t){return this.copySampleValue_(t-1)}}class z6t{constructor(t,e,n,i){if(void 0===t)throw new Error("THREE.KeyframeTrack: track name is undefined");if(void 0===e||0===e.length)throw new Error("THREE.KeyframeTrack: no keyframes in track named "+t);this.name=t,this.times=P6t.convertArray(e,this.TimeBufferType),this.values=P6t.convertArray(n,this.ValueBufferType),this.setInterpolation(i||this.DefaultInterpolation)}static toJSON(t){const e=t.constructor;let n;if(e.toJSON!==this.toJSON)n=e.toJSON(t);else{n={name:t.name,times:P6t.convertArray(t.times,Array),values:P6t.convertArray(t.values,Array)};const e=t.getInterpolation();e!==t.DefaultInterpolation&&(n.interpolation=e)}return n.type=t.ValueTypeName,n}InterpolantFactoryMethodDiscrete(t){return new O6t(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodLinear(t){return new R6t(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodSmooth(t){return new I6t(this.times,this.values,this.getValueSize(),t)}setInterpolation(t){let e;switch(t){case MZt:e=this.InterpolantFactoryMethodDiscrete;break;case EZt:e=this.InterpolantFactoryMethodLinear;break;case TZt:e=this.InterpolantFactoryMethodSmooth}if(void 0===e){const e="unsupported interpolation for "+this.ValueTypeName+" keyframe track named "+this.name;if(void 0===this.createInterpolant){if(t===this.DefaultInterpolation)throw new Error(e);this.setInterpolation(this.DefaultInterpolation)}return console.warn("THREE.KeyframeTrack:",e),this}return this.createInterpolant=e,this}getInterpolation(){switch(this.createInterpolant){case this.InterpolantFactoryMethodDiscrete:return MZt;case this.InterpolantFactoryMethodLinear:return EZt;case this.InterpolantFactoryMethodSmooth:return TZt}}getValueSize(){return this.values.length/this.times.length}shift(t){if(0!==t){const e=this.times;for(let n=0,i=e.length;n!==i;++n)e[n]+=t}return this}scale(t){if(1!==t){const e=this.times;for(let n=0,i=e.length;n!==i;++n)e[n]*=t}return this}trim(t,e){const n=this.times,i=n.length;let r=0,o=i-1;for(;r!==i&&n[r]<t;)++r;for(;-1!==o&&n[o]>e;)--o;if(++o,0!==r||o!==i){r>=o&&(o=Math.max(o,1),r=o-1);const t=this.getValueSize();this.times=P6t.arraySlice(n,r,o),this.values=P6t.arraySlice(this.values,r*t,o*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error("THREE.KeyframeTrack: Invalid value size in track.",this),t=!1);const n=this.times,i=this.values,r=n.length;0===r&&(console.error("THREE.KeyframeTrack: Track is empty.",this),t=!1);let o=null;for(let e=0;e!==r;e++){const i=n[e];if("number"==typeof i&&isNaN(i)){console.error("THREE.KeyframeTrack: Time is not a valid number.",this,e,i),t=!1;break}if(null!==o&&o>i){console.error("THREE.KeyframeTrack: Out of order keys.",this,e,i,o),t=!1;break}o=i}if(void 0!==i&&P6t.isTypedArray(i))for(let e=0,n=i.length;e!==n;++e){const n=i[e];if(isNaN(n)){console.error("THREE.KeyframeTrack: Value is not a valid number.",this,e,n),t=!1;break}}return t}optimize(){const t=P6t.arraySlice(this.times),e=P6t.arraySlice(this.values),n=this.getValueSize(),i=this.getInterpolation()===TZt,r=t.length-1;let o=1;for(let a=1;a<r;++a){let r=!1;const s=t[a];if(s!==t[a+1]&&(1!==a||s!==t[0]))if(i)r=!0;else{const t=a*n,i=t-n,o=t+n;for(let a=0;a!==n;++a){const n=e[t+a];if(n!==e[i+a]||n!==e[o+a]){r=!0;break}}}if(r){if(a!==o){t[o]=t[a];const i=a*n,r=o*n;for(let t=0;t!==n;++t)e[r+t]=e[i+t]}++o}}if(r>0){t[o]=t[r];for(let t=r*n,i=o*n,a=0;a!==n;++a)e[i+a]=e[t+a];++o}return o!==t.length?(this.times=P6t.arraySlice(t,0,o),this.values=P6t.arraySlice(e,0,o*n)):(this.times=t,this.values=e),this}clone(){const t=P6t.arraySlice(this.times,0),e=P6t.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}z6t.prototype.TimeBufferType=Float32Array,z6t.prototype.ValueBufferType=Float32Array,z6t.prototype.DefaultInterpolation=EZt;class D6t extends z6t{}D6t.prototype.ValueTypeName="bool",D6t.prototype.ValueBufferType=Array,D6t.prototype.DefaultInterpolation=MZt,D6t.prototype.InterpolantFactoryMethodLinear=void 0,D6t.prototype.InterpolantFactoryMethodSmooth=void 0;class B6t extends z6t{}B6t.prototype.ValueTypeName="color";class H6t extends z6t{}H6t.prototype.ValueTypeName="number";class F6t extends N6t{constructor(t,e,n,i){super(t,e,n,i)}interpolate_(t,e,n,i){const r=this.resultBuffer,o=this.sampleValues,a=this.valueSize,s=(n-e)/(i-e);let l=t*a;for(let t=l+a;l!==t;l+=4)TJt.slerpFlat(r,0,o,l-a,o,l,s);return r}}class V6t extends z6t{InterpolantFactoryMethodLinear(t){return new F6t(this.times,this.values,this.getValueSize(),t)}}V6t.prototype.ValueTypeName="quaternion",V6t.prototype.DefaultInterpolation=EZt,V6t.prototype.InterpolantFactoryMethodSmooth=void 0;class U6t extends z6t{}U6t.prototype.ValueTypeName="string",U6t.prototype.ValueBufferType=Array,U6t.prototype.DefaultInterpolation=MZt,U6t.prototype.InterpolantFactoryMethodLinear=void 0,U6t.prototype.InterpolantFactoryMethodSmooth=void 0;class j6t extends z6t{}j6t.prototype.ValueTypeName="vector";class G6t{constructor(t,e=-1,n,i=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=i,this.uuid=XZt(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,i=1/(t.fps||1);for(let t=0,r=n.length;t!==r;++t)e.push(W6t(n[t]).scale(i));const r=new this(t.name,t.duration,e,t.blendMode);return r.uuid=t.uuid,r}static toJSON(t){const e=[],n=t.tracks,i={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,i=n.length;t!==i;++t)e.push(z6t.toJSON(n[t]));return i}static CreateFromMorphTargetSequence(t,e,n,i){const r=e.length,o=[];for(let t=0;t<r;t++){let a=[],s=[];a.push((t+r-1)%r,t,(t+1)%r),s.push(0,1,0);const l=P6t.getKeyframeOrder(a);a=P6t.sortedArray(a,1,l),s=P6t.sortedArray(s,1,l),i||0!==a[0]||(a.push(r),s.push(s[0])),o.push(new H6t(".morphTargetInfluences["+e[t].name+"]",a,s).scale(1/n))}return new this(t,-1,o)}static findByName(t,e){let n=t;if(!Array.isArray(t)){const e=t;n=e.geometry&&e.geometry.animations||e.animations}for(let t=0;t<n.length;t++)if(n[t].name===e)return n[t];return null}static CreateClipsFromMorphTargetSequences(t,e,n){const i={},r=/^([\w-]*?)([\d]+)$/;for(let e=0,n=t.length;e<n;e++){const n=t[e],o=n.name.match(r);if(o&&o.length>1){const t=o[1];let e=i[t];e||(i[t]=e=[]),e.push(n)}}const o=[];for(const t in i)o.push(this.CreateFromMorphTargetSequence(t,i[t],e,n));return o}static parseAnimation(t,e){if(!t)return console.error("THREE.AnimationClip: No animation in JSONLoader data."),null;const n=function(t,e,n,i,r){if(0!==n.length){const o=[],a=[];P6t.flattenJSON(n,o,a,i),0!==o.length&&r.push(new t(e,o,a))}},i=[],r=t.name||"default",o=t.fps||30,a=t.blendMode;let s=t.length||-1;const l=t.hierarchy||[];for(let t=0;t<l.length;t++){const r=l[t].keys;if(r&&0!==r.length)if(r[0].morphTargets){const t={};let e;for(e=0;e<r.length;e++)if(r[e].morphTargets)for(let n=0;n<r[e].morphTargets.length;n++)t[r[e].morphTargets[n]]=-1;for(const n in t){const t=[],o=[];for(let i=0;i!==r[e].morphTargets.length;++i){const i=r[e];t.push(i.time),o.push(i.morphTarget===n?1:0)}i.push(new H6t(".morphTargetInfluence["+n+"]",t,o))}s=t.length*(o||1)}else{const o=".bones["+e[t].name+"]";n(j6t,o+".position",r,"pos",i),n(V6t,o+".quaternion",r,"rot",i),n(j6t,o+".scale",r,"scl",i)}}return 0===i.length?null:new this(r,s,i,a)}resetDuration(){let t=0;for(let e=0,n=this.tracks.length;e!==n;++e){const n=this.tracks[e];t=Math.max(t,n.times[n.times.length-1])}return this.duration=t,this}trim(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].trim(0,this.duration);return this}validate(){let t=!0;for(let e=0;e<this.tracks.length;e++)t=t&&this.tracks[e].validate();return t}optimize(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].optimize();return this}clone(){const t=[];for(let e=0;e<this.tracks.length;e++)t.push(this.tracks[e].clone());return new this.constructor(this.name,this.duration,t,this.blendMode)}toJSON(){return this.constructor.toJSON(this)}}function W6t(t){if(void 0===t.type)throw new Error("THREE.KeyframeTrack: track type undefined, can not parse");const e=(function n(t){switch(t.toLowerCase()){case"scalar":case"double":case"float":case"number":case"integer":return H6t;case"vector":case"vector2":case"vector3":case"vector4":return j6t;case"color":return B6t;case"quaternion":return V6t;case"bool":case"boolean":return D6t;case"string":return U6t}throw new Error("THREE.KeyframeTrack: Unsupported typeName: "+t)})(t.type);if(void 0===t.times){const e=[],n=[];P6t.flattenJSON(t.keys,e,n,"value"),t.times=e,t.values=n}return void 0!==e.parse?e.parse(t):new e(t.name,t.times,t.values,t.interpolation)}const q6t={enabled:!1,files:{},add:function(t,e){!1!==this.enabled&&(this.files[t]=e)},get:function(t){if(!1!==this.enabled)return this.files[t]},remove:function(t){delete this.files[t]},clear:function(){this.files={}}};class Y6t{constructor(t,e,n){const i=this;let r,o=!1,a=0,s=0;const l=[];this.onStart=void 0,this.onLoad=t,this.onProgress=e,this.onError=n,this.itemStart=function(t){s++,!1===o&&void 0!==i.onStart&&i.onStart(t,a,s),o=!0},this.itemEnd=function(t){a++,void 0!==i.onProgress&&i.onProgress(t,a,s),a===s&&(o=!1,void 0!==i.onLoad&&i.onLoad())},this.itemError=function(t){void 0!==i.onError&&i.onError(t)},this.resolveURL=function(t){return r?r(t):t},this.setURLModifier=function(t){return r=t,this},this.addHandler=function(t,e){return l.push(t,e),this},this.removeHandler=function(t){const e=l.indexOf(t);return-1!==e&&l.splice(e,2),this},this.getHandler=function(t){for(let e=0,n=l.length;e<n;e+=2){const n=l[e],i=l[e+1];if(n.global&&(n.lastIndex=0),n.test(t))return i}return null}}}const X6t=new Y6t;class $6t{constructor(t){this.manager=void 0!==t?t:X6t,this.crossOrigin="anonymous",this.withCredentials=!1,this.path="",this.resourcePath="",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(i,r){n.load(t,i,e,r)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}const K6t={};class Z6t extends $6t{constructor(t){super(t)}load(t,e,n,i){void 0===t&&(t=""),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,o=q6t.get(t);if(void 0!==o)return r.manager.itemStart(t),setTimeout((function(){e&&e(o),r.manager.itemEnd(t)}),0),o;if(void 0!==K6t[t])return void K6t[t].push({onLoad:e,onProgress:n,onError:i});const a=t.match(/^data:(.*?)(;base64)?,(.*)$/);let s;if(a){const n=a[1],o=!!a[2];let s=a[3];s=decodeURIComponent(s),o&&(s=atob(s));try{let i;const o=(this.responseType||"").toLowerCase();switch(o){case"arraybuffer":case"blob":const t=new Uint8Array(s.length);for(let e=0;e<s.length;e++)t[e]=s.charCodeAt(e);i="blob"===o?new Blob([t.buffer],{type:n}):t.buffer;break;case"document":const e=new DOMParser;i=e.parseFromString(s,n);break;case"json":i=JSON.parse(s);break;default:i=s}setTimeout((function(){e&&e(i),r.manager.itemEnd(t)}),0)}catch(e){setTimeout((function(){i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)}),0)}}else{K6t[t]=[],K6t[t].push({onLoad:e,onProgress:n,onError:i}),s=new XMLHttpRequest,s.open("GET",t,!0),s.addEventListener("load",(function(e){const n=this.response,i=K6t[t];if(delete K6t[t],200===this.status||0===this.status){0===this.status&&console.warn("THREE.FileLoader: HTTP Status 0 received."),q6t.add(t,n);for(let t=0,e=i.length;t<e;t++){const e=i[t];e.onLoad&&e.onLoad(n)}r.manager.itemEnd(t)}else{for(let t=0,n=i.length;t<n;t++){const n=i[t];n.onError&&n.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}}),!1),s.addEventListener("progress",(function(e){const n=K6t[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onProgress&&i.onProgress(e)}}),!1),s.addEventListener("error",(function(e){const n=K6t[t];delete K6t[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onError&&i.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}),!1),s.addEventListener("abort",(function(e){const n=K6t[t];delete K6t[t];for(let t=0,i=n.length;t<i;t++){const i=n[t];i.onError&&i.onError(e)}r.manager.itemError(t),r.manager.itemEnd(t)}),!1),void 0!==this.responseType&&(s.responseType=this.responseType),void 0!==this.withCredentials&&(s.withCredentials=this.withCredentials),s.overrideMimeType&&s.overrideMimeType(void 0!==this.mimeType?this.mimeType:"text/plain");for(const t in this.requestHeader)s.setRequestHeader(t,this.requestHeader[t]);s.send(null)}return r.manager.itemStart(t),s}setResponseType(t){return this.responseType=t,this}setMimeType(t){return this.mimeType=t,this}}class J6t extends $6t{constructor(t){super(t)}load(t,e,n,i){void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,o=q6t.get(t);if(void 0!==o)return r.manager.itemStart(t),setTimeout((function(){e&&e(o),r.manager.itemEnd(t)}),0),o;const a=document.createElementNS("http://www.w3.org/1999/xhtml","img");function s(){a.removeEventListener("load",s,!1),a.removeEventListener("error",l,!1),q6t.add(t,this),e&&e(this),r.manager.itemEnd(t)}function l(e){a.removeEventListener("load",s,!1),a.removeEventListener("error",l,!1),i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)}return a.addEventListener("load",s,!1),a.addEventListener("error",l,!1),"data:"!==t.substr(0,5)&&void 0!==this.crossOrigin&&(a.crossOrigin=this.crossOrigin),r.manager.itemStart(t),a.src=t,a}}class Q6t extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=new $1t,o=new J6t(this.manager);o.setCrossOrigin(this.crossOrigin),o.setPath(this.path);let a=0;function s(n){o.load(t[n],(function(t){r.images[n]=t,a++,6===a&&(r.needsUpdate=!0,e&&e(r))}),void 0,i)}for(let e=0;e<t.length;++e)s(e);return r}}class t9t extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new M3t,a=new Z6t(this.manager);return a.setResponseType("arraybuffer"),a.setRequestHeader(this.requestHeader),a.setPath(this.path),a.setWithCredentials(r.withCredentials),a.load(t,(function(t){const n=r.parse(t);n&&(void 0!==n.image?o.image=n.image:void 0!==n.data&&(o.image.width=n.width,o.image.height=n.height,o.image.data=n.data),o.wrapS=void 0!==n.wrapS?n.wrapS:KKt,o.wrapT=void 0!==n.wrapT?n.wrapT:KKt,o.magFilter=void 0!==n.magFilter?n.magFilter:eZt,o.minFilter=void 0!==n.minFilter?n.minFilter:eZt,o.anisotropy=void 0!==n.anisotropy?n.anisotropy:1,void 0!==n.encoding&&(o.encoding=n.encoding),void 0!==n.flipY&&(o.flipY=n.flipY),void 0!==n.format&&(o.format=n.format),void 0!==n.type&&(o.type=n.type),void 0!==n.mipmaps&&(o.mipmaps=n.mipmaps,o.minFilter=iZt),1===n.mipmapCount&&(o.minFilter=eZt),void 0!==n.generateMipmaps&&(o.generateMipmaps=n.generateMipmaps),o.needsUpdate=!0,e&&e(o,n))}),n,i),o}}class e9t extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=new bJt,o=new J6t(this.manager);return o.setCrossOrigin(this.crossOrigin),o.setPath(this.path),o.load(t,(function(n){r.image=n;const i=t.search(/\.jpe?g($|\?)/i)>0||0===t.search(/^data\:image\/jpeg/);r.format=i?uZt:hZt,r.needsUpdate=!0,void 0!==e&&e(r)}),n,i),r}}class n9t extends h4t{constructor(){super(),this.type="CurvePath",this.curves=[],this.autoClose=!1}add(t){this.curves.push(t)}closePath(){const t=this.curves[0].getPoint(0),e=this.curves[this.curves.length-1].getPoint(1);t.equals(e)||this.curves.push(new E4t(e,t))}getPoint(t){const e=t*this.getLength(),n=this.getCurveLengths();let i=0;for(;i<n.length;){if(n[i]>=e){const t=n[i]-e,r=this.curves[i],o=r.getLength();return r.getPointAt(0===o?0:1-t/o)}i++}return null}getLength(){const t=this.getCurveLengths();return t[t.length-1]}updateArcLengths(){this.needsUpdate=!0,this.cacheLengths=null,this.getCurveLengths()}getCurveLengths(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;const t=[];let e=0;for(let n=0,i=this.curves.length;n<i;n++)e+=this.curves[n].getLength(),t.push(e);return this.cacheLengths=t,t}getSpacedPoints(t=40){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return this.autoClose&&e.push(e[0]),e}getPoints(t=12){const e=[];let n;for(let i=0,r=this.curves;i<r.length;i++){const o=r[i],a=o.getPoints(o&&o.isEllipseCurve?2*t:o&&(o.isLineCurve||o.isLineCurve3)?1:o&&o.isSplineCurve?t*o.points.length:t);for(let t=0;t<a.length;t++){const i=a[t];n&&n.equals(i)||(e.push(i),n=i)}}return this.autoClose&&e.length>1&&!e[e.length-1].equals(e[0])&&e.push(e[0]),e}copy(t){super.copy(t),this.curves=[];for(let e=0,n=t.curves.length;e<n;e++)this.curves.push(t.curves[e].clone());return this.autoClose=t.autoClose,this}toJSON(){const t=super.toJSON();t.autoClose=this.autoClose,t.curves=[];for(let e=0,n=this.curves.length;e<n;e++)t.curves.push(this.curves[e].toJSON());return t}fromJSON(t){super.fromJSON(t),this.autoClose=t.autoClose,this.curves=[];for(let e=0,n=t.curves.length;e<n;e++){const n=t.curves[e];this.curves.push((new L4t[n.type]).fromJSON(n))}return this}}class i9t extends n9t{constructor(t){super(),this.type="Path",this.currentPoint=new mJt,t&&this.setFromPoints(t)}setFromPoints(t){this.moveTo(t[0].x,t[0].y);for(let e=1,n=t.length;e<n;e++)this.lineTo(t[e].x,t[e].y);return this}moveTo(t,e){return this.currentPoint.set(t,e),this}lineTo(t,e){const n=new E4t(this.currentPoint.clone(),new mJt(t,e));return this.curves.push(n),this.currentPoint.set(t,e),this}quadraticCurveTo(t,e,n,i){const r=new C4t(this.currentPoint.clone(),new mJt(t,e),new mJt(n,i));return this.curves.push(r),this.currentPoint.set(n,i),this}bezierCurveTo(t,e,n,i,r,o){const a=new S4t(this.currentPoint.clone(),new mJt(t,e),new mJt(n,i),new mJt(r,o));return this.curves.push(a),this.currentPoint.set(r,o),this}splineThru(t){const e=[this.currentPoint.clone()].concat(t),n=new k4t(e);return this.curves.push(n),this.currentPoint.copy(t[t.length-1]),this}arc(t,e,n,i,r,o){return this.absarc(t+this.currentPoint.x,e+this.currentPoint.y,n,i,r,o),this}absarc(t,e,n,i,r,o){return this.absellipse(t,e,n,n,i,r,o),this}ellipse(t,e,n,i,r,o,a,s){return this.absellipse(t+this.currentPoint.x,e+this.currentPoint.y,n,i,r,o,a,s),this}absellipse(t,e,n,i,r,o,a,s){const l=new d4t(t,e,n,i,r,o,a,s);if(this.curves.length>0){const t=l.getPoint(0);t.equals(this.currentPoint)||this.lineTo(t.x,t.y)}this.curves.push(l);const c=l.getPoint(1);return this.currentPoint.copy(c),this}copy(t){return super.copy(t),this.currentPoint.copy(t.currentPoint),this}toJSON(){const t=super.toJSON();return t.currentPoint=this.currentPoint.toArray(),t}fromJSON(t){return super.fromJSON(t),this.currentPoint.fromArray(t.currentPoint),this}}class r9t extends i9t{constructor(t){super(t),this.uuid=XZt(),this.type="Shape",this.holes=[]}getPointsHoles(t){const e=[];for(let n=0,i=this.holes.length;n<i;n++)e[n]=this.holes[n].getPoints(t);return e}extractPoints(t){return{shape:this.getPoints(t),holes:this.getPointsHoles(t)}}copy(t){super.copy(t),this.holes=[];for(let e=0,n=t.holes.length;e<n;e++)this.holes.push(t.holes[e].clone());return this}toJSON(){const t=super.toJSON();t.uuid=this.uuid,t.holes=[];for(let e=0,n=this.holes.length;e<n;e++)t.holes.push(this.holes[e].toJSON());return t}fromJSON(t){super.fromJSON(t),this.uuid=t.uuid,this.holes=[];for(let e=0,n=t.holes.length;e<n;e++){const n=t.holes[e];this.holes.push((new i9t).fromJSON(n))}return this}}class o9t extends kQt{constructor(t,e=1){super(),this.type="Light",this.color=new $Qt(t),this.intensity=e}dispose(){}copy(t){return super.copy(t),this.color.copy(t.color),this.intensity=t.intensity,this}toJSON(t){const e=super.toJSON(t);return e.object.color=this.color.getHex(),e.object.intensity=this.intensity,void 0!==this.groundColor&&(e.object.groundColor=this.groundColor.getHex()),void 0!==this.distance&&(e.object.distance=this.distance),void 0!==this.angle&&(e.object.angle=this.angle),void 0!==this.decay&&(e.object.decay=this.decay),void 0!==this.penumbra&&(e.object.penumbra=this.penumbra),void 0!==this.shadow&&(e.object.shadow=this.shadow.toJSON()),e}}o9t.prototype.isLight=!0;class a9t extends o9t{constructor(t,e,n){super(t,n),this.type="HemisphereLight",this.position.copy(kQt.DefaultUp),this.updateMatrix(),this.groundColor=new $Qt(e)}copy(t){return o9t.prototype.copy.call(this,t),this.groundColor.copy(t.groundColor),this}}a9t.prototype.isHemisphereLight=!0;const s9t=new rQt,l9t=new CJt,c9t=new CJt;class u9t{constructor(t){this.camera=t,this.bias=0,this.normalBias=0,this.radius=1,this.mapSize=new mJt(512,512),this.map=null,this.mapPass=null,this.matrix=new rQt,this.autoUpdate=!0,this.needsUpdate=!1,this._frustum=new i0t,this._frameExtents=new mJt(1,1),this._viewportCount=1,this._viewports=[new wJt(0,0,1,1)]}getViewportCount(){return this._viewportCount}getFrustum(){return this._frustum}updateMatrices(t){const e=this.camera,n=this.matrix;l9t.setFromMatrixPosition(t.matrixWorld),e.position.copy(l9t),c9t.setFromMatrixPosition(t.target.matrixWorld),e.lookAt(c9t),e.updateMatrixWorld(),s9t.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),this._frustum.setFromProjectionMatrix(s9t),n.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),n.multiply(e.projectionMatrix),n.multiply(e.matrixWorldInverse)}getViewport(t){return this._viewports[t]}getFrameExtents(){return this._frameExtents}dispose(){this.map&&this.map.dispose(),this.mapPass&&this.mapPass.dispose()}copy(t){return this.camera=t.camera.clone(),this.bias=t.bias,this.radius=t.radius,this.mapSize.copy(t.mapSize),this}clone(){return(new this.constructor).copy(this)}toJSON(){const t={};return 0!==this.bias&&(t.bias=this.bias),0!==this.normalBias&&(t.normalBias=this.normalBias),1!==this.radius&&(t.radius=this.radius),512===this.mapSize.x&&512===this.mapSize.y||(t.mapSize=this.mapSize.toArray()),t.camera=this.camera.toJSON(!1).object,delete t.camera.matrix,t}}class h9t extends u9t{constructor(){super(new q1t(50,1,.5,500)),this.focus=1}updateMatrices(t){const e=this.camera,n=2*YZt*t.angle*this.focus,i=this.mapSize.width/this.mapSize.height,r=t.distance||e.far;n===e.fov&&i===e.aspect&&r===e.far||(e.fov=n,e.aspect=i,e.far=r,e.updateProjectionMatrix()),super.updateMatrices(t)}copy(t){return super.copy(t),this.focus=t.focus,this}}h9t.prototype.isSpotLightShadow=!0;class d9t extends o9t{constructor(t,e,n=0,i=Math.PI/3,r=0,o=1){super(t,e),this.type="SpotLight",this.position.copy(kQt.DefaultUp),this.updateMatrix(),this.target=new kQt,this.distance=n,this.angle=i,this.penumbra=r,this.decay=o,this.shadow=new h9t}get power(){return this.intensity*Math.PI}set power(t){this.intensity=t/Math.PI}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.angle=t.angle,this.penumbra=t.penumbra,this.decay=t.decay,this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}d9t.prototype.isSpotLight=!0;const p9t=new rQt,f9t=new CJt,m9t=new CJt;class g9t extends u9t{constructor(){super(new q1t(90,1,.5,500)),this._frameExtents=new mJt(4,2),this._viewportCount=6,this._viewports=[new wJt(2,1,1,1),new wJt(0,1,1,1),new wJt(3,1,1,1),new wJt(1,1,1,1),new wJt(3,0,1,1),new wJt(1,0,1,1)],this._cubeDirections=[new CJt(1,0,0),new CJt(-1,0,0),new CJt(0,0,1),new CJt(0,0,-1),new CJt(0,1,0),new CJt(0,-1,0)],this._cubeUps=[new CJt(0,1,0),new CJt(0,1,0),new CJt(0,1,0),new CJt(0,1,0),new CJt(0,0,1),new CJt(0,0,-1)]}updateMatrices(t,e=0){const n=this.camera,i=this.matrix,r=t.distance||n.far;r!==n.far&&(n.far=r,n.updateProjectionMatrix()),f9t.setFromMatrixPosition(t.matrixWorld),n.position.copy(f9t),m9t.copy(n.position),m9t.add(this._cubeDirections[e]),n.up.copy(this._cubeUps[e]),n.lookAt(m9t),n.updateMatrixWorld(),i.makeTranslation(-f9t.x,-f9t.y,-f9t.z),p9t.multiplyMatrices(n.projectionMatrix,n.matrixWorldInverse),this._frustum.setFromProjectionMatrix(p9t)}}g9t.prototype.isPointLightShadow=!0;class _9t extends o9t{constructor(t,e,n=0,i=1){super(t,e),this.type="PointLight",this.distance=n,this.decay=i,this.shadow=new g9t}get power(){return 4*this.intensity*Math.PI}set power(t){this.intensity=t/(4*Math.PI)}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.decay=t.decay,this.shadow=t.shadow.clone(),this}}_9t.prototype.isPointLight=!0;class y9t extends u9t{constructor(){super(new g0t(-5,5,5,-5,.5,500))}}y9t.prototype.isDirectionalLightShadow=!0;class v9t extends o9t{constructor(t,e){super(t,e),this.type="DirectionalLight",this.position.copy(kQt.DefaultUp),this.updateMatrix(),this.target=new kQt,this.shadow=new y9t}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}v9t.prototype.isDirectionalLight=!0;class b9t extends o9t{constructor(t,e){super(t,e),this.type="AmbientLight"}}b9t.prototype.isAmbientLight=!0;class x9t extends o9t{constructor(t,e,n=10,i=10){super(t,e),this.type="RectAreaLight",this.width=n,this.height=i}copy(t){return super.copy(t),this.width=t.width,this.height=t.height,this}toJSON(t){const e=super.toJSON(t);return e.object.width=this.width,e.object.height=this.height,e}}x9t.prototype.isRectAreaLight=!0;class w9t{constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new CJt)}set(t){for(let e=0;e<9;e++)this.coefficients[e].copy(t[e]);return this}zero(){for(let t=0;t<9;t++)this.coefficients[t].set(0,0,0);return this}getAt(t,e){const n=t.x,i=t.y,r=t.z,o=this.coefficients;return e.copy(o[0]).multiplyScalar(.282095),e.addScaledVector(o[1],.488603*i),e.addScaledVector(o[2],.488603*r),e.addScaledVector(o[3],.488603*n),e.addScaledVector(o[4],n*i*1.092548),e.addScaledVector(o[5],i*r*1.092548),e.addScaledVector(o[6],.315392*(3*r*r-1)),e.addScaledVector(o[7],n*r*1.092548),e.addScaledVector(o[8],.546274*(n*n-i*i)),e}getIrradianceAt(t,e){const n=t.x,i=t.y,r=t.z,o=this.coefficients;return e.copy(o[0]).multiplyScalar(.886227),e.addScaledVector(o[1],1.023328*i),e.addScaledVector(o[2],1.023328*r),e.addScaledVector(o[3],1.023328*n),e.addScaledVector(o[4],.858086*n*i),e.addScaledVector(o[5],.858086*i*r),e.addScaledVector(o[6],.743125*r*r-.247708),e.addScaledVector(o[7],.858086*n*r),e.addScaledVector(o[8],.429043*(n*n-i*i)),e}add(t){for(let e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].fromArray(t,e+3*i);return this}toArray(t=[],e=0){const n=this.coefficients;for(let i=0;i<9;i++)n[i].toArray(t,e+3*i);return t}static getBasisAt(t,e){const n=t.x,i=t.y,r=t.z;e[0]=.282095,e[1]=.488603*i,e[2]=.488603*r,e[3]=.488603*n,e[4]=1.092548*n*i,e[5]=1.092548*i*r,e[6]=.315392*(3*r*r-1),e[7]=1.092548*n*r,e[8]=.546274*(n*n-i*i)}}w9t.prototype.isSphericalHarmonics3=!0;class S9t extends o9t{constructor(t=new w9t,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}S9t.prototype.isLightProbe=!0;class M9t extends $6t{constructor(t){super(t),this.textures={}}load(t,e,n,i){const r=this,o=new Z6t(r.manager);o.setPath(r.path),o.setRequestHeader(r.requestHeader),o.setWithCredentials(r.withCredentials),o.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e=this.textures;function n(t){return void 0===e[t]&&console.warn("THREE.MaterialLoader: Undefined texture",t),e[t]}const i=new L6t[t.type];if(void 0!==t.uuid&&(i.uuid=t.uuid),void 0!==t.name&&(i.name=t.name),void 0!==t.color&&void 0!==i.color&&i.color.setHex(t.color),void 0!==t.roughness&&(i.roughness=t.roughness),void 0!==t.metalness&&(i.metalness=t.metalness),void 0!==t.sheen&&(i.sheen=(new $Qt).setHex(t.sheen)),void 0!==t.emissive&&void 0!==i.emissive&&i.emissive.setHex(t.emissive),void 0!==t.specular&&void 0!==i.specular&&i.specular.setHex(t.specular),void 0!==t.specularIntensity&&(i.specularIntensity=t.specularIntensity),void 0!==t.specularTint&&void 0!==i.specularTint&&i.specularTint.setHex(t.specularTint),void 0!==t.shininess&&(i.shininess=t.shininess),void 0!==t.clearcoat&&(i.clearcoat=t.clearcoat),void 0!==t.clearcoatRoughness&&(i.clearcoatRoughness=t.clearcoatRoughness),void 0!==t.transmission&&(i.transmission=t.transmission),void 0!==t.thickness&&(i.thickness=t.thickness),void 0!==t.attenuationDistance&&(i.attenuationDistance=t.attenuationDistance),void 0!==t.attenuationTint&&void 0!==i.attenuationTint&&i.attenuationTint.setHex(t.attenuationTint),void 0!==t.fog&&(i.fog=t.fog),void 0!==t.flatShading&&(i.flatShading=t.flatShading),void 0!==t.blending&&(i.blending=t.blending),void 0!==t.combine&&(i.combine=t.combine),void 0!==t.side&&(i.side=t.side),void 0!==t.shadowSide&&(i.shadowSide=t.shadowSide),void 0!==t.opacity&&(i.opacity=t.opacity),void 0!==t.transparent&&(i.transparent=t.transparent),void 0!==t.alphaTest&&(i.alphaTest=t.alphaTest),void 0!==t.depthTest&&(i.depthTest=t.depthTest),void 0!==t.depthWrite&&(i.depthWrite=t.depthWrite),void 0!==t.colorWrite&&(i.colorWrite=t.colorWrite),void 0!==t.stencilWrite&&(i.stencilWrite=t.stencilWrite),void 0!==t.stencilWriteMask&&(i.stencilWriteMask=t.stencilWriteMask),void 0!==t.stencilFunc&&(i.stencilFunc=t.stencilFunc),void 0!==t.stencilRef&&(i.stencilRef=t.stencilRef),void 0!==t.stencilFuncMask&&(i.stencilFuncMask=t.stencilFuncMask),void 0!==t.stencilFail&&(i.stencilFail=t.stencilFail),void 0!==t.stencilZFail&&(i.stencilZFail=t.stencilZFail),void 0!==t.stencilZPass&&(i.stencilZPass=t.stencilZPass),void 0!==t.wireframe&&(i.wireframe=t.wireframe),void 0!==t.wireframeLinewidth&&(i.wireframeLinewidth=t.wireframeLinewidth),void 0!==t.wireframeLinecap&&(i.wireframeLinecap=t.wireframeLinecap),void 0!==t.wireframeLinejoin&&(i.wireframeLinejoin=t.wireframeLinejoin),void 0!==t.rotation&&(i.rotation=t.rotation),1!==t.linewidth&&(i.linewidth=t.linewidth),void 0!==t.dashSize&&(i.dashSize=t.dashSize),void 0!==t.gapSize&&(i.gapSize=t.gapSize),void 0!==t.scale&&(i.scale=t.scale),void 0!==t.polygonOffset&&(i.polygonOffset=t.polygonOffset),void 0!==t.polygonOffsetFactor&&(i.polygonOffsetFactor=t.polygonOffsetFactor),void 0!==t.polygonOffsetUnits&&(i.polygonOffsetUnits=t.polygonOffsetUnits),void 0!==t.dithering&&(i.dithering=t.dithering),void 0!==t.alphaToCoverage&&(i.alphaToCoverage=t.alphaToCoverage),void 0!==t.premultipliedAlpha&&(i.premultipliedAlpha=t.premultipliedAlpha),void 0!==t.visible&&(i.visible=t.visible),void 0!==t.toneMapped&&(i.toneMapped=t.toneMapped),void 0!==t.userData&&(i.userData=t.userData),void 0!==t.vertexColors&&(i.vertexColors="number"==typeof t.vertexColors?t.vertexColors>0:t.vertexColors),void 0!==t.uniforms)for(const e in t.uniforms){const r=t.uniforms[e];switch(i.uniforms[e]={},r.type){case"t":i.uniforms[e].value=n(r.value);break;case"c":i.uniforms[e].value=(new $Qt).setHex(r.value);break;case"v2":i.uniforms[e].value=(new mJt).fromArray(r.value);break;case"v3":i.uniforms[e].value=(new CJt).fromArray(r.value);break;case"v4":i.uniforms[e].value=(new wJt).fromArray(r.value);break;case"m3":i.uniforms[e].value=(new gJt).fromArray(r.value);break;case"m4":i.uniforms[e].value=(new rQt).fromArray(r.value);break;default:i.uniforms[e].value=r.value}}if(void 0!==t.defines&&(i.defines=t.defines),void 0!==t.vertexShader&&(i.vertexShader=t.vertexShader),void 0!==t.fragmentShader&&(i.fragmentShader=t.fragmentShader),void 0!==t.extensions)for(const e in t.extensions)i.extensions[e]=t.extensions[e];if(void 0!==t.shading&&(i.flatShading=1===t.shading),void 0!==t.size&&(i.size=t.size),void 0!==t.sizeAttenuation&&(i.sizeAttenuation=t.sizeAttenuation),void 0!==t.map&&(i.map=n(t.map)),void 0!==t.matcap&&(i.matcap=n(t.matcap)),void 0!==t.alphaMap&&(i.alphaMap=n(t.alphaMap)),void 0!==t.bumpMap&&(i.bumpMap=n(t.bumpMap)),void 0!==t.bumpScale&&(i.bumpScale=t.bumpScale),void 0!==t.normalMap&&(i.normalMap=n(t.normalMap)),void 0!==t.normalMapType&&(i.normalMapType=t.normalMapType),void 0!==t.normalScale){let e=t.normalScale;!1===Array.isArray(e)&&(e=[e,e]),i.normalScale=(new mJt).fromArray(e)}return void 0!==t.displacementMap&&(i.displacementMap=n(t.displacementMap)),void 0!==t.displacementScale&&(i.displacementScale=t.displacementScale),void 0!==t.displacementBias&&(i.displacementBias=t.displacementBias),void 0!==t.roughnessMap&&(i.roughnessMap=n(t.roughnessMap)),void 0!==t.metalnessMap&&(i.metalnessMap=n(t.metalnessMap)),void 0!==t.emissiveMap&&(i.emissiveMap=n(t.emissiveMap)),void 0!==t.emissiveIntensity&&(i.emissiveIntensity=t.emissiveIntensity),void 0!==t.specularMap&&(i.specularMap=n(t.specularMap)),void 0!==t.specularIntensityMap&&(i.specularIntensityMap=n(t.specularIntensityMap)),void 0!==t.specularTintMap&&(i.specularTintMap=n(t.specularTintMap)),void 0!==t.envMap&&(i.envMap=n(t.envMap)),void 0!==t.envMapIntensity&&(i.envMapIntensity=t.envMapIntensity),void 0!==t.reflectivity&&(i.reflectivity=t.reflectivity),void 0!==t.refractionRatio&&(i.refractionRatio=t.refractionRatio),void 0!==t.lightMap&&(i.lightMap=n(t.lightMap)),void 0!==t.lightMapIntensity&&(i.lightMapIntensity=t.lightMapIntensity),void 0!==t.aoMap&&(i.aoMap=n(t.aoMap)),void 0!==t.aoMapIntensity&&(i.aoMapIntensity=t.aoMapIntensity),void 0!==t.gradientMap&&(i.gradientMap=n(t.gradientMap)),void 0!==t.clearcoatMap&&(i.clearcoatMap=n(t.clearcoatMap)),void 0!==t.clearcoatRoughnessMap&&(i.clearcoatRoughnessMap=n(t.clearcoatRoughnessMap)),void 0!==t.clearcoatNormalMap&&(i.clearcoatNormalMap=n(t.clearcoatNormalMap)),void 0!==t.clearcoatNormalScale&&(i.clearcoatNormalScale=(new mJt).fromArray(t.clearcoatNormalScale)),void 0!==t.transmissionMap&&(i.transmissionMap=n(t.transmissionMap)),void 0!==t.thicknessMap&&(i.thicknessMap=n(t.thicknessMap)),i}setTextures(t){return this.textures=t,this}}class E9t{static decodeText(t){if("undefined"!=typeof TextDecoder)return(new TextDecoder).decode(t);let e="";for(let n=0,i=t.length;n<i;n++)e+=String.fromCharCode(t[n]);try{return decodeURIComponent(escape(e))}catch(t){return e}}static extractUrlBase(t){const e=t.lastIndexOf("/");return-1===e?"./":t.substr(0,e+1)}}class T9t extends b1t{constructor(){super(),this.type="InstancedBufferGeometry",this.instanceCount=1/0}copy(t){return super.copy(t),this.instanceCount=t.instanceCount,this}clone(){return(new this.constructor).copy(this)}toJSON(){const t=super.toJSON(this);return t.instanceCount=this.instanceCount,t.isInstancedBufferGeometry=!0,t}}T9t.prototype.isInstancedBufferGeometry=!0;class C9t extends QQt{constructor(t,e,n,i=1){"number"==typeof n&&(i=n,n=!1,console.error("THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument.")),super(t,e,n),this.meshPerAttribute=i}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}toJSON(){const t=super.toJSON();return t.meshPerAttribute=this.meshPerAttribute,t.isInstancedBufferAttribute=!0,t}}C9t.prototype.isInstancedBufferAttribute=!0;class A9t extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new Z6t(r.manager);o.setPath(r.path),o.setRequestHeader(r.requestHeader),o.setWithCredentials(r.withCredentials),o.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e={},n={};function i(t,i){if(void 0!==e[i])return e[i];const r=t.interleavedBuffers[i],o=(function a(t,e){if(void 0!==n[e])return n[e];const i=new Uint32Array(t.arrayBuffers[e]).buffer;return n[e]=i,i})(t,r.buffer),s=d1t(r.type,o),l=new $5t(s,r.stride);return l.uuid=r.uuid,e[i]=l,l}const r=t.isInstancedBufferGeometry?new T9t:new b1t,o=t.data.index;if(void 0!==o){const t=d1t(o.type,o.array);r.setIndex(new QQt(t,1))}const a=t.data.attributes;for(const e in a){const n=a[e];let o;if(n.isInterleavedBufferAttribute){const e=i(t.data,n.data);o=new Z5t(e,n.itemSize,n.offset,n.normalized)}else{const t=d1t(n.type,n.array);o=new(n.isInstancedBufferAttribute?C9t:QQt)(t,n.itemSize,n.normalized)}void 0!==n.name&&(o.name=n.name),void 0!==n.usage&&o.setUsage(n.usage),void 0!==n.updateRange&&(o.updateRange.offset=n.updateRange.offset,o.updateRange.count=n.updateRange.count),r.setAttribute(e,o)}const s=t.data.morphAttributes;if(s)for(const e in s){const n=s[e],o=[];for(let e=0,r=n.length;e<r;e++){const r=n[e];let a;if(r.isInterleavedBufferAttribute){const e=i(t.data,r.data);a=new Z5t(e,r.itemSize,r.offset,r.normalized)}else{const t=d1t(r.type,r.array);a=new QQt(t,r.itemSize,r.normalized)}void 0!==r.name&&(a.name=r.name),o.push(a)}r.morphAttributes[e]=o}t.data.morphTargetsRelative&&(r.morphTargetsRelative=!0);const l=t.data.groups||t.data.drawcalls||t.data.offsets;if(void 0!==l)for(let t=0,e=l.length;t!==e;++t){const e=l[t];r.addGroup(e.start,e.count,e.materialIndex)}const c=t.data.boundingSphere;if(void 0!==c){const t=new CJt;void 0!==c.center&&t.fromArray(c.center),r.boundingSphere=new $Jt(t,c.radius)}return t.name&&(r.name=t.name),t.userData&&(r.userData=t.userData),r}}const k9t={UVMapping:UKt,CubeReflectionMapping:jKt,CubeRefractionMapping:GKt,EquirectangularReflectionMapping:WKt,EquirectangularRefractionMapping:qKt,CubeUVReflectionMapping:YKt,CubeUVRefractionMapping:XKt},L9t={RepeatWrapping:$Kt,ClampToEdgeWrapping:KKt,MirroredRepeatWrapping:ZKt},P9t={NearestFilter:JKt,NearestMipmapNearestFilter:QKt,NearestMipmapLinearFilter:tZt,LinearFilter:eZt,LinearMipmapNearestFilter:nZt,LinearMipmapLinearFilter:iZt};class N9t extends $6t{constructor(t){super(t),"undefined"==typeof createImageBitmap&&console.warn("THREE.ImageBitmapLoader: createImageBitmap() not supported."),"undefined"==typeof fetch&&console.warn("THREE.ImageBitmapLoader: fetch() not supported."),this.options={premultiplyAlpha:"none"}}setOptions(t){return this.options=t,this}load(t,e,n,i){void 0===t&&(t=""),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const r=this,o=q6t.get(t);if(void 0!==o)return r.manager.itemStart(t),setTimeout((function(){e&&e(o),r.manager.itemEnd(t)}),0),o;const a={};a.credentials="anonymous"===this.crossOrigin?"same-origin":"include",a.headers=this.requestHeader,fetch(t,a).then((function(t){return t.blob()})).then((function(t){return createImageBitmap(t,Object.assign(r.options,{colorSpaceConversion:"none"}))})).then((function(n){q6t.add(t,n),e&&e(n),r.manager.itemEnd(t)})).catch((function(e){i&&i(e),r.manager.itemError(t),r.manager.itemEnd(t)})),r.manager.itemStart(t)}}N9t.prototype.isImageBitmapLoader=!0;class I9t{constructor(){this.type="ShapePath",this.color=new $Qt,this.subPaths=[],this.currentPath=null}moveTo(t,e){return this.currentPath=new i9t,this.subPaths.push(this.currentPath),this.currentPath.moveTo(t,e),this}lineTo(t,e){return this.currentPath.lineTo(t,e),this}quadraticCurveTo(t,e,n,i){return this.currentPath.quadraticCurveTo(t,e,n,i),this}bezierCurveTo(t,e,n,i,r,o){return this.currentPath.bezierCurveTo(t,e,n,i,r,o),this}splineThru(t){return this.currentPath.splineThru(t),this}toShapes(t,e){function n(t){const e=[];for(let n=0,i=t.length;n<i;n++){const i=t[n],r=new r9t;r.curves=i.curves,e.push(r)}return e}function i(t,e){const n=e.length;let i=!1;for(let r=n-1,o=0;o<n;r=o++){let n=e[r],a=e[o],s=a.x-n.x,l=a.y-n.y;if(Math.abs(l)>Number.EPSILON){if(l<0&&(n=e[o],s=-s,a=e[r],l=-l),t.y<n.y||t.y>a.y)continue;if(t.y===n.y){if(t.x===n.x)return!0}else{const e=l*(t.x-n.x)-s*(t.y-n.y);if(0===e)return!0;if(e<0)continue;i=!i}}else{if(t.y!==n.y)continue;if(a.x<=t.x&&t.x<=n.x||n.x<=t.x&&t.x<=a.x)return!0}}return i}const r=e6t.isClockWise,o=this.subPaths;if(0===o.length)return[];if(!0===e)return n(o);let a,s,l;const c=[];if(1===o.length)return s=o[0],l=new r9t,l.curves=s.curves,c.push(l),c;let u=!r(o[0].getPoints());u=t?!u:u;const h=[],d=[];let p,f,m=[],g=0;d[g]=void 0,m[g]=[];for(let e=0,n=o.length;e<n;e++)s=o[e],p=s.getPoints(),a=r(p),a=t?!a:a,a?(!u&&d[g]&&g++,d[g]={s:new r9t,p:p},d[g].s.curves=s.curves,u&&g++,m[g]=[]):m[g].push({h:s,p:p[0]});if(!d[0])return n(o);if(d.length>1){let t=!1;const e=[];for(let t=0,e=d.length;t<e;t++)h[t]=[];for(let n=0,r=d.length;n<r;n++){const r=m[n];for(let o=0;o<r.length;o++){const a=r[o];let s=!0;for(let r=0;r<d.length;r++)i(a.p,d[r].p)&&(n!==r&&e.push({froms:n,tos:r,hole:o}),s?(s=!1,h[r].push(a)):t=!0);s&&h[n].push(a)}}e.length>0&&(t||(m=h))}for(let t=0,e=d.length;t<e;t++){l=d[t].s,c.push(l),f=m[t];for(let t=0,e=f.length;t<e;t++)l.holes.push(f[t].h)}return c}}class R9t{constructor(t){this.type="Font",this.data=t}generateShapes(t,e=100){const n=[],i=(function r(t,e,n){const i=Array.from(t),r=e/n.resolution,o=(n.boundingBox.yMax-n.boundingBox.yMin+n.underlineThickness)*r,a=[];let s=0,l=0;for(let t=0;t<i.length;t++){const e=i[t];if("\n"===e)s=0,l-=o;else{const t=O9t(e,r,s,l,n);s+=t.offsetX,a.push(t.path)}}return a})(t,e,this.data);for(let t=0,e=i.length;t<e;t++)Array.prototype.push.apply(n,i[t].toShapes());return n}}function O9t(t,e,n,i,r){const o=r.glyphs[t]||r.glyphs["?"];if(!o)return void console.error('THREE.Font: character "'+t+'" does not exists in font family '+r.familyName+".");const a=new I9t;let s,l,c,u,h,d,p,f;if(o.o){const t=o._cachedOutline||(o._cachedOutline=o.o.split(" "));for(let r=0,o=t.length;r<o;)switch(t[r++]){case"m":s=t[r++]*e+n,l=t[r++]*e+i,a.moveTo(s,l);break;case"l":s=t[r++]*e+n,l=t[r++]*e+i,a.lineTo(s,l);break;case"q":c=t[r++]*e+n,u=t[r++]*e+i,h=t[r++]*e+n,d=t[r++]*e+i,a.quadraticCurveTo(h,d,c,u);break;case"b":c=t[r++]*e+n,u=t[r++]*e+i,h=t[r++]*e+n,d=t[r++]*e+i,p=t[r++]*e+n,f=t[r++]*e+i,a.bezierCurveTo(h,d,p,f,c,u)}}return{offsetX:o.ha*e,path:a}}let z9t;R9t.prototype.isFont=!0;const D9t={getContext:function(){return void 0===z9t&&(z9t=new(window.AudioContext||window.webkitAudioContext)),z9t},setContext:function(t){z9t=t}};class B9t extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new Z6t(this.manager);o.setResponseType("arraybuffer"),o.setPath(this.path),o.setRequestHeader(this.requestHeader),o.setWithCredentials(this.withCredentials),o.load(t,(function(n){try{const t=n.slice(0);D9t.getContext().decodeAudioData(t,(function(t){e(t)}))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}}class H9t extends S9t{constructor(t,e,n=1){super(void 0,n);const i=(new $Qt).set(t),r=(new $Qt).set(e),o=new CJt(i.r,i.g,i.b),a=new CJt(r.r,r.g,r.b),s=Math.sqrt(Math.PI),l=s*Math.sqrt(.75);this.sh.coefficients[0].copy(o).add(a).multiplyScalar(s),this.sh.coefficients[1].copy(o).sub(a).multiplyScalar(l)}}H9t.prototype.isHemisphereLightProbe=!0;class F9t extends S9t{constructor(t,e=1){super(void 0,e);const n=(new $Qt).set(t);this.sh.coefficients[0].set(n.r,n.g,n.b).multiplyScalar(2*Math.sqrt(Math.PI))}}F9t.prototype.isAmbientLightProbe=!0;const V9t=new rQt,U9t=new rQt;class j9t{constructor(t=!0){this.autoStart=t,this.startTime=0,this.oldTime=0,this.elapsedTime=0,this.running=!1}start(){this.startTime=G9t(),this.oldTime=this.startTime,this.elapsedTime=0,this.running=!0}stop(){this.getElapsedTime(),this.running=!1,this.autoStart=!1}getElapsedTime(){return this.getDelta(),this.elapsedTime}getDelta(){let t=0;if(this.autoStart&&!this.running)return this.start(),0;if(this.running){const e=G9t();t=(e-this.oldTime)/1e3,this.oldTime=e,this.elapsedTime+=t}return t}}function G9t(){return("undefined"==typeof performance?Date:performance).now()}const W9t=new CJt,q9t=new TJt,Y9t=new CJt,X9t=new CJt;class $9t extends kQt{constructor(t){super(),this.type="Audio",this.listener=t,this.context=t.context,this.gain=this.context.createGain(),this.gain.connect(t.getInput()),this.autoplay=!1,this.buffer=null,this.detune=0,this.loop=!1,this.loopStart=0,this.loopEnd=0,this.offset=0,this.duration=void 0,this.playbackRate=1,this.isPlaying=!1,this.hasPlaybackControl=!0,this.source=null,this.sourceType="empty",this._startedAt=0,this._progress=0,this._connected=!1,this.filters=[]}getOutput(){return this.gain}setNodeSource(t){return this.hasPlaybackControl=!1,this.sourceType="audioNode",this.source=t,this.connect(),this}setMediaElementSource(t){return this.hasPlaybackControl=!1,this.sourceType="mediaNode",this.source=this.context.createMediaElementSource(t),this.connect(),this}setMediaStreamSource(t){return this.hasPlaybackControl=!1,this.sourceType="mediaStreamNode",this.source=this.context.createMediaStreamSource(t),this.connect(),this}setBuffer(t){return this.buffer=t,this.sourceType="buffer",this.autoplay&&this.play(),this}play(t=0){if(!0===this.isPlaying)return void console.warn("THREE.Audio: Audio is already playing.");if(!1===this.hasPlaybackControl)return void console.warn("THREE.Audio: this Audio has no playback control.");this._startedAt=this.context.currentTime+t;const e=this.context.createBufferSource();return e.buffer=this.buffer,e.loop=this.loop,e.loopStart=this.loopStart,e.loopEnd=this.loopEnd,e.onended=this.onEnded.bind(this),e.start(this._startedAt,this._progress+this.offset,this.duration),this.isPlaying=!0,this.source=e,this.setDetune(this.detune),this.setPlaybackRate(this.playbackRate),this.connect()}pause(){if(!1!==this.hasPlaybackControl)return!0===this.isPlaying&&(this._progress+=Math.max(this.context.currentTime-this._startedAt,0)*this.playbackRate,!0===this.loop&&(this._progress=this._progress%(this.duration||this.buffer.duration)),this.source.stop(),this.source.onended=null,this.isPlaying=!1),this;console.warn("THREE.Audio: this Audio has no playback control.")}stop(){if(!1!==this.hasPlaybackControl)return this._progress=0,this.source.stop(),this.source.onended=null,this.isPlaying=!1,this;console.warn("THREE.Audio: this Audio has no playback control.")}connect(){if(this.filters.length>0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].connect(this.filters[t]);this.filters[this.filters.length-1].connect(this.getOutput())}else this.source.connect(this.getOutput());return this._connected=!0,this}disconnect(){if(this.filters.length>0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].disconnect(this.filters[t]);this.filters[this.filters.length-1].disconnect(this.getOutput())}else this.source.disconnect(this.getOutput());return this._connected=!1,this}getFilters(){return this.filters}setFilters(t){return t||(t=[]),!0===this._connected?(this.disconnect(),this.filters=t.slice(),this.connect()):this.filters=t.slice(),this}setDetune(t){if(this.detune=t,void 0!==this.source.detune)return!0===this.isPlaying&&this.source.detune.setTargetAtTime(this.detune,this.context.currentTime,.01),this}getDetune(){return this.detune}getFilter(){return this.getFilters()[0]}setFilter(t){return this.setFilters(t?[t]:[])}setPlaybackRate(t){if(!1!==this.hasPlaybackControl)return this.playbackRate=t,!0===this.isPlaying&&this.source.playbackRate.setTargetAtTime(this.playbackRate,this.context.currentTime,.01),this;console.warn("THREE.Audio: this Audio has no playback control.")}getPlaybackRate(){return this.playbackRate}onEnded(){this.isPlaying=!1}getLoop(){return!1===this.hasPlaybackControl?(console.warn("THREE.Audio: this Audio has no playback control."),!1):this.loop}setLoop(t){if(!1!==this.hasPlaybackControl)return this.loop=t,!0===this.isPlaying&&(this.source.loop=this.loop),this;console.warn("THREE.Audio: this Audio has no playback control.")}setLoopStart(t){return this.loopStart=t,this}setLoopEnd(t){return this.loopEnd=t,this}getVolume(){return this.gain.gain.value}setVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}}const K9t=new CJt,Z9t=new TJt,J9t=new CJt,Q9t=new CJt;class t8t{constructor(t,e=2048){this.analyser=t.context.createAnalyser(),this.analyser.fftSize=e,this.data=new Uint8Array(this.analyser.frequencyBinCount),t.getOutput().connect(this.analyser)}getFrequencyData(){return this.analyser.getByteFrequencyData(this.data),this.data}getAverageFrequency(){let t=0;const e=this.getFrequencyData();for(let n=0;n<e.length;n++)t+=e[n];return t/e.length}}class e8t{constructor(t,e,n){let i,r,o;switch(this.binding=t,this.valueSize=n,e){case"quaternion":i=this._slerp,r=this._slerpAdditive,o=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case"string":case"bool":i=this._select,r=this._select,o=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:i=this._lerp,r=this._lerpAdditive,o=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=i,this._mixBufferRegionAdditive=r,this._setIdentity=o,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(t,e){const n=this.buffer,i=this.valueSize,r=t*i+i;let o=this.cumulativeWeight;if(0===o){for(let t=0;t!==i;++t)n[r+t]=n[t];o=e}else o+=e,this._mixBufferRegion(n,r,0,e/o,i);this.cumulativeWeight=o}accumulateAdditive(t){const e=this.buffer,n=this.valueSize,i=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(e,i,0,t,n),this.cumulativeWeightAdditive+=t}apply(t){const e=this.valueSize,n=this.buffer,i=t*e+e,r=this.cumulativeWeight,o=this.cumulativeWeightAdditive,a=this.binding;this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,r<1&&this._mixBufferRegion(n,i,e*this._origIndex,1-r,e),o>0&&this._mixBufferRegionAdditive(n,i,this._addIndex*e,1,e);for(let t=e,r=e+e;t!==r;++t)if(n[t]!==n[t+e]){a.setValue(n,i);break}}saveOriginalState(){const t=this.buffer,e=this.valueSize,n=e*this._origIndex;this.binding.getValue(t,n);for(let i=e,r=n;i!==r;++i)t[i]=t[n+i%e];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){this.binding.setValue(this.buffer,3*this.valueSize)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n<e;n++)this.buffer[n]=0}_setAdditiveIdentityQuaternion(){this._setAdditiveIdentityNumeric(),this.buffer[this._addIndex*this.valueSize+3]=1}_setAdditiveIdentityOther(){const t=this._origIndex*this.valueSize,e=this._addIndex*this.valueSize;for(let n=0;n<this.valueSize;n++)this.buffer[e+n]=this.buffer[t+n]}_select(t,e,n,i,r){if(i>=.5)for(let i=0;i!==r;++i)t[e+i]=t[n+i]}_slerp(t,e,n,i){TJt.slerpFlat(t,e,t,e,t,n,i)}_slerpAdditive(t,e,n,i,r){const o=this._workIndex*r;TJt.multiplyQuaternionsFlat(t,o,t,e,t,n),TJt.slerpFlat(t,e,t,e,t,o,i)}_lerp(t,e,n,i,r){const o=1-i;for(let a=0;a!==r;++a){const r=e+a;t[r]=t[r]*o+t[n+a]*i}}_lerpAdditive(t,e,n,i,r){for(let o=0;o!==r;++o){const r=e+o;t[r]=t[r]+t[n+o]*i}}}const n8t=new RegExp("[\\[\\]\\.:\\/]","g"),i8t="[^\\[\\]\\.:\\/]",r8t="[^"+"\\[\\]\\.:\\/".replace("\\.","")+"]",o8t=/((?:WC+[\/:])*)/.source.replace("WC",i8t),a8t=/(WCOD+)?/.source.replace("WCOD",r8t),s8t=/(?:\.(WC+)(?:\[(.+)\])?)?/.source.replace("WC",i8t),l8t=/\.(WC+)(?:\[(.+)\])?/.source.replace("WC",i8t),c8t=new RegExp("^"+o8t+a8t+s8t+l8t+"$"),u8t=["material","materials","bones"];class h8t{constructor(t,e,n){this.path=e,this.parsedPath=n||h8t.parseTrackName(e),this.node=h8t.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new h8t.Composite(t,e,n):new h8t(t,e,n)}static sanitizeNodeName(t){return t.replace(/\s/g,"_").replace(n8t,"")}static parseTrackName(t){const e=c8t.exec(t);if(!e)throw new Error("PropertyBinding: Cannot parse trackName: "+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},i=n.nodeName&&n.nodeName.lastIndexOf(".");if(void 0!==i&&-1!==i){const t=n.nodeName.substring(i+1);-1!==u8t.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,i),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error("PropertyBinding: can not parse propertyName from trackName: "+t);return n}static findNode(t,e){if(!e||""===e||"."===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let i=0;i<t.length;i++){const r=t[i];if(r.name===e||r.uuid===e)return r;const o=n(r.children);if(o)return o}return null},i=n(t.children);if(i)return i}return null}_getValue_unavailable(){}_setValue_unavailable(){}_getValue_direct(t,e){t[e]=this.node[this.propertyName]}_getValue_array(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)t[e++]=n[i]}_getValue_arrayElement(t,e){t[e]=this.resolvedProperty[this.propertyIndex]}_getValue_toArray(t,e){this.resolvedProperty.toArray(t,e)}_setValue_direct(t,e){this.targetObject[this.propertyName]=t[e]}_setValue_direct_setNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.needsUpdate=!0}_setValue_direct_setMatrixWorldNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_array(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++]}_setValue_array_setNeedsUpdate(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++];this.targetObject.needsUpdate=!0}_setValue_array_setMatrixWorldNeedsUpdate(t,e){const n=this.resolvedProperty;for(let i=0,r=n.length;i!==r;++i)n[i]=t[e++];this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_arrayElement(t,e){this.resolvedProperty[this.propertyIndex]=t[e]}_setValue_arrayElement_setNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.needsUpdate=!0}_setValue_arrayElement_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_fromArray(t,e){this.resolvedProperty.fromArray(t,e)}_setValue_fromArray_setNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.needsUpdate=!0}_setValue_fromArray_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.matrixWorldNeedsUpdate=!0}_getValue_unbound(t,e){this.bind(),this.getValue(t,e)}_setValue_unbound(t,e){this.bind(),this.setValue(t,e)}bind(){let t=this.node;const e=this.parsedPath,n=e.objectName,i=e.propertyName;let r=e.propertyIndex;if(t||(t=h8t.findNode(this.rootNode,e.nodeName)||this.rootNode,this.node=t),this.getValue=this._getValue_unavailable,this.setValue=this._setValue_unavailable,!t)return void console.error("THREE.PropertyBinding: Trying to update node for track: "+this.path+" but it wasn't found.");if(n){let i=e.objectIndex;switch(n){case"materials":if(!t.material)return void console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.",this);if(!t.material.materials)return void console.error("THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.",this);t=t.material.materials;break;case"bones":if(!t.skeleton)return void console.error("THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.",this);t=t.skeleton.bones;for(let e=0;e<t.length;e++)if(t[e].name===i){i=e;break}break;default:if(void 0===t[n])return void console.error("THREE.PropertyBinding: Can not bind to objectName of node undefined.",this);t=t[n]}if(void 0!==i){if(void 0===t[i])return void console.error("THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.",this,t);t=t[i]}}const o=t[i];if(void 0===o)return void console.error("THREE.PropertyBinding: Trying to update property for track: "+e.nodeName+"."+i+" but it wasn't found.",t);let a=this.Versioning.None;this.targetObject=t,void 0!==t.needsUpdate?a=this.Versioning.NeedsUpdate:void 0!==t.matrixWorldNeedsUpdate&&(a=this.Versioning.MatrixWorldNeedsUpdate);let s=this.BindingType.Direct;if(void 0!==r){if("morphTargetInfluences"===i){if(!t.geometry)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.",this);if(!t.geometry.isBufferGeometry)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences on THREE.Geometry. Use THREE.BufferGeometry instead.",this);if(!t.geometry.morphAttributes)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.",this);void 0!==t.morphTargetDictionary[r]&&(r=t.morphTargetDictionary[r])}s=this.BindingType.ArrayElement,this.resolvedProperty=o,this.propertyIndex=r}else void 0!==o.fromArray&&void 0!==o.toArray?(s=this.BindingType.HasFromToArray,this.resolvedProperty=o):Array.isArray(o)?(s=this.BindingType.EntireArray,this.resolvedProperty=o):this.propertyName=i;this.getValue=this.GetterByBindingType[s],this.setValue=this.SetterByBindingTypeAndVersioning[s][a]}unbind(){this.node=null,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}}h8t.Composite=class{constructor(t,e,n){const i=n||h8t.parseTrackName(e);this._targetGroup=t,this._bindings=t.subscribe_(e,i)}getValue(t,e){this.bind();const n=this._bindings[this._targetGroup.nCachedObjects_];void 0!==n&&n.getValue(t,e)}setValue(t,e){const n=this._bindings;for(let i=this._targetGroup.nCachedObjects_,r=n.length;i!==r;++i)n[i].setValue(t,e)}bind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].bind()}unbind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].unbind()}},h8t.prototype.BindingType={Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3},h8t.prototype.Versioning={None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2},h8t.prototype.GetterByBindingType=[h8t.prototype._getValue_direct,h8t.prototype._getValue_array,h8t.prototype._getValue_arrayElement,h8t.prototype._getValue_toArray],h8t.prototype.SetterByBindingTypeAndVersioning=[[h8t.prototype._setValue_direct,h8t.prototype._setValue_direct_setNeedsUpdate,h8t.prototype._setValue_direct_setMatrixWorldNeedsUpdate],[h8t.prototype._setValue_array,h8t.prototype._setValue_array_setNeedsUpdate,h8t.prototype._setValue_array_setMatrixWorldNeedsUpdate],[h8t.prototype._setValue_arrayElement,h8t.prototype._setValue_arrayElement_setNeedsUpdate,h8t.prototype._setValue_arrayElement_setMatrixWorldNeedsUpdate],[h8t.prototype._setValue_fromArray,h8t.prototype._setValue_fromArray_setNeedsUpdate,h8t.prototype._setValue_fromArray_setMatrixWorldNeedsUpdate]];class d8t{constructor(){this.uuid=XZt(),this._objects=Array.prototype.slice.call(arguments),this.nCachedObjects_=0;const t={};this._indicesByUUID=t;for(let e=0,n=arguments.length;e!==n;++e)t[arguments[e].uuid]=e;this._paths=[],this._parsedPaths=[],this._bindings=[],this._bindingsIndicesByPath={};const e=this;this.stats={objects:{get total(){return e._objects.length},get inUse(){return this.total-e.nCachedObjects_}},get bindingsPerObject(){return e._bindings.length}}}add(){const t=this._objects,e=this._indicesByUUID,n=this._paths,i=this._parsedPaths,r=this._bindings,o=r.length;let a,s=t.length,l=this.nCachedObjects_;for(let c=0,u=arguments.length;c!==u;++c){const u=arguments[c],h=u.uuid;let d=e[h];if(void 0===d){d=s++,e[h]=d,t.push(u);for(let t=0,e=o;t!==e;++t)r[t].push(new h8t(u,n[t],i[t]))}else if(d<l){a=t[d];const s=--l,c=t[s];e[c.uuid]=d,t[d]=c,e[h]=s,t[s]=u;for(let t=0,e=o;t!==e;++t){const e=r[t];let o=e[d];e[d]=e[s],void 0===o&&(o=new h8t(u,n[t],i[t])),e[s]=o}}else t[d]!==a&&console.error("THREE.AnimationObjectGroup: Different objects with the same UUID detected. Clean the caches or recreate your infrastructure when reloading scenes.")}this.nCachedObjects_=l}remove(){const t=this._objects,e=this._indicesByUUID,n=this._bindings,i=n.length;let r=this.nCachedObjects_;for(let o=0,a=arguments.length;o!==a;++o){const a=arguments[o],s=a.uuid,l=e[s];if(void 0!==l&&l>=r){const o=r++,c=t[o];e[c.uuid]=l,t[l]=c,e[s]=o,t[o]=a;for(let t=0,e=i;t!==e;++t){const e=n[t],i=e[l];e[l]=e[o],e[o]=i}}}this.nCachedObjects_=r}uncache(){const t=this._objects,e=this._indicesByUUID,n=this._bindings,i=n.length;let r=this.nCachedObjects_,o=t.length;for(let a=0,s=arguments.length;a!==s;++a){const s=arguments[a].uuid,l=e[s];if(void 0!==l)if(delete e[s],l<r){const a=--r,s=t[a],c=--o,u=t[c];e[s.uuid]=l,t[l]=s,e[u.uuid]=a,t[a]=u,t.pop();for(let t=0,e=i;t!==e;++t){const e=n[t],i=e[c];e[l]=e[a],e[a]=i,e.pop()}}else{const r=--o,a=t[r];r>0&&(e[a.uuid]=l),t[l]=a,t.pop();for(let t=0,e=i;t!==e;++t){const e=n[t];e[l]=e[r],e.pop()}}}this.nCachedObjects_=r}subscribe_(t,e){const n=this._bindingsIndicesByPath;let i=n[t];const r=this._bindings;if(void 0!==i)return r[i];const o=this._paths,a=this._parsedPaths,s=this._objects,l=this.nCachedObjects_,c=new Array(s.length);i=r.length,n[t]=i,o.push(t),a.push(e),r.push(c);for(let n=l,i=s.length;n!==i;++n)c[n]=new h8t(s[n],t,e);return c}unsubscribe_(t){const e=this._bindingsIndicesByPath,n=e[t];if(void 0!==n){const i=this._paths,r=this._parsedPaths,o=this._bindings,a=o.length-1,s=o[a];e[t[a]]=n,o[n]=s,o.pop(),r[n]=r[a],r.pop(),i[n]=i[a],i.pop()}}}d8t.prototype.isAnimationObjectGroup=!0;class p8t{constructor(t,e,n=null,i=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=i;const r=e.tracks,o=r.length,a=new Array(o),s={endingStart:CZt,endingEnd:CZt};for(let t=0;t!==o;++t){const e=r[t].createInterpolant(null);a[t]=e,e.settings=s}this._interpolantSettings=s,this._interpolants=a,this._propertyBindings=new Array(o),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=2201,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(t){return this._startTime=t,this}setLoop(t,e){return this.loop=t,this.repetitions=e,this}setEffectiveWeight(t){return this.weight=t,this._effectiveWeight=this.enabled?t:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(t){return this._scheduleFading(t,0,1)}fadeOut(t){return this._scheduleFading(t,1,0)}crossFadeFrom(t,e,n){if(t.fadeOut(e),this.fadeIn(e),n){const n=this._clip.duration,i=t._clip.duration,r=n/i;t.warp(1,i/n,e),this.warp(r,1,e)}return this}crossFadeTo(t,e,n){return t.crossFadeFrom(this,e,n)}stopFading(){const t=this._weightInterpolant;return null!==t&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}setEffectiveTimeScale(t){return this.timeScale=t,this._effectiveTimeScale=this.paused?0:t,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(t){return this.timeScale=this._clip.duration/t,this.stopWarping()}syncWith(t){return this.time=t.time,this.timeScale=t.timeScale,this.stopWarping()}halt(t){return this.warp(this._effectiveTimeScale,0,t)}warp(t,e,n){const i=this._mixer,r=i.time,o=this.timeScale;let a=this._timeScaleInterpolant;null===a&&(a=i._lendControlInterpolant(),this._timeScaleInterpolant=a);const s=a.parameterPositions,l=a.sampleValues;return s[0]=r,s[1]=r+n,l[0]=t/o,l[1]=e/o,this}stopWarping(){const t=this._timeScaleInterpolant;return null!==t&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(t,e,n,i){if(!this.enabled)return void this._updateWeight(t);const r=this._startTime;if(null!==r){const i=(t-r)*n;if(i<0||0===n)return;this._startTime=null,e=n*i}e*=this._updateTimeScale(t);const o=this._updateTime(e),a=this._updateWeight(t);if(a>0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case PZt:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(o),e[n].accumulateAdditive(a);break;case LZt:default:for(let n=0,r=t.length;n!==r;++n)t[n].evaluate(o),e[n].accumulate(i,a)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const i=n.evaluate(t)[0];e*=i,t>n.parameterPositions[1]&&(this.stopFading(),0===i&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;null!==n&&(e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e))}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let i=this.time+t,r=this._loopCount;const o=2202===n;if(0===t)return-1===r?i:o&&1==(1&r)?e-i:i;if(2200===n){-1===r&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(i>=e)i=e;else{if(!(i<0)){this.time=i;break t}i=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=i,this._mixer.dispatchEvent({type:"finished",action:this,direction:t<0?-1:1})}}else{if(-1===r&&(t>=0?(r=0,this._setEndings(!0,0===this.repetitions,o)):this._setEndings(0===this.repetitions,!0,o)),i>=e||i<0){const n=Math.floor(i/e);i-=e*n,r+=Math.abs(n);const a=this.repetitions-r;if(a<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,i=t>0?e:0,this.time=i,this._mixer.dispatchEvent({type:"finished",action:this,direction:t>0?1:-1});else{if(1===a){const e=t<0;this._setEndings(e,!e,o)}else this._setEndings(!1,!1,o);this._loopCount=r,this.time=i,this._mixer.dispatchEvent({type:"loop",action:this,loopDelta:n})}}else this.time=i;if(o&&1==(1&r))return e-i}return i}_setEndings(t,e,n){const i=this._interpolantSettings;n?(i.endingStart=AZt,i.endingEnd=AZt):(i.endingStart=t?this.zeroSlopeAtStart?AZt:CZt:kZt,i.endingEnd=e?this.zeroSlopeAtEnd?AZt:CZt:kZt)}_scheduleFading(t,e,n){const i=this._mixer,r=i.time;let o=this._weightInterpolant;null===o&&(o=i._lendControlInterpolant(),this._weightInterpolant=o);const a=o.parameterPositions,s=o.sampleValues;return a[0]=r,s[0]=e,a[1]=r+t,s[1]=n,this}}class f8t extends jZt{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,i=t._clip.tracks,r=i.length,o=t._propertyBindings,a=t._interpolants,s=n.uuid,l=this._bindingsByRootAndName;let c=l[s];void 0===c&&(c={},l[s]=c);for(let t=0;t!==r;++t){const r=i[t],l=r.name;let u=c[l];if(void 0!==u)o[t]=u;else{if(u=o[t],void 0!==u){null===u._cacheIndex&&(++u.referenceCount,this._addInactiveBinding(u,s,l));continue}u=new e8t(h8t.create(n,l,e&&e._propertyBindings[t].binding.parsedPath),r.ValueTypeName,r.getValueSize()),++u.referenceCount,this._addInactiveBinding(u,s,l),o[t]=u}a[t].resultBuffer=u.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,i=this._actionsByClip[n];this._bindAction(t,i&&i.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e<this._nActiveActions}_addInactiveAction(t,e,n){const i=this._actions,r=this._actionsByClip;let o=r[e];if(void 0===o)o={knownActions:[t],actionByRoot:{}},t._byClipCacheIndex=0,r[e]=o;else{const e=o.knownActions;t._byClipCacheIndex=e.length,e.push(t)}t._cacheIndex=i.length,i.push(t),o.actionByRoot[n]=t}_removeInactiveAction(t){const e=this._actions,n=e[e.length-1],i=t._cacheIndex;n._cacheIndex=i,e[i]=n,e.pop(),t._cacheIndex=null;const r=t._clip.uuid,o=this._actionsByClip,a=o[r],s=a.knownActions,l=s[s.length-1],c=t._byClipCacheIndex;l._byClipCacheIndex=c,s[c]=l,s.pop(),t._byClipCacheIndex=null,delete a.actionByRoot[(t._localRoot||this._root).uuid],0===s.length&&delete o[r],this._removeInactiveBindingsForAction(t)}_removeInactiveBindingsForAction(t){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.referenceCount&&this._removeInactiveBinding(n)}}_lendAction(t){const e=this._actions,n=t._cacheIndex,i=this._nActiveActions++,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_takeBackAction(t){const e=this._actions,n=t._cacheIndex,i=--this._nActiveActions,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_addInactiveBinding(t,e,n){const i=this._bindingsByRootAndName,r=this._bindings;let o=i[e];void 0===o&&(o={},i[e]=o),o[n]=t,t._cacheIndex=r.length,r.push(t)}_removeInactiveBinding(t){const e=this._bindings,n=t.binding,i=n.rootNode.uuid,r=n.path,o=this._bindingsByRootAndName,a=o[i],s=e[e.length-1],l=t._cacheIndex;s._cacheIndex=l,e[l]=s,e.pop(),delete a[r],0===Object.keys(a).length&&delete o[i]}_lendBinding(t){const e=this._bindings,n=t._cacheIndex,i=this._nActiveBindings++,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_takeBackBinding(t){const e=this._bindings,n=t._cacheIndex,i=--this._nActiveBindings,r=e[i];t._cacheIndex=i,e[i]=t,r._cacheIndex=n,e[n]=r}_lendControlInterpolant(){const t=this._controlInterpolants,e=this._nActiveControlInterpolants++;let n=t[e];return void 0===n&&(n=new R6t(new Float32Array(2),new Float32Array(2),1,this._controlInterpolantsResultBuffer),n.__cacheIndex=e,t[e]=n),n}_takeBackControlInterpolant(t){const e=this._controlInterpolants,n=t.__cacheIndex,i=--this._nActiveControlInterpolants,r=e[i];t.__cacheIndex=i,e[i]=t,r.__cacheIndex=n,e[n]=r}clipAction(t,e,n){const i=e||this._root,r=i.uuid;let o="string"==typeof t?G6t.findByName(i,t):t;const a=null!==o?o.uuid:t,s=this._actionsByClip[a];let l=null;if(void 0===n&&(n=null!==o?o.blendMode:LZt),void 0!==s){const t=s.actionByRoot[r];if(void 0!==t&&t.blendMode===n)return t;l=s.knownActions[0],null===o&&(o=l._clip)}if(null===o)return null;const c=new p8t(this,o,e,n);return this._bindAction(c,l),this._addInactiveAction(c,a,r),c}existingAction(t,e){const n=e||this._root,i=n.uuid,r="string"==typeof t?G6t.findByName(n,t):t,o=this._actionsByClip[r?r.uuid:t];return void 0!==o&&o.actionByRoot[i]||null}stopAllAction(){const t=this._actions;for(let e=this._nActiveActions-1;e>=0;--e)t[e].stop();return this}update(t){const e=this._actions,n=this._nActiveActions,i=this.time+=t*=this.timeScale,r=Math.sign(t),o=this._accuIndex^=1;for(let a=0;a!==n;++a)e[a]._update(i,t,r,o);const a=this._bindings,s=this._nActiveBindings;for(let t=0;t!==s;++t)a[t].apply(o);return this}setTime(t){this.time=0;for(let t=0;t<this._actions.length;t++)this._actions[t].time=0;return this.update(t)}getRoot(){return this._root}uncacheClip(t){const e=this._actions,n=t.uuid,i=this._actionsByClip,r=i[n];if(void 0!==r){const t=r.knownActions;for(let n=0,i=t.length;n!==i;++n){const i=t[n];this._deactivateAction(i);const r=i._cacheIndex,o=e[e.length-1];i._cacheIndex=null,i._byClipCacheIndex=null,o._cacheIndex=r,e[r]=o,e.pop(),this._removeInactiveBindingsForAction(i)}delete i[n]}}uncacheRoot(t){const e=t.uuid,n=this._actionsByClip;for(const t in n){const i=n[t].actionByRoot[e];void 0!==i&&(this._deactivateAction(i),this._removeInactiveAction(i))}const i=this._bindingsByRootAndName[e];if(void 0!==i)for(const t in i){const e=i[t];e.restoreOriginalState(),this._removeInactiveBinding(e)}}uncacheAction(t,e){const n=this.existingAction(t,e);null!==n&&(this._deactivateAction(n),this._removeInactiveAction(n))}}f8t.prototype._controlInterpolantsResultBuffer=new Float32Array(1);class m8t{constructor(t){"string"==typeof t&&(console.warn("THREE.Uniform: Type parameter is no longer needed."),t=arguments[1]),this.value=t}clone(){return new m8t(void 0===this.value.clone?this.value:this.value.clone())}}class g8t extends $5t{constructor(t,e,n=1){super(t,e),this.meshPerAttribute=n}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}clone(t){const e=super.clone(t);return e.meshPerAttribute=this.meshPerAttribute,e}toJSON(t){const e=super.toJSON(t);return e.isInstancedInterleavedBuffer=!0,e.meshPerAttribute=this.meshPerAttribute,e}}g8t.prototype.isInstancedInterleavedBuffer=!0;class _8t{constructor(t,e,n,i,r){this.buffer=t,this.type=e,this.itemSize=n,this.elementSize=i,this.count=r,this.version=0}set needsUpdate(t){!0===t&&this.version++}setBuffer(t){return this.buffer=t,this}setType(t,e){return this.type=t,this.elementSize=e,this}setItemSize(t){return this.itemSize=t,this}setCount(t){return this.count=t,this}}function y8t(t,e){return t.distance-e.distance}function v8t(t,e,n,i){if(t.layers.test(e.layers)&&t.raycast(e,n),!0===i){const i=t.children;for(let t=0,r=i.length;t<r;t++)v8t(i[t],e,n,!0)}}_8t.prototype.isGLBufferAttribute=!0;class b8t{constructor(t=1,e=0,n=0){return this.radius=t,this.phi=e,this.theta=n,this}set(t,e,n){return this.radius=t,this.phi=e,this.theta=n,this}copy(t){return this.radius=t.radius,this.phi=t.phi,this.theta=t.theta,this}makeSafe(){const t=1e-6;return this.phi=Math.max(t,Math.min(Math.PI-t,this.phi)),this}setFromVector3(t){return this.setFromCartesianCoords(t.x,t.y,t.z)}setFromCartesianCoords(t,e,n){return this.radius=Math.sqrt(t*t+e*e+n*n),0===this.radius?(this.theta=0,this.phi=0):(this.theta=Math.atan2(t,n),this.phi=Math.acos($Zt(e/this.radius,-1,1))),this}clone(){return(new this.constructor).copy(this)}}const x8t=new mJt;class w8t{constructor(t=new mJt(1/0,1/0),e=new mJt(-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;e<n;e++)this.expandByPoint(t[e]);return this}setFromCenterAndSize(t,e){const n=x8t.copy(e).multiplyScalar(.5);return this.min.copy(t).sub(n),this.max.copy(t).add(n),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.min.copy(t.min),this.max.copy(t.max),this}makeEmpty(){return this.min.x=this.min.y=1/0,this.max.x=this.max.y=-1/0,this}isEmpty(){return this.max.x<this.min.x||this.max.y<this.min.y}getCenter(t){return this.isEmpty()?t.set(0,0):t.addVectors(this.min,this.max).multiplyScalar(.5)}getSize(t){return this.isEmpty()?t.set(0,0):t.subVectors(this.max,this.min)}expandByPoint(t){return this.min.min(t),this.max.max(t),this}expandByVector(t){return this.min.sub(t),this.max.add(t),this}expandByScalar(t){return this.min.addScalar(-t),this.max.addScalar(t),this}containsPoint(t){return!(t.x<this.min.x||t.x>this.max.x||t.y<this.min.y||t.y>this.max.y)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y))}intersectsBox(t){return!(t.max.x<this.min.x||t.min.x>this.max.x||t.max.y<this.min.y||t.min.y>this.max.y)}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return x8t.copy(t).clamp(this.min,this.max).sub(t).length()}intersect(t){return this.min.max(t.min),this.max.min(t.max),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}w8t.prototype.isBox2=!0;const S8t=new CJt,M8t=new CJt;class E8t{constructor(t=new CJt,e=new CJt){this.start=t,this.end=e}set(t,e){return this.start.copy(t),this.end.copy(e),this}copy(t){return this.start.copy(t.start),this.end.copy(t.end),this}getCenter(t){return t.addVectors(this.start,this.end).multiplyScalar(.5)}delta(t){return t.subVectors(this.end,this.start)}distanceSq(){return this.start.distanceToSquared(this.end)}distance(){return this.start.distanceTo(this.end)}at(t,e){return this.delta(e).multiplyScalar(t).add(this.start)}closestPointToPointParameter(t,e){S8t.subVectors(t,this.start),M8t.subVectors(this.end,this.start);const n=M8t.dot(M8t);let i=M8t.dot(S8t)/n;return e&&(i=$Zt(i,0,1)),i}closestPointToPoint(t,e,n){const i=this.closestPointToPointParameter(t,e);return this.delta(n).multiplyScalar(i).add(this.start)}applyMatrix4(t){return this.start.applyMatrix4(t),this.end.applyMatrix4(t),this}equals(t){return t.start.equals(this.start)&&t.end.equals(this.end)}clone(){return(new this.constructor).copy(this)}}class T8t extends kQt{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}T8t.prototype.isImmediateRenderObject=!0;const C8t=new CJt,A8t=new CJt,k8t=new rQt,L8t=new rQt;class P8t extends U3t{constructor(t){const e=N8t(t),n=new b1t,i=[],r=[],o=new $Qt(0,0,1),a=new $Qt(0,1,0);for(let t=0;t<e.length;t++){const n=e[t];n.parent&&n.parent.isBone&&(i.push(0,0,0),i.push(0,0,0),r.push(o.r,o.g,o.b),r.push(a.r,a.g,a.b))}n.setAttribute("position",new l1t(i,3)),n.setAttribute("color",new l1t(r,3)),super(n,new I3t({vertexColors:!0,depthTest:!1,depthWrite:!1,toneMapped:!1,transparent:!0})),this.type="SkeletonHelper",this.isSkeletonHelper=!0,this.root=t,this.bones=e,this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1}updateMatrixWorld(t){const e=this.bones,n=this.geometry,i=n.getAttribute("position");L8t.copy(this.root.matrixWorld).invert();for(let t=0,n=0;t<e.length;t++){const r=e[t];r.parent&&r.parent.isBone&&(k8t.multiplyMatrices(L8t,r.matrixWorld),A8t.setFromMatrixPosition(k8t),i.setXYZ(n,A8t.x,A8t.y,A8t.z),k8t.multiplyMatrices(L8t,r.parent.matrixWorld),A8t.setFromMatrixPosition(k8t),i.setXYZ(n+1,A8t.x,A8t.y,A8t.z),n+=2)}n.getAttribute("position").needsUpdate=!0,super.updateMatrixWorld(t)}}function N8t(t){const e=[];t&&t.isBone&&e.push(t);for(let n=0;n<t.children.length;n++)e.push.apply(e,N8t(t.children[n]));return e}const I8t=new CJt,R8t=new $Qt,O8t=new $Qt;class z8t extends U3t{constructor(t=10,e=10,n=4473924,i=8947848){n=new $Qt(n),i=new $Qt(i);const r=e/2,o=t/e,a=t/2,s=[],l=[];for(let t=0,c=0,u=-a;t<=e;t++,u+=o){s.push(-a,0,u,a,0,u),s.push(u,0,-a,u,0,a);const e=t===r?n:i;e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3}const c=new b1t;c.setAttribute("position",new l1t(s,3)),c.setAttribute("color",new l1t(l,3)),super(c,new I3t({vertexColors:!0,toneMapped:!1})),this.type="GridHelper"}}const D8t=new CJt,B8t=new CJt,H8t=new CJt,F8t=new CJt,V8t=new W1t;function U8t(t,e,n,i,r,o,a){F8t.set(r,o,a).unproject(i);const s=e[t];if(void 0!==s){const t=n.getAttribute("position");for(let e=0,n=s.length;e<n;e++)t.setXYZ(s[e],F8t.x,F8t.y,F8t.z)}}const j8t=new LJt;class G8t extends U3t{constructor(t,e=16776960){const n=new Uint16Array([0,1,1,2,2,3,3,0,4,5,5,6,6,7,7,4,0,4,1,5,2,6,3,7]),i=new Float32Array(24),r=new b1t;r.setIndex(new QQt(n,1)),r.setAttribute("position",new QQt(i,3)),super(r,new I3t({color:e,toneMapped:!1})),this.object=t,this.type="BoxHelper",this.matrixAutoUpdate=!1,this.update()}update(t){if(void 0!==t&&console.warn("THREE.BoxHelper: .update() has no longer arguments."),void 0!==this.object&&j8t.setFromObject(this.object),j8t.isEmpty())return;const e=j8t.min,n=j8t.max,i=this.geometry.attributes.position,r=i.array;r[0]=n.x,r[1]=n.y,r[2]=n.z,r[3]=e.x,r[4]=n.y,r[5]=n.z,r[6]=e.x,r[7]=e.y,r[8]=n.z,r[9]=n.x,r[10]=e.y,r[11]=n.z,r[12]=n.x,r[13]=n.y,r[14]=e.z,r[15]=e.x,r[16]=n.y,r[17]=e.z,r[18]=e.x,r[19]=e.y,r[20]=e.z,r[21]=n.x,r[22]=e.y,r[23]=e.z,i.needsUpdate=!0,this.geometry.computeBoundingSphere()}setFromObject(t){return this.object=t,this.update(),this}copy(t){return U3t.prototype.copy.call(this,t),this.object=t.object,this}}const W8t=new CJt;let q8t,Y8t;class X8t extends U3t{constructor(t=1){const e=[0,0,0,t,0,0,0,0,0,0,t,0,0,0,0,0,0,t],n=new b1t;n.setAttribute("position",new l1t(e,3)),n.setAttribute("color",new l1t([1,0,0,1,.6,0,0,1,0,.6,1,0,0,0,1,0,.6,1],3)),super(n,new I3t({vertexColors:!0,toneMapped:!1})),this.type="AxesHelper"}setColors(t,e,n){const i=new $Qt,r=this.geometry.attributes.color.array;return i.set(t),i.toArray(r,0),i.toArray(r,3),i.set(e),i.toArray(r,6),i.toArray(r,9),i.set(n),i.toArray(r,12),i.toArray(r,15),this.geometry.attributes.color.needsUpdate=!0,this}dispose(){this.geometry.dispose(),this.material.dispose()}}const $8t=new Float32Array(1),K8t=new Int32Array($8t.buffer);h4t.create=function(t,e){return console.log("THREE.Curve.create() has been deprecated"),t.prototype=Object.create(h4t.prototype),t.prototype.constructor=t,t.prototype.getPoint=e,t},i9t.prototype.fromPoints=function(t){return console.warn("THREE.Path: .fromPoints() has been renamed to .setFromPoints()."),this.setFromPoints(t)},z8t.prototype.setColors=function(){console.error("THREE.GridHelper: setColors() has been deprecated, pass them in the constructor instead.")},P8t.prototype.update=function(){console.error("THREE.SkeletonHelper: update() no longer needs to be called.")},$6t.prototype.extractUrlBase=function(t){return console.warn("THREE.Loader: .extractUrlBase() has been deprecated. Use THREE.LoaderUtils.extractUrlBase() instead."),E9t.extractUrlBase(t)},$6t.Handlers={add:function(){console.error("THREE.Loader: Handlers.add() has been removed. Use LoadingManager.addHandler() instead.")},get:function(){console.error("THREE.Loader: Handlers.get() has been removed. Use LoadingManager.getHandler() instead.")}},w8t.prototype.center=function(t){return console.warn("THREE.Box2: .center() has been renamed to .getCenter()."),this.getCenter(t)},w8t.prototype.empty=function(){return console.warn("THREE.Box2: .empty() has been renamed to .isEmpty()."),this.isEmpty()},w8t.prototype.isIntersectionBox=function(t){return console.warn("THREE.Box2: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},w8t.prototype.size=function(t){return console.warn("THREE.Box2: .size() has been renamed to .getSize()."),this.getSize(t)},LJt.prototype.center=function(t){return console.warn("THREE.Box3: .center() has been renamed to .getCenter()."),this.getCenter(t)},LJt.prototype.empty=function(){return console.warn("THREE.Box3: .empty() has been renamed to .isEmpty()."),this.isEmpty()},LJt.prototype.isIntersectionBox=function(t){return console.warn("THREE.Box3: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},LJt.prototype.isIntersectionSphere=function(t){return console.warn("THREE.Box3: .isIntersectionSphere() has been renamed to .intersectsSphere()."),this.intersectsSphere(t)},LJt.prototype.size=function(t){return console.warn("THREE.Box3: .size() has been renamed to .getSize()."),this.getSize(t)},$Jt.prototype.empty=function(){return console.warn("THREE.Sphere: .empty() has been renamed to .isEmpty()."),this.isEmpty()},i0t.prototype.setFromMatrix=function(t){return console.warn("THREE.Frustum: .setFromMatrix() has been renamed to .setFromProjectionMatrix()."),this.setFromProjectionMatrix(t)},E8t.prototype.center=function(t){return console.warn("THREE.Line3: .center() has been renamed to .getCenter()."),this.getCenter(t)},gJt.prototype.flattenToArrayOffset=function(t,e){return console.warn("THREE.Matrix3: .flattenToArrayOffset() has been deprecated. Use .toArray() instead."),this.toArray(t,e)},gJt.prototype.multiplyVector3=function(t){return console.warn("THREE.Matrix3: .multiplyVector3() has been removed. Use vector.applyMatrix3( matrix ) instead."),t.applyMatrix3(this)},gJt.prototype.multiplyVector3Array=function(){console.error("THREE.Matrix3: .multiplyVector3Array() has been removed.")},gJt.prototype.applyToBufferAttribute=function(t){return console.warn("THREE.Matrix3: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix3( matrix ) instead."),t.applyMatrix3(this)},gJt.prototype.applyToVector3Array=function(){console.error("THREE.Matrix3: .applyToVector3Array() has been removed.")},gJt.prototype.getInverse=function(t){return console.warn("THREE.Matrix3: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead."),this.copy(t).invert()},rQt.prototype.extractPosition=function(t){return console.warn("THREE.Matrix4: .extractPosition() has been renamed to .copyPosition()."),this.copyPosition(t)},rQt.prototype.flattenToArrayOffset=function(t,e){return console.warn("THREE.Matrix4: .flattenToArrayOffset() has been deprecated. Use .toArray() instead."),this.toArray(t,e)},rQt.prototype.getPosition=function(){return console.warn("THREE.Matrix4: .getPosition() has been removed. Use Vector3.setFromMatrixPosition( matrix ) instead."),(new CJt).setFromMatrixColumn(this,3)},rQt.prototype.setRotationFromQuaternion=function(t){return console.warn("THREE.Matrix4: .setRotationFromQuaternion() has been renamed to .makeRotationFromQuaternion()."),this.makeRotationFromQuaternion(t)},rQt.prototype.multiplyToArray=function(){console.warn("THREE.Matrix4: .multiplyToArray() has been removed.")},rQt.prototype.multiplyVector3=function(t){return console.warn("THREE.Matrix4: .multiplyVector3() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},rQt.prototype.multiplyVector4=function(t){return console.warn("THREE.Matrix4: .multiplyVector4() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},rQt.prototype.multiplyVector3Array=function(){console.error("THREE.Matrix4: .multiplyVector3Array() has been removed.")},rQt.prototype.rotateAxis=function(t){console.warn("THREE.Matrix4: .rotateAxis() has been removed. Use Vector3.transformDirection( matrix ) instead."),t.transformDirection(this)},rQt.prototype.crossVector=function(t){return console.warn("THREE.Matrix4: .crossVector() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},rQt.prototype.translate=function(){console.error("THREE.Matrix4: .translate() has been removed.")},rQt.prototype.rotateX=function(){console.error("THREE.Matrix4: .rotateX() has been removed.")},rQt.prototype.rotateY=function(){console.error("THREE.Matrix4: .rotateY() has been removed.")},rQt.prototype.rotateZ=function(){console.error("THREE.Matrix4: .rotateZ() has been removed.")},rQt.prototype.rotateByAxis=function(){console.error("THREE.Matrix4: .rotateByAxis() has been removed.")},rQt.prototype.applyToBufferAttribute=function(t){return console.warn("THREE.Matrix4: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},rQt.prototype.applyToVector3Array=function(){console.error("THREE.Matrix4: .applyToVector3Array() has been removed.")},rQt.prototype.makeFrustum=function(t,e,n,i,r,o){return console.warn("THREE.Matrix4: .makeFrustum() has been removed. Use .makePerspective( left, right, top, bottom, near, far ) instead."),this.makePerspective(t,e,i,n,r,o)},rQt.prototype.getInverse=function(t){return console.warn("THREE.Matrix4: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead."),this.copy(t).invert()},t0t.prototype.isIntersectionLine=function(t){return console.warn("THREE.Plane: .isIntersectionLine() has been renamed to .intersectsLine()."),this.intersectsLine(t)},TJt.prototype.multiplyVector3=function(t){return console.warn("THREE.Quaternion: .multiplyVector3() has been removed. Use is now vector.applyQuaternion( quaternion ) instead."),t.applyQuaternion(this)},TJt.prototype.inverse=function(){return console.warn("THREE.Quaternion: .inverse() has been renamed to invert()."),this.invert()},iQt.prototype.isIntersectionBox=function(t){return console.warn("THREE.Ray: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},iQt.prototype.isIntersectionPlane=function(t){return console.warn("THREE.Ray: .isIntersectionPlane() has been renamed to .intersectsPlane()."),this.intersectsPlane(t)},iQt.prototype.isIntersectionSphere=function(t){return console.warn("THREE.Ray: .isIntersectionSphere() has been renamed to .intersectsSphere()."),this.intersectsSphere(t)},FQt.prototype.area=function(){return console.warn("THREE.Triangle: .area() has been renamed to .getArea()."),this.getArea()},FQt.prototype.barycoordFromPoint=function(t,e){return console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord()."),this.getBarycoord(t,e)},FQt.prototype.midpoint=function(t){return console.warn("THREE.Triangle: .midpoint() has been renamed to .getMidpoint()."),this.getMidpoint(t)},FQt.prototypenormal=function(t){return console.warn("THREE.Triangle: .normal() has been renamed to .getNormal()."),this.getNormal(t)},FQt.prototype.plane=function(t){return console.warn("THREE.Triangle: .plane() has been renamed to .getPlane()."),this.getPlane(t)},FQt.barycoordFromPoint=function(t,e,n,i,r){return console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord()."),FQt.getBarycoord(t,e,n,i,r)},FQt.normal=function(t,e,n,i){return console.warn("THREE.Triangle: .normal() has been renamed to .getNormal()."),FQt.getNormal(t,e,n,i)},r9t.prototype.extractAllPoints=function(t){return console.warn("THREE.Shape: .extractAllPoints() has been removed. Use .extractPoints() instead."),this.extractPoints(t)},r9t.prototype.extrude=function(t){return console.warn("THREE.Shape: .extrude() has been removed. Use ExtrudeGeometry() instead."),new r6t(this,t)},r9t.prototype.makeGeometry=function(t){return console.warn("THREE.Shape: .makeGeometry() has been removed. Use ShapeGeometry() instead."),new h6t(this,t)},mJt.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector2: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},mJt.prototype.distanceToManhattan=function(t){return console.warn("THREE.Vector2: .distanceToManhattan() has been renamed to .manhattanDistanceTo()."),this.manhattanDistanceTo(t)},mJt.prototype.lengthManhattan=function(){return console.warn("THREE.Vector2: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},CJt.prototype.setEulerFromRotationMatrix=function(){console.error("THREE.Vector3: .setEulerFromRotationMatrix() has been removed. Use Euler.setFromRotationMatrix() instead.")},CJt.prototype.setEulerFromQuaternion=function(){console.error("THREE.Vector3: .setEulerFromQuaternion() has been removed. Use Euler.setFromQuaternion() instead.")},CJt.prototype.getPositionFromMatrix=function(t){return console.warn("THREE.Vector3: .getPositionFromMatrix() has been renamed to .setFromMatrixPosition()."),this.setFromMatrixPosition(t)},CJt.prototype.getScaleFromMatrix=function(t){return console.warn("THREE.Vector3: .getScaleFromMatrix() has been renamed to .setFromMatrixScale()."),this.setFromMatrixScale(t)},CJt.prototype.getColumnFromMatrix=function(t,e){return console.warn("THREE.Vector3: .getColumnFromMatrix() has been renamed to .setFromMatrixColumn()."),this.setFromMatrixColumn(e,t)},CJt.prototype.applyProjection=function(t){return console.warn("THREE.Vector3: .applyProjection() has been removed. Use .applyMatrix4( m ) instead."),this.applyMatrix4(t)},CJt.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector3: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},CJt.prototype.distanceToManhattan=function(t){return console.warn("THREE.Vector3: .distanceToManhattan() has been renamed to .manhattanDistanceTo()."),this.manhattanDistanceTo(t)},CJt.prototype.lengthManhattan=function(){return console.warn("THREE.Vector3: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},wJt.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector4: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},wJt.prototype.lengthManhattan=function(){return console.warn("THREE.Vector4: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},kQt.prototype.getChildByName=function(t){return console.warn("THREE.Object3D: .getChildByName() has been renamed to .getObjectByName()."),this.getObjectByName(t)},kQt.prototype.renderDepth=function(){console.warn("THREE.Object3D: .renderDepth has been removed. Use .renderOrder, instead.")},kQt.prototype.translate=function(t,e){return console.warn("THREE.Object3D: .translate() has been removed. Use .translateOnAxis( axis, distance ) instead."),this.translateOnAxis(e,t)},kQt.prototype.getWorldRotation=function(){console.error("THREE.Object3D: .getWorldRotation() has been removed. Use THREE.Object3D.getWorldQuaternion( target ) instead.")},kQt.prototype.applyMatrix=function(t){return console.warn("THREE.Object3D: .applyMatrix() has been renamed to .applyMatrix4()."),this.applyMatrix4(t)},Object.defineProperties(kQt.prototype,{eulerOrder:{get:function(){return console.warn("THREE.Object3D: .eulerOrder is now .rotation.order."),this.rotation.order},set:function(t){console.warn("THREE.Object3D: .eulerOrder is now .rotation.order."),this.rotation.order=t}},useQuaternion:{get:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")},set:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")}}}),B1t.prototype.setDrawMode=function(){console.error("THREE.Mesh: .setDrawMode() has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.")},Object.defineProperties(B1t.prototype,{drawMode:{get:function(){return console.error("THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode."),0},set:function(){console.error("THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.")}}}),w3t.prototype.initBones=function(){console.error("THREE.SkinnedMesh: initBones() has been removed.")},q1t.prototype.setLens=function(t,e){console.warn("THREE.PerspectiveCamera.setLens is deprecated. Use .setFocalLength and .filmGauge for a photographic setup."),void 0!==e&&(this.filmGauge=e),this.setFocalLength(t)},Object.defineProperties(o9t.prototype,{onlyShadow:{set:function(){console.warn("THREE.Light: .onlyShadow has been removed.")}},shadowCameraFov:{set:function(t){console.warn("THREE.Light: .shadowCameraFov is now .shadow.camera.fov."),this.shadow.camera.fov=t}},shadowCameraLeft:{set:function(t){console.warn("THREE.Light: .shadowCameraLeft is now .shadow.camera.left."),this.shadow.camera.left=t}},shadowCameraRight:{set:function(t){console.warn("THREE.Light: .shadowCameraRight is now .shadow.camera.right."),this.shadow.camera.right=t}},shadowCameraTop:{set:function(t){console.warn("THREE.Light: .shadowCameraTop is now .shadow.camera.top."),this.shadow.camera.top=t}},shadowCameraBottom:{set:function(t){console.warn("THREE.Light: .shadowCameraBottom is now .shadow.camera.bottom."),this.shadow.camera.bottom=t}},shadowCameraNear:{set:function(t){console.warn("THREE.Light: .shadowCameraNear is now .shadow.camera.near."),this.shadow.camera.near=t}},shadowCameraFar:{set:function(t){console.warn("THREE.Light: .shadowCameraFar is now .shadow.camera.far."),this.shadow.camera.far=t}},shadowCameraVisible:{set:function(){console.warn("THREE.Light: .shadowCameraVisible has been removed. Use new THREE.CameraHelper( light.shadow.camera ) instead.")}},shadowBias:{set:function(t){console.warn("THREE.Light: .shadowBias is now .shadow.bias."),this.shadow.bias=t}},shadowDarkness:{set:function(){console.warn("THREE.Light: .shadowDarkness has been removed.")}},shadowMapWidth:{set:function(t){console.warn("THREE.Light: .shadowMapWidth is now .shadow.mapSize.width."),this.shadow.mapSize.width=t}},shadowMapHeight:{set:function(t){console.warn("THREE.Light: .shadowMapHeight is now .shadow.mapSize.height."),this.shadow.mapSize.height=t}}}),Object.defineProperties(QQt.prototype,{length:{get:function(){return console.warn("THREE.BufferAttribute: .length has been deprecated. Use .count instead."),this.array.length}},dynamic:{get:function(){return console.warn("THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead."),this.usage===VZt},set:function(){console.warn("THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead."),this.setUsage(VZt)}}}),QQt.prototype.setDynamic=function(t){return console.warn("THREE.BufferAttribute: .setDynamic() has been deprecated. Use .setUsage() instead."),this.setUsage(!0===t?VZt:FZt),this},QQt.prototype.copyIndicesArray=function(){console.error("THREE.BufferAttribute: .copyIndicesArray() has been removed.")},QQt.prototype.setArray=function(){console.error("THREE.BufferAttribute: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers")},b1t.prototype.addIndex=function(t){console.warn("THREE.BufferGeometry: .addIndex() has been renamed to .setIndex()."),this.setIndex(t)},b1t.prototype.addAttribute=function(t,e){return console.warn("THREE.BufferGeometry: .addAttribute() has been renamed to .setAttribute()."),e&&e.isBufferAttribute||e&&e.isInterleavedBufferAttribute?"index"===t?(console.warn("THREE.BufferGeometry.addAttribute: Use .setIndex() for index attribute."),this.setIndex(e),this):this.setAttribute(t,e):(console.warn("THREE.BufferGeometry: .addAttribute() now expects ( name, attribute )."),this.setAttribute(t,new QQt(arguments[1],arguments[2])))},b1t.prototype.addDrawCall=function(t,e,n){void 0!==n&&console.warn("THREE.BufferGeometry: .addDrawCall() no longer supports indexOffset."),console.warn("THREE.BufferGeometry: .addDrawCall() is now .addGroup()."),this.addGroup(t,e)},b1t.prototype.clearDrawCalls=function(){console.warn("THREE.BufferGeometry: .clearDrawCalls() is now .clearGroups()."),this.clearGroups()},b1t.prototype.computeOffsets=function(){console.warn("THREE.BufferGeometry: .computeOffsets() has been removed.")},b1t.prototype.removeAttribute=function(t){return console.warn("THREE.BufferGeometry: .removeAttribute() has been renamed to .deleteAttribute()."),this.deleteAttribute(t)},b1t.prototype.applyMatrix=function(t){return console.warn("THREE.BufferGeometry: .applyMatrix() has been renamed to .applyMatrix4()."),this.applyMatrix4(t)},Object.defineProperties(b1t.prototype,{drawcalls:{get:function(){return console.error("THREE.BufferGeometry: .drawcalls has been renamed to .groups."),this.groups}},offsets:{get:function(){return console.warn("THREE.BufferGeometry: .offsets has been renamed to .groups."),this.groups}}}),$5t.prototype.setDynamic=function(t){return console.warn("THREE.InterleavedBuffer: .setDynamic() has been deprecated. Use .setUsage() instead."),this.setUsage(!0===t?VZt:FZt),this},$5t.prototype.setArray=function(){console.error("THREE.InterleavedBuffer: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers")},r6t.prototype.getArrays=function(){console.error("THREE.ExtrudeGeometry: .getArrays() has been removed.")},r6t.prototype.addShapeList=function(){console.error("THREE.ExtrudeGeometry: .addShapeList() has been removed.")},r6t.prototype.addShape=function(){console.error("THREE.ExtrudeGeometry: .addShape() has been removed.")},X5t.prototype.dispose=function(){console.error("THREE.Scene: .dispose() has been removed.")},m8t.prototype.onUpdate=function(){return console.warn("THREE.Uniform: .onUpdate() has been removed. Use object.onBeforeRender() instead."),this},Object.defineProperties(UQt.prototype,{wrapAround:{get:function(){console.warn("THREE.Material: .wrapAround has been removed.")},set:function(){console.warn("THREE.Material: .wrapAround has been removed.")}},overdraw:{get:function(){console.warn("THREE.Material: .overdraw has been removed.")},set:function(){console.warn("THREE.Material: .overdraw has been removed.")}},wrapRGB:{get:function(){return console.warn("THREE.Material: .wrapRGB has been removed."),new $Qt}},shading:{get:function(){console.error("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead.")},set:function(t){console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead."),this.flatShading=1===t}},stencilMask:{get:function(){return console.warn("THREE."+this.type+": .stencilMask has been removed. Use .stencilFuncMask instead."),this.stencilFuncMask},set:function(t){console.warn("THREE."+this.type+": .stencilMask has been removed. Use .stencilFuncMask instead."),this.stencilFuncMask=t}},vertexTangents:{get:function(){console.warn("THREE."+this.type+": .vertexTangents has been removed.")},set:function(){console.warn("THREE."+this.type+": .vertexTangents has been removed.")}}}),Object.defineProperties(G1t.prototype,{derivatives:{get:function(){return console.warn("THREE.ShaderMaterial: .derivatives has been moved to .extensions.derivatives."),this.extensions.derivatives},set:function(t){console.warn("THREE. ShaderMaterial: .derivatives has been moved to .extensions.derivatives."),this.extensions.derivatives=t}}}),G5t.prototype.clearTarget=function(t,e,n,i){console.warn("THREE.WebGLRenderer: .clearTarget() has been deprecated. Use .setRenderTarget() and .clear() instead."),this.setRenderTarget(t),this.clear(e,n,i)},G5t.prototype.animate=function(t){console.warn("THREE.WebGLRenderer: .animate() is now .setAnimationLoop()."),this.setAnimationLoop(t)},G5t.prototype.getCurrentRenderTarget=function(){return console.warn("THREE.WebGLRenderer: .getCurrentRenderTarget() is now .getRenderTarget()."),this.getRenderTarget()},G5t.prototype.getMaxAnisotropy=function(){return console.warn("THREE.WebGLRenderer: .getMaxAnisotropy() is now .capabilities.getMaxAnisotropy()."),this.capabilities.getMaxAnisotropy()},G5t.prototype.getPrecision=function(){return console.warn("THREE.WebGLRenderer: .getPrecision() is now .capabilities.precision."),this.capabilities.precision},G5t.prototype.resetGLState=function(){return console.warn("THREE.WebGLRenderer: .resetGLState() is now .state.reset()."),this.state.reset()},G5t.prototype.supportsFloatTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsFloatTextures() is now .extensions.get( 'OES_texture_float' )."),this.extensions.get("OES_texture_float")},G5t.prototype.supportsHalfFloatTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsHalfFloatTextures() is now .extensions.get( 'OES_texture_half_float' )."),this.extensions.get("OES_texture_half_float")},G5t.prototype.supportsStandardDerivatives=function(){return console.warn("THREE.WebGLRenderer: .supportsStandardDerivatives() is now .extensions.get( 'OES_standard_derivatives' )."),this.extensions.get("OES_standard_derivatives")},G5t.prototype.supportsCompressedTextureS3TC=function(){return console.warn("THREE.WebGLRenderer: .supportsCompressedTextureS3TC() is now .extensions.get( 'WEBGL_compressed_texture_s3tc' )."),this.extensions.get("WEBGL_compressed_texture_s3tc")},G5t.prototype.supportsCompressedTexturePVRTC=function(){return console.warn("THREE.WebGLRenderer: .supportsCompressedTexturePVRTC() is now .extensions.get( 'WEBGL_compressed_texture_pvrtc' )."),this.extensions.get("WEBGL_compressed_texture_pvrtc")},G5t.prototype.supportsBlendMinMax=function(){return console.warn("THREE.WebGLRenderer: .supportsBlendMinMax() is now .extensions.get( 'EXT_blend_minmax' )."),this.extensions.get("EXT_blend_minmax")},G5t.prototype.supportsVertexTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsVertexTextures() is now .capabilities.vertexTextures."),this.capabilities.vertexTextures},G5t.prototype.supportsInstancedArrays=function(){return console.warn("THREE.WebGLRenderer: .supportsInstancedArrays() is now .extensions.get( 'ANGLE_instanced_arrays' )."),this.extensions.get("ANGLE_instanced_arrays")},G5t.prototype.enableScissorTest=function(t){console.warn("THREE.WebGLRenderer: .enableScissorTest() is now .setScissorTest()."),this.setScissorTest(t)},G5t.prototype.initMaterial=function(){console.warn("THREE.WebGLRenderer: .initMaterial() has been removed.")},G5t.prototype.addPrePlugin=function(){console.warn("THREE.WebGLRenderer: .addPrePlugin() has been removed.")},G5t.prototype.addPostPlugin=function(){console.warn("THREE.WebGLRenderer: .addPostPlugin() has been removed.")},G5t.prototype.updateShadowMap=function(){console.warn("THREE.WebGLRenderer: .updateShadowMap() has been removed.")},G5t.prototype.setFaceCulling=function(){console.warn("THREE.WebGLRenderer: .setFaceCulling() has been removed.")},G5t.prototype.allocTextureUnit=function(){console.warn("THREE.WebGLRenderer: .allocTextureUnit() has been removed.")},G5t.prototype.setTexture=function(){console.warn("THREE.WebGLRenderer: .setTexture() has been removed.")},G5t.prototype.setTexture2D=function(){console.warn("THREE.WebGLRenderer: .setTexture2D() has been removed.")},G5t.prototype.setTextureCube=function(){console.warn("THREE.WebGLRenderer: .setTextureCube() has been removed.")},G5t.prototype.getActiveMipMapLevel=function(){return console.warn("THREE.WebGLRenderer: .getActiveMipMapLevel() is now .getActiveMipmapLevel()."),this.getActiveMipmapLevel()},Object.defineProperties(G5t.prototype,{shadowMapEnabled:{get:function(){return this.shadowMap.enabled},set:function(t){console.warn("THREE.WebGLRenderer: .shadowMapEnabled is now .shadowMap.enabled."),this.shadowMap.enabled=t}},shadowMapType:{get:function(){return this.shadowMap.type},set:function(t){console.warn("THREE.WebGLRenderer: .shadowMapType is now .shadowMap.type."),this.shadowMap.type=t}},shadowMapCullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")}},context:{get:function(){return console.warn("THREE.WebGLRenderer: .context has been removed. Use .getContext() instead."),this.getContext()}},vr:{get:function(){return console.warn("THREE.WebGLRenderer: .vr has been renamed to .xr"),this.xr}},gammaInput:{get:function(){return console.warn("THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead."),!1},set:function(){console.warn("THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead.")}},gammaOutput:{get:function(){return console.warn("THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead."),!1},set:function(t){console.warn("THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead."),this.outputEncoding=!0===t?IZt:NZt}},toneMappingWhitePoint:{get:function(){return console.warn("THREE.WebGLRenderer: .toneMappingWhitePoint has been removed."),1},set:function(){console.warn("THREE.WebGLRenderer: .toneMappingWhitePoint has been removed.")}}}),Object.defineProperties(R5t.prototype,{cullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")}},renderReverseSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")}},renderSingleSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")}}}),Object.defineProperties(SJt.prototype,{wrapS:{get:function(){return console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS."),this.texture.wrapS},set:function(t){console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS."),this.texture.wrapS=t}},wrapT:{get:function(){return console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT."),this.texture.wrapT},set:function(t){console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT."),this.texture.wrapT=t}},magFilter:{get:function(){return console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter."),this.texture.magFilter},set:function(t){console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter."),this.texture.magFilter=t}},minFilter:{get:function(){return console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter."),this.texture.minFilter},set:function(t){console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter."),this.texture.minFilter=t}},anisotropy:{get:function(){return console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy."),this.texture.anisotropy},set:function(t){console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy."),this.texture.anisotropy=t}},offset:{get:function(){return console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset."),this.texture.offset},set:function(t){console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset."),this.texture.offset=t}},repeat:{get:function(){return console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat."),this.texture.repeat},set:function(t){console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat."),this.texture.repeat=t}},format:{get:function(){return console.warn("THREE.WebGLRenderTarget: .format is now .texture.format."),this.texture.format},set:function(t){console.warn("THREE.WebGLRenderTarget: .format is now .texture.format."),this.texture.format=t}},type:{get:function(){return console.warn("THREE.WebGLRenderTarget: .type is now .texture.type."),this.texture.type},set:function(t){console.warn("THREE.WebGLRenderTarget: .type is now .texture.type."),this.texture.type=t}},generateMipmaps:{get:function(){return console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps."),this.texture.generateMipmaps},set:function(t){console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps."),this.texture.generateMipmaps=t}}}),$9t.prototype.load=function(t){console.warn("THREE.Audio: .load has been deprecated. Use THREE.AudioLoader instead.");const e=this;return(new B9t).load(t,(function(t){e.setBuffer(t)})),this},t8t.prototype.getData=function(){return console.warn("THREE.AudioAnalyser: .getData() is now .getFrequencyData()."),this.getFrequencyData()},X1t.prototype.updateCubeMap=function(t,e){return console.warn("THREE.CubeCamera: .updateCubeMap() is now .update()."),this.update(t,e)},X1t.prototype.clear=function(t,e,n,i){return console.warn("THREE.CubeCamera: .clear() is now .renderTarget.clear()."),this.renderTarget.clear(t,e,n,i)},yJt.crossOrigin=void 0,yJt.loadTexture=function(t,e,n,i){console.warn("THREE.ImageUtils.loadTexture has been deprecated. Use THREE.TextureLoader() instead.");const r=new e9t;r.setCrossOrigin(this.crossOrigin);const o=r.load(t,n,void 0,i);return e&&(o.mapping=e),o},yJt.loadTextureCube=function(t,e,n,i){console.warn("THREE.ImageUtils.loadTextureCube has been deprecated. Use THREE.CubeTextureLoader() instead.");const r=new Q6t;r.setCrossOrigin(this.crossOrigin);const o=r.load(t,n,void 0,i);return e&&(o.mapping=e),o},yJt.loadCompressedTexture=function(){console.error("THREE.ImageUtils.loadCompressedTexture has been removed. Use THREE.DDSLoader instead.")},yJt.loadCompressedTextureCube=function(){console.error("THREE.ImageUtils.loadCompressedTextureCube has been removed. Use THREE.DDSLoader instead.")};const Z8t={createMultiMaterialObject:function(){console.error("THREE.SceneUtils has been moved to /examples/jsm/utils/SceneUtils.js")},detach:function(){console.error("THREE.SceneUtils has been moved to /examples/jsm/utils/SceneUtils.js")},attach:function(){console.error("THREE.SceneUtils has been moved to /examples/jsm/utils/SceneUtils.js")}};"undefined"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("register",{detail:{revision:BKt}})),"undefined"!=typeof window&&(window.__THREE__?console.warn("WARNING: Multiple instances of Three.js being imported."):window.__THREE__=BKt);var J8t=Object.freeze({__proto__:null,ACESFilmicToneMapping:4,AddEquation:VKt,AddOperation:2,AdditiveAnimationBlendMode:PZt,AdditiveBlending:2,AlphaFormat:1021,AlwaysDepth:1,AlwaysStencilFunc:519,AmbientLight:b9t,AmbientLightProbe:F9t,AnimationClip:G6t,AnimationLoader:class extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new Z6t(this.manager);o.setPath(this.path),o.setRequestHeader(this.requestHeader),o.setWithCredentials(this.withCredentials),o.load(t,(function(n){try{e(r.parse(JSON.parse(n)))}catch(e){i?i(e):console.error(e),r.manager.itemError(t)}}),n,i)}parse(t){const e=[];for(let n=0;n<t.length;n++){const i=G6t.parse(t[n]);e.push(i)}return e}},AnimationMixer:f8t,AnimationObjectGroup:d8t,AnimationUtils:P6t,ArcCurve:p4t,ArrayCamera:B5t,ArrowHelper:class extends kQt{constructor(t=new CJt(0,0,1),e=new CJt(0,0,0),n=1,i=16776960,r=.2*n,o=.2*r){super(),this.type="ArrowHelper",void 0===q8t&&(q8t=new b1t,q8t.setAttribute("position",new l1t([0,0,0,0,1,0],3)),Y8t=new n4t(0,.5,1,5,1),Y8t.translate(0,-.5,0)),this.position.copy(e),this.line=new H3t(q8t,new I3t({color:i,toneMapped:!1})),this.line.matrixAutoUpdate=!1,this.add(this.line),this.cone=new B1t(Y8t,new KQt({color:i,toneMapped:!1})),this.cone.matrixAutoUpdate=!1,this.add(this.cone),this.setDirection(t),this.setLength(n,r,o)}setDirection(t){if(t.y>.99999)this.quaternion.set(0,0,0,1);else if(t.y<-.99999)this.quaternion.set(1,0,0,0);else{W8t.set(t.z,0,-t.x).normalize();const e=Math.acos(t.y);this.quaternion.setFromAxisAngle(W8t,e)}}setLength(t,e=.2*t,n=.2*e){this.line.scale.set(1,Math.max(1e-4,t-e),1),this.line.updateMatrix(),this.cone.scale.set(n,e,n),this.cone.position.y=t,this.cone.updateMatrix()}setColor(t){this.line.material.color.set(t),this.cone.material.color.set(t)}copy(t){return super.copy(t,!1),this.line.copy(t.line),this.cone.copy(t.cone),this}},Audio:$9t,AudioAnalyser:t8t,AudioContext:D9t,AudioListener:class extends kQt{constructor(){super(),this.type="AudioListener",this.context=D9t.getContext(),this.gain=this.context.createGain(),this.gain.connect(this.context.destination),this.filter=null,this.timeDelta=0,this._clock=new j9t}getInput(){return this.gain}removeFilter(){return null!==this.filter&&(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination),this.gain.connect(this.context.destination),this.filter=null),this}getFilter(){return this.filter}setFilter(t){return null!==this.filter?(this.gain.disconnect(this.filter),this.filter.disconnect(this.context.destination)):this.gain.disconnect(this.context.destination),this.filter=t,this.gain.connect(this.filter),this.filter.connect(this.context.destination),this}getMasterVolume(){return this.gain.gain.value}setMasterVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}updateMatrixWorld(t){super.updateMatrixWorld(t);const e=this.context.listener,n=this.up;if(this.timeDelta=this._clock.getDelta(),this.matrixWorld.decompose(W9t,q9t,Y9t),X9t.set(0,0,-1).applyQuaternion(q9t),e.positionX){const t=this.context.currentTime+this.timeDelta;e.positionX.linearRampToValueAtTime(W9t.x,t),e.positionY.linearRampToValueAtTime(W9t.y,t),e.positionZ.linearRampToValueAtTime(W9t.z,t),e.forwardX.linearRampToValueAtTime(X9t.x,t),e.forwardY.linearRampToValueAtTime(X9t.y,t),e.forwardZ.linearRampToValueAtTime(X9t.z,t),e.upX.linearRampToValueAtTime(n.x,t),e.upY.linearRampToValueAtTime(n.y,t),e.upZ.linearRampToValueAtTime(n.z,t)}else e.setPosition(W9t.x,W9t.y,W9t.z),e.setOrientation(X9t.x,X9t.y,X9t.z,n.x,n.y,n.z)}},AudioLoader:B9t,AxesHelper:X8t,AxisHelper:function Q8t(t){return console.warn("THREE.AxisHelper has been renamed to THREE.AxesHelper."),new X8t(t)},BackSide:1,BasicDepthPacking:3200,BasicShadowMap:0,BinaryTextureLoader:function t7t(t){return console.warn("THREE.BinaryTextureLoader has been renamed to THREE.DataTextureLoader."),new t9t(t)},Bone:S3t,BooleanKeyframeTrack:D6t,BoundingBoxHelper:function e7t(t,e){return console.warn("THREE.BoundingBoxHelper has been deprecated. Creating a THREE.BoxHelper instead."),new G8t(t,e)},Box2:w8t,Box3:LJt,Box3Helper:class extends U3t{constructor(t,e=16776960){const n=new Uint16Array([0,1,1,2,2,3,3,0,4,5,5,6,6,7,7,4,0,4,1,5,2,6,3,7]),i=new b1t;i.setIndex(new QQt(n,1)),i.setAttribute("position",new l1t([1,1,1,-1,1,1,-1,-1,1,1,-1,1,1,1,-1,-1,1,-1,-1,-1,-1,1,-1,-1],3)),super(i,new I3t({color:e,toneMapped:!1})),this.box=t,this.type="Box3Helper",this.geometry.computeBoundingSphere()}updateMatrixWorld(t){const e=this.box;e.isEmpty()||(e.getCenter(this.position),e.getSize(this.scale),this.scale.multiplyScalar(.5),super.updateMatrixWorld(t))}},BoxBufferGeometry:F1t,BoxGeometry:F1t,BoxHelper:G8t,BufferAttribute:QQt,BufferGeometry:b1t,BufferGeometryLoader:A9t,ByteType:1010,Cache:q6t,Camera:W1t,CameraHelper:class extends U3t{constructor(t){const e=new b1t,n=new I3t({color:16777215,vertexColors:!0,toneMapped:!1}),i=[],r=[],o={},a=new $Qt(16755200),s=new $Qt(16711680),l=new $Qt(43775),c=new $Qt(16777215),u=new $Qt(3355443);function h(t,e,n){d(t,n),d(e,n)}function d(t,e){i.push(0,0,0),r.push(e.r,e.g,e.b),void 0===o[t]&&(o[t]=[]),o[t].push(i.length/3-1)}h("n1","n2",a),h("n2","n4",a),h("n4","n3",a),h("n3","n1",a),h("f1","f2",a),h("f2","f4",a),h("f4","f3",a),h("f3","f1",a),h("n1","f1",a),h("n2","f2",a),h("n3","f3",a),h("n4","f4",a),h("p","n1",s),h("p","n2",s),h("p","n3",s),h("p","n4",s),h("u1","u2",l),h("u2","u3",l),h("u3","u1",l),h("c","t",c),h("p","c",u),h("cn1","cn2",u),h("cn3","cn4",u),h("cf1","cf2",u),h("cf3","cf4",u),e.setAttribute("position",new l1t(i,3)),e.setAttribute("color",new l1t(r,3)),super(e,n),this.type="CameraHelper",this.camera=t,this.camera.updateProjectionMatrix&&this.camera.updateProjectionMatrix(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.pointMap=o,this.update()}update(){const t=this.geometry,e=this.pointMap;V8t.projectionMatrixInverse.copy(this.camera.projectionMatrixInverse),U8t("c",e,t,V8t,0,0,-1),U8t("t",e,t,V8t,0,0,1),U8t("n1",e,t,V8t,-1,-1,-1),U8t("n2",e,t,V8t,1,-1,-1),U8t("n3",e,t,V8t,-1,1,-1),U8t("n4",e,t,V8t,1,1,-1),U8t("f1",e,t,V8t,-1,-1,1),U8t("f2",e,t,V8t,1,-1,1),U8t("f3",e,t,V8t,-1,1,1),U8t("f4",e,t,V8t,1,1,1),U8t("u1",e,t,V8t,.7,1.1,-1),U8t("u2",e,t,V8t,-.7,1.1,-1),U8t("u3",e,t,V8t,0,2,-1),U8t("cf1",e,t,V8t,-1,0,1),U8t("cf2",e,t,V8t,1,0,1),U8t("cf3",e,t,V8t,0,-1,1),U8t("cf4",e,t,V8t,0,1,1),U8t("cn1",e,t,V8t,-1,0,-1),U8t("cn2",e,t,V8t,1,0,-1),U8t("cn3",e,t,V8t,0,-1,-1),U8t("cn4",e,t,V8t,0,1,-1),t.getAttribute("position").needsUpdate=!0}dispose(){this.geometry.dispose(),this.material.dispose()}},CanvasRenderer:function n7t(){console.error("THREE.CanvasRenderer has been removed")},CanvasTexture:Q3t,CatmullRomCurve3:v4t,CineonToneMapping:3,CircleBufferGeometry:e4t,CircleGeometry:e4t,ClampToEdgeWrapping:KKt,Clock:j9t,Color:$Qt,ColorKeyframeTrack:B6t,CompressedTexture:J3t,CompressedTextureLoader:class extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=this,o=[],a=new J3t,s=new Z6t(this.manager);s.setPath(this.path),s.setResponseType("arraybuffer"),s.setRequestHeader(this.requestHeader),s.setWithCredentials(r.withCredentials);let l=0;function c(c){s.load(t[c],(function(t){const n=r.parse(t,!0);o[c]={width:n.width,height:n.height,format:n.format,mipmaps:n.mipmaps},l+=1,6===l&&(1===n.mipmapCount&&(a.minFilter=eZt),a.image=o,a.format=n.format,a.needsUpdate=!0,e&&e(a))}),n,i)}if(Array.isArray(t))for(let e=0,n=t.length;e<n;++e)c(e);else s.load(t,(function(t){const n=r.parse(t,!0);if(n.isCubemap){const t=n.mipmaps.length/n.mipmapCount;for(let e=0;e<t;e++){o[e]={mipmaps:[]};for(let t=0;t<n.mipmapCount;t++)o[e].mipmaps.push(n.mipmaps[e*n.mipmapCount+t]),o[e].format=n.format,o[e].width=n.width,o[e].height=n.height}a.image=o}else a.image.width=n.width,a.image.height=n.height,a.mipmaps=n.mipmaps;1===n.mipmapCount&&(a.minFilter=eZt),a.format=n.format,a.needsUpdate=!0,e&&e(a)}),n,i);return a}},ConeBufferGeometry:i4t,ConeGeometry:i4t,CubeCamera:X1t,CubeReflectionMapping:jKt,CubeRefractionMapping:GKt,CubeTexture:$1t,CubeTextureLoader:Q6t,CubeUVReflectionMapping:YKt,CubeUVRefractionMapping:XKt,CubicBezierCurve:S4t,CubicBezierCurve3:M4t,CubicInterpolant:I6t,CullFaceBack:1,CullFaceFront:2,CullFaceFrontBack:3,CullFaceNone:0,Curve:h4t,CurvePath:n9t,CustomBlending:5,CustomToneMapping:5,CylinderBufferGeometry:n4t,CylinderGeometry:n4t,Cylindrical:class{constructor(t=1,e=0,n=0){return this.radius=t,this.theta=e,this.y=n,this}set(t,e,n){return this.radius=t,this.theta=e,this.y=n,this}copy(t){return this.radius=t.radius,this.theta=t.theta,this.y=t.y,this}setFromVector3(t){return this.setFromCartesianCoords(t.x,t.y,t.z)}setFromCartesianCoords(t,e,n){return this.radius=Math.sqrt(t*t+n*n),this.theta=Math.atan2(t,n),this.y=e,this}clone(){return(new this.constructor).copy(this)}},DataTexture:M3t,DataTexture2DArray:K0t,DataTexture3D:Z0t,DataTextureLoader:t9t,DataUtils:class{static toHalfFloat(t){$8t[0]=t;const e=K8t[0];let n=e>>16&32768,i=e>>12&2047;const r=e>>23&255;return r<103?n:r>142?(n|=31744,n|=(255==r?0:1)&&8388607&e,n):r<113?(i|=2048,n|=(i>>114-r)+(i>>113-r&1),n):(n|=r-112<<10|i>>1,n+=1&i,n)}},DecrementStencilOp:7683,DecrementWrapStencilOp:34056,DefaultLoadingManager:X6t,DepthFormat:dZt,DepthStencilFormat:pZt,DepthTexture:t4t,DirectionalLight:v9t,DirectionalLightHelper:class extends kQt{constructor(t,e,n){super(),this.light=t,this.light.updateMatrixWorld(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.color=n,void 0===e&&(e=1);let i=new b1t;i.setAttribute("position",new l1t([-e,e,0,e,e,0,e,-e,0,-e,-e,0,-e,e,0],3));const r=new I3t({fog:!1,toneMapped:!1});this.lightPlane=new H3t(i,r),this.add(this.lightPlane),i=new b1t,i.setAttribute("position",new l1t([0,0,0,0,0,1],3)),this.targetLine=new H3t(i,r),this.add(this.targetLine),this.update()}dispose(){this.lightPlane.geometry.dispose(),this.lightPlane.material.dispose(),this.targetLine.geometry.dispose(),this.targetLine.material.dispose()}update(){D8t.setFromMatrixPosition(this.light.matrixWorld),B8t.setFromMatrixPosition(this.light.target.matrixWorld),H8t.subVectors(B8t,D8t),this.lightPlane.lookAt(B8t),void 0!==this.color?(this.lightPlane.material.color.set(this.color),this.targetLine.material.color.set(this.color)):(this.lightPlane.material.color.copy(this.light.color),this.targetLine.material.color.copy(this.light.color)),this.targetLine.lookAt(B8t),this.targetLine.scale.z=H8t.length()}},DiscreteInterpolant:O6t,DodecahedronBufferGeometry:o4t,DodecahedronGeometry:o4t,DoubleSide:2,DstAlphaFactor:206,DstColorFactor:208,DynamicBufferAttribute:function i7t(t,e){return console.warn("THREE.DynamicBufferAttribute has been removed. Use new THREE.BufferAttribute().setUsage( THREE.DynamicDrawUsage ) instead."),new QQt(t,e).setUsage(VZt)},DynamicCopyUsage:35050,DynamicDrawUsage:VZt,DynamicReadUsage:35049,EdgesGeometry:u4t,EdgesHelper:function r7t(t,e){return console.warn("THREE.EdgesHelper has been removed. Use THREE.EdgesGeometry instead."),new U3t(new u4t(t.geometry),new I3t({color:void 0!==e?e:16777215}))},EllipseCurve:d4t,EqualDepth:4,EqualStencilFunc:514,EquirectangularReflectionMapping:WKt,EquirectangularRefractionMapping:qKt,Euler:fQt,EventDispatcher:jZt,ExtrudeBufferGeometry:r6t,ExtrudeGeometry:r6t,FaceColors:1,FileLoader:Z6t,FlatShading:1,Float16BufferAttribute:s1t,Float32Attribute:function o7t(t,e){return console.warn("THREE.Float32Attribute has been removed. Use new THREE.Float32BufferAttribute() instead."),new l1t(t,e)},Float32BufferAttribute:l1t,Float64Attribute:function a7t(t,e){return console.warn("THREE.Float64Attribute has been removed. Use new THREE.Float64BufferAttribute() instead."),new c1t(t,e)},Float64BufferAttribute:c1t,FloatType:sZt,Fog:Y5t,FogExp2:q5t,Font:R9t,FontLoader:class extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=this,o=new Z6t(this.manager);o.setPath(this.path),o.setRequestHeader(this.requestHeader),o.setWithCredentials(r.withCredentials),o.load(t,(function(t){let n;try{n=JSON.parse(t)}catch(e){console.warn("THREE.FontLoader: typeface.js support is being deprecated. Use typeface.json instead."),n=JSON.parse(t.substring(65,t.length-2))}const i=r.parse(n);e&&e(i)}),n,i)}parse(t){return new R9t(t)}},FrontSide:0,Frustum:i0t,GLBufferAttribute:_8t,GLSL1:"100",GLSL3:UZt,GammaEncoding:RZt,GreaterDepth:6,GreaterEqualDepth:5,GreaterEqualStencilFunc:518,GreaterStencilFunc:516,GridHelper:z8t,Group:H5t,HalfFloatType:lZt,HemisphereLight:a9t,HemisphereLightHelper:class extends kQt{constructor(t,e,n){super(),this.light=t,this.light.updateMatrixWorld(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.color=n;const i=new l6t(e);i.rotateY(.5*Math.PI),this.material=new KQt({wireframe:!0,fog:!1,toneMapped:!1}),void 0===this.color&&(this.material.vertexColors=!0);const r=i.getAttribute("position"),o=new Float32Array(3*r.count);i.setAttribute("color",new QQt(o,3)),this.add(new B1t(i,this.material)),this.update()}dispose(){this.children[0].geometry.dispose(),this.children[0].material.dispose()}update(){const t=this.children[0];if(void 0!==this.color)this.material.color.set(this.color);else{const e=t.geometry.getAttribute("color");R8t.copy(this.light.color),O8t.copy(this.light.groundColor);for(let t=0,n=e.count;t<n;t++){const i=t<n/2?R8t:O8t;e.setXYZ(t,i.r,i.g,i.b)}e.needsUpdate=!0}t.lookAt(I8t.setFromMatrixPosition(this.light.matrixWorld).negate())}},HemisphereLightProbe:H9t,IcosahedronBufferGeometry:a6t,IcosahedronGeometry:a6t,ImageBitmapLoader:N9t,ImageLoader:J6t,ImageUtils:yJt,ImmediateRenderObject:T8t,IncrementStencilOp:7682,IncrementWrapStencilOp:34055,InstancedBufferAttribute:C9t,InstancedBufferGeometry:T9t,InstancedInterleavedBuffer:g8t,InstancedMesh:N3t,Int16Attribute:function s7t(t,e){return console.warn("THREE.Int16Attribute has been removed. Use new THREE.Int16BufferAttribute() instead."),new i1t(t,e)},Int16BufferAttribute:i1t,Int32Attribute:function l7t(t,e){return console.warn("THREE.Int32Attribute has been removed. Use new THREE.Int32BufferAttribute() instead."),new o1t(t,e)},Int32BufferAttribute:o1t,Int8Attribute:function c7t(t,e){return console.warn("THREE.Int8Attribute has been removed. 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Use an Array instead."),t.isMultiMaterial=!0,t.materials=t,t.clone=function(){return t.slice()},t},MultiplyBlending:4,MultiplyOperation:0,NearestFilter:JKt,NearestMipMapLinearFilter:1005,NearestMipMapNearestFilter:1004,NearestMipmapLinearFilter:tZt,NearestMipmapNearestFilter:QKt,NeverDepth:0,NeverStencilFunc:512,NoBlending:0,NoColors:0,NoToneMapping:0,NormalAnimationBlendMode:LZt,NormalBlending:1,NotEqualDepth:7,NotEqualStencilFunc:517,NumberKeyframeTrack:H6t,Object3D:kQt,ObjectLoader:class extends $6t{constructor(t){super(t)}load(t,e,n,i){const r=this,o=""===this.path?E9t.extractUrlBase(t):this.path;this.resourcePath=this.resourcePath||o;const a=new Z6t(this.manager);a.setPath(this.path),a.setRequestHeader(this.requestHeader),a.setWithCredentials(this.withCredentials),a.load(t,(function(n){let o=null;try{o=JSON.parse(n)}catch(e){return void 0!==i&&i(e),void console.error("THREE:ObjectLoader: Can't parse "+t+".",e.message)}const a=o.metadata;void 0!==a&&void 0!==a.type&&"geometry"!==a.type.toLowerCase()?r.parse(o,e):console.error("THREE.ObjectLoader: Can't load "+t)}),n,i)}async loadAsync(t,e){const n=""===this.path?E9t.extractUrlBase(t):this.path;this.resourcePath=this.resourcePath||n;const i=new Z6t(this.manager);i.setPath(this.path),i.setRequestHeader(this.requestHeader),i.setWithCredentials(this.withCredentials);const r=await i.loadAsync(t,e),o=JSON.parse(r),a=o.metadata;if(void 0===a||void 0===a.type||"geometry"===a.type.toLowerCase())throw new Error("THREE.ObjectLoader: Can't load "+t);return await this.parseAsync(o)}parse(t,e){const n=this.parseAnimations(t.animations),i=this.parseShapes(t.shapes),r=this.parseGeometries(t.geometries,i),o=this.parseImages(t.images,(function(){void 0!==e&&e(l)})),a=this.parseTextures(t.textures,o),s=this.parseMaterials(t.materials,a),l=this.parseObject(t.object,r,s,a,n),c=this.parseSkeletons(t.skeletons,l);if(this.bindSkeletons(l,c),void 0!==e){let t=!1;for(const e in o)if(o[e]instanceof HTMLImageElement){t=!0;break}!1===t&&e(l)}return l}async parseAsync(t){const e=this.parseAnimations(t.animations),n=this.parseShapes(t.shapes),i=this.parseGeometries(t.geometries,n),r=await this.parseImagesAsync(t.images),o=this.parseTextures(t.textures,r),a=this.parseMaterials(t.materials,o),s=this.parseObject(t.object,i,a,o,e),l=this.parseSkeletons(t.skeletons,s);return this.bindSkeletons(s,l),s}parseShapes(t){const e={};if(void 0!==t)for(let n=0,i=t.length;n<i;n++){const i=(new r9t).fromJSON(t[n]);e[i.uuid]=i}return e}parseSkeletons(t,e){const n={},i={};if(e.traverse((function(t){t.isBone&&(i[t.uuid]=t)})),void 0!==t)for(let e=0,r=t.length;e<r;e++){const r=(new C3t).fromJSON(t[e],i);n[r.uuid]=r}return n}parseGeometries(t,e){const n={};if(void 0!==t){const i=new A9t;for(let r=0,o=t.length;r<o;r++){let o;const a=t[r];switch(a.type){case"BufferGeometry":case"InstancedBufferGeometry":o=i.parse(a);break;case"Geometry":console.error("THREE.ObjectLoader: The legacy Geometry type is no longer supported.");break;default:a.type in b6t?o=b6t[a.type].fromJSON(a,e):console.warn(`THREE.ObjectLoader: Unsupported geometry type "${a.type}"`)}o.uuid=a.uuid,void 0!==a.name&&(o.name=a.name),!0===o.isBufferGeometry&&void 0!==a.userData&&(o.userData=a.userData),n[a.uuid]=o}}return n}parseMaterials(t,e){const n={},i={};if(void 0!==t){const r=new M9t;r.setTextures(e);for(let e=0,o=t.length;e<o;e++){const o=t[e];if("MultiMaterial"===o.type){const t=[];for(let e=0;e<o.materials.length;e++){const i=o.materials[e];void 0===n[i.uuid]&&(n[i.uuid]=r.parse(i)),t.push(n[i.uuid])}i[o.uuid]=t}else void 0===n[o.uuid]&&(n[o.uuid]=r.parse(o)),i[o.uuid]=n[o.uuid]}}return i}parseAnimations(t){const e={};if(void 0!==t)for(let n=0;n<t.length;n++){const i=G6t.parse(t[n]);e[i.uuid]=i}return e}parseImages(t,e){const n=this,i={};let r;function o(t){if("string"==typeof t){const e=t;return(function i(t){return n.manager.itemStart(t),r.load(t,(function(){n.manager.itemEnd(t)}),void 0,(function(){n.manager.itemError(t),n.manager.itemEnd(t)}))})(/^(\/\/)|([a-z]+:(\/\/)?)/i.test(e)?e:n.resourcePath+e)}return t.data?{data:d1t(t.type,t.data),width:t.width,height:t.height}:null}if(void 0!==t&&t.length>0){const n=new Y6t(e);r=new J6t(n),r.setCrossOrigin(this.crossOrigin);for(let e=0,n=t.length;e<n;e++){const n=t[e],r=n.url;if(Array.isArray(r)){i[n.uuid]=[];for(let t=0,e=r.length;t<e;t++){const e=o(r[t]);null!==e&&(e instanceof HTMLImageElement?i[n.uuid].push(e):i[n.uuid].push(new M3t(e.data,e.width,e.height)))}}else{const t=o(n.url);null!==t&&(i[n.uuid]=t)}}}return i}async parseImagesAsync(t){const e=this,n={};let i;async function r(t){if("string"==typeof t){const n=t,r=/^(\/\/)|([a-z]+:(\/\/)?)/i.test(n)?n:e.resourcePath+n;return await i.loadAsync(r)}return t.data?{data:d1t(t.type,t.data),width:t.width,height:t.height}:null}if(void 0!==t&&t.length>0){i=new J6t(this.manager),i.setCrossOrigin(this.crossOrigin);for(let e=0,i=t.length;e<i;e++){const i=t[e],o=i.url;if(Array.isArray(o)){n[i.uuid]=[];for(let t=0,e=o.length;t<e;t++){const e=o[t],a=await r(e);null!==a&&(a instanceof HTMLImageElement?n[i.uuid].push(a):n[i.uuid].push(new M3t(a.data,a.width,a.height)))}}else{const t=await r(i.url);null!==t&&(n[i.uuid]=t)}}}return n}parseTextures(t,e){function n(t,e){return"number"==typeof t?t:(console.warn("THREE.ObjectLoader.parseTexture: Constant should be in numeric form.",t),e[t])}const i={};if(void 0!==t)for(let r=0,o=t.length;r<o;r++){const o=t[r];let a;void 0===o.image&&console.warn('THREE.ObjectLoader: No "image" specified for',o.uuid),void 0===e[o.image]&&console.warn("THREE.ObjectLoader: Undefined image",o.image);const s=e[o.image];Array.isArray(s)?(a=new $1t(s),6===s.length&&(a.needsUpdate=!0)):(a=s&&s.data?new M3t(s.data,s.width,s.height):new bJt(s),s&&(a.needsUpdate=!0)),a.uuid=o.uuid,void 0!==o.name&&(a.name=o.name),void 0!==o.mapping&&(a.mapping=n(o.mapping,k9t)),void 0!==o.offset&&a.offset.fromArray(o.offset),void 0!==o.repeat&&a.repeat.fromArray(o.repeat),void 0!==o.center&&a.center.fromArray(o.center),void 0!==o.rotation&&(a.rotation=o.rotation),void 0!==o.wrap&&(a.wrapS=n(o.wrap[0],L9t),a.wrapT=n(o.wrap[1],L9t)),void 0!==o.format&&(a.format=o.format),void 0!==o.type&&(a.type=o.type),void 0!==o.encoding&&(a.encoding=o.encoding),void 0!==o.minFilter&&(a.minFilter=n(o.minFilter,P9t)),void 0!==o.magFilter&&(a.magFilter=n(o.magFilter,P9t)),void 0!==o.anisotropy&&(a.anisotropy=o.anisotropy),void 0!==o.flipY&&(a.flipY=o.flipY),void 0!==o.premultiplyAlpha&&(a.premultiplyAlpha=o.premultiplyAlpha),void 0!==o.unpackAlignment&&(a.unpackAlignment=o.unpackAlignment),i[o.uuid]=a}return i}parseObject(t,e,n,i,r){let o,a,s;function l(t){return void 0===e[t]&&console.warn("THREE.ObjectLoader: Undefined geometry",t),e[t]}function c(t){if(void 0!==t){if(Array.isArray(t)){const e=[];for(let i=0,r=t.length;i<r;i++){const r=t[i];void 0===n[r]&&console.warn("THREE.ObjectLoader: Undefined material",r),e.push(n[r])}return e}return void 0===n[t]&&console.warn("THREE.ObjectLoader: Undefined material",t),n[t]}}function u(t){return void 0===i[t]&&console.warn("THREE.ObjectLoader: Undefined texture",t),i[t]}switch(t.type){case"Scene":o=new X5t,void 0!==t.background&&(o.background=Number.isInteger(t.background)?new $Qt(t.background):u(t.background)),void 0!==t.environment&&(o.environment=u(t.environment)),void 0!==t.fog&&("Fog"===t.fog.type?o.fog=new Y5t(t.fog.color,t.fog.near,t.fog.far):"FogExp2"===t.fog.type&&(o.fog=new q5t(t.fog.color,t.fog.density)));break;case"PerspectiveCamera":o=new q1t(t.fov,t.aspect,t.near,t.far),void 0!==t.focus&&(o.focus=t.focus),void 0!==t.zoom&&(o.zoom=t.zoom),void 0!==t.filmGauge&&(o.filmGauge=t.filmGauge),void 0!==t.filmOffset&&(o.filmOffset=t.filmOffset),void 0!==t.view&&(o.view=Object.assign({},t.view));break;case"OrthographicCamera":o=new g0t(t.left,t.right,t.top,t.bottom,t.near,t.far),void 0!==t.zoom&&(o.zoom=t.zoom),void 0!==t.view&&(o.view=Object.assign({},t.view));break;case"AmbientLight":o=new b9t(t.color,t.intensity);break;case"DirectionalLight":o=new v9t(t.color,t.intensity);break;case"PointLight":o=new _9t(t.color,t.intensity,t.distance,t.decay);break;case"RectAreaLight":o=new x9t(t.color,t.intensity,t.width,t.height);break;case"SpotLight":o=new d9t(t.color,t.intensity,t.distance,t.angle,t.penumbra,t.decay);break;case"HemisphereLight":o=new a9t(t.color,t.groundColor,t.intensity);break;case"LightProbe":o=(new S9t).fromJSON(t);break;case"SkinnedMesh":a=l(t.geometry),s=c(t.material),o=new w3t(a,s),void 0!==t.bindMode&&(o.bindMode=t.bindMode),void 0!==t.bindMatrix&&o.bindMatrix.fromArray(t.bindMatrix),void 0!==t.skeleton&&(o.skeleton=t.skeleton);break;case"Mesh":a=l(t.geometry),s=c(t.material),o=new B1t(a,s);break;case"InstancedMesh":a=l(t.geometry),s=c(t.material);const e=t.instanceMatrix,n=t.instanceColor;o=new N3t(a,s,t.count),o.instanceMatrix=new QQt(new Float32Array(e.array),16),void 0!==n&&(o.instanceColor=new QQt(new Float32Array(n.array),n.itemSize));break;case"LOD":o=new g3t;break;case"Line":o=new H3t(l(t.geometry),c(t.material));break;case"LineLoop":o=new j3t(l(t.geometry),c(t.material));break;case"LineSegments":o=new U3t(l(t.geometry),c(t.material));break;case"PointCloud":case"Points":o=new $3t(l(t.geometry),c(t.material));break;case"Sprite":o=new d3t(c(t.material));break;case"Group":o=new H5t;break;case"Bone":o=new S3t;break;default:o=new kQt}if(o.uuid=t.uuid,void 0!==t.name&&(o.name=t.name),void 0!==t.matrix?(o.matrix.fromArray(t.matrix),void 0!==t.matrixAutoUpdate&&(o.matrixAutoUpdate=t.matrixAutoUpdate),o.matrixAutoUpdate&&o.matrix.decompose(o.position,o.quaternion,o.scale)):(void 0!==t.position&&o.position.fromArray(t.position),void 0!==t.rotation&&o.rotation.fromArray(t.rotation),void 0!==t.quaternion&&o.quaternion.fromArray(t.quaternion),void 0!==t.scale&&o.scale.fromArray(t.scale)),void 0!==t.castShadow&&(o.castShadow=t.castShadow),void 0!==t.receiveShadow&&(o.receiveShadow=t.receiveShadow),t.shadow&&(void 0!==t.shadow.bias&&(o.shadow.bias=t.shadow.bias),void 0!==t.shadow.normalBias&&(o.shadow.normalBias=t.shadow.normalBias),void 0!==t.shadow.radius&&(o.shadow.radius=t.shadow.radius),void 0!==t.shadow.mapSize&&o.shadow.mapSize.fromArray(t.shadow.mapSize),void 0!==t.shadow.camera&&(o.shadow.camera=this.parseObject(t.shadow.camera))),void 0!==t.visible&&(o.visible=t.visible),void 0!==t.frustumCulled&&(o.frustumCulled=t.frustumCulled),void 0!==t.renderOrder&&(o.renderOrder=t.renderOrder),void 0!==t.userData&&(o.userData=t.userData),void 0!==t.layers&&(o.layers.mask=t.layers),void 0!==t.children){const a=t.children;for(let t=0;t<a.length;t++)o.add(this.parseObject(a[t],e,n,i,r))}if(void 0!==t.animations){const e=t.animations;for(let t=0;t<e.length;t++)o.animations.push(r[e[t]])}if("LOD"===t.type){void 0!==t.autoUpdate&&(o.autoUpdate=t.autoUpdate);const e=t.levels;for(let t=0;t<e.length;t++){const n=e[t],i=o.getObjectByProperty("uuid",n.object);void 0!==i&&o.addLevel(i,n.distance)}}return o}bindSkeletons(t,e){0!==Object.keys(e).length&&t.traverse((function(t){if(!0===t.isSkinnedMesh&&void 0!==t.skeleton){const n=e[t.skeleton];void 0===n?console.warn("THREE.ObjectLoader: No skeleton found with UUID:",t.skeleton):t.bind(n,t.bindMatrix)}}))}setTexturePath(t){return console.warn("THREE.ObjectLoader: .setTexturePath() has been renamed to .setResourcePath()."),this.setResourcePath(t)}},ObjectSpaceNormalMap:1,OctahedronBufferGeometry:l6t,OctahedronGeometry:l6t,OneFactor:201,OneMinusDstAlphaFactor:207,OneMinusDstColorFactor:209,OneMinusSrcAlphaFactor:205,OneMinusSrcColorFactor:203,OrthographicCamera:g0t,PCFShadowMap:1,PCFSoftShadowMap:2,PMREMGenerator:R0t,ParametricBufferGeometry:c6t,ParametricGeometry:c6t,Particle:function f7t(t){return console.warn("THREE.Particle has been renamed to THREE.Sprite."),new d3t(t)},ParticleBasicMaterial:function m7t(t){return console.warn("THREE.ParticleBasicMaterial has been renamed to THREE.PointsMaterial."),new G3t(t)},ParticleSystem:function g7t(t,e){return console.warn("THREE.ParticleSystem has been renamed to THREE.Points."),new $3t(t,e)},ParticleSystemMaterial:function _7t(t){return console.warn("THREE.ParticleSystemMaterial has been renamed to THREE.PointsMaterial."),new G3t(t)},Path:i9t,PerspectiveCamera:q1t,Plane:t0t,PlaneBufferGeometry:a0t,PlaneGeometry:a0t,PlaneHelper:class extends H3t{constructor(t,e=1,n=16776960){const i=n,r=new b1t;r.setAttribute("position",new l1t([1,-1,1,-1,1,1,-1,-1,1,1,1,1,-1,1,1,-1,-1,1,1,-1,1,1,1,1,0,0,1,0,0,0],3)),r.computeBoundingSphere(),super(r,new I3t({color:i,toneMapped:!1})),this.type="PlaneHelper",this.plane=t,this.size=e;const o=new b1t;o.setAttribute("position",new l1t([1,1,1,-1,1,1,-1,-1,1,1,1,1,-1,-1,1,1,-1,1],3)),o.computeBoundingSphere(),this.add(new B1t(o,new KQt({color:i,opacity:.2,transparent:!0,depthWrite:!1,toneMapped:!1})))}updateMatrixWorld(t){let e=-this.plane.constant;Math.abs(e)<1e-8&&(e=1e-8),this.scale.set(.5*this.size,.5*this.size,e),this.children[0].material.side=e<0?1:0,this.lookAt(this.plane.normal),super.updateMatrixWorld(t)}},PointCloud:function y7t(t,e){return console.warn("THREE.PointCloud has been renamed to THREE.Points."),new $3t(t,e)},PointCloudMaterial:function v7t(t){return console.warn("THREE.PointCloudMaterial has been renamed to THREE.PointsMaterial."),new G3t(t)},PointLight:_9t,PointLightHelper:class extends B1t{constructor(t,e,n){super(new d6t(e,4,2),new KQt({wireframe:!0,fog:!1,toneMapped:!1})),this.light=t,this.light.updateMatrixWorld(),this.color=n,this.type="PointLightHelper",this.matrix=this.light.matrixWorld,this.matrixAutoUpdate=!1,this.update()}dispose(){this.geometry.dispose(),this.material.dispose()}update(){void 0!==this.color?this.material.color.set(this.color):this.material.color.copy(this.light.color)}},Points:$3t,PointsMaterial:G3t,PolarGridHelper:class extends U3t{constructor(t=10,e=16,n=8,i=64,r=4473924,o=8947848){r=new $Qt(r),o=new $Qt(o);const a=[],s=[];for(let n=0;n<=e;n++){const i=n/e*(2*Math.PI),l=Math.sin(i)*t,c=Math.cos(i)*t;a.push(0,0,0),a.push(l,0,c);const u=1&n?r:o;s.push(u.r,u.g,u.b),s.push(u.r,u.g,u.b)}for(let e=0;e<=n;e++){const l=1&e?r:o,c=t-t/n*e;for(let t=0;t<i;t++){let e=t/i*(2*Math.PI),n=Math.sin(e)*c,r=Math.cos(e)*c;a.push(n,0,r),s.push(l.r,l.g,l.b),e=(t+1)/i*(2*Math.PI),n=Math.sin(e)*c,r=Math.cos(e)*c,a.push(n,0,r),s.push(l.r,l.g,l.b)}}const l=new b1t;l.setAttribute("position",new l1t(a,3)),l.setAttribute("color",new l1t(s,3)),super(l,new I3t({vertexColors:!0,toneMapped:!1})),this.type="PolarGridHelper"}},PolyhedronBufferGeometry:r4t,PolyhedronGeometry:r4t,PositionalAudio:class extends $9t{constructor(t){super(t),this.panner=this.context.createPanner(),this.panner.panningModel="HRTF",this.panner.connect(this.gain)}getOutput(){return this.panner}getRefDistance(){return this.panner.refDistance}setRefDistance(t){return this.panner.refDistance=t,this}getRolloffFactor(){return this.panner.rolloffFactor}setRolloffFactor(t){return this.panner.rolloffFactor=t,this}getDistanceModel(){return this.panner.distanceModel}setDistanceModel(t){return this.panner.distanceModel=t,this}getMaxDistance(){return this.panner.maxDistance}setMaxDistance(t){return this.panner.maxDistance=t,this}setDirectionalCone(t,e,n){return this.panner.coneInnerAngle=t,this.panner.coneOuterAngle=e,this.panner.coneOuterGain=n,this}updateMatrixWorld(t){if(super.updateMatrixWorld(t),!0===this.hasPlaybackControl&&!1===this.isPlaying)return;this.matrixWorld.decompose(K9t,Z9t,J9t),Q9t.set(0,0,1).applyQuaternion(Z9t);const e=this.panner;if(e.positionX){const t=this.context.currentTime+this.listener.timeDelta;e.positionX.linearRampToValueAtTime(K9t.x,t),e.positionY.linearRampToValueAtTime(K9t.y,t),e.positionZ.linearRampToValueAtTime(K9t.z,t),e.orientationX.linearRampToValueAtTime(Q9t.x,t),e.orientationY.linearRampToValueAtTime(Q9t.y,t),e.orientationZ.linearRampToValueAtTime(Q9t.z,t)}else e.setPosition(K9t.x,K9t.y,K9t.z),e.setOrientation(Q9t.x,Q9t.y,Q9t.z)}},PropertyBinding:h8t,PropertyMixer:e8t,QuadraticBezierCurve:C4t,QuadraticBezierCurve3:A4t,Quaternion:TJt,QuaternionKeyframeTrack:V6t,QuaternionLinearInterpolant:F6t,REVISION:BKt,RGBADepthPacking:3201,RGBAFormat:hZt,RGBAIntegerFormat:1033,RGBA_ASTC_10x10_Format:37819,RGBA_ASTC_10x5_Format:37816,RGBA_ASTC_10x6_Format:37817,RGBA_ASTC_10x8_Format:37818,RGBA_ASTC_12x10_Format:37820,RGBA_ASTC_12x12_Format:37821,RGBA_ASTC_4x4_Format:37808,RGBA_ASTC_5x4_Format:37809,RGBA_ASTC_5x5_Format:37810,RGBA_ASTC_6x5_Format:37811,RGBA_ASTC_6x6_Format:37812,RGBA_ASTC_8x5_Format:37813,RGBA_ASTC_8x6_Format:37814,RGBA_ASTC_8x8_Format:37815,RGBA_BPTC_Format:36492,RGBA_ETC2_EAC_Format:SZt,RGBA_PVRTC_2BPPV1_Format:xZt,RGBA_PVRTC_4BPPV1_Format:bZt,RGBA_S3TC_DXT1_Format:mZt,RGBA_S3TC_DXT3_Format:gZt,RGBA_S3TC_DXT5_Format:_Zt,RGBDEncoding:BZt,RGBEEncoding:OZt,RGBEFormat:1023,RGBFormat:uZt,RGBIntegerFormat:1032,RGBM16Encoding:DZt,RGBM7Encoding:zZt,RGB_ETC1_Format:36196,RGB_ETC2_Format:wZt,RGB_PVRTC_2BPPV1_Format:vZt,RGB_PVRTC_4BPPV1_Format:yZt,RGB_S3TC_DXT1_Format:fZt,RGFormat:1030,RGIntegerFormat:1031,RawShaderMaterial:_0t,Ray:iQt,Raycaster:class{constructor(t,e,n=0,i=1/0){this.ray=new iQt(t,e),this.near=n,this.far=i,this.camera=null,this.layers=new mQt,this.params={Mesh:{},Line:{threshold:1},LOD:{},Points:{threshold:1},Sprite:{}}}set(t,e){this.ray.set(t,e)}setFromCamera(t,e){e&&e.isPerspectiveCamera?(this.ray.origin.setFromMatrixPosition(e.matrixWorld),this.ray.direction.set(t.x,t.y,.5).unproject(e).sub(this.ray.origin).normalize(),this.camera=e):e&&e.isOrthographicCamera?(this.ray.origin.set(t.x,t.y,(e.near+e.far)/(e.near-e.far)).unproject(e),this.ray.direction.set(0,0,-1).transformDirection(e.matrixWorld),this.camera=e):console.error("THREE.Raycaster: Unsupported camera type: "+e.type)}intersectObject(t,e=!1,n=[]){return v8t(t,this,n,e),n.sort(y8t),n}intersectObjects(t,e=!1,n=[]){for(let i=0,r=t.length;i<r;i++)v8t(t[i],this,n,e);return n.sort(y8t),n}},RectAreaLight:x9t,RedFormat:1028,RedIntegerFormat:1029,ReinhardToneMapping:2,RepeatWrapping:$Kt,ReplaceStencilOp:7681,ReverseSubtractEquation:102,RingBufferGeometry:u6t,RingGeometry:u6t,SRGB8_ALPHA8_ASTC_10x10_Format:37851,SRGB8_ALPHA8_ASTC_10x5_Format:37848,SRGB8_ALPHA8_ASTC_10x6_Format:37849,SRGB8_ALPHA8_ASTC_10x8_Format:37850,SRGB8_ALPHA8_ASTC_12x10_Format:37852,SRGB8_ALPHA8_ASTC_12x12_Format:37853,SRGB8_ALPHA8_ASTC_4x4_Format:37840,SRGB8_ALPHA8_ASTC_5x4_Format:37841,SRGB8_ALPHA8_ASTC_5x5_Format:37842,SRGB8_ALPHA8_ASTC_6x5_Format:37843,SRGB8_ALPHA8_ASTC_6x6_Format:37844,SRGB8_ALPHA8_ASTC_8x5_Format:37845,SRGB8_ALPHA8_ASTC_8x6_Format:37846,SRGB8_ALPHA8_ASTC_8x8_Format:37847,Scene:X5t,SceneUtils:Z8t,ShaderChunk:s0t,ShaderLib:c0t,ShaderMaterial:G1t,ShadowMaterial:x6t,Shape:r9t,ShapeBufferGeometry:h6t,ShapeGeometry:h6t,ShapePath:I9t,ShapeUtils:e6t,ShortType:1011,Skeleton:C3t,SkeletonHelper:P8t,SkinnedMesh:w3t,SmoothShading:2,Sphere:$Jt,SphereBufferGeometry:d6t,SphereGeometry:d6t,Spherical:b8t,SphericalHarmonics3:w9t,SplineCurve:k4t,SpotLight:d9t,SpotLightHelper:class extends kQt{constructor(t,e){super(),this.light=t,this.light.updateMatrixWorld(),this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1,this.color=e;const n=new b1t,i=[0,0,0,0,0,1,0,0,0,1,0,1,0,0,0,-1,0,1,0,0,0,0,1,1,0,0,0,0,-1,1];for(let t=0,e=1,n=32;t<n;t++,e++){const r=t/n*Math.PI*2,o=e/n*Math.PI*2;i.push(Math.cos(r),Math.sin(r),1,Math.cos(o),Math.sin(o),1)}n.setAttribute("position",new l1t(i,3));const r=new I3t({fog:!1,toneMapped:!1});this.cone=new U3t(n,r),this.add(this.cone),this.update()}dispose(){this.cone.geometry.dispose(),this.cone.material.dispose()}update(){this.light.updateMatrixWorld();const t=this.light.distance?this.light.distance:1e3,e=t*Math.tan(this.light.angle);this.cone.scale.set(e,e,t),C8t.setFromMatrixPosition(this.light.target.matrixWorld),this.cone.lookAt(C8t),void 0!==this.color?this.cone.material.color.set(this.color):this.cone.material.color.copy(this.light.color)}},Sprite:d3t,SpriteMaterial:J5t,SrcAlphaFactor:204,SrcAlphaSaturateFactor:210,SrcColorFactor:202,StaticCopyUsage:35046,StaticDrawUsage:FZt,StaticReadUsage:35045,StereoCamera:class{constructor(){this.type="StereoCamera",this.aspect=1,this.eyeSep=.064,this.cameraL=new q1t,this.cameraL.layers.enable(1),this.cameraL.matrixAutoUpdate=!1,this.cameraR=new q1t,this.cameraR.layers.enable(2),this.cameraR.matrixAutoUpdate=!1,this._cache={focus:null,fov:null,aspect:null,near:null,far:null,zoom:null,eyeSep:null}}update(t){const e=this._cache;if(e.focus!==t.focus||e.fov!==t.fov||e.aspect!==t.aspect*this.aspect||e.near!==t.near||e.far!==t.far||e.zoom!==t.zoom||e.eyeSep!==this.eyeSep){e.focus=t.focus,e.fov=t.fov,e.aspect=t.aspect*this.aspect,e.near=t.near,e.far=t.far,e.zoom=t.zoom,e.eyeSep=this.eyeSep;const n=t.projectionMatrix.clone(),i=e.eyeSep/2,r=i*e.near/e.focus,o=e.near*Math.tan(qZt*e.fov*.5)/e.zoom;let a,s;U9t.elements[12]=-i,V9t.elements[12]=i,a=-o*e.aspect+r,s=o*e.aspect+r,n.elements[0]=2*e.near/(s-a),n.elements[8]=(s+a)/(s-a),this.cameraL.projectionMatrix.copy(n),a=-o*e.aspect-r,s=o*e.aspect-r,n.elements[0]=2*e.near/(s-a),n.elements[8]=(s+a)/(s-a),this.cameraR.projectionMatrix.copy(n)}this.cameraL.matrixWorld.copy(t.matrixWorld).multiply(U9t),this.cameraR.matrixWorld.copy(t.matrixWorld).multiply(V9t)}},StreamCopyUsage:35042,StreamDrawUsage:35040,StreamReadUsage:35041,StringKeyframeTrack:U6t,SubtractEquation:101,SubtractiveBlending:3,TOUCH:FKt,TangentSpaceNormalMap:0,TetrahedronBufferGeometry:p6t,TetrahedronGeometry:p6t,TextBufferGeometry:f6t,TextGeometry:f6t,Texture:bJt,TextureLoader:e9t,TorusBufferGeometry:m6t,TorusGeometry:m6t,TorusKnotBufferGeometry:g6t,TorusKnotGeometry:g6t,Triangle:FQt,TriangleFanDrawMode:2,TriangleStripDrawMode:1,TrianglesDrawMode:0,TubeBufferGeometry:_6t,TubeGeometry:_6t,UVMapping:UKt,Uint16Attribute:function b7t(t,e){return console.warn("THREE.Uint16Attribute has been removed. Use new THREE.Uint16BufferAttribute() instead."),new r1t(t,e)},Uint16BufferAttribute:r1t,Uint32Attribute:function x7t(t,e){return console.warn("THREE.Uint32Attribute has been removed. Use new THREE.Uint32BufferAttribute() instead."),new a1t(t,e)},Uint32BufferAttribute:a1t,Uint8Attribute:function w7t(t,e){return console.warn("THREE.Uint8Attribute has been removed. Use new THREE.Uint8BufferAttribute() instead."),new e1t(t,e)},Uint8BufferAttribute:e1t,Uint8ClampedAttribute:function S7t(t,e){return console.warn("THREE.Uint8ClampedAttribute has been removed. Use new THREE.Uint8ClampedBufferAttribute() instead."),new n1t(t,e)},Uint8ClampedBufferAttribute:n1t,Uniform:m8t,UniformsLib:l0t,UniformsUtils:j1t,UnsignedByteType:rZt,UnsignedInt248Type:cZt,UnsignedIntType:aZt,UnsignedShort4444Type:1017,UnsignedShort5551Type:1018,UnsignedShort565Type:1019,UnsignedShortType:oZt,VSMShadowMap:3,Vector2:mJt,Vector3:CJt,Vector4:wJt,VectorKeyframeTrack:j6t,Vertex:function M7t(t,e,n){return console.warn("THREE.Vertex has been removed. Use THREE.Vector3 instead."),new CJt(t,e,n)},VertexColors:2,VideoTexture:Z3t,WebGL1Renderer:W5t,WebGLCubeRenderTarget:K1t,WebGLMultipleRenderTargets:MJt,WebGLMultisampleRenderTarget:EJt,WebGLRenderTarget:SJt,WebGLRenderTargetCube:function E7t(t,e,n){return console.warn("THREE.WebGLRenderTargetCube( width, height, options ) is now WebGLCubeRenderTarget( size, options )."),new K1t(t,n)},WebGLRenderer:G5t,WebGLUtils:D5t,WireframeGeometry:y6t,WireframeHelper:function T7t(t,e){return console.warn("THREE.WireframeHelper has been removed. Use THREE.WireframeGeometry instead."),new U3t(new y6t(t.geometry),new I3t({color:void 0!==e?e:16777215}))},WrapAroundEnding:kZt,XHRLoader:function C7t(t){return console.warn("THREE.XHRLoader has been renamed to THREE.FileLoader."),new Z6t(t)},ZeroCurvatureEnding:CZt,ZeroFactor:200,ZeroSlopeEnding:AZt,ZeroStencilOp:0,sRGBEncoding:IZt});const A7t={type:"change"},k7t={type:"start"},L7t={type:"end"};class P7t extends jZt{constructor(t,e){super(),void 0===e&&console.warn('THREE.OrbitControls: The second parameter "domElement" is now mandatory.'),e===document&&console.error('THREE.OrbitControls: "document" should not be used as the target "domElement". Please use "renderer.domElement" instead.'),this.object=t,this.domElement=e,this.domElement.style.touchAction="none",this.enabled=!0,this.target=new CJt,this.minDistance=0,this.maxDistance=1/0,this.minZoom=0,this.maxZoom=1/0,this.minPolarAngle=0,this.maxPolarAngle=Math.PI,this.minAzimuthAngle=-1/0,this.maxAzimuthAngle=1/0,this.enableDamping=!1,this.dampingFactor=.05,this.enableZoom=!0,this.zoomSpeed=1,this.enableRotate=!0,this.rotateSpeed=1,this.enablePan=!0,this.panSpeed=1,this.screenSpacePanning=!0,this.keyPanSpeed=7,this.autoRotate=!1,this.autoRotateSpeed=2,this.keys={LEFT:"ArrowLeft",UP:"ArrowUp",RIGHT:"ArrowRight",BOTTOM:"ArrowDown"},this.mouseButtons={LEFT:HKt.ROTATE,MIDDLE:HKt.DOLLY,RIGHT:HKt.PAN},this.touches={ONE:FKt.ROTATE,TWO:FKt.DOLLY_PAN},this.target0=this.target.clone(),this.position0=this.object.position.clone(),this.zoom0=this.object.zoom,this._domElementKeyEvents=null,this.getPolarAngle=function(){return a.phi},this.getAzimuthalAngle=function(){return a.theta},this.getDistance=function(){return this.object.position.distanceTo(this.target)},this.listenToKeyEvents=function(t){t.addEventListener("keydown",j),this._domElementKeyEvents=t},this.saveState=function(){n.target0.copy(n.target),n.position0.copy(n.object.position),n.zoom0=n.object.zoom},this.reset=function(){n.target.copy(n.target0),n.object.position.copy(n.position0),n.object.zoom=n.zoom0,n.object.updateProjectionMatrix(),n.dispatchEvent(A7t),n.update(),r=i.NONE},this.update=(function(){const e=new CJt,h=(new TJt).setFromUnitVectors(t.up,new CJt(0,1,0)),d=h.clone().invert(),p=new CJt,f=new TJt,m=2*Math.PI;return function t(){const g=n.object.position;e.copy(g).sub(n.target),e.applyQuaternion(h),a.setFromVector3(e),n.autoRotate&&r===i.NONE&&S((function _(){return 2*Math.PI/60/60*n.autoRotateSpeed})()),n.enableDamping?(a.theta+=s.theta*n.dampingFactor,a.phi+=s.phi*n.dampingFactor):(a.theta+=s.theta,a.phi+=s.phi);let y=n.minAzimuthAngle,v=n.maxAzimuthAngle;return isFinite(y)&&isFinite(v)&&(y<-Math.PI?y+=m:y>Math.PI&&(y-=m),v<-Math.PI?v+=m:v>Math.PI&&(v-=m),a.theta=y<=v?Math.max(y,Math.min(v,a.theta)):a.theta>(y+v)/2?Math.max(y,a.theta):Math.min(v,a.theta)),a.phi=Math.max(n.minPolarAngle,Math.min(n.maxPolarAngle,a.phi)),a.makeSafe(),a.radius*=l,a.radius=Math.max(n.minDistance,Math.min(n.maxDistance,a.radius)),!0===n.enableDamping?n.target.addScaledVector(c,n.dampingFactor):n.target.add(c),e.setFromSpherical(a),e.applyQuaternion(d),g.copy(n.target).add(e),n.object.lookAt(n.target),!0===n.enableDamping?(s.theta*=1-n.dampingFactor,s.phi*=1-n.dampingFactor,c.multiplyScalar(1-n.dampingFactor)):(s.set(0,0,0),c.set(0,0,0)),l=1,!!(u||p.distanceToSquared(n.object.position)>o||8*(1-f.dot(n.object.quaternion))>o)&&(n.dispatchEvent(A7t),p.copy(n.object.position),f.copy(n.object.quaternion),u=!1,!0)}})(),this.dispose=function(){n.domElement.removeEventListener("contextmenu",G),n.domElement.removeEventListener("pointerdown",B),n.domElement.removeEventListener("pointercancel",V),n.domElement.removeEventListener("wheel",U),n.domElement.removeEventListener("pointermove",H),n.domElement.removeEventListener("pointerup",F),null!==n._domElementKeyEvents&&n._domElementKeyEvents.removeEventListener("keydown",j)};const n=this,i={NONE:-1,ROTATE:0,DOLLY:1,PAN:2,TOUCH_ROTATE:3,TOUCH_PAN:4,TOUCH_DOLLY_PAN:5,TOUCH_DOLLY_ROTATE:6};let r=i.NONE;const o=1e-6,a=new b8t,s=new b8t;let l=1;const c=new CJt;let u=!1;const h=new mJt,d=new mJt,p=new mJt,f=new mJt,m=new mJt,g=new mJt,_=new mJt,y=new mJt,v=new mJt,b=[],x={};function w(){return Math.pow(.95,n.zoomSpeed)}function S(t){s.theta-=t}function M(t){s.phi-=t}const E=(function(){const t=new CJt;return function e(n,i){t.setFromMatrixColumn(i,0),t.multiplyScalar(-n),c.add(t)}})(),T=(function(){const t=new CJt;return function e(i,r){!0===n.screenSpacePanning?t.setFromMatrixColumn(r,1):(t.setFromMatrixColumn(r,0),t.crossVectors(n.object.up,t)),t.multiplyScalar(i),c.add(t)}})(),C=(function(){const t=new CJt;return function e(i,r){const o=n.domElement;if(n.object.isPerspectiveCamera){t.copy(n.object.position).sub(n.target);let e=t.length();e*=Math.tan(n.object.fov/2*Math.PI/180),E(2*i*e/o.clientHeight,n.object.matrix),T(2*r*e/o.clientHeight,n.object.matrix)}else n.object.isOrthographicCamera?(E(i*(n.object.right-n.object.left)/n.object.zoom/o.clientWidth,n.object.matrix),T(r*(n.object.top-n.object.bottom)/n.object.zoom/o.clientHeight,n.object.matrix)):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - pan disabled."),n.enablePan=!1)}})();function A(t){n.object.isPerspectiveCamera?l/=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom*t)),n.object.updateProjectionMatrix(),u=!0):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled."),n.enableZoom=!1)}function k(t){n.object.isPerspectiveCamera?l*=t:n.object.isOrthographicCamera?(n.object.zoom=Math.max(n.minZoom,Math.min(n.maxZoom,n.object.zoom/t)),n.object.updateProjectionMatrix(),u=!0):(console.warn("WARNING: OrbitControls.js encountered an unknown camera type - dolly/zoom disabled."),n.enableZoom=!1)}function L(t){h.set(t.clientX,t.clientY)}function P(t){f.set(t.clientX,t.clientY)}function N(){1===b.length?h.set(b[0].pageX,b[0].pageY):h.set(.5*(b[0].pageX+b[1].pageX),.5*(b[0].pageY+b[1].pageY))}function I(){1===b.length?f.set(b[0].pageX,b[0].pageY):f.set(.5*(b[0].pageX+b[1].pageX),.5*(b[0].pageY+b[1].pageY))}function R(){const t=b[0].pageX-b[1].pageX,e=b[0].pageY-b[1].pageY,n=Math.sqrt(t*t+e*e);_.set(0,n)}function O(t){if(1==b.length)d.set(t.pageX,t.pageY);else{const e=Y(t);d.set(.5*(t.pageX+e.x),.5*(t.pageY+e.y))}p.subVectors(d,h).multiplyScalar(n.rotateSpeed);const e=n.domElement;S(2*Math.PI*p.x/e.clientHeight),M(2*Math.PI*p.y/e.clientHeight),h.copy(d)}function z(t){if(1===b.length)m.set(t.pageX,t.pageY);else{const e=Y(t);m.set(.5*(t.pageX+e.x),.5*(t.pageY+e.y))}g.subVectors(m,f).multiplyScalar(n.panSpeed),C(g.x,g.y),f.copy(m)}function D(t){const e=Y(t),i=t.pageX-e.x,r=t.pageY-e.y,o=Math.sqrt(i*i+r*r);y.set(0,o),v.set(0,Math.pow(y.y/_.y,n.zoomSpeed)),A(v.y),_.copy(y)}function B(t){!1!==n.enabled&&(0===b.length&&(n.domElement.setPointerCapture(t.pointerId),n.domElement.addEventListener("pointermove",H),n.domElement.addEventListener("pointerup",F)),(function e(t){b.push(t)})(t),"touch"===t.pointerType?(function o(t){switch(q(t),b.length){case 1:switch(n.touches.ONE){case FKt.ROTATE:if(!1===n.enableRotate)return;N(),r=i.TOUCH_ROTATE;break;case FKt.PAN:if(!1===n.enablePan)return;I(),r=i.TOUCH_PAN;break;default:r=i.NONE}break;case 2:switch(n.touches.TWO){case FKt.DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;!(function t(){n.enableZoom&&R(),n.enablePan&&I()})(),r=i.TOUCH_DOLLY_PAN;break;case FKt.DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;!(function e(){n.enableZoom&&R(),n.enableRotate&&N()})(),r=i.TOUCH_DOLLY_ROTATE;break;default:r=i.NONE}break;default:r=i.NONE}r!==i.NONE&&n.dispatchEvent(k7t)})(t):(function a(t){let e;switch(t.button){case 0:e=n.mouseButtons.LEFT;break;case 1:e=n.mouseButtons.MIDDLE;break;case 2:e=n.mouseButtons.RIGHT;break;default:e=-1}switch(e){case HKt.DOLLY:if(!1===n.enableZoom)return;!(function e(t){_.set(t.clientX,t.clientY)})(t),r=i.DOLLY;break;case HKt.ROTATE:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enablePan)return;P(t),r=i.PAN}else{if(!1===n.enableRotate)return;L(t),r=i.ROTATE}break;case HKt.PAN:if(t.ctrlKey||t.metaKey||t.shiftKey){if(!1===n.enableRotate)return;L(t),r=i.ROTATE}else{if(!1===n.enablePan)return;P(t),r=i.PAN}break;default:r=i.NONE}r!==i.NONE&&n.dispatchEvent(k7t)})(t))}function H(t){!1!==n.enabled&&("touch"===t.pointerType?(function e(t){switch(q(t),r){case i.TOUCH_ROTATE:if(!1===n.enableRotate)return;O(t),n.update();break;case i.TOUCH_PAN:if(!1===n.enablePan)return;z(t),n.update();break;case i.TOUCH_DOLLY_PAN:if(!1===n.enableZoom&&!1===n.enablePan)return;!(function e(t){n.enableZoom&&D(t),n.enablePan&&z(t)})(t),n.update();break;case i.TOUCH_DOLLY_ROTATE:if(!1===n.enableZoom&&!1===n.enableRotate)return;!(function o(t){n.enableZoom&&D(t),n.enableRotate&&O(t)})(t),n.update();break;default:r=i.NONE}})(t):(function o(t){if(!1!==n.enabled)switch(r){case i.ROTATE:if(!1===n.enableRotate)return;!(function e(t){d.set(t.clientX,t.clientY),p.subVectors(d,h).multiplyScalar(n.rotateSpeed);const e=n.domElement;S(2*Math.PI*p.x/e.clientHeight),M(2*Math.PI*p.y/e.clientHeight),h.copy(d),n.update()})(t);break;case i.DOLLY:if(!1===n.enableZoom)return;!(function r(t){y.set(t.clientX,t.clientY),v.subVectors(y,_),v.y>0?A(w()):v.y<0&&k(w()),_.copy(y),n.update()})(t);break;case i.PAN:if(!1===n.enablePan)return;!(function o(t){m.set(t.clientX,t.clientY),g.subVectors(m,f).multiplyScalar(n.panSpeed),C(g.x,g.y),f.copy(m),n.update()})(t)}})(t))}function F(t){!1!==n.enabled&&("touch"===t.pointerType?(function e(t){n.dispatchEvent(L7t),r=i.NONE})():(function o(t){n.dispatchEvent(L7t),r=i.NONE})(),W(t),0===b.length&&(n.domElement.releasePointerCapture(t.pointerId),n.domElement.removeEventListener("pointermove",H),n.domElement.removeEventListener("pointerup",F)))}function V(t){W(t)}function U(t){!1===n.enabled||!1===n.enableZoom||r!==i.NONE&&r!==i.ROTATE||(t.preventDefault(),n.dispatchEvent(k7t),(function e(t){t.deltaY<0?k(w()):t.deltaY>0&&A(w()),n.update()})(t),n.dispatchEvent(L7t))}function j(t){!1!==n.enabled&&!1!==n.enablePan&&(function e(t){let e=!1;switch(t.code){case n.keys.UP:C(0,n.keyPanSpeed),e=!0;break;case n.keys.BOTTOM:C(0,-n.keyPanSpeed),e=!0;break;case n.keys.LEFT:C(n.keyPanSpeed,0),e=!0;break;case n.keys.RIGHT:C(-n.keyPanSpeed,0),e=!0}e&&(t.preventDefault(),n.update())})(t)}function G(t){!1!==n.enabled&&t.preventDefault()}function W(t){delete x[t.pointerId];for(let e=0;e<b.length;e++)if(b[e].pointerId==t.pointerId)return void b.splice(e,1)}function q(t){let e=x[t.pointerId];void 0===e&&(e=new mJt,x[t.pointerId]=e),e.set(t.pageX,t.pageY)}function Y(t){return x[(t.pointerId===b[0].pointerId?b[1]:b[0]).pointerId]}n.domElement.addEventListener("contextmenu",G),n.domElement.addEventListener("pointerdown",B),n.domElement.addEventListener("pointercancel",V),n.domElement.addEventListener("wheel",U,{passive:!1}),this.update()}}class N7t extends jZt{constructor(t){super(),this._lastMesh=null,this._clock=new j9t,this._canvasSize=null,this._layersConfig=null,this._runColor=t}_isObject(t){return"object"==typeof t&&null!=t&&!Array.isArray(t)}_applyDefaults(t,e){let n={};const i=[t,e];for(let t=0;t<i.length;t++){const e=i[t];for(let t in e){const i=t in n;this._isObject(e[t])?n[t]=this._applyDefaults(n[t]||{},e[t]):i||(n[t]=e[t])}}return n}_createLayers(){if(this._layersConfig&&this._scene&&this._lastMesh){if(this._layersConfig.showBoundingBox){var t=new G8t(this._lastMesh,new $Qt("rgb(0, 0, 255)"));this._scene.add(t)}if(this._layersConfig.showAxes){var e=new X8t(5);this._scene.add(e)}}}setLayersConfig(t){this._layersConfig=this._applyDefaults(t,this._layersConfig||{})}_createWorld(t,e){if(this.isReady())return;this._scene=new X5t;var n=new J8t[t.camera.cls](t.camera.fov,this._canvasSize.width/this._canvasSize.height,t.camera.near,t.camera.far);this._camera=n;var i=new P7t(n,e);const r=i;r.lookSpeed=.4,r.movementSpeed=20,r.noFly=!0,r.lookVertical=!0,r.constrainVertical=!0,r.verticalMin=1,r.verticalMax=2,r.addEventListener("change",this._onCameraPositionChange.bind(this)),this._cameraControls=i,this._renderer=new G5t({antialias:!0}),this._renderer.setPixelRatio(window.devicePixelRatio),this._renderer.setSize(this._canvasSize.width,this._canvasSize.height),this._renderer.setClearColor(16777215,1)}_clearScene(){for(;this._scene.children.length>0;)this._scene.remove(this._scene.children[0])}getRenderer(){return this._renderer}getCameraControls(){return this._cameraControls}isReady(){return!!this._camera&&!!this._cameraControls}getCameraPosition(){return{far:this._camera.far,position:this._camera.position.clone(),target:this._cameraControls.target.clone()}}setCanvasSize(t){this._canvasSize=t}draw(){this._animationFrameIndex&&cancelAnimationFrame(this._animationFrameIndex),this._camera.aspect=this._canvasSize.width/this._canvasSize.height,this._camera.updateProjectionMatrix(),this._renderer.setSize(this._canvasSize.width,this._canvasSize.height);const t=function(){var e=this._clock.getDelta();this._cameraControls.update(e),this._animationFrameIndex=requestAnimationFrame(t),this._renderer.render(this._scene,this._camera)}.bind(this);t()}updateScene(t,e){let n={};"config"in t&&t.config&&(n=JSON.parse(t.config)),this.dispatchEvent({type:"beforeUpdateScene"}),n=this._applyDefaults(n,{camera:{cls:"PerspectiveCamera",fov:75,near:.1,far:1e3},lights:[{cls:"AmbientLight",color:"#ffffff",intensity:.75},{cls:"DirectionalLight",color:"#ffffff",intensity:.75,position:[0,-1,2]}]}),this._createWorld(n,e),this._clearScene(),this._createLights(this._scene,n),this._createGeometry(t,n),this._createLayers(),this.draw()}resetView(t){if(!this.isReady())return;let e;this._cameraControls.reset(),!t&&this._lastMesh&&(e=this._lastMesh),e&&(this._fitObjectToViewport(e),this._lastMesh=e),this._cameraControls.update()}_createGeometry(t,e){const n=t.mesh;n.vertices&&n.faces&&n.faces.length?this._createMesh(n,e):this._createPointCloud(n,e)}_createPointCloud(t,e){const n=t.vertices,i=t.colors;let r={material:{cls:"PointsMaterial",size:.005}};i&&i.length==n.length?r.material.vertexColors=!0:r.material.color=this._runColor;const o=this._applyDefaults(e,r),a=new b1t,s=new Float32Array(n.flat());if(a.setAttribute("position",new QQt(s,3)),i&&i.length==n.length){const t=new Float32Array(i.flat());for(let e=0;e<t.length;e++)t[e]=t[e]/255;a.setAttribute("color",new QQt(t,3))}var l=new J8t[o.material.cls](o.material),c=new $3t(a,l);this._scene.add(c),this._lastMesh=c}setCameraViewpoint(t,e,n){this._silent=!0,this._camera.far=e,this._camera.position.set(t.x,t.y,t.z),this._camera.lookAt(n.clone()),this._camera.updateProjectionMatrix(),this._cameraControls.target=n.clone(),this._cameraControls.update(),this._silent=!1}_onCameraPositionChange(t){this._silent||this.dispatchEvent({type:"cameraPositionChange",event:t})}_fitObjectToViewport(t){const e=new LJt,n=new CJt,i=new CJt;e.setFromObject(t),e.getCenter(n),e.getSize(i);const r=Math.max(i.x,i.y,i.z),o=this._camera.fov*(Math.PI/180);let a=1.25*Math.abs(r/(2*Math.tan(o/2)));const s=e.min.z;this.setCameraViewpoint({x:n.x,y:n.y,z:a},3*(s<0?-s+a:a-s),n)}_createMesh(t,e){const n=t.vertices,i=t.faces,r=t.colors,o=this._applyDefaults(e,{material:{cls:"MeshStandardMaterial",color:"#a0a0a0",roughness:1,metalness:0}}),a=new b1t,s=new Float32Array(n.flat());a.setAttribute("position",new QQt(s,3));const l=new Uint16Array(i.flat());if(r&&r.length){const t=r.flat();for(let e=0;e<t.length;e++)t[e]=t[e]/255;a.setAttribute("color",new QQt(new Float32Array(t),3)),o.material=o.material||{},o.material.vertexColors=!0}a.center(),a.computeBoundingSphere(),a.setIndex(new QQt(l,1)),a.computeVertexNormals();let c=new J8t[o.material.cls](o.material),u=new B1t(a,c);u.castShadow=!0,u.receiveShadow=!0,this._scene.add(u),this._lastMesh=u}_createLights(t,e){for(let n=0;n<e.lights.length;n++){const i=e.lights[n];let r=new J8t[i.cls](i.color,i.intensity);i.position&&r.position.set(i.position[0],i.position[1],i.position[2]),t.add(r)}}}let I7t=class extends(er(ye)){constructor(){super(...arguments),this.selectedView="all",this.active=!1,this._colorScaleFunction=GR,this._steps=[],this._meshViewerAttached=!1,this._cameraPositionInitialized=!1,this._isMeshLoading=!1}get _runColor(){return this._colorScaleFunction(this.run)}connectedCallback(){super.connectedCallback(),this._dataProvider=new DKt(this.requestManager);const t=new N7t(this._runColor);t.addEventListener("beforeUpdateScene",this._updateCanvasSize.bind(this)),t.addEventListener("cameraPositionChange",this._onCameraPositionChange.bind(this)),this._meshViewer=t}reload(){this.active&&this._dataProvider&&(this._isMeshLoading=!0,this._dataProvider.reload(this.run,this.tag,this.sample).then((t=>{t&&(this._steps=t,this._stepIndex=t.length-1)})).catch((t=>{if(!t||!t.code||t.code!=RKt.CANCELLED)throw t=t||"Response processing failed.",new Error(t)})))}_updateScene(){const t=this._currentStep;t&&t.mesh&&(this._meshViewer.updateScene(t,this),this._cameraPositionInitialized||(this._meshViewer.resetView(),this._cameraPositionInitialized=!0),this._meshViewerAttached||(this.shadowRoot.appendChild(this._meshViewer.getRenderer().domElement),this._meshViewerAttached=!0))}_debouncedFetchMesh(){this.debounce("fetchMesh",(()=>this._maybeFetchMesh()),100)}_maybeFetchMesh(){return n(this,void 0,void 0,(function*(){const t=this._currentStep;if(t&&!t.mesh&&!t.meshFetching){t.meshFetching=!0,this._isMeshLoading=!0;try{const e=yield this._dataProvider.fetchData(t,this.run,this.tag,this.sample);t.mesh=e[0],this.notifyPath("_currentStep.mesh")}catch(t){if(!t||!t.code||t.code!=RKt.CANCELLED)throw t=t||"Response processing failed.",new Error(t)}finally{this._isMeshLoading=!1,t.meshFetching=!1}}}))}_onCameraPositionChange(){if(!this._meshViewer.isReady())return;const t=new CustomEvent("camera-position-change",{detail:this._meshViewer.getCameraPosition()});this.dispatchEvent(t)}setCameraViewpoint(t,e,n){this._meshViewer.setCameraViewpoint(t,e,n)}_updateCanvasSize(){const t=this.offsetWidth,e=t,n=this.$$(".tf-mesh-loader-header").offsetHeight;this._meshViewer.setCanvasSize({width:t,height:e-n})}redraw(){this._updateCanvasSize(),this.isConnected&&this._meshViewer.draw()}_hasAtLeastOneStep(t){return!!t&&t.length>0}_hasMultipleSteps(t){return!!t&&t.length>1}get _currentStep(){return this._steps[this._stepIndex]||null}get _stepValue(){const t=this._currentStep;return t?t.step:0}get _currentWallTime(){const t=this._currentStep;return t?KR(t.wall_time):""}_getMaxStepIndex(t){return t.length-1}_getSampleText(t){return String(t+1)}_hasMultipleSamples(t){return t>1}_updateView(){this._meshViewer&&"all"==this.selectedView&&this._meshViewer.resetView()}toLocaleString_(t){return t.toLocaleString()}};I7t.template=_e`
    <tf-card-heading color="[[_runColor]]" class="tf-mesh-loader-header">
      <template is="dom-if" if="[[_hasMultipleSamples(ofSamples)]]">
        <div>sample: [[_getSampleText(sample)]] of [[ofSamples]]</div>
      </template>
      <template is="dom-if" if="[[_hasAtLeastOneStep(_steps)]]">
        <div class="heading-row">
          <div class="heading-label">
            step
            <span style="font-weight: bold"
              >[[toLocaleString_(_stepValue)]]</span
            >
          </div>
          <div class="heading-label heading-right">
            <template is="dom-if" if="[[_currentWallTime]]">
              [[_currentWallTime]]
            </template>
          </div>
          <div class="label right">
            <paper-spinner-lite active hidden$="[[!_isMeshLoading]]">
            </paper-spinner-lite>
          </div>
        </div>
      </template>
      <template is="dom-if" if="[[_hasMultipleSteps(_steps)]]">
        <div>
          <paper-slider
            id="steps"
            immediate-value="{{_stepIndex}}"
            max="[[_getMaxStepIndex(_steps)]]"
            max-markers="[[_getMaxStepIndex(_steps)]]"
            snaps
            step="1"
            value="{{_stepIndex}}"
          ></paper-slider>
        </div>
      </template>
    </tf-card-heading>
    <style>
      paper-slider {
        width: 100%;
        margin-left: 1px;
        margin-right: 1px;
      }
      .tf-mesh-loader-header {
        display: block;
        height: 105px;
      }
      [hidden] {
        display: none;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],I7t.prototype,"run",void 0),t([o({type:String}),e("design:type",String)],I7t.prototype,"tag",void 0),t([o({type:Number}),e("design:type",Number)],I7t.prototype,"sample",void 0),t([o({type:Number}),e("design:type",Number)],I7t.prototype,"ofSamples",void 0),t([o({type:String}),e("design:type",String)],I7t.prototype,"selectedView",void 0),t([o({type:Boolean}),e("design:type",Boolean)],I7t.prototype,"active",void 0),t([o({type:Object}),e("design:type",dr)],I7t.prototype,"requestManager",void 0),t([o({type:Object}),e("design:type",N7t)],I7t.prototype,"_meshViewer",void 0),t([o({type:Object}),e("design:type",DKt)],I7t.prototype,"_dataProvider",void 0),t([o({type:Object}),e("design:type",Object)],I7t.prototype,"_colorScaleFunction",void 0),t([o({type:Array,notify:!0}),e("design:type",Array)],I7t.prototype,"_steps",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],I7t.prototype,"_stepIndex",void 0),t([o({type:Boolean}),e("design:type",Boolean)],I7t.prototype,"_meshViewerAttached",void 0),t([o({type:Boolean}),e("design:type",Boolean)],I7t.prototype,"_cameraPositionInitialized",void 0),t([o({type:Boolean}),e("design:type",Boolean)],I7t.prototype,"_isMeshLoading",void 0),t([s("run"),e("design:type",String),e("design:paramtypes",[])],I7t.prototype,"_runColor",null),t([a("run","tag","active","_dataProvider","_meshViewer"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],I7t.prototype,"reload",null),t([a("_currentStep.*","_meshViewer"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],I7t.prototype,"_updateScene",null),t([a("_currentStep"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],I7t.prototype,"_debouncedFetchMesh",null),t([s("_steps","_stepIndex"),e("design:type",Object),e("design:paramtypes",[])],I7t.prototype,"_currentStep",null),t([s("_currentStep"),e("design:type",Number),e("design:paramtypes",[])],I7t.prototype,"_stepValue",null),t([s("_currentStep"),e("design:type",String),e("design:paramtypes",[])],I7t.prototype,"_currentWallTime",null),t([a("selectedView"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],I7t.prototype,"_updateView",null),I7t=t([i("tf-mesh-loader")],I7t);let R7t=class extends ye{constructor(){super(),this.reloadOnReady=!0,this._tagFilter=".*",this._selectedView="all",this._requestManager=new dr,window.addEventListener("resize",(()=>{this._handleWindowResize()}),!1),this.reloadOnReady&&this.reload()}_getAllChildren(){return Array.from(this.shadowRoot.querySelectorAll("tf-mesh-loader"))}_onCameraPositionChanged(t){"share"==this._selectedView&&this._getAllChildren().forEach((e=>{t.target!=e&&e.setCameraViewpoint(t.detail.position,t.detail.far,t.detail.target)}))}_shouldOpen(t){return t<=2}reload(){this._fetchTags().then(this._reloadMeshes.bind(this))}_handleWindowResize(){this._getAllChildren().forEach((t=>{t.redraw()}))}_fetchTags(){const t=_r().pluginRoute("mesh","/tags");return this._requestManager.request(t).then((t=>{if(Se.exports.isEqual(t,this._runToTagInfo))return;const e=ar(Se.exports.mapValues(t,(t=>Object.keys(t))));this._dataNotFound=0===e.length,this._runToTagInfo=t}))}_reloadMeshes(){this._getAllChildren().forEach((t=>{t.reload()}))}get _categories(){var t=this._runToTagInfo,e=this._selectedRuns,n=this._tagFilter;function i(e){const n=t[e.run][e.tag].samples;return Se.exports.range(n).map((t=>Object.assign({},e,{sample:t,ofSamples:n})))}return Mr(Se.exports.mapValues(t,(t=>Object.keys(t))),e,n).map((t=>Object.assign({},t,{items:[].concat.apply([],t.items.map(i))})))}};R7t.template=_e`
    <tf-dashboard-layout>
      <div slot="sidebar" class="all-controls">
        <div class="settings">
          <div class="sidebar-section view-control">
            <h3 class="title">Point of view</h3>
            <div>
              <paper-radio-group
                id="view-radio-group"
                selected="{{_selectedView}}"
              >
                <paper-radio-button id="all-radio-button" name="all">
                  Display all points
                </paper-radio-button>
                <paper-tooltip
                  animation-delay="0"
                  for="all-radio-button"
                  position="right"
                  offset="0"
                >
                  Zoom and center camera to display all points at once. Note,
                  that some points could be too far (i.e. too small) to be
                  visible.
                </paper-tooltip>
                <paper-radio-button id="user-radio-button" name="user">
                  Current view
                </paper-radio-button>
                <paper-tooltip
                  animation-delay="0"
                  for="user-radio-button"
                  position="right"
                  offset="0"
                >
                  Keep current camera position and zoom level.
                </paper-tooltip>
                <paper-radio-button id="share-radio-button" name="share">
                  Share viewpoint
                </paper-radio-button>
                <paper-tooltip
                  animation-delay="0"
                  for="share-radio-button"
                  position="right"
                  offset="0"
                >
                  Share viewpoint among all cameras.
                </paper-tooltip>
              </paper-radio-group>
            </div>
          </div>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No point cloud data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any point cloud data to your event files.
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>

            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
            >
              <template>
                <tf-mesh-loader
                  active="[[active]]"
                  selected-view="[[_selectedView]]"
                  run="[[item.run]]"
                  tag="[[item.tag]]"
                  sample="[[item.sample]]"
                  of-samples="[[item.ofSamples]]"
                  request-manager="[[_requestManager]]"
                  class="tf-mesh-loader-container"
                  on-camera-position-change="_onCameraPositionChanged"
                >
                </tf-mesh-loader>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }
      paper-radio-button {
        display: block;
        padding: 5px;
      }
      .sidebar-section h3 {
        margin: 0;
        font-weight: normal;
        font-size: 14px;
        margin-bottom: 5px;
      }

      .runs-selector {
        flex-grow: 1;
      }

      tf-runs-selector {
        display: flex;
      }

      .view-control {
        display: block !important;
      }

      .view-control h3.title {
        padding-top: 16px;
        padding-bottom: 16px;
      }

      .allcontrols .view-control paper-radio-group {
        margin-top: 5px;
      }
      /* Layout must be horizontal, i.e. items arranged in a row. If items cannot fit in a row,
       * they should be moved to next line. All items must be square at all times. Minimum size of
       * the item is 480px. This means that maximum size of the item must be 480px + 479px = 959px.
       * */
      .horizontal {
        display: flex;
        flex-direction: row;
        flex-wrap: wrap;
      }
      tf-mesh-loader {
        width: 480px;
        flex-basis: 480px;
        flex-grow: 1;
        display: block;
      }
    </style>
  `,t([o({type:Boolean}),e("design:type",Boolean)],R7t.prototype,"reloadOnReady",void 0),t([o({type:Array}),e("design:type",Array)],R7t.prototype,"_selectedRuns",void 0),t([o({type:Object}),e("design:type",Object)],R7t.prototype,"_runToTagInfo",void 0),t([o({type:Boolean}),e("design:type",Boolean)],R7t.prototype,"_dataNotFound",void 0),t([o({type:String}),e("design:type",String)],R7t.prototype,"_tagFilter",void 0),t([o({type:String,notify:!0}),e("design:type",String)],R7t.prototype,"_selectedView",void 0),t([o({type:Object}),e("design:type",Object)],R7t.prototype,"_requestManager",void 0),t([s("_runToTagInfo","_selectedRuns","_tagFilter"),e("design:type",Array),e("design:paramtypes",[])],R7t.prototype,"_categories",null),R7t=t([i("mesh-dashboard"),e("design:paramtypes",[])],R7t);let O7t=class extends ye{constructor(){super(...arguments),this._expanded=!1,this._runToPrCurveEntry={},this._previousRunToPrCurveEntry={},this._colorScaleFunction={scale:GR},this._canceller=new XR,this._xComponentsCreationMethod=()=>{const t=new rO.Scales.Linear;return{scale:t,axis:new rO.Axes.Numeric(t,"bottom"),accessor:t=>t.recall}},this._yValueAccessor=t=>t.precision,this._tooltipColumns=(()=>{const t=wTt(4),e=e=>isNaN(e)?"NaN":t(e);return[{title:"Run",evaluate:t=>t.dataset.metadata().name},{title:"Threshold",evaluate:t=>e(t.datum.thresholds)},{title:"Precision",evaluate:t=>e(t.datum.precision)},{title:"Recall",evaluate:t=>e(t.datum.recall)},{title:"TP",evaluate:t=>t.datum.true_positives},{title:"FP",evaluate:t=>t.datum.false_positives},{title:"TN",evaluate:t=>t.datum.true_negatives},{title:"FN",evaluate:t=>t.datum.false_negatives}]})(),this._seriesDataFields=["thresholds","precision","recall","true_positives","false_positives","true_negatives","false_negatives"],this._defaultXRange=[-.05,1.05],this._defaultYRange=[-.05,1.05],this._requestData=(t,e,n)=>{const i=_r().pluginRoute("pr_curves","/pr_curves");Promise.all(t.map((t=>{const n=iO(i,{tag:this.tag,run:t});return this.requestManager.request(n).then((n=>{e({item:t,data:n})}))}))).finally((()=>{n()}))},this._smoothingEnabled=!1}_createProcessDataFunction(){return(t,e,n)=>{this.set("_runToDataOverTime",Object.assign({},this._runToDataOverTime,n))}}_computeRunColor(t){return GR(t)}connectedCallback(){super.connectedCallback(),this._attached=!0,this.reload()}_getChartDataLoader(){return this.shadowRoot.querySelector("tf-line-chart-data-loader")}reload(){this._attached&&(0!==this.runs.length?this._getChartDataLoader().reload():this.set("_runToDataOverTime",{}))}_setChartData(){var t=this._runToPrCurveEntry,e=this._previousRunToPrCurveEntry,n=this._setOfRelevantRuns;Se.exports.forOwn(t,((i,r)=>{const o=e[r];o&&t[r].step===o.step||(n[r]?this._updateSeriesDataForRun(r,i):this._clearSeriesData(r))}))}_updateSeriesDataForRun(t,e){const n=Se.exports.reduce(this._seriesDataFields,((t,n)=>(t[n]=e[n].slice().reverse(),t)),{}),i=new Array(n[this._seriesDataFields[0]].length);for(let t=0;t<i.length;t++)i[t]=Se.exports.mapValues(n,(e=>e[t]));const r=this._getChartDataLoader();r.setSeriesData(t,i),r.commitChanges()}_clearSeriesData(t){const e=this._getChartDataLoader();e.setSeriesData(t,[]),e.commitChanges()}_updateRunToPrCurveEntry(){var t=this.runToStepCap;const e={};Se.exports.forOwn(this._runToDataOverTime,((n,i)=>{n&&n.length&&(e[i]=this._computeEntryClosestOrEqualToStepCap(t[i],n))})),this.set("_previousRunToPrCurveEntry",this._runToPrCurveEntry),this.set("_runToPrCurveEntry",e)}_notifyDataChange(){this.onDataChange&&this.onDataChange(this._runToDataOverTime)}_computeEntryClosestOrEqualToStepCap(t,e){const n=Math.min(Se.exports.sortedIndex(e.map((t=>t.step)),t),e.length-1);return e[n]}get _runsWithStepAvailable(){var t=this._runToPrCurveEntry;return Se.exports.filter(this.runs,(e=>t[e])).sort()}get _setOfRelevantRuns(){const t={};return Se.exports.forEach(this._runsWithStepAvailable,(e=>{t[e]=!0})),t}_computeCurrentStepForRun(t,e){const n=t[e];return n?n.step:null}_computeCurrentWallTimeForRun(t,e){const n=t[e];return n?new Date(1e3*n.wall_time).toString():null}_toggleExpanded(t){this.set("_expanded",!this._expanded),this.redraw()}_resetDomain(){this._getChartDataLoader().resetDomain()}redraw(){this._getChartDataLoader().redraw()}};O7t.template=_e`
    <tf-card-heading
      tag="[[tag]]"
      display-name="[[tagMetadata.displayName]]"
      description="[[tagMetadata.description]]"
    ></tf-card-heading>

    <tf-line-chart-data-loader
      x-components-creation-method="[[_xComponentsCreationMethod]]"
      y-value-accessor="[[_yValueAccessor]]"
      tooltip-columns="[[_tooltipColumns]]"
      color-scale="[[_colorScaleFunction]]"
      default-x-range="[[_defaultXRange]]"
      default-y-range="[[_defaultYRange]]"
      smoothing-enabled="[[_smoothingEnabled]]"
      request-manager="[[requestManager]]"
      data-to-load="[[runs]]"
      data-series="[[runs]]"
      load-key="[[tag]]"
      request-data="[[_requestData]]"
      load-data-callback="[[_createProcessDataFunction()]]"
      active="[[active]]"
    ></tf-line-chart-data-loader>

    <div id="buttons-row">
      <paper-icon-button
        selected$="[[_expanded]]"
        icon="fullscreen"
        on-tap="_toggleExpanded"
      ></paper-icon-button>
      <paper-icon-button
        icon="settings-overscan"
        on-tap="_resetDomain"
        title="Reset axes to [0, 1]."
      ></paper-icon-button>
    </div>

    <div id="step-legend">
      <template is="dom-repeat" items="[[_runsWithStepAvailable]]" as="run">
        <div class="legend-row">
          <div
            class="color-box"
            style="background: [[_computeRunColor(run)]];"
          ></div>
          [[run]] is at
          <span class="step-label-text">
            step [[_computeCurrentStepForRun(_runToPrCurveEntry, run)]] </span
          ><br />
          <span class="wall-time-label-text">
            ([[_computeCurrentWallTimeForRun(_runToPrCurveEntry, run)]])
          </span>
        </div>
      </template>
    </div>

    <style>
      :host {
        display: flex;
        flex-direction: column;
        width: 500px;
        margin-right: 10px;
        margin-bottom: 25px;
      }
      :host([_expanded]) {
        width: 100%;
      }
      tf-line-chart-data-loader {
        height: 300px;
        position: relative;
      }
      :host([_expanded]) tf-line-chart-data-loader {
        height: 600px;
      }
      #buttons-row {
        display: flex;
        flex-direction: row;
      }
      #buttons-row paper-icon-button {
        color: #2196f3;
        border-radius: 100%;
        width: 32px;
        height: 32px;
        padding: 4px;
      }
      #buttons-row paper-icon-button[selected] {
        background: var(--tb-ui-light-accent);
      }
      #step-legend {
        box-sizing: border-box;
        font-size: 0.8em;
        max-height: 200px;
        overflow-y: auto;
        padding: 0 0 0 10px;
        width: 100%;
      }
      .legend-row {
        margin: 5px 0 5px 0;
        width: 100%;
      }
      .color-box {
        display: inline-block;
        border-radius: 1px;
        width: 10px;
        height: 10px;
      }
      .step-label-text {
        font-weight: bold;
      }
      .wall-time-label-text {
        color: #888;
        font-size: 0.8em;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],O7t.prototype,"runs",void 0),t([o({type:String}),e("design:type",String)],O7t.prototype,"tag",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"tagMetadata",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"runToStepCap",void 0),t([o({type:Object}),e("design:type",dr)],O7t.prototype,"requestManager",void 0),t([o({type:Boolean}),e("design:type",Boolean)],O7t.prototype,"active",void 0),t([o({type:Boolean,reflectToAttribute:!0}),e("design:type",Boolean)],O7t.prototype,"_expanded",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"_runToPrCurveEntry",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"_previousRunToPrCurveEntry",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"_runToDataOverTime",void 0),t([o({type:Object}),e("design:type",Function)],O7t.prototype,"onDataChange",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"_colorScaleFunction",void 0),t([o({type:Object}),e("design:type",XR)],O7t.prototype,"_canceller",void 0),t([o({type:Boolean}),e("design:type",Boolean)],O7t.prototype,"_attached",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"_xComponentsCreationMethod",void 0),t([o({type:Object}),e("design:type",Object)],O7t.prototype,"_yValueAccessor",void 0),t([o({type:Array}),e("design:type",Array)],O7t.prototype,"_tooltipColumns",void 0),t([o({type:Array}),e("design:type",Array)],O7t.prototype,"_seriesDataFields",void 0),t([o({type:Array}),e("design:type",Array)],O7t.prototype,"_defaultXRange",void 0),t([o({type:Array}),e("design:type",Array)],O7t.prototype,"_defaultYRange",void 0),t([o({type:Object}),e("design:type",Function)],O7t.prototype,"_requestData",void 0),t([o({type:Boolean}),e("design:type",Boolean)],O7t.prototype,"_smoothingEnabled",void 0),t([a("runs","tag"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],O7t.prototype,"reload",null),t([a("_runToPrCurveEntry","_previousRunToPrCurveEntry","_setOfRelevantRuns"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],O7t.prototype,"_setChartData",null),t([a("_runToDataOverTime","runToStepCap"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],O7t.prototype,"_updateRunToPrCurveEntry",null),t([a("_runToDataOverTime"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],O7t.prototype,"_notifyDataChange",null),t([s("runs","_runToPrCurveEntry"),e("design:type",Array),e("design:paramtypes",[])],O7t.prototype,"_runsWithStepAvailable",null),t([s("_runsWithStepAvailable"),e("design:type",Object),e("design:paramtypes",[])],O7t.prototype,"_setOfRelevantRuns",null),O7t=t([i("tf-pr-curve-card")],O7t);let z7t=class extends ye{constructor(){super(...arguments),this._runToStepIndex={}}_computeColorForRun(t){return GR(t)}_computeTimeTextForRun(t,e,n,i){const r=e[n];if(!Se.exports.isNumber(r))return"";const o=t[n];if(!o)return"";const a=o[r][i];if("step"===i)return`step ${a}`;if("relative"===i)return a<1?`${(1e3*a).toFixed(2)} ms`:`${a.toFixed(2)} s`;if("wall_time"===i)return new Date(1e3*a).toString();throw new Error(`The display type of ${i} is not recognized.`)}_sliderValueChanged(t){const e=t.target.dataset.run,n=t.target.immediateValue,i=Object.assign({},this._runToStepIndex);isNaN(n)?delete i[e]:i[e]=t.target.immediateValue,this._runToStepIndex=i}_computeMaxStepIndexForRun(t,e){const n=t[e];return n&&n.length?n.length-1:0}_updateStepsForNewRuns(){var t=this.runToAvailableTimeEntries;const e=Object.assign({},this._runToStepIndex);Se.exports.forOwn(t,((t,n)=>{Se.exports.isNumber(e[n])||(e[n]=t.length-1)})),this._runToStepIndex=e}_getStep(t,e){return this._runToStepIndex?this._runToStepIndex[e]:0}_computeRunToStep(t,e){const n={};return Se.exports.forOwn(e,((e,i)=>{const r=t[i];r&&(n[i]=r[e].step)})),n}get _runsWithSliders(){var t=this.runToAvailableTimeEntries;return this.runs.filter((e=>t[e]))}};z7t.template=_e`
    <template is="dom-repeat" items="[[_runsWithSliders]]" as="run">
      <div class="run-widget">
        <div class="run-display-container">
          <div
            class="run-color-box"
            style="background:[[_computeColorForRun(run)]];"
          ></div>
          <div class="run-text">[[run]]</div>
        </div>
        <div class="step-display-container">
          [[_computeTimeTextForRun(runToAvailableTimeEntries, _runToStepIndex,
          run, timeDisplayType)]]
        </div>
        <paper-slider
          data-run$="[[run]]"
          step="1"
          type="number"
          min="0"
          max="[[_computeMaxStepIndexForRun(runToAvailableTimeEntries, run)]]"
          value="[[_getStep(_runToStepIndex, run)]]"
          on-immediate-value-changed="_sliderValueChanged"
        ></paper-slider>
      </div>
    </template>
    <style>
      .run-widget {
        margin: 10px 0 0 0;
      }
      paper-slider {
        margin: -8px 0 0 -15px;
        width: 100%;
      }
      .step-display-container {
        font-size: 0.9em;
        margin: 0 15px 0 0;
      }
      .run-text {
        display: inline-block;
      }
      .run-color-box {
        width: 12px;
        height: 12px;
        border-radius: 3px;
        display: inline-block;
      }
    </style>
  `,t([o({type:Array}),e("design:type",Array)],z7t.prototype,"runs",void 0),t([o({type:Object}),e("design:type",Object)],z7t.prototype,"runToAvailableTimeEntries",void 0),t([o({type:Object,notify:!0,computed:"_computeRunToStep(runToAvailableTimeEntries, _runToStepIndex)"}),e("design:type",Object)],z7t.prototype,"runToStep",void 0),t([o({type:String}),e("design:type",String)],z7t.prototype,"timeDisplayType",void 0),t([o({type:Object}),e("design:type",Object)],z7t.prototype,"_runToStepIndex",void 0),t([a("runToAvailableTimeEntries"),e("design:type",Function),e("design:paramtypes",[]),e("design:returntype",void 0)],z7t.prototype,"_updateStepsForNewRuns",null),t([s("runs","runToAvailableTimeEntries"),e("design:type",Array),e("design:paramtypes",[])],z7t.prototype,"_runsWithSliders",null),z7t=t([i("tf-pr-curve-steps-selector")],z7t);let D7t=class extends(er(ye)){constructor(){super(...arguments),this.reloadOnReady=!0,this._timeDisplayType="step",this._selectedRuns=[],this._runToTagInfo={},this._tagToRunToData={},this._getCategoryItemKey=t=>t.tag,this._requestManager=new dr,this._step=0}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){Promise.all([this._fetchTags()]).then((()=>{this._reloadCards()}))}_shouldOpen(t){return t<=2}_fetchTags(){const t=_r().pluginRoute("pr_curves","/tags");return this._requestManager.request(t).then((t=>{if(Se.exports.isEqual(t,this._runToTagInfo))return;const e=ar(Se.exports.mapValues(t,(t=>Se.exports.keys(t))));this.set("_dataNotFound",0===e.length),this.set("_runToTagInfo",t),this.async((()=>{this.set("_categoriesDomReady",!0)}))}))}_reloadCards(){Se.exports.forEach(this.root.querySelectorAll("tf-pr-curve-card"),(t=>{t.reload()}))}get _categories(){var t=this._selectedRuns,e=this._tagFilter;return wr(Se.exports.mapValues(this._runToTagInfo,(t=>Object.keys(t))),t,e)}get _relevantSelectedRuns(){var t=this._runToTagInfo;return this._selectedRuns.filter((e=>t[e]))}_tagMetadata(t,e,n){const i={};e.forEach((e=>{i[e]=t[e][n]}));const r=n.replace(/\/pr_curves$/,"");return eO(i,r)}_createDataChangeCallback(t){return e=>{this.set("_tagToRunToData",Object.assign(Object.assign({},this._tagToRunToData),{[t]:e}))}}get _runToAvailableTimeEntries(){var t=this._tagToRunToData;const e={};for(const[n,i]of Object.entries(t))for(const[t]of Object.entries(i))(null==e[t]||n<e[t])&&(e[t]=n);const n={};for(const[i,r]of Object.entries(e)){const e=t[r][i];n[i]=e.map((t=>({step:t.step,wall_time:t.wall_time,relative:t.wall_time-e[0].wall_time})))}return n}};D7t.template=_e`
    <tf-dashboard-layout>
      <div class="sidebar" slot="sidebar">
        <div class="settings">
          <div class="sidebar-section">
            <tf-option-selector
              id="time-type-selector"
              name="Time Display Type"
              selected-id="{{_timeDisplayType}}"
            >
              <paper-button id="step">step</paper-button>
              <!--
            -->
              <paper-button id="relative">relative</paper-button>
              <!--
            -->
              <paper-button id="wall_time">wall</paper-button>
            </tf-option-selector>
          </div>
          <template is="dom-if" if="[[_runToAvailableTimeEntries]]">
            <div class="sidebar-section" id="steps-selector-container">
              <tf-pr-curve-steps-selector
                runs="[[_relevantSelectedRuns]]"
                run-to-step="{{_runToStep}}"
                run-to-available-time-entries="[[_runToAvailableTimeEntries]]"
                time-display-type="[[_timeDisplayType]]"
              >
              </tf-pr-curve-steps-selector>
            </div>
          </template>
        </div>
        <div class="sidebar-section runs-selector">
          <tf-runs-selector selected-runs="{{_selectedRuns}}">
          </tf-runs-selector>
        </div>
      </div>
      <div class="center" slot="center">
        <template is="dom-if" if="[[_dataNotFound]]">
          <div class="no-data-warning">
            <h3>No precision–recall curve data was found.</h3>
            <p>Probable causes:</p>
            <ul>
              <li>
                You haven’t written any precision–recall data to your event
                files.
              </li>
              <li>TensorBoard can’t find your event files.</li>
            </ul>
            <p>
              If you’re new to using TensorBoard, and want to find out how to
              add data and set up your event files, check out the
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md"
                >README</a
              >
              and perhaps the
              <a
                href="https://www.tensorflow.org/get_started/summaries_and_tensorboard"
                >TensorBoard tutorial</a
              >.
            </p>

            <p>
              If you think TensorBoard is configured properly, please see
              <a
                href="https://github.com/tensorflow/tensorboard/blob/master/README.md#my-tensorboard-isnt-showing-any-data-whats-wrong"
                >the section of the README devoted to missing data problems</a
              >
              and consider filing an issue on GitHub.
            </p>
          </div>
        </template>
        <template is="dom-if" if="[[!_dataNotFound]]">
          <tf-tag-filterer tag-filter="{{_tagFilter}}"></tf-tag-filterer>
          <template is="dom-repeat" items="[[_categories]]" as="category">
            <tf-category-paginated-view
              category="[[category]]"
              initial-opened="[[_shouldOpen(index)]]"
              get-category-item-key="[[_getCategoryItemKey]]"
            >
              <template>
                <tf-pr-curve-card
                  active="[[active]]"
                  runs="[[item.runs]]"
                  tag="[[item.tag]]"
                  tag-metadata="[[_tagMetadata(_runToTagInfo, item.runs, item.tag)]]"
                  request-manager="[[_requestManager]]"
                  run-to-step-cap="[[_runToStep]]"
                  on-data-change="[[_createDataChangeCallback(item.tag)]]"
                ></tf-pr-curve-card>
              </template>
            </tf-category-paginated-view>
          </template>
        </template>
      </div>
    </tf-dashboard-layout>

    <style include="dashboard-style"></style>
    <style>
      .no-data-warning {
        max-width: 540px;
        margin: 80px auto 0 auto;
      }

      /** Do not let the steps selector occlude the run selector. */
      #steps-selector-container {
        max-height: 60%;
        overflow-y: auto;
      }
    </style>
  `,t([o({type:Boolean}),e("design:type",Boolean)],D7t.prototype,"reloadOnReady",void 0),t([o({type:String}),e("design:type",String)],D7t.prototype,"_timeDisplayType",void 0),t([o({type:Array}),e("design:type",Array)],D7t.prototype,"_selectedRuns",void 0),t([o({type:Object}),e("design:type",Object)],D7t.prototype,"_runToTagInfo",void 0),t([o({type:Object}),e("design:type",Object)],D7t.prototype,"_tagToRunToData",void 0),t([o({type:Object,notify:!0}),e("design:type",Object)],D7t.prototype,"_runToStep",void 0),t([o({type:Boolean}),e("design:type",Boolean)],D7t.prototype,"_dataNotFound",void 0),t([o({type:String}),e("design:type",String)],D7t.prototype,"_tagFilter",void 0),t([o({type:Boolean}),e("design:type",Boolean)],D7t.prototype,"_categoriesDomReady",void 0),t([o({type:Object}),e("design:type",Object)],D7t.prototype,"_getCategoryItemKey",void 0),t([o({type:Object}),e("design:type",dr)],D7t.prototype,"_requestManager",void 0),t([o({type:Number,notify:!0}),e("design:type",Number)],D7t.prototype,"_step",void 0),t([s("_runToTagInfo","_selectedRuns","_tagFilter","_categoriesDomReady"),e("design:type",Array),e("design:paramtypes",[])],D7t.prototype,"_categories",null),t([s("_selectedRuns","_runToTagInfo"),e("design:type",Array),e("design:paramtypes",[])],D7t.prototype,"_relevantSelectedRuns",null),t([s("_tagToRunToData"),e("design:type",Object),e("design:paramtypes",[])],D7t.prototype,"_runToAvailableTimeEntries",null),D7t=t([i("tf-pr-curve-dashboard")],D7t);let B7t=class extends(er(ye)){constructor(){super(...arguments),this._installCommand="pip install -U tensorboard-plugin-profile"}_copyInstallCommand(){return n(this,void 0,void 0,(function*(){const t=()=>n(this,void 0,void 0,(function*(){this.$.commandTextarea.select();try{yield navigator.clipboard.writeText(this._installCommand)}catch(t){if(!document.execCommand("copy"))return Promise.reject()}})),e=this.$.copiedMessage;try{yield t(),e.innerText="Copied."}catch(t){e.innerText="Failed to copy to clipboard."}}))}_removeCopiedMessage(){this.$.copiedMessage.innerText=""}};B7t.template=_e`
    <div class="message">
      <h3>The profile plugin has moved.</h3>
      <p>
        Please install the new version of the profile plugin from PyPI by
        running the following command from the machine running TensorBoard:
      </p>
      <textarea
        id="commandTextarea"
        readonly=""
        rows="1"
        on-blur="_removeCopiedMessage"
      >
[[_installCommand]]</textarea
      >
      <div id="copyContainer">
        <span id="copiedMessage"></span>
        <paper-button raised="" on-tap="_copyInstallCommand"
          >Copy to clipboard</paper-button
        >
      </div>
    </div>

    <style>
      :host {
        display: flex;
      }

      .message {
        margin: 80px auto 0 auto;
        max-width: 540px;
      }
      #commandTextarea {
        margin-top: 1ex;
        padding: 1ex 1em;
        resize: vertical;
        width: 100%;
      }
      #copyContainer {
        display: flex;
      }
      #copiedMessage {
        align-self: center;
        flex-grow: 1;
        font-style: italic;
        padding-right: 1em;
        text-align: right;
      }
    </style>
  `,t([o({type:String}),e("design:type",String)],B7t.prototype,"_installCommand",void 0),B7t=t([i("tf-profile-redirect-dashboard")],B7t);let H7t=class extends(er(hl)){constructor(){super(...arguments),this.reloadOnReady=!0,this._showDownloadLinks=Hs("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}).call(this),this._smoothingWeight=Gs("_smoothingWeight",{defaultValue:.6}).call(this),this._ignoreYOutliers=Hs("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0}).call(this),this._xType=xTt.STEP,this._selectedRuns=[],this._tagFilter="",this._categories=[],this._getCategoryItemKey=t=>t.tag,this._requestManager=new dr(50),this._showDownloadLinksObserver=Fs("_showDownloadLinks",{defaultValue:!1,useLocalStorage:!0}),this._smoothingWeightObserver=Ws("_smoothingWeight",{defaultValue:.6}),this._ignoreYOutliersObserver=Fs("_ignoreYOutliers",{defaultValue:!0,useLocalStorage:!0})}get _smoothingEnabled(){return this._smoothingWeight>0}_getCategoryKey(t){return t.metadata.type==br.SEARCH_RESULTS?"":t.name}_shouldOpen(t){return t<=2}ready(){super.ready(),this.reloadOnReady&&this.reload()}reload(){this._fetchTags().then((()=>{this._reloadCharts()}))}_fetchTags(){const t=_r().pluginRoute("scalars","/tags");return this._requestManager.request(t).then((t=>{if(Se.exports.isEqual(t,this._runToTagInfo))return;const e=ar(Se.exports.mapValues(t,(t=>Object.keys(t))));this.set("_dataNotFound",0===e.length),this.set("_runToTagInfo",t),this.async((()=>{this.set("_categoriesDomReady",!0)}))}))}_reloadCharts(){this.root.querySelectorAll("tf-scalar-card").forEach((t=>{t.reload()}))}_updateCategories(){var t=this._selectedRuns;let e,n=this._tagFilter;e=wr(Se.exports.mapValues(this._runToTagInfo,(t=>Object.keys(t))),t,n),e.forEach((t=>{t.items=t.items.map((t=>({tag:t.tag,series:t.runs.map((e=>({run:e,tag:t.tag})))})))})),this.updateArrayProp("_categories",e,this._getCategoryKey)}_tagMetadata(t,e,n){const i=t.name,r=n.tag,o={};n.series.forEach((({run:t})=>{o[t]=e[t][r]}));const a=r.replace(/\/scalar_summary$/,"");let{description:s,displayName:l}=eO(o,a);return t.metadata.type==br.PREFIX_GROUP&&l.startsWith(i+"/")&&(l=l.slice(i.length+1)),{description:s,displayName:l}}};H7t.template=_e`
    <tf-dashboard-layout>
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            <div class="line-item">
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                checked="{{_ignoreYOutliers}}"
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              >
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                  smoothing-enabled="[[_smoothingEnabled]]"
                  smoothing-weight="[[_smoothingWeight]]"
                  tag-metadata="[[_tagMetadata(category, _runToTagInfo, item)]]"
                  tag="[[item.tag]]"
                  tooltip-sorting-method="[[_tooltipSortingMethod]]"
                  x-type="[[_xType]]"
                  batch-size="[[featureFlags.scalarsBatchSize]]"
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                ></tf-scalar-card>
              </template>
            </tf-category-paginated-view>
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        </template>
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        margin-top: 15px;
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    </style>
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    </tf-card-heading>
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      elevation="1"
      id="steps-container"
      class="container scrollbar"
    >
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        </paper-material>
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    <style include="scrollbar-style"></style>
    <style>
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        display: flex;
        flex-direction: column;
        width: 100%;
        height: auto;
        margin-right: 10px;
        margin-bottom: 15px;
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        will-change: transform;
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      #steps-container {
        border-radius: 3px;
        border: 2px solid /* color computed and set as inline style */;
        display: block;
        max-height: 500px;
        overflow: auto;
        padding: 10px;
        border-color: var(--tb-text-loader-outline);
      }
      .text {
        background-color: inherit;
        border-radius: 0 3px 3px 3px;
        padding: 5px;
        word-break: break-word;
      }
      .step-container {
        background-color: var(--tb-ui-light-accent);
        border-bottom: none;
        border-radius: 3px 3px 0 0;
        border: 1px solid var(--tb-ui-border);
        display: inline-block;
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        font-style: italic;
        margin-left: -1px; /* to correct for border */
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      }
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        margin-top: 15px;
      }

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          <tf-runs-selector selected-runs="{{_selectedRuns}}">
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            <p>Probable causes:</p>
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            <p>
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                  run="[[item.run]]"
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!(function(){
/*! *****************************************************************************
    Copyright (c) Microsoft Corporation.

    Permission to use, copy, modify, and/or distribute this software for any
    purpose with or without fee is hereby granted.

    THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
    REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
    AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
    INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
    LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
    OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
    PERFORMANCE OF THIS SOFTWARE.
    ***************************************************************************** */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const ea=-1;class na{constructor(t,e,n){this.factory=t,this.resolving=!1,ngDevMode&&hn(t,"Factory not specified"),ngDevMode&&ln(typeof t,"function","Expected factory function."),this.canSeeViewProviders=e,this.injectImpl=n}}function oa(t){let e="";return 1&t&&(e+="|Text"),2&t&&(e+="|Element"),4&t&&(e+="|Container"),8&t&&(e+="|ElementContainer"),16&t&&(e+="|Projection"),32&t&&(e+="|IcuContainer"),64&t&&(e+="|Placeholder"),e.length>0?e.substring(1):e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function ia(t,e,n){hn(t,"should be called with a TNode"),0==(t.type&e)&&bn(n||`Expected [${oa(e)}] but got ${oa(t.type)}.`)}function aa(t,e,n){const o=$o(t);let i=0;for(;i<n.length;){const a=n[i];if("number"==typeof a){if(0!==a)break;i++;const r=n[i++],s=n[i++],l=n[i++];ngDevMode&&ngDevMode.rendererSetAttribute++,o?t.setAttribute(e,s,l,r):e.setAttributeNS(r,s,l)}else{const r=a,s=n[++i];ngDevMode&&ngDevMode.rendererSetAttribute++,sa(r)?o&&t.setProperty(e,r,s):o?t.setAttribute(e,r,s):e.setAttribute(r,s),i++}}return i}function ra(t){return 3===t||4===t||6===t}function sa(t){return 64===t.charCodeAt(0)}function la(t,e){if(null===e||0===e.length);else if(null===t||0===t.length)t=e.slice();else{let n=-1;for(let o=0;o<e.length;o++){const i=e[o];"number"==typeof i?n=i:0===n||ca(t,n,i,null,-1===n||2===n?e[++o]:null)}}return t}function ca(t,e,n,o,i){let a=0,r=t.length;if(-1===e)r=-1;else for(;a<t.length;){const n=t[a++];if("number"==typeof n){if(n===e){r=-1;break}if(n>e){r=a-1;break}}}for(;a<t.length;){const e=t[a];if("number"==typeof e)break;if(e===n){if(null===o)return void(null!==i&&(t[a+1]=i));if(o===t[a+1])return void(t[a+2]=i)}a++,null!==o&&a++,null!==i&&a++}-1!==r&&(t.splice(r,0,e),a=r+1),t.splice(a++,0,n),null!==o&&t.splice(a++,0,o),null!==i&&t.splice(a++,0,i)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function da(t){return t!==ea}function pa(t){return ngDevMode&&an(t,"Number expected"),ngDevMode&&cn(t,-1,"Not a valid state."),ngDevMode&&fn(32767&t,go,"Parent injector must be pointing past HEADER_OFFSET."),32767&t}function ma(t,e){let n=(function o(t){return t>>16})(t),i=e;for(;n>0;)i=i[15],n--;return i}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let ua=!0;function fa(t){const e=ua;return ua=t,e}let ga=0;function ha(t,e){const n=ya(t,e);if(-1!==n)return n;const o=e[1];o.firstCreatePass&&(t.injectorIndex=e.length,ba(o.data,t),ba(e,null),ba(o.blueprint,null));const i=_a(t,e),a=t.injectorIndex;if(da(i)){const t=pa(i),n=ma(i,e),o=n[1].data;for(let i=0;i<8;i++)e[a+i]=n[t+i]|o[t+i]}return e[a+8]=i,a}function ba(t,e){t.push(0,0,0,0,0,0,0,0,e)}function ya(t,e){return-1===t.injectorIndex||t.parent&&t.parent.injectorIndex===t.injectorIndex||null===e[t.injectorIndex+8]?-1:(ngDevMode&&_n(e,t.injectorIndex),t.injectorIndex)}function _a(t,e){if(t.parent&&-1!==t.parent.injectorIndex)return t.parent.injectorIndex;let n=0,o=null,i=e;for(;null!==i;){const t=i[1],e=t.type;if(2===e?(ngDevMode&&hn(t.declTNode,"Embedded TNodes should have declaration parents."),o=t.declTNode):1===e?o=i[6]:(ngDevMode&&ln(t.type,0,"Root type expected"),o=null),null===o)return ea;if(ngDevMode&&o&&Oo(o,i[15]),n++,i=i[15],-1!==o.injectorIndex)return o.injectorIndex|n<<16}return ea}function Ca(t,e,n){!(function o(t,e,n){let o;ngDevMode&&ln(e.firstCreatePass,!0,"expected firstCreatePass to be true"),"string"==typeof n?o=n.charCodeAt(0)||0:n.hasOwnProperty(Qn)&&(o=n[Qn]),null==o&&(o=n[Qn]=ga++);const i=255&o;e.data[t+(i>>5)]|=1<<i})(t,e,n)}function Ma(t,e,n){if(n&En.Optional)return t;on(e,"NodeInjector")}function va(t,e,n,o){if(n&En.Optional&&void 0===o&&(o=null),0==(n&(En.Self|En.Host))){const i=t[9],a=An(void 0);try{return i?i.get(e,o,n&En.Optional):Tn(e,o,n&En.Optional)}finally{An(a)}}return Ma(o,e,n)}function xa(t,e,n,o=En.Default,i){if(null!==t){const a=(function a(t){if(ngDevMode&&hn(t,"token must be defined"),"string"==typeof t)return t.charCodeAt(0)||0;const e=t.hasOwnProperty(Qn)?t[Qn]:void 0;return"number"==typeof e?e>=0?255&e:(ngDevMode&&ln(e,-1,"Expecting to get Special Injector Id"),Pa):e})(n);if("function"==typeof a){if(!Hi(e,t,o))return o&En.Host?Ma(i,n,o):va(e,n,o,i);try{const t=a(o);if(null!=t||o&En.Optional)return t;on(n)}finally{ji()}}else if("number"==typeof a){let i=null,r=ya(t,e),s=ea,l=o&En.Host?e[16][6]:null;for((-1===r||o&En.SkipSelf)&&(s=-1===r?_a(t,e):e[r+8],s!==ea&&Ea(o,!1)?(i=e[1],r=pa(s),e=ma(s,e)):r=-1);-1!==r;){ngDevMode&&Ho(e,r);const t=e[1];if(ngDevMode&&Oo(t.data[r+8],e),Da(a,r,t.data)){const t=wa(r,e,n,i,o,l);if(t!==Oa)return t}s=e[r+8],s!==ea&&Ea(o,e[1].data[r+8]===l)&&Da(a,r,e)?(i=t,r=pa(s),e=ma(s,e)):r=-1}}}return va(e,n,o,i)}const Oa={};function Pa(){return new Ra(bi(),fi())}function wa(t,e,n,o,i,a){const r=e[1],s=r.data[t+8],l=ka(s,r,n,null==o?Mo(s)&&ua:o!=r&&0!=(3&s.type),i&En.Host&&a===s);return null!==l?Sa(e,r,l,s):Oa}function ka(t,e,n,o,i){const a=t.providerIndexes,r=e.data,s=1048575&a,l=t.directiveStart,c=a>>20,d=i?s+c:t.directiveEnd;for(let t=o?s:s+c;t<d;t++){const e=r[t];if(t<l&&n===e||t>=l&&e.type===n)return t}if(i){const t=r[l];if(t&&xo(t)&&t.type===n)return l}return null}function Sa(t,e,n,o){let i=t[n];const a=e.data;if((function r(t){return t instanceof na})(i)){const r=i;r.resolving&&en(tn(a[n]));const s=fa(r.canSeeViewProviders);r.resolving=!0;const l=r.injectImpl?An(r.injectImpl):null,c=Hi(t,o,En.Default);ngDevMode&&ln(c,!0,"Because flags do not contain `SkipSelf' we expect this to always succeed.");try{i=t[n]=r.factory(void 0,a,t,o),e.firstCreatePass&&n>=o.directiveStart&&(ngDevMode&&(function s(t){void 0!==t.type&&null!=t.selectors&&void 0!==t.inputs||bn("Expected a DirectiveDef/ComponentDef and this object does not seem to have the expected shape.")})(a[n]),(
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function l(t,e,n){ngDevMode&&Ao(n);const{ngOnChanges:o,ngOnInit:i,ngDoCheck:a}=e.type.prototype;if(o){const o=Vo(e);(n.preOrderHooks||(n.preOrderHooks=[])).push(t,o),(n.preOrderCheckHooks||(n.preOrderCheckHooks=[])).push(t,o)}i&&(n.preOrderHooks||(n.preOrderHooks=[])).push(0-t,i),a&&((n.preOrderHooks||(n.preOrderHooks=[])).push(t,a),(n.preOrderCheckHooks||(n.preOrderCheckHooks=[])).push(t,a))})(n,a[n],e))}finally{null!==l&&An(l),fa(s),r.resolving=!1,ji()}}return i}function Da(t,e,n){return!!(n[e+(t>>5)]&1<<t)}function Ea(t,e){return!(t&En.Self||t&En.Host&&e)}class Ra{constructor(t,e){this._tNode=t,this._lView=e}get(t,e){return xa(this._tNode,this._lView,t,void 0,e)}}function Aa(t){return Nn((()=>{const e=t.prototype.constructor,n=e[Jn]||Ta(e),o=Object.prototype;let i=Object.getPrototypeOf(t.prototype).constructor;for(;i&&i!==o;){const t=i[Jn]||Ta(i);if(t&&t!==n)return t;i=Object.getPrototypeOf(i)}return t=>new t}))}function Ta(t){return Xe(t)?()=>{const e=Ta(Ze(t));return e&&e()}:Fo(t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Na(t){return(function e(t,n){if(ngDevMode&&ia(t,15),ngDevMode&&hn(t,"expecting tNode"),"class"===n)return t.classes;if("style"===n)return t.styles;const o=t.attrs;if(o){const t=o.length;let e=0;for(;e<t;){const i=o[e];if(ra(i))break;if(0===i)e+=2;else if("number"==typeof i)for(e++;e<t&&"string"==typeof o[e];)e++;else{if(i===n)return o[e+1];e+=2}}}return null})(bi(),t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const za="__annotations__",Ia="__parameters__",Ha="__prop__metadata__";function Fa(t,e,n,o,i){return Nn((()=>{const a=La(e);function r(...t){if(this instanceof r)return a.call(this,...t),this;const e=new r(...t);return function n(a){return i&&i(a,...t),(a.hasOwnProperty(za)?a[za]:Object.defineProperty(a,za,{value:[]})[za]).push(e),o&&o(a),a}}return n&&(r.prototype=Object.create(n.prototype)),r.prototype.ngMetadataName=t,r.annotationCls=r,r}))}function La(t){return function e(...n){if(t){const e=t(...n);for(const t in e)this[t]=e[t]}}}function Ba(t,e,n){return Nn((()=>{const o=La(e);function i(...t){if(this instanceof i)return o.apply(this,t),this;const e=new i(...t);return n.annotation=e,n;function n(t,n,o){const i=t.hasOwnProperty(Ia)?t[Ia]:Object.defineProperty(t,Ia,{value:[]})[Ia];for(;i.length<=o;)i.push(null);return(i[o]=i[o]||[]).push(e),t}}return n&&(i.prototype=Object.create(n.prototype)),i.prototype.ngMetadataName=t,i.annotationCls=i,i}))}function Va(t,e,n,o){return Nn((()=>{const i=La(e);function a(...t){if(this instanceof a)return i.apply(this,t),this;const e=new a(...t);return function n(i,a){const r=i.constructor,s=r.hasOwnProperty(Ha)?r[Ha]:Object.defineProperty(r,Ha,{value:{}})[Ha];s[a]=s.hasOwnProperty(a)&&s[a]||[],s[a].unshift(e),o&&o(i,a,...t)}}return n&&(a.prototype=Object.create(n.prototype)),a.prototype.ngMetadataName=t,a.annotationCls=a,a}))}const ja=(function Ua(){return Ba("Attribute",(t=>({attributeName:t,__NG_ELEMENT_ID__:()=>Na(t)})))})();
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Ga{constructor(t,e){this._desc=t,this.ngMetadataName="InjectionToken",this.ɵprov=void 0,"number"==typeof e?(("undefined"==typeof ngDevMode||ngDevMode)&&mn(e,0,"Only negative numbers are supported here"),this.__NG_ELEMENT_ID__=e):void 0!==e&&(this.ɵprov=Mn({token:this,providedIn:e.providedIn||"root",factory:e.factory}))}toString(){return`InjectionToken ${this._desc}`}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */new Ga("AnalyzeForEntryComponents");class Wa{}const Ya=Va("ContentChildren",((t,e={})=>Object.assign({selector:t,first:!1,isViewQuery:!1,descendants:!1,emitDistinctChangesOnly:!0},e)),Wa),qa=Va("ContentChild",((t,e={})=>Object.assign({selector:t,first:!0,isViewQuery:!1,descendants:!0},e)),Wa);Va("ViewChildren",((t,e={})=>Object.assign({selector:t,first:!1,isViewQuery:!0,descendants:!0,emitDistinctChangesOnly:!0},e)),Wa);const Za=Va("ViewChild",((t,e)=>Object.assign({selector:t,first:!0,isViewQuery:!0,descendants:!0},e)),Wa);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
var Xa,Ka;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function Ja(t){const e=jn.ng;if(e&&e.ɵcompilerFacade)return e.ɵcompilerFacade;if("undefined"==typeof ngDevMode||ngDevMode){console.error(`JIT compilation failed for ${t.kind}`,t.type);let e=`The ${t.kind} '${t.type.name}' needs to be compiled using the JIT compiler, but '@angular/compiler' is not available.\n\n`;throw 1===t.usage?(e+=`The ${t.kind} is part of a library that has been partially compiled.\n`,e+="However, the Angular Linker has not processed the library such that JIT compilation is used as fallback.\n",e+="\n",e+="Ideally, the library is processed using the Angular Linker to become fully AOT compiled.\n"):e+="JIT compilation is discouraged for production use-cases! Consider using AOT mode instead.\n",e+="Alternatively, the JIT compiler should be loaded by bootstrapping using '@angular/platform-browser-dynamic' or '@angular/platform-server',\n",e+="or manually provide the compiler with 'import \"@angular/compiler\";' before bootstrapping.",new Error(e)}throw new Error("JIT compiler unavailable")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t,e,1)}const lr=/^function\s+\S+\(\)\s*{[\s\S]+\.apply\(this,\s*(arguments|(?:[^()]+\(\[\],)?[^()]+\(arguments\))\)/,cr=/^class\s+[A-Za-z\d$_]*\s*extends\s+[^{]+{/,dr=/^class\s+[A-Za-z\d$_]*\s*extends\s+[^{]+{[\s\S]*constructor\s*\(/,pr=/^class\s+[A-Za-z\d$_]*\s*extends\s+[^{]+{[\s\S]*constructor\s*\(\)\s*{\s*super\(\.\.\.arguments\)/;class mr{constructor(t){this._reflect=t||jn.Reflect}isReflectionEnabled(){return!0}factory(t){return(...e)=>new t(...e)}_zipTypesAndAnnotations(t,e){let n;n=ir(void 0===t?e.length:t.length);for(let o=0;o<n.length;o++)n[o]=void 0===t?[]:t[o]&&t[o]!=Object?[t[o]]:[],e&&null!=e[o]&&(n[o]=n[o].concat(e[o]));return n}_ownParameters(t,e){if((function n(t){return lr.test(t)||pr.test(t)||cr.test(t)&&!dr.test(t)})(t.toString()))return null;if(t.parameters&&t.parameters!==e.parameters)return t.parameters;const o=t.ctorParameters;if(o&&o!==e.ctorParameters){const t="function"==typeof o?o():o,e=t.map((t=>t&&t.type)),n=t.map((t=>t&&ur(t.decorators)));return this._zipTypesAndAnnotations(e,n)}const i=t.hasOwnProperty(Ia)&&t[Ia],a=this._reflect&&this._reflect.getOwnMetadata&&this._reflect.getOwnMetadata("design:paramtypes",t);return a||i?this._zipTypesAndAnnotations(a,i):ir(t.length)}parameters(t){if(!$a(t))return[];const e=fr(t);let n=this._ownParameters(t,e);return n||e===Object||(n=this.parameters(e)),n||[]}_ownAnnotations(t,e){if(t.annotations&&t.annotations!==e.annotations){let e=t.annotations;return"function"==typeof e&&e.annotations&&(e=e.annotations),e}return t.decorators&&t.decorators!==e.decorators?ur(t.decorators):t.hasOwnProperty(za)?t[za]:null}annotations(t){if(!$a(t))return[];const e=fr(t),n=this._ownAnnotations(t,e)||[];return(e!==Object?this.annotations(e):[]).concat(n)}_ownPropMetadata(t,e){if(t.propMetadata&&t.propMetadata!==e.propMetadata){let e=t.propMetadata;return"function"==typeof e&&e.propMetadata&&(e=e.propMetadata),e}if(t.propDecorators&&t.propDecorators!==e.propDecorators){const e=t.propDecorators,n={};return Object.keys(e).forEach((t=>{n[t]=ur(e[t])})),n}return t.hasOwnProperty(Ha)?t[Ha]:null}propMetadata(t){if(!$a(t))return{};const e=fr(t),n={};if(e!==Object){const t=this.propMetadata(e);Object.keys(t).forEach((e=>{n[e]=t[e]}))}const o=this._ownPropMetadata(t,e);return o&&Object.keys(o).forEach((t=>{const e=[];n.hasOwnProperty(t)&&e.push(...n[t]),e.push(...o[t]),n[t]=e})),n}ownPropMetadata(t){return $a(t)&&this._ownPropMetadata(t,fr(t))||{}}hasLifecycleHook(t,e){return t instanceof Qa&&e in t.prototype}guards(t){return{}}getter(t){return new Function("o","return o."+t+";")}setter(t){return new Function("o","v","return o."+t+" = v;")}method(t){return new Function("o","args",`if (!o.${t}) throw new Error('"${t}" is undefined');\n        return o.${t}.apply(o, args);`)}importUri(t){return"object"==typeof t&&t.filePath?t.filePath:`./${Ge(t)}`}resourceUri(t){return`./${Ge(t)}`}resolveIdentifier(t,e,n,o){return o}resolveEnum(t,e){return t[e]}}function ur(t){return t?t.map((t=>new(0,t.type.annotationCls)(...t.args?t.args:[]))):[]}function fr(t){const e=t.prototype?Object.getPrototypeOf(t.prototype):null;return(e?e.constructor:null)||Object}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const gr={},hr=/\n/gm,br="__source",yr=je({provide:String,useValue:je});let _r;function Cr(t){const e=_r;return _r=t,e}function Mr(t,e=En.Default){if(void 0===_r)throw new Error("inject() must be called from an injection context");return null===_r?Tn(t,void 0,e):_r.get(t,e&En.Optional?null:void 0,e)}function vr(t,e=En.Default){return((function n(){return Rn})()||Mr)(Ze(t),e)}function xr(t){const e=ngDevMode?`This constructor is not compatible with Angular Dependency Injection because its dependency at index ${t} of the parameter list is invalid.\nThis can happen if the dependency type is a primitive like a string or if an ancestor of this class is missing an Angular decorator.\n\nPlease check that 1) the type for the parameter at index ${t} is correct and 2) the correct Angular decorators are defined for this class and its ancestors.`:"invalid";throw new Error(e)}const Or=vr;function Pr(t){const e=[];for(let n=0;n<t.length;n++){const o=Ze(t[n]);if(Array.isArray(o)){if(0===o.length)throw new Error("Arguments array must have arguments.");let t,n=En.Default;for(let e=0;e<o.length;e++){const i=o[e],a=i.__NG_DI_FLAG__;"number"==typeof a?-1===a?t=i.token:n|=a:t=i}e.push(vr(t,n))}else e.push(vr(o))}return e}function wr(t,e){return t.__NG_DI_FLAG__=e,t.prototype.__NG_DI_FLAG__=e,t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const kr=wr(Ba("Inject",(t=>({token:t}))),-1),Sr=wr(Ba("Optional"),8),Dr=wr(Ba("Self"),2),Er=wr(Ba("SkipSelf"),4),Rr=wr(Ba("Host"),1);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let Ar=null;function Tr(){return Ar=Ar||new mr}function Nr(t){return zr(Tr().parameters(t))}function zr(t){return t.map((t=>(function e(t){const e={token:null,attribute:null,host:!1,optional:!1,self:!1,skipSelf:!1};if(Array.isArray(t)&&t.length>0)for(let n=0;n<t.length;n++){const o=t[n];if(void 0===o)continue;const i=Object.getPrototypeOf(o);if(o instanceof Sr||"Optional"===i.ngMetadataName)e.optional=!0;else if(o instanceof Er||"SkipSelf"===i.ngMetadataName)e.skipSelf=!0;else if(o instanceof Dr||"Self"===i.ngMetadataName)e.self=!0;else if(o instanceof Rr||"Host"===i.ngMetadataName)e.host=!0;else if(o instanceof kr)e.token=o.token;else if(o instanceof ja){if(void 0===o.attributeName)throw new Error("Attribute name must be defined.");e.attribute=o.attributeName}else e.token=o}else e.token=void 0===t||Array.isArray(t)&&0===t.length?null:t;return e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let Br,Vr;function jr(){if(void 0===Br&&(Br=null,jn.trustedTypes))try{Br=jn.trustedTypes.createPolicy("angular",{createHTML:t=>t,createScript:t=>t,createScriptURL:t=>t})}catch(t){}return Br}function Ur(t){var e;return(null===(e=jr())||void 0===e?void 0:e.createHTML(t))||t}function Gr(){if(void 0===Vr&&(Vr=null,jn.trustedTypes))try{Vr=jn.trustedTypes.createPolicy("angular#unsafe-bypass",{createHTML:t=>t,createScript:t=>t,createScriptURL:t=>t})}catch(t){}return Vr}function Wr(t){var e;return(null===(e=Gr())||void 0===e?void 0:e.createHTML(t))||t}function Yr(t){var e;return(null===(e=Gr())||void 0===e?void 0:e.createScript(t))||t}function qr(t){var e;return(null===(e=Gr())||void 0===e?void 0:e.createScriptURL(t))||t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function os(t){const e=new as(t);return(function n(){try{return!!(new window.DOMParser).parseFromString(Ur(""),"text/html")}catch(t){return!1}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function ds(t){const e={};for(const n of t.split(","))e[n]=!0;return e}function ps(...t){const e={};for(const n of t)for(const t in n)n.hasOwnProperty(t)&&(e[t]=!0);return e}const ms=ds("area,br,col,hr,img,wbr"),us=ds("colgroup,dd,dt,li,p,tbody,td,tfoot,th,thead,tr"),fs=ds("rp,rt"),gs=ps(fs,us),hs=ps(us,ds("address,article,aside,blockquote,caption,center,del,details,dialog,dir,div,dl,figure,figcaption,footer,h1,h2,h3,h4,h5,h6,header,hgroup,hr,ins,main,map,menu,nav,ol,pre,section,summary,table,ul")),bs=ps(fs,ds("a,abbr,acronym,audio,b,bdi,bdo,big,br,cite,code,del,dfn,em,font,i,img,ins,kbd,label,map,mark,picture,q,ruby,rp,rt,s,samp,small,source,span,strike,strong,sub,sup,time,track,tt,u,var,video")),ys=ps(ms,hs,bs,gs),_s=ds("background,cite,href,itemtype,longdesc,poster,src,xlink:href"),Cs=ds("srcset"),Ms=ds("abbr,accesskey,align,alt,autoplay,axis,bgcolor,border,cellpadding,cellspacing,class,clear,color,cols,colspan,compact,controls,coords,datetime,default,dir,download,face,headers,height,hidden,hreflang,hspace,ismap,itemscope,itemprop,kind,label,lang,language,loop,media,muted,nohref,nowrap,open,preload,rel,rev,role,rows,rowspan,rules,scope,scrolling,shape,size,sizes,span,srclang,start,summary,tabindex,target,title,translate,type,usemap,valign,value,vspace,width"),vs=ds("aria-activedescendant,aria-atomic,aria-autocomplete,aria-busy,aria-checked,aria-colcount,aria-colindex,aria-colspan,aria-controls,aria-current,aria-describedby,aria-details,aria-disabled,aria-dropeffect,aria-errormessage,aria-expanded,aria-flowto,aria-grabbed,aria-haspopup,aria-hidden,aria-invalid,aria-keyshortcuts,aria-label,aria-labelledby,aria-level,aria-live,aria-modal,aria-multiline,aria-multiselectable,aria-orientation,aria-owns,aria-placeholder,aria-posinset,aria-pressed,aria-readonly,aria-relevant,aria-required,aria-roledescription,aria-rowcount,aria-rowindex,aria-rowspan,aria-selected,aria-setsize,aria-sort,aria-valuemax,aria-valuemin,aria-valuenow,aria-valuetext"),xs=ps(_s,Cs,Ms,vs),Os=ds("script,style,template");class Ps{constructor(){this.sanitizedSomething=!1,this.buf=[]}sanitizeChildren(t){let e=t.firstChild,n=!0;for(;e;)if(e.nodeType===Node.ELEMENT_NODE?n=this.startElement(e):e.nodeType===Node.TEXT_NODE?this.chars(e.nodeValue):this.sanitizedSomething=!0,n&&e.firstChild)e=e.firstChild;else for(;e;){e.nodeType===Node.ELEMENT_NODE&&this.endElement(e);let t=this.checkClobberedElement(e,e.nextSibling);if(t){e=t;break}e=this.checkClobberedElement(e,e.parentNode)}return this.buf.join("")}startElement(t){const e=t.nodeName.toLowerCase();if(!ys.hasOwnProperty(e))return this.sanitizedSomething=!0,!Os.hasOwnProperty(e);this.buf.push("<"),this.buf.push(e);const n=t.attributes;for(let t=0;t<n.length;t++){const e=n.item(t),o=e.name,i=o.toLowerCase();if(!xs.hasOwnProperty(i)){this.sanitizedSomething=!0;continue}let a=e.value;_s[i]&&(a=ls(a)),Cs[i]&&(a=cs(a)),this.buf.push(" ",o,'="',Ss(a),'"')}return this.buf.push(">"),!0}endElement(t){const e=t.nodeName.toLowerCase();ys.hasOwnProperty(e)&&!ms.hasOwnProperty(e)&&(this.buf.push("</"),this.buf.push(e),this.buf.push(">"))}chars(t){this.buf.push(Ss(t))}checkClobberedElement(t,e){if(e&&(t.compareDocumentPosition(e)&Node.DOCUMENT_POSITION_CONTAINED_BY)===Node.DOCUMENT_POSITION_CONTAINED_BY)throw new Error(`Failed to sanitize html because the element is clobbered: ${t.outerHTML}`);return e}}const ws=/[\uD800-\uDBFF][\uDC00-\uDFFF]/g,ks=/([^\#-~ |!])/g;function Ss(t){return t.replace(/&/g,"&amp;").replace(ws,(function(t){return"&#"+(1024*(t.charCodeAt(0)-55296)+(t.charCodeAt(1)-56320)+65536)+";"})).replace(ks,(function(t){return"&#"+t.charCodeAt(0)+";"})).replace(/</g,"&lt;").replace(/>/g,"&gt;")}let Ds;function Es(t,e){let n=null;try{Ds=Ds||os(t);let o=e?String(e):"";n=Ds.getInertBodyElement(o);let i=5,a=o;do{if(0===i)throw new Error("Failed to sanitize html because the input is unstable");i--,o=a,a=n.innerHTML,n=Ds.getInertBodyElement(o)}while(o!==a);const r=new Ps,s=r.sanitizeChildren(Rs(n)||n);return("undefined"==typeof ngDevMode||ngDevMode)&&r.sanitizedSomething&&console.warn("WARNING: sanitizing HTML stripped some content, see https://g.co/ng/security#xss"),Ur(s)}finally{if(n){const t=Rs(n)||n;for(;t.firstChild;)t.removeChild(t.firstChild)}}}function Rs(t){return"content"in t&&(function e(t){return t.nodeType===Node.ELEMENT_NODE&&"TEMPLATE"===t.nodeName}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t)?t.content:null}var As;function Ts(t){const e=zs();return e?e.sanitize(As.URL,t)||"":es(t,"URL")?ts(t):ls($e(t))}function Ns(t){const e=zs();if(e)return qr(e.sanitize(As.RESOURCE_URL,t)||"");if(es(t,"ResourceURL"))return qr(ts(t));throw new Error("unsafe value used in a resource URL context (see https://g.co/ng/security#xss)")}function zs(){const t=fi();return t&&t[12]}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Is(t){let e=Bs(t);if(e){if(Array.isArray(e)){const n=e;let o,i,a;if((function n(t){return t&&t.constructor&&t.constructor.ɵcmp})(t)){if(o=Gs(n,t),-1==o)throw new Error("The provided component was not found in the application");i=t}else if((function o(t){return t&&t.constructor&&t.constructor.ɵdir})(t)){if(o=(function i(t,e){let n=t[1].firstChild;for(;n;){const o=n.directiveEnd;for(let i=n.directiveStart;i<o;i++)if(t[i]===e)return n.index;n=Us(n)}return-1})(n,t),-1==o)throw new Error("The provided directive was not found in the application");a=Ws(o,n,!1)}else if(o=js(n,t),-1==o)return null;const r=ei(n[o]),s=Bs(r),l=s&&!Array.isArray(s)?s:Hs(n,o,r);if(i&&void 0===l.component&&(l.component=i,Ls(l.component,l)),a&&void 0===l.directives){l.directives=a;for(let t=0;t<a.length;t++)Ls(a[t],l)}Ls(l.native,l),e=l}}else{const n=t;ngDevMode&&yn(n);let o=n;for(;o=o.parentNode;){const t=Bs(o);if(t){let o;if(o=Array.isArray(t)?t:t.lView,!o)return null;const i=js(o,n);if(i>=0){const t=ei(o[i]),n=Hs(o,i,t);Ls(t,n),e=n;break}}}}return e||null}function Hs(t,e,n){return{lView:t,nodeIndex:e,native:n,component:void 0,directives:void 0,localRefs:void 0}}function Fs(t){let e,n=Bs(t);if(Array.isArray(n)){const o=Gs(n,t);e=ri(o,n);const i=Hs(n,o,e[0]);i.component=t,Ls(t,i),Ls(i.native,i)}else e=ri(n.nodeIndex,n.lView);return e}function Ls(t,e){ngDevMode&&hn(t,"Target expected"),t.__ngContext__=e}function Bs(t){return ngDevMode&&hn(t,"Target expected"),t.__ngContext__||null}function Vs(t){const e=Bs(t);return e?Array.isArray(e)?e:e.lView:null}function js(t,e){const n=t[1];for(let o=go;o<n.bindingStartIndex;o++)if(ei(t[o])===e)return o;return-1}function Us(t){if(t.child)return t.child;if(t.next)return t.next;for(;t.parent&&!t.parent.next;)t=t.parent;return t.parent&&t.parent.next}function Gs(t,e){const n=t[1].components;if(n)for(let o=0;o<n.length;o++){const i=n[o];if(ri(i,t)[8]===e)return i}else if(ri(go,t)[8]===e)return go;return-1}function Ws(t,e,n){const o=e[1].data[t];let i=o.directiveStart;return 0==i?Wn:(!n&&2&o.flags&&i++,e.slice(i,o.directiveEnd))}function Ys(t){return t.ngOriginalError}function qs(t,...e){t.error(...e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */!(function(t){t[t.NONE=0]="NONE",t[t.HTML=1]="HTML",t[t.STYLE=2]="STYLE",t[t.SCRIPT=3]="SCRIPT",t[t.URL=4]="URL",t[t.RESOURCE_URL=5]="RESOURCE_URL"})(As||(As={}));class Zs{constructor(){this._console=console}handleError(t){const e=this._findOriginalError(t),n=this._findContext(t),o=(function i(t){return t&&t.ngErrorLogger||qs})(t);o(this._console,"ERROR",t),e&&o(this._console,"ORIGINAL ERROR",e),n&&o(this._console,"ERROR CONTEXT",n)}_findContext(t){return t?(function e(t){return t.ngDebugContext})(t)||this._findContext(Ys(t)):null}_findOriginalError(t){let e=t&&Ys(t);for(;e&&Ys(e);)e=Ys(e);return e||null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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var cl;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let dl;function pl(t,e){return dl(t,e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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function ml(t){ngDevMode&&Ro(t);const e=t[3];return _o(e)?e[3]:e}function ul(t){const e=(function n(t){ngDevMode&&hn(t,"component");let e=yo(t)?t:Vs(t);for(;e&&!(512&e[2]);)e=ml(e);return ngDevMode&&Ro(e),e})(t);return ngDevMode&&hn(e[8],"RootView has no context. 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function ql(t,e,n){ngDevMode&&cn(e,"",'can not look for "" string.');let o=t.length;for(;;){const i=t.indexOf(e,n);if(-1===i)return i;if(0===i||t.charCodeAt(i-1)<=32){const n=e.length;if(i+n===o||t.charCodeAt(i+n)<=32)return i}n=i+1}}const Zl="ng-template";function Xl(t,e,n){ngDevMode&&ln(e,e.toLowerCase(),"Class name expected to be lowercase.");let o=0;for(;o<t.length;){let i=t[o++];if(n&&"class"===i){if(i=t[o],-1!==ql(i.toLowerCase(),e,0))return!0}else if(1===i){for(;o<t.length&&"string"==typeof(i=t[o++]);)if(i.toLowerCase()===e)return!0;return!1}}return!1}function Kl(t){return 4===t.type&&t.value!==Zl}function Jl(t,e,n){return e===(4!==t.type||n?t.value:Zl)}function Ql(t,e,n){ngDevMode&&hn(e[0],"Selector should have a tag name");let o=4;const i=t.attrs||[],a=(function r(t){for(let e=0;e<t.length;e++)if(ra(t[e]))return e;return t.length})(i);let s=!1;for(let r=0;r<e.length;r++){const l=e[r];if("number"!=typeof l){if(!s)if(4&o){if(o=2|1&o,""!==l&&!Jl(t,l,n)||""===l&&1===e.length){if($l(o))return!1;s=!0}}else{const c=8&o?l:e[++r];if(8&o&&null!==t.attrs){if(!Xl(t.attrs,c,n)){if($l(o))return!1;s=!0}continue}const d=tc(8&o?"class":l,i,Kl(t),n);if(-1===d){if($l(o))return!1;s=!0;continue}if(""!==c){let t;d>a?t="":(ngDevMode&&cn(i[d],0,"We do not match directives on namespaced attributes"),t=i[d+1].toLowerCase());const e=8&o?t:null;if(e&&-1!==ql(e,c,0)||2&o&&c!==t){if($l(o))return!1;s=!0}}}}else{if(!s&&!$l(o)&&!$l(l))return!1;if(s&&$l(l))continue;s=!1,o=l|1&o}}return $l(o)||s}function $l(t){return 0==(1&t)}function tc(t,e,n,o){if(null===e)return-1;let i=0;if(o||!n){let n=!1;for(;i<e.length;){const o=e[i];if(o===t)return i;if(3===o||6===o)n=!0;else{if(1===o||2===o){let t=e[++i];for(;"string"==typeof t;)t=e[++i];continue}if(4===o)break;if(0===o){i+=4;continue}}i+=n?1:2}return-1}return(function a(t,e){let n=t.indexOf(4);if(n>-1)for(n++;n<t.length;){const o=t[n];if("number"==typeof o)return-1;if(o===e)return n;n++}return-1})(e,t)}function ec(t,e,n=!1){for(let o=0;o<e.length;o++)if(Ql(t,e[o],n))return!0;return!1}function nc(t,e){t:for(let n=0;n<e.length;n++){const o=e[n];if(t.length===o.length){for(let e=0;e<t.length;e++)if(t[e]!==o[e])continue t;return!0}}return!1}function oc(t,e){return t?":not("+e.trim()+")":e}function ic(t){let e=t[0],n=1,o=2,i="",a=!1;for(;n<t.length;){let r=t[n];if("string"==typeof r)if(2&o){const e=t[++n];i+="["+r+(e.length>0?'="'+e+'"':"")+"]"}else 8&o?i+="."+r:4&o&&(i+=" "+r);else""===i||$l(r)||(e+=oc(a,i),i=""),o=r,a=a||!$l(o);n++}return""!==i&&(e+=oc(a,i)),e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const ac="undefined"==typeof ngDevMode||ngDevMode?{__brand__:"NO_CHANGE"}:{};
/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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     * Copyright Google LLC All Rights Reserved.
     *
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     */function lc(t,e){return ngDevMode&&rn(t,0,32767),ngDevMode&&rn(e,0,32767),t<<17|e<<2}function cc(t){return ngDevMode&&an(t,"expected number"),t>>17&32767}function dc(t){return ngDevMode&&an(t,"expected number"),2==(2&t)}function pc(t){return ngDevMode&&an(t,"expected number"),2|t}function mc(t){return ngDevMode&&an(t,"expected number"),(131068&t)>>2}function uc(t,e){return ngDevMode&&an(t,"expected number"),ngDevMode&&rn(e,0,32767),-131069&t|e<<2}function fc(t){return ngDevMode&&an(t,"expected number"),1==(1&t)}function gc(t){return ngDevMode&&an(t,"expected number"),1|t}
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function hc(t,e){if(!ngDevMode)throw new Error("This method should be guarded with `ngDevMode` so that it can be tree shaken in production!");Object.defineProperty(t,"debug",{value:e,enumerable:!1})}function bc(t,e){if(!ngDevMode)throw new Error("This method should be guarded with `ngDevMode` so that it can be tree shaken in production!");Object.defineProperty(t,"debug",{get:e,enumerable:!1})}
/**
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     * Copyright Google LLC All Rights Reserved.
     *
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t}function zc(t,e=!1){const n=ei(t);if(n)switch(n.nodeType){case Node.TEXT_NODE:return n.textContent;case Node.COMMENT_NODE:return`\x3c!--${n.textContent}--\x3e`;case Node.ELEMENT_NODE:const t=n.outerHTML;return e?t:t.split(">"+n.innerHTML+"<")[0]+">"}return null}class Ic{constructor(t){this._raw_lView=t}get flags(){const t=this._raw_lView[2];return{__raw__flags__:t,initPhaseState:3&t,creationMode:!!(4&t),firstViewPass:!!(8&t),checkAlways:!!(16&t),dirty:!!(64&t),attached:!!(128&t),destroyed:!!(256&t),isRoot:!!(512&t),indexWithinInitPhase:t>>11}}get parent(){return Nc(this._raw_lView[3])}get hostHTML(){return zc(this._raw_lView[0],!0)}get html(){return(this.nodes||[]).map(Hc).join("")}get context(){return this._raw_lView[8]}get nodes(){const t=this._raw_lView;return Lc(t[1].firstChild,t)}get template(){return this.tView.template_}get tView(){return this._raw_lView[1]}get cleanup(){return this._raw_lView[7]}get injector(){return this._raw_lView[9]}get rendererFactory(){return 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a=n;a<o;a++)i.push({index:a,t:t.data[a],l:e[a]});return{start:n,end:o,length:o-n,content:i}}function Lc(t,e){if(t){const n=[];let o=t;for(;o;)n.push(Bc(o,e)),o=o.next;return n}return[]}function Bc(t,e){const n=ei(e[t.index]),o=[],i=[],a=e[1];for(let n=t.directiveStart;n<t.directiveEnd;n++)o.push(a.data[n].type),i.push(e[n]);return{html:zc(n),type:oa(t.type),tNode:t,native:n,children:Lc(t.child,e),factories:o,instances:i,injector:Vc(t,a,e),get injectorResolutionPath(){return t.debugNodeInjectorPath(e)}}}function Vc(t,e,n){const o=[];for(let n=t.providerIndexStart_;n<t.providerIndexEnd_;n++)o.push(e.data[n]);const i=[];for(let n=t.providerIndexEnd_;n<t.directiveEnd;n++)i.push(e.data[n]);return{bloom:Uc(n,t.injectorIndex),cumulativeBloom:Uc(e.data,t.injectorIndex),providers:i,viewProviders:o,parentInjectorIndex:n[t.providerIndexStart_-1]}}function jc(t,e){const n=t[e];if("number"!=typeof n)return"????????";const o="00000000"+n.toString(2);return o.substring(o.length-8)}function Uc(t,e){return e<0?"NO_NODE_INJECTOR":`${jc(t,e+7)}_${jc(t,e+6)}_${jc(t,e+5)}_${jc(t,e+4)}_${jc(t,e+3)}_${jc(t,e+2)}_${jc(t,e+1)}_${jc(t,e+0)}`}class Gc{constructor(t){this._raw_lContainer=t}get hasTransplantedViews(){return this._raw_lContainer[2]}get views(){return this._raw_lContainer.slice(bo).map(Nc)}get parent(){return Nc(this._raw_lContainer[3])}get movedViews(){return this._raw_lContainer[9]}get host(){return this._raw_lContainer[0]}get native(){return this._raw_lContainer[7]}get next(){return Nc(this._raw_lContainer[4])}}const Wc=Promise.resolve(null);function Yc(t,e){const n=t.contentQueries;if(null!==n)for(let o=0;o<n.length;o+=2){const i=n[o],a=n[o+1];if(-1!==a){const n=t.data[a];ngDevMode&&hn(n,"DirectiveDef not found."),ngDevMode&&hn(n.contentQueries,"contentQueries function should be defined"),zi(i),n.contentQueries(2,e[a],a)}}}function qc(t,e,n,o,i,a,r,s,l,c){const d=ngDevMode?(function p(t){return(function e(t,n){switch(t){case 0:return void 0===Mc&&(Mc=new(tl("LRootView"))),Mc;case 1:void 0===_c&&(_c=new Map);let t=_c.get(n);return void 0===t&&(t=new(tl("LComponentView"+vc(n))),_c.set(n,t)),t;case 2:void 0===Cc&&(Cc=new Map);let e=Cc.get(n);return void 0===e&&(e=new(tl("LEmbeddedView"+vc(n))),Cc.set(n,e)),e}})(t.type,t.template&&t.template.name).concat(t.blueprint)})(e):e.blueprint.slice();return d[0]=i,d[2]=140|o,di(d),ngDevMode&&e.declTNode&&t&&Oo(e.declTNode,t),d[3]=d[15]=t,d[8]=n,d[10]=r||t&&t[10],ngDevMode&&hn(d[10],"RendererFactory is required"),d[11]=s||t&&t[11],ngDevMode&&hn(d[11],"Renderer is required"),d[12]=l||t&&t[12]||null,d[9]=c||t&&t[9]||null,d[6]=a,ngDevMode&&ln(2!=e.type||null!==t,!0,"Embedded views must have parentLView"),d[16]=2==e.type?t[16]:d,ngDevMode&&(function m(t){hc(t,new Ic(t))})(d),d}function Zc(t,e,n,o,i){ngDevMode&&0!==e&&gn(e,go,"TNodes can't be in the LView header."),ngDevMode&&(function a(t){2!==t&&1!==t&&4!==t&&8!==t&&32!==t&&16!==t&&64!==t&&bn(`Expected TNodeType to have only a single type selected, but got ${oa(t)}.`)})(n);let r=t.data[e];if(null===r)r=Xc(t,e,n,o,i),(function s(){return mi.lFrame.inI18n})()&&(r.flags|=64);else if(64&r.type){r.type=n,r.value=o,r.attrs=i;const a=_i();r.injectorIndex=null===a?-1:a.injectorIndex,ngDevMode&&Po(r,t),ngDevMode&&ln(e,r.index,"Expecting same index")}return Ci(r,!0),r}function Xc(t,e,n,o,i){const a=yi(),r=Mi(),s=t.data[e]=(function l(t,e,n,o,i,a){ngDevMode&&0!==o&&gn(o,go,"TNodes can't be in the LView header."),ngDevMode&&pn(a,void 0,"'undefined' is not valid value for 'attrs'"),ngDevMode&&ngDevMode.tNode++,ngDevMode&&e&&Po(e,t);let r=e?e.injectorIndex:-1;const s=ngDevMode?new class{constructor(t,e,n,o,i,a,r,s,l,c,d,p,m,u,f,g,h,b,y,_,C,M,v,x,O,P,w,k,S,D,E,R){this.tView_=t,this.type=e,this.index=n,this.insertBeforeIndex=o,this.injectorIndex=i,this.directiveStart=a,this.directiveEnd=r,this.directiveStylingLast=s,this.propertyBindings=l,this.flags=c,this.providerIndexes=d,this.value=p,this.attrs=m,this.mergedAttrs=u,this.localNames=f,this.initialInputs=g,this.inputs=h,this.outputs=b,this.tViews=y,this.next=_,this.projectionNext=C,this.child=M,this.parent=v,this.projection=x,this.styles=O,this.stylesWithoutHost=P,this.residualStyles=w,this.classes=k,this.classesWithoutHost=S,this.residualClasses=D,this.classBindings=E,this.styleBindings=R}debugNodeInjectorPath(t){const e=[];let n=ya(this,t);if(-1===n){const e=_a(this,t);e!==ea&&(n=pa(e),t=ma(e,t))}for(;-1!==n;){ngDevMode&&Ho(t,n),e.push(Bc(t[1].data[n+8],t));const o=t[n+8];o===ea?n=-1:(n=pa(o),t=ma(o,t))}return e}get type_(){return oa(this.type)||`TNodeType.?${this.type}?`}get flags_(){const t=[];return 16&this.flags&&t.push("TNodeFlags.hasClassInput"),8&this.flags&&t.push("TNodeFlags.hasContentQuery"),32&this.flags&&t.push("TNodeFlags.hasStyleInput"),128&this.flags&&t.push("TNodeFlags.hasHostBindings"),2&this.flags&&t.push("TNodeFlags.isComponentHost"),1&this.flags&&t.push("TNodeFlags.isDirectiveHost"),64&this.flags&&t.push("TNodeFlags.isDetached"),4&this.flags&&t.push("TNodeFlags.isProjected"),t.join("|")}get template_(){if(1&this.type)return this.value;const t=[],e="string"==typeof this.value&&this.value||this.type_;if(t.push("<",e),this.flags&&t.push(" ",this.flags_),this.attrs)for(let e=0;e<this.attrs.length;){const n=this.attrs[e++];if("number"==typeof n)break;const o=this.attrs[e++];t.push(" ",n,'="',o,'"')}return t.push(">"),Oc(this.child,t),t.push("</",e,">"),t.join("")}get styleBindings_(){return xc(this,!1)}get classBindings_(){return xc(this,!0)}get providerIndexStart_(){return 1048575&this.providerIndexes}get providerIndexEnd_(){return this.providerIndexStart_+(this.providerIndexes>>>20)}}(t,n,o,null,r,-1,-1,-1,null,0,0,i,a,null,null,void 0,null,null,null,null,null,null,e,null,null,null,void 0,null,null,void 0,0,0):{type:n,index:o,insertBeforeIndex:null,injectorIndex:r,directiveStart:-1,directiveEnd:-1,directiveStylingLast:-1,propertyBindings:null,flags:0,providerIndexes:0,value:i,attrs:a,mergedAttrs:null,localNames:null,initialInputs:void 0,inputs:null,outputs:null,tViews:null,next:null,projectionNext:null,child:null,parent:e,projection:null,styles:null,stylesWithoutHost:null,residualStyles:void 0,classes:null,classesWithoutHost:null,residualClasses:void 0,classBindings:0,styleBindings:0};return ngDevMode&&Object.seal(s),s})(t,r?a:a&&a.parent,n,e,o,i);return null===t.firstChild&&(t.firstChild=s),null!==a&&(r?null==a.child&&null!==s.parent&&(a.child=s):null===a.next&&(a.next=s)),s}function Kc(t,e,n,o){if(0===n)return-1;ngDevMode&&(Ao(t),dn(t,e[1],"`LView` must be associated with `TView`!"),ln(t.data.length,e.length,"Expecting LView to be same size as TView"),ln(t.data.length,t.blueprint.length,"Expecting Blueprint to be same size as TView"),To(t));const i=e.length;for(let i=0;i<n;i++)e.push(o),t.blueprint.push(o),t.data.push(null);return i}function Jc(t,e,n){ngDevMode&&ln(si(e),!0,"Should be run in creation mode"),Fi(e);try{const o=t.viewQuery;null!==o&&Nd(1,o,n);const i=t.template;null!==i&&td(t,e,i,1,n),t.firstCreatePass&&(t.firstCreatePass=!1),t.staticContentQueries&&Yc(t,e),t.staticViewQueries&&Nd(2,t.viewQuery,n);const a=t.components;null!==a&&(function o(t,e){for(let n=0;n<e.length;n++)Sd(t,e[n])})(e,a)}catch(e){throw t.firstCreatePass&&(t.incompleteFirstPass=!0,t.firstCreatePass=!1),e}finally{e[2]&=-5,Ui()}}function Qc(t,e,n,o){ngDevMode&&ln(si(e),!1,"Should be run in update mode");const i=e[2];if(256==(256&i))return;Fi(e);const a=xi();try{di(e),ki(t.bindingStartIndex),null!==n&&td(t,e,n,2,o);const r=3==(3&i);if(!a)if(r){const n=t.preOrderCheckHooks;null!==n&&Ki(e,n,null)}else{const n=t.preOrderHooks;null!==n&&Ji(e,n,0,null),Qi(e,0)}if((function r(t){for(let e=fl(t);null!==e;e=gl(e)){if(!e[2])continue;const t=e[9];ngDevMode&&hn(t,"Transplanted View flags set but missing MOVED_VIEWS");for(let e=0;e<t.length;e++){const n=t[e],o=n[3];ngDevMode&&Do(o),0==(1024&n[2])&&pi(o,1),n[2]|=1024}}})(e),(function s(t){for(let e=fl(t);null!==e;e=gl(e))for(let t=bo;t<e.length;t++){const n=e[t],o=n[1];ngDevMode&&hn(o,"TView must be allocated"),li(n)&&Qc(o,n,o.template,n[8])}})(e),null!==t.contentQueries&&Yc(t,e),!a)if(r){const n=t.contentCheckHooks;null!==n&&Ki(e,n)}else{const n=t.contentHooks;null!==n&&Ji(e,n,1),Qi(e,1)}!(function l(t,e){const n=t.hostBindingOpCodes;if(null!==n)try{for(let t=0;t<n.length;t++){const o=n[t];if(o<0)Wi(~o);else{const i=o,a=n[++t],r=n[++t];Ri(a,i),r(2,e[i])}}}finally{Wi(-1)}})(t,e);const s=t.components;null!==s&&(function c(t,e){for(let n=0;n<e.length;n++)wd(t,e[n])})(e,s);const l=t.viewQuery;if(null!==l&&Nd(2,l,o),!a)if(r){const n=t.viewCheckHooks;null!==n&&Ki(e,n)}else{const n=t.viewHooks;null!==n&&Ji(e,n,2),Qi(e,2)}!0===t.firstUpdatePass&&(t.firstUpdatePass=!1),a||(e[2]&=-73),1024&e[2]&&(e[2]&=-1025,pi(e[3],-1))}finally{Ui()}}function $c(t,e,n,o){const i=e[10],a=!xi(),r=si(e);try{a&&!r&&i.begin&&i.begin(),r&&Jc(t,e,o),Qc(t,e,n,o)}finally{a&&!r&&i.end&&i.end()}}function td(t,e,n,o,i){const a=Gi(),r=2&o;try{Wi(-1),r&&e.length>go&&sc(t,e,go,xi()),qo(r?2:0,i),n(o,i)}finally{Wi(a),qo(r?3:1,i)}}function ed(t,e,n){if(Co(e)){const o=e.directiveEnd;for(let i=e.directiveStart;i<o;i++){const e=t.data[i];e.contentQueries&&e.contentQueries(1,n[i],i)}}}function nd(t,e,n){ui()&&((function o(t,e,n,i){const a=n.directiveStart,r=n.directiveEnd;t.firstCreatePass||ha(n,e),Ls(i,e);const s=n.initialInputs;for(let o=a;o<r;o++){const i=t.data[o],r=xo(i);r&&(ngDevMode&&ia(n,3),_d(e,n,i));const l=Sa(e,t,o,n);Ls(l,e),null!==s&&vd(e,o-a,l,i,n,s),r&&(ri(n.index,e)[8]=l)}})(t,e,n,oi(n,e)),128==(128&n.flags)&&(function i(t,e,n){const o=n.directiveStart,i=n.directiveEnd,a=n.index,r=(function s(){return mi.lFrame.currentDirectiveIndex})();try{Wi(a);for(let n=o;n<i;n++){const o=t.data[n],i=e[n];Ai(n),null===o.hostBindings&&0===o.hostVars&&null===o.hostAttrs||fd(o,i)}}finally{Wi(-1),Ai(r)}})(t,e,n))}function od(t,e,n=oi){const o=e.localNames;if(null!==o){let i=e.index+1;for(let a=0;a<o.length;a+=2){const r=o[a+1],s=-1===r?n(e,t):t[r];t[i++]=s}}}function id(t){const e=t.tView;return null===e||e.incompleteFirstPass?t.tView=ad(1,null,t.template,t.decls,t.vars,t.directiveDefs,t.pipeDefs,t.viewQuery,t.schemas,t.consts):e}function ad(t,e,n,o,i,a,r,s,l,c){ngDevMode&&ngDevMode.tView++;const d=go+o,p=d+i,m=(function u(t,e){const n=ngDevMode?new kc:[];for(let o=0;o<e;o++)n.push(o<t?null:ac);return n})(d,p),f="function"==typeof c?c():c,g=m[1]=ngDevMode?new class{constructor(t,e,n,o,i,a,r,s,l,c,d,p,m,u,f,g,h,b,y,_,C,M,v,x,O,P,w,k,S,D,E,R){this.type=t,this.blueprint=e,this.template=n,this.queries=o,this.viewQuery=i,this.declTNode=a,this.data=r,this.bindingStartIndex=s,this.expandoStartIndex=l,this.hostBindingOpCodes=c,this.firstCreatePass=d,this.firstUpdatePass=p,this.staticViewQueries=m,this.staticContentQueries=u,this.preOrderHooks=f,this.preOrderCheckHooks=g,this.contentHooks=h,this.contentCheckHooks=b,this.viewHooks=y,this.viewCheckHooks=_,this.destroyHooks=C,this.cleanup=M,this.contentQueries=v,this.components=x,this.directiveRegistry=O,this.pipeRegistry=P,this.firstChild=w,this.schemas=k,this.consts=S,this.incompleteFirstPass=D,this._decls=E,this._vars=R}get template_(){const t=[];return Oc(this.firstChild,t),t.join("")}get type_(){return ho[this.type]||`TViewType.?${this.type}?`}}(t,m,n,null,s,e,(function h(t){return void 0===wc&&(wc=new Pc),wc.concat(t)})(m).fill(null,d),d,p,null,!0,!0,!1,!1,null,null,null,null,null,null,null,null,null,null,"function"==typeof a?a():a,"function"==typeof r?r():r,null,l,f,!1,o,i):{type:t,blueprint:m,template:n,queries:null,viewQuery:s,declTNode:e,data:m.slice().fill(null,d),bindingStartIndex:d,expandoStartIndex:p,hostBindingOpCodes:null,firstCreatePass:!0,firstUpdatePass:!0,staticViewQueries:!1,staticContentQueries:!1,preOrderHooks:null,preOrderCheckHooks:null,contentHooks:null,contentCheckHooks:null,viewHooks:null,viewCheckHooks:null,destroyHooks:null,cleanup:null,contentQueries:null,components:null,directiveRegistry:"function"==typeof a?a():a,pipeRegistry:"function"==typeof r?r():r,firstChild:null,schemas:l,consts:f,incompleteFirstPass:!1};return ngDevMode&&Object.seal(g),g}function rd(t,e,n,o){const i=Hd(e);null===n?(ngDevMode&&Object.freeze(Fd(t)),i.push(o)):(i.push(n),t.firstCreatePass&&Fd(t).push(o,i.length-1))}function sd(t,e,n){for(let o in t)if(t.hasOwnProperty(o)){const i=t[o];(n=null===n?{}:n).hasOwnProperty(o)?n[o].push(e,i):n[o]=[e,i]}return n}function ld(t,e,n,o,i,a,r,s){ngDevMode&&pn(i,ac,"Incoming value should never be NO_CHANGE.");const l=oi(e,n);let c,d=e.inputs;if(!s&&null!=d&&(c=d[o]))Vd(t,n,c,o,i),Mo(e)&&(function p(t,e){ngDevMode&&Ro(t);const n=ri(e,t);16&n[2]||(n[2]|=64)})(n,e.index),ngDevMode&&(function m(t,e,n,o,i){if(7&n)for(let a=0;a<o.length;a+=2)cd(t,e,n,o[a+1],i)})(n,l,e.type,c,i);else if(3&e.type){if(o=(function u(t){return"class"===t?"className":"for"===t?"htmlFor":"formaction"===t?"formAction":"innerHtml"===t?"innerHTML":"readonly"===t?"readOnly":"tabindex"===t?"tabIndex":t})(o),ngDevMode){if((function f(t){if(t.toLowerCase().startsWith("on")){const e=`Binding to event property '${t}' is disallowed for security reasons, please use (${t.slice(2)})=...\nIf '${t}' is a directive input, make sure the directive is imported by the current module.`;throw new Error(e)}})(o),!(function g(t,e,n,o){return!(null!==t.schemas&&!(dd(t,o.value)||n in e||sa(n))&&"undefined"!=typeof Node&&null!==Node&&e instanceof Node)})(t,l,o,e))return void pd(o,e);ngDevMode.rendererSetProperty++}i=null!=r?r(i,e.value||"",o):i,$o(a)?a.setProperty(l,o,i):sa(o)||(l.setProperty?l.setProperty(o,i):l[o]=i)}else 12&e.type&&ngDevMode&&!dd(t,e.value)&&pd(o,e)}function cd(t,e,n,o,i){const a=t[11];o=(function r(t){return`ng-reflect-${t=(function e(t){return t.replace(el,((...t)=>"-"+t[1].toLowerCase()))})(t.replace(/[$@]/g,"_"))}`})(o);const s=(function l(t){try{return null!=t?t.toString().slice(0,30):t}catch(t){return"[ERROR] Exception while trying to serialize the value"}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(i);if(3&n)null==i?$o(a)?a.removeAttribute(e,o):e.removeAttribute(o):$o(a)?a.setAttribute(e,o,s):e.setAttribute(o,s);else{const t=$s(`bindings=${JSON.stringify({[o]:s},null,2)}`);$o(a)?a.setValue(e,t):e.textContent=t}}function dd(t,e){const n=t.schemas;if(null!==n)for(let t=0;t<n.length;t++){const o=n[t];if(o===Ks||o===Xs&&e&&e.indexOf("-")>-1)return!0}return!1}function pd(t,e){console.error(Qe("303",`Can't bind to '${t}' since it isn't a known property of '${e.value}'.`))}function md(t,e,n,o){ngDevMode&&Ao(t);let i=!1;if(ui()){const a=(function a(t,e,n){ngDevMode&&Ao(t),ngDevMode&&ia(n,15);const o=t.directiveRegistry;let i=null;if(o)for(let a=0;a<o.length;a++){const r=o[a];ec(n,r.selectors,!1)&&(i||(i=ngDevMode?new Sc:[]),Ca(ha(n,e),t,r.type),xo(r)?(ngDevMode&&(ia(n,2,`"${n.value}" tags cannot be used as component hosts. 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Ud(t,e,n){ngDevMode&&Ao(gi(),"Expecting to be called in first template pass only");let o=n?t.styles:null,i=n?t.classes:null,a=0;if(null!==e)for(let t=0;t<e.length;t++){const n=e[t];"number"==typeof n?a=n:1==a?i=We(i,n):2==a&&(o=We(o,n+": "+e[++t]+";"))}n?t.styles=o:t.stylesWithoutHost=o,n?t.classes=i:t.classesWithoutHost=i}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Gd=new Ga("INJECTOR",-1);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Wd{get(t,e=gr){if(e===gr){const e=new Error(`NullInjectorError: No provider for ${Ge(t)}!`);throw e.name="NullInjectorError",e}return e}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Yd=new Ga("Set Injector scope."),qd={},Zd={};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let Xd;function Kd(){return void 0===Xd&&(Xd=new Wd),Xd}function Jd(t,e=null,n=null,o){return new Qd(t,n,e||Kd(),o)}class Qd{constructor(t,e,n,o=null){this.parent=n,this.records=new Map,this.injectorDefTypes=new Set,this.onDestroy=new Set,this._destroyed=!1;const i=[];e&&er(e,(n=>this.processProvider(n,t,e))),er([t],(t=>this.processInjectorType(t,[],i))),this.records.set(Gd,ep(void 0,this));const a=this.records.get(Yd);this.scope=null!=a?a.value:null,this.source=o||("object"==typeof t?null:Ge(t))}get destroyed(){return this._destroyed}destroy(){this.assertNotDestroyed(),this._destroyed=!0;try{this.onDestroy.forEach((t=>t.ngOnDestroy()))}finally{this.records.clear(),this.onDestroy.clear(),this.injectorDefTypes.clear()}}get(t,e=gr,n=En.Default){this.assertNotDestroyed();const o=Cr(this),i=An(void 0);try{if(!(n&En.SkipSelf)){let e=this.records.get(t);if(void 0===e){const n=(function o(t){return"function"==typeof t||"object"==typeof t&&t instanceof Ga})(t)&&xn(t);e=n&&this.injectableDefInScope(n)?ep($d(t),qd):null,this.records.set(t,e)}if(null!=e)return this.hydrate(t,e)}return(n&En.Self?Kd():this.parent).get(t,e=n&En.Optional&&e===gr?null:e)}catch(e){if("NullInjectorError"===e.name){if((e.ngTempTokenPath=e.ngTempTokenPath||[]).unshift(Ge(t)),o)throw e;return(function n(t,e,o,i){const a=t.ngTempTokenPath;throw e[br]&&a.unshift(e[br]),t.message=(function r(t,e,n,o=null){t=t&&"\n"===t.charAt(0)&&"ɵ"==t.charAt(1)?t.substr(2):t;let i=Ge(e);if(Array.isArray(e))i=e.map(Ge).join(" -> ");else if("object"==typeof e){let t=[];for(let n in e)if(e.hasOwnProperty(n)){let o=e[n];t.push(n+":"+("string"==typeof o?JSON.stringify(o):Ge(o)))}i=`{${t.join(", ")}}`}return`${n}${o?"("+o+")":""}[${i}]: ${t.replace(hr,"\n  ")}`})("\n"+t.message,a,o,i),t.ngTokenPath=a,t.ngTempTokenPath=null,t})(e,t,"R3InjectorError",this.source)}throw e}finally{An(i),Cr(o)}}_resolveInjectorDefTypes(){this.injectorDefTypes.forEach((t=>this.get(t)))}toString(){const t=[];return this.records.forEach(((e,n)=>t.push(Ge(n)))),`R3Injector[${t.join(", ")}]`}assertNotDestroyed(){if(this._destroyed)throw new Error("Injector has already been destroyed.")}processInjectorType(t,e,n){if(!(t=Ze(t)))return!1;let o=Pn(t);const i=null==o&&t.ngModule||void 0,a=void 0===i?t:i;ngDevMode&&-1!==e.indexOf(a)&&en(Ge(a),e.map(Ge));const r=-1!==n.indexOf(a);if(void 0!==i&&(o=Pn(i)),null==o)return!1;if(null!=o.imports&&!r){let t;ngDevMode&&e.push(a),n.push(a);try{er(o.imports,(o=>{this.processInjectorType(o,e,n)&&(void 0===t&&(t=[]),t.push(o))}))}finally{ngDevMode&&e.pop()}if(void 0!==t)for(let e=0;e<t.length;e++){const{ngModule:n,providers:o}=t[e];er(o,(t=>this.processProvider(t,n,o||Wn)))}}this.injectorDefTypes.add(a);const s=Fo(a)||(()=>new a);this.records.set(a,ep(s,qd));const l=o.providers;if(null!=l&&!r){const e=t;er(l,(t=>this.processProvider(t,e,l)))}return void 0!==i&&void 0!==t.providers}processProvider(t,e,n){let o=op(t=Ze(t))?t:Ze(t&&t.provide);const i=(function a(t,e,n){return np(t)?ep(void 0,t.useValue):ep(tp(t,e,n),qd)})(t,e,n);if(op(t)||!0!==t.multi){const t=this.records.get(o);ngDevMode&&t&&void 0!==t.multi&&nn()}else{let e=this.records.get(o);e?ngDevMode&&void 0===e.multi&&nn():(e=ep(void 0,qd,!0),e.factory=()=>Pr(e.multi),this.records.set(o,e)),o=t,e.multi.push(t)}this.records.set(o,i)}hydrate(t,e){return ngDevMode&&e.value===Zd?en(Ge(t)):e.value===qd&&(e.value=Zd,e.value=e.factory()),"object"==typeof e.value&&e.value&&(function n(t){return null!==t&&"object"==typeof t&&"function"==typeof t.ngOnDestroy})(e.value)&&this.onDestroy.add(e.value),e.value}injectableDefInScope(t){if(!t.providedIn)return!1;const e=Ze(t.providedIn);return"string"==typeof e?"any"===e||e===this.scope:this.injectorDefTypes.has(e)}}function $d(t){const e=xn(t),n=null!==e?e.factory:Fo(t);if(null!==n)return n;if(t instanceof Ga)throw new Error(`Token ${Ge(t)} is missing a ɵprov definition.`);if(t instanceof Function)return(function o(t){const e=t.length;if(e>0){const n=ir(e,"?");throw new Error(`Can't resolve all parameters for ${Ge(t)}: (${n.join(", ")}).`)}const n=(function o(t){const e=t&&(t[wn]||t[Sn]);if(e){const n=(function n(t){if(t.hasOwnProperty("name"))return t.name;const e=(""+t).match(/^function\s*([^\s(]+)/);return null===e?"":e[1]})(t);return console.warn(`DEPRECATED: DI is instantiating a token "${n}" that inherits its @Injectable decorator but does not provide one itself.\nThis will become an error in a future version of Angular. Please add @Injectable() to the "${n}" class.`),e}return null})(t);return null!==n?()=>n.factory(t):()=>new t})(t);throw new Error("unreachable")}function tp(t,e,n){let o;if(op(t)){const e=Ze(t);return Fo(e)||$d(e)}if(np(t))o=()=>Ze(t.useValue);else if((function i(t){return!(!t||!t.useFactory)})(t))o=()=>t.useFactory(...Pr(t.deps||[]));else if((function a(t){return!(!t||!t.useExisting)})(t))o=()=>vr(Ze(t.useExisting));else{const i=Ze(t&&(t.useClass||t.provide));if(ngDevMode&&!i&&(function r(t,e,n){let o="";throw t&&e&&(o=` - only instances of Provider and Type are allowed, got: [${e.map((t=>t==n?"?"+n+"?":"...")).join(", ")}]`),new Error(`Invalid provider for the NgModule '${Ge(t)}'`+o)})(e,n,t),!(function s(t){return!!t.deps})(t))return Fo(i)||$d(i);o=()=>new i(...Pr(t.deps))}return o}function ep(t,e,n=!1){return{factory:t,value:e,multi:n?[]:void 0}}function np(t){return null!==t&&"object"==typeof t&&yr in t}function op(t){return"function"==typeof t}const ip=function ap(t,e,n){return(function o(t,e=null,n=null,i){const a=Jd(t,e,n,i);return a._resolveInjectorDefTypes(),a})({name:n},e,t,n)};class rp{static create(t,e){return Array.isArray(t)?ip(t,e,""):ip(t.providers,t.parent,t.name||"")}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function sp(t){bp(t);const e=Is(t);return null===e?null:(void 0===e.component&&(e.component=(function n(t,e){const n=e[1].data[t];return 2&n.flags?e[n.directiveStart]:null})(e.nodeIndex,e.lView)),e.component)}function lp(t){bp(t);const e=Is(t);return null===e?null:e.lView[8]}function cp(t){const e=Is(t);if(null===e)return null;let n,o=e.lView;for(ngDevMode&&Ro(o);2===o[1].type&&(n=ml(o));)o=n;return 512&o[2]?null:o[8]}function dp(t){return[...ul(t).components]}function pp(t){const e=Is(t);return null===e?rp.NULL:new Ra(e.lView[1].data[e.nodeIndex],e.lView)}function mp(t){if(t instanceof Text)return[];const e=Is(t);if(null===e)return[];const n=e.lView,o=n[1],i=e.nodeIndex;return(null==o?void 0:o.data[i])?(void 0===e.directives&&(e.directives=Ws(i,n,!1)),null===e.directives?[]:[...e.directives]):[]}function up(t){const{constructor:e}=t;if(!e)throw new Error("Unable to find the instance constructor");const n=po(e);if(n)return{inputs:n.inputs,outputs:n.outputs,encapsulation:n.encapsulation,changeDetection:n.onPush?zn.OnPush:zn.Default};const o=mo(e);return o?{inputs:o.inputs,outputs:o.outputs}:null}function fp(t){return Is(t).native}function gp(t){bp(t);const e=Is(t);if(null===e)return[];const n=e.lView,o=n[7],i=n[1].cleanup,a=[];if(i&&o)for(let e=0;e<i.length;){const r=i[e++],s=i[e++];if("string"==typeof r){const l=r,c=ei(n[s]),d=o[i[e++]],p=i[e++],m="boolean"==typeof p||p>=0?"dom":"output",u="boolean"==typeof p&&p;t==c&&a.push({element:t,name:l,callback:d,useCapture:u,type:m})}}return a.sort(hp),a}function hp(t,e){return t.name==e.name?0:t.name<e.name?-1:1}function bp(t){if("undefined"!=typeof Element&&!(t instanceof Element))throw new Error("Expecting instance of DOM Element")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function yp(t){(function e(t){ngDevMode&&hn(t,"component");const e=Ed(Fs(t));ngDevMode&&hn(e[8],"rootContext should be defined"),(function n(t,e){if(0===t.flags&&t.clean==Wc){let n;t.flags|=e,t.clean=new Promise((t=>n=t)),t.scheduler((()=>{if(1&t.flags&&(t.flags&=-2,Rd(t)),2&t.flags){t.flags&=-3;const e=t.playerHandler;e&&e.flushPlayers()}t.clean=Wc,n(null)}))}})(e[8],1)})(t),dp(t).forEach((t=>(function e(t){const e=Fs(t);Ad(e[1],e,t)})(t)))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */rp.THROW_IF_NOT_FOUND=gr,rp.NULL=new Wd,rp.ɵprov=Mn({token:rp,providedIn:"any",factory:()=>vr(Gd)}),rp.__NG_ELEMENT_ID__=-1;let _p=!1;function Cp(t,e){if("undefined"==typeof COMPILED||!COMPILED){const n=jn;if(ngDevMode&&hn(e,"function not defined"),n){let o=n.ng;o||(o=n.ng={}),o[t]=e}}}function Mp(t,e){const n=Vs(t);ngDevMode&&hn(n,"LView is required");const o=n[1],i=bi();ngDevMode&&hn(i,"TNode is required"),Xi(o,i)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Sp=["providersResolver"],Dp=["template","decls","consts","vars","onPush","ngContentSelectors","styles","encapsulation","schemas"];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let Ep=null;function Rp(){if(!Ep){const t=jn.Symbol;if(t&&t.iterator)Ep=t.iterator;else{const t=Object.getOwnPropertyNames(Map.prototype);for(let e=0;e<t.length;++e){const n=t[e];"entries"!==n&&"size"!==n&&Map.prototype[n]===Map.prototype.entries&&(Ep=n)}}}return Ep}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Ap(t,e){const n=Np(t),o=Np(e);if(n&&o)return(function i(t,e,n){const o=t[Rp()](),i=e[Rp()]();for(;;){const t=o.next(),e=i.next();if(t.done&&e.done)return!0;if(t.done||e.done)return!1;if(!n(t.value,e.value))return!1}})(t,e,Ap);{const i=t&&("object"==typeof t||"function"==typeof t),a=e&&("object"==typeof e||"function"==typeof e);return!(n||!i||o||!a)||Object.is(t,e)}}class Tp{constructor(t){this.wrapped=t}static wrap(t){return new Tp(t)}static unwrap(t){return Tp.isWrapped(t)?t.wrapped:t}static isWrapped(t){return t instanceof Tp}}function Np(t){return!!zp(t)&&(Array.isArray(t)||!(t instanceof Map)&&Rp()in t)}function zp(t){return null!==t&&("function"==typeof t||"object"==typeof t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Ip(t,e,n){return t[e]=n}function Hp(t,e){return ngDevMode&&_n(t,e),ngDevMode&&pn(t[e],ac,"Stored value should never be NO_CHANGE."),t[e]}function Fp(t,e,n){ngDevMode&&pn(n,ac,"Incoming value should never be NO_CHANGE."),ngDevMode&&mn(e,t.length,"Slot should have been initialized to NO_CHANGE");const o=t[e];if(Object.is(o,n))return!1;if(ngDevMode&&xi()){const i=o!==ac?o:void 0;if(!Ap(i,n)){const a=(function i(t,e,n,o){const i=t[1].data,a=i[e];if("string"==typeof a)return a.indexOf(al)>-1?ll(t,e,e,a,o):{propName:a,oldValue:n,newValue:o};if(null===a){let n=e-1;for(;"string"!=typeof i[n]&&null===i[n+1];)n--;const a=i[n];if("string"==typeof a){const i=a.match(new RegExp(al,"g"));if(i&&i.length-1>e-n)return ll(t,n,e,a,o)}}return{propName:void 0,oldValue:n,newValue:o}})(t,e,i,n);!(function a(t,e,n,o){let i=`ExpressionChangedAfterItHasBeenCheckedError: Expression has changed after it was checked. Previous value${o?` for '${o}'`:""}: '${e}'. Current value: '${n}'.`;throw t&&(i+=" It seems like the view has been created after its parent and its children have been dirty checked. Has it been created in a change detection hook?"),new Ke("100",i)})(o===ac,a.oldValue,a.newValue,a.propName)}return!1}return t[e]=n,!0}function Lp(t,e,n,o){const i=Fp(t,e,n);return Fp(t,e+1,o)||i}function Bp(t,e,n,o,i){const a=Lp(t,e,n,o);return Fp(t,e+2,i)||a}function Vp(t,e,n,o,i,a){const r=Lp(t,e,n,o);return Lp(t,e+2,i,a)||r}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function jp(t,e,n,o){const i=fi(),a=Si();if(Fp(i,a,e)){const r=gi(),s=Yi();Cd(s,i,t,e,n,o),ngDevMode&&zd(r.data,s,"attr."+t,a)}return jp}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Up(t,e){ngDevMode&&mn(2,e.length,"should have at least 3 values"),ngDevMode&&ln(e.length%2,1,"should have an odd number of values");let n=!1,o=wi();for(let i=1;i<e.length;i+=2)n=Fp(t,o++,e[i])||n;if(ki(o),!n)return ac;let i=e[0];for(let t=1;t<e.length;t+=2)i+=$e(e[t])+e[t+1];return i}function Gp(t,e,n,o){return Fp(t,Si(),n)?e+$e(n)+o:ac}function Wp(t,e,n,o,i,a){const r=Lp(t,wi(),n,i);return Di(2),r?e+$e(n)+o+$e(i)+a:ac}function Yp(t,e,n,o,i,a,r,s){const l=Bp(t,wi(),n,i,r);return Di(3),l?e+$e(n)+o+$e(i)+a+$e(r)+s:ac}function qp(t,e,n,o,i,a,r,s,l,c){const d=Vp(t,wi(),n,i,r,l);return Di(4),d?e+$e(n)+o+$e(i)+a+$e(r)+s+$e(l)+c:ac}function Zp(t,e,n,o,i,a,r,s,l,c,d,p){const m=wi();let u=Vp(t,m,n,i,r,l);return u=Fp(t,m+4,d)||u,Di(5),u?e+$e(n)+o+$e(i)+a+$e(r)+s+$e(l)+c+$e(d)+p:ac}function Xp(t,e,n,o,i,a,r,s,l,c,d,p,m,u){const f=wi();let g=Vp(t,f,n,i,r,l);return g=Lp(t,f+4,d,m)||g,Di(6),g?e+$e(n)+o+$e(i)+a+$e(r)+s+$e(l)+c+$e(d)+p+$e(m)+u:ac}function Kp(t,e,n,o,i,a,r,s,l,c,d,p,m,u,f,g){const h=wi();let b=Vp(t,h,n,i,r,l);return b=Bp(t,h+4,d,m,f)||b,Di(7),b?e+$e(n)+o+$e(i)+a+$e(r)+s+$e(l)+c+$e(d)+p+$e(m)+u+$e(f)+g:ac}function Jp(t,e,n,o,i,a,r,s,l,c,d,p,m,u,f,g,h,b){const y=wi();let _=Vp(t,y,n,i,r,l);return _=Vp(t,y+4,d,m,f,h)||_,Di(8),_?e+$e(n)+o+$e(i)+a+$e(r)+s+$e(l)+c+$e(d)+p+$e(m)+u+$e(f)+g+$e(h)+b:ac}function Qp(t,e,n,o,i,a,r,s){const l=fi(),c=gi(),d=t+go,p=c.firstCreatePass?(
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function m(t,e,n,o,i,a,r,s,l){ngDevMode&&Ao(e),ngDevMode&&ngDevMode.firstCreatePass++;const c=e.consts,d=Zc(e,t,4,r||null,ci(c,s));md(e,n,d,ci(c,l)),Xi(e,d);const p=d.tViews=ad(2,d,o,i,a,e.directiveRegistry,e.pipeRegistry,null,e.schemas,c);return null!==e.queries&&(e.queries.template(e,d),p.queries=e.queries.embeddedTView(d)),d})(d,c,l,e,n,o,i,a,r):c.data[d];Ci(p,!1);const u=l[11].createComment(ngDevMode?"container":"");Hl(c,l,u,p),Ls(u,l),Dd(l,l[d]=Pd(u,l,u,p)),vo(p)&&nd(c,l,p),null!=r&&od(l,p,s)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function $p(t){return ai((function e(){return mi.lFrame.contextLView})(),go+t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const tm={"ɵɵdefineInjectable":Mn,"ɵɵdefineInjector":vn,"ɵɵinject":vr,"ɵɵinvalidFactoryDep":xr,resolveForwardRef:Ze},em=je({provide:String,useValue:je});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function nm(t){return void 0!==t.useClass}function om(t){return void 0!==t.useFactory}je({provide:String,useValue:je});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const im=Fa("Injectable",void 0,void 0,void 0,((t,e)=>am(t,e))),am=function rm(t,e){let n=null,o=null;t.hasOwnProperty(wn)||Object.defineProperty(t,wn,{get:()=>{if(null===n){const o=Ja({usage:0,kind:"injectable",type:t});n=o.compileInjectable(tm,`ng:///${t.name}/ɵprov.js`,(function o(t,e){const n=e||{providedIn:null},o={name:t.name,type:t,typeArgumentCount:0,providedIn:n.providedIn};return(nm(n)||om(n))&&void 0!==n.deps&&(o.deps=zr(n.deps)),nm(n)?o.useClass=n.useClass:(function i(t){return em in t})(n)?o.useValue=n.useValue:om(n)?o.useFactory=n.useFactory:(function a(t){return void 0!==t.useExisting})(n)&&(o.useExisting=n.useExisting),o}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function e(t){const e=[];for(let n=0;n<t.length;++n){if(e.indexOf(t[n])>-1)return e.push(t[n]),e;e.push(t[n])}return e})(t.slice().reverse()).map((t=>Ge(t.token))).join(" -> ")+")":""}function lm(t,e,n,o){const i=[e],a=n(i),r=o?(function s(t,e){const n=`${t} caused by: ${e instanceof Error?e.message:e}`,o=Error(n);return o.ngOriginalError=e,o})(a,o):Error(a);return r.addKey=cm,r.keys=i,r.injectors=[t],r.constructResolvingMessage=n,r.ngOriginalError=o,r}function cm(t,e){this.injectors.push(t),this.keys.push(e),this.message=this.constructResolvingMessage(this.keys)}function dm(t,e){const n=[];for(let t=0,o=e.length;t<o;t++){const o=e[t];n.push(o&&0!=o.length?o.map(Ge).join(" "):"?")}return Error("Cannot resolve all parameters for '"+Ge(t)+"'("+n.join(", ")+"). Make sure that all the parameters are decorated with Inject or have valid type annotations and that '"+Ge(t)+"' is decorated with Injectable.")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class pm{constructor(t,e){if(this.token=t,this.id=e,!t)throw new Error("Token must be defined!");this.displayName=Ge(this.token)}static get(t){return mm.get(Ze(t))}static get numberOfKeys(){return mm.numberOfKeys}}const mm=new class{constructor(){this._allKeys=new Map}get(t){if(t instanceof pm)return t;if(this._allKeys.has(t))return this._allKeys.get(t);const e=new pm(t,pm.numberOfKeys);return this._allKeys.set(t,e),e}get numberOfKeys(){return this._allKeys.size}},um=new
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class{constructor(t){this.reflectionCapabilities=t}updateCapabilities(t){this.reflectionCapabilities=t}factory(t){return this.reflectionCapabilities.factory(t)}parameters(t){return this.reflectionCapabilities.parameters(t)}annotations(t){return this.reflectionCapabilities.annotations(t)}propMetadata(t){return this.reflectionCapabilities.propMetadata(t)}hasLifecycleHook(t,e){return this.reflectionCapabilities.hasLifecycleHook(t,e)}getter(t){return this.reflectionCapabilities.getter(t)}setter(t){return this.reflectionCapabilities.setter(t)}method(t){return this.reflectionCapabilities.method(t)}importUri(t){return this.reflectionCapabilities.importUri(t)}resourceUri(t){return this.reflectionCapabilities.resourceUri(t)}resolveIdentifier(t,e,n,o){return this.reflectionCapabilities.resolveIdentifier(t,e,n,o)}resolveEnum(t,e){return this.reflectionCapabilities.resolveEnum(t,e)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */(new mr);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class fm{constructor(t,e,n){this.key=t,this.optional=e,this.visibility=n}static fromKey(t){return new fm(t,!1,null)}}const gm=[];class hm{constructor(t,e,n){this.key=t,this.resolvedFactories=e,this.multiProvider=n,this.resolvedFactory=this.resolvedFactories[0]}}class bm{constructor(t,e){this.factory=t,this.dependencies=e}}function ym(t){let e,n;if(t.useClass){const o=Ze(t.useClass);e=um.factory(o),n=vm(o)}else t.useExisting?(e=t=>t,n=[fm.fromKey(pm.get(t.useExisting))]):t.useFactory?(e=t.useFactory,n=(function o(t,e){if(e){const n=e.map((t=>[t]));return e.map((e=>xm(t,e,n)))}return vm(t)})(t.useFactory,t.deps)):(e=()=>t.useValue,n=gm);return new bm(e,n)}function _m(t){return new hm(pm.get(t.provide),[ym(t)],t.multi||!1)}function Cm(t){const e=(function n(t,e){for(let n=0;n<t.length;n++){const o=t[n],i=e.get(o.key.id);if(i){if(o.multiProvider!==i.multiProvider)throw Error(`Cannot mix multi providers and regular providers, got: ${i} ${o}`);if(o.multiProvider)for(let t=0;t<o.resolvedFactories.length;t++)i.resolvedFactories.push(o.resolvedFactories[t]);else e.set(o.key.id,o)}else{let t;t=o.multiProvider?new hm(o.key,o.resolvedFactories.slice(),o.multiProvider):o,e.set(o.key.id,t)}}return e})(Mm(t,[]).map(_m),new Map);return Array.from(e.values())}function Mm(t,e){return t.forEach((t=>{if(t instanceof Qa)e.push({provide:t,useClass:t});else if(t&&"object"==typeof t&&void 0!==t.provide)e.push(t);else{if(!Array.isArray(t))throw(function n(t){return Error(`Invalid provider - only instances of Provider and Type are allowed, got: ${t}`)})(t);Mm(t,e)}})),e}function vm(t){const e=um.parameters(t);if(!e)return[];if(e.some((t=>null==t)))throw dm(t,e);return e.map((n=>xm(t,n,e)))}function xm(t,e,n){let o=null,i=!1;if(!Array.isArray(e))return Om(e instanceof kr?e.token:e,i,null);let a=null;for(let t=0;t<e.length;++t){const n=e[t];n instanceof Qa?o=n:n instanceof kr?o=n.token:n instanceof Sr?i=!0:n instanceof Dr||n instanceof Er?a=n:n instanceof Ga&&(o=n)}if(o=Ze(o),null!=o)return Om(o,i,a);throw dm(t,n)}function Om(t,e,n){return new fm(pm.get(t),e,n)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Pm={};class wm{static resolve(t){return Cm(t)}static resolveAndCreate(t,e){const n=wm.resolve(t);return wm.fromResolvedProviders(n,e)}static fromResolvedProviders(t,e){return new km(t,e)}}class km{constructor(t,e){this._constructionCounter=0,this._providers=t,this.parent=e||null;const n=t.length;this.keyIds=[],this.objs=[];for(let e=0;e<n;e++)this.keyIds[e]=t[e].key.id,this.objs[e]=Pm}get(t,e=gr){return this._getByKey(pm.get(t),null,e)}resolveAndCreateChild(t){const e=wm.resolve(t);return this.createChildFromResolved(e)}createChildFromResolved(t){const e=new km(t);return e.parent=this,e}resolveAndInstantiate(t){return this.instantiateResolved(wm.resolve([t])[0])}instantiateResolved(t){return this._instantiateProvider(t)}getProviderAtIndex(t){if(t<0||t>=this._providers.length)throw(function e(t){return Error(`Index ${t} is out-of-bounds.`)})(t);return this._providers[t]}_new(t){if(this._constructionCounter++>this._getMaxNumberOfObjects())throw(function e(t,n){return lm(t,n,(function(t){return`Cannot instantiate cyclic dependency!${sm(t)}`}))})(this,t.key);return this._instantiateProvider(t)}_getMaxNumberOfObjects(){return this.objs.length}_instantiateProvider(t){if(t.multiProvider){const e=[];for(let n=0;n<t.resolvedFactories.length;++n)e[n]=this._instantiate(t,t.resolvedFactories[n]);return e}return this._instantiate(t,t.resolvedFactories[0])}_instantiate(t,e){const n=e.factory;let o,i;try{o=e.dependencies.map((t=>this._getByReflectiveDependency(t)))}catch(e){throw e.addKey&&e.addKey(this,t.key),e}try{i=n(...o)}catch(e){throw(function n(t,e,o,i){return lm(t,i,(function(t){const n=Ge(t[0].token);return`${e.message}: Error during instantiation of ${n}!${sm(t)}.`}),e)})(this,e,0,t.key)}return i}_getByReflectiveDependency(t){return this._getByKey(t.key,t.visibility,t.optional?null:gr)}_getByKey(t,e,n){return t===km.INJECTOR_KEY?this:e instanceof Dr?this._getByKeySelf(t,n):this._getByKeyDefault(t,n,e)}_getObjByKeyId(t){for(let e=0;e<this.keyIds.length;e++)if(this.keyIds[e]===t)return this.objs[e]===Pm&&(this.objs[e]=this._new(this._providers[e])),this.objs[e];return Pm}_throwOrNull(t,e){if(e!==gr)return e;throw(function n(t,e){return lm(t,e,(function(t){return`No provider for ${Ge(t[0].token)}!${sm(t)}`}))})(this,t)}_getByKeySelf(t,e){const n=this._getObjByKeyId(t.id);return n!==Pm?n:this._throwOrNull(t,e)}_getByKeyDefault(t,e,n){let o;for(o=n instanceof Er?this.parent:this;o instanceof km;){const e=o,n=e._getObjByKeyId(t.id);if(n!==Pm)return n;o=e.parent}return null!==o?o.get(t.token,e):this._throwOrNull(t,e)}get displayName(){return`ReflectiveInjector(providers: [${(function t(e,n){const o=[];for(let t=0;t<e._providers.length;++t)o[t]=n(e.getProviderAtIndex(t));return o}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(this,(t=>' "'+t.key.displayName+'" ')).join(", ")}])`}toString(){return this.displayName}}function Sm(t,e=En.Default){const n=fi();return null===n?(ngDevMode&&(function o(t){ngDevMode&&cn(Rn,t,"Calling ɵɵinject would cause infinite recursion")})(Sm),vr(t,e)):xa(bi(),n,Ze(t),e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function Dm(t,e,n){const o=fi(),i=Si();if(Fp(o,i,e)){const a=gi(),r=Yi();ld(a,r,o,t,e,o[11],n,!1),ngDevMode&&zd(a.data,r,t,i)}return Dm}function Em(t,e,n,o,i){const a=i?"class":"style";Vd(t,n,e.inputs[a],a,o)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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     * Copyright Google LLC All Rights Reserved.
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     * Copyright Google LLC All Rights Reserved.
     *
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     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
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     * Copyright Google LLC All Rights Reserved.
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     * Copyright Google LLC All Rights Reserved.
     *
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     * Copyright Google LLC All Rights Reserved.
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     * Copyright Google LLC All Rights Reserved.
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     * Copyright Google LLC All Rights Reserved.
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
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/**
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     * Copyright Google LLC All Rights Reserved.
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     * found in the LICENSE file at https://angular.io/license
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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     * Copyright Google LLC All Rights Reserved.
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let Zu=Gu;function Xu(t){hn(t,"Expected localeId to be defined"),"string"==typeof t&&(Zu=t.toLowerCase().replace(/_/g,"-"))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function Ku(t,e,n){const o=e.insertBeforeIndex,i=Array.isArray(o)?o[0]:o;return null===i?Tl(t,0,n):(ngDevMode&&_n(n,i),ei(n[i]))}function Ju(t,e,n,o,i){const a=e.insertBeforeIndex;if(Array.isArray(a)){ngDevMode&&yn(o);let r=o,s=null;if(3&e.type||(s=r,r=i),null!==r&&0==(2&e.flags))for(let e=1;e<a.length;e++)Sl(t,r,n[a[e]],s,!1)}}
/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let df=0,pf=0;function mf(t,e,n,o){ngDevMode&&yn(o);const i=n[11];let a,r=null;for(let s=0;s<e.length;s++){const l=e[s];if("string"==typeof l){const t=e[++s];null===n[t]&&(ngDevMode&&ngDevMode.rendererCreateTextNode++,ngDevMode&&_n(n,t),n[t]=yl(i,l))}else if("number"==typeof l)switch(1&l){case 0:const c=sf(l);let d,p;if(null===r&&(r=c,a=Rl(i,o)),c===r?(d=o,p=a):(d=null,p=ei(n[c])),null!==p){ngDevMode&&yn(p);const e=lf(l);ngDevMode&&fn(e,go,"Missing ref");const o=n[e];ngDevMode&&yn(o),Sl(i,p,o,d,!1);const a=of(t,e);if(null!==a&&"object"==typeof a){ngDevMode&&ko(a);const e=rf(a,n);null!==e&&mf(t,a.create[e],n,n[a.anchorIdx])}}break;case 1:const m=e[++s],u=e[++s];Md(i,ni(l>>>1,n),null,null,m,u,null);break;default:throw new Error(`Unable to determine the type of mutate operation for "${l}"`)}else switch(l){case Yu:const t=e[++s],o=e[++s];null===n[o]&&(ngDevMode&&ln(typeof t,"string",`Expected "${t}" to be a comment node value`),ngDevMode&&ngDevMode.rendererCreateComment++,ngDevMode&&No(n,o),Ls(n[o]=Cl(i,t),n));break;case Wu:const a=e[++s],r=e[++s];null===n[r]&&(ngDevMode&&ln(typeof a,"string",`Expected "${a}" to be an element node tag name`),ngDevMode&&ngDevMode.rendererCreateElement++,ngDevMode&&No(n,r),Ls(n[r]=Ml(i,a,null),n));break;default:ngDevMode&&bn(`Unable to determine the type of mutate operation for "${l}"`)}}}function uf(t,e,n,o,i){for(let a=0;a<n.length;a++){const r=n[a],s=n[++a];if(r&i){let i="";for(let r=a+1;r<=a+s;r++){const a=n[r];if("string"==typeof a)i+=a;else if("number"==typeof a)if(a<0)i+=$e(e[o-a]);else{const s=a>>>2;switch(3&a){case 1:const a=n[++r],l=n[++r],c=t.data[s];ngDevMode&&hn(c,"Experting TNode or string"),"string"==typeof c?Md(e[11],e[s],null,c,a,i,l):ld(t,c,e,a,i,e[11],l,!1);break;case 0:const d=e[s];null!==d&&_l(e[11],d,i);break;case 2:gf(t,of(t,s),e,i);break;case 3:ff(t,of(t,s),o,e)}}}}else{const i=n[a+1];if(i>0&&3==(3&i)){const n=of(t,i>>>2);e[n.currentCaseLViewIndex]<0&&ff(t,n,o,e)}}a+=s}}function ff(t,e,n,o){ngDevMode&&_n(o,e.currentCaseLViewIndex);let i=o[e.currentCaseLViewIndex];if(null!==i){let a=df;i<0&&(i=o[e.currentCaseLViewIndex]=~i,a=-1),uf(t,o,e.update[i],n,a)}}function gf(t,e,n,o){const i=(function a(t,e){let n=t.cases.indexOf(e);if(-1===n)switch(t.type){case 1:{const o=(function i(t,e){const n=Bu(e)(parseInt(t,10)),o=Uu[n];return void 0!==o?o:"other"})(e,(function o(){return Zu})());n=t.cases.indexOf(o),-1===n&&"other"!==o&&(n=t.cases.indexOf("other"));break}case 0:n=t.cases.indexOf("other")}return-1===n?null:n}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function yf(t){const e=t||(Array.isArray(this)?this:[]);let n=[];for(let t=0;t<e.length;t++){const o=e[t++],i=(o&qu.APPEND_EAGERLY)===qu.APPEND_EAGERLY,a=o>>>qu.SHIFT;n.push(`lView[${a}] = document.${(o&qu.COMMENT)===qu.COMMENT?"createComment":"createText"}(${JSON.stringify(e[t])});`),i&&n.push(`parent.appendChild(lView[${a}]);`)}return n}function _f(t){const e=new vf(t||(Array.isArray(this)?this:[]));let n=[];function o(t){const n=t>>>2;switch(3&t){case 0:return`(lView[${n}] as Text).textContent = $$$`;case 1:const t=e.consumeString(),o=e.consumeFunction();return`(lView[${n}] as Element).setAttribute('${t}', ${o?`(${o})($$$)`:"$$$"})`;case 2:return`icuSwitchCase(${n}, $$$)`;case 3:return`icuUpdateCase(${n})`}throw new Error("unexpected OpCode")}for(;e.hasMore();){let t=e.consumeNumber(),i=e.consumeNumber();const a=e.i+i,r=[];let s="";for(;e.i<a;){let t=e.consumeNumberOrString();if("string"==typeof t)s+=t;else if(t<0)s+="${lView[i"+t+"]}";else{const e=o(t);r.push(e.replace("$$$","`"+s+"`")+";"),s=""}}n.push(`if (mask & 0b${t.toString(2)}) { ${r.join(" ")} }`)}return n}function Cf(t){const e=new vf(t||(Array.isArray(this)?this:[]));let n=[];function o(t){const n=sf(t),o=lf(t);switch(cf(t)){case 0:return`(lView[${n}] as Element).appendChild(lView[${i}])`;case 1:return`(lView[${o}] as Element).setAttribute("${e.consumeString()}", "${e.consumeString()}")`}throw new Error("Unexpected OpCode: "+cf(t))}let i=-1;for(;e.hasMore();){let t=e.consumeNumberStringOrMarker();if(t===Yu){const t=e.consumeString();i=e.consumeNumber(),n.push(`lView[${i}] = document.createComment("${t}")`)}else if(t===Wu){const t=e.consumeString();i=e.consumeNumber(),n.push(`lView[${i}] = document.createElement("${t}")`)}else if("string"==typeof t)i=e.consumeNumber(),n.push(`lView[${i}] = document.createTextNode("${t}")`);else{if("number"!=typeof t)throw new Error("Unexpected value");{const e=o(t);e&&n.push(e)}}}return n}function Mf(t){const e=t||(Array.isArray(this)?this:[]);let n=[];for(let t=0;t<e.length;t++){const o=e[t];n.push(o>0?`remove(lView[${o}])`:`removeNestedICU(${~o})`)}return n}class vf{constructor(t){this.i=0,this.codes=t}hasMore(){return this.i<this.codes.length}consumeNumber(){let t=this.codes[this.i++];return an(t,"expecting number in OpCode"),t}consumeString(){let t=this.codes[this.i++];return sn(t,"expecting string in OpCode"),t}consumeFunction(){let t=this.codes[this.i++];if(null===t||"function"==typeof t)return t;throw new Error("expecting function in OpCode")}consumeNumberOrString(){let t=this.codes[this.i++];return"string"==typeof t||an(t,"expecting number or string in OpCode"),t}consumeNumberStringOrMarker(){let t=this.codes[this.i++];return"string"==typeof t||"number"==typeof t||t==Yu||t==Wu||an(t,"expecting number, string, ICU_MARKER or ELEMENT_MARKER in OpCode"),t}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function Qf(t,e,n=-1){const o=gi(),i=fi(),a=go+t;ngDevMode&&hn(o,"tView should be defined");const r=ci(o.consts,e),s=_i();o.firstCreatePass&&(function l(t,e,n,o,i,a){const r=_i(),s=[],l=[],c=[[]];ngDevMode&&(bc(s,yf),bc(l,_f));const d=(function m(t){return t.replace(Df," ")})(i=(function p(t,e){if((function n(t){return-1===t})(e))return zf(t);{const n=t.indexOf(`:${e}�`)+2+e.toString().length,o=t.search(new RegExp(`�\\/\\*\\d+:${e}�`));return zf(t.substring(n,o))}})(i,a)).split(Sf);for(let a=0;a<d.length;a++){let p=d[a];if(0==(1&a)){const a=Ff(p);for(let d=0;d<a.length;d++){let p=a[d];if(0==(1&d)){const e=p;ngDevMode&&sn(e,"Parsed ICU part should be string"),""!==e&&Rf(t,r,c[0],s,l,n,e)}else{const a=p;if("object"!=typeof a)throw new Error(`Unable to parse ICU expression in "${i}" message.`);const d=Ef(t,r,c[0],n,s,ngDevMode?`ICU ${o}:${a.mainBinding}`:"",!0).index;ngDevMode&&gn(d,go,"Index must be in absolute LView offset"),If(t,n,l,e,a,d)}}}else{const e=47===p.charCodeAt(0),n=p.charCodeAt(e?1:0);ngDevMode&&Cn(n,42,35);const o=go+Number.parseInt(p.substring(e?2:1));if(e)c.shift(),Ci(_i(),!1);else{const e=af(t,c[0],o);c.unshift([]),Ci(e,!0)}}}t.data[o]={create:s,update:l}})(o,null===s?0:s.index,i,a,r,n);const c=o.data[a],d=kl(o,s===i[6]?null:s,i);!(function p(t,e,n,o){const i=t[11];for(let a=0;a<e.length;a++){const r=e[a++],s=e[a],l=(r&qu.COMMENT)===qu.COMMENT,c=(r&qu.APPEND_EAGERLY)===qu.APPEND_EAGERLY,d=r>>>qu.SHIFT;let p=t[d];null===p&&(p=t[d]=l?i.createComment(s):yl(i,s)),c&&null!==n&&Sl(i,n,p,o,!1)}})(i,c.create,d,s&&8&s.type?i[s.index]:null),Ei(!0)}function $f(){Ei(!1)}function tg(t,e,n){Qf(t,e,n),$f()}function eg(t){return(function e(t){t&&(df|=1<<Math.min(pf,31)),pf++})(Fp(fi(),Si(),t)),eg}function ng(t){!(function e(t,n,o){if(pf>0){ngDevMode&&hn(t,"tView should be defined");const e=t.data[o];uf(t,n,Array.isArray(e)?e:e.update,wi()-pf-1,df)}df=0,pf=0})(gi(),fi(),t+go)}function og(t,e={}){return(function n(t,e={}){let n=t;if(Wf.test(t)){const t={},e=[0];n=n.replace(Yf,((n,o,i)=>{const a=o||i,r=t[a]||[];if(r.length||(a.split("|").forEach((t=>{const e=t.match(Jf),n=e?parseInt(e[1],10):0,o=Kf.test(t);r.push([n,o,t])})),t[a]=r),!r.length)throw new Error(`i18n postprocess: unmatched placeholder - ${a}`);const s=e[e.length-1];let l=0;for(let t=0;t<r.length;t++)if(r[t][0]===s){l=t;break}const[c,d,p]=r[l];return d?e.pop():s!==c&&e.push(c),r.splice(l,1),p}))}return Object.keys(e).length?(n=n.replace(qf,((t,n,o,i,a,r)=>e.hasOwnProperty(o)?`${n}${e[o]}${r}`:t)),n=n.replace(Zf,((t,n)=>e.hasOwnProperty(n)?e[n]:t)),n=n.replace(Xf,((t,n)=>{if(e.hasOwnProperty(n)){const o=e[n];if(!o.length)throw new Error(`i18n postprocess: unmatched ICU - ${t} with key: ${n}`);return o.shift()}return t})),n):n})(t,e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function ig(t,e,n,o,i){if(t=Ze(t),Array.isArray(t))for(let a=0;a<t.length;a++)ig(t[a],e,n,o,i);else{const a=gi(),r=fi();let s=op(t)?t:Ze(t.provide),l=tp(t);const c=bi(),d=1048575&c.providerIndexes,p=c.directiveStart,m=c.providerIndexes>>20;if(op(t)||!t.multi){const o=new na(l,i,Sm),u=sg(s,e,i?d:d+m,p);-1===u?(Ca(ha(c,r),a,s),ag(a,t,e.length),e.push(s),c.directiveStart++,c.directiveEnd++,i&&(c.providerIndexes+=1048576),n.push(o),r.push(o)):(n[u]=o,r[u]=o)}else{const u=sg(s,e,d+m,p),f=sg(s,e,d,d+m),g=u>=0&&n[u],h=f>=0&&n[f];if(i&&!h||!i&&!g){Ca(ha(c,r),a,s);const d=(function a(t,e,n,o,i){const a=new na(t,n,Sm);return a.multi=[],a.index=e,a.componentProviders=0,rg(a,i,o&&!n),a})(i?cg:lg,n.length,i,o,l);!i&&h&&(n[f].providerFactory=d),ag(a,t,e.length,0),e.push(s),c.directiveStart++,c.directiveEnd++,i&&(c.providerIndexes+=1048576),n.push(d),r.push(d)}else ag(a,t,u>-1?u:f,rg(n[i?f:u],l,!i&&o));!i&&o&&h&&n[f].componentProviders++}}}function ag(t,e,n,o){const i=op(e);if(i||(function a(t){return!!t.useClass})(e)){const a=(e.useClass||e).prototype.ngOnDestroy;if(a){const r=t.destroyHooks||(t.destroyHooks=[]);if(!i&&e.multi){ngDevMode&&hn(o,"indexInFactory when registering multi factory destroy hook");const t=r.indexOf(n);-1===t?r.push(n,[o,a]):r[t+1].push(o,a)}else r.push(n,a)}}}function rg(t,e,n){return n&&t.componentProviders++,t.multi.push(e)-1}function sg(t,e,n,o){for(let i=n;i<o;i++)if(e[i]===t)return i;return-1}function lg(t,e,n,o){return dg(this.multi,[])}function cg(t,e,n,o){const i=this.multi;let a;if(this.providerFactory){const t=this.providerFactory.componentProviders,e=Sa(n,n[1],this.providerFactory.index,o);a=e.slice(0,t),dg(i,a);for(let n=t;n<e.length;n++)a.push(e[n])}else a=[],dg(i,a);return a}function dg(t,e){for(let n=0;n<t.length;n++)e.push((0,t[n])());return e}function pg(t,e=[]){return n=>{n.providersResolver=(n,o)=>(function i(t,e,n){const o=gi();if(o.firstCreatePass){const i=xo(t);ig(n,o.data,o.blueprint,i,!0),ig(e,o.data,o.blueprint,i,!1)}})(n,o?o(t):t,e)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class mg{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class ug{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function fg(...t){}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function gg(t,e){return new hg(oi(t,e))}ug.NULL=new class{resolveComponentFactory(t){throw(function e(t){const e=Error(`No component factory found for ${Ge(t)}. Did you add it to @NgModule.entryComponents?`);return e.ngComponent=t,e})(t)}};class hg{constructor(t){this.nativeElement=t}}function bg(t){return t instanceof hg?t.nativeElement:t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */hg.__NG_ELEMENT_ID__=function yg(){return gg(bi(),fi())},new Ga("Renderer2Interceptor");class _g{}class Cg{}Cg.__NG_ELEMENT_ID__=()=>Mg();const Mg=function vg(){const t=fi(),e=ri(bi().index,t);return(function n(t){const e=t[11];if(ngDevMode&&!$o(e))throw new Error("Cannot inject Renderer2 when the application uses Renderer3!");return e})(yo(e)?e:t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */;class xg{}xg.ɵprov=Mn({token:xg,providedIn:"root",factory:()=>null});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Og{constructor(t){this.full=t,this.major=t.split(".")[0],this.minor=t.split(".")[1],this.patch=t.split(".").slice(2).join(".")}}const Pg=new Og("12.2.1");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class wg{constructor(){}supports(t){return Np(t)}create(t){return new Sg(t)}}const kg=(t,e)=>e;class Sg{constructor(t){this.length=0,this._linkedRecords=null,this._unlinkedRecords=null,this._previousItHead=null,this._itHead=null,this._itTail=null,this._additionsHead=null,this._additionsTail=null,this._movesHead=null,this._movesTail=null,this._removalsHead=null,this._removalsTail=null,this._identityChangesHead=null,this._identityChangesTail=null,this._trackByFn=t||kg}forEachItem(t){let e;for(e=this._itHead;null!==e;e=e._next)t(e)}forEachOperation(t){let e=this._itHead,n=this._removalsHead,o=0,i=null;for(;e||n;){const a=!n||e&&e.currentIndex<Ag(n,o,i)?e:n,r=Ag(a,o,i),s=a.currentIndex;if(a===n)o--,n=n._nextRemoved;else if(e=e._next,null==a.previousIndex)o++;else{i||(i=[]);const t=r-o,e=s-o;if(t!=e){for(let n=0;n<t;n++){const o=n<i.length?i[n]:i[n]=0,a=o+n;e<=a&&a<t&&(i[n]=o+1)}i[a.previousIndex]=e-t}}r!==s&&t(a,r,s)}}forEachPreviousItem(t){let e;for(e=this._previousItHead;null!==e;e=e._nextPrevious)t(e)}forEachAddedItem(t){let e;for(e=this._additionsHead;null!==e;e=e._nextAdded)t(e)}forEachMovedItem(t){let e;for(e=this._movesHead;null!==e;e=e._nextMoved)t(e)}forEachRemovedItem(t){let e;for(e=this._removalsHead;null!==e;e=e._nextRemoved)t(e)}forEachIdentityChange(t){let e;for(e=this._identityChangesHead;null!==e;e=e._nextIdentityChange)t(e)}diff(t){if(null==t&&(t=[]),!Np(t))throw new Error(`Error trying to diff '${Ge(t)}'. Only arrays and iterables are allowed`);return this.check(t)?this:null}onDestroy(){}check(t){this._reset();let e,n,o,i=this._itHead,a=!1;if(Array.isArray(t)){this.length=t.length;for(let e=0;e<this.length;e++)n=t[e],o=this._trackByFn(e,n),null!==i&&Object.is(i.trackById,o)?(a&&(i=this._verifyReinsertion(i,n,o,e)),Object.is(i.item,n)||this._addIdentityChange(i,n)):(i=this._mismatch(i,n,o,e),a=!0),i=i._next}else e=0,(function r(t,e){if(Array.isArray(t))for(let n=0;n<t.length;n++)e(t[n]);else{const n=t[Rp()]();let o;for(;!(o=n.next()).done;)e(o.value)}})(t,(t=>{o=this._trackByFn(e,t),null!==i&&Object.is(i.trackById,o)?(a&&(i=this._verifyReinsertion(i,t,o,e)),Object.is(i.item,t)||this._addIdentityChange(i,t)):(i=this._mismatch(i,t,o,e),a=!0),i=i._next,e++})),this.length=e;return this._truncate(i),this.collection=t,this.isDirty}get isDirty(){return null!==this._additionsHead||null!==this._movesHead||null!==this._removalsHead||null!==this._identityChangesHead}_reset(){if(this.isDirty){let t;for(t=this._previousItHead=this._itHead;null!==t;t=t._next)t._nextPrevious=t._next;for(t=this._additionsHead;null!==t;t=t._nextAdded)t.previousIndex=t.currentIndex;for(this._additionsHead=this._additionsTail=null,t=this._movesHead;null!==t;t=t._nextMoved)t.previousIndex=t.currentIndex;this._movesHead=this._movesTail=null,this._removalsHead=this._removalsTail=null,this._identityChangesHead=this._identityChangesTail=null}}_mismatch(t,e,n,o){let i;return null===t?i=this._itTail:(i=t._prev,this._remove(t)),null!==(t=null===this._unlinkedRecords?null:this._unlinkedRecords.get(n,null))?(Object.is(t.item,e)||this._addIdentityChange(t,e),this._reinsertAfter(t,i,o)):null!==(t=null===this._linkedRecords?null:this._linkedRecords.get(n,o))?(Object.is(t.item,e)||this._addIdentityChange(t,e),this._moveAfter(t,i,o)):t=this._addAfter(new Dg(e,n),i,o),t}_verifyReinsertion(t,e,n,o){let i=null===this._unlinkedRecords?null:this._unlinkedRecords.get(n,null);return null!==i?t=this._reinsertAfter(i,t._prev,o):t.currentIndex!=o&&(t.currentIndex=o,this._addToMoves(t,o)),t}_truncate(t){for(;null!==t;){const e=t._next;this._addToRemovals(this._unlink(t)),t=e}null!==this._unlinkedRecords&&this._unlinkedRecords.clear(),null!==this._additionsTail&&(this._additionsTail._nextAdded=null),null!==this._movesTail&&(this._movesTail._nextMoved=null),null!==this._itTail&&(this._itTail._next=null),null!==this._removalsTail&&(this._removalsTail._nextRemoved=null),null!==this._identityChangesTail&&(this._identityChangesTail._nextIdentityChange=null)}_reinsertAfter(t,e,n){null!==this._unlinkedRecords&&this._unlinkedRecords.remove(t);const o=t._prevRemoved,i=t._nextRemoved;return null===o?this._removalsHead=i:o._nextRemoved=i,null===i?this._removalsTail=o:i._prevRemoved=o,this._insertAfter(t,e,n),this._addToMoves(t,n),t}_moveAfter(t,e,n){return this._unlink(t),this._insertAfter(t,e,n),this._addToMoves(t,n),t}_addAfter(t,e,n){return this._insertAfter(t,e,n),this._additionsTail=null===this._additionsTail?this._additionsHead=t:this._additionsTail._nextAdded=t,t}_insertAfter(t,e,n){const o=null===e?this._itHead:e._next;return t._next=o,t._prev=e,null===o?this._itTail=t:o._prev=t,null===e?this._itHead=t:e._next=t,null===this._linkedRecords&&(this._linkedRecords=new Rg),this._linkedRecords.put(t),t.currentIndex=n,t}_remove(t){return this._addToRemovals(this._unlink(t))}_unlink(t){null!==this._linkedRecords&&this._linkedRecords.remove(t);const e=t._prev,n=t._next;return null===e?this._itHead=n:e._next=n,null===n?this._itTail=e:n._prev=e,t}_addToMoves(t,e){return t.previousIndex===e||(this._movesTail=null===this._movesTail?this._movesHead=t:this._movesTail._nextMoved=t),t}_addToRemovals(t){return null===this._unlinkedRecords&&(this._unlinkedRecords=new Rg),this._unlinkedRecords.put(t),t.currentIndex=null,t._nextRemoved=null,null===this._removalsTail?(this._removalsTail=this._removalsHead=t,t._prevRemoved=null):(t._prevRemoved=this._removalsTail,this._removalsTail=this._removalsTail._nextRemoved=t),t}_addIdentityChange(t,e){return t.item=e,this._identityChangesTail=null===this._identityChangesTail?this._identityChangesHead=t:this._identityChangesTail._nextIdentityChange=t,t}}class Dg{constructor(t,e){this.item=t,this.trackById=e,this.currentIndex=null,this.previousIndex=null,this._nextPrevious=null,this._prev=null,this._next=null,this._prevDup=null,this._nextDup=null,this._prevRemoved=null,this._nextRemoved=null,this._nextAdded=null,this._nextMoved=null,this._nextIdentityChange=null}}class Eg{constructor(){this._head=null,this._tail=null}add(t){null===this._head?(this._head=this._tail=t,t._nextDup=null,t._prevDup=null):(this._tail._nextDup=t,t._prevDup=this._tail,t._nextDup=null,this._tail=t)}get(t,e){let n;for(n=this._head;null!==n;n=n._nextDup)if((null===e||e<=n.currentIndex)&&Object.is(n.trackById,t))return n;return null}remove(t){const e=t._prevDup,n=t._nextDup;return null===e?this._head=n:e._nextDup=n,null===n?this._tail=e:n._prevDup=e,null===this._head}}class Rg{constructor(){this.map=new Map}put(t){const e=t.trackById;let n=this.map.get(e);n||(n=new Eg,this.map.set(e,n)),n.add(t)}get(t,e){const n=this.map.get(t);return n?n.get(t,e):null}remove(t){const e=t.trackById;return this.map.get(e).remove(t)&&this.map.delete(e),t}get isEmpty(){return 0===this.map.size}clear(){this.map.clear()}}function Ag(t,e,n){const o=t.previousIndex;if(null===o)return o;let i=0;return n&&o<n.length&&(i=n[o]),o+e+i}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Tg{constructor(){}supports(t){return t instanceof Map||zp(t)}create(){return new Ng}}class Ng{constructor(){this._records=new Map,this._mapHead=null,this._appendAfter=null,this._previousMapHead=null,this._changesHead=null,this._changesTail=null,this._additionsHead=null,this._additionsTail=null,this._removalsHead=null,this._removalsTail=null}get isDirty(){return null!==this._additionsHead||null!==this._changesHead||null!==this._removalsHead}forEachItem(t){let e;for(e=this._mapHead;null!==e;e=e._next)t(e)}forEachPreviousItem(t){let e;for(e=this._previousMapHead;null!==e;e=e._nextPrevious)t(e)}forEachChangedItem(t){let e;for(e=this._changesHead;null!==e;e=e._nextChanged)t(e)}forEachAddedItem(t){let e;for(e=this._additionsHead;null!==e;e=e._nextAdded)t(e)}forEachRemovedItem(t){let e;for(e=this._removalsHead;null!==e;e=e._nextRemoved)t(e)}diff(t){if(t){if(!(t instanceof Map||zp(t)))throw new Error(`Error trying to diff '${Ge(t)}'. Only maps and objects are allowed`)}else t=new Map;return this.check(t)?this:null}onDestroy(){}check(t){this._reset();let e=this._mapHead;if(this._appendAfter=null,this._forEach(t,((t,n)=>{if(e&&e.key===n)this._maybeAddToChanges(e,t),this._appendAfter=e,e=e._next;else{const o=this._getOrCreateRecordForKey(n,t);e=this._insertBeforeOrAppend(e,o)}})),e){e._prev&&(e._prev._next=null),this._removalsHead=e;for(let t=e;null!==t;t=t._nextRemoved)t===this._mapHead&&(this._mapHead=null),this._records.delete(t.key),t._nextRemoved=t._next,t.previousValue=t.currentValue,t.currentValue=null,t._prev=null,t._next=null}return this._changesTail&&(this._changesTail._nextChanged=null),this._additionsTail&&(this._additionsTail._nextAdded=null),this.isDirty}_insertBeforeOrAppend(t,e){if(t){const n=t._prev;return e._next=t,e._prev=n,t._prev=e,n&&(n._next=e),t===this._mapHead&&(this._mapHead=e),this._appendAfter=t,t}return this._appendAfter?(this._appendAfter._next=e,e._prev=this._appendAfter):this._mapHead=e,this._appendAfter=e,null}_getOrCreateRecordForKey(t,e){if(this._records.has(t)){const n=this._records.get(t);this._maybeAddToChanges(n,e);const o=n._prev,i=n._next;return o&&(o._next=i),i&&(i._prev=o),n._next=null,n._prev=null,n}const n=new zg(t);return this._records.set(t,n),n.currentValue=e,this._addToAdditions(n),n}_reset(){if(this.isDirty){let t;for(this._previousMapHead=this._mapHead,t=this._previousMapHead;null!==t;t=t._next)t._nextPrevious=t._next;for(t=this._changesHead;null!==t;t=t._nextChanged)t.previousValue=t.currentValue;for(t=this._additionsHead;null!=t;t=t._nextAdded)t.previousValue=t.currentValue;this._changesHead=this._changesTail=null,this._additionsHead=this._additionsTail=null,this._removalsHead=null}}_maybeAddToChanges(t,e){Object.is(e,t.currentValue)||(t.previousValue=t.currentValue,t.currentValue=e,this._addToChanges(t))}_addToAdditions(t){null===this._additionsHead?this._additionsHead=this._additionsTail=t:(this._additionsTail._nextAdded=t,this._additionsTail=t)}_addToChanges(t){null===this._changesHead?this._changesHead=this._changesTail=t:(this._changesTail._nextChanged=t,this._changesTail=t)}_forEach(t,e){t instanceof Map?t.forEach(e):Object.keys(t).forEach((n=>e(t[n],n)))}}class zg{constructor(t){this.key=t,this.previousValue=null,this.currentValue=null,this._nextPrevious=null,this._next=null,this._prev=null,this._nextAdded=null,this._nextRemoved=null,this._nextChanged=null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Ig(){return new Hg([new wg])}class Hg{constructor(t){this.factories=t}static create(t,e){if(null!=e){const n=e.factories.slice();t=t.concat(n)}return new Hg(t)}static extend(t){return{provide:Hg,useFactory:e=>Hg.create(t,e||Ig()),deps:[[Hg,new Er,new Sr]]}}find(t){const e=this.factories.find((e=>e.supports(t)));if(null!=e)return e;throw new Error(`Cannot find a differ supporting object '${t}' of type '${(function n(t){return t.name||typeof t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t)}'`)}}function Fg(){return new Lg([new Tg])}Hg.ɵprov=Mn({token:Hg,providedIn:"root",factory:Ig});class Lg{constructor(t){this.factories=t}static create(t,e){if(e){const n=e.factories.slice();t=t.concat(n)}return new Lg(t)}static extend(t){return{provide:Lg,useFactory:e=>Lg.create(t,e||Fg()),deps:[[Lg,new Er,new Sr]]}}find(t){const e=this.factories.find((e=>e.supports(t)));if(e)return e;throw new Error(`Cannot find a differ supporting object '${t}'`)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function Bg(t,e,n,o,i=!1){for(;null!==n;){ngDevMode&&ia(n,63);const a=e[n.index];if(null!==a&&o.push(ei(a)),_o(a))for(let t=bo;t<a.length;t++){const e=a[t],n=e[1].firstChild;null!==n&&Bg(e[1],e,n,o)}const r=n.type;if(8&r)Bg(t,e,n.child,o);else if(32&r){const t=pl(n,e);let i;for(;i=t();)o.push(i)}else if(16&r){const t=Ll(e,n);if(Array.isArray(t))o.push(...t);else{const n=ml(e[16]);ngDevMode&&Io(n),Bg(n[1],n,t,o,!0)}}n=i?n.projectionNext:n.next}return o}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */Lg.ɵprov=Mn({token:Lg,providedIn:"root",factory:Fg});class Vg{constructor(t,e){this._lView=t,this._cdRefInjectingView=e,this._appRef=null,this._attachedToViewContainer=!1}get rootNodes(){const t=this._lView,e=t[1];return Bg(e,t,e.firstChild,[])}get context(){return this._lView[8]}set context(t){this._lView[8]=t}get destroyed(){return 256==(256&this._lView[2])}destroy(){if(this._appRef)this._appRef.detachView(this);else if(this._attachedToViewContainer){const t=this._lView[3];if(_o(t)){const e=t[8],n=e?e.indexOf(this):-1;n>-1&&(ngDevMode&&ln(n,t.indexOf(this._lView)-bo,"An attached view should be in the same position within its container as its ViewRef in the VIEW_REFS array."),xl(t,n),or(e,n))}this._attachedToViewContainer=!1}Ol(this._lView[1],this._lView)}onDestroy(t){rd(this._lView[1],this._lView,null,t)}markForCheck(){Ed(this._cdRefInjectingView||this._lView)}detach(){this._lView[2]&=-129}reattach(){this._lView[2]|=128}detectChanges(){Ad(this._lView[1],this._lView,this.context)}checkNoChanges(){!(function t(e,n,o){Oi(!0);try{Ad(e,n,o)}finally{Oi(!1)}})(this._lView[1],this._lView,this.context)}attachToViewContainerRef(){if(this._appRef)throw new Error("This view is already attached directly to the ApplicationRef!");this._attachedToViewContainer=!0}detachFromAppRef(){this._appRef=null,(function t(e,n){Ul(e,n,n[11],2,null,null)})(this._lView[1],this._lView)}attachToAppRef(t){if(this._attachedToViewContainer)throw new Error("This view is already attached to a ViewContainer!");this._appRef=t}}class jg extends Vg{constructor(t){super(t),this._view=t}detectChanges(){Td(this._view)}checkNoChanges(){!(function t(e){Oi(!0);try{Td(e)}finally{Oi(!1)}})(this._view)}get context(){return null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Ug{}Ug.__NG_ELEMENT_ID__=function Gg(t){return(function e(t,n,o){if(Mo(t)&&!o){const e=ri(t.index,n);return new Vg(e,e)}return 47&t.type?new Vg(n[16],n):null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(bi(),fi(),16==(16&t))};const Wg=[new Tg],Yg=[new wg],qg=new Hg(Yg),Zg=new Lg(Wg);class Xg{}Xg.__NG_ELEMENT_ID__=function Kg(){return $g(bi(),fi())};const Jg=Xg,Qg=class extends Jg{constructor(t,e,n){super(),this._declarationLView=t,this._declarationTContainer=e,this.elementRef=n}createEmbeddedView(t){const e=this._declarationTContainer.tViews,n=qc(this._declarationLView,e,t,16,null,e.declTNode,null,null,null,null),o=this._declarationLView[this._declarationTContainer.index];ngDevMode&&Do(o),n[17]=o;const i=this._declarationLView[19];return null!==i&&(n[19]=i.createEmbeddedView(e)),Jc(e,n,t),new Vg(n)}};function $g(t,e){return 4&t.type?(ngDevMode&&hn(t.tViews,"TView must be allocated"),new Qg(e,t,gg(t,e))):null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class th{}class eh{}eh.__NG_ELEMENT_ID__=function nh(){return sh(bi(),fi())};const oh=eh,ih=class extends oh{constructor(t,e,n){super(),this._lContainer=t,this._hostTNode=e,this._hostLView=n}get element(){return gg(this._hostTNode,this._hostLView)}get injector(){return new Ra(this._hostTNode,this._hostLView)}get parentInjector(){const t=_a(this._hostTNode,this._hostLView);if(da(t)){const e=ma(t,this._hostLView),n=pa(t);return ngDevMode&&Ho(e,n),new Ra(e[1].data[n+8],e)}return new Ra(null,this._hostLView)}clear(){for(;this.length>0;)this.remove(this.length-1)}get(t){const e=ah(this._lContainer);return null!==e&&e[t]||null}get length(){return this._lContainer.length-bo}createEmbeddedView(t,e,n){const o=t.createEmbeddedView(e||{});return this.insert(o,n),o}createComponent(t,e,n,o,i){const a=n||this.parentInjector;if(!i&&null==t.ngModule&&a){const t=a.get(th,null);t&&(i=t)}const r=t.create(a,o,void 0,i);return this.insert(r.hostView,e),r}insert(t,e){const n=t._lView,o=n[1];if(ngDevMode&&t.destroyed)throw new Error("Cannot insert a destroyed View in a ViewContainer!");if((function i(t){return _o(t[3])})(n)){const e=this.indexOf(t);if(-1!==e)this.detach(e);else{const e=n[3];ngDevMode&&ln(_o(e),!0,"An attached view should have its PARENT point to a container.");const o=new ih(e,e[6],e[3]);o.detach(o.indexOf(t))}}const a=this._adjustIndex(e),r=this._lContainer;!(function s(t,e,n,o){ngDevMode&&Ro(e),ngDevMode&&Do(n);const i=bo+o,a=n.length;o>0&&(n[i-1][4]=e),o<a-bo?(e[4]=n[i],nr(n,bo+o,e)):(n.push(e),e[4]=null),e[3]=n;const r=e[17];null!==r&&n!==r&&(function s(t,e){ngDevMode&&hn(e,"LView required"),ngDevMode&&Do(t);const n=t[9],o=e[3];ngDevMode&&Do(o);const i=o[3][16];ngDevMode&&hn(i,"Missing insertedComponentLView");const a=e[16];ngDevMode&&hn(a,"Missing declaredComponentLView"),a!==i&&(t[2]=!0),null===n?t[9]=[e]:n.push(e)})(r,e);const l=e[19];null!==l&&l.insertView(t),e[2]|=128})(o,n,r,a);const l=Bl(a,r),c=n[11],d=Rl(c,r[7]);return null!==d&&(function p(t,e,n,o,i,a){o[0]=i,o[6]=e,Ul(t,o,n,1,i,a)})(o,r[6],c,n,d,l),t.attachToViewContainerRef(),nr(rh(r),a,t),t}move(t,e){if(ngDevMode&&t.destroyed)throw new Error("Cannot move a destroyed View in a ViewContainer!");return this.insert(t,e)}indexOf(t){const e=ah(this._lContainer);return null!==e?e.indexOf(t):-1}remove(t){const e=this._adjustIndex(t,-1),n=xl(this._lContainer,e);n&&(or(rh(this._lContainer),e),Ol(n[1],n))}detach(t){const e=this._adjustIndex(t,-1),n=xl(this._lContainer,e);return n&&null!=or(rh(this._lContainer),e)?new Vg(n):null}_adjustIndex(t,e=0){return null==t?this.length+e:(ngDevMode&&(fn(t,-1,`ViewRef index must be positive, got ${t}`),mn(t,this.length+1+e,"index")),t)}};function ah(t){return t[8]}function rh(t){return t[8]||(t[8]=[])}function sh(t,e){let n;ngDevMode&&ia(t,15);const o=e[t.index];if(_o(o))n=o;else{let i;if(8&t.type)i=ei(o);else{const n=e[11];ngDevMode&&ngDevMode.rendererCreateComment++,i=n.createComment(ngDevMode?"container":"");const o=oi(t,e);Sl(n,Rl(n,o),i,(function i(t,e){return $o(t)?t.nextSibling(e):e.nextSibling})(n,o),!1)}e[t.index]=n=Pd(o,e,i,t),Dd(e,n)}return new ih(n,t,e)}const lh=new Map;function ch(t){let e=lh.get(t);return e||(e=Ge(t)+"_"+lh.size,lh.set(t,e)),e}ch(rp),ch(Gd),ch(th),
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
ch(Cg),ch(hg),ch(eh),ch(Xg),ch(Ug),ch(rp),ch(Gd);const dh={};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class ph extends ug{constructor(t){super(),this.ngModule=t}resolveComponentFactory(t){ngDevMode&&(function e(t,n="Type passed in is not ComponentType, it does not have 'ɵcmp' property."){po(t)||bn(n)})(t);const n=po(t);return new fh(n,this.ngModule)}}function mh(t){const e=[];for(let n in t)t.hasOwnProperty(n)&&e.push({propName:t[n],templateName:n});return e}const uh=new Ga("SCHEDULER_TOKEN",{providedIn:"root",factory:()=>nl});class fh extends mg{constructor(t,e){super(),this.componentDef=t,this.ngModule=e,this.componentType=t.type,this.selector=(function n(t){return t.map(ic).join(",")})(t.selectors),this.ngContentSelectors=t.ngContentSelectors?t.ngContentSelectors:[],this.isBoundToModule=!!e}get inputs(){return mh(this.componentDef.inputs)}get outputs(){return mh(this.componentDef.outputs)}create(t,e,n,o){const i=(o=o||this.ngModule)?(function a(t,e){return{get:(n,o,i)=>{const a=t.get(n,dh,i);return a!==dh||o===dh?a:e.get(n,o,i)}}})(t,o.injector):t,r=i.get(_g,ti),s=i.get(xg,null),l=r.createRenderer(null,this.componentDef),c=this.componentDef.selectors[0][0]||"div",d=n?(function p(t,e,n){if($o(t))return t.selectRootElement(e,n===Hn.ShadowDom);let o="string"==typeof e?t.querySelector(e):e;return ngDevMode&&(function i(t,e){if(!t)throw(function n(t,e){return new Error(`Renderer: ${t} [${tn(e)}]`)})("string"==typeof e?"Host node with selector not found:":"Host node is required:",e)})(o,e),o.textContent="",o})(l,n,this.componentDef.encapsulation):Ml(r.createRenderer(null,this.componentDef),c,(function m(t){const e=t.toLowerCase();return"svg"===e?Zo:"math"===e?Xo:null})(c)),u=this.componentDef.onPush?576:528,f=(function g(t,e){return{components:[],scheduler:t||nl,clean:Id,playerHandler:e||null,flags:0}})(),h=ad(0,null,null,1,0,null,null,null,null,null),b=qc(null,h,f,u,null,null,r,l,s,i);let y,_;Fi(b);try{const t=(function o(t,e,n,i,a,r){const s=n[1];ngDevMode&&_n(n,20),n[20]=t;const l=Zc(s,20,2,"#host",null),c=l.mergedAttrs=e.hostAttrs;null!==c&&(Ud(l,c,!0),null!==t&&(aa(a,t,c),null!==l.classes&&Yl(a,t,l.classes),null!==l.styles&&Wl(a,t,l.styles)));const d=i.createRenderer(t,e),p=qc(n,id(e),null,e.onPush?64:16,n[20],l,i,d,r||null,null);return s.firstCreatePass&&(Ca(ha(l,n),s,e.type),gd(s,l),bd(l,n.length,1)),Dd(n,p),n[20]=p})(d,this.componentDef,b,r,l);if(d)if(n)aa(l,d,["ng-version",Pg.full]);else{const{attrs:t,classes:e}=(function n(t){const e=[],n=[];let o=1,i=2;for(;o<t.length;){let a=t[o];if("string"==typeof a)2===i?""!==a&&e.push(a,t[++o]):8===i&&n.push(a);else{if(!$l(i))break;i=a}o++}return{attrs:e,classes:n}})(this.componentDef.selectors[0]);t&&aa(l,d,t),e&&e.length>0&&Yl(l,d,e.join(" "))}if(_=ii(h,go),void 0!==e){const t=_.projection=[];for(let n=0;n<this.ngContentSelectors.length;n++){const o=e[n];t.push(null!=o?Array.from(o):null)}}y=(function i(t,e,n,o,a){const r=n[1],s=(function l(t,e,n){const o=bi();if(t.firstCreatePass){n.providersResolver&&n.providersResolver(n);const i=Kc(t,e,1,null);ngDevMode&&ln(i,o.directiveStart,"Because this is a root component the allocated expando should match the TNode component."),yd(t,o,e,i,n)}const i=Sa(e,t,o.directiveStart,o);Ls(i,e);const a=oi(o,e);return a&&Ls(a,e),i})(r,n,e);if(o.components.push(s),t[8]=s,a&&a.forEach((t=>t(s,e))),e.contentQueries){const t=bi();ngDevMode&&hn(t,"TNode expected"),e.contentQueries(1,s,t.directiveStart)}const c=bi();return ngDevMode&&hn(c,"tNode should have been already created"),!r.firstCreatePass||null===e.hostBindings&&null===e.hostAttrs||(Wi(c.index),ud(n[1],c,0,c.directiveStart,c.directiveEnd,e),fd(e,s)),s})(t,this.componentDef,b,f,[Mp]),Jc(h,b,null)}finally{Ui()}return new gh(this.componentType,y,gg(_,b),b,_)}}new ph;class gh extends class{}{constructor(t,e,n,o,i){super(),this.location=n,this._rootLView=o,this._tNode=i,this.instance=e,this.hostView=this.changeDetectorRef=new jg(o),this.componentType=t}get injector(){return new Ra(this._tNode,this._rootLView)}destroy(){this.hostView.destroy()}onDestroy(t){this.hostView.onDestroy(t)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function hh(t,e,n,o){return Nn((()=>{const i=t;null!==e&&(i.hasOwnProperty("decorators")&&void 0!==i.decorators?i.decorators.push(...e):i.decorators=e),null!==n&&(i.ctorParameters=n),null!==o&&(i.propDecorators=i.hasOwnProperty("propDecorators")&&void 0!==i.propDecorators?Object.assign(Object.assign({},i.propDecorators),o):o)}))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const bh=new Map;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class yh extends th{constructor(t,e){super(),this._parent=e,this._bootstrapComponents=[],this.injector=this,this.destroyCbs=[],this.componentFactoryResolver=new ph(this);const n=fo(t);ngDevMode&&hn(n,`NgModule '${Ge(t)}' is not a subtype of 'NgModuleType'.`);const o=(function i(t){return t[Kn]||null})(t);o&&Xu(o),this._bootstrapComponents=rl(n.bootstrap),this._r3Injector=Jd(t,e,[{provide:th,useValue:this},{provide:ug,useValue:this.componentFactoryResolver}],Ge(t)),this._r3Injector._resolveInjectorDefTypes(),this.instance=this.get(t)}get(t,e=rp.THROW_IF_NOT_FOUND,n=En.Default){return t===rp||t===th||t===Gd?this:this._r3Injector.get(t,e,n)}destroy(){ngDevMode&&hn(this.destroyCbs,"NgModule already destroyed");const t=this._r3Injector;!t.destroyed&&t.destroy(),this.destroyCbs.forEach((t=>t())),this.destroyCbs=null}onDestroy(t){ngDevMode&&hn(this.destroyCbs,"NgModule already destroyed"),this.destroyCbs.push(t)}}class _h extends class{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */{constructor(t){super(),this.moduleType=t,null!==fo(t)&&(function e(t){const e=new Set;!(function t(n){const o=fo(n,!0),i=o.id;null!==i&&((function a(t,e,n){if(e&&e!==n)throw new Error(`Duplicate module registered for ${t} - ${Ge(e)} vs ${Ge(e.name)}`)})(i,bh.get(i),n),bh.set(i,n));const r=rl(o.imports);for(const n of r)e.has(n)||(e.add(n),t(n))})(t)})(t)}create(t){return new yh(this.moduleType,t)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Ch(t,e,n){const o=Pi()+t,i=fi();return i[o]===ac?Ip(i,o,n?e.call(n):e()):Hp(i,o)}function Mh(t,e,n,o){return kh(fi(),Pi(),t,e,n,o)}function vh(t,e,n,o,i){return Sh(fi(),Pi(),t,e,n,o,i)}function xh(t,e,n,o,i,a){return Dh(fi(),Pi(),t,e,n,o,i,a)}function Oh(t,e,n,o,i,a,r){return Eh(fi(),Pi(),t,e,n,o,i,a,r)}function Ph(t,e,n,o,i,a,r,s){const l=Pi()+t,c=fi(),d=Vp(c,l,n,o,i,a);return Fp(c,l+4,r)||d?Ip(c,l+5,s?e.call(s,n,o,i,a,r):e(n,o,i,a,r)):Hp(c,l+5)}function wh(t,e){ngDevMode&&_n(t,e);const n=t[e];return n===ac?void 0:n}function kh(t,e,n,o,i,a){const r=e+n;return Fp(t,r,i)?Ip(t,r+1,a?o.call(a,i):o(i)):wh(t,r+1)}function Sh(t,e,n,o,i,a,r){const s=e+n;return Lp(t,s,i,a)?Ip(t,s+2,r?o.call(r,i,a):o(i,a)):wh(t,s+2)}function Dh(t,e,n,o,i,a,r,s){const l=e+n;return Bp(t,l,i,a,r)?Ip(t,l+3,s?o.call(s,i,a,r):o(i,a,r)):wh(t,l+3)}function Eh(t,e,n,o,i,a,r,s,l){const c=e+n;return Vp(t,c,i,a,r,s)?Ip(t,c+4,l?o.call(l,i,a,r,s):o(i,a,r,s)):wh(t,c+4)}function Rh(t,e,n,o,i,a){let r=e+n,s=!1;for(let e=0;e<i.length;e++)Fp(t,r++,i[e])&&(s=!0);return s?Ip(t,r,o.apply(a,i)):wh(t,r)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Fh(t){return e=>{setTimeout(t,void 0,e)}}const Lh=class extends I{constructor(t=!1){super(),this.__isAsync=t}emit(t){super.next(t)}subscribe(t,e,n){var o,i,a;let r=t,s=e||(()=>null),l=n;if(t&&"object"==typeof t){const e=t;r=null===(o=e.next)||void 0===o?void 0:o.bind(e),s=null===(i=e.error)||void 0===i?void 0:i.bind(e),l=null===(a=e.complete)||void 0===a?void 0:a.bind(e)}this.__isAsync&&(s=Fh(s),r&&(r=Fh(r)),l&&(l=Fh(l)));const c=super.subscribe({next:r,error:s,complete:l});return t instanceof m&&t.add(c),c}};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Bh(){return this._results[Rp()]()}class Vh{constructor(t=!1){this._emitDistinctChangesOnly=t,this.dirty=!0,this._results=[],this._changesDetected=!1,this._changes=null,this.length=0,this.first=void 0,this.last=void 0;const e=Rp(),n=Vh.prototype;n[e]||(n[e]=Bh)}get changes(){return this._changes||(this._changes=new Lh)}get(t){return this._results[t]}map(t){return this._results.map(t)}filter(t){return this._results.filter(t)}find(t){return this._results.find(t)}reduce(t,e){return this._results.reduce(t,e)}forEach(t){this._results.forEach(t)}some(t){return this._results.some(t)}toArray(){return this._results.slice()}toString(){return this._results.toString()}reset(t,e){const n=this;n.dirty=!1;const o=tr(t);(this._changesDetected=!(function i(t,e,n){if(t.length!==e.length)return!1;for(let o=0;o<t.length;o++){let 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function ib(t,e){return $g(t,e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const ab={"ɵɵattribute":jp,"ɵɵattributeInterpolate1":function t(e,n,o,i,a,r){const s=fi(),l=Gp(s,n,o,i);if(l!==ac){const t=Yi();Cd(t,s,e,l,a,r),ngDevMode&&zd(gi().data,t,"attr."+e,wi()-1,n,i)}return t},"ɵɵattributeInterpolate2":function t(e,n,o,i,a,r,s,l){const c=fi(),d=Wp(c,n,o,i,a,r);if(d!==ac){const t=Yi();Cd(t,c,e,d,s,l),ngDevMode&&zd(gi().data,t,"attr."+e,wi()-2,n,i,r)}return t},"ɵɵattributeInterpolate3":function t(e,n,o,i,a,r,s,l,c,d){const p=fi(),m=Yp(p,n,o,i,a,r,s,l);if(m!==ac){const t=Yi();Cd(t,p,e,m,c,d),ngDevMode&&zd(gi().data,t,"attr."+e,wi()-3,n,i,r,l)}return t},"ɵɵattributeInterpolate4":function t(e,n,o,i,a,r,s,l,c,d,p,m){const u=fi(),f=qp(u,n,o,i,a,r,s,l,c,d);if(f!==ac){const t=Yi();Cd(t,u,e,f,p,m),ngDevMode&&zd(gi().data,t,"attr."+e,wi()-4,n,i,r,l,d)}return t},"ɵɵattributeInterpolate5":function t(e,n,o,i,a,r,s,l,c,d,p,m,u,f){const g=fi(),h=Zp(g,n,o,i,a,r,s,l,c,d,p,m);if(h!==ac){const 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
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/**
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     * Copyright Google LLC All Rights Reserved.
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/**
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     * Copyright Google LLC All Rights Reserved.
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     * found in the LICENSE file at https://angular.io/license
     */
class Zy{constructor(t,e){this.ngModuleFactory=t,this.componentFactories=e}}const Xy=function(t){return new _h(t)},Ky=Xy,Jy=function(t){return Promise.resolve(Xy(t))},Qy=function(t){const e=Xy(t),n=rl(fo(t).declarations).reduce(((t,e)=>{const n=po(e);return n&&t.push(new fh(n)),t}),[]);return new Zy(e,n)},$y=Qy,t_=function(t){return Promise.resolve(Qy(t))};class e_{constructor(){this.compileModuleSync=Ky,this.compileModuleAsync=Jy,this.compileModuleAndAllComponentsSync=$y,this.compileModuleAndAllComponentsAsync=t_}clearCache(){}clearCacheFor(t){}getModuleId(t){}}e_.ɵfac=function t(e){return new(e||e_)},e_.ɵprov=Mn({token:e_,factory:e_.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(e_,[{type:im}],(function(){return[]}),null);const n_=new Ga("compilerOptions"),o_=Promise.resolve(0);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function i_(t){"undefined"==typeof Zone?o_.then((()=>{t&&t.apply(null,null)})):Zone.current.scheduleMicroTask("scheduleMicrotask",t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class a_{constructor({enableLongStackTrace:t=!1,shouldCoalesceEventChangeDetection:e=!1,shouldCoalesceRunChangeDetection:n=!1}){if(this.hasPendingMacrotasks=!1,this.hasPendingMicrotasks=!1,this.isStable=!0,this.onUnstable=new Lh(!1),this.onMicrotaskEmpty=new Lh(!1),this.onStable=new Lh(!1),this.onError=new Lh(!1),"undefined"==typeof Zone)throw new Error("In this configuration Angular requires Zone.js");Zone.assertZonePatched();const o=this;o._nesting=0,o._outer=o._inner=Zone.current,Zone.TaskTrackingZoneSpec&&(o._inner=o._inner.fork(new Zone.TaskTrackingZoneSpec)),t&&Zone.longStackTraceZoneSpec&&(o._inner=o._inner.fork(Zone.longStackTraceZoneSpec)),o.shouldCoalesceEventChangeDetection=!n&&e,o.shouldCoalesceRunChangeDetection=n,o.lastRequestAnimationFrameId=-1,o.nativeRequestAnimationFrame=(function i(){let t=jn.requestAnimationFrame,e=jn.cancelAnimationFrame;if("undefined"!=typeof Zone&&t&&e){const n=t[Zone.__symbol__("OriginalDelegate")];n&&(t=n);const o=e[Zone.__symbol__("OriginalDelegate")];o&&(e=o)}return{nativeRequestAnimationFrame:t,nativeCancelAnimationFrame:e}})().nativeRequestAnimationFrame,(function a(t){const e=()=>{!(function e(t){t.isCheckStableRunning||-1!==t.lastRequestAnimationFrameId||(t.lastRequestAnimationFrameId=t.nativeRequestAnimationFrame.call(jn,(()=>{t.fakeTopEventTask||(t.fakeTopEventTask=Zone.root.scheduleEventTask("fakeTopEventTask",(()=>{t.lastRequestAnimationFrameId=-1,l_(t),t.isCheckStableRunning=!0,s_(t),t.isCheckStableRunning=!1}),void 0,(()=>{}),(()=>{}))),t.fakeTopEventTask.invoke()})),l_(t))})(t)};t._inner=t._inner.fork({name:"angular",properties:{isAngularZone:!0},onInvokeTask:(n,o,i,a,r,s)=>{try{return c_(t),n.invokeTask(i,a,r,s)}finally{(t.shouldCoalesceEventChangeDetection&&"eventTask"===a.type||t.shouldCoalesceRunChangeDetection)&&e(),d_(t)}},onInvoke:(n,o,i,a,r,s,l)=>{try{return c_(t),n.invoke(i,a,r,s,l)}finally{t.shouldCoalesceRunChangeDetection&&e(),d_(t)}},onHasTask:(e,n,o,i)=>{e.hasTask(o,i),n===o&&("microTask"==i.change?(t._hasPendingMicrotasks=i.microTask,l_(t),s_(t)):"macroTask"==i.change&&(t.hasPendingMacrotasks=i.macroTask))},onHandleError:(e,n,o,i)=>(e.handleError(o,i),t.runOutsideAngular((()=>t.onError.emit(i))),!1)})})(o)}static isInAngularZone(){return!0===Zone.current.get("isAngularZone")}static assertInAngularZone(){if(!a_.isInAngularZone())throw new Error("Expected to be in Angular Zone, but it is not!")}static assertNotInAngularZone(){if(a_.isInAngularZone())throw new Error("Expected to not be in Angular Zone, but it is!")}run(t,e,n){return this._inner.run(t,e,n)}runTask(t,e,n,o){const i=this._inner,a=i.scheduleEventTask("NgZoneEvent: "+o,t,r_,fg,fg);try{return i.runTask(a,e,n)}finally{i.cancelTask(a)}}runGuarded(t,e,n){return this._inner.runGuarded(t,e,n)}runOutsideAngular(t){return this._outer.run(t)}}const r_={};function s_(t){if(0==t._nesting&&!t.hasPendingMicrotasks&&!t.isStable)try{t._nesting++,t.onMicrotaskEmpty.emit(null)}finally{if(t._nesting--,!t.hasPendingMicrotasks)try{t.runOutsideAngular((()=>t.onStable.emit(null)))}finally{t.isStable=!0}}}function l_(t){t.hasPendingMicrotasks=!!(t._hasPendingMicrotasks||(t.shouldCoalesceEventChangeDetection||t.shouldCoalesceRunChangeDetection)&&-1!==t.lastRequestAnimationFrameId)}function c_(t){t._nesting++,t.isStable&&(t.isStable=!1,t.onUnstable.emit(null))}function d_(t){t._nesting--,s_(t)}class p_{constructor(){this.hasPendingMicrotasks=!1,this.hasPendingMacrotasks=!1,this.isStable=!0,this.onUnstable=new Lh,this.onMicrotaskEmpty=new Lh,this.onStable=new Lh,this.onError=new Lh}run(t,e,n){return t.apply(e,n)}runGuarded(t,e,n){return t.apply(e,n)}runOutsideAngular(t){return t()}runTask(t,e,n,o){return t.apply(e,n)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class m_{constructor(t){this._ngZone=t,this._pendingCount=0,this._isZoneStable=!0,this._didWork=!1,this._callbacks=[],this.taskTrackingZone=null,this._watchAngularEvents(),t.run((()=>{this.taskTrackingZone="undefined"==typeof Zone?null:Zone.current.get("TaskTrackingZone")}))}_watchAngularEvents(){this._ngZone.onUnstable.subscribe({next:()=>{this._didWork=!0,this._isZoneStable=!1}}),this._ngZone.runOutsideAngular((()=>{this._ngZone.onStable.subscribe({next:()=>{a_.assertNotInAngularZone(),i_((()=>{this._isZoneStable=!0,this._runCallbacksIfReady()}))}})}))}increasePendingRequestCount(){return this._pendingCount+=1,this._didWork=!0,this._pendingCount}decreasePendingRequestCount(){if(this._pendingCount-=1,this._pendingCount<0)throw new Error("pending async requests below zero");return this._runCallbacksIfReady(),this._pendingCount}isStable(){return this._isZoneStable&&0===this._pendingCount&&!this._ngZone.hasPendingMacrotasks}_runCallbacksIfReady(){if(this.isStable())i_((()=>{for(;0!==this._callbacks.length;){let t=this._callbacks.pop();clearTimeout(t.timeoutId),t.doneCb(this._didWork)}this._didWork=!1}));else{let t=this.getPendingTasks();this._callbacks=this._callbacks.filter((e=>!e.updateCb||!e.updateCb(t)||(clearTimeout(e.timeoutId),!1))),this._didWork=!0}}getPendingTasks(){return this.taskTrackingZone?this.taskTrackingZone.macroTasks.map((t=>({source:t.source,creationLocation:t.creationLocation,data:t.data}))):[]}addCallback(t,e,n){let o=-1;e&&e>0&&(o=setTimeout((()=>{this._callbacks=this._callbacks.filter((t=>t.timeoutId!==o)),t(this._didWork,this.getPendingTasks())}),e)),this._callbacks.push({doneCb:t,timeoutId:o,updateCb:n})}whenStable(t,e,n){if(n&&!this.taskTrackingZone)throw new Error('Task tracking zone is required when passing an update callback to whenStable(). Is "zone.js/plugins/task-tracking" loaded?');this.addCallback(t,e,n),this._runCallbacksIfReady()}getPendingRequestCount(){return this._pendingCount}findProviders(t,e,n){return[]}}m_.ɵfac=function t(e){return new(e||m_)(vr(a_))},m_.ɵprov=Mn({token:m_,factory:m_.ɵfac}),m_.ctorParameters=()=>[{type:a_}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(m_,[{type:im}],(function(){return[{type:a_}]}),null);class u_{constructor(){this._applications=new Map,g_.addToWindow(this)}registerApplication(t,e){this._applications.set(t,e)}unregisterApplication(t){this._applications.delete(t)}unregisterAllApplications(){this._applications.clear()}getTestability(t){return this._applications.get(t)||null}getAllTestabilities(){return Array.from(this._applications.values())}getAllRootElements(){return Array.from(this._applications.keys())}findTestabilityInTree(t,e=!0){return g_.findTestabilityInTree(this,t,e)}}u_.ɵfac=function t(e){return new(e||u_)},u_.ɵprov=Mn({token:u_,factory:u_.ɵfac}),u_.ctorParameters=()=>[],("undefined"==typeof ngDevMode||ngDevMode)&&hh(u_,[{type:im}],(function(){return[]}),null);let f_,g_=new class{addToWindow(t){}findTestabilityInTree(t,e,n){return null}},h_=!0,b_=!1;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function y_(){return b_=!0,h_}const __=new Ga("AllowMultipleToken");function C_(t,e,n=[]){const o=`Platform: ${e}`,i=new Ga(o);return(e=[])=>{let a=M_();if(!a||a.injector.get(__,!1))if(t)t(n.concat(e).concat({provide:i,useValue:!0}));else{const t=n.concat(e).concat({provide:i,useValue:!0},{provide:Yd,useValue:"platform"});!(function r(t){if(f_&&!f_.destroyed&&!f_.injector.get(__,!1))throw new Error("There can be only one platform. Destroy the previous one to create a new one.");(function e(){ngDevMode&&(function t(){_p||(_p=!0,Cp("ɵsetProfiler",Yo),Cp("getDirectiveMetadata",up),Cp("getComponent",sp),Cp("getContext",lp),Cp("getListeners",gp),Cp("getOwningComponent",cp),Cp("getHostElement",fp),Cp("getInjector",pp),Cp("getRootComponents",dp),Cp("getDirectives",mp),Cp("applyChanges",yp))})()})(),f_=t.get(v_);const n=t.get(Vy,null);n&&n.forEach((t=>t()))})(rp.create({providers:t,name:o}))}return(function s(t){const e=M_();if(!e)throw new Error("No platform exists!");if(!e.injector.get(t,null))throw new Error("A platform with a different configuration has been created. Please destroy it first.");return e})(i)}}function M_(){return f_&&!f_.destroyed?f_:null}class v_{constructor(t){this._injector=t,this._modules=[],this._destroyListeners=[],this._destroyed=!1}bootstrapModuleFactory(t,e){const n=(function o(t,e){let n;return n="noop"===t?new p_:("zone.js"===t?void 0:t)||new a_({enableLongStackTrace:y_(),shouldCoalesceEventChangeDetection:!!(null==e?void 0:e.ngZoneEventCoalescing),shouldCoalesceRunChangeDetection:!!(null==e?void 0:e.ngZoneRunCoalescing)}),n})(e?e.ngZone:void 0,{ngZoneEventCoalescing:e&&e.ngZoneEventCoalescing||!1,ngZoneRunCoalescing:e&&e.ngZoneRunCoalescing||!1}),i=[{provide:a_,useValue:n}];return n.run((()=>{const e=rp.create({providers:i,parent:this.injector,name:t.moduleType.name}),o=t.create(e),a=o.injector.get(Zs,null);if(!a)throw new Error("No ErrorHandler. Is platform module (BrowserModule) included?");return n.runOutsideAngular((()=>{const t=n.onError.subscribe({next:t=>{a.handleError(t)}});o.onDestroy((()=>{P_(this._modules,o),t.unsubscribe()}))})),(function r(t,e,n){try{const o=n();return Fm(o)?o.catch((n=>{throw e.runOutsideAngular((()=>t.handleError(n))),n})):o}catch(n){throw e.runOutsideAngular((()=>t.handleError(n))),n}})(a,n,(()=>{const t=o.injector.get(Iy);return t.runInitializers(),t.donePromise.then((()=>(Xu(o.injector.get(Wy,Gu)||Gu),this._moduleDoBootstrap(o),o)))}))}))}bootstrapModule(t,e=[]){const n=x_({},e);return(function o(t,e,n){ngDevMode&&(function o(t,e="Type passed in is not NgModuleType, it does not have 'ɵmod' property."){fo(t)||bn(e)})(n);const i=new _h(n);if("undefined"!=typeof ngJitMode&&!ngJitMode)return Promise.resolve(i);const a=t.get(n_,[]).concat(e);if((function r(t){if(null!==jb){if(t.defaultEncapsulation!==jb.defaultEncapsulation)return void(ngDevMode&&console.error("Provided value for `defaultEncapsulation` can not be changed once it has been set."));if(t.preserveWhitespaces!==jb.preserveWhitespaces)return void(ngDevMode&&console.error("Provided value for `preserveWhitespaces` can not be changed once it has been set."))}jb=t})({defaultEncapsulation:w_(a.map((t=>t.defaultEncapsulation))),preserveWhitespaces:w_(a.map((t=>t.preserveWhitespaces)))}),(function s(){return 0===Ir.size})())return Promise.resolve(i);const l=(function c(t){const e=[];return t.forEach((t=>t&&e.push(...t))),e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(a.map((t=>t.providers)));if(0===l.length)return Promise.resolve(i);const d=Ja({usage:0,kind:"NgModule",type:n}),p=rp.create({providers:l}).get(d.ResourceLoader);return(function m(t){const e=[],n=new Map;function o(e){let o=n.get(e);if(!o){const i=t(e);n.set(e,o=i.then(Lr))}return o}return Ir.forEach(((t,n)=>{const i=[];t.templateUrl&&i.push(o(t.templateUrl).then((e=>{t.template=e})));const a=t.styleUrls,r=t.styles||(t.styles=[]),s=t.styles.length;a&&a.forEach(((e,n)=>{r.push(""),i.push(o(e).then((o=>{r[s+n]=o,a.splice(a.indexOf(e),1),0==a.length&&(t.styleUrls=void 0)})))}));const l=Promise.all(i).then((()=>(function t(e){Hr.delete(e)})(n)));e.push(l)})),(function i(){Ir=new Map})(),Promise.all(e).then((()=>{}))})((t=>Promise.resolve(p.get(t)))).then((()=>i))})(this.injector,n,t).then((t=>this.bootstrapModuleFactory(t,n)))}_moduleDoBootstrap(t){const e=t.injector.get(O_);if(t._bootstrapComponents.length>0)t._bootstrapComponents.forEach((t=>e.bootstrap(t)));else{if(!t.instance.ngDoBootstrap)throw new Error(`The module ${Ge(t.instance.constructor)} was bootstrapped, but it does not declare "@NgModule.bootstrap" components nor a "ngDoBootstrap" method. Please define one of these.`);t.instance.ngDoBootstrap(e)}this._modules.push(t)}onDestroy(t){this._destroyListeners.push(t)}get injector(){return this._injector}destroy(){if(this._destroyed)throw new Error("The platform has already been destroyed!");this._modules.slice().forEach((t=>t.destroy())),this._destroyListeners.forEach((t=>t())),this._destroyed=!0}get destroyed(){return this._destroyed}}function x_(t,e){return Array.isArray(e)?e.reduce(x_,t):Object.assign(Object.assign({},t),e)}v_.ɵfac=function t(e){return new(e||v_)(vr(rp))},v_.ɵprov=Mn({token:v_,factory:v_.ɵfac}),v_.ctorParameters=()=>[{type:rp}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(v_,[{type:im}],(function(){return[{type:rp}]}),null);class O_{constructor(t,e,n,o,i){this._zone=t,this._injector=e,this._exceptionHandler=n,this._componentFactoryResolver=o,this._initStatus=i,this._bootstrapListeners=[],this._views=[],this._runningTick=!1,this._stable=!0,this.componentTypes=[],this.components=[],this._onMicrotaskEmptySubscription=this._zone.onMicrotaskEmpty.subscribe({next:()=>{this._zone.run((()=>{this.tick()}))}});const a=new D((t=>{this._stable=this._zone.isStable&&!this._zone.hasPendingMacrotasks&&!this._zone.hasPendingMicrotasks,this._zone.runOutsideAngular((()=>{t.next(this._stable),t.complete()}))})),r=new D((t=>{let e;this._zone.runOutsideAngular((()=>{e=this._zone.onStable.subscribe((()=>{a_.assertNotInAngularZone(),i_((()=>{this._stable||this._zone.hasPendingMacrotasks||this._zone.hasPendingMicrotasks||(this._stable=!0,t.next(!0))}))}))}));const n=this._zone.onUnstable.subscribe((()=>{a_.assertInAngularZone(),this._stable&&(this._stable=!1,this._zone.runOutsideAngular((()=>{t.next(!1)})))}));return()=>{e.unsubscribe(),n.unsubscribe()}}));this.isStable=re(a,r.pipe(Ee()))}bootstrap(t,e){if(!this._initStatus.done)throw new Error("Cannot bootstrap as there are still asynchronous initializers running. Bootstrap components in the `ngDoBootstrap` method of the root module.");let n;n=t instanceof mg?t:this._componentFactoryResolver.resolveComponentFactory(t),this.componentTypes.push(n.componentType);const o=(function i(t){return t.isBoundToModule})(n)?void 0:this._injector.get(th),a=n.create(rp.NULL,[],e||n.selector,o),r=a.location.nativeElement,s=a.injector.get(m_,null),l=s&&a.injector.get(u_);return s&&l&&l.registerApplication(r,s),a.onDestroy((()=>{this.detachView(a.hostView),P_(this.components,a),l&&l.unregisterApplication(r)})),this._loadComponent(a),("undefined"==typeof ngDevMode||ngDevMode)&&y_()&&this._injector.get(Gy).log("Angular is running in development mode. Call enableProdMode() to enable production mode."),a}tick(){if(this._runningTick)throw new Error("ApplicationRef.tick is called recursively");try{this._runningTick=!0;for(let t of this._views)t.detectChanges();if(("undefined"==typeof ngDevMode||ngDevMode)&&y_())for(let t of this._views)t.checkNoChanges()}catch(t){this._zone.runOutsideAngular((()=>this._exceptionHandler.handleError(t)))}finally{this._runningTick=!1}}attachView(t){const e=t;this._views.push(e),e.attachToAppRef(this)}detachView(t){const e=t;P_(this._views,e),e.detachFromAppRef()}_loadComponent(t){this.attachView(t.hostView),this.tick(),this.components.push(t),this._injector.get(Uy,[]).concat(this._bootstrapListeners).forEach((e=>e(t)))}ngOnDestroy(){this._views.slice().forEach((t=>t.destroy())),this._onMicrotaskEmptySubscription.unsubscribe()}get viewCount(){return this._views.length}}function P_(t,e){const n=t.indexOf(e);n>-1&&t.splice(n,1)}function w_(t){for(let e=t.length-1;e>=0;e--)if(void 0!==t[e])return t[e]}O_.ɵfac=function t(e){return new(e||O_)(vr(a_),vr(rp),vr(Zs),vr(ug),vr(Iy))},O_.ɵprov=Mn({token:O_,factory:O_.ɵfac}),O_.ctorParameters=()=>[{type:a_},{type:rp},{type:Zs},{type:ug},{type:Iy}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(O_,[{type:im}],(function(){return[{type:a_},{type:rp},{type:Zs},{type:ug},{type:Iy}]}),null);class k_{}const S_={factoryPathPrefix:"",factoryPathSuffix:".ngfactory"};class D_{constructor(t,e){this._compiler=t,this._config=e||S_}load(t){return this.loadAndCompile(t)}loadAndCompile(t){let[e,n]=t.split("#");return void 0===n&&(n="default"),System.import(e).then((t=>t[n])).then((t=>E_(t,e,n))).then((t=>this._compiler.compileModuleAsync(t)))}loadFactory(t){let[e,n]=t.split("#"),o="NgFactory";return void 0===n&&(n="default",o=""),System.import(this._config.factoryPathPrefix+e+this._config.factoryPathSuffix).then((t=>t[n+o])).then((t=>E_(t,e,n)))}}function E_(t,e,n){if(!t)throw new Error(`Cannot find '${n}' in '${e}'`);return t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */D_.ɵfac=function t(e){return new(e||D_)(vr(e_),vr(k_,8))},D_.ɵprov=Mn({token:D_,factory:D_.ɵfac}),D_.ctorParameters=()=>[{type:e_},{type:k_,decorators:[{type:Sr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(D_,[{type:im}],(function(){return[{type:e_},{type:k_,decorators:[{type:Sr}]}]}),null);const R_=C_(null,"core",[{provide:jy,useValue:"unknown"},{provide:v_,deps:[rp]},{provide:u_,deps:[]},{provide:Gy,deps:[]}]),A_=[{provide:O_,useClass:O_,deps:[a_,rp,Zs,ug,Iy]},{provide:uh,deps:[a_],useFactory:function T_(t){let e=[];return t.onStable.subscribe((()=>{for(;e.length;)e.pop()()})),function(t){e.push(t)}}},{provide:Iy,useClass:Iy,deps:[[new Sr,zy]]},{provide:e_,useClass:e_,deps:[]},Fy,{provide:Hg,useFactory:
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function N_(){return qg},deps:[]},{provide:Lg,useFactory:function z_(){return Zg},deps:[]},{provide:Wy,useFactory:function I_(t){return Xu(t=t||(function e(){return"undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode&&"undefined"!=typeof goog&&"en"!==goog.getLocale()?goog.getLocale():"undefined"!=typeof $localize&&$localize.locale||Gu})()),t},deps:[[new kr(Wy),new Sr,new Er]]},{provide:Yy,useValue:"USD"}];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class H_{constructor(t){}}var F_,L_;H_.ɵfac=function t(e){return new(e||H_)(vr(O_))},H_.ɵmod=ao({type:H_}),H_.ɵinj=vn({providers:A_}),H_.ctorParameters=()=>[{type:O_}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(H_,[{type:Ay,args:[{providers:A_}]}],(function(){return[{type:O_}]}),null),(function(t){t[t.CreateViewNodes=0]="CreateViewNodes",t[t.CheckNoChanges=1]="CheckNoChanges",t[t.CheckNoChangesProjectedViews=2]="CheckNoChangesProjectedViews",t[t.CheckAndUpdate=3]="CheckAndUpdate",t[t.CheckAndUpdateProjectedViews=4]="CheckAndUpdateProjectedViews",t[t.Destroy=5]="Destroy"})(F_||(F_={})),(function(t){t[t.create=0]="create",t[t.detectChanges=1]="detectChanges",t[t.checkNoChanges=2]="checkNoChanges",t[t.destroy=3]="destroy",t[t.handleEvent=4]="handleEvent"})(L_||(L_={})),
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
"undefined"!=typeof ngDevMode&&ngDevMode&&(jn.$localize=jn.$localize||function(){throw new Error("It looks like your application or one of its dependencies is using i18n.\nAngular 9 introduced a global `$localize()` function that needs to be loaded.\nPlease run `ng add @angular/localize` from the Angular CLI.\n(For non-CLI projects, add `import '@angular/localize/init';` to your `polyfills.ts` file.\nFor server-side rendering applications add the import to your `main.server.ts` file.)")})
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
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/**
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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     * found in the LICENSE file at https://angular.io/license
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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     * Copyright Google LLC All Rights Reserved.
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     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */var mC,uC,fC,gC,hC,bC,yC;function _C(t,e){return wC(Lu(t)[ju.DateFormat],e)}function CC(t,e){return wC(Lu(t)[ju.TimeFormat],e)}function MC(t,e){return wC(Lu(t)[ju.DateTimeFormat],e)}function vC(t,e){const n=Lu(t),o=n[ju.NumberSymbols][e];if(void 0===o){if(e===bC.CurrencyDecimal)return n[ju.NumberSymbols][bC.Decimal];if(e===bC.CurrencyGroup)return n[ju.NumberSymbols][bC.Group]}return o}function xC(t,e){return Lu(t)[ju.NumberFormats][e]}!(function(t){t[t.Decimal=0]="Decimal",t[t.Percent=1]="Percent",t[t.Currency=2]="Currency",t[t.Scientific=3]="Scientific"})(mC||(mC={})),(function(t){t[t.Zero=0]="Zero",t[t.One=1]="One",t[t.Two=2]="Two",t[t.Few=3]="Few",t[t.Many=4]="Many",t[t.Other=5]="Other"})(uC||(uC={})),(function(t){t[t.Format=0]="Format",t[t.Standalone=1]="Standalone"})(fC||(fC={})),(function(t){t[t.Narrow=0]="Narrow",t[t.Abbreviated=1]="Abbreviated",t[t.Wide=2]="Wide",t[t.Short=3]="Short"})(gC||(gC={})),(function(t){t[t.Short=0]="Short",t[t.Medium=1]="Medium",t[t.Long=2]="Long",t[t.Full=3]="Full"})(hC||(hC={})),(function(t){t[t.Decimal=0]="Decimal",t[t.Group=1]="Group",t[t.List=2]="List",t[t.PercentSign=3]="PercentSign",t[t.PlusSign=4]="PlusSign",t[t.MinusSign=5]="MinusSign",t[t.Exponential=6]="Exponential",t[t.SuperscriptingExponent=7]="SuperscriptingExponent",t[t.PerMille=8]="PerMille",t[t[1/0]=9]="Infinity",t[t.NaN=10]="NaN",t[t.TimeSeparator=11]="TimeSeparator",t[t.CurrencyDecimal=12]="CurrencyDecimal",t[t.CurrencyGroup=13]="CurrencyGroup"})(bC||(bC={})),(function(t){t[t.Sunday=0]="Sunday",t[t.Monday=1]="Monday",t[t.Tuesday=2]="Tuesday",t[t.Wednesday=3]="Wednesday",t[t.Thursday=4]="Thursday",t[t.Friday=5]="Friday",t[t.Saturday=6]="Saturday"})(yC||(yC={}));const OC=Bu;function PC(t){if(!t[ju.ExtraData])throw new Error(`Missing extra locale data for the locale "${t[ju.LocaleId]}". Use "registerLocaleData" to load new data. See the "I18n guide" on angular.io to know more.`)}function wC(t,e){for(let n=e;n>-1;n--)if(void 0!==t[n])return t[n];throw new Error("Locale data API: locale data undefined")}function kC(t){const[e,n]=t.split(":");return{hours:+e,minutes:+n}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const SC=/^(\d{4})-?(\d\d)-?(\d\d)(?:T(\d\d)(?::?(\d\d)(?::?(\d\d)(?:\.(\d+))?)?)?(Z|([+-])(\d\d):?(\d\d))?)?$/,DC={},EC=/((?:[^BEGHLMOSWYZabcdhmswyz']+)|(?:'(?:[^']|'')*')|(?:G{1,5}|y{1,4}|Y{1,4}|M{1,5}|L{1,5}|w{1,2}|W{1}|d{1,2}|E{1,6}|c{1,6}|a{1,5}|b{1,5}|B{1,5}|h{1,2}|H{1,2}|m{1,2}|s{1,2}|S{1,3}|z{1,4}|Z{1,5}|O{1,4}))([\s\S]*)/;var RC,AC,TC;function NC(t,e,n,o){let i=(function a(t){if(qC(t))return t;if("number"==typeof t&&!isNaN(t))return new Date(t);if("string"==typeof t){if(t=t.trim(),/^(\d{4}(-\d{1,2}(-\d{1,2})?)?)$/.test(t)){const[e,n=1,o=1]=t.split("-").map((t=>+t));return zC(e,n-1,o)}const e=parseFloat(t);if(!isNaN(t-e))return new Date(e);let n;if(n=t.match(SC))return(function e(t){const e=new Date(0);let n=0,o=0;const i=t[8]?e.setUTCFullYear:e.setFullYear,a=t[8]?e.setUTCHours:e.setHours;t[9]&&(n=Number(t[9]+t[10]),o=Number(t[9]+t[11])),i.call(e,Number(t[1]),Number(t[2])-1,Number(t[3]));const 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e;switch(t){case"G":case"GG":case"GGG":e=BC(TC.Eras,gC.Abbreviated);break;case"GGGG":e=BC(TC.Eras,gC.Wide);break;case"GGGGG":e=BC(TC.Eras,gC.Narrow);break;case"y":e=LC(AC.FullYear,1,0,!1,!0);break;case"yy":e=LC(AC.FullYear,2,0,!0,!0);break;case"yyy":e=LC(AC.FullYear,3,0,!1,!0);break;case"yyyy":e=LC(AC.FullYear,4,0,!1,!0);break;case"Y":e=GC(1);break;case"YY":e=GC(2,!0);break;case"YYY":e=GC(3);break;case"YYYY":e=GC(4);break;case"M":case"L":e=LC(AC.Month,1,1);break;case"MM":case"LL":e=LC(AC.Month,2,1);break;case"MMM":e=BC(TC.Months,gC.Abbreviated);break;case"MMMM":e=BC(TC.Months,gC.Wide);break;case"MMMMM":e=BC(TC.Months,gC.Narrow);break;case"LLL":e=BC(TC.Months,gC.Abbreviated,fC.Standalone);break;case"LLLL":e=BC(TC.Months,gC.Wide,fC.Standalone);break;case"LLLLL":e=BC(TC.Months,gC.Narrow,fC.Standalone);break;case"w":e=UC(1);break;case"ww":e=UC(2);break;case"W":e=UC(1,!0);break;case"d":e=LC(AC.Date,1);break;case"dd":e=LC(AC.Date,2);break;case"c":case"cc":e=LC(AC.Day,1);break;case"ccc":e=BC(TC.Days,gC.Abbreviated,fC.Standalone);break;case"cccc":e=BC(TC.Days,gC.Wide,fC.Standalone);break;case"ccccc":e=BC(TC.Days,gC.Narrow,fC.Standalone);break;case"cccccc":e=BC(TC.Days,gC.Short,fC.Standalone);break;case"E":case"EE":case"EEE":e=BC(TC.Days,gC.Abbreviated);break;case"EEEE":e=BC(TC.Days,gC.Wide);break;case"EEEEE":e=BC(TC.Days,gC.Narrow);break;case"EEEEEE":e=BC(TC.Days,gC.Short);break;case"a":case"aa":case"aaa":e=BC(TC.DayPeriods,gC.Abbreviated);break;case"aaaa":e=BC(TC.DayPeriods,gC.Wide);break;case"aaaaa":e=BC(TC.DayPeriods,gC.Narrow);break;case"b":case"bb":case"bbb":e=BC(TC.DayPeriods,gC.Abbreviated,fC.Standalone,!0);break;case"bbbb":e=BC(TC.DayPeriods,gC.Wide,fC.Standalone,!0);break;case"bbbbb":e=BC(TC.DayPeriods,gC.Narrow,fC.Standalone,!0);break;case"B":case"BB":case"BBB":e=BC(TC.DayPeriods,gC.Abbreviated,fC.Format,!0);break;case"BBBB":e=BC(TC.DayPeriods,gC.Wide,fC.Format,!0);break;case"BBBBB":e=BC(TC.DayPeriods,gC.Narrow,fC.Format,!0);break;case"h":e=LC(AC.Hours,1,-12);break;case"hh":e=LC(AC.Hours,2,-12);break;case"H":e=LC(AC.Hours,1);break;case"HH":e=LC(AC.Hours,2);break;case"m":e=LC(AC.Minutes,1);break;case"mm":e=LC(AC.Minutes,2);break;case"s":e=LC(AC.Seconds,1);break;case"ss":e=LC(AC.Seconds,2);break;case"S":e=LC(AC.FractionalSeconds,1);break;case"SS":e=LC(AC.FractionalSeconds,2);break;case"SSS":e=LC(AC.FractionalSeconds,3);break;case"Z":case"ZZ":case"ZZZ":e=VC(RC.Short);break;case"ZZZZZ":e=VC(RC.Extended);break;case"O":case"OO":case"OOO":case"z":case"zz":case"zzz":e=VC(RC.ShortGMT);break;case"OOOO":case"ZZZZ":case"zzzz":e=VC(RC.Long);break;default:return null}return WC[t]=e,e})(t);d+=e?e(i,n,l):"''"===t?"'":t.replace(/(^'|'$)/g,"").replace(/''/g,"'")})),d}function zC(t,e,n){const o=new Date(0);return o.setFullYear(t,e,n),o.setHours(0,0,0),o}function IC(t,e){const n=(function o(t){return Lu(t)[ju.LocaleId]})(t);if(DC[n]=DC[n]||{},DC[n][e])return DC[n][e];let i="";switch(e){case"shortDate":i=_C(t,hC.Short);break;case"mediumDate":i=_C(t,hC.Medium);break;case"longDate":i=_C(t,hC.Long);break;case"fullDate":i=_C(t,hC.Full);break;case"shortTime":i=CC(t,hC.Short);break;case"mediumTime":i=CC(t,hC.Medium);break;case"longTime":i=CC(t,hC.Long);break;case"fullTime":i=CC(t,hC.Full);break;case"short":const e=IC(t,"shortTime"),n=IC(t,"shortDate");i=HC(MC(t,hC.Short),[e,n]);break;case"medium":const o=IC(t,"mediumTime"),a=IC(t,"mediumDate");i=HC(MC(t,hC.Medium),[o,a]);break;case"long":const r=IC(t,"longTime"),s=IC(t,"longDate");i=HC(MC(t,hC.Long),[r,s]);break;case"full":const l=IC(t,"fullTime"),c=IC(t,"fullDate");i=HC(MC(t,hC.Full),[l,c])}return i&&(DC[n][e]=i),i}function HC(t,e){return e&&(t=t.replace(/\{([^}]+)}/g,(function(t,n){return null!=e&&n in e?e[n]:t}))),t}function FC(t,e,n="-",o,i){let a="";(t<0||i&&t<=0)&&(i?t=1-t:(t=-t,a=n));let r=String(t);for(;r.length<e;)r="0"+r;return o&&(r=r.substr(r.length-e)),a+r}function LC(t,e,n=0,o=!1,i=!1){return function(a,r){let s=(function l(t,e){switch(t){case AC.FullYear:return e.getFullYear();case AC.Month:return e.getMonth();case AC.Date:return e.getDate();case AC.Hours:return e.getHours();case AC.Minutes:return e.getMinutes();case AC.Seconds:return e.getSeconds();case AC.FractionalSeconds:return e.getMilliseconds();case AC.Day:return e.getDay();default:throw new Error(`Unknown DateType value "${t}".`)}})(t,a);if((n>0||s>-n)&&(s+=n),t===AC.Hours)0===s&&-12===n&&(s=12);else if(t===AC.FractionalSeconds)return(function c(t,e){return FC(t,3).substr(0,e)})(s,e);const d=vC(r,bC.MinusSign);return FC(s,e,d,o,i)}}function BC(t,e,n=fC.Format,o=!1){return function(i,a){return(function r(t,e,n,o,i,a){switch(n){case TC.Months:return(function r(t,e,n){const o=Lu(t),i=wC([o[ju.MonthsFormat],o[ju.MonthsStandalone]],e);return wC(i,n)})(e,i,o)[t.getMonth()];case TC.Days:return(function s(t,e,n){const o=Lu(t),i=wC([o[ju.DaysFormat],o[ju.DaysStandalone]],e);return wC(i,n)})(e,i,o)[t.getDay()];case TC.DayPeriods:const r=t.getHours(),s=t.getMinutes();if(a){const t=(function l(t){const e=Lu(t);return PC(e),(e[ju.ExtraData][2]||[]).map((t=>"string"==typeof t?kC(t):[kC(t[0]),kC(t[1])]))})(e),n=(function c(t,e,n){const o=Lu(t);PC(o);const i=wC([o[ju.ExtraData][0],o[ju.ExtraData][1]],e)||[];return wC(i,n)||[]})(e,i,o),a=t.findIndex((t=>{if(Array.isArray(t)){const[e,n]=t,o=r>=e.hours&&s>=e.minutes,i=r<n.hours||r===n.hours&&s<n.minutes;if(e.hours<n.hours){if(o&&i)return!0}else if(o||i)return!0}else if(t.hours===r&&t.minutes===s)return!0;return!1}));if(-1!==a)return n[a]}return(function d(t,e,n){const o=Lu(t),i=wC([o[ju.DayPeriodsFormat],o[ju.DayPeriodsStandalone]],e);return wC(i,n)})(e,i,o)[r<12?0:1];case TC.Eras:return(function p(t,e){return wC(Lu(t)[ju.Eras],e)})(e,o)[t.getFullYear()<=0?0:1];default:throw new Error(`unexpected translation type ${n}`)}})(i,a,t,e,n,o)}}function VC(t){return function(e,n,o){const i=-1*o,a=vC(n,bC.MinusSign),r=i>0?Math.floor(i/60):Math.ceil(i/60);switch(t){case RC.Short:return(i>=0?"+":"")+FC(r,2,a)+FC(Math.abs(i%60),2,a);case RC.ShortGMT:return"GMT"+(i>=0?"+":"")+FC(r,1,a);case RC.Long:return"GMT"+(i>=0?"+":"")+FC(r,2,a)+":"+FC(Math.abs(i%60),2,a);case RC.Extended:return 0===o?"Z":(i>=0?"+":"")+FC(r,2,a)+":"+FC(Math.abs(i%60),2,a);default:throw new Error(`Unknown zone width "${t}"`)}}}function jC(t){return zC(t.getFullYear(),t.getMonth(),t.getDate()+(4-t.getDay()))}function UC(t,e=!1){return function(n,o){let i;if(e){const t=new Date(n.getFullYear(),n.getMonth(),1).getDay()-1,e=n.getDate();i=1+Math.floor((e+t)/7)}else{const t=jC(n),e=(function a(t){const e=zC(t,0,1).getDay();return zC(t,0,1+(e<=4?4:11)-e)})(t.getFullYear()),o=t.getTime()-e.getTime();i=1+Math.round(o/6048e5)}return FC(i,t,vC(o,bC.MinusSign))}}function GC(t,e=!1){return function(n,o){return FC(jC(n).getFullYear(),t,vC(o,bC.MinusSign),e)}}!(function(t){t[t.Short=0]="Short",t[t.ShortGMT=1]="ShortGMT",t[t.Long=2]="Long",t[t.Extended=3]="Extended"})(RC||(RC={})),(function(t){t[t.FullYear=0]="FullYear",t[t.Month=1]="Month",t[t.Date=2]="Date",t[t.Hours=3]="Hours",t[t.Minutes=4]="Minutes",t[t.Seconds=5]="Seconds",t[t.FractionalSeconds=6]="FractionalSeconds",t[t.Day=7]="Day"})(AC||(AC={})),(function(t){t[t.DayPeriods=0]="DayPeriods",t[t.Days=1]="Days",t[t.Months=2]="Months",t[t.Eras=3]="Eras"})(TC||(TC={}));const WC={};function YC(t,e){t=t.replace(/:/g,"");const n=Date.parse("Jan 01, 1970 00:00:00 "+t)/6e4;return isNaN(n)?e:n}function qC(t){return t instanceof Date&&!isNaN(t.valueOf())}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const ZC=/^(\d+)?\.((\d+)(-(\d+))?)?$/,XC=".",KC="0";function JC(t,e,n,o,i,a,r=!1){let s="",l=!1;if(isFinite(t)){let c=(function c(t){let e,n,o,i,a,r=Math.abs(t)+"",s=0;for((n=r.indexOf(XC))>-1&&(r=r.replace(XC,"")),(o=r.search(/e/i))>0?(n<0&&(n=o),n+=+r.slice(o+1),r=r.substring(0,o)):n<0&&(n=r.length),o=0;r.charAt(o)===KC;o++);if(o===(a=r.length))e=[0],n=1;else{for(a--;r.charAt(a)===KC;)a--;for(n-=o,e=[],i=0;o<=a;o++,i++)e[i]=Number(r.charAt(o))}return n>22&&(e=e.splice(0,21),s=n-1,n=1),{digits:e,exponent:s,integerLen:n}})(t);r&&(c=(function d(t){if(0===t.digits[0])return t;const e=t.digits.length-t.integerLen;return t.exponent?t.exponent+=2:(0===e?t.digits.push(0,0):1===e&&t.digits.push(0),t.integerLen+=2),t})(c));let d=e.minInt,p=e.minFrac,m=e.maxFrac;if(a){const t=a.match(ZC);if(null===t)throw new Error(`${a} is not a valid digit info`);const e=t[1],n=t[3],o=t[5];null!=e&&(d=tM(e)),null!=n&&(p=tM(n)),null!=o?m=tM(o):null!=n&&p>m&&(m=p)}!(function p(t,e,n){if(e>n)throw new Error(`The minimum number of digits after fraction (${e}) is higher than the maximum (${n}).`);let o=t.digits,i=o.length-t.integerLen;const a=Math.min(Math.max(e,i),n);let r=a+t.integerLen,s=o[r];if(r>0){o.splice(Math.max(t.integerLen,r));for(let t=r;t<o.length;t++)o[t]=0}else{i=Math.max(0,i),t.integerLen=1,o.length=Math.max(1,r=a+1),o[0]=0;for(let t=1;t<r;t++)o[t]=0}if(s>=5)if(r-1<0){for(let e=0;e>r;e--)o.unshift(0),t.integerLen++;o.unshift(1),t.integerLen++}else o[r-1]++;for(;i<Math.max(0,a);i++)o.push(0);let l=0!==a;const c=e+t.integerLen,d=o.reduceRight((function(t,e,n,o){return o[n]=(e+=t)<10?e:e-10,l&&(0===o[n]&&n>=c?o.pop():l=!1),e>=10?1:0}),0);d&&(o.unshift(d),t.integerLen++)})(c,p,m);let u=c.digits,f=c.integerLen;const g=c.exponent;let h=[];for(l=u.every((t=>!t));f<d;f++)u.unshift(0);for(;f<0;f++)u.unshift(0);f>0?h=u.splice(f,u.length):(h=u,u=[0]);const b=[];for(u.length>=e.lgSize&&b.unshift(u.splice(-e.lgSize,u.length).join(""));u.length>e.gSize;)b.unshift(u.splice(-e.gSize,u.length).join(""));u.length&&b.unshift(u.join("")),s=b.join(vC(n,o)),h.length&&(s+=vC(n,i)+h.join("")),g&&(s+=vC(n,bC.Exponential)+"+"+g)}else s=vC(n,bC.Infinity);return s=t<0&&!l?e.negPre+s+e.negSuf:e.posPre+s+e.posSuf,s}function QC(t,e,n){return JC(t,$C(xC(e,mC.Decimal),vC(e,bC.MinusSign)),e,bC.Group,bC.Decimal,n)}function $C(t,e="-"){const n={minInt:1,minFrac:0,maxFrac:0,posPre:"",posSuf:"",negPre:"",negSuf:"",gSize:0,lgSize:0},o=t.split(";"),i=o[0],a=o[1],r=-1!==i.indexOf(XC)?i.split(XC):[i.substring(0,i.lastIndexOf(KC)+1),i.substring(i.lastIndexOf(KC)+1)],s=r[0],l=r[1]||"";n.posPre=s.substr(0,s.indexOf("#"));for(let t=0;t<l.length;t++){const e=l.charAt(t);e===KC?n.minFrac=n.maxFrac=t+1:"#"===e?n.maxFrac=t+1:n.posSuf+=e}const c=s.split(",");if(n.gSize=c[1]?c[1].length:0,n.lgSize=c[2]||c[1]?(c[2]||c[1]).length:0,a){const t=i.length-n.posPre.length-n.posSuf.length,e=a.indexOf("#");n.negPre=a.substr(0,e).replace(/'/g,""),n.negSuf=a.substr(e+t).replace(/'/g,"")}else n.negPre=e+n.posPre,n.negSuf=n.posSuf;return n}function tM(t){const e=parseInt(t);if(isNaN(e))throw new Error("Invalid integer literal when parsing "+t);return e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class eM{}function nM(t,e,n,o){let i=`=${t}`;if(e.indexOf(i)>-1)return i;if(i=n.getPluralCategory(t,o),e.indexOf(i)>-1)return i;if(e.indexOf("other")>-1)return"other";throw new Error(`No plural message found for value "${t}"`)}class oM extends eM{constructor(t){super(),this.locale=t}getPluralCategory(t,e){switch(OC(e||this.locale)(t)){case uC.Zero:return"zero";case uC.One:return"one";case uC.Two:return"two";case uC.Few:return"few";case uC.Many:return"many";default:return"other"}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function iM(t,e){e=encodeURIComponent(e);for(const n of t.split(";")){const t=n.indexOf("="),[o,i]=-1==t?[n,""]:[n.slice(0,t),n.slice(t+1)];if(o.trim()===e)return decodeURIComponent(i)}return null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */oM.ɵfac=function t(e){return new(e||oM)(vr(Wy))},oM.ɵprov=Mn({token:oM,factory:oM.ɵfac}),oM.ctorParameters=()=>[{type:String,decorators:[{type:kr,args:[Wy]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(oM,[{type:im}],(function(){return[{type:String,decorators:[{type:kr,args:[Wy]}]}]}),null);class aM{constructor(t,e,n,o){this._iterableDiffers=t,this._keyValueDiffers=e,this._ngEl=n,this._renderer=o,this._iterableDiffer=null,this._keyValueDiffer=null,this._initialClasses=[],this._rawClass=null}set klass(t){this._removeClasses(this._initialClasses),this._initialClasses="string"==typeof t?t.split(/\s+/):[],this._applyClasses(this._initialClasses),this._applyClasses(this._rawClass)}set ngClass(t){this._removeClasses(this._rawClass),this._applyClasses(this._initialClasses),this._iterableDiffer=null,this._keyValueDiffer=null,this._rawClass="string"==typeof t?t.split(/\s+/):t,this._rawClass&&(Np(this._rawClass)?this._iterableDiffer=this._iterableDiffers.find(this._rawClass).create():this._keyValueDiffer=this._keyValueDiffers.find(this._rawClass).create())}ngDoCheck(){if(this._iterableDiffer){const t=this._iterableDiffer.diff(this._rawClass);t&&this._applyIterableChanges(t)}else if(this._keyValueDiffer){const t=this._keyValueDiffer.diff(this._rawClass);t&&this._applyKeyValueChanges(t)}}_applyKeyValueChanges(t){t.forEachAddedItem((t=>this._toggleClass(t.key,t.currentValue))),t.forEachChangedItem((t=>this._toggleClass(t.key,t.currentValue))),t.forEachRemovedItem((t=>{t.previousValue&&this._toggleClass(t.key,!1)}))}_applyIterableChanges(t){t.forEachAddedItem((t=>{if("string"!=typeof t.item)throw new Error(`NgClass can only toggle CSS classes expressed as strings, got ${Ge(t.item)}`);this._toggleClass(t.item,!0)})),t.forEachRemovedItem((t=>this._toggleClass(t.item,!1)))}_applyClasses(t){t&&(Array.isArray(t)||t instanceof Set?t.forEach((t=>this._toggleClass(t,!0))):Object.keys(t).forEach((e=>this._toggleClass(e,!!t[e]))))}_removeClasses(t){t&&(Array.isArray(t)||t instanceof Set?t.forEach((t=>this._toggleClass(t,!1))):Object.keys(t).forEach((t=>this._toggleClass(t,!1))))}_toggleClass(t,e){(t=t.trim())&&t.split(/\s+/g).forEach((t=>{e?this._renderer.addClass(this._ngEl.nativeElement,t):this._renderer.removeClass(this._ngEl.nativeElement,t)}))}}aM.ɵfac=function t(e){return new(e||aM)(Sm(Hg),Sm(Lg),Sm(hg),Sm(Cg))},aM.ɵdir=lo({type:aM,selectors:[["","ngClass",""]],inputs:{klass:["class","klass"],ngClass:"ngClass"}}),aM.ctorParameters=()=>[{type:Hg},{type:Lg},{type:hg},{type:Cg}],aM.propDecorators={klass:[{type:xy,args:["class"]}],ngClass:[{type:xy,args:["ngClass"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(aM,[{type:Cy,args:[{selector:"[ngClass]"}]}],(function(){return[{type:Hg},{type:Lg},{type:hg},{type:Cg}]}),{klass:[{type:xy,args:["class"]}],ngClass:[{type:xy,args:["ngClass"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class rM{constructor(t){this._viewContainerRef=t,this._componentRef=null,this._moduleRef=null}ngOnChanges(t){if(this._viewContainerRef.clear(),this._componentRef=null,this.ngComponentOutlet){const e=this.ngComponentOutletInjector||this._viewContainerRef.parentInjector;if(t.ngComponentOutletNgModuleFactory)if(this._moduleRef&&this._moduleRef.destroy(),this.ngComponentOutletNgModuleFactory){const t=e.get(th);this._moduleRef=this.ngComponentOutletNgModuleFactory.create(t.injector)}else this._moduleRef=null;const n=(this._moduleRef?this._moduleRef.componentFactoryResolver:e.get(ug)).resolveComponentFactory(this.ngComponentOutlet);this._componentRef=this._viewContainerRef.createComponent(n,this._viewContainerRef.length,e,this.ngComponentOutletContent)}}ngOnDestroy(){this._moduleRef&&this._moduleRef.destroy()}}rM.ɵfac=function t(e){return new(e||rM)(Sm(eh))},rM.ɵdir=lo({type:rM,selectors:[["","ngComponentOutlet",""]],inputs:{ngComponentOutlet:"ngComponentOutlet",ngComponentOutletInjector:"ngComponentOutletInjector",ngComponentOutletContent:"ngComponentOutletContent",ngComponentOutletNgModuleFactory:"ngComponentOutletNgModuleFactory"},features:[Bo]}),rM.ctorParameters=()=>[{type:eh}],rM.propDecorators={ngComponentOutlet:[{type:xy}],ngComponentOutletInjector:[{type:xy}],ngComponentOutletContent:[{type:xy}],ngComponentOutletNgModuleFactory:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(rM,[{type:Cy,args:[{selector:"[ngComponentOutlet]"}]}],(function(){return[{type:eh}]}),{ngComponentOutlet:[{type:xy}],ngComponentOutletInjector:[{type:xy}],ngComponentOutletContent:[{type:xy}],ngComponentOutletNgModuleFactory:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class sM{constructor(t,e,n,o){this.$implicit=t,this.ngForOf=e,this.index=n,this.count=o}get first(){return 0===this.index}get last(){return this.index===this.count-1}get even(){return this.index%2==0}get odd(){return!this.even}}class lM{constructor(t,e,n){this._viewContainer=t,this._template=e,this._differs=n,this._ngForOf=null,this._ngForOfDirty=!0,this._differ=null}set ngForOf(t){this._ngForOf=t,this._ngForOfDirty=!0}set ngForTrackBy(t){"undefined"!=typeof ngDevMode&&!ngDevMode||null==t||"function"==typeof t||console&&console.warn&&console.warn(`trackBy must be a function, but received ${JSON.stringify(t)}. See https://angular.io/api/common/NgForOf#change-propagation for more information.`),this._trackByFn=t}get ngForTrackBy(){return this._trackByFn}set ngForTemplate(t){t&&(this._template=t)}ngDoCheck(){if(this._ngForOfDirty){this._ngForOfDirty=!1;const t=this._ngForOf;if(!this._differ&&t)try{this._differ=this._differs.find(t).create(this.ngForTrackBy)}catch(e){throw new Error(`Cannot find a differ supporting object '${t}' of type '${(function n(t){return t.name||typeof t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t)}'. NgFor only supports binding to Iterables such as Arrays.`)}}if(this._differ){const t=this._differ.diff(this._ngForOf);t&&this._applyChanges(t)}}_applyChanges(t){const e=[];t.forEachOperation(((t,n,o)=>{if(null==t.previousIndex){const n=this._viewContainer.createEmbeddedView(this._template,new sM(null,this._ngForOf,-1,-1),null===o?void 0:o),i=new cM(t,n);e.push(i)}else if(null==o)this._viewContainer.remove(null===n?void 0:n);else if(null!==n){const i=this._viewContainer.get(n);this._viewContainer.move(i,o);const a=new cM(t,i);e.push(a)}}));for(let t=0;t<e.length;t++)this._perViewChange(e[t].view,e[t].record);for(let t=0,e=this._viewContainer.length;t<e;t++){const n=this._viewContainer.get(t);n.context.index=t,n.context.count=e,n.context.ngForOf=this._ngForOf}t.forEachIdentityChange((t=>{this._viewContainer.get(t.currentIndex).context.$implicit=t.item}))}_perViewChange(t,e){t.context.$implicit=e.item}static ngTemplateContextGuard(t,e){return!0}}lM.ɵfac=function t(e){return new(e||lM)(Sm(eh),Sm(Xg),Sm(Hg))},lM.ɵdir=lo({type:lM,selectors:[["","ngFor","","ngForOf",""]],inputs:{ngForOf:"ngForOf",ngForTrackBy:"ngForTrackBy",ngForTemplate:"ngForTemplate"}}),lM.ctorParameters=()=>[{type:eh},{type:Xg},{type:Hg}],lM.propDecorators={ngForOf:[{type:xy}],ngForTrackBy:[{type:xy}],ngForTemplate:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(lM,[{type:Cy,args:[{selector:"[ngFor][ngForOf]"}]}],(function(){return[{type:eh},{type:Xg},{type:Hg}]}),{ngForOf:[{type:xy}],ngForTrackBy:[{type:xy}],ngForTemplate:[{type:xy}]});class cM{constructor(t,e){this.record=t,this.view=e}}class dM{constructor(t,e){this._viewContainer=t,this._context=new pM,this._thenTemplateRef=null,this._elseTemplateRef=null,this._thenViewRef=null,this._elseViewRef=null,this._thenTemplateRef=e}set ngIf(t){this._context.$implicit=this._context.ngIf=t,this._updateView()}set ngIfThen(t){mM("ngIfThen",t),this._thenTemplateRef=t,this._thenViewRef=null,this._updateView()}set ngIfElse(t){mM("ngIfElse",t),this._elseTemplateRef=t,this._elseViewRef=null,this._updateView()}_updateView(){this._context.$implicit?this._thenViewRef||(this._viewContainer.clear(),this._elseViewRef=null,this._thenTemplateRef&&(this._thenViewRef=this._viewContainer.createEmbeddedView(this._thenTemplateRef,this._context))):this._elseViewRef||(this._viewContainer.clear(),this._thenViewRef=null,this._elseTemplateRef&&(this._elseViewRef=this._viewContainer.createEmbeddedView(this._elseTemplateRef,this._context)))}static ngTemplateContextGuard(t,e){return!0}}dM.ɵfac=function t(e){return new(e||dM)(Sm(eh),Sm(Xg))},dM.ɵdir=lo({type:dM,selectors:[["","ngIf",""]],inputs:{ngIf:"ngIf",ngIfThen:"ngIfThen",ngIfElse:"ngIfElse"}}),dM.ctorParameters=()=>[{type:eh},{type:Xg}],dM.propDecorators={ngIf:[{type:xy}],ngIfThen:[{type:xy}],ngIfElse:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(dM,[{type:Cy,args:[{selector:"[ngIf]"}]}],(function(){return[{type:eh},{type:Xg}]}),{ngIf:[{type:xy}],ngIfThen:[{type:xy}],ngIfElse:[{type:xy}]});class pM{constructor(){this.$implicit=null,this.ngIf=null}}function mM(t,e){if(e&&!e.createEmbeddedView)throw new Error(`${t} must be a TemplateRef, but received '${Ge(e)}'.`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class uM{constructor(t,e){this._viewContainerRef=t,this._templateRef=e,this._created=!1}create(){this._created=!0,this._viewContainerRef.createEmbeddedView(this._templateRef)}destroy(){this._created=!1,this._viewContainerRef.clear()}enforceState(t){t&&!this._created?this.create():!t&&this._created&&this.destroy()}}class fM{constructor(){this._defaultUsed=!1,this._caseCount=0,this._lastCaseCheckIndex=0,this._lastCasesMatched=!1}set ngSwitch(t){this._ngSwitch=t,0===this._caseCount&&this._updateDefaultCases(!0)}_addCase(){return this._caseCount++}_addDefault(t){this._defaultViews||(this._defaultViews=[]),this._defaultViews.push(t)}_matchCase(t){const e=t==this._ngSwitch;return this._lastCasesMatched=this._lastCasesMatched||e,this._lastCaseCheckIndex++,this._lastCaseCheckIndex===this._caseCount&&(this._updateDefaultCases(!this._lastCasesMatched),this._lastCaseCheckIndex=0,this._lastCasesMatched=!1),e}_updateDefaultCases(t){if(this._defaultViews&&t!==this._defaultUsed){this._defaultUsed=t;for(let e=0;e<this._defaultViews.length;e++)this._defaultViews[e].enforceState(t)}}}fM.ɵfac=function t(e){return new(e||fM)},fM.ɵdir=lo({type:fM,selectors:[["","ngSwitch",""]],inputs:{ngSwitch:"ngSwitch"}}),fM.propDecorators={ngSwitch:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(fM,[{type:Cy,args:[{selector:"[ngSwitch]"}]}],(function(){return[]}),{ngSwitch:[{type:xy}]});class gM{constructor(t,e,n){this.ngSwitch=n,"undefined"!=typeof ngDevMode&&!ngDevMode||n||bM("ngSwitchCase","NgSwitchCase"),n._addCase(),this._view=new uM(t,e)}ngDoCheck(){this._view.enforceState(this.ngSwitch._matchCase(this.ngSwitchCase))}}gM.ɵfac=function t(e){return new(e||gM)(Sm(eh),Sm(Xg),Sm(fM,9))},gM.ɵdir=lo({type:gM,selectors:[["","ngSwitchCase",""]],inputs:{ngSwitchCase:"ngSwitchCase"}}),gM.ctorParameters=()=>[{type:eh},{type:Xg},{type:fM,decorators:[{type:Sr},{type:Rr}]}],gM.propDecorators={ngSwitchCase:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(gM,[{type:Cy,args:[{selector:"[ngSwitchCase]"}]}],(function(){return[{type:eh},{type:Xg},{type:fM,decorators:[{type:Sr},{type:Rr}]}]}),{ngSwitchCase:[{type:xy}]});class hM{constructor(t,e,n){"undefined"!=typeof ngDevMode&&!ngDevMode||n||bM("ngSwitchDefault","NgSwitchDefault"),n._addDefault(new uM(t,e))}}function bM(t,e){throw new Ke("305",`An element with the "${t}" attribute (matching the "${e}" directive) must be located inside an element with the "ngSwitch" attribute (matching "NgSwitch" directive)`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */hM.ɵfac=function t(e){return new(e||hM)(Sm(eh),Sm(Xg),Sm(fM,9))},hM.ɵdir=lo({type:hM,selectors:[["","ngSwitchDefault",""]]}),hM.ctorParameters=()=>[{type:eh},{type:Xg},{type:fM,decorators:[{type:Sr},{type:Rr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(hM,[{type:Cy,args:[{selector:"[ngSwitchDefault]"}]}],(function(){return[{type:eh},{type:Xg},{type:fM,decorators:[{type:Sr},{type:Rr}]}]}),null);class yM{constructor(t){this._localization=t,this._caseViews={}}set ngPlural(t){this._switchValue=t,this._updateView()}addCase(t,e){this._caseViews[t]=e}_updateView(){this._clearViews();const t=Object.keys(this._caseViews),e=nM(this._switchValue,t,this._localization);this._activateView(this._caseViews[e])}_clearViews(){this._activeView&&this._activeView.destroy()}_activateView(t){t&&(this._activeView=t,this._activeView.create())}}yM.ɵfac=function t(e){return new(e||yM)(Sm(eM))},yM.ɵdir=lo({type:yM,selectors:[["","ngPlural",""]],inputs:{ngPlural:"ngPlural"}}),yM.ctorParameters=()=>[{type:eM}],yM.propDecorators={ngPlural:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(yM,[{type:Cy,args:[{selector:"[ngPlural]"}]}],(function(){return[{type:eM}]}),{ngPlural:[{type:xy}]});class _M{constructor(t,e,n,o){this.value=t;const i=!isNaN(Number(t));o.addCase(i?`=${t}`:t,new uM(n,e))}}_M.ɵfac=function t(e){return new(e||_M)(Na("ngPluralCase"),Sm(Xg),Sm(eh),Sm(yM,1))},_M.ɵdir=lo({type:_M,selectors:[["","ngPluralCase",""]]}),_M.ctorParameters=()=>[{type:String,decorators:[{type:ja,args:["ngPluralCase"]}]},{type:Xg},{type:eh},{type:yM,decorators:[{type:Rr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(_M,[{type:Cy,args:[{selector:"[ngPluralCase]"}]}],(function(){return[{type:String,decorators:[{type:ja,args:["ngPluralCase"]}]},{type:Xg},{type:eh},{type:yM,decorators:[{type:Rr}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class CM{constructor(t,e,n){this._ngEl=t,this._differs=e,this._renderer=n,this._ngStyle=null,this._differ=null}set ngStyle(t){this._ngStyle=t,!this._differ&&t&&(this._differ=this._differs.find(t).create())}ngDoCheck(){if(this._differ){const t=this._differ.diff(this._ngStyle);t&&this._applyChanges(t)}}_setStyle(t,e){const[n,o]=t.split(".");null!=(e=null!=e&&o?`${e}${o}`:e)?this._renderer.setStyle(this._ngEl.nativeElement,n,e):this._renderer.removeStyle(this._ngEl.nativeElement,n)}_applyChanges(t){t.forEachRemovedItem((t=>this._setStyle(t.key,null))),t.forEachAddedItem((t=>this._setStyle(t.key,t.currentValue))),t.forEachChangedItem((t=>this._setStyle(t.key,t.currentValue)))}}CM.ɵfac=function t(e){return new(e||CM)(Sm(hg),Sm(Lg),Sm(Cg))},CM.ɵdir=lo({type:CM,selectors:[["","ngStyle",""]],inputs:{ngStyle:"ngStyle"}}),CM.ctorParameters=()=>[{type:hg},{type:Lg},{type:Cg}],CM.propDecorators={ngStyle:[{type:xy,args:["ngStyle"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(CM,[{type:Cy,args:[{selector:"[ngStyle]"}]}],(function(){return[{type:hg},{type:Lg},{type:Cg}]}),{ngStyle:[{type:xy,args:["ngStyle"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class MM{constructor(t){this._viewContainerRef=t,this._viewRef=null,this.ngTemplateOutletContext=null,this.ngTemplateOutlet=null}ngOnChanges(t){if(t.ngTemplateOutlet){const t=this._viewContainerRef;this._viewRef&&t.remove(t.indexOf(this._viewRef)),this._viewRef=this.ngTemplateOutlet?t.createEmbeddedView(this.ngTemplateOutlet,this.ngTemplateOutletContext):null}else this._viewRef&&t.ngTemplateOutletContext&&this.ngTemplateOutletContext&&(this._viewRef.context=this.ngTemplateOutletContext)}}MM.ɵfac=function t(e){return new(e||MM)(Sm(eh))},MM.ɵdir=lo({type:MM,selectors:[["","ngTemplateOutlet",""]],inputs:{ngTemplateOutletContext:"ngTemplateOutletContext",ngTemplateOutlet:"ngTemplateOutlet"},features:[Bo]}),MM.ctorParameters=()=>[{type:eh}],MM.propDecorators={ngTemplateOutletContext:[{type:xy}],ngTemplateOutlet:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(MM,[{type:Cy,args:[{selector:"[ngTemplateOutlet]"}]}],(function(){return[{type:eh}]}),{ngTemplateOutletContext:[{type:xy}],ngTemplateOutlet:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const vM=[aM,rM,lM,dM,MM,CM,fM,gM,hM,yM,_M];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function xM(t,e){return Error(`InvalidPipeArgument: '${e}' for pipe '${Ge(t)}'`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const OM=new class{createSubscription(t,e){return t.then(e,(t=>{throw t}))}dispose(t){}onDestroy(t){}},PM=new class{createSubscription(t,e){return t.subscribe({next:e,error:t=>{throw t}})}dispose(t){t.unsubscribe()}onDestroy(t){t.unsubscribe()}};class wM{constructor(t){this._ref=t,this._latestValue=null,this._subscription=null,this._obj=null,this._strategy=null}ngOnDestroy(){this._subscription&&this._dispose()}transform(t){return this._obj?t!==this._obj?(this._dispose(),this.transform(t)):this._latestValue:(t&&this._subscribe(t),this._latestValue)}_subscribe(t){this._obj=t,this._strategy=this._selectStrategy(t),this._subscription=this._strategy.createSubscription(t,(e=>this._updateLatestValue(t,e)))}_selectStrategy(t){if(Fm(t))return OM;if(Lm(t))return PM;throw xM(wM,t)}_dispose(){this._strategy.dispose(this._subscription),this._latestValue=null,this._subscription=null,this._obj=null}_updateLatestValue(t,e){t===this._obj&&(this._latestValue=e,this._ref.markForCheck())}}wM.ɵfac=function t(e){return new(e||wM)(Sm(Ug,16))},wM.ɵpipe=co({name:"async",type:wM,pure:!1}),wM.ctorParameters=()=>[{type:Ug}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(wM,[{type:vy,args:[{name:"async",pure:!1}]}],(function(){return[{type:Ug}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class kM{transform(t){if(null==t)return null;if("string"!=typeof t)throw xM(kM,t);return t.toLowerCase()}}kM.ɵfac=function t(e){return new(e||kM)},kM.ɵpipe=co({name:"lowercase",type:kM,pure:!0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(kM,[{type:vy,args:[{name:"lowercase"}]}],null,null);const 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class RM{constructor(t){this.locale=t}transform(t,e="mediumDate",n,o){if(null==t||""===t||t!=t)return null;try{return NC(t,e,o||this.locale,n)}catch(t){throw xM(RM,t.message)}}}RM.ɵfac=function t(e){return new(e||RM)(Sm(Wy,16))},RM.ɵpipe=co({name:"date",type:RM,pure:!0}),RM.ctorParameters=()=>[{type:String,decorators:[{type:kr,args:[Wy]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(RM,[{type:vy,args:[{name:"date",pure:!0}]}],(function(){return[{type:String,decorators:[{type:kr,args:[Wy]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const AM=/#/g;class TM{constructor(t){this._localization=t}transform(t,e,n){if(null==t)return"";if("object"!=typeof e||null===e)throw xM(TM,e);return e[nM(t,Object.keys(e),this._localization,n)].replace(AM,t.toString())}}TM.ɵfac=function t(e){return new(e||TM)(Sm(eM,16))},TM.ɵpipe=co({name:"i18nPlural",type:TM,pure:!0}),TM.ctorParameters=()=>[{type:eM}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(TM,[{type:vy,args:[{name:"i18nPlural",pure:!0}]}],(function(){return[{type:eM}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class NM{transform(t,e){if(null==t)return"";if("object"!=typeof e||"string"!=typeof t)throw xM(NM,e);return e.hasOwnProperty(t)?e[t]:e.hasOwnProperty("other")?e.other:""}}NM.ɵfac=function t(e){return new(e||NM)},NM.ɵpipe=co({name:"i18nSelect",type:NM,pure:!0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(NM,[{type:vy,args:[{name:"i18nSelect",pure:!0}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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class zM{transform(t){return JSON.stringify(t,null,2)}}zM.ɵfac=function t(e){return new(e||zM)},zM.ɵpipe=co({name:"json",type:zM,pure:!1}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(zM,[{type:vy,args:[{name:"json",pure:!1}]}],null,null);class IM{constructor(t){this.differs=t,this.keyValues=[],this.compareFn=HM}transform(t,e=HM){if(!t||!(t instanceof Map)&&"object"!=typeof t)return null;this.differ||(this.differ=this.differs.find(t).create());const n=this.differ.diff(t),o=e!==this.compareFn;return n&&(this.keyValues=[],n.forEachItem((t=>{this.keyValues.push((
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */BM.ɵfac=function t(e){return new(e||BM)(Sm(Wy,16),Sm(Yy,16))},BM.ɵpipe=co({name:"currency",type:BM,pure:!0}),BM.ctorParameters=()=>[{type:String,decorators:[{type:kr,args:[Wy]}]},{type:String,decorators:[{type:kr,args:[Yy]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(BM,[{type:vy,args:[{name:"currency"}]}],(function(){return[{type:String,decorators:[{type:kr,args:[Wy]}]},{type:String,decorators:[{type:kr,args:[Yy]}]}]}),null);class UM{transform(t,e,n){if(null==t)return null;if(!this.supports(t))throw xM(UM,t);return t.slice(e,n)}supports(t){return"string"==typeof t||Array.isArray(t)}}UM.ɵfac=function t(e){return new(e||UM)},UM.ɵpipe=co({name:"slice",type:UM,pure:!1}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(UM,[{type:vy,args:[{name:"slice",pure:!1}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const GM=[wM,EM,kM,zM,UM,FM,LM,DM,BM,RM,TM,NM,IM];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class WM{}WM.ɵfac=function t(e){return new(e||WM)},WM.ɵmod=ao({type:WM}),WM.ɵinj=vn({providers:[{provide:eM,useClass:oM}]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(WM,[{type:Ay,args:[{declarations:[vM,GM],exports:[vM,GM],providers:[{provide:eM,useClass:oM}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(WM,{declarations:[aM,rM,lM,dM,MM,CM,fM,gM,hM,yM,_M,wM,EM,kM,zM,UM,FM,LM,DM,BM,RM,TM,NM,IM],exports:[aM,rM,lM,dM,MM,CM,fM,gM,hM,yM,_M,wM,EM,kM,zM,UM,FM,LM,DM,BM,RM,TM,NM,IM]}),
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
new Og("12.2.1");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class YM{}YM.ɵprov=Mn({token:YM,providedIn:"root",factory:()=>new qM(vr(Z_),window)});class qM{constructor(t,e){this.document=t,this.window=e,this.offset=()=>[0,0]}setOffset(t){this.offset=Array.isArray(t)?()=>t:t}getScrollPosition(){return this.supportsScrolling()?[this.window.pageXOffset,this.window.pageYOffset]:[0,0]}scrollToPosition(t){this.supportsScrolling()&&this.window.scrollTo(t[0],t[1])}scrollToAnchor(t){if(!this.supportsScrolling())return;const e=(function n(t,e){const n=t.getElementById(e)||t.getElementsByName(e)[0];if(n)return n;if("function"==typeof t.createTreeWalker&&t.body&&(t.body.createShadowRoot||t.body.attachShadow)){const n=t.createTreeWalker(t.body,NodeFilter.SHOW_ELEMENT);let o=n.currentNode;for(;o;){const t=o.shadowRoot;if(t){const n=t.getElementById(e)||t.querySelector(`[name="${e}"]`);if(n)return n}o=n.nextNode()}}return null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(this.document,t);e&&(this.scrollToElement(e),this.attemptFocus(e))}setHistoryScrollRestoration(t){if(this.supportScrollRestoration()){const e=this.window.history;e&&e.scrollRestoration&&(e.scrollRestoration=t)}}scrollToElement(t){const e=t.getBoundingClientRect(),n=e.left+this.window.pageXOffset,o=e.top+this.window.pageYOffset,i=this.offset();this.window.scrollTo(n-i[0],o-i[1])}attemptFocus(t){return t.focus(),this.document.activeElement===t}supportScrollRestoration(){try{if(!this.supportsScrolling())return!1;const t=ZM(this.window.history)||ZM(Object.getPrototypeOf(this.window.history));return!(!t||!t.writable&&!t.set)}catch(t){return!1}}supportsScrolling(){try{return!!this.window&&!!this.window.scrollTo&&"pageXOffset"in this.window}catch(t){return!1}}}function ZM(t){return Object.getOwnPropertyDescriptor(t,"scrollRestoration")}class XM{}
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class KM extends class extends class{}{constructor(){super(...arguments),this.supportsDOMEvents=!0}}{static makeCurrent(){!(function t(e){Y_||(Y_=e)})(new KM)}onAndCancel(t,e,n){return t.addEventListener(e,n,!1),()=>{t.removeEventListener(e,n,!1)}}dispatchEvent(t,e){t.dispatchEvent(e)}remove(t){t.parentNode&&t.parentNode.removeChild(t)}createElement(t,e){return(e=e||this.getDefaultDocument()).createElement(t)}createHtmlDocument(){return document.implementation.createHTMLDocument("fakeTitle")}getDefaultDocument(){return document}isElementNode(t){return t.nodeType===Node.ELEMENT_NODE}isShadowRoot(t){return t instanceof DocumentFragment}getGlobalEventTarget(t,e){return"window"===e?window:"document"===e?t:"body"===e?t.body:null}getBaseHref(t){const e=(function n(){return QM=QM||document.querySelector("base"),QM?QM.getAttribute("href"):null})();return null==e?null:(function o(t){JM=JM||document.createElement("a"),JM.setAttribute("href",t);const e=JM.pathname;return"/"===e.charAt(0)?e:`/${e}`}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(e)}resetBaseElement(){QM=null}getUserAgent(){return window.navigator.userAgent}getCookie(t){return iM(document.cookie,t)}}let JM,QM=null;const $M=new Ga("TRANSITION_ID"),tv=[{provide:zy,useFactory:function ev(t,e,n){return()=>{n.get(Iy).donePromise.then((()=>{const n=q_();Array.prototype.slice.apply(e.querySelectorAll("style[ng-transition]")).filter((e=>e.getAttribute("ng-transition")===t)).forEach((t=>n.remove(t)))}))}},deps:[$M,Z_,rp],multi:!0}];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class nv{static init(){!(function t(e){g_=e})(new nv)}addToWindow(t){jn.getAngularTestability=(e,n=!0)=>{const o=t.findTestabilityInTree(e,n);if(null==o)throw new Error("Could not find testability for element.");return o},jn.getAllAngularTestabilities=()=>t.getAllTestabilities(),jn.getAllAngularRootElements=()=>t.getAllRootElements(),jn.frameworkStabilizers||(jn.frameworkStabilizers=[]),jn.frameworkStabilizers.push((t=>{const e=jn.getAllAngularTestabilities();let n=e.length,o=!1;const i=function(e){o=o||e,n--,0==n&&t(o)};e.forEach((function(t){t.whenStable(i)}))}))}findTestabilityInTree(t,e,n){if(null==e)return null;const o=t.getTestability(e);return null!=o?o:n?q_().isShadowRoot(e)?this.findTestabilityInTree(t,e.host,!0):this.findTestabilityInTree(t,e.parentElement,!0):null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class ov{build(){return new XMLHttpRequest}}ov.ɵfac=function t(e){return new(e||ov)},ov.ɵprov=Mn({token:ov,factory:ov.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ov,[{type:im}],null,null),new Sr;const iv=new Ga("EventManagerPlugins");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class av{constructor(t,e){this._zone=e,this._eventNameToPlugin=new Map,t.forEach((t=>t.manager=this)),this._plugins=t.slice().reverse()}addEventListener(t,e,n){return this._findPluginFor(e).addEventListener(t,e,n)}addGlobalEventListener(t,e,n){return this._findPluginFor(e).addGlobalEventListener(t,e,n)}getZone(){return this._zone}_findPluginFor(t){const e=this._eventNameToPlugin.get(t);if(e)return e;const n=this._plugins;for(let e=0;e<n.length;e++){const o=n[e];if(o.supports(t))return this._eventNameToPlugin.set(t,o),o}throw new Error(`No event manager plugin found for event ${t}`)}}av.ɵfac=function t(e){return new(e||av)(vr(iv),vr(a_))},av.ɵprov=Mn({token:av,factory:av.ɵfac}),av.ctorParameters=()=>[{type:Array,decorators:[{type:kr,args:[iv]}]},{type:a_}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(av,[{type:im}],(function(){return[{type:Array,decorators:[{type:kr,args:[iv]}]},{type:a_}]}),null);class rv{constructor(t){this._doc=t}addGlobalEventListener(t,e,n){const o=q_().getGlobalEventTarget(this._doc,t);if(!o)throw new Error(`Unsupported event target ${o} for event ${e}`);return this.addEventListener(o,e,n)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class sv{constructor(){this._stylesSet=new Set}addStyles(t){const e=new Set;t.forEach((t=>{this._stylesSet.has(t)||(this._stylesSet.add(t),e.add(t))})),this.onStylesAdded(e)}onStylesAdded(t){}getAllStyles(){return Array.from(this._stylesSet)}}sv.ɵfac=function t(e){return new(e||sv)},sv.ɵprov=Mn({token:sv,factory:sv.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(sv,[{type:im}],(function(){return[]}),null);class lv extends sv{constructor(t){super(),this._doc=t,this._hostNodes=new Map,this._hostNodes.set(t.head,[])}_addStylesToHost(t,e,n){t.forEach((t=>{const o=this._doc.createElement("style");o.textContent=t,n.push(e.appendChild(o))}))}addHost(t){const e=[];this._addStylesToHost(this._stylesSet,t,e),this._hostNodes.set(t,e)}removeHost(t){const e=this._hostNodes.get(t);e&&e.forEach(cv),this._hostNodes.delete(t)}onStylesAdded(t){this._hostNodes.forEach(((e,n)=>{this._addStylesToHost(t,n,e)}))}ngOnDestroy(){this._hostNodes.forEach((t=>t.forEach(cv)))}}function cv(t){q_().remove(t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class vv extends rv{constructor(t){super(t)}supports(t){return!0}addEventListener(t,e,n){return t.addEventListener(e,n,!1),()=>this.removeEventListener(t,e,n)}removeEventListener(t,e,n){return t.removeEventListener(e,n)}}vv.ɵfac=function t(e){return new(e||vv)(vr(Z_))},vv.ɵprov=Mn({token:vv,factory:vv.ɵfac}),vv.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(vv,[{type:im}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const xv={pan:!0,panstart:!0,panmove:!0,panend:!0,pancancel:!0,panleft:!0,panright:!0,panup:!0,pandown:!0,pinch:!0,pinchstart:!0,pinchmove:!0,pinchend:!0,pinchcancel:!0,pinchin:!0,pinchout:!0,press:!0,pressup:!0,rotate:!0,rotatestart:!0,rotatemove:!0,rotateend:!0,rotatecancel:!0,swipe:!0,swipeleft:!0,swiperight:!0,swipeup:!0,swipedown:!0,tap:!0,doubletap:!0},Ov=new Ga("HammerGestureConfig"),Pv=new Ga("HammerLoader");class wv{constructor(){this.events=[],this.overrides={}}buildHammer(t){const e=new Hammer(t,this.options);e.get("pinch").set({enable:!0}),e.get("rotate").set({enable:!0});for(const t in this.overrides)e.get(t).set(this.overrides[t]);return e}}wv.ɵfac=function t(e){return new(e||wv)},wv.ɵprov=Mn({token:wv,factory:wv.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(wv,[{type:im}],(function(){return[]}),null);class kv extends rv{constructor(t,e,n,o){super(t),this._config=e,this.console=n,this.loader=o,this._loaderPromise=null}supports(t){return!(!xv.hasOwnProperty(t.toLowerCase())&&!this.isCustomEvent(t)||!window.Hammer&&!this.loader&&(("undefined"==typeof ngDevMode||ngDevMode)&&this.console.warn(`The "${t}" event cannot be bound because Hammer.JS is not loaded and no custom loader has been specified.`),1))}addEventListener(t,e,n){const o=this.manager.getZone();if(e=e.toLowerCase(),!window.Hammer&&this.loader){this._loaderPromise=this._loaderPromise||this.loader();let o=!1,i=()=>{o=!0};return this._loaderPromise.then((()=>{if(!window.Hammer)return("undefined"==typeof ngDevMode||ngDevMode)&&this.console.warn("The custom HAMMER_LOADER completed, but Hammer.JS is not present."),void(i=()=>{});o||(i=this.addEventListener(t,e,n))})).catch((()=>{("undefined"==typeof ngDevMode||ngDevMode)&&this.console.warn(`The "${e}" event cannot be bound because the custom Hammer.JS loader failed.`),i=()=>{}})),()=>{i()}}return o.runOutsideAngular((()=>{const i=this._config.buildHammer(t),a=function(t){o.runGuarded((function(){n(t)}))};return i.on(e,a),()=>{i.off(e,a),"function"==typeof i.destroy&&i.destroy()}}))}isCustomEvent(t){return this._config.events.indexOf(t)>-1}}kv.ɵfac=function t(e){return new(e||kv)(vr(Z_),vr(Ov),vr(Gy),vr(Pv,8))},kv.ɵprov=Mn({token:kv,factory:kv.ɵfac}),kv.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wv,decorators:[{type:kr,args:[Ov]}]},{type:Gy},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Pv]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(kv,[{type:im}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wv,decorators:[{type:kr,args:[Ov]}]},{type:Gy},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Pv]}]}]}),null);const Sv=[{provide:iv,useClass:kv,multi:!0,deps:[Z_,Ov,Gy,[new Sr,Pv]]},{provide:Ov,useClass:wv,deps:[]}];class Dv{}Dv.ɵfac=function t(e){return new(e||Dv)},Dv.ɵmod=ao({type:Dv}),Dv.ɵinj=vn({providers:Sv}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Dv,[{type:Ay,args:[{providers:Sv}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Ev=["alt","control","meta","shift"],Rv={"\b":"Backspace","\t":"Tab","":"Delete","":"Escape",Del:"Delete",Esc:"Escape",Left:"ArrowLeft",Right:"ArrowRight",Up:"ArrowUp",Down:"ArrowDown",Menu:"ContextMenu",Scroll:"ScrollLock",Win:"OS"},Av={A:"1",B:"2",C:"3",D:"4",E:"5",F:"6",G:"7",H:"8",I:"9",J:"*",K:"+",M:"-",N:".",O:"/","`":"0","":"NumLock"},Tv={alt:t=>t.altKey,control:t=>t.ctrlKey,meta:t=>t.metaKey,shift:t=>t.shiftKey};class Nv extends rv{constructor(t){super(t)}supports(t){return null!=Nv.parseEventName(t)}addEventListener(t,e,n){const o=Nv.parseEventName(e),i=Nv.eventCallback(o.fullKey,n,this.manager.getZone());return this.manager.getZone().runOutsideAngular((()=>q_().onAndCancel(t,o.domEventName,i)))}static parseEventName(t){const e=t.toLowerCase().split("."),n=e.shift();if(0===e.length||"keydown"!==n&&"keyup"!==n)return null;const o=Nv._normalizeKey(e.pop());let i="";if(Ev.forEach((t=>{const n=e.indexOf(t);n>-1&&(e.splice(n,1),i+=t+".")})),i+=o,0!=e.length||0===o.length)return null;const a={};return a.domEventName=n,a.fullKey=i,a}static getEventFullKey(t){let e="",n=(function o(t){let e=t.key;if(null==e){if(e=t.keyIdentifier,null==e)return"Unidentified";e.startsWith("U+")&&(e=String.fromCharCode(parseInt(e.substring(2),16)),3===t.location&&Av.hasOwnProperty(e)&&(e=Av[e]))}return Rv[e]||e}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
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     * Copyright Google LLC All Rights Reserved.
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     * Copyright Google LLC All Rights Reserved.
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license Angular v12.0.0-next.0
     * (c) 2010-2020 Google LLC. https://angular.io/
     * License: MIT
     */!(function(){
/**
         * @license
         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
         * found in the LICENSE file at https://angular.io/license
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/**
         * @license
         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
         * found in the LICENSE file at https://angular.io/license
         */
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/**
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         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
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/**
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         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
         * found in the LICENSE file at https://angular.io/license
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/**
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         * Copyright Google LLC All Rights Reserved.
         *
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/**
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         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
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/**
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         * Copyright Google LLC All Rights Reserved.
         *
         * Use of this source code is governed by an MIT-style license that can be
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         * Copyright Google LLC All Rights Reserved.
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/**
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         * Copyright Google LLC All Rights Reserved.
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         * Copyright Google LLC All Rights Reserved.
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         * Copyright Google LLC All Rights Reserved.
         *
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/**
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     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
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     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class mx{constructor(t=0,e=0){this._onDoneFns=[],this._onStartFns=[],this._onDestroyFns=[],this._started=!1,this._destroyed=!1,this._finished=!1,this._position=0,this.parentPlayer=null,this.totalTime=t+e}_onFinish(){this._finished||(this._finished=!0,this._onDoneFns.forEach((t=>t())),this._onDoneFns=[])}onStart(t){this._onStartFns.push(t)}onDone(t){this._onDoneFns.push(t)}onDestroy(t){this._onDestroyFns.push(t)}hasStarted(){return this._started}init(){}play(){this.hasStarted()||(this._onStart(),this.triggerMicrotask()),this._started=!0}triggerMicrotask(){px((()=>this._onFinish()))}_onStart(){this._onStartFns.forEach((t=>t())),this._onStartFns=[]}pause(){}restart(){}finish(){this._onFinish()}destroy(){this._destroyed||(this._destroyed=!0,this.hasStarted()||this._onStart(),this.finish(),this._onDestroyFns.forEach((t=>t())),this._onDestroyFns=[])}reset(){this._started=!1}setPosition(t){this._position=this.totalTime?t*this.totalTime:1}getPosition(){return this.totalTime?this._position/this.totalTime:1}triggerCallback(t){const e="start"==t?this._onStartFns:this._onDoneFns;e.forEach((t=>t())),e.length=0}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class ux{constructor(t){this._onDoneFns=[],this._onStartFns=[],this._finished=!1,this._started=!1,this._destroyed=!1,this._onDestroyFns=[],this.parentPlayer=null,this.totalTime=0,this.players=t;let e=0,n=0,o=0;const i=this.players.length;0==i?px((()=>this._onFinish())):this.players.forEach((t=>{t.onDone((()=>{++e==i&&this._onFinish()})),t.onDestroy((()=>{++n==i&&this._onDestroy()})),t.onStart((()=>{++o==i&&this._onStart()}))})),this.totalTime=this.players.reduce(((t,e)=>Math.max(t,e.totalTime)),0)}_onFinish(){this._finished||(this._finished=!0,this._onDoneFns.forEach((t=>t())),this._onDoneFns=[])}init(){this.players.forEach((t=>t.init()))}onStart(t){this._onStartFns.push(t)}_onStart(){this.hasStarted()||(this._started=!0,this._onStartFns.forEach((t=>t())),this._onStartFns=[])}onDone(t){this._onDoneFns.push(t)}onDestroy(t){this._onDestroyFns.push(t)}hasStarted(){return this._started}play(){this.parentPlayer||this.init(),this._onStart(),this.players.forEach((t=>t.play()))}pause(){this.players.forEach((t=>t.pause()))}restart(){this.players.forEach((t=>t.restart()))}finish(){this._onFinish(),this.players.forEach((t=>t.finish()))}destroy(){this._onDestroy()}_onDestroy(){this._destroyed||(this._destroyed=!0,this._onFinish(),this.players.forEach((t=>t.destroy())),this._onDestroyFns.forEach((t=>t())),this._onDestroyFns=[])}reset(){this.players.forEach((t=>t.reset())),this._destroyed=!1,this._finished=!1,this._started=!1}setPosition(t){const e=t*this.totalTime;this.players.forEach((t=>{const n=t.totalTime?Math.min(1,e/t.totalTime):1;t.setPosition(n)}))}getPosition(){const t=this.players.reduce(((t,e)=>null===t||e.totalTime>t.totalTime?e:t),null);return null!=t?t.getPosition():0}beforeDestroy(){this.players.forEach((t=>{t.beforeDestroy&&t.beforeDestroy()}))}triggerCallback(t){const e="start"==t?this._onStartFns:this._onDoneFns;e.forEach((t=>t())),e.length=0}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Nx{validateStyleProperty(t){return Dx(t)}matchesElement(t,e){return Ex(t,e)}containsElement(t,e){return Rx(t,e)}query(t,e,n){return Ax(t,e,n)}computeStyle(t,e,n){return n||""}animate(t,e,n,o,i,a=[],r){return new mx(n,o)}}Nx.ɵfac=function t(e){return new(e||Nx)},Nx.ɵprov=Mn({token:Nx,factory:Nx.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Nx,[{type:im}],null,null);class zx{}zx.NOOP=new Nx;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const vO=new RegExp(":enter","g"),xO=new RegExp(":leave","g");function OO(t,e,n,o,i,a={},r={},s,l,c=[]){return(new PO).buildKeyframes(t,e,n,o,i,a,r,s,l,c)}class PO{buildKeyframes(t,e,n,o,i,a,r,s,l,c=[]){l=l||new MO;const d=new kO(t,e,l,o,i,c,[]);d.options=s,d.currentTimeline.setStyles([a],null,d.errors,s),rO(this,n,d);const p=d.timelines.filter((t=>t.containsAnimation()));if(p.length&&Object.keys(r).length){const t=p[p.length-1];t.allowOnlyTimelineStyles()||t.setStyles([r],null,d.errors,s)}return p.length?p.map((t=>t.buildKeyframes())):[CO(e,[],[],[],0,0,"",!1)]}visitTrigger(t,e){}visitState(t,e){}visitTransition(t,e){}visitAnimateChild(t,e){const n=e.subInstructions.consume(e.element);if(n){const o=e.createSubContext(t.options),i=e.currentTimeline.currentTime,a=this._visitSubInstructions(n,o,o.options);i!=a&&e.transformIntoNewTimeline(a)}e.previousNode=t}visitAnimateRef(t,e){const n=e.createSubContext(t.options);n.transformIntoNewTimeline(),this.visitReference(t.animation,n),e.transformIntoNewTimeline(n.currentTimeline.currentTime),e.previousNode=t}_visitSubInstructions(t,e,n){let o=e.currentTimeline.currentTime;const i=null!=n.duration?jx(n.duration):null,a=null!=n.delay?jx(n.delay):null;return 0!==i&&t.forEach((t=>{const n=e.appendInstructionToTimeline(t,i,a);o=Math.max(o,n.duration+n.delay)})),o}visitReference(t,e){e.updateOptions(t.options,!0),rO(this,t.animation,e),e.previousNode=t}visitSequence(t,e){const n=e.subContextCount;let o=e;const i=t.options;if(i&&(i.params||i.delay)&&(o=e.createSubContext(i),o.transformIntoNewTimeline(),null!=i.delay)){6==o.previousNode.type&&(o.currentTimeline.snapshotCurrentStyles(),o.previousNode=wO);const t=jx(i.delay);o.delayNextStep(t)}t.steps.length&&(t.steps.forEach((t=>rO(this,t,o))),o.currentTimeline.applyStylesToKeyframe(),o.subContextCount>n&&o.transformIntoNewTimeline()),e.previousNode=t}visitGroup(t,e){const n=[];let o=e.currentTimeline.currentTime;const i=t.options&&t.options.delay?jx(t.options.delay):0;t.steps.forEach((a=>{const r=e.createSubContext(t.options);i&&r.delayNextStep(i),rO(this,a,r),o=Math.max(o,r.currentTimeline.currentTime),n.push(r.currentTimeline)})),n.forEach((t=>e.currentTimeline.mergeTimelineCollectedStyles(t))),e.transformIntoNewTimeline(o),e.previousNode=t}_visitTiming(t,e){if(t.dynamic){const n=t.strValue;return Gx(e.params?tO(n,e.params,e.errors):n,e.errors)}return{duration:t.duration,delay:t.delay,easing:t.easing}}visitAnimate(t,e){const n=e.currentAnimateTimings=this._visitTiming(t.timings,e),o=e.currentTimeline;n.delay&&(e.incrementTime(n.delay),o.snapshotCurrentStyles());const i=t.style;5==i.type?this.visitKeyframes(i,e):(e.incrementTime(n.duration),this.visitStyle(i,e),o.applyStylesToKeyframe()),e.currentAnimateTimings=null,e.previousNode=t}visitStyle(t,e){const n=e.currentTimeline,o=e.currentAnimateTimings;!o&&n.getCurrentStyleProperties().length&&n.forwardFrame();const i=o&&o.easing||t.easing;t.isEmptyStep?n.applyEmptyStep(i):n.setStyles(t.styles,i,e.errors,e.options),e.previousNode=t}visitKeyframes(t,e){const n=e.currentAnimateTimings,o=e.currentTimeline.duration,i=n.duration,a=e.createSubContext().currentTimeline;a.easing=n.easing,t.styles.forEach((t=>{a.forwardTime((t.offset||0)*i),a.setStyles(t.styles,t.easing,e.errors,e.options),a.applyStylesToKeyframe()})),e.currentTimeline.mergeTimelineCollectedStyles(a),e.transformIntoNewTimeline(o+i),e.previousNode=t}visitQuery(t,e){const n=e.currentTimeline.currentTime,o=t.options||{},i=o.delay?jx(o.delay):0;i&&(6===e.previousNode.type||0==n&&e.currentTimeline.getCurrentStyleProperties().length)&&(e.currentTimeline.snapshotCurrentStyles(),e.previousNode=wO);let a=n;const r=e.invokeQuery(t.selector,t.originalSelector,t.limit,t.includeSelf,!!o.optional,e.errors);e.currentQueryTotal=r.length;let s=null;r.forEach(((n,o)=>{e.currentQueryIndex=o;const r=e.createSubContext(t.options,n);i&&r.delayNextStep(i),n===e.element&&(s=r.currentTimeline),rO(this,t.animation,r),r.currentTimeline.applyStylesToKeyframe(),a=Math.max(a,r.currentTimeline.currentTime)})),e.currentQueryIndex=0,e.currentQueryTotal=0,e.transformIntoNewTimeline(a),s&&(e.currentTimeline.mergeTimelineCollectedStyles(s),e.currentTimeline.snapshotCurrentStyles()),e.previousNode=t}visitStagger(t,e){const n=e.parentContext,o=e.currentTimeline,i=t.timings,a=Math.abs(i.duration),r=a*(e.currentQueryTotal-1);let s=a*e.currentQueryIndex;switch(i.duration<0?"reverse":i.easing){case"reverse":s=r-s;break;case"full":s=n.currentStaggerTime}const l=e.currentTimeline;s&&l.delayNextStep(s);const c=l.currentTime;rO(this,t.animation,e),e.previousNode=t,n.currentStaggerTime=o.currentTime-c+(o.startTime-n.currentTimeline.startTime)}}const wO={};class kO{constructor(t,e,n,o,i,a,r,s){this._driver=t,this.element=e,this.subInstructions=n,this._enterClassName=o,this._leaveClassName=i,this.errors=a,this.timelines=r,this.parentContext=null,this.currentAnimateTimings=null,this.previousNode=wO,this.subContextCount=0,this.options={},this.currentQueryIndex=0,this.currentQueryTotal=0,this.currentStaggerTime=0,this.currentTimeline=s||new SO(this._driver,e,0),r.push(this.currentTimeline)}get params(){return this.options.params}updateOptions(t,e){if(!t)return;const n=t;let o=this.options;null!=n.duration&&(o.duration=jx(n.duration)),null!=n.delay&&(o.delay=jx(n.delay));const i=n.params;if(i){let t=o.params;t||(t=this.options.params={}),Object.keys(i).forEach((n=>{e&&t.hasOwnProperty(n)||(t[n]=tO(i[n],t,this.errors))}))}}_copyOptions(){const t={};if(this.options){const e=this.options.params;if(e){const n=t.params={};Object.keys(e).forEach((t=>{n[t]=e[t]}))}}return t}createSubContext(t=null,e,n){const o=e||this.element,i=new kO(this._driver,o,this.subInstructions,this._enterClassName,this._leaveClassName,this.errors,this.timelines,this.currentTimeline.fork(o,n||0));return i.previousNode=this.previousNode,i.currentAnimateTimings=this.currentAnimateTimings,i.options=this._copyOptions(),i.updateOptions(t),i.currentQueryIndex=this.currentQueryIndex,i.currentQueryTotal=this.currentQueryTotal,i.parentContext=this,this.subContextCount++,i}transformIntoNewTimeline(t){return this.previousNode=wO,this.currentTimeline=this.currentTimeline.fork(this.element,t),this.timelines.push(this.currentTimeline),this.currentTimeline}appendInstructionToTimeline(t,e,n){const o={duration:null!=e?e:t.duration,delay:this.currentTimeline.currentTime+(null!=n?n:0)+t.delay,easing:""},i=new DO(this._driver,t.element,t.keyframes,t.preStyleProps,t.postStyleProps,o,t.stretchStartingKeyframe);return this.timelines.push(i),o}incrementTime(t){this.currentTimeline.forwardTime(this.currentTimeline.duration+t)}delayNextStep(t){t>0&&this.currentTimeline.delayNextStep(t)}invokeQuery(t,e,n,o,i,a){let r=[];if(o&&r.push(this.element),t.length>0){t=(t=t.replace(vO,"."+this._enterClassName)).replace(xO,"."+this._leaveClassName);let e=this._driver.query(this.element,t,1!=n);0!==n&&(e=n<0?e.slice(e.length+n,e.length):e.slice(0,n)),r.push(...e)}return i||0!=r.length||a.push(`\`query("${e}")\` returned zero elements. (Use \`query("${e}", { optional: true })\` if you wish to allow this.)`),r}}class SO{constructor(t,e,n,o){this._driver=t,this.element=e,this.startTime=n,this._elementTimelineStylesLookup=o,this.duration=0,this._previousKeyframe={},this._currentKeyframe={},this._keyframes=new Map,this._styleSummary={},this._pendingStyles={},this._backFill={},this._currentEmptyStepKeyframe=null,this._elementTimelineStylesLookup||(this._elementTimelineStylesLookup=new Map),this._localTimelineStyles=Object.create(this._backFill,{}),this._globalTimelineStyles=this._elementTimelineStylesLookup.get(e),this._globalTimelineStyles||(this._globalTimelineStyles=this._localTimelineStyles,this._elementTimelineStylesLookup.set(e,this._localTimelineStyles)),this._loadKeyframe()}containsAnimation(){switch(this._keyframes.size){case 0:return!1;case 1:return this.getCurrentStyleProperties().length>0;default:return!0}}getCurrentStyleProperties(){return Object.keys(this._currentKeyframe)}get currentTime(){return this.startTime+this.duration}delayNextStep(t){const e=1==this._keyframes.size&&Object.keys(this._pendingStyles).length;this.duration||e?(this.forwardTime(this.currentTime+t),e&&this.snapshotCurrentStyles()):this.startTime+=t}fork(t,e){return this.applyStylesToKeyframe(),new SO(this._driver,t,e||this.currentTime,this._elementTimelineStylesLookup)}_loadKeyframe(){this._currentKeyframe&&(this._previousKeyframe=this._currentKeyframe),this._currentKeyframe=this._keyframes.get(this.duration),this._currentKeyframe||(this._currentKeyframe=Object.create(this._backFill,{}),this._keyframes.set(this.duration,this._currentKeyframe))}forwardFrame(){this.duration+=1,this._loadKeyframe()}forwardTime(t){this.applyStylesToKeyframe(),this.duration=t,this._loadKeyframe()}_updateStyle(t,e){this._localTimelineStyles[t]=e,this._globalTimelineStyles[t]=e,this._styleSummary[t]={time:this.currentTime,value:e}}allowOnlyTimelineStyles(){return this._currentEmptyStepKeyframe!==this._currentKeyframe}applyEmptyStep(t){t&&(this._previousKeyframe.easing=t),Object.keys(this._globalTimelineStyles).forEach((t=>{this._backFill[t]=this._globalTimelineStyles[t]||ex,this._currentKeyframe[t]=ex})),this._currentEmptyStepKeyframe=this._currentKeyframe}setStyles(t,e,n,o){e&&(this._previousKeyframe.easing=e);const i=o&&o.params||{},a=(function r(t,e){const n={};let o;return t.forEach((t=>{"*"===t?(o=o||Object.keys(e),o.forEach((t=>{n[t]=ex}))):Yx(t,!1,n)})),n}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t,this._globalTimelineStyles);Object.keys(a).forEach((t=>{const e=tO(a[t],i,n);this._pendingStyles[t]=e,this._localTimelineStyles.hasOwnProperty(t)||(this._backFill[t]=this._globalTimelineStyles.hasOwnProperty(t)?this._globalTimelineStyles[t]:ex),this._updateStyle(t,e)}))}applyStylesToKeyframe(){const t=this._pendingStyles,e=Object.keys(t);0!=e.length&&(this._pendingStyles={},e.forEach((e=>{this._currentKeyframe[e]=t[e]})),Object.keys(this._localTimelineStyles).forEach((t=>{this._currentKeyframe.hasOwnProperty(t)||(this._currentKeyframe[t]=this._localTimelineStyles[t])})))}snapshotCurrentStyles(){Object.keys(this._localTimelineStyles).forEach((t=>{const e=this._localTimelineStyles[t];this._pendingStyles[t]=e,this._updateStyle(t,e)}))}getFinalKeyframe(){return this._keyframes.get(this.duration)}get properties(){const t=[];for(let e in this._currentKeyframe)t.push(e);return t}mergeTimelineCollectedStyles(t){Object.keys(t._styleSummary).forEach((e=>{const n=this._styleSummary[e],o=t._styleSummary[e];(!n||o.time>n.time)&&this._updateStyle(e,o.value)}))}buildKeyframes(){this.applyStylesToKeyframe();const t=new Set,e=new Set,n=1===this._keyframes.size&&0===this.duration;let o=[];this._keyframes.forEach(((i,a)=>{const r=Yx(i,!0);Object.keys(r).forEach((n=>{const o=r[n];"!"==o?t.add(n):o==ex&&e.add(n)})),n||(r.offset=a/this.duration),o.push(r)}));const i=t.size?eO(t.values()):[],a=e.size?eO(e.values()):[];if(n){const t=o[0],e=Wx(t);t.offset=0,e.offset=1,o=[t,e]}return CO(this.element,o,i,a,this.duration,this.startTime,this.easing,!1)}}class DO extends SO{constructor(t,e,n,o,i,a,r=!1){super(t,e,a.delay),this.keyframes=n,this.preStyleProps=o,this.postStyleProps=i,this._stretchStartingKeyframe=r,this.timings={duration:a.duration,delay:a.delay,easing:a.easing}}containsAnimation(){return this.keyframes.length>1}buildKeyframes(){let t=this.keyframes,{delay:e,duration:n,easing:o}=this.timings;if(this._stretchStartingKeyframe&&e){const i=[],a=n+e,r=e/a,s=Yx(t[0],!1);s.offset=0,i.push(s);const l=Yx(t[0],!1);l.offset=EO(r),i.push(l);const c=t.length-1;for(let o=1;o<=c;o++){let r=Yx(t[o],!1);r.offset=EO((e+r.offset*n)/a),i.push(r)}n=a,e=0,o="",t=i}return CO(this.element,t,this.preStyleProps,this.postStyleProps,n,e,o,!0)}}function EO(t,e=3){const n=Math.pow(10,e-1);return Math.round(t*n)/n}class RO{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class AO extends RO{normalizePropertyName(t,e){return oO(t)}normalizeStyleValue(t,e,n,o){let i="";const a=n.toString().trim();if(TO[e]&&0!==n&&"0"!==n)if("number"==typeof n)i="px";else{const e=n.match(/^[+-]?[\d\.]+([a-z]*)$/);e&&0==e[1].length&&o.push(`Please provide a CSS unit value for ${t}:${n}`)}return a+i}}const TO=(function NO(t){const e={};return t.forEach((t=>e[t]=!0)),e})("width,height,minWidth,minHeight,maxWidth,maxHeight,left,top,bottom,right,fontSize,outlineWidth,outlineOffset,paddingTop,paddingLeft,paddingBottom,paddingRight,marginTop,marginLeft,marginBottom,marginRight,borderRadius,borderWidth,borderTopWidth,borderLeftWidth,borderRightWidth,borderBottomWidth,textIndent,perspective".split(","));function zO(t,e,n,o,i,a,r,s,l,c,d,p,m){return{type:0,element:t,triggerName:e,isRemovalTransition:i,fromState:n,fromStyles:a,toState:o,toStyles:r,timelines:s,queriedElements:l,preStyleProps:c,postStyleProps:d,totalTime:p,errors:m}}const IO={};class HO{constructor(t,e,n){this._triggerName=t,this.ast=e,this._stateStyles=n}match(t,e,n,o){return(function i(t,e,n,o,a){return t.some((t=>t(e,n,o,a)))})(this.ast.matchers,t,e,n,o)}buildStyles(t,e,n){const o=this._stateStyles["*"],i=this._stateStyles[t],a=o?o.buildStyles(e,n):{};return i?i.buildStyles(e,n):a}build(t,e,n,o,i,a,r,s,l,c){const d=[],p=this.ast.options&&this.ast.options.params||IO,m=this.buildStyles(n,r&&r.params||IO,d),u=s&&s.params||IO,f=this.buildStyles(o,u,d),g=new Set,h=new Map,b=new Map,y="void"===o,_={params:Object.assign(Object.assign({},p),u)},C=c?[]:OO(t,e,this.ast.animation,i,a,m,f,_,l,d);let M=0;if(C.forEach((t=>{M=Math.max(t.duration+t.delay,M)})),d.length)return zO(e,this._triggerName,n,o,y,m,f,[],[],h,b,M,d);C.forEach((t=>{const n=t.element,o=Mx(h,n,{});t.preStyleProps.forEach((t=>o[t]=!0));const i=Mx(b,n,{});t.postStyleProps.forEach((t=>i[t]=!0)),n!==e&&g.add(n)}));const v=eO(g.values());return zO(e,this._triggerName,n,o,y,m,f,C,v,h,b,M)}}class FO{constructor(t,e,n){this.styles=t,this.defaultParams=e,this.normalizer=n}buildStyles(t,e){const n={},o=Wx(this.defaultParams);return Object.keys(t).forEach((e=>{const n=t[e];null!=n&&(o[e]=n)})),this.styles.styles.forEach((t=>{if("string"!=typeof t){const i=t;Object.keys(i).forEach((t=>{let a=i[t];a.length>1&&(a=tO(a,o,e));const r=this.normalizer.normalizePropertyName(t,e);a=this.normalizer.normalizeStyleValue(t,r,a,e),n[r]=a}))}})),n}}class LO{constructor(t,e,n){this.name=t,this.ast=e,this._normalizer=n,this.transitionFactories=[],this.states={},e.states.forEach((t=>{this.states[t.name]=new FO(t.style,t.options&&t.options.params||{},n)})),BO(this.states,"true","1"),BO(this.states,"false","0"),e.transitions.forEach((e=>{this.transitionFactories.push(new HO(t,e,this.states))})),this.fallbackTransition=(function o(t,e,n){return new HO(t,{type:1,animation:{type:2,steps:[],options:null},matchers:[(t,e)=>!0],options:null,queryCount:0,depCount:0},e)})(t,this.states)}get containsQueries(){return this.ast.queryCount>0}matchTransition(t,e,n,o){return this.transitionFactories.find((i=>i.match(t,e,n,o)))||null}matchStyles(t,e,n){return this.fallbackTransition.buildStyles(t,e,n)}}function BO(t,e,n){t.hasOwnProperty(e)?t.hasOwnProperty(n)||(t[n]=t[e]):t.hasOwnProperty(n)&&(t[e]=t[n])}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const VO=new MO;class jO{constructor(t,e,n){this.bodyNode=t,this._driver=e,this._normalizer=n,this._animations={},this._playersById={},this.players=[]}register(t,e){const n=[],o=fO(this._driver,e,n);if(n.length)throw new Error(`Unable to build the animation due to the following errors: ${n.join("\n")}`);this._animations[t]=o}_buildPlayer(t,e,n){const o=t.element,i=bx(0,this._normalizer,0,t.keyframes,e,n);return this._driver.animate(o,i,t.duration,t.delay,t.easing,[],!0)}create(t,e,n={}){const o=[],i=this._animations[t];let a;const r=new Map;if(i?(a=OO(this._driver,e,i,Ix,Hx,{},{},n,VO,o),a.forEach((t=>{const e=Mx(r,t.element,{});t.postStyleProps.forEach((t=>e[t]=null))}))):(o.push("The requested animation doesn't exist or has already been destroyed"),a=[]),o.length)throw new Error(`Unable to create the animation due to the following errors: ${o.join("\n")}`);r.forEach(((t,e)=>{Object.keys(t).forEach((n=>{t[n]=this._driver.computeStyle(e,n,ex)}))}));const s=hx(a.map((t=>{const e=r.get(t.element);return this._buildPlayer(t,{},e)})));return this._playersById[t]=s,s.onDestroy((()=>this.destroy(t))),this.players.push(s),s}destroy(t){const e=this._getPlayer(t);e.destroy(),delete this._playersById[t];const n=this.players.indexOf(e);n>=0&&this.players.splice(n,1)}_getPlayer(t){const e=this._playersById[t];if(!e)throw new Error(`Unable to find the timeline player referenced by ${t}`);return e}listen(t,e,n,o){const i=Cx(e,"","","");return yx(this._getPlayer(t),n,i,o),()=>{}}command(t,e,n,o){if("register"==n)return void this.register(t,o[0]);if("create"==n)return void this.create(t,e,o[0]||{});const i=this._getPlayer(t);switch(n){case"play":i.play();break;case"pause":i.pause();break;case"reset":i.reset();break;case"restart":i.restart();break;case"finish":i.finish();break;case"init":i.init();break;case"setPosition":i.setPosition(parseFloat(o[0]));break;case"destroy":this.destroy(t)}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const UO="ng-animate-queued",GO="ng-animate-disabled",WO=".ng-animate-disabled",YO=[],qO={namespaceId:"",setForRemoval:!1,setForMove:!1,hasAnimation:!1,removedBeforeQueried:!1},ZO={namespaceId:"",setForMove:!1,setForRemoval:!1,hasAnimation:!1,removedBeforeQueried:!0};class XO{constructor(t,e=""){this.namespaceId=e;const n=t&&t.hasOwnProperty("value");if(this.value=(function o(t){return null!=t?t:null})(n?t.value:t),n){const e=Wx(t);delete e.value,this.options=e}else this.options={};this.options.params||(this.options.params={})}get params(){return this.options.params}absorbOptions(t){const e=t.params;if(e){const t=this.options.params;Object.keys(e).forEach((n=>{null==t[n]&&(t[n]=e[n])}))}}}const KO="void",JO=new XO(KO);class QO{constructor(t,e,n){this.id=t,this.hostElement=e,this._engine=n,this.players=[],this._triggers={},this._queue=[],this._elementListeners=new Map,this._hostClassName="ng-tns-"+t,aP(e,this._hostClassName)}listen(t,e,n,o){if(!this._triggers.hasOwnProperty(e))throw new Error(`Unable to listen on the animation trigger event "${n}" because the animation trigger "${e}" doesn't exist!`);if(null==n||0==n.length)throw new Error(`Unable to listen on the animation trigger "${e}" because the provided event is undefined!`);if(!(function i(t){return"start"==t||"done"==t})(n))throw new Error(`The provided animation trigger event "${n}" for the animation trigger "${e}" is not supported!`);const a=Mx(this._elementListeners,t,[]),r={name:e,phase:n,callback:o};a.push(r);const s=Mx(this._engine.statesByElement,t,{});return s.hasOwnProperty(e)||(aP(t,Fx),aP(t,"ng-trigger-"+e),s[e]=JO),()=>{this._engine.afterFlush((()=>{const t=a.indexOf(r);t>=0&&a.splice(t,1),this._triggers[e]||delete s[e]}))}}register(t,e){return!this._triggers[t]&&(this._triggers[t]=e,!0)}_getTrigger(t){const e=this._triggers[t];if(!e)throw new Error(`The provided animation trigger "${t}" has not been registered!`);return e}trigger(t,e,n,o=!0){const i=this._getTrigger(e),a=new tP(this.id,e,t);let r=this._engine.statesByElement.get(t);r||(aP(t,Fx),aP(t,"ng-trigger-"+e),this._engine.statesByElement.set(t,r={}));let s=r[e];const l=new XO(n,this.id);if(!(n&&n.hasOwnProperty("value"))&&s&&l.absorbOptions(s.options),r[e]=l,s||(s=JO),l.value!==KO&&s.value===l.value){if(!(function e(t,n){const o=Object.keys(t),i=Object.keys(n);if(o.length!=i.length)return!1;for(let e=0;e<o.length;e++){const i=o[e];if(!n.hasOwnProperty(i)||t[i]!==n[i])return!1}return!0})(s.params,l.params)){const e=[],n=i.matchStyles(s.value,s.params,e),o=i.matchStyles(l.value,l.params,e);e.length?this._engine.reportError(e):this._engine.afterFlush((()=>{Kx(t,n),Xx(t,o)}))}return}const c=Mx(this._engine.playersByElement,t,[]);c.forEach((t=>{t.namespaceId==this.id&&t.triggerName==e&&t.queued&&t.destroy()}));let d=i.matchTransition(s.value,l.value,t,l.params),p=!1;if(!d){if(!o)return;d=i.fallbackTransition,p=!0}return this._engine.totalQueuedPlayers++,this._queue.push({element:t,triggerName:e,transition:d,fromState:s,toState:l,player:a,isFallbackTransition:p}),p||(aP(t,UO),a.onStart((()=>{rP(t,UO)}))),a.onDone((()=>{let e=this.players.indexOf(a);e>=0&&this.players.splice(e,1);const n=this._engine.playersByElement.get(t);if(n){let t=n.indexOf(a);t>=0&&n.splice(t,1)}})),this.players.push(a),c.push(a),a}deregister(t){delete this._triggers[t],this._engine.statesByElement.forEach(((e,n)=>{delete e[t]})),this._elementListeners.forEach(((e,n)=>{this._elementListeners.set(n,e.filter((e=>e.name!=t)))}))}clearElementCache(t){this._engine.statesByElement.delete(t),this._elementListeners.delete(t);const e=this._engine.playersByElement.get(t);e&&(e.forEach((t=>t.destroy())),this._engine.playersByElement.delete(t))}_signalRemovalForInnerTriggers(t,e){const n=this._engine.driver.query(t,Lx,!0);n.forEach((t=>{if(t.__ng_removed)return;const n=this._engine.fetchNamespacesByElement(t);n.size?n.forEach((n=>n.triggerLeaveAnimation(t,e,!1,!0))):this.clearElementCache(t)})),this._engine.afterFlushAnimationsDone((()=>n.forEach((t=>this.clearElementCache(t)))))}triggerLeaveAnimation(t,e,n,o){const i=this._engine.statesByElement.get(t);if(i){const a=[];if(Object.keys(i).forEach((e=>{if(this._triggers[e]){const n=this.trigger(t,e,KO,o);n&&a.push(n)}})),a.length)return this._engine.markElementAsRemoved(this.id,t,!0,e),n&&hx(a).onDone((()=>this._engine.processLeaveNode(t))),!0}return!1}prepareLeaveAnimationListeners(t){const e=this._elementListeners.get(t),n=this._engine.statesByElement.get(t);if(e&&n){const o=new Set;e.forEach((e=>{const i=e.name;if(o.has(i))return;o.add(i);const a=this._triggers[i].fallbackTransition,r=n[i]||JO,s=new XO(KO),l=new tP(this.id,i,t);this._engine.totalQueuedPlayers++,this._queue.push({element:t,triggerName:i,transition:a,fromState:r,toState:s,player:l,isFallbackTransition:!0})}))}}removeNode(t,e){const n=this._engine;if(t.childElementCount&&this._signalRemovalForInnerTriggers(t,e),this.triggerLeaveAnimation(t,e,!0))return;let o=!1;if(n.totalAnimations){const e=n.players.length?n.playersByQueriedElement.get(t):[];if(e&&e.length)o=!0;else{let e=t;for(;e=e.parentNode;)if(n.statesByElement.get(e)){o=!0;break}}}if(this.prepareLeaveAnimationListeners(t),o)n.markElementAsRemoved(this.id,t,!1,e);else{const o=t.__ng_removed;o&&o!==qO||(n.afterFlush((()=>this.clearElementCache(t))),n.destroyInnerAnimations(t),n._onRemovalComplete(t,e))}}insertNode(t,e){aP(t,this._hostClassName)}drainQueuedTransitions(t){const e=[];return this._queue.forEach((n=>{const o=n.player;if(o.destroyed)return;const i=n.element,a=this._elementListeners.get(i);a&&a.forEach((e=>{if(e.name==n.triggerName){const o=Cx(i,n.triggerName,n.fromState.value,n.toState.value);o._data=t,yx(n.player,e.phase,o,e.callback)}})),o.markedForDestroy?this._engine.afterFlush((()=>{o.destroy()})):e.push(n)})),this._queue=[],e.sort(((t,e)=>{const n=t.transition.ast.depCount,o=e.transition.ast.depCount;return 0==n||0==o?n-o:this._engine.driver.containsElement(t.element,e.element)?1:-1}))}destroy(t){this.players.forEach((t=>t.destroy())),this._signalRemovalForInnerTriggers(this.hostElement,t)}elementContainsData(t){let e=!1;return this._elementListeners.has(t)&&(e=!0),e=!!this._queue.find((e=>e.element===t))||e,e}}class $O{constructor(t,e,n){this.bodyNode=t,this.driver=e,this._normalizer=n,this.players=[],this.newHostElements=new Map,this.playersByElement=new Map,this.playersByQueriedElement=new Map,this.statesByElement=new Map,this.disabledNodes=new Set,this.totalAnimations=0,this.totalQueuedPlayers=0,this._namespaceLookup={},this._namespaceList=[],this._flushFns=[],this._whenQuietFns=[],this.namespacesByHostElement=new Map,this.collectedEnterElements=[],this.collectedLeaveElements=[],this.onRemovalComplete=(t,e)=>{}}_onRemovalComplete(t,e){this.onRemovalComplete(t,e)}get queuedPlayers(){const t=[];return this._namespaceList.forEach((e=>{e.players.forEach((e=>{e.queued&&t.push(e)}))})),t}createNamespace(t,e){const n=new QO(t,e,this);return this.bodyNode&&this.driver.containsElement(this.bodyNode,e)?this._balanceNamespaceList(n,e):(this.newHostElements.set(e,n),this.collectEnterElement(e)),this._namespaceLookup[t]=n}_balanceNamespaceList(t,e){const n=this._namespaceList.length-1;if(n>=0){let o=!1;for(let i=n;i>=0;i--)if(this.driver.containsElement(this._namespaceList[i].hostElement,e)){this._namespaceList.splice(i+1,0,t),o=!0;break}o||this._namespaceList.splice(0,0,t)}else this._namespaceList.push(t);return this.namespacesByHostElement.set(e,t),t}register(t,e){let n=this._namespaceLookup[t];return n||(n=this.createNamespace(t,e)),n}registerTrigger(t,e,n){let o=this._namespaceLookup[t];o&&o.register(e,n)&&this.totalAnimations++}destroy(t,e){if(!t)return;const n=this._fetchNamespace(t);this.afterFlush((()=>{this.namespacesByHostElement.delete(n.hostElement),delete this._namespaceLookup[t];const e=this._namespaceList.indexOf(n);e>=0&&this._namespaceList.splice(e,1)})),this.afterFlushAnimationsDone((()=>n.destroy(e)))}_fetchNamespace(t){return this._namespaceLookup[t]}fetchNamespacesByElement(t){const e=new Set,n=this.statesByElement.get(t);if(n){const t=Object.keys(n);for(let o=0;o<t.length;o++){const i=n[t[o]].namespaceId;if(i){const t=this._fetchNamespace(i);t&&e.add(t)}}}return e}trigger(t,e,n,o){if(eP(e)){const i=this._fetchNamespace(t);if(i)return i.trigger(e,n,o),!0}return!1}insertNode(t,e,n,o){if(!eP(e))return;const i=e.__ng_removed;if(i&&i.setForRemoval){i.setForRemoval=!1,i.setForMove=!0;const t=this.collectedLeaveElements.indexOf(e);t>=0&&this.collectedLeaveElements.splice(t,1)}if(t){const o=this._fetchNamespace(t);o&&o.insertNode(e,n)}o&&this.collectEnterElement(e)}collectEnterElement(t){this.collectedEnterElements.push(t)}markElementAsDisabled(t,e){e?this.disabledNodes.has(t)||(this.disabledNodes.add(t),aP(t,GO)):this.disabledNodes.has(t)&&(this.disabledNodes.delete(t),rP(t,GO))}removeNode(t,e,n,o){if(eP(e)){const i=t?this._fetchNamespace(t):null;if(i?i.removeNode(e,o):this.markElementAsRemoved(t,e,!1,o),n){const n=this.namespacesByHostElement.get(e);n&&n.id!==t&&n.removeNode(e,o)}}else this._onRemovalComplete(e,o)}markElementAsRemoved(t,e,n,o){this.collectedLeaveElements.push(e),e.__ng_removed={namespaceId:t,setForRemoval:o,hasAnimation:n,removedBeforeQueried:!1}}listen(t,e,n,o,i){return eP(e)?this._fetchNamespace(t).listen(e,n,o,i):()=>{}}_buildInstruction(t,e,n,o,i){return t.transition.build(this.driver,t.element,t.fromState.value,t.toState.value,n,o,t.fromState.options,t.toState.options,e,i)}destroyInnerAnimations(t){let e=this.driver.query(t,Lx,!0);e.forEach((t=>this.destroyActiveAnimationsForElement(t))),0!=this.playersByQueriedElement.size&&(e=this.driver.query(t,Vx,!0),e.forEach((t=>this.finishActiveQueriedAnimationOnElement(t))))}destroyActiveAnimationsForElement(t){const e=this.playersByElement.get(t);e&&e.forEach((t=>{t.queued?t.markedForDestroy=!0:t.destroy()}))}finishActiveQueriedAnimationOnElement(t){const e=this.playersByQueriedElement.get(t);e&&e.forEach((t=>t.finish()))}whenRenderingDone(){return new Promise((t=>{if(this.players.length)return hx(this.players).onDone((()=>t()));t()}))}processLeaveNode(t){const e=t.__ng_removed;if(e&&e.setForRemoval){if(t.__ng_removed=qO,e.namespaceId){this.destroyInnerAnimations(t);const n=this._fetchNamespace(e.namespaceId);n&&n.clearElementCache(t)}this._onRemovalComplete(t,e.setForRemoval)}this.driver.matchesElement(t,WO)&&this.markElementAsDisabled(t,!1),this.driver.query(t,WO,!0).forEach((t=>{this.markElementAsDisabled(t,!1)}))}flush(t=-1){let e=[];if(this.newHostElements.size&&(this.newHostElements.forEach(((t,e)=>this._balanceNamespaceList(t,e))),this.newHostElements.clear()),this.totalAnimations&&this.collectedEnterElements.length)for(let t=0;t<this.collectedEnterElements.length;t++)aP(this.collectedEnterElements[t],"ng-star-inserted");if(this._namespaceList.length&&(this.totalQueuedPlayers||this.collectedLeaveElements.length)){const n=[];try{e=this._flushAnimations(n,t)}finally{for(let t=0;t<n.length;t++)n[t]()}}else for(let t=0;t<this.collectedLeaveElements.length;t++)this.processLeaveNode(this.collectedLeaveElements[t]);if(this.totalQueuedPlayers=0,this.collectedEnterElements.length=0,this.collectedLeaveElements.length=0,this._flushFns.forEach((t=>t())),this._flushFns=[],this._whenQuietFns.length){const t=this._whenQuietFns;this._whenQuietFns=[],e.length?hx(e).onDone((()=>{t.forEach((t=>t()))})):t.forEach((t=>t()))}}reportError(t){throw new Error(`Unable to process animations due to the following failed trigger transitions\n ${t.join("\n")}`)}_flushAnimations(t,e){const n=new MO,o=[],i=new Map,a=[],r=new Map,s=new Map,l=new Map,c=new Set;this.disabledNodes.forEach((t=>{c.add(t);const e=this.driver.query(t,".ng-animate-queued",!0);for(let t=0;t<e.length;t++)c.add(e[t])}));const d=this.bodyNode,p=Array.from(this.statesByElement.keys()),m=iP(p,this.collectedEnterElements),u=new Map;let f=0;m.forEach(((t,e)=>{const n=Ix+f++;u.set(e,n),t.forEach((t=>aP(t,n)))}));const g=[],h=new Set,b=new Set;for(let t=0;t<this.collectedLeaveElements.length;t++){const e=this.collectedLeaveElements[t],n=e.__ng_removed;n&&n.setForRemoval&&(g.push(e),h.add(e),n.hasAnimation?this.driver.query(e,".ng-star-inserted",!0).forEach((t=>h.add(t))):b.add(e))}const y=new Map,_=iP(p,Array.from(h));_.forEach(((t,e)=>{const n=Hx+f++;y.set(e,n),t.forEach((t=>aP(t,n)))})),t.push((()=>{m.forEach(((t,e)=>{const n=u.get(e);t.forEach((t=>rP(t,n)))})),_.forEach(((t,e)=>{const n=y.get(e);t.forEach((t=>rP(t,n)))})),g.forEach((t=>{this.processLeaveNode(t)}))}));const C=[],M=[];for(let t=this._namespaceList.length-1;t>=0;t--)this._namespaceList[t].drainQueuedTransitions(e).forEach((t=>{const e=t.player,i=t.element;if(C.push(e),this.collectedEnterElements.length){const t=i.__ng_removed;if(t&&t.setForMove)return void e.destroy()}const c=!d||!this.driver.containsElement(d,i),p=y.get(i),m=u.get(i),f=this._buildInstruction(t,n,m,p,c);if(f.errors&&f.errors.length)M.push(f);else{if(c)return e.onStart((()=>Kx(i,f.fromStyles))),e.onDestroy((()=>Xx(i,f.toStyles))),void o.push(e);if(t.isFallbackTransition)return e.onStart((()=>Kx(i,f.fromStyles))),e.onDestroy((()=>Xx(i,f.toStyles))),void o.push(e);f.timelines.forEach((t=>t.stretchStartingKeyframe=!0)),n.append(i,f.timelines),a.push({instruction:f,player:e,element:i}),f.queriedElements.forEach((t=>Mx(r,t,[]).push(e))),f.preStyleProps.forEach(((t,e)=>{const n=Object.keys(t);if(n.length){let t=s.get(e);t||s.set(e,t=new Set),n.forEach((e=>t.add(e)))}})),f.postStyleProps.forEach(((t,e)=>{const n=Object.keys(t);let o=l.get(e);o||l.set(e,o=new Set),n.forEach((t=>o.add(t)))}))}}));if(M.length){const t=[];M.forEach((e=>{t.push(`@${e.triggerName} has failed due to:\n`),e.errors.forEach((e=>t.push(`- ${e}\n`)))})),C.forEach((t=>t.destroy())),this.reportError(t)}const v=new Map,x=new Map;a.forEach((t=>{const e=t.element;n.has(e)&&(x.set(e,e),this._beforeAnimationBuild(t.player.namespaceId,t.instruction,v))})),o.forEach((t=>{const e=t.element;this._getPreviousPlayers(e,!1,t.namespaceId,t.triggerName,null).forEach((t=>{Mx(v,e,[]).push(t),t.destroy()}))}));const O=g.filter((t=>cP(t,s,l))),P=new Map;oP(P,this.driver,b,l,ex).forEach((t=>{cP(t,s,l)&&O.push(t)}));const w=new Map;m.forEach(((t,e)=>{oP(w,this.driver,new Set(t),s,"!")})),O.forEach((t=>{const e=P.get(t),n=w.get(t);P.set(t,Object.assign(Object.assign({},e),n))}));const k=[],S=[],D={};a.forEach((t=>{const{element:e,player:a,instruction:r}=t;if(n.has(e)){if(c.has(e))return a.onDestroy((()=>Xx(e,r.toStyles))),a.disabled=!0,a.overrideTotalTime(r.totalTime),void o.push(a);let t=D;if(x.size>1){let n=e;const o=[];for(;n=n.parentNode;){const e=x.get(n);if(e){t=e;break}o.push(n)}o.forEach((e=>x.set(e,t)))}const n=this._buildAnimation(a.namespaceId,r,v,i,w,P);if(a.setRealPlayer(n),t===D)k.push(a);else{const e=this.playersByElement.get(t);e&&e.length&&(a.parentPlayer=hx(e)),o.push(a)}}else Kx(e,r.fromStyles),a.onDestroy((()=>Xx(e,r.toStyles))),S.push(a),c.has(e)&&o.push(a)})),S.forEach((t=>{const e=i.get(t.element);if(e&&e.length){const n=hx(e);t.setRealPlayer(n)}})),o.forEach((t=>{t.parentPlayer?t.syncPlayerEvents(t.parentPlayer):t.destroy()}));for(let t=0;t<g.length;t++){const e=g[t],n=e.__ng_removed;if(rP(e,Hx),n&&n.hasAnimation)continue;let o=[];if(r.size){let t=r.get(e);t&&t.length&&o.push(...t);let n=this.driver.query(e,Vx,!0);for(let t=0;t<n.length;t++){let e=r.get(n[t]);e&&e.length&&o.push(...e)}}const i=o.filter((t=>!t.destroyed));i.length?sP(this,e,i):this.processLeaveNode(e)}return g.length=0,k.forEach((t=>{this.players.push(t),t.onDone((()=>{t.destroy();const e=this.players.indexOf(t);this.players.splice(e,1)})),t.play()})),k}elementContainsData(t,e){let n=!1;const o=e.__ng_removed;return o&&o.setForRemoval&&(n=!0),this.playersByElement.has(e)&&(n=!0),this.playersByQueriedElement.has(e)&&(n=!0),this.statesByElement.has(e)&&(n=!0),this._fetchNamespace(t).elementContainsData(e)||n}afterFlush(t){this._flushFns.push(t)}afterFlushAnimationsDone(t){this._whenQuietFns.push(t)}_getPreviousPlayers(t,e,n,o,i){let a=[];if(e){const e=this.playersByQueriedElement.get(t);e&&(a=e)}else{const e=this.playersByElement.get(t);if(e){const t=!i||i==KO;e.forEach((e=>{e.queued||(t||e.triggerName==o)&&a.push(e)}))}}return(n||o)&&(a=a.filter((t=>!(n&&n!=t.namespaceId||o&&o!=t.triggerName)))),a}_beforeAnimationBuild(t,e,n){const o=e.element,i=e.isRemovalTransition?void 0:t,a=e.isRemovalTransition?void 0:e.triggerName;for(const t of e.timelines){const r=t.element,s=r!==o,l=Mx(n,r,[]);this._getPreviousPlayers(r,s,i,a,e.toState).forEach((t=>{const e=t.getRealPlayer();e.beforeDestroy&&e.beforeDestroy(),t.destroy(),l.push(t)}))}Kx(o,e.fromStyles)}_buildAnimation(t,e,n,o,i,a){const r=e.triggerName,s=e.element,l=[],c=new Set,d=new Set,p=e.timelines.map((e=>{const p=e.element;c.add(p);const m=p.__ng_removed;if(m&&m.removedBeforeQueried)return new mx(e.duration,e.delay);const u=p!==s,f=(function g(t){const e=[];return lP(t,e),e})((n.get(p)||YO).map((t=>t.getRealPlayer()))).filter((t=>!!t.element&&t.element===p)),h=i.get(p),b=a.get(p),y=bx(0,this._normalizer,0,e.keyframes,h,b),_=this._buildPlayer(e,y,f);if(e.subTimeline&&o&&d.add(p),u){const e=new tP(t,r,p);e.setRealPlayer(_),l.push(e)}return _}));l.forEach((t=>{Mx(this.playersByQueriedElement,t.element,[]).push(t),t.onDone((()=>(function e(t,n,o){let i;if(t instanceof Map){if(i=t.get(n),i){if(i.length){const t=i.indexOf(o);i.splice(t,1)}0==i.length&&t.delete(n)}}else if(i=t[n],i){if(i.length){const t=i.indexOf(o);i.splice(t,1)}0==i.length&&delete t[n]}return i})(this.playersByQueriedElement,t.element,t)))})),c.forEach((t=>aP(t,Bx)));const m=hx(p);return m.onDestroy((()=>{c.forEach((t=>rP(t,Bx))),Xx(s,e.toStyles)})),d.forEach((t=>{Mx(o,t,[]).push(m)})),m}_buildPlayer(t,e,n){return e.length>0?this.driver.animate(t.element,e,t.duration,t.delay,t.easing,n):new mx(t.duration,t.delay)}}class tP{constructor(t,e,n){this.namespaceId=t,this.triggerName=e,this.element=n,this._player=new mx,this._containsRealPlayer=!1,this._queuedCallbacks={},this.destroyed=!1,this.markedForDestroy=!1,this.disabled=!1,this.queued=!0,this.totalTime=0}setRealPlayer(t){this._containsRealPlayer||(this._player=t,Object.keys(this._queuedCallbacks).forEach((e=>{this._queuedCallbacks[e].forEach((n=>yx(t,e,void 0,n)))})),this._queuedCallbacks={},this._containsRealPlayer=!0,this.overrideTotalTime(t.totalTime),this.queued=!1)}getRealPlayer(){return this._player}overrideTotalTime(t){this.totalTime=t}syncPlayerEvents(t){const e=this._player;e.triggerCallback&&t.onStart((()=>e.triggerCallback("start"))),t.onDone((()=>this.finish())),t.onDestroy((()=>this.destroy()))}_queueEvent(t,e){Mx(this._queuedCallbacks,t,[]).push(e)}onDone(t){this.queued&&this._queueEvent("done",t),this._player.onDone(t)}onStart(t){this.queued&&this._queueEvent("start",t),this._player.onStart(t)}onDestroy(t){this.queued&&this._queueEvent("destroy",t),this._player.onDestroy(t)}init(){this._player.init()}hasStarted(){return!this.queued&&this._player.hasStarted()}play(){!this.queued&&this._player.play()}pause(){!this.queued&&this._player.pause()}restart(){!this.queued&&this._player.restart()}finish(){this._player.finish()}destroy(){this.destroyed=!0,this._player.destroy()}reset(){!this.queued&&this._player.reset()}setPosition(t){this.queued||this._player.setPosition(t)}getPosition(){return this.queued?0:this._player.getPosition()}triggerCallback(t){const e=this._player;e.triggerCallback&&e.triggerCallback(t)}}function eP(t){return t&&1===t.nodeType}function nP(t,e){const n=t.style.display;return t.style.display=null!=e?e:"none",n}function oP(t,e,n,o,i){const a=[];n.forEach((t=>a.push(nP(t))));const r=[];o.forEach(((n,o)=>{const a={};n.forEach((t=>{const n=a[t]=e.computeStyle(o,t,i);n&&0!=n.length||(o.__ng_removed=ZO,r.push(o))})),t.set(o,a)}));let s=0;return n.forEach((t=>nP(t,a[s++]))),r}function iP(t,e){const n=new Map;if(t.forEach((t=>n.set(t,[]))),0==e.length)return n;const o=new Set(e),i=new Map;function a(t){if(!t)return 1;let e=i.get(t);if(e)return e;const r=t.parentNode;return e=n.has(r)?r:o.has(r)?1:a(r),i.set(t,e),e}return e.forEach((t=>{const e=a(t);1!==e&&n.get(e).push(t)})),n}function aP(t,e){if(t.classList)t.classList.add(e);else{let n=t.$$classes;n||(n=t.$$classes={}),n[e]=!0}}function rP(t,e){if(t.classList)t.classList.remove(e);else{let n=t.$$classes;n&&delete n[e]}}function sP(t,e,n){hx(n).onDone((()=>t.processLeaveNode(e)))}function lP(t,e){for(let n=0;n<t.length;n++){const o=t[n];o instanceof ux?lP(o.players,e):e.push(o)}}function cP(t,e,n){const o=n.get(t);if(!o)return!1;let i=e.get(t);return i?o.forEach((t=>i.add(t))):e.set(t,o),n.delete(t),!0}class dP{constructor(t,e,n){this.bodyNode=t,this._driver=e,this._normalizer=n,this._triggerCache={},this.onRemovalComplete=(t,e)=>{},this._transitionEngine=new $O(t,e,n),this._timelineEngine=new jO(t,e,n),this._transitionEngine.onRemovalComplete=(t,e)=>this.onRemovalComplete(t,e)}registerTrigger(t,e,n,o,i){const a=t+"-"+o;let r=this._triggerCache[a];if(!r){const t=[],e=fO(this._driver,i,t);if(t.length)throw new Error(`The animation trigger "${o}" has failed to build due to the following errors:\n - ${t.join("\n - ")}`);r=(function n(t,e,o){return new LO(t,e,o)})(o,e,this._normalizer),this._triggerCache[a]=r}this._transitionEngine.registerTrigger(e,o,r)}register(t,e){this._transitionEngine.register(t,e)}destroy(t,e){this._transitionEngine.destroy(t,e)}onInsert(t,e,n,o){this._transitionEngine.insertNode(t,e,n,o)}onRemove(t,e,n,o){this._transitionEngine.removeNode(t,e,o||!1,n)}disableAnimations(t,e){this._transitionEngine.markElementAsDisabled(t,e)}process(t,e,n,o){if("@"==n.charAt(0)){const[t,i]=vx(n);this._timelineEngine.command(t,e,i,o)}else this._transitionEngine.trigger(t,e,n,o)}listen(t,e,n,o,i){if("@"==n.charAt(0)){const[t,o]=vx(n);return this._timelineEngine.listen(t,e,o,i)}return this._transitionEngine.listen(t,e,n,o,i)}flush(t=-1){this._transitionEngine.flush(t)}get players(){return this._transitionEngine.players.concat(this._timelineEngine.players)}whenRenderingDone(){return this._transitionEngine.whenRenderingDone()}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function pP(t,e){let n=null,o=null;return Array.isArray(e)&&e.length?(n=uP(e[0]),e.length>1&&(o=uP(e[e.length-1]))):e&&(n=uP(e)),n||o?new mP(t,n,o):null}class mP{constructor(t,e,n){this._element=t,this._startStyles=e,this._endStyles=n,this._state=0;let o=mP.initialStylesByElement.get(t);o||mP.initialStylesByElement.set(t,o={}),this._initialStyles=o}start(){this._state<1&&(this._startStyles&&Xx(this._element,this._startStyles,this._initialStyles),this._state=1)}finish(){this.start(),this._state<2&&(Xx(this._element,this._initialStyles),this._endStyles&&(Xx(this._element,this._endStyles),this._endStyles=null),this._state=1)}destroy(){this.finish(),this._state<3&&(mP.initialStylesByElement.delete(this._element),this._startStyles&&(Kx(this._element,this._startStyles),this._endStyles=null),this._endStyles&&(Kx(this._element,this._endStyles),this._endStyles=null),Xx(this._element,this._initialStyles),this._state=3)}}function uP(t){let e=null;const n=Object.keys(t);for(let o=0;o<n.length;o++){const i=n[o];fP(i)&&(e=e||{},e[i]=t[i])}return e}function fP(t){return"display"===t||"position"===t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */mP.initialStylesByElement=new WeakMap;const gP="animation",hP="animationend";class bP{constructor(t,e,n,o,i,a,r){this._element=t,this._name=e,this._duration=n,this._delay=o,this._easing=i,this._fillMode=a,this._onDoneFn=r,this._finished=!1,this._destroyed=!1,this._startTime=0,this._position=0,this._eventFn=t=>this._handleCallback(t)}apply(){!(function t(e,n){const o=xP(e,"").trim();let i=0;o.length&&((function a(t,e){let n=0;for(let o=0;o<t.length;o++)t.charAt(o)===e&&n++;return n})(o,",")+1,n=`${o}, ${n}`),vP(e,"",n)})(this._element,`${this._duration}ms ${this._easing} ${this._delay}ms 1 normal ${this._fillMode} ${this._name}`),MP(this._element,this._eventFn,!1),this._startTime=Date.now()}pause(){yP(this._element,this._name,"paused")}resume(){yP(this._element,this._name,"running")}setPosition(t){const e=_P(this._element,this._name);this._position=t*this._duration,vP(this._element,"Delay",`-${this._position}ms`,e)}getPosition(){return this._position}_handleCallback(t){const e=t._ngTestManualTimestamp||Date.now(),n=1e3*parseFloat(t.elapsedTime.toFixed(3));t.animationName==this._name&&Math.max(e-this._startTime,0)>=this._delay&&n>=this._duration&&this.finish()}finish(){this._finished||(this._finished=!0,this._onDoneFn(),MP(this._element,this._eventFn,!0))}destroy(){this._destroyed||(this._destroyed=!0,this.finish(),(function t(e,n){const o=xP(e,"").split(","),i=CP(o,n);i>=0&&(o.splice(i,1),vP(e,"",o.join(",")))})(this._element,this._name))}}function yP(t,e,n){vP(t,"PlayState",n,_P(t,e))}function _P(t,e){const n=xP(t,"");return n.indexOf(",")>0?CP(n.split(","),e):CP([n],e)}function CP(t,e){for(let n=0;n<t.length;n++)if(t[n].indexOf(e)>=0)return n;return-1}function MP(t,e,n){n?t.removeEventListener(hP,e):t.addEventListener(hP,e)}function vP(t,e,n,o){const i=gP+e;if(null!=o){const e=t.style[i];if(e.length){const t=e.split(",");t[o]=n,n=t.join(",")}}t.style[i]=n}function xP(t,e){return t.style[gP+e]||""}class OP{constructor(t,e,n,o,i,a,r,s){this.element=t,this.keyframes=e,this.animationName=n,this._duration=o,this._delay=i,this._finalStyles=r,this._specialStyles=s,this._onDoneFns=[],this._onStartFns=[],this._onDestroyFns=[],this.currentSnapshot={},this._state=0,this.easing=a||"linear",this.totalTime=o+i,this._buildStyler()}onStart(t){this._onStartFns.push(t)}onDone(t){this._onDoneFns.push(t)}onDestroy(t){this._onDestroyFns.push(t)}destroy(){this.init(),this._state>=4||(this._state=4,this._styler.destroy(),this._flushStartFns(),this._flushDoneFns(),this._specialStyles&&this._specialStyles.destroy(),this._onDestroyFns.forEach((t=>t())),this._onDestroyFns=[])}_flushDoneFns(){this._onDoneFns.forEach((t=>t())),this._onDoneFns=[]}_flushStartFns(){this._onStartFns.forEach((t=>t())),this._onStartFns=[]}finish(){this.init(),this._state>=3||(this._state=3,this._styler.finish(),this._flushStartFns(),this._specialStyles&&this._specialStyles.finish(),this._flushDoneFns())}setPosition(t){this._styler.setPosition(t)}getPosition(){return this._styler.getPosition()}hasStarted(){return this._state>=2}init(){this._state>=1||(this._state=1,this._styler.apply(),this._delay&&this._styler.pause())}play(){this.init(),this.hasStarted()||(this._flushStartFns(),this._state=2,this._specialStyles&&this._specialStyles.start()),this._styler.resume()}pause(){this.init(),this._styler.pause()}restart(){this.reset(),this.play()}reset(){this._state=0,this._styler.destroy(),this._buildStyler(),this._styler.apply()}_buildStyler(){this._styler=new bP(this.element,this.animationName,this._duration,this._delay,this.easing,"forwards",(()=>this.finish()))}triggerCallback(t){const e="start"==t?this._onStartFns:this._onDoneFns;e.forEach((t=>t())),e.length=0}beforeDestroy(){this.init();const t={};if(this.hasStarted()){const e=this._state>=3;Object.keys(this._finalStyles).forEach((n=>{"offset"!=n&&(t[n]=e?this._finalStyles[n]:sO(this.element,n))}))}this.currentSnapshot=t}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class PP extends mx{constructor(t,e){super(),this.element=t,this._startingStyles={},this.__initialized=!1,this._styles=Tx(e)}init(){!this.__initialized&&this._startingStyles&&(this.__initialized=!0,Object.keys(this._styles).forEach((t=>{this._startingStyles[t]=this.element.style[t]})),super.init())}play(){this._startingStyles&&(this.init(),Object.keys(this._styles).forEach((t=>this.element.style.setProperty(t,this._styles[t]))),super.play())}destroy(){this._startingStyles&&(Object.keys(this._startingStyles).forEach((t=>{const e=this._startingStyles[t];e?this.element.style.setProperty(t,e):this.element.style.removeProperty(t)})),this._startingStyles=null,super.destroy())}}class wP{constructor(){this._count=0}validateStyleProperty(t){return Dx(t)}matchesElement(t,e){return Ex(t,e)}containsElement(t,e){return Rx(t,e)}query(t,e,n){return Ax(t,e,n)}computeStyle(t,e,n){return window.getComputedStyle(t)[e]}buildKeyframeElement(t,e,n){n=n.map((t=>Tx(t)));let o=`@keyframes ${e} {\n`,i="";n.forEach((t=>{i=" ";const e=parseFloat(t.offset);o+=`${i}${100*e}% {\n`,i+=" ",Object.keys(t).forEach((e=>{const n=t[e];switch(e){case"offset":return;case"easing":return void(n&&(o+=`${i}animation-timing-function: ${n};\n`));default:return void(o+=`${i}${e}: ${n};\n`)}})),o+=`${i}}\n`})),o+="}\n";const a=document.createElement("style");return a.textContent=o,a}animate(t,e,n,o,i,a=[],r){("undefined"==typeof ngDevMode||ngDevMode)&&r&&(function s(){kP||(console.warn("@angular/animations: please load the web-animations.js polyfill to allow programmatic access...\n","  visit https://bit.ly/IWukam to learn more about using the web-animation-js polyfill."),kP=!0)})();const l=a.filter((t=>t instanceof OP)),c={};iO(n,o)&&l.forEach((t=>{let e=t.currentSnapshot;Object.keys(e).forEach((t=>c[t]=e[t]))}));const d=(function p(t){let e={};return t&&(Array.isArray(t)?t:[t]).forEach((t=>{Object.keys(t).forEach((n=>{"offset"!=n&&"easing"!=n&&(e[n]=t[n])}))})),e})(e=aO(t,e,c));if(0==n)return new PP(t,d);const m="gen_css_kf_"+this._count++,u=this.buildKeyframeElement(t,m,e);(function f(t){var e;const n=null===(e=t.getRootNode)||void 0===e?void 0:e.call(t);return"undefined"!=typeof ShadowRoot&&n instanceof ShadowRoot?n:document.head})(t).appendChild(u);const g=pP(t,e),h=new OP(t,e,m,n,o,i,d,g);return h.onDestroy((()=>(function t(e){e.parentNode.removeChild(e)})(u))),h}}let kP=!1;class SP{constructor(t,e,n,o){this.element=t,this.keyframes=e,this.options=n,this._specialStyles=o,this._onDoneFns=[],this._onStartFns=[],this._onDestroyFns=[],this._initialized=!1,this._finished=!1,this._started=!1,this._destroyed=!1,this.time=0,this.parentPlayer=null,this.currentSnapshot={},this._duration=n.duration,this._delay=n.delay||0,this.time=this._duration+this._delay}_onFinish(){this._finished||(this._finished=!0,this._onDoneFns.forEach((t=>t())),this._onDoneFns=[])}init(){this._buildPlayer(),this._preparePlayerBeforeStart()}_buildPlayer(){if(this._initialized)return;this._initialized=!0;const t=this.keyframes;this.domPlayer=this._triggerWebAnimation(this.element,t,this.options),this._finalKeyframe=t.length?t[t.length-1]:{},this.domPlayer.addEventListener("finish",(()=>this._onFinish()))}_preparePlayerBeforeStart(){this._delay?this._resetDomPlayerState():this.domPlayer.pause()}_triggerWebAnimation(t,e,n){return t.animate(e,n)}onStart(t){this._onStartFns.push(t)}onDone(t){this._onDoneFns.push(t)}onDestroy(t){this._onDestroyFns.push(t)}play(){this._buildPlayer(),this.hasStarted()||(this._onStartFns.forEach((t=>t())),this._onStartFns=[],this._started=!0,this._specialStyles&&this._specialStyles.start()),this.domPlayer.play()}pause(){this.init(),this.domPlayer.pause()}finish(){this.init(),this._specialStyles&&this._specialStyles.finish(),this._onFinish(),this.domPlayer.finish()}reset(){this._resetDomPlayerState(),this._destroyed=!1,this._finished=!1,this._started=!1}_resetDomPlayerState(){this.domPlayer&&this.domPlayer.cancel()}restart(){this.reset(),this.play()}hasStarted(){return this._started}destroy(){this._destroyed||(this._destroyed=!0,this._resetDomPlayerState(),this._onFinish(),this._specialStyles&&this._specialStyles.destroy(),this._onDestroyFns.forEach((t=>t())),this._onDestroyFns=[])}setPosition(t){void 0===this.domPlayer&&this.init(),this.domPlayer.currentTime=t*this.time}getPosition(){return this.domPlayer.currentTime/this.time}get totalTime(){return this._delay+this._duration}beforeDestroy(){const t={};this.hasStarted()&&Object.keys(this._finalKeyframe).forEach((e=>{"offset"!=e&&(t[e]=this._finished?this._finalKeyframe[e]:sO(this.element,e))})),this.currentSnapshot=t}triggerCallback(t){const e="start"==t?this._onStartFns:this._onDoneFns;e.forEach((t=>t())),e.length=0}}class DP{constructor(){this._isNativeImpl=/\{\s*\[native\s+code\]\s*\}/.test(EP().toString()),this._cssKeyframesDriver=new wP}validateStyleProperty(t){return Dx(t)}matchesElement(t,e){return Ex(t,e)}containsElement(t,e){return Rx(t,e)}query(t,e,n){return Ax(t,e,n)}computeStyle(t,e,n){return window.getComputedStyle(t)[e]}overrideWebAnimationsSupport(t){this._isNativeImpl=t}animate(t,e,n,o,i,a=[],r){if(!r&&!this._isNativeImpl)return this._cssKeyframesDriver.animate(t,e,n,o,i,a);const s={duration:n,delay:o,fill:0==o?"both":"forwards"};i&&(s.easing=i);const l={},c=a.filter((t=>t instanceof SP));iO(n,o)&&c.forEach((t=>{let e=t.currentSnapshot;Object.keys(e).forEach((t=>l[t]=e[t]))}));const d=pP(t,e=aO(t,e=e.map((t=>Yx(t,!1))),l));return new SP(t,e,s,d)}}function EP(){return fx()&&Element.prototype.animate||{}}
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */class RP extends tx{constructor(t,e){super(),this._nextAnimationId=0,this._renderer=t.createRenderer(e.body,{id:"0",encapsulation:Hn.None,styles:[],data:{animation:[]}})}build(t){const e=this._nextAnimationId.toString();this._nextAnimationId++;const n=Array.isArray(t)?ix(t):t;return NP(this._renderer,null,e,"register",[n]),new AP(e,this._renderer)}}RP.ɵfac=function t(e){return new(e||RP)(vr(_g),vr(Z_))},RP.ɵprov=Mn({token:RP,factory:RP.ɵfac}),RP.ctorParameters=()=>[{type:_g},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(RP,[{type:im}],(function(){return[{type:_g},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);class AP extends class{}{constructor(t,e){super(),this._id=t,this._renderer=e}create(t,e){return new TP(this._id,t,e||{},this._renderer)}}class TP{constructor(t,e,n,o){this.id=t,this.element=e,this._renderer=o,this.parentPlayer=null,this._started=!1,this.totalTime=0,this._command("create",n)}_listen(t,e){return this._renderer.listen(this.element,`@@${this.id}:${t}`,e)}_command(t,...e){return NP(this._renderer,this.element,this.id,t,e)}onDone(t){this._listen("done",t)}onStart(t){this._listen("start",t)}onDestroy(t){this._listen("destroy",t)}init(){this._command("init")}hasStarted(){return this._started}play(){this._command("play"),this._started=!0}pause(){this._command("pause")}restart(){this._command("restart")}finish(){this._command("finish")}destroy(){this._command("destroy")}reset(){this._command("reset"),this._started=!1}setPosition(t){this._command("setPosition",t)}getPosition(){var t,e;return null!==(e=null===(t=this._renderer.engine.players[+this.id])||void 0===t?void 0:t.getPosition())&&void 0!==e?e:0}}function NP(t,e,n,o,i){return t.setProperty(e,`@@${n}:${o}`,i)}const zP="@",IP="@.disabled";class HP{constructor(t,e,n){this.delegate=t,this.engine=e,this._zone=n,this._currentId=0,this._microtaskId=1,this._animationCallbacksBuffer=[],this._rendererCache=new Map,this._cdRecurDepth=0,this.promise=Promise.resolve(0),e.onRemovalComplete=(t,e)=>{e&&e.parentNode(t)&&e.removeChild(t.parentNode,t)}}createRenderer(t,e){const n=this.delegate.createRenderer(t,e);if(!(t&&e&&e.data&&e.data.animation)){let t=this._rendererCache.get(n);return t||(t=new FP("",n,this.engine),this._rendererCache.set(n,t)),t}const o=e.id,i=e.id+"-"+this._currentId;this._currentId++,this.engine.register(i,t);const a=e=>{Array.isArray(e)?e.forEach(a):this.engine.registerTrigger(o,i,t,e.name,e)};return e.data.animation.forEach(a),new LP(this,i,n,this.engine)}begin(){this._cdRecurDepth++,this.delegate.begin&&this.delegate.begin()}_scheduleCountTask(){this.promise.then((()=>{this._microtaskId++}))}scheduleListenerCallback(t,e,n){t>=0&&t<this._microtaskId?this._zone.run((()=>e(n))):(0==this._animationCallbacksBuffer.length&&Promise.resolve(null).then((()=>{this._zone.run((()=>{this._animationCallbacksBuffer.forEach((t=>{const[e,n]=t;e(n)})),this._animationCallbacksBuffer=[]}))})),this._animationCallbacksBuffer.push([e,n]))}end(){this._cdRecurDepth--,0==this._cdRecurDepth&&this._zone.runOutsideAngular((()=>{this._scheduleCountTask(),this.engine.flush(this._microtaskId)})),this.delegate.end&&this.delegate.end()}whenRenderingDone(){return this.engine.whenRenderingDone()}}HP.ɵfac=function t(e){return new(e||HP)(vr(_g),vr(dP),vr(a_))},HP.ɵprov=Mn({token:HP,factory:HP.ɵfac}),HP.ctorParameters=()=>[{type:_g},{type:dP},{type:a_}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(HP,[{type:im}],(function(){return[{type:_g},{type:dP},{type:a_}]}),null);class FP{constructor(t,e,n){this.namespaceId=t,this.delegate=e,this.engine=n,this.destroyNode=this.delegate.destroyNode?t=>e.destroyNode(t):null}get data(){return this.delegate.data}destroy(){this.engine.destroy(this.namespaceId,this.delegate),this.delegate.destroy()}createElement(t,e){return this.delegate.createElement(t,e)}createComment(t){return this.delegate.createComment(t)}createText(t){return this.delegate.createText(t)}appendChild(t,e){this.delegate.appendChild(t,e),this.engine.onInsert(this.namespaceId,e,t,!1)}insertBefore(t,e,n,o=!0){this.delegate.insertBefore(t,e,n),this.engine.onInsert(this.namespaceId,e,t,o)}removeChild(t,e,n){this.engine.onRemove(this.namespaceId,e,this.delegate,n)}selectRootElement(t,e){return this.delegate.selectRootElement(t,e)}parentNode(t){return this.delegate.parentNode(t)}nextSibling(t){return this.delegate.nextSibling(t)}setAttribute(t,e,n,o){this.delegate.setAttribute(t,e,n,o)}removeAttribute(t,e,n){this.delegate.removeAttribute(t,e,n)}addClass(t,e){this.delegate.addClass(t,e)}removeClass(t,e){this.delegate.removeClass(t,e)}setStyle(t,e,n,o){this.delegate.setStyle(t,e,n,o)}removeStyle(t,e,n){this.delegate.removeStyle(t,e,n)}setProperty(t,e,n){e.charAt(0)==zP&&e==IP?this.disableAnimations(t,!!n):this.delegate.setProperty(t,e,n)}setValue(t,e){this.delegate.setValue(t,e)}listen(t,e,n){return this.delegate.listen(t,e,n)}disableAnimations(t,e){this.engine.disableAnimations(t,e)}}class LP extends FP{constructor(t,e,n,o){super(e,n,o),this.factory=t,this.namespaceId=e}setProperty(t,e,n){e.charAt(0)==zP?"."==e.charAt(1)&&e==IP?this.disableAnimations(t,n=void 0===n||!!n):this.engine.process(this.namespaceId,t,e.substr(1),n):this.delegate.setProperty(t,e,n)}listen(t,e,n){if(e.charAt(0)==zP){const o=(function i(t){switch(t){case"body":return document.body;case"document":return document;case"window":return window;default:return t}})(t);let a=e.substr(1),r="";return a.charAt(0)!=zP&&([a,r]=(function s(t){const e=t.indexOf(".");return[t.substring(0,e),t.substr(e+1)]}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class ZP{static withConfig(t){return{ngModule:ZP,providers:t.disableAnimations?qP:WP}}}ZP.ɵfac=function t(e){return new(e||ZP)},ZP.ɵmod=ao({type:ZP}),ZP.ɵinj=vn({providers:WP,imports:[Uv]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ZP,[{type:Ay,args:[{exports:[Uv],providers:WP}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(ZP,{exports:function(){return[Uv]}});class XP{}XP.ɵfac=function t(e){return new(e||XP)},XP.ɵmod=ao({type:XP}),XP.ɵinj=vn({providers:qP,imports:[Uv]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(XP,[{type:Ay,args:[{exports:[Uv],providers:qP}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(XP,{exports:function(){return[Uv]}});const KP={};function JP(t,e){if(KP[t]=(KP[t]||0)+1,"function"==typeof e)return QP(t,((...n)=>Object.assign(Object.assign({},e(...n)),{type:t})));switch(e?e._as:"empty"){case"empty":return QP(t,(()=>({type:t})));case"props":return QP(t,(e=>Object.assign(Object.assign({},e),{type:t})));default:throw new Error("Unexpected config.")}}function QP(t,e){return Object.defineProperty(e,"type",{value:t,writable:!1})}const $P="@ngrx/store/init";class tw extends F{constructor(){super({type:$P})}next(t){if("function"==typeof t)throw new TypeError("\n        Dispatch expected an object, instead it received a function.\n        If you're using the createAction function, make sure to invoke the function\n        before dispatching the action. 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Array.isArray(e)&&e.length>0&&(t=Ow.apply(null,[...e,t])),(e,n)=>{const o=t(e);return(t,e)=>o(t=void 0===t?n:t,e)}}class ww extends D{}class kw extends tw{}class Sw extends F{constructor(t,e,n,o){super(o(n,e)),this.dispatcher=t,this.initialState=e,this.reducers=n,this.reducerFactory=o}get currentReducers(){return this.reducers}addFeature(t){this.addFeatures([t])}addFeatures(t){const e=t.reduce(((t,{reducers:e,reducerFactory:n,metaReducers:o,initialState:i,key:a})=>{const r="function"==typeof e?(function s(t){const e=Array.isArray(t)&&t.length>0?Ow(...t):t=>t;return(t,n)=>(t=e(t),(e,o)=>t(e=void 0===e?n:e,o))})(o)(e,i):Pw(n,o)(e,i);return 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Ow(wk,xk)(t)}function wk(t){return(function e(t){return t.constructor.hasOwnProperty(Ok)})(t)?t.constructor[Ok]:[]}function kk(t,e,n=10){return t.pipe(pe((o=>(e&&e.handleError(o),n<=1?t:kk(t,e,n-1)))))}class Sk extends D{constructor(t){super(),t&&(this.source=t)}lift(t){const e=new Sk;return e.source=this,e.operator=t,e}}function Dk(...t){return ce((e=>t.some((t=>"string"==typeof t?t===e.type:t.type===e.type))))}function Ek(t){return Rk(t,"ngrxOnInitEffects")}function Rk(t,e){return t&&e in t&&"function"==typeof t[e]}Sk.ɵfac=function t(e){return new(e||Sk)(vr(Ew))},Sk.ɵprov=Mn({token:Sk,factory:Sk.ɵfac}),Sk.ctorParameters=()=>[{type:D,decorators:[{type:kr,args:[Ew]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Sk,[{type:im}],(function(){return[{type:D,decorators:[{type:kr,args:[Ew]}]}]}),null);const Ak=new Ga("@ngrx/effects Internal Root Guard"),Tk=new Ga("@ngrx/effects User Provided Effects"),Nk=new Ga("@ngrx/effects Internal Root Effects"),zk=new Ga("@ngrx/effects Root Effects"),Ik=new Ga("@ngrx/effects Internal Feature Effects"),Hk=new Ga("@ngrx/effects Feature Effects"),Fk=new Ga("@ngrx/effects Effects Error Handler");class Lk extends I{constructor(t,e){super(),this.errorHandler=t,this.effectsErrorHandler=e}addEffects(t){this.next(t)}toActions(){return this.pipe(we(xk),Zt((t=>t.pipe(we(Bk)))),Zt((t=>re(t.pipe(Pe((t=>(function e(t,n){return e=>{const o=(function i(t,e,n){const o=xk(t).constructor.name;return re(...(function i(t){return[Pk,vk].reduce(((e,n)=>e.concat(n(t))),[])})(t).map((({propertyName:i,dispatch:a,useEffectsErrorHandler:r})=>{const s="function"==typeof t[i]?t[i]():t[i],l=r?n(s,e):s;return!1===a?l.pipe(ye()):l.pipe((function c(){return R((function(t,e){t.subscribe(new T(e,(function(t){e.next(At.createNext(t))}),(function(){e.next(At.createComplete()),e.complete()}),(function(t){e.next(At.createError(t)),e.complete()})))}))})()).pipe(It((e=>({effect:t[i],notification:e,propertyName:i,sourceName:o,sourceInstance:t}))))})))})(e,t,n);return(function a(t){return Rk(t,"ngrxOnRunEffects")})(e)?e.ngrxOnRunEffects(o):o}})(this.errorHandler,this.effectsErrorHandler)(t))),It((t=>((function e(t,n){if("N"===t.notification.kind){const e=t.notification.value;!(function o(t){return"function"!=typeof t&&t&&t.type&&"string"==typeof t.type})(e)&&n.handleError(new Error(`Effect ${(function i({propertyName:t,sourceInstance:e,sourceName:n}){const o="function"==typeof e[t];return`"${n}.${String(t)}${o?"()":""}"`})(t)} dispatched an invalid action: ${(function a(t){try{return JSON.stringify(t)}catch(e){return t}})(e)}`))}})(t,this.errorHandler),t.notification))),ce((t=>"N"===t.kind&&null!=t.value)),(function e(){return R((function(t,e){t.subscribe(new T(e,(function(t){return Tt(t,e)})))}))})()),t.pipe(be(1),ce(Ek),It((t=>t.ngrxOnInitEffects())))))))}}function Bk(t){return(function e(t){return Rk(t,"ngrxOnIdentifyEffects")})(t)?t.ngrxOnIdentifyEffects():""}Lk.ɵfac=function t(e){return new(e||Lk)(vr(Zs),vr(Fk))},Lk.ɵprov=Mn({token:Lk,factory:Lk.ɵfac}),Lk.ctorParameters=()=>[{type:Zs},{type:void 0,decorators:[{type:kr,args:[Fk]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Lk,[{type:im}],(function(){return[{type:Zs},{type:void 0,decorators:[{type:kr,args:[Fk]}]}]}),null);class Vk{constructor(t,e){this.effectSources=t,this.store=e,this.effectsSubscription=null}start(){this.effectsSubscription||(this.effectsSubscription=this.effectSources.toActions().subscribe(this.store))}ngOnDestroy(){this.effectsSubscription&&(this.effectsSubscription.unsubscribe(),this.effectsSubscription=null)}}Vk.ɵfac=function t(e){return new(e||Vk)(vr(Lk),vr(Iw))},Vk.ɵprov=Mn({token:Vk,factory:Vk.ɵfac}),Vk.ctorParameters=()=>[{type:Lk},{type:Iw}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Vk,[{type:im}],(function(){return[{type:Lk},{type:Iw}]}),null);const jk="@ngrx/effects/init";JP(jk);class Uk{constructor(t,e,n,o,i,a,r){this.sources=t,e.start(),o.forEach((e=>t.addEffects(e))),n.dispatch({type:jk})}addEffects(t){this.sources.addEffects(t)}}Uk.ɵfac=function t(e){return new(e||Uk)(vr(Lk),vr(Vk),vr(Iw),vr(zk),vr(lk,8),vr(ck,8),vr(Ak,8))},Uk.ɵmod=ao({type:Uk}),Uk.ɵinj=vn({}),Uk.ctorParameters=()=>[{type:Lk},{type:Vk},{type:Iw},{type:Array,decorators:[{type:kr,args:[zk]}]},{type:lk,decorators:[{type:Sr}]},{type:ck,decorators:[{type:Sr}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Ak]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Uk,[{type:Ay,args:[{}]}],(function(){return[{type:Lk},{type:Vk},{type:Iw},{type:Array,decorators:[{type:kr,args:[zk]}]},{type:lk,decorators:[{type:Sr}]},{type:ck,decorators:[{type:Sr}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Ak]}]}]}),null);class Gk{constructor(t,e,n,o){e.forEach((e=>e.forEach((e=>t.addEffects(e)))))}}Gk.ɵfac=function t(e){return new(e||Gk)(vr(Uk),vr(Hk),vr(lk,8),vr(ck,8))},Gk.ɵmod=ao({type:Gk}),Gk.ɵinj=vn({}),Gk.ctorParameters=()=>[{type:Uk},{type:Array,decorators:[{type:kr,args:[Hk]}]},{type:lk,decorators:[{type:Sr}]},{type:ck,decorators:[{type:Sr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Gk,[{type:Ay,args:[{}]}],(function(){return[{type:Uk},{type:Array,decorators:[{type:kr,args:[Hk]}]},{type:lk,decorators:[{type:Sr}]},{type:ck,decorators:[{type:Sr}]}]}),null);class Wk{static forFeature(t=[]){return{ngModule:Gk,providers:[t,{provide:Ik,multi:!0,useValue:t},{provide:Tk,multi:!0,useValue:[]},{provide:Hk,multi:!0,useFactory:Yk,deps:[rp,Ik,Tk]}]}}static forRoot(t=[]){return{ngModule:Uk,providers:[{provide:Fk,useValue:kk},Vk,Lk,Sk,t,{provide:Nk,useValue:[t]},{provide:Ak,useFactory:qk,deps:[[Vk,new Sr,new Er],[Nk,new Dr]]},{provide:Tk,multi:!0,useValue:[]},{provide:zk,useFactory:Yk,deps:[rp,Nk,Tk]}]}}}function Yk(t,e,n){const o=[];for(const t of e)o.push(...t);for(const t of n)o.push(...t);return(function i(t,e){return e.map((e=>t.get(e)))})(t,o)}function qk(t,e){if((1!==e.length||0!==e[0].length)&&t)throw new TypeError("EffectsModule.forRoot() called twice. Feature modules should use EffectsModule.forFeature() instead.");return"guarded"}var Zk;Wk.ɵfac=function t(e){return new(e||Wk)},Wk.ɵmod=ao({type:Wk}),Wk.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Wk,[{type:Ay,args:[{}]}],null,null),(function(t){t[t.UNKNOWN=0]="UNKNOWN",t[t.EXPERIMENTS=1]="EXPERIMENTS",t[t.EXPERIMENT=2]="EXPERIMENT",t[t.COMPARE_EXPERIMENT=3]="COMPARE_EXPERIMENT",t[t.NOT_SET=4]="NOT_SET"})(Zk||(Zk={}));const Xk="defaultExperimentId";function Kk(t){return t.split(",").map((t=>{const e=t.indexOf(":");if(e<0)throw new Error(`Expect colon delimiting name and ID: ${t}`);const n=t.slice(0,e),o=t.slice(e+1);if(!o)throw new Error(`Expect id to be non-falsy: ${t}`);return{name:n,id:o}}))}function Jk(t,e){switch(t){case Zk.EXPERIMENT:return Object.prototype.hasOwnProperty.call(e,"experimentId")?[e.experimentId]:[Xk];case Zk.COMPARE_EXPERIMENT:return Kk(e.experimentIds).map((({id:t})=>t));case Zk.EXPERIMENTS:default:return null}}function Qk(t,e){var n;switch(t){case Zk.COMPARE_EXPERIMENT:case Zk.EXPERIMENT:{const o=null!==(n=Jk(t,e))&&void 0!==n?n:[];return o.sort(),`${t}/${o.join(",")}`}case Zk.EXPERIMENTS:return String(t);case Zk.NOT_SET:return"__not_set";default:return""}}const $k=()=>window.location.href;class tS{getHref(){return $k()}getSearch(){const t=new URLSearchParams(window.location.search),e=[];return t.forEach(((t,n)=>{e.push({key:n,value:t})})),e}getHash(){return window.location.hash}getPath(){return window.location.pathname}replaceState(t){window.history.replaceState(null,"",t)}pushState(t){window.history.pushState(null,"",t)}onPopState(){return oe(window,"popstate").pipe(It((()=>({pathname:this.getPath(),queryParams:this.getSearch()}))))}getResolvedPath(t){return new URL(t,$k()).pathname}getFullPathFromRouteOrNav(t,e){const n=this.getResolvedPath(t.pathname);let o="";return!(function i(t){return t.hasOwnProperty("pathname")&&!t.hasOwnProperty("queryParams")})(t)&&t.queryParams.length&&(o="?"+(function a(t){const e=new URLSearchParams;for(const{key:n,value:o}of t)e.append(n,o);return e})(t.queryParams).toString()),`${n}${o}${e?this.getHash():""}`}}tS.ɵfac=function t(e){return new(e||tS)},tS.ɵprov=Mn({token:tS,factory:tS.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(tS,[{type:im}],null,null);class eS{constructor(t){this.appRoot=this.getAppRootFromMetaElement(t)}getAppRootFromMetaElement(t){const e=document.querySelector('head meta[name="tb-relative-root"]');if(!e)return"/";const{pathname:n}=new URL(e.content,t.getHref());return n.replace(/\/*$/,"/")}getAbsPathnameWithAppRoot(t){return this.appRoot.slice(0,-1)+t}getAppRootlessPathname(t){return t.startsWith(this.appRoot)?"/"+t.slice(this.appRoot.length):t}}eS.ɵfac=function t(e){return new(e||eS)(vr(tS))},eS.ɵprov=Mn({token:eS,factory:eS.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(eS,[{type:im}],(function(){return[{type:tS}]}),null);class nS extends eS{getAppRoot(){return this.appRoot}setAppRoot(t){this.appRoot=t}}nS.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(nS)))(n||nS)}})(),nS.ɵprov=Mn({token:nS,factory:nS.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(nS,[{type:im}],null,null);class oS{}oS.ɵfac=function t(e){return new(e||oS)},oS.ɵmod=ao({type:oS}),oS.ɵinj=vn({providers:[tS]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(oS,[{type:Ay,args:[{providers:[tS]}]}],null,null);class iS{}iS.ɵfac=function t(e){return new(e||iS)},iS.ɵmod=ao({type:iS}),iS.ɵinj=vn({providers:[eS],imports:[[oS]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(iS,[{type:Ay,args:[{imports:[oS],providers:[eS]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(iS,{imports:[oS]});const aS=JP("[App Routing] Discarding Unsaved Updates"),rS=JP("[App Routing] State Rehydrated From Url",{_as:"props",_p:void 0}),sS=JP("[App Routing] Route Config Loaded",{_as:"props",_p:void 0}),lS=JP("[App Routing] In App Navigation Requested",{_as:"props",_p:void 0}),cS=JP("[App Routing] In App Navigating",{_as:"props",_p:void 0}),dS=JP("[App Routing] In App Navigated",{_as:"props",_p:void 0}),pS=new Ga("[App Routing] Dirty Updates");class mS{constructor(t){this.dirtyUpdatesSelectorFactories=t}getDirtyUpdatesSelectors(){var t;return null!==(t=this.dirtyUpdatesSelectorFactories)&&void 0!==t?t:[]}static registerDirtyUpdates(t){return{ngModule:mS,providers:[{provide:pS,multi:!0,useFactory:t}]}}}mS.ɵfac=function t(e){return new(e||mS)(vr(pS,8))},mS.ɵmod=ao({type:mS}),mS.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(mS,[{type:Ay}],(function(){return[{type:Array,decorators:[{type:Sr},{type:kr,args:[pS]}]}]}),null);const uS=new Ga("[App Routing] Programmatical Navigation Provider");class fS{constructor(t){this.providers=new Map;for(const e of t||[]){if(this.providers.has(e.actionCreator.type))throw new RangeError(`"${e.actionCreator.type}" is already registered for nav. Multiple navigations on same kick is not allowed.`);this.providers.set(e.actionCreator.type,e.lambda)}}getNavigation(t){const e=this.providers.get(t.type);return e?e(t):null}static registerProgrammaticalNavigation(t){return{ngModule:fS,providers:[{provide:uS,multi:!0,useFactory:t}]}}}function gS(t){return null!=t.routeKind}function hS(t){return vS(t).map((t=>{const e=t.startsWith(":");return e?{pathPart:t,isParam:!0,paramName:t.slice(1)}:{pathPart:t,isParam:e}}))}fS.ɵfac=function t(e){return new(e||fS)(vr(uS,8))},fS.ɵmod=ao({type:fS}),fS.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(fS,[{type:Ay,args:[{}]}],(function(){return[{type:void 0,decorators:[{type:Sr},{type:kr,args:[uS]}]}]}),null);class bS{constructor(t){this.validateConfig(t),this.pathFragments=hS(t.path),this.pathMatchers=this.getPathMatchers(this.pathFragments)}static getMatcher(t){return gS(t)?new yS(t):(function e(t){return void 0!==t.redirectionPath})(t)?new _S(t):new CS(t)}validateConfig({path:t}){if(!t.startsWith("/"))throw new RangeError(`config.path should start with '/'. ${t}`);let e=0;for(;(e=t.indexOf(":",e+1))>=0;){if("/"!==t[e-1])throw new RangeError(`config.path parameter should come after '/'. ${t}`);if(void 0===t[e+1]||"/"===t[e+1])throw new RangeError(`config.path parameter should have non-empty name. ${t}`)}}getPathMatchers(t){return t.map((t=>{const{pathPart:e}=t;return t.isParam?e=>({isParamPathPart:!0,partMatched:!0,paramName:t.paramName,paramValue:e}):t=>({isParamPathPart:!1,partMatched:t===e})}))}match(t){let e={};if(this.pathMatchers.length!==t.length)return{result:!1};let n=0;for(const o of this.pathMatchers){const i=o(t[n++]);if(!i.partMatched)return{result:!1};i.isParamPathPart&&(e=Object.assign(Object.assign({},e),{[i.paramName]:i.paramValue}))}return{result:!0,params:e,pathParts:t,isRedirection:!1}}matchByParams(t){return{result:!0,params:t,pathParts:this.reprojectPathByParams(this.pathFragments,t),isRedirection:!1}}reprojectPathByParams(t,e){const n=[];for(const o of t)if(o.isParam){const{paramName:t}=o;if(!e.hasOwnProperty(t))throw new RangeError(`Failed to reproject parameter. "${t}" parameter should be present.`);n.push(e[t])}else n.push(o.pathPart);return n}}class yS extends bS{constructor(t){super(t),this.definition=t}}class _S extends bS{constructor(t){super(t),this.definition=t,this.redirectionFragments=hS(t.redirectionPath)}match(t){const e=super.match(t);if(!e.result)return e;const n=this.reprojectPathByParams(this.redirectionFragments,e.params);return{result:!0,params:e.params,pathParts:n,isRedirection:!0}}}class CS extends bS{constructor(t){super(t),this.definition=t}match(t){const e=super.match(t);if(!e.result)return e;const{pathParts:n,queryParams:o}=this.definition.redirector(t);return{result:!0,params:e.params,pathParts:n,isRedirection:!0,redirectionQueryParams:o}}}class MS{constructor(t,e=3){if(this.maxRedirection=e,e<0)throw new RangeError("maxRedirection has to be non-negative number");this.validateRouteConfigs(t),this.defaultRouteConfig=null,this.routeKindToConcreteConfigMatchers=new Map,this.configMatchers=[];for(const e of t){const t=bS.getMatcher(e);this.configMatchers.push(t),t instanceof yS&&(this.routeKindToConcreteConfigMatchers.set(t.definition.routeKind,t),t.definition.defaultRoute&&(this.defaultRouteConfig=t))}}validateRouteConfigs(t){const e=t.filter(gS),n=e.filter((t=>t.defaultRoute));if(n.length>1){const t=n.map((({path:t})=>t)).join(", ");throw new RangeError(`There are more than one defaultRoutes. ${t}`)}if(1===n.length){const{path:t}=n[0];if(Boolean(hS(t).find((({isParam:t})=>t))))throw new RangeError(`A defaultRoute cannot have any params. ${t}`)}const o=new Set;for(const{routeKind:t}of e){if(o.has(t))throw new RangeError(`Multiple route configuration for kind: ${t}. Configurations should have unique routeKinds`);o.add(t)}}match(t){var e;if(!t.pathname.startsWith("/"))throw new RangeError('Navigation has to made with pathname that starts with "/"');let n,o=vS(t.pathname),i=0,a=!1;for(;;){let t=!1;for(const e of this.configMatchers){const i=e.match(o);if(i.result){t=!0;const{params:r,pathParts:s,isRedirection:l}=i;if(l){o=s,a=!0,n=i.redirectionQueryParams;break}if(!(e instanceof yS))throw new RangeError("No concrete route definition `match` return redirection");const{definition:c}=e,d={routeKind:c.routeKind,params:r,pathname:xS(s),deepLinkProvider:c.deepLinkProvider||null};return Object.assign(Object.assign({},d),a?{originateFromRedirection:!0,redirectionOnlyQueryParams:n}:{originateFromRedirection:!1})}}if(a&&i++,!t||i>this.maxRedirection)break}if(i>this.maxRedirection)throw new Error(`Potential redirection loop (redirecting more than ${this.maxRedirection} times. Please do not have cycles in the routes.`);if(this.defaultRouteConfig){const{definition:t}=this.defaultRouteConfig;return{routeKind:t.routeKind,deepLinkProvider:null!==(e=t.deepLinkProvider)&&void 0!==e?e:null,pathname:t.path,params:{},originateFromRedirection:a}}return null}matchByRouteKind(t,e){const n=this.routeKindToConcreteConfigMatchers.get(t);if(!n)throw new RangeError(`Requires configuration for routeKind: ${t}`);return{routeKind:t,params:e,pathname:xS(n.matchByParams(e).pathParts),deepLinkProvider:n.definition.deepLinkProvider||null,originateFromRedirection:!1}}}function vS(t){return t.split("/").slice(1)}function xS(t){return"/"+t.join("/")}const OS=new Ga("[App Routing] Route Config");class PS{constructor(t){if(this.routeConfigs=null,this.routeKindToNgComponent=new Map,!t)return;const e=[];for(const n of t)for(const t of n)e.push(t);this.routeConfigs=new MS(e),e.forEach((t=>{gS(t)&&this.routeKindToNgComponent.set(t.routeKind,t.ngComponent)}))}getRegisteredRouteKinds(){return this.routeKindToNgComponent.keys()}getRouteConfigs(){return this.routeConfigs}getNgComponentByRouteKind(t){return this.routeKindToNgComponent.get(t)||null}static registerRoutes(t){return{ngModule:PS,providers:[{provide:OS,multi:!0,useFactory:t}]}}}PS.ɵfac=function t(e){return new(e||PS)(vr(OS,8))},PS.ɵmod=ao({type:PS}),PS.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(PS,[{type:Ay,args:[{}]}],(function(){return[{type:void 0,decorators:[{type:Sr},{type:kr,args:[OS]}]}]}),null);const wS="app_routing",kS=Kw(wS),SS=Zw(kS,(t=>t.activeRoute)),DS=Zw(kS,(t=>t.nextRoute)),ES=Zw(kS,(t=>t.registeredRouteKeys)),RS=Zw(SS,(t=>t?t.routeKind:Zk.NOT_SET)),AS=Zw(SS,(t=>t?t.params:{})),TS=Zw(RS,AS,((t,e)=>Jk(t,e))),NS=Zw(RS,AS,((t,e)=>Qk(t,e))),zS=Zw(RS,AS,((t,e)=>{if(t!==Zk.COMPARE_EXPERIMENT)return{};const n=(function o(t){const e=new Map,n=Kk(t.experimentIds);for(const{id:t,name:o}of n)o&&e.set(t,o);return e})(e);return Object.fromEntries(n.entries())})),IS=JP("[App Routing] Effects Init");class HS{constructor(t,e,n,o,i,a,r){this.actions$=t,this.store=e,this.location=n,this.dirtyUpdatesRegistry=o,this.registry=i,this.programmaticalNavModule=a,this.appRootProvider=r,this.onNavigationRequested$=this.actions$.pipe(Dk(lS),It((t=>{const e=t.pathname.startsWith("/")?this.appRootProvider.getAbsPathnameWithAppRoot(t.pathname):this.location.getResolvedPath(t.pathname);return Object.assign(Object.assign({},t),{pathname:e})}))),this.bootstrapReducers$=Mk((()=>this.actions$.pipe(Dk(IS),It((()=>sS({routeKinds:new Set(this.registry.getRegisteredRouteKinds())})))))),this.onInit$=this.actions$.pipe(Dk(IS)).pipe(Ce(0),It((()=>({pathname:this.location.getPath(),queryParams:this.location.getSearch(),replaceState:!0,browserInitiated:!0})))),this.userInitNavRoute$=re(this.onNavigationRequested$,this.onInit$,this.location.onPopState().pipe(It((t=>({pathname:t.pathname,replaceState:t.replaceState,browserInitiated:!0}))))).pipe(It((t=>{if(!t.pathname.startsWith("/"))throw new Error(`[App routing] pathname must start with '/'. Got: ${t.pathname}`);return Object.assign(Object.assign({},t),{pathname:this.appRootProvider.getAppRootlessPathname(t.pathname)})})),It((t=>({routeMatch:this.routeConfigs?this.routeConfigs.match(t):null,options:{replaceState:t.replaceState,browserInitiated:t.browserInitiated}})))),this.programmticalNavRoute$=this.actions$.pipe(It((t=>this.programmaticalNavModule.getNavigation(t))),ce((t=>null!==t)),It((t=>{const e=t,n=e.routeKind;let o;switch(e.routeKind){case Zk.COMPARE_EXPERIMENT:o={experimentIds:(i=e.routeParams.aliasAndExperimentIds,i.map((({alias:t,id:e})=>`${t}:${e}`)).join(","))};break;default:o=e.routeParams}var i;return{routeKind:n,routeParams:o}})),It((({routeKind:t,routeParams:e})=>({routeMatch:this.routeConfigs?this.routeConfigs.matchByRouteKind(t,e):null,options:{replaceState:!1,browserInitiated:!1}})))),this.validatedRoute$=re(this.userInitNavRoute$,this.programmticalNavRoute$).pipe(ce((({routeMatch:t})=>Boolean(t))),It((t=>({routeMatch:t.routeMatch,options:t.options})))),this.navigate$=Mk((()=>this.validatedRoute$.pipe(Ve(this.store.select(SS)),Zt((([t,e])=>{const n=null!==e&&Qk(t.routeMatch.routeKind,t.routeMatch.params)===Qk(e.routeKind,e.params),o=this.dirtyUpdatesRegistry.getDirtyUpdatesSelectors();return n||!o.length?Et(t):$t(this.dirtyUpdatesRegistry.getDirtyUpdatesSelectors().map((t=>this.store.select(t).pipe(be(1))))).pipe(It((t=>void 0!==t[0].experimentIds&&t[0].experimentIds.length>0)),ce((t=>{if(t){const t=window.confirm("You have unsaved edits, are you sure you want to discard them?");return t&&this.store.dispatch(aS()),t}return!0})),It((()=>t)))})),Fe((({routeMatch:t,options:e})=>{if(e.browserInitiated&&t.deepLinkProvider){const e=t.originateFromRedirection&&t.redirectionOnlyQueryParams?t.redirectionOnlyQueryParams:this.location.getSearch(),n=t.deepLinkProvider.deserializeQueryParams(e);this.store.dispatch(rS({routeKind:t.routeKind,partialState:n}))}})),ze((({routeMatch:t,options:e})=>{var n;const o={replaceState:null!==(n=e.replaceState)&&void 0!==n&&n},i=Et({routeKind:t.routeKind,params:t.params,pathname:t.pathname,queryParams:[],navigationOptions:o});return null===t.deepLinkProvider?i:t.deepLinkProvider.serializeStateToQueryParams(this.store).pipe(It(((e,n)=>({routeKind:t.routeKind,params:t.params,pathname:t.pathname,queryParams:e,navigationOptions:0===n?o:Object.assign(Object.assign({},o),{replaceState:!0})}))))})),Fe((t=>{this.store.dispatch(cS({after:t}))})),ge(0)).pipe(Ve(this.store.select(SS)),It((([t,e])=>({preserveHash:null===e||null===t||Qk(e.routeKind,e.params)===Qk(t.routeKind,t.params),route:t}))),Fe((({preserveHash:t,route:e})=>{!(function n(t,e){return t.pathname===e.pathname&&t.queryParams.length===e.queryParams.length&&t.queryParams.every(((t,n)=>{const o=e.queryParams[n];return t.key===o.key&&t.value===o.value}))})(e,{pathname:this.appRootProvider.getAppRootlessPathname(this.location.getPath()),queryParams:this.location.getSearch()})&&(e.navigationOptions.replaceState?this.location.replaceState(this.appRootProvider.getAbsPathnameWithAppRoot(this.location.getFullPathFromRouteOrNav(e,t))):this.location.pushState(this.appRootProvider.getAbsPathnameWithAppRoot(this.location.getFullPathFromRouteOrNav(e,t))))}))).pipe(Ve(this.store.select(SS)),It((([{route:t},e])=>dS({before:e,after:t})))))),this.routeConfigs=i.getRouteConfigs()}ngrxOnInitEffects(){return IS()}}HS.ɵfac=function t(e){return new(e||HS)(vr(Sk),vr(Iw),vr(tS),vr(mS),vr(PS),vr(fS),vr(eS))},HS.ɵprov=Mn({token:HS,factory:HS.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(HS,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:tS},{type:mS},{type:PS},{type:fS},{type:eS}]}),null);const FS=yk({activeRoute:null,nextRoute:null,registeredRouteKeys:new Set},bk(cS,((t,{after:e})=>Object.assign(Object.assign({},t),{nextRoute:e}))),bk(dS,((t,{after:e})=>Object.assign(Object.assign({},t),{activeRoute:e,nextRoute:null}))),bk(sS,((t,{routeKinds:e})=>Object.assign(Object.assign({},t),{registeredRouteKeys:e}))));function LS(t,e){return FS(t,e)}class BS{}BS.ɵfac=function t(e){return new(e||BS)},BS.ɵmod=ao({type:BS}),BS.ɵinj=vn({providers:[mS,fS],imports:[[PS,dk.forFeature(wS,LS),Wk.forFeature([HS]),iS,oS]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(BS,[{type:Ay,args:[{imports:[PS,dk.forFeature(wS,LS),Wk.forFeature([HS]),iS,oS],providers:[mS,fS]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(BS,{imports:[PS,ck,Gk,iS,oS]});class VS{}const jS="__tab__";class US{constructor(){this.tfStorage=document.createElement("tf-storage"),document.createElement("tf-globals").tf_globals.setUseHash(!0),this.tfStorage.tf_storage.migrateLegacyURLScheme()}getString(t){return this.tfStorage.tf_storage.getString(t)}setString(t,e,n){this.tfStorage.tf_storage.setString(t,e,n)}getPluginId(){return this.getString(jS)}setPluginId(t,e){this.setString(jS,t,e)}}var GS;US.ɵfac=function t(e){return new(e||US)},US.ɵprov=Mn({token:US,factory:US.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(US,[{type:im}],(function(){return[]}),null),(function(t){t.BROWSER_DEFAULT="browser_default",t.LIGHT="light",t.DARK="dark"})(GS||(GS={}));const WS=JP("[Persistent Settings] Global Settings Loaded",{_as:"props",_p:void 0}),YS=new Ga("[Persistent Settings] Global Settings");class qS{constructor(t){this.globalSettingSelectors=[],t&&(this.globalSettingSelectors=t.map((t=>t())))}getGlobalSettingSelectors(){var t;return null!==(t=this.globalSettingSelectors)&&void 0!==t?t:[]}static defineGlobalSetting(t){return{ngModule:qS,providers:[{provide:YS,multi:!0,useValue:t}]}}}qS.ɵfac=function t(e){return new(e||qS)(vr(YS,8))},qS.ɵmod=ao({type:qS}),qS.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(qS,[{type:Ay}],(function(){return[{type:Array,decorators:[{type:Sr},{type:kr,args:[YS]}]}]}),null);class ZS{setItem(t,e){localStorage.setItem(t,e)}getItem(t){return localStorage.getItem(t)}removeItem(t){localStorage.removeItem(t)}}ZS.ɵfac=function t(e){return new(e||ZS)},ZS.ɵprov=Mn({token:ZS,factory:ZS.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ZS,[{type:im}],null,null);class XS{}XS.ɵfac=function t(e){return new(e||XS)},XS.ɵmod=ao({type:XS}),XS.ɵinj=vn({providers:[ZS]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(XS,[{type:Ay,args:[{providers:[ZS]}]}],null,null);const KS="_tb_global_settings.timeseries",JS="_tb_global_settings",QS="notificationLastReadTimestamp";class $S{}$S.ɵfac=function t(e){return new(e||$S)},$S.ɵprov=Mn({token:$S,factory:$S.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh($S,[{type:im}],null,null);class tD{}tD.ɵfac=function t(e){return new(e||tD)},tD.ɵprov=Mn({token:tD,factory:tD.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(tD,[{type:im}],null,null);class eD extends tD{uiToBackend(t){return{ignoreOutliers:t.ignoreOutliers,scalarSmoothing:t.scalarSmoothing,tooltipSort:t.tooltipSortString,autoReload:t.autoReload,autoReloadPeriodInMs:t.autoReloadPeriodInMs,paginationSize:t.pageSize,theme:t.themeOverride,notificationLastReadTimeInMs:t.notificationLastReadTimeInMs,sideBarWidthInPercent:t.sideBarWidthInPercent,timeSeriesPromotionDismissed:t.timeSeriesPromotionDismissed,timeSeriesSettingsPaneOpened:t.timeSeriesSettingsPaneOpened}}backendToUi(t){const e={};return t.hasOwnProperty("scalarSmoothing")&&"number"==typeof t.scalarSmoothing&&(e.scalarSmoothing=t.scalarSmoothing),t.hasOwnProperty("ignoreOutliers")&&"boolean"==typeof t.ignoreOutliers&&(e.ignoreOutliers=t.ignoreOutliers),t.hasOwnProperty("tooltipSort")&&"string"==typeof t.tooltipSort&&(e.tooltipSortString=t.tooltipSort),t.hasOwnProperty("autoReload")&&"boolean"==typeof t.autoReload&&(e.autoReload=t.autoReload),t.hasOwnProperty("autoReloadPeriodInMs")&&"number"==typeof t.autoReloadPeriodInMs&&(e.autoReloadPeriodInMs=t.autoReloadPeriodInMs),t.hasOwnProperty("paginationSize")&&"number"==typeof t.paginationSize&&(e.pageSize=t.paginationSize),t.hasOwnProperty("theme")&&"string"==typeof t.theme&&new Set(Object.values(GS)).has(t.theme)&&(e.themeOverride=t.theme),t.hasOwnProperty("notificationLastReadTimeInMs")&&"number"==typeof t.notificationLastReadTimeInMs&&(e.notificationLastReadTimeInMs=t.notificationLastReadTimeInMs),t.hasOwnProperty("sideBarWidthInPercent")&&"number"==typeof t.sideBarWidthInPercent&&(e.sideBarWidthInPercent=t.sideBarWidthInPercent),t.hasOwnProperty("timeSeriesPromotionDismissed")&&"boolean"==typeof t.timeSeriesPromotionDismissed&&(e.timeSeriesPromotionDismissed=t.timeSeriesPromotionDismissed),t.hasOwnProperty("timeSeriesSettingsPaneOpened")&&"boolean"==typeof t.timeSeriesSettingsPaneOpened&&(e.timeSeriesSettingsPaneOpened=t.timeSeriesSettingsPaneOpened),e}}eD.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(eD)))(n||eD)}})(),eD.ɵprov=Mn({token:eD,factory:eD.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(eD,[{type:im}],null,null);class nD{constructor(t,e){this.localStorage=t,this.converter=e}setSettings(t){return Object.keys(t)?this.getSettings().pipe(Fe((e=>{this.localStorage.setItem(JS,JSON.stringify(this.converter.uiToBackend(Object.assign(Object.assign({},e),t)))),this.localStorage.removeItem(KS),this.localStorage.removeItem(QS)})),It((()=>{}))):rt}deserialize(t){try{return JSON.parse(t)}catch(t){return{}}}getSettings(){var t,e;const n=this.localStorage.getItem(QS),o=this.converter.backendToUi(this.deserialize(n?JSON.stringify({notificationLastReadTimeInMs:Number(n)}):"{}")),i=this.converter.backendToUi(this.deserialize(null!==(t=this.localStorage.getItem(KS))&&void 0!==t?t:"{}")),a=this.converter.backendToUi(this.deserialize(null!==(e=this.localStorage.getItem(JS))&&void 0!==e?e:"{}"));return Et(Object.assign(Object.assign(Object.assign({},o),i),a))}}nD.ɵfac=function t(e){return new(e||nD)(vr(ZS),vr(tD))},nD.ɵprov=Mn({token:nD,factory:nD.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(nD,[{type:im}],(function(){return[{type:ZS},{type:tD}]}),null);class oD{}oD.ɵfac=function t(e){return new(e||oD)},oD.ɵmod=ao({type:oD}),oD.ɵinj=vn({providers:[{provide:$S,useClass:nD},{provide:tD,useClass:eD}],imports:[[XS]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(oD,[{type:Ay,args:[{imports:[XS],providers:[{provide:$S,useClass:nD},{provide:tD,useClass:eD}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(oD,{imports:[XS]});const iD=JP("[Persistent Settings] Effects Init");class aD{constructor(t,e,n,o){this.actions$=t,this.store=e,this.configModule=n,this.dataSource=o,this.initializeAndUpdateSettings$=Mk((()=>{const t=this.actions$.pipe(Dk(iD),Zt((()=>this.dataSource.getSettings())),Fe((t=>{this.store.dispatch(WS({partialSettings:t}))})),Ce(0),Zt((()=>re(...this.configModule.getGlobalSettingSelectors().map((t=>this.store.select(t).pipe(Me(((t,e)=>{const n=Object.values(t),o=Object.values(e);return n.length===o.length&&n.every(((t,e)=>t===o[e]))})),Te(1))))))),Ee());return t.pipe((function e(t){return R((function(e,n){var o=[];return e.subscribe(new T(n,(function(t){return o.push(t)}),(function(){n.next(o),n.complete()}))),t.subscribe(new T(n,(function(){var t=o;o=[],n.next(t)}),y)),function(){o=null}}))})(t.pipe(ge(500))),Zt((t=>{const e={};for(const n of t)Object.assign(e,n);return this.dataSource.setSettings(e)})))}),{dispatch:!1})}ngrxOnInitEffects(){return iD()}}aD.ɵfac=function t(e){return new(e||aD)(vr(Sk),vr(Iw),vr(qS),vr($S))},aD.ɵprov=Mn({token:aD,factory:aD.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(aD,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:qS},{type:$S}]}),null);class rD{}rD.ɵfac=function t(e){return new(e||rD)},rD.ɵmod=ao({type:rD}),rD.ɵinj=vn({providers:[qS],imports:[[Wk.forFeature([aD]),oD]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(rD,[{type:Ay,args:[{imports:[Wk.forFeature([aD]),oD],providers:[qS]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(rD,{imports:[Gk,oD]});
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
class sD{}class lD{}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class cD{constructor(t){this.normalizedNames=new Map,this.lazyUpdate=null,t?this.lazyInit="string"==typeof t?()=>{this.headers=new Map,t.split("\n").forEach((t=>{const e=t.indexOf(":");if(e>0){const n=t.slice(0,e),o=n.toLowerCase(),i=t.slice(e+1).trim();this.maybeSetNormalizedName(n,o),this.headers.has(o)?this.headers.get(o).push(i):this.headers.set(o,[i])}}))}:()=>{this.headers=new Map,Object.keys(t).forEach((e=>{let n=t[e];const o=e.toLowerCase();"string"==typeof n&&(n=[n]),n.length>0&&(this.headers.set(o,n),this.maybeSetNormalizedName(e,o))}))}:this.headers=new Map}has(t){return this.init(),this.headers.has(t.toLowerCase())}get(t){this.init();const e=this.headers.get(t.toLowerCase());return e&&e.length>0?e[0]:null}keys(){return this.init(),Array.from(this.normalizedNames.values())}getAll(t){return this.init(),this.headers.get(t.toLowerCase())||null}append(t,e){return this.clone({name:t,value:e,op:"a"})}set(t,e){return this.clone({name:t,value:e,op:"s"})}delete(t,e){return this.clone({name:t,value:e,op:"d"})}maybeSetNormalizedName(t,e){this.normalizedNames.has(e)||this.normalizedNames.set(e,t)}init(){this.lazyInit&&(this.lazyInit instanceof cD?this.copyFrom(this.lazyInit):this.lazyInit(),this.lazyInit=null,this.lazyUpdate&&(this.lazyUpdate.forEach((t=>this.applyUpdate(t))),this.lazyUpdate=null))}copyFrom(t){t.init(),Array.from(t.headers.keys()).forEach((e=>{this.headers.set(e,t.headers.get(e)),this.normalizedNames.set(e,t.normalizedNames.get(e))}))}clone(t){const e=new cD;return e.lazyInit=this.lazyInit&&this.lazyInit instanceof cD?this.lazyInit:this,e.lazyUpdate=(this.lazyUpdate||[]).concat([t]),e}applyUpdate(t){const e=t.name.toLowerCase();switch(t.op){case"a":case"s":let n=t.value;if("string"==typeof n&&(n=[n]),0===n.length)return;this.maybeSetNormalizedName(t.name,e);const o=("a"===t.op?this.headers.get(e):void 0)||[];o.push(...n),this.headers.set(e,o);break;case"d":const i=t.value;if(i){let t=this.headers.get(e);if(!t)return;t=t.filter((t=>-1===i.indexOf(t))),0===t.length?(this.headers.delete(e),this.normalizedNames.delete(e)):this.headers.set(e,t)}else this.headers.delete(e),this.normalizedNames.delete(e)}}forEach(t){this.init(),Array.from(this.normalizedNames.keys()).forEach((e=>t(this.normalizedNames.get(e),this.headers.get(e))))}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class dD{encodeKey(t){return pD(t)}encodeValue(t){return pD(t)}decodeKey(t){return decodeURIComponent(t)}decodeValue(t){return decodeURIComponent(t)}}function pD(t){return encodeURIComponent(t).replace(/%40/gi,"@").replace(/%3A/gi,":").replace(/%24/gi,"$").replace(/%2C/gi,",").replace(/%3B/gi,";").replace(/%2B/gi,"+").replace(/%3D/gi,"=").replace(/%3F/gi,"?").replace(/%2F/gi,"/")}function mD(t){return`${t}`}class uD{constructor(t={}){if(this.updates=null,this.cloneFrom=null,this.encoder=t.encoder||new dD,t.fromString){if(t.fromObject)throw new Error("Cannot specify both fromString and fromObject.");this.map=(function e(t,n){const o=new Map;return t.length>0&&t.replace(/^\?/,"").split("&").forEach((t=>{const e=t.indexOf("="),[i,a]=-1==e?[n.decodeKey(t),""]:[n.decodeKey(t.slice(0,e)),n.decodeValue(t.slice(e+1))],r=o.get(i)||[];r.push(a),o.set(i,r)})),o})(t.fromString,this.encoder)}else t.fromObject?(this.map=new Map,Object.keys(t.fromObject).forEach((e=>{const n=t.fromObject[e];this.map.set(e,Array.isArray(n)?n:[n])}))):this.map=null}has(t){return this.init(),this.map.has(t)}get(t){this.init();const e=this.map.get(t);return e?e[0]:null}getAll(t){return this.init(),this.map.get(t)||null}keys(){return this.init(),Array.from(this.map.keys())}append(t,e){return this.clone({param:t,value:e,op:"a"})}appendAll(t){const e=[];return Object.keys(t).forEach((n=>{const o=t[n];Array.isArray(o)?o.forEach((t=>{e.push({param:n,value:t,op:"a"})})):e.push({param:n,value:o,op:"a"})})),this.clone(e)}set(t,e){return this.clone({param:t,value:e,op:"s"})}delete(t,e){return this.clone({param:t,value:e,op:"d"})}toString(){return this.init(),this.keys().map((t=>{const e=this.encoder.encodeKey(t);return this.map.get(t).map((t=>e+"="+this.encoder.encodeValue(t))).join("&")})).filter((t=>""!==t)).join("&")}clone(t){const e=new uD({encoder:this.encoder});return e.cloneFrom=this.cloneFrom||this,e.updates=(this.updates||[]).concat(t),e}init(){null===this.map&&(this.map=new Map),null!==this.cloneFrom&&(this.cloneFrom.init(),this.cloneFrom.keys().forEach((t=>this.map.set(t,this.cloneFrom.map.get(t)))),this.updates.forEach((t=>{switch(t.op){case"a":case"s":const e=("a"===t.op?this.map.get(t.param):void 0)||[];e.push(mD(t.value)),this.map.set(t.param,e);break;case"d":if(void 0===t.value){this.map.delete(t.param);break}{let e=this.map.get(t.param)||[];const n=e.indexOf(mD(t.value));-1!==n&&e.splice(n,1),e.length>0?this.map.set(t.param,e):this.map.delete(t.param)}}})),this.cloneFrom=this.updates=null)}}class fD{constructor(){this.map=new Map}set(t,e){return this.map.set(t,e),this}get(t){return this.map.has(t)||this.map.set(t,t.defaultValue()),this.map.get(t)}delete(t){return this.map.delete(t),this}keys(){return this.map.keys()}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function gD(t){return"undefined"!=typeof ArrayBuffer&&t instanceof ArrayBuffer}function hD(t){return"undefined"!=typeof Blob&&t instanceof Blob}function bD(t){return"undefined"!=typeof FormData&&t instanceof FormData}class yD{constructor(t,e,n,o){let i;if(this.url=e,this.body=null,this.reportProgress=!1,this.withCredentials=!1,this.responseType="json",this.method=t.toUpperCase(),(function a(t){switch(t){case"DELETE":case"GET":case"HEAD":case"OPTIONS":case"JSONP":return!1;default:return!0}})(this.method)||o?(this.body=void 0!==n?n:null,i=o):i=n,i&&(this.reportProgress=!!i.reportProgress,this.withCredentials=!!i.withCredentials,i.responseType&&(this.responseType=i.responseType),i.headers&&(this.headers=i.headers),i.context&&(this.context=i.context),i.params&&(this.params=i.params)),this.headers||(this.headers=new cD),this.context||(this.context=new fD),this.params){const t=this.params.toString();if(0===t.length)this.urlWithParams=e;else{const n=e.indexOf("?");this.urlWithParams=e+(-1===n?"?":n<e.length-1?"&":"")+t}}else this.params=new uD,this.urlWithParams=e}serializeBody(){return null===this.body?null:gD(this.body)||hD(this.body)||bD(this.body)||(function t(e){return"undefined"!=typeof URLSearchParams&&e instanceof URLSearchParams})(this.body)||"string"==typeof this.body?this.body:this.body instanceof uD?this.body.toString():"object"==typeof this.body||"boolean"==typeof this.body||Array.isArray(this.body)?JSON.stringify(this.body):this.body.toString()}detectContentTypeHeader(){return null===this.body||bD(this.body)?null:hD(this.body)?this.body.type||null:gD(this.body)?null:"string"==typeof this.body?"text/plain":this.body instanceof uD?"application/x-www-form-urlencoded;charset=UTF-8":"object"==typeof this.body||"number"==typeof this.body||"boolean"==typeof this.body?"application/json":null}clone(t={}){var e;const n=t.method||this.method,o=t.url||this.url,i=t.responseType||this.responseType,a=void 0!==t.body?t.body:this.body,r=void 0!==t.withCredentials?t.withCredentials:this.withCredentials,s=void 0!==t.reportProgress?t.reportProgress:this.reportProgress;let l=t.headers||this.headers,c=t.params||this.params;const d=null!==(e=t.context)&&void 0!==e?e:this.context;return void 0!==t.setHeaders&&(l=Object.keys(t.setHeaders).reduce(((e,n)=>e.set(n,t.setHeaders[n])),l)),t.setParams&&(c=Object.keys(t.setParams).reduce(((e,n)=>e.set(n,t.setParams[n])),c)),new yD(n,o,a,{params:c,headers:l,context:d,reportProgress:s,responseType:i,withCredentials:r})}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */var _D;!(function(t){t[t.Sent=0]="Sent",t[t.UploadProgress=1]="UploadProgress",t[t.ResponseHeader=2]="ResponseHeader",t[t.DownloadProgress=3]="DownloadProgress",t[t.Response=4]="Response",t[t.User=5]="User"})(_D||(_D={}));class CD{constructor(t,e=200,n="OK"){this.headers=t.headers||new cD,this.status=void 0!==t.status?t.status:e,this.statusText=t.statusText||n,this.url=t.url||null,this.ok=this.status>=200&&this.status<300}}class MD extends CD{constructor(t={}){super(t),this.type=_D.ResponseHeader}clone(t={}){return new MD({headers:t.headers||this.headers,status:void 0!==t.status?t.status:this.status,statusText:t.statusText||this.statusText,url:t.url||this.url||void 0})}}class vD extends CD{constructor(t={}){super(t),this.type=_D.Response,this.body=void 0!==t.body?t.body:null}clone(t={}){return new vD({body:void 0!==t.body?t.body:this.body,headers:t.headers||this.headers,status:void 0!==t.status?t.status:this.status,statusText:t.statusText||this.statusText,url:t.url||this.url||void 0})}}class xD extends CD{constructor(t){super(t,0,"Unknown Error"),this.name="HttpErrorResponse",this.ok=!1,this.message=this.status>=200&&this.status<300?`Http failure during parsing for ${t.url||"(unknown url)"}`:`Http failure response for ${t.url||"(unknown url)"}: ${t.status} ${t.statusText}`,this.error=t.error||null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function OD(t,e){return{body:e,headers:t.headers,context:t.context,observe:t.observe,params:t.params,reportProgress:t.reportProgress,responseType:t.responseType,withCredentials:t.withCredentials}}class PD{constructor(t){this.handler=t}request(t,e,n={}){let o;if(t instanceof yD)o=t;else{let i,a;i=n.headers instanceof cD?n.headers:new cD(n.headers),n.params&&(a=n.params instanceof uD?n.params:new uD({fromObject:n.params})),o=new yD(t,e,void 0!==n.body?n.body:null,{headers:i,context:n.context,params:a,reportProgress:n.reportProgress,responseType:n.responseType||"json",withCredentials:n.withCredentials})}const i=Et(o).pipe((function a(t,e){return l(e)?Zt(t,e,1):Zt(t,1)})((t=>this.handler.handle(t))));if(t instanceof yD||"events"===n.observe)return i;const r=i.pipe(ce((t=>t instanceof vD)));switch(n.observe||"body"){case"body":switch(o.responseType){case"arraybuffer":return r.pipe(It((t=>{if(null!==t.body&&!(t.body instanceof ArrayBuffer))throw new Error("Response is not an ArrayBuffer.");return t.body})));case"blob":return r.pipe(It((t=>{if(null!==t.body&&!(t.body instanceof Blob))throw new Error("Response is not a Blob.");return t.body})));case"text":return r.pipe(It((t=>{if(null!==t.body&&"string"!=typeof t.body)throw new Error("Response is not a string.");return t.body})));case"json":default:return r.pipe(It((t=>t.body)))}case"response":return r;default:throw new Error(`Unreachable: unhandled observe type ${n.observe}}`)}}delete(t,e={}){return this.request("DELETE",t,e)}get(t,e={}){return this.request("GET",t,e)}head(t,e={}){return this.request("HEAD",t,e)}jsonp(t,e){return this.request("JSONP",t,{params:(new uD).append(e,"JSONP_CALLBACK"),observe:"body",responseType:"json"})}options(t,e={}){return this.request("OPTIONS",t,e)}patch(t,e,n={}){return this.request("PATCH",t,OD(n,e))}post(t,e,n={}){return this.request("POST",t,OD(n,e))}put(t,e,n={}){return this.request("PUT",t,OD(n,e))}}PD.ɵfac=function t(e){return new(e||PD)(vr(sD))},PD.ɵprov=Mn({token:PD,factory:PD.ɵfac}),PD.ctorParameters=()=>[{type:sD}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(PD,[{type:im}],(function(){return[{type:sD}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class wD{constructor(t,e){this.next=t,this.interceptor=e}handle(t){return this.interceptor.intercept(t,this.next)}}const kD=new Ga("HTTP_INTERCEPTORS");class SD{intercept(t,e){return e.handle(t)}}SD.ɵfac=function t(e){return new(e||SD)},SD.ɵprov=Mn({token:SD,factory:SD.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(SD,[{type:im}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let DD=0;class ED{}class RD{constructor(t,e){this.callbackMap=t,this.document=e,this.resolvedPromise=Promise.resolve()}nextCallback(){return"ng_jsonp_callback_"+DD++}handle(t){if("JSONP"!==t.method)throw new Error("JSONP requests must use JSONP request method.");if("json"!==t.responseType)throw new Error("JSONP requests must use Json response type.");return new D((e=>{const n=this.nextCallback(),o=t.urlWithParams.replace(/=JSONP_CALLBACK(&|$)/,`=${n}$1`),i=this.document.createElement("script");i.src=o;let a=null,r=!1,s=!1;this.callbackMap[n]=t=>{delete this.callbackMap[n],s||(a=t,r=!0)};const l=()=>{i.parentNode&&i.parentNode.removeChild(i),delete this.callbackMap[n]},c=t=>{s||this.resolvedPromise.then((()=>{l(),r?(e.next(new vD({body:a,status:200,statusText:"OK",url:o})),e.complete()):e.error(new xD({url:o,status:0,statusText:"JSONP Error",error:new Error("JSONP injected script did not invoke callback.")}))}))},d=t=>{s||(l(),e.error(new xD({error:t,status:0,statusText:"JSONP Error",url:o})))};return i.addEventListener("load",c),i.addEventListener("error",d),this.document.body.appendChild(i),e.next({type:_D.Sent}),()=>{s=!0,i.removeEventListener("load",c),i.removeEventListener("error",d),l()}}))}}RD.ɵfac=function t(e){return new(e||RD)(vr(ED),vr(Z_))},RD.ɵprov=Mn({token:RD,factory:RD.ɵfac}),RD.ctorParameters=()=>[{type:ED},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(RD,[{type:im}],(function(){return[{type:ED},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);class AD{constructor(t){this.jsonp=t}intercept(t,e){return"JSONP"===t.method?this.jsonp.handle(t):e.handle(t)}}AD.ɵfac=function t(e){return new(e||AD)(vr(RD))},AD.ɵprov=Mn({token:AD,factory:AD.ɵfac}),AD.ctorParameters=()=>[{type:RD}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(AD,[{type:im}],(function(){return[{type:RD}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const TD=/^\)\]\}',?\n/;class ND{constructor(t){this.xhrFactory=t}handle(t){if("JSONP"===t.method)throw new Error("Attempted to construct Jsonp request without HttpClientJsonpModule installed.");return new D((e=>{const n=this.xhrFactory.build();if(n.open(t.method,t.urlWithParams),t.withCredentials&&(n.withCredentials=!0),t.headers.forEach(((t,e)=>n.setRequestHeader(t,e.join(",")))),t.headers.has("Accept")||n.setRequestHeader("Accept","application/json, text/plain, */*"),!t.headers.has("Content-Type")){const e=t.detectContentTypeHeader();null!==e&&n.setRequestHeader("Content-Type",e)}if(t.responseType){const e=t.responseType.toLowerCase();n.responseType="json"!==e?e:"text"}const o=t.serializeBody();let i=null;const a=()=>{if(null!==i)return i;const e=1223===n.status?204:n.status,o=n.statusText||"OK",a=new cD(n.getAllResponseHeaders()),r=(function s(t){return"responseURL"in t&&t.responseURL?t.responseURL:/^X-Request-URL:/m.test(t.getAllResponseHeaders())?t.getResponseHeader("X-Request-URL"):null})(n)||t.url;return i=new MD({headers:a,status:e,statusText:o,url:r}),i},r=()=>{let{headers:o,status:i,statusText:r,url:s}=a(),l=null;204!==i&&(l=void 0===n.response?n.responseText:n.response),0===i&&(i=l?200:0);let c=i>=200&&i<300;if("json"===t.responseType&&"string"==typeof l){const t=l;l=l.replace(TD,"");try{l=""!==l?JSON.parse(l):null}catch(e){l=t,c&&(c=!1,l={error:e,text:l})}}c?(e.next(new vD({body:l,headers:o,status:i,statusText:r,url:s||void 0})),e.complete()):e.error(new xD({error:l,headers:o,status:i,statusText:r,url:s||void 0}))},s=t=>{const{url:o}=a(),i=new xD({error:t,status:n.status||0,statusText:n.statusText||"Unknown Error",url:o||void 0});e.error(i)};let l=!1;const c=o=>{l||(e.next(a()),l=!0);let i={type:_D.DownloadProgress,loaded:o.loaded};o.lengthComputable&&(i.total=o.total),"text"===t.responseType&&n.responseText&&(i.partialText=n.responseText),e.next(i)},d=t=>{let n={type:_D.UploadProgress,loaded:t.loaded};t.lengthComputable&&(n.total=t.total),e.next(n)};return n.addEventListener("load",r),n.addEventListener("error",s),n.addEventListener("timeout",s),n.addEventListener("abort",s),t.reportProgress&&(n.addEventListener("progress",c),null!==o&&n.upload&&n.upload.addEventListener("progress",d)),n.send(o),e.next({type:_D.Sent}),()=>{n.removeEventListener("error",s),n.removeEventListener("abort",s),n.removeEventListener("load",r),n.removeEventListener("timeout",s),t.reportProgress&&(n.removeEventListener("progress",c),null!==o&&n.upload&&n.upload.removeEventListener("progress",d)),n.readyState!==n.DONE&&n.abort()}}))}}ND.ɵfac=function t(e){return new(e||ND)(vr(XM))},ND.ɵprov=Mn({token:ND,factory:ND.ɵfac}),ND.ctorParameters=()=>[{type:XM}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(ND,[{type:im}],(function(){return[{type:XM}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const zD=new Ga("XSRF_COOKIE_NAME"),ID=new Ga("XSRF_HEADER_NAME");class HD{}class FD{constructor(t,e,n){this.doc=t,this.platform=e,this.cookieName=n,this.lastCookieString="",this.lastToken=null,this.parseCount=0}getToken(){if("server"===this.platform)return null;const t=this.doc.cookie||"";return t!==this.lastCookieString&&(this.parseCount++,this.lastToken=iM(t,this.cookieName),this.lastCookieString=t),this.lastToken}}FD.ɵfac=function t(e){return new(e||FD)(vr(Z_),vr(jy),vr(zD))},FD.ɵprov=Mn({token:FD,factory:FD.ɵfac}),FD.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:String,decorators:[{type:kr,args:[jy]}]},{type:String,decorators:[{type:kr,args:[zD]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(FD,[{type:im}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:String,decorators:[{type:kr,args:[jy]}]},{type:String,decorators:[{type:kr,args:[zD]}]}]}),null);class LD{constructor(t,e){this.tokenService=t,this.headerName=e}intercept(t,e){const n=t.url.toLowerCase();if("GET"===t.method||"HEAD"===t.method||n.startsWith("http://")||n.startsWith("https://"))return e.handle(t);const o=this.tokenService.getToken();return null===o||t.headers.has(this.headerName)||(t=t.clone({headers:t.headers.set(this.headerName,o)})),e.handle(t)}}LD.ɵfac=function t(e){return new(e||LD)(vr(HD),vr(ID))},LD.ɵprov=Mn({token:LD,factory:LD.ɵfac}),LD.ctorParameters=()=>[{type:HD},{type:String,decorators:[{type:kr,args:[ID]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(LD,[{type:im}],(function(){return[{type:HD},{type:String,decorators:[{type:kr,args:[ID]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class BD{constructor(t,e){this.backend=t,this.injector=e,this.chain=null}handle(t){if(null===this.chain){const t=this.injector.get(kD,[]);this.chain=t.reduceRight(((t,e)=>new wD(t,e)),this.backend)}return this.chain.handle(t)}}function VD(){return"object"==typeof window?window:{}}BD.ɵfac=function t(e){return new(e||BD)(vr(lD),vr(rp))},BD.ɵprov=Mn({token:BD,factory:BD.ɵfac}),BD.ctorParameters=()=>[{type:lD},{type:rp}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(BD,[{type:im}],(function(){return[{type:lD},{type:rp}]}),null);class jD{static disable(){return{ngModule:jD,providers:[{provide:LD,useClass:SD}]}}static withOptions(t={}){return{ngModule:jD,providers:[t.cookieName?{provide:zD,useValue:t.cookieName}:[],t.headerName?{provide:ID,useValue:t.headerName}:[]]}}}jD.ɵfac=function t(e){return new(e||jD)},jD.ɵmod=ao({type:jD}),jD.ɵinj=vn({providers:[LD,{provide:kD,useExisting:LD,multi:!0},{provide:HD,useClass:FD},{provide:zD,useValue:"XSRF-TOKEN"},{provide:ID,useValue:"X-XSRF-TOKEN"}]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(jD,[{type:Ay,args:[{providers:[LD,{provide:kD,useExisting:LD,multi:!0},{provide:HD,useClass:FD},{provide:zD,useValue:"XSRF-TOKEN"},{provide:ID,useValue:"X-XSRF-TOKEN"}]}]}],null,null);class UD{}UD.ɵfac=function t(e){return new(e||UD)},UD.ɵmod=ao({type:UD}),UD.ɵinj=vn({providers:[PD,{provide:sD,useClass:BD},ND,{provide:lD,useExisting:ND}],imports:[[jD.withOptions({cookieName:"XSRF-TOKEN",headerName:"X-XSRF-TOKEN"})]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(UD,[{type:Ay,args:[{imports:[jD.withOptions({cookieName:"XSRF-TOKEN",headerName:"X-XSRF-TOKEN"})],providers:[PD,{provide:sD,useClass:BD},ND,{provide:lD,useExisting:ND}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(UD,{imports:[jD]});class GD{}GD.ɵfac=function t(e){return new(e||GD)},GD.ɵmod=ao({type:GD}),GD.ɵinj=vn({providers:[RD,{provide:ED,useFactory:VD},{provide:kD,useClass:AD,multi:!0}]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(GD,[{type:Ay,args:[{providers:[RD,{provide:ED,useFactory:VD},{provide:kD,useClass:AD,multi:!0}]}]}],null,null);const WD="feature",YD=Kw(WD),qD=Zw(YD,(t=>t.isFeatureFlagsLoaded)),ZD=Zw(YD,(t=>Object.assign(Object.assign({},t.defaultFlags),t.flagOverrides))),XD=Zw(YD,(t=>t.flagOverrides||{})),KD=Zw(ZD,(t=>t.isAutoDarkModeAllowed)),JD=Zw(ZD,(t=>null!==t.enableDarkModeOverride?t.enableDarkModeOverride:t.defaultEnableDarkMode)),QD=Zw(ZD,(t=>t.enableDarkModeOverride)),$D=Zw(ZD,(t=>t.enabledExperimentalPlugins)),tE=Zw(ZD,(t=>t.inColab)),eE=Zw(ZD,(t=>t.enabledColorGroup)),nE=Zw(ZD,(t=>t.enabledColorGroupByRegex)),oE=Zw(ZD,(t=>t.metricsImageSupportEnabled)),iE=Zw(ZD,(t=>t.enabledLinkedTime)),aE=Zw(ZD,(t=>t.enableTimeSeriesPromotion));function rE(t){const e={};for(const[n,o]of t.entries())e[n]=o;return e}function sE(t){let e=t.headers||new cD;return e=e.append("X-XSRF-Protected","1"),Object.assign(Object.assign({},t),{headers:e})}class lE{constructor(t,e,n){this.appRootProvider=t,this.http=e,this.store=n}resolveAppRoot(t){return t.startsWith("/")?this.appRootProvider.getAbsPathnameWithAppRoot(t):t}get(t,e={}){return this.http.get(this.resolveAppRoot(t),e)}post(t,e,n={}){return n=sE(n),this.store.select(qD).pipe(ce((t=>Boolean(t))),be(1),Ve(this.store.select(tE)),Zt((([,o])=>{const i=this.resolveAppRoot(t);return o?this.http.get(i,{headers:n.headers,params:rE(e)}):this.http.post(i,e,n)})))}put(t,e,n={}){return this.http.put(this.resolveAppRoot(t),e,sE(n))}delete(t,e={}){return this.http.delete(this.resolveAppRoot(t),sE(e))}}lE.ɵfac=function t(e){return new(e||lE)(vr(eS),vr(PD),vr(Iw))},lE.ɵprov=Mn({token:lE,factory:lE.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lE,[{type:im}],(function(){return[{type:eS},{type:PD},{type:Iw}]}),null);class cE{}var dE,pE,mE;cE.ɵfac=function t(e){return new(e||cE)},cE.ɵmod=ao({type:cE}),cE.ɵinj=vn({providers:[lE],imports:[[UD,iS]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(cE,[{type:Ay,args:[{imports:[UD,iS],providers:[lE]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(cE,{imports:[UD,iS]}),(function(t){t.STEP="step",t.WALL_TIME="wall_time",t.RELATIVE="relative"})(dE||(dE={})),(function(t){t.OFFSET="offset",t.OVERLAY="overlay"})(pE||(pE={})),(function(t){t.UNKNOWN="UNKNOWN",t.NOT_FOUND="NOT_FOUND"})(mE||(mE={}));const uE=new Ga("TensorBoard brand name");function fE(t){let e=mE.UNKNOWN;return t instanceof xD&&404===t.status&&(e=mE.NOT_FOUND),Rt(new gE(e))}class gE{constructor(t){this.failureCode=t}}class hE{constructor(t){this.http=t,this.tfBackend=document.createElement("tf-backend").tf_backend}fetchPluginsListing(t){const e=(function n(t){if(!t.length)return null;const e=new URLSearchParams;for(const n of t)e.append("experimentalPlugin",n);return e})(t),o=e?`data/plugins_listing?${e.toString()}`:"data/plugins_listing";return this.http.get(o).pipe(pe(fE))}fetchEnvironment(){return $t([this.http.get("data/environment"),Ct(this.tfBackend.environmentStore.refresh())]).pipe(It((([t])=>t)),pe(fE))}}hE.ɵfac=function t(e){return new(e||hE)(vr(lE))},hE.ɵprov=Mn({token:hE,factory:hE.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(hE,[{type:im}],(function(){return[{type:lE}]}),null);class bE{}var yE;bE.ɵfac=function t(e){return new(e||bE)},bE.ɵmod=ao({type:bE}),bE.ɵinj=vn({providers:[hE],imports:[[cE]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(bE,[{type:Ay,args:[{imports:[cE],providers:[hE]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(bE,{imports:[cE]}),(function(t){t[t.NOT_LOADED=0]="NOT_LOADED",t[t.LOADED=1]="LOADED",t[t.LOADING=2]="LOADING",t[t.FAILED=3]="FAILED"})(yE||(yE={}));const _E=JP("[Core] Plugin Changed",{_as:"props",_p:void 0}),CE=JP("[Core] Plugin Url Hash Changed",{_as:"props",_p:void 0}),ME=JP("[Core] Loaded"),vE=JP("[Core] User Triggered Reload"),xE=JP("[Core] Auto Reload"),OE=JP("[Core] PluginListing Fetch Requested"),PE=JP("[Core] PluginListing Fetch Successful",{_as:"props",_p:void 0}),wE=JP("[Core] PluginListing Fetch Failed",{_as:"props",_p:void 0}),kE=JP("[Core] Polymer Component Runs Fetch Requested"),SE=JP("[Core] Polymer Component Runs Fetch Successful"),DE=JP("[Core] Polymer Component Runs Fetch Failed"),EE=JP("[Core] Environment Fetch Successful",{_as:"props",_p:void 0}),RE=JP("[Core] Run Selection Changed",{_as:"props",_p:void 0}),AE=JP("[Core] Run Fetch Successful",{_as:"props",_p:void 0}),TE=JP("[Core] Side Bar Width Changed",{_as:"props",_p:void 0}),NE=JP("[Metrics] Metrics Settings Pane Closed"),zE=JP("[Metrics] Metrics Settings Pane Toggled"),IE=JP("[Metrics] Metrics Tag Metadata Requested"),HE=JP("[Metrics] Metrics Tag Metadata Loaded",{_as:"props",_p:void 0}),FE=JP("[Metrics] Metrics Tag Metadata Failed"),LE=JP("[Metrics] Metrics Settings Change Tooltip",{_as:"props",_p:void 0});JP("[Metrics] Metrics Settings Toggle Show Data Download");const BE=JP("[Metrics] Metrics Setting Toggle Ignore Outlier"),VE=JP("[Metrics] Metrics Setting Change X Axis Type",{_as:"props",_p:void 0}),jE=JP("[Metrics] Metrics Setting Change Scalar Smoothing",{_as:"props",_p:void 0}),UE=JP("[Metrics] Metrics Setting Partition Non Monotonic X Toggled"),GE=JP("[Metrics] Metrics Setting Change Image Brightness",{_as:"props",_p:void 0}),WE=JP("[Metrics] Metrics Setting Change Image Contrast",{_as:"props",_p:void 0}),YE=JP("[Metrics] Image Brightness Setting Reset"),qE=JP("[Metrics] Image Contrast Setting Reset"),ZE=JP("[Metrics] Metrics Setting Toggle Image Show Actual Size"),XE=JP("[Metrics] Metrics Setting Change Histogram Mode",{_as:"props",_p:void 0}),KE=JP("[Metrics] Multiple Time Series Requested",{_as:"props",_p:void 0}),JE=JP("[Metrics] Fetch Time Series Request Failed",{_as:"props",_p:void 0}),QE=JP("[Metrics] Fetch Time Series Response Loaded",{_as:"props",_p:void 0}),$E=JP("[Metrics] Card Visibility Changed",{_as:"props",_p:void 0}),tR=JP("[Metrics] Card Step Slider Changed",{_as:"props",_p:void 0}),eR=JP("[Metrics] Tag Filter Changed",{_as:"props",_p:void 0}),nR=JP("[Metrics] Metrics Tag Group Expansion Changed",{_as:"props",_p:void 0}),oR=JP("[Metrics] Card Pin State Toggled",{_as:"props",_p:void 0}),iR=JP("[Metrics] Toggle Visible Plugin",{_as:"props",_p:void 0}),aR=JP("[Metrics] Toggle Show All Plugins"),rR=JP("[Metrics] Linked Time Selection Changed",{_as:"props",_p:void 0}),sR=JP("[Metrics] Linked Time Selection Cleared"),lR=JP("[Metrics] Select Time Enable Toggle"),cR=JP("[Metrics] Use Range Select Time Toggle"),dR=JP("[Metrics] Metrics Promo Dismissed"),pR=JP("[Metrics] Metrics Promo Go To Scalars"),mR="core",uR={activePlugin:null,plugins:{},coreDataLoadState:{state:yE.NOT_LOADED,lastLoadedTimeInMs:null},pluginsListLoaded:{state:yE.NOT_LOADED,lastLoadedTimeInMs:null,failureCode:null},environment:{data_location:"",window_title:""},polymerRunsLoadState:{state:yE.NOT_LOADED,lastLoadedTimeInMs:null},polymerInteropRuns:[],polymerInteropRunSelection:new Set,sideBarWidthInPercent:20},fR=yk(uR,bk(_E,CE,((t,{plugin:e})=>Object.assign(Object.assign({},t),{activePlugin:e}))),bk(OE,(t=>Object.assign(Object.assign({},t),{coreDataLoadState:Object.assign(Object.assign({},t.coreDataLoadState),{state:yE.LOADING}),pluginsListLoaded:Object.assign(Object.assign({},t.pluginsListLoaded),{state:yE.LOADING})}))),bk(wE,((t,{failureCode:e})=>Object.assign(Object.assign({},t),{coreDataLoadState:Object.assign(Object.assign({},t.coreDataLoadState),{state:yE.FAILED}),pluginsListLoaded:Object.assign(Object.assign({},t.pluginsListLoaded),{state:yE.FAILED,failureCode:e})}))),bk(PE,((t,{plugins:e})=>{const n=Object.keys(e).find((t=>e[t].enabled))||null,o=t.activePlugin||n,i=Date.now();let a=t.coreDataLoadState;return t.polymerRunsLoadState.state===yE.LOADED&&(a={state:yE.LOADED,lastLoadedTimeInMs:i}),Object.assign(Object.assign({},t),{activePlugin:o,coreDataLoadState:a,plugins:e,pluginsListLoaded:{state:yE.LOADED,lastLoadedTimeInMs:i,failureCode:null}})})),bk(kE,(t=>Object.assign(Object.assign({},t),{coreDataLoadState:Object.assign(Object.assign({},t.coreDataLoadState),{state:yE.LOADING}),polymerRunsLoadState:Object.assign(Object.assign({},t.polymerRunsLoadState),{state:yE.LOADING})}))),bk(SE,(t=>{const e=Date.now();let n=t.coreDataLoadState;return t.pluginsListLoaded.state===yE.LOADED&&(n={state:yE.LOADED,lastLoadedTimeInMs:e}),Object.assign(Object.assign({},t),{coreDataLoadState:n,polymerRunsLoadState:Object.assign(Object.assign({},t.polymerRunsLoadState),{state:yE.LOADED,lastLoadedTimeInMs:e})})})),bk(DE,(t=>Object.assign(Object.assign({},t),{coreDataLoadState:Object.assign(Object.assign({},t.coreDataLoadState),{state:yE.FAILED}),polymerRunsLoadState:Object.assign(Object.assign({},t.polymerRunsLoadState),{state:yE.FAILED})}))),bk(EE,((t,{environment:e})=>Object.assign(Object.assign({},t),{environment:e}))),bk(AE,((t,{runs:e})=>Object.assign(Object.assign({},t),{polymerInteropRuns:e}))),bk(RE,((t,{nextSelection:e})=>Object.assign(Object.assign({},t),{polymerInteropRunSelection:new Set(e)}))),bk(TE,((t,{widthInPercent:e})=>Object.assign(Object.assign({},t),{sideBarWidthInPercent:Math.min(Math.max(0,e),100)}))),bk(WS,((t,{partialSettings:e})=>{const n=Object.assign({},t),o=e.sideBarWidthInPercent;return"number"==typeof o&&o>=0&&o<=100&&(n.sideBarWidthInPercent=o),n})),bk(pR,(t=>Object.assign(Object.assign({},t),{activePlugin:"scalars"}))));function gR(t,e){return fR(t,e)}const hR=Kw(mR),bR=Zw(hR,(t=>t.pluginsListLoaded)),yR=Zw(hR,(t=>t.polymerRunsLoadState)),_R=Zw(hR,(t=>t.coreDataLoadState.state)),CR=Zw(hR,(t=>t.coreDataLoadState.lastLoadedTimeInMs)),MR=Zw(hR,(t=>t.activePlugin)),vR=Zw(hR,(t=>t.plugins)),xR=Zw(hR,(t=>t.environment)),OR=Zw(hR,(t=>t.sideBarWidthInPercent)),PR=new Set([Zk.COMPARE_EXPERIMENT,Zk.EXPERIMENT,Zk.NOT_SET]);class wR{constructor(t,e,n){this.actions$=t,this.store=e,this.webappDataSource=n,this.tfBackend={ref:document.createElement("tf-backend").tf_backend},this.onDashboardLoad$=re(this.actions$.pipe(Dk(ME,dS),Ve(this.store.select(NS)),Me((([,t],[,e])=>t===e))),this.actions$.pipe(Dk(xE,vE))).pipe(Ve(this.store.select(RS)),ce((([,t])=>PR.has(t))),Be(1,void 0,{leading:!0})),this.fetchWebAppData$=Mk((()=>re(this.onDashboardLoad$.pipe(Ve(this.store.select(bR),this.store.select($D)),ce((([,{state:t}])=>t!==yE.LOADING)),Fe((()=>this.store.dispatch(OE()))),Zt((([,,t])=>(function e(){for(var t=[],e=0;e<arguments.length;e++)t[e]=arguments[e];var n=kt(t),o=le(t);return o.length?new D((function(t){var e=o.map((function(){return[]})),r=o.map((function(){return!1}));t.add((function(){e=r=null}));for(var s=function(s){Mt(o[s]).subscribe(new T(t,(function(o){if(e[s].push(o),e.every((function(t){return t.length}))){var l=e.map((function(t){return t.shift()}));t.next(n?n.apply(void 0,a([],i(l))):l),e.some((function(t,e){return!t.length&&r[e]}))&&t.complete()}}),(function(){r[s]=!0,!e[s].length&&t.complete()})))},l=0;!t.closed&&l<o.length;l++)s(l);return function(){e=r=null}})):rt})(this.webappDataSource.fetchPluginsListing(t),this.fetchEnvironment()).pipe(It((([t])=>{this.store.dispatch(PE({plugins:t}))})),pe((t=>(this.store.dispatch(wE(t instanceof gE?{failureCode:t.failureCode}:{failureCode:mE.UNKNOWN})),rt))))))),this.onDashboardLoad$.pipe(It((([,t])=>t)),ze((t=>t!==Zk.COMPARE_EXPERIMENT?Et([]):this.store.select(zS).pipe(Me(((t,e)=>{const n=Object.entries(t),o=new Map(Object.entries(e));if(n.length!==o.size)return!1;for(const[t,e]of n)if(o.get(t)!==e)return!1;return!0})),Ce(0),Be(500,void 0,{leading:!0,trailing:!0})))),Ve(this.store.select(RS),this.store.select(yR)),ce((([,t,e])=>PR.has(t)&&e.state!==yE.LOADING)),Fe((()=>{this.store.dispatch(kE())})),ze((()=>this.refreshPolymerRuns())),Fe((()=>{this.store.dispatch(SE())})),pe((()=>(this.store.dispatch(DE()),rt)))))),{dispatch:!1}),this.dispatchChangePlugin$=Mk((()=>re(this.onDashboardLoad$,this.actions$.pipe(Dk(PE))).pipe(Ve(this.store.select(MR)),It((([,t])=>t)),Me(),ce((t=>null!==t)),be(1),Fe((t=>{this.store.dispatch(_E({plugin:t}))})))),{dispatch:!1})}refreshPolymerRuns(){return Ct(this.tfBackend.ref.runsStore.refresh())}fetchEnvironment(){return this.webappDataSource.fetchEnvironment().pipe(Fe((t=>{this.store.dispatch(EE({environment:t}))})))}}wR.ɵfac=function t(e){return new(e||wR)(vr(Sk),vr(Iw),vr(hE))},wR.ɵprov=Mn({token:wR,factory:wR.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(wR,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:hE}]}),null);const kR=new Ga("Core Feature Config");function SR(t){return{initialState:Object.assign(Object.assign({},uR),{activePlugin:t.getPluginId()||null})}}function DR(){return Zw(OR,(t=>({sideBarWidthInPercent:t})))}class ER{}ER.ɵfac=function t(e){return new(e||ER)},ER.ɵmod=ao({type:ER}),ER.ɵinj=vn({providers:[{provide:kR,deps:[VS],useFactory:SR}],imports:[[Wk.forFeature([wR]),dk.forFeature(mR,gR,kR),bE,qS.defineGlobalSetting(DR)]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ER,[{type:Ay,args:[{imports:[Wk.forFeature([wR]),dk.forFeature(mR,gR,kR),bE,qS.defineGlobalSetting(DR)],providers:[{provide:kR,deps:[VS],useFactory:SR}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(ER,{imports:[Gk,ck,bE,qS]});const RR=new Ga("[Alert] Action-To-Alert Provider");class AR{constructor(t){this.providers=new Map;for(const e of t||[])for(const t of e){if(this.providers.has(t.actionCreator.type))throw new RangeError(`"${t.actionCreator.type}" is already registered for alerts. Multiple alerts for the same action is not allowed.`);this.providers.set(t.actionCreator.type,t.alertFromAction)}}getAlertFromAction(t){const e=this.providers.get(t.type);return e?e(t):null}static registerAlertActions(t){return{ngModule:AR,providers:[{provide:RR,multi:!0,useFactory:t}]}}}AR.ɵfac=function t(e){return new(e||AR)(vr(RR,8))},AR.ɵmod=ao({type:AR}),AR.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(AR,[{type:Ay,args:[{}]}],(function(){return[{type:void 0,decorators:[{type:Sr},{type:kr,args:[RR]}]}]}),null);const TR="hparams",NR=JP("[Runs] Fetch Runs Requested",{_as:"props",_p:void 0}),zR=JP("[Runs] Fetch Runs Succeeded",{_as:"props",_p:void 0}),IR=JP("[Runs] Fetch Runs Failed",{_as:"props",_p:void 0}),HR=JP("[Runs] Run Selection Toggled",{_as:"props",_p:void 0}),FR=JP("[Runs] Run Page Selection Toggled",{_as:"props",_p:void 0}),LR=JP("[Runs] Run Selector Pagination Option Changed",{_as:"props",_p:void 0}),BR=JP("[Runs] Run Selector Sort Changed",{_as:"props",_p:void 0}),VR=JP("[Runs] Run Selector Regex Filter Changed",{_as:"props",_p:void 0}),jR=JP("[Runs] Run Color Changed",{_as:"props",_p:void 0}),UR=JP("[Runs] Run Table Shown",{_as:"props",_p:void 0}),GR=JP("[Runs] Run Group By Changed",{_as:"props",_p:void 0});var WR,YR,qR,ZR;!(function(t){t.DATASET_UNKNOWN="DATASET_UNKNOWN",t.DATASET_TRAINING="DATASET_TRAINING",t.DATASET_VALIDATION="DATASET_VALIDATION"})(WR||(WR={})),(function(t){t.STATUS_UNKNOWN="STATUS_UNKNOWN",t.STATUS_SUCCESS="STATUS_SUCCESS",t.STATUS_FAILURE="STATUS_FAILURE",t.STATUS_RUNNING="STATUS_RUNNING"})(YR||(YR={})),(function(t){t.DATA_TYPE_UNSET="DATA_TYPE_UNSET",t.DATA_TYPE_STRING="DATA_TYPE_STRING",t.DATA_TYPE_BOOL="DATA_TYPE_BOOL",t.DATA_TYPE_FLOAT64="DATA_TYPE_FLOAT64"})(qR||(qR={})),(function(t){t[t.DISCRETE=0]="DISCRETE",t[t.INTERVAL=1]="INTERVAL"})(ZR||(ZR={}));class XR{}XR.ɵfac=function t(e){return new(e||XR)},XR.ɵprov=Mn({token:XR,factory:XR.ɵfac,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(XR,[{type:im,args:[{providedIn:"root"}]}],null,null);const KR=JP("[Hparams] Hparams Discrete Hparam Filter Changed",{_as:"props",_p:void 0}),JR=JP("[Hparams] Hparams Interval Hparam Filter Changed",{_as:"props",_p:void 0}),QR=JP("[Hparams] Hparams Metric Filter Changed",{_as:"props",_p:void 0});function $R(t){return JSON.stringify([...t].sort())}function tA(t){var e,n,o,i;const a=new Map,r=new Map,s=new Map;for(const a of t)for(const[t,l]of a)if(l.type===ZR.DISCRETE){const{possibleValues:e,values:n}=r.get(t)||{possibleValues:new Set,values:new Set};for(const t of l.filterValues)n.add(t);for(const t of l.possibleValues)e.add(t);r.set(t,{possibleValues:e,values:n})}else{const a=s.get(t);s.set(t,{filterLowerValue:Math.min(l.filterLowerValue,null!==(e=null==a?void 0:a.filterLowerValue)&&void 0!==e?e:1/0),filterUpperValue:Math.max(l.filterUpperValue,null!==(n=null==a?void 0:a.filterUpperValue)&&void 0!==n?n:-1/0),minValue:Math.min(l.minValue,null!==(o=null==a?void 0:a.minValue)&&void 0!==o?o:1/0),maxValue:Math.max(l.maxValue,null!==(i=null==a?void 0:a.maxValue)&&void 0!==i?i:-1/0)})}for(const[t,{values:e,possibleValues:n}]of r)a.set(t,{type:ZR.DISCRETE,includeUndefined:!0,possibleValues:[...n],filterValues:[...e]});for(const[t,{minValue:e,maxValue:n,filterLowerValue:o,filterUpperValue:i}]of s){if(a.has(t)){const e=a.get(t);if(e.type===ZR.DISCRETE&&e.possibleValues.some((t=>t)))throw new RangeError(`Cannot combine hparam, ${t}, as it is of mixed types.`)}a.set(t,{type:ZR.INTERVAL,includeUndefined:!0,minValue:e,maxValue:n,filterLowerValue:o,filterUpperValue:i})}return a}function eA(t){var e,n,o,i;const a=new Map;for(const r of t)for(const[t,s]of r){const r=a.get(t);a.set(t,Object.assign(Object.assign({type:ZR.INTERVAL,includeUndefined:!0},r),{minValue:Math.min(s.minValue,null!==(e=null==r?void 0:r.minValue)&&void 0!==e?e:1/0),maxValue:Math.max(s.maxValue,null!==(n=null==r?void 0:r.maxValue)&&void 0!==n?n:-1/0),filterLowerValue:Math.min(s.filterLowerValue,null!==(o=null==r?void 0:r.filterLowerValue)&&void 0!==o?o:1/0),filterUpperValue:Math.max(s.filterUpperValue,null!==(i=null==r?void 0:r.filterUpperValue)&&void 0!==i?i:-1/0)}))}return a}const nA=yk({specs:{},filters:{}},bk(KR,((t,e)=>{var n;const{experimentIds:o,hparamName:i,filterValues:a,includeUndefined:r}=e,s=$R(o),l=null!==(n=t.filters[s])&&void 0!==n?n:{hparams:new Map},c=l.hparams.get(i);if(c&&c.type!==ZR.DISCRETE)throw new RangeError(`New discrete filter of ${i} conflicts existing filter of `+ZR[c.type]);const d=tA(o.filter((e=>Boolean(t.specs[e]))).map((e=>t.specs[e].hparam.defaultFilters))).get(i);if(!d)throw new Error(`Cannot set hparam, ${i}, when it is not known for experimentIds: ${o.join(", ")}`);if(d.type!==ZR.DISCRETE)throw new Error(`Cannot set ${i} when default filter is not of discrete type.`);const p=new Set(d.possibleValues),m=[...a].filter((t=>!p.has(t)));if(m.length)throw new Error(`New filter for ${i} has more than one value that is not present in the spec. Bad values: ${m.join(", ")}`);const u=new Map(l.hparams);return u.set(i,Object.assign(Object.assign({},c),{type:ZR.DISCRETE,includeUndefined:r,possibleValues:[...p],filterValues:a})),Object.assign(Object.assign({},t),{filters:Object.assign(Object.assign({},t.filters),{[s]:Object.assign(Object.assign({},l),{hparams:u})})})})),bk(JR,((t,e)=>{var n;const{experimentIds:o,hparamName:i,filterLowerValue:a,filterUpperValue:r,includeUndefined:s}=e,l=$R(o),c=null!==(n=t.filters[l])&&void 0!==n?n:{metrics:new Map,hparams:new Map},d=c.hparams.get(i);if(d&&d.type!==ZR.INTERVAL)throw new RangeError(`New interval filter of ${i} conflicts existing filter of `+ZR[d.type]);const p=tA(o.filter((e=>Boolean(t.specs[e]))).map((e=>t.specs[e].hparam.defaultFilters))).get(i);if(!p)throw new Error(`Cannot set hpara, ${i}, when it is not known for experimentIds: ${o.join(", ")}`);if(p.type!==ZR.INTERVAL)throw new Error(`Cannot set ${i} when default filter is not of interval type.`);const m=new Map(c.hparams);return m.set(i,Object.assign(Object.assign({},d),{type:ZR.INTERVAL,includeUndefined:s,minValue:p.minValue,maxValue:p.maxValue,filterLowerValue:a,filterUpperValue:r})),Object.assign(Object.assign({},t),{filters:Object.assign(Object.assign({},t.filters),{[l]:Object.assign(Object.assign({},c),{hparams:m})})})})),bk(QR,((t,e)=>{var n;const{experimentIds:o,metricTag:i,filterLowerValue:a,filterUpperValue:r,includeUndefined:s}=e,l=$R(o),c=null!==(n=t.filters[l])&&void 0!==n?n:{metrics:new Map,hparams:new Map},d=eA(o.filter((e=>Boolean(t.specs[e]))).map((e=>t.specs[e].metric.defaultFilters))).get(i);if(!d)throw new Error(`Cannot set metric, ${i}, when it is not known for experimentIds: ${o.join(", ")}`);const p=c.metrics.get(i),m=new Map(c.metrics);return m.set(i,Object.assign(Object.assign({},p),{type:ZR.INTERVAL,includeUndefined:s,minValue:d.minValue,maxValue:d.maxValue,filterLowerValue:a,filterUpperValue:r})),Object.assign(Object.assign({},t),{filters:Object.assign(Object.assign({},t.filters),{[l]:Object.assign(Object.assign({},c),{metrics:m})})})})),bk(zR,((t,e)=>{var n,o,i,a;if(0===Object.keys(e.newRunsAndMetadata).length)return t;const r=Object.assign({},t.specs),s=new Map,l=new Set;for(const t of Object.keys(e.newRunsAndMetadata)){const c=new Map,d=new Map,p=new Map,m=new Map,{runs:u,metadata:f}=e.newRunsAndMetadata[t];for(const t of u){const e=f.runToHparamsAndMetrics[t.id];if(e)for(const t of e.metrics){const e=s.get(t.tag);s.set(t.tag,{min:e?Math.min(e.min,t.value):t.value,max:e?Math.max(e.max,t.value):t.value})}}for(const{name:t,domain:e}of f.hparamSpecs)if(e.type===ZR.DISCRETE){const n=p.get(t)||new Set;for(const t of e.values)n.add(t);p.set(t,n)}else{const n=m.get(t);m.set(t,{minValue:n?Math.min(e.minValue,n.minValue):e.minValue,maxValue:n?Math.max(e.maxValue,n.maxValue):e.maxValue})}for(const t of f.metricSpecs)l.add(t.tag);for(const[t,e]of p)c.set(t,{type:ZR.DISCRETE,includeUndefined:!0,possibleValues:[...e],filterValues:[...e]});for(const[t,{minValue:e,maxValue:n}]of m)c.set(t,{type:ZR.INTERVAL,includeUndefined:!0,minValue:e,maxValue:n,filterLowerValue:e,filterUpperValue:n});for(const t of l){const e=s.get(t),i=null!==(n=null==e?void 0:e.min)&&void 0!==n?n:0,a=null!==(o=null==e?void 0:e.max)&&void 0!==o?o:0;d.set(t,{type:ZR.INTERVAL,includeUndefined:!0,minValue:i,maxValue:a,filterLowerValue:i,filterUpperValue:a})}r[t]={hparam:Object.assign(Object.assign({},null===(i=r[t])||void 0===i?void 0:i.hparam),{specs:f.hparamSpecs,defaultFilters:c}),metric:Object.assign(Object.assign({},null===(a=r[t])||void 0===a?void 0:a.metric),{specs:f.metricSpecs,defaultFilters:d})}}return Object.assign(Object.assign({},t),{specs:r})})));function oA(t,e){return nA(t,e)}class iA{}iA.ɵfac=function t(e){return new(e||iA)},iA.ɵmod=ao({type:iA}),iA.ɵinj=vn({imports:[[dk.forFeature(TR,oA)]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(iA,[{type:Ay,args:[{imports:[dk.forFeature(TR,oA)]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(iA,{imports:[ck]});class aA{}function rA(t,e){return`${e}/${t}`}aA.ɵfac=function t(e){return new(e||aA)},aA.ɵmod=ao({type:aA}),aA.ɵinj=vn({imports:[[iA]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(aA,[{type:Ay,args:[{imports:[iA]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(aA,{imports:[iA]});class sA{constructor(t){this.http=t}fetchRuns(t){return this.http.get("data/runs").pipe(It((e=>e.map((e=>({id:rA(e,t),name:e,startTime:0}))))))}fetchHparamsMetadata(t){return Et({hparamSpecs:[],metricSpecs:[],runToHparamsAndMetrics:{}})}}sA.ɵfac=function t(e){return new(e||sA)(vr(lE))},sA.ɵprov=Mn({token:sA,factory:sA.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(sA,[{type:im}],(function(){return[{type:lE}]}),null);class lA{}lA.ɵfac=function t(e){return new(e||lA)},lA.ɵmod=ao({type:lA}),lA.ɵinj=vn({providers:[{provide:XR,useClass:sA}]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lA,[{type:Ay,args:[{providers:[{provide:XR,useClass:sA}]}]}],null,null);const cA="alerts",dA=Zw(Kw(cA),(t=>t.latestAlert)),pA="experiments",mA=Zw(Kw(pA),(t=>t.data)),uA=Zw(mA,((t,e)=>{const{experimentId:n}=e;return t.experimentMap[n]||null}));
/*! *****************************************************************************
    Copyright (c) Microsoft Corporation.

    Permission to use, copy, modify, and/or distribute this software for any
    purpose with or without fee is hereby granted.

    THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH
    REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY
    AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,
    INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM
    LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR
    OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR
    PERFORMANCE OF THIS SOFTWARE.
    ***************************************************************************** */
function fA(t,e){var n={};for(var o in t)Object.prototype.hasOwnProperty.call(t,o)&&e.indexOf(o)<0&&(n[o]=t[o]);if(null!=t&&"function"==typeof Object.getOwnPropertySymbols){var i=0;for(o=Object.getOwnPropertySymbols(t);i<o.length;i++)e.indexOf(o[i])<0&&Object.prototype.propertyIsEnumerable.call(t,o[i])&&(n[o[i]]=t[o[i]])}return n}function gA(t,e,n,o){return new(n||(n=Promise))((function(i,a){function r(t){try{l(o.next(t))}catch(t){a(t)}}function s(t){try{l(o.throw(t))}catch(t){a(t)}}function l(t){t.done?i(t.value):(function e(t){return t instanceof n?t:new n((function(e){e(t)}))})(t.value).then(r,s)}l((o=o.apply(t,e||[])).next())}))}var hA,bA,yA;!(function(t){t.SCALARS="scalars",t.HISTOGRAMS="histograms",t.IMAGES="images"})(hA||(hA={})),(function(t){t.DEFAULT="default",t.ASCENDING="ascending",t.DESCENDING="descending",t.NEAREST="nearest"})(bA||(bA={})),(function(t){t[t.STEP=0]="STEP",t[t.RELATIVE=1]="RELATIVE",t[t.WALL_TIME=2]="WALL_TIME"})(yA||(yA={}));const _A="timeseries",CA=[hA.IMAGES];function MA(t){return CA.includes(t)}const vA=[hA.HISTOGRAMS,hA.IMAGES];function xA(t){return vA.includes(t)}function OA(t){return xA(t.plugin)}class PA{}function wA(t){return t.hasOwnProperty("error")}const kA="data/plugin/timeseries";function SA(t){const e=t.indexOf("/");return{run:t.substring(e+1),experimentId:t.substring(0,e)}}function DA(t,e){return`${e}/${t}`}function EA(t,e){const{runToSeries:n,run:o}=t,i=fA(t,["runToSeries","run"]),a=Object.assign({},i);return n&&(a.runToSeries=RA(n,e)),o&&(a.runId=DA(o,e)),a}function RA(t,e){const n={};for(const o in t)t.hasOwnProperty(o)&&(n[DA(o,e)]=t[o]);return n}class AA{constructor(t,e){this.http=t,this.store=e}fetchTagMetadata(t){const e=t.map((t=>this.http.get(`/experiment/${t}/${kA}/tags`).pipe(It((e=>(function n(t,e){const n={};for(const o of Object.keys(t)){const i=o;if(MA(i)){const o=t[i],{tagRunSampledInfo:a}=o,r=fA(o,["tagRunSampledInfo"]),s={};for(const t in 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n&&i.push(t.experimentAlias,`${t.experimentAlias}/${t.runName}`),i.some((t=>o.test(t)))}const NN=Zw((t=>{const e=TS(t);return e?mN(t,{experimentIds:e}):null}),gN,(t=>{var e;const n=null!==(e=TS(t))&&void 0!==e?e:[],o=zS(t),i=new Map;for(const e of n){const n=uA(t,{experimentId:e});if(!n)continue;const a=lN(t,{experimentId:e});for(const t of a)i.set(t.id,{runName:t.name,experimentName:n.name,experimentAlias:o[e]})}return i}),RS,((t,e,n,o)=>{if(!t)return null;const i=o===Zk.COMPARE_EXPERIMENT,a=new Map;for(const[o,r]of t.entries()){const t=n.get(o);a.set(o,TN(t,e,i)&&r)}return a})),zN=Zw(AN,CN,_N,JD,((t,e,n,o)=>{const i={};return e.forEach(((e,a)=>{let r=o?t.inactive.darkHex:t.inactive.lightHex;if(n.has(a))r=n.get(a);else if(e>=0){const n=t.colors[e%t.colors.length];r=o?n.darkHex:n.lightHex}i[a]=r})),i}));class IN{constructor(t,e,n){this.actions$=t,this.store=e,this.runsDataSource=n,this.loadRunsOnRunTableShown$=Mk((()=>this.actions$.pipe(Dk(UR),Zt((({experimentIds:t})=>this.getExperimentsWithLoadState(t,(t=>t===yE.FAILED||t===yE.NOT_LOADED)).pipe(ce((t=>!!t.length)),Zt((e=>this.fetchAllRunsList(t,e)))))))),{dispatch:!1}),this.experimentsWithStaleRunsOnRouteChange$=this.actions$.pipe(Dk(dS),Ve(this.store.select(NS)),Me((([,t],[,e])=>t===e)),Ve(this.store.select(TS)),ce((([,t])=>!!t)),It((([,t])=>t)),Zt((t=>this.getExperimentsWithLoadState(t,(t=>t===yE.FAILED||t===yE.NOT_LOADED)).pipe(It((e=>({experimentIds:t,experimentIdsToBeFetched:e}))))))),this.experimentsWithStaleRunsOnReload$=this.actions$.pipe(Dk(xE,vE),Ve(this.store.select(TS)),ce((([,t])=>!!t)),It((([,t])=>t)),Zt((t=>this.getExperimentsWithLoadState(t,(t=>t!==yE.LOADING)).pipe(It((e=>({experimentIds:t,experimentIdsToBeFetched:e}))))))),this.loadRunsOnNavigationOrReload$=Mk((()=>re(this.experimentsWithStaleRunsOnRouteChange$,this.experimentsWithStaleRunsOnReload$).pipe(Zt((({experimentIds:t,experimentIdsToBeFetched:e})=>this.fetchAllRunsList(t,e))))),{dispatch:!1})}getRunsListLoadState(t){return this.store.select(pN,{experimentId:t}).pipe(be(1))}getExperimentsWithLoadState(t,e){return $t(t.map((t=>this.getRunsListLoadState(t)))).pipe(It((n=>t.filter(((t,o)=>e(n[o].state))))))}fetchAllRunsList(t,e){return Et({experimentIds:t,experimentIdsToBeFetched:e}).pipe(Fe((()=>{this.store.dispatch(NR({experimentIds:t,requestedExperimentIds:e}))})),Zt((()=>{const n=new Set(e);return $t(t.map((t=>n.has(t)?this.fetchRunsForExperiment(t):this.maybeWaitForRunsAndGetRuns(t))))})),It((t=>{const e={},n=[];for(const o of t)n.push(...o.runs),o.fromRemote&&(e[o.experimentId]={runs:o.runs,metadata:o.metadata});return{newRunsAndMetadata:e,runsForAllExperiments:n}})),Fe((({newRunsAndMetadata:e,runsForAllExperiments:n})=>{this.store.dispatch(zR({experimentIds:t,newRunsAndMetadata:e,runsForAllExperiments:n}))})),pe((n=>(this.store.dispatch(IR({experimentIds:t,requestedExperimentIds:e})),Et(null)))),It((()=>null)))}maybeWaitForRunsAndGetRuns(t){return this.store.select(pN,{experimentId:t}).pipe(ce((t=>t.state!==yE.LOADING)),be(1),Zt((t=>t.state===yE.FAILED?Rt(new Error("Pending request failed")):Et(t))),Ve(this.store.select(lN,{experimentId:t})),It((([,e])=>({fromRemote:!1,experimentId:t,runs:e}))))}fetchRunsForExperiment(t){return $t([this.runsDataSource.fetchRuns(t),this.runsDataSource.fetchHparamsMetadata(t)]).pipe(It((([e,n])=>({fromRemote:!0,experimentId:t,runs:e,metadata:n}))))}}function HN(t,e,n){const o=Object.keys(t),i=Object.assign(Object.assign(Object.assign({},t),e),{privateRouteContextedState:{}});return{initialState:i,reducers:yk(i,bk(dS,((e,{before:i,after:a})=>{const r=Qk(a.routeKind,a.params),s=i?Qk(i.routeKind,i.params):null;if(s===r)return e;let l=Object.assign({},e.privateRouteContextedState);if(s){const t={};for(const n of o)t[n]=e[n];l=Object.assign(Object.assign({},l),{[s]:t})}let c=e.privateRouteContextedState&&e.privateRouteContextedState[r]?e.privateRouteContextedState[r]:null;s&&null===c&&(c=t);const d=Object.assign(Object.assign(Object.assign({},e),c),{privateRouteContextedState:l});return n?n(d,a):d})))}}var FN,LN;function BN(...t){return(e,n)=>{let o=e;for(const e of t)o=e(o,n);return o}}IN.ɵfac=function t(e){return new(e||IN)(vr(Sk),vr(Iw),vr(XR))},IN.ɵprov=Mn({token:IN,factory:IN.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(IN,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:XR}]}),null),(function(t){t.ASC="asc",t.DESC="desc",t.UNSET=""})(FN||(FN={})),(function(t){t.EXPERIMENT_ID="id",t.USER="user",t.BEFORE="before",t.AFTER="after",t.REGEX="regex"})(LN||(LN={}));const{initialState:VN,reducers:jN}=HN({runColorOverrideForGroupBy:new Map,defaultRunColorIdForGroupBy:new Map,groupKeyToColorId:new Map,initialGroupBy:{key:tN.RUN},userSetGroupByKey:null,colorGroupRegexString:"",regexFilter:""},{runIds:{},runIdToExpId:{},runMetadata:{},runsLoadState:{},selectionState:new Map},((t,e)=>Object.assign(Object.assign({},t),{initialGroupBy:{key:e.routeKind===Zk.COMPARE_EXPERIMENT?tN.EXPERIMENT:tN.RUN}}))),UN=BN(yk(VN,bk(rS,((t,{routeKind:e,partialState:n})=>{var o;if(e!==Zk.COMPARE_EXPERIMENT&&e!==Zk.EXPERIMENT)return t;const i=n.runs.groupBy;if(!i)return t;const a=i.key===tN.REGEX?i.regexString:t.colorGroupRegexString;return Object.assign(Object.assign({},t),{colorGroupRegexString:a,userSetGroupByKey:null!==(o=i.key)&&void 0!==o?o:null})})),bk(NR,((t,e)=>{const n=Object.assign({},t.runsLoadState);for(const t of e.requestedExperimentIds)n[t]=n[t]?Object.assign(Object.assign({},n[t]),{state:yE.LOADING}):{lastLoadedTimeInMs:null,state:yE.LOADING};return Object.assign(Object.assign({},t),{runsLoadState:n})})),bk(zR,((t,e)=>{var n;const o=Object.assign({},t.runIds),i=Object.assign({},t.runMetadata),a=Object.assign({},t.runIdToExpId),r=Object.assign({},t.runsLoadState),s=new Map(t.selectionState);for(const t of Object.keys(e.newRunsAndMetadata)){const{runs:n,metadata:s}=e.newRunsAndMetadata[t];o[t]=n.map((({id:t})=>t)),r[t]=Object.assign(Object.assign({},r[t]),{lastLoadedTimeInMs:Date.now(),state:yE.LOADED});for(const e of n){const n=s.runToHparamsAndMetrics[e.id];i[e.id]=Object.assign(Object.assign({},e),{hparams:n?n.hparams:null,metrics:n?n.metrics:null}),a[e.id]=t}}const l=eN(e.experimentIds),c=new Map(null!==(n=s.get(l))&&void 0!==n?n:[]),d=e.runsForAllExperiments.length<=500;for(const t of e.runsForAllExperiments)c.has(t.id)||c.set(t.id,d);return s.set(l,c),Object.assign(Object.assign({},t),{runIds:o,runIdToExpId:a,runMetadata:i,runsLoadState:r,selectionState:s})})),bk(IR,((t,e)=>{const n=Object.assign({},t.runsLoadState);for(const t of e.requestedExperimentIds)n[t]=n[t]?Object.assign(Object.assign({},n[t]),{state:yE.FAILED}):{lastLoadedTimeInMs:null,state:yE.FAILED};return Object.assign(Object.assign({},t),{runsLoadState:n})})),bk(HR,((t,{experimentIds:e,runId:n})=>{var o;const i=eN(e),a=new Map(t.selectionState),r=new Map(null!==(o=a.get(i))&&void 0!==o?o:[]);return r.set(n,!Boolean(r.get(n))),a.set(i,r),Object.assign(Object.assign({},t),{selectionState:a})})),bk(FR,((t,{experimentIds:e,runIds:n})=>{var o;const i=eN(e),a=new Map(t.selectionState),r=new Map(null!==(o=a.get(i))&&void 0!==o?o:[]),s=!n.every((t=>Boolean(r.get(t))));for(const t of n)r.set(t,s);return a.set(i,r),Object.assign(Object.assign({},t),{selectionState:a})})),bk(zR,((t,{runsForAllExperiments:e})=>{const n=new Map(t.groupKeyToColorId),o=new Map(t.defaultRunColorIdForGroupBy);let i=t.initialGroupBy;null!==t.userSetGroupByKey&&(i=oN(t.userSetGroupByKey,t.colorGroupRegexString));const a=nN(i,e,t.runIdToExpId);Object.entries(a.matches).forEach((([t,e])=>{var i;const a=null!==(i=n.get(t))&&void 0!==i?i:n.size;n.set(t,a);for(const t of e)o.set(t.id,a)}));for(const t of a.nonMatches)o.set(t.id,-1);return Object.assign(Object.assign({},t),{defaultRunColorIdForGroupBy:o,groupKeyToColorId:n})})),bk(GR,((t,{experimentIds:e,groupBy:n})=>{const o=new Map,i=new Map(t.defaultRunColorIdForGroupBy),a=nN(n,e.flatMap((e=>t.runIds[e])).map((e=>t.runMetadata[e])),t.runIdToExpId);Object.entries(a.matches).forEach((([t,e])=>{var n;const a=null!==(n=o.get(t))&&void 0!==n?n:o.size;o.set(t,a);for(const t of e)i.set(t.id,a)}));for(const t of a.nonMatches)i.set(t.id,-1);const r=n.key===tN.REGEX?n.regexString:t.colorGroupRegexString;return Object.assign(Object.assign({},t),{colorGroupRegexString:r,userSetGroupByKey:n.key,defaultRunColorIdForGroupBy:i,groupKeyToColorId:o,runColorOverrideForGroupBy:new Map})})),bk(jR,((t,{runId:e,newColor:n})=>{const o=new Map(t.runColorOverrideForGroupBy);return o.set(e,n),Object.assign(Object.assign({},t),{runColorOverrideForGroupBy:o})})),bk(VR,((t,e)=>Object.assign(Object.assign({},t),{regexFilter:e.regexString})))),jN),GN={key:null,direction:FN.UNSET},{initialState:WN,reducers:YN}=HN({paginationOption:{pageIndex:0,pageSize:10},sort:GN},{}),qN=BN(yk(WN,bk(LR,((t,{pageSize:e,pageIndex:n})=>Object.assign(Object.assign({},t),{paginationOption:{pageSize:e,pageIndex:n}}))),bk(VR,((t,e)=>Object.assign(Object.assign({},t),{paginationOption:Object.assign(Object.assign({},t.paginationOption),{pageIndex:0})}))),bk(BR,((t,e)=>Object.assign(Object.assign({},t),{sort:{key:e.key,direction:e.direction}})))),YN);function ZN(t,e){return xw({data:UN,ui:qN})(t,e)}function XN(){return[{actionCreator:IR,alertFromAction:()=>({localizedMessage:"Failed to fetch runs"})}]}class KN{}var JN;KN.ɵfac=function t(e){return new(e||KN)},KN.ɵmod=ao({type:KN}),KN.ɵinj=vn({imports:[[dk.forFeature(QT,ZN),Wk.forFeature([IN]),lA,AR.registerAlertActions(XN),aA]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(KN,[{type:Ay,args:[{imports:[dk.forFeature(QT,ZN),Wk.forFeature([IN]),lA,AR.registerAlertActions(XN),aA]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(KN,{imports:[ck,Gk,lA,AR,aA]}),(function(t){t.RUNS_CHANGED="experimental.RunsChanged",t.GET_RUNS="experimental.GetRuns",t.GET_URL_DATA="experimental.GetURLPluginData",t.DATA_RELOADED="experimental.DataReloaded"})(JN||(JN={}));class QN{constructor(t){this.port=t,this.id=0,this.responseWaits=new Map,this.listeners=new Map,this.port.addEventListener("message",(t=>this.onMessage(t)))}listen(t,e){this.listeners.set(t,e)}unlisten(t){this.listeners.delete(t)}onMessage(t){return gA(this,void 0,void 0,(function*(){const e=JSON.parse(t.data),n=e.type,o=e.id,i=e.payload,a=e.error;if(e.isReply){if(!this.responseWaits.has(o))return;const{resolve:t,reject:e}=this.responseWaits.get(o);return this.responseWaits.delete(o),void(a?e(new Error(a)):t(i))}let r=null,s=null;if(this.listeners.has(n)){const t=this.listeners.get(n);try{r=yield t(i)}catch(t){s=t}}this.postMessage({type:n,id:o,payload:r,error:s,isReply:!0})}))}postMessage(t){this.port.postMessage(JSON.stringify(t))}sendMessage(t,e){const n=this.id++;return this.postMessage({type:t,id:n,payload:e,error:null,isReply:!1}),new Promise(((t,e)=>{this.responseWaits.set(n,{resolve:t,reject:e})}))}}const $N=new WeakMap,tz=new Set,ez=new Map,nz=new Map;function oz(t,e){return n=>{const o=nz.get(e),i=$N.get(o)||null;return t(i,n)}}window.addEventListener("message",(t=>{if("experimental.bootstrap"!==t.data)return;const e=t.ports[0];if(!e)return;const n=t.source?t.source.frameElement:null;n&&(function o(t,e){const n=new QN(t);tz.add(n),nz.set(n,e),t.start();for(const[t,e]of ez){const o=oz(e,n);n.listen(t,o)}})(e,n)}));class iz{broadcast(t,e){return(function n(t,e){for(const t of tz)nz.get(t).isConnected||(tz.delete(t),nz.delete(t));const n=[...tz].map((n=>n.sendMessage(t,e)));return Promise.all(n)})(t,e)}listen(t,e){!(function n(t,e){ez.set(t,e);for(const n of tz){const o=oz(e,n);n.listen(t,o)}})(t,e)}unlisten(t){!(function e(t){ez.delete(t);for(const e of tz)e.unlisten(t)})(t)}}iz.ɵfac=function t(e){return new(e||iz)},iz.ɵprov=Mn({token:iz,factory:iz.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(iz,[{type:im}],null,null);class az{constructor(t,e){this.ipc=t,this.store=e}init(){const t=document.createElement("tf-storage");this.ipc.listen(JN.GET_URL_DATA,(e=>{if(!e)return;const n=`p.${e.pluginName}.`,o={},i=t.tf_storage.getUrlHashDict();for(let t in i)t.startsWith(n)&&(o[t.substring(n.length)]=i[t]);return o})),this.store.select(CR).pipe(ce((t=>null!==t)),Me()).subscribe((()=>{this.ipc.broadcast(JN.DATA_RELOADED,void 0)}))}}az.ɵfac=function t(e){return new(e||az)(vr(iz),vr(Iw))},az.ɵprov=Mn({token:az,factory:az.ɵfac,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(az,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:iz},{type:Iw}]}),null);class rz{constructor(t,e){this.ipc=t,this.store=e}init(){const t=this.store.select(TS).pipe(Zt((t=>t?Wt(t.map((t=>this.store.select(lN,{experimentId:t})))).pipe(It((t=>t.flat())),Me(((t,e)=>t.length===e.length&&t.every(((t,n)=>e[n].id===t.id)))),It((t=>t.map((({name:t})=>t))))):Et([]))));t.subscribe((t=>{this.ipc.broadcast(JN.RUNS_CHANGED,t)})),this.ipc.listen(JN.GET_RUNS,(()=>t.pipe(be(1)).toPromise()))}}rz.ɵfac=function t(e){return new(e||rz)(vr(iz),vr(Iw))},rz.ɵprov=Mn({token:rz,factory:rz.ɵfac,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(rz,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:iz},{type:Iw}]}),null);class sz{constructor(t,e){e.init(),t.init()}registerPluginIframe(t,e){!(function n(t,e){$N.set(t,{pluginName:e})})(t,e)}}sz.ɵfac=function t(e){return new(e||sz)(vr(rz),vr(az))},sz.ɵmod=ao({type:sz}),sz.ɵinj=vn({providers:[iz,az,rz],imports:[[BS,ER,KN]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(sz,[{type:Ay,args:[{providers:[iz,az,rz],imports:[BS,ER,KN]}]}],(function(){return[{type:rz},{type:az}]}),null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(sz,{imports:[BS,ER,KN]});const lz=JP("[Alert] Alert Reported",{_as:"props",_p:void 0});class cz{constructor(t,e,n){this.actions$=t,this.store=e,this.alertActionModule=n,this.reportRegisteredActionAlerts$=Mk((()=>this.actions$.pipe(Fe((t=>{const e=this.alertActionModule.getAlertFromAction(t);e&&this.store.dispatch(lz(e))})))),{dispatch:!1})}}cz.ɵfac=function t(e){return new(e||cz)(vr(Sk),vr(Iw),vr(AR))},cz.ɵprov=Mn({token:cz,factory:cz.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(cz,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:AR}]}),null);const dz=yk({latestAlert:null},bk(lz,((t,{localizedMessage:e,followupAction:n})=>Object.assign(Object.assign({},t),{latestAlert:{localizedMessage:e,followupAction:n,created:Date.now()}}))));function pz(t,e){return dz(t,e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const mz=13,uz=27,fz=32,gz=38,hz=40;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function bz(t,...e){return e.length?e.some((e=>t[e])):t.altKey||t.shiftKey||t.ctrlKey||t.metaKey}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function yz(t){return null!=t&&"false"!=`${t}`}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function _z(t,e=0){return Cz(t)?Number(t):e}function Cz(t){return!isNaN(parseFloat(t))&&!isNaN(Number(t))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Mz(t){return Array.isArray(t)?t:[t]}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function vz(t){return null==t?"":"string"==typeof t?t:`${t}px`}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function xz(t){return t instanceof hg?t.nativeElement:t}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let Oz,Pz;try{Oz="undefined"!=typeof Intl&&Intl.v8BreakIterator}catch(jCt){Oz=!1}class wz{constructor(t){this._platformId=t,this.isBrowser=this._platformId?(function e(t){return"browser"===t})(this._platformId):"object"==typeof document&&!!document,this.EDGE=this.isBrowser&&/(edge)/i.test(navigator.userAgent),this.TRIDENT=this.isBrowser&&/(msie|trident)/i.test(navigator.userAgent),this.BLINK=this.isBrowser&&!(!window.chrome&&!Oz)&&"undefined"!=typeof CSS&&!this.EDGE&&!this.TRIDENT,this.WEBKIT=this.isBrowser&&/AppleWebKit/i.test(navigator.userAgent)&&!this.BLINK&&!this.EDGE&&!this.TRIDENT,this.IOS=this.isBrowser&&/iPad|iPhone|iPod/.test(navigator.userAgent)&&!("MSStream"in window),this.FIREFOX=this.isBrowser&&/(firefox|minefield)/i.test(navigator.userAgent),this.ANDROID=this.isBrowser&&/android/i.test(navigator.userAgent)&&!this.TRIDENT,this.SAFARI=this.isBrowser&&/safari/i.test(navigator.userAgent)&&this.WEBKIT}}wz.ɵfac=function t(e){return new(e||wz)(vr(jy))},wz.ɵprov=Mn({factory:function t(){return new wz(vr(jy))},token:wz,providedIn:"root"}),wz.ctorParameters=()=>[{type:Object,decorators:[{type:kr,args:[jy]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(wz,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:Object,decorators:[{type:kr,args:[jy]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class kz{}kz.ɵfac=function t(e){return new(e||kz)},kz.ɵmod=ao({type:kz}),kz.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(kz,[{type:Ay,args:[{}]}],null,null);const Sz=["color","button","checkbox","date","datetime-local","email","file","hidden","image","month","number","password","radio","range","reset","search","submit","tel","text","time","url","week"];function Dz(){if(Pz)return Pz;if("object"!=typeof document||!document)return Pz=new Set(Sz),Pz;let t=document.createElement("input");return Pz=new Set(Sz.filter((e=>(t.setAttribute("type",e),t.type===e)))),Pz}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let Ez,Rz,Az,Tz;function Nz(t){return(function e(){if(null==Ez&&"undefined"!=typeof window)try{window.addEventListener("test",null,Object.defineProperty({},"passive",{get:()=>Ez=!0}))}finally{Ez=Ez||!1}return Ez})()?t:!!t.capture}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function zz(){if(null==Az){if("object"!=typeof document||!document||"function"!=typeof Element||!Element)return Az=!1,Az;if("scrollBehavior"in document.documentElement.style)Az=!0;else{const t=Element.prototype.scrollTo;Az=!!t&&!/\{\s*\[native code\]\s*\}/.test(t.toString())}}return Az}function Iz(){if("object"!=typeof document||!document)return 0;if(null==Rz){const t=document.createElement("div"),e=t.style;t.dir="rtl",e.width="1px",e.overflow="auto",e.visibility="hidden",e.pointerEvents="none",e.position="absolute";const n=document.createElement("div"),o=n.style;o.width="2px",o.height="1px",t.appendChild(n),document.body.appendChild(t),Rz=0,0===t.scrollLeft&&(t.scrollLeft=1,Rz=0===t.scrollLeft?1:2),t.parentNode.removeChild(t)}return Rz}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Hz(t){if((function e(){if(null==Tz){const t="undefined"!=typeof document?document.head:null;Tz=!(!t||!t.createShadowRoot&&!t.attachShadow)}return Tz})()){const e=t.getRootNode?t.getRootNode():null;if("undefined"!=typeof ShadowRoot&&ShadowRoot&&e instanceof ShadowRoot)return e}return null}function Fz(){let t="undefined"!=typeof document&&document?document.activeElement:null;for(;t&&t.shadowRoot;){const e=t.shadowRoot.activeElement;if(e===t)break;t=e}return t}function Lz(t){return t.composedPath?t.composedPath()[0]:t.target}class Bz{create(t){return"undefined"==typeof MutationObserver?null:new MutationObserver(t)}}Bz.ɵfac=function t(e){return new(e||Bz)},Bz.ɵprov=Mn({factory:function t(){return new Bz},token:Bz,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Bz,[{type:im,args:[{providedIn:"root"}]}],null,null);class Vz{constructor(t){this._mutationObserverFactory=t,this._observedElements=new Map}ngOnDestroy(){this._observedElements.forEach(((t,e)=>this._cleanupObserver(e)))}observe(t){const e=xz(t);return new D((t=>{const n=this._observeElement(e).subscribe(t);return()=>{n.unsubscribe(),this._unobserveElement(e)}}))}_observeElement(t){if(this._observedElements.has(t))this._observedElements.get(t).count++;else{const e=new I,n=this._mutationObserverFactory.create((t=>e.next(t)));n&&n.observe(t,{characterData:!0,childList:!0,subtree:!0}),this._observedElements.set(t,{observer:n,stream:e,count:1})}return this._observedElements.get(t).stream}_unobserveElement(t){this._observedElements.has(t)&&(this._observedElements.get(t).count--,this._observedElements.get(t).count||this._cleanupObserver(t))}_cleanupObserver(t){if(this._observedElements.has(t)){const{observer:e,stream:n}=this._observedElements.get(t);e&&e.disconnect(),n.complete(),this._observedElements.delete(t)}}}Vz.ɵfac=function t(e){return new(e||Vz)(vr(Bz))},Vz.ɵprov=Mn({factory:function t(){return new Vz(vr(Bz))},token:Vz,providedIn:"root"}),Vz.ctorParameters=()=>[{type:Bz}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Vz,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:Bz}]}),null);class jz{constructor(t,e,n){this._contentObserver=t,this._elementRef=e,this._ngZone=n,this.event=new Lh,this._disabled=!1,this._currentSubscription=null}get disabled(){return this._disabled}set disabled(t){this._disabled=yz(t),this._disabled?this._unsubscribe():this._subscribe()}get debounce(){return this._debounce}set debounce(t){this._debounce=_z(t),this._subscribe()}ngAfterContentInit(){this._currentSubscription||this.disabled||this._subscribe()}ngOnDestroy(){this._unsubscribe()}_subscribe(){this._unsubscribe();const t=this._contentObserver.observe(this._elementRef);this._ngZone.runOutsideAngular((()=>{this._currentSubscription=(this.debounce?t.pipe(ge(this.debounce)):t).subscribe(this.event)}))}_unsubscribe(){var t;null===(t=this._currentSubscription)||void 0===t||t.unsubscribe()}}jz.ɵfac=function t(e){return new(e||jz)(Sm(Vz),Sm(hg),Sm(a_))},jz.ɵdir=lo({type:jz,selectors:[["","cdkObserveContent",""]],inputs:{disabled:["cdkObserveContentDisabled","disabled"],debounce:"debounce"},outputs:{event:"cdkObserveContent"},exportAs:["cdkObserveContent"]}),jz.ctorParameters=()=>[{type:Vz},{type:hg},{type:a_}],jz.propDecorators={event:[{type:Oy,args:["cdkObserveContent"]}],disabled:[{type:xy,args:["cdkObserveContentDisabled"]}],debounce:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(jz,[{type:Cy,args:[{selector:"[cdkObserveContent]",exportAs:"cdkObserveContent"}]}],(function(){return[{type:Vz},{type:hg},{type:a_}]}),{event:[{type:Oy,args:["cdkObserveContent"]}],disabled:[{type:xy,args:["cdkObserveContentDisabled"]}],debounce:[{type:xy}]});class Uz{}function Gz(t,e){return(t.getAttribute(e)||"").match(/\S+/g)||[]}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */Uz.ɵfac=function t(e){return new(e||Uz)},Uz.ɵmod=ao({type:Uz}),Uz.ɵinj=vn({providers:[Bz]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Uz,[{type:Ay,args:[{exports:[jz],declarations:[jz],providers:[Bz]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Uz,{declarations:[jz],exports:[jz]});const Wz="cdk-describedby-message-container",Yz="cdk-describedby-host";let qz=0;const Zz=new Map;let Xz=null;class Kz{constructor(t){this._document=t}describe(t,e,n){if(!this._canBeDescribed(t,e))return;const o=Jz(e,n);"string"!=typeof e?(Qz(e),Zz.set(o,{messageElement:e,referenceCount:0})):Zz.has(o)||this._createMessageElement(e,n),this._isElementDescribedByMessage(t,o)||this._addMessageReference(t,o)}removeDescription(t,e,n){if(!e||!this._isElementNode(t))return;const o=Jz(e,n);if(this._isElementDescribedByMessage(t,o)&&this._removeMessageReference(t,o),"string"==typeof e){const t=Zz.get(o);t&&0===t.referenceCount&&this._deleteMessageElement(o)}Xz&&0===Xz.childNodes.length&&this._deleteMessagesContainer()}ngOnDestroy(){const t=this._document.querySelectorAll("[cdk-describedby-host]");for(let e=0;e<t.length;e++)this._removeCdkDescribedByReferenceIds(t[e]),t[e].removeAttribute(Yz);Xz&&this._deleteMessagesContainer(),Zz.clear()}_createMessageElement(t,e){const n=this._document.createElement("div");Qz(n),n.textContent=t,e&&n.setAttribute("role",e),this._createMessagesContainer(),Xz.appendChild(n),Zz.set(Jz(t,e),{messageElement:n,referenceCount:0})}_deleteMessageElement(t){const e=Zz.get(t),n=e&&e.messageElement;Xz&&n&&Xz.removeChild(n),Zz.delete(t)}_createMessagesContainer(){if(!Xz){const t=this._document.getElementById(Wz);t&&t.parentNode&&t.parentNode.removeChild(t),Xz=this._document.createElement("div"),Xz.id=Wz,Xz.style.visibility="hidden",Xz.classList.add("cdk-visually-hidden"),this._document.body.appendChild(Xz)}}_deleteMessagesContainer(){Xz&&Xz.parentNode&&(Xz.parentNode.removeChild(Xz),Xz=null)}_removeCdkDescribedByReferenceIds(t){const e=Gz(t,"aria-describedby").filter((t=>0!=t.indexOf("cdk-describedby-message")));t.setAttribute("aria-describedby",e.join(" "))}_addMessageReference(t,e){const n=Zz.get(e);!(function o(t,e,n){const o=Gz(t,e);o.some((t=>t.trim()==n.trim()))||(o.push(n.trim()),t.setAttribute(e,o.join(" ")))})(t,"aria-describedby",n.messageElement.id),t.setAttribute(Yz,""),n.referenceCount++}_removeMessageReference(t,e){const n=Zz.get(e);n.referenceCount--,(function o(t,e,n){const o=Gz(t,e).filter((t=>t!=n.trim()));o.length?t.setAttribute(e,o.join(" ")):t.removeAttribute(e)})(t,"aria-describedby",n.messageElement.id),t.removeAttribute(Yz)}_isElementDescribedByMessage(t,e){const n=Gz(t,"aria-describedby"),o=Zz.get(e),i=o&&o.messageElement.id;return!!i&&-1!=n.indexOf(i)}_canBeDescribed(t,e){if(!this._isElementNode(t))return!1;if(e&&"object"==typeof e)return!0;const n=null==e?"":`${e}`.trim(),o=t.getAttribute("aria-label");return!(!n||o&&o.trim()===n)}_isElementNode(t){return t.nodeType===this._document.ELEMENT_NODE}}function Jz(t,e){return"string"==typeof t?`${e||""}/${t}`:t}function Qz(t){t.id||(t.id="cdk-describedby-message-"+qz++)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */Kz.ɵfac=function t(e){return new(e||Kz)(vr(Z_))},Kz.ɵprov=Mn({factory:function t(){return new Kz(vr(Z_))},token:Kz,providedIn:"root"}),Kz.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Kz,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);class $z{constructor(t){this._items=t,this._activeItemIndex=-1,this._activeItem=null,this._wrap=!1,this._letterKeyStream=new I,this._typeaheadSubscription=m.EMPTY,this._vertical=!0,this._allowedModifierKeys=[],this._homeAndEnd=!1,this._skipPredicateFn=t=>t.disabled,this._pressedLetters=[],this.tabOut=new I,this.change=new I,t instanceof Vh&&t.changes.subscribe((t=>{if(this._activeItem){const e=t.toArray().indexOf(this._activeItem);e>-1&&e!==this._activeItemIndex&&(this._activeItemIndex=e)}}))}skipPredicate(t){return this._skipPredicateFn=t,this}withWrap(t=!0){return this._wrap=t,this}withVerticalOrientation(t=!0){return this._vertical=t,this}withHorizontalOrientation(t){return this._horizontal=t,this}withAllowedModifierKeys(t){return this._allowedModifierKeys=t,this}withTypeAhead(t=200){if(("undefined"==typeof ngDevMode||ngDevMode)&&this._items.length&&this._items.some((t=>"function"!=typeof t.getLabel)))throw Error("ListKeyManager items in typeahead mode must implement the `getLabel` method.");return this._typeaheadSubscription.unsubscribe(),this._typeaheadSubscription=this._letterKeyStream.pipe(Fe((t=>this._pressedLetters.push(t))),ge(t),ce((()=>this._pressedLetters.length>0)),It((()=>this._pressedLetters.join("")))).subscribe((t=>{const e=this._getItemsArray();for(let n=1;n<e.length+1;n++){const o=(this._activeItemIndex+n)%e.length,i=e[o];if(!this._skipPredicateFn(i)&&0===i.getLabel().toUpperCase().trim().indexOf(t)){this.setActiveItem(o);break}}this._pressedLetters=[]})),this}withHomeAndEnd(t=!0){return this._homeAndEnd=t,this}setActiveItem(t){const e=this._activeItem;this.updateActiveItem(t),this._activeItem!==e&&this.change.next(this._activeItemIndex)}onKeydown(t){const e=t.keyCode,n=["altKey","ctrlKey","metaKey","shiftKey"].every((e=>!t[e]||this._allowedModifierKeys.indexOf(e)>-1));switch(e){case 9:return void this.tabOut.next();case hz:if(this._vertical&&n){this.setNextItemActive();break}return;case gz:if(this._vertical&&n){this.setPreviousItemActive();break}return;case 39:if(this._horizontal&&n){"rtl"===this._horizontal?this.setPreviousItemActive():this.setNextItemActive();break}return;case 37:if(this._horizontal&&n){"rtl"===this._horizontal?this.setNextItemActive():this.setPreviousItemActive();break}return;case 36:if(this._homeAndEnd&&n){this.setFirstItemActive();break}return;case 35:if(this._homeAndEnd&&n){this.setLastItemActive();break}return;default:return void((n||bz(t,"shiftKey"))&&(t.key&&1===t.key.length?this._letterKeyStream.next(t.key.toLocaleUpperCase()):(e>=65&&e<=90||e>=48&&e<=57)&&this._letterKeyStream.next(String.fromCharCode(e))))}this._pressedLetters=[],t.preventDefault()}get activeItemIndex(){return this._activeItemIndex}get activeItem(){return this._activeItem}isTyping(){return this._pressedLetters.length>0}setFirstItemActive(){this._setActiveItemByIndex(0,1)}setLastItemActive(){this._setActiveItemByIndex(this._items.length-1,-1)}setNextItemActive(){this._activeItemIndex<0?this.setFirstItemActive():this._setActiveItemByDelta(1)}setPreviousItemActive(){this._activeItemIndex<0&&this._wrap?this.setLastItemActive():this._setActiveItemByDelta(-1)}updateActiveItem(t){const e=this._getItemsArray(),n="number"==typeof t?t:e.indexOf(t),o=e[n];this._activeItem=null==o?null:o,this._activeItemIndex=n}_setActiveItemByDelta(t){this._wrap?this._setActiveInWrapMode(t):this._setActiveInDefaultMode(t)}_setActiveInWrapMode(t){const e=this._getItemsArray();for(let n=1;n<=e.length;n++){const o=(this._activeItemIndex+t*n+e.length)%e.length;if(!this._skipPredicateFn(e[o]))return void this.setActiveItem(o)}}_setActiveInDefaultMode(t){this._setActiveItemByIndex(this._activeItemIndex+t,t)}_setActiveItemByIndex(t,e){const n=this._getItemsArray();if(n[t]){for(;this._skipPredicateFn(n[t]);)if(!n[t+=e])return;this.setActiveItem(t)}}_getItemsArray(){return this._items instanceof Vh?this._items.toArray():this._items}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class tI extends $z{setActiveItem(t){this.activeItem&&this.activeItem.setInactiveStyles(),super.setActiveItem(t),this.activeItem&&this.activeItem.setActiveStyles()}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class eI extends $z{constructor(){super(...arguments),this._origin="program"}setFocusOrigin(t){return this._origin=t,this}setActiveItem(t){super.setActiveItem(t),this.activeItem&&this.activeItem.focus(this._origin)}}class nI{constructor(t){this._platform=t}isDisabled(t){return t.hasAttribute("disabled")}isVisible(t){return(function e(t){return!!(t.offsetWidth||t.offsetHeight||"function"==typeof t.getClientRects&&t.getClientRects().length)})(t)&&"visible"===getComputedStyle(t).visibility}isTabbable(t){if(!this._platform.isBrowser)return!1;const e=(function o(t){try{return t.frameElement}catch(t){return null}})((function n(t){return t.ownerDocument&&t.ownerDocument.defaultView||window}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class lI extends aI{constructor(t,e,n,o,i,a,r){super(t,e,n,o,r.defer),this._focusTrapManager=i,this._inertStrategy=a,this._focusTrapManager.register(this)}get enabled(){return this._enabled}set enabled(t){this._enabled=t,this._enabled?this._focusTrapManager.register(this):this._focusTrapManager.deregister(this)}destroy(){this._focusTrapManager.deregister(this),super.destroy()}_enable(){this._inertStrategy.preventFocus(this),this.toggleAnchors(!0)}_disable(){this._inertStrategy.allowFocus(this),this.toggleAnchors(!1)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const cI=new Ga("FOCUS_TRAP_INERT_STRATEGY"),dI="undefined"!=typeof Element&&!!Element.prototype.closest;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function pI(t,e){return t.matches?t.matches(e):t.msMatchesSelector(e)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class mI{constructor(){this._listener=null}preventFocus(t){this._listener&&t._document.removeEventListener("focus",this._listener,!0),this._listener=e=>this._trapFocus(t,e),t._ngZone.runOutsideAngular((()=>{t._document.addEventListener("focus",this._listener,!0)}))}allowFocus(t){this._listener&&(t._document.removeEventListener("focus",this._listener,!0),this._listener=null)}_trapFocus(t,e){const n=e.target,o=t._element;o.contains(n)||null!==(function i(t,e){if(!(t instanceof Node))return null;let n=t;for(;null!=n&&!(n instanceof Element);)n=n.parentNode;return n&&(dI?n.closest(e):(function o(t,e){let n=t;for(;null!=n&&!(n instanceof Element&&pI(n,e));)n=n.parentNode;return n||null})(n,e))})(n,"div.cdk-overlay-pane")||setTimeout((()=>{t.enabled&&!o.contains(t._document.activeElement)&&t.focusFirstTabbableElement()}))}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class uI{constructor(){this._focusTrapStack=[]}register(t){this._focusTrapStack=this._focusTrapStack.filter((e=>e!==t));let e=this._focusTrapStack;e.length&&e[e.length-1]._disable(),e.push(t),t._enable()}deregister(t){t._disable();const e=this._focusTrapStack,n=e.indexOf(t);-1!==n&&(e.splice(n,1),e.length&&e[e.length-1]._enable())}}uI.ɵfac=function t(e){return new(e||uI)},uI.ɵprov=Mn({factory:function t(){return new uI},token:uI,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(uI,[{type:im,args:[{providedIn:"root"}]}],(function(){return[]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class fI{constructor(t,e,n,o,i){this._checker=t,this._ngZone=e,this._focusTrapManager=n,this._document=o,this._inertStrategy=i||new mI}create(t,e={defer:!1}){let n;return n="boolean"==typeof e?{defer:e}:e,new lI(t,this._checker,this._ngZone,this._document,this._focusTrapManager,this._inertStrategy,n)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function gI(t){return 0===t.offsetX&&0===t.offsetY}function hI(t){const e=t.touches&&t.touches[0]||t.changedTouches&&t.changedTouches[0];return!(!e||-1!==e.identifier||null!=e.radiusX&&1!==e.radiusX||null!=e.radiusY&&1!==e.radiusY)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */fI.ɵfac=function t(e){return new(e||fI)(vr(nI),vr(a_),vr(uI),vr(Z_),vr(cI,8))},fI.ɵprov=Mn({factory:function t(){return new fI(vr(nI),vr(a_),vr(uI),vr(Z_),vr(cI,8))},token:fI,providedIn:"root"}),fI.ctorParameters=()=>[{type:nI},{type:a_},{type:uI},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[cI]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(fI,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:nI},{type:a_},{type:uI},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[cI]}]}]}),null);const bI=new Ga("cdk-input-modality-detector-options"),yI={ignoreKeys:[18,17,224,91,16]},_I=Nz({passive:!0,capture:!0});class CI{constructor(t,e,n,o){this._platform=t,this._mostRecentTarget=null,this._modality=new F(null),this._lastTouchMs=0,this._onKeydown=t=>{var e,n;(null===(n=null===(e=this._options)||void 0===e?void 0:e.ignoreKeys)||void 0===n?void 0:n.some((e=>e===t.keyCode)))||(this._modality.next("keyboard"),this._mostRecentTarget=Lz(t))},this._onMousedown=t=>{Date.now()-this._lastTouchMs<650||(this._modality.next(gI(t)?"keyboard":"mouse"),this._mostRecentTarget=Lz(t))},this._onTouchstart=t=>{hI(t)?this._modality.next("keyboard"):(this._lastTouchMs=Date.now(),this._modality.next("touch"),this._mostRecentTarget=Lz(t))},this._options=Object.assign(Object.assign({},yI),o),this.modalityDetected=this._modality.pipe(Te(1)),this.modalityChanged=this.modalityDetected.pipe(Me()),t.isBrowser&&e.runOutsideAngular((()=>{n.addEventListener("keydown",this._onKeydown,_I),n.addEventListener("mousedown",this._onMousedown,_I),n.addEventListener("touchstart",this._onTouchstart,_I)}))}get mostRecentModality(){return this._modality.value}ngOnDestroy(){this._platform.isBrowser&&(document.removeEventListener("keydown",this._onKeydown,_I),document.removeEventListener("mousedown",this._onMousedown,_I),document.removeEventListener("touchstart",this._onTouchstart,_I))}}CI.ɵfac=function t(e){return new(e||CI)(vr(wz),vr(a_),vr(Z_),vr(bI,8))},CI.ɵprov=Mn({factory:function t(){return new CI(vr(wz),vr(a_),vr(Z_),vr(bI,8))},token:CI,providedIn:"root"}),CI.ctorParameters=()=>[{type:wz},{type:a_},{type:Document,decorators:[{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[bI]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(CI,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:wz},{type:a_},{type:Document,decorators:[{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[bI]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const MI=new Ga("liveAnnouncerElement",{providedIn:"root",factory:function vI(){return null}}),xI=new Ga("LIVE_ANNOUNCER_DEFAULT_OPTIONS");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class OI{constructor(t,e,n,o){this._ngZone=e,this._defaultOptions=o,this._document=n,this._liveElement=t||this._createLiveElement()}announce(t,...e){const n=this._defaultOptions;let o,i;return 1===e.length&&"number"==typeof e[0]?i=e[0]:[o,i]=e,this.clear(),clearTimeout(this._previousTimeout),o||(o=n&&n.politeness?n.politeness:"polite"),null==i&&n&&(i=n.duration),this._liveElement.setAttribute("aria-live",o),this._ngZone.runOutsideAngular((()=>new Promise((e=>{clearTimeout(this._previousTimeout),this._previousTimeout=setTimeout((()=>{this._liveElement.textContent=t,e(),"number"==typeof i&&(this._previousTimeout=setTimeout((()=>this.clear()),i))}),100)}))))}clear(){this._liveElement&&(this._liveElement.textContent="")}ngOnDestroy(){clearTimeout(this._previousTimeout),this._liveElement&&this._liveElement.parentNode&&(this._liveElement.parentNode.removeChild(this._liveElement),this._liveElement=null)}_createLiveElement(){const t="cdk-live-announcer-element",e=this._document.getElementsByClassName(t),n=this._document.createElement("div");for(let t=0;t<e.length;t++)e[t].parentNode.removeChild(e[t]);return n.classList.add(t),n.classList.add("cdk-visually-hidden"),n.setAttribute("aria-atomic","true"),n.setAttribute("aria-live","polite"),this._document.body.appendChild(n),n}}OI.ɵfac=function t(e){return new(e||OI)(vr(MI,8),vr(a_),vr(Z_),vr(xI,8))},OI.ɵprov=Mn({factory:function t(){return new OI(vr(MI,8),vr(a_),vr(Z_),vr(xI,8))},token:OI,providedIn:"root"}),OI.ctorParameters=()=>[{type:void 0,decorators:[{type:Sr},{type:kr,args:[MI]}]},{type:a_},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[xI]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(OI,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:Sr},{type:kr,args:[MI]}]},{type:a_},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[xI]}]}]}),null);class PI{constructor(t,e,n,o){this._elementRef=t,this._liveAnnouncer=e,this._contentObserver=n,this._ngZone=o,this._politeness="polite"}get politeness(){return this._politeness}set politeness(t){this._politeness="off"===t||"assertive"===t?t:"polite","off"===this._politeness?this._subscription&&(this._subscription.unsubscribe(),this._subscription=null):this._subscription||(this._subscription=this._ngZone.runOutsideAngular((()=>this._contentObserver.observe(this._elementRef).subscribe((()=>{const t=this._elementRef.nativeElement.textContent;t!==this._previousAnnouncedText&&(this._liveAnnouncer.announce(t,this._politeness),this._previousAnnouncedText=t)})))))}ngOnDestroy(){this._subscription&&this._subscription.unsubscribe()}}PI.ɵfac=function t(e){return new(e||PI)(Sm(hg),Sm(OI),Sm(Vz),Sm(a_))},PI.ɵdir=lo({type:PI,selectors:[["","cdkAriaLive",""]],inputs:{politeness:["cdkAriaLive","politeness"]},exportAs:["cdkAriaLive"]}),PI.ctorParameters=()=>[{type:hg},{type:OI},{type:Vz},{type:a_}],PI.propDecorators={politeness:[{type:xy,args:["cdkAriaLive"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(PI,[{type:Cy,args:[{selector:"[cdkAriaLive]",exportAs:"cdkAriaLive"}]}],(function(){return[{type:hg},{type:OI},{type:Vz},{type:a_}]}),{politeness:[{type:xy,args:["cdkAriaLive"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const wI=new Ga("cdk-focus-monitor-default-options"),kI=Nz({passive:!0,capture:!0});class SI{constructor(t,e,n,o,i){this._ngZone=t,this._platform=e,this._inputModalityDetector=n,this._origin=null,this._windowFocused=!1,this._originFromTouchInteraction=!1,this._elementInfo=new Map,this._monitoredElementCount=0,this._rootNodeFocusListenerCount=new Map,this._windowFocusListener=()=>{this._windowFocused=!0,this._windowFocusTimeoutId=setTimeout((()=>this._windowFocused=!1))},this._stopInputModalityDetector=new I,this._rootNodeFocusAndBlurListener=t=>{const e=Lz(t),n="focus"===t.type?this._onFocus:this._onBlur;for(let o=e;o;o=o.parentElement)n.call(this,t,o)},this._document=o,this._detectionMode=(null==i?void 0:i.detectionMode)||0}monitor(t,e=!1){const n=xz(t);if(!this._platform.isBrowser||1!==n.nodeType)return Et(null);const o=Hz(n)||this._getDocument(),i=this._elementInfo.get(n);if(i)return e&&(i.checkChildren=!0),i.subject;const a={checkChildren:e,subject:new I,rootNode:o};return this._elementInfo.set(n,a),this._registerGlobalListeners(a),a.subject}stopMonitoring(t){const e=xz(t),n=this._elementInfo.get(e);n&&(n.subject.complete(),this._setClasses(e),this._elementInfo.delete(e),this._removeGlobalListeners(n))}focusVia(t,e,n){const o=xz(t);o===this._getDocument().activeElement?this._getClosestElementsInfo(o).forEach((([t,n])=>this._originChanged(t,e,n))):(this._setOrigin(e),"function"==typeof o.focus&&o.focus(n))}ngOnDestroy(){this._elementInfo.forEach(((t,e)=>this.stopMonitoring(e)))}_getDocument(){return this._document||document}_getWindow(){return this._getDocument().defaultView||window}_toggleClass(t,e,n){n?t.classList.add(e):t.classList.remove(e)}_getFocusOrigin(t){return this._origin?this._originFromTouchInteraction?this._shouldBeAttributedToTouch(t)?"touch":"program":this._origin:this._windowFocused&&this._lastFocusOrigin?this._lastFocusOrigin:"program"}_shouldBeAttributedToTouch(t){return 1===this._detectionMode||!!(null==t?void 0:t.contains(this._inputModalityDetector._mostRecentTarget))}_setClasses(t,e){this._toggleClass(t,"cdk-focused",!!e),this._toggleClass(t,"cdk-touch-focused","touch"===e),this._toggleClass(t,"cdk-keyboard-focused","keyboard"===e),this._toggleClass(t,"cdk-mouse-focused","mouse"===e),this._toggleClass(t,"cdk-program-focused","program"===e)}_setOrigin(t,e=!1){this._ngZone.runOutsideAngular((()=>{this._origin=t,this._originFromTouchInteraction="touch"===t&&e,0===this._detectionMode&&(clearTimeout(this._originTimeoutId),this._originTimeoutId=setTimeout((()=>this._origin=null),this._originFromTouchInteraction?650:1))}))}_onFocus(t,e){const n=this._elementInfo.get(e),o=Lz(t);n&&(n.checkChildren||e===o)&&this._originChanged(e,this._getFocusOrigin(o),n)}_onBlur(t,e){const n=this._elementInfo.get(e);!n||n.checkChildren&&t.relatedTarget instanceof Node&&e.contains(t.relatedTarget)||(this._setClasses(e),this._emitOrigin(n.subject,null))}_emitOrigin(t,e){this._ngZone.run((()=>t.next(e)))}_registerGlobalListeners(t){if(!this._platform.isBrowser)return;const e=t.rootNode,n=this._rootNodeFocusListenerCount.get(e)||0;n||this._ngZone.runOutsideAngular((()=>{e.addEventListener("focus",this._rootNodeFocusAndBlurListener,kI),e.addEventListener("blur",this._rootNodeFocusAndBlurListener,kI)})),this._rootNodeFocusListenerCount.set(e,n+1),1==++this._monitoredElementCount&&(this._ngZone.runOutsideAngular((()=>{this._getWindow().addEventListener("focus",this._windowFocusListener)})),this._inputModalityDetector.modalityDetected.pipe(Ie(this._stopInputModalityDetector)).subscribe((t=>{this._setOrigin(t,!0)})))}_removeGlobalListeners(t){const e=t.rootNode;if(this._rootNodeFocusListenerCount.has(e)){const t=this._rootNodeFocusListenerCount.get(e);t>1?this._rootNodeFocusListenerCount.set(e,t-1):(e.removeEventListener("focus",this._rootNodeFocusAndBlurListener,kI),e.removeEventListener("blur",this._rootNodeFocusAndBlurListener,kI),this._rootNodeFocusListenerCount.delete(e))}--this._monitoredElementCount||(this._getWindow().removeEventListener("focus",this._windowFocusListener),this._stopInputModalityDetector.next(),clearTimeout(this._windowFocusTimeoutId),clearTimeout(this._originTimeoutId))}_originChanged(t,e,n){this._setClasses(t,e),this._emitOrigin(n.subject,e),this._lastFocusOrigin=e}_getClosestElementsInfo(t){const e=[];return this._elementInfo.forEach(((n,o)=>{(o===t||n.checkChildren&&o.contains(t))&&e.push([o,n])})),e}}SI.ɵfac=function t(e){return new(e||SI)(vr(a_),vr(wz),vr(CI),vr(Z_,8),vr(wI,8))},SI.ɵprov=Mn({factory:function t(){return new SI(vr(a_),vr(wz),vr(CI),vr(Z_,8),vr(wI,8))},token:SI,providedIn:"root"}),SI.ctorParameters=()=>[{type:a_},{type:wz},{type:CI},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wI]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(SI,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:a_},{type:wz},{type:CI},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wI]}]}]}),null);class DI{constructor(t,e){this._elementRef=t,this._focusMonitor=e,this.cdkFocusChange=new Lh}ngAfterViewInit(){const t=this._elementRef.nativeElement;this._monitorSubscription=this._focusMonitor.monitor(t,1===t.nodeType&&t.hasAttribute("cdkMonitorSubtreeFocus")).subscribe((t=>this.cdkFocusChange.emit(t)))}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef),this._monitorSubscription&&this._monitorSubscription.unsubscribe()}}DI.ɵfac=function t(e){return new(e||DI)(Sm(hg),Sm(SI))},DI.ɵdir=lo({type:DI,selectors:[["","cdkMonitorElementFocus",""],["","cdkMonitorSubtreeFocus",""]],outputs:{cdkFocusChange:"cdkFocusChange"}}),DI.ctorParameters=()=>[{type:hg},{type:SI}],DI.propDecorators={cdkFocusChange:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(DI,[{type:Cy,args:[{selector:"[cdkMonitorElementFocus], [cdkMonitorSubtreeFocus]"}]}],(function(){return[{type:hg},{type:SI}]}),{cdkFocusChange:[{type:Oy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const EI="cdk-high-contrast-black-on-white",RI="cdk-high-contrast-white-on-black",AI="cdk-high-contrast-active";class TI{constructor(t,e){this._platform=t,this._document=e}getHighContrastMode(){if(!this._platform.isBrowser)return 0;const t=this._document.createElement("div");t.style.backgroundColor="rgb(1,2,3)",t.style.position="absolute",this._document.body.appendChild(t);const e=this._document.defaultView||window,n=e&&e.getComputedStyle?e.getComputedStyle(t):null,o=(n&&n.backgroundColor||"").replace(/ /g,"");switch(this._document.body.removeChild(t),o){case"rgb(0,0,0)":return 2;case"rgb(255,255,255)":return 1}return 0}_applyBodyHighContrastModeCssClasses(){if(!this._hasCheckedHighContrastMode&&this._platform.isBrowser&&this._document.body){const t=this._document.body.classList;t.remove(AI),t.remove(EI),t.remove(RI),this._hasCheckedHighContrastMode=!0;const e=this.getHighContrastMode();1===e?(t.add(AI),t.add(EI)):2===e&&(t.add(AI),t.add(RI))}}}TI.ɵfac=function t(e){return new(e||TI)(vr(wz),vr(Z_))},TI.ɵprov=Mn({factory:function t(){return new TI(vr(wz),vr(Z_))},token:TI,providedIn:"root"}),TI.ctorParameters=()=>[{type:wz},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(TI,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:wz},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class NI{constructor(t){t._applyBodyHighContrastModeCssClasses()}}NI.ɵfac=function t(e){return new(e||NI)(vr(TI))},NI.ɵmod=ao({type:NI}),NI.ɵinj=vn({imports:[[kz,Uz]]}),NI.ctorParameters=()=>[{type:TI}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(NI,[{type:Ay,args:[{imports:[kz,Uz],declarations:[PI,sI,DI],exports:[PI,sI,DI]}]}],(function(){return[{type:TI}]}),null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(NI,{declarations:function(){return[PI,sI,DI]},imports:function(){return[kz,Uz]},exports:function(){return[PI,sI,DI]}});const zI=new Ga("cdk-dir-doc",{providedIn:"root",factory:function II(){return Or(Z_)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */});class HI{constructor(t){if(this.value="ltr",this.change=new Lh,t){const e=t.documentElement?t.documentElement.dir:null,n=(t.body?t.body.dir:null)||e;this.value="ltr"===n||"rtl"===n?n:"ltr"}}ngOnDestroy(){this.change.complete()}}HI.ɵfac=function t(e){return new(e||HI)(vr(zI,8))},HI.ɵprov=Mn({factory:function t(){return new HI(vr(zI,8))},token:HI,providedIn:"root"}),HI.ctorParameters=()=>[{type:void 0,decorators:[{type:Sr},{type:kr,args:[zI]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(HI,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:Sr},{type:kr,args:[zI]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class FI{constructor(){this._dir="ltr",this._isInitialized=!1,this.change=new Lh}get dir(){return this._dir}set dir(t){const e=this._dir,n=t?t.toLowerCase():t;this._rawDir=t,this._dir="ltr"===n||"rtl"===n?n:"ltr",e!==this._dir&&this._isInitialized&&this.change.emit(this._dir)}get value(){return this.dir}ngAfterContentInit(){this._isInitialized=!0}ngOnDestroy(){this.change.complete()}}FI.ɵfac=function t(e){return new(e||FI)},FI.ɵdir=lo({type:FI,selectors:[["","dir",""]],hostVars:1,hostBindings:function t(e,n){2&e&&jp("dir",n._rawDir)},inputs:{dir:"dir"},outputs:{change:"dirChange"},exportAs:["dir"],features:[pg([{provide:HI,useExisting:FI}])]}),FI.propDecorators={change:[{type:Oy,args:["dirChange"]}],dir:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(FI,[{type:Cy,args:[{selector:"[dir]",providers:[{provide:HI,useExisting:FI}],host:{"[attr.dir]":"_rawDir"},exportAs:"dir"}]}],(function(){return[]}),{change:[{type:Oy,args:["dirChange"]}],dir:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class LI{}LI.ɵfac=function t(e){return new(e||LI)},LI.ɵmod=ao({type:LI}),LI.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(LI,[{type:Ay,args:[{exports:[FI],declarations:[FI]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(LI,{declarations:[FI],exports:[FI]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const BI=new Og("12.2.1"),VI=["*",[["mat-option"],["ng-container"]]];function jI(t,e){if(1&t&&Tm(0,"mat-pseudo-checkbox",4),2&t){const t=Ym();Dm("state",t.selected?"checked":"unchecked")("disabled",t.disabled)}}function UI(t,e){if(1&t&&(Rm(0,"span",5),ku(1),Am()),2&t){const t=Ym();rc(1),Du("(",t.group.label,")")}}new Og("12.2.3");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class GI{}GI.STANDARD_CURVE="cubic-bezier(0.4,0.0,0.2,1)",GI.DECELERATION_CURVE="cubic-bezier(0.0,0.0,0.2,1)",GI.ACCELERATION_CURVE="cubic-bezier(0.4,0.0,1,1)",GI.SHARP_CURVE="cubic-bezier(0.4,0.0,0.6,1)";class WI{}WI.COMPLEX="375ms",WI.ENTERING="225ms",WI.EXITING="195ms";
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const YI=new Og("12.2.3"),qI=new Ga("mat-sanity-checks",{providedIn:"root",factory:function ZI(){return!0}});class XI{constructor(t,e,n){this._hasDoneGlobalChecks=!1,this._document=n,t._applyBodyHighContrastModeCssClasses(),this._sanityChecks=e,this._hasDoneGlobalChecks||(this._checkDoctypeIsDefined(),this._checkThemeIsPresent(),this._checkCdkVersionMatch(),this._hasDoneGlobalChecks=!0)}_getWindow(){const t=this._document.defaultView||window;return"object"==typeof t&&t?t:null}_checkIsEnabled(t){return!(!y_()||this._isTestEnv())&&("boolean"==typeof this._sanityChecks?this._sanityChecks:!!this._sanityChecks[t])}_isTestEnv(){const t=this._getWindow();return t&&(t.__karma__||t.jasmine)}_checkDoctypeIsDefined(){this._checkIsEnabled("doctype")&&!this._document.doctype&&console.warn("Current document does not have a doctype. This may cause some Angular Material components not to behave as expected.")}_checkThemeIsPresent(){if(!this._checkIsEnabled("theme")||!this._document.body||"function"!=typeof getComputedStyle)return;const t=this._document.createElement("div");t.classList.add("mat-theme-loaded-marker"),this._document.body.appendChild(t);const e=getComputedStyle(t);e&&"none"!==e.display&&console.warn("Could not find Angular Material core theme. Most Material components may not work as expected. For more info refer to the theming guide: https://material.angular.io/guide/theming"),this._document.body.removeChild(t)}_checkCdkVersionMatch(){this._checkIsEnabled("version")&&YI.full!==BI.full&&console.warn("The Angular Material version ("+YI.full+") does not match the Angular CDK version ("+BI.full+").\nPlease ensure the versions of these two packages exactly match.")}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function KI(t){return class extends t{constructor(...t){super(...t),this._disabled=!1}get disabled(){return this._disabled}set disabled(t){this._disabled=yz(t)}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function JI(t,e){return class extends t{constructor(...t){super(...t),this.defaultColor=e,this.color=e}get color(){return this._color}set color(t){const e=t||this.defaultColor;e!==this._color&&(this._color&&this._elementRef.nativeElement.classList.remove(`mat-${this._color}`),e&&this._elementRef.nativeElement.classList.add(`mat-${e}`),this._color=e)}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function QI(t){return class extends t{constructor(...t){super(...t),this._disableRipple=!1}get disableRipple(){return this._disableRipple}set disableRipple(t){this._disableRipple=yz(t)}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function $I(t,e=0){return class extends t{constructor(...t){super(...t),this._tabIndex=e,this.defaultTabIndex=e}get tabIndex(){return this.disabled?-1:this._tabIndex}set tabIndex(t){this._tabIndex=null!=t?_z(t):this.defaultTabIndex}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function tH(t){return class extends t{constructor(...t){super(...t),this.stateChanges=new I,this.errorState=!1}updateErrorState(){const t=this.errorState,e=(this.errorStateMatcher||this._defaultErrorStateMatcher).isErrorState(this.ngControl?this.ngControl.control:null,this._parentFormGroup||this._parentForm);e!==t&&(this.errorState=e,this.stateChanges.next())}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function eH(t){return class extends t{constructor(...t){super(...t),this._isInitialized=!1,this._pendingSubscribers=[],this.initialized=new D((t=>{this._isInitialized?this._notifySubscriber(t):this._pendingSubscribers.push(t)}))}_markInitialized(){if(this._isInitialized&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("This directive has already been marked as initialized and should not be called twice.");this._isInitialized=!0,this._pendingSubscribers.forEach(this._notifySubscriber),this._pendingSubscribers=null}_notifySubscriber(t){t.next(),t.complete()}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */XI.ɵfac=function t(e){return new(e||XI)(vr(TI),vr(qI,8),vr(Z_))},XI.ɵmod=ao({type:XI}),XI.ɵinj=vn({imports:[[LI],LI]}),XI.ctorParameters=()=>[{type:TI},{type:void 0,decorators:[{type:Sr},{type:kr,args:[qI]}]},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(XI,[{type:Ay,args:[{imports:[LI],exports:[LI]}]}],(function(){return[{type:TI},{type:void 0,decorators:[{type:Sr},{type:kr,args:[qI]}]},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(XI,{imports:function(){return[LI]},exports:function(){return[LI]}});const nH=new Ga("MAT_DATE_LOCALE",{providedIn:"root",factory:function oH(){return Or(Wy)}});class iH{constructor(){this._localeChanges=new I,this.localeChanges=this._localeChanges}getValidDateOrNull(t){return this.isDateInstance(t)&&this.isValid(t)?t:null}deserialize(t){return null==t||this.isDateInstance(t)&&this.isValid(t)?t:this.invalid()}setLocale(t){this.locale=t,this._localeChanges.next()}compareDate(t,e){return this.getYear(t)-this.getYear(e)||this.getMonth(t)-this.getMonth(e)||this.getDate(t)-this.getDate(e)}sameDate(t,e){if(t&&e){let n=this.isValid(t),o=this.isValid(e);return n&&o?!this.compareDate(t,e):n==o}return t==e}clampDate(t,e,n){return e&&this.compareDate(t,e)<0?e:n&&this.compareDate(t,n)>0?n:t}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const aH=new Ga("mat-date-formats");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let rH;try{rH="undefined"!=typeof Intl}catch(jCt){rH=!1}const sH={long:["January","February","March","April","May","June","July","August","September","October","November","December"],short:["Jan","Feb","Mar","Apr","May","Jun","Jul","Aug","Sep","Oct","Nov","Dec"],narrow:["J","F","M","A","M","J","J","A","S","O","N","D"]},lH=pH(31,(t=>String(t+1))),cH={long:["Sunday","Monday","Tuesday","Wednesday","Thursday","Friday","Saturday"],short:["Sun","Mon","Tue","Wed","Thu","Fri","Sat"],narrow:["S","M","T","W","T","F","S"]},dH=/^\d{4}-\d{2}-\d{2}(?:T\d{2}:\d{2}:\d{2}(?:\.\d+)?(?:Z|(?:(?:\+|-)\d{2}:\d{2}))?)?$/;function pH(t,e){const n=Array(t);for(let o=0;o<t;o++)n[o]=e(o);return n}class mH extends iH{constructor(t,e){super(),this.useUtcForDisplay=!0,super.setLocale(t),this.useUtcForDisplay=!e.TRIDENT,this._clampDate=e.TRIDENT||e.EDGE}getYear(t){return t.getFullYear()}getMonth(t){return t.getMonth()}getDate(t){return t.getDate()}getDayOfWeek(t){return t.getDay()}getMonthNames(t){if(rH){const e=new Intl.DateTimeFormat(this.locale,{month:t,timeZone:"utc"});return pH(12,(t=>this._stripDirectionalityCharacters(this._format(e,new Date(2017,t,1)))))}return sH[t]}getDateNames(){if(rH){const t=new Intl.DateTimeFormat(this.locale,{day:"numeric",timeZone:"utc"});return pH(31,(e=>this._stripDirectionalityCharacters(this._format(t,new Date(2017,0,e+1)))))}return lH}getDayOfWeekNames(t){if(rH){const e=new Intl.DateTimeFormat(this.locale,{weekday:t,timeZone:"utc"});return pH(7,(t=>this._stripDirectionalityCharacters(this._format(e,new Date(2017,0,t+1)))))}return cH[t]}getYearName(t){if(rH){const e=new Intl.DateTimeFormat(this.locale,{year:"numeric",timeZone:"utc"});return this._stripDirectionalityCharacters(this._format(e,t))}return String(this.getYear(t))}getFirstDayOfWeek(){return 0}getNumDaysInMonth(t){return this.getDate(this._createDateWithOverflow(this.getYear(t),this.getMonth(t)+1,0))}clone(t){return new Date(t.getTime())}createDate(t,e,n){if("undefined"==typeof ngDevMode||ngDevMode){if(e<0||e>11)throw Error(`Invalid month index "${e}". Month index has to be between 0 and 11.`);if(n<1)throw Error(`Invalid date "${n}". Date has to be greater than 0.`)}let o=this._createDateWithOverflow(t,e,n);if(o.getMonth()!=e&&("undefined"==typeof ngDevMode||ngDevMode))throw Error(`Invalid date "${n}" for month with index "${e}".`);return o}today(){return new Date}parse(t){return"number"==typeof t?new Date(t):t?new Date(Date.parse(t)):null}format(t,e){if(!this.isValid(t))throw Error("NativeDateAdapter: Cannot format invalid date.");if(rH){this._clampDate&&(t.getFullYear()<1||t.getFullYear()>9999)&&(t=this.clone(t)).setFullYear(Math.max(1,Math.min(9999,t.getFullYear()))),e=Object.assign(Object.assign({},e),{timeZone:"utc"});const n=new Intl.DateTimeFormat(this.locale,e);return this._stripDirectionalityCharacters(this._format(n,t))}return this._stripDirectionalityCharacters(t.toDateString())}addCalendarYears(t,e){return this.addCalendarMonths(t,12*e)}addCalendarMonths(t,e){let n=this._createDateWithOverflow(this.getYear(t),this.getMonth(t)+e,this.getDate(t));return this.getMonth(n)!=((this.getMonth(t)+e)%12+12)%12&&(n=this._createDateWithOverflow(this.getYear(n),this.getMonth(n),0)),n}addCalendarDays(t,e){return this._createDateWithOverflow(this.getYear(t),this.getMonth(t),this.getDate(t)+e)}toIso8601(t){return[t.getUTCFullYear(),this._2digit(t.getUTCMonth()+1),this._2digit(t.getUTCDate())].join("-")}deserialize(t){if("string"==typeof t){if(!t)return null;if(dH.test(t)){let e=new Date(t);if(this.isValid(e))return e}}return super.deserialize(t)}isDateInstance(t){return t instanceof Date}isValid(t){return!isNaN(t.getTime())}invalid(){return new Date(NaN)}_createDateWithOverflow(t,e,n){const o=new Date;return o.setFullYear(t,e,n),o.setHours(0,0,0,0),o}_2digit(t){return("00"+t).slice(-2)}_stripDirectionalityCharacters(t){return t.replace(/[\u200e\u200f]/g,"")}_format(t,e){const n=new Date;return n.setUTCFullYear(e.getFullYear(),e.getMonth(),e.getDate()),n.setUTCHours(e.getHours(),e.getMinutes(),e.getSeconds(),e.getMilliseconds()),t.format(n)}}mH.ɵfac=function t(e){return new(e||mH)(vr(nH,8),vr(wz))},mH.ɵprov=Mn({token:mH,factory:mH.ɵfac}),mH.ctorParameters=()=>[{type:String,decorators:[{type:Sr},{type:kr,args:[nH]}]},{type:wz}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(mH,[{type:im}],(function(){return[{type:String,decorators:[{type:Sr},{type:kr,args:[nH]}]},{type:wz}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class uH{}uH.ɵfac=function t(e){return new(e||uH)},uH.ɵmod=ao({type:uH}),uH.ɵinj=vn({providers:[{provide:iH,useClass:mH}],imports:[[kz]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(uH,[{type:Ay,args:[{imports:[kz],providers:[{provide:iH,useClass:mH}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(uH,{imports:function(){return[kz]}});const fH={parse:{dateInput:null},display:{dateInput:{year:"numeric",month:"numeric",day:"numeric"},monthYearLabel:{year:"numeric",month:"short"},dateA11yLabel:{year:"numeric",month:"long",day:"numeric"},monthYearA11yLabel:{year:"numeric",month:"long"}}};class gH{}gH.ɵfac=function t(e){return new(e||gH)},gH.ɵmod=ao({type:gH}),gH.ɵinj=vn({providers:[{provide:aH,useValue:fH}],imports:[[uH]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(gH,[{type:Ay,args:[{imports:[uH],providers:[{provide:aH,useValue:fH}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(gH,{imports:[uH]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class hH{isErrorState(t,e){return!!(t&&t.invalid&&(t.dirty||e&&e.submitted))}}hH.ɵfac=function t(e){return new(e||hH)},hH.ɵprov=Mn({token:hH,factory:hH.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(hH,[{type:im}],null,null);class bH{isErrorState(t,e){return!!(t&&t.invalid&&(t.touched||e&&e.submitted))}}bH.ɵfac=function t(e){return new(e||bH)},bH.ɵprov=Mn({factory:function t(){return new bH},token:bH,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(bH,[{type:im,args:[{providedIn:"root"}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class yH{}yH.ɵfac=function t(e){return new(e||yH)},yH.ɵdir=lo({type:yH,selectors:[["","mat-line",""],["","matLine",""]],hostAttrs:[1,"mat-line"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(yH,[{type:Cy,args:[{selector:"[mat-line], [matLine]",host:{class:"mat-line"}}]}],null,null);class _H{}_H.ɵfac=function t(e){return new(e||_H)},_H.ɵmod=ao({type:_H}),_H.ɵinj=vn({imports:[[XI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(_H,[{type:Ay,args:[{imports:[XI],exports:[yH,XI],declarations:[yH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(_H,{declarations:[yH],imports:[XI],exports:[yH,XI]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class CH{constructor(t,e,n){this._renderer=t,this.element=e,this.config=n,this.state=3}fadeOut(){this._renderer.fadeOutRipple(this)}}const MH={enterDuration:225,exitDuration:150},vH=Nz({passive:!0}),xH=["mousedown","touchstart"],OH=["mouseup","mouseleave","touchend","touchcancel"];class PH{constructor(t,e,n,o){this._target=t,this._ngZone=e,this._isPointerDown=!1,this._activeRipples=new Set,this._pointerUpEventsRegistered=!1,o.isBrowser&&(this._containerElement=xz(n))}fadeInRipple(t,e,n={}){const o=this._containerRect=this._containerRect||this._containerElement.getBoundingClientRect(),i=Object.assign(Object.assign({},MH),n.animation);n.centered&&(t=o.left+o.width/2,e=o.top+o.height/2);const a=n.radius||(function r(t,e,n){const o=Math.max(Math.abs(t-n.left),Math.abs(t-n.right)),i=Math.max(Math.abs(e-n.top),Math.abs(e-n.bottom));return Math.sqrt(o*o+i*i)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t,e,o),s=t-o.left,l=e-o.top,c=i.enterDuration,d=document.createElement("div");d.classList.add("mat-ripple-element"),d.style.left=s-a+"px",d.style.top=l-a+"px",d.style.height=2*a+"px",d.style.width=2*a+"px",null!=n.color&&(d.style.backgroundColor=n.color),d.style.transitionDuration=`${c}ms`,this._containerElement.appendChild(d),(function p(t){window.getComputedStyle(t).getPropertyValue("opacity")})(d),d.style.transform="scale(1)";const m=new CH(this,d,n);return m.state=0,this._activeRipples.add(m),n.persistent||(this._mostRecentTransientRipple=m),this._runTimeoutOutsideZone((()=>{const t=m===this._mostRecentTransientRipple;m.state=1,n.persistent||t&&this._isPointerDown||m.fadeOut()}),c),m}fadeOutRipple(t){const e=this._activeRipples.delete(t);if(t===this._mostRecentTransientRipple&&(this._mostRecentTransientRipple=null),this._activeRipples.size||(this._containerRect=null),!e)return;const n=t.element,o=Object.assign(Object.assign({},MH),t.config.animation);n.style.transitionDuration=`${o.exitDuration}ms`,n.style.opacity="0",t.state=2,this._runTimeoutOutsideZone((()=>{t.state=3,n.parentNode.removeChild(n)}),o.exitDuration)}fadeOutAll(){this._activeRipples.forEach((t=>t.fadeOut()))}fadeOutAllNonPersistent(){this._activeRipples.forEach((t=>{t.config.persistent||t.fadeOut()}))}setupTriggerEvents(t){const e=xz(t);e&&e!==this._triggerElement&&(this._removeTriggerEvents(),this._triggerElement=e,this._registerEvents(xH))}handleEvent(t){"mousedown"===t.type?this._onMousedown(t):"touchstart"===t.type?this._onTouchStart(t):this._onPointerUp(),this._pointerUpEventsRegistered||(this._registerEvents(OH),this._pointerUpEventsRegistered=!0)}_onMousedown(t){const e=gI(t),n=this._lastTouchStartEvent&&Date.now()<this._lastTouchStartEvent+800;this._target.rippleDisabled||e||n||(this._isPointerDown=!0,this.fadeInRipple(t.clientX,t.clientY,this._target.rippleConfig))}_onTouchStart(t){if(!this._target.rippleDisabled&&!hI(t)){this._lastTouchStartEvent=Date.now(),this._isPointerDown=!0;const e=t.changedTouches;for(let t=0;t<e.length;t++)this.fadeInRipple(e[t].clientX,e[t].clientY,this._target.rippleConfig)}}_onPointerUp(){this._isPointerDown&&(this._isPointerDown=!1,this._activeRipples.forEach((t=>{!t.config.persistent&&(1===t.state||t.config.terminateOnPointerUp&&0===t.state)&&t.fadeOut()})))}_runTimeoutOutsideZone(t,e=0){this._ngZone.runOutsideAngular((()=>setTimeout(t,e)))}_registerEvents(t){this._ngZone.runOutsideAngular((()=>{t.forEach((t=>{this._triggerElement.addEventListener(t,this,vH)}))}))}_removeTriggerEvents(){this._triggerElement&&(xH.forEach((t=>{this._triggerElement.removeEventListener(t,this,vH)})),this._pointerUpEventsRegistered&&OH.forEach((t=>{this._triggerElement.removeEventListener(t,this,vH)})))}}const wH=new Ga("mat-ripple-global-options");class kH{constructor(t,e,n,o,i){this._elementRef=t,this._animationMode=i,this.radius=0,this._disabled=!1,this._isInitialized=!1,this._globalOptions=o||{},this._rippleRenderer=new PH(this,e,t,n)}get disabled(){return this._disabled}set disabled(t){t&&this.fadeOutAllNonPersistent(),this._disabled=t,this._setupTriggerEventsIfEnabled()}get trigger(){return this._trigger||this._elementRef.nativeElement}set trigger(t){this._trigger=t,this._setupTriggerEventsIfEnabled()}ngOnInit(){this._isInitialized=!0,this._setupTriggerEventsIfEnabled()}ngOnDestroy(){this._rippleRenderer._removeTriggerEvents()}fadeOutAll(){this._rippleRenderer.fadeOutAll()}fadeOutAllNonPersistent(){this._rippleRenderer.fadeOutAllNonPersistent()}get rippleConfig(){return{centered:this.centered,radius:this.radius,color:this.color,animation:Object.assign(Object.assign(Object.assign({},this._globalOptions.animation),"NoopAnimations"===this._animationMode?{enterDuration:0,exitDuration:0}:{}),this.animation),terminateOnPointerUp:this._globalOptions.terminateOnPointerUp}}get rippleDisabled(){return this.disabled||!!this._globalOptions.disabled}_setupTriggerEventsIfEnabled(){!this.disabled&&this._isInitialized&&this._rippleRenderer.setupTriggerEvents(this.trigger)}launch(t,e=0,n){return"number"==typeof t?this._rippleRenderer.fadeInRipple(t,e,Object.assign(Object.assign({},this.rippleConfig),n)):this._rippleRenderer.fadeInRipple(0,0,Object.assign(Object.assign({},this.rippleConfig),t))}}kH.ɵfac=function t(e){return new(e||kH)(Sm(hg),Sm(a_),Sm(wz),Sm(wH,8),Sm(VP,8))},kH.ɵdir=lo({type:kH,selectors:[["","mat-ripple",""],["","matRipple",""]],hostAttrs:[1,"mat-ripple"],hostVars:2,hostBindings:function t(e,n){2&e&&pu("mat-ripple-unbounded",n.unbounded)},inputs:{radius:["matRippleRadius","radius"],disabled:["matRippleDisabled","disabled"],trigger:["matRippleTrigger","trigger"],color:["matRippleColor","color"],unbounded:["matRippleUnbounded","unbounded"],centered:["matRippleCentered","centered"],animation:["matRippleAnimation","animation"]},exportAs:["matRipple"]}),kH.ctorParameters=()=>[{type:hg},{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],kH.propDecorators={color:[{type:xy,args:["matRippleColor"]}],unbounded:[{type:xy,args:["matRippleUnbounded"]}],centered:[{type:xy,args:["matRippleCentered"]}],radius:[{type:xy,args:["matRippleRadius"]}],animation:[{type:xy,args:["matRippleAnimation"]}],disabled:[{type:xy,args:["matRippleDisabled"]}],trigger:[{type:xy,args:["matRippleTrigger"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(kH,[{type:Cy,args:[{selector:"[mat-ripple], [matRipple]",exportAs:"matRipple",host:{class:"mat-ripple","[class.mat-ripple-unbounded]":"unbounded"}}]}],(function(){return[{type:hg},{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{radius:[{type:xy,args:["matRippleRadius"]}],disabled:[{type:xy,args:["matRippleDisabled"]}],trigger:[{type:xy,args:["matRippleTrigger"]}],color:[{type:xy,args:["matRippleColor"]}],unbounded:[{type:xy,args:["matRippleUnbounded"]}],centered:[{type:xy,args:["matRippleCentered"]}],animation:[{type:xy,args:["matRippleAnimation"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class SH{}SH.ɵfac=function t(e){return new(e||SH)},SH.ɵmod=ao({type:SH}),SH.ɵinj=vn({imports:[[XI,kz],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(SH,[{type:Ay,args:[{imports:[XI,kz],exports:[kH,XI],declarations:[kH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(SH,{declarations:function(){return[kH]},imports:function(){return[XI,kz]},exports:function(){return[kH,XI]}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class DH{constructor(t){this._animationMode=t,this.state="unchecked",this.disabled=!1}}DH.ɵfac=function t(e){return new(e||DH)(Sm(VP,8))},DH.ɵcmp=to({type:DH,selectors:[["mat-pseudo-checkbox"]],hostAttrs:[1,"mat-pseudo-checkbox"],hostVars:8,hostBindings:function t(e,n){2&e&&pu("mat-pseudo-checkbox-indeterminate","indeterminate"===n.state)("mat-pseudo-checkbox-checked","checked"===n.state)("mat-pseudo-checkbox-disabled",n.disabled)("_mat-animation-noopable","NoopAnimations"===n._animationMode)},inputs:{state:"state",disabled:"disabled"},decls:0,vars:0,template:function t(e,n){},styles:['.mat-pseudo-checkbox{width:16px;height:16px;border:2px solid;border-radius:2px;cursor:pointer;display:inline-block;vertical-align:middle;box-sizing:border-box;position:relative;flex-shrink:0;transition:border-color 90ms cubic-bezier(0, 0, 0.2, 0.1),background-color 90ms cubic-bezier(0, 0, 0.2, 0.1)}.mat-pseudo-checkbox::after{position:absolute;opacity:0;content:"";border-bottom:2px solid currentColor;transition:opacity 90ms cubic-bezier(0, 0, 0.2, 0.1)}.mat-pseudo-checkbox.mat-pseudo-checkbox-checked,.mat-pseudo-checkbox.mat-pseudo-checkbox-indeterminate{border-color:transparent}._mat-animation-noopable.mat-pseudo-checkbox{transition:none;animation:none}._mat-animation-noopable.mat-pseudo-checkbox::after{transition:none}.mat-pseudo-checkbox-disabled{cursor:default}.mat-pseudo-checkbox-indeterminate::after{top:5px;left:1px;width:10px;opacity:1;border-radius:2px}.mat-pseudo-checkbox-checked::after{top:2.4px;left:1px;width:8px;height:3px;border-left:2px solid currentColor;transform:rotate(-45deg);opacity:1;box-sizing:content-box}\n'],encapsulation:2,changeDetection:0}),DH.ctorParameters=()=>[{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],DH.propDecorators={state:[{type:xy}],disabled:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(DH,[{type:My,args:[{encapsulation:Hn.None,changeDetection:zn.OnPush,selector:"mat-pseudo-checkbox",template:"",host:{class:"mat-pseudo-checkbox","[class.mat-pseudo-checkbox-indeterminate]":'state === "indeterminate"',"[class.mat-pseudo-checkbox-checked]":'state === "checked"',"[class.mat-pseudo-checkbox-disabled]":"disabled","[class._mat-animation-noopable]":'_animationMode === "NoopAnimations"'},styles:['.mat-pseudo-checkbox{width:16px;height:16px;border:2px solid;border-radius:2px;cursor:pointer;display:inline-block;vertical-align:middle;box-sizing:border-box;position:relative;flex-shrink:0;transition:border-color 90ms cubic-bezier(0, 0, 0.2, 0.1),background-color 90ms cubic-bezier(0, 0, 0.2, 0.1)}.mat-pseudo-checkbox::after{position:absolute;opacity:0;content:"";border-bottom:2px solid currentColor;transition:opacity 90ms cubic-bezier(0, 0, 0.2, 0.1)}.mat-pseudo-checkbox.mat-pseudo-checkbox-checked,.mat-pseudo-checkbox.mat-pseudo-checkbox-indeterminate{border-color:transparent}._mat-animation-noopable.mat-pseudo-checkbox{transition:none;animation:none}._mat-animation-noopable.mat-pseudo-checkbox::after{transition:none}.mat-pseudo-checkbox-disabled{cursor:default}.mat-pseudo-checkbox-indeterminate::after{top:5px;left:1px;width:10px;opacity:1;border-radius:2px}.mat-pseudo-checkbox-checked::after{top:2.4px;left:1px;width:8px;height:3px;border-left:2px solid currentColor;transform:rotate(-45deg);opacity:1;box-sizing:content-box}\n']}]}],(function(){return[{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{state:[{type:xy}],disabled:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class EH{}EH.ɵfac=function t(e){return new(e||EH)},EH.ɵmod=ao({type:EH}),EH.ɵinj=vn({imports:[[XI]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(EH,[{type:Ay,args:[{imports:[XI],exports:[DH],declarations:[DH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(EH,{declarations:[DH],imports:[XI],exports:[DH]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const RH=new Ga("MAT_OPTION_PARENT_COMPONENT"),AH=KI(class{});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let TH=0;class NH extends AH{constructor(t){var e;super(),this._labelId="mat-optgroup-label-"+TH++,this._inert=null!==(e=null==t?void 0:t.inertGroups)&&void 0!==e&&e}}NH.ɵfac=function t(e){return new(e||NH)(Sm(RH,8))},NH.ɵdir=lo({type:NH,inputs:{label:"label"},features:[xp]}),NH.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[RH]},{type:Sr}]}],NH.propDecorators={label:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(NH,[{type:Cy}],(function(){return[{type:void 0,decorators:[{type:kr,args:[RH]},{type:Sr}]}]}),{label:[{type:xy}]});const zH=new Ga("MatOptgroup");class IH extends NH{}IH.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(IH)))(n||IH)}})(),IH.ɵcmp=to({type:IH,selectors:[["mat-optgroup"]],hostAttrs:[1,"mat-optgroup"],hostVars:5,hostBindings:function t(e,n){2&e&&(jp("role",n._inert?null:"group")("aria-disabled",n._inert?null:n.disabled.toString())("aria-labelledby",n._inert?null:n._labelId),pu("mat-optgroup-disabled",n.disabled))},inputs:{disabled:"disabled"},exportAs:["matOptgroup"],features:[pg([{provide:zH,useExisting:IH}]),xp],ngContentSelectors:["*","mat-option, ng-container"],decls:4,vars:2,consts:[["aria-hidden","true",1,"mat-optgroup-label",3,"id"]],template:function t(e,n){1&e&&(Zm(VI),Rm(0,"span",0),ku(1),Xm(2),Am(),Xm(3,1)),2&e&&(Dm("id",n._labelId),rc(1),Du("",n.label," "))},styles:[".mat-optgroup-label{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-optgroup-label[disabled]{cursor:default}[dir=rtl] .mat-optgroup-label{text-align:right}.mat-optgroup-label .mat-icon{margin-right:16px;vertical-align:middle}.mat-optgroup-label .mat-icon svg{vertical-align:top}[dir=rtl] .mat-optgroup-label .mat-icon{margin-left:16px;margin-right:0}\n"],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(IH,[{type:My,args:[{selector:"mat-optgroup",exportAs:"matOptgroup",template:'<span class="mat-optgroup-label" aria-hidden="true" [id]="_labelId">{{ label }} <ng-content></ng-content></span>\n<ng-content select="mat-option, ng-container"></ng-content>\n',encapsulation:Hn.None,changeDetection:zn.OnPush,inputs:["disabled"],host:{class:"mat-optgroup","[attr.role]":'_inert ? null : "group"',"[attr.aria-disabled]":"_inert ? null : disabled.toString()","[attr.aria-labelledby]":"_inert ? null : _labelId","[class.mat-optgroup-disabled]":"disabled"},providers:[{provide:zH,useExisting:IH}],styles:[".mat-optgroup-label{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-optgroup-label[disabled]{cursor:default}[dir=rtl] .mat-optgroup-label{text-align:right}.mat-optgroup-label .mat-icon{margin-right:16px;vertical-align:middle}.mat-optgroup-label .mat-icon svg{vertical-align:top}[dir=rtl] .mat-optgroup-label .mat-icon{margin-left:16px;margin-right:0}\n"]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let HH=0;class FH{constructor(t,e=!1){this.source=t,this.isUserInput=e}}class LH{constructor(t,e,n,o){this._element=t,this._changeDetectorRef=e,this._parent=n,this.group=o,this._selected=!1,this._active=!1,this._disabled=!1,this._mostRecentViewValue="",this.id="mat-option-"+HH++,this.onSelectionChange=new Lh,this._stateChanges=new I}get multiple(){return this._parent&&this._parent.multiple}get selected(){return this._selected}get disabled(){return this.group&&this.group.disabled||this._disabled}set disabled(t){this._disabled=yz(t)}get disableRipple(){return this._parent&&this._parent.disableRipple}get active(){return this._active}get viewValue(){return(this._getHostElement().textContent||"").trim()}select(){this._selected||(this._selected=!0,this._changeDetectorRef.markForCheck(),this._emitSelectionChangeEvent())}deselect(){this._selected&&(this._selected=!1,this._changeDetectorRef.markForCheck(),this._emitSelectionChangeEvent())}focus(t,e){const n=this._getHostElement();"function"==typeof n.focus&&n.focus(e)}setActiveStyles(){this._active||(this._active=!0,this._changeDetectorRef.markForCheck())}setInactiveStyles(){this._active&&(this._active=!1,this._changeDetectorRef.markForCheck())}getLabel(){return this.viewValue}_handleKeydown(t){t.keyCode!==mz&&t.keyCode!==fz||bz(t)||(this._selectViaInteraction(),t.preventDefault())}_selectViaInteraction(){this.disabled||(this._selected=!this.multiple||!this._selected,this._changeDetectorRef.markForCheck(),this._emitSelectionChangeEvent(!0))}_getAriaSelected(){return this.selected||!this.multiple&&null}_getTabIndex(){return this.disabled?"-1":"0"}_getHostElement(){return this._element.nativeElement}ngAfterViewChecked(){if(this._selected){const t=this.viewValue;t!==this._mostRecentViewValue&&(this._mostRecentViewValue=t,this._stateChanges.next())}}ngOnDestroy(){this._stateChanges.complete()}_emitSelectionChangeEvent(t=!1){this.onSelectionChange.emit(new FH(this,t))}}LH.ɵfac=function t(e){return new(e||LH)(Sm(hg),Sm(Ug),Sm(void 0),Sm(NH))},LH.ɵdir=lo({type:LH,inputs:{id:"id",disabled:"disabled",value:"value"},outputs:{onSelectionChange:"onSelectionChange"}}),LH.ctorParameters=()=>[{type:hg},{type:Ug},{type:void 0},{type:NH}],LH.propDecorators={value:[{type:xy}],id:[{type:xy}],disabled:[{type:xy}],onSelectionChange:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(LH,[{type:Cy}],(function(){return[{type:hg},{type:Ug},{type:void 0},{type:NH}]}),{id:[{type:xy}],onSelectionChange:[{type:Oy}],disabled:[{type:xy}],value:[{type:xy}]});class BH extends LH{constructor(t,e,n,o){super(t,e,n,o)}}function VH(t,e,n){if(n.length){let o=e.toArray(),i=n.toArray(),a=0;for(let e=0;e<t+1;e++)o[e].group&&o[e].group===i[a]&&a++;return a}return 0}function jH(t,e,n,o){return t<n?t:t+e>n+o?Math.max(0,t-o+e):n}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */BH.ɵfac=function t(e){return new(e||BH)(Sm(hg),Sm(Ug),Sm(RH,8),Sm(zH,8))},BH.ɵcmp=to({type:BH,selectors:[["mat-option"]],hostAttrs:["role","option",1,"mat-option","mat-focus-indicator"],hostVars:12,hostBindings:function t(e,n){1&e&&Vm("click",(function t(){return n._selectViaInteraction()}))("keydown",(function t(e){return n._handleKeydown(e)})),2&e&&(Tu("id",n.id),jp("tabindex",n._getTabIndex())("aria-selected",n._getAriaSelected())("aria-disabled",n.disabled.toString()),pu("mat-selected",n.selected)("mat-option-multiple",n.multiple)("mat-active",n.active)("mat-option-disabled",n.disabled))},exportAs:["matOption"],features:[xp],ngContentSelectors:["*"],decls:5,vars:4,consts:[["class","mat-option-pseudo-checkbox",3,"state","disabled",4,"ngIf"],[1,"mat-option-text"],["class","cdk-visually-hidden",4,"ngIf"],["mat-ripple","",1,"mat-option-ripple",3,"matRippleTrigger","matRippleDisabled"],[1,"mat-option-pseudo-checkbox",3,"state","disabled"],[1,"cdk-visually-hidden"]],template:function t(e,n){1&e&&(Zm(),Qp(0,jI,1,2,"mat-pseudo-checkbox",0),Rm(1,"span",1),Xm(2),Am(),Qp(3,UI,2,1,"span",2),Tm(4,"div",3)),2&e&&(Dm("ngIf",n.multiple),rc(3),Dm("ngIf",n.group&&n.group._inert),rc(1),Dm("matRippleTrigger",n._getHostElement())("matRippleDisabled",n.disabled||n.disableRipple))},directives:[dM,kH,DH],styles:[".mat-option{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;position:relative;cursor:pointer;outline:none;display:flex;flex-direction:row;max-width:100%;box-sizing:border-box;align-items:center;-webkit-tap-highlight-color:transparent}.mat-option[disabled]{cursor:default}[dir=rtl] .mat-option{text-align:right}.mat-option .mat-icon{margin-right:16px;vertical-align:middle}.mat-option .mat-icon svg{vertical-align:top}[dir=rtl] .mat-option .mat-icon{margin-left:16px;margin-right:0}.mat-option[aria-disabled=true]{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-optgroup .mat-option:not(.mat-option-multiple){padding-left:32px}[dir=rtl] .mat-optgroup .mat-option:not(.mat-option-multiple){padding-left:16px;padding-right:32px}.cdk-high-contrast-active .mat-option{margin:0 1px}.cdk-high-contrast-active .mat-option.mat-active{border:solid 1px currentColor;margin:0}.cdk-high-contrast-active .mat-option[aria-disabled=true]{opacity:.5}.mat-option-text{display:inline-block;flex-grow:1;overflow:hidden;text-overflow:ellipsis}.mat-option .mat-option-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none}.mat-option-pseudo-checkbox{margin-right:8px}[dir=rtl] .mat-option-pseudo-checkbox{margin-left:8px;margin-right:0}\n"],encapsulation:2,changeDetection:0}),BH.ctorParameters=()=>[{type:hg},{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[RH]}]},{type:IH,decorators:[{type:Sr},{type:kr,args:[zH]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(BH,[{type:My,args:[{selector:"mat-option",exportAs:"matOption",host:{role:"option","[attr.tabindex]":"_getTabIndex()","[class.mat-selected]":"selected","[class.mat-option-multiple]":"multiple","[class.mat-active]":"active","[id]":"id","[attr.aria-selected]":"_getAriaSelected()","[attr.aria-disabled]":"disabled.toString()","[class.mat-option-disabled]":"disabled","(click)":"_selectViaInteraction()","(keydown)":"_handleKeydown($event)",class:"mat-option mat-focus-indicator"},template:'<mat-pseudo-checkbox *ngIf="multiple" class="mat-option-pseudo-checkbox"\n    [state]="selected ? \'checked\' : \'unchecked\'" [disabled]="disabled"></mat-pseudo-checkbox>\n\n<span class="mat-option-text"><ng-content></ng-content></span>\n\n\x3c!-- See a11y notes inside optgroup.ts for context behind this element. --\x3e\n<span class="cdk-visually-hidden" *ngIf="group && group._inert">({{ group.label }})</span>\n\n<div class="mat-option-ripple" mat-ripple\n     [matRippleTrigger]="_getHostElement()"\n     [matRippleDisabled]="disabled || disableRipple">\n</div>\n',encapsulation:Hn.None,changeDetection:zn.OnPush,styles:[".mat-option{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;position:relative;cursor:pointer;outline:none;display:flex;flex-direction:row;max-width:100%;box-sizing:border-box;align-items:center;-webkit-tap-highlight-color:transparent}.mat-option[disabled]{cursor:default}[dir=rtl] .mat-option{text-align:right}.mat-option .mat-icon{margin-right:16px;vertical-align:middle}.mat-option .mat-icon svg{vertical-align:top}[dir=rtl] .mat-option .mat-icon{margin-left:16px;margin-right:0}.mat-option[aria-disabled=true]{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-optgroup .mat-option:not(.mat-option-multiple){padding-left:32px}[dir=rtl] .mat-optgroup .mat-option:not(.mat-option-multiple){padding-left:16px;padding-right:32px}.cdk-high-contrast-active .mat-option{margin:0 1px}.cdk-high-contrast-active .mat-option.mat-active{border:solid 1px currentColor;margin:0}.cdk-high-contrast-active .mat-option[aria-disabled=true]{opacity:.5}.mat-option-text{display:inline-block;flex-grow:1;overflow:hidden;text-overflow:ellipsis}.mat-option .mat-option-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none}.mat-option-pseudo-checkbox{margin-right:8px}[dir=rtl] .mat-option-pseudo-checkbox{margin-left:8px;margin-right:0}\n"]}]}],(function(){return[{type:hg},{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[RH]}]},{type:IH,decorators:[{type:Sr},{type:kr,args:[zH]}]}]}),null);class UH{}UH.ɵfac=function t(e){return new(e||UH)},UH.ɵmod=ao({type:UH}),UH.ɵinj=vn({imports:[[SH,WM,XI,EH]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(UH,[{type:Ay,args:[{imports:[SH,WM,XI,EH],exports:[BH,IH],declarations:[BH,IH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(UH,{declarations:function(){return[BH,IH]},imports:function(){return[SH,WM,XI,EH]},exports:function(){return[BH,IH]}});const GH=["mat-button",""],WH=["*"],YH=".mat-button .mat-button-focus-overlay,.mat-icon-button .mat-button-focus-overlay{opacity:0}.mat-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay,.mat-stroked-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay{opacity:.04}@media(hover: none){.mat-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay,.mat-stroked-button:hover:not(.mat-button-disabled) 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.mat-button-focus-overlay,.mat-mini-fab.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-mini-fab::-moz-focus-inner{border:0}._mat-animation-noopable.mat-mini-fab{transition:none;animation:none}.mat-mini-fab .mat-button-wrapper{padding:8px 0;display:inline-block;line-height:24px}.mat-icon-button{padding:0;min-width:0;width:40px;height:40px;flex-shrink:0;line-height:40px;border-radius:50%}.mat-icon-button i,.mat-icon-button .mat-icon{line-height:24px}.mat-button-ripple.mat-ripple,.mat-button-focus-overlay{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none;border-radius:inherit}.mat-button-ripple.mat-ripple:not(:empty){transform:translateZ(0)}.mat-button-focus-overlay{opacity:0;transition:opacity 200ms cubic-bezier(0.35, 0, 0.25, 1),background-color 200ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable .mat-button-focus-overlay{transition:none}.mat-button-ripple-round{border-radius:50%;z-index:1}.mat-button .mat-button-wrapper>*,.mat-flat-button .mat-button-wrapper>*,.mat-stroked-button .mat-button-wrapper>*,.mat-raised-button .mat-button-wrapper>*,.mat-icon-button .mat-button-wrapper>*,.mat-fab .mat-button-wrapper>*,.mat-mini-fab .mat-button-wrapper>*{vertical-align:middle}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon-button,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon-button{display:inline-flex;justify-content:center;align-items:center;font-size:inherit;width:2.5em;height:2.5em}.cdk-high-contrast-active .mat-button,.cdk-high-contrast-active .mat-flat-button,.cdk-high-contrast-active .mat-raised-button,.cdk-high-contrast-active .mat-icon-button,.cdk-high-contrast-active .mat-fab,.cdk-high-contrast-active .mat-mini-fab{outline:solid 1px}.cdk-high-contrast-active .mat-button-base.cdk-keyboard-focused,.cdk-high-contrast-active .mat-button-base.cdk-program-focused{outline:solid 3px}\n",qH=["mat-button","mat-flat-button","mat-icon-button","mat-raised-button","mat-stroked-button","mat-mini-fab","mat-fab"],ZH=JI(KI(QI(class{constructor(t){this._elementRef=t}})));class XH extends ZH{constructor(t,e,n){super(t),this._focusMonitor=e,this._animationMode=n,this.isRoundButton=this._hasHostAttributes("mat-fab","mat-mini-fab"),this.isIconButton=this._hasHostAttributes("mat-icon-button");for(const t of qH)this._hasHostAttributes(t)&&this._getHostElement().classList.add(t);t.nativeElement.classList.add("mat-button-base"),this.isRoundButton&&(this.color="accent")}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0)}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef)}focus(t,e){t?this._focusMonitor.focusVia(this._getHostElement(),t,e):this._getHostElement().focus(e)}_getHostElement(){return this._elementRef.nativeElement}_isRippleDisabled(){return this.disableRipple||this.disabled}_hasHostAttributes(...t){return t.some((t=>this._getHostElement().hasAttribute(t)))}}XH.ɵfac=function t(e){return new(e||XH)(Sm(hg),Sm(SI),Sm(VP,8))},XH.ɵcmp=to({type:XH,selectors:[["button","mat-button",""],["button","mat-raised-button",""],["button","mat-icon-button",""],["button","mat-fab",""],["button","mat-mini-fab",""],["button","mat-stroked-button",""],["button","mat-flat-button",""]],viewQuery:function t(e,n){if(1&e&&Qh(kH,5),2&e){let t;Jh(t=tb())&&(n.ripple=t.first)}},hostAttrs:[1,"mat-focus-indicator"],hostVars:5,hostBindings:function t(e,n){2&e&&(jp("disabled",n.disabled||null),pu("_mat-animation-noopable","NoopAnimations"===n._animationMode)("mat-button-disabled",n.disabled))},inputs:{disabled:"disabled",disableRipple:"disableRipple",color:"color"},exportAs:["matButton"],features:[xp],attrs:GH,ngContentSelectors:WH,decls:4,vars:5,consts:[[1,"mat-button-wrapper"],["matRipple","",1,"mat-button-ripple",3,"matRippleDisabled","matRippleCentered","matRippleTrigger"],[1,"mat-button-focus-overlay"]],template:function t(e,n){1&e&&(Zm(),Rm(0,"span",0),Xm(1),Am(),Tm(2,"span",1),Tm(3,"span",2)),2&e&&(rc(2),pu("mat-button-ripple-round",n.isRoundButton||n.isIconButton),Dm("matRippleDisabled",n._isRippleDisabled())("matRippleCentered",n.isIconButton)("matRippleTrigger",n._getHostElement()))},directives:[kH],styles:[YH],encapsulation:2,changeDetection:0}),XH.ctorParameters=()=>[{type:hg},{type:SI},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],XH.propDecorators={ripple:[{type:Za,args:[kH]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(XH,[{type:My,args:[{selector:"button[mat-button], button[mat-raised-button], button[mat-icon-button],\n             button[mat-fab], button[mat-mini-fab], button[mat-stroked-button],\n             button[mat-flat-button]",exportAs:"matButton",host:{"[attr.disabled]":"disabled || null","[class._mat-animation-noopable]":'_animationMode === "NoopAnimations"',"[class.mat-button-disabled]":"disabled",class:"mat-focus-indicator"},template:'<span class="mat-button-wrapper"><ng-content></ng-content></span>\n<span matRipple class="mat-button-ripple"\n      [class.mat-button-ripple-round]="isRoundButton || isIconButton"\n      [matRippleDisabled]="_isRippleDisabled()"\n      [matRippleCentered]="isIconButton"\n      [matRippleTrigger]="_getHostElement()"></span>\n<span class="mat-button-focus-overlay"></span>\n',inputs:["disabled","disableRipple","color"],encapsulation:Hn.None,changeDetection:zn.OnPush,styles:[".mat-button .mat-button-focus-overlay,.mat-icon-button .mat-button-focus-overlay{opacity:0}.mat-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay,.mat-stroked-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay{opacity:.04}@media(hover: none){.mat-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay,.mat-stroked-button:hover:not(.mat-button-disabled) .mat-button-focus-overlay{opacity:0}}.mat-button,.mat-icon-button,.mat-stroked-button,.mat-flat-button{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 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.mat-mini-fab{outline:solid 1px}.cdk-high-contrast-active .mat-button-base.cdk-keyboard-focused,.cdk-high-contrast-active .mat-button-base.cdk-program-focused{outline:solid 3px}\n"]}]}],(function(){return[{type:hg},{type:SI},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{ripple:[{type:Za,args:[kH]}]});class KH extends XH{constructor(t,e,n){super(e,t,n)}_haltDisabledEvents(t){this.disabled&&(t.preventDefault(),t.stopImmediatePropagation())}}KH.ɵfac=function t(e){return new(e||KH)(Sm(SI),Sm(hg),Sm(VP,8))},KH.ɵcmp=to({type:KH,selectors:[["a","mat-button",""],["a","mat-raised-button",""],["a","mat-icon-button",""],["a","mat-fab",""],["a","mat-mini-fab",""],["a","mat-stroked-button",""],["a","mat-flat-button",""]],hostAttrs:[1,"mat-focus-indicator"],hostVars:7,hostBindings:function t(e,n){1&e&&Vm("click",(function t(e){return 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.mat-button-focus-overlay{opacity:.12}.mat-fab::-moz-focus-inner{border:0}._mat-animation-noopable.mat-fab{transition:none;animation:none}.mat-fab .mat-button-wrapper{padding:16px 0;display:inline-block;line-height:24px}.mat-mini-fab{box-sizing:border-box;position:relative;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;display:inline-block;white-space:nowrap;text-decoration:none;vertical-align:baseline;text-align:center;margin:0;min-width:64px;line-height:36px;padding:0 16px;border-radius:4px;overflow:visible;transform:translate3d(0, 0, 0);transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);min-width:0;border-radius:50%;width:40px;height:40px;padding:0;flex-shrink:0}.mat-mini-fab::-moz-focus-inner{border:0}.mat-mini-fab.mat-button-disabled{cursor:default}.mat-mini-fab.cdk-keyboard-focused .mat-button-focus-overlay,.mat-mini-fab.cdk-program-focused .mat-button-focus-overlay{opacity:.12}.mat-mini-fab::-moz-focus-inner{border:0}._mat-animation-noopable.mat-mini-fab{transition:none;animation:none}.mat-mini-fab .mat-button-wrapper{padding:8px 0;display:inline-block;line-height:24px}.mat-icon-button{padding:0;min-width:0;width:40px;height:40px;flex-shrink:0;line-height:40px;border-radius:50%}.mat-icon-button i,.mat-icon-button .mat-icon{line-height:24px}.mat-button-ripple.mat-ripple,.mat-button-focus-overlay{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none;border-radius:inherit}.mat-button-ripple.mat-ripple:not(:empty){transform:translateZ(0)}.mat-button-focus-overlay{opacity:0;transition:opacity 200ms cubic-bezier(0.35, 0, 0.25, 1),background-color 200ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable .mat-button-focus-overlay{transition:none}.mat-button-ripple-round{border-radius:50%;z-index:1}.mat-button .mat-button-wrapper>*,.mat-flat-button .mat-button-wrapper>*,.mat-stroked-button .mat-button-wrapper>*,.mat-raised-button .mat-button-wrapper>*,.mat-icon-button .mat-button-wrapper>*,.mat-fab .mat-button-wrapper>*,.mat-mini-fab .mat-button-wrapper>*{vertical-align:middle}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon-button,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon-button{display:inline-flex;justify-content:center;align-items:center;font-size:inherit;width:2.5em;height:2.5em}.cdk-high-contrast-active .mat-button,.cdk-high-contrast-active .mat-flat-button,.cdk-high-contrast-active .mat-raised-button,.cdk-high-contrast-active .mat-icon-button,.cdk-high-contrast-active .mat-fab,.cdk-high-contrast-active .mat-mini-fab{outline:solid 1px}.cdk-high-contrast-active .mat-button-base.cdk-keyboard-focused,.cdk-high-contrast-active .mat-button-base.cdk-program-focused{outline:solid 3px}\n"]}]}],(function(){return[{type:SI},{type:hg},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{tabIndex:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class JH{}JH.ɵfac=function t(e){return new(e||JH)},JH.ɵmod=ao({type:JH}),JH.ɵinj=vn({imports:[[SH,XI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(JH,[{type:Ay,args:[{imports:[SH,XI],exports:[XH,KH,XI],declarations:[XH,KH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(JH,{declarations:function(){return[XH,KH]},imports:function(){return[SH,XI]},exports:function(){return[XH,KH,XI]}});class QH{}function $H(t){return t&&"function"==typeof t.connect}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class tF extends QH{constructor(t){super(),this._data=t}connect(){return Nt(this._data)?this._data:Et(this._data)}disconnect(){}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class eF{applyChanges(t,e,n,o,i){t.forEachOperation(((t,o,a)=>{let r,s;if(null==t.previousIndex){const i=n(t,o,a);r=e.createEmbeddedView(i.templateRef,i.context,i.index),s=1}else null==a?(e.remove(o),s=3):(r=e.get(o),e.move(r,a),s=2);i&&i({context:null==r?void 0:r.context,operation:s,record:t})}))}detach(){}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class nF{constructor(){this.viewCacheSize=20,this._viewCache=[]}applyChanges(t,e,n,o,i){t.forEachOperation(((t,a,r)=>{let s,l;null==t.previousIndex?(s=this._insertView((()=>n(t,a,r)),r,e,o(t)),l=s?1:0):null==r?(this._detachAndCacheView(a,e),l=3):(s=this._moveView(a,r,e,o(t)),l=2),i&&i({context:null==s?void 0:s.context,operation:l,record:t})}))}detach(){for(const t of this._viewCache)t.destroy();this._viewCache=[]}_insertView(t,e,n,o){const i=this._insertViewFromCache(e,n);if(i)return void(i.context.$implicit=o);const a=t();return n.createEmbeddedView(a.templateRef,a.context,a.index)}_detachAndCacheView(t,e){const n=e.detach(t);this._maybeCacheView(n,e)}_moveView(t,e,n,o){const i=n.get(t);return n.move(i,e),i.context.$implicit=o,i}_maybeCacheView(t,e){if(this._viewCache.length<this.viewCacheSize)this._viewCache.push(t);else{const n=e.indexOf(t);-1===n?t.destroy():e.remove(n)}}_insertViewFromCache(t,e){const n=this._viewCache.pop();return n&&e.insert(n,t),n||null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class oF{constructor(t=!1,e,n=!0){this._multiple=t,this._emitChanges=n,this._selection=new Set,this._deselectedToEmit=[],this._selectedToEmit=[],this.changed=new I,e&&e.length&&(t?e.forEach((t=>this._markSelected(t))):this._markSelected(e[0]),this._selectedToEmit.length=0)}get selected(){return this._selected||(this._selected=Array.from(this._selection.values())),this._selected}select(...t){this._verifyValueAssignment(t),t.forEach((t=>this._markSelected(t))),this._emitChangeEvent()}deselect(...t){this._verifyValueAssignment(t),t.forEach((t=>this._unmarkSelected(t))),this._emitChangeEvent()}toggle(t){this.isSelected(t)?this.deselect(t):this.select(t)}clear(){this._unmarkAll(),this._emitChangeEvent()}isSelected(t){return this._selection.has(t)}isEmpty(){return 0===this._selection.size}hasValue(){return!this.isEmpty()}sort(t){this._multiple&&this.selected&&this._selected.sort(t)}isMultipleSelection(){return this._multiple}_emitChangeEvent(){this._selected=null,(this._selectedToEmit.length||this._deselectedToEmit.length)&&(this.changed.next({source:this,added:this._selectedToEmit,removed:this._deselectedToEmit}),this._deselectedToEmit=[],this._selectedToEmit=[])}_markSelected(t){this.isSelected(t)||(this._multiple||this._unmarkAll(),this._selection.add(t),this._emitChanges&&this._selectedToEmit.push(t))}_unmarkSelected(t){this.isSelected(t)&&(this._selection.delete(t),this._emitChanges&&this._deselectedToEmit.push(t))}_unmarkAll(){this.isEmpty()||this._selection.forEach((t=>this._unmarkSelected(t)))}_verifyValueAssignment(t){if(t.length>1&&!this._multiple&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("Cannot pass multiple values into SelectionModel with single-value mode.")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)()}}class iF{constructor(){this._listeners=[]}notify(t,e){for(let n of this._listeners)n(t,e)}listen(t){return this._listeners.push(t),()=>{this._listeners=this._listeners.filter((e=>t!==e))}}ngOnDestroy(){this._listeners=[]}}iF.ɵfac=function t(e){return new(e||iF)},iF.ɵprov=Mn({factory:function t(){return new iF},token:iF,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(iF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const aF=new Ga("_ViewRepeater"),rF=["contentWrapper"],sF=new Ga("VIRTUAL_SCROLL_STRATEGY");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class lF{constructor(t,e,n){this._scrolledIndexChange=new I,this.scrolledIndexChange=this._scrolledIndexChange.pipe(Me()),this._viewport=null,this._itemSize=t,this._minBufferPx=e,this._maxBufferPx=n}attach(t){this._viewport=t,this._updateTotalContentSize(),this._updateRenderedRange()}detach(){this._scrolledIndexChange.complete(),this._viewport=null}updateItemAndBufferSize(t,e,n){if(n<e&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("CDK virtual scroll: maxBufferPx must be greater than or equal to minBufferPx");this._itemSize=t,this._minBufferPx=e,this._maxBufferPx=n,this._updateTotalContentSize(),this._updateRenderedRange()}onContentScrolled(){this._updateRenderedRange()}onDataLengthChanged(){this._updateTotalContentSize(),this._updateRenderedRange()}onContentRendered(){}onRenderedOffsetChanged(){}scrollToIndex(t,e){this._viewport&&this._viewport.scrollToOffset(t*this._itemSize,e)}_updateTotalContentSize(){this._viewport&&this._viewport.setTotalContentSize(this._viewport.getDataLength()*this._itemSize)}_updateRenderedRange(){if(!this._viewport)return;const t=this._viewport.getRenderedRange(),e={start:t.start,end:t.end},n=this._viewport.getViewportSize(),o=this._viewport.getDataLength();let i=this._viewport.measureScrollOffset(),a=this._itemSize>0?i/this._itemSize:0;if(e.end>o){const t=Math.ceil(n/this._itemSize),r=Math.max(0,Math.min(a,o-t));a!=r&&(a=r,i=r*this._itemSize,e.start=Math.floor(a)),e.end=Math.max(0,Math.min(o,e.start+t))}const r=i-e.start*this._itemSize;if(r<this._minBufferPx&&0!=e.start){const t=Math.ceil((this._maxBufferPx-r)/this._itemSize);e.start=Math.max(0,e.start-t),e.end=Math.min(o,Math.ceil(a+(n+this._minBufferPx)/this._itemSize))}else{const t=e.end*this._itemSize-(i+n);if(t<this._minBufferPx&&e.end!=o){const n=Math.ceil((this._maxBufferPx-t)/this._itemSize);n>0&&(e.end=Math.min(o,e.end+n),e.start=Math.max(0,Math.floor(a-this._minBufferPx/this._itemSize)))}}this._viewport.setRenderedRange(e),this._viewport.setRenderedContentOffset(this._itemSize*e.start),this._scrolledIndexChange.next(Math.floor(a))}}function cF(t){return t._scrollStrategy}class dF{constructor(){this._itemSize=20,this._minBufferPx=100,this._maxBufferPx=200,this._scrollStrategy=new lF(this.itemSize,this.minBufferPx,this.maxBufferPx)}get itemSize(){return this._itemSize}set itemSize(t){this._itemSize=_z(t)}get minBufferPx(){return this._minBufferPx}set minBufferPx(t){this._minBufferPx=_z(t)}get maxBufferPx(){return this._maxBufferPx}set maxBufferPx(t){this._maxBufferPx=_z(t)}ngOnChanges(){this._scrollStrategy.updateItemAndBufferSize(this.itemSize,this.minBufferPx,this.maxBufferPx)}}dF.ɵfac=function t(e){return new(e||dF)},dF.ɵdir=lo({type:dF,selectors:[["cdk-virtual-scroll-viewport","itemSize",""]],inputs:{itemSize:"itemSize",minBufferPx:"minBufferPx",maxBufferPx:"maxBufferPx"},features:[pg([{provide:sF,useFactory:cF,deps:[qe((()=>dF))]}]),Bo]}),dF.propDecorators={itemSize:[{type:xy}],minBufferPx:[{type:xy}],maxBufferPx:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(dF,[{type:Cy,args:[{selector:"cdk-virtual-scroll-viewport[itemSize]",providers:[{provide:sF,useFactory:cF,deps:[qe((()=>dF))]}]}]}],(function(){return[]}),{itemSize:[{type:xy}],minBufferPx:[{type:xy}],maxBufferPx:[{type:xy}]});class pF{constructor(t,e,n){this._ngZone=t,this._platform=e,this._scrolled=new I,this._globalSubscription=null,this._scrolledCount=0,this.scrollContainers=new Map,this._document=n}register(t){this.scrollContainers.has(t)||this.scrollContainers.set(t,t.elementScrolled().subscribe((()=>this._scrolled.next(t))))}deregister(t){const e=this.scrollContainers.get(t);e&&(e.unsubscribe(),this.scrollContainers.delete(t))}scrolled(t=20){return this._platform.isBrowser?new D((e=>{this._globalSubscription||this._addGlobalListener();const n=t>0?this._scrolled.pipe(de(t)).subscribe(e):this._scrolled.subscribe(e);return this._scrolledCount++,()=>{n.unsubscribe(),this._scrolledCount--,this._scrolledCount||this._removeGlobalListener()}})):Et()}ngOnDestroy(){this._removeGlobalListener(),this.scrollContainers.forEach(((t,e)=>this.deregister(e))),this._scrolled.complete()}ancestorScrolled(t,e){const n=this.getAncestorScrollContainers(t);return this.scrolled(e).pipe(ce((t=>!t||n.indexOf(t)>-1)))}getAncestorScrollContainers(t){const e=[];return this.scrollContainers.forEach(((n,o)=>{this._scrollableContainsElement(o,t)&&e.push(o)})),e}_getWindow(){return this._document.defaultView||window}_scrollableContainsElement(t,e){let n=xz(e),o=t.getElementRef().nativeElement;do{if(n==o)return!0}while(n=n.parentElement);return!1}_addGlobalListener(){this._globalSubscription=this._ngZone.runOutsideAngular((()=>oe(this._getWindow().document,"scroll").subscribe((()=>this._scrolled.next()))))}_removeGlobalListener(){this._globalSubscription&&(this._globalSubscription.unsubscribe(),this._globalSubscription=null)}}pF.ɵfac=function t(e){return new(e||pF)(vr(a_),vr(wz),vr(Z_,8))},pF.ɵprov=Mn({factory:function t(){return new pF(vr(a_),vr(wz),vr(Z_,8))},token:pF,providedIn:"root"}),pF.ctorParameters=()=>[{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(pF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class mF{constructor(t,e,n,o){this.elementRef=t,this.scrollDispatcher=e,this.ngZone=n,this.dir=o,this._destroyed=new I,this._elementScrolled=new D((t=>this.ngZone.runOutsideAngular((()=>oe(this.elementRef.nativeElement,"scroll").pipe(Ie(this._destroyed)).subscribe(t)))))}ngOnInit(){this.scrollDispatcher.register(this)}ngOnDestroy(){this.scrollDispatcher.deregister(this),this._destroyed.next(),this._destroyed.complete()}elementScrolled(){return this._elementScrolled}getElementRef(){return this.elementRef}scrollTo(t){const e=this.elementRef.nativeElement,n=this.dir&&"rtl"==this.dir.value;null==t.left&&(t.left=n?t.end:t.start),null==t.right&&(t.right=n?t.start:t.end),null!=t.bottom&&(t.top=e.scrollHeight-e.clientHeight-t.bottom),n&&0!=Iz()?(null!=t.left&&(t.right=e.scrollWidth-e.clientWidth-t.left),2==Iz()?t.left=t.right:1==Iz()&&(t.left=t.right?-t.right:t.right)):null!=t.right&&(t.left=e.scrollWidth-e.clientWidth-t.right),this._applyScrollToOptions(t)}_applyScrollToOptions(t){const e=this.elementRef.nativeElement;zz()?e.scrollTo(t):(null!=t.top&&(e.scrollTop=t.top),null!=t.left&&(e.scrollLeft=t.left))}measureScrollOffset(t){const e="left",n="right",o=this.elementRef.nativeElement;if("top"==t)return o.scrollTop;if("bottom"==t)return o.scrollHeight-o.clientHeight-o.scrollTop;const i=this.dir&&"rtl"==this.dir.value;return"start"==t?t=i?n:e:"end"==t&&(t=i?e:n),i&&2==Iz()?t==e?o.scrollWidth-o.clientWidth-o.scrollLeft:o.scrollLeft:i&&1==Iz()?t==e?o.scrollLeft+o.scrollWidth-o.clientWidth:-o.scrollLeft:t==e?o.scrollLeft:o.scrollWidth-o.clientWidth-o.scrollLeft}}mF.ɵfac=function t(e){return new(e||mF)(Sm(hg),Sm(pF),Sm(a_),Sm(HI,8))},mF.ɵdir=lo({type:mF,selectors:[["","cdk-scrollable",""],["","cdkScrollable",""]]}),mF.ctorParameters=()=>[{type:hg},{type:pF},{type:a_},{type:HI,decorators:[{type:Sr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(mF,[{type:Cy,args:[{selector:"[cdk-scrollable], [cdkScrollable]"}]}],(function(){return[{type:hg},{type:pF},{type:a_},{type:HI,decorators:[{type:Sr}]}]}),null);class uF{constructor(t,e,n){this._platform=t,this._change=new I,this._changeListener=t=>{this._change.next(t)},this._document=n,e.runOutsideAngular((()=>{if(t.isBrowser){const t=this._getWindow();t.addEventListener("resize",this._changeListener),t.addEventListener("orientationchange",this._changeListener)}this.change().subscribe((()=>this._viewportSize=null))}))}ngOnDestroy(){if(this._platform.isBrowser){const t=this._getWindow();t.removeEventListener("resize",this._changeListener),t.removeEventListener("orientationchange",this._changeListener)}this._change.complete()}getViewportSize(){this._viewportSize||this._updateViewportSize();const t={width:this._viewportSize.width,height:this._viewportSize.height};return this._platform.isBrowser||(this._viewportSize=null),t}getViewportRect(){const t=this.getViewportScrollPosition(),{width:e,height:n}=this.getViewportSize();return{top:t.top,left:t.left,bottom:t.top+n,right:t.left+e,height:n,width:e}}getViewportScrollPosition(){if(!this._platform.isBrowser)return{top:0,left:0};const t=this._document,e=this._getWindow(),n=t.documentElement,o=n.getBoundingClientRect();return{top:-o.top||t.body.scrollTop||e.scrollY||n.scrollTop||0,left:-o.left||t.body.scrollLeft||e.scrollX||n.scrollLeft||0}}change(t=20){return t>0?this._change.pipe(de(t)):this._change}_getWindow(){return this._document.defaultView||window}_updateViewportSize(){const t=this._getWindow();this._viewportSize=this._platform.isBrowser?{width:t.innerWidth,height:t.innerHeight}:{width:0,height:0}}}uF.ɵfac=function t(e){return new(e||uF)(vr(wz),vr(a_),vr(Z_,8))},uF.ɵprov=Mn({factory:function t(){return new uF(vr(wz),vr(a_),vr(Z_,8))},token:uF,providedIn:"root"}),uF.ctorParameters=()=>[{type:wz},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(uF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:wz},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]}]}),null);const fF="undefined"!=typeof requestAnimationFrame?at:$;class gF extends mF{constructor(t,e,n,o,i,a,r){if(super(t,a,n,i),this.elementRef=t,this._changeDetectorRef=e,this._scrollStrategy=o,this._detachedSubject=new I,this._renderedRangeSubject=new I,this._orientation="vertical",this._appendOnly=!1,this.scrolledIndexChange=new D((t=>this._scrollStrategy.scrolledIndexChange.subscribe((e=>Promise.resolve().then((()=>this.ngZone.run((()=>t.next(e))))))))),this.renderedRangeStream=this._renderedRangeSubject,this._totalContentSize=0,this._totalContentWidth="",this._totalContentHeight="",this._renderedRange={start:0,end:0},this._dataLength=0,this._viewportSize=0,this._renderedContentOffset=0,this._renderedContentOffsetNeedsRewrite=!1,this._isChangeDetectionPending=!1,this._runAfterChangeDetection=[],this._viewportChanges=m.EMPTY,!o&&("undefined"==typeof ngDevMode||ngDevMode))throw Error('Error: cdk-virtual-scroll-viewport requires the "itemSize" property to be set.');this._viewportChanges=r.change().subscribe((()=>{this.checkViewportSize()}))}get orientation(){return this._orientation}set orientation(t){this._orientation!==t&&(this._orientation=t,this._calculateSpacerSize())}get appendOnly(){return this._appendOnly}set appendOnly(t){this._appendOnly=yz(t)}ngOnInit(){super.ngOnInit(),this.ngZone.runOutsideAngular((()=>Promise.resolve().then((()=>{this._measureViewportSize(),this._scrollStrategy.attach(this),this.elementScrolled().pipe(Ne(null),de(0,fF)).subscribe((()=>this._scrollStrategy.onContentScrolled())),this._markChangeDetectionNeeded()}))))}ngOnDestroy(){this.detach(),this._scrollStrategy.detach(),this._renderedRangeSubject.complete(),this._detachedSubject.complete(),this._viewportChanges.unsubscribe(),super.ngOnDestroy()}attach(t){if(this._forOf&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("CdkVirtualScrollViewport is already attached.");this.ngZone.runOutsideAngular((()=>{this._forOf=t,this._forOf.dataStream.pipe(Ie(this._detachedSubject)).subscribe((t=>{const e=t.length;e!==this._dataLength&&(this._dataLength=e,this._scrollStrategy.onDataLengthChanged()),this._doChangeDetection()}))}))}detach(){this._forOf=null,this._detachedSubject.next()}getDataLength(){return this._dataLength}getViewportSize(){return this._viewportSize}getRenderedRange(){return this._renderedRange}setTotalContentSize(t){this._totalContentSize!==t&&(this._totalContentSize=t,this._calculateSpacerSize(),this._markChangeDetectionNeeded())}setRenderedRange(t){(
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function e(t,n){return t.start==n.start&&t.end==n.end})(this._renderedRange,t)||(this.appendOnly&&(t={start:0,end:Math.max(this._renderedRange.end,t.end)}),this._renderedRangeSubject.next(this._renderedRange=t),this._markChangeDetectionNeeded((()=>this._scrollStrategy.onContentRendered())))}getOffsetToRenderedContentStart(){return this._renderedContentOffsetNeedsRewrite?null:this._renderedContentOffset}setRenderedContentOffset(t,e="to-start"){const n="horizontal"==this.orientation,o=n?"X":"Y";let i=`translate${o}(${Number((n&&this.dir&&"rtl"==this.dir.value?-1:1)*t)}px)`;this._renderedContentOffset=t,"to-end"===e&&(i+=` translate${o}(-100%)`,this._renderedContentOffsetNeedsRewrite=!0),this._renderedContentTransform!=i&&(this._renderedContentTransform=i,this._markChangeDetectionNeeded((()=>{this._renderedContentOffsetNeedsRewrite?(this._renderedContentOffset-=this.measureRenderedContentSize(),this._renderedContentOffsetNeedsRewrite=!1,this.setRenderedContentOffset(this._renderedContentOffset)):this._scrollStrategy.onRenderedOffsetChanged()})))}scrollToOffset(t,e="auto"){const n={behavior:e};"horizontal"===this.orientation?n.start=t:n.top=t,this.scrollTo(n)}scrollToIndex(t,e="auto"){this._scrollStrategy.scrollToIndex(t,e)}measureScrollOffset(t){return super.measureScrollOffset(t||("horizontal"===this.orientation?"start":"top"))}measureRenderedContentSize(){const t=this._contentWrapper.nativeElement;return"horizontal"===this.orientation?t.offsetWidth:t.offsetHeight}measureRangeSize(t){return this._forOf?this._forOf.measureRangeSize(t,this.orientation):0}checkViewportSize(){this._measureViewportSize(),this._scrollStrategy.onDataLengthChanged()}_measureViewportSize(){const t=this.elementRef.nativeElement;this._viewportSize="horizontal"===this.orientation?t.clientWidth:t.clientHeight}_markChangeDetectionNeeded(t){t&&this._runAfterChangeDetection.push(t),this._isChangeDetectionPending||(this._isChangeDetectionPending=!0,this.ngZone.runOutsideAngular((()=>Promise.resolve().then((()=>{this._doChangeDetection()})))))}_doChangeDetection(){this._isChangeDetectionPending=!1,this._contentWrapper.nativeElement.style.transform=this._renderedContentTransform,this.ngZone.run((()=>this._changeDetectorRef.markForCheck()));const t=this._runAfterChangeDetection;this._runAfterChangeDetection=[];for(const e of t)e()}_calculateSpacerSize(){this._totalContentHeight="horizontal"===this.orientation?"":`${this._totalContentSize}px`,this._totalContentWidth="horizontal"===this.orientation?`${this._totalContentSize}px`:""}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function hF(t,e,n){if(!n.getBoundingClientRect)return 0;const o=n.getBoundingClientRect();return"horizontal"===t?"start"===e?o.left:o.right:"start"===e?o.top:o.bottom}gF.ɵfac=function t(e){return new(e||gF)(Sm(hg),Sm(Ug),Sm(a_),Sm(sF,8),Sm(HI,8),Sm(pF),Sm(uF))},gF.ɵcmp=to({type:gF,selectors:[["cdk-virtual-scroll-viewport"]],viewQuery:function t(e,n){if(1&e&&Qh(rF,7),2&e){let t;Jh(t=tb())&&(n._contentWrapper=t.first)}},hostAttrs:[1,"cdk-virtual-scroll-viewport"],hostVars:4,hostBindings:function t(e,n){2&e&&pu("cdk-virtual-scroll-orientation-horizontal","horizontal"===n.orientation)("cdk-virtual-scroll-orientation-vertical","horizontal"!==n.orientation)},inputs:{orientation:"orientation",appendOnly:"appendOnly"},outputs:{scrolledIndexChange:"scrolledIndexChange"},features:[pg([{provide:mF,useExisting:gF}]),xp],ngContentSelectors:["*"],decls:4,vars:4,consts:[[1,"cdk-virtual-scroll-content-wrapper"],["contentWrapper",""],[1,"cdk-virtual-scroll-spacer"]],template:function t(e,n){1&e&&(Zm(),Rm(0,"div",0,1),Xm(2),Am(),Tm(3,"div",2)),2&e&&(rc(3),du("width",n._totalContentWidth)("height",n._totalContentHeight))},styles:["cdk-virtual-scroll-viewport{display:block;position:relative;overflow:auto;contain:strict;transform:translateZ(0);will-change:scroll-position;-webkit-overflow-scrolling:touch}.cdk-virtual-scroll-content-wrapper{position:absolute;top:0;left:0;contain:content}[dir=rtl] .cdk-virtual-scroll-content-wrapper{right:0;left:auto}.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper{min-height:100%}.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>dl:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>ol:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>table:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>ul:not([cdkVirtualFor]){padding-left:0;padding-right:0;margin-left:0;margin-right:0;border-left-width:0;border-right-width:0;outline:none}.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper{min-width:100%}.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>dl:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>ol:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>table:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>ul:not([cdkVirtualFor]){padding-top:0;padding-bottom:0;margin-top:0;margin-bottom:0;border-top-width:0;border-bottom-width:0;outline:none}.cdk-virtual-scroll-spacer{position:absolute;top:0;left:0;height:1px;width:1px;transform-origin:0 0}[dir=rtl] .cdk-virtual-scroll-spacer{right:0;left:auto;transform-origin:100% 0}\n"],encapsulation:2,changeDetection:0}),gF.ctorParameters=()=>[{type:hg},{type:Ug},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[sF]}]},{type:HI,decorators:[{type:Sr}]},{type:pF},{type:uF}],gF.propDecorators={orientation:[{type:xy}],appendOnly:[{type:xy}],scrolledIndexChange:[{type:Oy}],_contentWrapper:[{type:Za,args:["contentWrapper",{static:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(gF,[{type:My,args:[{selector:"cdk-virtual-scroll-viewport",template:'\x3c!--\n  Wrap the rendered content in an element that will be used to offset it based on the scroll\n  position.\n--\x3e\n<div #contentWrapper class="cdk-virtual-scroll-content-wrapper">\n  <ng-content></ng-content>\n</div>\n\x3c!--\n  Spacer used to force the scrolling container to the correct size for the *total* number of items\n  so that the scrollbar captures the size of the entire data set.\n--\x3e\n<div class="cdk-virtual-scroll-spacer"\n     [style.width]="_totalContentWidth" [style.height]="_totalContentHeight"></div>\n',host:{class:"cdk-virtual-scroll-viewport","[class.cdk-virtual-scroll-orientation-horizontal]":'orientation === "horizontal"',"[class.cdk-virtual-scroll-orientation-vertical]":'orientation !== "horizontal"'},encapsulation:Hn.None,changeDetection:zn.OnPush,providers:[{provide:mF,useExisting:gF}],styles:["cdk-virtual-scroll-viewport{display:block;position:relative;overflow:auto;contain:strict;transform:translateZ(0);will-change:scroll-position;-webkit-overflow-scrolling:touch}.cdk-virtual-scroll-content-wrapper{position:absolute;top:0;left:0;contain:content}[dir=rtl] .cdk-virtual-scroll-content-wrapper{right:0;left:auto}.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper{min-height:100%}.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>dl:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>ol:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>table:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-horizontal .cdk-virtual-scroll-content-wrapper>ul:not([cdkVirtualFor]){padding-left:0;padding-right:0;margin-left:0;margin-right:0;border-left-width:0;border-right-width:0;outline:none}.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper{min-width:100%}.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>dl:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>ol:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>table:not([cdkVirtualFor]),.cdk-virtual-scroll-orientation-vertical .cdk-virtual-scroll-content-wrapper>ul:not([cdkVirtualFor]){padding-top:0;padding-bottom:0;margin-top:0;margin-bottom:0;border-top-width:0;border-bottom-width:0;outline:none}.cdk-virtual-scroll-spacer{position:absolute;top:0;left:0;height:1px;width:1px;transform-origin:0 0}[dir=rtl] .cdk-virtual-scroll-spacer{right:0;left:auto;transform-origin:100% 0}\n"]}]}],(function(){return[{type:hg},{type:Ug},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[sF]}]},{type:HI,decorators:[{type:Sr}]},{type:pF},{type:uF}]}),{scrolledIndexChange:[{type:Oy}],orientation:[{type:xy}],appendOnly:[{type:xy}],_contentWrapper:[{type:Za,args:["contentWrapper",{static:!0}]}]});class bF{constructor(t,e,n,o,i,a){this._viewContainerRef=t,this._template=e,this._differs=n,this._viewRepeater=o,this._viewport=i,this.viewChange=new I,this._dataSourceChanges=new I,this.dataStream=this._dataSourceChanges.pipe(Ne(null),De(),ze((([t,e])=>this._changeDataSource(t,e))),Ae(1)),this._differ=null,this._needsUpdate=!1,this._destroyed=new I,this.dataStream.subscribe((t=>{this._data=t,this._onRenderedDataChange()})),this._viewport.renderedRangeStream.pipe(Ie(this._destroyed)).subscribe((t=>{this._renderedRange=t,a.run((()=>this.viewChange.next(this._renderedRange))),this._onRenderedDataChange()})),this._viewport.attach(this)}get cdkVirtualForOf(){return this._cdkVirtualForOf}set cdkVirtualForOf(t){this._cdkVirtualForOf=t,$H(t)?this._dataSourceChanges.next(t):this._dataSourceChanges.next(new tF(Nt(t)?t:Array.from(t||[])))}get cdkVirtualForTrackBy(){return this._cdkVirtualForTrackBy}set cdkVirtualForTrackBy(t){this._needsUpdate=!0,this._cdkVirtualForTrackBy=t?(e,n)=>t(e+(this._renderedRange?this._renderedRange.start:0),n):void 0}set cdkVirtualForTemplate(t){t&&(this._needsUpdate=!0,this._template=t)}get cdkVirtualForTemplateCacheSize(){return this._viewRepeater.viewCacheSize}set cdkVirtualForTemplateCacheSize(t){this._viewRepeater.viewCacheSize=_z(t)}measureRangeSize(t,e){if(t.start>=t.end)return 0;if((t.start<this._renderedRange.start||t.end>this._renderedRange.end)&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Error: attempted to measure an item that isn't rendered.");const n=t.start-this._renderedRange.start,o=t.end-t.start;let i,a;for(let t=0;t<o;t++){const e=this._viewContainerRef.get(t+n);if(e&&e.rootNodes.length){i=a=e.rootNodes[0];break}}for(let t=o-1;t>-1;t--){const e=this._viewContainerRef.get(t+n);if(e&&e.rootNodes.length){a=e.rootNodes[e.rootNodes.length-1];break}}return i&&a?hF(e,"end",a)-hF(e,"start",i):0}ngDoCheck(){if(this._differ&&this._needsUpdate){const t=this._differ.diff(this._renderedItems);t?this._applyChanges(t):this._updateContext(),this._needsUpdate=!1}}ngOnDestroy(){this._viewport.detach(),this._dataSourceChanges.next(void 0),this._dataSourceChanges.complete(),this.viewChange.complete(),this._destroyed.next(),this._destroyed.complete(),this._viewRepeater.detach()}_onRenderedDataChange(){this._renderedRange&&(this._renderedItems=this._data.slice(this._renderedRange.start,this._renderedRange.end),this._differ||(this._differ=this._differs.find(this._renderedItems).create(((t,e)=>this.cdkVirtualForTrackBy?this.cdkVirtualForTrackBy(t,e):e))),this._needsUpdate=!0)}_changeDataSource(t,e){return t&&t.disconnect(this),this._needsUpdate=!0,e?e.connect(this):Et()}_updateContext(){const t=this._data.length;let e=this._viewContainerRef.length;for(;e--;){const n=this._viewContainerRef.get(e);n.context.index=this._renderedRange.start+e,n.context.count=t,this._updateComputedContextProperties(n.context),n.detectChanges()}}_applyChanges(t){this._viewRepeater.applyChanges(t,this._viewContainerRef,((t,e,n)=>this._getEmbeddedViewArgs(t,n)),(t=>t.item)),t.forEachIdentityChange((t=>{this._viewContainerRef.get(t.currentIndex).context.$implicit=t.item}));const e=this._data.length;let n=this._viewContainerRef.length;for(;n--;){const t=this._viewContainerRef.get(n);t.context.index=this._renderedRange.start+n,t.context.count=e,this._updateComputedContextProperties(t.context)}}_updateComputedContextProperties(t){t.first=0===t.index,t.last=t.index===t.count-1,t.even=t.index%2==0,t.odd=!t.even}_getEmbeddedViewArgs(t,e){return{templateRef:this._template,context:{$implicit:t.item,cdkVirtualForOf:this._cdkVirtualForOf,index:-1,count:-1,first:!1,last:!1,odd:!1,even:!1},index:e}}}bF.ɵfac=function t(e){return new(e||bF)(Sm(eh),Sm(Xg),Sm(Hg),Sm(aF),Sm(gF,4),Sm(a_))},bF.ɵdir=lo({type:bF,selectors:[["","cdkVirtualFor","","cdkVirtualForOf",""]],inputs:{cdkVirtualForOf:"cdkVirtualForOf",cdkVirtualForTrackBy:"cdkVirtualForTrackBy",cdkVirtualForTemplate:"cdkVirtualForTemplate",cdkVirtualForTemplateCacheSize:"cdkVirtualForTemplateCacheSize"},features:[pg([{provide:aF,useClass:nF}])]}),bF.ctorParameters=()=>[{type:eh},{type:Xg},{type:Hg},{type:nF,decorators:[{type:kr,args:[aF]}]},{type:gF,decorators:[{type:Er}]},{type:a_}],bF.propDecorators={cdkVirtualForOf:[{type:xy}],cdkVirtualForTrackBy:[{type:xy}],cdkVirtualForTemplate:[{type:xy}],cdkVirtualForTemplateCacheSize:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(bF,[{type:Cy,args:[{selector:"[cdkVirtualFor][cdkVirtualForOf]",providers:[{provide:aF,useClass:nF}]}]}],(function(){return[{type:eh},{type:Xg},{type:Hg},{type:nF,decorators:[{type:kr,args:[aF]}]},{type:gF,decorators:[{type:Er}]},{type:a_}]}),{cdkVirtualForOf:[{type:xy}],cdkVirtualForTrackBy:[{type:xy}],cdkVirtualForTemplate:[{type:xy}],cdkVirtualForTemplateCacheSize:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class yF{}yF.ɵfac=function t(e){return new(e||yF)},yF.ɵmod=ao({type:yF}),yF.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(yF,[{type:Ay,args:[{exports:[mF],declarations:[mF]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(yF,{declarations:[mF],exports:[mF]});class _F{}function CF(){throw Error("Host already has a portal attached")}_F.ɵfac=function t(e){return new(e||_F)},_F.ɵmod=ao({type:_F}),_F.ɵinj=vn({imports:[[LI,kz,yF],LI,yF]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(_F,[{type:Ay,args:[{imports:[LI,kz,yF],exports:[LI,yF,dF,bF,gF],declarations:[dF,bF,gF]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(_F,{declarations:function(){return[dF,bF,gF]},imports:function(){return[LI,kz,yF]},exports:function(){return[LI,yF,dF,bF,gF]}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class MF{attach(t){return("undefined"==typeof ngDevMode||ngDevMode)&&(null==t&&(function e(){throw Error("Attempting to attach a portal to a null PortalOutlet")})(),t.hasAttached()&&CF()),this._attachedHost=t,t.attach(this)}detach(){let t=this._attachedHost;null!=t?(this._attachedHost=null,t.detach()):("undefined"==typeof ngDevMode||ngDevMode)&&(function e(){throw Error("Attempting to detach a portal that is not attached to a host")})()}get isAttached(){return null!=this._attachedHost}setAttachedHost(t){this._attachedHost=t}}class vF extends MF{constructor(t,e,n,o){super(),this.component=t,this.viewContainerRef=e,this.injector=n,this.componentFactoryResolver=o}}class xF extends MF{constructor(t,e,n){super(),this.templateRef=t,this.viewContainerRef=e,this.context=n}get origin(){return this.templateRef.elementRef}attach(t,e=this.context){return this.context=e,super.attach(t)}detach(){return this.context=void 0,super.detach()}}class OF extends MF{constructor(t){super(),this.element=t instanceof hg?t.nativeElement:t}}class PF{constructor(){this._isDisposed=!1,this.attachDomPortal=null}hasAttached(){return!!this._attachedPortal}attach(t){return("undefined"==typeof ngDevMode||ngDevMode)&&(t||(function e(){throw Error("Must provide a portal to attach")})(),this.hasAttached()&&CF(),this._isDisposed&&(function n(){throw Error("This PortalOutlet has already been disposed")})()),t instanceof vF?(this._attachedPortal=t,this.attachComponentPortal(t)):t instanceof xF?(this._attachedPortal=t,this.attachTemplatePortal(t)):this.attachDomPortal&&t instanceof OF?(this._attachedPortal=t,this.attachDomPortal(t)):void(("undefined"==typeof ngDevMode||ngDevMode)&&(function o(){throw Error("Attempting to attach an unknown Portal type. BasePortalOutlet accepts either a ComponentPortal or a TemplatePortal.")})())}detach(){this._attachedPortal&&(this._attachedPortal.setAttachedHost(null),this._attachedPortal=null),this._invokeDisposeFn()}dispose(){this.hasAttached()&&this.detach(),this._invokeDisposeFn(),this._isDisposed=!0}setDisposeFn(t){this._disposeFn=t}_invokeDisposeFn(){this._disposeFn&&(this._disposeFn(),this._disposeFn=null)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class wF extends PF{constructor(t,e,n,o,i){super(),this.outletElement=t,this._componentFactoryResolver=e,this._appRef=n,this._defaultInjector=o,this.attachDomPortal=t=>{if(!this._document&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Cannot attach DOM portal without _document constructor parameter");const e=t.element;if(!e.parentNode&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("DOM portal content must be attached to a parent node.");const n=this._document.createComment("dom-portal");e.parentNode.insertBefore(n,e),this.outletElement.appendChild(e),this._attachedPortal=t,super.setDisposeFn((()=>{n.parentNode&&n.parentNode.replaceChild(e,n)}))},this._document=i}attachComponentPortal(t){const e=(t.componentFactoryResolver||this._componentFactoryResolver).resolveComponentFactory(t.component);let n;return t.viewContainerRef?(n=t.viewContainerRef.createComponent(e,t.viewContainerRef.length,t.injector||t.viewContainerRef.injector),this.setDisposeFn((()=>n.destroy()))):(n=e.create(t.injector||this._defaultInjector),this._appRef.attachView(n.hostView),this.setDisposeFn((()=>{this._appRef.detachView(n.hostView),n.destroy()}))),this.outletElement.appendChild(this._getComponentRootNode(n)),this._attachedPortal=t,n}attachTemplatePortal(t){let e=t.viewContainerRef,n=e.createEmbeddedView(t.templateRef,t.context);return n.rootNodes.forEach((t=>this.outletElement.appendChild(t))),n.detectChanges(),this.setDisposeFn((()=>{let t=e.indexOf(n);-1!==t&&e.remove(t)})),this._attachedPortal=t,n}dispose(){super.dispose(),null!=this.outletElement.parentNode&&this.outletElement.parentNode.removeChild(this.outletElement)}_getComponentRootNode(t){return t.hostView.rootNodes[0]}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class kF extends xF{constructor(t,e){super(t,e)}}kF.ɵfac=function t(e){return new(e||kF)(Sm(Xg),Sm(eh))},kF.ɵdir=lo({type:kF,selectors:[["","cdkPortal",""]],exportAs:["cdkPortal"],features:[xp]}),kF.ctorParameters=()=>[{type:Xg},{type:eh}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(kF,[{type:Cy,args:[{selector:"[cdkPortal]",exportAs:"cdkPortal"}]}],(function(){return[{type:Xg},{type:eh}]}),null);class SF extends kF{}SF.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(SF)))(n||SF)}})(),SF.ɵdir=lo({type:SF,selectors:[["","cdk-portal",""],["","portal",""]],exportAs:["cdkPortal"],features:[pg([{provide:kF,useExisting:SF}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(SF,[{type:Cy,args:[{selector:"[cdk-portal], [portal]",exportAs:"cdkPortal",providers:[{provide:kF,useExisting:SF}]}]}],null,null);class DF extends PF{constructor(t,e,n){super(),this._componentFactoryResolver=t,this._viewContainerRef=e,this._isInitialized=!1,this.attached=new Lh,this.attachDomPortal=t=>{if(!this._document&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Cannot attach DOM portal without _document constructor parameter");const e=t.element;if(!e.parentNode&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("DOM portal content must be attached to a parent node.");const n=this._document.createComment("dom-portal");t.setAttachedHost(this),e.parentNode.insertBefore(n,e),this._getRootNode().appendChild(e),this._attachedPortal=t,super.setDisposeFn((()=>{n.parentNode&&n.parentNode.replaceChild(e,n)}))},this._document=n}get portal(){return this._attachedPortal}set portal(t){(!this.hasAttached()||t||this._isInitialized)&&(this.hasAttached()&&super.detach(),t&&super.attach(t),this._attachedPortal=t)}get attachedRef(){return this._attachedRef}ngOnInit(){this._isInitialized=!0}ngOnDestroy(){super.dispose(),this._attachedPortal=null,this._attachedRef=null}attachComponentPortal(t){t.setAttachedHost(this);const e=null!=t.viewContainerRef?t.viewContainerRef:this._viewContainerRef,n=(t.componentFactoryResolver||this._componentFactoryResolver).resolveComponentFactory(t.component),o=e.createComponent(n,e.length,t.injector||e.injector);return e!==this._viewContainerRef&&this._getRootNode().appendChild(o.hostView.rootNodes[0]),super.setDisposeFn((()=>o.destroy())),this._attachedPortal=t,this._attachedRef=o,this.attached.emit(o),o}attachTemplatePortal(t){t.setAttachedHost(this);const e=this._viewContainerRef.createEmbeddedView(t.templateRef,t.context);return super.setDisposeFn((()=>this._viewContainerRef.clear())),this._attachedPortal=t,this._attachedRef=e,this.attached.emit(e),e}_getRootNode(){const t=this._viewContainerRef.element.nativeElement;return t.nodeType===t.ELEMENT_NODE?t:t.parentNode}}DF.ɵfac=function t(e){return new(e||DF)(Sm(ug),Sm(eh),Sm(Z_))},DF.ɵdir=lo({type:DF,selectors:[["","cdkPortalOutlet",""]],inputs:{portal:["cdkPortalOutlet","portal"]},outputs:{attached:"attached"},exportAs:["cdkPortalOutlet"],features:[xp]}),DF.ctorParameters=()=>[{type:ug},{type:eh},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],DF.propDecorators={attached:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(DF,[{type:Cy,args:[{selector:"[cdkPortalOutlet]",exportAs:"cdkPortalOutlet",inputs:["portal: cdkPortalOutlet"]}]}],(function(){return[{type:ug},{type:eh},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),{attached:[{type:Oy}]});class EF extends DF{}EF.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(EF)))(n||EF)}})(),EF.ɵdir=lo({type:EF,selectors:[["","cdkPortalHost",""],["","portalHost",""]],inputs:{portal:["cdkPortalHost","portal"]},exportAs:["cdkPortalHost"],features:[pg([{provide:DF,useExisting:EF}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(EF,[{type:Cy,args:[{selector:"[cdkPortalHost], [portalHost]",exportAs:"cdkPortalHost",inputs:["portal: cdkPortalHost"],providers:[{provide:DF,useExisting:EF}]}]}],null,null);class RF{}RF.ɵfac=function t(e){return new(e||RF)},RF.ɵmod=ao({type:RF}),RF.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(RF,[{type:Ay,args:[{exports:[kF,DF,SF,EF],declarations:[kF,DF,SF,EF]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(RF,{declarations:[kF,DF,SF,EF],exports:[kF,DF,SF,EF]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const AF=zz();class TF{constructor(t,e){this._viewportRuler=t,this._previousHTMLStyles={top:"",left:""},this._isEnabled=!1,this._document=e}attach(){}enable(){if(this._canBeEnabled()){const t=this._document.documentElement;this._previousScrollPosition=this._viewportRuler.getViewportScrollPosition(),this._previousHTMLStyles.left=t.style.left||"",this._previousHTMLStyles.top=t.style.top||"",t.style.left=vz(-this._previousScrollPosition.left),t.style.top=vz(-this._previousScrollPosition.top),t.classList.add("cdk-global-scrollblock"),this._isEnabled=!0}}disable(){if(this._isEnabled){const t=this._document.documentElement,e=t.style,n=this._document.body.style,o=e.scrollBehavior||"",i=n.scrollBehavior||"";this._isEnabled=!1,e.left=this._previousHTMLStyles.left,e.top=this._previousHTMLStyles.top,t.classList.remove("cdk-global-scrollblock"),AF&&(e.scrollBehavior=n.scrollBehavior="auto"),window.scroll(this._previousScrollPosition.left,this._previousScrollPosition.top),AF&&(e.scrollBehavior=o,n.scrollBehavior=i)}}_canBeEnabled(){if(this._document.documentElement.classList.contains("cdk-global-scrollblock")||this._isEnabled)return!1;const t=this._document.body,e=this._viewportRuler.getViewportSize();return t.scrollHeight>e.height||t.scrollWidth>e.width}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function NF(){return Error("Scroll strategy has already been attached.")}class zF{constructor(t,e,n,o){this._scrollDispatcher=t,this._ngZone=e,this._viewportRuler=n,this._config=o,this._scrollSubscription=null,this._detach=()=>{this.disable(),this._overlayRef.hasAttached()&&this._ngZone.run((()=>this._overlayRef.detach()))}}attach(t){if(this._overlayRef&&("undefined"==typeof ngDevMode||ngDevMode))throw NF();this._overlayRef=t}enable(){if(this._scrollSubscription)return;const t=this._scrollDispatcher.scrolled(0);this._config&&this._config.threshold&&this._config.threshold>1?(this._initialScrollPosition=this._viewportRuler.getViewportScrollPosition().top,this._scrollSubscription=t.subscribe((()=>{const t=this._viewportRuler.getViewportScrollPosition().top;Math.abs(t-this._initialScrollPosition)>this._config.threshold?this._detach():this._overlayRef.updatePosition()}))):this._scrollSubscription=t.subscribe(this._detach)}disable(){this._scrollSubscription&&(this._scrollSubscription.unsubscribe(),this._scrollSubscription=null)}detach(){this.disable(),this._overlayRef=null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class IF{enable(){}disable(){}attach(){}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function HF(t,e){return e.some((e=>t.bottom<e.top||t.top>e.bottom||t.right<e.left||t.left>e.right))}function FF(t,e){return e.some((e=>t.top<e.top||t.bottom>e.bottom||t.left<e.left||t.right>e.right))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class LF{constructor(t,e,n,o){this._scrollDispatcher=t,this._viewportRuler=e,this._ngZone=n,this._config=o,this._scrollSubscription=null}attach(t){if(this._overlayRef&&("undefined"==typeof ngDevMode||ngDevMode))throw NF();this._overlayRef=t}enable(){this._scrollSubscription||(this._scrollSubscription=this._scrollDispatcher.scrolled(this._config?this._config.scrollThrottle:0).subscribe((()=>{if(this._overlayRef.updatePosition(),this._config&&this._config.autoClose){const t=this._overlayRef.overlayElement.getBoundingClientRect(),{width:e,height:n}=this._viewportRuler.getViewportSize();HF(t,[{width:e,height:n,bottom:n,right:e,top:0,left:0}])&&(this.disable(),this._ngZone.run((()=>this._overlayRef.detach())))}})))}disable(){this._scrollSubscription&&(this._scrollSubscription.unsubscribe(),this._scrollSubscription=null)}detach(){this.disable(),this._overlayRef=null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class BF{constructor(t,e,n,o){this._scrollDispatcher=t,this._viewportRuler=e,this._ngZone=n,this.noop=()=>new IF,this.close=t=>new zF(this._scrollDispatcher,this._ngZone,this._viewportRuler,t),this.block=()=>new TF(this._viewportRuler,this._document),this.reposition=t=>new LF(this._scrollDispatcher,this._viewportRuler,this._ngZone,t),this._document=o}}BF.ɵfac=function t(e){return new(e||BF)(vr(pF),vr(uF),vr(a_),vr(Z_))},BF.ɵprov=Mn({factory:function t(){return new BF(vr(pF),vr(uF),vr(a_),vr(Z_))},token:BF,providedIn:"root"}),BF.ctorParameters=()=>[{type:pF},{type:uF},{type:a_},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(BF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:pF},{type:uF},{type:a_},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class VF{constructor(t){if(this.scrollStrategy=new IF,this.panelClass="",this.hasBackdrop=!1,this.backdropClass="cdk-overlay-dark-backdrop",this.disposeOnNavigation=!1,t){const e=Object.keys(t);for(const n of e)void 0!==t[n]&&(this[n]=t[n])}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class jF{constructor(t,e,n,o,i){this.offsetX=n,this.offsetY=o,this.panelClass=i,this.originX=t.originX,this.originY=t.originY,this.overlayX=e.overlayX,this.overlayY=e.overlayY}}class UF{}class GF{constructor(t,e){this.connectionPair=t,this.scrollableViewProperties=e}}function WF(t,e){if("top"!==e&&"bottom"!==e&&"center"!==e)throw Error(`ConnectedPosition: Invalid ${t} "${e}". Expected "top", "bottom" or "center".`)}function YF(t,e){if("start"!==e&&"end"!==e&&"center"!==e)throw Error(`ConnectedPosition: Invalid ${t} "${e}". Expected "start", "end" or "center".`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */GF.ctorParameters=()=>[{type:jF},{type:UF,decorators:[{type:Sr}]}];class qF{constructor(t){this._attachedOverlays=[],this._document=t}ngOnDestroy(){this.detach()}add(t){this.remove(t),this._attachedOverlays.push(t)}remove(t){const e=this._attachedOverlays.indexOf(t);e>-1&&this._attachedOverlays.splice(e,1),0===this._attachedOverlays.length&&this.detach()}}qF.ɵfac=function t(e){return new(e||qF)(vr(Z_))},qF.ɵprov=Mn({factory:function t(){return new qF(vr(Z_))},token:qF,providedIn:"root"}),qF.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(qF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class ZF extends qF{constructor(t){super(t),this._keydownListener=t=>{const e=this._attachedOverlays;for(let n=e.length-1;n>-1;n--)if(e[n]._keydownEvents.observers.length>0){e[n]._keydownEvents.next(t);break}}}add(t){super.add(t),this._isAttached||(this._document.body.addEventListener("keydown",this._keydownListener),this._isAttached=!0)}detach(){this._isAttached&&(this._document.body.removeEventListener("keydown",this._keydownListener),this._isAttached=!1)}}ZF.ɵfac=function t(e){return new(e||ZF)(vr(Z_))},ZF.ɵprov=Mn({factory:function t(){return new ZF(vr(Z_))},token:ZF,providedIn:"root"}),ZF.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(ZF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class XF extends qF{constructor(t,e){super(t),this._platform=e,this._cursorStyleIsSet=!1,this._clickListener=t=>{const e=Lz(t),n=this._attachedOverlays.slice();for(let o=n.length-1;o>-1;o--){const i=n[o];if(!(i._outsidePointerEvents.observers.length<1)&&i.hasAttached()){if(i.overlayElement.contains(e))break;i._outsidePointerEvents.next(t)}}}}add(t){if(super.add(t),!this._isAttached){const t=this._document.body;t.addEventListener("click",this._clickListener,!0),t.addEventListener("auxclick",this._clickListener,!0),t.addEventListener("contextmenu",this._clickListener,!0),this._platform.IOS&&!this._cursorStyleIsSet&&(this._cursorOriginalValue=t.style.cursor,t.style.cursor="pointer",this._cursorStyleIsSet=!0),this._isAttached=!0}}detach(){if(this._isAttached){const t=this._document.body;t.removeEventListener("click",this._clickListener,!0),t.removeEventListener("auxclick",this._clickListener,!0),t.removeEventListener("contextmenu",this._clickListener,!0),this._platform.IOS&&this._cursorStyleIsSet&&(t.style.cursor=this._cursorOriginalValue,this._cursorStyleIsSet=!1),this._isAttached=!1}}}XF.ɵfac=function t(e){return new(e||XF)(vr(Z_),vr(wz))},XF.ɵprov=Mn({factory:function t(){return new XF(vr(Z_),vr(wz))},token:XF,providedIn:"root"}),XF.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(XF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const KF="undefined"!=typeof window?window:{},JF=void 0!==KF.__karma__&&!!KF.__karma__||void 0!==KF.jasmine&&!!KF.jasmine||void 0!==KF.jest&&!!KF.jest||void 0!==KF.Mocha&&!!KF.Mocha;class QF{constructor(t,e){this._platform=e,this._document=t}ngOnDestroy(){const t=this._containerElement;t&&t.parentNode&&t.parentNode.removeChild(t)}getContainerElement(){return this._containerElement||this._createContainer(),this._containerElement}_createContainer(){const t="cdk-overlay-container";if(this._platform.isBrowser||JF){const e=this._document.querySelectorAll(`.${t}[platform="server"], .${t}[platform="test"]`);for(let t=0;t<e.length;t++)e[t].parentNode.removeChild(e[t])}const e=this._document.createElement("div");e.classList.add(t),JF?e.setAttribute("platform","test"):this._platform.isBrowser||e.setAttribute("platform","server"),this._document.body.appendChild(e),this._containerElement=e}}QF.ɵfac=function t(e){return new(e||QF)(vr(Z_),vr(wz))},QF.ɵprov=Mn({factory:function t(){return new QF(vr(Z_),vr(wz))},token:QF,providedIn:"root"}),QF.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(QF,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class $F{constructor(t,e,n,o,i,a,r,s,l){this._portalOutlet=t,this._host=e,this._pane=n,this._config=o,this._ngZone=i,this._keyboardDispatcher=a,this._document=r,this._location=s,this._outsideClickDispatcher=l,this._backdropElement=null,this._backdropClick=new I,this._attachments=new I,this._detachments=new I,this._locationChanges=m.EMPTY,this._backdropClickHandler=t=>this._backdropClick.next(t),this._keydownEvents=new I,this._outsidePointerEvents=new I,o.scrollStrategy&&(this._scrollStrategy=o.scrollStrategy,this._scrollStrategy.attach(this)),this._positionStrategy=o.positionStrategy}get overlayElement(){return this._pane}get backdropElement(){return this._backdropElement}get hostElement(){return this._host}attach(t){let e=this._portalOutlet.attach(t);return!this._host.parentElement&&this._previousHostParent&&this._previousHostParent.appendChild(this._host),this._positionStrategy&&this._positionStrategy.attach(this),this._updateStackingOrder(),this._updateElementSize(),this._updateElementDirection(),this._scrollStrategy&&this._scrollStrategy.enable(),this._ngZone.onStable.pipe(be(1)).subscribe((()=>{this.hasAttached()&&this.updatePosition()})),this._togglePointerEvents(!0),this._config.hasBackdrop&&this._attachBackdrop(),this._config.panelClass&&this._toggleClasses(this._pane,this._config.panelClass,!0),this._attachments.next(),this._keyboardDispatcher.add(this),this._config.disposeOnNavigation&&(this._locationChanges=this._location.subscribe((()=>this.dispose()))),this._outsideClickDispatcher.add(this),e}detach(){if(!this.hasAttached())return;this.detachBackdrop(),this._togglePointerEvents(!1),this._positionStrategy&&this._positionStrategy.detach&&this._positionStrategy.detach(),this._scrollStrategy&&this._scrollStrategy.disable();const t=this._portalOutlet.detach();return this._detachments.next(),this._keyboardDispatcher.remove(this),this._detachContentWhenStable(),this._locationChanges.unsubscribe(),this._outsideClickDispatcher.remove(this),t}dispose(){const t=this.hasAttached();this._positionStrategy&&this._positionStrategy.dispose(),this._disposeScrollStrategy(),this.detachBackdrop(),this._locationChanges.unsubscribe(),this._keyboardDispatcher.remove(this),this._portalOutlet.dispose(),this._attachments.complete(),this._backdropClick.complete(),this._keydownEvents.complete(),this._outsidePointerEvents.complete(),this._outsideClickDispatcher.remove(this),this._host&&this._host.parentNode&&(this._host.parentNode.removeChild(this._host),this._host=null),this._previousHostParent=this._pane=null,t&&this._detachments.next(),this._detachments.complete()}hasAttached(){return this._portalOutlet.hasAttached()}backdropClick(){return this._backdropClick}attachments(){return this._attachments}detachments(){return this._detachments}keydownEvents(){return this._keydownEvents}outsidePointerEvents(){return this._outsidePointerEvents}getConfig(){return this._config}updatePosition(){this._positionStrategy&&this._positionStrategy.apply()}updatePositionStrategy(t){t!==this._positionStrategy&&(this._positionStrategy&&this._positionStrategy.dispose(),this._positionStrategy=t,this.hasAttached()&&(t.attach(this),this.updatePosition()))}updateSize(t){this._config=Object.assign(Object.assign({},this._config),t),this._updateElementSize()}setDirection(t){this._config=Object.assign(Object.assign({},this._config),{direction:t}),this._updateElementDirection()}addPanelClass(t){this._pane&&this._toggleClasses(this._pane,t,!0)}removePanelClass(t){this._pane&&this._toggleClasses(this._pane,t,!1)}getDirection(){const t=this._config.direction;return t?"string"==typeof t?t:t.value:"ltr"}updateScrollStrategy(t){t!==this._scrollStrategy&&(this._disposeScrollStrategy(),this._scrollStrategy=t,this.hasAttached()&&(t.attach(this),t.enable()))}_updateElementDirection(){this._host.setAttribute("dir",this.getDirection())}_updateElementSize(){if(!this._pane)return;const t=this._pane.style;t.width=vz(this._config.width),t.height=vz(this._config.height),t.minWidth=vz(this._config.minWidth),t.minHeight=vz(this._config.minHeight),t.maxWidth=vz(this._config.maxWidth),t.maxHeight=vz(this._config.maxHeight)}_togglePointerEvents(t){this._pane.style.pointerEvents=t?"":"none"}_attachBackdrop(){const t="cdk-overlay-backdrop-showing";this._backdropElement=this._document.createElement("div"),this._backdropElement.classList.add("cdk-overlay-backdrop"),this._config.backdropClass&&this._toggleClasses(this._backdropElement,this._config.backdropClass,!0),this._host.parentElement.insertBefore(this._backdropElement,this._host),this._backdropElement.addEventListener("click",this._backdropClickHandler),"undefined"!=typeof requestAnimationFrame?this._ngZone.runOutsideAngular((()=>{requestAnimationFrame((()=>{this._backdropElement&&this._backdropElement.classList.add(t)}))})):this._backdropElement.classList.add(t)}_updateStackingOrder(){this._host.nextSibling&&this._host.parentNode.appendChild(this._host)}detachBackdrop(){let t,e=this._backdropElement;if(!e)return;let n=()=>{e&&(e.removeEventListener("click",this._backdropClickHandler),e.removeEventListener("transitionend",n),e.parentNode&&e.parentNode.removeChild(e)),this._backdropElement==e&&(this._backdropElement=null),this._config.backdropClass&&this._toggleClasses(e,this._config.backdropClass,!1),clearTimeout(t)};e.classList.remove("cdk-overlay-backdrop-showing"),this._ngZone.runOutsideAngular((()=>{e.addEventListener("transitionend",n)})),e.style.pointerEvents="none",t=this._ngZone.runOutsideAngular((()=>setTimeout(n,500)))}_toggleClasses(t,e,n){const o=t.classList;Mz(e).forEach((t=>{t&&(n?o.add(t):o.remove(t))}))}_detachContentWhenStable(){this._ngZone.runOutsideAngular((()=>{const t=this._ngZone.onStable.pipe(Ie(re(this._attachments,this._detachments))).subscribe((()=>{this._pane&&this._host&&0!==this._pane.children.length||(this._pane&&this._config.panelClass&&this._toggleClasses(this._pane,this._config.panelClass,!1),this._host&&this._host.parentElement&&(this._previousHostParent=this._host.parentElement,this._previousHostParent.removeChild(this._host)),t.unsubscribe())}))}))}_disposeScrollStrategy(){const t=this._scrollStrategy;t&&(t.disable(),t.detach&&t.detach())}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const tL="cdk-overlay-connected-position-bounding-box",eL=/([A-Za-z%]+)$/;class nL{constructor(t,e,n,o,i){this._viewportRuler=e,this._document=n,this._platform=o,this._overlayContainer=i,this._lastBoundingBoxSize={width:0,height:0},this._isPushed=!1,this._canPush=!0,this._growAfterOpen=!1,this._hasFlexibleDimensions=!0,this._positionLocked=!1,this._viewportMargin=0,this._scrollables=[],this._preferredPositions=[],this._positionChanges=new I,this._resizeSubscription=m.EMPTY,this._offsetX=0,this._offsetY=0,this._appliedPanelClasses=[],this.positionChanges=this._positionChanges,this.setOrigin(t)}get positions(){return this._preferredPositions}attach(t){if(this._overlayRef&&t!==this._overlayRef&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("This position strategy is already attached to an overlay");this._validatePositions(),t.hostElement.classList.add(tL),this._overlayRef=t,this._boundingBox=t.hostElement,this._pane=t.overlayElement,this._isDisposed=!1,this._isInitialRender=!0,this._lastPosition=null,this._resizeSubscription.unsubscribe(),this._resizeSubscription=this._viewportRuler.change().subscribe((()=>{this._isInitialRender=!0,this.apply()}))}apply(){if(this._isDisposed||!this._platform.isBrowser)return;if(!this._isInitialRender&&this._positionLocked&&this._lastPosition)return void this.reapplyLastPosition();this._clearPanelClasses(),this._resetOverlayElementStyles(),this._resetBoundingBoxStyles(),this._viewportRect=this._getNarrowedViewportRect(),this._originRect=this._getOriginRect(),this._overlayRect=this._pane.getBoundingClientRect();const t=this._originRect,e=this._overlayRect,n=this._viewportRect,o=[];let i;for(let a of this._preferredPositions){let r=this._getOriginPoint(t,a),s=this._getOverlayPoint(r,e,a),l=this._getOverlayFit(s,e,n,a);if(l.isCompletelyWithinViewport)return this._isPushed=!1,void this._applyPosition(a,r);this._canFitWithFlexibleDimensions(l,s,n)?o.push({position:a,origin:r,overlayRect:e,boundingBoxRect:this._calculateBoundingBoxRect(r,a)}):(!i||i.overlayFit.visibleArea<l.visibleArea)&&(i={overlayFit:l,overlayPoint:s,originPoint:r,position:a,overlayRect:e})}if(o.length){let t=null,e=-1;for(const n of o){const o=n.boundingBoxRect.width*n.boundingBoxRect.height*(n.position.weight||1);o>e&&(e=o,t=n)}return this._isPushed=!1,void this._applyPosition(t.position,t.origin)}if(this._canPush)return this._isPushed=!0,void this._applyPosition(i.position,i.originPoint);this._applyPosition(i.position,i.originPoint)}detach(){this._clearPanelClasses(),this._lastPosition=null,this._previousPushAmount=null,this._resizeSubscription.unsubscribe()}dispose(){this._isDisposed||(this._boundingBox&&oL(this._boundingBox.style,{top:"",left:"",right:"",bottom:"",height:"",width:"",alignItems:"",justifyContent:""}),this._pane&&this._resetOverlayElementStyles(),this._overlayRef&&this._overlayRef.hostElement.classList.remove(tL),this.detach(),this._positionChanges.complete(),this._overlayRef=this._boundingBox=null,this._isDisposed=!0)}reapplyLastPosition(){if(!this._isDisposed&&(!this._platform||this._platform.isBrowser)){this._originRect=this._getOriginRect(),this._overlayRect=this._pane.getBoundingClientRect(),this._viewportRect=this._getNarrowedViewportRect();const t=this._lastPosition||this._preferredPositions[0],e=this._getOriginPoint(this._originRect,t);this._applyPosition(t,e)}}withScrollableContainers(t){return this._scrollables=t,this}withPositions(t){return this._preferredPositions=t,-1===t.indexOf(this._lastPosition)&&(this._lastPosition=null),this._validatePositions(),this}withViewportMargin(t){return this._viewportMargin=t,this}withFlexibleDimensions(t=!0){return this._hasFlexibleDimensions=t,this}withGrowAfterOpen(t=!0){return this._growAfterOpen=t,this}withPush(t=!0){return this._canPush=t,this}withLockedPosition(t=!0){return this._positionLocked=t,this}setOrigin(t){return this._origin=t,this}withDefaultOffsetX(t){return this._offsetX=t,this}withDefaultOffsetY(t){return this._offsetY=t,this}withTransformOriginOn(t){return this._transformOriginSelector=t,this}_getOriginPoint(t,e){let n,o;if("center"==e.originX)n=t.left+t.width/2;else{const o=this._isRtl()?t.right:t.left,i=this._isRtl()?t.left:t.right;n="start"==e.originX?o:i}return o="center"==e.originY?t.top+t.height/2:"top"==e.originY?t.top:t.bottom,{x:n,y:o}}_getOverlayPoint(t,e,n){let o,i;return o="center"==n.overlayX?-e.width/2:"start"===n.overlayX?this._isRtl()?-e.width:0:this._isRtl()?0:-e.width,i="center"==n.overlayY?-e.height/2:"top"==n.overlayY?0:-e.height,{x:t.x+o,y:t.y+i}}_getOverlayFit(t,e,n,o){const i=aL(e);let{x:a,y:r}=t,s=this._getOffset(o,"x"),l=this._getOffset(o,"y");s&&(a+=s),l&&(r+=l);let c=0-r,d=r+i.height-n.height,p=this._subtractOverflows(i.width,0-a,a+i.width-n.width),m=this._subtractOverflows(i.height,c,d),u=p*m;return{visibleArea:u,isCompletelyWithinViewport:i.width*i.height===u,fitsInViewportVertically:m===i.height,fitsInViewportHorizontally:p==i.width}}_canFitWithFlexibleDimensions(t,e,n){if(this._hasFlexibleDimensions){const o=n.bottom-e.y,i=n.right-e.x,a=iL(this._overlayRef.getConfig().minHeight),r=iL(this._overlayRef.getConfig().minWidth),s=t.fitsInViewportHorizontally||null!=r&&r<=i;return(t.fitsInViewportVertically||null!=a&&a<=o)&&s}return!1}_pushOverlayOnScreen(t,e,n){if(this._previousPushAmount&&this._positionLocked)return{x:t.x+this._previousPushAmount.x,y:t.y+this._previousPushAmount.y};const o=aL(e),i=this._viewportRect,a=Math.max(t.x+o.width-i.width,0),r=Math.max(t.y+o.height-i.height,0),s=Math.max(i.top-n.top-t.y,0),l=Math.max(i.left-n.left-t.x,0);let c=0,d=0;return c=o.width<=i.width?l||-a:t.x<this._viewportMargin?i.left-n.left-t.x:0,d=o.height<=i.height?s||-r:t.y<this._viewportMargin?i.top-n.top-t.y:0,this._previousPushAmount={x:c,y:d},{x:t.x+c,y:t.y+d}}_applyPosition(t,e){if(this._setTransformOrigin(t),this._setOverlayElementStyles(e,t),this._setBoundingBoxStyles(e,t),t.panelClass&&this._addPanelClasses(t.panelClass),this._lastPosition=t,this._positionChanges.observers.length){const e=this._getScrollVisibility(),n=new GF(t,e);this._positionChanges.next(n)}this._isInitialRender=!1}_setTransformOrigin(t){if(!this._transformOriginSelector)return;const e=this._boundingBox.querySelectorAll(this._transformOriginSelector);let n,o=t.overlayY;n="center"===t.overlayX?"center":this._isRtl()?"start"===t.overlayX?"right":"left":"start"===t.overlayX?"left":"right";for(let t=0;t<e.length;t++)e[t].style.transformOrigin=`${n} ${o}`}_calculateBoundingBoxRect(t,e){const n=this._viewportRect,o=this._isRtl();let i,a,r,s,l,c;if("top"===e.overlayY)a=t.y,i=n.height-a+this._viewportMargin;else if("bottom"===e.overlayY)r=n.height-t.y+2*this._viewportMargin,i=n.height-r+this._viewportMargin;else{const e=Math.min(n.bottom-t.y+n.top,t.y),o=this._lastBoundingBoxSize.height;i=2*e,a=t.y-e,i>o&&!this._isInitialRender&&!this._growAfterOpen&&(a=t.y-o/2)}if("end"===e.overlayX&&!o||"start"===e.overlayX&&o)c=n.width-t.x+this._viewportMargin,s=t.x-this._viewportMargin;else if("start"===e.overlayX&&!o||"end"===e.overlayX&&o)l=t.x,s=n.right-t.x;else{const e=Math.min(n.right-t.x+n.left,t.x),o=this._lastBoundingBoxSize.width;s=2*e,l=t.x-e,s>o&&!this._isInitialRender&&!this._growAfterOpen&&(l=t.x-o/2)}return{top:a,left:l,bottom:r,right:c,width:s,height:i}}_setBoundingBoxStyles(t,e){const n=this._calculateBoundingBoxRect(t,e);this._isInitialRender||this._growAfterOpen||(n.height=Math.min(n.height,this._lastBoundingBoxSize.height),n.width=Math.min(n.width,this._lastBoundingBoxSize.width));const o={};if(this._hasExactPosition())o.top=o.left="0",o.bottom=o.right=o.maxHeight=o.maxWidth="",o.width=o.height="100%";else{const t=this._overlayRef.getConfig().maxHeight,i=this._overlayRef.getConfig().maxWidth;o.height=vz(n.height),o.top=vz(n.top),o.bottom=vz(n.bottom),o.width=vz(n.width),o.left=vz(n.left),o.right=vz(n.right),o.alignItems="center"===e.overlayX?"center":"end"===e.overlayX?"flex-end":"flex-start",o.justifyContent="center"===e.overlayY?"center":"bottom"===e.overlayY?"flex-end":"flex-start",t&&(o.maxHeight=vz(t)),i&&(o.maxWidth=vz(i))}this._lastBoundingBoxSize=n,oL(this._boundingBox.style,o)}_resetBoundingBoxStyles(){oL(this._boundingBox.style,{top:"0",left:"0",right:"0",bottom:"0",height:"",width:"",alignItems:"",justifyContent:""})}_resetOverlayElementStyles(){oL(this._pane.style,{top:"",left:"",bottom:"",right:"",position:"",transform:""})}_setOverlayElementStyles(t,e){const n={},o=this._hasExactPosition(),i=this._hasFlexibleDimensions,a=this._overlayRef.getConfig();if(o){const o=this._viewportRuler.getViewportScrollPosition();oL(n,this._getExactOverlayY(e,t,o)),oL(n,this._getExactOverlayX(e,t,o))}else n.position="static";let r="",s=this._getOffset(e,"x"),l=this._getOffset(e,"y");s&&(r+=`translateX(${s}px) `),l&&(r+=`translateY(${l}px)`),n.transform=r.trim(),a.maxHeight&&(o?n.maxHeight=vz(a.maxHeight):i&&(n.maxHeight="")),a.maxWidth&&(o?n.maxWidth=vz(a.maxWidth):i&&(n.maxWidth="")),oL(this._pane.style,n)}_getExactOverlayY(t,e,n){let o={top:"",bottom:""},i=this._getOverlayPoint(e,this._overlayRect,t);this._isPushed&&(i=this._pushOverlayOnScreen(i,this._overlayRect,n));let a=this._overlayContainer.getContainerElement().getBoundingClientRect().top;return i.y-=a,"bottom"===t.overlayY?o.bottom=this._document.documentElement.clientHeight-(i.y+this._overlayRect.height)+"px":o.top=vz(i.y),o}_getExactOverlayX(t,e,n){let o,i={left:"",right:""},a=this._getOverlayPoint(e,this._overlayRect,t);return this._isPushed&&(a=this._pushOverlayOnScreen(a,this._overlayRect,n)),o=this._isRtl()?"end"===t.overlayX?"left":"right":"end"===t.overlayX?"right":"left","right"===o?i.right=this._document.documentElement.clientWidth-(a.x+this._overlayRect.width)+"px":i.left=vz(a.x),i}_getScrollVisibility(){const t=this._getOriginRect(),e=this._pane.getBoundingClientRect(),n=this._scrollables.map((t=>t.getElementRef().nativeElement.getBoundingClientRect()));return{isOriginClipped:FF(t,n),isOriginOutsideView:HF(t,n),isOverlayClipped:FF(e,n),isOverlayOutsideView:HF(e,n)}}_subtractOverflows(t,...e){return e.reduce(((t,e)=>t-Math.max(e,0)),t)}_getNarrowedViewportRect(){const t=this._document.documentElement.clientWidth,e=this._document.documentElement.clientHeight,n=this._viewportRuler.getViewportScrollPosition();return{top:n.top+this._viewportMargin,left:n.left+this._viewportMargin,right:n.left+t-this._viewportMargin,bottom:n.top+e-this._viewportMargin,width:t-2*this._viewportMargin,height:e-2*this._viewportMargin}}_isRtl(){return"rtl"===this._overlayRef.getDirection()}_hasExactPosition(){return!this._hasFlexibleDimensions||this._isPushed}_getOffset(t,e){return"x"===e?null==t.offsetX?this._offsetX:t.offsetX:null==t.offsetY?this._offsetY:t.offsetY}_validatePositions(){if("undefined"==typeof ngDevMode||ngDevMode){if(!this._preferredPositions.length)throw Error("FlexibleConnectedPositionStrategy: At least one position is required.");this._preferredPositions.forEach((t=>{YF("originX",t.originX),WF("originY",t.originY),YF("overlayX",t.overlayX),WF("overlayY",t.overlayY)}))}}_addPanelClasses(t){this._pane&&Mz(t).forEach((t=>{""!==t&&-1===this._appliedPanelClasses.indexOf(t)&&(this._appliedPanelClasses.push(t),this._pane.classList.add(t))}))}_clearPanelClasses(){this._pane&&(this._appliedPanelClasses.forEach((t=>{this._pane.classList.remove(t)})),this._appliedPanelClasses=[])}_getOriginRect(){const t=this._origin;if(t instanceof hg)return t.nativeElement.getBoundingClientRect();if(t instanceof Element)return t.getBoundingClientRect();const e=t.width||0,n=t.height||0;return{top:t.y,bottom:t.y+n,left:t.x,right:t.x+e,height:n,width:e}}}function oL(t,e){for(let n in e)e.hasOwnProperty(n)&&(t[n]=e[n]);return t}function iL(t){if("number"!=typeof t&&null!=t){const[e,n]=t.split(eL);return n&&"px"!==n?null:parseFloat(e)}return t||null}function aL(t){return{top:Math.floor(t.top),right:Math.floor(t.right),bottom:Math.floor(t.bottom),left:Math.floor(t.left),width:Math.floor(t.width),height:Math.floor(t.height)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class rL{constructor(t,e,n,o,i,a,r){this._preferredPositions=[],this._positionStrategy=new nL(n,o,i,a,r).withFlexibleDimensions(!1).withPush(!1).withViewportMargin(0),this.withFallbackPosition(t,e),this.onPositionChange=this._positionStrategy.positionChanges}get positions(){return this._preferredPositions}attach(t){this._overlayRef=t,this._positionStrategy.attach(t),this._direction&&(t.setDirection(this._direction),this._direction=null)}dispose(){this._positionStrategy.dispose()}detach(){this._positionStrategy.detach()}apply(){this._positionStrategy.apply()}recalculateLastPosition(){this._positionStrategy.reapplyLastPosition()}withScrollableContainers(t){this._positionStrategy.withScrollableContainers(t)}withFallbackPosition(t,e,n,o){const i=new jF(t,e,n,o);return this._preferredPositions.push(i),this._positionStrategy.withPositions(this._preferredPositions),this}withDirection(t){return this._overlayRef?this._overlayRef.setDirection(t):this._direction=t,this}withOffsetX(t){return this._positionStrategy.withDefaultOffsetX(t),this}withOffsetY(t){return this._positionStrategy.withDefaultOffsetY(t),this}withLockedPosition(t){return this._positionStrategy.withLockedPosition(t),this}withPositions(t){return this._preferredPositions=t.slice(),this._positionStrategy.withPositions(this._preferredPositions),this}setOrigin(t){return this._positionStrategy.setOrigin(t),this}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const sL="cdk-global-overlay-wrapper";class lL{constructor(){this._cssPosition="static",this._topOffset="",this._bottomOffset="",this._leftOffset="",this._rightOffset="",this._alignItems="",this._justifyContent="",this._width="",this._height=""}attach(t){const e=t.getConfig();this._overlayRef=t,this._width&&!e.width&&t.updateSize({width:this._width}),this._height&&!e.height&&t.updateSize({height:this._height}),t.hostElement.classList.add(sL),this._isDisposed=!1}top(t=""){return this._bottomOffset="",this._topOffset=t,this._alignItems="flex-start",this}left(t=""){return this._rightOffset="",this._leftOffset=t,this._justifyContent="flex-start",this}bottom(t=""){return this._topOffset="",this._bottomOffset=t,this._alignItems="flex-end",this}right(t=""){return this._leftOffset="",this._rightOffset=t,this._justifyContent="flex-end",this}width(t=""){return this._overlayRef?this._overlayRef.updateSize({width:t}):this._width=t,this}height(t=""){return this._overlayRef?this._overlayRef.updateSize({height:t}):this._height=t,this}centerHorizontally(t=""){return this.left(t),this._justifyContent="center",this}centerVertically(t=""){return this.top(t),this._alignItems="center",this}apply(){if(!this._overlayRef||!this._overlayRef.hasAttached())return;const t=this._overlayRef.overlayElement.style,e=this._overlayRef.hostElement.style,n=this._overlayRef.getConfig(),{width:o,height:i,maxWidth:a,maxHeight:r}=n,s=!("100%"!==o&&"100vw"!==o||a&&"100%"!==a&&"100vw"!==a),l=!("100%"!==i&&"100vh"!==i||r&&"100%"!==r&&"100vh"!==r);t.position=this._cssPosition,t.marginLeft=s?"0":this._leftOffset,t.marginTop=l?"0":this._topOffset,t.marginBottom=this._bottomOffset,t.marginRight=this._rightOffset,s?e.justifyContent="flex-start":"center"===this._justifyContent?e.justifyContent="center":"rtl"===this._overlayRef.getConfig().direction?"flex-start"===this._justifyContent?e.justifyContent="flex-end":"flex-end"===this._justifyContent&&(e.justifyContent="flex-start"):e.justifyContent=this._justifyContent,e.alignItems=l?"flex-start":this._alignItems}dispose(){if(this._isDisposed||!this._overlayRef)return;const t=this._overlayRef.overlayElement.style,e=this._overlayRef.hostElement,n=e.style;e.classList.remove(sL),n.justifyContent=n.alignItems=t.marginTop=t.marginBottom=t.marginLeft=t.marginRight=t.position="",this._overlayRef=null,this._isDisposed=!0}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class cL{constructor(t,e,n,o){this._viewportRuler=t,this._document=e,this._platform=n,this._overlayContainer=o}global(){return new lL}connectedTo(t,e,n){return new rL(e,n,t,this._viewportRuler,this._document,this._platform,this._overlayContainer)}flexibleConnectedTo(t){return new nL(t,this._viewportRuler,this._document,this._platform,this._overlayContainer)}}cL.ɵfac=function t(e){return new(e||cL)(vr(uF),vr(Z_),vr(wz),vr(QF))},cL.ɵprov=Mn({factory:function t(){return new cL(vr(uF),vr(Z_),vr(wz),vr(QF))},token:cL,providedIn:"root"}),cL.ctorParameters=()=>[{type:uF},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz},{type:QF}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(cL,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:uF},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz},{type:QF}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let dL=0;class pL{constructor(t,e,n,o,i,a,r,s,l,c,d){this.scrollStrategies=t,this._overlayContainer=e,this._componentFactoryResolver=n,this._positionBuilder=o,this._keyboardDispatcher=i,this._injector=a,this._ngZone=r,this._document=s,this._directionality=l,this._location=c,this._outsideClickDispatcher=d}create(t){const e=this._createHostElement(),n=this._createPaneElement(e),o=this._createPortalOutlet(n),i=new VF(t);return i.direction=i.direction||this._directionality.value,new $F(o,e,n,i,this._ngZone,this._keyboardDispatcher,this._document,this._location,this._outsideClickDispatcher)}position(){return this._positionBuilder}_createPaneElement(t){const e=this._document.createElement("div");return e.id="cdk-overlay-"+dL++,e.classList.add("cdk-overlay-pane"),t.appendChild(e),e}_createHostElement(){const t=this._document.createElement("div");return this._overlayContainer.getContainerElement().appendChild(t),t}_createPortalOutlet(t){return this._appRef||(this._appRef=this._injector.get(O_)),new wF(t,this._componentFactoryResolver,this._appRef,this._injector,this._document)}}pL.ɵfac=function t(e){return new(e||pL)(vr(BF),vr(QF),vr(ug),vr(cL),vr(ZF),vr(rp),vr(a_),vr(Z_),vr(HI),vr(lC),vr(XF))},pL.ɵprov=Mn({token:pL,factory:pL.ɵfac}),pL.ctorParameters=()=>[{type:BF},{type:QF},{type:ug},{type:cL},{type:ZF},{type:rp},{type:a_},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:HI},{type:lC},{type:XF}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(pL,[{type:im}],(function(){return[{type:BF},{type:QF},{type:ug},{type:cL},{type:ZF},{type:rp},{type:a_},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:HI},{type:lC},{type:XF}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const mL=[{originX:"start",originY:"bottom",overlayX:"start",overlayY:"top"},{originX:"start",originY:"top",overlayX:"start",overlayY:"bottom"},{originX:"end",originY:"top",overlayX:"end",overlayY:"bottom"},{originX:"end",originY:"bottom",overlayX:"end",overlayY:"top"}],uL=new Ga("cdk-connected-overlay-scroll-strategy");class fL{constructor(t){this.elementRef=t}}fL.ɵfac=function t(e){return new(e||fL)(Sm(hg))},fL.ɵdir=lo({type:fL,selectors:[["","cdk-overlay-origin",""],["","overlay-origin",""],["","cdkOverlayOrigin",""]],exportAs:["cdkOverlayOrigin"]}),fL.ctorParameters=()=>[{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(fL,[{type:Cy,args:[{selector:"[cdk-overlay-origin], [overlay-origin], [cdkOverlayOrigin]",exportAs:"cdkOverlayOrigin"}]}],(function(){return[{type:hg}]}),null);class gL{constructor(t,e,n,o,i){this._overlay=t,this._dir=i,this._hasBackdrop=!1,this._lockPosition=!1,this._growAfterOpen=!1,this._flexibleDimensions=!1,this._push=!1,this._backdropSubscription=m.EMPTY,this._attachSubscription=m.EMPTY,this._detachSubscription=m.EMPTY,this._positionSubscription=m.EMPTY,this.viewportMargin=0,this.open=!1,this.disableClose=!1,this.backdropClick=new Lh,this.positionChange=new Lh,this.attach=new Lh,this.detach=new Lh,this.overlayKeydown=new Lh,this.overlayOutsideClick=new Lh,this._templatePortal=new xF(e,n),this._scrollStrategyFactory=o,this.scrollStrategy=this._scrollStrategyFactory()}get offsetX(){return this._offsetX}set offsetX(t){this._offsetX=t,this._position&&this._updatePositionStrategy(this._position)}get offsetY(){return this._offsetY}set offsetY(t){this._offsetY=t,this._position&&this._updatePositionStrategy(this._position)}get hasBackdrop(){return this._hasBackdrop}set hasBackdrop(t){this._hasBackdrop=yz(t)}get lockPosition(){return this._lockPosition}set lockPosition(t){this._lockPosition=yz(t)}get flexibleDimensions(){return this._flexibleDimensions}set flexibleDimensions(t){this._flexibleDimensions=yz(t)}get growAfterOpen(){return this._growAfterOpen}set growAfterOpen(t){this._growAfterOpen=yz(t)}get push(){return this._push}set push(t){this._push=yz(t)}get overlayRef(){return this._overlayRef}get dir(){return this._dir?this._dir.value:"ltr"}ngOnDestroy(){this._attachSubscription.unsubscribe(),this._detachSubscription.unsubscribe(),this._backdropSubscription.unsubscribe(),this._positionSubscription.unsubscribe(),this._overlayRef&&this._overlayRef.dispose()}ngOnChanges(t){this._position&&(this._updatePositionStrategy(this._position),this._overlayRef.updateSize({width:this.width,minWidth:this.minWidth,height:this.height,minHeight:this.minHeight}),t.origin&&this.open&&this._position.apply()),t.open&&(this.open?this._attachOverlay():this._detachOverlay())}_createOverlay(){this.positions&&this.positions.length||(this.positions=mL);const t=this._overlayRef=this._overlay.create(this._buildConfig());this._attachSubscription=t.attachments().subscribe((()=>this.attach.emit())),this._detachSubscription=t.detachments().subscribe((()=>this.detach.emit())),t.keydownEvents().subscribe((t=>{this.overlayKeydown.next(t),t.keyCode!==uz||this.disableClose||bz(t)||(t.preventDefault(),this._detachOverlay())})),this._overlayRef.outsidePointerEvents().subscribe((t=>{this.overlayOutsideClick.next(t)}))}_buildConfig(){const t=this._position=this.positionStrategy||this._createPositionStrategy(),e=new VF({direction:this._dir,positionStrategy:t,scrollStrategy:this.scrollStrategy,hasBackdrop:this.hasBackdrop});return(this.width||0===this.width)&&(e.width=this.width),(this.height||0===this.height)&&(e.height=this.height),(this.minWidth||0===this.minWidth)&&(e.minWidth=this.minWidth),(this.minHeight||0===this.minHeight)&&(e.minHeight=this.minHeight),this.backdropClass&&(e.backdropClass=this.backdropClass),this.panelClass&&(e.panelClass=this.panelClass),e}_updatePositionStrategy(t){const e=this.positions.map((t=>({originX:t.originX,originY:t.originY,overlayX:t.overlayX,overlayY:t.overlayY,offsetX:t.offsetX||this.offsetX,offsetY:t.offsetY||this.offsetY,panelClass:t.panelClass||void 0})));return t.setOrigin(this.origin.elementRef).withPositions(e).withFlexibleDimensions(this.flexibleDimensions).withPush(this.push).withGrowAfterOpen(this.growAfterOpen).withViewportMargin(this.viewportMargin).withLockedPosition(this.lockPosition).withTransformOriginOn(this.transformOriginSelector)}_createPositionStrategy(){const t=this._overlay.position().flexibleConnectedTo(this.origin.elementRef);return this._updatePositionStrategy(t),t}_attachOverlay(){this._overlayRef?this._overlayRef.getConfig().hasBackdrop=this.hasBackdrop:this._createOverlay(),this._overlayRef.hasAttached()||this._overlayRef.attach(this._templatePortal),this.hasBackdrop?this._backdropSubscription=this._overlayRef.backdropClick().subscribe((t=>{this.backdropClick.emit(t)})):this._backdropSubscription.unsubscribe(),this._positionSubscription.unsubscribe(),this.positionChange.observers.length>0&&(this._positionSubscription=this._position.positionChanges.pipe(He((()=>this.positionChange.observers.length>0))).subscribe((t=>{this.positionChange.emit(t),0===this.positionChange.observers.length&&this._positionSubscription.unsubscribe()})))}_detachOverlay(){this._overlayRef&&this._overlayRef.detach(),this._backdropSubscription.unsubscribe(),this._positionSubscription.unsubscribe()}}gL.ɵfac=function t(e){return new(e||gL)(Sm(pL),Sm(Xg),Sm(eh),Sm(uL),Sm(HI,8))},gL.ɵdir=lo({type:gL,selectors:[["","cdk-connected-overlay",""],["","connected-overlay",""],["","cdkConnectedOverlay",""]],inputs:{viewportMargin:["cdkConnectedOverlayViewportMargin","viewportMargin"],open:["cdkConnectedOverlayOpen","open"],disableClose:["cdkConnectedOverlayDisableClose","disableClose"],scrollStrategy:["cdkConnectedOverlayScrollStrategy","scrollStrategy"],offsetX:["cdkConnectedOverlayOffsetX","offsetX"],offsetY:["cdkConnectedOverlayOffsetY","offsetY"],hasBackdrop:["cdkConnectedOverlayHasBackdrop","hasBackdrop"],lockPosition:["cdkConnectedOverlayLockPosition","lockPosition"],flexibleDimensions:["cdkConnectedOverlayFlexibleDimensions","flexibleDimensions"],growAfterOpen:["cdkConnectedOverlayGrowAfterOpen","growAfterOpen"],push:["cdkConnectedOverlayPush","push"],positions:["cdkConnectedOverlayPositions","positions"],origin:["cdkConnectedOverlayOrigin","origin"],positionStrategy:["cdkConnectedOverlayPositionStrategy","positionStrategy"],width:["cdkConnectedOverlayWidth","width"],height:["cdkConnectedOverlayHeight","height"],minWidth:["cdkConnectedOverlayMinWidth","minWidth"],minHeight:["cdkConnectedOverlayMinHeight","minHeight"],backdropClass:["cdkConnectedOverlayBackdropClass","backdropClass"],panelClass:["cdkConnectedOverlayPanelClass","panelClass"],transformOriginSelector:["cdkConnectedOverlayTransformOriginOn","transformOriginSelector"]},outputs:{backdropClick:"backdropClick",positionChange:"positionChange",attach:"attach",detach:"detach",overlayKeydown:"overlayKeydown",overlayOutsideClick:"overlayOutsideClick"},exportAs:["cdkConnectedOverlay"],features:[Bo]}),gL.ctorParameters=()=>[{type:pL},{type:Xg},{type:eh},{type:void 0,decorators:[{type:kr,args:[uL]}]},{type:HI,decorators:[{type:Sr}]}],gL.propDecorators={origin:[{type:xy,args:["cdkConnectedOverlayOrigin"]}],positions:[{type:xy,args:["cdkConnectedOverlayPositions"]}],positionStrategy:[{type:xy,args:["cdkConnectedOverlayPositionStrategy"]}],offsetX:[{type:xy,args:["cdkConnectedOverlayOffsetX"]}],offsetY:[{type:xy,args:["cdkConnectedOverlayOffsetY"]}],width:[{type:xy,args:["cdkConnectedOverlayWidth"]}],height:[{type:xy,args:["cdkConnectedOverlayHeight"]}],minWidth:[{type:xy,args:["cdkConnectedOverlayMinWidth"]}],minHeight:[{type:xy,args:["cdkConnectedOverlayMinHeight"]}],backdropClass:[{type:xy,args:["cdkConnectedOverlayBackdropClass"]}],panelClass:[{type:xy,args:["cdkConnectedOverlayPanelClass"]}],viewportMargin:[{type:xy,args:["cdkConnectedOverlayViewportMargin"]}],scrollStrategy:[{type:xy,args:["cdkConnectedOverlayScrollStrategy"]}],open:[{type:xy,args:["cdkConnectedOverlayOpen"]}],disableClose:[{type:xy,args:["cdkConnectedOverlayDisableClose"]}],transformOriginSelector:[{type:xy,args:["cdkConnectedOverlayTransformOriginOn"]}],hasBackdrop:[{type:xy,args:["cdkConnectedOverlayHasBackdrop"]}],lockPosition:[{type:xy,args:["cdkConnectedOverlayLockPosition"]}],flexibleDimensions:[{type:xy,args:["cdkConnectedOverlayFlexibleDimensions"]}],growAfterOpen:[{type:xy,args:["cdkConnectedOverlayGrowAfterOpen"]}],push:[{type:xy,args:["cdkConnectedOverlayPush"]}],backdropClick:[{type:Oy}],positionChange:[{type:Oy}],attach:[{type:Oy}],detach:[{type:Oy}],overlayKeydown:[{type:Oy}],overlayOutsideClick:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(gL,[{type:Cy,args:[{selector:"[cdk-connected-overlay], [connected-overlay], [cdkConnectedOverlay]",exportAs:"cdkConnectedOverlay"}]}],(function(){return[{type:pL},{type:Xg},{type:eh},{type:void 0,decorators:[{type:kr,args:[uL]}]},{type:HI,decorators:[{type:Sr}]}]}),{viewportMargin:[{type:xy,args:["cdkConnectedOverlayViewportMargin"]}],open:[{type:xy,args:["cdkConnectedOverlayOpen"]}],disableClose:[{type:xy,args:["cdkConnectedOverlayDisableClose"]}],backdropClick:[{type:Oy}],positionChange:[{type:Oy}],attach:[{type:Oy}],detach:[{type:Oy}],overlayKeydown:[{type:Oy}],overlayOutsideClick:[{type:Oy}],scrollStrategy:[{type:xy,args:["cdkConnectedOverlayScrollStrategy"]}],offsetX:[{type:xy,args:["cdkConnectedOverlayOffsetX"]}],offsetY:[{type:xy,args:["cdkConnectedOverlayOffsetY"]}],hasBackdrop:[{type:xy,args:["cdkConnectedOverlayHasBackdrop"]}],lockPosition:[{type:xy,args:["cdkConnectedOverlayLockPosition"]}],flexibleDimensions:[{type:xy,args:["cdkConnectedOverlayFlexibleDimensions"]}],growAfterOpen:[{type:xy,args:["cdkConnectedOverlayGrowAfterOpen"]}],push:[{type:xy,args:["cdkConnectedOverlayPush"]}],positions:[{type:xy,args:["cdkConnectedOverlayPositions"]}],origin:[{type:xy,args:["cdkConnectedOverlayOrigin"]}],positionStrategy:[{type:xy,args:["cdkConnectedOverlayPositionStrategy"]}],width:[{type:xy,args:["cdkConnectedOverlayWidth"]}],height:[{type:xy,args:["cdkConnectedOverlayHeight"]}],minWidth:[{type:xy,args:["cdkConnectedOverlayMinWidth"]}],minHeight:[{type:xy,args:["cdkConnectedOverlayMinHeight"]}],backdropClass:[{type:xy,args:["cdkConnectedOverlayBackdropClass"]}],panelClass:[{type:xy,args:["cdkConnectedOverlayPanelClass"]}],transformOriginSelector:[{type:xy,args:["cdkConnectedOverlayTransformOriginOn"]}]});const hL={provide:uL,deps:[pL],useFactory:function bL(t){return()=>t.scrollStrategies.reposition()}};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class yL{}yL.ɵfac=function t(e){return new(e||yL)},yL.ɵmod=ao({type:yL}),yL.ɵinj=vn({providers:[pL,hL],imports:[[LI,RF,_F],_F]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(yL,[{type:Ay,args:[{imports:[LI,RF,_F],exports:[gL,fL,_F],declarations:[gL,fL],providers:[pL,hL]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(yL,{declarations:function(){return[gL,fL]},imports:function(){return[LI,RF,_F]},exports:function(){return[gL,fL,_F]}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class _L extends QF{constructor(t,e){super(t,e)}ngOnDestroy(){super.ngOnDestroy(),this._fullScreenEventName&&this._fullScreenListener&&this._document.removeEventListener(this._fullScreenEventName,this._fullScreenListener)}_createContainer(){super._createContainer(),this._adjustParentForFullscreenChange(),this._addFullscreenChangeListener((()=>this._adjustParentForFullscreenChange()))}_adjustParentForFullscreenChange(){this._containerElement&&(this.getFullscreenElement()||this._document.body).appendChild(this._containerElement)}_addFullscreenChangeListener(t){const e=this._getEventName();e&&(this._fullScreenListener&&this._document.removeEventListener(e,this._fullScreenListener),this._document.addEventListener(e,t),this._fullScreenListener=t)}_getEventName(){if(!this._fullScreenEventName){const t=this._document;t.fullscreenEnabled?this._fullScreenEventName="fullscreenchange":t.webkitFullscreenEnabled?this._fullScreenEventName="webkitfullscreenchange":t.mozFullScreenEnabled?this._fullScreenEventName="mozfullscreenchange":t.msFullscreenEnabled&&(this._fullScreenEventName="MSFullscreenChange")}return this._fullScreenEventName}getFullscreenElement(){const t=this._document;return t.fullscreenElement||t.webkitFullscreenElement||t.mozFullScreenElement||t.msFullscreenElement||null}}_L.ɵfac=function t(e){return new(e||_L)(vr(Z_),vr(wz))},_L.ɵprov=Mn({factory:function t(){return new _L(vr(Z_),vr(wz))},token:_L,providedIn:"root"}),_L.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(_L,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz}]}),null);class CL{}CL.ɵfac=function t(e){return new(e||CL)},CL.ɵmod=ao({type:CL}),CL.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(CL,[{type:Ay,args:[{}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const ML=new Set;let vL;class xL{constructor(t){this._platform=t,this._matchMedia=this._platform.isBrowser&&window.matchMedia?window.matchMedia.bind(window):OL}matchMedia(t){return this._platform.WEBKIT&&(function e(t){if(!ML.has(t))try{vL||(vL=document.createElement("style"),vL.setAttribute("type","text/css"),document.head.appendChild(vL)),vL.sheet&&(vL.sheet.insertRule(`@media ${t} {.fx-query-test{ }}`,0),ML.add(t))}catch(t){console.error(t)}})(t),this._matchMedia(t)}}function OL(t){return{matches:"all"===t||""===t,media:t,addListener:()=>{},removeListener:()=>{}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */xL.ɵfac=function t(e){return new(e||xL)(vr(wz))},xL.ɵprov=Mn({factory:function t(){return new xL(vr(wz))},token:xL,providedIn:"root"}),xL.ctorParameters=()=>[{type:wz}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(xL,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:wz}]}),null);class PL{constructor(t,e){this._mediaMatcher=t,this._zone=e,this._queries=new Map,this._destroySubject=new I}ngOnDestroy(){this._destroySubject.next(),this._destroySubject.complete()}isMatched(t){return wL(Mz(t)).some((t=>this._registerQuery(t).mql.matches))}observe(t){let e=Wt(wL(Mz(t)).map((t=>this._registerQuery(t).observable)));return e=Jt(e.pipe(be(1)),e.pipe(Te(1),ge(0))),e.pipe(It((t=>{const e={matches:!1,breakpoints:{}};return t.forEach((({matches:t,query:n})=>{e.matches=e.matches||t,e.breakpoints[n]=t})),e})))}_registerQuery(t){if(this._queries.has(t))return this._queries.get(t);const e=this._mediaMatcher.matchMedia(t),n={observable:new D((t=>{const n=e=>this._zone.run((()=>t.next(e)));return e.addListener(n),()=>{e.removeListener(n)}})).pipe(Ne(e),It((({matches:e})=>({query:t,matches:e}))),Ie(this._destroySubject)),mql:e};return this._queries.set(t,n),n}}function wL(t){return t.map((t=>t.split(","))).reduce(((t,e)=>t.concat(e))).map((t=>t.trim()))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function kL(t,e){if(1&t){const t=Hm();Rm(0,"div",1),Rm(1,"button",2),Vm("click",(function e(){return hi(t),Ym().action()})),ku(2),Am(),Am()}if(2&t){const t=Ym();rc(2),Su(t.data.action)}}function SL(t,e){}PL.ɵfac=function t(e){return new(e||PL)(vr(xL),vr(a_))},PL.ɵprov=Mn({factory:function t(){return new PL(vr(xL),vr(a_))},token:PL,providedIn:"root"}),PL.ctorParameters=()=>[{type:xL},{type:a_}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(PL,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:xL},{type:a_}]}),null);const DL=new Ga("MatSnackBarData");class EL{constructor(){this.politeness="assertive",this.announcementMessage="",this.duration=0,this.data=null,this.horizontalPosition="center",this.verticalPosition="bottom"}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const RL=Math.pow(2,31)-1;class AL{constructor(t,e){this._overlayRef=e,this._afterDismissed=new I,this._afterOpened=new I,this._onAction=new I,this._dismissedByAction=!1,this.containerInstance=t,this.onAction().subscribe((()=>this.dismiss())),t._onExit.subscribe((()=>this._finishDismiss()))}dismiss(){this._afterDismissed.closed||this.containerInstance.exit(),clearTimeout(this._durationTimeoutId)}dismissWithAction(){this._onAction.closed||(this._dismissedByAction=!0,this._onAction.next(),this._onAction.complete()),clearTimeout(this._durationTimeoutId)}closeWithAction(){this.dismissWithAction()}_dismissAfter(t){this._durationTimeoutId=setTimeout((()=>this.dismiss()),Math.min(t,RL))}_open(){this._afterOpened.closed||(this._afterOpened.next(),this._afterOpened.complete())}_finishDismiss(){this._overlayRef.dispose(),this._onAction.closed||this._onAction.complete(),this._afterDismissed.next({dismissedByAction:this._dismissedByAction}),this._afterDismissed.complete(),this._dismissedByAction=!1}afterDismissed(){return this._afterDismissed}afterOpened(){return this.containerInstance._onEnter}onAction(){return this._onAction}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class TL{constructor(t,e){this.snackBarRef=t,this.data=e}action(){this.snackBarRef.dismissWithAction()}get hasAction(){return!!this.data.action}}TL.ɵfac=function t(e){return new(e||TL)(Sm(AL),Sm(DL))},TL.ɵcmp=to({type:TL,selectors:[["simple-snack-bar"]],hostAttrs:[1,"mat-simple-snackbar"],decls:3,vars:2,consts:[["class","mat-simple-snackbar-action",4,"ngIf"],[1,"mat-simple-snackbar-action"],["mat-button","",3,"click"]],template:function t(e,n){1&e&&(Rm(0,"span"),ku(1),Am(),Qp(2,kL,3,1,"div",0)),2&e&&(rc(1),Su(n.data.message),rc(1),Dm("ngIf",n.hasAction))},directives:[dM,XH],styles:[".mat-simple-snackbar{display:flex;justify-content:space-between;align-items:center;line-height:20px;opacity:1}.mat-simple-snackbar-action{flex-shrink:0;margin:-8px -8px -8px 8px}.mat-simple-snackbar-action button{max-height:36px;min-width:0}[dir=rtl] .mat-simple-snackbar-action{margin-left:-8px;margin-right:8px}\n"],encapsulation:2,changeDetection:0}),TL.ctorParameters=()=>[{type:AL},{type:void 0,decorators:[{type:kr,args:[DL]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(TL,[{type:My,args:[{selector:"simple-snack-bar",template:'<span>{{data.message}}</span>\n<div class="mat-simple-snackbar-action"  *ngIf="hasAction">\n  <button mat-button (click)="action()">{{data.action}}</button>\n</div>\n',encapsulation:Hn.None,changeDetection:zn.OnPush,host:{class:"mat-simple-snackbar"},styles:[".mat-simple-snackbar{display:flex;justify-content:space-between;align-items:center;line-height:20px;opacity:1}.mat-simple-snackbar-action{flex-shrink:0;margin:-8px -8px -8px 8px}.mat-simple-snackbar-action button{max-height:36px;min-width:0}[dir=rtl] .mat-simple-snackbar-action{margin-left:-8px;margin-right:8px}\n"]}]}],(function(){return[{type:AL},{type:void 0,decorators:[{type:kr,args:[DL]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const NL={snackBarState:nx("state",[rx("void, hidden",ax({transform:"scale(0.8)",opacity:0})),rx("visible",ax({transform:"scale(1)",opacity:1})),lx("* => visible",ox("150ms cubic-bezier(0, 0, 0.2, 1)")),lx("* => void, * => hidden",ox("75ms cubic-bezier(0.4, 0.0, 1, 1)",ax({opacity:0})))])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class zL extends PF{constructor(t,e,n,o,i){super(),this._ngZone=t,this._elementRef=e,this._changeDetectorRef=n,this._platform=o,this.snackBarConfig=i,this._announceDelay=150,this._destroyed=!1,this._onAnnounce=new I,this._onExit=new I,this._onEnter=new I,this._animationState="void",this.attachDomPortal=t=>(this._assertNotAttached(),this._applySnackBarClasses(),this._portalOutlet.attachDomPortal(t)),this._live="assertive"!==i.politeness||i.announcementMessage?"off"===i.politeness?"off":"polite":"assertive",this._platform.FIREFOX&&("polite"===this._live&&(this._role="status"),"assertive"===this._live&&(this._role="alert"))}attachComponentPortal(t){return this._assertNotAttached(),this._applySnackBarClasses(),this._portalOutlet.attachComponentPortal(t)}attachTemplatePortal(t){return this._assertNotAttached(),this._applySnackBarClasses(),this._portalOutlet.attachTemplatePortal(t)}onAnimationEnd(t){const{fromState:e,toState:n}=t;if(("void"===n&&"void"!==e||"hidden"===n)&&this._completeExit(),"visible"===n){const t=this._onEnter;this._ngZone.run((()=>{t.next(),t.complete()}))}}enter(){this._destroyed||(this._animationState="visible",this._changeDetectorRef.detectChanges(),this._screenReaderAnnounce())}exit(){return this._animationState="hidden",this._elementRef.nativeElement.setAttribute("mat-exit",""),clearTimeout(this._announceTimeoutId),this._onExit}ngOnDestroy(){this._destroyed=!0,this._completeExit()}_completeExit(){this._ngZone.onMicrotaskEmpty.pipe(be(1)).subscribe((()=>{this._onExit.next(),this._onExit.complete()}))}_applySnackBarClasses(){const t=this._elementRef.nativeElement,e=this.snackBarConfig.panelClass;e&&(Array.isArray(e)?e.forEach((e=>t.classList.add(e))):t.classList.add(e)),"center"===this.snackBarConfig.horizontalPosition&&t.classList.add("mat-snack-bar-center"),"top"===this.snackBarConfig.verticalPosition&&t.classList.add("mat-snack-bar-top")}_assertNotAttached(){if(this._portalOutlet.hasAttached()&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Attempting to attach snack bar content after content is already attached")}_screenReaderAnnounce(){this._announceTimeoutId||this._ngZone.runOutsideAngular((()=>{this._announceTimeoutId=setTimeout((()=>{const t=this._elementRef.nativeElement.querySelector("[aria-hidden]"),e=this._elementRef.nativeElement.querySelector("[aria-live]");if(t&&e){let n=null;this._platform.isBrowser&&document.activeElement instanceof HTMLElement&&t.contains(document.activeElement)&&(n=document.activeElement),t.removeAttribute("aria-hidden"),e.appendChild(t),null==n||n.focus(),this._onAnnounce.next(),this._onAnnounce.complete()}}),this._announceDelay)}))}}zL.ɵfac=function t(e){return new(e||zL)(Sm(a_),Sm(hg),Sm(Ug),Sm(wz),Sm(EL))},zL.ɵcmp=to({type:zL,selectors:[["snack-bar-container"]],viewQuery:function t(e,n){if(1&e&&Qh(DF,7),2&e){let t;Jh(t=tb())&&(n._portalOutlet=t.first)}},hostAttrs:[1,"mat-snack-bar-container"],hostVars:1,hostBindings:function t(e,n){1&e&&jm("@state.done",(function t(e){return n.onAnimationEnd(e)})),2&e&&Nu("@state",n._animationState)},features:[xp],decls:3,vars:2,consts:[["aria-hidden","true"],["cdkPortalOutlet",""]],template:function t(e,n){1&e&&(Rm(0,"div",0),Qp(1,SL,0,0,"ng-template",1),Am(),Tm(2,"div")),2&e&&(rc(2),jp("aria-live",n._live)("role",n._role))},directives:[DF],styles:[".mat-snack-bar-container{border-radius:4px;box-sizing:border-box;display:block;margin:24px;max-width:33vw;min-width:344px;padding:14px 16px;min-height:48px;transform-origin:center}.cdk-high-contrast-active .mat-snack-bar-container{border:solid 1px}.mat-snack-bar-handset{width:100%}.mat-snack-bar-handset .mat-snack-bar-container{margin:8px;max-width:100%;min-width:0;width:100%}\n"],encapsulation:2,data:{animation:[NL.snackBarState]}}),zL.ctorParameters=()=>[{type:a_},{type:hg},{type:Ug},{type:wz},{type:EL}],zL.propDecorators={_portalOutlet:[{type:Za,args:[DF,{static:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(zL,[{type:My,args:[{selector:"snack-bar-container",template:'\x3c!-- Initially holds the snack bar content, will be empty after announcing to screen readers. --\x3e\n<div aria-hidden="true">\n  <ng-template cdkPortalOutlet></ng-template>\n</div>\n\n\x3c!-- Will receive the snack bar content from the non-live div, move will happen a short delay after opening --\x3e\n<div [attr.aria-live]="_live" [attr.role]="_role"></div>\n',changeDetection:zn.Default,encapsulation:Hn.None,animations:[NL.snackBarState],host:{class:"mat-snack-bar-container","[@state]":"_animationState","(@state.done)":"onAnimationEnd($event)"},styles:[".mat-snack-bar-container{border-radius:4px;box-sizing:border-box;display:block;margin:24px;max-width:33vw;min-width:344px;padding:14px 16px;min-height:48px;transform-origin:center}.cdk-high-contrast-active .mat-snack-bar-container{border:solid 1px}.mat-snack-bar-handset{width:100%}.mat-snack-bar-handset .mat-snack-bar-container{margin:8px;max-width:100%;min-width:0;width:100%}\n"]}]}],(function(){return[{type:a_},{type:hg},{type:Ug},{type:wz},{type:EL}]}),{_portalOutlet:[{type:Za,args:[DF,{static:!0}]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class IL{}IL.ɵfac=function t(e){return new(e||IL)},IL.ɵmod=ao({type:IL}),IL.ɵinj=vn({imports:[[yL,RF,WM,JH,XI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(IL,[{type:Ay,args:[{imports:[yL,RF,WM,JH,XI],exports:[zL,XI],declarations:[zL,TL],entryComponents:[zL,TL]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(IL,{declarations:function(){return[zL,TL]},imports:function(){return[yL,RF,WM,JH,XI]},exports:function(){return[zL,XI]}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const HL=new Ga("mat-snack-bar-default-options",{providedIn:"root",factory:function FL(){return new EL}});class LL{constructor(t,e,n,o,i,a){this._overlay=t,this._live=e,this._injector=n,this._breakpointObserver=o,this._parentSnackBar=i,this._defaultConfig=a,this._snackBarRefAtThisLevel=null,this.simpleSnackBarComponent=TL,this.snackBarContainerComponent=zL,this.handsetCssClass="mat-snack-bar-handset"}get _openedSnackBarRef(){const t=this._parentSnackBar;return t?t._openedSnackBarRef:this._snackBarRefAtThisLevel}set _openedSnackBarRef(t){this._parentSnackBar?this._parentSnackBar._openedSnackBarRef=t:this._snackBarRefAtThisLevel=t}openFromComponent(t,e){return this._attach(t,e)}openFromTemplate(t,e){return this._attach(t,e)}open(t,e="",n){const o=Object.assign(Object.assign({},this._defaultConfig),n);return o.data={message:t,action:e},o.announcementMessage===t&&(o.announcementMessage=void 0),this.openFromComponent(this.simpleSnackBarComponent,o)}dismiss(){this._openedSnackBarRef&&this._openedSnackBarRef.dismiss()}ngOnDestroy(){this._snackBarRefAtThisLevel&&this._snackBarRefAtThisLevel.dismiss()}_attachSnackBarContainer(t,e){const n=rp.create({parent:e&&e.viewContainerRef&&e.viewContainerRef.injector||this._injector,providers:[{provide:EL,useValue:e}]}),o=new vF(this.snackBarContainerComponent,e.viewContainerRef,n),i=t.attach(o);return i.instance.snackBarConfig=e,i.instance}_attach(t,e){const n=Object.assign(Object.assign(Object.assign({},new EL),this._defaultConfig),e),o=this._createOverlay(n),i=this._attachSnackBarContainer(o,n),a=new AL(i,o);if(t instanceof Xg){const e=new xF(t,null,{$implicit:n.data,snackBarRef:a});a.instance=i.attachTemplatePortal(e)}else{const e=this._createInjector(n,a),o=new vF(t,void 0,e),r=i.attachComponentPortal(o);a.instance=r.instance}return this._breakpointObserver.observe("(max-width: 599.98px) and (orientation: portrait)").pipe(Ie(o.detachments())).subscribe((t=>{const e=o.overlayElement.classList;t.matches?e.add(this.handsetCssClass):e.remove(this.handsetCssClass)})),n.announcementMessage&&i._onAnnounce.subscribe((()=>{this._live.announce(n.announcementMessage,n.politeness)})),this._animateSnackBar(a,n),this._openedSnackBarRef=a,this._openedSnackBarRef}_animateSnackBar(t,e){t.afterDismissed().subscribe((()=>{this._openedSnackBarRef==t&&(this._openedSnackBarRef=null),e.announcementMessage&&this._live.clear()})),this._openedSnackBarRef?(this._openedSnackBarRef.afterDismissed().subscribe((()=>{t.containerInstance.enter()})),this._openedSnackBarRef.dismiss()):t.containerInstance.enter(),e.duration&&e.duration>0&&t.afterOpened().subscribe((()=>t._dismissAfter(e.duration)))}_createOverlay(t){const e=new VF;e.direction=t.direction;let n=this._overlay.position().global();const o="rtl"===t.direction,i="left"===t.horizontalPosition||"start"===t.horizontalPosition&&!o||"end"===t.horizontalPosition&&o,a=!i&&"center"!==t.horizontalPosition;return i?n.left("0"):a?n.right("0"):n.centerHorizontally(),"top"===t.verticalPosition?n.top("0"):n.bottom("0"),e.positionStrategy=n,this._overlay.create(e)}_createInjector(t,e){return rp.create({parent:t&&t.viewContainerRef&&t.viewContainerRef.injector||this._injector,providers:[{provide:AL,useValue:e},{provide:DL,useValue:t.data}]})}}LL.ɵfac=function t(e){return new(e||LL)(vr(pL),vr(OI),vr(rp),vr(PL),vr(LL,12),vr(HL))},LL.ɵprov=Mn({factory:function t(){return new LL(vr(pL),vr(OI),vr(Gd),vr(PL),vr(LL,12),vr(HL))},token:LL,providedIn:IL}),LL.ctorParameters=()=>[{type:pL},{type:OI},{type:rp},{type:PL},{type:LL,decorators:[{type:Sr},{type:Er}]},{type:EL,decorators:[{type:kr,args:[HL]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(LL,[{type:im,args:[{providedIn:IL}]}],(function(){return[{type:pL},{type:OI},{type:rp},{type:PL},{type:LL,decorators:[{type:Sr},{type:Er}]},{type:EL,decorators:[{type:kr,args:[HL]}]}]}),null);const BL=/[\\^$.*+?()[\]{}|]/g,VL="\\u0000-\\u0020\\u007f-\\u009f",jL=new RegExp("(?:[a-zA-Z][a-zA-Z0-9+.-]{2,}:\\/\\/|data:|www\\.)[^\\s"+VL+'"]{2,}[^\\s'+VL+"\"')}\\],:;.!?]","gu");function UL(t){return(function e(t,n){n.flags.includes("g")||(n=new RegExp(n,n.flags+"g"));const o=[];let i=0;for(const e of t.matchAll(n)){const n=e.index,a=e[0];n>i&&o.push({index:i,text:t.substring(i,n),matchesRegex:!1}),o.push({index:n,text:a,matchesRegex:!0}),i=n+a.length}return t.length>i&&o.push({index:i,text:t.substring(i,t.length),matchesRegex:!1}),o})(t,jL).map((({matchesRegex:t,text:e})=>({isURL:t,text:e})))}function GL(t,e){if(1&t&&(Nm(0),ku(1),zm()),2&t){const t=Ym().$implicit;rc(1),Du(" ",t.text," ")}}function WL(t,e){if(1&t&&(Rm(0,"a",7),ku(1),Am()),2&t){const t=Ym().$implicit;Km("href",t.text,Ts),rc(1),Su(t.text)}}function YL(t,e){if(1&t&&(Nm(0),Qp(1,GL,2,1,"ng-container",5),Qp(2,WL,2,2,"ng-template",null,6,ib),zm()),2&t){const t=e.$implicit,n=$p(3);rc(1),Dm("ngIf",!t.isURL)("ngIfElse",n)}}function qL(t,e){if(1&t){const t=Hm();Rm(0,"button",8),Vm("click",(function e(){return hi(t),Ym().onActionButtonClicked()})),ku(1),Am()}if(2&t){const t=Ym();rc(1),Du(" ",t.alert.followupAction.localizedLabel," ")}}class ZL{constructor(t,e,n){this.snackBarRef=t,this.unknownData=e,this.store=n,this.splitByURL=UL,this.alert=e}onActionButtonClicked(){return gA(this,void 0,void 0,(function*(){this.snackBarRef.dismiss();const t=yield this.alert.followupAction.getFollowupAction(this.store);this.store.dispatch(t)}))}onCloseButtonClicked(){this.snackBarRef.dismiss()}}ZL.ɵfac=function t(e){return new(e||ZL)(Sm(AL),Sm(DL),Sm(Iw))},ZL.ɵcmp=to({type:ZL,selectors:[["alert-display-snackbar"]],decls:6,vars:2,consts:function(){let t;return t="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Dismiss"):$localize`:A button to close the snackbar message␟ea4d9fe61420a3fce81cf54c4c615e3c19c646a6␟1536087519743707362:Dismiss`,[[1,"message"],[4,"ngFor","ngForOf"],[1,"controls"],["mat-button","","class","followup-button",3,"click",4,"ngIf"],["mat-button","","aria-label",t,1,"dismiss-button",3,"click"],[4,"ngIf","ngIfElse"],["linkPiece",""],["rel","noreferrer noopener","target","_blank",3,"href"],["mat-button","",1,"followup-button",3,"click"]]},template:function t(e,n){1&e&&(Rm(0,"div",0),Qp(1,YL,4,2,"ng-container",1),Am(),Rm(2,"div",2),Qp(3,qL,2,1,"button",3),Rm(4,"button",4),Vm("click",(function t(){return n.onCloseButtonClicked()})),ku(5," Dismiss "),Am(),Am()),2&e&&(rc(1),Dm("ngForOf",n.splitByURL(n.alert.localizedMessage)),rc(2),Dm("ngIf",n.alert.followupAction))},directives:[lM,dM,XH],styles:["[_nghost-%COMP%]{display:flex;flex-wrap:wrap}.message[_ngcontent-%COMP%]{font-size:14px;align-self:center;margin:5px 0;word-break:break-word}.message[_ngcontent-%COMP%]   a[_ngcontent-%COMP%]{color:inherit}.controls[_ngcontent-%COMP%]{white-space:nowrap;margin-left:auto}button[_ngcontent-%COMP%]{text-transform:uppercase}"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ZL,[{type:My,args:[{selector:"alert-display-snackbar",templateUrl:"./alert_display_snackbar_container.ng.html",styleUrls:["./alert_display_snackbar_container.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:AL},{type:void 0,decorators:[{type:kr,args:[DL]}]},{type:Iw}]}),null);class XL{constructor(t,e){this.store=t,this.snackBar=e,this.ngUnsubscribe=new I}ngOnInit(){this.store.select(dA).pipe(Ie(this.ngUnsubscribe),ce((t=>Boolean(t)))).subscribe((t=>{this.showAlert(t)}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}showAlert(t){this.snackBar.openFromComponent(ZL,{duration:5e3,horizontalPosition:"start",verticalPosition:"bottom",data:t})}}XL.ɵfac=function t(e){return new(e||XL)(Sm(Iw),Sm(LL))},XL.ɵcmp=to({type:XL,selectors:[["alert-snackbar"]],decls:0,vars:0,template:function t(e,n){},encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(XL,[{type:My,args:[{selector:"alert-snackbar",template:"",changeDetection:zn.OnPush}]}],(function(){return[{type:Iw},{type:LL}]}),null);class KL{}KL.ɵfac=function t(e){return new(e||KL)},KL.ɵmod=ao({type:KL}),KL.ɵinj=vn({imports:[[WM,JH,IL]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(KL,[{type:Ay,args:[{declarations:[XL,ZL],exports:[XL],imports:[WM,JH,IL],entryComponents:[ZL]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(KL,{declarations:[XL,ZL],imports:[WM,JH,IL],exports:[XL]});class JL{}JL.ɵfac=function t(e){return new(e||JL)},JL.ɵmod=ao({type:JL}),JL.ɵinj=vn({imports:[[AR,KL,dk.forFeature(cA,pz),Wk.forFeature([cz])]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(JL,[{type:Ay,args:[{imports:[AR,KL,dk.forFeature(cA,pz),Wk.forFeature([cz])]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(JL,{imports:[AR,KL,ck,Gk]});const QL=["*",[["mat-toolbar-row"]]],$L=JI(class{constructor(t){this._elementRef=t}});class tB{}tB.ɵfac=function t(e){return new(e||tB)},tB.ɵdir=lo({type:tB,selectors:[["mat-toolbar-row"]],hostAttrs:[1,"mat-toolbar-row"],exportAs:["matToolbarRow"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(tB,[{type:Cy,args:[{selector:"mat-toolbar-row",exportAs:"matToolbarRow",host:{class:"mat-toolbar-row"}}]}],null,null);class eB extends $L{constructor(t,e,n){super(t),this._platform=e,this._document=n}ngAfterViewInit(){this._platform.isBrowser&&(this._checkToolbarMixedModes(),this._toolbarRows.changes.subscribe((()=>this._checkToolbarMixedModes())))}_checkToolbarMixedModes(){this._toolbarRows.length&&("undefined"==typeof ngDevMode||ngDevMode)&&Array.from(this._elementRef.nativeElement.childNodes).filter((t=>!(t.classList&&t.classList.contains("mat-toolbar-row")))).filter((t=>t.nodeType!==(this._document?this._document.COMMENT_NODE:8))).some((t=>!(!t.textContent||!t.textContent.trim())))&&(function t(){throw Error("MatToolbar: Attempting to combine different toolbar modes. Either specify multiple `<mat-toolbar-row>` elements explicitly or just place content inside of a `<mat-toolbar>` for a single row.")}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)()}}eB.ɵfac=function t(e){return new(e||eB)(Sm(hg),Sm(wz),Sm(Z_))},eB.ɵcmp=to({type:eB,selectors:[["mat-toolbar"]],contentQueries:function t(e,n,o){if(1&e&&$h(o,tB,5),2&e){let t;Jh(t=tb())&&(n._toolbarRows=t)}},hostAttrs:[1,"mat-toolbar"],hostVars:4,hostBindings:function t(e,n){2&e&&pu("mat-toolbar-multiple-rows",n._toolbarRows.length>0)("mat-toolbar-single-row",0===n._toolbarRows.length)},inputs:{color:"color"},exportAs:["matToolbar"],features:[xp],ngContentSelectors:["*","mat-toolbar-row"],decls:2,vars:0,template:function t(e,n){1&e&&(Zm(QL),Xm(0),Xm(1,1))},styles:[".cdk-high-contrast-active .mat-toolbar{outline:solid 1px}.mat-toolbar-row,.mat-toolbar-single-row{display:flex;box-sizing:border-box;padding:0 16px;width:100%;flex-direction:row;align-items:center;white-space:nowrap}.mat-toolbar-multiple-rows{display:flex;box-sizing:border-box;flex-direction:column;width:100%}\n"],encapsulation:2,changeDetection:0}),eB.ctorParameters=()=>[{type:hg},{type:wz},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],eB.propDecorators={_toolbarRows:[{type:Ya,args:[tB,{descendants:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(eB,[{type:My,args:[{selector:"mat-toolbar",exportAs:"matToolbar",template:'<ng-content></ng-content>\n<ng-content select="mat-toolbar-row"></ng-content>\n',inputs:["color"],host:{class:"mat-toolbar","[class.mat-toolbar-multiple-rows]":"_toolbarRows.length > 0","[class.mat-toolbar-single-row]":"_toolbarRows.length === 0"},changeDetection:zn.OnPush,encapsulation:Hn.None,styles:[".cdk-high-contrast-active .mat-toolbar{outline:solid 1px}.mat-toolbar-row,.mat-toolbar-single-row{display:flex;box-sizing:border-box;padding:0 16px;width:100%;flex-direction:row;align-items:center;white-space:nowrap}.mat-toolbar-multiple-rows{display:flex;box-sizing:border-box;flex-direction:column;width:100%}\n"]}]}],(function(){return[{type:hg},{type:wz},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),{_toolbarRows:[{type:Ya,args:[tB,{descendants:!0}]}]});class nB{}function oB(t,e){1&t&&Xm(0)}nB.ɵfac=function t(e){return new(e||nB)},nB.ɵmod=ao({type:nB}),nB.ɵinj=vn({imports:[[XI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(nB,[{type:Ay,args:[{imports:[XI],exports:[eB,tB,XI],declarations:[eB,tB]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(nB,{declarations:function(){return[eB,tB]},imports:function(){return[XI]},exports:function(){return[eB,tB,XI]}});const iB=["*"];function aB(t,e){}const rB=function(t){return{animationDuration:t}},sB=function(t,e){return{value:t,params:e}},lB=["tabBodyWrapper"],cB=["tabHeader"];function dB(t,e){}function pB(t,e){1&t&&Qp(0,dB,0,0,"ng-template",9),2&t&&Dm("cdkPortalOutlet",Ym().$implicit.templateLabel)}function mB(t,e){1&t&&ku(0),2&t&&Su(Ym().$implicit.textLabel)}function uB(t,e){if(1&t){const t=Hm();Rm(0,"div",6),Vm("click",(function e(){const n=hi(t),o=n.$implicit,i=n.index,a=Ym(),r=$p(1);return a._handleClick(o,r,i)}))("cdkFocusChange",(function e(n){const o=hi(t).index;return Ym()._tabFocusChanged(n,o)})),Rm(1,"div",7),Qp(2,pB,1,1,"ng-template",8),Qp(3,mB,1,1,"ng-template",8),Am(),Am()}if(2&t){const t=e.$implicit,n=e.index,o=Ym();pu("mat-tab-label-active",o.selectedIndex==n),Dm("id",o._getTabLabelId(n))("disabled",t.disabled)("matRippleDisabled",t.disabled||o.disableRipple),jp("tabIndex",o._getTabIndex(t,n))("aria-posinset",n+1)("aria-setsize",o._tabs.length)("aria-controls",o._getTabContentId(n))("aria-selected",o.selectedIndex==n)("aria-label",t.ariaLabel||null)("aria-labelledby",!t.ariaLabel&&t.ariaLabelledby?t.ariaLabelledby:null),rc(2),Dm("ngIf",t.templateLabel),rc(1),Dm("ngIf",!t.templateLabel)}}function fB(t,e){if(1&t){const t=Hm();Rm(0,"mat-tab-body",10),Vm("_onCentered",(function e(){return hi(t),Ym()._removeTabBodyWrapperHeight()}))("_onCentering",(function e(n){return hi(t),Ym()._setTabBodyWrapperHeight(n)})),Am()}if(2&t){const t=e.$implicit,n=e.index,o=Ym();pu("mat-tab-body-active",o.selectedIndex===n),Dm("id",o._getTabContentId(n))("content",t.content)("position",t.position)("origin",t.origin)("animationDuration",o.animationDuration),jp("tabindex",null!=o.contentTabIndex&&o.selectedIndex===n?o.contentTabIndex:null)("aria-labelledby",o._getTabLabelId(n))}}const gB=["tabListContainer"],hB=["tabList"],bB=["nextPaginator"],yB=["previousPaginator"],_B=new Ga("MatInkBarPositioner",{providedIn:"root",factory:function CB(){return t=>({left:t?(t.offsetLeft||0)+"px":"0",width:t?(t.offsetWidth||0)+"px":"0"})}});class MB{constructor(t,e,n,o){this._elementRef=t,this._ngZone=e,this._inkBarPositioner=n,this._animationMode=o}alignToElement(t){this.show(),"undefined"!=typeof requestAnimationFrame?this._ngZone.runOutsideAngular((()=>{requestAnimationFrame((()=>this._setStyles(t)))})):this._setStyles(t)}show(){this._elementRef.nativeElement.style.visibility="visible"}hide(){this._elementRef.nativeElement.style.visibility="hidden"}_setStyles(t){const e=this._inkBarPositioner(t),n=this._elementRef.nativeElement;n.style.left=e.left,n.style.width=e.width}}MB.ɵfac=function t(e){return new(e||MB)(Sm(hg),Sm(a_),Sm(_B),Sm(VP,8))},MB.ɵdir=lo({type:MB,selectors:[["mat-ink-bar"]],hostAttrs:[1,"mat-ink-bar"],hostVars:2,hostBindings:function t(e,n){2&e&&pu("_mat-animation-noopable","NoopAnimations"===n._animationMode)}}),MB.ctorParameters=()=>[{type:hg},{type:a_},{type:void 0,decorators:[{type:kr,args:[_B]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(MB,[{type:Cy,args:[{selector:"mat-ink-bar",host:{class:"mat-ink-bar","[class._mat-animation-noopable]":"_animationMode === 'NoopAnimations'"}}]}],(function(){return[{type:hg},{type:a_},{type:void 0,decorators:[{type:kr,args:[_B]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const vB=new Ga("MatTabContent");class xB{constructor(t){this.template=t}}xB.ɵfac=function t(e){return new(e||xB)(Sm(Xg))},xB.ɵdir=lo({type:xB,selectors:[["","matTabContent",""]],features:[pg([{provide:vB,useExisting:xB}])]}),xB.ctorParameters=()=>[{type:Xg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(xB,[{type:Cy,args:[{selector:"[matTabContent]",providers:[{provide:vB,useExisting:xB}]}]}],(function(){return[{type:Xg}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const OB=new Ga("MatTabLabel");class PB extends kF{}PB.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(PB)))(n||PB)}})(),PB.ɵdir=lo({type:PB,selectors:[["","mat-tab-label",""],["","matTabLabel",""]],features:[pg([{provide:OB,useExisting:PB}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(PB,[{type:Cy,args:[{selector:"[mat-tab-label], [matTabLabel]",providers:[{provide:OB,useExisting:PB}]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const wB=KI(class{}),kB=new Ga("MAT_TAB_GROUP");class SB extends wB{constructor(t,e){super(),this._viewContainerRef=t,this._closestTabGroup=e,this.textLabel="",this._contentPortal=null,this._stateChanges=new I,this.position=null,this.origin=null,this.isActive=!1}get templateLabel(){return this._templateLabel}set templateLabel(t){this._setTemplateLabelInput(t)}get content(){return this._contentPortal}ngOnChanges(t){(t.hasOwnProperty("textLabel")||t.hasOwnProperty("disabled"))&&this._stateChanges.next()}ngOnDestroy(){this._stateChanges.complete()}ngOnInit(){this._contentPortal=new xF(this._explicitContent||this._implicitContent,this._viewContainerRef)}_setTemplateLabelInput(t){t&&(this._templateLabel=t)}}SB.ɵfac=function t(e){return new(e||SB)(Sm(eh),Sm(kB,8))},SB.ɵcmp=to({type:SB,selectors:[["mat-tab"]],contentQueries:function t(e,n,o){if(1&e&&($h(o,OB,5),$h(o,vB,7,Xg)),2&e){let t;Jh(t=tb())&&(n.templateLabel=t.first),Jh(t=tb())&&(n._explicitContent=t.first)}},viewQuery:function t(e,n){if(1&e&&Qh(Xg,7),2&e){let t;Jh(t=tb())&&(n._implicitContent=t.first)}},inputs:{disabled:"disabled",textLabel:["label","textLabel"],ariaLabel:["aria-label","ariaLabel"],ariaLabelledby:["aria-labelledby","ariaLabelledby"]},exportAs:["matTab"],features:[xp,Bo],ngContentSelectors:iB,decls:1,vars:0,template:function t(e,n){1&e&&(Zm(),Qp(0,oB,1,0,"ng-template"))},encapsulation:2}),SB.ctorParameters=()=>[{type:eh},{type:void 0,decorators:[{type:kr,args:[kB]},{type:Sr}]}],SB.propDecorators={templateLabel:[{type:qa,args:[OB]}],_explicitContent:[{type:qa,args:[vB,{read:Xg,static:!0}]}],_implicitContent:[{type:Za,args:[Xg,{static:!0}]}],textLabel:[{type:xy,args:["label"]}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(SB,[{type:My,args:[{selector:"mat-tab",template:"\x3c!-- Create a template for the content of the <mat-tab> so that we can grab a reference to this\n    TemplateRef and use it in a Portal to render the tab content in the appropriate place in the\n    tab-group. --\x3e\n<ng-template><ng-content></ng-content></ng-template>\n",inputs:["disabled"],changeDetection:zn.Default,encapsulation:Hn.None,exportAs:"matTab"}]}],(function(){return[{type:eh},{type:void 0,decorators:[{type:kr,args:[kB]},{type:Sr}]}]}),{textLabel:[{type:xy,args:["label"]}],templateLabel:[{type:qa,args:[OB]}],_explicitContent:[{type:qa,args:[vB,{read:Xg,static:!0}]}],_implicitContent:[{type:Za,args:[Xg,{static:!0}]}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const DB={translateTab:nx("translateTab",[rx("center, void, left-origin-center, right-origin-center",ax({transform:"none"})),rx("left",ax({transform:"translate3d(-100%, 0, 0)",minHeight:"1px"})),rx("right",ax({transform:"translate3d(100%, 0, 0)",minHeight:"1px"})),lx("* => left, * => right, left => center, right => center",ox("{{animationDuration}} cubic-bezier(0.35, 0, 0.25, 1)")),lx("void => left-origin-center",[ax({transform:"translate3d(-100%, 0, 0)"}),ox("{{animationDuration}} cubic-bezier(0.35, 0, 0.25, 1)")]),lx("void => right-origin-center",[ax({transform:"translate3d(100%, 0, 0)"}),ox("{{animationDuration}} cubic-bezier(0.35, 0, 0.25, 1)")])])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class EB extends DF{constructor(t,e,n,o){super(t,e,o),this._host=n,this._centeringSub=m.EMPTY,this._leavingSub=m.EMPTY}ngOnInit(){super.ngOnInit(),this._centeringSub=this._host._beforeCentering.pipe(Ne(this._host._isCenterPosition(this._host._position))).subscribe((t=>{t&&!this.hasAttached()&&this.attach(this._host._content)})),this._leavingSub=this._host._afterLeavingCenter.subscribe((()=>{this.detach()}))}ngOnDestroy(){super.ngOnDestroy(),this._centeringSub.unsubscribe(),this._leavingSub.unsubscribe()}}EB.ɵfac=function t(e){return new(e||EB)(Sm(ug),Sm(eh),Sm(qe((()=>AB))),Sm(Z_))},EB.ɵdir=lo({type:EB,selectors:[["","matTabBodyHost",""]],features:[xp]}),EB.ctorParameters=()=>[{type:ug},{type:eh},{type:AB,decorators:[{type:kr,args:[qe((()=>AB))]}]},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(EB,[{type:Cy,args:[{selector:"[matTabBodyHost]"}]}],(function(){return[{type:ug},{type:eh},{type:AB,decorators:[{type:kr,args:[qe((()=>AB))]}]},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);class RB{constructor(t,e,n){this._elementRef=t,this._dir=e,this._dirChangeSubscription=m.EMPTY,this._translateTabComplete=new I,this._onCentering=new Lh,this._beforeCentering=new Lh,this._afterLeavingCenter=new Lh,this._onCentered=new Lh(!0),this.animationDuration="500ms",e&&(this._dirChangeSubscription=e.change.subscribe((t=>{this._computePositionAnimationState(t),n.markForCheck()}))),this._translateTabComplete.pipe(Me(((t,e)=>t.fromState===e.fromState&&t.toState===e.toState))).subscribe((t=>{this._isCenterPosition(t.toState)&&this._isCenterPosition(this._position)&&this._onCentered.emit(),this._isCenterPosition(t.fromState)&&!this._isCenterPosition(this._position)&&this._afterLeavingCenter.emit()}))}set position(t){this._positionIndex=t,this._computePositionAnimationState()}ngOnInit(){"center"==this._position&&null!=this.origin&&(this._position=this._computePositionFromOrigin(this.origin))}ngOnDestroy(){this._dirChangeSubscription.unsubscribe(),this._translateTabComplete.complete()}_onTranslateTabStarted(t){const e=this._isCenterPosition(t.toState);this._beforeCentering.emit(e),e&&this._onCentering.emit(this._elementRef.nativeElement.clientHeight)}_getLayoutDirection(){return this._dir&&"rtl"===this._dir.value?"rtl":"ltr"}_isCenterPosition(t){return"center"==t||"left-origin-center"==t||"right-origin-center"==t}_computePositionAnimationState(t=this._getLayoutDirection()){this._position=this._positionIndex<0?"ltr"==t?"left":"right":this._positionIndex>0?"ltr"==t?"right":"left":"center"}_computePositionFromOrigin(t){const e=this._getLayoutDirection();return"ltr"==e&&t<=0||"rtl"==e&&t>0?"left-origin-center":"right-origin-center"}}RB.ɵfac=function t(e){return new(e||RB)(Sm(hg),Sm(HI,8),Sm(Ug))},RB.ɵdir=lo({type:RB,inputs:{animationDuration:"animationDuration",position:"position",_content:["content","_content"],origin:"origin"},outputs:{_onCentering:"_onCentering",_beforeCentering:"_beforeCentering",_afterLeavingCenter:"_afterLeavingCenter",_onCentered:"_onCentered"}}),RB.ctorParameters=()=>[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:Ug}],RB.propDecorators={_onCentering:[{type:Oy}],_beforeCentering:[{type:Oy}],_afterLeavingCenter:[{type:Oy}],_onCentered:[{type:Oy}],_content:[{type:xy,args:["content"]}],origin:[{type:xy}],animationDuration:[{type:xy}],position:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(RB,[{type:Cy}],(function(){return[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:Ug}]}),{_onCentering:[{type:Oy}],_beforeCentering:[{type:Oy}],_afterLeavingCenter:[{type:Oy}],_onCentered:[{type:Oy}],animationDuration:[{type:xy}],position:[{type:xy}],_content:[{type:xy,args:["content"]}],origin:[{type:xy}]});class AB extends RB{constructor(t,e,n){super(t,e,n)}}AB.ɵfac=function t(e){return new(e||AB)(Sm(hg),Sm(HI,8),Sm(Ug))},AB.ɵcmp=to({type:AB,selectors:[["mat-tab-body"]],viewQuery:function t(e,n){if(1&e&&Qh(DF,5),2&e){let t;Jh(t=tb())&&(n._portalHost=t.first)}},hostAttrs:[1,"mat-tab-body"],features:[xp],decls:3,vars:6,consts:[["cdkScrollable","",1,"mat-tab-body-content"],["content",""],["matTabBodyHost",""]],template:function t(e,n){1&e&&(Rm(0,"div",0,1),Vm("@translateTab.start",(function t(e){return n._onTranslateTabStarted(e)}))("@translateTab.done",(function t(e){return n._translateTabComplete.next(e)})),Qp(2,aB,0,0,"ng-template",2),Am()),2&e&&Dm("@translateTab",vh(3,sB,n._position,Mh(1,rB,n.animationDuration)))},directives:[EB],styles:[".mat-tab-body-content{height:100%;overflow:auto}.mat-tab-group-dynamic-height .mat-tab-body-content{overflow:hidden}\n"],encapsulation:2,data:{animation:[DB.translateTab]}}),AB.ctorParameters=()=>[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:Ug}],AB.propDecorators={_portalHost:[{type:Za,args:[DF]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(AB,[{type:My,args:[{selector:"mat-tab-body",template:'<div class="mat-tab-body-content" #content\n     [@translateTab]="{\n        value: _position,\n        params: {animationDuration: animationDuration}\n     }"\n     (@translateTab.start)="_onTranslateTabStarted($event)"\n     (@translateTab.done)="_translateTabComplete.next($event)"\n     cdkScrollable>\n  <ng-template matTabBodyHost></ng-template>\n</div>\n',encapsulation:Hn.None,changeDetection:zn.Default,animations:[DB.translateTab],host:{class:"mat-tab-body"},styles:[".mat-tab-body-content{height:100%;overflow:auto}.mat-tab-group-dynamic-height .mat-tab-body-content{overflow:hidden}\n"]}]}],(function(){return[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:Ug}]}),{_portalHost:[{type:Za,args:[DF]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const TB=new Ga("MAT_TABS_CONFIG");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let NB=0;class zB{}const IB=JI(QI(class{constructor(t){this._elementRef=t}}),"primary");class HB extends IB{constructor(t,e,n,o){var i;super(t),this._changeDetectorRef=e,this._animationMode=o,this._tabs=new Vh,this._indexToSelect=0,this._tabBodyWrapperHeight=0,this._tabsSubscription=m.EMPTY,this._tabLabelSubscription=m.EMPTY,this._selectedIndex=null,this.headerPosition="above",this.selectedIndexChange=new Lh,this.focusChange=new Lh,this.animationDone=new Lh,this.selectedTabChange=new Lh(!0),this._groupId=NB++,this.animationDuration=n&&n.animationDuration?n.animationDuration:"500ms",this.disablePagination=!(!n||null==n.disablePagination)&&n.disablePagination,this.dynamicHeight=!(!n||null==n.dynamicHeight)&&n.dynamicHeight,this.contentTabIndex=null!==(i=null==n?void 0:n.contentTabIndex)&&void 0!==i?i:null}get dynamicHeight(){return this._dynamicHeight}set dynamicHeight(t){this._dynamicHeight=yz(t)}get selectedIndex(){return this._selectedIndex}set selectedIndex(t){this._indexToSelect=_z(t,null)}get animationDuration(){return this._animationDuration}set animationDuration(t){this._animationDuration=/^\d+$/.test(t)?t+"ms":t}get contentTabIndex(){return this._contentTabIndex}set contentTabIndex(t){this._contentTabIndex=_z(t,null)}get backgroundColor(){return this._backgroundColor}set backgroundColor(t){const e=this._elementRef.nativeElement;e.classList.remove(`mat-background-${this.backgroundColor}`),t&&e.classList.add(`mat-background-${t}`),this._backgroundColor=t}ngAfterContentChecked(){const t=this._indexToSelect=this._clampTabIndex(this._indexToSelect);if(this._selectedIndex!=t){const e=null==this._selectedIndex;if(!e){this.selectedTabChange.emit(this._createChangeEvent(t));const e=this._tabBodyWrapper.nativeElement;e.style.minHeight=e.clientHeight+"px"}Promise.resolve().then((()=>{this._tabs.forEach(((e,n)=>e.isActive=n===t)),e||(this.selectedIndexChange.emit(t),this._tabBodyWrapper.nativeElement.style.minHeight="")}))}this._tabs.forEach(((e,n)=>{e.position=n-t,null==this._selectedIndex||0!=e.position||e.origin||(e.origin=t-this._selectedIndex)})),this._selectedIndex!==t&&(this._selectedIndex=t,this._changeDetectorRef.markForCheck())}ngAfterContentInit(){this._subscribeToAllTabChanges(),this._subscribeToTabLabels(),this._tabsSubscription=this._tabs.changes.subscribe((()=>{if(this._clampTabIndex(this._indexToSelect)===this._selectedIndex){const t=this._tabs.toArray();for(let e=0;e<t.length;e++)if(t[e].isActive){this._indexToSelect=this._selectedIndex=e;break}}this._changeDetectorRef.markForCheck()}))}_subscribeToAllTabChanges(){this._allTabs.changes.pipe(Ne(this._allTabs)).subscribe((t=>{this._tabs.reset(t.filter((t=>t._closestTabGroup===this||!t._closestTabGroup))),this._tabs.notifyOnChanges()}))}ngOnDestroy(){this._tabs.destroy(),this._tabsSubscription.unsubscribe(),this._tabLabelSubscription.unsubscribe()}realignInkBar(){this._tabHeader&&this._tabHeader._alignInkBarToSelectedTab()}focusTab(t){const e=this._tabHeader;e&&(e.focusIndex=t)}_focusChanged(t){this.focusChange.emit(this._createChangeEvent(t))}_createChangeEvent(t){const e=new zB;return e.index=t,this._tabs&&this._tabs.length&&(e.tab=this._tabs.toArray()[t]),e}_subscribeToTabLabels(){this._tabLabelSubscription&&this._tabLabelSubscription.unsubscribe(),this._tabLabelSubscription=re(...this._tabs.map((t=>t._stateChanges))).subscribe((()=>this._changeDetectorRef.markForCheck()))}_clampTabIndex(t){return Math.min(this._tabs.length-1,Math.max(t||0,0))}_getTabLabelId(t){return`mat-tab-label-${this._groupId}-${t}`}_getTabContentId(t){return`mat-tab-content-${this._groupId}-${t}`}_setTabBodyWrapperHeight(t){if(!this._dynamicHeight||!this._tabBodyWrapperHeight)return;const e=this._tabBodyWrapper.nativeElement;e.style.height=this._tabBodyWrapperHeight+"px",this._tabBodyWrapper.nativeElement.offsetHeight&&(e.style.height=t+"px")}_removeTabBodyWrapperHeight(){const t=this._tabBodyWrapper.nativeElement;this._tabBodyWrapperHeight=t.clientHeight,t.style.height="",this.animationDone.emit()}_handleClick(t,e,n){t.disabled||(this.selectedIndex=e.focusIndex=n)}_getTabIndex(t,e){return t.disabled?null:this.selectedIndex===e?0:-1}_tabFocusChanged(t,e){t&&"mouse"!==t&&"touch"!==t&&(this._tabHeader.focusIndex=e)}}HB.ɵfac=function t(e){return new(e||HB)(Sm(hg),Sm(Ug),Sm(TB,8),Sm(VP,8))},HB.ɵdir=lo({type:HB,inputs:{headerPosition:"headerPosition",animationDuration:"animationDuration",disablePagination:"disablePagination",dynamicHeight:"dynamicHeight",contentTabIndex:"contentTabIndex",selectedIndex:"selectedIndex",backgroundColor:"backgroundColor"},outputs:{selectedIndexChange:"selectedIndexChange",focusChange:"focusChange",animationDone:"animationDone",selectedTabChange:"selectedTabChange"},features:[xp]}),HB.ctorParameters=()=>[{type:hg},{type:Ug},{type:void 0,decorators:[{type:kr,args:[TB]},{type:Sr}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],HB.propDecorators={dynamicHeight:[{type:xy}],selectedIndex:[{type:xy}],headerPosition:[{type:xy}],animationDuration:[{type:xy}],contentTabIndex:[{type:xy}],disablePagination:[{type:xy}],backgroundColor:[{type:xy}],selectedIndexChange:[{type:Oy}],focusChange:[{type:Oy}],animationDone:[{type:Oy}],selectedTabChange:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(HB,[{type:Cy}],(function(){return[{type:hg},{type:Ug},{type:void 0,decorators:[{type:kr,args:[TB]},{type:Sr}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{headerPosition:[{type:xy}],selectedIndexChange:[{type:Oy}],focusChange:[{type:Oy}],animationDone:[{type:Oy}],selectedTabChange:[{type:Oy}],animationDuration:[{type:xy}],disablePagination:[{type:xy}],dynamicHeight:[{type:xy}],contentTabIndex:[{type:xy}],selectedIndex:[{type:xy}],backgroundColor:[{type:xy}]});class FB extends HB{constructor(t,e,n,o){super(t,e,n,o)}}FB.ɵfac=function t(e){return new(e||FB)(Sm(hg),Sm(Ug),Sm(TB,8),Sm(VP,8))},FB.ɵcmp=to({type:FB,selectors:[["mat-tab-group"]],contentQueries:function t(e,n,o){if(1&e&&$h(o,SB,5),2&e){let t;Jh(t=tb())&&(n._allTabs=t)}},viewQuery:function t(e,n){if(1&e&&(Qh(lB,5),Qh(cB,5)),2&e){let t;Jh(t=tb())&&(n._tabBodyWrapper=t.first),Jh(t=tb())&&(n._tabHeader=t.first)}},hostAttrs:[1,"mat-tab-group"],hostVars:4,hostBindings:function t(e,n){2&e&&pu("mat-tab-group-dynamic-height",n.dynamicHeight)("mat-tab-group-inverted-header","below"===n.headerPosition)},inputs:{color:"color",disableRipple:"disableRipple"},exportAs:["matTabGroup"],features:[pg([{provide:kB,useExisting:FB}]),xp],decls:6,vars:7,consts:[[3,"selectedIndex","disableRipple","disablePagination","indexFocused","selectFocusedIndex"],["tabHeader",""],["class","mat-tab-label mat-focus-indicator","role","tab","matTabLabelWrapper","","mat-ripple","","cdkMonitorElementFocus","",3,"id","mat-tab-label-active","disabled","matRippleDisabled","click","cdkFocusChange",4,"ngFor","ngForOf"],[1,"mat-tab-body-wrapper"],["tabBodyWrapper",""],["role","tabpanel",3,"id","mat-tab-body-active","content","position","origin","animationDuration","_onCentered","_onCentering",4,"ngFor","ngForOf"],["role","tab","matTabLabelWrapper","","mat-ripple","","cdkMonitorElementFocus","",1,"mat-tab-label","mat-focus-indicator",3,"id","disabled","matRippleDisabled","click","cdkFocusChange"],[1,"mat-tab-label-content"],[3,"ngIf"],[3,"cdkPortalOutlet"],["role","tabpanel",3,"id","content","position","origin","animationDuration","_onCentered","_onCentering"]],template:function t(e,n){1&e&&(Rm(0,"mat-tab-header",0,1),Vm("indexFocused",(function t(e){return n._focusChanged(e)}))("selectFocusedIndex",(function t(e){return n.selectedIndex=e})),Qp(2,uB,4,14,"div",2),Am(),Rm(3,"div",3,4),Qp(5,fB,1,9,"mat-tab-body",5),Am()),2&e&&(Dm("selectedIndex",n.selectedIndex||0)("disableRipple",n.disableRipple)("disablePagination",n.disablePagination),rc(2),Dm("ngForOf",n._tabs),rc(1),pu("_mat-animation-noopable","NoopAnimations"===n._animationMode),rc(2),Dm("ngForOf",n._tabs))},directives:function(){return[GB,lM,BB,kH,DI,dM,DF,AB]},styles:[".mat-tab-group{display:flex;flex-direction:column;max-width:100%}.mat-tab-group.mat-tab-group-inverted-header{flex-direction:column-reverse}.mat-tab-label{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;position:relative}.mat-tab-label:focus{outline:none}.mat-tab-label:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-label:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-label.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-label.mat-tab-disabled{opacity:.5}.mat-tab-label .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-label{opacity:1}@media(max-width: 599px){.mat-tab-label{padding:0 12px}}@media(max-width: 959px){.mat-tab-label{padding:0 12px}}.mat-tab-group[mat-stretch-tabs]>.mat-tab-header .mat-tab-label{flex-basis:0;flex-grow:1}.mat-tab-body-wrapper{position:relative;overflow:hidden;display:flex;transition:height 500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-tab-body-wrapper{transition:none;animation:none}.mat-tab-body{top:0;left:0;right:0;bottom:0;position:absolute;display:block;overflow:hidden;outline:0;flex-basis:100%}.mat-tab-body.mat-tab-body-active{position:relative;overflow-x:hidden;overflow-y:auto;z-index:1;flex-grow:1}.mat-tab-group.mat-tab-group-dynamic-height .mat-tab-body.mat-tab-body-active{overflow-y:hidden}\n"],encapsulation:2}),FB.ctorParameters=()=>[{type:hg},{type:Ug},{type:void 0,decorators:[{type:kr,args:[TB]},{type:Sr}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],FB.propDecorators={_allTabs:[{type:Ya,args:[SB,{descendants:!0}]}],_tabBodyWrapper:[{type:Za,args:["tabBodyWrapper"]}],_tabHeader:[{type:Za,args:["tabHeader"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(FB,[{type:My,args:[{selector:"mat-tab-group",exportAs:"matTabGroup",template:'<mat-tab-header #tabHeader\n               [selectedIndex]="selectedIndex || 0"\n               [disableRipple]="disableRipple"\n               [disablePagination]="disablePagination"\n               (indexFocused)="_focusChanged($event)"\n               (selectFocusedIndex)="selectedIndex = $event">\n  <div class="mat-tab-label mat-focus-indicator" role="tab" matTabLabelWrapper mat-ripple cdkMonitorElementFocus\n       *ngFor="let tab of _tabs; let i = index"\n       [id]="_getTabLabelId(i)"\n       [attr.tabIndex]="_getTabIndex(tab, i)"\n       [attr.aria-posinset]="i + 1"\n       [attr.aria-setsize]="_tabs.length"\n       [attr.aria-controls]="_getTabContentId(i)"\n       [attr.aria-selected]="selectedIndex == i"\n       [attr.aria-label]="tab.ariaLabel || null"\n       [attr.aria-labelledby]="(!tab.ariaLabel && tab.ariaLabelledby) ? tab.ariaLabelledby : null"\n       [class.mat-tab-label-active]="selectedIndex == i"\n       [disabled]="tab.disabled"\n       [matRippleDisabled]="tab.disabled || disableRipple"\n       (click)="_handleClick(tab, tabHeader, i)"\n       (cdkFocusChange)="_tabFocusChanged($event, i)">\n\n\n    <div class="mat-tab-label-content">\n      \x3c!-- If there is a label template, use it. --\x3e\n      <ng-template [ngIf]="tab.templateLabel">\n        <ng-template [cdkPortalOutlet]="tab.templateLabel"></ng-template>\n      </ng-template>\n\n      \x3c!-- If there is not a label template, fall back to the text label. --\x3e\n      <ng-template [ngIf]="!tab.templateLabel">{{tab.textLabel}}</ng-template>\n    </div>\n  </div>\n</mat-tab-header>\n\n<div\n  class="mat-tab-body-wrapper"\n  [class._mat-animation-noopable]="_animationMode === \'NoopAnimations\'"\n  #tabBodyWrapper>\n  <mat-tab-body role="tabpanel"\n               *ngFor="let tab of _tabs; let i = index"\n               [id]="_getTabContentId(i)"\n               [attr.tabindex]="(contentTabIndex != null && selectedIndex === i) ? contentTabIndex : null"\n               [attr.aria-labelledby]="_getTabLabelId(i)"\n               [class.mat-tab-body-active]="selectedIndex === i"\n               [content]="tab.content!"\n               [position]="tab.position!"\n               [origin]="tab.origin"\n               [animationDuration]="animationDuration"\n               (_onCentered)="_removeTabBodyWrapperHeight()"\n               (_onCentering)="_setTabBodyWrapperHeight($event)">\n  </mat-tab-body>\n</div>\n',encapsulation:Hn.None,changeDetection:zn.Default,inputs:["color","disableRipple"],providers:[{provide:kB,useExisting:FB}],host:{class:"mat-tab-group","[class.mat-tab-group-dynamic-height]":"dynamicHeight","[class.mat-tab-group-inverted-header]":'headerPosition === "below"'},styles:[".mat-tab-group{display:flex;flex-direction:column;max-width:100%}.mat-tab-group.mat-tab-group-inverted-header{flex-direction:column-reverse}.mat-tab-label{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;position:relative}.mat-tab-label:focus{outline:none}.mat-tab-label:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-label:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-label.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-label.mat-tab-disabled{opacity:.5}.mat-tab-label .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-label{opacity:1}@media(max-width: 599px){.mat-tab-label{padding:0 12px}}@media(max-width: 959px){.mat-tab-label{padding:0 12px}}.mat-tab-group[mat-stretch-tabs]>.mat-tab-header .mat-tab-label{flex-basis:0;flex-grow:1}.mat-tab-body-wrapper{position:relative;overflow:hidden;display:flex;transition:height 500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-tab-body-wrapper{transition:none;animation:none}.mat-tab-body{top:0;left:0;right:0;bottom:0;position:absolute;display:block;overflow:hidden;outline:0;flex-basis:100%}.mat-tab-body.mat-tab-body-active{position:relative;overflow-x:hidden;overflow-y:auto;z-index:1;flex-grow:1}.mat-tab-group.mat-tab-group-dynamic-height .mat-tab-body.mat-tab-body-active{overflow-y:hidden}\n"]}]}],(function(){return[{type:hg},{type:Ug},{type:void 0,decorators:[{type:kr,args:[TB]},{type:Sr}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{_allTabs:[{type:Ya,args:[SB,{descendants:!0}]}],_tabBodyWrapper:[{type:Za,args:["tabBodyWrapper"]}],_tabHeader:[{type:Za,args:["tabHeader"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const LB=KI(class{});class BB extends LB{constructor(t){super(),this.elementRef=t}focus(){this.elementRef.nativeElement.focus()}getOffsetLeft(){return this.elementRef.nativeElement.offsetLeft}getOffsetWidth(){return this.elementRef.nativeElement.offsetWidth}}BB.ɵfac=function t(e){return new(e||BB)(Sm(hg))},BB.ɵdir=lo({type:BB,selectors:[["","matTabLabelWrapper",""]],hostVars:3,hostBindings:function t(e,n){2&e&&(jp("aria-disabled",!!n.disabled),pu("mat-tab-disabled",n.disabled))},inputs:{disabled:"disabled"},features:[xp]}),BB.ctorParameters=()=>[{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(BB,[{type:Cy,args:[{selector:"[matTabLabelWrapper]",inputs:["disabled"],host:{"[class.mat-tab-disabled]":"disabled","[attr.aria-disabled]":"!!disabled"}}]}],(function(){return[{type:hg}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const VB=Nz({passive:!0});class jB{constructor(t,e,n,o,i,a,r){this._elementRef=t,this._changeDetectorRef=e,this._viewportRuler=n,this._dir=o,this._ngZone=i,this._platform=a,this._animationMode=r,this._scrollDistance=0,this._selectedIndexChanged=!1,this._destroyed=new I,this._showPaginationControls=!1,this._disableScrollAfter=!0,this._disableScrollBefore=!0,this._stopScrolling=new I,this.disablePagination=!1,this._selectedIndex=0,this.selectFocusedIndex=new Lh,this.indexFocused=new Lh,i.runOutsideAngular((()=>{oe(t.nativeElement,"mouseleave").pipe(Ie(this._destroyed)).subscribe((()=>{this._stopInterval()}))}))}get selectedIndex(){return this._selectedIndex}set selectedIndex(t){t=_z(t),this._selectedIndex!=t&&(this._selectedIndexChanged=!0,this._selectedIndex=t,this._keyManager&&this._keyManager.updateActiveItem(t))}ngAfterViewInit(){oe(this._previousPaginator.nativeElement,"touchstart",VB).pipe(Ie(this._destroyed)).subscribe((()=>{this._handlePaginatorPress("before")})),oe(this._nextPaginator.nativeElement,"touchstart",VB).pipe(Ie(this._destroyed)).subscribe((()=>{this._handlePaginatorPress("after")}))}ngAfterContentInit(){const t=this._dir?this._dir.change:Et("ltr"),e=this._viewportRuler.change(150),n=()=>{this.updatePagination(),this._alignInkBarToSelectedTab()};this._keyManager=new eI(this._items).withHorizontalOrientation(this._getLayoutDirection()).withHomeAndEnd().withWrap(),this._keyManager.updateActiveItem(this._selectedIndex),"undefined"!=typeof requestAnimationFrame?requestAnimationFrame(n):n(),re(t,e,this._items.changes).pipe(Ie(this._destroyed)).subscribe((()=>{this._ngZone.run((()=>Promise.resolve().then(n))),this._keyManager.withHorizontalOrientation(this._getLayoutDirection())})),this._keyManager.change.pipe(Ie(this._destroyed)).subscribe((t=>{this.indexFocused.emit(t),this._setTabFocus(t)}))}ngAfterContentChecked(){this._tabLabelCount!=this._items.length&&(this.updatePagination(),this._tabLabelCount=this._items.length,this._changeDetectorRef.markForCheck()),this._selectedIndexChanged&&(this._scrollToLabel(this._selectedIndex),this._checkScrollingControls(),this._alignInkBarToSelectedTab(),this._selectedIndexChanged=!1,this._changeDetectorRef.markForCheck()),this._scrollDistanceChanged&&(this._updateTabScrollPosition(),this._scrollDistanceChanged=!1,this._changeDetectorRef.markForCheck())}ngOnDestroy(){this._destroyed.next(),this._destroyed.complete(),this._stopScrolling.complete()}_handleKeydown(t){if(!bz(t))switch(t.keyCode){case mz:case fz:this.focusIndex!==this.selectedIndex&&(this.selectFocusedIndex.emit(this.focusIndex),this._itemSelected(t));break;default:this._keyManager.onKeydown(t)}}_onContentChanges(){const t=this._elementRef.nativeElement.textContent;t!==this._currentTextContent&&(this._currentTextContent=t||"",this._ngZone.run((()=>{this.updatePagination(),this._alignInkBarToSelectedTab(),this._changeDetectorRef.markForCheck()})))}updatePagination(){this._checkPaginationEnabled(),this._checkScrollingControls(),this._updateTabScrollPosition()}get focusIndex(){return this._keyManager?this._keyManager.activeItemIndex:0}set focusIndex(t){this._isValidIndex(t)&&this.focusIndex!==t&&this._keyManager&&this._keyManager.setActiveItem(t)}_isValidIndex(t){if(!this._items)return!0;const e=this._items?this._items.toArray()[t]:null;return!!e&&!e.disabled}_setTabFocus(t){if(this._showPaginationControls&&this._scrollToLabel(t),this._items&&this._items.length){this._items.toArray()[t].focus();const e=this._tabListContainer.nativeElement,n=this._getLayoutDirection();e.scrollLeft="ltr"==n?0:e.scrollWidth-e.offsetWidth}}_getLayoutDirection(){return this._dir&&"rtl"===this._dir.value?"rtl":"ltr"}_updateTabScrollPosition(){if(this.disablePagination)return;const t=this.scrollDistance,e="ltr"===this._getLayoutDirection()?-t:t;this._tabList.nativeElement.style.transform=`translateX(${Math.round(e)}px)`,(this._platform.TRIDENT||this._platform.EDGE)&&(this._tabListContainer.nativeElement.scrollLeft=0)}get scrollDistance(){return this._scrollDistance}set scrollDistance(t){this._scrollTo(t)}_scrollHeader(t){return this._scrollTo(this._scrollDistance+("before"==t?-1:1)*this._tabListContainer.nativeElement.offsetWidth/3)}_handlePaginatorClick(t){this._stopInterval(),this._scrollHeader(t)}_scrollToLabel(t){if(this.disablePagination)return;const e=this._items?this._items.toArray()[t]:null;if(!e)return;const n=this._tabListContainer.nativeElement.offsetWidth,{offsetLeft:o,offsetWidth:i}=e.elementRef.nativeElement;let a,r;"ltr"==this._getLayoutDirection()?(a=o,r=a+i):(r=this._tabList.nativeElement.offsetWidth-o,a=r-i);const s=this.scrollDistance,l=this.scrollDistance+n;a<s?this.scrollDistance-=s-a+60:r>l&&(this.scrollDistance+=r-l+60)}_checkPaginationEnabled(){if(this.disablePagination)this._showPaginationControls=!1;else{const t=this._tabList.nativeElement.scrollWidth>this._elementRef.nativeElement.offsetWidth;t||(this.scrollDistance=0),t!==this._showPaginationControls&&this._changeDetectorRef.markForCheck(),this._showPaginationControls=t}}_checkScrollingControls(){this.disablePagination?this._disableScrollAfter=this._disableScrollBefore=!0:(this._disableScrollBefore=0==this.scrollDistance,this._disableScrollAfter=this.scrollDistance==this._getMaxScrollDistance(),this._changeDetectorRef.markForCheck())}_getMaxScrollDistance(){return this._tabList.nativeElement.scrollWidth-this._tabListContainer.nativeElement.offsetWidth||0}_alignInkBarToSelectedTab(){const t=this._items&&this._items.length?this._items.toArray()[this.selectedIndex]:null,e=t?t.elementRef.nativeElement:null;e?this._inkBar.alignToElement(e):this._inkBar.hide()}_stopInterval(){this._stopScrolling.next()}_handlePaginatorPress(t,e){e&&null!=e.button&&0!==e.button||(this._stopInterval(),ae(650,100).pipe(Ie(re(this._stopScrolling,this._destroyed))).subscribe((()=>{const{maxScrollDistance:e,distance:n}=this._scrollHeader(t);(0===n||n>=e)&&this._stopInterval()})))}_scrollTo(t){if(this.disablePagination)return{maxScrollDistance:0,distance:0};const e=this._getMaxScrollDistance();return this._scrollDistance=Math.max(0,Math.min(e,t)),this._scrollDistanceChanged=!0,this._checkScrollingControls(),{maxScrollDistance:e,distance:this._scrollDistance}}}jB.ɵfac=function t(e){return new(e||jB)(Sm(hg),Sm(Ug),Sm(uF),Sm(HI,8),Sm(a_),Sm(wz),Sm(VP,8))},jB.ɵdir=lo({type:jB,inputs:{disablePagination:"disablePagination"}}),jB.ctorParameters=()=>[{type:hg},{type:Ug},{type:uF},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],jB.propDecorators={disablePagination:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(jB,[{type:Cy}],(function(){return[{type:hg},{type:Ug},{type:uF},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{disablePagination:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class UB extends jB{constructor(t,e,n,o,i,a,r){super(t,e,n,o,i,a,r),this._disableRipple=!1}get disableRipple(){return this._disableRipple}set disableRipple(t){this._disableRipple=yz(t)}_itemSelected(t){t.preventDefault()}}UB.ɵfac=function t(e){return new(e||UB)(Sm(hg),Sm(Ug),Sm(uF),Sm(HI,8),Sm(a_),Sm(wz),Sm(VP,8))},UB.ɵdir=lo({type:UB,inputs:{disableRipple:"disableRipple"},features:[xp]}),UB.ctorParameters=()=>[{type:hg},{type:Ug},{type:uF},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],UB.propDecorators={disableRipple:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(UB,[{type:Cy}],(function(){return[{type:hg},{type:Ug},{type:uF},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{disableRipple:[{type:xy}]});class GB extends UB{constructor(t,e,n,o,i,a,r){super(t,e,n,o,i,a,r)}}GB.ɵfac=function t(e){return new(e||GB)(Sm(hg),Sm(Ug),Sm(uF),Sm(HI,8),Sm(a_),Sm(wz),Sm(VP,8))},GB.ɵcmp=to({type:GB,selectors:[["mat-tab-header"]],contentQueries:function t(e,n,o){if(1&e&&$h(o,BB,4),2&e){let t;Jh(t=tb())&&(n._items=t)}},viewQuery:function t(e,n){if(1&e&&(Qh(MB,7),Qh(gB,7),Qh(hB,7),Qh(bB,5),Qh(yB,5)),2&e){let t;Jh(t=tb())&&(n._inkBar=t.first),Jh(t=tb())&&(n._tabListContainer=t.first),Jh(t=tb())&&(n._tabList=t.first),Jh(t=tb())&&(n._nextPaginator=t.first),Jh(t=tb())&&(n._previousPaginator=t.first)}},hostAttrs:[1,"mat-tab-header"],hostVars:4,hostBindings:function t(e,n){2&e&&pu("mat-tab-header-pagination-controls-enabled",n._showPaginationControls)("mat-tab-header-rtl","rtl"==n._getLayoutDirection())},inputs:{selectedIndex:"selectedIndex"},outputs:{selectFocusedIndex:"selectFocusedIndex",indexFocused:"indexFocused"},features:[xp],ngContentSelectors:iB,decls:13,vars:8,consts:[["aria-hidden","true","mat-ripple","",1,"mat-tab-header-pagination","mat-tab-header-pagination-before","mat-elevation-z4",3,"matRippleDisabled","click","mousedown","touchend"],["previousPaginator",""],[1,"mat-tab-header-pagination-chevron"],[1,"mat-tab-label-container",3,"keydown"],["tabListContainer",""],["role","tablist",1,"mat-tab-list",3,"cdkObserveContent"],["tabList",""],[1,"mat-tab-labels"],["aria-hidden","true","mat-ripple","",1,"mat-tab-header-pagination","mat-tab-header-pagination-after","mat-elevation-z4",3,"matRippleDisabled","mousedown","click","touchend"],["nextPaginator",""]],template:function t(e,n){1&e&&(Zm(),Rm(0,"div",0,1),Vm("click",(function t(){return n._handlePaginatorClick("before")}))("mousedown",(function t(e){return n._handlePaginatorPress("before",e)}))("touchend",(function t(){return n._stopInterval()})),Tm(2,"div",2),Am(),Rm(3,"div",3,4),Vm("keydown",(function t(e){return n._handleKeydown(e)})),Rm(5,"div",5,6),Vm("cdkObserveContent",(function t(){return n._onContentChanges()})),Rm(7,"div",7),Xm(8),Am(),Tm(9,"mat-ink-bar"),Am(),Am(),Rm(10,"div",8,9),Vm("mousedown",(function t(e){return n._handlePaginatorPress("after",e)}))("click",(function t(){return n._handlePaginatorClick("after")}))("touchend",(function t(){return n._stopInterval()})),Tm(12,"div",2),Am()),2&e&&(pu("mat-tab-header-pagination-disabled",n._disableScrollBefore),Dm("matRippleDisabled",n._disableScrollBefore||n.disableRipple),rc(5),pu("_mat-animation-noopable","NoopAnimations"===n._animationMode),rc(5),pu("mat-tab-header-pagination-disabled",n._disableScrollAfter),Dm("matRippleDisabled",n._disableScrollAfter||n.disableRipple))},directives:[kH,jz,MB],styles:['.mat-tab-header{display:flex;overflow:hidden;position:relative;flex-shrink:0}.mat-tab-header-pagination{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;position:relative;display:none;justify-content:center;align-items:center;min-width:32px;cursor:pointer;z-index:2;-webkit-tap-highlight-color:transparent;touch-action:none}.mat-tab-header-pagination-controls-enabled .mat-tab-header-pagination{display:flex}.mat-tab-header-pagination-before,.mat-tab-header-rtl .mat-tab-header-pagination-after{padding-left:4px}.mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-rtl .mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(-135deg)}.mat-tab-header-rtl .mat-tab-header-pagination-before,.mat-tab-header-pagination-after{padding-right:4px}.mat-tab-header-rtl .mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(45deg)}.mat-tab-header-pagination-chevron{border-style:solid;border-width:2px 2px 0 0;content:"";height:8px;width:8px}.mat-tab-header-pagination-disabled{box-shadow:none;cursor:default}.mat-tab-list{flex-grow:1;position:relative;transition:transform 500ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-ink-bar{position:absolute;bottom:0;height:2px;transition:500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-ink-bar{transition:none;animation:none}.mat-tab-group-inverted-header .mat-ink-bar{bottom:auto;top:0}.cdk-high-contrast-active .mat-ink-bar{outline:solid 2px;height:0}.mat-tab-labels{display:flex}[mat-align-tabs=center]>.mat-tab-header .mat-tab-labels{justify-content:center}[mat-align-tabs=end]>.mat-tab-header .mat-tab-labels{justify-content:flex-end}.mat-tab-label-container{display:flex;flex-grow:1;overflow:hidden;z-index:1}._mat-animation-noopable.mat-tab-list{transition:none;animation:none}.mat-tab-label{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;position:relative}.mat-tab-label:focus{outline:none}.mat-tab-label:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-label:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-label.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-label.mat-tab-disabled{opacity:.5}.mat-tab-label .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-label{opacity:1}@media(max-width: 599px){.mat-tab-label{min-width:72px}}\n'],encapsulation:2}),GB.ctorParameters=()=>[{type:hg},{type:Ug},{type:uF},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],GB.propDecorators={_items:[{type:Ya,args:[BB,{descendants:!1}]}],_inkBar:[{type:Za,args:[MB,{static:!0}]}],_tabListContainer:[{type:Za,args:["tabListContainer",{static:!0}]}],_tabList:[{type:Za,args:["tabList",{static:!0}]}],_nextPaginator:[{type:Za,args:["nextPaginator"]}],_previousPaginator:[{type:Za,args:["previousPaginator"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(GB,[{type:My,args:[{selector:"mat-tab-header",template:'<div class="mat-tab-header-pagination mat-tab-header-pagination-before mat-elevation-z4"\n     #previousPaginator\n     aria-hidden="true"\n     mat-ripple [matRippleDisabled]="_disableScrollBefore || disableRipple"\n     [class.mat-tab-header-pagination-disabled]="_disableScrollBefore"\n     (click)="_handlePaginatorClick(\'before\')"\n     (mousedown)="_handlePaginatorPress(\'before\', $event)"\n     (touchend)="_stopInterval()">\n  <div class="mat-tab-header-pagination-chevron"></div>\n</div>\n\n<div class="mat-tab-label-container" #tabListContainer (keydown)="_handleKeydown($event)">\n  <div\n    #tabList\n    class="mat-tab-list"\n    [class._mat-animation-noopable]="_animationMode === \'NoopAnimations\'"\n    role="tablist"\n    (cdkObserveContent)="_onContentChanges()">\n    <div class="mat-tab-labels">\n      <ng-content></ng-content>\n    </div>\n    <mat-ink-bar></mat-ink-bar>\n  </div>\n</div>\n\n<div class="mat-tab-header-pagination mat-tab-header-pagination-after mat-elevation-z4"\n     #nextPaginator\n     aria-hidden="true"\n     mat-ripple [matRippleDisabled]="_disableScrollAfter || disableRipple"\n     [class.mat-tab-header-pagination-disabled]="_disableScrollAfter"\n     (mousedown)="_handlePaginatorPress(\'after\', $event)"\n     (click)="_handlePaginatorClick(\'after\')"\n     (touchend)="_stopInterval()">\n  <div class="mat-tab-header-pagination-chevron"></div>\n</div>\n',inputs:["selectedIndex"],outputs:["selectFocusedIndex","indexFocused"],encapsulation:Hn.None,changeDetection:zn.Default,host:{class:"mat-tab-header","[class.mat-tab-header-pagination-controls-enabled]":"_showPaginationControls","[class.mat-tab-header-rtl]":"_getLayoutDirection() == 'rtl'"},styles:['.mat-tab-header{display:flex;overflow:hidden;position:relative;flex-shrink:0}.mat-tab-header-pagination{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;position:relative;display:none;justify-content:center;align-items:center;min-width:32px;cursor:pointer;z-index:2;-webkit-tap-highlight-color:transparent;touch-action:none}.mat-tab-header-pagination-controls-enabled .mat-tab-header-pagination{display:flex}.mat-tab-header-pagination-before,.mat-tab-header-rtl .mat-tab-header-pagination-after{padding-left:4px}.mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-rtl .mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(-135deg)}.mat-tab-header-rtl .mat-tab-header-pagination-before,.mat-tab-header-pagination-after{padding-right:4px}.mat-tab-header-rtl .mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(45deg)}.mat-tab-header-pagination-chevron{border-style:solid;border-width:2px 2px 0 0;content:"";height:8px;width:8px}.mat-tab-header-pagination-disabled{box-shadow:none;cursor:default}.mat-tab-list{flex-grow:1;position:relative;transition:transform 500ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-ink-bar{position:absolute;bottom:0;height:2px;transition:500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-ink-bar{transition:none;animation:none}.mat-tab-group-inverted-header .mat-ink-bar{bottom:auto;top:0}.cdk-high-contrast-active .mat-ink-bar{outline:solid 2px;height:0}.mat-tab-labels{display:flex}[mat-align-tabs=center]>.mat-tab-header .mat-tab-labels{justify-content:center}[mat-align-tabs=end]>.mat-tab-header .mat-tab-labels{justify-content:flex-end}.mat-tab-label-container{display:flex;flex-grow:1;overflow:hidden;z-index:1}._mat-animation-noopable.mat-tab-list{transition:none;animation:none}.mat-tab-label{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;position:relative}.mat-tab-label:focus{outline:none}.mat-tab-label:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-label:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-label.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-label.mat-tab-disabled{opacity:.5}.mat-tab-label .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-label{opacity:1}@media(max-width: 599px){.mat-tab-label{min-width:72px}}\n']}]}],(function(){return[{type:hg},{type:Ug},{type:uF},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{_items:[{type:Ya,args:[BB,{descendants:!1}]}],_inkBar:[{type:Za,args:[MB,{static:!0}]}],_tabListContainer:[{type:Za,args:["tabListContainer",{static:!0}]}],_tabList:[{type:Za,args:["tabList",{static:!0}]}],_nextPaginator:[{type:Za,args:["nextPaginator"]}],_previousPaginator:[{type:Za,args:["previousPaginator"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class WB extends jB{constructor(t,e,n,o,i,a,r){super(t,o,i,e,n,a,r),this._disableRipple=!1,this.color="primary"}get backgroundColor(){return this._backgroundColor}set backgroundColor(t){const e=this._elementRef.nativeElement.classList;e.remove(`mat-background-${this.backgroundColor}`),t&&e.add(`mat-background-${t}`),this._backgroundColor=t}get disableRipple(){return this._disableRipple}set disableRipple(t){this._disableRipple=yz(t)}_itemSelected(){}ngAfterContentInit(){this._items.changes.pipe(Ne(null),Ie(this._destroyed)).subscribe((()=>{this.updateActiveLink()})),super.ngAfterContentInit()}updateActiveLink(){if(!this._items)return;const t=this._items.toArray();for(let e=0;e<t.length;e++)if(t[e].active)return this.selectedIndex=e,void this._changeDetectorRef.markForCheck();this.selectedIndex=-1,this._inkBar.hide()}}WB.ɵfac=function t(e){return new(e||WB)(Sm(hg),Sm(HI,8),Sm(a_),Sm(Ug),Sm(uF),Sm(wz),Sm(VP,8))},WB.ɵdir=lo({type:WB,inputs:{color:"color",backgroundColor:"backgroundColor",disableRipple:"disableRipple"},features:[xp]}),WB.ctorParameters=()=>[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:Ug},{type:uF},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],WB.propDecorators={backgroundColor:[{type:xy}],disableRipple:[{type:xy}],color:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(WB,[{type:Cy}],(function(){return[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:Ug},{type:uF},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{color:[{type:xy}],backgroundColor:[{type:xy}],disableRipple:[{type:xy}]});class YB extends WB{constructor(t,e,n,o,i,a,r){super(t,e,n,o,i,a,r)}}YB.ɵfac=function t(e){return new(e||YB)(Sm(hg),Sm(HI,8),Sm(a_),Sm(Ug),Sm(uF),Sm(wz),Sm(VP,8))},YB.ɵcmp=to({type:YB,selectors:[["","mat-tab-nav-bar",""]],contentQueries:function t(e,n,o){if(1&e&&$h(o,XB,5),2&e){let t;Jh(t=tb())&&(n._items=t)}},viewQuery:function t(e,n){if(1&e&&(Qh(MB,7),Qh(gB,7),Qh(hB,7),Qh(bB,5),Qh(yB,5)),2&e){let t;Jh(t=tb())&&(n._inkBar=t.first),Jh(t=tb())&&(n._tabListContainer=t.first),Jh(t=tb())&&(n._tabList=t.first),Jh(t=tb())&&(n._nextPaginator=t.first),Jh(t=tb())&&(n._previousPaginator=t.first)}},hostAttrs:[1,"mat-tab-nav-bar","mat-tab-header"],hostVars:10,hostBindings:function t(e,n){2&e&&pu("mat-tab-header-pagination-controls-enabled",n._showPaginationControls)("mat-tab-header-rtl","rtl"==n._getLayoutDirection())("mat-primary","warn"!==n.color&&"accent"!==n.color)("mat-accent","accent"===n.color)("mat-warn","warn"===n.color)},inputs:{color:"color"},exportAs:["matTabNavBar","matTabNav"],features:[xp],attrs:["mat-tab-nav-bar",""],ngContentSelectors:iB,decls:13,vars:8,consts:[["aria-hidden","true","mat-ripple","",1,"mat-tab-header-pagination","mat-tab-header-pagination-before","mat-elevation-z4",3,"matRippleDisabled","click","mousedown","touchend"],["previousPaginator",""],[1,"mat-tab-header-pagination-chevron"],[1,"mat-tab-link-container",3,"keydown"],["tabListContainer",""],[1,"mat-tab-list",3,"cdkObserveContent"],["tabList",""],[1,"mat-tab-links"],["aria-hidden","true","mat-ripple","",1,"mat-tab-header-pagination","mat-tab-header-pagination-after","mat-elevation-z4",3,"matRippleDisabled","mousedown","click","touchend"],["nextPaginator",""]],template:function t(e,n){1&e&&(Zm(),Rm(0,"div",0,1),Vm("click",(function t(){return n._handlePaginatorClick("before")}))("mousedown",(function t(e){return n._handlePaginatorPress("before",e)}))("touchend",(function t(){return n._stopInterval()})),Tm(2,"div",2),Am(),Rm(3,"div",3,4),Vm("keydown",(function t(e){return n._handleKeydown(e)})),Rm(5,"div",5,6),Vm("cdkObserveContent",(function t(){return n._onContentChanges()})),Rm(7,"div",7),Xm(8),Am(),Tm(9,"mat-ink-bar"),Am(),Am(),Rm(10,"div",8,9),Vm("mousedown",(function t(e){return n._handlePaginatorPress("after",e)}))("click",(function t(){return n._handlePaginatorClick("after")}))("touchend",(function t(){return n._stopInterval()})),Tm(12,"div",2),Am()),2&e&&(pu("mat-tab-header-pagination-disabled",n._disableScrollBefore),Dm("matRippleDisabled",n._disableScrollBefore||n.disableRipple),rc(5),pu("_mat-animation-noopable","NoopAnimations"===n._animationMode),rc(5),pu("mat-tab-header-pagination-disabled",n._disableScrollAfter),Dm("matRippleDisabled",n._disableScrollAfter||n.disableRipple))},directives:[kH,jz,MB],styles:['.mat-tab-header{display:flex;overflow:hidden;position:relative;flex-shrink:0}.mat-tab-header-pagination{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;position:relative;display:none;justify-content:center;align-items:center;min-width:32px;cursor:pointer;z-index:2;-webkit-tap-highlight-color:transparent;touch-action:none}.mat-tab-header-pagination-controls-enabled .mat-tab-header-pagination{display:flex}.mat-tab-header-pagination-before,.mat-tab-header-rtl .mat-tab-header-pagination-after{padding-left:4px}.mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-rtl .mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(-135deg)}.mat-tab-header-rtl .mat-tab-header-pagination-before,.mat-tab-header-pagination-after{padding-right:4px}.mat-tab-header-rtl .mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(45deg)}.mat-tab-header-pagination-chevron{border-style:solid;border-width:2px 2px 0 0;content:"";height:8px;width:8px}.mat-tab-header-pagination-disabled{box-shadow:none;cursor:default}.mat-tab-list{flex-grow:1;position:relative;transition:transform 500ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-tab-links{display:flex}[mat-align-tabs=center]>.mat-tab-link-container .mat-tab-links{justify-content:center}[mat-align-tabs=end]>.mat-tab-link-container .mat-tab-links{justify-content:flex-end}.mat-ink-bar{position:absolute;bottom:0;height:2px;transition:500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-ink-bar{transition:none;animation:none}.mat-tab-group-inverted-header .mat-ink-bar{bottom:auto;top:0}.cdk-high-contrast-active .mat-ink-bar{outline:solid 2px;height:0}.mat-tab-link-container{display:flex;flex-grow:1;overflow:hidden;z-index:1}.mat-tab-link{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;vertical-align:top;text-decoration:none;position:relative;overflow:hidden;-webkit-tap-highlight-color:transparent}.mat-tab-link:focus{outline:none}.mat-tab-link:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-link:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-link.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-link.mat-tab-disabled{opacity:.5}.mat-tab-link .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-link{opacity:1}[mat-stretch-tabs] .mat-tab-link{flex-basis:0;flex-grow:1}.mat-tab-link.mat-tab-disabled{pointer-events:none}@media(max-width: 599px){.mat-tab-link{min-width:72px}}\n'],encapsulation:2}),YB.ctorParameters=()=>[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:Ug},{type:uF},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],YB.propDecorators={_items:[{type:Ya,args:[qe((()=>XB)),{descendants:!0}]}],_inkBar:[{type:Za,args:[MB,{static:!0}]}],_tabListContainer:[{type:Za,args:["tabListContainer",{static:!0}]}],_tabList:[{type:Za,args:["tabList",{static:!0}]}],_nextPaginator:[{type:Za,args:["nextPaginator"]}],_previousPaginator:[{type:Za,args:["previousPaginator"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(YB,[{type:My,args:[{selector:"[mat-tab-nav-bar]",exportAs:"matTabNavBar, matTabNav",inputs:["color"],template:'<div class="mat-tab-header-pagination mat-tab-header-pagination-before mat-elevation-z4"\n     #previousPaginator\n     aria-hidden="true"\n     mat-ripple [matRippleDisabled]="_disableScrollBefore || disableRipple"\n     [class.mat-tab-header-pagination-disabled]="_disableScrollBefore"\n     (click)="_handlePaginatorClick(\'before\')"\n     (mousedown)="_handlePaginatorPress(\'before\', $event)"\n     (touchend)="_stopInterval()">\n  <div class="mat-tab-header-pagination-chevron"></div>\n</div>\n\n<div class="mat-tab-link-container" #tabListContainer (keydown)="_handleKeydown($event)">\n  <div\n    class="mat-tab-list"\n    [class._mat-animation-noopable]="_animationMode === \'NoopAnimations\'"\n    #tabList\n    (cdkObserveContent)="_onContentChanges()">\n    <div class="mat-tab-links">\n      <ng-content></ng-content>\n    </div>\n    <mat-ink-bar></mat-ink-bar>\n  </div>\n</div>\n\n<div class="mat-tab-header-pagination mat-tab-header-pagination-after mat-elevation-z4"\n     #nextPaginator\n     aria-hidden="true"\n     mat-ripple [matRippleDisabled]="_disableScrollAfter || disableRipple"\n     [class.mat-tab-header-pagination-disabled]="_disableScrollAfter"\n     (mousedown)="_handlePaginatorPress(\'after\', $event)"\n     (click)="_handlePaginatorClick(\'after\')"\n     (touchend)="_stopInterval()">\n  <div class="mat-tab-header-pagination-chevron"></div>\n</div>\n',host:{class:"mat-tab-nav-bar mat-tab-header","[class.mat-tab-header-pagination-controls-enabled]":"_showPaginationControls","[class.mat-tab-header-rtl]":"_getLayoutDirection() == 'rtl'","[class.mat-primary]":'color !== "warn" && color !== "accent"',"[class.mat-accent]":'color === "accent"',"[class.mat-warn]":'color === "warn"'},encapsulation:Hn.None,changeDetection:zn.Default,styles:['.mat-tab-header{display:flex;overflow:hidden;position:relative;flex-shrink:0}.mat-tab-header-pagination{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;position:relative;display:none;justify-content:center;align-items:center;min-width:32px;cursor:pointer;z-index:2;-webkit-tap-highlight-color:transparent;touch-action:none}.mat-tab-header-pagination-controls-enabled .mat-tab-header-pagination{display:flex}.mat-tab-header-pagination-before,.mat-tab-header-rtl .mat-tab-header-pagination-after{padding-left:4px}.mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-rtl .mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(-135deg)}.mat-tab-header-rtl .mat-tab-header-pagination-before,.mat-tab-header-pagination-after{padding-right:4px}.mat-tab-header-rtl .mat-tab-header-pagination-before .mat-tab-header-pagination-chevron,.mat-tab-header-pagination-after .mat-tab-header-pagination-chevron{transform:rotate(45deg)}.mat-tab-header-pagination-chevron{border-style:solid;border-width:2px 2px 0 0;content:"";height:8px;width:8px}.mat-tab-header-pagination-disabled{box-shadow:none;cursor:default}.mat-tab-list{flex-grow:1;position:relative;transition:transform 500ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-tab-links{display:flex}[mat-align-tabs=center]>.mat-tab-link-container .mat-tab-links{justify-content:center}[mat-align-tabs=end]>.mat-tab-link-container .mat-tab-links{justify-content:flex-end}.mat-ink-bar{position:absolute;bottom:0;height:2px;transition:500ms cubic-bezier(0.35, 0, 0.25, 1)}._mat-animation-noopable.mat-ink-bar{transition:none;animation:none}.mat-tab-group-inverted-header .mat-ink-bar{bottom:auto;top:0}.cdk-high-contrast-active .mat-ink-bar{outline:solid 2px;height:0}.mat-tab-link-container{display:flex;flex-grow:1;overflow:hidden;z-index:1}.mat-tab-link{height:48px;padding:0 24px;cursor:pointer;box-sizing:border-box;opacity:.6;min-width:160px;text-align:center;display:inline-flex;justify-content:center;align-items:center;white-space:nowrap;vertical-align:top;text-decoration:none;position:relative;overflow:hidden;-webkit-tap-highlight-color:transparent}.mat-tab-link:focus{outline:none}.mat-tab-link:focus:not(.mat-tab-disabled){opacity:1}.cdk-high-contrast-active .mat-tab-link:focus{outline:dotted 2px;outline-offset:-2px}.mat-tab-link.mat-tab-disabled{cursor:default}.cdk-high-contrast-active .mat-tab-link.mat-tab-disabled{opacity:.5}.mat-tab-link .mat-tab-label-content{display:inline-flex;justify-content:center;align-items:center;white-space:nowrap}.cdk-high-contrast-active .mat-tab-link{opacity:1}[mat-stretch-tabs] .mat-tab-link{flex-basis:0;flex-grow:1}.mat-tab-link.mat-tab-disabled{pointer-events:none}@media(max-width: 599px){.mat-tab-link{min-width:72px}}\n']}]}],(function(){return[{type:hg},{type:HI,decorators:[{type:Sr}]},{type:a_},{type:Ug},{type:uF},{type:wz},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{_items:[{type:Ya,args:[qe((()=>XB)),{descendants:!0}]}],_inkBar:[{type:Za,args:[MB,{static:!0}]}],_tabListContainer:[{type:Za,args:["tabListContainer",{static:!0}]}],_tabList:[{type:Za,args:["tabList",{static:!0}]}],_nextPaginator:[{type:Za,args:["nextPaginator"]}],_previousPaginator:[{type:Za,args:["previousPaginator"]}]});const qB=$I(QI(KI(class{})));class ZB extends qB{constructor(t,e,n,o,i,a){super(),this._tabNavBar=t,this.elementRef=e,this._focusMonitor=i,this._isActive=!1,this.rippleConfig=n||{},this.tabIndex=parseInt(o)||0,"NoopAnimations"===a&&(this.rippleConfig.animation={enterDuration:0,exitDuration:0})}get active(){return this._isActive}set active(t){yz(t)!==this._isActive&&(this._isActive=t,this._tabNavBar.updateActiveLink())}get rippleDisabled(){return this.disabled||this.disableRipple||this._tabNavBar.disableRipple||!!this.rippleConfig.disabled}focus(){this.elementRef.nativeElement.focus()}ngAfterViewInit(){this._focusMonitor.monitor(this.elementRef)}ngOnDestroy(){this._focusMonitor.stopMonitoring(this.elementRef)}_handleFocus(){this._tabNavBar.focusIndex=this._tabNavBar._items.toArray().indexOf(this)}}ZB.ɵfac=function t(e){return new(e||ZB)(Sm(WB),Sm(hg),Sm(wH,8),Na("tabindex"),Sm(SI),Sm(VP,8))},ZB.ɵdir=lo({type:ZB,inputs:{active:"active"},features:[xp]}),ZB.ctorParameters=()=>[{type:WB},{type:hg},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:SI},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],ZB.propDecorators={active:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(ZB,[{type:Cy}],(function(){return[{type:WB},{type:hg},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:SI},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),{active:[{type:xy}]});class XB extends ZB{constructor(t,e,n,o,i,a,r,s){super(t,e,i,a,r,s),this._tabLinkRipple=new PH(this,n,e,o),this._tabLinkRipple.setupTriggerEvents(e.nativeElement)}ngOnDestroy(){super.ngOnDestroy(),this._tabLinkRipple._removeTriggerEvents()}}XB.ɵfac=function t(e){return new(e||XB)(Sm(YB),Sm(hg),Sm(a_),Sm(wz),Sm(wH,8),Na("tabindex"),Sm(SI),Sm(VP,8))},XB.ɵdir=lo({type:XB,selectors:[["","mat-tab-link",""],["","matTabLink",""]],hostAttrs:[1,"mat-tab-link","mat-focus-indicator"],hostVars:7,hostBindings:function t(e,n){1&e&&Vm("focus",(function t(){return n._handleFocus()})),2&e&&(jp("aria-current",n.active?"page":null)("aria-disabled",n.disabled)("tabIndex",n.tabIndex),pu("mat-tab-disabled",n.disabled)("mat-tab-label-active",n.active))},inputs:{disabled:"disabled",disableRipple:"disableRipple",tabIndex:"tabIndex"},exportAs:["matTabLink"],features:[xp]}),XB.ctorParameters=()=>[{type:YB},{type:hg},{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:SI},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(XB,[{type:Cy,args:[{selector:"[mat-tab-link], [matTabLink]",exportAs:"matTabLink",inputs:["disabled","disableRipple","tabIndex"],host:{class:"mat-tab-link mat-focus-indicator","[attr.aria-current]":'active ? "page" : null',"[attr.aria-disabled]":"disabled","[attr.tabIndex]":"tabIndex","[class.mat-tab-disabled]":"disabled","[class.mat-tab-label-active]":"active","(focus)":"_handleFocus()"}}]}],(function(){return[{type:YB},{type:hg},{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:SI},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class KB{}KB.ɵfac=function t(e){return new(e||KB)},KB.ɵmod=ao({type:KB}),KB.ɵinj=vn({imports:[[WM,XI,RF,SH,Uz,NI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(KB,[{type:Ay,args:[{imports:[WM,XI,RF,SH,Uz,NI],exports:[XI,FB,PB,SB,YB,XB,xB],declarations:[FB,PB,SB,MB,BB,YB,XB,AB,EB,GB,xB]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(KB,{declarations:function(){return[FB,PB,SB,MB,BB,YB,XB,AB,EB,GB,xB]},imports:function(){return[WM,XI,RF,SH,Uz,NI]},exports:function(){return[XI,FB,PB,SB,YB,XB,xB]}});const JB=["underline"],QB=["connectionContainer"],$B=["inputContainer"],tV=["label"];function eV(t,e){1&t&&(Nm(0),Rm(1,"div",14),Tm(2,"div",15),Tm(3,"div",16),Tm(4,"div",17),Am(),Rm(5,"div",18),Tm(6,"div",15),Tm(7,"div",16),Tm(8,"div",17),Am(),zm())}function nV(t,e){1&t&&(Rm(0,"div",19),Xm(1,1),Am())}function oV(t,e){if(1&t&&(Nm(0),Xm(1,2),Rm(2,"span"),ku(3),Am(),zm()),2&t){const t=Ym(2);rc(3),Su(t._control.placeholder)}}function iV(t,e){1&t&&Xm(0,3,["*ngSwitchCase","true"])}function aV(t,e){1&t&&(Rm(0,"span",23),ku(1," *"),Am())}function rV(t,e){if(1&t){const t=Hm();Rm(0,"label",20,21),Vm("cdkObserveContent",(function e(){return hi(t),Ym().updateOutlineGap()})),Qp(2,oV,4,1,"ng-container",12),Qp(3,iV,1,0,"ng-content",12),Qp(4,aV,2,0,"span",22),Am()}if(2&t){const t=Ym();pu("mat-empty",t._control.empty&&!t._shouldAlwaysFloat())("mat-form-field-empty",t._control.empty&&!t._shouldAlwaysFloat())("mat-accent","accent"==t.color)("mat-warn","warn"==t.color),Dm("cdkObserveContentDisabled","outline"!=t.appearance)("id",t._labelId)("ngSwitch",t._hasLabel()),jp("for",t._control.id)("aria-owns",t._control.id),rc(2),Dm("ngSwitchCase",!1),rc(1),Dm("ngSwitchCase",!0),rc(1),Dm("ngIf",!t.hideRequiredMarker&&t._control.required&&!t._control.disabled)}}function sV(t,e){1&t&&(Rm(0,"div",24),Xm(1,4),Am())}function lV(t,e){if(1&t&&(Rm(0,"div",25,26),Tm(2,"span",27),Am()),2&t){const t=Ym();rc(2),pu("mat-accent","accent"==t.color)("mat-warn","warn"==t.color)}}function cV(t,e){1&t&&(Rm(0,"div"),Xm(1,5),Am()),2&t&&Dm("@transitionMessages",Ym()._subscriptAnimationState)}function dV(t,e){if(1&t&&(Rm(0,"div",31),ku(1),Am()),2&t){const t=Ym(2);Dm("id",t._hintLabelId),rc(1),Su(t.hintLabel)}}function pV(t,e){if(1&t&&(Rm(0,"div",28),Qp(1,dV,2,2,"div",29),Xm(2,6),Tm(3,"div",30),Xm(4,7),Am()),2&t){const t=Ym();Dm("@transitionMessages",t._subscriptAnimationState),rc(1),Dm("ngIf",t.hintLabel)}}const mV=["*",[["","matPrefix",""]],[["mat-placeholder"]],[["mat-label"]],[["","matSuffix",""]],[["mat-error"]],[["mat-hint",3,"align","end"]],[["mat-hint","align","end"]]];let uV=0;const fV=new Ga("MatError");class gV{constructor(t,e){this.id="mat-error-"+uV++,t||e.nativeElement.setAttribute("aria-live","polite")}}gV.ɵfac=function t(e){return new(e||gV)(Na("aria-live"),Sm(hg))},gV.ɵdir=lo({type:gV,selectors:[["mat-error"]],hostAttrs:["aria-atomic","true",1,"mat-error"],hostVars:1,hostBindings:function t(e,n){2&e&&jp("id",n.id)},inputs:{id:"id"},features:[pg([{provide:fV,useExisting:gV}])]}),gV.ctorParameters=()=>[{type:String,decorators:[{type:ja,args:["aria-live"]}]},{type:hg}],gV.propDecorators={id:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(gV,[{type:Cy,args:[{selector:"mat-error",host:{class:"mat-error","[attr.id]":"id","aria-atomic":"true"},providers:[{provide:fV,useExisting:gV}]}]}],(function(){return[{type:String,decorators:[{type:ja,args:["aria-live"]}]},{type:hg}]}),{id:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const hV={transitionMessages:nx("transitionMessages",[rx("enter",ax({opacity:1,transform:"translateY(0%)"})),lx("void => enter",[ax({opacity:0,transform:"translateY(-5px)"}),ox("300ms cubic-bezier(0.55, 0, 0.55, 0.2)")])])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class bV{}function yV(t){return Error(`A hint was already declared for 'align="${t}"'.`)}bV.ɵfac=function t(e){return new(e||bV)},bV.ɵdir=lo({type:bV}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(bV,[{type:Cy}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let _V=0;const CV=new Ga("MatHint");class MV{constructor(){this.align="start",this.id="mat-hint-"+_V++}}MV.ɵfac=function t(e){return new(e||MV)},MV.ɵdir=lo({type:MV,selectors:[["mat-hint"]],hostAttrs:[1,"mat-hint"],hostVars:4,hostBindings:function t(e,n){2&e&&(jp("id",n.id)("align",null),pu("mat-form-field-hint-end","end"===n.align))},inputs:{align:"align",id:"id"},features:[pg([{provide:CV,useExisting:MV}])]}),MV.propDecorators={align:[{type:xy}],id:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(MV,[{type:Cy,args:[{selector:"mat-hint",host:{class:"mat-hint","[class.mat-form-field-hint-end]":'align === "end"',"[attr.id]":"id","[attr.align]":"null"},providers:[{provide:CV,useExisting:MV}]}]}],(function(){return[]}),{align:[{type:xy}],id:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class vV{}vV.ɵfac=function t(e){return new(e||vV)},vV.ɵdir=lo({type:vV,selectors:[["mat-label"]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(vV,[{type:Cy,args:[{selector:"mat-label"}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class xV{}xV.ɵfac=function t(e){return new(e||xV)},xV.ɵdir=lo({type:xV,selectors:[["mat-placeholder"]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(xV,[{type:Cy,args:[{selector:"mat-placeholder"}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const OV=new Ga("MatPrefix");class PV{}PV.ɵfac=function t(e){return new(e||PV)},PV.ɵdir=lo({type:PV,selectors:[["","matPrefix",""]],features:[pg([{provide:OV,useExisting:PV}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(PV,[{type:Cy,args:[{selector:"[matPrefix]",providers:[{provide:OV,useExisting:PV}]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const wV=new Ga("MatSuffix");class kV{}kV.ɵfac=function t(e){return new(e||kV)},kV.ɵdir=lo({type:kV,selectors:[["","matSuffix",""]],features:[pg([{provide:wV,useExisting:kV}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(kV,[{type:Cy,args:[{selector:"[matSuffix]",providers:[{provide:wV,useExisting:kV}]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let SV=0;const DV=JI(class{constructor(t){this._elementRef=t}},"primary"),EV=new Ga("MAT_FORM_FIELD_DEFAULT_OPTIONS"),RV=new Ga("MatFormField");class AV extends DV{constructor(t,e,n,o,i,a,r,s){super(t),this._changeDetectorRef=e,this._dir=o,this._defaults=i,this._platform=a,this._ngZone=r,this._outlineGapCalculationNeededImmediately=!1,this._outlineGapCalculationNeededOnStable=!1,this._destroyed=new I,this._showAlwaysAnimate=!1,this._subscriptAnimationState="",this._hintLabel="",this._hintLabelId="mat-hint-"+SV++,this._labelId="mat-form-field-label-"+SV++,this.floatLabel=this._getDefaultFloatLabelState(),this._animationsEnabled="NoopAnimations"!==s,this.appearance=i&&i.appearance?i.appearance:"legacy",this._hideRequiredMarker=!(!i||null==i.hideRequiredMarker)&&i.hideRequiredMarker}get appearance(){return this._appearance}set appearance(t){const e=this._appearance;this._appearance=t||this._defaults&&this._defaults.appearance||"legacy","outline"===this._appearance&&e!==t&&(this._outlineGapCalculationNeededOnStable=!0)}get hideRequiredMarker(){return this._hideRequiredMarker}set hideRequiredMarker(t){this._hideRequiredMarker=yz(t)}_shouldAlwaysFloat(){return"always"===this.floatLabel&&!this._showAlwaysAnimate}_canLabelFloat(){return"never"!==this.floatLabel}get hintLabel(){return this._hintLabel}set hintLabel(t){this._hintLabel=t,this._processHints()}get floatLabel(){return"legacy"!==this.appearance&&"never"===this._floatLabel?"auto":this._floatLabel}set floatLabel(t){t!==this._floatLabel&&(this._floatLabel=t||this._getDefaultFloatLabelState(),this._changeDetectorRef.markForCheck())}get _control(){return this._explicitFormFieldControl||this._controlNonStatic||this._controlStatic}set _control(t){this._explicitFormFieldControl=t}getLabelId(){return this._hasFloatingLabel()?this._labelId:null}getConnectedOverlayOrigin(){return this._connectionContainerRef||this._elementRef}ngAfterContentInit(){this._validateControlChild();const t=this._control;t.controlType&&this._elementRef.nativeElement.classList.add(`mat-form-field-type-${t.controlType}`),t.stateChanges.pipe(Ne(null)).subscribe((()=>{this._validatePlaceholders(),this._syncDescribedByIds(),this._changeDetectorRef.markForCheck()})),t.ngControl&&t.ngControl.valueChanges&&t.ngControl.valueChanges.pipe(Ie(this._destroyed)).subscribe((()=>this._changeDetectorRef.markForCheck())),this._ngZone.runOutsideAngular((()=>{this._ngZone.onStable.pipe(Ie(this._destroyed)).subscribe((()=>{this._outlineGapCalculationNeededOnStable&&this.updateOutlineGap()}))})),re(this._prefixChildren.changes,this._suffixChildren.changes).subscribe((()=>{this._outlineGapCalculationNeededOnStable=!0,this._changeDetectorRef.markForCheck()})),this._hintChildren.changes.pipe(Ne(null)).subscribe((()=>{this._processHints(),this._changeDetectorRef.markForCheck()})),this._errorChildren.changes.pipe(Ne(null)).subscribe((()=>{this._syncDescribedByIds(),this._changeDetectorRef.markForCheck()})),this._dir&&this._dir.change.pipe(Ie(this._destroyed)).subscribe((()=>{"function"==typeof requestAnimationFrame?this._ngZone.runOutsideAngular((()=>{requestAnimationFrame((()=>this.updateOutlineGap()))})):this.updateOutlineGap()}))}ngAfterContentChecked(){this._validateControlChild(),this._outlineGapCalculationNeededImmediately&&this.updateOutlineGap()}ngAfterViewInit(){this._subscriptAnimationState="enter",this._changeDetectorRef.detectChanges()}ngOnDestroy(){this._destroyed.next(),this._destroyed.complete()}_shouldForward(t){const e=this._control?this._control.ngControl:null;return e&&e[t]}_hasPlaceholder(){return!!(this._control&&this._control.placeholder||this._placeholderChild)}_hasLabel(){return!(!this._labelChildNonStatic&&!this._labelChildStatic)}_shouldLabelFloat(){return this._canLabelFloat()&&(this._control&&this._control.shouldLabelFloat||this._shouldAlwaysFloat())}_hideControlPlaceholder(){return"legacy"===this.appearance&&!this._hasLabel()||this._hasLabel()&&!this._shouldLabelFloat()}_hasFloatingLabel(){return this._hasLabel()||"legacy"===this.appearance&&this._hasPlaceholder()}_getDisplayedMessages(){return this._errorChildren&&this._errorChildren.length>0&&this._control.errorState?"error":"hint"}_animateAndLockLabel(){this._hasFloatingLabel()&&this._canLabelFloat()&&(this._animationsEnabled&&this._label&&(this._showAlwaysAnimate=!0,oe(this._label.nativeElement,"transitionend").pipe(be(1)).subscribe((()=>{this._showAlwaysAnimate=!1}))),this.floatLabel="always",this._changeDetectorRef.markForCheck())}_validatePlaceholders(){if(this._control.placeholder&&this._placeholderChild&&("undefined"==typeof ngDevMode||ngDevMode))
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
throw(function t(){return Error("Placeholder attribute and child element were both specified.")})()}_processHints(){this._validateHints(),this._syncDescribedByIds()}_validateHints(){if(this._hintChildren&&("undefined"==typeof ngDevMode||ngDevMode)){let t,e;this._hintChildren.forEach((n=>{if("start"===n.align){if(t||this.hintLabel)throw yV("start");t=n}else if("end"===n.align){if(e)throw yV("end");e=n}}))}}_getDefaultFloatLabelState(){return this._defaults&&this._defaults.floatLabel||"auto"}_syncDescribedByIds(){if(this._control){let t=[];if(this._control.userAriaDescribedBy&&"string"==typeof this._control.userAriaDescribedBy&&t.push(...this._control.userAriaDescribedBy.split(" ")),"hint"===this._getDisplayedMessages()){const e=this._hintChildren?this._hintChildren.find((t=>"start"===t.align)):null,n=this._hintChildren?this._hintChildren.find((t=>"end"===t.align)):null;e?t.push(e.id):this._hintLabel&&t.push(this._hintLabelId),n&&t.push(n.id)}else this._errorChildren&&t.push(...this._errorChildren.map((t=>t.id)));this._control.setDescribedByIds(t)}}_validateControlChild(){if(!this._control&&("undefined"==typeof ngDevMode||ngDevMode))throw(function t(){return Error("mat-form-field must contain a MatFormFieldControl.")})()}updateOutlineGap(){const t=this._label?this._label.nativeElement:null;if("outline"!==this.appearance||!t||!t.children.length||!t.textContent.trim())return;if(!this._platform.isBrowser)return;if(!this._isAttachedToDOM())return void(this._outlineGapCalculationNeededImmediately=!0);let e=0,n=0;const o=this._connectionContainerRef.nativeElement,i=o.querySelectorAll(".mat-form-field-outline-start"),a=o.querySelectorAll(".mat-form-field-outline-gap");if(this._label&&this._label.nativeElement.children.length){const i=o.getBoundingClientRect();if(0===i.width&&0===i.height)return this._outlineGapCalculationNeededOnStable=!0,void(this._outlineGapCalculationNeededImmediately=!1);const a=this._getStartEnd(i),r=t.children,s=this._getStartEnd(r[0].getBoundingClientRect());let l=0;for(let t=0;t<r.length;t++)l+=r[t].offsetWidth;e=Math.abs(s-a)-5,n=l>0?.75*l+10:0}for(let t=0;t<i.length;t++)i[t].style.width=`${e}px`;for(let t=0;t<a.length;t++)a[t].style.width=`${n}px`;this._outlineGapCalculationNeededOnStable=this._outlineGapCalculationNeededImmediately=!1}_getStartEnd(t){return this._dir&&"rtl"===this._dir.value?t.right:t.left}_isAttachedToDOM(){const t=this._elementRef.nativeElement;if(t.getRootNode){const e=t.getRootNode();return e&&e!==t}return document.documentElement.contains(t)}}AV.ɵfac=function t(e){return new(e||AV)(Sm(hg),Sm(Ug),Sm(hg),Sm(HI,8),Sm(EV,8),Sm(wz),Sm(a_),Sm(VP,8))},AV.ɵcmp=to({type:AV,selectors:[["mat-form-field"]],contentQueries:function t(e,n,o){if(1&e&&($h(o,bV,5),$h(o,bV,7),$h(o,vV,5),$h(o,vV,7),$h(o,xV,5),$h(o,fV,5),$h(o,CV,5),$h(o,OV,5),$h(o,wV,5)),2&e){let t;Jh(t=tb())&&(n._controlNonStatic=t.first),Jh(t=tb())&&(n._controlStatic=t.first),Jh(t=tb())&&(n._labelChildNonStatic=t.first),Jh(t=tb())&&(n._labelChildStatic=t.first),Jh(t=tb())&&(n._placeholderChild=t.first),Jh(t=tb())&&(n._errorChildren=t),Jh(t=tb())&&(n._hintChildren=t),Jh(t=tb())&&(n._prefixChildren=t),Jh(t=tb())&&(n._suffixChildren=t)}},viewQuery:function t(e,n){if(1&e&&(Qh(JB,5),Qh(QB,7),Qh($B,5),Qh(tV,5)),2&e){let t;Jh(t=tb())&&(n.underlineRef=t.first),Jh(t=tb())&&(n._connectionContainerRef=t.first),Jh(t=tb())&&(n._inputContainerRef=t.first),Jh(t=tb())&&(n._label=t.first)}},hostAttrs:[1,"mat-form-field"],hostVars:40,hostBindings:function t(e,n){2&e&&pu("mat-form-field-appearance-standard","standard"==n.appearance)("mat-form-field-appearance-fill","fill"==n.appearance)("mat-form-field-appearance-outline","outline"==n.appearance)("mat-form-field-appearance-legacy","legacy"==n.appearance)("mat-form-field-invalid",n._control.errorState)("mat-form-field-can-float",n._canLabelFloat())("mat-form-field-should-float",n._shouldLabelFloat())("mat-form-field-has-label",n._hasFloatingLabel())("mat-form-field-hide-placeholder",n._hideControlPlaceholder())("mat-form-field-disabled",n._control.disabled)("mat-form-field-autofilled",n._control.autofilled)("mat-focused",n._control.focused)("ng-untouched",n._shouldForward("untouched"))("ng-touched",n._shouldForward("touched"))("ng-pristine",n._shouldForward("pristine"))("ng-dirty",n._shouldForward("dirty"))("ng-valid",n._shouldForward("valid"))("ng-invalid",n._shouldForward("invalid"))("ng-pending",n._shouldForward("pending"))("_mat-animation-noopable",!n._animationsEnabled)},inputs:{color:"color",floatLabel:"floatLabel",appearance:"appearance",hideRequiredMarker:"hideRequiredMarker",hintLabel:"hintLabel"},exportAs:["matFormField"],features:[pg([{provide:RV,useExisting:AV}]),xp],ngContentSelectors:["*","[matPrefix]","mat-placeholder","mat-label","[matSuffix]","mat-error","mat-hint:not([align='end'])","mat-hint[align='end']"],decls:15,vars:8,consts:[[1,"mat-form-field-wrapper"],[1,"mat-form-field-flex",3,"click"],["connectionContainer",""],[4,"ngIf"],["class","mat-form-field-prefix",4,"ngIf"],[1,"mat-form-field-infix"],["inputContainer",""],[1,"mat-form-field-label-wrapper"],["class","mat-form-field-label",3,"cdkObserveContentDisabled","id","mat-empty","mat-form-field-empty","mat-accent","mat-warn","ngSwitch","cdkObserveContent",4,"ngIf"],["class","mat-form-field-suffix",4,"ngIf"],["class","mat-form-field-underline",4,"ngIf"],[1,"mat-form-field-subscript-wrapper",3,"ngSwitch"],[4,"ngSwitchCase"],["class","mat-form-field-hint-wrapper",4,"ngSwitchCase"],[1,"mat-form-field-outline"],[1,"mat-form-field-outline-start"],[1,"mat-form-field-outline-gap"],[1,"mat-form-field-outline-end"],[1,"mat-form-field-outline","mat-form-field-outline-thick"],[1,"mat-form-field-prefix"],[1,"mat-form-field-label",3,"cdkObserveContentDisabled","id","ngSwitch","cdkObserveContent"],["label",""],["class","mat-placeholder-required mat-form-field-required-marker","aria-hidden","true",4,"ngIf"],["aria-hidden","true",1,"mat-placeholder-required","mat-form-field-required-marker"],[1,"mat-form-field-suffix"],[1,"mat-form-field-underline"],["underline",""],[1,"mat-form-field-ripple"],[1,"mat-form-field-hint-wrapper"],["class","mat-hint",3,"id",4,"ngIf"],[1,"mat-form-field-hint-spacer"],[1,"mat-hint",3,"id"]],template:function t(e,n){1&e&&(Zm(mV),Rm(0,"div",0),Rm(1,"div",1,2),Vm("click",(function t(e){return n._control.onContainerClick&&n._control.onContainerClick(e)})),Qp(3,eV,9,0,"ng-container",3),Qp(4,nV,2,0,"div",4),Rm(5,"div",5,6),Xm(7),Rm(8,"span",7),Qp(9,rV,5,16,"label",8),Am(),Am(),Qp(10,sV,2,0,"div",9),Am(),Qp(11,lV,3,4,"div",10),Rm(12,"div",11),Qp(13,cV,2,1,"div",12),Qp(14,pV,5,2,"div",13),Am(),Am()),2&e&&(rc(3),Dm("ngIf","outline"==n.appearance),rc(1),Dm("ngIf",n._prefixChildren.length),rc(5),Dm("ngIf",n._hasFloatingLabel()),rc(1),Dm("ngIf",n._suffixChildren.length),rc(1),Dm("ngIf","outline"!=n.appearance),rc(1),Dm("ngSwitch",n._getDisplayedMessages()),rc(1),Dm("ngSwitchCase","error"),rc(1),Dm("ngSwitchCase","hint"))},directives:[dM,fM,gM,jz],styles:[".mat-form-field{display:inline-block;position:relative;text-align:left}[dir=rtl] 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class TV{}TV.ɵfac=function t(e){return new(e||TV)},TV.ɵmod=ao({type:TV}),TV.ɵinj=vn({imports:[[WM,XI,Uz],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(TV,[{type:Ay,args:[{declarations:[gV,AV,MV,vV,xV,PV,kV],imports:[WM,XI,Uz],exports:[XI,gV,AV,MV,vV,xV,PV,kV]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(TV,{declarations:function(){return[gV,AV,MV,vV,xV,PV,kV]},imports:function(){return[WM,XI,Uz]},exports:function(){return[XI,gV,AV,MV,vV,xV,PV,kV]}});
/**
     * @license Angular v12.2.1
     * (c) 2010-2021 Google LLC. https://angular.io/
     * License: MIT
     */
class NV{constructor(t,e){this._renderer=t,this._elementRef=e,this.onChange=t=>{},this.onTouched=()=>{}}setProperty(t,e){this._renderer.setProperty(this._elementRef.nativeElement,t,e)}registerOnTouched(t){this.onTouched=t}registerOnChange(t){this.onChange=t}setDisabledState(t){this.setProperty("disabled",t)}}NV.ɵfac=function t(e){return new(e||NV)(Sm(Cg),Sm(hg))},NV.ɵdir=lo({type:NV}),NV.ctorParameters=()=>[{type:Cg},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(NV,[{type:Cy}],(function(){return[{type:Cg},{type:hg}]}),null);class zV extends NV{}zV.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(zV)))(n||zV)}})(),zV.ɵdir=lo({type:zV,features:[xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(zV,[{type:Cy}],null,null);const IV=new Ga("NgValueAccessor"),HV={provide:IV,useExisting:qe((()=>FV)),multi:!0};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class FV extends zV{writeValue(t){this.setProperty("checked",t)}}FV.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(FV)))(n||FV)}})(),FV.ɵdir=lo({type:FV,selectors:[["input","type","checkbox","formControlName",""],["input","type","checkbox","formControl",""],["input","type","checkbox","ngModel",""]],hostBindings:function t(e,n){1&e&&Vm("change",(function t(e){return n.onChange(e.target.checked)}))("blur",(function t(){return n.onTouched()}))},features:[pg([HV]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(FV,[{type:Cy,args:[{selector:"input[type=checkbox][formControlName],input[type=checkbox][formControl],input[type=checkbox][ngModel]",host:{"(change)":"onChange($event.target.checked)","(blur)":"onTouched()"},providers:[HV]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function jV(t){return null==t||0===t.length}function UV(t){return null!=t&&"number"==typeof t.length}VV.ɵfac=function t(e){return new(e||VV)(Sm(Cg),Sm(hg),Sm(BV,8))},VV.ɵdir=lo({type:VV,selectors:[["input","formControlName","",3,"type","checkbox"],["textarea","formControlName",""],["input","formControl","",3,"type","checkbox"],["textarea","formControl",""],["input","ngModel","",3,"type","checkbox"],["textarea","ngModel",""],["","ngDefaultControl",""]],hostBindings:function t(e,n){1&e&&Vm("input",(function t(e){return n._handleInput(e.target.value)}))("blur",(function t(){return n.onTouched()}))("compositionstart",(function t(){return n._compositionStart()}))("compositionend",(function t(e){return n._compositionEnd(e.target.value)}))},features:[pg([LV]),xp]}),VV.ctorParameters=()=>[{type:Cg},{type:hg},{type:Boolean,decorators:[{type:Sr},{type:kr,args:[BV]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(VV,[{type:Cy,args:[{selector:"input:not([type=checkbox])[formControlName],textarea[formControlName],input:not([type=checkbox])[formControl],textarea[formControl],input:not([type=checkbox])[ngModel],textarea[ngModel],[ngDefaultControl]",host:{"(input)":"$any(this)._handleInput($event.target.value)","(blur)":"onTouched()","(compositionstart)":"$any(this)._compositionStart()","(compositionend)":"$any(this)._compositionEnd($event.target.value)"},providers:[LV]}]}],(function(){return[{type:Cg},{type:hg},{type:Boolean,decorators:[{type:Sr},{type:kr,args:[BV]}]}]}),null);const GV=new Ga("NgValidators"),WV=new Ga("NgAsyncValidators"),YV=/^(?=.{1,254}$)(?=.{1,64}@)[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+(?:\.[a-zA-Z0-9!#$%&'*+/=?^_`{|}~-]+)*@[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?(?:\.[a-zA-Z0-9](?:[a-zA-Z0-9-]{0,61}[a-zA-Z0-9])?)*$/;class qV{static min(t){return ZV(t)}static max(t){return XV(t)}static required(t){return KV(t)}static requiredTrue(t){return JV(t)}static email(t){return QV(t)}static minLength(t){return $V(t)}static maxLength(t){return tj(t)}static pattern(t){return ej(t)}static nullValidator(t){return null}static compose(t){return lj(t)}static composeAsync(t){return dj(t)}}function ZV(t){return e=>{if(jV(e.value)||jV(t))return null;const n=parseFloat(e.value);return!isNaN(n)&&n<t?{min:{min:t,actual:e.value}}:null}}function XV(t){return e=>{if(jV(e.value)||jV(t))return null;const n=parseFloat(e.value);return!isNaN(n)&&n>t?{max:{max:t,actual:e.value}}:null}}function KV(t){return jV(t.value)?{required:!0}:null}function JV(t){return!0===t.value?null:{required:!0}}function QV(t){return jV(t.value)||YV.test(t.value)?null:{email:!0}}function $V(t){return e=>jV(e.value)||!UV(e.value)?null:e.value.length<t?{minlength:{requiredLength:t,actualLength:e.value.length}}:null}function tj(t){return e=>UV(e.value)&&e.value.length>t?{maxlength:{requiredLength:t,actualLength:e.value.length}}:null}function ej(t){if(!t)return nj;let e,n;return"string"==typeof t?(n="","^"!==t.charAt(0)&&(n+="^"),n+=t,"$"!==t.charAt(t.length-1)&&(n+="$"),e=new RegExp(n)):(n=t.toString(),e=t),t=>{if(jV(t.value))return null;const o=t.value;return e.test(o)?null:{pattern:{requiredPattern:n,actualValue:o}}}}function nj(t){return null}function oj(t){return null!=t}function ij(t){const e=Fm(t)?Ct(t):t;if(!Bm(e)&&("undefined"==typeof ngDevMode||ngDevMode))throw new Error("Expected validator to return Promise or Observable.");return e}function aj(t){let e={};return t.forEach((t=>{e=null!=t?Object.assign(Object.assign({},e),t):e})),0===Object.keys(e).length?null:e}function rj(t,e){return e.map((e=>e(t)))}function sj(t){return t.map((t=>(function e(t){return!t.validate})(t)?t:e=>t.validate(e)))}function lj(t){if(!t)return null;const e=t.filter(oj);return 0==e.length?null:function(t){return aj(rj(t,e))}}function cj(t){return null!=t?lj(sj(t)):null}function dj(t){if(!t)return null;const e=t.filter(oj);return 0==e.length?null:function(t){return $t(rj(t,e).map(ij)).pipe(It(aj))}}function pj(t){return null!=t?dj(sj(t)):null}function mj(t,e){return null===t?[e]:Array.isArray(t)?[...t,e]:[t,e]}function uj(t){return t._rawValidators}function fj(t){return t._rawAsyncValidators}function gj(t){return t?Array.isArray(t)?t:[t]:[]}function hj(t,e){return Array.isArray(t)?t.includes(e):t===e}function bj(t,e){const n=gj(e);return gj(t).forEach((t=>{hj(n,t)||n.push(t)})),n}function yj(t,e){return gj(e).filter((e=>!hj(t,e)))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class _j{constructor(){this._rawValidators=[],this._rawAsyncValidators=[],this._onDestroyCallbacks=[]}get value(){return this.control?this.control.value:null}get valid(){return this.control?this.control.valid:null}get invalid(){return this.control?this.control.invalid:null}get pending(){return this.control?this.control.pending:null}get disabled(){return this.control?this.control.disabled:null}get enabled(){return this.control?this.control.enabled:null}get errors(){return this.control?this.control.errors:null}get pristine(){return this.control?this.control.pristine:null}get dirty(){return this.control?this.control.dirty:null}get touched(){return this.control?this.control.touched:null}get status(){return this.control?this.control.status:null}get untouched(){return this.control?this.control.untouched:null}get statusChanges(){return this.control?this.control.statusChanges:null}get valueChanges(){return this.control?this.control.valueChanges:null}get path(){return null}_setValidators(t){this._rawValidators=t||[],this._composedValidatorFn=cj(this._rawValidators)}_setAsyncValidators(t){this._rawAsyncValidators=t||[],this._composedAsyncValidatorFn=pj(this._rawAsyncValidators)}get validator(){return this._composedValidatorFn||null}get asyncValidator(){return this._composedAsyncValidatorFn||null}_registerOnDestroy(t){this._onDestroyCallbacks.push(t)}_invokeOnDestroyCallbacks(){this._onDestroyCallbacks.forEach((t=>t())),this._onDestroyCallbacks=[]}reset(t){this.control&&this.control.reset(t)}hasError(t,e){return!!this.control&&this.control.hasError(t,e)}getError(t,e){return this.control?this.control.getError(t,e):null}}_j.ɵfac=function t(e){return new(e||_j)},_j.ɵdir=lo({type:_j});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Cj extends _j{get formDirective(){return null}get path(){return null}}Cj.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(Cj)))(n||Cj)}})(),Cj.ɵdir=lo({type:Cj,features:[xp]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Mj extends _j{constructor(){super(...arguments),this._parent=null,this.name=null,this.valueAccessor=null}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class vj{constructor(t){this._cd=t}is(t){var e,n,o;return"submitted"===t?!!(null===(e=this._cd)||void 0===e?void 0:e.submitted):!!(null===(o=null===(n=this._cd)||void 0===n?void 0:n.control)||void 0===o?void 0:o[t])}}class xj extends vj{constructor(t){super(t)}}xj.ɵfac=function t(e){return new(e||xj)(Sm(Mj,2))},xj.ɵdir=lo({type:xj,selectors:[["","formControlName",""],["","ngModel",""],["","formControl",""]],hostVars:14,hostBindings:function t(e,n){2&e&&pu("ng-untouched",n.is("untouched"))("ng-touched",n.is("touched"))("ng-pristine",n.is("pristine"))("ng-dirty",n.is("dirty"))("ng-valid",n.is("valid"))("ng-invalid",n.is("invalid"))("ng-pending",n.is("pending"))},features:[xp]}),xj.ctorParameters=()=>[{type:Mj,decorators:[{type:Dr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(xj,[{type:Cy,args:[{selector:"[formControlName],[ngModel],[formControl]",host:{"[class.ng-untouched]":'is("untouched")',"[class.ng-touched]":'is("touched")',"[class.ng-pristine]":'is("pristine")',"[class.ng-dirty]":'is("dirty")',"[class.ng-valid]":'is("valid")',"[class.ng-invalid]":'is("invalid")',"[class.ng-pending]":'is("pending")'}}]}],(function(){return[{type:Mj,decorators:[{type:Dr}]}]}),null);class Oj extends vj{constructor(t){super(t)}}Oj.ɵfac=function t(e){return new(e||Oj)(Sm(Cj,10))},Oj.ɵdir=lo({type:Oj,selectors:[["","formGroupName",""],["","formArrayName",""],["","ngModelGroup",""],["","formGroup",""],["form",3,"ngNoForm",""],["","ngForm",""]],hostVars:16,hostBindings:function t(e,n){2&e&&pu("ng-untouched",n.is("untouched"))("ng-touched",n.is("touched"))("ng-pristine",n.is("pristine"))("ng-dirty",n.is("dirty"))("ng-valid",n.is("valid"))("ng-invalid",n.is("invalid"))("ng-pending",n.is("pending"))("ng-submitted",n.is("submitted"))},features:[xp]}),Oj.ctorParameters=()=>[{type:Cj,decorators:[{type:Sr},{type:Dr}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Oj,[{type:Cy,args:[{selector:"[formGroupName],[formArrayName],[ngModelGroup],[formGroup],form:not([ngNoForm]),[ngForm]",host:{"[class.ng-untouched]":'is("untouched")',"[class.ng-touched]":'is("touched")',"[class.ng-pristine]":'is("pristine")',"[class.ng-dirty]":'is("dirty")',"[class.ng-valid]":'is("valid")',"[class.ng-invalid]":'is("invalid")',"[class.ng-pending]":'is("pending")',"[class.ng-submitted]":'is("submitted")'}}]}],(function(){return[{type:Cj,decorators:[{type:Sr},{type:Dr}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Pj='\n    <div [formGroup]="myGroup">\n      <input formControlName="firstName">\n    </div>\n\n    In your class:\n\n    this.myGroup = new FormGroup({\n       firstName: new FormControl()\n    });',wj='\n    <div [formGroup]="myGroup">\n       <div formGroupName="person">\n          <input formControlName="firstName">\n       </div>\n    </div>\n\n    In your class:\n\n    this.myGroup = new FormGroup({\n       person: new FormGroup({ firstName: new FormControl() })\n    });',kj='\n    <form>\n       <div ngModelGroup="person">\n          <input [(ngModel)]="person.name" name="firstName">\n       </div>\n    </form>';
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Sj{static controlParentException(){throw new Error(`formControlName must be used with a parent formGroup directive.  You'll want to add a formGroup\n       directive and pass it an existing FormGroup instance (you can create one in your class).\n\n      Example:\n\n      ${Pj}`)}static ngModelGroupException(){throw new Error(`formControlName cannot be used with an ngModelGroup parent. It is only compatible with parents\n       that also have a "form" prefix: formGroupName, formArrayName, or formGroup.\n\n       Option 1:  Update the parent to be formGroupName (reactive form strategy)\n\n        ${wj}\n\n        Option 2: Use ngModel instead of formControlName (template-driven strategy)\n\n        ${kj}`)}static missingFormException(){throw new Error(`formGroup expects a FormGroup instance. Please pass one in.\n\n       Example:\n\n       ${Pj}`)}static groupParentException(){throw new Error(`formGroupName must be used with a parent formGroup directive.  You'll want to add a formGroup\n      directive and pass it an existing FormGroup instance (you can create one in your class).\n\n      Example:\n\n      ${wj}`)}static arrayParentException(){throw new Error('formArrayName must be used with a parent formGroup directive.  You\'ll want to add a formGroup\n       directive and pass it an existing FormGroup instance (you can create one in your class).\n\n        Example:\n\n        \n    <div [formGroup]="myGroup">\n      <div formArrayName="cities">\n        <div *ngFor="let city of cityArray.controls; index as i">\n          <input [formControlName]="i">\n        </div>\n      </div>\n    </div>\n\n    In your class:\n\n    this.cityArray = new FormArray([new FormControl(\'SF\')]);\n    this.myGroup = new FormGroup({\n      cities: this.cityArray\n    });')}static disabledAttrWarning(){console.warn("\n      It looks like you're using the disabled attribute with a reactive form directive. If you set disabled to true\n      when you set up this control in your component class, the disabled attribute will actually be set in the DOM for\n      you. We recommend using this approach to avoid 'changed after checked' errors.\n\n      Example:\n      form = new FormGroup({\n        first: new FormControl({value: 'Nancy', disabled: true}, Validators.required),\n        last: new FormControl('Drew', Validators.required)\n      });\n    ")}static ngModelWarning(t){console.warn(`\n    It looks like you're using ngModel on the same form field as ${t}.\n    Support for using the ngModel input property and ngModelChange event with\n    reactive form directives has been deprecated in Angular v6 and will be removed\n    in a future version of Angular.\n\n    For more information on this, see our API docs here:\n    https://angular.io/api/forms/${"formControl"===t?"FormControlDirective":"FormControlName"}#use-with-ngmodel\n    `)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Dj(t,e){return[...e.path,t]}function Ej(t,e){("undefined"==typeof ngDevMode||ngDevMode)&&(t||Hj(e,"Cannot find control with"),e.valueAccessor||Hj(e,"No value accessor for form control with")),Tj(t,e),e.valueAccessor.writeValue(t.value),(function n(t,e){e.valueAccessor.registerOnChange((n=>{t._pendingValue=n,t._pendingChange=!0,t._pendingDirty=!0,"change"===t.updateOn&&zj(t,e)}))})(t,e),(function o(t,e){const n=(t,n)=>{e.valueAccessor.writeValue(t),n&&e.viewToModelUpdate(t)};t.registerOnChange(n),e._registerOnDestroy((()=>{t._unregisterOnChange(n)}))})(t,e),(function i(t,e){e.valueAccessor.registerOnTouched((()=>{t._pendingTouched=!0,"blur"===t.updateOn&&t._pendingChange&&zj(t,e),"submit"!==t.updateOn&&t.markAsTouched()}))})(t,e),(function a(t,e){if(e.valueAccessor.setDisabledState){const n=t=>{e.valueAccessor.setDisabledState(t)};t.registerOnDisabledChange(n),e._registerOnDestroy((()=>{t._unregisterOnDisabledChange(n)}))}})(t,e)}function Rj(t,e,n=!0){const o=()=>{n&&("undefined"==typeof ngDevMode||ngDevMode)&&(function t(e){Hj(e,"There is no FormControl instance attached to form control element with")})(e)};e.valueAccessor&&(e.valueAccessor.registerOnChange(o),e.valueAccessor.registerOnTouched(o)),Nj(t,e),t&&(e._invokeOnDestroyCallbacks(),t._registerOnCollectionChange((()=>{})))}function Aj(t,e){t.forEach((t=>{t.registerOnValidatorChange&&t.registerOnValidatorChange(e)}))}function Tj(t,e){const n=uj(t);null!==e.validator?t.setValidators(mj(n,e.validator)):"function"==typeof n&&t.setValidators([n]);const o=fj(t);null!==e.asyncValidator?t.setAsyncValidators(mj(o,e.asyncValidator)):"function"==typeof o&&t.setAsyncValidators([o]);const i=()=>t.updateValueAndValidity();Aj(e._rawValidators,i),Aj(e._rawAsyncValidators,i)}function Nj(t,e){let n=!1;if(null!==t){if(null!==e.validator){const o=uj(t);if(Array.isArray(o)&&o.length>0){const i=o.filter((t=>t!==e.validator));i.length!==o.length&&(n=!0,t.setValidators(i))}}if(null!==e.asyncValidator){const o=fj(t);if(Array.isArray(o)&&o.length>0){const i=o.filter((t=>t!==e.asyncValidator));i.length!==o.length&&(n=!0,t.setAsyncValidators(i))}}}const o=()=>{};return Aj(e._rawValidators,o),Aj(e._rawAsyncValidators,o),n}function zj(t,e){t._pendingDirty&&t.markAsDirty(),t.setValue(t._pendingValue,{emitModelToViewChange:!1}),e.viewToModelUpdate(t._pendingValue),t._pendingChange=!1}function Ij(t,e){null!=t||"undefined"!=typeof ngDevMode&&!ngDevMode||Hj(e,"Cannot find control with"),Tj(t,e)}function Hj(t,e){let n;throw n=t.path.length>1?`path: '${t.path.join(" -> ")}'`:t.path[0]?`name: '${t.path}'`:"unspecified name attribute",new Error(`${e} ${n}`)}function Fj(t,e){if(!t.hasOwnProperty("model"))return!1;const n=t.model;return!!n.isFirstChange()||!Object.is(e,n.currentValue)}function Lj(t,e){t._syncPendingControls(),e.forEach((t=>{const e=t.control;"submit"===e.updateOn&&e._pendingChange&&(t.viewToModelUpdate(e._pendingValue),e._pendingChange=!1)}))}function Bj(t,e){if(!e)return null;let n,o,i;return Array.isArray(e)||"undefined"!=typeof ngDevMode&&!ngDevMode||Hj(t,"Value accessor was not provided as an array for form control with"),e.forEach((e=>{e.constructor===VV?n=e:(function a(t){return Object.getPrototypeOf(t.constructor)===zV})(e)?(o&&("undefined"==typeof ngDevMode||ngDevMode)&&Hj(t,"More than one built-in value accessor matches form control with"),o=e):(i&&("undefined"==typeof ngDevMode||ngDevMode)&&Hj(t,"More than one custom value accessor matches form control with"),i=e)})),i||o||n||(("undefined"==typeof ngDevMode||ngDevMode)&&Hj(t,"No valid value accessor for form control with"),null)}function Vj(t,e){const n=t.indexOf(e);n>-1&&t.splice(n,1)}function jj(t,e,n,o){"never"!==o&&((null!==o&&"once"!==o||e._ngModelWarningSentOnce)&&("always"!==o||n._ngModelWarningSent)||(Sj.ngModelWarning(t),e._ngModelWarningSentOnce=!0,n._ngModelWarningSent=!0))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const Uj="VALID",Gj="INVALID",Wj="PENDING",Yj="DISABLED";function qj(t){return(Jj(t)?t.validators:t)||null}function Zj(t){return Array.isArray(t)?cj(t):t||null}function Xj(t,e){return(Jj(e)?e.asyncValidators:t)||null}function Kj(t){return Array.isArray(t)?pj(t):t||null}function Jj(t){return null!=t&&!Array.isArray(t)&&"object"==typeof t}class Qj{constructor(t,e){this._hasOwnPendingAsyncValidator=!1,this._onCollectionChange=()=>{},this._parent=null,this.pristine=!0,this.touched=!1,this._onDisabledChange=[],this._rawValidators=t,this._rawAsyncValidators=e,this._composedValidatorFn=Zj(this._rawValidators),this._composedAsyncValidatorFn=Kj(this._rawAsyncValidators)}get validator(){return this._composedValidatorFn}set validator(t){this._rawValidators=this._composedValidatorFn=t}get asyncValidator(){return this._composedAsyncValidatorFn}set asyncValidator(t){this._rawAsyncValidators=this._composedAsyncValidatorFn=t}get parent(){return this._parent}get valid(){return this.status===Uj}get invalid(){return this.status===Gj}get pending(){return this.status==Wj}get disabled(){return this.status===Yj}get enabled(){return this.status!==Yj}get dirty(){return!this.pristine}get untouched(){return!this.touched}get updateOn(){return this._updateOn?this._updateOn:this.parent?this.parent.updateOn:"change"}setValidators(t){this._rawValidators=t,this._composedValidatorFn=Zj(t)}setAsyncValidators(t){this._rawAsyncValidators=t,this._composedAsyncValidatorFn=Kj(t)}addValidators(t){this.setValidators(bj(t,this._rawValidators))}addAsyncValidators(t){this.setAsyncValidators(bj(t,this._rawAsyncValidators))}removeValidators(t){this.setValidators(yj(t,this._rawValidators))}removeAsyncValidators(t){this.setAsyncValidators(yj(t,this._rawAsyncValidators))}hasValidator(t){return hj(this._rawValidators,t)}hasAsyncValidator(t){return hj(this._rawAsyncValidators,t)}clearValidators(){this.validator=null}clearAsyncValidators(){this.asyncValidator=null}markAsTouched(t={}){this.touched=!0,this._parent&&!t.onlySelf&&this._parent.markAsTouched(t)}markAllAsTouched(){this.markAsTouched({onlySelf:!0}),this._forEachChild((t=>t.markAllAsTouched()))}markAsUntouched(t={}){this.touched=!1,this._pendingTouched=!1,this._forEachChild((t=>{t.markAsUntouched({onlySelf:!0})})),this._parent&&!t.onlySelf&&this._parent._updateTouched(t)}markAsDirty(t={}){this.pristine=!1,this._parent&&!t.onlySelf&&this._parent.markAsDirty(t)}markAsPristine(t={}){this.pristine=!0,this._pendingDirty=!1,this._forEachChild((t=>{t.markAsPristine({onlySelf:!0})})),this._parent&&!t.onlySelf&&this._parent._updatePristine(t)}markAsPending(t={}){this.status=Wj,!1!==t.emitEvent&&this.statusChanges.emit(this.status),this._parent&&!t.onlySelf&&this._parent.markAsPending(t)}disable(t={}){const e=this._parentMarkedDirty(t.onlySelf);this.status=Yj,this.errors=null,this._forEachChild((e=>{e.disable(Object.assign(Object.assign({},t),{onlySelf:!0}))})),this._updateValue(),!1!==t.emitEvent&&(this.valueChanges.emit(this.value),this.statusChanges.emit(this.status)),this._updateAncestors(Object.assign(Object.assign({},t),{skipPristineCheck:e})),this._onDisabledChange.forEach((t=>t(!0)))}enable(t={}){const e=this._parentMarkedDirty(t.onlySelf);this.status=Uj,this._forEachChild((e=>{e.enable(Object.assign(Object.assign({},t),{onlySelf:!0}))})),this.updateValueAndValidity({onlySelf:!0,emitEvent:t.emitEvent}),this._updateAncestors(Object.assign(Object.assign({},t),{skipPristineCheck:e})),this._onDisabledChange.forEach((t=>t(!1)))}_updateAncestors(t){this._parent&&!t.onlySelf&&(this._parent.updateValueAndValidity(t),t.skipPristineCheck||this._parent._updatePristine(),this._parent._updateTouched())}setParent(t){this._parent=t}updateValueAndValidity(t={}){this._setInitialStatus(),this._updateValue(),this.enabled&&(this._cancelExistingSubscription(),this.errors=this._runValidator(),this.status=this._calculateStatus(),this.status!==Uj&&this.status!==Wj||this._runAsyncValidator(t.emitEvent)),!1!==t.emitEvent&&(this.valueChanges.emit(this.value),this.statusChanges.emit(this.status)),this._parent&&!t.onlySelf&&this._parent.updateValueAndValidity(t)}_updateTreeValidity(t={emitEvent:!0}){this._forEachChild((e=>e._updateTreeValidity(t))),this.updateValueAndValidity({onlySelf:!0,emitEvent:t.emitEvent})}_setInitialStatus(){this.status=this._allControlsDisabled()?Yj:Uj}_runValidator(){return this.validator?this.validator(this):null}_runAsyncValidator(t){if(this.asyncValidator){this.status=Wj,this._hasOwnPendingAsyncValidator=!0;const e=ij(this.asyncValidator(this));this._asyncValidationSubscription=e.subscribe((e=>{this._hasOwnPendingAsyncValidator=!1,this.setErrors(e,{emitEvent:t})}))}}_cancelExistingSubscription(){this._asyncValidationSubscription&&(this._asyncValidationSubscription.unsubscribe(),this._hasOwnPendingAsyncValidator=!1)}setErrors(t,e={}){this.errors=t,this._updateControlsErrors(!1!==e.emitEvent)}get(t){return(function e(t,n,o){if(null==n)return null;if(Array.isArray(n)||(n=n.split(o)),Array.isArray(n)&&0===n.length)return null;let i=t;return n.forEach((t=>{i=i instanceof tU?i.controls.hasOwnProperty(t)?i.controls[t]:null:i instanceof eU&&i.at(t)||null})),i})(this,t,".")}getError(t,e){const n=e?this.get(e):this;return n&&n.errors?n.errors[t]:null}hasError(t,e){return!!this.getError(t,e)}get root(){let t=this;for(;t._parent;)t=t._parent;return t}_updateControlsErrors(t){this.status=this._calculateStatus(),t&&this.statusChanges.emit(this.status),this._parent&&this._parent._updateControlsErrors(t)}_initObservables(){this.valueChanges=new Lh,this.statusChanges=new Lh}_calculateStatus(){return this._allControlsDisabled()?Yj:this.errors?Gj:this._hasOwnPendingAsyncValidator||this._anyControlsHaveStatus(Wj)?Wj:this._anyControlsHaveStatus(Gj)?Gj:Uj}_anyControlsHaveStatus(t){return this._anyControls((e=>e.status===t))}_anyControlsDirty(){return this._anyControls((t=>t.dirty))}_anyControlsTouched(){return this._anyControls((t=>t.touched))}_updatePristine(t={}){this.pristine=!this._anyControlsDirty(),this._parent&&!t.onlySelf&&this._parent._updatePristine(t)}_updateTouched(t={}){this.touched=this._anyControlsTouched(),this._parent&&!t.onlySelf&&this._parent._updateTouched(t)}_isBoxedValue(t){return"object"==typeof t&&null!==t&&2===Object.keys(t).length&&"value"in t&&"disabled"in t}_registerOnCollectionChange(t){this._onCollectionChange=t}_setUpdateStrategy(t){Jj(t)&&null!=t.updateOn&&(this._updateOn=t.updateOn)}_parentMarkedDirty(t){return!t&&!(!this._parent||!this._parent.dirty)&&!this._parent._anyControlsDirty()}}class $j extends Qj{constructor(t=null,e,n){super(qj(e),Xj(n,e)),this._onChange=[],this._applyFormState(t),this._setUpdateStrategy(e),this._initObservables(),this.updateValueAndValidity({onlySelf:!0,emitEvent:!!this.asyncValidator})}setValue(t,e={}){this.value=this._pendingValue=t,this._onChange.length&&!1!==e.emitModelToViewChange&&this._onChange.forEach((t=>t(this.value,!1!==e.emitViewToModelChange))),this.updateValueAndValidity(e)}patchValue(t,e={}){this.setValue(t,e)}reset(t=null,e={}){this._applyFormState(t),this.markAsPristine(e),this.markAsUntouched(e),this.setValue(this.value,e),this._pendingChange=!1}_updateValue(){}_anyControls(t){return!1}_allControlsDisabled(){return this.disabled}registerOnChange(t){this._onChange.push(t)}_unregisterOnChange(t){Vj(this._onChange,t)}registerOnDisabledChange(t){this._onDisabledChange.push(t)}_unregisterOnDisabledChange(t){Vj(this._onDisabledChange,t)}_forEachChild(t){}_syncPendingControls(){return!("submit"!==this.updateOn||(this._pendingDirty&&this.markAsDirty(),this._pendingTouched&&this.markAsTouched(),!this._pendingChange)||(this.setValue(this._pendingValue,{onlySelf:!0,emitModelToViewChange:!1}),0))}_applyFormState(t){this._isBoxedValue(t)?(this.value=this._pendingValue=t.value,t.disabled?this.disable({onlySelf:!0,emitEvent:!1}):this.enable({onlySelf:!0,emitEvent:!1})):this.value=this._pendingValue=t}}class tU extends Qj{constructor(t,e,n){super(qj(e),Xj(n,e)),this.controls=t,this._initObservables(),this._setUpdateStrategy(e),this._setUpControls(),this.updateValueAndValidity({onlySelf:!0,emitEvent:!!this.asyncValidator})}registerControl(t,e){return this.controls[t]?this.controls[t]:(this.controls[t]=e,e.setParent(this),e._registerOnCollectionChange(this._onCollectionChange),e)}addControl(t,e,n={}){this.registerControl(t,e),this.updateValueAndValidity({emitEvent:n.emitEvent}),this._onCollectionChange()}removeControl(t,e={}){this.controls[t]&&this.controls[t]._registerOnCollectionChange((()=>{})),delete this.controls[t],this.updateValueAndValidity({emitEvent:e.emitEvent}),this._onCollectionChange()}setControl(t,e,n={}){this.controls[t]&&this.controls[t]._registerOnCollectionChange((()=>{})),delete this.controls[t],e&&this.registerControl(t,e),this.updateValueAndValidity({emitEvent:n.emitEvent}),this._onCollectionChange()}contains(t){return this.controls.hasOwnProperty(t)&&this.controls[t].enabled}setValue(t,e={}){this._checkAllValuesPresent(t),Object.keys(t).forEach((n=>{this._throwIfControlMissing(n),this.controls[n].setValue(t[n],{onlySelf:!0,emitEvent:e.emitEvent})})),this.updateValueAndValidity(e)}patchValue(t,e={}){null!=t&&(Object.keys(t).forEach((n=>{this.controls[n]&&this.controls[n].patchValue(t[n],{onlySelf:!0,emitEvent:e.emitEvent})})),this.updateValueAndValidity(e))}reset(t={},e={}){this._forEachChild(((n,o)=>{n.reset(t[o],{onlySelf:!0,emitEvent:e.emitEvent})})),this._updatePristine(e),this._updateTouched(e),this.updateValueAndValidity(e)}getRawValue(){return this._reduceChildren({},((t,e,n)=>(t[n]=e instanceof $j?e.value:e.getRawValue(),t)))}_syncPendingControls(){let t=this._reduceChildren(!1,((t,e)=>!!e._syncPendingControls()||t));return t&&this.updateValueAndValidity({onlySelf:!0}),t}_throwIfControlMissing(t){if(!Object.keys(this.controls).length)throw new Error("\n        There are no form controls registered with this group yet. If you're using ngModel,\n        you may want to check next tick (e.g. use setTimeout).\n      ");if(!this.controls[t])throw new Error(`Cannot find form control with name: ${t}.`)}_forEachChild(t){Object.keys(this.controls).forEach((e=>{const n=this.controls[e];n&&t(n,e)}))}_setUpControls(){this._forEachChild((t=>{t.setParent(this),t._registerOnCollectionChange(this._onCollectionChange)}))}_updateValue(){this.value=this._reduceValue()}_anyControls(t){for(const e of Object.keys(this.controls)){const n=this.controls[e];if(this.contains(e)&&t(n))return!0}return!1}_reduceValue(){return this._reduceChildren({},((t,e,n)=>((e.enabled||this.disabled)&&(t[n]=e.value),t)))}_reduceChildren(t,e){let n=t;return this._forEachChild(((t,o)=>{n=e(n,t,o)})),n}_allControlsDisabled(){for(const t of Object.keys(this.controls))if(this.controls[t].enabled)return!1;return Object.keys(this.controls).length>0||this.disabled}_checkAllValuesPresent(t){this._forEachChild(((e,n)=>{if(void 0===t[n])throw new Error(`Must supply a value for form control with name: '${n}'.`)}))}}class eU extends Qj{constructor(t,e,n){super(qj(e),Xj(n,e)),this.controls=t,this._initObservables(),this._setUpdateStrategy(e),this._setUpControls(),this.updateValueAndValidity({onlySelf:!0,emitEvent:!!this.asyncValidator})}at(t){return this.controls[t]}push(t,e={}){this.controls.push(t),this._registerControl(t),this.updateValueAndValidity({emitEvent:e.emitEvent}),this._onCollectionChange()}insert(t,e,n={}){this.controls.splice(t,0,e),this._registerControl(e),this.updateValueAndValidity({emitEvent:n.emitEvent})}removeAt(t,e={}){this.controls[t]&&this.controls[t]._registerOnCollectionChange((()=>{})),this.controls.splice(t,1),this.updateValueAndValidity({emitEvent:e.emitEvent})}setControl(t,e,n={}){this.controls[t]&&this.controls[t]._registerOnCollectionChange((()=>{})),this.controls.splice(t,1),e&&(this.controls.splice(t,0,e),this._registerControl(e)),this.updateValueAndValidity({emitEvent:n.emitEvent}),this._onCollectionChange()}get length(){return this.controls.length}setValue(t,e={}){this._checkAllValuesPresent(t),t.forEach(((t,n)=>{this._throwIfControlMissing(n),this.at(n).setValue(t,{onlySelf:!0,emitEvent:e.emitEvent})})),this.updateValueAndValidity(e)}patchValue(t,e={}){null!=t&&(t.forEach(((t,n)=>{this.at(n)&&this.at(n).patchValue(t,{onlySelf:!0,emitEvent:e.emitEvent})})),this.updateValueAndValidity(e))}reset(t=[],e={}){this._forEachChild(((n,o)=>{n.reset(t[o],{onlySelf:!0,emitEvent:e.emitEvent})})),this._updatePristine(e),this._updateTouched(e),this.updateValueAndValidity(e)}getRawValue(){return this.controls.map((t=>t instanceof $j?t.value:t.getRawValue()))}clear(t={}){this.controls.length<1||(this._forEachChild((t=>t._registerOnCollectionChange((()=>{})))),this.controls.splice(0),this.updateValueAndValidity({emitEvent:t.emitEvent}))}_syncPendingControls(){let t=this.controls.reduce(((t,e)=>!!e._syncPendingControls()||t),!1);return t&&this.updateValueAndValidity({onlySelf:!0}),t}_throwIfControlMissing(t){if(!this.controls.length)throw new Error("\n        There are no form controls registered with this array yet. If you're using ngModel,\n        you may want to check next tick (e.g. use setTimeout).\n      ");if(!this.at(t))throw new Error(`Cannot find form control at index ${t}`)}_forEachChild(t){this.controls.forEach(((e,n)=>{t(e,n)}))}_updateValue(){this.value=this.controls.filter((t=>t.enabled||this.disabled)).map((t=>t.value))}_anyControls(t){return this.controls.some((e=>e.enabled&&t(e)))}_setUpControls(){this._forEachChild((t=>this._registerControl(t)))}_checkAllValuesPresent(t){this._forEachChild(((e,n)=>{if(void 0===t[n])throw new Error(`Must supply a value for form control at index: ${n}.`)}))}_allControlsDisabled(){for(const t of this.controls)if(t.enabled)return!1;return this.controls.length>0||this.disabled}_registerControl(t){t.setParent(this),t._registerOnCollectionChange(this._onCollectionChange)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const nU={provide:Cj,useExisting:qe((()=>iU))},oU=Promise.resolve(null);class iU extends Cj{constructor(t,e){super(),this.submitted=!1,this._directives=[],this.ngSubmit=new Lh,this.form=new tU({},cj(t),pj(e))}ngAfterViewInit(){this._setUpdateStrategy()}get formDirective(){return this}get control(){return this.form}get path(){return[]}get controls(){return this.form.controls}addControl(t){oU.then((()=>{const e=this._findContainer(t.path);t.control=e.registerControl(t.name,t.control),Ej(t.control,t),t.control.updateValueAndValidity({emitEvent:!1}),this._directives.push(t)}))}getControl(t){return this.form.get(t.path)}removeControl(t){oU.then((()=>{const e=this._findContainer(t.path);e&&e.removeControl(t.name),Vj(this._directives,t)}))}addFormGroup(t){oU.then((()=>{const e=this._findContainer(t.path),n=new tU({});Ij(n,t),e.registerControl(t.name,n),n.updateValueAndValidity({emitEvent:!1})}))}removeFormGroup(t){oU.then((()=>{const e=this._findContainer(t.path);e&&e.removeControl(t.name)}))}getFormGroup(t){return this.form.get(t.path)}updateModel(t,e){oU.then((()=>{this.form.get(t.path).setValue(e)}))}setValue(t){this.control.setValue(t)}onSubmit(t){return this.submitted=!0,Lj(this.form,this._directives),this.ngSubmit.emit(t),!1}onReset(){this.resetForm()}resetForm(t){this.form.reset(t),this.submitted=!1}_setUpdateStrategy(){this.options&&null!=this.options.updateOn&&(this.form._updateOn=this.options.updateOn)}_findContainer(t){return t.pop(),t.length?this.form.get(t):this.form}}iU.ɵfac=function t(e){return new(e||iU)(Sm(GV,10),Sm(WV,10))},iU.ɵdir=lo({type:iU,selectors:[["form",3,"ngNoForm","",3,"formGroup",""],["ng-form"],["","ngForm",""]],hostBindings:function t(e,n){1&e&&Vm("submit",(function t(e){return n.onSubmit(e)}))("reset",(function t(){return n.onReset()}))},inputs:{options:["ngFormOptions","options"]},outputs:{ngSubmit:"ngSubmit"},exportAs:["ngForm"],features:[pg([nU]),xp]}),iU.ctorParameters=()=>[{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}],iU.propDecorators={options:[{type:xy,args:["ngFormOptions"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(iU,[{type:Cy,args:[{selector:"form:not([ngNoForm]):not([formGroup]),ng-form,[ngForm]",providers:[nU],host:{"(submit)":"onSubmit($event)","(reset)":"onReset()"},outputs:["ngSubmit"],exportAs:"ngForm"}]}],(function(){return[{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}]}),{options:[{type:xy,args:["ngFormOptions"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class aU extends Cj{ngOnInit(){this._checkParentType(),this.formDirective.addFormGroup(this)}ngOnDestroy(){this.formDirective&&this.formDirective.removeFormGroup(this)}get control(){return this.formDirective.getFormGroup(this)}get path(){return Dj(null==this.name?this.name:this.name.toString(),this._parent)}get formDirective(){return this._parent?this._parent.formDirective:null}_checkParentType(){}}aU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(aU)))(n||aU)}})(),aU.ɵdir=lo({type:aU,features:[xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(aU,[{type:Cy}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class rU{static modelParentException(){throw new Error(`\n      ngModel cannot be used to register form controls with a parent formGroup directive.  Try using\n      formGroup's partner directive "formControlName" instead.  Example:\n\n      ${Pj}\n\n      Or, if you'd like to avoid registering this form control, indicate that it's standalone in ngModelOptions:\n\n      Example:\n\n      \n    <div [formGroup]="myGroup">\n       <input formControlName="firstName">\n       <input [(ngModel)]="showMoreControls" [ngModelOptions]="{standalone: true}">\n    </div>\n  `)}static formGroupNameException(){throw new Error(`\n      ngModel cannot be used to register form controls with a parent formGroupName or formArrayName directive.\n\n      Option 1: Use formControlName instead of ngModel (reactive strategy):\n\n      ${wj}\n\n      Option 2:  Update ngModel's parent be ngModelGroup (template-driven strategy):\n\n      ${kj}`)}static missingNameException(){throw new Error('If ngModel is used within a form tag, either the name attribute must be set or the form\n      control must be defined as \'standalone\' in ngModelOptions.\n\n      Example 1: <input [(ngModel)]="person.firstName" name="first">\n      Example 2: <input [(ngModel)]="person.firstName" [ngModelOptions]="{standalone: true}">')}static modelGroupParentException(){throw new Error(`\n      ngModelGroup cannot be used with a parent formGroup directive.\n\n      Option 1: Use formGroupName instead of ngModelGroup (reactive strategy):\n\n      ${wj}\n\n      Option 2:  Use a regular form tag instead of the formGroup directive (template-driven strategy):\n\n      ${kj}`)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const sU={provide:Cj,useExisting:qe((()=>lU))};class lU extends aU{constructor(t,e,n){super(),this._parent=t,this._setValidators(e),this._setAsyncValidators(n)}_checkParentType(){this._parent instanceof lU||this._parent instanceof iU||"undefined"!=typeof ngDevMode&&!ngDevMode||rU.modelGroupParentException()}}lU.ɵfac=function t(e){return new(e||lU)(Sm(Cj,5),Sm(GV,10),Sm(WV,10))},lU.ɵdir=lo({type:lU,selectors:[["","ngModelGroup",""]],inputs:{name:["ngModelGroup","name"]},exportAs:["ngModelGroup"],features:[pg([sU]),xp]}),lU.ctorParameters=()=>[{type:Cj,decorators:[{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}],lU.propDecorators={name:[{type:xy,args:["ngModelGroup"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(lU,[{type:Cy,args:[{selector:"[ngModelGroup]",providers:[sU],exportAs:"ngModelGroup"}]}],(function(){return[{type:Cj,decorators:[{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}]}),{name:[{type:xy,args:["ngModelGroup"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const cU={provide:Mj,useExisting:qe((()=>pU))},dU=Promise.resolve(null);class pU extends Mj{constructor(t,e,n,o){super(),this.control=new $j,this._registered=!1,this.update=new Lh,this._parent=t,this._setValidators(e),this._setAsyncValidators(n),this.valueAccessor=Bj(this,o)}ngOnChanges(t){this._checkForErrors(),this._registered||this._setUpControl(),"isDisabled"in t&&this._updateDisabled(t),Fj(t,this.viewModel)&&(this._updateValue(this.model),this.viewModel=this.model)}ngOnDestroy(){this.formDirective&&this.formDirective.removeControl(this)}get path(){return this._parent?Dj(this.name,this._parent):[this.name]}get formDirective(){return this._parent?this._parent.formDirective:null}viewToModelUpdate(t){this.viewModel=t,this.update.emit(t)}_setUpControl(){this._setUpdateStrategy(),this._isStandalone()?this._setUpStandalone():this.formDirective.addControl(this),this._registered=!0}_setUpdateStrategy(){this.options&&null!=this.options.updateOn&&(this.control._updateOn=this.options.updateOn)}_isStandalone(){return!this._parent||!(!this.options||!this.options.standalone)}_setUpStandalone(){Ej(this.control,this),this.control.updateValueAndValidity({emitEvent:!1})}_checkForErrors(){this._isStandalone()||this._checkParentType(),this._checkName()}_checkParentType(){("undefined"==typeof ngDevMode||ngDevMode)&&(!(this._parent instanceof lU)&&this._parent instanceof aU?rU.formGroupNameException():this._parent instanceof lU||this._parent instanceof iU||rU.modelParentException())}_checkName(){this.options&&this.options.name&&(this.name=this.options.name),this._isStandalone()||this.name||"undefined"!=typeof ngDevMode&&!ngDevMode||rU.missingNameException()}_updateValue(t){dU.then((()=>{this.control.setValue(t,{emitViewToModelChange:!1})}))}_updateDisabled(t){const e=t.isDisabled.currentValue,n=""===e||e&&"false"!==e;dU.then((()=>{n&&!this.control.disabled?this.control.disable():!n&&this.control.disabled&&this.control.enable()}))}}pU.ɵfac=function t(e){return new(e||pU)(Sm(Cj,9),Sm(GV,10),Sm(WV,10),Sm(IV,10))},pU.ɵdir=lo({type:pU,selectors:[["","ngModel","",3,"formControlName","",3,"formControl",""]],inputs:{name:"name",isDisabled:["disabled","isDisabled"],model:["ngModel","model"],options:["ngModelOptions","options"]},outputs:{update:"ngModelChange"},exportAs:["ngModel"],features:[pg([cU]),xp,Bo]}),pU.ctorParameters=()=>[{type:Cj,decorators:[{type:Sr},{type:Rr}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[IV]}]}],pU.propDecorators={name:[{type:xy}],isDisabled:[{type:xy,args:["disabled"]}],model:[{type:xy,args:["ngModel"]}],options:[{type:xy,args:["ngModelOptions"]}],update:[{type:Oy,args:["ngModelChange"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(pU,[{type:Cy,args:[{selector:"[ngModel]:not([formControlName]):not([formControl])",providers:[cU],exportAs:"ngModel"}]}],(function(){return[{type:Cj,decorators:[{type:Sr},{type:Rr}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[IV]}]}]}),{update:[{type:Oy,args:["ngModelChange"]}],name:[{type:xy}],isDisabled:[{type:xy,args:["disabled"]}],model:[{type:xy,args:["ngModel"]}],options:[{type:xy,args:["ngModelOptions"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class mU{}mU.ɵfac=function t(e){return new(e||mU)},mU.ɵdir=lo({type:mU,selectors:[["form",3,"ngNoForm","",3,"ngNativeValidate",""]],hostAttrs:["novalidate",""]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(mU,[{type:Cy,args:[{selector:"form:not([ngNoForm]):not([ngNativeValidate])",host:{novalidate:""}}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const uU={provide:IV,useExisting:qe((()=>fU)),multi:!0};class fU extends zV{writeValue(t){this.setProperty("value",null==t?"":t)}registerOnChange(t){this.onChange=e=>{t(""==e?null:parseFloat(e))}}}fU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(fU)))(n||fU)}})(),fU.ɵdir=lo({type:fU,selectors:[["input","type","number","formControlName",""],["input","type","number","formControl",""],["input","type","number","ngModel",""]],hostBindings:function t(e,n){1&e&&Vm("input",(function t(e){return n.onChange(e.target.value)}))("blur",(function t(){return n.onTouched()}))},features:[pg([uU]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(fU,[{type:Cy,args:[{selector:"input[type=number][formControlName],input[type=number][formControl],input[type=number][ngModel]",host:{"(input)":"onChange($event.target.value)","(blur)":"onTouched()"},providers:[uU]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const _U={provide:IV,useExisting:qe((()=>CU)),multi:!0};class CU extends zV{writeValue(t){this.setProperty("value",parseFloat(t))}registerOnChange(t){this.onChange=e=>{t(""==e?null:parseFloat(e))}}}CU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(CU)))(n||CU)}})(),CU.ɵdir=lo({type:CU,selectors:[["input","type","range","formControlName",""],["input","type","range","formControl",""],["input","type","range","ngModel",""]],hostBindings:function t(e,n){1&e&&Vm("change",(function t(e){return n.onChange(e.target.value)}))("input",(function t(e){return n.onChange(e.target.value)}))("blur",(function t(){return n.onTouched()}))},features:[pg([_U]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(CU,[{type:Cy,args:[{selector:"input[type=range][formControlName],input[type=range][formControl],input[type=range][ngModel]",host:{"(change)":"onChange($event.target.value)","(input)":"onChange($event.target.value)","(blur)":"onTouched()"},providers:[_U]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const MU=new Ga("NgModelWithFormControlWarning"),vU={provide:Mj,useExisting:qe((()=>xU))};class xU extends Mj{constructor(t,e,n,o){super(),this._ngModelWarningConfig=o,this.update=new Lh,this._ngModelWarningSent=!1,this._setValidators(t),this._setAsyncValidators(e),this.valueAccessor=Bj(this,n)}set isDisabled(t){("undefined"==typeof ngDevMode||ngDevMode)&&Sj.disabledAttrWarning()}ngOnChanges(t){if(this._isControlChanged(t)){const e=t.form.previousValue;e&&Rj(e,this,!1),Ej(this.form,this),this.control.disabled&&this.valueAccessor.setDisabledState&&this.valueAccessor.setDisabledState(!0),this.form.updateValueAndValidity({emitEvent:!1})}Fj(t,this.viewModel)&&(("undefined"==typeof ngDevMode||ngDevMode)&&jj("formControl",xU,this,this._ngModelWarningConfig),this.form.setValue(this.model),this.viewModel=this.model)}ngOnDestroy(){this.form&&Rj(this.form,this,!1)}get path(){return[]}get control(){return this.form}viewToModelUpdate(t){this.viewModel=t,this.update.emit(t)}_isControlChanged(t){return t.hasOwnProperty("form")}}xU.ɵfac=function t(e){return new(e||xU)(Sm(GV,10),Sm(WV,10),Sm(IV,10),Sm(MU,8))},xU.ɵdir=lo({type:xU,selectors:[["","formControl",""]],inputs:{isDisabled:["disabled","isDisabled"],form:["formControl","form"],model:["ngModel","model"]},outputs:{update:"ngModelChange"},exportAs:["ngForm"],features:[pg([vU]),xp,Bo]}),xU._ngModelWarningSentOnce=!1,xU.ctorParameters=()=>[{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[IV]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[MU]}]}],xU.propDecorators={form:[{type:xy,args:["formControl"]}],isDisabled:[{type:xy,args:["disabled"]}],model:[{type:xy,args:["ngModel"]}],update:[{type:Oy,args:["ngModelChange"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(xU,[{type:Cy,args:[{selector:"[formControl]",providers:[vU],exportAs:"ngForm"}]}],(function(){return[{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[IV]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[MU]}]}]}),{update:[{type:Oy,args:["ngModelChange"]}],isDisabled:[{type:xy,args:["disabled"]}],form:[{type:xy,args:["formControl"]}],model:[{type:xy,args:["ngModel"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const OU={provide:Cj,useExisting:qe((()=>PU))};class PU extends Cj{constructor(t,e){super(),this.validators=t,this.asyncValidators=e,this.submitted=!1,this._onCollectionChange=()=>this._updateDomValue(),this.directives=[],this.form=null,this.ngSubmit=new Lh,this._setValidators(t),this._setAsyncValidators(e)}ngOnChanges(t){this._checkFormPresent(),t.hasOwnProperty("form")&&(this._updateValidators(),this._updateDomValue(),this._updateRegistrations(),this._oldForm=this.form)}ngOnDestroy(){this.form&&(Nj(this.form,this),this.form._onCollectionChange===this._onCollectionChange&&this.form._registerOnCollectionChange((()=>{})))}get formDirective(){return this}get control(){return this.form}get path(){return[]}addControl(t){const e=this.form.get(t.path);return Ej(e,t),e.updateValueAndValidity({emitEvent:!1}),this.directives.push(t),e}getControl(t){return this.form.get(t.path)}removeControl(t){Rj(t.control||null,t,!1),Vj(this.directives,t)}addFormGroup(t){this._setUpFormContainer(t)}removeFormGroup(t){this._cleanUpFormContainer(t)}getFormGroup(t){return this.form.get(t.path)}addFormArray(t){this._setUpFormContainer(t)}removeFormArray(t){this._cleanUpFormContainer(t)}getFormArray(t){return this.form.get(t.path)}updateModel(t,e){this.form.get(t.path).setValue(e)}onSubmit(t){return this.submitted=!0,Lj(this.form,this.directives),this.ngSubmit.emit(t),!1}onReset(){this.resetForm()}resetForm(t){this.form.reset(t),this.submitted=!1}_updateDomValue(){this.directives.forEach((t=>{const e=t.control,n=this.form.get(t.path);e!==n&&(Rj(e||null,t),n instanceof $j&&(Ej(n,t),t.control=n))})),this.form._updateTreeValidity({emitEvent:!1})}_setUpFormContainer(t){const e=this.form.get(t.path);Ij(e,t),e.updateValueAndValidity({emitEvent:!1})}_cleanUpFormContainer(t){if(this.form){const e=this.form.get(t.path);e&&(function n(t,e){return Nj(t,e)})(e,t)&&e.updateValueAndValidity({emitEvent:!1})}}_updateRegistrations(){this.form._registerOnCollectionChange(this._onCollectionChange),this._oldForm&&this._oldForm._registerOnCollectionChange((()=>{}))}_updateValidators(){Tj(this.form,this),this._oldForm&&Nj(this._oldForm,this)}_checkFormPresent(){this.form||"undefined"!=typeof ngDevMode&&!ngDevMode||Sj.missingFormException()}}PU.ɵfac=function t(e){return new(e||PU)(Sm(GV,10),Sm(WV,10))},PU.ɵdir=lo({type:PU,selectors:[["","formGroup",""]],hostBindings:function t(e,n){1&e&&Vm("submit",(function t(e){return n.onSubmit(e)}))("reset",(function t(){return n.onReset()}))},inputs:{form:["formGroup","form"]},outputs:{ngSubmit:"ngSubmit"},exportAs:["ngForm"],features:[pg([OU]),xp,Bo]}),PU.ctorParameters=()=>[{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}],PU.propDecorators={form:[{type:xy,args:["formGroup"]}],ngSubmit:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(PU,[{type:Cy,args:[{selector:"[formGroup]",providers:[OU],host:{"(submit)":"onSubmit($event)","(reset)":"onReset()"},exportAs:"ngForm"}]}],(function(){return[{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}]}),{form:[{type:xy,args:["formGroup"]}],ngSubmit:[{type:Oy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const wU={provide:Cj,useExisting:qe((()=>kU))};class kU extends aU{constructor(t,e,n){super(),this._parent=t,this._setValidators(e),this._setAsyncValidators(n)}_checkParentType(){EU(this._parent)&&("undefined"==typeof ngDevMode||ngDevMode)&&Sj.groupParentException()}}kU.ɵfac=function t(e){return new(e||kU)(Sm(Cj,13),Sm(GV,10),Sm(WV,10))},kU.ɵdir=lo({type:kU,selectors:[["","formGroupName",""]],inputs:{name:["formGroupName","name"]},features:[pg([wU]),xp]}),kU.ctorParameters=()=>[{type:Cj,decorators:[{type:Sr},{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}],kU.propDecorators={name:[{type:xy,args:["formGroupName"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(kU,[{type:Cy,args:[{selector:"[formGroupName]",providers:[wU]}]}],(function(){return[{type:Cj,decorators:[{type:Sr},{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}]}),{name:[{type:xy,args:["formGroupName"]}]});const SU={provide:Cj,useExisting:qe((()=>DU))};class DU extends Cj{constructor(t,e,n){super(),this._parent=t,this._setValidators(e),this._setAsyncValidators(n)}ngOnInit(){this._checkParentType(),this.formDirective.addFormArray(this)}ngOnDestroy(){this.formDirective&&this.formDirective.removeFormArray(this)}get control(){return this.formDirective.getFormArray(this)}get formDirective(){return this._parent?this._parent.formDirective:null}get path(){return Dj(null==this.name?this.name:this.name.toString(),this._parent)}_checkParentType(){EU(this._parent)&&("undefined"==typeof ngDevMode||ngDevMode)&&Sj.arrayParentException()}}function EU(t){return!(t instanceof kU||t instanceof PU||t instanceof DU)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */DU.ɵfac=function t(e){return new(e||DU)(Sm(Cj,13),Sm(GV,10),Sm(WV,10))},DU.ɵdir=lo({type:DU,selectors:[["","formArrayName",""]],inputs:{name:["formArrayName","name"]},features:[pg([SU]),xp]}),DU.ctorParameters=()=>[{type:Cj,decorators:[{type:Sr},{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}],DU.propDecorators={name:[{type:xy,args:["formArrayName"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(DU,[{type:Cy,args:[{selector:"[formArrayName]",providers:[SU]}]}],(function(){return[{type:Cj,decorators:[{type:Sr},{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]}]}),{name:[{type:xy,args:["formArrayName"]}]});const RU={provide:Mj,useExisting:qe((()=>AU))};class AU extends Mj{constructor(t,e,n,o,i){super(),this._ngModelWarningConfig=i,this._added=!1,this.update=new Lh,this._ngModelWarningSent=!1,this._parent=t,this._setValidators(e),this._setAsyncValidators(n),this.valueAccessor=Bj(this,o)}set isDisabled(t){("undefined"==typeof ngDevMode||ngDevMode)&&Sj.disabledAttrWarning()}ngOnChanges(t){this._added||this._setUpControl(),Fj(t,this.viewModel)&&(("undefined"==typeof ngDevMode||ngDevMode)&&jj("formControlName",AU,this,this._ngModelWarningConfig),this.viewModel=this.model,this.formDirective.updateModel(this,this.model))}ngOnDestroy(){this.formDirective&&this.formDirective.removeControl(this)}viewToModelUpdate(t){this.viewModel=t,this.update.emit(t)}get path(){return Dj(null==this.name?this.name:this.name.toString(),this._parent)}get formDirective(){return this._parent?this._parent.formDirective:null}_checkParentType(){("undefined"==typeof ngDevMode||ngDevMode)&&(!(this._parent instanceof kU)&&this._parent instanceof aU?Sj.ngModelGroupException():this._parent instanceof kU||this._parent instanceof PU||this._parent instanceof DU||Sj.controlParentException())}_setUpControl(){this._checkParentType(),this.control=this.formDirective.addControl(this),this.control.disabled&&this.valueAccessor.setDisabledState&&this.valueAccessor.setDisabledState(!0),this._added=!0}}AU.ɵfac=function t(e){return new(e||AU)(Sm(Cj,13),Sm(GV,10),Sm(WV,10),Sm(IV,10),Sm(MU,8))},AU.ɵdir=lo({type:AU,selectors:[["","formControlName",""]],inputs:{isDisabled:["disabled","isDisabled"],name:["formControlName","name"],model:["ngModel","model"]},outputs:{update:"ngModelChange"},features:[pg([RU]),xp,Bo]}),AU._ngModelWarningSentOnce=!1,AU.ctorParameters=()=>[{type:Cj,decorators:[{type:Sr},{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[IV]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[MU]}]}],AU.propDecorators={name:[{type:xy,args:["formControlName"]}],isDisabled:[{type:xy,args:["disabled"]}],model:[{type:xy,args:["ngModel"]}],update:[{type:Oy,args:["ngModelChange"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(AU,[{type:Cy,args:[{selector:"[formControlName]",providers:[RU]}]}],(function(){return[{type:Cj,decorators:[{type:Sr},{type:Rr},{type:Er}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[GV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[WV]}]},{type:Array,decorators:[{type:Sr},{type:Dr},{type:kr,args:[IV]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[MU]}]}]}),{update:[{type:Oy,args:["ngModelChange"]}],isDisabled:[{type:xy,args:["disabled"]}],name:[{type:xy,args:["formControlName"]}],model:[{type:xy,args:["ngModel"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const TU={provide:IV,useExisting:qe((()=>zU)),multi:!0};function NU(t,e){return null==t?`${e}`:(e&&"object"==typeof e&&(e="Object"),`${t}: ${e}`.slice(0,50))}class zU extends zV{constructor(){super(...arguments),this._optionMap=new Map,this._idCounter=0,this._compareWith=Object.is}set compareWith(t){if("function"!=typeof t&&("undefined"==typeof ngDevMode||ngDevMode))throw new Error(`compareWith must be a function, but received ${JSON.stringify(t)}`);this._compareWith=t}writeValue(t){this.value=t;const e=this._getOptionId(t);null==e&&this.setProperty("selectedIndex",-1);const n=NU(e,t);this.setProperty("value",n)}registerOnChange(t){this.onChange=e=>{this.value=this._getOptionValue(e),t(this.value)}}_registerOption(){return(this._idCounter++).toString()}_getOptionId(t){for(const e of Array.from(this._optionMap.keys()))if(this._compareWith(this._optionMap.get(e),t))return e;return null}_getOptionValue(t){const e=(function n(t){return t.split(":")[0]})(t);return this._optionMap.has(e)?this._optionMap.get(e):t}}zU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(zU)))(n||zU)}})(),zU.ɵdir=lo({type:zU,selectors:[["select","formControlName","",3,"multiple",""],["select","formControl","",3,"multiple",""],["select","ngModel","",3,"multiple",""]],hostBindings:function t(e,n){1&e&&Vm("change",(function t(e){return n.onChange(e.target.value)}))("blur",(function t(){return n.onTouched()}))},inputs:{compareWith:"compareWith"},features:[pg([TU]),xp]}),zU.propDecorators={compareWith:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(zU,[{type:Cy,args:[{selector:"select:not([multiple])[formControlName],select:not([multiple])[formControl],select:not([multiple])[ngModel]",host:{"(change)":"onChange($event.target.value)","(blur)":"onTouched()"},providers:[TU]}]}],null,{compareWith:[{type:xy}]});class IU{constructor(t,e,n){this._element=t,this._renderer=e,this._select=n,this._select&&(this.id=this._select._registerOption())}set ngValue(t){null!=this._select&&(this._select._optionMap.set(this.id,t),this._setElementValue(NU(this.id,t)),this._select.writeValue(this._select.value))}set value(t){this._setElementValue(t),this._select&&this._select.writeValue(this._select.value)}_setElementValue(t){this._renderer.setProperty(this._element.nativeElement,"value",t)}ngOnDestroy(){this._select&&(this._select._optionMap.delete(this.id),this._select.writeValue(this._select.value))}}IU.ɵfac=function t(e){return new(e||IU)(Sm(hg),Sm(Cg),Sm(zU,9))},IU.ɵdir=lo({type:IU,selectors:[["option"]],inputs:{ngValue:"ngValue",value:"value"}}),IU.ctorParameters=()=>[{type:hg},{type:Cg},{type:zU,decorators:[{type:Sr},{type:Rr}]}],IU.propDecorators={ngValue:[{type:xy,args:["ngValue"]}],value:[{type:xy,args:["value"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(IU,[{type:Cy,args:[{selector:"option"}]}],(function(){return[{type:hg},{type:Cg},{type:zU,decorators:[{type:Sr},{type:Rr}]}]}),{ngValue:[{type:xy,args:["ngValue"]}],value:[{type:xy,args:["value"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const HU={provide:IV,useExisting:qe((()=>LU)),multi:!0};function FU(t,e){return null==t?`${e}`:("string"==typeof e&&(e=`'${e}'`),e&&"object"==typeof e&&(e="Object"),`${t}: ${e}`.slice(0,50))}class LU extends zV{constructor(){super(...arguments),this._optionMap=new Map,this._idCounter=0,this._compareWith=Object.is}set compareWith(t){if("function"!=typeof t&&("undefined"==typeof ngDevMode||ngDevMode))throw new Error(`compareWith must be a function, but received ${JSON.stringify(t)}`);this._compareWith=t}writeValue(t){let e;if(this.value=t,Array.isArray(t)){const n=t.map((t=>this._getOptionId(t)));e=(t,e)=>{t._setSelected(n.indexOf(e.toString())>-1)}}else e=(t,e)=>{t._setSelected(!1)};this._optionMap.forEach(e)}registerOnChange(t){this.onChange=e=>{const n=[];if(void 0!==e.selectedOptions){const t=e.selectedOptions;for(let e=0;e<t.length;e++){const o=t.item(e),i=this._getOptionValue(o.value);n.push(i)}}else{const t=e.options;for(let e=0;e<t.length;e++){const o=t.item(e);if(o.selected){const t=this._getOptionValue(o.value);n.push(t)}}}this.value=n,t(n)}}_registerOption(t){const e=(this._idCounter++).toString();return this._optionMap.set(e,t),e}_getOptionId(t){for(const e of Array.from(this._optionMap.keys()))if(this._compareWith(this._optionMap.get(e)._value,t))return e;return null}_getOptionValue(t){const e=(function n(t){return t.split(":")[0]})(t);return this._optionMap.has(e)?this._optionMap.get(e)._value:t}}LU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(LU)))(n||LU)}})(),LU.ɵdir=lo({type:LU,selectors:[["select","multiple","","formControlName",""],["select","multiple","","formControl",""],["select","multiple","","ngModel",""]],hostBindings:function t(e,n){1&e&&Vm("change",(function t(e){return n.onChange(e.target)}))("blur",(function t(){return n.onTouched()}))},inputs:{compareWith:"compareWith"},features:[pg([HU]),xp]}),LU.propDecorators={compareWith:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(LU,[{type:Cy,args:[{selector:"select[multiple][formControlName],select[multiple][formControl],select[multiple][ngModel]",host:{"(change)":"onChange($event.target)","(blur)":"onTouched()"},providers:[HU]}]}],null,{compareWith:[{type:xy}]});class BU{constructor(t,e,n){this._element=t,this._renderer=e,this._select=n,this._select&&(this.id=this._select._registerOption(this))}set ngValue(t){null!=this._select&&(this._value=t,this._setElementValue(FU(this.id,t)),this._select.writeValue(this._select.value))}set value(t){this._select?(this._value=t,this._setElementValue(FU(this.id,t)),this._select.writeValue(this._select.value)):this._setElementValue(t)}_setElementValue(t){this._renderer.setProperty(this._element.nativeElement,"value",t)}_setSelected(t){this._renderer.setProperty(this._element.nativeElement,"selected",t)}ngOnDestroy(){this._select&&(this._select._optionMap.delete(this.id),this._select.writeValue(this._select.value))}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function VU(t){return"number"==typeof t?t:parseInt(t,10)}BU.ɵfac=function t(e){return new(e||BU)(Sm(hg),Sm(Cg),Sm(LU,9))},BU.ɵdir=lo({type:BU,selectors:[["option"]],inputs:{ngValue:"ngValue",value:"value"}}),BU.ctorParameters=()=>[{type:hg},{type:Cg},{type:LU,decorators:[{type:Sr},{type:Rr}]}],BU.propDecorators={ngValue:[{type:xy,args:["ngValue"]}],value:[{type:xy,args:["value"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(BU,[{type:Cy,args:[{selector:"option"}]}],(function(){return[{type:hg},{type:Cg},{type:LU,decorators:[{type:Sr},{type:Rr}]}]}),{ngValue:[{type:xy,args:["ngValue"]}],value:[{type:xy,args:["value"]}]});class jU{constructor(){this._validator=nj}handleChanges(t){if(this.inputName in t){const e=this.normalizeInput(t[this.inputName].currentValue);this._validator=this.createValidator(e),this._onChange&&this._onChange()}}validate(t){return this._validator(t)}registerOnValidatorChange(t){this._onChange=t}}jU.ɵfac=function t(e){return new(e||jU)},jU.ɵdir=lo({type:jU}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(jU,[{type:Cy}],(function(){return[]}),null);const UU={provide:GV,useExisting:qe((()=>GU)),multi:!0};class GU extends jU{constructor(){super(...arguments),this.inputName="max",this.normalizeInput=t=>parseFloat(t),this.createValidator=t=>XV(t)}ngOnChanges(t){this.handleChanges(t)}}GU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(GU)))(n||GU)}})(),GU.ɵdir=lo({type:GU,selectors:[["input","type","number","max","","formControlName",""],["input","type","number","max","","formControl",""],["input","type","number","max","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){if(2&e){let t;jp("max",null!==(t=n.max)&&void 0!==t?t:null)}},inputs:{max:"max"},features:[pg([UU]),xp,Bo]}),GU.propDecorators={max:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(GU,[{type:Cy,args:[{selector:"input[type=number][max][formControlName],input[type=number][max][formControl],input[type=number][max][ngModel]",providers:[UU],host:{"[attr.max]":"max ?? null"}}]}],null,{max:[{type:xy}]});const WU={provide:GV,useExisting:qe((()=>YU)),multi:!0};class YU extends jU{constructor(){super(...arguments),this.inputName="min",this.normalizeInput=t=>parseFloat(t),this.createValidator=t=>ZV(t)}ngOnChanges(t){this.handleChanges(t)}}YU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(YU)))(n||YU)}})(),YU.ɵdir=lo({type:YU,selectors:[["input","type","number","min","","formControlName",""],["input","type","number","min","","formControl",""],["input","type","number","min","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){if(2&e){let t;jp("min",null!==(t=n.min)&&void 0!==t?t:null)}},inputs:{min:"min"},features:[pg([WU]),xp,Bo]}),YU.propDecorators={min:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(YU,[{type:Cy,args:[{selector:"input[type=number][min][formControlName],input[type=number][min][formControl],input[type=number][min][ngModel]",providers:[WU],host:{"[attr.min]":"min ?? null"}}]}],null,{min:[{type:xy}]});const qU={provide:GV,useExisting:qe((()=>XU)),multi:!0},ZU={provide:GV,useExisting:qe((()=>KU)),multi:!0};class XU{constructor(){this._required=!1}get required(){return this._required}set required(t){this._required=null!=t&&!1!==t&&"false"!=`${t}`,this._onChange&&this._onChange()}validate(t){return this.required?KV(t):null}registerOnValidatorChange(t){this._onChange=t}}XU.ɵfac=function t(e){return new(e||XU)},XU.ɵdir=lo({type:XU,selectors:[["","required","","formControlName","",3,"type","checkbox"],["","required","","formControl","",3,"type","checkbox"],["","required","","ngModel","",3,"type","checkbox"]],hostVars:1,hostBindings:function t(e,n){2&e&&jp("required",n.required?"":null)},inputs:{required:"required"},features:[pg([qU])]}),XU.propDecorators={required:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(XU,[{type:Cy,args:[{selector:":not([type=checkbox])[required][formControlName],:not([type=checkbox])[required][formControl],:not([type=checkbox])[required][ngModel]",providers:[qU],host:{"[attr.required]":'required ? "" : null'}}]}],(function(){return[]}),{required:[{type:xy}]});class KU extends XU{validate(t){return this.required?JV(t):null}}KU.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(KU)))(n||KU)}})(),KU.ɵdir=lo({type:KU,selectors:[["input","type","checkbox","required","","formControlName",""],["input","type","checkbox","required","","formControl",""],["input","type","checkbox","required","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){2&e&&jp("required",n.required?"":null)},features:[pg([ZU]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(KU,[{type:Cy,args:[{selector:"input[type=checkbox][required][formControlName],input[type=checkbox][required][formControl],input[type=checkbox][required][ngModel]",providers:[ZU],host:{"[attr.required]":'required ? "" : null'}}]}],null,null);const JU={provide:GV,useExisting:qe((()=>QU)),multi:!0};class QU{constructor(){this._enabled=!1}set email(t){this._enabled=""===t||!0===t||"true"===t,this._onChange&&this._onChange()}validate(t){return this._enabled?QV(t):null}registerOnValidatorChange(t){this._onChange=t}}QU.ɵfac=function t(e){return new(e||QU)},QU.ɵdir=lo({type:QU,selectors:[["","email","","formControlName",""],["","email","","formControl",""],["","email","","ngModel",""]],inputs:{email:"email"},features:[pg([JU])]}),QU.propDecorators={email:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(QU,[{type:Cy,args:[{selector:"[email][formControlName],[email][formControl],[email][ngModel]",providers:[JU]}]}],(function(){return[]}),{email:[{type:xy}]});const $U={provide:GV,useExisting:qe((()=>tG)),multi:!0};class tG{constructor(){this._validator=nj}ngOnChanges(t){"minlength"in t&&(this._createValidator(),this._onChange&&this._onChange())}validate(t){return this.enabled()?this._validator(t):null}registerOnValidatorChange(t){this._onChange=t}_createValidator(){this._validator=this.enabled()?$V(VU(this.minlength)):nj}enabled(){return null!=this.minlength}}tG.ɵfac=function t(e){return new(e||tG)},tG.ɵdir=lo({type:tG,selectors:[["","minlength","","formControlName",""],["","minlength","","formControl",""],["","minlength","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){2&e&&jp("minlength",n.enabled()?n.minlength:null)},inputs:{minlength:"minlength"},features:[pg([$U]),Bo]}),tG.propDecorators={minlength:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(tG,[{type:Cy,args:[{selector:"[minlength][formControlName],[minlength][formControl],[minlength][ngModel]",providers:[$U],host:{"[attr.minlength]":"enabled() ? minlength : null"}}]}],(function(){return[]}),{minlength:[{type:xy}]});const eG={provide:GV,useExisting:qe((()=>nG)),multi:!0};class nG{constructor(){this._validator=nj}ngOnChanges(t){"maxlength"in t&&(this._createValidator(),this._onChange&&this._onChange())}validate(t){return this.enabled()?this._validator(t):null}registerOnValidatorChange(t){this._onChange=t}_createValidator(){this._validator=this.enabled()?tj(VU(this.maxlength)):nj}enabled(){return null!=this.maxlength}}nG.ɵfac=function t(e){return new(e||nG)},nG.ɵdir=lo({type:nG,selectors:[["","maxlength","","formControlName",""],["","maxlength","","formControl",""],["","maxlength","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){2&e&&jp("maxlength",n.enabled()?n.maxlength:null)},inputs:{maxlength:"maxlength"},features:[pg([eG]),Bo]}),nG.propDecorators={maxlength:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(nG,[{type:Cy,args:[{selector:"[maxlength][formControlName],[maxlength][formControl],[maxlength][ngModel]",providers:[eG],host:{"[attr.maxlength]":"enabled() ? maxlength : null"}}]}],(function(){return[]}),{maxlength:[{type:xy}]});const oG={provide:GV,useExisting:qe((()=>iG)),multi:!0};class iG{constructor(){this._validator=nj}ngOnChanges(t){"pattern"in t&&(this._createValidator(),this._onChange&&this._onChange())}validate(t){return this._validator(t)}registerOnValidatorChange(t){this._onChange=t}_createValidator(){this._validator=ej(this.pattern)}}iG.ɵfac=function t(e){return new(e||iG)},iG.ɵdir=lo({type:iG,selectors:[["","pattern","","formControlName",""],["","pattern","","formControl",""],["","pattern","","ngModel",""]],hostVars:1,hostBindings:function t(e,n){2&e&&jp("pattern",n.pattern?n.pattern:null)},inputs:{pattern:"pattern"},features:[pg([oG]),Bo]}),iG.propDecorators={pattern:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(iG,[{type:Cy,args:[{selector:"[pattern][formControlName],[pattern][formControl],[pattern][ngModel]",providers:[oG],host:{"[attr.pattern]":"pattern ? pattern : null"}}]}],(function(){return[]}),{pattern:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const aG=[mU,IU,BU,VV,fU,CU,FV,zU,LU,yU,xj,Oj,XU,tG,nG,iG,KU,QU,YU,GU],rG=[pU,lU,iU],sG=[xU,PU,AU,kU,DU];class lG{}lG.ɵfac=function t(e){return new(e||lG)},lG.ɵmod=ao({type:lG}),lG.ɵinj=vn({imports:[[hU]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lG,[{type:Ay,args:[{declarations:aG,imports:[hU],exports:aG}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(lG,{declarations:[mU,IU,BU,VV,fU,CU,FV,zU,LU,yU,xj,Oj,XU,tG,nG,iG,KU,QU,YU,GU],imports:[hU],exports:[mU,IU,BU,VV,fU,CU,FV,zU,LU,yU,xj,Oj,XU,tG,nG,iG,KU,QU,YU,GU]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class cG{}cG.ɵfac=function t(e){return new(e||cG)},cG.ɵmod=ao({type:cG}),cG.ɵinj=vn({imports:[lG]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(cG,[{type:Ay,args:[{declarations:rG,exports:[lG,rG]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(cG,{declarations:[pU,lU,iU],exports:[lG,pU,lU,iU]});class dG{static withConfig(t){return{ngModule:dG,providers:[{provide:MU,useValue:t.warnOnNgModelWithFormControl}]}}}dG.ɵfac=function t(e){return new(e||dG)},dG.ɵmod=ao({type:dG}),dG.ɵinj=vn({imports:[lG]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(dG,[{type:Ay,args:[{declarations:[sG],exports:[lG,sG]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(dG,{declarations:[xU,PU,AU,kU,DU],exports:[lG,xU,PU,AU,kU,DU]});class pG{group(t,e=null){const n=this._reduceControls(t);let o,i=null,a=null;return null!=e&&((
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function r(t){return void 0!==t.asyncValidators||void 0!==t.validators||void 0!==t.updateOn})(e)?(i=null!=e.validators?e.validators:null,a=null!=e.asyncValidators?e.asyncValidators:null,o=null!=e.updateOn?e.updateOn:void 0):(i=null!=e.validator?e.validator:null,a=null!=e.asyncValidator?e.asyncValidator:null)),new tU(n,{asyncValidators:a,updateOn:o,validators:i})}control(t,e,n){return new $j(t,e,n)}array(t,e,n){const o=t.map((t=>this._createControl(t)));return new eU(o,e,n)}_reduceControls(t){const e={};return Object.keys(t).forEach((n=>{e[n]=this._createControl(t[n])})),e}_createControl(t){return t instanceof $j||t instanceof tU||t instanceof eU?t:Array.isArray(t)?this.control(t[0],t.length>1?t[1]:null,t.length>2?t[2]:null):this.control(t)}}pG.ɵfac=function t(e){return new(e||pG)},pG.ɵprov=Mn({factory:function t(){return new pG},token:pG,providedIn:dG}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(pG,[{type:im,args:[{providedIn:dG}]}],null,null),
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
new Og("12.2.1");const mG=["trigger"],uG=["panel"];function fG(t,e){if(1&t&&(Rm(0,"span",8),ku(1),Am()),2&t){const t=Ym();rc(1),Su(t.placeholder)}}function gG(t,e){if(1&t&&(Rm(0,"span",12),ku(1),Am()),2&t){const t=Ym(2);rc(1),Su(t.triggerValue)}}function hG(t,e){1&t&&Xm(0,0,["*ngSwitchCase","true"])}function bG(t,e){1&t&&(Rm(0,"span",9),Qp(1,gG,2,1,"span",10),Qp(2,hG,1,0,"ng-content",11),Am()),2&t&&(Dm("ngSwitch",!!Ym().customTrigger),rc(2),Dm("ngSwitchCase",!0))}function yG(t,e){if(1&t){const t=Hm();Rm(0,"div",13),Rm(1,"div",14,15),Vm("@transformPanel.done",(function e(n){return hi(t),Ym()._panelDoneAnimatingStream.next(n.toState)}))("keydown",(function e(n){return hi(t),Ym()._handleKeydown(n)})),Xm(3,1),Am(),Am()}if(2&t){const t=Ym();Dm("@transformPanelWrap",void 0),rc(1),Au("mat-select-panel ",t._getPanelTheme(),""),du("transform-origin",t._transformOrigin)("font-size",t._triggerFontSize,"px"),Dm("ngClass",t.panelClass)("@transformPanel",t.multiple?"showing-multiple":"showing"),jp("id",t.id+"-panel")("aria-multiselectable",t.multiple)("aria-label",t.ariaLabel||null)("aria-labelledby",t._getPanelAriaLabelledby())}}const _G=[[["mat-select-trigger"]],"*"],CG={transformPanelWrap:nx("transformPanelWrap",[lx("* => void",dx("@transformPanel",[cx()],{optional:!0}))]),transformPanel:nx("transformPanel",[rx("void",ax({transform:"scaleY(0.8)",minWidth:"100%",opacity:0})),rx("showing",ax({opacity:1,minWidth:"calc(100% + 32px)",transform:"scaleY(1)"})),rx("showing-multiple",ax({opacity:1,minWidth:"calc(100% + 64px)",transform:"scaleY(1)"})),lx("void => *",ox("120ms cubic-bezier(0, 0, 0.2, 1)")),lx("* => void",ox("100ms 25ms linear",ax({opacity:0})))])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let MG=0;const vG=256,xG=new Ga("mat-select-scroll-strategy"),OG=new Ga("MAT_SELECT_CONFIG"),PG={provide:xG,deps:[pL],useFactory:function wG(t){return()=>t.scrollStrategies.reposition()}};class kG{constructor(t,e){this.source=t,this.value=e}}const SG=QI($I(KI(tH(class{constructor(t,e,n,o,i){this._elementRef=t,this._defaultErrorStateMatcher=e,this._parentForm=n,this._parentFormGroup=o,this.ngControl=i}})))),DG=new Ga("MatSelectTrigger");class EG{}EG.ɵfac=function t(e){return new(e||EG)},EG.ɵdir=lo({type:EG,selectors:[["mat-select-trigger"]],features:[pg([{provide:DG,useExisting:EG}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(EG,[{type:Cy,args:[{selector:"mat-select-trigger",providers:[{provide:DG,useExisting:EG}]}]}],null,null);class RG extends SG{constructor(t,e,n,o,i,a,r,s,l,c,d,p,m,u){var f,g,h;super(i,o,r,s,c),this._viewportRuler=t,this._changeDetectorRef=e,this._ngZone=n,this._dir=a,this._parentFormField=l,this._liveAnnouncer=m,this._defaultOptions=u,this._panelOpen=!1,this._compareWith=(t,e)=>t===e,this._uid="mat-select-"+MG++,this._triggerAriaLabelledBy=null,this._destroy=new I,this._onChange=()=>{},this._onTouched=()=>{},this._valueId="mat-select-value-"+MG++,this._panelDoneAnimatingStream=new I,this._overlayPanelClass=(null===(f=this._defaultOptions)||void 0===f?void 0:f.overlayPanelClass)||"",this._focused=!1,this.controlType="mat-select",this._required=!1,this._multiple=!1,this._disableOptionCentering=null!==(h=null===(g=this._defaultOptions)||void 0===g?void 0:g.disableOptionCentering)&&void 0!==h&&h,this.ariaLabel="",this.optionSelectionChanges=Qt((()=>{const t=this.options;return t?t.changes.pipe(Ne(t),ze((()=>re(...t.map((t=>t.onSelectionChange)))))):this._ngZone.onStable.pipe(be(1),ze((()=>this.optionSelectionChanges)))})),this.openedChange=new Lh,this._openedStream=this.openedChange.pipe(ce((t=>t)),It((()=>{}))),this._closedStream=this.openedChange.pipe(ce((t=>!t)),It((()=>{}))),this.selectionChange=new Lh,this.valueChange=new Lh,this.ngControl&&(this.ngControl.valueAccessor=this),null!=(null==u?void 0:u.typeaheadDebounceInterval)&&(this._typeaheadDebounceInterval=u.typeaheadDebounceInterval),this._scrollStrategyFactory=p,this._scrollStrategy=this._scrollStrategyFactory(),this.tabIndex=parseInt(d)||0,this.id=this.id}get focused(){return this._focused||this._panelOpen}get placeholder(){return this._placeholder}set placeholder(t){this._placeholder=t,this.stateChanges.next()}get required(){return this._required}set required(t){this._required=yz(t),this.stateChanges.next()}get multiple(){return this._multiple}set multiple(t){if(this._selectionModel&&("undefined"==typeof ngDevMode||ngDevMode))
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
throw(function e(){return Error("Cannot change `multiple` mode of select after initialization.")})();this._multiple=yz(t)}get disableOptionCentering(){return this._disableOptionCentering}set disableOptionCentering(t){this._disableOptionCentering=yz(t)}get compareWith(){return this._compareWith}set compareWith(t){if("function"!=typeof t&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("`compareWith` must be a function.")})();this._compareWith=t,this._selectionModel&&this._initializeSelection()}get value(){return this._value}set value(t){(t!==this._value||this._multiple&&Array.isArray(t))&&(this.options&&this._setSelectionByValue(t),this._value=t)}get typeaheadDebounceInterval(){return this._typeaheadDebounceInterval}set typeaheadDebounceInterval(t){this._typeaheadDebounceInterval=_z(t)}get id(){return this._id}set id(t){this._id=t||this._uid,this.stateChanges.next()}ngOnInit(){this._selectionModel=new oF(this.multiple),this.stateChanges.next(),this._panelDoneAnimatingStream.pipe(Me(),Ie(this._destroy)).subscribe((()=>this._panelDoneAnimating(this.panelOpen)))}ngAfterContentInit(){this._initKeyManager(),this._selectionModel.changed.pipe(Ie(this._destroy)).subscribe((t=>{t.added.forEach((t=>t.select())),t.removed.forEach((t=>t.deselect()))})),this.options.changes.pipe(Ne(null),Ie(this._destroy)).subscribe((()=>{this._resetOptions(),this._initializeSelection()}))}ngDoCheck(){const t=this._getTriggerAriaLabelledby();if(t!==this._triggerAriaLabelledBy){const e=this._elementRef.nativeElement;this._triggerAriaLabelledBy=t,t?e.setAttribute("aria-labelledby",t):e.removeAttribute("aria-labelledby")}this.ngControl&&this.updateErrorState()}ngOnChanges(t){t.disabled&&this.stateChanges.next(),t.typeaheadDebounceInterval&&this._keyManager&&this._keyManager.withTypeAhead(this._typeaheadDebounceInterval)}ngOnDestroy(){this._destroy.next(),this._destroy.complete(),this.stateChanges.complete()}toggle(){this.panelOpen?this.close():this.open()}open(){this._canOpen()&&(this._panelOpen=!0,this._keyManager.withHorizontalOrientation(null),this._highlightCorrectOption(),this._changeDetectorRef.markForCheck())}close(){this._panelOpen&&(this._panelOpen=!1,this._keyManager.withHorizontalOrientation(this._isRtl()?"rtl":"ltr"),this._changeDetectorRef.markForCheck(),this._onTouched())}writeValue(t){this.value=t}registerOnChange(t){this._onChange=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t,this._changeDetectorRef.markForCheck(),this.stateChanges.next()}get panelOpen(){return this._panelOpen}get selected(){return this.multiple?this._selectionModel.selected:this._selectionModel.selected[0]}get triggerValue(){if(this.empty)return"";if(this._multiple){const t=this._selectionModel.selected.map((t=>t.viewValue));return this._isRtl()&&t.reverse(),t.join(", ")}return this._selectionModel.selected[0].viewValue}_isRtl(){return!!this._dir&&"rtl"===this._dir.value}_handleKeydown(t){this.disabled||(this.panelOpen?this._handleOpenKeydown(t):this._handleClosedKeydown(t))}_handleClosedKeydown(t){const e=t.keyCode,n=e===hz||e===gz||37===e||39===e,o=e===mz||e===fz,i=this._keyManager;if(!i.isTyping()&&o&&!bz(t)||(this.multiple||t.altKey)&&n)t.preventDefault(),this.open();else if(!this.multiple){const e=this.selected;i.onKeydown(t);const n=this.selected;n&&e!==n&&this._liveAnnouncer.announce(n.viewValue,1e4)}}_handleOpenKeydown(t){const e=this._keyManager,n=t.keyCode,o=n===hz||n===gz,i=e.isTyping();if(o&&t.altKey)t.preventDefault(),this.close();else if(i||n!==mz&&n!==fz||!e.activeItem||bz(t))if(!i&&this._multiple&&65===n&&t.ctrlKey){t.preventDefault();const e=this.options.some((t=>!t.disabled&&!t.selected));this.options.forEach((t=>{t.disabled||(e?t.select():t.deselect())}))}else{const n=e.activeItemIndex;e.onKeydown(t),this._multiple&&o&&t.shiftKey&&e.activeItem&&e.activeItemIndex!==n&&e.activeItem._selectViaInteraction()}else t.preventDefault(),e.activeItem._selectViaInteraction()}_onFocus(){this.disabled||(this._focused=!0,this.stateChanges.next())}_onBlur(){this._focused=!1,this.disabled||this.panelOpen||(this._onTouched(),this._changeDetectorRef.markForCheck(),this.stateChanges.next())}_onAttached(){this._overlayDir.positionChange.pipe(be(1)).subscribe((()=>{this._changeDetectorRef.detectChanges(),this._positioningSettled()}))}_getPanelTheme(){return this._parentFormField?`mat-${this._parentFormField.color}`:""}get empty(){return!this._selectionModel||this._selectionModel.isEmpty()}_initializeSelection(){Promise.resolve().then((()=>{this._setSelectionByValue(this.ngControl?this.ngControl.value:this._value),this.stateChanges.next()}))}_setSelectionByValue(t){if(this._selectionModel.selected.forEach((t=>t.setInactiveStyles())),this._selectionModel.clear(),this.multiple&&t){if(!Array.isArray(t)&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("Value must be an array in multiple-selection mode.")})();t.forEach((t=>this._selectValue(t))),this._sortValues()}else{const e=this._selectValue(t);e?this._keyManager.updateActiveItem(e):this.panelOpen||this._keyManager.updateActiveItem(-1)}this._changeDetectorRef.markForCheck()}_selectValue(t){const e=this.options.find((e=>{if(this._selectionModel.isSelected(e))return!1;try{return null!=e.value&&this._compareWith(e.value,t)}catch(t){return("undefined"==typeof ngDevMode||ngDevMode)&&console.warn(t),!1}}));return e&&this._selectionModel.select(e),e}_initKeyManager(){this._keyManager=new tI(this.options).withTypeAhead(this._typeaheadDebounceInterval).withVerticalOrientation().withHorizontalOrientation(this._isRtl()?"rtl":"ltr").withHomeAndEnd().withAllowedModifierKeys(["shiftKey"]),this._keyManager.tabOut.pipe(Ie(this._destroy)).subscribe((()=>{this.panelOpen&&(!this.multiple&&this._keyManager.activeItem&&this._keyManager.activeItem._selectViaInteraction(),this.focus(),this.close())})),this._keyManager.change.pipe(Ie(this._destroy)).subscribe((()=>{this._panelOpen&&this.panel?this._scrollOptionIntoView(this._keyManager.activeItemIndex||0):this._panelOpen||this.multiple||!this._keyManager.activeItem||this._keyManager.activeItem._selectViaInteraction()}))}_resetOptions(){const t=re(this.options.changes,this._destroy);this.optionSelectionChanges.pipe(Ie(t)).subscribe((t=>{this._onSelect(t.source,t.isUserInput),t.isUserInput&&!this.multiple&&this._panelOpen&&(this.close(),this.focus())})),re(...this.options.map((t=>t._stateChanges))).pipe(Ie(t)).subscribe((()=>{this._changeDetectorRef.markForCheck(),this.stateChanges.next()}))}_onSelect(t,e){const n=this._selectionModel.isSelected(t);null!=t.value||this._multiple?(n!==t.selected&&(t.selected?this._selectionModel.select(t):this._selectionModel.deselect(t)),e&&this._keyManager.setActiveItem(t),this.multiple&&(this._sortValues(),e&&this.focus())):(t.deselect(),this._selectionModel.clear(),null!=this.value&&this._propagateChanges(t.value)),n!==this._selectionModel.isSelected(t)&&this._propagateChanges(),this.stateChanges.next()}_sortValues(){if(this.multiple){const t=this.options.toArray();this._selectionModel.sort(((e,n)=>this.sortComparator?this.sortComparator(e,n,t):t.indexOf(e)-t.indexOf(n))),this.stateChanges.next()}}_propagateChanges(t){let e=null;e=this.multiple?this.selected.map((t=>t.value)):this.selected?this.selected.value:t,this._value=e,this.valueChange.emit(e),this._onChange(e),this.selectionChange.emit(this._getChangeEvent(e)),this._changeDetectorRef.markForCheck()}_highlightCorrectOption(){this._keyManager&&(this.empty?this._keyManager.setFirstItemActive():this._keyManager.setActiveItem(this._selectionModel.selected[0]))}_canOpen(){var t;return!this._panelOpen&&!this.disabled&&(null===(t=this.options)||void 0===t?void 0:t.length)>0}focus(t){this._elementRef.nativeElement.focus(t)}_getPanelAriaLabelledby(){var t;if(this.ariaLabel)return null;const e=null===(t=this._parentFormField)||void 0===t?void 0:t.getLabelId();return this.ariaLabelledby?(e?e+" ":"")+this.ariaLabelledby:e}_getAriaActiveDescendant(){return this.panelOpen&&this._keyManager&&this._keyManager.activeItem?this._keyManager.activeItem.id:null}_getTriggerAriaLabelledby(){var t;if(this.ariaLabel)return null;const e=null===(t=this._parentFormField)||void 0===t?void 0:t.getLabelId();let n=(e?e+" ":"")+this._valueId;return this.ariaLabelledby&&(n+=" "+this.ariaLabelledby),n}_panelDoneAnimating(t){this.openedChange.emit(t)}setDescribedByIds(t){this._ariaDescribedby=t.join(" ")}onContainerClick(){this.focus(),this.open()}get shouldLabelFloat(){return this._panelOpen||!this.empty||this._focused&&!!this._placeholder}}RG.ɵfac=function t(e){return new(e||RG)(Sm(uF),Sm(Ug),Sm(a_),Sm(bH),Sm(hg),Sm(HI,8),Sm(iU,8),Sm(PU,8),Sm(RV,8),Sm(Mj,10),Na("tabindex"),Sm(xG),Sm(OI),Sm(OG,8))},RG.ɵdir=lo({type:RG,viewQuery:function t(e,n){if(1&e&&(Qh(mG,5),Qh(uG,5),Qh(gL,5)),2&e){let t;Jh(t=tb())&&(n.trigger=t.first),Jh(t=tb())&&(n.panel=t.first),Jh(t=tb())&&(n._overlayDir=t.first)}},inputs:{ariaLabel:["aria-label","ariaLabel"],id:"id",placeholder:"placeholder",required:"required",multiple:"multiple",disableOptionCentering:"disableOptionCentering",compareWith:"compareWith",value:"value",typeaheadDebounceInterval:"typeaheadDebounceInterval",panelClass:"panelClass",ariaLabelledby:["aria-labelledby","ariaLabelledby"],errorStateMatcher:"errorStateMatcher",sortComparator:"sortComparator"},outputs:{openedChange:"openedChange",_openedStream:"opened",_closedStream:"closed",selectionChange:"selectionChange",valueChange:"valueChange"},features:[xp,Bo]}),RG.ctorParameters=()=>[{type:uF},{type:Ug},{type:a_},{type:bH},{type:hg},{type:HI,decorators:[{type:Sr}]},{type:iU,decorators:[{type:Sr}]},{type:PU,decorators:[{type:Sr}]},{type:AV,decorators:[{type:Sr},{type:kr,args:[RV]}]},{type:Mj,decorators:[{type:Dr},{type:Sr}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:void 0,decorators:[{type:kr,args:[xG]}]},{type:OI},{type:void 0,decorators:[{type:Sr},{type:kr,args:[OG]}]}],RG.propDecorators={trigger:[{type:Za,args:["trigger"]}],panel:[{type:Za,args:["panel"]}],_overlayDir:[{type:Za,args:[gL]}],panelClass:[{type:xy}],placeholder:[{type:xy}],required:[{type:xy}],multiple:[{type:xy}],disableOptionCentering:[{type:xy}],compareWith:[{type:xy}],value:[{type:xy}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],errorStateMatcher:[{type:xy}],typeaheadDebounceInterval:[{type:xy}],sortComparator:[{type:xy}],id:[{type:xy}],openedChange:[{type:Oy}],_openedStream:[{type:Oy,args:["opened"]}],_closedStream:[{type:Oy,args:["closed"]}],selectionChange:[{type:Oy}],valueChange:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(RG,[{type:Cy}],(function(){return[{type:uF},{type:Ug},{type:a_},{type:bH},{type:hg},{type:HI,decorators:[{type:Sr}]},{type:iU,decorators:[{type:Sr}]},{type:PU,decorators:[{type:Sr}]},{type:AV,decorators:[{type:Sr},{type:kr,args:[RV]}]},{type:Mj,decorators:[{type:Dr},{type:Sr}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:void 0,decorators:[{type:kr,args:[xG]}]},{type:OI},{type:void 0,decorators:[{type:Sr},{type:kr,args:[OG]}]}]}),{ariaLabel:[{type:xy,args:["aria-label"]}],openedChange:[{type:Oy}],_openedStream:[{type:Oy,args:["opened"]}],_closedStream:[{type:Oy,args:["closed"]}],selectionChange:[{type:Oy}],valueChange:[{type:Oy}],id:[{type:xy}],placeholder:[{type:xy}],required:[{type:xy}],multiple:[{type:xy}],disableOptionCentering:[{type:xy}],compareWith:[{type:xy}],value:[{type:xy}],typeaheadDebounceInterval:[{type:xy}],trigger:[{type:Za,args:["trigger"]}],panel:[{type:Za,args:["panel"]}],_overlayDir:[{type:Za,args:[gL]}],panelClass:[{type:xy}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],errorStateMatcher:[{type:xy}],sortComparator:[{type:xy}]});class AG extends RG{constructor(){super(...arguments),this._scrollTop=0,this._triggerFontSize=0,this._transformOrigin="top",this._offsetY=0,this._positions=[{originX:"start",originY:"top",overlayX:"start",overlayY:"top"},{originX:"start",originY:"bottom",overlayX:"start",overlayY:"bottom"}]}_calculateOverlayScroll(t,e,n){const o=this._getItemHeight();return Math.min(Math.max(0,o*t-e+o/2),n)}ngOnInit(){super.ngOnInit(),this._viewportRuler.change().pipe(Ie(this._destroy)).subscribe((()=>{this.panelOpen&&(this._triggerRect=this.trigger.nativeElement.getBoundingClientRect(),this._changeDetectorRef.markForCheck())}))}open(){super._canOpen()&&(super.open(),this._triggerRect=this.trigger.nativeElement.getBoundingClientRect(),this._triggerFontSize=parseInt(getComputedStyle(this.trigger.nativeElement).fontSize||"0"),this._calculateOverlayPosition(),this._ngZone.onStable.pipe(be(1)).subscribe((()=>{this._triggerFontSize&&this._overlayDir.overlayRef&&this._overlayDir.overlayRef.overlayElement&&(this._overlayDir.overlayRef.overlayElement.style.fontSize=`${this._triggerFontSize}px`)})))}_scrollOptionIntoView(t){const e=VH(t,this.options,this.optionGroups),n=this._getItemHeight();this.panel.nativeElement.scrollTop=0===t&&1===e?0:jH((t+e)*n,n,this.panel.nativeElement.scrollTop,vG)}_positioningSettled(){this._calculateOverlayOffsetX(),this.panel.nativeElement.scrollTop=this._scrollTop}_panelDoneAnimating(t){this.panelOpen?this._scrollTop=0:(this._overlayDir.offsetX=0,this._changeDetectorRef.markForCheck()),super._panelDoneAnimating(t)}_getChangeEvent(t){return new kG(this,t)}_calculateOverlayOffsetX(){const t=this._overlayDir.overlayRef.overlayElement.getBoundingClientRect(),e=this._viewportRuler.getViewportSize(),n=this._isRtl(),o=this.multiple?56:32;let i;if(this.multiple)i=40;else if(this.disableOptionCentering)i=16;else{let t=this._selectionModel.selected[0]||this.options.first;i=t&&t.group?32:16}n||(i*=-1);const a=0-(t.left+i-(n?o:0)),r=t.right+i-e.width+(n?0:o);a>0?i+=a+8:r>0&&(i-=r+8),this._overlayDir.offsetX=Math.round(i),this._overlayDir.overlayRef.updatePosition()}_calculateOverlayOffsetY(t,e,n){const o=this._getItemHeight(),i=(o-this._triggerRect.height)/2,a=Math.floor(vG/o);let r;return this.disableOptionCentering?0:(r=0===this._scrollTop?t*o:this._scrollTop===n?(t-(this._getItemCount()-a))*o+(o-(this._getItemCount()*o-vG)%o):e-o/2,Math.round(-1*r-i))}_checkOverlayWithinViewport(t){const e=this._getItemHeight(),n=this._viewportRuler.getViewportSize(),o=this._triggerRect.top-8,i=n.height-this._triggerRect.bottom-8,a=Math.abs(this._offsetY),r=Math.min(this._getItemCount()*e,vG)-a-this._triggerRect.height;r>i?this._adjustPanelUp(r,i):a>o?this._adjustPanelDown(a,o,t):this._transformOrigin=this._getOriginBasedOnOption()}_adjustPanelUp(t,e){const n=Math.round(t-e);this._scrollTop-=n,this._offsetY-=n,this._transformOrigin=this._getOriginBasedOnOption(),this._scrollTop<=0&&(this._scrollTop=0,this._offsetY=0,this._transformOrigin="50% bottom 0px")}_adjustPanelDown(t,e,n){const o=Math.round(t-e);if(this._scrollTop+=o,this._offsetY+=o,this._transformOrigin=this._getOriginBasedOnOption(),this._scrollTop>=n)return this._scrollTop=n,this._offsetY=0,void(this._transformOrigin="50% top 0px")}_calculateOverlayPosition(){const t=this._getItemHeight(),e=this._getItemCount(),n=Math.min(e*t,vG),o=e*t-n;let i;i=this.empty?0:Math.max(this.options.toArray().indexOf(this._selectionModel.selected[0]),0),i+=VH(i,this.options,this.optionGroups);const a=n/2;this._scrollTop=this._calculateOverlayScroll(i,a,o),this._offsetY=this._calculateOverlayOffsetY(i,a,o),this._checkOverlayWithinViewport(o)}_getOriginBasedOnOption(){const t=this._getItemHeight(),e=(t-this._triggerRect.height)/2;return`50% ${Math.abs(this._offsetY)-e+t/2}px 0px`}_getItemHeight(){return 3*this._triggerFontSize}_getItemCount(){return this.options.length+this.optionGroups.length}}AG.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(AG)))(n||AG)}})(),AG.ɵcmp=to({type:AG,selectors:[["mat-select"]],contentQueries:function t(e,n,o){if(1&e&&($h(o,DG,5),$h(o,BH,5),$h(o,zH,5)),2&e){let t;Jh(t=tb())&&(n.customTrigger=t.first),Jh(t=tb())&&(n.options=t),Jh(t=tb())&&(n.optionGroups=t)}},hostAttrs:["role","combobox","aria-autocomplete","none","aria-haspopup","true",1,"mat-select"],hostVars:20,hostBindings:function t(e,n){1&e&&Vm("keydown",(function t(e){return n._handleKeydown(e)}))("focus",(function t(){return n._onFocus()}))("blur",(function t(){return n._onBlur()})),2&e&&(jp("id",n.id)("tabindex",n.tabIndex)("aria-controls",n.panelOpen?n.id+"-panel":null)("aria-expanded",n.panelOpen)("aria-label",n.ariaLabel||null)("aria-required",n.required.toString())("aria-disabled",n.disabled.toString())("aria-invalid",n.errorState)("aria-describedby",n._ariaDescribedby||null)("aria-activedescendant",n._getAriaActiveDescendant()),pu("mat-select-disabled",n.disabled)("mat-select-invalid",n.errorState)("mat-select-required",n.required)("mat-select-empty",n.empty)("mat-select-multiple",n.multiple))},inputs:{disabled:"disabled",disableRipple:"disableRipple",tabIndex:"tabIndex"},exportAs:["matSelect"],features:[pg([{provide:bV,useExisting:AG},{provide:RH,useExisting:AG}]),xp],ngContentSelectors:["mat-select-trigger","*"],decls:9,vars:12,consts:[["cdk-overlay-origin","",1,"mat-select-trigger",3,"click"],["origin","cdkOverlayOrigin","trigger",""],[1,"mat-select-value",3,"ngSwitch"],["class","mat-select-placeholder mat-select-min-line",4,"ngSwitchCase"],["class","mat-select-value-text",3,"ngSwitch",4,"ngSwitchCase"],[1,"mat-select-arrow-wrapper"],[1,"mat-select-arrow"],["cdk-connected-overlay","","cdkConnectedOverlayLockPosition","","cdkConnectedOverlayHasBackdrop","","cdkConnectedOverlayBackdropClass","cdk-overlay-transparent-backdrop",3,"cdkConnectedOverlayPanelClass","cdkConnectedOverlayScrollStrategy","cdkConnectedOverlayOrigin","cdkConnectedOverlayOpen","cdkConnectedOverlayPositions","cdkConnectedOverlayMinWidth","cdkConnectedOverlayOffsetY","backdropClick","attach","detach"],[1,"mat-select-placeholder","mat-select-min-line"],[1,"mat-select-value-text",3,"ngSwitch"],["class","mat-select-min-line",4,"ngSwitchDefault"],[4,"ngSwitchCase"],[1,"mat-select-min-line"],[1,"mat-select-panel-wrap"],["role","listbox","tabindex","-1",3,"ngClass","keydown"],["panel",""]],template:function t(e,n){if(1&e&&(Zm(_G),Rm(0,"div",0,1),Vm("click",(function t(){return n.toggle()})),Rm(3,"div",2),Qp(4,fG,2,1,"span",3),Qp(5,bG,3,2,"span",4),Am(),Rm(6,"div",5),Tm(7,"div",6),Am(),Am(),Qp(8,yG,4,14,"ng-template",7),Vm("backdropClick",(function t(){return n.close()}))("attach",(function t(){return n._onAttached()}))("detach",(function t(){return n.close()}))),2&e){const t=$p(1);jp("aria-owns",n.panelOpen?n.id+"-panel":null),rc(3),Dm("ngSwitch",n.empty),jp("id",n._valueId),rc(1),Dm("ngSwitchCase",!0),rc(1),Dm("ngSwitchCase",!1),rc(3),Dm("cdkConnectedOverlayPanelClass",n._overlayPanelClass)("cdkConnectedOverlayScrollStrategy",n._scrollStrategy)("cdkConnectedOverlayOrigin",t)("cdkConnectedOverlayOpen",n.panelOpen)("cdkConnectedOverlayPositions",n._positions)("cdkConnectedOverlayMinWidth",null==n._triggerRect?null:n._triggerRect.width)("cdkConnectedOverlayOffsetY",n._offsetY)}},directives:[fL,fM,gM,gL,hM,aM],styles:['.mat-select{display:inline-block;width:100%;outline:none}.mat-select-trigger{display:inline-table;cursor:pointer;position:relative;box-sizing:border-box}.mat-select-disabled .mat-select-trigger{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-select-value{display:table-cell;max-width:0;width:100%;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.mat-select-value-text{white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.mat-select-arrow-wrapper{display:table-cell;vertical-align:middle}.mat-form-field-appearance-fill .mat-select-arrow-wrapper{transform:translateY(-50%)}.mat-form-field-appearance-outline .mat-select-arrow-wrapper{transform:translateY(-25%)}.mat-form-field-appearance-standard.mat-form-field-has-label .mat-select:not(.mat-select-empty) .mat-select-arrow-wrapper{transform:translateY(-50%)}.mat-form-field-appearance-standard .mat-select.mat-select-empty .mat-select-arrow-wrapper{transition:transform 400ms cubic-bezier(0.25, 0.8, 0.25, 1)}._mat-animation-noopable.mat-form-field-appearance-standard .mat-select.mat-select-empty .mat-select-arrow-wrapper{transition:none}.mat-select-arrow{width:0;height:0;border-left:5px solid transparent;border-right:5px solid transparent;border-top:5px solid;margin:0 4px}.mat-select-panel-wrap{flex-basis:100%}.mat-select-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;padding-top:0;padding-bottom:0;max-height:256px;min-width:100%;border-radius:4px;outline:0}.cdk-high-contrast-active .mat-select-panel{outline:solid 1px}.mat-select-panel .mat-optgroup-label,.mat-select-panel .mat-option{font-size:inherit;line-height:3em;height:3em}.mat-form-field-type-mat-select:not(.mat-form-field-disabled) .mat-form-field-flex{cursor:pointer}.mat-form-field-type-mat-select .mat-form-field-label{width:calc(100% - 18px)}.mat-select-placeholder{transition:color 400ms 133.3333333333ms cubic-bezier(0.25, 0.8, 0.25, 1)}._mat-animation-noopable .mat-select-placeholder{transition:none}.mat-form-field-hide-placeholder .mat-select-placeholder{color:transparent;-webkit-text-fill-color:transparent;transition:none;display:block}.mat-select-min-line:empty::before{content:" ";white-space:pre;width:1px;display:inline-block;opacity:0}\n'],encapsulation:2,data:{animation:[CG.transformPanelWrap,CG.transformPanel]},changeDetection:0}),AG.propDecorators={options:[{type:Ya,args:[BH,{descendants:!0}]}],optionGroups:[{type:Ya,args:[zH,{descendants:!0}]}],customTrigger:[{type:qa,args:[DG]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(AG,[{type:My,args:[{selector:"mat-select",exportAs:"matSelect",template:'\x3c!--\n Note that the select trigger element specifies `aria-owns` pointing to the listbox overlay.\n While aria-owns is not required for the ARIA 1.2 `role="combobox"` interaction pattern,\n it fixes an issue with VoiceOver when the select appears inside of an `aria-model="true"`\n element (e.g. a dialog). Without this `aria-owns`, the `aria-modal` on a dialog prevents\n VoiceOver from "seeing" the select\'s listbox overlay for aria-activedescendant.\n Using `aria-owns` re-parents the select overlay so that it works again.\n See https://github.com/angular/components/issues/20694\n--\x3e\n<div cdk-overlay-origin\n     [attr.aria-owns]="panelOpen ? id + \'-panel\' : null"\n     class="mat-select-trigger"\n     (click)="toggle()"\n     #origin="cdkOverlayOrigin"\n     #trigger>\n  <div class="mat-select-value" [ngSwitch]="empty" [attr.id]="_valueId">\n    <span class="mat-select-placeholder mat-select-min-line" *ngSwitchCase="true">{{placeholder}}</span>\n    <span class="mat-select-value-text" *ngSwitchCase="false" [ngSwitch]="!!customTrigger">\n      <span class="mat-select-min-line" *ngSwitchDefault>{{triggerValue}}</span>\n      <ng-content select="mat-select-trigger" *ngSwitchCase="true"></ng-content>\n    </span>\n  </div>\n\n  <div class="mat-select-arrow-wrapper"><div class="mat-select-arrow"></div></div>\n</div>\n\n<ng-template\n  cdk-connected-overlay\n  cdkConnectedOverlayLockPosition\n  cdkConnectedOverlayHasBackdrop\n  cdkConnectedOverlayBackdropClass="cdk-overlay-transparent-backdrop"\n  [cdkConnectedOverlayPanelClass]="_overlayPanelClass"\n  [cdkConnectedOverlayScrollStrategy]="_scrollStrategy"\n  [cdkConnectedOverlayOrigin]="origin"\n  [cdkConnectedOverlayOpen]="panelOpen"\n  [cdkConnectedOverlayPositions]="_positions"\n  [cdkConnectedOverlayMinWidth]="_triggerRect?.width!"\n  [cdkConnectedOverlayOffsetY]="_offsetY"\n  (backdropClick)="close()"\n  (attach)="_onAttached()"\n  (detach)="close()">\n  <div class="mat-select-panel-wrap" [@transformPanelWrap]>\n    <div\n      #panel\n      role="listbox"\n      tabindex="-1"\n      class="mat-select-panel {{ _getPanelTheme() }}"\n      [attr.id]="id + \'-panel\'"\n      [attr.aria-multiselectable]="multiple"\n      [attr.aria-label]="ariaLabel || null"\n      [attr.aria-labelledby]="_getPanelAriaLabelledby()"\n      [ngClass]="panelClass"\n      [@transformPanel]="multiple ? \'showing-multiple\' : \'showing\'"\n      (@transformPanel.done)="_panelDoneAnimatingStream.next($event.toState)"\n      [style.transformOrigin]="_transformOrigin"\n      [style.font-size.px]="_triggerFontSize"\n      (keydown)="_handleKeydown($event)">\n      <ng-content></ng-content>\n    </div>\n  </div>\n</ng-template>\n',inputs:["disabled","disableRipple","tabIndex"],encapsulation:Hn.None,changeDetection:zn.OnPush,host:{role:"combobox","aria-autocomplete":"none","aria-haspopup":"true",class:"mat-select","[attr.id]":"id","[attr.tabindex]":"tabIndex","[attr.aria-controls]":'panelOpen ? id + "-panel" : null',"[attr.aria-expanded]":"panelOpen","[attr.aria-label]":"ariaLabel || null","[attr.aria-required]":"required.toString()","[attr.aria-disabled]":"disabled.toString()","[attr.aria-invalid]":"errorState","[attr.aria-describedby]":"_ariaDescribedby || null","[attr.aria-activedescendant]":"_getAriaActiveDescendant()","[class.mat-select-disabled]":"disabled","[class.mat-select-invalid]":"errorState","[class.mat-select-required]":"required","[class.mat-select-empty]":"empty","[class.mat-select-multiple]":"multiple","(keydown)":"_handleKeydown($event)","(focus)":"_onFocus()","(blur)":"_onBlur()"},animations:[CG.transformPanelWrap,CG.transformPanel],providers:[{provide:bV,useExisting:AG},{provide:RH,useExisting:AG}],styles:['.mat-select{display:inline-block;width:100%;outline:none}.mat-select-trigger{display:inline-table;cursor:pointer;position:relative;box-sizing:border-box}.mat-select-disabled .mat-select-trigger{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:default}.mat-select-value{display:table-cell;max-width:0;width:100%;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.mat-select-value-text{white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.mat-select-arrow-wrapper{display:table-cell;vertical-align:middle}.mat-form-field-appearance-fill .mat-select-arrow-wrapper{transform:translateY(-50%)}.mat-form-field-appearance-outline .mat-select-arrow-wrapper{transform:translateY(-25%)}.mat-form-field-appearance-standard.mat-form-field-has-label .mat-select:not(.mat-select-empty) .mat-select-arrow-wrapper{transform:translateY(-50%)}.mat-form-field-appearance-standard .mat-select.mat-select-empty .mat-select-arrow-wrapper{transition:transform 400ms cubic-bezier(0.25, 0.8, 0.25, 1)}._mat-animation-noopable.mat-form-field-appearance-standard .mat-select.mat-select-empty .mat-select-arrow-wrapper{transition:none}.mat-select-arrow{width:0;height:0;border-left:5px solid transparent;border-right:5px solid transparent;border-top:5px solid;margin:0 4px}.mat-select-panel-wrap{flex-basis:100%}.mat-select-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;padding-top:0;padding-bottom:0;max-height:256px;min-width:100%;border-radius:4px;outline:0}.cdk-high-contrast-active .mat-select-panel{outline:solid 1px}.mat-select-panel .mat-optgroup-label,.mat-select-panel .mat-option{font-size:inherit;line-height:3em;height:3em}.mat-form-field-type-mat-select:not(.mat-form-field-disabled) .mat-form-field-flex{cursor:pointer}.mat-form-field-type-mat-select .mat-form-field-label{width:calc(100% - 18px)}.mat-select-placeholder{transition:color 400ms 133.3333333333ms cubic-bezier(0.25, 0.8, 0.25, 1)}._mat-animation-noopable .mat-select-placeholder{transition:none}.mat-form-field-hide-placeholder .mat-select-placeholder{color:transparent;-webkit-text-fill-color:transparent;transition:none;display:block}.mat-select-min-line:empty::before{content:" ";white-space:pre;width:1px;display:inline-block;opacity:0}\n']}]}],null,{options:[{type:Ya,args:[BH,{descendants:!0}]}],optionGroups:[{type:Ya,args:[zH,{descendants:!0}]}],customTrigger:[{type:qa,args:[DG]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class TG{}function NG(t,e){if(1&t){const t=Hm();Rm(0,"span",5),Vm("click",(function e(n){hi(t);const o=Ym().$implicit;return Ym().onActivePluginSelection(n,o.id)})),ku(1),Am()}if(2&t){const t=Ym().$implicit;jp("data-plugin-id",t.id),rc(1),Du(" ",t.tab_name," ")}}function zG(t,e){1&t&&(Rm(0,"mat-tab",3),Qp(1,NG,2,2,"ng-template",4),Am()),2&t&&Dm("disabled",!e.$implicit.enabled)}function IG(t,e){if(1&t&&(Rm(0,"mat-option",9),ku(1),Am()),2&t){const t=e.$implicit;Dm("value",t.id),jp("data-plugin-id",t.id),rc(1),Du(" ",t.tab_name," ")}}function HG(t,e){if(1&t){const t=Hm();Rm(0,"mat-form-field",6),Rm(1,"mat-label"),ku(2,"Inactive"),Am(),Rm(3,"mat-select",7),Vm("selectionChange",(function e(n){return hi(t),Ym().onDisabledPluginSelectionChanged(n)})),Qp(4,IG,2,3,"mat-option",8),Am(),Am()}if(2&t){const t=Ym();rc(3),Dm("value",t.selectedPlugin),rc(1),Dm("ngForOf",t.disabledPlugins)}}TG.ɵfac=function t(e){return new(e||TG)},TG.ɵmod=ao({type:TG}),TG.ɵinj=vn({providers:[PG],imports:[[WM,yL,UH,XI],yF,TV,UH,XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(TG,[{type:Ay,args:[{imports:[WM,yL,UH,XI],exports:[yF,TV,AG,EG,UH,XI],declarations:[AG,EG],providers:[PG]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(TG,{declarations:function(){return[AG,EG]},imports:function(){return[WM,yL,UH,XI]},exports:function(){return[yF,TV,AG,EG,UH,XI]}});class FG{constructor(){this.onPluginSelectionChanged=new Lh}getActivePluginIndex(){return this.activePlugins.findIndex((({id:t})=>t===this.selectedPlugin))}onActivePluginSelection(t,e){t.stopPropagation(),this.onPluginSelectionChanged.emit(e)}onDisabledPluginSelectionChanged(t){this.onPluginSelectionChanged.emit(t.value)}}FG.ɵfac=function t(e){return new(e||FG)},FG.ɵcmp=to({type:FG,selectors:[["plugin-selector-component"]],inputs:{activePlugins:"activePlugins",disabledPlugins:"disabledPlugins",selectedPlugin:"selectedPlugin"},outputs:{onPluginSelectionChanged:"onPluginSelectionChanged"},decls:3,vars:3,consts:[["animationDuration","100ms",1,"active-plugin-list",3,"selectedIndex"],[3,"disabled",4,"ngFor","ngForOf"],["floatLabel","never",4,"ngIf"],[3,"disabled"],["mat-tab-label",""],[1,"plugin-name",3,"click"],["floatLabel","never"],[3,"value","selectionChange"],[3,"value",4,"ngFor","ngForOf"],[3,"value"]],template:function t(e,n){1&e&&(Rm(0,"mat-tab-group",0),Qp(1,zG,2,1,"mat-tab",1),Am(),Qp(2,HG,5,2,"mat-form-field",2)),2&e&&(Dm("selectedIndex",n.getActivePluginIndex()),rc(1),Dm("ngForOf",n.activePlugins),rc(1),Dm("ngIf",n.disabledPlugins.length>0))},directives:[FB,lM,dM,SB,PB,AV,vV,AG,BH],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", 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.mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}input.mat-input-element[_ngcontent-%COMP%]{margin-top:-0.0625em}.mat-menu-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:400}.mat-paginator[_ngcontent-%COMP%], .mat-paginator-page-size[_ngcontent-%COMP%]   .mat-select-trigger[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px}.mat-radio-button[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action[_ngcontent-%COMP%]{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node[_ngcontent-%COMP%], .mat-nested-tree-node[_ngcontent-%COMP%]{font-weight:400;font-size:14px}.mat-ripple[_ngcontent-%COMP%]{overflow:hidden;position:relative}.mat-ripple[_ngcontent-%COMP%]:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded[_ngcontent-%COMP%]{overflow:visible}.mat-ripple-element[_ngcontent-%COMP%]{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms cubic-bezier(0, 0, 0.2, 1);transform:scale(0)}.cdk-high-contrast-active[_ngcontent-%COMP%]   .mat-ripple-element[_ngcontent-%COMP%]{display:none}.cdk-visually-hidden[_ngcontent-%COMP%]{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container[_ngcontent-%COMP%], .cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{align-items:center;display:flex;flex:1 1 auto;font-size:14px;height:100%;overflow:hidden}mat-form-field[_ngcontent-%COMP%]{flex:0 0;margin-top:5px;width:130px}mat-label[_ngcontent-%COMP%], mat-select[_ngcontent-%COMP%], mat-option[_ngcontent-%COMP%]{font-weight:500;text-transform:uppercase}.active-plugin-list[_ngcontent-%COMP%]{align-self:stretch;flex:1 1 auto;overflow:hidden}.plugin-name[_ngcontent-%COMP%]{align-items:center;display:inline-flex;height:100%;justify-content:center;padding:0 12px;width:100%}[_nghost-%COMP%]     .active-plugin-list.mat-primary .mat-tab-list .mat-ink-bar{background-color:currentColor}[_nghost-%COMP%]     .active-plugin-list .mat-tab-label, [_nghost-%COMP%]     .active-plugin-list .mat-tab-link{color:inherit;opacity:.7}[_nghost-%COMP%]     .active-plugin-list .mat-tab-label.mat-tab-label-active, [_nghost-%COMP%]     .active-plugin-list .mat-tab-link.mat-tab-label-active{opacity:1}[_nghost-%COMP%]     .active-plugin-list .mat-tab-header-pagination-chevron{border-color:currentColor}[_nghost-%COMP%]     .active-plugin-list .mat-tab-header-pagination-disabled{visibility:hidden}[_nghost-%COMP%]     .active-plugin-list .mat-tab-disabled{display:none}[_nghost-%COMP%]     .active-plugin-list mat-tab-list, [_nghost-%COMP%]     .active-plugin-list .mat-tab-header, [_nghost-%COMP%]     .active-plugin-list .mat-tab-labels, [_nghost-%COMP%]     .active-plugin-list .mat-tab-label{height:100%}[_nghost-%COMP%]     .active-plugin-list .mat-tab-label{min-width:48px;padding:0;text-transform:uppercase}[_nghost-%COMP%]     .active-plugin-list .mat-tab-label-content{height:100%}[_nghost-%COMP%]     .active-plugin-list mat-tab-header .mat-tab-list{padding:0 36px}[_nghost-%COMP%]     .active-plugin-list mat-tab-header>:first-child, [_nghost-%COMP%]     .active-plugin-list mat-tab-header>.mat-tab-label-container, [_nghost-%COMP%]     .active-plugin-list mat-tab-header>:last-child{bottom:0;position:absolute;top:0}[_nghost-%COMP%]     .active-plugin-list mat-tab-header>:first-child, [_nghost-%COMP%]     .active-plugin-list mat-tab-header>.mat-tab-label-container{left:0}[_nghost-%COMP%]     .active-plugin-list mat-tab-header>:last-child, [_nghost-%COMP%]     .active-plugin-list mat-tab-header>.mat-tab-label-container{right:0}[_nghost-%COMP%]     .active-plugin-list mat-tab-header>.mat-tab-header-pagination{background-color:#f57c00}body.dark-mode   [_nghost-%COMP%]     .active-plugin-list mat-tab-header>.mat-tab-header-pagination{background-color:#ef6c00}']}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(FG,[{type:My,args:[{selector:"plugin-selector-component",templateUrl:"./plugin_selector_component.ng.html",styleUrls:["./plugin_selector_component.css"]}]}],null,{activePlugins:[{type:xy}],disabledPlugins:[{type:xy}],selectedPlugin:[{type:xy}],onPluginSelectionChanged:[{type:Oy}]});const LG=Zw(vR,(t=>Object.keys(t).map((e=>Object.assign({},{id:e},t[e]))))),BG=Zw(LG,(t=>t.filter((t=>!t.enabled))));class VG{constructor(t){this.store=t,this.activePlugin$=this.store.pipe(Fw(MR)),this.plugins$=this.store.pipe(Fw(LG)),this.disabledPlugins$=this.store.pipe(Fw(BG))}onPluginSelectionChange(t){this.store.dispatch(_E({plugin:t}))}}function jG(t,e){}VG.ɵfac=function t(e){return new(e||VG)(Sm(Iw))},VG.ɵcmp=to({type:VG,selectors:[["plugin-selector"]],decls:4,vars:9,consts:[[3,"activePlugins","disabledPlugins","selectedPlugin","onPluginSelectionChanged"]],template:function t(e,n){1&e&&(Rm(0,"plugin-selector-component",0),Vm("onPluginSelectionChanged",(function t(e){return n.onPluginSelectionChange(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Am()),2&e&&Dm("activePlugins",Th(1,3,n.plugins$))("disabledPlugins",Th(2,5,n.disabledPlugins$))("selectedPlugin",Th(3,7,n.activePlugin$))},directives:[FG],pipes:[wM],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(VG,[{type:My,args:[{selector:"plugin-selector",template:'\n    <plugin-selector-component\n      [activePlugins]="plugins$ | async"\n      [disabledPlugins]="disabledPlugins$ | async"\n      [selectedPlugin]="activePlugin$ | async"\n      (onPluginSelectionChanged)="onPluginSelectionChange($event)"\n    ></plugin-selector-component>\n  '}]}],(function(){return[{type:Iw}]}),null);class UG{constructor(){this.role="dialog",this.panelClass="",this.hasBackdrop=!0,this.backdropClass="",this.disableClose=!1,this.width="",this.height="",this.maxWidth="80vw",this.data=null,this.ariaDescribedBy=null,this.ariaLabelledBy=null,this.ariaLabel=null,this.autoFocus=!0,this.restoreFocus=!0,this.closeOnNavigation=!0}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const GG={dialogContainer:nx("dialogContainer",[rx("void, exit",ax({opacity:0,transform:"scale(0.7)"})),rx("enter",ax({transform:"none"})),lx("* => enter",ox("150ms cubic-bezier(0, 0, 0.2, 1)",ax({transform:"none",opacity:1}))),lx("* => void, * => exit",ox("75ms cubic-bezier(0.4, 0.0, 0.2, 1)",ax({opacity:0})))])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function WG(){throw Error("Attempting to attach dialog content after content is already attached")}class YG extends PF{constructor(t,e,n,o,i,a){super(),this._elementRef=t,this._focusTrapFactory=e,this._changeDetectorRef=n,this._config=i,this._focusMonitor=a,this._animationStateChanged=new Lh,this._elementFocusedBeforeDialogWasOpened=null,this._closeInteractionType=null,this.attachDomPortal=t=>(this._portalOutlet.hasAttached()&&("undefined"==typeof ngDevMode||ngDevMode)&&WG(),this._portalOutlet.attachDomPortal(t)),this._ariaLabelledBy=i.ariaLabelledBy||null,this._document=o}_initializeWithAttachedContent(){this._setupFocusTrap(),this._capturePreviouslyFocusedElement(),this._focusDialogContainer()}attachComponentPortal(t){return this._portalOutlet.hasAttached()&&("undefined"==typeof ngDevMode||ngDevMode)&&WG(),this._portalOutlet.attachComponentPortal(t)}attachTemplatePortal(t){return this._portalOutlet.hasAttached()&&("undefined"==typeof ngDevMode||ngDevMode)&&WG(),this._portalOutlet.attachTemplatePortal(t)}_recaptureFocus(){this._containsFocus()||(!this._config.autoFocus||!this._focusTrap.focusInitialElement())&&this._elementRef.nativeElement.focus()}_trapFocus(){this._config.autoFocus?this._focusTrap.focusInitialElementWhenReady():this._containsFocus()||this._elementRef.nativeElement.focus()}_restoreFocus(){const t=this._elementFocusedBeforeDialogWasOpened;if(this._config.restoreFocus&&t&&"function"==typeof t.focus){const e=Fz(),n=this._elementRef.nativeElement;e&&e!==this._document.body&&e!==n&&!n.contains(e)||(this._focusMonitor?(this._focusMonitor.focusVia(t,this._closeInteractionType),this._closeInteractionType=null):t.focus())}this._focusTrap&&this._focusTrap.destroy()}_setupFocusTrap(){this._focusTrap=this._focusTrapFactory.create(this._elementRef.nativeElement)}_capturePreviouslyFocusedElement(){this._document&&(this._elementFocusedBeforeDialogWasOpened=Fz())}_focusDialogContainer(){this._elementRef.nativeElement.focus&&this._elementRef.nativeElement.focus()}_containsFocus(){const t=this._elementRef.nativeElement,e=Fz();return t===e||t.contains(e)}}YG.ɵfac=function t(e){return new(e||YG)(Sm(hg),Sm(rI),Sm(Ug),Sm(Z_,8),Sm(UG),Sm(SI))},YG.ɵdir=lo({type:YG,viewQuery:function t(e,n){if(1&e&&Qh(DF,7),2&e){let t;Jh(t=tb())&&(n._portalOutlet=t.first)}},features:[xp]}),YG.ctorParameters=()=>[{type:hg},{type:rI},{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:UG},{type:SI}],YG.propDecorators={_portalOutlet:[{type:Za,args:[DF,{static:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(YG,[{type:Cy}],(function(){return[{type:hg},{type:rI},{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:UG},{type:SI}]}),{_portalOutlet:[{type:Za,args:[DF,{static:!0}]}]});class qG extends YG{constructor(){super(...arguments),this._state="enter"}_onAnimationDone({toState:t,totalTime:e}){"enter"===t?(this._trapFocus(),this._animationStateChanged.next({state:"opened",totalTime:e})):"exit"===t&&(this._restoreFocus(),this._animationStateChanged.next({state:"closed",totalTime:e}))}_onAnimationStart({toState:t,totalTime:e}){"enter"===t?this._animationStateChanged.next({state:"opening",totalTime:e}):"exit"!==t&&"void"!==t||this._animationStateChanged.next({state:"closing",totalTime:e})}_startExitAnimation(){this._state="exit",this._changeDetectorRef.markForCheck()}}qG.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(qG)))(n||qG)}})(),qG.ɵcmp=to({type:qG,selectors:[["mat-dialog-container"]],hostAttrs:["tabindex","-1","aria-modal","true",1,"mat-dialog-container"],hostVars:6,hostBindings:function t(e,n){1&e&&jm("@dialogContainer.start",(function t(e){return n._onAnimationStart(e)}))("@dialogContainer.done",(function t(e){return n._onAnimationDone(e)})),2&e&&(Tu("id",n._id),jp("role",n._config.role)("aria-labelledby",n._config.ariaLabel?null:n._ariaLabelledBy)("aria-label",n._config.ariaLabel)("aria-describedby",n._config.ariaDescribedBy||null),Nu("@dialogContainer",n._state))},features:[xp],decls:1,vars:0,consts:[["cdkPortalOutlet",""]],template:function t(e,n){1&e&&Qp(0,jG,0,0,"ng-template",0)},directives:[DF],styles:[".mat-dialog-container{display:block;padding:24px;border-radius:4px;box-sizing:border-box;overflow:auto;outline:0;width:100%;height:100%;min-height:inherit;max-height:inherit}.cdk-high-contrast-active .mat-dialog-container{outline:solid 1px}.mat-dialog-content{display:block;margin:0 -24px;padding:0 24px;max-height:65vh;overflow:auto;-webkit-overflow-scrolling:touch}.mat-dialog-title{margin:0 0 20px;display:block}.mat-dialog-actions{padding:8px 0;display:flex;flex-wrap:wrap;min-height:52px;align-items:center;box-sizing:content-box;margin-bottom:-24px}.mat-dialog-actions[align=end]{justify-content:flex-end}.mat-dialog-actions[align=center]{justify-content:center}.mat-dialog-actions .mat-button-base+.mat-button-base,.mat-dialog-actions .mat-mdc-button-base+.mat-mdc-button-base{margin-left:8px}[dir=rtl] .mat-dialog-actions .mat-button-base+.mat-button-base,[dir=rtl] .mat-dialog-actions .mat-mdc-button-base+.mat-mdc-button-base{margin-left:0;margin-right:8px}\n"],encapsulation:2,data:{animation:[GG.dialogContainer]}}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(qG,[{type:My,args:[{selector:"mat-dialog-container",template:"<ng-template cdkPortalOutlet></ng-template>\n",encapsulation:Hn.None,changeDetection:zn.Default,animations:[GG.dialogContainer],host:{class:"mat-dialog-container",tabindex:"-1","aria-modal":"true","[id]":"_id","[attr.role]":"_config.role","[attr.aria-labelledby]":"_config.ariaLabel ? null : _ariaLabelledBy","[attr.aria-label]":"_config.ariaLabel","[attr.aria-describedby]":"_config.ariaDescribedBy || null","[@dialogContainer]":"_state","(@dialogContainer.start)":"_onAnimationStart($event)","(@dialogContainer.done)":"_onAnimationDone($event)"},styles:[".mat-dialog-container{display:block;padding:24px;border-radius:4px;box-sizing:border-box;overflow:auto;outline:0;width:100%;height:100%;min-height:inherit;max-height:inherit}.cdk-high-contrast-active .mat-dialog-container{outline:solid 1px}.mat-dialog-content{display:block;margin:0 -24px;padding:0 24px;max-height:65vh;overflow:auto;-webkit-overflow-scrolling:touch}.mat-dialog-title{margin:0 0 20px;display:block}.mat-dialog-actions{padding:8px 0;display:flex;flex-wrap:wrap;min-height:52px;align-items:center;box-sizing:content-box;margin-bottom:-24px}.mat-dialog-actions[align=end]{justify-content:flex-end}.mat-dialog-actions[align=center]{justify-content:center}.mat-dialog-actions .mat-button-base+.mat-button-base,.mat-dialog-actions .mat-mdc-button-base+.mat-mdc-button-base{margin-left:8px}[dir=rtl] .mat-dialog-actions .mat-button-base+.mat-button-base,[dir=rtl] .mat-dialog-actions .mat-mdc-button-base+.mat-mdc-button-base{margin-left:0;margin-right:8px}\n"]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let ZG=0;class XG{constructor(t,e,n="mat-dialog-"+ZG++){this._overlayRef=t,this._containerInstance=e,this.id=n,this.disableClose=this._containerInstance._config.disableClose,this._afterOpened=new I,this._afterClosed=new I,this._beforeClosed=new I,this._state=0,e._id=n,e._animationStateChanged.pipe(ce((t=>"opened"===t.state)),be(1)).subscribe((()=>{this._afterOpened.next(),this._afterOpened.complete()})),e._animationStateChanged.pipe(ce((t=>"closed"===t.state)),be(1)).subscribe((()=>{clearTimeout(this._closeFallbackTimeout),this._finishDialogClose()})),t.detachments().subscribe((()=>{this._beforeClosed.next(this._result),this._beforeClosed.complete(),this._afterClosed.next(this._result),this._afterClosed.complete(),this.componentInstance=null,this._overlayRef.dispose()})),t.keydownEvents().pipe(ce((t=>t.keyCode===uz&&!this.disableClose&&!bz(t)))).subscribe((t=>{t.preventDefault(),KG(this,"keyboard")})),t.backdropClick().subscribe((()=>{this.disableClose?this._containerInstance._recaptureFocus():KG(this,"mouse")}))}close(t){this._result=t,this._containerInstance._animationStateChanged.pipe(ce((t=>"closing"===t.state)),be(1)).subscribe((e=>{this._beforeClosed.next(t),this._beforeClosed.complete(),this._overlayRef.detachBackdrop(),this._closeFallbackTimeout=setTimeout((()=>this._finishDialogClose()),e.totalTime+100)})),this._state=1,this._containerInstance._startExitAnimation()}afterOpened(){return this._afterOpened}afterClosed(){return this._afterClosed}beforeClosed(){return this._beforeClosed}backdropClick(){return this._overlayRef.backdropClick()}keydownEvents(){return this._overlayRef.keydownEvents()}updatePosition(t){let e=this._getPositionStrategy();return t&&(t.left||t.right)?t.left?e.left(t.left):e.right(t.right):e.centerHorizontally(),t&&(t.top||t.bottom)?t.top?e.top(t.top):e.bottom(t.bottom):e.centerVertically(),this._overlayRef.updatePosition(),this}updateSize(t="",e=""){return this._overlayRef.updateSize({width:t,height:e}),this._overlayRef.updatePosition(),this}addPanelClass(t){return this._overlayRef.addPanelClass(t),this}removePanelClass(t){return this._overlayRef.removePanelClass(t),this}getState(){return this._state}_finishDialogClose(){this._state=2,this._overlayRef.dispose()}_getPositionStrategy(){return this._overlayRef.getConfig().positionStrategy}}function KG(t,e,n){return void 0!==t._containerInstance&&(t._containerInstance._closeInteractionType=e),t.close(n)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const JG=new Ga("MatDialogData"),QG=new Ga("mat-dialog-default-options"),$G=new Ga("mat-dialog-scroll-strategy"),tW={provide:$G,deps:[pL],useFactory:function eW(t){return()=>t.scrollStrategies.block()}};class nW{constructor(t,e,n,o,i,a,r,s,l){this._overlay=t,this._injector=e,this._defaultOptions=n,this._parentDialog=o,this._overlayContainer=i,this._dialogRefConstructor=r,this._dialogContainerType=s,this._dialogDataToken=l,this._openDialogsAtThisLevel=[],this._afterAllClosedAtThisLevel=new I,this._afterOpenedAtThisLevel=new I,this._ariaHiddenElements=new Map,this.afterAllClosed=Qt((()=>this.openDialogs.length?this._getAfterAllClosed():this._getAfterAllClosed().pipe(Ne(void 0)))),this._scrollStrategy=a}get openDialogs(){return this._parentDialog?this._parentDialog.openDialogs:this._openDialogsAtThisLevel}get afterOpened(){return this._parentDialog?this._parentDialog.afterOpened:this._afterOpenedAtThisLevel}_getAfterAllClosed(){const t=this._parentDialog;return t?t._getAfterAllClosed():this._afterAllClosedAtThisLevel}open(t,e){if((e=(function n(t,e){return Object.assign(Object.assign({},e),t)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(e,this._defaultOptions||new UG)).id&&this.getDialogById(e.id)&&("undefined"==typeof ngDevMode||ngDevMode))throw Error(`Dialog with id "${e.id}" exists already. The dialog id must be unique.`);const o=this._createOverlay(e),i=this._attachDialogContainer(o,e),a=this._attachDialogContent(t,i,o,e);return this.openDialogs.length||this._hideNonDialogContentFromAssistiveTechnology(),this.openDialogs.push(a),a.afterClosed().subscribe((()=>this._removeOpenDialog(a))),this.afterOpened.next(a),i._initializeWithAttachedContent(),a}closeAll(){this._closeDialogs(this.openDialogs)}getDialogById(t){return this.openDialogs.find((e=>e.id===t))}ngOnDestroy(){this._closeDialogs(this._openDialogsAtThisLevel),this._afterAllClosedAtThisLevel.complete(),this._afterOpenedAtThisLevel.complete()}_createOverlay(t){const e=this._getOverlayConfig(t);return this._overlay.create(e)}_getOverlayConfig(t){const e=new VF({positionStrategy:this._overlay.position().global(),scrollStrategy:t.scrollStrategy||this._scrollStrategy(),panelClass:t.panelClass,hasBackdrop:t.hasBackdrop,direction:t.direction,minWidth:t.minWidth,minHeight:t.minHeight,maxWidth:t.maxWidth,maxHeight:t.maxHeight,disposeOnNavigation:t.closeOnNavigation});return t.backdropClass&&(e.backdropClass=t.backdropClass),e}_attachDialogContainer(t,e){const n=rp.create({parent:e&&e.viewContainerRef&&e.viewContainerRef.injector||this._injector,providers:[{provide:UG,useValue:e}]}),o=new vF(this._dialogContainerType,e.viewContainerRef,n,e.componentFactoryResolver);return t.attach(o).instance}_attachDialogContent(t,e,n,o){const i=new this._dialogRefConstructor(n,e,o.id);if(t instanceof Xg)e.attachTemplatePortal(new xF(t,null,{$implicit:o.data,dialogRef:i}));else{const n=this._createInjector(o,i,e),a=e.attachComponentPortal(new vF(t,o.viewContainerRef,n));i.componentInstance=a.instance}return i.updateSize(o.width,o.height).updatePosition(o.position),i}_createInjector(t,e,n){const o=t&&t.viewContainerRef&&t.viewContainerRef.injector,i=[{provide:this._dialogContainerType,useValue:n},{provide:this._dialogDataToken,useValue:t.data},{provide:this._dialogRefConstructor,useValue:e}];return!t.direction||o&&o.get(HI,null,En.Optional)||i.push({provide:HI,useValue:{value:t.direction,change:Et()}}),rp.create({parent:o||this._injector,providers:i})}_removeOpenDialog(t){const e=this.openDialogs.indexOf(t);e>-1&&(this.openDialogs.splice(e,1),this.openDialogs.length||(this._ariaHiddenElements.forEach(((t,e)=>{t?e.setAttribute("aria-hidden",t):e.removeAttribute("aria-hidden")})),this._ariaHiddenElements.clear(),this._getAfterAllClosed().next()))}_hideNonDialogContentFromAssistiveTechnology(){const t=this._overlayContainer.getContainerElement();if(t.parentElement){const e=t.parentElement.children;for(let n=e.length-1;n>-1;n--){let o=e[n];o===t||"SCRIPT"===o.nodeName||"STYLE"===o.nodeName||o.hasAttribute("aria-live")||(this._ariaHiddenElements.set(o,o.getAttribute("aria-hidden")),o.setAttribute("aria-hidden","true"))}}}_closeDialogs(t){let e=t.length;for(;e--;)t[e].close()}}nW.ɵfac=function t(e){return new(e||nW)(Sm(pL),Sm(rp),Sm(void 0),Sm(void 0),Sm(QF),Sm(void 0),Sm(Qa),Sm(Qa),Sm(Ga))},nW.ɵdir=lo({type:nW}),nW.ctorParameters=()=>[{type:pL},{type:rp},{type:void 0},{type:void 0},{type:QF},{type:void 0},{type:Qa},{type:Qa},{type:Ga}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(nW,[{type:Cy}],(function(){return[{type:pL},{type:rp},{type:void 0},{type:void 0},{type:QF},{type:void 0},{type:Qa},{type:Qa},{type:Ga}]}),null);class oW extends nW{constructor(t,e,n,o,i,a,r){super(t,e,o,a,r,i,XG,qG,JG)}}oW.ɵfac=function t(e){return new(e||oW)(vr(pL),vr(rp),vr(lC,8),vr(QG,8),vr($G),vr(oW,12),vr(QF))},oW.ɵprov=Mn({token:oW,factory:oW.ɵfac}),oW.ctorParameters=()=>[{type:pL},{type:rp},{type:lC,decorators:[{type:Sr}]},{type:UG,decorators:[{type:Sr},{type:kr,args:[QG]}]},{type:void 0,decorators:[{type:kr,args:[$G]}]},{type:oW,decorators:[{type:Sr},{type:Er}]},{type:QF}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(oW,[{type:im}],(function(){return[{type:pL},{type:rp},{type:lC,decorators:[{type:Sr}]},{type:UG,decorators:[{type:Sr},{type:kr,args:[QG]}]},{type:void 0,decorators:[{type:kr,args:[$G]}]},{type:oW,decorators:[{type:Sr},{type:Er}]},{type:QF}]}),null);let iW=0;class aW{constructor(t,e,n){this.dialogRef=t,this._elementRef=e,this._dialog=n,this.type="button"}ngOnInit(){this.dialogRef||(this.dialogRef=cW(this._elementRef,this._dialog.openDialogs))}ngOnChanges(t){const e=t._matDialogClose||t._matDialogCloseResult;e&&(this.dialogResult=e.currentValue)}_onButtonClick(t){KG(this.dialogRef,0===t.screenX&&0===t.screenY?"keyboard":"mouse",this.dialogResult)}}aW.ɵfac=function t(e){return new(e||aW)(Sm(XG,8),Sm(hg),Sm(oW))},aW.ɵdir=lo({type:aW,selectors:[["","mat-dialog-close",""],["","matDialogClose",""]],hostVars:2,hostBindings:function t(e,n){1&e&&Vm("click",(function t(e){return n._onButtonClick(e)})),2&e&&jp("aria-label",n.ariaLabel||null)("type",n.type)},inputs:{type:"type",dialogResult:["mat-dialog-close","dialogResult"],ariaLabel:["aria-label","ariaLabel"],_matDialogClose:["matDialogClose","_matDialogClose"]},exportAs:["matDialogClose"],features:[Bo]}),aW.ctorParameters=()=>[{type:XG,decorators:[{type:Sr}]},{type:hg},{type:oW}],aW.propDecorators={ariaLabel:[{type:xy,args:["aria-label"]}],type:[{type:xy}],dialogResult:[{type:xy,args:["mat-dialog-close"]}],_matDialogClose:[{type:xy,args:["matDialogClose"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(aW,[{type:Cy,args:[{selector:"[mat-dialog-close], [matDialogClose]",exportAs:"matDialogClose",host:{"(click)":"_onButtonClick($event)","[attr.aria-label]":"ariaLabel || null","[attr.type]":"type"}}]}],(function(){return[{type:XG,decorators:[{type:Sr}]},{type:hg},{type:oW}]}),{type:[{type:xy}],dialogResult:[{type:xy,args:["mat-dialog-close"]}],ariaLabel:[{type:xy,args:["aria-label"]}],_matDialogClose:[{type:xy,args:["matDialogClose"]}]});class rW{constructor(t,e,n){this._dialogRef=t,this._elementRef=e,this._dialog=n,this.id="mat-dialog-title-"+iW++}ngOnInit(){this._dialogRef||(this._dialogRef=cW(this._elementRef,this._dialog.openDialogs)),this._dialogRef&&Promise.resolve().then((()=>{const t=this._dialogRef._containerInstance;t&&!t._ariaLabelledBy&&(t._ariaLabelledBy=this.id)}))}}rW.ɵfac=function t(e){return new(e||rW)(Sm(XG,8),Sm(hg),Sm(oW))},rW.ɵdir=lo({type:rW,selectors:[["","mat-dialog-title",""],["","matDialogTitle",""]],hostAttrs:[1,"mat-dialog-title"],hostVars:1,hostBindings:function t(e,n){2&e&&Tu("id",n.id)},inputs:{id:"id"},exportAs:["matDialogTitle"]}),rW.ctorParameters=()=>[{type:XG,decorators:[{type:Sr}]},{type:hg},{type:oW}],rW.propDecorators={id:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(rW,[{type:Cy,args:[{selector:"[mat-dialog-title], [matDialogTitle]",exportAs:"matDialogTitle",host:{class:"mat-dialog-title","[id]":"id"}}]}],(function(){return[{type:XG,decorators:[{type:Sr}]},{type:hg},{type:oW}]}),{id:[{type:xy}]});class sW{}sW.ɵfac=function t(e){return new(e||sW)},sW.ɵdir=lo({type:sW,selectors:[["","mat-dialog-content",""],["mat-dialog-content"],["","matDialogContent",""]],hostAttrs:[1,"mat-dialog-content"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(sW,[{type:Cy,args:[{selector:"[mat-dialog-content], mat-dialog-content, [matDialogContent]",host:{class:"mat-dialog-content"}}]}],null,null);class lW{}function cW(t,e){let n=t.nativeElement.parentElement;for(;n&&!n.classList.contains("mat-dialog-container");)n=n.parentElement;return n?e.find((t=>t.id===n.id)):null}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */lW.ɵfac=function t(e){return new(e||lW)},lW.ɵdir=lo({type:lW,selectors:[["","mat-dialog-actions",""],["mat-dialog-actions"],["","matDialogActions",""]],hostAttrs:[1,"mat-dialog-actions"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lW,[{type:Cy,args:[{selector:"[mat-dialog-actions], mat-dialog-actions, [matDialogActions]",host:{class:"mat-dialog-actions"}}]}],null,null);class dW{}dW.ɵfac=function t(e){return new(e||dW)},dW.ɵmod=ao({type:dW}),dW.ɵinj=vn({providers:[oW,tW],imports:[[yL,RF,XI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(dW,[{type:Ay,args:[{imports:[yL,RF,XI],exports:[qG,aW,rW,sW,lW,XI],declarations:[qG,aW,rW,lW,sW],providers:[oW,tW],entryComponents:[qG]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(dW,{declarations:function(){return[qG,aW,rW,lW,sW]},imports:function(){return[yL,RF,XI]},exports:function(){return[qG,aW,rW,sW,lW,XI]}});class pW{constructor(t,e){this._document=e;const n=this._textarea=this._document.createElement("textarea"),o=n.style;o.position="fixed",o.top=o.opacity="0",o.left="-999em",n.setAttribute("aria-hidden","true"),n.value=t,this._document.body.appendChild(n)}copy(){const t=this._textarea;let e=!1;try{if(t){const n=this._document.activeElement;t.select(),t.setSelectionRange(0,t.value.length),e=this._document.execCommand("copy"),n&&n.focus()}}catch(t){}return e}destroy(){const t=this._textarea;t&&(t.parentNode&&t.parentNode.removeChild(t),this._textarea=void 0)}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class mW{constructor(t){this._document=t}copy(t){const e=this.beginCopy(t),n=e.copy();return e.destroy(),n}beginCopy(t){return new pW(t,this._document)}}mW.ɵfac=function t(e){return new(e||mW)(vr(Z_))},mW.ɵprov=Mn({factory:function t(){return new mW(vr(Z_))},token:mW,providedIn:"root"}),mW.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(mW,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const uW=new Ga("CDK_COPY_TO_CLIPBOARD_CONFIG");class fW{constructor(t,e,n){this._clipboard=t,this._ngZone=e,this.text="",this.attempts=1,this.copied=new Lh,this._pending=new Set,n&&null!=n.attempts&&(this.attempts=n.attempts)}copy(t=this.attempts){if(t>1){let e=t;const n=this._clipboard.beginCopy(this.text);this._pending.add(n);const o=()=>{const t=n.copy();t||!--e||this._destroyed?(this._currentTimeout=null,this._pending.delete(n),n.destroy(),this.copied.emit(t)):this._currentTimeout=this._ngZone.runOutsideAngular((()=>setTimeout(o,1)))};o()}else this.copied.emit(this._clipboard.copy(this.text))}ngOnDestroy(){this._currentTimeout&&clearTimeout(this._currentTimeout),this._pending.forEach((t=>t.destroy())),this._pending.clear(),this._destroyed=!0}}fW.ɵfac=function t(e){return new(e||fW)(Sm(mW),Sm(a_),Sm(uW,8))},fW.ɵdir=lo({type:fW,selectors:[["","cdkCopyToClipboard",""]],hostBindings:function t(e,n){1&e&&Vm("click",(function t(){return n.copy()}))},inputs:{text:["cdkCopyToClipboard","text"],attempts:["cdkCopyToClipboardAttempts","attempts"]},outputs:{copied:"cdkCopyToClipboardCopied"}}),fW.ctorParameters=()=>[{type:mW},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[uW]}]}],fW.propDecorators={text:[{type:xy,args:["cdkCopyToClipboard"]}],attempts:[{type:xy,args:["cdkCopyToClipboardAttempts"]}],copied:[{type:Oy,args:["cdkCopyToClipboardCopied"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(fW,[{type:Cy,args:[{selector:"[cdkCopyToClipboard]",host:{"(click)":"copy()"}}]}],(function(){return[{type:mW},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[uW]}]}]}),{text:[{type:xy,args:["cdkCopyToClipboard"]}],attempts:[{type:xy,args:["cdkCopyToClipboardAttempts"]}],copied:[{type:Oy,args:["cdkCopyToClipboardCopied"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class gW{}function hW(t){return Error(`Unable to find icon with the name "${t}"`)}function bW(t){return Error(`The URL provided to MatIconRegistry was not trusted as a resource URL via Angular's DomSanitizer. Attempted URL was "${t}".`)}function yW(t){return Error(`The literal provided to MatIconRegistry was not trusted as safe HTML by Angular's DomSanitizer. Attempted literal was "${t}".`)}gW.ɵfac=function t(e){return new(e||gW)},gW.ɵmod=ao({type:gW}),gW.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(gW,[{type:Ay,args:[{declarations:[fW],exports:[fW]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(gW,{declarations:[fW],exports:[fW]});class _W{constructor(t,e,n){this.url=t,this.svgText=e,this.options=n}}class CW{constructor(t,e,n,o){this._httpClient=t,this._sanitizer=e,this._errorHandler=o,this._svgIconConfigs=new Map,this._iconSetConfigs=new Map,this._cachedIconsByUrl=new Map,this._inProgressUrlFetches=new Map,this._fontCssClassesByAlias=new Map,this._resolvers=[],this._defaultFontSetClass="material-icons",this._document=n}addSvgIcon(t,e,n){return this.addSvgIconInNamespace("",t,e,n)}addSvgIconLiteral(t,e,n){return this.addSvgIconLiteralInNamespace("",t,e,n)}addSvgIconInNamespace(t,e,n,o){return this._addSvgIconConfig(t,e,new _W(n,null,o))}addSvgIconResolver(t){return this._resolvers.push(t),this}addSvgIconLiteralInNamespace(t,e,n,o){const i=this._sanitizer.sanitize(As.HTML,n);if(!i)throw yW(n);return this._addSvgIconConfig(t,e,new _W("",i,o))}addSvgIconSet(t,e){return this.addSvgIconSetInNamespace("",t,e)}addSvgIconSetLiteral(t,e){return this.addSvgIconSetLiteralInNamespace("",t,e)}addSvgIconSetInNamespace(t,e,n){return this._addSvgIconSetConfig(t,new _W(e,null,n))}addSvgIconSetLiteralInNamespace(t,e,n){const o=this._sanitizer.sanitize(As.HTML,e);if(!o)throw yW(e);return this._addSvgIconSetConfig(t,new _W("",o,n))}registerFontClassAlias(t,e=t){return this._fontCssClassesByAlias.set(t,e),this}classNameForFontAlias(t){return this._fontCssClassesByAlias.get(t)||t}setDefaultFontSetClass(t){return this._defaultFontSetClass=t,this}getDefaultFontSetClass(){return this._defaultFontSetClass}getSvgIconFromUrl(t){const e=this._sanitizer.sanitize(As.RESOURCE_URL,t);if(!e)throw bW(t);const n=this._cachedIconsByUrl.get(e);return n?Et(MW(n)):this._loadSvgIconFromConfig(new _W(t,null)).pipe(Fe((t=>this._cachedIconsByUrl.set(e,t))),It((t=>MW(t))))}getNamedSvgIcon(t,e=""){const n=vW(e,t);let o=this._svgIconConfigs.get(n);if(o)return this._getSvgFromConfig(o);if(o=this._getIconConfigFromResolvers(e,t),o)return this._svgIconConfigs.set(n,o),this._getSvgFromConfig(o);const i=this._iconSetConfigs.get(e);return i?this._getSvgFromIconSetConfigs(t,i):Rt(hW(n))}ngOnDestroy(){this._resolvers=[],this._svgIconConfigs.clear(),this._iconSetConfigs.clear(),this._cachedIconsByUrl.clear()}_getSvgFromConfig(t){return t.svgText?Et(MW(this._svgElementFromConfig(t))):this._loadSvgIconFromConfig(t).pipe(It((t=>MW(t))))}_getSvgFromIconSetConfigs(t,e){const n=this._extractIconWithNameFromAnySet(t,e);return n?Et(n):$t(e.filter((t=>!t.svgText)).map((t=>this._loadSvgIconSetFromConfig(t).pipe(pe((e=>{const n=this._sanitizer.sanitize(As.RESOURCE_URL,t.url);return this._errorHandler.handleError(new Error(`Loading icon set URL: ${n} failed: ${e.message}`)),Et(null)})))))).pipe(It((()=>{const n=this._extractIconWithNameFromAnySet(t,e);if(!n)throw hW(t);return n})))}_extractIconWithNameFromAnySet(t,e){for(let n=e.length-1;n>=0;n--){const o=e[n];if(o.svgText&&o.svgText.indexOf(t)>-1){const e=this._svgElementFromConfig(o),n=this._extractSvgIconFromSet(e,t,o.options);if(n)return n}}return null}_loadSvgIconFromConfig(t){return this._fetchIcon(t).pipe(Fe((e=>t.svgText=e)),It((()=>this._svgElementFromConfig(t))))}_loadSvgIconSetFromConfig(t){return t.svgText?Et(null):this._fetchIcon(t).pipe(Fe((e=>t.svgText=e)))}_extractSvgIconFromSet(t,e,n){const o=t.querySelector(`[id="${e}"]`);if(!o)return null;const i=o.cloneNode(!0);if(i.removeAttribute("id"),"svg"===i.nodeName.toLowerCase())return this._setSvgAttributes(i,n);if("symbol"===i.nodeName.toLowerCase())return this._setSvgAttributes(this._toSvgElement(i),n);const a=this._svgElementFromString("<svg></svg>");return a.appendChild(i),this._setSvgAttributes(a,n)}_svgElementFromString(t){const e=this._document.createElement("DIV");e.innerHTML=t;const n=e.querySelector("svg");if(!n)throw Error("<svg> tag not found");return n}_toSvgElement(t){const e=this._svgElementFromString("<svg></svg>"),n=t.attributes;for(let t=0;t<n.length;t++){const{name:o,value:i}=n[t];"id"!==o&&e.setAttribute(o,i)}for(let n=0;n<t.childNodes.length;n++)t.childNodes[n].nodeType===this._document.ELEMENT_NODE&&e.appendChild(t.childNodes[n].cloneNode(!0));return e}_setSvgAttributes(t,e){return t.setAttribute("fit",""),t.setAttribute("height","100%"),t.setAttribute("width","100%"),t.setAttribute("preserveAspectRatio","xMidYMid meet"),t.setAttribute("focusable","false"),e&&e.viewBox&&t.setAttribute("viewBox",e.viewBox),t}_fetchIcon(t){var e;const{url:n,options:o}=t,i=null!==(e=null==o?void 0:o.withCredentials)&&void 0!==e&&e;if(!this._httpClient)throw(function a(){return Error("Could not find HttpClient provider for use with Angular Material icons. Please include the HttpClientModule from @angular/common/http in your app imports.")})();if(null==n)throw Error(`Cannot fetch icon from URL "${n}".`);const r=this._sanitizer.sanitize(As.RESOURCE_URL,n);if(!r)throw bW(n);const s=this._inProgressUrlFetches.get(r);if(s)return s;const l=this._httpClient.get(r,{responseType:"text",withCredentials:i}).pipe((function c(t){return R((function(e,n){try{e.subscribe(n)}finally{n.add(t)}}))})((()=>this._inProgressUrlFetches.delete(r))),Ee());return this._inProgressUrlFetches.set(r,l),l}_addSvgIconConfig(t,e,n){return this._svgIconConfigs.set(vW(t,e),n),this}_addSvgIconSetConfig(t,e){const n=this._iconSetConfigs.get(t);return n?n.push(e):this._iconSetConfigs.set(t,[e]),this}_svgElementFromConfig(t){if(!t.svgElement){const e=this._svgElementFromString(t.svgText);this._setSvgAttributes(e,t.options),t.svgElement=e}return t.svgElement}_getIconConfigFromResolvers(t,e){for(let o=0;o<this._resolvers.length;o++){const i=this._resolvers[o](e,t);if(i)return(n=i).url&&n.options?new _W(i.url,null,i.options):new _W(i,null)}var n;
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */}}function MW(t){return t.cloneNode(!0)}function vW(t,e){return t+":"+e}CW.ɵfac=function t(e){return new(e||CW)(vr(PD,8),vr(zv),vr(Z_,8),vr(Zs))},CW.ɵprov=Mn({factory:function t(){return new CW(vr(PD,8),vr(zv),vr(Z_,8),vr(Zs))},token:CW,providedIn:"root"}),CW.ctorParameters=()=>[{type:PD,decorators:[{type:Sr}]},{type:zv},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:Zs}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(CW,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:PD,decorators:[{type:Sr}]},{type:zv},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:Zs}]}),null),new Sr,new Er,new Sr,new Sr;const xW=JI(class{constructor(t){this._elementRef=t}}),OW=new Ga("mat-icon-location",{providedIn:"root",factory:function PW(){const t=Or(Z_),e=t?t.location:null;return{getPathname:()=>e?e.pathname+e.search:""}}}),wW=["clip-path","color-profile","src","cursor","fill","filter","marker","marker-start","marker-mid","marker-end","mask","stroke"],kW=wW.map((t=>`[${t}]`)).join(", "),SW=/^url\(['"]?#(.*?)['"]?\)$/;class DW extends xW{constructor(t,e,n,o,i){super(t),this._iconRegistry=e,this._location=o,this._errorHandler=i,this._inline=!1,this._currentIconFetch=m.EMPTY,n||t.nativeElement.setAttribute("aria-hidden","true")}get inline(){return this._inline}set inline(t){this._inline=yz(t)}get svgIcon(){return this._svgIcon}set svgIcon(t){t!==this._svgIcon&&(t?this._updateSvgIcon(t):this._svgIcon&&this._clearSvgElement(),this._svgIcon=t)}get fontSet(){return this._fontSet}set fontSet(t){const e=this._cleanupFontValue(t);e!==this._fontSet&&(this._fontSet=e,this._updateFontIconClasses())}get fontIcon(){return this._fontIcon}set fontIcon(t){const e=this._cleanupFontValue(t);e!==this._fontIcon&&(this._fontIcon=e,this._updateFontIconClasses())}_splitIconName(t){if(!t)return["",""];const e=t.split(":");switch(e.length){case 1:return["",e[0]];case 2:return e;default:throw Error(`Invalid icon name: "${t}"`)}}ngOnInit(){this._updateFontIconClasses()}ngAfterViewChecked(){const t=this._elementsWithExternalReferences;if(t&&t.size){const t=this._location.getPathname();t!==this._previousPath&&(this._previousPath=t,this._prependPathToReferences(t))}}ngOnDestroy(){this._currentIconFetch.unsubscribe(),this._elementsWithExternalReferences&&this._elementsWithExternalReferences.clear()}_usingFontIcon(){return!this.svgIcon}_setSvgElement(t){this._clearSvgElement();const e=t.querySelectorAll("style");for(let t=0;t<e.length;t++)e[t].textContent+=" ";const n=this._location.getPathname();this._previousPath=n,this._cacheChildrenWithExternalReferences(t),this._prependPathToReferences(n),this._elementRef.nativeElement.appendChild(t)}_clearSvgElement(){const t=this._elementRef.nativeElement;let e=t.childNodes.length;for(this._elementsWithExternalReferences&&this._elementsWithExternalReferences.clear();e--;){const n=t.childNodes[e];1===n.nodeType&&"svg"!==n.nodeName.toLowerCase()||t.removeChild(n)}}_updateFontIconClasses(){if(!this._usingFontIcon())return;const t=this._elementRef.nativeElement,e=this.fontSet?this._iconRegistry.classNameForFontAlias(this.fontSet):this._iconRegistry.getDefaultFontSetClass();e!=this._previousFontSetClass&&(this._previousFontSetClass&&t.classList.remove(this._previousFontSetClass),e&&t.classList.add(e),this._previousFontSetClass=e),this.fontIcon!=this._previousFontIconClass&&(this._previousFontIconClass&&t.classList.remove(this._previousFontIconClass),this.fontIcon&&t.classList.add(this.fontIcon),this._previousFontIconClass=this.fontIcon)}_cleanupFontValue(t){return"string"==typeof t?t.trim().split(" ")[0]:t}_prependPathToReferences(t){const e=this._elementsWithExternalReferences;e&&e.forEach(((e,n)=>{e.forEach((e=>{n.setAttribute(e.name,`url('${t}#${e.value}')`)}))}))}_cacheChildrenWithExternalReferences(t){const e=t.querySelectorAll(kW),n=this._elementsWithExternalReferences=this._elementsWithExternalReferences||new Map;for(let t=0;t<e.length;t++)wW.forEach((o=>{const i=e[t],a=i.getAttribute(o),r=a?a.match(SW):null;if(r){let t=n.get(i);t||(t=[],n.set(i,t)),t.push({name:o,value:r[1]})}}))}_updateSvgIcon(t){if(this._svgNamespace=null,this._svgName=null,this._currentIconFetch.unsubscribe(),t){const[e,n]=this._splitIconName(t);e&&(this._svgNamespace=e),n&&(this._svgName=n),this._currentIconFetch=this._iconRegistry.getNamedSvgIcon(n,e).pipe(be(1)).subscribe((t=>this._setSvgElement(t)),(t=>{this._errorHandler.handleError(new Error(`Error retrieving icon ${e}:${n}! ${t.message}`))}))}}}DW.ɵfac=function t(e){return new(e||DW)(Sm(hg),Sm(CW),Na("aria-hidden"),Sm(OW),Sm(Zs))},DW.ɵcmp=to({type:DW,selectors:[["mat-icon"]],hostAttrs:["role","img",1,"mat-icon","notranslate"],hostVars:7,hostBindings:function t(e,n){2&e&&(jp("data-mat-icon-type",n._usingFontIcon()?"font":"svg")("data-mat-icon-name",n._svgName||n.fontIcon)("data-mat-icon-namespace",n._svgNamespace||n.fontSet),pu("mat-icon-inline",n.inline)("mat-icon-no-color","primary"!==n.color&&"accent"!==n.color&&"warn"!==n.color))},inputs:{color:"color",inline:"inline",svgIcon:"svgIcon",fontSet:"fontSet",fontIcon:"fontIcon"},exportAs:["matIcon"],features:[xp],ngContentSelectors:["*"],decls:1,vars:0,template:function t(e,n){1&e&&(Zm(),Xm(0))},styles:[".mat-icon{background-repeat:no-repeat;display:inline-block;fill:currentColor;height:24px;width:24px}.mat-icon.mat-icon-inline{font-size:inherit;height:inherit;line-height:inherit;width:inherit}[dir=rtl] .mat-icon-rtl-mirror{transform:scale(-1, 1)}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon{display:block}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon-button .mat-icon,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon-button .mat-icon{margin:auto}\n"],encapsulation:2,changeDetection:0}),DW.ctorParameters=()=>[{type:hg},{type:CW},{type:String,decorators:[{type:ja,args:["aria-hidden"]}]},{type:void 0,decorators:[{type:kr,args:[OW]}]},{type:Zs}],DW.propDecorators={inline:[{type:xy}],svgIcon:[{type:xy}],fontSet:[{type:xy}],fontIcon:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(DW,[{type:My,args:[{template:"<ng-content></ng-content>",selector:"mat-icon",exportAs:"matIcon",inputs:["color"],host:{role:"img",class:"mat-icon notranslate","[attr.data-mat-icon-type]":'_usingFontIcon() ? "font" : "svg"',"[attr.data-mat-icon-name]":"_svgName || fontIcon","[attr.data-mat-icon-namespace]":"_svgNamespace || fontSet","[class.mat-icon-inline]":"inline","[class.mat-icon-no-color]":'color !== "primary" && color !== "accent" && color !== "warn"'},encapsulation:Hn.None,changeDetection:zn.OnPush,styles:[".mat-icon{background-repeat:no-repeat;display:inline-block;fill:currentColor;height:24px;width:24px}.mat-icon.mat-icon-inline{font-size:inherit;height:inherit;line-height:inherit;width:inherit}[dir=rtl] .mat-icon-rtl-mirror{transform:scale(-1, 1)}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon{display:block}.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-prefix .mat-icon-button .mat-icon,.mat-form-field:not(.mat-form-field-appearance-legacy) .mat-form-field-suffix .mat-icon-button .mat-icon{margin:auto}\n"]}]}],(function(){return[{type:hg},{type:CW},{type:String,decorators:[{type:ja,args:["aria-hidden"]}]},{type:void 0,decorators:[{type:kr,args:[OW]}]},{type:Zs}]}),{inline:[{type:xy}],svgIcon:[{type:xy}],fontSet:[{type:xy}],fontIcon:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class EW{}EW.ɵfac=function t(e){return new(e||EW)},EW.ɵmod=ao({type:EW}),EW.ɵinj=vn({imports:[[XI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(EW,[{type:Ay,args:[{imports:[XI],exports:[DW,XI],declarations:[DW]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(EW,{declarations:function(){return[DW]},imports:function(){return[XI]},exports:function(){return[DW,XI]}});class RW{constructor(t){this.dialogRef=t,this.tensorboardDotDevUrl="https://tensorboard.dev/?utm_source=tensorboard"}onClose(){this.dialogRef.close()}getCommandText(){return this.logdir?"tensorboard dev upload --logdir \\\n    '"+this.logdir.replace(/'/g,"'\\''")+"'":"tensorboard dev upload --logdir {logdir}"}}RW.ɵfac=function t(e){return new(e||RW)(Sm(XG))},RW.ɵcmp=to({type:RW,selectors:[["tbdev-upload-dialog-component"]],inputs:{logdir:"logdir"},decls:21,vars:4,consts:[["target","_blank","rel","noreferrer noopener",1,"anchor-text",3,"href"],[1,"command"],["mat-icon-button","","title","Click to copy the command",1,"command-copy",3,"cdkCopyToClipboard"],["svgIcon","content_copy_24px"],[1,"bottom-buttons"],["mat-flat-button","",1,"close-button",3,"click"],["mat-flat-button","","target","_blank","rel","noreferrer noopener",1,"learn-more-button",3,"href"]],template:function t(e,n){1&e&&(Rm(0,"h3"),ku(1,"Upload to TensorBoard.dev"),Am(),Rm(2,"p"),Rm(3,"a",0),ku(4," TensorBoard.dev"),Am(),ku(5," enables you to easily host, track, and share your ML experiments with everyone. You can share a link to the uploaded TensorBoard in papers, blog posts, and social media. This can showcase the results more effectively and helps reproducibility.\n"),Am(),Rm(6,"p"),ku(7,"To upload a logdir to TensorBoard.dev, run the command:"),Am(),Rm(8,"div",1),Rm(9,"pre"),Rm(10,"code"),ku(11),Am(),Am(),Rm(12,"button",2),Tm(13,"mat-icon",3),Am(),Am(),Rm(14,"p"),ku(15," Only certain plugins are currently supported. Uploaded TensorBoards are public and visible to everyone; do not upload sensitive data.\n"),Am(),Rm(16,"div",4),Rm(17,"button",5),Vm("click",(function t(){return n.onClose()})),ku(18," Close "),Am(),Rm(19,"a",6),ku(20," Learn more "),Am(),Am()),2&e&&(rc(3),Km("href",n.tensorboardDotDevUrl,Ts),rc(8),Su(n.getCommandText()),rc(1),Dm("cdkCopyToClipboard",n.getCommandText()),rc(7),Km("href",n.tensorboardDotDevUrl,Ts))},directives:[XH,fW,DW,KH],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", 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ngDevMode||ngDevMode)&&hh(IW,[{type:My,args:[{selector:"tbdev-upload-button",templateUrl:"./tbdev_upload_button_component.ng.html",styleUrls:["./tbdev_upload_button_component.css"]}]}],(function(){return[{type:Window,decorators:[{type:kr,args:["window"]}]},{type:oW}]}),{shown:[{type:Py,args:["class.shown"]}]});const FW=["*"];function LW(t,e){if(1&t){const t=Hm();Rm(0,"div",0),Vm("keydown",(function e(n){return hi(t),Ym()._handleKeydown(n)}))("click",(function e(){return hi(t),Ym().closed.emit("click")}))("@transformMenu.start",(function e(n){return hi(t),Ym()._onAnimationStart(n)}))("@transformMenu.done",(function e(n){return hi(t),Ym()._onAnimationDone(n)})),Rm(1,"div",1),Xm(2),Am(),Am()}if(2&t){const t=Ym();Dm("id",t.panelId)("ngClass",t._classList)("@transformMenu",t._panelAnimationState),jp("aria-label",t.ariaLabel||null)("aria-labelledby",t.ariaLabelledby||null)("aria-describedby",t.ariaDescribedby||null)}}const BW={transformMenu:nx("transformMenu",[rx("void",ax({opacity:0,transform:"scale(0.8)"})),lx("void => enter",ox("120ms cubic-bezier(0, 0, 0.2, 1)",ax({opacity:1,transform:"scale(1)"}))),lx("* => void",ox("100ms 25ms linear",ax({opacity:0})))]),fadeInItems:nx("fadeInItems",[rx("showing",ax({opacity:1})),lx("void => *",[ax({opacity:0}),ox("400ms 100ms cubic-bezier(0.55, 0, 0.55, 0.2)")])])},VW=new Ga("MatMenuContent");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class jW{constructor(t,e,n,o,i,a,r){this._template=t,this._componentFactoryResolver=e,this._appRef=n,this._injector=o,this._viewContainerRef=i,this._document=a,this._changeDetectorRef=r,this._attached=new I}attach(t={}){this._portal||(this._portal=new xF(this._template,this._viewContainerRef)),this.detach(),this._outlet||(this._outlet=new wF(this._document.createElement("div"),this._componentFactoryResolver,this._appRef,this._injector));const e=this._template.elementRef.nativeElement;e.parentNode.insertBefore(this._outlet.outletElement,e),this._changeDetectorRef&&this._changeDetectorRef.markForCheck(),this._portal.attach(this._outlet,t),this._attached.next()}detach(){this._portal.isAttached&&this._portal.detach()}ngOnDestroy(){this._outlet&&this._outlet.dispose()}}jW.ɵfac=function t(e){return new(e||jW)(Sm(Xg),Sm(ug),Sm(O_),Sm(rp),Sm(eh),Sm(Z_),Sm(Ug))},jW.ɵdir=lo({type:jW,selectors:[["ng-template","matMenuContent",""]],features:[pg([{provide:VW,useExisting:jW}])]}),jW.ctorParameters=()=>[{type:Xg},{type:ug},{type:O_},{type:rp},{type:eh},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:Ug}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(jW,[{type:Cy,args:[{selector:"ng-template[matMenuContent]",providers:[{provide:VW,useExisting:jW}]}]}],(function(){return[{type:Xg},{type:ug},{type:O_},{type:rp},{type:eh},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:Ug}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const UW=new Ga("MAT_MENU_PANEL"),GW=QI(KI(class{}));
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class WW extends GW{constructor(t,e,n,o,i){super(),this._elementRef=t,this._focusMonitor=n,this._parentMenu=o,this._changeDetectorRef=i,this.role="menuitem",this._hovered=new I,this._focused=new I,this._highlighted=!1,this._triggersSubmenu=!1,o&&o.addItem&&o.addItem(this)}focus(t,e){this._focusMonitor&&t?this._focusMonitor.focusVia(this._getHostElement(),t,e):this._getHostElement().focus(e),this._focused.next(this)}ngAfterViewInit(){this._focusMonitor&&this._focusMonitor.monitor(this._elementRef,!1)}ngOnDestroy(){this._focusMonitor&&this._focusMonitor.stopMonitoring(this._elementRef),this._parentMenu&&this._parentMenu.removeItem&&this._parentMenu.removeItem(this),this._hovered.complete(),this._focused.complete()}_getTabIndex(){return this.disabled?"-1":"0"}_getHostElement(){return this._elementRef.nativeElement}_checkDisabled(t){this.disabled&&(t.preventDefault(),t.stopPropagation())}_handleMouseEnter(){this._hovered.next(this)}getLabel(){var t,e;const n=this._elementRef.nativeElement.cloneNode(!0),o=n.querySelectorAll("mat-icon, .material-icons");for(let e=0;e<o.length;e++){const n=o[e];null===(t=n.parentNode)||void 0===t||t.removeChild(n)}return(null===(e=n.textContent)||void 0===e?void 0:e.trim())||""}_setHighlighted(t){var e;this._highlighted=t,null===(e=this._changeDetectorRef)||void 0===e||e.markForCheck()}}WW.ɵfac=function t(e){return new(e||WW)(Sm(hg),Sm(Z_),Sm(SI),Sm(UW,8),Sm(Ug))},WW.ɵcmp=to({type:WW,selectors:[["","mat-menu-item",""]],hostAttrs:[1,"mat-focus-indicator"],hostVars:10,hostBindings:function t(e,n){1&e&&Vm("click",(function t(e){return n._checkDisabled(e)}))("mouseenter",(function t(){return n._handleMouseEnter()})),2&e&&(jp("role",n.role)("tabindex",n._getTabIndex())("aria-disabled",n.disabled.toString())("disabled",n.disabled||null),pu("mat-menu-item",!0)("mat-menu-item-highlighted",n._highlighted)("mat-menu-item-submenu-trigger",n._triggersSubmenu))},inputs:{disabled:"disabled",disableRipple:"disableRipple",role:"role"},exportAs:["matMenuItem"],features:[xp],attrs:["mat-menu-item",""],ngContentSelectors:FW,decls:3,vars:3,consts:[["matRipple","",1,"mat-menu-ripple",3,"matRippleDisabled","matRippleTrigger"],["class","mat-menu-submenu-icon","viewBox","0 0 5 10","focusable","false",4,"ngIf"],["viewBox","0 0 5 10","focusable","false",1,"mat-menu-submenu-icon"],["points","0,0 5,5 0,10"]],template:function t(e,n){1&e&&(Zm(),Xm(0),Tm(1,"div",0),Qp(2,HW,2,0,"svg",1)),2&e&&(rc(1),Dm("matRippleDisabled",n.disableRipple||n.disabled)("matRippleTrigger",n._getHostElement()),rc(1),Dm("ngIf",n._triggersSubmenu))},directives:[kH,dM],encapsulation:2,changeDetection:0}),WW.ctorParameters=()=>[{type:hg},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:SI},{type:void 0,decorators:[{type:kr,args:[UW]},{type:Sr}]},{type:Ug}],WW.propDecorators={role:[{type:xy}],_checkDisabled:[{type:wy,args:["click",["$event"]]}],_handleMouseEnter:[{type:wy,args:["mouseenter"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(WW,[{type:My,args:[{selector:"[mat-menu-item]",exportAs:"matMenuItem",inputs:["disabled","disableRipple"],host:{"[attr.role]":"role","[class.mat-menu-item]":"true","[class.mat-menu-item-highlighted]":"_highlighted","[class.mat-menu-item-submenu-trigger]":"_triggersSubmenu","[attr.tabindex]":"_getTabIndex()","[attr.aria-disabled]":"disabled.toString()","[attr.disabled]":"disabled || null",class:"mat-focus-indicator"},changeDetection:zn.OnPush,encapsulation:Hn.None,template:'<ng-content></ng-content>\n<div class="mat-menu-ripple" matRipple\n     [matRippleDisabled]="disableRipple || disabled"\n     [matRippleTrigger]="_getHostElement()">\n</div>\n\n<svg\n  *ngIf="_triggersSubmenu"\n  class="mat-menu-submenu-icon"\n  viewBox="0 0 5 10"\n  focusable="false"><polygon points="0,0 5,5 0,10"/></svg>\n'}]}],(function(){return[{type:hg},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:SI},{type:void 0,decorators:[{type:kr,args:[UW]},{type:Sr}]},{type:Ug}]}),{role:[{type:xy}],_checkDisabled:[{type:wy,args:["click",["$event"]]}],_handleMouseEnter:[{type:wy,args:["mouseenter"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const YW=new Ga("mat-menu-default-options",{providedIn:"root",factory:function qW(){return{overlapTrigger:!1,xPosition:"after",yPosition:"below",backdropClass:"cdk-overlay-transparent-backdrop"}}});let ZW=0;class XW{constructor(t,e,n){this._elementRef=t,this._ngZone=e,this._defaultOptions=n,this._xPosition=this._defaultOptions.xPosition,this._yPosition=this._defaultOptions.yPosition,this._directDescendantItems=new Vh,this._tabSubscription=m.EMPTY,this._classList={},this._panelAnimationState="void",this._animationDone=new I,this.overlayPanelClass=this._defaultOptions.overlayPanelClass||"",this.backdropClass=this._defaultOptions.backdropClass,this._overlapTrigger=this._defaultOptions.overlapTrigger,this._hasBackdrop=this._defaultOptions.hasBackdrop,this.closed=new Lh,this.close=this.closed,this.panelId="mat-menu-panel-"+ZW++}get xPosition(){return this._xPosition}set xPosition(t){"before"===t||"after"===t||"undefined"!=typeof ngDevMode&&!ngDevMode||(function e(){throw Error('xPosition value must be either \'before\' or after\'.\n      Example: <mat-menu xPosition="before" #menu="matMenu"></mat-menu>')})(),this._xPosition=t,this.setPositionClasses()}get yPosition(){return this._yPosition}set yPosition(t){"above"===t||"below"===t||"undefined"!=typeof ngDevMode&&!ngDevMode||(function e(){throw Error('yPosition value must be either \'above\' or below\'.\n      Example: <mat-menu yPosition="above" #menu="matMenu"></mat-menu>')})(),this._yPosition=t,this.setPositionClasses()}get overlapTrigger(){return this._overlapTrigger}set overlapTrigger(t){this._overlapTrigger=yz(t)}get hasBackdrop(){return this._hasBackdrop}set hasBackdrop(t){this._hasBackdrop=yz(t)}set panelClass(t){const e=this._previousPanelClass;e&&e.length&&e.split(" ").forEach((t=>{this._classList[t]=!1})),this._previousPanelClass=t,t&&t.length&&(t.split(" ").forEach((t=>{this._classList[t]=!0})),this._elementRef.nativeElement.className="")}get classList(){return this.panelClass}set classList(t){this.panelClass=t}ngOnInit(){this.setPositionClasses()}ngAfterContentInit(){this._updateDirectDescendants(),this._keyManager=new eI(this._directDescendantItems).withWrap().withTypeAhead().withHomeAndEnd(),this._tabSubscription=this._keyManager.tabOut.subscribe((()=>this.closed.emit("tab"))),this._directDescendantItems.changes.pipe(Ne(this._directDescendantItems),ze((t=>re(...t.map((t=>t._focused)))))).subscribe((t=>this._keyManager.updateActiveItem(t)))}ngOnDestroy(){this._directDescendantItems.destroy(),this._tabSubscription.unsubscribe(),this.closed.complete()}_hovered(){return this._directDescendantItems.changes.pipe(Ne(this._directDescendantItems),ze((t=>re(...t.map((t=>t._hovered))))))}addItem(t){}removeItem(t){}_handleKeydown(t){const e=t.keyCode,n=this._keyManager;switch(e){case uz:bz(t)||(t.preventDefault(),this.closed.emit("keydown"));break;case 37:this.parentMenu&&"ltr"===this.direction&&this.closed.emit("keydown");break;case 39:this.parentMenu&&"rtl"===this.direction&&this.closed.emit("keydown");break;default:e!==gz&&e!==hz||n.setFocusOrigin("keyboard"),n.onKeydown(t)}}focusFirstItem(t="program"){this.lazyContent?this._ngZone.onStable.pipe(be(1)).subscribe((()=>this._focusFirstItem(t))):this._focusFirstItem(t)}_focusFirstItem(t){const e=this._keyManager;if(e.setFocusOrigin(t).setFirstItemActive(),!e.activeItem&&this._directDescendantItems.length){let t=this._directDescendantItems.first._getHostElement().parentElement;for(;t;){if("menu"===t.getAttribute("role")){t.focus();break}t=t.parentElement}}}resetActiveItem(){this._keyManager.setActiveItem(-1)}setElevation(t){const e=Math.min(this._baseElevation+t,24),n=`${this._elevationPrefix}${e}`,o=Object.keys(this._classList).find((t=>t.startsWith(this._elevationPrefix)));o&&o!==this._previousElevation||(this._previousElevation&&(this._classList[this._previousElevation]=!1),this._classList[n]=!0,this._previousElevation=n)}setPositionClasses(t=this.xPosition,e=this.yPosition){const n=this._classList;n["mat-menu-before"]="before"===t,n["mat-menu-after"]="after"===t,n["mat-menu-above"]="above"===e,n["mat-menu-below"]="below"===e}_startAnimation(){this._panelAnimationState="enter"}_resetAnimation(){this._panelAnimationState="void"}_onAnimationDone(t){this._animationDone.next(t),this._isAnimating=!1}_onAnimationStart(t){this._isAnimating=!0,"enter"===t.toState&&0===this._keyManager.activeItemIndex&&(t.element.scrollTop=0)}_updateDirectDescendants(){this._allItems.changes.pipe(Ne(this._allItems)).subscribe((t=>{this._directDescendantItems.reset(t.filter((t=>t._parentMenu===this))),this._directDescendantItems.notifyOnChanges()}))}}XW.ɵfac=function t(e){return new(e||XW)(Sm(hg),Sm(a_),Sm(YW))},XW.ɵdir=lo({type:XW,contentQueries:function t(e,n,o){if(1&e&&($h(o,VW,5),$h(o,WW,5),$h(o,WW,4)),2&e){let t;Jh(t=tb())&&(n.lazyContent=t.first),Jh(t=tb())&&(n._allItems=t),Jh(t=tb())&&(n.items=t)}},viewQuery:function t(e,n){if(1&e&&Qh(Xg,5),2&e){let t;Jh(t=tb())&&(n.templateRef=t.first)}},inputs:{backdropClass:"backdropClass",xPosition:"xPosition",yPosition:"yPosition",overlapTrigger:"overlapTrigger",hasBackdrop:"hasBackdrop",panelClass:["class","panelClass"],classList:"classList",ariaLabel:["aria-label","ariaLabel"],ariaLabelledby:["aria-labelledby","ariaLabelledby"],ariaDescribedby:["aria-describedby","ariaDescribedby"]},outputs:{closed:"closed",close:"close"}}),XW.ctorParameters=()=>[{type:hg},{type:a_},{type:void 0,decorators:[{type:kr,args:[YW]}]}],XW.propDecorators={_allItems:[{type:Ya,args:[WW,{descendants:!0}]}],backdropClass:[{type:xy}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],ariaDescribedby:[{type:xy,args:["aria-describedby"]}],xPosition:[{type:xy}],yPosition:[{type:xy}],templateRef:[{type:Za,args:[Xg]}],items:[{type:Ya,args:[WW,{descendants:!1}]}],lazyContent:[{type:qa,args:[VW]}],overlapTrigger:[{type:xy}],hasBackdrop:[{type:xy}],panelClass:[{type:xy,args:["class"]}],classList:[{type:xy}],closed:[{type:Oy}],close:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(XW,[{type:Cy}],(function(){return[{type:hg},{type:a_},{type:void 0,decorators:[{type:kr,args:[YW]}]}]}),{backdropClass:[{type:xy}],closed:[{type:Oy}],close:[{type:Oy}],xPosition:[{type:xy}],yPosition:[{type:xy}],overlapTrigger:[{type:xy}],hasBackdrop:[{type:xy}],panelClass:[{type:xy,args:["class"]}],classList:[{type:xy}],_allItems:[{type:Ya,args:[WW,{descendants:!0}]}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],ariaDescribedby:[{type:xy,args:["aria-describedby"]}],templateRef:[{type:Za,args:[Xg]}],items:[{type:Ya,args:[WW,{descendants:!1}]}],lazyContent:[{type:qa,args:[VW]}]});class KW extends XW{constructor(t,e,n){super(t,e,n),this._elevationPrefix="mat-elevation-z",this._baseElevation=4}}KW.ɵfac=function t(e){return new(e||KW)(Sm(hg),Sm(a_),Sm(YW))},KW.ɵcmp=to({type:KW,selectors:[["mat-menu"]],hostVars:3,hostBindings:function t(e,n){2&e&&jp("aria-label",null)("aria-labelledby",null)("aria-describedby",null)},exportAs:["matMenu"],features:[pg([{provide:UW,useExisting:KW}]),xp],ngContentSelectors:FW,decls:1,vars:0,consts:[["tabindex","-1","role","menu",1,"mat-menu-panel",3,"id","ngClass","keydown","click"],[1,"mat-menu-content"]],template:function t(e,n){1&e&&(Zm(),Qp(0,LW,3,6,"ng-template"))},directives:[aM],styles:["mat-menu{display:none}.mat-menu-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;max-height:calc(100vh - 48px);border-radius:4px;outline:0;min-height:64px}.mat-menu-panel.ng-animating{pointer-events:none}.cdk-high-contrast-active .mat-menu-panel{outline:solid 1px}.mat-menu-content:not(:empty){padding-top:8px;padding-bottom:8px}.mat-menu-item{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;position:relative}.mat-menu-item::-moz-focus-inner{border:0}.mat-menu-item[disabled]{cursor:default}[dir=rtl] .mat-menu-item{text-align:right}.mat-menu-item .mat-icon{margin-right:16px;vertical-align:middle}.mat-menu-item .mat-icon svg{vertical-align:top}[dir=rtl] .mat-menu-item .mat-icon{margin-left:16px;margin-right:0}.mat-menu-item[disabled]{pointer-events:none}.cdk-high-contrast-active .mat-menu-item{margin-top:1px}.cdk-high-contrast-active .mat-menu-item.cdk-program-focused,.cdk-high-contrast-active .mat-menu-item.cdk-keyboard-focused,.cdk-high-contrast-active .mat-menu-item-highlighted{outline:dotted 1px}.mat-menu-item-submenu-trigger{padding-right:32px}[dir=rtl] .mat-menu-item-submenu-trigger{padding-right:16px;padding-left:32px}.mat-menu-submenu-icon{position:absolute;top:50%;right:16px;transform:translateY(-50%);width:5px;height:10px;fill:currentColor}[dir=rtl] .mat-menu-submenu-icon{right:auto;left:16px;transform:translateY(-50%) scaleX(-1)}.cdk-high-contrast-active .mat-menu-submenu-icon{fill:CanvasText}button.mat-menu-item{width:100%}.mat-menu-item .mat-menu-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none}\n"],encapsulation:2,data:{animation:[BW.transformMenu,BW.fadeInItems]},changeDetection:0}),KW.ctorParameters=()=>[{type:hg},{type:a_},{type:void 0,decorators:[{type:kr,args:[YW]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(KW,[{type:My,args:[{selector:"mat-menu",template:'<ng-template>\n  <div\n    class="mat-menu-panel"\n    [id]="panelId"\n    [ngClass]="_classList"\n    (keydown)="_handleKeydown($event)"\n    (click)="closed.emit(\'click\')"\n    [@transformMenu]="_panelAnimationState"\n    (@transformMenu.start)="_onAnimationStart($event)"\n    (@transformMenu.done)="_onAnimationDone($event)"\n    tabindex="-1"\n    role="menu"\n    [attr.aria-label]="ariaLabel || null"\n    [attr.aria-labelledby]="ariaLabelledby || null"\n    [attr.aria-describedby]="ariaDescribedby || null">\n    <div class="mat-menu-content">\n      <ng-content></ng-content>\n    </div>\n  </div>\n</ng-template>\n',changeDetection:zn.OnPush,encapsulation:Hn.None,exportAs:"matMenu",host:{"[attr.aria-label]":"null","[attr.aria-labelledby]":"null","[attr.aria-describedby]":"null"},animations:[BW.transformMenu,BW.fadeInItems],providers:[{provide:UW,useExisting:KW}],styles:["mat-menu{display:none}.mat-menu-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;max-height:calc(100vh - 48px);border-radius:4px;outline:0;min-height:64px}.mat-menu-panel.ng-animating{pointer-events:none}.cdk-high-contrast-active .mat-menu-panel{outline:solid 1px}.mat-menu-content:not(:empty){padding-top:8px;padding-bottom:8px}.mat-menu-item{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:pointer;outline:none;border:none;-webkit-tap-highlight-color:transparent;white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;line-height:48px;height:48px;padding:0 16px;text-align:left;text-decoration:none;max-width:100%;position:relative}.mat-menu-item::-moz-focus-inner{border:0}.mat-menu-item[disabled]{cursor:default}[dir=rtl] .mat-menu-item{text-align:right}.mat-menu-item .mat-icon{margin-right:16px;vertical-align:middle}.mat-menu-item .mat-icon svg{vertical-align:top}[dir=rtl] .mat-menu-item .mat-icon{margin-left:16px;margin-right:0}.mat-menu-item[disabled]{pointer-events:none}.cdk-high-contrast-active .mat-menu-item{margin-top:1px}.cdk-high-contrast-active .mat-menu-item.cdk-program-focused,.cdk-high-contrast-active .mat-menu-item.cdk-keyboard-focused,.cdk-high-contrast-active .mat-menu-item-highlighted{outline:dotted 1px}.mat-menu-item-submenu-trigger{padding-right:32px}[dir=rtl] .mat-menu-item-submenu-trigger{padding-right:16px;padding-left:32px}.mat-menu-submenu-icon{position:absolute;top:50%;right:16px;transform:translateY(-50%);width:5px;height:10px;fill:currentColor}[dir=rtl] .mat-menu-submenu-icon{right:auto;left:16px;transform:translateY(-50%) scaleX(-1)}.cdk-high-contrast-active .mat-menu-submenu-icon{fill:CanvasText}button.mat-menu-item{width:100%}.mat-menu-item .mat-menu-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none}\n"]}]}],(function(){return[{type:hg},{type:a_},{type:void 0,decorators:[{type:kr,args:[YW]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const JW=new Ga("mat-menu-scroll-strategy"),QW={provide:JW,deps:[pL],useFactory:function $W(t){return()=>t.scrollStrategies.reposition()}},tY=Nz({passive:!0});class eY{constructor(t,e,n,o,i,a,r,s){this._overlay=t,this._element=e,this._viewContainerRef=n,this._menuItemInstance=a,this._dir=r,this._focusMonitor=s,this._overlayRef=null,this._menuOpen=!1,this._closingActionsSubscription=m.EMPTY,this._hoverSubscription=m.EMPTY,this._menuCloseSubscription=m.EMPTY,this._handleTouchStart=t=>{hI(t)||(this._openedBy="touch")},this._openedBy=void 0,this.restoreFocus=!0,this.menuOpened=new Lh,this.onMenuOpen=this.menuOpened,this.menuClosed=new Lh,this.onMenuClose=this.menuClosed,this._scrollStrategy=o,this._parentMaterialMenu=i instanceof XW?i:void 0,e.nativeElement.addEventListener("touchstart",this._handleTouchStart,tY),a&&(a._triggersSubmenu=this.triggersSubmenu())}get _deprecatedMatMenuTriggerFor(){return this.menu}set _deprecatedMatMenuTriggerFor(t){this.menu=t}get menu(){return this._menu}set menu(t){t!==this._menu&&(this._menu=t,this._menuCloseSubscription.unsubscribe(),t&&(t!==this._parentMaterialMenu||"undefined"!=typeof ngDevMode&&!ngDevMode||(function e(){throw Error("matMenuTriggerFor: menu cannot contain its own trigger. Assign a menu that is not a parent of the trigger or move the trigger outside of the menu.")})(),this._menuCloseSubscription=t.close.subscribe((t=>{this._destroyMenu(t),"click"!==t&&"tab"!==t||!this._parentMaterialMenu||this._parentMaterialMenu.closed.emit(t)}))))}ngAfterContentInit(){this._checkMenu(),this._handleHover()}ngOnDestroy(){this._overlayRef&&(this._overlayRef.dispose(),this._overlayRef=null),this._element.nativeElement.removeEventListener("touchstart",this._handleTouchStart,tY),this._menuCloseSubscription.unsubscribe(),this._closingActionsSubscription.unsubscribe(),this._hoverSubscription.unsubscribe()}get menuOpen(){return this._menuOpen}get dir(){return this._dir&&"rtl"===this._dir.value?"rtl":"ltr"}triggersSubmenu(){return!(!this._menuItemInstance||!this._parentMaterialMenu)}toggleMenu(){return this._menuOpen?this.closeMenu():this.openMenu()}openMenu(){if(this._menuOpen)return;this._checkMenu();const t=this._createOverlay(),e=t.getConfig();this._setPosition(e.positionStrategy),e.hasBackdrop=null==this.menu.hasBackdrop?!this.triggersSubmenu():this.menu.hasBackdrop,t.attach(this._getPortal()),this.menu.lazyContent&&this.menu.lazyContent.attach(this.menuData),this._closingActionsSubscription=this._menuClosingActions().subscribe((()=>this.closeMenu())),this._initMenu(),this.menu instanceof XW&&this.menu._startAnimation()}closeMenu(){this.menu.close.emit()}focus(t,e){this._focusMonitor&&t?this._focusMonitor.focusVia(this._element,t,e):this._element.nativeElement.focus(e)}updatePosition(){var t;null===(t=this._overlayRef)||void 0===t||t.updatePosition()}_destroyMenu(t){if(!this._overlayRef||!this.menuOpen)return;const e=this.menu;this._closingActionsSubscription.unsubscribe(),this._overlayRef.detach(),!this.restoreFocus||"keydown"!==t&&this._openedBy&&this.triggersSubmenu()||this.focus(this._openedBy),this._openedBy=void 0,e instanceof XW?(e._resetAnimation(),e.lazyContent?e._animationDone.pipe(ce((t=>"void"===t.toState)),be(1),Ie(e.lazyContent._attached)).subscribe({next:()=>e.lazyContent.detach(),complete:()=>this._setIsMenuOpen(!1)}):this._setIsMenuOpen(!1)):(this._setIsMenuOpen(!1),e.lazyContent&&e.lazyContent.detach())}_initMenu(){this.menu.parentMenu=this.triggersSubmenu()?this._parentMaterialMenu:void 0,this.menu.direction=this.dir,this._setMenuElevation(),this.menu.focusFirstItem(this._openedBy||"program"),this._setIsMenuOpen(!0)}_setMenuElevation(){if(this.menu.setElevation){let t=0,e=this.menu.parentMenu;for(;e;)t++,e=e.parentMenu;this.menu.setElevation(t)}}_setIsMenuOpen(t){this._menuOpen=t,this._menuOpen?this.menuOpened.emit():this.menuClosed.emit(),this.triggersSubmenu()&&this._menuItemInstance._setHighlighted(t)}_checkMenu(){this.menu||"undefined"!=typeof ngDevMode&&!ngDevMode||(
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
function t(){throw Error('matMenuTriggerFor: must pass in an mat-menu instance.\n\n    Example:\n      <mat-menu #menu="matMenu"></mat-menu>\n      <button [matMenuTriggerFor]="menu"></button>')})()}_createOverlay(){if(!this._overlayRef){const t=this._getOverlayConfig();this._subscribeToPositions(t.positionStrategy),this._overlayRef=this._overlay.create(t),this._overlayRef.keydownEvents().subscribe()}return this._overlayRef}_getOverlayConfig(){return new VF({positionStrategy:this._overlay.position().flexibleConnectedTo(this._element).withLockedPosition().withGrowAfterOpen().withTransformOriginOn(".mat-menu-panel, .mat-mdc-menu-panel"),backdropClass:this.menu.backdropClass||"cdk-overlay-transparent-backdrop",panelClass:this.menu.overlayPanelClass,scrollStrategy:this._scrollStrategy(),direction:this._dir})}_subscribeToPositions(t){this.menu.setPositionClasses&&t.positionChanges.subscribe((t=>{this.menu.setPositionClasses("start"===t.connectionPair.overlayX?"after":"before","top"===t.connectionPair.overlayY?"below":"above")}))}_setPosition(t){let[e,n]="before"===this.menu.xPosition?["end","start"]:["start","end"],[o,i]="above"===this.menu.yPosition?["bottom","top"]:["top","bottom"],[a,r]=[o,i],[s,l]=[e,n],c=0;this.triggersSubmenu()?(l=e="before"===this.menu.xPosition?"start":"end",n=s="end"===e?"start":"end",c="bottom"===o?8:-8):this.menu.overlapTrigger||(a="top"===o?"bottom":"top",r="top"===i?"bottom":"top"),t.withPositions([{originX:e,originY:a,overlayX:s,overlayY:o,offsetY:c},{originX:n,originY:a,overlayX:l,overlayY:o,offsetY:c},{originX:e,originY:r,overlayX:s,overlayY:i,offsetY:-c},{originX:n,originY:r,overlayX:l,overlayY:i,offsetY:-c}])}_menuClosingActions(){const t=this._overlayRef.backdropClick(),e=this._overlayRef.detachments();return re(t,this._parentMaterialMenu?this._parentMaterialMenu.closed:Et(),this._parentMaterialMenu?this._parentMaterialMenu._hovered().pipe(ce((t=>t!==this._menuItemInstance)),ce((()=>this._menuOpen))):Et(),e)}_handleMousedown(t){gI(t)||(this._openedBy=0===t.button?"mouse":void 0,this.triggersSubmenu()&&t.preventDefault())}_handleKeydown(t){const e=t.keyCode;e!==mz&&e!==fz||(this._openedBy="keyboard"),this.triggersSubmenu()&&(39===e&&"ltr"===this.dir||37===e&&"rtl"===this.dir)&&(this._openedBy="keyboard",this.openMenu())}_handleClick(t){this.triggersSubmenu()?(t.stopPropagation(),this.openMenu()):this.toggleMenu()}_handleHover(){this.triggersSubmenu()&&this._parentMaterialMenu&&(this._hoverSubscription=this._parentMaterialMenu._hovered().pipe(ce((t=>t===this._menuItemInstance&&!t.disabled)),Ce(0,$)).subscribe((()=>{this._openedBy="mouse",this.menu instanceof XW&&this.menu._isAnimating?this.menu._animationDone.pipe(be(1),Ce(0,$),Ie(this._parentMaterialMenu._hovered())).subscribe((()=>this.openMenu())):this.openMenu()})))}_getPortal(){return this._portal&&this._portal.templateRef===this.menu.templateRef||(this._portal=new xF(this.menu.templateRef,this._viewContainerRef)),this._portal}}eY.ɵfac=function t(e){return new(e||eY)(Sm(pL),Sm(hg),Sm(eh),Sm(JW),Sm(UW,8),Sm(WW,10),Sm(HI,8),Sm(SI))},eY.ɵdir=lo({type:eY,selectors:[["","mat-menu-trigger-for",""],["","matMenuTriggerFor",""]],hostAttrs:["aria-haspopup","true",1,"mat-menu-trigger"],hostVars:2,hostBindings:function t(e,n){1&e&&Vm("mousedown",(function t(e){return n._handleMousedown(e)}))("keydown",(function t(e){return n._handleKeydown(e)}))("click",(function t(e){return n._handleClick(e)})),2&e&&jp("aria-expanded",n.menuOpen||null)("aria-controls",n.menuOpen?n.menu.panelId:null)},inputs:{restoreFocus:["matMenuTriggerRestoreFocus","restoreFocus"],_deprecatedMatMenuTriggerFor:["mat-menu-trigger-for","_deprecatedMatMenuTriggerFor"],menu:["matMenuTriggerFor","menu"],menuData:["matMenuTriggerData","menuData"]},outputs:{menuOpened:"menuOpened",onMenuOpen:"onMenuOpen",menuClosed:"menuClosed",onMenuClose:"onMenuClose"},exportAs:["matMenuTrigger"]}),eY.ctorParameters=()=>[{type:pL},{type:hg},{type:eh},{type:void 0,decorators:[{type:kr,args:[JW]}]},{type:void 0,decorators:[{type:kr,args:[UW]},{type:Sr}]},{type:WW,decorators:[{type:Sr},{type:Dr}]},{type:HI,decorators:[{type:Sr}]},{type:SI}],eY.propDecorators={_deprecatedMatMenuTriggerFor:[{type:xy,args:["mat-menu-trigger-for"]}],menu:[{type:xy,args:["matMenuTriggerFor"]}],menuData:[{type:xy,args:["matMenuTriggerData"]}],restoreFocus:[{type:xy,args:["matMenuTriggerRestoreFocus"]}],menuOpened:[{type:Oy}],onMenuOpen:[{type:Oy}],menuClosed:[{type:Oy}],onMenuClose:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(eY,[{type:Cy,args:[{selector:"[mat-menu-trigger-for], [matMenuTriggerFor]",host:{class:"mat-menu-trigger","aria-haspopup":"true","[attr.aria-expanded]":"menuOpen || null","[attr.aria-controls]":"menuOpen ? menu.panelId : null","(mousedown)":"_handleMousedown($event)","(keydown)":"_handleKeydown($event)","(click)":"_handleClick($event)"},exportAs:"matMenuTrigger"}]}],(function(){return[{type:pL},{type:hg},{type:eh},{type:void 0,decorators:[{type:kr,args:[JW]}]},{type:void 0,decorators:[{type:kr,args:[UW]},{type:Sr}]},{type:WW,decorators:[{type:Sr},{type:Dr}]},{type:HI,decorators:[{type:Sr}]},{type:SI}]}),{restoreFocus:[{type:xy,args:["matMenuTriggerRestoreFocus"]}],menuOpened:[{type:Oy}],onMenuOpen:[{type:Oy}],menuClosed:[{type:Oy}],onMenuClose:[{type:Oy}],_deprecatedMatMenuTriggerFor:[{type:xy,args:["mat-menu-trigger-for"]}],menu:[{type:xy,args:["matMenuTriggerFor"]}],menuData:[{type:xy,args:["matMenuTriggerData"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class nY{}nY.ɵfac=function t(e){return new(e||nY)},nY.ɵmod=ao({type:nY}),nY.ɵinj=vn({providers:[QW],imports:[XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(nY,[{type:Ay,args:[{exports:[eY,jW,XI],declarations:[eY,jW],providers:[QW]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(nY,{declarations:function(){return[eY,jW]},exports:function(){return[eY,jW,XI]}});class oY{}function iY(t,e){1&t&&Tm(0,"mat-icon",8)}function aY(t,e){1&t&&Tm(0,"mat-icon",9)}function rY(t,e){1&t&&Tm(0,"mat-icon",10)}var sY;oY.ɵfac=function t(e){return new(e||oY)},oY.ɵmod=ao({type:oY}),oY.ɵinj=vn({providers:[QW],imports:[[WM,XI,SH,yL,nY],yF,XI,nY]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(oY,[{type:Ay,args:[{imports:[WM,XI,SH,yL,nY],exports:[yF,XI,KW,WW,nY],declarations:[KW,WW],providers:[QW]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(oY,{declarations:function(){return[KW,WW]},imports:function(){return[WM,XI,SH,yL,nY]},exports:function(){return[yF,XI,KW,WW,nY]}}),(function(t){t[t.DEFAULT=0]="DEFAULT",t[t.DARK_MODE_ON=1]="DARK_MODE_ON",t[t.DARK_MODE_OFF=2]="DARK_MODE_OFF"})(sY||(sY={}));class lY{constructor(){this.DarkModeOverride=sY,this.onOverrideChanged=new Lh}getButtonTitle(){let t;switch(this.darkModeOverride){case sY.DEFAULT:t="Browser default";break;case sY.DARK_MODE_ON:t="Dark mode";break;case sY.DARK_MODE_OFF:t="Light mode"}return`Current mode: [${t}]. Switch between browser default, light, or dark theme.`}}lY.ɵfac=function t(e){return new(e||lY)},lY.ɵcmp=to({type:lY,selectors:[["app-header-dark-mode-toggle-component"]],inputs:{darkModeOverride:"darkModeOverride"},outputs:{onOverrideChanged:"onOverrideChanged"},decls:15,vars:6,consts:[["mat-icon-button","","aria-label","Menu for changing light or dark theme",3,"matMenuTriggerFor","ngSwitch","title"],["svgIcon","brightness_6_24px",4,"ngSwitchCase"],["svgIcon","light_mode_24px",4,"ngSwitchCase"],["svgIcon","dark_mode_24px",4,"ngSwitchCase"],["menu","matMenu"],["mat-menu-item","","title","Set the theme to match the default mode in the browser.",3,"click"],["mat-menu-item","","title","Force light TensorBoard theme.",3,"click"],["mat-menu-item","","title","Force dark TensorBoard theme.",3,"click"],["svgIcon","brightness_6_24px"],["svgIcon","light_mode_24px"],["svgIcon","dark_mode_24px"]],template:function t(e,n){1&e&&(Rm(0,"button",0),Qp(1,iY,1,0,"mat-icon",1),Qp(2,aY,1,0,"mat-icon",2),Qp(3,rY,1,0,"mat-icon",3),Am(),Rm(4,"mat-menu",null,4),Rm(6,"button",5),Vm("click",(function t(){return n.onOverrideChanged.emit(n.DarkModeOverride.DEFAULT)})),Rm(7,"label"),ku(8,"Browser default"),Am(),Am(),Rm(9,"button",6),Vm("click",(function t(){return n.onOverrideChanged.emit(n.DarkModeOverride.DARK_MODE_OFF)})),Rm(10,"label"),ku(11,"Light"),Am(),Am(),Rm(12,"button",7),Vm("click",(function t(){return n.onOverrideChanged.emit(n.DarkModeOverride.DARK_MODE_ON)})),Rm(13,"label"),ku(14,"Dark"),Am(),Am(),Am()),2&e&&(Dm("matMenuTriggerFor",$p(5))("ngSwitch",n.darkModeOverride)("title",n.getButtonTitle()),rc(1),Dm("ngSwitchCase",n.DarkModeOverride.DEFAULT),rc(1),Dm("ngSwitchCase",n.DarkModeOverride.DARK_MODE_OFF),rc(1),Dm("ngSwitchCase",n.DarkModeOverride.DARK_MODE_ON))},directives:[XH,eY,fM,gM,KW,WW,DW],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lY,[{type:My,args:[{selector:"app-header-dark-mode-toggle-component",template:'\n    <button\n      mat-icon-button\n      [matMenuTriggerFor]="menu"\n      aria-label="Menu for changing light or dark theme"\n      [ngSwitch]="darkModeOverride"\n      [title]="getButtonTitle()"\n    >\n      <mat-icon\n        *ngSwitchCase="DarkModeOverride.DEFAULT"\n        svgIcon="brightness_6_24px"\n      ></mat-icon>\n      <mat-icon\n        *ngSwitchCase="DarkModeOverride.DARK_MODE_OFF"\n        svgIcon="light_mode_24px"\n      ></mat-icon>\n      <mat-icon\n        *ngSwitchCase="DarkModeOverride.DARK_MODE_ON"\n        svgIcon="dark_mode_24px"\n      ></mat-icon>\n    </button>\n    <mat-menu #menu="matMenu">\n      <button\n        mat-menu-item\n        title="Set the theme to match the default mode in the browser."\n        (click)="onOverrideChanged.emit(DarkModeOverride.DEFAULT)"\n      >\n        <label>Browser default</label>\n      </button>\n      <button\n        mat-menu-item\n        title="Force light TensorBoard theme."\n        (click)="onOverrideChanged.emit(DarkModeOverride.DARK_MODE_OFF)"\n      >\n        <label>Light</label>\n      </button>\n      <button\n        mat-menu-item\n        title="Force dark TensorBoard theme."\n        (click)="onOverrideChanged.emit(DarkModeOverride.DARK_MODE_ON)"\n      >\n        <label>Dark</label>\n      </button>\n    </mat-menu>\n  '}]}],null,{darkModeOverride:[{type:xy}],onOverrideChanged:[{type:Oy}]});class cY{constructor(t){this.store=t,this.darkModeOverride$=this.store.select(QD).pipe(It((t=>null===t?sY.DEFAULT:t?sY.DARK_MODE_ON:sY.DARK_MODE_OFF)))}changeDarkMode(t){let e=null;switch(t){case sY.DEFAULT:e=null;break;case sY.DARK_MODE_OFF:e=!1;break;case sY.DARK_MODE_ON:e=!0}this.store.dispatch(WA({enableDarkMode:e}))}}cY.ɵfac=function t(e){return new(e||cY)(Sm(Iw))},cY.ɵcmp=to({type:cY,selectors:[["app-header-dark-mode-toggle"]],decls:2,vars:3,consts:[[3,"darkModeOverride","onOverrideChanged"]],template:function t(e,n){1&e&&(Rm(0,"app-header-dark-mode-toggle-component",0),Vm("onOverrideChanged",(function t(e){return n.changeDarkMode(e)})),Ah(1,"async"),Am()),2&e&&Dm("darkModeOverride",Th(1,1,n.darkModeOverride$))},directives:[lY],pipes:[wM],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(cY,[{type:My,args:[{selector:"app-header-dark-mode-toggle",template:'\n    <app-header-dark-mode-toggle-component\n      [darkModeOverride]="darkModeOverride$ | async"\n      (onOverrideChanged)="changeDarkMode($event)"\n    >\n    </app-header-dark-mode-toggle-component>\n  '}]}],(function(){return[{type:Iw}]}),null);const dY=Zw(vR,MR,((t,e)=>!(!e||!t[e])&&t[e].disable_reload));class pY{constructor(t){this.store=t,this.reloadDisabled$=this.store.select(dY),this.isReloading$=this.store.select(_R).pipe(fe(this.reloadDisabled$),It((([t,e])=>!e&&t===yE.LOADING))),this.lastLoadedTimeInMs$=this.store.select(CR)}triggerReload(){this.store.dispatch(vE())}getReloadTitle(t){return t?`Last Updated: ${t}`:"Loading..."}}function mY(t){return t.state!==yE.NOT_LOADED&&t.state!==yE.LOADING}pY.ɵfac=function t(e){return new(e||pY)(Sm(Iw))},pY.ɵcmp=to({type:pY,selectors:[["app-header-reload"]],decls:6,vars:13,consts:[["mat-icon-button","",1,"reload-button",3,"title","disabled","click"],["svgIcon","refresh_24px",1,"refresh-icon"]],template:function t(e,n){1&e&&(Rm(0,"button",0),Vm("click",(function t(){return n.triggerReload()})),Ah(1,"async"),Ah(2,"date"),Ah(3,"async"),Ah(4,"async"),Tm(5,"mat-icon",1),Am()),2&e&&(pu("loading",Th(1,4,n.isReloading$)),Dm("title",n.getReloadTitle(Nh(2,6,Th(3,9,n.lastLoadedTimeInMs$),"medium")))("disabled",Th(4,11,n.reloadDisabled$)))},directives:[XH,DW],pipes:[wM,RM],styles:[".reload-button[_ngcontent-%COMP%], .refresh-icon[_ngcontent-%COMP%] {\n        align-items: center;\n        display: flex;\n        justify-content: center;\n      }\n\n      .reload-button.loading[_ngcontent-%COMP%] {\n        animation: rotate 2s linear infinite;\n      }\n\n      @keyframes rotate {\n        0% {\n          transform: rotate(0deg);\n        }\n        50% {\n          transform: rotate(180deg);\n        }\n        100% {\n          transform: rotate(360deg);\n        }\n      }"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(pY,[{type:My,args:[{selector:"app-header-reload",template:'\n    <button\n      class="reload-button"\n      [class.loading]="isReloading$ | async"\n      mat-icon-button\n      (click)="triggerReload()"\n      [title]="getReloadTitle(lastLoadedTimeInMs$ | async | date: \'medium\')"\n      [disabled]="reloadDisabled$ | async"\n    >\n      <mat-icon class="refresh-icon" svgIcon="refresh_24px"></mat-icon>\n    </button>\n  ',styles:["\n      .reload-button,\n      .refresh-icon {\n        align-items: center;\n        display: flex;\n        justify-content: center;\n      }\n\n      .reload-button.loading {\n        animation: rotate 2s linear infinite;\n      }\n\n      @keyframes rotate {\n        0% {\n          transform: rotate(0deg);\n        }\n        50% {\n          transform: rotate(180deg);\n        }\n        100% {\n          transform: rotate(360deg);\n        }\n      }\n    "]}]}],(function(){return[{type:Iw}]}),null);const uY=yk(wN,bk(vN,(t=>mY(t)?Object.assign(Object.assign({},t),{settings:Object.assign(Object.assign({},t.settings),{reloadEnabled:!t.settings.reloadEnabled})}):t)),bk(xN,((t,{periodInMs:e})=>{if(!mY(t))return t;const n=e>=3e4?e:t.settings.reloadPeriodInMs;return Object.assign(Object.assign({},t),{settings:Object.assign(Object.assign({},t.settings),{reloadPeriodInMs:n})})})),bk(ON,((t,{size:e})=>{if(!mY(t))return t;const n=e>0?e:t.settings.pageSize;return Object.assign(Object.assign({},t),{settings:Object.assign(Object.assign({},t.settings),{pageSize:n})})})),bk(WS,((t,{partialSettings:e})=>{const n={};return Number.isFinite(e.pageSize)&&e.pageSize>0&&(n.pageSize=e.pageSize),"boolean"==typeof e.autoReload&&(n.reloadEnabled=e.autoReload),Number.isFinite(e.autoReloadPeriodInMs)&&e.autoReloadPeriodInMs>3e4&&(n.reloadPeriodInMs=e.autoReloadPeriodInMs),Object.assign(Object.assign({},t),{settings:Object.assign(Object.assign({},t.settings),n)})})));function fY(t,e){return uY(t,e)}const gY=["input"],hY=function(t){return{enterDuration:t}},bY=new Ga("mat-checkbox-default-options",{providedIn:"root",factory:function yY(){return{color:"accent",clickAction:"check-indeterminate"}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */});let _Y=0;const CY={color:"accent",clickAction:"check-indeterminate"},MY={provide:IV,useExisting:qe((()=>OY)),multi:!0};class vY{}const xY=$I(JI(QI(KI(class{constructor(t){this._elementRef=t}}))));class OY extends xY{constructor(t,e,n,o,i,a,r){super(t),this._changeDetectorRef=e,this._focusMonitor=n,this._ngZone=o,this._animationMode=a,this._options=r,this.ariaLabel="",this.ariaLabelledby=null,this._uniqueId="mat-checkbox-"+ ++_Y,this.id=this._uniqueId,this.labelPosition="after",this.name=null,this.change=new Lh,this.indeterminateChange=new Lh,this._onTouched=()=>{},this._currentAnimationClass="",this._currentCheckState=0,this._controlValueAccessorChangeFn=()=>{},this._checked=!1,this._disabled=!1,this._indeterminate=!1,this._options=this._options||CY,this.color=this.defaultColor=this._options.color||CY.color,this.tabIndex=parseInt(i)||0}get inputId(){return`${this.id||this._uniqueId}-input`}get required(){return this._required}set required(t){this._required=yz(t)}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0).subscribe((t=>{t||Promise.resolve().then((()=>{this._onTouched(),this._changeDetectorRef.markForCheck()}))})),this._syncIndeterminate(this._indeterminate)}ngAfterViewChecked(){}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef)}get checked(){return this._checked}set checked(t){t!=this.checked&&(this._checked=t,this._changeDetectorRef.markForCheck())}get disabled(){return this._disabled}set disabled(t){const e=yz(t);e!==this.disabled&&(this._disabled=e,this._changeDetectorRef.markForCheck())}get indeterminate(){return this._indeterminate}set indeterminate(t){const e=t!=this._indeterminate;this._indeterminate=yz(t),e&&(this._transitionCheckState(this._indeterminate?3:this.checked?1:2),this.indeterminateChange.emit(this._indeterminate)),this._syncIndeterminate(this._indeterminate)}_isRippleDisabled(){return this.disableRipple||this.disabled}_onLabelTextChange(){this._changeDetectorRef.detectChanges()}writeValue(t){this.checked=!!t}registerOnChange(t){this._controlValueAccessorChangeFn=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t}_getAriaChecked(){return this.checked?"true":this.indeterminate?"mixed":"false"}_transitionCheckState(t){let e=this._currentCheckState,n=this._elementRef.nativeElement;if(e!==t&&(this._currentAnimationClass.length>0&&n.classList.remove(this._currentAnimationClass),this._currentAnimationClass=this._getAnimationClassForCheckStateTransition(e,t),this._currentCheckState=t,this._currentAnimationClass.length>0)){n.classList.add(this._currentAnimationClass);const t=this._currentAnimationClass;this._ngZone.runOutsideAngular((()=>{setTimeout((()=>{n.classList.remove(t)}),1e3)}))}}_emitChangeEvent(){const t=new 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n="";switch(t){case 0:if(1===e)n="unchecked-checked";else{if(3!=e)return"";n="unchecked-indeterminate"}break;case 2:n=1===e?"unchecked-checked":"unchecked-indeterminate";break;case 1:n=2===e?"checked-unchecked":"checked-indeterminate";break;case 3:n=1===e?"indeterminate-checked":"indeterminate-unchecked"}return`mat-checkbox-anim-${n}`}_syncIndeterminate(t){const e=this._inputElement;e&&(e.nativeElement.indeterminate=t)}}OY.ɵfac=function t(e){return new(e||OY)(Sm(hg),Sm(Ug),Sm(SI),Sm(a_),Na("tabindex"),Sm(VP,8),Sm(bY,8))},OY.ɵcmp=to({type:OY,selectors:[["mat-checkbox"]],viewQuery:function t(e,n){if(1&e&&(Qh(gY,5),Qh(kH,5)),2&e){let t;Jh(t=tb())&&(n._inputElement=t.first),Jh(t=tb())&&(n.ripple=t.first)}},hostAttrs:[1,"mat-checkbox"],hostVars:12,hostBindings:function 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0 24 24",0,"xml","space","preserve","aria-hidden","true",1,"mat-checkbox-checkmark"],["fill","none","stroke","white","d","M4.1,12.7 9,17.6 20.3,6.3",1,"mat-checkbox-checkmark-path"],[1,"mat-checkbox-mixedmark"],[1,"mat-checkbox-label",3,"cdkObserveContent"],["checkboxLabel",""],[2,"display","none"]],template:function t(e,n){if(1&e&&(Zm(),Rm(0,"label",0,1),Rm(2,"span",2),Rm(3,"input",3,4),Vm("change",(function t(e){return n._onInteractionEvent(e)}))("click",(function t(e){return n._onInputClick(e)})),Am(),Rm(5,"span",5),Tm(6,"span",6),Am(),Tm(7,"span",7),Rm(8,"span",8),qi(),Rm(9,"svg",9),Tm(10,"path",10),Am(),Zi(),Tm(11,"span",11),Am(),Am(),Rm(12,"span",12,13),Vm("cdkObserveContent",(function t(){return n._onLabelTextChange()})),Rm(14,"span",14),ku(15," "),Am(),Xm(16),Am(),Am()),2&e){const t=$p(1),e=$p(13);jp("for",n.inputId),rc(2),pu("mat-checkbox-inner-container-no-side-margin",!e.textContent||!e.textContent.trim()),rc(1),Dm("id",n.inputId)("required",n.required)("checked",n.checked)("disabled",n.disabled)("tabIndex",n.tabIndex),jp("value",n.value)("name",n.name)("aria-label",n.ariaLabel||null)("aria-labelledby",n.ariaLabelledby)("aria-checked",n._getAriaChecked())("aria-describedby",n.ariaDescribedby),rc(2),Dm("matRippleTrigger",t)("matRippleDisabled",n._isRippleDisabled())("matRippleRadius",20)("matRippleCentered",!0)("matRippleAnimation",Mh(19,hY,"NoopAnimations"===n._animationMode?0:150))}},directives:[kH,jz],styles:["@keyframes mat-checkbox-fade-in-background{0%{opacity:0}50%{opacity:1}}@keyframes mat-checkbox-fade-out-background{0%,50%{opacity:1}100%{opacity:0}}@keyframes mat-checkbox-unchecked-checked-checkmark-path{0%,50%{stroke-dashoffset:22.910259}50%{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1)}100%{stroke-dashoffset:0}}@keyframes mat-checkbox-unchecked-indeterminate-mixedmark{0%,68.2%{transform:scaleX(0)}68.2%{animation-timing-function:cubic-bezier(0, 0, 0, 1)}100%{transform:scaleX(1)}}@keyframes mat-checkbox-checked-unchecked-checkmark-path{from{animation-timing-function:cubic-bezier(0.4, 0, 1, 1);stroke-dashoffset:0}to{stroke-dashoffset:-22.910259}}@keyframes mat-checkbox-checked-indeterminate-checkmark{from{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1);opacity:1;transform:rotate(0deg)}to{opacity:0;transform:rotate(45deg)}}@keyframes mat-checkbox-indeterminate-checked-checkmark{from{animation-timing-function:cubic-bezier(0.14, 0, 0, 1);opacity:0;transform:rotate(45deg)}to{opacity:1;transform:rotate(360deg)}}@keyframes mat-checkbox-checked-indeterminate-mixedmark{from{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1);opacity:0;transform:rotate(-45deg)}to{opacity:1;transform:rotate(0deg)}}@keyframes mat-checkbox-indeterminate-checked-mixedmark{from{animation-timing-function:cubic-bezier(0.14, 0, 0, 1);opacity:1;transform:rotate(0deg)}to{opacity:0;transform:rotate(315deg)}}@keyframes mat-checkbox-indeterminate-unchecked-mixedmark{0%{animation-timing-function:linear;opacity:1;transform:scaleX(1)}32.8%,100%{opacity:0;transform:scaleX(0)}}.mat-checkbox-background,.mat-checkbox-frame{top:0;left:0;right:0;bottom:0;position:absolute;border-radius:2px;box-sizing:border-box;pointer-events:none}.mat-checkbox{display:inline-block;transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);cursor:pointer;-webkit-tap-highlight-color:transparent}._mat-animation-noopable.mat-checkbox{transition:none;animation:none}.mat-checkbox .mat-ripple-element:not(.mat-checkbox-persistent-ripple){opacity:.16}.mat-checkbox .mat-checkbox-ripple{position:absolute;left:calc(50% - 20px);top:calc(50% - 20px);height:40px;width:40px;z-index:1;pointer-events:none}.cdk-high-contrast-active .mat-checkbox.cdk-keyboard-focused .mat-checkbox-ripple{outline:solid 3px}.mat-checkbox-layout{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:inherit;align-items:baseline;vertical-align:middle;display:inline-flex;white-space:nowrap}.mat-checkbox-label{-webkit-user-select:auto;-moz-user-select:auto;-ms-user-select:auto;user-select:auto}.mat-checkbox-inner-container{display:inline-block;height:16px;line-height:0;margin:auto;margin-right:8px;order:0;position:relative;vertical-align:middle;white-space:nowrap;width:16px;flex-shrink:0}[dir=rtl] .mat-checkbox-inner-container{margin-left:8px;margin-right:auto}.mat-checkbox-inner-container-no-side-margin{margin-left:0;margin-right:0}.mat-checkbox-frame{background-color:transparent;transition:border-color 90ms cubic-bezier(0, 0, 0.2, 0.1);border-width:2px;border-style:solid}._mat-animation-noopable .mat-checkbox-frame{transition:none}.mat-checkbox-background{align-items:center;display:inline-flex;justify-content:center;transition:background-color 90ms cubic-bezier(0, 0, 0.2, 0.1),opacity 90ms cubic-bezier(0, 0, 0.2, 0.1);-webkit-print-color-adjust:exact;color-adjust:exact}._mat-animation-noopable .mat-checkbox-background{transition:none}.cdk-high-contrast-active .mat-checkbox .mat-checkbox-background{background:none}.mat-checkbox-persistent-ripple{display:block;width:100%;height:100%;transform:none}.mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{opacity:.04}.mat-checkbox.cdk-keyboard-focused .mat-checkbox-persistent-ripple{opacity:.12}.mat-checkbox-persistent-ripple,.mat-checkbox.mat-checkbox-disabled .mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{opacity:0}@media(hover: none){.mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{display:none}}.mat-checkbox-checkmark{top:0;left:0;right:0;bottom:0;position:absolute;width:100%}.mat-checkbox-checkmark-path{stroke-dashoffset:22.910259;stroke-dasharray:22.910259;stroke-width:2.1333333333px}.cdk-high-contrast-black-on-white .mat-checkbox-checkmark-path{stroke:#000 !important}.mat-checkbox-mixedmark{width:calc(100% - 6px);height:2px;opacity:0;transform:scaleX(0) rotate(0deg);border-radius:2px}.cdk-high-contrast-active .mat-checkbox-mixedmark{height:0;border-top:solid 2px;margin-top:2px}.mat-checkbox-label-before .mat-checkbox-inner-container{order:1;margin-left:8px;margin-right:auto}[dir=rtl] .mat-checkbox-label-before .mat-checkbox-inner-container{margin-left:auto;margin-right:8px}.mat-checkbox-checked .mat-checkbox-checkmark{opacity:1}.mat-checkbox-checked .mat-checkbox-checkmark-path{stroke-dashoffset:0}.mat-checkbox-checked .mat-checkbox-mixedmark{transform:scaleX(1) rotate(-45deg)}.mat-checkbox-indeterminate .mat-checkbox-checkmark{opacity:0;transform:rotate(45deg)}.mat-checkbox-indeterminate .mat-checkbox-checkmark-path{stroke-dashoffset:0}.mat-checkbox-indeterminate .mat-checkbox-mixedmark{opacity:1;transform:scaleX(1) rotate(0deg)}.mat-checkbox-unchecked .mat-checkbox-background{background-color:transparent}.mat-checkbox-disabled{cursor:default}.cdk-high-contrast-active .mat-checkbox-disabled{opacity:.5}.mat-checkbox-anim-unchecked-checked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-in-background}.mat-checkbox-anim-unchecked-checked .mat-checkbox-checkmark-path{animation:180ms linear 0ms mat-checkbox-unchecked-checked-checkmark-path}.mat-checkbox-anim-unchecked-indeterminate .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-in-background}.mat-checkbox-anim-unchecked-indeterminate .mat-checkbox-mixedmark{animation:90ms linear 0ms mat-checkbox-unchecked-indeterminate-mixedmark}.mat-checkbox-anim-checked-unchecked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-out-background}.mat-checkbox-anim-checked-unchecked .mat-checkbox-checkmark-path{animation:90ms linear 0ms mat-checkbox-checked-unchecked-checkmark-path}.mat-checkbox-anim-checked-indeterminate .mat-checkbox-checkmark{animation:90ms linear 0ms mat-checkbox-checked-indeterminate-checkmark}.mat-checkbox-anim-checked-indeterminate .mat-checkbox-mixedmark{animation:90ms linear 0ms mat-checkbox-checked-indeterminate-mixedmark}.mat-checkbox-anim-indeterminate-checked .mat-checkbox-checkmark{animation:500ms linear 0ms mat-checkbox-indeterminate-checked-checkmark}.mat-checkbox-anim-indeterminate-checked .mat-checkbox-mixedmark{animation:500ms linear 0ms mat-checkbox-indeterminate-checked-mixedmark}.mat-checkbox-anim-indeterminate-unchecked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-out-background}.mat-checkbox-anim-indeterminate-unchecked .mat-checkbox-mixedmark{animation:300ms linear 0ms mat-checkbox-indeterminate-unchecked-mixedmark}.mat-checkbox-input{bottom:0;left:50%}\n"],encapsulation:2,changeDetection:0}),OY.ctorParameters=()=>[{type:hg},{type:Ug},{type:SI},{type:a_},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[bY]}]}],OY.propDecorators={ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],ariaDescribedby:[{type:xy,args:["aria-describedby"]}],id:[{type:xy}],required:[{type:xy}],labelPosition:[{type:xy}],name:[{type:xy}],change:[{type:Oy}],indeterminateChange:[{type:Oy}],value:[{type:xy}],_inputElement:[{type:Za,args:["input"]}],ripple:[{type:Za,args:[kH]}],checked:[{type:xy}],disabled:[{type:xy}],indeterminate:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(OY,[{type:My,args:[{selector:"mat-checkbox",template:'<label [attr.for]="inputId" class="mat-checkbox-layout" #label>\n  <span class="mat-checkbox-inner-container"\n       [class.mat-checkbox-inner-container-no-side-margin]="!checkboxLabel.textContent || !checkboxLabel.textContent.trim()">\n    <input #input\n           class="mat-checkbox-input cdk-visually-hidden" type="checkbox"\n           [id]="inputId"\n           [required]="required"\n           [checked]="checked"\n           [attr.value]="value"\n           [disabled]="disabled"\n           [attr.name]="name"\n           [tabIndex]="tabIndex"\n           [attr.aria-label]="ariaLabel || null"\n           [attr.aria-labelledby]="ariaLabelledby"\n           [attr.aria-checked]="_getAriaChecked()"\n           [attr.aria-describedby]="ariaDescribedby"\n           (change)="_onInteractionEvent($event)"\n           (click)="_onInputClick($event)">\n    <span matRipple class="mat-checkbox-ripple mat-focus-indicator"\n         [matRippleTrigger]="label"\n         [matRippleDisabled]="_isRippleDisabled()"\n         [matRippleRadius]="20"\n         [matRippleCentered]="true"\n         [matRippleAnimation]="{enterDuration: _animationMode === \'NoopAnimations\' ? 0 : 150}">\n      <span class="mat-ripple-element mat-checkbox-persistent-ripple"></span>\n    </span>\n    <span class="mat-checkbox-frame"></span>\n    <span class="mat-checkbox-background">\n      <svg version="1.1"\n           focusable="false"\n           class="mat-checkbox-checkmark"\n           viewBox="0 0 24 24"\n           xml:space="preserve"\n           aria-hidden="true">\n        <path class="mat-checkbox-checkmark-path"\n              fill="none"\n              stroke="white"\n              d="M4.1,12.7 9,17.6 20.3,6.3"/>\n      </svg>\n      \x3c!-- Element for rendering the indeterminate state checkbox. --\x3e\n      <span class="mat-checkbox-mixedmark"></span>\n    </span>\n  </span>\n  <span class="mat-checkbox-label" #checkboxLabel (cdkObserveContent)="_onLabelTextChange()">\n    \x3c!-- Add an invisible span so JAWS can read the label --\x3e\n    <span style="display:none">&nbsp;</span>\n    <ng-content></ng-content>\n  </span>\n</label>\n',exportAs:"matCheckbox",host:{class:"mat-checkbox","[id]":"id","[attr.tabindex]":"null","[class.mat-checkbox-indeterminate]":"indeterminate","[class.mat-checkbox-checked]":"checked","[class.mat-checkbox-disabled]":"disabled","[class.mat-checkbox-label-before]":'labelPosition == "before"',"[class._mat-animation-noopable]":"_animationMode === 'NoopAnimations'"},providers:[MY],inputs:["disableRipple","color","tabIndex"],encapsulation:Hn.None,changeDetection:zn.OnPush,styles:["@keyframes mat-checkbox-fade-in-background{0%{opacity:0}50%{opacity:1}}@keyframes mat-checkbox-fade-out-background{0%,50%{opacity:1}100%{opacity:0}}@keyframes mat-checkbox-unchecked-checked-checkmark-path{0%,50%{stroke-dashoffset:22.910259}50%{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1)}100%{stroke-dashoffset:0}}@keyframes mat-checkbox-unchecked-indeterminate-mixedmark{0%,68.2%{transform:scaleX(0)}68.2%{animation-timing-function:cubic-bezier(0, 0, 0, 1)}100%{transform:scaleX(1)}}@keyframes mat-checkbox-checked-unchecked-checkmark-path{from{animation-timing-function:cubic-bezier(0.4, 0, 1, 1);stroke-dashoffset:0}to{stroke-dashoffset:-22.910259}}@keyframes mat-checkbox-checked-indeterminate-checkmark{from{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1);opacity:1;transform:rotate(0deg)}to{opacity:0;transform:rotate(45deg)}}@keyframes mat-checkbox-indeterminate-checked-checkmark{from{animation-timing-function:cubic-bezier(0.14, 0, 0, 1);opacity:0;transform:rotate(45deg)}to{opacity:1;transform:rotate(360deg)}}@keyframes mat-checkbox-checked-indeterminate-mixedmark{from{animation-timing-function:cubic-bezier(0, 0, 0.2, 0.1);opacity:0;transform:rotate(-45deg)}to{opacity:1;transform:rotate(0deg)}}@keyframes mat-checkbox-indeterminate-checked-mixedmark{from{animation-timing-function:cubic-bezier(0.14, 0, 0, 1);opacity:1;transform:rotate(0deg)}to{opacity:0;transform:rotate(315deg)}}@keyframes mat-checkbox-indeterminate-unchecked-mixedmark{0%{animation-timing-function:linear;opacity:1;transform:scaleX(1)}32.8%,100%{opacity:0;transform:scaleX(0)}}.mat-checkbox-background,.mat-checkbox-frame{top:0;left:0;right:0;bottom:0;position:absolute;border-radius:2px;box-sizing:border-box;pointer-events:none}.mat-checkbox{display:inline-block;transition:background 400ms cubic-bezier(0.25, 0.8, 0.25, 1),box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);cursor:pointer;-webkit-tap-highlight-color:transparent}._mat-animation-noopable.mat-checkbox{transition:none;animation:none}.mat-checkbox .mat-ripple-element:not(.mat-checkbox-persistent-ripple){opacity:.16}.mat-checkbox .mat-checkbox-ripple{position:absolute;left:calc(50% - 20px);top:calc(50% - 20px);height:40px;width:40px;z-index:1;pointer-events:none}.cdk-high-contrast-active .mat-checkbox.cdk-keyboard-focused .mat-checkbox-ripple{outline:solid 3px}.mat-checkbox-layout{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;cursor:inherit;align-items:baseline;vertical-align:middle;display:inline-flex;white-space:nowrap}.mat-checkbox-label{-webkit-user-select:auto;-moz-user-select:auto;-ms-user-select:auto;user-select:auto}.mat-checkbox-inner-container{display:inline-block;height:16px;line-height:0;margin:auto;margin-right:8px;order:0;position:relative;vertical-align:middle;white-space:nowrap;width:16px;flex-shrink:0}[dir=rtl] .mat-checkbox-inner-container{margin-left:8px;margin-right:auto}.mat-checkbox-inner-container-no-side-margin{margin-left:0;margin-right:0}.mat-checkbox-frame{background-color:transparent;transition:border-color 90ms cubic-bezier(0, 0, 0.2, 0.1);border-width:2px;border-style:solid}._mat-animation-noopable .mat-checkbox-frame{transition:none}.mat-checkbox-background{align-items:center;display:inline-flex;justify-content:center;transition:background-color 90ms cubic-bezier(0, 0, 0.2, 0.1),opacity 90ms cubic-bezier(0, 0, 0.2, 0.1);-webkit-print-color-adjust:exact;color-adjust:exact}._mat-animation-noopable .mat-checkbox-background{transition:none}.cdk-high-contrast-active .mat-checkbox .mat-checkbox-background{background:none}.mat-checkbox-persistent-ripple{display:block;width:100%;height:100%;transform:none}.mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{opacity:.04}.mat-checkbox.cdk-keyboard-focused .mat-checkbox-persistent-ripple{opacity:.12}.mat-checkbox-persistent-ripple,.mat-checkbox.mat-checkbox-disabled .mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{opacity:0}@media(hover: none){.mat-checkbox-inner-container:hover .mat-checkbox-persistent-ripple{display:none}}.mat-checkbox-checkmark{top:0;left:0;right:0;bottom:0;position:absolute;width:100%}.mat-checkbox-checkmark-path{stroke-dashoffset:22.910259;stroke-dasharray:22.910259;stroke-width:2.1333333333px}.cdk-high-contrast-black-on-white .mat-checkbox-checkmark-path{stroke:#000 !important}.mat-checkbox-mixedmark{width:calc(100% - 6px);height:2px;opacity:0;transform:scaleX(0) rotate(0deg);border-radius:2px}.cdk-high-contrast-active .mat-checkbox-mixedmark{height:0;border-top:solid 2px;margin-top:2px}.mat-checkbox-label-before .mat-checkbox-inner-container{order:1;margin-left:8px;margin-right:auto}[dir=rtl] .mat-checkbox-label-before .mat-checkbox-inner-container{margin-left:auto;margin-right:8px}.mat-checkbox-checked .mat-checkbox-checkmark{opacity:1}.mat-checkbox-checked .mat-checkbox-checkmark-path{stroke-dashoffset:0}.mat-checkbox-checked .mat-checkbox-mixedmark{transform:scaleX(1) rotate(-45deg)}.mat-checkbox-indeterminate .mat-checkbox-checkmark{opacity:0;transform:rotate(45deg)}.mat-checkbox-indeterminate .mat-checkbox-checkmark-path{stroke-dashoffset:0}.mat-checkbox-indeterminate .mat-checkbox-mixedmark{opacity:1;transform:scaleX(1) rotate(0deg)}.mat-checkbox-unchecked .mat-checkbox-background{background-color:transparent}.mat-checkbox-disabled{cursor:default}.cdk-high-contrast-active .mat-checkbox-disabled{opacity:.5}.mat-checkbox-anim-unchecked-checked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-in-background}.mat-checkbox-anim-unchecked-checked .mat-checkbox-checkmark-path{animation:180ms linear 0ms mat-checkbox-unchecked-checked-checkmark-path}.mat-checkbox-anim-unchecked-indeterminate .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-in-background}.mat-checkbox-anim-unchecked-indeterminate .mat-checkbox-mixedmark{animation:90ms linear 0ms mat-checkbox-unchecked-indeterminate-mixedmark}.mat-checkbox-anim-checked-unchecked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-out-background}.mat-checkbox-anim-checked-unchecked .mat-checkbox-checkmark-path{animation:90ms linear 0ms mat-checkbox-checked-unchecked-checkmark-path}.mat-checkbox-anim-checked-indeterminate .mat-checkbox-checkmark{animation:90ms linear 0ms mat-checkbox-checked-indeterminate-checkmark}.mat-checkbox-anim-checked-indeterminate .mat-checkbox-mixedmark{animation:90ms linear 0ms mat-checkbox-checked-indeterminate-mixedmark}.mat-checkbox-anim-indeterminate-checked .mat-checkbox-checkmark{animation:500ms linear 0ms mat-checkbox-indeterminate-checked-checkmark}.mat-checkbox-anim-indeterminate-checked .mat-checkbox-mixedmark{animation:500ms linear 0ms mat-checkbox-indeterminate-checked-mixedmark}.mat-checkbox-anim-indeterminate-unchecked .mat-checkbox-background{animation:180ms linear 0ms mat-checkbox-fade-out-background}.mat-checkbox-anim-indeterminate-unchecked .mat-checkbox-mixedmark{animation:300ms linear 0ms mat-checkbox-indeterminate-unchecked-mixedmark}.mat-checkbox-input{bottom:0;left:50%}\n"]}]}],(function(){return[{type:hg},{type:Ug},{type:SI},{type:a_},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[bY]}]}]}),{ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],id:[{type:xy}],labelPosition:[{type:xy}],name:[{type:xy}],change:[{type:Oy}],indeterminateChange:[{type:Oy}],required:[{type:xy}],checked:[{type:xy}],disabled:[{type:xy}],indeterminate:[{type:xy}],ariaDescribedby:[{type:xy,args:["aria-describedby"]}],value:[{type:xy}],_inputElement:[{type:Za,args:["input"]}],ripple:[{type:Za,args:[kH]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const PY={provide:GV,useExisting:qe((()=>wY)),multi:!0};class wY extends KU{}wY.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(wY)))(n||wY)}})(),wY.ɵdir=lo({type:wY,selectors:[["mat-checkbox","required","","formControlName",""],["mat-checkbox","required","","formControl",""],["mat-checkbox","required","","ngModel",""]],features:[pg([PY]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(wY,[{type:Cy,args:[{selector:"mat-checkbox[required][formControlName],\n             mat-checkbox[required][formControl], mat-checkbox[required][ngModel]",providers:[PY]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class kY{}kY.ɵfac=function t(e){return new(e||kY)},kY.ɵmod=ao({type:kY}),kY.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(kY,[{type:Ay,args:[{exports:[wY],declarations:[wY]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(kY,{declarations:[wY],exports:[wY]});class SY{}SY.ɵfac=function t(e){return new(e||SY)},SY.ɵmod=ao({type:SY}),SY.ɵinj=vn({imports:[[SH,XI,Uz,kY],XI,kY]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(SY,[{type:Ay,args:[{imports:[SH,XI,Uz,kY],exports:[OY,XI,kY],declarations:[OY]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(SY,{declarations:function(){return[OY]},imports:function(){return[SH,XI,Uz,kY]},exports:function(){return[OY,XI,kY]}});const DY=Nz({passive:!0});class EY{constructor(t,e){this._platform=t,this._ngZone=e,this._monitoredElements=new Map}monitor(t){if(!this._platform.isBrowser)return rt;const e=xz(t),n=this._monitoredElements.get(e);if(n)return n.subject;const o=new I,i="cdk-text-field-autofilled",a=t=>{"cdk-text-field-autofill-start"!==t.animationName||e.classList.contains(i)?"cdk-text-field-autofill-end"===t.animationName&&e.classList.contains(i)&&(e.classList.remove(i),this._ngZone.run((()=>o.next({target:t.target,isAutofilled:!1})))):(e.classList.add(i),this._ngZone.run((()=>o.next({target:t.target,isAutofilled:!0}))))};return this._ngZone.runOutsideAngular((()=>{e.addEventListener("animationstart",a,DY),e.classList.add("cdk-text-field-autofill-monitored")})),this._monitoredElements.set(e,{subject:o,unlisten:()=>{e.removeEventListener("animationstart",a,DY)}}),o}stopMonitoring(t){const e=xz(t),n=this._monitoredElements.get(e);n&&(n.unlisten(),n.subject.complete(),e.classList.remove("cdk-text-field-autofill-monitored"),e.classList.remove("cdk-text-field-autofilled"),this._monitoredElements.delete(e))}ngOnDestroy(){this._monitoredElements.forEach(((t,e)=>this.stopMonitoring(e)))}}EY.ɵfac=function t(e){return new(e||EY)(vr(wz),vr(a_))},EY.ɵprov=Mn({factory:function t(){return new EY(vr(wz),vr(a_))},token:EY,providedIn:"root"}),EY.ctorParameters=()=>[{type:wz},{type:a_}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(EY,[{type:im,args:[{providedIn:"root"}]}],(function(){return[{type:wz},{type:a_}]}),null);class RY{constructor(t,e){this._elementRef=t,this._autofillMonitor=e,this.cdkAutofill=new Lh}ngOnInit(){this._autofillMonitor.monitor(this._elementRef).subscribe((t=>this.cdkAutofill.emit(t)))}ngOnDestroy(){this._autofillMonitor.stopMonitoring(this._elementRef)}}RY.ɵfac=function t(e){return new(e||RY)(Sm(hg),Sm(EY))},RY.ɵdir=lo({type:RY,selectors:[["","cdkAutofill",""]],outputs:{cdkAutofill:"cdkAutofill"}}),RY.ctorParameters=()=>[{type:hg},{type:EY}],RY.propDecorators={cdkAutofill:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(RY,[{type:Cy,args:[{selector:"[cdkAutofill]"}]}],(function(){return[{type:hg},{type:EY}]}),{cdkAutofill:[{type:Oy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class AY{constructor(t,e,n,o){this._elementRef=t,this._platform=e,this._ngZone=n,this._destroyed=new I,this._enabled=!0,this._previousMinRows=-1,this._isViewInited=!1,this._handleFocusEvent=t=>{this._hasFocus="focus"===t.type},this._document=o,this._textareaElement=this._elementRef.nativeElement}get minRows(){return this._minRows}set minRows(t){this._minRows=_z(t),this._setMinHeight()}get maxRows(){return this._maxRows}set maxRows(t){this._maxRows=_z(t),this._setMaxHeight()}get enabled(){return this._enabled}set enabled(t){t=yz(t),this._enabled!==t&&((this._enabled=t)?this.resizeToFitContent(!0):this.reset())}get placeholder(){return this._textareaElement.placeholder}set placeholder(t){this._cachedPlaceholderHeight=void 0,this._textareaElement.placeholder=t,this._cacheTextareaPlaceholderHeight()}_setMinHeight(){const t=this.minRows&&this._cachedLineHeight?this.minRows*this._cachedLineHeight+"px":null;t&&(this._textareaElement.style.minHeight=t)}_setMaxHeight(){const t=this.maxRows&&this._cachedLineHeight?this.maxRows*this._cachedLineHeight+"px":null;t&&(this._textareaElement.style.maxHeight=t)}ngAfterViewInit(){this._platform.isBrowser&&(this._initialHeight=this._textareaElement.style.height,this.resizeToFitContent(),this._ngZone.runOutsideAngular((()=>{oe(this._getWindow(),"resize").pipe(de(16),Ie(this._destroyed)).subscribe((()=>this.resizeToFitContent(!0))),this._textareaElement.addEventListener("focus",this._handleFocusEvent),this._textareaElement.addEventListener("blur",this._handleFocusEvent)})),this._isViewInited=!0,this.resizeToFitContent(!0))}ngOnDestroy(){this._textareaElement.removeEventListener("focus",this._handleFocusEvent),this._textareaElement.removeEventListener("blur",this._handleFocusEvent),this._destroyed.next(),this._destroyed.complete()}_cacheTextareaLineHeight(){if(this._cachedLineHeight)return;let t=this._textareaElement.cloneNode(!1);t.rows=1,t.style.position="absolute",t.style.visibility="hidden",t.style.border="none",t.style.padding="0",t.style.height="",t.style.minHeight="",t.style.maxHeight="",t.style.overflow="hidden",this._textareaElement.parentNode.appendChild(t),this._cachedLineHeight=t.clientHeight,this._textareaElement.parentNode.removeChild(t),this._setMinHeight(),this._setMaxHeight()}_measureScrollHeight(){const t=this._textareaElement,e=t.style.marginBottom||"",n=this._platform.FIREFOX,o=n&&this._hasFocus,i=n?"cdk-textarea-autosize-measuring-firefox":"cdk-textarea-autosize-measuring";o&&(t.style.marginBottom=`${t.clientHeight}px`),t.classList.add(i);const a=t.scrollHeight-4;return t.classList.remove(i),o&&(t.style.marginBottom=e),a}_cacheTextareaPlaceholderHeight(){if(!this._isViewInited||null!=this._cachedPlaceholderHeight)return;if(!this.placeholder)return void(this._cachedPlaceholderHeight=0);const t=this._textareaElement.value;this._textareaElement.value=this._textareaElement.placeholder,this._cachedPlaceholderHeight=this._measureScrollHeight(),this._textareaElement.value=t}ngDoCheck(){this._platform.isBrowser&&this.resizeToFitContent()}resizeToFitContent(t=!1){if(!this._enabled)return;if(this._cacheTextareaLineHeight(),this._cacheTextareaPlaceholderHeight(),!this._cachedLineHeight)return;const e=this._elementRef.nativeElement,n=e.value;if(!t&&this._minRows===this._previousMinRows&&n===this._previousValue)return;const o=this._measureScrollHeight(),i=Math.max(o,this._cachedPlaceholderHeight||0);e.style.height=`${i}px`,this._ngZone.runOutsideAngular((()=>{"undefined"!=typeof requestAnimationFrame?requestAnimationFrame((()=>this._scrollToCaretPosition(e))):setTimeout((()=>this._scrollToCaretPosition(e)))})),this._previousValue=n,this._previousMinRows=this._minRows}reset(){void 0!==this._initialHeight&&(this._textareaElement.style.height=this._initialHeight)}_noopInputHandler(){}_getDocument(){return this._document||document}_getWindow(){return this._getDocument().defaultView||window}_scrollToCaretPosition(t){const{selectionStart:e,selectionEnd:n}=t;!this._destroyed.isStopped&&this._hasFocus&&t.setSelectionRange(e,n)}}AY.ɵfac=function t(e){return new(e||AY)(Sm(hg),Sm(wz),Sm(a_),Sm(Z_,8))},AY.ɵdir=lo({type:AY,selectors:[["textarea","cdkTextareaAutosize",""]],hostAttrs:["rows","1",1,"cdk-textarea-autosize"],hostBindings:function t(e,n){1&e&&Vm("input",(function t(){return n._noopInputHandler()}))},inputs:{minRows:["cdkAutosizeMinRows","minRows"],maxRows:["cdkAutosizeMaxRows","maxRows"],enabled:["cdkTextareaAutosize","enabled"],placeholder:"placeholder"},exportAs:["cdkTextareaAutosize"]}),AY.ctorParameters=()=>[{type:hg},{type:wz},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]}],AY.propDecorators={minRows:[{type:xy,args:["cdkAutosizeMinRows"]}],maxRows:[{type:xy,args:["cdkAutosizeMaxRows"]}],enabled:[{type:xy,args:["cdkTextareaAutosize"]}],placeholder:[{type:xy}],_noopInputHandler:[{type:wy,args:["input"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(AY,[{type:Cy,args:[{selector:"textarea[cdkTextareaAutosize]",exportAs:"cdkTextareaAutosize",host:{class:"cdk-textarea-autosize",rows:"1"}}]}],(function(){return[{type:hg},{type:wz},{type:a_},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]}]}),{minRows:[{type:xy,args:["cdkAutosizeMinRows"]}],maxRows:[{type:xy,args:["cdkAutosizeMaxRows"]}],enabled:[{type:xy,args:["cdkTextareaAutosize"]}],placeholder:[{type:xy}],_noopInputHandler:[{type:wy,args:["input"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class TY{}TY.ɵfac=function t(e){return new(e||TY)},TY.ɵmod=ao({type:TY}),TY.ɵinj=vn({imports:[[kz]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(TY,[{type:Ay,args:[{declarations:[RY,AY],imports:[kz],exports:[RY,AY]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(TY,{declarations:function(){return[RY,AY]},imports:function(){return[kz]},exports:function(){return[RY,AY]}});class NY extends AY{get matAutosizeMinRows(){return this.minRows}set matAutosizeMinRows(t){this.minRows=t}get matAutosizeMaxRows(){return this.maxRows}set matAutosizeMaxRows(t){this.maxRows=t}get matAutosize(){return this.enabled}set matAutosize(t){this.enabled=t}get matTextareaAutosize(){return this.enabled}set matTextareaAutosize(t){this.enabled=t}}NY.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(NY)))(n||NY)}})(),NY.ɵdir=lo({type:NY,selectors:[["textarea","mat-autosize",""],["textarea","matTextareaAutosize",""]],hostAttrs:["rows","1",1,"cdk-textarea-autosize","mat-autosize"],inputs:{cdkAutosizeMinRows:"cdkAutosizeMinRows",cdkAutosizeMaxRows:"cdkAutosizeMaxRows",matAutosizeMinRows:"matAutosizeMinRows",matAutosizeMaxRows:"matAutosizeMaxRows",matAutosize:["mat-autosize","matAutosize"],matTextareaAutosize:"matTextareaAutosize"},exportAs:["matTextareaAutosize"],features:[xp]}),NY.propDecorators={matAutosizeMinRows:[{type:xy}],matAutosizeMaxRows:[{type:xy}],matAutosize:[{type:xy,args:["mat-autosize"]}],matTextareaAutosize:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(NY,[{type:Cy,args:[{selector:"textarea[mat-autosize], textarea[matTextareaAutosize]",exportAs:"matTextareaAutosize",inputs:["cdkAutosizeMinRows","cdkAutosizeMaxRows"],host:{class:"cdk-textarea-autosize mat-autosize",rows:"1"}}]}],null,{matAutosizeMinRows:[{type:xy}],matAutosizeMaxRows:[{type:xy}],matAutosize:[{type:xy,args:["mat-autosize"]}],matTextareaAutosize:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const zY=new Ga("MAT_INPUT_VALUE_ACCESSOR"),IY=["button","checkbox","file","hidden","image","radio","range","reset","submit"];
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let HY=0;const FY=tH(class{constructor(t,e,n,o){this._defaultErrorStateMatcher=t,this._parentForm=e,this._parentFormGroup=n,this.ngControl=o}});class LY extends FY{constructor(t,e,n,o,i,a,r,s,l,c){super(a,o,i,n),this._elementRef=t,this._platform=e,this._autofillMonitor=s,this._formField=c,this._uid="mat-input-"+HY++,this.focused=!1,this.stateChanges=new I,this.controlType="mat-input",this.autofilled=!1,this._disabled=!1,this._required=!1,this._type="text",this._readonly=!1,this._neverEmptyInputTypes=["date","datetime","datetime-local","month","time","week"].filter((t=>Dz().has(t)));const d=this._elementRef.nativeElement,p=d.nodeName.toLowerCase();this._inputValueAccessor=r||d,this._previousNativeValue=this.value,this.id=this.id,e.IOS&&l.runOutsideAngular((()=>{t.nativeElement.addEventListener("keyup",(t=>{const e=t.target;e.value||0!==e.selectionStart||0!==e.selectionEnd||(e.setSelectionRange(1,1),e.setSelectionRange(0,0))}))})),this._isServer=!this._platform.isBrowser,this._isNativeSelect="select"===p,this._isTextarea="textarea"===p,this._isInFormField=!!c,this._isNativeSelect&&(this.controlType=d.multiple?"mat-native-select-multiple":"mat-native-select")}get disabled(){return this.ngControl&&null!==this.ngControl.disabled?this.ngControl.disabled:this._disabled}set disabled(t){this._disabled=yz(t),this.focused&&(this.focused=!1,this.stateChanges.next())}get id(){return this._id}set id(t){this._id=t||this._uid}get required(){return this._required}set required(t){this._required=yz(t)}get type(){return this._type}set type(t){this._type=t||"text",this._validateType(),!this._isTextarea&&Dz().has(this._type)&&(this._elementRef.nativeElement.type=this._type)}get value(){return this._inputValueAccessor.value}set value(t){t!==this.value&&(this._inputValueAccessor.value=t,this.stateChanges.next())}get readonly(){return this._readonly}set readonly(t){this._readonly=yz(t)}ngAfterViewInit(){this._platform.isBrowser&&this._autofillMonitor.monitor(this._elementRef.nativeElement).subscribe((t=>{this.autofilled=t.isAutofilled,this.stateChanges.next()}))}ngOnChanges(){this.stateChanges.next()}ngOnDestroy(){this.stateChanges.complete(),this._platform.isBrowser&&this._autofillMonitor.stopMonitoring(this._elementRef.nativeElement)}ngDoCheck(){this.ngControl&&this.updateErrorState(),this._dirtyCheckNativeValue(),this._dirtyCheckPlaceholder()}focus(t){this._elementRef.nativeElement.focus(t)}_focusChanged(t){t!==this.focused&&(this.focused=t,this.stateChanges.next())}_onInput(){}_dirtyCheckPlaceholder(){var t,e;const n=(null===(e=null===(t=this._formField)||void 0===t?void 0:t._hideControlPlaceholder)||void 0===e?void 0:e.call(t))?null:this.placeholder;if(n!==this._previousPlaceholder){const t=this._elementRef.nativeElement;this._previousPlaceholder=n,n?t.setAttribute("placeholder",n):t.removeAttribute("placeholder")}}_dirtyCheckNativeValue(){const t=this._elementRef.nativeElement.value;this._previousNativeValue!==t&&(this._previousNativeValue=t,this.stateChanges.next())}_validateType(){if(IY.indexOf(this._type)>-1&&("undefined"==typeof ngDevMode||ngDevMode))
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
throw(function t(e){return Error(`Input type "${e}" isn't supported by matInput.`)})(this._type)}_isNeverEmpty(){return this._neverEmptyInputTypes.indexOf(this._type)>-1}_isBadInput(){let t=this._elementRef.nativeElement.validity;return t&&t.badInput}get empty(){return!(this._isNeverEmpty()||this._elementRef.nativeElement.value||this._isBadInput()||this.autofilled)}get shouldLabelFloat(){if(this._isNativeSelect){const t=this._elementRef.nativeElement,e=t.options[0];return this.focused||t.multiple||!this.empty||!!(t.selectedIndex>-1&&e&&e.label)}return this.focused||!this.empty}setDescribedByIds(t){t.length?this._elementRef.nativeElement.setAttribute("aria-describedby",t.join(" ")):this._elementRef.nativeElement.removeAttribute("aria-describedby")}onContainerClick(){this.focused||this.focus()}}LY.ɵfac=function t(e){return new(e||LY)(Sm(hg),Sm(wz),Sm(Mj,10),Sm(iU,8),Sm(PU,8),Sm(bH),Sm(zY,10),Sm(EY),Sm(a_),Sm(RV,8))},LY.ɵdir=lo({type:LY,selectors:[["input","matInput",""],["textarea","matInput",""],["select","matNativeControl",""],["input","matNativeControl",""],["textarea","matNativeControl",""]],hostAttrs:[1,"mat-input-element","mat-form-field-autofill-control"],hostVars:9,hostBindings:function t(e,n){1&e&&Vm("focus",(function t(){return n._focusChanged(!0)}))("blur",(function t(){return n._focusChanged(!1)}))("input",(function t(){return n._onInput()})),2&e&&(Tu("disabled",n.disabled)("required",n.required),jp("id",n.id)("data-placeholder",n.placeholder)("readonly",n.readonly&&!n._isNativeSelect||null)("aria-invalid",n.empty&&n.required?null:n.errorState)("aria-required",n.required),pu("mat-input-server",n._isServer))},inputs:{id:"id",disabled:"disabled",required:"required",type:"type",value:"value",readonly:"readonly",placeholder:"placeholder",errorStateMatcher:"errorStateMatcher",userAriaDescribedBy:["aria-describedby","userAriaDescribedBy"]},exportAs:["matInput"],features:[pg([{provide:bV,useExisting:LY}]),xp,Bo]}),LY.ctorParameters=()=>[{type:hg},{type:wz},{type:Mj,decorators:[{type:Sr},{type:Dr}]},{type:iU,decorators:[{type:Sr}]},{type:PU,decorators:[{type:Sr}]},{type:bH},{type:void 0,decorators:[{type:Sr},{type:Dr},{type:kr,args:[zY]}]},{type:EY},{type:a_},{type:AV,decorators:[{type:Sr},{type:kr,args:[RV]}]}],LY.propDecorators={disabled:[{type:xy}],id:[{type:xy}],placeholder:[{type:xy}],required:[{type:xy}],type:[{type:xy}],errorStateMatcher:[{type:xy}],userAriaDescribedBy:[{type:xy,args:["aria-describedby"]}],value:[{type:xy}],readonly:[{type:xy}],_focusChanged:[{type:wy,args:["focus",["true"]]},{type:wy,args:["blur",["false"]]}],_onInput:[{type:wy,args:["input"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(LY,[{type:Cy,args:[{selector:"input[matInput], textarea[matInput], select[matNativeControl],\n      input[matNativeControl], textarea[matNativeControl]",exportAs:"matInput",host:{class:"mat-input-element mat-form-field-autofill-control","[class.mat-input-server]":"_isServer","[attr.id]":"id","[attr.data-placeholder]":"placeholder","[disabled]":"disabled","[required]":"required","[attr.readonly]":"readonly && !_isNativeSelect || null","[attr.aria-invalid]":"(empty && required) ? null : errorState","[attr.aria-required]":"required"},providers:[{provide:bV,useExisting:LY}]}]}],(function(){return[{type:hg},{type:wz},{type:Mj,decorators:[{type:Sr},{type:Dr}]},{type:iU,decorators:[{type:Sr}]},{type:PU,decorators:[{type:Sr}]},{type:bH},{type:void 0,decorators:[{type:Sr},{type:Dr},{type:kr,args:[zY]}]},{type:EY},{type:a_},{type:AV,decorators:[{type:Sr},{type:kr,args:[RV]}]}]}),{id:[{type:xy}],disabled:[{type:xy}],required:[{type:xy}],type:[{type:xy}],value:[{type:xy}],readonly:[{type:xy}],_focusChanged:[{type:wy,args:["focus",["true"]]},{type:wy,args:["blur",["false"]]}],_onInput:[{type:wy,args:["input"]}],placeholder:[{type:xy}],errorStateMatcher:[{type:xy}],userAriaDescribedBy:[{type:xy,args:["aria-describedby"]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class BY{}function VY(t,e){if(1&t&&(Rm(0,"mat-error"),ku(1),Am()),2&t){const t=Ym();rc(1),Du(" Reload period has to be minimum of ",t.MIN_RELOAD_PERIOD_IN_S," seconds. ")}}function jY(t,e){1&t&&(Rm(0,"mat-error"),ku(1," Page size has to be a positive integer. "),Am())}BY.ɵfac=function t(e){return new(e||BY)},BY.ɵmod=ao({type:BY}),BY.ɵinj=vn({providers:[bH],imports:[[TY,TV,XI],TY,TV]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(BY,[{type:Ay,args:[{declarations:[LY,NY],imports:[TY,TV,XI],exports:[TY,TV,LY,NY],providers:[bH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(BY,{declarations:function(){return[LY,NY]},imports:function(){return[TY,TV,XI]},exports:function(){return[TY,TV,LY,NY]}});class UY{constructor(){this.reloadToggled=new Lh,this.reloadPeriodInMsChanged=new Lh,this.pageSizeChanged=new Lh,this.MIN_RELOAD_PERIOD_IN_S=30,this.reloadPeriodControl=new $j(this.MIN_RELOAD_PERIOD_IN_S,[qV.required,qV.min(this.MIN_RELOAD_PERIOD_IN_S)]),this.paginationControl=new $j(1,[qV.required,qV.min(1),t=>{const e=Number(t.value);return Math.round(e)===t.value?null:{integer:{value:t.value}}}]),this.ngUnsubscribe=new I}ngOnInit(){this.reloadPeriodControl.valueChanges.pipe(Ie(this.ngUnsubscribe),ge(500),ce((()=>this.reloadPeriodControl.valid))).subscribe((()=>{this.reloadPeriodControl.valid&&this.reloadPeriodInMsChanged.emit(1e3*this.reloadPeriodControl.value)})),this.paginationControl.valueChanges.pipe(Ie(this.ngUnsubscribe),ge(500),ce((()=>this.paginationControl.valid))).subscribe((()=>{this.pageSizeChanged.emit(this.paginationControl.value)}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}ngOnChanges(t){if(t.reloadPeriodInMs){const e=t.reloadPeriodInMs;e.previousValue!==e.currentValue&&this.reloadPeriodControl.setValue(e.currentValue/1e3)}if(t.reloadEnabled&&(t.reloadEnabled.currentValue?this.reloadPeriodControl.enable():this.reloadPeriodControl.disable()),t.pageSize){const e=t.pageSize;e.previousValue!==e.currentValue&&this.paginationControl.setValue(e.currentValue)}}onReloadToggle(){this.reloadToggled.emit()}}UY.ɵfac=function t(e){return new(e||UY)},UY.ɵcmp=to({type:UY,selectors:[["settings-dialog-component"]],inputs:{reloadEnabled:"reloadEnabled",reloadPeriodInMs:"reloadPeriodInMs",pageSize:"pageSize"},outputs:{reloadToggled:"reloadToggled",reloadPeriodInMsChanged:"reloadPeriodInMsChanged",pageSizeChanged:"pageSizeChanged"},features:[Bo],decls:14,vars:5,consts:[[1,"reload-toggle"],[3,"checked","change"],["matInput","","type","number","placeholder","Reload Period",1,"reload-period",3,"formControl"],[4,"ngIf"],["matInput","","type","number","placeholder","Pagination Limit",1,"page-size",3,"formControl"]],template:function t(e,n){1&e&&(Rm(0,"h3"),ku(1,"Settings"),Am(),Rm(2,"div"),Rm(3,"div",0),Rm(4,"mat-checkbox",1),Vm("change",(function t(){return n.onReloadToggle()})),ku(5,"Reload data"),Am(),Am(),Rm(6,"div"),Rm(7,"mat-form-field"),Tm(8,"input",2),Am(),Qp(9,VY,2,1,"mat-error",3),Am(),Am(),Rm(10,"div"),Rm(11,"mat-form-field"),Tm(12,"input",4),Am(),Qp(13,jY,2,0,"mat-error",3),Am()),2&e&&(rc(4),Dm("checked",n.reloadEnabled),rc(4),Dm("formControl",n.reloadPeriodControl),rc(1),Dm("ngIf",n.reloadPeriodControl.hasError("min")||n.reloadPeriodControl.hasError("required")),rc(3),Dm("formControl",n.paginationControl),rc(1),Dm("ngIf",n.paginationControl.invalid))},directives:[OY,AV,LY,fU,VV,xj,xU,dM,gV],styles:["[_nghost-%COMP%] {\n  font-size: 15px;\n}\n\n[_nghost-%COMP%]    > div[_ngcontent-%COMP%] {\n  margin: 10px 0;\n}\n\n[_nghost-%COMP%]    > [_ngcontent-%COMP%]:first-child {\n  margin-top: 0;\n}\n\n[_nghost-%COMP%]    > [_ngcontent-%COMP%]:last-child {\n  margin-bottom: 0;\n}\n\nh3[_ngcontent-%COMP%] {\n  font-size: 20px;\n}\n\n.reload-toggle[_ngcontent-%COMP%] {\n  margin-bottom: 10px;\n}"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(UY,[{type:My,args:[{selector:"settings-dialog-component",templateUrl:"settings_dialog_component.ng.html",styleUrls:["./settings_dialog_component.css"]}]}],null,{reloadEnabled:[{type:xy}],reloadPeriodInMs:[{type:xy}],pageSize:[{type:xy}],reloadToggled:[{type:Oy}],reloadPeriodInMsChanged:[{type:Oy}],pageSizeChanged:[{type:Oy}]});class GY{constructor(t){this.store=t,this.reloadEnabled$=this.store.select(DN),this.reloadPeriodInMs$=this.store.select(EN),this.pageSize$=this.store.select(RN)}onReloadToggled(){this.store.dispatch(vN())}onReloadPeriodInMsChanged(t){this.store.dispatch(xN({periodInMs:t}))}onPageSizeChanged(t){this.store.dispatch(ON({size:t}))}}GY.ɵfac=function t(e){return new(e||GY)(Sm(Iw))},GY.ɵcmp=to({type:GY,selectors:[["settings-dialog"]],decls:4,vars:9,consts:[[3,"reloadEnabled","reloadPeriodInMs","pageSize","reloadToggled","reloadPeriodInMsChanged","pageSizeChanged"]],template:function t(e,n){1&e&&(Rm(0,"settings-dialog-component",0),Vm("reloadToggled",(function t(){return n.onReloadToggled()}))("reloadPeriodInMsChanged",(function t(e){return n.onReloadPeriodInMsChanged(e)}))("pageSizeChanged",(function t(e){return n.onPageSizeChanged(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Am()),2&e&&Dm("reloadEnabled",Th(1,3,n.reloadEnabled$))("reloadPeriodInMs",Th(2,5,n.reloadPeriodInMs$))("pageSize",Th(3,7,n.pageSize$))},directives:[UY],pipes:[wM],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(GY,[{type:My,args:[{selector:"settings-dialog",template:'\n    <settings-dialog-component\n      [reloadEnabled]="reloadEnabled$ | async"\n      [reloadPeriodInMs]="reloadPeriodInMs$ | async"\n      [pageSize]="pageSize$ | async"\n      (reloadToggled)="onReloadToggled()"\n      (reloadPeriodInMsChanged)="onReloadPeriodInMsChanged($event)"\n      (pageSizeChanged)="onPageSizeChanged($event)"\n    ></settings-dialog-component>\n  '}]}],(function(){return[{type:Iw}]}),null);class WY{constructor(t){this.dialog=t}isButtonDisabled(){return this.settingsLoadState===yE.NOT_LOADED||this.settingsLoadState===yE.LOADING}openDialog(){this.dialog.open(GY,{width:"400px"})}}WY.ɵfac=function t(e){return new(e||WY)(Sm(oW))},WY.ɵcmp=to({type:WY,selectors:[["settings-button-component"]],inputs:{settingsLoadState:"settingsLoadState"},decls:2,vars:1,consts:[["mat-icon-button","",3,"disabled","click"],["svgIcon","settings_24px"]],template:function t(e,n){1&e&&(Rm(0,"button",0),Vm("click",(function t(){return n.openDialog()})),Tm(1,"mat-icon",1),Am()),2&e&&Dm("disabled",n.isButtonDisabled())},directives:[XH,DW],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(WY,[{type:My,args:[{selector:"settings-button-component",template:'\n    <button\n      mat-icon-button\n      [disabled]="isButtonDisabled()"\n      (click)="openDialog()"\n    >\n      <mat-icon svgIcon="settings_24px"></mat-icon>\n    </button>\n  '}]}],(function(){return[{type:oW}]}),{settingsLoadState:[{type:xy}]});class YY{constructor(t){this.store=t,this.settingsLoadState$=this.store.select(SN)}}YY.ɵfac=function t(e){return new(e||YY)(Sm(Iw))},YY.ɵcmp=to({type:YY,selectors:[["settings-button"]],decls:2,vars:3,consts:[[3,"settingsLoadState"]],template:function t(e,n){1&e&&(Tm(0,"settings-button-component",0),Ah(1,"async")),2&e&&Dm("settingsLoadState",Th(1,1,n.settingsLoadState$))},directives:[WY],pipes:[wM],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(YY,[{type:My,args:[{selector:"settings-button",template:'\n    <settings-button-component\n      [settingsLoadState]="settingsLoadState$ | async"\n    ></settings-button-component>\n  '}]}],(function(){return[{type:Iw}]}),null);class qY{}qY.ɵfac=function t(e){return new(e||qY)},qY.ɵcmp=to({type:qY,selectors:[["app-header"]],decls:10,vars:0,consts:[[1,"brand"],[1,"plugins"],["mat-icon-button","","href","https://github.com/tensorflow/tensorboard/blob/master/README.md","rel","noopener noreferrer","target","_blank","aria-label","Help",1,"readme"],["svgIcon","help_outline_24px"]],template:function t(e,n){1&e&&(Rm(0,"mat-toolbar"),Rm(1,"span",0),ku(2,"TensorBoard"),Am(),Tm(3,"plugin-selector",1),Tm(4,"tbdev-upload-button"),Tm(5,"app-header-dark-mode-toggle"),Tm(6,"app-header-reload"),Tm(7,"settings-button"),Rm(8,"a",2),Tm(9,"mat-icon",3),Am(),Am())},directives:[eB,VG,IW,cY,pY,YY,KH,DW],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], 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.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28122em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-form-field-autofill-control[_ngcontent-%COMP%]:-webkit-autofill + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28121em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.2812em) scale(0.75)}}.mat-form-field-appearance-fill[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:.25em 0 .75em 0}.mat-form-field-appearance-fill[_ngcontent-%COMP%]  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new(e||Sq)},Sq.ɵmod=ao({type:Sq}),Sq.ɵinj=vn({providers:[kq],imports:[[cE]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Sq,[{type:Ay,args:[{imports:[cE],providers:[kq]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Sq,{imports:[cE]});class Dq{}Dq.ɵfac=function t(e){return new(e||Dq)},Dq.ɵcmp=to({type:Dq,selectors:[["inactive-component"]],decls:54,vars:0,consts:[[1,"container"],[1,"title"],[1,"code"],[1,"arg"],[1,"exhibits-container"],[1,"exhibit"],[1,"screenshot"],["src","data:image/png;base64,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"],[1,"description"],["src","data:image/png;base64,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0!==o&&(i.graphExecutions=Object.assign(Object.assign({},t.graphExecutions),{focusIndex:o})),i}function xZ(t,e){return _Z(t,e)}const OZ=Kw(pZ),PZ=Zw(OZ,(t=>t.runs)),wZ=Zw(OZ,(t=>t.runsLoaded)),kZ=Zw(OZ,(t=>t.activeRunId)),SZ=Zw(OZ,(t=>t.lastDataPollOnsetTimeMs-t.lastNonEmptyPollDataTimeMs)),DZ=Zw(OZ,(t=>t.alerts)),EZ=Zw(DZ,(t=>t.alertsLoaded)),RZ=Zw(DZ,(t=>t.numAlerts)),AZ=Zw(DZ,(t=>t.focusType)),TZ=Zw(DZ,(t=>null===t.focusType?0:t.alertsBreakdown[t.focusType]||0)),NZ=Zw(DZ,(t=>null===t.focusType||void 0===t.alerts[t.focusType]?null:t.alerts[t.focusType])),zZ=Zw(DZ,(t=>t.alertsBreakdown)),IZ=Zw(OZ,(t=>t.executions)),HZ=Zw(IZ,(t=>t.numExecutionsLoaded)),FZ=Zw(IZ,(t=>t.executionDigestsLoaded)),LZ=Zw(IZ,(t=>t.executionDigestsLoaded.numExecutions)),BZ=Zw(IZ,(t=>t.scrollBeginIndex)),VZ=Zw(IZ,(t=>t.pageSize)),jZ=Zw(IZ,(t=>t.displayCount)),UZ=Zw(IZ,(t=>{const e=[];for(let n=t.scrollBeginIndex;n<t.scrollBeginIndex+t.displayCount;++n)e.push(n in 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a=e[t].graph_id,r=Math.max(0,t-200);for(let s=t-1;s>=r;--s)if(void 0!==e[s])for(let t=0;t<n.length&&(o[t]||e[s].graph_id!==a||e[s].op_name!==n[t].op_name||e[s].output_slot!==n[t].output_slot||(i.push(s),o[t]=!0,i.length!==n.length));++t);return i})),iX=Zw(tX,(t=>{const{focusedOp:e,ops:n}=t;if(null!==e&&void 0!==n[e.graphId]&&n[e.graphId].has(e.opName)){const t=n[e.graphId],{consumers:o}=t.get(e.opName);return o.map((e=>e.map((e=>{const n=Object.assign({},e);return t.has(e.op_name)&&(n.data=t.get(e.op_name)),n}))))}return null})),aX=Zw(OZ,(t=>{const e=t.executions.scrollBeginIndex,n=t.executions.scrollBeginIndex+t.executions.displayCount,o=new Array(n-e).fill(null),i=t.alerts.focusType;if(null===i)return o;const a=t.alerts.executionIndices[i];if(void 0===a)return o;for(let i=e;i<n;++i)a.includes(i)&&(o[i-e]=t.alerts.focusType);return o})),rX=Zw(OZ,(t=>t.executions)),sX=Zw(rX,(t=>t.focusIndex)),lX=Zw(rX,(t=>{if(null===t.focusIndex)return null;const{focusIndex:e,scrollBeginIndex:n,displayCount:o}=t;return e<n||e>=n+o?null:e-n})),cX=Zw(rX,(t=>t.executionData)),dX=Zw(OZ,(t=>t.graphs.loadingOps)),pX=Zw(OZ,(t=>t.stackFrames)),mX=Zw(rX,(t=>{const{focusIndex:e,executionData:n}=t;return null===e||void 0===n[e]?null:n[e]})),uX=Zw(OZ,sX,mX,eX,((t,e,n,o)=>{const{codeLocationFocusType:i}=t;return null===i?null:i===fZ.EXECUTION?null===e||null===n?null:{codeLocationType:fZ.EXECUTION,opType:n.op_type,executionIndex:e}:null===o?null:{codeLocationType:fZ.GRAPH_OP_CREATION,opType:o.op_type,opName:o.op_name}})),fX=Zw(OZ,gZ),gX=Zw(OZ,(t=>t.sourceCode)),hX=Zw(gX,(t=>t.sourceFileListLoaded));Zw(gX,(t=>t.sourceFileList));const bX=Zw(gX,(t=>{const{sourceFileList:e,focusLineSpec:n}=t;return null===n?-1:hZ(e,n)})),yX=Zw(gX,bX,((t,e)=>-1===e?null:t.fileContents[e]||null)),_X=Zw(OZ,(t=>t.sourceCode.focusLineSpec)),CX=Zw(OZ,(t=>t.stickToBottommostFrameInFocusedFile)),MX=function(t){return[t]};function vX(t,e){if(1&t){const t=Hm();Rm(0,"div",7),Vm("click",(function e(){const n=hi(t).$implicit;return Ym().onToggleFocusType.emit(n.type)})),Rm(1,"div",8),ku(2),Am(),Rm(3,"div",9),ku(4),Am(),Tm(5,"div"),Am()}if(2&t){const t=e.$implicit,n=Ym();Dm("ngClass",Mh(4,MX,t.type===n.focusType?"focus":"")),rc(2),Su(t.displayName),rc(2),Eu(" ",t.displaySymbol,": ",t.count," ")}}class xX{constructor(){this.numAlerts=0,this.alertsBreakdown=[],this.focusType=null,this.onToggleFocusType=new Lh}}xX.ɵfac=function t(e){return new(e||xX)},xX.ɵcmp=to({type:xX,selectors:[["alerts-component"]],inputs:{numAlerts:"numAlerts",alertsBreakdown:"alertsBreakdown",focusType:"focusType"},outputs:{onToggleFocusType:"onToggleFocusType"},decls:10,vars:5,consts:[[1,"alerts-container"],[1,"debugging-title"],[1,"num-alerts-container"],[1,"num-alerts-label"],[1,"num-alerts-value",3,"ngClass"],[1,"alerts-breakdown-container"],["class","alerts-breakdown-type",3,"ngClass","click",4,"ngFor","ngForOf"],[1,"alerts-breakdown-type",3,"ngClass","click"],[1,"alert-type-name"],[1,"alert-type-count"]],template:function t(e,n){1&e&&(Rm(0,"div",0),Rm(1,"div",1),ku(2,"Debugging"),Am(),Rm(3,"div",2),Rm(4,"div",3),ku(5,"Alerts"),Am(),Rm(6,"div",4),ku(7),Am(),Am(),Rm(8,"div",5),Qp(9,vX,6,6,"div",6),Am(),Am()),2&e&&(rc(6),Dm("ngClass",Mh(3,MX,n.numAlerts>0?"non-zero":"")),rc(1),Du(" ",n.numAlerts," "),rc(2),Dm("ngForOf",n.alertsBreakdown))},directives:[aM,lM],styles:[".alerts-breakdown-container[_ngcontent-%COMP%] {\n  font-size: 13px;\n  padding: 10px 10px 10px;\n  position: relative;\n}\n\n.alerts-breakdown-type[_ngcontent-%COMP%] {\n  border-radius: 0 10px 10px 0;\n  cursor: pointer;\n  display: flex;\n  padding: 6px 0 6px 50px;\n  vertical-align: middle;\n}\n\n.alerts-breakdown-type.focus[_ngcontent-%COMP%] {\n  background-color: #ffeee0;\n}\n\n.alerts-container[_ngcontent-%COMP%] {\n  font-family: 'Roboto', Arial, Helvetica, sans-serif;\n}\n\n.alert-type-count[_ngcontent-%COMP%] {\n  \n  background-color: #e52592;\n  border-radius: 3px;\n  color: #fff;\n  display: inline-block;\n  padding: 3px;\n  position: absolute;\n  right: 20px;\n  vertical-align: middle;\n}\n\n.alert-type-name[_ngcontent-%COMP%] {\n  display: inline-block;\n  padding: 3px;\n  vertical-align: middle;\n}\n\n.debugging-title[_ngcontent-%COMP%] {\n  font-size: 18px;\n}\n\n.num-alerts-container[_ngcontent-%COMP%] {\n  font-weight: bold;\n  padding: 10px 10px 10px 30px;\n  position: relative;\n}\n\n.num-alerts-label[_ngcontent-%COMP%] {\n  display: inline-block;\n  font-size: 13px;\n}\n\n.num-alerts-value[_ngcontent-%COMP%] {\n  border-radius: 12px;\n  display: inline-block;\n  font-size: 13px;\n  font-weight: normal;\n  line-height: 24px;\n  position: absolute;\n  right: 20px;\n  text-align: center;\n  vertical-align: middle;\n  width: 24px;\n}\n\n.num-alerts-value.non-zero[_ngcontent-%COMP%] {\n  background-color: #ffb780;\n  font-weight: bold;\n}"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(xX,[{type:My,args:[{selector:"alerts-component",templateUrl:"./alerts_component.ng.html",styleUrls:["./alerts_component.css"]}]}],null,{numAlerts:[{type:xy}],alertsBreakdown:[{type:xy}],focusType:[{type:xy}],onToggleFocusType:[{type:Oy}]});const OX={[uZ.FUNCTION_RECOMPILE_ALERT]:{displayName:"Function recompiles",displaySymbol:"C"},[uZ.INF_NAN_ALERT]:{displayName:"NaN/∞",displaySymbol:"∞"},[uZ.TENSOR_SHAPE_ALERT]:{displayName:"Tensor shape",displaySymbol:"■"}};class PX{constructor(t){this.store=t,this.numAlerts$=this.store.pipe(Fw(RZ)),this.alertsBreakdown$=this.store.pipe(Fw(Zw(zZ,(t=>{const e=Object.keys(t);return e.sort(),e.map((e=>Object.assign(Object.assign({type:e},OX[e]),{count:t[e]})))})))),this.focusType$=this.store.pipe(Fw(AZ))}onToggleFocusType(t){this.store.dispatch(Bq({alertType:t}))}}PX.ɵfac=function t(e){return new(e||PX)(Sm(Iw))},PX.ɵcmp=to({type:PX,selectors:[["tf-debugger-v2-alerts"]],decls:4,vars:9,consts:[[3,"numAlerts","alertsBreakdown","focusType","onToggleFocusType"]],template:function t(e,n){1&e&&(Rm(0,"alerts-component",0),Vm("onToggleFocusType",(function t(e){return n.onToggleFocusType(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Am()),2&e&&Dm("numAlerts",Th(1,3,n.numAlerts$))("alertsBreakdown",Th(2,5,n.alertsBreakdown$))("focusType",Th(3,7,n.focusType$))},directives:[xX],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(PX,[{type:My,args:[{selector:"tf-debugger-v2-alerts",template:'\n    <alerts-component\n      [numAlerts]="numAlerts$ | async"\n      [alertsBreakdown]="alertsBreakdown$ | async"\n      [focusType]="focusType$ | async"\n      (onToggleFocusType)="onToggleFocusType($event)"\n    >\n    </alerts-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);const wX=["sliderWrapper"],kX=Nz({passive:!1}),SX={provide:IV,useExisting:qe((()=>RX)),multi:!0};class DX{}const EX=$I(JI(KI(class{constructor(t){this._elementRef=t}}),"accent"));class RX extends EX{constructor(t,e,n,o,i,a,r,s){super(t),this._focusMonitor=e,this._changeDetectorRef=n,this._dir=o,this._ngZone=a,this._animationMode=s,this._invert=!1,this._max=100,this._min=0,this._step=1,this._thumbLabel=!1,this._tickInterval=0,this._value=null,this._vertical=!1,this.change=new Lh,this.input=new Lh,this.valueChange=new Lh,this.onTouched=()=>{},this._percent=0,this._isSliding=null,this._isActive=!1,this._tickIntervalPercent=0,this._sliderDimensions=null,this._controlValueAccessorChangeFn=()=>{},this._dirChangeSubscription=m.EMPTY,this._pointerDown=t=>{this.disabled||this._isSliding||!AX(t)&&0!==t.button||this._ngZone.run((()=>{this._touchId=AX(t)?(function e(t,n){for(let e=0;e<t.touches.length;e++){const o=t.touches[e].target;if(n===o||n.contains(o))return t.touches[e].identifier}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t,this._elementRef.nativeElement):void 0;const n=TX(t,this._touchId);if(n){const e=this.value;this._isSliding="pointer",this._lastPointerEvent=t,t.preventDefault(),this._focusHostElement(),this._onMouseenter(),this._bindGlobalEvents(t),this._focusHostElement(),this._updateValueFromPosition(n),this._valueOnSlideStart=e,e!=this.value&&this._emitInputEvent()}}))},this._pointerMove=t=>{if("pointer"===this._isSliding){const e=TX(t,this._touchId);if(e){t.preventDefault();const n=this.value;this._lastPointerEvent=t,this._updateValueFromPosition(e),n!=this.value&&this._emitInputEvent()}}},this._pointerUp=t=>{"pointer"===this._isSliding&&(AX(t)&&"number"==typeof this._touchId&&!NX(t.changedTouches,this._touchId)||(t.preventDefault(),this._removeGlobalEvents(),this._isSliding=null,this._touchId=void 0,this._valueOnSlideStart==this.value||this.disabled||this._emitChangeEvent(),this._valueOnSlideStart=this._lastPointerEvent=null))},this._windowBlur=()=>{this._lastPointerEvent&&this._pointerUp(this._lastPointerEvent)},this._document=r,this.tabIndex=parseInt(i)||0,a.runOutsideAngular((()=>{const e=t.nativeElement;e.addEventListener("mousedown",this._pointerDown,kX),e.addEventListener("touchstart",this._pointerDown,kX)}))}get invert(){return this._invert}set invert(t){this._invert=yz(t)}get max(){return this._max}set max(t){this._max=_z(t,this._max),this._percent=this._calculatePercentage(this._value),this._changeDetectorRef.markForCheck()}get min(){return this._min}set min(t){this._min=_z(t,this._min),this._percent=this._calculatePercentage(this._value),this._changeDetectorRef.markForCheck()}get step(){return this._step}set step(t){this._step=_z(t,this._step),this._step%1!=0&&(this._roundToDecimal=this._step.toString().split(".").pop().length),this._changeDetectorRef.markForCheck()}get thumbLabel(){return this._thumbLabel}set thumbLabel(t){this._thumbLabel=yz(t)}get tickInterval(){return this._tickInterval}set tickInterval(t){this._tickInterval="auto"===t?"auto":"number"==typeof t||"string"==typeof t?_z(t,this._tickInterval):0}get value(){return null===this._value&&(this.value=this._min),this._value}set value(t){if(t!==this._value){let e=_z(t,0);this._roundToDecimal&&e!==this.min&&e!==this.max&&(e=parseFloat(e.toFixed(this._roundToDecimal))),this._value=e,this._percent=this._calculatePercentage(this._value),this._changeDetectorRef.markForCheck()}}get vertical(){return this._vertical}set vertical(t){this._vertical=yz(t)}get displayValue(){return this.displayWith?this.displayWith(this.value):this._roundToDecimal&&this.value&&this.value%1!=0?this.value.toFixed(this._roundToDecimal):this.value||0}focus(t){this._focusHostElement(t)}blur(){this._blurHostElement()}get percent(){return this._clamp(this._percent)}_shouldInvertAxis(){return this.vertical?!this.invert:this.invert}_isMinValue(){return 0===this.percent}_getThumbGap(){return this.disabled?7:this._isMinValue()&&!this.thumbLabel?this._isActive?10:7:0}_getTrackBackgroundStyles(){const t=this.vertical?`1, ${1-this.percent}, 1`:1-this.percent+", 1, 1";return{transform:`translate${this.vertical?"Y":"X"}(${this._shouldInvertMouseCoords()?"-":""}${this._getThumbGap()}px) scale3d(${t})`}}_getTrackFillStyles(){const t=this.percent,e=this.vertical?`1, ${t}, 1`:`${t}, 1, 1`;return{transform:`translate${this.vertical?"Y":"X"}(${this._shouldInvertMouseCoords()?"":"-"}${this._getThumbGap()}px) scale3d(${e})`,display:0===t?"none":""}}_getTicksContainerStyles(){return{transform:`translate${this.vertical?"Y":"X"}(${this.vertical||"rtl"!=this._getDirection()?"-":""}${this._tickIntervalPercent/2*100}%)`}}_getTicksStyles(){let t=100*this._tickIntervalPercent,e={backgroundSize:this.vertical?`2px ${t}%`:`${t}% 2px`,transform:`translateZ(0) translate${this.vertical?"Y":"X"}(${this.vertical||"rtl"!=this._getDirection()?"":"-"}${t/2}%)${this.vertical||"rtl"!=this._getDirection()?"":" rotate(180deg)"}`};if(this._isMinValue()&&this._getThumbGap()){const t=this._shouldInvertAxis();let n;n=this.vertical?t?"Bottom":"Top":t?"Right":"Left",e[`padding${n}`]=`${this._getThumbGap()}px`}return e}_getThumbContainerStyles(){const t=this._shouldInvertAxis();return{transform:`translate${this.vertical?"Y":"X"}(-${100*(("rtl"!=this._getDirection()||this.vertical?t:!t)?this.percent:1-this.percent)}%)`}}_shouldInvertMouseCoords(){const t=this._shouldInvertAxis();return"rtl"!=this._getDirection()||this.vertical?t:!t}_getDirection(){return this._dir&&"rtl"==this._dir.value?"rtl":"ltr"}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0).subscribe((t=>{this._isActive=!!t&&"keyboard"!==t,this._changeDetectorRef.detectChanges()})),this._dir&&(this._dirChangeSubscription=this._dir.change.subscribe((()=>{this._changeDetectorRef.markForCheck()})))}ngOnDestroy(){const t=this._elementRef.nativeElement;t.removeEventListener("mousedown",this._pointerDown,kX),t.removeEventListener("touchstart",this._pointerDown,kX),this._lastPointerEvent=null,this._removeGlobalEvents(),this._focusMonitor.stopMonitoring(this._elementRef),this._dirChangeSubscription.unsubscribe()}_onMouseenter(){this.disabled||(this._sliderDimensions=this._getSliderDimensions(),this._updateTickIntervalPercent())}_onFocus(){this._sliderDimensions=this._getSliderDimensions(),this._updateTickIntervalPercent()}_onBlur(){this.onTouched()}_onKeydown(t){if(this.disabled||bz(t)||this._isSliding&&"keyboard"!==this._isSliding)return;const e=this.value;switch(t.keyCode){case 33:this._increment(10);break;case 34:this._increment(-10);break;case 35:this.value=this.max;break;case 36:this.value=this.min;break;case 37:this._increment("rtl"==this._getDirection()?1:-1);break;case gz:this._increment(1);break;case 39:this._increment("rtl"==this._getDirection()?-1:1);break;case hz:this._increment(-1);break;default:return}e!=this.value&&(this._emitInputEvent(),this._emitChangeEvent()),this._isSliding="keyboard",t.preventDefault()}_onKeyup(){"keyboard"===this._isSliding&&(this._isSliding=null)}_getWindow(){return this._document.defaultView||window}_bindGlobalEvents(t){const e=this._document,n=AX(t),o=n?"touchend":"mouseup";e.addEventListener(n?"touchmove":"mousemove",this._pointerMove,kX),e.addEventListener(o,this._pointerUp,kX),n&&e.addEventListener("touchcancel",this._pointerUp,kX);const i=this._getWindow();void 0!==i&&i&&i.addEventListener("blur",this._windowBlur)}_removeGlobalEvents(){const t=this._document;t.removeEventListener("mousemove",this._pointerMove,kX),t.removeEventListener("mouseup",this._pointerUp,kX),t.removeEventListener("touchmove",this._pointerMove,kX),t.removeEventListener("touchend",this._pointerUp,kX),t.removeEventListener("touchcancel",this._pointerUp,kX);const e=this._getWindow();void 0!==e&&e&&e.removeEventListener("blur",this._windowBlur)}_increment(t){this.value=this._clamp((this.value||0)+this.step*t,this.min,this.max)}_updateValueFromPosition(t){if(!this._sliderDimensions)return;let e=this._clamp(((this.vertical?t.y:t.x)-(this.vertical?this._sliderDimensions.top:this._sliderDimensions.left))/(this.vertical?this._sliderDimensions.height:this._sliderDimensions.width));if(this._shouldInvertMouseCoords()&&(e=1-e),0===e)this.value=this.min;else if(1===e)this.value=this.max;else{const t=this._calculateValue(e),n=Math.round((t-this.min)/this.step)*this.step+this.min;this.value=this._clamp(n,this.min,this.max)}}_emitChangeEvent(){this._controlValueAccessorChangeFn(this.value),this.valueChange.emit(this.value),this.change.emit(this._createChangeEvent())}_emitInputEvent(){this.input.emit(this._createChangeEvent())}_updateTickIntervalPercent(){if(this.tickInterval&&this._sliderDimensions)if("auto"==this.tickInterval){let t=this.vertical?this._sliderDimensions.height:this._sliderDimensions.width,e=Math.ceil(30/(t*this.step/(this.max-this.min)));this._tickIntervalPercent=e*this.step/t}else this._tickIntervalPercent=this.tickInterval*this.step/(this.max-this.min)}_createChangeEvent(t=this.value){let e=new DX;return e.source=this,e.value=t,e}_calculatePercentage(t){return((t||0)-this.min)/(this.max-this.min)}_calculateValue(t){return this.min+t*(this.max-this.min)}_clamp(t,e=0,n=1){return Math.max(e,Math.min(t,n))}_getSliderDimensions(){return this._sliderWrapper?this._sliderWrapper.nativeElement.getBoundingClientRect():null}_focusHostElement(t){this._elementRef.nativeElement.focus(t)}_blurHostElement(){this._elementRef.nativeElement.blur()}writeValue(t){this.value=t}registerOnChange(t){this._controlValueAccessorChangeFn=t}registerOnTouched(t){this.onTouched=t}setDisabledState(t){this.disabled=t}}function AX(t){return"t"===t.type[0]}function TX(t,e){let n;return n=AX(t)?"number"==typeof e?NX(t.touches,e)||NX(t.changedTouches,e):t.touches[0]||t.changedTouches[0]:t,n?{x:n.clientX,y:n.clientY}:void 0}function NX(t,e){for(let n=0;n<t.length;n++)if(t[n].identifier===e)return t[n]}RX.ɵfac=function t(e){return new(e||RX)(Sm(hg),Sm(SI),Sm(Ug),Sm(HI,8),Na("tabindex"),Sm(a_),Sm(Z_),Sm(VP,8))},RX.ɵcmp=to({type:RX,selectors:[["mat-slider"]],viewQuery:function t(e,n){if(1&e&&Qh(wX,5),2&e){let t;Jh(t=tb())&&(n._sliderWrapper=t.first)}},hostAttrs:["role","slider",1,"mat-slider","mat-focus-indicator"],hostVars:29,hostBindings:function t(e,n){1&e&&Vm("focus",(function t(){return n._onFocus()}))("blur",(function t(){return n._onBlur()}))("keydown",(function t(e){return n._onKeydown(e)}))("keyup",(function t(){return n._onKeyup()}))("mouseenter",(function t(){return n._onMouseenter()}))("selectstart",(function t(e){return e.preventDefault()})),2&e&&(Tu("tabIndex",n.tabIndex),jp("aria-disabled",n.disabled)("aria-valuemax",n.max)("aria-valuemin",n.min)("aria-valuenow",n.value)("aria-valuetext",null==n.valueText?n.displayValue:n.valueText)("aria-orientation",n.vertical?"vertical":"horizontal"),pu("mat-slider-disabled",n.disabled)("mat-slider-has-ticks",n.tickInterval)("mat-slider-horizontal",!n.vertical)("mat-slider-axis-inverted",n._shouldInvertAxis())("mat-slider-invert-mouse-coords",n._shouldInvertMouseCoords())("mat-slider-sliding",n._isSliding)("mat-slider-thumb-label-showing",n.thumbLabel)("mat-slider-vertical",n.vertical)("mat-slider-min-value",n._isMinValue())("mat-slider-hide-last-tick",n.disabled||n._isMinValue()&&n._getThumbGap()&&n._shouldInvertAxis())("_mat-animation-noopable","NoopAnimations"===n._animationMode))},inputs:{disabled:"disabled",color:"color",tabIndex:"tabIndex",invert:"invert",max:"max",min:"min",step:"step",thumbLabel:"thumbLabel",tickInterval:"tickInterval",value:"value",vertical:"vertical",displayWith:"displayWith",valueText:"valueText"},outputs:{change:"change",input:"input",valueChange:"valueChange"},exportAs:["matSlider"],features:[pg([SX]),xp],decls:13,vars:6,consts:[[1,"mat-slider-wrapper"],["sliderWrapper",""],[1,"mat-slider-track-wrapper"],[1,"mat-slider-track-background",3,"ngStyle"],[1,"mat-slider-track-fill",3,"ngStyle"],[1,"mat-slider-ticks-container",3,"ngStyle"],[1,"mat-slider-ticks",3,"ngStyle"],[1,"mat-slider-thumb-container",3,"ngStyle"],[1,"mat-slider-focus-ring"],[1,"mat-slider-thumb"],[1,"mat-slider-thumb-label"],[1,"mat-slider-thumb-label-text"]],template:function 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CK{constructor(){this.activeRunId=null,this.loadingNumExecutions=!1,this.numExecutions=0,this.scrollBeginIndex=0,this.scrollBeginIndexUpperLimit=0,this.pageSize=0,this.displayCount=0,this.displayExecutionDigests=[],this.displayFocusedAlertTypes=[],this.focusedExecutionIndex=null,this.focusedExecutionDisplayIndex=null,this.focusedExecutionData=null,this.onNavigateLeft=new Lh,this.onNavigateRight=new Lh,this.onExecutionDigestClicked=new Lh,this.onSliderChange=new Lh}}CK.ɵfac=function t(e){return new(e||CK)},CK.ɵcmp=to({type:CK,selectors:[["timeline-component"]],inputs:{activeRunId:"activeRunId",loadingNumExecutions:"loadingNumExecutions",numExecutions:"numExecutions",scrollBeginIndex:"scrollBeginIndex",scrollBeginIndexUpperLimit:"scrollBeginIndexUpperLimit",pageSize:"pageSize",displayCount:"displayCount",displayExecutionDigests:"displayExecutionDigests",displayFocusedAlertTypes:"displayFocusedAlertTypes",focusedExecutionIndex:"focusedExecutionIndex",focusedExecutionDisplayIndex:"focusedExecutionDisplayIndex",focusedExecutionData:"focusedExecutionData"},outputs:{onNavigateLeft:"onNavigateLeft",onNavigateRight:"onNavigateRight",onExecutionDigestClicked:"onExecutionDigestClicked",onSliderChange:"onSliderChange"},decls:9,vars:4,consts:[[1,"timeline-title"],[1,"execution-count"],[1,"top-level-executions"],["class","navigation-section",4,"ngIf"],["class","execution-timeline",4,"ngIf"],[4,"ngIf"],[1,"navigation-section"],["mat-button","",1,"navigation-button-left",3,"click"],[1,"navigation-position-info"],["mat-button","",1,"navigation-button-right",3,"click"],["class","timeline-slider","step","1",3,"min","max","value","input",4,"ngIf"],["step","1",1,"timeline-slider",3,"min","max","value","input"],[1,"execution-timeline"],[3,"click",4,"ngFor","ngForOf"],[3,"click"],[1,"execution-digest",3,"ngClass","title"],[3,"focusedExecutionIndex"]],template:function 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vK{constructor(t){this.store=t,this.activeRunId$=this.store.pipe(Fw(kZ)),this.loadingNumExecutions$=this.store.pipe(Fw(Zw(HZ,(t=>t.state==yE.LOADING)))),this.scrollBeginIndex$=this.store.pipe(Fw(BZ)),this.scrollBeginIndexUpperLimit$=this.store.pipe(Fw(Zw(LZ,jZ,((t,e)=>Math.max(0,t-e))))),this.pageSize$=this.store.pipe(Fw(VZ)),this.displayCount$=this.store.pipe(Fw(jZ)),this.displayExecutionDigests$=this.store.pipe(Fw(Zw(UZ,(t=>t.map((t=>(function e(t,n=1){if(!t)return{op_type:"(N/A)",short_op_type:"..",is_graph:!1};const o=MK.filter((e=>t.op_type.startsWith(e)));if(o.length){const 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wK(t,e){if(1&t&&(Rm(0,"div",9),ku(1),Am()),2&t){const t=Ym();rc(1),Du(" ",t.opData.op_type," ")}}function kK(t,e){1&t&&(Rm(0,"span",10),ku(1," (Op info unavailable.) 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e(){hi(t);const n=Ym().$implicit,o=Ym(2);return o.onTensorNameClick.emit({index:n,graph_id:o.graphExecutionData[n].graph_id,op_name:o.graphExecutionData[n].op_name})})),ku(3),Am(),Rm(4,"div",15),ku(5),Am(),Am(),Qp(6,GK,1,4,"debug-tensor-value",16),Am()}if(2&t){const t=Ym().$implicit,e=Ym(2);rc(2),Km("title",e.getTensorName(t)),rc(1),Du(" ",e.getTensorName(t)," "),rc(2),Su(e.graphExecutionData[t].op_type),rc(1),Dm("ngIf",null!==e.graphExecutionData[t].debug_tensor_value)}}function YK(t,e){1&t&&(Rm(0,"div",18),ku(1," Loading... "),Am())}const qK=function(t){return{"input-of-focus":t}};function ZK(t,e){if(1&t&&(Rm(0,"div",5),Rm(1,"div",6),Rm(2,"div",7),Qp(3,jK,2,0,"div",8),ku(4),Am(),Qp(5,WK,7,4,"div",9),Qp(6,YK,2,0,"ng-template",10,11,ib),Am(),Am()),2&t){const t=e.$implicit,n=$p(7),o=Ym(2);rc(1),Dm("ngClass",Mh(5,qK,o.isInputOfFocus(t))),rc(2),Dm("ngIf",t===o.focusIndex),rc(1),Du(" ",t," "),rc(1),Dm("ngIf",o.graphExecutionData[t])("ngIfElse",n)}}function XK(t,e){if(1&t){const t=Hm();Rm(0,"cdk-virtual-scroll-viewport",3),Vm("scrolledIndexChange",(function e(n){return hi(t),Ym().onScrolledIndexChange.emit(n)})),Qp(1,ZK,8,7,"div",4),Am()}if(2&t){const t=Ym();rc(1),Dm("cdkVirtualForOf",t.graphExecutionIndices)}}class KK{constructor(){this.onScrolledIndexChange=new Lh,this.onTensorNameClick=new Lh,this.parseDebugTensorValue=HX,this.TEST_ONLY={getViewPort:()=>this.viewPort}}ngOnChanges(t){if(this.viewPort&&t.focusIndex&&null!==t.focusIndex.currentValue){const 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SJ{constructor(t){this.store=t,this.codeLocationType$=this.store.pipe(Fw(Zw(uX,(t=>null===t?null:t.codeLocationType)))),this.opType$=this.store.pipe(Fw(Zw(uX,(t=>null===t?null:t.opType)))),this.opName$=this.store.pipe(Fw(Zw(uX,(t=>null===t||t.codeLocationType!==fZ.GRAPH_OP_CREATION?null:t.opName)))),this.executionIndex$=this.store.pipe(Fw(Zw(uX,(t=>null===t||t.codeLocationType!==fZ.EXECUTION?null:t.executionIndex)))),this.stickToBottommostFrameInFocusedFile$=this.store.pipe(Fw(CX)),this.stackFramesForDisplay$=this.store.pipe(Fw(Zw(fX,_X,((t,e)=>{if(null===t)return null;const n=[];for(const o of t){const{host_name:t,file_path:i,lineno:a,function_name:r}=o,s=i.split("/"),l=null!==e&&t===e.host_name&&i===e.file_path;n.push({host_name:t,file_path:i,concise_file_path:s[s.length-1],lineno:a,function_name:r,belongsToFocusedFile:l,focused:l&&a===e.lineno})}return n}))))}onSourceLineClicked(t){const{host_name:e,file_path:n,lineno:o,function_name:i}=t;this.store.dispatch(sZ({stackFrame:{host_name:e,file_path:n,lineno:o,function_name:i}}))}}function DJ(t,e){1&t&&Tm(0,"tf-debugger-v2-inactive")}function EJ(t,e){1&t&&(Rm(0,"div",3),Tm(1,"tf-debugger-v2-alerts"),Rm(2,"div",4),Tm(3,"tf-debugger-v2-timeline"),Tm(4,"tf-debugger-v2-graph"),Am(),Tm(5,"tf-debugger-v2-graph-executions"),Am(),Rm(6,"div",5),Tm(7,"tf-debugger-v2-source-files"),Tm(8,"tf-debugger-v2-stack-trace"),Am())}SJ.ɵfac=function t(e){return new(e||SJ)(Sm(Iw))},SJ.ɵcmp=to({type:SJ,selectors:[["tf-debugger-v2-stack-trace"]],decls:7,vars:18,consts:[[3,"codeLocationType","opType","opName","executionIndex","stickToBottommostFrameInFocusedFile","stackFramesForDisplay","onSourceLineClicked"]],template:function t(e,n){1&e&&(Rm(0,"stack-trace-component",0),Vm("onSourceLineClicked",(function t(e){return n.onSourceLineClicked(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Am()),2&e&&Dm("codeLocationType",Th(1,6,n.codeLocationType$))("opType",Th(2,8,n.opType$))("opName",Th(3,10,n.opName$))("executionIndex",Th(4,12,n.executionIndex$))("stickToBottommostFrameInFocusedFile",Th(5,14,n.stickToBottommostFrameInFocusedFile$))("stackFramesForDisplay",Th(6,16,n.stackFramesForDisplay$))},directives:[kJ],pipes:[wM],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(SJ,[{type:My,args:[{selector:"tf-debugger-v2-stack-trace",template:'\n    <stack-trace-component\n      [codeLocationType]="codeLocationType$ | async"\n      [opType]="opType$ | async"\n      [opName]="opName$ | async"\n      [executionIndex]="executionIndex$ | async"\n      [stickToBottommostFrameInFocusedFile]="\n        stickToBottommostFrameInFocusedFile$ | async\n      "\n      [stackFramesForDisplay]="stackFramesForDisplay$ | async"\n      (onSourceLineClicked)="onSourceLineClicked($event)"\n    ></stack-trace-component>\n  '}]}],(function(){return[{type:Iw}]}),null);class RJ{constructor(){this.runs={},this.runIds=[],this.activeRunId=null}}RJ.ɵfac=function t(e){return new(e||RJ)},RJ.ɵcmp=to({type:RJ,selectors:[["debugger-component"]],inputs:{runs:"runs",runIds:"runIds",activeRunId:"activeRunId"},decls:4,vars:2,consts:[[1,"debugger-container"],[4,"ngIf","ngIfElse"],["dataAvailable",""],[1,"top-section"],[1,"top-center-section"],[1,"bottom-section"]],template:function t(e,n){if(1&e&&(Rm(0,"div",0),Qp(1,DJ,1,0,"tf-debugger-v2-inactive",1),Qp(2,EJ,9,0,"ng-template",null,2,ib),Am()),2&e){const t=$p(3);rc(1),Dm("ngIf",0===n.runIds.length)("ngIfElse",t)}},directives:[dM,Eq,PX,vK,VK,JK,pJ,SJ],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   .mat-chip-remove.mat-icon[_ngcontent-%COMP%]{font-size:18px}.mat-table[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-header-cell[_ngcontent-%COMP%]{font-size:12px;font-weight:500}.mat-cell[_ngcontent-%COMP%], .mat-footer-cell[_ngcontent-%COMP%]{font-size:14px}.mat-calendar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-calendar-body[_ngcontent-%COMP%]{font-size:13px}.mat-calendar-body-label[_ngcontent-%COMP%], .mat-calendar-period-button[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-calendar-table-header[_ngcontent-%COMP%]   th[_ngcontent-%COMP%]{font-size:11px;font-weight:400}.mat-dialog-title[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-expansion-panel-header[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", 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.mat-icon[_ngcontent-%COMP%]{height:1.125em;line-height:1.125}.mat-form-field-infix[_ngcontent-%COMP%]{padding:.5em 0;border-top:.84375em solid transparent}.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.34375em) scale(0.75);width:133.3333333333%}.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.34374em) 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.mat-form-field-label[_ngcontent-%COMP%]{top:1.28125em}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   .mat-form-field-underline[_ngcontent-%COMP%]{bottom:1.25em}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   .mat-form-field-subscript-wrapper[_ngcontent-%COMP%]{margin-top:.5416666667em;top:calc(100% - 1.6666666667em)}@media print{.mat-form-field-appearance-legacy.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28122em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-form-field-autofill-control[_ngcontent-%COMP%]:-webkit-autofill + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   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.mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59374em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:1em 0 1em 0}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{top:1.84375em;margin-top:-0.25em}.mat-form-field-appearance-outline.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59374em) scale(0.75);width:133.3333433333%}.mat-grid-tile-header[_ngcontent-%COMP%], .mat-grid-tile-footer[_ngcontent-%COMP%]{font-size:14px}.mat-grid-tile-header[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%], .mat-grid-tile-footer[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-grid-tile-header[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2), .mat-grid-tile-footer[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}input.mat-input-element[_ngcontent-%COMP%]{margin-top:-0.0625em}.mat-menu-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:400}.mat-paginator[_ngcontent-%COMP%], .mat-paginator-page-size[_ngcontent-%COMP%]   .mat-select-trigger[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px}.mat-radio-button[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", 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#555}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(RJ,[{type:My,args:[{selector:"debugger-component",templateUrl:"./debugger_component.ng.html",styleUrls:["./debugger_component.css"],changeDetection:zn.OnPush}]}],null,{runs:[{type:xy}],runIds:[{type:xy}],activeRunId:[{type:xy}]});class AJ{constructor(t){this.store=t,this.runs$=this.store.pipe(Fw(PZ)),this.runsIds$=this.store.pipe(Fw(Zw(PZ,(t=>Object.keys(t))))),this.activeRunId$=this.store.pipe(Fw(kZ))}ngOnInit(){this.store.dispatch(Rq())}ngOnDestroy(){this.store.dispatch(Aq())}}AJ.ɵfac=function t(e){return new(e||AJ)(Sm(Iw))},AJ.ɵcmp=to({type:AJ,selectors:[["tf-debugger-v2"]],decls:4,vars:9,consts:[[3,"runs","runIds","activeRunId"]],template:function t(e,n){1&e&&(Tm(0,"debugger-component",0),Ah(1,"async"),Ah(2,"async"),Ah(3,"async")),2&e&&Dm("runs",Th(1,3,n.runs$))("runIds",Th(2,5,n.runsIds$))("activeRunId",Th(3,7,n.activeRunId$))},directives:[RJ],pipes:[wM],styles:["[_nghost-%COMP%] {\n        display: block;\n        height: 100%;\n      }"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(AJ,[{type:My,args:[{selector:"tf-debugger-v2",template:'\n    <debugger-component\n      [runs]="runs$ | async"\n      [runIds]="runsIds$ | async"\n      [activeRunId]="activeRunId$ | async"\n    ></debugger-component>\n  ',styles:["\n      :host {\n        display: block;\n        height: 100%;\n      }\n    "]}]}],(function(){return[{type:Iw}]}),null);const TJ="debugger-v2";function NJ(t,e,n,o,i){if(n<=0||!Number.isInteger(n))throw new Error(`Invalid pageSize: ${n}`);if(e>o)throw new Error(`end index (${e}) exceeds total number of items (${o})`);if(e-t>n)throw new Error("begin-end span exceeds page size, which is not allowed");const a=[],r=Math.floor(t/n);(!(r in i)||i[r]<n&&r*n+i[r]<o)&&a.push(r);const s=Math.floor((e-1)/n);return s!==r&&(!(s in i)||s*n+i[s]<e&&e<o)&&a.push(s),a}class zJ{constructor(t,e,n){this.actions$=t,this.store=e,this.dataSource=n,this.loadData$=Mk((()=>{const t=this.loadDebuggerRuns(re(this.onDebuggerDataPoll(),this.onCoreReload())).pipe(Ee()),e=this.loadSourceFileList(t),n=this.createNumExecutionLoader(t),o=this.createNumAlertsAndBreakdownLoader(t),i=this.onAlertTypeFocused(),a=this.fetchExecutionDigestsForAlertTypeFocus(i),r=this.createInitialExecutionDetector(n).pipe(Ee()),s=this.createExecutionDigestLoader(re(this.onExecutionScroll(),this.createInitialExecutionDigest(r),a)),l=this.createExecutionDataAndStackFramesLoader(re(this.onExecutionDigestFocused(),r.pipe(Ve(this.store.select(kZ),this.store.select(cX)),It((([,t,e])=>({activeRunId:t,loadedExecutionData:e,focusIndex:0}))))));return re(o,s,l,this.createNumGraphExecutionLoader(t),e,this.onSourceFileFocused(),this.loadGraphExecutionPages(this.onGraphExecutionScroll()),this.loadGraphOpStackFrames(this.loadGraphOpInfo())).pipe(It((()=>({}))))}),{dispatch:!1})}onDebuggerDataPoll(){return this.actions$.pipe(Dk(Rq),ze((t=>(function e(t,n,o){return t.pipe((function i(t){return R((function(e,n){var o,i,a=!1,r=!1,s=!1,l=function(){return s&&r&&(n.complete(),!0)},c=function(){s=!1,o=e.subscribe(new T(n,void 0,(function(){s=!0,!l()&&(i||(i=new I,t(i).subscribe(new T(n,(function(){o?c():a=!0}),(function(){r=!0,l()})))),i).next()}))),a&&(o.unsubscribe(),o=null,a=!1,c())};c()}))})((t=>t.pipe(Ve(n),_e((([,t])=>ae(t)))))),Ie(o),It((()=>{})))})(Et(t),this.store.select(SZ).pipe(It((t=>(function e(t){return t>6e4?6e4:t>4e3?t:2e3})(t)))),this.actions$.pipe(Dk(Aq))))),Fe((()=>this.store.dispatch(Tq()))),It((()=>{})))}onCoreReload(){return re(this.actions$.pipe(Dk(vE,xE)),this.actions$.pipe(Dk(_E)).pipe(Ve(this.store.select(wZ)),ce((([,t])=>t.state===yE.NOT_LOADED||t.state===yE.FAILED&&null===t.lastLoadedTimeInMs)))).pipe(Ve(this.store.select(MR)),ce((([,t])=>t===TJ)),Fe((()=>this.store.dispatch(Tq()))),It((()=>{})))}loadDebuggerRuns(t){return t.pipe(Ve(this.store.select(wZ)),ce((([,{state:t}])=>t!==yE.LOADING)),Fe((()=>this.store.dispatch(Nq()))),Zt((()=>this.dataSource.fetchRuns().pipe(Fe((t=>{this.store.dispatch(zq({runs:t}))})),It((()=>{}))))))}createNumExecutionLoader(t){return t.pipe(Ve(this.store.select(PZ),this.store.select(HZ)),ce((([,t,e])=>Object.keys(t).length>0&&e.state!==yE.LOADING)),Fe((()=>this.store.dispatch(Vq()))),Zt((([,t])=>{const e=Object.keys(t)[0];return this.dataSource.fetchExecutionDigests(e,0,0).pipe(Fe((t=>{this.store.dispatch(jq({numExecutions:t.num_digests}))})),It((()=>{})))})))}createNumGraphExecutionLoader(t){return t.pipe(Ve(this.store.select(PZ),this.store.select(WZ)),ce((([,t,e])=>Object.keys(t).length>0&&e.state!==yE.LOADING)),Fe((()=>this.store.dispatch(Kq()))),Zt((([,t])=>{const e=Object.keys(t)[0];return this.dataSource.fetchGraphExecutionDigests(e,0,0).pipe(Fe((t=>{this.store.dispatch(Jq({numGraphExecutions:t.num_digests}))})),It((()=>{})))})))}createNumAlertsAndBreakdownLoader(t){return t.pipe(Ve(this.store.select(PZ),this.store.select(EZ)),ce((([,t,e])=>Object.keys(t).length>0&&e.state!==yE.LOADING)),Fe((()=>this.store.dispatch(Hq()))),Zt((([,t])=>{const e=Object.keys(t)[0];return this.dataSource.fetchAlerts(e,0,0).pipe(Fe((t=>{this.store.dispatch(Fq({numAlerts:t.num_alerts,alertsBreakdown:t.alerts_breakdown}))})),It((()=>{})))})))}createInitialExecutionDetector(t){return t.pipe(Ve(this.store.select(LZ),this.store.select(FZ)),ce((([,t,e])=>t>0&&0===Object.keys(e.pageLoadedSizes).length)),It((()=>{})))}createInitialExecutionDigest(t){return t.pipe(Ve(this.store.select(LZ),this.store.select(kZ),this.store.select(VZ)),ce((([,,t])=>null!==t)),It((([,t,e,n])=>({begin:0,end:Math.min(t,n),runId:e}))))}onExecutionScroll(){return this.actions$.pipe(Dk(Wq,Yq,qq),Ve(this.store.select(kZ),this.store.select(BZ),this.store.select(LZ),this.store.select(jZ),this.store.select(VZ)),ce((([t])=>null!==t)),It((([,t,e,n,o,i])=>({runId:t,begin:e,end:Math.min(n,e+o),pageSize:i}))),Ve(this.store.select(FZ)),It((([t,e])=>({props:t,loaded:e,missingPages:NJ(t.begin,t.end,t.pageSize,e.numExecutions,e.pageLoadedSizes)}))),ce((({missingPages:t})=>t.length>0)),It((({props:t,loaded:e,missingPages:n})=>{const{runId:o,pageSize:i}=t;return{begin:n[0]*i,end:Math.min(e.numExecutions,(n[n.length-1]+1)*i),runId:o}})))}createExecutionDigestLoader(t){return t.pipe(Ve(this.store.select(FZ)),ce((([{begin:t,end:e},n])=>e>t&&!(function o(t,e,n){if(e>=n)throw new Error(`Expected begin to be less than end, but got begin=${e}, end=${n}`);return-1!==t.findIndex((t=>t.begin>=e&&t.end<=n))})(n.loadingRanges,t,e))),Fe((([{begin:t,end:e}])=>{this.store.dispatch(Uq({begin:t,end:e}))})),Zt((([{runId:t,begin:e,end:n}])=>this.dataSource.fetchExecutionDigests(t,e,n).pipe(Fe((t=>{this.store.dispatch(Gq(t))})),It((()=>{}))))))}onExecutionDigestFocused(){return this.actions$.pipe(Dk(Zq),Ve(this.store.select(kZ),this.store.select(cX),this.store.select(BZ)),It((([t,e,n,o])=>({activeRunId:e,loadedExecutionData:n,focusIndex:o+t.displayIndex}))))}createExecutionDataAndStackFramesLoader(t){return t.pipe(ce((({activeRunId:t,loadedExecutionData:e,focusIndex:n})=>null!==t&&null!==n&&void 0===e[n])),Zt((({activeRunId:t,focusIndex:e})=>{const n=e,o=n+1;return this.dataSource.fetchExecutionData(t,n,o).pipe(Fe((t=>{this.store.dispatch(Xq(t))})),It((t=>({executionData:t,begin:n,end:o}))))})),It((({executionData:t})=>t.executions[0])),Ve(this.store.select(kZ),this.store.select(pX)),ce((([t,e,n])=>{if(null===e)return!1;for(const e of t.stack_frame_ids)if(void 0===n[e])return!0;return!1})),Zt((([t,e])=>{const n=t.stack_frame_ids;return this.dataSource.fetchStackFrames(e,n).pipe(Fe((t=>{const e={};for(let o=0;o<n.length;++o)e[n[o]]=t.stack_frames[o];this.store.dispatch(dZ({stackFrames:e}))})),It((()=>{})))})))}onGraphExecutionScroll(){return this.actions$.pipe(Dk(tZ),ge(100),Ve(this.store.select(kZ),this.store.select(YZ),this.store.select(qZ)),ce((([,t,e])=>null!==t&&e>0)),It((([,t,e,n])=>({runId:t,numGraphExecutions:e,scrollBeginIndex:n}))),Ve(this.store.select(XZ),this.store.select(ZZ),this.store.select(KZ),this.store.select(JZ)),It((([{runId:t,numGraphExecutions:e,scrollBeginIndex:n},o,i,a,r])=>{let s=NJ(n,Math.min(n+i,e),o,e,r);return s=s.filter((t=>-1===a.indexOf(t))),{runId:t,missingPages:s,pageSize:o,numGraphExecutions:e}})))}loadGraphExecutionPages(t){return t.pipe(ce((({missingPages:t})=>t.length>0)),Fe((({missingPages:t})=>{t.forEach((t=>{this.store.dispatch(Qq({pageIndex:t}))}))})),Zt((({runId:t,missingPages:e,pageSize:n,numGraphExecutions:o})=>{const i=e[0]*n,a=Math.min((e[e.length-1]+1)*n,o);return this.dataSource.fetchGraphExecutionData(t,i,a).pipe(Fe((t=>{this.store.dispatch($q(t))})),It((()=>{})))})))}loadGraphOpInfo(){return this.actions$.pipe(Dk(nZ,eZ),Ve(this.store.select(kZ),this.store.select(dX)),ce((([t,e,n])=>{const{graph_id:o,op_name:i}=t;return!(null===e||void 0!==n[o]&&n[o].has(i)&&(n[o].get(i)===yE.LOADING||n[o].get(i)===yE.LOADED))})),Fe((([{graph_id:t,op_name:e}])=>this.store.dispatch(oZ({graph_id:t,op_name:e})))),Zt((([t,e])=>{const{graph_id:n,op_name:o}=t;return this.dataSource.fetchGraphOpInfo(e,n,o).pipe(Fe((t=>this.store.dispatch(iZ({graphOpInfoResponse:t})))),It((t=>({runId:e,stackFrameIds:t.stack_frame_ids}))))})))}loadGraphOpStackFrames(t){return t.pipe(Ve(this.store.select(pX)),It((([{runId:t,stackFrameIds:e},n])=>({runId:t,missingStackFrameIds:e.filter((t=>void 0===n[t]))}))),ce((({runId:t,missingStackFrameIds:e})=>null!==t&&e.length>0)),Zt((({runId:t,missingStackFrameIds:e})=>this.dataSource.fetchStackFrames(t,e).pipe(Fe((t=>{const n={};for(let o=0;o<e.length;++o)n[e[o]]=t.stack_frames[o];this.store.dispatch(dZ({stackFrames:n}))})),It((()=>{}))))))}onAlertTypeFocused(){return this.actions$.pipe(Dk(Bq),Ve(this.store.select(kZ),this.store.select(AZ),this.store.select(TZ),this.store.select(NZ),this.store.select(EZ)),ce((([,t,e,n,o,i])=>null!==t&&null!==e&&n>0&&(null===o||Object.keys(o).length<n)&&i.state!==yE.LOADING)),Fe((()=>this.store.dispatch(Hq()))),Zt((([,t,e])=>this.dataSource.fetchAlerts(t,0,-1,e))),Fe((({num_alerts:t,alerts_breakdown:e,alert_type:n,begin:o,end:i,alerts:a})=>{this.store.dispatch(Lq({numAlerts:t,alertsBreakdown:e,alertType:n,begin:o,end:i,alerts:a}))})))}fetchExecutionDigestsForAlertTypeFocus(t){return t.pipe(Ve(this.store.select(VZ),this.store.select(jZ),this.store.select(LZ),this.store.select(FZ),this.store.select(kZ)),It((([t,e,n,o,i,a])=>{const r=t.alerts[0].execution_index,s=NJ(Math.max(0,r-Math.floor(n/2)),Math.min(r+Math.floor(n/2),o),e,o,i.pageLoadedSizes);return 0===s.length?{runId:a,begin:0,end:0}:{runId:a,begin:s[0]*e,end:Math.min(i.numExecutions,(s[s.length-1]+1)*e)}})))}loadSourceFileList(t){return t.pipe(Ve(this.store.select(kZ),this.store.select(hX)),ce((([,t,e])=>null!==t&&e.state!==yE.LOADING)),Fe((()=>this.store.dispatch(aZ()))),Zt((([,t])=>this.dataSource.fetchSourceFileList(t).pipe(Fe((t=>{const e=[];t.forEach((([t,n])=>{e.push({host_name:t,file_path:n})})),this.store.dispatch(rZ({sourceFiles:e}))})),It((()=>{}))))))}onSourceFileFocused(){return this.actions$.pipe(Dk(sZ),Ve(this.store.select(kZ),this.store.select(bX),this.store.select(yX)),It((([t,e,n,o])=>({runId:e,stackFrame:t.stackFrame,fileIndex:n,fileContent:o}))),ce((({runId:t,fileContent:e})=>null!==t&&null!==e&&e.loadState===yE.NOT_LOADED)),Fe((({stackFrame:t})=>this.store.dispatch(lZ({host_name:t.host_name,file_path:t.file_path})))),Zt((({fileIndex:t,runId:e})=>this.dataSource.fetchSourceFile(e,t).pipe(Fe((t=>{this.store.dispatch(cZ(t))})),It((()=>{}))))))}}zJ.ɵfac=function t(e){return new(e||zJ)(vr(Sk),vr(Iw),vr(kq))},zJ.ɵprov=Mn({token:zJ,factory:zJ.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(zJ,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:kq}]}),null);class IJ{}IJ.ɵfac=function t(e){return new(e||IJ)},IJ.ɵmod=ao({type:IJ}),IJ.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(IJ,[{type:Ay,args:[{declarations:[xX,PX],imports:[WM],exports:[PX]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(IJ,{declarations:[xX,PX],imports:[WM],exports:[PX]});class HJ{}HJ.ɵfac=function t(e){return new(e||HJ)},HJ.ɵmod=ao({type:HJ}),HJ.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(HJ,[{type:Ay,args:[{declarations:[BK,DK,VK],imports:[WM],exports:[VK]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(HJ,{declarations:[BK,DK,VK],imports:[WM],exports:[VK]});class FJ{}FJ.ɵfac=function t(e){return new(e||FJ)},FJ.ɵmod=ao({type:FJ}),FJ.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(FJ,[{type:Ay,args:[{declarations:[tK,iK,oK,eK,nK,aK],imports:[WM],exports:[aK]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(FJ,{declarations:[tK,iK,oK,eK,nK,aK],imports:[WM],exports:[aK]});class LJ{}LJ.ɵfac=function t(e){return new(e||LJ)},LJ.ɵmod=ao({type:LJ}),LJ.ɵinj=vn({imports:[[WM,FJ,_F]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(LJ,[{type:Ay,args:[{declarations:[KK,JK],imports:[WM,FJ,_F],exports:[JK]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(LJ,{declarations:[KK,JK],imports:[WM,FJ,_F],exports:[JK]});class BJ{}BJ.ɵfac=function t(e){return new(e||BJ)},BJ.ɵmod=ao({type:BJ}),BJ.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(BJ,[{type:Ay,args:[{declarations:[Dq,Eq],exports:[Eq]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(BJ,{declarations:[Dq,Eq],exports:[Eq]});class VJ{}VJ.ɵfac=function t(e){return new(e||VJ)},VJ.ɵmod=ao({type:VJ}),VJ.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(VJ,[{type:Ay,args:[{exports:[oJ],declarations:[oJ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(VJ,{declarations:[oJ],exports:[oJ]});const jJ=["codeViewerContainer"];class UJ{constructor(){this.firstText=null,this.secondText=null,this.renderSideBySide=!0,this.monaco=null,this.editor=null,this.RESIZE_DEBOUNCE_INTERVAL_MS=50}onResize(){this.editor&&this.editor.layout()}ngOnChanges(t){if(!this.monaco)return;const e=!this.editor;e&&(this.editor=this.monaco.editor.createDiffEditor(this.codeViewerContainer.nativeElement,{readOnly:!0,fontSize:10,minimap:{enabled:!0},renderSideBySide:this.renderSideBySide,theme:nJ(this.useDarkMode)})),(e||t.firstText||t.secondText)&&this.editor.setModel({original:this.monaco.editor.createModel(this.firstText||""),modified:this.monaco.editor.createModel(this.secondText||"")}),t.renderSideBySide&&this.editor.updateOptions({renderSideBySide:this.renderSideBySide}),t.useDarkMode&&this.monaco.editor.setTheme(nJ(this.useDarkMode))}}UJ.ɵfac=function t(e){return new(e||UJ)},UJ.ɵcmp=to({type:UJ,selectors:[["source-code-diff-component"]],viewQuery:function t(e,n){if(1&e&&Qh(jJ,7,hg),2&e){let t;Jh(t=tb())&&(n.codeViewerContainer=t.first)}},inputs:{firstText:"firstText",secondText:"secondText",renderSideBySide:"renderSideBySide",monaco:"monaco",useDarkMode:"useDarkMode"},features:[Bo],decls:2,vars:1,consts:[["detectResize","",1,"code-viewer-container",3,"resizeEventDebouncePeriodInMs","onResize"],["codeViewerContainer",""]],template:function t(e,n){1&e&&(Rm(0,"div",0,1),Vm("onResize",(function t(){return n.onResize()})),Am()),2&e&&Dm("resizeEventDebouncePeriodInMs",n.RESIZE_DEBOUNCE_INTERVAL_MS)},directives:[oJ],styles:[".code-viewer-container[_ngcontent-%COMP%] {\n        height: 100%;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(UJ,[{type:My,args:[{selector:"source-code-diff-component",template:'\n    <div\n      #codeViewerContainer\n      class="code-viewer-container"\n      detectResize\n      [resizeEventDebouncePeriodInMs]="RESIZE_DEBOUNCE_INTERVAL_MS"\n      (onResize)="onResize()"\n    ></div>\n  ',styles:["\n      .code-viewer-container {\n        height: 100%;\n      }\n    "],changeDetection:zn.OnPush}]}],null,{firstText:[{type:xy}],secondText:[{type:xy}],renderSideBySide:[{type:xy}],monaco:[{type:xy}],useDarkMode:[{type:xy}],codeViewerContainer:[{type:Za,args:["codeViewerContainer",{static:!0,read:hg}]}]});class GJ{constructor(){this.firstText=null,this.secondText=null,this.renderSideBySide=!0,this.useDarkMode=!1,this.monaco$=null}ngOnInit(){this.monaco$=Ct(eJ()).pipe(It((()=>window.monaco)))}}GJ.ɵfac=function t(e){return new(e||GJ)},GJ.ɵcmp=to({type:GJ,selectors:[["source-code-diff"]],inputs:{firstText:"firstText",secondText:"secondText",renderSideBySide:"renderSideBySide",useDarkMode:"useDarkMode"},decls:2,vars:7,consts:[[3,"firstText","secondText","renderSideBySide","monaco","useDarkMode"]],template:function t(e,n){1&e&&(Tm(0,"source-code-diff-component",0),Ah(1,"async")),2&e&&Dm("firstText",n.firstText)("secondText",n.secondText)("renderSideBySide",n.renderSideBySide)("monaco",Th(1,5,n.monaco$))("useDarkMode",n.useDarkMode)},directives:[UJ],pipes:[wM],styles:["source-code-diff-component[_ngcontent-%COMP%] {\n        display: block;\n        height: 100%;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(GJ,[{type:My,args:[{selector:"source-code-diff",template:'\n    <source-code-diff-component\n      [firstText]="firstText"\n      [secondText]="secondText"\n      [renderSideBySide]="renderSideBySide"\n      [monaco]="monaco$ | async"\n      [useDarkMode]="useDarkMode"\n    ></source-code-diff-component>\n  ',styles:["\n      source-code-diff-component {\n        display: block;\n        height: 100%;\n      }\n    "],changeDetection:zn.OnPush}]}],null,{firstText:[{type:xy}],secondText:[{type:xy}],renderSideBySide:[{type:xy}],useDarkMode:[{type:xy}]});class WJ{}WJ.ɵfac=function t(e){return new(e||WJ)},WJ.ɵmod=ao({type:WJ}),WJ.ɵinj=vn({imports:[[WM,VJ]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(WJ,[{type:Ay,args:[{declarations:[aJ,rJ,UJ,GJ],imports:[WM,VJ],exports:[rJ,GJ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(WJ,{declarations:[aJ,rJ,UJ,GJ],imports:[WM,VJ],exports:[rJ,GJ]});class YJ{}YJ.ɵfac=function t(e){return new(e||YJ)},YJ.ɵmod=ao({type:YJ}),YJ.ɵinj=vn({imports:[[WM,WJ]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(YJ,[{type:Ay,args:[{declarations:[dJ,pJ],imports:[WM,WJ],exports:[pJ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(YJ,{declarations:[dJ,pJ],imports:[WM,WJ],exports:[pJ]});class qJ{}qJ.ɵfac=function t(e){return new(e||qJ)},qJ.ɵmod=ao({type:qJ}),qJ.ɵinj=vn({imports:[[WM,WJ]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(qJ,[{type:Ay,args:[{declarations:[kJ,SJ],imports:[WM,WJ],exports:[SJ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(qJ,{declarations:[kJ,SJ],imports:[WM,WJ],exports:[SJ]});class ZJ{}ZJ.ɵfac=function t(e){return new(e||ZJ)},ZJ.ɵmod=ao({type:ZJ}),ZJ.ɵinj=vn({imports:[[WM,FJ]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ZJ,[{type:Ay,args:[{declarations:[pK,uK],imports:[WM,FJ],exports:[uK]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(ZJ,{declarations:[pK,uK],imports:[WM,FJ],exports:[uK]});class XJ{}XJ.ɵfac=function t(e){return new(e||XJ)},XJ.ɵmod=ao({type:XJ}),XJ.ɵinj=vn({imports:[[WM,ZJ,JH,zX]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(XJ,[{type:Ay,args:[{declarations:[CK,vK],imports:[WM,ZJ,JH,zX],exports:[vK]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(XJ,{declarations:[CK,vK],imports:[WM,ZJ,JH,zX],exports:[vK]});class KJ{}function JJ(t,e){const n={};for(const o of Object.keys(t))n[o]=e(t[o],o);return n}function QJ(t,e,n){const{plugin:o,tag:i,runId:a,sample:r}=e[t],s=nT(n,o,i,r);if(s){if(null!==a&&s.runToSeries.hasOwnProperty(a)){const t=s.runToSeries[a].length;return t>0?t-1:null}const t=Object.values(s.runToSeries).map((t=>t.length));if(t.length)return Math.max(...t)-1}return null}function $J(t,e,n,o){const i=Object.assign({},e);for(const a in t){if(!t.hasOwnProperty(a))continue;const r=QJ(a,t,n);if(null===r){e.hasOwnProperty(a)&&(i[a]=null);continue}const s=e.hasOwnProperty(a)?e[a]:null,l=QJ(a,t,o),c=null!==s&&s===l,d=null===s||c;(null!==s&&s>r||d)&&(i[a]=r)}return i}function tQ(t){const e=JJ(t.runToLoadState,(t=>t===yE.LOADING?yE.LOADING:yE.NOT_LOADED));return Object.assign(Object.assign({},t),{runToLoadState:e})}function eQ(t,e,n,o){return JSON.stringify([t,e,n||"",o])}KJ.ɵfac=function t(e){return new(e||KJ)},KJ.ɵmod=ao({type:KJ}),KJ.ɵinj=vn({imports:[[IJ,WM,ER,LJ,HJ,BJ,YJ,qJ,Sq,XJ,dk.forFeature(pZ,xZ),Wk.forFeature([zJ]),wq.forPlugin(TJ,AJ)]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(KJ,[{type:Ay,args:[{declarations:[RJ,AJ],imports:[IJ,WM,ER,LJ,HJ,BJ,YJ,qJ,Sq,XJ,dk.forFeature(pZ,xZ),Wk.forFeature([zJ]),wq.forPlugin(TJ,AJ)],exports:[AJ],entryComponents:[AJ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(KJ,{declarations:[RJ,AJ],imports:[IJ,WM,ER,LJ,HJ,BJ,YJ,qJ,Sq,XJ,ck,Gk,wq],exports:[AJ]});const{initialState:nQ,reducers:oQ}=HN({tagMetadataLoadState:{state:yE.NOT_LOADED,lastLoadedTimeInMs:null},tagMetadata:{scalars:{tagDescriptions:{},tagToRuns:{}},histograms:{tagDescriptions:{},tagToRuns:{}},images:{tagDescriptions:{},tagRunSampledInfo:{}}},cardList:[],cardToPinnedCopy:new Map,pinnedCardToOriginal:new Map,unresolvedImportedPinnedCards:[],cardMetadataMap:{},cardStepIndex:{},tagFilter:"",tagGroupExpanded:new Map,selectedTime:null,selectTimeEnabled:!1,useRangeSelectTime:!1,filteredPluginTypes:new Set,stepMinMax:{min:1/0,max:-1/0}},{isSettingsPaneOpen:!0,promoteTimeSeries:!0,timeSeriesData:{scalars:{},histograms:{},images:{}},settings:pT,settingOverrides:{},visibleCardMap:new Map},(t=>Object.assign(Object.assign({},t),{visibleCardMap:new Map}))),iQ=nQ,aQ=yk(nQ,bk(rS,((t,{routeKind:e,partialState:n})=>{if(e!==Zk.EXPERIMENT&&e!==Zk.COMPARE_EXPERIMENT)return t;const o=new Set;for(const e of t.pinnedCardToOriginal.keys()){const{plugin:n,tag:i,runId:a,sample:r}=t.cardMetadataMap[e];o.add(eQ(n,i,a,r))}const i=n,a=[];for(const e of[...t.unresolvedImportedPinnedCards,...i.metrics.pinnedCards]){const t=eQ(e.plugin,e.tag,e.runId,e.sample);o.has(t)||(o.add(t),a.push(e))}const r=sT(a,t.cardList,t.cardMetadataMap,t.cardToPinnedCopy,t.pinnedCardToOriginal,t.cardStepIndex),s=i.metrics.smoothing;let l=t.settingOverrides;if(Number.isFinite(s)&&null!==s){const e=Math.max(0,Math.min(.999,Number(s.toPrecision(3))));l=Object.assign(Object.assign({},t.settingOverrides),{scalarSmoothing:e})}const 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Object.keys(n[t])){const{maxSamplesPerStep:a}=n[t][i];for(let n=0;n<a;n++)e.push({plugin:o,tag:t,runId:i,sample:n,numSample:a})}}}else if(xA(o)){i=t[o].tagToRuns;for(const t of Object.keys(i))for(const n of i[t])e.push({plugin:o,tag:t,runId:n})}else{i=t[o].tagToRuns;for(const t of Object.keys(i))e.push({plugin:o,tag:t,runId:null})}}return e})(n),r=[];for(const e of i){const n=iT(e);t.cardMetadataMap.hasOwnProperty(n)||(o[n]=e,r.push(n))}const s=[...t.cardList,...r],l=sT(t.unresolvedImportedPinnedCards,r,o,t.cardToPinnedCopy,t.pinnedCardToOriginal,t.cardStepIndex);return Object.assign(Object.assign(Object.assign({},t),l),{tagMetadataLoadState:{state:yE.LOADED,lastLoadedTimeInMs:Date.now()},tagMetadata:n,cardList:s})})),bk(eR,((t,{tagFilter:e})=>Object.assign(Object.assign({},t),{tagFilter:e}))),bk(LE,((t,{sort:e})=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{tooltipSort:e})}))),bk(BE,(t=>{var e;const n=!(null!==(e=t.settingOverrides.ignoreOutliers)&&void 0!==e?e:t.settings.ignoreOutliers);return Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{ignoreOutliers:n})})})),bk(VE,((t,{xAxisType:e})=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{xAxisType:e})}))),bk(jE,((t,{smoothing:e})=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{scalarSmoothing:e})}))),bk(UE,(t=>{var e;const n=!(null!==(e=t.settingOverrides.scalarPartitionNonMonotonicX)&&void 0!==e?e:t.settings.scalarPartitionNonMonotonicX);return Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{scalarPartitionNonMonotonicX:n})})})),bk(GE,((t,{brightnessInMilli:e})=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{imageBrightnessInMilli:e})}))),bk(WE,((t,{contrastInMilli:e})=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{imageContrastInMilli:e})}))),bk(YE,(t=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{imageBrightnessInMilli:void 0})}))),bk(qE,(t=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{imageContrastInMilli:void 0})}))),bk(ZE,(t=>{var e;const n=!(null!==(e=t.settingOverrides.imageShowActualSize)&&void 0!==e?e:t.settings.imageShowActualSize);return Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{imageShowActualSize:n})})})),bk(XE,((t,{histogramMode:e})=>Object.assign(Object.assign({},t),{settingOverrides:Object.assign(Object.assign({},t.settingOverrides),{histogramMode:e})}))),bk(KE,((t,{requests:e})=>{if(!e.length)return t;const n=Object.assign({},t.timeSeriesData);for(const o of e){const{plugin:e,tag:i,sample:a}=o;n[e]=oT(n,e,i,a);const r=nT(n,e,i,a),s=OA(o)?[o.runId]:rT(t.tagMetadata,e,i,a);r.runToLoadState=aT(yE.LOADING,s,r.runToLoadState)}return Object.assign(Object.assign({},t),{timeSeriesData:n})})),bk(JE,((t,{request:e})=>{const n=Object.assign({},t.timeSeriesData),{plugin:o,tag:i,sample:a}=e;n[o]=oT(n,o,i,a);const r=nT(n,o,i,a),s=OA(e)?[e.runId]:rT(t.tagMetadata,o,i,a);return r.runToLoadState=aT(yE.FAILED,s,r.runToLoadState),Object.assign(Object.assign({},t),{timeSeriesData:n})})),bk(QE,((t,{response:e})=>{const n=Object.assign({},t.stepMinMax),o=Object.assign({},t.timeSeriesData),{plugin:i,tag:a,runId:r,sample:s}=e;o[i]=oT(o,i,a,s);const l=nT(o,i,a,s);if(wA(e)){const e=r?[r]:rT(t.tagMetadata,i,a,s);l.runToLoadState=aT(yE.FAILED,e,l.runToLoadState)}else{const t=e.runToSeries;l.runToSeries=Object.assign({},l.runToSeries),l.runToLoadState=Object.assign({},l.runToLoadState);for(const e in t)if(t.hasOwnProperty(e)){l.runToSeries[e]=t[e],l.runToLoadState[e]=yE.LOADED;for(const o of t[e])n.min=Math.min(n.min,o.step),n.max=Math.max(n.max,o.step)}}return Object.assign(Object.assign({},t),{timeSeriesData:o,cardStepIndex:$J(t.cardMetadataMap,t.cardStepIndex,o,t.timeSeriesData),stepMinMax:n})})),bk(tR,((t,{cardId:e,stepIndex:n})=>{const o=QJ(e,t.cardMetadataMap,t.timeSeriesData);let i=n;return null===o?i=null:n>o&&(i=o),Object.assign(Object.assign({},t),{cardStepIndex:Object.assign(Object.assign({},t.cardStepIndex),{[e]:i})})})),bk(nR,((t,{tagGroup:e})=>{const n=new Map(t.tagGroupExpanded);return n.set(e,!n.get(e)),Object.assign(Object.assign({},t),{tagGroupExpanded:n})})),bk($E,((t,{enteredCards:e,exitedCards:n})=>{if(!e.length&&!n.length)return t;const o=new Map(t.visibleCardMap);return e.forEach((({elementId:t,cardId:e})=>{var n;const i=null!==(n=o.get(t))&&void 0!==n?n:null;if(null!==i&&i!==e)throw new Error("A DOM element cannot be reused for more than 1 unique card metadata");o.set(t,e)})),n.forEach((({elementId:t})=>{o.delete(t)})),Object.assign(Object.assign({},t),{visibleCardMap:o})})),bk(oR,((t,{cardId:e})=>{const n=t.pinnedCardToOriginal.has(e),o=!n&&!t.cardToPinnedCopy.has(e);if(o&&!cT(t))return t;let i=new Map(t.cardToPinnedCopy),a=new Map(t.pinnedCardToOriginal),r=Object.assign({},t.cardMetadataMap),s=Object.assign({},t.cardStepIndex);if(n){const n=t.pinnedCardToOriginal.get(e);i.delete(n),a.delete(e),delete r[e],delete s[e]}else if(o){const t=lT(e,i,a,s,r);i=t.cardToPinnedCopy,a=t.pinnedCardToOriginal,r=t.cardMetadataMap,s=t.cardStepIndex}else{const n=t.cardToPinnedCopy.get(e);i.delete(e),a.delete(n),delete r[n],delete s[n]}return Object.assign(Object.assign({},t),{cardMetadataMap:r,cardStepIndex:s,cardToPinnedCopy:i,pinnedCardToOriginal:a})})),bk(lR,(t=>Object.assign(Object.assign({},t),{selectTimeEnabled:!t.selectTimeEnabled}))),bk(rR,((t,e)=>{var n,o,i,a;const r=e.startStep;let s=null!==(a=null!==(n=e.endStep)&&void 0!==n?n:null===(i=null===(o=t.selectedTime)||void 0===o?void 0:o.end)||void 0===i?void 0:i.step)&&void 0!==a?a:t.stepMinMax.max;return r>s&&(s=r),Object.assign(Object.assign({},t),{selectTimeEnabled:!0,selectedTime:{start:{step:r},end:{step:s}}})})),bk(cR,(t=>Object.assign(Object.assign({},t),{useRangeSelectTime:!t.useRangeSelectTime}))),bk(sR,(t=>Object.assign(Object.assign({},t),{selectedTime:null}))),bk(iR,((t,{plugin:e})=>{let n=new Set(t.filteredPluginTypes);return n.has(e)?n.delete(e):n.add(e),Object.values(hA).every((t=>n.has(t)))&&(n=new Set),Object.assign(Object.assign({},t),{filteredPluginTypes:n})})),bk(aR,(t=>Object.assign(Object.assign({},t),{filteredPluginTypes:new Set}))),bk(dR,(t=>Object.assign(Object.assign({},t),{promoteTimeSeries:!1}))),bk(zE,(t=>Object.assign(Object.assign({},t),{isSettingsPaneOpen:!t.isSettingsPaneOpen}))),bk(NE,(t=>Object.assign(Object.assign({},t),{isSettingsPaneOpen:!1}))));function rQ(t,e){return BN(aQ,oQ)(t,e)}function sQ(t,e){return{tagDescriptions:t[e].tagDescriptions,tagToRuns:lQ(t[e].runTagInfo)}}function lQ(t){const e={};for(const n in t)for(const o of t[n])e[o]=[...e[o]||[],n];return e}const cQ=Zw(gT,yT,((t,e,n)=>e?Object.assign(Object.assign({},e),{loadState:t,id:n}):null)),dQ=JP("[Metrics Effects] Init");class pQ{constructor(t,e,n){this.actions$=t,this.store=e,this.dataSource=n,this.dashboardShownWithoutData$=this.actions$.pipe(Dk(dQ,_E,PE,dS),Ve(this.store.select(MR),this.store.select(uT)),ce((([,t,e])=>t===_A&&e.state===yE.NOT_LOADED))),this.reloadRequestedWhileShown$=this.actions$.pipe(Dk(xE,vE),Ve(this.store.select(MR)),ce((([,t])=>t===_A))),this.loadTagMetadata$=re(this.dashboardShownWithoutData$,this.reloadRequestedWhileShown$).pipe(Ve(this.store.select(uT),this.store.select(TS)),ce((([,t,e])=>t.state!==yE.LOADING&&null!==e)),Fe((()=>{this.store.dispatch(IE())})),ze((([,,t])=>this.dataSource.fetchTagMetadata(t).pipe(Fe((t=>{this.store.dispatch(HE({tagMetadata:t}))})),pe((()=>(this.store.dispatch(FE()),Et(null)))))))),this.visibleCardsWithoutDataChanged$=this.actions$.pipe(Dk($E),ze((()=>this.getVisibleCardFetchInfos().pipe(be(1)))),It((t=>t.filter((t=>t.loadState===yE.NOT_LOADED))))),this.visibleCardsReloaded$=this.reloadRequestedWhileShown$.pipe(ze((()=>this.getVisibleCardFetchInfos().pipe(be(1)))),It((t=>t.filter((t=>t.loadState!==yE.LOADING))))),this.loadTimeSeries$=re(this.visibleCardsWithoutDataChanged$,this.visibleCardsReloaded$).pipe(ce((t=>t.length>0)),Ve(this.store.select(TS).pipe(ce((t=>null!==t)))),Zt((([t,e])=>this.fetchTimeSeriesForCards(t,e)))),this.dataEffects$=Mk((()=>re(this.loadTagMetadata$,this.loadTimeSeries$)),{dispatch:!1})}ngrxOnInitEffects(){return dQ()}getVisibleCardFetchInfos(){return this.store.select(CT).pipe(ze((t=>t.size?$t([...t].map((t=>this.store.select(cQ,t).pipe(be(1))))):Et([]))),It((t=>t.filter(Boolean))))}fetchTimeSeries(t){return this.dataSource.fetchTimeSeries([t]).pipe(Fe((t=>{const e=t.filter(wA);e.length&&console.error("Time series response contained errors:",e),this.store.dispatch(QE({response:t[0]}))})),pe((()=>(this.store.dispatch(JE({request:t})),Et(null)))))}fetchTimeSeriesForCards(t,e){return Et(t.map((t=>{const{plugin:n,tag:o,runId:i,sample:a}=t;return xA(n)?{plugin:n,tag:o,sample:a,runId:i}:{plugin:n,tag:o,sample:a,experimentIds:e}}))).pipe(Fe((t=>{this.store.dispatch(KE({requests:t}))})),Zt((t=>re(...t.map((t=>this.fetchTimeSeries(t)))))))}}pQ.ɵfac=function t(e){return new(e||pQ)(vr(Sk),vr(Iw),vr(PA))},pQ.ɵprov=Mn({token:pQ,factory:pQ.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(pQ,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:PA}]}),null);const mQ=new Ga("Metrics Store Config"),uQ=new Ga("Metrics Initial Settings Config");function fQ(t){return t?{initialState:Object.assign(Object.assign({},iQ),{settings:t})}:{initialState:iQ}}var gQ;!(function(t){t[t.LEFT=1]="LEFT",t[t.RIGHT=2]="RIGHT",t[t.MIDDLE=4]="MIDDLE",t[t.FOURTH=8]="FOURTH",t[t.FIFTH=32]="FIFTH"})(gQ||(gQ={}));let hQ=0;function bQ(t,e){if(1&t){const t=Hm();Rm(0,"button",3),Vm("click",(function e(){return hi(t),Ym().expandSidebar()})),Tm(1,"mat-icon",4),Am()}}function yQ(t,e){if(1&t&&(Rm(0,"nav",5),Ah(1,"async"),Xm(2,1),Am()),2&t){const t=Ym();du("width",Th(1,4,t.width$),"%")("min-width",t.MINIMUM_SIDEBAR_WIDTH_IN_PX,"px")}}function _Q(t,e){if(1&t){const t=Hm();Rm(0,"div",6),Vm("mousedown",(function e(){return hi(t),Ym().resizeGrabbed()})),Tm(1,"mat-icon",7),Am()}}const CQ=[[["","main",""]],[["","sidebar",""]]];class MQ{constructor(t,e){this.store=t,this.width$=this.store.select(OR),this.ngUnsubscribe=new I,this.resizing=!1,this.MINIMUM_SIDEBAR_WIDTH_IN_PX=75,oe(e.nativeElement,"mousemove").pipe(Ie(this.ngUnsubscribe),ce((()=>this.resizing))).subscribe((t=>{if((t.buttons&gQ.LEFT)!==gQ.LEFT)return void(this.resizing=!1);t.preventDefault();const{width:n}=e.nativeElement.getBoundingClientRect();this.store.dispatch(TE({widthInPercent:t.clientX<=this.MINIMUM_SIDEBAR_WIDTH_IN_PX?0:t.clientX/n*100}))})),oe(e.nativeElement,"mouseup",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((()=>{this.resizing=!1}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}resizeGrabbed(){this.resizing=!0}expandSidebar(){this.store.dispatch(TE({widthInPercent:20}))}}var vQ;MQ.ɵfac=function t(e){return new(e||MQ)(Sm(Iw),Sm(hg))},MQ.ɵcmp=to({type:MQ,selectors:[["tb-dashboard-layout"]],ngContentSelectors:["[main]","[sidebar]"],decls:7,vars:9,consts:[["class","expand",3,"click",4,"ngIf"],["class","sidebar",3,"width","minWidth",4,"ngIf"],["class","resizer",3,"mousedown",4,"ngIf"],[1,"expand",3,"click"],["svgIcon","expand_more_24px"],[1,"sidebar"],[1,"resizer",3,"mousedown"],["svgIcon","drag_indicator_24px"]],template:function t(e,n){1&e&&(Zm(CQ),Qp(0,bQ,2,0,"button",0),Ah(1,"async"),Qp(2,yQ,3,6,"nav",1),Ah(3,"async"),Qp(4,_Q,2,0,"div",2),Ah(5,"async"),Xm(6)),2&e&&(Dm("ngIf",0===Th(1,3,n.width$)),rc(2),Dm("ngIf",Th(3,5,n.width$)>0),rc(2),Dm("ngIf",Th(5,7,n.width$)>0))},directives:[dM,DW],pipes:[wM],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   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sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action[_ngcontent-%COMP%]{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node[_ngcontent-%COMP%], .mat-nested-tree-node[_ngcontent-%COMP%]{font-weight:400;font-size:14px}.mat-ripple[_ngcontent-%COMP%]{overflow:hidden;position:relative}.mat-ripple[_ngcontent-%COMP%]:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded[_ngcontent-%COMP%]{overflow:visible}.mat-ripple-element[_ngcontent-%COMP%]{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms 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.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes 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Got: ${e.datasetType} vs. ${n.datasetType}`)}else o.set(t.tag,Object.assign({},t))}if(a.length)throw new Error(`Validation error:\n${a.join("\n")}`);return{hparams:[...e].map((([t,e])=>Object.assign(Object.assign({},e),{displayName:[...n.get(t)].join(" or ")}))),metrics:[...o].map((([t,e])=>Object.assign(Object.assign({},e),{displayName:[...i.get(t)].join(" or ")})))}})(...e.experimentIds.map((e=>{const n=t.specs[e];return n?{hparams:n.hparam.specs,metrics:n.metric.specs}:null})).filter(Boolean)))),DQ={tooltipState:nx("state",[rx("initial, void, hidden",ax({opacity:0,transform:"scale(0)"})),rx("visible",ax({transform:"scale(1)"})),lx("* => visible",ox("200ms cubic-bezier(0, 0, 0.2, 1)",sx([ax({opacity:0,transform:"scale(0)",offset:0}),ax({opacity:.5,transform:"scale(0.99)",offset:.5}),ax({opacity:1,transform:"scale(1)",offset:1})]))),lx("* => hidden",ox("100ms cubic-bezier(0, 0, 0.2, 1)",ax({opacity:0})))])},EQ=Nz({passive:!0});function RQ(t){return Error(`Tooltip position "${t}" is invalid.`)}const AQ=new Ga("mat-tooltip-scroll-strategy"),TQ={provide:AQ,deps:[pL],useFactory:function NQ(t){return()=>t.scrollStrategies.reposition({scrollThrottle:20})}},zQ=new Ga("mat-tooltip-default-options",{providedIn:"root",factory:function IQ(){return{showDelay:0,hideDelay:0,touchendHideDelay:1500}}});class HQ{constructor(t,e,n,o,i,a,r,s,l,c,d,p){this._overlay=t,this._elementRef=e,this._scrollDispatcher=n,this._viewContainerRef=o,this._ngZone=i,this._platform=a,this._ariaDescriber=r,this._focusMonitor=s,this._dir=c,this._defaultOptions=d,this._position="below",this._disabled=!1,this._viewInitialized=!1,this._pointerExitEventsInitialized=!1,this._viewportMargin=8,this._cssClassPrefix="mat",this.showDelay=this._defaultOptions.showDelay,this.hideDelay=this._defaultOptions.hideDelay,this.touchGestures="auto",this._message="",this._passiveListeners=[],this._destroyed=new I,this._handleKeydown=t=>{this._isTooltipVisible()&&t.keyCode===uz&&!bz(t)&&(t.preventDefault(),t.stopPropagation(),this._ngZone.run((()=>this.hide(0))))},this._scrollStrategy=l,this._document=p,d&&(d.position&&(this.position=d.position),d.touchGestures&&(this.touchGestures=d.touchGestures)),c.change.pipe(Ie(this._destroyed)).subscribe((()=>{this._overlayRef&&this._updatePosition(this._overlayRef)})),i.runOutsideAngular((()=>{e.nativeElement.addEventListener("keydown",this._handleKeydown)}))}get position(){return this._position}set position(t){var e;t!==this._position&&(this._position=t,this._overlayRef&&(this._updatePosition(this._overlayRef),null===(e=this._tooltipInstance)||void 0===e||e.show(0),this._overlayRef.updatePosition()))}get disabled(){return this._disabled}set disabled(t){this._disabled=yz(t),this._disabled?this.hide(0):this._setupPointerEnterEventsIfNeeded()}get message(){return this._message}set message(t){this._ariaDescriber.removeDescription(this._elementRef.nativeElement,this._message,"tooltip"),this._message=null!=t?String(t).trim():"",!this._message&&this._isTooltipVisible()?this.hide(0):(this._setupPointerEnterEventsIfNeeded(),this._updateTooltipMessage(),this._ngZone.runOutsideAngular((()=>{Promise.resolve().then((()=>{this._ariaDescriber.describe(this._elementRef.nativeElement,this.message,"tooltip")}))})))}get tooltipClass(){return this._tooltipClass}set tooltipClass(t){this._tooltipClass=t,this._tooltipInstance&&this._setTooltipClass(this._tooltipClass)}ngAfterViewInit(){this._viewInitialized=!0,this._setupPointerEnterEventsIfNeeded(),this._focusMonitor.monitor(this._elementRef).pipe(Ie(this._destroyed)).subscribe((t=>{t?"keyboard"===t&&this._ngZone.run((()=>this.show())):this._ngZone.run((()=>this.hide(0)))}))}ngOnDestroy(){const t=this._elementRef.nativeElement;clearTimeout(this._touchstartTimeout),this._overlayRef&&(this._overlayRef.dispose(),this._tooltipInstance=null),t.removeEventListener("keydown",this._handleKeydown),this._passiveListeners.forEach((([e,n])=>{t.removeEventListener(e,n,EQ)})),this._passiveListeners.length=0,this._destroyed.next(),this._destroyed.complete(),this._ariaDescriber.removeDescription(t,this.message,"tooltip"),this._focusMonitor.stopMonitoring(t)}show(t=this.showDelay){if(this.disabled||!this.message||this._isTooltipVisible()&&!this._tooltipInstance._showTimeoutId&&!this._tooltipInstance._hideTimeoutId)return;const e=this._createOverlay();this._detach(),this._portal=this._portal||new vF(this._tooltipComponent,this._viewContainerRef),this._tooltipInstance=e.attach(this._portal).instance,this._tooltipInstance.afterHidden().pipe(Ie(this._destroyed)).subscribe((()=>this._detach())),this._setTooltipClass(this._tooltipClass),this._updateTooltipMessage(),this._tooltipInstance.show(t)}hide(t=this.hideDelay){this._tooltipInstance&&this._tooltipInstance.hide(t)}toggle(){this._isTooltipVisible()?this.hide():this.show()}_isTooltipVisible(){return!!this._tooltipInstance&&this._tooltipInstance.isVisible()}_createOverlay(){if(this._overlayRef)return this._overlayRef;const t=this._scrollDispatcher.getAncestorScrollContainers(this._elementRef),e=this._overlay.position().flexibleConnectedTo(this._elementRef).withTransformOriginOn(`.${this._cssClassPrefix}-tooltip`).withFlexibleDimensions(!1).withViewportMargin(this._viewportMargin).withScrollableContainers(t);return e.positionChanges.pipe(Ie(this._destroyed)).subscribe((t=>{this._updateCurrentPositionClass(t.connectionPair),this._tooltipInstance&&t.scrollableViewProperties.isOverlayClipped&&this._tooltipInstance.isVisible()&&this._ngZone.run((()=>this.hide(0)))})),this._overlayRef=this._overlay.create({direction:this._dir,positionStrategy:e,panelClass:`${this._cssClassPrefix}-tooltip-panel`,scrollStrategy:this._scrollStrategy()}),this._updatePosition(this._overlayRef),this._overlayRef.detachments().pipe(Ie(this._destroyed)).subscribe((()=>this._detach())),this._overlayRef.outsidePointerEvents().pipe(Ie(this._destroyed)).subscribe((()=>{var t;return null===(t=this._tooltipInstance)||void 0===t?void 0:t._handleBodyInteraction()})),this._overlayRef}_detach(){this._overlayRef&&this._overlayRef.hasAttached()&&this._overlayRef.detach(),this._tooltipInstance=null}_updatePosition(t){const e=t.getConfig().positionStrategy,n=this._getOrigin(),o=this._getOverlayPosition();e.withPositions([this._addOffset(Object.assign(Object.assign({},n.main),o.main)),this._addOffset(Object.assign(Object.assign({},n.fallback),o.fallback))])}_addOffset(t){return t}_getOrigin(){const t=!this._dir||"ltr"==this._dir.value,e=this.position;let n;if("above"==e||"below"==e)n={originX:"center",originY:"above"==e?"top":"bottom"};else if("before"==e||"left"==e&&t||"right"==e&&!t)n={originX:"start",originY:"center"};else if("after"==e||"right"==e&&t||"left"==e&&!t)n={originX:"end",originY:"center"};else if("undefined"==typeof ngDevMode||ngDevMode)throw RQ(e);const{x:o,y:i}=this._invertPosition(n.originX,n.originY);return{main:n,fallback:{originX:o,originY:i}}}_getOverlayPosition(){const t=!this._dir||"ltr"==this._dir.value,e=this.position;let n;if("above"==e)n={overlayX:"center",overlayY:"bottom"};else if("below"==e)n={overlayX:"center",overlayY:"top"};else if("before"==e||"left"==e&&t||"right"==e&&!t)n={overlayX:"end",overlayY:"center"};else if("after"==e||"right"==e&&t||"left"==e&&!t)n={overlayX:"start",overlayY:"center"};else if("undefined"==typeof ngDevMode||ngDevMode)throw RQ(e);const{x:o,y:i}=this._invertPosition(n.overlayX,n.overlayY);return{main:n,fallback:{overlayX:o,overlayY:i}}}_updateTooltipMessage(){this._tooltipInstance&&(this._tooltipInstance.message=this.message,this._tooltipInstance._markForCheck(),this._ngZone.onMicrotaskEmpty.pipe(be(1),Ie(this._destroyed)).subscribe((()=>{this._tooltipInstance&&this._overlayRef.updatePosition()})))}_setTooltipClass(t){this._tooltipInstance&&(this._tooltipInstance.tooltipClass=t,this._tooltipInstance._markForCheck())}_invertPosition(t,e){return"above"===this.position||"below"===this.position?"top"===e?e="bottom":"bottom"===e&&(e="top"):"end"===t?t="start":"start"===t&&(t="end"),{x:t,y:e}}_updateCurrentPositionClass(t){const{overlayY:e,originX:n,originY:o}=t;let i;if(i="center"===e?this._dir&&"rtl"===this._dir.value?"end"===n?"left":"right":"start"===n?"left":"right":"bottom"===e&&"top"===o?"above":"below",i!==this._currentPosition){const t=this._overlayRef;if(t){const 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0,decorators:[{type:kr,args:[Z_]}]}],HQ.propDecorators={position:[{type:xy,args:["matTooltipPosition"]}],disabled:[{type:xy,args:["matTooltipDisabled"]}],showDelay:[{type:xy,args:["matTooltipShowDelay"]}],hideDelay:[{type:xy,args:["matTooltipHideDelay"]}],touchGestures:[{type:xy,args:["matTooltipTouchGestures"]}],message:[{type:xy,args:["matTooltip"]}],tooltipClass:[{type:xy,args:["matTooltipClass"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(HQ,[{type:Cy}],(function(){return[{type:pL},{type:hg},{type:pF},{type:eh},{type:a_},{type:wz},{type:Kz},{type:SI},{type:void 0},{type:HI},{type:void 0},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),{showDelay:[{type:xy,args:["matTooltipShowDelay"]}],hideDelay:[{type:xy,args:["matTooltipHideDelay"]}],touchGestures:[{type:xy,args:["matTooltipTouchGestures"]}],position:[{type:xy,args:["matTooltipPosition"]}],disabled:[{type:xy,args:["matTooltipDisabled"]}],message:[{type:xy,args:["matTooltip"]}],tooltipClass:[{type:xy,args:["matTooltipClass"]}]});class FQ extends HQ{constructor(t,e,n,o,i,a,r,s,l,c,d,p){super(t,e,n,o,i,a,r,s,l,c,d,p),this._tooltipComponent=BQ}}FQ.ɵfac=function t(e){return new(e||FQ)(Sm(pL),Sm(hg),Sm(pF),Sm(eh),Sm(a_),Sm(wz),Sm(Kz),Sm(SI),Sm(AQ),Sm(HI,8),Sm(zQ,8),Sm(Z_))},FQ.ɵdir=lo({type:FQ,selectors:[["","matTooltip",""]],hostAttrs:[1,"mat-tooltip-trigger"],exportAs:["matTooltip"],features:[xp]}),FQ.ctorParameters=()=>[{type:pL},{type:hg},{type:pF},{type:eh},{type:a_},{type:wz},{type:Kz},{type:SI},{type:void 0,decorators:[{type:kr,args:[AQ]}]},{type:HI,decorators:[{type:Sr}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[zQ]}]},{type:void 0,decorators:[{type:kr,args:[Z_]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(FQ,[{type:Cy,args:[{selector:"[matTooltip]",exportAs:"matTooltip",host:{class:"mat-tooltip-trigger"}}]}],(function(){return[{type:pL},{type:hg},{type:pF},{type:eh},{type:a_},{type:wz},{type:Kz},{type:SI},{type:void 0,decorators:[{type:kr,args:[AQ]}]},{type:HI,decorators:[{type:Sr}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[zQ]}]},{type:void 0,decorators:[{type:kr,args:[Z_]}]}]}),null);class LQ{constructor(t){this._changeDetectorRef=t,this._visibility="initial",this._closeOnInteraction=!1,this._onHide=new I}show(t){clearTimeout(this._hideTimeoutId),this._closeOnInteraction=!0,this._showTimeoutId=setTimeout((()=>{this._visibility="visible",this._showTimeoutId=void 0,this._onShow(),this._markForCheck()}),t)}hide(t){clearTimeout(this._showTimeoutId),this._hideTimeoutId=setTimeout((()=>{this._visibility="hidden",this._hideTimeoutId=void 0,this._markForCheck()}),t)}afterHidden(){return this._onHide}isVisible(){return"visible"===this._visibility}ngOnDestroy(){clearTimeout(this._showTimeoutId),clearTimeout(this._hideTimeoutId),this._onHide.complete()}_animationStart(){this._closeOnInteraction=!1}_animationDone(t){const e=t.toState;"hidden"!==e||this.isVisible()||this._onHide.next(),"visible"!==e&&"hidden"!==e||(this._closeOnInteraction=!0)}_handleBodyInteraction(){this._closeOnInteraction&&this.hide(0)}_markForCheck(){this._changeDetectorRef.markForCheck()}_onShow(){}}LQ.ɵfac=function t(e){return new(e||LQ)(Sm(Ug))},LQ.ɵdir=lo({type:LQ}),LQ.ctorParameters=()=>[{type:Ug}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(LQ,[{type:Cy}],(function(){return[{type:Ug}]}),null);class BQ extends LQ{constructor(t,e){super(t),this._breakpointObserver=e,this._isHandset=this._breakpointObserver.observe("(max-width: 599.98px) and (orientation: portrait), (max-width: 959.98px) and (orientation: landscape)")}}BQ.ɵfac=function t(e){return new(e||BQ)(Sm(Ug),Sm(PL))},BQ.ɵcmp=to({type:BQ,selectors:[["mat-tooltip-component"]],hostAttrs:["aria-hidden","true"],hostVars:2,hostBindings:function t(e,n){2&e&&du("zoom","visible"===n._visibility?1:null)},features:[xp],decls:3,vars:7,consts:[[1,"mat-tooltip",3,"ngClass"]],template:function t(e,n){if(1&e&&(Rm(0,"div",0),Vm("@state.start",(function t(){return n._animationStart()}))("@state.done",(function t(e){return n._animationDone(e)})),Ah(1,"async"),ku(2),Am()),2&e){let t;pu("mat-tooltip-handset",null==(t=Th(1,5,n._isHandset))?null:t.matches),Dm("ngClass",n.tooltipClass)("@state",n._visibility),rc(2),Su(n.message)}},directives:[aM],pipes:[wM],styles:[".mat-tooltip-panel{pointer-events:none !important}.mat-tooltip{color:#fff;border-radius:4px;margin:14px;max-width:250px;padding-left:8px;padding-right:8px;overflow:hidden;text-overflow:ellipsis}.cdk-high-contrast-active .mat-tooltip{outline:solid 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1px}.mat-tooltip-handset{margin:24px;padding-left:16px;padding-right:16px}\n"]}]}],(function(){return[{type:Ug},{type:PL}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class VQ{}function jQ(t,e){if(1&t&&(Rm(0,"mat-option",19),ku(1),Am()),2&t){const t=e.$implicit;Dm("value",t),rc(1),Du(" ",t," ")}}function UQ(t,e){if(1&t){const t=Hm();Rm(0,"mat-form-field",16),Rm(1,"mat-select",17),Vm("selectionChange",(function e(n){return hi(t),Ym(2)._changePageSize(n.value)})),Qp(2,jQ,2,2,"mat-option",18),Am(),Am()}if(2&t){const t=Ym(2);Dm("appearance",t._formFieldAppearance)("color",t.color),rc(1),Dm("value",t.pageSize)("disabled",t.disabled)("aria-label",t._intl.itemsPerPageLabel),rc(1),Dm("ngForOf",t._displayedPageSizeOptions)}}function GQ(t,e){if(1&t&&(Rm(0,"div",20),ku(1),Am()),2&t){const t=Ym(2);rc(1),Su(t.pageSize)}}function WQ(t,e){if(1&t&&(Rm(0,"div",12),Rm(1,"div",13),ku(2),Am(),Qp(3,UQ,3,6,"mat-form-field",14),Qp(4,GQ,2,1,"div",15),Am()),2&t){const t=Ym();rc(2),Du(" ",t._intl.itemsPerPageLabel," "),rc(1),Dm("ngIf",t._displayedPageSizeOptions.length>1),rc(1),Dm("ngIf",t._displayedPageSizeOptions.length<=1)}}function YQ(t,e){if(1&t){const t=Hm();Rm(0,"button",21),Vm("click",(function e(){return hi(t),Ym().firstPage()})),qi(),Rm(1,"svg",7),Tm(2,"path",22),Am(),Am()}if(2&t){const t=Ym();Dm("matTooltip",t._intl.firstPageLabel)("matTooltipDisabled",t._previousButtonsDisabled())("matTooltipPosition","above")("disabled",t._previousButtonsDisabled()),jp("aria-label",t._intl.firstPageLabel)}}function qQ(t,e){if(1&t){const t=Hm();qi(),Zi(),Rm(0,"button",23),Vm("click",(function e(){return hi(t),Ym().lastPage()})),qi(),Rm(1,"svg",7),Tm(2,"path",24),Am(),Am()}if(2&t){const t=Ym();Dm("matTooltip",t._intl.lastPageLabel)("matTooltipDisabled",t._nextButtonsDisabled())("matTooltipPosition","above")("disabled",t._nextButtonsDisabled()),jp("aria-label",t._intl.lastPageLabel)}}VQ.ɵfac=function t(e){return new(e||VQ)},VQ.ɵmod=ao({type:VQ}),VQ.ɵinj=vn({providers:[TQ],imports:[[NI,WM,yL,XI],XI,yF]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(VQ,[{type:Ay,args:[{imports:[NI,WM,yL,XI],exports:[FQ,BQ,XI,yF],declarations:[FQ,BQ],entryComponents:[BQ],providers:[TQ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(VQ,{declarations:function(){return[FQ,BQ]},imports:function(){return[NI,WM,yL,XI]},exports:function(){return[FQ,BQ,XI,yF]}});class ZQ{constructor(){this.changes=new I,this.itemsPerPageLabel="Items per page:",this.nextPageLabel="Next page",this.previousPageLabel="Previous page",this.firstPageLabel="First page",this.lastPageLabel="Last page",this.getRangeLabel=(t,e,n)=>{if(0==n||0==e)return`0 of ${n}`;const o=t*e;return`${o+1} – ${o<(n=Math.max(n,0))?Math.min(o+e,n):o+e} of ${n}`}}}ZQ.ɵfac=function t(e){return new(e||ZQ)},ZQ.ɵprov=Mn({factory:function t(){return new ZQ},token:ZQ,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ZQ,[{type:im,args:[{providedIn:"root"}]}],(function(){return[]}),null);const XQ={provide:ZQ,deps:[[new Sr,new Er,ZQ]],useFactory:function KQ(t){return t||new ZQ}},JQ=new Ga("MAT_PAGINATOR_DEFAULT_OPTIONS"),QQ=KI(eH(class{}));
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class $Q extends QQ{constructor(t,e,n){if(super(),this._intl=t,this._changeDetectorRef=e,this._pageIndex=0,this._length=0,this._pageSizeOptions=[],this._hidePageSize=!1,this._showFirstLastButtons=!1,this.page=new Lh,this._intlChanges=t.changes.subscribe((()=>this._changeDetectorRef.markForCheck())),n){const{pageSize:t,pageSizeOptions:e,hidePageSize:o,showFirstLastButtons:i}=n;null!=t&&(this._pageSize=t),null!=e&&(this._pageSizeOptions=e),null!=o&&(this._hidePageSize=o),null!=i&&(this._showFirstLastButtons=i)}}get pageIndex(){return this._pageIndex}set pageIndex(t){this._pageIndex=Math.max(_z(t),0),this._changeDetectorRef.markForCheck()}get length(){return this._length}set length(t){this._length=_z(t),this._changeDetectorRef.markForCheck()}get pageSize(){return this._pageSize}set pageSize(t){this._pageSize=Math.max(_z(t),0),this._updateDisplayedPageSizeOptions()}get pageSizeOptions(){return this._pageSizeOptions}set pageSizeOptions(t){this._pageSizeOptions=(t||[]).map((t=>_z(t))),this._updateDisplayedPageSizeOptions()}get hidePageSize(){return this._hidePageSize}set hidePageSize(t){this._hidePageSize=yz(t)}get showFirstLastButtons(){return this._showFirstLastButtons}set showFirstLastButtons(t){this._showFirstLastButtons=yz(t)}ngOnInit(){this._initialized=!0,this._updateDisplayedPageSizeOptions(),this._markInitialized()}ngOnDestroy(){this._intlChanges.unsubscribe()}nextPage(){if(!this.hasNextPage())return;const t=this.pageIndex;this.pageIndex++,this._emitPageEvent(t)}previousPage(){if(!this.hasPreviousPage())return;const t=this.pageIndex;this.pageIndex--,this._emitPageEvent(t)}firstPage(){if(!this.hasPreviousPage())return;const t=this.pageIndex;this.pageIndex=0,this._emitPageEvent(t)}lastPage(){if(!this.hasNextPage())return;const t=this.pageIndex;this.pageIndex=this.getNumberOfPages()-1,this._emitPageEvent(t)}hasPreviousPage(){return this.pageIndex>=1&&0!=this.pageSize}hasNextPage(){const t=this.getNumberOfPages()-1;return this.pageIndex<t&&0!=this.pageSize}getNumberOfPages(){return this.pageSize?Math.ceil(this.length/this.pageSize):0}_changePageSize(t){const e=this.pageIndex;this.pageIndex=Math.floor(this.pageIndex*this.pageSize/t)||0,this.pageSize=t,this._emitPageEvent(e)}_nextButtonsDisabled(){return this.disabled||!this.hasNextPage()}_previousButtonsDisabled(){return this.disabled||!this.hasPreviousPage()}_updateDisplayedPageSizeOptions(){this._initialized&&(this.pageSize||(this._pageSize=0!=this.pageSizeOptions.length?this.pageSizeOptions[0]:50),this._displayedPageSizeOptions=this.pageSizeOptions.slice(),-1===this._displayedPageSizeOptions.indexOf(this.pageSize)&&this._displayedPageSizeOptions.push(this.pageSize),this._displayedPageSizeOptions.sort(((t,e)=>t-e)),this._changeDetectorRef.markForCheck())}_emitPageEvent(t){this.page.emit({previousPageIndex:t,pageIndex:this.pageIndex,pageSize:this.pageSize,length:this.length})}}$Q.ɵfac=function t(e){return new(e||$Q)(Sm(ZQ),Sm(Ug),Sm(void 0))},$Q.ɵdir=lo({type:$Q,inputs:{pageIndex:"pageIndex",length:"length",pageSize:"pageSize",pageSizeOptions:"pageSizeOptions",hidePageSize:"hidePageSize",showFirstLastButtons:"showFirstLastButtons",color:"color"},outputs:{page:"page"},features:[xp]}),$Q.ctorParameters=()=>[{type:ZQ},{type:Ug},{type:void 0}],$Q.propDecorators={color:[{type:xy}],pageIndex:[{type:xy}],length:[{type:xy}],pageSize:[{type:xy}],pageSizeOptions:[{type:xy}],hidePageSize:[{type:xy}],showFirstLastButtons:[{type:xy}],page:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh($Q,[{type:Cy}],(function(){return[{type:ZQ},{type:Ug},{type:void 0}]}),{page:[{type:Oy}],pageIndex:[{type:xy}],length:[{type:xy}],pageSize:[{type:xy}],pageSizeOptions:[{type:xy}],hidePageSize:[{type:xy}],showFirstLastButtons:[{type:xy}],color:[{type:xy}]});class t$ extends $Q{constructor(t,e,n){super(t,e,n),n&&null!=n.formFieldAppearance&&(this._formFieldAppearance=n.formFieldAppearance)}}t$.ɵfac=function t(e){return new(e||t$)(Sm(ZQ),Sm(Ug),Sm(JQ,8))},t$.ɵcmp=to({type:t$,selectors:[["mat-paginator"]],hostAttrs:["role","group",1,"mat-paginator"],inputs:{disabled:"disabled"},exportAs:["matPaginator"],features:[xp],decls:14,vars:14,consts:[[1,"mat-paginator-outer-container"],[1,"mat-paginator-container"],["class","mat-paginator-page-size",4,"ngIf"],[1,"mat-paginator-range-actions"],[1,"mat-paginator-range-label"],["mat-icon-button","","type","button","class","mat-paginator-navigation-first",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click",4,"ngIf"],["mat-icon-button","","type","button",1,"mat-paginator-navigation-previous",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click"],["viewBox","0 0 24 24","focusable","false",1,"mat-paginator-icon"],["d","M15.41 7.41L14 6l-6 6 6 6 1.41-1.41L10.83 12z"],["mat-icon-button","","type","button",1,"mat-paginator-navigation-next",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click"],["d","M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z"],["mat-icon-button","","type","button","class","mat-paginator-navigation-last",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click",4,"ngIf"],[1,"mat-paginator-page-size"],[1,"mat-paginator-page-size-label"],["class","mat-paginator-page-size-select",3,"appearance","color",4,"ngIf"],["class","mat-paginator-page-size-value",4,"ngIf"],[1,"mat-paginator-page-size-select",3,"appearance","color"],[3,"value","disabled","aria-label","selectionChange"],[3,"value",4,"ngFor","ngForOf"],[3,"value"],[1,"mat-paginator-page-size-value"],["mat-icon-button","","type","button",1,"mat-paginator-navigation-first",3,"matTooltip","matTooltipDisabled","matTooltipPosition","disabled","click"],["d","M18.41 16.59L13.82 12l4.59-4.59L17 6l-6 6 6 6zM6 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4px;width:56px}.mat-paginator-page-size-select.mat-form-field-appearance-outline{width:64px}.mat-paginator-page-size-select.mat-form-field-appearance-fill{width:64px}.mat-paginator-range-label{margin:0 32px 0 24px}.mat-paginator-range-actions{display:flex;align-items:center}.mat-paginator-icon{width:28px;fill:currentColor}[dir=rtl] .mat-paginator-icon{transform:rotate(180deg)}.cdk-high-contrast-active .mat-paginator-icon{fill:CanvasText}\n"],encapsulation:2,changeDetection:0}),t$.ctorParameters=()=>[{type:ZQ},{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[JQ]}]}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(t$,[{type:My,args:[{selector:"mat-paginator",exportAs:"matPaginator",template:'<div class="mat-paginator-outer-container">\n  <div class="mat-paginator-container">\n    <div class="mat-paginator-page-size" *ngIf="!hidePageSize">\n      <div class="mat-paginator-page-size-label">\n        {{_intl.itemsPerPageLabel}}\n      </div>\n\n      <mat-form-field\n        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      (click)="firstPage()"\n              [attr.aria-label]="_intl.firstPageLabel"\n              [matTooltip]="_intl.firstPageLabel"\n              [matTooltipDisabled]="_previousButtonsDisabled()"\n              [matTooltipPosition]="\'above\'"\n              [disabled]="_previousButtonsDisabled()"\n              *ngIf="showFirstLastButtons">\n        <svg class="mat-paginator-icon" viewBox="0 0 24 24" focusable="false">\n          <path d="M18.41 16.59L13.82 12l4.59-4.59L17 6l-6 6 6 6zM6 6h2v12H6z"/>\n        </svg>\n      </button>\n      <button mat-icon-button type="button"\n              class="mat-paginator-navigation-previous"\n              (click)="previousPage()"\n              [attr.aria-label]="_intl.previousPageLabel"\n              [matTooltip]="_intl.previousPageLabel"\n              [matTooltipDisabled]="_previousButtonsDisabled()"\n              [matTooltipPosition]="\'above\'"\n              [disabled]="_previousButtonsDisabled()">\n        <svg class="mat-paginator-icon" viewBox="0 0 24 24" focusable="false">\n          <path d="M15.41 7.41L14 6l-6 6 6 6 1.41-1.41L10.83 12z"/>\n        </svg>\n      </button>\n      <button mat-icon-button type="button"\n              class="mat-paginator-navigation-next"\n              (click)="nextPage()"\n              [attr.aria-label]="_intl.nextPageLabel"\n              [matTooltip]="_intl.nextPageLabel"\n              [matTooltipDisabled]="_nextButtonsDisabled()"\n              [matTooltipPosition]="\'above\'"\n              [disabled]="_nextButtonsDisabled()">\n        <svg class="mat-paginator-icon" viewBox="0 0 24 24" focusable="false">\n          <path d="M10 6L8.59 7.41 13.17 12l-4.58 4.59L10 18l6-6z"/>\n        </svg>\n      </button>\n      <button mat-icon-button type="button"\n              class="mat-paginator-navigation-last"\n              (click)="lastPage()"\n              [attr.aria-label]="_intl.lastPageLabel"\n              [matTooltip]="_intl.lastPageLabel"\n          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4px;width:56px}.mat-paginator-page-size-select.mat-form-field-appearance-outline{width:64px}.mat-paginator-page-size-select.mat-form-field-appearance-fill{width:64px}.mat-paginator-range-label{margin:0 32px 0 24px}.mat-paginator-range-actions{display:flex;align-items:center}.mat-paginator-icon{width:28px;fill:currentColor}[dir=rtl] .mat-paginator-icon{transform:rotate(180deg)}.cdk-high-contrast-active .mat-paginator-icon{fill:CanvasText}\n"]}]}],(function(){return[{type:ZQ},{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[JQ]}]}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class e${}function n$(t,e){if(1&t){const t=Hm();Rm(0,"div",3),Vm("@arrowPosition.start",(function e(){return hi(t),Ym()._disableViewStateAnimation=!0}))("@arrowPosition.done",(function e(){return hi(t),Ym()._disableViewStateAnimation=!1})),Tm(1,"div",4),Rm(2,"div",5),Tm(3,"div",6),Tm(4,"div",7),Tm(5,"div",8),Am(),Am()}if(2&t){const t=Ym();Dm("@arrowOpacity",t._getArrowViewState())("@arrowPosition",t._getArrowViewState())("@allowChildren",t._getArrowDirectionState()),rc(2),Dm("@indicator",t._getArrowDirectionState()),rc(1),Dm("@leftPointer",t._getArrowDirectionState()),rc(1),Dm("@rightPointer",t._getArrowDirectionState())}}e$.ɵfac=function t(e){return new(e||e$)},e$.ɵmod=ao({type:e$}),e$.ɵinj=vn({providers:[XQ],imports:[[WM,JH,TG,VQ,XI]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(e$,[{type:Ay,args:[{imports:[WM,JH,TG,VQ,XI],exports:[t$],declarations:[t$],providers:[XQ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(e$,{declarations:function(){return[t$]},imports:function(){return[WM,JH,TG,VQ,XI]},exports:function(){return[t$]}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const o$=new Ga("MAT_SORT_DEFAULT_OPTIONS"),i$=eH(KI(class{}));class a$ extends i${constructor(t){super(),this._defaultOptions=t,this.sortables=new Map,this._stateChanges=new I,this.start="asc",this._direction="",this.sortChange=new Lh}get direction(){return this._direction}set direction(t){if(t&&"asc"!==t&&"desc"!==t&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(t){return Error(`${t} is not a valid sort direction ('asc' or 'desc').`)})(t);this._direction=t}get disableClear(){return this._disableClear}set disableClear(t){this._disableClear=yz(t)}register(t){if("undefined"==typeof ngDevMode||ngDevMode){if(!t.id)throw(function e(){return Error("MatSortHeader must be provided with a unique id.")})();if(this.sortables.has(t.id))throw(function n(t){return Error(`Cannot have two MatSortables with the same id (${t}).`)})(t.id)}this.sortables.set(t.id,t)}deregister(t){this.sortables.delete(t.id)}sort(t){this.active!=t.id?(this.active=t.id,this.direction=t.start?t.start:this.start):this.direction=this.getNextSortDirection(t),this.sortChange.emit({active:this.active,direction:this.direction})}getNextSortDirection(t){var e,n,o;if(!t)return"";const i=null!==(n=null!==(e=null==t?void 0:t.disableClear)&&void 0!==e?e:this.disableClear)&&void 0!==n?n:!!(null===(o=this._defaultOptions)||void 0===o?void 0:o.disableClear);let a=(function r(t,e){let n=["asc","desc"];return"desc"==t&&n.reverse(),e||n.push(""),n}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)(t.start||this.start,i),s=a.indexOf(this.direction)+1;return s>=a.length&&(s=0),a[s]}ngOnInit(){this._markInitialized()}ngOnChanges(){this._stateChanges.next()}ngOnDestroy(){this._stateChanges.complete()}}a$.ɵfac=function t(e){return new(e||a$)(Sm(o$,8))},a$.ɵdir=lo({type:a$,selectors:[["","matSort",""]],hostAttrs:[1,"mat-sort"],inputs:{disabled:["matSortDisabled","disabled"],start:["matSortStart","start"],direction:["matSortDirection","direction"],disableClear:["matSortDisableClear","disableClear"],active:["matSortActive","active"]},outputs:{sortChange:"matSortChange"},exportAs:["matSort"],features:[xp,Bo]}),a$.ctorParameters=()=>[{type:void 0,decorators:[{type:Sr},{type:kr,args:[o$]}]}],a$.propDecorators={active:[{type:xy,args:["matSortActive"]}],start:[{type:xy,args:["matSortStart"]}],direction:[{type:xy,args:["matSortDirection"]}],disableClear:[{type:xy,args:["matSortDisableClear"]}],sortChange:[{type:Oy,args:["matSortChange"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(a$,[{type:Cy,args:[{selector:"[matSort]",exportAs:"matSort",host:{class:"mat-sort"},inputs:["disabled: matSortDisabled"]}]}],(function(){return[{type:void 0,decorators:[{type:Sr},{type:kr,args:[o$]}]}]}),{start:[{type:xy,args:["matSortStart"]}],sortChange:[{type:Oy,args:["matSortChange"]}],direction:[{type:xy,args:["matSortDirection"]}],disableClear:[{type:xy,args:["matSortDisableClear"]}],active:[{type:xy,args:["matSortActive"]}]});const r$=WI.ENTERING+" "+GI.STANDARD_CURVE,s$={indicator:nx("indicator",[rx("active-asc, asc",ax({transform:"translateY(0px)"})),rx("active-desc, desc",ax({transform:"translateY(10px)"})),lx("active-asc <=> active-desc",ox(r$))]),leftPointer:nx("leftPointer",[rx("active-asc, asc",ax({transform:"rotate(-45deg)"})),rx("active-desc, desc",ax({transform:"rotate(45deg)"})),lx("active-asc <=> active-desc",ox(r$))]),rightPointer:nx("rightPointer",[rx("active-asc, asc",ax({transform:"rotate(45deg)"})),rx("active-desc, desc",ax({transform:"rotate(-45deg)"})),lx("active-asc <=> active-desc",ox(r$))]),arrowOpacity:nx("arrowOpacity",[rx("desc-to-active, asc-to-active, active",ax({opacity:1})),rx("desc-to-hint, asc-to-hint, hint",ax({opacity:.54})),rx("hint-to-desc, active-to-desc, desc, hint-to-asc, active-to-asc, asc, void",ax({opacity:0})),lx("* => asc, * => desc, * => active, * => hint, * => void",ox("0ms")),lx("* <=> *",ox(r$))]),arrowPosition:nx("arrowPosition",[lx("* => desc-to-hint, * => desc-to-active",ox(r$,sx([ax({transform:"translateY(-25%)"}),ax({transform:"translateY(0)"})]))),lx("* => hint-to-desc, * => active-to-desc",ox(r$,sx([ax({transform:"translateY(0)"}),ax({transform:"translateY(25%)"})]))),lx("* => asc-to-hint, * => asc-to-active",ox(r$,sx([ax({transform:"translateY(25%)"}),ax({transform:"translateY(0)"})]))),lx("* => hint-to-asc, * => active-to-asc",ox(r$,sx([ax({transform:"translateY(0)"}),ax({transform:"translateY(-25%)"})]))),rx("desc-to-hint, asc-to-hint, hint, desc-to-active, asc-to-active, active",ax({transform:"translateY(0)"})),rx("hint-to-desc, active-to-desc, desc",ax({transform:"translateY(-25%)"})),rx("hint-to-asc, active-to-asc, asc",ax({transform:"translateY(25%)"}))]),allowChildren:nx("allowChildren",[lx("* <=> *",[dx("@*",cx(),{optional:!0})])])};
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class l${constructor(){this.changes=new I}}l$.ɵfac=function t(e){return new(e||l$)},l$.ɵprov=Mn({factory:function t(){return new l$},token:l$,providedIn:"root"}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(l$,[{type:im,args:[{providedIn:"root"}]}],(function(){return[]}),null);const c$={provide:l$,deps:[[new Sr,new Er,l$]],useFactory:function d$(t){return t||new l$}},p$=KI(class{});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class m$ extends p${constructor(t,e,n,o,i,a){if(super(),this._intl=t,this._changeDetectorRef=e,this._sort=n,this._columnDef=o,this._focusMonitor=i,this._elementRef=a,this._showIndicatorHint=!1,this._viewState={},this._arrowDirection="",this._disableViewStateAnimation=!1,this.arrowPosition="after",!n&&("undefined"==typeof ngDevMode||ngDevMode))throw(function r(){return Error("MatSortHeader must be placed within a parent element with the MatSort directive.")})();this._handleStateChanges()}get disableClear(){return this._disableClear}set disableClear(t){this._disableClear=yz(t)}ngOnInit(){!this.id&&this._columnDef&&(this.id=this._columnDef.name),this._updateArrowDirection(),this._setAnimationTransitionState({toState:this._isSorted()?"active":this._arrowDirection}),this._sort.register(this)}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0).subscribe((t=>{const e=!!t;e!==this._showIndicatorHint&&(this._setIndicatorHintVisible(e),this._changeDetectorRef.markForCheck())}))}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef),this._sort.deregister(this),this._rerenderSubscription.unsubscribe()}_setIndicatorHintVisible(t){this._isDisabled()&&t||(this._showIndicatorHint=t,this._isSorted()||(this._updateArrowDirection(),this._setAnimationTransitionState(this._showIndicatorHint?{fromState:this._arrowDirection,toState:"hint"}:{fromState:"hint",toState:this._arrowDirection})))}_setAnimationTransitionState(t){this._viewState=t||{},this._disableViewStateAnimation&&(this._viewState={toState:t.toState})}_toggleOnInteraction(){this._sort.sort(this),"hint"!==this._viewState.toState&&"active"!==this._viewState.toState||(this._disableViewStateAnimation=!0)}_handleClick(){this._isDisabled()||this._sort.sort(this)}_handleKeydown(t){this._isDisabled()||t.keyCode!==fz&&t.keyCode!==mz||(t.preventDefault(),this._toggleOnInteraction())}_isSorted(){return this._sort.active==this.id&&("asc"===this._sort.direction||"desc"===this._sort.direction)}_getArrowDirectionState(){return`${this._isSorted()?"active-":""}${this._arrowDirection}`}_getArrowViewState(){const t=this._viewState.fromState;return(t?`${t}-to-`:"")+this._viewState.toState}_updateArrowDirection(){this._arrowDirection=this._isSorted()?this._sort.direction:this.start||this._sort.start}_isDisabled(){return this._sort.disabled||this.disabled}_getAriaSortAttribute(){return this._isSorted()?"asc"==this._sort.direction?"ascending":"descending":"none"}_renderArrow(){return!this._isDisabled()||this._isSorted()}_handleStateChanges(){this._rerenderSubscription=re(this._sort.sortChange,this._sort._stateChanges,this._intl.changes).subscribe((()=>{this._isSorted()&&(this._updateArrowDirection(),"hint"!==this._viewState.toState&&"active"!==this._viewState.toState||(this._disableViewStateAnimation=!0),this._setAnimationTransitionState({fromState:this._arrowDirection,toState:"active"}),this._showIndicatorHint=!1),!this._isSorted()&&this._viewState&&"active"===this._viewState.toState&&(this._disableViewStateAnimation=!1,this._setAnimationTransitionState({fromState:"active",toState:this._arrowDirection})),this._changeDetectorRef.markForCheck()}))}}m$.ɵfac=function t(e){return new(e||m$)(Sm(l$),Sm(Ug),Sm(a$,8),Sm("MAT_SORT_HEADER_COLUMN_DEF",8),Sm(SI),Sm(hg))},m$.ɵcmp=to({type:m$,selectors:[["","mat-sort-header",""]],hostAttrs:[1,"mat-sort-header"],hostVars:3,hostBindings:function t(e,n){1&e&&Vm("click",(function t(){return n._handleClick()}))("keydown",(function t(e){return n._handleKeydown(e)}))("mouseenter",(function t(){return n._setIndicatorHintVisible(!0)}))("mouseleave",(function t(){return n._setIndicatorHintVisible(!1)})),2&e&&(jp("aria-sort",n._getAriaSortAttribute()),pu("mat-sort-header-disabled",n._isDisabled()))},inputs:{disabled:"disabled",arrowPosition:"arrowPosition",disableClear:"disableClear",id:["mat-sort-header","id"],start:"start"},exportAs:["matSortHeader"],features:[xp],attrs:["mat-sort-header",""],ngContentSelectors:["*"],decls:4,vars:6,consts:[["role","button",1,"mat-sort-header-container","mat-focus-indicator"],[1,"mat-sort-header-content"],["class","mat-sort-header-arrow",4,"ngIf"],[1,"mat-sort-header-arrow"],[1,"mat-sort-header-stem"],[1,"mat-sort-header-indicator"],[1,"mat-sort-header-pointer-left"],[1,"mat-sort-header-pointer-right"],[1,"mat-sort-header-pointer-middle"]],template:function t(e,n){1&e&&(Zm(),Rm(0,"div",0),Rm(1,"div",1),Xm(2),Am(),Qp(3,n$,6,6,"div",2),Am()),2&e&&(pu("mat-sort-header-sorted",n._isSorted())("mat-sort-header-position-before","before"==n.arrowPosition),jp("tabindex",n._isDisabled()?null:0),rc(3),Dm("ngIf",n._renderArrow()))},directives:[dM],styles:[".mat-sort-header-container{display:flex;cursor:pointer;align-items:center;letter-spacing:normal;outline:0}[mat-sort-header].cdk-keyboard-focused .mat-sort-header-container,[mat-sort-header].cdk-program-focused .mat-sort-header-container{border-bottom:solid 1px currentColor}.mat-sort-header-disabled .mat-sort-header-container{cursor:default}.mat-sort-header-content{text-align:center;display:flex;align-items:center}.mat-sort-header-position-before{flex-direction:row-reverse}.mat-sort-header-arrow{height:12px;width:12px;min-width:12px;position:relative;display:flex;opacity:0}.mat-sort-header-arrow,[dir=rtl] .mat-sort-header-position-before .mat-sort-header-arrow{margin:0 0 0 6px}.mat-sort-header-position-before .mat-sort-header-arrow,[dir=rtl] .mat-sort-header-arrow{margin:0 6px 0 0}.mat-sort-header-stem{background:currentColor;height:10px;width:2px;margin:auto;display:flex;align-items:center}.cdk-high-contrast-active .mat-sort-header-stem{width:0;border-left:solid 2px}.mat-sort-header-indicator{width:100%;height:2px;display:flex;align-items:center;position:absolute;top:0;left:0}.mat-sort-header-pointer-middle{margin:auto;height:2px;width:2px;background:currentColor;transform:rotate(45deg)}.cdk-high-contrast-active .mat-sort-header-pointer-middle{width:0;height:0;border-top:solid 2px;border-left:solid 2px}.mat-sort-header-pointer-left,.mat-sort-header-pointer-right{background:currentColor;width:6px;height:2px;position:absolute;top:0}.cdk-high-contrast-active .mat-sort-header-pointer-left,.cdk-high-contrast-active .mat-sort-header-pointer-right{width:0;height:0;border-left:solid 6px;border-top:solid 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class u${}u$.ɵfac=function t(e){return new(e||u$)},u$.ɵmod=ao({type:u$}),u$.ɵinj=vn({providers:[c$],imports:[[WM,XI]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(u$,[{type:Ay,args:[{imports:[WM,XI],exports:[a$,m$],declarations:[a$,m$],providers:[c$]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(u$,{declarations:function(){return[a$,m$]},imports:function(){return[WM,XI]},exports:function(){return[a$,m$]}});const f$=[[["caption"]],[["colgroup"],["col"]]];function g$(t,e){if(1&t&&(Rm(0,"th",3),ku(1),Am()),2&t){const t=Ym();du("text-align",t.justify),rc(1),Du(" ",t.headerText," ")}}function h$(t,e){if(1&t&&(Rm(0,"td",4),ku(1),Am()),2&t){const t=e.$implicit,n=Ym();du("text-align",n.justify),rc(1),Du(" ",n.dataAccessor(t,n.name)," ")}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function b$(t){return class extends t{constructor(...t){super(...t),this._sticky=!1,this._hasStickyChanged=!1}get sticky(){return this._sticky}set sticky(t){const e=this._sticky;this._sticky=yz(t),this._hasStickyChanged=e!==this._sticky}hasStickyChanged(){const t=this._hasStickyChanged;return this._hasStickyChanged=!1,t}resetStickyChanged(){this._hasStickyChanged=!1}}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */const y$=new Ga("CDK_TABLE"),_$=new Ga("text-column-options");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class C${constructor(t){this.template=t}}C$.ɵfac=function t(e){return new(e||C$)(Sm(Xg))},C$.ɵdir=lo({type:C$,selectors:[["","cdkCellDef",""]]}),C$.ctorParameters=()=>[{type:Xg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(C$,[{type:Cy,args:[{selector:"[cdkCellDef]"}]}],(function(){return[{type:Xg}]}),null);class M${constructor(t){this.template=t}}M$.ɵfac=function t(e){return new(e||M$)(Sm(Xg))},M$.ɵdir=lo({type:M$,selectors:[["","cdkHeaderCellDef",""]]}),M$.ctorParameters=()=>[{type:Xg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(M$,[{type:Cy,args:[{selector:"[cdkHeaderCellDef]"}]}],(function(){return[{type:Xg}]}),null);class v${constructor(t){this.template=t}}v$.ɵfac=function t(e){return new(e||v$)(Sm(Xg))},v$.ɵdir=lo({type:v$,selectors:[["","cdkFooterCellDef",""]]}),v$.ctorParameters=()=>[{type:Xg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(v$,[{type:Cy,args:[{selector:"[cdkFooterCellDef]"}]}],(function(){return[{type:Xg}]}),null);const x$=b$(class{});class O$ extends x${constructor(t){super(),this._table=t,this._stickyEnd=!1}get name(){return this._name}set name(t){this._setNameInput(t)}get stickyEnd(){return this._stickyEnd}set stickyEnd(t){const e=this._stickyEnd;this._stickyEnd=yz(t),this._hasStickyChanged=e!==this._stickyEnd}_updateColumnCssClassName(){this._columnCssClassName=[`cdk-column-${this.cssClassFriendlyName}`]}_setNameInput(t){t&&(this._name=t,this.cssClassFriendlyName=t.replace(/[^a-z0-9_-]/gi,"-"),this._updateColumnCssClassName())}}O$.ɵfac=function t(e){return new(e||O$)(Sm(y$,8))},O$.ɵdir=lo({type:O$,selectors:[["","cdkColumnDef",""]],contentQueries:function t(e,n,o){if(1&e&&($h(o,C$,5),$h(o,M$,5),$h(o,v$,5)),2&e){let t;Jh(t=tb())&&(n.cell=t.first),Jh(t=tb())&&(n.headerCell=t.first),Jh(t=tb())&&(n.footerCell=t.first)}},inputs:{sticky:"sticky",name:["cdkColumnDef","name"],stickyEnd:"stickyEnd"},features:[pg([{provide:"MAT_SORT_HEADER_COLUMN_DEF",useExisting:O$}]),xp]}),O$.ctorParameters=()=>[{type:void 0,decorators:[{type:kr,args:[y$]},{type:Sr}]}],O$.propDecorators={name:[{type:xy,args:["cdkColumnDef"]}],stickyEnd:[{type:xy,args:["stickyEnd"]}],cell:[{type:qa,args:[C$]}],headerCell:[{type:qa,args:[M$]}],footerCell:[{type:qa,args:[v$]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(O$,[{type:Cy,args:[{selector:"[cdkColumnDef]",inputs:["sticky"],providers:[{provide:"MAT_SORT_HEADER_COLUMN_DEF",useExisting:O$}]}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[y$]},{type:Sr}]}]}),{name:[{type:xy,args:["cdkColumnDef"]}],stickyEnd:[{type:xy,args:["stickyEnd"]}],cell:[{type:qa,args:[C$]}],headerCell:[{type:qa,args:[M$]}],footerCell:[{type:qa,args:[v$]}]});class P${constructor(t,e){const n=e.nativeElement.classList;for(const e of t._columnCssClassName)n.add(e)}}class w$ extends P${constructor(t,e){super(t,e)}}w$.ɵfac=function t(e){return new(e||w$)(Sm(O$),Sm(hg))},w$.ɵdir=lo({type:w$,selectors:[["cdk-header-cell"],["th","cdk-header-cell",""]],hostAttrs:["role","columnheader",1,"cdk-header-cell"],features:[xp]}),w$.ctorParameters=()=>[{type:O$},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(w$,[{type:Cy,args:[{selector:"cdk-header-cell, th[cdk-header-cell]",host:{class:"cdk-header-cell",role:"columnheader"}}]}],(function(){return[{type:O$},{type:hg}]}),null);class k$ extends P${constructor(t,e){var n;if(super(t,e),1===(null===(n=t._table)||void 0===n?void 0:n._elementRef.nativeElement.nodeType)){const n=t._table._elementRef.nativeElement.getAttribute("role");e.nativeElement.setAttribute("role","grid"===n||"treegrid"===n?"gridcell":"cell")}}}k$.ɵfac=function t(e){return new(e||k$)(Sm(O$),Sm(hg))},k$.ɵdir=lo({type:k$,selectors:[["cdk-footer-cell"],["td","cdk-footer-cell",""]],hostAttrs:[1,"cdk-footer-cell"],features:[xp]}),k$.ctorParameters=()=>[{type:O$},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(k$,[{type:Cy,args:[{selector:"cdk-footer-cell, td[cdk-footer-cell]",host:{class:"cdk-footer-cell"}}]}],(function(){return[{type:O$},{type:hg}]}),null);class S$ extends P${constructor(t,e){var n;if(super(t,e),1===(null===(n=t._table)||void 0===n?void 0:n._elementRef.nativeElement.nodeType)){const n=t._table._elementRef.nativeElement.getAttribute("role");e.nativeElement.setAttribute("role","grid"===n||"treegrid"===n?"gridcell":"cell")}}}S$.ɵfac=function t(e){return new(e||S$)(Sm(O$),Sm(hg))},S$.ɵdir=lo({type:S$,selectors:[["cdk-cell"],["td","cdk-cell",""]],hostAttrs:[1,"cdk-cell"],features:[xp]}),S$.ctorParameters=()=>[{type:O$},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(S$,[{type:Cy,args:[{selector:"cdk-cell, td[cdk-cell]",host:{class:"cdk-cell"}}]}],(function(){return[{type:O$},{type:hg}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class D${constructor(){this.tasks=[],this.endTasks=[]}}const E$=new Ga("_COALESCED_STYLE_SCHEDULER");class R${constructor(t){this._ngZone=t,this._currentSchedule=null,this._destroyed=new I}schedule(t){this._createScheduleIfNeeded(),this._currentSchedule.tasks.push(t)}scheduleEnd(t){this._createScheduleIfNeeded(),this._currentSchedule.endTasks.push(t)}ngOnDestroy(){this._destroyed.next(),this._destroyed.complete()}_createScheduleIfNeeded(){this._currentSchedule||(this._currentSchedule=new D$,this._getScheduleObservable().pipe(Ie(this._destroyed)).subscribe((()=>{for(;this._currentSchedule.tasks.length||this._currentSchedule.endTasks.length;){const t=this._currentSchedule;this._currentSchedule=new D$;for(const e of t.tasks)e();for(const e of t.endTasks)e()}this._currentSchedule=null})))}_getScheduleObservable(){return this._ngZone.isStable?Ct(Promise.resolve(void 0)):this._ngZone.onStable.pipe(be(1))}}R$.ɵfac=function t(e){return new(e||R$)(vr(a_))},R$.ɵprov=Mn({token:R$,factory:R$.ɵfac}),R$.ctorParameters=()=>[{type:a_}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(R$,[{type:im}],(function(){return[{type:a_}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const A$="<ng-container cdkCellOutlet></ng-container>";class T${constructor(t,e){this.template=t,this._differs=e}ngOnChanges(t){if(!this._columnsDiffer){const e=t.columns&&t.columns.currentValue||[];this._columnsDiffer=this._differs.find(e).create(),this._columnsDiffer.diff(e)}}getColumnsDiff(){return this._columnsDiffer.diff(this.columns)}extractCellTemplate(t){return this instanceof z$?t.headerCell.template:this instanceof H$?t.footerCell.template:t.cell.template}}T$.ɵfac=function t(e){return new(e||T$)(Sm(Xg),Sm(Hg))},T$.ɵdir=lo({type:T$,features:[Bo]}),T$.ctorParameters=()=>[{type:Xg},{type:Hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(T$,[{type:Cy}],(function(){return[{type:Xg},{type:Hg}]}),null);const N$=b$(class extends T${});class z$ extends N${constructor(t,e,n){super(t,e),this._table=n}ngOnChanges(t){super.ngOnChanges(t)}}z$.ɵfac=function t(e){return 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U${constructor(t){this.templateRef=t}}U$.ɵfac=function t(e){return new(e||U$)(Sm(Xg))},U$.ɵdir=lo({type:U$,selectors:[["ng-template","cdkNoDataRow",""]]}),U$.ctorParameters=()=>[{type:Xg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(U$,[{type:Cy,args:[{selector:"ng-template[cdkNoDataRow]"}]}],(function(){return[{type:Xg}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const G$=["top","bottom","left","right"];class W${constructor(t,e,n,o,i=!0,a=!0,r){this._isNativeHtmlTable=t,this._stickCellCss=e,this.direction=n,this._coalescedStyleScheduler=o,this._isBrowser=i,this._needsPositionStickyOnElement=a,this._positionListener=r,this._cachedCellWidths=[],this._borderCellCss={top:`${e}-border-elem-top`,bottom:`${e}-border-elem-bottom`,left:`${e}-border-elem-left`,right:`${e}-border-elem-right`}}clearStickyPositioning(t,e){const n=[];for(const e of t)if(e.nodeType===e.ELEMENT_NODE){n.push(e);for(let t=0;t<e.children.length;t++)n.push(e.children[t])}this._coalescedStyleScheduler.schedule((()=>{for(const t of n)this._removeStickyStyle(t,e)}))}updateStickyColumns(t,e,n,o=!0){if(!t.length||!this._isBrowser||!e.some((t=>t))&&!n.some((t=>t)))return void(this._positionListener&&(this._positionListener.stickyColumnsUpdated({sizes:[]}),this._positionListener.stickyEndColumnsUpdated({sizes:[]})));const i=t[0],a=i.children.length,r=this._getCellWidths(i,o),s=this._getStickyStartColumnPositions(r,e),l=this._getStickyEndColumnPositions(r,n),c=e.lastIndexOf(!0),d=n.indexOf(!0);this._coalescedStyleScheduler.schedule((()=>{const o="rtl"===this.direction,i=o?"right":"left",p=o?"left":"right";for(const o of t)for(let t=0;t<a;t++){const a=o.children[t];e[t]&&this._addStickyStyle(a,i,s[t],t===c),n[t]&&this._addStickyStyle(a,p,l[t],t===d)}this._positionListener&&(this._positionListener.stickyColumnsUpdated({sizes:-1===c?[]:r.slice(0,c+1).map(((t,n)=>e[n]?t:null))}),this._positionListener.stickyEndColumnsUpdated({sizes:-1===d?[]:r.slice(d).map(((t,e)=>n[e+d]?t:null)).reverse()}))}))}stickRows(t,e,n){if(!this._isBrowser)return;const o="bottom"===n?t.slice().reverse():t,i="bottom"===n?e.slice().reverse():e,a=[],r=[],s=[];for(let t=0,e=0;t<o.length;t++){if(!i[t])continue;a[t]=e;const n=o[t];s[t]=this._isNativeHtmlTable?Array.from(n.children):[n];const l=n.getBoundingClientRect().height;e+=l,r[t]=l}const l=i.lastIndexOf(!0);this._coalescedStyleScheduler.schedule((()=>{var t,e;for(let t=0;t<o.length;t++){if(!i[t])continue;const e=a[t],o=t===l;for(const i of s[t])this._addStickyStyle(i,n,e,o)}"top"===n?null===(t=this._positionListener)||void 0===t||t.stickyHeaderRowsUpdated({sizes:r,offsets:a,elements:s}):null===(e=this._positionListener)||void 0===e||e.stickyFooterRowsUpdated({sizes:r,offsets:a,elements:s})}))}updateStickyFooterContainer(t,e){if(!this._isNativeHtmlTable)return;const n=t.querySelector("tfoot");this._coalescedStyleScheduler.schedule((()=>{e.some((t=>!t))?this._removeStickyStyle(n,["bottom"]):this._addStickyStyle(n,"bottom",0,!1)}))}_removeStickyStyle(t,e){for(const n of e)t.style[n]="",t.classList.remove(this._borderCellCss[n]);G$.some((n=>-1===e.indexOf(n)&&t.style[n]))?t.style.zIndex=this._getCalculatedZIndex(t):(t.style.zIndex="",this._needsPositionStickyOnElement&&(t.style.position=""),t.classList.remove(this._stickCellCss))}_addStickyStyle(t,e,n,o){t.classList.add(this._stickCellCss),o&&t.classList.add(this._borderCellCss[e]),t.style[e]=`${n}px`,t.style.zIndex=this._getCalculatedZIndex(t),this._needsPositionStickyOnElement&&(t.style.cssText+="position: -webkit-sticky; position: sticky; ")}_getCalculatedZIndex(t){const e={top:100,bottom:10,left:1,right:1};let n=0;for(const o of G$)t.style[o]&&(n+=e[o]);return n?`${n}`:""}_getCellWidths(t,e=!0){if(!e&&this._cachedCellWidths.length)return this._cachedCellWidths;const n=[],o=t.children;for(let t=0;t<o.length;t++)n.push(o[t].getBoundingClientRect().width);return this._cachedCellWidths=n,n}_getStickyStartColumnPositions(t,e){const n=[];let o=0;for(let i=0;i<t.length;i++)e[i]&&(n[i]=o,o+=t[i]);return n}_getStickyEndColumnPositions(t,e){const n=[];let o=0;for(let i=t.length;i>0;i--)e[i]&&(n[i]=o,o+=t[i]);return n}}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */function Y$(t){return Error(`Could not find column with id "${t}".`)}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const q$=new Ga("CDK_SPL");
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */class Z${}Z$.ɵfac=function t(e){return new(e||Z$)},Z$.ɵdir=lo({type:Z$,selectors:[["cdk-table","recycleRows",""],["table","cdk-table","","recycleRows",""]],features:[pg([{provide:aF,useClass:nF}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Z$,[{type:Cy,args:[{selector:"cdk-table[recycleRows], table[cdk-table][recycleRows]",providers:[{provide:aF,useClass:nF}]}]}],null,null);class X${constructor(t,e){this.viewContainer=t,this.elementRef=e}}X$.ɵfac=function t(e){return new(e||X$)(Sm(eh),Sm(hg))},X$.ɵdir=lo({type:X$,selectors:[["","rowOutlet",""]]}),X$.ctorParameters=()=>[{type:eh},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(X$,[{type:Cy,args:[{selector:"[rowOutlet]"}]}],(function(){return[{type:eh},{type:hg}]}),null);class K${constructor(t,e){this.viewContainer=t,this.elementRef=e}}K$.ɵfac=function t(e){return new(e||K$)(Sm(eh),Sm(hg))},K$.ɵdir=lo({type:K$,selectors:[["","headerRowOutlet",""]]}),K$.ctorParameters=()=>[{type:eh},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(K$,[{type:Cy,args:[{selector:"[headerRowOutlet]"}]}],(function(){return[{type:eh},{type:hg}]}),null);class J${constructor(t,e){this.viewContainer=t,this.elementRef=e}}J$.ɵfac=function t(e){return new(e||J$)(Sm(eh),Sm(hg))},J$.ɵdir=lo({type:J$,selectors:[["","footerRowOutlet",""]]}),J$.ctorParameters=()=>[{type:eh},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(J$,[{type:Cy,args:[{selector:"[footerRowOutlet]"}]}],(function(){return[{type:eh},{type:hg}]}),null);class Q${constructor(t,e){this.viewContainer=t,this.elementRef=e}}Q$.ɵfac=function t(e){return new(e||Q$)(Sm(eh),Sm(hg))},Q$.ɵdir=lo({type:Q$,selectors:[["","noDataRowOutlet",""]]}),Q$.ctorParameters=()=>[{type:eh},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Q$,[{type:Cy,args:[{selector:"[noDataRowOutlet]"}]}],(function(){return[{type:eh},{type:hg}]}),null);const $$='\n  <ng-content select="caption"></ng-content>\n  <ng-content select="colgroup, col"></ng-content>\n  <ng-container headerRowOutlet></ng-container>\n  <ng-container rowOutlet></ng-container>\n  <ng-container noDataRowOutlet></ng-container>\n  <ng-container footerRowOutlet></ng-container>\n';class t0{constructor(t,e,n,o,i,a,r,s,l,c,d){this._differs=t,this._changeDetectorRef=e,this._elementRef=n,this._dir=i,this._platform=r,this._viewRepeater=s,this._coalescedStyleScheduler=l,this._viewportRuler=c,this._stickyPositioningListener=d,this._onDestroy=new I,this._columnDefsByName=new Map,this._customColumnDefs=new Set,this._customRowDefs=new Set,this._customHeaderRowDefs=new Set,this._customFooterRowDefs=new Set,this._headerRowDefChanged=!0,this._footerRowDefChanged=!0,this._stickyColumnStylesNeedReset=!0,this._forceRecalculateCellWidths=!0,this._cachedRenderRowsMap=new Map,this.stickyCssClass="cdk-table-sticky",this.needsPositionStickyOnElement=!0,this._isShowingNoDataRow=!1,this._multiTemplateDataRows=!1,this._fixedLayout=!1,this.contentChanged=new Lh,this.viewChange=new F({start:0,end:Number.MAX_VALUE}),o||this._elementRef.nativeElement.setAttribute("role","table"),this._document=a,this._isNativeHtmlTable="TABLE"===this._elementRef.nativeElement.nodeName}get trackBy(){return this._trackByFn}set trackBy(t){"undefined"!=typeof ngDevMode&&!ngDevMode||null==t||"function"==typeof t||console.warn(`trackBy must be a function, but received ${JSON.stringify(t)}.`),this._trackByFn=t}get dataSource(){return this._dataSource}set dataSource(t){this._dataSource!==t&&this._switchDataSource(t)}get multiTemplateDataRows(){return this._multiTemplateDataRows}set multiTemplateDataRows(t){this._multiTemplateDataRows=yz(t),this._rowOutlet&&this._rowOutlet.viewContainer.length&&(this._forceRenderDataRows(),this.updateStickyColumnStyles())}get fixedLayout(){return this._fixedLayout}set fixedLayout(t){this._fixedLayout=yz(t),this._forceRecalculateCellWidths=!0,this._stickyColumnStylesNeedReset=!0}ngOnInit(){this._setupStickyStyler(),this._isNativeHtmlTable&&this._applyNativeTableSections(),this._dataDiffer=this._differs.find([]).create(((t,e)=>this.trackBy?this.trackBy(e.dataIndex,e.data):e)),this._viewportRuler.change().pipe(Ie(this._onDestroy)).subscribe((()=>{this._forceRecalculateCellWidths=!0}))}ngAfterContentChecked(){if(this._cacheRowDefs(),this._cacheColumnDefs(),!this._headerRowDefs.length&&!this._footerRowDefs.length&&!this._rowDefs.length&&("undefined"==typeof ngDevMode||ngDevMode))throw(function t(){return Error("Missing definitions for header, footer, and row; cannot determine which columns should be rendered.")})();const e=this._renderUpdatedColumns()||this._headerRowDefChanged||this._footerRowDefChanged;this._stickyColumnStylesNeedReset=this._stickyColumnStylesNeedReset||e,this._forceRecalculateCellWidths=e,this._headerRowDefChanged&&(this._forceRenderHeaderRows(),this._headerRowDefChanged=!1),this._footerRowDefChanged&&(this._forceRenderFooterRows(),this._footerRowDefChanged=!1),this.dataSource&&this._rowDefs.length>0&&!this._renderChangeSubscription?this._observeRenderChanges():this._stickyColumnStylesNeedReset&&this.updateStickyColumnStyles(),this._checkStickyStates()}ngOnDestroy(){this._rowOutlet.viewContainer.clear(),this._noDataRowOutlet.viewContainer.clear(),this._headerRowOutlet.viewContainer.clear(),this._footerRowOutlet.viewContainer.clear(),this._cachedRenderRowsMap.clear(),this._onDestroy.next(),this._onDestroy.complete(),$H(this.dataSource)&&this.dataSource.disconnect(this)}renderRows(){this._renderRows=this._getAllRenderRows();const t=this._dataDiffer.diff(this._renderRows);if(!t)return this._updateNoDataRow(),void this.contentChanged.next();const e=this._rowOutlet.viewContainer;this._viewRepeater.applyChanges(t,e,((t,e,n)=>this._getEmbeddedViewArgs(t.item,n)),(t=>t.item.data),(t=>{1===t.operation&&t.context&&this._renderCellTemplateForItem(t.record.item.rowDef,t.context)})),this._updateRowIndexContext(),t.forEachIdentityChange((t=>{e.get(t.currentIndex).context.$implicit=t.item.data})),this._updateNoDataRow(),this.updateStickyColumnStyles(),this.contentChanged.next()}addColumnDef(t){this._customColumnDefs.add(t)}removeColumnDef(t){this._customColumnDefs.delete(t)}addRowDef(t){this._customRowDefs.add(t)}removeRowDef(t){this._customRowDefs.delete(t)}addHeaderRowDef(t){this._customHeaderRowDefs.add(t),this._headerRowDefChanged=!0}removeHeaderRowDef(t){this._customHeaderRowDefs.delete(t),this._headerRowDefChanged=!0}addFooterRowDef(t){this._customFooterRowDefs.add(t),this._footerRowDefChanged=!0}removeFooterRowDef(t){this._customFooterRowDefs.delete(t),this._footerRowDefChanged=!0}setNoDataRow(t){this._customNoDataRow=t}updateStickyHeaderRowStyles(){const t=this._getRenderedRows(this._headerRowOutlet),e=this._elementRef.nativeElement.querySelector("thead");e&&(e.style.display=t.length?"":"none");const n=this._headerRowDefs.map((t=>t.sticky));this._stickyStyler.clearStickyPositioning(t,["top"]),this._stickyStyler.stickRows(t,n,"top"),this._headerRowDefs.forEach((t=>t.resetStickyChanged()))}updateStickyFooterRowStyles(){const t=this._getRenderedRows(this._footerRowOutlet),e=this._elementRef.nativeElement.querySelector("tfoot");e&&(e.style.display=t.length?"":"none");const n=this._footerRowDefs.map((t=>t.sticky));this._stickyStyler.clearStickyPositioning(t,["bottom"]),this._stickyStyler.stickRows(t,n,"bottom"),this._stickyStyler.updateStickyFooterContainer(this._elementRef.nativeElement,n),this._footerRowDefs.forEach((t=>t.resetStickyChanged()))}updateStickyColumnStyles(){const t=this._getRenderedRows(this._headerRowOutlet),e=this._getRenderedRows(this._rowOutlet),n=this._getRenderedRows(this._footerRowOutlet);(this._isNativeHtmlTable&&!this._fixedLayout||this._stickyColumnStylesNeedReset)&&(this._stickyStyler.clearStickyPositioning([...t,...e,...n],["left","right"]),this._stickyColumnStylesNeedReset=!1),t.forEach(((t,e)=>{this._addStickyColumnStyles([t],this._headerRowDefs[e])})),this._rowDefs.forEach((t=>{const n=[];for(let o=0;o<e.length;o++)this._renderRows[o].rowDef===t&&n.push(e[o]);this._addStickyColumnStyles(n,t)})),n.forEach(((t,e)=>{this._addStickyColumnStyles([t],this._footerRowDefs[e])})),Array.from(this._columnDefsByName.values()).forEach((t=>t.resetStickyChanged()))}_getAllRenderRows(){const t=[],e=this._cachedRenderRowsMap;this._cachedRenderRowsMap=new Map;for(let n=0;n<this._data.length;n++){let o=this._data[n];const i=this._getRenderRowsForData(o,n,e.get(o));this._cachedRenderRowsMap.has(o)||this._cachedRenderRowsMap.set(o,new WeakMap);for(let e=0;e<i.length;e++){let n=i[e];const o=this._cachedRenderRowsMap.get(n.data);o.has(n.rowDef)?o.get(n.rowDef).push(n):o.set(n.rowDef,[n]),t.push(n)}}return t}_getRenderRowsForData(t,e,n){return this._getRowDefs(t,e).map((o=>{const i=n&&n.has(o)?n.get(o):[];if(i.length){const t=i.shift();return t.dataIndex=e,t}return{data:t,rowDef:o,dataIndex:e}}))}_cacheColumnDefs(){this._columnDefsByName.clear(),e0(this._getOwnDefs(this._contentColumnDefs),this._customColumnDefs).forEach((t=>{if(this._columnDefsByName.has(t.name)&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(t){return Error(`Duplicate column definition name provided: "${t}".`)})(t.name);this._columnDefsByName.set(t.name,t)}))}_cacheRowDefs(){this._headerRowDefs=e0(this._getOwnDefs(this._contentHeaderRowDefs),this._customHeaderRowDefs),this._footerRowDefs=e0(this._getOwnDefs(this._contentFooterRowDefs),this._customFooterRowDefs),this._rowDefs=e0(this._getOwnDefs(this._contentRowDefs),this._customRowDefs);const t=this._rowDefs.filter((t=>!t.when));if(!this.multiTemplateDataRows&&t.length>1&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("There can only be one default row without a when predicate function.")})();this._defaultRowDef=t[0]}_renderUpdatedColumns(){const t=(t,e)=>t||!!e.getColumnsDiff(),e=this._rowDefs.reduce(t,!1);e&&this._forceRenderDataRows();const n=this._headerRowDefs.reduce(t,!1);n&&this._forceRenderHeaderRows();const o=this._footerRowDefs.reduce(t,!1);return o&&this._forceRenderFooterRows(),e||n||o}_switchDataSource(t){this._data=[],$H(this.dataSource)&&this.dataSource.disconnect(this),this._renderChangeSubscription&&(this._renderChangeSubscription.unsubscribe(),this._renderChangeSubscription=null),t||(this._dataDiffer&&this._dataDiffer.diff([]),this._rowOutlet.viewContainer.clear()),this._dataSource=t}_observeRenderChanges(){if(!this.dataSource)return;let t;if($H(this.dataSource)?t=this.dataSource.connect(this):Nt(this.dataSource)?t=this.dataSource:Array.isArray(this.dataSource)&&(t=Et(this.dataSource)),void 0===t&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("Provided data source did not match an array, Observable, or DataSource")})();this._renderChangeSubscription=t.pipe(Ie(this._onDestroy)).subscribe((t=>{this._data=t||[],this.renderRows()}))}_forceRenderHeaderRows(){this._headerRowOutlet.viewContainer.length>0&&this._headerRowOutlet.viewContainer.clear(),this._headerRowDefs.forEach(((t,e)=>this._renderRow(this._headerRowOutlet,t,e))),this.updateStickyHeaderRowStyles()}_forceRenderFooterRows(){this._footerRowOutlet.viewContainer.length>0&&this._footerRowOutlet.viewContainer.clear(),this._footerRowDefs.forEach(((t,e)=>this._renderRow(this._footerRowOutlet,t,e))),this.updateStickyFooterRowStyles()}_addStickyColumnStyles(t,e){const n=Array.from(e.columns||[]).map((t=>{const e=this._columnDefsByName.get(t);if(!e&&("undefined"==typeof ngDevMode||ngDevMode))throw Y$(t);return e})),o=n.map((t=>t.sticky)),i=n.map((t=>t.stickyEnd));this._stickyStyler.updateStickyColumns(t,o,i,!this._fixedLayout||this._forceRecalculateCellWidths)}_getRenderedRows(t){const e=[];for(let n=0;n<t.viewContainer.length;n++){const o=t.viewContainer.get(n);e.push(o.rootNodes[0])}return e}_getRowDefs(t,e){if(1==this._rowDefs.length)return[this._rowDefs[0]];let n=[];if(this.multiTemplateDataRows)n=this._rowDefs.filter((n=>!n.when||n.when(e,t)));else{let o=this._rowDefs.find((n=>n.when&&n.when(e,t)))||this._defaultRowDef;o&&n.push(o)}if(!n.length&&("undefined"==typeof ngDevMode||ngDevMode))throw(function o(t){return Error(`Could not find a matching row definition for theprovided row data: ${JSON.stringify(t)}`)})(t);return n}_getEmbeddedViewArgs(t,e){return{templateRef:t.rowDef.template,context:{$implicit:t.data},index:e}}_renderRow(t,e,n,o={}){const i=t.viewContainer.createEmbeddedView(e.template,o,n);return this._renderCellTemplateForItem(e,o),i}_renderCellTemplateForItem(t,e){for(let n of this._getCellTemplates(t))L$.mostRecentCellOutlet&&L$.mostRecentCellOutlet._viewContainer.createEmbeddedView(n,e);this._changeDetectorRef.markForCheck()}_updateRowIndexContext(){const t=this._rowOutlet.viewContainer;for(let e=0,n=t.length;e<n;e++){const o=t.get(e).context;o.count=n,o.first=0===e,o.last=e===n-1,o.even=e%2==0,o.odd=!o.even,this.multiTemplateDataRows?(o.dataIndex=this._renderRows[e].dataIndex,o.renderIndex=e):o.index=this._renderRows[e].dataIndex}}_getCellTemplates(t){return t&&t.columns?Array.from(t.columns,(e=>{const n=this._columnDefsByName.get(e);if(!n&&("undefined"==typeof ngDevMode||ngDevMode))throw Y$(e);return t.extractCellTemplate(n)})):[]}_applyNativeTableSections(){const t=this._document.createDocumentFragment(),e=[{tag:"thead",outlets:[this._headerRowOutlet]},{tag:"tbody",outlets:[this._rowOutlet,this._noDataRowOutlet]},{tag:"tfoot",outlets:[this._footerRowOutlet]}];for(const n of e){const e=this._document.createElement(n.tag);e.setAttribute("role","rowgroup");for(const t of n.outlets)e.appendChild(t.elementRef.nativeElement);t.appendChild(e)}this._elementRef.nativeElement.appendChild(t)}_forceRenderDataRows(){this._dataDiffer.diff([]),this._rowOutlet.viewContainer.clear(),this.renderRows()}_checkStickyStates(){const t=(t,e)=>t||e.hasStickyChanged();this._headerRowDefs.reduce(t,!1)&&this.updateStickyHeaderRowStyles(),this._footerRowDefs.reduce(t,!1)&&this.updateStickyFooterRowStyles(),Array.from(this._columnDefsByName.values()).reduce(t,!1)&&(this._stickyColumnStylesNeedReset=!0,this.updateStickyColumnStyles())}_setupStickyStyler(){this._stickyStyler=new W$(this._isNativeHtmlTable,this.stickyCssClass,this._dir?this._dir.value:"ltr",this._coalescedStyleScheduler,this._platform.isBrowser,this.needsPositionStickyOnElement,this._stickyPositioningListener),(this._dir?this._dir.change:Et()).pipe(Ie(this._onDestroy)).subscribe((t=>{this._stickyStyler.direction=t,this.updateStickyColumnStyles()}))}_getOwnDefs(t){return t.filter((t=>!t._table||t._table===this))}_updateNoDataRow(){const t=this._customNoDataRow||this._noDataRow;if(t){const e=0===this._rowOutlet.viewContainer.length;if(e!==this._isShowingNoDataRow){const n=this._noDataRowOutlet.viewContainer;e?n.createEmbeddedView(t.templateRef):n.clear(),this._isShowingNoDataRow=e}}}}function e0(t,e){return t.concat(Array.from(e))}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */t0.ɵfac=function t(e){return new(e||t0)(Sm(Hg),Sm(Ug),Sm(hg),Na("role"),Sm(HI,8),Sm(Z_),Sm(wz),Sm(aF),Sm(E$),Sm(uF),Sm(q$,12))},t0.ɵcmp=to({type:t0,selectors:[["cdk-table"],["table","cdk-table",""]],contentQueries:function t(e,n,o){if(1&e&&($h(o,U$,5),$h(o,O$,5),$h(o,F$,5),$h(o,z$,5),$h(o,H$,5)),2&e){let t;Jh(t=tb())&&(n._noDataRow=t.first),Jh(t=tb())&&(n._contentColumnDefs=t),Jh(t=tb())&&(n._contentRowDefs=t),Jh(t=tb())&&(n._contentHeaderRowDefs=t),Jh(t=tb())&&(n._contentFooterRowDefs=t)}},viewQuery:function t(e,n){if(1&e&&(Qh(X$,7),Qh(K$,7),Qh(J$,7),Qh(Q$,7)),2&e){let t;Jh(t=tb())&&(n._rowOutlet=t.first),Jh(t=tb())&&(n._headerRowOutlet=t.first),Jh(t=tb())&&(n._footerRowOutlet=t.first),Jh(t=tb())&&(n._noDataRowOutlet=t.first)}},hostAttrs:[1,"cdk-table"],hostVars:2,hostBindings:function t(e,n){2&e&&pu("cdk-table-fixed-layout",n.fixedLayout)},inputs:{trackBy:"trackBy",dataSource:"dataSource",multiTemplateDataRows:"multiTemplateDataRows",fixedLayout:"fixedLayout"},outputs:{contentChanged:"contentChanged"},exportAs:["cdkTable"],features:[pg([{provide:y$,useExisting:t0},{provide:aF,useClass:eF},{provide:E$,useClass:R$},{provide:q$,useValue:null}])],ngContentSelectors:["caption","colgroup, col"],decls:6,vars:0,consts:[["headerRowOutlet",""],["rowOutlet",""],["noDataRowOutlet",""],["footerRowOutlet",""]],template:function t(e,n){1&e&&(Zm(f$),Xm(0),Xm(1,1),Im(2,0),Im(3,1),Im(4,2),Im(5,3))},directives:[K$,X$,Q$,J$],styles:[".cdk-table-fixed-layout{table-layout:fixed}\n"],encapsulation:2}),t0.ctorParameters=()=>[{type:Hg},{type:Ug},{type:hg},{type:String,decorators:[{type:ja,args:["role"]}]},{type:HI,decorators:[{type:Sr}]},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:wz},{type:void 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const o0=[t0,F$,C$,L$,M$,v$,O$,S$,j$,w$,k$,B$,z$,V$,H$,X$,K$,J$,n0,U$,Z$,Q$];class i0{}i0.ɵfac=function t(e){return new(e||i0)},i0.ɵmod=ao({type:i0}),i0.ɵinj=vn({imports:[[_F]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(i0,[{type:Ay,args:[{exports:o0,declarations:o0,imports:[_F]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(i0,{declarations:function(){return[t0,F$,C$,L$,M$,v$,O$,S$,j$,w$,k$,B$,z$,V$,H$,X$,K$,J$,n0,U$,Z$,Q$]},imports:function(){return[_F]},exports:function(){return[t0,F$,C$,L$,M$,v$,O$,S$,j$,w$,k$,B$,z$,V$,H$,X$,K$,J$,n0,U$,Z$,Q$]}});const a0=[[["caption"]],[["colgroup"],["col"]]];function r0(t,e){if(1&t&&(Rm(0,"th",3),ku(1),Am()),2&t){const t=Ym();du("text-align",t.justify),rc(1),Du(" ",t.headerText," ")}}function s0(t,e){if(1&t&&(Rm(0,"td",4),ku(1),Am()),2&t){const t=e.$implicit,n=Ym();du("text-align",n.justify),rc(1),Du(" ",n.dataAccessor(t,n.name)," ")}}class l0{}l0.ɵfac=function t(e){return new(e||l0)},l0.ɵdir=lo({type:l0,selectors:[["mat-table","recycleRows",""],["table","mat-table","","recycleRows",""]],features:[pg([{provide:aF,useClass:nF}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(l0,[{type:Cy,args:[{selector:"mat-table[recycleRows], table[mat-table][recycleRows]",providers:[{provide:aF,useClass:nF}]}]}],null,null);class c0 extends t0{constructor(){super(...arguments),this.stickyCssClass="mat-table-sticky",this.needsPositionStickyOnElement=!1}}c0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(c0)))(n||c0)}})(),c0.ɵcmp=to({type:c0,selectors:[["mat-table"],["table","mat-table",""]],hostAttrs:[1,"mat-table"],hostVars:2,hostBindings:function t(e,n){2&e&&pu("mat-table-fixed-layout",n.fixedLayout)},exportAs:["matTable"],features:[pg([{provide:aF,useClass:eF},{provide:t0,useExisting:c0},{provide:y$,useExisting:c0},{provide:E$,useClass:R$},{provide:q$,useValue:null}]),xp],ngContentSelectors:["caption","colgroup, col"],decls:6,vars:0,consts:[["headerRowOutlet",""],["rowOutlet",""],["noDataRowOutlet",""],["footerRowOutlet",""]],template:function t(e,n){1&e&&(Zm(a0),Xm(0),Xm(1,1),Im(2,0),Im(3,1),Im(4,2),Im(5,3))},directives:[K$,X$,Q$,J$],styles:['mat-table{display:block}mat-header-row{min-height:56px}mat-row,mat-footer-row{min-height:48px}mat-row,mat-header-row,mat-footer-row{display:flex;border-width:0;border-bottom-width:1px;border-style:solid;align-items:center;box-sizing:border-box}mat-row::after,mat-header-row::after,mat-footer-row::after{display:inline-block;min-height:inherit;content:""}mat-cell:first-of-type,mat-header-cell:first-of-type,mat-footer-cell:first-of-type{padding-left:24px}[dir=rtl] mat-cell:first-of-type:not(:only-of-type),[dir=rtl] mat-header-cell:first-of-type:not(:only-of-type),[dir=rtl] mat-footer-cell:first-of-type:not(:only-of-type){padding-left:0;padding-right:24px}mat-cell:last-of-type,mat-header-cell:last-of-type,mat-footer-cell:last-of-type{padding-right:24px}[dir=rtl] mat-cell:last-of-type:not(:only-of-type),[dir=rtl] mat-header-cell:last-of-type:not(:only-of-type),[dir=rtl] mat-footer-cell:last-of-type:not(:only-of-type){padding-right:0;padding-left:24px}mat-cell,mat-header-cell,mat-footer-cell{flex:1;display:flex;align-items:center;overflow:hidden;word-wrap:break-word;min-height:inherit}table.mat-table{border-spacing:0}tr.mat-header-row{height:56px}tr.mat-row,tr.mat-footer-row{height:48px}th.mat-header-cell{text-align:left}[dir=rtl] th.mat-header-cell{text-align:right}th.mat-header-cell,td.mat-cell,td.mat-footer-cell{padding:0;border-bottom-width:1px;border-bottom-style:solid}th.mat-header-cell:first-of-type,td.mat-cell:first-of-type,td.mat-footer-cell:first-of-type{padding-left:24px}[dir=rtl] th.mat-header-cell:first-of-type:not(:only-of-type),[dir=rtl] 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table[mat-table]",exportAs:"matTable",template:$$,host:{class:"mat-table","[class.mat-table-fixed-layout]":"fixedLayout"},providers:[{provide:aF,useClass:eF},{provide:t0,useExisting:c0},{provide:y$,useExisting:c0},{provide:E$,useClass:R$},{provide:q$,useValue:null}],encapsulation:Hn.None,changeDetection:zn.Default,styles:['mat-table{display:block}mat-header-row{min-height:56px}mat-row,mat-footer-row{min-height:48px}mat-row,mat-header-row,mat-footer-row{display:flex;border-width:0;border-bottom-width:1px;border-style:solid;align-items:center;box-sizing:border-box}mat-row::after,mat-header-row::after,mat-footer-row::after{display:inline-block;min-height:inherit;content:""}mat-cell:first-of-type,mat-header-cell:first-of-type,mat-footer-cell:first-of-type{padding-left:24px}[dir=rtl] mat-cell:first-of-type:not(:only-of-type),[dir=rtl] mat-header-cell:first-of-type:not(:only-of-type),[dir=rtl] mat-footer-cell:first-of-type:not(:only-of-type){padding-left:0;padding-right:24px}mat-cell:last-of-type,mat-header-cell:last-of-type,mat-footer-cell:last-of-type{padding-right:24px}[dir=rtl] mat-cell:last-of-type:not(:only-of-type),[dir=rtl] mat-header-cell:last-of-type:not(:only-of-type),[dir=rtl] mat-footer-cell:last-of-type:not(:only-of-type){padding-right:0;padding-left:24px}mat-cell,mat-header-cell,mat-footer-cell{flex:1;display:flex;align-items:center;overflow:hidden;word-wrap:break-word;min-height:inherit}table.mat-table{border-spacing:0}tr.mat-header-row{height:56px}tr.mat-row,tr.mat-footer-row{height:48px}th.mat-header-cell{text-align:left}[dir=rtl] th.mat-header-cell{text-align:right}th.mat-header-cell,td.mat-cell,td.mat-footer-cell{padding:0;border-bottom-width:1px;border-bottom-style:solid}th.mat-header-cell:first-of-type,td.mat-cell:first-of-type,td.mat-footer-cell:first-of-type{padding-left:24px}[dir=rtl] th.mat-header-cell:first-of-type:not(:only-of-type),[dir=rtl] td.mat-cell:first-of-type:not(:only-of-type),[dir=rtl] td.mat-footer-cell:first-of-type:not(:only-of-type){padding-left:0;padding-right:24px}th.mat-header-cell:last-of-type,td.mat-cell:last-of-type,td.mat-footer-cell:last-of-type{padding-right:24px}[dir=rtl] th.mat-header-cell:last-of-type:not(:only-of-type),[dir=rtl] td.mat-cell:last-of-type:not(:only-of-type),[dir=rtl] td.mat-footer-cell:last-of-type:not(:only-of-type){padding-right:0;padding-left:24px}.mat-table-sticky{position:-webkit-sticky !important;position:sticky !important}.mat-table-fixed-layout{table-layout:fixed}\n']}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class d0 extends C${}d0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(d0)))(n||d0)}})(),d0.ɵdir=lo({type:d0,selectors:[["","matCellDef",""]],features:[pg([{provide:C$,useExisting:d0}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(d0,[{type:Cy,args:[{selector:"[matCellDef]",providers:[{provide:C$,useExisting:d0}]}]}],null,null);class p0 extends M${}p0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(p0)))(n||p0)}})(),p0.ɵdir=lo({type:p0,selectors:[["","matHeaderCellDef",""]],features:[pg([{provide:M$,useExisting:p0}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(p0,[{type:Cy,args:[{selector:"[matHeaderCellDef]",providers:[{provide:M$,useExisting:p0}]}]}],null,null);class m0 extends v${}m0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(m0)))(n||m0)}})(),m0.ɵdir=lo({type:m0,selectors:[["","matFooterCellDef",""]],features:[pg([{provide:v$,useExisting:m0}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(m0,[{type:Cy,args:[{selector:"[matFooterCellDef]",providers:[{provide:v$,useExisting:m0}]}]}],null,null);class u0 extends O${get name(){return this._name}set name(t){this._setNameInput(t)}_updateColumnCssClassName(){super._updateColumnCssClassName(),this._columnCssClassName.push(`mat-column-${this.cssClassFriendlyName}`)}}u0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(u0)))(n||u0)}})(),u0.ɵdir=lo({type:u0,selectors:[["","matColumnDef",""]],inputs:{sticky:"sticky",name:["matColumnDef","name"]},features:[pg([{provide:O$,useExisting:u0},{provide:"MAT_SORT_HEADER_COLUMN_DEF",useExisting:u0}]),xp]}),u0.propDecorators={name:[{type:xy,args:["matColumnDef"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(u0,[{type:Cy,args:[{selector:"[matColumnDef]",inputs:["sticky"],providers:[{provide:O$,useExisting:u0},{provide:"MAT_SORT_HEADER_COLUMN_DEF",useExisting:u0}]}]}],null,{name:[{type:xy,args:["matColumnDef"]}]});class f0 extends w${}f0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(f0)))(n||f0)}})(),f0.ɵdir=lo({type:f0,selectors:[["mat-header-cell"],["th","mat-header-cell",""]],hostAttrs:["role","columnheader",1,"mat-header-cell"],features:[xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(f0,[{type:Cy,args:[{selector:"mat-header-cell, th[mat-header-cell]",host:{class:"mat-header-cell",role:"columnheader"}}]}],null,null);class g0 extends k${}g0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(g0)))(n||g0)}})(),g0.ɵdir=lo({type:g0,selectors:[["mat-footer-cell"],["td","mat-footer-cell",""]],hostAttrs:["role","gridcell",1,"mat-footer-cell"],features:[xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(g0,[{type:Cy,args:[{selector:"mat-footer-cell, td[mat-footer-cell]",host:{class:"mat-footer-cell",role:"gridcell"}}]}],null,null);class h0 extends S${}h0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(h0)))(n||h0)}})(),h0.ɵdir=lo({type:h0,selectors:[["mat-cell"],["td","mat-cell",""]],hostAttrs:["role","gridcell",1,"mat-cell"],features:[xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(h0,[{type:Cy,args:[{selector:"mat-cell, td[mat-cell]",host:{class:"mat-cell",role:"gridcell"}}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class b0 extends z${}b0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(b0)))(n||b0)}})(),b0.ɵdir=lo({type:b0,selectors:[["","matHeaderRowDef",""]],inputs:{columns:["matHeaderRowDef","columns"],sticky:["matHeaderRowDefSticky","sticky"]},features:[pg([{provide:z$,useExisting:b0}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(b0,[{type:Cy,args:[{selector:"[matHeaderRowDef]",providers:[{provide:z$,useExisting:b0}],inputs:["columns: matHeaderRowDef","sticky: matHeaderRowDefSticky"]}]}],null,null);class y0 extends H${}y0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(y0)))(n||y0)}})(),y0.ɵdir=lo({type:y0,selectors:[["","matFooterRowDef",""]],inputs:{columns:["matFooterRowDef","columns"],sticky:["matFooterRowDefSticky","sticky"]},features:[pg([{provide:H$,useExisting:y0}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(y0,[{type:Cy,args:[{selector:"[matFooterRowDef]",providers:[{provide:H$,useExisting:y0}],inputs:["columns: matFooterRowDef","sticky: matFooterRowDefSticky"]}]}],null,null);class _0 extends F${}_0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(_0)))(n||_0)}})(),_0.ɵdir=lo({type:_0,selectors:[["","matRowDef",""]],inputs:{columns:["matRowDefColumns","columns"],when:["matRowDefWhen","when"]},features:[pg([{provide:F$,useExisting:_0}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(_0,[{type:Cy,args:[{selector:"[matRowDef]",providers:[{provide:F$,useExisting:_0}],inputs:["columns: matRowDefColumns","when: matRowDefWhen"]}]}],null,null);class C0 extends B${}C0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(C0)))(n||C0)}})(),C0.ɵcmp=to({type:C0,selectors:[["mat-header-row"],["tr","mat-header-row",""]],hostAttrs:["role","row",1,"mat-header-row"],exportAs:["matHeaderRow"],features:[pg([{provide:B$,useExisting:C0}]),xp],decls:1,vars:0,consts:[["cdkCellOutlet",""]],template:function t(e,n){1&e&&Im(0,0)},directives:[L$],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(C0,[{type:My,args:[{selector:"mat-header-row, tr[mat-header-row]",template:A$,host:{class:"mat-header-row",role:"row"},changeDetection:zn.Default,encapsulation:Hn.None,exportAs:"matHeaderRow",providers:[{provide:B$,useExisting:C0}]}]}],null,null);class M0 extends V${}M0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(M0)))(n||M0)}})(),M0.ɵcmp=to({type:M0,selectors:[["mat-footer-row"],["tr","mat-footer-row",""]],hostAttrs:["role","row",1,"mat-footer-row"],exportAs:["matFooterRow"],features:[pg([{provide:V$,useExisting:M0}]),xp],decls:1,vars:0,consts:[["cdkCellOutlet",""]],template:function t(e,n){1&e&&Im(0,0)},directives:[L$],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(M0,[{type:My,args:[{selector:"mat-footer-row, tr[mat-footer-row]",template:A$,host:{class:"mat-footer-row",role:"row"},changeDetection:zn.Default,encapsulation:Hn.None,exportAs:"matFooterRow",providers:[{provide:V$,useExisting:M0}]}]}],null,null);class v0 extends j${}v0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(v0)))(n||v0)}})(),v0.ɵcmp=to({type:v0,selectors:[["mat-row"],["tr","mat-row",""]],hostAttrs:["role","row",1,"mat-row"],exportAs:["matRow"],features:[pg([{provide:j$,useExisting:v0}]),xp],decls:1,vars:0,consts:[["cdkCellOutlet",""]],template:function t(e,n){1&e&&Im(0,0)},directives:[L$],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(v0,[{type:My,args:[{selector:"mat-row, tr[mat-row]",template:A$,host:{class:"mat-row",role:"row"},changeDetection:zn.Default,encapsulation:Hn.None,exportAs:"matRow",providers:[{provide:j$,useExisting:v0}]}]}],null,null);class x0 extends U${}x0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(x0)))(n||x0)}})(),x0.ɵdir=lo({type:x0,selectors:[["ng-template","matNoDataRow",""]],features:[pg([{provide:U$,useExisting:x0}]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(x0,[{type:Cy,args:[{selector:"ng-template[matNoDataRow]",providers:[{provide:U$,useExisting:x0}]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class O0 extends n0{}O0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(O0)))(n||O0)}})(),O0.ɵcmp=to({type:O0,selectors:[["mat-text-column"]],features:[xp],decls:3,vars:0,consts:[["matColumnDef",""],["mat-header-cell","",3,"text-align",4,"matHeaderCellDef"],["mat-cell","",3,"text-align",4,"matCellDef"],["mat-header-cell",""],["mat-cell",""]],template:function t(e,n){1&e&&(Nm(0,0),Qp(1,r0,2,3,"th",1),Qp(2,s0,2,3,"td",2),zm())},directives:[u0,p0,d0,f0,h0],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(O0,[{type:My,args:[{selector:"mat-text-column",template:'\n    <ng-container matColumnDef>\n      <th mat-header-cell *matHeaderCellDef [style.text-align]="justify">\n        {{headerText}}\n      </th>\n      <td mat-cell *matCellDef="let data" [style.text-align]="justify">\n        {{dataAccessor(data, name)}}\n      </td>\n    </ng-container>\n  ',encapsulation:Hn.None,changeDetection:zn.Default}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const P0=[c0,l0,p0,b0,u0,d0,_0,m0,y0,f0,h0,g0,C0,v0,M0,x0,O0];class w0{}w0.ɵfac=function t(e){return new(e||w0)},w0.ɵmod=ao({type:w0}),w0.ɵinj=vn({imports:[[i0,XI],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(w0,[{type:Ay,args:[{imports:[i0,XI],exports:[XI,P0],declarations:P0}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(w0,{declarations:function(){return[c0,l0,p0,b0,u0,d0,_0,m0,y0,f0,h0,g0,C0,v0,M0,x0,O0]},imports:function(){return[i0,XI]},exports:function(){return[XI,c0,l0,p0,b0,u0,d0,_0,m0,y0,f0,h0,g0,C0,v0,M0,x0,O0]}});class k0 extends QH{constructor(t=[]){super(),this._renderData=new F([]),this._filter=new F(""),this._internalPageChanges=new I,this._renderChangesSubscription=null,this.sortingDataAccessor=(t,e)=>{const n=t[e];if(Cz(n)){const t=Number(n);return t<9007199254740991?t:n}return n},this.sortData=(t,e)=>{const n=e.active,o=e.direction;return n&&""!=o?t.sort(((t,e)=>{let i=this.sortingDataAccessor(t,n),a=this.sortingDataAccessor(e,n);const r=typeof i,s=typeof a;r!==s&&("number"===r&&(i+=""),"number"===s&&(a+=""));let l=0;return null!=i&&null!=a?i>a?l=1:i<a&&(l=-1):null!=i?l=1:null!=a&&(l=-1),l*("asc"==o?1:-1)})):t},this.filterPredicate=(t,e)=>{const n=Object.keys(t).reduce(((e,n)=>e+t[n]+"◬"),"").toLowerCase(),o=e.trim().toLowerCase();return-1!=n.indexOf(o)},this._data=new F(t),this._updateChangeSubscription()}get data(){return this._data.value}set data(t){this._data.next(t),this._renderChangesSubscription||this._filterData(t)}get filter(){return this._filter.value}set filter(t){this._filter.next(t),this._renderChangesSubscription||this._filterData(this.data)}get sort(){return this._sort}set sort(t){this._sort=t,this._updateChangeSubscription()}get paginator(){return this._paginator}set paginator(t){this._paginator=t,this._updateChangeSubscription()}_updateChangeSubscription(){var t;const e=this._sort?re(this._sort.sortChange,this._sort.initialized):Et(null),n=this._paginator?re(this._paginator.page,this._internalPageChanges,this._paginator.initialized):Et(null),o=Wt([this._data,this._filter]).pipe(It((([t])=>this._filterData(t)))),i=Wt([o,e]).pipe(It((([t])=>this._orderData(t)))),a=Wt([i,n]).pipe(It((([t])=>this._pageData(t))));null===(t=this._renderChangesSubscription)||void 0===t||t.unsubscribe(),this._renderChangesSubscription=a.subscribe((t=>this._renderData.next(t)))}_filterData(t){return this.filteredData=null==this.filter||""===this.filter?t:t.filter((t=>this.filterPredicate(t,this.filter))),this.paginator&&this._updatePaginator(this.filteredData.length),this.filteredData}_orderData(t){return this.sort?this.sortData(t.slice(),this.sort):t}_pageData(t){if(!this.paginator)return t;const e=this.paginator.pageIndex*this.paginator.pageSize;return t.slice(e,e+this.paginator.pageSize)}_updatePaginator(t){Promise.resolve().then((()=>{const e=this.paginator;if(e&&(e.length=t,e.pageIndex>0)){const t=Math.ceil(e.length/e.pageSize)-1||0,n=Math.min(e.pageIndex,t);n!==e.pageIndex&&(e.pageIndex=n,this._internalPageChanges.next())}}))}connect(){return this._renderChangesSubscription||this._updateChangeSubscription(),this._renderData}disconnect(){var t;null===(t=this._renderChangesSubscription)||void 0===t||t.unsubscribe(),this._renderChangesSubscription=null}}class S0 extends k0{}const D0=["panel"];function E0(t,e){if(1&t&&(Rm(0,"div",0,1),Xm(2),Am()),2&t){const t=e.id,n=Ym();Dm("id",n.id)("ngClass",n._classList),jp("aria-label",n.ariaLabel||null)("aria-labelledby",n._getPanelAriaLabelledby(t))}}let R0=0;class A0{constructor(t,e){this.source=t,this.option=e}}const T0=QI(class{}),N0=new Ga("mat-autocomplete-default-options",{providedIn:"root",factory:function z0(){return{autoActiveFirstOption:!1}}});class I0 extends T0{constructor(t,e,n,o){super(),this._changeDetectorRef=t,this._elementRef=e,this._activeOptionChanges=m.EMPTY,this.showPanel=!1,this._isOpen=!1,this.displayWith=null,this.optionSelected=new Lh,this.opened=new Lh,this.closed=new Lh,this.optionActivated=new Lh,this._classList={},this.id="mat-autocomplete-"+R0++,this.inertGroups=(null==o?void 0:o.SAFARI)||!1,this._autoActiveFirstOption=!!n.autoActiveFirstOption}get isOpen(){return this._isOpen&&this.showPanel}get autoActiveFirstOption(){return this._autoActiveFirstOption}set autoActiveFirstOption(t){this._autoActiveFirstOption=yz(t)}set classList(t){this._classList=t&&t.length?(function e(t,n=/\s+/){const o=[];if(null!=t){const e=Array.isArray(t)?t:`${t}`.split(n);for(const t of e){const e=`${t}`.trim();e&&o.push(e)}}return o})(t).reduce(((t,e)=>(t[e]=!0,t)),{}):{},this._setVisibilityClasses(this._classList),this._elementRef.nativeElement.className=""}ngAfterContentInit(){this._keyManager=new tI(this.options).withWrap(),this._activeOptionChanges=this._keyManager.change.subscribe((t=>{this.optionActivated.emit({source:this,option:this.options.toArray()[t]||null})})),this._setVisibility()}ngOnDestroy(){this._activeOptionChanges.unsubscribe()}_setScrollTop(t){this.panel&&(this.panel.nativeElement.scrollTop=t)}_getScrollTop(){return this.panel?this.panel.nativeElement.scrollTop:0}_setVisibility(){this.showPanel=!!this.options.length,this._setVisibilityClasses(this._classList),this._changeDetectorRef.markForCheck()}_emitSelectEvent(t){const e=new A0(this,t);this.optionSelected.emit(e)}_getPanelAriaLabelledby(t){return this.ariaLabel?null:this.ariaLabelledby?(t?t+" ":"")+this.ariaLabelledby:t}_setVisibilityClasses(t){t[this._visibleClass]=this.showPanel,t[this._hiddenClass]=!this.showPanel}}I0.ɵfac=function t(e){return new(e||I0)(Sm(Ug),Sm(hg),Sm(N0),Sm(wz))},I0.ɵdir=lo({type:I0,viewQuery:function t(e,n){if(1&e&&(Qh(Xg,7),Qh(D0,5)),2&e){let t;Jh(t=tb())&&(n.template=t.first),Jh(t=tb())&&(n.panel=t.first)}},inputs:{displayWith:"displayWith",autoActiveFirstOption:"autoActiveFirstOption",classList:["class","classList"],ariaLabel:["aria-label","ariaLabel"],ariaLabelledby:["aria-labelledby","ariaLabelledby"],panelWidth:"panelWidth"},outputs:{optionSelected:"optionSelected",opened:"opened",closed:"closed",optionActivated:"optionActivated"},features:[xp]}),I0.ctorParameters=()=>[{type:Ug},{type:hg},{type:void 0,decorators:[{type:kr,args:[N0]}]},{type:wz}],I0.propDecorators={template:[{type:Za,args:[Xg,{static:!0}]}],panel:[{type:Za,args:["panel"]}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],displayWith:[{type:xy}],autoActiveFirstOption:[{type:xy}],panelWidth:[{type:xy}],optionSelected:[{type:Oy}],opened:[{type:Oy}],closed:[{type:Oy}],optionActivated:[{type:Oy}],classList:[{type:xy,args:["class"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(I0,[{type:Cy}],(function(){return[{type:Ug},{type:hg},{type:void 0,decorators:[{type:kr,args:[N0]}]},{type:wz}]}),{displayWith:[{type:xy}],optionSelected:[{type:Oy}],opened:[{type:Oy}],closed:[{type:Oy}],optionActivated:[{type:Oy}],autoActiveFirstOption:[{type:xy}],classList:[{type:xy,args:["class"]}],template:[{type:Za,args:[Xg,{static:!0}]}],panel:[{type:Za,args:["panel"]}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],panelWidth:[{type:xy}]});class H0 extends I0{constructor(){super(...arguments),this._visibleClass="mat-autocomplete-visible",this._hiddenClass="mat-autocomplete-hidden"}}H0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(H0)))(n||H0)}})(),H0.ɵcmp=to({type:H0,selectors:[["mat-autocomplete"]],contentQueries:function t(e,n,o){if(1&e&&($h(o,zH,5),$h(o,BH,5)),2&e){let t;Jh(t=tb())&&(n.optionGroups=t),Jh(t=tb())&&(n.options=t)}},hostAttrs:[1,"mat-autocomplete"],inputs:{disableRipple:"disableRipple"},exportAs:["matAutocomplete"],features:[pg([{provide:RH,useExisting:H0}]),xp],ngContentSelectors:["*"],decls:1,vars:0,consts:[["role","listbox",1,"mat-autocomplete-panel",3,"id","ngClass"],["panel",""]],template:function t(e,n){1&e&&(Zm(),Qp(0,E0,3,4,"ng-template"))},directives:[aM],styles:[".mat-autocomplete-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;visibility:hidden;max-width:none;max-height:256px;position:relative;width:100%;border-bottom-left-radius:4px;border-bottom-right-radius:4px}.mat-autocomplete-panel.mat-autocomplete-visible{visibility:visible}.mat-autocomplete-panel.mat-autocomplete-hidden{visibility:hidden}.mat-autocomplete-panel-above .mat-autocomplete-panel{border-radius:0;border-top-left-radius:4px;border-top-right-radius:4px}.mat-autocomplete-panel .mat-divider-horizontal{margin-top:-1px}.cdk-high-contrast-active .mat-autocomplete-panel{outline:solid 1px}mat-autocomplete{display:none}\n"],encapsulation:2,changeDetection:0}),H0.propDecorators={optionGroups:[{type:Ya,args:[zH,{descendants:!0}]}],options:[{type:Ya,args:[BH,{descendants:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(H0,[{type:My,args:[{selector:"mat-autocomplete",template:'<ng-template let-formFieldId="id">\n  <div class="mat-autocomplete-panel"\n       role="listbox"\n       [id]="id"\n       [attr.aria-label]="ariaLabel || null"\n       [attr.aria-labelledby]="_getPanelAriaLabelledby(formFieldId)"\n       [ngClass]="_classList"\n       #panel>\n    <ng-content></ng-content>\n  </div>\n</ng-template>\n',encapsulation:Hn.None,changeDetection:zn.OnPush,exportAs:"matAutocomplete",inputs:["disableRipple"],host:{class:"mat-autocomplete"},providers:[{provide:RH,useExisting:H0}],styles:[".mat-autocomplete-panel{min-width:112px;max-width:280px;overflow:auto;-webkit-overflow-scrolling:touch;visibility:hidden;max-width:none;max-height:256px;position:relative;width:100%;border-bottom-left-radius:4px;border-bottom-right-radius:4px}.mat-autocomplete-panel.mat-autocomplete-visible{visibility:visible}.mat-autocomplete-panel.mat-autocomplete-hidden{visibility:hidden}.mat-autocomplete-panel-above .mat-autocomplete-panel{border-radius:0;border-top-left-radius:4px;border-top-right-radius:4px}.mat-autocomplete-panel .mat-divider-horizontal{margin-top:-1px}.cdk-high-contrast-active .mat-autocomplete-panel{outline:solid 1px}mat-autocomplete{display:none}\n"]}]}],null,{optionGroups:[{type:Ya,args:[zH,{descendants:!0}]}],options:[{type:Ya,args:[BH,{descendants:!0}]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class F0{constructor(t){this.elementRef=t}}F0.ɵfac=function t(e){return new(e||F0)(Sm(hg))},F0.ɵdir=lo({type:F0}),F0.ctorParameters=()=>[{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(F0,[{type:Cy}],(function(){return[{type:hg}]}),null);class L0 extends F0{}L0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(L0)))(n||L0)}})(),L0.ɵdir=lo({type:L0,selectors:[["","matAutocompleteOrigin",""]],exportAs:["matAutocompleteOrigin"],features:[xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(L0,[{type:Cy,args:[{selector:"[matAutocompleteOrigin]",exportAs:"matAutocompleteOrigin"}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const B0=new Ga("mat-autocomplete-scroll-strategy"),V0={provide:B0,deps:[pL],useFactory:function j0(t){return()=>t.scrollStrategies.reposition()}},U0={provide:IV,useExisting:qe((()=>W0)),multi:!0};class G0{constructor(t,e,n,o,i,a,r,s,l,c,d){this._element=t,this._overlay=e,this._viewContainerRef=n,this._zone=o,this._changeDetectorRef=i,this._dir=r,this._formField=s,this._document=l,this._viewportRuler=c,this._defaults=d,this._componentDestroyed=!1,this._autocompleteDisabled=!1,this._manuallyFloatingLabel=!1,this._viewportSubscription=m.EMPTY,this._canOpenOnNextFocus=!0,this._closeKeyEventStream=new I,this._windowBlurHandler=()=>{this._canOpenOnNextFocus=this._document.activeElement!==this._element.nativeElement||this.panelOpen},this._onChange=()=>{},this._onTouched=()=>{},this.position="auto",this.autocompleteAttribute="off",this._overlayAttached=!1,this.optionSelections=Qt((()=>this.autocomplete&&this.autocomplete.options?re(...this.autocomplete.options.map((t=>t.onSelectionChange))):this._zone.onStable.pipe(be(1),ze((()=>this.optionSelections))))),this._scrollStrategy=a}get autocompleteDisabled(){return this._autocompleteDisabled}set autocompleteDisabled(t){this._autocompleteDisabled=yz(t)}ngAfterViewInit(){const t=this._getWindow();void 0!==t&&this._zone.runOutsideAngular((()=>t.addEventListener("blur",this._windowBlurHandler)))}ngOnChanges(t){t.position&&this._positionStrategy&&(this._setStrategyPositions(this._positionStrategy),this.panelOpen&&this._overlayRef.updatePosition())}ngOnDestroy(){const t=this._getWindow();void 0!==t&&t.removeEventListener("blur",this._windowBlurHandler),this._viewportSubscription.unsubscribe(),this._componentDestroyed=!0,this._destroyPanel(),this._closeKeyEventStream.complete()}get panelOpen(){return this._overlayAttached&&this.autocomplete.showPanel}openPanel(){this._attachOverlay(),this._floatLabel()}closePanel(){this._resetLabel(),this._overlayAttached&&(this.panelOpen&&this.autocomplete.closed.emit(),this.autocomplete._isOpen=this._overlayAttached=!1,this._overlayRef&&this._overlayRef.hasAttached()&&(this._overlayRef.detach(),this._closingActionsSubscription.unsubscribe()),this._componentDestroyed||this._changeDetectorRef.detectChanges())}updatePosition(){this._overlayAttached&&this._overlayRef.updatePosition()}get panelClosingActions(){return re(this.optionSelections,this.autocomplete._keyManager.tabOut.pipe(ce((()=>this._overlayAttached))),this._closeKeyEventStream,this._getOutsideClickStream(),this._overlayRef?this._overlayRef.detachments().pipe(ce((()=>this._overlayAttached))):Et()).pipe(It((t=>t instanceof FH?t:null)))}get activeOption(){return this.autocomplete&&this.autocomplete._keyManager?this.autocomplete._keyManager.activeItem:null}_getOutsideClickStream(){return re(oe(this._document,"click"),oe(this._document,"auxclick"),oe(this._document,"touchend")).pipe(ce((t=>{const e=Lz(t),n=this._formField?this._formField._elementRef.nativeElement:null,o=this.connectedTo?this.connectedTo.elementRef.nativeElement:null;return this._overlayAttached&&e!==this._element.nativeElement&&(!n||!n.contains(e))&&(!o||!o.contains(e))&&!!this._overlayRef&&!this._overlayRef.overlayElement.contains(e)})))}writeValue(t){Promise.resolve(null).then((()=>this._setTriggerValue(t)))}registerOnChange(t){this._onChange=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this._element.nativeElement.disabled=t}_handleKeydown(t){const e=t.keyCode;if(e!==uz||bz(t)||t.preventDefault(),this.activeOption&&e===mz&&this.panelOpen)this.activeOption._selectViaInteraction(),this._resetActiveItem(),t.preventDefault();else if(this.autocomplete){const n=this.autocomplete._keyManager.activeItem,o=e===gz||e===hz;this.panelOpen||9===e?this.autocomplete._keyManager.onKeydown(t):o&&this._canOpen()&&this.openPanel(),(o||this.autocomplete._keyManager.activeItem!==n)&&this._scrollToOption(this.autocomplete._keyManager.activeItemIndex||0)}}_handleInput(t){let e=t.target,n=e.value;"number"===e.type&&(n=""==n?null:parseFloat(n)),this._previousValue!==n&&(this._previousValue=n,this._onChange(n),this._canOpen()&&this._document.activeElement===t.target&&this.openPanel())}_handleFocus(){this._canOpenOnNextFocus?this._canOpen()&&(this._previousValue=this._element.nativeElement.value,this._attachOverlay(),this._floatLabel(!0)):this._canOpenOnNextFocus=!0}_floatLabel(t=!1){this._formField&&"auto"===this._formField.floatLabel&&(t?this._formField._animateAndLockLabel():this._formField.floatLabel="always",this._manuallyFloatingLabel=!0)}_resetLabel(){this._manuallyFloatingLabel&&(this._formField.floatLabel="auto",this._manuallyFloatingLabel=!1)}_subscribeToClosingActions(){return re(this._zone.onStable.pipe(be(1)),this.autocomplete.options.changes.pipe(Fe((()=>this._positionStrategy.reapplyLastPosition())),Ce(0))).pipe(ze((()=>{const t=this.panelOpen;return this._resetActiveItem(),this.autocomplete._setVisibility(),this.panelOpen&&(this._overlayRef.updatePosition(),t!==this.panelOpen&&this.autocomplete.opened.emit()),this.panelClosingActions})),be(1)).subscribe((t=>this._setValueAndClose(t)))}_destroyPanel(){this._overlayRef&&(this.closePanel(),this._overlayRef.dispose(),this._overlayRef=null)}_setTriggerValue(t){const e=this.autocomplete&&this.autocomplete.displayWith?this.autocomplete.displayWith(t):t,n=null!=e?e:"";this._formField?this._formField._control.value=n:this._element.nativeElement.value=n,this._previousValue=n}_setValueAndClose(t){t&&t.source&&(this._clearPreviousSelectedOption(t.source),this._setTriggerValue(t.source.value),this._onChange(t.source.value),this._element.nativeElement.focus(),this.autocomplete._emitSelectEvent(t.source)),this.closePanel()}_clearPreviousSelectedOption(t){this.autocomplete.options.forEach((e=>{e!==t&&e.selected&&e.deselect()}))}_attachOverlay(){var t;if(!this.autocomplete&&("undefined"==typeof ngDevMode||ngDevMode))throw(function e(){return Error("Attempting to open an undefined instance of `mat-autocomplete`. Make sure that the id passed to the `matAutocomplete` is correct and that you're attempting to open it after the ngAfterContentInit hook.")})();let n=this._overlayRef;n?(this._positionStrategy.setOrigin(this._getConnectedElement()),n.updateSize({width:this._getPanelWidth()})):(this._portal=new xF(this.autocomplete.template,this._viewContainerRef,{id:null===(t=this._formField)||void 0===t?void 0:t.getLabelId()}),n=this._overlay.create(this._getOverlayConfig()),this._overlayRef=n,n.keydownEvents().subscribe((t=>{(t.keyCode===uz&&!bz(t)||t.keyCode===gz&&bz(t,"altKey"))&&(this._resetActiveItem(),this._closeKeyEventStream.next(),t.stopPropagation(),t.preventDefault())})),this._viewportSubscription=this._viewportRuler.change().subscribe((()=>{this.panelOpen&&n&&n.updateSize({width:this._getPanelWidth()})}))),n&&!n.hasAttached()&&(n.attach(this._portal),this._closingActionsSubscription=this._subscribeToClosingActions());const o=this.panelOpen;this.autocomplete._setVisibility(),this.autocomplete._isOpen=this._overlayAttached=!0,this.panelOpen&&o!==this.panelOpen&&this.autocomplete.opened.emit()}_getOverlayConfig(){var t;return new VF({positionStrategy:this._getOverlayPosition(),scrollStrategy:this._scrollStrategy(),width:this._getPanelWidth(),direction:this._dir,panelClass:null===(t=this._defaults)||void 0===t?void 0:t.overlayPanelClass})}_getOverlayPosition(){const t=this._overlay.position().flexibleConnectedTo(this._getConnectedElement()).withFlexibleDimensions(!1).withPush(!1);return this._setStrategyPositions(t),this._positionStrategy=t,t}_setStrategyPositions(t){const e=[{originX:"start",originY:"bottom",overlayX:"start",overlayY:"top"},{originX:"end",originY:"bottom",overlayX:"end",overlayY:"top"}],n=this._aboveClass,o=[{originX:"start",originY:"top",overlayX:"start",overlayY:"bottom",panelClass:n},{originX:"end",originY:"top",overlayX:"end",overlayY:"bottom",panelClass:n}];let i;i="above"===this.position?o:"below"===this.position?e:[...e,...o],t.withPositions(i)}_getConnectedElement(){return this.connectedTo?this.connectedTo.elementRef:this._formField?this._formField.getConnectedOverlayOrigin():this._element}_getPanelWidth(){return this.autocomplete.panelWidth||this._getHostWidth()}_getHostWidth(){return this._getConnectedElement().nativeElement.getBoundingClientRect().width}_resetActiveItem(){const t=this.autocomplete;t.autoActiveFirstOption?t._keyManager.setFirstItemActive():t._keyManager.setActiveItem(-1)}_canOpen(){const t=this._element.nativeElement;return!t.readOnly&&!t.disabled&&!this._autocompleteDisabled}_getWindow(){var t;return(null===(t=this._document)||void 0===t?void 0:t.defaultView)||window}_scrollToOption(t){const e=this.autocomplete,n=VH(t,e.options,e.optionGroups);if(0===t&&1===n)e._setScrollTop(0);else if(e.panel){const n=e.options.toArray()[t];if(n){const t=n._getHostElement(),o=jH(t.offsetTop,t.offsetHeight,e._getScrollTop(),e.panel.nativeElement.offsetHeight);e._setScrollTop(o)}}}}G0.ɵfac=function t(e){return new(e||G0)(Sm(hg),Sm(pL),Sm(eh),Sm(a_),Sm(Ug),Sm(B0),Sm(HI,8),Sm(RV,9),Sm(Z_,8),Sm(uF),Sm(N0,8))},G0.ɵdir=lo({type:G0,inputs:{position:["matAutocompletePosition","position"],autocompleteAttribute:["autocomplete","autocompleteAttribute"],autocompleteDisabled:["matAutocompleteDisabled","autocompleteDisabled"],autocomplete:["matAutocomplete","autocomplete"],connectedTo:["matAutocompleteConnectedTo","connectedTo"]},features:[Bo]}),G0.ctorParameters=()=>[{type:hg},{type:pL},{type:eh},{type:a_},{type:Ug},{type:void 0,decorators:[{type:kr,args:[B0]}]},{type:HI,decorators:[{type:Sr}]},{type:AV,decorators:[{type:Sr},{type:kr,args:[RV]},{type:Rr}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:uF},{type:void 0,decorators:[{type:Sr},{type:kr,args:[N0]}]}],G0.propDecorators={autocomplete:[{type:xy,args:["matAutocomplete"]}],position:[{type:xy,args:["matAutocompletePosition"]}],connectedTo:[{type:xy,args:["matAutocompleteConnectedTo"]}],autocompleteAttribute:[{type:xy,args:["autocomplete"]}],autocompleteDisabled:[{type:xy,args:["matAutocompleteDisabled"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(G0,[{type:Cy}],(function(){return[{type:hg},{type:pL},{type:eh},{type:a_},{type:Ug},{type:void 0,decorators:[{type:kr,args:[B0]}]},{type:HI,decorators:[{type:Sr}]},{type:AV,decorators:[{type:Sr},{type:kr,args:[RV]},{type:Rr}]},{type:void 0,decorators:[{type:Sr},{type:kr,args:[Z_]}]},{type:uF},{type:void 0,decorators:[{type:Sr},{type:kr,args:[N0]}]}]}),{position:[{type:xy,args:["matAutocompletePosition"]}],autocompleteAttribute:[{type:xy,args:["autocomplete"]}],autocompleteDisabled:[{type:xy,args:["matAutocompleteDisabled"]}],autocomplete:[{type:xy,args:["matAutocomplete"]}],connectedTo:[{type:xy,args:["matAutocompleteConnectedTo"]}]});class W0 extends G0{constructor(){super(...arguments),this._aboveClass="mat-autocomplete-panel-above"}}W0.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(W0)))(n||W0)}})(),W0.ɵdir=lo({type:W0,selectors:[["input","matAutocomplete",""],["textarea","matAutocomplete",""]],hostAttrs:[1,"mat-autocomplete-trigger"],hostVars:7,hostBindings:function t(e,n){1&e&&Vm("focusin",(function t(){return n._handleFocus()}))("blur",(function t(){return n._onTouched()}))("input",(function t(e){return n._handleInput(e)}))("keydown",(function t(e){return n._handleKeydown(e)})),2&e&&jp("autocomplete",n.autocompleteAttribute)("role",n.autocompleteDisabled?null:"combobox")("aria-autocomplete",n.autocompleteDisabled?null:"list")("aria-activedescendant",n.panelOpen&&n.activeOption?n.activeOption.id:null)("aria-expanded",n.autocompleteDisabled?null:n.panelOpen.toString())("aria-owns",n.autocompleteDisabled||!n.panelOpen||null==n.autocomplete?null:n.autocomplete.id)("aria-haspopup",!n.autocompleteDisabled)},exportAs:["matAutocompleteTrigger"],features:[pg([U0]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(W0,[{type:Cy,args:[{selector:"input[matAutocomplete], textarea[matAutocomplete]",host:{class:"mat-autocomplete-trigger","[attr.autocomplete]":"autocompleteAttribute","[attr.role]":'autocompleteDisabled ? null : "combobox"',"[attr.aria-autocomplete]":'autocompleteDisabled ? null : "list"',"[attr.aria-activedescendant]":"(panelOpen && activeOption) ? activeOption.id : null","[attr.aria-expanded]":"autocompleteDisabled ? null : panelOpen.toString()","[attr.aria-owns]":"(autocompleteDisabled || !panelOpen) ? null : autocomplete?.id","[attr.aria-haspopup]":"!autocompleteDisabled","(focusin)":"_handleFocus()","(blur)":"_onTouched()","(input)":"_handleInput($event)","(keydown)":"_handleKeydown($event)"},exportAs:"matAutocompleteTrigger",providers:[U0]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class Y0{}Y0.ɵfac=function t(e){return new(e||Y0)},Y0.ɵmod=ao({type:Y0}),Y0.ɵinj=vn({providers:[V0],imports:[[yL,UH,XI,WM],yF,UH,XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Y0,[{type:Ay,args:[{imports:[yL,UH,XI,WM],exports:[H0,W0,L0,yF,UH,XI],declarations:[H0,W0,L0],providers:[V0]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Y0,{declarations:function(){return[H0,W0,L0]},imports:function(){return[yL,UH,XI,WM]},exports:function(){return[H0,W0,L0,yF,UH,XI]}});class q0{constructor(){this.value="",this.placeholder=""}onInputKeyUp(t){"Enter"===t.key&&this.autocompleteTrigger.closePanel()}}function Z0(t,e){if(1&t&&(qi(),Tm(0,"circle",3)),2&t){const t=Ym();du("animation-name","mat-progress-spinner-stroke-rotate-"+t._spinnerAnimationLabel)("stroke-dashoffset",t._getStrokeDashOffset(),"px")("stroke-dasharray",t._getStrokeCircumference(),"px")("stroke-width",t._getCircleStrokeWidth(),"%"),jp("r",t._getCircleRadius())}}function X0(t,e){if(1&t&&(qi(),Tm(0,"circle",3)),2&t){const t=Ym();du("stroke-dashoffset",t._getStrokeDashOffset(),"px")("stroke-dasharray",t._getStrokeCircumference(),"px")("stroke-width",t._getCircleStrokeWidth(),"%"),jp("r",t._getCircleRadius())}}function K0(t,e){if(1&t&&(qi(),Tm(0,"circle",3)),2&t){const t=Ym();du("animation-name","mat-progress-spinner-stroke-rotate-"+t._spinnerAnimationLabel)("stroke-dashoffset",t._getStrokeDashOffset(),"px")("stroke-dasharray",t._getStrokeCircumference(),"px")("stroke-width",t._getCircleStrokeWidth(),"%"),jp("r",t._getCircleRadius())}}function J0(t,e){if(1&t&&(qi(),Tm(0,"circle",3)),2&t){const t=Ym();du("stroke-dashoffset",t._getStrokeDashOffset(),"px")("stroke-dasharray",t._getStrokeCircumference(),"px")("stroke-width",t._getCircleStrokeWidth(),"%"),jp("r",t._getCircleRadius())}}q0.ɵfac=function t(e){return new(e||q0)},q0.ɵcmp=to({type:q0,selectors:[["tb-filter-input"]],viewQuery:function t(e,n){if(1&e&&Qh(W0,5),2&e){let t;Jh(t=tb())&&(n.autocompleteTrigger=t.first)}},inputs:{value:"value",matAutocomplete:"matAutocomplete",placeholder:"placeholder"},decls:2,vars:4,consts:[["svgIcon","search_24px"],["type","text","autocomplete","off",3,"placeholder","matAutocomplete","matAutocompleteDisabled","value","keyup"]],template:function t(e,n){1&e&&(Tm(0,"mat-icon",0),Rm(1,"input",1),Vm("keyup",(function t(e){return n.onInputKeyUp(e)})),Am()),2&e&&(rc(1),Dm("placeholder",n.placeholder)("matAutocomplete",n.matAutocomplete)("matAutocompleteDisabled",!n.matAutocomplete)("value",n.value))},directives:[DW,W0],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   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 .mat-form-field-label[_ngcontent-%COMP%]{top:1.09375em;margin-top:-0.5em}.mat-form-field-appearance-fill.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59374em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:1em 0 1em 0}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   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h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 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.mat-ripple-element[_ngcontent-%COMP%]{display:none}.cdk-visually-hidden[_ngcontent-%COMP%]{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container[_ngcontent-%COMP%], .cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{display:flex;font-size:13px}mat-icon[_ngcontent-%COMP%]{color:#616161;flex:none;margin-right:5px}body.dark-mode[_nghost-%COMP%]   mat-icon[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   mat-icon[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}input[_ngcontent-%COMP%]{background-color:inherit;caret-color:currentColor;color:currentColor;font:inherit;border:none;outline:none;padding:0;flex-grow:1}']}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(q0,[{type:My,args:[{selector:"tb-filter-input",template:'\n    <mat-icon svgIcon="search_24px"></mat-icon>\n\n    \x3c!-- Note: to allow falsy \'matAutocomplete\' values, we need \'matAutocompleteDisabled\'\n    to prevent runtime errors. --\x3e\n    <input\n      type="text"\n      autocomplete="off"\n      [placeholder]="placeholder"\n      [matAutocomplete]="matAutocomplete"\n      [matAutocompleteDisabled]="!matAutocomplete"\n      [value]="value"\n      (keyup)="onInputKeyUp($event)"\n    />\n  ',styleUrls:["filter_input_component.css"]}]}],null,{value:[{type:xy}],matAutocomplete:[{type:xy}],placeholder:[{type:xy}],autocompleteTrigger:[{type:Za,args:[W0]}]});const Q0=".mat-progress-spinner{display:block;position:relative;overflow:hidden}.mat-progress-spinner svg{position:absolute;transform:rotate(-90deg);top:0;left:0;transform-origin:center;overflow:visible}.mat-progress-spinner circle{fill:transparent;transform-origin:center;transition:stroke-dashoffset 225ms linear}._mat-animation-noopable.mat-progress-spinner circle{transition:none;animation:none}.cdk-high-contrast-active .mat-progress-spinner circle{stroke:currentColor;stroke:CanvasText}.mat-progress-spinner.mat-progress-spinner-indeterminate-animation[mode=indeterminate] svg{animation:mat-progress-spinner-linear-rotate 2000ms linear infinite}._mat-animation-noopable.mat-progress-spinner.mat-progress-spinner-indeterminate-animation[mode=indeterminate] svg{transition:none;animation:none}.mat-progress-spinner.mat-progress-spinner-indeterminate-animation[mode=indeterminate] circle{transition-property:stroke;animation-duration:4000ms;animation-timing-function:cubic-bezier(0.35, 0, 0.25, 1);animation-iteration-count:infinite}._mat-animation-noopable.mat-progress-spinner.mat-progress-spinner-indeterminate-animation[mode=indeterminate] circle{transition:none;animation:none}.mat-progress-spinner.mat-progress-spinner-indeterminate-fallback-animation[mode=indeterminate] svg{animation:mat-progress-spinner-stroke-rotate-fallback 10000ms cubic-bezier(0.87, 0.03, 0.33, 1) infinite}._mat-animation-noopable.mat-progress-spinner.mat-progress-spinner-indeterminate-fallback-animation[mode=indeterminate] svg{transition:none;animation:none}.mat-progress-spinner.mat-progress-spinner-indeterminate-fallback-animation[mode=indeterminate] circle{transition-property:stroke}._mat-animation-noopable.mat-progress-spinner.mat-progress-spinner-indeterminate-fallback-animation[mode=indeterminate] circle{transition:none;animation:none}@keyframes mat-progress-spinner-linear-rotate{0%{transform:rotate(0deg)}100%{transform:rotate(360deg)}}@keyframes mat-progress-spinner-stroke-rotate-100{0%{stroke-dashoffset:268.606171575px;transform:rotate(0)}12.5%{stroke-dashoffset:56.5486677px;transform:rotate(0)}12.5001%{stroke-dashoffset:56.5486677px;transform:rotateX(180deg) rotate(72.5deg)}25%{stroke-dashoffset:268.606171575px;transform:rotateX(180deg) rotate(72.5deg)}25.0001%{stroke-dashoffset:268.606171575px;transform:rotate(270deg)}37.5%{stroke-dashoffset:56.5486677px;transform:rotate(270deg)}37.5001%{stroke-dashoffset:56.5486677px;transform:rotateX(180deg) rotate(161.5deg)}50%{stroke-dashoffset:268.606171575px;transform:rotateX(180deg) rotate(161.5deg)}50.0001%{stroke-dashoffset:268.606171575px;transform:rotate(180deg)}62.5%{stroke-dashoffset:56.5486677px;transform:rotate(180deg)}62.5001%{stroke-dashoffset:56.5486677px;transform:rotateX(180deg) rotate(251.5deg)}75%{stroke-dashoffset:268.606171575px;transform:rotateX(180deg) rotate(251.5deg)}75.0001%{stroke-dashoffset:268.606171575px;transform:rotate(90deg)}87.5%{stroke-dashoffset:56.5486677px;transform:rotate(90deg)}87.5001%{stroke-dashoffset:56.5486677px;transform:rotateX(180deg) rotate(341.5deg)}100%{stroke-dashoffset:268.606171575px;transform:rotateX(180deg) rotate(341.5deg)}}@keyframes mat-progress-spinner-stroke-rotate-fallback{0%{transform:rotate(0deg)}25%{transform:rotate(1170deg)}50%{transform:rotate(2340deg)}75%{transform:rotate(3510deg)}100%{transform:rotate(4680deg)}}\n",$0=JI(class{constructor(t){this._elementRef=t}},"primary"),t1=new Ga("mat-progress-spinner-default-options",{providedIn:"root",factory:function e1(){return{diameter:100}}});class n1 extends $0{constructor(t,e,n,o,i){super(t),this._document=n,this._diameter=100,this._value=0,this._fallbackAnimation=!1,this.mode="determinate";const a=n1._diameters;this._spinnerAnimationLabel=this._getSpinnerAnimationLabel(),a.has(n.head)||a.set(n.head,new Set([100])),this._fallbackAnimation=e.EDGE||e.TRIDENT,this._noopAnimations="NoopAnimations"===o&&!!i&&!i._forceAnimations,i&&(i.diameter&&(this.diameter=i.diameter),i.strokeWidth&&(this.strokeWidth=i.strokeWidth))}get diameter(){return this._diameter}set diameter(t){this._diameter=_z(t),this._spinnerAnimationLabel=this._getSpinnerAnimationLabel(),!this._fallbackAnimation&&this._styleRoot&&this._attachStyleNode()}get strokeWidth(){return this._strokeWidth||this.diameter/10}set strokeWidth(t){this._strokeWidth=_z(t)}get value(){return"determinate"===this.mode?this._value:0}set value(t){this._value=Math.max(0,Math.min(100,_z(t)))}ngOnInit(){const t=this._elementRef.nativeElement;this._styleRoot=Hz(t)||this._document.head,this._attachStyleNode(),t.classList.add(`mat-progress-spinner-indeterminate${this._fallbackAnimation?"-fallback":""}-animation`)}_getCircleRadius(){return(this.diameter-10)/2}_getViewBox(){const t=2*this._getCircleRadius()+this.strokeWidth;return`0 0 ${t} ${t}`}_getStrokeCircumference(){return 2*Math.PI*this._getCircleRadius()}_getStrokeDashOffset(){return"determinate"===this.mode?this._getStrokeCircumference()*(100-this._value)/100:this._fallbackAnimation&&"indeterminate"===this.mode?.2*this._getStrokeCircumference():null}_getCircleStrokeWidth(){return this.strokeWidth/this.diameter*100}_attachStyleNode(){const t=this._styleRoot,e=this._diameter,n=n1._diameters;let o=n.get(t);if(!o||!o.has(e)){const i=this._document.createElement("style");i.setAttribute("mat-spinner-animation",this._spinnerAnimationLabel),i.textContent=this._getAnimationText(),t.appendChild(i),o||(o=new Set,n.set(t,o)),o.add(e)}}_getAnimationText(){const t=this._getStrokeCircumference();return"\n @keyframes mat-progress-spinner-stroke-rotate-DIAMETER {\n    0%      { stroke-dashoffset: START_VALUE;  transform: rotate(0); }\n    12.5%   { stroke-dashoffset: END_VALUE;    transform: rotate(0); }\n    12.5001%  { stroke-dashoffset: END_VALUE;    transform: rotateX(180deg) rotate(72.5deg); }\n    25%     { stroke-dashoffset: START_VALUE;  transform: rotateX(180deg) rotate(72.5deg); }\n\n    25.0001%   { stroke-dashoffset: START_VALUE;  transform: rotate(270deg); }\n    37.5%   { stroke-dashoffset: END_VALUE;    transform: rotate(270deg); }\n    37.5001%  { stroke-dashoffset: END_VALUE;    transform: rotateX(180deg) rotate(161.5deg); }\n    50%     { stroke-dashoffset: START_VALUE;  transform: rotateX(180deg) rotate(161.5deg); }\n\n    50.0001%  { stroke-dashoffset: START_VALUE;  transform: rotate(180deg); }\n    62.5%   { stroke-dashoffset: END_VALUE;    transform: rotate(180deg); }\n    62.5001%  { stroke-dashoffset: END_VALUE;    transform: rotateX(180deg) rotate(251.5deg); }\n    75%     { stroke-dashoffset: START_VALUE;  transform: rotateX(180deg) rotate(251.5deg); }\n\n    75.0001%  { stroke-dashoffset: START_VALUE;  transform: rotate(90deg); }\n    87.5%   { stroke-dashoffset: END_VALUE;    transform: rotate(90deg); }\n    87.5001%  { stroke-dashoffset: END_VALUE;    transform: rotateX(180deg) rotate(341.5deg); }\n    100%    { stroke-dashoffset: START_VALUE;  transform: rotateX(180deg) rotate(341.5deg); }\n  }\n".replace(/START_VALUE/g,""+.95*t).replace(/END_VALUE/g,""+.2*t).replace(/DIAMETER/g,`${this._spinnerAnimationLabel}`)}_getSpinnerAnimationLabel(){return this.diameter.toString().replace(".","_")}}n1.ɵfac=function t(e){return new(e||n1)(Sm(hg),Sm(wz),Sm(Z_,8),Sm(VP,8),Sm(t1))},n1.ɵcmp=to({type:n1,selectors:[["mat-progress-spinner"]],hostAttrs:["role","progressbar","tabindex","-1",1,"mat-progress-spinner"],hostVars:10,hostBindings:function 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class i1{}i1.ɵfac=function t(e){return new(e||i1)},i1.ɵmod=ao({type:i1}),i1.ɵinj=vn({imports:[[XI,WM],XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(i1,[{type:Ay,args:[{imports:[XI,WM],exports:[n1,o1,XI],declarations:[n1,o1]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(i1,{declarations:function(){return[n1,o1]},imports:function(){return[XI,WM]},exports:function(){return[n1,o1,XI]}});const a1=["regexStringInput"];function r1(t,e){if(1&t&&(Nm(0),Rm(1,"li",22),ku(2),Am(),zm()),2&t){const t=e.$implicit;rc(1),Dm("title",t.name),rc(1),Su(t.name)}}function s1(t,e){if(1&t&&(Rm(0,"li",23),Rm(1,"em"),ku(2),Ah(3,"number"),Am(),Am()),2&t){const t=Ym().$implicit;rc(2),Du("and ",Th(3,1,t.runs.length-5)," more")}}function l1(t,e){1&t&&(Rm(0,"li",24),Rm(1,"em"),ku(2,"No runs are in the group"),Am(),Am())}const c1=function(t){return{borderColor:t}},d1=function(t){return{backgroundColor:t}};function p1(t,e){if(1&t&&(Rm(0,"ul",16),Rm(1,"li"),Rm(2,"label"),Tm(3,"span",17),Rm(4,"code",18),ku(5),Am(),Am(),Rm(6,"ul"),Qp(7,r1,3,2,"ng-container",19),Ah(8,"slice"),Qp(9,s1,4,3,"li",20),Qp(10,l1,3,0,"li",21),Am(),Am(),Am()),2&t){const t=e.$implicit;Dm("ngStyle",Mh(11,c1,t.color)),rc(3),Dm("ngStyle",Mh(13,d1,t.color)),rc(1),Dm("title",t.groupId),rc(1),Su(t.groupId),rc(2),Dm("ngForOf",zh(8,7,t.runs,0,5)),rc(2),Dm("ngIf",t.runs.length>5),rc(1),Dm("ngIf",0===t.runs.length)}}function m1(t,e){if(1&t&&(Rm(0,"div",14),Qp(1,p1,11,15,"ul",15),Am()),2&t){const t=Ym(2);rc(1),Dm("ngForOf",t.colorRunPairList)}}function u1(t,e){if(1&t&&(Rm(0,"div",25),ku(1," There are no runs matching the regex, "),Rm(2,"code"),ku(3),Am(),ku(4,". 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 [_nghost-%COMP%]   .grouping-preview[_ngcontent-%COMP%]{border:1px solid #555}.match-container[_ngcontent-%COMP%]{align-items:flex-start;display:grid;flex-wrap:wrap;gap:10px;grid-template-columns:repeat(2, minmax(50%, 1fr))}.color-swatch[_ngcontent-%COMP%]{border-radius:50%;box-shadow:0 0 2px #000;display:inline-block;height:15px;width:15px}ul[_ngcontent-%COMP%]{list-style-type:none;padding:0}mat-form-field[_ngcontent-%COMP%]{width:100%}.group[_ngcontent-%COMP%]{border:1px solid #ebebeb;border-radius:3px;margin:0;padding:10px}body.dark-mode[_nghost-%COMP%]   .group[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group[_ngcontent-%COMP%]{border:1px solid #555}.group[_ngcontent-%COMP%]   label[_ngcontent-%COMP%]{border-bottom:1px solid #ebebeb;align-items:center;display:grid;gap:10px;grid-template-columns:max-content auto;padding:5px 0}body.dark-mode[_nghost-%COMP%]   .group[_ngcontent-%COMP%]   label[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group[_ngcontent-%COMP%]   label[_ngcontent-%COMP%]{border-bottom:1px solid #555}.group[_ngcontent-%COMP%]   label[_ngcontent-%COMP%]   .group-id[_ngcontent-%COMP%]{font-size:.95em;overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.group[_ngcontent-%COMP%]   ul[_ngcontent-%COMP%]{font-size:.9em}.group[_ngcontent-%COMP%]   ul[_ngcontent-%COMP%]   li[_ngcontent-%COMP%]{overflow:hidden;text-overflow:ellipsis;white-space:nowrap}.group[_ngcontent-%COMP%]   .more[_ngcontent-%COMP%], .group[_ngcontent-%COMP%]   .no-match[_ngcontent-%COMP%]{color:#616161;margin-top:5px}body.dark-mode[_nghost-%COMP%]   .group[_ngcontent-%COMP%]   .more[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group[_ngcontent-%COMP%]   .more[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}body.dark-mode[_nghost-%COMP%]   .group[_ngcontent-%COMP%]   .no-match[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group[_ngcontent-%COMP%]   .no-match[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(g1,[{type:My,args:[{selector:"regex-edit-dialog-component",templateUrl:"regex_edit_dialog.ng.html",styleUrls:["regex_edit_dialog_component.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:XG},{type:hg}]}),{regexString:[{type:xy}],colorRunPairList:[{type:xy}],onSave:[{type:Oy}],regexInputOnChange:[{type:Oy}],regexStringInput:[{type:Za,args:["regexStringInput",{static:!0}]}]});class h1{constructor(t,e,n){this.store=t,this.dialogRef=e,this.tentativeRegexString$=new I,this.groupByRegexString$=Qt((()=>re(this.store.select(MN).pipe(be(1)),this.tentativeRegexString$))).pipe(Ne("")),this.colorRunPairList$=Qt((()=>this.groupByRegexString$.pipe(ge(500),ce((t=>{try{const e=new RegExp(t);return Boolean(e)}catch(t){return!1}})),fe(this.allRuns$,this.runIdToEid$,this.store.select(AN),this.store.select(JD)),It((([t,e,n,o,i])=>{const a=nN({key:tN.REGEX,regexString:t},e,n),r=new Map,s=[];for(const[t,e]of Object.entries(a.matches)){let n=r.get(t);if(!n){const e=o.colors[r.size%o.colors.length];n=i?e.darkHex:e.lightHex,r.set(t,n)}s.push({groupId:t,color:n,runs:e})}return s}))))).pipe(Ne([])),this.experimentIds=n.experimentIds,this.runIdToEid$=Wt(this.experimentIds.map((t=>this.store.select(cN,{experimentId:t}).pipe(It((e=>({experimentId:t,runIds:e}))))))).pipe(It((t=>{const e={};for(const{runIds:n,experimentId:o}of t)for(const t of n)e[t]=o;return e}))),this.allRuns$=Wt(this.experimentIds.map((t=>this.store.select(lN,{experimentId:t})))).pipe(It((t=>t.flat())))}onRegexInputOnChange(t){this.tentativeRegexString$.next(t)}onSave(t){this.store.dispatch(GR({experimentIds:this.experimentIds,groupBy:{key:tN.REGEX,regexString:t}}))}}function b1(t,e){1&t&&Tm(0,"mat-icon",11)}function y1(t,e){if(1&t){const t=Hm();Rm(0,"button",10),Vm("click",(function e(){hi(t);const n=Ym();return n.onGroupByChange.emit({key:n.GroupByKey.EXPERIMENT})})),Rm(1,"span"),Qp(2,b1,1,0,"mat-icon",7),Am(),Rm(3,"label"),ku(4,"Experiment"),Am(),Am()}if(2&t){const t=Ym();jp("aria-checked",t.selectedGroupBy.key===t.GroupByKey.EXPERIMENT),rc(2),Dm("ngIf",t.selectedGroupBy.key===t.GroupByKey.EXPERIMENT)}}function _1(t,e){1&t&&Tm(0,"mat-icon",11)}function C1(t,e){1&t&&Tm(0,"mat-icon",11)}function M1(t,e){if(1&t){const t=Hm();Rm(0,"button",12),Vm("click",(function e(){return hi(t),Ym().onGroupByRegexClick()})),Rm(1,"span"),Qp(2,C1,1,0,"mat-icon",7),Am(),Rm(3,"label"),ku(4,"Regex"),Am(),Am()}if(2&t){const t=Ym();jp("aria-checked",t.selectedGroupBy.key===t.GroupByKey.REGEX),rc(2),Dm("ngIf",t.selectedGroupBy.key===t.GroupByKey.REGEX)}}function v1(t,e){if(1&t&&(Rm(0,"label"),ku(1),Am()),2&t){const t=Ym(2);rc(1),Su(t.regexString)}}function x1(t,e){1&t&&(Rm(0,"label",17),ku(1,"(none set)"),Am())}function O1(t,e){if(1&t){const t=Hm();Rm(0,"button",13),Vm("click",(function e(){return hi(t),Ym().onRegexStringEdit()})),Rm(1,"span"),Tm(2,"mat-icon",14),Am(),Qp(3,v1,2,1,"label",15),Qp(4,x1,2,0,"label",16),Am()}if(2&t){const t=Ym();rc(3),Dm("ngIf",t.regexString),rc(1),Dm("ngIf",!t.regexString)}}h1.ɵfac=function t(e){return new(e||h1)(Sm(Iw),Sm(XG),Sm(JG))},h1.ɵcmp=to({type:h1,selectors:[["regex-edit-dialog"]],decls:3,vars:6,consts:[[3,"regexString","colorRunPairList","onSave","regexInputOnChange"]],template:function t(e,n){1&e&&(Rm(0,"regex-edit-dialog-component",0),Vm("onSave",(function t(e){return n.onSave(e)}))("regexInputOnChange",(function t(e){return n.onRegexInputOnChange(e)})),Ah(1,"async"),Ah(2,"async"),Am()),2&e&&Dm("regexString",Th(1,2,n.groupByRegexString$))("colorRunPairList",Th(2,4,n.colorRunPairList$))},directives:[g1],pipes:[wM],styles:["[_nghost-%COMP%], regex-edit-dialog-component[_ngcontent-%COMP%] {\n        display: block;\n        height: 100%;\n        width: 100%;\n      }"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(h1,[{type:My,args:[{selector:"regex-edit-dialog",template:'<regex-edit-dialog-component\n    [regexString]="groupByRegexString$ | async"\n    [colorRunPairList]="colorRunPairList$ | async"\n    (onSave)="onSave($event)"\n    (regexInputOnChange)="onRegexInputOnChange($event)"\n  ></regex-edit-dialog-component>',styles:["\n      :host,\n      regex-edit-dialog-component {\n        display: block;\n        height: 100%;\n        width: 100%;\n      }\n    "]}]}],(function(){return[{type:Iw},{type:XG},{type:void 0,decorators:[{type:kr,args:[JG]}]}]}),null);class P1{constructor(t){this.dialog=t,this.GroupByKey=tN,this.onGroupByChange=new Lh}onRegexStringEdit(){this.dialog.open(h1,{maxHeight:"95vh",maxWidth:"80vw",data:{experimentIds:this.experimentIds}})}onGroupByRegexClick(){this.regexString?this.onGroupByChange.emit({key:tN.REGEX,regexString:this.regexString}):this.onRegexStringEdit()}}P1.ɵfac=function t(e){return new(e||P1)(Sm(oW))},P1.ɵcmp=to({type:P1,selectors:[["runs-group-menu-button-component"]],inputs:{showExperimentsGroupBy:"showExperimentsGroupBy",experimentIds:"experimentIds",regexString:"regexString",selectedGroupBy:"selectedGroupBy",showGroupByRegex:"showGroupByRegex"},outputs:{onGroupByChange:"onGroupByChange"},decls:14,vars:6,consts:[["mat-icon-button","","title","Color runs by...",3,"matMenuTriggerFor"],["svgIcon","palette_24px"],[1,"run-table-color-group-by"],["groupByMenu","matMenu"],[1,"label"],["mat-menu-item","","role","menuitemradio","data-value","experiment",3,"click",4,"ngIf"],["mat-menu-item","","role","menuitemradio","data-value","run",3,"click"],["svgIcon","done_24px",4,"ngIf"],["mat-menu-item","","role","menuitemradio","data-value","regex",3,"click",4,"ngIf"],["mat-menu-item","","role","menuitem","data-value","regex-edit","class","display-regex-string",3,"click",4,"ngIf"],["mat-menu-item","","role","menuitemradio","data-value","experiment",3,"click"],["svgIcon","done_24px"],["mat-menu-item","","role","menuitemradio","data-value","regex",3,"click"],["mat-menu-item","","role","menuitem","data-value","regex-edit",1,"display-regex-string",3,"click"],["svgIcon","edit_24px"],[4,"ngIf"],["class","none-set-string",4,"ngIf"],[1,"none-set-string"]],template:function t(e,n){1&e&&(Rm(0,"button",0),Tm(1,"mat-icon",1),Am(),Rm(2,"mat-menu",2,3),Rm(4,"div",4),ku(5,"Color runs by"),Am(),Qp(6,y1,5,2,"button",5),Rm(7,"button",6),Vm("click",(function t(){return n.onGroupByChange.emit({key:n.GroupByKey.RUN})})),Rm(8,"span"),Qp(9,_1,1,0,"mat-icon",7),Am(),Rm(10,"label"),ku(11,"Run"),Am(),Am(),Qp(12,M1,5,2,"button",8),Qp(13,O1,5,2,"button",9),Am()),2&e&&(Dm("matMenuTriggerFor",$p(3)),rc(6),Dm("ngIf",n.showExperimentsGroupBy),rc(1),jp("aria-checked",n.selectedGroupBy.key===n.GroupByKey.RUN),rc(2),Dm("ngIf",n.selectedGroupBy.key===n.GroupByKey.RUN),rc(3),Dm("ngIf",n.showGroupByRegex),rc(1),Dm("ngIf",n.showGroupByRegex))},directives:[XH,eY,DW,KW,dM,WW],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   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scale(0.75)}}.mat-form-field-appearance-fill[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:.25em 0 .75em 0}.mat-form-field-appearance-fill[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{top:1.09375em;margin-top:-0.5em}.mat-form-field-appearance-fill.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59374em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:1em 0 1em 0}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{top:1.84375em;margin-top:-0.25em}.mat-form-field-appearance-outline.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59374em) 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sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   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.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}  .run-table-color-group-by{font-size:16px}  .run-table-color-group-by .label{color:#616161;font-size:.9em;margin:10px 0;padding:0 16px;pointer-events:none}  .run-table-color-group-by button{display:grid;gap:2px 10px;grid-template-columns:20px auto}  .run-table-color-group-by mat-icon{height:20px;width:20px}  .run-table-color-group-by .display-regex-string{padding-left:40px}  .run-table-color-group-by .display-regex-string .none-set-string{color:#616161}body.dark-mode[_nghost-%COMP%]     .run-table-color-group-by .display-regex-string .none-set-string, body.dark-mode   [_nghost-%COMP%]     .run-table-color-group-by .display-regex-string .none-set-string{color:rgba(255,255,255,.7)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(P1,[{type:My,args:[{selector:"runs-group-menu-button-component",templateUrl:"runs_group_menu_button_component.ng.html",styleUrls:["runs_group_menu_button_component.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:oW}]}),{showExperimentsGroupBy:[{type:xy}],experimentIds:[{type:xy}],regexString:[{type:xy}],selectedGroupBy:[{type:xy}],showGroupByRegex:[{type:xy}],onGroupByChange:[{type:Oy}]});class w1{constructor(t){this.store=t,this.showGroupByRegex$=this.store.select(nE),this.showExperimentsGroupBy$=this.store.select(ES).pipe(It((t=>t.has(Zk.COMPARE_EXPERIMENT)))),this.selectedGroupBy$=this.store.select(fN),this.groupByRegexString$=this.store.select(MN)}onGroupByChange(t){this.store.dispatch(GR({experimentIds:this.experimentIds,groupBy:t}))}}w1.ɵfac=function t(e){return new(e||w1)(Sm(Iw))},w1.ɵcmp=to({type:w1,selectors:[["runs-group-menu-button"]],inputs:{experimentIds:"experimentIds"},decls:5,vars:13,consts:[[3,"regexString","selectedGroupBy","showGroupByRegex","showExperimentsGroupBy","experimentIds","onGroupByChange"]],template:function t(e,n){1&e&&(Rm(0,"runs-group-menu-button-component",0),Vm("onGroupByChange",(function t(e){return n.onGroupByChange(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Am()),2&e&&Dm("regexString",Th(1,5,n.groupByRegexString$))("selectedGroupBy",Th(2,7,n.selectedGroupBy$))("showGroupByRegex",Th(3,9,n.showGroupByRegex$))("showExperimentsGroupBy",Th(4,11,n.showExperimentsGroupBy$))("experimentIds",n.experimentIds)},directives:[P1],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(w1,[{type:My,args:[{selector:"runs-group-menu-button",template:'\n    <runs-group-menu-button-component\n      [regexString]="groupByRegexString$ | async"\n      [selectedGroupBy]="selectedGroupBy$ | async"\n      [showGroupByRegex]="showGroupByRegex$ | async"\n      [showExperimentsGroupBy]="showExperimentsGroupBy$ | async"\n      [experimentIds]="experimentIds"\n      (onGroupByChange)="onGroupByChange($event)"\n    ></runs-group-menu-button-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{experimentIds:[{type:xy}]});const k1=["container"];var S1;!(function(t){t[t.NONE=0]="NONE",t[t.LEFT=1]="LEFT",t[t.RIGHT=2]="RIGHT"})(S1||(S1={}));class D1{constructor(t){this.changeDetector=t,this.tickCount=20,this.value=new Lh,this.Position=S1,this.activeThumb=S1.NONE,this.offsetXFromOriginOfActiveThumb=0,this.ngUnsubscribe=new I}getThumbPosition(t){const e=this.getClippedValue(t),n=this.max-this.min;return n<=0?"50%":(e-this.min)/n*100+"%"}getTrackWidth(){const t=this.max-this.min;return t<=0?"0%":(this.getClippedValue(this.upperValue)-this.getClippedValue(this.lowerValue))/t*100+"%"}getClippedValue(t){return Math.min(Math.max(t,this.min),this.max)}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}ngOnInit(){oe(document,"mousemove",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{this.handleMouseMove(t)})),oe(document,"mouseup",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{this.handleMouseOut(t)}))}handleMouseDown(t,e){this.activeThumb=e,this.offsetXFromOriginOfActiveThumb=6-t.offsetX}calculateValueFromMouseEvent(t){const{left:e,right:n}=this.container.nativeElement.getBoundingClientRect(),o=t.clientX-e+this.offsetXFromOriginOfActiveThumb;let i;if(null!==this.tickCount&&this.tickCount>0){const t=(n-e)/this.tickCount;i=Math.round(o/t)*t/(n-e)}else i=o/(n-e);const a=this.getClippedValue(this.min+(this.max-this.min)*i);return Number(a.toFixed(10))}handleMouseMove(t){if(this.activeThumb===S1.NONE)return;const e=this.calculateValueFromMouseEvent(t);let n=[this.lowerValue,this.upperValue];this.activeThumb===S1.LEFT?(e>this.upperValue&&(this.activeThumb=S1.RIGHT),n=[e,this.upperValue]):(e<this.lowerValue&&(this.activeThumb=S1.LEFT),n=[this.lowerValue,e]),this.maybeNotifyNextValue(n),this.changeDetector.markForCheck()}maybeNotifyNextValue(t){const[e,n]=t.sort(((t,e)=>t-e));this.lowerValue===e&&this.upperValue===n||this.value.emit({lowerValue:e,upperValue:n})}handleMouseOut(t){this.activeThumb!==S1.NONE&&(this.activeThumb=S1.NONE,this.changeDetector.markForCheck())}handleInputChange(t,e){const n=this.getClippedValue(Number(t.target.value));if(isNaN(n))return;let o=[this.lowerValue,this.upperValue];o=e===S1.LEFT?[n,this.upperValue]:[this.lowerValue,n],this.maybeNotifyNextValue(o)}isThumbActive(t){return this.activeThumb===t}}D1.ɵfac=function t(e){return new(e||D1)(Sm(Ug))},D1.ɵcmp=to({type:D1,selectors:[["tb-range-input"]],viewQuery:function t(e,n){if(1&e&&Qh(k1,7,hg),2&e){let t;Jh(t=tb())&&(n.container=t.first)}},inputs:{min:"min",max:"max",lowerValue:"lowerValue",upperValue:"upperValue",tickCount:"tickCount"},outputs:{value:"value"},decls:8,vars:14,consts:[["type","number",1,"lower-input",3,"value","change"],["type","number",1,"upper-input",3,"value","change"],[1,"container"],["container",""],[1,"slider-track"],[1,"slider-track-fill"],[1,"thumb",3,"mousedown"]],template:function t(e,n){1&e&&(Rm(0,"input",0),Vm("change",(function t(e){return n.handleInputChange(e,n.Position.LEFT)})),Am(),Rm(1,"input",1),Vm("change",(function t(e){return n.handleInputChange(e,n.Position.RIGHT)})),Am(),Rm(2,"span",2,3),Tm(4,"span",4),Tm(5,"span",5),Rm(6,"span",6),Vm("mousedown",(function t(e){return n.handleMouseDown(e,n.Position.LEFT)})),Am(),Rm(7,"span",6),Vm("mousedown",(function t(e){return n.handleMouseDown(e,n.Position.RIGHT)})),Am(),Am()),2&e&&(Dm("value",n.lowerValue),rc(1),Dm("value",n.upperValue),rc(4),du("left",n.getThumbPosition(n.lowerValue))("width",n.getTrackWidth()),rc(1),du("left",n.getThumbPosition(n.lowerValue)),pu("active",n.isThumbActive(n.Position.LEFT)),rc(1),du("left",n.getThumbPosition(n.upperValue)),pu("active",n.isThumbActive(n.Position.RIGHT)))},styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], 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g3{constructor(){this.active=null}setActive(t){this.active&&this.active!==t&&"inline"!==this.active.cpDialogDisplay&&this.active.closeDialog(),this.active=t}hsva2hsla(t){const e=t.h,n=t.s,o=t.v,i=t.a;if(0===o)return new c3(e,0,0,i);if(0===n&&1===o)return new c3(e,1,1,i);{const t=o*(2-n)/2;return new c3(e,o*n/(1-Math.abs(2*t-1)),t,i)}}hsla2hsva(t){const e=Math.min(t.h,1),n=Math.min(t.s,1),o=Math.min(t.l,1),i=Math.min(t.a,1);if(0===o)return new l3(e,0,0,i);{const t=o+n*(1-Math.abs(2*o-1))/2;return new l3(e,2*(t-o)/t,t,i)}}hsvaToRgba(t){let e,n,o;const i=t.h,a=t.s,r=t.v,s=t.a,l=Math.floor(6*i),c=6*i-l,d=r*(1-a),p=r*(1-c*a),m=r*(1-(1-c)*a);switch(l%6){case 0:e=r,n=m,o=d;break;case 1:e=p,n=r,o=d;break;case 2:e=d,n=r,o=m;break;case 3:e=d,n=p,o=r;break;case 4:e=m,n=d,o=r;break;case 5:e=r,n=d,o=p;break;default:e=0,n=0,o=0}return new s3(e,n,o,s)}cmykToRgb(t){return new s3((1-t.c)*(1-t.k),(1-t.m)*(1-t.k),(1-t.y)*(1-t.k),t.a)}rgbaToCmyk(t){const e=1-Math.max(t.r,t.g,t.b);return 1===e?new d3(0,0,0,1,t.a):new d3((1-t.r-e)/(1-e),(1-t.g-e)/(1-e),(1-t.b-e)/(1-e),e,t.a)}rgbaToHsva(t){let e,n;const o=Math.min(t.r,1),i=Math.min(t.g,1),a=Math.min(t.b,1),r=Math.min(t.a,1),s=Math.max(o,i,a),l=Math.min(o,i,a),c=s,d=s-l;if(n=0===s?0:d/s,s===l)e=0;else{switch(s){case o:e=(i-a)/d+(i<a?6:0);break;case i:e=(a-o)/d+2;break;case a:e=(o-i)/d+4;break;default:e=0}e/=6}return new l3(e,n,c,r)}rgbaToHex(t,e){let n="#"+(1<<24|t.r<<16|t.g<<8|t.b).toString(16).substr(1);return e&&(n+=(256|Math.round(255*t.a)).toString(16).substr(1)),n}normalizeCMYK(t){return new d3(t.c/100,t.m/100,t.y/100,t.k/100,t.a)}denormalizeCMYK(t){return new d3(Math.floor(100*t.c),Math.floor(100*t.m),Math.floor(100*t.y),Math.floor(100*t.k),t.a)}denormalizeRGBA(t){return new s3(Math.round(255*t.r),Math.round(255*t.g),Math.round(255*t.b),t.a)}stringToHsva(t="",e=!1){let n=null;t=(t||"").toLowerCase();const o=[{re:/(rgb)a?\(\s*(\d{1,3})\s*,\s*(\d{1,3})\s*%?,\s*(\d{1,3})\s*%?(?:,\s*(\d+(?:\.\d+)?)\s*)?\)/,parse:function(t){return new s3(parseInt(t[2],10)/255,parseInt(t[3],10)/255,parseInt(t[4],10)/255,isNaN(parseFloat(t[5]))?1:parseFloat(t[5]))}},{re:/(hsl)a?\(\s*(\d{1,3})\s*,\s*(\d{1,3})%\s*,\s*(\d{1,3})%\s*(?:,\s*(\d+(?:\.\d+)?)\s*)?\)/,parse:function(t){return new c3(parseInt(t[2],10)/360,parseInt(t[3],10)/100,parseInt(t[4],10)/100,isNaN(parseFloat(t[5]))?1:parseFloat(t[5]))}}];o.push(e?{re:/#([a-fA-F0-9]{2})([a-fA-F0-9]{2})([a-fA-F0-9]{2})([a-fA-F0-9]{2})?$/,parse:function(t){return new s3(parseInt(t[1],16)/255,parseInt(t[2],16)/255,parseInt(t[3],16)/255,parseInt(t[4]||"FF",16)/255)}}:{re:/#([a-fA-F0-9]{2})([a-fA-F0-9]{2})([a-fA-F0-9]{2})$/,parse:function(t){return new s3(parseInt(t[1],16)/255,parseInt(t[2],16)/255,parseInt(t[3],16)/255,1)}}),o.push({re:/#([a-fA-F0-9])([a-fA-F0-9])([a-fA-F0-9])$/,parse:function(t){return new s3(parseInt(t[1]+t[1],16)/255,parseInt(t[2]+t[2],16)/255,parseInt(t[3]+t[3],16)/255,1)}});for(const e in o)if(o.hasOwnProperty(e)){const i=o[e],a=i.re.exec(t),r=a&&i.parse(a);if(r)return r instanceof s3?n=this.rgbaToHsva(r):r instanceof c3&&(n=this.hsla2hsva(r)),n}return n}outputFormat(t,e,n){switch("auto"===e&&(e=t.a<1?"rgba":"hex"),e){case"hsla":const e=this.hsva2hsla(t),o=new c3(Math.round(360*e.h),Math.round(100*e.s),Math.round(100*e.l),Math.round(100*e.a)/100);return t.a<1||"always"===n?"hsla("+o.h+","+o.s+"%,"+o.l+"%,"+o.a+")":"hsl("+o.h+","+o.s+"%,"+o.l+"%)";case"rgba":const i=this.denormalizeRGBA(this.hsvaToRgba(t));return t.a<1||"always"===n?"rgba("+i.r+","+i.g+","+i.b+","+Math.round(100*i.a)/100+")":"rgb("+i.r+","+i.g+","+i.b+")";default:const a="always"===n||"forced"===n;return this.rgbaToHex(this.denormalizeRGBA(this.hsvaToRgba(t)),a)}}}g3.ɵfac=function t(e){return new(e||g3)},g3.ɵprov=Mn({token:g3,factory:g3.ɵfac}),g3.ctorParameters=()=>[],("undefined"==typeof ngDevMode||ngDevMode)&&hh(g3,[{type:im}],(function(){return[]}),null);class h3{constructor(t,e,n){this.elRef=t,this.cdRef=e,this.service=n,this.isIE10=!1,this.dialogArrowSize=10,this.dialogArrowOffset=15,this.dialogInputFields=[r3.HEX,r3.RGBA,r3.HSLA,r3.CMYK],this.useRootViewContainer=!1}handleEsc(t){this.show&&"popup"===this.cpDialogDisplay&&this.onCancelColor(t)}handleEnter(t){this.show&&"popup"===this.cpDialogDisplay&&this.onAcceptColor(t)}ngOnInit(){this.slider=new u3(0,0,0,0),this.sliderDimMax=new f3(this.hueSlider.nativeElement.offsetWidth||140,this.cpWidth,130,this.alphaSlider.nativeElement.offsetWidth||140),this.format=this.cpCmykEnabled?r3.CMYK:"rgba"===this.cpOutputFormat?r3.RGBA:"hsla"===this.cpOutputFormat?r3.HSLA:r3.HEX,this.listenerMouseDown=t=>{this.onMouseDown(t)},this.listenerResize=()=>{this.onResize()},this.openDialog(this.initialColor,!1)}ngOnDestroy(){this.closeDialog()}ngAfterViewInit(){230===this.cpWidth&&"inline"!==this.cpDialogDisplay||(this.sliderDimMax=new f3(this.hueSlider.nativeElement.offsetWidth||140,this.cpWidth,130,this.alphaSlider.nativeElement.offsetWidth||140),this.updateColorPicker(!1),this.cdRef.detectChanges())}openDialog(t,e=!0){this.service.setActive(this),this.width||(this.cpWidth=this.directiveElementRef.nativeElement.offsetWidth),this.height||(this.height=320),this.setInitialColor(t),this.setColorFromString(t,e),this.openColorPicker()}closeDialog(){this.closeColorPicker()}setupDialog(t,e,n,o,i,a,r,s,l,c,d,p,m,u,f,g,h,b,y,_,C,M,v,x,O,P,w,k,S,D,E,R,A,T,N,z){this.setInitialColor(n),this.setColorMode(s),this.isIE10=10===(function I(){let t="";"undefined"!=typeof navigator&&(t=navigator.userAgent.toLowerCase());const e=t.indexOf("msie ");return e>0&&parseInt(t.substring(e+5,t.indexOf(".",e)),10)})(),this.directiveInstance=t,this.directiveElementRef=e,this.cpDisableInput=p,this.cpCmykEnabled=l,this.cpAlphaChannel=c,this.cpOutputFormat=d,this.cpDialogDisplay=a,this.cpIgnoredElements=m,this.cpSaveClickOutside=u,this.cpCloseClickOutside=f,this.useRootViewContainer=g,this.width=this.cpWidth=parseInt(o,10),this.height=this.cpHeight=parseInt(i,10),this.cpPosition=h,this.cpPositionOffset=parseInt(b,10),this.cpOKButton=P,this.cpOKButtonText=k,this.cpOKButtonClass=w,this.cpCancelButton=S,this.cpCancelButtonText=E,this.cpCancelButtonClass=D,this.fallbackColor=r||"#fff",this.setPresetConfig(_,C),this.cpPresetColorsClass=M,this.cpMaxPresetColorsLength=v,this.cpPresetEmptyMessage=x,this.cpPresetEmptyMessageClass=O,this.cpAddColorButton=R,this.cpAddColorButtonText=T,this.cpAddColorButtonClass=A,this.cpRemoveColorButtonClass=N,this.cpTriggerElement=z,y||(this.dialogArrowOffset=0),"inline"===a&&(this.dialogArrowSize=0,this.dialogArrowOffset=0),"hex"===d&&"always"!==c&&"forced"!==c&&(this.cpAlphaChannel="disabled")}setColorMode(t){switch(t.toString().toUpperCase()){case"1":case"C":case"COLOR":this.cpColorMode=1;break;case"2":case"G":case"GRAYSCALE":this.cpColorMode=2;break;case"3":case"P":case"PRESETS":this.cpColorMode=3;break;default:this.cpColorMode=1}}setInitialColor(t){this.initialColor=t}setPresetConfig(t,e){this.cpPresetLabel=t,this.cpPresetColors=e}setColorFromString(t,e=!0,n=!0){let o;"always"===this.cpAlphaChannel||"forced"===this.cpAlphaChannel?(o=this.service.stringToHsva(t,!0),o||this.hsva||(o=this.service.stringToHsva(t,!1))):o=this.service.stringToHsva(t,!1),o||this.hsva||(o=this.service.stringToHsva(this.fallbackColor,!1)),o&&(this.hsva=o,this.sliderH=this.hsva.h,"hex"===this.cpOutputFormat&&"disabled"===this.cpAlphaChannel&&(this.hsva.a=1),this.updateColorPicker(e,n))}onResize(){"fixed"===this.position?this.setDialogPosition():"inline"!==this.cpDialogDisplay&&this.closeColorPicker()}onDragEnd(t){this.directiveInstance.sliderDragEnd({slider:t,color:this.outputColor})}onDragStart(t){this.directiveInstance.sliderDragStart({slider:t,color:this.outputColor})}onMouseDown(t){!this.show||this.isIE10||"popup"!==this.cpDialogDisplay||t.target===this.directiveElementRef.nativeElement||this.isDescendant(this.elRef.nativeElement,t.target)||this.isDescendant(this.directiveElementRef.nativeElement,t.target)||0!==this.cpIgnoredElements.filter((e=>e===t.target)).length||(this.cpSaveClickOutside?this.directiveInstance.colorSelected(this.outputColor):(this.hsva=null,this.setColorFromString(this.initialColor,!1),this.cpCmykEnabled&&this.directiveInstance.cmykChanged(this.cmykColor),this.directiveInstance.colorChanged(this.initialColor),this.directiveInstance.colorCanceled()),this.cpCloseClickOutside&&this.closeColorPicker())}onAcceptColor(t){t.stopPropagation(),this.outputColor&&this.directiveInstance.colorSelected(this.outputColor),"popup"===this.cpDialogDisplay&&this.closeColorPicker()}onCancelColor(t){this.hsva=null,t.stopPropagation(),this.directiveInstance.colorCanceled(),this.setColorFromString(this.initialColor,!0),"popup"===this.cpDialogDisplay&&(this.cpCmykEnabled&&this.directiveInstance.cmykChanged(this.cmykColor),this.directiveInstance.colorChanged(this.initialColor,!0),this.closeColorPicker())}onFormatToggle(t){const e=this.dialogInputFields.length-(this.cpCmykEnabled?0:1),n=((this.dialogInputFields.indexOf(this.format)+t)%e+e)%e;this.format=this.dialogInputFields[n]}onColorChange(t){this.hsva.s=t.s/t.rgX,this.hsva.v=t.v/t.rgY,this.updateColorPicker(),this.directiveInstance.sliderChanged({slider:"lightness",value:this.hsva.v,color:this.outputColor}),this.directiveInstance.sliderChanged({slider:"saturation",value:this.hsva.s,color:this.outputColor})}onHueChange(t){this.hsva.h=t.v/t.rgX,this.sliderH=this.hsva.h,this.updateColorPicker(),this.directiveInstance.sliderChanged({slider:"hue",value:this.hsva.h,color:this.outputColor})}onValueChange(t){this.hsva.v=t.v/t.rgX,this.updateColorPicker(),this.directiveInstance.sliderChanged({slider:"value",value:this.hsva.v,color:this.outputColor})}onAlphaChange(t){this.hsva.a=t.v/t.rgX,this.updateColorPicker(),this.directiveInstance.sliderChanged({slider:"alpha",value:this.hsva.a,color:this.outputColor})}onHexInput(t){if(null===t)this.updateColorPicker();else{t&&"#"!==t[0]&&(t="#"+t);let e=/^#([a-f0-9]{3}|[a-f0-9]{6})$/gi;"always"===this.cpAlphaChannel&&(e=/^#([a-f0-9]{3}|[a-f0-9]{6}|[a-f0-9]{8})$/gi);const n=e.test(t);n&&(t.length<5&&(t="#"+t.substring(1).split("").map((t=>t+t)).join("")),"forced"===this.cpAlphaChannel&&(t+=Math.round(255*this.hsva.a).toString(16)),this.setColorFromString(t,!0,!1)),this.directiveInstance.inputChanged({input:"hex",valid:n,value:t,color:this.outputColor})}}onRedInput(t){const e=this.service.hsvaToRgba(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.r=t.v/t.rg,this.hsva=this.service.rgbaToHsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"red",valid:n,value:e.r,color:this.outputColor})}onBlueInput(t){const e=this.service.hsvaToRgba(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.b=t.v/t.rg,this.hsva=this.service.rgbaToHsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"blue",valid:n,value:e.b,color:this.outputColor})}onGreenInput(t){const e=this.service.hsvaToRgba(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.g=t.v/t.rg,this.hsva=this.service.rgbaToHsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"green",valid:n,value:e.g,color:this.outputColor})}onHueInput(t){const e=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;e&&(this.hsva.h=t.v/t.rg,this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"hue",valid:e,value:this.hsva.h,color:this.outputColor})}onValueInput(t){const e=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;e&&(this.hsva.v=t.v/t.rg,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"value",valid:e,value:this.hsva.v,color:this.outputColor})}onAlphaInput(t){const e=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;e&&(this.hsva.a=t.v/t.rg,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"alpha",valid:e,value:this.hsva.a,color:this.outputColor})}onLightnessInput(t){const e=this.service.hsva2hsla(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.l=t.v/t.rg,this.hsva=this.service.hsla2hsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"lightness",valid:n,value:e.l,color:this.outputColor})}onSaturationInput(t){const e=this.service.hsva2hsla(this.hsva),n=!isNaN(t.v)&&t.v>=0&&t.v<=t.rg;n&&(e.s=t.v/t.rg,this.hsva=this.service.hsla2hsva(e),this.sliderH=this.hsva.h,this.updateColorPicker()),this.directiveInstance.inputChanged({input:"saturation",valid:n,value:e.s,color:this.outputColor})}onCyanInput(t){!isNaN(t.v)&&t.v>=0&&t.v<=t.rg&&(this.cmyk.c=t.v,this.updateColorPicker(!1,!0,!0)),this.directiveInstance.inputChanged({input:"cyan",valid:!0,value:this.cmyk.c,color:this.outputColor})}onMagentaInput(t){!isNaN(t.v)&&t.v>=0&&t.v<=t.rg&&(this.cmyk.m=t.v,this.updateColorPicker(!1,!0,!0)),this.directiveInstance.inputChanged({input:"magenta",valid:!0,value:this.cmyk.m,color:this.outputColor})}onYellowInput(t){!isNaN(t.v)&&t.v>=0&&t.v<=t.rg&&(this.cmyk.y=t.v,this.updateColorPicker(!1,!0,!0)),this.directiveInstance.inputChanged({input:"yellow",valid:!0,value:this.cmyk.y,color:this.outputColor})}onBlackInput(t){!isNaN(t.v)&&t.v>=0&&t.v<=t.rg&&(this.cmyk.k=t.v,this.updateColorPicker(!1,!0,!0)),this.directiveInstance.inputChanged({input:"black",valid:!0,value:this.cmyk.k,color:this.outputColor})}onAddPresetColor(t,e){t.stopPropagation(),this.cpPresetColors.filter((t=>t===e)).length||(this.cpPresetColors=this.cpPresetColors.concat(e),this.directiveInstance.presetColorsChanged(this.cpPresetColors))}onRemovePresetColor(t,e){t.stopPropagation(),this.cpPresetColors=this.cpPresetColors.filter((t=>t!==e)),this.directiveInstance.presetColorsChanged(this.cpPresetColors)}openColorPicker(){this.show||(this.show=!0,this.hidden=!0,setTimeout((()=>{this.hidden=!1,this.setDialogPosition(),this.cdRef.detectChanges()}),0),this.directiveInstance.stateChanged(!0),this.isIE10||(document.addEventListener("mousedown",this.listenerMouseDown),document.addEventListener("touchstart",this.listenerMouseDown)),window.addEventListener("resize",this.listenerResize))}closeColorPicker(){this.show&&(this.show=!1,this.directiveInstance.stateChanged(!1),this.isIE10||(document.removeEventListener("mousedown",this.listenerMouseDown),document.removeEventListener("touchstart",this.listenerMouseDown)),window.removeEventListener("resize",this.listenerResize),this.cdRef.destroyed||this.cdRef.detectChanges())}updateColorPicker(t=!0,e=!0,n=!1){if(this.sliderDimMax){let o,i,a;2===this.cpColorMode&&(this.hsva.s=0);const r=this.outputColor;if(i=this.service.hsva2hsla(this.hsva),this.cpCmykEnabled?(n?(a=this.service.cmykToRgb(this.service.normalizeCMYK(this.cmyk)),this.hsva=this.service.rgbaToHsva(a)):(a=this.service.hsvaToRgba(this.hsva),this.cmyk=this.service.denormalizeCMYK(this.service.rgbaToCmyk(a))),a=this.service.denormalizeRGBA(a),this.sliderH=this.hsva.h):a=this.service.denormalizeRGBA(this.service.hsvaToRgba(this.hsva)),o=this.service.denormalizeRGBA(this.service.hsvaToRgba(new l3(this.sliderH||this.hsva.h,1,1,1))),e&&(this.hslaText=new c3(Math.round(360*i.h),Math.round(100*i.s),Math.round(100*i.l),Math.round(100*i.a)/100),this.rgbaText=new s3(a.r,a.g,a.b,Math.round(100*a.a)/100),this.cpCmykEnabled&&(this.cmykText=new d3(this.cmyk.c,this.cmyk.m,this.cmyk.y,this.cmyk.k,Math.round(100*this.cmyk.a)/100)),this.hexText=this.service.rgbaToHex(a,"always"===this.cpAlphaChannel),this.hexAlpha=this.rgbaText.a),"auto"===this.cpOutputFormat&&this.format!==r3.RGBA&&this.format!==r3.CMYK&&this.hsva.a<1&&(this.format=this.hsva.a<1?r3.RGBA:r3.HEX),this.hueSliderColor="rgb("+o.r+","+o.g+","+o.b+")",this.alphaSliderColor="rgb("+a.r+","+a.g+","+a.b+")",this.outputColor=this.service.outputFormat(this.hsva,this.cpOutputFormat,this.cpAlphaChannel),this.selectedColor=this.service.outputFormat(this.hsva,"rgba",null),this.format!==r3.CMYK)this.cmykColor="";else if("always"===this.cpAlphaChannel||"enabled"===this.cpAlphaChannel||"forced"===this.cpAlphaChannel){const t=Math.round(100*this.cmyk.a)/100;this.cmykColor=`cmyka(${this.cmyk.c},${this.cmyk.m},${this.cmyk.y},${this.cmyk.k},${t})`}else this.cmykColor=`cmyk(${this.cmyk.c},${this.cmyk.m},${this.cmyk.y},${this.cmyk.k})`;this.slider=new u3((this.sliderH||this.hsva.h)*this.sliderDimMax.h-8,this.hsva.s*this.sliderDimMax.s-8,(1-this.hsva.v)*this.sliderDimMax.v-8,this.hsva.a*this.sliderDimMax.a-8),t&&r!==this.outputColor&&(this.cpCmykEnabled&&this.directiveInstance.cmykChanged(this.cmykColor),this.directiveInstance.colorChanged(this.outputColor))}}setDialogPosition(){if("inline"===this.cpDialogDisplay)this.position="relative";else{let t,e="static",n="",o=null,i=null,a=this.directiveElementRef.nativeElement.parentNode;const r=this.dialogElement.nativeElement.offsetHeight;for(;null!==a&&"HTML"!==a.tagName;){if(t=window.getComputedStyle(a),e=t.getPropertyValue("position"),n=t.getPropertyValue("transform"),"static"!==e&&null===o&&(o=a),n&&"none"!==n&&null===i&&(i=a),"fixed"===e){o=i;break}a=a.parentNode}const s=this.createDialogBox(this.directiveElementRef.nativeElement,"fixed"!==e);if(this.useRootViewContainer||"fixed"===e&&(!o||o instanceof HTMLUnknownElement))this.top=s.top,this.left=s.left;else{null===o&&(o=a);const t=this.createDialogBox(o,"fixed"!==e);this.top=s.top-t.top,this.left=s.left-t.left}"fixed"===e&&(this.position="fixed");let l=this.cpPosition;"auto"===this.cpPosition&&(l=(function c(t,e){let n="right",o="bottom";const{height:i,width:a}=t,{top:r,left:s}=e,l=r+e.height,c=s+e.width,d=r-i<0,p=l+i>(window.innerHeight||document.documentElement.clientHeight),m=s-a<0,u=c+a>(window.innerWidth||document.documentElement.clientWidth);return p&&(o="top"),d&&(o="bottom"),m&&(n="right"),u&&(n="left"),d&&p&&m&&u?["left","right","top","bottom"].reduce(((e,n)=>t[e]>t[n]?e:n)):m&&u?d?"bottom":p||r>l?"top":"bottom":d&&p?m?"right":u||s>c?"left":"right":`${o}-${n}`})(this.dialogElement.nativeElement.getBoundingClientRect(),this.cpTriggerElement.nativeElement.getBoundingClientRect())),"top"===l?(this.arrowTop=r-1,this.top-=r+this.dialogArrowSize,this.left+=this.cpPositionOffset/100*s.width-this.dialogArrowOffset):"bottom"===l?(this.top+=s.height+this.dialogArrowSize,this.left+=this.cpPositionOffset/100*s.width-this.dialogArrowOffset):"top-left"===l||"left-top"===l?(this.top-=r-s.height+s.height*this.cpPositionOffset/100,this.left-=this.cpWidth+this.dialogArrowSize-2-this.dialogArrowOffset):"top-right"===l||"right-top"===l?(this.top-=r-s.height+s.height*this.cpPositionOffset/100,this.left+=s.width+this.dialogArrowSize-2-this.dialogArrowOffset):"left"===l||"bottom-left"===l||"left-bottom"===l?(this.top+=s.height*this.cpPositionOffset/100-this.dialogArrowOffset,this.left-=this.cpWidth+this.dialogArrowSize-2):(this.top+=s.height*this.cpPositionOffset/100-this.dialogArrowOffset,this.left+=s.width+this.dialogArrowSize-2),this.cpUsePosition=l}}isDescendant(t,e){let 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");background-size:100% 100%;border:none;cursor:pointer;direction:ltr;height:130px;touch-action:manipulation;width:100%}.color-picker .cp-add-color-button-class{background:transparent;border:0;cursor:pointer;display:inline;margin:3px -3px;padding:0;position:absolute}.color-picker .cp-add-color-button-class:hover{text-decoration:underline}.color-picker .cp-add-color-button-class:disabled{color:#999;cursor:not-allowed}.color-picker .cp-add-color-button-class:disabled:hover{text-decoration:none}.color-picker .cp-remove-color-button-class{background:#fff;border-radius:50%;box-shadow:1px 1px 5px #333;cursor:pointer;display:block;height:10px;position:absolute;right:-5px;text-align:center;top:-5px;width:10px}.color-picker .cp-remove-color-button-class:before{bottom:3.5px;content:"x";display:inline-block;font-size:10px;position:relative}'],encapsulation:2}),h3.ctorParameters=()=>[{type:hg},{type:Ug},{type:g3}],h3.propDecorators={dialogElement:[{type:Za,args:["dialogPopup",{static:!0}]}],hueSlider:[{type:Za,args:["hueSlider",{static:!0}]}],alphaSlider:[{type:Za,args:["alphaSlider",{static:!0}]}],handleEsc:[{type:wy,args:["document:keyup.esc",["$event"]]}],handleEnter:[{type:wy,args:["document:keyup.enter",["$event"]]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(h3,[{type:My,args:[{selector:"color-picker",template:'<div #dialogPopup class="color-picker" [class.open]="show" [style.display]="!show ? \'none\' : \'block\'" [style.visibility]="hidden ? \'hidden\' : \'visible\'" [style.top.px]="top" [style.left.px]="left" [style.position]="position" [style.height.px]="cpHeight" [style.width.px]="cpWidth" (click)="$event.stopPropagation()">\n  <div *ngIf="cpDialogDisplay==\'popup\'" class="arrow arrow-{{cpUsePosition}}" [style.top.px]="arrowTop"></div>\n\n  <div *ngIf="(cpColorMode || 1) === 1" class="saturation-lightness" [slider] [rgX]="1" [rgY]="1" [style.background-color]="hueSliderColor" (newValue)="onColorChange($event)" (dragStart)="onDragStart(\'saturation-lightness\')" (dragEnd)="onDragEnd(\'saturation-lightness\')">\n    <div class="cursor" [style.top.px]="slider?.v" [style.left.px]="slider?.s"></div>\n  </div>\n\n  <div class="hue-alpha box">\n    <div class="left">\n      <div class="selected-color-background"></div>\n\n      <div class="selected-color" [style.background-color]="selectedColor"></div>\n\n      <button *ngIf="cpAddColorButton" type="button" class="{{cpAddColorButtonClass}}" [disabled]="cpPresetColors && cpPresetColors.length >= cpMaxPresetColorsLength" (click)="onAddPresetColor($event, selectedColor)">\n        {{cpAddColorButtonText}}\n      </button>\n    </div>\n\n    <div class="right">\n      <div *ngIf="cpAlphaChannel===\'disabled\'" style="height: 16px;"></div>\n\n      <div #hueSlider class="hue" [slider] [rgX]="1" [style.display]="(cpColorMode || 1) === 1 ? \'block\' : \'none\'" (newValue)="onHueChange($event)" (dragStart)="onDragStart(\'hue\')" (dragEnd)="onDragEnd(\'hue\')">\n        <div class="cursor" [style.left.px]="slider?.h"></div>\n      </div>\n\n      <div #valueSlider class="value" [slider] [rgX]="1" [style.display]="(cpColorMode || 1) === 2 ? \'block\': \'none\'" (newValue)="onValueChange($event)" (dragStart)="onDragStart(\'value\')" (dragEnd)="onDragEnd(\'value\')">\n        <div class="cursor" [style.right.px]="slider?.v"></div>\n      </div>\n\n      <div #alphaSlider class="alpha" [slider] [rgX]="1" [style.display]="cpAlphaChannel === \'disabled\' ? \'none\' : \'block\'" [style.background-color]="alphaSliderColor" (newValue)="onAlphaChange($event)" (dragStart)="onDragStart(\'alpha\')" (dragEnd)="onDragEnd(\'alpha\')">\n        <div class="cursor" [style.left.px]="slider?.a"></div>\n      </div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1" class="cmyk-text" [style.display]="format !== 3 ? \'none\' : \'block\'">\n    <div class="box">\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="cmykText?.c" (keyup.enter)="onAcceptColor($event)" (newValue)="onCyanInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="cmykText?.m" (keyup.enter)="onAcceptColor($event)" (newValue)="onMagentaInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="cmykText?.y" (keyup.enter)="onAcceptColor($event)" (newValue)="onYellowInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="cmykText?.k" (keyup.enter)="onAcceptColor($event)" (newValue)="onBlackInput($event)" />\n      <input *ngIf="cpAlphaChannel!==\'disabled\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1" [text] [rg]="1" [value]="cmykText?.a" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)" />\n    </div>\n\n     <div class="box">\n      <div>C</div><div>M</div><div>Y</div><div>K</div><div *ngIf="cpAlphaChannel!==\'disabled\'" >A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1 " class="hsla-text" [style.display]="format !== 2 ? \'none\' : \'block\'">\n    <div class="box">\n      <input type="number" pattern="[0-9]*" min="0" max="360" [text] [rg]="360" [value]="hslaText?.h" (keyup.enter)="onAcceptColor($event)" (newValue)="onHueInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="hslaText?.s" (keyup.enter)="onAcceptColor($event)" (newValue)="onSaturationInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="hslaText?.l" (keyup.enter)="onAcceptColor($event)" (newValue)="onLightnessInput($event)" />\n      <input *ngIf="cpAlphaChannel!==\'disabled\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1" [text] [rg]="1" [value]="hslaText?.a" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)" />\n    </div>\n\n    <div class="box">\n      <div>H</div><div>S</div><div>L</div><div *ngIf="cpAlphaChannel!==\'disabled\'">A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1 " [style.display]="format !== 1 ? \'none\' : \'block\'" class="rgba-text">\n    <div class="box">\n      <input type="number" pattern="[0-9]*" min="0" max="255" [text] [rg]="255" [value]="rgbaText?.r" (keyup.enter)="onAcceptColor($event)" (newValue)="onRedInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="255" [text] [rg]="255" [value]="rgbaText?.g" (keyup.enter)="onAcceptColor($event)" (newValue)="onGreenInput($event)" />\n      <input type="number" pattern="[0-9]*" min="0" max="255" [text] [rg]="255" [value]="rgbaText?.b" (keyup.enter)="onAcceptColor($event)" (newValue)="onBlueInput($event)" />\n      <input *ngIf="cpAlphaChannel!==\'disabled\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1" [text] [rg]="1" [value]="rgbaText?.a" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)" />\n    </div>\n\n    <div class="box">\n      <div>R</div><div>G</div><div>B</div><div *ngIf="cpAlphaChannel!==\'disabled\'" >A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1" class="hex-text" [class.hex-alpha]="cpAlphaChannel===\'forced\'"\n    [style.display]="format !== 0 ? \'none\' : \'block\'">\n    <div class="box">\n      <input [text] [value]="hexText" (blur)="onHexInput(null)" (keyup.enter)="onAcceptColor($event)" (newValue)="onHexInput($event)"/>\n      <input *ngIf="cpAlphaChannel===\'forced\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1" [text] [rg]="1" [value]="hexAlpha" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)"/>\n    </div>\n\n    <div class="box">\n      <div>Hex</div>\n      <div *ngIf="cpAlphaChannel===\'forced\'">A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 2" class="value-text">\n    <div class="box">\n      <input type="number" pattern="[0-9]*" min="0" max="100" [text] [rg]="100" [value]="hslaText?.l" (keyup.enter)="onAcceptColor($event)" (newValue)="onValueInput($event)" />\n      <input *ngIf="cpAlphaChannel!==\'disabled\'" type="number" pattern="[0-9]+([\\.,][0-9]{1,2})?" min="0" max="1" step="0.1"  [text] [rg]="1" [value]="hslaText?.a" (keyup.enter)="onAcceptColor($event)" (newValue)="onAlphaInput($event)" />\n    </div>\n\n    <div class="box">\n      <div>V</div><div>A</div>\n    </div>\n  </div>\n\n  <div *ngIf="!cpDisableInput && (cpColorMode || 1) === 1" class="type-policy">\n    <span class="type-policy-arrow" (click)="onFormatToggle(-1)"></span>\n    <span class="type-policy-arrow" (click)="onFormatToggle(1)"></span>\n  </div>\n\n  <div *ngIf="cpPresetColors?.length || cpAddColorButton" class="preset-area">\n    <hr>\n\n    <div class="preset-label">{{cpPresetLabel}}</div>\n\n    <div *ngIf="cpPresetColors?.length" class="{{cpPresetColorsClass}}">\n      <div *ngFor="let color of cpPresetColors" class="preset-color" [style.backgroundColor]="color" (click)="setColorFromString(color)">\n        <span *ngIf="cpAddColorButton" class="{{cpRemoveColorButtonClass}}" (click)="onRemovePresetColor($event, color)"></span>\n      </div>\n    </div>\n\n    <div *ngIf="!cpPresetColors?.length && cpAddColorButton" class="{{cpPresetEmptyMessageClass}}">{{cpPresetEmptyMessage}}</div>\n  </div>\n\n  <div *ngIf="cpOKButton || cpCancelButton" class="button-area">\n    <button *ngIf="cpCancelButton" type="button" class="{{cpCancelButtonClass}}" (click)="onCancelColor($event)">{{cpCancelButtonText}}</button>\n\n    <button *ngIf="cpOKButton" type="button" class="{{cpOKButtonClass}}" (click)="onAcceptColor($event)">{{cpOKButtonText}}</button>\n  </div>\n</div>\n',encapsulation:Hn.None,styles:['.color-picker{-moz-user-select:none;-ms-user-select:none;-webkit-user-select:none;background-color:#fff;border:1px solid #777;cursor:default;height:auto;position:absolute;user-select:none;width:230px;z-index:1000}.color-picker *{box-sizing:border-box;font-size:11px;margin:0}.color-picker input{color:#000;font-size:13px;height:26px;min-width:0;text-align:center;width:0}.color-picker input:-moz-submit-invalid,.color-picker input:-moz-ui-invalid,.color-picker input:invalid{box-shadow:none}.color-picker input::-webkit-inner-spin-button,.color-picker input::-webkit-outer-spin-button{-webkit-appearance:none;margin:0}.color-picker .arrow{border-style:solid;height:0;position:absolute;width:0;z-index:999999}.color-picker .arrow.arrow-top{border-color:#777 transparent transparent;border-width:10px 5px;left:8px}.color-picker .arrow.arrow-bottom{border-color:transparent transparent #777;border-width:10px 5px;left:8px;top:-20px}.color-picker .arrow.arrow-left-top,.color-picker .arrow.arrow-top-left{border-color:transparent transparent transparent #777;border-width:5px 10px;bottom:8px;right:-21px}.color-picker .arrow.arrow-right-top,.color-picker .arrow.arrow-top-right{border-color:transparent #777 transparent transparent;border-width:5px 10px;bottom:8px;left:-20px}.color-picker .arrow.arrow-bottom-left,.color-picker .arrow.arrow-left,.color-picker .arrow.arrow-left-bottom{border-color:transparent transparent transparent #777;border-width:5px 10px;right:-21px;top:8px}.color-picker .arrow.arrow-bottom-right,.color-picker .arrow.arrow-right,.color-picker .arrow.arrow-right-bottom{border-color:transparent #777 transparent transparent;border-width:5px 10px;left:-20px;top:8px}.color-picker .cursor{border:2px solid #222;border-radius:50%;cursor:default;height:16px;position:relative;width:16px}.color-picker .box{display:flex;padding:4px 8px}.color-picker .left{padding:16px 8px;position:relative}.color-picker .right{flex:1 1 auto;padding:12px 8px}.color-picker .button-area{padding:0 16px 16px;text-align:right}.color-picker .button-area button{margin-left:8px}.color-picker .preset-area{padding:4px 15px}.color-picker .preset-area .preset-label{color:#555;font-size:11px;overflow:hidden;padding:4px;text-align:left;text-overflow:ellipsis;white-space:nowrap;width:100%}.color-picker .preset-area .preset-color{border:1px solid #a9a9a9;border-radius:25%;cursor:pointer;display:inline-block;height:18px;margin:4px 6px 8px;position:relative;width:18px}.color-picker .preset-area .preset-empty-message{font-style:italic;margin-bottom:8px;margin-top:4px;min-height:18px;text-align:center}.color-picker .hex-text{font-size:11px;padding:4px 8px;width:100%}.color-picker .hex-text .box{padding:0 24px 8px 8px}.color-picker .hex-text .box div{clear:left;color:#555;flex:1 1 auto;float:left;text-align:center}.color-picker .hex-text .box input{border:1px solid #a9a9a9;flex:1 1 auto;padding:1px}.color-picker .hex-alpha .box div:first-child,.color-picker .hex-alpha .box input:first-child{flex-grow:3;margin-right:8px}.color-picker .cmyk-text,.color-picker .hsla-text,.color-picker .rgba-text,.color-picker .value-text{font-size:11px;padding:4px 8px;width:100%}.color-picker .cmyk-text .box,.color-picker .hsla-text .box,.color-picker .rgba-text .box{padding:0 24px 8px 8px}.color-picker .value-text .box{padding:0 8px 8px}.color-picker .cmyk-text .box div,.color-picker .hsla-text .box div,.color-picker .rgba-text .box div,.color-picker .value-text .box div{color:#555;flex:1 1 auto;margin-right:8px;text-align:center}.color-picker .cmyk-text .box div:last-child,.color-picker .hsla-text .box div:last-child,.color-picker .rgba-text .box div:last-child,.color-picker .value-text .box div:last-child{margin-right:0}.color-picker .cmyk-text .box input,.color-picker .hsla-text .box input,.color-picker .rgba-text .box input,.color-picker .value-text .box input{border:1px solid #a9a9a9;flex:1;float:left;margin:0 8px 0 0;padding:1px}.color-picker .cmyk-text .box input:last-child,.color-picker .hsla-text .box input:last-child,.color-picker .rgba-text .box input:last-child,.color-picker .value-text .box input:last-child{margin-right:0}.color-picker .hue-alpha{align-items:center;margin-bottom:3px}.color-picker .hue{background-image:url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAJYAAAAQCAYAAAD06IYnAAAABmJLR0QA/wD/AP+gvaeTAAAACXBIWXMAAAsTAAALEwEAmpwYAAAAB3RJTUUH4AIWDwkUFWbCCAAAAFxJREFUaN7t0kEKg0AQAME2x83/n2qu5qCgD1iDhCoYdpnbQC9bbY1qVO/jvc6k3ad91s7/7F1/csgPrujuQ17BDYSFsBAWwgJhISyEBcJCWAgLhIWwEBYIi2f7Ar/1TCgFH2X9AAAAAElFTkSuQmCC");direction:ltr}.color-picker .hue,.color-picker .value{background-size:100% 100%;border:none;cursor:pointer;height:16px;margin-bottom:16px;width:100%}.color-picker .value{background-image:url("data:image/png;base64,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");direction:rtl}.color-picker .alpha{background-image:url("data:image/png;base64,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");background-size:100% 100%;border:none;cursor:pointer;direction:ltr;height:16px;width:100%}.color-picker .type-policy{background-image:url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABIAAAAgCAYAAAAffCjxAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAACewAAAnsB01CO3AAAABl0RVh0U29mdHdhcmUAd3d3Lmlua3NjYXBlLm9yZ5vuPBoAAAIASURBVEiJ7ZY9axRRFIafsxMStrLQJpAgpBFhi+C9w1YSo00I6RZ/g9vZpBf/QOr4GyRgkSKNSrAadsZqQGwCkuAWyRZJsySwvhZ7N/vhzrgbLH3Ld8597jlzz50zJokyxXH8DqDVar0qi6v8BbItqSGpEcfxdlmsFWXkvX8AfAVWg3UKPEnT9GKujMzsAFgZsVaCN1VTQd77XUnrgE1kv+6935268WRpzrnHZvYRWC7YvC3pRZZl3wozqtVqiyH9IgjAspkd1Gq1xUJQtVrdB9ZKIAOthdg/Qc65LUk7wNIMoCVJO865rYFhkqjX6/d7vV4GPJwBMqofURS5JEk6FYBer/eeYb/Mo9WwFnPOvQbeAvfuAAK4BN4sAJtAG/gJIElmNuiJyba3EGNmZiPeZuEVmVell/Y/6N+CzDn3AXhEOOo7Hv/3BeAz8IzQkMPnJbuPx1wC+yYJ7/0nYIP5S/0FHKdp+rwCEEXRS/rf5Hl1Gtb2M0iSpCOpCZzPATmX1EySpHMLAsiy7MjMDoHrGSDXZnaYZdnRwBh7J91utwmczAA6CbG3GgPleX4jqUH/a1CktqRGnuc3hSCAMB32gKspkCtgb3KCQMmkjeP4WNJThrNNZval1WptTIsv7JtQ4tmIdRa8qSoEpWl6YWZNoAN0zKxZNPehpLSBZv2t+Q0CJ9lLnARQLAAAAABJRU5ErkJggg==");background-position:50%;background-repeat:no-repeat;background-size:8px 16px;height:24px;position:absolute;right:12px;top:218px;width:16px}.color-picker .type-policy .type-policy-arrow{display:block;height:50%;width:100%}.color-picker .selected-color{border:1px solid #a9a9a9;border-radius:50%;height:40px;left:8px;position:absolute;top:16px;width:40px}.color-picker .selected-color-background{background-image:url("data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACgAAAAoCAYAAACM/rhtAAAAh0lEQVRYR+2W0QlAMQgD60zdfwOdqa8TmI/wQMr5K0I5bZLIzLOa2nt37VVVbd+dDx5obgCC3KBLwJ2ff4PnVidkf+ucIhw80HQaCLo3DMH3CRK3iFsmAWVl6hPNDwt8EvNE5q+YuEXcMgkonVM6SdyCoEvAnZ8v1Hjx817MilmxSUB5rdLJDycZgUAZUch/AAAAAElFTkSuQmCC");border-radius:50%;height:40px;width:40px}.color-picker 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");background-size:100% 100%;border:none;cursor:pointer;direction:ltr;height:130px;touch-action:manipulation;width:100%}.color-picker .cp-add-color-button-class{background:transparent;border:0;cursor:pointer;display:inline;margin:3px -3px;padding:0;position:absolute}.color-picker .cp-add-color-button-class:hover{text-decoration:underline}.color-picker .cp-add-color-button-class:disabled{color:#999;cursor:not-allowed}.color-picker .cp-add-color-button-class:disabled:hover{text-decoration:none}.color-picker .cp-remove-color-button-class{background:#fff;border-radius:50%;box-shadow:1px 1px 5px #333;cursor:pointer;display:block;height:10px;position:absolute;right:-5px;text-align:center;top:-5px;width:10px}.color-picker .cp-remove-color-button-class:before{bottom:3.5px;content:"x";display:inline-block;font-size:10px;position:relative}']}]}],(function(){return[{type:hg},{type:Ug},{type:g3}]}),{handleEsc:[{type:wy,args:["document:keyup.esc",["$event"]]}],handleEnter:[{type:wy,args:["document:keyup.enter",["$event"]]}],dialogElement:[{type:Za,args:["dialogPopup",{static:!0}]}],hueSlider:[{type:Za,args:["hueSlider",{static:!0}]}],alphaSlider:[{type:Za,args:["alphaSlider",{static:!0}]}]});class b3{constructor(t,e,n,o,i,a){this.injector=t,this.cfr=e,this.appRef=n,this.vcRef=o,this.elRef=i,this._service=a,this.dialogCreated=!1,this.ignoreChanges=!1,this.viewAttachedToAppRef=!1,this.cpWidth="230px",this.cpHeight="auto",this.cpToggle=!1,this.cpDisabled=!1,this.cpIgnoredElements=[],this.cpFallbackColor="",this.cpColorMode="color",this.cpCmykEnabled=!1,this.cpOutputFormat="auto",this.cpAlphaChannel="enabled",this.cpDisableInput=!1,this.cpDialogDisplay="popup",this.cpSaveClickOutside=!0,this.cpCloseClickOutside=!0,this.cpUseRootViewContainer=!1,this.cpPosition="auto",this.cpPositionOffset="0%",this.cpPositionRelativeToArrow=!1,this.cpOKButton=!1,this.cpOKButtonText="OK",this.cpOKButtonClass="cp-ok-button-class",this.cpCancelButton=!1,this.cpCancelButtonText="Cancel",this.cpCancelButtonClass="cp-cancel-button-class",this.cpPresetLabel="Preset colors",this.cpPresetColorsClass="cp-preset-colors-class",this.cpMaxPresetColorsLength=6,this.cpPresetEmptyMessage="No colors added",this.cpPresetEmptyMessageClass="preset-empty-message",this.cpAddColorButton=!1,this.cpAddColorButtonText="Add color",this.cpAddColorButtonClass="cp-add-color-button-class",this.cpRemoveColorButtonClass="cp-remove-color-button-class",this.cpInputChange=new Lh(!0),this.cpToggleChange=new Lh(!0),this.cpSliderChange=new Lh(!0),this.cpSliderDragEnd=new Lh(!0),this.cpSliderDragStart=new Lh(!0),this.colorPickerOpen=new Lh(!0),this.colorPickerClose=new Lh(!0),this.colorPickerCancel=new Lh(!0),this.colorPickerSelect=new Lh(!0),this.colorPickerChange=new Lh(!1),this.cpCmykColorChange=new Lh(!0),this.cpPresetColorsChange=new Lh(!0)}handleClick(){this.inputFocus()}handleFocus(){this.inputFocus()}handleInput(t){this.inputChange(t)}ngOnDestroy(){null!=this.cmpRef&&(this.viewAttachedToAppRef&&this.appRef.detachView(this.cmpRef.hostView),this.cmpRef.destroy(),this.cmpRef=null,this.dialog=null)}ngOnChanges(t){t.cpToggle&&!this.cpDisabled&&(t.cpToggle.currentValue?this.openDialog():t.cpToggle.currentValue||this.closeDialog()),t.colorPicker&&(this.dialog&&!this.ignoreChanges&&("inline"===this.cpDialogDisplay&&this.dialog.setInitialColor(t.colorPicker.currentValue),this.dialog.setColorFromString(t.colorPicker.currentValue,!1),this.cpUseRootViewContainer&&"inline"!==this.cpDialogDisplay&&this.cmpRef.changeDetectorRef.detectChanges()),this.ignoreChanges=!1),(t.cpPresetLabel||t.cpPresetColors)&&this.dialog&&this.dialog.setPresetConfig(this.cpPresetLabel,this.cpPresetColors)}openDialog(){if(this.dialogCreated)this.dialog&&this.dialog.openDialog(this.colorPicker);else{let t=this.vcRef;if(this.dialogCreated=!0,this.viewAttachedToAppRef=!1,this.cpUseRootViewContainer&&"inline"!==this.cpDialogDisplay){const e=this.injector.get(this.appRef.componentTypes[0],rp.NULL);e!==rp.NULL?(t=e.vcRef||e.viewContainerRef||this.vcRef,t===this.vcRef&&console.warn("You are using cpUseRootViewContainer, but the root component is not exposing viewContainerRef!Please expose it by adding 'public vcRef: ViewContainerRef' to the constructor.")):this.viewAttachedToAppRef=!0}const e=this.cfr.resolveComponentFactory(h3);if(this.viewAttachedToAppRef)this.cmpRef=e.create(this.injector),this.appRef.attachView(this.cmpRef.hostView),document.body.appendChild(this.cmpRef.hostView.rootNodes[0]);else{const n=wm.fromResolvedProviders([],t.parentInjector);this.cmpRef=t.createComponent(e,0,n,[])}this.cmpRef.instance.setupDialog(this,this.elRef,this.colorPicker,this.cpWidth,this.cpHeight,this.cpDialogDisplay,this.cpFallbackColor,this.cpColorMode,this.cpCmykEnabled,this.cpAlphaChannel,this.cpOutputFormat,this.cpDisableInput,this.cpIgnoredElements,this.cpSaveClickOutside,this.cpCloseClickOutside,this.cpUseRootViewContainer,this.cpPosition,this.cpPositionOffset,this.cpPositionRelativeToArrow,this.cpPresetLabel,this.cpPresetColors,this.cpPresetColorsClass,this.cpMaxPresetColorsLength,this.cpPresetEmptyMessage,this.cpPresetEmptyMessageClass,this.cpOKButton,this.cpOKButtonClass,this.cpOKButtonText,this.cpCancelButton,this.cpCancelButtonClass,this.cpCancelButtonText,this.cpAddColorButton,this.cpAddColorButtonClass,this.cpAddColorButtonText,this.cpRemoveColorButtonClass,this.elRef),this.dialog=this.cmpRef.instance,this.vcRef!==t&&this.cmpRef.changeDetectorRef.detectChanges()}}closeDialog(){this.dialog&&"popup"===this.cpDialogDisplay&&this.dialog.closeDialog()}cmykChanged(t){this.cpCmykColorChange.emit(t)}stateChanged(t){this.cpToggleChange.emit(t),t?this.colorPickerOpen.emit(this.colorPicker):this.colorPickerClose.emit(this.colorPicker)}colorChanged(t,e=!0){this.ignoreChanges=e,this.colorPickerChange.emit(t)}colorSelected(t){this.colorPickerSelect.emit(t)}colorCanceled(){this.colorPickerCancel.emit()}inputFocus(){const t=this.elRef.nativeElement,e=this.cpIgnoredElements.filter((e=>e===t));this.cpDisabled||e.length||("undefined"!=typeof document&&t===document.activeElement?this.openDialog():this.dialog&&this.dialog.show?this.closeDialog():this.openDialog())}inputChange(t){this.dialog?this.dialog.setColorFromString(t.target.value,!0):(this.colorPicker=t.target.value,this.colorPickerChange.emit(this.colorPicker))}inputChanged(t){this.cpInputChange.emit(t)}sliderChanged(t){this.cpSliderChange.emit(t)}sliderDragEnd(t){this.cpSliderDragEnd.emit(t)}sliderDragStart(t){this.cpSliderDragStart.emit(t)}presetColorsChanged(t){this.cpPresetColorsChange.emit(t)}}b3.ɵfac=function t(e){return new(e||b3)(Sm(rp),Sm(ug),Sm(O_),Sm(eh),Sm(hg),Sm(g3))},b3.ɵdir=lo({type:b3,selectors:[["","colorPicker",""]],hostBindings:function t(e,n){1&e&&Vm("click",(function t(){return n.handleClick()}))("focus",(function t(){return n.handleFocus()}))("input",(function t(e){return n.handleInput(e)}))},inputs:{cpWidth:"cpWidth",cpHeight:"cpHeight",cpToggle:"cpToggle",cpDisabled:"cpDisabled",cpIgnoredElements:"cpIgnoredElements",cpFallbackColor:"cpFallbackColor",cpColorMode:"cpColorMode",cpCmykEnabled:"cpCmykEnabled",cpOutputFormat:"cpOutputFormat",cpAlphaChannel:"cpAlphaChannel",cpDisableInput:"cpDisableInput",cpDialogDisplay:"cpDialogDisplay",cpSaveClickOutside:"cpSaveClickOutside",cpCloseClickOutside:"cpCloseClickOutside",cpUseRootViewContainer:"cpUseRootViewContainer",cpPosition:"cpPosition",cpPositionOffset:"cpPositionOffset",cpPositionRelativeToArrow:"cpPositionRelativeToArrow",cpOKButton:"cpOKButton",cpOKButtonText:"cpOKButtonText",cpOKButtonClass:"cpOKButtonClass",cpCancelButton:"cpCancelButton",cpCancelButtonText:"cpCancelButtonText",cpCancelButtonClass:"cpCancelButtonClass",cpPresetLabel:"cpPresetLabel",cpPresetColorsClass:"cpPresetColorsClass",cpMaxPresetColorsLength:"cpMaxPresetColorsLength",cpPresetEmptyMessage:"cpPresetEmptyMessage",cpPresetEmptyMessageClass:"cpPresetEmptyMessageClass",cpAddColorButton:"cpAddColorButton",cpAddColorButtonText:"cpAddColorButtonText",cpAddColorButtonClass:"cpAddColorButtonClass",cpRemoveColorButtonClass:"cpRemoveColorButtonClass",colorPicker:"colorPicker",cpPresetColors:"cpPresetColors"},outputs:{cpInputChange:"cpInputChange",cpToggleChange:"cpToggleChange",cpSliderChange:"cpSliderChange",cpSliderDragEnd:"cpSliderDragEnd",cpSliderDragStart:"cpSliderDragStart",colorPickerOpen:"colorPickerOpen",colorPickerClose:"colorPickerClose",colorPickerCancel:"colorPickerCancel",colorPickerSelect:"colorPickerSelect",colorPickerChange:"colorPickerChange",cpCmykColorChange:"cpCmykColorChange",cpPresetColorsChange:"cpPresetColorsChange"},exportAs:["ngxColorPicker"],features:[Bo]}),b3.ctorParameters=()=>[{type:rp},{type:ug},{type:O_},{type:eh},{type:hg},{type:g3}],b3.propDecorators={colorPicker:[{type:xy}],cpWidth:[{type:xy}],cpHeight:[{type:xy}],cpToggle:[{type:xy}],cpDisabled:[{type:xy}],cpIgnoredElements:[{type:xy}],cpFallbackColor:[{type:xy}],cpColorMode:[{type:xy}],cpCmykEnabled:[{type:xy}],cpOutputFormat:[{type:xy}],cpAlphaChannel:[{type:xy}],cpDisableInput:[{type:xy}],cpDialogDisplay:[{type:xy}],cpSaveClickOutside:[{type:xy}],cpCloseClickOutside:[{type:xy}],cpUseRootViewContainer:[{type:xy}],cpPosition:[{type:xy}],cpPositionOffset:[{type:xy}],cpPositionRelativeToArrow:[{type:xy}],cpOKButton:[{type:xy}],cpOKButtonText:[{type:xy}],cpOKButtonClass:[{type:xy}],cpCancelButton:[{type:xy}],cpCancelButtonText:[{type:xy}],cpCancelButtonClass:[{type:xy}],cpPresetLabel:[{type:xy}],cpPresetColors:[{type:xy}],cpPresetColorsClass:[{type:xy}],cpMaxPresetColorsLength:[{type:xy}],cpPresetEmptyMessage:[{type:xy}],cpPresetEmptyMessageClass:[{type:xy}],cpAddColorButton:[{type:xy}],cpAddColorButtonText:[{type:xy}],cpAddColorButtonClass:[{type:xy}],cpRemoveColorButtonClass:[{type:xy}],cpInputChange:[{type:Oy}],cpToggleChange:[{type:Oy}],cpSliderChange:[{type:Oy}],cpSliderDragEnd:[{type:Oy}],cpSliderDragStart:[{type:Oy}],colorPickerOpen:[{type:Oy}],colorPickerClose:[{type:Oy}],colorPickerCancel:[{type:Oy}],colorPickerSelect:[{type:Oy}],colorPickerChange:[{type:Oy}],cpCmykColorChange:[{type:Oy}],cpPresetColorsChange:[{type:Oy}],handleClick:[{type:wy,args:["click"]}],handleFocus:[{type:wy,args:["focus"]}],handleInput:[{type:wy,args:["input",["$event"]]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(b3,[{type:Cy,args:[{selector:"[colorPicker]",exportAs:"ngxColorPicker"}]}],(function(){return[{type:rp},{type:ug},{type:O_},{type:eh},{type:hg},{type:g3}]}),{cpWidth:[{type:xy}],cpHeight:[{type:xy}],cpToggle:[{type:xy}],cpDisabled:[{type:xy}],cpIgnoredElements:[{type:xy}],cpFallbackColor:[{type:xy}],cpColorMode:[{type:xy}],cpCmykEnabled:[{type:xy}],cpOutputFormat:[{type:xy}],cpAlphaChannel:[{type:xy}],cpDisableInput:[{type:xy}],cpDialogDisplay:[{type:xy}],cpSaveClickOutside:[{type:xy}],cpCloseClickOutside:[{type:xy}],cpUseRootViewContainer:[{type:xy}],cpPosition:[{type:xy}],cpPositionOffset:[{type:xy}],cpPositionRelativeToArrow:[{type:xy}],cpOKButton:[{type:xy}],cpOKButtonText:[{type:xy}],cpOKButtonClass:[{type:xy}],cpCancelButton:[{type:xy}],cpCancelButtonText:[{type:xy}],cpCancelButtonClass:[{type:xy}],cpPresetLabel:[{type:xy}],cpPresetColorsClass:[{type:xy}],cpMaxPresetColorsLength:[{type:xy}],cpPresetEmptyMessage:[{type:xy}],cpPresetEmptyMessageClass:[{type:xy}],cpAddColorButton:[{type:xy}],cpAddColorButtonText:[{type:xy}],cpAddColorButtonClass:[{type:xy}],cpRemoveColorButtonClass:[{type:xy}],cpInputChange:[{type:Oy}],cpToggleChange:[{type:Oy}],cpSliderChange:[{type:Oy}],cpSliderDragEnd:[{type:Oy}],cpSliderDragStart:[{type:Oy}],colorPickerOpen:[{type:Oy}],colorPickerClose:[{type:Oy}],colorPickerCancel:[{type:Oy}],colorPickerSelect:[{type:Oy}],colorPickerChange:[{type:Oy}],cpCmykColorChange:[{type:Oy}],cpPresetColorsChange:[{type:Oy}],handleClick:[{type:wy,args:["click"]}],handleFocus:[{type:wy,args:["focus"]}],handleInput:[{type:wy,args:["input",["$event"]]}],colorPicker:[{type:xy}],cpPresetColors:[{type:xy}]});class y3{}y3.ɵfac=function t(e){return new(e||y3)},y3.ɵmod=ao({type:y3}),y3.ɵinj=vn({providers:[g3],imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(y3,[{type:Ay,args:[{imports:[WM],exports:[b3],providers:[g3],declarations:[h3,b3,p3,m3],entryComponents:[h3]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(y3,{declarations:function(){return[h3,b3,p3,m3]},imports:function(){return[WM]},exports:function(){return[b3]}});const _3=["filter"];function C3(t,e){1&t&&Im(0)}function M3(t,e){1&t&&Im(0)}const v3=function(t){return{item:t}};function x3(t,e){if(1&t&&(Nm(0),Qp(1,M3,1,0,"ng-container",12),zm()),2&t){const t=e.$implicit;Ym();const n=$p(14);rc(1),Dm("ngTemplateOutlet",n)("ngTemplateOutletContext",Mh(2,v3,t))}}function O3(t,e){1&t&&(Rm(0,"div",13),Tm(1,"mat-spinner",14),Am())}function P3(t,e){1&t&&(Rm(0,"div",15),ku(1,"No Runs"),Am())}function w3(t,e){if(1&t&&(Rm(0,"div",15),Rm(1,"span"),ku(2,'No runs match "'),Rm(3,"code"),ku(4),Am(),ku(5,'"'),Am(),Am()),2&t){const t=Ym();rc(4),Su(t.regexFilter)}}const k3=function(){return[5,10,20]};function S3(t,e){if(1&t){const t=Hm();Rm(0,"mat-paginator",16),Vm("page",(function e(n){return hi(t),Ym().onPaginationChange.emit(n)})),Am()}if(2&t){const t=Ym();Dm("pageSizeOptions",Ch(4,k3))("pageIndex",t.paginationOption.pageIndex)("pageSize",t.paginationOption.pageSize)("length",t.filteredItemsLength)}}function D3(t,e){if(1&t){const t=Hm();Rm(0,"mat-checkbox",26),Vm("change",(function e(){return hi(t),Ym(3).handlePageToggle()})),Am()}if(2&t){const t=Ym(3);Dm("checked",t.allPageItemsSelected())("indeterminate",!t.allPageItemsSelected()&&t.somePageItemsSelected())}}const E3=function(t){return{type:t}};function R3(t,e){if(1&t&&(Rm(0,"span",27),ku(1,"Experiment"),Am()),2&t){const t=Ym(3);Dm("mat-sort-header",Mh(1,E3,t.SortType.EXPERIMENT_NAME))}}function A3(t,e){if(1&t&&(Rm(0,"span",27),ku(1,"Run"),Am()),2&t){const t=Ym(3);Dm("mat-sort-header",Mh(1,E3,t.SortType.RUN_NAME))}}function T3(t,e){1&t&&Tm(0,"runs-group-menu-button",29),2&t&&Dm("experimentIds",Ym(4).experimentIds)}function N3(t,e){if(1&t&&(Rm(0,"span"),Qp(1,T3,1,1,"runs-group-menu-button",28),Am()),2&t){const t=Ym(3);rc(1),Dm("ngIf",t.showGroupControl)}}const z3=function(t){return["column",t]};function I3(t,e){if(1&t&&(Rm(0,"span",21),Nm(1,22),Qp(2,D3,1,2,"mat-checkbox",23),Qp(3,R3,2,3,"span",24),Qp(4,A3,2,3,"span",24),Qp(5,N3,2,1,"span",25),zm(),Am()),2&t){const t=e.$implicit,n=Ym(2);Dm("ngClass",Mh(6,z3,"tb-column-"+t)),rc(1),Dm("ngSwitch",t),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.CHECKBOX),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.EXPERIMENT_NAME),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.RUN_NAME),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.RUN_COLOR)}}function H3(t,e){if(1&t){const t=Hm();Nm(0),Rm(1,"div",38),Vm("click",(function t(e){return e.stopPropagation()})),Rm(2,"tb-range-input",39),Vm("value",(function e(n){hi(t);const o=Ym(2).$implicit;return Ym(2).handleHparamIntervalChanged(o,n)})),Am(),Am(),zm()}if(2&t){const t=Ym(2).$implicit;rc(2),Dm("min",t.filter.minValue)("max",t.filter.maxValue)("lowerValue",t.filter.filterLowerValue)("upperValue",t.filter.filterUpperValue)}}function F3(t,e){if(1&t){const t=Hm();Rm(0,"div",41),Vm("click",(function t(e){return e.stopPropagation()})),Rm(1,"mat-checkbox",37),Vm("change",(function e(){const n=hi(t).$implicit,o=Ym(3).$implicit;return Ym(2).handleHparamDiscreteChanged(o,n)})),Rm(2,"span"),ku(3),Am(),Am(),Am()}if(2&t){const t=e.$implicit,n=Ym(3).$implicit;rc(1),Dm("checked",n.filter.filterValues.includes(t)),rc(2),Su(t)}}function L3(t,e){if(1&t&&(Nm(0),Qp(1,F3,4,2,"div",40),zm()),2&t){const t=Ym(2).$implicit;rc(1),Dm("ngForOf",t.filter.possibleValues)}}function B3(t,e){if(1&t){const t=Hm();Nm(0),Rm(1,"button",33),Vm("click",(function t(e){return e.stopPropagation()})),Tm(2,"mat-icon",34),Am(),Rm(3,"mat-menu",null,35),Rm(5,"div",36),Vm("click",(function t(e){return e.stopPropagation()})),Rm(6,"mat-checkbox",37),Vm("change",(function e(){hi(t);const n=Ym().$implicit;return Ym(2).handleHparamIncludeUndefinedToggled(n)})),Rm(7,"span"),ku(8,"(show empty value)"),Am(),Am(),Am(),Qp(9,H3,3,4,"ng-container",32),Qp(10,L3,2,1,"ng-container",32),Am(),zm()}if(2&t){const t=$p(4),e=Ym().$implicit,n=Ym(2);rc(1),Dm("matMenuTriggerFor",t),jp("aria-label","Filter hparam "+(e.displayName||e.name)),rc(5),Dm("checked",e.filter.includeUndefined),rc(3),Dm("ngIf",e.filter.type===n.DomainType.INTERVAL),rc(1),Dm("ngIf",e.filter.type===n.DomainType.DISCRETE)}}const V3=function(t,e){return{type:t,name:e}};function j3(t,e){if(1&t&&(Rm(0,"span",30),Rm(1,"span",31),ku(2),Am(),Qp(3,B3,11,5,"ng-container",32),Am()),2&t){const t=e.$implicit,n=Ym(2);Dm("mat-sort-header",vh(3,V3,n.SortType.HPARAM,t.name)),rc(2),Su(t.displayName||t.name),rc(1),Dm("ngIf",t.filter)}}function U3(t,e){if(1&t){const t=Hm();Nm(0),Rm(1,"button",33),Vm("click",(function t(e){return e.stopPropagation()})),Tm(2,"mat-icon",34),Am(),Rm(3,"mat-menu",null,35),Rm(5,"div",36),Vm("click",(function t(e){return e.stopPropagation()})),Rm(6,"mat-checkbox",37),Vm("change",(function e(){hi(t);const n=Ym().$implicit;return Ym(2).handleMetricIncludeUndefinedChanged(n)})),Rm(7,"span"),ku(8,"(show empty value)"),Am(),Am(),Am(),Rm(9,"div",38),Vm("click",(function t(e){return e.stopPropagation()})),Rm(10,"tb-range-input",39),Vm("value",(function e(n){hi(t);const o=Ym().$implicit;return Ym(2).handleMetricFilterChanged(o,n)})),Am(),Am(),Am(),zm()}if(2&t){const t=$p(4),e=Ym().$implicit;rc(1),Dm("matMenuTriggerFor",t),jp("aria-label","Filter metric "+(e.displayName||e.tag)),rc(5),Dm("checked",e.filter.includeUndefined),rc(4),Dm("min",e.filter.minValue)("max",e.filter.maxValue)("lowerValue",e.filter.filterLowerValue)("upperValue",e.filter.filterUpperValue)}}const G3=function(t,e){return{type:t,tag:e}};function W3(t,e){if(1&t&&(Rm(0,"span",30),Rm(1,"span",31),ku(2),Am(),Qp(3,U3,11,7,"ng-container",32),Am()),2&t){const t=e.$implicit,n=Ym(2);Dm("mat-sort-header",vh(3,G3,n.SortType.METRIC,t.tag)),rc(2),Su(t.displayName||t.tag),rc(1),Dm("ngIf",t.filter)}}function Y3(t,e){if(1&t){const t=Hm();Rm(0,"div",17),Rm(1,"div",18),Vm("matSortChange",(function e(n){return hi(t),Ym().handleSortChange(n)})),Qp(2,I3,6,8,"span",19),Qp(3,j3,4,6,"span",20),Qp(4,W3,4,6,"span",20),Am(),Am()}if(2&t){const t=Ym();rc(1),Dm("matSortActive",t.sortOption.column),rc(1),Dm("ngForOf",t.columns),rc(1),Dm("ngForOf",t.hparamColumns)("ngForTrackBy",t.trackByHparamColumn),rc(1),Dm("ngForOf",t.metricColumns)("ngForTrackBy",t.trackByMetricColumn)}}function q3(t,e){if(1&t){const t=Hm();Rm(0,"span"),Rm(1,"mat-checkbox",37),Vm("change",(function e(){hi(t);const n=Ym(2).item;return Ym().onSelectionToggle.emit(n)})),Am(),Am()}if(2&t){const t=Ym(2).item;rc(1),Dm("checked",t.selected)}}function Z3(t,e){if(1&t&&(Rm(0,"span",31),ku(1),Am()),2&t){const t=Ym(2).item;jp("title",t.experimentName),rc(1),Su(t.experimentAlias)}}function X3(t,e){if(1&t&&(Rm(0,"span",31),ku(1),Am()),2&t){const t=Ym(2).item;rc(1),Su(t.run.name)}}const K3=function(t){return{"run-color-swatch":!0,"no-color":t}};function J3(t,e){if(1&t){const t=Hm();Rm(0,"span"),Rm(1,"button",47),Vm("colorPickerChange",(function e(n){hi(t);const o=Ym(2).item;return Ym().onRunColorChange.emit({runId:o.run.id,newColor:n})})),Am(),Am()}if(2&t){const t=Ym(2).item;rc(1),du("background",t.runColor),Dm("ngClass",Mh(8,K3,!t.runColor))("colorPicker",t.runColor)("cpDialogDisplay","popup")("cpPositionOffset",-20)("cpUseRootViewContainer",!0)("cpOutputFormat","hex")}}function Q3(t,e){if(1&t&&(Rm(0,"span",45),Nm(1,22),Qp(2,q3,2,1,"span",25),Qp(3,Z3,2,2,"span",46),Qp(4,X3,2,1,"span",46),Qp(5,J3,2,10,"span",25),zm(),Am()),2&t){const t=e.$implicit,n=Ym(2);Dm("ngClass",Mh(6,z3,"tb-column-"+t)),rc(1),Dm("ngSwitch",t),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.CHECKBOX),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.EXPERIMENT_NAME),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.RUN_NAME),rc(1),Dm("ngSwitchCase",n.RunsTableColumn.RUN_COLOR)}}function $3(t,e){if(1&t&&(Rm(0,"span",48),ku(1),Am()),2&t){const t=e.$implicit,n=Ym().item;rc(1),Su(n.hparams.get(t.name))}}function t2(t,e){if(1&t&&(Rm(0,"span",48),ku(1),Am()),2&t){const t=e.$implicit,n=Ym().item;rc(1),Su(n.metrics.get(t.tag))}}function e2(t,e){if(1&t&&(Rm(0,"div",42),Qp(1,Q3,6,8,"span",43),Qp(2,$3,2,1,"span",44),Qp(3,t2,2,1,"span",44),Am()),2&t){const t=e.item,n=Ym();jp("data-id",t.run.id),rc(1),Dm("ngForOf",n.columns),rc(1),Dm("ngForOf",n.hparamColumns),rc(1),Dm("ngForOf",n.metricColumns)}}class n2 extends ZQ{constructor(){super(...arguments),this.itemsPerPageLabel="Show runs:"}}n2.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(n2)))(n||n2)}})(),n2.ɵprov=Mn({token:n2,factory:n2.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(n2,[{type:im}],null,null);class o2{constructor(){this.dataSource=new S0,this.DomainType=ZR,this.RunsTableColumn=vQ,this.SortType=$T,this.onRegexFilterChange=new Lh,this.onSelectionToggle=new Lh,this.onPageSelectionToggle=new Lh,this.onPaginationChange=new Lh,this.onSortChange=new Lh,this.onRunColorChange=new Lh,this.onHparamDiscreteFilterChanged=new Lh,this.onHparamIntervalFilterChanged=new Lh,this.onMetricFilterChanged=new Lh}ngOnChanges(){this.dataSource.data=this.pageItems}getHparamColumnId(t){return`h:${t.name}`}getMetricColumnId(t){return`m:${t.tag}`}getColumnIds(){return[...this.columns,...this.hparamColumns.map(this.getHparamColumnId),...this.metricColumns.map(this.getMetricColumnId)]}allPageItemsSelected(){return Boolean(this.pageItems.length)&&this.pageItems.every((t=>t.selected))}somePageItemsSelected(){return this.pageItems.some((t=>t.selected))}handlePageToggle(){this.onPageSelectionToggle.emit({items:this.pageItems})}handleSortChange(t){let e;switch(t.direction){case"asc":e=FN.ASC;break;case"desc":e=FN.DESC;break;default:e=FN.UNSET}this.onSortChange.emit({key:t.active,direction:e})}onFilterKeyUp(t){this.onRegexFilterChange.emit(t.target.value)}tableTrackBy(t,e){return e.run.id}handleHparamIncludeUndefinedToggled(t){const{name:e,filter:n}=t;if(!n)throw new RangeError("Invariant error: require filter to exist for it to change");n.type===ZR.DISCRETE?this.onHparamDiscreteFilterChanged.emit({hparamName:e,includeUndefined:!n.includeUndefined,filterValues:n.filterValues}):this.onHparamIntervalFilterChanged.emit({name:e,includeUndefined:!n.includeUndefined,filterLowerValue:n.filterLowerValue,filterUpperValue:n.filterUpperValue})}handleHparamIntervalChanged(t,e){const{name:n,filter:o}=t;if(!o)throw new RangeError("Invariant error: require filter to exist for it to change");this.onHparamIntervalFilterChanged.emit({name:n,includeUndefined:o.includeUndefined,filterLowerValue:e.lowerValue,filterUpperValue:e.upperValue})}handleHparamDiscreteChanged(t,e){const{name:n,filter:o}=t;if(!o)throw new RangeError("Invariant error: require filter to exist for it to change");if(o.type!==ZR.DISCRETE)throw new RangeError(`Invariant error: expected discrete domain for ${n}`);const i=new Set([...o.filterValues]);i.has(e)?i.delete(e):i.add(e),this.onHparamDiscreteFilterChanged.emit({hparamName:n,includeUndefined:o.includeUndefined,filterValues:[...i]})}handleMetricIncludeUndefinedChanged(t){if(!t.filter)throw new RangeError("Invariant error: require filter to exist for it to change");this.onMetricFilterChanged.emit({name:t.tag,includeUndefined:!t.filter.includeUndefined,filterLowerValue:t.filter.filterLowerValue,filterUpperValue:t.filter.filterUpperValue})}handleMetricFilterChanged(t,e){if(!t.filter)throw new RangeError("Invariant error: require filter to exist for it to change");this.onMetricFilterChanged.emit({name:t.tag,includeUndefined:t.filter.includeUndefined,filterLowerValue:e.lowerValue,filterUpperValue:e.upperValue})}trackByHparamColumn(t){return t.name}trackByMetricColumn(t){return t.tag}}o2.ɵfac=function t(e){return new(e||o2)},o2.ɵcmp=to({type:o2,selectors:[["runs-table-component"]],viewQuery:function t(e,n){if(1&e&&(Qh(_3,7,hg),Qh(t$,7),Qh(a$,7)),2&e){let t;Jh(t=tb())&&(n.filter=t.first),Jh(t=tb())&&(n.paginator=t.first),Jh(t=tb())&&(n.sort=t.first)}},hostVars:2,hostBindings:function t(e,n){2&e&&pu("flex-layout",n.useFlexibleLayout)},inputs:{experimentIds:"experimentIds",showExperimentName:"showExperimentName",columns:"columns",hparamColumns:"hparamColumns",metricColumns:"metricColumns",allItemsLength:"allItemsLength",filteredItemsLength:"filteredItemsLength",useFlexibleLayout:"useFlexibleLayout",usePagination:"usePagination",showGroupControl:"showGroupControl",pageItems:"pageItems",loading:"loading",numSelectedItems:"numSelectedItems",sortOption:"sortOption",paginationOption:"paginationOption",regexFilter:"regexFilter"},outputs:{onRegexFilterChange:"onRegexFilterChange",onSelectionToggle:"onSelectionToggle",onPageSelectionToggle:"onPageSelectionToggle",onPaginationChange:"onPaginationChange",onSortChange:"onSortChange",onRunColorChange:"onRunColorChange",onHparamDiscreteFilterChanged:"onHparamDiscreteFilterChanged",onHparamIntervalFilterChanged:"onHparamIntervalFilterChanged",onMetricFilterChanged:"onMetricFilterChanged"},features:[pg([{provide:ZQ,useClass:n2}]),Bo],decls:15,vars:7,consts:[[1,"filter-row"],["placeholder","Filter runs 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t(e,n){if(1&e&&(Rm(0,"div",0),Rm(1,"tb-filter-input",1),Vm("keyup",(function t(e){return n.onFilterKeyUp(e)})),Am(),Am(),Rm(2,"div",2),Rm(3,"div",3),Qp(4,C3,1,0,"ng-container",4),Rm(5,"div",5),Qp(6,x3,2,4,"ng-container",6),Am(),Am(),Qp(7,O3,2,0,"div",7),Qp(8,P3,2,0,"div",8),Qp(9,w3,6,1,"div",8),Am(),Qp(10,S3,1,5,"mat-paginator",9),Qp(11,Y3,5,6,"ng-template",null,10,ib),Qp(13,e2,4,4,"ng-template",null,11,ib)),2&e){const t=$p(12);rc(4),Dm("ngTemplateOutlet",t),rc(2),Dm("ngForOf",n.pageItems)("ngForTrackBy",n.tableTrackBy),rc(1),Dm("ngIf",n.loading),rc(1),Dm("ngIf",!n.loading&&0===n.allItemsLength),rc(1),Dm("ngIf",!n.loading&&n.allItemsLength>0&&0===n.filteredItemsLength),rc(1),Dm("ngIf",n.usePagination)}},directives:[q0,MM,lM,dM,o1,t$,a$,aM,fM,gM,OY,m$,w1,XH,eY,DW,KW,WW,D1,b3],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   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ngDevMode||ngDevMode)&&hh(o2,[{type:My,args:[{selector:"runs-table-component",templateUrl:"runs_table_component.ng.html",host:{"[class.flex-layout]":"useFlexibleLayout"},styleUrls:["runs_table_component.css"],changeDetection:zn.OnPush,providers:[{provide:ZQ,useClass:n2}]}]}],null,{experimentIds:[{type:xy}],showExperimentName:[{type:xy}],columns:[{type:xy}],hparamColumns:[{type:xy}],metricColumns:[{type:xy}],allItemsLength:[{type:xy}],filteredItemsLength:[{type:xy}],useFlexibleLayout:[{type:xy}],usePagination:[{type:xy}],showGroupControl:[{type:xy}],pageItems:[{type:xy}],loading:[{type:xy}],numSelectedItems:[{type:xy}],sortOption:[{type:xy}],paginationOption:[{type:xy}],regexFilter:[{type:xy}],onRegexFilterChange:[{type:Oy}],onSelectionToggle:[{type:Oy}],onPageSelectionToggle:[{type:Oy}],onPaginationChange:[{type:Oy}],onSortChange:[{type:Oy}],onRunColorChange:[{type:Oy}],onHparamDiscreteFilterChanged:[{type:Oy}],onHparamIntervalFilterChanged:[{type:Oy}],onMetricFilterChanged:[{type:Oy}],filter:[{type:Za,args:["filter",{static:!0,read:hg}]}],paginator:[{type:Za,args:[t$,{static:!0}]}],sort:[{type:Za,args:[a$,{static:!0}]}]});const i2=Zw(pN,(t=>t.state===yE.LOADING));function a2(t,e){switch(e.type){case $T.EXPERIMENT_NAME:return[t.experimentAlias,t.run.name,t.run.id];case $T.RUN_NAME:return[t.run.name,t.experimentAlias,t.run.id];case $T.HPARAM:return[t.hparams.get(e.name),t.run.name,t.experimentAlias,t.run.id];case $T.METRIC:return[t.metrics.get(e.tag),t.run.name,t.experimentAlias,t.run.id];default:throw new Error(`Not yet implemented: ${e}`)}}function r2(t,e){return void 0===e?t.includeUndefined:t.type===ZR.DISCRETE?t.filterValues.includes(e):t.type===ZR.INTERVAL&&t.filterLowerValue<=e&&e<=t.filterUpperValue}class s2{constructor(t){this.store=t,this.loading$=null,this.hparamColumns$=Et([]),this.metricColumns$=Et([]),this.useFlexibleLayout=!1,this.usePagination=!1,this.columns=[vQ.RUN_NAME],this.showHparamsAndMetrics=!1,this.sortOption$=this.store.select(yN),this.paginationOption$=this.store.select(bN),this.regexFilter$=this.store.select(gN),this.ngUnsubscribe=new I}isExperimentNameVisible(){return this.columns.some((t=>t===vQ.EXPERIMENT_NAME))}ngOnInit(){const t=Wt(this.experimentIds.map((t=>this.getRunTableItemsForExperiment(t)))).pipe(It((t=>[].concat(...t))));this.allUnsortedRunTableItems$=t.pipe(Ie(this.ngUnsubscribe),Ae(1)),this.allItemsLength$=this.allUnsortedRunTableItems$.pipe(It((t=>t.length)));const e=this.getFilteredItems$(this.allUnsortedRunTableItems$).pipe(Ie(this.ngUnsubscribe),Ae(1));this.filteredItemsLength$=e.pipe(It((t=>t.length))),this.pageItems$=this.sortedAndSlicedItems$(e),this.numSelectedItems$=this.allUnsortedRunTableItems$.pipe(It((t=>t.reduce(((t,e)=>t+Number(e.selected)),0))));const n=this.experimentIds.map((t=>this.store.select(i2,{experimentId:t})));if(this.loading$=Wt(n).pipe(It((t=>t.some((t=>t))))),this.showHparamsAndMetrics){const t=this.store.select(SQ,{experimentIds:this.experimentIds});this.hparamColumns$=Wt([this.store.select(PQ,this.experimentIds),t]).pipe(It((([t,{hparams:e}])=>e.map((({name:e,displayName:n})=>{const o=t.get(e);if(!o)throw new RangeError(`Invariant error: a filter for ${e} must exist when the hparam exists`);return{displayName:n,name:e,filter:o}}))))),this.metricColumns$=Wt([this.store.select(kQ,this.experimentIds),t]).pipe(It((([t,{metrics:e}])=>e.map((({tag:e,displayName:n})=>{const o=t.get(e);if(!o)throw new RangeError(`Invariant error: a filter for ${e} must exist when the metric exists`);return{displayName:n,tag:e,filter:o}})))))}this.columns.includes(vQ.CHECKBOX)&&this.store.select(NS).pipe(Ie(this.ngUnsubscribe),ze((()=>t.pipe(ce((t=>t.length>500)),be(1))))).subscribe((()=>{this.store.dispatch(lz({localizedMessage:"The number of runs exceeds 500. New runs are unselected for performance reasons."}))})),this.showGroupControl$=this.store.select(eE).pipe(It((t=>t&&this.columns.includes(vQ.RUN_COLOR)))),this.store.dispatch(UR({experimentIds:this.experimentIds}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}getFilteredItems$(t){return Wt([t,this.store.select(gN)]).pipe(It((([t,e])=>{if(!e)return t;const n=this.columns.includes(vQ.EXPERIMENT_NAME);return t.filter((t=>TN({runName:t.run.name,experimentAlias:t.experimentAlias,experimentName:t.experimentName},e,n)))})),ze((t=>this.showHparamsAndMetrics?Wt(this.store.select(PQ,this.experimentIds),this.store.select(kQ,this.experimentIds)).pipe(It((([e,n])=>t.filter((({hparams:t,metrics:o})=>[...e.entries()].every((([e,n])=>r2(n,t.get(e))))&&[...n.entries()].every((([t,e])=>r2(e,o.get(t))))))))):Et(t))))}sortedAndSlicedItems$(t){const e=Wt([t,this.store.select(yN)]).pipe(It((([t,e])=>(function n(t,e){const n=e.key,o=[...t];return null===n||e.direction===FN.UNSET||o.sort(((t,o)=>{const i=a2(t,n),a=a2(o,n);if(i.length!==a.length)throw new Error(`Invariant error: a given sort should result in same number of items: ${e}`);for(let t=0;t<i.length;t++){const n=i[t],o=a[t];if(n!==o){if(void 0===n||void 0===o)return void 0===o?-1:1;if(typeof n!=typeof o)throw new Error(`Cannot compare values of different types: ${typeof n} vs. ${typeof o}`);return n<o==(e.direction===FN.ASC)?-1:1}}return 0})),o})(t,e))));return Wt([e,this.store.select(bN)]).pipe(It((([t,e])=>{if(!this.usePagination)return t.slice();const{pageSize:n,pageIndex:o}=e;return t.slice(o*n,(o+1)*n)})),Ne([]))}getRunTableItemsForExperiment(t){return Wt([this.store.select(lN,{experimentId:t}),this.store.select(uA,{experimentId:t}),this.store.select(NN),this.store.select(zN),this.store.select(zS)]).pipe(It((([e,n,o,i,a])=>e.map((e=>{const r=new Map;(e.hparams||[]).forEach((t=>{r.set(t.name,t.value)}));const s=new Map;return(e.metrics||[]).forEach((t=>{s.set(t.tag,t.value)})),{run:e,experimentName:(null==n?void 0:n.name)||"",experimentAlias:a[t],selected:Boolean(o&&o.get(e.id)),runColor:i[e.id],hparams:r,metrics:s}})))))}onRunSelectionToggle(t){this.store.dispatch(HR({experimentIds:this.experimentIds,runId:t.run.id}))}onPageSelectionToggle(t){const{items:e}=t,n=e.map((({run:t})=>t.id));this.store.dispatch(FR({experimentIds:this.experimentIds,runIds:n}))}onPaginationChange(t){if(!this.usePagination)throw new Error("Pagination events cannot be dispatched when pagination is disabled");const{pageIndex:e,pageSize:n}=t;this.store.dispatch(LR({pageIndex:e,pageSize:n}))}onSortChange(t){this.store.dispatch(BR(t))}onRegexFilterChange(t){this.store.dispatch(VR({regexString:t}))}onRunColorChange({runId:t,newColor:e}){this.store.dispatch(jR({runId:t,newColor:e}))}onHparamDiscreteFilterChanged(t){const{hparamName:e,filterValues:n,includeUndefined:o}=t;this.store.dispatch(KR({experimentIds:this.experimentIds,hparamName:e,filterValues:n,includeUndefined:o}))}onHparamIntervalFilterChanged(t){const{name:e,filterLowerValue:n,filterUpperValue:o,includeUndefined:i}=t;this.store.dispatch(JR({experimentIds:this.experimentIds,hparamName:e,filterLowerValue:n,filterUpperValue:o,includeUndefined:i}))}onMetricFilterChanged(t){const{name:e,includeUndefined:n,filterLowerValue:o,filterUpperValue:i}=t;this.store.dispatch(QR({experimentIds:this.experimentIds,metricTag:e,includeUndefined:n,filterLowerValue:o,filterUpperValue:i}))}}s2.ɵfac=function t(e){return new(e||s2)(Sm(Iw))},s2.ɵcmp=to({type:s2,selectors:[["runs-table"]],hostVars:2,hostBindings:function t(e,n){2&e&&pu("flex-layout",n.useFlexibleLayout)},inputs:{useFlexibleLayout:"useFlexibleLayout",usePagination:"usePagination",columns:"columns",experimentIds:"experimentIds",showHparamsAndMetrics:"showHparamsAndMetrics"},decls:12,vars:38,consts:[[3,"experimentIds","useFlexibleLayout","numSelectedItems","columns","hparamColumns","metricColumns","showExperimentName","pageItems","filteredItemsLength","allItemsLength","loading","paginationOption","regexFilter","showGroupControl","sortOption","usePagination","onSelectionToggle","onPageSelectionToggle","onPaginationChange","onRegexFilterChange","onSortChange","onRunColorChange","onHparamIntervalFilterChanged","onHparamDiscreteFilterChanged","onMetricFilterChanged"]],template:function t(e,n){1&e&&(Rm(0,"runs-table-component",0),Vm("onSelectionToggle",(function t(e){return n.onRunSelectionToggle(e)}))("onPageSelectionToggle",(function t(e){return n.onPageSelectionToggle(e)}))("onPaginationChange",(function t(e){return n.onPaginationChange(e)}))("onRegexFilterChange",(function t(e){return n.onRegexFilterChange(e)}))("onSortChange",(function t(e){return n.onSortChange(e)}))("onRunColorChange",(function t(e){return n.onRunColorChange(e)}))("onHparamIntervalFilterChanged",(function t(e){return n.onHparamIntervalFilterChanged(e)}))("onHparamDiscreteFilterChanged",(function t(e){return n.onHparamDiscreteFilterChanged(e)}))("onMetricFilterChanged",(function t(e){return n.onMetricFilterChanged(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Ah(7,"async"),Ah(8,"async"),Ah(9,"async"),Ah(10,"async"),Ah(11,"async"),Am()),2&e&&Dm("experimentIds",n.experimentIds)("useFlexibleLayout",n.useFlexibleLayout)("numSelectedItems",Th(1,16,n.numSelectedItems$))("columns",n.columns)("hparamColumns",Th(2,18,n.hparamColumns$))("metricColumns",Th(3,20,n.metricColumns$))("showExperimentName",n.isExperimentNameVisible())("pageItems",Th(4,22,n.pageItems$))("filteredItemsLength",Th(5,24,n.filteredItemsLength$))("allItemsLength",Th(6,26,n.allItemsLength$))("loading",Th(7,28,n.loading$))("paginationOption",Th(8,30,n.paginationOption$))("regexFilter",Th(9,32,n.regexFilter$))("showGroupControl",Th(10,34,n.showGroupControl$))("sortOption",Th(11,36,n.sortOption$))("usePagination",n.usePagination)},directives:[o2],pipes:[wM],styles:[".flex-layout[_nghost-%COMP%] {\n        display: flex;\n      }\n\n      .flex-layout[_nghost-%COMP%]    > runs-table-component[_ngcontent-%COMP%] {\n        width: 100%;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(s2,[{type:My,args:[{selector:"runs-table",template:'\n    <runs-table-component\n      [experimentIds]="experimentIds"\n      [useFlexibleLayout]="useFlexibleLayout"\n      [numSelectedItems]="numSelectedItems$ | async"\n      [columns]="columns"\n      [hparamColumns]="hparamColumns$ | async"\n      [metricColumns]="metricColumns$ | async"\n      [showExperimentName]="isExperimentNameVisible()"\n      [pageItems]="pageItems$ | async"\n      [filteredItemsLength]="filteredItemsLength$ | async"\n      [allItemsLength]="allItemsLength$ | async"\n      [loading]="loading$ | async"\n      [paginationOption]="paginationOption$ | async"\n      [regexFilter]="regexFilter$ | async"\n      [showGroupControl]="showGroupControl$ | async"\n      [sortOption]="sortOption$ | async"\n      [usePagination]="usePagination"\n      (onSelectionToggle)="onRunSelectionToggle($event)"\n      (onPageSelectionToggle)="onPageSelectionToggle($event)"\n      (onPaginationChange)="onPaginationChange($event)"\n      (onRegexFilterChange)="onRegexFilterChange($event)"\n      (onSortChange)="onSortChange($event)"\n      (onRunColorChange)="onRunColorChange($event)"\n      (onHparamIntervalFilterChanged)="onHparamIntervalFilterChanged($event)"\n      (onHparamDiscreteFilterChanged)="onHparamDiscreteFilterChanged($event)"\n      (onMetricFilterChanged)="onMetricFilterChanged($event)"\n    ></runs-table-component>\n  ',host:{"[class.flex-layout]":"useFlexibleLayout"},styles:["\n      :host.flex-layout {\n        display: flex;\n      }\n\n      :host.flex-layout > runs-table-component {\n        width: 100%;\n      }\n    "],changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{useFlexibleLayout:[{type:xy}],usePagination:[{type:xy}],columns:[{type:xy}],experimentIds:[{type:xy}],showHparamsAndMetrics:[{type:xy}]});class l2{}l2.ɵfac=function t(e){return new(e||l2)},l2.ɵcmp=to({type:l2,selectors:[["runs-selector-component"]],inputs:{experimentIds:"experimentIds",showHparamsAndMetrics:"showHparamsAndMetrics",columns:"columns"},decls:1,vars:4,consts:[[3,"useFlexibleLayout","columns","experimentIds","showHparamsAndMetrics"]],template:function t(e,n){1&e&&Tm(0,"runs-table",0),2&e&&Dm("useFlexibleLayout",!0)("columns",n.columns)("experimentIds",n.experimentIds)("showHparamsAndMetrics",n.showHparamsAndMetrics)},directives:[s2],styles:["runs-table[_ngcontent-%COMP%] {\n        height: 100%;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(l2,[{type:My,args:[{selector:"runs-selector-component",template:'\n    <runs-table\n      [useFlexibleLayout]="true"\n      [columns]="columns"\n      [experimentIds]="experimentIds"\n      [showHparamsAndMetrics]="showHparamsAndMetrics"\n    ></runs-table>\n  ',styles:["\n      runs-table {\n        height: 100%;\n      }\n    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sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   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.mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action[_ngcontent-%COMP%]{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node[_ngcontent-%COMP%], .mat-nested-tree-node[_ngcontent-%COMP%]{font-weight:400;font-size:14px}.mat-ripple[_ngcontent-%COMP%]{overflow:hidden;position:relative}.mat-ripple[_ngcontent-%COMP%]:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded[_ngcontent-%COMP%]{overflow:visible}.mat-ripple-element[_ngcontent-%COMP%]{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms cubic-bezier(0, 0, 0.2, 1);transform:scale(0)}.cdk-high-contrast-active[_ngcontent-%COMP%]   .mat-ripple-element[_ngcontent-%COMP%]{display:none}.cdk-visually-hidden[_ngcontent-%COMP%]{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container[_ngcontent-%COMP%], .cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}.tag-filter[_ngcontent-%COMP%]{display:flex;position:relative}tb-filter-input[_ngcontent-%COMP%]{flex-grow:1}[_nghost-%COMP%]{color:#212121}body.dark-mode   [_nghost-%COMP%]{color:#fff}[_nghost-%COMP%]:not(.valid){color:#c62828}[_nghost-%COMP%]:not(.valid)   .error-icon[_ngcontent-%COMP%]{color:#c62828;position:absolute;right:0}  .tag-options .option,   .tag-options .and-more{-webkit-box-orient:vertical;-webkit-line-clamp:3;display:-webkit-box;font-size:14px;line-height:1.4;padding:8px 16px}  .tag-options .and-more{color:#616161}body.dark-mode[_nghost-%COMP%]     .tag-options .and-more, body.dark-mode   [_nghost-%COMP%]     .tag-options .and-more{color:rgba(255,255,255,.7)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(v2,[{type:My,args:[{selector:"metrics-tag-filter-component",templateUrl:"filter_input_component.ng.html",styleUrls:["filter_input_component.css"],changeDetection:zn.OnPush}]}],null,{regexFilterValue:[{type:xy}],isRegexFilterValid:[{type:Py,args:["class.valid"]},{type:xy}],completions:[{type:xy}],onRegexFilterValueChange:[{type:Oy}]});class x2{constructor(t){this.store=t,this.tagFilter$=this.store.select(BT),this.isTagFilterRegexValid$=this.tagFilter$.pipe(It((t=>{try{return new RegExp(t),!0}catch(t){return!1}}))),this.completions$=this.store.select(MT).pipe(fe(this.store.select(qT)),It((([t,e])=>t.filter((({plugin:t})=>!e.size||e.has(t))).map((({tag:t})=>t)))),It((t=>[...new Set(t)])),It((t=>t.sort(g2))),fe(this.store.select(BT)),It((([t,e])=>{try{return[t,new RegExp(e,"i")]}catch(e){return[t,null]}})),ce((([,t])=>null!==t)),It((([t,e])=>t.filter((t=>e.test(t))))))}onTagFilterChange(t){this.store.dispatch(eR({tagFilter:t}))}}x2.ɵfac=function t(e){return new(e||x2)(Sm(Iw))},x2.ɵcmp=to({type:x2,selectors:[["metrics-tag-filter"]],decls:4,vars:9,consts:[[3,"regexFilterValue","isRegexFilterValid","completions","onRegexFilterValueChange"]],template:function t(e,n){1&e&&(Rm(0,"metrics-tag-filter-component",0),Vm("onRegexFilterValueChange",(function t(e){return n.onTagFilterChange(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Am()),2&e&&Dm("regexFilterValue",Th(1,3,n.tagFilter$))("isRegexFilterValid",Th(2,5,n.isTagFilterRegexValid$))("completions",Th(3,7,n.completions$))},directives:[v2],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(x2,[{type:My,args:[{selector:"metrics-tag-filter",template:'\n    <metrics-tag-filter-component\n      [regexFilterValue]="tagFilter$ | async"\n      [isRegexFilterValid]="isTagFilterRegexValid$ | async"\n      [completions]="completions$ | async"\n      (onRegexFilterValueChange)="onTagFilterChange($event)"\n    ></metrics-tag-filter-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);const O2=["button"],P2=new Ga("MAT_BUTTON_TOGGLE_DEFAULT_OPTIONS"),w2=new Ga("MatButtonToggleGroup"),k2={provide:IV,useExisting:qe((()=>E2)),multi:!0};let S2=0;class D2{constructor(t,e){this.source=t,this.value=e}}class E2{constructor(t,e){this._changeDetector=t,this._vertical=!1,this._multiple=!1,this._disabled=!1,this._controlValueAccessorChangeFn=()=>{},this._onTouched=()=>{},this._name="mat-button-toggle-group-"+S2++,this.valueChange=new Lh,this.change=new Lh,this.appearance=e&&e.appearance?e.appearance:"standard"}get name(){return this._name}set name(t){this._name=t,this._buttonToggles&&this._buttonToggles.forEach((t=>{t.name=this._name,t._markForCheck()}))}get vertical(){return this._vertical}set vertical(t){this._vertical=yz(t)}get value(){const t=this._selectionModel?this._selectionModel.selected:[];return this.multiple?t.map((t=>t.value)):t[0]?t[0].value:void 0}set value(t){this._setSelectionByValue(t),this.valueChange.emit(this.value)}get selected(){const t=this._selectionModel?this._selectionModel.selected:[];return this.multiple?t:t[0]||null}get multiple(){return this._multiple}set multiple(t){this._multiple=yz(t)}get disabled(){return this._disabled}set disabled(t){this._disabled=yz(t),this._buttonToggles&&this._buttonToggles.forEach((t=>t._markForCheck()))}ngOnInit(){this._selectionModel=new oF(this.multiple,void 0,!1)}ngAfterContentInit(){this._selectionModel.select(...this._buttonToggles.filter((t=>t.checked)))}writeValue(t){this.value=t,this._changeDetector.markForCheck()}registerOnChange(t){this._controlValueAccessorChangeFn=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t}_emitChangeEvent(){const t=this.selected,e=Array.isArray(t)?t[t.length-1]:t,n=new D2(e,this.value);this._controlValueAccessorChangeFn(n.value),this.change.emit(n)}_syncButtonToggle(t,e,n=!1,o=!1){this.multiple||!this.selected||t.checked||(this.selected.checked=!1),this._selectionModel?e?this._selectionModel.select(t):this._selectionModel.deselect(t):o=!0,o?Promise.resolve().then((()=>this._updateModelValue(n))):this._updateModelValue(n)}_isSelected(t){return this._selectionModel&&this._selectionModel.isSelected(t)}_isPrechecked(t){return void 0!==this._rawValue&&(this.multiple&&Array.isArray(this._rawValue)?this._rawValue.some((e=>null!=t.value&&e===t.value)):t.value===this._rawValue)}_setSelectionByValue(t){if(this._rawValue=t,this._buttonToggles)if(this.multiple&&t){if(!Array.isArray(t)&&("undefined"==typeof ngDevMode||ngDevMode))throw Error("Value must be an array in multiple-selection mode.");this._clearSelection(),t.forEach((t=>this._selectValue(t)))}else this._clearSelection(),this._selectValue(t)}_clearSelection(){this._selectionModel.clear(),this._buttonToggles.forEach((t=>t.checked=!1))}_selectValue(t){const e=this._buttonToggles.find((e=>null!=e.value&&e.value===t));e&&(e.checked=!0,this._selectionModel.select(e))}_updateModelValue(t){t&&this._emitChangeEvent(),this.valueChange.emit(this.value)}}E2.ɵfac=function t(e){return new(e||E2)(Sm(Ug),Sm(P2,8))},E2.ɵdir=lo({type:E2,selectors:[["mat-button-toggle-group"]],contentQueries:function t(e,n,o){if(1&e&&$h(o,A2,5),2&e){let t;Jh(t=tb())&&(n._buttonToggles=t)}},hostAttrs:["role","group",1,"mat-button-toggle-group"],hostVars:5,hostBindings:function t(e,n){2&e&&(jp("aria-disabled",n.disabled),pu("mat-button-toggle-vertical",n.vertical)("mat-button-toggle-group-appearance-standard","standard"===n.appearance))},inputs:{appearance:"appearance",name:"name",vertical:"vertical",value:"value",multiple:"multiple",disabled:"disabled"},outputs:{valueChange:"valueChange",change:"change"},exportAs:["matButtonToggleGroup"],features:[pg([k2,{provide:w2,useExisting:E2}])]}),E2.ctorParameters=()=>[{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[P2]}]}],E2.propDecorators={_buttonToggles:[{type:Ya,args:[qe((()=>A2)),{descendants:!0}]}],appearance:[{type:xy}],name:[{type:xy}],vertical:[{type:xy}],value:[{type:xy}],valueChange:[{type:Oy}],multiple:[{type:xy}],disabled:[{type:xy}],change:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(E2,[{type:Cy,args:[{selector:"mat-button-toggle-group",providers:[k2,{provide:w2,useExisting:E2}],host:{role:"group",class:"mat-button-toggle-group","[attr.aria-disabled]":"disabled","[class.mat-button-toggle-vertical]":"vertical","[class.mat-button-toggle-group-appearance-standard]":'appearance === "standard"'},exportAs:"matButtonToggleGroup"}]}],(function(){return[{type:Ug},{type:void 0,decorators:[{type:Sr},{type:kr,args:[P2]}]}]}),{valueChange:[{type:Oy}],change:[{type:Oy}],appearance:[{type:xy}],name:[{type:xy}],vertical:[{type:xy}],value:[{type:xy}],multiple:[{type:xy}],disabled:[{type:xy}],_buttonToggles:[{type:Ya,args:[qe((()=>A2)),{descendants:!0}]}]});const R2=QI(class{});class A2 extends R2{constructor(t,e,n,o,i,a){super(),this._changeDetectorRef=e,this._elementRef=n,this._focusMonitor=o,this._isSingleSelector=!1,this._checked=!1,this.ariaLabelledby=null,this._disabled=!1,this.change=new Lh;const r=Number(i);this.tabIndex=r||0===r?r:null,this.buttonToggleGroup=t,this.appearance=a&&a.appearance?a.appearance:"standard"}get buttonId(){return`${this.id}-button`}get appearance(){return this.buttonToggleGroup?this.buttonToggleGroup.appearance:this._appearance}set appearance(t){this._appearance=t}get checked(){return this.buttonToggleGroup?this.buttonToggleGroup._isSelected(this):this._checked}set checked(t){const e=yz(t);e!==this._checked&&(this._checked=e,this.buttonToggleGroup&&this.buttonToggleGroup._syncButtonToggle(this,this._checked),this._changeDetectorRef.markForCheck())}get disabled(){return this._disabled||this.buttonToggleGroup&&this.buttonToggleGroup.disabled}set disabled(t){this._disabled=yz(t)}ngOnInit(){const t=this.buttonToggleGroup;this._isSingleSelector=t&&!t.multiple,this.id=this.id||"mat-button-toggle-"+S2++,this._isSingleSelector&&(this.name=t.name),t&&(t._isPrechecked(this)?this.checked=!0:t._isSelected(this)!==this._checked&&t._syncButtonToggle(this,this._checked))}ngAfterViewInit(){this._focusMonitor.monitor(this._elementRef,!0)}ngOnDestroy(){const t=this.buttonToggleGroup;this._focusMonitor.stopMonitoring(this._elementRef),t&&t._isSelected(this)&&t._syncButtonToggle(this,!1,!1,!0)}focus(t){this._buttonElement.nativeElement.focus(t)}_onButtonClick(){const t=!!this._isSingleSelector||!this._checked;t!==this._checked&&(this._checked=t,this.buttonToggleGroup&&(this.buttonToggleGroup._syncButtonToggle(this,this._checked,!0),this.buttonToggleGroup._onTouched())),this.change.emit(new D2(this,this.value))}_markForCheck(){this._changeDetectorRef.markForCheck()}}A2.ɵfac=function t(e){return new(e||A2)(Sm(w2,8),Sm(Ug),Sm(hg),Sm(SI),Na("tabindex"),Sm(P2,8))},A2.ɵcmp=to({type:A2,selectors:[["mat-button-toggle"]],viewQuery:function t(e,n){if(1&e&&Qh(O2,5),2&e){let t;Jh(t=tb())&&(n._buttonElement=t.first)}},hostAttrs:["role","presentation",1,"mat-button-toggle"],hostVars:12,hostBindings:function t(e,n){1&e&&Vm("focus",(function t(){return n.focus()})),2&e&&(jp("aria-label",null)("aria-labelledby",null)("id",n.id)("name",null),pu("mat-button-toggle-standalone",!n.buttonToggleGroup)("mat-button-toggle-checked",n.checked)("mat-button-toggle-disabled",n.disabled)("mat-button-toggle-appearance-standard","standard"===n.appearance))},inputs:{disableRipple:"disableRipple",ariaLabelledby:["aria-labelledby","ariaLabelledby"],tabIndex:"tabIndex",appearance:"appearance",checked:"checked",disabled:"disabled",id:"id",name:"name",ariaLabel:["aria-label","ariaLabel"],value:"value"},outputs:{change:"change"},exportAs:["matButtonToggle"],features:[xp],ngContentSelectors:["*"],decls:6,vars:9,consts:[["type","button",1,"mat-button-toggle-button","mat-focus-indicator",3,"id","disabled","click"],["button",""],[1,"mat-button-toggle-label-content"],[1,"mat-button-toggle-focus-overlay"],["matRipple","",1,"mat-button-toggle-ripple",3,"matRippleTrigger","matRippleDisabled"]],template:function t(e,n){if(1&e&&(Zm(),Rm(0,"button",0,1),Vm("click",(function t(){return n._onButtonClick()})),Rm(2,"span",2),Xm(3),Am(),Am(),Tm(4,"span",3),Tm(5,"span",4)),2&e){const t=$p(1);Dm("id",n.buttonId)("disabled",n.disabled||null),jp("tabindex",n.disabled?-1:n.tabIndex)("aria-pressed",n.checked)("name",n.name||null)("aria-label",n.ariaLabel)("aria-labelledby",n.ariaLabelledby),rc(5),Dm("matRippleTrigger",t)("matRippleDisabled",n.disableRipple||n.disabled)}},directives:[kH],styles:[".mat-button-toggle-standalone,.mat-button-toggle-group{position:relative;display:inline-flex;flex-direction:row;white-space:nowrap;overflow:hidden;border-radius:2px;-webkit-tap-highlight-color:transparent}.cdk-high-contrast-active .mat-button-toggle-standalone,.cdk-high-contrast-active .mat-button-toggle-group{outline:solid 1px}.mat-button-toggle-standalone.mat-button-toggle-appearance-standard,.mat-button-toggle-group-appearance-standard{border-radius:4px}.cdk-high-contrast-active 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this.disabled">\n</span>\n',encapsulation:Hn.None,exportAs:"matButtonToggle",changeDetection:zn.OnPush,inputs:["disableRipple"],host:{"[class.mat-button-toggle-standalone]":"!buttonToggleGroup","[class.mat-button-toggle-checked]":"checked","[class.mat-button-toggle-disabled]":"disabled","[class.mat-button-toggle-appearance-standard]":'appearance === "standard"',class:"mat-button-toggle","[attr.aria-label]":"null","[attr.aria-labelledby]":"null","[attr.id]":"id","[attr.name]":"null","(focus)":"focus()",role:"presentation"},styles:[".mat-button-toggle-standalone,.mat-button-toggle-group{position:relative;display:inline-flex;flex-direction:row;white-space:nowrap;overflow:hidden;border-radius:2px;-webkit-tap-highlight-color:transparent}.cdk-high-contrast-active .mat-button-toggle-standalone,.cdk-high-contrast-active .mat-button-toggle-group{outline:solid 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
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ngDevMode||ngDevMode)&&hh(z2,[{type:My,args:[{selector:"tb-truncated-path",template:'\n    <span *ngIf="firstTextPart().length > 0" class="first-text-part">{{\n      firstTextPart()\n    }}</span>\n    <span class="second-text-part">{{ secondTextPart() }}</span>\n  ',styleUrls:["truncated_path_component.css"]}]}],null,{value:[{type:xy}]});class F2{}F2.ɵfac=function t(e){return new(e||F2)},F2.ɵcmp=to({type:F2,selectors:[["card-run-name-component"]],inputs:{name:"name"},decls:1,vars:1,template:function t(e,n){1&e&&ku(0),2&e&&Su(n.name)},styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, 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0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{display:flex;flex-direction:column;box-sizing:border-box;height:100%;overflow:auto;padding:16px;padding-top:4px}.actual-size[_nghost-%COMP%]{height:auto}.heading[_ngcontent-%COMP%]{align-items:center;font-size:14px;margin-bottom:4px;position:relative}.line[_ngcontent-%COMP%]{align-items:center;display:grid;grid-template-columns:1fr max-content}.metadata[_ngcontent-%COMP%]{display:flex;flex-wrap:wrap;gap:5px;justify-content:flex-end;max-width:175px;text-align:end}.tag[_ngcontent-%COMP%]{overflow:hidden}.pin-button[_ngcontent-%COMP%]   mat-icon[_ngcontent-%COMP%]{height:18px}.run[_ngcontent-%COMP%]{align-self:baseline;display:flex;overflow:hidden;white-space:nowrap}.run[_ngcontent-%COMP%]   .dot[_ngcontent-%COMP%]{flex:none;display:inline-block;width:13px;height:13px;border-radius:50%;margin-right:4px}.run[_ngcontent-%COMP%]   .run-text[_ngcontent-%COMP%]{overflow:hidden;text-overflow:ellipsis;max-width:120px}.run[_ngcontent-%COMP%], .sample[_ngcontent-%COMP%], .step[_ngcontent-%COMP%]{color:#616161;font-size:13px}body.dark-mode[_nghost-%COMP%]   .run[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .run[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}body.dark-mode[_nghost-%COMP%]   .sample[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .sample[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}body.dark-mode[_nghost-%COMP%]   .step[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .step[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.controls[_ngcontent-%COMP%]{color:#616161;white-space:nowrap;justify-self:flex-end;flex-shrink:0;margin-right:-12px}body.dark-mode[_nghost-%COMP%]   .controls[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .controls[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.img-container[_ngcontent-%COMP%]{flex:1 1 0;overflow-y:auto;position:relative}.img-container[_ngcontent-%COMP%]   img[_ngcontent-%COMP%]{image-rendering:-moz-crisp-edges;image-rendering:pixelated}.actual-size[_nghost-%COMP%]   .img-container[_ngcontent-%COMP%]{overflow:auto;flex:none}[_nghost-%COMP%]:not(.actual-size)   img[_ngcontent-%COMP%]{position:absolute;max-height:100%;max-width:100%;width:auto;height:100%;object-fit:contain}.slider-row[_ngcontent-%COMP%]{display:flex;align-items:center;height:24px}.step-slider[_ngcontent-%COMP%]{flex:1}[_nghost-%COMP%]     .mat-slider-min-value .mat-slider-thumb{background-color:#f57c00}.empty-message[_ngcontent-%COMP%]{margin-top:1em;font-size:13px}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Z2,[{type:My,args:[{selector:"image-card-component",templateUrl:"image_card_component.ng.html",styleUrls:["image_card_component.css"],host:{"[class.actual-size]":"showActualSize"},changeDetection:zn.OnPush}]}],null,{loadState:[{type:xy}],title:[{type:xy}],tag:[{type:xy}],runId:[{type:xy}],sample:[{type:xy}],numSample:[{type:xy}],imageUrl:[{type:xy}],stepIndex:[{type:xy}],stepValues:[{type:xy}],brightnessInMilli:[{type:xy}],contrastInMilli:[{type:xy}],showActualSize:[{type:xy}],runColorScale:[{type:xy}],allowToggleActualSize:[{type:xy}],isPinned:[{type:xy}],onActualSizeToggle:[{type:Oy}],stepIndexChange:[{type:Oy}],onPinClicked:[{type:Oy}]});class X2{constructor(t,e){this.store=t,this.dataSource=e,this.fullWidthChanged=new Lh,this.pinStateChanged=new Lh,this.brightnessInMilli$=this.store.select(HT),this.contrastInMilli$=this.store.select(FT),this.actualSizeGlobalSetting$=this.store.select(LT),this.showActualSize=!1,this.actualSizeUiToggled=!1,this.actualSizeUiToggleSubject=new F(this.actualSizeUiToggled),this.ngUnsubscribe=new I}onStepIndexChanged(t){this.store.dispatch(tR({cardId:this.cardId,stepIndex:t}))}isImageCardMetadata(t){const{plugin:e}=t;return e===hA.IMAGES}onActualSizeToggle(){this.actualSizeUiToggled=!this.actualSizeUiToggled,this.actualSizeUiToggleSubject.next(this.actualSizeUiToggled)}ngOnInit(){Wt([this.actualSizeGlobalSetting$,this.actualSizeUiToggleSubject]).pipe(Ie(this.ngUnsubscribe),Fe((([t,e])=>{this.showActualSize=t||e,this.fullWidthChanged.emit(this.showActualSize)}))).subscribe((()=>{}));const t=this.store.select(yT,this.cardId).pipe(Ie(this.ngUnsubscribe),ce((t=>!!t&&this.isImageCardMetadata(t))),It((t=>t)),Ae(1)),e=Wt([t,this.store.select(hT,this.cardId)]).pipe(Ie(this.ngUnsubscribe),It((([t,e])=>{const n=t.runId;return e&&e.hasOwnProperty(n)?e[n]:[]})),Me(((t,e)=>t.length===e.length&&0===t.length||t===e)),Ae(1));this.stepIndex$=this.store.select(vT,this.cardId),this.loadState$=this.store.select(gT,this.cardId);const n=Wt([e,this.stepIndex$]).pipe(It((([t,e])=>null!==e&&t[e]?t[e]:null)));this.tag$=t.pipe(It((t=>t.tag))),this.title$=this.tag$.pipe(It((t=>f2(t,this.groupName)))),this.runId$=t.pipe(It((t=>t.runId))),this.sample$=t.pipe(It((t=>t.sample))),this.numSample$=t.pipe(It((t=>t.numSample))),this.imageUrl$=n.pipe(It((t=>t?this.dataSource.imageUrl(t.imageId):null))),this.stepValues$=e.pipe(It((t=>t.map((t=>t.step))))),this.isPinned$=this.store.select(wT,this.cardId)}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}}var K2,J2,Q2;function $2(t,e){1&t&&(Rm(0,"span"),ku(1,"scalar"),Am())}function t5(t,e){1&t&&(Rm(0,"span"),ku(1,"histogram"),Am())}function e5(t,e){1&t&&(Rm(0,"span"),ku(1,"unknown"),Am())}function n5(t,e){if(1&t&&(Nm(0,13),Qp(1,$2,2,0,"span",14),Qp(2,t5,2,0,"span",14),Qp(3,e5,2,0,"span",15),zm()),2&t){const t=Ym(2);Dm("ngSwitch",t.cardMetadata.plugin),rc(1),Dm("ngSwitchCase",t.PluginType.SCALARS),rc(1),Dm("ngSwitchCase",t.PluginType.HISTOGRAMS)}}function o5(t,e){1&t&&Im(0)}function i5(t,e){if(1&t&&(Rm(0,"option",16),ku(1),Am()),2&t){const t=e.$implicit;Dm("value",t.id),rc(1),Su(t.name)}}function a5(t,e){if(1&t){const t=Hm();Nm(0),Rm(1,"h2"),Qp(2,n5,4,3,"ng-template",null,2,ib),Rm(4,"span"),ku(5,"Download "),Am(),Qp(6,o5,1,0,"ng-container",3),Rm(7,"span"),ku(8," data for "),Am(),Rm(9,"code",4),ku(10),Am(),Am(),Rm(11,"mat-dialog-content"),Rm(12,"mat-form-field",5),Rm(13,"mat-label"),ku(14,"Select a run to download a data for a series"),Am(),Rm(15,"select",6),Vm("change",(function e(n){return hi(t),Ym().runSelected.emit(n.target.value)})),Rm(16,"option",7),ku(17,"-"),Am(),Qp(18,i5,2,2,"option",8),Am(),Am(),Rm(19,"div",9),Rm(20,"span"),ku(21,"Download as…"),Am(),ku(22," "),Rm(23,"a",10),ku(24,"JSON"),Am(),Rm(25,"a",10),ku(26,"CSV"),Am(),Am(),Am(),Rm(27,"mat-dialog-actions",11),Rm(28,"button",12),ku(29,"Close"),Am(),Am(),zm()}if(2&t){const t=$p(3),e=Ym();rc(6),Dm("ngTemplateOutlet",t),rc(3),Dm("title",e.cardMetadata.tag),rc(1),Su(e.cardMetadata.tag),rc(5),Dm("value",e.selectedRunId||""),rc(1),Dm("value",""),rc(2),Dm("ngForOf",e.runs),rc(5),Dm("disabled",!e.downloadUrlJson)("download",e.getDownloadName("json")),jp("href",e.downloadUrlJson,Ts),rc(2),Dm("disabled",!e.downloadUrlCsv)("download",e.getDownloadName("csv")),jp("href",e.downloadUrlCsv,Ts)}}function r5(t,e){1&t&&ku(0,"Loading...")}X2.ɵfac=function t(e){return new(e||X2)(Sm(Iw),Sm(PA))},X2.ɵcmp=to({type:X2,selectors:[["image-card"]],inputs:{cardId:"cardId",groupName:"groupName",runColorScale:"runColorScale"},outputs:{fullWidthChanged:"fullWidthChanged",pinStateChanged:"pinStateChanged"},decls:14,vars:41,consts:[[3,"loadState","title","tag","runId","sample","numSample","imageUrl","stepIndex","stepValues","brightnessInMilli","contrastInMilli","runColorScale","showActualSize","allowToggleActualSize","isPinned","stepIndexChange","onActualSizeToggle","onPinClicked"]],template:function t(e,n){1&e&&(Rm(0,"image-card-component",0),Vm("stepIndexChange",(function t(e){return n.onStepIndexChanged(e)}))("onActualSizeToggle",(function t(){return n.onActualSizeToggle()}))("onPinClicked",(function t(e){return n.pinStateChanged.emit(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Ah(7,"async"),Ah(8,"async"),Ah(9,"async"),Ah(10,"async"),Ah(11,"async"),Ah(12,"async"),Ah(13,"async"),Am()),2&e&&Dm("loadState",Th(1,15,n.loadState$))("title",Th(2,17,n.title$))("tag",Th(3,19,n.tag$))("runId",Th(4,21,n.runId$))("sample",Th(5,23,n.sample$))("numSample",Th(6,25,n.numSample$))("imageUrl",Th(7,27,n.imageUrl$))("stepIndex",Th(8,29,n.stepIndex$))("stepValues",Th(9,31,n.stepValues$))("brightnessInMilli",Th(10,33,n.brightnessInMilli$))("contrastInMilli",Th(11,35,n.contrastInMilli$))("runColorScale",n.runColorScale)("showActualSize",n.showActualSize)("allowToggleActualSize",!1===Th(12,37,n.actualSizeGlobalSetting$))("isPinned",Th(13,39,n.isPinned$))},directives:[Z2],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(X2,[{type:My,args:[{selector:"image-card",template:'\n    <image-card-component\n      [loadState]="loadState$ | async"\n      [title]="title$ | async"\n      [tag]="tag$ | async"\n      [runId]="runId$ | async"\n      [sample]="sample$ | async"\n      [numSample]="numSample$ | async"\n      [imageUrl]="imageUrl$ | async"\n      [stepIndex]="stepIndex$ | async"\n      [stepValues]="stepValues$ | async"\n      (stepIndexChange)="onStepIndexChanged($event)"\n      [brightnessInMilli]="brightnessInMilli$ | async"\n      [contrastInMilli]="contrastInMilli$ | async"\n      [runColorScale]="runColorScale"\n      [showActualSize]="showActualSize"\n      [allowToggleActualSize]="(actualSizeGlobalSetting$ | async) === false"\n      [isPinned]="isPinned$ | async"\n      (onActualSizeToggle)="onActualSizeToggle()"\n      (onPinClicked)="pinStateChanged.emit($event)"\n    ></image-card-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw},{type:PA}]}),{cardId:[{type:xy}],groupName:[{type:xy}],runColorScale:[{type:xy}],fullWidthChanged:[{type:Oy}],pinStateChanged:[{type:Oy}]}),(function(t){t[t.SVG=0]="SVG",t[t.WEBGL=1]="WEBGL"})(K2||(K2={})),(function(t){t[t.LINEAR=0]="LINEAR",t[t.LOG10=1]="LOG10",t[t.TIME=2]="TIME"})(J2||(J2={}));class s5{constructor(){this.runSelected=new Lh,this.PluginType=hA}getDownloadName(t){const e=this.runs.find((t=>t.id===this.selectedRunId));return e?`${e.name}.${t}`:""}}s5.ɵfac=function t(e){return new(e||s5)},s5.ɵcmp=to({type:s5,selectors:[["data_download_dialog_component"]],inputs:{cardMetadata:"cardMetadata",runs:"runs",selectedRunId:"selectedRunId",downloadUrlCsv:"downloadUrlCsv",downloadUrlJson:"downloadUrlJson"},outputs:{runSelected:"runSelected"},decls:3,vars:2,consts:[[4,"ngIf","ngIfElse"],["noCardMetadata",""],["dataName",""],[4,"ngTemplateOutlet"],[1,"tag-name",3,"title"],["appearance","fill",1,"run-selector"],["matNativeControl","","name","run","cdkFocusInitial","","required","",3,"value","change"],["selected","",3,"value"],[3,"value",4,"ngFor","ngForOf"],[1,"download-controls"],["mat-stroked-button","",3,"disabled","download"],["align","end"],["mat-button","","mat-dialog-close",""],[3,"ngSwitch"],[4,"ngSwitchCase"],[4,"NgSwitchDefault"],[3,"value"]],template:function t(e,n){if(1&e&&(Qp(0,a5,30,12,"ng-container",0),Qp(1,r5,1,0,"ng-template",null,1,ib)),2&e){const t=$p(2);Dm("ngIf",n.cardMetadata)("ngIfElse",t)}},directives:[dM,MM,sW,AV,vV,LY,IU,BU,lM,KH,lW,XH,aW,fM,gM],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, 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.mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], 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/**
     * @license
     * Copyright 2010-2021 Three.js Authors
     * SPDX-License-Identifier: MIT
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this.set(1,0,0,0,1,0,0,0,1),this}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],this}extractBasis(t,e,n){return t.setFromMatrix3Column(this,0),e.setFromMatrix3Column(this,1),n.setFromMatrix3Column(this,2),this}setFromMatrix4(t){const e=t.elements;return this.set(e[0],e[4],e[8],e[1],e[5],e[9],e[2],e[6],e[10]),this}multiply(t){return this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,o=e.elements,i=this.elements,a=n[0],r=n[3],s=n[6],l=n[1],c=n[4],d=n[7],p=n[2],m=n[5],u=n[8],f=o[0],g=o[3],h=o[6],b=o[1],y=o[4],_=o[7],C=o[2],M=o[5],v=o[8];return i[0]=a*f+r*b+s*C,i[3]=a*g+r*y+s*M,i[6]=a*h+r*_+s*v,i[1]=l*f+c*b+d*C,i[4]=l*g+c*y+d*M,i[7]=l*h+c*_+d*v,i[2]=p*f+m*b+u*C,i[5]=p*g+m*y+u*M,i[8]=p*h+m*_+u*v,this}multiplyScalar(t){const e=this.elements;return 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this.set(n*s,n*l,-n*(s*a+l*r)+a+t,-o*l,o*s,-o*(-l*a+s*r)+r+e,0,0,1),this}scale(t,e){const n=this.elements;return n[0]*=t,n[3]*=t,n[6]*=t,n[1]*=e,n[4]*=e,n[7]*=e,this}rotate(t){const e=Math.cos(t),n=Math.sin(t),o=this.elements,i=o[0],a=o[3],r=o[6],s=o[1],l=o[4],c=o[7];return o[0]=e*i+n*s,o[3]=e*a+n*l,o[6]=e*r+n*c,o[1]=-n*i+e*s,o[4]=-n*a+e*l,o[7]=-n*r+e*c,this}translate(t,e){const n=this.elements;return n[0]+=t*n[2],n[3]+=t*n[5],n[6]+=t*n[8],n[1]+=e*n[2],n[4]+=e*n[5],n[7]+=e*n[8],this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<9;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<9;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t}clone(){return(new this.constructor).fromArray(this.elements)}}let tat;$it.prototype.isMatrix3=!0;class eat{static getDataURL(t){if(/^data:/i.test(t.src))return t.src;if("undefined"==typeof HTMLCanvasElement)return t.src;let e;if(t instanceof HTMLCanvasElement)e=t;else{void 0===tat&&(tat=document.createElementNS("http://www.w3.org/1999/xhtml","canvas")),tat.width=t.width,tat.height=t.height;const n=tat.getContext("2d");t instanceof ImageData?n.putImageData(t,0,0):n.drawImage(t,0,0,t.width,t.height),e=tat}return e.width>2048||e.height>2048?(console.warn("THREE.ImageUtils.getDataURL: Image converted to jpg for performance reasons",t),e.toDataURL("image/jpeg",.6)):e.toDataURL("image/png")}}let nat=0;class oat extends Uit{constructor(t=oat.DEFAULT_IMAGE,e=oat.DEFAULT_MAPPING,n=1001,o=1001,i=1006,a=1008,r=1023,s=1009,l=1,c=3e3){super(),Object.defineProperty(this,"id",{value:nat++}),this.uuid=qit(),this.name="",this.image=t,this.mipmaps=[],this.mapping=e,this.wrapS=n,this.wrapT=o,this.magFilter=i,this.minFilter=a,this.anisotropy=l,this.format=r,this.internalFormat=null,this.type=s,this.offset=new Qit(0,0),this.repeat=new Qit(1,1),this.center=new Qit(0,0),this.rotation=0,this.matrixAutoUpdate=!0,this.matrix=new $it,this.generateMipmaps=!0,this.premultiplyAlpha=!1,this.flipY=!0,this.unpackAlignment=4,this.encoding=c,this.version=0,this.onUpdate=null,this.isRenderTargetTexture=!1}updateMatrix(){this.matrix.setUvTransform(this.offset.x,this.offset.y,this.repeat.x,this.repeat.y,this.rotation,this.center.x,this.center.y)}clone(){return(new this.constructor).copy(this)}copy(t){return this.name=t.name,this.image=t.image,this.mipmaps=t.mipmaps.slice(0),this.mapping=t.mapping,this.wrapS=t.wrapS,this.wrapT=t.wrapT,this.magFilter=t.magFilter,this.minFilter=t.minFilter,this.anisotropy=t.anisotropy,this.format=t.format,this.internalFormat=t.internalFormat,this.type=t.type,this.offset.copy(t.offset),this.repeat.copy(t.repeat),this.center.copy(t.center),this.rotation=t.rotation,this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrix.copy(t.matrix),this.generateMipmaps=t.generateMipmaps,this.premultiplyAlpha=t.premultiplyAlpha,this.flipY=t.flipY,this.unpackAlignment=t.unpackAlignment,this.encoding=t.encoding,this}toJSON(t){const e=void 0===t||"string"==typeof t;if(!e&&void 0!==t.textures[this.uuid])return t.textures[this.uuid];const n={metadata:{version:4.5,type:"Texture",generator:"Texture.toJSON"},uuid:this.uuid,name:this.name,mapping:this.mapping,repeat:[this.repeat.x,this.repeat.y],offset:[this.offset.x,this.offset.y],center:[this.center.x,this.center.y],rotation:this.rotation,wrap:[this.wrapS,this.wrapT],format:this.format,type:this.type,encoding:this.encoding,minFilter:this.minFilter,magFilter:this.magFilter,anisotropy:this.anisotropy,flipY:this.flipY,premultiplyAlpha:this.premultiplyAlpha,unpackAlignment:this.unpackAlignment};if(void 0!==this.image){const o=this.image;if(void 0===o.uuid&&(o.uuid=qit()),!e&&void 0===t.images[o.uuid]){let e;if(Array.isArray(o)){e=[];for(let t=0,n=o.length;t<n;t++)e.push(iat(o[t].isDataTexture?o[t].image:o[t]))}else e=iat(o);t.images[o.uuid]={uuid:o.uuid,url:e}}n.image=o.uuid}return e||(t.textures[this.uuid]=n),n}dispose(){this.dispatchEvent({type:"dispose"})}transformUv(t){if(300!==this.mapping)return t;if(t.applyMatrix3(this.matrix),t.x<0||t.x>1)switch(this.wrapS){case uit:t.x=t.x-Math.floor(t.x);break;case fit:t.x=t.x<0?0:1;break;case git:t.x=1===Math.abs(Math.floor(t.x)%2)?Math.ceil(t.x)-t.x:t.x-Math.floor(t.x)}if(t.y<0||t.y>1)switch(this.wrapT){case uit:t.y=t.y-Math.floor(t.y);break;case fit:t.y=t.y<0?0:1;break;case git:t.y=1===Math.abs(Math.floor(t.y)%2)?Math.ceil(t.y)-t.y:t.y-Math.floor(t.y)}return this.flipY&&(t.y=1-t.y),t}set needsUpdate(t){!0===t&&this.version++}}function iat(t){return"undefined"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||"undefined"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||"undefined"!=typeof ImageBitmap&&t instanceof ImageBitmap?eat.getDataURL(t):t.data?{data:Array.prototype.slice.call(t.data),width:t.width,height:t.height,type:t.data.constructor.name}:(console.warn("THREE.Texture: Unable to serialize Texture."),{})}oat.DEFAULT_IMAGE=void 0,oat.DEFAULT_MAPPING=300,oat.prototype.isTexture=!0;class aat{constructor(t=0,e=0,n=0,o=1){this.x=t,this.y=e,this.z=n,this.w=o}get width(){return this.z}set width(t){this.z=t}get height(){return this.w}set height(t){this.w=t}set(t,e,n,o){return this.x=t,this.y=e,this.z=n,this.w=o,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this.w=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setW(t){return this.w=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;case 3:this.w=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;case 3:return this.w;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y,this.z,this.w)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this.w=void 0!==t.w?t.w:1,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector4: .add() now only accepts one argument. 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Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this.w-=t.w,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this.w-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this.w=t.w-e.w,this}multiply(t){return this.x*=t.x,this.y*=t.y,this.z*=t.z,this.w*=t.w,this}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this.w*=t,this}applyMatrix4(t){const e=this.x,n=this.y,o=this.z,i=this.w,a=t.elements;return this.x=a[0]*e+a[4]*n+a[8]*o+a[12]*i,this.y=a[1]*e+a[5]*n+a[9]*o+a[13]*i,this.z=a[2]*e+a[6]*n+a[10]*o+a[14]*i,this.w=a[3]*e+a[7]*n+a[11]*o+a[15]*i,this}divideScalar(t){return this.multiplyScalar(1/t)}setAxisAngleFromQuaternion(t){this.w=2*Math.acos(t.w);const e=Math.sqrt(1-t.w*t.w);return e<1e-4?(this.x=1,this.y=0,this.z=0):(this.x=t.x/e,this.y=t.y/e,this.z=t.z/e),this}setAxisAngleFromRotationMatrix(t){let e,n,o,i;const a=.01,r=.1,s=t.elements,l=s[0],c=s[4],d=s[8],p=s[1],m=s[5],u=s[9],f=s[2],g=s[6],h=s[10];if(Math.abs(c-p)<a&&Math.abs(d-f)<a&&Math.abs(u-g)<a){if(Math.abs(c+p)<r&&Math.abs(d+f)<r&&Math.abs(u+g)<r&&Math.abs(l+m+h-3)<r)return this.set(1,0,0,0),this;e=Math.PI;const t=(l+1)/2,s=(m+1)/2,b=(h+1)/2,y=(c+p)/4,_=(d+f)/4,C=(u+g)/4;return t>s&&t>b?t<a?(n=0,o=.707106781,i=.707106781):(n=Math.sqrt(t),o=y/n,i=_/n):s>b?s<a?(n=.707106781,o=0,i=.707106781):(o=Math.sqrt(s),n=y/o,i=C/o):b<a?(n=.707106781,o=.707106781,i=0):(i=Math.sqrt(b),n=_/i,o=C/i),this.set(n,o,i,e),this}let b=Math.sqrt((g-u)*(g-u)+(d-f)*(d-f)+(p-c)*(p-c));return Math.abs(b)<.001&&(b=1),this.x=(g-u)/b,this.y=(d-f)/b,this.z=(p-c)/b,this.w=Math.acos((l+m+h-1)/2),this}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this.w=Math.min(this.w,t.w),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this.w=Math.max(this.w,t.w),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this.w=Math.max(t.w,Math.min(e.w,this.w)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this.w=Math.max(t,Math.min(e,this.w)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this.w=Math.floor(this.w),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this.w=Math.ceil(this.w),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this.w=Math.round(this.w),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this.w=this.w<0?Math.ceil(this.w):Math.floor(this.w),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this.w=-this.w,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z+this.w*t.w}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z+this.w*this.w)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)+Math.abs(this.w)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this.w+=(t.w-this.w)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this.w=t.w+(e.w-t.w)*n,this}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z&&t.w===this.w}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this.w=t[e+3],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t[e+3]=this.w,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector4: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this.w=t.getW(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this.w=Math.random(),this}}aat.prototype.isVector4=!0;class rat extends Uit{constructor(t,e,n={}){super(),this.width=t,this.height=e,this.depth=1,this.scissor=new aat(0,0,t,e),this.scissorTest=!1,this.viewport=new aat(0,0,t,e),this.texture=new oat(void 0,n.mapping,n.wrapS,n.wrapT,n.magFilter,n.minFilter,n.format,n.type,n.anisotropy,n.encoding),this.texture.isRenderTargetTexture=!0,this.texture.image={width:t,height:e,depth:1},this.texture.generateMipmaps=void 0!==n.generateMipmaps&&n.generateMipmaps,this.texture.minFilter=void 0!==n.minFilter?n.minFilter:bit,this.depthBuffer=void 0===n.depthBuffer||n.depthBuffer,this.stencilBuffer=void 0!==n.stencilBuffer&&n.stencilBuffer,this.depthTexture=void 0!==n.depthTexture?n.depthTexture:null}setTexture(t){t.image={width:this.width,height:this.height,depth:this.depth},this.texture=t}setSize(t,e,n=1){this.width===t&&this.height===e&&this.depth===n||(this.width=t,this.height=e,this.depth=n,this.texture.image.width=t,this.texture.image.height=e,this.texture.image.depth=n,this.dispose()),this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e)}clone(){return(new this.constructor).copy(this)}copy(t){return this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.copy(t.viewport),this.texture=t.texture.clone(),this.texture.image={...this.texture.image},this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this}dispose(){this.dispatchEvent({type:"dispose"})}}rat.prototype.isWebGLRenderTarget=!0,class extends rat{constructor(t,e,n){super(t,e);const o=this.texture;this.texture=[];for(let t=0;t<n;t++)this.texture[t]=o.clone()}setSize(t,e,n=1){if(this.width!==t||this.height!==e||this.depth!==n){this.width=t,this.height=e,this.depth=n;for(let o=0,i=this.texture.length;o<i;o++)this.texture[o].image.width=t,this.texture[o].image.height=e,this.texture[o].image.depth=n;this.dispose()}return this.viewport.set(0,0,t,e),this.scissor.set(0,0,t,e),this}copy(t){this.dispose(),this.width=t.width,this.height=t.height,this.depth=t.depth,this.viewport.set(0,0,this.width,this.height),this.scissor.set(0,0,this.width,this.height),this.depthBuffer=t.depthBuffer,this.stencilBuffer=t.stencilBuffer,this.depthTexture=t.depthTexture,this.texture.length=0;for(let e=0,n=t.texture.length;e<n;e++)this.texture[e]=t.texture[e].clone();return this}}.prototype.isWebGLMultipleRenderTargets=!0;class sat extends rat{constructor(t,e,n){super(t,e,n),this.samples=4}copy(t){return super.copy.call(this,t),this.samples=t.samples,this}}sat.prototype.isWebGLMultisampleRenderTarget=!0;class lat{constructor(t=0,e=0,n=0,o=1){this._x=t,this._y=e,this._z=n,this._w=o}static slerp(t,e,n,o){return console.warn("THREE.Quaternion: Static .slerp() has been deprecated. Use qm.slerpQuaternions( qa, qb, t ) instead."),n.slerpQuaternions(t,e,o)}static slerpFlat(t,e,n,o,i,a,r){let s=n[o+0],l=n[o+1],c=n[o+2],d=n[o+3];const p=i[a+0],m=i[a+1],u=i[a+2],f=i[a+3];if(0===r)return t[e+0]=s,t[e+1]=l,t[e+2]=c,void(t[e+3]=d);if(1===r)return t[e+0]=p,t[e+1]=m,t[e+2]=u,void(t[e+3]=f);if(d!==f||s!==p||l!==m||c!==u){let t=1-r;const e=s*p+l*m+c*u+d*f,n=e>=0?1:-1,o=1-e*e;if(o>Number.EPSILON){const i=Math.sqrt(o),a=Math.atan2(i,e*n);t=Math.sin(t*a)/i,r=Math.sin(r*a)/i}const i=r*n;if(s=s*t+p*i,l=l*t+m*i,c=c*t+u*i,d=d*t+f*i,t===1-r){const t=1/Math.sqrt(s*s+l*l+c*c+d*d);s*=t,l*=t,c*=t,d*=t}}t[e]=s,t[e+1]=l,t[e+2]=c,t[e+3]=d}static multiplyQuaternionsFlat(t,e,n,o,i,a){const r=n[o],s=n[o+1],l=n[o+2],c=n[o+3],d=i[a],p=i[a+1],m=i[a+2],u=i[a+3];return t[e]=r*u+c*d+s*m-l*p,t[e+1]=s*u+c*p+l*d-r*m,t[e+2]=l*u+c*m+r*p-s*d,t[e+3]=c*u-r*d-s*p-l*m,t}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get w(){return this._w}set w(t){this._w=t,this._onChangeCallback()}set(t,e,n,o){return this._x=t,this._y=e,this._z=n,this._w=o,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._w)}copy(t){return this._x=t.x,this._y=t.y,this._z=t.z,this._w=t.w,this._onChangeCallback(),this}setFromEuler(t,e){if(!t||!t.isEuler)throw new Error("THREE.Quaternion: .setFromEuler() now expects an Euler rotation rather than a Vector3 and order.");const n=t._x,o=t._y,i=t._z,a=t._order,r=Math.cos,s=Math.sin,l=r(n/2),c=r(o/2),d=r(i/2),p=s(n/2),m=s(o/2),u=s(i/2);switch(a){case"XYZ":this._x=p*c*d+l*m*u,this._y=l*m*d-p*c*u,this._z=l*c*u+p*m*d,this._w=l*c*d-p*m*u;break;case"YXZ":this._x=p*c*d+l*m*u,this._y=l*m*d-p*c*u,this._z=l*c*u-p*m*d,this._w=l*c*d+p*m*u;break;case"ZXY":this._x=p*c*d-l*m*u,this._y=l*m*d+p*c*u,this._z=l*c*u+p*m*d,this._w=l*c*d-p*m*u;break;case"ZYX":this._x=p*c*d-l*m*u,this._y=l*m*d+p*c*u,this._z=l*c*u-p*m*d,this._w=l*c*d+p*m*u;break;case"YZX":this._x=p*c*d+l*m*u,this._y=l*m*d+p*c*u,this._z=l*c*u-p*m*d,this._w=l*c*d-p*m*u;break;case"XZY":this._x=p*c*d-l*m*u,this._y=l*m*d-p*c*u,this._z=l*c*u+p*m*d,this._w=l*c*d+p*m*u;break;default:console.warn("THREE.Quaternion: .setFromEuler() encountered an unknown order: "+a)}return!1!==e&&this._onChangeCallback(),this}setFromAxisAngle(t,e){const n=e/2,o=Math.sin(n);return this._x=t.x*o,this._y=t.y*o,this._z=t.z*o,this._w=Math.cos(n),this._onChangeCallback(),this}setFromRotationMatrix(t){const e=t.elements,n=e[0],o=e[4],i=e[8],a=e[1],r=e[5],s=e[9],l=e[2],c=e[6],d=e[10],p=n+r+d;if(p>0){const t=.5/Math.sqrt(p+1);this._w=.25/t,this._x=(c-s)*t,this._y=(i-l)*t,this._z=(a-o)*t}else if(n>r&&n>d){const t=2*Math.sqrt(1+n-r-d);this._w=(c-s)/t,this._x=.25*t,this._y=(o+a)/t,this._z=(i+l)/t}else if(r>d){const t=2*Math.sqrt(1+r-n-d);this._w=(i-l)/t,this._x=(o+a)/t,this._y=.25*t,this._z=(s+c)/t}else{const t=2*Math.sqrt(1+d-n-r);this._w=(a-o)/t,this._x=(i+l)/t,this._y=(s+c)/t,this._z=.25*t}return this._onChangeCallback(),this}setFromUnitVectors(t,e){let n=t.dot(e)+1;return n<Number.EPSILON?(n=0,Math.abs(t.x)>Math.abs(t.z)?(this._x=-t.y,this._y=t.x,this._z=0,this._w=n):(this._x=0,this._y=-t.z,this._z=t.y,this._w=n)):(this._x=t.y*e.z-t.z*e.y,this._y=t.z*e.x-t.x*e.z,this._z=t.x*e.y-t.y*e.x,this._w=n),this.normalize()}angleTo(t){return 2*Math.acos(Math.abs(Zit(this.dot(t),-1,1)))}rotateTowards(t,e){const n=this.angleTo(t);if(0===n)return this;const o=Math.min(1,e/n);return this.slerp(t,o),this}identity(){return this.set(0,0,0,1)}invert(){return this.conjugate()}conjugate(){return this._x*=-1,this._y*=-1,this._z*=-1,this._onChangeCallback(),this}dot(t){return this._x*t._x+this._y*t._y+this._z*t._z+this._w*t._w}lengthSq(){return this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w}length(){return Math.sqrt(this._x*this._x+this._y*this._y+this._z*this._z+this._w*this._w)}normalize(){let t=this.length();return 0===t?(this._x=0,this._y=0,this._z=0,this._w=1):(t=1/t,this._x=this._x*t,this._y=this._y*t,this._z=this._z*t,this._w=this._w*t),this._onChangeCallback(),this}multiply(t,e){return void 0!==e?(console.warn("THREE.Quaternion: .multiply() now only accepts one argument. Use .multiplyQuaternions( a, b ) instead."),this.multiplyQuaternions(t,e)):this.multiplyQuaternions(this,t)}premultiply(t){return this.multiplyQuaternions(t,this)}multiplyQuaternions(t,e){const n=t._x,o=t._y,i=t._z,a=t._w,r=e._x,s=e._y,l=e._z,c=e._w;return this._x=n*c+a*r+o*l-i*s,this._y=o*c+a*s+i*r-n*l,this._z=i*c+a*l+n*s-o*r,this._w=a*c-n*r-o*s-i*l,this._onChangeCallback(),this}slerp(t,e){if(0===e)return this;if(1===e)return this.copy(t);const n=this._x,o=this._y,i=this._z,a=this._w;let r=a*t._w+n*t._x+o*t._y+i*t._z;if(r<0?(this._w=-t._w,this._x=-t._x,this._y=-t._y,this._z=-t._z,r=-r):this.copy(t),r>=1)return this._w=a,this._x=n,this._y=o,this._z=i,this;const s=1-r*r;if(s<=Number.EPSILON){const t=1-e;return this._w=t*a+e*this._w,this._x=t*n+e*this._x,this._y=t*o+e*this._y,this._z=t*i+e*this._z,this.normalize(),this._onChangeCallback(),this}const l=Math.sqrt(s),c=Math.atan2(l,r),d=Math.sin((1-e)*c)/l,p=Math.sin(e*c)/l;return this._w=a*d+this._w*p,this._x=n*d+this._x*p,this._y=o*d+this._y*p,this._z=i*d+this._z*p,this._onChangeCallback(),this}slerpQuaternions(t,e,n){this.copy(t).slerp(e,n)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._w===this._w}fromArray(t,e=0){return this._x=t[e],this._y=t[e+1],this._z=t[e+2],this._w=t[e+3],this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._w,t}fromBufferAttribute(t,e){return this._x=t.getX(e),this._y=t.getY(e),this._z=t.getZ(e),this._w=t.getW(e),this}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}lat.prototype.isQuaternion=!0;class cat{constructor(t=0,e=0,n=0){this.x=t,this.y=e,this.z=n}set(t,e,n){return void 0===n&&(n=this.z),this.x=t,this.y=e,this.z=n,this}setScalar(t){return this.x=t,this.y=t,this.z=t,this}setX(t){return this.x=t,this}setY(t){return this.y=t,this}setZ(t){return this.z=t,this}setComponent(t,e){switch(t){case 0:this.x=e;break;case 1:this.y=e;break;case 2:this.z=e;break;default:throw new Error("index is out of range: "+t)}return this}getComponent(t){switch(t){case 0:return this.x;case 1:return this.y;case 2:return this.z;default:throw new Error("index is out of range: "+t)}}clone(){return new this.constructor(this.x,this.y,this.z)}copy(t){return this.x=t.x,this.y=t.y,this.z=t.z,this}add(t,e){return void 0!==e?(console.warn("THREE.Vector3: .add() now only accepts one argument. Use .addVectors( a, b ) instead."),this.addVectors(t,e)):(this.x+=t.x,this.y+=t.y,this.z+=t.z,this)}addScalar(t){return this.x+=t,this.y+=t,this.z+=t,this}addVectors(t,e){return this.x=t.x+e.x,this.y=t.y+e.y,this.z=t.z+e.z,this}addScaledVector(t,e){return this.x+=t.x*e,this.y+=t.y*e,this.z+=t.z*e,this}sub(t,e){return void 0!==e?(console.warn("THREE.Vector3: .sub() now only accepts one argument. Use .subVectors( a, b ) instead."),this.subVectors(t,e)):(this.x-=t.x,this.y-=t.y,this.z-=t.z,this)}subScalar(t){return this.x-=t,this.y-=t,this.z-=t,this}subVectors(t,e){return this.x=t.x-e.x,this.y=t.y-e.y,this.z=t.z-e.z,this}multiply(t,e){return void 0!==e?(console.warn("THREE.Vector3: .multiply() now only accepts one argument. Use .multiplyVectors( a, b ) instead."),this.multiplyVectors(t,e)):(this.x*=t.x,this.y*=t.y,this.z*=t.z,this)}multiplyScalar(t){return this.x*=t,this.y*=t,this.z*=t,this}multiplyVectors(t,e){return this.x=t.x*e.x,this.y=t.y*e.y,this.z=t.z*e.z,this}applyEuler(t){return t&&t.isEuler||console.error("THREE.Vector3: .applyEuler() now expects an Euler rotation rather than a Vector3 and order."),this.applyQuaternion(pat.setFromEuler(t))}applyAxisAngle(t,e){return this.applyQuaternion(pat.setFromAxisAngle(t,e))}applyMatrix3(t){const e=this.x,n=this.y,o=this.z,i=t.elements;return this.x=i[0]*e+i[3]*n+i[6]*o,this.y=i[1]*e+i[4]*n+i[7]*o,this.z=i[2]*e+i[5]*n+i[8]*o,this}applyNormalMatrix(t){return this.applyMatrix3(t).normalize()}applyMatrix4(t){const e=this.x,n=this.y,o=this.z,i=t.elements,a=1/(i[3]*e+i[7]*n+i[11]*o+i[15]);return this.x=(i[0]*e+i[4]*n+i[8]*o+i[12])*a,this.y=(i[1]*e+i[5]*n+i[9]*o+i[13])*a,this.z=(i[2]*e+i[6]*n+i[10]*o+i[14])*a,this}applyQuaternion(t){const e=this.x,n=this.y,o=this.z,i=t.x,a=t.y,r=t.z,s=t.w,l=s*e+a*o-r*n,c=s*n+r*e-i*o,d=s*o+i*n-a*e,p=-i*e-a*n-r*o;return this.x=l*s+p*-i+c*-r-d*-a,this.y=c*s+p*-a+d*-i-l*-r,this.z=d*s+p*-r+l*-a-c*-i,this}project(t){return this.applyMatrix4(t.matrixWorldInverse).applyMatrix4(t.projectionMatrix)}unproject(t){return this.applyMatrix4(t.projectionMatrixInverse).applyMatrix4(t.matrixWorld)}transformDirection(t){const e=this.x,n=this.y,o=this.z,i=t.elements;return this.x=i[0]*e+i[4]*n+i[8]*o,this.y=i[1]*e+i[5]*n+i[9]*o,this.z=i[2]*e+i[6]*n+i[10]*o,this.normalize()}divide(t){return this.x/=t.x,this.y/=t.y,this.z/=t.z,this}divideScalar(t){return this.multiplyScalar(1/t)}min(t){return this.x=Math.min(this.x,t.x),this.y=Math.min(this.y,t.y),this.z=Math.min(this.z,t.z),this}max(t){return this.x=Math.max(this.x,t.x),this.y=Math.max(this.y,t.y),this.z=Math.max(this.z,t.z),this}clamp(t,e){return this.x=Math.max(t.x,Math.min(e.x,this.x)),this.y=Math.max(t.y,Math.min(e.y,this.y)),this.z=Math.max(t.z,Math.min(e.z,this.z)),this}clampScalar(t,e){return this.x=Math.max(t,Math.min(e,this.x)),this.y=Math.max(t,Math.min(e,this.y)),this.z=Math.max(t,Math.min(e,this.z)),this}clampLength(t,e){const n=this.length();return this.divideScalar(n||1).multiplyScalar(Math.max(t,Math.min(e,n)))}floor(){return this.x=Math.floor(this.x),this.y=Math.floor(this.y),this.z=Math.floor(this.z),this}ceil(){return this.x=Math.ceil(this.x),this.y=Math.ceil(this.y),this.z=Math.ceil(this.z),this}round(){return this.x=Math.round(this.x),this.y=Math.round(this.y),this.z=Math.round(this.z),this}roundToZero(){return this.x=this.x<0?Math.ceil(this.x):Math.floor(this.x),this.y=this.y<0?Math.ceil(this.y):Math.floor(this.y),this.z=this.z<0?Math.ceil(this.z):Math.floor(this.z),this}negate(){return this.x=-this.x,this.y=-this.y,this.z=-this.z,this}dot(t){return this.x*t.x+this.y*t.y+this.z*t.z}lengthSq(){return this.x*this.x+this.y*this.y+this.z*this.z}length(){return Math.sqrt(this.x*this.x+this.y*this.y+this.z*this.z)}manhattanLength(){return Math.abs(this.x)+Math.abs(this.y)+Math.abs(this.z)}normalize(){return this.divideScalar(this.length()||1)}setLength(t){return this.normalize().multiplyScalar(t)}lerp(t,e){return this.x+=(t.x-this.x)*e,this.y+=(t.y-this.y)*e,this.z+=(t.z-this.z)*e,this}lerpVectors(t,e,n){return this.x=t.x+(e.x-t.x)*n,this.y=t.y+(e.y-t.y)*n,this.z=t.z+(e.z-t.z)*n,this}cross(t,e){return void 0!==e?(console.warn("THREE.Vector3: .cross() now only accepts one argument. Use .crossVectors( a, b ) instead."),this.crossVectors(t,e)):this.crossVectors(this,t)}crossVectors(t,e){const n=t.x,o=t.y,i=t.z,a=e.x,r=e.y,s=e.z;return this.x=o*s-i*r,this.y=i*a-n*s,this.z=n*r-o*a,this}projectOnVector(t){const e=t.lengthSq();if(0===e)return this.set(0,0,0);const n=t.dot(this)/e;return this.copy(t).multiplyScalar(n)}projectOnPlane(t){return dat.copy(this).projectOnVector(t),this.sub(dat)}reflect(t){return this.sub(dat.copy(t).multiplyScalar(2*this.dot(t)))}angleTo(t){const e=Math.sqrt(this.lengthSq()*t.lengthSq());if(0===e)return Math.PI/2;const n=this.dot(t)/e;return Math.acos(Zit(n,-1,1))}distanceTo(t){return Math.sqrt(this.distanceToSquared(t))}distanceToSquared(t){const e=this.x-t.x,n=this.y-t.y,o=this.z-t.z;return e*e+n*n+o*o}manhattanDistanceTo(t){return Math.abs(this.x-t.x)+Math.abs(this.y-t.y)+Math.abs(this.z-t.z)}setFromSpherical(t){return this.setFromSphericalCoords(t.radius,t.phi,t.theta)}setFromSphericalCoords(t,e,n){const o=Math.sin(e)*t;return this.x=o*Math.sin(n),this.y=Math.cos(e)*t,this.z=o*Math.cos(n),this}setFromCylindrical(t){return this.setFromCylindricalCoords(t.radius,t.theta,t.y)}setFromCylindricalCoords(t,e,n){return this.x=t*Math.sin(e),this.y=n,this.z=t*Math.cos(e),this}setFromMatrixPosition(t){const e=t.elements;return this.x=e[12],this.y=e[13],this.z=e[14],this}setFromMatrixScale(t){const e=this.setFromMatrixColumn(t,0).length(),n=this.setFromMatrixColumn(t,1).length(),o=this.setFromMatrixColumn(t,2).length();return this.x=e,this.y=n,this.z=o,this}setFromMatrixColumn(t,e){return this.fromArray(t.elements,4*e)}setFromMatrix3Column(t,e){return this.fromArray(t.elements,3*e)}equals(t){return t.x===this.x&&t.y===this.y&&t.z===this.z}fromArray(t,e=0){return this.x=t[e],this.y=t[e+1],this.z=t[e+2],this}toArray(t=[],e=0){return t[e]=this.x,t[e+1]=this.y,t[e+2]=this.z,t}fromBufferAttribute(t,e,n){return void 0!==n&&console.warn("THREE.Vector3: offset has been removed from .fromBufferAttribute()."),this.x=t.getX(e),this.y=t.getY(e),this.z=t.getZ(e),this}random(){return this.x=Math.random(),this.y=Math.random(),this.z=Math.random(),this}}cat.prototype.isVector3=!0;const dat=new cat,pat=new lat;class mat{constructor(t=new cat(1/0,1/0,1/0),e=new cat(-1/0,-1/0,-1/0)){this.min=t,this.max=e}set(t,e){return this.min.copy(t),this.max.copy(e),this}setFromArray(t){let e=1/0,n=1/0,o=1/0,i=-1/0,a=-1/0,r=-1/0;for(let s=0,l=t.length;s<l;s+=3){const l=t[s],c=t[s+1],d=t[s+2];l<e&&(e=l),c<n&&(n=c),d<o&&(o=d),l>i&&(i=l),c>a&&(a=c),d>r&&(r=d)}return this.min.set(e,n,o),this.max.set(i,a,r),this}setFromBufferAttribute(t){let e=1/0,n=1/0,o=1/0,i=-1/0,a=-1/0,r=-1/0;for(let s=0,l=t.count;s<l;s++){const l=t.getX(s),c=t.getY(s),d=t.getZ(s);l<e&&(e=l),c<n&&(n=c),d<o&&(o=d),l>i&&(i=l),c>a&&(a=c),d>r&&(r=d)}return this.min.set(e,n,o),this.max.set(i,a,r),this}setFromPoints(t){this.makeEmpty();for(let e=0,n=t.length;e<n;e++)this.expandByPoint(t[e]);return this}setFromCenterAndSize(t,e){const n=fat.copy(e).multiplyScalar(.5);return this.min.copy(t).sub(n),this.max.copy(t).add(n),this}setFromObject(t){return this.makeEmpty(),this.expandByObject(t)}clone(){return(new this.constructor).copy(this)}copy(t){return this.min.copy(t.min),this.max.copy(t.max),this}makeEmpty(){return this.min.x=this.min.y=this.min.z=1/0,this.max.x=this.max.y=this.max.z=-1/0,this}isEmpty(){return this.max.x<this.min.x||this.max.y<this.min.y||this.max.z<this.min.z}getCenter(t){return this.isEmpty()?t.set(0,0,0):t.addVectors(this.min,this.max).multiplyScalar(.5)}getSize(t){return this.isEmpty()?t.set(0,0,0):t.subVectors(this.max,this.min)}expandByPoint(t){return this.min.min(t),this.max.max(t),this}expandByVector(t){return this.min.sub(t),this.max.add(t),this}expandByScalar(t){return this.min.addScalar(-t),this.max.addScalar(t),this}expandByObject(t){t.updateWorldMatrix(!1,!1);const e=t.geometry;void 0!==e&&(null===e.boundingBox&&e.computeBoundingBox(),gat.copy(e.boundingBox),gat.applyMatrix4(t.matrixWorld),this.union(gat));const n=t.children;for(let t=0,e=n.length;t<e;t++)this.expandByObject(n[t]);return this}containsPoint(t){return!(t.x<this.min.x||t.x>this.max.x||t.y<this.min.y||t.y>this.max.y||t.z<this.min.z||t.z>this.max.z)}containsBox(t){return this.min.x<=t.min.x&&t.max.x<=this.max.x&&this.min.y<=t.min.y&&t.max.y<=this.max.y&&this.min.z<=t.min.z&&t.max.z<=this.max.z}getParameter(t,e){return e.set((t.x-this.min.x)/(this.max.x-this.min.x),(t.y-this.min.y)/(this.max.y-this.min.y),(t.z-this.min.z)/(this.max.z-this.min.z))}intersectsBox(t){return!(t.max.x<this.min.x||t.min.x>this.max.x||t.max.y<this.min.y||t.min.y>this.max.y||t.max.z<this.min.z||t.min.z>this.max.z)}intersectsSphere(t){return this.clampPoint(t.center,fat),fat.distanceToSquared(t.center)<=t.radius*t.radius}intersectsPlane(t){let e,n;return t.normal.x>0?(e=t.normal.x*this.min.x,n=t.normal.x*this.max.x):(e=t.normal.x*this.max.x,n=t.normal.x*this.min.x),t.normal.y>0?(e+=t.normal.y*this.min.y,n+=t.normal.y*this.max.y):(e+=t.normal.y*this.max.y,n+=t.normal.y*this.min.y),t.normal.z>0?(e+=t.normal.z*this.min.z,n+=t.normal.z*this.max.z):(e+=t.normal.z*this.max.z,n+=t.normal.z*this.min.z),e<=-t.constant&&n>=-t.constant}intersectsTriangle(t){if(this.isEmpty())return!1;this.getCenter(vat),xat.subVectors(this.max,vat),hat.subVectors(t.a,vat),bat.subVectors(t.b,vat),yat.subVectors(t.c,vat),_at.subVectors(bat,hat),Cat.subVectors(yat,bat),Mat.subVectors(hat,yat);let e=[0,-_at.z,_at.y,0,-Cat.z,Cat.y,0,-Mat.z,Mat.y,_at.z,0,-_at.x,Cat.z,0,-Cat.x,Mat.z,0,-Mat.x,-_at.y,_at.x,0,-Cat.y,Cat.x,0,-Mat.y,Mat.x,0];return!!wat(e,hat,bat,yat,xat)&&(e=[1,0,0,0,1,0,0,0,1],!!wat(e,hat,bat,yat,xat)&&(Oat.crossVectors(_at,Cat),e=[Oat.x,Oat.y,Oat.z],wat(e,hat,bat,yat,xat)))}clampPoint(t,e){return e.copy(t).clamp(this.min,this.max)}distanceToPoint(t){return fat.copy(t).clamp(this.min,this.max).sub(t).length()}getBoundingSphere(t){return this.getCenter(t.center),t.radius=.5*this.getSize(fat).length(),t}intersect(t){return this.min.max(t.min),this.max.min(t.max),this.isEmpty()&&this.makeEmpty(),this}union(t){return this.min.min(t.min),this.max.max(t.max),this}applyMatrix4(t){return this.isEmpty()||(uat[0].set(this.min.x,this.min.y,this.min.z).applyMatrix4(t),uat[1].set(this.min.x,this.min.y,this.max.z).applyMatrix4(t),uat[2].set(this.min.x,this.max.y,this.min.z).applyMatrix4(t),uat[3].set(this.min.x,this.max.y,this.max.z).applyMatrix4(t),uat[4].set(this.max.x,this.min.y,this.min.z).applyMatrix4(t),uat[5].set(this.max.x,this.min.y,this.max.z).applyMatrix4(t),uat[6].set(this.max.x,this.max.y,this.min.z).applyMatrix4(t),uat[7].set(this.max.x,this.max.y,this.max.z).applyMatrix4(t),this.setFromPoints(uat)),this}translate(t){return this.min.add(t),this.max.add(t),this}equals(t){return t.min.equals(this.min)&&t.max.equals(this.max)}}mat.prototype.isBox3=!0;const uat=[new cat,new cat,new cat,new cat,new cat,new cat,new cat,new cat],fat=new cat,gat=new mat,hat=new cat,bat=new cat,yat=new cat,_at=new cat,Cat=new cat,Mat=new cat,vat=new cat,xat=new cat,Oat=new cat,Pat=new cat;function wat(t,e,n,o,i){for(let a=0,r=t.length-3;a<=r;a+=3){Pat.fromArray(t,a);const r=i.x*Math.abs(Pat.x)+i.y*Math.abs(Pat.y)+i.z*Math.abs(Pat.z),s=e.dot(Pat),l=n.dot(Pat),c=o.dot(Pat);if(Math.max(-Math.max(s,l,c),Math.min(s,l,c))>r)return!1}return!0}const kat=new mat,Sat=new cat,Dat=new cat,Eat=new cat;class Rat{constructor(t=new cat,e=-1){this.center=t,this.radius=e}set(t,e){return this.center.copy(t),this.radius=e,this}setFromPoints(t,e){const n=this.center;void 0!==e?n.copy(e):kat.setFromPoints(t).getCenter(n);let o=0;for(let e=0,i=t.length;e<i;e++)o=Math.max(o,n.distanceToSquared(t[e]));return this.radius=Math.sqrt(o),this}copy(t){return this.center.copy(t.center),this.radius=t.radius,this}isEmpty(){return this.radius<0}makeEmpty(){return this.center.set(0,0,0),this.radius=-1,this}containsPoint(t){return t.distanceToSquared(this.center)<=this.radius*this.radius}distanceToPoint(t){return t.distanceTo(this.center)-this.radius}intersectsSphere(t){const e=this.radius+t.radius;return t.center.distanceToSquared(this.center)<=e*e}intersectsBox(t){return t.intersectsSphere(this)}intersectsPlane(t){return Math.abs(t.distanceToPoint(this.center))<=this.radius}clampPoint(t,e){const n=this.center.distanceToSquared(t);return e.copy(t),n>this.radius*this.radius&&(e.sub(this.center).normalize(),e.multiplyScalar(this.radius).add(this.center)),e}getBoundingBox(t){return this.isEmpty()?(t.makeEmpty(),t):(t.set(this.center,this.center),t.expandByScalar(this.radius),t)}applyMatrix4(t){return this.center.applyMatrix4(t),this.radius=this.radius*t.getMaxScaleOnAxis(),this}translate(t){return this.center.add(t),this}expandByPoint(t){Eat.subVectors(t,this.center);const e=Eat.lengthSq();if(e>this.radius*this.radius){const t=Math.sqrt(e),n=.5*(t-this.radius);this.center.add(Eat.multiplyScalar(n/t)),this.radius+=n}return this}union(t){return Dat.subVectors(t.center,this.center).normalize().multiplyScalar(t.radius),this.expandByPoint(Sat.copy(t.center).add(Dat)),this.expandByPoint(Sat.copy(t.center).sub(Dat)),this}equals(t){return t.center.equals(this.center)&&t.radius===this.radius}clone(){return(new this.constructor).copy(this)}}const Aat=new cat,Tat=new cat,Nat=new cat,zat=new cat,Iat=new cat,Hat=new cat,Fat=new cat;class Lat{constructor(t=new cat,e=new cat(0,0,-1)){this.origin=t,this.direction=e}set(t,e){return this.origin.copy(t),this.direction.copy(e),this}copy(t){return this.origin.copy(t.origin),this.direction.copy(t.direction),this}at(t,e){return e.copy(this.direction).multiplyScalar(t).add(this.origin)}lookAt(t){return this.direction.copy(t).sub(this.origin).normalize(),this}recast(t){return this.origin.copy(this.at(t,Aat)),this}closestPointToPoint(t,e){e.subVectors(t,this.origin);const n=e.dot(this.direction);return n<0?e.copy(this.origin):e.copy(this.direction).multiplyScalar(n).add(this.origin)}distanceToPoint(t){return Math.sqrt(this.distanceSqToPoint(t))}distanceSqToPoint(t){const e=Aat.subVectors(t,this.origin).dot(this.direction);return e<0?this.origin.distanceToSquared(t):(Aat.copy(this.direction).multiplyScalar(e).add(this.origin),Aat.distanceToSquared(t))}distanceSqToSegment(t,e,n,o){Tat.copy(t).add(e).multiplyScalar(.5),Nat.copy(e).sub(t).normalize(),zat.copy(this.origin).sub(Tat);const i=.5*t.distanceTo(e),a=-this.direction.dot(Nat),r=zat.dot(this.direction),s=-zat.dot(Nat),l=zat.lengthSq(),c=Math.abs(1-a*a);let d,p,m,u;if(c>0)if(d=a*s-r,p=a*r-s,u=i*c,d>=0)if(p>=-u)if(p<=u){const t=1/c;d*=t,p*=t,m=d*(d+a*p+2*r)+p*(a*d+p+2*s)+l}else p=i,d=Math.max(0,-(a*p+r)),m=-d*d+p*(p+2*s)+l;else p=-i,d=Math.max(0,-(a*p+r)),m=-d*d+p*(p+2*s)+l;else p<=-u?(d=Math.max(0,-(-a*i+r)),p=d>0?-i:Math.min(Math.max(-i,-s),i),m=-d*d+p*(p+2*s)+l):p<=u?(d=0,p=Math.min(Math.max(-i,-s),i),m=p*(p+2*s)+l):(d=Math.max(0,-(a*i+r)),p=d>0?i:Math.min(Math.max(-i,-s),i),m=-d*d+p*(p+2*s)+l);else p=a>0?-i:i,d=Math.max(0,-(a*p+r)),m=-d*d+p*(p+2*s)+l;return n&&n.copy(this.direction).multiplyScalar(d).add(this.origin),o&&o.copy(Nat).multiplyScalar(p).add(Tat),m}intersectSphere(t,e){Aat.subVectors(t.center,this.origin);const n=Aat.dot(this.direction),o=Aat.dot(Aat)-n*n,i=t.radius*t.radius;if(o>i)return null;const a=Math.sqrt(i-o),r=n-a,s=n+a;return r<0&&s<0?null:this.at(r<0?s:r,e)}intersectsSphere(t){return this.distanceSqToPoint(t.center)<=t.radius*t.radius}distanceToPlane(t){const e=t.normal.dot(this.direction);if(0===e)return 0===t.distanceToPoint(this.origin)?0:null;const n=-(this.origin.dot(t.normal)+t.constant)/e;return n>=0?n:null}intersectPlane(t,e){const n=this.distanceToPlane(t);return null===n?null:this.at(n,e)}intersectsPlane(t){const e=t.distanceToPoint(this.origin);return 0===e||t.normal.dot(this.direction)*e<0}intersectBox(t,e){let n,o,i,a,r,s;const l=1/this.direction.x,c=1/this.direction.y,d=1/this.direction.z,p=this.origin;return l>=0?(n=(t.min.x-p.x)*l,o=(t.max.x-p.x)*l):(n=(t.max.x-p.x)*l,o=(t.min.x-p.x)*l),c>=0?(i=(t.min.y-p.y)*c,a=(t.max.y-p.y)*c):(i=(t.max.y-p.y)*c,a=(t.min.y-p.y)*c),n>a||i>o?null:((i>n||n!=n)&&(n=i),(a<o||o!=o)&&(o=a),d>=0?(r=(t.min.z-p.z)*d,s=(t.max.z-p.z)*d):(r=(t.max.z-p.z)*d,s=(t.min.z-p.z)*d),n>s||r>o?null:((r>n||n!=n)&&(n=r),(s<o||o!=o)&&(o=s),o<0?null:this.at(n>=0?n:o,e)))}intersectsBox(t){return null!==this.intersectBox(t,Aat)}intersectTriangle(t,e,n,o,i){Iat.subVectors(e,t),Hat.subVectors(n,t),Fat.crossVectors(Iat,Hat);let a,r=this.direction.dot(Fat);if(r>0){if(o)return null;a=1}else{if(!(r<0))return null;a=-1,r=-r}zat.subVectors(this.origin,t);const s=a*this.direction.dot(Hat.crossVectors(zat,Hat));if(s<0)return null;const l=a*this.direction.dot(Iat.cross(zat));if(l<0)return null;if(s+l>r)return null;const c=-a*zat.dot(Fat);return c<0?null:this.at(c/r,i)}applyMatrix4(t){return this.origin.applyMatrix4(t),this.direction.transformDirection(t),this}equals(t){return t.origin.equals(this.origin)&&t.direction.equals(this.direction)}clone(){return(new this.constructor).copy(this)}}class Bat{constructor(){this.elements=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],arguments.length>0&&console.error("THREE.Matrix4: the constructor no longer reads arguments. use .set() instead.")}set(t,e,n,o,i,a,r,s,l,c,d,p,m,u,f,g){const h=this.elements;return h[0]=t,h[4]=e,h[8]=n,h[12]=o,h[1]=i,h[5]=a,h[9]=r,h[13]=s,h[2]=l,h[6]=c,h[10]=d,h[14]=p,h[3]=m,h[7]=u,h[11]=f,h[15]=g,this}identity(){return this.set(1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1),this}clone(){return(new Bat).fromArray(this.elements)}copy(t){const e=this.elements,n=t.elements;return e[0]=n[0],e[1]=n[1],e[2]=n[2],e[3]=n[3],e[4]=n[4],e[5]=n[5],e[6]=n[6],e[7]=n[7],e[8]=n[8],e[9]=n[9],e[10]=n[10],e[11]=n[11],e[12]=n[12],e[13]=n[13],e[14]=n[14],e[15]=n[15],this}copyPosition(t){const e=this.elements,n=t.elements;return e[12]=n[12],e[13]=n[13],e[14]=n[14],this}setFromMatrix3(t){const e=t.elements;return this.set(e[0],e[3],e[6],0,e[1],e[4],e[7],0,e[2],e[5],e[8],0,0,0,0,1),this}extractBasis(t,e,n){return t.setFromMatrixColumn(this,0),e.setFromMatrixColumn(this,1),n.setFromMatrixColumn(this,2),this}makeBasis(t,e,n){return this.set(t.x,e.x,n.x,0,t.y,e.y,n.y,0,t.z,e.z,n.z,0,0,0,0,1),this}extractRotation(t){const e=this.elements,n=t.elements,o=1/Vat.setFromMatrixColumn(t,0).length(),i=1/Vat.setFromMatrixColumn(t,1).length(),a=1/Vat.setFromMatrixColumn(t,2).length();return e[0]=n[0]*o,e[1]=n[1]*o,e[2]=n[2]*o,e[3]=0,e[4]=n[4]*i,e[5]=n[5]*i,e[6]=n[6]*i,e[7]=0,e[8]=n[8]*a,e[9]=n[9]*a,e[10]=n[10]*a,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromEuler(t){t&&t.isEuler||console.error("THREE.Matrix4: .makeRotationFromEuler() now expects a Euler rotation rather than a Vector3 and order.");const e=this.elements,n=t.x,o=t.y,i=t.z,a=Math.cos(n),r=Math.sin(n),s=Math.cos(o),l=Math.sin(o),c=Math.cos(i),d=Math.sin(i);if("XYZ"===t.order){const t=a*c,n=a*d,o=r*c,i=r*d;e[0]=s*c,e[4]=-s*d,e[8]=l,e[1]=n+o*l,e[5]=t-i*l,e[9]=-r*s,e[2]=i-t*l,e[6]=o+n*l,e[10]=a*s}else if("YXZ"===t.order){const t=s*c,n=s*d,o=l*c,i=l*d;e[0]=t+i*r,e[4]=o*r-n,e[8]=a*l,e[1]=a*d,e[5]=a*c,e[9]=-r,e[2]=n*r-o,e[6]=i+t*r,e[10]=a*s}else if("ZXY"===t.order){const t=s*c,n=s*d,o=l*c,i=l*d;e[0]=t-i*r,e[4]=-a*d,e[8]=o+n*r,e[1]=n+o*r,e[5]=a*c,e[9]=i-t*r,e[2]=-a*l,e[6]=r,e[10]=a*s}else if("ZYX"===t.order){const t=a*c,n=a*d,o=r*c,i=r*d;e[0]=s*c,e[4]=o*l-n,e[8]=t*l+i,e[1]=s*d,e[5]=i*l+t,e[9]=n*l-o,e[2]=-l,e[6]=r*s,e[10]=a*s}else if("YZX"===t.order){const t=a*s,n=a*l,o=r*s,i=r*l;e[0]=s*c,e[4]=i-t*d,e[8]=o*d+n,e[1]=d,e[5]=a*c,e[9]=-r*c,e[2]=-l*c,e[6]=n*d+o,e[10]=t-i*d}else if("XZY"===t.order){const t=a*s,n=a*l,o=r*s,i=r*l;e[0]=s*c,e[4]=-d,e[8]=l*c,e[1]=t*d+i,e[5]=a*c,e[9]=n*d-o,e[2]=o*d-n,e[6]=r*c,e[10]=i*d+t}return e[3]=0,e[7]=0,e[11]=0,e[12]=0,e[13]=0,e[14]=0,e[15]=1,this}makeRotationFromQuaternion(t){return this.compose(Uat,t,Gat)}lookAt(t,e,n){const o=this.elements;return qat.subVectors(t,e),0===qat.lengthSq()&&(qat.z=1),qat.normalize(),Wat.crossVectors(n,qat),0===Wat.lengthSq()&&(1===Math.abs(n.z)?qat.x+=1e-4:qat.z+=1e-4,qat.normalize(),Wat.crossVectors(n,qat)),Wat.normalize(),Yat.crossVectors(qat,Wat),o[0]=Wat.x,o[4]=Yat.x,o[8]=qat.x,o[1]=Wat.y,o[5]=Yat.y,o[9]=qat.y,o[2]=Wat.z,o[6]=Yat.z,o[10]=qat.z,this}multiply(t,e){return void 0!==e?(console.warn("THREE.Matrix4: .multiply() now only accepts one argument. Use .multiplyMatrices( a, b ) instead."),this.multiplyMatrices(t,e)):this.multiplyMatrices(this,t)}premultiply(t){return this.multiplyMatrices(t,this)}multiplyMatrices(t,e){const n=t.elements,o=e.elements,i=this.elements,a=n[0],r=n[4],s=n[8],l=n[12],c=n[1],d=n[5],p=n[9],m=n[13],u=n[2],f=n[6],g=n[10],h=n[14],b=n[3],y=n[7],_=n[11],C=n[15],M=o[0],v=o[4],x=o[8],O=o[12],P=o[1],w=o[5],k=o[9],S=o[13],D=o[2],E=o[6],R=o[10],A=o[14],T=o[3],N=o[7],z=o[11],I=o[15];return i[0]=a*M+r*P+s*D+l*T,i[4]=a*v+r*w+s*E+l*N,i[8]=a*x+r*k+s*R+l*z,i[12]=a*O+r*S+s*A+l*I,i[1]=c*M+d*P+p*D+m*T,i[5]=c*v+d*w+p*E+m*N,i[9]=c*x+d*k+p*R+m*z,i[13]=c*O+d*S+p*A+m*I,i[2]=u*M+f*P+g*D+h*T,i[6]=u*v+f*w+g*E+h*N,i[10]=u*x+f*k+g*R+h*z,i[14]=u*O+f*S+g*A+h*I,i[3]=b*M+y*P+_*D+C*T,i[7]=b*v+y*w+_*E+C*N,i[11]=b*x+y*k+_*R+C*z,i[15]=b*O+y*S+_*A+C*I,this}multiplyScalar(t){const e=this.elements;return e[0]*=t,e[4]*=t,e[8]*=t,e[12]*=t,e[1]*=t,e[5]*=t,e[9]*=t,e[13]*=t,e[2]*=t,e[6]*=t,e[10]*=t,e[14]*=t,e[3]*=t,e[7]*=t,e[11]*=t,e[15]*=t,this}determinant(){const t=this.elements,e=t[0],n=t[4],o=t[8],i=t[12],a=t[1],r=t[5],s=t[9],l=t[13],c=t[2],d=t[6],p=t[10],m=t[14];return t[3]*(+i*s*d-o*l*d-i*r*p+n*l*p+o*r*m-n*s*m)+t[7]*(+e*s*m-e*l*p+i*a*p-o*a*m+o*l*c-i*s*c)+t[11]*(+e*l*d-e*r*m-i*a*d+n*a*m+i*r*c-n*l*c)+t[15]*(-o*r*c-e*s*d+e*r*p+o*a*d-n*a*p+n*s*c)}transpose(){const t=this.elements;let e;return e=t[1],t[1]=t[4],t[4]=e,e=t[2],t[2]=t[8],t[8]=e,e=t[6],t[6]=t[9],t[9]=e,e=t[3],t[3]=t[12],t[12]=e,e=t[7],t[7]=t[13],t[13]=e,e=t[11],t[11]=t[14],t[14]=e,this}setPosition(t,e,n){const o=this.elements;return t.isVector3?(o[12]=t.x,o[13]=t.y,o[14]=t.z):(o[12]=t,o[13]=e,o[14]=n),this}invert(){const t=this.elements,e=t[0],n=t[1],o=t[2],i=t[3],a=t[4],r=t[5],s=t[6],l=t[7],c=t[8],d=t[9],p=t[10],m=t[11],u=t[12],f=t[13],g=t[14],h=t[15],b=d*g*l-f*p*l+f*s*m-r*g*m-d*s*h+r*p*h,y=u*p*l-c*g*l-u*s*m+a*g*m+c*s*h-a*p*h,_=c*f*l-u*d*l+u*r*m-a*f*m-c*r*h+a*d*h,C=u*d*s-c*f*s-u*r*p+a*f*p+c*r*g-a*d*g,M=e*b+n*y+o*_+i*C;if(0===M)return this.set(0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0);const v=1/M;return t[0]=b*v,t[1]=(f*p*i-d*g*i-f*o*m+n*g*m+d*o*h-n*p*h)*v,t[2]=(r*g*i-f*s*i+f*o*l-n*g*l-r*o*h+n*s*h)*v,t[3]=(d*s*i-r*p*i-d*o*l+n*p*l+r*o*m-n*s*m)*v,t[4]=y*v,t[5]=(c*g*i-u*p*i+u*o*m-e*g*m-c*o*h+e*p*h)*v,t[6]=(u*s*i-a*g*i-u*o*l+e*g*l+a*o*h-e*s*h)*v,t[7]=(a*p*i-c*s*i+c*o*l-e*p*l-a*o*m+e*s*m)*v,t[8]=_*v,t[9]=(u*d*i-c*f*i-u*n*m+e*f*m+c*n*h-e*d*h)*v,t[10]=(a*f*i-u*r*i+u*n*l-e*f*l-a*n*h+e*r*h)*v,t[11]=(c*r*i-a*d*i-c*n*l+e*d*l+a*n*m-e*r*m)*v,t[12]=C*v,t[13]=(c*f*o-u*d*o+u*n*p-e*f*p-c*n*g+e*d*g)*v,t[14]=(u*r*o-a*f*o-u*n*s+e*f*s+a*n*g-e*r*g)*v,t[15]=(a*d*o-c*r*o+c*n*s-e*d*s-a*n*p+e*r*p)*v,this}scale(t){const e=this.elements,n=t.x,o=t.y,i=t.z;return e[0]*=n,e[4]*=o,e[8]*=i,e[1]*=n,e[5]*=o,e[9]*=i,e[2]*=n,e[6]*=o,e[10]*=i,e[3]*=n,e[7]*=o,e[11]*=i,this}getMaxScaleOnAxis(){const t=this.elements;return Math.sqrt(Math.max(t[0]*t[0]+t[1]*t[1]+t[2]*t[2],t[4]*t[4]+t[5]*t[5]+t[6]*t[6],t[8]*t[8]+t[9]*t[9]+t[10]*t[10]))}makeTranslation(t,e,n){return this.set(1,0,0,t,0,1,0,e,0,0,1,n,0,0,0,1),this}makeRotationX(t){const e=Math.cos(t),n=Math.sin(t);return this.set(1,0,0,0,0,e,-n,0,0,n,e,0,0,0,0,1),this}makeRotationY(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,0,n,0,0,1,0,0,-n,0,e,0,0,0,0,1),this}makeRotationZ(t){const e=Math.cos(t),n=Math.sin(t);return this.set(e,-n,0,0,n,e,0,0,0,0,1,0,0,0,0,1),this}makeRotationAxis(t,e){const n=Math.cos(e),o=Math.sin(e),i=1-n,a=t.x,r=t.y,s=t.z,l=i*a,c=i*r;return this.set(l*a+n,l*r-o*s,l*s+o*r,0,l*r+o*s,c*r+n,c*s-o*a,0,l*s-o*r,c*s+o*a,i*s*s+n,0,0,0,0,1),this}makeScale(t,e,n){return this.set(t,0,0,0,0,e,0,0,0,0,n,0,0,0,0,1),this}makeShear(t,e,n,o,i,a){return this.set(1,n,i,0,t,1,a,0,e,o,1,0,0,0,0,1),this}compose(t,e,n){const o=this.elements,i=e._x,a=e._y,r=e._z,s=e._w,l=i+i,c=a+a,d=r+r,p=i*l,m=i*c,u=i*d,f=a*c,g=a*d,h=r*d,b=s*l,y=s*c,_=s*d,C=n.x,M=n.y,v=n.z;return o[0]=(1-(f+h))*C,o[1]=(m+_)*C,o[2]=(u-y)*C,o[3]=0,o[4]=(m-_)*M,o[5]=(1-(p+h))*M,o[6]=(g+b)*M,o[7]=0,o[8]=(u+y)*v,o[9]=(g-b)*v,o[10]=(1-(p+f))*v,o[11]=0,o[12]=t.x,o[13]=t.y,o[14]=t.z,o[15]=1,this}decompose(t,e,n){const o=this.elements;let i=Vat.set(o[0],o[1],o[2]).length();const a=Vat.set(o[4],o[5],o[6]).length(),r=Vat.set(o[8],o[9],o[10]).length();this.determinant()<0&&(i=-i),t.x=o[12],t.y=o[13],t.z=o[14],jat.copy(this);const s=1/i,l=1/a,c=1/r;return jat.elements[0]*=s,jat.elements[1]*=s,jat.elements[2]*=s,jat.elements[4]*=l,jat.elements[5]*=l,jat.elements[6]*=l,jat.elements[8]*=c,jat.elements[9]*=c,jat.elements[10]*=c,e.setFromRotationMatrix(jat),n.x=i,n.y=a,n.z=r,this}makePerspective(t,e,n,o,i,a){void 0===a&&console.warn("THREE.Matrix4: .makePerspective() has been redefined and has a new signature. Please check the docs.");const r=this.elements,s=2*i/(n-o),l=(e+t)/(e-t),c=(n+o)/(n-o),d=-(a+i)/(a-i),p=-2*a*i/(a-i);return r[0]=2*i/(e-t),r[4]=0,r[8]=l,r[12]=0,r[1]=0,r[5]=s,r[9]=c,r[13]=0,r[2]=0,r[6]=0,r[10]=d,r[14]=p,r[3]=0,r[7]=0,r[11]=-1,r[15]=0,this}makeOrthographic(t,e,n,o,i,a){const r=this.elements,s=1/(e-t),l=1/(n-o),c=1/(a-i),d=(e+t)*s,p=(n+o)*l,m=(a+i)*c;return r[0]=2*s,r[4]=0,r[8]=0,r[12]=-d,r[1]=0,r[5]=2*l,r[9]=0,r[13]=-p,r[2]=0,r[6]=0,r[10]=-2*c,r[14]=-m,r[3]=0,r[7]=0,r[11]=0,r[15]=1,this}equals(t){const e=this.elements,n=t.elements;for(let t=0;t<16;t++)if(e[t]!==n[t])return!1;return!0}fromArray(t,e=0){for(let n=0;n<16;n++)this.elements[n]=t[n+e];return this}toArray(t=[],e=0){const n=this.elements;return t[e]=n[0],t[e+1]=n[1],t[e+2]=n[2],t[e+3]=n[3],t[e+4]=n[4],t[e+5]=n[5],t[e+6]=n[6],t[e+7]=n[7],t[e+8]=n[8],t[e+9]=n[9],t[e+10]=n[10],t[e+11]=n[11],t[e+12]=n[12],t[e+13]=n[13],t[e+14]=n[14],t[e+15]=n[15],t}}Bat.prototype.isMatrix4=!0;const Vat=new cat,jat=new Bat,Uat=new cat(0,0,0),Gat=new cat(1,1,1),Wat=new cat,Yat=new cat,qat=new cat,Zat=new Bat,Xat=new lat;class Kat{constructor(t=0,e=0,n=0,o=Kat.DefaultOrder){this._x=t,this._y=e,this._z=n,this._order=o}get x(){return this._x}set x(t){this._x=t,this._onChangeCallback()}get y(){return this._y}set y(t){this._y=t,this._onChangeCallback()}get z(){return this._z}set z(t){this._z=t,this._onChangeCallback()}get order(){return this._order}set order(t){this._order=t,this._onChangeCallback()}set(t,e,n,o=this._order){return this._x=t,this._y=e,this._z=n,this._order=o,this._onChangeCallback(),this}clone(){return new this.constructor(this._x,this._y,this._z,this._order)}copy(t){return this._x=t._x,this._y=t._y,this._z=t._z,this._order=t._order,this._onChangeCallback(),this}setFromRotationMatrix(t,e=this._order,n=!0){const o=t.elements,i=o[0],a=o[4],r=o[8],s=o[1],l=o[5],c=o[9],d=o[2],p=o[6],m=o[10];switch(e){case"XYZ":this._y=Math.asin(Zit(r,-1,1)),Math.abs(r)<.9999999?(this._x=Math.atan2(-c,m),this._z=Math.atan2(-a,i)):(this._x=Math.atan2(p,l),this._z=0);break;case"YXZ":this._x=Math.asin(-Zit(c,-1,1)),Math.abs(c)<.9999999?(this._y=Math.atan2(r,m),this._z=Math.atan2(s,l)):(this._y=Math.atan2(-d,i),this._z=0);break;case"ZXY":this._x=Math.asin(Zit(p,-1,1)),Math.abs(p)<.9999999?(this._y=Math.atan2(-d,m),this._z=Math.atan2(-a,l)):(this._y=0,this._z=Math.atan2(s,i));break;case"ZYX":this._y=Math.asin(-Zit(d,-1,1)),Math.abs(d)<.9999999?(this._x=Math.atan2(p,m),this._z=Math.atan2(s,i)):(this._x=0,this._z=Math.atan2(-a,l));break;case"YZX":this._z=Math.asin(Zit(s,-1,1)),Math.abs(s)<.9999999?(this._x=Math.atan2(-c,l),this._y=Math.atan2(-d,i)):(this._x=0,this._y=Math.atan2(r,m));break;case"XZY":this._z=Math.asin(-Zit(a,-1,1)),Math.abs(a)<.9999999?(this._x=Math.atan2(p,l),this._y=Math.atan2(r,i)):(this._x=Math.atan2(-c,m),this._y=0);break;default:console.warn("THREE.Euler: .setFromRotationMatrix() encountered an unknown order: "+e)}return this._order=e,!0===n&&this._onChangeCallback(),this}setFromQuaternion(t,e,n){return Zat.makeRotationFromQuaternion(t),this.setFromRotationMatrix(Zat,e,n)}setFromVector3(t,e=this._order){return this.set(t.x,t.y,t.z,e)}reorder(t){return Xat.setFromEuler(this),this.setFromQuaternion(Xat,t)}equals(t){return t._x===this._x&&t._y===this._y&&t._z===this._z&&t._order===this._order}fromArray(t){return this._x=t[0],this._y=t[1],this._z=t[2],void 0!==t[3]&&(this._order=t[3]),this._onChangeCallback(),this}toArray(t=[],e=0){return t[e]=this._x,t[e+1]=this._y,t[e+2]=this._z,t[e+3]=this._order,t}toVector3(t){return t?t.set(this._x,this._y,this._z):new cat(this._x,this._y,this._z)}_onChange(t){return this._onChangeCallback=t,this}_onChangeCallback(){}}Kat.prototype.isEuler=!0,Kat.DefaultOrder="XYZ",Kat.RotationOrders=["XYZ","YZX","ZXY","XZY","YXZ","ZYX"];class Jat{constructor(){this.mask=1}set(t){this.mask=1<<t|0}enable(t){this.mask|=1<<t|0}enableAll(){this.mask=-1}toggle(t){this.mask^=1<<t|0}disable(t){this.mask&=~(1<<t|0)}disableAll(){this.mask=0}test(t){return 0!=(this.mask&t.mask)}}let Qat=0;const $at=new cat,trt=new lat,ert=new Bat,nrt=new cat,ort=new cat,irt=new cat,art=new lat,rrt=new cat(1,0,0),srt=new cat(0,1,0),lrt=new cat(0,0,1),crt={type:"added"},drt={type:"removed"};class prt extends Uit{constructor(){super(),Object.defineProperty(this,"id",{value:Qat++}),this.uuid=qit(),this.name="",this.type="Object3D",this.parent=null,this.children=[],this.up=prt.DefaultUp.clone();const t=new cat,e=new Kat,n=new lat,o=new cat(1,1,1);e._onChange((function i(){n.setFromEuler(e,!1)})),n._onChange((function a(){e.setFromQuaternion(n,void 0,!1)})),Object.defineProperties(this,{position:{configurable:!0,enumerable:!0,value:t},rotation:{configurable:!0,enumerable:!0,value:e},quaternion:{configurable:!0,enumerable:!0,value:n},scale:{configurable:!0,enumerable:!0,value:o},modelViewMatrix:{value:new Bat},normalMatrix:{value:new $it}}),this.matrix=new Bat,this.matrixWorld=new Bat,this.matrixAutoUpdate=prt.DefaultMatrixAutoUpdate,this.matrixWorldNeedsUpdate=!1,this.layers=new Jat,this.visible=!0,this.castShadow=!1,this.receiveShadow=!1,this.frustumCulled=!0,this.renderOrder=0,this.animations=[],this.userData={}}onBeforeRender(){}onAfterRender(){}applyMatrix4(t){this.matrixAutoUpdate&&this.updateMatrix(),this.matrix.premultiply(t),this.matrix.decompose(this.position,this.quaternion,this.scale)}applyQuaternion(t){return this.quaternion.premultiply(t),this}setRotationFromAxisAngle(t,e){this.quaternion.setFromAxisAngle(t,e)}setRotationFromEuler(t){this.quaternion.setFromEuler(t,!0)}setRotationFromMatrix(t){this.quaternion.setFromRotationMatrix(t)}setRotationFromQuaternion(t){this.quaternion.copy(t)}rotateOnAxis(t,e){return trt.setFromAxisAngle(t,e),this.quaternion.multiply(trt),this}rotateOnWorldAxis(t,e){return trt.setFromAxisAngle(t,e),this.quaternion.premultiply(trt),this}rotateX(t){return this.rotateOnAxis(rrt,t)}rotateY(t){return this.rotateOnAxis(srt,t)}rotateZ(t){return this.rotateOnAxis(lrt,t)}translateOnAxis(t,e){return $at.copy(t).applyQuaternion(this.quaternion),this.position.add($at.multiplyScalar(e)),this}translateX(t){return this.translateOnAxis(rrt,t)}translateY(t){return this.translateOnAxis(srt,t)}translateZ(t){return this.translateOnAxis(lrt,t)}localToWorld(t){return t.applyMatrix4(this.matrixWorld)}worldToLocal(t){return t.applyMatrix4(ert.copy(this.matrixWorld).invert())}lookAt(t,e,n){t.isVector3?nrt.copy(t):nrt.set(t,e,n);const o=this.parent;this.updateWorldMatrix(!0,!1),ort.setFromMatrixPosition(this.matrixWorld),this.isCamera||this.isLight?ert.lookAt(ort,nrt,this.up):ert.lookAt(nrt,ort,this.up),this.quaternion.setFromRotationMatrix(ert),o&&(ert.extractRotation(o.matrixWorld),trt.setFromRotationMatrix(ert),this.quaternion.premultiply(trt.invert()))}add(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.add(arguments[t]);return this}return t===this?(console.error("THREE.Object3D.add: object can't be added as a child of itself.",t),this):(t&&t.isObject3D?(null!==t.parent&&t.parent.remove(t),t.parent=this,this.children.push(t),t.dispatchEvent(crt)):console.error("THREE.Object3D.add: object not an instance of THREE.Object3D.",t),this)}remove(t){if(arguments.length>1){for(let t=0;t<arguments.length;t++)this.remove(arguments[t]);return this}const e=this.children.indexOf(t);return-1!==e&&(t.parent=null,this.children.splice(e,1),t.dispatchEvent(drt)),this}removeFromParent(){const t=this.parent;return null!==t&&t.remove(this),this}clear(){for(let t=0;t<this.children.length;t++){const e=this.children[t];e.parent=null,e.dispatchEvent(drt)}return this.children.length=0,this}attach(t){return this.updateWorldMatrix(!0,!1),ert.copy(this.matrixWorld).invert(),null!==t.parent&&(t.parent.updateWorldMatrix(!0,!1),ert.multiply(t.parent.matrixWorld)),t.applyMatrix4(ert),this.add(t),t.updateWorldMatrix(!1,!0),this}getObjectById(t){return this.getObjectByProperty("id",t)}getObjectByName(t){return this.getObjectByProperty("name",t)}getObjectByProperty(t,e){if(this[t]===e)return this;for(let n=0,o=this.children.length;n<o;n++){const o=this.children[n].getObjectByProperty(t,e);if(void 0!==o)return o}}getWorldPosition(t){return this.updateWorldMatrix(!0,!1),t.setFromMatrixPosition(this.matrixWorld)}getWorldQuaternion(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(ort,t,irt),t}getWorldScale(t){return this.updateWorldMatrix(!0,!1),this.matrixWorld.decompose(ort,art,t),t}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(e[8],e[9],e[10]).normalize()}raycast(){}traverse(t){t(this);const e=this.children;for(let n=0,o=e.length;n<o;n++)e[n].traverse(t)}traverseVisible(t){if(!1===this.visible)return;t(this);const e=this.children;for(let n=0,o=e.length;n<o;n++)e[n].traverseVisible(t)}traverseAncestors(t){const e=this.parent;null!==e&&(t(e),e.traverseAncestors(t))}updateMatrix(){this.matrix.compose(this.position,this.quaternion,this.scale),this.matrixWorldNeedsUpdate=!0}updateMatrixWorld(t){this.matrixAutoUpdate&&this.updateMatrix(),(this.matrixWorldNeedsUpdate||t)&&(null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),this.matrixWorldNeedsUpdate=!1,t=!0);const e=this.children;for(let n=0,o=e.length;n<o;n++)e[n].updateMatrixWorld(t)}updateWorldMatrix(t,e){const n=this.parent;if(!0===t&&null!==n&&n.updateWorldMatrix(!0,!1),this.matrixAutoUpdate&&this.updateMatrix(),null===this.parent?this.matrixWorld.copy(this.matrix):this.matrixWorld.multiplyMatrices(this.parent.matrixWorld,this.matrix),!0===e){const t=this.children;for(let e=0,n=t.length;e<n;e++)t[e].updateWorldMatrix(!1,!0)}}toJSON(t){const e=void 0===t||"string"==typeof t,n={};e&&(t={geometries:{},materials:{},textures:{},images:{},shapes:{},skeletons:{},animations:{}},n.metadata={version:4.5,type:"Object",generator:"Object3D.toJSON"});const o={};function i(e,n){return void 0===e[n.uuid]&&(e[n.uuid]=n.toJSON(t)),n.uuid}if(o.uuid=this.uuid,o.type=this.type,""!==this.name&&(o.name=this.name),!0===this.castShadow&&(o.castShadow=!0),!0===this.receiveShadow&&(o.receiveShadow=!0),!1===this.visible&&(o.visible=!1),!1===this.frustumCulled&&(o.frustumCulled=!1),0!==this.renderOrder&&(o.renderOrder=this.renderOrder),"{}"!==JSON.stringify(this.userData)&&(o.userData=this.userData),o.layers=this.layers.mask,o.matrix=this.matrix.toArray(),!1===this.matrixAutoUpdate&&(o.matrixAutoUpdate=!1),this.isInstancedMesh&&(o.type="InstancedMesh",o.count=this.count,o.instanceMatrix=this.instanceMatrix.toJSON(),null!==this.instanceColor&&(o.instanceColor=this.instanceColor.toJSON())),this.isScene)this.background&&(this.background.isColor?o.background=this.background.toJSON():this.background.isTexture&&(o.background=this.background.toJSON(t).uuid)),this.environment&&this.environment.isTexture&&(o.environment=this.environment.toJSON(t).uuid);else if(this.isMesh||this.isLine||this.isPoints){o.geometry=i(t.geometries,this.geometry);const e=this.geometry.parameters;if(void 0!==e&&void 0!==e.shapes){const n=e.shapes;if(Array.isArray(n))for(let e=0,o=n.length;e<o;e++)i(t.shapes,n[e]);else i(t.shapes,n)}}if(this.isSkinnedMesh&&(o.bindMode=this.bindMode,o.bindMatrix=this.bindMatrix.toArray(),void 0!==this.skeleton&&(i(t.skeletons,this.skeleton),o.skeleton=this.skeleton.uuid)),void 0!==this.material)if(Array.isArray(this.material)){const e=[];for(let n=0,o=this.material.length;n<o;n++)e.push(i(t.materials,this.material[n]));o.material=e}else o.material=i(t.materials,this.material);if(this.children.length>0){o.children=[];for(let e=0;e<this.children.length;e++)o.children.push(this.children[e].toJSON(t).object)}if(this.animations.length>0){o.animations=[];for(let e=0;e<this.animations.length;e++)o.animations.push(i(t.animations,this.animations[e]))}if(e){const e=a(t.geometries),o=a(t.materials),i=a(t.textures),r=a(t.images),s=a(t.shapes),l=a(t.skeletons),c=a(t.animations);e.length>0&&(n.geometries=e),o.length>0&&(n.materials=o),i.length>0&&(n.textures=i),r.length>0&&(n.images=r),s.length>0&&(n.shapes=s),l.length>0&&(n.skeletons=l),c.length>0&&(n.animations=c)}return n.object=o,n;function a(t){const e=[];for(const n in t){const o=t[n];delete o.metadata,e.push(o)}return e}}clone(t){return(new this.constructor).copy(this,t)}copy(t,e=!0){if(this.name=t.name,this.up.copy(t.up),this.position.copy(t.position),this.rotation.order=t.rotation.order,this.quaternion.copy(t.quaternion),this.scale.copy(t.scale),this.matrix.copy(t.matrix),this.matrixWorld.copy(t.matrixWorld),this.matrixAutoUpdate=t.matrixAutoUpdate,this.matrixWorldNeedsUpdate=t.matrixWorldNeedsUpdate,this.layers.mask=t.layers.mask,this.visible=t.visible,this.castShadow=t.castShadow,this.receiveShadow=t.receiveShadow,this.frustumCulled=t.frustumCulled,this.renderOrder=t.renderOrder,this.userData=JSON.parse(JSON.stringify(t.userData)),!0===e)for(let e=0;e<t.children.length;e++)this.add(t.children[e].clone());return this}}prt.DefaultUp=new cat(0,1,0),prt.DefaultMatrixAutoUpdate=!0,prt.prototype.isObject3D=!0;const mrt=new cat,urt=new cat,frt=new cat,grt=new cat,hrt=new cat,brt=new cat,yrt=new cat,_rt=new cat,Crt=new cat,Mrt=new cat;class vrt{constructor(t=new cat,e=new cat,n=new cat){this.a=t,this.b=e,this.c=n}static getNormal(t,e,n,o){o.subVectors(n,e),mrt.subVectors(t,e),o.cross(mrt);const i=o.lengthSq();return i>0?o.multiplyScalar(1/Math.sqrt(i)):o.set(0,0,0)}static getBarycoord(t,e,n,o,i){mrt.subVectors(o,e),urt.subVectors(n,e),frt.subVectors(t,e);const a=mrt.dot(mrt),r=mrt.dot(urt),s=mrt.dot(frt),l=urt.dot(urt),c=urt.dot(frt),d=a*l-r*r;if(0===d)return i.set(-2,-1,-1);const p=1/d,m=(l*s-r*c)*p,u=(a*c-r*s)*p;return i.set(1-m-u,u,m)}static containsPoint(t,e,n,o){return this.getBarycoord(t,e,n,o,grt),grt.x>=0&&grt.y>=0&&grt.x+grt.y<=1}static getUV(t,e,n,o,i,a,r,s){return this.getBarycoord(t,e,n,o,grt),s.set(0,0),s.addScaledVector(i,grt.x),s.addScaledVector(a,grt.y),s.addScaledVector(r,grt.z),s}static isFrontFacing(t,e,n,o){return mrt.subVectors(n,e),urt.subVectors(t,e),mrt.cross(urt).dot(o)<0}set(t,e,n){return this.a.copy(t),this.b.copy(e),this.c.copy(n),this}setFromPointsAndIndices(t,e,n,o){return this.a.copy(t[e]),this.b.copy(t[n]),this.c.copy(t[o]),this}clone(){return(new this.constructor).copy(this)}copy(t){return this.a.copy(t.a),this.b.copy(t.b),this.c.copy(t.c),this}getArea(){return mrt.subVectors(this.c,this.b),urt.subVectors(this.a,this.b),.5*mrt.cross(urt).length()}getMidpoint(t){return t.addVectors(this.a,this.b).add(this.c).multiplyScalar(1/3)}getNormal(t){return vrt.getNormal(this.a,this.b,this.c,t)}getPlane(t){return t.setFromCoplanarPoints(this.a,this.b,this.c)}getBarycoord(t,e){return vrt.getBarycoord(t,this.a,this.b,this.c,e)}getUV(t,e,n,o,i){return vrt.getUV(t,this.a,this.b,this.c,e,n,o,i)}containsPoint(t){return vrt.containsPoint(t,this.a,this.b,this.c)}isFrontFacing(t){return vrt.isFrontFacing(this.a,this.b,this.c,t)}intersectsBox(t){return t.intersectsTriangle(this)}closestPointToPoint(t,e){const n=this.a,o=this.b,i=this.c;let a,r;hrt.subVectors(o,n),brt.subVectors(i,n),_rt.subVectors(t,n);const s=hrt.dot(_rt),l=brt.dot(_rt);if(s<=0&&l<=0)return e.copy(n);Crt.subVectors(t,o);const c=hrt.dot(Crt),d=brt.dot(Crt);if(c>=0&&d<=c)return e.copy(o);const p=s*d-c*l;if(p<=0&&s>=0&&c<=0)return a=s/(s-c),e.copy(n).addScaledVector(hrt,a);Mrt.subVectors(t,i);const m=hrt.dot(Mrt),u=brt.dot(Mrt);if(u>=0&&m<=u)return e.copy(i);const f=m*l-s*u;if(f<=0&&l>=0&&u<=0)return r=l/(l-u),e.copy(n).addScaledVector(brt,r);const g=c*u-m*d;if(g<=0&&d-c>=0&&m-u>=0)return yrt.subVectors(i,o),r=(d-c)/(d-c+(m-u)),e.copy(o).addScaledVector(yrt,r);const h=1/(g+f+p);return a=f*h,r=p*h,e.copy(n).addScaledVector(hrt,a).addScaledVector(brt,r)}equals(t){return t.a.equals(this.a)&&t.b.equals(this.b)&&t.c.equals(this.c)}}let xrt=0;class Ort extends Uit{constructor(){super(),Object.defineProperty(this,"id",{value:xrt++}),this.uuid=qit(),this.name="",this.type="Material",this.fog=!0,this.blending=1,this.side=0,this.vertexColors=!1,this.opacity=1,this.transparent=!1,this.blendSrc=204,this.blendDst=205,this.blendEquation=cit,this.blendSrcAlpha=null,this.blendDstAlpha=null,this.blendEquationAlpha=null,this.depthFunc=3,this.depthTest=!0,this.depthWrite=!0,this.stencilWriteMask=255,this.stencilFunc=519,this.stencilRef=0,this.stencilFuncMask=255,this.stencilFail=Lit,this.stencilZFail=Lit,this.stencilZPass=Lit,this.stencilWrite=!1,this.clippingPlanes=null,this.clipIntersection=!1,this.clipShadows=!1,this.shadowSide=null,this.colorWrite=!0,this.precision=null,this.polygonOffset=!1,this.polygonOffsetFactor=0,this.polygonOffsetUnits=0,this.dithering=!1,this.alphaTest=0,this.alphaToCoverage=!1,this.premultipliedAlpha=!1,this.visible=!0,this.toneMapped=!0,this.userData={},this.version=0}onBuild(){}onBeforeCompile(){}customProgramCacheKey(){return this.onBeforeCompile.toString()}setValues(t){if(void 0!==t)for(const e in t){const n=t[e];if(void 0===n){console.warn("THREE.Material: '"+e+"' parameter is undefined.");continue}if("shading"===e){console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead."),this.flatShading=1===n;continue}const o=this[e];void 0!==o?o&&o.isColor?o.set(n):o&&o.isVector3&&n&&n.isVector3?o.copy(n):this[e]=n:console.warn("THREE."+this.type+": '"+e+"' is not a property of this material.")}}toJSON(t){const e=void 0===t||"string"==typeof t;e&&(t={textures:{},images:{}});const n={metadata:{version:4.5,type:"Material",generator:"Material.toJSON"}};function o(t){const e=[];for(const n in t){const o=t[n];delete o.metadata,e.push(o)}return e}if(n.uuid=this.uuid,n.type=this.type,""!==this.name&&(n.name=this.name),this.color&&this.color.isColor&&(n.color=this.color.getHex()),void 0!==this.roughness&&(n.roughness=this.roughness),void 0!==this.metalness&&(n.metalness=this.metalness),this.sheen&&this.sheen.isColor&&(n.sheen=this.sheen.getHex()),this.emissive&&this.emissive.isColor&&(n.emissive=this.emissive.getHex()),this.emissiveIntensity&&1!==this.emissiveIntensity&&(n.emissiveIntensity=this.emissiveIntensity),this.specular&&this.specular.isColor&&(n.specular=this.specular.getHex()),void 0!==this.specularIntensity&&(n.specularIntensity=this.specularIntensity),this.specularTint&&this.specularTint.isColor&&(n.specularTint=this.specularTint.getHex()),void 0!==this.shininess&&(n.shininess=this.shininess),void 0!==this.clearcoat&&(n.clearcoat=this.clearcoat),void 0!==this.clearcoatRoughness&&(n.clearcoatRoughness=this.clearcoatRoughness),this.clearcoatMap&&this.clearcoatMap.isTexture&&(n.clearcoatMap=this.clearcoatMap.toJSON(t).uuid),this.clearcoatRoughnessMap&&this.clearcoatRoughnessMap.isTexture&&(n.clearcoatRoughnessMap=this.clearcoatRoughnessMap.toJSON(t).uuid),this.clearcoatNormalMap&&this.clearcoatNormalMap.isTexture&&(n.clearcoatNormalMap=this.clearcoatNormalMap.toJSON(t).uuid,n.clearcoatNormalScale=this.clearcoatNormalScale.toArray()),this.map&&this.map.isTexture&&(n.map=this.map.toJSON(t).uuid),this.matcap&&this.matcap.isTexture&&(n.matcap=this.matcap.toJSON(t).uuid),this.alphaMap&&this.alphaMap.isTexture&&(n.alphaMap=this.alphaMap.toJSON(t).uuid),this.lightMap&&this.lightMap.isTexture&&(n.lightMap=this.lightMap.toJSON(t).uuid,n.lightMapIntensity=this.lightMapIntensity),this.aoMap&&this.aoMap.isTexture&&(n.aoMap=this.aoMap.toJSON(t).uuid,n.aoMapIntensity=this.aoMapIntensity),this.bumpMap&&this.bumpMap.isTexture&&(n.bumpMap=this.bumpMap.toJSON(t).uuid,n.bumpScale=this.bumpScale),this.normalMap&&this.normalMap.isTexture&&(n.normalMap=this.normalMap.toJSON(t).uuid,n.normalMapType=this.normalMapType,n.normalScale=this.normalScale.toArray()),this.displacementMap&&this.displacementMap.isTexture&&(n.displacementMap=this.displacementMap.toJSON(t).uuid,n.displacementScale=this.displacementScale,n.displacementBias=this.displacementBias),this.roughnessMap&&this.roughnessMap.isTexture&&(n.roughnessMap=this.roughnessMap.toJSON(t).uuid),this.metalnessMap&&this.metalnessMap.isTexture&&(n.metalnessMap=this.metalnessMap.toJSON(t).uuid),this.emissiveMap&&this.emissiveMap.isTexture&&(n.emissiveMap=this.emissiveMap.toJSON(t).uuid),this.specularMap&&this.specularMap.isTexture&&(n.specularMap=this.specularMap.toJSON(t).uuid),this.specularIntensityMap&&this.specularIntensityMap.isTexture&&(n.specularIntensityMap=this.specularIntensityMap.toJSON(t).uuid),this.specularTintMap&&this.specularTintMap.isTexture&&(n.specularTintMap=this.specularTintMap.toJSON(t).uuid),this.envMap&&this.envMap.isTexture&&(n.envMap=this.envMap.toJSON(t).uuid,void 0!==this.combine&&(n.combine=this.combine)),void 0!==this.envMapIntensity&&(n.envMapIntensity=this.envMapIntensity),void 0!==this.reflectivity&&(n.reflectivity=this.reflectivity),void 0!==this.refractionRatio&&(n.refractionRatio=this.refractionRatio),this.gradientMap&&this.gradientMap.isTexture&&(n.gradientMap=this.gradientMap.toJSON(t).uuid),void 0!==this.transmission&&(n.transmission=this.transmission),this.transmissionMap&&this.transmissionMap.isTexture&&(n.transmissionMap=this.transmissionMap.toJSON(t).uuid),void 0!==this.thickness&&(n.thickness=this.thickness),this.thicknessMap&&this.thicknessMap.isTexture&&(n.thicknessMap=this.thicknessMap.toJSON(t).uuid),void 0!==this.attenuationDistance&&(n.attenuationDistance=this.attenuationDistance),void 0!==this.attenuationTint&&(n.attenuationTint=this.attenuationTint.getHex()),void 0!==this.size&&(n.size=this.size),null!==this.shadowSide&&(n.shadowSide=this.shadowSide),void 0!==this.sizeAttenuation&&(n.sizeAttenuation=this.sizeAttenuation),1!==this.blending&&(n.blending=this.blending),0!==this.side&&(n.side=this.side),this.vertexColors&&(n.vertexColors=!0),this.opacity<1&&(n.opacity=this.opacity),!0===this.transparent&&(n.transparent=this.transparent),n.depthFunc=this.depthFunc,n.depthTest=this.depthTest,n.depthWrite=this.depthWrite,n.colorWrite=this.colorWrite,n.stencilWrite=this.stencilWrite,n.stencilWriteMask=this.stencilWriteMask,n.stencilFunc=this.stencilFunc,n.stencilRef=this.stencilRef,n.stencilFuncMask=this.stencilFuncMask,n.stencilFail=this.stencilFail,n.stencilZFail=this.stencilZFail,n.stencilZPass=this.stencilZPass,this.rotation&&0!==this.rotation&&(n.rotation=this.rotation),!0===this.polygonOffset&&(n.polygonOffset=!0),0!==this.polygonOffsetFactor&&(n.polygonOffsetFactor=this.polygonOffsetFactor),0!==this.polygonOffsetUnits&&(n.polygonOffsetUnits=this.polygonOffsetUnits),this.linewidth&&1!==this.linewidth&&(n.linewidth=this.linewidth),void 0!==this.dashSize&&(n.dashSize=this.dashSize),void 0!==this.gapSize&&(n.gapSize=this.gapSize),void 0!==this.scale&&(n.scale=this.scale),!0===this.dithering&&(n.dithering=!0),this.alphaTest>0&&(n.alphaTest=this.alphaTest),!0===this.alphaToCoverage&&(n.alphaToCoverage=this.alphaToCoverage),!0===this.premultipliedAlpha&&(n.premultipliedAlpha=this.premultipliedAlpha),!0===this.wireframe&&(n.wireframe=this.wireframe),this.wireframeLinewidth>1&&(n.wireframeLinewidth=this.wireframeLinewidth),"round"!==this.wireframeLinecap&&(n.wireframeLinecap=this.wireframeLinecap),"round"!==this.wireframeLinejoin&&(n.wireframeLinejoin=this.wireframeLinejoin),!0===this.flatShading&&(n.flatShading=this.flatShading),!1===this.visible&&(n.visible=!1),!1===this.toneMapped&&(n.toneMapped=!1),"{}"!==JSON.stringify(this.userData)&&(n.userData=this.userData),e){const e=o(t.textures),i=o(t.images);e.length>0&&(n.textures=e),i.length>0&&(n.images=i)}return n}clone(){return(new this.constructor).copy(this)}copy(t){this.name=t.name,this.fog=t.fog,this.blending=t.blending,this.side=t.side,this.vertexColors=t.vertexColors,this.opacity=t.opacity,this.transparent=t.transparent,this.blendSrc=t.blendSrc,this.blendDst=t.blendDst,this.blendEquation=t.blendEquation,this.blendSrcAlpha=t.blendSrcAlpha,this.blendDstAlpha=t.blendDstAlpha,this.blendEquationAlpha=t.blendEquationAlpha,this.depthFunc=t.depthFunc,this.depthTest=t.depthTest,this.depthWrite=t.depthWrite,this.stencilWriteMask=t.stencilWriteMask,this.stencilFunc=t.stencilFunc,this.stencilRef=t.stencilRef,this.stencilFuncMask=t.stencilFuncMask,this.stencilFail=t.stencilFail,this.stencilZFail=t.stencilZFail,this.stencilZPass=t.stencilZPass,this.stencilWrite=t.stencilWrite;const e=t.clippingPlanes;let n=null;if(null!==e){const t=e.length;n=new Array(t);for(let o=0;o!==t;++o)n[o]=e[o].clone()}return this.clippingPlanes=n,this.clipIntersection=t.clipIntersection,this.clipShadows=t.clipShadows,this.shadowSide=t.shadowSide,this.colorWrite=t.colorWrite,this.precision=t.precision,this.polygonOffset=t.polygonOffset,this.polygonOffsetFactor=t.polygonOffsetFactor,this.polygonOffsetUnits=t.polygonOffsetUnits,this.dithering=t.dithering,this.alphaTest=t.alphaTest,this.alphaToCoverage=t.alphaToCoverage,this.premultipliedAlpha=t.premultipliedAlpha,this.visible=t.visible,this.toneMapped=t.toneMapped,this.userData=JSON.parse(JSON.stringify(t.userData)),this}dispose(){this.dispatchEvent({type:"dispose"})}set needsUpdate(t){!0===t&&this.version++}}Ort.prototype.isMaterial=!0;const Prt={aliceblue:15792383,antiquewhite:16444375,aqua:65535,aquamarine:8388564,azure:15794175,beige:16119260,bisque:16770244,black:0,blanchedalmond:16772045,blue:255,blueviolet:9055202,brown:10824234,burlywood:14596231,cadetblue:6266528,chartreuse:8388352,chocolate:13789470,coral:16744272,cornflowerblue:6591981,cornsilk:16775388,crimson:14423100,cyan:65535,darkblue:139,darkcyan:35723,darkgoldenrod:12092939,darkgray:11119017,darkgreen:25600,darkgrey:11119017,darkkhaki:12433259,darkmagenta:9109643,darkolivegreen:5597999,darkorange:16747520,darkorchid:10040012,darkred:9109504,darksalmon:15308410,darkseagreen:9419919,darkslateblue:4734347,darkslategray:3100495,darkslategrey:3100495,darkturquoise:52945,darkviolet:9699539,deeppink:16716947,deepskyblue:49151,dimgray:6908265,dimgrey:6908265,dodgerblue:2003199,firebrick:11674146,floralwhite:16775920,forestgreen:2263842,fuchsia:16711935,gainsboro:14474460,ghostwhite:16316671,gold:16766720,goldenrod:14329120,gray:8421504,green:32768,greenyellow:11403055,grey:8421504,honeydew:15794160,hotpink:16738740,indianred:13458524,indigo:4915330,ivory:16777200,khaki:15787660,lavender:15132410,lavenderblush:16773365,lawngreen:8190976,lemonchiffon:16775885,lightblue:11393254,lightcoral:15761536,lightcyan:14745599,lightgoldenrodyellow:16448210,lightgray:13882323,lightgreen:9498256,lightgrey:13882323,lightpink:16758465,lightsalmon:16752762,lightseagreen:2142890,lightskyblue:8900346,lightslategray:7833753,lightslategrey:7833753,lightsteelblue:11584734,lightyellow:16777184,lime:65280,limegreen:3329330,linen:16445670,magenta:16711935,maroon:8388608,mediumaquamarine:6737322,mediumblue:205,mediumorchid:12211667,mediumpurple:9662683,mediumseagreen:3978097,mediumslateblue:8087790,mediumspringgreen:64154,mediumturquoise:4772300,mediumvioletred:13047173,midnightblue:1644912,mintcream:16121850,mistyrose:16770273,moccasin:16770229,navajowhite:16768685,navy:128,oldlace:16643558,olive:8421376,olivedrab:7048739,orange:16753920,orangered:16729344,orchid:14315734,palegoldenrod:15657130,palegreen:10025880,paleturquoise:11529966,palevioletred:14381203,papayawhip:16773077,peachpuff:16767673,peru:13468991,pink:16761035,plum:14524637,powderblue:11591910,purple:8388736,rebeccapurple:6697881,red:16711680,rosybrown:12357519,royalblue:4286945,saddlebrown:9127187,salmon:16416882,sandybrown:16032864,seagreen:3050327,seashell:16774638,sienna:10506797,silver:12632256,skyblue:8900331,slateblue:6970061,slategray:7372944,slategrey:7372944,snow:16775930,springgreen:65407,steelblue:4620980,tan:13808780,teal:32896,thistle:14204888,tomato:16737095,turquoise:4251856,violet:15631086,wheat:16113331,white:16777215,whitesmoke:16119285,yellow:16776960,yellowgreen:10145074},wrt={h:0,s:0,l:0},krt={h:0,s:0,l:0};function Srt(t,e,n){return n<0&&(n+=1),n>1&&(n-=1),n<1/6?t+6*(e-t)*n:n<.5?e:n<2/3?t+6*(e-t)*(2/3-n):t}function Drt(t){return t<.04045?.0773993808*t:Math.pow(.9478672986*t+.0521327014,2.4)}function Ert(t){return t<.0031308?12.92*t:1.055*Math.pow(t,.41666)-.055}class Rrt{constructor(t,e,n){return void 0===e&&void 0===n?this.set(t):this.setRGB(t,e,n)}set(t){return t&&t.isColor?this.copy(t):"number"==typeof t?this.setHex(t):"string"==typeof t&&this.setStyle(t),this}setScalar(t){return this.r=t,this.g=t,this.b=t,this}setHex(t){return t=Math.floor(t),this.r=(t>>16&255)/255,this.g=(t>>8&255)/255,this.b=(255&t)/255,this}setRGB(t,e,n){return this.r=t,this.g=e,this.b=n,this}setHSL(t,e,n){if(t=(function o(t,e){return(t%e+e)%e})(t,1),e=Zit(e,0,1),n=Zit(n,0,1),0===e)this.r=this.g=this.b=n;else{const o=n<=.5?n*(1+e):n+e-n*e,i=2*n-o;this.r=Srt(i,o,t+1/3),this.g=Srt(i,o,t),this.b=Srt(i,o,t-1/3)}return this}setStyle(t){function e(e){void 0!==e&&parseFloat(e)<1&&console.warn("THREE.Color: Alpha component of "+t+" will be ignored.")}let n;if(n=/^((?:rgb|hsl)a?)\(([^\)]*)\)/.exec(t)){let t;const o=n[2];switch(n[1]){case"rgb":case"rgba":if(t=/^\s*(\d+)\s*,\s*(\d+)\s*,\s*(\d+)\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(o))return this.r=Math.min(255,parseInt(t[1],10))/255,this.g=Math.min(255,parseInt(t[2],10))/255,this.b=Math.min(255,parseInt(t[3],10))/255,e(t[4]),this;if(t=/^\s*(\d+)\%\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(o))return this.r=Math.min(100,parseInt(t[1],10))/100,this.g=Math.min(100,parseInt(t[2],10))/100,this.b=Math.min(100,parseInt(t[3],10))/100,e(t[4]),this;break;case"hsl":case"hsla":if(t=/^\s*(\d*\.?\d+)\s*,\s*(\d+)\%\s*,\s*(\d+)\%\s*(?:,\s*(\d*\.?\d+)\s*)?$/.exec(o)){const n=parseFloat(t[1])/360,o=parseInt(t[2],10)/100,i=parseInt(t[3],10)/100;return e(t[4]),this.setHSL(n,o,i)}}}else if(n=/^\#([A-Fa-f\d]+)$/.exec(t)){const t=n[1],e=t.length;if(3===e)return this.r=parseInt(t.charAt(0)+t.charAt(0),16)/255,this.g=parseInt(t.charAt(1)+t.charAt(1),16)/255,this.b=parseInt(t.charAt(2)+t.charAt(2),16)/255,this;if(6===e)return this.r=parseInt(t.charAt(0)+t.charAt(1),16)/255,this.g=parseInt(t.charAt(2)+t.charAt(3),16)/255,this.b=parseInt(t.charAt(4)+t.charAt(5),16)/255,this}return t&&t.length>0?this.setColorName(t):this}setColorName(t){const e=Prt[t.toLowerCase()];return void 0!==e?this.setHex(e):console.warn("THREE.Color: Unknown color "+t),this}clone(){return new this.constructor(this.r,this.g,this.b)}copy(t){return this.r=t.r,this.g=t.g,this.b=t.b,this}copyGammaToLinear(t,e=2){return this.r=Math.pow(t.r,e),this.g=Math.pow(t.g,e),this.b=Math.pow(t.b,e),this}copyLinearToGamma(t,e=2){const n=e>0?1/e:1;return this.r=Math.pow(t.r,n),this.g=Math.pow(t.g,n),this.b=Math.pow(t.b,n),this}convertGammaToLinear(t){return this.copyGammaToLinear(this,t),this}convertLinearToGamma(t){return this.copyLinearToGamma(this,t),this}copySRGBToLinear(t){return this.r=Drt(t.r),this.g=Drt(t.g),this.b=Drt(t.b),this}copyLinearToSRGB(t){return this.r=Ert(t.r),this.g=Ert(t.g),this.b=Ert(t.b),this}convertSRGBToLinear(){return this.copySRGBToLinear(this),this}convertLinearToSRGB(){return this.copyLinearToSRGB(this),this}getHex(){return 255*this.r<<16^255*this.g<<8^255*this.b<<0}getHexString(){return("000000"+this.getHex().toString(16)).slice(-6)}getHSL(t){const e=this.r,n=this.g,o=this.b,i=Math.max(e,n,o),a=Math.min(e,n,o);let r,s;const l=(a+i)/2;if(a===i)r=0,s=0;else{const t=i-a;switch(s=l<=.5?t/(i+a):t/(2-i-a),i){case e:r=(n-o)/t+(n<o?6:0);break;case n:r=(o-e)/t+2;break;case o:r=(e-n)/t+4}r/=6}return t.h=r,t.s=s,t.l=l,t}getStyle(){return"rgb("+(255*this.r|0)+","+(255*this.g|0)+","+(255*this.b|0)+")"}offsetHSL(t,e,n){return this.getHSL(wrt),wrt.h+=t,wrt.s+=e,wrt.l+=n,this.setHSL(wrt.h,wrt.s,wrt.l),this}add(t){return this.r+=t.r,this.g+=t.g,this.b+=t.b,this}addColors(t,e){return this.r=t.r+e.r,this.g=t.g+e.g,this.b=t.b+e.b,this}addScalar(t){return this.r+=t,this.g+=t,this.b+=t,this}sub(t){return this.r=Math.max(0,this.r-t.r),this.g=Math.max(0,this.g-t.g),this.b=Math.max(0,this.b-t.b),this}multiply(t){return this.r*=t.r,this.g*=t.g,this.b*=t.b,this}multiplyScalar(t){return this.r*=t,this.g*=t,this.b*=t,this}lerp(t,e){return this.r+=(t.r-this.r)*e,this.g+=(t.g-this.g)*e,this.b+=(t.b-this.b)*e,this}lerpColors(t,e,n){return this.r=t.r+(e.r-t.r)*n,this.g=t.g+(e.g-t.g)*n,this.b=t.b+(e.b-t.b)*n,this}lerpHSL(t,e){this.getHSL(wrt),t.getHSL(krt);const n=Xit(wrt.h,krt.h,e),o=Xit(wrt.s,krt.s,e),i=Xit(wrt.l,krt.l,e);return this.setHSL(n,o,i),this}equals(t){return t.r===this.r&&t.g===this.g&&t.b===this.b}fromArray(t,e=0){return this.r=t[e],this.g=t[e+1],this.b=t[e+2],this}toArray(t=[],e=0){return t[e]=this.r,t[e+1]=this.g,t[e+2]=this.b,t}fromBufferAttribute(t,e){return this.r=t.getX(e),this.g=t.getY(e),this.b=t.getZ(e),!0===t.normalized&&(this.r/=255,this.g/=255,this.b/=255),this}toJSON(){return this.getHex()}}Rrt.NAMES=Prt,Rrt.prototype.isColor=!0,Rrt.prototype.r=1,Rrt.prototype.g=1,Rrt.prototype.b=1;class Art extends Ort{constructor(t){super(),this.type="MeshBasicMaterial",this.color=new Rrt(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}Art.prototype.isMeshBasicMaterial=!0;const Trt=new cat,Nrt=new Qit;class zrt{constructor(t,e,n){if(Array.isArray(t))throw new TypeError("THREE.BufferAttribute: array should be a Typed Array.");this.name="",this.array=t,this.itemSize=e,this.count=void 0!==t?t.length/e:0,this.normalized=!0===n,this.usage=Bit,this.updateRange={offset:0,count:-1},this.version=0}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.name=t.name,this.array=new t.array.constructor(t.array),this.itemSize=t.itemSize,this.count=t.count,this.normalized=t.normalized,this.usage=t.usage,this}copyAt(t,e,n){t*=this.itemSize,n*=e.itemSize;for(let o=0,i=this.itemSize;o<i;o++)this.array[t+o]=e.array[n+o];return this}copyArray(t){return this.array.set(t),this}copyColorsArray(t){const e=this.array;let n=0;for(let o=0,i=t.length;o<i;o++){let i=t[o];void 0===i&&(console.warn("THREE.BufferAttribute.copyColorsArray(): color is undefined",o),i=new Rrt),e[n++]=i.r,e[n++]=i.g,e[n++]=i.b}return this}copyVector2sArray(t){const e=this.array;let n=0;for(let o=0,i=t.length;o<i;o++){let i=t[o];void 0===i&&(console.warn("THREE.BufferAttribute.copyVector2sArray(): vector is undefined",o),i=new Qit),e[n++]=i.x,e[n++]=i.y}return this}copyVector3sArray(t){const e=this.array;let n=0;for(let o=0,i=t.length;o<i;o++){let i=t[o];void 0===i&&(console.warn("THREE.BufferAttribute.copyVector3sArray(): vector is undefined",o),i=new cat),e[n++]=i.x,e[n++]=i.y,e[n++]=i.z}return this}copyVector4sArray(t){const e=this.array;let n=0;for(let o=0,i=t.length;o<i;o++){let i=t[o];void 0===i&&(console.warn("THREE.BufferAttribute.copyVector4sArray(): vector is undefined",o),i=new aat),e[n++]=i.x,e[n++]=i.y,e[n++]=i.z,e[n++]=i.w}return this}applyMatrix3(t){if(2===this.itemSize)for(let e=0,n=this.count;e<n;e++)Nrt.fromBufferAttribute(this,e),Nrt.applyMatrix3(t),this.setXY(e,Nrt.x,Nrt.y);else if(3===this.itemSize)for(let e=0,n=this.count;e<n;e++)Trt.fromBufferAttribute(this,e),Trt.applyMatrix3(t),this.setXYZ(e,Trt.x,Trt.y,Trt.z);return this}applyMatrix4(t){for(let e=0,n=this.count;e<n;e++)Trt.x=this.getX(e),Trt.y=this.getY(e),Trt.z=this.getZ(e),Trt.applyMatrix4(t),this.setXYZ(e,Trt.x,Trt.y,Trt.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)Trt.x=this.getX(e),Trt.y=this.getY(e),Trt.z=this.getZ(e),Trt.applyNormalMatrix(t),this.setXYZ(e,Trt.x,Trt.y,Trt.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)Trt.x=this.getX(e),Trt.y=this.getY(e),Trt.z=this.getZ(e),Trt.transformDirection(t),this.setXYZ(e,Trt.x,Trt.y,Trt.z);return this}set(t,e=0){return this.array.set(t,e),this}getX(t){return this.array[t*this.itemSize]}setX(t,e){return this.array[t*this.itemSize]=e,this}getY(t){return this.array[t*this.itemSize+1]}setY(t,e){return this.array[t*this.itemSize+1]=e,this}getZ(t){return this.array[t*this.itemSize+2]}setZ(t,e){return this.array[t*this.itemSize+2]=e,this}getW(t){return this.array[t*this.itemSize+3]}setW(t,e){return this.array[t*this.itemSize+3]=e,this}setXY(t,e,n){return this.array[0+(t*=this.itemSize)]=e,this.array[t+1]=n,this}setXYZ(t,e,n,o){return this.array[0+(t*=this.itemSize)]=e,this.array[t+1]=n,this.array[t+2]=o,this}setXYZW(t,e,n,o,i){return this.array[0+(t*=this.itemSize)]=e,this.array[t+1]=n,this.array[t+2]=o,this.array[t+3]=i,this}onUpload(t){return this.onUploadCallback=t,this}clone(){return new this.constructor(this.array,this.itemSize).copy(this)}toJSON(){const t={itemSize:this.itemSize,type:this.array.constructor.name,array:Array.prototype.slice.call(this.array),normalized:this.normalized};return""!==this.name&&(t.name=this.name),this.usage!==Bit&&(t.usage=this.usage),0===this.updateRange.offset&&-1===this.updateRange.count||(t.updateRange=this.updateRange),t}}zrt.prototype.isBufferAttribute=!0;class Irt extends zrt{constructor(t,e,n){super(new Uint16Array(t),e,n)}}class Hrt extends zrt{constructor(t,e,n){super(new Uint32Array(t),e,n)}}(class extends zrt{constructor(t,e,n){super(new Uint16Array(t),e,n)}}).prototype.isFloat16BufferAttribute=!0;class Frt extends zrt{constructor(t,e,n){super(new Float32Array(t),e,n)}}function Lrt(t){if(0===t.length)return-1/0;let e=t[0];for(let n=1,o=t.length;n<o;++n)t[n]>e&&(e=t[n]);return e}let Brt=0;const Vrt=new Bat,jrt=new prt,Urt=new cat,Grt=new mat,Wrt=new mat,Yrt=new cat;class qrt extends Uit{constructor(){super(),Object.defineProperty(this,"id",{value:Brt++}),this.uuid=qit(),this.name="",this.type="BufferGeometry",this.index=null,this.attributes={},this.morphAttributes={},this.morphTargetsRelative=!1,this.groups=[],this.boundingBox=null,this.boundingSphere=null,this.drawRange={start:0,count:1/0},this.userData={}}getIndex(){return this.index}setIndex(t){return this.index=Array.isArray(t)?new(Lrt(t)>65535?Hrt:Irt)(t,1):t,this}getAttribute(t){return this.attributes[t]}setAttribute(t,e){return this.attributes[t]=e,this}deleteAttribute(t){return delete this.attributes[t],this}hasAttribute(t){return void 0!==this.attributes[t]}addGroup(t,e,n=0){this.groups.push({start:t,count:e,materialIndex:n})}clearGroups(){this.groups=[]}setDrawRange(t,e){this.drawRange.start=t,this.drawRange.count=e}applyMatrix4(t){const e=this.attributes.position;void 0!==e&&(e.applyMatrix4(t),e.needsUpdate=!0);const n=this.attributes.normal;if(void 0!==n){const e=(new $it).getNormalMatrix(t);n.applyNormalMatrix(e),n.needsUpdate=!0}const o=this.attributes.tangent;return void 0!==o&&(o.transformDirection(t),o.needsUpdate=!0),null!==this.boundingBox&&this.computeBoundingBox(),null!==this.boundingSphere&&this.computeBoundingSphere(),this}applyQuaternion(t){return Vrt.makeRotationFromQuaternion(t),this.applyMatrix4(Vrt),this}rotateX(t){return Vrt.makeRotationX(t),this.applyMatrix4(Vrt),this}rotateY(t){return Vrt.makeRotationY(t),this.applyMatrix4(Vrt),this}rotateZ(t){return Vrt.makeRotationZ(t),this.applyMatrix4(Vrt),this}translate(t,e,n){return Vrt.makeTranslation(t,e,n),this.applyMatrix4(Vrt),this}scale(t,e,n){return Vrt.makeScale(t,e,n),this.applyMatrix4(Vrt),this}lookAt(t){return jrt.lookAt(t),jrt.updateMatrix(),this.applyMatrix4(jrt.matrix),this}center(){return this.computeBoundingBox(),this.boundingBox.getCenter(Urt).negate(),this.translate(Urt.x,Urt.y,Urt.z),this}setFromPoints(t){const e=[];for(let n=0,o=t.length;n<o;n++){const o=t[n];e.push(o.x,o.y,o.z||0)}return this.setAttribute("position",new Frt(e,3)),this}computeBoundingBox(){null===this.boundingBox&&(this.boundingBox=new mat);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingBox(): GLBufferAttribute requires a manual bounding box. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingBox.set(new cat(-1/0,-1/0,-1/0),new cat(1/0,1/0,1/0));if(void 0!==t){if(this.boundingBox.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++)Grt.setFromBufferAttribute(e[t]),this.morphTargetsRelative?(Yrt.addVectors(this.boundingBox.min,Grt.min),this.boundingBox.expandByPoint(Yrt),Yrt.addVectors(this.boundingBox.max,Grt.max),this.boundingBox.expandByPoint(Yrt)):(this.boundingBox.expandByPoint(Grt.min),this.boundingBox.expandByPoint(Grt.max))}else this.boundingBox.makeEmpty();(isNaN(this.boundingBox.min.x)||isNaN(this.boundingBox.min.y)||isNaN(this.boundingBox.min.z))&&console.error('THREE.BufferGeometry.computeBoundingBox(): Computed min/max have NaN values. The "position" attribute is likely to have NaN values.',this)}computeBoundingSphere(){null===this.boundingSphere&&(this.boundingSphere=new Rat);const t=this.attributes.position,e=this.morphAttributes.position;if(t&&t.isGLBufferAttribute)return console.error('THREE.BufferGeometry.computeBoundingSphere(): GLBufferAttribute requires a manual bounding sphere. Alternatively set "mesh.frustumCulled" to "false".',this),void this.boundingSphere.set(new cat,1/0);if(t){const n=this.boundingSphere.center;if(Grt.setFromBufferAttribute(t),e)for(let t=0,n=e.length;t<n;t++)Wrt.setFromBufferAttribute(e[t]),this.morphTargetsRelative?(Yrt.addVectors(Grt.min,Wrt.min),Grt.expandByPoint(Yrt),Yrt.addVectors(Grt.max,Wrt.max),Grt.expandByPoint(Yrt)):(Grt.expandByPoint(Wrt.min),Grt.expandByPoint(Wrt.max));Grt.getCenter(n);let o=0;for(let e=0,i=t.count;e<i;e++)Yrt.fromBufferAttribute(t,e),o=Math.max(o,n.distanceToSquared(Yrt));if(e)for(let i=0,a=e.length;i<a;i++){const a=e[i],r=this.morphTargetsRelative;for(let e=0,i=a.count;e<i;e++)Yrt.fromBufferAttribute(a,e),r&&(Urt.fromBufferAttribute(t,e),Yrt.add(Urt)),o=Math.max(o,n.distanceToSquared(Yrt))}this.boundingSphere.radius=Math.sqrt(o),isNaN(this.boundingSphere.radius)&&console.error('THREE.BufferGeometry.computeBoundingSphere(): Computed radius is NaN. The "position" attribute is likely to have NaN values.',this)}}computeFaceNormals(){}computeTangents(){const t=this.index,e=this.attributes;if(null===t||void 0===e.position||void 0===e.normal||void 0===e.uv)return void console.error("THREE.BufferGeometry: .computeTangents() failed. Missing required attributes (index, position, normal or uv)");const n=t.array,o=e.position.array,i=e.normal.array,a=e.uv.array,r=o.length/3;void 0===e.tangent&&this.setAttribute("tangent",new zrt(new Float32Array(4*r),4));const s=e.tangent.array,l=[],c=[];for(let t=0;t<r;t++)l[t]=new cat,c[t]=new cat;const d=new cat,p=new cat,m=new cat,u=new Qit,f=new Qit,g=new Qit,h=new cat,b=new cat;function y(t,e,n){d.fromArray(o,3*t),p.fromArray(o,3*e),m.fromArray(o,3*n),u.fromArray(a,2*t),f.fromArray(a,2*e),g.fromArray(a,2*n),p.sub(d),m.sub(d),f.sub(u),g.sub(u);const i=1/(f.x*g.y-g.x*f.y);isFinite(i)&&(h.copy(p).multiplyScalar(g.y).addScaledVector(m,-f.y).multiplyScalar(i),b.copy(m).multiplyScalar(f.x).addScaledVector(p,-g.x).multiplyScalar(i),l[t].add(h),l[e].add(h),l[n].add(h),c[t].add(b),c[e].add(b),c[n].add(b))}let _=this.groups;0===_.length&&(_=[{start:0,count:n.length}]);for(let t=0,e=_.length;t<e;++t){const e=_[t],o=e.start;for(let t=o,i=o+e.count;t<i;t+=3)y(n[t+0],n[t+1],n[t+2])}const C=new cat,M=new cat,v=new cat,x=new cat;function O(t){v.fromArray(i,3*t),x.copy(v);const e=l[t];C.copy(e),C.sub(v.multiplyScalar(v.dot(e))).normalize(),M.crossVectors(x,e);const n=M.dot(c[t])<0?-1:1;s[4*t]=C.x,s[4*t+1]=C.y,s[4*t+2]=C.z,s[4*t+3]=n}for(let t=0,e=_.length;t<e;++t){const e=_[t],o=e.start;for(let t=o,i=o+e.count;t<i;t+=3)O(n[t+0]),O(n[t+1]),O(n[t+2])}}computeVertexNormals(){const t=this.index,e=this.getAttribute("position");if(void 0!==e){let n=this.getAttribute("normal");if(void 0===n)n=new zrt(new Float32Array(3*e.count),3),this.setAttribute("normal",n);else for(let t=0,e=n.count;t<e;t++)n.setXYZ(t,0,0,0);const o=new cat,i=new cat,a=new cat,r=new cat,s=new cat,l=new cat,c=new cat,d=new cat;if(t)for(let p=0,m=t.count;p<m;p+=3){const m=t.getX(p+0),u=t.getX(p+1),f=t.getX(p+2);o.fromBufferAttribute(e,m),i.fromBufferAttribute(e,u),a.fromBufferAttribute(e,f),c.subVectors(a,i),d.subVectors(o,i),c.cross(d),r.fromBufferAttribute(n,m),s.fromBufferAttribute(n,u),l.fromBufferAttribute(n,f),r.add(c),s.add(c),l.add(c),n.setXYZ(m,r.x,r.y,r.z),n.setXYZ(u,s.x,s.y,s.z),n.setXYZ(f,l.x,l.y,l.z)}else for(let t=0,r=e.count;t<r;t+=3)o.fromBufferAttribute(e,t+0),i.fromBufferAttribute(e,t+1),a.fromBufferAttribute(e,t+2),c.subVectors(a,i),d.subVectors(o,i),c.cross(d),n.setXYZ(t+0,c.x,c.y,c.z),n.setXYZ(t+1,c.x,c.y,c.z),n.setXYZ(t+2,c.x,c.y,c.z);this.normalizeNormals(),n.needsUpdate=!0}}merge(t,e){if(!t||!t.isBufferGeometry)return void console.error("THREE.BufferGeometry.merge(): geometry not an instance of THREE.BufferGeometry.",t);void 0===e&&(e=0,console.warn("THREE.BufferGeometry.merge(): Overwriting original geometry, starting at offset=0. Use BufferGeometryUtils.mergeBufferGeometries() for lossless merge."));const n=this.attributes;for(const o in n){if(void 0===t.attributes[o])continue;const i=n[o].array,a=t.attributes[o],r=a.array,s=a.itemSize*e,l=Math.min(r.length,i.length-s);for(let t=0,e=s;t<l;t++,e++)i[e]=r[t]}return this}normalizeNormals(){const t=this.attributes.normal;for(let e=0,n=t.count;e<n;e++)Yrt.fromBufferAttribute(t,e),Yrt.normalize(),t.setXYZ(e,Yrt.x,Yrt.y,Yrt.z)}toNonIndexed(){function t(t,e){const n=t.array,o=t.itemSize,i=t.normalized,a=new n.constructor(e.length*o);let r=0,s=0;for(let i=0,l=e.length;i<l;i++){r=t.isInterleavedBufferAttribute?e[i]*t.data.stride+t.offset:e[i]*o;for(let t=0;t<o;t++)a[s++]=n[r++]}return new zrt(a,o,i)}if(null===this.index)return console.warn("THREE.BufferGeometry.toNonIndexed(): BufferGeometry is already non-indexed."),this;const e=new qrt,n=this.index.array,o=this.attributes;for(const i in o){const a=t(o[i],n);e.setAttribute(i,a)}const i=this.morphAttributes;for(const o in i){const a=[],r=i[o];for(let e=0,o=r.length;e<o;e++){const o=t(r[e],n);a.push(o)}e.morphAttributes[o]=a}e.morphTargetsRelative=this.morphTargetsRelative;const a=this.groups;for(let t=0,n=a.length;t<n;t++){const n=a[t];e.addGroup(n.start,n.count,n.materialIndex)}return e}toJSON(){const t={metadata:{version:4.5,type:"BufferGeometry",generator:"BufferGeometry.toJSON"}};if(t.uuid=this.uuid,t.type=this.type,""!==this.name&&(t.name=this.name),Object.keys(this.userData).length>0&&(t.userData=this.userData),void 0!==this.parameters){const e=this.parameters;for(const n in e)void 0!==e[n]&&(t[n]=e[n]);return t}t.data={attributes:{}};const e=this.index;null!==e&&(t.data.index={type:e.array.constructor.name,array:Array.prototype.slice.call(e.array)});const n=this.attributes;for(const e in n)t.data.attributes[e]=n[e].toJSON(t.data);const o={};let i=!1;for(const e in this.morphAttributes){const n=this.morphAttributes[e],a=[];for(let e=0,o=n.length;e<o;e++)a.push(n[e].toJSON(t.data));a.length>0&&(o[e]=a,i=!0)}i&&(t.data.morphAttributes=o,t.data.morphTargetsRelative=this.morphTargetsRelative);const a=this.groups;a.length>0&&(t.data.groups=JSON.parse(JSON.stringify(a)));const r=this.boundingSphere;return null!==r&&(t.data.boundingSphere={center:r.center.toArray(),radius:r.radius}),t}clone(){return(new qrt).copy(this)}copy(t){this.index=null,this.attributes={},this.morphAttributes={},this.groups=[],this.boundingBox=null,this.boundingSphere=null;const e={};this.name=t.name;const n=t.index;null!==n&&this.setIndex(n.clone(e));const o=t.attributes;for(const t in o)this.setAttribute(t,o[t].clone(e));const i=t.morphAttributes;for(const t in i){const n=[],o=i[t];for(let t=0,i=o.length;t<i;t++)n.push(o[t].clone(e));this.morphAttributes[t]=n}this.morphTargetsRelative=t.morphTargetsRelative;const a=t.groups;for(let t=0,e=a.length;t<e;t++){const e=a[t];this.addGroup(e.start,e.count,e.materialIndex)}const r=t.boundingBox;null!==r&&(this.boundingBox=r.clone());const s=t.boundingSphere;return null!==s&&(this.boundingSphere=s.clone()),this.drawRange.start=t.drawRange.start,this.drawRange.count=t.drawRange.count,this.userData=t.userData,this}dispose(){this.dispatchEvent({type:"dispose"})}}qrt.prototype.isBufferGeometry=!0;const Zrt=new Bat,Xrt=new Lat,Krt=new Rat,Jrt=new cat,Qrt=new cat,$rt=new cat,tst=new cat,est=new cat,nst=new cat,ost=new cat,ist=new cat,ast=new cat,rst=new Qit,sst=new Qit,lst=new Qit,cst=new cat,dst=new cat;class pst extends prt{constructor(t=new qrt,e=new Art){super(),this.type="Mesh",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),void 0!==t.morphTargetInfluences&&(this.morphTargetInfluences=t.morphTargetInfluences.slice()),void 0!==t.morphTargetDictionary&&(this.morphTargetDictionary=Object.assign({},t.morphTargetDictionary)),this.material=t.material,this.geometry=t.geometry,this}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Mesh.updateMorphTargets() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}}raycast(t,e){const n=this.geometry,o=this.material,i=this.matrixWorld;if(void 0===o)return;if(null===n.boundingSphere&&n.computeBoundingSphere(),Krt.copy(n.boundingSphere),Krt.applyMatrix4(i),!1===t.ray.intersectsSphere(Krt))return;if(Zrt.copy(i).invert(),Xrt.copy(t.ray).applyMatrix4(Zrt),null!==n.boundingBox&&!1===Xrt.intersectsBox(n.boundingBox))return;let a;if(n.isBufferGeometry){const i=n.index,r=n.attributes.position,s=n.morphAttributes.position,l=n.morphTargetsRelative,c=n.attributes.uv,d=n.attributes.uv2,p=n.groups,m=n.drawRange;if(null!==i)if(Array.isArray(o))for(let n=0,u=p.length;n<u;n++){const u=p[n],f=o[u.materialIndex];for(let n=Math.max(u.start,m.start),o=Math.min(u.start+u.count,m.start+m.count);n<o;n+=3){const o=i.getX(n),p=i.getX(n+1),m=i.getX(n+2);a=mst(this,f,t,Xrt,r,s,l,c,d,o,p,m),a&&(a.faceIndex=Math.floor(n/3),a.face.materialIndex=u.materialIndex,e.push(a))}}else for(let n=Math.max(0,m.start),p=Math.min(i.count,m.start+m.count);n<p;n+=3){const p=i.getX(n),m=i.getX(n+1),u=i.getX(n+2);a=mst(this,o,t,Xrt,r,s,l,c,d,p,m,u),a&&(a.faceIndex=Math.floor(n/3),e.push(a))}else if(void 0!==r)if(Array.isArray(o))for(let n=0,i=p.length;n<i;n++){const i=p[n],u=o[i.materialIndex];for(let n=Math.max(i.start,m.start),o=Math.min(i.start+i.count,m.start+m.count);n<o;n+=3)a=mst(this,u,t,Xrt,r,s,l,c,d,n,n+1,n+2),a&&(a.faceIndex=Math.floor(n/3),a.face.materialIndex=i.materialIndex,e.push(a))}else for(let n=Math.max(0,m.start),i=Math.min(r.count,m.start+m.count);n<i;n+=3)a=mst(this,o,t,Xrt,r,s,l,c,d,n,n+1,n+2),a&&(a.faceIndex=Math.floor(n/3),e.push(a))}else n.isGeometry&&console.error("THREE.Mesh.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}}function mst(t,e,n,o,i,a,r,s,l,c,d,p){Jrt.fromBufferAttribute(i,c),Qrt.fromBufferAttribute(i,d),$rt.fromBufferAttribute(i,p);const m=t.morphTargetInfluences;if(a&&m){ost.set(0,0,0),ist.set(0,0,0),ast.set(0,0,0);for(let t=0,e=a.length;t<e;t++){const e=m[t],n=a[t];0!==e&&(tst.fromBufferAttribute(n,c),est.fromBufferAttribute(n,d),nst.fromBufferAttribute(n,p),r?(ost.addScaledVector(tst,e),ist.addScaledVector(est,e),ast.addScaledVector(nst,e)):(ost.addScaledVector(tst.sub(Jrt),e),ist.addScaledVector(est.sub(Qrt),e),ast.addScaledVector(nst.sub($rt),e)))}Jrt.add(ost),Qrt.add(ist),$rt.add(ast)}t.isSkinnedMesh&&(t.boneTransform(c,Jrt),t.boneTransform(d,Qrt),t.boneTransform(p,$rt));const u=(function f(t,e,n,o,i,a,r,s){let l;if(l=1===e.side?o.intersectTriangle(r,a,i,!0,s):o.intersectTriangle(i,a,r,2!==e.side,s),null===l)return null;dst.copy(s),dst.applyMatrix4(t.matrixWorld);const c=n.ray.origin.distanceTo(dst);return c<n.near||c>n.far?null:{distance:c,point:dst.clone(),object:t}})(t,e,n,o,Jrt,Qrt,$rt,cst);if(u){s&&(rst.fromBufferAttribute(s,c),sst.fromBufferAttribute(s,d),lst.fromBufferAttribute(s,p),u.uv=vrt.getUV(cst,Jrt,Qrt,$rt,rst,sst,lst,new Qit)),l&&(rst.fromBufferAttribute(l,c),sst.fromBufferAttribute(l,d),lst.fromBufferAttribute(l,p),u.uv2=vrt.getUV(cst,Jrt,Qrt,$rt,rst,sst,lst,new Qit));const t={a:c,b:d,c:p,normal:new cat,materialIndex:0};vrt.getNormal(Jrt,Qrt,$rt,t.normal),u.face=t}return u}pst.prototype.isMesh=!0;class ust extends qrt{constructor(t=1,e=1,n=1,o=1,i=1,a=1){super(),this.type="BoxGeometry",this.parameters={width:t,height:e,depth:n,widthSegments:o,heightSegments:i,depthSegments:a};const r=this;o=Math.floor(o),i=Math.floor(i),a=Math.floor(a);const s=[],l=[],c=[],d=[];let p=0,m=0;function u(t,e,n,o,i,a,u,f,g,h,b){const y=a/g,_=u/h,C=a/2,M=u/2,v=f/2,x=g+1,O=h+1;let P=0,w=0;const k=new cat;for(let a=0;a<O;a++){const r=a*_-M;for(let s=0;s<x;s++)k[t]=(s*y-C)*o,k[e]=r*i,k[n]=v,l.push(k.x,k.y,k.z),k[t]=0,k[e]=0,k[n]=f>0?1:-1,c.push(k.x,k.y,k.z),d.push(s/g),d.push(1-a/h),P+=1}for(let t=0;t<h;t++)for(let e=0;e<g;e++){const n=p+e+x*(t+1),o=p+(e+1)+x*(t+1),i=p+(e+1)+x*t;s.push(p+e+x*t,n,i),s.push(n,o,i),w+=6}r.addGroup(m,w,b),m+=w,p+=P}u("z","y","x",-1,-1,n,e,t,a,i,0),u("z","y","x",1,-1,n,e,-t,a,i,1),u("x","z","y",1,1,t,n,e,o,a,2),u("x","z","y",1,-1,t,n,-e,o,a,3),u("x","y","z",1,-1,t,e,n,o,i,4),u("x","y","z",-1,-1,t,e,-n,o,i,5),this.setIndex(s),this.setAttribute("position",new Frt(l,3)),this.setAttribute("normal",new Frt(c,3)),this.setAttribute("uv",new Frt(d,2))}static fromJSON(t){return new ust(t.width,t.height,t.depth,t.widthSegments,t.heightSegments,t.depthSegments)}}function fst(t){const e={};for(const n in t){e[n]={};for(const o in t[n]){const i=t[n][o];e[n][o]=i&&(i.isColor||i.isMatrix3||i.isMatrix4||i.isVector2||i.isVector3||i.isVector4||i.isTexture||i.isQuaternion)?i.clone():Array.isArray(i)?i.slice():i}}return e}function gst(t){const e={};for(let n=0;n<t.length;n++){const o=fst(t[n]);for(const t in o)e[t]=o[t]}return e}const hst={clone:fst,merge:gst};class bst extends Ort{constructor(t){super(),this.type="ShaderMaterial",this.defines={},this.uniforms={},this.vertexShader="void main() {\n\tgl_Position = projectionMatrix * modelViewMatrix * vec4( position, 1.0 );\n}",this.fragmentShader="void main() {\n\tgl_FragColor = vec4( 1.0, 0.0, 0.0, 1.0 );\n}",this.linewidth=1,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.lights=!1,this.clipping=!1,this.extensions={derivatives:!1,fragDepth:!1,drawBuffers:!1,shaderTextureLOD:!1},this.defaultAttributeValues={color:[1,1,1],uv:[0,0],uv2:[0,0]},this.index0AttributeName=void 0,this.uniformsNeedUpdate=!1,this.glslVersion=null,void 0!==t&&(void 0!==t.attributes&&console.error("THREE.ShaderMaterial: attributes should now be defined in THREE.BufferGeometry instead."),this.setValues(t))}copy(t){return super.copy(t),this.fragmentShader=t.fragmentShader,this.vertexShader=t.vertexShader,this.uniforms=fst(t.uniforms),this.defines=Object.assign({},t.defines),this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.lights=t.lights,this.clipping=t.clipping,this.extensions=Object.assign({},t.extensions),this.glslVersion=t.glslVersion,this}toJSON(t){const e=super.toJSON(t);e.glslVersion=this.glslVersion,e.uniforms={};for(const n in this.uniforms){const o=this.uniforms[n].value;e.uniforms[n]=o&&o.isTexture?{type:"t",value:o.toJSON(t).uuid}:o&&o.isColor?{type:"c",value:o.getHex()}:o&&o.isVector2?{type:"v2",value:o.toArray()}:o&&o.isVector3?{type:"v3",value:o.toArray()}:o&&o.isVector4?{type:"v4",value:o.toArray()}:o&&o.isMatrix3?{type:"m3",value:o.toArray()}:o&&o.isMatrix4?{type:"m4",value:o.toArray()}:{value:o}}Object.keys(this.defines).length>0&&(e.defines=this.defines),e.vertexShader=this.vertexShader,e.fragmentShader=this.fragmentShader;const n={};for(const t in this.extensions)!0===this.extensions[t]&&(n[t]=!0);return Object.keys(n).length>0&&(e.extensions=n),e}}bst.prototype.isShaderMaterial=!0;class yst extends prt{constructor(){super(),this.type="Camera",this.matrixWorldInverse=new Bat,this.projectionMatrix=new Bat,this.projectionMatrixInverse=new Bat}copy(t,e){return super.copy(t,e),this.matrixWorldInverse.copy(t.matrixWorldInverse),this.projectionMatrix.copy(t.projectionMatrix),this.projectionMatrixInverse.copy(t.projectionMatrixInverse),this}getWorldDirection(t){this.updateWorldMatrix(!0,!1);const e=this.matrixWorld.elements;return t.set(-e[8],-e[9],-e[10]).normalize()}updateMatrixWorld(t){super.updateMatrixWorld(t),this.matrixWorldInverse.copy(this.matrixWorld).invert()}updateWorldMatrix(t,e){super.updateWorldMatrix(t,e),this.matrixWorldInverse.copy(this.matrixWorld).invert()}clone(){return(new this.constructor).copy(this)}}yst.prototype.isCamera=!0;class _st extends yst{constructor(t=50,e=1,n=.1,o=2e3){super(),this.type="PerspectiveCamera",this.fov=t,this.zoom=1,this.near=n,this.far=o,this.focus=10,this.aspect=e,this.view=null,this.filmGauge=35,this.filmOffset=0,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.fov=t.fov,this.zoom=t.zoom,this.near=t.near,this.far=t.far,this.focus=t.focus,this.aspect=t.aspect,this.view=null===t.view?null:Object.assign({},t.view),this.filmGauge=t.filmGauge,this.filmOffset=t.filmOffset,this}setFocalLength(t){const e=.5*this.getFilmHeight()/t;this.fov=2*Yit*Math.atan(e),this.updateProjectionMatrix()}getFocalLength(){const t=Math.tan(.5*Wit*this.fov);return.5*this.getFilmHeight()/t}getEffectiveFOV(){return 2*Yit*Math.atan(Math.tan(.5*Wit*this.fov)/this.zoom)}getFilmWidth(){return this.filmGauge*Math.min(this.aspect,1)}getFilmHeight(){return this.filmGauge/Math.max(this.aspect,1)}setViewOffset(t,e,n,o,i,a){this.aspect=t/e,null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=o,this.view.width=i,this.view.height=a,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=this.near;let e=t*Math.tan(.5*Wit*this.fov)/this.zoom,n=2*e,o=this.aspect*n,i=-.5*o;const a=this.view;if(null!==this.view&&this.view.enabled){const t=a.fullWidth,r=a.fullHeight;i+=a.offsetX*o/t,e-=a.offsetY*n/r,o*=a.width/t,n*=a.height/r}const r=this.filmOffset;0!==r&&(i+=t*r/this.getFilmWidth()),this.projectionMatrix.makePerspective(i,i+o,e,e-n,t,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.fov=this.fov,e.object.zoom=this.zoom,e.object.near=this.near,e.object.far=this.far,e.object.focus=this.focus,e.object.aspect=this.aspect,null!==this.view&&(e.object.view=Object.assign({},this.view)),e.object.filmGauge=this.filmGauge,e.object.filmOffset=this.filmOffset,e}}_st.prototype.isPerspectiveCamera=!0;const Cst=90;class Mst extends prt{constructor(t,e,n){if(super(),this.type="CubeCamera",!0!==n.isWebGLCubeRenderTarget)return void console.error("THREE.CubeCamera: The constructor now expects an instance of WebGLCubeRenderTarget as third parameter.");this.renderTarget=n;const o=new _st(Cst,1,t,e);o.layers=this.layers,o.up.set(0,-1,0),o.lookAt(new cat(1,0,0)),this.add(o);const i=new _st(Cst,1,t,e);i.layers=this.layers,i.up.set(0,-1,0),i.lookAt(new cat(-1,0,0)),this.add(i);const a=new _st(Cst,1,t,e);a.layers=this.layers,a.up.set(0,0,1),a.lookAt(new cat(0,1,0)),this.add(a);const r=new _st(Cst,1,t,e);r.layers=this.layers,r.up.set(0,0,-1),r.lookAt(new cat(0,-1,0)),this.add(r);const s=new _st(Cst,1,t,e);s.layers=this.layers,s.up.set(0,-1,0),s.lookAt(new cat(0,0,1)),this.add(s);const l=new _st(Cst,1,t,e);l.layers=this.layers,l.up.set(0,-1,0),l.lookAt(new cat(0,0,-1)),this.add(l)}update(t,e){null===this.parent&&this.updateMatrixWorld();const n=this.renderTarget,[o,i,a,r,s,l]=this.children,c=t.xr.enabled,d=t.getRenderTarget();t.xr.enabled=!1;const p=n.texture.generateMipmaps;n.texture.generateMipmaps=!1,t.setRenderTarget(n,0),t.render(e,o),t.setRenderTarget(n,1),t.render(e,i),t.setRenderTarget(n,2),t.render(e,a),t.setRenderTarget(n,3),t.render(e,r),t.setRenderTarget(n,4),t.render(e,s),n.texture.generateMipmaps=p,t.setRenderTarget(n,5),t.render(e,l),t.setRenderTarget(d),t.xr.enabled=c}}class vst extends oat{constructor(t,e,n,o,i,a,r,s,l,c){super(t=void 0!==t?t:[],e=void 0!==e?e:dit,n,o,i,a,r=void 0!==r?r:Pit,s,l,c),this.flipY=!1}get images(){return this.image}set images(t){this.image=t}}vst.prototype.isCubeTexture=!0;class xst extends rat{constructor(t,e,n){Number.isInteger(e)&&(console.warn("THREE.WebGLCubeRenderTarget: constructor signature is now WebGLCubeRenderTarget( size, options )"),e=n),super(t,t,e),this.texture=new vst(void 0,(e=e||{}).mapping,e.wrapS,e.wrapT,e.magFilter,e.minFilter,e.format,e.type,e.anisotropy,e.encoding),this.texture.isRenderTargetTexture=!0,this.texture.generateMipmaps=void 0!==e.generateMipmaps&&e.generateMipmaps,this.texture.minFilter=void 0!==e.minFilter?e.minFilter:bit,this.texture._needsFlipEnvMap=!1}fromEquirectangularTexture(t,e){this.texture.type=e.type,this.texture.format=wit,this.texture.encoding=e.encoding,this.texture.generateMipmaps=e.generateMipmaps,this.texture.minFilter=e.minFilter,this.texture.magFilter=e.magFilter;const n=new ust(5,5,5),o=new bst({name:"CubemapFromEquirect",uniforms:fst({tEquirect:{value:null}}),vertexShader:"\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\tvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\n\t\t\t\t\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n\n\t\t\t\t}\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvWorldDirection = transformDirection( position, modelMatrix );\n\n\t\t\t\t\t#include <begin_vertex>\n\t\t\t\t\t#include <project_vertex>\n\n\t\t\t\t}\n\t\t\t",fragmentShader:"\n\n\t\t\t\tuniform sampler2D tEquirect;\n\n\t\t\t\tvarying vec3 vWorldDirection;\n\n\t\t\t\t#include <common>\n\n\t\t\t\tvoid main() {\n\n\t\t\t\t\tvec3 direction = normalize( vWorldDirection );\n\n\t\t\t\t\tvec2 sampleUV = equirectUv( direction );\n\n\t\t\t\t\tgl_FragColor = texture2D( tEquirect, sampleUV );\n\n\t\t\t\t}\n\t\t\t",side:1,blending:0});o.uniforms.tEquirect.value=e;const i=new pst(n,o),a=e.minFilter;return e.minFilter===yit&&(e.minFilter=bit),new Mst(1,10,this).update(t,i),e.minFilter=a,i.geometry.dispose(),i.material.dispose(),this}clear(t,e,n,o){const i=t.getRenderTarget();for(let i=0;i<6;i++)t.setRenderTarget(this,i),t.clear(e,n,o);t.setRenderTarget(i)}}xst.prototype.isWebGLCubeRenderTarget=!0;const Ost=new cat,Pst=new cat,wst=new $it;class kst{constructor(t=new cat(1,0,0),e=0){this.normal=t,this.constant=e}set(t,e){return this.normal.copy(t),this.constant=e,this}setComponents(t,e,n,o){return this.normal.set(t,e,n),this.constant=o,this}setFromNormalAndCoplanarPoint(t,e){return this.normal.copy(t),this.constant=-e.dot(this.normal),this}setFromCoplanarPoints(t,e,n){const o=Ost.subVectors(n,e).cross(Pst.subVectors(t,e)).normalize();return this.setFromNormalAndCoplanarPoint(o,t),this}copy(t){return this.normal.copy(t.normal),this.constant=t.constant,this}normalize(){const t=1/this.normal.length();return this.normal.multiplyScalar(t),this.constant*=t,this}negate(){return this.constant*=-1,this.normal.negate(),this}distanceToPoint(t){return this.normal.dot(t)+this.constant}distanceToSphere(t){return this.distanceToPoint(t.center)-t.radius}projectPoint(t,e){return e.copy(this.normal).multiplyScalar(-this.distanceToPoint(t)).add(t)}intersectLine(t,e){const n=t.delta(Ost),o=this.normal.dot(n);if(0===o)return 0===this.distanceToPoint(t.start)?e.copy(t.start):null;const i=-(t.start.dot(this.normal)+this.constant)/o;return i<0||i>1?null:e.copy(n).multiplyScalar(i).add(t.start)}intersectsLine(t){const e=this.distanceToPoint(t.start),n=this.distanceToPoint(t.end);return e<0&&n>0||n<0&&e>0}intersectsBox(t){return t.intersectsPlane(this)}intersectsSphere(t){return t.intersectsPlane(this)}coplanarPoint(t){return t.copy(this.normal).multiplyScalar(-this.constant)}applyMatrix4(t,e){const n=e||wst.getNormalMatrix(t),o=this.coplanarPoint(Ost).applyMatrix4(t),i=this.normal.applyMatrix3(n).normalize();return this.constant=-o.dot(i),this}translate(t){return this.constant-=t.dot(this.normal),this}equals(t){return t.normal.equals(this.normal)&&t.constant===this.constant}clone(){return(new this.constructor).copy(this)}}kst.prototype.isPlane=!0;const Sst=new Rat,Dst=new cat;class Est{constructor(t=new kst,e=new kst,n=new kst,o=new kst,i=new kst,a=new kst){this.planes=[t,e,n,o,i,a]}set(t,e,n,o,i,a){const r=this.planes;return r[0].copy(t),r[1].copy(e),r[2].copy(n),r[3].copy(o),r[4].copy(i),r[5].copy(a),this}copy(t){const e=this.planes;for(let n=0;n<6;n++)e[n].copy(t.planes[n]);return this}setFromProjectionMatrix(t){const e=this.planes,n=t.elements,o=n[0],i=n[1],a=n[2],r=n[3],s=n[4],l=n[5],c=n[6],d=n[7],p=n[8],m=n[9],u=n[10],f=n[11],g=n[12],h=n[13],b=n[14],y=n[15];return e[0].setComponents(r-o,d-s,f-p,y-g).normalize(),e[1].setComponents(r+o,d+s,f+p,y+g).normalize(),e[2].setComponents(r+i,d+l,f+m,y+h).normalize(),e[3].setComponents(r-i,d-l,f-m,y-h).normalize(),e[4].setComponents(r-a,d-c,f-u,y-b).normalize(),e[5].setComponents(r+a,d+c,f+u,y+b).normalize(),this}intersectsObject(t){const e=t.geometry;return null===e.boundingSphere&&e.computeBoundingSphere(),Sst.copy(e.boundingSphere).applyMatrix4(t.matrixWorld),this.intersectsSphere(Sst)}intersectsSprite(t){return Sst.center.set(0,0,0),Sst.radius=.7071067811865476,Sst.applyMatrix4(t.matrixWorld),this.intersectsSphere(Sst)}intersectsSphere(t){const e=this.planes,n=t.center,o=-t.radius;for(let t=0;t<6;t++)if(e[t].distanceToPoint(n)<o)return!1;return!0}intersectsBox(t){const e=this.planes;for(let n=0;n<6;n++){const o=e[n];if(Dst.x=o.normal.x>0?t.max.x:t.min.x,Dst.y=o.normal.y>0?t.max.y:t.min.y,Dst.z=o.normal.z>0?t.max.z:t.min.z,o.distanceToPoint(Dst)<0)return!1}return!0}containsPoint(t){const e=this.planes;for(let n=0;n<6;n++)if(e[n].distanceToPoint(t)<0)return!1;return!0}clone(){return(new this.constructor).copy(this)}}function Rst(){let t=null,e=!1,n=null,o=null;function i(e,a){n(e,a),o=t.requestAnimationFrame(i)}return{start:function(){!0!==e&&null!==n&&(o=t.requestAnimationFrame(i),e=!0)},stop:function(){t.cancelAnimationFrame(o),e=!1},setAnimationLoop:function(t){n=t},setContext:function(e){t=e}}}function Ast(t,e){const n=e.isWebGL2,o=new WeakMap;return{get:function i(t){return t.isInterleavedBufferAttribute&&(t=t.data),o.get(t)},remove:function a(e){e.isInterleavedBufferAttribute&&(e=e.data);const n=o.get(e);n&&(t.deleteBuffer(n.buffer),o.delete(e))},update:function r(e,i){if(e.isGLBufferAttribute){const t=o.get(e);return void((!t||t.version<e.version)&&o.set(e,{buffer:e.buffer,type:e.type,bytesPerElement:e.elementSize,version:e.version}))}e.isInterleavedBufferAttribute&&(e=e.data);const a=o.get(e);void 0===a?o.set(e,(function r(e,o){const i=e.array,a=e.usage,r=t.createBuffer();t.bindBuffer(o,r),t.bufferData(o,i,a),e.onUploadCallback();let s=5126;return i instanceof Float32Array?s=5126:i instanceof Float64Array?console.warn("THREE.WebGLAttributes: Unsupported data buffer format: Float64Array."):i instanceof Uint16Array?e.isFloat16BufferAttribute?n?s=5131:console.warn("THREE.WebGLAttributes: Usage of Float16BufferAttribute requires WebGL2."):s=5123:i instanceof Int16Array?s=5122:i instanceof Uint32Array?s=5125:i instanceof Int32Array?s=5124:i instanceof Int8Array?s=5120:(i instanceof Uint8Array||i instanceof Uint8ClampedArray)&&(s=5121),{buffer:r,type:s,bytesPerElement:i.BYTES_PER_ELEMENT,version:e.version}})(e,i)):a.version<e.version&&((function s(e,o,i){const a=o.array,r=o.updateRange;t.bindBuffer(i,e),-1===r.count?t.bufferSubData(i,0,a):(n?t.bufferSubData(i,r.offset*a.BYTES_PER_ELEMENT,a,r.offset,r.count):t.bufferSubData(i,r.offset*a.BYTES_PER_ELEMENT,a.subarray(r.offset,r.offset+r.count)),r.count=-1)})(a.buffer,e,i),a.version=e.version)}}}class Tst extends qrt{constructor(t=1,e=1,n=1,o=1){super(),this.type="PlaneGeometry",this.parameters={width:t,height:e,widthSegments:n,heightSegments:o};const i=t/2,a=e/2,r=Math.floor(n),s=Math.floor(o),l=r+1,c=s+1,d=t/r,p=e/s,m=[],u=[],f=[],g=[];for(let t=0;t<c;t++){const e=t*p-a;for(let n=0;n<l;n++)u.push(n*d-i,-e,0),f.push(0,0,1),g.push(n/r),g.push(1-t/s)}for(let t=0;t<s;t++)for(let e=0;e<r;e++){const n=e+l*(t+1),o=e+1+l*(t+1),i=e+1+l*t;m.push(e+l*t,n,i),m.push(n,o,i)}this.setIndex(m),this.setAttribute("position",new Frt(u,3)),this.setAttribute("normal",new Frt(f,3)),this.setAttribute("uv",new Frt(g,2))}static fromJSON(t){return new Tst(t.width,t.height,t.widthSegments,t.heightSegments)}}const Nst={alphamap_fragment:"#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, vUv ).g;\n#endif",alphamap_pars_fragment:"#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",alphatest_fragment:"#ifdef ALPHATEST\n\tif ( diffuseColor.a < ALPHATEST ) discard;\n#endif",aomap_fragment:"#ifdef USE_AOMAP\n\tfloat ambientOcclusion = ( texture2D( aoMap, vUv2 ).r - 1.0 ) * aoMapIntensity + 1.0;\n\treflectedLight.indirectDiffuse *= ambientOcclusion;\n\t#if defined( USE_ENVMAP ) && defined( STANDARD )\n\t\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular *= computeSpecularOcclusion( dotNV, ambientOcclusion, material.specularRoughness );\n\t#endif\n#endif",aomap_pars_fragment:"#ifdef USE_AOMAP\n\tuniform sampler2D aoMap;\n\tuniform float aoMapIntensity;\n#endif",begin_vertex:"vec3 transformed = vec3( position );",beginnormal_vertex:"vec3 objectNormal = vec3( normal );\n#ifdef USE_TANGENT\n\tvec3 objectTangent = vec3( tangent.xyz );\n#endif",bsdfs:"vec2 integrateSpecularBRDF( const in float dotNV, const in float roughness ) {\n\tconst vec4 c0 = vec4( - 1, - 0.0275, - 0.572, 0.022 );\n\tconst vec4 c1 = vec4( 1, 0.0425, 1.04, - 0.04 );\n\tvec4 r = roughness * c0 + c1;\n\tfloat a004 = min( r.x * r.x, exp2( - 9.28 * dotNV ) ) * r.x + r.y;\n\treturn vec2( -1.04, 1.04 ) * a004 + r.zw;\n}\nfloat punctualLightIntensityToIrradianceFactor( const in float lightDistance, const in float cutoffDistance, const in float decayExponent ) {\n#if defined ( PHYSICALLY_CORRECT_LIGHTS )\n\tfloat distanceFalloff = 1.0 / max( pow( lightDistance, decayExponent ), 0.01 );\n\tif( cutoffDistance > 0.0 ) {\n\t\tdistanceFalloff *= pow2( saturate( 1.0 - pow4( lightDistance / cutoffDistance ) ) );\n\t}\n\treturn distanceFalloff;\n#else\n\tif( cutoffDistance > 0.0 && decayExponent > 0.0 ) {\n\t\treturn pow( saturate( -lightDistance / cutoffDistance + 1.0 ), decayExponent );\n\t}\n\treturn 1.0;\n#endif\n}\nvec3 BRDF_Diffuse_Lambert( const in vec3 diffuseColor ) {\n\treturn RECIPROCAL_PI * diffuseColor;\n}\nvec3 F_Schlick( const in vec3 f0, const in vec3 f90, const in float dotVH ) {\n\tfloat fresnel = exp2( ( -5.55473 * dotVH - 6.98316 ) * dotVH );\n\treturn ( f90 - f0 ) * fresnel + f0;\n}\nvec3 F_Schlick_RoughnessDependent( const in vec3 F0, const in float dotNV, const in float roughness ) {\n\tfloat fresnel = exp2( ( -5.55473 * dotNV - 6.98316 ) * dotNV );\n\tvec3 Fr = max( vec3( 1.0 - roughness ), F0 ) - F0;\n\treturn Fr * fresnel + F0;\n}\nfloat G_GGX_Smith( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 = pow2( alpha );\n\tfloat gl = dotNL + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\tfloat gv = dotNV + sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\treturn 1.0 / ( gl * gv );\n}\nfloat G_GGX_SmithCorrelated( const in float alpha, const in float dotNL, const in float dotNV ) {\n\tfloat a2 = pow2( alpha );\n\tfloat gv = dotNL * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNV ) );\n\tfloat gl = dotNV * sqrt( a2 + ( 1.0 - a2 ) * pow2( dotNL ) );\n\treturn 0.5 / max( gv + gl, EPSILON );\n}\nfloat D_GGX( const in float alpha, const in float dotNH ) {\n\tfloat a2 = pow2( alpha );\n\tfloat denom = pow2( dotNH ) * ( a2 - 1.0 ) + 1.0;\n\treturn RECIPROCAL_PI * a2 / pow2( denom );\n}\nvec3 BRDF_Specular_GGX( const in IncidentLight incidentLight, const in vec3 viewDir, const in vec3 normal, const in vec3 f0, const in vec3 f90, const in float roughness ) {\n\tfloat alpha = pow2( roughness );\n\tvec3 halfDir = normalize( incidentLight.direction + viewDir );\n\tfloat dotNL = saturate( dot( normal, incidentLight.direction ) );\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tfloat dotNH = saturate( dot( normal, halfDir ) );\n\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F = F_Schlick( f0, f90, dotLH );\n\tfloat G = G_GGX_SmithCorrelated( alpha, dotNL, dotNV );\n\tfloat D = D_GGX( alpha, dotNH );\n\treturn F * ( G * D );\n}\nvec2 LTC_Uv( const in vec3 N, const in vec3 V, const in float roughness ) {\n\tconst float LUT_SIZE = 64.0;\n\tconst float LUT_SCALE = ( LUT_SIZE - 1.0 ) / LUT_SIZE;\n\tconst float LUT_BIAS = 0.5 / LUT_SIZE;\n\tfloat dotNV = saturate( dot( N, V ) );\n\tvec2 uv = vec2( roughness, sqrt( 1.0 - dotNV ) );\n\tuv = uv * LUT_SCALE + LUT_BIAS;\n\treturn uv;\n}\nfloat LTC_ClippedSphereFormFactor( const in vec3 f ) {\n\tfloat l = length( f );\n\treturn max( ( l * l + f.z ) / ( l + 1.0 ), 0.0 );\n}\nvec3 LTC_EdgeVectorFormFactor( const in vec3 v1, const in vec3 v2 ) {\n\tfloat x = dot( v1, v2 );\n\tfloat y = abs( x );\n\tfloat a = 0.8543985 + ( 0.4965155 + 0.0145206 * y ) * y;\n\tfloat b = 3.4175940 + ( 4.1616724 + y ) * y;\n\tfloat v = a / b;\n\tfloat theta_sintheta = ( x > 0.0 ) ? v : 0.5 * inversesqrt( max( 1.0 - x * x, 1e-7 ) ) - v;\n\treturn cross( v1, v2 ) * theta_sintheta;\n}\nvec3 LTC_Evaluate( const in vec3 N, const in vec3 V, const in vec3 P, const in mat3 mInv, const in vec3 rectCoords[ 4 ] ) {\n\tvec3 v1 = rectCoords[ 1 ] - rectCoords[ 0 ];\n\tvec3 v2 = rectCoords[ 3 ] - rectCoords[ 0 ];\n\tvec3 lightNormal = cross( v1, v2 );\n\tif( dot( lightNormal, P - rectCoords[ 0 ] ) < 0.0 ) return vec3( 0.0 );\n\tvec3 T1, T2;\n\tT1 = normalize( V - N * dot( V, N ) );\n\tT2 = - cross( N, T1 );\n\tmat3 mat = mInv * transposeMat3( mat3( T1, T2, N ) );\n\tvec3 coords[ 4 ];\n\tcoords[ 0 ] = mat * ( rectCoords[ 0 ] - P );\n\tcoords[ 1 ] = mat * ( rectCoords[ 1 ] - P );\n\tcoords[ 2 ] = mat * ( rectCoords[ 2 ] - P );\n\tcoords[ 3 ] = mat * ( rectCoords[ 3 ] - P );\n\tcoords[ 0 ] = normalize( coords[ 0 ] );\n\tcoords[ 1 ] = normalize( coords[ 1 ] );\n\tcoords[ 2 ] = normalize( coords[ 2 ] );\n\tcoords[ 3 ] = normalize( coords[ 3 ] );\n\tvec3 vectorFormFactor = vec3( 0.0 );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 0 ], coords[ 1 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 1 ], coords[ 2 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 2 ], coords[ 3 ] );\n\tvectorFormFactor += LTC_EdgeVectorFormFactor( coords[ 3 ], coords[ 0 ] );\n\tfloat result = LTC_ClippedSphereFormFactor( vectorFormFactor );\n\treturn vec3( result );\n}\nvec3 BRDF_Specular_GGX_Environment( const in vec3 viewDir, const in vec3 normal, const in vec3 specularColor, const in float roughness ) {\n\tfloat dotNV = saturate( dot( normal, viewDir ) );\n\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\n\treturn specularColor * brdf.x + brdf.y;\n}\nvoid BRDF_Specular_Multiscattering_Environment( const in GeometricContext geometry, const in vec3 specularColor, const in float roughness, inout vec3 singleScatter, inout vec3 multiScatter ) {\n\tfloat dotNV = saturate( dot( geometry.normal, geometry.viewDir ) );\n\tvec3 F = F_Schlick_RoughnessDependent( specularColor, dotNV, roughness );\n\tvec2 brdf = integrateSpecularBRDF( dotNV, roughness );\n\tvec3 FssEss = F * brdf.x + brdf.y;\n\tfloat Ess = brdf.x + brdf.y;\n\tfloat Ems = 1.0 - Ess;\n\tvec3 Favg = specularColor + ( 1.0 - specularColor ) * 0.047619;\tvec3 Fms = FssEss * Favg / ( 1.0 - Ems * Favg );\n\tsingleScatter += FssEss;\n\tmultiScatter += Fms * Ems;\n}\nfloat G_BlinnPhong_Implicit( ) {\n\treturn 0.25;\n}\nfloat D_BlinnPhong( const in float shininess, const in float dotNH ) {\n\treturn RECIPROCAL_PI * ( shininess * 0.5 + 1.0 ) * pow( dotNH, shininess );\n}\nvec3 BRDF_Specular_BlinnPhong( const in IncidentLight incidentLight, const in GeometricContext geometry, const in vec3 specularColor, const in float shininess ) {\n\tvec3 halfDir = normalize( incidentLight.direction + geometry.viewDir );\n\tfloat dotNH = saturate( dot( geometry.normal, halfDir ) );\n\tfloat dotLH = saturate( dot( incidentLight.direction, halfDir ) );\n\tvec3 F = F_Schlick( specularColor, vec3( 1.0 ), dotLH );\n\tfloat G = G_BlinnPhong_Implicit( );\n\tfloat D = D_BlinnPhong( shininess, dotNH );\n\treturn F * ( G * D );\n}\nfloat GGXRoughnessToBlinnExponent( const in float ggxRoughness ) {\n\treturn ( 2.0 / pow2( ggxRoughness + 0.0001 ) - 2.0 );\n}\nfloat BlinnExponentToGGXRoughness( const in float blinnExponent ) {\n\treturn sqrt( 2.0 / ( blinnExponent + 2.0 ) );\n}\n#if defined( USE_SHEEN )\nfloat D_Charlie(float roughness, float NoH) {\n\tfloat invAlpha = 1.0 / roughness;\n\tfloat cos2h = NoH * NoH;\n\tfloat sin2h = max(1.0 - cos2h, 0.0078125);\treturn (2.0 + invAlpha) * pow(sin2h, invAlpha * 0.5) / (2.0 * PI);\n}\nfloat V_Neubelt(float NoV, float NoL) {\n\treturn saturate(1.0 / (4.0 * (NoL + NoV - NoL * NoV)));\n}\nvec3 BRDF_Specular_Sheen( const in float roughness, const in vec3 L, const in GeometricContext geometry, vec3 specularColor ) {\n\tvec3 N = geometry.normal;\n\tvec3 V = geometry.viewDir;\n\tvec3 H = normalize( V + L );\n\tfloat dotNH = saturate( dot( N, H ) );\n\treturn specularColor * D_Charlie( roughness, dotNH ) * V_Neubelt( dot(N, V), dot(N, L) );\n}\n#endif",bumpmap_pars_fragment:"#ifdef USE_BUMPMAP\n\tuniform sampler2D bumpMap;\n\tuniform float bumpScale;\n\tvec2 dHdxy_fwd() {\n\t\tvec2 dSTdx = dFdx( vUv );\n\t\tvec2 dSTdy = dFdy( vUv );\n\t\tfloat Hll = bumpScale * texture2D( bumpMap, vUv ).x;\n\t\tfloat dBx = bumpScale * texture2D( bumpMap, vUv + dSTdx ).x - Hll;\n\t\tfloat dBy = bumpScale * texture2D( bumpMap, vUv + dSTdy ).x - Hll;\n\t\treturn vec2( dBx, dBy );\n\t}\n\tvec3 perturbNormalArb( vec3 surf_pos, vec3 surf_norm, vec2 dHdxy, float faceDirection ) {\n\t\tvec3 vSigmaX = vec3( dFdx( surf_pos.x ), dFdx( surf_pos.y ), dFdx( surf_pos.z ) );\n\t\tvec3 vSigmaY = vec3( dFdy( surf_pos.x ), dFdy( surf_pos.y ), dFdy( surf_pos.z ) );\n\t\tvec3 vN = surf_norm;\n\t\tvec3 R1 = cross( vSigmaY, vN );\n\t\tvec3 R2 = cross( vN, vSigmaX );\n\t\tfloat fDet = dot( vSigmaX, R1 ) * faceDirection;\n\t\tvec3 vGrad = sign( fDet ) * ( dHdxy.x * R1 + dHdxy.y * R2 );\n\t\treturn normalize( abs( fDet ) * surf_norm - vGrad );\n\t}\n#endif",clipping_planes_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvec4 plane;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < UNION_CLIPPING_PLANES; i ++ ) {\n\t\tplane = clippingPlanes[ i ];\n\t\tif ( dot( vClipPosition, plane.xyz ) > plane.w ) discard;\n\t}\n\t#pragma unroll_loop_end\n\t#if UNION_CLIPPING_PLANES < NUM_CLIPPING_PLANES\n\t\tbool clipped = true;\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = UNION_CLIPPING_PLANES; i < NUM_CLIPPING_PLANES; i ++ ) {\n\t\t\tplane = clippingPlanes[ i ];\n\t\t\tclipped = ( dot( vClipPosition, plane.xyz ) > plane.w ) && clipped;\n\t\t}\n\t\t#pragma unroll_loop_end\n\t\tif ( clipped ) discard;\n\t#endif\n#endif",clipping_planes_pars_fragment:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n\tuniform vec4 clippingPlanes[ NUM_CLIPPING_PLANES ];\n#endif",clipping_planes_pars_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvarying vec3 vClipPosition;\n#endif",clipping_planes_vertex:"#if NUM_CLIPPING_PLANES > 0\n\tvClipPosition = - mvPosition.xyz;\n#endif",color_fragment:"#if defined( USE_COLOR_ALPHA )\n\tdiffuseColor *= vColor;\n#elif defined( USE_COLOR )\n\tdiffuseColor.rgb *= vColor;\n#endif",color_pars_fragment:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR )\n\tvarying vec3 vColor;\n#endif",color_pars_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvarying vec4 vColor;\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvarying vec3 vColor;\n#endif",color_vertex:"#if defined( USE_COLOR_ALPHA )\n\tvColor = vec4( 1.0 );\n#elif defined( USE_COLOR ) || defined( USE_INSTANCING_COLOR )\n\tvColor = vec3( 1.0 );\n#endif\n#ifdef USE_COLOR\n\tvColor *= color;\n#endif\n#ifdef USE_INSTANCING_COLOR\n\tvColor.xyz *= instanceColor.xyz;\n#endif",common:"#define PI 3.141592653589793\n#define PI2 6.283185307179586\n#define PI_HALF 1.5707963267948966\n#define RECIPROCAL_PI 0.3183098861837907\n#define RECIPROCAL_PI2 0.15915494309189535\n#define EPSILON 1e-6\n#ifndef saturate\n#define saturate(a) clamp( a, 0.0, 1.0 )\n#endif\n#define whiteComplement(a) ( 1.0 - saturate( a ) )\nfloat pow2( const in float x ) { return x*x; }\nfloat pow3( const in float x ) { return x*x*x; }\nfloat pow4( const in float x ) { float x2 = x*x; return x2*x2; }\nfloat average( const in vec3 color ) { return dot( color, vec3( 0.3333 ) ); }\nhighp float rand( const in vec2 uv ) {\n\tconst highp float a = 12.9898, b = 78.233, c = 43758.5453;\n\thighp float dt = dot( uv.xy, vec2( a,b ) ), sn = mod( dt, PI );\n\treturn fract(sin(sn) * c);\n}\n#ifdef HIGH_PRECISION\n\tfloat precisionSafeLength( vec3 v ) { return length( v ); }\n#else\n\tfloat max3( vec3 v ) { return max( max( v.x, v.y ), v.z ); }\n\tfloat precisionSafeLength( vec3 v ) {\n\t\tfloat maxComponent = max3( abs( v ) );\n\t\treturn length( v / maxComponent ) * maxComponent;\n\t}\n#endif\nstruct IncidentLight {\n\tvec3 color;\n\tvec3 direction;\n\tbool visible;\n};\nstruct ReflectedLight {\n\tvec3 directDiffuse;\n\tvec3 directSpecular;\n\tvec3 indirectDiffuse;\n\tvec3 indirectSpecular;\n};\nstruct GeometricContext {\n\tvec3 position;\n\tvec3 normal;\n\tvec3 viewDir;\n#ifdef CLEARCOAT\n\tvec3 clearcoatNormal;\n#endif\n};\nvec3 transformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( matrix * vec4( dir, 0.0 ) ).xyz );\n}\nvec3 inverseTransformDirection( in vec3 dir, in mat4 matrix ) {\n\treturn normalize( ( vec4( dir, 0.0 ) * matrix ).xyz );\n}\nvec3 projectOnPlane(in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\tfloat distance = dot( planeNormal, point - pointOnPlane );\n\treturn - distance * planeNormal + point;\n}\nfloat sideOfPlane( in vec3 point, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn sign( dot( point - pointOnPlane, planeNormal ) );\n}\nvec3 linePlaneIntersect( in vec3 pointOnLine, in vec3 lineDirection, in vec3 pointOnPlane, in vec3 planeNormal ) {\n\treturn lineDirection * ( dot( planeNormal, pointOnPlane - pointOnLine ) / dot( planeNormal, lineDirection ) ) + pointOnLine;\n}\nmat3 transposeMat3( const in mat3 m ) {\n\tmat3 tmp;\n\ttmp[ 0 ] = vec3( m[ 0 ].x, m[ 1 ].x, m[ 2 ].x );\n\ttmp[ 1 ] = vec3( m[ 0 ].y, m[ 1 ].y, m[ 2 ].y );\n\ttmp[ 2 ] = vec3( m[ 0 ].z, m[ 1 ].z, m[ 2 ].z );\n\treturn tmp;\n}\nfloat linearToRelativeLuminance( const in vec3 color ) {\n\tvec3 weights = vec3( 0.2126, 0.7152, 0.0722 );\n\treturn dot( weights, color.rgb );\n}\nbool isPerspectiveMatrix( mat4 m ) {\n\treturn m[ 2 ][ 3 ] == - 1.0;\n}\nvec2 equirectUv( in vec3 dir ) {\n\tfloat u = atan( dir.z, dir.x ) * RECIPROCAL_PI2 + 0.5;\n\tfloat v = asin( clamp( dir.y, - 1.0, 1.0 ) ) * RECIPROCAL_PI + 0.5;\n\treturn vec2( u, v );\n}",cube_uv_reflection_fragment:"#ifdef ENVMAP_TYPE_CUBE_UV\n\t#define cubeUV_maxMipLevel 8.0\n\t#define cubeUV_minMipLevel 4.0\n\t#define cubeUV_maxTileSize 256.0\n\t#define cubeUV_minTileSize 16.0\n\tfloat getFace( vec3 direction ) {\n\t\tvec3 absDirection = abs( direction );\n\t\tfloat face = - 1.0;\n\t\tif ( absDirection.x > absDirection.z ) {\n\t\t\tif ( absDirection.x > absDirection.y )\n\t\t\t\tface = direction.x > 0.0 ? 0.0 : 3.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t} else {\n\t\t\tif ( absDirection.z > absDirection.y )\n\t\t\t\tface = direction.z > 0.0 ? 2.0 : 5.0;\n\t\t\telse\n\t\t\t\tface = direction.y > 0.0 ? 1.0 : 4.0;\n\t\t}\n\t\treturn face;\n\t}\n\tvec2 getUV( vec3 direction, float face ) {\n\t\tvec2 uv;\n\t\tif ( face == 0.0 ) {\n\t\t\tuv = vec2( direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 1.0 ) {\n\t\t\tuv = vec2( - direction.x, - direction.z ) / abs( direction.y );\n\t\t} else if ( face == 2.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.y ) / abs( direction.z );\n\t\t} else if ( face == 3.0 ) {\n\t\t\tuv = vec2( - direction.z, direction.y ) / abs( direction.x );\n\t\t} else if ( face == 4.0 ) {\n\t\t\tuv = vec2( - direction.x, direction.z ) / abs( direction.y );\n\t\t} else {\n\t\t\tuv = vec2( direction.x, direction.y ) / abs( direction.z );\n\t\t}\n\t\treturn 0.5 * ( uv + 1.0 );\n\t}\n\tvec3 bilinearCubeUV( sampler2D envMap, vec3 direction, float mipInt ) {\n\t\tfloat face = getFace( direction );\n\t\tfloat filterInt = max( cubeUV_minMipLevel - mipInt, 0.0 );\n\t\tmipInt = max( mipInt, cubeUV_minMipLevel );\n\t\tfloat faceSize = exp2( mipInt );\n\t\tfloat texelSize = 1.0 / ( 3.0 * cubeUV_maxTileSize );\n\t\tvec2 uv = getUV( direction, face ) * ( faceSize - 1.0 );\n\t\tvec2 f = fract( uv );\n\t\tuv += 0.5 - f;\n\t\tif ( face > 2.0 ) {\n\t\t\tuv.y += faceSize;\n\t\t\tface -= 3.0;\n\t\t}\n\t\tuv.x += face * faceSize;\n\t\tif ( mipInt < cubeUV_maxMipLevel ) {\n\t\t\tuv.y += 2.0 * cubeUV_maxTileSize;\n\t\t}\n\t\tuv.y += filterInt * 2.0 * cubeUV_minTileSize;\n\t\tuv.x += 3.0 * max( 0.0, cubeUV_maxTileSize - 2.0 * faceSize );\n\t\tuv *= texelSize;\n\t\tvec3 tl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.x += texelSize;\n\t\tvec3 tr = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.y += texelSize;\n\t\tvec3 br = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tuv.x -= texelSize;\n\t\tvec3 bl = envMapTexelToLinear( texture2D( envMap, uv ) ).rgb;\n\t\tvec3 tm = mix( tl, tr, f.x );\n\t\tvec3 bm = mix( bl, br, f.x );\n\t\treturn mix( tm, bm, f.y );\n\t}\n\t#define r0 1.0\n\t#define v0 0.339\n\t#define m0 - 2.0\n\t#define r1 0.8\n\t#define v1 0.276\n\t#define m1 - 1.0\n\t#define r4 0.4\n\t#define v4 0.046\n\t#define m4 2.0\n\t#define r5 0.305\n\t#define v5 0.016\n\t#define m5 3.0\n\t#define r6 0.21\n\t#define v6 0.0038\n\t#define m6 4.0\n\tfloat roughnessToMip( float roughness ) {\n\t\tfloat mip = 0.0;\n\t\tif ( roughness >= r1 ) {\n\t\t\tmip = ( r0 - roughness ) * ( m1 - m0 ) / ( r0 - r1 ) + m0;\n\t\t} else if ( roughness >= r4 ) {\n\t\t\tmip = ( r1 - roughness ) * ( m4 - m1 ) / ( r1 - r4 ) + m1;\n\t\t} else if ( roughness >= r5 ) {\n\t\t\tmip = ( r4 - roughness ) * ( m5 - m4 ) / ( r4 - r5 ) + m4;\n\t\t} else if ( roughness >= r6 ) {\n\t\t\tmip = ( r5 - roughness ) * ( m6 - m5 ) / ( r5 - r6 ) + m5;\n\t\t} else {\n\t\t\tmip = - 2.0 * log2( 1.16 * roughness );\t\t}\n\t\treturn mip;\n\t}\n\tvec4 textureCubeUV( sampler2D envMap, vec3 sampleDir, float roughness ) {\n\t\tfloat mip = clamp( roughnessToMip( roughness ), m0, cubeUV_maxMipLevel );\n\t\tfloat mipF = fract( mip );\n\t\tfloat mipInt = floor( mip );\n\t\tvec3 color0 = bilinearCubeUV( envMap, sampleDir, mipInt );\n\t\tif ( mipF == 0.0 ) {\n\t\t\treturn vec4( color0, 1.0 );\n\t\t} else {\n\t\t\tvec3 color1 = bilinearCubeUV( envMap, sampleDir, mipInt + 1.0 );\n\t\t\treturn vec4( mix( color0, color1, mipF ), 1.0 );\n\t\t}\n\t}\n#endif",defaultnormal_vertex:"vec3 transformedNormal = objectNormal;\n#ifdef USE_INSTANCING\n\tmat3 m = mat3( instanceMatrix );\n\ttransformedNormal /= vec3( dot( m[ 0 ], m[ 0 ] ), dot( m[ 1 ], m[ 1 ] ), dot( m[ 2 ], m[ 2 ] ) );\n\ttransformedNormal = m * transformedNormal;\n#endif\ntransformedNormal = normalMatrix * transformedNormal;\n#ifdef FLIP_SIDED\n\ttransformedNormal = - transformedNormal;\n#endif\n#ifdef USE_TANGENT\n\tvec3 transformedTangent = ( modelViewMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#ifdef FLIP_SIDED\n\t\ttransformedTangent = - transformedTangent;\n\t#endif\n#endif",displacementmap_pars_vertex:"#ifdef USE_DISPLACEMENTMAP\n\tuniform sampler2D displacementMap;\n\tuniform float displacementScale;\n\tuniform float displacementBias;\n#endif",displacementmap_vertex:"#ifdef USE_DISPLACEMENTMAP\n\ttransformed += normalize( objectNormal ) * ( texture2D( displacementMap, vUv ).x * displacementScale + displacementBias );\n#endif",emissivemap_fragment:"#ifdef USE_EMISSIVEMAP\n\tvec4 emissiveColor = texture2D( emissiveMap, vUv );\n\temissiveColor.rgb = emissiveMapTexelToLinear( emissiveColor ).rgb;\n\ttotalEmissiveRadiance *= emissiveColor.rgb;\n#endif",emissivemap_pars_fragment:"#ifdef USE_EMISSIVEMAP\n\tuniform sampler2D emissiveMap;\n#endif",encodings_fragment:"gl_FragColor = linearToOutputTexel( gl_FragColor );",encodings_pars_fragment:"\nvec4 LinearToLinear( in vec4 value ) {\n\treturn value;\n}\nvec4 GammaToLinear( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( gammaFactor ) ), value.a );\n}\nvec4 LinearToGamma( in vec4 value, in float gammaFactor ) {\n\treturn vec4( pow( value.rgb, vec3( 1.0 / gammaFactor ) ), value.a );\n}\nvec4 sRGBToLinear( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb * 0.9478672986 + vec3( 0.0521327014 ), vec3( 2.4 ) ), value.rgb * 0.0773993808, vec3( lessThanEqual( value.rgb, vec3( 0.04045 ) ) ) ), value.a );\n}\nvec4 LinearTosRGB( in vec4 value ) {\n\treturn vec4( mix( pow( value.rgb, vec3( 0.41666 ) ) * 1.055 - vec3( 0.055 ), value.rgb * 12.92, vec3( lessThanEqual( value.rgb, vec3( 0.0031308 ) ) ) ), value.a );\n}\nvec4 RGBEToLinear( in vec4 value ) {\n\treturn vec4( value.rgb * exp2( value.a * 255.0 - 128.0 ), 1.0 );\n}\nvec4 LinearToRGBE( in vec4 value ) {\n\tfloat maxComponent = max( max( value.r, value.g ), value.b );\n\tfloat fExp = clamp( ceil( log2( maxComponent ) ), -128.0, 127.0 );\n\treturn vec4( value.rgb / exp2( fExp ), ( fExp + 128.0 ) / 255.0 );\n}\nvec4 RGBMToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * value.a * maxRange, 1.0 );\n}\nvec4 LinearToRGBM( in vec4 value, in float maxRange ) {\n\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\n\tfloat M = clamp( maxRGB / maxRange, 0.0, 1.0 );\n\tM = ceil( M * 255.0 ) / 255.0;\n\treturn vec4( value.rgb / ( M * maxRange ), M );\n}\nvec4 RGBDToLinear( in vec4 value, in float maxRange ) {\n\treturn vec4( value.rgb * ( ( maxRange / 255.0 ) / value.a ), 1.0 );\n}\nvec4 LinearToRGBD( in vec4 value, in float maxRange ) {\n\tfloat maxRGB = max( value.r, max( value.g, value.b ) );\n\tfloat D = max( maxRange / maxRGB, 1.0 );\n\tD = clamp( floor( D ) / 255.0, 0.0, 1.0 );\n\treturn vec4( value.rgb * ( D * ( 255.0 / maxRange ) ), D );\n}\nconst mat3 cLogLuvM = mat3( 0.2209, 0.3390, 0.4184, 0.1138, 0.6780, 0.7319, 0.0102, 0.1130, 0.2969 );\nvec4 LinearToLogLuv( in vec4 value ) {\n\tvec3 Xp_Y_XYZp = cLogLuvM * value.rgb;\n\tXp_Y_XYZp = max( Xp_Y_XYZp, vec3( 1e-6, 1e-6, 1e-6 ) );\n\tvec4 vResult;\n\tvResult.xy = Xp_Y_XYZp.xy / Xp_Y_XYZp.z;\n\tfloat Le = 2.0 * log2(Xp_Y_XYZp.y) + 127.0;\n\tvResult.w = fract( Le );\n\tvResult.z = ( Le - ( floor( vResult.w * 255.0 ) ) / 255.0 ) / 255.0;\n\treturn vResult;\n}\nconst mat3 cLogLuvInverseM = mat3( 6.0014, -2.7008, -1.7996, -1.3320, 3.1029, -5.7721, 0.3008, -1.0882, 5.6268 );\nvec4 LogLuvToLinear( in vec4 value ) {\n\tfloat Le = value.z * 255.0 + value.w;\n\tvec3 Xp_Y_XYZp;\n\tXp_Y_XYZp.y = exp2( ( Le - 127.0 ) / 2.0 );\n\tXp_Y_XYZp.z = Xp_Y_XYZp.y / value.y;\n\tXp_Y_XYZp.x = value.x * Xp_Y_XYZp.z;\n\tvec3 vRGB = cLogLuvInverseM * Xp_Y_XYZp.rgb;\n\treturn vec4( max( vRGB, 0.0 ), 1.0 );\n}",envmap_fragment:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvec3 cameraToFrag;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToFrag = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToFrag = normalize( vWorldPosition - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( normal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( cameraToFrag, worldNormal );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( cameraToFrag, worldNormal, refractionRatio );\n\t\t#endif\n\t#else\n\t\tvec3 reflectVec = vReflect;\n\t#endif\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tvec4 envColor = textureCube( envMap, vec3( flipEnvMap * reflectVec.x, reflectVec.yz ) );\n\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\tvec4 envColor = textureCubeUV( envMap, reflectVec, 0.0 );\n\t#else\n\t\tvec4 envColor = vec4( 0.0 );\n\t#endif\n\t#ifndef ENVMAP_TYPE_CUBE_UV\n\t\tenvColor = envMapTexelToLinear( envColor );\n\t#endif\n\t#ifdef ENVMAP_BLENDING_MULTIPLY\n\t\toutgoingLight = mix( outgoingLight, outgoingLight * envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_MIX )\n\t\toutgoingLight = mix( outgoingLight, envColor.xyz, specularStrength * reflectivity );\n\t#elif defined( ENVMAP_BLENDING_ADD )\n\t\toutgoingLight += envColor.xyz * specularStrength * reflectivity;\n\t#endif\n#endif",envmap_common_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float envMapIntensity;\n\tuniform float flipEnvMap;\n\tuniform int maxMipLevel;\n\t#ifdef ENVMAP_TYPE_CUBE\n\t\tuniform samplerCube envMap;\n\t#else\n\t\tuniform sampler2D envMap;\n\t#endif\n\t\n#endif",envmap_pars_fragment:"#ifdef USE_ENVMAP\n\tuniform float reflectivity;\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) || defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\tvarying vec3 vWorldPosition;\n\t\tuniform float refractionRatio;\n\t#else\n\t\tvarying vec3 vReflect;\n\t#endif\n#endif",envmap_pars_vertex:"#ifdef USE_ENVMAP\n\t#if defined( USE_BUMPMAP ) || defined( USE_NORMALMAP ) ||defined( PHONG )\n\t\t#define ENV_WORLDPOS\n\t#endif\n\t#ifdef ENV_WORLDPOS\n\t\t\n\t\tvarying vec3 vWorldPosition;\n\t#else\n\t\tvarying vec3 vReflect;\n\t\tuniform float refractionRatio;\n\t#endif\n#endif",envmap_physical_pars_fragment:"#if defined( USE_ENVMAP )\n\t#ifdef ENVMAP_MODE_REFRACTION\n\t\tuniform float refractionRatio;\n\t#endif\n\tvec3 getLightProbeIndirectIrradiance( const in GeometricContext geometry, const in int maxMIPLevel ) {\n\t\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryVec = vec3( flipEnvMap * worldNormal.x, worldNormal.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor = textureCubeLodEXT( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor = textureCube( envMap, queryVec, float( maxMIPLevel ) );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, worldNormal, 1.0 );\n\t\t#else\n\t\t\tvec4 envMapColor = vec4( 0.0 );\n\t\t#endif\n\t\treturn PI * envMapColor.rgb * envMapIntensity;\n\t}\n\tfloat getSpecularMIPLevel( const in float roughness, const in int maxMIPLevel ) {\n\t\tfloat maxMIPLevelScalar = float( maxMIPLevel );\n\t\tfloat sigma = PI * roughness * roughness / ( 1.0 + roughness );\n\t\tfloat desiredMIPLevel = maxMIPLevelScalar + log2( sigma );\n\t\treturn clamp( desiredMIPLevel, 0.0, maxMIPLevelScalar );\n\t}\n\tvec3 getLightProbeIndirectRadiance( const in vec3 viewDir, const in vec3 normal, const in float roughness, const in int maxMIPLevel ) {\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvec3 reflectVec = reflect( -viewDir, normal );\n\t\t\treflectVec = normalize( mix( reflectVec, normal, roughness * roughness) );\n\t\t#else\n\t\t\tvec3 reflectVec = refract( -viewDir, normal, refractionRatio );\n\t\t#endif\n\t\treflectVec = inverseTransformDirection( reflectVec, viewMatrix );\n\t\tfloat specularMIPLevel = getSpecularMIPLevel( roughness, maxMIPLevel );\n\t\t#ifdef ENVMAP_TYPE_CUBE\n\t\t\tvec3 queryReflectVec = vec3( flipEnvMap * reflectVec.x, reflectVec.yz );\n\t\t\t#ifdef TEXTURE_LOD_EXT\n\t\t\t\tvec4 envMapColor = textureCubeLodEXT( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#else\n\t\t\t\tvec4 envMapColor = textureCube( envMap, queryReflectVec, specularMIPLevel );\n\t\t\t#endif\n\t\t\tenvMapColor.rgb = envMapTexelToLinear( envMapColor ).rgb;\n\t\t#elif defined( ENVMAP_TYPE_CUBE_UV )\n\t\t\tvec4 envMapColor = textureCubeUV( envMap, reflectVec, roughness );\n\t\t#endif\n\t\treturn envMapColor.rgb * envMapIntensity;\n\t}\n#endif",envmap_vertex:"#ifdef USE_ENVMAP\n\t#ifdef ENV_WORLDPOS\n\t\tvWorldPosition = worldPosition.xyz;\n\t#else\n\t\tvec3 cameraToVertex;\n\t\tif ( isOrthographic ) {\n\t\t\tcameraToVertex = normalize( vec3( - viewMatrix[ 0 ][ 2 ], - viewMatrix[ 1 ][ 2 ], - viewMatrix[ 2 ][ 2 ] ) );\n\t\t} else {\n\t\t\tcameraToVertex = normalize( worldPosition.xyz - cameraPosition );\n\t\t}\n\t\tvec3 worldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\t#ifdef ENVMAP_MODE_REFLECTION\n\t\t\tvReflect = reflect( cameraToVertex, worldNormal );\n\t\t#else\n\t\t\tvReflect = refract( cameraToVertex, worldNormal, refractionRatio );\n\t\t#endif\n\t#endif\n#endif",fog_vertex:"#ifdef USE_FOG\n\tfogDepth = - mvPosition.z;\n#endif",fog_pars_vertex:"#ifdef USE_FOG\n\tvarying float fogDepth;\n#endif",fog_fragment:"#ifdef USE_FOG\n\t#ifdef FOG_EXP2\n\t\tfloat fogFactor = 1.0 - exp( - fogDensity * fogDensity * fogDepth * fogDepth );\n\t#else\n\t\tfloat fogFactor = smoothstep( fogNear, fogFar, fogDepth );\n\t#endif\n\tgl_FragColor.rgb = mix( gl_FragColor.rgb, fogColor, fogFactor );\n#endif",fog_pars_fragment:"#ifdef USE_FOG\n\tuniform vec3 fogColor;\n\tvarying float fogDepth;\n\t#ifdef FOG_EXP2\n\t\tuniform float fogDensity;\n\t#else\n\t\tuniform float fogNear;\n\t\tuniform float fogFar;\n\t#endif\n#endif",gradientmap_pars_fragment:"#ifdef USE_GRADIENTMAP\n\tuniform sampler2D gradientMap;\n#endif\nvec3 getGradientIrradiance( vec3 normal, vec3 lightDirection ) {\n\tfloat dotNL = dot( normal, lightDirection );\n\tvec2 coord = vec2( dotNL * 0.5 + 0.5, 0.0 );\n\t#ifdef USE_GRADIENTMAP\n\t\treturn texture2D( gradientMap, coord ).rgb;\n\t#else\n\t\treturn ( coord.x < 0.7 ) ? vec3( 0.7 ) : vec3( 1.0 );\n\t#endif\n}",lightmap_fragment:"#ifdef USE_LIGHTMAP\n\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\treflectedLight.indirectDiffuse += PI * lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n#endif",lightmap_pars_fragment:"#ifdef USE_LIGHTMAP\n\tuniform sampler2D lightMap;\n\tuniform float lightMapIntensity;\n#endif",lights_lambert_vertex:"vec3 diffuse = vec3( 1.0 );\nGeometricContext geometry;\ngeometry.position = mvPosition.xyz;\ngeometry.normal = normalize( transformedNormal );\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( -mvPosition.xyz );\nGeometricContext backGeometry;\nbackGeometry.position = geometry.position;\nbackGeometry.normal = -geometry.normal;\nbackGeometry.viewDir = geometry.viewDir;\nvLightFront = vec3( 0.0 );\nvIndirectFront = vec3( 0.0 );\n#ifdef DOUBLE_SIDED\n\tvLightBack = vec3( 0.0 );\n\tvIndirectBack = vec3( 0.0 );\n#endif\nIncidentLight directLight;\nfloat dotNL;\nvec3 directLightColor_Diffuse;\nvIndirectFront += getAmbientLightIrradiance( ambientLightColor );\nvIndirectFront += getLightProbeIrradiance( lightProbe, geometry );\n#ifdef DOUBLE_SIDED\n\tvIndirectBack += getAmbientLightIrradiance( ambientLightColor );\n\tvIndirectBack += getLightProbeIrradiance( lightProbe, backGeometry );\n#endif\n#if NUM_POINT_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tgetPointDirectLightIrradiance( pointLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tgetSpotDirectLightIrradiance( spotLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_DIR_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tgetDirectionalDirectLightIrradiance( directionalLights[ i ], geometry, directLight );\n\t\tdotNL = dot( geometry.normal, directLight.direction );\n\t\tdirectLightColor_Diffuse = PI * directLight.color;\n\t\tvLightFront += saturate( dotNL ) * directLightColor_Diffuse;\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvLightBack += saturate( -dotNL ) * directLightColor_Diffuse;\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\tvIndirectFront += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\tvIndirectBack += getHemisphereLightIrradiance( hemisphereLights[ i ], backGeometry );\n\t\t#endif\n\t}\n\t#pragma unroll_loop_end\n#endif",lights_pars_begin:"uniform bool receiveShadow;\nuniform vec3 ambientLightColor;\nuniform vec3 lightProbe[ 9 ];\nvec3 shGetIrradianceAt( in vec3 normal, in vec3 shCoefficients[ 9 ] ) {\n\tfloat x = normal.x, y = normal.y, z = normal.z;\n\tvec3 result = shCoefficients[ 0 ] * 0.886227;\n\tresult += shCoefficients[ 1 ] * 2.0 * 0.511664 * y;\n\tresult += shCoefficients[ 2 ] * 2.0 * 0.511664 * z;\n\tresult += shCoefficients[ 3 ] * 2.0 * 0.511664 * x;\n\tresult += shCoefficients[ 4 ] * 2.0 * 0.429043 * x * y;\n\tresult += shCoefficients[ 5 ] * 2.0 * 0.429043 * y * z;\n\tresult += shCoefficients[ 6 ] * ( 0.743125 * z * z - 0.247708 );\n\tresult += shCoefficients[ 7 ] * 2.0 * 0.429043 * x * z;\n\tresult += shCoefficients[ 8 ] * 0.429043 * ( x * x - y * y );\n\treturn result;\n}\nvec3 getLightProbeIrradiance( const in vec3 lightProbe[ 9 ], const in GeometricContext geometry ) {\n\tvec3 worldNormal = inverseTransformDirection( geometry.normal, viewMatrix );\n\tvec3 irradiance = shGetIrradianceAt( worldNormal, lightProbe );\n\treturn irradiance;\n}\nvec3 getAmbientLightIrradiance( const in vec3 ambientLightColor ) {\n\tvec3 irradiance = ambientLightColor;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treturn irradiance;\n}\n#if NUM_DIR_LIGHTS > 0\n\tstruct DirectionalLight {\n\t\tvec3 direction;\n\t\tvec3 color;\n\t};\n\tuniform DirectionalLight directionalLights[ NUM_DIR_LIGHTS ];\n\tvoid getDirectionalDirectLightIrradiance( const in DirectionalLight directionalLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tdirectLight.color = directionalLight.color;\n\t\tdirectLight.direction = directionalLight.direction;\n\t\tdirectLight.visible = true;\n\t}\n#endif\n#if NUM_POINT_LIGHTS > 0\n\tstruct PointLight {\n\t\tvec3 position;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t};\n\tuniform PointLight pointLights[ NUM_POINT_LIGHTS ];\n\tvoid getPointDirectLightIrradiance( const in PointLight pointLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tvec3 lVector = pointLight.position - geometry.position;\n\t\tdirectLight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tdirectLight.color = pointLight.color;\n\t\tdirectLight.color *= punctualLightIntensityToIrradianceFactor( lightDistance, pointLight.distance, pointLight.decay );\n\t\tdirectLight.visible = ( directLight.color != vec3( 0.0 ) );\n\t}\n#endif\n#if NUM_SPOT_LIGHTS > 0\n\tstruct SpotLight {\n\t\tvec3 position;\n\t\tvec3 direction;\n\t\tvec3 color;\n\t\tfloat distance;\n\t\tfloat decay;\n\t\tfloat coneCos;\n\t\tfloat penumbraCos;\n\t};\n\tuniform SpotLight spotLights[ NUM_SPOT_LIGHTS ];\n\tvoid getSpotDirectLightIrradiance( const in SpotLight spotLight, const in GeometricContext geometry, out IncidentLight directLight ) {\n\t\tvec3 lVector = spotLight.position - geometry.position;\n\t\tdirectLight.direction = normalize( lVector );\n\t\tfloat lightDistance = length( lVector );\n\t\tfloat angleCos = dot( directLight.direction, spotLight.direction );\n\t\tif ( angleCos > spotLight.coneCos ) {\n\t\t\tfloat spotEffect = smoothstep( spotLight.coneCos, spotLight.penumbraCos, angleCos );\n\t\t\tdirectLight.color = spotLight.color;\n\t\t\tdirectLight.color *= spotEffect * punctualLightIntensityToIrradianceFactor( lightDistance, spotLight.distance, spotLight.decay );\n\t\t\tdirectLight.visible = true;\n\t\t} else {\n\t\t\tdirectLight.color = vec3( 0.0 );\n\t\t\tdirectLight.visible = false;\n\t\t}\n\t}\n#endif\n#if NUM_RECT_AREA_LIGHTS > 0\n\tstruct RectAreaLight {\n\t\tvec3 color;\n\t\tvec3 position;\n\t\tvec3 halfWidth;\n\t\tvec3 halfHeight;\n\t};\n\tuniform sampler2D ltc_1;\tuniform sampler2D ltc_2;\n\tuniform RectAreaLight rectAreaLights[ NUM_RECT_AREA_LIGHTS ];\n#endif\n#if NUM_HEMI_LIGHTS > 0\n\tstruct HemisphereLight {\n\t\tvec3 direction;\n\t\tvec3 skyColor;\n\t\tvec3 groundColor;\n\t};\n\tuniform HemisphereLight hemisphereLights[ NUM_HEMI_LIGHTS ];\n\tvec3 getHemisphereLightIrradiance( const in HemisphereLight hemiLight, const in GeometricContext geometry ) {\n\t\tfloat dotNL = dot( geometry.normal, hemiLight.direction );\n\t\tfloat hemiDiffuseWeight = 0.5 * dotNL + 0.5;\n\t\tvec3 irradiance = mix( hemiLight.groundColor, hemiLight.skyColor, hemiDiffuseWeight );\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tirradiance *= PI;\n\t\t#endif\n\t\treturn irradiance;\n\t}\n#endif",lights_toon_fragment:"ToonMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;",lights_toon_pars_fragment:"varying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\nstruct ToonMaterial {\n\tvec3 diffuseColor;\n};\nvoid RE_Direct_Toon( const in IncidentLight directLight, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\tvec3 irradiance = getGradientIrradiance( geometry.normal, directLight.direction ) * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Toon( const in vec3 irradiance, const in GeometricContext geometry, const in ToonMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_Toon\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Toon\n#define Material_LightProbeLOD( material )\t(0)",lights_phong_fragment:"BlinnPhongMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb;\nmaterial.specularColor = specular;\nmaterial.specularShininess = shininess;\nmaterial.specularStrength = specularStrength;",lights_phong_pars_fragment:"varying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\nstruct BlinnPhongMaterial {\n\tvec3 diffuseColor;\n\tvec3 specularColor;\n\tfloat specularShininess;\n\tfloat specularStrength;\n};\nvoid RE_Direct_BlinnPhong( const in IncidentLight directLight, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\treflectedLight.directDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n\treflectedLight.directSpecular += irradiance * BRDF_Specular_BlinnPhong( directLight, geometry, material.specularColor, material.specularShininess ) * material.specularStrength;\n}\nvoid RE_IndirectDiffuse_BlinnPhong( const in vec3 irradiance, const in GeometricContext geometry, const in BlinnPhongMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\n#define RE_Direct\t\t\t\tRE_Direct_BlinnPhong\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_BlinnPhong\n#define Material_LightProbeLOD( material )\t(0)",lights_physical_fragment:"PhysicalMaterial material;\nmaterial.diffuseColor = diffuseColor.rgb * ( 1.0 - metalnessFactor );\nvec3 dxy = max( abs( dFdx( geometryNormal ) ), abs( dFdy( geometryNormal ) ) );\nfloat geometryRoughness = max( max( dxy.x, dxy.y ), dxy.z );\nmaterial.specularRoughness = max( roughnessFactor, 0.0525 );material.specularRoughness += geometryRoughness;\nmaterial.specularRoughness = min( material.specularRoughness, 1.0 );\n#ifdef REFLECTIVITY\n\t#ifdef SPECULAR\n\t\tvec3 specularIntensityFactor = vec3( specularIntensity );\n\t\tvec3 specularTintFactor = specularTint;\n\t\t#ifdef USE_SPECULARINTENSITYMAP\n\t\t\tspecularIntensityFactor *= texture2D( specularIntensityMap, vUv ).a;\n\t\t#endif\n\t\t#ifdef USE_SPECULARTINTMAP\n\t\t\tspecularTintFactor *= specularTintMapTexelToLinear( texture2D( specularTintMap, vUv ) ).rgb;\n\t\t#endif\n\t\tmaterial.specularColorF90 = mix( specularIntensityFactor, vec3( 1.0 ), metalnessFactor );\n\t#else\n\t\tvec3 specularIntensityFactor = vec3( 1.0 );\n\t\tvec3 specularTintFactor = vec3( 1.0 );\n\t\tmaterial.specularColorF90 = vec3( 1.0 );\n\t#endif\n\tmaterial.specularColor = mix( min( vec3( MAXIMUM_SPECULAR_COEFFICIENT * pow2( reflectivity ) ) * specularTintFactor, vec3( 1.0 ) ) * specularIntensityFactor, diffuseColor.rgb, metalnessFactor );\n#else\n\tmaterial.specularColor = mix( vec3( DEFAULT_SPECULAR_COEFFICIENT ), diffuseColor.rgb, metalnessFactor );\n\tmaterial.specularColorF90 = vec3( 1.0 );\n#endif\n#ifdef CLEARCOAT\n\tmaterial.clearcoat = clearcoat;\n\tmaterial.clearcoatRoughness = clearcoatRoughness;\n\t#ifdef USE_CLEARCOATMAP\n\t\tmaterial.clearcoat *= texture2D( clearcoatMap, vUv ).x;\n\t#endif\n\t#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\t\tmaterial.clearcoatRoughness *= texture2D( clearcoatRoughnessMap, vUv ).y;\n\t#endif\n\tmaterial.clearcoat = saturate( material.clearcoat );\tmaterial.clearcoatRoughness = max( material.clearcoatRoughness, 0.0525 );\n\tmaterial.clearcoatRoughness += geometryRoughness;\n\tmaterial.clearcoatRoughness = min( material.clearcoatRoughness, 1.0 );\n#endif\n#ifdef USE_SHEEN\n\tmaterial.sheenColor = sheen;\n#endif",lights_physical_pars_fragment:"struct PhysicalMaterial {\n\tvec3 diffuseColor;\n\tfloat specularRoughness;\n\tvec3 specularColor;\n\tvec3 specularColorF90;\n#ifdef CLEARCOAT\n\tfloat clearcoat;\n\tfloat clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tvec3 sheenColor;\n#endif\n};\n#define MAXIMUM_SPECULAR_COEFFICIENT 0.16\n#define DEFAULT_SPECULAR_COEFFICIENT 0.04\nfloat clearcoatDHRApprox( const in float roughness, const in float dotNL ) {\n\treturn DEFAULT_SPECULAR_COEFFICIENT + ( 1.0 - DEFAULT_SPECULAR_COEFFICIENT ) * ( pow( 1.0 - dotNL, 5.0 ) * pow( 1.0 - roughness, 2.0 ) );\n}\n#if NUM_RECT_AREA_LIGHTS > 0\n\tvoid RE_Direct_RectArea_Physical( const in RectAreaLight rectAreaLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\t\tvec3 normal = geometry.normal;\n\t\tvec3 viewDir = geometry.viewDir;\n\t\tvec3 position = geometry.position;\n\t\tvec3 lightPos = rectAreaLight.position;\n\t\tvec3 halfWidth = rectAreaLight.halfWidth;\n\t\tvec3 halfHeight = rectAreaLight.halfHeight;\n\t\tvec3 lightColor = rectAreaLight.color;\n\t\tfloat roughness = material.specularRoughness;\n\t\tvec3 rectCoords[ 4 ];\n\t\trectCoords[ 0 ] = lightPos + halfWidth - halfHeight;\t\trectCoords[ 1 ] = lightPos - halfWidth - halfHeight;\n\t\trectCoords[ 2 ] = lightPos - halfWidth + halfHeight;\n\t\trectCoords[ 3 ] = lightPos + halfWidth + halfHeight;\n\t\tvec2 uv = LTC_Uv( normal, viewDir, roughness );\n\t\tvec4 t1 = texture2D( ltc_1, uv );\n\t\tvec4 t2 = texture2D( ltc_2, uv );\n\t\tmat3 mInv = mat3(\n\t\t\tvec3( t1.x, 0, t1.y ),\n\t\t\tvec3(    0, 1,    0 ),\n\t\t\tvec3( t1.z, 0, t1.w )\n\t\t);\n\t\tvec3 fresnel = ( material.specularColor * t2.x + ( vec3( 1.0 ) - material.specularColor ) * t2.y );\n\t\treflectedLight.directSpecular += lightColor * fresnel * LTC_Evaluate( normal, viewDir, position, mInv, rectCoords );\n\t\treflectedLight.directDiffuse += lightColor * material.diffuseColor * LTC_Evaluate( normal, viewDir, position, mat3( 1.0 ), rectCoords );\n\t}\n#endif\nvoid RE_Direct_Physical( const in IncidentLight directLight, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\tfloat dotNL = saturate( dot( geometry.normal, directLight.direction ) );\n\tvec3 irradiance = dotNL * directLight.color;\n\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\tirradiance *= PI;\n\t#endif\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNL = saturate( dot( geometry.clearcoatNormal, directLight.direction ) );\n\t\tvec3 ccIrradiance = ccDotNL * directLight.color;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tccIrradiance *= PI;\n\t\t#endif\n\t\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t\treflectedLight.directSpecular += ccIrradiance * material.clearcoat * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), vec3( 1.0 ), material.clearcoatRoughness );\n\t#else\n\t\tfloat clearcoatDHR = 0.0;\n\t#endif\n\t#ifdef USE_SHEEN\n\t\treflectedLight.directSpecular += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_Sheen(\n\t\t\tmaterial.specularRoughness,\n\t\t\tdirectLight.direction,\n\t\t\tgeometry,\n\t\t\tmaterial.sheenColor\n\t\t);\n\t#else\n\t\treflectedLight.directSpecular += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Specular_GGX( directLight, geometry.viewDir, geometry.normal, material.specularColor, material.specularColorF90, material.specularRoughness);\n\t#endif\n\treflectedLight.directDiffuse += ( 1.0 - clearcoatDHR ) * irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectDiffuse_Physical( const in vec3 irradiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight ) {\n\treflectedLight.indirectDiffuse += irradiance * BRDF_Diffuse_Lambert( material.diffuseColor );\n}\nvoid RE_IndirectSpecular_Physical( const in vec3 radiance, const in vec3 irradiance, const in vec3 clearcoatRadiance, const in GeometricContext geometry, const in PhysicalMaterial material, inout ReflectedLight reflectedLight) {\n\t#ifdef CLEARCOAT\n\t\tfloat ccDotNV = saturate( dot( geometry.clearcoatNormal, geometry.viewDir ) );\n\t\treflectedLight.indirectSpecular += clearcoatRadiance * material.clearcoat * BRDF_Specular_GGX_Environment( geometry.viewDir, geometry.clearcoatNormal, vec3( DEFAULT_SPECULAR_COEFFICIENT ), material.clearcoatRoughness );\n\t\tfloat ccDotNL = ccDotNV;\n\t\tfloat clearcoatDHR = material.clearcoat * clearcoatDHRApprox( material.clearcoatRoughness, ccDotNL );\n\t#else\n\t\tfloat clearcoatDHR = 0.0;\n\t#endif\n\tfloat clearcoatInv = 1.0 - clearcoatDHR;\n\tvec3 singleScattering = vec3( 0.0 );\n\tvec3 multiScattering = vec3( 0.0 );\n\tvec3 cosineWeightedIrradiance = irradiance * RECIPROCAL_PI;\n\tBRDF_Specular_Multiscattering_Environment( geometry, material.specularColor, material.specularRoughness, singleScattering, multiScattering );\n\tvec3 diffuse = material.diffuseColor * ( 1.0 - ( singleScattering + multiScattering ) );\n\treflectedLight.indirectSpecular += clearcoatInv * radiance * singleScattering;\n\treflectedLight.indirectSpecular += multiScattering * cosineWeightedIrradiance;\n\treflectedLight.indirectDiffuse += diffuse * cosineWeightedIrradiance;\n}\n#define RE_Direct\t\t\t\tRE_Direct_Physical\n#define RE_Direct_RectArea\t\tRE_Direct_RectArea_Physical\n#define RE_IndirectDiffuse\t\tRE_IndirectDiffuse_Physical\n#define RE_IndirectSpecular\t\tRE_IndirectSpecular_Physical\nfloat computeSpecularOcclusion( const in float dotNV, const in float ambientOcclusion, const in float roughness ) {\n\treturn saturate( pow( dotNV + ambientOcclusion, exp2( - 16.0 * roughness - 1.0 ) ) - 1.0 + ambientOcclusion );\n}",lights_fragment_begin:"\nGeometricContext geometry;\ngeometry.position = - vViewPosition;\ngeometry.normal = normal;\ngeometry.viewDir = ( isOrthographic ) ? vec3( 0, 0, 1 ) : normalize( vViewPosition );\n#ifdef CLEARCOAT\n\tgeometry.clearcoatNormal = clearcoatNormal;\n#endif\nIncidentLight directLight;\n#if ( NUM_POINT_LIGHTS > 0 ) && defined( RE_Direct )\n\tPointLight pointLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHTS; i ++ ) {\n\t\tpointLight = pointLights[ i ];\n\t\tgetPointDirectLightIrradiance( pointLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_POINT_LIGHT_SHADOWS )\n\t\tpointLightShadow = pointLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getPointShadow( pointShadowMap[ i ], pointLightShadow.shadowMapSize, pointLightShadow.shadowBias, pointLightShadow.shadowRadius, vPointShadowCoord[ i ], pointLightShadow.shadowCameraNear, pointLightShadow.shadowCameraFar ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_SPOT_LIGHTS > 0 ) && defined( RE_Direct )\n\tSpotLight spotLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHTS; i ++ ) {\n\t\tspotLight = spotLights[ i ];\n\t\tgetSpotDirectLightIrradiance( spotLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_SPOT_LIGHT_SHADOWS )\n\t\tspotLightShadow = spotLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( spotShadowMap[ i ], spotLightShadow.shadowMapSize, spotLightShadow.shadowBias, spotLightShadow.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_DIR_LIGHTS > 0 ) && defined( RE_Direct )\n\tDirectionalLight directionalLight;\n\t#if defined( USE_SHADOWMAP ) && NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLightShadow;\n\t#endif\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHTS; i ++ ) {\n\t\tdirectionalLight = directionalLights[ i ];\n\t\tgetDirectionalDirectLightIrradiance( directionalLight, geometry, directLight );\n\t\t#if defined( USE_SHADOWMAP ) && ( UNROLLED_LOOP_INDEX < NUM_DIR_LIGHT_SHADOWS )\n\t\tdirectionalLightShadow = directionalLightShadows[ i ];\n\t\tdirectLight.color *= all( bvec2( directLight.visible, receiveShadow ) ) ? getShadow( directionalShadowMap[ i ], directionalLightShadow.shadowMapSize, directionalLightShadow.shadowBias, directionalLightShadow.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t\t#endif\n\t\tRE_Direct( directLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if ( NUM_RECT_AREA_LIGHTS > 0 ) && defined( RE_Direct_RectArea )\n\tRectAreaLight rectAreaLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_RECT_AREA_LIGHTS; i ++ ) {\n\t\trectAreaLight = rectAreaLights[ i ];\n\t\tRE_Direct_RectArea( rectAreaLight, geometry, material, reflectedLight );\n\t}\n\t#pragma unroll_loop_end\n#endif\n#if defined( RE_IndirectDiffuse )\n\tvec3 iblIrradiance = vec3( 0.0 );\n\tvec3 irradiance = getAmbientLightIrradiance( ambientLightColor );\n\tirradiance += getLightProbeIrradiance( lightProbe, geometry );\n\t#if ( NUM_HEMI_LIGHTS > 0 )\n\t\t#pragma unroll_loop_start\n\t\tfor ( int i = 0; i < NUM_HEMI_LIGHTS; i ++ ) {\n\t\t\tirradiance += getHemisphereLightIrradiance( hemisphereLights[ i ], geometry );\n\t\t}\n\t\t#pragma unroll_loop_end\n\t#endif\n#endif\n#if defined( RE_IndirectSpecular )\n\tvec3 radiance = vec3( 0.0 );\n\tvec3 clearcoatRadiance = vec3( 0.0 );\n#endif",lights_fragment_maps:"#if defined( RE_IndirectDiffuse )\n\t#ifdef USE_LIGHTMAP\n\t\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\t\tvec3 lightMapIrradiance = lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n\t\t#ifndef PHYSICALLY_CORRECT_LIGHTS\n\t\t\tlightMapIrradiance *= PI;\n\t\t#endif\n\t\tirradiance += lightMapIrradiance;\n\t#endif\n\t#if defined( USE_ENVMAP ) && defined( STANDARD ) && defined( ENVMAP_TYPE_CUBE_UV )\n\t\tiblIrradiance += getLightProbeIndirectIrradiance( geometry, maxMipLevel );\n\t#endif\n#endif\n#if defined( USE_ENVMAP ) && defined( RE_IndirectSpecular )\n\tradiance += getLightProbeIndirectRadiance( geometry.viewDir, geometry.normal, material.specularRoughness, maxMipLevel );\n\t#ifdef CLEARCOAT\n\t\tclearcoatRadiance += getLightProbeIndirectRadiance( geometry.viewDir, geometry.clearcoatNormal, material.clearcoatRoughness, maxMipLevel );\n\t#endif\n#endif",lights_fragment_end:"#if defined( RE_IndirectDiffuse )\n\tRE_IndirectDiffuse( irradiance, geometry, material, reflectedLight );\n#endif\n#if defined( RE_IndirectSpecular )\n\tRE_IndirectSpecular( radiance, iblIrradiance, clearcoatRadiance, geometry, material, reflectedLight );\n#endif",logdepthbuf_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tgl_FragDepthEXT = vIsPerspective == 0.0 ? gl_FragCoord.z : log2( vFragDepth ) * logDepthBufFC * 0.5;\n#endif",logdepthbuf_pars_fragment:"#if defined( USE_LOGDEPTHBUF ) && defined( USE_LOGDEPTHBUF_EXT )\n\tuniform float logDepthBufFC;\n\tvarying float vFragDepth;\n\tvarying float vIsPerspective;\n#endif",logdepthbuf_pars_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvarying float vFragDepth;\n\t\tvarying float vIsPerspective;\n\t#else\n\t\tuniform float logDepthBufFC;\n\t#endif\n#endif",logdepthbuf_vertex:"#ifdef USE_LOGDEPTHBUF\n\t#ifdef USE_LOGDEPTHBUF_EXT\n\t\tvFragDepth = 1.0 + gl_Position.w;\n\t\tvIsPerspective = float( isPerspectiveMatrix( projectionMatrix ) );\n\t#else\n\t\tif ( isPerspectiveMatrix( projectionMatrix ) ) {\n\t\t\tgl_Position.z = log2( max( EPSILON, gl_Position.w + 1.0 ) ) * logDepthBufFC - 1.0;\n\t\t\tgl_Position.z *= gl_Position.w;\n\t\t}\n\t#endif\n#endif",map_fragment:"#ifdef USE_MAP\n\tvec4 texelColor = texture2D( map, vUv );\n\ttexelColor = mapTexelToLinear( texelColor );\n\tdiffuseColor *= texelColor;\n#endif",map_pars_fragment:"#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif",map_particle_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tvec2 uv = ( uvTransform * vec3( gl_PointCoord.x, 1.0 - gl_PointCoord.y, 1 ) ).xy;\n#endif\n#ifdef USE_MAP\n\tvec4 mapTexel = texture2D( map, uv );\n\tdiffuseColor *= mapTexelToLinear( mapTexel );\n#endif\n#ifdef USE_ALPHAMAP\n\tdiffuseColor.a *= texture2D( alphaMap, uv ).g;\n#endif",map_particle_pars_fragment:"#if defined( USE_MAP ) || defined( USE_ALPHAMAP )\n\tuniform mat3 uvTransform;\n#endif\n#ifdef USE_MAP\n\tuniform sampler2D map;\n#endif\n#ifdef USE_ALPHAMAP\n\tuniform sampler2D alphaMap;\n#endif",metalnessmap_fragment:"float metalnessFactor = metalness;\n#ifdef USE_METALNESSMAP\n\tvec4 texelMetalness = texture2D( metalnessMap, vUv );\n\tmetalnessFactor *= texelMetalness.b;\n#endif",metalnessmap_pars_fragment:"#ifdef USE_METALNESSMAP\n\tuniform sampler2D metalnessMap;\n#endif",morphnormal_vertex:"#ifdef USE_MORPHNORMALS\n\tobjectNormal *= morphTargetBaseInfluence;\n\tobjectNormal += morphNormal0 * morphTargetInfluences[ 0 ];\n\tobjectNormal += morphNormal1 * morphTargetInfluences[ 1 ];\n\tobjectNormal += morphNormal2 * morphTargetInfluences[ 2 ];\n\tobjectNormal += morphNormal3 * morphTargetInfluences[ 3 ];\n#endif",morphtarget_pars_vertex:"#ifdef USE_MORPHTARGETS\n\tuniform float morphTargetBaseInfluence;\n\t#ifndef USE_MORPHNORMALS\n\t\tuniform float morphTargetInfluences[ 8 ];\n\t#else\n\t\tuniform float morphTargetInfluences[ 4 ];\n\t#endif\n#endif",morphtarget_vertex:"#ifdef USE_MORPHTARGETS\n\ttransformed *= morphTargetBaseInfluence;\n\ttransformed += morphTarget0 * morphTargetInfluences[ 0 ];\n\ttransformed += morphTarget1 * morphTargetInfluences[ 1 ];\n\ttransformed += morphTarget2 * morphTargetInfluences[ 2 ];\n\ttransformed += morphTarget3 * morphTargetInfluences[ 3 ];\n\t#ifndef USE_MORPHNORMALS\n\t\ttransformed += morphTarget4 * morphTargetInfluences[ 4 ];\n\t\ttransformed += morphTarget5 * morphTargetInfluences[ 5 ];\n\t\ttransformed += morphTarget6 * morphTargetInfluences[ 6 ];\n\t\ttransformed += morphTarget7 * morphTargetInfluences[ 7 ];\n\t#endif\n#endif",normal_fragment_begin:"float faceDirection = gl_FrontFacing ? 1.0 : - 1.0;\n#ifdef FLAT_SHADED\n\tvec3 fdx = vec3( dFdx( vViewPosition.x ), dFdx( vViewPosition.y ), dFdx( vViewPosition.z ) );\n\tvec3 fdy = vec3( dFdy( vViewPosition.x ), dFdy( vViewPosition.y ), dFdy( vViewPosition.z ) );\n\tvec3 normal = normalize( cross( fdx, fdy ) );\n#else\n\tvec3 normal = normalize( vNormal );\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\t#ifdef USE_TANGENT\n\t\tvec3 tangent = normalize( vTangent );\n\t\tvec3 bitangent = normalize( vBitangent );\n\t\t#ifdef DOUBLE_SIDED\n\t\t\ttangent = tangent * faceDirection;\n\t\t\tbitangent = bitangent * faceDirection;\n\t\t#endif\n\t\t#if defined( TANGENTSPACE_NORMALMAP ) || defined( USE_CLEARCOAT_NORMALMAP )\n\t\t\tmat3 vTBN = mat3( tangent, bitangent, normal );\n\t\t#endif\n\t#endif\n#endif\nvec3 geometryNormal = normal;",normal_fragment_maps:"#ifdef OBJECTSPACE_NORMALMAP\n\tnormal = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\t#ifdef FLIP_SIDED\n\t\tnormal = - normal;\n\t#endif\n\t#ifdef DOUBLE_SIDED\n\t\tnormal = normal * faceDirection;\n\t#endif\n\tnormal = normalize( normalMatrix * normal );\n#elif defined( TANGENTSPACE_NORMALMAP )\n\tvec3 mapN = texture2D( normalMap, vUv ).xyz * 2.0 - 1.0;\n\tmapN.xy *= normalScale;\n\t#ifdef USE_TANGENT\n\t\tnormal = normalize( vTBN * mapN );\n\t#else\n\t\tnormal = perturbNormal2Arb( -vViewPosition, normal, mapN, faceDirection );\n\t#endif\n#elif defined( USE_BUMPMAP )\n\tnormal = perturbNormalArb( -vViewPosition, normal, dHdxy_fwd(), faceDirection );\n#endif",normalmap_pars_fragment:"#ifdef USE_NORMALMAP\n\tuniform sampler2D normalMap;\n\tuniform vec2 normalScale;\n#endif\n#ifdef OBJECTSPACE_NORMALMAP\n\tuniform mat3 normalMatrix;\n#endif\n#if ! defined ( USE_TANGENT ) && ( defined ( TANGENTSPACE_NORMALMAP ) || defined ( USE_CLEARCOAT_NORMALMAP ) )\n\tvec3 perturbNormal2Arb( vec3 eye_pos, vec3 surf_norm, vec3 mapN, float faceDirection ) {\n\t\tvec3 q0 = vec3( dFdx( eye_pos.x ), dFdx( eye_pos.y ), dFdx( eye_pos.z ) );\n\t\tvec3 q1 = vec3( dFdy( eye_pos.x ), dFdy( eye_pos.y ), dFdy( eye_pos.z ) );\n\t\tvec2 st0 = dFdx( vUv.st );\n\t\tvec2 st1 = dFdy( vUv.st );\n\t\tvec3 N = surf_norm;\n\t\tvec3 q1perp = cross( q1, N );\n\t\tvec3 q0perp = cross( N, q0 );\n\t\tvec3 T = q1perp * st0.x + q0perp * st1.x;\n\t\tvec3 B = q1perp * st0.y + q0perp * st1.y;\n\t\tfloat det = max( dot( T, T ), dot( B, B ) );\n\t\tfloat scale = ( det == 0.0 ) ? 0.0 : faceDirection * inversesqrt( det );\n\t\treturn normalize( T * ( mapN.x * scale ) + B * ( mapN.y * scale ) + N * mapN.z );\n\t}\n#endif",clearcoat_normal_fragment_begin:"#ifdef CLEARCOAT\n\tvec3 clearcoatNormal = geometryNormal;\n#endif",clearcoat_normal_fragment_maps:"#ifdef USE_CLEARCOAT_NORMALMAP\n\tvec3 clearcoatMapN = texture2D( clearcoatNormalMap, vUv ).xyz * 2.0 - 1.0;\n\tclearcoatMapN.xy *= clearcoatNormalScale;\n\t#ifdef USE_TANGENT\n\t\tclearcoatNormal = normalize( vTBN * clearcoatMapN );\n\t#else\n\t\tclearcoatNormal = perturbNormal2Arb( - vViewPosition, clearcoatNormal, clearcoatMapN, faceDirection );\n\t#endif\n#endif",clearcoat_pars_fragment:"#ifdef USE_CLEARCOATMAP\n\tuniform sampler2D clearcoatMap;\n#endif\n#ifdef USE_CLEARCOAT_ROUGHNESSMAP\n\tuniform sampler2D clearcoatRoughnessMap;\n#endif\n#ifdef USE_CLEARCOAT_NORMALMAP\n\tuniform sampler2D clearcoatNormalMap;\n\tuniform vec2 clearcoatNormalScale;\n#endif",packing:"vec3 packNormalToRGB( const in vec3 normal ) {\n\treturn normalize( normal ) * 0.5 + 0.5;\n}\nvec3 unpackRGBToNormal( const in vec3 rgb ) {\n\treturn 2.0 * rgb.xyz - 1.0;\n}\nconst float PackUpscale = 256. / 255.;const float UnpackDownscale = 255. / 256.;\nconst vec3 PackFactors = vec3( 256. * 256. * 256., 256. * 256., 256. );\nconst vec4 UnpackFactors = UnpackDownscale / vec4( PackFactors, 1. );\nconst float ShiftRight8 = 1. / 256.;\nvec4 packDepthToRGBA( const in float v ) {\n\tvec4 r = vec4( fract( v * PackFactors ), v );\n\tr.yzw -= r.xyz * ShiftRight8;\treturn r * PackUpscale;\n}\nfloat unpackRGBAToDepth( const in vec4 v ) {\n\treturn dot( v, UnpackFactors );\n}\nvec4 pack2HalfToRGBA( vec2 v ) {\n\tvec4 r = vec4( v.x, fract( v.x * 255.0 ), v.y, fract( v.y * 255.0 ));\n\treturn vec4( r.x - r.y / 255.0, r.y, r.z - r.w / 255.0, r.w);\n}\nvec2 unpackRGBATo2Half( vec4 v ) {\n\treturn vec2( v.x + ( v.y / 255.0 ), v.z + ( v.w / 255.0 ) );\n}\nfloat viewZToOrthographicDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn ( viewZ + near ) / ( near - far );\n}\nfloat orthographicDepthToViewZ( const in float linearClipZ, const in float near, const in float far ) {\n\treturn linearClipZ * ( near - far ) - near;\n}\nfloat viewZToPerspectiveDepth( const in float viewZ, const in float near, const in float far ) {\n\treturn (( near + viewZ ) * far ) / (( far - near ) * viewZ );\n}\nfloat perspectiveDepthToViewZ( const in float invClipZ, const in float near, const in float far ) {\n\treturn ( near * far ) / ( ( far - near ) * invClipZ - far );\n}",premultiplied_alpha_fragment:"#ifdef PREMULTIPLIED_ALPHA\n\tgl_FragColor.rgb *= gl_FragColor.a;\n#endif",project_vertex:"vec4 mvPosition = vec4( transformed, 1.0 );\n#ifdef USE_INSTANCING\n\tmvPosition = instanceMatrix * mvPosition;\n#endif\nmvPosition = modelViewMatrix * mvPosition;\ngl_Position = projectionMatrix * mvPosition;",dithering_fragment:"#ifdef DITHERING\n\tgl_FragColor.rgb = dithering( gl_FragColor.rgb );\n#endif",dithering_pars_fragment:"#ifdef DITHERING\n\tvec3 dithering( vec3 color ) {\n\t\tfloat grid_position = rand( gl_FragCoord.xy );\n\t\tvec3 dither_shift_RGB = vec3( 0.25 / 255.0, -0.25 / 255.0, 0.25 / 255.0 );\n\t\tdither_shift_RGB = mix( 2.0 * dither_shift_RGB, -2.0 * dither_shift_RGB, grid_position );\n\t\treturn color + dither_shift_RGB;\n\t}\n#endif",roughnessmap_fragment:"float roughnessFactor = roughness;\n#ifdef USE_ROUGHNESSMAP\n\tvec4 texelRoughness = texture2D( roughnessMap, vUv );\n\troughnessFactor *= texelRoughness.g;\n#endif",roughnessmap_pars_fragment:"#ifdef USE_ROUGHNESSMAP\n\tuniform sampler2D roughnessMap;\n#endif",shadowmap_pars_fragment:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D directionalShadowMap[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D spotShadowMap[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform sampler2D pointShadowMap[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n\tfloat texture2DCompare( sampler2D depths, vec2 uv, float compare ) {\n\t\treturn step( compare, unpackRGBAToDepth( texture2D( depths, uv ) ) );\n\t}\n\tvec2 texture2DDistribution( sampler2D shadow, vec2 uv ) {\n\t\treturn unpackRGBATo2Half( texture2D( shadow, uv ) );\n\t}\n\tfloat VSMShadow (sampler2D shadow, vec2 uv, float compare ){\n\t\tfloat occlusion = 1.0;\n\t\tvec2 distribution = texture2DDistribution( shadow, uv );\n\t\tfloat hard_shadow = step( compare , distribution.x );\n\t\tif (hard_shadow != 1.0 ) {\n\t\t\tfloat distance = compare - distribution.x ;\n\t\t\tfloat variance = max( 0.00000, distribution.y * distribution.y );\n\t\t\tfloat softness_probability = variance / (variance + distance * distance );\t\t\tsoftness_probability = clamp( ( softness_probability - 0.3 ) / ( 0.95 - 0.3 ), 0.0, 1.0 );\t\t\tocclusion = clamp( max( hard_shadow, softness_probability ), 0.0, 1.0 );\n\t\t}\n\t\treturn occlusion;\n\t}\n\tfloat getShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord ) {\n\t\tfloat shadow = 1.0;\n\t\tshadowCoord.xyz /= shadowCoord.w;\n\t\tshadowCoord.z += shadowBias;\n\t\tbvec4 inFrustumVec = bvec4 ( shadowCoord.x >= 0.0, shadowCoord.x <= 1.0, shadowCoord.y >= 0.0, shadowCoord.y <= 1.0 );\n\t\tbool inFrustum = all( inFrustumVec );\n\t\tbvec2 frustumTestVec = bvec2( inFrustum, shadowCoord.z <= 1.0 );\n\t\tbool frustumTest = all( frustumTestVec );\n\t\tif ( frustumTest ) {\n\t\t#if defined( SHADOWMAP_TYPE_PCF )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx0 = - texelSize.x * shadowRadius;\n\t\t\tfloat dy0 = - texelSize.y * shadowRadius;\n\t\t\tfloat dx1 = + texelSize.x * shadowRadius;\n\t\t\tfloat dy1 = + texelSize.y * shadowRadius;\n\t\t\tfloat dx2 = dx0 / 2.0;\n\t\t\tfloat dy2 = dy0 / 2.0;\n\t\t\tfloat dx3 = dx1 / 2.0;\n\t\t\tfloat dy3 = dy1 / 2.0;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy2 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx2, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx3, dy3 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( 0.0, dy1 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, shadowCoord.xy + vec2( dx1, dy1 ), shadowCoord.z )\n\t\t\t) * ( 1.0 / 17.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_PCF_SOFT )\n\t\t\tvec2 texelSize = vec2( 1.0 ) / shadowMapSize;\n\t\t\tfloat dx = texelSize.x;\n\t\t\tfloat dy = texelSize.y;\n\t\t\tvec2 uv = shadowCoord.xy;\n\t\t\tvec2 f = fract( uv * shadowMapSize + 0.5 );\n\t\t\tuv -= f * texelSize;\n\t\t\tshadow = (\n\t\t\t\ttexture2DCompare( shadowMap, uv, shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( dx, 0.0 ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + vec2( 0.0, dy ), shadowCoord.z ) +\n\t\t\t\ttexture2DCompare( shadowMap, uv + texelSize, shadowCoord.z ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, 0.0 ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 0.0 ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( -dx, dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, dy ), shadowCoord.z ),\n\t\t\t\t\t f.x ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( 0.0, -dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( 0.0, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( texture2DCompare( shadowMap, uv + vec2( dx, -dy ), shadowCoord.z ), \n\t\t\t\t\t texture2DCompare( shadowMap, uv + vec2( dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t f.y ) +\n\t\t\t\tmix( mix( texture2DCompare( shadowMap, uv + vec2( -dx, -dy ), shadowCoord.z ), \n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, -dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t mix( texture2DCompare( shadowMap, uv + vec2( -dx, 2.0 * dy ), shadowCoord.z ), \n\t\t\t\t\t\t  texture2DCompare( shadowMap, uv + vec2( 2.0 * dx, 2.0 * dy ), shadowCoord.z ),\n\t\t\t\t\t\t  f.x ),\n\t\t\t\t\t f.y )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#elif defined( SHADOWMAP_TYPE_VSM )\n\t\t\tshadow = VSMShadow( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#else\n\t\t\tshadow = texture2DCompare( shadowMap, shadowCoord.xy, shadowCoord.z );\n\t\t#endif\n\t\t}\n\t\treturn shadow;\n\t}\n\tvec2 cubeToUV( vec3 v, float texelSizeY ) {\n\t\tvec3 absV = abs( v );\n\t\tfloat scaleToCube = 1.0 / max( absV.x, max( absV.y, absV.z ) );\n\t\tabsV *= scaleToCube;\n\t\tv *= scaleToCube * ( 1.0 - 2.0 * texelSizeY );\n\t\tvec2 planar = v.xy;\n\t\tfloat almostATexel = 1.5 * texelSizeY;\n\t\tfloat almostOne = 1.0 - almostATexel;\n\t\tif ( absV.z >= almostOne ) {\n\t\t\tif ( v.z > 0.0 )\n\t\t\t\tplanar.x = 4.0 - v.x;\n\t\t} else if ( absV.x >= almostOne ) {\n\t\t\tfloat signX = sign( v.x );\n\t\t\tplanar.x = v.z * signX + 2.0 * signX;\n\t\t} else if ( absV.y >= almostOne ) {\n\t\t\tfloat signY = sign( v.y );\n\t\t\tplanar.x = v.x + 2.0 * signY + 2.0;\n\t\t\tplanar.y = v.z * signY - 2.0;\n\t\t}\n\t\treturn vec2( 0.125, 0.25 ) * planar + vec2( 0.375, 0.75 );\n\t}\n\tfloat getPointShadow( sampler2D shadowMap, vec2 shadowMapSize, float shadowBias, float shadowRadius, vec4 shadowCoord, float shadowCameraNear, float shadowCameraFar ) {\n\t\tvec2 texelSize = vec2( 1.0 ) / ( shadowMapSize * vec2( 4.0, 2.0 ) );\n\t\tvec3 lightToPosition = shadowCoord.xyz;\n\t\tfloat dp = ( length( lightToPosition ) - shadowCameraNear ) / ( shadowCameraFar - shadowCameraNear );\t\tdp += shadowBias;\n\t\tvec3 bd3D = normalize( lightToPosition );\n\t\t#if defined( SHADOWMAP_TYPE_PCF ) || defined( SHADOWMAP_TYPE_PCF_SOFT ) || defined( SHADOWMAP_TYPE_VSM )\n\t\t\tvec2 offset = vec2( - 1, 1 ) * shadowRadius * texelSize.y;\n\t\t\treturn (\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yyx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxy, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.xxx, texelSize.y ), dp ) +\n\t\t\t\ttexture2DCompare( shadowMap, cubeToUV( bd3D + offset.yxx, texelSize.y ), dp )\n\t\t\t) * ( 1.0 / 9.0 );\n\t\t#else\n\t\t\treturn texture2DCompare( shadowMap, cubeToUV( bd3D, texelSize.y ), dp );\n\t\t#endif\n\t}\n#endif",shadowmap_pars_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t\tuniform mat4 directionalShadowMatrix[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tvarying vec4 vDirectionalShadowCoord[ NUM_DIR_LIGHT_SHADOWS ];\n\t\tstruct DirectionalLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform DirectionalLightShadow directionalLightShadows[ NUM_DIR_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 spotShadowMatrix[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vSpotShadowCoord[ NUM_SPOT_LIGHT_SHADOWS ];\n\t\tstruct SpotLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t};\n\t\tuniform SpotLightShadow spotLightShadows[ NUM_SPOT_LIGHT_SHADOWS ];\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t\tuniform mat4 pointShadowMatrix[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tvarying vec4 vPointShadowCoord[ NUM_POINT_LIGHT_SHADOWS ];\n\t\tstruct PointLightShadow {\n\t\t\tfloat shadowBias;\n\t\t\tfloat shadowNormalBias;\n\t\t\tfloat shadowRadius;\n\t\t\tvec2 shadowMapSize;\n\t\t\tfloat shadowCameraNear;\n\t\t\tfloat shadowCameraFar;\n\t\t};\n\t\tuniform PointLightShadow pointLightShadows[ NUM_POINT_LIGHT_SHADOWS ];\n\t#endif\n#endif",shadowmap_vertex:"#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0 || NUM_SPOT_LIGHT_SHADOWS > 0 || NUM_POINT_LIGHT_SHADOWS > 0\n\t\tvec3 shadowWorldNormal = inverseTransformDirection( transformedNormal, viewMatrix );\n\t\tvec4 shadowWorldPosition;\n\t#endif\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * directionalLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvDirectionalShadowCoord[ i ] = directionalShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * spotLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvSpotShadowCoord[ i ] = spotShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tshadowWorldPosition = worldPosition + vec4( shadowWorldNormal * pointLightShadows[ i ].shadowNormalBias, 0 );\n\t\tvPointShadowCoord[ i ] = pointShadowMatrix[ i ] * shadowWorldPosition;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n#endif",shadowmask_pars_fragment:"float getShadowMask() {\n\tfloat shadow = 1.0;\n\t#ifdef USE_SHADOWMAP\n\t#if NUM_DIR_LIGHT_SHADOWS > 0\n\tDirectionalLightShadow directionalLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_DIR_LIGHT_SHADOWS; i ++ ) {\n\t\tdirectionalLight = directionalLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( directionalShadowMap[ i ], directionalLight.shadowMapSize, directionalLight.shadowBias, directionalLight.shadowRadius, vDirectionalShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_SPOT_LIGHT_SHADOWS > 0\n\tSpotLightShadow spotLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_SPOT_LIGHT_SHADOWS; i ++ ) {\n\t\tspotLight = spotLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getShadow( spotShadowMap[ i ], spotLight.shadowMapSize, spotLight.shadowBias, spotLight.shadowRadius, vSpotShadowCoord[ i ] ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#if NUM_POINT_LIGHT_SHADOWS > 0\n\tPointLightShadow pointLight;\n\t#pragma unroll_loop_start\n\tfor ( int i = 0; i < NUM_POINT_LIGHT_SHADOWS; i ++ ) {\n\t\tpointLight = pointLightShadows[ i ];\n\t\tshadow *= receiveShadow ? getPointShadow( pointShadowMap[ i ], pointLight.shadowMapSize, pointLight.shadowBias, pointLight.shadowRadius, vPointShadowCoord[ i ], pointLight.shadowCameraNear, pointLight.shadowCameraFar ) : 1.0;\n\t}\n\t#pragma unroll_loop_end\n\t#endif\n\t#endif\n\treturn shadow;\n}",skinbase_vertex:"#ifdef USE_SKINNING\n\tmat4 boneMatX = getBoneMatrix( skinIndex.x );\n\tmat4 boneMatY = getBoneMatrix( skinIndex.y );\n\tmat4 boneMatZ = getBoneMatrix( skinIndex.z );\n\tmat4 boneMatW = getBoneMatrix( skinIndex.w );\n#endif",skinning_pars_vertex:"#ifdef USE_SKINNING\n\tuniform mat4 bindMatrix;\n\tuniform mat4 bindMatrixInverse;\n\t#ifdef BONE_TEXTURE\n\t\tuniform highp sampler2D boneTexture;\n\t\tuniform int boneTextureSize;\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tfloat j = i * 4.0;\n\t\t\tfloat x = mod( j, float( boneTextureSize ) );\n\t\t\tfloat y = floor( j / float( boneTextureSize ) );\n\t\t\tfloat dx = 1.0 / float( boneTextureSize );\n\t\t\tfloat dy = 1.0 / float( boneTextureSize );\n\t\t\ty = dy * ( y + 0.5 );\n\t\t\tvec4 v1 = texture2D( boneTexture, vec2( dx * ( x + 0.5 ), y ) );\n\t\t\tvec4 v2 = texture2D( boneTexture, vec2( dx * ( x + 1.5 ), y ) );\n\t\t\tvec4 v3 = texture2D( boneTexture, vec2( dx * ( x + 2.5 ), y ) );\n\t\t\tvec4 v4 = texture2D( boneTexture, vec2( dx * ( x + 3.5 ), y ) );\n\t\t\tmat4 bone = mat4( v1, v2, v3, v4 );\n\t\t\treturn bone;\n\t\t}\n\t#else\n\t\tuniform mat4 boneMatrices[ MAX_BONES ];\n\t\tmat4 getBoneMatrix( const in float i ) {\n\t\t\tmat4 bone = boneMatrices[ int(i) ];\n\t\t\treturn bone;\n\t\t}\n\t#endif\n#endif",skinning_vertex:"#ifdef USE_SKINNING\n\tvec4 skinVertex = bindMatrix * vec4( transformed, 1.0 );\n\tvec4 skinned = vec4( 0.0 );\n\tskinned += boneMatX * skinVertex * skinWeight.x;\n\tskinned += boneMatY * skinVertex * skinWeight.y;\n\tskinned += boneMatZ * skinVertex * skinWeight.z;\n\tskinned += boneMatW * skinVertex * skinWeight.w;\n\ttransformed = ( bindMatrixInverse * skinned ).xyz;\n#endif",skinnormal_vertex:"#ifdef USE_SKINNING\n\tmat4 skinMatrix = mat4( 0.0 );\n\tskinMatrix += skinWeight.x * boneMatX;\n\tskinMatrix += skinWeight.y * boneMatY;\n\tskinMatrix += skinWeight.z * boneMatZ;\n\tskinMatrix += skinWeight.w * boneMatW;\n\tskinMatrix = bindMatrixInverse * skinMatrix * bindMatrix;\n\tobjectNormal = vec4( skinMatrix * vec4( objectNormal, 0.0 ) ).xyz;\n\t#ifdef USE_TANGENT\n\t\tobjectTangent = vec4( skinMatrix * vec4( objectTangent, 0.0 ) ).xyz;\n\t#endif\n#endif",specularmap_fragment:"float specularStrength;\n#ifdef USE_SPECULARMAP\n\tvec4 texelSpecular = texture2D( specularMap, vUv );\n\tspecularStrength = texelSpecular.r;\n#else\n\tspecularStrength = 1.0;\n#endif",specularmap_pars_fragment:"#ifdef USE_SPECULARMAP\n\tuniform sampler2D specularMap;\n#endif",tonemapping_fragment:"#if defined( TONE_MAPPING )\n\tgl_FragColor.rgb = toneMapping( gl_FragColor.rgb );\n#endif",tonemapping_pars_fragment:"#ifndef saturate\n#define saturate(a) clamp( a, 0.0, 1.0 )\n#endif\nuniform float toneMappingExposure;\nvec3 LinearToneMapping( vec3 color ) {\n\treturn toneMappingExposure * color;\n}\nvec3 ReinhardToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\treturn saturate( color / ( vec3( 1.0 ) + color ) );\n}\nvec3 OptimizedCineonToneMapping( vec3 color ) {\n\tcolor *= toneMappingExposure;\n\tcolor = max( vec3( 0.0 ), color - 0.004 );\n\treturn pow( ( color * ( 6.2 * color + 0.5 ) ) / ( color * ( 6.2 * color + 1.7 ) + 0.06 ), vec3( 2.2 ) );\n}\nvec3 RRTAndODTFit( vec3 v ) {\n\tvec3 a = v * ( v + 0.0245786 ) - 0.000090537;\n\tvec3 b = v * ( 0.983729 * v + 0.4329510 ) + 0.238081;\n\treturn a / b;\n}\nvec3 ACESFilmicToneMapping( vec3 color ) {\n\tconst mat3 ACESInputMat = mat3(\n\t\tvec3( 0.59719, 0.07600, 0.02840 ),\t\tvec3( 0.35458, 0.90834, 0.13383 ),\n\t\tvec3( 0.04823, 0.01566, 0.83777 )\n\t);\n\tconst mat3 ACESOutputMat = mat3(\n\t\tvec3(  1.60475, -0.10208, -0.00327 ),\t\tvec3( -0.53108,  1.10813, -0.07276 ),\n\t\tvec3( -0.07367, -0.00605,  1.07602 )\n\t);\n\tcolor *= toneMappingExposure / 0.6;\n\tcolor = ACESInputMat * color;\n\tcolor = RRTAndODTFit( color );\n\tcolor = ACESOutputMat * color;\n\treturn saturate( color );\n}\nvec3 CustomToneMapping( vec3 color ) { return color; }",transmission_fragment:"#ifdef USE_TRANSMISSION\n\tfloat transmissionFactor = transmission;\n\tfloat thicknessFactor = thickness;\n\t#ifdef USE_TRANSMISSIONMAP\n\t\ttransmissionFactor *= texture2D( transmissionMap, vUv ).r;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tthicknessFactor *= texture2D( thicknessMap, vUv ).g;\n\t#endif\n\tvec3 pos = vWorldPosition.xyz / vWorldPosition.w;\n\tvec3 v = normalize( cameraPosition - pos );\n\tvec3 n = inverseTransformDirection( normal, viewMatrix );\n\tfloat ior = ( 1.0 + 0.4 * reflectivity ) / ( 1.0 - 0.4 * reflectivity );\n\tvec3 transmission = transmissionFactor * getIBLVolumeRefraction(\n\t\tn, v, roughnessFactor, material.diffuseColor, material.specularColor,\n\t\tpos, modelMatrix, viewMatrix, projectionMatrix, ior, thicknessFactor,\n\t\tattenuationTint, attenuationDistance );\n\ttotalDiffuse = mix( totalDiffuse, transmission, transmissionFactor );\n#endif",transmission_pars_fragment:"#ifdef USE_TRANSMISSION\n\t#ifdef USE_TRANSMISSIONMAP\n\t\tuniform sampler2D transmissionMap;\n\t#endif\n\t#ifdef USE_THICKNESSMAP\n\t\tuniform sampler2D thicknessMap;\n\t#endif\n\tuniform vec2 transmissionSamplerSize;\n\tuniform sampler2D transmissionSamplerMap;\n\tuniform mat4 modelMatrix;\n\tuniform mat4 projectionMatrix;\n\tvarying vec4 vWorldPosition;\n\tvec3 getVolumeTransmissionRay(vec3 n, vec3 v, float thickness, float ior, mat4 modelMatrix) {\n\t\tvec3 refractionVector = refract(-v, normalize(n), 1.0 / ior);\n\t\tvec3 modelScale;\n\t\tmodelScale.x = length(vec3(modelMatrix[0].xyz));\n\t\tmodelScale.y = length(vec3(modelMatrix[1].xyz));\n\t\tmodelScale.z = length(vec3(modelMatrix[2].xyz));\n\t\treturn normalize(refractionVector) * thickness * modelScale;\n\t}\n\tfloat applyIorToRoughness(float roughness, float ior) {\n\t\treturn roughness * clamp(ior * 2.0 - 2.0, 0.0, 1.0);\n\t}\n\tvec3 getTransmissionSample(vec2 fragCoord, float roughness, float ior) {\n\t\tfloat framebufferLod = log2(transmissionSamplerSize.x) * applyIorToRoughness(roughness, ior);\n\t\treturn texture2DLodEXT(transmissionSamplerMap, fragCoord.xy, framebufferLod).rgb;\n\t}\n\tvec3 applyVolumeAttenuation(vec3 radiance, float transmissionDistance, vec3 attenuationColor, float attenuationDistance) {\n\t\tif (attenuationDistance == 0.0) {\n\t\t\treturn radiance;\n\t\t} else {\n\t\t\tvec3 attenuationCoefficient = -log(attenuationColor) / attenuationDistance;\n\t\t\tvec3 transmittance = exp(-attenuationCoefficient * transmissionDistance);\t\t\treturn transmittance * radiance;\n\t\t}\n\t}\n\tvec3 getIBLVolumeRefraction(vec3 n, vec3 v, float perceptualRoughness, vec3 baseColor, vec3 specularColor,\n\t\tvec3 position, mat4 modelMatrix, mat4 viewMatrix, mat4 projMatrix, float ior, float thickness,\n\t\tvec3 attenuationColor, float attenuationDistance) {\n\t\tvec3 transmissionRay = getVolumeTransmissionRay(n, v, thickness, ior, modelMatrix);\n\t\tvec3 refractedRayExit = position + transmissionRay;\n\t\tvec4 ndcPos = projMatrix * viewMatrix * vec4(refractedRayExit, 1.0);\n\t\tvec2 refractionCoords = ndcPos.xy / ndcPos.w;\n\t\trefractionCoords += 1.0;\n\t\trefractionCoords /= 2.0;\n\t\tvec3 transmittedLight = getTransmissionSample(refractionCoords, perceptualRoughness, ior);\n\t\tvec3 attenuatedColor = applyVolumeAttenuation(transmittedLight, length(transmissionRay), attenuationColor, attenuationDistance);\n\t\treturn (1.0 - specularColor) * attenuatedColor * baseColor;\n\t}\n#endif",uv_pars_fragment:"#if ( defined( USE_UV ) && ! defined( UVS_VERTEX_ONLY ) )\n\tvarying vec2 vUv;\n#endif",uv_pars_vertex:"#ifdef USE_UV\n\t#ifdef UVS_VERTEX_ONLY\n\t\tvec2 vUv;\n\t#else\n\t\tvarying vec2 vUv;\n\t#endif\n\tuniform mat3 uvTransform;\n#endif",uv_vertex:"#ifdef USE_UV\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n#endif",uv2_pars_fragment:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvarying vec2 vUv2;\n#endif",uv2_pars_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tattribute vec2 uv2;\n\tvarying vec2 vUv2;\n\tuniform mat3 uv2Transform;\n#endif",uv2_vertex:"#if defined( USE_LIGHTMAP ) || defined( USE_AOMAP )\n\tvUv2 = ( uv2Transform * vec3( uv2, 1 ) ).xy;\n#endif",worldpos_vertex:"#if defined( USE_ENVMAP ) || defined( DISTANCE ) || defined ( USE_SHADOWMAP ) || defined ( USE_TRANSMISSION )\n\tvec4 worldPosition = vec4( transformed, 1.0 );\n\t#ifdef USE_INSTANCING\n\t\tworldPosition = instanceMatrix * worldPosition;\n\t#endif\n\tworldPosition = modelMatrix * worldPosition;\n#endif",background_frag:"uniform sampler2D t2D;\nvarying vec2 vUv;\nvoid main() {\n\tvec4 texColor = texture2D( t2D, vUv );\n\tgl_FragColor = mapTexelToLinear( texColor );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",background_vert:"varying vec2 vUv;\nuniform mat3 uvTransform;\nvoid main() {\n\tvUv = ( uvTransform * vec3( uv, 1 ) ).xy;\n\tgl_Position = vec4( position.xy, 1.0, 1.0 );\n}",cube_frag:"#include <envmap_common_pars_fragment>\nuniform float opacity;\nvarying vec3 vWorldDirection;\n#include <cube_uv_reflection_fragment>\nvoid main() {\n\tvec3 vReflect = vWorldDirection;\n\t#include <envmap_fragment>\n\tgl_FragColor = envColor;\n\tgl_FragColor.a *= opacity;\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",cube_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\tgl_Position.z = gl_Position.w;\n}",depth_frag:"#if DEPTH_PACKING == 3200\n\tuniform float opacity;\n#endif\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#if DEPTH_PACKING == 3200\n\t\tdiffuseColor.a = opacity;\n\t#endif\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <logdepthbuf_fragment>\n\tfloat fragCoordZ = 0.5 * vHighPrecisionZW[0] / vHighPrecisionZW[1] + 0.5;\n\t#if DEPTH_PACKING == 3200\n\t\tgl_FragColor = vec4( vec3( 1.0 - fragCoordZ ), opacity );\n\t#elif DEPTH_PACKING == 3201\n\t\tgl_FragColor = packDepthToRGBA( fragCoordZ );\n\t#endif\n}",depth_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvarying vec2 vHighPrecisionZW;\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvHighPrecisionZW = gl_Position.zw;\n}",distanceRGBA_frag:"#define DISTANCE\nuniform vec3 referencePosition;\nuniform float nearDistance;\nuniform float farDistance;\nvarying vec3 vWorldPosition;\n#include <common>\n#include <packing>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main () {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( 1.0 );\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\tfloat dist = length( vWorldPosition - referencePosition );\n\tdist = ( dist - nearDistance ) / ( farDistance - nearDistance );\n\tdist = saturate( dist );\n\tgl_FragColor = packDepthToRGBA( dist );\n}",distanceRGBA_vert:"#define DISTANCE\nvarying vec3 vWorldPosition;\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <skinbase_vertex>\n\t#ifdef USE_DISPLACEMENTMAP\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <clipping_planes_vertex>\n\tvWorldPosition = worldPosition.xyz;\n}",equirect_frag:"uniform sampler2D tEquirect;\nvarying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvec3 direction = normalize( vWorldDirection );\n\tvec2 sampleUV = equirectUv( direction );\n\tvec4 texColor = texture2D( tEquirect, sampleUV );\n\tgl_FragColor = mapTexelToLinear( texColor );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n}",equirect_vert:"varying vec3 vWorldDirection;\n#include <common>\nvoid main() {\n\tvWorldDirection = transformDirection( position, modelMatrix );\n\t#include <begin_vertex>\n\t#include <project_vertex>\n}",linedashed_frag:"uniform vec3 diffuse;\nuniform float opacity;\nuniform float dashSize;\nuniform float totalSize;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tif ( mod( vLineDistance, totalSize ) > dashSize ) {\n\t\tdiscard;\n\t}\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <color_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",linedashed_vert:"uniform float scale;\nattribute float lineDistance;\nvarying float vLineDistance;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\tvLineDistance = scale * lineDistance;\n\t#include <color_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n}",meshbasic_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <fog_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\t#ifdef USE_LIGHTMAP\n\t\n\t\tvec4 lightMapTexel= texture2D( lightMap, vUv2 );\n\t\treflectedLight.indirectDiffuse += lightMapTexelToLinear( lightMapTexel ).rgb * lightMapIntensity;\n\t#else\n\t\treflectedLight.indirectDiffuse += vec3( 1.0 );\n\t#endif\n\t#include <aomap_fragment>\n\treflectedLight.indirectDiffuse *= diffuseColor.rgb;\n\tvec3 outgoingLight = reflectedLight.indirectDiffuse;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshbasic_vert:"#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#if defined ( USE_ENVMAP ) || defined ( USE_SKINNING )\n\t\t#include <beginnormal_vertex>\n\t\t#include <morphnormal_vertex>\n\t\t#include <skinbase_vertex>\n\t\t#include <skinnormal_vertex>\n\t\t#include <defaultnormal_vertex>\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <fog_vertex>\n}",meshlambert_frag:"uniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <fog_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <shadowmask_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <emissivemap_fragment>\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.indirectDiffuse += ( gl_FrontFacing ) ? vIndirectFront : vIndirectBack;\n\t#else\n\t\treflectedLight.indirectDiffuse += vIndirectFront;\n\t#endif\n\t#include <lightmap_fragment>\n\treflectedLight.indirectDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb );\n\t#ifdef DOUBLE_SIDED\n\t\treflectedLight.directDiffuse = ( gl_FrontFacing ) ? vLightFront : vLightBack;\n\t#else\n\t\treflectedLight.directDiffuse = vLightFront;\n\t#endif\n\treflectedLight.directDiffuse *= BRDF_Diffuse_Lambert( diffuseColor.rgb ) * getShadowMask();\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshlambert_vert:"#define LAMBERT\nvarying vec3 vLightFront;\nvarying vec3 vIndirectFront;\n#ifdef DOUBLE_SIDED\n\tvarying vec3 vLightBack;\n\tvarying vec3 vIndirectBack;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <envmap_pars_vertex>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <lights_lambert_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshmatcap_frag:"#define MATCAP\nuniform vec3 diffuse;\nuniform float opacity;\nuniform sampler2D matcap;\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tvec3 viewDir = normalize( vViewPosition );\n\tvec3 x = normalize( vec3( viewDir.z, 0.0, - viewDir.x ) );\n\tvec3 y = cross( viewDir, x );\n\tvec2 uv = vec2( dot( x, normal ), dot( y, normal ) ) * 0.495 + 0.5;\n\t#ifdef USE_MATCAP\n\t\tvec4 matcapColor = texture2D( matcap, uv );\n\t\tmatcapColor = matcapTexelToLinear( matcapColor );\n\t#else\n\t\tvec4 matcapColor = vec4( 1.0 );\n\t#endif\n\tvec3 outgoingLight = diffuseColor.rgb * matcapColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshmatcap_vert:"#define MATCAP\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <color_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#ifndef FLAT_SHADED\n\t\tvNormal = normalize( transformedNormal );\n\t\t#ifdef USE_TANGENT\n\t\t\tvTangent = normalize( transformedTangent );\n\t\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t\t#endif\n\t#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n\tvViewPosition = - mvPosition.xyz;\n}",meshtoon_frag:"#define TOON\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <gradientmap_pars_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <lights_toon_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_toon_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + totalEmissiveRadiance;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshtoon_vert:"#define TOON\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshphong_frag:"#define PHONG\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform vec3 specular;\nuniform float shininess;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <lights_phong_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <specularmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <specularmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_phong_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 outgoingLight = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse + reflectedLight.directSpecular + reflectedLight.indirectSpecular + totalEmissiveRadiance;\n\t#include <envmap_fragment>\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshphong_vert:"#define PHONG\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <envmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <envmap_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",meshphysical_frag:"#define STANDARD\n#ifdef PHYSICAL\n\t#define REFLECTIVITY\n\t#define CLEARCOAT\n\t#define SPECULAR\n#endif\nuniform vec3 diffuse;\nuniform vec3 emissive;\nuniform float roughness;\nuniform float metalness;\nuniform float opacity;\n#ifdef USE_TRANSMISSION\n\tuniform float transmission;\n\tuniform float thickness;\n\tuniform float attenuationDistance;\n\tuniform vec3 attenuationTint;\n#endif\n#ifdef REFLECTIVITY\n\tuniform float reflectivity;\n#endif\n#ifdef SPECULAR\n\tuniform float specularIntensity;\n\tuniform vec3 specularTint;\n\t#ifdef USE_SPECULARINTENSITYMAP\n\t\tuniform sampler2D specularIntensityMap;\n\t#endif\n\t#ifdef USE_SPECULARTINTMAP\n\t\tuniform sampler2D specularTintMap;\n\t#endif\n#endif\n#ifdef CLEARCOAT\n\tuniform float clearcoat;\n\tuniform float clearcoatRoughness;\n#endif\n#ifdef USE_SHEEN\n\tuniform vec3 sheen;\n#endif\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <packing>\n#include <dithering_pars_fragment>\n#include <color_pars_fragment>\n#include <uv_pars_fragment>\n#include <uv2_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <aomap_pars_fragment>\n#include <lightmap_pars_fragment>\n#include <emissivemap_pars_fragment>\n#include <bsdfs>\n#include <transmission_pars_fragment>\n#include <cube_uv_reflection_fragment>\n#include <envmap_common_pars_fragment>\n#include <envmap_physical_pars_fragment>\n#include <fog_pars_fragment>\n#include <lights_pars_begin>\n#include <lights_physical_pars_fragment>\n#include <shadowmap_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <clearcoat_pars_fragment>\n#include <roughnessmap_pars_fragment>\n#include <metalnessmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\tReflectedLight reflectedLight = ReflectedLight( vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ), vec3( 0.0 ) );\n\tvec3 totalEmissiveRadiance = emissive;\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <color_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\t#include <roughnessmap_fragment>\n\t#include <metalnessmap_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\t#include <clearcoat_normal_fragment_begin>\n\t#include <clearcoat_normal_fragment_maps>\n\t#include <emissivemap_fragment>\n\t#include <lights_physical_fragment>\n\t#include <lights_fragment_begin>\n\t#include <lights_fragment_maps>\n\t#include <lights_fragment_end>\n\t#include <aomap_fragment>\n\tvec3 totalDiffuse = reflectedLight.directDiffuse + reflectedLight.indirectDiffuse;\n\tvec3 totalSpecular = reflectedLight.directSpecular + reflectedLight.indirectSpecular;\n\t#include <transmission_fragment>\n\tvec3 outgoingLight = totalDiffuse + totalSpecular + totalEmissiveRadiance;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n\t#include <dithering_fragment>\n}",meshphysical_vert:"#define STANDARD\nvarying vec3 vViewPosition;\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#ifdef USE_TRANSMISSION\n\tvarying vec4 vWorldPosition;\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <uv2_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <shadowmap_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <uv2_vertex>\n\t#include <color_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\tvViewPosition = - mvPosition.xyz;\n\t#include <worldpos_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n#ifdef USE_TRANSMISSION\n\tvWorldPosition = worldPosition;\n#endif\n}",normal_frag:"#define NORMAL\nuniform float opacity;\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <packing>\n#include <uv_pars_fragment>\n#include <bumpmap_pars_fragment>\n#include <normalmap_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\t#include <logdepthbuf_fragment>\n\t#include <normal_fragment_begin>\n\t#include <normal_fragment_maps>\n\tgl_FragColor = vec4( packNormalToRGB( normal ), opacity );\n}",normal_vert:"#define NORMAL\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvarying vec3 vViewPosition;\n#endif\n#ifndef FLAT_SHADED\n\tvarying vec3 vNormal;\n\t#ifdef USE_TANGENT\n\t\tvarying vec3 vTangent;\n\t\tvarying vec3 vBitangent;\n\t#endif\n#endif\n#include <common>\n#include <uv_pars_vertex>\n#include <displacementmap_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <skinning_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n#ifndef FLAT_SHADED\n\tvNormal = normalize( transformedNormal );\n\t#ifdef USE_TANGENT\n\t\tvTangent = normalize( transformedTangent );\n\t\tvBitangent = normalize( cross( vNormal, vTangent ) * tangent.w );\n\t#endif\n#endif\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <skinning_vertex>\n\t#include <displacementmap_vertex>\n\t#include <project_vertex>\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n#if defined( FLAT_SHADED ) || defined( USE_BUMPMAP ) || defined( TANGENTSPACE_NORMALMAP )\n\tvViewPosition = - mvPosition.xyz;\n#endif\n}",points_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include <common>\n#include <color_pars_fragment>\n#include <map_particle_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_particle_fragment>\n\t#include <color_fragment>\n\t#include <alphatest_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n\t#include <premultiplied_alpha_fragment>\n}",points_vert:"uniform float size;\nuniform float scale;\n#include <common>\n#include <color_pars_vertex>\n#include <fog_pars_vertex>\n#include <morphtarget_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <color_vertex>\n\t#include <begin_vertex>\n\t#include <morphtarget_vertex>\n\t#include <project_vertex>\n\tgl_PointSize = size;\n\t#ifdef USE_SIZEATTENUATION\n\t\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\n\t\tif ( isPerspective ) gl_PointSize *= ( scale / - mvPosition.z );\n\t#endif\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <worldpos_vertex>\n\t#include <fog_vertex>\n}",shadow_frag:"uniform vec3 color;\nuniform float opacity;\n#include <common>\n#include <packing>\n#include <fog_pars_fragment>\n#include <bsdfs>\n#include <lights_pars_begin>\n#include <shadowmap_pars_fragment>\n#include <shadowmask_pars_fragment>\nvoid main() {\n\tgl_FragColor = vec4( color, opacity * ( 1.0 - getShadowMask() ) );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n}",shadow_vert:"#include <common>\n#include <fog_pars_vertex>\n#include <shadowmap_pars_vertex>\nvoid main() {\n\t#include <begin_vertex>\n\t#include <project_vertex>\n\t#include <worldpos_vertex>\n\t#include <beginnormal_vertex>\n\t#include <morphnormal_vertex>\n\t#include <skinbase_vertex>\n\t#include <skinnormal_vertex>\n\t#include <defaultnormal_vertex>\n\t#include <shadowmap_vertex>\n\t#include <fog_vertex>\n}",sprite_frag:"uniform vec3 diffuse;\nuniform float opacity;\n#include <common>\n#include <uv_pars_fragment>\n#include <map_pars_fragment>\n#include <alphamap_pars_fragment>\n#include <fog_pars_fragment>\n#include <logdepthbuf_pars_fragment>\n#include <clipping_planes_pars_fragment>\nvoid main() {\n\t#include <clipping_planes_fragment>\n\tvec3 outgoingLight = vec3( 0.0 );\n\tvec4 diffuseColor = vec4( diffuse, opacity );\n\t#include <logdepthbuf_fragment>\n\t#include <map_fragment>\n\t#include <alphamap_fragment>\n\t#include <alphatest_fragment>\n\toutgoingLight = diffuseColor.rgb;\n\tgl_FragColor = vec4( outgoingLight, diffuseColor.a );\n\t#include <tonemapping_fragment>\n\t#include <encodings_fragment>\n\t#include <fog_fragment>\n}",sprite_vert:"uniform float rotation;\nuniform vec2 center;\n#include <common>\n#include <uv_pars_vertex>\n#include <fog_pars_vertex>\n#include <logdepthbuf_pars_vertex>\n#include <clipping_planes_pars_vertex>\nvoid main() {\n\t#include <uv_vertex>\n\tvec4 mvPosition = modelViewMatrix * vec4( 0.0, 0.0, 0.0, 1.0 );\n\tvec2 scale;\n\tscale.x = length( vec3( modelMatrix[ 0 ].x, modelMatrix[ 0 ].y, modelMatrix[ 0 ].z ) );\n\tscale.y = length( vec3( modelMatrix[ 1 ].x, modelMatrix[ 1 ].y, modelMatrix[ 1 ].z ) );\n\t#ifndef USE_SIZEATTENUATION\n\t\tbool isPerspective = isPerspectiveMatrix( projectionMatrix );\n\t\tif ( isPerspective ) scale *= - mvPosition.z;\n\t#endif\n\tvec2 alignedPosition = ( position.xy - ( center - vec2( 0.5 ) ) ) * scale;\n\tvec2 rotatedPosition;\n\trotatedPosition.x = cos( rotation ) * alignedPosition.x - sin( rotation ) * alignedPosition.y;\n\trotatedPosition.y = sin( rotation ) * alignedPosition.x + cos( rotation ) * alignedPosition.y;\n\tmvPosition.xy += rotatedPosition;\n\tgl_Position = projectionMatrix * mvPosition;\n\t#include <logdepthbuf_vertex>\n\t#include <clipping_planes_vertex>\n\t#include <fog_vertex>\n}"},zst={common:{diffuse:{value:new Rrt(16777215)},opacity:{value:1},map:{value:null},uvTransform:{value:new $it},uv2Transform:{value:new $it},alphaMap:{value:null}},specularmap:{specularMap:{value:null}},envmap:{envMap:{value:null},flipEnvMap:{value:-1},reflectivity:{value:1},refractionRatio:{value:.98},maxMipLevel:{value:0}},aomap:{aoMap:{value:null},aoMapIntensity:{value:1}},lightmap:{lightMap:{value:null},lightMapIntensity:{value:1}},emissivemap:{emissiveMap:{value:null}},bumpmap:{bumpMap:{value:null},bumpScale:{value:1}},normalmap:{normalMap:{value:null},normalScale:{value:new Qit(1,1)}},displacementmap:{displacementMap:{value:null},displacementScale:{value:1},displacementBias:{value:0}},roughnessmap:{roughnessMap:{value:null}},metalnessmap:{metalnessMap:{value:null}},gradientmap:{gradientMap:{value:null}},fog:{fogDensity:{value:25e-5},fogNear:{value:1},fogFar:{value:2e3},fogColor:{value:new Rrt(16777215)}},lights:{ambientLightColor:{value:[]},lightProbe:{value:[]},directionalLights:{value:[],properties:{direction:{},color:{}}},directionalLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},directionalShadowMap:{value:[]},directionalShadowMatrix:{value:[]},spotLights:{value:[],properties:{color:{},position:{},direction:{},distance:{},coneCos:{},penumbraCos:{},decay:{}}},spotLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{}}},spotShadowMap:{value:[]},spotShadowMatrix:{value:[]},pointLights:{value:[],properties:{color:{},position:{},decay:{},distance:{}}},pointLightShadows:{value:[],properties:{shadowBias:{},shadowNormalBias:{},shadowRadius:{},shadowMapSize:{},shadowCameraNear:{},shadowCameraFar:{}}},pointShadowMap:{value:[]},pointShadowMatrix:{value:[]},hemisphereLights:{value:[],properties:{direction:{},skyColor:{},groundColor:{}}},rectAreaLights:{value:[],properties:{color:{},position:{},width:{},height:{}}},ltc_1:{value:null},ltc_2:{value:null}},points:{diffuse:{value:new Rrt(16777215)},opacity:{value:1},size:{value:1},scale:{value:1},map:{value:null},alphaMap:{value:null},uvTransform:{value:new $it}},sprite:{diffuse:{value:new Rrt(16777215)},opacity:{value:1},center:{value:new Qit(.5,.5)},rotation:{value:0},map:{value:null},alphaMap:{value:null},uvTransform:{value:new $it}}},Ist={basic:{uniforms:gst([zst.common,zst.specularmap,zst.envmap,zst.aomap,zst.lightmap,zst.fog]),vertexShader:Nst.meshbasic_vert,fragmentShader:Nst.meshbasic_frag},lambert:{uniforms:gst([zst.common,zst.specularmap,zst.envmap,zst.aomap,zst.lightmap,zst.emissivemap,zst.fog,zst.lights,{emissive:{value:new Rrt(0)}}]),vertexShader:Nst.meshlambert_vert,fragmentShader:Nst.meshlambert_frag},phong:{uniforms:gst([zst.common,zst.specularmap,zst.envmap,zst.aomap,zst.lightmap,zst.emissivemap,zst.bumpmap,zst.normalmap,zst.displacementmap,zst.fog,zst.lights,{emissive:{value:new Rrt(0)},specular:{value:new Rrt(1118481)},shininess:{value:30}}]),vertexShader:Nst.meshphong_vert,fragmentShader:Nst.meshphong_frag},standard:{uniforms:gst([zst.common,zst.envmap,zst.aomap,zst.lightmap,zst.emissivemap,zst.bumpmap,zst.normalmap,zst.displacementmap,zst.roughnessmap,zst.metalnessmap,zst.fog,zst.lights,{emissive:{value:new Rrt(0)},roughness:{value:1},metalness:{value:0},envMapIntensity:{value:1}}]),vertexShader:Nst.meshphysical_vert,fragmentShader:Nst.meshphysical_frag},toon:{uniforms:gst([zst.common,zst.aomap,zst.lightmap,zst.emissivemap,zst.bumpmap,zst.normalmap,zst.displacementmap,zst.gradientmap,zst.fog,zst.lights,{emissive:{value:new Rrt(0)}}]),vertexShader:Nst.meshtoon_vert,fragmentShader:Nst.meshtoon_frag},matcap:{uniforms:gst([zst.common,zst.bumpmap,zst.normalmap,zst.displacementmap,zst.fog,{matcap:{value:null}}]),vertexShader:Nst.meshmatcap_vert,fragmentShader:Nst.meshmatcap_frag},points:{uniforms:gst([zst.points,zst.fog]),vertexShader:Nst.points_vert,fragmentShader:Nst.points_frag},dashed:{uniforms:gst([zst.common,zst.fog,{scale:{value:1},dashSize:{value:1},totalSize:{value:2}}]),vertexShader:Nst.linedashed_vert,fragmentShader:Nst.linedashed_frag},depth:{uniforms:gst([zst.common,zst.displacementmap]),vertexShader:Nst.depth_vert,fragmentShader:Nst.depth_frag},normal:{uniforms:gst([zst.common,zst.bumpmap,zst.normalmap,zst.displacementmap,{opacity:{value:1}}]),vertexShader:Nst.normal_vert,fragmentShader:Nst.normal_frag},sprite:{uniforms:gst([zst.sprite,zst.fog]),vertexShader:Nst.sprite_vert,fragmentShader:Nst.sprite_frag},background:{uniforms:{uvTransform:{value:new $it},t2D:{value:null}},vertexShader:Nst.background_vert,fragmentShader:Nst.background_frag},cube:{uniforms:gst([zst.envmap,{opacity:{value:1}}]),vertexShader:Nst.cube_vert,fragmentShader:Nst.cube_frag},equirect:{uniforms:{tEquirect:{value:null}},vertexShader:Nst.equirect_vert,fragmentShader:Nst.equirect_frag},distanceRGBA:{uniforms:gst([zst.common,zst.displacementmap,{referencePosition:{value:new cat},nearDistance:{value:1},farDistance:{value:1e3}}]),vertexShader:Nst.distanceRGBA_vert,fragmentShader:Nst.distanceRGBA_frag},shadow:{uniforms:gst([zst.lights,zst.fog,{color:{value:new Rrt(0)},opacity:{value:1}}]),vertexShader:Nst.shadow_vert,fragmentShader:Nst.shadow_frag}};function Hst(t,e,n,o,i){const a=new Rrt(0);let r,s,l=0,c=null,d=0,p=null;function m(t,e){n.buffers.color.setClear(t.r,t.g,t.b,e,i)}return{getClearColor:function(){return a},setClearColor:function(t,e=1){a.set(t),l=e,m(a,l)},getClearAlpha:function(){return l},setClearAlpha:function(t){l=t,m(a,l)},render:function u(n,i){let u=!1,f=!0===i.isScene?i.background:null;f&&f.isTexture&&(f=e.get(f));const g=t.xr,h=g.getSession&&g.getSession();h&&"additive"===h.environmentBlendMode&&(f=null),null===f?m(a,l):f&&f.isColor&&(m(f,1),u=!0),(t.autoClear||u)&&t.clear(t.autoClearColor,t.autoClearDepth,t.autoClearStencil),f&&(f.isCubeTexture||f.mapping===mit)?(void 0===s&&(s=new pst(new ust(1,1,1),new bst({name:"BackgroundCubeMaterial",uniforms:fst(Ist.cube.uniforms),vertexShader:Ist.cube.vertexShader,fragmentShader:Ist.cube.fragmentShader,side:1,depthTest:!1,depthWrite:!1,fog:!1})),s.geometry.deleteAttribute("normal"),s.geometry.deleteAttribute("uv"),s.onBeforeRender=function(t,e,n){this.matrixWorld.copyPosition(n.matrixWorld)},Object.defineProperty(s.material,"envMap",{get:function(){return this.uniforms.envMap.value}}),o.update(s)),s.material.uniforms.envMap.value=f,s.material.uniforms.flipEnvMap.value=f.isCubeTexture&&!1===f.isRenderTargetTexture?-1:1,c===f&&d===f.version&&p===t.toneMapping||(s.material.needsUpdate=!0,c=f,d=f.version,p=t.toneMapping),n.unshift(s,s.geometry,s.material,0,0,null)):f&&f.isTexture&&(void 0===r&&(r=new pst(new Tst(2,2),new bst({name:"BackgroundMaterial",uniforms:fst(Ist.background.uniforms),vertexShader:Ist.background.vertexShader,fragmentShader:Ist.background.fragmentShader,side:0,depthTest:!1,depthWrite:!1,fog:!1})),r.geometry.deleteAttribute("normal"),Object.defineProperty(r.material,"map",{get:function(){return this.uniforms.t2D.value}}),o.update(r)),r.material.uniforms.t2D.value=f,!0===f.matrixAutoUpdate&&f.updateMatrix(),r.material.uniforms.uvTransform.value.copy(f.matrix),c===f&&d===f.version&&p===t.toneMapping||(r.material.needsUpdate=!0,c=f,d=f.version,p=t.toneMapping),n.unshift(r,r.geometry,r.material,0,0,null))}}}function Fst(t,e,n,o){const i=t.getParameter(34921),a=o.isWebGL2?null:e.get("OES_vertex_array_object"),r=o.isWebGL2||null!==a,s={},l=m(null);let c=l;function d(e){return o.isWebGL2?t.bindVertexArray(e):a.bindVertexArrayOES(e)}function p(e){return o.isWebGL2?t.deleteVertexArray(e):a.deleteVertexArrayOES(e)}function m(t){const e=[],n=[],o=[];for(let t=0;t<i;t++)e[t]=0,n[t]=0,o[t]=0;return{geometry:null,program:null,wireframe:!1,newAttributes:e,enabledAttributes:n,attributeDivisors:o,object:t,attributes:{},index:null}}function u(){const t=c.newAttributes;for(let e=0,n=t.length;e<n;e++)t[e]=0}function f(t){g(t,0)}function g(n,i){const a=c.enabledAttributes,r=c.attributeDivisors;c.newAttributes[n]=1,0===a[n]&&(t.enableVertexAttribArray(n),a[n]=1),r[n]!==i&&((o.isWebGL2?t:e.get("ANGLE_instanced_arrays"))[o.isWebGL2?"vertexAttribDivisor":"vertexAttribDivisorANGLE"](n,i),r[n]=i)}function h(){const e=c.newAttributes,n=c.enabledAttributes;for(let o=0,i=n.length;o<i;o++)n[o]!==e[o]&&(t.disableVertexAttribArray(o),n[o]=0)}function b(e,n,i,a,r,s){!0!==o.isWebGL2||5124!==i&&5125!==i?t.vertexAttribPointer(e,n,i,a,r,s):t.vertexAttribIPointer(e,n,i,r,s)}function y(){_(),c!==l&&(c=l,d(c.object))}function _(){l.geometry=null,l.program=null,l.wireframe=!1}return{setup:function C(i,l,p,y,_){let C=!1;if(r){const e=(function M(e,n,i){const r=!0===i.wireframe;let l=s[e.id];void 0===l&&(l={},s[e.id]=l);let c=l[n.id];void 0===c&&(c={},l[n.id]=c);let d=c[r];return void 0===d&&(d=m((function p(){return o.isWebGL2?t.createVertexArray():a.createVertexArrayOES()})()),c[r]=d),d})(y,p,l);c!==e&&(c=e,d(c.object)),C=(function v(t,e){const n=c.attributes,o=t.attributes;let i=0;for(const t in o){const e=n[t],a=o[t];if(void 0===e)return!0;if(e.attribute!==a)return!0;if(e.data!==a.data)return!0;i++}return c.attributesNum!==i||c.index!==e})(y,_),C&&(function x(t,e){const n={},o=t.attributes;let i=0;for(const t in o){const e=o[t],a={};a.attribute=e,e.data&&(a.data=e.data),n[t]=a,i++}c.attributes=n,c.attributesNum=i,c.index=e})(y,_)}else{const t=!0===l.wireframe;c.geometry===y.id&&c.program===p.id&&c.wireframe===t||(c.geometry=y.id,c.program=p.id,c.wireframe=t,C=!0)}!0===i.isInstancedMesh&&(C=!0),null!==_&&n.update(_,34963),C&&((function O(i,a,r,s){if(!1===o.isWebGL2&&(i.isInstancedMesh||s.isInstancedBufferGeometry)&&null===e.get("ANGLE_instanced_arrays"))return;u();const l=s.attributes,c=r.getAttributes(),d=a.defaultAttributeValues;for(const e in c){const o=c[e];if(o>=0){const a=l[e];if(void 0!==a){const e=a.normalized,i=a.itemSize,r=n.get(a);if(void 0===r)continue;const l=r.buffer,c=r.type,d=r.bytesPerElement;if(a.isInterleavedBufferAttribute){const n=a.data,r=n.stride,p=a.offset;n&&n.isInstancedInterleavedBuffer?(g(o,n.meshPerAttribute),void 0===s._maxInstanceCount&&(s._maxInstanceCount=n.meshPerAttribute*n.count)):f(o),t.bindBuffer(34962,l),b(o,i,c,e,r*d,p*d)}else a.isInstancedBufferAttribute?(g(o,a.meshPerAttribute),void 0===s._maxInstanceCount&&(s._maxInstanceCount=a.meshPerAttribute*a.count)):f(o),t.bindBuffer(34962,l),b(o,i,c,e,0,0)}else if("instanceMatrix"===e){const e=n.get(i.instanceMatrix);if(void 0===e)continue;const a=e.buffer,r=e.type;g(o+0,1),g(o+1,1),g(o+2,1),g(o+3,1),t.bindBuffer(34962,a),t.vertexAttribPointer(o+0,4,r,!1,64,0),t.vertexAttribPointer(o+1,4,r,!1,64,16),t.vertexAttribPointer(o+2,4,r,!1,64,32),t.vertexAttribPointer(o+3,4,r,!1,64,48)}else if("instanceColor"===e){const e=n.get(i.instanceColor);if(void 0===e)continue;const a=e.buffer,r=e.type;g(o,1),t.bindBuffer(34962,a),t.vertexAttribPointer(o,3,r,!1,12,0)}else if(void 0!==d){const n=d[e];if(void 0!==n)switch(n.length){case 2:t.vertexAttrib2fv(o,n);break;case 3:t.vertexAttrib3fv(o,n);break;case 4:t.vertexAttrib4fv(o,n);break;default:t.vertexAttrib1fv(o,n)}}}}h()})(i,l,p,y),null!==_&&t.bindBuffer(34963,n.get(_).buffer))},reset:y,resetDefaultState:_,dispose:function M(){y();for(const t in s){const e=s[t];for(const t in e){const n=e[t];for(const t in n)p(n[t].object),delete n[t];delete e[t]}delete s[t]}},releaseStatesOfGeometry:function v(t){if(void 0===s[t.id])return;const e=s[t.id];for(const t in e){const n=e[t];for(const t in n)p(n[t].object),delete n[t];delete e[t]}delete s[t.id]},releaseStatesOfProgram:function x(t){for(const e in s){const n=s[e];if(void 0===n[t.id])continue;const o=n[t.id];for(const t in o)p(o[t].object),delete o[t];delete n[t.id]}},initAttributes:u,enableAttribute:f,disableUnusedAttributes:h}}function Lst(t,e,n,o){const i=o.isWebGL2;let a;this.setMode=function r(t){a=t},this.render=function s(e,o){t.drawArrays(a,e,o),n.update(o,a,1)},this.renderInstances=function l(o,r,s){if(0===s)return;let l,c;if(i)l=t,c="drawArraysInstanced";else if(l=e.get("ANGLE_instanced_arrays"),c="drawArraysInstancedANGLE",null===l)return void console.error("THREE.WebGLBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.");l[c](a,o,r,s),n.update(r,a,s)}}function Bst(t,e,n){let o;function i(e){if("highp"===e){if(t.getShaderPrecisionFormat(35633,36338).precision>0&&t.getShaderPrecisionFormat(35632,36338).precision>0)return"highp";e="mediump"}return"mediump"===e&&t.getShaderPrecisionFormat(35633,36337).precision>0&&t.getShaderPrecisionFormat(35632,36337).precision>0?"mediump":"lowp"}const a="undefined"!=typeof WebGL2RenderingContext&&t instanceof WebGL2RenderingContext||"undefined"!=typeof WebGL2ComputeRenderingContext&&t instanceof WebGL2ComputeRenderingContext;let r=void 0!==n.precision?n.precision:"highp";const s=i(r);s!==r&&(console.warn("THREE.WebGLRenderer:",r,"not supported, using",s,"instead."),r=s);const l=a||e.has("WEBGL_draw_buffers"),c=!0===n.logarithmicDepthBuffer,d=t.getParameter(34930),p=t.getParameter(35660),m=t.getParameter(3379),u=t.getParameter(34076),f=t.getParameter(34921),g=t.getParameter(36347),h=t.getParameter(36348),b=t.getParameter(36349),y=p>0,_=a||e.has("OES_texture_float");return{isWebGL2:a,drawBuffers:l,getMaxAnisotropy:function C(){if(void 0!==o)return o;if(!0===e.has("EXT_texture_filter_anisotropic")){const n=e.get("EXT_texture_filter_anisotropic");o=t.getParameter(n.MAX_TEXTURE_MAX_ANISOTROPY_EXT)}else o=0;return o},getMaxPrecision:i,precision:r,logarithmicDepthBuffer:c,maxTextures:d,maxVertexTextures:p,maxTextureSize:m,maxCubemapSize:u,maxAttributes:f,maxVertexUniforms:g,maxVaryings:h,maxFragmentUniforms:b,vertexTextures:y,floatFragmentTextures:_,floatVertexTextures:y&&_,maxSamples:a?t.getParameter(36183):0}}function Vst(t){const e=this;let n=null,o=0,i=!1,a=!1;const r=new kst,s=new $it,l={value:null,needsUpdate:!1};function c(){l.value!==n&&(l.value=n,l.needsUpdate=o>0),e.numPlanes=o,e.numIntersection=0}function d(t,n,o,i){const a=null!==t?t.length:0;let c=null;if(0!==a){if(c=l.value,!0!==i||null===c){const e=o+4*a,i=n.matrixWorldInverse;s.getNormalMatrix(i),(null===c||c.length<e)&&(c=new Float32Array(e));for(let e=0,n=o;e!==a;++e,n+=4)r.copy(t[e]).applyMatrix4(i,s),r.normal.toArray(c,n),c[n+3]=r.constant}l.value=c,l.needsUpdate=!0}return e.numPlanes=a,e.numIntersection=0,c}this.uniform=l,this.numPlanes=0,this.numIntersection=0,this.init=function(t,e,a){const r=0!==t.length||e||0!==o||i;return i=e,n=d(t,a,0),o=t.length,r},this.beginShadows=function(){a=!0,d(null)},this.endShadows=function(){a=!1,c()},this.setState=function(e,r,s){const p=e.clippingPlanes,m=e.clipIntersection,u=e.clipShadows,f=t.get(e);if(!i||null===p||0===p.length||a&&!u)a?d(null):c();else{const t=a?0:o,e=4*t;let i=f.clippingState||null;l.value=i,i=d(p,r,e,s);for(let t=0;t!==e;++t)i[t]=n[t];f.clippingState=i,this.numIntersection=m?this.numPlanes:0,this.numPlanes+=t}}}function jst(t){let e=new WeakMap;function n(t,e){return 303===e?t.mapping=dit:304===e&&(t.mapping=pit),t}function o(t){const n=t.target;n.removeEventListener("dispose",o);const i=e.get(n);void 0!==i&&(e.delete(n),i.dispose())}return{get:function i(a){if(a&&a.isTexture&&!1===a.isRenderTargetTexture){const i=a.mapping;if(303===i||304===i){if(e.has(a))return n(e.get(a).texture,a.mapping);{const i=a.image;if(i&&i.height>0){const r=t.getRenderTarget(),s=new xst(i.height/2);return s.fromEquirectangularTexture(t,a),e.set(a,s),t.setRenderTarget(r),a.addEventListener("dispose",o),n(s.texture,a.mapping)}return null}}}return a},dispose:function a(){e=new WeakMap}}}Ist.physical={uniforms:gst([Ist.standard.uniforms,{clearcoat:{value:0},clearcoatMap:{value:null},clearcoatRoughness:{value:0},clearcoatRoughnessMap:{value:null},clearcoatNormalScale:{value:new Qit(1,1)},clearcoatNormalMap:{value:null},sheen:{value:new Rrt(0)},transmission:{value:0},transmissionMap:{value:null},transmissionSamplerSize:{value:new Qit},transmissionSamplerMap:{value:null},thickness:{value:0},thicknessMap:{value:null},attenuationDistance:{value:0},attenuationTint:{value:new Rrt(0)},specularIntensity:{value:0},specularIntensityMap:{value:null},specularTint:{value:new Rrt(1,1,1)},specularTintMap:{value:null}}]),vertexShader:Nst.meshphysical_vert,fragmentShader:Nst.meshphysical_frag};class Ust extends yst{constructor(t=-1,e=1,n=1,o=-1,i=.1,a=2e3){super(),this.type="OrthographicCamera",this.zoom=1,this.view=null,this.left=t,this.right=e,this.top=n,this.bottom=o,this.near=i,this.far=a,this.updateProjectionMatrix()}copy(t,e){return super.copy(t,e),this.left=t.left,this.right=t.right,this.top=t.top,this.bottom=t.bottom,this.near=t.near,this.far=t.far,this.zoom=t.zoom,this.view=null===t.view?null:Object.assign({},t.view),this}setViewOffset(t,e,n,o,i,a){null===this.view&&(this.view={enabled:!0,fullWidth:1,fullHeight:1,offsetX:0,offsetY:0,width:1,height:1}),this.view.enabled=!0,this.view.fullWidth=t,this.view.fullHeight=e,this.view.offsetX=n,this.view.offsetY=o,this.view.width=i,this.view.height=a,this.updateProjectionMatrix()}clearViewOffset(){null!==this.view&&(this.view.enabled=!1),this.updateProjectionMatrix()}updateProjectionMatrix(){const t=(this.right-this.left)/(2*this.zoom),e=(this.top-this.bottom)/(2*this.zoom),n=(this.right+this.left)/2,o=(this.top+this.bottom)/2;let i=n-t,a=n+t,r=o+e,s=o-e;if(null!==this.view&&this.view.enabled){const t=(this.right-this.left)/this.view.fullWidth/this.zoom,e=(this.top-this.bottom)/this.view.fullHeight/this.zoom;i+=t*this.view.offsetX,a=i+t*this.view.width,r-=e*this.view.offsetY,s=r-e*this.view.height}this.projectionMatrix.makeOrthographic(i,a,r,s,this.near,this.far),this.projectionMatrixInverse.copy(this.projectionMatrix).invert()}toJSON(t){const e=super.toJSON(t);return e.object.zoom=this.zoom,e.object.left=this.left,e.object.right=this.right,e.object.top=this.top,e.object.bottom=this.bottom,e.object.near=this.near,e.object.far=this.far,null!==this.view&&(e.object.view=Object.assign({},this.view)),e}}Ust.prototype.isOrthographicCamera=!0;class Gst extends bst{constructor(t){super(t),this.type="RawShaderMaterial"}}Gst.prototype.isRawShaderMaterial=!0;const Wst=Math.pow(2,8),Yst=[.125,.215,.35,.446,.526,.582],qst=5+Yst.length,Zst={[zit]:0,[Iit]:1,[Fit]:2,3004:3,3005:4,3006:5,[Hit]:6},Xst=new Art({side:1,depthWrite:!1,depthTest:!1}),Kst=new pst(new ust,Xst),Jst=new Ust,{_lodPlanes:Qst,_sizeLods:$st,_sigmas:tlt}=clt(),elt=new Rrt;let nlt=null;const olt=(1+Math.sqrt(5))/2,ilt=1/olt,alt=[new cat(1,1,1),new cat(-1,1,1),new cat(1,1,-1),new cat(-1,1,-1),new cat(0,olt,ilt),new cat(0,olt,-ilt),new cat(ilt,0,olt),new cat(-ilt,0,olt),new cat(olt,ilt,0),new cat(-olt,ilt,0)];function rlt(t){const e=Math.max(t.r,t.g,t.b),n=Math.min(Math.max(Math.ceil(Math.log2(e)),-128),127);return t.multiplyScalar(Math.pow(2,-n)),(n+128)/255}class slt{constructor(t){this._renderer=t,this._pingPongRenderTarget=null,this._blurMaterial=(function e(t){const e=new Float32Array(t),n=new cat(0,1,0);return new Gst({name:"SphericalGaussianBlur",defines:{n:t},uniforms:{envMap:{value:null},samples:{value:1},weights:{value:e},latitudinal:{value:!1},dTheta:{value:0},mipInt:{value:0},poleAxis:{value:n},inputEncoding:{value:Zst[3e3]},outputEncoding:{value:Zst[3e3]}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute vec3 position;\n\t\tattribute vec2 uv;\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\t\t\tuniform int samples;\n\t\t\tuniform float weights[ n ];\n\t\t\tuniform bool latitudinal;\n\t\t\tuniform float dTheta;\n\t\t\tuniform float mipInt;\n\t\t\tuniform vec3 poleAxis;\n\n\t\t\t\n\n\t\tuniform int inputEncoding;\n\t\tuniform int outputEncoding;\n\n\t\t#include <encodings_pars_fragment>\n\n\t\tvec4 inputTexelToLinear( vec4 value ) {\n\n\t\t\tif ( inputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( inputEncoding == 1 ) {\n\n\t\t\t\treturn sRGBToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 2 ) {\n\n\t\t\t\treturn RGBEToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 3 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 7.0 );\n\n\t\t\t} else if ( inputEncoding == 4 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 16.0 );\n\n\t\t\t} else if ( inputEncoding == 5 ) {\n\n\t\t\t\treturn RGBDToLinear( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn GammaToLinear( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 linearToOutputTexel( vec4 value ) {\n\n\t\t\tif ( outputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( outputEncoding == 1 ) {\n\n\t\t\t\treturn LinearTosRGB( value );\n\n\t\t\t} else if ( outputEncoding == 2 ) {\n\n\t\t\t\treturn LinearToRGBE( value );\n\n\t\t\t} else if ( outputEncoding == 3 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 7.0 );\n\n\t\t\t} else if ( outputEncoding == 4 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 16.0 );\n\n\t\t\t} else if ( outputEncoding == 5 ) {\n\n\t\t\t\treturn LinearToRGBD( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn LinearToGamma( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 envMapTexelToLinear( vec4 color ) {\n\n\t\t\treturn inputTexelToLinear( color );\n\n\t\t}\n\t\n\n\t\t\t#define ENVMAP_TYPE_CUBE_UV\n\t\t\t#include <cube_uv_reflection_fragment>\n\n\t\t\tvec3 getSample( float theta, vec3 axis ) {\n\n\t\t\t\tfloat cosTheta = cos( theta );\n\t\t\t\t// Rodrigues' axis-angle rotation\n\t\t\t\tvec3 sampleDirection = vOutputDirection * cosTheta\n\t\t\t\t\t+ cross( axis, vOutputDirection ) * sin( theta )\n\t\t\t\t\t+ axis * dot( axis, vOutputDirection ) * ( 1.0 - cosTheta );\n\n\t\t\t\treturn bilinearCubeUV( envMap, sampleDirection, mipInt );\n\n\t\t\t}\n\n\t\t\tvoid main() {\n\n\t\t\t\tvec3 axis = latitudinal ? poleAxis : cross( poleAxis, vOutputDirection );\n\n\t\t\t\tif ( all( equal( axis, vec3( 0.0 ) ) ) ) {\n\n\t\t\t\t\taxis = vec3( vOutputDirection.z, 0.0, - vOutputDirection.x );\n\n\t\t\t\t}\n\n\t\t\t\taxis = normalize( axis );\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t\t\t\tgl_FragColor.rgb += weights[ 0 ] * getSample( 0.0, axis );\n\n\t\t\t\tfor ( int i = 1; i < n; i++ ) {\n\n\t\t\t\t\tif ( i >= samples ) {\n\n\t\t\t\t\t\tbreak;\n\n\t\t\t\t\t}\n\n\t\t\t\t\tfloat theta = dTheta * float( i );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( -1.0 * theta, axis );\n\t\t\t\t\tgl_FragColor.rgb += weights[ i ] * getSample( theta, axis );\n\n\t\t\t\t}\n\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t",blending:0,depthTest:!1,depthWrite:!1})})(20),this._equirectShader=null,this._cubemapShader=null,this._compileMaterial(this._blurMaterial)}fromScene(t,e=0,n=.1,o=100){nlt=this._renderer.getRenderTarget();const i=this._allocateTargets();return this._sceneToCubeUV(t,n,o,i),e>0&&this._blur(i,0,0,e),this._applyPMREM(i),this._cleanup(i),i}fromEquirectangular(t){return this._fromTexture(t)}fromCubemap(t){return this._fromTexture(t)}compileCubemapShader(){null===this._cubemapShader&&(this._cubemapShader=ult(),this._compileMaterial(this._cubemapShader))}compileEquirectangularShader(){null===this._equirectShader&&(this._equirectShader=mlt(),this._compileMaterial(this._equirectShader))}dispose(){this._blurMaterial.dispose(),null!==this._cubemapShader&&this._cubemapShader.dispose(),null!==this._equirectShader&&this._equirectShader.dispose();for(let t=0;t<Qst.length;t++)Qst[t].dispose()}_cleanup(t){this._pingPongRenderTarget.dispose(),this._renderer.setRenderTarget(nlt),t.scissorTest=!1,plt(t,0,0,t.width,t.height)}_fromTexture(t){nlt=this._renderer.getRenderTarget();const e=this._allocateTargets(t);return this._textureToCubeUV(t,e),this._applyPMREM(e),this._cleanup(e),e}_allocateTargets(t){const e={magFilter:hit,minFilter:hit,generateMipmaps:!1,type:_it,format:1023,encoding:llt(t)?t.encoding:Fit,depthBuffer:!1},n=dlt(e);return n.depthBuffer=!t,this._pingPongRenderTarget=dlt(e),n}_compileMaterial(t){const e=new pst(Qst[0],t);this._renderer.compile(e,Jst)}_sceneToCubeUV(t,e,n,o){const i=new _st(90,1,e,n),a=[1,-1,1,1,1,1],r=[1,1,1,-1,-1,-1],s=this._renderer,l=s.autoClear,c=s.outputEncoding,d=s.toneMapping;s.getClearColor(elt),s.toneMapping=0,s.outputEncoding=zit,s.autoClear=!1;let p=!1;const m=t.background;if(m){if(m.isColor){Xst.color.copy(m).convertSRGBToLinear(),t.background=null;const e=rlt(Xst.color);Xst.opacity=e,p=!0}}else{Xst.color.copy(elt).convertSRGBToLinear();const t=rlt(Xst.color);Xst.opacity=t,p=!0}for(let e=0;e<6;e++){const n=e%3;0==n?(i.up.set(0,a[e],0),i.lookAt(r[e],0,0)):1==n?(i.up.set(0,0,a[e]),i.lookAt(0,r[e],0)):(i.up.set(0,a[e],0),i.lookAt(0,0,r[e])),plt(o,n*Wst,e>2?Wst:0,Wst,Wst),s.setRenderTarget(o),p&&s.render(Kst,i),s.render(t,i)}s.toneMapping=d,s.outputEncoding=c,s.autoClear=l}_textureToCubeUV(t,e){const n=this._renderer;t.isCubeTexture?null==this._cubemapShader&&(this._cubemapShader=ult()):null==this._equirectShader&&(this._equirectShader=mlt());const o=t.isCubeTexture?this._cubemapShader:this._equirectShader,i=new pst(Qst[0],o),a=o.uniforms;a.envMap.value=t,t.isCubeTexture||a.texelSize.value.set(1/t.image.width,1/t.image.height),a.inputEncoding.value=Zst[t.encoding],a.outputEncoding.value=Zst[e.texture.encoding],plt(e,0,0,3*Wst,2*Wst),n.setRenderTarget(e),n.render(i,Jst)}_applyPMREM(t){const e=this._renderer,n=e.autoClear;e.autoClear=!1;for(let e=1;e<qst;e++){const n=Math.sqrt(tlt[e]*tlt[e]-tlt[e-1]*tlt[e-1]);this._blur(t,e-1,e,n,alt[(e-1)%alt.length])}e.autoClear=n}_blur(t,e,n,o,i){const a=this._pingPongRenderTarget;this._halfBlur(t,a,e,n,o,"latitudinal",i),this._halfBlur(a,t,n,n,o,"longitudinal",i)}_halfBlur(t,e,n,o,i,a,r){const s=this._renderer,l=this._blurMaterial;"latitudinal"!==a&&"longitudinal"!==a&&console.error("blur direction must be either latitudinal or longitudinal!");const c=new pst(Qst[o],l),d=l.uniforms,p=$st[n]-1,m=isFinite(i)?Math.PI/(2*p):2*Math.PI/39,u=i/m,f=isFinite(i)?1+Math.floor(3*u):20;f>20&&console.warn(`sigmaRadians, ${i}, is too large and will clip, as it requested ${f} samples when the maximum is set to 20`);const g=[];let h=0;for(let t=0;t<20;++t){const e=t/u,n=Math.exp(-e*e/2);g.push(n),0==t?h+=n:t<f&&(h+=2*n)}for(let t=0;t<g.length;t++)g[t]=g[t]/h;d.envMap.value=t.texture,d.samples.value=f,d.weights.value=g,d.latitudinal.value="latitudinal"===a,r&&(d.poleAxis.value=r),d.dTheta.value=m,d.mipInt.value=8-n,d.inputEncoding.value=Zst[t.texture.encoding],d.outputEncoding.value=Zst[t.texture.encoding];const b=$st[o];plt(e,3*Math.max(0,Wst-2*b),(0===o?0:2*Wst)+2*b*(o>4?o-8+4:0),3*b,2*b),s.setRenderTarget(e),s.render(c,Jst)}}function llt(t){return void 0!==t&&t.type===_it&&(t.encoding===zit||t.encoding===Iit||t.encoding===Hit)}function clt(){const t=[],e=[],n=[];let o=8;for(let i=0;i<qst;i++){const a=Math.pow(2,o);e.push(a);let r=1/a;i>4?r=Yst[i-8+4-1]:0==i&&(r=0),n.push(r);const s=1/(a-1),l=-s/2,c=1+s/2,d=[l,l,c,l,c,c,l,l,c,c,l,c],p=6,m=6,u=3,f=2,g=1,h=new Float32Array(u*m*p),b=new Float32Array(f*m*p),y=new Float32Array(g*m*p);for(let t=0;t<p;t++){const e=t%3*2/3-1,n=t>2?0:-1;h.set([e,n,0,e+2/3,n,0,e+2/3,n+1,0,e,n,0,e+2/3,n+1,0,e,n+1,0],u*m*t),b.set(d,f*m*t),y.set([t,t,t,t,t,t],g*m*t)}const _=new qrt;_.setAttribute("position",new zrt(h,u)),_.setAttribute("uv",new zrt(b,f)),_.setAttribute("faceIndex",new zrt(y,g)),t.push(_),o>4&&o--}return{_lodPlanes:t,_sizeLods:e,_sigmas:n}}function dlt(t){const e=new rat(3*Wst,3*Wst,t);return e.texture.mapping=mit,e.texture.name="PMREM.cubeUv",e.scissorTest=!0,e}function plt(t,e,n,o,i){t.viewport.set(e,n,o,i),t.scissor.set(e,n,o,i)}function mlt(){const t=new Qit(1,1);return new Gst({name:"EquirectangularToCubeUV",uniforms:{envMap:{value:null},texelSize:{value:t},inputEncoding:{value:Zst[3e3]},outputEncoding:{value:Zst[3e3]}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute vec3 position;\n\t\tattribute vec2 uv;\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform sampler2D envMap;\n\t\t\tuniform vec2 texelSize;\n\n\t\t\t\n\n\t\tuniform int inputEncoding;\n\t\tuniform int outputEncoding;\n\n\t\t#include <encodings_pars_fragment>\n\n\t\tvec4 inputTexelToLinear( vec4 value ) {\n\n\t\t\tif ( inputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( inputEncoding == 1 ) {\n\n\t\t\t\treturn sRGBToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 2 ) {\n\n\t\t\t\treturn RGBEToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 3 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 7.0 );\n\n\t\t\t} else if ( inputEncoding == 4 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 16.0 );\n\n\t\t\t} else if ( inputEncoding == 5 ) {\n\n\t\t\t\treturn RGBDToLinear( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn GammaToLinear( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 linearToOutputTexel( vec4 value ) {\n\n\t\t\tif ( outputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( outputEncoding == 1 ) {\n\n\t\t\t\treturn LinearTosRGB( value );\n\n\t\t\t} else if ( outputEncoding == 2 ) {\n\n\t\t\t\treturn LinearToRGBE( value );\n\n\t\t\t} else if ( outputEncoding == 3 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 7.0 );\n\n\t\t\t} else if ( outputEncoding == 4 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 16.0 );\n\n\t\t\t} else if ( outputEncoding == 5 ) {\n\n\t\t\t\treturn LinearToRGBD( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn LinearToGamma( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 envMapTexelToLinear( vec4 color ) {\n\n\t\t\treturn inputTexelToLinear( color );\n\n\t\t}\n\t\n\n\t\t\t#include <common>\n\n\t\t\tvoid main() {\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\n\t\t\t\tvec3 outputDirection = normalize( vOutputDirection );\n\t\t\t\tvec2 uv = equirectUv( outputDirection );\n\n\t\t\t\tvec2 f = fract( uv / texelSize - 0.5 );\n\t\t\t\tuv -= f * texelSize;\n\t\t\t\tvec3 tl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.x += texelSize.x;\n\t\t\t\tvec3 tr = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.y += texelSize.y;\n\t\t\t\tvec3 br = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\t\t\t\tuv.x -= texelSize.x;\n\t\t\t\tvec3 bl = envMapTexelToLinear( texture2D ( envMap, uv ) ).rgb;\n\n\t\t\t\tvec3 tm = mix( tl, tr, f.x );\n\t\t\t\tvec3 bm = mix( bl, br, f.x );\n\t\t\t\tgl_FragColor.rgb = mix( tm, bm, f.y );\n\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t",blending:0,depthTest:!1,depthWrite:!1})}function ult(){return new Gst({name:"CubemapToCubeUV",uniforms:{envMap:{value:null},inputEncoding:{value:Zst[3e3]},outputEncoding:{value:Zst[3e3]}},vertexShader:"\n\n\t\tprecision mediump float;\n\t\tprecision mediump int;\n\n\t\tattribute vec3 position;\n\t\tattribute vec2 uv;\n\t\tattribute float faceIndex;\n\n\t\tvarying vec3 vOutputDirection;\n\n\t\t// RH coordinate system; PMREM face-indexing convention\n\t\tvec3 getDirection( vec2 uv, float face ) {\n\n\t\t\tuv = 2.0 * uv - 1.0;\n\n\t\t\tvec3 direction = vec3( uv, 1.0 );\n\n\t\t\tif ( face == 0.0 ) {\n\n\t\t\t\tdirection = direction.zyx; // ( 1, v, u ) pos x\n\n\t\t\t} else if ( face == 1.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xz *= -1.0; // ( -u, 1, -v ) pos y\n\n\t\t\t} else if ( face == 2.0 ) {\n\n\t\t\t\tdirection.x *= -1.0; // ( -u, v, 1 ) pos z\n\n\t\t\t} else if ( face == 3.0 ) {\n\n\t\t\t\tdirection = direction.zyx;\n\t\t\t\tdirection.xz *= -1.0; // ( -1, v, -u ) neg x\n\n\t\t\t} else if ( face == 4.0 ) {\n\n\t\t\t\tdirection = direction.xzy;\n\t\t\t\tdirection.xy *= -1.0; // ( -u, -1, v ) neg y\n\n\t\t\t} else if ( face == 5.0 ) {\n\n\t\t\t\tdirection.z *= -1.0; // ( u, v, -1 ) neg z\n\n\t\t\t}\n\n\t\t\treturn direction;\n\n\t\t}\n\n\t\tvoid main() {\n\n\t\t\tvOutputDirection = getDirection( uv, faceIndex );\n\t\t\tgl_Position = vec4( position, 1.0 );\n\n\t\t}\n\t",fragmentShader:"\n\n\t\t\tprecision mediump float;\n\t\t\tprecision mediump int;\n\n\t\t\tvarying vec3 vOutputDirection;\n\n\t\t\tuniform samplerCube envMap;\n\n\t\t\t\n\n\t\tuniform int inputEncoding;\n\t\tuniform int outputEncoding;\n\n\t\t#include <encodings_pars_fragment>\n\n\t\tvec4 inputTexelToLinear( vec4 value ) {\n\n\t\t\tif ( inputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( inputEncoding == 1 ) {\n\n\t\t\t\treturn sRGBToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 2 ) {\n\n\t\t\t\treturn RGBEToLinear( value );\n\n\t\t\t} else if ( inputEncoding == 3 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 7.0 );\n\n\t\t\t} else if ( inputEncoding == 4 ) {\n\n\t\t\t\treturn RGBMToLinear( value, 16.0 );\n\n\t\t\t} else if ( inputEncoding == 5 ) {\n\n\t\t\t\treturn RGBDToLinear( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn GammaToLinear( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 linearToOutputTexel( vec4 value ) {\n\n\t\t\tif ( outputEncoding == 0 ) {\n\n\t\t\t\treturn value;\n\n\t\t\t} else if ( outputEncoding == 1 ) {\n\n\t\t\t\treturn LinearTosRGB( value );\n\n\t\t\t} else if ( outputEncoding == 2 ) {\n\n\t\t\t\treturn LinearToRGBE( value );\n\n\t\t\t} else if ( outputEncoding == 3 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 7.0 );\n\n\t\t\t} else if ( outputEncoding == 4 ) {\n\n\t\t\t\treturn LinearToRGBM( value, 16.0 );\n\n\t\t\t} else if ( outputEncoding == 5 ) {\n\n\t\t\t\treturn LinearToRGBD( value, 256.0 );\n\n\t\t\t} else {\n\n\t\t\t\treturn LinearToGamma( value, 2.2 );\n\n\t\t\t}\n\n\t\t}\n\n\t\tvec4 envMapTexelToLinear( vec4 color ) {\n\n\t\t\treturn inputTexelToLinear( color );\n\n\t\t}\n\t\n\n\t\t\tvoid main() {\n\n\t\t\t\tgl_FragColor = vec4( 0.0, 0.0, 0.0, 1.0 );\n\t\t\t\tgl_FragColor.rgb = envMapTexelToLinear( textureCube( envMap, vec3( - vOutputDirection.x, vOutputDirection.yz ) ) ).rgb;\n\t\t\t\tgl_FragColor = linearToOutputTexel( gl_FragColor );\n\n\t\t\t}\n\t\t",blending:0,depthTest:!1,depthWrite:!1})}function flt(t){let e=new WeakMap,n=null;function o(t){const n=t.target;n.removeEventListener("dispose",o);const i=e.get(n);void 0!==i&&(i.delete(n),i.dispose())}return{get:function i(a){if(a&&a.isTexture&&!1===a.isRenderTargetTexture){const i=a.mapping,r=303===i||304===i,s=i===dit||i===pit;if(r||s){if(e.has(a))return e.get(a).texture;{const i=a.image;if(r&&i&&i.height>0||s&&i&&(function r(t){let e=0;for(let n=0;n<6;n++)void 0!==t[n]&&e++;return 6===e})(i)){const i=t.getRenderTarget();null===n&&(n=new slt(t));const s=r?n.fromEquirectangular(a):n.fromCubemap(a);return e.set(a,s),t.setRenderTarget(i),a.addEventListener("dispose",o),s.texture}return null}}}return a},dispose:function a(){e=new WeakMap,null!==n&&(n.dispose(),n=null)}}}function glt(t){const e={};function n(n){if(void 0!==e[n])return e[n];let o;switch(n){case"WEBGL_depth_texture":o=t.getExtension("WEBGL_depth_texture")||t.getExtension("MOZ_WEBGL_depth_texture")||t.getExtension("WEBKIT_WEBGL_depth_texture");break;case"EXT_texture_filter_anisotropic":o=t.getExtension("EXT_texture_filter_anisotropic")||t.getExtension("MOZ_EXT_texture_filter_anisotropic")||t.getExtension("WEBKIT_EXT_texture_filter_anisotropic");break;case"WEBGL_compressed_texture_s3tc":o=t.getExtension("WEBGL_compressed_texture_s3tc")||t.getExtension("MOZ_WEBGL_compressed_texture_s3tc")||t.getExtension("WEBKIT_WEBGL_compressed_texture_s3tc");break;case"WEBGL_compressed_texture_pvrtc":o=t.getExtension("WEBGL_compressed_texture_pvrtc")||t.getExtension("WEBKIT_WEBGL_compressed_texture_pvrtc");break;default:o=t.getExtension(n)}return e[n]=o,o}return{has:function(t){return null!==n(t)},init:function(t){t.isWebGL2?n("EXT_color_buffer_float"):(n("WEBGL_depth_texture"),n("OES_texture_float"),n("OES_texture_half_float"),n("OES_texture_half_float_linear"),n("OES_standard_derivatives"),n("OES_element_index_uint"),n("OES_vertex_array_object"),n("ANGLE_instanced_arrays")),n("OES_texture_float_linear"),n("EXT_color_buffer_half_float")},get:function(t){const e=n(t);return null===e&&console.warn("THREE.WebGLRenderer: "+t+" extension not supported."),e}}}function hlt(t,e,n,o){const i={},a=new WeakMap;function r(t){const s=t.target;null!==s.index&&e.remove(s.index);for(const t in s.attributes)e.remove(s.attributes[t]);s.removeEventListener("dispose",r),delete i[s.id];const l=a.get(s);l&&(e.remove(l),a.delete(s)),o.releaseStatesOfGeometry(s),!0===s.isInstancedBufferGeometry&&delete s._maxInstanceCount,n.memory.geometries--}function s(t){const n=[],o=t.index,i=t.attributes.position;let r=0;if(null!==o){const t=o.array;r=o.version;for(let e=0,o=t.length;e<o;e+=3){const o=t[e+0],i=t[e+1],a=t[e+2];n.push(o,i,i,a,a,o)}}else{r=i.version;for(let t=0,e=i.array.length/3-1;t<e;t+=3){const e=t+0,o=t+1,i=t+2;n.push(e,o,o,i,i,e)}}const s=new(Lrt(n)>65535?Hrt:Irt)(n,1);s.version=r;const l=a.get(t);l&&e.remove(l),a.set(t,s)}return{get:function l(t,e){return!0===i[e.id]||(e.addEventListener("dispose",r),i[e.id]=!0,n.memory.geometries++),e},update:function c(t){const n=t.attributes;for(const t in n)e.update(n[t],34962);const o=t.morphAttributes;for(const t in o){const n=o[t];for(let t=0,o=n.length;t<o;t++)e.update(n[t],34962)}},getWireframeAttribute:function d(t){const e=a.get(t);if(e){const n=t.index;null!==n&&e.version<n.version&&s(t)}else s(t);return a.get(t)}}}function blt(t,e,n,o){const i=o.isWebGL2;let a,r,s;this.setMode=function l(t){a=t},this.setIndex=function c(t){r=t.type,s=t.bytesPerElement},this.render=function d(e,o){t.drawElements(a,o,r,e*s),n.update(o,a,1)},this.renderInstances=function p(o,l,c){if(0===c)return;let d,p;if(i)d=t,p="drawElementsInstanced";else if(d=e.get("ANGLE_instanced_arrays"),p="drawElementsInstancedANGLE",null===d)return void console.error("THREE.WebGLIndexedBufferRenderer: using THREE.InstancedBufferGeometry but hardware does not support extension ANGLE_instanced_arrays.");d[p](a,l,r,o*s,c),n.update(l,a,c)}}function ylt(t){const e={frame:0,calls:0,triangles:0,points:0,lines:0};return{memory:{geometries:0,textures:0},render:e,programs:null,autoReset:!0,reset:function n(){e.frame++,e.calls=0,e.triangles=0,e.points=0,e.lines=0},update:function o(t,n,i){switch(e.calls++,n){case 4:e.triangles+=i*(t/3);break;case 1:e.lines+=i*(t/2);break;case 3:e.lines+=i*(t-1);break;case 2:e.lines+=i*t;break;case 0:e.points+=i*t;break;default:console.error("THREE.WebGLInfo: Unknown draw mode:",n)}}}}function _lt(t,e){return t[0]-e[0]}function Clt(t,e){return Math.abs(e[1])-Math.abs(t[1])}function Mlt(t){const e={},n=new Float32Array(8),o=[];for(let t=0;t<8;t++)o[t]=[t,0];return{update:function i(a,r,s,l){const c=a.morphTargetInfluences,d=void 0===c?0:c.length;let p=e[r.id];if(void 0===p||p.length!==d){p=[];for(let t=0;t<d;t++)p[t]=[t,0];e[r.id]=p}for(let t=0;t<d;t++){const e=p[t];e[0]=t,e[1]=c[t]}p.sort(Clt);for(let t=0;t<8;t++)t<d&&p[t][1]?(o[t][0]=p[t][0],o[t][1]=p[t][1]):(o[t][0]=Number.MAX_SAFE_INTEGER,o[t][1]=0);o.sort(_lt);const m=r.morphAttributes.position,u=r.morphAttributes.normal;let f=0;for(let t=0;t<8;t++){const e=o[t],i=e[0],a=e[1];i!==Number.MAX_SAFE_INTEGER&&a?(m&&r.getAttribute("morphTarget"+t)!==m[i]&&r.setAttribute("morphTarget"+t,m[i]),u&&r.getAttribute("morphNormal"+t)!==u[i]&&r.setAttribute("morphNormal"+t,u[i]),n[t]=a,f+=a):(m&&!0===r.hasAttribute("morphTarget"+t)&&r.deleteAttribute("morphTarget"+t),u&&!0===r.hasAttribute("morphNormal"+t)&&r.deleteAttribute("morphNormal"+t),n[t]=0)}const g=r.morphTargetsRelative?1:1-f;l.getUniforms().setValue(t,"morphTargetBaseInfluence",g),l.getUniforms().setValue(t,"morphTargetInfluences",n)}}}function vlt(t,e,n,o){let i=new WeakMap;function a(t){const e=t.target;e.removeEventListener("dispose",a),n.remove(e.instanceMatrix),null!==e.instanceColor&&n.remove(e.instanceColor)}return{update:function r(t){const r=o.render.frame,s=e.get(t,t.geometry);return i.get(s)!==r&&(e.update(s),i.set(s,r)),t.isInstancedMesh&&(!1===t.hasEventListener("dispose",a)&&t.addEventListener("dispose",a),n.update(t.instanceMatrix,34962),null!==t.instanceColor&&n.update(t.instanceColor,34962)),s},dispose:function s(){i=new WeakMap}}}class xlt extends oat{constructor(t=null,e=1,n=1,o=1){super(null),this.image={data:t,width:e,height:n,depth:o},this.magFilter=hit,this.minFilter=hit,this.wrapR=fit,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}xlt.prototype.isDataTexture2DArray=!0;class Olt extends oat{constructor(t=null,e=1,n=1,o=1){super(null),this.image={data:t,width:e,height:n,depth:o},this.magFilter=hit,this.minFilter=hit,this.wrapR=fit,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}Olt.prototype.isDataTexture3D=!0;const Plt=new oat,wlt=new xlt,klt=new Olt,Slt=new vst,Dlt=[],Elt=[],Rlt=new Float32Array(16),Alt=new Float32Array(9),Tlt=new Float32Array(4);function Nlt(t,e,n){const o=t[0];if(o<=0||o>0)return t;const i=e*n;let a=Dlt[i];if(void 0===a&&(a=new Float32Array(i),Dlt[i]=a),0!==e){o.toArray(a,0);for(let o=1,i=0;o!==e;++o)i+=n,t[o].toArray(a,i)}return a}function zlt(t,e){if(t.length!==e.length)return!1;for(let n=0,o=t.length;n<o;n++)if(t[n]!==e[n])return!1;return!0}function Ilt(t,e){for(let n=0,o=e.length;n<o;n++)t[n]=e[n]}function Hlt(t,e){let n=Elt[e];void 0===n&&(n=new Int32Array(e),Elt[e]=n);for(let o=0;o!==e;++o)n[o]=t.allocateTextureUnit();return n}function Flt(t,e){const n=this.cache;n[0]!==e&&(t.uniform1f(this.addr,e),n[0]=e)}function Llt(t,e){const n=this.cache;if(void 0!==e.x)n[0]===e.x&&n[1]===e.y||(t.uniform2f(this.addr,e.x,e.y),n[0]=e.x,n[1]=e.y);else{if(zlt(n,e))return;t.uniform2fv(this.addr,e),Ilt(n,e)}}function Blt(t,e){const n=this.cache;if(void 0!==e.x)n[0]===e.x&&n[1]===e.y&&n[2]===e.z||(t.uniform3f(this.addr,e.x,e.y,e.z),n[0]=e.x,n[1]=e.y,n[2]=e.z);else if(void 0!==e.r)n[0]===e.r&&n[1]===e.g&&n[2]===e.b||(t.uniform3f(this.addr,e.r,e.g,e.b),n[0]=e.r,n[1]=e.g,n[2]=e.b);else{if(zlt(n,e))return;t.uniform3fv(this.addr,e),Ilt(n,e)}}function Vlt(t,e){const n=this.cache;if(void 0!==e.x)n[0]===e.x&&n[1]===e.y&&n[2]===e.z&&n[3]===e.w||(t.uniform4f(this.addr,e.x,e.y,e.z,e.w),n[0]=e.x,n[1]=e.y,n[2]=e.z,n[3]=e.w);else{if(zlt(n,e))return;t.uniform4fv(this.addr,e),Ilt(n,e)}}function jlt(t,e){const n=this.cache,o=e.elements;if(void 0===o){if(zlt(n,e))return;t.uniformMatrix2fv(this.addr,!1,e),Ilt(n,e)}else{if(zlt(n,o))return;Tlt.set(o),t.uniformMatrix2fv(this.addr,!1,Tlt),Ilt(n,o)}}function Ult(t,e){const n=this.cache,o=e.elements;if(void 0===o){if(zlt(n,e))return;t.uniformMatrix3fv(this.addr,!1,e),Ilt(n,e)}else{if(zlt(n,o))return;Alt.set(o),t.uniformMatrix3fv(this.addr,!1,Alt),Ilt(n,o)}}function Glt(t,e){const n=this.cache,o=e.elements;if(void 0===o){if(zlt(n,e))return;t.uniformMatrix4fv(this.addr,!1,e),Ilt(n,e)}else{if(zlt(n,o))return;Rlt.set(o),t.uniformMatrix4fv(this.addr,!1,Rlt),Ilt(n,o)}}function Wlt(t,e){const n=this.cache;n[0]!==e&&(t.uniform1i(this.addr,e),n[0]=e)}function Ylt(t,e){const n=this.cache;zlt(n,e)||(t.uniform2iv(this.addr,e),Ilt(n,e))}function qlt(t,e){const n=this.cache;zlt(n,e)||(t.uniform3iv(this.addr,e),Ilt(n,e))}function Zlt(t,e){const n=this.cache;zlt(n,e)||(t.uniform4iv(this.addr,e),Ilt(n,e))}function Xlt(t,e){const n=this.cache;n[0]!==e&&(t.uniform1ui(this.addr,e),n[0]=e)}function Klt(t,e){const n=this.cache;zlt(n,e)||(t.uniform2uiv(this.addr,e),Ilt(n,e))}function Jlt(t,e){const n=this.cache;zlt(n,e)||(t.uniform3uiv(this.addr,e),Ilt(n,e))}function Qlt(t,e){const n=this.cache;zlt(n,e)||(t.uniform4uiv(this.addr,e),Ilt(n,e))}function $lt(t,e,n){const o=this.cache,i=n.allocateTextureUnit();o[0]!==i&&(t.uniform1i(this.addr,i),o[0]=i),n.safeSetTexture2D(e||Plt,i)}function tct(t,e,n){const o=this.cache,i=n.allocateTextureUnit();o[0]!==i&&(t.uniform1i(this.addr,i),o[0]=i),n.setTexture3D(e||klt,i)}function ect(t,e,n){const o=this.cache,i=n.allocateTextureUnit();o[0]!==i&&(t.uniform1i(this.addr,i),o[0]=i),n.safeSetTextureCube(e||Slt,i)}function nct(t,e,n){const o=this.cache,i=n.allocateTextureUnit();o[0]!==i&&(t.uniform1i(this.addr,i),o[0]=i),n.setTexture2DArray(e||wlt,i)}function oct(t,e){t.uniform1fv(this.addr,e)}function ict(t,e){const n=Nlt(e,this.size,2);t.uniform2fv(this.addr,n)}function act(t,e){const n=Nlt(e,this.size,3);t.uniform3fv(this.addr,n)}function rct(t,e){const n=Nlt(e,this.size,4);t.uniform4fv(this.addr,n)}function sct(t,e){const n=Nlt(e,this.size,4);t.uniformMatrix2fv(this.addr,!1,n)}function lct(t,e){const n=Nlt(e,this.size,9);t.uniformMatrix3fv(this.addr,!1,n)}function cct(t,e){const n=Nlt(e,this.size,16);t.uniformMatrix4fv(this.addr,!1,n)}function dct(t,e){t.uniform1iv(this.addr,e)}function pct(t,e){t.uniform2iv(this.addr,e)}function mct(t,e){t.uniform3iv(this.addr,e)}function uct(t,e){t.uniform4iv(this.addr,e)}function fct(t,e){t.uniform1uiv(this.addr,e)}function gct(t,e){t.uniform2uiv(this.addr,e)}function hct(t,e){t.uniform3uiv(this.addr,e)}function bct(t,e){t.uniform4uiv(this.addr,e)}function yct(t,e,n){const o=e.length,i=Hlt(n,o);t.uniform1iv(this.addr,i);for(let t=0;t!==o;++t)n.safeSetTexture2D(e[t]||Plt,i[t])}function _ct(t,e,n){const o=e.length,i=Hlt(n,o);t.uniform1iv(this.addr,i);for(let t=0;t!==o;++t)n.safeSetTextureCube(e[t]||Slt,i[t])}function Cct(t,e,n){this.id=t,this.addr=n,this.cache=[],this.setValue=(function o(t){switch(t){case 5126:return Flt;case 35664:return Llt;case 35665:return Blt;case 35666:return Vlt;case 35674:return jlt;case 35675:return Ult;case 35676:return Glt;case 5124:case 35670:return Wlt;case 35667:case 35671:return Ylt;case 35668:case 35672:return qlt;case 35669:case 35673:return Zlt;case 5125:return Xlt;case 36294:return Klt;case 36295:return Jlt;case 36296:return Qlt;case 35678:case 36198:case 36298:case 36306:case 35682:return $lt;case 35679:case 36299:case 36307:return tct;case 35680:case 36300:case 36308:case 36293:return ect;case 36289:case 36303:case 36311:case 36292:return nct}})(e.type)}function Mct(t,e,n){this.id=t,this.addr=n,this.cache=[],this.size=e.size,this.setValue=(function o(t){switch(t){case 5126:return oct;case 35664:return ict;case 35665:return act;case 35666:return rct;case 35674:return sct;case 35675:return lct;case 35676:return cct;case 5124:case 35670:return dct;case 35667:case 35671:return pct;case 35668:case 35672:return mct;case 35669:case 35673:return uct;case 5125:return fct;case 36294:return gct;case 36295:return hct;case 36296:return bct;case 35678:case 36198:case 36298:case 36306:case 35682:return yct;case 35680:case 36300:case 36308:case 36293:return _ct}})(e.type)}function vct(t){this.id=t,this.seq=[],this.map={}}Mct.prototype.updateCache=function(t){const e=this.cache;t instanceof Float32Array&&e.length!==t.length&&(this.cache=new Float32Array(t.length)),Ilt(e,t)},vct.prototype.setValue=function(t,e,n){const o=this.seq;for(let i=0,a=o.length;i!==a;++i){const a=o[i];a.setValue(t,e[a.id],n)}};const xct=/(\w+)(\])?(\[|\.)?/g;function Oct(t,e){t.seq.push(e),t.map[e.id]=e}function Pct(t,e,n){const o=t.name,i=o.length;for(xct.lastIndex=0;;){const a=xct.exec(o),r=xct.lastIndex;let s=a[1];const l=a[3];if("]"===a[2]&&(s|=0),void 0===l||"["===l&&r+2===i){Oct(n,void 0===l?new Cct(s,t,e):new Mct(s,t,e));break}{let t=n.map[s];void 0===t&&(t=new vct(s),Oct(n,t)),n=t}}}function wct(t,e){this.seq=[],this.map={};const n=t.getProgramParameter(e,35718);for(let o=0;o<n;++o){const n=t.getActiveUniform(e,o);Pct(n,t.getUniformLocation(e,n.name),this)}}function kct(t,e,n){const o=t.createShader(e);return t.shaderSource(o,n),t.compileShader(o),o}wct.prototype.setValue=function(t,e,n,o){const i=this.map[e];void 0!==i&&i.setValue(t,n,o)},wct.prototype.setOptional=function(t,e,n){const o=e[n];void 0!==o&&this.setValue(t,n,o)},wct.upload=function(t,e,n,o){for(let i=0,a=e.length;i!==a;++i){const a=e[i],r=n[a.id];!1!==r.needsUpdate&&a.setValue(t,r.value,o)}},wct.seqWithValue=function(t,e){const n=[];for(let o=0,i=t.length;o!==i;++o){const i=t[o];i.id in e&&n.push(i)}return n};let Sct=0;function Dct(t){switch(t){case zit:return["Linear","( value )"];case Iit:return["sRGB","( value )"];case Fit:return["RGBE","( value )"];case 3004:return["RGBM","( value, 7.0 )"];case 3005:return["RGBM","( value, 16.0 )"];case 3006:return["RGBD","( value, 256.0 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0!==a.depthPacking&&a.depthPacking,index0AttributeName:a.index0AttributeName,extensionDerivatives:a.extensions&&a.extensions.derivatives,extensionFragDepth:a.extensions&&a.extensions.fragDepth,extensionDrawBuffers:a.extensions&&a.extensions.drawBuffers,extensionShaderTextureLOD:a.extensions&&a.extensions.shaderTextureLOD,rendererExtensionFragDepth:l||o.has("EXT_frag_depth"),rendererExtensionDrawBuffers:l||o.has("WEBGL_draw_buffers"),rendererExtensionShaderTextureLod:l||o.has("EXT_shader_texture_lod"),customProgramCacheKey:a.customProgramCacheKey()}},getProgramCacheKey:function y(e){const n=[];if(e.shaderID?n.push(e.shaderID):(n.push(e.fragmentShader),n.push(e.vertexShader)),void 0!==e.defines)for(const t in e.defines)n.push(t),n.push(e.defines[t]);if(!1===e.isRawShaderMaterial){for(let t=0;t<g.length;t++)n.push(e[g[t]]);n.push(t.outputEncoding),n.push(t.gammaFactor)}return n.push(e.customProgramCacheKey),n.join()},getUniforms:function _(t){const e=f[t.type];let n;return n=e?hst.clone(Ist[e].uniforms):t.uniforms,n},acquireProgram:function C(e,n){let o;for(let t=0,e=s.length;t<e;t++){const e=s[t];if(e.cacheKey===n){o=e,++o.usedTimes;break}}return void 0===o&&(o=new Yct(t,n,e,a),s.push(o)),o},releaseProgram:function M(t){if(0==--t.usedTimes){const e=s.indexOf(t);s[e]=s[s.length-1],s.pop(),t.destroy()}},programs:s}}function Zct(){let t=new WeakMap;return{get:function e(n){let o=t.get(n);return void 0===o&&(o={},t.set(n,o)),o},remove:function n(e){t.delete(e)},update:function o(e,n,i){t.get(e)[n]=i},dispose:function i(){t=new WeakMap}}}function Xct(t,e){return t.groupOrder!==e.groupOrder?t.groupOrder-e.groupOrder:t.renderOrder!==e.renderOrder?t.renderOrder-e.renderOrder:t.program!==e.program?t.program.id-e.program.id:t.material.id!==e.material.id?t.material.id-e.material.id:t.z!==e.z?t.z-e.z:t.id-e.id}function Kct(t,e){return t.groupOrder!==e.groupOrder?t.groupOrder-e.groupOrder:t.renderOrder!==e.renderOrder?t.renderOrder-e.renderOrder:t.z!==e.z?e.z-t.z:t.id-e.id}function Jct(t){const e=[];let n=0;const o=[],i=[],a=[],r={id:-1};function s(o,i,a,s,l,c){let d=e[n];const p=t.get(a);return void 0===d?(d={id:o.id,object:o,geometry:i,material:a,program:p.program||r,groupOrder:s,renderOrder:o.renderOrder,z:l,group:c},e[n]=d):(d.id=o.id,d.object=o,d.geometry=i,d.material=a,d.program=p.program||r,d.groupOrder=s,d.renderOrder=o.renderOrder,d.z=l,d.group=c),n++,d}return{opaque:o,transmissive:i,transparent:a,init:function l(){n=0,o.length=0,i.length=0,a.length=0},push:function c(t,e,n,r,l,d){const p=s(t,e,n,r,l,d);n.transmission>0?i.push(p):!0===n.transparent?a.push(p):o.push(p)},unshift:function d(t,e,n,r,l,c){const d=s(t,e,n,r,l,c);n.transmission>0?i.unshift(d):!0===n.transparent?a.unshift(d):o.unshift(d)},finish:function p(){for(let t=n,o=e.length;t<o;t++){const n=e[t];if(null===n.id)break;n.id=null,n.object=null,n.geometry=null,n.material=null,n.program=null,n.group=null}},sort:function m(t,e){o.length>1&&o.sort(t||Xct),i.length>1&&i.sort(e||Kct),a.length>1&&a.sort(e||Kct)}}}function Qct(t){let e=new WeakMap;return{get:function n(o,i){let a;return!1===e.has(o)?(a=new Jct(t),e.set(o,[a])):i>=e.get(o).length?(a=new Jct(t),e.get(o).push(a)):a=e.get(o)[i],a},dispose:function o(){e=new WeakMap}}}function $ct(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case"DirectionalLight":n={direction:new cat,color:new Rrt};break;case"SpotLight":n={position:new cat,direction:new cat,color:new Rrt,distance:0,coneCos:0,penumbraCos:0,decay:0};break;case"PointLight":n={position:new cat,color:new Rrt,distance:0,decay:0};break;case"HemisphereLight":n={direction:new cat,skyColor:new Rrt,groundColor:new Rrt};break;case"RectAreaLight":n={color:new Rrt,position:new cat,halfWidth:new cat,halfHeight:new cat}}return t[e.id]=n,n}}}let tdt=0;function edt(t,e){return(e.castShadow?1:0)-(t.castShadow?1:0)}function ndt(t,e){const n=new $ct,o=(function i(){const t={};return{get:function(e){if(void 0!==t[e.id])return t[e.id];let n;switch(e.type){case"DirectionalLight":case"SpotLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new Qit};break;case"PointLight":n={shadowBias:0,shadowNormalBias:0,shadowRadius:1,shadowMapSize:new Qit,shadowCameraNear:1,shadowCameraFar:1e3}}return t[e.id]=n,n}}})(),a={version:0,hash:{directionalLength:-1,pointLength:-1,spotLength:-1,rectAreaLength:-1,hemiLength:-1,numDirectionalShadows:-1,numPointShadows:-1,numSpotShadows:-1},ambient:[0,0,0],probe:[],directional:[],directionalShadow:[],directionalShadowMap:[],directionalShadowMatrix:[],spot:[],spotShadow:[],spotShadowMap:[],spotShadowMatrix:[],rectArea:[],rectAreaLTC1:null,rectAreaLTC2:null,point:[],pointShadow:[],pointShadowMap:[],pointShadowMatrix:[],hemi:[]};for(let t=0;t<9;t++)a.probe.push(new cat);const r=new cat,s=new Bat,l=new Bat;return{setup:function c(i){let r=0,s=0,l=0;for(let t=0;t<9;t++)a.probe[t].set(0,0,0);let c=0,d=0,p=0,m=0,u=0,f=0,g=0,h=0;i.sort(edt);for(let t=0,e=i.length;t<e;t++){const e=i[t],b=e.color,y=e.intensity,_=e.distance,C=e.shadow&&e.shadow.map?e.shadow.map.texture:null;if(e.isAmbientLight)r+=b.r*y,s+=b.g*y,l+=b.b*y;else if(e.isLightProbe)for(let t=0;t<9;t++)a.probe[t].addScaledVector(e.sh.coefficients[t],y);else if(e.isDirectionalLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity),e.castShadow){const t=e.shadow,n=o.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,a.directionalShadow[c]=n,a.directionalShadowMap[c]=C,a.directionalShadowMatrix[c]=e.shadow.matrix,f++}a.directional[c]=t,c++}else if(e.isSpotLight){const t=n.get(e);if(t.position.setFromMatrixPosition(e.matrixWorld),t.color.copy(b).multiplyScalar(y),t.distance=_,t.coneCos=Math.cos(e.angle),t.penumbraCos=Math.cos(e.angle*(1-e.penumbra)),t.decay=e.decay,e.castShadow){const t=e.shadow,n=o.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,a.spotShadow[p]=n,a.spotShadowMap[p]=C,a.spotShadowMatrix[p]=e.shadow.matrix,h++}a.spot[p]=t,p++}else if(e.isRectAreaLight){const t=n.get(e);t.color.copy(b).multiplyScalar(y),t.halfWidth.set(.5*e.width,0,0),t.halfHeight.set(0,.5*e.height,0),a.rectArea[m]=t,m++}else if(e.isPointLight){const t=n.get(e);if(t.color.copy(e.color).multiplyScalar(e.intensity),t.distance=e.distance,t.decay=e.decay,e.castShadow){const t=e.shadow,n=o.get(e);n.shadowBias=t.bias,n.shadowNormalBias=t.normalBias,n.shadowRadius=t.radius,n.shadowMapSize=t.mapSize,n.shadowCameraNear=t.camera.near,n.shadowCameraFar=t.camera.far,a.pointShadow[d]=n,a.pointShadowMap[d]=C,a.pointShadowMatrix[d]=e.shadow.matrix,g++}a.point[d]=t,d++}else if(e.isHemisphereLight){const t=n.get(e);t.skyColor.copy(e.color).multiplyScalar(y),t.groundColor.copy(e.groundColor).multiplyScalar(y),a.hemi[u]=t,u++}}m>0&&(e.isWebGL2||!0===t.has("OES_texture_float_linear")?(a.rectAreaLTC1=zst.LTC_FLOAT_1,a.rectAreaLTC2=zst.LTC_FLOAT_2):!0===t.has("OES_texture_half_float_linear")?(a.rectAreaLTC1=zst.LTC_HALF_1,a.rectAreaLTC2=zst.LTC_HALF_2):console.error("THREE.WebGLRenderer: Unable to use RectAreaLight. Missing WebGL extensions.")),a.ambient[0]=r,a.ambient[1]=s,a.ambient[2]=l;const b=a.hash;b.directionalLength===c&&b.pointLength===d&&b.spotLength===p&&b.rectAreaLength===m&&b.hemiLength===u&&b.numDirectionalShadows===f&&b.numPointShadows===g&&b.numSpotShadows===h||(a.directional.length=c,a.spot.length=p,a.rectArea.length=m,a.point.length=d,a.hemi.length=u,a.directionalShadow.length=f,a.directionalShadowMap.length=f,a.pointShadow.length=g,a.pointShadowMap.length=g,a.spotShadow.length=h,a.spotShadowMap.length=h,a.directionalShadowMatrix.length=f,a.pointShadowMatrix.length=g,a.spotShadowMatrix.length=h,b.directionalLength=c,b.pointLength=d,b.spotLength=p,b.rectAreaLength=m,b.hemiLength=u,b.numDirectionalShadows=f,b.numPointShadows=g,b.numSpotShadows=h,a.version=tdt++)},setupView:function d(t,e){let n=0,o=0,i=0,c=0,d=0;const p=e.matrixWorldInverse;for(let e=0,m=t.length;e<m;e++){const m=t[e];if(m.isDirectionalLight){const t=a.directional[n];t.direction.setFromMatrixPosition(m.matrixWorld),r.setFromMatrixPosition(m.target.matrixWorld),t.direction.sub(r),t.direction.transformDirection(p),n++}else if(m.isSpotLight){const t=a.spot[i];t.position.setFromMatrixPosition(m.matrixWorld),t.position.applyMatrix4(p),t.direction.setFromMatrixPosition(m.matrixWorld),r.setFromMatrixPosition(m.target.matrixWorld),t.direction.sub(r),t.direction.transformDirection(p),i++}else if(m.isRectAreaLight){const t=a.rectArea[c];t.position.setFromMatrixPosition(m.matrixWorld),t.position.applyMatrix4(p),l.identity(),s.copy(m.matrixWorld),s.premultiply(p),l.extractRotation(s),t.halfWidth.set(.5*m.width,0,0),t.halfHeight.set(0,.5*m.height,0),t.halfWidth.applyMatrix4(l),t.halfHeight.applyMatrix4(l),c++}else if(m.isPointLight){const t=a.point[o];t.position.setFromMatrixPosition(m.matrixWorld),t.position.applyMatrix4(p),o++}else if(m.isHemisphereLight){const t=a.hemi[d];t.direction.setFromMatrixPosition(m.matrixWorld),t.direction.transformDirection(p),t.direction.normalize(),d++}}},state:a}}function odt(t,e){const n=new ndt(t,e),o=[],i=[];return{init:function a(){o.length=0,i.length=0},state:{lightsArray:o,shadowsArray:i,lights:n},setupLights:function r(){n.setup(o)},setupLightsView:function s(t){n.setupView(o,t)},pushLight:function l(t){o.push(t)},pushShadow:function c(t){i.push(t)}}}function idt(t,e){let n=new WeakMap;return{get:function o(i,a=0){let r;return!1===n.has(i)?(r=new odt(t,e),n.set(i,[r])):a>=n.get(i).length?(r=new odt(t,e),n.get(i).push(r)):r=n.get(i)[a],r},dispose:function i(){n=new WeakMap}}}class adt extends Ort{constructor(t){super(),this.type="MeshDepthMaterial",this.depthPacking=3200,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.depthPacking=t.depthPacking,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this}}adt.prototype.isMeshDepthMaterial=!0;class rdt extends Ort{constructor(t){super(),this.type="MeshDistanceMaterial",this.referencePosition=new cat,this.nearDistance=1,this.farDistance=1e3,this.map=null,this.alphaMap=null,this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.fog=!1,this.setValues(t)}copy(t){return super.copy(t),this.referencePosition.copy(t.referencePosition),this.nearDistance=t.nearDistance,this.farDistance=t.farDistance,this.map=t.map,this.alphaMap=t.alphaMap,this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this}}function sdt(t,e,n){let o=new Est;const i=new Qit,a=new Qit,r=new aat,s=new adt({depthPacking:3201}),l=new rdt,c={},d=n.maxTextureSize,p={0:1,1:0,2:2},m=new bst({defines:{SAMPLE_RATE:2/8,HALF_SAMPLE_RATE:1/8},uniforms:{shadow_pass:{value:null},resolution:{value:new Qit},radius:{value:4}},vertexShader:"void main() {\n\tgl_Position = vec4( position, 1.0 );\n}",fragmentShader:"uniform sampler2D shadow_pass;\nuniform vec2 resolution;\nuniform float radius;\n#include <packing>\nvoid main() {\n\tfloat mean = 0.0;\n\tfloat squared_mean = 0.0;\n\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy ) / resolution ) );\n\tfor ( float i = -1.0; i < 1.0 ; i += SAMPLE_RATE) {\n\t\t#ifdef HORIZONTAL_PASS\n\t\t\tvec2 distribution = unpackRGBATo2Half( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( i, 0.0 ) * radius ) / resolution ) );\n\t\t\tmean += distribution.x;\n\t\t\tsquared_mean += distribution.y * distribution.y + distribution.x * distribution.x;\n\t\t#else\n\t\t\tfloat depth = unpackRGBAToDepth( texture2D( shadow_pass, ( gl_FragCoord.xy + vec2( 0.0, i ) * radius ) / resolution ) );\n\t\t\tmean += depth;\n\t\t\tsquared_mean += depth * depth;\n\t\t#endif\n\t}\n\tmean = mean * HALF_SAMPLE_RATE;\n\tsquared_mean = squared_mean * HALF_SAMPLE_RATE;\n\tfloat std_dev = sqrt( squared_mean - mean * mean );\n\tgl_FragColor = pack2HalfToRGBA( vec2( mean, std_dev ) );\n}"}),u=m.clone();u.defines.HORIZONTAL_PASS=1;const f=new qrt;f.setAttribute("position",new zrt(new Float32Array([-1,-1,.5,3,-1,.5,-1,3,.5]),3));const g=new pst(f,m),h=this;function b(n,o){const i=e.update(g);m.uniforms.shadow_pass.value=n.map.texture,m.uniforms.resolution.value=n.mapSize,m.uniforms.radius.value=n.radius,t.setRenderTarget(n.mapPass),t.clear(),t.renderBufferDirect(o,null,i,m,g,null),u.uniforms.shadow_pass.value=n.mapPass.texture,u.uniforms.resolution.value=n.mapSize,u.uniforms.radius.value=n.radius,t.setRenderTarget(n.map),t.clear(),t.renderBufferDirect(o,null,i,u,g,null)}function y(e,n,o,i,a,r,d){let m=null;const u=!0===i.isPointLight?e.customDistanceMaterial:e.customDepthMaterial;if(m=void 0!==u?u:!0===i.isPointLight?l:s,t.localClippingEnabled&&!0===o.clipShadows&&0!==o.clippingPlanes.length){const t=m.uuid,e=o.uuid;let n=c[t];void 0===n&&(n={},c[t]=n);let i=n[e];void 0===i&&(i=m.clone(),n[e]=i),m=i}return m.visible=o.visible,m.wireframe=o.wireframe,m.side=3===d?null!==o.shadowSide?o.shadowSide:o.side:null!==o.shadowSide?o.shadowSide:p[o.side],m.clipShadows=o.clipShadows,m.clippingPlanes=o.clippingPlanes,m.clipIntersection=o.clipIntersection,m.wireframeLinewidth=o.wireframeLinewidth,m.linewidth=o.linewidth,!0===i.isPointLight&&!0===m.isMeshDistanceMaterial&&(m.referencePosition.setFromMatrixPosition(i.matrixWorld),m.nearDistance=a,m.farDistance=r),m}function _(n,i,a,r,s){if(!1===n.visible)return;if(n.layers.test(i.layers)&&(n.isMesh||n.isLine||n.isPoints)&&(n.castShadow||n.receiveShadow&&3===s)&&(!n.frustumCulled||o.intersectsObject(n))){n.modelViewMatrix.multiplyMatrices(a.matrixWorldInverse,n.matrixWorld);const o=e.update(n),i=n.material;if(Array.isArray(i)){const e=o.groups;for(let l=0,c=e.length;l<c;l++){const c=e[l],d=i[c.materialIndex];if(d&&d.visible){const e=y(n,0,d,r,a.near,a.far,s);t.renderBufferDirect(a,null,o,e,n,c)}}}else if(i.visible){const e=y(n,0,i,r,a.near,a.far,s);t.renderBufferDirect(a,null,o,e,n,null)}}const l=n.children;for(let t=0,e=l.length;t<e;t++)_(l[t],i,a,r,s)}this.enabled=!1,this.autoUpdate=!0,this.needsUpdate=!1,this.type=1,this.render=function(e,n,s){if(!1===h.enabled)return;if(!1===h.autoUpdate&&!1===h.needsUpdate)return;if(0===e.length)return;const l=t.getRenderTarget(),c=t.getActiveCubeFace(),p=t.getActiveMipmapLevel(),m=t.state;m.setBlending(0),m.buffers.color.setClear(1,1,1,1),m.buffers.depth.setTest(!0),m.setScissorTest(!1);for(let l=0,c=e.length;l<c;l++){const c=e[l],p=c.shadow;if(void 0===p){console.warn("THREE.WebGLShadowMap:",c,"has no shadow.");continue}if(!1===p.autoUpdate&&!1===p.needsUpdate)continue;i.copy(p.mapSize);const u=p.getFrameExtents();if(i.multiply(u),a.copy(p.mapSize),(i.x>d||i.y>d)&&(i.x>d&&(a.x=Math.floor(d/u.x),i.x=a.x*u.x,p.mapSize.x=a.x),i.y>d&&(a.y=Math.floor(d/u.y),i.y=a.y*u.y,p.mapSize.y=a.y)),null===p.map&&!p.isPointLightShadow&&3===this.type){const t={minFilter:bit,magFilter:bit,format:wit};p.map=new rat(i.x,i.y,t),p.map.texture.name=c.name+".shadowMap",p.mapPass=new rat(i.x,i.y,t),p.camera.updateProjectionMatrix()}null===p.map&&(p.map=new 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n.texImage2D(3553,0,M,u,g,p),e.__maxMipLevel=0;y(o,m)&&_(r,o,p.width,p.height),e.__version=o.version,o.onUpdate&&o.onUpdate(o)}function A(e,i,r,s,l){const c=a.convert(r.format),d=a.convert(r.type),p=C(r.internalFormat,c,d);32879===l||35866===l?n.texImage3D(l,0,p,i.width,i.height,i.depth,0,c,d,null):n.texImage2D(l,0,p,i.width,i.height,0,c,d,null),n.bindFramebuffer(36160,e),t.framebufferTexture2D(36160,s,l,o.get(r).__webglTexture,0),n.bindFramebuffer(36160,null)}function T(e,n,o){if(t.bindRenderbuffer(36161,e),n.depthBuffer&&!n.stencilBuffer){let i=33189;if(o){const e=n.depthTexture;e&&e.isDepthTexture&&(e.type===vit?i=36012:e.type===Mit&&(i=33190));const o=N(n);t.renderbufferStorageMultisample(36161,o,i,n.width,n.height)}else t.renderbufferStorage(36161,i,n.width,n.height);t.framebufferRenderbuffer(36160,36096,36161,e)}else if(n.depthBuffer&&n.stencilBuffer){if(o){const e=N(n);t.renderbufferStorageMultisample(36161,e,35056,n.width,n.height)}else t.renderbufferStorage(36161,34041,n.width,n.height);t.framebufferRenderbuffer(36160,33306,36161,e)}else{const e=!0===n.isWebGLMultipleRenderTargets?n.texture[0]:n.texture,i=a.convert(e.format),r=a.convert(e.type),s=C(e.internalFormat,i,r);if(o){const e=N(n);t.renderbufferStorageMultisample(36161,e,s,n.width,n.height)}else t.renderbufferStorage(36161,s,n.width,n.height)}t.bindRenderbuffer(36161,null)}function N(t){return s&&t.isWebGLMultisampleRenderTarget?Math.min(p,t.samples):0}let z=!1,I=!1;this.allocateTextureUnit=function H(){const t=O;return t>=l&&console.warn("THREE.WebGLTextures: Trying to use "+t+" texture units while this GPU supports only "+l),O+=1,t},this.resetTextureUnits=function F(){O=0},this.setTexture2D=P,this.setTexture2DArray=function L(t,e){const i=o.get(t);t.version>0&&i.__version!==t.version?R(i,t,e):(n.activeTexture(33984+e),n.bindTexture(35866,i.__webglTexture))},this.setTexture3D=function B(t,e){const i=o.get(t);t.version>0&&i.__version!==t.version?R(i,t,e):(n.activeTexture(33984+e),n.bindTexture(32879,i.__webglTexture))},this.setTextureCube=w,this.setupRenderTarget=function V(e){const l=e.texture,c=o.get(e),d=o.get(l);e.addEventListener("dispose",x),!0!==e.isWebGLMultipleRenderTargets&&(d.__webglTexture=t.createTexture(),d.__version=l.version,r.memory.textures++);const p=!0===e.isWebGLCubeRenderTarget,m=!0===e.isWebGLMultipleRenderTargets,u=!0===e.isWebGLMultisampleRenderTarget,f=l.isDataTexture3D||l.isDataTexture2DArray,g=b(e)||s;if(!s||l.format!==Pit||l.type!==vit&&l.type!==xit||(l.format=wit,console.warn("THREE.WebGLRenderer: Rendering to textures with RGB format is not supported. Using RGBA format instead.")),p){c.__webglFramebuffer=[];for(let e=0;e<6;e++)c.__webglFramebuffer[e]=t.createFramebuffer()}else if(c.__webglFramebuffer=t.createFramebuffer(),m)if(i.drawBuffers){const n=e.texture;for(let e=0,i=n.length;e<i;e++){const i=o.get(n[e]);void 0===i.__webglTexture&&(i.__webglTexture=t.createTexture(),r.memory.textures++)}}else console.warn("THREE.WebGLRenderer: WebGLMultipleRenderTargets can only be used with WebGL2 or WEBGL_draw_buffers extension.");else if(u)if(s){c.__webglMultisampledFramebuffer=t.createFramebuffer(),c.__webglColorRenderbuffer=t.createRenderbuffer(),t.bindRenderbuffer(36161,c.__webglColorRenderbuffer);const o=a.convert(l.format),i=a.convert(l.type),r=C(l.internalFormat,o,i),s=N(e);t.renderbufferStorageMultisample(36161,s,r,e.width,e.height),n.bindFramebuffer(36160,c.__webglMultisampledFramebuffer),t.framebufferRenderbuffer(36160,36064,36161,c.__webglColorRenderbuffer),t.bindRenderbuffer(36161,null),e.depthBuffer&&(c.__webglDepthRenderbuffer=t.createRenderbuffer(),T(c.__webglDepthRenderbuffer,e,!0)),n.bindFramebuffer(36160,null)}else console.warn("THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.");if(p){n.bindTexture(34067,d.__webglTexture),D(34067,l,g);for(let t=0;t<6;t++)A(c.__webglFramebuffer[t],e,l,36064,34069+t);y(l,g)&&_(34067,l,e.width,e.height),n.bindTexture(34067,null)}else if(m){const t=e.texture;for(let i=0,a=t.length;i<a;i++){const a=t[i],r=o.get(a);n.bindTexture(3553,r.__webglTexture),D(3553,a,g),A(c.__webglFramebuffer,e,a,36064+i,3553),y(a,g)&&_(3553,a,e.width,e.height)}n.bindTexture(3553,null)}else{let t=3553;f&&(s?t=l.isDataTexture3D?32879:35866:console.warn("THREE.DataTexture3D and THREE.DataTexture2DArray only supported with WebGL2.")),n.bindTexture(t,d.__webglTexture),D(t,l,g),A(c.__webglFramebuffer,e,l,36064,t),y(l,g)&&_(t,l,e.width,e.height,e.depth),n.bindTexture(t,null)}e.depthBuffer&&(function h(e){const i=o.get(e),a=!0===e.isWebGLCubeRenderTarget;if(e.depthTexture){if(a)throw new Error("target.depthTexture not supported in Cube render targets");!(function r(e,i){if(i&&i.isWebGLCubeRenderTarget)throw new Error("Depth Texture with cube render targets is not supported");if(n.bindFramebuffer(36160,e),!i.depthTexture||!i.depthTexture.isDepthTexture)throw new Error("renderTarget.depthTexture must be an instance of THREE.DepthTexture");o.get(i.depthTexture).__webglTexture&&i.depthTexture.image.width===i.width&&i.depthTexture.image.height===i.height||(i.depthTexture.image.width=i.width,i.depthTexture.image.height=i.height,i.depthTexture.needsUpdate=!0),P(i.depthTexture,0);const a=o.get(i.depthTexture).__webglTexture;if(i.depthTexture.format===kit)t.framebufferTexture2D(36160,36096,3553,a,0);else{if(i.depthTexture.format!==Sit)throw new Error("Unknown depthTexture format");t.framebufferTexture2D(36160,33306,3553,a,0)}})(i.__webglFramebuffer,e)}else if(a){i.__webglDepthbuffer=[];for(let o=0;o<6;o++)n.bindFramebuffer(36160,i.__webglFramebuffer[o]),i.__webglDepthbuffer[o]=t.createRenderbuffer(),T(i.__webglDepthbuffer[o],e,!1)}else n.bindFramebuffer(36160,i.__webglFramebuffer),i.__webglDepthbuffer=t.createRenderbuffer(),T(i.__webglDepthbuffer,e,!1);n.bindFramebuffer(36160,null)})(e)},this.updateRenderTargetMipmap=function j(t){const e=b(t)||s,i=!0===t.isWebGLMultipleRenderTargets?t.texture:[t.texture];for(let a=0,r=i.length;a<r;a++){const r=i[a];if(y(r,e)){const e=t.isWebGLCubeRenderTarget?34067:3553,i=o.get(r).__webglTexture;n.bindTexture(e,i),_(e,r,t.width,t.height),n.bindTexture(e,null)}}},this.updateMultisampleRenderTarget=function U(e){if(e.isWebGLMultisampleRenderTarget)if(s){const i=e.width,a=e.height;let r=16384;e.depthBuffer&&(r|=256),e.stencilBuffer&&(r|=1024);const s=o.get(e);n.bindFramebuffer(36008,s.__webglMultisampledFramebuffer),n.bindFramebuffer(36009,s.__webglFramebuffer),t.blitFramebuffer(0,0,i,a,0,0,i,a,r,9728),n.bindFramebuffer(36008,null),n.bindFramebuffer(36009,s.__webglMultisampledFramebuffer)}else console.warn("THREE.WebGLRenderer: WebGLMultisampleRenderTarget can only be used with WebGL2.")},this.safeSetTexture2D=function G(t,e){t&&t.isWebGLRenderTarget&&(!1===z&&(console.warn("THREE.WebGLTextures.safeSetTexture2D: don't use render targets as textures. Use their .texture property instead."),z=!0),t=t.texture),P(t,e)},this.safeSetTextureCube=function W(t,e){t&&t.isWebGLCubeRenderTarget&&(!1===I&&(console.warn("THREE.WebGLTextures.safeSetTextureCube: don't use cube render targets as textures. Use their .texture property instead."),I=!0),t=t.texture),w(t,e)}}function ddt(t,e,n){const o=n.isWebGL2;return{convert:function i(t){let n;if(t===_it)return 5121;if(1017===t)return 32819;if(1018===t)return 32820;if(1019===t)return 33635;if(1010===t)return 5120;if(1011===t)return 5122;if(t===Cit)return 5123;if(1013===t)return 5124;if(t===Mit)return 5125;if(t===vit)return 5126;if(t===xit)return o?5131:(n=e.get("OES_texture_half_float"),null!==n?n.HALF_FLOAT_OES:null);if(1021===t)return 6406;if(t===Pit)return 6407;if(t===wit)return 6408;if(1024===t)return 6409;if(1025===t)return 6410;if(t===kit)return 6402;if(t===Sit)return 34041;if(1028===t)return 6403;if(1029===t)return 36244;if(1030===t)return 33319;if(1031===t)return 33320;if(1032===t)return 36248;if(1033===t)return 36249;if(33776===t||33777===t||33778===t||33779===t){if(n=e.get("WEBGL_compressed_texture_s3tc"),null===n)return null;if(33776===t)return n.COMPRESSED_RGB_S3TC_DXT1_EXT;if(33777===t)return n.COMPRESSED_RGBA_S3TC_DXT1_EXT;if(33778===t)return n.COMPRESSED_RGBA_S3TC_DXT3_EXT;if(33779===t)return n.COMPRESSED_RGBA_S3TC_DXT5_EXT}if(35840===t||35841===t||35842===t||35843===t){if(n=e.get("WEBGL_compressed_texture_pvrtc"),null===n)return null;if(35840===t)return n.COMPRESSED_RGB_PVRTC_4BPPV1_IMG;if(35841===t)return n.COMPRESSED_RGB_PVRTC_2BPPV1_IMG;if(35842===t)return n.COMPRESSED_RGBA_PVRTC_4BPPV1_IMG;if(35843===t)return n.COMPRESSED_RGBA_PVRTC_2BPPV1_IMG}if(36196===t)return n=e.get("WEBGL_compressed_texture_etc1"),null!==n?n.COMPRESSED_RGB_ETC1_WEBGL:null;if((37492===t||37496===t)&&(n=e.get("WEBGL_compressed_texture_etc"),null!==n)){if(37492===t)return n.COMPRESSED_RGB8_ETC2;if(37496===t)return n.COMPRESSED_RGBA8_ETC2_EAC}return 37808===t||37809===t||37810===t||37811===t||37812===t||37813===t||37814===t||37815===t||37816===t||37817===t||37818===t||37819===t||37820===t||37821===t||37840===t||37841===t||37842===t||37843===t||37844===t||37845===t||37846===t||37847===t||37848===t||37849===t||37850===t||37851===t||37852===t||37853===t?(n=e.get("WEBGL_compressed_texture_astc"),null!==n?t:null):36492===t?(n=e.get("EXT_texture_compression_bptc"),null!==n?t:null):t===Oit?o?34042:(n=e.get("WEBGL_depth_texture"),null!==n?n.UNSIGNED_INT_24_8_WEBGL:null):void 0}}}rdt.prototype.isMeshDistanceMaterial=!0;class pdt extends _st{constructor(t=[]){super(),this.cameras=t}}pdt.prototype.isArrayCamera=!0;class mdt extends prt{constructor(){super(),this.type="Group"}}mdt.prototype.isGroup=!0;const udt={type:"move"};class fdt{constructor(){this._targetRay=null,this._grip=null,this._hand=null}getHandSpace(){return null===this._hand&&(this._hand=new mdt,this._hand.matrixAutoUpdate=!1,this._hand.visible=!1,this._hand.joints={},this._hand.inputState={pinching:!1}),this._hand}getTargetRaySpace(){return null===this._targetRay&&(this._targetRay=new mdt,this._targetRay.matrixAutoUpdate=!1,this._targetRay.visible=!1,this._targetRay.hasLinearVelocity=!1,this._targetRay.linearVelocity=new cat,this._targetRay.hasAngularVelocity=!1,this._targetRay.angularVelocity=new cat),this._targetRay}getGripSpace(){return null===this._grip&&(this._grip=new mdt,this._grip.matrixAutoUpdate=!1,this._grip.visible=!1,this._grip.hasLinearVelocity=!1,this._grip.linearVelocity=new cat,this._grip.hasAngularVelocity=!1,this._grip.angularVelocity=new cat),this._grip}dispatchEvent(t){return null!==this._targetRay&&this._targetRay.dispatchEvent(t),null!==this._grip&&this._grip.dispatchEvent(t),null!==this._hand&&this._hand.dispatchEvent(t),this}disconnect(t){return this.dispatchEvent({type:"disconnected",data:t}),null!==this._targetRay&&(this._targetRay.visible=!1),null!==this._grip&&(this._grip.visible=!1),null!==this._hand&&(this._hand.visible=!1),this}update(t,e,n){let o=null,i=null,a=null;const r=this._targetRay,s=this._grip,l=this._hand;if(t&&"visible-blurred"!==e.session.visibilityState)if(null!==r&&(o=e.getPose(t.targetRaySpace,n),null!==o&&(r.matrix.fromArray(o.transform.matrix),r.matrix.decompose(r.position,r.rotation,r.scale),o.linearVelocity?(r.hasLinearVelocity=!0,r.linearVelocity.copy(o.linearVelocity)):r.hasLinearVelocity=!1,o.angularVelocity?(r.hasAngularVelocity=!0,r.angularVelocity.copy(o.angularVelocity)):r.hasAngularVelocity=!1,this.dispatchEvent(udt))),l&&t.hand){a=!0;for(const o of t.hand.values()){const t=e.getJointPose(o,n);if(void 0===l.joints[o.jointName]){const t=new mdt;t.matrixAutoUpdate=!1,t.visible=!1,l.joints[o.jointName]=t,l.add(t)}const i=l.joints[o.jointName];null!==t&&(i.matrix.fromArray(t.transform.matrix),i.matrix.decompose(i.position,i.rotation,i.scale),i.jointRadius=t.radius),i.visible=null!==t}const o=l.joints["index-finger-tip"].position.distanceTo(l.joints["thumb-tip"].position),i=.02,r=.005;l.inputState.pinching&&o>i+r?(l.inputState.pinching=!1,this.dispatchEvent({type:"pinchend",handedness:t.handedness,target:this})):!l.inputState.pinching&&o<=i-r&&(l.inputState.pinching=!0,this.dispatchEvent({type:"pinchstart",handedness:t.handedness,target:this}))}else null!==s&&t.gripSpace&&(i=e.getPose(t.gripSpace,n),null!==i&&(s.matrix.fromArray(i.transform.matrix),s.matrix.decompose(s.position,s.rotation,s.scale),i.linearVelocity?(s.hasLinearVelocity=!0,s.linearVelocity.copy(i.linearVelocity)):s.hasLinearVelocity=!1,i.angularVelocity?(s.hasAngularVelocity=!0,s.angularVelocity.copy(i.angularVelocity)):s.hasAngularVelocity=!1));return null!==r&&(r.visible=null!==o),null!==s&&(s.visible=null!==i),null!==l&&(l.visible=null!==a),this}}class gdt extends Uit{constructor(t,e){super();const n=this,o=t.state;let i=null,a=1,r=null,s="local-floor",l=null,c=null,d=null,p=null,m=null;const u=[],f=new Map,g=new _st;g.layers.enable(1),g.viewport=new aat;const h=new _st;h.layers.enable(2),h.viewport=new aat;const b=[g,h],y=new pdt;y.layers.enable(1),y.layers.enable(2);let _=null,C=null;function M(t){const e=f.get(t.inputSource);e&&e.dispatchEvent({type:t.type,data:t.inputSource})}function v(){f.forEach((function(t,e){t.disconnect(e)})),f.clear(),_=null,C=null,o.bindXRFramebuffer(null),t.setRenderTarget(t.getRenderTarget()),S.stop(),n.isPresenting=!1,n.dispatchEvent({type:"sessionend"})}function x(t){const e=i.inputSources;for(let t=0;t<u.length;t++)f.set(e[t],u[t]);for(let e=0;e<t.removed.length;e++){const n=t.removed[e],o=f.get(n);o&&(o.dispatchEvent({type:"disconnected",data:n}),f.delete(n))}for(let e=0;e<t.added.length;e++){const n=t.added[e],o=f.get(n);o&&o.dispatchEvent({type:"connected",data:n})}}this.cameraAutoUpdate=!0,this.enabled=!1,this.isPresenting=!1,this.getController=function(t){let e=u[t];return void 0===e&&(e=new fdt,u[t]=e),e.getTargetRaySpace()},this.getControllerGrip=function(t){let e=u[t];return void 0===e&&(e=new fdt,u[t]=e),e.getGripSpace()},this.getHand=function(t){let e=u[t];return void 0===e&&(e=new fdt,u[t]=e),e.getHandSpace()},this.setFramebufferScaleFactor=function(t){a=t,!0===n.isPresenting&&console.warn("THREE.WebXRManager: Cannot change framebuffer scale while presenting.")},this.setReferenceSpaceType=function(t){s=t,!0===n.isPresenting&&console.warn("THREE.WebXRManager: Cannot change reference space type while presenting.")},this.getReferenceSpace=function(){return r},this.getSession=function(){return i},this.setSession=async function(t){if(i=t,null!==i){i.addEventListener("select",M),i.addEventListener("selectstart",M),i.addEventListener("selectend",M),i.addEventListener("squeeze",M),i.addEventListener("squeezestart",M),i.addEventListener("squeezeend",M),i.addEventListener("end",v),i.addEventListener("inputsourceschange",x);const t=e.getContextAttributes();if(!0!==t.xrCompatible&&await e.makeXRCompatible(),void 0===i.renderState.layers)m=new XRWebGLLayer(i,e,{antialias:t.antialias,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:a}),i.updateRenderState({baseLayer:m});else{let n=0;if(t.antialias)m=new XRWebGLLayer(i,e,{antialias:!0,alpha:t.alpha,depth:t.depth,stencil:t.stencil,framebufferScaleFactor:a}),i.updateRenderState({layers:[m]});else{t.depth&&(n=t.stencil?34041:6402);const o={colorFormat:t.alpha?6408:6407,depthFormat:n,scaleFactor:a};c=new XRWebGLBinding(i,e),p=c.createProjectionLayer(o),d=e.createFramebuffer(),i.updateRenderState({layers:[p]})}}r=await i.requestReferenceSpace(s),S.setContext(i),S.start(),n.isPresenting=!0,n.dispatchEvent({type:"sessionstart"})}};const O=new cat,P=new cat;function w(t,e){null===e?t.matrixWorld.copy(t.matrix):t.matrixWorld.multiplyMatrices(e.matrixWorld,t.matrix),t.matrixWorldInverse.copy(t.matrixWorld).invert()}this.updateCamera=function(t){if(null===i)return;y.near=h.near=g.near=t.near,y.far=h.far=g.far=t.far,_===y.near&&C===y.far||(i.updateRenderState({depthNear:y.near,depthFar:y.far}),_=y.near,C=y.far);const e=t.parent,n=y.cameras;w(y,e);for(let t=0;t<n.length;t++)w(n[t],e);y.matrixWorld.decompose(y.position,y.quaternion,y.scale),t.position.copy(y.position),t.quaternion.copy(y.quaternion),t.scale.copy(y.scale),t.matrix.copy(y.matrix),t.matrixWorld.copy(y.matrixWorld);const o=t.children;for(let t=0,e=o.length;t<e;t++)o[t].updateMatrixWorld(!0);2===n.length?(function a(t,e,n){O.setFromMatrixPosition(e.matrixWorld),P.setFromMatrixPosition(n.matrixWorld);const o=O.distanceTo(P),i=e.projectionMatrix.elements,a=n.projectionMatrix.elements,r=i[14]/(i[10]-1),s=i[14]/(i[10]+1),l=(i[9]+1)/i[5],c=(i[9]-1)/i[5],d=(i[8]-1)/i[0],p=(a[8]+1)/a[0],m=r*d,u=r*p,f=o/(-d+p),g=f*-d;e.matrixWorld.decompose(t.position,t.quaternion,t.scale),t.translateX(g),t.translateZ(f),t.matrixWorld.compose(t.position,t.quaternion,t.scale),t.matrixWorldInverse.copy(t.matrixWorld).invert();const h=r+f,b=s+f;t.projectionMatrix.makePerspective(m-g,u+(o-g),l*s/b*h,c*s/b*h,h,b)})(y,g,h):y.projectionMatrix.copy(g.projectionMatrix)},this.getCamera=function(){return y},this.getFoveation=function(){return null!==p?p.fixedFoveation:null!==m?m.fixedFoveation:void 0},this.setFoveation=function(t){null!==p&&(p.fixedFoveation=t),null!==m&&void 0!==m.fixedFoveation&&(m.fixedFoveation=t)};let k=null;const S=new Rst;S.setAnimationLoop((function D(t,n){if(l=n.getViewerPose(r),null!==l){const t=l.views;null!==m&&o.bindXRFramebuffer(m.framebuffer);let n=!1;t.length!==y.cameras.length&&(y.cameras.length=0,n=!0);for(let i=0;i<t.length;i++){const a=t[i];let r=null;if(null!==m)r=m.getViewport(a);else{const t=c.getViewSubImage(p,a);o.bindXRFramebuffer(d),void 0!==t.depthStencilTexture&&e.framebufferTexture2D(36160,36096,3553,t.depthStencilTexture,0),e.framebufferTexture2D(36160,36064,3553,t.colorTexture,0),r=t.viewport}const s=b[i];s.matrix.fromArray(a.transform.matrix),s.projectionMatrix.fromArray(a.projectionMatrix),s.viewport.set(r.x,r.y,r.width,r.height),0===i&&y.matrix.copy(s.matrix),!0===n&&y.cameras.push(s)}}const a=i.inputSources;for(let t=0;t<u.length;t++)u[t].update(a[t],n,r);k&&k(t,n)})),this.setAnimationLoop=function(t){k=t},this.dispose=function(){}}}function hdt(t){function e(e,n){e.opacity.value=n.opacity,n.color&&e.diffuse.value.copy(n.color),n.emissive&&e.emissive.value.copy(n.emissive).multiplyScalar(n.emissiveIntensity),n.map&&(e.map.value=n.map),n.alphaMap&&(e.alphaMap.value=n.alphaMap),n.specularMap&&(e.specularMap.value=n.specularMap);const o=t.get(n).envMap;if(o){e.envMap.value=o,e.flipEnvMap.value=o.isCubeTexture&&!1===o.isRenderTargetTexture?-1:1,e.reflectivity.value=n.reflectivity,e.refractionRatio.value=n.refractionRatio;const i=t.get(o).__maxMipLevel;void 0!==i&&(e.maxMipLevel.value=i)}let i,a;n.lightMap&&(e.lightMap.value=n.lightMap,e.lightMapIntensity.value=n.lightMapIntensity),n.aoMap&&(e.aoMap.value=n.aoMap,e.aoMapIntensity.value=n.aoMapIntensity),n.map?i=n.map:n.specularMap?i=n.specularMap:n.displacementMap?i=n.displacementMap:n.normalMap?i=n.normalMap:n.bumpMap?i=n.bumpMap:n.roughnessMap?i=n.roughnessMap:n.metalnessMap?i=n.metalnessMap:n.alphaMap?i=n.alphaMap:n.emissiveMap?i=n.emissiveMap:n.clearcoatMap?i=n.clearcoatMap:n.clearcoatNormalMap?i=n.clearcoatNormalMap:n.clearcoatRoughnessMap?i=n.clearcoatRoughnessMap:n.specularIntensityMap?i=n.specularIntensityMap:n.specularTintMap&&(i=n.specularTintMap),void 0!==i&&(i.isWebGLRenderTarget&&(i=i.texture),!0===i.matrixAutoUpdate&&i.updateMatrix(),e.uvTransform.value.copy(i.matrix)),n.aoMap?a=n.aoMap:n.lightMap&&(a=n.lightMap),void 0!==a&&(a.isWebGLRenderTarget&&(a=a.texture),!0===a.matrixAutoUpdate&&a.updateMatrix(),e.uv2Transform.value.copy(a.matrix))}function n(e,n){e.roughness.value=n.roughness,e.metalness.value=n.metalness,n.roughnessMap&&(e.roughnessMap.value=n.roughnessMap),n.metalnessMap&&(e.metalnessMap.value=n.metalnessMap),n.emissiveMap&&(e.emissiveMap.value=n.emissiveMap),n.bumpMap&&(e.bumpMap.value=n.bumpMap,e.bumpScale.value=n.bumpScale,1===n.side&&(e.bumpScale.value*=-1)),n.normalMap&&(e.normalMap.value=n.normalMap,e.normalScale.value.copy(n.normalScale),1===n.side&&e.normalScale.value.negate()),n.displacementMap&&(e.displacementMap.value=n.displacementMap,e.displacementScale.value=n.displacementScale,e.displacementBias.value=n.displacementBias),t.get(n).envMap&&(e.envMapIntensity.value=n.envMapIntensity)}return{refreshFogUniforms:function o(t,e){t.fogColor.value.copy(e.color),e.isFog?(t.fogNear.value=e.near,t.fogFar.value=e.far):e.isFogExp2&&(t.fogDensity.value=e.density)},refreshMaterialUniforms:function i(t,o,a,r,s){o.isMeshBasicMaterial?e(t,o):o.isMeshLambertMaterial?(e(t,o),(function l(t,e){e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap)})(t,o)):o.isMeshToonMaterial?(e(t,o),(function c(t,e){e.gradientMap&&(t.gradientMap.value=e.gradientMap),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap),e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1)),e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate()),e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,o)):o.isMeshPhongMaterial?(e(t,o),(function d(t,e){t.specular.value.copy(e.specular),t.shininess.value=Math.max(e.shininess,1e-4),e.emissiveMap&&(t.emissiveMap.value=e.emissiveMap),e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1)),e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate()),e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,o)):o.isMeshStandardMaterial?(e(t,o),o.isMeshPhysicalMaterial?(function p(t,e,o){n(t,e),t.reflectivity.value=e.reflectivity,t.clearcoat.value=e.clearcoat,t.clearcoatRoughness.value=e.clearcoatRoughness,e.sheen&&t.sheen.value.copy(e.sheen),e.clearcoatMap&&(t.clearcoatMap.value=e.clearcoatMap),e.clearcoatRoughnessMap&&(t.clearcoatRoughnessMap.value=e.clearcoatRoughnessMap),e.clearcoatNormalMap&&(t.clearcoatNormalScale.value.copy(e.clearcoatNormalScale),t.clearcoatNormalMap.value=e.clearcoatNormalMap,1===e.side&&t.clearcoatNormalScale.value.negate()),t.transmission.value=e.transmission,e.transmissionMap&&(t.transmissionMap.value=e.transmissionMap),e.transmission>0&&(t.transmissionSamplerMap.value=o.texture,t.transmissionSamplerSize.value.set(o.width,o.height)),t.thickness.value=e.thickness,e.thicknessMap&&(t.thicknessMap.value=e.thicknessMap),t.attenuationDistance.value=e.attenuationDistance,t.attenuationTint.value.copy(e.attenuationTint),t.specularIntensity.value=e.specularIntensity,t.specularTint.value.copy(e.specularTint),e.specularIntensityMap&&(t.specularIntensityMap.value=e.specularIntensityMap),e.specularTintMap&&(t.specularTintMap.value=e.specularTintMap)})(t,o,s):n(t,o)):o.isMeshMatcapMaterial?(e(t,o),(function m(t,e){e.matcap&&(t.matcap.value=e.matcap),e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1)),e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate()),e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,o)):o.isMeshDepthMaterial?(e(t,o),(function u(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,o)):o.isMeshDistanceMaterial?(e(t,o),(function f(t,e){e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias),t.referencePosition.value.copy(e.referencePosition),t.nearDistance.value=e.nearDistance,t.farDistance.value=e.farDistance})(t,o)):o.isMeshNormalMaterial?(e(t,o),(function g(t,e){e.bumpMap&&(t.bumpMap.value=e.bumpMap,t.bumpScale.value=e.bumpScale,1===e.side&&(t.bumpScale.value*=-1)),e.normalMap&&(t.normalMap.value=e.normalMap,t.normalScale.value.copy(e.normalScale),1===e.side&&t.normalScale.value.negate()),e.displacementMap&&(t.displacementMap.value=e.displacementMap,t.displacementScale.value=e.displacementScale,t.displacementBias.value=e.displacementBias)})(t,o)):o.isLineBasicMaterial?((function h(t,e){t.diffuse.value.copy(e.color),t.opacity.value=e.opacity})(t,o),o.isLineDashedMaterial&&(function b(t,e){t.dashSize.value=e.dashSize,t.totalSize.value=e.dashSize+e.gapSize,t.scale.value=e.scale})(t,o)):o.isPointsMaterial?(function y(t,e,n,o){let i;t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.size.value=e.size*n,t.scale.value=.5*o,e.map&&(t.map.value=e.map),e.alphaMap&&(t.alphaMap.value=e.alphaMap),e.map?i=e.map:e.alphaMap&&(i=e.alphaMap),void 0!==i&&(!0===i.matrixAutoUpdate&&i.updateMatrix(),t.uvTransform.value.copy(i.matrix))})(t,o,a,r):o.isSpriteMaterial?(function _(t,e){let n;t.diffuse.value.copy(e.color),t.opacity.value=e.opacity,t.rotation.value=e.rotation,e.map&&(t.map.value=e.map),e.alphaMap&&(t.alphaMap.value=e.alphaMap),e.map?n=e.map:e.alphaMap&&(n=e.alphaMap),void 0!==n&&(!0===n.matrixAutoUpdate&&n.updateMatrix(),t.uvTransform.value.copy(n.matrix))})(t,o):o.isShadowMaterial?(t.color.value.copy(o.color),t.opacity.value=o.opacity):o.isShaderMaterial&&(o.uniformsNeedUpdate=!1)}}}function bdt(t={}){const e=void 0!==t.canvas?t.canvas:(function n(){const t=document.createElementNS("http://www.w3.org/1999/xhtml","canvas");return t.style.display="block",t})(),o=void 0!==t.context?t.context:null,i=void 0!==t.alpha&&t.alpha,a=void 0===t.depth||t.depth,r=void 0===t.stencil||t.stencil,s=void 0!==t.antialias&&t.antialias,l=void 0===t.premultipliedAlpha||t.premultipliedAlpha,c=void 0!==t.preserveDrawingBuffer&&t.preserveDrawingBuffer,d=void 0!==t.powerPreference?t.powerPreference:"default",p=void 0!==t.failIfMajorPerformanceCaveat&&t.failIfMajorPerformanceCaveat;let m=null,u=null;const f=[],g=[];this.domElement=e,this.debug={checkShaderErrors:!0},this.autoClear=!0,this.autoClearColor=!0,this.autoClearDepth=!0,this.autoClearStencil=!0,this.sortObjects=!0,this.clippingPlanes=[],this.localClippingEnabled=!1,this.gammaFactor=2,this.outputEncoding=zit,this.physicallyCorrectLights=!1,this.toneMapping=0,this.toneMappingExposure=1;const h=this;let b=!1,y=0,_=0,C=null,M=-1,v=null;const x=new aat,O=new aat;let P=null,w=e.width,k=e.height,S=1,D=null,E=null;const R=new aat(0,0,w,k),A=new aat(0,0,w,k);let T=!1;const N=[],z=new Est;let I=!1,H=!1,F=null;const L=new Bat,B=new cat,V={background:null,fog:null,environment:null,overrideMaterial:null,isScene:!0};function j(){return null===C?S:1}let U,G,W,Y,q,Z,X,K,J,Q,$,tt,et,nt,ot,it,at,rt,st,lt,ct,dt,pt,mt=o;function ut(t,n){for(let o=0;o<t.length;o++){const i=e.getContext(t[o],n);if(null!==i)return i}return null}try{const t={alpha:i,depth:a,stencil:r,antialias:s,premultipliedAlpha:l,preserveDrawingBuffer:c,powerPreference:d,failIfMajorPerformanceCaveat:p};if(e.addEventListener("webglcontextlost",ht,!1),e.addEventListener("webglcontextrestored",bt,!1),null===mt){const e=["webgl2","webgl","experimental-webgl"];if(!0===h.isWebGL1Renderer&&e.shift(),mt=ut(e,t),null===mt)throw ut(e)?new Error("Error creating WebGL context with your selected attributes."):new Error("Error creating WebGL context.")}void 0===mt.getShaderPrecisionFormat&&(mt.getShaderPrecisionFormat=function(){return{rangeMin:1,rangeMax:1,precision:1}})}catch(t){throw console.error("THREE.WebGLRenderer: "+t.message),t}function ft(){U=new glt(mt),G=new Bst(mt,U,t),U.init(G),dt=new ddt(mt,U,G),W=new ldt(mt,U,G),N[0]=1029,Y=new ylt(mt),q=new Zct,Z=new cdt(mt,U,W,q,G,dt,Y),X=new jst(h),K=new flt(h),J=new Ast(mt,G),pt=new Fst(mt,U,J,G),Q=new hlt(mt,J,Y,pt),$=new vlt(mt,Q,J,Y),st=new Mlt(mt),it=new Vst(q),tt=new qct(h,X,K,U,G,pt,it),et=new hdt(q),nt=new Qct(q),ot=new idt(U,G),rt=new Hst(h,X,W,$,l),at=new sdt(h,$,G),lt=new Lst(mt,U,Y,G),ct=new blt(mt,U,Y,G),Y.programs=tt.programs,h.capabilities=G,h.extensions=U,h.properties=q,h.renderLists=nt,h.shadowMap=at,h.state=W,h.info=Y}ft();const gt=new gdt(h,mt);function ht(t){t.preventDefault(),console.log("THREE.WebGLRenderer: Context Lost."),b=!0}function bt(){console.log("THREE.WebGLRenderer: Context Restored."),b=!1;const t=Y.autoReset,e=at.enabled,n=at.autoUpdate,o=at.needsUpdate,i=at.type;ft(),Y.autoReset=t,at.enabled=e,at.autoUpdate=n,at.needsUpdate=o,at.type=i}function yt(t){const e=t.target;e.removeEventListener("dispose",yt),(function n(t){(function e(t){const e=q.get(t).programs;void 0!==e&&e.forEach((function(t){tt.releaseProgram(t)}))})(t),q.remove(t)})(e)}this.xr=gt,this.getContext=function(){return mt},this.getContextAttributes=function(){return mt.getContextAttributes()},this.forceContextLoss=function(){const t=U.get("WEBGL_lose_context");t&&t.loseContext()},this.forceContextRestore=function(){const t=U.get("WEBGL_lose_context");t&&t.restoreContext()},this.getPixelRatio=function(){return S},this.setPixelRatio=function(t){void 0!==t&&(S=t,this.setSize(w,k,!1))},this.getSize=function(t){return t.set(w,k)},this.setSize=function(t,n,o){gt.isPresenting?console.warn("THREE.WebGLRenderer: Can't change size while VR device is presenting."):(w=t,k=n,e.width=Math.floor(t*S),e.height=Math.floor(n*S),!1!==o&&(e.style.width=t+"px",e.style.height=n+"px"),this.setViewport(0,0,t,n))},this.getDrawingBufferSize=function(t){return t.set(w*S,k*S).floor()},this.setDrawingBufferSize=function(t,n,o){w=t,k=n,S=o,e.width=Math.floor(t*o),e.height=Math.floor(n*o),this.setViewport(0,0,t,n)},this.getCurrentViewport=function(t){return t.copy(x)},this.getViewport=function(t){return t.copy(R)},this.setViewport=function(t,e,n,o){t.isVector4?R.set(t.x,t.y,t.z,t.w):R.set(t,e,n,o),W.viewport(x.copy(R).multiplyScalar(S).floor())},this.getScissor=function(t){return t.copy(A)},this.setScissor=function(t,e,n,o){t.isVector4?A.set(t.x,t.y,t.z,t.w):A.set(t,e,n,o),W.scissor(O.copy(A).multiplyScalar(S).floor())},this.getScissorTest=function(){return T},this.setScissorTest=function(t){W.setScissorTest(T=t)},this.setOpaqueSort=function(t){D=t},this.setTransparentSort=function(t){E=t},this.getClearColor=function(t){return t.copy(rt.getClearColor())},this.setClearColor=function(){rt.setClearColor.apply(rt,arguments)},this.getClearAlpha=function(){return 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o=e.getAttributes();t.hasPositions&&(mt.bindBuffer(34962,n.position),mt.bufferData(34962,t.positionArray,35048),pt.enableAttribute(o.position),mt.vertexAttribPointer(o.position,3,5126,!1,0,0)),t.hasNormals&&(mt.bindBuffer(34962,n.normal),mt.bufferData(34962,t.normalArray,35048),pt.enableAttribute(o.normal),mt.vertexAttribPointer(o.normal,3,5126,!1,0,0)),t.hasUvs&&(mt.bindBuffer(34962,n.uv),mt.bufferData(34962,t.uvArray,35048),pt.enableAttribute(o.uv),mt.vertexAttribPointer(o.uv,2,5126,!1,0,0)),t.hasColors&&(mt.bindBuffer(34962,n.color),mt.bufferData(34962,t.colorArray,35048),pt.enableAttribute(o.color),mt.vertexAttribPointer(o.color,3,5126,!1,0,0)),pt.disableUnusedAttributes(),mt.drawArrays(4,0,t.count),t.count=0},this.renderBufferDirect=function(t,e,n,o,i,a){null===e&&(e=V);const r=i.isMesh&&i.matrixWorld.determinant()<0,s=St(t,e,o,i);W.setMaterial(o,r);let l=n.index;const c=n.attributes.position;if(null===l){if(void 0===c||0===c.count)return}else if(0===l.count)return;let 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if((t.isMesh||t.isLine||t.isPoints)&&(t.isSkinnedMesh&&t.skeleton.frame!==Y.render.frame&&(t.skeleton.update(),t.skeleton.frame=Y.render.frame),!t.frustumCulled||z.intersectsObject(t))){o&&B.setFromMatrixPosition(t.matrixWorld).applyMatrix4(L);const e=$.update(t),i=t.material;if(Array.isArray(i)){const o=e.groups;for(let a=0,r=o.length;a<r;a++){const r=o[a],s=i[r.materialIndex];s&&s.visible&&m.push(t,e,s,n,B.z,r)}}else i.visible&&m.push(t,e,i,n,B.z,null)}const i=t.children;for(let t=0,a=i.length;t<a;t++)xt(i[t],e,n,o)}function Ot(t,e,n){const o=!0===e.isScene?e.overrideMaterial:null;if(n.isArrayCamera){const i=n.cameras;for(let n=0,a=i.length;n<a;n++){const a=i[n];W.viewport(x.copy(a.viewport)),u.setupLightsView(a);for(let n=0,i=t.length;n<i;n++){const i=t[n],r=i.object,s=i.geometry,l=null===o?i.material:o,c=i.group;r.layers.test(a.layers)&&Pt(r,e,a,s,l,c)}}}else for(let i=0,a=t.length;i<a;i++){const a=t[i];Pt(a.object,e,n,a.geometry,null===o?a.material:o,a.group)}}function Pt(t,e,n,o,i,a){if(t.onBeforeRender(h,e,n,o,i,a),t.modelViewMatrix.multiplyMatrices(n.matrixWorldInverse,t.matrixWorld),t.normalMatrix.getNormalMatrix(t.modelViewMatrix),t.isImmediateRenderObject){const o=St(n,e,i,t);W.setMaterial(i),pt.reset(),(function r(t,e){t.render((function(t){h.renderBufferImmediate(t,e)}))})(t,o)}else!0===i.transparent&&2===i.side?(i.side=1,i.needsUpdate=!0,h.renderBufferDirect(n,e,o,i,t,a),i.side=0,i.needsUpdate=!0,h.renderBufferDirect(n,e,o,i,t,a),i.side=2):h.renderBufferDirect(n,e,o,i,t,a);t.onAfterRender(h,e,n,o,i,a)}function wt(t,e,n){!0!==e.isScene&&(e=V);const o=q.get(t),i=u.state.lights,a=i.state.version,r=tt.getParameters(t,i.state,u.state.shadowsArray,e,n),s=tt.getProgramCacheKey(r);let l=o.programs;o.environment=t.isMeshStandardMaterial?e.environment:null,o.fog=e.fog,o.envMap=(t.isMeshStandardMaterial?K:X).get(t.envMap||o.environment),void 0===l&&(t.addEventListener("dispose",yt),l=new Map,o.programs=l);let c=l.get(s);if(void 0!==c){if(o.currentProgram===c&&o.lightsStateVersion===a)return kt(t,r),c}else r.uniforms=tt.getUniforms(t),t.onBuild(r,h),t.onBeforeCompile(r,h),c=tt.acquireProgram(r,s),l.set(s,c),o.uniforms=r.uniforms;const d=o.uniforms;(t.isShaderMaterial||t.isRawShaderMaterial)&&!0!==t.clipping||(d.clippingPlanes=it.uniform),kt(t,r),o.needsLights=(function p(t){return t.isMeshLambertMaterial||t.isMeshToonMaterial||t.isMeshPhongMaterial||t.isMeshStandardMaterial||t.isShadowMaterial||t.isShaderMaterial&&!0===t.lights})(t),o.lightsStateVersion=a,o.needsLights&&(d.ambientLightColor.value=i.state.ambient,d.lightProbe.value=i.state.probe,d.directionalLights.value=i.state.directional,d.directionalLightShadows.value=i.state.directionalShadow,d.spotLights.value=i.state.spot,d.spotLightShadows.value=i.state.spotShadow,d.rectAreaLights.value=i.state.rectArea,d.ltc_1.value=i.state.rectAreaLTC1,d.ltc_2.value=i.state.rectAreaLTC2,d.pointLights.value=i.state.point,d.pointLightShadows.value=i.state.pointShadow,d.hemisphereLights.value=i.state.hemi,d.directionalShadowMap.value=i.state.directionalShadowMap,d.directionalShadowMatrix.value=i.state.directionalShadowMatrix,d.spotShadowMap.value=i.state.spotShadowMap,d.spotShadowMatrix.value=i.state.spotShadowMatrix,d.pointShadowMap.value=i.state.pointShadowMap,d.pointShadowMatrix.value=i.state.pointShadowMatrix);const m=c.getUniforms(),f=wct.seqWithValue(m.seq,d);return o.currentProgram=c,o.uniformsList=f,c}function kt(t,e){const n=q.get(t);n.outputEncoding=e.outputEncoding,n.instancing=e.instancing,n.skinning=e.skinning,n.morphTargets=e.morphTargets,n.morphNormals=e.morphNormals,n.numClippingPlanes=e.numClippingPlanes,n.numIntersection=e.numClipIntersection,n.vertexAlphas=e.vertexAlphas,n.vertexTangents=e.vertexTangents}function St(t,e,n,o){!0!==e.isScene&&(e=V),Z.resetTextureUnits();const i=e.fog,a=null===C?h.outputEncoding:C.texture.encoding,r=(n.isMeshStandardMaterial?K:X).get(n.envMap||(n.isMeshStandardMaterial?e.environment:null)),s=!0===n.vertexColors&&!!o.geometry&&!!o.geometry.attributes.color&&4===o.geometry.attributes.color.itemSize,l=!!o.geometry&&!!o.geometry.attributes.tangent,c=!!o.geometry&&!!o.geometry.morphAttributes.position,d=!!o.geometry&&!!o.geometry.morphAttributes.normal,p=q.get(n),m=u.state.lights;!0!==I||!0!==H&&t===v||it.setState(n,t,t===v&&n.id===M);let f=!1;n.version===p.__version?p.needsLights&&p.lightsStateVersion!==m.state.version||p.outputEncoding!==a||o.isInstancedMesh&&!1===p.instancing?f=!0:o.isInstancedMesh||!0!==p.instancing?o.isSkinnedMesh&&!1===p.skinning?f=!0:o.isSkinnedMesh||!0!==p.skinning?p.envMap!==r||n.fog&&p.fog!==i?f=!0:void 0===p.numClippingPlanes||p.numClippingPlanes===it.numPlanes&&p.numIntersection===it.numIntersection?(p.vertexAlphas!==s||p.vertexTangents!==l||p.morphTargets!==c||p.morphNormals!==d)&&(f=!0):f=!0:f=!0:f=!0:(f=!0,p.__version=n.version);let g=p.currentProgram;!0===f&&(g=wt(n,e,o));let b=!1,y=!1,_=!1;const x=g.getUniforms(),O=p.uniforms;if(W.useProgram(g.program)&&(b=!0,y=!0,_=!0),n.id!==M&&(M=n.id,y=!0),b||v!==t){if(x.setValue(mt,"projectionMatrix",t.projectionMatrix),G.logarithmicDepthBuffer&&x.setValue(mt,"logDepthBufFC",2/(Math.log(t.far+1)/Math.LN2)),v!==t&&(v=t,y=!0,_=!0),n.isShaderMaterial||n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshStandardMaterial||n.envMap){const e=x.map.cameraPosition;void 0!==e&&e.setValue(mt,B.setFromMatrixPosition(t.matrixWorld))}(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial)&&x.setValue(mt,"isOrthographic",!0===t.isOrthographicCamera),(n.isMeshPhongMaterial||n.isMeshToonMaterial||n.isMeshLambertMaterial||n.isMeshBasicMaterial||n.isMeshStandardMaterial||n.isShaderMaterial||n.isShadowMaterial||o.isSkinnedMesh)&&x.setValue(mt,"viewMatrix",t.matrixWorldInverse)}if(o.isSkinnedMesh){x.setOptional(mt,o,"bindMatrix"),x.setOptional(mt,o,"bindMatrixInverse");const t=o.skeleton;t&&(G.floatVertexTextures?(null===t.boneTexture&&t.computeBoneTexture(),x.setValue(mt,"boneTexture",t.boneTexture,Z),x.setValue(mt,"boneTextureSize",t.boneTextureSize)):x.setOptional(mt,t,"boneMatrices"))}return(y||p.receiveShadow!==o.receiveShadow)&&(p.receiveShadow=o.receiveShadow,x.setValue(mt,"receiveShadow",o.receiveShadow)),y&&(x.setValue(mt,"toneMappingExposure",h.toneMappingExposure),p.needsLights&&(function 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window&&vt.setContext(window),this.setAnimationLoop=function(t){_t=t,gt.setAnimationLoop(t),null===t?vt.stop():vt.start()},gt.addEventListener("sessionstart",Ct),gt.addEventListener("sessionend",Mt),this.render=function(t,e){if(void 0!==e&&!0!==e.isCamera)return void console.error("THREE.WebGLRenderer.render: camera is not an instance of THREE.Camera.");if(!0===b)return;!0===t.autoUpdate&&t.updateMatrixWorld(),null===e.parent&&e.updateMatrixWorld(),!0===gt.enabled&&!0===gt.isPresenting&&(!0===gt.cameraAutoUpdate&&gt.updateCamera(e),e=gt.getCamera()),!0===t.isScene&&t.onBeforeRender(h,t,e,C),u=ot.get(t,g.length),u.init(),g.push(u),L.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),z.setFromProjectionMatrix(L),H=this.localClippingEnabled,I=it.init(this.clippingPlanes,H,e),m=nt.get(t,f.length),m.init(),f.push(m),xt(t,e,0,h.sortObjects),m.finish(),!0===h.sortObjects&&m.sort(D,E),!0===I&&it.beginShadows(),at.render(u.state.shadowsArray,t,e),u.setupLights(),u.setupLightsView(e),!0===I&&it.endShadows(),!0===this.info.autoReset&&this.info.reset(),rt.render(m,t);const n=m.opaque,o=m.transmissive,i=m.transparent;n.length>0&&Ot(n,t,e),o.length>0&&(function a(t,e,n,o){null===F&&(F=new(!0===s&&!0===G.isWebGL2?sat:rat)(1024,1024,{generateMipmaps:!0,type:null!==dt.convert(xit)?xit:_it,minFilter:yit,magFilter:hit,wrapS:fit,wrapT:fit}));const i=h.getRenderTarget();h.setRenderTarget(F),h.clear();const a=h.toneMapping;h.toneMapping=0,Ot(t,n,o),h.toneMapping=a,Z.updateMultisampleRenderTarget(F),Z.updateRenderTargetMipmap(F),h.setRenderTarget(i),Ot(e,n,o)})(n,o,t,e),i.length>0&&Ot(i,t,e),null!==C&&(Z.updateMultisampleRenderTarget(C),Z.updateRenderTargetMipmap(C)),!0===t.isScene&&t.onAfterRender(h,t,e),W.buffers.depth.setTest(!0),W.buffers.depth.setMask(!0),W.buffers.color.setMask(!0),W.setPolygonOffset(!1),pt.resetDefaultState(),M=-1,v=null,g.pop(),u=g.length>0?g[g.length-1]:null,f.pop(),m=f.length>0?f[f.length-1]:null},this.getActiveCubeFace=function(){return y},this.getActiveMipmapLevel=function(){return _},this.getRenderTarget=function(){return C},this.setRenderTarget=function(t,e=0,n=0){C=t,y=e,_=n,t&&void 0===q.get(t).__webglFramebuffer&&Z.setupRenderTarget(t);let o=null,i=!1,a=!1;if(t){const n=t.texture;(n.isDataTexture3D||n.isDataTexture2DArray)&&(a=!0);const r=q.get(t).__webglFramebuffer;t.isWebGLCubeRenderTarget?(o=r[e],i=!0):o=t.isWebGLMultisampleRenderTarget?q.get(t).__webglMultisampledFramebuffer:r,x.copy(t.viewport),O.copy(t.scissor),P=t.scissorTest}else x.copy(R).multiplyScalar(S).floor(),O.copy(A).multiplyScalar(S).floor(),P=T;if(W.bindFramebuffer(36160,o)&&G.drawBuffers){let e=!1;if(t)if(t.isWebGLMultipleRenderTargets){const n=t.texture;if(N.length!==n.length||36064!==N[0]){for(let t=0,e=n.length;t<e;t++)N[t]=36064+t;N.length=n.length,e=!0}}else 1===N.length&&36064===N[0]||(N[0]=36064,N.length=1,e=!0);else 1===N.length&&1029===N[0]||(N[0]=1029,N.length=1,e=!0);e&&(G.isWebGL2?mt.drawBuffers(N):U.get("WEBGL_draw_buffers").drawBuffersWEBGL(N))}if(W.viewport(x),W.scissor(O),W.setScissorTest(P),i){const o=q.get(t.texture);mt.framebufferTexture2D(36160,36064,34069+e,o.__webglTexture,n)}else if(a){const o=q.get(t.texture);mt.framebufferTextureLayer(36160,36064,o.__webglTexture,n||0,e||0)}},this.readRenderTargetPixels=function(t,e,n,o,i,a,r){if(!t||!t.isWebGLRenderTarget)return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not THREE.WebGLRenderTarget.");let s=q.get(t).__webglFramebuffer;if(t.isWebGLCubeRenderTarget&&void 0!==r&&(s=s[r]),s){W.bindFramebuffer(36160,s);try{const r=t.texture,s=r.format,l=r.type;if(s!==wit&&dt.convert(s)!==mt.getParameter(35739))return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in RGBA or implementation defined format.");const c=l===xit&&(U.has("EXT_color_buffer_half_float")||G.isWebGL2&&U.has("EXT_color_buffer_float"));if(!(l===_it||dt.convert(l)===mt.getParameter(35738)||l===vit&&(G.isWebGL2||U.has("OES_texture_float")||U.has("WEBGL_color_buffer_float"))||c))return void console.error("THREE.WebGLRenderer.readRenderTargetPixels: renderTarget is not in UnsignedByteType or implementation defined type.");36053===mt.checkFramebufferStatus(36160)?e>=0&&e<=t.width-o&&n>=0&&n<=t.height-i&&mt.readPixels(e,n,o,i,dt.convert(s),dt.convert(l),a):console.error("THREE.WebGLRenderer.readRenderTargetPixels: readPixels from renderTarget failed. Framebuffer not complete.")}finally{const t=null!==C?q.get(C).__webglFramebuffer:null;W.bindFramebuffer(36160,t)}}},this.copyFramebufferToTexture=function(t,e,n=0){const o=Math.pow(2,-n),i=Math.floor(e.image.width*o),a=Math.floor(e.image.height*o);let r=dt.convert(e.format);G.isWebGL2&&(6407===r&&(r=32849),6408===r&&(r=32856)),Z.setTexture2D(e,0),mt.copyTexImage2D(3553,n,r,t.x,t.y,i,a,0),W.unbindTexture()},this.copyTextureToTexture=function(t,e,n,o=0){const i=e.image.width,a=e.image.height,r=dt.convert(n.format),s=dt.convert(n.type);Z.setTexture2D(n,0),mt.pixelStorei(37440,n.flipY),mt.pixelStorei(37441,n.premultiplyAlpha),mt.pixelStorei(3317,n.unpackAlignment),e.isDataTexture?mt.texSubImage2D(3553,o,t.x,t.y,i,a,r,s,e.image.data):e.isCompressedTexture?mt.compressedTexSubImage2D(3553,o,t.x,t.y,e.mipmaps[0].width,e.mipmaps[0].height,r,e.mipmaps[0].data):mt.texSubImage2D(3553,o,t.x,t.y,r,s,e.image),0===o&&n.generateMipmaps&&mt.generateMipmap(3553),W.unbindTexture()},this.copyTextureToTexture3D=function(t,e,n,o,i=0){if(h.isWebGL1Renderer)return void console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: can only be used with WebGL2.");const a=t.max.x-t.min.x+1,r=t.max.y-t.min.y+1,s=t.max.z-t.min.z+1,l=dt.convert(o.format),c=dt.convert(o.type);let d;if(o.isDataTexture3D)Z.setTexture3D(o,0),d=32879;else{if(!o.isDataTexture2DArray)return void console.warn("THREE.WebGLRenderer.copyTextureToTexture3D: only supports THREE.DataTexture3D and 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super.copy(t,e),null!==t.background&&(this.background=t.background.clone()),null!==t.environment&&(this.environment=t.environment.clone()),null!==t.fog&&(this.fog=t.fog.clone()),null!==t.overrideMaterial&&(this.overrideMaterial=t.overrideMaterial.clone()),this.autoUpdate=t.autoUpdate,this.matrixAutoUpdate=t.matrixAutoUpdate,this}toJSON(t){const e=super.toJSON(t);return null!==this.fog&&(e.object.fog=this.fog.toJSON()),e}}ydt.prototype.isScene=!0;class _dt{constructor(t,e){this.array=t,this.stride=e,this.count=void 0!==t?t.length/e:0,this.usage=Bit,this.updateRange={offset:0,count:-1},this.version=0,this.uuid=qit()}onUploadCallback(){}set needsUpdate(t){!0===t&&this.version++}setUsage(t){return this.usage=t,this}copy(t){return this.array=new t.array.constructor(t.array),this.count=t.count,this.stride=t.stride,this.usage=t.usage,this}copyAt(t,e,n){t*=this.stride,n*=e.stride;for(let o=0,i=this.stride;o<i;o++)this.array[t+o]=e.array[n+o];return this}set(t,e=0){return this.array.set(t,e),this}clone(t){void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=qit()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=this.array.slice(0).buffer);const e=new this.array.constructor(t.arrayBuffers[this.array.buffer._uuid]),n=new this.constructor(e,this.stride);return n.setUsage(this.usage),n}onUpload(t){return this.onUploadCallback=t,this}toJSON(t){return void 0===t.arrayBuffers&&(t.arrayBuffers={}),void 0===this.array.buffer._uuid&&(this.array.buffer._uuid=qit()),void 0===t.arrayBuffers[this.array.buffer._uuid]&&(t.arrayBuffers[this.array.buffer._uuid]=Array.prototype.slice.call(new Uint32Array(this.array.buffer))),{uuid:this.uuid,buffer:this.array.buffer._uuid,type:this.array.constructor.name,stride:this.stride}}}_dt.prototype.isInterleavedBuffer=!0;const Cdt=new cat;class Mdt{constructor(t,e,n,o=!1){this.name="",this.data=t,this.itemSize=e,this.offset=n,this.normalized=!0===o}get count(){return this.data.count}get array(){return this.data.array}set needsUpdate(t){this.data.needsUpdate=t}applyMatrix4(t){for(let e=0,n=this.data.count;e<n;e++)Cdt.x=this.getX(e),Cdt.y=this.getY(e),Cdt.z=this.getZ(e),Cdt.applyMatrix4(t),this.setXYZ(e,Cdt.x,Cdt.y,Cdt.z);return this}applyNormalMatrix(t){for(let e=0,n=this.count;e<n;e++)Cdt.x=this.getX(e),Cdt.y=this.getY(e),Cdt.z=this.getZ(e),Cdt.applyNormalMatrix(t),this.setXYZ(e,Cdt.x,Cdt.y,Cdt.z);return this}transformDirection(t){for(let e=0,n=this.count;e<n;e++)Cdt.x=this.getX(e),Cdt.y=this.getY(e),Cdt.z=this.getZ(e),Cdt.transformDirection(t),this.setXYZ(e,Cdt.x,Cdt.y,Cdt.z);return this}setX(t,e){return this.data.array[t*this.data.stride+this.offset]=e,this}setY(t,e){return this.data.array[t*this.data.stride+this.offset+1]=e,this}setZ(t,e){return this.data.array[t*this.data.stride+this.offset+2]=e,this}setW(t,e){return this.data.array[t*this.data.stride+this.offset+3]=e,this}getX(t){return this.data.array[t*this.data.stride+this.offset]}getY(t){return this.data.array[t*this.data.stride+this.offset+1]}getZ(t){return this.data.array[t*this.data.stride+this.offset+2]}getW(t){return this.data.array[t*this.data.stride+this.offset+3]}setXY(t,e,n){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this}setXYZ(t,e,n,o){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=o,this}setXYZW(t,e,n,o,i){return this.data.array[(t=t*this.data.stride+this.offset)+0]=e,this.data.array[t+1]=n,this.data.array[t+2]=o,this.data.array[t+3]=i,this}clone(t){if(void 0===t){console.log("THREE.InterleavedBufferAttribute.clone(): Cloning an interlaved buffer attribute will deinterleave buffer data.");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return new zrt(new this.array.constructor(t),this.itemSize,this.normalized)}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.clone(t)),new Mdt(t.interleavedBuffers[this.data.uuid],this.itemSize,this.offset,this.normalized)}toJSON(t){if(void 0===t){console.log("THREE.InterleavedBufferAttribute.toJSON(): Serializing an interlaved buffer attribute will deinterleave buffer data.");const t=[];for(let e=0;e<this.count;e++){const n=e*this.data.stride+this.offset;for(let e=0;e<this.itemSize;e++)t.push(this.data.array[n+e])}return{itemSize:this.itemSize,type:this.array.constructor.name,array:t,normalized:this.normalized}}return void 0===t.interleavedBuffers&&(t.interleavedBuffers={}),void 0===t.interleavedBuffers[this.data.uuid]&&(t.interleavedBuffers[this.data.uuid]=this.data.toJSON(t)),{isInterleavedBufferAttribute:!0,itemSize:this.itemSize,data:this.data.uuid,offset:this.offset,normalized:this.normalized}}}Mdt.prototype.isInterleavedBufferAttribute=!0;class vdt extends Ort{constructor(t){super(),this.type="SpriteMaterial",this.color=new Rrt(16777215),this.map=null,this.alphaMap=null,this.rotation=0,this.sizeAttenuation=!0,this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.rotation=t.rotation,this.sizeAttenuation=t.sizeAttenuation,this}}let xdt;vdt.prototype.isSpriteMaterial=!0;const Odt=new cat,Pdt=new cat,wdt=new cat,kdt=new Qit,Sdt=new Qit,Ddt=new Bat,Edt=new cat,Rdt=new cat,Adt=new cat,Tdt=new Qit,Ndt=new Qit,zdt=new Qit;function Idt(t,e,n,o,i,a){kdt.subVectors(t,n).addScalar(.5).multiply(o),void 0!==i?(Sdt.x=a*kdt.x-i*kdt.y,Sdt.y=i*kdt.x+a*kdt.y):Sdt.copy(kdt),t.copy(e),t.x+=Sdt.x,t.y+=Sdt.y,t.applyMatrix4(Ddt)}(class extends prt{constructor(t){if(super(),this.type="Sprite",void 0===xdt){xdt=new qrt;const t=new Float32Array([-.5,-.5,0,0,0,.5,-.5,0,1,0,.5,.5,0,1,1,-.5,.5,0,0,1]),e=new _dt(t,5);xdt.setIndex([0,1,2,0,2,3]),xdt.setAttribute("position",new Mdt(e,3,0,!1)),xdt.setAttribute("uv",new Mdt(e,2,3,!1))}this.geometry=xdt,this.material=void 0!==t?t:new vdt,this.center=new Qit(.5,.5)}raycast(t,e){null===t.camera&&console.error('THREE.Sprite: "Raycaster.camera" needs to be set in order to raycast against sprites.'),Pdt.setFromMatrixScale(this.matrixWorld),Ddt.copy(t.camera.matrixWorld),this.modelViewMatrix.multiplyMatrices(t.camera.matrixWorldInverse,this.matrixWorld),wdt.setFromMatrixPosition(this.modelViewMatrix),t.camera.isPerspectiveCamera&&!1===this.material.sizeAttenuation&&Pdt.multiplyScalar(-wdt.z);const n=this.material.rotation;let o,i;0!==n&&(i=Math.cos(n),o=Math.sin(n));const a=this.center;Idt(Edt.set(-.5,-.5,0),wdt,a,Pdt,o,i),Idt(Rdt.set(.5,-.5,0),wdt,a,Pdt,o,i),Idt(Adt.set(.5,.5,0),wdt,a,Pdt,o,i),Tdt.set(0,0),Ndt.set(1,0),zdt.set(1,1);let r=t.ray.intersectTriangle(Edt,Rdt,Adt,!1,Odt);if(null===r&&(Idt(Rdt.set(-.5,.5,0),wdt,a,Pdt,o,i),Ndt.set(0,1),r=t.ray.intersectTriangle(Edt,Adt,Rdt,!1,Odt),null===r))return;const s=t.ray.origin.distanceTo(Odt);s<t.near||s>t.far||e.push({distance:s,point:Odt.clone(),uv:vrt.getUV(Odt,Edt,Rdt,Adt,Tdt,Ndt,zdt,new Qit),face:null,object:this})}copy(t){return super.copy(t),void 0!==t.center&&this.center.copy(t.center),this.material=t.material,this}}).prototype.isSprite=!0;const Hdt=new cat,Fdt=new aat,Ldt=new aat,Bdt=new cat,Vdt=new Bat;class jdt extends pst{constructor(t,e){super(t,e),this.type="SkinnedMesh",this.bindMode="attached",this.bindMatrix=new Bat,this.bindMatrixInverse=new Bat}copy(t){return super.copy(t),this.bindMode=t.bindMode,this.bindMatrix.copy(t.bindMatrix),this.bindMatrixInverse.copy(t.bindMatrixInverse),this.skeleton=t.skeleton,this}bind(t,e){this.skeleton=t,void 0===e&&(this.updateMatrixWorld(!0),this.skeleton.calculateInverses(),e=this.matrixWorld),this.bindMatrix.copy(e),this.bindMatrixInverse.copy(e).invert()}pose(){this.skeleton.pose()}normalizeSkinWeights(){const t=new aat,e=this.geometry.attributes.skinWeight;for(let n=0,o=e.count;n<o;n++){t.x=e.getX(n),t.y=e.getY(n),t.z=e.getZ(n),t.w=e.getW(n);const o=1/t.manhattanLength();o!==1/0?t.multiplyScalar(o):t.set(1,0,0,0),e.setXYZW(n,t.x,t.y,t.z,t.w)}}updateMatrixWorld(t){super.updateMatrixWorld(t),"attached"===this.bindMode?this.bindMatrixInverse.copy(this.matrixWorld).invert():"detached"===this.bindMode?this.bindMatrixInverse.copy(this.bindMatrix).invert():console.warn("THREE.SkinnedMesh: Unrecognized bindMode: "+this.bindMode)}boneTransform(t,e){const n=this.skeleton,o=this.geometry;Fdt.fromBufferAttribute(o.attributes.skinIndex,t),Ldt.fromBufferAttribute(o.attributes.skinWeight,t),Hdt.fromBufferAttribute(o.attributes.position,t).applyMatrix4(this.bindMatrix),e.set(0,0,0);for(let t=0;t<4;t++){const o=Ldt.getComponent(t);if(0!==o){const i=Fdt.getComponent(t);Vdt.multiplyMatrices(n.bones[i].matrixWorld,n.boneInverses[i]),e.addScaledVector(Bdt.copy(Hdt).applyMatrix4(Vdt),o)}}return e.applyMatrix4(this.bindMatrixInverse)}}jdt.prototype.isSkinnedMesh=!0,class extends prt{constructor(){super(),this.type="Bone"}}.prototype.isBone=!0,class extends oat{constructor(t=null,e=1,n=1,o,i,a,r,s,l=1003,c=1003,d,p){super(null,a,r,s,l,c,o,i,d,p),this.image={data:t,width:e,height:n},this.magFilter=l,this.minFilter=c,this.generateMipmaps=!1,this.flipY=!1,this.unpackAlignment=1,this.needsUpdate=!0}}.prototype.isDataTexture=!0;const Udt=new Bat,Gdt=new Bat,Wdt=[],Ydt=new pst;(class extends pst{constructor(t,e,n){super(t,e),this.instanceMatrix=new zrt(new Float32Array(16*n),16),this.instanceColor=null,this.count=n,this.frustumCulled=!1}copy(t){return super.copy(t),this.instanceMatrix.copy(t.instanceMatrix),null!==t.instanceColor&&(this.instanceColor=t.instanceColor.clone()),this.count=t.count,this}getColorAt(t,e){e.fromArray(this.instanceColor.array,3*t)}getMatrixAt(t,e){e.fromArray(this.instanceMatrix.array,16*t)}raycast(t,e){const n=this.matrixWorld,o=this.count;if(Ydt.geometry=this.geometry,Ydt.material=this.material,void 0!==Ydt.material)for(let i=0;i<o;i++){this.getMatrixAt(i,Udt),Gdt.multiplyMatrices(n,Udt),Ydt.matrixWorld=Gdt,Ydt.raycast(t,Wdt);for(let t=0,n=Wdt.length;t<n;t++){const n=Wdt[t];n.instanceId=i,n.object=this,e.push(n)}Wdt.length=0}}setColorAt(t,e){null===this.instanceColor&&(this.instanceColor=new zrt(new Float32Array(3*this.instanceMatrix.count),3)),e.toArray(this.instanceColor.array,3*t)}setMatrixAt(t,e){e.toArray(this.instanceMatrix.array,16*t)}updateMorphTargets(){}dispose(){this.dispatchEvent({type:"dispose"})}}).prototype.isInstancedMesh=!0;class qdt extends Ort{constructor(t){super(),this.type="LineBasicMaterial",this.color=new Rrt(16777215),this.linewidth=1,this.linecap="round",this.linejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.linewidth=t.linewidth,this.linecap=t.linecap,this.linejoin=t.linejoin,this}}qdt.prototype.isLineBasicMaterial=!0;const Zdt=new cat,Xdt=new cat,Kdt=new Bat,Jdt=new Lat,Qdt=new Rat;class $dt extends prt{constructor(t=new qrt,e=new qdt){super(),this.type="Line",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[0];for(let t=1,o=e.count;t<o;t++)Zdt.fromBufferAttribute(e,t-1),Xdt.fromBufferAttribute(e,t),n[t]=n[t-1],n[t]+=Zdt.distanceTo(Xdt);t.setAttribute("lineDistance",new Frt(n,1))}else console.warn("THREE.Line.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else t.isGeometry&&console.error("THREE.Line.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");return this}raycast(t,e){const n=this.geometry,o=this.matrixWorld,i=t.params.Line.threshold,a=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),Qdt.copy(n.boundingSphere),Qdt.applyMatrix4(o),Qdt.radius+=i,!1===t.ray.intersectsSphere(Qdt))return;Kdt.copy(o).invert(),Jdt.copy(t.ray).applyMatrix4(Kdt);const r=i/((this.scale.x+this.scale.y+this.scale.z)/3),s=r*r,l=new cat,c=new cat,d=new cat,p=new cat,m=this.isLineSegments?2:1;if(n.isBufferGeometry){const o=n.index,i=n.attributes.position;if(null!==o)for(let n=Math.max(0,a.start),r=Math.min(o.count,a.start+a.count)-1;n<r;n+=m){const a=o.getX(n),r=o.getX(n+1);if(l.fromBufferAttribute(i,a),c.fromBufferAttribute(i,r),Jdt.distanceSqToSegment(l,c,p,d)>s)continue;p.applyMatrix4(this.matrixWorld);const m=t.ray.origin.distanceTo(p);m<t.near||m>t.far||e.push({distance:m,point:d.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}else for(let n=Math.max(0,a.start),o=Math.min(i.count,a.start+a.count)-1;n<o;n+=m){if(l.fromBufferAttribute(i,n),c.fromBufferAttribute(i,n+1),Jdt.distanceSqToSegment(l,c,p,d)>s)continue;p.applyMatrix4(this.matrixWorld);const o=t.ray.origin.distanceTo(p);o<t.near||o>t.far||e.push({distance:o,point:d.clone().applyMatrix4(this.matrixWorld),index:n,face:null,faceIndex:null,object:this})}}else n.isGeometry&&console.error("THREE.Line.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Line.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.")}}}$dt.prototype.isLine=!0;const tpt=new cat,ept=new cat;class npt extends $dt{constructor(t,e){super(t,e),this.type="LineSegments"}computeLineDistances(){const t=this.geometry;if(t.isBufferGeometry)if(null===t.index){const e=t.attributes.position,n=[];for(let t=0,o=e.count;t<o;t+=2)tpt.fromBufferAttribute(e,t),ept.fromBufferAttribute(e,t+1),n[t]=0===t?0:n[t-1],n[t+1]=n[t]+tpt.distanceTo(ept);t.setAttribute("lineDistance",new Frt(n,1))}else console.warn("THREE.LineSegments.computeLineDistances(): Computation only possible with non-indexed BufferGeometry.");else t.isGeometry&&console.error("THREE.LineSegments.computeLineDistances() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.");return this}}npt.prototype.isLineSegments=!0,class extends $dt{constructor(t,e){super(t,e),this.type="LineLoop"}}.prototype.isLineLoop=!0;class opt extends Ort{constructor(t){super(),this.type="PointsMaterial",this.color=new Rrt(16777215),this.map=null,this.alphaMap=null,this.size=1,this.sizeAttenuation=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.alphaMap=t.alphaMap,this.size=t.size,this.sizeAttenuation=t.sizeAttenuation,this}}opt.prototype.isPointsMaterial=!0;const ipt=new Bat,apt=new Lat,rpt=new Rat,spt=new cat;function lpt(t,e,n,o,i,a,r){const s=apt.distanceSqToPoint(t);if(s<n){const n=new cat;apt.closestPointToPoint(t,n),n.applyMatrix4(o);const l=i.ray.origin.distanceTo(n);if(l<i.near||l>i.far)return;a.push({distance:l,distanceToRay:Math.sqrt(s),point:n,index:e,face:null,object:r})}}(class extends prt{constructor(t=new qrt,e=new opt){super(),this.type="Points",this.geometry=t,this.material=e,this.updateMorphTargets()}copy(t){return super.copy(t),this.material=t.material,this.geometry=t.geometry,this}raycast(t,e){const n=this.geometry,o=this.matrixWorld,i=t.params.Points.threshold,a=n.drawRange;if(null===n.boundingSphere&&n.computeBoundingSphere(),rpt.copy(n.boundingSphere),rpt.applyMatrix4(o),rpt.radius+=i,!1===t.ray.intersectsSphere(rpt))return;ipt.copy(o).invert(),apt.copy(t.ray).applyMatrix4(ipt);const r=i/((this.scale.x+this.scale.y+this.scale.z)/3),s=r*r;if(n.isBufferGeometry){const i=n.index,r=n.attributes.position;if(null!==i)for(let n=Math.max(0,a.start),l=Math.min(i.count,a.start+a.count);n<l;n++){const a=i.getX(n);spt.fromBufferAttribute(r,a),lpt(spt,a,s,o,t,e,this)}else for(let n=Math.max(0,a.start),i=Math.min(r.count,a.start+a.count);n<i;n++)spt.fromBufferAttribute(r,n),lpt(spt,n,s,o,t,e,this)}else console.error("THREE.Points.raycast() no longer supports THREE.Geometry. Use THREE.BufferGeometry instead.")}updateMorphTargets(){const t=this.geometry;if(t.isBufferGeometry){const e=t.morphAttributes,n=Object.keys(e);if(n.length>0){const t=e[n[0]];if(void 0!==t){this.morphTargetInfluences=[],this.morphTargetDictionary={};for(let e=0,n=t.length;e<n;e++){const n=t[e].name||String(e);this.morphTargetInfluences.push(0),this.morphTargetDictionary[n]=e}}}}else{const e=t.morphTargets;void 0!==e&&e.length>0&&console.error("THREE.Points.updateMorphTargets() does not support THREE.Geometry. Use THREE.BufferGeometry instead.")}}}).prototype.isPoints=!0,class extends oat{constructor(t,e,n,o,i,a,r,s,l){super(t,e,n,o,i,a,r,s,l),this.format=void 0!==r?r:Pit,this.minFilter=void 0!==a?a:bit,this.magFilter=void 0!==i?i:bit,this.generateMipmaps=!1;const c=this;"requestVideoFrameCallback"in t&&t.requestVideoFrameCallback((function e(){c.needsUpdate=!0,t.requestVideoFrameCallback(e)}))}clone(){return new this.constructor(this.image).copy(this)}update(){const t=this.image;0=="requestVideoFrameCallback"in t&&t.readyState>=t.HAVE_CURRENT_DATA&&(this.needsUpdate=!0)}}.prototype.isVideoTexture=!0,class extends oat{constructor(t,e,n,o,i,a,r,s,l,c,d,p){super(null,a,r,s,l,c,o,i,d,p),this.image={width:e,height:n},this.mipmaps=t,this.flipY=!1,this.generateMipmaps=!1}}.prototype.isCompressedTexture=!0,class extends oat{constructor(t,e,n,o,i,a,r,s,l){super(t,e,n,o,i,a,r,s,l),this.needsUpdate=!0}}.prototype.isCanvasTexture=!0,class extends oat{constructor(t,e,n,o,i,a,r,s,l,c){if((c=void 0!==c?c:kit)!==kit&&c!==Sit)throw new Error("DepthTexture format must be either THREE.DepthFormat or THREE.DepthStencilFormat");void 0===n&&c===kit&&(n=Cit),void 0===n&&c===Sit&&(n=Oit),super(null,o,i,a,r,s,c,n,l),this.image={width:t,height:e},this.magFilter=void 0!==r?r:hit,this.minFilter=void 0!==s?s:hit,this.flipY=!1,this.generateMipmaps=!1}}.prototype.isDepthTexture=!0;class cpt extends qrt{constructor(t=1,e=8,n=0,o=2*Math.PI){super(),this.type="CircleGeometry",this.parameters={radius:t,segments:e,thetaStart:n,thetaLength:o},e=Math.max(3,e);const i=[],a=[],r=[],s=[],l=new cat,c=new Qit;a.push(0,0,0),r.push(0,0,1),s.push(.5,.5);for(let i=0,d=3;i<=e;i++,d+=3){const p=n+i/e*o;l.x=t*Math.cos(p),l.y=t*Math.sin(p),a.push(l.x,l.y,l.z),r.push(0,0,1),c.x=(a[d]/t+1)/2,c.y=(a[d+1]/t+1)/2,s.push(c.x,c.y)}for(let t=1;t<=e;t++)i.push(t,t+1,0);this.setIndex(i),this.setAttribute("position",new Frt(a,3)),this.setAttribute("normal",new Frt(r,3)),this.setAttribute("uv",new Frt(s,2))}static fromJSON(t){return new cpt(t.radius,t.segments,t.thetaStart,t.thetaLength)}}new cat,new cat,new cat,new vrt;class dpt{constructor(){this.type="Curve",this.arcLengthDivisions=200}getPoint(){return console.warn("THREE.Curve: .getPoint() not implemented."),null}getPointAt(t,e){const n=this.getUtoTmapping(t);return this.getPoint(n,e)}getPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return e}getSpacedPoints(t=5){const e=[];for(let n=0;n<=t;n++)e.push(this.getPointAt(n/t));return e}getLength(){const t=this.getLengths();return t[t.length-1]}getLengths(t=this.arcLengthDivisions){if(this.cacheArcLengths&&this.cacheArcLengths.length===t+1&&!this.needsUpdate)return this.cacheArcLengths;this.needsUpdate=!1;const e=[];let n,o=this.getPoint(0),i=0;e.push(0);for(let a=1;a<=t;a++)n=this.getPoint(a/t),i+=n.distanceTo(o),e.push(i),o=n;return this.cacheArcLengths=e,e}updateArcLengths(){this.needsUpdate=!0,this.getLengths()}getUtoTmapping(t,e){const n=this.getLengths();let o=0;const i=n.length;let a;a=e||t*n[i-1];let r,s=0,l=i-1;for(;s<=l;)if(o=Math.floor(s+(l-s)/2),r=n[o]-a,r<0)s=o+1;else{if(!(r>0)){l=o;break}l=o-1}if(o=l,n[o]===a)return o/(i-1);const c=n[o];return(o+(a-c)/(n[o+1]-c))/(i-1)}getTangent(t,e){const n=1e-4;let o=t-n,i=t+n;o<0&&(o=0),i>1&&(i=1);const a=this.getPoint(o),r=this.getPoint(i),s=e||(a.isVector2?new Qit:new cat);return s.copy(r).sub(a).normalize(),s}getTangentAt(t,e){const n=this.getUtoTmapping(t);return this.getTangent(n,e)}computeFrenetFrames(t,e){const n=new cat,o=[],i=[],a=[],r=new cat,s=new Bat;for(let e=0;e<=t;e++)o[e]=this.getTangentAt(e/t,new cat),o[e].normalize();i[0]=new cat,a[0]=new cat;let l=Number.MAX_VALUE;const c=Math.abs(o[0].x),d=Math.abs(o[0].y),p=Math.abs(o[0].z);c<=l&&(l=c,n.set(1,0,0)),d<=l&&(l=d,n.set(0,1,0)),p<=l&&n.set(0,0,1),r.crossVectors(o[0],n).normalize(),i[0].crossVectors(o[0],r),a[0].crossVectors(o[0],i[0]);for(let e=1;e<=t;e++){if(i[e]=i[e-1].clone(),a[e]=a[e-1].clone(),r.crossVectors(o[e-1],o[e]),r.length()>Number.EPSILON){r.normalize();const t=Math.acos(Zit(o[e-1].dot(o[e]),-1,1));i[e].applyMatrix4(s.makeRotationAxis(r,t))}a[e].crossVectors(o[e],i[e])}if(!0===e){let e=Math.acos(Zit(i[0].dot(i[t]),-1,1));e/=t,o[0].dot(r.crossVectors(i[0],i[t]))>0&&(e=-e);for(let n=1;n<=t;n++)i[n].applyMatrix4(s.makeRotationAxis(o[n],e*n)),a[n].crossVectors(o[n],i[n])}return{tangents:o,normals:i,binormals:a}}clone(){return(new this.constructor).copy(this)}copy(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}toJSON(){const t={metadata:{version:4.5,type:"Curve",generator:"Curve.toJSON"}};return t.arcLengthDivisions=this.arcLengthDivisions,t.type=this.type,t}fromJSON(t){return this.arcLengthDivisions=t.arcLengthDivisions,this}}class ppt extends dpt{constructor(t=0,e=0,n=1,o=1,i=0,a=2*Math.PI,r=!1,s=0){super(),this.type="EllipseCurve",this.aX=t,this.aY=e,this.xRadius=n,this.yRadius=o,this.aStartAngle=i,this.aEndAngle=a,this.aClockwise=r,this.aRotation=s}getPoint(t,e){const n=e||new Qit,o=2*Math.PI;let i=this.aEndAngle-this.aStartAngle;const a=Math.abs(i)<Number.EPSILON;for(;i<0;)i+=o;for(;i>o;)i-=o;i<Number.EPSILON&&(i=a?0:o),!0!==this.aClockwise||a||(i===o?i=-o:i-=o);const r=this.aStartAngle+t*i;let s=this.aX+this.xRadius*Math.cos(r),l=this.aY+this.yRadius*Math.sin(r);if(0!==this.aRotation){const t=Math.cos(this.aRotation),e=Math.sin(this.aRotation),n=s-this.aX,o=l-this.aY;s=n*t-o*e+this.aX,l=n*e+o*t+this.aY}return n.set(s,l)}copy(t){return super.copy(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}toJSON(){const t=super.toJSON();return t.aX=this.aX,t.aY=this.aY,t.xRadius=this.xRadius,t.yRadius=this.yRadius,t.aStartAngle=this.aStartAngle,t.aEndAngle=this.aEndAngle,t.aClockwise=this.aClockwise,t.aRotation=this.aRotation,t}fromJSON(t){return super.fromJSON(t),this.aX=t.aX,this.aY=t.aY,this.xRadius=t.xRadius,this.yRadius=t.yRadius,this.aStartAngle=t.aStartAngle,this.aEndAngle=t.aEndAngle,this.aClockwise=t.aClockwise,this.aRotation=t.aRotation,this}}ppt.prototype.isEllipseCurve=!0;class mpt extends ppt{constructor(t,e,n,o,i,a){super(t,e,n,n,o,i,a),this.type="ArcCurve"}}function upt(){let t=0,e=0,n=0,o=0;function i(i,a,r,s){t=i,e=r,n=-3*i+3*a-2*r-s,o=2*i-2*a+r+s}return{initCatmullRom:function(t,e,n,o,a){i(e,n,a*(n-t),a*(o-e))},initNonuniformCatmullRom:function(t,e,n,o,a,r,s){let l=(e-t)/a-(n-t)/(a+r)+(n-e)/r,c=(n-e)/r-(o-e)/(r+s)+(o-n)/s;l*=r,c*=r,i(e,n,l,c)},calc:function(i){const a=i*i;return t+e*i+n*a+o*(a*i)}}}mpt.prototype.isArcCurve=!0;const fpt=new cat,gpt=new upt,hpt=new upt,bpt=new upt;class ypt extends dpt{constructor(t=[],e=!1,n="centripetal",o=.5){super(),this.type="CatmullRomCurve3",this.points=t,this.closed=e,this.curveType=n,this.tension=o}getPoint(t,e=new cat){const n=e,o=this.points,i=o.length,a=(i-(this.closed?0:1))*t;let r,s,l=Math.floor(a),c=a-l;this.closed?l+=l>0?0:(Math.floor(Math.abs(l)/i)+1)*i:0===c&&l===i-1&&(l=i-2,c=1),this.closed||l>0?r=o[(l-1)%i]:(fpt.subVectors(o[0],o[1]).add(o[0]),r=fpt);const d=o[l%i],p=o[(l+1)%i];if(this.closed||l+2<i?s=o[(l+2)%i]:(fpt.subVectors(o[i-1],o[i-2]).add(o[i-1]),s=fpt),"centripetal"===this.curveType||"chordal"===this.curveType){const t="chordal"===this.curveType?.5:.25;let e=Math.pow(r.distanceToSquared(d),t),n=Math.pow(d.distanceToSquared(p),t),o=Math.pow(p.distanceToSquared(s),t);n<1e-4&&(n=1),e<1e-4&&(e=n),o<1e-4&&(o=n),gpt.initNonuniformCatmullRom(r.x,d.x,p.x,s.x,e,n,o),hpt.initNonuniformCatmullRom(r.y,d.y,p.y,s.y,e,n,o),bpt.initNonuniformCatmullRom(r.z,d.z,p.z,s.z,e,n,o)}else"catmullrom"===this.curveType&&(gpt.initCatmullRom(r.x,d.x,p.x,s.x,this.tension),hpt.initCatmullRom(r.y,d.y,p.y,s.y,this.tension),bpt.initCatmullRom(r.z,d.z,p.z,s.z,this.tension));return n.set(gpt.calc(c),hpt.calc(c),bpt.calc(c)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++)this.points.push(t.points[e].clone());return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++)t.points.push(this.points[e].toArray());return t.closed=this.closed,t.curveType=this.curveType,t.tension=this.tension,t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new cat).fromArray(n))}return this.closed=t.closed,this.curveType=t.curveType,this.tension=t.tension,this}}function _pt(t,e,n,o,i){const a=.5*(o-e),r=.5*(i-n),s=t*t;return(2*n-2*o+a+r)*(t*s)+(-3*n+3*o-2*a-r)*s+a*t+n}function Cpt(t,e,n,o){return(function i(t,e){const n=1-t;return n*n*e})(t,e)+(function a(t,e){return 2*(1-t)*t*e})(t,n)+(function r(t,e){return t*t*e})(t,o)}function Mpt(t,e,n,o,i){return(function a(t,e){const n=1-t;return n*n*n*e})(t,e)+(function r(t,e){const n=1-t;return 3*n*n*t*e})(t,n)+(function s(t,e){return 3*(1-t)*t*t*e})(t,o)+(function l(t,e){return t*t*t*e})(t,i)}ypt.prototype.isCatmullRomCurve3=!0;class vpt extends dpt{constructor(t=new Qit,e=new Qit,n=new Qit,o=new Qit){super(),this.type="CubicBezierCurve",this.v0=t,this.v1=e,this.v2=n,this.v3=o}getPoint(t,e=new Qit){const n=e,o=this.v0,i=this.v1,a=this.v2,r=this.v3;return n.set(Mpt(t,o.x,i.x,a.x,r.x),Mpt(t,o.y,i.y,a.y,r.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}vpt.prototype.isCubicBezierCurve=!0;class xpt extends dpt{constructor(t=new cat,e=new cat,n=new cat,o=new cat){super(),this.type="CubicBezierCurve3",this.v0=t,this.v1=e,this.v2=n,this.v3=o}getPoint(t,e=new cat){const n=e,o=this.v0,i=this.v1,a=this.v2,r=this.v3;return n.set(Mpt(t,o.x,i.x,a.x,r.x),Mpt(t,o.y,i.y,a.y,r.y),Mpt(t,o.z,i.z,a.z,r.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this.v3.copy(t.v3),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t.v3=this.v3.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this.v3.fromArray(t.v3),this}}xpt.prototype.isCubicBezierCurve3=!0;class Opt extends dpt{constructor(t=new Qit,e=new Qit){super(),this.type="LineCurve",this.v1=t,this.v2=e}getPoint(t,e=new Qit){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}getTangent(t,e){const n=e||new Qit;return n.copy(this.v2).sub(this.v1).normalize(),n}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}Opt.prototype.isLineCurve=!0;class Ppt extends dpt{constructor(t=new Qit,e=new Qit,n=new Qit){super(),this.type="QuadraticBezierCurve",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new Qit){const n=e,o=this.v0,i=this.v1,a=this.v2;return n.set(Cpt(t,o.x,i.x,a.x),Cpt(t,o.y,i.y,a.y)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}Ppt.prototype.isQuadraticBezierCurve=!0;class wpt extends dpt{constructor(t=new cat,e=new cat,n=new cat){super(),this.type="QuadraticBezierCurve3",this.v0=t,this.v1=e,this.v2=n}getPoint(t,e=new cat){const n=e,o=this.v0,i=this.v1,a=this.v2;return n.set(Cpt(t,o.x,i.x,a.x),Cpt(t,o.y,i.y,a.y),Cpt(t,o.z,i.z,a.z)),n}copy(t){return super.copy(t),this.v0.copy(t.v0),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v0=this.v0.toArray(),t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v0.fromArray(t.v0),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}}wpt.prototype.isQuadraticBezierCurve3=!0;class kpt extends dpt{constructor(t=[]){super(),this.type="SplineCurve",this.points=t}getPoint(t,e=new Qit){const n=e,o=this.points,i=(o.length-1)*t,a=Math.floor(i),r=i-a,s=o[0===a?a:a-1],l=o[a],c=o[a>o.length-2?o.length-1:a+1],d=o[a>o.length-3?o.length-1:a+2];return n.set(_pt(r,s.x,l.x,c.x,d.x),_pt(r,s.y,l.y,c.y,d.y)),n}copy(t){super.copy(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++)this.points.push(t.points[e].clone());return this}toJSON(){const t=super.toJSON();t.points=[];for(let e=0,n=this.points.length;e<n;e++)t.points.push(this.points[e].toArray());return t}fromJSON(t){super.fromJSON(t),this.points=[];for(let e=0,n=t.points.length;e<n;e++){const n=t.points[e];this.points.push((new Qit).fromArray(n))}return this}}kpt.prototype.isSplineCurve=!0;var Spt=Object.freeze({__proto__:null,ArcCurve:mpt,CatmullRomCurve3:ypt,CubicBezierCurve:vpt,CubicBezierCurve3:xpt,EllipseCurve:ppt,LineCurve:Opt,LineCurve3:class extends dpt{constructor(t=new cat,e=new cat){super(),this.type="LineCurve3",this.isLineCurve3=!0,this.v1=t,this.v2=e}getPoint(t,e=new cat){const n=e;return 1===t?n.copy(this.v2):(n.copy(this.v2).sub(this.v1),n.multiplyScalar(t).add(this.v1)),n}getPointAt(t,e){return this.getPoint(t,e)}copy(t){return super.copy(t),this.v1.copy(t.v1),this.v2.copy(t.v2),this}toJSON(){const t=super.toJSON();return t.v1=this.v1.toArray(),t.v2=this.v2.toArray(),t}fromJSON(t){return super.fromJSON(t),this.v1.fromArray(t.v1),this.v2.fromArray(t.v2),this}},QuadraticBezierCurve:Ppt,QuadraticBezierCurve3:wpt,SplineCurve:kpt});function Dpt(t,e,n,o,i){let a,r;if(i===(function s(t,e,n,o){let i=0;for(let a=e,r=n-o;a<n;a+=o)i+=(t[r]-t[a])*(t[a+1]+t[r+1]),r=a;return i})(t,e,n,o)>0)for(a=e;a<n;a+=o)r=Kpt(a,t[a],t[a+1],r);else for(a=n-o;a>=e;a-=o)r=Kpt(a,t[a],t[a+1],r);return r&&Gpt(r,r.next)&&(Jpt(r),r=r.next),r}function Ept(t,e){if(!t)return t;e||(e=t);let n,o=t;do{if(n=!1,o.steiner||!Gpt(o,o.next)&&0!==Upt(o.prev,o,o.next))o=o.next;else{if(Jpt(o),o=e=o.prev,o===o.next)break;n=!0}}while(n||o!==e);return e}function Rpt(t,e,n,o,i,a,r){if(!t)return;!r&&a&&(function s(t,e,n,o){let i=t;do{null===i.z&&(i.z=Lpt(i.x,i.y,e,n,o)),i.prevZ=i.prev,i.nextZ=i.next,i=i.next}while(i!==t);i.prevZ.nextZ=null,i.prevZ=null,(function a(t){let e,n,o,i,a,r,s,l,c=1;do{for(n=t,t=null,a=null,r=0;n;){for(r++,o=n,s=0,e=0;e<c&&(s++,o=o.nextZ,o);e++);for(l=c;s>0||l>0&&o;)0!==s&&(0===l||!o||n.z<=o.z)?(i=n,n=n.nextZ,s--):(i=o,o=o.nextZ,l--),a?a.nextZ=i:t=i,i.prevZ=a,a=i;n=o}a.nextZ=null,c*=2}while(r>1)})(i)})(t,o,i,a);let l,c,d=t;for(;t.prev!==t.next;)if(l=t.prev,c=t.next,a?Tpt(t,o,i,a):Apt(t))e.push(l.i/n),e.push(t.i/n),e.push(c.i/n),Jpt(t),t=c.next,d=c.next;else if((t=c)===d){r?1===r?Rpt(t=Npt(Ept(t),e,n),e,n,o,i,a,2):2===r&&zpt(t,e,n,o,i,a):Rpt(Ept(t),e,n,o,i,a,1);break}}function Apt(t){const e=t.prev,n=t,o=t.next;if(Upt(e,n,o)>=0)return!1;let i=t.next.next;for(;i!==t.prev;){if(Vpt(e.x,e.y,n.x,n.y,o.x,o.y,i.x,i.y)&&Upt(i.prev,i,i.next)>=0)return!1;i=i.next}return!0}function Tpt(t,e,n,o){const i=t.prev,a=t,r=t.next;if(Upt(i,a,r)>=0)return!1;const s=i.x>a.x?i.x>r.x?i.x:r.x:a.x>r.x?a.x:r.x,l=i.y>a.y?i.y>r.y?i.y:r.y:a.y>r.y?a.y:r.y,c=Lpt(i.x<a.x?i.x<r.x?i.x:r.x:a.x<r.x?a.x:r.x,i.y<a.y?i.y<r.y?i.y:r.y:a.y<r.y?a.y:r.y,e,n,o),d=Lpt(s,l,e,n,o);let p=t.prevZ,m=t.nextZ;for(;p&&p.z>=c&&m&&m.z<=d;){if(p!==t.prev&&p!==t.next&&Vpt(i.x,i.y,a.x,a.y,r.x,r.y,p.x,p.y)&&Upt(p.prev,p,p.next)>=0)return!1;if(p=p.prevZ,m!==t.prev&&m!==t.next&&Vpt(i.x,i.y,a.x,a.y,r.x,r.y,m.x,m.y)&&Upt(m.prev,m,m.next)>=0)return!1;m=m.nextZ}for(;p&&p.z>=c;){if(p!==t.prev&&p!==t.next&&Vpt(i.x,i.y,a.x,a.y,r.x,r.y,p.x,p.y)&&Upt(p.prev,p,p.next)>=0)return!1;p=p.prevZ}for(;m&&m.z<=d;){if(m!==t.prev&&m!==t.next&&Vpt(i.x,i.y,a.x,a.y,r.x,r.y,m.x,m.y)&&Upt(m.prev,m,m.next)>=0)return!1;m=m.nextZ}return!0}function Npt(t,e,n){let o=t;do{const i=o.prev,a=o.next.next;!Gpt(i,a)&&Wpt(i,o,o.next,a)&&Zpt(i,a)&&Zpt(a,i)&&(e.push(i.i/n),e.push(o.i/n),e.push(a.i/n),Jpt(o),Jpt(o.next),o=t=a),o=o.next}while(o!==t);return Ept(o)}function zpt(t,e,n,o,i,a){let r=t;do{let t=r.next.next;for(;t!==r.prev;){if(r.i!==t.i&&jpt(r,t)){let s=Xpt(r,t);return r=Ept(r,r.next),s=Ept(s,s.next),Rpt(r,e,n,o,i,a),void Rpt(s,e,n,o,i,a)}t=t.next}r=r.next}while(r!==t)}function Ipt(t,e){return t.x-e.x}function Hpt(t,e){if(e=(function n(t,e){let n=e;const o=t.x,i=t.y;let a,r=-1/0;do{if(i<=n.y&&i>=n.next.y&&n.next.y!==n.y){const t=n.x+(i-n.y)*(n.next.x-n.x)/(n.next.y-n.y);if(t<=o&&t>r){if(r=t,t===o){if(i===n.y)return n;if(i===n.next.y)return n.next}a=n.x<n.next.x?n:n.next}}n=n.next}while(n!==e);if(!a)return null;if(o===r)return a;const s=a,l=a.x,c=a.y;let d,p=1/0;n=a;do{o>=n.x&&n.x>=l&&o!==n.x&&Vpt(i<c?o:r,i,l,c,i<c?r:o,i,n.x,n.y)&&(d=Math.abs(i-n.y)/(o-n.x),Zpt(n,t)&&(d<p||d===p&&(n.x>a.x||n.x===a.x&&Fpt(a,n)))&&(a=n,p=d)),n=n.next}while(n!==s);return a})(t,e)){const n=Xpt(e,t);Ept(e,e.next),Ept(n,n.next)}}function Fpt(t,e){return Upt(t.prev,t,e.prev)<0&&Upt(e.next,t,t.next)<0}function Lpt(t,e,n,o,i){return(t=1431655765&((t=858993459&((t=252645135&((t=16711935&((t=32767*(t-n)*i)|t<<8))|t<<4))|t<<2))|t<<1))|(e=1431655765&((e=858993459&((e=252645135&((e=16711935&((e=32767*(e-o)*i)|e<<8))|e<<4))|e<<2))|e<<1))<<1}function Bpt(t){let e=t,n=t;do{(e.x<n.x||e.x===n.x&&e.y<n.y)&&(n=e),e=e.next}while(e!==t);return n}function Vpt(t,e,n,o,i,a,r,s){return(i-r)*(e-s)-(t-r)*(a-s)>=0&&(t-r)*(o-s)-(n-r)*(e-s)>=0&&(n-r)*(a-s)-(i-r)*(o-s)>=0}function jpt(t,e){return t.next.i!==e.i&&t.prev.i!==e.i&&!(function n(t,e){let n=t;do{if(n.i!==t.i&&n.next.i!==t.i&&n.i!==e.i&&n.next.i!==e.i&&Wpt(n,n.next,t,e))return!0;n=n.next}while(n!==t);return!1})(t,e)&&(Zpt(t,e)&&Zpt(e,t)&&(function o(t,e){let n=t,o=!1;const i=(t.x+e.x)/2,a=(t.y+e.y)/2;do{n.y>a!=n.next.y>a&&n.next.y!==n.y&&i<(n.next.x-n.x)*(a-n.y)/(n.next.y-n.y)+n.x&&(o=!o),n=n.next}while(n!==t);return o})(t,e)&&(Upt(t.prev,t,e.prev)||Upt(t,e.prev,e))||Gpt(t,e)&&Upt(t.prev,t,t.next)>0&&Upt(e.prev,e,e.next)>0)}function Upt(t,e,n){return(e.y-t.y)*(n.x-e.x)-(e.x-t.x)*(n.y-e.y)}function Gpt(t,e){return t.x===e.x&&t.y===e.y}function Wpt(t,e,n,o){const i=qpt(Upt(t,e,n)),a=qpt(Upt(t,e,o)),r=qpt(Upt(n,o,t)),s=qpt(Upt(n,o,e));return i!==a&&r!==s||!(0!==i||!Ypt(t,n,e))||!(0!==a||!Ypt(t,o,e))||!(0!==r||!Ypt(n,t,o))||!(0!==s||!Ypt(n,e,o))}function Ypt(t,e,n){return e.x<=Math.max(t.x,n.x)&&e.x>=Math.min(t.x,n.x)&&e.y<=Math.max(t.y,n.y)&&e.y>=Math.min(t.y,n.y)}function qpt(t){return t>0?1:t<0?-1:0}function Zpt(t,e){return Upt(t.prev,t,t.next)<0?Upt(t,e,t.next)>=0&&Upt(t,t.prev,e)>=0:Upt(t,e,t.prev)<0||Upt(t,t.next,e)<0}function Xpt(t,e){const n=new Qpt(t.i,t.x,t.y),o=new Qpt(e.i,e.x,e.y),i=t.next,a=e.prev;return t.next=e,e.prev=t,n.next=i,i.prev=n,o.next=n,n.prev=o,a.next=o,o.prev=a,o}function Kpt(t,e,n,o){const i=new Qpt(t,e,n);return o?(i.next=o.next,i.prev=o,o.next.prev=i,o.next=i):(i.prev=i,i.next=i),i}function Jpt(t){t.next.prev=t.prev,t.prev.next=t.next,t.prevZ&&(t.prevZ.nextZ=t.nextZ),t.nextZ&&(t.nextZ.prevZ=t.prevZ)}function Qpt(t,e,n){this.i=t,this.x=e,this.y=n,this.prev=null,this.next=null,this.z=null,this.prevZ=null,this.nextZ=null,this.steiner=!1}class $pt{static area(t){const e=t.length;let n=0;for(let o=e-1,i=0;i<e;o=i++)n+=t[o].x*t[i].y-t[i].x*t[o].y;return.5*n}static isClockWise(t){return $pt.area(t)<0}static triangulateShape(t,e){const n=[],o=[],i=[];tmt(t),emt(n,t);let a=t.length;e.forEach(tmt);for(let t=0;t<e.length;t++)o.push(a),a+=e[t].length,emt(n,e[t]);const r=(function(t,e,n=2){const o=e&&e.length,i=o?e[0]*n:t.length;let a=Dpt(t,0,i,n,!0);const r=[];if(!a||a.next===a.prev)return r;let s,l,c,d,p,m,u;if(o&&(a=(function f(t,e,n,o){const i=[];let a,r,s,l,c;for(a=0,r=e.length;a<r;a++)s=e[a]*o,l=a<r-1?e[a+1]*o:t.length,c=Dpt(t,s,l,o,!1),c===c.next&&(c.steiner=!0),i.push(Bpt(c));for(i.sort(Ipt),a=0;a<i.length;a++)Hpt(i[a],n),n=Ept(n,n.next);return n})(t,e,a,n)),t.length>80*n){s=c=t[0],l=d=t[1];for(let e=n;e<i;e+=n)p=t[e],m=t[e+1],p<s&&(s=p),m<l&&(l=m),p>c&&(c=p),m>d&&(d=m);u=Math.max(c-s,d-l),u=0!==u?1/u:0}return Rpt(a,r,n,s,l,u),r})(n,o);for(let t=0;t<r.length;t+=3)i.push(r.slice(t,t+3));return i}}function tmt(t){const e=t.length;e>2&&t[e-1].equals(t[0])&&t.pop()}function emt(t,e){for(let n=0;n<e.length;n++)t.push(e[n].x),t.push(e[n].y)}class nmt extends qrt{constructor(t,e){super(),this.type="ExtrudeGeometry",this.parameters={shapes:t,options:e},t=Array.isArray(t)?t:[t];const n=this,o=[],i=[];for(let e=0,n=t.length;e<n;e++)a(t[e]);function a(t){const a=[],r=void 0!==e.curveSegments?e.curveSegments:12,s=void 0!==e.steps?e.steps:1;let l=void 0!==e.depth?e.depth:100,c=void 0===e.bevelEnabled||e.bevelEnabled,d=void 0!==e.bevelThickness?e.bevelThickness:6,p=void 0!==e.bevelSize?e.bevelSize:d-2,m=void 0!==e.bevelOffset?e.bevelOffset:0,u=void 0!==e.bevelSegments?e.bevelSegments:3;const f=e.extrudePath,g=void 0!==e.UVGenerator?e.UVGenerator:omt;void 0!==e.amount&&(console.warn("THREE.ExtrudeBufferGeometry: amount has been renamed to depth."),l=e.amount);let h,b,y,_,C,M=!1;f&&(h=f.getSpacedPoints(s),M=!0,c=!1,b=f.computeFrenetFrames(s,!1),y=new cat,_=new cat,C=new cat),c||(u=0,d=0,p=0,m=0);const v=t.extractPoints(r);let x=v.shape;const O=v.holes;if(!$pt.isClockWise(x)){x=x.reverse();for(let t=0,e=O.length;t<e;t++){const e=O[t];$pt.isClockWise(e)&&(O[t]=e.reverse())}}const P=$pt.triangulateShape(x,O),w=x;for(let t=0,e=O.length;t<e;t++)x=x.concat(O[t]);function k(t,e,n){return e||console.error("THREE.ExtrudeGeometry: vec does not exist"),e.clone().multiplyScalar(n).add(t)}const S=x.length,D=P.length;function E(t,e,n){let o,i,a;const r=t.x-e.x,s=t.y-e.y,l=n.x-t.x,c=n.y-t.y,d=r*r+s*s;if(Math.abs(r*c-s*l)>Number.EPSILON){const p=Math.sqrt(d),m=Math.sqrt(l*l+c*c),u=e.x-s/p,f=e.y+r/p,g=((n.x-c/m-u)*c-(n.y+l/m-f)*l)/(r*c-s*l);o=u+r*g-t.x,i=f+s*g-t.y;const h=o*o+i*i;if(h<=2)return new Qit(o,i);a=Math.sqrt(h/2)}else{let t=!1;r>Number.EPSILON?l>Number.EPSILON&&(t=!0):r<-Number.EPSILON?l<-Number.EPSILON&&(t=!0):Math.sign(s)===Math.sign(c)&&(t=!0),t?(o=-s,i=r,a=Math.sqrt(d)):(o=r,i=s,a=Math.sqrt(d/2))}return new Qit(o/a,i/a)}const R=[];for(let t=0,e=w.length,n=e-1,o=t+1;t<e;t++,n++,o++)n===e&&(n=0),o===e&&(o=0),R[t]=E(w[t],w[n],w[o]);const A=[];let T,N=R.concat();for(let t=0,e=O.length;t<e;t++){const e=O[t];T=[];for(let t=0,n=e.length,o=n-1,i=t+1;t<n;t++,o++,i++)o===n&&(o=0),i===n&&(i=0),T[t]=E(e[t],e[o],e[i]);A.push(T),N=N.concat(T)}for(let t=0;t<u;t++){const e=t/u,n=d*Math.cos(e*Math.PI/2),o=p*Math.sin(e*Math.PI/2)+m;for(let t=0,e=w.length;t<e;t++){const e=k(w[t],R[t],o);H(e.x,e.y,-n)}for(let t=0,e=O.length;t<e;t++){const e=O[t];T=A[t];for(let t=0,i=e.length;t<i;t++){const i=k(e[t],T[t],o);H(i.x,i.y,-n)}}}const z=p+m;for(let t=0;t<S;t++){const e=c?k(x[t],N[t],z):x[t];M?(_.copy(b.normals[0]).multiplyScalar(e.x),y.copy(b.binormals[0]).multiplyScalar(e.y),C.copy(h[0]).add(_).add(y),H(C.x,C.y,C.z)):H(e.x,e.y,0)}for(let t=1;t<=s;t++)for(let e=0;e<S;e++){const n=c?k(x[e],N[e],z):x[e];M?(_.copy(b.normals[t]).multiplyScalar(n.x),y.copy(b.binormals[t]).multiplyScalar(n.y),C.copy(h[t]).add(_).add(y),H(C.x,C.y,C.z)):H(n.x,n.y,l/s*t)}for(let t=u-1;t>=0;t--){const e=t/u,n=d*Math.cos(e*Math.PI/2),o=p*Math.sin(e*Math.PI/2)+m;for(let t=0,e=w.length;t<e;t++){const e=k(w[t],R[t],o);H(e.x,e.y,l+n)}for(let t=0,e=O.length;t<e;t++){const e=O[t];T=A[t];for(let t=0,i=e.length;t<i;t++){const i=k(e[t],T[t],o);M?H(i.x,i.y+h[s-1].y,h[s-1].x+n):H(i.x,i.y,l+n)}}}function I(t,e){let n=t.length;for(;--n>=0;){const o=n;let i=n-1;i<0&&(i=t.length-1);for(let t=0,n=s+2*u;t<n;t++){const n=S*t,a=S*(t+1);L(e+o+n,e+i+n,e+i+a,e+o+a)}}}function H(t,e,n){a.push(t),a.push(e),a.push(n)}function F(t,e,i){B(t),B(e),B(i);const a=o.length/3,r=g.generateTopUV(n,o,a-3,a-2,a-1);V(r[0]),V(r[1]),V(r[2])}function L(t,e,i,a){B(t),B(e),B(a),B(e),B(i),B(a);const r=o.length/3,s=g.generateSideWallUV(n,o,r-6,r-3,r-2,r-1);V(s[0]),V(s[1]),V(s[3]),V(s[1]),V(s[2]),V(s[3])}function B(t){o.push(a[3*t+0]),o.push(a[3*t+1]),o.push(a[3*t+2])}function V(t){i.push(t.x),i.push(t.y)}!(function j(){const t=o.length/3;if(c){let t=0,e=S*t;for(let t=0;t<D;t++){const n=P[t];F(n[2]+e,n[1]+e,n[0]+e)}t=s+2*u,e=S*t;for(let t=0;t<D;t++){const n=P[t];F(n[0]+e,n[1]+e,n[2]+e)}}else{for(let t=0;t<D;t++){const e=P[t];F(e[2],e[1],e[0])}for(let t=0;t<D;t++){const e=P[t];F(e[0]+S*s,e[1]+S*s,e[2]+S*s)}}n.addGroup(t,o.length/3-t,0)})(),(function U(){const t=o.length/3;let e=0;I(w,e),e+=w.length;for(let t=0,n=O.length;t<n;t++){const n=O[t];I(n,e),e+=n.length}n.addGroup(t,o.length/3-t,1)})()}this.setAttribute("position",new Frt(o,3)),this.setAttribute("uv",new Frt(i,2)),this.computeVertexNormals()}toJSON(){const t=super.toJSON();return(function e(t,n,o){if(o.shapes=[],Array.isArray(t))for(let e=0,n=t.length;e<n;e++)o.shapes.push(t[e].uuid);else o.shapes.push(t.uuid);return void 0!==n.extrudePath&&(o.options.extrudePath=n.extrudePath.toJSON()),o})(this.parameters.shapes,this.parameters.options,t)}static fromJSON(t,e){const n=[];for(let o=0,i=t.shapes.length;o<i;o++)n.push(e[t.shapes[o]]);const o=t.options.extrudePath;return void 0!==o&&(t.options.extrudePath=(new Spt[o.type]).fromJSON(o)),new nmt(n,t.options)}}const omt={generateTopUV:function(t,e,n,o,i){const a=e[3*o],r=e[3*o+1],s=e[3*i],l=e[3*i+1];return[new Qit(e[3*n],e[3*n+1]),new Qit(a,r),new Qit(s,l)]},generateSideWallUV:function(t,e,n,o,i,a){const r=e[3*n],s=e[3*n+1],l=e[3*n+2],c=e[3*o],d=e[3*o+1],p=e[3*o+2],m=e[3*i],u=e[3*i+1],f=e[3*i+2],g=e[3*a],h=e[3*a+1],b=e[3*a+2];return Math.abs(s-d)<Math.abs(r-c)?[new Qit(r,1-l),new Qit(c,1-p),new Qit(m,1-f),new Qit(g,1-b)]:[new Qit(s,1-l),new Qit(d,1-p),new Qit(u,1-f),new Qit(h,1-b)]}};class imt extends qrt{constructor(t,e=12){super(),this.type="ShapeGeometry",this.parameters={shapes:t,curveSegments:e};const n=[],o=[],i=[],a=[];let r=0,s=0;if(!1===Array.isArray(t))l(t);else for(let e=0;e<t.length;e++)l(t[e]),this.addGroup(r,s,e),r+=s,s=0;function l(t){const r=o.length/3,l=t.extractPoints(e);let c=l.shape;const d=l.holes;!1===$pt.isClockWise(c)&&(c=c.reverse());for(let t=0,e=d.length;t<e;t++){const e=d[t];!0===$pt.isClockWise(e)&&(d[t]=e.reverse())}const p=$pt.triangulateShape(c,d);for(let t=0,e=d.length;t<e;t++)c=c.concat(d[t]);for(let t=0,e=c.length;t<e;t++){const e=c[t];o.push(e.x,e.y,0),i.push(0,0,1),a.push(e.x,e.y)}for(let t=0,e=p.length;t<e;t++){const e=p[t];n.push(e[0]+r,e[1]+r,e[2]+r),s+=3}}this.setIndex(n),this.setAttribute("position",new Frt(o,3)),this.setAttribute("normal",new Frt(i,3)),this.setAttribute("uv",new Frt(a,2))}toJSON(){const t=super.toJSON();return(function e(t,n){if(n.shapes=[],Array.isArray(t))for(let e=0,o=t.length;e<o;e++)n.shapes.push(t[e].uuid);else n.shapes.push(t.uuid);return n})(this.parameters.shapes,t)}static fromJSON(t,e){const n=[];for(let o=0,i=t.shapes.length;o<i;o++)n.push(e[t.shapes[o]]);return new imt(n,t.curveSegments)}}(class extends Ort{constructor(t){super(),this.type="ShadowMaterial",this.color=new Rrt(0),this.transparent=!0,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this}}).prototype.isShadowMaterial=!0;class amt extends Ort{constructor(t){super(),this.defines={STANDARD:""},this.type="MeshStandardMaterial",this.color=new Rrt(16777215),this.roughness=1,this.metalness=0,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Rrt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new Qit(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.roughnessMap=null,this.metalnessMap=null,this.alphaMap=null,this.envMap=null,this.envMapIntensity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:""},this.color.copy(t.color),this.roughness=t.roughness,this.metalness=t.metalness,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.roughnessMap=t.roughnessMap,this.metalnessMap=t.metalnessMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.envMapIntensity=t.envMapIntensity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}amt.prototype.isMeshStandardMaterial=!0,class extends amt{constructor(t){super(),this.defines={STANDARD:"",PHYSICAL:""},this.type="MeshPhysicalMaterial",this.clearcoat=0,this.clearcoatMap=null,this.clearcoatRoughness=0,this.clearcoatRoughnessMap=null,this.clearcoatNormalScale=new Qit(1,1),this.clearcoatNormalMap=null,this.reflectivity=.5,Object.defineProperty(this,"ior",{get:function(){return(1+.4*this.reflectivity)/(1-.4*this.reflectivity)},set:function(t){this.reflectivity=Zit(2.5*(t-1)/(t+1),0,1)}}),this.sheen=null,this.transmission=0,this.transmissionMap=null,this.thickness=.01,this.thicknessMap=null,this.attenuationDistance=0,this.attenuationTint=new Rrt(1,1,1),this.specularIntensity=1,this.specularIntensityMap=null,this.specularTint=new Rrt(1,1,1),this.specularTintMap=null,this.setValues(t)}copy(t){return super.copy(t),this.defines={STANDARD:"",PHYSICAL:""},this.clearcoat=t.clearcoat,this.clearcoatMap=t.clearcoatMap,this.clearcoatRoughness=t.clearcoatRoughness,this.clearcoatRoughnessMap=t.clearcoatRoughnessMap,this.clearcoatNormalMap=t.clearcoatNormalMap,this.clearcoatNormalScale.copy(t.clearcoatNormalScale),this.reflectivity=t.reflectivity,this.sheen=t.sheen?(this.sheen||new Rrt).copy(t.sheen):null,this.transmission=t.transmission,this.transmissionMap=t.transmissionMap,this.thickness=t.thickness,this.thicknessMap=t.thicknessMap,this.attenuationDistance=t.attenuationDistance,this.attenuationTint.copy(t.attenuationTint),this.specularIntensity=t.specularIntensity,this.specularIntensityMap=t.specularIntensityMap,this.specularTint.copy(t.specularTint),this.specularTintMap=t.specularTintMap,this}}.prototype.isMeshPhysicalMaterial=!0,class extends Ort{constructor(t){super(),this.type="MeshPhongMaterial",this.color=new Rrt(16777215),this.specular=new Rrt(1118481),this.shininess=30,this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Rrt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new Qit(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.specular.copy(t.specular),this.shininess=t.shininess,this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this.flatShading=t.flatShading,this}}.prototype.isMeshPhongMaterial=!0,class extends Ort{constructor(t){super(),this.defines={TOON:""},this.type="MeshToonMaterial",this.color=new Rrt(16777215),this.map=null,this.gradientMap=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Rrt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new Qit(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.gradientMap=t.gradientMap,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}.prototype.isMeshToonMaterial=!0,class extends Ort{constructor(t){super(),this.type="MeshNormalMaterial",this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new Qit(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.wireframe=!1,this.wireframeLinewidth=1,this.fog=!1,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.flatShading=t.flatShading,this}}.prototype.isMeshNormalMaterial=!0,class extends Ort{constructor(t){super(),this.type="MeshLambertMaterial",this.color=new Rrt(16777215),this.map=null,this.lightMap=null,this.lightMapIntensity=1,this.aoMap=null,this.aoMapIntensity=1,this.emissive=new Rrt(0),this.emissiveIntensity=1,this.emissiveMap=null,this.specularMap=null,this.alphaMap=null,this.envMap=null,this.combine=0,this.reflectivity=1,this.refractionRatio=.98,this.wireframe=!1,this.wireframeLinewidth=1,this.wireframeLinecap="round",this.wireframeLinejoin="round",this.setValues(t)}copy(t){return super.copy(t),this.color.copy(t.color),this.map=t.map,this.lightMap=t.lightMap,this.lightMapIntensity=t.lightMapIntensity,this.aoMap=t.aoMap,this.aoMapIntensity=t.aoMapIntensity,this.emissive.copy(t.emissive),this.emissiveMap=t.emissiveMap,this.emissiveIntensity=t.emissiveIntensity,this.specularMap=t.specularMap,this.alphaMap=t.alphaMap,this.envMap=t.envMap,this.combine=t.combine,this.reflectivity=t.reflectivity,this.refractionRatio=t.refractionRatio,this.wireframe=t.wireframe,this.wireframeLinewidth=t.wireframeLinewidth,this.wireframeLinecap=t.wireframeLinecap,this.wireframeLinejoin=t.wireframeLinejoin,this}}.prototype.isMeshLambertMaterial=!0,class extends Ort{constructor(t){super(),this.defines={MATCAP:""},this.type="MeshMatcapMaterial",this.color=new Rrt(16777215),this.matcap=null,this.map=null,this.bumpMap=null,this.bumpScale=1,this.normalMap=null,this.normalMapType=0,this.normalScale=new Qit(1,1),this.displacementMap=null,this.displacementScale=1,this.displacementBias=0,this.alphaMap=null,this.flatShading=!1,this.setValues(t)}copy(t){return super.copy(t),this.defines={MATCAP:""},this.color.copy(t.color),this.matcap=t.matcap,this.map=t.map,this.bumpMap=t.bumpMap,this.bumpScale=t.bumpScale,this.normalMap=t.normalMap,this.normalMapType=t.normalMapType,this.normalScale.copy(t.normalScale),this.displacementMap=t.displacementMap,this.displacementScale=t.displacementScale,this.displacementBias=t.displacementBias,this.alphaMap=t.alphaMap,this.flatShading=t.flatShading,this}}.prototype.isMeshMatcapMaterial=!0,class extends qdt{constructor(t){super(),this.type="LineDashedMaterial",this.scale=1,this.dashSize=3,this.gapSize=1,this.setValues(t)}copy(t){return super.copy(t),this.scale=t.scale,this.dashSize=t.dashSize,this.gapSize=t.gapSize,this}}.prototype.isLineDashedMaterial=!0;const rmt={arraySlice:function(t,e,n){return rmt.isTypedArray(t)?new t.constructor(t.subarray(e,void 0!==n?n:t.length)):t.slice(e,n)},convertArray:function(t,e,n){return!t||!n&&t.constructor===e?t:"number"==typeof e.BYTES_PER_ELEMENT?new e(t):Array.prototype.slice.call(t)},isTypedArray:function(t){return ArrayBuffer.isView(t)&&!(t instanceof DataView)},getKeyframeOrder:function(t){const e=t.length,n=new Array(e);for(let t=0;t!==e;++t)n[t]=t;return n.sort((function o(e,n){return t[e]-t[n]})),n},sortedArray:function(t,e,n){const o=t.length,i=new t.constructor(o);for(let a=0,r=0;r!==o;++a){const o=n[a]*e;for(let n=0;n!==e;++n)i[r++]=t[o+n]}return i},flattenJSON:function(t,e,n,o){let i=1,a=t[0];for(;void 0!==a&&void 0===a[o];)a=t[i++];if(void 0===a)return;let r=a[o];if(void 0!==r)if(Array.isArray(r))do{r=a[o],void 0!==r&&(e.push(a.time),n.push.apply(n,r)),a=t[i++]}while(void 0!==a);else if(void 0!==r.toArray)do{r=a[o],void 0!==r&&(e.push(a.time),r.toArray(n,n.length)),a=t[i++]}while(void 0!==a);else do{r=a[o],void 0!==r&&(e.push(a.time),n.push(r)),a=t[i++]}while(void 0!==a)},subclip:function(t,e,n,o,i=30){const a=t.clone();a.name=e;const r=[];for(let t=0;t<a.tracks.length;++t){const e=a.tracks[t],s=e.getValueSize(),l=[],c=[];for(let t=0;t<e.times.length;++t){const a=e.times[t]*i;if(!(a<n||a>=o)){l.push(e.times[t]);for(let n=0;n<s;++n)c.push(e.values[t*s+n])}}0!==l.length&&(e.times=rmt.convertArray(l,e.times.constructor),e.values=rmt.convertArray(c,e.values.constructor),r.push(e))}a.tracks=r;let s=1/0;for(let t=0;t<a.tracks.length;++t)s>a.tracks[t].times[0]&&(s=a.tracks[t].times[0]);for(let t=0;t<a.tracks.length;++t)a.tracks[t].shift(-1*s);return a.resetDuration(),a},makeClipAdditive:function(t,e=0,n=t,o=30){o<=0&&(o=30);const i=n.tracks.length,a=e/o;for(let e=0;e<i;++e){const o=n.tracks[e],i=o.ValueTypeName;if("bool"===i||"string"===i)continue;const r=t.tracks.find((function(t){return t.name===o.name&&t.ValueTypeName===i}));if(void 0===r)continue;let s=0;const l=o.getValueSize();o.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(s=l/3);let c=0;const d=r.getValueSize();r.createInterpolant.isInterpolantFactoryMethodGLTFCubicSpline&&(c=d/3);const p=o.times.length-1;let m;if(a<=o.times[0])m=rmt.arraySlice(o.values,s,l-s);else if(a>=o.times[p]){const t=p*l+s;m=rmt.arraySlice(o.values,t,t+l-s)}else{const t=o.createInterpolant(),e=s,n=l-s;t.evaluate(a),m=rmt.arraySlice(t.resultBuffer,e,n)}"quaternion"===i&&(new lat).fromArray(m).normalize().conjugate().toArray(m);const u=r.times.length;for(let t=0;t<u;++t){const e=t*d+c;if("quaternion"===i)lat.multiplyQuaternionsFlat(r.values,e,m,0,r.values,e);else{const t=d-2*c;for(let n=0;n<t;++n)r.values[e+n]-=m[n]}}}return t.blendMode=2501,t}};class smt{constructor(t,e,n,o){this.parameterPositions=t,this._cachedIndex=0,this.resultBuffer=void 0!==o?o:new e.constructor(n),this.sampleValues=e,this.valueSize=n,this.settings=null,this.DefaultSettings_={}}evaluate(t){const e=this.parameterPositions;let n=this._cachedIndex,o=e[n],i=e[n-1];t:{e:{let a;n:{o:if(!(t<o)){for(let a=n+2;;){if(void 0===o){if(t<i)break o;return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,t,i)}if(n===a)break;if(i=o,o=e[++n],t<o)break e}a=e.length;break n}if(t>=i)break t;{const r=e[1];t<r&&(n=2,i=r);for(let a=n-2;;){if(void 0===i)return this._cachedIndex=0,this.beforeStart_(0,t,o);if(n===a)break;if(o=i,i=e[--n-1],t>=i)break e}a=n,n=0}}for(;n<a;){const o=n+a>>>1;t<e[o]?a=o:n=o+1}if(o=e[n],i=e[n-1],void 0===i)return this._cachedIndex=0,this.beforeStart_(0,t,o);if(void 0===o)return n=e.length,this._cachedIndex=n,this.afterEnd_(n-1,i,t)}this._cachedIndex=n,this.intervalChanged_(n,i,o)}return this.interpolate_(n,i,t,o)}getSettings_(){return this.settings||this.DefaultSettings_}copySampleValue_(t){const e=this.resultBuffer,n=this.sampleValues,o=this.valueSize,i=t*o;for(let t=0;t!==o;++t)e[t]=n[i+t];return e}interpolate_(){throw new Error("call to abstract method")}intervalChanged_(){}}smt.prototype.beforeStart_=smt.prototype.copySampleValue_,smt.prototype.afterEnd_=smt.prototype.copySampleValue_;class lmt extends smt{constructor(t,e,n,o){super(t,e,n,o),this._weightPrev=-0,this._offsetPrev=-0,this._weightNext=-0,this._offsetNext=-0,this.DefaultSettings_={endingStart:Ait,endingEnd:Ait}}intervalChanged_(t,e,n){const o=this.parameterPositions;let i=t-2,a=t+1,r=o[i],s=o[a];if(void 0===r)switch(this.getSettings_().endingStart){case Tit:i=t,r=2*e-n;break;case Nit:i=o.length-2,r=e+o[i]-o[i+1];break;default:i=t,r=n}if(void 0===s)switch(this.getSettings_().endingEnd){case Tit:a=t,s=2*n-e;break;case Nit:a=1,s=n+o[1]-o[0];break;default:a=t-1,s=e}const l=.5*(n-e),c=this.valueSize;this._weightPrev=l/(e-r),this._weightNext=l/(s-n),this._offsetPrev=i*c,this._offsetNext=a*c}interpolate_(t,e,n,o){const i=this.resultBuffer,a=this.sampleValues,r=this.valueSize,s=t*r,l=s-r,c=this._offsetPrev,d=this._offsetNext,p=this._weightPrev,m=this._weightNext,u=(n-e)/(o-e),f=u*u,g=f*u,h=-p*g+2*p*f-p*u,b=(1+p)*g+(-1.5-2*p)*f+(-.5+p)*u+1,y=(-1-m)*g+(1.5+m)*f+.5*u,_=m*g-m*f;for(let t=0;t!==r;++t)i[t]=h*a[c+t]+b*a[l+t]+y*a[s+t]+_*a[d+t];return i}}class cmt extends smt{constructor(t,e,n,o){super(t,e,n,o)}interpolate_(t,e,n,o){const i=this.resultBuffer,a=this.sampleValues,r=this.valueSize,s=t*r,l=s-r,c=(n-e)/(o-e),d=1-c;for(let t=0;t!==r;++t)i[t]=a[l+t]*d+a[s+t]*c;return i}}class dmt extends smt{constructor(t,e,n,o){super(t,e,n,o)}interpolate_(t){return this.copySampleValue_(t-1)}}class pmt{constructor(t,e,n,o){if(void 0===t)throw new Error("THREE.KeyframeTrack: track name is undefined");if(void 0===e||0===e.length)throw new Error("THREE.KeyframeTrack: no keyframes in track named "+t);this.name=t,this.times=rmt.convertArray(e,this.TimeBufferType),this.values=rmt.convertArray(n,this.ValueBufferType),this.setInterpolation(o||this.DefaultInterpolation)}static toJSON(t){const e=t.constructor;let n;if(e.toJSON!==this.toJSON)n=e.toJSON(t);else{n={name:t.name,times:rmt.convertArray(t.times,Array),values:rmt.convertArray(t.values,Array)};const e=t.getInterpolation();e!==t.DefaultInterpolation&&(n.interpolation=e)}return n.type=t.ValueTypeName,n}InterpolantFactoryMethodDiscrete(t){return new dmt(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodLinear(t){return new cmt(this.times,this.values,this.getValueSize(),t)}InterpolantFactoryMethodSmooth(t){return new lmt(this.times,this.values,this.getValueSize(),t)}setInterpolation(t){let e;switch(t){case Dit:e=this.InterpolantFactoryMethodDiscrete;break;case Eit:e=this.InterpolantFactoryMethodLinear;break;case Rit:e=this.InterpolantFactoryMethodSmooth}if(void 0===e){const e="unsupported interpolation for "+this.ValueTypeName+" keyframe track named "+this.name;if(void 0===this.createInterpolant){if(t===this.DefaultInterpolation)throw new Error(e);this.setInterpolation(this.DefaultInterpolation)}return console.warn("THREE.KeyframeTrack:",e),this}return this.createInterpolant=e,this}getInterpolation(){switch(this.createInterpolant){case this.InterpolantFactoryMethodDiscrete:return Dit;case this.InterpolantFactoryMethodLinear:return Eit;case this.InterpolantFactoryMethodSmooth:return Rit}}getValueSize(){return this.values.length/this.times.length}shift(t){if(0!==t){const e=this.times;for(let n=0,o=e.length;n!==o;++n)e[n]+=t}return this}scale(t){if(1!==t){const e=this.times;for(let n=0,o=e.length;n!==o;++n)e[n]*=t}return this}trim(t,e){const n=this.times,o=n.length;let i=0,a=o-1;for(;i!==o&&n[i]<t;)++i;for(;-1!==a&&n[a]>e;)--a;if(++a,0!==i||a!==o){i>=a&&(a=Math.max(a,1),i=a-1);const t=this.getValueSize();this.times=rmt.arraySlice(n,i,a),this.values=rmt.arraySlice(this.values,i*t,a*t)}return this}validate(){let t=!0;const e=this.getValueSize();e-Math.floor(e)!=0&&(console.error("THREE.KeyframeTrack: Invalid value size in track.",this),t=!1);const n=this.times,o=this.values,i=n.length;0===i&&(console.error("THREE.KeyframeTrack: Track is empty.",this),t=!1);let a=null;for(let e=0;e!==i;e++){const o=n[e];if("number"==typeof o&&isNaN(o)){console.error("THREE.KeyframeTrack: Time is not a valid number.",this,e,o),t=!1;break}if(null!==a&&a>o){console.error("THREE.KeyframeTrack: Out of order keys.",this,e,o,a),t=!1;break}a=o}if(void 0!==o&&rmt.isTypedArray(o))for(let e=0,n=o.length;e!==n;++e){const n=o[e];if(isNaN(n)){console.error("THREE.KeyframeTrack: Value is not a valid number.",this,e,n),t=!1;break}}return t}optimize(){const t=rmt.arraySlice(this.times),e=rmt.arraySlice(this.values),n=this.getValueSize(),o=this.getInterpolation()===Rit,i=t.length-1;let a=1;for(let r=1;r<i;++r){let i=!1;const s=t[r];if(s!==t[r+1]&&(1!==r||s!==t[0]))if(o)i=!0;else{const t=r*n,o=t-n,a=t+n;for(let r=0;r!==n;++r){const n=e[t+r];if(n!==e[o+r]||n!==e[a+r]){i=!0;break}}}if(i){if(r!==a){t[a]=t[r];const o=r*n,i=a*n;for(let t=0;t!==n;++t)e[i+t]=e[o+t]}++a}}if(i>0){t[a]=t[i];for(let t=i*n,o=a*n,r=0;r!==n;++r)e[o+r]=e[t+r];++a}return a!==t.length?(this.times=rmt.arraySlice(t,0,a),this.values=rmt.arraySlice(e,0,a*n)):(this.times=t,this.values=e),this}clone(){const t=rmt.arraySlice(this.times,0),e=rmt.arraySlice(this.values,0),n=new(0,this.constructor)(this.name,t,e);return n.createInterpolant=this.createInterpolant,n}}pmt.prototype.TimeBufferType=Float32Array,pmt.prototype.ValueBufferType=Float32Array,pmt.prototype.DefaultInterpolation=Eit;class mmt extends pmt{}mmt.prototype.ValueTypeName="bool",mmt.prototype.ValueBufferType=Array,mmt.prototype.DefaultInterpolation=Dit,mmt.prototype.InterpolantFactoryMethodLinear=void 0,mmt.prototype.InterpolantFactoryMethodSmooth=void 0;class umt extends pmt{}umt.prototype.ValueTypeName="color";class fmt extends pmt{}fmt.prototype.ValueTypeName="number";class gmt extends smt{constructor(t,e,n,o){super(t,e,n,o)}interpolate_(t,e,n,o){const i=this.resultBuffer,a=this.sampleValues,r=this.valueSize,s=(n-e)/(o-e);let l=t*r;for(let t=l+r;l!==t;l+=4)lat.slerpFlat(i,0,a,l-r,a,l,s);return i}}class hmt extends pmt{InterpolantFactoryMethodLinear(t){return new gmt(this.times,this.values,this.getValueSize(),t)}}hmt.prototype.ValueTypeName="quaternion",hmt.prototype.DefaultInterpolation=Eit,hmt.prototype.InterpolantFactoryMethodSmooth=void 0;class bmt extends pmt{}bmt.prototype.ValueTypeName="string",bmt.prototype.ValueBufferType=Array,bmt.prototype.DefaultInterpolation=Dit,bmt.prototype.InterpolantFactoryMethodLinear=void 0,bmt.prototype.InterpolantFactoryMethodSmooth=void 0;class ymt extends pmt{}ymt.prototype.ValueTypeName="vector";class _mt{constructor(t,e=-1,n,o=2500){this.name=t,this.tracks=n,this.duration=e,this.blendMode=o,this.uuid=qit(),this.duration<0&&this.resetDuration()}static parse(t){const e=[],n=t.tracks,o=1/(t.fps||1);for(let t=0,i=n.length;t!==i;++t)e.push(Cmt(n[t]).scale(o));const i=new this(t.name,t.duration,e,t.blendMode);return i.uuid=t.uuid,i}static toJSON(t){const e=[],n=t.tracks,o={name:t.name,duration:t.duration,tracks:e,uuid:t.uuid,blendMode:t.blendMode};for(let t=0,o=n.length;t!==o;++t)e.push(pmt.toJSON(n[t]));return o}static CreateFromMorphTargetSequence(t,e,n,o){const i=e.length,a=[];for(let t=0;t<i;t++){let r=[],s=[];r.push((t+i-1)%i,t,(t+1)%i),s.push(0,1,0);const l=rmt.getKeyframeOrder(r);r=rmt.sortedArray(r,1,l),s=rmt.sortedArray(s,1,l),o||0!==r[0]||(r.push(i),s.push(s[0])),a.push(new fmt(".morphTargetInfluences["+e[t].name+"]",r,s).scale(1/n))}return new this(t,-1,a)}static findByName(t,e){let n=t;if(!Array.isArray(t)){const e=t;n=e.geometry&&e.geometry.animations||e.animations}for(let t=0;t<n.length;t++)if(n[t].name===e)return n[t];return null}static CreateClipsFromMorphTargetSequences(t,e,n){const o={},i=/^([\w-]*?)([\d]+)$/;for(let e=0,n=t.length;e<n;e++){const n=t[e],a=n.name.match(i);if(a&&a.length>1){const t=a[1];let e=o[t];e||(o[t]=e=[]),e.push(n)}}const a=[];for(const t in o)a.push(this.CreateFromMorphTargetSequence(t,o[t],e,n));return a}static parseAnimation(t,e){if(!t)return console.error("THREE.AnimationClip: No animation in JSONLoader data."),null;const n=function(t,e,n,o,i){if(0!==n.length){const a=[],r=[];rmt.flattenJSON(n,a,r,o),0!==a.length&&i.push(new t(e,a,r))}},o=[],i=t.name||"default",a=t.fps||30,r=t.blendMode;let s=t.length||-1;const l=t.hierarchy||[];for(let t=0;t<l.length;t++){const i=l[t].keys;if(i&&0!==i.length)if(i[0].morphTargets){const t={};let e;for(e=0;e<i.length;e++)if(i[e].morphTargets)for(let n=0;n<i[e].morphTargets.length;n++)t[i[e].morphTargets[n]]=-1;for(const n in t){const t=[],a=[];for(let o=0;o!==i[e].morphTargets.length;++o){const o=i[e];t.push(o.time),a.push(o.morphTarget===n?1:0)}o.push(new fmt(".morphTargetInfluence["+n+"]",t,a))}s=t.length*(a||1)}else{const a=".bones["+e[t].name+"]";n(ymt,a+".position",i,"pos",o),n(hmt,a+".quaternion",i,"rot",o),n(ymt,a+".scale",i,"scl",o)}}return 0===o.length?null:new this(i,s,o,r)}resetDuration(){let t=0;for(let e=0,n=this.tracks.length;e!==n;++e){const n=this.tracks[e];t=Math.max(t,n.times[n.times.length-1])}return this.duration=t,this}trim(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].trim(0,this.duration);return this}validate(){let t=!0;for(let e=0;e<this.tracks.length;e++)t=t&&this.tracks[e].validate();return t}optimize(){for(let t=0;t<this.tracks.length;t++)this.tracks[t].optimize();return this}clone(){const t=[];for(let e=0;e<this.tracks.length;e++)t.push(this.tracks[e].clone());return new this.constructor(this.name,this.duration,t,this.blendMode)}toJSON(){return this.constructor.toJSON(this)}}function Cmt(t){if(void 0===t.type)throw new Error("THREE.KeyframeTrack: track type undefined, can not parse");const e=(function n(t){switch(t.toLowerCase()){case"scalar":case"double":case"float":case"number":case"integer":return fmt;case"vector":case"vector2":case"vector3":case"vector4":return ymt;case"color":return umt;case"quaternion":return hmt;case"bool":case"boolean":return mmt;case"string":return bmt}throw new Error("THREE.KeyframeTrack: Unsupported typeName: "+t)})(t.type);if(void 0===t.times){const e=[],n=[];rmt.flattenJSON(t.keys,e,n,"value"),t.times=e,t.values=n}return void 0!==e.parse?e.parse(t):new e(t.name,t.times,t.values,t.interpolation)}const Mmt={enabled:!1,files:{},add:function(t,e){!1!==this.enabled&&(this.files[t]=e)},get:function(t){if(!1!==this.enabled)return this.files[t]},remove:function(t){delete this.files[t]},clear:function(){this.files={}}},vmt=new class{constructor(t,e,n){const o=this;let i,a=!1,r=0,s=0;const l=[];this.onStart=void 0,this.onLoad=t,this.onProgress=e,this.onError=n,this.itemStart=function(t){s++,!1===a&&void 0!==o.onStart&&o.onStart(t,r,s),a=!0},this.itemEnd=function(t){r++,void 0!==o.onProgress&&o.onProgress(t,r,s),r===s&&(a=!1,void 0!==o.onLoad&&o.onLoad())},this.itemError=function(t){void 0!==o.onError&&o.onError(t)},this.resolveURL=function(t){return i?i(t):t},this.setURLModifier=function(t){return i=t,this},this.addHandler=function(t,e){return l.push(t,e),this},this.removeHandler=function(t){const e=l.indexOf(t);return-1!==e&&l.splice(e,2),this},this.getHandler=function(t){for(let e=0,n=l.length;e<n;e+=2){const n=l[e],o=l[e+1];if(n.global&&(n.lastIndex=0),n.test(t))return o}return null}}};class xmt{constructor(t){this.manager=void 0!==t?t:vmt,this.crossOrigin="anonymous",this.withCredentials=!1,this.path="",this.resourcePath="",this.requestHeader={}}load(){}loadAsync(t,e){const n=this;return new Promise((function(o,i){n.load(t,o,e,i)}))}parse(){}setCrossOrigin(t){return this.crossOrigin=t,this}setWithCredentials(t){return this.withCredentials=t,this}setPath(t){return this.path=t,this}setResourcePath(t){return this.resourcePath=t,this}setRequestHeader(t){return this.requestHeader=t,this}}const Omt={};class Pmt extends xmt{constructor(t){super(t)}load(t,e,n,o){void 0===t&&(t=""),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const i=this,a=Mmt.get(t);if(void 0!==a)return i.manager.itemStart(t),setTimeout((function(){e&&e(a),i.manager.itemEnd(t)}),0),a;if(void 0!==Omt[t])return void Omt[t].push({onLoad:e,onProgress:n,onError:o});const r=t.match(/^data:(.*?)(;base64)?,(.*)$/);let s;if(r){const n=r[1],a=!!r[2];let s=r[3];s=decodeURIComponent(s),a&&(s=atob(s));try{let o;const a=(this.responseType||"").toLowerCase();switch(a){case"arraybuffer":case"blob":const t=new Uint8Array(s.length);for(let e=0;e<s.length;e++)t[e]=s.charCodeAt(e);o="blob"===a?new Blob([t.buffer],{type:n}):t.buffer;break;case"document":const e=new DOMParser;o=e.parseFromString(s,n);break;case"json":o=JSON.parse(s);break;default:o=s}setTimeout((function(){e&&e(o),i.manager.itemEnd(t)}),0)}catch(e){setTimeout((function(){o&&o(e),i.manager.itemError(t),i.manager.itemEnd(t)}),0)}}else{Omt[t]=[],Omt[t].push({onLoad:e,onProgress:n,onError:o}),s=new XMLHttpRequest,s.open("GET",t,!0),s.addEventListener("load",(function(e){const n=this.response,o=Omt[t];if(delete Omt[t],200===this.status||0===this.status){0===this.status&&console.warn("THREE.FileLoader: HTTP Status 0 received."),Mmt.add(t,n);for(let t=0,e=o.length;t<e;t++){const e=o[t];e.onLoad&&e.onLoad(n)}i.manager.itemEnd(t)}else{for(let t=0,n=o.length;t<n;t++){const n=o[t];n.onError&&n.onError(e)}i.manager.itemError(t),i.manager.itemEnd(t)}}),!1),s.addEventListener("progress",(function(e){const n=Omt[t];for(let t=0,o=n.length;t<o;t++){const o=n[t];o.onProgress&&o.onProgress(e)}}),!1),s.addEventListener("error",(function(e){const n=Omt[t];delete Omt[t];for(let t=0,o=n.length;t<o;t++){const o=n[t];o.onError&&o.onError(e)}i.manager.itemError(t),i.manager.itemEnd(t)}),!1),s.addEventListener("abort",(function(e){const n=Omt[t];delete Omt[t];for(let t=0,o=n.length;t<o;t++){const o=n[t];o.onError&&o.onError(e)}i.manager.itemError(t),i.manager.itemEnd(t)}),!1),void 0!==this.responseType&&(s.responseType=this.responseType),void 0!==this.withCredentials&&(s.withCredentials=this.withCredentials),s.overrideMimeType&&s.overrideMimeType(void 0!==this.mimeType?this.mimeType:"text/plain");for(const t in this.requestHeader)s.setRequestHeader(t,this.requestHeader[t]);s.send(null)}return i.manager.itemStart(t),s}setResponseType(t){return this.responseType=t,this}setMimeType(t){return this.mimeType=t,this}}class wmt extends xmt{constructor(t){super(t)}load(t,e,n,o){void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const i=this,a=Mmt.get(t);if(void 0!==a)return i.manager.itemStart(t),setTimeout((function(){e&&e(a),i.manager.itemEnd(t)}),0),a;const r=document.createElementNS("http://www.w3.org/1999/xhtml","img");function s(){r.removeEventListener("load",s,!1),r.removeEventListener("error",l,!1),Mmt.add(t,this),e&&e(this),i.manager.itemEnd(t)}function l(e){r.removeEventListener("load",s,!1),r.removeEventListener("error",l,!1),o&&o(e),i.manager.itemError(t),i.manager.itemEnd(t)}return r.addEventListener("load",s,!1),r.addEventListener("error",l,!1),"data:"!==t.substr(0,5)&&void 0!==this.crossOrigin&&(r.crossOrigin=this.crossOrigin),i.manager.itemStart(t),r.src=t,r}}class kmt extends xmt{constructor(t){super(t)}load(t,e,n,o){const i=new vst,a=new wmt(this.manager);a.setCrossOrigin(this.crossOrigin),a.setPath(this.path);let r=0;function s(n){a.load(t[n],(function(t){i.images[n]=t,r++,6===r&&(i.needsUpdate=!0,e&&e(i))}),void 0,o)}for(let e=0;e<t.length;++e)s(e);return i}}class Smt extends xmt{constructor(t){super(t)}load(t,e,n,o){const i=new oat,a=new wmt(this.manager);return a.setCrossOrigin(this.crossOrigin),a.setPath(this.path),a.load(t,(function(n){i.image=n;const o=t.search(/\.jpe?g($|\?)/i)>0||0===t.search(/^data\:image\/jpeg/);i.format=o?Pit:wit,i.needsUpdate=!0,void 0!==e&&e(i)}),n,o),i}}class Dmt extends dpt{constructor(){super(),this.type="CurvePath",this.curves=[],this.autoClose=!1}add(t){this.curves.push(t)}closePath(){const t=this.curves[0].getPoint(0),e=this.curves[this.curves.length-1].getPoint(1);t.equals(e)||this.curves.push(new Opt(e,t))}getPoint(t){const e=t*this.getLength(),n=this.getCurveLengths();let o=0;for(;o<n.length;){if(n[o]>=e){const t=n[o]-e,i=this.curves[o],a=i.getLength();return i.getPointAt(0===a?0:1-t/a)}o++}return null}getLength(){const t=this.getCurveLengths();return t[t.length-1]}updateArcLengths(){this.needsUpdate=!0,this.cacheLengths=null,this.getCurveLengths()}getCurveLengths(){if(this.cacheLengths&&this.cacheLengths.length===this.curves.length)return this.cacheLengths;const t=[];let e=0;for(let n=0,o=this.curves.length;n<o;n++)e+=this.curves[n].getLength(),t.push(e);return this.cacheLengths=t,t}getSpacedPoints(t=40){const e=[];for(let n=0;n<=t;n++)e.push(this.getPoint(n/t));return this.autoClose&&e.push(e[0]),e}getPoints(t=12){const e=[];let n;for(let o=0,i=this.curves;o<i.length;o++){const a=i[o],r=a.getPoints(a&&a.isEllipseCurve?2*t:a&&(a.isLineCurve||a.isLineCurve3)?1:a&&a.isSplineCurve?t*a.points.length:t);for(let t=0;t<r.length;t++){const o=r[t];n&&n.equals(o)||(e.push(o),n=o)}}return this.autoClose&&e.length>1&&!e[e.length-1].equals(e[0])&&e.push(e[0]),e}copy(t){super.copy(t),this.curves=[];for(let e=0,n=t.curves.length;e<n;e++)this.curves.push(t.curves[e].clone());return this.autoClose=t.autoClose,this}toJSON(){const t=super.toJSON();t.autoClose=this.autoClose,t.curves=[];for(let e=0,n=this.curves.length;e<n;e++)t.curves.push(this.curves[e].toJSON());return t}fromJSON(t){super.fromJSON(t),this.autoClose=t.autoClose,this.curves=[];for(let e=0,n=t.curves.length;e<n;e++){const n=t.curves[e];this.curves.push((new Spt[n.type]).fromJSON(n))}return this}}class Emt extends Dmt{constructor(t){super(),this.type="Path",this.currentPoint=new Qit,t&&this.setFromPoints(t)}setFromPoints(t){this.moveTo(t[0].x,t[0].y);for(let e=1,n=t.length;e<n;e++)this.lineTo(t[e].x,t[e].y);return this}moveTo(t,e){return this.currentPoint.set(t,e),this}lineTo(t,e){const n=new Opt(this.currentPoint.clone(),new Qit(t,e));return this.curves.push(n),this.currentPoint.set(t,e),this}quadraticCurveTo(t,e,n,o){const i=new Ppt(this.currentPoint.clone(),new Qit(t,e),new Qit(n,o));return this.curves.push(i),this.currentPoint.set(n,o),this}bezierCurveTo(t,e,n,o,i,a){const r=new vpt(this.currentPoint.clone(),new Qit(t,e),new Qit(n,o),new Qit(i,a));return this.curves.push(r),this.currentPoint.set(i,a),this}splineThru(t){const e=[this.currentPoint.clone()].concat(t),n=new kpt(e);return this.curves.push(n),this.currentPoint.copy(t[t.length-1]),this}arc(t,e,n,o,i,a){return this.absarc(t+this.currentPoint.x,e+this.currentPoint.y,n,o,i,a),this}absarc(t,e,n,o,i,a){return this.absellipse(t,e,n,n,o,i,a),this}ellipse(t,e,n,o,i,a,r,s){return this.absellipse(t+this.currentPoint.x,e+this.currentPoint.y,n,o,i,a,r,s),this}absellipse(t,e,n,o,i,a,r,s){const l=new ppt(t,e,n,o,i,a,r,s);if(this.curves.length>0){const t=l.getPoint(0);t.equals(this.currentPoint)||this.lineTo(t.x,t.y)}this.curves.push(l);const c=l.getPoint(1);return this.currentPoint.copy(c),this}copy(t){return super.copy(t),this.currentPoint.copy(t.currentPoint),this}toJSON(){const t=super.toJSON();return t.currentPoint=this.currentPoint.toArray(),t}fromJSON(t){return super.fromJSON(t),this.currentPoint.fromArray(t.currentPoint),this}}class Rmt extends Emt{constructor(t){super(t),this.uuid=qit(),this.type="Shape",this.holes=[]}getPointsHoles(t){const e=[];for(let n=0,o=this.holes.length;n<o;n++)e[n]=this.holes[n].getPoints(t);return e}extractPoints(t){return{shape:this.getPoints(t),holes:this.getPointsHoles(t)}}copy(t){super.copy(t),this.holes=[];for(let e=0,n=t.holes.length;e<n;e++)this.holes.push(t.holes[e].clone());return this}toJSON(){const t=super.toJSON();t.uuid=this.uuid,t.holes=[];for(let e=0,n=this.holes.length;e<n;e++)t.holes.push(this.holes[e].toJSON());return t}fromJSON(t){super.fromJSON(t),this.uuid=t.uuid,this.holes=[];for(let e=0,n=t.holes.length;e<n;e++){const n=t.holes[e];this.holes.push((new Emt).fromJSON(n))}return this}}class Amt extends prt{constructor(t,e=1){super(),this.type="Light",this.color=new Rrt(t),this.intensity=e}dispose(){}copy(t){return super.copy(t),this.color.copy(t.color),this.intensity=t.intensity,this}toJSON(t){const e=super.toJSON(t);return e.object.color=this.color.getHex(),e.object.intensity=this.intensity,void 0!==this.groundColor&&(e.object.groundColor=this.groundColor.getHex()),void 0!==this.distance&&(e.object.distance=this.distance),void 0!==this.angle&&(e.object.angle=this.angle),void 0!==this.decay&&(e.object.decay=this.decay),void 0!==this.penumbra&&(e.object.penumbra=this.penumbra),void 0!==this.shadow&&(e.object.shadow=this.shadow.toJSON()),e}}Amt.prototype.isLight=!0,class extends Amt{constructor(t,e,n){super(t,n),this.type="HemisphereLight",this.position.copy(prt.DefaultUp),this.updateMatrix(),this.groundColor=new Rrt(e)}copy(t){return Amt.prototype.copy.call(this,t),this.groundColor.copy(t.groundColor),this}}.prototype.isHemisphereLight=!0;const Tmt=new Bat,Nmt=new cat,zmt=new cat;class Imt{constructor(t){this.camera=t,this.bias=0,this.normalBias=0,this.radius=1,this.mapSize=new Qit(512,512),this.map=null,this.mapPass=null,this.matrix=new Bat,this.autoUpdate=!0,this.needsUpdate=!1,this._frustum=new Est,this._frameExtents=new Qit(1,1),this._viewportCount=1,this._viewports=[new aat(0,0,1,1)]}getViewportCount(){return this._viewportCount}getFrustum(){return this._frustum}updateMatrices(t){const e=this.camera,n=this.matrix;Nmt.setFromMatrixPosition(t.matrixWorld),e.position.copy(Nmt),zmt.setFromMatrixPosition(t.target.matrixWorld),e.lookAt(zmt),e.updateMatrixWorld(),Tmt.multiplyMatrices(e.projectionMatrix,e.matrixWorldInverse),this._frustum.setFromProjectionMatrix(Tmt),n.set(.5,0,0,.5,0,.5,0,.5,0,0,.5,.5,0,0,0,1),n.multiply(e.projectionMatrix),n.multiply(e.matrixWorldInverse)}getViewport(t){return this._viewports[t]}getFrameExtents(){return this._frameExtents}dispose(){this.map&&this.map.dispose(),this.mapPass&&this.mapPass.dispose()}copy(t){return this.camera=t.camera.clone(),this.bias=t.bias,this.radius=t.radius,this.mapSize.copy(t.mapSize),this}clone(){return(new this.constructor).copy(this)}toJSON(){const t={};return 0!==this.bias&&(t.bias=this.bias),0!==this.normalBias&&(t.normalBias=this.normalBias),1!==this.radius&&(t.radius=this.radius),512===this.mapSize.x&&512===this.mapSize.y||(t.mapSize=this.mapSize.toArray()),t.camera=this.camera.toJSON(!1).object,delete t.camera.matrix,t}}class Hmt extends Imt{constructor(){super(new _st(50,1,.5,500)),this.focus=1}updateMatrices(t){const e=this.camera,n=2*Yit*t.angle*this.focus,o=this.mapSize.width/this.mapSize.height,i=t.distance||e.far;n===e.fov&&o===e.aspect&&i===e.far||(e.fov=n,e.aspect=o,e.far=i,e.updateProjectionMatrix()),super.updateMatrices(t)}copy(t){return super.copy(t),this.focus=t.focus,this}}Hmt.prototype.isSpotLightShadow=!0,class extends Amt{constructor(t,e,n=0,o=Math.PI/3,i=0,a=1){super(t,e),this.type="SpotLight",this.position.copy(prt.DefaultUp),this.updateMatrix(),this.target=new prt,this.distance=n,this.angle=o,this.penumbra=i,this.decay=a,this.shadow=new Hmt}get power(){return this.intensity*Math.PI}set power(t){this.intensity=t/Math.PI}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.angle=t.angle,this.penumbra=t.penumbra,this.decay=t.decay,this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}.prototype.isSpotLight=!0;const Fmt=new Bat,Lmt=new cat,Bmt=new cat;class Vmt extends Imt{constructor(){super(new _st(90,1,.5,500)),this._frameExtents=new Qit(4,2),this._viewportCount=6,this._viewports=[new aat(2,1,1,1),new aat(0,1,1,1),new aat(3,1,1,1),new aat(1,1,1,1),new aat(3,0,1,1),new aat(1,0,1,1)],this._cubeDirections=[new cat(1,0,0),new cat(-1,0,0),new cat(0,0,1),new cat(0,0,-1),new cat(0,1,0),new cat(0,-1,0)],this._cubeUps=[new cat(0,1,0),new cat(0,1,0),new cat(0,1,0),new cat(0,1,0),new cat(0,0,1),new cat(0,0,-1)]}updateMatrices(t,e=0){const n=this.camera,o=this.matrix,i=t.distance||n.far;i!==n.far&&(n.far=i,n.updateProjectionMatrix()),Lmt.setFromMatrixPosition(t.matrixWorld),n.position.copy(Lmt),Bmt.copy(n.position),Bmt.add(this._cubeDirections[e]),n.up.copy(this._cubeUps[e]),n.lookAt(Bmt),n.updateMatrixWorld(),o.makeTranslation(-Lmt.x,-Lmt.y,-Lmt.z),Fmt.multiplyMatrices(n.projectionMatrix,n.matrixWorldInverse),this._frustum.setFromProjectionMatrix(Fmt)}}Vmt.prototype.isPointLightShadow=!0,class extends Amt{constructor(t,e,n=0,o=1){super(t,e),this.type="PointLight",this.distance=n,this.decay=o,this.shadow=new Vmt}get power(){return 4*this.intensity*Math.PI}set power(t){this.intensity=t/(4*Math.PI)}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.distance=t.distance,this.decay=t.decay,this.shadow=t.shadow.clone(),this}}.prototype.isPointLight=!0;class jmt extends Imt{constructor(){super(new Ust(-5,5,5,-5,.5,500))}}jmt.prototype.isDirectionalLightShadow=!0,class extends Amt{constructor(t,e){super(t,e),this.type="DirectionalLight",this.position.copy(prt.DefaultUp),this.updateMatrix(),this.target=new prt,this.shadow=new jmt}dispose(){this.shadow.dispose()}copy(t){return super.copy(t),this.target=t.target.clone(),this.shadow=t.shadow.clone(),this}}.prototype.isDirectionalLight=!0,class extends Amt{constructor(t,e){super(t,e),this.type="AmbientLight"}}.prototype.isAmbientLight=!0,class extends Amt{constructor(t,e,n=10,o=10){super(t,e),this.type="RectAreaLight",this.width=n,this.height=o}copy(t){return super.copy(t),this.width=t.width,this.height=t.height,this}toJSON(t){const e=super.toJSON(t);return e.object.width=this.width,e.object.height=this.height,e}}.prototype.isRectAreaLight=!0;class Umt{constructor(){this.coefficients=[];for(let t=0;t<9;t++)this.coefficients.push(new cat)}set(t){for(let e=0;e<9;e++)this.coefficients[e].copy(t[e]);return this}zero(){for(let t=0;t<9;t++)this.coefficients[t].set(0,0,0);return this}getAt(t,e){const n=t.x,o=t.y,i=t.z,a=this.coefficients;return e.copy(a[0]).multiplyScalar(.282095),e.addScaledVector(a[1],.488603*o),e.addScaledVector(a[2],.488603*i),e.addScaledVector(a[3],.488603*n),e.addScaledVector(a[4],n*o*1.092548),e.addScaledVector(a[5],o*i*1.092548),e.addScaledVector(a[6],.315392*(3*i*i-1)),e.addScaledVector(a[7],n*i*1.092548),e.addScaledVector(a[8],.546274*(n*n-o*o)),e}getIrradianceAt(t,e){const n=t.x,o=t.y,i=t.z,a=this.coefficients;return e.copy(a[0]).multiplyScalar(.886227),e.addScaledVector(a[1],1.023328*o),e.addScaledVector(a[2],1.023328*i),e.addScaledVector(a[3],1.023328*n),e.addScaledVector(a[4],.858086*n*o),e.addScaledVector(a[5],.858086*o*i),e.addScaledVector(a[6],.743125*i*i-.247708),e.addScaledVector(a[7],.858086*n*i),e.addScaledVector(a[8],.429043*(n*n-o*o)),e}add(t){for(let e=0;e<9;e++)this.coefficients[e].add(t.coefficients[e]);return this}addScaledSH(t,e){for(let n=0;n<9;n++)this.coefficients[n].addScaledVector(t.coefficients[n],e);return this}scale(t){for(let e=0;e<9;e++)this.coefficients[e].multiplyScalar(t);return this}lerp(t,e){for(let n=0;n<9;n++)this.coefficients[n].lerp(t.coefficients[n],e);return this}equals(t){for(let e=0;e<9;e++)if(!this.coefficients[e].equals(t.coefficients[e]))return!1;return!0}copy(t){return this.set(t.coefficients)}clone(){return(new this.constructor).copy(this)}fromArray(t,e=0){const n=this.coefficients;for(let o=0;o<9;o++)n[o].fromArray(t,e+3*o);return this}toArray(t=[],e=0){const n=this.coefficients;for(let o=0;o<9;o++)n[o].toArray(t,e+3*o);return t}static getBasisAt(t,e){const n=t.x,o=t.y,i=t.z;e[0]=.282095,e[1]=.488603*o,e[2]=.488603*i,e[3]=.488603*n,e[4]=1.092548*n*o,e[5]=1.092548*o*i,e[6]=.315392*(3*i*i-1),e[7]=1.092548*n*i,e[8]=.546274*(n*n-o*o)}}Umt.prototype.isSphericalHarmonics3=!0;class Gmt extends Amt{constructor(t=new Umt,e=1){super(void 0,e),this.sh=t}copy(t){return super.copy(t),this.sh.copy(t.sh),this}fromJSON(t){return this.intensity=t.intensity,this.sh.fromArray(t.sh),this}toJSON(t){const e=super.toJSON(t);return e.object.sh=this.sh.toArray(),e}}let Wmt;Gmt.prototype.isLightProbe=!0,class extends qrt{constructor(){super(),this.type="InstancedBufferGeometry",this.instanceCount=1/0}copy(t){return super.copy(t),this.instanceCount=t.instanceCount,this}clone(){return(new this.constructor).copy(this)}toJSON(){const t=super.toJSON(this);return t.instanceCount=this.instanceCount,t.isInstancedBufferGeometry=!0,t}}.prototype.isInstancedBufferGeometry=!0,class extends zrt{constructor(t,e,n,o=1){"number"==typeof n&&(o=n,n=!1,console.error("THREE.InstancedBufferAttribute: The constructor now expects normalized as the third argument.")),super(t,e,n),this.meshPerAttribute=o}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}toJSON(){const t=super.toJSON();return t.meshPerAttribute=this.meshPerAttribute,t.isInstancedBufferAttribute=!0,t}}.prototype.isInstancedBufferAttribute=!0,class extends xmt{constructor(t){super(t),"undefined"==typeof createImageBitmap&&console.warn("THREE.ImageBitmapLoader: createImageBitmap() not supported."),"undefined"==typeof fetch&&console.warn("THREE.ImageBitmapLoader: fetch() not supported."),this.options={premultiplyAlpha:"none"}}setOptions(t){return this.options=t,this}load(t,e,n,o){void 0===t&&(t=""),void 0!==this.path&&(t=this.path+t),t=this.manager.resolveURL(t);const i=this,a=Mmt.get(t);if(void 0!==a)return i.manager.itemStart(t),setTimeout((function(){e&&e(a),i.manager.itemEnd(t)}),0),a;const r={};r.credentials="anonymous"===this.crossOrigin?"same-origin":"include",r.headers=this.requestHeader,fetch(t,r).then((function(t){return t.blob()})).then((function(t){return createImageBitmap(t,Object.assign(i.options,{colorSpaceConversion:"none"}))})).then((function(n){Mmt.add(t,n),e&&e(n),i.manager.itemEnd(t)})).catch((function(e){o&&o(e),i.manager.itemError(t),i.manager.itemEnd(t)})),i.manager.itemStart(t)}}.prototype.isImageBitmapLoader=!0;class Ymt extends xmt{constructor(t){super(t)}load(t,e,n,o){const i=this,a=new Pmt(this.manager);a.setResponseType("arraybuffer"),a.setPath(this.path),a.setRequestHeader(this.requestHeader),a.setWithCredentials(this.withCredentials),a.load(t,(function(n){try{const t=n.slice(0);(void 0===Wmt&&(Wmt=new(window.AudioContext||window.webkitAudioContext)),Wmt).decodeAudioData(t,(function(t){e(t)}))}catch(e){o?o(e):console.error(e),i.manager.itemError(t)}}),n,o)}}(class extends Gmt{constructor(t,e,n=1){super(void 0,n);const o=(new Rrt).set(t),i=(new Rrt).set(e),a=new cat(o.r,o.g,o.b),r=new cat(i.r,i.g,i.b),s=Math.sqrt(Math.PI),l=s*Math.sqrt(.75);this.sh.coefficients[0].copy(a).add(r).multiplyScalar(s),this.sh.coefficients[1].copy(a).sub(r).multiplyScalar(l)}}).prototype.isHemisphereLightProbe=!0,class extends Gmt{constructor(t,e=1){super(void 0,e);const n=(new Rrt).set(t);this.sh.coefficients[0].set(n.r,n.g,n.b).multiplyScalar(2*Math.sqrt(Math.PI))}}.prototype.isAmbientLightProbe=!0;class qmt{constructor(t,e,n){let o,i,a;switch(this.binding=t,this.valueSize=n,e){case"quaternion":o=this._slerp,i=this._slerpAdditive,a=this._setAdditiveIdentityQuaternion,this.buffer=new Float64Array(6*n),this._workIndex=5;break;case"string":case"bool":o=this._select,i=this._select,a=this._setAdditiveIdentityOther,this.buffer=new Array(5*n);break;default:o=this._lerp,i=this._lerpAdditive,a=this._setAdditiveIdentityNumeric,this.buffer=new Float64Array(5*n)}this._mixBufferRegion=o,this._mixBufferRegionAdditive=i,this._setIdentity=a,this._origIndex=3,this._addIndex=4,this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,this.useCount=0,this.referenceCount=0}accumulate(t,e){const n=this.buffer,o=this.valueSize,i=t*o+o;let a=this.cumulativeWeight;if(0===a){for(let t=0;t!==o;++t)n[i+t]=n[t];a=e}else a+=e,this._mixBufferRegion(n,i,0,e/a,o);this.cumulativeWeight=a}accumulateAdditive(t){const e=this.buffer,n=this.valueSize,o=n*this._addIndex;0===this.cumulativeWeightAdditive&&this._setIdentity(),this._mixBufferRegionAdditive(e,o,0,t,n),this.cumulativeWeightAdditive+=t}apply(t){const e=this.valueSize,n=this.buffer,o=t*e+e,i=this.cumulativeWeight,a=this.cumulativeWeightAdditive,r=this.binding;this.cumulativeWeight=0,this.cumulativeWeightAdditive=0,i<1&&this._mixBufferRegion(n,o,e*this._origIndex,1-i,e),a>0&&this._mixBufferRegionAdditive(n,o,this._addIndex*e,1,e);for(let t=e,i=e+e;t!==i;++t)if(n[t]!==n[t+e]){r.setValue(n,o);break}}saveOriginalState(){const t=this.buffer,e=this.valueSize,n=e*this._origIndex;this.binding.getValue(t,n);for(let o=e,i=n;o!==i;++o)t[o]=t[n+o%e];this._setIdentity(),this.cumulativeWeight=0,this.cumulativeWeightAdditive=0}restoreOriginalState(){this.binding.setValue(this.buffer,3*this.valueSize)}_setAdditiveIdentityNumeric(){const t=this._addIndex*this.valueSize,e=t+this.valueSize;for(let n=t;n<e;n++)this.buffer[n]=0}_setAdditiveIdentityQuaternion(){this._setAdditiveIdentityNumeric(),this.buffer[this._addIndex*this.valueSize+3]=1}_setAdditiveIdentityOther(){const t=this._origIndex*this.valueSize,e=this._addIndex*this.valueSize;for(let n=0;n<this.valueSize;n++)this.buffer[e+n]=this.buffer[t+n]}_select(t,e,n,o,i){if(o>=.5)for(let o=0;o!==i;++o)t[e+o]=t[n+o]}_slerp(t,e,n,o){lat.slerpFlat(t,e,t,e,t,n,o)}_slerpAdditive(t,e,n,o,i){const a=this._workIndex*i;lat.multiplyQuaternionsFlat(t,a,t,e,t,n),lat.slerpFlat(t,e,t,e,t,a,o)}_lerp(t,e,n,o,i){const a=1-o;for(let r=0;r!==i;++r){const i=e+r;t[i]=t[i]*a+t[n+r]*o}}_lerpAdditive(t,e,n,o,i){for(let a=0;a!==i;++a){const i=e+a;t[i]=t[i]+t[n+a]*o}}}const Zmt=new RegExp("[\\[\\]\\.:\\/]","g"),Xmt="[^\\[\\]\\.:\\/]",Kmt="[^"+"\\[\\]\\.:\\/".replace("\\.","")+"]",Jmt=/((?:WC+[\/:])*)/.source.replace("WC",Xmt),Qmt=/(WCOD+)?/.source.replace("WCOD",Kmt),$mt=/(?:\.(WC+)(?:\[(.+)\])?)?/.source.replace("WC",Xmt),tut=/\.(WC+)(?:\[(.+)\])?/.source.replace("WC",Xmt),eut=new RegExp("^"+Jmt+Qmt+$mt+tut+"$"),nut=["material","materials","bones"];class out{constructor(t,e,n){this.path=e,this.parsedPath=n||out.parseTrackName(e),this.node=out.findNode(t,this.parsedPath.nodeName)||t,this.rootNode=t,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}static create(t,e,n){return t&&t.isAnimationObjectGroup?new out.Composite(t,e,n):new out(t,e,n)}static sanitizeNodeName(t){return t.replace(/\s/g,"_").replace(Zmt,"")}static parseTrackName(t){const e=eut.exec(t);if(!e)throw new Error("PropertyBinding: Cannot parse trackName: "+t);const n={nodeName:e[2],objectName:e[3],objectIndex:e[4],propertyName:e[5],propertyIndex:e[6]},o=n.nodeName&&n.nodeName.lastIndexOf(".");if(void 0!==o&&-1!==o){const t=n.nodeName.substring(o+1);-1!==nut.indexOf(t)&&(n.nodeName=n.nodeName.substring(0,o),n.objectName=t)}if(null===n.propertyName||0===n.propertyName.length)throw new Error("PropertyBinding: can not parse propertyName from trackName: "+t);return n}static findNode(t,e){if(!e||""===e||"."===e||-1===e||e===t.name||e===t.uuid)return t;if(t.skeleton){const n=t.skeleton.getBoneByName(e);if(void 0!==n)return n}if(t.children){const n=function(t){for(let o=0;o<t.length;o++){const i=t[o];if(i.name===e||i.uuid===e)return i;const a=n(i.children);if(a)return a}return null},o=n(t.children);if(o)return o}return null}_getValue_unavailable(){}_setValue_unavailable(){}_getValue_direct(t,e){t[e]=this.node[this.propertyName]}_getValue_array(t,e){const n=this.resolvedProperty;for(let o=0,i=n.length;o!==i;++o)t[e++]=n[o]}_getValue_arrayElement(t,e){t[e]=this.resolvedProperty[this.propertyIndex]}_getValue_toArray(t,e){this.resolvedProperty.toArray(t,e)}_setValue_direct(t,e){this.targetObject[this.propertyName]=t[e]}_setValue_direct_setNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.needsUpdate=!0}_setValue_direct_setMatrixWorldNeedsUpdate(t,e){this.targetObject[this.propertyName]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_array(t,e){const n=this.resolvedProperty;for(let o=0,i=n.length;o!==i;++o)n[o]=t[e++]}_setValue_array_setNeedsUpdate(t,e){const n=this.resolvedProperty;for(let o=0,i=n.length;o!==i;++o)n[o]=t[e++];this.targetObject.needsUpdate=!0}_setValue_array_setMatrixWorldNeedsUpdate(t,e){const n=this.resolvedProperty;for(let o=0,i=n.length;o!==i;++o)n[o]=t[e++];this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_arrayElement(t,e){this.resolvedProperty[this.propertyIndex]=t[e]}_setValue_arrayElement_setNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.needsUpdate=!0}_setValue_arrayElement_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty[this.propertyIndex]=t[e],this.targetObject.matrixWorldNeedsUpdate=!0}_setValue_fromArray(t,e){this.resolvedProperty.fromArray(t,e)}_setValue_fromArray_setNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.needsUpdate=!0}_setValue_fromArray_setMatrixWorldNeedsUpdate(t,e){this.resolvedProperty.fromArray(t,e),this.targetObject.matrixWorldNeedsUpdate=!0}_getValue_unbound(t,e){this.bind(),this.getValue(t,e)}_setValue_unbound(t,e){this.bind(),this.setValue(t,e)}bind(){let t=this.node;const e=this.parsedPath,n=e.objectName,o=e.propertyName;let i=e.propertyIndex;if(t||(t=out.findNode(this.rootNode,e.nodeName)||this.rootNode,this.node=t),this.getValue=this._getValue_unavailable,this.setValue=this._setValue_unavailable,!t)return void console.error("THREE.PropertyBinding: Trying to update node for track: "+this.path+" but it wasn't found.");if(n){let o=e.objectIndex;switch(n){case"materials":if(!t.material)return void console.error("THREE.PropertyBinding: Can not bind to material as node does not have a material.",this);if(!t.material.materials)return void console.error("THREE.PropertyBinding: Can not bind to material.materials as node.material does not have a materials array.",this);t=t.material.materials;break;case"bones":if(!t.skeleton)return void console.error("THREE.PropertyBinding: Can not bind to bones as node does not have a skeleton.",this);t=t.skeleton.bones;for(let e=0;e<t.length;e++)if(t[e].name===o){o=e;break}break;default:if(void 0===t[n])return void console.error("THREE.PropertyBinding: Can not bind to objectName of node undefined.",this);t=t[n]}if(void 0!==o){if(void 0===t[o])return void console.error("THREE.PropertyBinding: Trying to bind to objectIndex of objectName, but is undefined.",this,t);t=t[o]}}const a=t[o];if(void 0===a)return void console.error("THREE.PropertyBinding: Trying to update property for track: "+e.nodeName+"."+o+" but it wasn't found.",t);let r=this.Versioning.None;this.targetObject=t,void 0!==t.needsUpdate?r=this.Versioning.NeedsUpdate:void 0!==t.matrixWorldNeedsUpdate&&(r=this.Versioning.MatrixWorldNeedsUpdate);let s=this.BindingType.Direct;if(void 0!==i){if("morphTargetInfluences"===o){if(!t.geometry)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.",this);if(!t.geometry.isBufferGeometry)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences on THREE.Geometry. Use THREE.BufferGeometry instead.",this);if(!t.geometry.morphAttributes)return void console.error("THREE.PropertyBinding: Can not bind to morphTargetInfluences because node does not have a geometry.morphAttributes.",this);void 0!==t.morphTargetDictionary[i]&&(i=t.morphTargetDictionary[i])}s=this.BindingType.ArrayElement,this.resolvedProperty=a,this.propertyIndex=i}else void 0!==a.fromArray&&void 0!==a.toArray?(s=this.BindingType.HasFromToArray,this.resolvedProperty=a):Array.isArray(a)?(s=this.BindingType.EntireArray,this.resolvedProperty=a):this.propertyName=o;this.getValue=this.GetterByBindingType[s],this.setValue=this.SetterByBindingTypeAndVersioning[s][r]}unbind(){this.node=null,this.getValue=this._getValue_unbound,this.setValue=this._setValue_unbound}}out.Composite=class{constructor(t,e,n){const o=n||out.parseTrackName(e);this._targetGroup=t,this._bindings=t.subscribe_(e,o)}getValue(t,e){this.bind();const n=this._bindings[this._targetGroup.nCachedObjects_];void 0!==n&&n.getValue(t,e)}setValue(t,e){const n=this._bindings;for(let o=this._targetGroup.nCachedObjects_,i=n.length;o!==i;++o)n[o].setValue(t,e)}bind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].bind()}unbind(){const t=this._bindings;for(let e=this._targetGroup.nCachedObjects_,n=t.length;e!==n;++e)t[e].unbind()}},out.prototype.BindingType={Direct:0,EntireArray:1,ArrayElement:2,HasFromToArray:3},out.prototype.Versioning={None:0,NeedsUpdate:1,MatrixWorldNeedsUpdate:2},out.prototype.GetterByBindingType=[out.prototype._getValue_direct,out.prototype._getValue_array,out.prototype._getValue_arrayElement,out.prototype._getValue_toArray],out.prototype.SetterByBindingTypeAndVersioning=[[out.prototype._setValue_direct,out.prototype._setValue_direct_setNeedsUpdate,out.prototype._setValue_direct_setMatrixWorldNeedsUpdate],[out.prototype._setValue_array,out.prototype._setValue_array_setNeedsUpdate,out.prototype._setValue_array_setMatrixWorldNeedsUpdate],[out.prototype._setValue_arrayElement,out.prototype._setValue_arrayElement_setNeedsUpdate,out.prototype._setValue_arrayElement_setMatrixWorldNeedsUpdate],[out.prototype._setValue_fromArray,out.prototype._setValue_fromArray_setNeedsUpdate,out.prototype._setValue_fromArray_setMatrixWorldNeedsUpdate]];class iut{constructor(t,e,n=null,o=e.blendMode){this._mixer=t,this._clip=e,this._localRoot=n,this.blendMode=o;const i=e.tracks,a=i.length,r=new Array(a),s={endingStart:Ait,endingEnd:Ait};for(let t=0;t!==a;++t){const e=i[t].createInterpolant(null);r[t]=e,e.settings=s}this._interpolantSettings=s,this._interpolants=r,this._propertyBindings=new Array(a),this._cacheIndex=null,this._byClipCacheIndex=null,this._timeScaleInterpolant=null,this._weightInterpolant=null,this.loop=2201,this._loopCount=-1,this._startTime=null,this.time=0,this.timeScale=1,this._effectiveTimeScale=1,this.weight=1,this._effectiveWeight=1,this.repetitions=1/0,this.paused=!1,this.enabled=!0,this.clampWhenFinished=!1,this.zeroSlopeAtStart=!0,this.zeroSlopeAtEnd=!0}play(){return this._mixer._activateAction(this),this}stop(){return this._mixer._deactivateAction(this),this.reset()}reset(){return this.paused=!1,this.enabled=!0,this.time=0,this._loopCount=-1,this._startTime=null,this.stopFading().stopWarping()}isRunning(){return this.enabled&&!this.paused&&0!==this.timeScale&&null===this._startTime&&this._mixer._isActiveAction(this)}isScheduled(){return this._mixer._isActiveAction(this)}startAt(t){return this._startTime=t,this}setLoop(t,e){return this.loop=t,this.repetitions=e,this}setEffectiveWeight(t){return this.weight=t,this._effectiveWeight=this.enabled?t:0,this.stopFading()}getEffectiveWeight(){return this._effectiveWeight}fadeIn(t){return this._scheduleFading(t,0,1)}fadeOut(t){return this._scheduleFading(t,1,0)}crossFadeFrom(t,e,n){if(t.fadeOut(e),this.fadeIn(e),n){const n=this._clip.duration,o=t._clip.duration,i=n/o;t.warp(1,o/n,e),this.warp(i,1,e)}return this}crossFadeTo(t,e,n){return t.crossFadeFrom(this,e,n)}stopFading(){const t=this._weightInterpolant;return null!==t&&(this._weightInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}setEffectiveTimeScale(t){return this.timeScale=t,this._effectiveTimeScale=this.paused?0:t,this.stopWarping()}getEffectiveTimeScale(){return this._effectiveTimeScale}setDuration(t){return this.timeScale=this._clip.duration/t,this.stopWarping()}syncWith(t){return this.time=t.time,this.timeScale=t.timeScale,this.stopWarping()}halt(t){return this.warp(this._effectiveTimeScale,0,t)}warp(t,e,n){const o=this._mixer,i=o.time,a=this.timeScale;let r=this._timeScaleInterpolant;null===r&&(r=o._lendControlInterpolant(),this._timeScaleInterpolant=r);const s=r.parameterPositions,l=r.sampleValues;return s[0]=i,s[1]=i+n,l[0]=t/a,l[1]=e/a,this}stopWarping(){const t=this._timeScaleInterpolant;return null!==t&&(this._timeScaleInterpolant=null,this._mixer._takeBackControlInterpolant(t)),this}getMixer(){return this._mixer}getClip(){return this._clip}getRoot(){return this._localRoot||this._mixer._root}_update(t,e,n,o){if(!this.enabled)return void this._updateWeight(t);const i=this._startTime;if(null!==i){const o=(t-i)*n;if(o<0||0===n)return;this._startTime=null,e=n*o}e*=this._updateTimeScale(t);const a=this._updateTime(e),r=this._updateWeight(t);if(r>0){const t=this._interpolants,e=this._propertyBindings;switch(this.blendMode){case 2501:for(let n=0,o=t.length;n!==o;++n)t[n].evaluate(a),e[n].accumulateAdditive(r);break;case 2500:default:for(let n=0,i=t.length;n!==i;++n)t[n].evaluate(a),e[n].accumulate(o,r)}}}_updateWeight(t){let e=0;if(this.enabled){e=this.weight;const n=this._weightInterpolant;if(null!==n){const o=n.evaluate(t)[0];e*=o,t>n.parameterPositions[1]&&(this.stopFading(),0===o&&(this.enabled=!1))}}return this._effectiveWeight=e,e}_updateTimeScale(t){let e=0;if(!this.paused){e=this.timeScale;const n=this._timeScaleInterpolant;null!==n&&(e*=n.evaluate(t)[0],t>n.parameterPositions[1]&&(this.stopWarping(),0===e?this.paused=!0:this.timeScale=e))}return this._effectiveTimeScale=e,e}_updateTime(t){const e=this._clip.duration,n=this.loop;let o=this.time+t,i=this._loopCount;const a=2202===n;if(0===t)return-1===i?o:a&&1==(1&i)?e-o:o;if(2200===n){-1===i&&(this._loopCount=0,this._setEndings(!0,!0,!1));t:{if(o>=e)o=e;else{if(!(o<0)){this.time=o;break t}o=0}this.clampWhenFinished?this.paused=!0:this.enabled=!1,this.time=o,this._mixer.dispatchEvent({type:"finished",action:this,direction:t<0?-1:1})}}else{if(-1===i&&(t>=0?(i=0,this._setEndings(!0,0===this.repetitions,a)):this._setEndings(0===this.repetitions,!0,a)),o>=e||o<0){const n=Math.floor(o/e);o-=e*n,i+=Math.abs(n);const r=this.repetitions-i;if(r<=0)this.clampWhenFinished?this.paused=!0:this.enabled=!1,o=t>0?e:0,this.time=o,this._mixer.dispatchEvent({type:"finished",action:this,direction:t>0?1:-1});else{if(1===r){const e=t<0;this._setEndings(e,!e,a)}else this._setEndings(!1,!1,a);this._loopCount=i,this.time=o,this._mixer.dispatchEvent({type:"loop",action:this,loopDelta:n})}}else this.time=o;if(a&&1==(1&i))return e-o}return o}_setEndings(t,e,n){const o=this._interpolantSettings;n?(o.endingStart=Tit,o.endingEnd=Tit):(o.endingStart=t?this.zeroSlopeAtStart?Tit:Ait:Nit,o.endingEnd=e?this.zeroSlopeAtEnd?Tit:Ait:Nit)}_scheduleFading(t,e,n){const o=this._mixer,i=o.time;let a=this._weightInterpolant;null===a&&(a=o._lendControlInterpolant(),this._weightInterpolant=a);const r=a.parameterPositions,s=a.sampleValues;return r[0]=i,s[0]=e,r[1]=i+t,s[1]=n,this}}(class extends Uit{constructor(t){super(),this._root=t,this._initMemoryManager(),this._accuIndex=0,this.time=0,this.timeScale=1}_bindAction(t,e){const n=t._localRoot||this._root,o=t._clip.tracks,i=o.length,a=t._propertyBindings,r=t._interpolants,s=n.uuid,l=this._bindingsByRootAndName;let c=l[s];void 0===c&&(c={},l[s]=c);for(let t=0;t!==i;++t){const i=o[t],l=i.name;let d=c[l];if(void 0!==d)a[t]=d;else{if(d=a[t],void 0!==d){null===d._cacheIndex&&(++d.referenceCount,this._addInactiveBinding(d,s,l));continue}d=new qmt(out.create(n,l,e&&e._propertyBindings[t].binding.parsedPath),i.ValueTypeName,i.getValueSize()),++d.referenceCount,this._addInactiveBinding(d,s,l),a[t]=d}r[t].resultBuffer=d.buffer}}_activateAction(t){if(!this._isActiveAction(t)){if(null===t._cacheIndex){const e=(t._localRoot||this._root).uuid,n=t._clip.uuid,o=this._actionsByClip[n];this._bindAction(t,o&&o.knownActions[0]),this._addInactiveAction(t,n,e)}const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==n.useCount++&&(this._lendBinding(n),n.saveOriginalState())}this._lendAction(t)}}_deactivateAction(t){if(this._isActiveAction(t)){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.useCount&&(n.restoreOriginalState(),this._takeBackBinding(n))}this._takeBackAction(t)}}_initMemoryManager(){this._actions=[],this._nActiveActions=0,this._actionsByClip={},this._bindings=[],this._nActiveBindings=0,this._bindingsByRootAndName={},this._controlInterpolants=[],this._nActiveControlInterpolants=0;const t=this;this.stats={actions:{get total(){return t._actions.length},get inUse(){return t._nActiveActions}},bindings:{get total(){return t._bindings.length},get inUse(){return t._nActiveBindings}},controlInterpolants:{get total(){return t._controlInterpolants.length},get inUse(){return t._nActiveControlInterpolants}}}}_isActiveAction(t){const e=t._cacheIndex;return null!==e&&e<this._nActiveActions}_addInactiveAction(t,e,n){const o=this._actions,i=this._actionsByClip;let a=i[e];if(void 0===a)a={knownActions:[t],actionByRoot:{}},t._byClipCacheIndex=0,i[e]=a;else{const e=a.knownActions;t._byClipCacheIndex=e.length,e.push(t)}t._cacheIndex=o.length,o.push(t),a.actionByRoot[n]=t}_removeInactiveAction(t){const e=this._actions,n=e[e.length-1],o=t._cacheIndex;n._cacheIndex=o,e[o]=n,e.pop(),t._cacheIndex=null;const i=t._clip.uuid,a=this._actionsByClip,r=a[i],s=r.knownActions,l=s[s.length-1],c=t._byClipCacheIndex;l._byClipCacheIndex=c,s[c]=l,s.pop(),t._byClipCacheIndex=null,delete r.actionByRoot[(t._localRoot||this._root).uuid],0===s.length&&delete a[i],this._removeInactiveBindingsForAction(t)}_removeInactiveBindingsForAction(t){const e=t._propertyBindings;for(let t=0,n=e.length;t!==n;++t){const n=e[t];0==--n.referenceCount&&this._removeInactiveBinding(n)}}_lendAction(t){const e=this._actions,n=t._cacheIndex,o=this._nActiveActions++,i=e[o];t._cacheIndex=o,e[o]=t,i._cacheIndex=n,e[n]=i}_takeBackAction(t){const e=this._actions,n=t._cacheIndex,o=--this._nActiveActions,i=e[o];t._cacheIndex=o,e[o]=t,i._cacheIndex=n,e[n]=i}_addInactiveBinding(t,e,n){const o=this._bindingsByRootAndName,i=this._bindings;let a=o[e];void 0===a&&(a={},o[e]=a),a[n]=t,t._cacheIndex=i.length,i.push(t)}_removeInactiveBinding(t){const e=this._bindings,n=t.binding,o=n.rootNode.uuid,i=n.path,a=this._bindingsByRootAndName,r=a[o],s=e[e.length-1],l=t._cacheIndex;s._cacheIndex=l,e[l]=s,e.pop(),delete r[i],0===Object.keys(r).length&&delete a[o]}_lendBinding(t){const e=this._bindings,n=t._cacheIndex,o=this._nActiveBindings++,i=e[o];t._cacheIndex=o,e[o]=t,i._cacheIndex=n,e[n]=i}_takeBackBinding(t){const e=this._bindings,n=t._cacheIndex,o=--this._nActiveBindings,i=e[o];t._cacheIndex=o,e[o]=t,i._cacheIndex=n,e[n]=i}_lendControlInterpolant(){const t=this._controlInterpolants,e=this._nActiveControlInterpolants++;let n=t[e];return void 0===n&&(n=new cmt(new Float32Array(2),new Float32Array(2),1,this._controlInterpolantsResultBuffer),n.__cacheIndex=e,t[e]=n),n}_takeBackControlInterpolant(t){const e=this._controlInterpolants,n=t.__cacheIndex,o=--this._nActiveControlInterpolants,i=e[o];t.__cacheIndex=o,e[o]=t,i.__cacheIndex=n,e[n]=i}clipAction(t,e,n){const o=e||this._root,i=o.uuid;let a="string"==typeof t?_mt.findByName(o,t):t;const r=null!==a?a.uuid:t,s=this._actionsByClip[r];let l=null;if(void 0===n&&(n=null!==a?a.blendMode:2500),void 0!==s){const t=s.actionByRoot[i];if(void 0!==t&&t.blendMode===n)return t;l=s.knownActions[0],null===a&&(a=l._clip)}if(null===a)return null;const c=new iut(this,a,e,n);return this._bindAction(c,l),this._addInactiveAction(c,r,i),c}existingAction(t,e){const n=e||this._root,o=n.uuid,i="string"==typeof t?_mt.findByName(n,t):t,a=this._actionsByClip[i?i.uuid:t];return void 0!==a&&a.actionByRoot[o]||null}stopAllAction(){const t=this._actions;for(let e=this._nActiveActions-1;e>=0;--e)t[e].stop();return this}update(t){const e=this._actions,n=this._nActiveActions,o=this.time+=t*=this.timeScale,i=Math.sign(t),a=this._accuIndex^=1;for(let r=0;r!==n;++r)e[r]._update(o,t,i,a);const r=this._bindings,s=this._nActiveBindings;for(let t=0;t!==s;++t)r[t].apply(a);return this}setTime(t){this.time=0;for(let t=0;t<this._actions.length;t++)this._actions[t].time=0;return this.update(t)}getRoot(){return this._root}uncacheClip(t){const e=this._actions,n=t.uuid,o=this._actionsByClip,i=o[n];if(void 0!==i){const t=i.knownActions;for(let n=0,o=t.length;n!==o;++n){const o=t[n];this._deactivateAction(o);const i=o._cacheIndex,a=e[e.length-1];o._cacheIndex=null,o._byClipCacheIndex=null,a._cacheIndex=i,e[i]=a,e.pop(),this._removeInactiveBindingsForAction(o)}delete o[n]}}uncacheRoot(t){const e=t.uuid,n=this._actionsByClip;for(const t in n){const o=n[t].actionByRoot[e];void 0!==o&&(this._deactivateAction(o),this._removeInactiveAction(o))}const o=this._bindingsByRootAndName[e];if(void 0!==o)for(const t in o){const e=o[t];e.restoreOriginalState(),this._removeInactiveBinding(e)}}uncacheAction(t,e){const n=this.existingAction(t,e);null!==n&&(this._deactivateAction(n),this._removeInactiveAction(n))}}).prototype._controlInterpolantsResultBuffer=new Float32Array(1),class extends _dt{constructor(t,e,n=1){super(t,e),this.meshPerAttribute=n}copy(t){return super.copy(t),this.meshPerAttribute=t.meshPerAttribute,this}clone(t){const e=super.clone(t);return e.meshPerAttribute=this.meshPerAttribute,e}toJSON(t){const e=super.toJSON(t);return e.isInstancedInterleavedBuffer=!0,e.meshPerAttribute=this.meshPerAttribute,e}}.prototype.isInstancedInterleavedBuffer=!0,class extends prt{constructor(t){super(),this.material=t,this.render=function(){},this.hasPositions=!1,this.hasNormals=!1,this.hasColors=!1,this.hasUvs=!1,this.positionArray=null,this.normalArray=null,this.colorArray=null,this.uvArray=null,this.count=0}}.prototype.isImmediateRenderObject=!0;const aut=new cat,rut=new Bat,sut=new Bat;function lut(t){const e=[];t&&t.isBone&&e.push(t);for(let n=0;n<t.children.length;n++)e.push.apply(e,lut(t.children[n]));return e}const cut=new Float32Array(1);function dut(t,e,n){if(1===n)return new Rrt(e);const o=d7(e);if(!o)throw new Error(`d3 failed to recognize the color: ${e}`);return new Rrt(d8(o,t)(1-n))}var put,mut,uut,fut;function gut(t,e){const n=e.length/2;let o=t.attributes.position;o&&o.count===3*n||(o=new zrt(new Float32Array(3*n),3),t.setAttribute("position",o));const i=o.array;for(let t=0;t<n;t++)i[3*t]=e[2*t],i[3*t+1]=e[2*t+1];o.needsUpdate=!0,t.setDrawRange(0,3*n),t.computeBoundingSphere()}function hut(t,e,n){const o=Math.max(e.length/2-1,0),i=2*o*3,a=3*i;let r=t.attributes.position;r&&r.count===i||(r=new zrt(new Float32Array(a),3),t.setAttribute("position",r));const s=r.array;for(let t=0;t<o;t++){const[o,i,a,r]=[e[2*t],e[2*t+1],e[2*t+2],e[2*t+3]],l=new Qit(o,i),c=new Qit(a,r),d=new Qit(a-o,r-i),p=new Qit(-d.y,d.x).setLength(n/2),m=l.clone().add(p),u=l.clone().sub(p),f=c.clone().add(p),g=c.clone().sub(p),h=[m.x,m.y,0,u.x,u.y,0,f.x,f.y,0,f.x,f.y,0,u.x,u.y,0,g.x,g.y,0];s.set(h,t*h.length)}r.needsUpdate=!0,t.setDrawRange(0,a),t.computeBoundingSphere()}function but(t,e,n,o){const{visible:i,color:a,opacity:r}=o;if(Array.isArray(e.material))throw new Error("Invariant error: only expect one material on an object");const s=e.material;if(s.visible!==i&&(s.visible=i,s.needsUpdate=!0),!i)return!1;const l=dut(t,a,null!=r?r:1),c=n(e.geometry);return e.geometry!==c&&(e.geometry=c),s.color.equals(l)||(s.color.set(l),s.needsUpdate=!0),!0}new Int32Array(cut.buffer),dpt.create=function(t,e){return console.log("THREE.Curve.create() has been deprecated"),t.prototype=Object.create(dpt.prototype),t.prototype.constructor=t,t.prototype.getPoint=e,t},Emt.prototype.fromPoints=function(t){return console.warn("THREE.Path: .fromPoints() has been renamed to .setFromPoints()."),this.setFromPoints(t)},class extends npt{constructor(t=10,e=10,n=4473924,o=8947848){n=new Rrt(n),o=new Rrt(o);const i=e/2,a=t/e,r=t/2,s=[],l=[];for(let t=0,c=0,d=-r;t<=e;t++,d+=a){s.push(-r,0,d,r,0,d),s.push(d,0,-r,d,0,r);const e=t===i?n:o;e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3,e.toArray(l,c),c+=3}const c=new qrt;c.setAttribute("position",new Frt(s,3)),c.setAttribute("color",new Frt(l,3)),super(c,new qdt({vertexColors:!0,toneMapped:!1})),this.type="GridHelper"}}.prototype.setColors=function(){console.error("THREE.GridHelper: setColors() has been deprecated, pass them in the constructor instead.")},class extends npt{constructor(t){const e=lut(t),n=new qrt,o=[],i=[],a=new Rrt(0,0,1),r=new Rrt(0,1,0);for(let t=0;t<e.length;t++){const n=e[t];n.parent&&n.parent.isBone&&(o.push(0,0,0),o.push(0,0,0),i.push(a.r,a.g,a.b),i.push(r.r,r.g,r.b))}n.setAttribute("position",new Frt(o,3)),n.setAttribute("color",new Frt(i,3)),super(n,new qdt({vertexColors:!0,depthTest:!1,depthWrite:!1,toneMapped:!1,transparent:!0})),this.type="SkeletonHelper",this.isSkeletonHelper=!0,this.root=t,this.bones=e,this.matrix=t.matrixWorld,this.matrixAutoUpdate=!1}updateMatrixWorld(t){const e=this.bones,n=this.geometry,o=n.getAttribute("position");sut.copy(this.root.matrixWorld).invert();for(let t=0,n=0;t<e.length;t++){const i=e[t];i.parent&&i.parent.isBone&&(rut.multiplyMatrices(sut,i.matrixWorld),aut.setFromMatrixPosition(rut),o.setXYZ(n,aut.x,aut.y,aut.z),rut.multiplyMatrices(sut,i.parent.matrixWorld),aut.setFromMatrixPosition(rut),o.setXYZ(n+1,aut.x,aut.y,aut.z),n+=2)}n.getAttribute("position").needsUpdate=!0,super.updateMatrixWorld(t)}}.prototype.update=function(){console.error("THREE.SkeletonHelper: update() no longer needs to be called.")},xmt.prototype.extractUrlBase=function(t){return console.warn("THREE.Loader: .extractUrlBase() has been deprecated. Use THREE.LoaderUtils.extractUrlBase() instead."),class{static decodeText(t){if("undefined"!=typeof TextDecoder)return(new TextDecoder).decode(t);let e="";for(let n=0,o=t.length;n<o;n++)e+=String.fromCharCode(t[n]);try{return decodeURIComponent(escape(e))}catch(t){return e}}static extractUrlBase(t){const e=t.lastIndexOf("/");return-1===e?"./":t.substr(0,e+1)}}.extractUrlBase(t)},xmt.Handlers={add:function(){console.error("THREE.Loader: Handlers.add() has been removed. Use LoadingManager.addHandler() instead.")},get:function(){console.error("THREE.Loader: Handlers.get() has been removed. Use LoadingManager.getHandler() instead.")}},mat.prototype.center=function(t){return console.warn("THREE.Box3: .center() has been renamed to .getCenter()."),this.getCenter(t)},mat.prototype.empty=function(){return console.warn("THREE.Box3: .empty() has been renamed to .isEmpty()."),this.isEmpty()},mat.prototype.isIntersectionBox=function(t){return console.warn("THREE.Box3: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},mat.prototype.isIntersectionSphere=function(t){return console.warn("THREE.Box3: .isIntersectionSphere() has been renamed to .intersectsSphere()."),this.intersectsSphere(t)},mat.prototype.size=function(t){return console.warn("THREE.Box3: .size() has been renamed to .getSize()."),this.getSize(t)},Rat.prototype.empty=function(){return console.warn("THREE.Sphere: .empty() has been renamed to .isEmpty()."),this.isEmpty()},Est.prototype.setFromMatrix=function(t){return console.warn("THREE.Frustum: .setFromMatrix() has been renamed to .setFromProjectionMatrix()."),this.setFromProjectionMatrix(t)},$it.prototype.flattenToArrayOffset=function(t,e){return console.warn("THREE.Matrix3: .flattenToArrayOffset() has been deprecated. Use .toArray() instead."),this.toArray(t,e)},$it.prototype.multiplyVector3=function(t){return console.warn("THREE.Matrix3: .multiplyVector3() has been removed. Use vector.applyMatrix3( matrix ) instead."),t.applyMatrix3(this)},$it.prototype.multiplyVector3Array=function(){console.error("THREE.Matrix3: .multiplyVector3Array() has been removed.")},$it.prototype.applyToBufferAttribute=function(t){return console.warn("THREE.Matrix3: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix3( matrix ) instead."),t.applyMatrix3(this)},$it.prototype.applyToVector3Array=function(){console.error("THREE.Matrix3: .applyToVector3Array() has been removed.")},$it.prototype.getInverse=function(t){return console.warn("THREE.Matrix3: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead."),this.copy(t).invert()},Bat.prototype.extractPosition=function(t){return console.warn("THREE.Matrix4: .extractPosition() has been renamed to .copyPosition()."),this.copyPosition(t)},Bat.prototype.flattenToArrayOffset=function(t,e){return console.warn("THREE.Matrix4: .flattenToArrayOffset() has been deprecated. Use .toArray() instead."),this.toArray(t,e)},Bat.prototype.getPosition=function(){return console.warn("THREE.Matrix4: .getPosition() has been removed. Use Vector3.setFromMatrixPosition( matrix ) instead."),(new cat).setFromMatrixColumn(this,3)},Bat.prototype.setRotationFromQuaternion=function(t){return console.warn("THREE.Matrix4: .setRotationFromQuaternion() has been renamed to .makeRotationFromQuaternion()."),this.makeRotationFromQuaternion(t)},Bat.prototype.multiplyToArray=function(){console.warn("THREE.Matrix4: .multiplyToArray() has been removed.")},Bat.prototype.multiplyVector3=function(t){return console.warn("THREE.Matrix4: .multiplyVector3() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},Bat.prototype.multiplyVector4=function(t){return console.warn("THREE.Matrix4: .multiplyVector4() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},Bat.prototype.multiplyVector3Array=function(){console.error("THREE.Matrix4: .multiplyVector3Array() has been removed.")},Bat.prototype.rotateAxis=function(t){console.warn("THREE.Matrix4: .rotateAxis() has been removed. Use Vector3.transformDirection( matrix ) instead."),t.transformDirection(this)},Bat.prototype.crossVector=function(t){return console.warn("THREE.Matrix4: .crossVector() has been removed. Use vector.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},Bat.prototype.translate=function(){console.error("THREE.Matrix4: .translate() has been removed.")},Bat.prototype.rotateX=function(){console.error("THREE.Matrix4: .rotateX() has been removed.")},Bat.prototype.rotateY=function(){console.error("THREE.Matrix4: .rotateY() has been removed.")},Bat.prototype.rotateZ=function(){console.error("THREE.Matrix4: .rotateZ() has been removed.")},Bat.prototype.rotateByAxis=function(){console.error("THREE.Matrix4: .rotateByAxis() has been removed.")},Bat.prototype.applyToBufferAttribute=function(t){return console.warn("THREE.Matrix4: .applyToBufferAttribute() has been removed. Use attribute.applyMatrix4( matrix ) instead."),t.applyMatrix4(this)},Bat.prototype.applyToVector3Array=function(){console.error("THREE.Matrix4: .applyToVector3Array() has been removed.")},Bat.prototype.makeFrustum=function(t,e,n,o,i,a){return console.warn("THREE.Matrix4: .makeFrustum() has been removed. Use .makePerspective( left, right, top, bottom, near, far ) instead."),this.makePerspective(t,e,o,n,i,a)},Bat.prototype.getInverse=function(t){return console.warn("THREE.Matrix4: .getInverse() has been removed. Use matrixInv.copy( matrix ).invert(); instead."),this.copy(t).invert()},kst.prototype.isIntersectionLine=function(t){return console.warn("THREE.Plane: .isIntersectionLine() has been renamed to .intersectsLine()."),this.intersectsLine(t)},lat.prototype.multiplyVector3=function(t){return console.warn("THREE.Quaternion: .multiplyVector3() has been removed. Use is now vector.applyQuaternion( quaternion ) instead."),t.applyQuaternion(this)},lat.prototype.inverse=function(){return console.warn("THREE.Quaternion: .inverse() has been renamed to invert()."),this.invert()},Lat.prototype.isIntersectionBox=function(t){return console.warn("THREE.Ray: .isIntersectionBox() has been renamed to .intersectsBox()."),this.intersectsBox(t)},Lat.prototype.isIntersectionPlane=function(t){return console.warn("THREE.Ray: .isIntersectionPlane() has been renamed to .intersectsPlane()."),this.intersectsPlane(t)},Lat.prototype.isIntersectionSphere=function(t){return console.warn("THREE.Ray: .isIntersectionSphere() has been renamed to .intersectsSphere()."),this.intersectsSphere(t)},vrt.prototype.area=function(){return console.warn("THREE.Triangle: .area() has been renamed to .getArea()."),this.getArea()},vrt.prototype.barycoordFromPoint=function(t,e){return console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord()."),this.getBarycoord(t,e)},vrt.prototype.midpoint=function(t){return console.warn("THREE.Triangle: .midpoint() has been renamed to .getMidpoint()."),this.getMidpoint(t)},vrt.prototypenormal=function(t){return console.warn("THREE.Triangle: .normal() has been renamed to .getNormal()."),this.getNormal(t)},vrt.prototype.plane=function(t){return console.warn("THREE.Triangle: .plane() has been renamed to .getPlane()."),this.getPlane(t)},vrt.barycoordFromPoint=function(t,e,n,o,i){return console.warn("THREE.Triangle: .barycoordFromPoint() has been renamed to .getBarycoord()."),vrt.getBarycoord(t,e,n,o,i)},vrt.normal=function(t,e,n,o){return console.warn("THREE.Triangle: .normal() has been renamed to .getNormal()."),vrt.getNormal(t,e,n,o)},Rmt.prototype.extractAllPoints=function(t){return console.warn("THREE.Shape: .extractAllPoints() has been removed. Use .extractPoints() instead."),this.extractPoints(t)},Rmt.prototype.extrude=function(t){return console.warn("THREE.Shape: .extrude() has been removed. Use ExtrudeGeometry() instead."),new nmt(this,t)},Rmt.prototype.makeGeometry=function(t){return console.warn("THREE.Shape: .makeGeometry() has been removed. Use ShapeGeometry() instead."),new imt(this,t)},Qit.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector2: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},Qit.prototype.distanceToManhattan=function(t){return console.warn("THREE.Vector2: .distanceToManhattan() has been renamed to .manhattanDistanceTo()."),this.manhattanDistanceTo(t)},Qit.prototype.lengthManhattan=function(){return console.warn("THREE.Vector2: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},cat.prototype.setEulerFromRotationMatrix=function(){console.error("THREE.Vector3: .setEulerFromRotationMatrix() has been removed. Use Euler.setFromRotationMatrix() instead.")},cat.prototype.setEulerFromQuaternion=function(){console.error("THREE.Vector3: .setEulerFromQuaternion() has been removed. Use Euler.setFromQuaternion() instead.")},cat.prototype.getPositionFromMatrix=function(t){return console.warn("THREE.Vector3: .getPositionFromMatrix() has been renamed to .setFromMatrixPosition()."),this.setFromMatrixPosition(t)},cat.prototype.getScaleFromMatrix=function(t){return console.warn("THREE.Vector3: .getScaleFromMatrix() has been renamed to .setFromMatrixScale()."),this.setFromMatrixScale(t)},cat.prototype.getColumnFromMatrix=function(t,e){return console.warn("THREE.Vector3: .getColumnFromMatrix() has been renamed to .setFromMatrixColumn()."),this.setFromMatrixColumn(e,t)},cat.prototype.applyProjection=function(t){return console.warn("THREE.Vector3: .applyProjection() has been removed. Use .applyMatrix4( m ) instead."),this.applyMatrix4(t)},cat.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector3: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},cat.prototype.distanceToManhattan=function(t){return console.warn("THREE.Vector3: .distanceToManhattan() has been renamed to .manhattanDistanceTo()."),this.manhattanDistanceTo(t)},cat.prototype.lengthManhattan=function(){return console.warn("THREE.Vector3: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},aat.prototype.fromAttribute=function(t,e,n){return console.warn("THREE.Vector4: .fromAttribute() has been renamed to .fromBufferAttribute()."),this.fromBufferAttribute(t,e,n)},aat.prototype.lengthManhattan=function(){return console.warn("THREE.Vector4: .lengthManhattan() has been renamed to .manhattanLength()."),this.manhattanLength()},prt.prototype.getChildByName=function(t){return console.warn("THREE.Object3D: .getChildByName() has been renamed to .getObjectByName()."),this.getObjectByName(t)},prt.prototype.renderDepth=function(){console.warn("THREE.Object3D: .renderDepth has been removed. Use .renderOrder, instead.")},prt.prototype.translate=function(t,e){return console.warn("THREE.Object3D: .translate() has been removed. Use .translateOnAxis( axis, distance ) instead."),this.translateOnAxis(e,t)},prt.prototype.getWorldRotation=function(){console.error("THREE.Object3D: .getWorldRotation() has been removed. Use THREE.Object3D.getWorldQuaternion( target ) instead.")},prt.prototype.applyMatrix=function(t){return console.warn("THREE.Object3D: .applyMatrix() has been renamed to .applyMatrix4()."),this.applyMatrix4(t)},Object.defineProperties(prt.prototype,{eulerOrder:{get:function(){return console.warn("THREE.Object3D: .eulerOrder is now .rotation.order."),this.rotation.order},set:function(t){console.warn("THREE.Object3D: .eulerOrder is now .rotation.order."),this.rotation.order=t}},useQuaternion:{get:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")},set:function(){console.warn("THREE.Object3D: .useQuaternion has been removed. The library now uses quaternions by default.")}}}),pst.prototype.setDrawMode=function(){console.error("THREE.Mesh: .setDrawMode() has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.")},Object.defineProperties(pst.prototype,{drawMode:{get:function(){return console.error("THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode."),0},set:function(){console.error("THREE.Mesh: .drawMode has been removed. The renderer now always assumes THREE.TrianglesDrawMode. Transform your geometry via BufferGeometryUtils.toTrianglesDrawMode() if necessary.")}}}),jdt.prototype.initBones=function(){console.error("THREE.SkinnedMesh: initBones() has been removed.")},_st.prototype.setLens=function(t,e){console.warn("THREE.PerspectiveCamera.setLens is deprecated. Use .setFocalLength and .filmGauge for a photographic setup."),void 0!==e&&(this.filmGauge=e),this.setFocalLength(t)},Object.defineProperties(Amt.prototype,{onlyShadow:{set:function(){console.warn("THREE.Light: .onlyShadow has been removed.")}},shadowCameraFov:{set:function(t){console.warn("THREE.Light: .shadowCameraFov is now .shadow.camera.fov."),this.shadow.camera.fov=t}},shadowCameraLeft:{set:function(t){console.warn("THREE.Light: .shadowCameraLeft is now .shadow.camera.left."),this.shadow.camera.left=t}},shadowCameraRight:{set:function(t){console.warn("THREE.Light: .shadowCameraRight is now .shadow.camera.right."),this.shadow.camera.right=t}},shadowCameraTop:{set:function(t){console.warn("THREE.Light: .shadowCameraTop is now .shadow.camera.top."),this.shadow.camera.top=t}},shadowCameraBottom:{set:function(t){console.warn("THREE.Light: .shadowCameraBottom is now .shadow.camera.bottom."),this.shadow.camera.bottom=t}},shadowCameraNear:{set:function(t){console.warn("THREE.Light: .shadowCameraNear is now .shadow.camera.near."),this.shadow.camera.near=t}},shadowCameraFar:{set:function(t){console.warn("THREE.Light: .shadowCameraFar is now .shadow.camera.far."),this.shadow.camera.far=t}},shadowCameraVisible:{set:function(){console.warn("THREE.Light: .shadowCameraVisible has been removed. Use new THREE.CameraHelper( light.shadow.camera ) instead.")}},shadowBias:{set:function(t){console.warn("THREE.Light: .shadowBias is now .shadow.bias."),this.shadow.bias=t}},shadowDarkness:{set:function(){console.warn("THREE.Light: .shadowDarkness has been removed.")}},shadowMapWidth:{set:function(t){console.warn("THREE.Light: .shadowMapWidth is now .shadow.mapSize.width."),this.shadow.mapSize.width=t}},shadowMapHeight:{set:function(t){console.warn("THREE.Light: .shadowMapHeight is now .shadow.mapSize.height."),this.shadow.mapSize.height=t}}}),Object.defineProperties(zrt.prototype,{length:{get:function(){return console.warn("THREE.BufferAttribute: .length has been deprecated. Use .count instead."),this.array.length}},dynamic:{get:function(){return console.warn("THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead."),this.usage===Vit},set:function(){console.warn("THREE.BufferAttribute: .dynamic has been deprecated. Use .usage instead."),this.setUsage(Vit)}}}),zrt.prototype.setDynamic=function(t){return console.warn("THREE.BufferAttribute: .setDynamic() has been deprecated. Use .setUsage() instead."),this.setUsage(!0===t?Vit:Bit),this},zrt.prototype.copyIndicesArray=function(){console.error("THREE.BufferAttribute: .copyIndicesArray() has been removed.")},zrt.prototype.setArray=function(){console.error("THREE.BufferAttribute: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers")},qrt.prototype.addIndex=function(t){console.warn("THREE.BufferGeometry: .addIndex() has been renamed to .setIndex()."),this.setIndex(t)},qrt.prototype.addAttribute=function(t,e){return console.warn("THREE.BufferGeometry: .addAttribute() has been renamed to .setAttribute()."),e&&e.isBufferAttribute||e&&e.isInterleavedBufferAttribute?"index"===t?(console.warn("THREE.BufferGeometry.addAttribute: Use .setIndex() for index attribute."),this.setIndex(e),this):this.setAttribute(t,e):(console.warn("THREE.BufferGeometry: .addAttribute() now expects ( name, attribute )."),this.setAttribute(t,new zrt(arguments[1],arguments[2])))},qrt.prototype.addDrawCall=function(t,e,n){void 0!==n&&console.warn("THREE.BufferGeometry: .addDrawCall() no longer supports indexOffset."),console.warn("THREE.BufferGeometry: .addDrawCall() is now .addGroup()."),this.addGroup(t,e)},qrt.prototype.clearDrawCalls=function(){console.warn("THREE.BufferGeometry: .clearDrawCalls() is now .clearGroups()."),this.clearGroups()},qrt.prototype.computeOffsets=function(){console.warn("THREE.BufferGeometry: .computeOffsets() has been removed.")},qrt.prototype.removeAttribute=function(t){return console.warn("THREE.BufferGeometry: .removeAttribute() has been renamed to .deleteAttribute()."),this.deleteAttribute(t)},qrt.prototype.applyMatrix=function(t){return console.warn("THREE.BufferGeometry: .applyMatrix() has been renamed to .applyMatrix4()."),this.applyMatrix4(t)},Object.defineProperties(qrt.prototype,{drawcalls:{get:function(){return console.error("THREE.BufferGeometry: .drawcalls has been renamed to .groups."),this.groups}},offsets:{get:function(){return console.warn("THREE.BufferGeometry: .offsets has been renamed to .groups."),this.groups}}}),_dt.prototype.setDynamic=function(t){return console.warn("THREE.InterleavedBuffer: .setDynamic() has been deprecated. Use .setUsage() instead."),this.setUsage(!0===t?Vit:Bit),this},_dt.prototype.setArray=function(){console.error("THREE.InterleavedBuffer: .setArray has been removed. Use BufferGeometry .setAttribute to replace/resize attribute buffers")},nmt.prototype.getArrays=function(){console.error("THREE.ExtrudeGeometry: .getArrays() has been removed.")},nmt.prototype.addShapeList=function(){console.error("THREE.ExtrudeGeometry: .addShapeList() has been removed.")},nmt.prototype.addShape=function(){console.error("THREE.ExtrudeGeometry: .addShape() has been removed.")},ydt.prototype.dispose=function(){console.error("THREE.Scene: .dispose() has been removed.")},Object.defineProperties(Ort.prototype,{wrapAround:{get:function(){console.warn("THREE.Material: .wrapAround has been removed.")},set:function(){console.warn("THREE.Material: .wrapAround has been removed.")}},overdraw:{get:function(){console.warn("THREE.Material: .overdraw has been removed.")},set:function(){console.warn("THREE.Material: .overdraw has been removed.")}},wrapRGB:{get:function(){return console.warn("THREE.Material: .wrapRGB has been removed."),new Rrt}},shading:{get:function(){console.error("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead.")},set:function(t){console.warn("THREE."+this.type+": .shading has been removed. Use the boolean .flatShading instead."),this.flatShading=1===t}},stencilMask:{get:function(){return console.warn("THREE."+this.type+": .stencilMask has been removed. Use .stencilFuncMask instead."),this.stencilFuncMask},set:function(t){console.warn("THREE."+this.type+": .stencilMask has been removed. Use .stencilFuncMask instead."),this.stencilFuncMask=t}},vertexTangents:{get:function(){console.warn("THREE."+this.type+": .vertexTangents has been removed.")},set:function(){console.warn("THREE."+this.type+": .vertexTangents has been removed.")}}}),Object.defineProperties(bst.prototype,{derivatives:{get:function(){return console.warn("THREE.ShaderMaterial: .derivatives has been moved to .extensions.derivatives."),this.extensions.derivatives},set:function(t){console.warn("THREE. ShaderMaterial: .derivatives has been moved to .extensions.derivatives."),this.extensions.derivatives=t}}}),bdt.prototype.clearTarget=function(t,e,n,o){console.warn("THREE.WebGLRenderer: .clearTarget() has been deprecated. Use .setRenderTarget() and .clear() instead."),this.setRenderTarget(t),this.clear(e,n,o)},bdt.prototype.animate=function(t){console.warn("THREE.WebGLRenderer: .animate() is now .setAnimationLoop()."),this.setAnimationLoop(t)},bdt.prototype.getCurrentRenderTarget=function(){return console.warn("THREE.WebGLRenderer: .getCurrentRenderTarget() is now .getRenderTarget()."),this.getRenderTarget()},bdt.prototype.getMaxAnisotropy=function(){return console.warn("THREE.WebGLRenderer: .getMaxAnisotropy() is now .capabilities.getMaxAnisotropy()."),this.capabilities.getMaxAnisotropy()},bdt.prototype.getPrecision=function(){return console.warn("THREE.WebGLRenderer: .getPrecision() is now .capabilities.precision."),this.capabilities.precision},bdt.prototype.resetGLState=function(){return console.warn("THREE.WebGLRenderer: .resetGLState() is now .state.reset()."),this.state.reset()},bdt.prototype.supportsFloatTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsFloatTextures() is now .extensions.get( 'OES_texture_float' )."),this.extensions.get("OES_texture_float")},bdt.prototype.supportsHalfFloatTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsHalfFloatTextures() is now .extensions.get( 'OES_texture_half_float' )."),this.extensions.get("OES_texture_half_float")},bdt.prototype.supportsStandardDerivatives=function(){return console.warn("THREE.WebGLRenderer: .supportsStandardDerivatives() is now .extensions.get( 'OES_standard_derivatives' )."),this.extensions.get("OES_standard_derivatives")},bdt.prototype.supportsCompressedTextureS3TC=function(){return console.warn("THREE.WebGLRenderer: .supportsCompressedTextureS3TC() is now .extensions.get( 'WEBGL_compressed_texture_s3tc' )."),this.extensions.get("WEBGL_compressed_texture_s3tc")},bdt.prototype.supportsCompressedTexturePVRTC=function(){return console.warn("THREE.WebGLRenderer: .supportsCompressedTexturePVRTC() is now .extensions.get( 'WEBGL_compressed_texture_pvrtc' )."),this.extensions.get("WEBGL_compressed_texture_pvrtc")},bdt.prototype.supportsBlendMinMax=function(){return console.warn("THREE.WebGLRenderer: .supportsBlendMinMax() is now .extensions.get( 'EXT_blend_minmax' )."),this.extensions.get("EXT_blend_minmax")},bdt.prototype.supportsVertexTextures=function(){return console.warn("THREE.WebGLRenderer: .supportsVertexTextures() is now .capabilities.vertexTextures."),this.capabilities.vertexTextures},bdt.prototype.supportsInstancedArrays=function(){return console.warn("THREE.WebGLRenderer: .supportsInstancedArrays() is now .extensions.get( 'ANGLE_instanced_arrays' )."),this.extensions.get("ANGLE_instanced_arrays")},bdt.prototype.enableScissorTest=function(t){console.warn("THREE.WebGLRenderer: .enableScissorTest() is now .setScissorTest()."),this.setScissorTest(t)},bdt.prototype.initMaterial=function(){console.warn("THREE.WebGLRenderer: .initMaterial() has been removed.")},bdt.prototype.addPrePlugin=function(){console.warn("THREE.WebGLRenderer: .addPrePlugin() has been removed.")},bdt.prototype.addPostPlugin=function(){console.warn("THREE.WebGLRenderer: .addPostPlugin() has been removed.")},bdt.prototype.updateShadowMap=function(){console.warn("THREE.WebGLRenderer: .updateShadowMap() has been removed.")},bdt.prototype.setFaceCulling=function(){console.warn("THREE.WebGLRenderer: .setFaceCulling() has been removed.")},bdt.prototype.allocTextureUnit=function(){console.warn("THREE.WebGLRenderer: .allocTextureUnit() has been removed.")},bdt.prototype.setTexture=function(){console.warn("THREE.WebGLRenderer: .setTexture() has been removed.")},bdt.prototype.setTexture2D=function(){console.warn("THREE.WebGLRenderer: .setTexture2D() has been removed.")},bdt.prototype.setTextureCube=function(){console.warn("THREE.WebGLRenderer: .setTextureCube() has been removed.")},bdt.prototype.getActiveMipMapLevel=function(){return console.warn("THREE.WebGLRenderer: .getActiveMipMapLevel() is now .getActiveMipmapLevel()."),this.getActiveMipmapLevel()},Object.defineProperties(bdt.prototype,{shadowMapEnabled:{get:function(){return this.shadowMap.enabled},set:function(t){console.warn("THREE.WebGLRenderer: .shadowMapEnabled is now .shadowMap.enabled."),this.shadowMap.enabled=t}},shadowMapType:{get:function(){return this.shadowMap.type},set:function(t){console.warn("THREE.WebGLRenderer: .shadowMapType is now .shadowMap.type."),this.shadowMap.type=t}},shadowMapCullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMapCullFace has been removed. Set Material.shadowSide instead.")}},context:{get:function(){return console.warn("THREE.WebGLRenderer: .context has been removed. Use .getContext() instead."),this.getContext()}},vr:{get:function(){return console.warn("THREE.WebGLRenderer: .vr has been renamed to .xr"),this.xr}},gammaInput:{get:function(){return console.warn("THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead."),!1},set:function(){console.warn("THREE.WebGLRenderer: .gammaInput has been removed. Set the encoding for textures via Texture.encoding instead.")}},gammaOutput:{get:function(){return console.warn("THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead."),!1},set:function(t){console.warn("THREE.WebGLRenderer: .gammaOutput has been removed. Set WebGLRenderer.outputEncoding instead."),this.outputEncoding=!0===t?Iit:zit}},toneMappingWhitePoint:{get:function(){return console.warn("THREE.WebGLRenderer: .toneMappingWhitePoint has been removed."),1},set:function(){console.warn("THREE.WebGLRenderer: .toneMappingWhitePoint has been removed.")}}}),Object.defineProperties(sdt.prototype,{cullFace:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.cullFace has been removed. Set Material.shadowSide instead.")}},renderReverseSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderReverseSided has been removed. Set Material.shadowSide instead.")}},renderSingleSided:{get:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")},set:function(){console.warn("THREE.WebGLRenderer: .shadowMap.renderSingleSided has been removed. Set Material.shadowSide instead.")}}}),Object.defineProperties(rat.prototype,{wrapS:{get:function(){return console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS."),this.texture.wrapS},set:function(t){console.warn("THREE.WebGLRenderTarget: .wrapS is now .texture.wrapS."),this.texture.wrapS=t}},wrapT:{get:function(){return console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT."),this.texture.wrapT},set:function(t){console.warn("THREE.WebGLRenderTarget: .wrapT is now .texture.wrapT."),this.texture.wrapT=t}},magFilter:{get:function(){return console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter."),this.texture.magFilter},set:function(t){console.warn("THREE.WebGLRenderTarget: .magFilter is now .texture.magFilter."),this.texture.magFilter=t}},minFilter:{get:function(){return console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter."),this.texture.minFilter},set:function(t){console.warn("THREE.WebGLRenderTarget: .minFilter is now .texture.minFilter."),this.texture.minFilter=t}},anisotropy:{get:function(){return console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy."),this.texture.anisotropy},set:function(t){console.warn("THREE.WebGLRenderTarget: .anisotropy is now .texture.anisotropy."),this.texture.anisotropy=t}},offset:{get:function(){return console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset."),this.texture.offset},set:function(t){console.warn("THREE.WebGLRenderTarget: .offset is now .texture.offset."),this.texture.offset=t}},repeat:{get:function(){return console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat."),this.texture.repeat},set:function(t){console.warn("THREE.WebGLRenderTarget: .repeat is now .texture.repeat."),this.texture.repeat=t}},format:{get:function(){return console.warn("THREE.WebGLRenderTarget: .format is now .texture.format."),this.texture.format},set:function(t){console.warn("THREE.WebGLRenderTarget: .format is now .texture.format."),this.texture.format=t}},type:{get:function(){return console.warn("THREE.WebGLRenderTarget: .type is now .texture.type."),this.texture.type},set:function(t){console.warn("THREE.WebGLRenderTarget: .type is now .texture.type."),this.texture.type=t}},generateMipmaps:{get:function(){return console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps."),this.texture.generateMipmaps},set:function(t){console.warn("THREE.WebGLRenderTarget: .generateMipmaps is now .texture.generateMipmaps."),this.texture.generateMipmaps=t}}}),class extends prt{constructor(t){super(),this.type="Audio",this.listener=t,this.context=t.context,this.gain=this.context.createGain(),this.gain.connect(t.getInput()),this.autoplay=!1,this.buffer=null,this.detune=0,this.loop=!1,this.loopStart=0,this.loopEnd=0,this.offset=0,this.duration=void 0,this.playbackRate=1,this.isPlaying=!1,this.hasPlaybackControl=!0,this.source=null,this.sourceType="empty",this._startedAt=0,this._progress=0,this._connected=!1,this.filters=[]}getOutput(){return this.gain}setNodeSource(t){return this.hasPlaybackControl=!1,this.sourceType="audioNode",this.source=t,this.connect(),this}setMediaElementSource(t){return this.hasPlaybackControl=!1,this.sourceType="mediaNode",this.source=this.context.createMediaElementSource(t),this.connect(),this}setMediaStreamSource(t){return this.hasPlaybackControl=!1,this.sourceType="mediaStreamNode",this.source=this.context.createMediaStreamSource(t),this.connect(),this}setBuffer(t){return this.buffer=t,this.sourceType="buffer",this.autoplay&&this.play(),this}play(t=0){if(!0===this.isPlaying)return void console.warn("THREE.Audio: Audio is already playing.");if(!1===this.hasPlaybackControl)return void console.warn("THREE.Audio: this Audio has no playback control.");this._startedAt=this.context.currentTime+t;const e=this.context.createBufferSource();return e.buffer=this.buffer,e.loop=this.loop,e.loopStart=this.loopStart,e.loopEnd=this.loopEnd,e.onended=this.onEnded.bind(this),e.start(this._startedAt,this._progress+this.offset,this.duration),this.isPlaying=!0,this.source=e,this.setDetune(this.detune),this.setPlaybackRate(this.playbackRate),this.connect()}pause(){if(!1!==this.hasPlaybackControl)return!0===this.isPlaying&&(this._progress+=Math.max(this.context.currentTime-this._startedAt,0)*this.playbackRate,!0===this.loop&&(this._progress=this._progress%(this.duration||this.buffer.duration)),this.source.stop(),this.source.onended=null,this.isPlaying=!1),this;console.warn("THREE.Audio: this Audio has no playback control.")}stop(){if(!1!==this.hasPlaybackControl)return this._progress=0,this.source.stop(),this.source.onended=null,this.isPlaying=!1,this;console.warn("THREE.Audio: this Audio has no playback control.")}connect(){if(this.filters.length>0){this.source.connect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].connect(this.filters[t]);this.filters[this.filters.length-1].connect(this.getOutput())}else this.source.connect(this.getOutput());return this._connected=!0,this}disconnect(){if(this.filters.length>0){this.source.disconnect(this.filters[0]);for(let t=1,e=this.filters.length;t<e;t++)this.filters[t-1].disconnect(this.filters[t]);this.filters[this.filters.length-1].disconnect(this.getOutput())}else this.source.disconnect(this.getOutput());return this._connected=!1,this}getFilters(){return this.filters}setFilters(t){return t||(t=[]),!0===this._connected?(this.disconnect(),this.filters=t.slice(),this.connect()):this.filters=t.slice(),this}setDetune(t){if(this.detune=t,void 0!==this.source.detune)return!0===this.isPlaying&&this.source.detune.setTargetAtTime(this.detune,this.context.currentTime,.01),this}getDetune(){return this.detune}getFilter(){return this.getFilters()[0]}setFilter(t){return this.setFilters(t?[t]:[])}setPlaybackRate(t){if(!1!==this.hasPlaybackControl)return this.playbackRate=t,!0===this.isPlaying&&this.source.playbackRate.setTargetAtTime(this.playbackRate,this.context.currentTime,.01),this;console.warn("THREE.Audio: this Audio has no playback control.")}getPlaybackRate(){return this.playbackRate}onEnded(){this.isPlaying=!1}getLoop(){return!1===this.hasPlaybackControl?(console.warn("THREE.Audio: this Audio has no playback control."),!1):this.loop}setLoop(t){if(!1!==this.hasPlaybackControl)return this.loop=t,!0===this.isPlaying&&(this.source.loop=this.loop),this;console.warn("THREE.Audio: this Audio has no playback control.")}setLoopStart(t){return this.loopStart=t,this}setLoopEnd(t){return this.loopEnd=t,this}getVolume(){return this.gain.gain.value}setVolume(t){return this.gain.gain.setTargetAtTime(t,this.context.currentTime,.01),this}}.prototype.load=function(t){console.warn("THREE.Audio: .load has been deprecated. Use THREE.AudioLoader instead.");const e=this;return(new Ymt).load(t,(function(t){e.setBuffer(t)})),this},Mst.prototype.updateCubeMap=function(t,e){return console.warn("THREE.CubeCamera: .updateCubeMap() is now .update()."),this.update(t,e)},Mst.prototype.clear=function(t,e,n,o){return console.warn("THREE.CubeCamera: .clear() is now .renderTarget.clear()."),this.renderTarget.clear(t,e,n,o)},eat.crossOrigin=void 0,eat.loadTexture=function(t,e,n,o){console.warn("THREE.ImageUtils.loadTexture has been deprecated. Use THREE.TextureLoader() instead.");const i=new Smt;i.setCrossOrigin(this.crossOrigin);const a=i.load(t,n,void 0,o);return e&&(a.mapping=e),a},eat.loadTextureCube=function(t,e,n,o){console.warn("THREE.ImageUtils.loadTextureCube has been deprecated. Use THREE.CubeTextureLoader() instead.");const i=new kmt;i.setCrossOrigin(this.crossOrigin);const a=i.load(t,n,void 0,o);return e&&(a.mapping=e),a},eat.loadCompressedTexture=function(){console.error("THREE.ImageUtils.loadCompressedTexture has been removed. Use THREE.DDSLoader instead.")},eat.loadCompressedTextureCube=function(){console.error("THREE.ImageUtils.loadCompressedTextureCube has been removed. Use THREE.DDSLoader instead.")},"undefined"!=typeof __THREE_DEVTOOLS__&&__THREE_DEVTOOLS__.dispatchEvent(new CustomEvent("register",{detail:{revision:"131"}})),"undefined"!=typeof window&&(window.__THREE__?console.warn("WARNING: Multiple instances of Three.js being imported."):window.__THREE__="131"),(function(t){t[t.CIRCLE=0]="CIRCLE",t[t.LINE=1]="LINE",t[t.TRIANGLE=2]="TRIANGLE",t[t.TRAPEZOID=3]="TRAPEZOID"})(put||(put={}));class yut{constructor(t,e,n,o){this.coordinator=e,this.scene=new ydt,this.backgroundColor="#fff",iit()&&t instanceof OffscreenCanvas&&(t.style=t.style||{}),o&&t.addEventListener("webglcontextlost",o),this.renderer=new bdt({canvas:t,context:t.getContext("webgl2",{antialias:!0,precision:"highp",alpha:!0})}),this.renderer.setPixelRatio(n)}onResize(t){this.renderer.setSize(t.width,t.height)}destroyObject(t){const e=t.obj3d;if(this.scene.remove(e),e instanceof pst){e.geometry.dispose();const t=Array.isArray(e.material)?e.material:[e.material];for(const e of t)e.dispose()}}setUseDarkMode(t){this.backgroundColor=t?"#303030":"#fff"}createOrUpdateLineObject(t,e,n){var o;if(!t&&!n.visible)return null;const{visible:i,width:a}=n;if(!t){const t=dut(this.backgroundColor,n.color,null!==(o=n.opacity)&&void 0!==o?o:1),r=new qrt,s=new qdt({color:t}),l=new pst(r,s);return s.visible=i,hut(r,e,a),this.scene.add(l),{type:put.LINE,data:e,obj3d:l,width:a}}const{data:r,obj3d:s,width:l}=t;return but(this.backgroundColor,s,(t=>(a===l&&r&&ait(r,e)||hut(t,e,a),t)),n)?{type:put.LINE,data:e,obj3d:s,width:a}:t}createMesh(t,e){if(!e.visible)return null;const{visible:n,color:o,opacity:i}=e,a=dut(this.backgroundColor,o,null!=i?i:1),r=new Art({color:a,visible:n});return new pst(t,r)}createOrUpdateTriangleObject(t,e,n){const{size:o}=n,i=o*Math.sqrt(3)/2,a=new Float32Array([e.x-o/2,e.y-i/3,e.x+o/2,e.y-i/3,e.x,e.y+2*i/3]);if(!t){const t=new qrt;gut(t,a);const o=this.createMesh(t,n);return null===o?null:(this.scene.add(o),{type:put.TRIANGLE,data:e,obj3d:o})}return but(this.backgroundColor,t.obj3d,(t=>(gut(t,a),t)),n)?{type:put.TRIANGLE,data:e,obj3d:t.obj3d}:t}createOrUpdateCircleObject(t,e,n){const{radius:o}=n,i=new cpt(n.radius);if(!t){const t=this.createMesh(i,n);return null===t?null:(t.position.set(e.x,e.y,0),this.scene.add(t),{type:put.CIRCLE,data:{loc:e,radius:o},obj3d:t})}return but(this.backgroundColor,t.obj3d,(()=>i),n)?(t.obj3d.position.set(e.x,e.y,0),{type:put.CIRCLE,data:{loc:e,radius:o},obj3d:t.obj3d}):t}createOrUpdateTrapezoidObject(t,e,n,o){if(e.y!==n.y)throw new RangeError("Input error: start.y != end.y.");const{altitude:i}=o,a=2/Math.sqrt(3)*i,r=new Rmt([new Qit(e.x-a/2,e.y-i/2),new Qit(e.x,e.y+i/2),new Qit(n.x,n.y+i/2),new Qit(n.x+a/2,n.y-i/2)]);r.autoClose=!0;const s=new imt(r);if(!t){const t=this.createMesh(s,o);return null===t?null:(this.scene.add(t),{type:put.TRAPEZOID,data:[e,n],obj3d:t})}return but(this.backgroundColor,t.obj3d,(()=>s),o)?{type:put.TRAPEZOID,data:[e,n],obj3d:t.obj3d}:t}flush(){this.renderer.render(this.scene,this.coordinator.getCamera())}}class _ut{constructor(t,e){this.renderCache=t,this.renderer=e}setLine(t,e,n){const o=this.renderer.createOrUpdateLineObject(this.renderCache.getFromPreviousFrame(t),e,n);o&&this.renderCache.setToCurrentFrame(t,o)}setTriangle(t,e,n){const o=this.renderer.createOrUpdateTriangleObject(this.renderCache.getFromPreviousFrame(t),e,n);o&&this.renderCache.setToCurrentFrame(t,o)}setCircle(t,e,n){const o=this.renderer.createOrUpdateCircleObject(this.renderCache.getFromPreviousFrame(t),e,n);o&&this.renderCache.setToCurrentFrame(t,o)}setTrapezoid(t,e,n,o){const i=this.renderer.createOrUpdateTrapezoidObject(this.renderCache.getFromPreviousFrame(t),e,n,o);i&&this.renderCache.setToCurrentFrame(t,i)}}class Cut{constructor(){this.prevFrameCache=new Map,this.currFrameCache=new Map}getFromPreviousFrame(t){const e=this.prevFrameCache.get(t);return null!=e?e:null}setToCurrentFrame(t,e){this.currFrameCache.set(t,e)}finalizeFrameAndGetRemoved(){const t=[];for(const[e,n]of this.prevFrameCache.entries())this.currFrameCache.has(e)||t.push(n);return this.prevFrameCache=this.currFrameCache,this.currFrameCache=new Map,t}}!(function(t){t[t.NUMBER=0]="NUMBER",t[t.NAN=1]="NAN"})(mut||(mut={}));class Mut extends class{constructor(t){this.rawSeriesData=[],this.series=[],this.paintDirty=!0,this.renderCache=new Cut,this.coordinateIdentifier=null,this.layout={x:0,width:1,y:0,height:1},this.getMetadataMapImpl=t.getMetadataMap,this.coordinator=t.coordinator,this.renderer=t.renderer,this.paintBrush=new _ut(this.renderCache,this.renderer)}setLayoutRect(t){this.layout.x===t.x&&this.layout.width===t.width&&this.layout.y===t.y&&this.layout.height===t.height||(this.paintDirty=!0),this.layout=t}getLayoutRect(){return this.layout}getMetadataMap(){return this.getMetadataMapImpl()}markAsPaintDirty(){this.paintDirty=!0}render(){if(this.transformCoordinatesIfStale(),this.paintDirty){this.redraw();for(const t of this.renderCache.finalizeFrameAndGetRemoved())this.renderer.destroyObject(t);this.paintDirty=!1}}isCoordinateUpdated(){return this.coordinator.getUpdateIdentifier()!==this.coordinateIdentifier}clearCoordinateIdentifier(){this.coordinateIdentifier=null}setData(t){this.clearCoordinateIdentifier(),this.rawSeriesData=t}transformCoordinatesIfStale(){if(!this.isCoordinateUpdated())return;const t=this.getLayoutRect();this.series=new Array(this.rawSeriesData.length);for(let e=0;e<this.rawSeriesData.length;e++){const n=this.rawSeriesData[e];this.series[e]={id:n.id,polyline:new Float32Array(2*n.points.length)};for(let o=0;o<n.points.length;o++){const[i,a]=this.coordinator.transformDataToUiCoord(t,[n.points[o].x,n.points[o].y]);this.series[e].polyline[2*o]=i,this.series[e].polyline[2*o+1]=a}}this.coordinateIdentifier=this.coordinator.getUpdateIdentifier(),this.markAsPaintDirty()}}{recordPartition(t,e,n){return t?{type:mut.NUMBER,polyline:e}:{type:mut.NAN,polyline:e.map(((t,e)=>isNaN(t)?e%2==0?n.x:n.y:t))}}partitionPolyline(t){const e=[];let n=0,o=!1;const i=this.coordinator.transformDataToUiCoord(this.getLayoutRect(),[0,0]),a={x:i[0],y:i[1]};let r=null;for(let i=0;i<t.length;i+=2){const a=t[i],s=t[i+1],l=isNaN(a)||isNaN(s);l!==o&&n!==i&&(e.push(this.recordPartition(!o,t.slice(n,i),null===r?{x:a,y:s}:r)),n=i),l||(r={x:a,y:s}),o=l}return n!==t.length-1&&e.push(this.recordPartition(!o,t.slice(n,t.length),null!=r?r:a)),e}redraw(){var t,e,n;for(const o of this.series){const i=this.getMetadataMap()[o.id];if(!i)continue;if(o.polyline.length%2!=0)throw new Error(`Cannot have odd length-ed polyline: ${o.polyline.length}`);const a=this.partitionPolyline(o.polyline);for(const[r,{type:s,polyline:l}]of a.entries())if(s===mut.NUMBER)2===l.length?this.paintBrush.setCircle(JSON.stringify(["circle",o.id,r]),{x:l[0],y:l[1]},{color:i.color,visible:i.visible,opacity:null!==(t=i.opacity)&&void 0!==t?t:1,radius:4}):this.paintBrush.setLine(JSON.stringify(["line",o.id,r]),l,{color:i.color,visible:i.visible,opacity:null!==(e=i.opacity)&&void 0!==e?e:1,width:2});else if(!i.aux)for(let t=0;t<l.length;t+=2)this.paintBrush.setTriangle(JSON.stringify(["NaN",o.id,l[t],l[t+1]]),{x:l[t],y:l[t+1]},{color:i.color,visible:i.visible,opacity:null!==(n=i.opacity)&&void 0!==n?n:1,size:12})}}}class vut extends rit{constructor(){super(...arguments),this.camera=new Ust(0,1e3,1e3,0,0,100)}isYAxisPointedDown(){return!1}setDomContainerRect(t){super.setDomContainerRect(t),this.camera.left=t.x,this.camera.right=t.x+t.width,this.camera.top=t.y+t.height,this.camera.bottom=t.y,this.camera.updateProjectionMatrix()}getCamera(){return this.camera}}class xut{constructor(t){switch(this.metadataMap={},this.shouldRepaint=!1,this.callbacks=t.callbacks,t.type){case K2.SVG:this.coordinator=new rit,this.renderer=new lit(t.container);break;case K2.WEBGL:{const e=new vut;this.coordinator=e,this.renderer=new yut(t.container,e,t.devicePixelRatio,t.callbacks.onContextLost);break}}this.renderer.setUseDarkMode(t.useDarkMode),this.seriesLineView=new Mut({renderer:this.renderer,coordinator:this.coordinator,getMetadataMap:()=>this.metadataMap}),this.resize(t.domDimension)}dispose(){}setXScaleType(t){this.coordinator.setXScale($ot(t)),this.scheduleRepaint()}setYScaleType(t){this.coordinator.setYScale($ot(t)),this.scheduleRepaint()}resize(t){this.coordinator.setDomContainerRect(Object.assign({x:0,y:0},t)),this.renderer.onResize(Object.assign({x:0,y:0},t)),this.seriesLineView.setLayoutRect(Object.assign(Object.assign({},t),{x:0,y:0})),this.scheduleRepaint()}setMetadata(t){let e=!1;Object.entries(t).forEach((([t,n])=>{const o=this.metadataMap[t];o&&n.color===o.color&&n.visible===o.visible&&n.opacity===o.opacity||(e=!0),this.metadataMap[t]=n})),e&&this.seriesLineView.markAsPaintDirty(),this.scheduleRepaint()}setViewBox(t){this.coordinator.setViewBoxRect({x:t.x[0],width:t.x[1]-t.x[0],y:t.y[0],height:t.y[1]-t.y[0]}),this.scheduleRepaint()}setData(t){this.seriesLineView.setData(t),this.scheduleRepaint()}setUseDarkMode(t){this.renderer.setUseDarkMode(t),this.scheduleRepaint()}scheduleRepaint(){this.shouldRepaint||(this.shouldRepaint=!0,self.requestAnimationFrame((()=>{this.repaint(),this.shouldRepaint=!1})))}repaint(){this.seriesLineView.render(),this.renderer.flush(),this.callbacks.onDrawEnd()}}function Out(t){if(t.includes("/"))throw new RangeError("Worker factory only allows file name and no resource path.");return new Worker(t)}!(function(t){t[t.SERIES_DATA_UPDATED=0]="SERIES_DATA_UPDATED",t[t.SERIES_METADATA_CHANGED=1]="SERIES_METADATA_CHANGED",t[t.SCALE_UPDATED=2]="SCALE_UPDATED",t[t.VIEW_BOX_UPDATED=3]="VIEW_BOX_UPDATED",t[t.INIT=4]="INIT",t[t.DOM_RESIZED=5]="DOM_RESIZED",t[t.DARK_MODE_UPDATED=6]="DARK_MODE_UPDATED"})(uut||(uut={})),(function(t){t[t.ON_REDRAW_END=0]="ON_REDRAW_END",t[t.ON_CONTEXT_LOST=1]="ON_CONTEXT_LOST"})(fut||(fut={}));class Put{constructor(t){if(this.callbacks=t.callbacks,t.type!==K2.WEBGL)throw new RangeError(`Cannot use non WEBGL renderer for the offscreen line chart. Received ${K2[t.type]} `);const e=new MessageChannel;e.port1.onmessage=t=>{this.onMessageFromWorker(t.data)},this.txMessagePort=e.port1;const n=t.container.transferControlToOffscreen();this.workerInstance=Put.workerPool.getNext();const o={type:uut.INIT,canvas:n,devicePixelRatio:window.devicePixelRatio,dim:t.domDimension,rendererType:t.type,useDarkMode:t.useDarkMode};this.workerInstance.postMessage(o,[n,e.port2])}dispose(){this.workerInstance.free(),this.txMessagePort.close()}setXScaleType(t){this.sendMessage({type:uut.SCALE_UPDATED,axis:"x",scaleType:t})}setYScaleType(t){this.sendMessage({type:uut.SCALE_UPDATED,axis:"y",scaleType:t})}resize(t){this.sendMessage({type:uut.DOM_RESIZED,dim:t})}setMetadata(t){this.sendMessage({type:uut.SERIES_METADATA_CHANGED,metadata:t})}setViewBox(t){this.sendMessage({type:uut.VIEW_BOX_UPDATED,extent:t})}setData(t){const e=(function n(t){const e=t.reduce(((t,e)=>t+e.points.length),0);let n=0;const o=new Float64Array(2*e),i=[];for(const e of t){i.push({id:e.id,length:e.points.length});for(let t=0;t<e.points.length;t++)o[n++]=e.points[t].x,o[n++]=e.points[t].y}return{idsAndLengths:i,flattenedSeries:o.buffer}})(t);this.sendMessage({type:uut.SERIES_DATA_UPDATED,compactDataSeries:e},[e.flattenedSeries])}setUseDarkMode(t){this.sendMessage({type:uut.DARK_MODE_UPDATED,useDarkMode:t})}sendMessage(t,e){e?this.txMessagePort.postMessage(t,e):this.txMessagePort.postMessage(t)}onMessageFromWorker(t){switch(t.type){case fut.ON_REDRAW_END:this.callbacks.onDrawEnd();break;case fut.ON_CONTEXT_LOST:this.callbacks.onContextLost()}}}function wut(t,e){return"x"===e?[0,t.width]:[t.height,0]}function kut(t,e){const n=Math.floor(t/50);return Math.min(n,e)}function Sut(t,e){if(1&t&&(qi(),Tm(0,"line",2)),2&t){const t=e.$implicit,n=Ym();pu("zero",0===t),jp("x1",n.getDomX(t))("x2",n.getDomX(t))("y2",n.domDim.height)}}function Dut(t,e){if(1&t&&(qi(),Tm(0,"line",3)),2&t){const t=e.$implicit,n=Ym();pu("zero",0===t),jp("y1",n.getDomY(t))("x2",n.domDim.width)("y2",n.getDomY(t))}}Put.workerPool=new class{constructor(t,e=10,n=Out){this.workerResourcePath=t,this.maxPoolSize=e,this.workerFactory=n,this.workers=[]}getNext(){let t;if(this.workers.every((({activeCount:t})=>t>0))&&this.workers.length<this.maxPoolSize){const e=this.workerFactory(this.workerResourcePath);t={activeCount:0,postMessage:(t,n)=>{e.postMessage(t,n)},free:()=>{t.activeCount=Math.max(t.activeCount-1,0)}},this.workers.push(t)}else{const e=this.workers.map((({activeCount:t})=>t)),n=e.indexOf(Math.min(...e));t=this.workers[n]}return t.activeCount++,t}}("chart_worker.js?_file_hash=25338065");class Eut{getDomX(t){return this.xScale.forward(this.viewExtent.x,wut(this.domDim,"x"),t)}getDomY(t){return this.yScale.forward(this.viewExtent.y,wut(this.domDim,"y"),t)}getXTicks(){return this.xScale.ticks(this.viewExtent.x,kut(this.domDim.width,this.xGridCount))}getYTicks(){return this.yScale.ticks(this.viewExtent.y,kut(this.domDim.height,this.yGridCount))}}function Rut(t,e,n,o,i,a){let r;switch(t.deltaMode){case WheelEvent.DOM_DELTA_PIXEL:r=1;break;case WheelEvent.DOM_DELTA_LINE:r=8;break;case WheelEvent.DOM_DELTA_PAGE:r=20;break;default:r=1,console.warn(`Unknown WheelEvent deltaMode: ${t.deltaMode}.`)}const s=t.deltaY*r,l=s<0?Math.max(s*o,-.95):s*o,{width:c,height:d}=n,p=[i.reverse(e.x,[0,c],-t.offsetX*l),i.reverse(e.x,[0,c],c+(c-t.offsetX)*l)],m=[a.reverse(e.y,[d,0],-t.offsetY*l),a.reverse(e.y,[d,0],d+(d-t.offsetY)*l)];return{x:p[1]<p[0]?[p[1],p[0]]:p,y:m[1]<m[0]?[m[1],m[0]]:m}}Eut.ɵfac=function t(e){return new(e||Eut)},Eut.ɵcmp=to({type:Eut,selectors:[["line-chart-grid-view"]],inputs:{viewExtent:"viewExtent",xScale:"xScale",xGridCount:"xGridCount",yScale:"yScale",yGridCount:"yGridCount",domDim:"domDim"},decls:3,vars:2,consts:[["y1","0",3,"zero",4,"ngFor","ngForOf"],["x1","0",3,"zero",4,"ngFor","ngForOf"],["y1","0"],["x1","0"]],template:function t(e,n){1&e&&(qi(),Rm(0,"svg"),Qp(1,Sut,1,5,"line",0),Qp(2,Dut,1,5,"line",1),Am()),2&e&&(rc(1),Dm("ngForOf",n.getXTicks()),rc(1),Dm("ngForOf",n.getYTicks()))},directives:[lM],styles:["[_nghost-%COMP%] {\n        display: flex;\n        overflow: hidden;\n      }\n\n      svg[_ngcontent-%COMP%] {\n        height: 100%;\n        width: 100%;\n      }\n\n      line[_ngcontent-%COMP%] {\n        stroke: #ccc;\n        stroke-width: 1px;\n      }\n\n      .zero[_ngcontent-%COMP%] {\n        stroke: #aaa;\n        stroke-width: 1.5px;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Eut,[{type:My,args:[{selector:"line-chart-grid-view",template:'<svg>\n    <line\n      *ngFor="let tick of getXTicks()"\n      [class.zero]="tick === 0"\n      [attr.x1]="getDomX(tick)"\n      y1="0"\n      [attr.x2]="getDomX(tick)"\n      [attr.y2]="domDim.height"\n    ></line>\n    <line\n      *ngFor="let tick of getYTicks()"\n      [class.zero]="tick === 0"\n      x1="0"\n      [attr.y1]="getDomY(tick)"\n      [attr.x2]="domDim.width"\n      [attr.y2]="getDomY(tick)"\n    ></line>\n  </svg>',styles:["\n      :host {\n        display: flex;\n        overflow: hidden;\n      }\n\n      svg {\n        height: 100%;\n        width: 100%;\n      }\n\n      line {\n        stroke: #ccc;\n        stroke-width: 1px;\n      }\n\n      .zero {\n        stroke: #aaa;\n        stroke-width: 1.5px;\n      }\n    "],changeDetection:zn.OnPush}]}],null,{viewExtent:[{type:xy}],xScale:[{type:xy}],xGridCount:[{type:xy}],yScale:[{type:xy}],yGridCount:[{type:xy}],domDim:[{type:xy}]});const Aut=["dots"];function Tut(t,e){if(1&t&&(qi(),Tm(0,"circle",12)),2&t){const t=Ym().$implicit,e=Ym(2);jp("cx",e.getDomX(t.point.x))("cy",e.getDomY(t.point.y))("fill",t.metadata.color)}}function Nut(t,e){if(1&t&&(qi(),Nm(0),Qp(1,Tut,1,3,"circle",11),zm()),2&t){const t=e.$implicit,n=Ym(2);rc(1),Dm("ngIf",n.shouldRenderTooltipPoint(t.point))}}function zut(t,e){if(1&t&&(qi(),Nm(0),Qp(1,Nut,2,1,"ng-container",10),zm()),2&t){const t=Ym();rc(1),Dm("ngForOf",t.cursoredData)("ngForTrackBy",t.trackBySeriesName)}}function Iut(t,e){if(1&t&&(qi(),Tm(0,"rect",13)),2&t){const t=Ym();jp("x",t.zoomBoxInUiCoordinate.x)("width",t.zoomBoxInUiCoordinate.width)("y",t.zoomBoxInUiCoordinate.y)("height",t.zoomBoxInUiCoordinate.height)}}const Hut=function(t,e){return{data:t,cursorLocationInDataCoord:e}};function Fut(t,e){if(1&t&&(Rm(0,"div",14),Im(1,15),Am()),2&t){const t=Ym(),e=$p(11);rc(1),Dm("ngTemplateOutlet",t.tooltipTemplate?t.tooltipTemplate:e)("ngTemplateOutletContext",vh(2,Hut,t.cursoredData,t.cursorLocationInDataCoord))}}function Lut(t,e){if(1&t&&(Nm(0),Rm(1,"tr",17),Rm(2,"td",18),Tm(3,"span"),Am(),Rm(4,"td",19),ku(5),Am(),Rm(6,"td"),ku(7),Am(),Rm(8,"td"),ku(9),Am(),Am(),zm()),2&t){const t=e.$implicit;rc(3),du("background-color",t.metadata.color),rc(2),Su(t.metadata.displayName),rc(2),Su(t.point.y),rc(2),Su(t.point.x)}}function But(t,e){if(1&t&&(Rm(0,"table"),Rm(1,"thead"),Rm(2,"tr"),Tm(3,"th",16),Rm(4,"th"),ku(5,"Name"),Am(),Rm(6,"th"),ku(7,"Y"),Am(),Rm(8,"th"),ku(9,"X"),Am(),Am(),Am(),Rm(10,"tbody"),Qp(11,Lut,10,5,"ng-container",10),Am(),Am()),2&t){const t=e.data,n=Ym();rc(11),Dm("ngForOf",t)("ngForTrackBy",n.trackBySeriesName)}}var Vut;function jut(t){return t.scrollStrategies.reposition()}!(function(t){t[t.NONE=0]="NONE",t[t.DRAG_ZOOMING=1]="DRAG_ZOOMING",t[t.SCROLL_ZOOMING=2]="SCROLL_ZOOMING",t[t.PANNING=3]="PANNING"})(Vut||(Vut={}));class Uut{constructor(t,e){this.changeDetector=t,this.scrollStrategy=e,this.onViewExtentChange=new Lh,this.onViewExtentReset=new Lh,this.InteractionState=Vut,this.state=Vut.NONE,this.specialKeyPressed=!1,this.zoomBoxInUiCoordinate={x:0,width:0,height:0,y:0},this.tooltipPositions=[{offsetY:5,originX:"start",overlayX:"start",originY:"bottom",overlayY:"top"},{offsetY:5,originX:"end",overlayX:"end",originY:"bottom",overlayY:"top"},{offsetY:-15,originX:"start",overlayX:"start",originY:"top",overlayY:"bottom"},{offsetY:-15,originX:"end",overlayX:"end",originY:"top",overlayY:"bottom"},{offsetX:5,originX:"end",overlayX:"start",originY:"top",overlayY:"top"},{offsetX:-5,originX:"start",overlayX:"end",originY:"top",overlayY:"top"}],this.cursorLocationInDataCoord=null,this.cursoredData=[],this.tooltipDisplayAttached=!1,this.showZoomInstruction=!1,this.dragStartCoord=null,this.isCursorInside=!1,this.ngUnsubscribe=new I}ngAfterViewInit(){oe(this.dotsContainer.nativeElement,"dblclick",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((()=>{this.onViewExtentReset.emit(),this.state=Vut.NONE,this.changeDetector.markForCheck()})),oe(window,"keydown",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{const e=this.shouldPan(t);e!==this.specialKeyPressed&&(this.specialKeyPressed=e,this.changeDetector.markForCheck())})),oe(window,"keyup",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{const e=this.shouldPan(t);e!==this.specialKeyPressed&&(this.specialKeyPressed=e,this.changeDetector.markForCheck())})),oe(this.dotsContainer.nativeElement,"mousedown",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{const e=this.state,n=this.shouldPan(t)?Vut.PANNING:Vut.DRAG_ZOOMING;e===Vut.NONE&&n===Vut.DRAG_ZOOMING&&(this.dragStartCoord={x:t.offsetX,y:t.offsetY},this.zoomBoxInUiCoordinate={x:t.offsetX,width:0,y:t.offsetY,height:0}),e!==n&&(this.state=n,this.changeDetector.markForCheck())})),oe(this.dotsContainer.nativeElement,"mouseup",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{const e=(t.buttons&gQ.LEFT)===gQ.LEFT;this.dragStartCoord=null;const n=this.zoomBoxInUiCoordinate;if(!e&&this.state===Vut.DRAG_ZOOMING&&n.width>0&&n.height>0){const t=this.getDataX(n.x),e=this.getDataX(n.x+n.width),o=this.getDataY(n.y+n.height),i=this.getDataY(n.y);this.onViewExtentChange.emit({dataExtent:{x:[t,e],y:[o,i]}})}this.state!==Vut.NONE&&(this.state=Vut.NONE,this.changeDetector.markForCheck())})),oe(this.dotsContainer.nativeElement,"mouseenter",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{this.isCursorInside=!0,this.updateTooltip(t),this.changeDetector.markForCheck()})),oe(this.dotsContainer.nativeElement,"mouseleave",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{this.dragStartCoord=null,this.isCursorInside=!1,this.updateTooltip(t),this.state=Vut.NONE,this.changeDetector.markForCheck()})),oe(this.dotsContainer.nativeElement,"mousemove",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>{switch(this.state){case Vut.SCROLL_ZOOMING:this.state=Vut.NONE,this.updateTooltip(t),this.changeDetector.markForCheck();break;case Vut.NONE:this.updateTooltip(t),this.changeDetector.markForCheck();break;case Vut.PANNING:{const e=-t.movementX,n=-t.movementY,{width:o,height:i}=this.domDim,a=this.getDataX(e),r=this.getDataX(o+e),s=this.getDataY(i+n),l=this.getDataY(n);this.onViewExtentChange.emit({dataExtent:{x:[a,r],y:[s,l]}});break}case Vut.DRAG_ZOOMING:{if(!this.dragStartCoord)break;const e=[this.dragStartCoord.x,t.offsetX],n=[this.dragStartCoord.y,t.offsetY];this.zoomBoxInUiCoordinate={x:Math.min(...e),width:Math.max(...e)-Math.min(...e),y:Math.min(...n),height:Math.max(...n)-Math.min(...n)}}this.changeDetector.markForCheck()}})),oe(this.dotsContainer.nativeElement,"wheel",{passive:!1}).pipe(Ie(this.ngUnsubscribe),ze((t=>{const e=!t.ctrlKey&&!t.shiftKey&&t.altKey;return this.showZoomInstruction=!e,this.changeDetector.markForCheck(),e?(t.preventDefault(),Et(t)):ae(3e3).pipe(Fe((()=>{this.showZoomInstruction=!1,this.changeDetector.markForCheck()})),It((()=>null)))})),ce((t=>Boolean(t)))).subscribe((t=>{this.onViewExtentChange.emit({dataExtent:Rut(t,this.viewExtent,this.domDim,.01,this.xScale,this.yScale)}),this.state!==Vut.SCROLL_ZOOMING&&(this.state=Vut.SCROLL_ZOOMING,this.changeDetector.markForCheck())}))}ngOnChanges(){this.updateCursoredDataAndTooltipVisibility()}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}shouldPan(t){const e=t.shiftKey||t.altKey;if(t instanceof KeyboardEvent)return e;const n=(t.buttons&gQ.LEFT)===gQ.LEFT,o=(t.buttons&gQ.MIDDLE)===gQ.MIDDLE;return!(!n&&!o)&&(o&&!n||e)}trackBySeriesName(t,e){return e.id}getDomX(t){return this.xScale.forward(this.viewExtent.x,wut(this.domDim,"x"),t)}getDataX(t){return this.xScale.reverse(this.viewExtent.x,wut(this.domDim,"x"),t)}getDomY(t){return this.yScale.forward(this.viewExtent.y,wut(this.domDim,"y"),t)}getDataY(t){return this.yScale.reverse(this.viewExtent.y,wut(this.domDim,"y"),t)}shouldRenderTooltipPoint(t){return null!==t&&!isNaN(t.x)&&!isNaN(t.y)}updateTooltip(t){this.cursorLocationInDataCoord={x:this.getDataX(t.offsetX),y:this.getDataY(t.offsetY)},this.updateCursoredDataAndTooltipVisibility()}onTooltipDisplayDetached(){this.tooltipDisplayAttached=!1}updateCursoredDataAndTooltipVisibility(){const t=this.cursorLocationInDataCoord;if(null===t)return this.cursoredData=[],void(this.tooltipDisplayAttached=!1);this.cursoredData=this.isCursorInside?this.seriesData.map((t=>({seriesDatum:t,metadata:this.seriesMetadataMap[t.id]}))).filter((({metadata:t})=>t&&t.visible&&!Boolean(t.aux))).map((({seriesDatum:e,metadata:n})=>{const o=(function i(t,e){const n=Math.min(p5(t.map((({x:t})=>t)),e),t.length-1),o=Math.max(0,n-1);return Math.abs(t[o].x-e)-Math.abs(t[n].x-e)<=0?o:n})(e.points,t.x);return{id:e.id,closestPointIndex:o,point:e.points[o],metadata:n}})).filter((t=>t)):[],this.tooltipDisplayAttached=Boolean(this.cursoredData.length)}}function Gut(t,e,n,o){return{major:[],minor:t.ticks(o,n).map((t=>({value:t,tickFormattedString:e.formatTick(t)})))}}Uut.ɵfac=function t(e){return new(e||Uut)(Sm(Ug),Sm(LF))},Uut.ɵcmp=to({type:Uut,selectors:[["line-chart-interactive-view"]],viewQuery:function t(e,n){if(1&e&&(Qh(Aut,7,hg),Qh(gL,5)),2&e){let t;Jh(t=tb())&&(n.dotsContainer=t.first),Jh(t=tb())&&(n.overlay=t.first)}},hostVars:2,hostBindings:function t(e,n){2&e&&pu("show-zoom-instruction",n.showZoomInstruction)},inputs:{seriesData:"seriesData",seriesMetadataMap:"seriesMetadataMap",viewExtent:"viewExtent",xScale:"xScale",yScale:"yScale",domDim:"domDim",tooltipOriginEl:"tooltipOriginEl",tooltipTemplate:"tooltipTemplate"},outputs:{onViewExtentChange:"onViewExtentChange",onViewExtentReset:"onViewExtentReset"},features:[pg([{provide:LF,useFactory:jut,deps:[pL]}]),Bo],decls:12,vars:15,consts:[[1,"dots"],["dots",""],[4,"ngIf"],["class","zoom-box",4,"ngIf"],[1,"zoom-instruction"],[1,"instruction-content"],["cdkOverlayOrigin","",1,"tooltip-origin"],["tooltipOrigin","cdkOverlayOrigin"],["cdkConnectedOverlay","",3,"cdkConnectedOverlayOrigin","cdkConnectedOverlayOpen","cdkConnectedOverlayPositions","cdkConnectedOverlayScrollStrategy","cdkConnectedOverlayLockPosition","cdkConnectedOverlayFlexibleDimensions","cdkConnectedOverlayGrowAfterOpen","detach"],["defaultTooltip",""],[4,"ngFor","ngForOf","ngForTrackBy"],["r","4",4,"ngIf"],["r","4"],[1,"zoom-box"],[1,"tooltip-container"],[3,"ngTemplateOutlet","ngTemplateOutletContext"],[1,"circle-header"],[1,"tooltip-row"],[1,"tooltip-row-circle"],[1,"name"]],template:function t(e,n){1&e&&(qi(),Rm(0,"svg",0,1),Qp(2,zut,2,2,"ng-container",2),Qp(3,Iut,1,4,"rect",3),Am(),Zi(),Rm(4,"div",4),Rm(5,"span",5),ku(6,"Alt + Scroll to Zoom"),Am(),Am(),Tm(7,"div",6,7),Qp(9,Fut,2,5,"ng-template",8),Vm("detach",(function t(){return n.onTooltipDisplayDetached()})),Qp(10,But,12,2,"ng-template",null,9,ib)),2&e&&(pu("pannable",n.specialKeyPressed)("draggable",n.state===n.InteractionState.NONE||n.state===n.InteractionState.DRAG_ZOOMING)("panning",n.state===n.InteractionState.PANNING),rc(2),Dm("ngIf",n.state===n.InteractionState.NONE),rc(1),Dm("ngIf",n.state===n.InteractionState.DRAG_ZOOMING),rc(6),Dm("cdkConnectedOverlayOrigin",n.tooltipOriginEl)("cdkConnectedOverlayOpen",n.tooltipDisplayAttached&&n.state===n.InteractionState.NONE)("cdkConnectedOverlayPositions",n.tooltipPositions)("cdkConnectedOverlayScrollStrategy",n.scrollStrategy)("cdkConnectedOverlayLockPosition",!1)("cdkConnectedOverlayFlexibleDimensions",!0)("cdkConnectedOverlayGrowAfterOpen",!0))},directives:[dM,fL,gL,lM,MM],styles:["[_nghost-%COMP%]{display:flex;position:relative;user-select:none}.dots[_ngcontent-%COMP%]{height:100%;width:100%}.dots.draggable[_ngcontent-%COMP%]{cursor:crosshair}.dots.pannable[_ngcontent-%COMP%]{cursor:grab}.dots.panning[_ngcontent-%COMP%]{cursor:grabbing}.tooltip-row-circle[_ngcontent-%COMP%]{align-items:center;display:inline-flex;height:12px;width:12px}.tooltip-row-circle[_ngcontent-%COMP%] > span[_ngcontent-%COMP%]{border-radius:50%;border:1px solid rgba(255,255,255,.6);display:inline-block;height:10px;width:10px}.tooltip-origin[_ngcontent-%COMP%]{bottom:0;left:0;position:absolute;right:0}.tooltip-container[_ngcontent-%COMP%]{background:rgba(0,0,0,.85);border-radius:4px;color:#fff;contain:paint style layout;font-size:.9em;overflow:auto;padding:5px;pointer-events:none;width:100%}th[_ngcontent-%COMP%], td[_ngcontent-%COMP%]{padding:2px 5px;text-align:left}th[_ngcontent-%COMP%]{font-weight:500;padding-bottom:5px}.zoom-box[_ngcontent-%COMP%]{fill-opacity:.03;fill:#000;stroke:#ccc}.zoom-instruction[_ngcontent-%COMP%]{align-items:center;display:flex;justify-content:center;left:0;opacity:0;pointer-events:none;position:absolute;right:0;top:10px;transition:opacity .5s;z-index:1}.instruction-content[_ngcontent-%COMP%]{background:rgba(0,0,0,.6);border-radius:5px;color:#fff;padding:5px 10px;user-select:none}.show-zoom-instruction[_nghost-%COMP%]   .zoom-instruction[_ngcontent-%COMP%]{opacity:1}"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Uut,[{type:My,args:[{selector:"line-chart-interactive-view",templateUrl:"./line_chart_interactive_view.ng.html",styleUrls:["./line_chart_interactive_view.css"],changeDetection:zn.OnPush,providers:[{provide:LF,useFactory:jut,deps:[pL]}]}]}],(function(){return[{type:Ug},{type:LF}]}),{dotsContainer:[{type:Za,args:["dots",{static:!0,read:hg}]}],overlay:[{type:Za,args:[gL]}],seriesData:[{type:xy}],seriesMetadataMap:[{type:xy}],viewExtent:[{type:xy}],xScale:[{type:xy}],yScale:[{type:xy}],domDim:[{type:xy}],tooltipOriginEl:[{type:xy}],tooltipTemplate:[{type:xy}],onViewExtentChange:[{type:Oy}],onViewExtentReset:[{type:Oy}],showZoomInstruction:[{type:Py,args:["class.show-zoom-instruction"]}]});const Wut=document.createElement("canvas").getContext("2d");function Yut(t,e){if(1&t&&(qi(),Rm(0,"g",17),Rm(1,"text"),ku(2),Am(),Rm(3,"title"),ku(4),Am(),Am()),2&t){const t=e.$implicit,n=Ym();rc(1),du("font",n.axisFont),jp("x",n.textXPosition(t.value))("y",n.textYPosition(t.value)),rc(1),Du(" ",t.tickFormattedString," "),rc(2),Su(n.getFormatter().formatLong(t.value))}}function qut(t,e){if(1&t&&(Rm(0,"span",20),Rm(1,"span"),ku(2),Am(),Am()),2&t){const t=e.$implicit,n=e.index,o=e.last,i=Ym(2);du("left",i.getMajorXPosition(t),"px")("width",i.getMajorWidthString(t,o,i.majorTicks[n+1]))("bottom",i.getMajorYPosition(t),"px")("height",i.getMajorHeightString(t,o,i.majorTicks[n+1]))("font",i.axisFont),pu("major-label",!0)("last",o),Dm("title",i.getFormatter().formatLong(t.start)),rc(2),Su(t.tickFormattedString)}}function Zut(t,e){if(1&t&&(Rm(0,"div",18),Qp(1,qut,3,16,"span",19),Am()),2&t){const t=Ym();rc(1),Dm("ngForOf",t.majorTicks)("ngForTrackBy",t.trackByMajorTick)}}class Xut{constructor(){this.onViewExtentChange=new Lh,this.editMenuOpened=!1,this.majorTicks=[],this.minorTicks=[]}ngOnChanges(){let t=null;const e=kut("x"===this.axis?this.domDim.width:this.domDim.height,this.gridCount);t=this.scale instanceof tit?(function n(t,e,o,i){const[a,r]=i,s=Math.abs(r-a);if(s>.001)return Gut(t,e,o,i);const l=t.ticks([a,r],o),c=t.ticks([a,r],2),d=[];let p=(function m(t){const e=t.toExponential().split("e-",2);return 2===e.length?Number(e[1])-1:0})(s);s<1&&c.every((t=>{const e=Math.abs(t);return e>=0&&e<1}))&&(p+=1);const u=new Map;for(const t of c){const[n,o=""]=String(t).split(".",2),i=Number(n+"."+o.slice(0,p));u.set(i,{start:i,tickFormattedString:0===i?"—":e.formatReadable(i)})}const f=10*Math.pow(10,-p);for(const t of l)for(const n of[...u.keys()].reverse()){const o=t-n;if(o>=0&&o<f){if(0===n)d.push({value:t,tickFormattedString:e.formatTick(t)});else{const e=String(t).slice(String(n).length);d.push({value:t,tickFormattedString:`…${e||"0"}`})}break}}return{major:Array.from(u.values()),minor:d}})(this.scale,this.getFormatter(),e,this.axisExtent):this.scale instanceof nit?(function o(t,e,n,i){const[a,r]=i;let s=t.ticks(i,2);if(r-a>=864e5||s.length>2)return Gut(t,e,n,i);const l=t.ticks(i,n);return{major:s.map((t=>({start:t,tickFormattedString:e.formatShort(t)}))),minor:l.map((t=>({value:t,tickFormattedString:e.formatTick(t)})))}})(this.scale,this.getFormatter(),e,this.axisExtent):Gut(this.scale,this.getFormatter(),e,this.axisExtent),this.majorTicks=t.major,this.minorTicks=(function i(t,e,n,o,a=5){if(!t.length||!Wut)return t;const r="x"===n?1:-1;let s=null;return t.filter((t=>{const i=e(t);Wut.font=o;const l=Wut.measureText(t.tickFormattedString),c="x"===n?l.width:l.actualBoundingBoxAscent-l.actualBoundingBoxDescent;return null===s?!(i+r*c<0||(s=i+r*c,0)):!(r*(s+r*a-i)>0||(s=i+r*c,0))}))})(t.minor,(t=>this.getDomPos(t.value)),this.axis,"11px Roboto, sans-serif")}getFormatter(){var t;return null!==(t=this.customFormatter)&&void 0!==t?t:this.scale.defaultFormatter}trackByMinorTick(t){return t.value}trackByMajorTick(t){return t.start}getDomPos(t){return 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.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{display:flex;flex-direction:column;box-sizing:border-box;height:100%;overflow:auto;padding:16px;padding-top:4px}.heading[_ngcontent-%COMP%]{align-items:center;display:flex;font-size:14px;justify-content:space-between;margin-bottom:4px;position:relative}.heading[_ngcontent-%COMP%]   .name[_ngcontent-%COMP%]{align-items:center;display:grid;gap:5px;grid-template-columns:auto auto}.heading[_ngcontent-%COMP%]   vis-selected-time-clipped[_ngcontent-%COMP%]{font-size:1.2em;line-height:0}.tag[_ngcontent-%COMP%]{overflow:hidden}.pin-button[_ngcontent-%COMP%]   mat-icon[_ngcontent-%COMP%]{height:18px}.controls[_ngcontent-%COMP%]{color:#616161;white-space:nowrap;flex-shrink:0;margin-right:-12px}body.dark-mode[_nghost-%COMP%]   .controls[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .controls[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.chart-container[_ngcontent-%COMP%]{position:relative;flex:1}.chart-container[_ngcontent-%COMP%]   mat-spinner[_ngcontent-%COMP%]{position:absolute;right:11px;top:11px}.chart-container[_ngcontent-%COMP%]   line-chart[_ngcontent-%COMP%]{display:block;height:100%}.tooltip[_ngcontent-%COMP%]{border-spacing:4px;font-size:13px}.tooltip[_ngcontent-%COMP%]   th[_ngcontent-%COMP%]{text-align:left}.tooltip[_ngcontent-%COMP%]   .tooltip-row[_ngcontent-%COMP%]{white-space:nowrap}.tooltip[_ngcontent-%COMP%]   .tooltip-row-circle[_ngcontent-%COMP%]{align-items:center;display:inline-flex;height:12px;width:12px}.tooltip[_ngcontent-%COMP%]   .tooltip-row-circle[_ngcontent-%COMP%] > span[_ngcontent-%COMP%]{border-radius:50%;border:1px solid rgba(255,255,255,.4);display:inline-block;height:10px;width:10px}.tooltip[_ngcontent-%COMP%]   .closest[_ngcontent-%COMP%]   .tooltip-row-circle[_ngcontent-%COMP%] > span[_ngcontent-%COMP%]{border-color:#fff;box-shadow:inset 0 0 0 1px #fff}.out-of-selected-time[_ngcontent-%COMP%]{border:0 dashed currentColor;height:100%;position:absolute}.out-of-selected-time.start[_ngcontent-%COMP%]{border-right-width:2px;margin-left:-1px}.out-of-selected-time.start.range[_ngcontent-%COMP%]{left:0}.out-of-selected-time.end[_ngcontent-%COMP%]{border-left-width:2px;margin-right:-1px;right:0}.out-of-selected-time.range[_ngcontent-%COMP%]{background-color:rgba(255,255,255,.5)}body.dark-mode[_nghost-%COMP%]   .out-of-selected-time.range[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .out-of-selected-time.range[_ngcontent-%COMP%]{background-color:rgba(0,0,0,.4)}.linked-time-fob-container[_ngcontent-%COMP%]{display:inline-block;left:0;position:absolute;top:0}linked-time-fob[_ngcontent-%COMP%]{transform:translateX(-50%)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Tft,[{type:My,args:[{selector:"scalar-card-component",templateUrl:"scalar_card_component.ng.html",styleUrls:["scalar_card_component.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:hg},{type:oW}]}),{cardId:[{type:xy}],chartMetadataMap:[{type:xy}],DataDownloadComponent:[{type:xy}],dataSeries:[{type:xy}],ignoreOutliers:[{type:xy}],isCardVisible:[{type:xy}],isPinned:[{type:xy}],loadState:[{type:xy}],showFullSize:[{type:xy}],smoothingEnabled:[{type:xy}],tag:[{type:xy}],title:[{type:xy}],tooltipSort:[{type:xy}],xAxisType:[{type:xy}],xScaleType:[{type:xy}],useDarkMode:[{type:xy}],selectedTime:[{type:xy}],onFullSizeToggle:[{type:Oy}],onPinClicked:[{type:Oy}],lineChart:[{type:Za,args:[fft]}]});class Nft{constructor(t,e){this.ref=t,this.cdkScrollable=e,this.onVisibilityChange=new Lh,this.ngUnsubscribe$=new I,this.onEvent$=new I}ngOnInit(){const t=new IntersectionObserver((t=>{this.onEvent$.next(t)}),{root:this.cdkScrollable?this.cdkScrollable.getElementRef().nativeElement:null,rootMargin:this.intersectionObserverMargin});t.observe(this.ref.nativeElement),this.ngUnsubscribe$.subscribe((()=>{t.unobserve(this.ref.nativeElement)})),this.onEvent$.pipe(Ie(this.ngUnsubscribe$)).subscribe((t=>{const e=t.slice(-1)[0];this.onVisibilityChange.emit({visible:e.isIntersecting})}))}ngOnDestroy(){this.ngUnsubscribe$.next(),this.ngUnsubscribe$.complete()}waitForEventForTestOnly(){return new Promise((t=>this.onEvent$.pipe(be(1)).subscribe((()=>{t()}))))}}function zft(t,e){return t.length===e.length&&t.every(((t,n)=>{const o=e[n],i=t.points,a=o.points;return t.runId===o.runId&&i.length===a.length&&i.every(((t,e)=>{const n=a[e];return t.x===n.x&&t.y===n.y}))}))}Nft.ɵfac=function t(e){return new(e||Nft)(Sm(hg),Sm(mF,8))},Nft.ɵdir=lo({type:Nft,selectors:[["","observeIntersection",""]],inputs:{intersectionObserverMargin:"intersectionObserverMargin"},outputs:{onVisibilityChange:"onVisibilityChange"}}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Nft,[{type:Cy,args:[{selector:"[observeIntersection]"}]}],(function(){return[{type:hg},{type:mF,decorators:[{type:Sr}]}]}),{intersectionObserverMargin:[{type:xy}],onVisibilityChange:[{type:Oy}]});class Ift{constructor(t){this.store=t,this.DataDownloadComponent=l5,this.fullWidthChanged=new Lh,this.fullHeightChanged=new Lh,this.pinStateChanged=new Lh,this.isVisible=!1,this.useDarkMode$=this.store.select(JD),this.ignoreOutliers$=this.store.select(AT),this.tooltipSort$=this.store.select(RT),this.xAxisType$=this.store.select(TT),this.xScaleType$=this.store.select(TT).pipe(It((t=>{switch(t){case yA.STEP:case yA.RELATIVE:return J2.LINEAR;case yA.WALL_TIME:return J2.TIME;default:throw new Error(`Invalid xAxisType for line chart. ${t}`)}}))),this.scalarSmoothing$=this.store.select(zT),this.smoothingEnabled$=this.store.select(zT).pipe(It((t=>t>0))),this.showFullSize=!1,this.ngUnsubscribe=new I}onVisibilityChange({visible:t}){this.isVisible=t}isScalarCardMetadata(t){const{plugin:e}=t;return e===hA.SCALARS}onFullSizeToggle(){this.showFullSize=!this.showFullSize,this.fullWidthChanged.emit(this.showFullSize),this.fullHeightChanged.emit(this.showFullSize)}ngOnInit(){const t=this.store.select(yT,this.cardId).pipe(ce((t=>!!t&&this.isScalarCardMetadata(t))),It((t=>t)));function e(t){return JSON.stringify(["smoothed",t])}const n=this.store.select(hT,this.cardId).pipe(Ie(this.ngUnsubscribe),ce((t=>Boolean(t))),It((t=>t)),Ae(1)).pipe(fe(this.store.select(TT)),It((([t,e])=>Object.keys(t).map((n=>({runId:n,points:this.stepSeriesToLineSeries(t[n],e)}))))),Me(zft)).pipe(fe(this.store.select(IT)),Ie(this.ngUnsubscribe),It((([t,e])=>e?(function n(t){var e;const n=[];for(const o of t){const t=[];let i=Number.isFinite(null===(e=o.points[0])||void 0===e?void 0:e.x)?o.points[0].x:-1/0,a=[];for(const e of o.points)Number.isFinite(e.x)?(e.x<i&&(t.push({seriesId:JSON.stringify([o.runId,t.length]),runId:o.runId,points:a}),a=[]),a.push(e),i=e.x):a.push(e);t.push({seriesId:JSON.stringify([o.runId,t.length]),runId:o.runId,points:a});for(let e=0;e<t.length;e++)n.push(Object.assign(Object.assign({},t[e]),{partitionIndex:e,partitionSize:t.length}))}return n})(t):t.map((t=>Object.assign(Object.assign({},t),{seriesId:t.runId,partitionIndex:0,partitionSize:1}))))),It((t=>t.map((t=>{var e;const n=null===(e=t.points[0])||void 0===e?void 0:e.wallTime;return Object.assign(Object.assign({},t),{points:t.points.map((t=>Object.assign(Object.assign({},t),{relativeTimeInMs:t.wallTime-n})))})})))),fe(this.store.select(TT)),It((([t,e])=>t.map((t=>Object.assign(Object.assign({},t),{points:t.points.map((t=>{let n;switch(e){case yA.RELATIVE:n=t.relativeTimeInMs;break;case yA.WALL_TIME:n=t.wallTime;break;case yA.STEP:default:n=t.step}return Object.assign(Object.assign({},t),{x:n})}))}))))),Ae(1));this.dataSeries$=n.pipe(fe(this.store.select(zT)),ze((([t,n])=>{const o=t.map((({seriesId:t,points:e})=>({id:t,points:e})));return n<=0?Et(o):Ct((function i(t,e){var n;return gA(this,void 0,void 0,(function*(){Number.isFinite(e)||(e=0),e=Math.max(0,Math.min(e,1));const o=[];for(const i of t){const t=null===(n=i.points[0])||void 0===n?void 0:n.y;if(i.points.every((e=>e.y==t))){o.push(i);continue}let a=i.points.length>0?0:NaN,r=0;const s=i.points.map((t=>{const n=t.y;if(Number.isFinite(n)){a=a*e+(1-e)*n,r++;const o=1===e?1:1-Math.pow(e,r);return{x:t.x,y:a/o}}return{x:t.x,y:n}}));o.push({id:i.id,points:s})}return o}))})(o,n)).pipe(It((t=>{const n=o.map(((n,o)=>({id:e(n.id),points:t[o].points.map((({y:t},e)=>Object.assign(Object.assign({},n.points[e]),{y:t})))})));return[...o,...n]})))})),Ne([])),this.selectedTime$=Wt([n,this.store.select(YT),this.store.select(TT)]).pipe(It((([t,e,n])=>{if(n!==yA.STEP||!e)return null;let o=1/0,i=-1/0;for(const{points:e}of t)for(const t of e)o=o>t.x?t.x:o,i=i<t.x?t.x:i;return H2(e,o,i)}))),this.chartMetadataMap$=n.pipe(ze((t=>Wt(t.map((t=>this.getRunDisplayName(t.runId).pipe(It((e=>Object.assign(Object.assign({},t),{displayName:e}))))))))),fe(this.store.select(NN),this.store.select(zN),this.store.select(zT)),ge(0),It((([t,n,o,i])=>{var a;const r={},s=i>0;for(const e of t){const{seriesId:t,runId:i,displayName:s,partitionIndex:l,partitionSize:c}=e;r[t]={type:Q2.ORIGINAL,id:t,displayName:c>1?`${s}: ${l}`:s,visible:Boolean(n&&n.get(i)),color:null!==(a=o[i])&&void 0!==a?a:"#fff",aux:!1,opacity:1}}if(!s)return r;for(const[t,n]of Object.entries(r)){const o=e(t);r[o]=Object.assign(Object.assign({},n),{id:o,type:Q2.DERIVED,aux:!1,originalSeriesId:t}),n.aux=!0,n.opacity=.25}return r})),Ne({})),this.loadState$=this.store.select(gT,this.cardId),this.tag$=t.pipe(It((t=>t.tag))),this.title$=this.tag$.pipe(It((t=>f2(t,this.groupName)))),this.isPinned$=this.store.select(wT,this.cardId)}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}getRunDisplayName(t){return Wt([this.store.select(rN,{runId:t}),this.store.select(zS),this.store.select(sN,{runId:t})]).pipe(It((([e,n,o])=>I2(t,o,e?n[e]:null))))}stepSeriesToLineSeries(t,e){const n=e===yA.STEP;return t.map((t=>{const e=1e3*t.wallTime;return Object.assign(Object.assign({},t),{x:n?t.step:e,y:t.value,wallTime:e,relativeTimeInMs:0})}))}}function Hft(t,e,n){const o=[],{left:i,right:a}=e,r=(a-i)/n;let s=0,l=0;for(let e=0;e<n;e++){const a=i+e*r,c=a+r,d=e===n-1;let p=l;for(l=0;s<t.length;){const e=t[s],n=Fft(e,a,c,!d);if(p+=n.curr,l+=n.next,e.x+e.dx>c)break;s++}o.push({x:a,dx:r,y:p})}return o}function Fft(t,e,n,o){const i=t.x,a=t.x+t.dx;if(i>n||a<e)return{curr:0,next:0};if(0===t.dx)return o&&a===n?{curr:.5*t.y,next:.5*t.y}:{curr:t.y,next:0};const r=Math.min(a,n)-Math.max(i,e);return{curr:t.y*r/t.dx,next:0}}Ift.ɵfac=function t(e){return new(e||Ift)(Sm(Iw))},Ift.ɵcmp=to({type:Ift,selectors:[["scalar-card"]],inputs:{DataDownloadComponent:"DataDownloadComponent",cardId:"cardId",groupName:"groupName"},outputs:{fullWidthChanged:"fullWidthChanged",fullHeightChanged:"fullHeightChanged",pinStateChanged:"pinStateChanged"},decls:14,vars:43,consts:[["observeIntersection","",3,"cardId","chartMetadataMap","DataDownloadComponent","dataSeries","ignoreOutliers","isCardVisible","isPinned","loadState","showFullSize","smoothingEnabled","tag","title","tooltipSort","xAxisType","xScaleType","useDarkMode","selectedTime","onFullSizeToggle","onPinClicked","onVisibilityChange"]],template:function t(e,n){1&e&&(Rm(0,"scalar-card-component",0),Vm("onFullSizeToggle",(function t(){return n.onFullSizeToggle()}))("onPinClicked",(function t(e){return n.pinStateChanged.emit(e)}))("onVisibilityChange",(function t(e){return n.onVisibilityChange(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Ah(7,"async"),Ah(8,"async"),Ah(9,"async"),Ah(10,"async"),Ah(11,"async"),Ah(12,"async"),Ah(13,"async"),Am()),2&e&&Dm("cardId",n.cardId)("chartMetadataMap",Th(1,17,n.chartMetadataMap$))("DataDownloadComponent",n.DataDownloadComponent)("dataSeries",Th(2,19,n.dataSeries$))("ignoreOutliers",Th(3,21,n.ignoreOutliers$))("isCardVisible",n.isVisible)("isPinned",Th(4,23,n.isPinned$))("loadState",Th(5,25,n.loadState$))("showFullSize",n.showFullSize)("smoothingEnabled",Th(6,27,n.smoothingEnabled$))("tag",Th(7,29,n.tag$))("title",Th(8,31,n.title$))("tooltipSort",Th(9,33,n.tooltipSort$))("xAxisType",Th(10,35,n.xAxisType$))("xScaleType",Th(11,37,n.xScaleType$))("useDarkMode",Th(12,39,n.useDarkMode$))("selectedTime",Th(13,41,n.selectedTime$))},directives:[Tft,Nft],pipes:[wM],styles:["[_nghost-%COMP%] {\n        display: block;\n        height: 100%;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Ift,[{type:My,args:[{selector:"scalar-card",template:'\n    <scalar-card-component\n      [cardId]="cardId"\n      [chartMetadataMap]="chartMetadataMap$ | async"\n      [DataDownloadComponent]="DataDownloadComponent"\n      [dataSeries]="dataSeries$ | async"\n      [ignoreOutliers]="ignoreOutliers$ | async"\n      [isCardVisible]="isVisible"\n      [isPinned]="isPinned$ | async"\n      [loadState]="loadState$ | async"\n      [showFullSize]="showFullSize"\n      [smoothingEnabled]="smoothingEnabled$ | async"\n      [tag]="tag$ | async"\n      [title]="title$ | async"\n      [tooltipSort]="tooltipSort$ | async"\n      [xAxisType]="xAxisType$ | async"\n      [xScaleType]="xScaleType$ | async"\n      [useDarkMode]="useDarkMode$ | async"\n      [selectedTime]="selectedTime$ | async"\n      (onFullSizeToggle)="onFullSizeToggle()"\n      (onPinClicked)="pinStateChanged.emit($event)"\n      observeIntersection\n      (onVisibilityChange)="onVisibilityChange($event)"\n    ></scalar-card-component>\n  ',styles:["\n      :host {\n        display: block;\n        height: 100%;\n      }\n    "],changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{DataDownloadComponent:[{type:xy}],cardId:[{type:xy}],groupName:[{type:xy}],fullWidthChanged:[{type:Oy}],fullHeightChanged:[{type:Oy}],pinStateChanged:[{type:Oy}]});const Lft=["main"],Bft=["xAxis"],Vft=["yAxis"],jft=["content"],Uft=["histograms"];function Gft(t,e){if(1&t&&(qi(),Rm(0,"g"),Rm(1,"text"),ku(2),Am(),Am()),2&t){const t=Ym();du("transform",t.getCssTranslatePx(t.tooltipData.xAxis.position,9)),rc(2),Su(t.tooltipData.xAxis.label)}}function Wft(t,e){if(1&t&&(qi(),Rm(0,"g"),Rm(1,"text"),ku(2),Am(),Am()),2&t){const t=Ym();du("transform",t.getGroupTransform(t.tooltipData.closestDatum)),rc(1),jp("y",t.tooltipData.yAxis.position),rc(1),Du(" ",t.tooltipData.yAxis.label," ")}}function Yft(t,e){if(1&t&&(Rm(0,"div",16),Tm(1,"linked-time-fob",17),Am()),2&t){const t=Ym(2);du("transform",t.getCssTranslatePx(0,t.scales.temporalScale(t.linkedTime.endStep))),rc(1),Dm("step",t.linkedTime.endStep)}}function qft(t,e){if(1&t&&(qi(),Zi(),Nm(0),Rm(1,"div",16),Tm(2,"linked-time-fob",17),Am(),Qp(3,Yft,2,3,"div",18),zm()),2&t){const t=Ym();rc(1),du("transform",t.getCssTranslatePx(0,t.scales.temporalScale(t.linkedTime.startStep))),rc(1),Dm("step",t.linkedTime.startStep),rc(1),Dm("ngIf",t.linkedTime.endStep)}}function Zft(t,e){if(1&t&&(qi(),Rm(0,"g"),Tm(1,"line",19),Am()),2&t){const t=e.$implicit;du("transform",Ym().getCssTranslatePx(0,t))}}function Xft(t,e){1&t&&(qi(),Tm(0,"line",22))}function Kft(t,e){if(1&t&&(qi(),Tm(0,"circle",23)),2&t){const t=Ym().$implicit,e=Ym();du("transform",e.getCssTranslatePx(e.getUiCoordFromBinForContent(e.getClosestBinFromBinCoordinate(t,e.tooltipData.xPositionInBinCoord)).x,e.getUiCoordFromBinForContent(e.getClosestBinFromBinCoordinate(t,e.tooltipData.xPositionInBinCoord)).y))}}function Jft(t,e){if(1&t&&(qi(),Rm(0,"g"),Qp(1,Xft,1,0,"line",20),Tm(2,"path"),Qp(3,Kft,1,2,"circle",21),Am()),2&t){const t=e.$implicit,n=Ym();du("transform",n.getGroupTransform(t))("color",n.getHistogramFill(t)),pu("histogram",!0)("no-color",!n.isDatumInLinkedTimeRange(t)),rc(1),Dm("ngIf",n.mode===n.HistogramMode.OFFSET),rc(1),jp("d",n.getHistogramPath(t)),rc(1),Dm("ngIf",n.tooltipData)}}function Qft(t,e){if(1&t&&(qi(),Tm(0,"circle",23)),2&t){const t=Ym(2);jp("cx",t.getUiCoordFromBinForContent(t.tooltipData.closestBin).x)("cy",t.getUiCoordFromBinForContent(t.tooltipData.closestBin).y)}}function $ft(t,e){if(1&t&&(qi(),Rm(0,"g",4),Rm(1,"g"),Tm(2,"path"),Qp(3,Qft,1,2,"circle",24),Am(),Rm(4,"g",25),Rm(5,"text",26),ku(6),Am(),Am(),Am()),2&t){const t=Ym();rc(1),du("transform",t.getGroupTransform(t.tooltipData.closestDatum)),rc(1),jp("d",t.getHistogramPath(t.tooltipData.closestDatum)),rc(1),Dm("ngIf",t.tooltipData.closestBin),rc(1),du("transform",t.getCssTranslatePx(t.tooltipData.value.position.x,t.tooltipData.value.position.y)),rc(2),Su(t.tooltipData.value.label)}}class tgt{constructor(t){this.changeDetector=t,this.mode=pE.OFFSET,this.timeProperty=dE.STEP,this.linkedTime=null,this.HistogramMode=pE,this.TimeProperty=dE,this.tooltipData=null,this.ngUnsubscribe=new I,this.layout={histogramHeight:0,contentClientRect:{height:0,width:0}},this.scales=null,this.formatters={binNumber:Ett(".3~s"),count:Ett(".3n"),wallTime:Wet("%m/%d %X"),step:Ett(".0f"),relative:t=>Ett(".1r")(t/36e5)+"h"},this.domVisible=!1}ngOnChanges(){this.updateChartIfVisible()}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}ngAfterViewInit(){oe(this.main.nativeElement,"mousemove",{passive:!0}).pipe(Ie(this.ngUnsubscribe)).subscribe((t=>this.onMouseMove(t)))}getCssTranslatePx(t,e){return`translate(${t}px, ${e}px)`}getClosestBinFromBinCoordinate(t,e){if(!t.bins.length)return{x:0,dx:0,y:0};const n=t.bins[0],o=t.bins.slice(-1)[0];return e<n.x?n:e>=o.x+o.dx?o:t.bins.find((t=>t.x<=e&&e<t.x+t.dx))}getUiCoordFromBinForContent(t){return this.scales?{x:this.scales.binScale(ogt(t)),y:this.scales.countScale(t.y)}:{x:0,y:0}}getHistogramPath(t){if(!this.scales||!t.bins.length)return"";const e=this.scales.binScale,n=this.scales.countScale,o=t.bins[0],i=t.bins.slice(-1)[0],a=[`M${e(ogt(o))},${n(0)}`];for(const o of t.bins)a.push(`L${e(ogt(o))},${n(o.y)}`);return a.push(`L${e(ogt(i))},${n(0)}`),a.join("")}trackByWallTime(t){return t.wallTime}getGroupTransform(t){return this.scales&&this.mode!==pE.OVERLAY?this.getCssTranslatePx(0,this.scales.temporalScale(this.getTimeValue(t))):""}isLinkedTimeEnabled(t){return Boolean(this.mode===pE.OFFSET&&this.timeProperty===dE.STEP&&this.scales&&t)}isDatumInLinkedTimeRange(t){return!this.isLinkedTimeEnabled(this.linkedTime)||(null===this.linkedTime.endStep?this.linkedTime.startStep===t.step:this.linkedTime.startStep<=t.step&&this.linkedTime.endStep>=t.step)}getHistogramFill(t){return this.scales?this.scales.d3ColorScale(this.getTimeValue(t)):""}getGridTickYLocs(){if(!this.scales||this.mode===pE.OFFSET)return[];const t=this.scales.countScale;return t.ticks().map((e=>t(e)))}onResize(){this.updateClientRects(),this.updateChartIfVisible()}onVisibilityChange({visible:t}){this.domVisible=t,t&&(this.updateClientRects(),this.updateChartIfVisible())}getTimeValue(t){switch(this.timeProperty){case dE.WALL_TIME:return t.wallTime;case dE.STEP:return t.step;case dE.RELATIVE:return t.wallTime-this.data[0].wallTime}}updateClientRects(){this.content&&(this.layout.contentClientRect=this.content.nativeElement.getBoundingClientRect(),this.layout.histogramHeight=this.layout.contentClientRect.height/2.5)}updateChartIfVisible(){this.domVisible&&(this.scales=this.computeScales(this.data),this.renderXAxis(),this.renderYAxis(),this.changeDetector.detectChanges())}computeScales(t){const{width:e,height:n}=this.layout.contentClientRect,{min:o,max:i}=ngt(t,(t=>(function e(t,n){return t.reduce(((t,e)=>Math.min(t,n(e))),1/0)})(t.bins,(t=>t.x))),(t=>egt(t.bins,(({x:t,dx:e})=>t+e)))),a=egt(t,(t=>egt(t.bins,(({y:t})=>t)))),r=eet().domain([o,i]).nice(),s=this.mode!==pE.OVERLAY&&this.timeProperty==dE.WALL_TIME?fot():eet(),l=t.map((t=>this.getTimeValue(t))),{min:c,max:d}=ngt(l,(t=>t)),p=[c,d];s.domain(p);const m=eet();m.domain([0,a]);const u=S7(this.color||"#000"),f=eet();return f.domain(p),r.range([0,e]),f.range([u.brighter(),u.darker()]),f.interpolate(m8),this.mode===pE.OVERLAY?(s.range([n,n]),m.range([n,0])):(s.range([n-(this.mode===pE.OFFSET?n-this.layout.histogramHeight:0),n]),m.range([0,-this.layout.histogramHeight])),{binScale:r,d3ColorScale:f,countScale:m,temporalScale:s}}renderXAxis(){if(!this.scales)return;const{width:t}=this.layout.contentClientRect,e=z5(this.scales.binScale).ticks(Math.max(2,t/20));e.tickFormat(this.formatters.binNumber),e(J4(this.xAxis.nativeElement))}getYAxisFormatter(){if(this.mode===pE.OVERLAY)return this.formatters.count;switch(this.timeProperty){case dE.WALL_TIME:return this.formatters.wallTime;case dE.STEP:return this.formatters.step;case dE.RELATIVE:return this.formatters.relative;default:throw RangeError(`Y axis formatter for ${this.timeProperty} must be implemented`)}}renderYAxis(){if(!this.scales)return;const t=this.mode===pE.OVERLAY?this.scales.countScale:this.scales.temporalScale,{height:e}=this.layout.contentClientRect,n=N5(t).ticks(Math.max(2,e/15));n.tickFormat(this.getYAxisFormatter()),n(J4(this.yAxis.nativeElement))}findClosestDatumIndex(t){let e=t.target,n=e;for(;e&&e!==this.histograms.nativeElement;)n=e,e=e.parentElement;return e?Array.from(e.children).indexOf(n):-1}onMouseMoveForTestOnly(t){return this.onMouseMove(t)}onMouseMove(t){if(!this.scales)return;const e=t.offsetX,n=t.offsetY,o=this.findClosestDatumIndex(t);if(o<0)return;const i=this.scales.binScale.invert(e),a=this.data[o],r=this.getClosestBinFromBinCoordinate(a,i);this.tooltipData={value:{position:{x:e,y:n},label:this.mode===pE.OFFSET?this.formatters.count(r.y):`Step: ${this.formatters.step(a.step)}`},xAxis:{position:this.getUiCoordFromBinForContent(r).x,label:this.formatters.binNumber(ogt(r))},yAxis:{position:this.scales.countScale(this.mode===pE.OFFSET?0:r.y),label:this.mode===pE.OFFSET?this.getYAxisFormatter()(this.getTimeValue(a)):this.formatters.binNumber(r.y)},xPositionInBinCoord:i,closestDatum:a,closestBin:r},this.changeDetector.detectChanges()}}function egt(t,e){return t.reduce(((t,n)=>Math.max(t,e(n))),-1/0)}function ngt(t,e,n){n||(n=e);let o=1/0,i=-1/0;for(const a of t)o=Math.min(o,e(a)),i=Math.max(i,n(a));return{min:o,max:i}}function ogt(t){return t.x+.5*t.dx}function igt(t,e){1&t&&Tm(0,"vis-selected-time-clipped")}function agt(t,e){1&t&&(Rm(0,"span",14),Tm(1,"mat-spinner",15),Am())}tgt.ɵfac=function t(e){return new(e||tgt)(Sm(Ug))},tgt.ɵcmp=to({type:tgt,selectors:[["tb-histogram"]],viewQuery:function t(e,n){if(1&e&&(Qh(Lft,5),Qh(Bft,5),Qh(Vft,5),Qh(jft,5),Qh(Uft,5)),2&e){let 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auto;font-size:14px;margin-bottom:4px}.tag[_ngcontent-%COMP%]{align-items:center;display:flex;gap:5px;grid-area:tag;overflow:hidden}.tag[_ngcontent-%COMP%]   vis-selected-time-clipped[_ngcontent-%COMP%]{line-height:0}.pin-button[_ngcontent-%COMP%]   mat-icon[_ngcontent-%COMP%]{height:18px}.run[_ngcontent-%COMP%]{grid-area:run;display:flex;white-space:nowrap;font-size:13px}.run[_ngcontent-%COMP%]   .dot[_ngcontent-%COMP%]{flex:none;display:inline-block;width:13px;height:13px;border-radius:50%;margin-right:4px}.run[_ngcontent-%COMP%]   .run-text[_ngcontent-%COMP%]{overflow:hidden;text-overflow:ellipsis;max-width:120px}.controls[_ngcontent-%COMP%]{color:#616161;white-space:nowrap;grid-area:controls;justify-self:flex-end;flex-shrink:0;margin-right:-12px}body.dark-mode[_nghost-%COMP%]   .controls[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .controls[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.spinner[_ngcontent-%COMP%]{display:flex;grid-area:spinner;height:100%;justify-content:center;position:relative}mat-spinner[_ngcontent-%COMP%]{top:0;right:0;position:absolute}tb-histogram[_ngcontent-%COMP%]{flex-grow:1;overflow:hidden}.empty-message[_ngcontent-%COMP%]{margin-top:1em;font-size:13px}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(pgt,[{type:My,args:[{selector:"histogram-card-component",templateUrl:"histogram_card_component.ng.html",styleUrls:["histogram_card_component.css"],changeDetection:zn.OnPush}]}],null,{loadState:[{type:xy}],title:[{type:xy}],tag:[{type:xy}],runId:[{type:xy}],data:[{type:xy}],mode:[{type:xy}],xAxisType:[{type:xy}],runColorScale:[{type:xy}],showFullSize:[{type:xy}],isPinned:[{type:xy}],selectedTime:[{type:xy}],onFullSizeToggle:[{type:Oy}],onPinClicked:[{type:Oy}]});class mgt{constructor(t){this.store=t,this.fullWidthChanged=new Lh,this.fullHeightChanged=new Lh,this.pinStateChanged=new Lh,this.mode$=this.store.select(NT),this.xAxisType$=this.store.select(TT),this.showFullSize=!1}isHistogramCardMetadata(t){const{plugin:e}=t;return e===hA.HISTOGRAMS}onFullSizeToggle(){this.showFullSize=!this.showFullSize,this.fullWidthChanged.emit(this.showFullSize),this.fullHeightChanged.emit(this.showFullSize)}ngOnInit(){const t=this.store.select(yT,this.cardId).pipe(ce((t=>!!t&&this.isHistogramCardMetadata(t))),It((t=>t))),e=Wt([t,this.store.select(hT,this.cardId)]);this.data$=e.pipe(It((([t,e])=>{const n=t.runId;return e&&e.hasOwnProperty(n)?(function o(t,e=30){if(!t.length||e<1)return[];const n=(function o(t){let e=null,n=null;for(const{bins:o}of t){if(!o.length)continue;const t=o[o.length-1],i=o[0].x,a=t.x+t.dx;(null===e||i<e)&&(e=i),(null===n||a>n)&&(n=a)}return null===e||null===n?null:{left:e,right:n}})(t);return n&&n.left===n.right&&(n.right=1.1*n.right+1,n.left=n.left/1.1-1),t.map((t=>({step:t.step,wallTime:t.wallTime,bins:n?Hft(t.bins,n,e):[]})))})(e[n].map((t=>{const{wallTime:e,step:n}=t;return{wallTime:e,step:n,bins:t.bins.map((t=>({x:t.min,dx:t.max-t.min,y:t.count})))}}))):[]}))),this.selectedTime$=this.store.select(YT).pipe(fe(this.data$),It((([t,e])=>{if(!t)return null;let n=1/0,o=-1/0;for(const t of e)n=Math.min(t.step,n),o=Math.max(t.step,o);return H2(t,n,o)}))),this.loadState$=this.store.select(gT,this.cardId),this.tag$=t.pipe(It((t=>t.tag))),this.title$=this.tag$.pipe(It((t=>f2(t,this.groupName)))),this.runId$=t.pipe(It((t=>t.runId))),this.isPinned$=this.store.select(wT,this.cardId)}}function ugt(t,e){if(1&t){const t=Hm();Rm(0,"image-card",6),Vm("fullWidthChanged",(function e(n){return hi(t),Ym(2).onFullWidthChanged(n)}))("pinStateChanged",(function e(){return hi(t),Ym(2).onPinStateChanged()})),Am()}if(2&t){const t=Ym(2);Dm("cardId",t.cardId)("groupName",t.groupName)("runColorScale",t.runColorScale)}}function fgt(t,e){if(1&t){const t=Hm();Rm(0,"scalar-card",7),Vm("fullWidthChanged",(function e(n){return hi(t),Ym(2).onFullWidthChanged(n)}))("fullHeightChanged",(function e(n){return hi(t),Ym(2).onFullHeightChanged(n)}))("pinStateChanged",(function e(){return hi(t),Ym(2).onPinStateChanged()})),Am()}if(2&t){const t=Ym(2);Dm("cardId",t.cardId)("groupName",t.groupName)}}function ggt(t,e){if(1&t){const t=Hm();Rm(0,"histogram-card",8),Vm("fullWidthChanged",(function e(n){return hi(t),Ym(2).onFullWidthChanged(n)}))("fullHeightChanged",(function e(n){return hi(t),Ym(2).onFullHeightChanged(n)}))("pinStateChanged",(function e(){return hi(t),Ym(2).onPinStateChanged()})),Am()}if(2&t){const t=Ym(2);Dm("cardId",t.cardId)("groupName",t.groupName)("runColorScale",t.runColorScale)}}function hgt(t,e){if(1&t&&(Rm(0,"div"),ku(1),Am()),2&t){const t=Ym(2);rc(1),Du("Placeholder error for: ",t.cardId,"")}}function bgt(t,e){if(1&t&&(Nm(0,1),Qp(1,ugt,1,3,"image-card",2),Qp(2,fgt,1,2,"scalar-card",3),Qp(3,ggt,1,3,"histogram-card",4),Qp(4,hgt,2,1,"div",5),zm()),2&t){const t=Ym();Dm("ngSwitch",t.pluginType),rc(1),Dm("ngSwitchCase",t.PluginType.IMAGES),rc(1),Dm("ngSwitchCase",t.PluginType.SCALARS),rc(1),Dm("ngSwitchCase",t.PluginType.HISTOGRAMS)}}mgt.ɵfac=function t(e){return new(e||mgt)(Sm(Iw))},mgt.ɵcmp=to({type:mgt,selectors:[["histogram-card"]],inputs:{cardId:"cardId",groupName:"groupName",runColorScale:"runColorScale"},outputs:{fullWidthChanged:"fullWidthChanged",fullHeightChanged:"fullHeightChanged",pinStateChanged:"pinStateChanged"},decls:10,vars:29,consts:[[3,"loadState","title","tag","runId","data","mode","xAxisType","runColorScale","showFullSize","isPinned","selectedTime","onFullSizeToggle","onPinClicked"]],template:function t(e,n){1&e&&(Rm(0,"histogram-card-component",0),Vm("onFullSizeToggle",(function t(){return n.onFullSizeToggle()}))("onPinClicked",(function t(e){return n.pinStateChanged.emit(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Ah(7,"async"),Ah(8,"async"),Ah(9,"async"),Am()),2&e&&Dm("loadState",Th(1,11,n.loadState$))("title",Th(2,13,n.title$))("tag",Th(3,15,n.tag$))("runId",Th(4,17,n.runId$))("data",Th(5,19,n.data$))("mode",Th(6,21,n.mode$))("xAxisType",Th(7,23,n.xAxisType$))("runColorScale",n.runColorScale)("showFullSize",n.showFullSize)("isPinned",Th(8,25,n.isPinned$))("selectedTime",Th(9,27,n.selectedTime$))},directives:[pgt],pipes:[wM],styles:["[_nghost-%COMP%] {\n        display: block;\n        height: 100%;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(mgt,[{type:My,args:[{selector:"histogram-card",template:'\n    <histogram-card-component\n      [loadState]="loadState$ | async"\n      [title]="title$ | async"\n      [tag]="tag$ | async"\n      [runId]="runId$ | async"\n      [data]="data$ | async"\n      [mode]="mode$ | async"\n      [xAxisType]="xAxisType$ | async"\n      [runColorScale]="runColorScale"\n      [showFullSize]="showFullSize"\n      [isPinned]="isPinned$ | async"\n      [selectedTime]="selectedTime$ | async"\n      (onFullSizeToggle)="onFullSizeToggle()"\n      (onPinClicked)="pinStateChanged.emit($event)"\n    ></histogram-card-component>\n  ',styles:["\n      :host {\n        display: block;\n        height: 100%;\n      }\n    "],changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{cardId:[{type:xy}],groupName:[{type:xy}],runColorScale:[{type:xy}],fullWidthChanged:[{type:Oy}],fullHeightChanged:[{type:Oy}],pinStateChanged:[{type:Oy}]});class ygt{constructor(){this.PluginType=hA,this.fullWidthChanged=new Lh,this.fullHeightChanged=new Lh,this.pinStateChanged=new Lh}onFullWidthChanged(t){this.fullWidthChanged.emit(t)}onFullHeightChanged(t){this.fullHeightChanged.emit(t)}onPinStateChanged(){this.pinStateChanged.emit()}}ygt.ɵfac=function t(e){return new(e||ygt)},ygt.ɵcmp=to({type:ygt,selectors:[["card-view-component"]],inputs:{isEverVisible:"isEverVisible",cardId:"cardId",groupName:"groupName",pluginType:"pluginType",runColorScale:"runColorScale"},outputs:{fullWidthChanged:"fullWidthChanged",fullHeightChanged:"fullHeightChanged",pinStateChanged:"pinStateChanged"},decls:1,vars:1,consts:[[3,"ngSwitch",4,"ngIf"],[3,"ngSwitch"],[3,"cardId","groupName","runColorScale","fullWidthChanged","pinStateChanged",4,"ngSwitchCase"],[3,"cardId","groupName","fullWidthChanged","fullHeightChanged","pinStateChanged",4,"ngSwitchCase"],[3,"cardId","groupName","runColorScale","fullWidthChanged","fullHeightChanged","pinStateChanged",4,"ngSwitchCase"],[4,"ngSwitchDefault"],[3,"cardId","groupName","runColorScale","fullWidthChanged","pinStateChanged"],[3,"cardId","groupName","fullWidthChanged","fullHeightChanged","pinStateChanged"],[3,"cardId","groupName","runColorScale","fullWidthChanged","fullHeightChanged","pinStateChanged"]],template:function t(e,n){1&e&&Qp(0,bgt,5,4,"ng-container",0),2&e&&Dm("ngIf",n.isEverVisible)},directives:[dM,fM,gM,hM,X2,Ift,mgt],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ygt,[{type:My,args:[{selector:"card-view-component",templateUrl:"card_view_component.ng.html",changeDetection:zn.OnPush}]}],null,{isEverVisible:[{type:xy}],cardId:[{type:xy}],groupName:[{type:xy}],pluginType:[{type:xy}],runColorScale:[{type:xy}],fullWidthChanged:[{type:Oy}],fullHeightChanged:[{type:Oy}],pinStateChanged:[{type:Oy}]});class _gt{constructor(t){this.store=t,this.isEverVisible=!1,this.showFullWidth=!1,this.showFullHeight=!1,this.runColorScale$=this.store.select(zN).pipe(Be(350,void 0,{leading:!0,trailing:!0}),It((t=>e=>t.hasOwnProperty(e)?t[e]:"#fff")))}onVisibilityChange({visible:t}){this.isEverVisible=this.isEverVisible||t}onFullWidthChanged(t){this.showFullWidth=t}onFullHeightChanged(t){this.showFullHeight=t}onPinStateChanged(){this.store.select(wT,this.cardId).pipe(be(1),Ve(this.store.select(ST))).subscribe((([t,e])=>{this.store.dispatch(oR({cardId:this.cardId,canCreateNewPins:e,wasPinned:t}))}))}}function Cgt(t,e){1&t&&Im(0)}function Mgt(t,e){if(1&t&&Tm(0,"card-view",4),2&t){const t=e.$implicit,n=Ym();Dm("cardId",t.cardId)("groupName",n.groupName)("pluginType",t.plugin)("cardObserver",n.cardObserver)("cardLazyLoader",t.cardId)}}function vgt(t,e){1&t&&Im(0)}function xgt(t,e){if(1&t){const t=Hm();Rm(0,"button",13),Vm("click",(function e(n){hi(t);const o=Ym(3);return o.handlePageChange(o.pageIndex-1,n.target)})),ku(1," Previous "),Am()}2&t&&Dm("disabled",0===Ym(3).pageIndex)}function Ogt(t,e){1&t&&Tm(0,"mat-icon",17)}function Pgt(t,e){1&t&&Tm(0,"mat-icon",18)}function wgt(t,e){if(1&t){const t=Hm();Rm(0,"button",14),Vm("click",(function e(){return hi(t),Ym(3).groupExpansionToggled.emit()})),Qp(1,Ogt,1,0,"mat-icon",15),Qp(2,Pgt,1,0,"ng-template",null,16,ib),Am()}if(2&t){const t=$p(3),e=Ym(3);rc(1),Dm("ngIf",e.isGroupExpanded)("ngIfElse",t)}}function kgt(t,e){if(1&t){const t=Hm();Rm(0,"span",21),Rm(1,"input",22),Vm("input",(function e(n){return hi(t),Ym(4).onPaginationInputChange(n)}))("change",(function e(n){return hi(t),Ym(4).onPaginationInputChange(n)})),Am(),ku(2),Am()}if(2&t){const t=Ym(4);rc(1),Dm("value",t.pageIndex+1)("max",t.numPages),rc(1),Du(" of ",t.numPages,"")}}function Sgt(t,e){if(1&t){const t=Hm();Rm(0,"span"),Qp(1,kgt,3,3,"span",19),Rm(2,"button",20),Vm("click",(function e(n){hi(t);const o=Ym(3);return o.handlePageChange(o.pageIndex+1,n.target)})),ku(3," Next "),Am(),Am()}if(2&t){const t=Ym(2).isBottomControl,e=Ym();rc(1),Dm("ngIf",e.showPaginationInput(t)),rc(1),Dm("disabled",e.pageIndex+1>=e.numPages)}}function Dgt(t,e){if(1&t&&(Rm(0,"div",6),Rm(1,"span",7),Qp(2,xgt,2,1,"button",8),Am(),Rm(3,"span",9),Qp(4,wgt,4,2,"button",10),Am(),Rm(5,"span",11),Qp(6,Sgt,4,2,"span",12),Am(),Am()),2&t){const t=Ym().isBottomControl,e=Ym();rc(2),Dm("ngIf",e.showPaginationControls),rc(2),Dm("ngIf",e.showExpand(t)),rc(2),Dm("ngIf",e.showPaginationControls)}}function Egt(t,e){if(1&t&&Qp(0,Dgt,7,3,"div",5),2&t){const t=e.isBottomControl,n=Ym();Dm("ngIf",n.showPaginationControls||n.showExpand(t))}}_gt.ɵfac=function t(e){return new(e||_gt)(Sm(Iw))},_gt.ɵcmp=to({type:_gt,selectors:[["card-view"]],hostVars:4,hostBindings:function t(e,n){2&e&&pu("full-width",n.showFullWidth)("full-height",n.showFullHeight)},inputs:{cardId:"cardId",groupName:"groupName",pluginType:"pluginType"},decls:2,vars:7,consts:[["observeIntersection","","intersectionObserverMargin","200px 200px 200px 200px",3,"isEverVisible","cardId","groupName","pluginType","runColorScale","fullWidthChanged","fullHeightChanged","pinStateChanged","onVisibilityChange"]],template:function t(e,n){1&e&&(Rm(0,"card-view-component",0),Vm("fullWidthChanged",(function t(e){return n.onFullWidthChanged(e)}))("fullHeightChanged",(function t(e){return n.onFullHeightChanged(e)}))("pinStateChanged",(function t(){return n.onPinStateChanged()}))("onVisibilityChange",(function t(e){return n.onVisibilityChange(e)})),Ah(1,"async"),Am()),2&e&&Dm("isEverVisible",n.isEverVisible)("cardId",n.cardId)("groupName",n.groupName)("pluginType",n.pluginType)("runColorScale",Th(1,5,n.runColorScale$))},directives:[ygt,Nft],pipes:[wM],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   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.mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   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print{.mat-form-field-appearance-legacy.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28122em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-form-field-autofill-control[_ngcontent-%COMP%]:-webkit-autofill + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28121em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.2812em) 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sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action[_ngcontent-%COMP%]{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node[_ngcontent-%COMP%], .mat-nested-tree-node[_ngcontent-%COMP%]{font-weight:400;font-size:14px}.mat-ripple[_ngcontent-%COMP%]{overflow:hidden;position:relative}.mat-ripple[_ngcontent-%COMP%]:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded[_ngcontent-%COMP%]{overflow:visible}.mat-ripple-element[_ngcontent-%COMP%]{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms cubic-bezier(0, 0, 0.2, 1);transform:scale(0)}.cdk-high-contrast-active[_ngcontent-%COMP%]   .mat-ripple-element[_ngcontent-%COMP%]{display:none}.cdk-visually-hidden[_ngcontent-%COMP%]{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container[_ngcontent-%COMP%], .cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{background-color:#fff}body.dark-mode   [_nghost-%COMP%]{background-color:#303030}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(_gt,[{type:My,args:[{selector:"card-view",template:'\n    <card-view-component\n      [isEverVisible]="isEverVisible"\n      [cardId]="cardId"\n      [groupName]="groupName"\n      [pluginType]="pluginType"\n      [runColorScale]="runColorScale$ | async"\n      (fullWidthChanged)="onFullWidthChanged($event)"\n      (fullHeightChanged)="onFullHeightChanged($event)"\n      (pinStateChanged)="onPinStateChanged()"\n      observeIntersection\n      intersectionObserverMargin="200px 200px 200px 200px"\n      (onVisibilityChange)="onVisibilityChange($event)"\n    >\n    </card-view-component>\n  ',styleUrls:["card_view_container.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{cardId:[{type:xy}],groupName:[{type:xy}],pluginType:[{type:xy}],showFullWidth:[{type:Py,args:["class.full-width"]}],showFullHeight:[{type:Py,args:["class.full-height"]}]});const Rgt=function(){return{isBottomControl:!1}},Agt=function(){return{isBottomControl:!0}};class Tgt{constructor(t){this.cdkScrollable=t,this.PluginType=hA,this.pageIndexChanged=new Lh,this.groupExpansionToggled=new Lh}showExpand(t){return!!t&&this.isGroupExpandable}showPaginationInput(t){return t}handlePageChange(t,e){const n=e.getBoundingClientRect().top;setTimeout((()=>{this.scrollToKeepTargetPosition(e,n)}),0),this.pageIndexChanged.emit(t)}scrollToKeepTargetPosition(t,e){var n;const o=null===(n=this.cdkScrollable)||void 0===n?void 0:n.getElementRef().nativeElement;o&&o.scrollTo(0,t.getBoundingClientRect().top-e+o.scrollTop)}trackByCards(t,e){return e.cardId}onPaginationInputChange(t){const e=t.target;if("input"===t.type&&""===e.value)return;const n=Number(e.value)-1,o=Math.min(Math.max(0,n),this.numPages-1);e.value!==String(o+1)&&(e.value=String(o+1)),this.handlePageChange(o,e)}}Tgt.ɵfac=function t(e){return new(e||Tgt)(Sm(mF,8))},Tgt.ɵcmp=to({type:Tgt,selectors:[["metrics-card-grid-component"]],inputs:{isGroupExpandable:"isGroupExpandable",isGroupExpanded:"isGroupExpanded",groupName:"groupName",pageIndex:"pageIndex",numPages:"numPages",cardIdsWithMetadata:"cardIdsWithMetadata",cardObserver:"cardObserver",showPaginationControls:"showPaginationControls"},outputs:{pageIndexChanged:"pageIndexChanged",groupExpansionToggled:"groupExpansionToggled"},decls:6,vars:8,consts:function(){let t,e,n;return t="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Previous page"):$localize`:A button that sets a group to the previous page.␟575e782fd27f2ee70a034a775efe9ad162472250␟3629960544875360046:Previous page`,e="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Expand group"):$localize`:A button that allows user to expand a tag group.␟ffaa11471b878a6dffe2e68c6f37064a9e074853␟5386054325274779258:Expand group`,n="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Next page"):$localize`:A button that sets a group to the next page.␟ce3cefb1cd0099aa5003dda16ec9eb21fd8ba789␟3337301694210287595:Next page`,[[4,"ngTemplateOutlet","ngTemplateOutletContext"],[1,"card-grid"],[3,"cardId","groupName","pluginType","cardObserver","cardLazyLoader",4,"ngFor","ngForOf","ngForTrackBy"],["groupControls",""],[3,"cardId","groupName","pluginType","cardObserver","cardLazyLoader"],["class","group-controls",4,"ngIf"],[1,"group-controls"],[1,"prev-container"],["class","prev 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paint;grid-column-start:1;grid-column-end:-1}card-view.full-height[_ngcontent-%COMP%]{min-height:480px}.group-controls[_ngcontent-%COMP%]{color:#616161;display:grid;align-items:center;grid-template-columns:1fr 1fr 1fr;gap:16px;padding:0 16px}body.dark-mode[_nghost-%COMP%]   .group-controls[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group-controls[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.group-controls[_ngcontent-%COMP%]:first-of-type{padding-top:16px}.group-controls[_ngcontent-%COMP%]:last-of-type{padding-bottom:16px}.prev-container[_ngcontent-%COMP%]{justify-self:flex-start}.expand-container[_ngcontent-%COMP%]{justify-self:center}.input-and-next-container[_ngcontent-%COMP%]{justify-self:flex-end}.pagination-input[_ngcontent-%COMP%]{margin-right:16px}.pagination-input[_ngcontent-%COMP%]   input[_ngcontent-%COMP%]{background:transparent;border:1px solid currentColor;color:inherit;font:inherit}.expand-group-button[_ngcontent-%COMP%], .pagination-button[_ngcontent-%COMP%]{color:#616161;background-color:#fff}body.dark-mode[_nghost-%COMP%]   .expand-group-button[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .expand-group-button[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}body.dark-mode[_nghost-%COMP%]   .pagination-button[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .pagination-button[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.expand-group-button[_ngcontent-%COMP%]:disabled, .pagination-button[_ngcontent-%COMP%]:disabled{color:#757575}body.dark-mode[_nghost-%COMP%]   .expand-group-button[_ngcontent-%COMP%]:disabled, body.dark-mode   [_nghost-%COMP%]   .expand-group-button[_ngcontent-%COMP%]:disabled{color:#616161}body.dark-mode[_nghost-%COMP%]   .pagination-button[_ngcontent-%COMP%]:disabled, body.dark-mode   [_nghost-%COMP%]   .pagination-button[_ngcontent-%COMP%]:disabled{color:#616161}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Tgt,[{type:My,args:[{selector:"metrics-card-grid-component",templateUrl:"./card_grid_component.ng.html",styleUrls:["./card_grid_component.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:mF,decorators:[{type:Sr}]}]}),{isGroupExpandable:[{type:xy}],isGroupExpanded:[{type:xy}],groupName:[{type:xy}],pageIndex:[{type:xy}],numPages:[{type:xy}],cardIdsWithMetadata:[{type:xy}],cardObserver:[{type:xy}],showPaginationControls:[{type:xy}],pageIndexChanged:[{type:Oy}],groupExpansionToggled:[{type:Oy}]});class Ngt{constructor(t){this.store=t,this.groupName=null,this.groupName$=new F(null),this.pageIndex$=new F(0),this.items$=new F([]),this.ngUnsubscribe=new I,this.numPages$=Wt([this.items$,this.store.select(RN)]).pipe(It((([t,e])=>Math.ceil(t.length/e)))),this.isGroupExpanded$=this.groupName$.pipe(ze((t=>null!==t?this.store.select(VT,t):Et(!0)))),this.showPaginationControls$=Wt([this.numPages$,this.store.select(RN),this.isGroupExpanded$]).pipe(It((([t,e,n])=>!(t<=1)&&(e<=3||n)))),this.isGroupExpandable$=Wt([this.items$,this.store.select(RN)]).pipe(It((([t,e])=>!(null===this.groupName||e<=3||t.length<=3)))),this.normalizedPageIndex$=Wt([this.pageIndex$,this.numPages$]).pipe(Ie(this.ngUnsubscribe),Fe((([t,e])=>{0!==e&&(t>=e?this.pageIndex$.next(e-1):t<0&&this.pageIndex$.next(0))})),It((([t,e])=>Math.min(Math.max(t,0),e-1))),Ae(1)),this.pagedItems$=Wt([this.items$,this.store.select(RN),this.normalizedPageIndex$,this.isGroupExpanded$]).pipe(It((([t,e,n,o])=>{const i=e*n,a=e*n+Math.min(o?e:3,e);return t.slice(i,a)})))}ngOnChanges(t){t.cardIdsWithMetadata&&this.items$.next(this.cardIdsWithMetadata),t.groupName&&this.groupName$.next(this.groupName)}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}onPageIndexChanged(t){this.pageIndex$.next(t)}onGroupExpansionToggled(){if(null===this.groupName)throw new RangeError("Invariant error: expansion cannot be toggled when groupName is null");this.store.dispatch(nR({tagGroup:this.groupName}))}}function zgt(t,e){if(1&t&&(Rm(0,"span",8),ku(1),Am()),2&t){const t=Ym();rc(1),Du("",t.cardIdsWithMetadata.length," cards")}}function Igt(t,e){1&t&&(Rm(0,"span",9),ku(1,"New card pinned"),Am()),2&t&&jp("data-id",e.$implicit)}function Hgt(t,e){if(1&t&&Tm(0,"metrics-card-grid",10),2&t){const t=Ym();Dm("cardIdsWithMetadata",t.cardIdsWithMetadata)("cardObserver",t.cardObserver)}}function Fgt(t,e){1&t&&(Rm(0,"div",11),ku(1,"Pin cards for a quick view and comparison"),Am())}Ngt.ɵfac=function t(e){return new(e||Ngt)(Sm(Iw))},Ngt.ɵcmp=to({type:Ngt,selectors:[["metrics-card-grid"]],inputs:{groupName:"groupName",cardIdsWithMetadata:"cardIdsWithMetadata",cardObserver:"cardObserver"},features:[Bo],decls:7,vars:20,consts:[[3,"isGroupExpandable","isGroupExpanded","groupName","pageIndex","numPages","showPaginationControls","cardIdsWithMetadata","cardObserver","pageIndexChanged","groupExpansionToggled"]],template:function t(e,n){1&e&&(Rm(0,"metrics-card-grid-component",0),Vm("pageIndexChanged",(function t(e){return n.onPageIndexChanged(e)}))("groupExpansionToggled",(function t(){return n.onGroupExpansionToggled()})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Am()),2&e&&Dm("isGroupExpandable",Th(1,8,n.isGroupExpandable$))("isGroupExpanded",Th(2,10,n.isGroupExpanded$))("groupName",n.groupName)("pageIndex",Th(3,12,n.normalizedPageIndex$))("numPages",Th(4,14,n.numPages$))("showPaginationControls",Th(5,16,n.showPaginationControls$))("cardIdsWithMetadata",Th(6,18,n.pagedItems$))("cardObserver",n.cardObserver)},directives:[Tgt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Ngt,[{type:My,args:[{selector:"metrics-card-grid",template:'\n    <metrics-card-grid-component\n      [isGroupExpandable]="isGroupExpandable$ | async"\n      [isGroupExpanded]="isGroupExpanded$ | async"\n      [groupName]="groupName"\n      [pageIndex]="normalizedPageIndex$ | async"\n      [numPages]="numPages$ | async"\n      [showPaginationControls]="showPaginationControls$ | async"\n      [cardIdsWithMetadata]="pagedItems$ | async"\n      [cardObserver]="cardObserver"\n      (pageIndexChanged)="onPageIndexChanged($event)"\n      (groupExpansionToggled)="onGroupExpansionToggled()"\n    >\n    </metrics-card-grid-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{groupName:[{type:xy}],cardIdsWithMetadata:[{type:xy}],cardObserver:[{type:xy}]});class Lgt{}Lgt.ɵfac=function t(e){return new(e||Lgt)},Lgt.ɵcmp=to({type:Lgt,selectors:[["metrics-pinned-view-component"]],inputs:{cardObserver:"cardObserver",cardIdsWithMetadata:"cardIdsWithMetadata",newCardPinnedIds:"newCardPinnedIds"},decls:10,vars:4,consts:[[1,"group-toolbar"],["svgIcon","keep_24px"],[1,"group-text"],["aria-role","heading","aria-level","3",1,"group-title"],["class","group-card-count",4,"ngIf"],["class","new-card-pinned",4,"ngFor","ngForOf"],[3,"cardIdsWithMetadata","cardObserver",4,"ngIf","ngIfElse"],["emptyPinnedView",""],[1,"group-card-count"],[1,"new-card-pinned"],[3,"cardIdsWithMetadata","cardObserver"],[1,"empty-message"]],template:function t(e,n){if(1&e&&(Rm(0,"div",0),Tm(1,"mat-icon",1),Rm(2,"span",2),Rm(3,"span",3),ku(4,"Pinned"),Am(),Qp(5,zgt,2,1,"span",4),Qp(6,Igt,2,1,"span",5),Am(),Am(),Qp(7,Hgt,1,2,"metrics-card-grid",6),Qp(8,Fgt,2,0,"ng-template",null,7,ib)),2&e){const t=$p(9);rc(5),Dm("ngIf",n.cardIdsWithMetadata.length>1),rc(1),Dm("ngForOf",n.newCardPinnedIds),rc(1),Dm("ngIf",n.cardIdsWithMetadata.length)("ngIfElse",t)}},directives:[DW,dM,lM,Ngt],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   .mat-chip-remove.mat-icon[_ngcontent-%COMP%]{font-size:18px}.mat-table[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-header-cell[_ngcontent-%COMP%]{font-size:12px;font-weight:500}.mat-cell[_ngcontent-%COMP%], .mat-footer-cell[_ngcontent-%COMP%]{font-size:14px}.mat-calendar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-calendar-body[_ngcontent-%COMP%]{font-size:13px}.mat-calendar-body-label[_ngcontent-%COMP%], .mat-calendar-period-button[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-calendar-table-header[_ngcontent-%COMP%]   th[_ngcontent-%COMP%]{font-size:11px;font-weight:400}.mat-dialog-title[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-expansion-panel-header[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:15px;font-weight:400}.mat-expansion-panel-content[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-form-field[_ngcontent-%COMP%]{font-size:inherit;font-weight:400;line-height:1.125;font-family:Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-form-field-wrapper[_ngcontent-%COMP%]{padding-bottom:1.34375em}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%]{font-size:150%;line-height:1.125}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]{height:1.5em;width:1.5em}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%]{height:1.125em;line-height:1.125}.mat-form-field-infix[_ngcontent-%COMP%]{padding:.5em 0;border-top:.84375em solid transparent}.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.34375em) scale(0.75);width:133.3333333333%}.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + 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sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%] 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.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{background-color:#fff;border-bottom:1px solid #ebebeb;align-items:center;background-color:#fff;display:flex;flex:none;height:42px;margin-bottom:-1px;padding:0 16px;position:sticky;top:0;z-index:1;box-shadow:0px 2px 4px 0px rgba(0,0,0,.15)}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{background-color:#303030}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{border-bottom:1px solid #555}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{box-shadow:0px 2px 4px 0px rgba(255,255,255,.15)}mat-icon[_ngcontent-%COMP%]{color:#616161;flex:none;margin-right:5px}body.dark-mode[_nghost-%COMP%]   mat-icon[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   mat-icon[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.group-text[_ngcontent-%COMP%]{display:flex;align-items:baseline;gap:6px}.group-title[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.group-card-count[_ngcontent-%COMP%]{font-size:12px;font-weight:400;color:#616161}body.dark-mode[_nghost-%COMP%]   .group-card-count[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group-card-count[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.empty-message[_ngcontent-%COMP%]{color:#616161;font-size:13px;font-style:italic;padding:16px;text-align:center}body.dark-mode[_nghost-%COMP%]   .empty-message[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .empty-message[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}.new-card-pinned[_ngcontent-%COMP%]{animation:pinned-view-fade-out 3s linear;background:#f44336;border-radius:5px;color:#fff;display:inline-block;font-size:13px;opacity:0;padding:3px 5px}@keyframes pinned-view-fade-out{from{opacity:1}66%{opacity:.99}to{opacity:0}}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Lgt,[{type:My,args:[{selector:"metrics-pinned-view-component",template:'\n    <div class="group-toolbar">\n      <mat-icon svgIcon="keep_24px"></mat-icon>\n      <span class="group-text">\n        <span class="group-title" aria-role="heading" aria-level="3"\n          >Pinned</span\n        >\n        <span *ngIf="cardIdsWithMetadata.length > 1" class="group-card-count"\n          >{{ cardIdsWithMetadata.length }} cards</span\n        >\n        <span\n          *ngFor="let id of newCardPinnedIds"\n          [attr.data-id]="id"\n          class="new-card-pinned"\n          >New card pinned</span\n        >\n      </span>\n    </div>\n    <metrics-card-grid\n      *ngIf="cardIdsWithMetadata.length; else emptyPinnedView"\n      [cardIdsWithMetadata]="cardIdsWithMetadata"\n      [cardObserver]="cardObserver"\n    ></metrics-card-grid>\n    <ng-template #emptyPinnedView>\n      <div class="empty-message">Pin cards for a quick view and comparison</div>\n    </ng-template>\n  ',styleUrls:["pinned_view_component.css"],changeDetection:zn.OnPush}]}],null,{cardObserver:[{type:xy}],cardIdsWithMetadata:[{type:xy}],newCardPinnedIds:[{type:xy}]});class Bgt{constructor(t){this.store=t,this.cardIdsWithMetadata$=this.store.select(PT).pipe(Ne([])),this.newCardPinnedIds$=this.store.select(PT).pipe(Te(1),It((t=>t.map((t=>t.cardId)))),De(),It((([t,e])=>{const n=new Set(t),o=new Set(e);for(const t of o)if(!n.has(t))return Date.now();return null})),Ne(null),De(),It((([t,e])=>null===t&&null===e?null:null===e?[t]:[e])),ce((t=>null!==t)),It((t=>[t[0]])))}}Bgt.ɵfac=function t(e){return new(e||Bgt)(Sm(Iw))},Bgt.ɵcmp=to({type:Bgt,selectors:[["metrics-pinned-view"]],inputs:{cardObserver:"cardObserver"},decls:3,vars:7,consts:[[3,"cardIdsWithMetadata","newCardPinnedIds","cardObserver"]],template:function t(e,n){1&e&&(Tm(0,"metrics-pinned-view-component",0),Ah(1,"async"),Ah(2,"async")),2&e&&Dm("cardIdsWithMetadata",Th(1,3,n.cardIdsWithMetadata$))("newCardPinnedIds",Th(2,5,n.newCardPinnedIds$))("cardObserver",n.cardObserver)},directives:[Lgt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Bgt,[{type:My,args:[{selector:"metrics-pinned-view",template:'\n    <metrics-pinned-view-component\n      [cardIdsWithMetadata]="cardIdsWithMetadata$ | async"\n      [newCardPinnedIds]="newCardPinnedIds$ | async"\n      [cardObserver]="cardObserver"\n    ></metrics-pinned-view-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{cardObserver:[{type:xy}]});const Vgt=Zw(MT,NN,((t,e)=>t.filter((t=>!xA(t.plugin)||Boolean(e&&e.get(t.runId)))))),jgt=Zw(Vgt,(t=>t.sort(((t,e)=>g2(t.tag,e.tag)))));function Ugt(t,e){if(1&t&&(Rm(0,"span",6),ku(1),Ah(2,"number"),Am()),2&t){const t=Ym().$implicit;rc(1),Du("",Th(2,1,t.items.length)," cards")}}function Ggt(t,e){if(1&t&&(Rm(0,"div",1),Rm(1,"div",2),Rm(2,"span"),Rm(3,"span",3),ku(4),Am(),Qp(5,Ugt,3,3,"span",4),Am(),Am(),Tm(6,"metrics-card-grid",5),Am()),2&t){const t=e.$implicit,n=Ym();rc(3),Km("title",t.groupName),rc(1),Su(t.groupName),rc(1),Dm("ngIf",t.items.length>1),rc(1),Dm("cardIdsWithMetadata",t.items)("cardObserver",n.cardObserver)("groupName",t.groupName)}}class Wgt{constructor(){this.PluginType=hA}trackByGroup(t,e){return e.groupName}}Wgt.ɵfac=function t(e){return new(e||Wgt)},Wgt.ɵcmp=to({type:Wgt,selectors:[["metrics-card-groups-component"]],inputs:{cardGroups:"cardGroups",cardObserver:"cardObserver"},decls:1,vars:2,consts:[["class","card-group",4,"ngFor","ngForOf","ngForTrackBy"],[1,"card-group"],[1,"group-toolbar"],["aria-role","heading","aria-level","3",1,"group-title",3,"title"],["class","group-card-count",4,"ngIf"],[3,"cardIdsWithMetadata","cardObserver","groupName"],[1,"group-card-count"]],template:function t(e,n){1&e&&Qp(0,Ggt,7,6,"div",0),2&e&&Dm("ngForOf",n.cardGroups)("ngForTrackBy",n.trackByGroup)},directives:[lM,dM,Ngt],pipes:[FM],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   .mat-chip-remove.mat-icon[_ngcontent-%COMP%]{font-size:18px}.mat-table[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", 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!important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{background-color:#fff;border-bottom:1px solid #ebebeb;align-items:center;background-color:#fff;display:flex;flex:none;height:42px;margin-bottom:-1px;padding:0 16px;position:sticky;top:0;z-index:1;box-shadow:0px 2px 4px 0px rgba(0,0,0,.15)}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{background-color:#303030}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{border-bottom:1px solid #555}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{box-shadow:0px 2px 4px 0px rgba(255,255,255,.15)}[_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{border-top:1px solid #ebebeb;top:-1px}body.dark-mode   [_nghost-%COMP%]   .group-toolbar[_ngcontent-%COMP%]{border-top:1px solid #555}.card-group[_ngcontent-%COMP%]:first-of-type   .group-toolbar[_ngcontent-%COMP%]{border-top:none}.group-title[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.group-card-count[_ngcontent-%COMP%]{font-size:12px;font-weight:400;color:#616161;margin-left:6px}body.dark-mode[_nghost-%COMP%]   .group-card-count[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .group-card-count[_ngcontent-%COMP%]{color:rgba(255,255,255,.7)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Wgt,[{type:My,args:[{selector:"metrics-card-groups-component",template:'\n    <div\n      *ngFor="let group of cardGroups; trackBy: trackByGroup"\n      class="card-group"\n    >\n      <div class="group-toolbar">\n        <span>\n          <span\n            class="group-title"\n            aria-role="heading"\n            aria-level="3"\n            title="{{ group.groupName }}"\n            >{{ group.groupName }}</span\n          >\n          <span *ngIf="group.items.length > 1" class="group-card-count"\n            >{{ group.items.length | number }} cards</span\n          >\n        </span>\n      </div>\n      <metrics-card-grid\n        [cardIdsWithMetadata]="group.items"\n        [cardObserver]="cardObserver"\n        [groupName]="group.groupName"\n      ></metrics-card-grid>\n    </div>\n  ',styleUrls:["card_groups_component.css"],changeDetection:zn.OnPush}]}],null,{cardGroups:[{type:xy}],cardObserver:[{type:xy}]});class Ygt{constructor(t){this.store=t,this.cardGroups$=this.store.select(jgt).pipe(fe(this.store.select(qT)),It((([t,e])=>e.size?t.filter((t=>e.has(t.plugin))):t)),It((t=>(function e(t){const e=new Map,n=t.slice().sort(((t,e)=>g2(t.tag,e.tag)));for(const t of n){const n=t.tag.split("/",1)[0];e.has(n)||e.set(n,{groupName:n,items:[]}),e.get(n).items.push(t)}return[...e.values()]})(t))))}}function qgt(t,e){if(1&t&&(Rm(0,"span"),ku(1),Am()),2&t){const t=Ym();rc(1),Du(" and ",t.getPluginTypeFilterString(t.pluginTypes)," visualization filter")}}Ygt.ɵfac=function t(e){return new(e||Ygt)(Sm(Iw))},Ygt.ɵcmp=to({type:Ygt,selectors:[["metrics-card-groups"]],inputs:{cardObserver:"cardObserver"},decls:2,vars:4,consts:[[3,"cardGroups","cardObserver"]],template:function t(e,n){1&e&&(Tm(0,"metrics-card-groups-component",0),Ah(1,"async")),2&e&&Dm("cardGroups",Th(1,2,n.cardGroups$))("cardObserver",n.cardObserver)},directives:[Wgt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Ygt,[{type:My,args:[{selector:"metrics-card-groups",template:'\n    <metrics-card-groups-component\n      [cardGroups]="cardGroups$ | async"\n      [cardObserver]="cardObserver"\n    ></metrics-card-groups-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{cardObserver:[{type:xy}]});class Zgt{constructor(){this.PluginType=hA,this.listFormatter=new Intl.ListFormat(void 0,{style:"long",type:"disjunction"})}getPluginTypeFilterString(t){const e=[...t].map((t=>{switch(t){case hA.SCALARS:return"scalar";case hA.IMAGES:return"image";case hA.HISTOGRAMS:return"histogram";default:throw new RangeError(`Please implement human readable name for plugin type: ${t}`)}}));return this.listFormatter.format(e)}}Zgt.ɵfac=function t(e){return new(e||Zgt)},Zgt.ɵcmp=to({type:Zgt,selectors:[["metrics-empty-tag-match-component"]],inputs:{pluginTypes:"pluginTypes",tagFilterRegex:"tagFilterRegex",tagCounts:"tagCounts"},decls:6,vars:5,consts:[[4,"ngIf"]],template:function t(e,n){1&e&&(ku(0,"No matches for tag filter "),Rm(1,"code"),ku(2),Am(),Qp(3,qgt,2,1,"span",0),ku(4),Ah(5,"number")),2&e&&(rc(2),Du("/",n.tagFilterRegex,"/"),rc(1),Dm("ngIf",n.pluginTypes.size),rc(1),Du(" out of ",Th(5,3,n.tagCounts)," tags."))},directives:[dM],pipes:[FM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Zgt,[{type:My,args:[{selector:"metrics-empty-tag-match-component",template:'No matches for tag filter <code>/{{ tagFilterRegex }}/</code\n    ><span *ngIf="pluginTypes.size">\n      and {{ getPluginTypeFilterString(pluginTypes) }} visualization\n      filter</span\n    >\n    out of {{ tagCounts | number }} tags.',changeDetection:zn.OnPush}]}],null,{pluginTypes:[{type:xy}],tagFilterRegex:[{type:xy}],tagCounts:[{type:xy}]});class Xgt{constructor(t){this.store=t,this.pluginTypes$=this.store.select(qT),this.tagFilterRegex$=this.store.select(BT),this.tagCounts$=this.store.select(jgt).pipe(It((t=>new Set(t.map((({tag:t})=>t))).size)))}}function Kgt(t,e){if(1&t&&(Rm(0,"span",6),ku(1),Ah(2,"number"),Am()),2&t){const t=Ym();rc(1),Du("",Th(2,1,t.cardIdsWithMetadata.length)," cards")}}function Jgt(t,e){1&t&&Tm(0,"metrics-empty-tag-match",7)}Xgt.ɵfac=function t(e){return new(e||Xgt)(Sm(Iw))},Xgt.ɵcmp=to({type:Xgt,selectors:[["metrics-empty-tag-match"]],decls:4,vars:9,consts:[[3,"pluginTypes","tagFilterRegex","tagCounts"]],template:function t(e,n){1&e&&(Tm(0,"metrics-empty-tag-match-component",0),Ah(1,"async"),Ah(2,"async"),Ah(3,"async")),2&e&&Dm("pluginTypes",Th(1,3,n.pluginTypes$))("tagFilterRegex",Th(2,5,n.tagFilterRegex$))("tagCounts",Th(3,7,n.tagCounts$))},directives:[Zgt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Xgt,[{type:My,args:[{selector:"metrics-empty-tag-match",template:'\n    <metrics-empty-tag-match-component\n      [pluginTypes]="pluginTypes$ | async"\n      [tagFilterRegex]="tagFilterRegex$ | async"\n      [tagCounts]="tagCounts$ | async"\n    ></metrics-empty-tag-match-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);class Qgt{}Qgt.ɵfac=function t(e){return new(e||Qgt)},Qgt.ɵcmp=to({type:Qgt,selectors:[["metrics-filtered-view-component"]],inputs:{isEmptyMatch:"isEmptyMatch",cardObserver:"cardObserver",cardIdsWithMetadata:"cardIdsWithMetadata"},decls:7,vars:4,consts:[[1,"group-toolbar"],[1,"group-text"],["aria-role","heading","aria-level","3",1,"group-title"],["class","group-card-count",4,"ngIf"],["class","warn",4,"ngIf"],[3,"cardIdsWithMetadata","cardObserver"],[1,"group-card-count"],[1,"warn"]],template:function t(e,n){1&e&&(Rm(0,"div",0),Rm(1,"span",1),Rm(2,"span",2),ku(3,"Tags matching filter"),Am(),Qp(4,Kgt,3,3,"span",3),Am(),Am(),Qp(5,Jgt,1,0,"metrics-empty-tag-match",4),Tm(6,"metrics-card-grid",5)),2&e&&(rc(4),Dm("ngIf",n.cardIdsWithMetadata.length>1),rc(1),Dm("ngIf",n.isEmptyMatch),rc(1),Dm("cardIdsWithMetadata",n.cardIdsWithMetadata)("cardObserver",n.cardObserver))},directives:[dM,Ngt,Xgt],pipes:[FM],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   .mat-chip-remove.mat-icon[_ngcontent-%COMP%]{font-size:18px}.mat-table[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-header-cell[_ngcontent-%COMP%]{font-size:12px;font-weight:500}.mat-cell[_ngcontent-%COMP%], .mat-footer-cell[_ngcontent-%COMP%]{font-size:14px}.mat-calendar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-calendar-body[_ngcontent-%COMP%]{font-size:13px}.mat-calendar-body-label[_ngcontent-%COMP%], .mat-calendar-period-button[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-calendar-table-header[_ngcontent-%COMP%]   th[_ngcontent-%COMP%]{font-size:11px;font-weight:400}.mat-dialog-title[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-expansion-panel-header[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:15px;font-weight:400}.mat-expansion-panel-content[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-form-field[_ngcontent-%COMP%]{font-size:inherit;font-weight:400;line-height:1.125;font-family:Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-form-field-wrapper[_ngcontent-%COMP%]{padding-bottom:1.34375em}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%]{font-size:150%;line-height:1.125}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]{height:1.5em;width:1.5em}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   .mat-icon-button[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%]{height:1.125em;line-height:1.125}.mat-form-field-infix[_ngcontent-%COMP%]{padding:.5em 0;border-top:.84375em solid transparent}.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.34375em) scale(0.75);width:133.3333333333%}.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.34374em) scale(0.75);width:133.3333433333%}.mat-form-field-label-wrapper[_ngcontent-%COMP%]{top:-0.84375em;padding-top:.84375em}.mat-form-field-label[_ngcontent-%COMP%]{top:1.34375em}.mat-form-field-underline[_ngcontent-%COMP%]{bottom:1.34375em}.mat-form-field-subscript-wrapper[_ngcontent-%COMP%]{font-size:75%;margin-top:.6666666667em;top:calc(100% - 1.7916666667em)}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   .mat-form-field-wrapper[_ngcontent-%COMP%]{padding-bottom:1.25em}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:.4375em 0}.mat-form-field-appearance-legacy.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28125em) scale(0.75) perspective(100px) translateZ(0.001px);-ms-transform:translateY(-1.28125em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-form-field-autofill-control[_ngcontent-%COMP%]:-webkit-autofill + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28125em) scale(0.75) perspective(100px) translateZ(0.00101px);-ms-transform:translateY(-1.28124em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28125em) scale(0.75) perspective(100px) translateZ(0.00102px);-ms-transform:translateY(-1.28123em) scale(0.75);width:133.3333533333%}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{top:1.28125em}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   .mat-form-field-underline[_ngcontent-%COMP%]{bottom:1.25em}.mat-form-field-appearance-legacy[_ngcontent-%COMP%]   .mat-form-field-subscript-wrapper[_ngcontent-%COMP%]{margin-top:.5416666667em;top:calc(100% - 1.6666666667em)}@media print{.mat-form-field-appearance-legacy.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28122em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-form-field-autofill-control[_ngcontent-%COMP%]:-webkit-autofill + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.28121em) scale(0.75)}.mat-form-field-appearance-legacy.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.2812em) scale(0.75)}}.mat-form-field-appearance-fill[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:.25em 0 .75em 0}.mat-form-field-appearance-fill[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{top:1.09375em;margin-top:-0.5em}.mat-form-field-appearance-fill.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-fill.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-0.59374em) scale(0.75);width:133.3333433333%}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-infix[_ngcontent-%COMP%]{padding:1em 0 1em 0}.mat-form-field-appearance-outline[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{top:1.84375em;margin-top:-0.25em}.mat-form-field-appearance-outline.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59374em) scale(0.75);width:133.3333433333%}.mat-grid-tile-header[_ngcontent-%COMP%], .mat-grid-tile-footer[_ngcontent-%COMP%]{font-size:14px}.mat-grid-tile-header[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%], .mat-grid-tile-footer[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-grid-tile-header[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2), .mat-grid-tile-footer[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}input.mat-input-element[_ngcontent-%COMP%]{margin-top:-0.0625em}.mat-menu-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:400}.mat-paginator[_ngcontent-%COMP%], .mat-paginator-page-size[_ngcontent-%COMP%]   .mat-select-trigger[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px}.mat-radio-button[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select-trigger[_ngcontent-%COMP%]{height:1.125em}.mat-slide-toggle-content[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-slider-thumb-label-text[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-stepper-vertical[_ngcontent-%COMP%], .mat-stepper-horizontal[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-step-label[_ngcontent-%COMP%]{font-size:14px;font-weight:400}.mat-step-sub-label-error[_ngcontent-%COMP%]{font-weight:normal}.mat-step-label-error[_ngcontent-%COMP%]{font-size:14px}.mat-step-label-selected[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-tab-group[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tab-label[_ngcontent-%COMP%], .mat-tab-link[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-toolbar[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   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.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}mat-select[_ngcontent-%COMP%]{border:1px solid #8e98a3;border-radius:3px;box-sizing:border-box;padding:6px}mat-select[_ngcontent-%COMP%]:focus{outline-color:-webkit-focus-ring-color;outline-style:auto}  .mat-select-panel{max-width:70vw}  mat-option.mat-option{height:auto}  .mat-option-text{white-space:normal;word-break:break-all}']}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(eht,[{type:My,args:[{selector:"tb-dropdown",template:'\n    <mat-select\n      [value]="value"\n      (selectionChange)="selectionChange.emit($event.value)"\n    >\n      <mat-option\n        *ngFor="let option of options"\n        [value]="option.value"\n        [disabled]="option.disabled"\n      >\n        {{ option.displayText }}\n      </mat-option>\n    </mat-select>\n  ',styleUrls:["dropdown_component.css"]}]}],null,{value:[{type:xy}],options:[{type:xy}],selectionChange:[{type:Oy}]});class rht{constructor(t){this.locale=t,this.selectTimeEnableToggled=new Lh,this.useRangeSelectTimeToggled=new Lh,this.selectTimeChanged=new Lh,this.TooltipSortDropdownOptions=[{value:bA.DEFAULT,displayText:"Default"},{value:bA.ASCENDING,displayText:"Ascending"},{value:bA.DESCENDING,displayText:"Descending"},{value:bA.NEAREST,displayText:"Nearest"}],this.tooltipSortChanged=new Lh,this.ignoreOutliersChanged=new Lh,this.XAxisType=yA,this.XAxisTypeDropdownOptions=[{value:yA.STEP,displayText:"Step"},{value:yA.RELATIVE,displayText:"Relative"},{value:yA.WALL_TIME,displayText:"Wall"}],this.xAxisTypeChanged=new Lh,this.HistogramModeDropdownOptions=[{value:pE.OFFSET,displayText:"Offset"},{value:pE.OVERLAY,displayText:"Overlay"}],this.histogramModeChanged=new Lh,this.MAX_SMOOTHING_VALUE=.999,this.MAX_SMOOTHING_SLIDER_VALUE=.99,this.scalarSmoothingControlChanged$=new Lh,this.scalarSmoothingChanged=this.scalarSmoothingControlChanged$.pipe(de(250)),this.scalarPartitionXToggled=new Lh,this.imageBrightnessSliderChanged$=new Lh,this.imageBrightnessInMilliChanged=this.imageBrightnessSliderChanged$.pipe(de(250)),this.imageBrightnessReset=new Lh,this.imageContrastSliderChanged$=new Lh,this.imageContrastInMilliChanged=this.imageContrastSliderChanged$.pipe(de(250)),this.imageContrastReset=new Lh,this.imageShowActualSizeChanged=new Lh}onScalarSmoothingInput(t){const e=t.target;if(!e.value)return;const n=Math.min(Math.max(0,parseFloat(e.value)),.999);n!==parseFloat(e.value)&&(e.value=String(n)),this.scalarSmoothingControlChanged$.emit(n)}formatMilliToZeroth(t){return QC(t/1e3,this.locale||"en-US","1.0-2")}onStepStartChanged(t){this.selectTimeChanged.emit({start:{step:t},end:null})}onStepRangeChanged({lowerValue:t,upperValue:e}){this.selectTimeChanged.emit({start:{step:t},end:{step:e}})}}rht.ɵfac=function t(e){return new(e||rht)(Sm(Wy))},rht.ɵcmp=to({type:rht,selectors:[["metrics-dashboard-settings-component"]],inputs:{isLinkedTimeFeatureEnabled:"isLinkedTimeFeatureEnabled",selectTimeEnabled:"selectTimeEnabled",useRangeSelectTime:"useRangeSelectTime",selectedTime:"selectedTime",stepMinMax:"stepMinMax",isImageSupportEnabled:"isImageSupportEnabled",tooltipSort:"tooltipSort",ignoreOutliers:"ignoreOutliers",xAxisType:"xAxisType",histogramMode:"histogramMode",scalarSmoothing:"scalarSmoothing",scalarPartitionX:"scalarPartitionX",imageBrightnessInMilli:"imageBrightnessInMilli",imageContrastInMilli:"imageContrastInMilli",imageShowActualSize:"imageShowActualSize"},outputs:{selectTimeEnableToggled:"selectTimeEnableToggled",useRangeSelectTimeToggled:"useRangeSelectTimeToggled",selectTimeChanged:"selectTimeChanged",tooltipSortChanged:"tooltipSortChanged",ignoreOutliersChanged:"ignoreOutliersChanged",xAxisTypeChanged:"xAxisTypeChanged",histogramModeChanged:"histogramModeChanged",scalarSmoothingChanged:"scalarSmoothingChanged",scalarPartitionXToggled:"scalarPartitionXToggled",imageBrightnessInMilliChanged:"imageBrightnessInMilliChanged",imageBrightnessReset:"imageBrightnessReset",imageContrastInMilliChanged:"imageContrastInMilliChanged",imageContrastReset:"imageContrastReset",imageShowActualSizeChanged:"imageShowActualSizeChanged"},decls:36,vars:17,consts:function(){let t,e;return t="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Reset brightness"):$localize`:A button to reset the image brightness setting␟c482b3a47ea0975fa8be01afb3fbec9b76628bd7␟1189161857240378395:Reset brightness`,e="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Reset contrast"):$localize`:A button to reset the image contrast setting␟ed712a8b927041be15252b29eb521ebb1374bad8␟5370703342923611955:Reset contrast`,[[1,"general"],[1,"section-title"],[1,"control-row","x-axis-type"],["id","x-axis-type-label",1,"control-name"],[3,"value","options","selectionChange"],["class","control-row linked-time",4,"ngIf"],[1,"scalars"],[1,"control-row","scalars-smoothing"],["id","scalars-smoothing-label",1,"control-name"],[1,"slider-row"],["aria-labelledby","scalars-smoothing-label","color","primary",3,"max","min","step","value","thumbLabel","input"],["aria-labelledby","scalars-smoothing-label","type","number","min","0","step","0.001",1,"slider-input",3,"max","value","input"],[1,"control-row","tooltip-sort"],[1,"control-name"],[1,"control-row","scalars-ignore-outliers"],[3,"checked","change"],[1,"control-row","scalars-partition-x"],["svgIcon","help_outline_24px","title","Non-monotonic steps can occur when reusing a logdir with multiple summary writers and overlapping steps. Line charts, without this option enabled, can appear zig zagged. This is common when restarting from a checkpoint.\n\nWhen enabled, a non-monotonic time series composed of N monotonic pieces will be shown as N monotonic lines.",1,"info"],[1,"Histograms"],[1,"control-row","histogram-mode"],["class","image",4,"ngIf"],[1,"control-row","linked-time"],[1,"controls"],[1,"step-selector"],["color","primary",3,"disabled","min","max","step","value","thumbLabel","input",4,"ngIf","ngIfElse"],["range",""],["color","primary",3,"disabled","min","max","step","value","thumbLabel","input"],[3,"min","max","lowerValue","upperValue","value"],[1,"image"],[1,"control-row","image-brightness"],["id","image-brightness-label",1,"control-name"],["aria-labelledby","image-brightness-label","color","primary",3,"max","min","step","value","thumbLabel","displayWith","input"],["mat-icon-button","","aria-label",t,"title","Reset brightness",1,"reset-button",3,"click"],["svgIcon","settings_backup_restore_24px"],[1,"control-row","image-contrast"],["id","image-constrast-label",1,"control-name"],["aria-labelledby","image-constrast-label","color","primary",3,"max","min","step","value","thumbLabel","displayWith","input"],["mat-icon-button","","aria-label",e,"title","Reset contrast",1,"reset-button",3,"click"],[1,"control-row","image-show-actual-size"]]},template:function t(e,n){1&e&&(Rm(0,"section",0),Rm(1,"h3",1),ku(2,"General"),Am(),Rm(3,"div",2),Rm(4,"div",3),ku(5,"Horizontal Axis"),Am(),Rm(6,"tb-dropdown",4),Vm("selectionChange",(function t(e){return n.xAxisTypeChanged.emit(e)})),Am(),Am(),Qp(7,iht,14,4,"div",5),Am(),Rm(8,"section",6),Rm(9,"h3",1),ku(10,"Scalars"),Am(),Rm(11,"div",7),Rm(12,"div",8),ku(13,"Smoothing"),Am(),Rm(14,"div",9),Rm(15,"mat-slider",10),Vm("input",(function t(e){return n.scalarSmoothingControlChanged$.emit(e.value)})),Am(),Rm(16,"input",11),Vm("input",(function t(e){return n.onScalarSmoothingInput(e)})),Am(),Am(),Am(),Rm(17,"div",12),Rm(18,"div",13),ku(19,"Tooltip sorting method"),Am(),Rm(20,"tb-dropdown",4),Vm("selectionChange",(function t(e){return n.tooltipSortChanged.emit(e)})),Am(),Am(),Rm(21,"div",14),Rm(22,"mat-checkbox",15),Vm("change",(function t(e){return n.ignoreOutliersChanged.emit(e.checked)})),ku(23,"Ignore outliers in chart scaling"),Am(),Am(),Rm(24,"div",16),Rm(25,"mat-checkbox",15),Vm("change",(function t(){return n.scalarPartitionXToggled.emit()})),ku(26,"Partition non-monotonic X axis"),Am(),Tm(27,"mat-icon",17),Am(),Am(),Rm(28,"section",18),Rm(29,"h3",1),ku(30,"Histograms"),Am(),Rm(31,"div",19),Rm(32,"div",13),ku(33,"Mode"),Am(),Rm(34,"tb-dropdown",4),Vm("selectionChange",(function t(e){return 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.section-title[_ngcontent-%COMP%]{color:#fff}section[_ngcontent-%COMP%]   .control-row[_ngcontent-%COMP%]:not(:last-child){margin-bottom:12px}.control-name[_ngcontent-%COMP%]{margin-bottom:8px}.slider-row[_ngcontent-%COMP%]{display:flex;align-items:center;height:28px}.slider-row[_ngcontent-%COMP%]   .reset-button[_ngcontent-%COMP%]{margin-left:6px}.slider-row[_ngcontent-%COMP%]   .slider-input[_ngcontent-%COMP%]{background-color:inherit;border:1px solid #8e98a3;border-radius:2px;box-sizing:border-box;color:inherit;height:100%;margin-left:12px;padding:0 4px}body.dark-mode[_nghost-%COMP%]   .slider-row[_ngcontent-%COMP%]   .slider-input[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .slider-row[_ngcontent-%COMP%]   .slider-input[_ngcontent-%COMP%]{border-color:#425066}.scalars-smoothing[_ngcontent-%COMP%]   .slider-input[_ngcontent-%COMP%]{flex:none;width:5em}.scalars-partition-x[_ngcontent-%COMP%]{align-items:center;display:flex}.scalars-partition-x[_ngcontent-%COMP%]   .info[_ngcontent-%COMP%]{height:15px;margin-left:5px;width:15px}mat-slider[_ngcontent-%COMP%]{flex:1;margin-left:-8px;margin-right:-8px}tb-dropdown[_ngcontent-%COMP%]{display:block}.linked-time[_ngcontent-%COMP%]   .step-selector[_ngcontent-%COMP%]{padding:0 10px}.linked-time[_ngcontent-%COMP%]   mat-slider[_ngcontent-%COMP%], .linked-time[_ngcontent-%COMP%]   tb-range-input[_ngcontent-%COMP%]{width:100%}.linked-time[_ngcontent-%COMP%]   .controls[_ngcontent-%COMP%]{padding:5px}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(rht,[{type:My,args:[{selector:"metrics-dashboard-settings-component",templateUrl:"settings_view_component.ng.html",changeDetection:zn.OnPush,styleUrls:["settings_view_component.css"]}]}],(function(){return[{type:void 0,decorators:[{type:kr,args:[Wy]}]}]}),{isLinkedTimeFeatureEnabled:[{type:xy}],selectTimeEnabled:[{type:xy}],useRangeSelectTime:[{type:xy}],selectedTime:[{type:xy}],stepMinMax:[{type:xy}],selectTimeEnableToggled:[{type:Oy}],useRangeSelectTimeToggled:[{type:Oy}],selectTimeChanged:[{type:Oy}],isImageSupportEnabled:[{type:xy}],tooltipSort:[{type:xy}],tooltipSortChanged:[{type:Oy}],ignoreOutliers:[{type:xy}],ignoreOutliersChanged:[{type:Oy}],xAxisType:[{type:xy}],xAxisTypeChanged:[{type:Oy}],histogramMode:[{type:xy}],histogramModeChanged:[{type:Oy}],scalarSmoothing:[{type:xy}],scalarSmoothingChanged:[{type:Oy}],scalarPartitionX:[{type:xy}],scalarPartitionXToggled:[{type:Oy}],imageBrightnessInMilli:[{type:xy}],imageBrightnessInMilliChanged:[{type:Oy}],imageBrightnessReset:[{type:Oy}],imageContrastInMilli:[{type:xy}],imageContrastInMilliChanged:[{type:Oy}],imageContrastReset:[{type:Oy}],imageShowActualSize:[{type:xy}],imageShowActualSizeChanged:[{type:Oy}]});class sht{constructor(t){this.store=t,this.isLinkedTimeFeatureEnabled$=this.store.select(iE),this.selectTimeEnabled$=this.store.select(jT),this.useRangeSelectTime$=this.store.select(UT),this.selectedTime$=this.store.select(WT),this.stepMinMax$=this.store.select(GT),this.isImageSupportEnabled$=this.store.select(qD).pipe(ce(Boolean),be(1),Ve(this.store.select(oE)),It((([,t])=>t))),this.tooltipSort$=this.store.select(RT),this.ignoreOutliers$=this.store.select(AT),this.xAxisType$=this.store.select(TT),this.histogramMode$=this.store.select(NT),this.scalarSmoothing$=this.store.select(zT),this.scalarPartitionX$=this.store.select(IT),this.imageBrightnessInMilli$=this.store.select(HT),this.imageContrastInMilli$=this.store.select(FT),this.imageShowActualSize$=this.store.select(LT)}onTooltipSortChanged(t){this.store.dispatch(LE({sort:t}))}onIgnoreOutliersChanged(){this.store.dispatch(BE())}onXAxisTypeChanged(t){this.store.dispatch(VE({xAxisType:t}))}onHistogramModeChanged(t){this.store.dispatch(XE({histogramMode:t}))}onScalarSmoothingChanged(t){this.store.dispatch(jE({smoothing:t}))}onScalarPartitionXToggled(){this.store.dispatch(UE())}onImageBrightnessInMilliChanged(t){this.store.dispatch(GE({brightnessInMilli:t}))}onImageBrightnessReset(){this.store.dispatch(YE())}onImageContrastReset(){this.store.dispatch(qE())}onImageContrastInMilliChanged(t){this.store.dispatch(WE({contrastInMilli:t}))}onImageShowActualSizeChanged(){this.store.dispatch(ZE())}onSelectTimeEnableToggled(){this.store.dispatch(lR())}onUseRangeSelectTimeToggled(){this.store.dispatch(cR())}onSelectTimeChanged(t){var e;this.store.dispatch(rR({startStep:t.start.step,endStep:null===(e=t.end)||void 0===e?void 0:e.step}))}}sht.ɵfac=function t(e){return new(e||sht)(Sm(Iw))},sht.ɵcmp=to({type:sht,selectors:[["metrics-dashboard-settings"]],decls:16,vars:45,consts:[[3,"isImageSupportEnabled","tooltipSort","ignoreOutliers","xAxisType","histogramMode","scalarSmoothing","scalarPartitionX","imageBrightnessInMilli","imageContrastInMilli","imageShowActualSize","isLinkedTimeFeatureEnabled","selectTimeEnabled","selectedTime","useRangeSelectTime","stepMinMax","tooltipSortChanged","ignoreOutliersChanged","xAxisTypeChanged","histogramModeChanged","scalarSmoothingChanged","scalarPartitionXToggled","imageBrightnessInMilliChanged","imageBrightnessReset","imageContrastInMilliChanged","imageContrastReset","imageShowActualSizeChanged","selectTimeEnableToggled","useRangeSelectTimeToggled","selectTimeChanged"]],template:function t(e,n){1&e&&(Rm(0,"metrics-dashboard-settings-component",0),Vm("tooltipSortChanged",(function t(e){return n.onTooltipSortChanged(e)}))("ignoreOutliersChanged",(function t(){return n.onIgnoreOutliersChanged()}))("xAxisTypeChanged",(function t(e){return n.onXAxisTypeChanged(e)}))("histogramModeChanged",(function t(e){return n.onHistogramModeChanged(e)}))("scalarSmoothingChanged",(function t(e){return n.onScalarSmoothingChanged(e)}))("scalarPartitionXToggled",(function t(){return n.onScalarPartitionXToggled()}))("imageBrightnessInMilliChanged",(function t(e){return n.onImageBrightnessInMilliChanged(e)}))("imageBrightnessReset",(function t(){return n.onImageBrightnessReset()}))("imageContrastInMilliChanged",(function t(e){return n.onImageContrastInMilliChanged(e)}))("imageContrastReset",(function t(){return n.onImageContrastReset()}))("imageShowActualSizeChanged",(function t(){return n.onImageShowActualSizeChanged()}))("selectTimeEnableToggled",(function t(){return n.onSelectTimeEnableToggled()}))("useRangeSelectTimeToggled",(function t(){return n.onUseRangeSelectTimeToggled()}))("selectTimeChanged",(function t(e){return n.onSelectTimeChanged(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Ah(7,"async"),Ah(8,"async"),Ah(9,"async"),Ah(10,"async"),Ah(11,"async"),Ah(12,"async"),Ah(13,"async"),Ah(14,"async"),Ah(15,"async"),Am()),2&e&&Dm("isImageSupportEnabled",Th(1,15,n.isImageSupportEnabled$))("tooltipSort",Th(2,17,n.tooltipSort$))("ignoreOutliers",Th(3,19,n.ignoreOutliers$))("xAxisType",Th(4,21,n.xAxisType$))("histogramMode",Th(5,23,n.histogramMode$))("scalarSmoothing",Th(6,25,n.scalarSmoothing$))("scalarPartitionX",Th(7,27,n.scalarPartitionX$))("imageBrightnessInMilli",Th(8,29,n.imageBrightnessInMilli$))("imageContrastInMilli",Th(9,31,n.imageContrastInMilli$))("imageShowActualSize",Th(10,33,n.imageShowActualSize$))("isLinkedTimeFeatureEnabled",Th(11,35,n.isLinkedTimeFeatureEnabled$))("selectTimeEnabled",Th(12,37,n.selectTimeEnabled$))("selectedTime",Th(13,39,n.selectedTime$))("useRangeSelectTime",Th(14,41,n.useRangeSelectTime$))("stepMinMax",Th(15,43,n.stepMinMax$))},directives:[rht],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(sht,[{type:My,args:[{selector:"metrics-dashboard-settings",template:'\n    <metrics-dashboard-settings-component\n      [isImageSupportEnabled]="isImageSupportEnabled$ | async"\n      [tooltipSort]="tooltipSort$ | async"\n      (tooltipSortChanged)="onTooltipSortChanged($event)"\n      [ignoreOutliers]="ignoreOutliers$ | async"\n      (ignoreOutliersChanged)="onIgnoreOutliersChanged()"\n      [xAxisType]="xAxisType$ | async"\n      (xAxisTypeChanged)="onXAxisTypeChanged($event)"\n      [histogramMode]="histogramMode$ | async"\n      (histogramModeChanged)="onHistogramModeChanged($event)"\n      [scalarSmoothing]="scalarSmoothing$ | async"\n      (scalarSmoothingChanged)="onScalarSmoothingChanged($event)"\n      [scalarPartitionX]="scalarPartitionX$ | async"\n      (scalarPartitionXToggled)="onScalarPartitionXToggled()"\n      [imageBrightnessInMilli]="imageBrightnessInMilli$ | async"\n      (imageBrightnessInMilliChanged)="onImageBrightnessInMilliChanged($event)"\n      (imageBrightnessReset)="onImageBrightnessReset()"\n      [imageContrastInMilli]="imageContrastInMilli$ | async"\n      (imageContrastInMilliChanged)="onImageContrastInMilliChanged($event)"\n      (imageContrastReset)="onImageContrastReset()"\n      [imageShowActualSize]="imageShowActualSize$ | async"\n      (imageShowActualSizeChanged)="onImageShowActualSizeChanged()"\n      [isLinkedTimeFeatureEnabled]="isLinkedTimeFeatureEnabled$ | async"\n      [selectTimeEnabled]="selectTimeEnabled$ | async"\n      [selectedTime]="selectedTime$ | async"\n      [useRangeSelectTime]="useRangeSelectTime$ | async"\n      [stepMinMax]="stepMinMax$ | async"\n      (selectTimeEnableToggled)="onSelectTimeEnableToggled()"\n      (useRangeSelectTimeToggled)="onUseRangeSelectTimeToggled()"\n      (selectTimeChanged)="onSelectTimeChanged($event)"\n    >\n    </metrics-dashboard-settings-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);class lht{}function cht(t,e){1&t&&Tm(0,"metrics-filtered-view",9),2&t&&Dm("cardObserver",Ym().cardObserver)}function dht(t,e){1&t&&(Rm(0,"div",12),Tm(1,"mat-spinner",13),Am())}function pht(t,e){if(1&t){const t=Hm();Rm(0,"div",14),Rm(1,"div",15),Rm(2,"h2",16),ku(3,"Settings"),Am(),Rm(4,"button",17),Vm("click",(function e(){return hi(t),Ym().onCloseSidepaneButtonClicked.emit()})),Tm(5,"mat-icon",18),Am(),Am(),Tm(6,"metrics-dashboard-right-pane"),Am()}}lht.ɵfac=function t(e){return new(e||lht)},lht.ɵcmp=to({type:lht,selectors:[["metrics-dashboard-right-pane"]],decls:1,vars:0,template:function t(e,n){1&e&&Tm(0,"metrics-dashboard-settings")},directives:[sht],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lht,[{type:My,args:[{selector:"metrics-dashboard-right-pane",template:"<metrics-dashboard-settings></metrics-dashboard-settings>",changeDetection:zn.OnPush}]}],null,null);const mht=function(t){return{checked:t,"settings-button":!0}};class uht{constructor(t){this.host=t,this.onSettingsButtonClicked=new Lh,this.onCloseSidepaneButtonClicked=new Lh,this.onPluginTypeToggled=new Lh,this.onPluginTypeAllToggled=new Lh,this.PluginType=hA,this.cardObserver=new p2(this.host.nativeElement,"600px 0px 600px 0px")}}uht.ɵfac=function t(e){return new(e||uht)(Sm(hg))},uht.ɵcmp=to({type:uht,selectors:[["metrics-main-view-component"]],inputs:{showFilteredView:"showFilteredView",isSidepaneOpen:"isSidepaneOpen",filteredPluginTypes:"filteredPluginTypes",initialTagsLoading:"initialTagsLoading"},outputs:{onSettingsButtonClicked:"onSettingsButtonClicked",onCloseSidepaneButtonClicked:"onCloseSidepaneButtonClicked",onPluginTypeToggled:"onPluginTypeToggled",onPluginTypeAllToggled:"onPluginTypeAllToggled"},decls:22,vars:21,consts:function(){let t,e;return t="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Toggle settings side pane"):$localize`:Label on a toolbar button to toggle the settings side pane.␟d3516db6bbe6860a55beab66e4969dac625b8d72␟7659285445580838925:Toggle settings side pane`,e="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Close side pane"):$localize`:Label on a button to close the settings side pane.␟04521dc0b6a65cf5c382944c9a8b4b844a3e9598␟8156766997747165871:Close side pane`,[[1,"toolbar"],["multiple","","appearance","standard",1,"filter-view"],[3,"checked","click"],[1,"right-items"],["mat-stroked-button","","aria-label",t,3,"ngClass","click"],["svgIcon","settings_24px"],[1,"split-content"],["cdkScrollable",""],[3,"cardObserver",4,"ngIf"],[3,"cardObserver"],["class","loading-container",4,"ngIf"],["class","sidebar",4,"ngIf"],[1,"loading-container"],["diameter","36"],[1,"sidebar"],[1,"header"],[1,"title"],["mat-icon-button","","aria-label",e,3,"click"],["svgIcon","close_24px"]]},template:function t(e,n){1&e&&(Rm(0,"div",0),Tm(1,"metrics-tag-filter"),Rm(2,"mat-button-toggle-group",1),Rm(3,"mat-button-toggle",2),Vm("click",(function t(){return n.onPluginTypeAllToggled.emit()})),ku(4," All "),Am(),Rm(5,"mat-button-toggle",2),Vm("click",(function t(){return n.onPluginTypeToggled.emit(n.PluginType.SCALARS)})),ku(6," Scalars "),Am(),Rm(7,"mat-button-toggle",2),Vm("click",(function t(){return n.onPluginTypeToggled.emit(n.PluginType.IMAGES)})),ku(8," Image "),Am(),Rm(9,"mat-button-toggle",2),Vm("click",(function t(){return n.onPluginTypeToggled.emit(n.PluginType.HISTOGRAMS)})),ku(10," Histogram "),Am(),Am(),Rm(11,"div",3),Rm(12,"button",4),Vm("click",(function t(){return n.onSettingsButtonClicked.emit()})),Tm(13,"mat-icon",5),ku(14," Settings "),Am(),Am(),Am(),Rm(15,"div",6),Rm(16,"div",7),Qp(17,cht,1,1,"metrics-filtered-view",8),Tm(18,"metrics-pinned-view",9),Qp(19,dht,2,0,"div",10),Tm(20,"metrics-card-groups",9),Am(),Qp(21,pht,7,0,"div",11),Am()),2&e&&(rc(3),Dm("checked",0===n.filteredPluginTypes.size),rc(2),Dm("checked",n.filteredPluginTypes.has(n.PluginType.SCALARS)),rc(2),Dm("checked",n.filteredPluginTypes.has(n.PluginType.IMAGES)),rc(2),Dm("checked",n.filteredPluginTypes.has(n.PluginType.HISTOGRAMS)),rc(3),Dm("ngClass",Mh(19,mht,n.isSidepaneOpen)),jp("aria-pressed",n.isSidepaneOpen),rc(4),pu("main",!0)("filter-view",n.showFilteredView),rc(1),Dm("ngIf",n.showFilteredView),rc(1),du("display",n.showFilteredView?"none":""),Dm("cardObserver",n.cardObserver),rc(1),Dm("ngIf",n.initialTagsLoading),rc(1),du("display",n.showFilteredView?"none":""),Dm("cardObserver",n.cardObserver),rc(1),Dm("ngIf",n.isSidepaneOpen))},directives:[x2,E2,A2,XH,aM,DW,mF,dM,Bgt,Ygt,$gt,o1,lht],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   .mat-chip-remove.mat-icon[_ngcontent-%COMP%]{font-size:18px}.mat-table[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-header-cell[_ngcontent-%COMP%]{font-size:12px;font-weight:500}.mat-cell[_ngcontent-%COMP%], .mat-footer-cell[_ngcontent-%COMP%]{font-size:14px}.mat-calendar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-calendar-body[_ngcontent-%COMP%]{font-size:13px}.mat-calendar-body-label[_ngcontent-%COMP%], .mat-calendar-period-button[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-calendar-table-header[_ngcontent-%COMP%]   th[_ngcontent-%COMP%]{font-size:11px;font-weight:400}.mat-dialog-title[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", 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#ebebeb;margin-left:16px;padding-left:16px}body.dark-mode[_nghost-%COMP%]   .toolbar[_ngcontent-%COMP%]   .right-items[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .toolbar[_ngcontent-%COMP%]   .right-items[_ngcontent-%COMP%]{border-left:1px solid #555}.filter-view[_ngcontent-%COMP%]{flex:none;margin-right:5px}.filter-view[_ngcontent-%COMP%]   mat-button-toggle[_ngcontent-%COMP%]{height:25px;font-size:12px}.filter-view[_ngcontent-%COMP%]   mat-button-toggle[_ngcontent-%COMP%]     .mat-button-toggle-label-content{line-height:25px}.split-content[_ngcontent-%COMP%]{display:flex;overflow-y:auto;flex:1}.main[_ngcontent-%COMP%], .sidebar[_ngcontent-%COMP%]{contain:strict;overflow-x:hidden;overflow-y:auto;will-change:transform,scroll-position}.main[_ngcontent-%COMP%]{background-color:#f5f6f7;flex:1 1;display:flex;flex-direction:column}body.dark-mode[_nghost-%COMP%]   .main[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   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.settings-button.checked[_ngcontent-%COMP%]{background-color:#e0e0e0;border-color:#e0e0e0}body.dark-mode   [_nghost-%COMP%]   .settings-button.checked[_ngcontent-%COMP%]{background-color:#212121}[_nghost-%COMP%]   .settings-button[_ngcontent-%COMP%]     .mat-button-wrapper{display:inline-flex;align-items:center}[_nghost-%COMP%]   .settings-button[_ngcontent-%COMP%]   mat-icon[_ngcontent-%COMP%]{margin-right:4px}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(uht,[{type:My,args:[{selector:"metrics-main-view-component",templateUrl:"main_view_component.ng.html",styleUrls:["main_view_component.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:hg}]}),{showFilteredView:[{type:xy}],isSidepaneOpen:[{type:xy}],filteredPluginTypes:[{type:xy}],initialTagsLoading:[{type:xy}],onSettingsButtonClicked:[{type:Oy}],onCloseSidepaneButtonClicked:[{type:Oy}],onPluginTypeToggled:[{type:Oy}],onPluginTypeAllToggled:[{type:Oy}]});class fht{constructor(t){this.store=t,this.isSidepaneOpen$=this.store.select(XT),this.initialTagsLoading$=this.store.select(uT).pipe(He((t=>null===t.lastLoadedTimeInMs),!0),It((t=>t.state===yE.LOADING&&null===t.lastLoadedTimeInMs))),this.showFilteredView$=this.store.select(BT).pipe(It((t=>t.length>0))),this.filteredPluginTypes$=this.store.select(qT)}onSettingsButtonClicked(){this.store.dispatch(zE())}onCloseSidepaneButtonClicked(){this.store.dispatch(NE())}onPluginVisibilityToggled(t){this.store.dispatch(iR({plugin:t}))}onShowAllPlugins(){this.store.dispatch(aR())}}fht.ɵfac=function t(e){return new(e||fht)(Sm(Iw))},fht.ɵcmp=to({type:fht,selectors:[["metrics-main-view"]],decls:5,vars:12,consts:[[3,"showFilteredView","isSidepaneOpen","initialTagsLoading","filteredPluginTypes","onSettingsButtonClicked","onCloseSidepaneButtonClicked","onPluginTypeToggled","onPluginTypeAllToggled"]],template:function t(e,n){1&e&&(Rm(0,"metrics-main-view-component",0),Vm("onSettingsButtonClicked",(function t(){return n.onSettingsButtonClicked()}))("onCloseSidepaneButtonClicked",(function t(){return n.onCloseSidepaneButtonClicked()}))("onPluginTypeToggled",(function t(e){return n.onPluginVisibilityToggled(e)}))("onPluginTypeAllToggled",(function t(){return n.onShowAllPlugins()})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Am()),2&e&&Dm("showFilteredView",Th(1,4,n.showFilteredView$))("isSidepaneOpen",Th(2,6,n.isSidepaneOpen$))("initialTagsLoading",Th(3,8,n.initialTagsLoading$))("filteredPluginTypes",Th(4,10,n.filteredPluginTypes$))},directives:[uht],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(fht,[{type:My,args:[{selector:"metrics-main-view",template:'\n    <metrics-main-view-component\n      [showFilteredView]="showFilteredView$ | async"\n      [isSidepaneOpen]="isSidepaneOpen$ | async"\n      [initialTagsLoading]="initialTagsLoading$ | async"\n      [filteredPluginTypes]="filteredPluginTypes$ | async"\n      (onSettingsButtonClicked)="onSettingsButtonClicked()"\n      (onCloseSidepaneButtonClicked)="onCloseSidepaneButtonClicked()"\n      (onPluginTypeToggled)="onPluginVisibilityToggled($event)"\n      (onPluginTypeAllToggled)="onShowAllPlugins()"\n    ></metrics-main-view-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);const ght=new Ga("[Metrics] METRICS_Promo Message Component");function hht(t,e){1&t&&(Nm(0),Xm(1),zm())}class bht{constructor(t,e){this.viewContainerRef=t,this.componentFactoryResolver=e}ngOnInit(){if(this.customizableComponent){const t=this.componentFactoryResolver.resolveComponentFactory(this.customizableComponent.constructor);this.viewContainerRef.createComponent(t)}}}bht.ɵfac=function t(e){return new(e||bht)(Sm(eh),Sm(ug))},bht.ɵcmp=to({type:bht,selectors:[["tb-customization"]],inputs:{customizableComponent:"customizableComponent"},ngContentSelectors:["*"],decls:1,vars:1,consts:[[4,"ngIf"]],template:function t(e,n){1&e&&(Zm(),Qp(0,hht,2,0,"ng-container",0)),2&e&&Dm("ngIf",!n.customizableComponent)},directives:[dM],encapsulation:2}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(bht,[{type:My,args:[{selector:"tb-customization",template:'\n    <ng-container *ngIf="!customizableComponent">\n      <ng-content></ng-content>\n    </ng-container>\n  '}]}],(function(){return[{type:eh},{type:ug}]}),{customizableComponent:[{type:xy}]});class yht{constructor(t){this.customPromoMessage=t,this.onDismiss=new Lh,this.onGoToScalars=new Lh}}yht.ɵfac=function t(e){return new(e||yht)(Sm(ght,8))},yht.ɵcmp=to({type:yht,selectors:[["metrics-promo-notice-component"]],outputs:{onDismiss:"onDismiss",onGoToScalars:"onGoToScalars"},decls:7,vars:1,consts:[[1,"message",3,"customizableComponent"],[1,"go-to-scalars",3,"click"],[1,"dismiss",3,"click"],["inline","","svgIcon","close_24px"]],template:function t(e,n){1&e&&(Rm(0,"tb-customization",0),ku(1," Welcome to new default experience of TensorBoard. 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1}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Mht,[{type:My,args:[{selector:"metrics-dashboard",template:'\n    <metrics-promo-notice\n      *ngIf="isButterBarEnabled$ | async"\n      class="notice"\n    ></metrics-promo-notice>\n    <tb-dashboard-layout>\n      <runs-selector sidebar></runs-selector>\n      <metrics-main-view main></metrics-main-view>\n    </tb-dashboard-layout>\n  ',styleUrls:["metrics_container.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);class vht{}vht.ɵfac=function t(e){return new(e||vht)},vht.ɵmod=ao({type:vht}),vht.ɵinj=vn({imports:[[WM,EW,JH]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(vht,[{type:Ay,args:[{declarations:[MQ],exports:[MQ],imports:[WM,EW,JH]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(vht,{declarations:[MQ],imports:[WM,EW,JH],exports:[MQ]});class xht{}xht.ɵfac=function t(e){return new(e||xht)},xht.ɵmod=ao({type:xht}),xht.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(xht,[{type:Ay,args:[{imports:[WM],declarations:[bht],exports:[bht]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(xht,{declarations:[bht],imports:[WM],exports:[bht]});class Oht{}Oht.ɵfac=function t(e){return new(e||Oht)},Oht.ɵmod=ao({type:Oht}),Oht.ɵinj=vn({imports:[[WM,Y0,EW]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Oht,[{type:Ay,args:[{declarations:[q0],exports:[q0],imports:[WM,Y0,EW]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Oht,{declarations:[q0],imports:[WM,Y0,EW],exports:[q0]});class Pht{}Pht.ɵfac=function t(e){return new(e||Pht)},Pht.ɵmod=ao({type:Pht}),Pht.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Pht,[{type:Ay,args:[{imports:[WM],exports:[D1],declarations:[D1]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Pht,{declarations:[D1],imports:[WM],exports:[D1]});class wht{}wht.ɵfac=function t(e){return 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ngDevMode||ngDevMode)&&hh(Sht,[{type:Ay,args:[{exports:[Nft],declarations:[Nft]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Sht,{declarations:[Nft],exports:[Nft]});class Dht{}Dht.ɵfac=function t(e){return new(e||Dht)},Dht.ɵmod=ao({type:Dht}),Dht.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Dht,[{type:Ay,args:[{declarations:[hft],exports:[hft],imports:[WM]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Dht,{declarations:[hft],imports:[WM],exports:[hft]});class Eht{}Eht.ɵfac=function t(e){return new(e||Eht)},Eht.ɵmod=ao({type:Eht}),Eht.ɵinj=vn({imports:[[WM,VJ,Sht,Dht]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Eht,[{type:Ay,args:[{declarations:[tgt],exports:[tgt],imports:[WM,VJ,Sht,Dht]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Eht,{declarations:[tgt],imports:[WM,VJ,Sht,Dht],exports:[tgt]});class Rht{}Rht.ɵfac=function t(e){return new(e||Rht)},Rht.ɵmod=ao({type:Rht}),Rht.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Rht,[{type:Ay,args:[{declarations:[z2],exports:[z2],imports:[WM]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Rht,{declarations:[z2],imports:[WM],exports:[z2]});class Aht{}Aht.ɵfac=function t(e){return new(e||Aht)},Aht.ɵmod=ao({type:Aht}),Aht.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Aht,[{type:Ay,args:[{declarations:[L2,F2],exports:[L2],imports:[WM]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Aht,{declarations:[L2,F2],imports:[WM],exports:[L2]});class Tht{}Tht.ɵfac=function t(e){return new(e||Tht)},Tht.ɵmod=ao({type:Tht}),Tht.ɵinj=vn({imports:[[WM,EW]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Tht,[{type:Ay,args:[{declarations:[gft],exports:[gft],imports:[WM,EW]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Tht,{declarations:[gft],imports:[WM,EW],exports:[gft]});class Nht{}Nht.ɵfac=function t(e){return new(e||Nht)},Nht.ɵmod=ao({type:Nht}),Nht.ɵinj=vn({imports:[[WM,Eht,JH,EW,i1,Aht,Rht,Tht]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Nht,[{type:Ay,args:[{declarations:[mgt,pgt],exports:[mgt],imports:[WM,Eht,JH,EW,i1,Aht,Rht,Tht]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Nht,{declarations:[mgt,pgt],imports:[WM,Eht,JH,EW,i1,Aht,Rht,Tht],exports:[mgt]});class zht{}zht.ɵfac=function t(e){return new(e||zht)},zht.ɵmod=ao({type:zht}),zht.ɵinj=vn({imports:[[WM,JH,EW,i1,zX,Aht,Rht]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(zht,[{type:Ay,args:[{declarations:[X2,Z2],exports:[X2],imports:[WM,JH,EW,i1,zX,Aht,Rht]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(zht,{declarations:[X2,Z2],imports:[WM,JH,EW,i1,zX,Aht,Rht],exports:[X2]});class Iht{}Iht.ɵfac=function t(e){return new(e||Iht)},Iht.ɵmod=ao({type:Iht}),Iht.ɵinj=vn({imports:[[WM,yL,JH,EW,BY,oY]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Iht,[{type:Ay,args:[{declarations:[Xut,Uut,Eut],exports:[Xut,Uut,Eut],imports:[WM,yL,JH,EW,BY,oY]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Iht,{declarations:[Xut,Uut,Eut],imports:[WM,yL,JH,EW,BY,oY],exports:[Xut,Uut,Eut]});class Hht{}Hht.ɵfac=function t(e){return new(e||Hht)},Hht.ɵmod=ao({type:Hht}),Hht.ɵinj=vn({imports:[[WM,yL,Iht,VJ]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Hht,[{type:Ay,args:[{declarations:[fft],exports:[fft],imports:[WM,yL,Iht,VJ]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Hht,{declarations:[fft],imports:[WM,yL,Iht,VJ],exports:[fft]});class Fht{}Fht.ɵfac=function t(e){return new(e||Fht)},Fht.ɵmod=ao({type:Fht}),Fht.ɵinj=vn({imports:[[WM,cG,JH,dW,BY,TG,eT]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Fht,[{type:Ay,args:[{declarations:[l5,s5],exports:[l5],imports:[WM,cG,JH,dW,BY,TG,eT],entryComponents:[l5]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Fht,{declarations:[l5,s5],imports:[WM,cG,JH,dW,BY,TG,eT],exports:[l5]});class Lht{}Lht.ɵfac=function t(e){return new(e||Lht)},Lht.ɵmod=ao({type:Lht}),Lht.ɵinj=vn({imports:[[WM,Fht,Sht,Hht,Dht,JH,EW,oY,i1,VJ,Rht,Tht]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Lht,[{type:Ay,args:[{declarations:[Ift,Tft],exports:[Ift],imports:[WM,Fht,Sht,Hht,Dht,JH,EW,oY,i1,VJ,Rht,Tht]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Lht,{declarations:[Ift,Tft],imports:[WM,Fht,Sht,Hht,Dht,JH,EW,oY,i1,VJ,Rht,Tht],exports:[Ift]});class Bht{}Bht.ɵfac=function t(e){return new(e||Bht)},Bht.ɵmod=ao({type:Bht}),Bht.ɵinj=vn({imports:[[WM,zht,Lht,Nht,Sht]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Bht,[{type:Ay,args:[{declarations:[m2,ygt,_gt],exports:[m2,_gt],imports:[WM,zht,Lht,Nht,Sht]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Bht,{declarations:[m2,ygt,_gt],imports:[WM,zht,Lht,Nht,Sht],exports:[m2,_gt]});class Vht{}Vht.ɵfac=function t(e){return new(e||Vht)},Vht.ɵmod=ao({type:Vht}),Vht.ɵinj=vn({imports:[[WM,TG]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Vht,[{type:Ay,args:[{declarations:[eht],exports:[eht],imports:[WM,TG]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Vht,{declarations:[eht],imports:[WM,TG],exports:[eht]});class jht{}jht.ɵfac=function t(e){return new(e||jht)},jht.ɵmod=ao({type:jht}),jht.ɵinj=vn({imports:[[WM,Vht,JH,T2,SY,EW,TG,zX,tT,Pht]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(jht,[{type:Ay,args:[{declarations:[lht,rht,sht],exports:[lht],imports:[WM,Vht,JH,T2,SY,EW,TG,zX,tT,Pht]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(jht,{declarations:[lht,rht,sht],imports:[WM,Vht,JH,T2,SY,EW,TG,zX,tT,Pht],exports:[lht]});class Uht{}Uht.ɵfac=function t(e){return new(e||Uht)},Uht.ɵmod=ao({type:Uht}),Uht.ɵinj=vn({imports:[[Bht,WM,Oht,Y0,JH,T2,EW,BY,i1,jht,_F]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Uht,[{type:Ay,args:[{declarations:[Tgt,Ngt,Wgt,Ygt,Zgt,Xgt,Qgt,$gt,uht,fht,v2,x2,Lgt,Bgt],exports:[fht],imports:[Bht,WM,Oht,Y0,JH,T2,EW,BY,i1,jht,_F]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Uht,{declarations:[Tgt,Ngt,Wgt,Ygt,Zgt,Xgt,Qgt,$gt,uht,fht,v2,x2,Lgt,Bgt],imports:[Bht,WM,Oht,Y0,JH,T2,EW,BY,i1,jht,_F],exports:[fht]});class Ght{}function Wht(){return[{actionCreator:oR,alertFromAction:t=>{const{wasPinned:e,canCreateNewPins:n}=t;return e||n?null:{localizedMessage:"Max pin limit exceeded. 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0!==n&&(o=n-1,i=n+1),Object.assign(Object.assign({},t),{metricArithmetic:[...t.metricArithmetic.slice(0,o),...t.metricArithmetic.slice(i)],metricFilters:a})})),bk(Bbt,((t,{metric:e,max:n,min:o,includeNaN:i})=>t.metricFilters[e]?Object.assign(Object.assign({},t),{metricFilters:Object.assign(Object.assign({},t.metricFilters),{[e]:{max:n,min:o,includeNaN:i}})}):t)),bk(Vbt,((t,{metric:e})=>{const n={metric:e,order:nbt.DESCENDING};return t.sort.metric===e&&t.sort.order===nbt.DESCENDING&&(n.order=nbt.ASCENDNG),Object.assign(Object.assign({},t),{sort:n})})),bk(jbt,((t,{annotation:e})=>{const n={metric:e,order:nbt.SIMILAR};return t.sort.metric===e&&t.sort.order===nbt.SIMILAR&&(n.order=nbt.DISSIMILAR),Object.assign(Object.assign({},t),{sort:n})})),bk(Ubt,(t=>Object.assign(Object.assign({},t),{pcExpanded:!t.pcExpanded}))),bk(Gbt,(t=>Object.assign(Object.assign({},t),{annotationsExpanded:!t.annotationsExpanded}))),bk(Wbt,(t=>Object.assign(Object.assign({},t),{sidebarExpanded:!t.sidebarExpanded}))),bk(Ybt,(t=>Object.assign(Object.assign({},t),{showCounts:!t.showCounts}))),bk(qbt,(t=>Object.assign(Object.assign({},t),{showHiddenAnnotations:!t.showHiddenAnnotations}))),bk(Zbt,((t,{metric:e})=>{let n=ebt.EMBEDDINGS,o=e;return 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t(e,n){2&e&&pu("valid",n.isRegexFilterValid)},inputs:{completions:"completions",regexFilterValue:"regexFilterValue",isRegexFilterValid:"isRegexFilterValid"},outputs:{onRegexFilterValueChange:"onRegexFilterValueChange",onAddFilter:"onAddFilter"},decls:7,vars:4,consts:[["svgIcon","search_24px"],["matInput","","autocomplete","off","placeholder","Add Metric Filter",3,"value","matAutocomplete","input"],["matInput",""],["svgIcon","error_24px","class","error-icon","matTooltip","Invalid regex filter. 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!important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}mat-icon[_ngcontent-%COMP%]{flex:none;margin-right:5px}[_nghost-%COMP%]{display:flex;padding:0 10px;position:relative;font-size:.9em}[_nghost-%COMP%]:not(.valid){color:#c62828}[_nghost-%COMP%]:not(.valid)   input[_ngcontent-%COMP%]{caret-color:currentColor}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(syt,[{type:My,args:[{selector:"metric-search-component",templateUrl:"./metric_search_component.ng.html",styleUrls:["./metric_search_component.css"],changeDetection:zn.OnPush}]}],null,{completions:[{type:xy}],regexFilterValue:[{type:xy}],isRegexFilterValid:[{type:Py,args:["class.valid"]},{type:xy}],onRegexFilterValueChange:[{type:Oy}],onAddFilter:[{type:Oy}]});class lyt{constructor(t){this.store=t,this.metricsRegex$=this.store.select(ubt),this.activeRuns$=this.store.pipe(Fw(NN)).pipe(It((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.metricsForActiveRuns$=Wt(this.activeRuns$,this.store.select(sbt)).pipe(It((([t,e])=>{const n=new Set;for(const o of t)if(e[o])for(const t of e[o])n.add(t);return[...n]}))),this.isMetricsFilterValid$=this.metricsRegex$.pipe(It((t=>{try{return Boolean(new RegExp(t))}catch(t){return!1}}))),this.metricFilterKeys$=this.store.pipe(Fw(gbt)).pipe(It((t=>Object.keys(t)))),this.completions$=Wt(this.metricsForActiveRuns$,this.metricsRegex$,this.metricFilterKeys$).pipe(It((([t,e,n])=>{const o=t.filter((t=>!n.includes(t)));try{const t=new RegExp(e,"i");return o.filter((e=>t.test(e))).sort()}catch(t){return[]}})))}onFilterChange(t){this.store.dispatch(Hbt({regex:t}))}onAddFilter(t){this.store.dispatch(Fbt({metric:t})),this.store.dispatch(Hbt({regex:""}))}}function cyt(t,e,n){const o=[[e,...n]];if(!n.length||!t.length)return"data:text/csv;charset=utf-8,"+o.map((t=>t.join(","))).join("\n");const i=n.map((t=>nyt(t)));for(const[n,a]of t){const t=a.filter((t=>t.run===e));if(t.length){const e=[n];for(const n of i){const o=t.find((t=>t.metric===n));e.push(void 0===o?"null":`${o.nPMIValue}`)}o.push(e)}}return"data:text/csv;charset=utf-8,"+o.map((t=>t.join(","))).join("\n")}lyt.ɵfac=function t(e){return new(e||lyt)(Sm(Iw))},lyt.ɵcmp=to({type:lyt,selectors:[["npmi-metric-search"]],decls:4,vars:9,consts:[[3,"regexFilterValue","completions","isRegexFilterValid","onRegexFilterValueChange","onAddFilter"]],template:function t(e,n){1&e&&(Rm(0,"metric-search-component",0),Vm("onRegexFilterValueChange",(function t(e){return n.onFilterChange(e)}))("onAddFilter",(function t(e){return n.onAddFilter(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Am()),2&e&&Dm("regexFilterValue",Th(1,3,n.metricsRegex$))("completions",Th(2,5,n.completions$))("isRegexFilterValid",Th(3,7,n.isMetricsFilterValid$))},directives:[syt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lyt,[{type:My,args:[{selector:"npmi-metric-search",template:'\n    <metric-search-component\n      [regexFilterValue]="metricsRegex$ | async"\n      [completions]="completions$ | async"\n      [isRegexFilterValid]="isMetricsFilterValid$ | async"\n      (onRegexFilterValueChange)="onFilterChange($event)"\n      (onAddFilter)="onAddFilter($event)"\n    ></metric-search-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);const dyt=function(t){return{"active-button":t}};class pyt{downloadResults(){for(const t of this.runs){const e=cyt(this.flaggedData,t,this.metrics),n=document.createElement("a");n.setAttribute("href",e),n.setAttribute("download",`report_${t}.csv`),n.click()}}}pyt.ɵfac=function t(e){return new(e||pyt)},pyt.ɵcmp=to({type:pyt,selectors:[["results-download-component"]],inputs:{numFlaggedAnnotations:"numFlaggedAnnotations",runs:"runs",flaggedData:"flaggedData",metrics:"metrics"},decls:4,vars:5,consts:[["mat-stroked-button","","title","Export CSV reports of all flagged annotations. Will generate one CSV per active run.",3,"disabled","ngClass","click"],[1,"button-contents"],["svgIcon","get_app_24px"]],template:function t(e,n){1&e&&(Rm(0,"button",0),Vm("click",(function t(){return n.downloadResults()})),Rm(1,"span",1),Tm(2,"mat-icon",2),ku(3),Am(),Am()),2&e&&(Dm("disabled",0===n.numFlaggedAnnotations)("ngClass",Mh(3,dyt,n.numFlaggedAnnotations>0)),rc(3),Du(" Flagged Rows (",n.numFlaggedAnnotations,") "))},directives:[XH,aM,DW],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 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ngDevMode||ngDevMode)&&hh(pyt,[{type:My,args:[{selector:"results-download-component",templateUrl:"./results_download_component.ng.html",styleUrls:["./results_download_component.css"],changeDetection:zn.OnPush}]}],null,{numFlaggedAnnotations:[{type:xy}],runs:[{type:xy}],flaggedData:[{type:xy}],metrics:[{type:xy}]});class myt{constructor(t){this.store=t,this.flaggedAnnotations$=this.store.select(dbt),this.numFlaggedAnnotations$=this.flaggedAnnotations$.pipe(It((t=>t.length))),this.activeRuns$=this.store.select(NN).pipe(It((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.flaggedData$=Wt([this.store.select(rbt),this.flaggedAnnotations$]).pipe(It((([t,e])=>{const n=new Set(e);return Object.entries(t).filter((t=>n.has(t[0])))}))),this.metrics$=Wt([this.store.select(sbt),this.activeRuns$,this.store.select(gbt)]).pipe(It((([t,e,n])=>{let o=Object.keys(n);for(const n of e)t[n]&&(o=o.concat(t[n].filter((t=>eyt(t)))));return o=[...new Set(o)],o})))}}myt.ɵfac=function t(e){return new(e||myt)(Sm(Iw))},myt.ɵcmp=to({type:myt,selectors:[["npmi-results-download"]],decls:5,vars:12,consts:[[3,"numFlaggedAnnotations","runs","flaggedData","metrics"]],template:function t(e,n){1&e&&(Tm(0,"results-download-component",0),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async")),2&e&&Dm("numFlaggedAnnotations",Th(1,4,n.numFlaggedAnnotations$))("runs",Th(2,6,n.activeRuns$))("flaggedData",Th(3,8,n.flaggedData$))("metrics",Th(4,10,n.metrics$))},directives:[pyt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(myt,[{type:My,args:[{selector:"npmi-results-download",template:'\n    <results-download-component\n      [numFlaggedAnnotations]="numFlaggedAnnotations$ | async"\n      [runs]="activeRuns$ | async"\n      [flaggedData]="flaggedData$ | async"\n      [metrics]="metrics$ | async"\n    ></results-download-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);const uyt=new Ga("MatChipRemove"),fyt=new Ga("MatChipAvatar"),gyt=new Ga("MatChipTrailingIcon"),hyt=$I(JI(QI(class{constructor(t){this._elementRef=t}}),"primary"),-1);class byt{}byt.ɵfac=function t(e){return new(e||byt)},byt.ɵdir=lo({type:byt,selectors:[["mat-chip-avatar"],["","matChipAvatar",""]],hostAttrs:[1,"mat-chip-avatar"],features:[pg([{provide:fyt,useExisting:byt}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(byt,[{type:Cy,args:[{selector:"mat-chip-avatar, [matChipAvatar]",host:{class:"mat-chip-avatar"},providers:[{provide:fyt,useExisting:byt}]}]}],null,null);class yyt{}yyt.ɵfac=function t(e){return new(e||yyt)},yyt.ɵdir=lo({type:yyt,selectors:[["mat-chip-trailing-icon"],["","matChipTrailingIcon",""]],hostAttrs:[1,"mat-chip-trailing-icon"],features:[pg([{provide:gyt,useExisting:yyt}])]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(yyt,[{type:Cy,args:[{selector:"mat-chip-trailing-icon, [matChipTrailingIcon]",host:{class:"mat-chip-trailing-icon"},providers:[{provide:gyt,useExisting:yyt}]}]}],null,null);class _yt extends hyt{constructor(t,e,n,o,i,a,r,s){super(t),this._ngZone=e,this._changeDetectorRef=i,this._hasFocus=!1,this.chipListSelectable=!0,this._chipListMultiple=!1,this._chipListDisabled=!1,this._selected=!1,this._selectable=!0,this._disabled=!1,this._removable=!0,this._onFocus=new I,this._onBlur=new I,this.selectionChange=new Lh,this.destroyed=new Lh,this.removed=new Lh,this._addHostClassName(),this._chipRippleTarget=a.createElement("div"),this._chipRippleTarget.classList.add("mat-chip-ripple"),this._elementRef.nativeElement.appendChild(this._chipRippleTarget),this._chipRipple=new PH(this,e,this._chipRippleTarget,n),this._chipRipple.setupTriggerEvents(t),this.rippleConfig=o||{},this._animationsDisabled="NoopAnimations"===r,this.tabIndex=null!=s&&parseInt(s)||-1}get rippleDisabled(){return this.disabled||this.disableRipple||this._animationsDisabled||!!this.rippleConfig.disabled}get selected(){return this._selected}set selected(t){const e=yz(t);e!==this._selected&&(this._selected=e,this._dispatchSelectionChange())}get value(){return void 0!==this._value?this._value:this._elementRef.nativeElement.textContent}set value(t){this._value=t}get selectable(){return this._selectable&&this.chipListSelectable}set selectable(t){this._selectable=yz(t)}get disabled(){return this._chipListDisabled||this._disabled}set disabled(t){this._disabled=yz(t)}get removable(){return this._removable}set removable(t){this._removable=yz(t)}get ariaSelected(){return this.selectable&&(this._chipListMultiple||this.selected)?this.selected.toString():null}_addHostClassName(){const t="mat-basic-chip",e=this._elementRef.nativeElement;e.hasAttribute(t)||e.tagName.toLowerCase()===t?e.classList.add(t):e.classList.add("mat-standard-chip")}ngOnDestroy(){this.destroyed.emit({chip:this}),this._chipRipple._removeTriggerEvents()}select(){this._selected||(this._selected=!0,this._dispatchSelectionChange(),this._changeDetectorRef.markForCheck())}deselect(){this._selected&&(this._selected=!1,this._dispatchSelectionChange(),this._changeDetectorRef.markForCheck())}selectViaInteraction(){this._selected||(this._selected=!0,this._dispatchSelectionChange(!0),this._changeDetectorRef.markForCheck())}toggleSelected(t=!1){return this._selected=!this.selected,this._dispatchSelectionChange(t),this._changeDetectorRef.markForCheck(),this.selected}focus(){this._hasFocus||(this._elementRef.nativeElement.focus(),this._onFocus.next({chip:this})),this._hasFocus=!0}remove(){this.removable&&this.removed.emit({chip:this})}_handleClick(t){this.disabled?t.preventDefault():t.stopPropagation()}_handleKeydown(t){if(!this.disabled)switch(t.keyCode){case 46:case 8:this.remove(),t.preventDefault();break;case fz:this.selectable&&this.toggleSelected(!0),t.preventDefault()}}_blur(){this._ngZone.onStable.pipe(be(1)).subscribe((()=>{this._ngZone.run((()=>{this._hasFocus=!1,this._onBlur.next({chip:this})}))}))}_dispatchSelectionChange(t=!1){this.selectionChange.emit({source:this,isUserInput:t,selected:this._selected})}}_yt.ɵfac=function t(e){return new(e||_yt)(Sm(hg),Sm(a_),Sm(wz),Sm(wH,8),Sm(Ug),Sm(Z_),Sm(VP,8),Na("tabindex"))},_yt.ɵdir=lo({type:_yt,selectors:[["mat-basic-chip"],["","mat-basic-chip",""],["mat-chip"],["","mat-chip",""]],contentQueries:function t(e,n,o){if(1&e&&($h(o,fyt,5),$h(o,gyt,5),$h(o,uyt,5)),2&e){let t;Jh(t=tb())&&(n.avatar=t.first),Jh(t=tb())&&(n.trailingIcon=t.first),Jh(t=tb())&&(n.removeIcon=t.first)}},hostAttrs:["role","option",1,"mat-chip","mat-focus-indicator"],hostVars:14,hostBindings:function t(e,n){1&e&&Vm("click",(function t(e){return n._handleClick(e)}))("keydown",(function t(e){return n._handleKeydown(e)}))("focus",(function t(){return n.focus()}))("blur",(function t(){return n._blur()})),2&e&&(jp("tabindex",n.disabled?null:n.tabIndex)("disabled",n.disabled||null)("aria-disabled",n.disabled.toString())("aria-selected",n.ariaSelected),pu("mat-chip-selected",n.selected)("mat-chip-with-avatar",n.avatar)("mat-chip-with-trailing-icon",n.trailingIcon||n.removeIcon)("mat-chip-disabled",n.disabled)("_mat-animation-noopable",n._animationsDisabled))},inputs:{color:"color",disableRipple:"disableRipple",tabIndex:"tabIndex",selected:"selected",value:"value",selectable:"selectable",disabled:"disabled",removable:"removable"},outputs:{selectionChange:"selectionChange",destroyed:"destroyed",removed:"removed"},exportAs:["matChip"],features:[xp]}),_yt.ctorParameters=()=>[{type:hg},{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:Ug},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]}],_yt.propDecorators={avatar:[{type:qa,args:[fyt]}],trailingIcon:[{type:qa,args:[gyt]}],removeIcon:[{type:qa,args:[uyt]}],selected:[{type:xy}],value:[{type:xy}],selectable:[{type:xy}],disabled:[{type:xy}],removable:[{type:xy}],selectionChange:[{type:Oy}],destroyed:[{type:Oy}],removed:[{type:Oy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(_yt,[{type:Cy,args:[{selector:"mat-basic-chip, [mat-basic-chip], mat-chip, [mat-chip]",inputs:["color","disableRipple","tabIndex"],exportAs:"matChip",host:{class:"mat-chip mat-focus-indicator","[attr.tabindex]":"disabled ? null : tabIndex",role:"option","[class.mat-chip-selected]":"selected","[class.mat-chip-with-avatar]":"avatar","[class.mat-chip-with-trailing-icon]":"trailingIcon || removeIcon","[class.mat-chip-disabled]":"disabled","[class._mat-animation-noopable]":"_animationsDisabled","[attr.disabled]":"disabled || null","[attr.aria-disabled]":"disabled.toString()","[attr.aria-selected]":"ariaSelected","(click)":"_handleClick($event)","(keydown)":"_handleKeydown($event)","(focus)":"focus()","(blur)":"_blur()"}}]}],(function(){return[{type:hg},{type:a_},{type:wz},{type:void 0,decorators:[{type:Sr},{type:kr,args:[wH]}]},{type:Ug},{type:void 0,decorators:[{type:kr,args:[Z_]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]},{type:String,decorators:[{type:ja,args:["tabindex"]}]}]}),{selectionChange:[{type:Oy}],destroyed:[{type:Oy}],removed:[{type:Oy}],selected:[{type:xy}],value:[{type:xy}],selectable:[{type:xy}],disabled:[{type:xy}],removable:[{type:xy}],avatar:[{type:qa,args:[fyt]}],trailingIcon:[{type:qa,args:[gyt]}],removeIcon:[{type:qa,args:[uyt]}]});class Cyt{constructor(t,e){this._parentChip=t,"BUTTON"===e.nativeElement.nodeName&&e.nativeElement.setAttribute("type","button")}_handleClick(t){const e=this._parentChip;e.removable&&!e.disabled&&e.remove(),t.stopPropagation()}}Cyt.ɵfac=function t(e){return new(e||Cyt)(Sm(_yt),Sm(hg))},Cyt.ɵdir=lo({type:Cyt,selectors:[["","matChipRemove",""]],hostAttrs:[1,"mat-chip-remove","mat-chip-trailing-icon"],hostBindings:function t(e,n){1&e&&Vm("click",(function t(e){return n._handleClick(e)}))},features:[pg([{provide:uyt,useExisting:Cyt}])]}),Cyt.ctorParameters=()=>[{type:_yt},{type:hg}],("undefined"==typeof ngDevMode||ngDevMode)&&hh(Cyt,[{type:Cy,args:[{selector:"[matChipRemove]",host:{class:"mat-chip-remove mat-chip-trailing-icon","(click)":"_handleClick($event)"},providers:[{provide:uyt,useExisting:Cyt}]}]}],(function(){return[{type:_yt},{type:hg}]}),null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Myt=new Ga("mat-chips-default-options"),vyt=tH(class{constructor(t,e,n,o){this._defaultErrorStateMatcher=t,this._parentForm=e,this._parentFormGroup=n,this.ngControl=o}});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */let xyt=0;class Oyt{constructor(t,e){this.source=t,this.value=e}}class Pyt extends vyt{constructor(t,e,n,o,i,a,r){super(a,o,i,r),this._elementRef=t,this._changeDetectorRef=e,this._dir=n,this.controlType="mat-chip-list",this._lastDestroyedChipIndex=null,this._destroyed=new I,this._uid="mat-chip-list-"+xyt++,this._tabIndex=0,this._userTabIndex=null,this._onTouched=()=>{},this._onChange=()=>{},this._multiple=!1,this._compareWith=(t,e)=>t===e,this._required=!1,this._disabled=!1,this.ariaOrientation="horizontal",this._selectable=!0,this.change=new Lh,this.valueChange=new Lh,this.ngControl&&(this.ngControl.valueAccessor=this)}get selected(){return this.multiple?this._selectionModel.selected:this._selectionModel.selected[0]}get role(){return this.empty?null:"listbox"}get multiple(){return this._multiple}set multiple(t){this._multiple=yz(t),this._syncChipsState()}get compareWith(){return this._compareWith}set compareWith(t){this._compareWith=t,this._selectionModel&&this._initializeSelection()}get value(){return this._value}set value(t){this.writeValue(t),this._value=t}get id(){return this._chipInput?this._chipInput.id:this._uid}get required(){return this._required}set required(t){this._required=yz(t),this.stateChanges.next()}get placeholder(){return this._chipInput?this._chipInput.placeholder:this._placeholder}set placeholder(t){this._placeholder=t,this.stateChanges.next()}get focused(){return this._chipInput&&this._chipInput.focused||this._hasFocusedChip()}get empty(){return(!this._chipInput||this._chipInput.empty)&&(!this.chips||0===this.chips.length)}get shouldLabelFloat(){return!this.empty||this.focused}get disabled(){return this.ngControl?!!this.ngControl.disabled:this._disabled}set disabled(t){this._disabled=yz(t),this._syncChipsState()}get selectable(){return this._selectable}set selectable(t){this._selectable=yz(t),this.chips&&this.chips.forEach((t=>t.chipListSelectable=this._selectable))}set tabIndex(t){this._userTabIndex=t,this._tabIndex=t}get chipSelectionChanges(){return re(...this.chips.map((t=>t.selectionChange)))}get chipFocusChanges(){return re(...this.chips.map((t=>t._onFocus)))}get chipBlurChanges(){return re(...this.chips.map((t=>t._onBlur)))}get chipRemoveChanges(){return re(...this.chips.map((t=>t.destroyed)))}ngAfterContentInit(){this._keyManager=new eI(this.chips).withWrap().withVerticalOrientation().withHomeAndEnd().withHorizontalOrientation(this._dir?this._dir.value:"ltr"),this._dir&&this._dir.change.pipe(Ie(this._destroyed)).subscribe((t=>this._keyManager.withHorizontalOrientation(t))),this._keyManager.tabOut.pipe(Ie(this._destroyed)).subscribe((()=>{this._allowFocusEscape()})),this.chips.changes.pipe(Ne(null),Ie(this._destroyed)).subscribe((()=>{this.disabled&&Promise.resolve().then((()=>{this._syncChipsState()})),this._resetChips(),this._initializeSelection(),this._updateTabIndex(),this._updateFocusForDestroyedChips(),this.stateChanges.next()}))}ngOnInit(){this._selectionModel=new oF(this.multiple,void 0,!1),this.stateChanges.next()}ngDoCheck(){this.ngControl&&(this.updateErrorState(),this.ngControl.disabled!==this._disabled&&(this.disabled=!!this.ngControl.disabled))}ngOnDestroy(){this._destroyed.next(),this._destroyed.complete(),this.stateChanges.complete(),this._dropSubscriptions()}registerInput(t){this._chipInput=t,this._elementRef.nativeElement.setAttribute("data-mat-chip-input",t.id)}setDescribedByIds(t){this._ariaDescribedby=t.join(" ")}writeValue(t){this.chips&&this._setSelectionByValue(t,!1)}registerOnChange(t){this._onChange=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t,this.stateChanges.next()}onContainerClick(t){this._originatesFromChip(t)||this.focus()}focus(t){this.disabled||this._chipInput&&this._chipInput.focused||(this.chips.length>0?(this._keyManager.setFirstItemActive(),this.stateChanges.next()):(this._focusInput(t),this.stateChanges.next()))}_focusInput(t){this._chipInput&&this._chipInput.focus(t)}_keydown(t){const e=t.target;e&&e.classList.contains("mat-chip")&&(this._keyManager.onKeydown(t),this.stateChanges.next())}_updateTabIndex(){this._tabIndex=this._userTabIndex||(0===this.chips.length?-1:0)}_updateFocusForDestroyedChips(){if(null!=this._lastDestroyedChipIndex)if(this.chips.length){const t=Math.min(this._lastDestroyedChipIndex,this.chips.length-1);this._keyManager.setActiveItem(t)}else this.focus();this._lastDestroyedChipIndex=null}_isValidIndex(t){return t>=0&&t<this.chips.length}_setSelectionByValue(t,e=!0){if(this._clearSelection(),this.chips.forEach((t=>t.deselect())),Array.isArray(t))t.forEach((t=>this._selectValue(t,e))),this._sortValues();else{const n=this._selectValue(t,e);n&&e&&this._keyManager.setActiveItem(n)}}_selectValue(t,e=!0){const n=this.chips.find((e=>null!=e.value&&this._compareWith(e.value,t)));return n&&(e?n.selectViaInteraction():n.select(),this._selectionModel.select(n)),n}_initializeSelection(){Promise.resolve().then((()=>{(this.ngControl||this._value)&&(this._setSelectionByValue(this.ngControl?this.ngControl.value:this._value,!1),this.stateChanges.next())}))}_clearSelection(t){this._selectionModel.clear(),this.chips.forEach((e=>{e!==t&&e.deselect()})),this.stateChanges.next()}_sortValues(){this._multiple&&(this._selectionModel.clear(),this.chips.forEach((t=>{t.selected&&this._selectionModel.select(t)})),this.stateChanges.next())}_propagateChanges(t){let e=null;e=Array.isArray(this.selected)?this.selected.map((t=>t.value)):this.selected?this.selected.value:t,this._value=e,this.change.emit(new Oyt(this,e)),this.valueChange.emit(e),this._onChange(e),this._changeDetectorRef.markForCheck()}_blur(){this._hasFocusedChip()||this._keyManager.setActiveItem(-1),this.disabled||(this._chipInput?setTimeout((()=>{this.focused||this._markAsTouched()})):this._markAsTouched())}_markAsTouched(){this._onTouched(),this._changeDetectorRef.markForCheck(),this.stateChanges.next()}_allowFocusEscape(){-1!==this._tabIndex&&(this._tabIndex=-1,setTimeout((()=>{this._tabIndex=this._userTabIndex||0,this._changeDetectorRef.markForCheck()})))}_resetChips(){this._dropSubscriptions(),this._listenToChipsFocus(),this._listenToChipsSelection(),this._listenToChipsRemoved()}_dropSubscriptions(){this._chipFocusSubscription&&(this._chipFocusSubscription.unsubscribe(),this._chipFocusSubscription=null),this._chipBlurSubscription&&(this._chipBlurSubscription.unsubscribe(),this._chipBlurSubscription=null),this._chipSelectionSubscription&&(this._chipSelectionSubscription.unsubscribe(),this._chipSelectionSubscription=null),this._chipRemoveSubscription&&(this._chipRemoveSubscription.unsubscribe(),this._chipRemoveSubscription=null)}_listenToChipsSelection(){this._chipSelectionSubscription=this.chipSelectionChanges.subscribe((t=>{t.source.selected?this._selectionModel.select(t.source):this._selectionModel.deselect(t.source),this.multiple||this.chips.forEach((t=>{!this._selectionModel.isSelected(t)&&t.selected&&t.deselect()})),t.isUserInput&&this._propagateChanges()}))}_listenToChipsFocus(){this._chipFocusSubscription=this.chipFocusChanges.subscribe((t=>{let e=this.chips.toArray().indexOf(t.chip);this._isValidIndex(e)&&this._keyManager.updateActiveItem(e),this.stateChanges.next()})),this._chipBlurSubscription=this.chipBlurChanges.subscribe((()=>{this._blur(),this.stateChanges.next()}))}_listenToChipsRemoved(){this._chipRemoveSubscription=this.chipRemoveChanges.subscribe((t=>{const e=t.chip,n=this.chips.toArray().indexOf(t.chip);this._isValidIndex(n)&&e._hasFocus&&(this._lastDestroyedChipIndex=n)}))}_originatesFromChip(t){let e=t.target;for(;e&&e!==this._elementRef.nativeElement;){if(e.classList.contains("mat-chip"))return!0;e=e.parentElement}return!1}_hasFocusedChip(){return this.chips&&this.chips.some((t=>t._hasFocus))}_syncChipsState(){this.chips&&this.chips.forEach((t=>{t._chipListDisabled=this._disabled,t._chipListMultiple=this.multiple}))}}Pyt.ɵfac=function t(e){return new(e||Pyt)(Sm(hg),Sm(Ug),Sm(HI,8),Sm(iU,8),Sm(PU,8),Sm(bH),Sm(Mj,10))},Pyt.ɵcmp=to({type:Pyt,selectors:[["mat-chip-list"]],contentQueries:function t(e,n,o){if(1&e&&$h(o,_yt,5),2&e){let t;Jh(t=tb())&&(n.chips=t)}},hostAttrs:[1,"mat-chip-list"],hostVars:15,hostBindings:function t(e,n){1&e&&Vm("focus",(function t(){return n.focus()}))("blur",(function t(){return n._blur()}))("keydown",(function t(e){return n._keydown(e)})),2&e&&(Tu("id",n._uid),jp("tabindex",n.disabled?null:n._tabIndex)("aria-describedby",n._ariaDescribedby||null)("aria-required",n.role?n.required:null)("aria-disabled",n.disabled.toString())("aria-invalid",n.errorState)("aria-multiselectable",n.multiple)("role",n.role)("aria-orientation",n.ariaOrientation),pu("mat-chip-list-disabled",n.disabled)("mat-chip-list-invalid",n.errorState)("mat-chip-list-required",n.required))},inputs:{ariaOrientation:["aria-orientation","ariaOrientation"],multiple:"multiple",compareWith:"compareWith",value:"value",required:"required",placeholder:"placeholder",disabled:"disabled",selectable:"selectable",tabIndex:"tabIndex",errorStateMatcher:"errorStateMatcher"},outputs:{change:"change",valueChange:"valueChange"},exportAs:["matChipList"],features:[pg([{provide:bV,useExisting:Pyt}]),xp],ngContentSelectors:["*"],decls:2,vars:0,consts:[[1,"mat-chip-list-wrapper"]],template:function t(e,n){1&e&&(Zm(),Rm(0,"div",0),Xm(1),Am())},styles:['.mat-chip{position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;transform:translateZ(0);border:none;-webkit-appearance:none;-moz-appearance:none}.mat-standard-chip{transition:box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);display:inline-flex;padding:7px 12px;border-radius:16px;align-items:center;cursor:default;min-height:32px;height:1px}._mat-animation-noopable.mat-standard-chip{transition:none;animation:none}.mat-standard-chip .mat-chip-remove.mat-icon{width:18px;height:18px}.mat-standard-chip::after{top:0;left:0;right:0;bottom:0;position:absolute;border-radius:inherit;opacity:0;content:"";pointer-events:none;transition:opacity 200ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-standard-chip:hover::after{opacity:.12}.mat-standard-chip:focus{outline:none}.mat-standard-chip:focus::after{opacity:.16}.cdk-high-contrast-active .mat-standard-chip{outline:solid 1px}.cdk-high-contrast-active .mat-standard-chip:focus{outline:dotted 2px}.mat-standard-chip.mat-chip-disabled::after{opacity:0}.mat-standard-chip.mat-chip-disabled .mat-chip-remove,.mat-standard-chip.mat-chip-disabled .mat-chip-trailing-icon{cursor:default}.mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar,.mat-standard-chip.mat-chip-with-avatar{padding-top:0;padding-bottom:0}.mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar{padding-right:8px;padding-left:0}[dir=rtl] .mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar{padding-left:8px;padding-right:0}.mat-standard-chip.mat-chip-with-trailing-icon{padding-top:7px;padding-bottom:7px;padding-right:8px;padding-left:12px}[dir=rtl] .mat-standard-chip.mat-chip-with-trailing-icon{padding-left:8px;padding-right:12px}.mat-standard-chip.mat-chip-with-avatar{padding-left:0;padding-right:12px}[dir=rtl] .mat-standard-chip.mat-chip-with-avatar{padding-right:0;padding-left:12px}.mat-standard-chip .mat-chip-avatar{width:24px;height:24px;margin-right:8px;margin-left:4px}[dir=rtl] .mat-standard-chip .mat-chip-avatar{margin-left:8px;margin-right:4px}.mat-standard-chip .mat-chip-remove,.mat-standard-chip .mat-chip-trailing-icon{width:18px;height:18px;cursor:pointer}.mat-standard-chip .mat-chip-remove,.mat-standard-chip .mat-chip-trailing-icon{margin-left:8px;margin-right:0}[dir=rtl] .mat-standard-chip .mat-chip-remove,[dir=rtl] .mat-standard-chip .mat-chip-trailing-icon{margin-right:8px;margin-left:0}.mat-chip-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none;border-radius:inherit;overflow:hidden}.mat-chip-list-wrapper{display:flex;flex-direction:row;flex-wrap:wrap;align-items:center;margin:-4px}.mat-chip-list-wrapper input.mat-input-element,.mat-chip-list-wrapper .mat-standard-chip{margin:4px}.mat-chip-list-stacked .mat-chip-list-wrapper{flex-direction:column;align-items:flex-start}.mat-chip-list-stacked .mat-chip-list-wrapper .mat-standard-chip{width:100%}.mat-chip-avatar{border-radius:50%;justify-content:center;align-items:center;display:flex;overflow:hidden;object-fit:cover}input.mat-chip-input{width:150px;margin:4px;flex:1 0 150px}\n'],encapsulation:2,changeDetection:0}),Pyt.ctorParameters=()=>[{type:hg},{type:Ug},{type:HI,decorators:[{type:Sr}]},{type:iU,decorators:[{type:Sr}]},{type:PU,decorators:[{type:Sr}]},{type:bH},{type:Mj,decorators:[{type:Sr},{type:Dr}]}],Pyt.propDecorators={errorStateMatcher:[{type:xy}],multiple:[{type:xy}],compareWith:[{type:xy}],value:[{type:xy}],required:[{type:xy}],placeholder:[{type:xy}],disabled:[{type:xy}],ariaOrientation:[{type:xy,args:["aria-orientation"]}],selectable:[{type:xy}],tabIndex:[{type:xy}],change:[{type:Oy}],valueChange:[{type:Oy}],chips:[{type:Ya,args:[_yt,{descendants:!0}]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(Pyt,[{type:My,args:[{selector:"mat-chip-list",template:'<div class="mat-chip-list-wrapper"><ng-content></ng-content></div>',exportAs:"matChipList",host:{"[attr.tabindex]":"disabled ? null : _tabIndex","[attr.aria-describedby]":"_ariaDescribedby || null","[attr.aria-required]":"role ? required : null","[attr.aria-disabled]":"disabled.toString()","[attr.aria-invalid]":"errorState","[attr.aria-multiselectable]":"multiple","[attr.role]":"role","[class.mat-chip-list-disabled]":"disabled","[class.mat-chip-list-invalid]":"errorState","[class.mat-chip-list-required]":"required","[attr.aria-orientation]":"ariaOrientation",class:"mat-chip-list","(focus)":"focus()","(blur)":"_blur()","(keydown)":"_keydown($event)","[id]":"_uid"},providers:[{provide:bV,useExisting:Pyt}],encapsulation:Hn.None,changeDetection:zn.OnPush,styles:['.mat-chip{position:relative;box-sizing:border-box;-webkit-tap-highlight-color:transparent;transform:translateZ(0);border:none;-webkit-appearance:none;-moz-appearance:none}.mat-standard-chip{transition:box-shadow 280ms cubic-bezier(0.4, 0, 0.2, 1);display:inline-flex;padding:7px 12px;border-radius:16px;align-items:center;cursor:default;min-height:32px;height:1px}._mat-animation-noopable.mat-standard-chip{transition:none;animation:none}.mat-standard-chip .mat-chip-remove.mat-icon{width:18px;height:18px}.mat-standard-chip::after{top:0;left:0;right:0;bottom:0;position:absolute;border-radius:inherit;opacity:0;content:"";pointer-events:none;transition:opacity 200ms cubic-bezier(0.35, 0, 0.25, 1)}.mat-standard-chip:hover::after{opacity:.12}.mat-standard-chip:focus{outline:none}.mat-standard-chip:focus::after{opacity:.16}.cdk-high-contrast-active .mat-standard-chip{outline:solid 1px}.cdk-high-contrast-active .mat-standard-chip:focus{outline:dotted 2px}.mat-standard-chip.mat-chip-disabled::after{opacity:0}.mat-standard-chip.mat-chip-disabled .mat-chip-remove,.mat-standard-chip.mat-chip-disabled .mat-chip-trailing-icon{cursor:default}.mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar,.mat-standard-chip.mat-chip-with-avatar{padding-top:0;padding-bottom:0}.mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar{padding-right:8px;padding-left:0}[dir=rtl] .mat-standard-chip.mat-chip-with-trailing-icon.mat-chip-with-avatar{padding-left:8px;padding-right:0}.mat-standard-chip.mat-chip-with-trailing-icon{padding-top:7px;padding-bottom:7px;padding-right:8px;padding-left:12px}[dir=rtl] .mat-standard-chip.mat-chip-with-trailing-icon{padding-left:8px;padding-right:12px}.mat-standard-chip.mat-chip-with-avatar{padding-left:0;padding-right:12px}[dir=rtl] .mat-standard-chip.mat-chip-with-avatar{padding-right:0;padding-left:12px}.mat-standard-chip .mat-chip-avatar{width:24px;height:24px;margin-right:8px;margin-left:4px}[dir=rtl] .mat-standard-chip .mat-chip-avatar{margin-left:8px;margin-right:4px}.mat-standard-chip .mat-chip-remove,.mat-standard-chip .mat-chip-trailing-icon{width:18px;height:18px;cursor:pointer}.mat-standard-chip .mat-chip-remove,.mat-standard-chip .mat-chip-trailing-icon{margin-left:8px;margin-right:0}[dir=rtl] .mat-standard-chip .mat-chip-remove,[dir=rtl] .mat-standard-chip .mat-chip-trailing-icon{margin-right:8px;margin-left:0}.mat-chip-ripple{top:0;left:0;right:0;bottom:0;position:absolute;pointer-events:none;border-radius:inherit;overflow:hidden}.mat-chip-list-wrapper{display:flex;flex-direction:row;flex-wrap:wrap;align-items:center;margin:-4px}.mat-chip-list-wrapper input.mat-input-element,.mat-chip-list-wrapper .mat-standard-chip{margin:4px}.mat-chip-list-stacked .mat-chip-list-wrapper{flex-direction:column;align-items:flex-start}.mat-chip-list-stacked .mat-chip-list-wrapper .mat-standard-chip{width:100%}.mat-chip-avatar{border-radius:50%;justify-content:center;align-items:center;display:flex;overflow:hidden;object-fit:cover}input.mat-chip-input{width:150px;margin:4px;flex:1 0 150px}\n']}]}],(function(){return[{type:hg},{type:Ug},{type:HI,decorators:[{type:Sr}]},{type:iU,decorators:[{type:Sr}]},{type:PU,decorators:[{type:Sr}]},{type:bH},{type:Mj,decorators:[{type:Sr},{type:Dr}]}]}),{ariaOrientation:[{type:xy,args:["aria-orientation"]}],change:[{type:Oy}],valueChange:[{type:Oy}],multiple:[{type:xy}],compareWith:[{type:xy}],value:[{type:xy}],required:[{type:xy}],placeholder:[{type:xy}],disabled:[{type:xy}],selectable:[{type:xy}],tabIndex:[{type:xy}],errorStateMatcher:[{type:xy}],chips:[{type:Ya,args:[_yt,{descendants:!0}]}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let wyt=0;class kyt{constructor(t,e){this._elementRef=t,this._defaultOptions=e,this.focused=!1,this._addOnBlur=!1,this.separatorKeyCodes=this._defaultOptions.separatorKeyCodes,this.chipEnd=new Lh,this.placeholder="",this.id="mat-chip-list-input-"+wyt++,this._disabled=!1,this.inputElement=this._elementRef.nativeElement}set chipList(t){t&&(this._chipList=t,this._chipList.registerInput(this))}get addOnBlur(){return this._addOnBlur}set addOnBlur(t){this._addOnBlur=yz(t)}get disabled(){return this._disabled||this._chipList&&this._chipList.disabled}set disabled(t){this._disabled=yz(t)}get empty(){return!this.inputElement.value}ngOnChanges(){this._chipList.stateChanges.next()}ngOnDestroy(){this.chipEnd.complete()}ngAfterContentInit(){this._focusLastChipOnBackspace=this.empty}_keydown(t){if(t){if(9!==t.keyCode||bz(t,"shiftKey")||this._chipList._allowFocusEscape(),8===t.keyCode&&this._focusLastChipOnBackspace)return this._chipList._keyManager.setLastItemActive(),void t.preventDefault();this._focusLastChipOnBackspace=!1}this._emitChipEnd(t)}_keyup(t){!this._focusLastChipOnBackspace&&8===t.keyCode&&this.empty&&(this._focusLastChipOnBackspace=!0,t.preventDefault())}_blur(){this.addOnBlur&&this._emitChipEnd(),this.focused=!1,this._chipList.focused||this._chipList._blur(),this._chipList.stateChanges.next()}_focus(){this.focused=!0,this._focusLastChipOnBackspace=this.empty,this._chipList.stateChanges.next()}_emitChipEnd(t){!this.inputElement.value&&t&&this._chipList._keydown(t),t&&!this._isSeparatorKey(t)||(this.chipEnd.emit({input:this.inputElement,value:this.inputElement.value,chipInput:this}),null==t||t.preventDefault())}_onInput(){this._chipList.stateChanges.next()}focus(t){this.inputElement.focus(t)}clear(){this.inputElement.value="",this._focusLastChipOnBackspace=!0}_isSeparatorKey(t){return!bz(t)&&new Set(this.separatorKeyCodes).has(t.keyCode)}}kyt.ɵfac=function t(e){return new(e||kyt)(Sm(hg),Sm(Myt))},kyt.ɵdir=lo({type:kyt,selectors:[["input","matChipInputFor",""]],hostAttrs:[1,"mat-chip-input","mat-input-element"],hostVars:5,hostBindings:function t(e,n){1&e&&Vm("keydown",(function t(e){return n._keydown(e)}))("keyup",(function t(e){return n._keyup(e)}))("blur",(function t(){return n._blur()}))("focus",(function t(){return n._focus()}))("input",(function t(){return n._onInput()})),2&e&&(Tu("id",n.id),jp("disabled",n.disabled||null)("placeholder",n.placeholder||null)("aria-invalid",n._chipList&&n._chipList.ngControl?n._chipList.ngControl.invalid:null)("aria-required",n._chipList&&n._chipList.required||null))},inputs:{separatorKeyCodes:["matChipInputSeparatorKeyCodes","separatorKeyCodes"],placeholder:"placeholder",id:"id",chipList:["matChipInputFor","chipList"],addOnBlur:["matChipInputAddOnBlur","addOnBlur"],disabled:"disabled"},outputs:{chipEnd:"matChipInputTokenEnd"},exportAs:["matChipInput","matChipInputFor"],features:[Bo]}),kyt.ctorParameters=()=>[{type:hg},{type:void 0,decorators:[{type:kr,args:[Myt]}]}],kyt.propDecorators={chipList:[{type:xy,args:["matChipInputFor"]}],addOnBlur:[{type:xy,args:["matChipInputAddOnBlur"]}],separatorKeyCodes:[{type:xy,args:["matChipInputSeparatorKeyCodes"]}],chipEnd:[{type:Oy,args:["matChipInputTokenEnd"]}],placeholder:[{type:xy}],id:[{type:xy}],disabled:[{type:xy}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(kyt,[{type:Cy,args:[{selector:"input[matChipInputFor]",exportAs:"matChipInput, matChipInputFor",host:{class:"mat-chip-input mat-input-element","(keydown)":"_keydown($event)","(keyup)":"_keyup($event)","(blur)":"_blur()","(focus)":"_focus()","(input)":"_onInput()","[id]":"id","[attr.disabled]":"disabled || null","[attr.placeholder]":"placeholder || null","[attr.aria-invalid]":"_chipList && _chipList.ngControl ? _chipList.ngControl.invalid : null","[attr.aria-required]":"_chipList && _chipList.required || null"}}]}],(function(){return[{type:hg},{type:void 0,decorators:[{type:kr,args:[Myt]}]}]}),{separatorKeyCodes:[{type:xy,args:["matChipInputSeparatorKeyCodes"]}],chipEnd:[{type:Oy,args:["matChipInputTokenEnd"]}],placeholder:[{type:xy}],id:[{type:xy}],chipList:[{type:xy,args:["matChipInputFor"]}],addOnBlur:[{type:xy,args:["matChipInputAddOnBlur"]}],disabled:[{type:xy}]});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const Syt=[Pyt,_yt,kyt,Cyt,byt,yyt],Dyt={separatorKeyCodes:[mz]};class Eyt{}Eyt.ɵfac=function t(e){return new(e||Eyt)},Eyt.ɵmod=ao({type:Eyt}),Eyt.ɵinj=vn({providers:[bH,{provide:Myt,useValue:Dyt}],imports:[[XI]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Eyt,[{type:Ay,args:[{imports:[XI],exports:Syt,declarations:Syt,providers:[bH,{provide:Myt,useValue:Dyt}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Eyt,{declarations:function(){return[Pyt,_yt,kyt,Cyt,byt,yyt]},imports:function(){return[XI]},exports:function(){return[Pyt,_yt,kyt,Cyt,byt,yyt]}});const Ryt=function(t,e){return{"embedding-selected":t,"embedding-unselected":e}};function Ayt(t,e){if(1&t){const t=Hm();Rm(0,"mat-icon",5),Vm("click",(function e(){hi(t);const n=Ym();return n.onSelect.emit(n.metric)})),Am()}if(2&t){const t=Ym();Dm("ngClass",vh(1,Ryt,t.embeddingsMetric===t.metric,t.embeddingsMetric!==t.metric))}}const Tyt=function(t){return{width:t}},Nyt=function(t){return{"value-invalid":t}};class zyt{constructor(){this.onRemove=new Lh,this.onSelect=new Lh,this.onFilterChange=new Lh,this.focusMin=!1,this.focusMax=!1,this.ngUnsubscribe=new I}ngOnInit(){this.minFormControl=new $j(this.filterValues.min,[qV.required,qV.min(-1),qV.max(1),this.minValueValidator.bind(this)]),this.maxFormControl=new $j(this.filterValues.max,[qV.required,qV.min(-1),qV.max(1),this.maxValueValidator.bind(this)]),this.minFormControl.valueChanges.pipe(Ie(this.ngUnsubscribe)).subscribe((()=>{this.minFormControl.valid&&this.maxFormControl.valid&&this.onFilterChange.emit({min:parseFloat(this.minFormControl.value),max:parseFloat(this.maxFormControl.value)})})),this.maxFormControl.valueChanges.pipe(Ie(this.ngUnsubscribe)).subscribe((()=>{this.minFormControl.valid&&this.maxFormControl.valid&&this.onFilterChange.emit({min:parseFloat(this.minFormControl.value),max:parseFloat(this.maxFormControl.value)})}))}ngOnChanges(t){this.minFormControl&&this.maxFormControl&&(this.minFormControl.setValue(this.filterValues.min,{emitEvent:!1}),this.maxFormControl.setValue(this.filterValues.max,{emitEvent:!1}))}ngOnDestroy(){this.ngUnsubscribe.next(),this.ngUnsubscribe.complete()}minValueValidator(t){return this.maxFormControl&&"NaN"!==t.value?isNaN(parseFloat(t.value))?{value:"the string you entered is neither NaN nor a number"}:parseFloat(t.value)>parseFloat(this.maxFormControl.value)?{value:"the number you entered is larger than the max value"}:null:null}maxValueValidator(t){return this.minFormControl?"NaN"===this.minFormControl.value&&"NaN"===t.value?null:isNaN(parseFloat(t.value))?{value:"the string you entered is neither NaN nor a number"}:t.value<this.minFormControl.value?{value:"the number you entered is smaller than the min value"}:null:null}getErrorDescription(t){if(t){const e=Object.keys(t)[0];return"required"===e?"you did not enter anything":"min"===e?"the number must be at least -1.0":"max"===e?"the number is bigger than 1.0":t[e]}return""}}zyt.ɵfac=function t(e){return new(e||zyt)},zyt.ɵcmp=to({type:zyt,selectors:[["metric-arithmetic-element-component"]],inputs:{metric:"metric",filterValues:"filterValues",hasEmbeddingsData:"hasEmbeddingsData",embeddingsMetric:"embeddingsMetric"},outputs:{onRemove:"onRemove",onSelect:"onSelect",onFilterChange:"onFilterChange"},features:[Bo],decls:10,vars:22,consts:[[1,"filter-chip",3,"removed"],["class","embeddings-button","svgIcon","group_work_24px",3,"ngClass","click",4,"ngIf"],[1,"metric-arithmetic-element-range",3,"keydown"],["matInput","",1,"input-field",3,"value","matTooltip","matTooltipDisabled","ngStyle","ngClass","formControl","focus","focusout"],["matChipRemove","","svgIcon","cancel_24px"],["svgIcon","group_work_24px",1,"embeddings-button",3,"ngClass","click"]],template:function t(e,n){1&e&&(Rm(0,"mat-chip",0),Vm("removed",(function t(){return n.onRemove.emit(n.metric)})),Qp(1,Ayt,1,4,"mat-icon",1),ku(2),Rm(3,"div",2),Vm("keydown",(function t(e){return e.stopPropagation()})),ku(4," [ "),Rm(5,"input",3),Vm("focus",(function t(){return n.focusMin=!0}))("focusout",(function t(){return n.focusMin=!1})),Am(),ku(6," ; "),Rm(7,"input",3),Vm("focus",(function t(){return n.focusMax=!0}))("focusout",(function t(){return n.focusMax=!1})),Am(),ku(8," ] "),Am(),Tm(9,"mat-icon",4),Am()),2&e&&(rc(1),Dm("ngIf",n.hasEmbeddingsData),rc(1),Du(" ",n.metric," "),rc(3),Dm("value",n.filterValues.min)("matTooltip",n.getErrorDescription(n.minFormControl.errors))("matTooltipDisabled",!n.minFormControl.invalid)("ngStyle",Mh(14,Tyt,n.focusMin?"100px":n.minFormControl.value.toString().length+"ch"))("ngClass",Mh(16,Nyt,!n.minFormControl.valid))("formControl",n.minFormControl),rc(2),Dm("value",n.filterValues.max)("matTooltip",n.getErrorDescription(n.maxFormControl.errors))("matTooltipDisabled",!n.maxFormControl.invalid)("ngStyle",Mh(18,Tyt,n.focusMax?"100px":n.maxFormControl.value.toString().length+"ch"))("ngClass",Mh(20,Nyt,!n.maxFormControl.valid))("formControl",n.maxFormControl))},directives:[_yt,dM,VV,FQ,CM,aM,xj,xU,DW,Cyt],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   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1s}.input-field[_ngcontent-%COMP%]:focus{background-color:rgba(0,0,0,.12);border:none;outline:none}.value-invalid[_ngcontent-%COMP%]{color:#f44336}.embedding-selected[_ngcontent-%COMP%]{color:#f57c00;opacity:1}.embedding-unselected[_ngcontent-%COMP%]{opacity:.4}.embeddings-button[_ngcontent-%COMP%]{width:18px;height:18px;margin-right:8px;cursor:pointer}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(zyt,[{type:My,args:[{selector:"metric-arithmetic-element-component",templateUrl:"./metric_arithmetic_element_component.ng.html",styleUrls:["./metric_arithmetic_element_component.css"],changeDetection:zn.OnPush}]}],null,{metric:[{type:xy}],filterValues:[{type:xy}],hasEmbeddingsData:[{type:xy}],embeddingsMetric:[{type:xy}],onRemove:[{type:Oy}],onSelect:[{type:Oy}],onFilterChange:[{type:Oy}]});class Iyt{constructor(t){this.store=t,this.filterValues$=this.store.pipe(Fw(gbt)).pipe(It((t=>{const e=t[this.metric];return e?{min:e.includeNaN?"NaN":this.roundToThreeDecimalPoints(e.min),max:e.max<e.min?"NaN":this.roundToThreeDecimalPoints(e.max)}:{min:-1,max:1}}))),this.hasEmbeddingsData$=this.store.pipe(Fw(lbt)).pipe(It((t=>void 0!==t))),this.embeddingsMetric$=this.store.pipe(Fw(Obt))}remove(t){this.store.dispatch(Lbt({metric:t}))}select(t){this.store.dispatch(Zbt({metric:t}))}filterChange(t){const e=isNaN(t.min)?-1:t.min,n=isNaN(t.max)?-2:t.max,o=isNaN(t.min);this.store.dispatch(Bbt({metric:this.metric,max:n,min:e,includeNaN:o}))}roundToThreeDecimalPoints(t){return Math.round(1e3*(t+Number.EPSILON))/1e3}}Iyt.ɵfac=function t(e){return new(e||Iyt)(Sm(Iw))},Iyt.ɵcmp=to({type:Iyt,selectors:[["npmi-metric-arithmetic-element"]],inputs:{metric:"metric"},decls:4,vars:10,consts:[[3,"metric","filterValues","hasEmbeddingsData","embeddingsMetric","onRemove","onSelect","onFilterChange"]],template:function t(e,n){1&e&&(Rm(0,"metric-arithmetic-element-component",0),Vm("onRemove",(function t(e){return n.remove(e)}))("onSelect",(function t(e){return n.select(e)}))("onFilterChange",(function t(e){return n.filterChange(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Am()),2&e&&Dm("metric",n.metric)("filterValues",Th(1,4,n.filterValues$))("hasEmbeddingsData",Th(2,6,n.hasEmbeddingsData$))("embeddingsMetric",Th(3,8,n.embeddingsMetric$))},directives:[zyt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Iyt,[{type:My,args:[{selector:"npmi-metric-arithmetic-element",template:'\n    <metric-arithmetic-element-component\n      [metric]="metric"\n      [filterValues]="filterValues$ | async"\n      [hasEmbeddingsData]="hasEmbeddingsData$ | async"\n      [embeddingsMetric]="embeddingsMetric$ | async"\n      (onRemove)="remove($event)"\n      (onSelect)="select($event)"\n      (onFilterChange)="filterChange($event)"\n    ></metric-arithmetic-element-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{metric:[{type:xy}]});class Hyt{constructor(){this.Operator=$ht}}function Fyt(t,e){1&t&&Tm(0,"npmi-metric-arithmetic-element",4),2&t&&Dm("metric",Ym().$implicit.metric)}function Lyt(t,e){1&t&&Tm(0,"npmi-metric-arithmetic-operator",5),2&t&&Dm("operator",Ym().$implicit.operator)}function Byt(t,e){if(1&t&&(Rm(0,"div"),Qp(1,Fyt,1,1,"npmi-metric-arithmetic-element",2),Qp(2,Lyt,1,1,"npmi-metric-arithmetic-operator",3),Am()),2&t){const t=e.$implicit,n=Ym();rc(1),Dm("ngIf",t.kind===n.ArithmeticKind.METRIC),rc(1),Dm("ngIf",t.kind===n.ArithmeticKind.OPERATOR)}}Hyt.ɵfac=function t(e){return new(e||Hyt)},Hyt.ɵcmp=to({type:Hyt,selectors:[["npmi-metric-arithmetic-operator"]],inputs:{operator:"operator"},decls:2,vars:1,template:function t(e,n){1&e&&(Rm(0,"mat-chip"),ku(1),Am()),2&e&&(rc(1),Du(" ",n.operator===n.Operator.AND?"&":""," "))},directives:[_yt],encapsulation:2,changeDetection:0}),("undefined"==typeof 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t=J4(this),e=att(this).selection;e?(t.selectAll(".selection").style("display",null).attr("x",e[0][0]).attr("y",e[0][1]).attr("width",e[1][0]-e[0][0]).attr("height",e[1][1]-e[0][1]),t.selectAll(".handle").style("display",null).attr("x",(function(t){return"e"===t.type[t.type.length-1]?e[1][0]-l/2:e[0][0]-l/2})).attr("y",(function(t){return"s"===t.type[0]?e[1][1]-l/2:e[0][1]-l/2})).attr("width",(function(t){return"n"===t.type||"s"===t.type?e[1][0]-e[0][0]+l:l})).attr("height",(function(t){return"e"===t.type||"w"===t.type?e[1][1]-e[0][1]+l:l}))):t.selectAll(".selection,.handle").style("display","none").attr("x",null).attr("y",null).attr("width",null).attr("height",null)}function p(t,e,n){var o=t.__brush.emitter;return!o||n&&o.clean?new m(t,e,n):o}function m(t,e,n){this.that=t,this.args=e,this.state=t.__brush,this.active=0,this.clean=n}function u(){if((!n||L4.touches)&&i.apply(this,arguments)){var 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qyt{constructor(t){this.store=t,this.activeRuns$=this.store.pipe(Fw(NN)).pipe(It((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.visibleAnnotations$=Wt([this.store.select(rbt),this.store.select(pbt),this.store.select(Mbt)]).pipe(It((([t,e,n])=>Gyt(t,e,n)))),this.chartWidth$=this.store.pipe(Fw(xbt)).pipe(It((t=>Math.max(150,t)))),this.runColorScale$=this.store.select(zN).pipe(It((t=>e=>{if(!t.hasOwnProperty(e))throw new Error(`[Color scale] unknown runId: ${e}.`);return t[e]})))}ngOnInit(){this.chartData$=Wt([this.visibleAnnotations$,this.activeRuns$]).pipe(It((([t,e])=>(function n(t,e,o){const i={},a={},r=new Set(e),s=nyt(o),l={max:-1,min:1};Object.values(t).forEach((t=>{t.forEach((t=>{const e=t.run;if(r.has(e)&&t.metric===s)if(null===t.nPMIValue)a[e]?a[e].push(null):a[e]=[null];else{const n=t.nPMIValue;l.max=l.max<n?n:l.max,l.min=l.min>n?n:l.min,i[t.run]?i[e].push(n):i[e]=[n]}}))}));const c={},d=O5().domain([l.min,l.max]).value((t=>t)),p=O5().domain([-1/0,1/0]).thresholds(0).value((t=>t));for(const t of r)if(c[t]=d(i[t]),a[t]){const e=p(a[t]);c[t].unshift(e[0])}return{violinData:c,extremes:l}})(t,e,this.metricName))))}removeMetric(){this.store.dispatch(Lbt({metric:this.metricName}))}updateFilter(t){this.store.dispatch(Bbt(Object.assign({metric:this.metricName},t)))}}function Zyt(t,e){if(1&t&&Tm(0,"npmi-violin-filter",8),2&t){const t=e.$implicit;Dm("metricName",t[0])("filter",t[1])}}function Xyt(t,e){1&t&&(Rm(0,"div",9),Rm(1,"span",10),ku(2," You can add more filters at the top. "),Am(),Am())}qyt.ɵfac=function t(e){return new(e||qyt)(Sm(Iw))},qyt.ɵcmp=to({type:qyt,selectors:[["npmi-violin-filter"]],inputs:{metricName:"metricName",filter:"filter"},decls:4,vars:11,consts:[[3,"metricName","filter","chartData","width","colorScale","onRemove","onUpdateFilter"]],template:function t(e,n){1&e&&(Rm(0,"violin-filter-component",0),Vm("onRemove",(function t(){return n.removeMetric()}))("onUpdateFilter",(function t(e){return n.updateFilter(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Am()),2&e&&Dm("metricName",n.metricName)("filter",n.filter)("chartData",Th(1,5,n.chartData$))("width",Th(2,7,n.chartWidth$))("colorScale",Th(3,9,n.runColorScale$))},directives:[Yyt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(qyt,[{type:My,args:[{selector:"npmi-violin-filter",template:'\n    <violin-filter-component\n      [metricName]="metricName"\n      [filter]="filter"\n      [chartData]="chartData$ | async"\n      [width]="chartWidth$ | async"\n      [colorScale]="runColorScale$ | async"\n      (onRemove)="removeMetric()"\n      (onUpdateFilter)="updateFilter($event)"\n    ></violin-filter-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{metricName:[{type:xy}],filter:[{type:xy}]});class Kyt{constructor(){this.toggleSidebarExpanded=new Lh}}Kyt.ɵfac=function t(e){return new(e||Kyt)},Kyt.ɵcmp=to({type:Kyt,selectors:[["violin-filters-component"]],inputs:{sidebarExpanded:"sidebarExpanded",metricFilters:"metricFilters"},outputs:{toggleSidebarExpanded:"toggleSidebarExpanded"},decls:9,vars:2,consts:function(){let t;return t="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Expand/Hide Sidebar"):$localize`:Label for a button that expands/hides the sidebar.␟48c29903ce881ab61088f8d49d827203716aaed4␟4658602991970260215:Expand/Hide 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.mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", 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.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes 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16px}.filters-hint-text[_ngcontent-%COMP%]{color:rgba(0,0,0,.38)}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Kyt,[{type:My,args:[{selector:"violin-filters-component",templateUrl:"./violin_filters_component.ng.html",styleUrls:["./violin_filters_component.css"],changeDetection:zn.OnPush}]}],null,{sidebarExpanded:[{type:xy}],metricFilters:[{type:xy}],toggleSidebarExpanded:[{type:Oy}]});class Jyt{constructor(t){this.store=t,this.sidebarExpanded$=this.store.select(_bt),this.metricFilters$=this.store.select(gbt).pipe(It((t=>Object.entries(t))))}onToggleSidebarExpanded(){this.store.dispatch(Wbt())}}function Qyt(t,e,n){return t.length!=e.length?n:e.map(((e,n)=>e-t[n])).map((t=>Math.pow(t,2))).reduce(((t,e)=>t+e),0)}Jyt.ɵfac=function t(e){return new(e||Jyt)(Sm(Iw))},Jyt.ɵcmp=to({type:Jyt,selectors:[["npmi-violin-filters"]],decls:3,vars:6,consts:[[3,"sidebarExpanded","metricFilters","toggleSidebarExpanded"]],template:function t(e,n){1&e&&(Rm(0,"violin-filters-component",0),Vm("toggleSidebarExpanded",(function t(){return n.onToggleSidebarExpanded()})),Ah(1,"async"),Ah(2,"async"),Am()),2&e&&Dm("sidebarExpanded",Th(1,2,n.sidebarExpanded$))("metricFilters",Th(2,4,n.metricFilters$))},directives:[Kyt],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Jyt,[{type:My,args:[{selector:"npmi-violin-filters",template:'\n    <violin-filters-component\n      [sidebarExpanded]="sidebarExpanded$ | async"\n      [metricFilters]="metricFilters$ | async"\n      (toggleSidebarExpanded)="onToggleSidebarExpanded()"\n    ></violin-filters-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);const $yt=["thumbContainer"],t_t=["toggleBar"],e_t=["input"],n_t=function(t){return{enterDuration:t}},o_t=new Ga("mat-slide-toggle-default-options",{providedIn:"root",factory:()=>({disableToggleValue:!1})});
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
let i_t=0;const a_t={provide:IV,useExisting:qe((()=>l_t)),multi:!0};class r_t{constructor(t,e){this.source=t,this.checked=e}}const s_t=$I(JI(QI(KI(class{constructor(t){this._elementRef=t}}))));class l_t extends s_t{constructor(t,e,n,o,i,a){super(t),this._focusMonitor=e,this._changeDetectorRef=n,this.defaults=i,this._onChange=t=>{},this._onTouched=()=>{},this._uniqueId="mat-slide-toggle-"+ ++i_t,this._required=!1,this._checked=!1,this.name=null,this.id=this._uniqueId,this.labelPosition="after",this.ariaLabel=null,this.ariaLabelledby=null,this.change=new Lh,this.toggleChange=new Lh,this.tabIndex=parseInt(o)||0,this.color=this.defaultColor=i.color||"accent",this._noopAnimations="NoopAnimations"===a}get required(){return this._required}set required(t){this._required=yz(t)}get checked(){return this._checked}set checked(t){this._checked=yz(t),this._changeDetectorRef.markForCheck()}get inputId(){return`${this.id||this._uniqueId}-input`}ngAfterContentInit(){this._focusMonitor.monitor(this._elementRef,!0).subscribe((t=>{"keyboard"===t||"program"===t?this._inputElement.nativeElement.focus():t||Promise.resolve().then((()=>this._onTouched()))}))}ngOnDestroy(){this._focusMonitor.stopMonitoring(this._elementRef)}_onChangeEvent(t){t.stopPropagation(),this.toggleChange.emit(),this.defaults.disableToggleValue?this._inputElement.nativeElement.checked=this.checked:(this.checked=this._inputElement.nativeElement.checked,this._emitChangeEvent())}_onInputClick(t){t.stopPropagation()}writeValue(t){this.checked=!!t}registerOnChange(t){this._onChange=t}registerOnTouched(t){this._onTouched=t}setDisabledState(t){this.disabled=t,this._changeDetectorRef.markForCheck()}focus(t,e){e?this._focusMonitor.focusVia(this._inputElement,e,t):this._inputElement.nativeElement.focus(t)}toggle(){this.checked=!this.checked,this._onChange(this.checked)}_emitChangeEvent(){this._onChange(this.checked),this.change.emit(new r_t(this,this.checked))}_onLabelTextChange(){this._changeDetectorRef.detectChanges()}}l_t.ɵfac=function t(e){return new(e||l_t)(Sm(hg),Sm(SI),Sm(Ug),Na("tabindex"),Sm(o_t),Sm(VP,8))},l_t.ɵcmp=to({type:l_t,selectors:[["mat-slide-toggle"]],viewQuery:function t(e,n){if(1&e&&(Qh($yt,5),Qh(t_t,5),Qh(e_t,5)),2&e){let t;Jh(t=tb())&&(n._thumbEl=t.first),Jh(t=tb())&&(n._thumbBarEl=t.first),Jh(t=tb())&&(n._inputElement=t.first)}},hostAttrs:[1,"mat-slide-toggle"],hostVars:12,hostBindings:function t(e,n){2&e&&(Tu("id",n.id),jp("tabindex",n.disabled?null:-1)("aria-label",null)("aria-labelledby",null),pu("mat-checked",n.checked)("mat-disabled",n.disabled)("mat-slide-toggle-label-before","before"==n.labelPosition)("_mat-animation-noopable",n._noopAnimations))},inputs:{disabled:"disabled",disableRipple:"disableRipple",color:"color",tabIndex:"tabIndex",name:"name",id:"id",labelPosition:"labelPosition",ariaLabel:["aria-label","ariaLabel"],ariaLabelledby:["aria-labelledby","ariaLabelledby"],required:"required",checked:"checked",ariaDescribedby:["aria-describedby","ariaDescribedby"]},outputs:{change:"change",toggleChange:"toggleChange"},exportAs:["matSlideToggle"],features:[pg([a_t]),xp],ngContentSelectors:["*"],decls:16,vars:20,consts:[[1,"mat-slide-toggle-label"],["label",""],[1,"mat-slide-toggle-bar"],["toggleBar",""],["type","checkbox","role","switch",1,"mat-slide-toggle-input","cdk-visually-hidden",3,"id","required","tabIndex","checked","disabled","change","click"],["input",""],[1,"mat-slide-toggle-thumb-container"],["thumbContainer",""],[1,"mat-slide-toggle-thumb"],["mat-ripple","",1,"mat-slide-toggle-ripple","mat-focus-indicator",3,"matRippleTrigger","matRippleDisabled","matRippleCentered","matRippleRadius","matRippleAnimation"],[1,"mat-ripple-element","mat-slide-toggle-persistent-ripple"],[1,"mat-slide-toggle-content",3,"cdkObserveContent"],["labelContent",""],[2,"display","none"]],template:function t(e,n){if(1&e&&(Zm(),Rm(0,"label",0,1),Rm(2,"div",2,3),Rm(4,"input",4,5),Vm("change",(function t(e){return n._onChangeEvent(e)}))("click",(function t(e){return n._onInputClick(e)})),Am(),Rm(6,"div",6,7),Tm(8,"div",8),Rm(9,"div",9),Tm(10,"div",10),Am(),Am(),Am(),Rm(11,"span",11,12),Vm("cdkObserveContent",(function t(){return n._onLabelTextChange()})),Rm(13,"span",13),ku(14," "),Am(),Xm(15),Am(),Am()),2&e){const t=$p(1),e=$p(12);jp("for",n.inputId),rc(2),pu("mat-slide-toggle-bar-no-side-margin",!e.textContent||!e.textContent.trim()),rc(2),Dm("id",n.inputId)("required",n.required)("tabIndex",n.tabIndex)("checked",n.checked)("disabled",n.disabled),jp("name",n.name)("aria-checked",n.checked.toString())("aria-label",n.ariaLabel)("aria-labelledby",n.ariaLabelledby)("aria-describedby",n.ariaDescribedby),rc(5),Dm("matRippleTrigger",t)("matRippleDisabled",n.disableRipple||n.disabled)("matRippleCentered",!0)("matRippleRadius",20)("matRippleAnimation",Mh(18,n_t,n._noopAnimations?0:150))}},directives:[kH,jz],styles:[".mat-slide-toggle{display:inline-block;height:24px;max-width:100%;line-height:24px;white-space:nowrap;outline:none;-webkit-tap-highlight-color:transparent}.mat-slide-toggle.mat-checked .mat-slide-toggle-thumb-container{transform:translate3d(16px, 0, 0)}[dir=rtl] .mat-slide-toggle.mat-checked .mat-slide-toggle-thumb-container{transform:translate3d(-16px, 0, 0)}.mat-slide-toggle.mat-disabled{opacity:.38}.mat-slide-toggle.mat-disabled .mat-slide-toggle-label,.mat-slide-toggle.mat-disabled .mat-slide-toggle-thumb-container{cursor:default}.mat-slide-toggle-label{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;display:flex;flex:1;flex-direction:row;align-items:center;height:inherit;cursor:pointer}.mat-slide-toggle-content{white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.mat-slide-toggle-label-before .mat-slide-toggle-label{order:1}.mat-slide-toggle-label-before .mat-slide-toggle-bar{order:2}[dir=rtl] .mat-slide-toggle-label-before .mat-slide-toggle-bar,.mat-slide-toggle-bar{margin-right:8px;margin-left:0}[dir=rtl] .mat-slide-toggle-bar,.mat-slide-toggle-label-before .mat-slide-toggle-bar{margin-left:8px;margin-right:0}.mat-slide-toggle-bar-no-side-margin{margin-left:0;margin-right:0}.mat-slide-toggle-thumb-container{position:absolute;z-index:1;width:20px;height:20px;top:-3px;left:0;transform:translate3d(0, 0, 0);transition:all 80ms linear;transition-property:transform}._mat-animation-noopable .mat-slide-toggle-thumb-container{transition:none}[dir=rtl] .mat-slide-toggle-thumb-container{left:auto;right:0}.mat-slide-toggle-thumb{height:20px;width:20px;border-radius:50%}.mat-slide-toggle-bar{position:relative;width:36px;height:14px;flex-shrink:0;border-radius:8px}.mat-slide-toggle-input{bottom:0;left:10px}[dir=rtl] .mat-slide-toggle-input{left:auto;right:10px}.mat-slide-toggle-bar,.mat-slide-toggle-thumb{transition:all 80ms linear;transition-property:background-color;transition-delay:50ms}._mat-animation-noopable .mat-slide-toggle-bar,._mat-animation-noopable .mat-slide-toggle-thumb{transition:none}.mat-slide-toggle .mat-slide-toggle-ripple{position:absolute;top:calc(50% - 20px);left:calc(50% - 20px);height:40px;width:40px;z-index:1;pointer-events:none}.mat-slide-toggle .mat-slide-toggle-ripple .mat-ripple-element:not(.mat-slide-toggle-persistent-ripple){opacity:.12}.mat-slide-toggle-persistent-ripple{width:100%;height:100%;transform:none}.mat-slide-toggle-bar:hover .mat-slide-toggle-persistent-ripple{opacity:.04}.mat-slide-toggle:not(.mat-disabled).cdk-keyboard-focused .mat-slide-toggle-persistent-ripple{opacity:.12}.mat-slide-toggle-persistent-ripple,.mat-slide-toggle.mat-disabled .mat-slide-toggle-bar:hover .mat-slide-toggle-persistent-ripple{opacity:0}@media(hover: none){.mat-slide-toggle-bar:hover .mat-slide-toggle-persistent-ripple{display:none}}.cdk-high-contrast-active .mat-slide-toggle-thumb,.cdk-high-contrast-active .mat-slide-toggle-bar{border:1px solid}.cdk-high-contrast-active .mat-slide-toggle.cdk-keyboard-focused .mat-slide-toggle-bar{outline:2px dotted;outline-offset:5px}\n"],encapsulation:2,changeDetection:0}),l_t.ctorParameters=()=>[{type:hg},{type:SI},{type:Ug},{type:String,decorators:[{type:ja,args:["tabindex"]}]},{type:void 0,decorators:[{type:kr,args:[o_t]}]},{type:String,decorators:[{type:Sr},{type:kr,args:[VP]}]}],l_t.propDecorators={_thumbEl:[{type:Za,args:["thumbContainer"]}],_thumbBarEl:[{type:Za,args:["toggleBar"]}],name:[{type:xy}],id:[{type:xy}],labelPosition:[{type:xy}],ariaLabel:[{type:xy,args:["aria-label"]}],ariaLabelledby:[{type:xy,args:["aria-labelledby"]}],ariaDescribedby:[{type:xy,args:["aria-describedby"]}],required:[{type:xy}],checked:[{type:xy}],change:[{type:Oy}],toggleChange:[{type:Oy}],_inputElement:[{type:Za,args:["input"]}]},("undefined"==typeof ngDevMode||ngDevMode)&&hh(l_t,[{type:My,args:[{selector:"mat-slide-toggle",exportAs:"matSlideToggle",host:{class:"mat-slide-toggle","[id]":"id","[attr.tabindex]":"disabled ? null : -1","[attr.aria-label]":"null","[attr.aria-labelledby]":"null","[class.mat-checked]":"checked","[class.mat-disabled]":"disabled","[class.mat-slide-toggle-label-before]":'labelPosition == "before"',"[class._mat-animation-noopable]":"_noopAnimations"},template:'<label [attr.for]="inputId" class="mat-slide-toggle-label" #label>\n  <div #toggleBar class="mat-slide-toggle-bar"\n       [class.mat-slide-toggle-bar-no-side-margin]="!labelContent.textContent || !labelContent.textContent.trim()">\n\n    <input #input class="mat-slide-toggle-input cdk-visually-hidden" type="checkbox"\n           role="switch"\n           [id]="inputId"\n           [required]="required"\n           [tabIndex]="tabIndex"\n           [checked]="checked"\n           [disabled]="disabled"\n           [attr.name]="name"\n           [attr.aria-checked]="checked.toString()"\n           [attr.aria-label]="ariaLabel"\n           [attr.aria-labelledby]="ariaLabelledby"\n           [attr.aria-describedby]="ariaDescribedby"\n           (change)="_onChangeEvent($event)"\n           (click)="_onInputClick($event)">\n\n    <div class="mat-slide-toggle-thumb-container" #thumbContainer>\n      <div class="mat-slide-toggle-thumb"></div>\n      <div class="mat-slide-toggle-ripple mat-focus-indicator" mat-ripple\n           [matRippleTrigger]="label"\n           [matRippleDisabled]="disableRipple || disabled"\n           [matRippleCentered]="true"\n           [matRippleRadius]="20"\n           [matRippleAnimation]="{enterDuration: _noopAnimations ? 0 : 150}">\n\n        <div class="mat-ripple-element mat-slide-toggle-persistent-ripple"></div>\n      </div>\n    </div>\n\n  </div>\n\n  <span class="mat-slide-toggle-content" #labelContent (cdkObserveContent)="_onLabelTextChange()">\n    \x3c!-- Add an invisible span so JAWS can read the label --\x3e\n    <span style="display:none">&nbsp;</span>\n    <ng-content></ng-content>\n  </span>\n</label>\n',providers:[a_t],inputs:["disabled","disableRipple","color","tabIndex"],encapsulation:Hn.None,changeDetection:zn.OnPush,styles:[".mat-slide-toggle{display:inline-block;height:24px;max-width:100%;line-height:24px;white-space:nowrap;outline:none;-webkit-tap-highlight-color:transparent}.mat-slide-toggle.mat-checked .mat-slide-toggle-thumb-container{transform:translate3d(16px, 0, 0)}[dir=rtl] .mat-slide-toggle.mat-checked .mat-slide-toggle-thumb-container{transform:translate3d(-16px, 0, 0)}.mat-slide-toggle.mat-disabled{opacity:.38}.mat-slide-toggle.mat-disabled .mat-slide-toggle-label,.mat-slide-toggle.mat-disabled .mat-slide-toggle-thumb-container{cursor:default}.mat-slide-toggle-label{-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;display:flex;flex:1;flex-direction:row;align-items:center;height:inherit;cursor:pointer}.mat-slide-toggle-content{white-space:nowrap;overflow:hidden;text-overflow:ellipsis}.mat-slide-toggle-label-before .mat-slide-toggle-label{order:1}.mat-slide-toggle-label-before .mat-slide-toggle-bar{order:2}[dir=rtl] .mat-slide-toggle-label-before .mat-slide-toggle-bar,.mat-slide-toggle-bar{margin-right:8px;margin-left:0}[dir=rtl] .mat-slide-toggle-bar,.mat-slide-toggle-label-before .mat-slide-toggle-bar{margin-left:8px;margin-right:0}.mat-slide-toggle-bar-no-side-margin{margin-left:0;margin-right:0}.mat-slide-toggle-thumb-container{position:absolute;z-index:1;width:20px;height:20px;top:-3px;left:0;transform:translate3d(0, 0, 0);transition:all 80ms linear;transition-property:transform}._mat-animation-noopable .mat-slide-toggle-thumb-container{transition:none}[dir=rtl] .mat-slide-toggle-thumb-container{left:auto;right:0}.mat-slide-toggle-thumb{height:20px;width:20px;border-radius:50%}.mat-slide-toggle-bar{position:relative;width:36px;height:14px;flex-shrink:0;border-radius:8px}.mat-slide-toggle-input{bottom:0;left:10px}[dir=rtl] 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/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
const c_t={provide:GV,useExisting:qe((()=>d_t)),multi:!0};class d_t extends KU{}d_t.ɵfac=(function(){let t;return function e(n){return(t||(t=Aa(d_t)))(n||d_t)}})(),d_t.ɵdir=lo({type:d_t,selectors:[["mat-slide-toggle","required","","formControlName",""],["mat-slide-toggle","required","","formControl",""],["mat-slide-toggle","required","","ngModel",""]],features:[pg([c_t]),xp]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(d_t,[{type:Cy,args:[{selector:"mat-slide-toggle[required][formControlName],\n             mat-slide-toggle[required][formControl], mat-slide-toggle[required][ngModel]",providers:[c_t]}]}],null,null);
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */
class p_t{}p_t.ɵfac=function t(e){return new(e||p_t)},p_t.ɵmod=ao({type:p_t}),p_t.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(p_t,[{type:Ay,args:[{exports:[d_t],declarations:[d_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(p_t,{declarations:[d_t],exports:[d_t]});class m_t{}function u_t(t,e){1&t&&Tm(0,"mat-icon",3)}m_t.ɵfac=function t(e){return new(e||m_t)},m_t.ɵmod=ao({type:m_t}),m_t.ɵinj=vn({imports:[[p_t,SH,XI,Uz],p_t,XI]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(m_t,[{type:Ay,args:[{imports:[p_t,SH,XI,Uz],exports:[p_t,l_t,XI],declarations:[l_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(m_t,{declarations:function(){return[l_t]},imports:function(){return[p_t,SH,XI,Uz]},exports:function(){return[p_t,l_t,XI]}});class f_t{constructor(){this.onRegexFilterValueChange=new Lh}}f_t.ɵfac=function t(e){return new(e||f_t)},f_t.ɵcmp=to({type:f_t,selectors:[["npmi-annotations-search-component"]],hostVars:2,hostBindings:function t(e,n){2&e&&pu("valid",n.isRegexFilterValid)},inputs:{regexFilterValue:"regexFilterValue",isRegexFilterValid:"isRegexFilterValid"},outputs:{onRegexFilterValueChange:"onRegexFilterValueChange"},decls:3,vars:2,consts:[["svgIcon","search_24px"],["autocomplete","off","placeholder","Filter Annotations",3,"value","input"],["svgIcon","error_24px","class","error-icon","matTooltip","Invalid regex filter. The result may be stale.",4,"ngIf"],["svgIcon","error_24px","matTooltip","Invalid regex filter. 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ngDevMode||ngDevMode)&&hh(f_t,[{type:My,args:[{selector:"npmi-annotations-search-component",templateUrl:"./annotations_search_component.ng.html",styleUrls:["./annotations_search_component.css"],changeDetection:zn.OnPush}]}],null,{regexFilterValue:[{type:xy}],onRegexFilterValueChange:[{type:Oy}],isRegexFilterValid:[{type:Py,args:["class.valid"]},{type:xy}]});class g_t{constructor(t){this.store=t,this.annotationsFilter$=this.store.select(mbt),this.isAnnotationsFilterValid$=this.annotationsFilter$.pipe(It((t=>{try{return new RegExp(t),!0}catch(t){return!1}})))}filterChange(t){this.store.dispatch(Ibt({regex:t}))}}function h_t(t,e){if(1&t){const t=Hm();Nm(0),Rm(1,"button",5),Vm("click",(function e(){hi(t);const n=Ym();return n.onFlagAnnotations.emit(n.selectedAnnotations)})),Tm(2,"mat-icon",6),Am(),Rm(3,"button",7),Vm("click",(function e(){hi(t);const n=Ym();return n.onHideAnnotations.emit(n.selectedAnnotations)})),Tm(4,"mat-icon",8),Am(),zm()}if(2&t){const t=Ym();rc(1),Dm("disabled",0===t.selectedAnnotations.length),rc(2),Dm("disabled",0===t.selectedAnnotations.length)}}function b_t(t,e){if(1&t){const t=Hm();Nm(0),Rm(1,"mat-slide-toggle",9),Vm("change",(function e(){return hi(t),Ym().onToggleShowCounts.emit()})),ku(2," Sample Count "),Am(),Rm(3,"mat-slide-toggle",10),Vm("change",(function e(){return hi(t),Ym().onToggleShowHidden.emit()})),ku(4," Show Hidden "),Am(),Tm(5,"npmi-annotations-search"),zm()}if(2&t){const t=Ym();rc(1),Dm("checked",t.showCounts),rc(2),Dm("checked",t.showHidden)}}g_t.ɵfac=function t(e){return new(e||g_t)(Sm(Iw))},g_t.ɵcmp=to({type:g_t,selectors:[["npmi-annotations-search"]],decls:3,vars:6,consts:[[3,"regexFilterValue","isRegexFilterValid","onRegexFilterValueChange"]],template:function t(e,n){1&e&&(Rm(0,"npmi-annotations-search-component",0),Vm("onRegexFilterValueChange",(function t(e){return 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new(e||y_t)},y_t.ɵcmp=to({type:y_t,selectors:[["npmi-annotations-list-toolbar-component"]],inputs:{numAnnotations:"numAnnotations",expanded:"expanded",selectedAnnotations:"selectedAnnotations",annotationsExpanded:"annotationsExpanded",showCounts:"showCounts",showHidden:"showHidden"},outputs:{onFlagAnnotations:"onFlagAnnotations",onHideAnnotations:"onHideAnnotations",onToggleExpanded:"onToggleExpanded",onToggleShowCounts:"onToggleShowCounts",onToggleShowHidden:"onToggleShowHidden"},decls:7,vars:4,consts:function(){let t,e,n;return t="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Hides/Shows the Annotations List"):$localize`:Label for a button that hides/shows the annotations list.␟b3603ba33e5308dd8c5e805e508b2f7233df89d4␟7336374413056342492:Hides/Shows the Annotations List`,e="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Flag Selected Annotations"):$localize`:Label for a button that flags selected 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ngDevMode||ngDevMode)&&hh(y_t,[{type:My,args:[{selector:"npmi-annotations-list-toolbar-component",templateUrl:"./annotations_list_toolbar_component.ng.html",styleUrls:["./annotations_list_toolbar_component.css"],changeDetection:zn.OnPush}]}],null,{numAnnotations:[{type:xy}],expanded:[{type:xy}],selectedAnnotations:[{type:xy}],annotationsExpanded:[{type:xy}],showCounts:[{type:xy}],showHidden:[{type:xy}],onFlagAnnotations:[{type:Oy}],onHideAnnotations:[{type:Oy}],onToggleExpanded:[{type:Oy}],onToggleShowCounts:[{type:Oy}],onToggleShowHidden:[{type:Oy}]});class __t{constructor(t){this.store=t,this.selectedAnnotations$=this.store.select(cbt),this.annotationsExpanded$=this.store.select(ybt),this.showCounts$=this.store.select(Cbt),this.showHidden$=this.store.select(Mbt),this.annotationsFilter$=this.store.select(mbt),this.isAnnotationsFilterValid$=this.annotationsFilter$.pipe(It((t=>{try{return Boolean(new RegExp(t))}catch(t){return!1}})))}filterChange(t){this.store.dispatch(Ibt({regex:t}))}flagAnnotations(t){this.store.dispatch(Nbt({annotations:t}))}hideAnnotations(t){this.store.dispatch(zbt({annotations:t}))}toggleExpanded(){this.store.dispatch(Gbt())}toggleShowCounts(){this.store.dispatch(Ybt())}toggleShowHidden(){this.store.dispatch(qbt())}}__t.ɵfac=function t(e){return new(e||__t)(Sm(Iw))},__t.ɵcmp=to({type:__t,selectors:[["npmi-annotations-list-toolbar"]],inputs:{numAnnotations:"numAnnotations",expanded:"expanded"},decls:5,vars:14,consts:[[3,"numAnnotations","expanded","selectedAnnotations","annotationsExpanded","showCounts","showHidden","onFlagAnnotations","onHideAnnotations","onToggleExpanded","onToggleShowCounts","onToggleShowHidden"]],template:function t(e,n){1&e&&(Rm(0,"npmi-annotations-list-toolbar-component",0),Vm("onFlagAnnotations",(function t(e){return n.flagAnnotations(e)}))("onHideAnnotations",(function t(e){return n.hideAnnotations(e)}))("onToggleExpanded",(function t(){return n.toggleExpanded()}))("onToggleShowCounts",(function t(){return n.toggleShowCounts()}))("onToggleShowHidden",(function t(){return n.toggleShowHidden()})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Am()),2&e&&Dm("numAnnotations",n.numAnnotations)("expanded",n.expanded)("selectedAnnotations",Th(1,6,n.selectedAnnotations$))("annotationsExpanded",Th(2,8,n.annotationsExpanded$))("showCounts",Th(3,10,n.showCounts$))("showHidden",Th(4,12,n.showHidden$))},directives:[y_t],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(__t,[{type:My,args:[{selector:"npmi-annotations-list-toolbar",template:'\n    <npmi-annotations-list-toolbar-component\n      [numAnnotations]="numAnnotations"\n      [expanded]="expanded"\n      [selectedAnnotations]="selectedAnnotations$ | async"\n      [annotationsExpanded]="annotationsExpanded$ | async"\n      [showCounts]="showCounts$ | async"\n      [showHidden]="showHidden$ | async"\n      (onFlagAnnotations)="flagAnnotations($event)"\n      (onHideAnnotations)="hideAnnotations($event)"\n      (onToggleExpanded)="toggleExpanded()"\n      (onToggleShowCounts)="toggleShowCounts()"\n      (onToggleShowHidden)="toggleShowHidden()"\n    ></npmi-annotations-list-toolbar-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{numAnnotations:[{type:xy}],expanded:[{type:xy}]});const C_t=["glyph"];class M_t{ngAfterViewInit(){this.svg=J4(this.glyphSVG.nativeElement),this.mainContainer=this.svg.append("g"),this.draw()}draw(){"circle"==this.shape?this.mainContainer.append("circle").attr("fill",this.color).attr("stroke","black").attr("cx",5).attr("cy",5).attr("r",5):"bar"==this.shape?this.mainContainer.append("rect").attr("fill",this.color).attr("x",0).attr("y",0).attr("width",10).attr("height",10):"runIndicator"==this.shape&&this.mainContainer.append("g").append("path").attr("fill",this.color).attr("stroke","black").attr("d","M 2 0 L 10 0 L 7 5 L 10 10 L 2 10 Z")}}M_t.ɵfac=function t(e){return new(e||M_t)},M_t.ɵcmp=to({type:M_t,selectors:[["npmi-legend-element"]],viewQuery:function t(e,n){if(1&e&&Qh(C_t,7,hg),2&e){let t;Jh(t=tb())&&(n.glyphSVG=t.first)}},inputs:{text:"text",color:"color",shape:"shape"},decls:4,vars:1,consts:[[1,"glyph"],["glyph",""],[1,"legend-element-title"]],template:function t(e,n){1&e&&(qi(),Tm(0,"svg",0,1),Zi(),Rm(2,"div",2),ku(3),Am()),2&e&&(rc(3),Su(n.text))},styles:["[_nghost-%COMP%]{align-items:center;display:flex;padding-right:10px}.legend-element-title[_ngcontent-%COMP%]{font-size:.8em;padding-left:5px}.glyph[_ngcontent-%COMP%]{width:10px;height:10px}"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(M_t,[{type:My,args:[{selector:"npmi-legend-element",templateUrl:"./legend_element_component.ng.html",styleUrls:["./legend_element_component.css"],changeDetection:zn.OnPush}]}],null,{text:[{type:xy}],color:[{type:xy}],shape:[{type:xy}],glyphSVG:[{type:Za,args:["glyph",{static:!0,read:hg}]}]});class v_t{}function x_t(t,e){if(1&t&&Tm(0,"mat-icon",8),2&t){const t=Ym(2);Dm("svgIcon",t.sort.order===t.SortOrder.DESCENDING?"arrow_downward_24px":"arrow_upward_24px")("ngClass",t.sort.order===t.SortOrder.DESCENDING?"down-icon":"up-icon")}}function O_t(t,e){if(1&t){const t=Hm();Rm(0,"div",4),Rm(1,"div",5),Rm(2,"div",6),Vm("click",(function e(){const n=hi(t).$implicit;return Ym().onChangeSort.emit(n)})),ku(3),Qp(4,x_t,1,2,"mat-icon",7),Am(),Am(),Am()}if(2&t){const t=e.$implicit,n=Ym();rc(3),Du(" ",n.stripMetric(t)," "),rc(1),Dm("ngIf",t===n.sort.metric)}}v_t.ɵfac=function t(e){return new(e||v_t)},v_t.ɵcmp=to({type:v_t,selectors:[["npmi-annotations-list-legend"]],decls:4,vars:0,consts:[["text","run indicator","color","rgb(0,0,0)","shape","runIndicator"],["text","positive correlation","color","rgb(109, 174, 213)","shape","bar"],["text","negative correlation","color","rgb(249, 105, 76)","shape","bar"],["text","sample count","color","rgb(151, 151, 151)","shape","circle"]],template:function t(e,n){1&e&&(Tm(0,"npmi-legend-element",0),Tm(1,"npmi-legend-element",1),Tm(2,"npmi-legend-element",2),Tm(3,"npmi-legend-element",3))},directives:[M_t],styles:["[_nghost-%COMP%]{display:flex;padding:0 16px}"]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(v_t,[{type:My,args:[{selector:"npmi-annotations-list-legend",templateUrl:"./legend_component.ng.html",styleUrls:["./legend_component.css"]}]}],null,null);class P_t{constructor(){this.onChangeSort=new Lh,this.onAllAnnotationsToggled=new Lh,this.SortOrder=nbt}stripMetric(t){return nyt(t)}}P_t.ɵfac=function t(e){return new(e||P_t)},P_t.ɵcmp=to({type:P_t,selectors:[["npmi-annotations-list-header-component"]],inputs:{numAnnotations:"numAnnotations",selectedAnnotations:"selectedAnnotations",activeMetrics:"activeMetrics",sort:"sort"},outputs:{onChangeSort:"onChangeSort",onAllAnnotationsToggled:"onAllAnnotationsToggled"},decls:4,vars:2,consts:[[1,"toggle-all-container"],[3,"checked","change"],[1,"annotations-header-containers"],["class","header-column",4,"ngFor","ngForOf"],[1,"header-column"],[1,"header-container"],["tabindex","0","role","button","title","Change the sort by clicking any of the metrics.",1,"header-clickable",3,"click"],["class","sort-icon",3,"svgIcon","ngClass",4,"ngIf"],[1,"sort-icon",3,"svgIcon","ngClass"]],template:function t(e,n){1&e&&(Rm(0,"div",0),Rm(1,"mat-checkbox",1),Vm("change",(function t(e){return 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ngDevMode||ngDevMode)&&hh(P_t,[{type:My,args:[{selector:"npmi-annotations-list-header-component",templateUrl:"./header_component.ng.html",styleUrls:["./header_component.css"],changeDetection:zn.OnPush}]}],null,{numAnnotations:[{type:xy}],selectedAnnotations:[{type:xy}],activeMetrics:[{type:xy}],sort:[{type:xy}],onChangeSort:[{type:Oy}],onAllAnnotationsToggled:[{type:Oy}]});class w_t{constructor(t){this.store=t,this.selectedAnnotations$=this.store.select(cbt),this.annotationSort$=this.store.select(hbt)}changeSort(t){this.store.dispatch(Vbt({metric:t}))}allAnnotationsToggled(t){this.store.dispatch(Abt(t?{annotations:Object.keys(this.annotations)}:{annotations:[]}))}}w_t.ɵfac=function t(e){return new(e||w_t)(Sm(Iw))},w_t.ɵcmp=to({type:w_t,selectors:[["npmi-annotations-list-header"]],inputs:{numAnnotations:"numAnnotations",annotations:"annotations",activeMetrics:"activeMetrics"},decls:3,vars:8,consts:[[3,"numAnnotations","selectedAnnotations","sort","activeMetrics","onChangeSort","onAllAnnotationsToggled"]],template:function t(e,n){1&e&&(Rm(0,"npmi-annotations-list-header-component",0),Vm("onChangeSort",(function t(e){return n.changeSort(e)}))("onAllAnnotationsToggled",(function t(e){return n.allAnnotationsToggled(e)})),Ah(1,"async"),Ah(2,"async"),Am()),2&e&&Dm("numAnnotations",n.numAnnotations)("selectedAnnotations",Th(1,4,n.selectedAnnotations$))("sort",Th(2,6,n.annotationSort$))("activeMetrics",n.activeMetrics)},directives:[P_t],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(w_t,[{type:My,args:[{selector:"npmi-annotations-list-header",template:'\n    <npmi-annotations-list-header-component\n      [numAnnotations]="numAnnotations"\n      [selectedAnnotations]="selectedAnnotations$ | async"\n      [sort]="annotationSort$ | async"\n      [activeMetrics]="activeMetrics"\n      (onChangeSort)="changeSort($event)"\n      (onAllAnnotationsToggled)="allAnnotationsToggled($event)"\n    ></npmi-annotations-list-header-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{numAnnotations:[{type:xy}],annotations:[{type:xy}],activeMetrics:[{type:xy}]});const k_t=["chart"],S_t=["hintClip"];function D_t(t,e){1&t&&Tm(0,"mat-icon",12)}function E_t(t,e){1&t&&Tm(0,"mat-icon",13)}function R_t(t,e){if(1&t&&Tm(0,"mat-icon",14),2&t){const t=Ym();Dm("svgIcon",t.sort.order===t.SortOrder.SIMILAR?"arrow_downward_24px":"arrow_upward_24px")("ngClass",t.sort.order===t.SortOrder.SIMILAR?"down-icon":"up-icon")}}class A_t{constructor(){this.selected=!1,this.onShowSimilarAnnotations=new Lh,this.SortOrder=nbt,this.width=10,this.chartWidth=10,this.chartHeight=10,this.maxDotRadius=10,this.countDotOffset=70,this.countTextPadding=2,this.margin={top:0,right:0,bottom:0,left:100},this.strokeColor="#fff",this.textClass="default-text",this.runs=[]}onResize(t){this.redraw()}ngAfterViewInit(){this.svg=J4(this.annotationContainer.nativeElement).select("svg"),this.xScale=Utt().padding(0),this.yScale=Utt().padding(0),this.sizeScale=eet().domain([0,1]),this.countSizeScale=eet().range([2,this.maxDotRadius]),this.mainContainer=this.svg.append("g").attr("transform",`translate(${this.margin.left}, ${this.margin.top})`),this.barsGroup=this.mainContainer.append("g"),this.countDotsGroup=this.mainContainer.append("g"),this.textsGroup=this.mainContainer.append("g"),this.countTextsGroup=this.mainContainer.append("g"),this.runHintGroup=this.svg.append("g"),this.redraw()}ngOnChanges(t){this.svg&&this.redraw()}redraw(){this.selected=this.selectedAnnotations.includes(this.annotation),this.updateDimensions(),this.setTextClass(),this.updateAxes(),this.draw()}updateDimensions(){const t=new Set;this.data.forEach((e=>{t.add(e.run)})),this.runs=[...t],this.svg.style("height",this.numActiveRuns*this.runHeight+"px"),this.chartHeight=this.runs.length*this.runHeight-this.margin.top-this.margin.bottom,this.width=this.annotationContainer.nativeElement.clientWidth||10,this.chartWidth=this.width-this.margin.left-this.margin.right}setTextClass(){this.textClass="default-text",this.flaggedAnnotations.includes(this.annotation)?this.textClass="flag-text":this.hiddenAnnotations.includes(this.annotation)&&(this.textClass="hidden-text")}updateAxes(){this.xScale.rangeRound([0,this.chartWidth-this.chartWidth/this.activeMetrics.length]).domain(this.activeMetrics.map((t=>nyt(t)))),this.yScale.rangeRound([0,this.chartHeight-this.runHeight]).domain(this.runs),this.sizeScale.range([0,this.chartWidth/this.activeMetrics.length]),this.countSizeScale.domain([0,this.maxCount])}draw(){this.drawRunIndicators(),this.drawRunHintTexts(),this.drawBars(),this.drawTexts(),this.showCounts?(this.drawCountDots(),this.drawCountTexts()):(this.countDotsGroup.selectAll(".count-dot").remove(),this.countTextsGroup.selectAll(".count-background-text").remove(),this.countTextsGroup.selectAll(".count-text").remove())}drawRunIndicators(){J4(this.clipPathElement.nativeElement).select("rect").attr("width",this.margin.left-30).attr("height",this.chartHeight);const t=this.runHintGroup.selectAll(".hint").data(this.runs),e=t.enter().append("g").attr("class","hint");e.append("path").attr("d","M 0 0 L 15 0 L 10 10 L 15 20 L 0 20 Z"),e.merge(t).attr("transform",function(t){return`translate(10, ${this.yScale(t)+5})`}.bind(this)).attr("fill",function(t){return this.colorScale(t)}.bind(this)),t.exit().remove()}drawRunHintTexts(){const t=this.runHintGroup.selectAll(".hint-text").data(this.runs);t.enter().append("text").attr("x",25).attr("font-size","10px").attr("alignment-baseline","middle").attr("clip-path","url(#hint-clip)").merge(t).attr("y",function(t){return this.yScale(t)+15}.bind(this)).attr("class",`hint-text ${this.textClass}`).text((t=>{var e;return(null===(e=this.runIdToRuns.get(t))||void 0===e?void 0:e.name)||""})),t.exit().remove()}drawBars(){const t=this.barsGroup.selectAll(".bar").data(this.data);t.enter().append("rect").attr("class","bar").attr("height",20).merge(t).attr("fill",(t=>null===t.nPMIValue?"":t.nPMIValue>=0?bot(t.nPMIValue):_ot(-1*t.nPMIValue))).attr("x",function(t){return this.xScale(t.metric)}.bind(this)).attr("y",function(t){return this.yScale(t.run)+5}.bind(this)).attr("width",function(t){return null===t.nPMIValue?0:this.sizeScale(Math.abs(t.nPMIValue))}.bind(this)),t.exit().remove()}drawCountDots(){const t=this.countDotsGroup.selectAll(".count-dot").data(this.data);t.enter().append("circle").attr("class","count-dot").attr("stroke","black").merge(t).attr("fill",function(t){return null===t.countValue?"":yot(t.countValue/this.maxCount)}.bind(this)).attr("cx",function(t){return this.xScale(t.metric)+this.countDotOffset}.bind(this)).attr("cy",function(t){return this.yScale(t.run)+this.runHeight/2}.bind(this)).attr("r",function(t){return null===t.countValue?0:this.countSizeScale(t.countValue)}.bind(this)),t.exit().remove()}drawTexts(){const t=this.textsGroup.selectAll(".npmi-background-text").data(this.data);t.enter().append("text").attr("class","npmi-background-text").attr("stroke-width",3).attr("stroke-linejoin","round").attr("stroke",this.strokeColor).attr("font-size","13px").attr("alignment-baseline","middle").merge(t).attr("x",function(t){return this.xScale(t.metric)+5}.bind(this)).attr("y",function(t){return this.yScale(t.run)+this.runHeight/2}.bind(this)).text((t=>null===t.nPMIValue?"null":Math.round(1e3*(t.nPMIValue+Number.EPSILON))/1e3)),t.exit().remove();const e=this.textsGroup.selectAll(".npmi-text").data(this.data);e.enter().append("text").attr("class","npmi-text").attr("font-size","13px").attr("alignment-baseline","middle").merge(e).attr("x",function(t){return this.xScale(t.metric)+5}.bind(this)).attr("y",function(t){return this.yScale(t.run)+this.runHeight/2}.bind(this)).text((t=>null===t.nPMIValue?"null":Math.round(1e3*(t.nPMIValue+Number.EPSILON))/1e3)),e.exit().remove()}drawCountTexts(){const t=this.countTextsGroup.selectAll(".count-background-text").data(this.data);t.enter().append("text").attr("class","count-background-text").attr("stroke-width",3).attr("stroke-linejoin","round").attr("stroke",this.strokeColor).attr("font-size","10px").attr("alignment-baseline","middle").merge(t).attr("x",function(t){return this.xScale(t.metric)+this.countDotOffset+this.countTextPadding+this.maxDotRadius}.bind(this)).attr("y",function(t){return this.yScale(t.run)+this.runHeight/2}.bind(this)).text((t=>null===t.countValue?"":Intl.NumberFormat().format(t.countValue))),t.exit().remove();const e=this.countTextsGroup.selectAll(".count-text").data(this.data);e.enter().append("text").attr("class","count-text").attr("font-size","10px").attr("alignment-baseline","middle").merge(e).attr("x",function(t){return this.xScale(t.metric)+this.countDotOffset+this.countTextPadding+this.maxDotRadius}.bind(this)).attr("y",function(t){return this.yScale(t.run)+this.runHeight/2}.bind(this)).text((t=>null===t.countValue?"":Intl.NumberFormat().format(t.countValue))),e.exit().remove()}similaritySort(t){this.hasEmbedding&&(t.stopPropagation(),this.onShowSimilarAnnotations.emit())}}A_t.ɵfac=function t(e){return new(e||A_t)},A_t.ɵcmp=to({type:A_t,selectors:[["annotation-component"]],viewQuery:function t(e,n){if(1&e&&(Qh(k_t,7,hg),Qh(S_t,7,hg)),2&e){let t;Jh(t=tb())&&(n.annotationContainer=t.first),Jh(t=tb())&&(n.clipPathElement=t.first)}},hostVars:2,hostBindings:function t(e,n){1&e&&Vm("resize",(function t(){return n.onResize()}),!1,ol),2&e&&pu("selected-row",n.selected)},inputs:{data:"data",maxCount:"maxCount",selectedAnnotations:"selectedAnnotations",flaggedAnnotations:"flaggedAnnotations",hiddenAnnotations:"hiddenAnnotations",activeMetrics:"activeMetrics",numActiveRuns:"numActiveRuns",showCounts:"showCounts",annotation:"annotation",runHeight:"runHeight",hasEmbedding:"hasEmbedding",sort:"sort",sidebarWidth:"sidebarWidth",colorScale:"colorScale",runIdToRuns:"runIdToRuns"},outputs:{onShowSimilarAnnotations:"onShowSimilarAnnotations"},features:[Bo],decls:14,vars:10,consts:[[1,"annotation-title"],[1,"annotation-checkbox",3,"checked","click"],[1,"annotation-button",3,"ngClass","click"],["class","flagged-icon","svgIcon","flag_24px",4,"ngIf"],["class","hidden-icon","svgIcon","visibility_off_24px",4,"ngIf"],["class","annotation-icon",3,"svgIcon","ngClass",4,"ngIf"],[1,"chart-div"],["chart",""],[1,"chart-svg"],["id","hint-clip"],["hintClip",""],["x","0","y","0"],["svgIcon","flag_24px",1,"flagged-icon"],["svgIcon","visibility_off_24px",1,"hidden-icon"],[1,"annotation-icon",3,"svgIcon","ngClass"]],template:function t(e,n){1&e&&(Rm(0,"div",0),Rm(1,"mat-checkbox",1),Vm("click",(function t(e){return e.preventDefault()})),Am(),Rm(2,"button",2),Vm("click",(function t(e){return n.similaritySort(e)})),ku(3),Am(),Qp(4,D_t,1,0,"mat-icon",3),Qp(5,E_t,1,0,"mat-icon",4),Qp(6,R_t,1,2,"mat-icon",5),Am(),Rm(7,"div",6,7),qi(),Rm(9,"svg",8),Rm(10,"defs"),Rm(11,"clipPath",9,10),Tm(13,"rect",11),Am(),Am(),Am(),Am()),2&e&&(pu("flagged-annotation",n.flaggedAnnotations.includes(n.annotation))("hidden-annotation",n.hiddenAnnotations.includes(n.annotation)&&!n.flaggedAnnotations.includes(n.annotation)),rc(1),Dm("checked",n.selectedAnnotations.includes(n.annotation)),rc(1),Dm("ngClass",n.hasEmbedding?"clickable-annotation":""),rc(1),Du(" ",n.annotation," 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ngDevMode||ngDevMode)&&hh(A_t,[{type:My,args:[{selector:"annotation-component",templateUrl:"./annotation_component.ng.html",styleUrls:["./annotation_component.css"],changeDetection:zn.OnPush,encapsulation:Hn.None}]}],null,{data:[{type:xy}],maxCount:[{type:xy}],selectedAnnotations:[{type:xy}],flaggedAnnotations:[{type:xy}],hiddenAnnotations:[{type:xy}],activeMetrics:[{type:xy}],numActiveRuns:[{type:xy}],showCounts:[{type:xy}],annotation:[{type:xy}],runHeight:[{type:xy}],hasEmbedding:[{type:xy}],sort:[{type:xy}],sidebarWidth:[{type:xy}],colorScale:[{type:xy}],runIdToRuns:[{type:xy}],annotationContainer:[{type:Za,args:["chart",{static:!0,read:hg}]}],clipPathElement:[{type:Za,args:["hintClip",{static:!0,read:hg}]}],selected:[{type:Py,args:["class.selected-row"]}],onResize:[{type:wy,args:["window:resize"]}],onShowSimilarAnnotations:[{type:Oy}]});class T_t{constructor(t){this.store=t,this.sort$=this.store.select(hbt),this.flaggedAnnotations$=this.store.select(dbt),this.hiddenAnnotations$=this.store.select(pbt),this.selectedAnnotations$=this.store.select(cbt),this.showCounts$=this.store.select(Cbt),this.sidebarWidth$=this.store.select(xbt),this.runColorScale$=this.store.select(zN).pipe(It((t=>e=>{if(!t.hasOwnProperty(e))throw new Error(`[Color scale] unknown runId: ${e}.`);return t[e]}))),this.runIdToRuns$=this.store.select(dN)}showSimilarAnnotations(){this.store.dispatch(jbt({annotation:this.annotation}))}}function N_t(t,e){if(1&t){const t=Hm();Rm(0,"npmi-annotation",5),Vm("click",(function e(n){const o=hi(t).$implicit;return Ym(2).rowClicked(n,o)})),Am()}if(2&t){const t=e.$implicit,n=Ym(2);Dm("data",n.annotations[t])("activeMetrics",n.activeMetrics)("numActiveRuns",n.numActiveRuns)("maxCount",n.maxCount)("annotation",t)("runHeight",n.runHeight)("hasEmbedding",n.embeddingData&&void 0!==n.embeddingData[t])}}function z_t(t,e){if(1&t&&(Nm(0),Tm(1,"npmi-annotations-list-legend"),Tm(2,"npmi-annotations-list-header",2),Rm(3,"cdk-virtual-scroll-viewport",3),Qp(4,N_t,1,7,"npmi-annotation",4),Am(),zm()),2&t){const t=Ym();rc(2),Dm("annotations",t.annotations)("numAnnotations",t.numAnnotations)("activeMetrics",t.activeMetrics),rc(1),Km("itemSize",t.numActiveRuns*t.runHeight+25),rc(1),Dm("cdkVirtualForOf",t.sortedAnnotations)}}T_t.ɵfac=function t(e){return new(e||T_t)(Sm(Iw))},T_t.ɵcmp=to({type:T_t,selectors:[["npmi-annotation"]],inputs:{data:"data",maxCount:"maxCount",activeMetrics:"activeMetrics",numActiveRuns:"numActiveRuns",annotation:"annotation",runHeight:"runHeight",hasEmbedding:"hasEmbedding"},decls:9,vars:31,consts:[[3,"data","maxCount","activeMetrics","numActiveRuns","annotation","runHeight","hasEmbedding","sort","selectedAnnotations","flaggedAnnotations","hiddenAnnotations","showCounts","sidebarWidth","colorScale","runIdToRuns","onShowSimilarAnnotations"]],template:function 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.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{background-color:#fff;border:1px solid #ebebeb;display:flex;flex-direction:column;height:calc(100% - 2px);width:calc(100% - 2px)}.annotation-rows[_ngcontent-%COMP%]{display:flex;flex-direction:column;flex:1 1;overflow-y:auto}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(I_t,[{type:My,args:[{selector:"annotations-list-component",templateUrl:"./annotations_list_component.ng.html",styleUrls:["./annotations_list_component.css"],changeDetection:zn.OnPush}]}],null,{annotations:[{type:xy}],embeddingData:[{type:xy}],annotationsExpanded:[{type:xy}],numAnnotations:[{type:xy}],annotationSort:[{type:xy}],activeMetrics:[{type:xy}],numActiveRuns:[{type:xy}],sortedAnnotations:[{type:xy}],selectedAnnotations:[{type:xy}],maxCount:[{type:xy}],onRowClick:[{type:Oy}]});class H_t{constructor(t){this.store=t,this.annotationsExpanded$=this.store.pipe(Fw(ybt)),this.activeRuns$=this.store.pipe(Fw(NN)).pipe(It((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.embeddingData$=this.store.pipe(Fw(lbt)),this.numActiveRuns$=this.activeRuns$.pipe(It((t=>t.length))),this.activeMetrics$=Wt([this.store.select(sbt),this.activeRuns$,this.store.select(gbt)]).pipe(It((([t,e,n])=>{let o=[];for(const n of e)t[n]&&(o=o.concat(t[n].filter((t=>eyt(t)))));return o=[...new Set([...Object.keys(n),...o])],o}))),this.visibleAnnotations$=Wt([this.store.select(rbt),this.store.select(pbt),this.store.select(Mbt)]).pipe(It((([t,e,n])=>Gyt(t,e,n)))),this.filteredAnnotations$=Wt([this.visibleAnnotations$,this.store.select(fbt),this.store.select(gbt),this.activeRuns$,this.activeMetrics$,this.store.select(mbt)]).pipe(It((([t,e,n,o,i,a])=>(function r(t,e,n,o,i,a){const r={},s=new Set(e),l=new Set(i.map((t=>nyt(t)))),c=new RegExp(a,"i");return Object.entries(t).forEach((t=>{if(!c.test(t[0]))return;let e=t[1];e=e.filter((t=>s.has(t.run)&&l.has(t.metric))),(function i(t,e,n){return t.every((t=>{if(t.kind===tbt.OPERATOR)return!0;const o=e[t.metric];return void 0===o||n.some((e=>e.metric===nyt(t.metric)&&(null===e.nPMIValue?o.includeNaN:e.nPMIValue<=o.max&&e.nPMIValue>=o.min)))}))})(n,o,e)&&0!==e.length&&(r[t[0]]=e)})),r})(t,o,e,n,i,a)))).pipe(Ee()),this.numAnnotations$=this.filteredAnnotations$.pipe(It((t=>Object.keys(t).length))),this.sortedAnnotations$=Wt([this.filteredAnnotations$,this.store.pipe(Fw(hbt)),this.embeddingData$]).pipe(It((([t,e,n])=>(function o(t,e,n){const o=Object.keys(t),i=e.order===nbt.DISSIMILAR||e.order===nbt.SIMILAR;return""===e.metric||(void 0===n||void 0===n.points[e.metric])&&i?o:(function s(t,e,n){return t.sort(n?(t,n)=>e[t]-e[n]:(t,n)=>e[n]-e[t])})(o,i?(function a(t,e,n){const o={};let i=Number.POSITIVE_INFINITY,a=Number.NEGATIVE_INFINITY;n.order===nbt.SIMILAR&&(i=Number.NEGATIVE_INFINITY,a=Number.POSITIVE_INFINITY);for(const r of t)o[r]=r===n.metric?i:void 0===e.points[r]?a:e.points[r].vector?Qyt(e.points[n.metric].vector,e.points[r].vector,a):a;return o})(o,n,e):(function r(t,e,n){const o=nyt(n.metric),i={};if(n.order===nbt.DESCENDING)for(const n of t)i[n]=Math.max(...e[n].filter((t=>t.metric===o)).map((t=>null===t.nPMIValue?-1/0:t.nPMIValue)));else for(const n of t)i[n]=Math.min(...e[n].filter((t=>t.metric===o)).map((t=>null===t.nPMIValue?1/0:t.nPMIValue)));return i})(o,t,e),e.order===nbt.ASCENDNG||e.order===nbt.SIMILAR)})(t,e,n)))),this.selectedAnnotations$=this.store.pipe(Fw(cbt)),this.maxCount$=this.filteredAnnotations$.pipe(It((t=>{let e=0;return Object.values(t).forEach((t=>{t.forEach((t=>{t.countValue&&(e=Math.max(e,t.countValue))}))})),e})))}rowClicked(t){this.store.dispatch(Rbt({annotations:t}))}}H_t.ɵfac=function t(e){return new(e||H_t)(Sm(Iw))},H_t.ɵcmp=to({type:H_t,selectors:[["npmi-annotations-list"]],decls:10,vars:27,consts:[[3,"annotations","embeddingData","annotationsExpanded","numAnnotations","activeMetrics","numActiveRuns","sortedAnnotations","selectedAnnotations","maxCount","onRowClick"]],template:function t(e,n){1&e&&(Rm(0,"annotations-list-component",0),Vm("onRowClick",(function t(e){return n.rowClicked(e)})),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Ah(7,"async"),Ah(8,"async"),Ah(9,"async"),Am()),2&e&&Dm("annotations",Th(1,9,n.filteredAnnotations$))("embeddingData",Th(2,11,n.embeddingData$))("annotationsExpanded",Th(3,13,n.annotationsExpanded$))("numAnnotations",Th(4,15,n.numAnnotations$))("activeMetrics",Th(5,17,n.activeMetrics$))("numActiveRuns",Th(6,19,n.numActiveRuns$))("sortedAnnotations",Th(7,21,n.sortedAnnotations$))("selectedAnnotations",Th(8,23,n.selectedAnnotations$))("maxCount",Th(9,25,n.maxCount$))},directives:[I_t],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(H_t,[{type:My,args:[{selector:"npmi-annotations-list",template:'\n    <annotations-list-component\n      [annotations]="filteredAnnotations$ | async"\n      [embeddingData]="embeddingData$ | async"\n      [annotationsExpanded]="annotationsExpanded$ | async"\n      [numAnnotations]="numAnnotations$ | async"\n      [activeMetrics]="activeMetrics$ | async"\n      [numActiveRuns]="numActiveRuns$ | async"\n      [sortedAnnotations]="sortedAnnotations$ | async"\n      [selectedAnnotations]="selectedAnnotations$ | async"\n      [maxCount]="maxCount$ | async"\n      (onRowClick)="rowClicked($event)"\n    ></annotations-list-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);const F_t=["chart"];class L_t{constructor(){this.width=0,this.chartWidth=0,this.height=300,this.margin={top:20,right:40,bottom:20,left:40},this.chartHeight=this.height-this.margin.top-this.margin.bottom}onResize(t){this.redraw()}ngAfterViewInit(){this.svg=J4(this.svgElement.nativeElement),this.mainContainer=this.svg.append("g").attr("transform",`translate(${this.margin.left}, ${this.margin.top})`),this.coordinatesGroup=this.mainContainer.append("g"),this.labelsGroup=this.mainContainer.append("g"),this.axisGroup=this.mainContainer.append("g"),this.xScale=Utt().padding(.1),this.yScale=eet().range([this.chartHeight,0]),this.yAxis=N5(this.yScale),this.redraw()}ngOnChanges(t){this.svg&&this.redraw()}redraw(){this.updateDimensions(),this.updateAxes(),this.draw()}updateDimensions(){this.width=this.svgElement.nativeElement.clientWidth||10,this.chartWidth=this.width-this.margin.left-this.margin.right}updateAxes(){this.xScale.rangeRound([0,this.chartWidth]).domain(this.activeMetrics),this.yScale.domain([this.coordinateData.extremes.min,this.coordinateData.extremes.max])}draw(){this.drawAxes(),this.drawAxisLabels(),this.drawCoordinates(),this.drawLabels()}drawAxes(){const t=this.axisGroup.selectAll(".axis-y").data(this.activeMetrics);t.enter().append("g").attr("class","axis-y").merge(t).attr("transform",function(t){return`translate(${this.xScale(t)}, 0)`}.bind(this)).call(this.yAxis),t.exit().remove()}drawAxisLabels(){const t=this.axisGroup.selectAll(".axis-bg-text").data(this.activeMetrics);t.enter().append("text").attr("class","axis-bg-text").attr("font-size","13px").attr("stroke-width",2).attr("stroke-linejoin","round").attr("stroke","white").merge(t).text((t=>t)).attr("transform",function(t){return`translate(${this.xScale(t)-5}, ${this.yScale(this.coordinateData.extremes.min)}) rotate(-90)`}.bind(this)),t.exit().remove();const e=this.axisGroup.selectAll(".axis-text").data(this.activeMetrics);e.enter().append("text").attr("font-size","13px").attr("class","axis-text").merge(e).text((t=>t)).attr("transform",function(t){return`translate(${this.xScale(t)-5}, ${this.yScale(this.coordinateData.extremes.min)}) rotate(-90)`}.bind(this)),e.exit().remove()}drawCoordinates(){const t=this.coordinatesGroup.selectAll(".coord").data(this.coordinateData.coordinates);t.enter().append("path").attr("class","coord").attr("fill","none").merge(t).attr("d",this.path.bind(this)).attr("stroke",function(t){return this.colorScale(t.runId)}.bind(this)),t.exit().remove();const e=this.coordinatesGroup.selectAll(".hiddenCoord").data(this.coordinateData.coordinates);e.enter().append("path").attr("class","hiddenCoord").attr("stroke-width","10px").attr("fill","none").attr("stroke","rgba(0, 0, 0, 0.0)").on("mouseover",this.handleCoordinateMouseOver.bind(this)).on("mouseout",this.handleCoordinateMouseOut.bind(this)).merge(e).attr("d",this.path.bind(this)),e.exit().remove()}path(t){return t.values.sort(((t,e)=>this.activeMetrics.indexOf(t.metric)-this.activeMetrics.indexOf(e.metric))),Pot()(t.values.map(function(t){let e=this.yScale(t.nPMIValue);return[this.xScale(t.metric),e]}.bind(this)))}handleCoordinateMouseOver(t,e){this.labelsGroup.selectAll(".coordinate-label").filter((function(e){return!(e.annotation===t.annotation)})).style("opacity",.1),this.coordinatesGroup.selectAll(".coord").filter((function(e){return!(e.annotation===t.annotation)})).style("opacity",.1)}handleCoordinateMouseOut(){this.labelsGroup.selectAll(".coordinate-label").style("opacity",1),this.coordinatesGroup.selectAll(".coord").style("opacity",1)}drawLabels(){const t=30/this.xScale.step(),e=this.coordinateData.coordinates.length<30?this.coordinateData.coordinates:[],n=this.labelsGroup.selectAll(".coordinate-label").data(e);n.enter().append("text").attr("class","coordinate-label").attr("font-size","10px").merge(n).text((function(t){return t.annotation})).attr("x",this.xScale(this.activeMetrics[0])+30).attr("y",function(e){const n=this.yScale(e.values[0].nPMIValue?e.values[0].nPMIValue:0),o=this.yScale(e.values[1].nPMIValue?e.values[1].nPMIValue:0);return(1-t)*n+t*o}.bind(this)),n.exit().remove()}}L_t.ɵfac=function t(e){return new(e||L_t)},L_t.ɵcmp=to({type:L_t,selectors:[["parallel-coordinates-component"]],viewQuery:function t(e,n){if(1&e&&Qh(F_t,7,hg),2&e){let t;Jh(t=tb())&&(n.svgElement=t.first)}},hostBindings:function t(e,n){1&e&&Vm("resize",(function t(e){return n.onResize(e)}),!1,ol)},inputs:{activeMetrics:"activeMetrics",coordinateData:"coordinateData",sidebarWidth:"sidebarWidth",colorScale:"colorScale"},features:[Bo],decls:2,vars:0,consts:[[1,"pc-chart"],["chart",""]],template:function t(e,n){1&e&&(qi(),Tm(0,"svg",0,1))},styles:[".pc-chart[_ngcontent-%COMP%]{height:300px;width:100%}"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(L_t,[{type:My,args:[{selector:"parallel-coordinates-component",templateUrl:"./parallel_coordinates_component.ng.html",styleUrls:["./parallel_coordinates_component.css"],changeDetection:zn.OnPush}]}],null,{activeMetrics:[{type:xy}],coordinateData:[{type:xy}],sidebarWidth:[{type:xy}],colorScale:[{type:xy}],svgElement:[{type:Za,args:["chart",{static:!0,read:hg}]}],onResize:[{type:wy,args:["window:resize",["$event"]]}]});class B_t{constructor(t){this.store=t,this.activeRuns$=this.store.pipe(Fw(NN)).pipe(It((t=>t?Array.from(t.entries()).filter((t=>t[1])).map((t=>t[0])):[]))),this.activeMetrics$=Wt(this.store.select(sbt),this.activeRuns$,this.store.select(gbt)).pipe(It((([t,e,n])=>{let o=[];for(const n of e)t[n]&&(o=o.concat(t[n].filter((t=>eyt(t)))));return o=[...new Set([...Object.keys(n),...o])],o.map((t=>nyt(t)))}))),this.coordinateData$=Wt([this.store.select(rbt),this.store.select(cbt),this.activeRuns$,this.activeMetrics$]).pipe(It((([t,e,n,o])=>(function i(t,e,n,o){const i=[],a={max:-1,min:1},r=new Set(n),s=new Set(o);return 0===r.size||0===s.size||0===Object.keys(t).length?{coordinates:[],extremes:{min:-1,max:1}}:(e.forEach((e=>{const n={};t[e].forEach((t=>{r.has(t.run)&&s.has(t.metric)&&(n[t.run]?n[t.run].push(t):n[t.run]=[t],null!==t.nPMIValue?(a.max=Math.max(a.max,t.nPMIValue),a.min=Math.min(a.min,t.nPMIValue)):(a.max=Math.max(a.max,0),a.min=Math.min(a.min,0)))}));for(const t of Object.keys(n))i.push({annotation:e,runId:t,values:n[t]})})),a.max<a.min&&(a.max=1,a.min=-1),{coordinates:i,extremes:a})})(t,e,n,o)))),this.sidebarWidth$=this.store.select(xbt),this.runColorScale$=this.store.select(zN).pipe(It((t=>e=>{if(!t.hasOwnProperty(e))throw new Error(`[Color scale] unknown runId: ${e}.`);return t[e]})))}}function V_t(t,e){1&t&&Tm(0,"mat-icon",9)}function j_t(t,e){1&t&&Tm(0,"mat-icon",10)}function U_t(t,e){1&t&&Tm(0,"npmi-parallel-coordinates")}B_t.ɵfac=function t(e){return new(e||B_t)(Sm(Iw))},B_t.ɵcmp=to({type:B_t,selectors:[["npmi-parallel-coordinates"]],decls:5,vars:12,consts:[[3,"activeMetrics","coordinateData","sidebarWidth","colorScale"]],template:function t(e,n){1&e&&(Tm(0,"parallel-coordinates-component",0),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async")),2&e&&Dm("activeMetrics",Th(1,4,n.activeMetrics$))("coordinateData",Th(2,6,n.coordinateData$))("sidebarWidth",Th(3,8,n.sidebarWidth$))("colorScale",Th(4,10,n.runColorScale$))},directives:[L_t],pipes:[wM],encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(B_t,[{type:My,args:[{selector:"npmi-parallel-coordinates",template:'\n    <parallel-coordinates-component\n      [activeMetrics]="activeMetrics$ | async"\n      [coordinateData]="coordinateData$ | async"\n      [sidebarWidth]="sidebarWidth$ | async"\n      [colorScale]="runColorScale$ | async"\n    ></parallel-coordinates-component>\n  ',changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),null);class G_t{constructor(){this.onClearSelectedAnnotations=new Lh,this.onToggleExpanded=new Lh}}G_t.ɵfac=function t(e){return new(e||G_t)},G_t.ɵcmp=to({type:G_t,selectors:[["selected-annotations-component"]],inputs:{pcExpanded:"pcExpanded",selectedAnnotations:"selectedAnnotations"},outputs:{onClearSelectedAnnotations:"onClearSelectedAnnotations",onToggleExpanded:"onToggleExpanded"},decls:11,vars:4,consts:function(){let t,e;return t="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Clear Annotation Selection"):$localize`:Label for a button that clears the annotation selection.␟2b52289f2c4b7f5b182ed23c9154722ecd46a2d4␟2494660520341308830:Clear Annotation Selection`,e="undefined"!=typeof ngI18nClosureMode&&ngI18nClosureMode?goog.getMsg("Expand/Hide Selected Annotations"):$localize`:Label for a button that expands or hides selected annotations.␟8f4ad305c19e3655f3189e3e264e83fb76f7bb95␟5670797784070952095:Expand/Hide Selected Annotations`,[[1,"pc-container"],[1,"pc-toolbar"],[1,"pc-title"],["mat-icon-button","","aria-label",t,"title","Deselects all selected annotations.",1,"clear-button",3,"disabled","click"],["svgIcon","clear_24px"],["mat-icon-button","","aria-label",e,1,"expand-button",3,"click"],["svgIcon","expand_less_24px","class","expand-less-icon",4,"ngIf","ngIfElse"],["notExpanded",""],[4,"ngIf"],["svgIcon","expand_less_24px",1,"expand-less-icon"],["svgIcon","expand_more_24px",1,"expand-icon"]]},template:function t(e,n){if(1&e&&(Rm(0,"div",0),Rm(1,"div",1),Rm(2,"h3",2),ku(3,"Selected Annotations"),Am(),Rm(4,"button",3),Vm("click",(function t(){return n.onClearSelectedAnnotations.emit()})),Tm(5,"mat-icon",4),Am(),Rm(6,"button",5),Vm("click",(function t(){return n.onToggleExpanded.emit()})),Qp(7,V_t,1,0,"mat-icon",6),Qp(8,j_t,1,0,"ng-template",null,7,ib),Am(),Am(),Qp(10,U_t,1,0,"npmi-parallel-coordinates",8),Am()),2&e){const t=$p(9);rc(4),Dm("disabled",0===n.selectedAnnotations.length),rc(3),Dm("ngIf",n.pcExpanded)("ngIfElse",t),rc(3),Dm("ngIf",n.pcExpanded)}},directives:[XH,DW,dM,B_t],styles:['.mat-badge-content[_ngcontent-%COMP%]{font-weight:600;font-size:12px;font-family:Roboto, "Helvetica Neue", sans-serif}.mat-badge-small[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:9px}.mat-badge-large[_ngcontent-%COMP%]   .mat-badge-content[_ngcontent-%COMP%]{font-size:24px}.mat-h1[_ngcontent-%COMP%], .mat-headline[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h1[_ngcontent-%COMP%]{font:400 24px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h2[_ngcontent-%COMP%], .mat-title[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h3[_ngcontent-%COMP%], .mat-subheading-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%]{font:400 16px/28px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h4[_ngcontent-%COMP%], .mat-subheading-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%]{font:400 15px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 16px}.mat-h5[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%]{font:400 calc(14px * 0.83)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-h6[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:400 calc(14px * 0.67)/20px Roboto, "Helvetica Neue", sans-serif;margin:0 0 12px}.mat-body-strong[_ngcontent-%COMP%], .mat-body-2[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-body[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-body-1[_ngcontent-%COMP%]   p[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   p[_ngcontent-%COMP%]{margin:0 0 12px}.mat-small[_ngcontent-%COMP%], .mat-caption[_ngcontent-%COMP%]{font:400 12px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-display-4[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-4[_ngcontent-%COMP%]{font:300 112px/112px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.05em;margin:0 0 56px}.mat-display-3[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-3[_ngcontent-%COMP%]{font:400 56px/56px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.02em;margin:0 0 64px}.mat-display-2[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-2[_ngcontent-%COMP%]{font:400 45px/48px Roboto, "Helvetica Neue", sans-serif;letter-spacing:-0.005em;margin:0 0 64px}.mat-display-1[_ngcontent-%COMP%], .mat-typography[_ngcontent-%COMP%]   .mat-display-1[_ngcontent-%COMP%]{font:400 34px/40px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0 0 64px}.mat-bottom-sheet-container[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-button[_ngcontent-%COMP%], .mat-raised-button[_ngcontent-%COMP%], .mat-icon-button[_ngcontent-%COMP%], .mat-stroked-button[_ngcontent-%COMP%], .mat-flat-button[_ngcontent-%COMP%], .mat-fab[_ngcontent-%COMP%], .mat-mini-fab[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-button-toggle[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-card-title[_ngcontent-%COMP%]{font-size:24px;font-weight:500}.mat-card-header[_ngcontent-%COMP%]   .mat-card-title[_ngcontent-%COMP%]{font-size:20px}.mat-card-subtitle[_ngcontent-%COMP%], .mat-card-content[_ngcontent-%COMP%]{font-size:14px}.mat-checkbox[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-checkbox-layout[_ngcontent-%COMP%]   .mat-checkbox-label[_ngcontent-%COMP%]{line-height:24px}.mat-chip[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-chip[_ngcontent-%COMP%]   .mat-chip-trailing-icon.mat-icon[_ngcontent-%COMP%], .mat-chip[_ngcontent-%COMP%]   .mat-chip-remove.mat-icon[_ngcontent-%COMP%]{font-size:18px}.mat-table[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-header-cell[_ngcontent-%COMP%]{font-size:12px;font-weight:500}.mat-cell[_ngcontent-%COMP%], .mat-footer-cell[_ngcontent-%COMP%]{font-size:14px}.mat-calendar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-calendar-body[_ngcontent-%COMP%]{font-size:13px}.mat-calendar-body-label[_ngcontent-%COMP%], .mat-calendar-period-button[_ngcontent-%COMP%]{font-size:14px;font-weight:500}.mat-calendar-table-header[_ngcontent-%COMP%]   th[_ngcontent-%COMP%]{font-size:11px;font-weight:400}.mat-dialog-title[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-expansion-panel-header[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:15px;font-weight:400}.mat-expansion-panel-content[_ngcontent-%COMP%]{font:400 14px/20px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-form-field[_ngcontent-%COMP%]{font-size:inherit;font-weight:400;line-height:1.125;font-family:Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-form-field-wrapper[_ngcontent-%COMP%]{padding-bottom:1.34375em}.mat-form-field-prefix[_ngcontent-%COMP%]   .mat-icon[_ngcontent-%COMP%], .mat-form-field-suffix[_ngcontent-%COMP%]   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Array.isArray(e)||(e=Array.from(e)),e}function JMt(t,e,n,o,i){if(5!==arguments.length)throw new RangeError("expected 4 arguments");if($Mt("startRow",e),$Mt("endRow",n),$Mt("startColumn",o),$Mt("endColumn",i),e>n||o>i||e<0||e>=t.rows||n<0||n>=t.rows||o<0||o>=t.columns||i<0||i>=t.columns)throw new RangeError("Submatrix indices are out of range")}function QMt(t,e=0){let n=[];for(let o=0;o<t;o++)n.push(e);return n}function $Mt(t,e){if("number"!=typeof e)throw new TypeError(`${t} must be a number`)}function tvt(t){if(t.isEmpty())throw new Error("Empty matrix has no elements to index")}class evt{static from1DArray(t,e,n){if(t*e!==n.length)throw new RangeError("data length does not match given dimensions");let o=new ivt(t,e);for(let i=0;i<t;i++)for(let t=0;t<e;t++)o.set(i,t,n[i*e+t]);return o}static rowVector(t){let e=new ivt(1,t.length);for(let n=0;n<t.length;n++)e.set(0,n,t[n]);return e}static columnVector(t){let e=new ivt(t.length,1);for(let n=0;n<t.length;n++)e.set(n,0,t[n]);return 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i=Math.min(o,e,n),a=this.zeros(e,n);for(let e=0;e<i;e++)a.set(e,e,t[e]);return a}static min(t,e){t=this.checkMatrix(t),e=this.checkMatrix(e);let n=t.rows,o=t.columns,i=new ivt(n,o);for(let a=0;a<n;a++)for(let n=0;n<o;n++)i.set(a,n,Math.min(t.get(a,n),e.get(a,n)));return i}static max(t,e){t=this.checkMatrix(t),e=this.checkMatrix(e);let n=t.rows,o=t.columns,i=new this(n,o);for(let a=0;a<n;a++)for(let n=0;n<o;n++)i.set(a,n,Math.max(t.get(a,n),e.get(a,n)));return i}static checkMatrix(t){return evt.isMatrix(t)?t:new ivt(t)}static isMatrix(t){return null!=t&&"Matrix"===t.klass}get size(){return this.rows*this.columns}apply(t){if("function"!=typeof t)throw new TypeError("callback must be a function");for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)t.call(this,e,n);return this}to1DArray(){let t=[];for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)t.push(this.get(e,n));return t}to2DArray(){let t=[];for(let e=0;e<this.rows;e++){t.push([]);for(let n=0;n<this.columns;n++)t[e].push(this.get(e,n))}return t}toJSON(){return this.to2DArray()}isRowVector(){return 1===this.rows}isColumnVector(){return 1===this.columns}isVector(){return 1===this.rows||1===this.columns}isSquare(){return this.rows===this.columns}isEmpty(){return 0===this.rows||0===this.columns}isSymmetric(){if(this.isSquare()){for(let t=0;t<this.rows;t++)for(let e=0;e<=t;e++)if(this.get(t,e)!==this.get(e,t))return!1;return!0}return!1}isEchelonForm(){let t=0,e=0,n=-1,o=!0,i=!1;for(;t<this.rows&&o;){for(e=0,i=!1;e<this.columns&&!1===i;)0===this.get(t,e)?e++:1===this.get(t,e)&&e>n?(i=!0,n=e):(o=!1,i=!0);t++}return o}isReducedEchelonForm(){let t=0,e=0,n=-1,o=!0,i=!1;for(;t<this.rows&&o;){for(e=0,i=!1;e<this.columns&&!1===i;)0===this.get(t,e)?e++:1===this.get(t,e)&&e>n?(i=!0,n=e):(o=!1,i=!0);for(let n=e+1;n<this.rows;n++)0!==this.get(t,n)&&(o=!1);t++}return o}echelonForm(){let t=this.clone(),e=0,n=0;for(;e<t.rows&&n<t.columns;){let o=e;for(let i=e;i<t.rows;i++)t.get(i,n)>t.get(o,n)&&(o=i);if(0===t.get(o,n))n++;else{t.swapRows(e,o);let i=t.get(e,n);for(let o=n;o<t.columns;o++)t.set(e,o,t.get(e,o)/i);for(let o=e+1;o<t.rows;o++){let i=t.get(o,n)/t.get(e,n);t.set(o,n,0);for(let a=n+1;a<t.columns;a++)t.set(o,a,t.get(o,a)-t.get(e,a)*i)}e++,n++}}return t}reducedEchelonForm(){let t=this.echelonForm(),e=t.columns,n=t.rows,o=n-1;for(;o>=0;)if(0===t.maxRow(o))o--;else{let i=0,a=!1;for(;i<n&&!1===a;)1===t.get(o,i)?a=!0:i++;for(let n=0;n<o;n++){let a=t.get(n,i);for(let r=i;r<e;r++){let e=t.get(n,r)-a*t.get(o,r);t.set(n,r,e)}}o--}return t}set(){throw new Error("set method is unimplemented")}get(){throw new Error("get method is unimplemented")}repeat(t={}){if("object"!=typeof t)throw new TypeError("options must be an object");const{rows:e=1,columns:n=1}=t;if(!Number.isInteger(e)||e<=0)throw new TypeError("rows must be a positive integer");if(!Number.isInteger(n)||n<=0)throw new TypeError("columns must be a positive integer");let o=new ivt(this.rows*e,this.columns*n);for(let t=0;t<e;t++)for(let e=0;e<n;e++)o.setSubMatrix(this,this.rows*t,this.columns*e);return o}fill(t){for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,t);return this}neg(){return this.mulS(-1)}getRow(t){WMt(this,t);let e=[];for(let n=0;n<this.columns;n++)e.push(this.get(t,n));return e}getRowVector(t){return ivt.rowVector(this.getRow(t))}setRow(t,e){WMt(this,t),e=qMt(this,e);for(let n=0;n<this.columns;n++)this.set(t,n,e[n]);return this}swapRows(t,e){WMt(this,t),WMt(this,e);for(let n=0;n<this.columns;n++){let o=this.get(t,n);this.set(t,n,this.get(e,n)),this.set(e,n,o)}return this}getColumn(t){YMt(this,t);let e=[];for(let n=0;n<this.rows;n++)e.push(this.get(n,t));return e}getColumnVector(t){return ivt.columnVector(this.getColumn(t))}setColumn(t,e){YMt(this,t),e=ZMt(this,e);for(let n=0;n<this.rows;n++)this.set(n,t,e[n]);return this}swapColumns(t,e){YMt(this,t),YMt(this,e);for(let n=0;n<this.rows;n++){let o=this.get(n,t);this.set(n,t,this.get(n,e)),this.set(n,e,o)}return this}addRowVector(t){t=qMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)+t[n]);return this}subRowVector(t){t=qMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)-t[n]);return this}mulRowVector(t){t=qMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)*t[n]);return this}divRowVector(t){t=qMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)/t[n]);return this}addColumnVector(t){t=ZMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)+t[e]);return this}subColumnVector(t){t=ZMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)-t[e]);return this}mulColumnVector(t){t=ZMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)*t[e]);return this}divColumnVector(t){t=ZMt(this,t);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.set(e,n,this.get(e,n)/t[e]);return this}mulRow(t,e){WMt(this,t);for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)*e);return this}mulColumn(t,e){YMt(this,t);for(let n=0;n<this.rows;n++)this.set(n,t,this.get(n,t)*e);return this}max(){if(this.isEmpty())return NaN;let t=this.get(0,0);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.get(e,n)>t&&(t=this.get(e,n));return t}maxIndex(){tvt(this);let t=this.get(0,0),e=[0,0];for(let n=0;n<this.rows;n++)for(let o=0;o<this.columns;o++)this.get(n,o)>t&&(t=this.get(n,o),e[0]=n,e[1]=o);return e}min(){if(this.isEmpty())return NaN;let t=this.get(0,0);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)this.get(e,n)<t&&(t=this.get(e,n));return t}minIndex(){tvt(this);let t=this.get(0,0),e=[0,0];for(let n=0;n<this.rows;n++)for(let o=0;o<this.columns;o++)this.get(n,o)<t&&(t=this.get(n,o),e[0]=n,e[1]=o);return e}maxRow(t){if(WMt(this,t),this.isEmpty())return NaN;let e=this.get(t,0);for(let n=1;n<this.columns;n++)this.get(t,n)>e&&(e=this.get(t,n));return e}maxRowIndex(t){WMt(this,t),tvt(this);let e=this.get(t,0),n=[t,0];for(let o=1;o<this.columns;o++)this.get(t,o)>e&&(e=this.get(t,o),n[1]=o);return n}minRow(t){if(WMt(this,t),this.isEmpty())return NaN;let e=this.get(t,0);for(let n=1;n<this.columns;n++)this.get(t,n)<e&&(e=this.get(t,n));return e}minRowIndex(t){WMt(this,t),tvt(this);let e=this.get(t,0),n=[t,0];for(let o=1;o<this.columns;o++)this.get(t,o)<e&&(e=this.get(t,o),n[1]=o);return n}maxColumn(t){if(YMt(this,t),this.isEmpty())return NaN;let e=this.get(0,t);for(let n=1;n<this.rows;n++)this.get(n,t)>e&&(e=this.get(n,t));return e}maxColumnIndex(t){YMt(this,t),tvt(this);let e=this.get(0,t),n=[0,t];for(let o=1;o<this.rows;o++)this.get(o,t)>e&&(e=this.get(o,t),n[0]=o);return n}minColumn(t){if(YMt(this,t),this.isEmpty())return NaN;let e=this.get(0,t);for(let n=1;n<this.rows;n++)this.get(n,t)<e&&(e=this.get(n,t));return e}minColumnIndex(t){YMt(this,t),tvt(this);let e=this.get(0,t),n=[0,t];for(let o=1;o<this.rows;o++)this.get(o,t)<e&&(e=this.get(o,t),n[0]=o);return n}diag(){let t=Math.min(this.rows,this.columns),e=[];for(let n=0;n<t;n++)e.push(this.get(n,n));return e}norm(t="frobenius"){let e=0;if("max"===t)return this.max();if("frobenius"===t){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)e+=this.get(t,n)*this.get(t,n);return Math.sqrt(e)}throw new RangeError(`unknown norm type: ${t}`)}cumulativeSum(){let t=0;for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)t+=this.get(e,n),this.set(e,n,t);return this}dot(t){evt.isMatrix(t)&&(t=t.to1DArray());let e=this.to1DArray();if(e.length!==t.length)throw new RangeError("vectors do not have the same size");let n=0;for(let o=0;o<e.length;o++)n+=e[o]*t[o];return n}mmul(t){t=ivt.checkMatrix(t);let e=this.rows,n=this.columns,o=t.columns,i=new ivt(e,o),a=new Float64Array(n);for(let r=0;r<o;r++){for(let e=0;e<n;e++)a[e]=t.get(e,r);for(let t=0;t<e;t++){let e=0;for(let o=0;o<n;o++)e+=this.get(t,o)*a[o];i.set(t,r,e)}}return i}strassen2x2(t){t=ivt.checkMatrix(t);let e=new ivt(2,2);const n=this.get(0,0),o=t.get(0,0),i=this.get(0,1),a=t.get(0,1),r=this.get(1,0),s=t.get(1,0),l=this.get(1,1),c=t.get(1,1),d=(n+l)*(o+c),p=(r+l)*o,m=n*(a-c),u=l*(s-o),f=(n+i)*c,g=m+f,h=p+u,b=d-p+m+(r-n)*(o+a);return e.set(0,0,d+u-f+(i-l)*(s+c)),e.set(0,1,g),e.set(1,0,h),e.set(1,1,b),e}strassen3x3(t){t=ivt.checkMatrix(t);let e=new ivt(3,3);const n=this.get(0,0),o=this.get(0,1),i=this.get(0,2),a=this.get(1,0),r=this.get(1,1),s=this.get(1,2),l=this.get(2,0),c=this.get(2,1),d=this.get(2,2),p=t.get(0,0),m=t.get(0,1),u=t.get(0,2),f=t.get(1,0),g=t.get(1,1),h=t.get(1,2),b=t.get(2,0),y=t.get(2,1),_=t.get(2,2),C=(n-a)*(-m+g),M=(-n+a+r)*(p-m+g),v=(a+r)*(-p+m),x=n*p,O=(-n+l+c)*(p-u+h),P=(-n+l)*(u-h),w=(l+c)*(-p+u),k=(-i+c+d)*(g+b-y),S=(i-d)*(g-y),D=i*b,E=(c+d)*(-b+y),R=(-i+r+s)*(h+b-_),A=(i-s)*(h-_),T=(r+s)*(-b+_),N=(n+o+i-a-r-c-d)*g+M+v+x+k+D+E,z=x+O+w+(n+o+i-r-s-l-c)*h+D+R+T,I=C+r*(-p+m+f-g-h-b+_)+M+x+D+R+A,H=C+M+v+x+s*y,F=D+R+A+T+a*u,L=x+O+P+c*(-p+u+f-g-h-b+y)+k+S+D,B=k+S+D+E+l*m,V=x+O+P+w+d*_;return e.set(0,0,x+D+o*f),e.set(0,1,N),e.set(0,2,z),e.set(1,0,I),e.set(1,1,H),e.set(1,2,F),e.set(2,0,L),e.set(2,1,B),e.set(2,2,V),e}mmulStrassen(t){t=ivt.checkMatrix(t);let e=this.clone(),n=e.rows,o=e.columns,i=t.rows,a=t.columns;function r(t,e,n){if(t.rows===e&&t.columns===n)return t;{let o=evt.zeros(e,n);return o=o.setSubMatrix(t,0,0),o}}o!==i&&console.warn(`Multiplying ${n} x ${o} and ${i} x ${a} matrix: dimensions do not match.`);let s=Math.max(n,i),l=Math.max(o,a);return e=r(e,s,l),(function t(e,n,o,i){if(o<=512||i<=512)return e.mmul(n);o%2==1&&i%2==1?(e=r(e,o+1,i+1),n=r(n,o+1,i+1)):o%2==1?(e=r(e,o+1,i),n=r(n,o+1,i)):i%2==1&&(e=r(e,o,i+1),n=r(n,o,i+1));let a=parseInt(e.rows/2,10),s=parseInt(e.columns/2,10),l=e.subMatrix(0,a-1,0,s-1),c=n.subMatrix(0,a-1,0,s-1),d=e.subMatrix(0,a-1,s,e.columns-1),p=n.subMatrix(0,a-1,s,n.columns-1),m=e.subMatrix(a,e.rows-1,0,s-1),u=n.subMatrix(a,n.rows-1,0,s-1),f=e.subMatrix(a,e.rows-1,s,e.columns-1),g=n.subMatrix(a,n.rows-1,s,n.columns-1),h=t(evt.add(l,f),evt.add(c,g),a,s),b=t(evt.add(m,f),c,a,s),y=t(l,evt.sub(p,g),a,s),_=t(f,evt.sub(u,c),a,s),C=t(evt.add(l,d),g,a,s),M=t(evt.sub(m,l),evt.add(c,p),a,s),v=t(evt.sub(d,f),evt.add(u,g),a,s),x=evt.add(h,_);x.sub(C),x.add(v);let O=evt.add(y,C),P=evt.add(b,_),w=evt.sub(h,b);w.add(y),w.add(M);let k=evt.zeros(2*x.rows,2*x.columns);return k=k.setSubMatrix(x,0,0),k=k.setSubMatrix(O,x.rows,0),k=k.setSubMatrix(P,0,x.columns),k=k.setSubMatrix(w,x.rows,x.columns),k.subMatrix(0,o-1,0,i-1)})(e,t=r(t,s,l),s,l)}scaleRows(t={}){if("object"!=typeof t)throw new TypeError("options must be an object");const{min:e=0,max:n=1}=t;if(!Number.isFinite(e))throw new TypeError("min must be a number");if(!Number.isFinite(n))throw new TypeError("max must be a number");if(e>=n)throw new RangeError("min must be smaller than max");let o=new ivt(this.rows,this.columns);for(let t=0;t<this.rows;t++){const i=this.getRow(t);i.length>0&&BMt(i,{min:e,max:n,output:i}),o.setRow(t,i)}return o}scaleColumns(t={}){if("object"!=typeof t)throw new TypeError("options must be an object");const{min:e=0,max:n=1}=t;if(!Number.isFinite(e))throw new TypeError("min must be a number");if(!Number.isFinite(n))throw new TypeError("max must be a number");if(e>=n)throw new RangeError("min must be smaller than max");let o=new ivt(this.rows,this.columns);for(let t=0;t<this.columns;t++){const i=this.getColumn(t);i.length&&BMt(i,{min:e,max:n,output:i}),o.setColumn(t,i)}return o}flipRows(){const t=Math.ceil(this.columns/2);for(let e=0;e<this.rows;e++)for(let n=0;n<t;n++){let t=this.get(e,n),o=this.get(e,this.columns-1-n);this.set(e,n,o),this.set(e,this.columns-1-n,t)}return this}flipColumns(){const t=Math.ceil(this.rows/2);for(let e=0;e<this.columns;e++)for(let n=0;n<t;n++){let t=this.get(n,e),o=this.get(this.rows-1-n,e);this.set(n,e,o),this.set(this.rows-1-n,e,t)}return this}kroneckerProduct(t){t=ivt.checkMatrix(t);let e=this.rows,n=this.columns,o=t.rows,i=t.columns,a=new ivt(e*o,n*i);for(let r=0;r<e;r++)for(let e=0;e<n;e++)for(let n=0;n<o;n++)for(let s=0;s<i;s++)a.set(o*r+n,i*e+s,this.get(r,e)*t.get(n,s));return a}kroneckerSum(t){if(t=ivt.checkMatrix(t),!this.isSquare()||!t.isSquare())throw new Error("Kronecker Sum needs two Square Matrices");let e=this.rows,n=t.rows,o=this.kroneckerProduct(ivt.eye(n,n)),i=ivt.eye(e,e).kroneckerProduct(t);return o.add(i)}transpose(){let t=new ivt(this.columns,this.rows);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)t.set(n,e,this.get(e,n));return t}sortRows(t=nvt){for(let e=0;e<this.rows;e++)this.setRow(e,this.getRow(e).sort(t));return this}sortColumns(t=nvt){for(let e=0;e<this.columns;e++)this.setColumn(e,this.getColumn(e).sort(t));return this}subMatrix(t,e,n,o){JMt(this,t,e,n,o);let i=new ivt(e-t+1,o-n+1);for(let a=t;a<=e;a++)for(let e=n;e<=o;e++)i.set(a-t,e-n,this.get(a,e));return i}subMatrixRow(t,e,n){if(void 0===e&&(e=0),void 0===n&&(n=this.columns-1),e>n||e<0||e>=this.columns||n<0||n>=this.columns)throw new RangeError("Argument out of range");let o=new ivt(t.length,n-e+1);for(let i=0;i<t.length;i++)for(let a=e;a<=n;a++){if(t[i]<0||t[i]>=this.rows)throw new RangeError(`Row index out of range: ${t[i]}`);o.set(i,a-e,this.get(t[i],a))}return o}subMatrixColumn(t,e,n){if(void 0===e&&(e=0),void 0===n&&(n=this.rows-1),e>n||e<0||e>=this.rows||n<0||n>=this.rows)throw new RangeError("Argument out of range");let o=new ivt(n-e+1,t.length);for(let i=0;i<t.length;i++)for(let a=e;a<=n;a++){if(t[i]<0||t[i]>=this.columns)throw new RangeError(`Column index out of range: ${t[i]}`);o.set(a-e,i,this.get(a,t[i]))}return o}setSubMatrix(t,e,n){if((t=ivt.checkMatrix(t)).isEmpty())return this;JMt(this,e,e+t.rows-1,n,n+t.columns-1);for(let o=0;o<t.rows;o++)for(let i=0;i<t.columns;i++)this.set(e+o,n+i,t.get(o,i));return this}selection(t,e){let n=(function o(t,e,n){return{row:XMt(t,e),column:KMt(t,n)}})(this,t,e),i=new ivt(t.length,e.length);for(let t=0;t<n.row.length;t++){let e=n.row[t];for(let o=0;o<n.column.length;o++)i.set(t,o,this.get(e,n.column[o]))}return i}trace(){let t=Math.min(this.rows,this.columns),e=0;for(let n=0;n<t;n++)e+=this.get(n,n);return e}clone(){let t=new ivt(this.rows,this.columns);for(let e=0;e<this.rows;e++)for(let n=0;n<this.columns;n++)t.set(e,n,this.get(e,n));return t}sum(t){switch(t){case"row":return(function e(t){let e=QMt(t.rows);for(let n=0;n<t.rows;++n)for(let o=0;o<t.columns;++o)e[n]+=t.get(n,o);return e})(this);case"column":return(function n(t){let e=QMt(t.columns);for(let n=0;n<t.rows;++n)for(let o=0;o<t.columns;++o)e[o]+=t.get(n,o);return e})(this);case void 0:return(function o(t){let e=0;for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)e+=t.get(n,o);return e})(this);default:throw new Error(`invalid option: ${t}`)}}product(t){switch(t){case"row":return(function e(t){let e=QMt(t.rows,1);for(let n=0;n<t.rows;++n)for(let o=0;o<t.columns;++o)e[n]*=t.get(n,o);return e})(this);case"column":return(function n(t){let e=QMt(t.columns,1);for(let n=0;n<t.rows;++n)for(let o=0;o<t.columns;++o)e[o]*=t.get(n,o);return e})(this);case void 0:return(function o(t){let e=1;for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)e*=t.get(n,o);return e})(this);default:throw new Error(`invalid option: ${t}`)}}mean(t){const e=this.sum(t);switch(t){case"row":for(let t=0;t<this.rows;t++)e[t]/=this.columns;return e;case"column":for(let t=0;t<this.columns;t++)e[t]/=this.rows;return e;case void 0:return e/this.size;default:throw new Error(`invalid option: ${t}`)}}variance(t,e={}){if("object"==typeof t&&(e=t,t=void 0),"object"!=typeof e)throw new TypeError("options must be an object");const{unbiased:n=!0,mean:o=this.mean(t)}=e;if("boolean"!=typeof n)throw new TypeError("unbiased must be a boolean");switch(t){case"row":if(!Array.isArray(o))throw new TypeError("mean must be an array");return(function e(t,n,o){const i=t.rows,a=t.columns,r=[];for(let e=0;e<i;e++){let i=0,s=0,l=0;for(let n=0;n<a;n++)l=t.get(e,n)-o[e],i+=l,s+=l*l;r.push(n?(s-i*i/a)/(a-1):(s-i*i/a)/a)}return r})(this,n,o);case"column":if(!Array.isArray(o))throw new TypeError("mean must be an array");return(function i(t,e,n){const o=t.rows,i=t.columns,a=[];for(let r=0;r<i;r++){let i=0,s=0,l=0;for(let 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n=0;n<t.columns;n++){let o=0;for(let e=0;e<t.rows;e++)o+=Math.pow(t.get(e,n),2)/(t.rows-1);e.push(Math.sqrt(o))}return e})(this);else if(!Array.isArray(n))throw new TypeError("scale must be an array");return(function a(t,e){for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)t.set(n,o,t.get(n,o)/e[o])})(this,n),this;case void 0:if(void 0===n)n=(function r(t){const e=t.size-1;let n=0;for(let o=0;o<t.columns;o++)for(let i=0;i<t.rows;i++)n+=Math.pow(t.get(i,o),2)/e;return Math.sqrt(n)})(this);else if("number"!=typeof n)throw new TypeError("scale must be a number");return(function s(t,e){for(let n=0;n<t.rows;n++)for(let o=0;o<t.columns;o++)t.set(n,o,t.get(n,o)/e)})(this,n),this;default:throw new Error(`invalid option: ${t}`)}}toString(t){return UMt(this,t)}}function nvt(t,e){return t-e}evt.prototype.klass="Matrix","undefined"!=typeof Symbol&&(evt.prototype[Symbol.for("nodejs.util.inspect.custom")]=function ovt(){return UMt(this)}),evt.random=evt.rand,evt.randomInt=evt.randInt,evt.diagonal=evt.diag,evt.prototype.diagonal=evt.prototype.diag,evt.identity=evt.eye,evt.prototype.negate=evt.prototype.neg,evt.prototype.tensorProduct=evt.prototype.kroneckerProduct;class ivt extends evt{constructor(t,e){if(super(),ivt.isMatrix(t))return t.clone();if(Number.isInteger(t)&&t>=0){if(this.data=[],!(Number.isInteger(e)&&e>=0))throw new TypeError("nColumns must be a positive integer");for(let n=0;n<t;n++)this.data.push(new Float64Array(e))}else{if(!Array.isArray(t))throw new TypeError("First argument must be a positive number or an array");{const n=t;if("number"!=typeof(e=(t=n.length)?n[0].length:0))throw new TypeError("Data must be a 2D array with at least one element");this.data=[];for(let o=0;o<t;o++){if(n[o].length!==e)throw new RangeError("Inconsistent array dimensions");this.data.push(Float64Array.from(n[o]))}}}this.rows=t,this.columns=e}set(t,e,n){return this.data[t][e]=n,this}get(t,e){return 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this},t.prototype.addM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)+n.get(t,e));return this},t.add=function t(n,o){return new e(n).add(o)},t.prototype.sub=function t(e){return"number"==typeof e?this.subS(e):this.subM(e)},t.prototype.subS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)-e);return this},t.prototype.subM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)-n.get(t,e));return this},t.sub=function t(n,o){return new 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new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)|n.get(t,e));return this},t.or=function t(n,o){return new e(n).or(o)},t.prototype.xor=function t(e){return"number"==typeof e?this.xorS(e):this.xorM(e)},t.prototype.xorS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)^e);return this},t.prototype.xorM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)^n.get(t,e));return this},t.xor=function t(n,o){return new e(n).xor(o)},t.prototype.leftShift=function t(e){return"number"==typeof e?this.leftShiftS(e):this.leftShiftM(e)},t.prototype.leftShiftS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)<<e);return this},t.prototype.leftShiftM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)<<n.get(t,e));return this},t.leftShift=function t(n,o){return new e(n).leftShift(o)},t.prototype.signPropagatingRightShift=function t(e){return"number"==typeof e?this.signPropagatingRightShiftS(e):this.signPropagatingRightShiftM(e)},t.prototype.signPropagatingRightShiftS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)>>e);return this},t.prototype.signPropagatingRightShiftM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)>>n.get(t,e));return this},t.signPropagatingRightShift=function t(n,o){return new e(n).signPropagatingRightShift(o)},t.prototype.rightShift=function t(e){return"number"==typeof e?this.rightShiftS(e):this.rightShiftM(e)},t.prototype.rightShiftS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,this.get(t,n)>>>e);return this},t.prototype.rightShiftM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,this.get(t,e)>>>n.get(t,e));return this},t.rightShift=function t(n,o){return new e(n).rightShift(o)},t.prototype.zeroFillRightShift=t.prototype.rightShift,t.prototype.zeroFillRightShiftS=t.prototype.rightShiftS,t.prototype.zeroFillRightShiftM=t.prototype.rightShiftM,t.zeroFillRightShift=t.rightShift,t.prototype.not=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,~this.get(t,e));return this},t.not=function t(n){return new 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t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.atan(this.get(t,e)));return this},t.atan=function t(n){return new e(n).atan()},t.prototype.atanh=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.atanh(this.get(t,e)));return this},t.atanh=function t(n){return new e(n).atanh()},t.prototype.cbrt=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.cbrt(this.get(t,e)));return this},t.cbrt=function t(n){return new e(n).cbrt()},t.prototype.ceil=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.ceil(this.get(t,e)));return this},t.ceil=function t(n){return new e(n).ceil()},t.prototype.clz32=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.clz32(this.get(t,e)));return this},t.clz32=function t(n){return new e(n).clz32()},t.prototype.cos=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.cos(this.get(t,e)));return this},t.cos=function t(n){return new e(n).cos()},t.prototype.cosh=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.cosh(this.get(t,e)));return this},t.cosh=function t(n){return new e(n).cosh()},t.prototype.exp=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.exp(this.get(t,e)));return this},t.exp=function t(n){return new e(n).exp()},t.prototype.expm1=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.expm1(this.get(t,e)));return this},t.expm1=function t(n){return new e(n).expm1()},t.prototype.floor=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.floor(this.get(t,e)));return this},t.floor=function t(n){return new e(n).floor()},t.prototype.fround=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.fround(this.get(t,e)));return this},t.fround=function t(n){return new e(n).fround()},t.prototype.log=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.log(this.get(t,e)));return this},t.log=function t(n){return new e(n).log()},t.prototype.log1p=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.log1p(this.get(t,e)));return this},t.log1p=function t(n){return new e(n).log1p()},t.prototype.log10=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.log10(this.get(t,e)));return this},t.log10=function t(n){return new e(n).log10()},t.prototype.log2=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.log2(this.get(t,e)));return this},t.log2=function t(n){return new e(n).log2()},t.prototype.round=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.round(this.get(t,e)));return this},t.round=function t(n){return new e(n).round()},t.prototype.sign=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sign(this.get(t,e)));return this},t.sign=function t(n){return new e(n).sign()},t.prototype.sin=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sin(this.get(t,e)));return this},t.sin=function t(n){return new e(n).sin()},t.prototype.sinh=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sinh(this.get(t,e)));return this},t.sinh=function t(n){return new e(n).sinh()},t.prototype.sqrt=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.sqrt(this.get(t,e)));return this},t.sqrt=function t(n){return new e(n).sqrt()},t.prototype.tan=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.tan(this.get(t,e)));return this},t.tan=function t(n){return new e(n).tan()},t.prototype.tanh=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.tanh(this.get(t,e)));return this},t.tanh=function t(n){return new e(n).tanh()},t.prototype.trunc=function t(){for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.trunc(this.get(t,e)));return this},t.trunc=function t(n){return new e(n).trunc()},t.pow=function t(n,o){return new e(n).pow(o)},t.prototype.pow=function t(e){return"number"==typeof e?this.powS(e):this.powM(e)},t.prototype.powS=function t(e){for(let t=0;t<this.rows;t++)for(let n=0;n<this.columns;n++)this.set(t,n,Math.pow(this.get(t,n),e));return this},t.prototype.powM=function t(n){if(n=e.checkMatrix(n),this.rows!==n.rows||this.columns!==n.columns)throw new RangeError("Matrices dimensions must be equal");for(let t=0;t<this.rows;t++)for(let e=0;e<this.columns;e++)this.set(t,e,Math.pow(this.get(t,e),n.get(t,e)));return this}})(evt,ivt);class rvt extends evt{constructor(t){super(),this.data=t,this.rows=t.length,this.columns=t[0].length}set(t,e,n){return this.data[t][e]=n,this}get(t,e){return this.data[t][e]}}class svt{constructor(t){let e,n,o,i,a,r,s,l,c,d=(t=rvt.checkMatrix(t)).clone(),p=d.rows,m=d.columns,u=new Float64Array(p),f=1;for(e=0;e<p;e++)u[e]=e;for(l=new Float64Array(p),n=0;n<m;n++){for(e=0;e<p;e++)l[e]=d.get(e,n);for(e=0;e<p;e++){for(c=Math.min(e,n),a=0,o=0;o<c;o++)a+=d.get(e,o)*l[o];l[e]-=a,d.set(e,n,l[e])}for(i=n,e=n+1;e<p;e++)Math.abs(l[e])>Math.abs(l[i])&&(i=e);if(i!==n){for(o=0;o<m;o++)r=d.get(i,o),d.set(i,o,d.get(n,o)),d.set(n,o,r);s=u[i],u[i]=u[n],u[n]=s,f=-f}if(n<p&&0!==d.get(n,n))for(e=n+1;e<p;e++)d.set(e,n,d.get(e,n)/d.get(n,n))}this.LU=d,this.pivotVector=u,this.pivotSign=f}isSingular(){let t=this.LU,e=t.columns;for(let n=0;n<e;n++)if(0===t.get(n,n))return!0;return!1}solve(t){t=ivt.checkMatrix(t);let e=this.LU;if(e.rows!==t.rows)throw new Error("Invalid matrix dimensions");if(this.isSingular())throw new Error("LU matrix is singular");let n,o,i,a=t.columns,r=t.subMatrixRow(this.pivotVector,0,a-1),s=e.columns;for(i=0;i<s;i++)for(n=i+1;n<s;n++)for(o=0;o<a;o++)r.set(n,o,r.get(n,o)-r.get(i,o)*e.get(n,i));for(i=s-1;i>=0;i--){for(o=0;o<a;o++)r.set(i,o,r.get(i,o)/e.get(i,i));for(n=0;n<i;n++)for(o=0;o<a;o++)r.set(n,o,r.get(n,o)-r.get(i,o)*e.get(n,i))}return r}get determinant(){let t=this.LU;if(!t.isSquare())throw new Error("Matrix must be square");let e=this.pivotSign,n=t.columns;for(let o=0;o<n;o++)e*=t.get(o,o);return e}get lowerTriangularMatrix(){let t=this.LU,e=t.rows,n=t.columns,o=new ivt(e,n);for(let i=0;i<e;i++)for(let e=0;e<n;e++)o.set(i,e,i>e?t.get(i,e):i===e?1:0);return o}get upperTriangularMatrix(){let t=this.LU,e=t.rows,n=t.columns,o=new ivt(e,n);for(let i=0;i<e;i++)for(let e=0;e<n;e++)o.set(i,e,i<=e?t.get(i,e):0);return o}get pivotPermutationVector(){return Array.from(this.pivotVector)}}function lvt(t,e){let n=0;return Math.abs(t)>Math.abs(e)?(n=e/t,Math.abs(t)*Math.sqrt(1+n*n)):0!==e?(n=t/e,Math.abs(e)*Math.sqrt(1+n*n)):0}class cvt{constructor(t){let e,n,o,i,a=(t=rvt.checkMatrix(t)).clone(),r=t.rows,s=t.columns,l=new Float64Array(s);for(o=0;o<s;o++){let t=0;for(e=o;e<r;e++)t=lvt(t,a.get(e,o));if(0!==t){for(a.get(o,o)<0&&(t=-t),e=o;e<r;e++)a.set(e,o,a.get(e,o)/t);for(a.set(o,o,a.get(o,o)+1),n=o+1;n<s;n++){for(i=0,e=o;e<r;e++)i+=a.get(e,o)*a.get(e,n);for(i=-i/a.get(o,o),e=o;e<r;e++)a.set(e,n,a.get(e,n)+i*a.get(e,o))}}l[o]=-t}this.QR=a,this.Rdiag=l}solve(t){t=ivt.checkMatrix(t);let e=this.QR,n=e.rows;if(t.rows!==n)throw new Error("Matrix row dimensions must agree");if(!this.isFullRank())throw new Error("Matrix is rank deficient");let o,i,a,r,s=t.columns,l=t.clone(),c=e.columns;for(a=0;a<c;a++)for(i=0;i<s;i++){for(r=0,o=a;o<n;o++)r+=e.get(o,a)*l.get(o,i);for(r=-r/e.get(a,a),o=a;o<n;o++)l.set(o,i,l.get(o,i)+r*e.get(o,a))}for(a=c-1;a>=0;a--){for(i=0;i<s;i++)l.set(a,i,l.get(a,i)/this.Rdiag[a]);for(o=0;o<a;o++)for(i=0;i<s;i++)l.set(o,i,l.get(o,i)-l.get(a,i)*e.get(o,a))}return l.subMatrix(0,c-1,0,s-1)}isFullRank(){let t=this.QR.columns;for(let e=0;e<t;e++)if(0===this.Rdiag[e])return!1;return!0}get upperTriangularMatrix(){let t,e,n=this.QR,o=n.columns,i=new ivt(o,o);for(t=0;t<o;t++)for(e=0;e<o;e++)i.set(t,e,t<e?n.get(t,e):t===e?this.Rdiag[t]:0);return i}get orthogonalMatrix(){let t,e,n,o,i=this.QR,a=i.rows,r=i.columns,s=new ivt(a,r);for(n=r-1;n>=0;n--){for(t=0;t<a;t++)s.set(t,n,0);for(s.set(n,n,1),e=n;e<r;e++)if(0!==i.get(n,n)){for(o=0,t=n;t<a;t++)o+=i.get(t,n)*s.get(t,e);for(o=-o/i.get(n,n),t=n;t<a;t++)s.set(t,e,s.get(t,e)+o*i.get(t,n))}}return s}}class dvt{constructor(t,e={}){if((t=rvt.checkMatrix(t)).isEmpty())throw new Error("Matrix must be non-empty");let n=t.rows,o=t.columns;const{computeLeftSingularVectors:i=!0,computeRightSingularVectors:a=!0,autoTranspose:r=!1}=e;let s,l=Boolean(i),c=Boolean(a),d=!1;if(n<o)if(r){s=t.transpose(),n=s.rows,o=s.columns,d=!0;let e=l;l=c,c=e}else s=t.clone(),console.warn("Computing SVD on a matrix with more columns than rows. Consider enabling autoTranspose");else s=t.clone();let p=Math.min(n,o),m=Math.min(n+1,o),u=new Float64Array(m),f=new ivt(n,p),g=new ivt(o,o),h=new Float64Array(o),b=new Float64Array(n),y=new Float64Array(m);for(let t=0;t<m;t++)y[t]=t;let _=Math.min(n-1,o),C=Math.max(0,Math.min(o-2,n)),M=Math.max(_,C);for(let t=0;t<M;t++){if(t<_){u[t]=0;for(let e=t;e<n;e++)u[t]=lvt(u[t],s.get(e,t));if(0!==u[t]){s.get(t,t)<0&&(u[t]=-u[t]);for(let e=t;e<n;e++)s.set(e,t,s.get(e,t)/u[t]);s.set(t,t,s.get(t,t)+1)}u[t]=-u[t]}for(let e=t+1;e<o;e++){if(t<_&&0!==u[t]){let o=0;for(let i=t;i<n;i++)o+=s.get(i,t)*s.get(i,e);o=-o/s.get(t,t);for(let i=t;i<n;i++)s.set(i,e,s.get(i,e)+o*s.get(i,t))}h[e]=s.get(t,e)}if(l&&t<_)for(let e=t;e<n;e++)f.set(e,t,s.get(e,t));if(t<C){h[t]=0;for(let e=t+1;e<o;e++)h[t]=lvt(h[t],h[e]);if(0!==h[t]){h[t+1]<0&&(h[t]=0-h[t]);for(let e=t+1;e<o;e++)h[e]/=h[t];h[t+1]+=1}if(h[t]=-h[t],t+1<n&&0!==h[t]){for(let e=t+1;e<n;e++)b[e]=0;for(let e=t+1;e<n;e++)for(let n=t+1;n<o;n++)b[e]+=h[n]*s.get(e,n);for(let e=t+1;e<o;e++){let o=-h[e]/h[t+1];for(let i=t+1;i<n;i++)s.set(i,e,s.get(i,e)+o*b[i])}}if(c)for(let e=t+1;e<o;e++)g.set(e,t,h[e])}}let v=Math.min(o,n+1);if(_<o&&(u[_]=s.get(_,_)),n<v&&(u[v-1]=0),C+1<v&&(h[C]=s.get(C,v-1)),h[v-1]=0,l){for(let t=_;t<p;t++){for(let e=0;e<n;e++)f.set(e,t,0);f.set(t,t,1)}for(let t=_-1;t>=0;t--)if(0!==u[t]){for(let e=t+1;e<p;e++){let o=0;for(let i=t;i<n;i++)o+=f.get(i,t)*f.get(i,e);o=-o/f.get(t,t);for(let i=t;i<n;i++)f.set(i,e,f.get(i,e)+o*f.get(i,t))}for(let e=t;e<n;e++)f.set(e,t,-f.get(e,t));f.set(t,t,1+f.get(t,t));for(let e=0;e<t-1;e++)f.set(e,t,0)}else{for(let e=0;e<n;e++)f.set(e,t,0);f.set(t,t,1)}}if(c)for(let t=o-1;t>=0;t--){if(t<C&&0!==h[t])for(let e=t+1;e<o;e++){let n=0;for(let i=t+1;i<o;i++)n+=g.get(i,t)*g.get(i,e);n=-n/g.get(t+1,t);for(let i=t+1;i<o;i++)g.set(i,e,g.get(i,e)+n*g.get(i,t))}for(let e=0;e<o;e++)g.set(e,t,0);g.set(t,t,1)}let x=v-1,O=Number.EPSILON;for(;v>0;){let t,e;for(t=v-2;t>=-1&&-1!==t;t--){const e=Number.MIN_VALUE+O*Math.abs(u[t]+Math.abs(u[t+1]));if(Math.abs(h[t])<=e||Number.isNaN(h[t])){h[t]=0;break}}if(t===v-2)e=4;else{let n;for(n=v-1;n>=t&&n!==t;n--){let e=(n!==v?Math.abs(h[n]):0)+(n!==t+1?Math.abs(h[n-1]):0);if(Math.abs(u[n])<=O*e){u[n]=0;break}}n===t?e=3:n===v-1?e=1:(e=2,t=n)}switch(t++,e){case 1:{let e=h[v-2];h[v-2]=0;for(let n=v-2;n>=t;n--){let i=lvt(u[n],e),a=u[n]/i,r=e/i;if(u[n]=i,n!==t&&(e=-r*h[n-1],h[n-1]=a*h[n-1]),c)for(let t=0;t<o;t++)i=a*g.get(t,n)+r*g.get(t,v-1),g.set(t,v-1,-r*g.get(t,n)+a*g.get(t,v-1)),g.set(t,n,i)}break}case 2:{let e=h[t-1];h[t-1]=0;for(let o=t;o<v;o++){let i=lvt(u[o],e),a=u[o]/i,r=e/i;if(u[o]=i,e=-r*h[o],h[o]=a*h[o],l)for(let e=0;e<n;e++)i=a*f.get(e,o)+r*f.get(e,t-1),f.set(e,t-1,-r*f.get(e,o)+a*f.get(e,t-1)),f.set(e,o,i)}break}case 3:{const e=Math.max(Math.abs(u[v-1]),Math.abs(u[v-2]),Math.abs(h[v-2]),Math.abs(u[t]),Math.abs(h[t])),i=u[v-1]/e,a=u[v-2]/e,r=h[v-2]/e,s=u[t]/e,d=h[t]/e,p=((a+i)*(a-i)+r*r)/2,m=i*r*(i*r);let b=0;0===p&&0===m||(b=p<0?0-Math.sqrt(p*p+m):Math.sqrt(p*p+m),b=m/(p+b));let y=(s+i)*(s-i)+b,_=s*d;for(let e=t;e<v-1;e++){let i=lvt(y,_);0===i&&(i=Number.MIN_VALUE);let a=y/i,r=_/i;if(e!==t&&(h[e-1]=i),y=a*u[e]+r*h[e],h[e]=a*h[e]-r*u[e],_=r*u[e+1],u[e+1]=a*u[e+1],c)for(let t=0;t<o;t++)i=a*g.get(t,e)+r*g.get(t,e+1),g.set(t,e+1,-r*g.get(t,e)+a*g.get(t,e+1)),g.set(t,e,i);if(i=lvt(y,_),0===i&&(i=Number.MIN_VALUE),a=y/i,r=_/i,u[e]=i,y=a*h[e]+r*u[e+1],u[e+1]=-r*h[e]+a*u[e+1],_=r*h[e+1],h[e+1]=a*h[e+1],l&&e<n-1)for(let t=0;t<n;t++)i=a*f.get(t,e)+r*f.get(t,e+1),f.set(t,e+1,-r*f.get(t,e)+a*f.get(t,e+1)),f.set(t,e,i)}h[v-2]=y;break}case 4:if(u[t]<=0&&(u[t]=u[t]<0?-u[t]:0,c))for(let e=0;e<=x;e++)g.set(e,t,-g.get(e,t));for(;t<x&&!(u[t]>=u[t+1]);){let e=u[t];if(u[t]=u[t+1],u[t+1]=e,c&&t<o-1)for(let n=0;n<o;n++)e=g.get(n,t+1),g.set(n,t+1,g.get(n,t)),g.set(n,t,e);if(l&&t<n-1)for(let o=0;o<n;o++)e=f.get(o,t+1),f.set(o,t+1,f.get(o,t)),f.set(o,t,e);t++}v--}}if(d){let t=g;g=f,f=t}this.m=n,this.n=o,this.s=u,this.U=f,this.V=g}solve(t){let e=t,n=this.threshold,o=this.s.length,i=ivt.zeros(o,o);for(let t=0;t<o;t++)Math.abs(this.s[t])<=n?i.set(t,t,0):i.set(t,t,1/this.s[t]);let a=this.U,r=this.rightSingularVectors,s=r.mmul(i),l=r.rows,c=a.rows,d=ivt.zeros(l,c);for(let t=0;t<l;t++)for(let e=0;e<c;e++){let n=0;for(let i=0;i<o;i++)n+=s.get(t,i)*a.get(e,i);d.set(t,e,n)}return d.mmul(e)}solveForDiagonal(t){return this.solve(ivt.diag(t))}inverse(){let t=this.V,e=this.threshold,n=t.rows,o=t.columns,i=new ivt(n,this.s.length);for(let a=0;a<n;a++)for(let n=0;n<o;n++)Math.abs(this.s[n])>e&&i.set(a,n,t.get(a,n)/this.s[n]);let a=this.U,r=a.rows,s=a.columns,l=new ivt(n,r);for(let t=0;t<n;t++)for(let e=0;e<r;e++){let n=0;for(let o=0;o<s;o++)n+=i.get(t,o)*a.get(e,o);l.set(t,e,n)}return l}get condition(){return this.s[0]/this.s[Math.min(this.m,this.n)-1]}get norm2(){return this.s[0]}get rank(){let t=Math.max(this.m,this.n)*this.s[0]*Number.EPSILON,e=0,n=this.s;for(let o=0,i=n.length;o<i;o++)n[o]>t&&e++;return e}get diagonal(){return Array.from(this.s)}get threshold(){return Number.EPSILON/2*Math.max(this.m,this.n)*this.s[0]}get leftSingularVectors(){return this.U}get rightSingularVectors(){return this.V}get diagonalMatrix(){return ivt.diag(this.s)}}function pvt(t,e,n,o,i){let a=ivt.eye(e.length,e.length,n*o*o);const r=i(e);let s=new Float64Array(t.x.length);for(let e=0;e<t.x.length;e++)s[e]=r(t.x[e]);let l=(function c(t,e,n,o,i){const a=n.length,r=t.x.length;let s=new Array(a);for(let l=0;l<a;l++){s[l]=new Array(r);let a=n.slice();a[l]+=o;let c=i(a);for(let n=0;n<r;n++)s[l][n]=e[n]-c(t.x[n])}return new ivt(s)})(t,s,e,o,i),d=(function p(t,e){const n=t.x.length;let o=new Array(n);for(let i=0;i<n;i++)o[i]=[t.y[i]-e[i]];return new ivt(o)})(t,s),m=(function u(t,e=!1){return t=rvt.checkMatrix(t),e?new dvt(t).inverse():(function n(t,e,o=!1){return t=rvt.checkMatrix(t),e=rvt.checkMatrix(e),o?new dvt(t).solve(e):t.isSquare()?new svt(t).solve(e):new cvt(t).solve(e)})(t,ivt.eye(t.rows))})(a.add(l.mmul(l.transpose())));return(e=(e=new ivt([e])).sub(m.mmul(l).mmul(d).mul(o).transpose())).to1DArray()}var mvt=$v(Object.freeze({__proto__:null,default:function uvt(t,e,n={}){let{maxIterations:o=100,gradientDifference:i=.1,damping:a=0,errorTolerance:r=.01,minValues:s,maxValues:l,initialValues:c}=n;if(a<=0)throw new Error("The damping option must be a positive number");if(!t.x||!t.y)throw new Error("The data parameter must have x and y elements");if(!NMt(t.x)||t.x.length<2||!NMt(t.y)||t.y.length<2)throw new Error("The data parameter elements must be an array with more than 2 points");if(t.x.length!==t.y.length)throw new Error("The data parameter elements must have the same size");let d=c||new Array(e.length).fill(1),p=d.length;if(l=l||new 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s=[],l=[],c=[],d=r.getAll();for(a=0;a<d.length;a++){var p=d[a];p.value&&(s.push(p.value),c.push(p.row),l.push(p.col))}return{head:l,tail:c,epochsPerSample:this.makeEpochsPerSample(s,e)}},t.prototype.makeEpochsPerSample=function(t,e){var n=Ovt.filled(t.length,-1),o=Ovt.max(t),i=t.map((function(t){return t/o*e}));return i.forEach((function(t,o){t>0&&(n[o]=e/i[o])})),n},t.prototype.assignOptimizationStateParameters=function(t){Object.assign(this.optimizationState,t)},t.prototype.prepareForOptimizationLoop=function(){var t=this,e=t.repulsionStrength,n=t.learningRate,o=t.negativeSampleRate,i=this.optimizationState,a=i.epochsPerSample,r=i.headEmbedding,s=r[0].length,l=r.length===i.tailEmbedding.length,c=a.map((function(t){return t/o})),d=bvt(c),p=bvt(a);this.assignOptimizationStateParameters({epochOfNextSample:p,epochOfNextNegativeSample:d,epochsPerNegativeSample:c,moveOther:l,initialAlpha:n,alpha:n,gamma:e,dim:s})},t.prototype.initializeOptimization=function(){var 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D=Ovt.tauRandInt(y,this.random),E=a[D],R=Tvt(M,E),A=0;if(R>0)A=2*u*g,A/=(.001+R)*(f*Math.pow(R,g)+1);else if(C===D)continue;for(P=0;P<h;P++)w=4,A>0&&(w=Avt(A*(M[P]-E[P]),4)),M[P]+=w*m}l[_]+=k*c[_]}return e.alpha=p*(1-t/b),e.currentEpoch+=1,i},t.prototype.optimizeLayoutAsync=function(t){var e=this;return void 0===t&&(t=function(){return!0}),new Promise((function(n,o){var i=function(){return fvt(e,void 0,void 0,(function(){var e,a,r,s,l;return gvt(this,(function(c){try{if(a=(e=this.optimizationState).nEpochs,this.embedding=this.optimizeLayoutStep(e.currentEpoch),s=!1===t(r=this.optimizationState.currentEpoch),l=r===a,s||l)return[2,n(l)];setTimeout((function(){return i()}),0)}catch(t){o(t)}return[2]}))}))};setTimeout((function(){return i()}),0)}))},t.prototype.optimizeLayout=function(t){void 0===t&&(t=function(){return!0});for(var e=!1,n=[];!e;){var o=this.optimizationState,i=o.nEpochs;n=this.optimizeLayoutStep(o.currentEpoch);var 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n=0,o=0;o<t.length;o++)n+=Math.pow(t[o]-e[o],2);return n}function Nvt(t,e){var n=Ovt.linear(0,3*t,300).map((function(t){return t<e?1:t})),o=Ovt.zeros(n.length).map((function(o,i){return n[i]>=e?Math.exp(-(n[i]-e)/t):o})),i=Pvt.default({x:n,y:o},(function(t){var e=hvt(t,2),n=e[0],o=e[1];return function(t){return 1/(1+n*Math.pow(t,2*o))}}),{damping:1.5,initialValues:[.5,.5],gradientDifference:.1,maxIterations:100,errorTolerance:.01}).parameterValues,a=hvt(i,2);return{a:a[0],b:a[1]}}function zvt(t,e,n,o){return void 0===n&&(n=1),void 0===o&&(o=5),t.map((function(t,i,a){return-1===e[i]||-1===e[a]?t*Math.exp(-n):e[i]!==e[a]?t*Math.exp(-o):t}))}function Ivt(t){t=Mvt.normalize(t,"max");var e=Mvt.transpose(t),n=Mvt.pairwiseMultiply(e,t);return t=Mvt.add(t,Mvt.subtract(e,n)),Mvt.eliminateZeros(t)}function Hvt(t,e,n){for(var o=Ovt.zeros(t.length).map((function(t){return Ovt.zeros(n[0].length)})),i=0;i<t.length;i++)for(var a=0;a<t[0].length;a++)for(var r=0;r<n[0].length;r++)o[i][r]+=e[i][a]*n[t[i][a]][r];return o}function Fvt(t){let e,n,o=t.length;for(;o;)n=Math.floor(Math.random()*o--),e=t[o],t[o]=t[n],t[n]=e;return t}function Lvt(t,e){return`${e}/${t}`}uCt.findABParams=Nvt,uCt.fastIntersection=zvt,uCt.resetLocalConnectivity=Ivt,uCt.initTransform=Hvt,Object.defineProperty(mCt,"__esModule",{value:!0}),mCt.UMAP=uCt.UMAP;class Bvt{constructor(t){this.http=t,this.httpPathPrefix="data/plugin/npmi"}fetchData(t){return $t(this.fetchAnnotations(t),this.fetchMetrics(t),this.fetchValues(t),this.fetchEmbeddings(t)).pipe(It((([t,e,n,o])=>{const i={},a={};let r,s=0;for(const r of Object.keys(t))for(const l in t[r]){const c=t[r][l];Object.keys(o).length&&!a[c]&&o[r][l]&&o[r][l].some((t=>0!==t))&&(a[c]={vector:o[r][l],index:s,name:c},s+=1);const d=new Map;for(const t in e[r]){const o=e[r][t],i=nyt(o);let a=d.get(i);a||(a={nPMIValue:null,countValue:null,annotation:c,metric:i,run:r},d.set(i,a)),$bt(o)?a.countValue=n[r][l][t]:tyt(o)&&(a.nPMIValue=n[r][l][t])}i[c]=[...i[c]?i[c]:[],...d.values()]}return Object.keys(a).length&&(r=(function l(t){const e=Object.keys(t);return{points:t,pointKeys:e,shuffledDataIndices:Fvt((n=e.length,[...new Array(n)].map(((t,e)=>e)))),hasUmapRun:!1};var n})(a)),{annotationData:i,metrics:e,embeddingDataSet:r}})),pe((t=>t instanceof xD&&400<=t.status&&t.status<500?Et({annotationData:{},metrics:{},embeddingDataSet:void 0}):Rt(t))))}fetchAnnotations(t){return $t(t.map((t=>this.http.get(`/experiment/${t}/${this.httpPathPrefix}/annotations`).pipe(It((e=>(function n(t,e){return Object.fromEntries(Object.entries(t).map((([t,n])=>[Lvt(t,e),n])))})(e,t))))))).pipe(It((t=>{let e={};for(const n of t)e=Object.assign(Object.assign({},e),n);return e})))}fetchMetrics(t){return $t(t.map((t=>this.http.get(`/experiment/${t}/${this.httpPathPrefix}/metrics`).pipe(It((e=>(function n(t,e){return Object.fromEntries(Object.entries(t).map((([t,n])=>[Lvt(t,e),n])))})(e,t))))))).pipe(It((t=>{let e={};for(const n of t)e=Object.assign(Object.assign({},e),n);return e})))}fetchValues(t){return $t(t.map((t=>this.http.get(`/experiment/${t}/${this.httpPathPrefix}/values`).pipe(It((e=>(function n(t,e){return Object.fromEntries(Object.entries(t).map((([t,n])=>[Lvt(t,e),n])))})(e,t))))))).pipe(It((t=>{let e={};for(const n of t)e=Object.assign(Object.assign({},e),n);return e})))}fetchEmbeddings(t){return $t(t.map((t=>this.http.get(`/experiment/${t}/${this.httpPathPrefix}/embeddings`).pipe(It((e=>(function n(t,e){return Object.fromEntries(Object.entries(t).map((([t,n])=>[Lvt(t,e),n])))})(e,t))))))).pipe(It((t=>{let e={};for(const n of t)e=Object.assign(Object.assign({},e),n);return e})))}}Bvt.ɵfac=function t(e){return new(e||Bvt)(vr(lE))},Bvt.ɵprov=Mn({token:Bvt,factory:Bvt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Bvt,[{type:im}],(function(){return[{type:lE}]}),null);class Vvt{}Vvt.ɵfac=function t(e){return new(e||Vvt)},Vvt.ɵmod=ao({type:Vvt}),Vvt.ɵinj=vn({providers:[Bvt],imports:[[cE]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Vvt,[{type:Ay,args:[{imports:[cE],providers:[Bvt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Vvt,{imports:[cE]});class jvt{constructor(t,e,n){this.actions$=t,this.store=e,this.dataSource=n,this.loadData$=Mk((()=>re(this.loadPluginData()).pipe(It((()=>({}))))),{dispatch:!1})}loadPluginData(){return this.actions$.pipe(Dk(kbt),Ve(this.store.select(abt),this.store.select(TS)),ce((([,t,e])=>t!==yE.LOADING&&null!==e)),Fe((()=>this.store.dispatch(Sbt()))),Zt((([,,t])=>this.dataSource.fetchData(t).pipe(Fe((t=>{this.store.dispatch(Dbt(t))})),It((()=>{})),pe((()=>(this.store.dispatch(Ebt()),rt)))))))}}jvt.ɵfac=function t(e){return new(e||jvt)(vr(Sk),vr(Iw),vr(Bvt))},jvt.ɵprov=Mn({token:jvt,factory:jvt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(jvt,[{type:im}],(function(){return[{type:Sk},{type:Iw},{type:Bvt}]}),null);class Uvt{}Uvt.ɵfac=function t(e){return new(e||Uvt)},Uvt.ɵmod=ao({type:Uvt}),Uvt.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Uvt,[{type:Ay,args:[{declarations:[obt],exports:[obt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Uvt,{declarations:[obt],exports:[obt]});class Gvt{}Gvt.ɵfac=function t(e){return new(e||Gvt)},Gvt.ɵmod=ao({type:Gvt}),Gvt.ɵinj=vn({imports:[[WM,EW,BY,Y0]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Gvt,[{type:Ay,args:[{declarations:[syt,lyt],imports:[WM,EW,BY,Y0],exports:[lyt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Gvt,{declarations:[syt,lyt],imports:[WM,EW,BY,Y0],exports:[lyt]});class Wvt{}Wvt.ɵfac=function t(e){return new(e||Wvt)},Wvt.ɵmod=ao({type:Wvt}),Wvt.ɵinj=vn({imports:[[WM,cG,dG,EW,Eyt,VQ]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Wvt,[{type:Ay,args:[{declarations:[zyt,Iyt],imports:[WM,cG,dG,EW,Eyt,VQ],exports:[Iyt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Wvt,{declarations:[zyt,Iyt],imports:[WM,cG,dG,EW,Eyt,VQ],exports:[Iyt]});class Yvt{}Yvt.ɵfac=function t(e){return new(e||Yvt)},Yvt.ɵmod=ao({type:Yvt}),Yvt.ɵinj=vn({imports:[[WM,Eyt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Yvt,[{type:Ay,args:[{declarations:[Hyt],imports:[WM,Eyt],exports:[Hyt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Yvt,{declarations:[Hyt],imports:[WM,Eyt],exports:[Hyt]});class qvt{}qvt.ɵfac=function t(e){return new(e||qvt)},qvt.ɵmod=ao({type:qvt}),qvt.ɵinj=vn({imports:[[WM,Eyt,Wvt,Yvt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(qvt,[{type:Ay,args:[{declarations:[Vyt,jyt],imports:[WM,Eyt,Wvt,Yvt],exports:[jyt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(qvt,{declarations:[Vyt,jyt],imports:[WM,Eyt,Wvt,Yvt],exports:[jyt]});class Zvt{}Zvt.ɵfac=function t(e){return new(e||Zvt)},Zvt.ɵmod=ao({type:Zvt}),Zvt.ɵinj=vn({imports:[[WM,EW,JH]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Zvt,[{type:Ay,args:[{declarations:[pyt,myt],imports:[WM,EW,JH],exports:[myt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Zvt,{declarations:[pyt,myt],imports:[WM,EW,JH],exports:[myt]});class Xvt{}Xvt.ɵfac=function t(e){return new(e||Xvt)},Xvt.ɵmod=ao({type:Xvt}),Xvt.ɵinj=vn({imports:[[WM,Gvt,qvt,Zvt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Xvt,[{type:Ay,args:[{declarations:[Uyt],imports:[WM,Gvt,qvt,Zvt],exports:[Uyt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Xvt,{declarations:[Uyt],imports:[WM,Gvt,qvt,Zvt],exports:[Uyt]});class Kvt{}Kvt.ɵfac=function t(e){return new(e||Kvt)},Kvt.ɵmod=ao({type:Kvt}),Kvt.ɵinj=vn({imports:[[WM,cG,JH,EW]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Kvt,[{type:Ay,args:[{declarations:[Yyt,qyt],imports:[WM,cG,JH,EW],exports:[qyt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Kvt,{declarations:[Yyt,qyt],imports:[WM,cG,JH,EW],exports:[qyt]});class Jvt{}Jvt.ɵfac=function t(e){return new(e||Jvt)},Jvt.ɵmod=ao({type:Jvt}),Jvt.ɵinj=vn({imports:[[WM,cG,EW,JH,Kvt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Jvt,[{type:Ay,args:[{declarations:[Kyt,Jyt],imports:[WM,cG,EW,JH,Kvt],exports:[Jyt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Jvt,{declarations:[Kyt,Jyt],imports:[WM,cG,EW,JH,Kvt],exports:[Jyt]});class Qvt{}Qvt.ɵfac=function t(e){return new(e||Qvt)},Qvt.ɵmod=ao({type:Qvt}),Qvt.ɵinj=vn({imports:[[WM,cG,EW,BY]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Qvt,[{type:Ay,args:[{declarations:[f_t,g_t],imports:[WM,cG,EW,BY],exports:[g_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Qvt,{declarations:[f_t,g_t],imports:[WM,cG,EW,BY],exports:[g_t]});class $vt{}$vt.ɵfac=function t(e){return new(e||$vt)},$vt.ɵmod=ao({type:$vt}),$vt.ɵinj=vn({imports:[[WM,cG,EW,JH,m_t,Qvt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh($vt,[{type:Ay,args:[{declarations:[y_t,__t],imports:[WM,cG,EW,JH,m_t,Qvt],exports:[__t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro($vt,{declarations:[y_t,__t],imports:[WM,cG,EW,JH,m_t,Qvt],exports:[__t]});class txt{}txt.ɵfac=function t(e){return new(e||txt)},txt.ɵmod=ao({type:txt}),txt.ɵinj=vn({imports:[[WM,cG,SY,EW]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(txt,[{type:Ay,args:[{declarations:[P_t,w_t],imports:[WM,cG,SY,EW],exports:[w_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(txt,{declarations:[P_t,w_t],imports:[WM,cG,SY,EW],exports:[w_t]});class ext{}ext.ɵfac=function t(e){return new(e||ext)},ext.ɵmod=ao({type:ext}),ext.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ext,[{type:Ay,args:[{declarations:[M_t],imports:[WM],exports:[M_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(ext,{declarations:[M_t],imports:[WM],exports:[M_t]});class nxt{}nxt.ɵfac=function t(e){return new(e||nxt)},nxt.ɵmod=ao({type:nxt}),nxt.ɵinj=vn({imports:[[ext]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(nxt,[{type:Ay,args:[{declarations:[v_t],imports:[ext],exports:[v_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(nxt,{declarations:[v_t],imports:[ext],exports:[v_t]});class oxt{}oxt.ɵfac=function t(e){return new(e||oxt)},oxt.ɵmod=ao({type:oxt}),oxt.ɵinj=vn({imports:[[WM,cG,SY,EW,JH]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(oxt,[{type:Ay,args:[{declarations:[T_t,A_t],imports:[WM,cG,SY,EW,JH],exports:[T_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(oxt,{declarations:[T_t,A_t],imports:[WM,cG,SY,EW,JH],exports:[T_t]});class ixt{}ixt.ɵfac=function t(e){return new(e||ixt)},ixt.ɵmod=ao({type:ixt}),ixt.ɵinj=vn({imports:[[WM,$vt,txt,nxt,_F,oxt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(ixt,[{type:Ay,args:[{declarations:[I_t,H_t],imports:[WM,$vt,txt,nxt,_F,oxt],exports:[H_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(ixt,{declarations:[I_t,H_t],imports:[WM,$vt,txt,nxt,_F,oxt],exports:[H_t]});class axt{}axt.ɵfac=function t(e){return new(e||axt)},axt.ɵmod=ao({type:axt}),axt.ɵinj=vn({imports:[[WM]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(axt,[{type:Ay,args:[{declarations:[L_t,B_t],imports:[WM],exports:[B_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(axt,{declarations:[L_t,B_t],imports:[WM],exports:[B_t]});class rxt{}rxt.ɵfac=function t(e){return new(e||rxt)},rxt.ɵmod=ao({type:rxt}),rxt.ɵinj=vn({imports:[[WM,EW,JH,axt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(rxt,[{type:Ay,args:[{declarations:[G_t,W_t],imports:[WM,EW,JH,axt],exports:[W_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(rxt,{declarations:[G_t,W_t],imports:[WM,EW,JH,axt],exports:[W_t]});class sxt{}sxt.ɵfac=function t(e){return new(e||sxt)},sxt.ɵmod=ao({type:sxt}),sxt.ɵinj=vn({imports:[[WM,cG,SY,EW,kht,Xvt,JH,Jvt,ixt,rxt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(sxt,[{type:Ay,args:[{declarations:[J_t,Q_t],imports:[WM,cG,SY,EW,kht,Xvt,JH,Jvt,ixt,rxt],exports:[Q_t]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(sxt,{declarations:[J_t,Q_t],imports:[WM,cG,SY,EW,kht,Xvt,JH,Jvt,ixt,rxt],exports:[Q_t]});class lxt{}lxt.ɵfac=function t(e){return new(e||lxt)},lxt.ɵmod=ao({type:lxt}),lxt.ɵinj=vn({imports:[[WM,cG,SY,EW,kht,Xvt,JH,ixt,rxt]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(lxt,[{type:Ay,args:[{declarations:[iCt,aCt],imports:[WM,cG,SY,EW,kht,Xvt,JH,ixt,rxt],exports:[aCt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(lxt,{declarations:[iCt,aCt],imports:[WM,cG,SY,EW,kht,Xvt,JH,ixt,rxt],exports:[aCt]});class cxt{}cxt.ɵfac=function t(e){return new(e||cxt)},cxt.ɵmod=ao({type:cxt}),cxt.ɵinj=vn({imports:[[WM,Uvt,sxt,lxt,Vvt,dk.forFeature(Qht,iyt),Wk.forFeature([jvt]),wq.forPlugin("npmi",pCt)]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(cxt,[{type:Ay,args:[{declarations:[dCt,pCt],imports:[WM,Uvt,sxt,lxt,Vvt,dk.forFeature(Qht,iyt),Wk.forFeature([jvt]),wq.forPlugin("npmi",pCt)],exports:[pCt],entryComponents:[pCt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(cxt,{declarations:[dCt,pCt],imports:[WM,Uvt,sxt,lxt,Vvt,ck,Gk,wq],exports:[pCt]});class dxt{}dxt.ɵfac=function t(e){return new(e||dxt)},dxt.ɵcmp=to({type:dxt,selectors:[["text-dashboard"]],decls:1,vars:0,template:function t(e,n){1&e&&ku(0," This is the text dashboard ")},encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(dxt,[{type:My,args:[{selector:"text-dashboard",template:" This is the text dashboard ",changeDetection:zn.OnPush}]}],null,null);class pxt{}pxt.ɵfac=function t(e){return new(e||pxt)},pxt.ɵmod=ao({type:pxt}),pxt.ɵinj=vn({}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(pxt,[{type:Ay,args:[{declarations:[dxt],exports:[dxt]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(pxt,{declarations:[dxt],exports:[dxt]});class mxt{}mxt.ɵfac=function t(e){return new(e||mxt)},mxt.ɵprov=Mn({token:mxt,factory:mxt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(mxt,[{type:im}],null,null);class uxt{constructor(t){this.http=t,this.httpPathPrefix="data/plugin/text_v2"}fetchRunToTag(){return this.http.get(this.httpPathPrefix+"/tags").pipe(It((t=>{const e=new Map;return Object.entries(t).forEach((([t,n])=>{e.set(t,n)})),e})))}fetchTextData(t,e){const n=new URLSearchParams({run:t,tag:e});return this.http.get(this.httpPathPrefix+`/text?${n.toString()}`).pipe(It((t=>t.map((t=>({originalShape:t.original_shape,step:t.step,stringArray:t.string_array,wallTimeInMs:1e3*t.wall_time,truncated:t.truncated}))))))}}uxt.ɵfac=function t(e){return new(e||uxt)(vr(lE))},uxt.ɵprov=Mn({token:uxt,factory:uxt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(uxt,[{type:im}],(function(){return[{type:lE}]}),null);class fxt{}fxt.ɵfac=function t(e){return new(e||fxt)},fxt.ɵmod=ao({type:fxt}),fxt.ɵinj=vn({providers:[uxt,{provide:mxt,useExisting:uxt}],imports:[[cE]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(fxt,[{type:Ay,args:[{imports:[cE],providers:[uxt,{provide:mxt,useExisting:uxt}]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(fxt,{imports:[cE]});const gxt=JP("[Text] Text Plugin Loaded"),hxt=JP("[Text] Runs To Tag Loaded",{_as:"props",_p:void 0}),bxt=JP("[Text] Tag Group Visibility Changed",{_as:"props",_p:void 0}),yxt=JP("[Text] Text Data Loaded Loaded",{_as:"props",_p:void 0}),_xt="text",Cxt=Kw(_xt);Zw(Cxt,(t=>t.runToTags));const Mxt=Zw(Cxt,(t=>{const e=new Set,n=new Set;for(const o of t.visibleRunTags.values())for(const t of o){const o=JSON.stringify(t);e.has(o)||(e.add(o),n.add(t))}return[...n]})),vxt=Zw(Cxt,((t,e)=>{const n=t.data.get(e.run);return n&&n.get(e.tag)||null}));class xxt{constructor(t,e,n){this.actions$=t,this.store=e,this.dataSource=n,this.loadRunToTags$=Mk((()=>this.actions$.pipe(Dk(gxt),ze((()=>this.dataSource.fetchRunToTag().pipe(Fe((t=>{this.store.dispatch(hxt({runToTags:t}))})),It((()=>{}))))))),{dispatch:!1}),this.loadData$=Mk((()=>re(this.actions$.pipe(Dk(bxt),ze((({visibleTextCards:t})=>$t(t.map((({run:t,tag:e})=>this.store.select(vxt,{run:t,tag:e}).pipe((function n(t,e){var n=arguments.length>=2;return function(o){return o.pipe(t?ce((function(e,n){return t(e,n,o)})):w,Se(1),n?he(e):xe((function(){return new 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.mat-form-field-label[_ngcontent-%COMP%]{top:1.84375em;margin-top:-0.25em}.mat-form-field-appearance-outline.mat-form-field-can-float.mat-form-field-should-float[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%], .mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[_ngcontent-%COMP%]:focus + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59375em) scale(0.75);width:133.3333333333%}.mat-form-field-appearance-outline.mat-form-field-can-float[_ngcontent-%COMP%]   .mat-input-server[label][_ngcontent-%COMP%]:not(:label-shown) + .mat-form-field-label-wrapper[_ngcontent-%COMP%]   .mat-form-field-label[_ngcontent-%COMP%]{transform:translateY(-1.59374em) scale(0.75);width:133.3333433333%}.mat-grid-tile-header[_ngcontent-%COMP%], .mat-grid-tile-footer[_ngcontent-%COMP%]{font-size:14px}.mat-grid-tile-header[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%], .mat-grid-tile-footer[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-grid-tile-header[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2), .mat-grid-tile-footer[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}input.mat-input-element[_ngcontent-%COMP%]{margin-top:-0.0625em}.mat-menu-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:400}.mat-paginator[_ngcontent-%COMP%], .mat-paginator-page-size[_ngcontent-%COMP%]   .mat-select-trigger[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px}.mat-radio-button[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-select[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", 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h1[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h2[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h3[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h4[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h5[_ngcontent-%COMP%], .mat-toolbar[_ngcontent-%COMP%]   h6[_ngcontent-%COMP%]{font:500 20px/32px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal;margin:0}.mat-tooltip[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:10px;padding-top:6px;padding-bottom:6px}.mat-tooltip-handset[_ngcontent-%COMP%]{font-size:14px;padding-top:8px;padding-bottom:8px}.mat-list-item[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:16px}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:14px}.mat-list-base[_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px;font-weight:500}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-item[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]{font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]{white-space:nowrap;overflow:hidden;text-overflow:ellipsis;display:block;box-sizing:border-box}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-list-option[_ngcontent-%COMP%]   .mat-line[_ngcontent-%COMP%]:nth-child(n+2){font-size:12px}.mat-list-base[dense][_ngcontent-%COMP%]   .mat-subheader[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:12px;font-weight:500}.mat-option[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:16px}.mat-optgroup-label[_ngcontent-%COMP%]{font:500 14px/24px Roboto, "Helvetica Neue", sans-serif;letter-spacing:normal}.mat-simple-snackbar[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif;font-size:14px}.mat-simple-snackbar-action[_ngcontent-%COMP%]{line-height:1;font-family:inherit;font-size:inherit;font-weight:500}.mat-tree[_ngcontent-%COMP%]{font-family:Roboto, "Helvetica Neue", sans-serif}.mat-tree-node[_ngcontent-%COMP%], .mat-nested-tree-node[_ngcontent-%COMP%]{font-weight:400;font-size:14px}.mat-ripple[_ngcontent-%COMP%]{overflow:hidden;position:relative}.mat-ripple[_ngcontent-%COMP%]:not(:empty){transform:translateZ(0)}.mat-ripple.mat-ripple-unbounded[_ngcontent-%COMP%]{overflow:visible}.mat-ripple-element[_ngcontent-%COMP%]{position:absolute;border-radius:50%;pointer-events:none;transition:opacity,transform 0ms cubic-bezier(0, 0, 0.2, 1);transform:scale(0)}.cdk-high-contrast-active[_ngcontent-%COMP%]   .mat-ripple-element[_ngcontent-%COMP%]{display:none}.cdk-visually-hidden[_ngcontent-%COMP%]{border:0;clip:rect(0 0 0 0);height:1px;margin:-1px;overflow:hidden;padding:0;position:absolute;width:1px;white-space:nowrap;outline:0;-webkit-appearance:none;-moz-appearance:none}.cdk-overlay-container[_ngcontent-%COMP%], .cdk-global-overlay-wrapper[_ngcontent-%COMP%]{pointer-events:none;top:0;left:0;height:100%;width:100%}.cdk-overlay-container[_ngcontent-%COMP%]{position:fixed;z-index:1000}.cdk-overlay-container[_ngcontent-%COMP%]:empty{display:none}.cdk-global-overlay-wrapper[_ngcontent-%COMP%]{display:flex;position:absolute;z-index:1000}.cdk-overlay-pane[_ngcontent-%COMP%]{position:absolute;pointer-events:auto;box-sizing:border-box;z-index:1000;display:flex;max-width:100%;max-height:100%}.cdk-overlay-backdrop[_ngcontent-%COMP%]{position:absolute;top:0;bottom:0;left:0;right:0;z-index:1000;pointer-events:auto;-webkit-tap-highlight-color:transparent;transition:opacity 400ms cubic-bezier(0.25, 0.8, 0.25, 1);opacity:0}.cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:1}.cdk-high-contrast-active[_ngcontent-%COMP%]   .cdk-overlay-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:.6}.cdk-overlay-dark-backdrop[_ngcontent-%COMP%]{background:rgba(0,0,0,.32)}.cdk-overlay-transparent-backdrop[_ngcontent-%COMP%], .cdk-overlay-transparent-backdrop.cdk-overlay-backdrop-showing[_ngcontent-%COMP%]{opacity:0}.cdk-overlay-connected-position-bounding-box[_ngcontent-%COMP%]{position:absolute;z-index:1000;display:flex;flex-direction:column;min-width:1px;min-height:1px}.cdk-global-scrollblock[_ngcontent-%COMP%]{position:fixed;width:100%;overflow-y:scroll}textarea.cdk-textarea-autosize[_ngcontent-%COMP%]{resize:none}textarea.cdk-textarea-autosize-measuring[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:auto !important;overflow:hidden !important}textarea.cdk-textarea-autosize-measuring-firefox[_ngcontent-%COMP%]{padding:2px 0 !important;box-sizing:content-box !important;height:0 !important}@keyframes cdk-text-field-autofill-start{}@keyframes cdk-text-field-autofill-end{}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:-webkit-autofill{animation:cdk-text-field-autofill-start 0s 1ms}.cdk-text-field-autofill-monitored[_ngcontent-%COMP%]:not(:-webkit-autofill){animation:cdk-text-field-autofill-end 0s 1ms}.mat-focus-indicator[_ngcontent-%COMP%]{position:relative}.mat-mdc-focus-indicator[_ngcontent-%COMP%]{position:relative}[_nghost-%COMP%]{background-color:#fff;color:#212121;display:block;position:relative}body.dark-mode   [_nghost-%COMP%]   .plugins.is-first-party-plugin[_ngcontent-%COMP%]{background-color:#303030;color:#fff}.plugins[_ngcontent-%COMP%]{height:100%;position:relative}.warning[_ngcontent-%COMP%]{background:#fff;bottom:0;left:0;position:absolute;right:0;top:0}body.dark-mode[_nghost-%COMP%]   .warning[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .warning[_ngcontent-%COMP%]{background:#303030}.warning-message[_ngcontent-%COMP%]{color:#212121;margin:80px auto 0;max-width:540px}body.dark-mode[_nghost-%COMP%]   .warning-message[_ngcontent-%COMP%], body.dark-mode   [_nghost-%COMP%]   .warning-message[_ngcontent-%COMP%]{color:#fff}.last-reload-time[_ngcontent-%COMP%]{font-style:italic}.plugins[_ngcontent-%COMP%]     iframe{border:0;display:block;height:100%;width:100%}'],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Uxt,[{type:My,args:[{selector:"plugins-component",templateUrl:"./plugins_component.ng.html",styleUrls:["plugins_component.css"],changeDetection:zn.OnPush}]}],(function(){return[{type:ug},{type:wq},{type:sz,decorators:[{type:Sr}]}]}),{pluginsContainer:[{type:Za,args:["pluginContainer",{static:!0,read:hg}]}],ngPluginContainer:[{type:Za,args:["ngPluginContainer",{static:!0,read:eh}]}],activePluginId:[{type:xy}],activeKnownPlugin:[{type:xy}],pluginLoadState:[{type:xy}],dataLocation:[{type:xy}],isFeatureFlagsLoaded:[{type:xy}],settingsLoadState:[{type:xy}],featureFlags:[{type:xy}],lastUpdated:[{type:xy}],environmentFailureNotFoundTemplate:[{type:xy}],environmentFailureUnknownTemplate:[{type:xy}]});const Gxt=Zw(vR,MR,((t,e)=>e&&t[e]?Object.assign({id:e},t[e]):null));class Wxt{constructor(t){this.store=t,this.activeKnownPlugin$=this.store.select(Gxt),this.activePluginId$=this.store.select(MR),this.pluginLoadState$=Wt(this.activeKnownPlugin$,this.activePluginId$,this.store.select(bR)).pipe(It((([t,e,n])=>null!==n.failureCode?n.failureCode===mE.NOT_FOUND?jxt.ENVIRONMENT_FAILURE_NOT_FOUND:jxt.ENVIRONMENT_FAILURE_UNKNOWN:null!==t?jxt.LOADED:null===n.lastLoadedTimeInMs&&n.state===yE.LOADING?jxt.LOADING:e?jxt.UNKNOWN_PLUGIN_ID:jxt.NO_ENABLED_PLUGINS))),this.lastLoadedTimeInMs$=this.store.select(CR),this.dataLocation$=this.store.select(xR).pipe(It((t=>t.data_location))),this.isFeatureFlagsLoaded$=this.store.select(qD),this.featureFlags$=this.store.select(ZD),this.settingsLoadState$=this.store.select(SN)}}Wxt.ɵfac=function t(e){return new(e||Wxt)(Sm(Iw))},Wxt.ɵcmp=to({type:Wxt,selectors:[["plugins"]],inputs:{environmentFailureNotFoundTemplate:"environmentFailureNotFoundTemplate",environmentFailureUnknownTemplate:"environmentFailureUnknownTemplate"},decls:9,vars:26,consts:[[3,"activeKnownPlugin","activePluginId","dataLocation","lastUpdated","pluginLoadState","isFeatureFlagsLoaded","settingsLoadState","featureFlags","environmentFailureNotFoundTemplate","environmentFailureUnknownTemplate"]],template:function t(e,n){1&e&&(Tm(0,"plugins-component",0),Ah(1,"async"),Ah(2,"async"),Ah(3,"async"),Ah(4,"async"),Ah(5,"async"),Ah(6,"async"),Ah(7,"async"),Ah(8,"async")),2&e&&Dm("activeKnownPlugin",Th(1,10,n.activeKnownPlugin$))("activePluginId",Th(2,12,n.activePluginId$))("dataLocation",Th(3,14,n.dataLocation$))("lastUpdated",Th(4,16,n.lastLoadedTimeInMs$))("pluginLoadState",Th(5,18,n.pluginLoadState$))("isFeatureFlagsLoaded",Th(6,20,n.isFeatureFlagsLoaded$))("settingsLoadState",Th(7,22,n.settingsLoadState$))("featureFlags",Th(8,24,n.featureFlags$))("environmentFailureNotFoundTemplate",n.environmentFailureNotFoundTemplate)("environmentFailureUnknownTemplate",n.environmentFailureUnknownTemplate)},styles:["plugins-component[_ngcontent-%COMP%] { height: 100%; }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Wxt,[{type:My,args:[{selector:"plugins",template:'\n    <plugins-component\n      [activeKnownPlugin]="activeKnownPlugin$ | async"\n      [activePluginId]="activePluginId$ | async"\n      [dataLocation]="dataLocation$ | async"\n      [lastUpdated]="lastLoadedTimeInMs$ | async"\n      [pluginLoadState]="pluginLoadState$ | async"\n      [isFeatureFlagsLoaded]="isFeatureFlagsLoaded$ | async"\n      [settingsLoadState]="settingsLoadState$ | async"\n      [featureFlags]="featureFlags$ | async"\n      [environmentFailureNotFoundTemplate]="environmentFailureNotFoundTemplate"\n      [environmentFailureUnknownTemplate]="environmentFailureUnknownTemplate"\n    ></plugins-component>\n  ',styles:["plugins-component { height: 100%; }"],changeDetection:zn.OnPush}]}],(function(){return[{type:Iw}]}),{environmentFailureNotFoundTemplate:[{type:xy}],environmentFailureUnknownTemplate:[{type:xy}]});class Yxt{}Yxt.ɵfac=function t(e){return new(e||Yxt)},Yxt.ɵmod=ao({type:Yxt}),Yxt.ɵinj=vn({imports:[[ER,WM,wq]]}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Yxt,[{type:Ay,args:[{declarations:[Wxt,Uxt],exports:[Wxt],imports:[ER,WM,wq]}]}],null,null),("undefined"==typeof ngJitMode||ngJitMode)&&ro(Yxt,{declarations:[Wxt,Uxt],imports:[ER,WM,wq],exports:[Wxt]}),eo(Wxt,[Uxt],[wM]);class qxt{constructor(t,e){this.store=t,this.document=e,this.onVisibilityChange=this.onVisibilityChangeImpl.bind(this),this.reloadEnabled$=this.store.pipe(Fw(DN)),this.reloadPeriodInMs$=this.store.pipe(Fw(EN)),this.reloadTimerId=null,this.missedAutoReload=!1}ngOnInit(){this.document.addEventListener("visibilitychange",this.onVisibilityChange),Wt(this.reloadEnabled$.pipe(Me()),this.reloadPeriodInMs$.pipe(Me())).subscribe((([t,e])=>{this.cancelLoad(),t&&this.load(e)}))}onVisibilityChangeImpl(){"visible"===this.document.visibilityState&&this.missedAutoReload&&(this.missedAutoReload=!1,this.store.dispatch(xE()))}load(t){this.reloadTimerId=setTimeout((()=>{"visible"===this.document.visibilityState?this.store.dispatch(xE()):this.missedAutoReload=!0,this.load(t)}),t)}cancelLoad(){null!==this.reloadTimerId&&clearTimeout(this.reloadTimerId),this.reloadTimerId=null}ngOnDestroy(){this.cancelLoad(),this.document.removeEventListener("visibilitychange",this.onVisibilityChange)}}qxt.ɵfac=function t(e){return new(e||qxt)(Sm(Iw),Sm(Z_))},qxt.ɵcmp=to({type:qxt,selectors:[["reloader"]],decls:0,vars:0,template:function t(e,n){},encapsulation:2,changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(qxt,[{type:My,args:[{selector:"reloader",template:"",changeDetection:zn.OnPush}]}],(function(){return[{type:Iw},{type:Document,decorators:[{type:kr,args:[Z_]}]}]}),null);class Zxt{}Zxt.ɵfac=function t(e){return new(e||Zxt)},Zxt.ɵcmp=to({type:Zxt,selectors:[["tensorboard-wrapper-component"]],decls:2,vars:0,consts:[[1,"plugins"]],template:function t(e,n){1&e&&(Tm(0,"plugins",0),Tm(1,"reloader"))},directives:[Wxt,qxt],styles:["[_nghost-%COMP%] {\n        display: flex;\n        flex-direction: column;\n        height: 100%;\n      }\n\n      .plugins[_ngcontent-%COMP%] {\n        flex: 1 1;\n        overflow: auto;\n        position: relative;\n      }"],changeDetection:0}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Zxt,[{type:My,args:[{selector:"tensorboard-wrapper-component",template:'\n    <plugins class="plugins"></plugins>\n    <reloader></reloader>\n  ',styles:["\n      :host {\n        display: flex;\n        flex-direction: column;\n        height: 100%;\n      }\n\n      .plugins {\n        flex: 1 1;\n        overflow: auto;\n        position: relative;\n      }\n    "],changeDetection:zn.OnPush}]}],null,null);class Xxt{}Xxt.ɵfac=function t(e){return new(e||Xxt)},Xxt.ɵprov=Mn({token:Xxt,factory:Xxt.ɵfac}),("undefined"==typeof ngDevMode||ngDevMode)&&hh(Xxt,[{type:im}],null,null);const Kxt="smoothing",Jxt="runColorGroup",Qxt="tagFilter",$xt="regex:";let tOt=class extends Xxt{getMetricsPinnedCards(t){return Wt([t.select(PT),t.select(kT)]).pipe(It((([t,e])=>{if(!t.length&&!e.length)return[];const n=[...t.map((({plugin:t,tag:e,sample:n,runId:o})=>{const i={plugin:t,tag:e};return xA(t)&&(i.runId=o),MA(t)&&(i.sample=n),i})),...e];return[{key:"pinnedCards",value:JSON.stringify(n)}]})))}getFeatureFlagStates(t){return Wt([t.select($D),t.select(XD)]).pipe(It((([t,e])=>{const n=t.map((t=>({key:NA,value:t})));return"boolean"==typeof e.enabledColorGroup&&n.push({key:IA,value:String(e.enabledColorGroup)}),"boolean"==typeof e.enabledColorGroupByRegex&&n.push({key:HA,value:String(e.enabledColorGroupByRegex)}),n})))}serializeStateToQueryParams(t){return Wt([this.getMetricsPinnedCards(t),t.select(BT).pipe(It((t=>t?[{key:Qxt,value:t}]:[]))),this.getFeatureFlagStates(t),t.select(ET).pipe(It((t=>Number.isFinite(t.scalarSmoothing)?[{key:Kxt,value:String(t.scalarSmoothing)}]:[]))),t.select(uN).pipe(It((t=>{if(!t)return[];let e;switch(t.key){case tN.EXPERIMENT:e="experiment";break;case tN.RUN:e="run";break;case tN.REGEX:e=`regex:${t.regexString}`;break;default:throw new RangeError("Serialization not implemented")}return[{key:Jxt,value:e}]})))]).pipe(It((t=>t.flat())))}deserializeQueryParams(t){let e=null,n=null,o=null,i=null;for(const{key:a,value:r}of t)switch(a){case"pinnedCards":e=eOt(r);break;case Kxt:n=Number(r);break;case Jxt:switch(r){case"experiment":i={key:tN.EXPERIMENT};break;case"run":i={key:tN.RUN}}if(r.startsWith($xt)){const t=r.slice($xt.length);i={key:tN.REGEX,regexString:t}}break;case Qxt:o=r}return{metrics:{pinnedCards:e||[],smoothing:n,tagFilter:o},runs:{groupBy:i}}}};function eOt(t){let e;try{e=JSON.parse(t)}catch(t){return null}if(!Array.isArray(e))return null;const n=[];for(const t of e){const e="string"==typeof t.runId,i="number"==typeof t.sample,a="string"==typeof t.tag,r=e||void 0===t.runId,s=i||void 0===t.sample;if(!("string"==typeof t.plugin&&a&&r&&s))continue;if((o=t.plugin)!==hA.SCALARS&&o!==hA.HISTOGRAMS&&o!==hA.IMAGES)continue;if(!t.tag)continue;if(xA(t.plugin)){if(!t.runId)continue}else 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ngJitMode||ngJitMode)&&ro(dOt,{declarations:[aq],imports:[tT,Uv,ZP,BS,sq,PS,JL,KL,lq,cOt,ER,gq,dq,vq,aA,xq,pq,rD,sz,Yxt,KN,Cq,sOt,kxt]}),"loading"!==document.readyState?Fv().bootstrapModule(dOt):window.addEventListener("DOMContentLoaded",(()=>{Fv().bootstrapModule(dOt)})),(function pOt(){if(b_)throw new Error("Cannot enable prod mode after platform setup.");(void 0===typeof ngDevMode||ngDevMode)&&(jn.ngDevMode=!1),h_=!1}
/**
     * @license
     * Copyright Google LLC All Rights Reserved.
     *
     * Use of this source code is governed by an MIT-style license that can be
     * found in the LICENSE file at https://angular.io/license
     */)()})();
",
+              "headers": [
+                [
+                  "content-type",
+                  "application/javascript; charset=utf-8"
+                ]
+              ],
+              "ok": true,
+              "status": 200,
+              "status_text": ""
+            }
+          }
+        },
+        "id": "_wpQGXu9aONh",
+        "outputId": "8edb7bb8-0907-4f1f-ee6e-459cc4b3c8dc"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/javascript": "\n        (async () => {\n            const url = new URL(await google.colab.kernel.proxyPort(6006, {'cache': true}));\n            url.searchParams.set('tensorboardColab', 'true');\n            const iframe = document.createElement('iframe');\n            iframe.src = url;\n            iframe.setAttribute('width', '100%');\n            iframe.setAttribute('height', '800');\n            iframe.setAttribute('frameborder', 0);\n            document.body.appendChild(iframe);\n        })();\n    ",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# load tensorboard in colab\n",
+        "%load_ext tensorboard\n",
+        "\n",
+        "# see curves in tensorboard\n",
+        "%tensorboard --logdir ./tutorial_exps"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "MfQ-yspZLuuI"
+      },
+      "source": [
+        "From the tensorboard, we can observe that changes of loss and learning rate. We can see the losses of each branch gradually decrease as the training goes by.\n",
+        "\n",
+        "## Test the Trained Detector\n",
+        "\n",
+        "After finetuning the detector, let's visualize the prediction results!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 20,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 443
+        },
+        "id": "_MuZurfGLq0p",
+        "outputId": "d035aec4-6bad-4e04-d105-d9557f058140"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/content/mmdetection/mmdet/datasets/utils.py:69: UserWarning: \"ImageToTensor\" pipeline is replaced by \"DefaultFormatBundle\" for batch inference. It is recommended to manually replace it in the test data pipeline in your config file.\n",
+            "  'data pipeline in your config file.', UserWarning)\n"
+          ]
+        },
+        {
+          "data": {
+            "image/png": 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",
+            "text/plain": [
+              "<Figure size 1242.01x375.01 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "needs_background": "light"
+          },
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "img = mmcv.imread('kitti_tiny/training/image_2/000068.jpeg')\n",
+        "\n",
+        "model.cfg = cfg\n",
+        "result = inference_detector(model, img)\n",
+        "show_result_pyplot(model, img, result)\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "F1L8o3rtc37M"
+      },
+      "source": [
+        "## What to Do Next?\n",
+        "\n",
+        "So far, we have learnt how to test and train a two-stage detector using MMDetection. To further explore MMDetection, you could do several other things as shown below:\n",
+        "\n",
+        "- Try single-stage detectors, e.g., [RetinaNet](https://github.com/open-mmlab/mmdetection/tree/master/configs/retinanet) and [SSD](https://github.com/open-mmlab/mmdetection/tree/master/configs/ssd) in [MMDetection model zoo](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md). Single-stage detectors are more commonly used than two-stage detectors in industry.\n",
+        "- Try anchor-free detectors, e.g., [FCOS](https://github.com/open-mmlab/mmdetection/tree/master/configs/fcos) and [RepPoints](https://github.com/open-mmlab/mmdetection/tree/master/configs/reppoints) in [MMDetection model zoo](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md). Anchor-free detector is a new trend in the object detection community.\n",
+        "- Try 3D object detection using [MMDetection3D](https://github.com/open-mmlab/mmdetection3d), also one of the OpenMMLab projects. In MMDetection3D, not only can you try all the methods supported in MMDetection but also some 3D object detectors.\n"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "collapsed_sections": [],
+      "name": "object_detection",
+      "provenance": [],
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.7"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
diff --git a/demo/create_result_gif.py b/demo/create_result_gif.py
new file mode 100644
index 0000000..d2356e9
--- /dev/null
+++ b/demo/create_result_gif.py
@@ -0,0 +1,163 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+import matplotlib.patches as mpatches
+import matplotlib.pyplot as plt
+import mmcv
+import numpy as np
+
+try:
+    import imageio
+except ImportError:
+    imageio = None
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Create GIF for demo')
+    parser.add_argument(
+        'image_dir',
+        help='directory where result '
+        'images save path generated by ‘analyze_results.py’')
+    parser.add_argument(
+        '--out',
+        type=str,
+        default='result.gif',
+        help='gif path where will be saved')
+    args = parser.parse_args()
+    return args
+
+
+def _generate_batch_data(sampler, batch_size):
+    batch = []
+    for idx in sampler:
+        batch.append(idx)
+        if len(batch) == batch_size:
+            yield batch
+            batch = []
+    if len(batch) > 0:
+        yield batch
+
+
+def create_gif(frames, gif_name, duration=2):
+    """Create gif through imageio.
+
+    Args:
+        frames (list[ndarray]): Image frames
+        gif_name (str): Saved gif name
+        duration (int): Display interval (s),
+            Default: 2
+    """
+    if imageio is None:
+        raise RuntimeError('imageio is not installed,'
+                           'Please use “pip install imageio” to install')
+    imageio.mimsave(gif_name, frames, 'GIF', duration=duration)
+
+
+def create_frame_by_matplotlib(image_dir,
+                               nrows=1,
+                               fig_size=(300, 300),
+                               font_size=15):
+    """Create gif frame image through matplotlib.
+
+    Args:
+        image_dir (str): Root directory of result images
+        nrows (int): Number of rows displayed, Default: 1
+        fig_size (tuple): Figure size of the pyplot figure.
+           Default: (300, 300)
+        font_size (int): Font size of texts. Default: 15
+
+    Returns:
+        list[ndarray]: image frames
+    """
+
+    result_dir_names = os.listdir(image_dir)
+    assert len(result_dir_names) == 2
+    # Longer length has higher priority
+    result_dir_names.reverse()
+
+    images_list = []
+    for dir_names in result_dir_names:
+        images_list.append(mmcv.scandir(osp.join(image_dir, dir_names)))
+
+    frames = []
+    for paths in _generate_batch_data(zip(*images_list), nrows):
+
+        fig, axes = plt.subplots(nrows=nrows, ncols=2)
+        fig.suptitle('Good/bad case selected according '
+                     'to the COCO mAP of the single image')
+
+        det_patch = mpatches.Patch(color='salmon', label='prediction')
+        gt_patch = mpatches.Patch(color='royalblue', label='ground truth')
+        # bbox_to_anchor may need to be finetuned
+        plt.legend(
+            handles=[det_patch, gt_patch],
+            bbox_to_anchor=(1, -0.18),
+            loc='lower right',
+            borderaxespad=0.)
+
+        if nrows == 1:
+            axes = [axes]
+
+        dpi = fig.get_dpi()
+        # set fig size and margin
+        fig.set_size_inches(
+            (fig_size[0] * 2 + fig_size[0] // 20) / dpi,
+            (fig_size[1] * nrows + fig_size[1] // 3) / dpi,
+        )
+
+        fig.tight_layout()
+        # set subplot margin
+        plt.subplots_adjust(
+            hspace=.05,
+            wspace=0.05,
+            left=0.02,
+            right=0.98,
+            bottom=0.02,
+            top=0.98)
+
+        for i, (path_tuple, ax_tuple) in enumerate(zip(paths, axes)):
+            image_path_left = osp.join(
+                osp.join(image_dir, result_dir_names[0], path_tuple[0]))
+            image_path_right = osp.join(
+                osp.join(image_dir, result_dir_names[1], path_tuple[1]))
+            image_left = mmcv.imread(image_path_left)
+            image_left = mmcv.rgb2bgr(image_left)
+            image_right = mmcv.imread(image_path_right)
+            image_right = mmcv.rgb2bgr(image_right)
+
+            if i == 0:
+                ax_tuple[0].set_title(
+                    result_dir_names[0], fontdict={'size': font_size})
+                ax_tuple[1].set_title(
+                    result_dir_names[1], fontdict={'size': font_size})
+            ax_tuple[0].imshow(
+                image_left, extent=(0, *fig_size, 0), interpolation='bilinear')
+            ax_tuple[0].axis('off')
+            ax_tuple[1].imshow(
+                image_right,
+                extent=(0, *fig_size, 0),
+                interpolation='bilinear')
+            ax_tuple[1].axis('off')
+
+        canvas = fig.canvas
+        s, (width, height) = canvas.print_to_buffer()
+        buffer = np.frombuffer(s, dtype='uint8')
+        img_rgba = buffer.reshape(height, width, 4)
+        rgb, alpha = np.split(img_rgba, [3], axis=2)
+        img = rgb.astype('uint8')
+
+        frames.append(img)
+
+    return frames
+
+
+def main():
+    args = parse_args()
+    frames = create_frame_by_matplotlib(args.image_dir)
+    create_gif(frames, args.out)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/demo/demo.jpg b/demo/demo.jpg
new file mode 100644
index 0000000..dd613ce
Binary files /dev/null and b/demo/demo.jpg differ
diff --git a/demo/demo.mp4 b/demo/demo.mp4
new file mode 100644
index 0000000..6c06d15
Binary files /dev/null and b/demo/demo.mp4 differ
diff --git a/demo/image_demo.py b/demo/image_demo.py
new file mode 100644
index 0000000..129b175
--- /dev/null
+++ b/demo/image_demo.py
@@ -0,0 +1,68 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import asyncio
+from argparse import ArgumentParser
+
+from mmdet.apis import (async_inference_detector, inference_detector,
+                        init_detector, show_result_pyplot)
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument('img', help='Image file')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument('--out-file', default=None, help='Path to output file')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--palette',
+        default='coco',
+        choices=['coco', 'voc', 'citys', 'random'],
+        help='Color palette used for visualization')
+    parser.add_argument(
+        '--score-thr', type=float, default=0.3, help='bbox score threshold')
+    parser.add_argument(
+        '--async-test',
+        action='store_true',
+        help='whether to set async options for async inference.')
+    args = parser.parse_args()
+    return args
+
+
+def main(args):
+    # build the model from a config file and a checkpoint file
+    model = init_detector(args.config, args.checkpoint, device=args.device)
+    # test a single image
+    result = inference_detector(model, args.img)
+    # show the results
+    show_result_pyplot(
+        model,
+        args.img,
+        result,
+        palette=args.palette,
+        score_thr=args.score_thr,
+        out_file=args.out_file)
+
+
+async def async_main(args):
+    # build the model from a config file and a checkpoint file
+    model = init_detector(args.config, args.checkpoint, device=args.device)
+    # test a single image
+    tasks = asyncio.create_task(async_inference_detector(model, args.img))
+    result = await asyncio.gather(tasks)
+    # show the results
+    show_result_pyplot(
+        model,
+        args.img,
+        result[0],
+        palette=args.palette,
+        score_thr=args.score_thr,
+        out_file=args.out_file)
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    if args.async_test:
+        asyncio.run(async_main(args))
+    else:
+        main(args)
diff --git a/demo/inference_demo.ipynb b/demo/inference_demo.ipynb
new file mode 100644
index 0000000..d18dced
--- /dev/null
+++ b/demo/inference_demo.ipynb
@@ -0,0 +1,100 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mmdet.apis import init_detector, inference_detector, show_result_pyplot\n",
+    "import mmcv"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "config_file = '../configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'\n",
+    "# download the checkpoint from model zoo and put it in `checkpoints/`\n",
+    "# url: https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth\n",
+    "checkpoint_file = '../checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# build the model from a config file and a checkpoint file\n",
+    "model = init_detector(config_file, checkpoint_file, device='cuda:0')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# test a single image\n",
+    "img = 'demo.jpg'\n",
+    "result = inference_detector(model, img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# show the results\n",
+    "show_result_pyplot(model, img, result)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.7"
+  },
+  "pycharm": {
+   "stem_cell": {
+    "cell_type": "raw",
+    "metadata": {
+     "collapsed": false
+    },
+    "source": []
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/demo/video_demo.py b/demo/video_demo.py
new file mode 100644
index 0000000..4ee1fa6
--- /dev/null
+++ b/demo/video_demo.py
@@ -0,0 +1,61 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+
+import cv2
+import mmcv
+
+from mmdet.apis import inference_detector, init_detector
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='MMDetection video demo')
+    parser.add_argument('video', help='Video file')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--score-thr', type=float, default=0.3, help='Bbox score threshold')
+    parser.add_argument('--out', type=str, help='Output video file')
+    parser.add_argument('--show', action='store_true', help='Show video')
+    parser.add_argument(
+        '--wait-time',
+        type=float,
+        default=1,
+        help='The interval of show (s), 0 is block')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    assert args.out or args.show, \
+        ('Please specify at least one operation (save/show the '
+         'video) with the argument "--out" or "--show"')
+
+    model = init_detector(args.config, args.checkpoint, device=args.device)
+
+    video_reader = mmcv.VideoReader(args.video)
+    video_writer = None
+    if args.out:
+        fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+        video_writer = cv2.VideoWriter(
+            args.out, fourcc, video_reader.fps,
+            (video_reader.width, video_reader.height))
+
+    for frame in mmcv.track_iter_progress(video_reader):
+        result = inference_detector(model, frame)
+        frame = model.show_result(frame, result, score_thr=args.score_thr)
+        if args.show:
+            cv2.namedWindow('video', 0)
+            mmcv.imshow(frame, 'video', args.wait_time)
+        if args.out:
+            video_writer.write(frame)
+
+    if video_writer:
+        video_writer.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/demo/video_gpuaccel_demo.py b/demo/video_gpuaccel_demo.py
new file mode 100644
index 0000000..e19eb98
--- /dev/null
+++ b/demo/video_gpuaccel_demo.py
@@ -0,0 +1,113 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+
+import cv2
+import mmcv
+import numpy as np
+import torch
+from torchvision.transforms import functional as F
+
+from mmdet.apis import init_detector
+from mmdet.datasets.pipelines import Compose
+
+try:
+    import ffmpegcv
+except ImportError:
+    raise ImportError(
+        'Please install ffmpegcv with:\n\n    pip install ffmpegcv')
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='MMDetection video demo with GPU acceleration')
+    parser.add_argument('video', help='Video file')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--score-thr', type=float, default=0.3, help='Bbox score threshold')
+    parser.add_argument('--out', type=str, help='Output video file')
+    parser.add_argument('--show', action='store_true', help='Show video')
+    parser.add_argument(
+        '--nvdecode', action='store_true', help='Use NVIDIA decoder')
+    parser.add_argument(
+        '--wait-time',
+        type=float,
+        default=1,
+        help='The interval of show (s), 0 is block')
+    args = parser.parse_args()
+    return args
+
+
+def prefetch_img_metas(cfg, ori_wh):
+    w, h = ori_wh
+    cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam'
+    test_pipeline = Compose(cfg.data.test.pipeline)
+    data = {'img': np.zeros((h, w, 3), dtype=np.uint8)}
+    data = test_pipeline(data)
+    img_metas = data['img_metas'][0].data
+    return img_metas
+
+
+def process_img(frame_resize, img_metas, device):
+    assert frame_resize.shape == img_metas['pad_shape']
+    frame_cuda = torch.from_numpy(frame_resize).to(device).float()
+    frame_cuda = frame_cuda.permute(2, 0, 1)  # HWC to CHW
+    mean = torch.from_numpy(img_metas['img_norm_cfg']['mean']).to(device)
+    std = torch.from_numpy(img_metas['img_norm_cfg']['std']).to(device)
+    frame_cuda = F.normalize(frame_cuda, mean=mean, std=std, inplace=True)
+    frame_cuda = frame_cuda[None, :, :, :]  # NCHW
+    data = {'img': [frame_cuda], 'img_metas': [[img_metas]]}
+    return data
+
+
+def main():
+    args = parse_args()
+    assert args.out or args.show, \
+        ('Please specify at least one operation (save/show the '
+         'video) with the argument "--out" or "--show"')
+
+    model = init_detector(args.config, args.checkpoint, device=args.device)
+
+    if args.nvdecode:
+        VideoCapture = ffmpegcv.VideoCaptureNV
+    else:
+        VideoCapture = ffmpegcv.VideoCapture
+    video_origin = VideoCapture(args.video)
+    img_metas = prefetch_img_metas(model.cfg,
+                                   (video_origin.width, video_origin.height))
+    resize_wh = img_metas['pad_shape'][1::-1]
+    video_resize = VideoCapture(
+        args.video,
+        resize=resize_wh,
+        resize_keepratio=True,
+        resize_keepratioalign='topleft',
+        pix_fmt='rgb24')
+    video_writer = None
+    if args.out:
+        video_writer = ffmpegcv.VideoWriter(args.out, fps=video_origin.fps)
+
+    with torch.no_grad():
+        for frame_resize, frame_origin in zip(
+                mmcv.track_iter_progress(video_resize), video_origin):
+            data = process_img(frame_resize, img_metas, args.device)
+            result = model(return_loss=False, rescale=True, **data)[0]
+            frame_mask = model.show_result(
+                frame_origin, result, score_thr=args.score_thr)
+            if args.show:
+                cv2.namedWindow('video', 0)
+                mmcv.imshow(frame_mask, 'video', args.wait_time)
+            if args.out:
+                video_writer.write(frame_mask)
+
+    if video_writer:
+        video_writer.release()
+    video_origin.release()
+    video_resize.release()
+
+    cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/demo/webcam_demo.py b/demo/webcam_demo.py
new file mode 100644
index 0000000..b9ead6e
--- /dev/null
+++ b/demo/webcam_demo.py
@@ -0,0 +1,47 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+
+import cv2
+import torch
+
+from mmdet.apis import inference_detector, init_detector
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='MMDetection webcam demo')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint', help='checkpoint file')
+    parser.add_argument(
+        '--device', type=str, default='cuda:0', help='CPU/CUDA device option')
+    parser.add_argument(
+        '--camera-id', type=int, default=0, help='camera device id')
+    parser.add_argument(
+        '--score-thr', type=float, default=0.5, help='bbox score threshold')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    device = torch.device(args.device)
+
+    model = init_detector(args.config, args.checkpoint, device=device)
+
+    camera = cv2.VideoCapture(args.camera_id)
+
+    print('Press "Esc", "q" or "Q" to exit.')
+    while True:
+        ret_val, img = camera.read()
+        result = inference_detector(model, img)
+
+        ch = cv2.waitKey(1)
+        if ch == 27 or ch == ord('q') or ch == ord('Q'):
+            break
+
+        model.show_result(
+            img, result, score_thr=args.score_thr, wait_time=1, show=True)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/docker/Dockerfile b/docker/Dockerfile
new file mode 100644
index 0000000..af53cb5
--- /dev/null
+++ b/docker/Dockerfile
@@ -0,0 +1,29 @@
+ARG PYTORCH="1.6.0"
+ARG CUDA="10.1"
+ARG CUDNN="7"
+
+FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
+
+ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX"
+ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"
+ENV CMAKE_PREFIX_PATH="$(dirname $(which conda))/../"
+
+# To fix GPG key error when running apt-get update
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+
+RUN apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install MMCV
+RUN pip install --no-cache-dir --upgrade pip wheel setuptools
+RUN pip install --no-cache-dir mmcv-full==1.3.17 -f https://download.openmmlab.com/mmcv/dist/cu101/torch1.6.0/index.html
+
+# Install MMDetection
+RUN conda clean --all
+RUN git clone https://github.com/open-mmlab/mmdetection.git /mmdetection
+WORKDIR /mmdetection
+ENV FORCE_CUDA="1"
+RUN pip install --no-cache-dir -r requirements/build.txt
+RUN pip install --no-cache-dir -e .
diff --git a/docker/serve/Dockerfile b/docker/serve/Dockerfile
new file mode 100644
index 0000000..c53613c
--- /dev/null
+++ b/docker/serve/Dockerfile
@@ -0,0 +1,49 @@
+ARG PYTORCH="1.6.0"
+ARG CUDA="10.1"
+ARG CUDNN="7"
+FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
+
+ARG MMCV="1.3.17"
+ARG MMDET="2.28.2"
+
+ENV PYTHONUNBUFFERED TRUE
+
+RUN apt-get update && \
+    DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \
+    ca-certificates \
+    g++ \
+    openjdk-11-jre-headless \
+    # MMDet Requirements
+    ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
+    && rm -rf /var/lib/apt/lists/*
+
+ENV PATH="/opt/conda/bin:$PATH"
+RUN export FORCE_CUDA=1
+
+# TORCHSEVER
+RUN pip install torchserve torch-model-archiver
+
+# MMLAB
+ARG PYTORCH
+ARG CUDA
+RUN ["/bin/bash", "-c", "pip install mmcv-full==${MMCV} -f https://download.openmmlab.com/mmcv/dist/cu${CUDA//./}/torch${PYTORCH}/index.html"]
+RUN pip install mmdet==${MMDET}
+
+RUN useradd -m model-server \
+    && mkdir -p /home/model-server/tmp
+
+COPY entrypoint.sh /usr/local/bin/entrypoint.sh
+
+RUN chmod +x /usr/local/bin/entrypoint.sh \
+    && chown -R model-server /home/model-server
+
+COPY config.properties /home/model-server/config.properties
+RUN mkdir /home/model-server/model-store && chown -R model-server /home/model-server/model-store
+
+EXPOSE 8080 8081 8082
+
+USER model-server
+WORKDIR /home/model-server
+ENV TEMP=/home/model-server/tmp
+ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]
+CMD ["serve"]
diff --git a/docker/serve/config.properties b/docker/serve/config.properties
new file mode 100644
index 0000000..efb9c47
--- /dev/null
+++ b/docker/serve/config.properties
@@ -0,0 +1,5 @@
+inference_address=http://0.0.0.0:8080
+management_address=http://0.0.0.0:8081
+metrics_address=http://0.0.0.0:8082
+model_store=/home/model-server/model-store
+load_models=all
diff --git a/docker/serve/entrypoint.sh b/docker/serve/entrypoint.sh
new file mode 100644
index 0000000..41ba00b
--- /dev/null
+++ b/docker/serve/entrypoint.sh
@@ -0,0 +1,12 @@
+#!/bin/bash
+set -e
+
+if [[ "$1" = "serve" ]]; then
+    shift 1
+    torchserve --start --ts-config /home/model-server/config.properties
+else
+    eval "$@"
+fi
+
+# prevent docker exit
+tail -f /dev/null
diff --git a/docs/en/1_exist_data_model.md b/docs/en/1_exist_data_model.md
new file mode 100644
index 0000000..28cd39a
--- /dev/null
+++ b/docs/en/1_exist_data_model.md
@@ -0,0 +1,697 @@
+# 1: Inference and train with existing models and standard datasets
+
+MMDetection provides hundreds of existing and existing detection models in [Model Zoo](https://mmdetection.readthedocs.io/en/latest/model_zoo.html)), and supports multiple standard datasets, including Pascal VOC, COCO, CityScapes, LVIS, etc. This note will show how to perform common tasks on these existing models and standard datasets, including:
+
+- Use existing models to inference on given images.
+- Test existing models on standard datasets.
+- Train predefined models on standard datasets.
+
+## Inference with existing models
+
+By inference, we mean using trained models to detect objects on images. In MMDetection, a model is defined by a configuration file and existing model parameters are save in a checkpoint file.
+
+To start with, we recommend [Faster RCNN](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn) with this [configuration file](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) and this [checkpoint file](https://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth). It is recommended to download the checkpoint file to `checkpoints` directory.
+
+### High-level APIs for inference
+
+MMDetection provide high-level Python APIs for inference on images. Here is an example of building the model and inference on given images or videos.
+
+```python
+from mmdet.apis import init_detector, inference_detector
+import mmcv
+
+# Specify the path to model config and checkpoint file
+config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
+
+# build the model from a config file and a checkpoint file
+model = init_detector(config_file, checkpoint_file, device='cuda:0')
+
+# test a single image and show the results
+img = 'test.jpg'  # or img = mmcv.imread(img), which will only load it once
+result = inference_detector(model, img)
+# visualize the results in a new window
+model.show_result(img, result)
+# or save the visualization results to image files
+model.show_result(img, result, out_file='result.jpg')
+
+# test a video and show the results
+video = mmcv.VideoReader('video.mp4')
+for frame in video:
+    result = inference_detector(model, frame)
+    model.show_result(frame, result, wait_time=1)
+```
+
+A notebook demo can be found in [demo/inference_demo.ipynb](https://github.com/open-mmlab/mmdetection/blob/master/demo/inference_demo.ipynb).
+
+Note:  `inference_detector` only supports single-image inference for now.
+
+### Asynchronous interface - supported for Python 3.7+
+
+For Python 3.7+, MMDetection also supports async interfaces.
+By utilizing CUDA streams, it allows not to block CPU on GPU bound inference code and enables better CPU/GPU utilization for single-threaded application. Inference can be done concurrently either between different input data samples or between different models of some inference pipeline.
+
+See `tests/async_benchmark.py` to compare the speed of synchronous and asynchronous interfaces.
+
+```python
+import asyncio
+import torch
+from mmdet.apis import init_detector, async_inference_detector
+from mmdet.utils.contextmanagers import concurrent
+
+async def main():
+    config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+    checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
+    device = 'cuda:0'
+    model = init_detector(config_file, checkpoint=checkpoint_file, device=device)
+
+    # queue is used for concurrent inference of multiple images
+    streamqueue = asyncio.Queue()
+    # queue size defines concurrency level
+    streamqueue_size = 3
+
+    for _ in range(streamqueue_size):
+        streamqueue.put_nowait(torch.cuda.Stream(device=device))
+
+    # test a single image and show the results
+    img = 'test.jpg'  # or img = mmcv.imread(img), which will only load it once
+
+    async with concurrent(streamqueue):
+        result = await async_inference_detector(model, img)
+
+    # visualize the results in a new window
+    model.show_result(img, result)
+    # or save the visualization results to image files
+    model.show_result(img, result, out_file='result.jpg')
+
+
+asyncio.run(main())
+
+```
+
+### Demos
+
+We also provide three demo scripts, implemented with high-level APIs and supporting functionality codes.
+Source codes are available [here](https://github.com/open-mmlab/mmdetection/tree/master/demo).
+
+#### Image demo
+
+This script performs inference on a single image.
+
+```shell
+python demo/image_demo.py \
+    ${IMAGE_FILE} \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    [--device ${GPU_ID}] \
+    [--score-thr ${SCORE_THR}]
+```
+
+Examples:
+
+```shell
+python demo/image_demo.py demo/demo.jpg \
+    configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+    checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+    --device cpu
+```
+
+#### Webcam demo
+
+This is a live demo from a webcam.
+
+```shell
+python demo/webcam_demo.py \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    [--device ${GPU_ID}] \
+    [--camera-id ${CAMERA-ID}] \
+    [--score-thr ${SCORE_THR}]
+```
+
+Examples:
+
+```shell
+python demo/webcam_demo.py \
+    configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+    checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
+```
+
+#### Video demo
+
+This script performs inference on a video.
+
+```shell
+python demo/video_demo.py \
+    ${VIDEO_FILE} \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    [--device ${GPU_ID}] \
+    [--score-thr ${SCORE_THR}] \
+    [--out ${OUT_FILE}] \
+    [--show] \
+    [--wait-time ${WAIT_TIME}]
+```
+
+Examples:
+
+```shell
+python demo/video_demo.py demo/demo.mp4 \
+    configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+    checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+    --out result.mp4
+```
+
+#### Video demo with GPU acceleration
+
+This script performs inference on a video with GPU acceleration.
+
+```shell
+python demo/video_gpuaccel_demo.py \
+    ${VIDEO_FILE} \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    [--device ${GPU_ID}] \
+    [--score-thr ${SCORE_THR}] \
+    [--nvdecode] \
+    [--out ${OUT_FILE}] \
+    [--show] \
+    [--wait-time ${WAIT_TIME}]
+```
+
+Examples:
+
+```shell
+python demo/video_gpuaccel_demo.py demo/demo.mp4 \
+    configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+    checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+    --nvdecode --out result.mp4
+```
+
+## Test existing models on standard datasets
+
+To evaluate a model's accuracy, one usually tests the model on some standard datasets.
+MMDetection supports multiple public datasets including COCO, Pascal VOC, CityScapes, and [more](https://github.com/open-mmlab/mmdetection/tree/master/configs/_base_/datasets).
+This section will show how to test existing models on supported datasets.
+
+### Prepare datasets
+
+Public datasets like [Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC/index.html) or mirror and [COCO](https://cocodataset.org/#download) are available from official websites or mirrors. Note: In the detection task, Pascal VOC 2012 is an extension of Pascal VOC 2007 without overlap, and we usually use them together.
+It is recommended to download and extract the dataset somewhere outside the project directory and symlink the dataset root to `$MMDETECTION/data` as below.
+If your folder structure is different, you may need to change the corresponding paths in config files.
+
+We provide a script to download datasets such as COCO , you can run `python tools/misc/download_dataset.py --dataset-name coco2017` to download COCO dataset.
+
+For more usage please refer to [dataset-download](https://github.com/open-mmlab/mmdetection/tree/master/docs/en/useful_tools.md#dataset-download)
+
+```text
+mmdetection
+├── mmdet
+├── tools
+├── configs
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+│   ├── cityscapes
+│   │   ├── annotations
+│   │   ├── leftImg8bit
+│   │   │   ├── train
+│   │   │   ├── val
+│   │   ├── gtFine
+│   │   │   ├── train
+│   │   │   ├── val
+│   ├── VOCdevkit
+│   │   ├── VOC2007
+│   │   ├── VOC2012
+```
+
+Some models require additional [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) datasets, such as HTC, DetectoRS and SCNet, you can download and unzip then move to the coco folder. The directory should be like this.
+
+```text
+mmdetection
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+│   │   ├── stuffthingmaps
+```
+
+Panoptic segmentation models like PanopticFPN require additional [COCO Panoptic](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip) datasets, you can download and unzip then move to the coco annotation folder. The directory should be like this.
+
+```text
+mmdetection
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   │   ├── panoptic_train2017.json
+│   │   │   ├── panoptic_train2017
+│   │   │   ├── panoptic_val2017.json
+│   │   │   ├── panoptic_val2017
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+```
+
+The [cityscapes](https://www.cityscapes-dataset.com/) annotations need to be converted into the coco format using `tools/dataset_converters/cityscapes.py`:
+
+```shell
+pip install cityscapesscripts
+
+python tools/dataset_converters/cityscapes.py \
+    ./data/cityscapes \
+    --nproc 8 \
+    --out-dir ./data/cityscapes/annotations
+```
+
+TODO: CHANGE TO THE NEW PATH
+
+### Test existing models
+
+We provide testing scripts for evaluating an existing model on the whole dataset (COCO, PASCAL VOC, Cityscapes, etc.).
+The following testing environments are supported:
+
+- single GPU
+- CPU
+- single node multiple GPUs
+- multiple nodes
+
+Choose the proper script to perform testing depending on the testing environment.
+
+```shell
+# single-gpu testing
+python tools/test.py \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    [--out ${RESULT_FILE}] \
+    [--eval ${EVAL_METRICS}] \
+    [--show]
+
+# CPU: disable GPUs and run single-gpu testing script
+export CUDA_VISIBLE_DEVICES=-1
+python tools/test.py \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    [--out ${RESULT_FILE}] \
+    [--eval ${EVAL_METRICS}] \
+    [--show]
+
+# multi-gpu testing
+bash tools/dist_test.sh \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    ${GPU_NUM} \
+    [--out ${RESULT_FILE}] \
+    [--eval ${EVAL_METRICS}]
+```
+
+`tools/dist_test.sh` also supports multi-node testing, but relies on PyTorch's [launch utility](https://pytorch.org/docs/stable/distributed.html#launch-utility).
+
+Optional arguments:
+
+- `RESULT_FILE`: Filename of the output results in pickle format. If not specified, the results will not be saved to a file.
+- `EVAL_METRICS`: Items to be evaluated on the results. Allowed values depend on the dataset, e.g., `proposal_fast`, `proposal`, `bbox`, `segm` are available for COCO, `mAP`, `recall` for PASCAL VOC. Cityscapes could be evaluated by `cityscapes` as well as all COCO metrics.
+- `--show`: If specified, detection results will be plotted on the images and shown in a new window. It is only applicable to single GPU testing and used for debugging and visualization. Please make sure that GUI is available in your environment. Otherwise, you may encounter an error like `cannot connect to X server`.
+- `--show-dir`: If specified, detection results will be plotted on the images and saved to the specified directory. It is only applicable to single GPU testing and used for debugging and visualization. You do NOT need a GUI available in your environment for using this option.
+- `--show-score-thr`: If specified, detections with scores below this threshold will be removed.
+- `--cfg-options`:  if specified, the key-value pair optional cfg will be merged into config file
+- `--eval-options`: if specified, the key-value pair optional eval cfg will be kwargs for dataset.evaluate() function, it's only for evaluation
+
+### Examples
+
+Assuming that you have already downloaded the checkpoints to the directory `checkpoints/`.
+
+1. Test Faster R-CNN and visualize the results. Press any key for the next image.
+   Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn).
+
+   ```shell
+   python tools/test.py \
+       configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+       checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+       --show
+   ```
+
+2. Test Faster R-CNN and save the painted images for future visualization.
+   Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn).
+
+   ```shell
+   python tools/test.py \
+       configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+       checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+       --show-dir faster_rcnn_r50_fpn_1x_results
+   ```
+
+3. Test Faster R-CNN on PASCAL VOC (without saving the test results) and evaluate the mAP.
+   Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc).
+
+   ```shell
+   python tools/test.py \
+       configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc.py \
+       checkpoints/faster_rcnn_r50_fpn_1x_voc0712_20200624-c9895d40.pth \
+       --eval mAP
+   ```
+
+4. Test Mask R-CNN with 8 GPUs, and evaluate the bbox and mask AP.
+   Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn).
+
+   ```shell
+   ./tools/dist_test.sh \
+       configs/mask_rcnn_r50_fpn_1x_coco.py \
+       checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \
+       8 \
+       --out results.pkl \
+       --eval bbox segm
+   ```
+
+5. Test Mask R-CNN with 8 GPUs, and evaluate the **classwise** bbox and mask AP.
+   Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn).
+
+   ```shell
+   ./tools/dist_test.sh \
+       configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \
+       checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \
+       8 \
+       --out results.pkl \
+       --eval bbox segm \
+       --options "classwise=True"
+   ```
+
+6. Test Mask R-CNN on COCO test-dev with 8 GPUs, and generate JSON files for submitting to the official evaluation server.
+   Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn).
+
+   ```shell
+   ./tools/dist_test.sh \
+       configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \
+       checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \
+       8 \
+       --format-only \
+       --options "jsonfile_prefix=./mask_rcnn_test-dev_results"
+   ```
+
+   This command generates two JSON files `mask_rcnn_test-dev_results.bbox.json` and `mask_rcnn_test-dev_results.segm.json`.
+
+7. Test Mask R-CNN on Cityscapes test with 8 GPUs, and generate txt and png files for submitting to the official evaluation server.
+   Config and checkpoint files are available [here](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes).
+
+   ```shell
+   ./tools/dist_test.sh \
+       configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py \
+       checkpoints/mask_rcnn_r50_fpn_1x_cityscapes_20200227-afe51d5a.pth \
+       8 \
+       --format-only \
+       --options "txtfile_prefix=./mask_rcnn_cityscapes_test_results"
+   ```
+
+   The generated png and txt would be under `./mask_rcnn_cityscapes_test_results` directory.
+
+### Test without Ground Truth Annotations
+
+MMDetection supports to test models without ground-truth annotations using `CocoDataset`. If your dataset format is not in COCO format, please convert them to COCO format. For example, if your dataset format is VOC, you can directly convert it to COCO format by the [script in tools.](https://github.com/open-mmlab/mmdetection/tree/master/tools/dataset_converters/pascal_voc.py) If your dataset format is Cityscapes, you can directly convert it to COCO format by the [script in tools.](https://github.com/open-mmlab/mmdetection/tree/master/tools/dataset_converters/cityscapes.py) The rest of the formats can be converted using [this script](https://github.com/open-mmlab/mmdetection/tree/master/tools/dataset_converters/images2coco.py).
+
+```shel
+python tools/dataset_converters/images2coco.py \
+    ${IMG_PATH} \
+    ${CLASSES} \
+    ${OUT} \
+    [--exclude-extensions]
+```
+
+arguments：
+
+- `IMG_PATH`: The root path of images.
+- `CLASSES`: The text file with a list of categories.
+- `OUT`: The output annotation json file name. The save dir is in the same directory as `IMG_PATH`.
+- `exclude-extensions`: The suffix of images to be excluded, such as 'png' and 'bmp'.
+
+After the conversion is complete, you can use the following command to test
+
+```shell
+# single-gpu testing
+python tools/test.py \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    --format-only \
+    --options ${JSONFILE_PREFIX} \
+    [--show]
+
+# CPU: disable GPUs and run single-gpu testing script
+export CUDA_VISIBLE_DEVICES=-1
+python tools/test.py \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    [--out ${RESULT_FILE}] \
+    [--eval ${EVAL_METRICS}] \
+    [--show]
+
+# multi-gpu testing
+bash tools/dist_test.sh \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    ${GPU_NUM} \
+    --format-only \
+    --options ${JSONFILE_PREFIX} \
+    [--show]
+```
+
+Assuming that the checkpoints in the [model zoo](https://mmdetection.readthedocs.io/en/latest/modelzoo_statistics.html) have been downloaded to the directory `checkpoints/`, we can test Mask R-CNN on COCO test-dev with 8 GPUs, and generate JSON files using the following command.
+
+```sh
+./tools/dist_test.sh \
+    configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \
+    checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \
+    8 \
+    --format-only \
+    --options "jsonfile_prefix=./mask_rcnn_test-dev_results"
+```
+
+This command generates two JSON files `mask_rcnn_test-dev_results.bbox.json` and `mask_rcnn_test-dev_results.segm.json`.
+
+### Batch Inference
+
+MMDetection supports inference with a single image or batched images in test mode. By default, we use single-image inference and you can use batch inference by modifying `samples_per_gpu` in the config of test data. You can do that either by modifying the config as below.
+
+```shell
+data = dict(train=dict(...), val=dict(...), test=dict(samples_per_gpu=2, ...))
+```
+
+Or you can set it through `--cfg-options` as `--cfg-options data.test.samples_per_gpu=2`
+
+### Deprecated ImageToTensor
+
+In test mode,  `ImageToTensor`  pipeline is deprecated, it's replaced by `DefaultFormatBundle` that recommended to manually replace it in the test data pipeline in your config file.  examples:
+
+```python
+# use ImageToTensor (deprecated)
+pipelines = [
+   dict(type='LoadImageFromFile'),
+   dict(
+       type='MultiScaleFlipAug',
+       img_scale=(1333, 800),
+       flip=False,
+       transforms=[
+           dict(type='Resize', keep_ratio=True),
+           dict(type='RandomFlip'),
+           dict(type='Normalize', mean=[0, 0, 0], std=[1, 1, 1]),
+           dict(type='Pad', size_divisor=32),
+           dict(type='ImageToTensor', keys=['img']),
+           dict(type='Collect', keys=['img']),
+       ])
+   ]
+
+# manually replace ImageToTensor to DefaultFormatBundle (recommended)
+pipelines = [
+   dict(type='LoadImageFromFile'),
+   dict(
+       type='MultiScaleFlipAug',
+       img_scale=(1333, 800),
+       flip=False,
+       transforms=[
+           dict(type='Resize', keep_ratio=True),
+           dict(type='RandomFlip'),
+           dict(type='Normalize', mean=[0, 0, 0], std=[1, 1, 1]),
+           dict(type='Pad', size_divisor=32),
+           dict(type='DefaultFormatBundle'),
+           dict(type='Collect', keys=['img']),
+       ])
+   ]
+```
+
+## Train predefined models on standard datasets
+
+MMDetection also provides out-of-the-box tools for training detection models.
+This section will show how to train _predefined_ models (under [configs](https://github.com/open-mmlab/mmdetection/tree/master/configs)) on standard datasets i.e. COCO.
+
+### Prepare datasets
+
+Training requires preparing datasets too. See section [Prepare datasets](#prepare-datasets) above for details.
+
+**Note**:
+Currently, the config files under `configs/cityscapes` use COCO pretrained weights to initialize.
+You could download the existing models in advance if the network connection is unavailable or slow. Otherwise, it would cause errors at the beginning of training.
+
+### Learning rate automatically scale
+
+**Important**: The default learning rate in config files is for 8 GPUs and 2 sample per gpu (batch size = 8 * 2 = 16). And it had been set to `auto_scale_lr.base_batch_size` in `config/_base_/default_runtime.py`. Learning rate will be automatically scaled base on this value when the batch size is `16`. Meanwhile, in order not to affect other codebase which based on mmdet, the flag `auto_scale_lr.enable` is set to `False` by default.
+
+If you want to enable this feature, you need to add argument `--auto-scale-lr`. And you need to check the config name which you want to use before you process the command, because the config name indicates the default batch size.
+By default, it is `8 x 2 = 16 batch size`, like `faster_rcnn_r50_caffe_fpn_90k_coco.py` or `pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py`. In other cases, you will see the config file name have `_NxM_` in dictating, like `cornernet_hourglass104_mstest_32x3_210e_coco.py` which batch size is `32 x 3 = 96`, or `scnet_x101_64x4d_fpn_8x1_20e_coco.py` which batch size is `8 x 1 = 8`.
+
+**Please remember to check the bottom of the specific config file you want to use, it will have `auto_scale_lr.base_batch_size` if the batch size is not `16`. If you can't find those values, check the config file which in `_base_=[xxx]` and you will find it. Please do not modify its values if you want to automatically scale the LR.**
+
+Learning rate automatically scale basic usage is as follows.
+
+```shell
+python tools/train.py \
+    ${CONFIG_FILE} \
+    --auto-scale-lr \
+    [optional arguments]
+```
+
+If you enabled this feature, the learning rate will be automatically scaled according to the number of GPUs of the machine and the batch size of training. See [linear scaling rule](https://arxiv.org/abs/1706.02677) for details. For example, If there are 4 GPUs and 2 pictures on each GPU, `lr = 0.01`, then if there are 16 GPUs and 4 pictures on each GPU, it will automatically scale to `lr = 0.08`.
+
+If you don't want to use it, you need to calculate the learning rate according to the [linear scaling rule](https://arxiv.org/abs/1706.02677) manually then change `optimizer.lr` in specific config file.
+
+### Training on a single GPU
+
+We provide `tools/train.py` to launch training jobs on a single GPU.
+The basic usage is as follows.
+
+```shell
+python tools/train.py \
+    ${CONFIG_FILE} \
+    [optional arguments]
+```
+
+During training, log files and checkpoints will be saved to the working directory, which is specified by `work_dir` in the config file or via CLI argument `--work-dir`.
+
+By default, the model is evaluated on the validation set every epoch, the evaluation interval can be specified in the config file as shown below.
+
+```python
+# evaluate the model every 12 epoch.
+evaluation = dict(interval=12)
+```
+
+This tool accepts several optional arguments, including:
+
+- `--no-validate` (**not suggested**): Disable evaluation during training.
+- `--work-dir ${WORK_DIR}`: Override the working directory.
+- `--resume-from ${CHECKPOINT_FILE}`: Resume from a previous checkpoint file.
+- `--options 'Key=value'`: Overrides other settings in the used config.
+
+**Note**:
+
+Difference between `resume-from` and `load-from`:
+
+`resume-from` loads both the model weights and optimizer status, and the epoch is also inherited from the specified checkpoint. It is usually used for resuming the training process that is interrupted accidentally.
+`load-from` only loads the model weights and the training epoch starts from 0. It is usually used for finetuning.
+
+### Training on CPU
+
+The process of training on the CPU is consistent with single GPU training. We just need to disable GPUs before the training process.
+
+```shell
+export CUDA_VISIBLE_DEVICES=-1
+```
+
+And then run the script [above](#training-on-a-single-GPU).
+
+**Note**:
+
+We do not recommend users to use CPU for training because it is too slow. We support this feature to allow users to debug on machines without GPU for convenience.
+
+### Training on multiple GPUs
+
+We provide `tools/dist_train.sh` to launch training on multiple GPUs.
+The basic usage is as follows.
+
+```shell
+bash ./tools/dist_train.sh \
+    ${CONFIG_FILE} \
+    ${GPU_NUM} \
+    [optional arguments]
+```
+
+Optional arguments remain the same as stated [above](#training-on-a-single-GPU).
+
+#### Launch multiple jobs simultaneously
+
+If you would like to launch multiple jobs on a single machine, e.g., 2 jobs of 4-GPU training on a machine with 8 GPUs,
+you need to specify different ports (29500 by default) for each job to avoid communication conflict.
+
+If you use `dist_train.sh` to launch training jobs, you can set the port in commands.
+
+```shell
+CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh ${CONFIG_FILE} 4
+CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 ./tools/dist_train.sh ${CONFIG_FILE} 4
+```
+
+### Train with multiple machines
+
+If you launch with multiple machines simply connected with ethernet, you can simply run following commands:
+
+On the first machine:
+
+```shell
+NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS
+```
+
+On the second machine:
+
+```shell
+NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS
+```
+
+Usually it is slow if you do not have high speed networking like InfiniBand.
+
+### Manage jobs with Slurm
+
+[Slurm](https://slurm.schedmd.com/) is a good job scheduling system for computing clusters.
+On a cluster managed by Slurm, you can use `slurm_train.sh` to spawn training jobs. It supports both single-node and multi-node training.
+
+The basic usage is as follows.
+
+```shell
+[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR}
+```
+
+Below is an example of using 16 GPUs to train Mask R-CNN on a Slurm partition named _dev_, and set the work-dir to some shared file systems.
+
+```shell
+GPUS=16 ./tools/slurm_train.sh dev mask_r50_1x configs/mask_rcnn_r50_fpn_1x_coco.py /nfs/xxxx/mask_rcnn_r50_fpn_1x
+```
+
+You can check [the source code](https://github.com/open-mmlab/mmdetection/blob/master/tools/slurm_train.sh) to review full arguments and environment variables.
+
+When using Slurm, the port option need to be set in one of the following ways:
+
+1. Set the port through `--options`. This is more recommended since it does not change the original configs.
+
+   ```shell
+   CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} --options 'dist_params.port=29500'
+   CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} --options 'dist_params.port=29501'
+   ```
+
+2. Modify the config files to set different communication ports.
+
+   In `config1.py`, set
+
+   ```python
+   dist_params = dict(backend='nccl', port=29500)
+   ```
+
+   In `config2.py`, set
+
+   ```python
+   dist_params = dict(backend='nccl', port=29501)
+   ```
+
+   Then you can launch two jobs with `config1.py` and `config2.py`.
+
+   ```shell
+   CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR}
+   CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR}
+   ```
diff --git a/docs/en/2_new_data_model.md b/docs/en/2_new_data_model.md
new file mode 100644
index 0000000..408e8d1
--- /dev/null
+++ b/docs/en/2_new_data_model.md
@@ -0,0 +1,266 @@
+# 2: Train with customized datasets
+
+In this note, you will know how to inference, test, and train predefined models with customized datasets. We use the [balloon dataset](https://github.com/matterport/Mask_RCNN/tree/master/samples/balloon) as an example to describe the whole process.
+
+The basic steps are as below:
+
+1. Prepare the customized dataset
+2. Prepare a config
+3. Train, test, inference models on the customized dataset.
+
+## Prepare the customized dataset
+
+There are three ways to support a new dataset in MMDetection:
+
+1. reorganize the dataset into COCO format.
+2. reorganize the dataset into a middle format.
+3. implement a new dataset.
+
+Usually we recommend to use the first two methods which are usually easier than the third.
+
+In this note, we give an example for converting the data into COCO format.
+
+**Note**: MMDetection only supports evaluating mask AP of dataset in COCO format for now.
+So for instance segmentation task users should convert the data into coco format.
+
+### COCO annotation format
+
+The necessary keys of COCO format for instance segmentation is as below, for the complete details, please refer [here](https://cocodataset.org/#format-data).
+
+```json
+{
+    "images": [image],
+    "annotations": [annotation],
+    "categories": [category]
+}
+
+
+image = {
+    "id": int,
+    "width": int,
+    "height": int,
+    "file_name": str,
+}
+
+annotation = {
+    "id": int,
+    "image_id": int,
+    "category_id": int,
+    "segmentation": RLE or [polygon],
+    "area": float,
+    "bbox": [x,y,width,height],
+    "iscrowd": 0 or 1,
+}
+
+categories = [{
+    "id": int,
+    "name": str,
+    "supercategory": str,
+}]
+```
+
+Assume we use the balloon dataset.
+After downloading the data, we need to implement a function to convert the annotation format into the COCO format. Then we can use implemented COCODataset to load the data and perform training and evaluation.
+
+If you take a look at the dataset, you will find the dataset format is as below:
+
+```json
+{'base64_img_data': '',
+ 'file_attributes': {},
+ 'filename': '34020010494_e5cb88e1c4_k.jpg',
+ 'fileref': '',
+ 'regions': {'0': {'region_attributes': {},
+   'shape_attributes': {'all_points_x': [1020,
+     1000,
+     994,
+     1003,
+     1023,
+     1050,
+     1089,
+     1134,
+     1190,
+     1265,
+     1321,
+     1361,
+     1403,
+     1428,
+     1442,
+     1445,
+     1441,
+     1427,
+     1400,
+     1361,
+     1316,
+     1269,
+     1228,
+     1198,
+     1207,
+     1210,
+     1190,
+     1177,
+     1172,
+     1174,
+     1170,
+     1153,
+     1127,
+     1104,
+     1061,
+     1032,
+     1020],
+    'all_points_y': [963,
+     899,
+     841,
+     787,
+     738,
+     700,
+     663,
+     638,
+     621,
+     619,
+     643,
+     672,
+     720,
+     765,
+     800,
+     860,
+     896,
+     942,
+     990,
+     1035,
+     1079,
+     1112,
+     1129,
+     1134,
+     1144,
+     1153,
+     1166,
+     1166,
+     1150,
+     1136,
+     1129,
+     1122,
+     1112,
+     1084,
+     1037,
+     989,
+     963],
+    'name': 'polygon'}}},
+ 'size': 1115004}
+```
+
+The annotation is a JSON file where each key indicates an image's all annotations.
+The code to convert the balloon dataset into coco format is as below.
+
+```python
+import os.path as osp
+import mmcv
+
+def convert_balloon_to_coco(ann_file, out_file, image_prefix):
+    data_infos = mmcv.load(ann_file)
+
+    annotations = []
+    images = []
+    obj_count = 0
+    for idx, v in enumerate(mmcv.track_iter_progress(data_infos.values())):
+        filename = v['filename']
+        img_path = osp.join(image_prefix, filename)
+        height, width = mmcv.imread(img_path).shape[:2]
+
+        images.append(dict(
+            id=idx,
+            file_name=filename,
+            height=height,
+            width=width))
+
+        bboxes = []
+        labels = []
+        masks = []
+        for _, obj in v['regions'].items():
+            assert not obj['region_attributes']
+            obj = obj['shape_attributes']
+            px = obj['all_points_x']
+            py = obj['all_points_y']
+            poly = [(x + 0.5, y + 0.5) for x, y in zip(px, py)]
+            poly = [p for x in poly for p in x]
+
+            x_min, y_min, x_max, y_max = (
+                min(px), min(py), max(px), max(py))
+
+
+            data_anno = dict(
+                image_id=idx,
+                id=obj_count,
+                category_id=0,
+                bbox=[x_min, y_min, x_max - x_min, y_max - y_min],
+                area=(x_max - x_min) * (y_max - y_min),
+                segmentation=[poly],
+                iscrowd=0)
+            annotations.append(data_anno)
+            obj_count += 1
+
+    coco_format_json = dict(
+        images=images,
+        annotations=annotations,
+        categories=[{'id':0, 'name': 'balloon'}])
+    mmcv.dump(coco_format_json, out_file)
+
+```
+
+Using the function above, users can successfully convert the annotation file into json format, then we can use `CocoDataset` to train and evaluate the model.
+
+## Prepare a config
+
+The second step is to prepare a config thus the dataset could be successfully loaded. Assume that we want to use Mask R-CNN with FPN, the config to train the detector on balloon dataset is as below. Assume the config is under directory `configs/balloon/` and named as `mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py`, the config is as below.
+
+```python
+# The new config inherits a base config to highlight the necessary modification
+_base_ = 'mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
+
+# We also need to change the num_classes in head to match the dataset's annotation
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(num_classes=1),
+        mask_head=dict(num_classes=1)))
+
+# Modify dataset related settings
+dataset_type = 'COCODataset'
+classes = ('balloon',)
+data = dict(
+    train=dict(
+        img_prefix='balloon/train/',
+        classes=classes,
+        ann_file='balloon/train/annotation_coco.json'),
+    val=dict(
+        img_prefix='balloon/val/',
+        classes=classes,
+        ann_file='balloon/val/annotation_coco.json'),
+    test=dict(
+        img_prefix='balloon/val/',
+        classes=classes,
+        ann_file='balloon/val/annotation_coco.json'))
+
+# We can use the pre-trained Mask RCNN model to obtain higher performance
+load_from = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'
+```
+
+This checkpoint file can be downloaded [here](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth)
+
+## Train a new model
+
+To train a model with the new config, you can simply run
+
+```shell
+python tools/train.py configs/balloon/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py
+```
+
+For more detailed usages, please refer to the [Case 1](1_exist_data_model.md).
+
+## Test and inference
+
+To test the trained model, you can simply run
+
+```shell
+python tools/test.py configs/balloon/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py work_dirs/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon/latest.pth --eval bbox segm
+```
+
+For more detailed usages, please refer to the [Case 1](1_exist_data_model.md).
diff --git a/docs/en/3_exist_data_new_model.md b/docs/en/3_exist_data_new_model.md
new file mode 100644
index 0000000..b34c133
--- /dev/null
+++ b/docs/en/3_exist_data_new_model.md
@@ -0,0 +1,283 @@
+# 3: Train with customized models and standard datasets
+
+In this note, you will know how to train, test and inference your own customized models under standard datasets. We use the cityscapes dataset to train a customized Cascade Mask R-CNN R50 model as an example to demonstrate the whole process, which using [`AugFPN`](https://github.com/Gus-Guo/AugFPN) to replace the default `FPN` as neck, and add `Rotate` or `Translate` as training-time auto augmentation.
+
+The basic steps are as below:
+
+1. Prepare the standard dataset
+2. Prepare your own customized model
+3. Prepare a config
+4. Train, test, and inference models on the standard dataset.
+
+## Prepare the standard dataset
+
+In this note, as we use the standard cityscapes dataset as an example.
+
+It is recommended to symlink the dataset root to `$MMDETECTION/data`.
+If your folder structure is different, you may need to change the corresponding paths in config files.
+
+```none
+mmdetection
+├── mmdet
+├── tools
+├── configs
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+│   ├── cityscapes
+│   │   ├── annotations
+│   │   ├── leftImg8bit
+│   │   │   ├── train
+│   │   │   ├── val
+│   │   ├── gtFine
+│   │   │   ├── train
+│   │   │   ├── val
+│   ├── VOCdevkit
+│   │   ├── VOC2007
+│   │   ├── VOC2012
+
+```
+
+Or you can set your dataset root through
+
+```bash
+export MMDET_DATASETS=$data_root
+```
+
+We will replace dataset root with `$MMDET_DATASETS`, so you don't have to modify the corresponding path in config files.
+
+The cityscapes annotations have to be converted into the coco format using `tools/dataset_converters/cityscapes.py`:
+
+```shell
+pip install cityscapesscripts
+python tools/dataset_converters/cityscapes.py ./data/cityscapes --nproc 8 --out-dir ./data/cityscapes/annotations
+```
+
+Currently the config files in `cityscapes` use COCO pre-trained weights to initialize.
+You could download the pre-trained models in advance if network is unavailable or slow, otherwise it would cause errors at the beginning of training.
+
+## Prepare your own customized model
+
+The second step is to use your own module or training setting. Assume that we want to implement a new neck called `AugFPN` to replace with the default `FPN` under the existing detector Cascade Mask R-CNN R50. The following implements`AugFPN` under MMDetection.
+
+### 1. Define a new neck (e.g. AugFPN)
+
+Firstly create a new file `mmdet/models/necks/augfpn.py`.
+
+```python
+from ..builder import NECKS
+
+@NECKS.register_module()
+class AugFPN(nn.Module):
+
+    def __init__(self,
+                in_channels,
+                out_channels,
+                num_outs,
+                start_level=0,
+                end_level=-1,
+                add_extra_convs=False):
+        pass
+
+    def forward(self, inputs):
+        # implementation is ignored
+        pass
+```
+
+### 2. Import the module
+
+You can either add the following line to `mmdet/models/necks/__init__.py`,
+
+```python
+from .augfpn import AugFPN
+```
+
+or alternatively add
+
+```python
+custom_imports = dict(
+    imports=['mmdet.models.necks.augfpn.py'],
+    allow_failed_imports=False)
+```
+
+to the config file and avoid modifying the original code.
+
+### 3. Modify the config file
+
+```python
+neck=dict(
+    type='AugFPN',
+    in_channels=[256, 512, 1024, 2048],
+    out_channels=256,
+    num_outs=5)
+```
+
+For more detailed usages about customize your own models (e.g. implement a new backbone, head, loss, etc) and runtime training settings (e.g. define a new optimizer, use gradient clip, customize training schedules and hooks, etc), please refer to the guideline [Customize Models](tutorials/customize_models.md) and [Customize Runtime Settings](tutorials/customize_runtime.md) respectively.
+
+## Prepare a config
+
+The third step is to prepare a config for your own training setting. Assume that we want to add `AugFPN` and `Rotate` or `Translate` augmentation to existing Cascade Mask R-CNN R50 to train the cityscapes dataset, and assume the config is under directory `configs/cityscapes/` and named as `cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py`, the config is as below.
+
+```python
+# The new config inherits the base configs to highlight the necessary modification
+_base_ = [
+    '../_base_/models/cascade_mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    # set None to avoid loading ImageNet pretrained backbone,
+    # instead here we set `load_from` to load from COCO pretrained detectors.
+    backbone=dict(init_cfg=None),
+    # replace neck from defaultly `FPN` to our new implemented module `AugFPN`
+    neck=dict(
+        type='AugFPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    # We also need to change the num_classes in head from 80 to 8, to match the
+    # cityscapes dataset's annotation. This modification involves `bbox_head` and `mask_head`.
+    roi_head=dict(
+        bbox_head=[
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                # change the number of classes from defaultly COCO to cityscapes
+                num_classes=8,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                # change the number of classes from defaultly COCO to cityscapes
+                num_classes=8,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                # change the number of classes from defaultly COCO to cityscapes
+                num_classes=8,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ],
+        mask_head=dict(
+            type='FCNMaskHead',
+            num_convs=4,
+            in_channels=256,
+            conv_out_channels=256,
+            # change the number of classes from defaultly COCO to cityscapes
+            num_classes=8,
+            loss_mask=dict(
+                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))))
+
+# over-write `train_pipeline` for new added `AutoAugment` training setting
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='AutoAugment',
+        policies=[
+            [dict(
+                 type='Rotate',
+                 level=5,
+                 img_fill_val=(124, 116, 104),
+                 prob=0.5,
+                 scale=1)
+            ],
+            [dict(type='Rotate', level=7, img_fill_val=(124, 116, 104)),
+             dict(
+                 type='Translate',
+                 level=5,
+                 prob=0.5,
+                 img_fill_val=(124, 116, 104))
+            ],
+        ]),
+    dict(
+        type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+
+# set batch_size per gpu, and set new training pipeline
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=3,
+    # over-write `pipeline` with new training pipeline setting
+    train=dict(dataset=dict(pipeline=train_pipeline)))
+
+# Set optimizer
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# Set customized learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[8])
+runner = dict(type='EpochBasedRunner', max_epochs=10)
+
+# We can use the COCO pretrained Cascade Mask R-CNN R50 model for more stable performance initialization
+load_from = 'https://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth'
+```
+
+## Train a new model
+
+To train a model with the new config, you can simply run
+
+```shell
+python tools/train.py configs/cityscapes/cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py
+```
+
+For more detailed usages, please refer to the [Case 1](1_exist_data_model.md).
+
+## Test and inference
+
+To test the trained model, you can simply run
+
+```shell
+python tools/test.py configs/cityscapes/cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py work_dirs/cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py/latest.pth --eval bbox segm
+```
+
+For more detailed usages, please refer to the [Case 1](1_exist_data_model.md).
diff --git a/docs/en/Makefile b/docs/en/Makefile
new file mode 100644
index 0000000..d4bb2cb
--- /dev/null
+++ b/docs/en/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/en/_static/css/readthedocs.css b/docs/en/_static/css/readthedocs.css
new file mode 100644
index 0000000..57ed0ad
--- /dev/null
+++ b/docs/en/_static/css/readthedocs.css
@@ -0,0 +1,6 @@
+.header-logo {
+    background-image: url("../image/mmdet-logo.png");
+    background-size: 156px 40px;
+    height: 40px;
+    width: 156px;
+}
diff --git a/docs/en/_static/image/mmdet-logo.png b/docs/en/_static/image/mmdet-logo.png
new file mode 100644
index 0000000..58e2b5e
Binary files /dev/null and b/docs/en/_static/image/mmdet-logo.png differ
diff --git a/docs/en/api.rst b/docs/en/api.rst
new file mode 100644
index 0000000..e61c663
--- /dev/null
+++ b/docs/en/api.rst
@@ -0,0 +1,108 @@
+mmdet.apis
+--------------
+.. automodule:: mmdet.apis
+    :members:
+
+mmdet.core
+--------------
+
+anchor
+^^^^^^^^^^
+.. automodule:: mmdet.core.anchor
+    :members:
+
+bbox
+^^^^^^^^^^
+.. automodule:: mmdet.core.bbox
+    :members:
+
+export
+^^^^^^^^^^
+.. automodule:: mmdet.core.export
+    :members:
+
+mask
+^^^^^^^^^^
+.. automodule:: mmdet.core.mask
+    :members:
+
+evaluation
+^^^^^^^^^^
+.. automodule:: mmdet.core.evaluation
+    :members:
+
+post_processing
+^^^^^^^^^^^^^^^
+.. automodule:: mmdet.core.post_processing
+    :members:
+
+utils
+^^^^^^^^^^
+.. automodule:: mmdet.core.utils
+    :members:
+
+mmdet.datasets
+--------------
+
+datasets
+^^^^^^^^^^
+.. automodule:: mmdet.datasets
+    :members:
+
+pipelines
+^^^^^^^^^^
+.. automodule:: mmdet.datasets.pipelines
+    :members:
+
+samplers
+^^^^^^^^^^
+.. automodule:: mmdet.datasets.samplers
+    :members:
+
+api_wrappers
+^^^^^^^^^^^^
+.. automodule:: mmdet.datasets.api_wrappers
+    :members:
+
+mmdet.models
+--------------
+
+detectors
+^^^^^^^^^^
+.. automodule:: mmdet.models.detectors
+    :members:
+
+backbones
+^^^^^^^^^^
+.. automodule:: mmdet.models.backbones
+    :members:
+
+necks
+^^^^^^^^^^^^
+.. automodule:: mmdet.models.necks
+    :members:
+
+dense_heads
+^^^^^^^^^^^^
+.. automodule:: mmdet.models.dense_heads
+    :members:
+
+roi_heads
+^^^^^^^^^^
+.. automodule:: mmdet.models.roi_heads
+    :members:
+
+losses
+^^^^^^^^^^
+.. automodule:: mmdet.models.losses
+    :members:
+
+utils
+^^^^^^^^^^
+.. automodule:: mmdet.models.utils
+    :members:
+
+mmdet.utils
+--------------
+.. automodule::mmdet.utils
+    :members:
diff --git a/docs/en/changelog.md b/docs/en/changelog.md
new file mode 100644
index 0000000..473aec3
--- /dev/null
+++ b/docs/en/changelog.md
@@ -0,0 +1,1897 @@
+## Changelog
+
+### v2.28.2 (24/2/2023)
+
+#### New Features and Improvements
+
+- Add Twitter, Discord, Medium and YouTube link (#9774)
+- Update `customize_runtime.md` (#9797)
+
+#### Bug Fixes
+
+- Fix `WIDERFace SSD` loss for Nan problem (#9734)
+- Fix missing API documentation in Readthedoc (#9729)
+- Fix the configuration file and log path of CenterNet (#9791)
+
+#### Contributors
+
+A total of 4 developers contributed to this release.
+Thanks @co63oc, @Ginray, @vansin, @RangiLyu
+
+### v2.28.1 (1/2/2023)
+
+#### Bug Fixes
+
+- Enable to set float mlp_ratio in SwinTransformer (#8670)
+- Fix import error that causes training failure (#9694)
+- Fix isort version in lint (#9685)
+- Fix init_cfg of YOLOF (#8243)
+
+#### Contributors
+
+A total of 4 developers contributed to this release.
+Thanks @triple-Mu, @i-aki-y, @twmht, @RangiLyu
+
+### v2.28.0 (28/1/2023)
+
+#### Highlights
+
+- Support Objects365 Dataset and Separated and Occluded COCO metric
+- Support acceleration of RetinaNet and SSD on Ascend
+- Deprecate the support of Python 3.6
+
+#### New Features and Improvements
+
+- Support Objects365 Dataset (#7525)
+- Support [Separated and Occluded COCO metric](https://arxiv.org/abs/2210.10046) (#9574)
+- Support acceleration of RetinaNet and SSD on Ascend with documentation (#9648, #9614)
+- Added missing `-` to `--format-only` in documentation.
+
+#### Deprecations
+
+- Upgrade the minimum Python version to 3.7, the support of Python 3.6 is no longer guaranteed (#9604)
+
+#### Bug Fixes
+
+- Fix validation loss logging by (#9663)
+- Fix inconsistent float precision between mmdet and mmcv (#9570)
+- Fix argument name for fp32 in `DeformableDETRHead` (#9607)
+- Fix typo of all config file path in Metafile.yml (#9627)
+
+#### Contributors
+
+A total of 11 developers contributed to this release.
+Thanks @eantono, @akstt, @@lpizzinidev, @RangiLyu, @kbumsik, @tianleiSHI, @nijkah, @BIGWangYuDong, @wangjiangben-hw, @@jamiechoi1995, @ZwwWayne
+
+## New Contributors
+
+- @kbumsik made their first contribution in https://github.com/open-mmlab/mmdetection/pull/9627
+- @akstt made their first contribution in https://github.com/open-mmlab/mmdetection/pull/9614
+- @lpizzinidev made their first contribution in https://github.com/open-mmlab/mmdetection/pull/9649
+- @eantono made their first contribution in https://github.com/open-mmlab/mmdetection/pull/9663
+
+### v2.27.0 (5/1/2023)
+
+#### Highlights
+
+- Support receptive field search of CNN models([TPAMI 2022: RF-Next](http://mftp.mmcheng.net/Papers/22TPAMI-ActionSeg.pdf)) (#8191)
+
+#### Bug Fixes
+
+- Fix deadlock issue related with MMDetWandbHook (#9476)
+
+#### Improvements
+
+- Add minimum GitHub token permissions for workflows (#8928)
+- Delete compatible code for parrots in roi extractor (#9503)
+- Deprecate np.bool Type Alias (#9498)
+- Replace numpy transpose with torch permute to speed-up data pre-processing (#9533)
+
+#### Documents
+
+- Fix typo in docs/zh_cn/tutorials/config.md (#9416)
+- Fix Faster RCNN FP16 config link in README (#9366)
+
+#### Contributors
+
+A total of 12 developers contributed to this release.
+Thanks @Min-Sheng, @gasvn, @lzyhha, @jbwang1997, @zachcoleman, @chenyuwang814, @MilkClouds, @Fizzez, @boahc077, @apatsekin, @zytx121, @DonggeunYu
+
+### v2.26.0 (23/11/2022)
+
+#### Highlights
+
+- Support training on [NPU](docs/en/device/npu.md) (#9267)
+
+#### Bug Fixes
+
+- Fix RPN visualization (#9151)
+- Fix readthedocs by freezing the dependency versions (#9154)
+- Fix device argument error in MMDet_Tutorial.ipynb (#9112)
+- Fix solov2 cannot dealing with empty gt image (#9185)
+- Fix random flipping ratio comparison of mixup image (#9336)
+
+#### Improvements
+
+- Complement necessary argument of seg_suffix of cityscapes (#9330)
+- Support copy paste based on bbox when there is no gt mask (#8905)
+- Make scipy as a default dependency in runtime (#9186)
+
+#### Documents
+
+- Delete redundant Chinese characters in docs (#9175)
+- Add MMEval in README (#9217)
+
+#### Contributors
+
+A total of 11 developers contributed to this release.
+Thanks @wangjiangben-hw, @motokimura, @AdorableJiang, @BainOuO, @JarvisKevin, @wanghonglie, @zytx121, @BIGWangYuDong, @hhaAndroid, @RangiLyu, @ZwwWayne
+
+### v2.25.3 (25/10/2022)
+
+#### Bug Fixes
+
+- Skip remote sync when wandb is offline (#8755)
+- Fix jpg to png bug when using seg maps (#9078)
+
+#### Improvements
+
+- Fix typo in warning (#8844)
+- Fix CI for timm, pycocotools, onnx (#9034)
+- Upgrade pre-commit hooks (#8964)
+
+#### Documents
+
+- Update BoundedIoULoss config in readme (#8808)
+- Fix Faster R-CNN Readme (#8803)
+- Update location of test_cfg and train_cfg (#8792)
+- Fix issue template (#8966)
+- Update random sampler docstring (#9033)
+- Fix wrong image link (#9054)
+- Fix FPG readme (#9041)
+
+#### Contributors
+
+A total of 13 developers contributed to this release.
+Thanks @Zheng-LinXiao, @i-aki-y, @fbagci, @sudoAimer, @Czm369, @DrRyanHuang, @RangiLyu, @wanghonglie, @shinya7y, @Ryoo72, @akshaygulabrao, @gy-7, @Neesky
+
+### v2.25.2 (15/9/2022)
+
+#### Bug Fixes
+
+- Fix DyDCNv2 RuntimeError (#8485)
+- Fix repeated import of CascadeRPNHead (#8578)
+- Fix absolute positional embedding of swin backbone (#8127)
+- Fix get train_pipeline method of val workflow (#8575)
+
+#### Improvements
+
+- Upgrade onnxsim to at least 0.4.0 (#8383)
+- Support tuple format in analyze_results script (#8549)
+- Fix floordiv warning (#8648)
+
+#### Documents
+
+- Fix typo in HTC link (#8487)
+- Fix docstring of `BboxOverlaps2D` (#8512)
+- Added missed Chinese tutorial link (#8564)
+- Fix mistakes in gaussian radius formula (#8607)
+- Update config documentation about how to Add WandB Hook (#8663)
+- Add mmengine link in readme (#8799)
+- Update issue template (#8802)
+
+#### Contributors
+
+A total of 16 developers contributed to this release.
+Thanks @daquexian, @lyq10085, @ZwwWayne, @fbagci, @BubblyYi, @fathomson, @ShunchiZhang, @ceasona, @Happylkx, @normster, @chhluo, @Lehsuby, @JiayuXu0, @Nourollah, @hewanru-bit, @RangiLyu
+
+### v2.25.1 (29/7/2022)
+
+#### Bug Fixes
+
+- Fix single GPU distributed training of cuda device specifying (#8176)
+- Fix PolygonMask bug in FilterAnnotations (#8136)
+- Fix mdformat version to support python3.6 (#8195)
+- Fix GPG key error in Dockerfile (#8215)
+- Fix `WandbLoggerHook` error (#8273)
+- Fix Pytorch 1.10 incompatibility issues (#8439)
+
+#### Improvements
+
+- Add `mim` to `extras_require` in setup.py (#8194)
+- Support get image shape on macOS (#8434)
+- Add test commands of `mim` in CI (#8230 & #8240)
+- Update `maskformer` to be compatible when cfg is a dictionary (#8263)
+- Clean `Pillow` version check in CI (#8229)
+
+#### Documents
+
+- Change example hook name in tutorials (#8118)
+- Update projects (#8120)
+- Update metafile and release new models (#8294)
+- Add download link in tutorials (#8391)
+
+#### Contributors
+
+A total of 15 developers contributed to this release.
+Thanks @ZwwWayne, @ayulockin, @Mxbonn, @p-mishra1, @Youth-Got, @MiXaiLL76, @chhluo, @jbwang1997, @atinfinity, @shinya7y, @duanzhihua, @STLAND-admin, @BIGWangYuDong, @grimoire, @xiaoyuan0203
+
+### v2.25.0 (31/5/2022)
+
+#### Highlights
+
+- Support dedicated `WandbLogger` hook
+- Support [ConvNeXt](configs/convnext), [DDOD](configs/ddod), [SOLOv2](configs/solov2)
+- Support [Mask2Former](configs/mask2former) for instance segmentation
+- Rename [config files of Mask2Former](configs/mask2former)
+
+#### Backwards incompatible changes
+
+- Rename [config files of Mask2Former](configs/mask2former) (#7571)
+
+  <table align="center">
+    <thead>
+        <tr align='center'>
+            <td>before v2.25.0</td>
+            <td>after v2.25.0</td>
+        </tr>
+    </thead>
+    <tbody><tr valign='top'>
+    <th>
+
+  - `mask2former_xxx_coco.py` represents config files for **panoptic segmentation**.
+
+  </th>
+    <th>
+
+  - `mask2former_xxx_coco.py` represents config files for **instance segmentation**.
+  - `mask2former_xxx_coco-panoptic.py` represents config files for **panoptic segmentation**.
+
+  </th></tr>
+  </tbody></table>
+
+#### New Features
+
+- Support [ConvNeXt](https://arxiv.org/abs/2201.03545) (#7281)
+- Support [DDOD](https://arxiv.org/abs/2107.02963) (#7279)
+- Support [SOLOv2](https://arxiv.org/abs/2003.10152) (#7441)
+- Support [Mask2Former](https://arxiv.org/abs/2112.01527) for instance segmentation (#7571, #8032)
+
+#### Bug Fixes
+
+- Enable YOLOX training on different devices (#7912)
+- Fix the log plot error when evaluation with `interval != 1` (#7784)
+- Fix RuntimeError of HTC (#8083)
+
+#### Improvements
+
+- Support dedicated `WandbLogger` hook (#7459)
+
+  Users can set
+
+  ```python
+  cfg.log_config.hooks = [
+    dict(type='MMDetWandbHook',
+         init_kwargs={'project': 'MMDetection-tutorial'},
+         interval=10,
+         log_checkpoint=True,
+         log_checkpoint_metadata=True,
+         num_eval_images=10)]
+  ```
+
+  in the config to use `MMDetWandbHook`. Example can be found in this [colab tutorial](https://colab.research.google.com/drive/1RCSXHZwDZvakFh3eo9RuNrJbCGqD0dru?usp=sharing#scrollTo=WTEdPDRaBz2C)
+
+- Add `AvoidOOM` to avoid OOM (#7434, #8091)
+
+  Try to use `AvoidCUDAOOM` to avoid GPU out of memory. It will first retry after calling `torch.cuda.empty_cache()`. If it still fails, it will then retry by converting the type of inputs to FP16 format. If it still fails, it will try to copy inputs from GPUs to CPUs to continue computing. Try AvoidOOM in code to make the code continue to run when GPU memory runs out:
+
+  ```python
+  from mmdet.utils import AvoidCUDAOOM
+
+  output = AvoidCUDAOOM.retry_if_cuda_oom(some_function)(input1, input2)
+  ```
+
+  Users can also try `AvoidCUDAOOM` as a decorator to make the code continue to run when GPU memory runs out:
+
+  ```python
+  from mmdet.utils import AvoidCUDAOOM
+
+  @AvoidCUDAOOM.retry_if_cuda_oom
+  def function(*args, **kwargs):
+      ...
+      return xxx
+  ```
+
+- Support reading `gpu_collect` from `cfg.evaluation.gpu_collect` (#7672)
+
+- Speedup the Video Inference by Accelerating data-loading Stage (#7832)
+
+- Support replacing the `${key}` with the value of `cfg.key` (#7492)
+
+- Accelerate result analysis in `analyze_result.py`. The evaluation time is speedup by 10 ~ 15 times and only tasks 10 ~ 15 minutes now. (#7891)
+
+- Support to set `block_dilations` in `DilatedEncoder` (#7812)
+
+- Support panoptic segmentation result analysis (#7922)
+
+- Release DyHead with Swin-Large backbone (#7733)
+
+- Documentations updating and adding
+
+  - Fix wrong default type of `act_cfg` in `SwinTransformer` (#7794)
+  - Fix text errors in the tutorials (#7959)
+  - Rewrite the [installation guide](docs/en/get_started.md) (#7897)
+  - [Useful hooks](docs/en/tutorials/useful_hooks.md) (#7810)
+  - Fix heading anchor in documentation  (#8006)
+  - Replace `markdownlint` with `mdformat` for avoiding installing ruby (#8009)
+
+#### Contributors
+
+A total of 20 developers contributed to this release.
+
+Thanks @ZwwWayne, @DarthThomas, @solyaH, @LutingWang, @chenxinfeng4, @Czm369, @Chenastron, @chhluo, @austinmw, @Shanyaliux @hellock, @Y-M-Y, @jbwang1997, @hhaAndroid, @Irvingao, @zhanggefan, @BIGWangYuDong, @Keiku, @PeterVennerstrom, @ayulockin
+
+### v2.24.0 (26/4/2022)
+
+#### Highlights
+
+- Support [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://arxiv.org/abs/2012.07177)
+- Support automatically scaling LR according to GPU number and samples per GPU
+- Support Class Aware Sampler that improves performance on OpenImages Dataset
+
+#### New Features
+
+- Support [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://arxiv.org/abs/2012.07177), see [example configs](configs/simple_copy_paste/mask_rcnn_r50_fpn_syncbn-all_rpn-2conv_ssj_scp_32x2_270k_coco.py) (#7501)
+
+- Support Class Aware Sampler, users can set
+
+  ```python
+  data=dict(train_dataloader=dict(class_aware_sampler=dict(num_sample_class=1))))
+  ```
+
+  in the config to use `ClassAwareSampler`. Examples can be found in [the configs of OpenImages Dataset](https://github.com/open-mmlab/mmdetection/tree/master/configs/openimages/faster_rcnn_r50_fpn_32x2_cas_1x_openimages.py).  (#7436)
+
+- Support automatically scaling LR according to GPU number and samples per GPU. (#7482)
+  In each config, there is a corresponding config of auto-scaling LR as below,
+
+  ```python
+  auto_scale_lr = dict(enable=True, base_batch_size=N)
+  ```
+
+  where `N` is the batch size used for the current learning rate in the config (also equals to `samples_per_gpu` * gpu number to train this config).
+  By default, we set `enable=False` so that the original usages will not be affected. Users can set `enable=True` in each config or add `--auto-scale-lr` after the command line to enable this feature and should check the correctness of `base_batch_size` in customized configs.
+
+- Support setting dataloader arguments in config and add functions to handle config compatibility. (#7668)
+  The comparison between the old and new usages is as below.
+
+  <table align="center">
+    <thead>
+        <tr align='center'>
+            <td>v2.23.0</td>
+            <td>v2.24.0</td>
+        </tr>
+    </thead>
+    <tbody><tr valign='top'>
+    <th>
+
+  ```python
+  data = dict(
+      samples_per_gpu=64, workers_per_gpu=4,
+      train=dict(type='xxx', ...),
+      val=dict(type='xxx', samples_per_gpu=4, ...),
+      test=dict(type='xxx', ...),
+  )
+  ```
+
+  </th>
+    <th>
+
+  ```python
+  # A recommended config that is clear
+  data = dict(
+      train=dict(type='xxx', ...),
+      val=dict(type='xxx', ...),
+      test=dict(type='xxx', ...),
+      # Use different batch size during inference.
+      train_dataloader=dict(samples_per_gpu=64, workers_per_gpu=4),
+      val_dataloader=dict(samples_per_gpu=8, workers_per_gpu=2),
+      test_dataloader=dict(samples_per_gpu=8, workers_per_gpu=2),
+  )
+
+  # Old style still works but allows to set more arguments about data loaders
+  data = dict(
+      samples_per_gpu=64,  # only works for train_dataloader
+      workers_per_gpu=4,  # only works for train_dataloader
+      train=dict(type='xxx', ...),
+      val=dict(type='xxx', ...),
+      test=dict(type='xxx', ...),
+      # Use different batch size during inference.
+      val_dataloader=dict(samples_per_gpu=8, workers_per_gpu=2),
+      test_dataloader=dict(samples_per_gpu=8, workers_per_gpu=2),
+  )
+  ```
+
+  </th></tr>
+  </tbody></table>
+
+- Support memory profile hook. Users can use it to monitor the memory usages during training as below (#7560)
+
+  ```python
+  custom_hooks = [
+      dict(type='MemoryProfilerHook', interval=50)
+  ]
+  ```
+
+- Support to run on PyTorch with MLU chip (#7578)
+
+- Support re-spliting data batch with tag (#7641)
+
+- Support the `DiceCost` used by [K-Net](https://arxiv.org/abs/2106.14855) in `MaskHungarianAssigner` (#7716)
+
+- Support splitting COCO data for Semi-supervised object detection (#7431)
+
+- Support Pathlib for Config.fromfile (#7685)
+
+- Support to use file client in OpenImages dataset (#7433)
+
+- Add a probability parameter to Mosaic transformation (#7371)
+
+- Support specifying interpolation mode in `Resize` pipeline (#7585)
+
+#### Bug Fixes
+
+- Avoid invalid bbox after deform_sampling (#7567)
+- Fix the issue that argument color_theme does not take effect when exporting confusion matrix (#7701)
+- Fix the `end_level` in Necks, which should be the index of the end input backbone level (#7502)
+- Fix the bug that `mix_results` may be None in `MultiImageMixDataset` (#7530)
+- Fix the bug in ResNet plugin when two plugins are used (#7797)
+
+#### Improvements
+
+- Enhance `load_json_logs` of analyze_logs.py for resumed training logs (#7732)
+- Add argument `out_file` in image_demo.py (#7676)
+- Allow mixed precision training with `SimOTAAssigner` (#7516)
+- Updated INF to 100000.0 to be the same as that in the official YOLOX (#7778)
+- Add documentations of:
+  - how to get channels of a new backbone (#7642)
+  - how to unfreeze the backbone network (#7570)
+  - how to train fast_rcnn model (#7549)
+  - proposals in Deformable DETR (#7690)
+  - from-scratch install script in get_started.md (#7575)
+- Release pre-trained models of
+  - [Mask2Former](configs/mask2former) (#7595, #7709)
+  - RetinaNet with ResNet-18 and release models (#7387)
+  - RetinaNet with EfficientNet backbone (#7646)
+
+#### Contributors
+
+A total of 27 developers contributed to this release.
+Thanks @jovialio, @zhangsanfeng2022, @HarryZJ, @jamiechoi1995, @nestiank, @PeterH0323, @RangeKing, @Y-M-Y, @mattcasey02, @weiji14, @Yulv-git, @xiefeifeihu, @FANG-MING, @meng976537406, @nijkah, @sudz123, @CCODING04, @SheffieldCao, @Czm369, @BIGWangYuDong, @zytx121, @jbwang1997, @chhluo, @jshilong, @RangiLyu, @hhaAndroid, @ZwwWayne
+
+### v2.23.0 (28/3/2022)
+
+#### Highlights
+
+- Support Mask2Former: [Masked-attention Mask Transformer for Universal Image Segmentation](https://arxiv.org/abs/2112.01527)
+- Support EfficientNet: [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://arxiv.org/abs/1905.11946)
+- Support setting data root through environment variable `MMDET_DATASETS`, users don't have to modify the corresponding path in config files anymore.
+- Find a good recipe for fine-tuning high precision ResNet backbone pre-trained by Torchvision.
+
+#### New Features
+
+- Support [Mask2Former](configs/mask2former)(#6938)(#7466)(#7471)
+- Support [EfficientNet](configs/efficientnet) (#7514)
+- Support setting data root through environment variable `MMDET_DATASETS`, users don't have to modify the corresponding path in config files anymore. (#7386)
+- Support setting different seeds to different ranks (#7432)
+- Update the `dist_train.sh` so that the script can be used to support launching multi-node training on machines without slurm (#7415)
+- Find a good recipe for fine-tuning high precision ResNet backbone pre-trained by Torchvision (#7489)
+
+#### Bug Fixes
+
+- Fix bug in VOC unit test which removes the data directory (#7270)
+- Adjust the order of `get_classes` and `FileClient` (#7276)
+- Force the inputs of `get_bboxes` in yolox_head to float32 (#7324)
+- Fix misplaced arguments in LoadPanopticAnnotations (#7388)
+- Fix reduction=mean in CELoss. (#7449)
+- Update unit test of CrossEntropyCost (#7537)
+- Fix memory leaking in panpotic segmentation evaluation (#7538)
+- Fix the bug of shape broadcast in YOLOv3 (#7551)
+
+#### Improvements
+
+- Add Chinese version of onnx2tensorrt.md (#7219)
+- Update colab tutorials (#7310)
+- Update information about Localization Distillation (#7350)
+- Add Chinese version of `finetune.md` (#7178)
+- Update YOLOX log for non square input (#7235)
+- Add `nproc` in `coco_panoptic.py` for panoptic quality computing (#7315)
+- Allow to set channel_order in LoadImageFromFile (#7258)
+- Take point sample related functions out of mask_point_head (#7353)
+- Add instance evaluation for coco_panoptic (#7313)
+- Enhance the robustness of analyze_logs.py (#7407)
+- Supplementary notes of sync_random_seed (#7440)
+- Update docstring of cross entropy loss (#7472)
+- Update pascal voc result (#7503)
+- We create How-to documentation to record any questions about How to xxx. In this version, we added
+  - How to use Mosaic augmentation (#7507)
+  - How to use backbone in mmcls (#7438)
+  - How to produce and submit the prediction results of panoptic segmentation models on COCO test-dev set (#7430))
+
+#### Contributors
+
+A total of 27 developers contributed to this release.
+Thanks @ZwwWayne, @haofanwang, @shinya7y, @chhluo, @yangrisheng, @triple-Mu, @jbwang1997, @HikariTJU, @imflash217, @274869388, @zytx121, @matrixgame2018, @jamiechoi1995, @BIGWangYuDong, @JingweiZhang12, @Xiangxu-0103, @hhaAndroid, @jshilong, @osbm, @ceroytres, @bunge-bedstraw-herb, @Youth-Got, @daavoo, @jiangyitong, @RangiLyu, @CCODING04, @yarkable
+
+### v2.22.0 (24/2/2022)
+
+#### Highlights
+
+- Support MaskFormer: [Per-Pixel Classification is Not All You Need for Semantic Segmentation](https://arxiv.org/abs/2107.06278) (#7212)
+- Support DyHead: [Dynamic Head: Unifying Object Detection Heads with Attentions](https://arxiv.org/abs/2106.08322) (#6823)
+- Release a good recipe of using ResNet in object detectors pre-trained by [ResNet Strikes Back](https://arxiv.org/abs/2110.00476), which consistently brings about 3~4 mAP improvements over RetinaNet, Faster/Mask/Cascade Mask R-CNN (#7001)
+- Support [Open Images Dataset](https://storage.googleapis.com/openimages/web/index.html) (#6331)
+- Support TIMM backbone: [PyTorch Image Models](https://github.com/rwightman/pytorch-image-models) (#7020)
+
+#### New Features
+
+- Support [MaskFormer](configs/maskformer) (#7212)
+- Support [DyHead](configs/dyhead) (#6823)
+- Support [ResNet Strikes Back](configs/resnet_strikes_back) (#7001)
+- Support [OpenImages Dataset](configs/openimages) (#6331)
+- Support [TIMM backbone](configs/timm_example) (#7020)
+- Support visualization for Panoptic Segmentation (#7041)
+
+#### Breaking Changes
+
+In order to support the visualization for Panoptic Segmentation, the `num_classes` can not be `None` when using the `get_palette` function to determine whether to use the panoptic palette.
+
+#### Bug Fixes
+
+- Fix bug for the best checkpoints can not be saved when the `key_score` is None (#7101)
+- Fix MixUp transform filter boxes failing case (#7080)
+- Add missing properties in SABLHead (#7091)
+- Fix bug when NaNs exist in confusion matrix (#7147)
+- Fix PALETTE AttributeError in downstream task (#7230)
+
+#### Improvements
+
+- Speed up SimOTA matching (#7098)
+- Add Chinese translation of `docs_zh-CN/tutorials/init_cfg.md` (#7188)
+
+#### Contributors
+
+A total of 20 developers contributed to this release.
+Thanks @ZwwWayne, @hhaAndroid, @RangiLyu, @AronLin, @BIGWangYuDong, @jbwang1997, @zytx121, @chhluo, @shinya7y, @LuooChen, @dvansa, @siatwangmin, @del-zhenwu, @vikashranjan26, @haofanwang, @jamiechoi1995, @HJoonKwon, @yarkable, @zhijian-liu, @RangeKing
+
+### v2.21.0 (8/2/2022)
+
+### Breaking Changes
+
+To standardize the contents in config READMEs and meta files of OpenMMLab projects, the READMEs and meta files in each config directory have been significantly changed. The template will be released in the future, for now, you can refer to the examples of README for [algorithm](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/README.md), [dataset](https://github.com/open-mmlab/mmdetection/blob/master/configs/deepfashion/README.md) and [backbone](https://github.com/open-mmlab/mmdetection/blob/master/configs/regnet/README.md). To align with the standard, the configs in dcn are put into to two directories named `dcn` and `dcnv2`.
+
+#### New Features
+
+- Allow to customize colors of different classes during visualization (#6716)
+- Support CPU training (#7016)
+- Add download script of COCO, LVIS, and VOC dataset (#7015)
+
+#### Bug Fixes
+
+- Fix weight conversion issue of RetinaNet with Swin-S (#6973)
+- Update `__repr__` of `Compose` (#6951)
+- Fix BadZipFile Error when build docker (#6966)
+- Fix bug in non-distributed multi-gpu training/testing (#7019)
+- Fix bbox clamp in PyTorch 1.10 (#7074)
+- Relax the requirement of PALETTE in dataset wrappers (#7085)
+- Keep the same weights before reassign in the PAA head (#7032)
+- Update code demo in doc (#7092)
+
+#### Improvements
+
+- Speed-up training by allow to set variables of multi-processing (#6974, #7036)
+- Add links of Chinese tutorials in readme (#6897)
+- Disable cv2 multiprocessing by default for acceleration (#6867)
+- Deprecate the support for "python setup.py test" (#6998)
+- Re-organize metafiles and config readmes (#7051)
+- Fix None grad problem during training TOOD by adding `SigmoidGeometricMean` (#7090)
+
+#### Contributors
+
+A total of 26 developers contributed to this release.
+Thanks @del-zhenwu, @zimoqingfeng, @srishilesh, @imyhxy, @jenhaoyang, @jliu-ac, @kimnamu, @ShengliLiu, @garvan2021, @ciusji, @DIYer22, @kimnamu, @q3394101, @zhouzaida, @gaotongxiao, @topsy404, @AntoAndGar, @jbwang1997, @nijkah, @ZwwWayne, @Czm369, @jshilong, @RangiLyu, @BIGWangYuDong, @hhaAndroid, @AronLin
+
+### v2.20.0 (27/12/2021)
+
+#### New Features
+
+- Support [TOOD](configs/tood/README.md): Task-aligned One-stage Object Detection (ICCV 2021 Oral) (#6746)
+- Support resuming from the latest checkpoint automatically (#6727)
+
+#### Bug Fixes
+
+- Fix wrong bbox `loss_weight` of the PAA head (#6744)
+- Fix the padding value of `gt_semantic_seg` in batch collating (#6837)
+- Fix test error of lvis when using `classwise` (#6845)
+- Avoid BC-breaking of `get_local_path`  (#6719)
+- Fix bug in `sync_norm_hook` when the BN layer does not exist (#6852)
+- Use pycocotools directly no matter what platform it is (#6838)
+
+#### Improvements
+
+- Add unit test for SimOTA with no valid bbox (#6770)
+- Use precommit to check readme (#6802)
+- Support selecting GPU-ids in non-distributed testing time (#6781)
+
+#### Contributors
+
+A total of 16 developers contributed to this release.
+Thanks @ZwwWayne, @Czm369, @jshilong, @RangiLyu, @BIGWangYuDong, @hhaAndroid, @jamiechoi1995, @AronLin, @Keiku, @gkagkos, @fcakyon, @www516717402, @vansin, @zactodd, @kimnamu, @jenhaoyang
+
+### v2.19.1 (14/12/2021)
+
+#### New Features
+
+- Release [YOLOX](configs/yolox/README.md) COCO pretrained models (#6698)
+
+#### Bug Fixes
+
+- Fix DCN initialization in DenseHead (#6625)
+- Fix initialization of ConvFCHead (#6624)
+- Fix PseudoSampler in RCNN (#6622)
+- Fix weight initialization in Swin and PVT (#6663)
+- Fix dtype bug in BaseDenseHead (#6767)
+- Fix SimOTA with no valid bbox (#6733)
+
+#### Improvements
+
+- Add an example of combining swin and one-stage models (#6621)
+- Add `get_ann_info` to dataset_wrappers (#6526)
+- Support keeping image ratio in the multi-scale training of YOLOX (#6732)
+- Support `bbox_clip_border` for the augmentations of YOLOX (#6730)
+
+#### Documents
+
+- Update metafile (#6717)
+- Add mmhuman3d in readme (#6699)
+- Update FAQ docs (#6587)
+- Add doc for `detect_anomalous_params` (#6697)
+
+#### Contributors
+
+A total of 11 developers contributed to this release.
+Thanks @ZwwWayne, @LJoson, @Czm369, @jshilong, @ZCMax, @RangiLyu, @BIGWangYuDong, @hhaAndroid, @zhaoxin111, @GT9505, @shinya7y
+
+### v2.19.0 (29/11/2021)
+
+#### Highlights
+
+- Support [Label Assignment Distillation](https://arxiv.org/abs/2108.10520)
+- Support `persistent_workers` for Pytorch >= 1.7
+- Align accuracy to the updated official YOLOX
+
+#### New Features
+
+- Support [Label Assignment Distillation](https://arxiv.org/abs/2108.10520) (#6342)
+- Support `persistent_workers` for Pytorch >= 1.7 (#6435)
+
+#### Bug Fixes
+
+- Fix repeatedly output warning message (#6584)
+- Avoid infinite GPU waiting in dist training (#6501)
+- Fix SSD512 config error (#6574)
+- Fix MMDetection model to ONNX command (#6558)
+
+#### Improvements
+
+- Refactor configs of FP16 models (#6592)
+- Align accuracy to the updated official YOLOX (#6443)
+- Speed up training and reduce memory cost when using PhotoMetricDistortion. (#6442)
+- Make OHEM work with seesaw loss (#6514)
+
+#### Documents
+
+- Update README.md (#6567)
+
+#### Contributors
+
+A total of 11 developers contributed to this release.
+Thanks @FloydHsiu, @RangiLyu, @ZwwWayne, @AndreaPi, @st9007a, @hachreak, @BIGWangYuDong, @hhaAndroid, @AronLin, @chhluo, @vealocia, @HarborYuan, @st9007a, @jshilong
+
+### v2.18.1 (15/11/2021)
+
+#### Highlights
+
+- Release [QueryInst](http://arxiv.org/abs/2105.01928) pre-trained weights (#6460)
+- Support plot confusion matrix (#6344)
+
+#### New Features
+
+- Release [QueryInst](http://arxiv.org/abs/2105.01928) pre-trained weights (#6460)
+- Support plot confusion matrix (#6344)
+
+#### Bug Fixes
+
+- Fix aug test error when the number of prediction bboxes is 0 (#6398)
+- Fix SpatialReductionAttention in PVT (#6488)
+- Fix wrong use of `trunc_normal_init` in PVT and Swin-Transformer (#6432)
+
+#### Improvements
+
+- Save the printed AP information of COCO API to logger (#6505)
+- Always map location to cpu when load checkpoint (#6405)
+- Set a random seed when the user does not set a seed (#6457)
+
+#### Documents
+
+- Chinese version of [Corruption Benchmarking](robustness_benchmarking.md) (#6375)
+- Fix config path in docs (#6396)
+- Update GRoIE readme (#6401)
+
+#### Contributors
+
+A total of 11 developers contributed to this release.
+Thanks @st9007a, @hachreak, @HarborYuan, @vealocia, @chhluo, @AndreaPi, @AronLin, @BIGWangYuDong, @hhaAndroid, @RangiLyu, @ZwwWayne
+
+### v2.18.0 (27/10/2021)
+
+#### Highlights
+
+- Support [QueryInst](http://arxiv.org/abs/2105.01928) (#6050)
+- Refactor dense heads to decouple onnx export logics from `get_bboxes` and speed up inference (#5317, #6003, #6369, #6268, #6315)
+
+#### New Features
+
+- Support [QueryInst](http://arxiv.org/abs/2105.01928) (#6050)
+- Support infinite sampler (#5996)
+
+#### Bug Fixes
+
+- Fix init_weight in fcn_mask_head (#6378)
+- Fix type error in imshow_bboxes of RPN (#6386)
+- Fix broken colab link in MMDetection Tutorial (#6382)
+- Make sure the device and dtype of scale_factor are the same as bboxes (#6374)
+- Remove sampling hardcode (#6317)
+- Fix RandomAffine bbox coordinate recorrection (#6293)
+- Fix init bug of final cls/reg layer in convfc head (#6279)
+- Fix img_shape broken in auto_augment (#6259)
+- Fix kwargs parameter missing error in two_stage (#6256)
+
+#### Improvements
+
+- Unify the interface of stuff head and panoptic head (#6308)
+- Polish readme (#6243)
+- Add code-spell pre-commit hook and fix a typo (#6306)
+- Fix typo (#6245, #6190)
+- Fix sampler unit test (#6284)
+- Fix `forward_dummy` of YOLACT to enable `get_flops` (#6079)
+- Fix link error in the config documentation (#6252)
+- Adjust the order to beautify the document (#6195)
+
+#### Refactors
+
+- Refactor one-stage get_bboxes logic (#5317)
+- Refactor ONNX export of One-Stage models (#6003, #6369)
+- Refactor dense_head and speedup (#6268)
+- Migrate to use prior_generator in training of dense heads (#6315)
+
+#### Contributors
+
+A total of 18 developers contributed to this release.
+Thanks @Boyden, @onnkeat, @st9007a, @vealocia, @yhcao6, @DapangpangX, @yellowdolphin, @cclauss, @kennymckormick,
+@pingguokiller, @collinzrj, @AndreaPi, @AronLin, @BIGWangYuDong, @hhaAndroid, @jshilong, @RangiLyu, @ZwwWayne
+
+### v2.17.0 (28/9/2021)
+
+#### Highlights
+
+- Support [PVT](https://arxiv.org/abs/2102.12122) and [PVTv2](https://arxiv.org/abs/2106.13797)
+- Support [SOLO](https://arxiv.org/abs/1912.04488)
+- Support large scale jittering and New Mask R-CNN baselines
+- Speed up `YOLOv3` inference
+
+#### New Features
+
+- Support [PVT](https://arxiv.org/abs/2102.12122) and [PVTv2](https://arxiv.org/abs/2106.13797) (#5780)
+- Support [SOLO](https://arxiv.org/abs/1912.04488) (#5832)
+- Support large scale jittering and New Mask R-CNN baselines (#6132)
+- Add a general data structure for the results of models (#5508)
+- Added a base class for one-stage instance segmentation (#5904)
+- Speed up `YOLOv3` inference (#5991)
+- Release Swin Transformer pre-trained models (#6100)
+- Support mixed precision training in `YOLOX` (#5983)
+- Support `val` workflow in `YOLACT` (#5986)
+- Add script to test `torchserve` (#5936)
+- Support `onnxsim` with dynamic input shape (#6117)
+
+#### Bug Fixes
+
+- Fix the function naming errors in `model_wrappers` (#5975)
+- Fix regression loss bug when the input is an empty tensor (#5976)
+- Fix scores not contiguous error in `centernet_head` (#6016)
+- Fix missing parameters bug in `imshow_bboxes` (#6034)
+- Fix bug in `aug_test` of `HTC` when the length of `det_bboxes` is 0 (#6088)
+- Fix empty proposal errors in the training of some two-stage models (#5941)
+- Fix `dynamic_axes` parameter error in `ONNX` dynamic shape export (#6104)
+- Fix `dynamic_shape` bug of `SyncRandomSizeHook` (#6144)
+- Fix the Swin Transformer config link error in the configuration (#6172)
+
+#### Improvements
+
+- Add filter rules in `Mosaic` transform (#5897)
+- Add size divisor in get flops to avoid some potential bugs (#6076)
+- Add Chinese translation of `docs_zh-CN/tutorials/customize_dataset.md` (#5915)
+- Add Chinese translation of `conventions.md` (#5825)
+- Add description of the output of data pipeline (#5886)
+- Add dataset information in the README file for `PanopticFPN` (#5996)
+- Add `extra_repr` for `DropBlock` layer to get details in the model printing (#6140)
+- Fix CI out of memory and add PyTorch1.9 Python3.9 unit tests (#5862)
+- Fix download links error of some model (#6069)
+- Improve the generalization of XML dataset (#5943)
+- Polish assertion error messages (#6017)
+- Remove `opencv-python-headless` dependency by `albumentations` (#5868)
+- Check dtype in transform unit tests (#5969)
+- Replace the default theme of documentation with PyTorch Sphinx Theme (#6146)
+- Update the paper and code fields in the metafile (#6043)
+- Support to customize padding value of segmentation map (#6152)
+- Support to resize multiple segmentation maps (#5747)
+
+#### Contributors
+
+A total of 24 developers contributed to this release.
+Thanks @morkovka1337, @HarborYuan, @guillaumefrd, @guigarfr, @www516717402, @gaotongxiao, @ypwhs, @MartaYang, @shinya7y, @justiceeem, @zhaojinjian0000, @VVsssssk, @aravind-anantha, @wangbo-zhao, @czczup, @whai362, @czczup, @marijnl, @AronLin, @BIGWangYuDong, @hhaAndroid, @jshilong, @RangiLyu, @ZwwWayne
+
+### v2.16.0 (30/8/2021)
+
+#### Highlights
+
+- Support [Panoptic FPN](https://arxiv.org/abs/1901.02446) and [Swin Transformer](https://arxiv.org/abs/2103.14030)
+
+#### New Features
+
+- Support [Panoptic FPN](https://arxiv.org/abs/1901.02446) and release models (#5577, #5902)
+- Support Swin Transformer backbone (#5748)
+- Release RetinaNet models pre-trained with multi-scale 3x schedule (#5636)
+- Add script to convert unlabeled image list to coco format (#5643)
+- Add hook to check whether the loss value is valid (#5674)
+- Add YOLO anchor optimizing tool (#5644)
+- Support export onnx models without post process. (#5851)
+- Support classwise evaluation in CocoPanopticDataset (#5896)
+- Adapt browse_dataset for concatenated datasets. (#5935)
+- Add `PatchEmbed` and `PatchMerging` with `AdaptivePadding` (#5952)
+
+#### Bug Fixes
+
+- Fix unit tests of YOLOX (#5859)
+- Fix lose randomness in `imshow_det_bboxes` (#5845)
+- Make output result of `ImageToTensor` contiguous (#5756)
+- Fix inference bug when calling `regress_by_class` in RoIHead in some cases (#5884)
+- Fix bug in CIoU loss where alpha should not have gradient. (#5835)
+- Fix the bug that `multiscale_output` is defined but not used in HRNet (#5887)
+- Set the priority of EvalHook to LOW. (#5882)
+- Fix a YOLOX bug when applying bbox rescaling in test mode (#5899)
+- Fix mosaic coordinate error (#5947)
+- Fix dtype of bbox in RandomAffine. (#5930)
+
+#### Improvements
+
+- Add Chinese version of `data_pipeline` and  (#5662)
+- Support to remove state dicts of EMA when publishing models. (#5858)
+- Refactor the loss function in HTC and SCNet (#5881)
+- Use warnings instead of logger.warning (#5540)
+- Use legacy coordinate in metric of VOC (#5627)
+- Add Chinese version of customize_losses (#5826)
+- Add Chinese version of model_zoo (#5827)
+
+#### Contributors
+
+A total of 19 developers contributed to this release.
+Thanks @ypwhs, @zywvvd, @collinzrj, @OceanPang, @ddonatien, @@haotian-liu, @viibridges, @Muyun99, @guigarfr, @zhaojinjian0000, @jbwang1997,@wangbo-zhao, @xvjiarui, @RangiLyu, @jshilong, @AronLin, @BIGWangYuDong, @hhaAndroid, @ZwwWayne
+
+### v2.15.1 (11/8/2021)
+
+#### Highlights
+
+- Support [YOLOX](https://arxiv.org/abs/2107.08430)
+
+#### New Features
+
+- Support [YOLOX](https://arxiv.org/abs/2107.08430)(#5756, #5758, #5760, #5767, #5770, #5774, #5777, #5808, #5828, #5848)
+
+#### Bug Fixes
+
+- Update correct SSD models. (#5789)
+- Fix casting error in mask structure (#5820)
+- Fix MMCV deployment documentation links. (#5790)
+
+#### Improvements
+
+- Use dynamic MMCV download link in TorchServe dockerfile (#5779)
+- Rename the function `upsample_like` to `interpolate_as` for more general usage (#5788)
+
+#### Contributors
+
+A total of 14 developers contributed to this release.
+Thanks @HAOCHENYE, @xiaohu2015, @HsLOL, @zhiqwang, @Adamdad, @shinya7y, @Johnson-Wang, @RangiLyu, @jshilong, @mmeendez8, @AronLin, @BIGWangYuDong, @hhaAndroid, @ZwwWayne
+
+### v2.15.0 (02/8/2021)
+
+#### Highlights
+
+- Support adding [MIM](https://github.com/open-mmlab/mim) dependencies during pip installation
+- Support MobileNetV2 for SSD-Lite and YOLOv3
+- Support Chinese Documentation
+
+#### New Features
+
+- Add function `upsample_like` (#5732)
+- Support to output pdf and epub format documentation (#5738)
+- Support and release Cascade Mask R-CNN 3x pre-trained models (#5645)
+- Add `ignore_index` to CrossEntropyLoss (#5646)
+- Support adding [MIM](https://github.com/open-mmlab/mim) dependencies during pip installation (#5676)
+- Add MobileNetV2 config and models for YOLOv3 (#5510)
+- Support COCO Panoptic Dataset (#5231)
+- Support ONNX export of cascade models (#5486)
+- Support DropBlock with RetinaNet (#5544)
+- Support MobileNetV2 SSD-Lite (#5526)
+
+#### Bug Fixes
+
+- Fix the device of label in multiclass_nms (#5673)
+- Fix error of backbone initialization from pre-trained checkpoint in config file (#5603, #5550)
+- Fix download links of RegNet pretrained weights (#5655)
+- Fix two-stage runtime error given empty proposal (#5559)
+- Fix flops count error in DETR (#5654)
+- Fix unittest for `NumClassCheckHook` when it is not used. (#5626)
+- Fix description bug of using custom dataset (#5546)
+- Fix bug of `multiclass_nms` that returns the global indices (#5592)
+- Fix `valid_mask` logic error in RPNHead (#5562)
+- Fix unit test error of pretrained configs (#5561)
+- Fix typo error in anchor_head.py (#5555)
+- Fix bug when using dataset wrappers (#5552)
+- Fix a typo error in demo/MMDet_Tutorial.ipynb (#5511)
+- Fixing crash in `get_root_logger` when `cfg.log_level` is not None (#5521)
+- Fix docker version (#5502)
+- Fix optimizer parameter error when using `IterBasedRunner` (#5490)
+
+#### Improvements
+
+- Add unit tests for MMTracking (#5620)
+- Add Chinese translation of documentation (#5718, #5618, #5558, #5423, #5593, #5421, #5408. #5369, #5419, #5530, #5531)
+- Update resource limit (#5697)
+- Update docstring for InstaBoost (#5640)
+- Support key `reduction_override` in all loss functions (#5515)
+- Use repeatdataset to accelerate CenterNet training (#5509)
+- Remove unnecessary code in autoassign (#5519)
+- Add documentation about `init_cfg` (#5273)
+
+#### Contributors
+
+A total of 18 developers contributed to this release.
+Thanks @OceanPang, @AronLin, @hellock, @Outsider565, @RangiLyu, @ElectronicElephant, @likyoo, @BIGWangYuDong, @hhaAndroid, @noobying, @yyz561, @likyoo,
+@zeakey, @ZwwWayne, @ChenyangLiu, @johnson-magic, @qingswu, @BuxianChen
+
+### v2.14.0 (29/6/2021)
+
+#### Highlights
+
+- Add `simple_test` to dense heads to improve the consistency of single-stage and two-stage detectors
+- Revert the `test_mixins` to single image test to improve efficiency and readability
+- Add Faster R-CNN and Mask R-CNN config using multi-scale training with 3x schedule
+
+#### New Features
+
+- Support pretrained models from MoCo v2 and SwAV (#5286)
+- Add Faster R-CNN and Mask R-CNN config using multi-scale training with 3x schedule (#5179, #5233)
+- Add `reduction_override` in MSELoss (#5437)
+- Stable support of exporting DETR to ONNX with dynamic shapes and batch inference (#5168)
+- Stable support of exporting PointRend to ONNX with dynamic shapes and batch inference (#5440)
+
+#### Bug Fixes
+
+- Fix size mismatch bug in `multiclass_nms` (#4980)
+- Fix the import path of `MultiScaleDeformableAttention` (#5338)
+- Fix errors in config of GCNet ResNext101 models (#5360)
+- Fix Grid-RCNN error when there is no bbox result (#5357)
+- Fix errors in `onnx_export` of bbox_head when setting reg_class_agnostic (#5468)
+- Fix type error of AutoAssign in the document (#5478)
+- Fix web links ending with `.md` (#5315)
+
+#### Improvements
+
+- Add `simple_test` to dense heads to improve the consistency of single-stage and two-stage detectors (#5264)
+- Add support for mask diagonal flip in TTA (#5403)
+- Revert the `test_mixins` to single image test to improve efficiency and readability (#5249)
+- Make YOLOv3 Neck more flexible (#5218)
+- Refactor SSD to make it more general (#5291)
+- Refactor `anchor_generator` and `point_generator` (#5349)
+- Allow to configure out the `mask_head` of the HTC algorithm (#5389)
+- Delete deprecated warning in FPN (#5311)
+- Move `model.pretrained` to `model.backbone.init_cfg` (#5370)
+- Make deployment tools more friendly to use (#5280)
+- Clarify installation documentation (#5316)
+- Add ImageNet Pretrained Models docs (#5268)
+- Add FAQ about training loss=nan solution and COCO AP or AR =-1 (# 5312, #5313)
+- Change all weight links of http to https (#5328)
+
+### v2.13.0 (01/6/2021)
+
+#### Highlights
+
+- Support new methods: [CenterNet](https://arxiv.org/abs/1904.07850), [Seesaw Loss](https://arxiv.org/abs/2008.10032), [MobileNetV2](https://arxiv.org/abs/1801.04381)
+
+#### New Features
+
+- Support paper [Objects as Points](https://arxiv.org/abs/1904.07850) (#4602)
+- Support paper [Seesaw Loss for Long-Tailed Instance Segmentation (CVPR 2021)](https://arxiv.org/abs/2008.10032) (#5128)
+- Support [MobileNetV2](https://arxiv.org/abs/1801.04381) backbone and inverted residual block (#5122)
+- Support [MIM](https://github.com/open-mmlab/mim) (#5143)
+- ONNX exportation with dynamic shapes of CornerNet (#5136)
+- Add `mask_soft` config option to allow non-binary masks (#4615)
+- Add PWC metafile (#5135)
+
+#### Bug Fixes
+
+- Fix YOLOv3 FP16 training error (#5172)
+- Fix Cacscade R-CNN TTA test error when `det_bboxes` length is 0  (#5221)
+- Fix `iou_thr` variable naming errors in VOC recall calculation function (#5195)
+- Fix Faster R-CNN performance dropped in ONNX Runtime (#5197)
+- Fix DETR dict changed error when using python 3.8 during iteration  (#5226)
+
+#### Improvements
+
+- Refactor ONNX export of two stage detector (#5205)
+- Replace MMDetection's EvalHook with MMCV's EvalHook for consistency  (#4806)
+- Update RoI extractor for ONNX (#5194)
+- Use better parameter initialization in YOLOv3 head for higher performance (#5181)
+- Release new DCN models of Mask R-CNN by mixed-precision training (#5201)
+- Update YOLOv3 model weights (#5229)
+- Add DetectoRS ResNet-101 model weights (#4960)
+- Discard bboxes with sizes equals to `min_bbox_size` (#5011)
+- Remove duplicated code in DETR head (#5129)
+- Remove unnecessary object in class definition (#5180)
+- Fix doc link (#5192)
+
+### v2.12.0 (01/5/2021)
+
+#### Highlights
+
+- Support new methods: [AutoAssign](https://arxiv.org/abs/2007.03496), [YOLOF](https://arxiv.org/abs/2103.09460), and [Deformable DETR](https://arxiv.org/abs/2010.04159)
+- Stable support of exporting models to ONNX with batched images and dynamic shape (#5039)
+
+#### Backwards Incompatible Changes
+
+MMDetection is going through big refactoring for more general and convenient usages during the releases from v2.12.0 to v2.15.0 (maybe longer).
+In v2.12.0 MMDetection inevitably brings some BC-breakings, including the MMCV dependency, model initialization, model registry, and mask AP evaluation.
+
+- MMCV version. MMDetection v2.12.0 relies on the newest features in MMCV 1.3.3, including `BaseModule` for unified parameter initialization, model registry, and the CUDA operator `MultiScaleDeformableAttn` for [Deformable DETR](https://arxiv.org/abs/2010.04159). Note that MMCV 1.3.2 already contains all the features used by MMDet but has known issues. Therefore, we recommend users skip MMCV v1.3.2 and use v1.3.3, though v1.3.2 might work for most cases.
+- Unified model initialization (#4750). To unify the parameter initialization in OpenMMLab projects, MMCV supports `BaseModule` that accepts `init_cfg` to allow the modules' parameters initialized in a flexible and unified manner. Now the users need to explicitly call `model.init_weights()` in the training script to initialize the model (as in [here](https://github.com/open-mmlab/mmdetection/blob/master/tools/train.py#L162), previously this was handled by the detector. The models in MMDetection have been re-benchmarked to ensure accuracy based on PR #4750. __The downstream projects should update their code accordingly to use MMDetection v2.12.0__.
+- Unified model registry (#5059). To easily use backbones implemented in other OpenMMLab projects, MMDetection migrates to inherit the model registry created in MMCV (#760). In this way, as long as the backbone is supported in an OpenMMLab project and that project also uses the registry in MMCV, users can use that backbone in MMDetection by simply modifying the config without copying the code of that backbone into MMDetection.
+- Mask AP evaluation (#4898). Previous versions calculate the areas of masks through the bounding boxes when calculating the mask AP of small, medium, and large instances. To indeed use the areas of masks, we pop the key `bbox` during mask AP calculation. This change does not affect the overall mask AP evaluation and aligns the mask AP of similar models in other projects like Detectron2.
+
+#### New Features
+
+- Support paper [AutoAssign: Differentiable Label Assignment for Dense Object Detection](https://arxiv.org/abs/2007.03496) (#4295)
+- Support paper [You Only Look One-level Feature](https://arxiv.org/abs/2103.09460) (#4295)
+- Support paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) (#4778)
+- Support calculating IoU with FP16 tensor in `bbox_overlaps` to save memory and keep speed (#4889)
+- Add `__repr__` in custom dataset to count the number of instances (#4756)
+- Add windows support by updating requirements.txt (#5052)
+- Stable support of exporting models to ONNX with batched images and dynamic shape, including SSD, FSAF,FCOS, YOLOv3, RetinaNet, Faster R-CNN, and Mask R-CNN (#5039)
+
+#### Improvements
+
+- Use MMCV `MODEL_REGISTRY` (#5059)
+- Unified parameter initialization for more flexible usage (#4750)
+- Rename variable names and fix docstring in anchor head (#4883)
+- Support training with empty GT in Cascade RPN (#4928)
+- Add more details of usage of `test_robustness` in documentation (#4917)
+- Changing to use `pycocotools` instead of `mmpycocotools` to fully support Detectron2 and MMDetection in one environment (#4939)
+- Update torch serve dockerfile to support dockers of more versions (#4954)
+- Add check for training with single class dataset (#4973)
+- Refactor transformer and DETR Head (#4763)
+- Update FPG model zoo (#5079)
+- More accurate mask AP of small/medium/large instances (#4898)
+
+#### Bug Fixes
+
+- Fix bug in mean_ap.py when calculating mAP by 11 points (#4875)
+- Fix error when key `meta` is not in old checkpoints (#4936)
+- Fix hanging bug when training with empty GT in VFNet, GFL, and FCOS by changing the place of `reduce_mean` (#4923, #4978, #5058)
+- Fix asyncronized inference error and provide related demo (#4941)
+- Fix IoU losses dimensionality unmatch error (#4982)
+- Fix torch.randperm whtn using PyTorch 1.8 (#5014)
+- Fix empty bbox error in `mask_head` when using CARAFE (#5062)
+- Fix `supplement_mask` bug when there are zero-size RoIs (#5065)
+- Fix testing with empty rois in RoI Heads (#5081)
+
+### v2.11.0 (01/4/2021)
+
+__Highlights__
+
+- Support new method: [Localization Distillation for Object Detection](https://arxiv.org/pdf/2102.12252.pdf)
+- Support Pytorch2ONNX with batch inference and dynamic shape
+
+__New Features__
+
+- Support [Localization Distillation for Object Detection](https://arxiv.org/pdf/2102.12252.pdf) (#4758)
+- Support Pytorch2ONNX with batch inference and dynamic shape for Faster-RCNN and mainstream one-stage detectors (#4796)
+
+__Improvements__
+
+- Support batch inference in head of RetinaNet (#4699)
+- Add batch dimension in second stage of Faster-RCNN (#4785)
+- Support batch inference in bbox coder (#4721)
+- Add check for `ann_ids` in `COCODataset` to ensure it is unique (#4789)
+- support for showing the FPN results (#4716)
+- support dynamic shape for grid_anchor (#4684)
+- Move pycocotools version check to when it is used (#4880)
+
+__Bug Fixes__
+
+- Fix a bug of TridentNet when doing the batch inference (#4717)
+- Fix a bug of Pytorch2ONNX in FASF (#4735)
+- Fix a bug when show the image with float type (#4732)
+
+### v2.10.0 (01/03/2021)
+
+#### Highlights
+
+- Support new methods: [FPG](https://arxiv.org/abs/2004.03580)
+- Support ONNX2TensorRT for SSD, FSAF, FCOS, YOLOv3, and Faster R-CNN.
+
+#### New Features
+
+- Support ONNX2TensorRT for SSD, FSAF, FCOS, YOLOv3, and Faster R-CNN (#4569)
+- Support [Feature Pyramid Grids (FPG)](https://arxiv.org/abs/2004.03580) (#4645)
+- Support video demo (#4420)
+- Add seed option for sampler (#4665)
+- Support to customize type of runner (#4570, #4669)
+- Support synchronizing BN buffer in `EvalHook` (#4582)
+- Add script for GIF demo (#4573)
+
+#### Bug Fixes
+
+- Fix ConfigDict AttributeError and add Colab link (#4643)
+- Avoid crash in empty gt training of GFL head (#4631)
+- Fix `iou_thrs` bug in RPN evaluation (#4581)
+- Fix syntax error of config when upgrading model version (#4584)
+
+#### Improvements
+
+- Refactor unit test file structures (#4600)
+- Refactor nms config (#4636)
+- Get loading pipeline by checking the class directly rather than through config strings (#4619)
+- Add doctests for mask target generation and mask structures (#4614)
+- Use deep copy when copying pipeline arguments (#4621)
+- Update documentations (#4642, #4650, #4620, #4630)
+- Remove redundant code calling `import_modules_from_strings` (#4601)
+- Clean deprecated FP16 API (#4571)
+- Check whether `CLASSES` is correctly initialized in the initialization of `XMLDataset` (#4555)
+- Support batch inference in the inference API (#4462, #4526)
+- Clean deprecated warning and fix 'meta' error (#4695)
+
+### v2.9.0 (01/02/2021)
+
+#### Highlights
+
+- Support new methods: [SCNet](https://arxiv.org/abs/2012.10150), [Sparse R-CNN](https://arxiv.org/abs/2011.12450)
+- Move `train_cfg` and `test_cfg` into model in configs
+- Support to visualize results based on prediction quality
+
+#### New Features
+
+- Support [SCNet](https://arxiv.org/abs/2012.10150) (#4356)
+- Support [Sparse R-CNN](https://arxiv.org/abs/2011.12450) (#4219)
+- Support evaluate mAP by multiple IoUs (#4398)
+- Support concatenate dataset for testing (#4452)
+- Support to visualize results based on prediction quality (#4441)
+- Add ONNX simplify option to Pytorch2ONNX script (#4468)
+- Add hook for checking compatibility of class numbers in heads and datasets (#4508)
+
+#### Bug Fixes
+
+- Fix CPU inference bug of Cascade RPN (#4410)
+- Fix NMS error of CornerNet when there is no prediction box (#4409)
+- Fix TypeError in CornerNet inference (#4411)
+- Fix bug of PAA when training with background images (#4391)
+- Fix the error that the window data is not destroyed when `out_file is not None` and `show==False` (#4442)
+- Fix order of NMS `score_factor` that will decrease the performance of YOLOv3 (#4473)
+- Fix bug in HTC TTA when the number of detection boxes is 0 (#4516)
+- Fix resize error in mask data structures (#4520)
+
+#### Improvements
+
+- Allow to customize classes in LVIS dataset (#4382)
+- Add tutorials for building new models with existing datasets (#4396)
+- Add CPU compatibility information in documentation (#4405)
+- Add documentation of deprecated `ImageToTensor` for batch inference (#4408)
+- Add more details in documentation for customizing dataset (#4430)
+- Switch `imshow_det_bboxes` visualization backend from OpenCV to Matplotlib (#4389)
+- Deprecate `ImageToTensor` in `image_demo.py` (#4400)
+- Move train_cfg/test_cfg into model (#4347, #4489)
+- Update docstring for `reg_decoded_bbox` option in bbox heads (#4467)
+- Update dataset information in documentation (#4525)
+- Release pre-trained R50 and R101 PAA detectors with multi-scale 3x training schedules (#4495)
+- Add guidance for speed benchmark (#4537)
+
+### v2.8.0 (04/01/2021)
+
+#### Highlights
+
+- Support new methods: [Cascade RPN](https://arxiv.org/abs/1909.06720), [TridentNet](https://arxiv.org/abs/1901.01892)
+
+#### New Features
+
+- Support [Cascade RPN](https://arxiv.org/abs/1909.06720) (#1900)
+- Support [TridentNet](https://arxiv.org/abs/1901.01892) (#3313)
+
+#### Bug Fixes
+
+- Fix bug of show result in async_benchmark (#4367)
+- Fix scale factor in MaskTestMixin (#4366)
+- Fix but when returning indices in `multiclass_nms` (#4362)
+- Fix bug of empirical attention in resnext backbone error (#4300)
+- Fix bug of `img_norm_cfg` in FCOS-HRNet models with updated performance and models (#4250)
+- Fix invalid checkpoint and log in Mask R-CNN models on Cityscapes dataset (#4287)
+- Fix bug in distributed sampler when dataset is too small (#4257)
+- Fix bug of 'PAFPN has no attribute extra_convs_on_inputs' (#4235)
+
+#### Improvements
+
+- Update model url from aws to aliyun (#4349)
+- Update ATSS for PyTorch 1.6+ (#4359)
+- Update script to install ruby in pre-commit installation (#4360)
+- Delete deprecated `mmdet.ops` (#4325)
+- Refactor hungarian assigner for more general usage in Sparse R-CNN (#4259)
+- Handle scipy import in DETR to reduce package dependencies (#4339)
+- Update documentation of usages for config options after MMCV (1.2.3) supports overriding list in config (#4326)
+- Update pre-train models of faster rcnn trained on COCO subsets (#4307)
+- Avoid zero or too small value for beta in Dynamic R-CNN (#4303)
+- Add doccumentation for Pytorch2ONNX (#4271)
+- Add deprecated warning FPN arguments (#4264)
+- Support returning indices of kept bboxes when using nms (#4251)
+- Update type and device requirements when creating tensors `GFLHead` (#4210)
+- Update device requirements when creating tensors in `CrossEntropyLoss` (#4224)
+
+### v2.7.0 (30/11/2020)
+
+- Support new method: [DETR](https://arxiv.org/abs/2005.12872), [ResNest](https://arxiv.org/abs/2004.08955), Faster R-CNN DC5.
+- Support YOLO, Mask R-CNN, and Cascade R-CNN models exportable to ONNX.
+
+#### New Features
+
+- Support [DETR](https://arxiv.org/abs/2005.12872) (#4201, #4206)
+- Support to link the best checkpoint in training (#3773)
+- Support to override config through options in inference.py (#4175)
+- Support YOLO, Mask R-CNN, and Cascade R-CNN models exportable to ONNX (#4087, #4083)
+- Support [ResNeSt](https://arxiv.org/abs/2004.08955) backbone (#2959)
+- Support unclip border bbox regression (#4076)
+- Add tpfp func in evaluating AP (#4069)
+- Support mixed precision training of SSD detector with other backbones (#4081)
+- Add Faster R-CNN DC5 models (#4043)
+
+#### Bug Fixes
+
+- Fix bug of `gpu_id` in distributed training mode (#4163)
+- Support Albumentations with version higher than 0.5 (#4032)
+- Fix num_classes bug in faster rcnn config (#4088)
+- Update code in docs/2_new_data_model.md (#4041)
+
+#### Improvements
+
+- Ensure DCN offset to have similar type as features in VFNet (#4198)
+- Add config links in README files of models (#4190)
+- Add tutorials for loss conventions (#3818)
+- Add solution to installation issues in 30-series GPUs (#4176)
+- Update docker version in get_started.md (#4145)
+- Add model statistics and polish some titles in configs README (#4140)
+- Clamp neg probability in FreeAnchor (#4082)
+- Speed up expanding large images (#4089)
+- Fix Pytorch 1.7 incompatibility issues (#4103)
+- Update trouble shooting page to resolve segmentation fault (#4055)
+- Update aLRP-Loss in project page (#4078)
+- Clean duplicated `reduce_mean` function (#4056)
+- Refactor Q&A (#4045)
+
+### v2.6.0 (1/11/2020)
+
+- Support new method: [VarifocalNet](https://arxiv.org/abs/2008.13367).
+- Refactored documentation with more tutorials.
+
+#### New Features
+
+- Support GIoU calculation in `BboxOverlaps2D`, and re-implement `giou_loss` using `bbox_overlaps` (#3936)
+- Support random sampling in CPU mode (#3948)
+- Support VarifocalNet (#3666, #4024)
+
+#### Bug Fixes
+
+- Fix SABL validating bug in Cascade R-CNN (#3913)
+- Avoid division by zero in PAA head when num_pos=0 (#3938)
+- Fix temporary directory bug of multi-node testing error (#4034, #4017)
+- Fix `--show-dir` option in test script (#4025)
+- Fix GA-RetinaNet r50 model url (#3983)
+- Update code in docs and fix broken urls (#3947)
+
+#### Improvements
+
+- Refactor pytorch2onnx API into `mmdet.core.export` and use `generate_inputs_and_wrap_model` for pytorch2onnx (#3857, #3912)
+- Update RPN upgrade scripts for v2.5.0 compatibility (#3986)
+- Use mmcv `tensor2imgs` (#4010)
+- Update test robustness (#4000)
+- Update trouble shooting page (#3994)
+- Accelerate PAA training speed (#3985)
+- Support batch_size > 1 in validation (#3966)
+- Use RoIAlign implemented in MMCV for inference in CPU mode (#3930)
+- Documentation refactoring (#4031)
+
+### v2.5.0 (5/10/2020)
+
+#### Highlights
+
+- Support new methods: [YOLACT](https://arxiv.org/abs/1904.02689), [CentripetalNet](https://arxiv.org/abs/2003.09119).
+- Add more documentations for easier and more clear usage.
+
+#### Backwards Incompatible Changes
+
+__FP16 related methods are imported from mmcv instead of mmdet. (#3766, #3822)__
+Mixed precision training utils in `mmdet.core.fp16` are moved to `mmcv.runner`, including `force_fp32`, `auto_fp16`, `wrap_fp16_model`, and `Fp16OptimizerHook`. A deprecation warning will be raised if users attempt to import those methods from `mmdet.core.fp16`, and will be finally removed in V2.10.0.
+
+__\[0, N-1\] represents foreground classes and N indicates background classes for all models. (#3221)__
+Before v2.5.0, the background label for RPN is 0, and N for other heads. Now the behavior is consistent for all models. Thus `self.background_labels` in `dense_heads` is removed and all heads use `self.num_classes` to indicate the class index of background labels.
+This change has no effect on the pre-trained models in the v2.x model zoo, but will affect the training of all models with RPN heads. Two-stage detectors whose RPN head uses softmax will be affected because the order of categories is changed.
+
+**Only call `get_subset_by_classes` when `test_mode=True` and `self.filter_empty_gt=True` (#3695)**
+Function `get_subset_by_classes` in dataset is refactored and only filters out images when `test_mode=True` and `self.filter_empty_gt=True`.
+In the original implementation, `get_subset_by_classes` is not related to the flag `self.filter_empty_gt` and will only be called when the classes is set during initialization no matter `test_mode` is `True` or `False`. This brings ambiguous behavior and potential bugs in many cases. After v2.5.0, if `filter_empty_gt=False`, no matter whether the classes are specified in a dataset, the dataset will use all the images in the annotations. If `filter_empty_gt=True` and `test_mode=True`, no matter whether the classes are specified, the dataset will call \`\`get_subset_by_classes\` to check the images and filter out images containing no GT boxes. Therefore, the users should be responsible for the data filtering/cleaning process for the test dataset.
+
+#### New Features
+
+- Test time augmentation for single stage detectors (#3844, #3638)
+- Support to show the name of experiments during training (#3764)
+- Add `Shear`, `Rotate`, `Translate` Augmentation (#3656, #3619, #3687)
+- Add image-only transformations including `Constrast`, `Equalize`, `Color`, and `Brightness`. (#3643)
+- Support [YOLACT](https://arxiv.org/abs/1904.02689) (#3456)
+- Support [CentripetalNet](https://arxiv.org/abs/2003.09119) (#3390)
+- Support PyTorch 1.6 in docker (#3905)
+
+#### Bug Fixes
+
+- Fix the bug of training ATSS when there is no ground truth boxes (#3702)
+- Fix the bug of using Focal Loss when there is `num_pos` is 0 (#3702)
+- Fix the label index mapping in dataset browser (#3708)
+- Fix Mask R-CNN training stuck problem when their is no positive rois (#3713)
+- Fix the bug of `self.rpn_head.test_cfg` in `RPNTestMixin` by using `self.rpn_head` in rpn head (#3808)
+- Fix deprecated `Conv2d` from mmcv.ops (#3791)
+- Fix device bug in RepPoints (#3836)
+- Fix SABL validating bug (#3849)
+- Use `https://download.openmmlab.com/mmcv/dist/index.html` for installing MMCV (#3840)
+- Fix nonzero in NMS for PyTorch 1.6.0 (#3867)
+- Fix the API change bug of PAA (#3883)
+- Fix typo in bbox_flip (#3886)
+- Fix cv2 import error of ligGL.so.1 in Dockerfile (#3891)
+
+#### Improvements
+
+- Change to use `mmcv.utils.collect_env` for collecting environment information to avoid duplicate codes (#3779)
+- Update checkpoint file names to v2.0 models in documentation (#3795)
+- Update tutorials for changing runtime settings (#3778), modifying loss (#3777)
+- Improve the function of `simple_test_bboxes` in SABL (#3853)
+- Convert mask to bool before using it as img's index for robustness and speedup (#3870)
+- Improve documentation of modules and dataset customization (#3821)
+
+### v2.4.0 (5/9/2020)
+
+__Highlights__
+
+- Fix lots of issues/bugs and reorganize the trouble shooting page
+- Support new methods [SABL](https://arxiv.org/abs/1912.04260), [YOLOv3](https://arxiv.org/abs/1804.02767), and [PAA Assign](https://arxiv.org/abs/2007.08103)
+- Support Batch Inference
+- Start to publish `mmdet` package to PyPI since v2.3.0
+- Switch model zoo to download.openmmlab.com
+
+__Backwards Incompatible Changes__
+
+- Support Batch Inference (#3564, #3686, #3705): Since v2.4.0, MMDetection could inference model with multiple images in a single GPU.
+  This change influences all the test APIs in MMDetection and downstream codebases. To help the users migrate their code, we use `replace_ImageToTensor` (#3686) to convert legacy test data pipelines during dataset initialization.
+- Support RandomFlip with horizontal/vertical/diagonal direction (#3608): Since v2.4.0, MMDetection supports horizontal/vertical/diagonal flip in the data augmentation. This influences bounding box, mask, and image transformations in data augmentation process and the process that will map those data back to the original format.
+- Migrate to use `mmlvis` and `mmpycocotools` for COCO and LVIS dataset (#3727). The APIs are fully compatible with the original `lvis` and `pycocotools`. Users need to uninstall the existing pycocotools and lvis packages in their environment first and install `mmlvis` & `mmpycocotools`.
+
+__Bug Fixes__
+
+- Fix default mean/std for onnx (#3491)
+- Fix coco evaluation and add metric items (#3497)
+- Fix typo for install.md (#3516)
+- Fix atss when sampler per gpu is 1 (#3528)
+- Fix import of fuse_conv_bn (#3529)
+- Fix bug of gaussian_target, update unittest of heatmap (#3543)
+- Fixed VOC2012 evaluate (#3553)
+- Fix scale factor bug of rescale (#3566)
+- Fix with_xxx_attributes in base detector (#3567)
+- Fix boxes scaling when number is 0 (#3575)
+- Fix rfp check when neck config is a list (#3591)
+- Fix import of fuse conv bn in benchmark.py (#3606)
+- Fix webcam demo (#3634)
+- Fix typo and itemize issues in tutorial (#3658)
+- Fix error in distributed training when some levels of FPN are not assigned with bounding boxes (#3670)
+- Fix the width and height orders of stride in valid flag generation (#3685)
+- Fix weight initialization bug in Res2Net DCN (#3714)
+- Fix bug in OHEMSampler (#3677)
+
+__New Features__
+
+- Support Cutout augmentation (#3521)
+- Support evaluation on multiple datasets through ConcatDataset (#3522)
+- Support [PAA assign](https://arxiv.org/abs/2007.08103) #(3547)
+- Support eval metric with pickle results (#3607)
+- Support [YOLOv3](https://arxiv.org/abs/1804.02767) (#3083)
+- Support [SABL](https://arxiv.org/abs/1912.04260) (#3603)
+- Support to publish to Pypi in github-action (#3510)
+- Support custom imports (#3641)
+
+__Improvements__
+
+- Refactor common issues in documentation (#3530)
+- Add pytorch 1.6 to CI config (#3532)
+- Add config to runner meta (#3534)
+- Add eval-option flag for testing (#3537)
+- Add init_eval to evaluation hook (#3550)
+- Add include_bkg in ClassBalancedDataset (#3577)
+- Using config's loading in inference_detector (#3611)
+- Add ATSS ResNet-101 models in model zoo (#3639)
+- Update urls to download.openmmlab.com (#3665)
+- Support non-mask training for CocoDataset (#3711)
+
+### v2.3.0 (5/8/2020)
+
+__Highlights__
+
+- The CUDA/C++ operators have been moved to `mmcv.ops`. For backward compatibility `mmdet.ops` is kept as warppers of `mmcv.ops`.
+- Support new methods [CornerNet](https://arxiv.org/abs/1808.01244), [DIOU](https://arxiv.org/abs/1911.08287)/[CIOU](https://arxiv.org/abs/2005.03572) loss, and new dataset: [LVIS V1](https://arxiv.org/abs/1908.03195)
+- Provide more detailed colab training tutorials and more complete documentation.
+- Support to convert RetinaNet from Pytorch to ONNX.
+
+__Bug Fixes__
+
+- Fix the model initialization bug of DetectoRS (#3187)
+- Fix the bug of module names in NASFCOSHead (#3205)
+- Fix the filename bug in publish_model.py (#3237)
+- Fix the dimensionality bug when `inside_flags.any()` is `False` in dense heads (#3242)
+- Fix the bug of forgetting to pass flip directions in `MultiScaleFlipAug` (#3262)
+- Fixed the bug caused by default value of `stem_channels` (#3333)
+- Fix the bug of model checkpoint loading for CPU inference (#3318, #3316)
+- Fix topk bug when box number is smaller than the expected topk number in ATSSAssigner (#3361)
+- Fix the gt priority bug in center_region_assigner.py (#3208)
+- Fix NaN issue of iou calculation in iou_loss.py (#3394)
+- Fix the bug that `iou_thrs` is not actually used during evaluation in coco.py (#3407)
+- Fix test-time augmentation of RepPoints (#3435)
+- Fix runtimeError caused by incontiguous tensor in Res2Net+DCN (#3412)
+
+__New Features__
+
+- Support [CornerNet](https://arxiv.org/abs/1808.01244) (#3036)
+- Support [DIOU](https://arxiv.org/abs/1911.08287)/[CIOU](https://arxiv.org/abs/2005.03572) loss (#3151)
+- Support [LVIS V1](https://arxiv.org/abs/1908.03195) dataset (#)
+- Support customized hooks in training (#3395)
+- Support fp16 training of generalized focal loss (#3410)
+- Support to convert RetinaNet from Pytorch to ONNX (#3075)
+
+__Improvements__
+
+- Support to process ignore boxes in ATSS assigner (#3082)
+- Allow to crop images without ground truth in `RandomCrop` (#3153)
+- Enable the the `Accuracy` module to set threshold (#3155)
+- Refactoring unit tests (#3206)
+- Unify the training settings of `to_float32` and `norm_cfg` in RegNets configs (#3210)
+- Add colab training tutorials for beginners (#3213, #3273)
+- Move CUDA/C++ operators into `mmcv.ops` and keep `mmdet.ops` as warppers for backward compatibility (#3232)(#3457)
+- Update installation scripts in documentation (#3290) and dockerfile (#3320)
+- Support to set image resize backend (#3392)
+- Remove git hash in version file (#3466)
+- Check mmcv version to force version compatibility (#3460)
+
+### v2.2.0 (1/7/2020)
+
+__Highlights__
+
+- Support new methods: [DetectoRS](https://arxiv.org/abs/2006.02334), [PointRend](https://arxiv.org/abs/1912.08193), [Generalized Focal Loss](https://arxiv.org/abs/2006.04388), [Dynamic R-CNN](https://arxiv.org/abs/2004.06002)
+
+__Bug Fixes__
+
+- Fix FreeAnchor when no gt in image (#3176)
+- Clean up deprecated usage of `register_module()` (#3092, #3161)
+- Fix pretrain bug in NAS FCOS (#3145)
+- Fix `num_classes` in SSD (#3142)
+- Fix FCOS warmup (#3119)
+- Fix `rstrip` in `tools/publish_model.py`
+- Fix `flip_ratio` default value in RandomFLip pipeline (#3106)
+- Fix cityscapes eval with ms_rcnn (#3112)
+- Fix RPN softmax (#3056)
+- Fix filename of LVIS@v0.5 (#2998)
+- Fix nan loss by filtering out-of-frame gt_bboxes in COCO (#2999)
+- Fix bug in FSAF (#3018)
+- Add FocalLoss `num_classes` check (#2964)
+- Fix PISA Loss when there are no gts (#2992)
+- Avoid nan in `iou_calculator` (#2975)
+- Prevent possible bugs in loading and transforms caused by shallow copy (#2967)
+
+__New Features__
+
+- Add DetectoRS (#3064)
+- Support Generalize Focal Loss (#3097)
+- Support PointRend (#2752)
+- Support Dynamic R-CNN (#3040)
+- Add DeepFashion dataset (#2968)
+- Implement FCOS training tricks (#2935)
+- Use BaseDenseHead as base class for anchor-base heads (#2963)
+- Add `with_cp` for BasicBlock (#2891)
+- Add `stem_channels` argument for ResNet (#2954)
+
+__Improvements__
+
+- Add anchor free base head (#2867)
+- Migrate to github action (#3137)
+- Add docstring for datasets, pipelines, core modules and methods (#3130, #3125, #3120)
+- Add VOC benchmark (#3060)
+- Add `concat` mode in GRoI (#3098)
+- Remove cmd arg `autorescale-lr` (#3080)
+- Use `len(data['img_metas'])` to indicate `num_samples` (#3073, #3053)
+- Switch to EpochBasedRunner (#2976)
+
+### v2.1.0 (8/6/2020)
+
+__Highlights__
+
+- Support new backbones: [RegNetX](https://arxiv.org/abs/2003.13678), [Res2Net](https://arxiv.org/abs/1904.01169)
+- Support new methods: [NASFCOS](https://arxiv.org/abs/1906.04423), [PISA](https://arxiv.org/abs/1904.04821), [GRoIE](https://arxiv.org/abs/2004.13665)
+- Support new dataset: [LVIS](https://arxiv.org/abs/1908.03195)
+
+__Bug Fixes__
+
+- Change the CLI argument `--validate` to `--no-validate` to enable validation after training epochs by default. (#2651)
+- Add missing cython to docker file (#2713)
+- Fix bug in nms cpu implementation (#2754)
+- Fix bug when showing mask results (#2763)
+- Fix gcc requirement (#2806)
+- Fix bug in async test (#2820)
+- Fix mask encoding-decoding bugs in test API (#2824)
+- Fix bug in test time augmentation (#2858, #2921, #2944)
+- Fix a typo in comment of apis/train (#2877)
+- Fix the bug of returning None when no gt bboxes are in the original image in `RandomCrop`. Fix the bug that misses to handle `gt_bboxes_ignore`, `gt_label_ignore`, and `gt_masks_ignore` in `RandomCrop`, `MinIoURandomCrop` and `Expand` modules. (#2810)
+- Fix bug of `base_channels` of regnet (#2917)
+- Fix the bug of logger when loading pre-trained weights in base detector (#2936)
+
+__New Features__
+
+- Add IoU models (#2666)
+- Add colab demo for inference
+- Support class agnostic nms (#2553)
+- Add benchmark gathering scripts for development only (#2676)
+- Add mmdet-based project links (#2736, #2767, #2895)
+- Add config dump in training (#2779)
+- Add ClassBalancedDataset (#2721)
+- Add res2net backbone (#2237)
+- Support RegNetX models (#2710)
+- Use `mmcv.FileClient` to support different storage backends (#2712)
+- Add ClassBalancedDataset (#2721)
+- Code Release: Prime Sample Attention in Object Detection (CVPR 2020) (#2626)
+- Implement NASFCOS (#2682)
+- Add class weight in CrossEntropyLoss (#2797)
+- Support LVIS dataset (#2088)
+- Support GRoIE (#2584)
+
+__Improvements__
+
+- Allow different x and y strides in anchor heads. (#2629)
+- Make FSAF loss more robust to no gt (#2680)
+- Compute pure inference time instead (#2657) and update inference speed (#2730)
+- Avoided the possibility that a patch with 0 area is cropped. (#2704)
+- Add warnings when deprecated `imgs_per_gpu` is used. (#2700)
+- Add a mask rcnn example for config (#2645)
+- Update model zoo (#2762, #2866, #2876, #2879, #2831)
+- Add `ori_filename` to img_metas and use it in test show-dir (#2612)
+- Use `img_fields` to handle multiple images during image transform (#2800)
+- Add upsample_cfg support in FPN (#2787)
+- Add `['img']` as default `img_fields` for back compatibility (#2809)
+- Rename the pretrained model from `open-mmlab://resnet50_caffe` and `open-mmlab://resnet50_caffe_bgr` to `open-mmlab://detectron/resnet50_caffe` and `open-mmlab://detectron2/resnet50_caffe`. (#2832)
+- Added sleep(2) in test.py to reduce hanging problem (#2847)
+- Support `c10::half` in CARAFE (#2890)
+- Improve documentations (#2918, #2714)
+- Use optimizer constructor in mmcv and clean the original implementation in `mmdet.core.optimizer` (#2947)
+
+### v2.0.0 (6/5/2020)
+
+In this release, we made lots of major refactoring and modifications.
+
+1. __Faster speed__. We optimize the training and inference speed for common models, achieving up to 30% speedup for training and 25% for inference. Please refer to [model zoo](model_zoo.md#comparison-with-detectron2) for details.
+
+2. __Higher performance__. We change some default hyperparameters with no additional cost, which leads to a gain of performance for most models. Please refer to [compatibility](compatibility.md#training-hyperparameters) for details.
+
+3. __More documentation and tutorials__. We add a bunch of documentation and tutorials to help users get started more smoothly. Read it [here](https://mmdetection.readthedocs.io/en/latest/).
+
+4. __Support PyTorch 1.5__. The support for 1.1 and 1.2 is dropped, and we switch to some new APIs.
+
+5. __Better configuration system__. Inheritance is supported to reduce the redundancy of configs.
+
+6. __Better modular design__. Towards the goal of simplicity and flexibility, we simplify some encapsulation while add more other configurable modules like BBoxCoder, IoUCalculator, OptimizerConstructor, RoIHead. Target computation is also included in heads and the call hierarchy is simpler.
+
+7. Support new methods: [FSAF](https://arxiv.org/abs/1903.00621) and PAFPN (part of [PAFPN](https://arxiv.org/abs/1803.01534)).
+
+__Breaking Changes__
+Models training with MMDetection 1.x are not fully compatible with 2.0, please refer to the [compatibility doc](compatibility.md) for the details and how to migrate to the new version.
+
+__Improvements__
+
+- Unify cuda and cpp API for custom ops. (#2277)
+- New config files with inheritance. (#2216)
+- Encapsulate the second stage into RoI heads. (#1999)
+- Refactor GCNet/EmpericalAttention into plugins. (#2345)
+- Set low quality match as an option in IoU-based bbox assigners. (#2375)
+- Change the codebase's coordinate system. (#2380)
+- Refactor the category order in heads. 0 means the first positive class instead of background now. (#2374)
+- Add bbox sampler and assigner registry. (#2419)
+- Speed up the inference of RPN. (#2420)
+- Add `train_cfg` and `test_cfg` as class members in all anchor heads. (#2422)
+- Merge target computation methods into heads. (#2429)
+- Add bbox coder to support different bbox encoding and losses. (#2480)
+- Unify the API for regression loss. (#2156)
+- Refactor Anchor Generator. (#2474)
+- Make `lr` an optional argument for optimizers. (#2509)
+- Migrate to modules and methods in MMCV. (#2502, #2511, #2569, #2572)
+- Support PyTorch 1.5. (#2524)
+- Drop the support for Python 3.5 and use F-string in the codebase. (#2531)
+
+__Bug Fixes__
+
+- Fix the scale factors for resized images without keep the aspect ratio. (#2039)
+- Check if max_num > 0 before slicing in NMS. (#2486)
+- Fix Deformable RoIPool when there is no instance. (#2490)
+- Fix the default value of assigned labels. (#2536)
+- Fix the evaluation of Cityscapes. (#2578)
+
+__New Features__
+
+- Add deep_stem and avg_down option to ResNet, i.e., support ResNetV1d. (#2252)
+- Add L1 loss. (#2376)
+- Support both polygon and bitmap for instance masks. (#2353, #2540)
+- Support CPU mode for inference. (#2385)
+- Add optimizer constructor for complicated configuration of optimizers. (#2397, #2488)
+- Implement PAFPN. (#2392)
+- Support empty tensor input for some modules. (#2280)
+- Support for custom dataset classes without overriding it. (#2408, #2443)
+- Support to train subsets of coco dataset. (#2340)
+- Add iou_calculator to potentially support more IoU calculation methods. (2405)
+- Support class wise mean AP (was removed in the last version). (#2459)
+- Add option to save the testing result images. (#2414)
+- Support MomentumUpdaterHook. (#2571)
+- Add a demo to inference a single image. (#2605)
+
+### v1.1.0 (24/2/2020)
+
+__Highlights__
+
+- Dataset evaluation is rewritten with a unified api, which is used by both evaluation hooks and test scripts.
+- Support new methods: [CARAFE](https://arxiv.org/abs/1905.02188).
+
+__Breaking Changes__
+
+- The new MMDDP inherits from the official DDP, thus the `__init__` api is changed to be the same as official DDP.
+- The `mask_head` field in HTC config files is modified.
+- The evaluation and testing script is updated.
+- In all transforms, instance masks are stored as a numpy array shaped (n, h, w) instead of a list of (h, w) arrays, where n is the number of instances.
+
+__Bug Fixes__
+
+- Fix IOU assigners when ignore_iof_thr > 0 and there is no pred boxes. (#2135)
+- Fix mAP evaluation when there are no ignored boxes. (#2116)
+- Fix the empty RoI input for Deformable RoI Pooling. (#2099)
+- Fix the dataset settings for multiple workflows. (#2103)
+- Fix the warning related to `torch.uint8` in PyTorch 1.4. (#2105)
+- Fix the inference demo on devices other than gpu:0. (#2098)
+- Fix Dockerfile. (#2097)
+- Fix the bug that `pad_val` is unused in Pad transform. (#2093)
+- Fix the albumentation transform when there is no ground truth bbox. (#2032)
+
+__Improvements__
+
+- Use torch instead of numpy for random sampling. (#2094)
+- Migrate to the new MMDDP implementation in MMCV v0.3. (#2090)
+- Add meta information in logs. (#2086)
+- Rewrite Soft NMS with pytorch extension and remove cython as a dependency. (#2056)
+- Rewrite dataset evaluation. (#2042, #2087, #2114, #2128)
+- Use numpy array for masks in transforms. (#2030)
+
+__New Features__
+
+- Implement "CARAFE: Content-Aware ReAssembly of FEatures". (#1583)
+- Add `worker_init_fn()` in data_loader when seed is set. (#2066, #2111)
+- Add logging utils. (#2035)
+
+### v1.0.0 (30/1/2020)
+
+This release mainly improves the code quality and add more docstrings.
+
+__Highlights__
+
+- Documentation is online now: <https://mmdetection.readthedocs.io>.
+- Support new models: [ATSS](https://arxiv.org/abs/1912.02424).
+- DCN is now available with the api `build_conv_layer` and `ConvModule` like the normal conv layer.
+- A tool to collect environment information is available for trouble shooting.
+
+__Bug Fixes__
+
+- Fix the incompatibility of the latest numpy and pycocotools. (#2024)
+- Fix the case when distributed package is unavailable, e.g., on Windows. (#1985)
+- Fix the dimension issue for `refine_bboxes()`. (#1962)
+- Fix the typo when `seg_prefix` is a list. (#1906)
+- Add segmentation map cropping to RandomCrop. (#1880)
+- Fix the return value of `ga_shape_target_single()`. (#1853)
+- Fix the loaded shape of empty proposals. (#1819)
+- Fix the mask data type when using albumentation. (#1818)
+
+__Improvements__
+
+- Enhance AssignResult and SamplingResult. (#1995)
+- Add ability to overwrite existing module in Registry. (#1982)
+- Reorganize requirements and make albumentations and imagecorruptions optional. (#1969)
+- Check NaN in `SSDHead`. (#1935)
+- Encapsulate the DCN in ResNe(X)t into a ConvModule & Conv_layers. (#1894)
+- Refactoring for mAP evaluation and support multiprocessing and logging. (#1889)
+- Init the root logger before constructing Runner to log more information. (#1865)
+- Split `SegResizeFlipPadRescale` into different existing transforms. (#1852)
+- Move `init_dist()` to MMCV. (#1851)
+- Documentation and docstring improvements. (#1971, #1938, #1869, #1838)
+- Fix the color of the same class for mask visualization. (#1834)
+- Remove the option `keep_all_stages` in HTC and Cascade R-CNN. (#1806)
+
+__New Features__
+
+- Add two test-time options `crop_mask` and `rle_mask_encode` for mask heads. (#2013)
+- Support loading grayscale images as single channel. (#1975)
+- Implement "Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection". (#1872)
+- Add sphinx generated docs. (#1859, #1864)
+- Add GN support for flops computation. (#1850)
+- Collect env info for trouble shooting. (#1812)
+
+### v1.0rc1 (13/12/2019)
+
+The RC1 release mainly focuses on improving the user experience, and fixing bugs.
+
+__Highlights__
+
+- Support new models: [FoveaBox](https://arxiv.org/abs/1904.03797), [RepPoints](https://arxiv.org/abs/1904.11490) and [FreeAnchor](https://arxiv.org/abs/1909.02466).
+- Add a Dockerfile.
+- Add a jupyter notebook demo and a webcam demo.
+- Setup the code style and CI.
+- Add lots of docstrings and unit tests.
+- Fix lots of bugs.
+
+__Breaking Changes__
+
+- There was a bug for computing COCO-style mAP w.r.t different scales (AP_s, AP_m, AP_l), introduced by #621. (#1679)
+
+__Bug Fixes__
+
+- Fix a sampling interval bug in Libra R-CNN. (#1800)
+- Fix the learning rate in SSD300 WIDER FACE. (#1781)
+- Fix the scaling issue when `keep_ratio=False`. (#1730)
+- Fix typos. (#1721, #1492, #1242, #1108, #1107)
+- Fix the shuffle argument in `build_dataloader`. (#1693)
+- Clip the proposal when computing mask targets. (#1688)
+- Fix the "index out of range" bug for samplers in some corner cases. (#1610, #1404)
+- Fix the NMS issue on devices other than GPU:0. (#1603)
+- Fix SSD Head and GHM Loss on CPU. (#1578)
+- Fix the OOM error when there are too many gt bboxes. (#1575)
+- Fix the wrong keyword argument `nms_cfg` in HTC. (#1573)
+- Process masks and semantic segmentation in Expand and MinIoUCrop transforms. (#1550, #1361)
+- Fix a scale bug in the Non Local op. (#1528)
+- Fix a bug in transforms when `gt_bboxes_ignore` is None. (#1498)
+- Fix a bug when `img_prefix` is None. (#1497)
+- Pass the device argument to `grid_anchors` and `valid_flags`. (#1478)
+- Fix the data pipeline for test_robustness. (#1476)
+- Fix the argument type of deformable pooling. (#1390)
+- Fix the coco_eval when there are only two classes. (#1376)
+- Fix a bug in Modulated DeformableConv when deformable_group>1. (#1359)
+- Fix the mask cropping in RandomCrop. (#1333)
+- Fix zero outputs in DeformConv when not running on cuda:0. (#1326)
+- Fix the type issue in Expand. (#1288)
+- Fix the inference API. (#1255)
+- Fix the inplace operation in Expand. (#1249)
+- Fix the from-scratch training config. (#1196)
+- Fix inplace add in RoIExtractor which cause an error in PyTorch 1.2. (#1160)
+- Fix FCOS when input images has no positive sample. (#1136)
+- Fix recursive imports. (#1099)
+
+__Improvements__
+
+- Print the config file and mmdet version in the log. (#1721)
+- Lint the code before compiling in travis CI. (#1715)
+- Add a probability argument for the `Expand` transform. (#1651)
+- Update the PyTorch and CUDA version in the docker file. (#1615)
+- Raise a warning when specifying `--validate` in non-distributed training. (#1624, #1651)
+- Beautify the mAP printing. (#1614)
+- Add pre-commit hook. (#1536)
+- Add the argument `in_channels` to backbones. (#1475)
+- Add lots of docstrings and unit tests, thanks to [@Erotemic](https://github.com/Erotemic). (#1603, #1517, #1506, #1505, #1491, #1479, #1477, #1475, #1474)
+- Add support for multi-node distributed test when there is no shared storage. (#1399)
+- Optimize Dockerfile to reduce the image size. (#1306)
+- Update new results of HRNet. (#1284, #1182)
+- Add an argument `no_norm_on_lateral` in FPN. (#1240)
+- Test the compiling in CI. (#1235)
+- Move docs to a separate folder. (#1233)
+- Add a jupyter notebook demo. (#1158)
+- Support different type of dataset for training. (#1133)
+- Use int64_t instead of long in cuda kernels. (#1131)
+- Support unsquare RoIs for bbox and mask heads. (#1128)
+- Manually add type promotion to make compatible to PyTorch 1.2. (#1114)
+- Allowing validation dataset for computing validation loss. (#1093)
+- Use `.scalar_type()` instead of `.type()` to suppress some warnings. (#1070)
+
+__New Features__
+
+- Add an option `--with_ap` to compute the AP for each class. (#1549)
+- Implement "FreeAnchor: Learning to Match Anchors for Visual Object Detection". (#1391)
+- Support [Albumentations](https://github.com/albumentations-team/albumentations) for augmentations in the data pipeline. (#1354)
+- Implement "FoveaBox: Beyond Anchor-based Object Detector". (#1339)
+- Support horizontal and vertical flipping. (#1273, #1115)
+- Implement "RepPoints: Point Set Representation for Object Detection". (#1265)
+- Add test-time augmentation to HTC and Cascade R-CNN. (#1251)
+- Add a COCO result analysis tool. (#1228)
+- Add Dockerfile. (#1168)
+- Add a webcam demo. (#1155, #1150)
+- Add FLOPs counter. (#1127)
+- Allow arbitrary layer order for ConvModule. (#1078)
+
+### v1.0rc0 (27/07/2019)
+
+- Implement lots of new methods and components (Mixed Precision Training, HTC, Libra R-CNN, Guided Anchoring, Empirical Attention, Mask Scoring R-CNN, Grid R-CNN (Plus), GHM, GCNet, FCOS, HRNet, Weight Standardization, etc.). Thank all collaborators!
+- Support two additional datasets: WIDER FACE and Cityscapes.
+- Refactoring for loss APIs and make it more flexible to adopt different losses and related hyper-parameters.
+- Speed up multi-gpu testing.
+- Integrate all compiling and installing in a single script.
+
+### v0.6.0 (14/04/2019)
+
+- Up to 30% speedup compared to the model zoo.
+- Support both PyTorch stable and nightly version.
+- Replace NMS and SigmoidFocalLoss with Pytorch CUDA extensions.
+
+### v0.6rc0(06/02/2019)
+
+- Migrate to PyTorch 1.0.
+
+### v0.5.7 (06/02/2019)
+
+- Add support for Deformable ConvNet v2. (Many thanks to the authors and [@chengdazhi](https://github.com/chengdazhi))
+- This is the last release based on PyTorch 0.4.1.
+
+### v0.5.6 (17/01/2019)
+
+- Add support for Group Normalization.
+- Unify RPNHead and single stage heads (RetinaHead, SSDHead) with AnchorHead.
+
+### v0.5.5 (22/12/2018)
+
+- Add SSD for COCO and PASCAL VOC.
+- Add ResNeXt backbones and detection models.
+- Refactoring for Samplers/Assigners and add OHEM.
+- Add VOC dataset and evaluation scripts.
+
+### v0.5.4 (27/11/2018)
+
+- Add SingleStageDetector and RetinaNet.
+
+### v0.5.3 (26/11/2018)
+
+- Add Cascade R-CNN and Cascade Mask R-CNN.
+- Add support for Soft-NMS in config files.
+
+### v0.5.2 (21/10/2018)
+
+- Add support for custom datasets.
+- Add a script to convert PASCAL VOC annotations to the expected format.
+
+### v0.5.1 (20/10/2018)
+
+- Add BBoxAssigner and BBoxSampler, the `train_cfg` field in config files are restructured.
+- `ConvFCRoIHead` / `SharedFCRoIHead` are renamed to `ConvFCBBoxHead` / `SharedFCBBoxHead` for consistency.
diff --git a/docs/en/compatibility.md b/docs/en/compatibility.md
new file mode 100644
index 0000000..a545a49
--- /dev/null
+++ b/docs/en/compatibility.md
@@ -0,0 +1,178 @@
+# Compatibility of MMDetection 2.x
+
+## MMDetection 2.25.0
+
+In order to support Mask2Former for instance segmentation, the original config files of Mask2Former for panpotic segmentation need to be renamed [PR #7571](https://github.com/open-mmlab/mmdetection/pull/7571).
+
+<table align="center">
+    <thead>
+        <tr align='center'>
+            <td>before v2.25.0</td>
+            <td>after v2.25.0</td>
+        </tr>
+    </thead>
+    <tbody><tr valign='top'>
+    <th>
+
+```
+'mask2former_xxx_coco.py' represents config files for **panoptic segmentation**.
+```
+
+</th>
+    <th>
+
+```
+'mask2former_xxx_coco.py' represents config files for **instance segmentation**.
+'mask2former_xxx_coco-panoptic.py' represents config files for **panoptic segmentation**.
+```
+
+</th></tr>
+  </tbody></table>
+
+## MMDetection 2.21.0
+
+In order to support CPU training, the logic of scatter in batch collating has been changed. We recommend to use
+MMCV v1.4.4 or higher. For more details, please refer to [MMCV PR #1621](https://github.com/open-mmlab/mmcv/pull/1621).
+
+## MMDetection 2.18.1
+
+### MMCV compatibility
+
+In order to fix the wrong weight reference bug in BaseTransformerLayer, the logic in batch first mode of MultiheadAttention has been changed.
+We recommend to use MMCV v1.3.17 or higher. For more details, please refer to [MMCV PR #1418](https://github.com/open-mmlab/mmcv/pull/1418).
+
+## MMDetection 2.18.0
+
+### DIIHead compatibility
+
+In order to support QueryInst, attn_feats is added into the returned tuple of DIIHead.
+
+## MMDetection 2.14.0
+
+### MMCV Version
+
+In order to fix the problem that the priority of EvalHook is too low, all hook priorities have been re-adjusted in 1.3.8, so MMDetection 2.14.0 needs to rely on the latest MMCV 1.3.8 version. For related information, please refer to [#1120](https://github.com/open-mmlab/mmcv/pull/1120), for related issues, please refer to [#5343](https://github.com/open-mmlab/mmdetection/issues/5343).
+
+### SSD compatibility
+
+In v2.14.0, to make SSD more flexible to use, [PR5291](https://github.com/open-mmlab/mmdetection/pull/5291) refactored its backbone, neck and head. The users can use the script `tools/model_converters/upgrade_ssd_version.py` to convert their models.
+
+```bash
+python tools/model_converters/upgrade_ssd_version.py ${OLD_MODEL_PATH} ${NEW_MODEL_PATH}
+```
+
+- OLD_MODEL_PATH: the path to load the old version SSD model.
+- NEW_MODEL_PATH: the path to save the converted model weights.
+
+## MMDetection 2.12.0
+
+MMDetection is going through big refactoring for more general and convenient usages during the releases from v2.12.0 to v2.18.0 (maybe longer).
+In v2.12.0 MMDetection inevitably brings some BC-breakings, including the MMCV dependency, model initialization, model registry, and mask AP evaluation.
+
+### MMCV Version
+
+MMDetection v2.12.0 relies on the newest features in MMCV 1.3.3, including `BaseModule` for unified parameter initialization, model registry, and the CUDA operator `MultiScaleDeformableAttn` for [Deformable DETR](https://arxiv.org/abs/2010.04159). Note that MMCV 1.3.2 already contains all the features used by MMDet but has known issues. Therefore, we recommend users to skip MMCV v1.3.2 and use v1.3.2, though v1.3.2 might work for most of the cases.
+
+### Unified model initialization
+
+To unify the parameter initialization in OpenMMLab projects, MMCV supports `BaseModule` that accepts `init_cfg` to allow the modules' parameters initialized in a flexible and unified manner. Now the users need to explicitly call `model.init_weights()` in the training script to initialize the model (as in [here](https://github.com/open-mmlab/mmdetection/blob/master/tools/train.py#L162), previously this was handled by the detector. **The downstream projects must update their model initialization accordingly to use MMDetection v2.12.0**. Please refer to PR #4750 for details.
+
+### Unified model registry
+
+To easily use backbones implemented in other OpenMMLab projects, MMDetection v2.12.0 inherits the model registry created in MMCV (#760). In this way, as long as the backbone is supported in an OpenMMLab project and that project also uses the registry in MMCV, users can use that backbone in MMDetection by simply modifying the config without copying the code of that backbone into MMDetection. Please refer to PR #5059 for more details.
+
+### Mask AP evaluation
+
+Before [PR 4898](https://github.com/open-mmlab/mmdetection/pull/4898) and V2.12.0, the mask AP of small, medium, and large instances is calculated based on the bounding box area rather than the real mask area. This leads to higher `APs` and `APm` but lower `APl` but will not affect the overall mask AP. [PR 4898](https://github.com/open-mmlab/mmdetection/pull/4898) change it to use mask areas by deleting `bbox` in mask AP calculation.
+The new calculation does not affect the overall mask AP evaluation and is consistent with [Detectron2](https://github.com/facebookresearch/detectron2/).
+
+## Compatibility with MMDetection 1.x
+
+MMDetection 2.0 goes through a big refactoring and addresses many legacy issues. It is not compatible with the 1.x version, i.e., running inference with the same model weights in these two versions will produce different results. Thus, MMDetection 2.0 re-benchmarks all the models and provides their links and logs in the model zoo.
+
+The major differences are in four folds: coordinate system, codebase conventions, training hyperparameters, and modular design.
+
+### Coordinate System
+
+The new coordinate system is consistent with [Detectron2](https://github.com/facebookresearch/detectron2/) and treats the center of the most left-top pixel as (0, 0) rather than the left-top corner of that pixel.
+Accordingly, the system interprets the coordinates in COCO bounding box and segmentation annotations as coordinates in range `[0, width]` or `[0, height]`.
+This modification affects all the computation related to the bbox and pixel selection,
+which is more natural and accurate.
+
+- The height and width of a box with corners (x1, y1) and (x2, y2) in the new coordinate system is computed as `width = x2 - x1` and `height = y2 - y1`.
+  In MMDetection 1.x and previous version, a "+ 1" was added both height and width.
+  This modification are in three folds:
+
+  1. Box transformation and encoding/decoding in regression.
+  2. IoU calculation. This affects the matching process between ground truth and bounding box and the NMS process. The effect to compatibility is very negligible, though.
+  3. The corners of bounding box is in float type and no longer quantized. This should provide more accurate bounding box results. This also makes the bounding box and RoIs not required to have minimum size of 1, whose effect is small, though.
+
+- The anchors are center-aligned to feature grid points and in float type.
+  In MMDetection 1.x and previous version, the anchors are in `int` type and not center-aligned.
+  This affects the anchor generation in RPN and all the anchor-based methods.
+
+- ROIAlign is better aligned with the image coordinate system. The new implementation is adopted from [Detectron2](https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/ROIAlign).
+  The RoIs are shifted by half a pixel by default when they are used to cropping RoI features, compared to MMDetection 1.x.
+  The old behavior is still available by setting `aligned=False` instead of `aligned=True`.
+
+- Mask cropping and pasting are more accurate.
+
+  1. We use the new RoIAlign to crop mask targets. In MMDetection 1.x, the bounding box is quantized before it is used to crop mask target, and the crop process is implemented by numpy. In new implementation, the bounding box for crop is not quantized and sent to RoIAlign. This implementation accelerates the training speed by a large margin (~0.1s per iter, ~2 hour when training Mask R50 for 1x schedule) and should be more accurate.
+
+  2. In MMDetection 2.0, the "`paste_mask()`" function is different and should be more accurate than those in previous versions. This change follows the modification in [Detectron2](https://github.com/facebookresearch/detectron2/blob/master/detectron2/structures/masks.py) and can improve mask AP on COCO by ~0.5% absolute.
+
+### Codebase Conventions
+
+- MMDetection 2.0 changes the order of class labels to reduce unused parameters in regression and mask branch more naturally (without +1 and -1).
+  This effect all the classification layers of the model to have a different ordering of class labels. The final layers of regression branch and mask head no longer keep K+1 channels for K categories, and their class orders are consistent with the classification branch.
+
+  - In MMDetection 2.0, label "K" means background, and labels \[0, K-1\] correspond to the K = num_categories object categories.
+
+  - In MMDetection 1.x and previous version, label "0" means background, and labels \[1, K\] correspond to the K categories.
+
+  - **Note**: The class order of softmax RPN is still the same as that in 1.x in versions\<=2.4.0 while sigmoid RPN is not affected. The class orders in all heads are unified since MMDetection v2.5.0.
+
+- Low quality matching in R-CNN is not used. In MMDetection 1.x and previous versions, the `max_iou_assigner` will match low quality boxes for each ground truth box in both RPN and R-CNN training. We observe this sometimes does not assign the most perfect GT box to some bounding boxes,
+  thus MMDetection 2.0 do not allow low quality matching by default in R-CNN training in the new system. This sometimes may slightly improve the box AP (~0.1% absolute).
+
+- Separate scale factors for width and height. In MMDetection 1.x and previous versions, the scale factor is a single float in mode `keep_ratio=True`. This is slightly inaccurate because the scale factors for width and height have slight difference. MMDetection 2.0 adopts separate scale factors for width and height, the improvement on AP ~0.1% absolute.
+
+- Configs name conventions are changed. MMDetection V2.0 adopts the new name convention to maintain the gradually growing model zoo as the following:
+
+  ```shell
+  [model]_(model setting)_[backbone]_[neck]_(norm setting)_(misc)_(gpu x batch)_[schedule]_[dataset].py,
+  ```
+
+  where the (`misc`) includes DCN and GCBlock, etc. More details are illustrated in the [documentation for config](tutorials/config)
+
+- MMDetection V2.0 uses new ResNet Caffe backbones to reduce warnings when loading pre-trained models. Most of the new backbones' weights are the same as the former ones but do not have `conv.bias`, except that they use a different `img_norm_cfg`. Thus, the new backbone will not cause warning of unexpected keys.
+
+### Training Hyperparameters
+
+The change in training hyperparameters does not affect
+model-level compatibility but slightly improves the performance. The major ones are:
+
+- The number of proposals after nms is changed from 2000 to 1000 by setting `nms_post=1000` and `max_num=1000`.
+  This slightly improves both mask AP and bbox AP by ~0.2% absolute.
+
+- The default box regression losses for Mask R-CNN, Faster R-CNN and RetinaNet are changed from smooth L1 Loss to L1 loss. This leads to an overall improvement in box AP (~0.6% absolute). However, using L1-loss for other methods such as Cascade R-CNN and HTC does not improve the performance, so we keep the original settings for these methods.
+
+- The sample num of RoIAlign layer is set to be 0 for simplicity. This leads to slightly improvement on mask AP (~0.2% absolute).
+
+- The default setting does not use gradient clipping anymore during training for faster training speed. This does not degrade performance of the most of models. For some models such as RepPoints we keep using gradient clipping to stabilize the training process and to obtain better performance.
+
+- The default warmup ratio is changed from 1/3 to 0.001 for a more smooth warming up process since the gradient clipping is usually not used. The effect is found negligible during our re-benchmarking, though.
+
+### Upgrade Models from 1.x to 2.0
+
+To convert the models trained by MMDetection V1.x to MMDetection V2.0, the users can use the script `tools/model_converters/upgrade_model_version.py` to convert
+their models. The converted models can be run in MMDetection V2.0 with slightly dropped performance (less than 1% AP absolute).
+Details can be found in `configs/legacy`.
+
+## pycocotools compatibility
+
+`mmpycocotools` is the OpenMMlab's fork of official `pycocotools`, which works for both MMDetection and Detectron2.
+Before [PR 4939](https://github.com/open-mmlab/mmdetection/pull/4939), since `pycocotools` and `mmpycocotool` have the same package name, if users already installed `pycocotools` (installed Detectron2 first under the same environment), then the setup of MMDetection will skip installing `mmpycocotool`. Thus MMDetection fails due to the missing `mmpycocotools`.
+If MMDetection is installed before Detectron2, they could work under the same environment.
+[PR 4939](https://github.com/open-mmlab/mmdetection/pull/4939) deprecates mmpycocotools in favor of official pycocotools.
+Users may install MMDetection and Detectron2 under the same environment after [PR 4939](https://github.com/open-mmlab/mmdetection/pull/4939), no matter what the installation order is.
diff --git a/docs/en/conf.py b/docs/en/conf.py
new file mode 100644
index 0000000..e902e3f
--- /dev/null
+++ b/docs/en/conf.py
@@ -0,0 +1,116 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import subprocess
+import sys
+
+import pytorch_sphinx_theme
+
+sys.path.insert(0, os.path.abspath('../..'))
+
+# -- Project information -----------------------------------------------------
+
+project = 'MMDetection'
+copyright = '2018-2021, OpenMMLab'
+author = 'MMDetection Authors'
+version_file = '../../mmdet/version.py'
+
+
+def get_version():
+    with open(version_file, 'r') as f:
+        exec(compile(f.read(), version_file, 'exec'))
+    return locals()['__version__']
+
+
+# The full version, including alpha/beta/rc tags
+release = get_version()
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.napoleon',
+    'sphinx.ext.viewcode',
+    'myst_parser',
+    'sphinx_markdown_tables',
+    'sphinx_copybutton',
+]
+
+myst_enable_extensions = ['colon_fence']
+myst_heading_anchors = 3
+
+autodoc_mock_imports = [
+    'matplotlib', 'pycocotools', 'terminaltables', 'mmdet.version', 'mmcv.ops'
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+#
+source_suffix = {
+    '.rst': 'restructuredtext',
+    '.md': 'markdown',
+}
+
+# The master toctree document.
+master_doc = 'index'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+# html_theme = 'sphinx_rtd_theme'
+html_theme = 'pytorch_sphinx_theme'
+html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]
+
+html_theme_options = {
+    'menu': [
+        {
+            'name': 'GitHub',
+            'url': 'https://github.com/open-mmlab/mmdetection'
+        },
+    ],
+    # Specify the language of shared menu
+    'menu_lang':
+    'en'
+}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+html_css_files = ['css/readthedocs.css']
+
+# -- Extension configuration -------------------------------------------------
+# Ignore >>> when copying code
+copybutton_prompt_text = r'>>> |\.\.\. '
+copybutton_prompt_is_regexp = True
+
+
+def builder_inited_handler(app):
+    subprocess.run(['./stat.py'])
+
+
+def setup(app):
+    app.connect('builder-inited', builder_inited_handler)
diff --git a/docs/en/conventions.md b/docs/en/conventions.md
new file mode 100644
index 0000000..97e5fd0
--- /dev/null
+++ b/docs/en/conventions.md
@@ -0,0 +1,78 @@
+# Conventions
+
+Please check the following conventions if you would like to modify MMDetection as your own project.
+
+## Loss
+
+In MMDetection, a `dict` containing losses and metrics will be returned by `model(**data)`.
+
+For example, in bbox head,
+
+```python
+class BBoxHead(nn.Module):
+    ...
+    def loss(self, ...):
+        losses = dict()
+        # classification loss
+        losses['loss_cls'] = self.loss_cls(...)
+        # classification accuracy
+        losses['acc'] = accuracy(...)
+        # bbox regression loss
+        losses['loss_bbox'] = self.loss_bbox(...)
+        return losses
+```
+
+`bbox_head.loss()` will be called during model forward.
+The returned dict contains `'loss_bbox'`, `'loss_cls'`, `'acc'` .
+Only `'loss_bbox'`, `'loss_cls'` will be used during back propagation,
+`'acc'` will only be used as a metric to monitor training process.
+
+By default, only values whose keys contain `'loss'` will be back propagated.
+This behavior could be changed by modifying `BaseDetector.train_step()`.
+
+## Empty Proposals
+
+In MMDetection, We have added special handling and unit test for empty proposals of two-stage. We need to deal with the empty proposals of the entire batch and single image at the same time. For example, in CascadeRoIHead,
+
+```python
+# simple_test method
+...
+# There is no proposal in the whole batch
+if rois.shape[0] == 0:
+    bbox_results = [[
+        np.zeros((0, 5), dtype=np.float32)
+        for _ in range(self.bbox_head[-1].num_classes)
+    ]] * num_imgs
+    if self.with_mask:
+        mask_classes = self.mask_head[-1].num_classes
+        segm_results = [[[] for _ in range(mask_classes)]
+                        for _ in range(num_imgs)]
+        results = list(zip(bbox_results, segm_results))
+    else:
+        results = bbox_results
+    return results
+...
+
+# There is no proposal in the single image
+for i in range(self.num_stages):
+    ...
+    if i < self.num_stages - 1:
+          for j in range(num_imgs):
+                   # Handle empty proposal
+                   if rois[j].shape[0] > 0:
+                       bbox_label = cls_score[j][:, :-1].argmax(dim=1)
+                       refine_roi = self.bbox_head[i].regress_by_class(
+                            rois[j], bbox_label, bbox_pred[j], img_metas[j])
+                       refine_roi_list.append(refine_roi)
+```
+
+If you have customized `RoIHead`, you can refer to the above method to deal with empty proposals.
+
+## Coco Panoptic Dataset
+
+In MMDetection, we have supported COCO Panoptic dataset. We clarify a few conventions about the implementation of `CocoPanopticDataset` here.
+
+1. For mmdet\<=2.16.0, the range of foreground and background labels in semantic segmentation are different from the default setting of MMDetection. The label `0` stands for `VOID` label and the category labels start from `1`.
+   Since mmdet=2.17.0, the category labels of semantic segmentation start from `0` and label `255` stands for `VOID` for consistency with labels of bounding boxes.
+   To achieve that, the `Pad` pipeline supports setting the padding value for `seg`.
+2. In the evaluation, the panoptic result is a map with the same shape as the original image. Each value in the result map has the format of `instance_id * INSTANCE_OFFSET + category_id`.
diff --git a/docs/en/device/npu.md b/docs/en/device/npu.md
new file mode 100644
index 0000000..b0129b7
--- /dev/null
+++ b/docs/en/device/npu.md
@@ -0,0 +1,55 @@
+# NPU (HUAWEI Ascend)
+
+## Usage
+
+Please refer to the [building documentation of MMCV](https://mmcv.readthedocs.io/en/latest/get_started/build.html#build-mmcv-full-on-ascend-npu-machine) to install MMCV on NPU devices
+
+Here we use 8 NPUs on your computer to train the model with the following command:
+
+```shell
+bash tools/dist_train.sh configs/ssd/ssd300_coco.py 8
+```
+
+Also, you can use only one NPU to train the model with the following command:
+
+```shell
+python tools/train.py configs/ssd/ssd300_coco.py
+```
+
+## Models Results
+
+|        Model         | box AP | mask AP | Config                                                                                                                        | Download                                                                                                     |
+| :------------------: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------- |
+|     [ssd300](<>)     |  25.6  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd/ssd300_fp16_coco.py)                               | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/ssd300_coco.log.json)                              |
+|     [ssd512](<>)     |  29.4  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd/ssd512_fp16_coco.py)                               | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/ssd512_coco.log.json)                              |
+| [ssdlite-mbv2\*](<>) |  20.2  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py)          | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/ssdlite_mobilenetv2_scratch_600e_coco.log.json)    |
+| [retinanet-r18](<>)  |  31.8  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/retinanet_r18_fpn_1x8_1x_coco.py)            | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/retinanet_r18_fpn_1x8_1x_coco.log.json)            |
+| [retinanet-r50](<>)  |  36.6  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py)           | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/retinanet_r50_fpn_1x_coco.log.json)                |
+|   [yolov3-608](<>)   |  34.7  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py)         | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/yolov3_d53_fp16_mstrain-608_273e_coco.log.json)    |
+|  [yolox-s\*\*](<>)   |  39.9  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolox/yolox_s_8x8_300e_coco.py)                        | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/yolox_s_8x8_300e_coco.log.json)                    |
+| [centernet-r18](<>)  |  26.1  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/centernet/centernet_resnet18_140e_cocoo.py)            | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/centernet_resnet18_140e_coco.log.jsonn)            |
+|   [fcos-r50\*](<>)   |  36.1  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_fp16_1x_bs8x8_coco.py) | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/fcos_r50_caffe_fpn_gn-head_1x_coco_bs8x8.log.json) |
+|   [solov2-r50](<>)   |  ---   |  34.7   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/solov2/solov2_r50_fpn_1x_coco.py)                      | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/solov2_r50_fpn_1x_coco.log.json)                   |
+
+**Notes:**
+
+- If not specially marked, the results on NPU are the same as those on the GPU with FP32.
+- (\*) The results on the NPU of these models are aligned with the results of the mixed-precision training on the GPU,
+  but are lower than the results of the FP32. This situation is mainly related to the phase of the model itself in
+  mixed-precision training, users may need to adjust the hyperparameters to achieve better results.
+- (\*\*) The accuracy of yolox-s on the GPU in mixed precision is 40.1, with `persister_woker=True` in the data loader config by default.
+  There are currently some bugs on NPUs that prevent the last few epochs from running, but the accuracy is less affected and the difference can be ignored.
+
+## High-performance Model Result on Ascend Device
+
+Introduction to optimization:
+
+1. Modify the loop calculation as a whole batch calculation to reduce the number of instructions issued.
+2. Modify the index calculation to mask calculation, because the SIMD architecture is good at processing continuous data calculation.
+
+|           Model            |                                                          Config                                                           | v100 iter time |       910A iter time       |
+| :------------------------: | :-----------------------------------------------------------------------------------------------------------------------: | :------------: | :------------------------: |
+|    [ascend-ssd300](<>)     |          [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd/ascend_ssd300_fp16_coco.py)           |  0.165s/iter   | 0.383s/iter -> 0.13s/iter  |
+| [ascend-retinanet-r18](<>) | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/ascend_retinanet_r18_fpn_1x8_1x_coco.py) |  0.567s/iter   | 0.780s/iter -> 0.420s/iter |
+
+**All above models are provided by Huawei Ascend group.**
diff --git a/docs/en/faq.md b/docs/en/faq.md
new file mode 100644
index 0000000..b8afefc
--- /dev/null
+++ b/docs/en/faq.md
@@ -0,0 +1,240 @@
+# Frequently Asked Questions
+
+We list some common troubles faced by many users and their corresponding solutions here. Feel free to enrich the list if you find any frequent issues and have ways to help others to solve them. If the contents here do not cover your issue, please create an issue using the [provided templates](https://github.com/open-mmlab/mmdetection/blob/master/.github/ISSUE_TEMPLATE/error-report.md/) and make sure you fill in all required information in the template.
+
+## Installation
+
+- Compatibility issue between MMCV and MMDetection; "ConvWS is already registered in conv layer"; "AssertionError: MMCV==xxx is used but incompatible. Please install mmcv>=xxx, \<=xxx."
+
+  Compatible MMDetection and MMCV versions are shown as below. Please choose the correct version of MMCV to avoid installation issues.
+
+| MMDetection version |        MMCV version        |
+| :-----------------: | :------------------------: |
+|       master        | mmcv-full>=1.3.17, \<1.8.0 |
+|       2.28.2        | mmcv-full>=1.3.17, \<1.8.0 |
+|       2.28.1        | mmcv-full>=1.3.17, \<1.8.0 |
+|       2.28.0        | mmcv-full>=1.3.17, \<1.8.0 |
+|       2.27.0        | mmcv-full>=1.3.17, \<1.8.0 |
+|       2.26.0        | mmcv-full>=1.3.17, \<1.8.0 |
+|       2.25.3        | mmcv-full>=1.3.17, \<1.7.0 |
+|       2.25.2        | mmcv-full>=1.3.17, \<1.7.0 |
+|       2.25.1        | mmcv-full>=1.3.17, \<1.6.0 |
+|       2.25.0        | mmcv-full>=1.3.17, \<1.6.0 |
+|       2.24.1        | mmcv-full>=1.3.17, \<1.6.0 |
+|       2.24.0        | mmcv-full>=1.3.17, \<1.6.0 |
+|       2.23.0        | mmcv-full>=1.3.17, \<1.5.0 |
+|       2.22.0        | mmcv-full>=1.3.17, \<1.5.0 |
+|       2.21.0        | mmcv-full>=1.3.17, \<1.5.0 |
+|       2.20.0        | mmcv-full>=1.3.17, \<1.5.0 |
+|       2.19.1        | mmcv-full>=1.3.17, \<1.5.0 |
+|       2.19.0        | mmcv-full>=1.3.17, \<1.5.0 |
+|       2.18.0        | mmcv-full>=1.3.17, \<1.4.0 |
+|       2.17.0        | mmcv-full>=1.3.14, \<1.4.0 |
+|       2.16.0        | mmcv-full>=1.3.8, \<1.4.0  |
+|       2.15.1        | mmcv-full>=1.3.8, \<1.4.0  |
+|       2.15.0        | mmcv-full>=1.3.8, \<1.4.0  |
+|       2.14.0        | mmcv-full>=1.3.8, \<1.4.0  |
+|       2.13.0        | mmcv-full>=1.3.3, \<1.4.0  |
+|       2.12.0        | mmcv-full>=1.3.3, \<1.4.0  |
+|       2.11.0        | mmcv-full>=1.2.4, \<1.4.0  |
+|       2.10.0        | mmcv-full>=1.2.4, \<1.4.0  |
+|        2.9.0        | mmcv-full>=1.2.4, \<1.4.0  |
+|        2.8.0        | mmcv-full>=1.2.4, \<1.4.0  |
+|        2.7.0        | mmcv-full>=1.1.5, \<1.4.0  |
+|        2.6.0        | mmcv-full>=1.1.5, \<1.4.0  |
+|        2.5.0        | mmcv-full>=1.1.5, \<1.4.0  |
+|        2.4.0        | mmcv-full>=1.1.1, \<1.4.0  |
+|        2.3.0        |      mmcv-full==1.0.5      |
+|      2.3.0rc0       |      mmcv-full>=1.0.2      |
+|        2.2.1        |        mmcv==0.6.2         |
+|        2.2.0        |        mmcv==0.6.2         |
+|        2.1.0        |   mmcv>=0.5.9, \<=0.6.1    |
+|        2.0.0        |   mmcv>=0.5.1, \<=0.5.8    |
+
+- "No module named 'mmcv.ops'"; "No module named 'mmcv.\_ext'".
+
+  1. Uninstall existing mmcv in the environment using `pip uninstall mmcv`.
+  2. Install mmcv-full following the [installation instruction](get_started#best-practices).
+
+- Using albumentations
+
+  If you would like to use `albumentations`, we suggest using `pip install -r requirements/albu.txt` or
+  `pip install -U albumentations --no-binary qudida,albumentations`.
+  If you simply use `pip install albumentations>=0.3.2`, it will install `opencv-python-headless` simultaneously (even though you have already installed `opencv-python`).
+  Please refer to the [official documentation](https://albumentations.ai/docs/getting_started/installation/#note-on-opencv-dependencies) for details.
+
+- ModuleNotFoundError is raised when using some algorithms
+
+  Some extra dependencies are required for Instaboost, Panoptic Segmentation, LVIS dataset, etc. Please note the error message and install corresponding packages, e.g.,
+
+  ```shell
+  # for instaboost
+  pip install instaboostfast
+  # for panoptic segmentation
+  pip install git+https://github.com/cocodataset/panopticapi.git
+  # for LVIS dataset
+  pip install git+https://github.com/lvis-dataset/lvis-api.git
+  ```
+
+## Coding
+
+- Do I need to reinstall mmdet after some code modifications
+
+  If you follow the best practice and install mmdet with `pip install -e .`, any local modifications made to the code will take effect without reinstallation.
+
+- How to develop with multiple MMDetection versions
+
+  You can have multiple folders like mmdet-2.21, mmdet-2.22.
+  When you run the train or test script, it will adopt the mmdet package in the current folder.
+
+  To use the default MMDetection installed in the environment rather than the one you are working with, you can remove the following line in those scripts:
+
+  ```shell
+  PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
+  ```
+
+## PyTorch/CUDA Environment
+
+- "RTX 30 series card fails when building MMCV or MMDet"
+
+  1. Temporary work-around: do `MMCV_WITH_OPS=1 MMCV_CUDA_ARGS='-gencode=arch=compute_80,code=sm_80' pip install -e .`.
+     The common issue is `nvcc fatal : Unsupported gpu architecture 'compute_86'`. This means that the compiler should optimize for sm_86, i.e., nvidia 30 series card, but such optimizations have not been supported by CUDA toolkit 11.0.
+     This work-around modifies the compile flag by adding `MMCV_CUDA_ARGS='-gencode=arch=compute_80,code=sm_80'`, which tells `nvcc` to optimize for **sm_80**, i.e., Nvidia A100. Although A100 is different from the 30 series card, they use similar ampere architecture. This may hurt the performance but it works.
+  2. PyTorch developers have updated that the default compiler flags should be fixed by [pytorch/pytorch#47585](https://github.com/pytorch/pytorch/pull/47585). So using PyTorch-nightly may also be able to solve the problem, though we have not tested it yet.
+
+- "invalid device function" or "no kernel image is available for execution".
+
+  1. Check if your cuda runtime version (under `/usr/local/`), `nvcc --version` and `conda list cudatoolkit` version match.
+  2. Run `python mmdet/utils/collect_env.py` to check whether PyTorch, torchvision, and MMCV are built for the correct GPU architecture.
+     You may need to set `TORCH_CUDA_ARCH_LIST` to reinstall MMCV.
+     The GPU arch table could be found [here](https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#gpu-feature-list),
+     i.e. run `TORCH_CUDA_ARCH_LIST=7.0 pip install mmcv-full` to build MMCV for Volta GPUs.
+     The compatibility issue could happen when using old GPUS, e.g., Tesla K80 (3.7) on colab.
+  3. Check whether the running environment is the same as that when mmcv/mmdet has compiled.
+     For example, you may compile mmcv using CUDA 10.0 but run it on CUDA 9.0 environments.
+
+- "undefined symbol" or "cannot open xxx.so".
+
+  1. If those symbols are CUDA/C++ symbols (e.g., libcudart.so or GLIBCXX), check whether the CUDA/GCC runtimes are the same as those used for compiling mmcv,
+     i.e. run `python mmdet/utils/collect_env.py` to see if `"MMCV Compiler"`/`"MMCV CUDA Compiler"` is the same as `"GCC"`/`"CUDA_HOME"`.
+  2. If those symbols are PyTorch symbols (e.g., symbols containing caffe, aten, and TH), check whether the PyTorch version is the same as that used for compiling mmcv.
+  3. Run `python mmdet/utils/collect_env.py` to check whether PyTorch, torchvision, and MMCV are built by and running on the same environment.
+
+- setuptools.sandbox.UnpickleableException: DistutilsSetupError("each element of 'ext_modules' option must be an Extension instance or 2-tuple")
+
+  1. If you are using miniconda rather than anaconda, check whether Cython is installed as indicated in [#3379](https://github.com/open-mmlab/mmdetection/issues/3379).
+     You need to manually install Cython first and then run command `pip install -r requirements.txt`.
+  2. You may also need to check the compatibility between the `setuptools`, `Cython`, and `PyTorch` in your environment.
+
+- "Segmentation fault".
+
+  1. Check you GCC version and use GCC 5.4. This usually caused by the incompatibility between PyTorch and the environment (e.g., GCC \< 4.9 for PyTorch). We also recommend the users to avoid using GCC 5.5 because many feedbacks report that GCC 5.5 will cause "segmentation fault" and simply changing it to GCC 5.4 could solve the problem.
+
+  2. Check whether PyTorch is correctly installed and could use CUDA op, e.g. type the following command in your terminal.
+
+     ```shell
+     python -c 'import torch; print(torch.cuda.is_available())'
+     ```
+
+     And see whether they could correctly output results.
+
+  3. If Pytorch is correctly installed, check whether MMCV is correctly installed.
+
+     ```shell
+     python -c 'import mmcv; import mmcv.ops'
+     ```
+
+     If MMCV is correctly installed, then there will be no issue of the above two commands.
+
+  4. If MMCV and Pytorch is correctly installed, you man use `ipdb`, `pdb` to set breakpoints or directly add 'print' in mmdetection code and see which part leads the segmentation fault.
+
+## Training
+
+- "Loss goes Nan"
+
+  1. Check if the dataset annotations are valid: zero-size bounding boxes will cause the regression loss to be Nan due to the commonly used transformation for box regression. Some small size (width or height are smaller than 1) boxes will also cause this problem after data augmentation (e.g., instaboost). So check the data and try to filter out those zero-size boxes and skip some risky augmentations on the small-size boxes when you face the problem.
+  2. Reduce the learning rate: the learning rate might be too large due to some reasons, e.g., change of batch size. You can rescale them to the value that could stably train the model.
+  3. Extend the warmup iterations: some models are sensitive to the learning rate at the start of the training. You can extend the warmup iterations, e.g., change the `warmup_iters` from 500 to 1000 or 2000.
+  4. Add gradient clipping: some models requires gradient clipping to stabilize the training process. The default of `grad_clip` is `None`, you can add gradient clippint to avoid gradients that are too large, i.e., set `optimizer_config=dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))` in your config file. If your config does not inherits from any basic config that contains `optimizer_config=dict(grad_clip=None)`, you can simply add `optimizer_config=dict(grad_clip=dict(max_norm=35, norm_type=2))`.
+
+- "GPU out of memory"
+
+  1. There are some scenarios when there are large amount of ground truth boxes, which may cause OOM during target assignment. You can set `gpu_assign_thr=N` in the config of assigner thus the assigner will calculate box overlaps through CPU when there are more than N GT boxes.
+
+  2. Set `with_cp=True` in the backbone. This uses the sublinear strategy in PyTorch to reduce GPU memory cost in the backbone.
+
+  3. Try mixed precision training using following the examples in `config/fp16`. The `loss_scale` might need further tuning for different models.
+
+  4. Try to use `AvoidCUDAOOM` to avoid GPU out of memory. It will first retry after calling `torch.cuda.empty_cache()`. If it still fails, it will then retry by converting the type of inputs to FP16 format. If it still fails, it will try to copy inputs from GPUs to CPUs to continue computing. Try AvoidOOM in you code to make the code continue to run when GPU memory runs out:
+
+     ```python
+     from mmdet.utils import AvoidCUDAOOM
+
+     output = AvoidCUDAOOM.retry_if_cuda_oom(some_function)(input1, input2)
+     ```
+
+     You can also try `AvoidCUDAOOM` as a decorator to make the code continue to run when GPU memory runs out:
+
+     ```python
+     from mmdet.utils import AvoidCUDAOOM
+
+     @AvoidCUDAOOM.retry_if_cuda_oom
+     def function(*args, **kwargs):
+         ...
+         return xxx
+     ```
+
+- "RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one"
+
+  1. This error indicates that your module has parameters that were not used in producing loss. This phenomenon may be caused by running different branches in your code in DDP mode.
+  2. You can set `find_unused_parameters = True` in the config to solve the above problems(but this will slow down the training speed.
+  3. If the version of your MMCV >= 1.4.1, you can get the name of those unused parameters with `detect_anomalous_params=True` in `optimizer_config` of config.
+
+- Save the best model
+
+  It can be turned on by configuring `evaluation = dict(save_best=‘auto’)`. In the case of the `auto` parameter, the first key in the returned evaluation result will be used as the basis for selecting the best model. You can also directly set the key in the evaluation result to manually set it, for example, `evaluation = dict(save_best='mAP' )`.
+
+- Resume training with `ExpMomentumEMAHook`
+
+  If you use `ExpMomentumEMAHook` in training, you can't just use command line parameters  `--resume-from` nor `--cfg-options resume_from` to restore model parameters during resume, i.e., the command `python tools/train.py configs/yolox/yolox_s_8x8_300e_coco.py --resume-from ./work_dir/yolox_s_8x8_300e_coco/epoch_x.pth` will not work. Since `ExpMomentumEMAHook` needs to reload the weights, taking the `yolox_s` algorithm as an example, you should modify the values of `resume_from` in two places of the config as below:
+
+  ```python
+  # Open configs/yolox/yolox_s_8x8_300e_coco.py directly and modify all resume_from fields
+  resume_from=./work_dir/yolox_s_8x8_300e_coco/epoch_x.pth
+  custom_hooks=[...
+      dict(
+          type='ExpMomentumEMAHook',
+          resume_from=./work_dir/yolox_s_8x8_300e_coco/epoch_x.pth,
+          momentum=0.0001,
+          priority=49)
+      ]
+  ```
+
+## Evaluation
+
+- COCO Dataset, AP or AR = -1
+  1. According to the definition of COCO dataset, the small and medium areas in an image are less than 1024 (32\*32), 9216 (96\*96), respectively.
+  2. If the corresponding area has no object, the result of AP and AR will set to -1.
+
+## Model
+
+- `style` in ResNet
+
+  The `style` parameter in ResNet allows either `pytorch` or `caffe` style. It indicates the difference in the Bottleneck module. Bottleneck is a stacking structure of `1x1-3x3-1x1` convolutional layers. In the case of `caffe` mode, the convolution layer with `stride=2` is the first `1x1` convolution, while in `pyorch` mode, it is the second `3x3` convolution has `stride=2`. A sample code is as below:
+
+  ```python
+  if self.style == 'pytorch':
+        self.conv1_stride = 1
+        self.conv2_stride = stride
+  else:
+        self.conv1_stride = stride
+        self.conv2_stride = 1
+  ```
+
+- ResNeXt parameter description
+
+  ResNeXt comes from the paper [`Aggregated Residual Transformations for Deep Neural Networks`](https://arxiv.org/abs/1611.05431). It introduces  group and uses “cardinality” to control the number of groups to achieve a balance between accuracy and complexity. It controls the basic width and grouping parameters of the internal Bottleneck module through two hyperparameters `baseWidth` and `cardinality`. An example configuration name in MMDetection is `mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py`, where `mask_rcnn` represents the algorithm using Mask R-CNN, `x101` represents the backbone network using ResNeXt-101, and `64x4d` represents that the bottleneck block has 64 group and each group has basic width of 4.
+
+- `norm_eval` in backbone
+
+  Since the detection model is usually large and the input image resolution is high, this will result in a small batch of the detection model, which will make the variance of the statistics calculated by BatchNorm during the training process very large and not as stable as the statistics obtained during the pre-training of the backbone network . Therefore, the `norm_eval=True` mode is generally used in training, and the BatchNorm statistics in the pre-trained backbone network are directly used. The few algorithms that use large batches are the `norm_eval=False` mode, such as NASFPN. For the backbone network without ImageNet pre-training and the batch is relatively small, you can consider using `SyncBN`.
diff --git a/docs/en/get_started.md b/docs/en/get_started.md
new file mode 100644
index 0000000..b7c6066
--- /dev/null
+++ b/docs/en/get_started.md
@@ -0,0 +1,208 @@
+# Prerequisites
+
+In this section we demonstrate how to prepare an environment with PyTorch.
+
+MMDetection works on Linux, Windows and macOS. It requires Python 3.7+, CUDA 9.2+ and PyTorch 1.5+.
+
+```{note}
+If you are experienced with PyTorch and have already installed it, just skip this part and jump to the [next section](#installation). Otherwise, you can follow these steps for the preparation.
+```
+
+**Step 0.** Download and install Miniconda from the [official website](https://docs.conda.io/en/latest/miniconda.html).
+
+**Step 1.** Create a conda environment and activate it.
+
+```shell
+conda create --name openmmlab python=3.8 -y
+conda activate openmmlab
+```
+
+**Step 2.** Install PyTorch following [official instructions](https://pytorch.org/get-started/locally/), e.g.
+
+On GPU platforms:
+
+```shell
+conda install pytorch torchvision -c pytorch
+```
+
+On CPU platforms:
+
+```shell
+conda install pytorch torchvision cpuonly -c pytorch
+```
+
+# Installation
+
+We recommend that users follow our best practices to install MMDetection. However, the whole process is highly customizable. See [Customize Installation](#customize-installation) section for more information.
+
+## Best Practices
+
+**Step 0.** Install [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim).
+
+```shell
+pip install -U openmim
+mim install mmcv-full
+```
+
+**Step 1.** Install MMDetection.
+
+Case a: If you develop and run mmdet directly, install it from source:
+
+```shell
+git clone https://github.com/open-mmlab/mmdetection.git
+cd mmdetection
+pip install -v -e .
+# "-v" means verbose, or more output
+# "-e" means installing a project in editable mode,
+# thus any local modifications made to the code will take effect without reinstallation.
+```
+
+Case b: If you use mmdet as a dependency or third-party package, install it with pip:
+
+```shell
+pip install mmdet
+```
+
+## Verify the installation
+
+To verify whether MMDetection is installed correctly, we provide some sample codes to run an inference demo.
+
+**Step 1.** We need to download config and checkpoint files.
+
+```shell
+mim download mmdet --config yolov3_mobilenetv2_320_300e_coco --dest .
+```
+
+The downloading will take several seconds or more, depending on your network environment. When it is done, you will find two files `yolov3_mobilenetv2_320_300e_coco.py` and `yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth` in your current folder.
+
+**Step 2.** Verify the inference demo.
+
+Option (a). If you install mmdetection from source, just run the following command.
+
+```shell
+python demo/image_demo.py demo/demo.jpg yolov3_mobilenetv2_320_300e_coco.py yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth --device cpu --out-file result.jpg
+```
+
+You will see a new image `result.jpg` on your current folder, where bounding boxes are plotted on cars, benches, etc.
+
+Option (b). If you install mmdetection with pip, open you python interpreter and copy&paste the following codes.
+
+```python
+from mmdet.apis import init_detector, inference_detector
+
+config_file = 'yolov3_mobilenetv2_320_300e_coco.py'
+checkpoint_file = 'yolov3_mobilenetv2_320_300e_coco_20210719_215349-d18dff72.pth'
+model = init_detector(config_file, checkpoint_file, device='cpu')  # or device='cuda:0'
+inference_detector(model, 'demo/demo.jpg')
+```
+
+You will see a list of arrays printed, indicating the detected bounding boxes.
+
+## Customize Installation
+
+### CUDA versions
+
+When installing PyTorch, you need to specify the version of CUDA. If you are not clear on which to choose, follow our recommendations:
+
+- For Ampere-based NVIDIA GPUs, such as GeForce 30 series and NVIDIA A100, CUDA 11 is a must.
+- For older NVIDIA GPUs, CUDA 11 is backward compatible, but CUDA 10.2 offers better compatibility and is more lightweight.
+
+Please make sure the GPU driver satisfies the minimum version requirements. See [this table](https://docs.nvidia.com/cuda/cuda-toolkit-release-notes/index.html#cuda-major-component-versions__table-cuda-toolkit-driver-versions) for more information.
+
+```{note}
+Installing CUDA runtime libraries is enough if you follow our best practices, because no CUDA code will be compiled locally. However if you hope to compile MMCV from source or develop other CUDA operators, you need to install the complete CUDA toolkit from NVIDIA's [website](https://developer.nvidia.com/cuda-downloads), and its version should match the CUDA version of PyTorch. i.e., the specified version of cudatoolkit in `conda install` command.
+```
+
+### Install MMCV without MIM
+
+MMCV contains C++ and CUDA extensions, thus depending on PyTorch in a complex way. MIM solves such dependencies automatically and makes the installation easier. However, it is not a must.
+
+To install MMCV with pip instead of MIM, please follow [MMCV installation guides](https://mmcv.readthedocs.io/en/latest/get_started/installation.html). This requires manually specifying a find-url based on PyTorch version and its CUDA version.
+
+For example, the following command install mmcv-full built for PyTorch 1.10.x and CUDA 11.3.
+
+```shell
+pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu113/torch1.10/index.html
+```
+
+### Install on CPU-only platforms
+
+MMDetection can be built for CPU only environment. In CPU mode you can train (requires MMCV version >= 1.4.4), test or inference a model.
+
+However some functionalities are gone in this mode:
+
+- Deformable Convolution
+- Modulated Deformable Convolution
+- ROI pooling
+- Deformable ROI pooling
+- CARAFE
+- SyncBatchNorm
+- CrissCrossAttention
+- MaskedConv2d
+- Temporal Interlace Shift
+- nms_cuda
+- sigmoid_focal_loss_cuda
+- bbox_overlaps
+
+If you try to train/test/inference a model containing above ops, an error will be raised.
+The following table lists affected algorithms.
+
+|                        Operator                         |                                          Model                                           |
+| :-----------------------------------------------------: | :--------------------------------------------------------------------------------------: |
+| Deformable Convolution/Modulated Deformable Convolution | DCN、Guided Anchoring、RepPoints、CentripetalNet、VFNet、CascadeRPN、NAS-FCOS、DetectoRS |
+|                      MaskedConv2d                       |                                     Guided Anchoring                                     |
+|                         CARAFE                          |                                          CARAFE                                          |
+|                      SyncBatchNorm                      |                                         ResNeSt                                          |
+
+### Install on Google Colab
+
+[Google Colab](https://research.google.com/) usually has PyTorch installed,
+thus we only need to install MMCV and MMDetection with the following commands.
+
+**Step 1.** Install [MMCV](https://github.com/open-mmlab/mmcv) using [MIM](https://github.com/open-mmlab/mim).
+
+```shell
+!pip3 install openmim
+!mim install mmcv-full
+```
+
+**Step 2.** Install MMDetection from the source.
+
+```shell
+!git clone https://github.com/open-mmlab/mmdetection.git
+%cd mmdetection
+!pip install -e .
+```
+
+**Step 3.** Verification.
+
+```python
+import mmdet
+print(mmdet.__version__)
+# Example output: 2.23.0
+```
+
+```{note}
+Within Jupyter, the exclamation mark `!` is used to call external executables and `%cd` is a [magic command](https://ipython.readthedocs.io/en/stable/interactive/magics.html#magic-cd) to change the current working directory of Python.
+```
+
+### Using MMDetection with Docker
+
+We provide a [Dockerfile](https://github.com/open-mmlab/mmdetection/blob/master/docker/Dockerfile) to build an image. Ensure that your [docker version](https://docs.docker.com/engine/install/) >=19.03.
+
+```shell
+# build an image with PyTorch 1.6, CUDA 10.1
+# If you prefer other versions, just modified the Dockerfile
+docker build -t mmdetection docker/
+```
+
+Run it with
+
+```shell
+docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmdetection/data mmdetection
+```
+
+## Trouble shooting
+
+If you have some issues during the installation, please first view the [FAQ](faq.md) page.
+You may [open an issue](https://github.com/open-mmlab/mmdetection/issues/new/choose) on GitHub if no solution is found.
diff --git a/docs/en/index.rst b/docs/en/index.rst
new file mode 100644
index 0000000..0089c87
--- /dev/null
+++ b/docs/en/index.rst
@@ -0,0 +1,63 @@
+Welcome to MMDetection's documentation!
+=======================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Get Started
+
+   get_started.md
+   modelzoo_statistics.md
+   model_zoo.md
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Quick Run
+
+   1_exist_data_model.md
+   2_new_data_model.md
+   3_exist_data_new_model.md
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Tutorials
+
+   tutorials/index.rst
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Useful Tools and Scripts
+
+   useful_tools.md
+
+.. toctree::
+   :maxdepth: 2
+   :caption: Notes
+
+   conventions.md
+   compatibility.md
+   projects.md
+   changelog.md
+   faq.md
+
+.. toctree::
+   :caption: Switch Language
+
+   switch_language.md
+
+.. toctree::
+   :maxdepth: 1
+   :caption: API Reference
+
+   api.rst
+
+.. toctree::
+   :maxdepth: 1
+   :caption: Device Support
+
+   device/npu.md
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`search`
diff --git a/docs/en/make.bat b/docs/en/make.bat
new file mode 100644
index 0000000..922152e
--- /dev/null
+++ b/docs/en/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/docs/en/model_zoo.md b/docs/en/model_zoo.md
new file mode 100644
index 0000000..6e2f3b6
--- /dev/null
+++ b/docs/en/model_zoo.md
@@ -0,0 +1,362 @@
+# Benchmark and Model Zoo
+
+## Mirror sites
+
+We only use aliyun to maintain the model zoo since MMDetection V2.0. The model zoo of V1.x has been deprecated.
+
+## Common settings
+
+- All models were trained on `coco_2017_train`, and tested on the `coco_2017_val`.
+- We use distributed training.
+- All pytorch-style pretrained backbones on ImageNet are from PyTorch model zoo, caffe-style pretrained backbones are converted from the newly released model from detectron2.
+- For fair comparison with other codebases, we report the GPU memory as the maximum value of `torch.cuda.max_memory_allocated()` for all 8 GPUs. Note that this value is usually less than what `nvidia-smi` shows.
+- We report the inference time as the total time of network forwarding and post-processing, excluding the data loading time. Results are obtained with the script [benchmark.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/benchmark.py) which computes the average time on 2000 images.
+
+## ImageNet Pretrained Models
+
+It is common to initialize from backbone models pre-trained on ImageNet classification task. All pre-trained  model links can be found at [open_mmlab](https://github.com/open-mmlab/mmcv/blob/master/mmcv/model_zoo/open_mmlab.json).  According to `img_norm_cfg` and source of weight, we can divide all the ImageNet  pre-trained  model weights into some cases:
+
+- TorchVision:  Corresponding to torchvision weight, including ResNet50, ResNet101. The `img_norm_cfg` is `dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)`.
+- Pycls:  Corresponding to [pycls](https://github.com/facebookresearch/pycls) weight, including RegNetX. The `img_norm_cfg` is `dict(   mean=[103.530, 116.280, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)`.
+- MSRA styles: Corresponding to [MSRA](https://github.com/KaimingHe/deep-residual-networks) weights, including ResNet50_Caffe and ResNet101_Caffe. The `img_norm_cfg` is `dict(   mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)`.
+- Caffe2 styles:  Currently only contains ResNext101_32x8d. The `img_norm_cfg` is `dict(mean=[103.530, 116.280, 123.675], std=[57.375, 57.120, 58.395], to_rgb=False)`.
+- Other styles: E.g SSD which corresponds to `img_norm_cfg` is `dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)` and YOLOv3 which corresponds to `img_norm_cfg` is `dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)`.
+
+The detailed table of the commonly used backbone models in MMDetection is listed below :
+
+| model            | source      | link                                                                                                                                                                                                   | description                                                                                                                                                                                                                                      |
+| ---------------- | ----------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| ResNet50         | TorchVision | [torchvision's ResNet-50](https://download.pytorch.org/models/resnet50-19c8e357.pth)                                                                                                                   | From [torchvision's ResNet-50](https://download.pytorch.org/models/resnet50-19c8e357.pth).                                                                                                                                                       |
+| ResNet101        | TorchVision | [torchvision's ResNet-101](https://download.pytorch.org/models/resnet101-5d3b4d8f.pth)                                                                                                                 | From [torchvision's ResNet-101](https://download.pytorch.org/models/resnet101-5d3b4d8f.pth).                                                                                                                                                     |
+| RegNetX          | Pycls       | [RegNetX_3.2gf](https://download.openmmlab.com/pretrain/third_party/regnetx_3.2gf-c2599b0f.pth), [RegNetX_800mf](https://download.openmmlab.com/pretrain/third_party/regnetx_800mf-1f4be4c7.pth). etc. | From [pycls](https://github.com/facebookresearch/pycls).                                                                                                                                                                                         |
+| ResNet50_Caffe   | MSRA        | [MSRA's ResNet-50](https://download.openmmlab.com/pretrain/third_party/resnet50_caffe-788b5fa3.pth)                                                                                                    | Converted copy of [Detectron2's R-50.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-50.pkl) model. The original weight comes from [MSRA's original ResNet-50](https://github.com/KaimingHe/deep-residual-networks).    |
+| ResNet101_Caffe  | MSRA        | [MSRA's ResNet-101](https://download.openmmlab.com/pretrain/third_party/resnet101_caffe-3ad79236.pth)                                                                                                  | Converted copy of [Detectron2's R-101.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-101.pkl) model. The original weight comes from [MSRA's original ResNet-101](https://github.com/KaimingHe/deep-residual-networks). |
+| ResNext101_32x8d | Caffe2      | [Caffe2 ResNext101_32x8d](https://download.openmmlab.com/pretrain/third_party/resnext101_32x8d-1516f1aa.pth)                                                                                           | Converted copy of [Detectron2's X-101-32x8d.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/FAIR/X-101-32x8d.pkl) model. The ResNeXt-101-32x8d model trained with Caffe2 at FB.                                                |
+
+## Baselines
+
+### RPN
+
+Please refer to [RPN](https://github.com/open-mmlab/mmdetection/blob/master/configs/rpn) for details.
+
+### Faster R-CNN
+
+Please refer to [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn) for details.
+
+### Mask R-CNN
+
+Please refer to [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn) for details.
+
+### Fast R-CNN (with pre-computed proposals)
+
+Please refer to [Fast R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/fast_rcnn) for details.
+
+### RetinaNet
+
+Please refer to [RetinaNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet) for details.
+
+### Cascade R-CNN and Cascade Mask R-CNN
+
+Please refer to [Cascade R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/cascade_rcnn) for details.
+
+### Hybrid Task Cascade (HTC)
+
+Please refer to [HTC](https://github.com/open-mmlab/mmdetection/blob/master/configs/htc) for details.
+
+### SSD
+
+Please refer to [SSD](https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd) for details.
+
+### Group Normalization (GN)
+
+Please refer to [Group Normalization](https://github.com/open-mmlab/mmdetection/blob/master/configs/gn) for details.
+
+### Weight Standardization
+
+Please refer to [Weight Standardization](https://github.com/open-mmlab/mmdetection/blob/master/configs/gn+ws) for details.
+
+### Deformable Convolution v2
+
+Please refer to [Deformable Convolutional Networks](https://github.com/open-mmlab/mmdetection/blob/master/configs/dcn) for details.
+
+### CARAFE: Content-Aware ReAssembly of FEatures
+
+Please refer to [CARAFE](https://github.com/open-mmlab/mmdetection/blob/master/configs/carafe) for details.
+
+### Instaboost
+
+Please refer to [Instaboost](https://github.com/open-mmlab/mmdetection/blob/master/configs/instaboost) for details.
+
+### Libra R-CNN
+
+Please refer to [Libra R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/libra_rcnn) for details.
+
+### Guided Anchoring
+
+Please refer to [Guided Anchoring](https://github.com/open-mmlab/mmdetection/blob/master/configs/guided_anchoring) for details.
+
+### FCOS
+
+Please refer to [FCOS](https://github.com/open-mmlab/mmdetection/blob/master/configs/fcos) for details.
+
+### FoveaBox
+
+Please refer to [FoveaBox](https://github.com/open-mmlab/mmdetection/blob/master/configs/foveabox) for details.
+
+### RepPoints
+
+Please refer to [RepPoints](https://github.com/open-mmlab/mmdetection/blob/master/configs/reppoints) for details.
+
+### FreeAnchor
+
+Please refer to [FreeAnchor](https://github.com/open-mmlab/mmdetection/blob/master/configs/free_anchor) for details.
+
+### Grid R-CNN (plus)
+
+Please refer to [Grid R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/grid_rcnn) for details.
+
+### GHM
+
+Please refer to [GHM](https://github.com/open-mmlab/mmdetection/blob/master/configs/ghm) for details.
+
+### GCNet
+
+Please refer to [GCNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/gcnet) for details.
+
+### HRNet
+
+Please refer to [HRNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/hrnet) for details.
+
+### Mask Scoring R-CNN
+
+Please refer to [Mask Scoring R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/ms_rcnn) for details.
+
+### Train from Scratch
+
+Please refer to [Rethinking ImageNet Pre-training](https://github.com/open-mmlab/mmdetection/blob/master/configs/scratch) for details.
+
+### NAS-FPN
+
+Please refer to [NAS-FPN](https://github.com/open-mmlab/mmdetection/blob/master/configs/nas_fpn) for details.
+
+### ATSS
+
+Please refer to [ATSS](https://github.com/open-mmlab/mmdetection/blob/master/configs/atss) for details.
+
+### FSAF
+
+Please refer to [FSAF](https://github.com/open-mmlab/mmdetection/blob/master/configs/fsaf) for details.
+
+### RegNetX
+
+Please refer to [RegNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/regnet) for details.
+
+### Res2Net
+
+Please refer to [Res2Net](https://github.com/open-mmlab/mmdetection/blob/master/configs/res2net) for details.
+
+### GRoIE
+
+Please refer to [GRoIE](https://github.com/open-mmlab/mmdetection/blob/master/configs/groie) for details.
+
+### Dynamic R-CNN
+
+Please refer to [Dynamic R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/dynamic_rcnn) for details.
+
+### PointRend
+
+Please refer to [PointRend](https://github.com/open-mmlab/mmdetection/blob/master/configs/point_rend) for details.
+
+### DetectoRS
+
+Please refer to [DetectoRS](https://github.com/open-mmlab/mmdetection/blob/master/configs/detectors) for details.
+
+### Generalized Focal Loss
+
+Please refer to [Generalized Focal Loss](https://github.com/open-mmlab/mmdetection/blob/master/configs/gfl) for details.
+
+### CornerNet
+
+Please refer to [CornerNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/cornernet) for details.
+
+### YOLOv3
+
+Please refer to [YOLOv3](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolo) for details.
+
+### PAA
+
+Please refer to [PAA](https://github.com/open-mmlab/mmdetection/blob/master/configs/paa) for details.
+
+### SABL
+
+Please refer to [SABL](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl) for details.
+
+### CentripetalNet
+
+Please refer to [CentripetalNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/centripetalnet) for details.
+
+### ResNeSt
+
+Please refer to [ResNeSt](https://github.com/open-mmlab/mmdetection/blob/master/configs/resnest) for details.
+
+### DETR
+
+Please refer to [DETR](https://github.com/open-mmlab/mmdetection/blob/master/configs/detr) for details.
+
+### Deformable DETR
+
+Please refer to [Deformable DETR](https://github.com/open-mmlab/mmdetection/blob/master/configs/deformable_detr) for details.
+
+### AutoAssign
+
+Please refer to [AutoAssign](https://github.com/open-mmlab/mmdetection/blob/master/configs/autoassign) for details.
+
+### YOLOF
+
+Please refer to [YOLOF](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolof) for details.
+
+### Seesaw Loss
+
+Please refer to [Seesaw Loss](https://github.com/open-mmlab/mmdetection/blob/master/configs/seesaw_loss) for details.
+
+### CenterNet
+
+Please refer to [CenterNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/centernet) for details.
+
+### YOLOX
+
+Please refer to [YOLOX](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolox) for details.
+
+### PVT
+
+Please refer to [PVT](https://github.com/open-mmlab/mmdetection/blob/master/configs/pvt) for details.
+
+### SOLO
+
+Please refer to [SOLO](https://github.com/open-mmlab/mmdetection/blob/master/configs/solo) for details.
+
+### QueryInst
+
+Please refer to [QueryInst](https://github.com/open-mmlab/mmdetection/blob/master/configs/queryinst) for details.
+
+### PanopticFPN
+
+Please refer to [PanopticFPN](https://github.com/open-mmlab/mmdetection/blob/master/configs/panoptic_fpn) for details.
+
+### MaskFormer
+
+Please refer to [MaskFormer](https://github.com/open-mmlab/mmdetection/blob/master/configs/maskformer) for details.
+
+### DyHead
+
+Please refer to [DyHead](https://github.com/open-mmlab/mmdetection/blob/master/configs/dyhead) for details.
+
+### Mask2Former
+
+Please refer to [Mask2Former](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask2former) for details.
+
+### Efficientnet
+
+Please refer to [Efficientnet](https://github.com/open-mmlab/mmdetection/blob/master/configs/efficientnet) for details.
+
+### RF-Next
+
+Please refer to [RF-Next](https://github.com/open-mmlab/mmdetection/blob/master/configs/rfnext) for details.
+
+### Other datasets
+
+We also benchmark some methods on [PASCAL VOC](https://github.com/open-mmlab/mmdetection/blob/master/configs/pascal_voc), [Cityscapes](https://github.com/open-mmlab/mmdetection/blob/master/configs/cityscapes), [OpenImages](https://github.com/open-mmlab/mmdetection/blob/master/configs/openimages) and [WIDER FACE](https://github.com/open-mmlab/mmdetection/blob/master/configs/wider_face).
+
+### Pre-trained Models
+
+We also train [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn) and [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn) using ResNet-50 and [RegNetX-3.2G](https://github.com/open-mmlab/mmdetection/blob/master/configs/regnet) with multi-scale training and longer schedules. These models serve as strong pre-trained models for downstream tasks for convenience.
+
+## Speed benchmark
+
+### Training Speed benchmark
+
+We provide [analyze_logs.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/analyze_logs.py) to get average time of iteration in training. You can find examples in [Log Analysis](https://mmdetection.readthedocs.io/en/latest/useful_tools.html#log-analysis).
+
+We compare the training speed of Mask R-CNN with some other popular frameworks (The data is copied from [detectron2](https://github.com/facebookresearch/detectron2/blob/master/docs/notes/benchmarks.md/)).
+For mmdetection, we benchmark with [mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py), which should have the same setting with [mask_rcnn_R_50_FPN_noaug_1x.yaml](https://github.com/facebookresearch/detectron2/blob/master/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml) of detectron2.
+We also provide the [checkpoint](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug_compare_20200518-10127928.pth) and [training log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug_20200518_105755.log.json) for reference. The throughput is computed as the average throughput in iterations 100-500 to skip GPU warmup time.
+
+| Implementation                                                                         | Throughput (img/s) |
+| -------------------------------------------------------------------------------------- | ------------------ |
+| [Detectron2](https://github.com/facebookresearch/detectron2)                           | 62                 |
+| [MMDetection](https://github.com/open-mmlab/mmdetection)                               | 61                 |
+| [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/)          | 53                 |
+| [tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) | 50                 |
+| [simpledet](https://github.com/TuSimple/simpledet/)                                    | 39                 |
+| [Detectron](https://github.com/facebookresearch/Detectron)                             | 19                 |
+| [matterport/Mask_RCNN](https://github.com/matterport/Mask_RCNN/)                       | 14                 |
+
+### Inference Speed Benchmark
+
+We provide [benchmark.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/benchmark.py) to benchmark the inference latency.
+The script benchmarkes the model with 2000 images and calculates the average time ignoring first 5 times. You can change the output log interval (defaults: 50) by setting `LOG-INTERVAL`.
+
+```shell
+python tools/benchmark.py ${CONFIG} ${CHECKPOINT} [--log-interval $[LOG-INTERVAL]] [--fuse-conv-bn]
+```
+
+The latency of all models in our model zoo is benchmarked without setting `fuse-conv-bn`, you can get a lower latency by setting it.
+
+## Comparison with Detectron2
+
+We compare mmdetection with [Detectron2](https://github.com/facebookresearch/detectron2.git) in terms of speed and performance.
+We use the commit id [185c27e](https://github.com/facebookresearch/detectron2/tree/185c27e4b4d2d4c68b5627b3765420c6d7f5a659)(30/4/2020) of detectron.
+For fair comparison, we install and run both frameworks on the same machine.
+
+### Hardware
+
+- 8 NVIDIA Tesla V100 (32G) GPUs
+- Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
+
+### Software environment
+
+- Python 3.7
+- PyTorch 1.4
+- CUDA 10.1
+- CUDNN 7.6.03
+- NCCL 2.4.08
+
+### Performance
+
+| Type                                                                                                                                   | Lr schd | Detectron2                                                                                                                             | mmdetection | Download                                                                                                                                                                                                                                                                                                                                                         |
+| -------------------------------------------------------------------------------------------------------------------------------------- | ------- | -------------------------------------------------------------------------------------------------------------------------------------- | ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py) | 1x      | [37.9](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml)                 | 38.0        | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-5324cff8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco_20200429_234554.log.json)             |
+| [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py)  | 1x      | [38.6 & 35.2](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml) | 38.8 & 35.4 | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco-dbecf295.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco_20200430_054239.log.json) |
+| [Retinanet](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py)        | 1x      | [36.5](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml)                   | 37.0        | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/retinanet_r50_caffe_fpn_mstrain_1x_coco/retinanet_r50_caffe_fpn_mstrain_1x_coco-586977a0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/retinanet_r50_caffe_fpn_mstrain_1x_coco/retinanet_r50_caffe_fpn_mstrain_1x_coco_20200430_014748.log.json)                     |
+
+### Training Speed
+
+The training speed is measure with s/iter. The lower, the better.
+
+| Type         | Detectron2 | mmdetection |
+| ------------ | ---------- | ----------- |
+| Faster R-CNN | 0.210      | 0.216       |
+| Mask R-CNN   | 0.261      | 0.265       |
+| Retinanet    | 0.200      | 0.205       |
+
+### Inference Speed
+
+The inference speed is measured with fps (img/s) on a single GPU, the higher, the better.
+To be consistent with Detectron2, we report the pure inference speed (without the time of data loading).
+For Mask R-CNN, we exclude the time of RLE encoding in post-processing.
+We also include the officially reported speed in the parentheses, which is slightly higher
+than the results tested on our server due to differences of hardwares.
+
+| Type         | Detectron2  | mmdetection |
+| ------------ | ----------- | ----------- |
+| Faster R-CNN | 25.6 (26.3) | 22.2        |
+| Mask R-CNN   | 22.5 (23.3) | 19.6        |
+| Retinanet    | 17.8 (18.2) | 20.6        |
+
+### Training memory
+
+| Type         | Detectron2 | mmdetection |
+| ------------ | ---------- | ----------- |
+| Faster R-CNN | 3.0        | 3.8         |
+| Mask R-CNN   | 3.4        | 3.9         |
+| Retinanet    | 3.9        | 3.4         |
diff --git a/docs/en/projects.md b/docs/en/projects.md
new file mode 100644
index 0000000..fa8ecb7
--- /dev/null
+++ b/docs/en/projects.md
@@ -0,0 +1,58 @@
+# Projects based on MMDetection
+
+There are many projects built upon MMDetection.
+We list some of them as examples of how to extend MMDetection for your own projects.
+As the page might not be completed, please feel free to create a PR to update this page.
+
+## Projects as an extension
+
+Some projects extend the boundary of MMDetection for deployment or other research fields.
+They reveal the potential of what MMDetection can do. We list several of them as below.
+
+- [OTEDetection](https://github.com/opencv/mmdetection): OpenVINO training extensions for object detection.
+- [MMDetection3d](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
+
+## Projects of papers
+
+There are also projects released with papers.
+Some of the papers are published in top-tier conferences (CVPR, ICCV, and ECCV), the others are also highly influential.
+To make this list also a reference for the community to develop and compare new object detection algorithms, we list them following the time order of top-tier conferences.
+Methods already supported and maintained by MMDetection are not listed.
+
+- Anchor Pruning for Object Detection, CVIU 2022. [\[paper\]](https://doi.org/10.1016/j.cviu.2022.103445)[\[github\]](https://github.com/Mxbonn/anchor_pruning)
+- Involution: Inverting the Inherence of Convolution for Visual Recognition, CVPR21. [\[paper\]](https://arxiv.org/abs/2103.06255)[\[github\]](https://github.com/d-li14/involution)
+- Multiple Instance Active Learning for Object Detection, CVPR 2021. [\[paper\]](https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan_Multiple_Instance_Active_Learning_for_Object_Detection_CVPR_2021_paper.pdf)[\[github\]](https://github.com/yuantn/MI-AOD)
+- Adaptive Class Suppression Loss for Long-Tail Object Detection, CVPR 2021. [\[paper\]](https://arxiv.org/abs/2104.00885)[\[github\]](https://github.com/CASIA-IVA-Lab/ACSL)
+- Generalizable Pedestrian Detection: The Elephant In The Room, CVPR2021. [\[paper\]](https://arxiv.org/abs/2003.08799)[\[github\]](https://github.com/hasanirtiza/Pedestron)
+- Group Fisher Pruning for Practical Network Compression, ICML2021. [\[paper\]](https://github.com/jshilong/FisherPruning/blob/main/resources/paper.pdf)[\[github\]](https://github.com/jshilong/FisherPruning)
+- Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax, CVPR2020. [\[paper\]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Overcoming_Classifier_Imbalance_for_Long-Tail_Object_Detection_With_Balanced_Group_CVPR_2020_paper.pdf)[\[github\]](https://github.com/FishYuLi/BalancedGroupSoftmax)
+- Coherent Reconstruction of Multiple Humans from a Single Image, CVPR2020. [\[paper\]](https://jiangwenpl.github.io/multiperson/)[\[github\]](https://github.com/JiangWenPL/multiperson)
+- Look-into-Object: Self-supervised Structure Modeling for Object Recognition, CVPR 2020. [\[paper\]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Look-Into-Object_Self-Supervised_Structure_Modeling_for_Object_Recognition_CVPR_2020_paper.pdf)[\[github\]](https://github.com/JDAI-CV/LIO)
+- Video Panoptic Segmentation, CVPR2020. [\[paper\]](https://arxiv.org/abs/2006.11339)[\[github\]](https://github.com/mcahny/vps)
+- D2Det: Towards High Quality Object Detection and Instance Segmentation, CVPR2020. [\[paper\]](http://openaccess.thecvf.com/content_CVPR_2020/html/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.html)[\[github\]](https://github.com/JialeCao001/D2Det)
+- CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection, CVPR2020. [\[paper\]](https://arxiv.org/abs/2003.09119)[\[github\]](https://github.com/KiveeDong/CentripetalNet)
+- Learning a Unified Sample Weighting Network for Object Detection, CVPR 2020. [\[paper\]](http://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Learning_a_Unified_Sample_Weighting_Network_for_Object_Detection_CVPR_2020_paper.html)[\[github\]](https://github.com/caiqi/sample-weighting-network)
+- Scale-equalizing Pyramid Convolution for Object Detection, CVPR2020. [\[paper\]](https://arxiv.org/abs/2005.03101) [\[github\]](https://github.com/jshilong/SEPC)
+- Revisiting the Sibling Head in Object Detector, CVPR2020. [\[paper\]](https://arxiv.org/abs/2003.07540)[\[github\]](https://github.com/Sense-X/TSD)
+- PolarMask: Single Shot Instance Segmentation with Polar Representation, CVPR2020. [\[paper\]](https://arxiv.org/abs/1909.13226)[\[github\]](https://github.com/xieenze/PolarMask)
+- Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection, CVPR2020. [\[paper\]](https://arxiv.org/abs/2003.11818)[\[github\]](https://github.com/ggjy/HitDet.pytorch)
+- ZeroQ: A Novel Zero Shot Quantization Framework, CVPR2020. [\[paper\]](https://arxiv.org/abs/2001.00281)[\[github\]](https://github.com/amirgholami/ZeroQ)
+- CBNet: A Novel Composite Backbone Network Architecture for Object Detection, AAAI2020. [\[paper\]](https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuY.1833.pdf)[\[github\]](https://github.com/VDIGPKU/CBNet)
+- RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation, AAAI2020. [\[paper\]](https://arxiv.org/abs/1912.05070)[\[github\]](https://github.com/wangsr126/RDSNet)
+- Training-Time-Friendly Network for Real-Time Object Detection, AAAI2020. [\[paper\]](https://arxiv.org/abs/1909.00700)[\[github\]](https://github.com/ZJULearning/ttfnet)
+- Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution, NeurIPS 2019. [\[paper\]](https://arxiv.org/abs/1909.06720)[\[github\]](https://github.com/thangvubk/Cascade-RPN)
+- Reasoning R-CNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection, CVPR2019. [\[paper\]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Reasoning-RCNN_Unifying_Adaptive_Global_Reasoning_Into_Large-Scale_Object_Detection_CVPR_2019_paper.pdf)[\[github\]](https://github.com/chanyn/Reasoning-RCNN)
+- Learning RoI Transformer for Oriented Object Detection in Aerial Images, CVPR2019. [\[paper\]](https://arxiv.org/abs/1812.00155)[\[github\]](https://github.com/dingjiansw101/AerialDetection)
+- SOLO: Segmenting Objects by Locations. [\[paper\]](https://arxiv.org/abs/1912.04488)[\[github\]](https://github.com/WXinlong/SOLO)
+- SOLOv2: Dynamic, Faster and Stronger. [\[paper\]](https://arxiv.org/abs/2003.10152)[\[github\]](https://github.com/WXinlong/SOLO)
+- Dense Peppoints: Representing Visual Objects with Dense Point Sets. [\[paper\]](https://arxiv.org/abs/1912.11473)[\[github\]](https://github.com/justimyhxu/Dense-RepPoints)
+- IterDet: Iterative Scheme for Object Detection in Crowded Environments. [\[paper\]](https://arxiv.org/abs/2005.05708)[\[github\]](https://github.com/saic-vul/iterdet)
+- Cross-Iteration Batch Normalization. [\[paper\]](https://arxiv.org/abs/2002.05712)[\[github\]](https://github.com/Howal/Cross-iterationBatchNorm)
+- A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection, NeurIPS2020 [\[paper\]](https://arxiv.org/abs/2009.13592)[\[github\]](https://github.com/kemaloksuz/aLRPLoss)
+- RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder, NeurIPS2020 [\[paper\]](https://arxiv.org/abs/2010.15831)[\[github\]](https://github.com/microsoft/RelationNet2)
+- Generalized Focal Loss V2: Learning Reliable Localization Quality Estimation for Dense Object Detection, CVPR2021[\[paper\]](https://arxiv.org/abs/2011.12885)[\[github\]](https://github.com/implus/GFocalV2)
+- Swin Transformer: Hierarchical Vision Transformer using Shifted Windows, ICCV2021[\[paper\]](https://arxiv.org/abs/2103.14030)[\[github\]](https://github.com/SwinTransformer/)
+- Focal Transformer: Focal Self-attention for Local-Global Interactions in Vision Transformers, NeurIPS2021[\[paper\]](https://arxiv.org/abs/2107.00641)[\[github\]](https://github.com/microsoft/Focal-Transformer)
+- End-to-End Semi-Supervised Object Detection with Soft Teacher, ICCV2021[\[paper\]](https://arxiv.org/abs/2106.09018)[\[github\]](https://github.com/microsoft/SoftTeacher)
+- CBNetV2: A Novel Composite Backbone Network Architecture for Object Detection [\[paper\]](http://arxiv.org/abs/2107.00420)[\[github\]](https://github.com/VDIGPKU/CBNetV2)
+- Instances as Queries, ICCV2021 [\[paper\]](https://openaccess.thecvf.com/content/ICCV2021/papers/Fang_Instances_As_Queries_ICCV_2021_paper.pdf)[\[github\]](https://github.com/hustvl/QueryInst)
diff --git a/docs/en/robustness_benchmarking.md b/docs/en/robustness_benchmarking.md
new file mode 100644
index 0000000..bb624ee
--- /dev/null
+++ b/docs/en/robustness_benchmarking.md
@@ -0,0 +1,110 @@
+# Corruption Benchmarking
+
+## Introduction
+
+We provide tools to test object detection and instance segmentation models on the image corruption benchmark defined in [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://arxiv.org/abs/1907.07484).
+This page provides basic tutorials how to use the benchmark.
+
+```latex
+@article{michaelis2019winter,
+  title={Benchmarking Robustness in Object Detection:
+    Autonomous Driving when Winter is Coming},
+  author={Michaelis, Claudio and Mitzkus, Benjamin and
+    Geirhos, Robert and Rusak, Evgenia and
+    Bringmann, Oliver and Ecker, Alexander S. and
+    Bethge, Matthias and Brendel, Wieland},
+  journal={arXiv:1907.07484},
+  year={2019}
+}
+```
+
+![image corruption example](../resources/corruptions_sev_3.png)
+
+## About the benchmark
+
+To submit results to the benchmark please visit the [benchmark homepage](https://github.com/bethgelab/robust-detection-benchmark)
+
+The benchmark is modelled after the [imagenet-c benchmark](https://github.com/hendrycks/robustness) which was originally
+published in [Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://arxiv.org/abs/1903.12261) (ICLR 2019) by Dan Hendrycks and Thomas Dietterich.
+
+The image corruption functions are included in this library but can be installed separately using:
+
+```shell
+pip install imagecorruptions
+```
+
+Compared to imagenet-c a few changes had to be made to handle images of arbitrary size and greyscale images.
+We also modified the 'motion blur' and 'snow' corruptions to remove dependency from a linux specific library,
+which would have to be installed separately otherwise. For details please refer to the [imagecorruptions repository](https://github.com/bethgelab/imagecorruptions).
+
+## Inference with pretrained models
+
+We provide a testing script to evaluate a models performance on any combination of the corruptions provided in the benchmark.
+
+### Test a dataset
+
+- [x] single GPU testing
+- [ ] multiple GPU testing
+- [ ] visualize detection results
+
+You can use the following commands to test a models performance under the 15 corruptions used in the benchmark.
+
+```shell
+# single-gpu testing
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
+```
+
+Alternatively different group of corruptions can be selected.
+
+```shell
+# noise
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions noise
+
+# blur
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions blur
+
+# wetaher
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions weather
+
+# digital
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions digital
+```
+
+Or a costom set of corruptions e.g.:
+
+```shell
+# gaussian noise, zoom blur and snow
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions gaussian_noise zoom_blur snow
+```
+
+Finally the corruption severities to evaluate can be chosen.
+Severity 0 corresponds to clean data and the effect increases from 1 to 5.
+
+```shell
+# severity 1
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 1
+
+# severities 0,2,4
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 0 2 4
+```
+
+## Results for modelzoo models
+
+The results on COCO 2017val are shown in the below table.
+
+|        Model        |      Backbone       |  Style  | Lr schd | box AP clean | box AP corr. | box % | mask AP clean | mask AP corr. | mask % |
+| :-----------------: | :-----------------: | :-----: | :-----: | :----------: | :----------: | :---: | :-----------: | :-----------: | :----: |
+|    Faster R-CNN     |      R-50-FPN       | pytorch |   1x    |     36.3     |     18.2     | 50.2  |       -       |       -       |   -    |
+|    Faster R-CNN     |      R-101-FPN      | pytorch |   1x    |     38.5     |     20.9     | 54.2  |       -       |       -       |   -    |
+|    Faster R-CNN     |   X-101-32x4d-FPN   | pytorch |   1x    |     40.1     |     22.3     | 55.5  |       -       |       -       |   -    |
+|    Faster R-CNN     |   X-101-64x4d-FPN   | pytorch |   1x    |     41.3     |     23.4     | 56.6  |       -       |       -       |   -    |
+|    Faster R-CNN     |    R-50-FPN-DCN     | pytorch |   1x    |     40.0     |     22.4     | 56.1  |       -       |       -       |   -    |
+|    Faster R-CNN     | X-101-32x4d-FPN-DCN | pytorch |   1x    |     43.4     |     26.7     | 61.6  |       -       |       -       |   -    |
+|     Mask R-CNN      |      R-50-FPN       | pytorch |   1x    |     37.3     |     18.7     | 50.1  |     34.2      |     16.8      |  49.1  |
+|     Mask R-CNN      |    R-50-FPN-DCN     | pytorch |   1x    |     41.1     |     23.3     | 56.7  |     37.2      |     20.7      |  55.7  |
+|    Cascade R-CNN    |      R-50-FPN       | pytorch |   1x    |     40.4     |     20.1     | 49.7  |       -       |       -       |   -    |
+| Cascade Mask R-CNN  |      R-50-FPN       | pytorch |   1x    |     41.2     |     20.7     | 50.2  |     35.7      |     17.6      |  49.3  |
+|      RetinaNet      |      R-50-FPN       | pytorch |   1x    |     35.6     |     17.8     | 50.1  |       -       |       -       |   -    |
+| Hybrid Task Cascade | X-101-64x4d-FPN-DCN | pytorch |   1x    |     50.6     |     32.7     | 64.7  |     43.8      |     28.1      |  64.0  |
+
+Results may vary slightly due to the stochastic application of the corruptions.
diff --git a/docs/en/stat.py b/docs/en/stat.py
new file mode 100755
index 0000000..427c27b
--- /dev/null
+++ b/docs/en/stat.py
@@ -0,0 +1,64 @@
+#!/usr/bin/env python
+import functools as func
+import glob
+import os.path as osp
+import re
+
+import numpy as np
+
+url_prefix = 'https://github.com/open-mmlab/mmdetection/blob/master/configs'
+
+files = sorted(glob.glob('../../configs/*/README.md'))
+
+stats = []
+titles = []
+num_ckpts = 0
+
+for f in files:
+    url = osp.dirname(f.replace('../../configs', url_prefix))
+
+    with open(f, 'r') as content_file:
+        content = content_file.read()
+
+    title = content.split('\n')[0].replace('# ', '').strip()
+    ckpts = set(x.lower().strip()
+                for x in re.findall(r'\[model\]\((https?.*)\)', content))
+
+    if len(ckpts) == 0:
+        continue
+
+    _papertype = [x for x in re.findall(r'\[([A-Z]+)\]', content)]
+    assert len(_papertype) > 0
+    papertype = _papertype[0]
+
+    paper = set([(papertype, title)])
+
+    titles.append(title)
+    num_ckpts += len(ckpts)
+
+    statsmsg = f"""
+\t* [{papertype}] [{title}]({url}) ({len(ckpts)} ckpts)
+"""
+    stats.append((paper, ckpts, statsmsg))
+
+allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _ in stats])
+msglist = '\n'.join(x for _, _, x in stats)
+
+papertypes, papercounts = np.unique([t for t, _ in allpapers],
+                                    return_counts=True)
+countstr = '\n'.join(
+    [f'   - {t}: {c}' for t, c in zip(papertypes, papercounts)])
+
+modelzoo = f"""
+# Model Zoo Statistics
+
+* Number of papers: {len(set(titles))}
+{countstr}
+
+* Number of checkpoints: {num_ckpts}
+
+{msglist}
+"""
+
+with open('modelzoo_statistics.md', 'w') as f:
+    f.write(modelzoo)
diff --git a/docs/en/switch_language.md b/docs/en/switch_language.md
new file mode 100644
index 0000000..b2c4ad9
--- /dev/null
+++ b/docs/en/switch_language.md
@@ -0,0 +1,3 @@
+## <a href='https://mmdetection.readthedocs.io/en/latest/'>English</a>
+
+## <a href='https://mmdetection.readthedocs.io/zh_CN/latest/'>简体中文</a>
diff --git a/docs/en/tutorials/config.md b/docs/en/tutorials/config.md
new file mode 100644
index 0000000..8fd37db
--- /dev/null
+++ b/docs/en/tutorials/config.md
@@ -0,0 +1,551 @@
+# Tutorial 1: Learn about Configs
+
+We incorporate modular and inheritance design into our config system, which is convenient to conduct various experiments.
+If you wish to inspect the config file, you may run `python tools/misc/print_config.py /PATH/TO/CONFIG` to see the complete config.
+
+## Modify config through script arguments
+
+When submitting jobs using "tools/train.py" or "tools/test.py", you may specify `--cfg-options` to in-place modify the config.
+
+- Update config keys of dict chains.
+
+  The config options can be specified following the order of the dict keys in the original config.
+  For example, `--cfg-options model.backbone.norm_eval=False` changes the all BN modules in model backbones to `train` mode.
+
+- Update keys inside a list of configs.
+
+  Some config dicts are composed as a list in your config. For example, the training pipeline `data.train.pipeline` is normally a list
+  e.g. `[dict(type='LoadImageFromFile'), ...]`. If you want to change `'LoadImageFromFile'` to `'LoadImageFromWebcam'` in the pipeline,
+  you may specify `--cfg-options data.train.pipeline.0.type=LoadImageFromWebcam`.
+
+- Update values of list/tuples.
+
+  If the value to be updated is a list or a tuple. For example, the config file normally sets `workflow=[('train', 1)]`. If you want to
+  change this key, you may specify `--cfg-options workflow="[(train,1),(val,1)]"`. Note that the quotation mark " is necessary to
+  support list/tuple data types, and that **NO** white space is allowed inside the quotation marks in the specified value.
+
+## Config File Structure
+
+There are 4 basic component types under `config/_base_`, dataset, model, schedule, default_runtime.
+Many methods could be easily constructed with one of each like Faster R-CNN, Mask R-CNN, Cascade R-CNN, RPN, SSD.
+The configs that are composed by components from `_base_` are called _primitive_.
+
+For all configs under the same folder, it is recommended to have only **one** _primitive_ config. All other configs should inherit from the _primitive_ config. In this way, the maximum of inheritance level is 3.
+
+For easy understanding, we recommend contributors to inherit from existing methods.
+For example, if some modification is made base on Faster R-CNN, user may first inherit the basic Faster R-CNN structure by specifying `_base_ = ../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py`, then modify the necessary fields in the config files.
+
+If you are building an entirely new method that does not share the structure with any of the existing methods, you may create a folder `xxx_rcnn` under `configs`,
+
+Please refer to [mmcv](https://mmcv.readthedocs.io/en/latest/understand_mmcv/config.html) for detailed documentation.
+
+## Config Name Style
+
+We follow the below style to name config files. Contributors are advised to follow the same style.
+
+```
+{model}_[model setting]_{backbone}_{neck}_[norm setting]_[misc]_[gpu x batch_per_gpu]_{schedule}_{dataset}
+```
+
+`{xxx}` is required field and `[yyy]` is optional.
+
+- `{model}`: model type like `faster_rcnn`, `mask_rcnn`, etc.
+- `[model setting]`: specific setting for some model, like `without_semantic` for `htc`, `moment` for `reppoints`, etc.
+- `{backbone}`: backbone type like `r50` (ResNet-50), `x101` (ResNeXt-101).
+- `{neck}`: neck type like `fpn`, `pafpn`, `nasfpn`, `c4`.
+- `[norm_setting]`: `bn` (Batch Normalization) is used unless specified, other norm layer type could be `gn` (Group Normalization), `syncbn` (Synchronized Batch Normalization).
+  `gn-head`/`gn-neck` indicates GN is applied in head/neck only, while `gn-all` means GN is applied in the entire model, e.g. backbone, neck, head.
+- `[misc]`: miscellaneous setting/plugins of model, e.g. `dconv`, `gcb`, `attention`, `albu`, `mstrain`.
+- `[gpu x batch_per_gpu]`: GPUs and samples per GPU, `8x2` is used by default.
+- `{schedule}`: training schedule, options are `1x`, `2x`, `20e`, etc.
+  `1x` and `2x` means 12 epochs and 24 epochs respectively.
+  `20e` is adopted in cascade models, which denotes 20 epochs.
+  For `1x`/`2x`, initial learning rate decays by a factor of 10 at the 8/16th and 11/22th epochs.
+  For `20e`, initial learning rate decays by a factor of 10 at the 16th and 19th epochs.
+- `{dataset}`: dataset like `coco`, `cityscapes`, `voc_0712`, `wider_face`.
+
+## Deprecated train_cfg/test_cfg
+
+The `train_cfg` and `test_cfg` are deprecated in config file, please specify them in the model config. The original config structure is as below.
+
+```python
+# deprecated
+model = dict(
+    type=...,
+    ...
+)
+train_cfg=dict(...)
+test_cfg=dict(...)
+```
+
+The migration example is as below.
+
+```python
+# recommended
+model = dict(
+    type=...,
+    ...
+train_cfg=dict(...),
+          test_cfg=dict(...),
+)
+```
+
+## An Example of Mask R-CNN
+
+To help the users have a basic idea of a complete config and the modules in a modern detection system,
+we make brief comments on the config of Mask R-CNN using ResNet50 and FPN as the following.
+For more detailed usage and the corresponding alternative for each modules, please refer to the API documentation.
+
+```python
+model = dict(
+    type='MaskRCNN',  # The name of detector
+    backbone=dict(  # The config of backbone
+        type='ResNet',  # The type of the backbone, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/backbones/resnet.py#L308 for more details.
+        depth=50,  # The depth of backbone, usually it is 50 or 101 for ResNet and ResNext backbones.
+        num_stages=4,  # Number of stages of the backbone.
+        out_indices=(0, 1, 2, 3),  # The index of output feature maps produced in each stages
+        frozen_stages=1,  # The weights in the first 1 stage are frozen
+        norm_cfg=dict(  # The config of normalization layers.
+            type='BN',  # Type of norm layer, usually it is BN or GN
+            requires_grad=True),  # Whether to train the gamma and beta in BN
+        norm_eval=True,  # Whether to freeze the statistics in BN
+        style='pytorch', # The style of backbone, 'pytorch' means that stride 2 layers are in 3x3 conv, 'caffe' means stride 2 layers are in 1x1 convs.
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),  # The ImageNet pretrained backbone to be loaded
+    neck=dict(
+        type='FPN',  # The neck of detector is FPN. We also support 'NASFPN', 'PAFPN', etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/necks/fpn.py#L10 for more details.
+        in_channels=[256, 512, 1024, 2048],  # The input channels, this is consistent with the output channels of backbone
+        out_channels=256,  # The output channels of each level of the pyramid feature map
+        num_outs=5),  # The number of output scales
+    rpn_head=dict(
+        type='RPNHead',  # The type of RPN head is 'RPNHead', we also support 'GARPNHead', etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/rpn_head.py#L12 for more details.
+        in_channels=256,  # The input channels of each input feature map, this is consistent with the output channels of neck
+        feat_channels=256,  # Feature channels of convolutional layers in the head.
+        anchor_generator=dict(  # The config of anchor generator
+            type='AnchorGenerator',  # Most of methods use AnchorGenerator, SSD Detectors uses `SSDAnchorGenerator`. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/anchor/anchor_generator.py#L10 for more details
+            scales=[8],  # Basic scale of the anchor, the area of the anchor in one position of a feature map will be scale * base_sizes
+            ratios=[0.5, 1.0, 2.0],  # The ratio between height and width.
+            strides=[4, 8, 16, 32, 64]),  # The strides of the anchor generator. This is consistent with the FPN feature strides. The strides will be taken as base_sizes if base_sizes is not set.
+        bbox_coder=dict(  # Config of box coder to encode and decode the boxes during training and testing
+            type='DeltaXYWHBBoxCoder',  # Type of box coder. 'DeltaXYWHBBoxCoder' is applied for most of methods. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py#L9 for more details.
+            target_means=[0.0, 0.0, 0.0, 0.0],  # The target means used to encode and decode boxes
+            target_stds=[1.0, 1.0, 1.0, 1.0]),  # The standard variance used to encode and decode boxes
+        loss_cls=dict(  # Config of loss function for the classification branch
+            type='CrossEntropyLoss',  # Type of loss for classification branch, we also support FocalLoss etc.
+            use_sigmoid=True,  # RPN usually perform two-class classification, so it usually uses sigmoid function.
+            loss_weight=1.0),  # Loss weight of the classification branch.
+        loss_bbox=dict(  # Config of loss function for the regression branch.
+            type='L1Loss',  # Type of loss, we also support many IoU Losses and smooth L1-loss, etc. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/smooth_l1_loss.py#L56 for implementation.
+            loss_weight=1.0)),  # Loss weight of the regression branch.
+    roi_head=dict(  # RoIHead encapsulates the second stage of two-stage/cascade detectors.
+        type='StandardRoIHead',  # Type of the RoI head. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/standard_roi_head.py#L10 for implementation.
+        bbox_roi_extractor=dict(  # RoI feature extractor for bbox regression.
+            type='SingleRoIExtractor',  # Type of the RoI feature extractor, most of methods uses SingleRoIExtractor. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/roi_extractors/single_level.py#L10 for details.
+            roi_layer=dict(  # Config of RoI Layer
+                type='RoIAlign',  # Type of RoI Layer, DeformRoIPoolingPack and ModulatedDeformRoIPoolingPack are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/roi_align/roi_align.py#L79 for details.
+                output_size=7,  # The output size of feature maps.
+                sampling_ratio=0),  # Sampling ratio when extracting the RoI features. 0 means adaptive ratio.
+            out_channels=256,  # output channels of the extracted feature.
+            featmap_strides=[4, 8, 16, 32]),  # Strides of multi-scale feature maps. It should be consistent to the architecture of the backbone.
+        bbox_head=dict(  # Config of box head in the RoIHead.
+            type='Shared2FCBBoxHead',  # Type of the bbox head, Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py#L177 for implementation details.
+            in_channels=256,  # Input channels for bbox head. This is consistent with the out_channels in roi_extractor
+            fc_out_channels=1024,  # Output feature channels of FC layers.
+            roi_feat_size=7,  # Size of RoI features
+            num_classes=80,  # Number of classes for classification
+            bbox_coder=dict(  # Box coder used in the second stage.
+                type='DeltaXYWHBBoxCoder',  # Type of box coder. 'DeltaXYWHBBoxCoder' is applied for most of methods.
+                target_means=[0.0, 0.0, 0.0, 0.0],  # Means used to encode and decode box
+                target_stds=[0.1, 0.1, 0.2, 0.2]),  # Standard variance for encoding and decoding. It is smaller since the boxes are more accurate. [0.1, 0.1, 0.2, 0.2] is a conventional setting.
+            reg_class_agnostic=False,  # Whether the regression is class agnostic.
+            loss_cls=dict(  # Config of loss function for the classification branch
+                type='CrossEntropyLoss',  # Type of loss for classification branch, we also support FocalLoss etc.
+                use_sigmoid=False,  # Whether to use sigmoid.
+                loss_weight=1.0),  # Loss weight of the classification branch.
+            loss_bbox=dict(  # Config of loss function for the regression branch.
+                type='L1Loss',  # Type of loss, we also support many IoU Losses and smooth L1-loss, etc.
+                loss_weight=1.0)),  # Loss weight of the regression branch.
+        mask_roi_extractor=dict(  # RoI feature extractor for mask generation.
+            type='SingleRoIExtractor',  # Type of the RoI feature extractor, most of methods uses SingleRoIExtractor.
+            roi_layer=dict(  # Config of RoI Layer that extracts features for instance segmentation
+                type='RoIAlign',  # Type of RoI Layer, DeformRoIPoolingPack and ModulatedDeformRoIPoolingPack are also supported
+                output_size=14,  # The output size of feature maps.
+                sampling_ratio=0),  # Sampling ratio when extracting the RoI features.
+            out_channels=256,  # Output channels of the extracted feature.
+            featmap_strides=[4, 8, 16, 32]),  # Strides of multi-scale feature maps.
+        mask_head=dict(  # Mask prediction head
+            type='FCNMaskHead',  # Type of mask head, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py#L21 for implementation details.
+            num_convs=4,  # Number of convolutional layers in mask head.
+            in_channels=256,  # Input channels, should be consistent with the output channels of mask roi extractor.
+            conv_out_channels=256,  # Output channels of the convolutional layer.
+            num_classes=80,  # Number of class to be segmented.
+            loss_mask=dict(  # Config of loss function for the mask branch.
+                type='CrossEntropyLoss',  # Type of loss used for segmentation
+                use_mask=True,  # Whether to only train the mask in the correct class.
+                loss_weight=1.0))),  # Loss weight of mask branch.
+    train_cfg = dict(  # Config of training hyperparameters for rpn and rcnn
+        rpn=dict(  # Training config of rpn
+            assigner=dict(  # Config of assigner
+                type='MaxIoUAssigner',  # Type of assigner, MaxIoUAssigner is used for many common detectors. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details.
+                pos_iou_thr=0.7,  # IoU >= threshold 0.7 will be taken as positive samples
+                neg_iou_thr=0.3,  # IoU < threshold 0.3 will be taken as negative samples
+                min_pos_iou=0.3,  # The minimal IoU threshold to take boxes as positive samples
+                match_low_quality=True,  # Whether to match the boxes under low quality (see API doc for more details).
+                ignore_iof_thr=-1),  # IoF threshold for ignoring bboxes
+            sampler=dict(  # Config of positive/negative sampler
+                type='RandomSampler',  # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
+                num=256,  # Number of samples
+                pos_fraction=0.5,  # The ratio of positive samples in the total samples.
+                neg_pos_ub=-1,  # The upper bound of negative samples based on the number of positive samples.
+                add_gt_as_proposals=False),  # Whether add GT as proposals after sampling.
+            allowed_border=-1,  # The border allowed after padding for valid anchors.
+            pos_weight=-1,  # The weight of positive samples during training.
+            debug=False),  # Whether to set the debug mode
+        rpn_proposal=dict(  # The config to generate proposals during training
+            nms_across_levels=False,  # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
+            nms_pre=2000,  # The number of boxes before NMS
+            nms_post=1000,  # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
+            max_per_img=1000,  # The number of boxes to be kept after NMS.
+            nms=dict( # Config of NMS
+                type='nms',  # Type of NMS
+                iou_threshold=0.7 # NMS threshold
+                ),
+            min_bbox_size=0),  # The allowed minimal box size
+        rcnn=dict(  # The config for the roi heads.
+            assigner=dict(  # Config of assigner for second stage, this is different for that in rpn
+                type='MaxIoUAssigner',  # Type of assigner, MaxIoUAssigner is used for all roi_heads for now. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10 for more details.
+                pos_iou_thr=0.5,  # IoU >= threshold 0.5 will be taken as positive samples
+                neg_iou_thr=0.5,  # IoU < threshold 0.5 will be taken as negative samples
+                min_pos_iou=0.5,  # The minimal IoU threshold to take boxes as positive samples
+                match_low_quality=False,  # Whether to match the boxes under low quality (see API doc for more details).
+                ignore_iof_thr=-1),  # IoF threshold for ignoring bboxes
+            sampler=dict(
+                type='RandomSampler',  # Type of sampler, PseudoSampler and other samplers are also supported. Refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8 for implementation details.
+                num=512,  # Number of samples
+                pos_fraction=0.25,  # The ratio of positive samples in the total samples.
+                neg_pos_ub=-1,  # The upper bound of negative samples based on the number of positive samples.
+                add_gt_as_proposals=True
+            ),  # Whether add GT as proposals after sampling.
+            mask_size=28,  # Size of mask
+            pos_weight=-1,  # The weight of positive samples during training.
+            debug=False)),  # Whether to set the debug mode
+    test_cfg = dict(  # Config for testing hyperparameters for rpn and rcnn
+        rpn=dict(  # The config to generate proposals during testing
+            nms_across_levels=False,  # Whether to do NMS for boxes across levels. Only work in `GARPNHead`, naive rpn does not support do nms cross levels.
+            nms_pre=1000,  # The number of boxes before NMS
+            nms_post=1000,  # The number of boxes to be kept by NMS, Only work in `GARPNHead`.
+            max_per_img=1000,  # The number of boxes to be kept after NMS.
+            nms=dict( # Config of NMS
+                type='nms',  #Type of NMS
+                iou_threshold=0.7 # NMS threshold
+                ),
+            min_bbox_size=0),  # The allowed minimal box size
+        rcnn=dict(  # The config for the roi heads.
+            score_thr=0.05,  # Threshold to filter out boxes
+            nms=dict(  # Config of NMS in the second stage
+                type='nms',  # Type of NMS
+                iou_thr=0.5),  # NMS threshold
+            max_per_img=100,  # Max number of detections of each image
+            mask_thr_binary=0.5)))  # Threshold of mask prediction
+
+dataset_type = 'CocoDataset'  # Dataset type, this will be used to define the dataset
+data_root = 'data/coco/'  # Root path of data
+img_norm_cfg = dict(  # Image normalization config to normalize the input images
+    mean=[123.675, 116.28, 103.53],  # Mean values used to pre-training the pre-trained backbone models
+    std=[58.395, 57.12, 57.375],  # Standard variance used to pre-training the pre-trained backbone models
+    to_rgb=True
+)  # The channel orders of image used to pre-training the pre-trained backbone models
+train_pipeline = [  # Training pipeline
+    dict(type='LoadImageFromFile'),  # First pipeline to load images from file path
+    dict(
+        type='LoadAnnotations',  # Second pipeline to load annotations for current image
+        with_bbox=True,  # Whether to use bounding box, True for detection
+        with_mask=True,  # Whether to use instance mask, True for instance segmentation
+        poly2mask=False),  # Whether to convert the polygon mask to instance mask, set False for acceleration and to save memory
+    dict(
+        type='Resize',  # Augmentation pipeline that resize the images and their annotations
+        img_scale=(1333, 800),  # The largest scale of image
+        keep_ratio=True
+    ),  # whether to keep the ratio between height and width.
+    dict(
+        type='RandomFlip',  # Augmentation pipeline that flip the images and their annotations
+        flip_ratio=0.5),  # The ratio or probability to flip
+    dict(
+        type='Normalize',  # Augmentation pipeline that normalize the input images
+        mean=[123.675, 116.28, 103.53],  # These keys are the same of img_norm_cfg since the
+        std=[58.395, 57.12, 57.375],  # keys of img_norm_cfg are used here as arguments
+        to_rgb=True),
+    dict(
+        type='Pad',  # Padding config
+        size_divisor=32),  # The number the padded images should be divisible
+    dict(type='DefaultFormatBundle'),  # Default format bundle to gather data in the pipeline
+    dict(
+        type='Collect',  # Pipeline that decides which keys in the data should be passed to the detector
+        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),  # First pipeline to load images from file path
+    dict(
+        type='MultiScaleFlipAug',  # An encapsulation that encapsulates the testing augmentations
+        img_scale=(1333, 800),  # Decides the largest scale for testing, used for the Resize pipeline
+        flip=False,  # Whether to flip images during testing
+        transforms=[
+            dict(type='Resize',  # Use resize augmentation
+                 keep_ratio=True),  # Whether to keep the ratio between height and width, the img_scale set here will be suppressed by the img_scale set above.
+            dict(type='RandomFlip'),  # Thought RandomFlip is added in pipeline, it is not used because flip=False
+            dict(
+                type='Normalize',  # Normalization config, the values are from img_norm_cfg
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(
+                type='Pad',  # Padding config to pad images divisible by 32.
+                size_divisor=32),
+            dict(
+                type='ImageToTensor',  # convert image to tensor
+                keys=['img']),
+            dict(
+                type='Collect',  # Collect pipeline that collect necessary keys for testing.
+                keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,  # Batch size of a single GPU
+    workers_per_gpu=2,  # Worker to pre-fetch data for each single GPU
+    train=dict(  # Train dataset config
+        type='CocoDataset',  # Type of dataset, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/coco.py#L19 for details.
+        ann_file='data/coco/annotations/instances_train2017.json',  # Path of annotation file
+        img_prefix='data/coco/train2017/',  # Prefix of image path
+        pipeline=[  # pipeline, this is passed by the train_pipeline created before.
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='LoadAnnotations',
+                with_bbox=True,
+                with_mask=True,
+                poly2mask=False),
+            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(
+                type='Collect',
+                keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+        ]),
+    val=dict(  # Validation dataset config
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[  # Pipeline is passed by test_pipeline created before
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(  # Test dataset config, modify the ann_file for test-dev/test submission
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[  # Pipeline is passed by test_pipeline created before
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ],
+        samples_per_gpu=2  # Batch size of a single GPU used in testing
+    ))
+evaluation = dict(  # The config to build the evaluation hook, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/evaluation/eval_hooks.py#L7 for more details.
+    interval=1,  # Evaluation interval
+    metric=['bbox', 'segm'])  # Metrics used during evaluation
+optimizer = dict(  # Config used to build optimizer, support all the optimizers in PyTorch whose arguments are also the same as those in PyTorch
+    type='SGD',  # Type of optimizers, refer to https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/optimizer/default_constructor.py#L13 for more details
+    lr=0.02,  # Learning rate of optimizers, see detail usages of the parameters in the documentation of PyTorch
+    momentum=0.9,  # Momentum
+    weight_decay=0.0001)  # Weight decay of SGD
+optimizer_config = dict(  # Config used to build the optimizer hook, refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8 for implementation details.
+    grad_clip=None)  # Most of the methods do not use gradient clip
+lr_config = dict(  # Learning rate scheduler config used to register LrUpdater hook
+    policy='step',  # The policy of scheduler, also support CosineAnnealing, Cyclic, etc. Refer to details of supported LrUpdater from https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9.
+    warmup='linear',  # The warmup policy, also support `exp` and `constant`.
+    warmup_iters=500,  # The number of iterations for warmup
+    warmup_ratio=
+    0.001,  # The ratio of the starting learning rate used for warmup
+    step=[8, 11])  # Steps to decay the learning rate
+runner = dict(
+    type='EpochBasedRunner', # Type of runner to use (i.e. IterBasedRunner or EpochBasedRunner)
+    max_epochs=12) # Runner that runs the workflow in total max_epochs. For IterBasedRunner use `max_iters`
+checkpoint_config = dict(  # Config to set the checkpoint hook, Refer to https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py for implementation.
+    interval=1)  # The save interval is 1
+log_config = dict(  # config to register logger hook
+    interval=50,  # Interval to print the log
+    hooks=[
+        dict(type='TextLoggerHook', by_epoch=False),
+        dict(type='TensorboardLoggerHook', by_epoch=False),
+        dict(type='MMDetWandbHook', by_epoch=False, # The Wandb logger is also supported, It requires `wandb` to be installed.
+             init_kwargs={'entity': "OpenMMLab", # The entity used to log on Wandb
+                          'project': "MMDet", # Project name in WandB
+                          'config': cfg_dict}), # Check https://docs.wandb.ai/ref/python/init for more init arguments.
+        # MMDetWandbHook is mmdet implementation of WandbLoggerHook. ClearMLLoggerHook, DvcliveLoggerHook, MlflowLoggerHook, NeptuneLoggerHook, PaviLoggerHook, SegmindLoggerHook are also supported based on MMCV implementation.
+    ])  # The logger used to record the training process.
+
+dist_params = dict(backend='nccl')  # Parameters to setup distributed training, the port can also be set.
+log_level = 'INFO'  # The level of logging.
+load_from = None  # load models as a pre-trained model from a given path. This will not resume training.
+resume_from = None  # Resume checkpoints from a given path, the training will be resumed from the epoch when the checkpoint's is saved.
+workflow = [('train', 1)]  # Workflow for runner. [('train', 1)] means there is only one workflow and the workflow named 'train' is executed once. The workflow trains the model by 12 epochs according to the total_epochs.
+work_dir = 'work_dir'  # Directory to save the model checkpoints and logs for the current experiments.
+```
+
+## FAQ
+
+### Ignore some fields in the base configs
+
+Sometimes, you may set `_delete_=True` to ignore some of fields in base configs.
+You may refer to [mmcv](https://mmcv.readthedocs.io/en/latest/understand_mmcv/config.html#inherit-from-base-config-with-ignored-fields) for simple illustration.
+
+In MMDetection, for example, to change the backbone of Mask R-CNN with the following config.
+
+```python
+model = dict(
+    type='MaskRCNN',
+    pretrained='torchvision://resnet50',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch'),
+    neck=dict(...),
+    rpn_head=dict(...),
+    roi_head=dict(...))
+```
+
+`ResNet` and `HRNet` use different keywords to construct.
+
+```python
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w32',
+    backbone=dict(
+        _delete_=True,
+        type='HRNet',
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(32, 64)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(32, 64, 128)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(32, 64, 128, 256)))),
+    neck=dict(...))
+```
+
+The `_delete_=True` would replace all old keys in `backbone` field with new keys.
+
+### Use intermediate variables in configs
+
+Some intermediate variables are used in the configs files, like `train_pipeline`/`test_pipeline` in datasets.
+It's worth noting that when modifying intermediate variables in the children configs, user need to pass the intermediate variables into corresponding fields again.
+For example, we would like to use multi scale strategy to train a Mask R-CNN. `train_pipeline`/`test_pipeline` are intermediate variable we would like modify.
+
+```python
+_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode="value",
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+```
+
+We first define the new `train_pipeline`/`test_pipeline` and pass them into `data`.
+
+Similarly, if we would like to switch from `SyncBN` to `BN` or `MMSyncBN`, we need to substitute every `norm_cfg` in the config.
+
+```python
+_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='BN', requires_grad=True)
+model = dict(
+    backbone=dict(norm_cfg=norm_cfg),
+    neck=dict(norm_cfg=norm_cfg),
+    ...)
+```
diff --git a/docs/en/tutorials/customize_dataset.md b/docs/en/tutorials/customize_dataset.md
new file mode 100644
index 0000000..3237f16
--- /dev/null
+++ b/docs/en/tutorials/customize_dataset.md
@@ -0,0 +1,542 @@
+# Tutorial 2: Customize Datasets
+
+## Support new data format
+
+To support a new data format, you can either convert them to existing formats (COCO format or PASCAL format) or directly convert them to the middle format. You could also choose to convert them offline (before training by a script) or online (implement a new dataset and do the conversion at training). In MMDetection, we recommend to convert the data into COCO formats and do the conversion offline, thus you only need to modify the config's data annotation paths and classes after the conversion of your data.
+
+### Reorganize new data formats to existing format
+
+The simplest way is to convert your dataset to existing dataset formats (COCO or PASCAL VOC).
+
+The annotation json files in COCO format has the following necessary keys:
+
+```python
+'images': [
+    {
+        'file_name': 'COCO_val2014_000000001268.jpg',
+        'height': 427,
+        'width': 640,
+        'id': 1268
+    },
+    ...
+],
+
+'annotations': [
+    {
+        'segmentation': [[192.81,
+            247.09,
+            ...
+            219.03,
+            249.06]],  # if you have mask labels
+        'area': 1035.749,
+        'iscrowd': 0,
+        'image_id': 1268,
+        'bbox': [192.81, 224.8, 74.73, 33.43],
+        'category_id': 16,
+        'id': 42986
+    },
+    ...
+],
+
+'categories': [
+    {'id': 0, 'name': 'car'},
+ ]
+```
+
+There are three necessary keys in the json file:
+
+- `images`: contains a list of images with their information like `file_name`, `height`, `width`, and `id`.
+- `annotations`: contains the list of instance annotations.
+- `categories`: contains the list of categories names and their ID.
+
+After the data pre-processing, there are two steps for users to train the customized new dataset with existing format (e.g. COCO format):
+
+1. Modify the config file for using the customized dataset.
+2. Check the annotations of the customized dataset.
+
+Here we give an example to show the above two steps, which uses a customized dataset of 5 classes with COCO format to train an existing Cascade Mask R-CNN R50-FPN detector.
+
+#### 1. Modify the config file for using the customized dataset
+
+There are two aspects involved in the modification of config file:
+
+1. The `data` field. Specifically, you need to explicitly add the `classes` fields in `data.train`, `data.val` and `data.test`.
+2. The `num_classes` field in the `model` part. Explicitly over-write all the `num_classes` from default value (e.g. 80 in COCO) to your classes number.
+
+In `configs/my_custom_config.py`:
+
+```python
+
+# the new config inherits the base configs to highlight the necessary modification
+_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
+
+# 1. dataset settings
+dataset_type = 'CocoDataset'
+classes = ('a', 'b', 'c', 'd', 'e')
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        # explicitly add your class names to the field `classes`
+        classes=classes,
+        ann_file='path/to/your/train/annotation_data',
+        img_prefix='path/to/your/train/image_data'),
+    val=dict(
+        type=dataset_type,
+        # explicitly add your class names to the field `classes`
+        classes=classes,
+        ann_file='path/to/your/val/annotation_data',
+        img_prefix='path/to/your/val/image_data'),
+    test=dict(
+        type=dataset_type,
+        # explicitly add your class names to the field `classes`
+        classes=classes,
+        ann_file='path/to/your/test/annotation_data',
+        img_prefix='path/to/your/test/image_data'))
+
+# 2. model settings
+
+# explicitly over-write all the `num_classes` field from default 80 to 5.
+model = dict(
+    roi_head=dict(
+        bbox_head=[
+            dict(
+                type='Shared2FCBBoxHead',
+                # explicitly over-write all the `num_classes` field from default 80 to 5.
+                num_classes=5),
+            dict(
+                type='Shared2FCBBoxHead',
+                # explicitly over-write all the `num_classes` field from default 80 to 5.
+                num_classes=5),
+            dict(
+                type='Shared2FCBBoxHead',
+                # explicitly over-write all the `num_classes` field from default 80 to 5.
+                num_classes=5)],
+    # explicitly over-write all the `num_classes` field from default 80 to 5.
+    mask_head=dict(num_classes=5)))
+```
+
+#### 2. Check the annotations of the customized dataset
+
+Assuming your customized dataset is COCO format, make sure you have the correct annotations in the customized dataset:
+
+1. The length for `categories` field in annotations should exactly equal the tuple length of `classes` fields in your config, meaning the number of classes (e.g. 5 in this example).
+2. The `classes` fields in your config file should have exactly the same elements and the same order with the `name` in `categories` of annotations. MMDetection automatically maps the uncontinuous `id` in `categories` to the continuous label indices, so the string order of `name` in `categories` field affects the order of label indices. Meanwhile, the string order of `classes` in config affects the label text during visualization of predicted bounding boxes.
+3. The `category_id` in `annotations` field should be valid, i.e., all values in `category_id` should belong to `id` in `categories`.
+
+Here is a valid example of annotations:
+
+```python
+
+'annotations': [
+    {
+        'segmentation': [[192.81,
+            247.09,
+            ...
+            219.03,
+            249.06]],  # if you have mask labels
+        'area': 1035.749,
+        'iscrowd': 0,
+        'image_id': 1268,
+        'bbox': [192.81, 224.8, 74.73, 33.43],
+        'category_id': 16,
+        'id': 42986
+    },
+    ...
+],
+
+# MMDetection automatically maps the uncontinuous `id` to the continuous label indices.
+'categories': [
+    {'id': 1, 'name': 'a'}, {'id': 3, 'name': 'b'}, {'id': 4, 'name': 'c'}, {'id': 16, 'name': 'd'}, {'id': 17, 'name': 'e'},
+ ]
+```
+
+We use this way to support CityScapes dataset. The script is in [cityscapes.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/dataset_converters/cityscapes.py) and we also provide the finetuning [configs](https://github.com/open-mmlab/mmdetection/blob/master/configs/cityscapes).
+
+**Note**
+
+1. For instance segmentation datasets, **MMDetection only supports evaluating mask AP of dataset in COCO format for now**.
+2. It is recommended to convert the data offline before training, thus you can still use `CocoDataset` and only need to modify the path of annotations and the training classes.
+
+### Reorganize new data format to middle format
+
+It is also fine if you do not want to convert the annotation format to COCO or PASCAL format.
+Actually, we define a simple annotation format and all existing datasets are
+processed to be compatible with it, either online or offline.
+
+The annotation of a dataset is a list of dict, each dict corresponds to an image.
+There are 3 field `filename` (relative path), `width`, `height` for testing,
+and an additional field `ann` for training. `ann` is also a dict containing at least 2 fields:
+`bboxes` and `labels`, both of which are numpy arrays. Some datasets may provide
+annotations like crowd/difficult/ignored bboxes, we use `bboxes_ignore` and `labels_ignore`
+to cover them.
+
+Here is an example.
+
+```python
+
+[
+    {
+        'filename': 'a.jpg',
+        'width': 1280,
+        'height': 720,
+        'ann': {
+            'bboxes': <np.ndarray, float32> (n, 4),
+            'labels': <np.ndarray, int64> (n, ),
+            'bboxes_ignore': <np.ndarray, float32> (k, 4),
+            'labels_ignore': <np.ndarray, int64> (k, ) (optional field)
+        }
+    },
+    ...
+]
+```
+
+There are two ways to work with custom datasets.
+
+- online conversion
+
+  You can write a new Dataset class inherited from `CustomDataset`, and overwrite two methods
+  `load_annotations(self, ann_file)` and `get_ann_info(self, idx)`,
+  like [CocoDataset](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/coco.py) and [VOCDataset](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/voc.py).
+
+- offline conversion
+
+  You can convert the annotation format to the expected format above and save it to
+  a pickle or json file, like [pascal_voc.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/dataset_converters/pascal_voc.py).
+  Then you can simply use `CustomDataset`.
+
+### An example of customized dataset
+
+Assume the annotation is in a new format in text files.
+The bounding boxes annotations are stored in text file `annotation.txt` as the following
+
+```
+#
+000001.jpg
+1280 720
+2
+10 20 40 60 1
+20 40 50 60 2
+#
+000002.jpg
+1280 720
+3
+50 20 40 60 2
+20 40 30 45 2
+30 40 50 60 3
+```
+
+We can create a new dataset in `mmdet/datasets/my_dataset.py` to load the data.
+
+```python
+import mmcv
+import numpy as np
+
+from .builder import DATASETS
+from .custom import CustomDataset
+
+
+@DATASETS.register_module()
+class MyDataset(CustomDataset):
+
+    CLASSES = ('person', 'bicycle', 'car', 'motorcycle')
+
+    def load_annotations(self, ann_file):
+        ann_list = mmcv.list_from_file(ann_file)
+
+        data_infos = []
+        for i, ann_line in enumerate(ann_list):
+            if ann_line != '#':
+                continue
+
+            img_shape = ann_list[i + 2].split(' ')
+            width = int(img_shape[0])
+            height = int(img_shape[1])
+            bbox_number = int(ann_list[i + 3])
+
+            anns = ann_line.split(' ')
+            bboxes = []
+            labels = []
+            for anns in ann_list[i + 4:i + 4 + bbox_number]:
+                bboxes.append([float(ann) for ann in anns[:4]])
+                labels.append(int(anns[4]))
+
+            data_infos.append(
+                dict(
+                    filename=ann_list[i + 1],
+                    width=width,
+                    height=height,
+                    ann=dict(
+                        bboxes=np.array(bboxes).astype(np.float32),
+                        labels=np.array(labels).astype(np.int64))
+                ))
+
+        return data_infos
+
+    def get_ann_info(self, idx):
+        return self.data_infos[idx]['ann']
+
+```
+
+Then in the config, to use `MyDataset` you can modify the config as the following
+
+```python
+dataset_A_train = dict(
+    type='MyDataset',
+    ann_file = 'image_list.txt',
+    pipeline=train_pipeline
+)
+```
+
+## Customize datasets by dataset wrappers
+
+MMDetection also supports many dataset wrappers to mix the dataset or modify the dataset distribution for training.
+Currently it supports to three dataset wrappers as below:
+
+- `RepeatDataset`: simply repeat the whole dataset.
+- `ClassBalancedDataset`: repeat dataset in a class balanced manner.
+- `ConcatDataset`: concat datasets.
+
+### Repeat dataset
+
+We use `RepeatDataset` as wrapper to repeat the dataset. For example, suppose the original dataset is `Dataset_A`, to repeat it, the config looks like the following
+
+```python
+dataset_A_train = dict(
+        type='RepeatDataset',
+        times=N,
+        dataset=dict(  # This is the original config of Dataset_A
+            type='Dataset_A',
+            ...
+            pipeline=train_pipeline
+        )
+    )
+```
+
+### Class balanced dataset
+
+We use `ClassBalancedDataset` as wrapper to repeat the dataset based on category
+frequency. The dataset to repeat needs to instantiate function `self.get_cat_ids(idx)`
+to support `ClassBalancedDataset`.
+For example, to repeat `Dataset_A` with `oversample_thr=1e-3`, the config looks like the following
+
+```python
+dataset_A_train = dict(
+        type='ClassBalancedDataset',
+        oversample_thr=1e-3,
+        dataset=dict(  # This is the original config of Dataset_A
+            type='Dataset_A',
+            ...
+            pipeline=train_pipeline
+        )
+    )
+```
+
+You may refer to [source code](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/dataset_wrappers.py#L211) for details.
+
+### Concatenate dataset
+
+There are three ways to concatenate the dataset.
+
+1. If the datasets you want to concatenate are in the same type with different annotation files, you can concatenate the dataset configs like the following.
+
+   ```python
+   dataset_A_train = dict(
+       type='Dataset_A',
+       ann_file = ['anno_file_1', 'anno_file_2'],
+       pipeline=train_pipeline
+   )
+   ```
+
+   If the concatenated dataset is used for test or evaluation, this manner supports to evaluate each dataset separately. To test the concatenated datasets as a whole, you can set `separate_eval=False` as below.
+
+   ```python
+   dataset_A_train = dict(
+       type='Dataset_A',
+       ann_file = ['anno_file_1', 'anno_file_2'],
+       separate_eval=False,
+       pipeline=train_pipeline
+   )
+   ```
+
+2. In case the dataset you want to concatenate is different, you can concatenate the dataset configs like the following.
+
+   ```python
+   dataset_A_train = dict()
+   dataset_B_train = dict()
+
+   data = dict(
+       imgs_per_gpu=2,
+       workers_per_gpu=2,
+       train = [
+           dataset_A_train,
+           dataset_B_train
+       ],
+       val = dataset_A_val,
+       test = dataset_A_test
+       )
+   ```
+
+   If the concatenated dataset is used for test or evaluation, this manner also supports to evaluate each dataset separately.
+
+3. We also support to define `ConcatDataset` explicitly as the following.
+
+   ```python
+   dataset_A_val = dict()
+   dataset_B_val = dict()
+
+   data = dict(
+       imgs_per_gpu=2,
+       workers_per_gpu=2,
+       train=dataset_A_train,
+       val=dict(
+           type='ConcatDataset',
+           datasets=[dataset_A_val, dataset_B_val],
+           separate_eval=False))
+   ```
+
+   This manner allows users to evaluate all the datasets as a single one by setting `separate_eval=False`.
+
+**Note:**
+
+1. The option `separate_eval=False` assumes the datasets use `self.data_infos` during evaluation. Therefore, COCO datasets do not support this behavior since COCO datasets do not fully rely on `self.data_infos` for evaluation. Combining different types of datasets and evaluating them as a whole is not tested thus is not suggested.
+2. Evaluating `ClassBalancedDataset` and `RepeatDataset` is not supported thus evaluating concatenated datasets of these types is also not supported.
+
+A more complex example that repeats `Dataset_A` and `Dataset_B` by N and M times, respectively, and then concatenates the repeated datasets is as the following.
+
+```python
+dataset_A_train = dict(
+    type='RepeatDataset',
+    times=N,
+    dataset=dict(
+        type='Dataset_A',
+        ...
+        pipeline=train_pipeline
+    )
+)
+dataset_A_val = dict(
+    ...
+    pipeline=test_pipeline
+)
+dataset_A_test = dict(
+    ...
+    pipeline=test_pipeline
+)
+dataset_B_train = dict(
+    type='RepeatDataset',
+    times=M,
+    dataset=dict(
+        type='Dataset_B',
+        ...
+        pipeline=train_pipeline
+    )
+)
+data = dict(
+    imgs_per_gpu=2,
+    workers_per_gpu=2,
+    train = [
+        dataset_A_train,
+        dataset_B_train
+    ],
+    val = dataset_A_val,
+    test = dataset_A_test
+)
+
+```
+
+## Modify Dataset Classes
+
+With existing dataset types, we can modify the class names of them to train subset of the annotations.
+For example, if you want to train only three classes of the current dataset,
+you can modify the classes of dataset.
+The dataset will filter out the ground truth boxes of other classes automatically.
+
+```python
+classes = ('person', 'bicycle', 'car')
+data = dict(
+    train=dict(classes=classes),
+    val=dict(classes=classes),
+    test=dict(classes=classes))
+```
+
+MMDetection V2.0 also supports to read the classes from a file, which is common in real applications.
+For example, assume the `classes.txt` contains the name of classes as the following.
+
+```
+person
+bicycle
+car
+```
+
+Users can set the classes as a file path, the dataset will load it and convert it to a list automatically.
+
+```python
+classes = 'path/to/classes.txt'
+data = dict(
+    train=dict(classes=classes),
+    val=dict(classes=classes),
+    test=dict(classes=classes))
+```
+
+**Note**:
+
+- Before MMDetection v2.5.0, the dataset will filter out the empty GT images automatically if the classes are set and there is no way to disable that through config. This is an undesirable behavior and introduces confusion because if the classes are not set, the dataset only filter the empty GT images when `filter_empty_gt=True` and `test_mode=False`. After MMDetection v2.5.0, we decouple the image filtering process and the classes modification, i.e., the dataset will only filter empty GT images when `filter_empty_gt=True` and `test_mode=False`, no matter whether the classes are set. Thus, setting the classes only influences the annotations of classes used for training and users could decide whether to filter empty GT images by themselves.
+- Since the middle format only has box labels and does not contain the class names, when using `CustomDataset`, users cannot filter out the empty GT images through configs but only do this offline.
+- Please remember to modify the `num_classes` in the head when specifying `classes` in dataset. We implemented [NumClassCheckHook](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/utils.py) to check whether the numbers are consistent since v2.9.0(after PR#4508).
+- The features for setting dataset classes and dataset filtering will be refactored to be more user-friendly in the future (depends on the progress).
+
+## COCO Panoptic Dataset
+
+Now we support COCO Panoptic Dataset, the format of panoptic annotations is different from COCO format.
+Both the foreground and the background will exist in the annotation file.
+The annotation json files in COCO Panoptic format has the following necessary keys:
+
+```python
+'images': [
+    {
+        'file_name': '000000001268.jpg',
+        'height': 427,
+        'width': 640,
+        'id': 1268
+    },
+    ...
+]
+
+'annotations': [
+    {
+        'filename': '000000001268.jpg',
+        'image_id': 1268,
+        'segments_info': [
+            {
+                'id':8345037,  # One-to-one correspondence with the id in the annotation map.
+                'category_id': 51,
+                'iscrowd': 0,
+                'bbox': (x1, y1, w, h),  # The bbox of the background is the outer rectangle of its mask.
+                'area': 24315
+            },
+            ...
+        ]
+    },
+    ...
+]
+
+'categories': [  # including both foreground categories and background categories
+    {'id': 0, 'name': 'person'},
+    ...
+ ]
+```
+
+Moreover, the `seg_prefix` must be set to the path of the panoptic annotation images.
+
+```python
+data = dict(
+    type='CocoPanopticDataset',
+    train=dict(
+        seg_prefix = 'path/to/your/train/panoptic/image_annotation_data'
+    ),
+    val=dict(
+        seg_prefix = 'path/to/your/train/panoptic/image_annotation_data'
+    )
+)
+```
diff --git a/docs/en/tutorials/customize_losses.md b/docs/en/tutorials/customize_losses.md
new file mode 100644
index 0000000..5c00368
--- /dev/null
+++ b/docs/en/tutorials/customize_losses.md
@@ -0,0 +1,126 @@
+# Tutorial 6: Customize Losses
+
+MMDetection provides users with different loss functions. But the default configuration may be not applicable for different datasets or models, so users may want to modify a specific loss to adapt the new situation.
+
+This tutorial first elaborate the computation pipeline of losses, then give some instructions about how to modify each step. The modification can be categorized as tweaking and weighting.
+
+## Computation pipeline of a loss
+
+Given the input prediction and target, as well as the weights, a loss function maps the input tensor to the final loss scalar. The mapping can be divided into five steps:
+
+1. Set the sampling method to sample positive and negative samples.
+
+2. Get **element-wise** or **sample-wise** loss by the loss kernel function.
+
+3. Weighting the loss with a weight tensor **element-wisely**.
+
+4. Reduce the loss tensor to a **scalar**.
+
+5. Weighting the loss with a **scalar**.
+
+## Set sampling method (step 1)
+
+For some loss functions, sampling strategies are needed to avoid imbalance between positive and negative samples.
+
+For example, when using `CrossEntropyLoss` in RPN head, we need to set `RandomSampler` in `train_cfg`
+
+```python
+train_cfg=dict(
+    rpn=dict(
+        sampler=dict(
+            type='RandomSampler',
+            num=256,
+            pos_fraction=0.5,
+            neg_pos_ub=-1,
+            add_gt_as_proposals=False))
+```
+
+For some other losses which have positive and negative sample balance mechanism such as Focal Loss, GHMC, and QualityFocalLoss, the sampler is no more necessary.
+
+## Tweaking loss
+
+Tweaking a loss is more related with step 2, 4, 5, and most modifications can be specified in the config.
+Here we take [Focal Loss (FL)](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/focal_loss.py) as an example.
+The following code sniper are the construction method and config of FL respectively, they are actually one to one correspondence.
+
+```python
+@LOSSES.register_module()
+class FocalLoss(nn.Module):
+
+    def __init__(self,
+                 use_sigmoid=True,
+                 gamma=2.0,
+                 alpha=0.25,
+                 reduction='mean',
+                 loss_weight=1.0):
+```
+
+```python
+loss_cls=dict(
+    type='FocalLoss',
+    use_sigmoid=True,
+    gamma=2.0,
+    alpha=0.25,
+    loss_weight=1.0)
+```
+
+### Tweaking hyper-parameters (step 2)
+
+`gamma` and `beta` are two hyper-parameters in the Focal Loss. Say if we want to change the value of `gamma` to be 1.5 and `alpha` to be 0.5, then we can specify them in the config as follows:
+
+```python
+loss_cls=dict(
+    type='FocalLoss',
+    use_sigmoid=True,
+    gamma=1.5,
+    alpha=0.5,
+    loss_weight=1.0)
+```
+
+### Tweaking the way of reduction (step 3)
+
+The default way of reduction is `mean` for FL. Say if we want to change the reduction from `mean` to `sum`, we can specify it in the config as follows:
+
+```python
+loss_cls=dict(
+    type='FocalLoss',
+    use_sigmoid=True,
+    gamma=2.0,
+    alpha=0.25,
+    loss_weight=1.0,
+    reduction='sum')
+```
+
+### Tweaking loss weight (step 5)
+
+The loss weight here is a scalar which controls the weight of different losses in multi-task learning, e.g. classification loss and regression loss. Say if we want to change to loss weight of classification loss to be 0.5, we can specify it in the config as follows:
+
+```python
+loss_cls=dict(
+    type='FocalLoss',
+    use_sigmoid=True,
+    gamma=2.0,
+    alpha=0.25,
+    loss_weight=0.5)
+```
+
+## Weighting loss (step 3)
+
+Weighting loss means we re-weight the loss element-wisely. To be more specific, we multiply the loss tensor with a weight tensor which has the same shape. As a result, different entries of the loss can be scaled differently, and so called element-wisely.
+The loss weight varies across different models and highly context related, but overall there are two kinds of loss weights, `label_weights` for classification loss and `bbox_weights` for bbox regression loss. You can find them in the `get_target` method of the corresponding head. Here we take [ATSSHead](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/atss_head.py#L530) as an example, which inherit [AnchorHead](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/anchor_head.py) but overwrite its `get_targets` method which yields different `label_weights` and `bbox_weights`.
+
+```
+class ATSSHead(AnchorHead):
+
+    ...
+
+    def get_targets(self,
+                    anchor_list,
+                    valid_flag_list,
+                    gt_bboxes_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None,
+                    gt_labels_list=None,
+                    label_channels=1,
+                    unmap_outputs=True):
+```
diff --git a/docs/en/tutorials/customize_models.md b/docs/en/tutorials/customize_models.md
new file mode 100644
index 0000000..81c3912
--- /dev/null
+++ b/docs/en/tutorials/customize_models.md
@@ -0,0 +1,363 @@
+# Tutorial 4: Customize Models
+
+We basically categorize model components into 5 types.
+
+- backbone: usually an FCN network to extract feature maps, e.g., ResNet, MobileNet.
+- neck: the component between backbones and heads, e.g., FPN, PAFPN.
+- head: the component for specific tasks, e.g., bbox prediction and mask prediction.
+- roi extractor: the part for extracting RoI features from feature maps, e.g., RoI Align.
+- loss: the component in head for calculating losses, e.g., FocalLoss, L1Loss, and GHMLoss.
+
+## Develop new components
+
+### Add a new backbone
+
+Here we show how to develop new components with an example of MobileNet.
+
+#### 1. Define a new backbone (e.g. MobileNet)
+
+Create a new file `mmdet/models/backbones/mobilenet.py`.
+
+```python
+import torch.nn as nn
+
+from ..builder import BACKBONES
+
+
+@BACKBONES.register_module()
+class MobileNet(nn.Module):
+
+    def __init__(self, arg1, arg2):
+        pass
+
+    def forward(self, x):  # should return a tuple
+        pass
+```
+
+#### 2. Import the module
+
+You can either add the following line to `mmdet/models/backbones/__init__.py`
+
+```python
+from .mobilenet import MobileNet
+```
+
+or alternatively add
+
+```python
+custom_imports = dict(
+    imports=['mmdet.models.backbones.mobilenet'],
+    allow_failed_imports=False)
+```
+
+to the config file to avoid modifying the original code.
+
+#### 3. Use the backbone in your config file
+
+```python
+model = dict(
+    ...
+    backbone=dict(
+        type='MobileNet',
+        arg1=xxx,
+        arg2=xxx),
+    ...
+```
+
+### Add new necks
+
+#### 1. Define a neck (e.g. PAFPN)
+
+Create a new file `mmdet/models/necks/pafpn.py`.
+
+```python
+from ..builder import NECKS
+
+@NECKS.register_module()
+class PAFPN(nn.Module):
+
+    def __init__(self,
+                in_channels,
+                out_channels,
+                num_outs,
+                start_level=0,
+                end_level=-1,
+                add_extra_convs=False):
+        pass
+
+    def forward(self, inputs):
+        # implementation is ignored
+        pass
+```
+
+#### 2. Import the module
+
+You can either add the following line to `mmdet/models/necks/__init__.py`,
+
+```python
+from .pafpn import PAFPN
+```
+
+or alternatively add
+
+```python
+custom_imports = dict(
+    imports=['mmdet.models.necks.pafpn.py'],
+    allow_failed_imports=False)
+```
+
+to the config file and avoid modifying the original code.
+
+#### 3. Modify the config file
+
+```python
+neck=dict(
+    type='PAFPN',
+    in_channels=[256, 512, 1024, 2048],
+    out_channels=256,
+    num_outs=5)
+```
+
+### Add new heads
+
+Here we show how to develop a new head with the example of [Double Head R-CNN](https://arxiv.org/abs/1904.06493) as the following.
+
+First, add a new bbox head in `mmdet/models/roi_heads/bbox_heads/double_bbox_head.py`.
+Double Head R-CNN implements a new bbox head for object detection.
+To implement a bbox head, basically we need to implement three functions of the new module as the following.
+
+```python
+from mmdet.models.builder import HEADS
+from .bbox_head import BBoxHead
+
+@HEADS.register_module()
+class DoubleConvFCBBoxHead(BBoxHead):
+    r"""Bbox head used in Double-Head R-CNN
+
+                                      /-> cls
+                  /-> shared convs ->
+                                      \-> reg
+    roi features
+                                      /-> cls
+                  \-> shared fc    ->
+                                      \-> reg
+    """  # noqa: W605
+
+    def __init__(self,
+                 num_convs=0,
+                 num_fcs=0,
+                 conv_out_channels=1024,
+                 fc_out_channels=1024,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 **kwargs):
+        kwargs.setdefault('with_avg_pool', True)
+        super(DoubleConvFCBBoxHead, self).__init__(**kwargs)
+
+
+    def forward(self, x_cls, x_reg):
+
+```
+
+Second, implement a new RoI Head if it is necessary. We plan to inherit the new `DoubleHeadRoIHead` from `StandardRoIHead`. We can find that a `StandardRoIHead` already implements the following functions.
+
+```python
+import torch
+
+from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
+from ..builder import HEADS, build_head, build_roi_extractor
+from .base_roi_head import BaseRoIHead
+from .test_mixins import BBoxTestMixin, MaskTestMixin
+
+
+@HEADS.register_module()
+class StandardRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin):
+    """Simplest base roi head including one bbox head and one mask head.
+    """
+
+    def init_assigner_sampler(self):
+
+    def init_bbox_head(self, bbox_roi_extractor, bbox_head):
+
+    def init_mask_head(self, mask_roi_extractor, mask_head):
+
+
+    def forward_dummy(self, x, proposals):
+
+
+    def forward_train(self,
+                      x,
+                      img_metas,
+                      proposal_list,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None,
+                      gt_masks=None):
+
+    def _bbox_forward(self, x, rois):
+
+    def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
+                            img_metas):
+
+    def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,
+                            img_metas):
+
+    def _mask_forward(self, x, rois=None, pos_inds=None, bbox_feats=None):
+
+
+    def simple_test(self,
+                    x,
+                    proposal_list,
+                    img_metas,
+                    proposals=None,
+                    rescale=False):
+        """Test without augmentation."""
+
+```
+
+Double Head's modification is mainly in the bbox_forward logic, and it inherits other logics from the `StandardRoIHead`.
+In the `mmdet/models/roi_heads/double_roi_head.py`, we implement the new RoI Head as the following:
+
+```python
+from ..builder import HEADS
+from .standard_roi_head import StandardRoIHead
+
+
+@HEADS.register_module()
+class DoubleHeadRoIHead(StandardRoIHead):
+    """RoI head for Double Head RCNN
+
+    https://arxiv.org/abs/1904.06493
+    """
+
+    def __init__(self, reg_roi_scale_factor, **kwargs):
+        super(DoubleHeadRoIHead, self).__init__(**kwargs)
+        self.reg_roi_scale_factor = reg_roi_scale_factor
+
+    def _bbox_forward(self, x, rois):
+        bbox_cls_feats = self.bbox_roi_extractor(
+            x[:self.bbox_roi_extractor.num_inputs], rois)
+        bbox_reg_feats = self.bbox_roi_extractor(
+            x[:self.bbox_roi_extractor.num_inputs],
+            rois,
+            roi_scale_factor=self.reg_roi_scale_factor)
+        if self.with_shared_head:
+            bbox_cls_feats = self.shared_head(bbox_cls_feats)
+            bbox_reg_feats = self.shared_head(bbox_reg_feats)
+        cls_score, bbox_pred = self.bbox_head(bbox_cls_feats, bbox_reg_feats)
+
+        bbox_results = dict(
+            cls_score=cls_score,
+            bbox_pred=bbox_pred,
+            bbox_feats=bbox_cls_feats)
+        return bbox_results
+```
+
+Last, the users need to add the module in
+`mmdet/models/bbox_heads/__init__.py` and `mmdet/models/roi_heads/__init__.py` thus the corresponding registry could find and load them.
+
+Alternatively, the users can add
+
+```python
+custom_imports=dict(
+    imports=['mmdet.models.roi_heads.double_roi_head', 'mmdet.models.bbox_heads.double_bbox_head'])
+```
+
+to the config file and achieve the same goal.
+
+The config file of Double Head R-CNN is as the following
+
+```python
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        type='DoubleHeadRoIHead',
+        reg_roi_scale_factor=1.3,
+        bbox_head=dict(
+            _delete_=True,
+            type='DoubleConvFCBBoxHead',
+            num_convs=4,
+            num_fcs=2,
+            in_channels=256,
+            conv_out_channels=1024,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=2.0),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=2.0))))
+
+```
+
+Since MMDetection 2.0, the config system supports to inherit configs such that the users can focus on the modification.
+The Double Head R-CNN mainly uses a new DoubleHeadRoIHead and a new
+`DoubleConvFCBBoxHead`, the arguments are set according to the `__init__` function of each module.
+
+### Add new loss
+
+Assume you want to add a new loss as `MyLoss`, for bounding box regression.
+To add a new loss function, the users need implement it in `mmdet/models/losses/my_loss.py`.
+The decorator `weighted_loss` enable the loss to be weighted for each element.
+
+```python
+import torch
+import torch.nn as nn
+
+from ..builder import LOSSES
+from .utils import weighted_loss
+
+@weighted_loss
+def my_loss(pred, target):
+    assert pred.size() == target.size() and target.numel() > 0
+    loss = torch.abs(pred - target)
+    return loss
+
+@LOSSES.register_module()
+class MyLoss(nn.Module):
+
+    def __init__(self, reduction='mean', loss_weight=1.0):
+        super(MyLoss, self).__init__()
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None):
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        loss_bbox = self.loss_weight * my_loss(
+            pred, target, weight, reduction=reduction, avg_factor=avg_factor)
+        return loss_bbox
+```
+
+Then the users need to add it in the `mmdet/models/losses/__init__.py`.
+
+```python
+from .my_loss import MyLoss, my_loss
+
+```
+
+Alternatively, you can add
+
+```python
+custom_imports=dict(
+    imports=['mmdet.models.losses.my_loss'])
+```
+
+to the config file and achieve the same goal.
+
+To use it, modify the `loss_xxx` field.
+Since MyLoss is for regression, you need to modify the `loss_bbox` field in the head.
+
+```python
+loss_bbox=dict(type='MyLoss', loss_weight=1.0))
+```
diff --git a/docs/en/tutorials/customize_runtime.md b/docs/en/tutorials/customize_runtime.md
new file mode 100644
index 0000000..f08d90b
--- /dev/null
+++ b/docs/en/tutorials/customize_runtime.md
@@ -0,0 +1,323 @@
+# Tutorial 5: Customize Runtime Settings
+
+## Customize optimization settings
+
+### Customize optimizer supported by Pytorch
+
+We already support to use all the optimizers implemented by PyTorch, and the only modification is to change the `optimizer` field of config files.
+For example, if you want to use `ADAM` (note that the performance could drop a lot), the modification could be as the following.
+
+```python
+optimizer = dict(type='Adam', lr=0.0003, weight_decay=0.0001)
+```
+
+To modify the learning rate of the model, the users only need to modify the `lr` in the config of optimizer. The users can directly set arguments following the [API doc](https://pytorch.org/docs/stable/optim.html?highlight=optim#module-torch.optim) of PyTorch.
+
+### Customize self-implemented optimizer
+
+#### 1. Define a new optimizer
+
+A customized optimizer could be defined as following.
+
+Assume you want to add a optimizer named `MyOptimizer`, which has arguments `a`, `b`, and `c`.
+You need to create a new directory named `mmdet/core/optimizer`.
+And then implement the new optimizer in a file, e.g., in `mmdet/core/optimizer/my_optimizer.py`:
+
+```python
+from .registry import OPTIMIZERS
+from torch.optim import Optimizer
+
+
+@OPTIMIZERS.register_module()
+class MyOptimizer(Optimizer):
+
+    def __init__(self, a, b, c)
+
+```
+
+#### 2. Add the optimizer to registry
+
+To find the above module defined above, this module should be imported into the main namespace at first. There are two options to achieve it.
+
+- Modify `mmdet/core/optimizer/__init__.py` to import it.
+
+  The newly defined module should be imported in `mmdet/core/optimizer/__init__.py` so that the registry will
+  find the new module and add it:
+
+```python
+from .my_optimizer import MyOptimizer
+```
+
+- Use `custom_imports` in the config to manually import it
+
+```python
+custom_imports = dict(imports=['mmdet.core.optimizer.my_optimizer'], allow_failed_imports=False)
+```
+
+The module `mmdet.core.optimizer.my_optimizer` will be imported at the beginning of the program and the class `MyOptimizer` is then automatically registered.
+Note that only the package containing the class `MyOptimizer` should be imported.
+`mmdet.core.optimizer.my_optimizer.MyOptimizer` **cannot** be imported directly.
+
+Actually users can use a totally different file directory structure using this importing method, as long as the module root can be located in `PYTHONPATH`.
+
+#### 3. Specify the optimizer in the config file
+
+Then you can use `MyOptimizer` in `optimizer` field of config files.
+In the configs, the optimizers are defined by the field `optimizer` like the following:
+
+```python
+optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
+```
+
+To use your own optimizer, the field can be changed to
+
+```python
+optimizer = dict(type='MyOptimizer', a=a_value, b=b_value, c=c_value)
+```
+
+### Customize optimizer constructor
+
+Some models may have some parameter-specific settings for optimization, e.g. weight decay for BatchNorm layers.
+The users can do those fine-grained parameter tuning through customizing optimizer constructor.
+
+```python
+from mmcv.utils import build_from_cfg
+
+from mmcv.runner.optimizer import OPTIMIZER_BUILDERS, OPTIMIZERS
+from mmdet.utils import get_root_logger
+from .my_optimizer import MyOptimizer
+
+
+@OPTIMIZER_BUILDERS.register_module()
+class MyOptimizerConstructor(object):
+
+    def __init__(self, optimizer_cfg, paramwise_cfg=None):
+
+    def __call__(self, model):
+
+        return my_optimizer
+
+```
+
+The default optimizer constructor is implemented [here](https://github.com/open-mmlab/mmcv/blob/9ecd6b0d5ff9d2172c49a182eaa669e9f27bb8e7/mmcv/runner/optimizer/default_constructor.py#L11), which could also serve as a template for new optimizer constructor.
+
+### Additional settings
+
+Tricks not implemented by the optimizer should be implemented through optimizer constructor (e.g., set parameter-wise learning rates) or hooks. We list some common settings that could stabilize the training or accelerate the training. Feel free to create PR, issue for more settings.
+
+- __Use gradient clip to stabilize training__:
+  Some models need gradient clip to clip the gradients to stabilize the training process. An example is as below:
+
+  ```python
+  optimizer_config = dict(
+      _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
+  ```
+
+  If your config inherits the base config which already sets the `optimizer_config`, you might need `_delete_=True` to override the unnecessary settings. See the [config documentation](https://mmdetection.readthedocs.io/en/latest/tutorials/config.html) for more details.
+
+- __Use momentum schedule to accelerate model convergence__:
+  We support momentum scheduler to modify model's momentum according to learning rate, which could make the model converge in a faster way.
+  Momentum scheduler is usually used with LR scheduler, for example, the following config is used in 3D detection to accelerate convergence.
+  For more details, please refer to the implementation of [CyclicLrUpdater](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/lr_updater.py#L327) and [CyclicMomentumUpdater](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/momentum_updater.py#L130).
+
+  ```python
+  lr_config = dict(
+      policy='cyclic',
+      target_ratio=(10, 1e-4),
+      cyclic_times=1,
+      step_ratio_up=0.4,
+  )
+  momentum_config = dict(
+      policy='cyclic',
+      target_ratio=(0.85 / 0.95, 1),
+      cyclic_times=1,
+      step_ratio_up=0.4,
+  )
+  ```
+
+## Customize training schedules
+
+By default we use step learning rate with 1x schedule, this calls [`StepLRHook`](https://github.com/open-mmlab/mmcv/blob/f48241a65aebfe07db122e9db320c31b685dc674/mmcv/runner/hooks/lr_updater.py#L153) in MMCV.
+We support many other learning rate schedule [here](https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py), such as `CosineAnnealing` and `Poly` schedule. Here are some examples
+
+- Poly schedule:
+
+  ```python
+  lr_config = dict(policy='poly', power=0.9, min_lr=1e-4, by_epoch=False)
+  ```
+
+- ConsineAnnealing schedule:
+
+  ```python
+  lr_config = dict(
+      policy='CosineAnnealing',
+      warmup='linear',
+      warmup_iters=1000,
+      warmup_ratio=1.0 / 10,
+      min_lr_ratio=1e-5)
+  ```
+
+## Customize workflow
+
+Workflow is a list of (phase, epochs) to specify the running order and epochs.
+By default it is set to be
+
+```python
+workflow = [('train', 1)]
+```
+
+which means running 1 epoch for training.
+Sometimes user may want to check some metrics (e.g. loss, accuracy) about the model on the validate set.
+In such case, we can set the workflow as
+
+```python
+[('train', 1), ('val', 1)]
+```
+
+so that 1 epoch for training and 1 epoch for validation will be run iteratively.
+
+**Note**:
+
+1. The parameters of model will not be updated during val epoch.
+2. Keyword `total_epochs` in the config only controls the number of training epochs and will not affect the validation workflow.
+3. Workflows `[('train', 1), ('val', 1)]` and `[('train', 1)]` will not change the behavior of `EvalHook` because `EvalHook` is called by `after_train_epoch` and validation workflow only affect hooks that are called through `after_val_epoch`. Therefore, the only difference between `[('train', 1), ('val', 1)]` and `[('train', 1)]` is that the runner will calculate losses on validation set after each training epoch.
+
+## Customize hooks
+
+### Customize self-implemented hooks
+
+#### 1. Implement a new hook
+
+There are some occasions when the users might need to implement a new hook. MMDetection supports customized hooks in training (#3395) since v2.3.0. Thus the users could implement a hook directly in mmdet or their mmdet-based codebases and use the hook by only modifying the config in training.
+Before v2.3.0, the users need to modify the code to get the hook registered before training starts.
+Here we give an example of creating a new hook in mmdet and using it in training.
+
+```python
+from mmcv.runner import HOOKS, Hook
+
+
+@HOOKS.register_module()
+class MyHook(Hook):
+
+    def __init__(self, a, b):
+        pass
+
+    def before_run(self, runner):
+        pass
+
+    def after_run(self, runner):
+        pass
+
+    def before_epoch(self, runner):
+        pass
+
+    def after_epoch(self, runner):
+        pass
+
+    def before_iter(self, runner):
+        pass
+
+    def after_iter(self, runner):
+        pass
+```
+
+Depending on the functionality of the hook, the users need to specify what the hook will do at each stage of the training in `before_run`, `after_run`, `before_epoch`, `after_epoch`, `before_iter`, and `after_iter`.
+
+#### 2. Register the new hook
+
+Then we need to make `MyHook` imported. Assuming the file is in `mmdet/core/utils/my_hook.py` there are two ways to do that:
+
+- Modify `mmdet/core/utils/__init__.py` to import it.
+
+  The newly defined module should be imported in `mmdet/core/utils/__init__.py` so that the registry will
+  find the new module and add it:
+
+```python
+from .my_hook import MyHook
+```
+
+- Use `custom_imports` in the config to manually import it
+
+```python
+custom_imports = dict(imports=['mmdet.core.utils.my_hook'], allow_failed_imports=False)
+```
+
+#### 3. Modify the config
+
+```python
+custom_hooks = [
+    dict(type='MyHook', a=a_value, b=b_value)
+]
+```
+
+You can also set the priority of the hook by adding key `priority` to `'NORMAL'` or `'HIGHEST'` as below
+
+```python
+custom_hooks = [
+    dict(type='MyHook', a=a_value, b=b_value, priority='NORMAL')
+]
+```
+
+By default the hook's priority is set as `NORMAL` during registration.
+
+### Use hooks implemented in MMCV
+
+If the hook is already implemented in MMCV, you can directly modify the config to use the hook as below
+
+#### 4. Example: `NumClassCheckHook`
+
+We implement a customized hook named  [NumClassCheckHook](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/utils.py) to check whether the `num_classes` in head matches the length of `CLASSES` in `dataset`.
+
+We set it in [default_runtime.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/_base_/default_runtime.py).
+
+```python
+custom_hooks = [dict(type='NumClassCheckHook')]
+```
+
+### Modify default runtime hooks
+
+There are some common hooks that are not registered through `custom_hooks`, they are
+
+- log_config
+- checkpoint_config
+- evaluation
+- lr_config
+- optimizer_config
+- momentum_config
+
+In those hooks, only the logger hook has the `VERY_LOW` priority, others' priority are `NORMAL`.
+The above-mentioned tutorials already covers how to modify `optimizer_config`, `momentum_config`, and `lr_config`.
+Here we reveal how what we can do with `log_config`, `checkpoint_config`, and `evaluation`.
+
+#### Checkpoint config
+
+The MMCV runner will use `checkpoint_config` to initialize [`CheckpointHook`](https://github.com/open-mmlab/mmcv/blob/9ecd6b0d5ff9d2172c49a182eaa669e9f27bb8e7/mmcv/runner/hooks/checkpoint.py#L9).
+
+```python
+checkpoint_config = dict(interval=1)
+```
+
+The users could set `max_keep_ckpts` to save only small number of checkpoints or decide whether to store state dict of optimizer by `save_optimizer`. More details of the arguments are [here](https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.CheckpointHook)
+
+#### Log config
+
+The `log_config` wraps multiple logger hooks and enables to set intervals. Now MMCV supports `WandbLoggerHook`, `MlflowLoggerHook`, and `TensorboardLoggerHook`.
+The detail usages can be found in the [doc](https://mmcv.readthedocs.io/en/latest/api.html#mmcv.runner.LoggerHook).
+
+```python
+log_config = dict(
+    interval=50,
+    hooks=[
+        dict(type='TextLoggerHook'),
+        dict(type='TensorboardLoggerHook')
+    ])
+```
+
+#### Evaluation config
+
+The config of `evaluation` will be used to initialize the [`EvalHook`](https://github.com/open-mmlab/mmdetection/blob/7a404a2c000620d52156774a5025070d9e00d918/mmdet/core/evaluation/eval_hooks.py#L8).
+Except the key `interval`, other arguments such as `metric` will be passed to the `dataset.evaluate()`
+
+```python
+evaluation = dict(interval=1, metric='bbox')
+```
diff --git a/docs/en/tutorials/data_pipeline.md b/docs/en/tutorials/data_pipeline.md
new file mode 100644
index 0000000..57a6db4
--- /dev/null
+++ b/docs/en/tutorials/data_pipeline.md
@@ -0,0 +1,199 @@
+# Tutorial 3: Customize Data Pipelines
+
+## Design of Data pipelines
+
+Following typical conventions, we use `Dataset` and `DataLoader` for data loading
+with multiple workers. `Dataset` returns a dict of data items corresponding
+the arguments of models' forward method.
+Since the data in object detection may not be the same size (image size, gt bbox size, etc.),
+we introduce a new `DataContainer` type in MMCV to help collect and distribute
+data of different size.
+See [here](https://github.com/open-mmlab/mmcv/blob/master/mmcv/parallel/data_container.py) for more details.
+
+The data preparation pipeline and the dataset is decomposed. Usually a dataset
+defines how to process the annotations and a data pipeline defines all the steps to prepare a data dict.
+A pipeline consists of a sequence of operations. Each operation takes a dict as input and also output a dict for the next transform.
+
+We present a classical pipeline in the following figure. The blue blocks are pipeline operations. With the pipeline going on, each operator can add new keys (marked as green) to the result dict or update the existing keys (marked as orange).
+![pipeline figure](../../../resources/data_pipeline.png)
+
+The operations are categorized into data loading, pre-processing, formatting and test-time augmentation.
+
+Here is a pipeline example for Faster R-CNN.
+
+```python
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+```
+
+For each operation, we list the related dict fields that are added/updated/removed.
+
+### Data loading
+
+`LoadImageFromFile`
+
+- add: img, img_shape, ori_shape
+
+`LoadAnnotations`
+
+- add: gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg, bbox_fields, mask_fields
+
+`LoadProposals`
+
+- add: proposals
+
+### Pre-processing
+
+`Resize`
+
+- add: scale, scale_idx, pad_shape, scale_factor, keep_ratio
+- update: img, img_shape, \*bbox_fields, \*mask_fields, \*seg_fields
+
+`RandomFlip`
+
+- add: flip
+- update: img, \*bbox_fields, \*mask_fields, \*seg_fields
+
+`Pad`
+
+- add: pad_fixed_size, pad_size_divisor
+- update: img, pad_shape, \*mask_fields, \*seg_fields
+
+`RandomCrop`
+
+- update: img, pad_shape, gt_bboxes, gt_labels, gt_masks, \*bbox_fields
+
+`Normalize`
+
+- add: img_norm_cfg
+- update: img
+
+`SegRescale`
+
+- update: gt_semantic_seg
+
+`PhotoMetricDistortion`
+
+- update: img
+
+`Expand`
+
+- update: img, gt_bboxes
+
+`MinIoURandomCrop`
+
+- update: img, gt_bboxes, gt_labels
+
+`Corrupt`
+
+- update: img
+
+### Formatting
+
+`ToTensor`
+
+- update: specified by `keys`.
+
+`ImageToTensor`
+
+- update: specified by `keys`.
+
+`Transpose`
+
+- update: specified by `keys`.
+
+`ToDataContainer`
+
+- update: specified by `fields`.
+
+`DefaultFormatBundle`
+
+- update: img, proposals, gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg
+
+`Collect`
+
+- add: img_meta (the keys of img_meta is specified by `meta_keys`)
+- remove: all other keys except for those specified by `keys`
+
+### Test time augmentation
+
+`MultiScaleFlipAug`
+
+## Extend and use custom pipelines
+
+1. Write a new pipeline in a file, e.g., in `my_pipeline.py`. It takes a dict as input and returns a dict.
+
+   ```python
+   import random
+   from mmdet.datasets import PIPELINES
+
+
+   @PIPELINES.register_module()
+   class MyTransform:
+       """Add your transform
+
+       Args:
+           p (float): Probability of shifts. Default 0.5.
+       """
+
+       def __init__(self, p=0.5):
+           self.p = p
+
+       def __call__(self, results):
+           if random.random() > self.p:
+               results['dummy'] = True
+           return results
+   ```
+
+2. Import and use the pipeline in your config file.
+   Make sure the import is relative to where your train script is located.
+
+   ```python
+   custom_imports = dict(imports=['path.to.my_pipeline'], allow_failed_imports=False)
+
+   img_norm_cfg = dict(
+       mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+   train_pipeline = [
+       dict(type='LoadImageFromFile'),
+       dict(type='LoadAnnotations', with_bbox=True),
+       dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+       dict(type='RandomFlip', flip_ratio=0.5),
+       dict(type='Normalize', **img_norm_cfg),
+       dict(type='Pad', size_divisor=32),
+       dict(type='MyTransform', p=0.2),
+       dict(type='DefaultFormatBundle'),
+       dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+   ]
+   ```
+
+3. Visualize the output of your augmentation pipeline
+
+   To visualize the output of your augmentation pipeline, `tools/misc/browse_dataset.py`
+   can help the user to browse a detection dataset (both images and bounding box annotations)
+   visually, or save the image to a designated directory. More details can refer to
+   [useful_tools](../useful_tools.md)
diff --git a/docs/en/tutorials/finetune.md b/docs/en/tutorials/finetune.md
new file mode 100644
index 0000000..afa5021
--- /dev/null
+++ b/docs/en/tutorials/finetune.md
@@ -0,0 +1,89 @@
+# Tutorial 7: Finetuning Models
+
+Detectors pre-trained on the COCO dataset can serve as a good pre-trained model for other datasets, e.g., CityScapes and KITTI Dataset.
+This tutorial provides instruction for users to use the models provided in the [Model Zoo](../model_zoo.md) for other datasets to obtain better performance.
+
+There are two steps to finetune a model on a new dataset.
+
+- Add support for the new dataset following [Tutorial 2: Customize Datasets](customize_dataset.md).
+- Modify the configs as will be discussed in this tutorial.
+
+Take the finetuning process on Cityscapes Dataset as an example, the users need to modify five parts in the config.
+
+## Inherit base configs
+
+To release the burden and reduce bugs in writing the whole configs, MMDetection V2.0 support inheriting configs from multiple existing configs. To finetune a Mask RCNN model, the new config needs to inherit
+`_base_/models/mask_rcnn_r50_fpn.py` to build the basic structure of the model. To use the Cityscapes Dataset, the new config can also simply inherit `_base_/datasets/cityscapes_instance.py`. For runtime settings such as training schedules, the new config needs to inherit `_base_/default_runtime.py`. This configs are in the `configs` directory and the users can also choose to write the whole contents rather than use inheritance.
+
+```python
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py'
+]
+```
+
+## Modify head
+
+Then the new config needs to modify the head according to the class numbers of the new datasets. By only changing `num_classes` in the roi_head, the weights of the pre-trained models are mostly reused except the final prediction head.
+
+```python
+model = dict(
+    pretrained=None,
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=8,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
+        mask_head=dict(
+            type='FCNMaskHead',
+            num_convs=4,
+            in_channels=256,
+            conv_out_channels=256,
+            num_classes=8,
+            loss_mask=dict(
+                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))))
+```
+
+## Modify dataset
+
+The users may also need to prepare the dataset and write the configs about dataset. MMDetection V2.0 already support VOC, WIDER FACE, COCO and Cityscapes Dataset.
+
+## Modify training schedule
+
+The finetuning hyperparameters vary from the default schedule. It usually requires smaller learning rate and less training epochs
+
+```python
+# optimizer
+# lr is set for a batch size of 8
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# learning policy
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[7])
+# the max_epochs and step in lr_config need specifically tuned for the customized dataset
+runner = dict(max_epochs=8)
+log_config = dict(interval=100)
+```
+
+## Use pre-trained model
+
+To use the pre-trained model, the new config add the link of pre-trained models in the `load_from`. The users might need to download the model weights before training to avoid the download time during training.
+
+```python
+load_from = 'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'  # noqa
+
+```
diff --git a/docs/en/tutorials/how_to.md b/docs/en/tutorials/how_to.md
new file mode 100644
index 0000000..c5184dc
--- /dev/null
+++ b/docs/en/tutorials/how_to.md
@@ -0,0 +1,204 @@
+# Tutorial 11: How to xxx
+
+This tutorial collects answers to any `How to xxx with MMDetection`. Feel free to update this doc if you meet new questions about `How to` and find the answers!
+
+## Use backbone network through MMClassification
+
+The model registry in MMDet, MMCls, MMSeg all inherit from the root registry in MMCV. This allows these repositories to directly use the modules already implemented by each other. Therefore, users can use backbone networks from MMClassification in MMDetection without implementing a network that already exists in MMClassification.
+
+### Use backbone network implemented in MMClassification
+
+Suppose you want to use `MobileNetV3-small` as the backbone network of `RetinaNet`, the example config is as the following.
+
+```python
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# please install mmcls>=0.20.0
+# import mmcls.models to trigger register_module in mmcls
+custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
+pretrained = 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth'
+model = dict(
+    backbone=dict(
+        _delete_=True, # Delete the backbone field in _base_
+        type='mmcls.MobileNetV3', # Using MobileNetV3 from mmcls
+        arch='small',
+        out_indices=(3, 8, 11), # Modify out_indices
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint=pretrained,
+            prefix='backbone.')), # The pre-trained weights of backbone network in MMCls have prefix='backbone.'. The prefix in the keys will be removed so that these weights can be normally loaded.
+    # Modify in_channels
+    neck=dict(in_channels=[24, 48, 96], start_level=0))
+```
+
+### Use backbone network in TIMM through MMClassification
+
+MMClassification also provides a wrapper for the PyTorch Image Models (timm) backbone network, users can directly use the backbone network in timm through MMClassification. Suppose you want to use EfficientNet-B1 as the backbone network of RetinaNet, the example config is as the following.
+
+```python
+# https://github.com/open-mmlab/mmdetection/blob/master/configs/timm_example/retinanet_timm_efficientnet_b1_fpn_1x_coco.py
+
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# please install mmcls>=0.20.0
+# import mmcls.models to trigger register_module in mmcls
+custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
+model = dict(
+    backbone=dict(
+        _delete_=True, # Delete the backbone field in _base_
+        type='mmcls.TIMMBackbone', # Using timm from mmcls
+        model_name='efficientnet_b1',
+        features_only=True,
+        pretrained=True,
+        out_indices=(1, 2, 3, 4)), # Modify out_indices
+    neck=dict(in_channels=[24, 40, 112, 320])) # Modify in_channels
+
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+```
+
+`type='mmcls.TIMMBackbone'` means use the `TIMMBackbone` class from MMClassification in MMDetection, and the model used is `EfficientNet-B1`, where `mmcls` means the MMClassification repo and `TIMMBackbone` means the TIMMBackbone wrapper implemented in MMClassification.
+
+For the principle of the Hierarchy Registry, please refer to the [MMCV document](https://github.com/open-mmlab/mmcv/blob/master/docs/en/understand_mmcv/registry.md#hierarchy-registry). For how to use other backbones in MMClassification, you can refer to the [MMClassification document](https://github.com/open-mmlab/mmclassification/blob/master/docs/en/tutorials/config.md).
+
+## Use Mosaic augmentation
+
+If you want to use `Mosaic` in training, please make sure that you use `MultiImageMixDataset` at the same time. Taking the 'Faster R-CNN' algorithm as an example, you should modify the values of `train_pipeline` and `train_dataset` in the config as below:
+
+```python
+# Open configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py directly and add the following fields
+data_root = 'data/coco/'
+dataset_type = 'CocoDataset'
+img_scale=(1333, 800)​
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+train_pipeline = [
+    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.1, 2),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)), # The image will be enlarged by 4 times after Mosaic processing,so we use affine transformation to restore the image size.
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+train_dataset = dict(
+    _delete_ = True, # remove unnecessary Settings
+    type='MultiImageMixDataset',
+    dataset=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ],
+        filter_empty_gt=False,
+    ),
+    pipeline=train_pipeline
+    )
+​
+data = dict(
+    train=train_dataset
+    )
+```
+
+## Unfreeze backbone network after freezing the backbone in the config
+
+If you have freezed the backbone network in the config and want to unfreeze it after some epoches, you can write a hook function to do it.  Taking the Faster R-CNN with the resnet backbone as an example, you can freeze one stage of the backbone network and  add a `custom_hooks` in the config as below:
+
+```python
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    # freeze one stage of the backbone network.
+    backbone=dict(frozen_stages=1),
+)
+custom_hooks = [dict(type="UnfreezeBackboneEpochBasedHook", unfreeze_epoch=1)]
+```
+
+Meanwhile write the hook class `UnfreezeBackboneEpochBasedHook` in `mmdet/core/hook/unfreeze_backbone_epoch_based_hook.py`
+
+```python
+from mmcv.parallel import is_module_wrapper
+from mmcv.runner.hooks import HOOKS, Hook
+
+
+@HOOKS.register_module()
+class UnfreezeBackboneEpochBasedHook(Hook):
+    """Unfreeze backbone network Hook.
+
+    Args:
+        unfreeze_epoch (int): The epoch unfreezing the backbone network.
+    """
+
+    def __init__(self, unfreeze_epoch=1):
+        self.unfreeze_epoch = unfreeze_epoch
+
+    def before_train_epoch(self, runner):
+        # Unfreeze the backbone network.
+        # Only valid for resnet.
+        if runner.epoch == self.unfreeze_epoch:
+            model = runner.model
+            if is_module_wrapper(model):
+                model = model.module
+            backbone = model.backbone
+            if backbone.frozen_stages >= 0:
+                if backbone.deep_stem:
+                    backbone.stem.train()
+                    for param in backbone.stem.parameters():
+                        param.requires_grad = True
+                else:
+                    backbone.norm1.train()
+                    for m in [backbone.conv1, backbone.norm1]:
+                        for param in m.parameters():
+                            param.requires_grad = True
+
+            for i in range(1, backbone.frozen_stages + 1):
+                m = getattr(backbone, f'layer{i}')
+                m.train()
+                for param in m.parameters():
+                    param.requires_grad = True
+```
+
+## Get the channels of a new backbone
+
+If you want to get the channels of a new backbone, you can build this backbone alone and input a pseudo image to get each stage output.
+
+Take `ResNet` as an example:
+
+```python
+from mmdet.models import ResNet
+import torch
+self = ResNet(depth=18)
+self.eval()
+inputs = torch.rand(1, 3, 32, 32)
+level_outputs = self.forward(inputs)
+for level_out in level_outputs:
+    print(tuple(level_out.shape))
+
+```
+
+Output of the above script is as below:
+
+```python
+(1, 64, 8, 8)
+(1, 128, 4, 4)
+(1, 256, 2, 2)
+(1, 512, 1, 1)
+```
+
+Users can get the channels of the new backbone by Replacing the `ResNet(depth=18)` in this script with their customized backbone.
diff --git a/docs/en/tutorials/index.rst b/docs/en/tutorials/index.rst
new file mode 100644
index 0000000..5513611
--- /dev/null
+++ b/docs/en/tutorials/index.rst
@@ -0,0 +1,17 @@
+.. toctree::
+   :maxdepth: 2
+
+   config.md
+   customize_dataset.md
+   data_pipeline.md
+   customize_models.md
+   customize_runtime.md
+   customize_losses.md
+   finetune.md
+   robustness_benchmarking.md
+   pytorch2onnx.md
+   onnx2tensorrt.md
+   init_cfg.md
+   how_to.md
+   test_results_submission.md
+   useful_hooks.md
diff --git a/docs/en/tutorials/init_cfg.md b/docs/en/tutorials/init_cfg.md
new file mode 100644
index 0000000..b46b494
--- /dev/null
+++ b/docs/en/tutorials/init_cfg.md
@@ -0,0 +1,161 @@
+# Tutorial 10: Weight initialization
+
+During training, a proper initialization strategy is beneficial to speeding up the training or obtaining a higher performance. [MMCV](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/weight_init.py) provide some commonly used methods for initializing modules like `nn.Conv2d`. Model initialization in MMdetection mainly uses `init_cfg`. Users can initialize models with following two steps:
+
+1. Define `init_cfg` for a model or its components in `model_cfg`,  but `init_cfg` of children components have higher priority and will override `init_cfg` of parents modules.
+2. Build model as usual, but call `model.init_weights()` method explicitly, and model parameters will be initialized as configuration.
+
+The high-level workflow of initialization in MMdetection is :
+
+model_cfg(init_cfg) -> build_from_cfg -> model -> init_weight() -> initialize(self, self.init_cfg) -> children's init_weight()
+
+### Description
+
+It is dict or list\[dict\], and contains the following keys and values:
+
+- `type` (str), containing the initializer name in `INTIALIZERS`, and followed by arguments of the initializer.
+- `layer` (str or list\[str\]), containing the names of basiclayers in Pytorch or MMCV with learnable parameters that will be initialized, e.g. `'Conv2d'`,`'DeformConv2d'`.
+- `override` (dict or list\[dict\]),  containing the sub-modules that not inherit from BaseModule and whose initialization configuration is different from other layers' which are in `'layer'` key. Initializer defined in `type` will work for all layers defined in `layer`, so if sub-modules are not derived Classes of `BaseModule` but can be initialized as same ways of layers in `layer`, it does not need to use `override`. `override` contains:
+  - `type` followed by arguments of initializer;
+  - `name` to indicate sub-module which will be initialized.
+
+### Initialize parameters
+
+Inherit a new model from `mmcv.runner.BaseModule` or `mmdet.models`  Here we show an example of FooModel.
+
+```python
+import torch.nn as nn
+from mmcv.runner import BaseModule
+
+class FooModel(BaseModule)
+	def __init__(self,
+                 arg1,
+                 arg2,
+                 init_cfg=None):
+    	super(FooModel, self).__init__(init_cfg)
+		...
+```
+
+- Initialize model by using `init_cfg` directly in code
+
+  ```python
+  import torch.nn as nn
+  from mmcv.runner import BaseModule
+  # or directly inherit mmdet models
+
+  class FooModel(BaseModule)
+  	def __init__(self,
+                  arg1,
+                  arg2,
+                  init_cfg=XXX):
+  		super(FooModel, self).__init__(init_cfg)
+  	    ...
+  ```
+
+- Initialize model by using `init_cfg` directly in `mmcv.Sequential` or `mmcv.ModuleList` code
+
+  ```python
+  from mmcv.runner import BaseModule, ModuleList
+
+  class FooModel(BaseModule)
+  	def __init__(self,
+              	arg1,
+              	arg2,
+              	init_cfg=None):
+  		super(FooModel, self).__init__(init_cfg)
+      	...
+      	self.conv1 = ModuleList(init_cfg=XXX)
+  ```
+
+- Initialize model by using `init_cfg` in config file
+
+  ```python
+  model = dict(
+  	...
+  	model = dict(
+      	type='FooModel',
+      	arg1=XXX,
+      	arg2=XXX,
+      	init_cfg=XXX),
+          ...
+  ```
+
+### Usage of init_cfg
+
+1. Initialize model by `layer` key
+
+   If we only define `layer`, it just initialize the layer in `layer` key.
+
+   NOTE: Value of `layer` key is the class name with attributes weights and bias of Pytorch, (so such as  `MultiheadAttention layer` is not supported).
+
+- Define `layer` key for initializing module with same configuration.
+
+  ```python
+  init_cfg = dict(type='Constant', layer=['Conv1d', 'Conv2d', 'Linear'], val=1)
+  # initialize whole module with same configuration
+  ```
+
+- Define `layer` key for initializing layer with different configurations.
+
+```python
+init_cfg = [dict(type='Constant', layer='Conv1d', val=1),
+            dict(type='Constant', layer='Conv2d', val=2),
+            dict(type='Constant', layer='Linear', val=3)]
+# nn.Conv1d will be initialized with dict(type='Constant', val=1)
+# nn.Conv2d will be initialized with dict(type='Constant', val=2)
+# nn.Linear will be initialized with dict(type='Constant', val=3)
+```
+
+2. Initialize model by `override` key
+
+- When initializing some specific part with its attribute name, we can use `override` key, and the value in `override` will ignore the value in init_cfg.
+
+  ```python
+  # layers：
+  # self.feat = nn.Conv1d(3, 1, 3)
+  # self.reg = nn.Conv2d(3, 3, 3)
+  # self.cls = nn.Linear(1,2)
+
+  init_cfg = dict(type='Constant',
+                  layer=['Conv1d','Conv2d'], val=1, bias=2,
+                  override=dict(type='Constant', name='reg', val=3, bias=4))
+  # self.feat and self.cls will be initialized with 	dict(type='Constant', val=1, bias=2)
+  # The module called 'reg' will be initialized with dict(type='Constant', val=3, bias=4)
+  ```
+
+- If `layer` is None in init_cfg, only sub-module with the name in override will be initialized, and type and other args in override can be omitted.
+
+  ```python
+  # layers：
+  # self.feat = nn.Conv1d(3, 1, 3)
+  # self.reg = nn.Conv2d(3, 3, 3)
+  # self.cls = nn.Linear(1,2)
+
+  init_cfg = dict(type='Constant', val=1, bias=2, 	override=dict(name='reg'))
+
+  # self.feat and self.cls will be initialized by Pytorch
+  # The module called 'reg' will be initialized with dict(type='Constant', val=1, bias=2)
+  ```
+
+- If we don't define `layer` key or `override` key, it will not initialize anything.
+
+- Invalid usage
+
+  ```python
+  # It is invalid that override don't have name key
+  init_cfg = dict(type='Constant', layer=['Conv1d','Conv2d'], val=1, bias=2,
+              	override=dict(type='Constant', val=3, bias=4))
+
+  # It is also invalid that override has name and other args except type
+  init_cfg = dict(type='Constant', layer=['Conv1d','Conv2d'], val=1, bias=2,
+                  override=dict(name='reg', val=3, bias=4))
+  ```
+
+3. Initialize model with the pretrained model
+
+   ```python
+   init_cfg = dict(type='Pretrained',
+               checkpoint='torchvision://resnet50')
+   ```
+
+More details can refer to the documentation in [MMCV](https://mmcv.readthedocs.io/en/latest/cnn.html#weight-initialization) and MMCV [PR #780](https://github.com/open-mmlab/mmcv/pull/780)
diff --git a/docs/en/tutorials/onnx2tensorrt.md b/docs/en/tutorials/onnx2tensorrt.md
new file mode 100644
index 0000000..3848bb7
--- /dev/null
+++ b/docs/en/tutorials/onnx2tensorrt.md
@@ -0,0 +1,106 @@
+# Tutorial 9: ONNX to TensorRT (Experimental)
+
+> ## [Try the new MMDeploy to deploy your model](https://mmdeploy.readthedocs.io/)
+
+<!-- TOC -->
+
+- [Tutorial 9: ONNX to TensorRT (Experimental)](#tutorial-9-onnx-to-tensorrt-experimental)
+  - [How to convert models from ONNX to TensorRT](#how-to-convert-models-from-onnx-to-tensorrt)
+    - [Prerequisite](#prerequisite)
+    - [Usage](#usage)
+  - [How to evaluate the exported models](#how-to-evaluate-the-exported-models)
+  - [List of supported models convertible to TensorRT](#list-of-supported-models-convertible-to-tensorrt)
+  - [Reminders](#reminders)
+  - [FAQs](#faqs)
+
+<!-- TOC -->
+
+## How to convert models from ONNX to TensorRT
+
+### Prerequisite
+
+1. Please refer to [get_started.md](https://mmdetection.readthedocs.io/en/latest/get_started.html) for installation of MMCV and MMDetection from source.
+2. Please refer to [ONNXRuntime in mmcv](https://mmcv.readthedocs.io/en/latest/deployment/onnxruntime_op.html) and [TensorRT plugin in mmcv](https://github.com/open-mmlab/mmcv/blob/master/docs/en/deployment/tensorrt_plugin.md/) to install `mmcv-full` with ONNXRuntime custom ops and TensorRT plugins.
+3. Use our tool [pytorch2onnx](https://mmdetection.readthedocs.io/en/latest/tutorials/pytorch2onnx.html) to convert the model from PyTorch to ONNX.
+
+### Usage
+
+```bash
+python tools/deployment/onnx2tensorrt.py \
+    ${CONFIG} \
+    ${MODEL} \
+    --trt-file ${TRT_FILE} \
+    --input-img ${INPUT_IMAGE_PATH} \
+    --shape ${INPUT_IMAGE_SHAPE} \
+    --min-shape ${MIN_IMAGE_SHAPE} \
+    --max-shape ${MAX_IMAGE_SHAPE} \
+    --workspace-size {WORKSPACE_SIZE} \
+    --show \
+    --verify \
+```
+
+Description of all arguments:
+
+- `config` : The path of a model config file.
+- `model` : The path of an ONNX model file.
+- `--trt-file`: The Path of output TensorRT engine file. If not specified, it will be set to `tmp.trt`.
+- `--input-img` : The path of an input image for tracing and conversion. By default, it will be set to `demo/demo.jpg`.
+- `--shape`: The height and width of model input. If not specified, it will be set to `400 600`.
+- `--min-shape`: The minimum height and width of model input. If not specified, it will be set to the same as `--shape`.
+- `--max-shape`: The maximum height and width of model input. If not specified, it will be set to the same as `--shape`.
+- `--workspace-size` : The required GPU workspace size in GiB to build TensorRT engine. If not specified, it will be set to `1` GiB.
+- `--show`: Determines whether to show the outputs of the model. If not specified, it will be set to `False`.
+- `--verify`: Determines whether to verify the correctness of models between ONNXRuntime and TensorRT. If not specified, it will be set to `False`.
+- `--verbose`: Determines whether to print logging messages. It's useful for debugging. If not specified, it will be set to `False`.
+
+Example:
+
+```bash
+python tools/deployment/onnx2tensorrt.py \
+    configs/retinanet/retinanet_r50_fpn_1x_coco.py \
+    checkpoints/retinanet_r50_fpn_1x_coco.onnx \
+    --trt-file checkpoints/retinanet_r50_fpn_1x_coco.trt \
+    --input-img demo/demo.jpg \
+    --shape 400 600 \
+    --show \
+    --verify \
+```
+
+## How to evaluate the exported models
+
+We prepare a tool `tools/deplopyment/test.py` to evaluate TensorRT models.
+
+Please refer to following links for more information.
+
+- [how-to-evaluate-the-exported-models](pytorch2onnx.md#how-to-evaluate-the-exported-models)
+- [results-and-models](pytorch2onnx.md#results-and-models)
+
+## List of supported models convertible to TensorRT
+
+The table below lists the models that are guaranteed to be convertible to TensorRT.
+
+|       Model        |                              Config                              | Dynamic Shape | Batch Inference | Note |
+| :----------------: | :--------------------------------------------------------------: | :-----------: | :-------------: | :--: |
+|        SSD         |                   `configs/ssd/ssd300_coco.py`                   |       Y       |        Y        |      |
+|        FSAF        |              `configs/fsaf/fsaf_r50_fpn_1x_coco.py`              |       Y       |        Y        |      |
+|        FCOS        |         `configs/fcos/fcos_r50_caffe_fpn_4x4_1x_coco.py`         |       Y       |        Y        |      |
+|       YOLOv3       |        `configs/yolo/yolov3_d53_mstrain-608_273e_coco.py`        |       Y       |        Y        |      |
+|     RetinaNet      |         `configs/retinanet/retinanet_r50_fpn_1x_coco.py`         |       Y       |        Y        |      |
+|    Faster R-CNN    |       `configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py`       |       Y       |        Y        |      |
+|   Cascade R-CNN    |      `configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py`      |       Y       |        Y        |      |
+|     Mask R-CNN     |         `configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py`         |       Y       |        Y        |      |
+| Cascade Mask R-CNN |   `configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py`    |       Y       |        Y        |      |
+|     PointRend      | `configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py` |       Y       |        Y        |      |
+
+Notes:
+
+- *All models above are tested with Pytorch==1.6.0, onnx==1.7.0 and TensorRT-7.2.1.6.Ubuntu-16.04.x86_64-gnu.cuda-10.2.cudnn8.0*
+
+## Reminders
+
+- If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, we may not provide much help here due to the limited resources. Please try to dig a little deeper and debug by yourself.
+- Because this feature is experimental and may change fast, please always try with the latest `mmcv` and `mmdetecion`.
+
+## FAQs
+
+- None
diff --git a/docs/en/tutorials/pytorch2onnx.md b/docs/en/tutorials/pytorch2onnx.md
new file mode 100644
index 0000000..3561178
--- /dev/null
+++ b/docs/en/tutorials/pytorch2onnx.md
@@ -0,0 +1,334 @@
+# Tutorial 8: Pytorch to ONNX (Experimental)
+
+> ## [Try the new MMDeploy to deploy your model](https://mmdeploy.readthedocs.io/)
+
+<!-- TOC -->
+
+- [Tutorial 8: Pytorch to ONNX (Experimental)](#tutorial-8-pytorch-to-onnx-experimental)
+  - [How to convert models from Pytorch to ONNX](#how-to-convert-models-from-pytorch-to-onnx)
+    - [Prerequisite](#prerequisite)
+    - [Usage](#usage)
+    - [Description of all arguments](#description-of-all-arguments)
+  - [How to evaluate the exported models](#how-to-evaluate-the-exported-models)
+    - [Prerequisite](#prerequisite-1)
+    - [Usage](#usage-1)
+    - [Description of all arguments](#description-of-all-arguments-1)
+    - [Results and Models](#results-and-models)
+  - [List of supported models exportable to ONNX](#list-of-supported-models-exportable-to-onnx)
+  - [The Parameters of Non-Maximum Suppression in ONNX Export](#the-parameters-of-non-maximum-suppression-in-onnx-export)
+  - [Reminders](#reminders)
+  - [FAQs](#faqs)
+
+<!-- TOC -->
+
+## How to convert models from Pytorch to ONNX
+
+### Prerequisite
+
+1. Install the prerequisites following [get_started.md/Prepare environment](../get_started.md).
+2. Build custom operators for ONNX Runtime and install MMCV manually following [How to build custom operators for ONNX Runtime](https://github.com/open-mmlab/mmcv/blob/master/docs/en/deployment/onnxruntime_op.md/#how-to-build-custom-operators-for-onnx-runtime)
+3. Install MMdetection manually following steps 2-3 in [get_started.md/Install MMdetection](../get_started.md).
+
+### Usage
+
+```bash
+python tools/deployment/pytorch2onnx.py \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    --output-file ${OUTPUT_FILE} \
+    --input-img ${INPUT_IMAGE_PATH} \
+    --shape ${IMAGE_SHAPE} \
+    --test-img ${TEST_IMAGE_PATH} \
+    --opset-version ${OPSET_VERSION} \
+    --cfg-options ${CFG_OPTIONS}
+    --dynamic-export \
+    --show \
+    --verify \
+    --simplify \
+```
+
+### Description of all arguments
+
+- `config` : The path of a model config file.
+- `checkpoint` : The path of a model checkpoint file.
+- `--output-file`: The path of output ONNX model. If not specified, it will be set to `tmp.onnx`.
+- `--input-img`: The path of an input image for tracing and conversion. By default, it will be set to `tests/data/color.jpg`.
+- `--shape`: The height and width of input tensor to the model. If not specified, it will be set to `800 1216`.
+- `--test-img` : The path of an image to verify the exported ONNX model. By default, it will be set to `None`, meaning it will use `--input-img` for verification.
+- `--opset-version` : The opset version of ONNX. If not specified, it will be set to `11`.
+- `--dynamic-export`: Determines whether to export ONNX model with dynamic input and output shapes. If not specified, it will be set to `False`.
+- `--show`: Determines whether to print the architecture of the exported model and whether to show detection outputs when `--verify` is set to `True`. If not specified, it will be set to `False`.
+- `--verify`: Determines whether to verify the correctness of an exported model. If not specified, it will be set to `False`.
+- `--simplify`: Determines whether to simplify the exported ONNX model. If not specified, it will be set to `False`.
+- `--cfg-options`: Override some settings in the used config file, the key-value pair in `xxx=yyy` format will be merged into config file.
+- `--skip-postprocess`: Determines whether export model without post process. If not specified, it will be set to `False`. Notice: This is an experimental option. Only work for some single stage models. Users need to implement the post-process by themselves. We do not guarantee the correctness of the exported model.
+
+Example:
+
+```bash
+python tools/deployment/pytorch2onnx.py \
+    configs/yolo/yolov3_d53_mstrain-608_273e_coco.py \
+    checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco.pth \
+    --output-file checkpoints/yolo/yolov3_d53_mstrain-608_273e_coco.onnx \
+    --input-img demo/demo.jpg \
+    --test-img tests/data/color.jpg \
+    --shape 608 608 \
+    --show \
+    --verify \
+    --dynamic-export \
+    --cfg-options \
+      model.test_cfg.deploy_nms_pre=-1 \
+```
+
+## How to evaluate the exported models
+
+We prepare a tool `tools/deplopyment/test.py` to evaluate ONNX models with ONNXRuntime and TensorRT.
+
+### Prerequisite
+
+- Install onnx and onnxruntime (CPU version)
+
+  ```shell
+  pip install onnx onnxruntime==1.5.1
+  ```
+
+- If you want to run the model on GPU, please remove the CPU version before using the GPU version.
+
+  ```shell
+  pip uninstall onnxruntime
+  pip install onnxruntime-gpu
+  ```
+
+  Note: onnxruntime-gpu is version-dependent on CUDA and CUDNN, please ensure that your
+  environment meets the requirements.
+
+- Build custom operators for ONNX Runtime following [How to build custom operators for ONNX Runtime](https://github.com/open-mmlab/mmcv/blob/master/docs/en/deployment/onnxruntime_op.md/#how-to-build-custom-operators-for-onnx-runtime)
+
+- Install TensorRT by referring to [How to build TensorRT plugins in MMCV](https://mmcv.readthedocs.io/en/latest/deployment/tensorrt_plugin.html#how-to-build-tensorrt-plugins-in-mmcv) (optional)
+
+### Usage
+
+```bash
+python tools/deployment/test.py \
+    ${CONFIG_FILE} \
+    ${MODEL_FILE} \
+    --out ${OUTPUT_FILE} \
+    --backend ${BACKEND} \
+    --format-only ${FORMAT_ONLY} \
+    --eval ${EVALUATION_METRICS} \
+    --show-dir ${SHOW_DIRECTORY} \
+    ----show-score-thr ${SHOW_SCORE_THRESHOLD} \
+    ----cfg-options ${CFG_OPTIONS} \
+    ----eval-options ${EVALUATION_OPTIONS} \
+```
+
+### Description of all arguments
+
+- `config`: The path of a model config file.
+- `model`: The path of an input model file.
+- `--out`: The path of output result file in pickle format.
+- `--backend`: Backend for input model to run and should be `onnxruntime` or `tensorrt`.
+- `--format-only` : Format the output results without perform evaluation. It is useful when you want to format the result to a specific format and submit it to the test server. If not specified, it will be set to `False`.
+- `--eval`: Evaluation metrics, which depends on the dataset, e.g., "bbox", "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC.
+- `--show-dir`: Directory where painted images will be saved
+- `--show-score-thr`: Score threshold. Default is set to `0.3`.
+- `--cfg-options`: Override some settings in the used config file, the key-value pair in `xxx=yyy` format will be merged into config file.
+- `--eval-options`: Custom options for evaluation, the key-value pair in `xxx=yyy` format will be kwargs for `dataset.evaluate()` function
+
+Notes:
+
+- If the deployed backend platform is TensorRT, please add environment variables before running the file:
+
+  ```bash
+  export ONNX_BACKEND=MMCVTensorRT
+  ```
+
+- If you want to use the `--dynamic-export` parameter in the TensorRT backend to export ONNX, please remove the `--simplify` parameter, and vice versa.
+
+### Results and Models
+
+<table border="1" class="docutils">
+	<tr>
+	    <th align="center">Model</th>
+	    <th align="center">Config</th>
+	    <th align="center">Metric</th>
+	    <th align="center">PyTorch</th>
+	    <th align="center">ONNX Runtime</th>
+	    <th align="center">TensorRT</th>
+	</tr >
+  <tr >
+	    <td align="center">FCOS</td>
+	    <td align="center"><code>configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">36.6</td>
+	    <td align="center">36.5</td>
+	    <td align="center">36.3</td>
+	</tr>
+  <tr >
+	    <td align="center">FSAF</td>
+	    <td align="center"><code>configs/fsaf/fsaf_r50_fpn_1x_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">36.0</td>
+	    <td align="center">36.0</td>
+	    <td align="center">35.9</td>
+	</tr>
+  <tr >
+	    <td align="center">RetinaNet</td>
+	    <td align="center"><code>configs/retinanet/retinanet_r50_fpn_1x_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">36.5</td>
+	    <td align="center">36.4</td>
+	    <td align="center">36.3</td>
+	</tr>
+	<tr >
+	    <td align="center" align="center" >SSD</td>
+	    <td align="center" align="center"><code>configs/ssd/ssd300_coco.py</code></td>
+	    <td align="center" align="center">Box AP</td>
+	    <td align="center" align="center">25.6</td>
+	    <td align="center" align="center">25.6</td>
+	    <td align="center" align="center">25.6</td>
+	</tr>
+  <tr >
+	    <td align="center">YOLOv3</td>
+	    <td align="center"><code>configs/yolo/yolov3_d53_mstrain-608_273e_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">33.5</td>
+	    <td align="center">33.5</td>
+	    <td align="center">33.5</td>
+	</tr>
+  <tr >
+	    <td align="center">Faster R-CNN</td>
+	    <td align="center"><code>configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">37.4</td>
+	    <td align="center">37.4</td>
+	    <td align="center">37.0</td>
+	</tr>
+  <tr >
+	    <td align="center">Cascade R-CNN</td>
+	    <td align="center"><code>configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">40.3</td>
+	    <td align="center">40.3</td>
+	    <td align="center">40.1</td>
+	</tr>
+
+<tr >
+	    <td align="center" rowspan="2">Mask R-CNN</td>
+	    <td align="center" rowspan="2"><code>configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">38.2</td>
+	    <td align="center">38.1</td>
+	    <td align="center">37.7</td>
+	</tr>
+	<tr>
+	    <td align="center">Mask AP</td>
+	    <td align="center">34.7</td>
+	    <td align="center">33.7</td>
+	    <td align="center">33.3</td>
+	</tr>
+  <tr >
+	    <td align="center" rowspan="2">Cascade Mask R-CNN</td>
+	    <td align="center" rowspan="2"><code>configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">41.2</td>
+	    <td align="center">41.2</td>
+	    <td align="center">40.9</td>
+	</tr>
+	<tr>
+	    <td align="center">Mask AP</td>
+	    <td align="center">35.9</td>
+	    <td align="center">34.8</td>
+	    <td align="center">34.5</td>
+	</tr>
+
+<tr >
+	    <td align="center">CornerNet</td>
+	    <td align="center"><code>configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">40.6</td>
+	    <td align="center">40.4</td>
+		<td align="center">-</td>
+	</tr>
+  <tr >
+	    <td align="center">DETR</td>
+	    <td align="center"><code>configs/detr/detr_r50_8x2_150e_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">40.1</td>
+	    <td align="center">40.1</td>
+		<td align="center">-</td>
+  </tr>
+  <tr >
+	    <td align="center" rowspan="2">PointRend</td>
+	    <td align="center" rowspan="2"><code>configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py</code></td>
+	    <td align="center">Box AP</td>
+	    <td align="center">38.4</td>
+	    <td align="center">38.4</td>
+	    <td align="center">-</td>
+  </tr>
+  <tr>
+	    <td align="center">Mask AP</td>
+	    <td align="center">36.3</td>
+	    <td align="center">35.2</td>
+	    <td align="center">-</td>
+  </tr>
+</table>
+
+Notes:
+
+- All ONNX models are evaluated with dynamic shape on coco dataset and images are preprocessed according to the original config file. Note that CornerNet is evaluated without test-time flip, since currently only single-scale evaluation is supported with ONNX Runtime.
+
+- Mask AP of Mask R-CNN drops by 1% for ONNXRuntime. The main reason is that the predicted masks are directly interpolated to original image in PyTorch, while they are at first interpolated to the preprocessed input image of the model and then to original image in other backend.
+
+## List of supported models exportable to ONNX
+
+The table below lists the models that are guaranteed to be exportable to ONNX and runnable in ONNX Runtime.
+
+|       Model        |                               Config                                | Dynamic Shape | Batch Inference |                                     Note                                      |
+| :----------------: | :-----------------------------------------------------------------: | :-----------: | :-------------: | :---------------------------------------------------------------------------: |
+|        FCOS        |      `configs/fcos/fcos_r50_caffe_fpn_gn-head_4x4_1x_coco.py`       |       Y       |        Y        |                                                                               |
+|        FSAF        |               `configs/fsaf/fsaf_r50_fpn_1x_coco.py`                |       Y       |        Y        |                                                                               |
+|     RetinaNet      |          `configs/retinanet/retinanet_r50_fpn_1x_coco.py`           |       Y       |        Y        |                                                                               |
+|        SSD         |                    `configs/ssd/ssd300_coco.py`                     |       Y       |        Y        |                                                                               |
+|       YOLOv3       |         `configs/yolo/yolov3_d53_mstrain-608_273e_coco.py`          |       Y       |        Y        |                                                                               |
+|    Faster R-CNN    |        `configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py`         |       Y       |        Y        |                                                                               |
+|   Cascade R-CNN    |       `configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py`        |       Y       |        Y        |                                                                               |
+|     Mask R-CNN     |          `configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py`           |       Y       |        Y        |                                                                               |
+| Cascade Mask R-CNN |     `configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py`     |       Y       |        Y        |                                                                               |
+|     CornerNet      | `configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py` |       Y       |        N        | no flip, no batch inference, tested with torch==1.7.0 and onnxruntime==1.5.1. |
+|        DETR        |              `configs/detr/detr_r50_8x2_150e_coco.py`               |       Y       |        Y        |                     batch inference is *not recommended*                      |
+|     PointRend      |  `configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py`   |       Y       |        Y        |                                                                               |
+
+Notes:
+
+- Minimum required version of MMCV is `1.3.5`
+
+- *All models above are tested with Pytorch==1.6.0 and onnxruntime==1.5.1*, except for CornerNet. For more details about the
+  torch version when exporting CornerNet to ONNX, which involves `mmcv::cummax`, please refer to the [Known Issues](https://github.com/open-mmlab/mmcv/blob/master/docs/en/deployment/onnxruntime_op.md#known-issues) in mmcv.
+
+- Though supported, it is *not recommended* to use batch inference in onnxruntime for `DETR`, because there is huge performance gap between ONNX and torch model (e.g. 33.5 vs 39.9 mAP on COCO for onnxruntime and torch respectively, with a batch size 2). The main reason for the gap is that these is non-negligible effect on the predicted regressions during batch inference for ONNX, since the predicted coordinates is normalized by `img_shape` (without padding) and should be converted to absolute format, but `img_shape` is not dynamically traceable thus the padded `img_shape_for_onnx` is used.
+
+- Currently only single-scale evaluation is supported with ONNX Runtime, also `mmcv::SoftNonMaxSuppression` is only supported for single image by now.
+
+## The Parameters of Non-Maximum Suppression in ONNX Export
+
+In the process of exporting the ONNX model, we set some parameters for the NMS op to control the number of output bounding boxes. The following will introduce the parameter setting of the NMS op in the supported models. You can set these parameters through `--cfg-options`.
+
+- `nms_pre`: The number of boxes before NMS. The default setting is `1000`.
+
+- `deploy_nms_pre`: The number of boxes before NMS when exporting to ONNX model. The default setting is `0`.
+
+- `max_per_img`: The number of boxes to be kept after NMS. The default setting is `100`.
+
+- `max_output_boxes_per_class`: Maximum number of output boxes per class of NMS. The default setting is `200`.
+
+## Reminders
+
+- When the input model has custom op such as `RoIAlign` and if you want to verify the exported ONNX model, you may have to build `mmcv` with [ONNXRuntime](https://mmcv.readthedocs.io/en/latest/deployment/onnxruntime_op.html) from source.
+- `mmcv.onnx.simplify` feature is based on [onnx-simplifier](https://github.com/daquexian/onnx-simplifier). If you want to try it, please refer to [onnx in `mmcv`](https://mmcv.readthedocs.io/en/latest/deployment/onnx.html) and [onnxruntime op in `mmcv`](https://mmcv.readthedocs.io/en/latest/deployment/onnxruntime_op.html) for more information.
+- If you meet any problem with the listed models above, please create an issue and it would be taken care of soon. For models not included in the list, please try to dig a little deeper and debug a little bit more and hopefully solve them by yourself.
+- Because this feature is experimental and may change fast, please always try with the latest `mmcv` and `mmdetecion`.
+
+## FAQs
+
+- None
diff --git a/docs/en/tutorials/test_results_submission.md b/docs/en/tutorials/test_results_submission.md
new file mode 100644
index 0000000..aed595c
--- /dev/null
+++ b/docs/en/tutorials/test_results_submission.md
@@ -0,0 +1,112 @@
+# Tutorial 12: Test Results Submission
+
+## Panoptic segmentation test results submission
+
+The following sections introduce how to produce the prediction results of panoptic segmentation models on the COCO test-dev set and submit the predictions to [COCO evaluation server](https://competitions.codalab.org/competitions/19507).
+
+### Prerequisites
+
+- Download [COCO test dataset images](http://images.cocodataset.org/zips/test2017.zip), [testing image info](http://images.cocodataset.org/annotations/image_info_test2017.zip), and [panoptic train/val annotations](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip), then unzip them, put 'test2017' to `data/coco/`, put json files and annotation files to `data/coco/annotations/`.
+
+```shell
+# suppose data/coco/ does not exist
+mkdir -pv data/coco/
+
+# download test2017
+wget -P data/coco/ http://images.cocodataset.org/zips/test2017.zip
+wget -P data/coco/ http://images.cocodataset.org/annotations/image_info_test2017.zip
+wget -P data/coco/ http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip
+
+# unzip them
+unzip data/coco/test2017.zip -d data/coco/
+unzip data/coco/image_info_test2017.zip -d data/coco/
+unzip data/coco/panoptic_annotations_trainval2017.zip -d data/coco/
+
+# remove zip files (optional)
+rm -rf data/coco/test2017.zip data/coco/image_info_test2017.zip data/coco/panoptic_annotations_trainval2017.zip
+```
+
+- Run the following code to update category information in testing image info. Since the attribute `isthing` is missing in category information of 'image_info_test-dev2017.json', we need to update it with the category information in 'panoptic_val2017.json'.
+
+```shell
+python tools/misc/gen_coco_panoptic_test_info.py data/coco/annotations
+```
+
+After completing the above preparations, your directory structure of `data` should be like this:
+
+```text
+data
+`-- coco
+    |-- annotations
+    |   |-- image_info_test-dev2017.json
+    |   |-- image_info_test2017.json
+    |   |-- panoptic_image_info_test-dev2017.json
+    |   |-- panoptic_train2017.json
+    |   |-- panoptic_train2017.zip
+    |   |-- panoptic_val2017.json
+    |   `-- panoptic_val2017.zip
+    `-- test2017
+```
+
+### Inference on coco test-dev
+
+The commands to perform inference on test2017 are as below:
+
+```shell
+# test with single gpu
+CUDA_VISIBLE_DEVICES=0 python tools/test.py \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    --format-only \
+    --cfg-options data.test.ann_file=data/coco/annotations/panoptic_image_info_test-dev2017.json data.test.img_prefix=data/coco/test2017 \
+    --eval-options jsonfile_prefix=${WORK_DIR}/results
+
+# test with four gpus
+CUDA_VISIBLE_DEVICES=0,1,3,4 bash tools/dist_test.sh \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    4 \ # four gpus
+    --format-only \
+    --cfg-options data.test.ann_file=data/coco/annotations/panoptic_image_info_test-dev2017.json data.test.img_prefix=data/coco/test2017 \
+    --eval-options jsonfile_prefix=${WORK_DIR}/results
+
+# test with slurm
+GPUS=8 tools/slurm_test.sh \
+    ${Partition} \
+    ${JOB_NAME} \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    --format-only \
+    --cfg-options data.test.ann_file=data/coco/annotations/panoptic_image_info_test-dev2017.json data.test.img_prefix=data/coco/test2017 \
+    --eval-options jsonfile_prefix=${WORK_DIR}/results
+```
+
+Example
+
+Suppose we perform inference on `test2017` using pretrained MaskFormer with ResNet-50 backbone.
+
+```shell
+# test with single gpu
+CUDA_VISIBLE_DEVICES=0 python tools/test.py \
+    configs/maskformer/maskformer_r50_mstrain_16x1_75e_coco.py \
+    checkpoints/maskformer_r50_mstrain_16x1_75e_coco_20220221_141956-bc2699cb.pth \
+    --format-only \
+    --cfg-options data.test.ann_file=data/coco/annotations/panoptic_image_info_test-dev2017.json data.test.img_prefix=data/coco/test2017 \
+    --eval-options jsonfile_prefix=work_dirs/maskformer/results
+```
+
+### Rename files and zip results
+
+After inference, the panoptic segmentation results (a json file and a directory where the masks are stored) will be in `WORK_DIR`. We should rename them according to the naming convention described on [COCO's Website](https://cocodataset.org/#upload). Finally, we need to compress the json and the directory where the masks are stored into a zip file, and rename the zip file according to the naming convention. Note that the zip file should **directly** contains the above two files.
+
+The commands to rename files and zip results:
+
+```shell
+# In WORK_DIR, we have panoptic segmentation results: 'panoptic' and 'results.panoptic.json'.
+cd ${WORK_DIR}
+
+# replace '[algorithm_name]' with the name of algorithm you used.
+mv ./panoptic ./panoptic_test-dev2017_[algorithm_name]_results
+mv ./results.panoptic.json ./panoptic_test-dev2017_[algorithm_name]_results.json
+zip panoptic_test-dev2017_[algorithm_name]_results.zip -ur panoptic_test-dev2017_[algorithm_name]_results panoptic_test-dev2017_[algorithm_name]_results.json
+```
diff --git a/docs/en/tutorials/useful_hooks.md b/docs/en/tutorials/useful_hooks.md
new file mode 100644
index 0000000..2c8bd55
--- /dev/null
+++ b/docs/en/tutorials/useful_hooks.md
@@ -0,0 +1,83 @@
+# Tutorial 13: Useful Hooks
+
+MMDetection and MMCV provide users with various useful hooks including log hooks, evaluation hooks, NumClassCheckHook, etc. This tutorial introduces the functionalities and usages of hooks implemented in MMDetection. For using hooks in MMCV, please read the [API documentation in MMCV](https://github.com/open-mmlab/mmcv/blob/master/docs/en/understand_mmcv/runner.md).
+
+## CheckInvalidLossHook
+
+## EvalHook and DistEvalHook
+
+## ExpMomentumEMAHook and LinearMomentumEMAHook
+
+## NumClassCheckHook
+
+## [MemoryProfilerHook](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/hook/memory_profiler_hook.py)
+
+Memory profiler hook records memory information including virtual memory, swap memory, and the memory of the current process. This hook helps grasp the memory usage of the system and discover potential memory leak bugs. To use this hook, users should install `memory_profiler` and `psutil` by `pip install memory_profiler psutil` first.
+
+### Usage
+
+To use this hook, users should add the following code to the config file.
+
+```python
+custom_hooks = [
+    dict(type='MemoryProfilerHook', interval=50)
+]
+```
+
+### Result
+
+During training, you can see the messages in the log recorded by `MemoryProfilerHook` as below. The system has 250 GB (246360 MB + 9407 MB) of memory and 8 GB (5740 MB + 2452 MB) of swap memory in total. Currently 9407 MB (4.4%) of memory and 5740 MB (29.9%) of swap memory were consumed. And the current training process consumed 5434 MB of memory.
+
+```text
+2022-04-21 08:49:56,881 - mmdet - INFO - Memory information available_memory: 246360 MB, used_memory: 9407 MB, memory_utilization: 4.4 %, available_swap_memory: 5740 MB, used_swap_memory: 2452 MB, swap_memory_utilization: 29.9 %, current_process_memory: 5434 MB
+```
+
+## SetEpochInfoHook
+
+## SyncNormHook
+
+## SyncRandomSizeHook
+
+## YOLOXLrUpdaterHook
+
+## YOLOXModeSwitchHook
+
+## How to implement a custom hook
+
+In general, there are 10 points where hooks can be inserted from the beginning to the end of model training. The users can implement custom hooks and insert them at different points in the process of training to do what they want.
+
+- global points: `before_run`, `after_run`
+- points in training: `before_train_epoch`, `before_train_iter`, `after_train_iter`, `after_train_epoch`
+- points in validation: `before_val_epoch`, `before_val_iter`, `after_val_iter`, `after_val_epoch`
+
+For example, users can implement a hook to check loss and terminate training when loss goes NaN. To achieve that, there are three steps to go:
+
+1. Implement a new hook that inherits the `Hook` class in MMCV, and implement `after_train_iter` method which checks whether loss goes NaN after every `n` training iterations.
+2. The implemented hook should be registered in `HOOKS` by `@HOOKS.register_module()` as shown in the code below.
+3. Add `custom_hooks = [dict(type='CheckInvalidLossHook', interval=50)]` in the config file.
+
+```python
+import torch
+from mmcv.runner.hooks import HOOKS, Hook
+
+
+@HOOKS.register_module()
+class CheckInvalidLossHook(Hook):
+    """Check invalid loss hook.
+    This hook will regularly check whether the loss is valid
+    during training.
+    Args:
+        interval (int): Checking interval (every k iterations).
+            Default: 50.
+    """
+
+    def __init__(self, interval=50):
+        self.interval = interval
+
+    def after_train_iter(self, runner):
+        if self.every_n_iters(runner, self.interval):
+            assert torch.isfinite(runner.outputs['loss']), \
+                runner.logger.info('loss become infinite or NaN!')
+```
+
+Please read [customize_runtime](https://mmdetection.readthedocs.io/en/latest/tutorials/customize_runtime.html#customize-self-implemented-hooks) for more about implementing a custom hook.
diff --git a/docs/en/useful_tools.md b/docs/en/useful_tools.md
new file mode 100644
index 0000000..8eacd72
--- /dev/null
+++ b/docs/en/useful_tools.md
@@ -0,0 +1,589 @@
+Apart from training/testing scripts, We provide lots of useful tools under the
+`tools/` directory.
+
+## Log Analysis
+
+`tools/analysis_tools/analyze_logs.py` plots loss/mAP curves given a training
+log file. Run `pip install seaborn` first to install the dependency.
+
+```shell
+python tools/analysis_tools/analyze_logs.py plot_curve [--keys ${KEYS}] [--eval-interval ${EVALUATION_INTERVAL}] [--title ${TITLE}] [--legend ${LEGEND}] [--backend ${BACKEND}] [--style ${STYLE}] [--out ${OUT_FILE}]
+```
+
+![loss curve image](../../resources/loss_curve.png)
+
+Examples:
+
+- Plot the classification loss of some run.
+
+  ```shell
+  python tools/analysis_tools/analyze_logs.py plot_curve log.json --keys loss_cls --legend loss_cls
+  ```
+
+- Plot the classification and regression loss of some run, and save the figure to a pdf.
+
+  ```shell
+  python tools/analysis_tools/analyze_logs.py plot_curve log.json --keys loss_cls loss_bbox --out losses.pdf
+  ```
+
+- Compare the bbox mAP of two runs in the same figure.
+
+  ```shell
+  python tools/analysis_tools/analyze_logs.py plot_curve log1.json log2.json --keys bbox_mAP --legend run1 run2
+  ```
+
+- Compute the average training speed.
+
+  ```shell
+  python tools/analysis_tools/analyze_logs.py cal_train_time log.json [--include-outliers]
+  ```
+
+  The output is expected to be like the following.
+
+  ```text
+  -----Analyze train time of work_dirs/some_exp/20190611_192040.log.json-----
+  slowest epoch 11, average time is 1.2024
+  fastest epoch 1, average time is 1.1909
+  time std over epochs is 0.0028
+  average iter time: 1.1959 s/iter
+  ```
+
+## Result Analysis
+
+`tools/analysis_tools/analyze_results.py` calculates single image mAP and saves or shows the topk images with the highest and lowest scores based on prediction results.
+
+**Usage**
+
+```shell
+python tools/analysis_tools/analyze_results.py \
+      ${CONFIG} \
+      ${PREDICTION_PATH} \
+      ${SHOW_DIR} \
+      [--show] \
+      [--wait-time ${WAIT_TIME}] \
+      [--topk ${TOPK}] \
+      [--show-score-thr ${SHOW_SCORE_THR}] \
+      [--cfg-options ${CFG_OPTIONS}]
+```
+
+Description of all arguments:
+
+- `config` : The path of a model config file.
+- `prediction_path`:  Output result file in pickle format from `tools/test.py`
+- `show_dir`: Directory where painted GT and detection images will be saved
+- `--show`：Determines whether to show painted images, If not specified, it will be set to `False`
+- `--wait-time`: The interval of show (s), 0 is block
+- `--topk`: The number of saved images that have the highest and lowest `topk` scores after sorting. If not specified, it will be set to `20`.
+- `--show-score-thr`:  Show score threshold. If not specified, it will be set to `0`.
+- `--cfg-options`: If specified, the key-value pair optional cfg will be merged into config file
+
+**Examples**:
+
+Assume that you have got result file in pickle format from `tools/test.py`  in the path './result.pkl'.
+
+1. Test Faster R-CNN and visualize the results, save images to the directory `results/`
+
+```shell
+python tools/analysis_tools/analyze_results.py \
+       configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+       result.pkl \
+       results \
+       --show
+```
+
+2. Test Faster R-CNN and specified topk to 50, save images to the directory `results/`
+
+```shell
+python tools/analysis_tools/analyze_results.py \
+       configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+       result.pkl \
+       results \
+       --topk 50
+```
+
+3. If you want to filter the low score prediction results, you can specify the `show-score-thr` parameter
+
+```shell
+python tools/analysis_tools/analyze_results.py \
+       configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+       result.pkl \
+       results \
+       --show-score-thr 0.3
+```
+
+## Visualization
+
+### Visualize Datasets
+
+`tools/misc/browse_dataset.py` helps the user to browse a detection dataset (both
+images and bounding box annotations) visually, or save the image to a
+designated directory.
+
+```shell
+python tools/misc/browse_dataset.py ${CONFIG} [-h] [--skip-type ${SKIP_TYPE[SKIP_TYPE...]}] [--output-dir ${OUTPUT_DIR}] [--not-show] [--show-interval ${SHOW_INTERVAL}]
+```
+
+### Visualize Models
+
+First, convert the model to ONNX as described
+[here](#convert-mmdetection-model-to-onnx-experimental).
+Note that currently only RetinaNet is supported, support for other models
+will be coming in later versions.
+The converted model could be visualized by tools like [Netron](https://github.com/lutzroeder/netron).
+
+### Visualize Predictions
+
+If you need a lightweight GUI for visualizing the detection results, you can refer [DetVisGUI project](https://github.com/Chien-Hung/DetVisGUI/tree/mmdetection).
+
+## Error Analysis
+
+`tools/analysis_tools/coco_error_analysis.py` analyzes COCO results per category and by
+different criterion. It can also make a plot to provide useful information.
+
+```shell
+python tools/analysis_tools/coco_error_analysis.py ${RESULT} ${OUT_DIR} [-h] [--ann ${ANN}] [--types ${TYPES[TYPES...]}]
+```
+
+Example:
+
+Assume that you have got [Mask R-CNN checkpoint file](https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_fpn_1x_coco/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth) in the path 'checkpoint'. For other checkpoints, please refer to our [model zoo](./model_zoo.md). You can use the following command to get the results bbox and segmentation json file.
+
+```shell
+# out: results.bbox.json and results.segm.json
+python tools/test.py \
+       configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \
+       checkpoint/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \
+       --format-only \
+       --options "jsonfile_prefix=./results"
+```
+
+1. Get COCO bbox error results per category , save analyze result images to the directory `results/`
+
+```shell
+python tools/analysis_tools/coco_error_analysis.py \
+       results.bbox.json \
+       results \
+       --ann=data/coco/annotations/instances_val2017.json \
+```
+
+2. Get COCO segmentation error results per category , save analyze result images to the directory `results/`
+
+```shell
+python tools/analysis_tools/coco_error_analysis.py \
+       results.segm.json \
+       results \
+       --ann=data/coco/annotations/instances_val2017.json \
+       --types='segm'
+```
+
+## Model Serving
+
+In order to serve an `MMDetection` model with [`TorchServe`](https://pytorch.org/serve/), you can follow the steps:
+
+### 1. Convert model from MMDetection to TorchServe
+
+```shell
+python tools/deployment/mmdet2torchserve.py ${CONFIG_FILE} ${CHECKPOINT_FILE} \
+--output-folder ${MODEL_STORE} \
+--model-name ${MODEL_NAME}
+```
+
+**Note**: ${MODEL_STORE} needs to be an absolute path to a folder.
+
+### 2. Build `mmdet-serve` docker image
+
+```shell
+docker build -t mmdet-serve:latest docker/serve/
+```
+
+### 3. Run `mmdet-serve`
+
+Check the official docs for [running TorchServe with docker](https://github.com/pytorch/serve/blob/master/docker/README.md#running-torchserve-in-a-production-docker-environment).
+
+In order to run in GPU, you need to install [nvidia-docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). You can omit the `--gpus` argument in order to run in CPU.
+
+Example:
+
+```shell
+docker run --rm \
+--cpus 8 \
+--gpus device=0 \
+-p8080:8080 -p8081:8081 -p8082:8082 \
+--mount type=bind,source=$MODEL_STORE,target=/home/model-server/model-store \
+mmdet-serve:latest
+```
+
+[Read the docs](https://github.com/pytorch/serve/blob/072f5d088cce9bb64b2a18af065886c9b01b317b/docs/rest_api.md/) about the Inference (8080), Management (8081) and Metrics (8082) APis
+
+### 4. Test deployment
+
+```shell
+curl -O curl -O https://raw.githubusercontent.com/pytorch/serve/master/docs/images/3dogs.jpg
+curl http://127.0.0.1:8080/predictions/${MODEL_NAME} -T 3dogs.jpg
+```
+
+You should obtain a response similar to:
+
+```json
+[
+  {
+    "class_name": "dog",
+    "bbox": [
+      294.63409423828125,
+      203.99111938476562,
+      417.048583984375,
+      281.62744140625
+    ],
+    "score": 0.9987992644309998
+  },
+  {
+    "class_name": "dog",
+    "bbox": [
+      404.26019287109375,
+      126.0080795288086,
+      574.5091552734375,
+      293.6662292480469
+    ],
+    "score": 0.9979367256164551
+  },
+  {
+    "class_name": "dog",
+    "bbox": [
+      197.2144775390625,
+      93.3067855834961,
+      307.8505554199219,
+      276.7560119628906
+    ],
+    "score": 0.993338406085968
+  }
+]
+```
+
+And you can use `test_torchserver.py` to compare result of torchserver and pytorch, and visualize them.
+
+```shell
+python tools/deployment/test_torchserver.py ${IMAGE_FILE} ${CONFIG_FILE} ${CHECKPOINT_FILE} ${MODEL_NAME}
+[--inference-addr ${INFERENCE_ADDR}] [--device ${DEVICE}] [--score-thr ${SCORE_THR}]
+```
+
+Example:
+
+```shell
+python tools/deployment/test_torchserver.py \
+demo/demo.jpg \
+configs/yolo/yolov3_d53_320_273e_coco.py \
+checkpoint/yolov3_d53_320_273e_coco-421362b6.pth \
+yolov3
+```
+
+## Model Complexity
+
+`tools/analysis_tools/get_flops.py` is a script adapted from [flops-counter.pytorch](https://github.com/sovrasov/flops-counter.pytorch) to compute the FLOPs and params of a given model.
+
+```shell
+python tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]
+```
+
+You will get the results like this.
+
+```text
+==============================
+Input shape: (3, 1280, 800)
+Flops: 239.32 GFLOPs
+Params: 37.74 M
+==============================
+```
+
+**Note**: This tool is still experimental and we do not guarantee that the
+number is absolutely correct. You may well use the result for simple
+comparisons, but double check it before you adopt it in technical reports or papers.
+
+1. FLOPs are related to the input shape while parameters are not. The default
+   input shape is (1, 3, 1280, 800).
+2. Some operators are not counted into FLOPs like GN and custom operators. Refer to [`mmcv.cnn.get_model_complexity_info()`](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/flops_counter.py) for details.
+3. The FLOPs of two-stage detectors is dependent on the number of proposals.
+
+## Model conversion
+
+### MMDetection model to ONNX (experimental)
+
+We provide a script to convert model to [ONNX](https://github.com/onnx/onnx) format. We also support comparing the output results between Pytorch and ONNX model for verification.
+
+```shell
+python tools/deployment/pytorch2onnx.py ${CONFIG_FILE} ${CHECKPOINT_FILE} --output-file ${ONNX_FILE} [--shape ${INPUT_SHAPE} --verify]
+```
+
+**Note**: This tool is still experimental. Some customized operators are not supported for now. For a detailed description of the usage and the list of supported models, please refer to [pytorch2onnx](tutorials/pytorch2onnx.md).
+
+### MMDetection 1.x model to MMDetection 2.x
+
+`tools/model_converters/upgrade_model_version.py` upgrades a previous MMDetection checkpoint
+to the new version. Note that this script is not guaranteed to work as some
+breaking changes are introduced in the new version. It is recommended to
+directly use the new checkpoints.
+
+```shell
+python tools/model_converters/upgrade_model_version.py ${IN_FILE} ${OUT_FILE} [-h] [--num-classes NUM_CLASSES]
+```
+
+### RegNet model to MMDetection
+
+`tools/model_converters/regnet2mmdet.py` convert keys in pycls pretrained RegNet models to
+MMDetection style.
+
+```shell
+python tools/model_converters/regnet2mmdet.py ${SRC} ${DST} [-h]
+```
+
+### Detectron ResNet to Pytorch
+
+`tools/model_converters/detectron2pytorch.py` converts keys in the original detectron pretrained
+ResNet models to PyTorch style.
+
+```shell
+python tools/model_converters/detectron2pytorch.py ${SRC} ${DST} ${DEPTH} [-h]
+```
+
+### Prepare a model for publishing
+
+`tools/model_converters/publish_model.py` helps users to prepare their model for publishing.
+
+Before you upload a model to AWS, you may want to
+
+1. convert model weights to CPU tensors
+2. delete the optimizer states and
+3. compute the hash of the checkpoint file and append the hash id to the
+   filename.
+
+```shell
+python tools/model_converters/publish_model.py ${INPUT_FILENAME} ${OUTPUT_FILENAME}
+```
+
+E.g.,
+
+```shell
+python tools/model_converters/publish_model.py work_dirs/faster_rcnn/latest.pth faster_rcnn_r50_fpn_1x_20190801.pth
+```
+
+The final output filename will be `faster_rcnn_r50_fpn_1x_20190801-{hash id}.pth`.
+
+## Dataset Conversion
+
+`tools/data_converters/` contains tools to convert the Cityscapes dataset
+and Pascal VOC dataset to the COCO format.
+
+```shell
+python tools/dataset_converters/cityscapes.py ${CITYSCAPES_PATH} [-h] [--img-dir ${IMG_DIR}] [--gt-dir ${GT_DIR}] [-o ${OUT_DIR}] [--nproc ${NPROC}]
+python tools/dataset_converters/pascal_voc.py ${DEVKIT_PATH} [-h] [-o ${OUT_DIR}]
+```
+
+## Dataset Download
+
+`tools/misc/download_dataset.py` supports downloading datasets such as COCO, VOC, and LVIS.
+
+```shell
+python tools/misc/download_dataset.py --dataset-name coco2017
+python tools/misc/download_dataset.py --dataset-name voc2007
+python tools/misc/download_dataset.py --dataset-name lvis
+```
+
+## Benchmark
+
+### Robust Detection Benchmark
+
+`tools/analysis_tools/test_robustness.py` and`tools/analysis_tools/robustness_eval.py`  helps users to evaluate model robustness. The core idea comes from [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://arxiv.org/abs/1907.07484). For more information how to evaluate models on corrupted images and results for a set of standard models please refer to [robustness_benchmarking.md](robustness_benchmarking.md).
+
+### FPS Benchmark
+
+`tools/analysis_tools/benchmark.py` helps users to calculate FPS. The FPS value includes model forward and post-processing. In order to get a more accurate value, currently only supports single GPU distributed startup mode.
+
+```shell
+python -m torch.distributed.launch --nproc_per_node=1 --master_port=${PORT} tools/analysis_tools/benchmark.py \
+    ${CONFIG} \
+    ${CHECKPOINT} \
+    [--repeat-num ${REPEAT_NUM}] \
+    [--max-iter ${MAX_ITER}] \
+    [--log-interval ${LOG_INTERVAL}] \
+    --launcher pytorch
+```
+
+Examples: Assuming that you have already downloaded the `Faster R-CNN` model checkpoint to the directory `checkpoints/`.
+
+```shell
+python -m torch.distributed.launch --nproc_per_node=1 --master_port=29500 tools/analysis_tools/benchmark.py \
+       configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+       checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+       --launcher pytorch
+```
+
+## Miscellaneous
+
+### Evaluating a metric
+
+`tools/analysis_tools/eval_metric.py` evaluates certain metrics of a pkl result file
+according to a config file.
+
+```shell
+python tools/analysis_tools/eval_metric.py ${CONFIG} ${PKL_RESULTS} [-h] [--format-only] [--eval ${EVAL[EVAL ...]}]
+                      [--cfg-options ${CFG_OPTIONS [CFG_OPTIONS ...]}]
+                      [--eval-options ${EVAL_OPTIONS [EVAL_OPTIONS ...]}]
+```
+
+### Print the entire config
+
+`tools/misc/print_config.py` prints the whole config verbatim, expanding all its
+imports.
+
+```shell
+python tools/misc/print_config.py ${CONFIG} [-h] [--options ${OPTIONS [OPTIONS...]}]
+```
+
+## Hyper-parameter Optimization
+
+### YOLO Anchor Optimization
+
+`tools/analysis_tools/optimize_anchors.py` provides two method to optimize YOLO anchors.
+
+One is k-means anchor cluster which refers from [darknet](https://github.com/AlexeyAB/darknet/blob/master/src/detector.c#L1421).
+
+```shell
+python tools/analysis_tools/optimize_anchors.py ${CONFIG} --algorithm k-means --input-shape ${INPUT_SHAPE [WIDTH HEIGHT]} --output-dir ${OUTPUT_DIR}
+```
+
+Another is using differential evolution to optimize anchors.
+
+```shell
+python tools/analysis_tools/optimize_anchors.py ${CONFIG} --algorithm differential_evolution --input-shape ${INPUT_SHAPE [WIDTH HEIGHT]} --output-dir ${OUTPUT_DIR}
+```
+
+E.g.,
+
+```shell
+python tools/analysis_tools/optimize_anchors.py configs/yolo/yolov3_d53_320_273e_coco.py --algorithm differential_evolution --input-shape 608 608 --device cuda --output-dir work_dirs
+```
+
+You will get:
+
+```
+loading annotations into memory...
+Done (t=9.70s)
+creating index...
+index created!
+2021-07-19 19:37:20,951 - mmdet - INFO - Collecting bboxes from annotation...
+[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 117266/117266, 15874.5 task/s, elapsed: 7s, ETA:     0s
+
+2021-07-19 19:37:28,753 - mmdet - INFO - Collected 849902 bboxes.
+differential_evolution step 1: f(x)= 0.506055
+differential_evolution step 2: f(x)= 0.506055
+......
+
+differential_evolution step 489: f(x)= 0.386625
+2021-07-19 19:46:40,775 - mmdet - INFO Anchor evolution finish. Average IOU: 0.6133754253387451
+2021-07-19 19:46:40,776 - mmdet - INFO Anchor differential evolution result:[[10, 12], [15, 30], [32, 22], [29, 59], [61, 46], [57, 116], [112, 89], [154, 198], [349, 336]]
+2021-07-19 19:46:40,798 - mmdet - INFO Result saved in work_dirs/anchor_optimize_result.json
+```
+
+## Confusion Matrix
+
+A confusion matrix is a summary of prediction results.
+
+`tools/analysis_tools/confusion_matrix.py` can analyze the prediction results and plot a confusion matrix table.
+
+First, run `tools/test.py` to save the `.pkl` detection results.
+
+Then, run
+
+```
+python tools/analysis_tools/confusion_matrix.py ${CONFIG}  ${DETECTION_RESULTS}  ${SAVE_DIR} --show
+```
+
+And you will get a confusion matrix like this:
+
+![confusion_matrix_example](https://user-images.githubusercontent.com/12907710/140513068-994cdbf4-3a4a-48f0-8fd8-2830d93fd963.png)
+
+## COCO Separated & Occluded Mask Metric
+
+Detecting occluded objects still remains a challenge for state-of-the-art object detectors.
+We implemented the metric presented in paper [A Tri-Layer Plugin to Improve Occluded Detection](https://arxiv.org/abs/2210.10046) to calculate the recall of separated and occluded masks.
+
+There are two ways to use this metric:
+
+### Offline evaluation
+
+We provide a script to calculate the metric with a dumped prediction file.
+
+First, use the `tools/test.py` script to dump the detection results:
+
+```shell
+python tools/test.py ${CONFIG} ${MODEL_PATH} --out results.pkl
+```
+
+Then, run the `tools/analysis_tools/coco_occluded_separated_recall.py` script to get the recall of separated and occluded masks:
+
+```shell
+python tools/analysis_tools/coco_occluded_separated_recall.py results.pkl --out occluded_separated_recall.json
+```
+
+The output should be like this:
+
+```
+loading annotations into memory...
+Done (t=0.51s)
+creating index...
+index created!
+processing detection results...
+[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5000/5000, 109.3 task/s, elapsed: 46s, ETA:     0s
+computing occluded mask recall...
+[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 5550/5550, 780.5 task/s, elapsed: 7s, ETA:     0s
+COCO occluded mask recall: 58.79%
+COCO occluded mask success num: 3263
+computing separated mask recall...
+[>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>] 3522/3522, 778.3 task/s, elapsed: 5s, ETA:     0s
+COCO separated mask recall: 31.94%
+COCO separated mask success num: 1125
+
++-----------+--------+-------------+
+| mask type | recall | num correct |
++-----------+--------+-------------+
+| occluded  | 58.79% | 3263        |
+| separated | 31.94% | 1125        |
++-----------+--------+-------------+
+Evaluation results have been saved to occluded_separated_recall.json.
+```
+
+### Online evaluation
+
+We implement `OccludedSeparatedCocoDataset` which inherited from the `CocoDataset`.
+To evaluate the recall of separated and occluded masks during training, just replace the validation dataset type with `'OccludedSeparatedCocoDataset'` in your config:
+
+```python
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=train_pipeline),
+    val=dict(
+        type='OccludedSeparatedCocoDataset',  # modify this
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline),
+    test=dict(
+        type='OccludedSeparatedCocoDataset',  # modify this
+        ann_file=data_root + 'annotations/instances_val2017.json',
+        img_prefix=data_root + 'val2017/',
+        pipeline=test_pipeline))
+```
+
+Please cite the paper if you use this metric:
+
+```latex
+@article{zhan2022triocc,
+    title={A Tri-Layer Plugin to Improve Occluded Detection},
+    author={Zhan, Guanqi and Xie, Weidi and Zisserman, Andrew},
+    journal={British Machine Vision Conference},
+    year={2022}
+}
+```
diff --git a/docs/zh_cn/1_exist_data_model.md b/docs/zh_cn/1_exist_data_model.md
new file mode 100644
index 0000000..e349343
--- /dev/null
+++ b/docs/zh_cn/1_exist_data_model.md
@@ -0,0 +1,678 @@
+# 1: 使用已有模型在标准数据集上进行推理
+
+MMDetection 在 [Model Zoo](https://mmdetection.readthedocs.io/en/latest/model_zoo.html) 中提供了数以百计的检测模型，并支持多种标准数据集，包括 Pascal VOC，COCO，Cityscapes，LVIS 等。这份文档将会讲述如何使用这些模型和标准数据集来运行一些常见的任务，包括：
+
+- 使用现有模型在给定图片上进行推理
+- 在标准数据集上测试现有模型
+- 在标准数据集上训练预定义的模型
+
+## 使用现有模型进行推理
+
+推理是指使用训练好的模型来检测图像上的目标。在 MMDetection 中，一个模型被定义为一个配置文件和对应的存储在 checkpoint 文件内的模型参数的集合。
+
+首先，我们建议从 [Faster RCNN](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn) 开始，其 [配置](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py) 文件和 [checkpoint](http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth) 文件在此。
+我们建议将 checkpoint 文件下载到 `checkpoints` 文件夹内。
+
+### 推理的高层编程接口
+
+MMDetection 为在图片上推理提供了 Python 的高层编程接口。下面是建立模型和在图像或视频上进行推理的例子。
+
+```python
+from mmdet.apis import init_detector, inference_detector
+import mmcv
+
+# 指定模型的配置文件和 checkpoint 文件路径
+config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
+
+# 根据配置文件和 checkpoint 文件构建模型
+model = init_detector(config_file, checkpoint_file, device='cuda:0')
+
+# 测试单张图片并展示结果
+img = 'test.jpg'  # 或者 img = mmcv.imread(img)，这样图片仅会被读一次
+result = inference_detector(model, img)
+# 在一个新的窗口中将结果可视化
+model.show_result(img, result)
+# 或者将可视化结果保存为图片
+model.show_result(img, result, out_file='result.jpg')
+
+# 测试视频并展示结果
+video = mmcv.VideoReader('video.mp4')
+for frame in video:
+    result = inference_detector(model, frame)
+    model.show_result(frame, result, wait_time=1)
+```
+
+jupyter notebook 上的演示样例在 [demo/inference_demo.ipynb](https://github.com/open-mmlab/mmdetection/blob/master/demo/inference_demo.ipynb) 。
+
+### 异步接口-支持 Python 3.7+
+
+对于 Python 3.7+，MMDetection 也有异步接口。利用 CUDA 流，绑定 GPU 的推理代码不会阻塞 CPU，从而使得 CPU/GPU 在单线程应用中能达到更高的利用率。在推理流程中，不同数据样本的推理和不同模型的推理都能并发地运行。
+
+您可以参考 `tests/async_benchmark.py` 来对比同步接口和异步接口的运行速度。
+
+```python
+import asyncio
+import torch
+from mmdet.apis import init_detector, async_inference_detector
+from mmdet.utils.contextmanagers import concurrent
+
+async def main():
+    config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+    checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
+    device = 'cuda:0'
+    model = init_detector(config_file, checkpoint=checkpoint_file, device=device)
+
+    # 此队列用于并行推理多张图像
+    streamqueue = asyncio.Queue()
+    # 队列大小定义了并行的数量
+    streamqueue_size = 3
+
+    for _ in range(streamqueue_size):
+        streamqueue.put_nowait(torch.cuda.Stream(device=device))
+
+    # 测试单张图片并展示结果
+    img = 'test.jpg'  # or 或者 img = mmcv.imread(img)，这样图片仅会被读一次
+
+    async with concurrent(streamqueue):
+        result = await async_inference_detector(model, img)
+
+    # 在一个新的窗口中将结果可视化
+    model.show_result(img, result)
+    # 或者将可视化结果保存为图片
+    model.show_result(img, result, out_file='result.jpg')
+
+
+asyncio.run(main())
+
+```
+
+### 演示样例
+
+我们还提供了三个演示脚本，它们是使用高层编程接口实现的。 [源码在此](https://github.com/open-mmlab/mmdetection/tree/master/demo) 。
+
+#### 图片样例
+
+这是在单张图片上进行推理的脚本，可以开启 `--async-test` 来进行异步推理。
+
+```shell
+python demo/image_demo.py \
+    ${IMAGE_FILE} \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    [--device ${GPU_ID}] \
+    [--score-thr ${SCORE_THR}] \
+    [--async-test]
+```
+
+运行样例：
+
+```shell
+python demo/image_demo.py demo/demo.jpg \
+    configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+    checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+    --device cpu
+```
+
+#### 摄像头样例
+
+这是使用摄像头实时图片的推理脚本。
+
+```shell
+python demo/webcam_demo.py \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    [--device ${GPU_ID}] \
+    [--camera-id ${CAMERA-ID}] \
+    [--score-thr ${SCORE_THR}]
+```
+
+运行样例：
+
+```shell
+python demo/webcam_demo.py \
+    configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+    checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
+```
+
+#### 视频样例
+
+这是在视频样例上进行推理的脚本。
+
+```shell
+python demo/video_demo.py \
+    ${VIDEO_FILE} \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    [--device ${GPU_ID}] \
+    [--score-thr ${SCORE_THR}] \
+    [--out ${OUT_FILE}] \
+    [--show] \
+    [--wait-time ${WAIT_TIME}]
+```
+
+运行样例：
+
+```shell
+python demo/video_demo.py demo/demo.mp4 \
+    configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+    checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+    --out result.mp4
+```
+
+#### 视频样例，显卡加速版本
+
+这是在视频样例上进行推理的脚本，使用显卡加速。
+
+```shell
+python demo/video_gpuaccel_demo.py \
+     ${VIDEO_FILE} \
+     ${CONFIG_FILE} \
+     ${CHECKPOINT_FILE} \
+     [--device ${GPU_ID}] \
+     [--score-thr ${SCORE_THR}] \
+     [--nvdecode] \
+     [--out ${OUT_FILE}] \
+     [--show] \
+     [--wait-time ${WAIT_TIME}]
+
+```
+
+运行样例：
+
+```shell
+python demo/video_gpuaccel_demo.py demo/demo.mp4 \
+    configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+    checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+    --nvdecode --out result.mp4
+```
+
+## 在标准数据集上测试现有模型
+
+为了测试一个模型的精度，我们通常会在标准数据集上对其进行测试。MMDetection 支持多个公共数据集，包括 [COCO](https://cocodataset.org/) ，
+[Pascal VOC](http://host.robots.ox.ac.uk/pascal/VOC) ，[Cityscapes](https://www.cityscapes-dataset.com/) 等等。
+这一部分将会介绍如何在支持的数据集上测试现有模型。
+
+### 数据集准备
+
+一些公共数据集，比如 Pascal VOC 及其镜像数据集，或者 COCO 等数据集都可以从官方网站或者镜像网站获取。
+注意：在检测任务中，Pascal VOC 2012 是 Pascal VOC 2007 的无交集扩展，我们通常将两者一起使用。
+我们建议将数据集下载，然后解压到项目外部的某个文件夹内，然后通过符号链接的方式，将数据集根目录链接到 `$MMDETECTION/data` 文件夹下，格式如下所示。
+如果你的文件夹结构和下方不同的话，你需要在配置文件中改变对应的路径。
+我们提供了下载 COCO 等数据集的脚本，你可以运行 `python tools/misc/download_dataset.py --dataset-name coco2017` 下载 COCO 数据集。
+
+```plain
+mmdetection
+├── mmdet
+├── tools
+├── configs
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+│   ├── cityscapes
+│   │   ├── annotations
+│   │   ├── leftImg8bit
+│   │   │   ├── train
+│   │   │   ├── val
+│   │   ├── gtFine
+│   │   │   ├── train
+│   │   │   ├── val
+│   ├── VOCdevkit
+│   │   ├── VOC2007
+│   │   ├── VOC2012
+```
+
+有些模型需要额外的 [COCO-stuff](http://calvin.inf.ed.ac.uk/wp-content/uploads/data/cocostuffdataset/stuffthingmaps_trainval2017.zip) 数据集，比如 HTC，DetectoRS 和 SCNet，你可以下载并解压它们到 `coco` 文件夹下。文件夹会是如下结构：
+
+```plain
+mmdetection
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+│   │   ├── stuffthingmaps
+```
+
+PanopticFPN 等全景分割模型需要额外的 [COCO Panoptic](http://images.cocodataset.org/annotations/panoptic_annotations_trainval2017.zip) 数据集，你可以下载并解压它们到 `coco/annotations` 文件夹下。文件夹会是如下结构：
+
+```text
+mmdetection
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   │   ├── panoptic_train2017.json
+│   │   │   ├── panoptic_train2017
+│   │   │   ├── panoptic_val2017.json
+│   │   │   ├── panoptic_val2017
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+```
+
+Cityscape 数据集的标注格式需要转换，以与 COCO 数据集标注格式保持一致，使用 `tools/dataset_converters/cityscapes.py` 来完成转换：
+
+```shell
+pip install cityscapesscripts
+
+python tools/dataset_converters/cityscapes.py \
+    ./data/cityscapes \
+    --nproc 8 \
+    --out-dir ./data/cityscapes/annotations
+```
+
+### 测试现有模型
+
+我们提供了测试脚本，能够测试一个现有模型在所有数据集（COCO，Pascal VOC，Cityscapes 等）上的性能。我们支持在如下环境下测试：
+
+- 单 GPU 测试
+- CPU 测试
+- 单节点多 GPU 测试
+- 多节点测试
+
+根据以上测试环境，选择合适的脚本来执行测试过程。
+
+```shell
+# 单 GPU 测试
+python tools/test.py \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    [--out ${RESULT_FILE}] \
+    [--eval ${EVAL_METRICS}] \
+    [--show]
+
+# CPU 测试：禁用 GPU 并运行单 GPU 测试脚本
+export CUDA_VISIBLE_DEVICES=-1
+python tools/test.py \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    [--out ${RESULT_FILE}] \
+    [--eval ${EVAL_METRICS}] \
+    [--show]
+
+# 单节点多 GPU 测试
+bash tools/dist_test.sh \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    ${GPU_NUM} \
+    [--out ${RESULT_FILE}] \
+    [--eval ${EVAL_METRICS}]
+```
+
+`tools/dist_test.sh` 也支持多节点测试，不过需要依赖 PyTorch 的 [启动工具](https://pytorch.org/docs/stable/distributed.html#launch-utility) 。
+
+可选参数：
+
+- `RESULT_FILE`: 结果文件名称，需以 .pkl 形式存储。如果没有声明，则不将结果存储到文件。
+- `EVAL_METRICS`: 需要测试的度量指标。可选值是取决于数据集的，比如 `proposal_fast`，`proposal`，`bbox`，`segm` 是 COCO 数据集的可选值，`mAP`，`recall` 是 Pascal VOC 数据集的可选值。Cityscapes 数据集可以测试 `cityscapes` 和所有 COCO 数据集支持的度量指标。
+- `--show`: 如果开启，检测结果将被绘制在图像上，以一个新窗口的形式展示。它只适用于单 GPU 的测试，是用于调试和可视化的。请确保使用此功能时，你的 GUI 可以在环境中打开。否则，你可能会遇到这么一个错误 `cannot connect to X server`。
+- `--show-dir`: 如果指明，检测结果将会被绘制在图像上并保存到指定目录。它只适用于单 GPU 的测试，是用于调试和可视化的。即使你的环境中没有 GUI，这个选项也可使用。
+- `--show-score-thr`: 如果指明，得分低于此阈值的检测结果将会被移除。
+- `--cfg-options`:  如果指明，这里的键值对将会被合并到配置文件中。
+- `--eval-options`: 如果指明，这里的键值对将会作为字典参数被传入 `dataset.evaluation()` 函数中，仅在测试阶段使用。
+
+### 样例
+
+假设你已经下载了 checkpoint 文件到 `checkpoints/` 文件下了。
+
+1. 测试 Faster R-CNN 并可视化其结果。按任意键继续下张图片的测试。配置文件和 checkpoint 文件 [在此](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn) 。
+
+   ```shell
+   python tools/test.py \
+       configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+       checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+       --show
+   ```
+
+2. 测试 Faster R-CNN，并为了之后的可视化保存绘制的图像。配置文件和 checkpoint 文件 [在此](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn) 。
+
+   ```shell
+   python tools/test.py \
+       configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py \
+       checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth \
+       --show-dir faster_rcnn_r50_fpn_1x_results
+   ```
+
+3. 在 Pascal VOC 数据集上测试 Faster R-CNN，不保存测试结果，测试 `mAP`。配置文件和 checkpoint 文件 [在此](https://github.com/open-mmlab/mmdetection/tree/master/configs/pascal_voc) 。
+
+   ```shell
+   python tools/test.py \
+       configs/pascal_voc/faster_rcnn_r50_fpn_1x_voc.py \
+       checkpoints/faster_rcnn_r50_fpn_1x_voc0712_20200624-c9895d40.pth \
+       --eval mAP
+   ```
+
+4. 使用 8 块 GPU 测试 Mask R-CNN，测试 `bbox` 和 `mAP` 。配置文件和 checkpoint 文件 [在此](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn) 。
+
+   ```shell
+   ./tools/dist_test.sh \
+       configs/mask_rcnn_r50_fpn_1x_coco.py \
+       checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \
+       8 \
+       --out results.pkl \
+       --eval bbox segm
+   ```
+
+5. 使用 8 块 GPU 测试 Mask R-CNN，测试**每类**的 `bbox` 和 `mAP`。配置文件和 checkpoint 文件 [在此](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn) 。
+
+   ```shell
+   ./tools/dist_test.sh \
+       configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \
+       checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \
+       8 \
+       --out results.pkl \
+       --eval bbox segm \
+       --options "classwise=True"
+   ```
+
+6. 在 COCO test-dev 数据集上，使用 8 块 GPU 测试 Mask R-CNN，并生成 JSON 文件提交到官方评测服务器。配置文件和 checkpoint 文件 [在此](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn) 。
+
+   ```shell
+   ./tools/dist_test.sh \
+       configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \
+       checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \
+       8 \
+       --format-only \
+       --options "jsonfile_prefix=./mask_rcnn_test-dev_results"
+   ```
+
+这行命令生成两个 JSON 文件 `mask_rcnn_test-dev_results.bbox.json` 和 `mask_rcnn_test-dev_results.segm.json`。
+
+7. 在 Cityscapes 数据集上，使用 8 块 GPU 测试 Mask R-CNN，生成 txt 和 png 文件，并上传到官方评测服务器。配置文件和 checkpoint 文件 [在此](https://github.com/open-mmlab/mmdetection/tree/master/configs/cityscapes) 。
+
+   ```shell
+   ./tools/dist_test.sh \
+       configs/cityscapes/mask_rcnn_r50_fpn_1x_cityscapes.py \
+       checkpoints/mask_rcnn_r50_fpn_1x_cityscapes_20200227-afe51d5a.pth \
+       8 \
+       --format-only \
+       --options "txtfile_prefix=./mask_rcnn_cityscapes_test_results"
+   ```
+
+生成的 png 和 txt 文件在 `./mask_rcnn_cityscapes_test_results` 文件夹下。
+
+### 不使用 Ground Truth 标注进行测试
+
+MMDetection 支持在不使用 ground-truth 标注的情况下对模型进行测试，这需要用到 `CocoDataset`。如果你的数据集格式不是 COCO 格式的，请将其转化成 COCO 格式。如果你的数据集格式是 VOC 或者 Cityscapes，你可以使用 [tools/dataset_converters](https://github.com/open-mmlab/mmdetection/tree/master/tools/dataset_converters) 内的脚本直接将其转化成 COCO 格式。如果是其他格式，可以使用 [images2coco 脚本](https://github.com/open-mmlab/mmdetection/tree/master/tools/dataset_converters/images2coco.py) 进行转换。
+
+```shell
+python tools/dataset_converters/images2coco.py \
+    ${IMG_PATH} \
+    ${CLASSES} \
+    ${OUT} \
+    [--exclude-extensions]
+```
+
+参数：
+
+- `IMG_PATH`: 图片根路径。
+- `CLASSES`: 类列表文本文件名。文本中每一行存储一个类别。
+- `OUT`: 输出 json 文件名。 默认保存目录和 `IMG_PATH` 在同一级。
+- `exclude-extensions`: 待排除的文件后缀名。
+
+在转换完成后，使用如下命令进行测试
+
+```shell
+# 单 GPU 测试
+python tools/test.py \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    --format-only \
+    --options ${JSONFILE_PREFIX} \
+    [--show]
+
+# CPU 测试：禁用 GPU 并运行单 GPU 测试脚本
+export CUDA_VISIBLE_DEVICES=-1
+python tools/test.py \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    [--out ${RESULT_FILE}] \
+    [--eval ${EVAL_METRICS}] \
+    [--show]
+
+# 单节点多 GPU 测试
+bash tools/dist_test.sh \
+    ${CONFIG_FILE} \
+    ${CHECKPOINT_FILE} \
+    ${GPU_NUM} \
+    --format-only \
+    --options ${JSONFILE_PREFIX} \
+    [--show]
+```
+
+假设 [model zoo](https://mmdetection.readthedocs.io/en/latest/modelzoo_statistics.html) 中的 checkpoint 文件被下载到了 `checkpoints/` 文件夹下，
+我们可以使用以下命令，用 8 块 GPU 在 COCO test-dev 数据集上测试 Mask R-CNN，并且生成 JSON 文件。
+
+```sh
+./tools/dist_test.sh \
+    configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py \
+    checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth \
+    8 \
+    -format-only \
+    --options "jsonfile_prefix=./mask_rcnn_test-dev_results"
+```
+
+这行命令生成两个 JSON 文件 `mask_rcnn_test-dev_results.bbox.json` 和 `mask_rcnn_test-dev_results.segm.json`。
+
+### 批量推理
+
+MMDetection 在测试模式下，既支持单张图片的推理，也支持对图像进行批量推理。默认情况下，我们使用单张图片的测试，你可以通过修改测试数据配置文件中的 `samples_per_gpu` 来开启批量测试。
+开启批量推理的配置文件修改方法为：
+
+```shell
+data = dict(train=dict(...), val=dict(...), test=dict(samples_per_gpu=2, ...))
+```
+
+或者你可以通过将 `--cfg-options` 设置为 `--cfg-options data.test.samples_per_gpu=2` 来开启它。
+
+### 弃用 ImageToTensor
+
+在测试模式下，弃用 `ImageToTensor` 流程，取而代之的是 `DefaultFormatBundle`。建议在你的测试数据流程的配置文件中手动替换它，如：
+
+```python
+# （已弃用）使用 ImageToTensor
+pipelines = [
+   dict(type='LoadImageFromFile'),
+   dict(
+       type='MultiScaleFlipAug',
+       img_scale=(1333, 800),
+       flip=False,
+       transforms=[
+           dict(type='Resize', keep_ratio=True),
+           dict(type='RandomFlip'),
+           dict(type='Normalize', mean=[0, 0, 0], std=[1, 1, 1]),
+           dict(type='Pad', size_divisor=32),
+           dict(type='ImageToTensor', keys=['img']),
+           dict(type='Collect', keys=['img']),
+       ])
+   ]
+
+# （建议使用）手动将 ImageToTensor 替换为 DefaultFormatBundle
+pipelines = [
+   dict(type='LoadImageFromFile'),
+   dict(
+       type='MultiScaleFlipAug',
+       img_scale=(1333, 800),
+       flip=False,
+       transforms=[
+           dict(type='Resize', keep_ratio=True),
+           dict(type='RandomFlip'),
+           dict(type='Normalize', mean=[0, 0, 0], std=[1, 1, 1]),
+           dict(type='Pad', size_divisor=32),
+           dict(type='DefaultFormatBundle'),
+           dict(type='Collect', keys=['img']),
+       ])
+   ]
+```
+
+## 在标准数据集上训练预定义的模型
+
+MMDetection 也为训练检测模型提供了开盖即食的工具。本节将展示在标准数据集（比如 COCO）上如何训练一个预定义的模型。
+
+### 数据集
+
+训练需要准备好数据集，细节请参考 [数据集准备](#%E6%95%B0%E6%8D%AE%E9%9B%86%E5%87%86%E5%A4%87) 。
+
+**注意**：
+目前，`configs/cityscapes` 文件夹下的配置文件都是使用 COCO 预训练权值进行初始化的。如果网络连接不可用或者速度很慢，你可以提前下载现存的模型。否则可能在训练的开始会有错误发生。
+
+### 学习率自动缩放
+
+**注意**：在配置文件中的学习率是在 8 块 GPU，每块 GPU 有 2 张图像（批大小为 8\*2=16）的情况下设置的。其已经设置在`config/_base_/default_runtime.py` 中的 `auto_scale_lr.base_batch_size`。当配置文件的批次大小为`16`时，学习率会基于该值进行自动缩放。同时，为了不影响其他基于 mmdet 的 codebase，启用自动缩放标志 `auto_scale_lr.enable` 默认设置为 `False`。
+
+如果要启用此功能，需在命令添加参数 `--auto-scale-lr`。并且在启动命令之前，请检查下即将使用的配置文件的名称，因为配置名称指示默认的批处理大小。
+在默认情况下，批次大小是 `8 x 2 = 16`，例如：`faster_rcnn_r50_caffe_fpn_90k_coco.py` 或者 `pisa_faster_rcnn_x101_32x4d_fpn_1x_coco.py`；若不是默认批次，你可以在配置文件看到像 `_NxM_` 字样的，例如：`cornernet_hourglass104_mstest_32x3_210e_coco.py` 的批次大小是 `32 x 3 = 96`, 或者 `scnet_x101_64x4d_fpn_8x1_20e_coco.py` 的批次大小是 `8 x 1 = 8`。
+
+**请记住：如果使用不是默认批次大小为`16`的配置文件，请检查配置文件中的底部，会有 `auto_scale_lr.base_batch_size`。如果找不到，可以在其继承的 `_base_=[xxx]` 文件中找到。另外，如果想使用自动缩放学习率的功能，请不要修改这些值。**
+
+学习率自动缩放基本用法如下：
+
+```shell
+python tools/train.py \
+    ${CONFIG_FILE} \
+    --auto-scale-lr \
+    [optional arguments]
+```
+
+执行命令之后，会根据机器的GPU数量和训练的批次大小对学习率进行自动缩放，缩放方式详见 [线性扩展规则](https://arxiv.org/abs/1706.02677) ，比如：在 4 块 GPU 并且每张 GPU 上有 2 张图片的情况下 `lr=0.01`，那么在 16 块 GPU 并且每张 GPU 上有 4 张图片的情况下, LR 会自动缩放至`lr=0.08`。
+
+如果不启用该功能，则需要根据 [线性扩展规则](https://arxiv.org/abs/1706.02677) 来手动计算并修改配置文件里面 `optimizer.lr` 的值。
+
+### 使用单 GPU 训练
+
+我们提供了 `tools/train.py` 来开启在单张 GPU 上的训练任务。基本使用如下：
+
+```shell
+python tools/train.py \
+    ${CONFIG_FILE} \
+    [optional arguments]
+```
+
+在训练期间，日志文件和 checkpoint 文件将会被保存在工作目录下，它需要通过配置文件中的 `work_dir` 或者 CLI 参数中的 `--work-dir` 来指定。
+
+默认情况下，模型将在每轮训练之后在 validation 集上进行测试，测试的频率可以通过设置配置文件来指定：
+
+```python
+# 每 12 轮迭代进行一次测试评估
+evaluation = dict(interval=12)
+```
+
+这个工具接受以下参数：
+
+- `--no-validate` (**不建议**): 在训练期间关闭测试.
+- `--work-dir ${WORK_DIR}`: 覆盖工作目录.
+- `--resume-from ${CHECKPOINT_FILE}`: 从某个 checkpoint 文件继续训练.
+- `--options 'Key=value'`: 覆盖使用的配置文件中的其他设置.
+
+**注意**：
+`resume-from` 和 `load-from` 的区别：
+
+`resume-from` 既加载了模型的权重和优化器的状态，也会继承指定 checkpoint 的迭代次数，不会重新开始训练。`load-from` 则是只加载模型的权重，它的训练是从头开始的，经常被用于微调模型。
+
+### 使用 CPU 训练
+
+使用 CPU 训练的流程和使用单 GPU 训练的流程一致，我们仅需要在训练流程开始前禁用 GPU。
+
+```shell
+export CUDA_VISIBLE_DEVICES=-1
+```
+
+之后运行单 GPU 训练脚本即可。
+
+**注意**：
+
+我们不推荐用户使用 CPU 进行训练，这太过缓慢。我们支持这个功能是为了方便用户在没有 GPU 的机器上进行调试。
+
+### 在多 GPU 上训练
+
+我们提供了 `tools/dist_train.sh` 来开启在多 GPU 上的训练。基本使用如下：
+
+```shell
+bash ./tools/dist_train.sh \
+    ${CONFIG_FILE} \
+    ${GPU_NUM} \
+    [optional arguments]
+```
+
+可选参数和单 GPU 训练的可选参数一致。
+
+#### 同时启动多个任务
+
+如果你想在一台机器上启动多个任务的话，比如在一个有 8 块 GPU 的机器上启动 2 个需要 4 块GPU的任务，你需要给不同的训练任务指定不同的端口（默认为 29500）来避免冲突。
+
+如果你使用 `dist_train.sh` 来启动训练任务，你可以使用命令来设置端口。
+
+```shell
+CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 ./tools/dist_train.sh ${CONFIG_FILE} 4
+CUDA_VISIBLE_DEVICES=4,5,6,7 PORT=29501 ./tools/dist_train.sh ${CONFIG_FILE} 4
+```
+
+### 使用多台机器训练
+
+如果您想使用由 ethernet 连接起来的多台机器， 您可以使用以下命令:
+
+在第一台机器上:
+
+```shell
+NNODES=2 NODE_RANK=0 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS
+```
+
+在第二台机器上:
+
+```shell
+NNODES=2 NODE_RANK=1 PORT=$MASTER_PORT MASTER_ADDR=$MASTER_ADDR sh tools/dist_train.sh $CONFIG $GPUS
+```
+
+但是，如果您不使用高速网路连接这几台机器的话，训练将会非常慢。
+
+### 使用 Slurm 来管理任务
+
+Slurm 是一个常见的计算集群调度系统。在 Slurm 管理的集群上，你可以使用 `slurm.sh` 来开启训练任务。它既支持单节点训练也支持多节点训练。
+
+基本使用如下：
+
+```shell
+[GPUS=${GPUS}] ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} ${CONFIG_FILE} ${WORK_DIR}
+```
+
+以下是在一个名称为 _dev_ 的 Slurm 分区上，使用 16 块 GPU 来训练 Mask R-CNN 的例子，并且将 `work-dir` 设置在了某些共享文件系统下。
+
+```shell
+GPUS=16 ./tools/slurm_train.sh dev mask_r50_1x configs/mask_rcnn_r50_fpn_1x_coco.py /nfs/xxxx/mask_rcnn_r50_fpn_1x
+```
+
+你可以查看 [源码](https://github.com/open-mmlab/mmdetection/blob/master/tools/slurm_train.sh) 来检查全部的参数和环境变量.
+
+在使用 Slurm 时，端口需要以下方的某个方法之一来设置。
+
+1. 通过 `--options` 来设置端口。我们非常建议用这种方法，因为它无需改变原始的配置文件。
+
+   ```shell
+   CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR} --options 'dist_params.port=29500'
+   CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR} --options 'dist_params.port=29501'
+   ```
+
+2. 修改配置文件来设置不同的交流端口。
+
+   在 `config1.py` 中，设置：
+
+   ```python
+   dist_params = dict(backend='nccl', port=29500)
+   ```
+
+   在 `config2.py` 中，设置：
+
+   ```python
+   dist_params = dict(backend='nccl', port=29501)
+   ```
+
+   然后你可以使用 `config1.py` 和 `config2.py` 来启动两个任务了。
+
+   ```shell
+   CUDA_VISIBLE_DEVICES=0,1,2,3 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config1.py ${WORK_DIR}
+   CUDA_VISIBLE_DEVICES=4,5,6,7 GPUS=4 ./tools/slurm_train.sh ${PARTITION} ${JOB_NAME} config2.py ${WORK_DIR}
+   ```
diff --git a/docs/zh_cn/2_new_data_model.md b/docs/zh_cn/2_new_data_model.md
new file mode 100644
index 0000000..f760c51
--- /dev/null
+++ b/docs/zh_cn/2_new_data_model.md
@@ -0,0 +1,267 @@
+# 2: 在自定义数据集上进行训练
+
+通过本文档，你将会知道如何使用自定义数据集对预先定义好的模型进行推理，测试以及训练。我们使用 [balloon dataset](https://github.com/matterport/Mask_RCNN/tree/master/samples/balloon) 作为例子来描述整个过程。
+
+基本步骤如下：
+
+1. 准备自定义数据集
+2. 准备配置文件
+3. 在自定义数据集上进行训练，测试和推理。
+
+## 准备自定义数据集
+
+MMDetection 一共支持三种形式应用新数据集：
+
+1. 将数据集重新组织为 COCO 格式。
+2. 将数据集重新组织为一个中间格式。
+3. 实现一个新的数据集。
+
+我们通常建议使用前面两种方法，因为它们通常来说比第三种方法要简单。
+
+在本文档中，我们展示一个例子来说明如何将数据转化为 COCO 格式。
+
+**注意**：MMDetection 现只支持对 COCO 格式的数据集进行 mask AP 的评测。
+
+所以用户如果要进行实例分割，只能将数据转成 COCO 格式。
+
+### COCO标注格式
+
+用于实例分割的 COCO 数据集格式如下所示，其中的键（key）都是必要的，参考[这里](https://cocodataset.org/#format-data)来获取更多细节。
+
+```json
+{
+    "images": [image],
+    "annotations": [annotation],
+    "categories": [category]
+}
+
+
+image = {
+    "id": int,
+    "width": int,
+    "height": int,
+    "file_name": str,
+}
+
+annotation = {
+    "id": int,
+    "image_id": int,
+    "category_id": int,
+    "segmentation": RLE or [polygon],
+    "area": float,
+    "bbox": [x,y,width,height],
+    "iscrowd": 0 or 1,
+}
+
+categories = [{
+    "id": int,
+    "name": str,
+    "supercategory": str,
+}]
+```
+
+现在假设我们使用 balloon dataset。
+
+下载了数据集之后，我们需要实现一个函数将标注格式转化为 COCO 格式。然后我们就可以使用已经实现的 `COCODataset` 类来加载数据并进行训练以及评测。
+
+如果你浏览过新数据集，你会发现格式如下：
+
+```json
+{'base64_img_data': '',
+ 'file_attributes': {},
+ 'filename': '34020010494_e5cb88e1c4_k.jpg',
+ 'fileref': '',
+ 'regions': {'0': {'region_attributes': {},
+   'shape_attributes': {'all_points_x': [1020,
+     1000,
+     994,
+     1003,
+     1023,
+     1050,
+     1089,
+     1134,
+     1190,
+     1265,
+     1321,
+     1361,
+     1403,
+     1428,
+     1442,
+     1445,
+     1441,
+     1427,
+     1400,
+     1361,
+     1316,
+     1269,
+     1228,
+     1198,
+     1207,
+     1210,
+     1190,
+     1177,
+     1172,
+     1174,
+     1170,
+     1153,
+     1127,
+     1104,
+     1061,
+     1032,
+     1020],
+    'all_points_y': [963,
+     899,
+     841,
+     787,
+     738,
+     700,
+     663,
+     638,
+     621,
+     619,
+     643,
+     672,
+     720,
+     765,
+     800,
+     860,
+     896,
+     942,
+     990,
+     1035,
+     1079,
+     1112,
+     1129,
+     1134,
+     1144,
+     1153,
+     1166,
+     1166,
+     1150,
+     1136,
+     1129,
+     1122,
+     1112,
+     1084,
+     1037,
+     989,
+     963],
+    'name': 'polygon'}}},
+ 'size': 1115004}
+```
+
+标注文件时是 JSON 格式的，其中所有键（key）组成了一张图片的所有标注。
+
+其中将 balloon dataset 转化为 COCO 格式的代码如下所示。
+
+```python
+
+import os.path as osp
+import mmcv
+
+def convert_balloon_to_coco(ann_file, out_file, image_prefix):
+    data_infos = mmcv.load(ann_file)
+
+    annotations = []
+    images = []
+    obj_count = 0
+    for idx, v in enumerate(mmcv.track_iter_progress(data_infos.values())):
+        filename = v['filename']
+        img_path = osp.join(image_prefix, filename)
+        height, width = mmcv.imread(img_path).shape[:2]
+
+        images.append(dict(
+            id=idx,
+            file_name=filename,
+            height=height,
+            width=width))
+
+        bboxes = []
+        labels = []
+        masks = []
+        for _, obj in v['regions'].items():
+            assert not obj['region_attributes']
+            obj = obj['shape_attributes']
+            px = obj['all_points_x']
+            py = obj['all_points_y']
+            poly = [(x + 0.5, y + 0.5) for x, y in zip(px, py)]
+            poly = [p for x in poly for p in x]
+
+            x_min, y_min, x_max, y_max = (
+                min(px), min(py), max(px), max(py))
+
+
+            data_anno = dict(
+                image_id=idx,
+                id=obj_count,
+                category_id=0,
+                bbox=[x_min, y_min, x_max - x_min, y_max - y_min],
+                area=(x_max - x_min) * (y_max - y_min),
+                segmentation=[poly],
+                iscrowd=0)
+            annotations.append(data_anno)
+            obj_count += 1
+
+    coco_format_json = dict(
+        images=images,
+        annotations=annotations,
+        categories=[{'id':0, 'name': 'balloon'}])
+    mmcv.dump(coco_format_json, out_file)
+```
+
+使用如上的函数，用户可以成功将标注文件转化为 JSON 格式，之后可以使用 `CocoDataset` 对模型进行训练和评测。
+
+## 准备配置文件
+
+第二步需要准备一个配置文件来成功加载数据集。假设我们想要用 balloon dataset 来训练配备了 FPN 的 Mask R-CNN ，如下是我们的配置文件。假设配置文件命名为 `mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py`，相应保存路径为 `configs/balloon/`，配置文件内容如下所示。
+
+```python
+# 这个新的配置文件继承自一个原始配置文件，只需要突出必要的修改部分即可
+_base_ = 'mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py'
+
+# 我们需要对头中的类别数量进行修改来匹配数据集的标注
+model = dict(
+    roi_head=dict(
+        bbox_head=dict(num_classes=1),
+        mask_head=dict(num_classes=1)))
+
+# 修改数据集相关设置
+dataset_type = 'CocoDataset'
+classes = ('balloon',)
+data = dict(
+    train=dict(
+        img_prefix='balloon/train/',
+        classes=classes,
+        ann_file='balloon/train/annotation_coco.json'),
+    val=dict(
+        img_prefix='balloon/val/',
+        classes=classes,
+        ann_file='balloon/val/annotation_coco.json'),
+    test=dict(
+        img_prefix='balloon/val/',
+        classes=classes,
+        ann_file='balloon/val/annotation_coco.json'))
+
+# 我们可以使用预训练的 Mask R-CNN 来获取更好的性能
+load_from = 'checkpoints/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'
+```
+
+## 训练一个新的模型
+
+为了使用新的配置方法来对模型进行训练，你只需要运行如下命令。
+
+```shell
+python tools/train.py configs/balloon/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py
+```
+
+参考[情况 1](./1_exist_data_model.md)来获取更多详细的使用方法。
+
+## 测试以及推理
+
+为了测试训练完毕的模型，你只需要运行如下命令。
+
+```shell
+python tools/test.py configs/balloon/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py work_dirs/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_balloon.py/latest.pth --eval bbox segm
+```
+
+参考[情况 1](./1_exist_data_model.md)来获取更多详细的使用方法。
diff --git a/docs/zh_cn/3_exist_data_new_model.md b/docs/zh_cn/3_exist_data_new_model.md
new file mode 100644
index 0000000..e32e373
--- /dev/null
+++ b/docs/zh_cn/3_exist_data_new_model.md
@@ -0,0 +1,283 @@
+# 3: 在标准数据集上训练自定义模型
+
+在本文中，你将知道如何在标准数据集上训练、测试和推理自定义模型。我们将在 cityscapes 数据集上以自定义 Cascade Mask R-CNN R50 模型为例演示整个过程，为了方便说明，我们将 neck 模块中的 `FPN` 替换为 `AugFPN`，并且在训练中的自动增强类中增加 `Rotate` 或 `Translate`。
+
+基本步骤如下所示：
+
+1. 准备标准数据集
+2. 准备你的自定义模型
+3. 准备配置文件
+4. 在标准数据集上对模型进行训练、测试和推理
+
+## 准备标准数据集
+
+在本文中，我们使用 cityscapes 标准数据集为例进行说明。
+
+推荐将数据集根路径采用符号链接方式链接到 `$MMDETECTION/data`。
+
+如果你的文件结构不同，你可能需要在配置文件中进行相应的路径更改。标准的文件组织格式如下所示：
+
+```none
+mmdetection
+├── mmdet
+├── tools
+├── configs
+├── data
+│   ├── coco
+│   │   ├── annotations
+│   │   ├── train2017
+│   │   ├── val2017
+│   │   ├── test2017
+│   ├── cityscapes
+│   │   ├── annotations
+│   │   ├── leftImg8bit
+│   │   │   ├── train
+│   │   │   ├── val
+│   │   ├── gtFine
+│   │   │   ├── train
+│   │   │   ├── val
+│   ├── VOCdevkit
+│   │   ├── VOC2007
+│   │   ├── VOC2012
+```
+
+你也可以通过如下方式设定数据集根路径
+
+```bash
+export MMDET_DATASETS=$data_root
+```
+
+我们将会使用环境便变量 `$MMDET_DATASETS` 作为数据集的根目录，因此你无需再修改相应配置文件的路径信息。
+
+你需要使用脚本 `tools/dataset_converters/cityscapes.py` 将 cityscapes 标注转化为 coco 标注格式。
+
+```shell
+pip install cityscapesscripts
+python tools/dataset_converters/cityscapes.py ./data/cityscapes --nproc 8 --out-dir ./data/cityscapes/annotations
+```
+
+目前在 `cityscapes `文件夹中的配置文件所对应模型是采用 COCO 预训练权重进行初始化的。
+
+如果你的网络不可用或者比较慢，建议你先手动下载对应的预训练权重，否则可能在训练开始时候出现错误。
+
+## 准备你的自定义模型
+
+第二步是准备你的自定义模型或者训练相关配置。假设你想在已有的  Cascade Mask R-CNN R50 检测模型基础上，新增一个新的 neck 模块 `AugFPN` 去代替默认的 `FPN`，以下是具体实现：
+
+### 1 定义新的 neck (例如 AugFPN)
+
+首先创建新文件  `mmdet/models/necks/augfpn.py`.
+
+```python
+from ..builder import NECKS
+
+@NECKS.register_module()
+class AugFPN(nn.Module):
+
+    def __init__(self,
+                in_channels,
+                out_channels,
+                num_outs,
+                start_level=0,
+                end_level=-1,
+                add_extra_convs=False):
+        pass
+
+    def forward(self, inputs):
+        # implementation is ignored
+        pass
+```
+
+### 2 导入模块
+
+你可以采用两种方式导入模块，第一种是在  `mmdet/models/necks/__init__.py` 中添加如下内容
+
+```python
+from .augfpn import AugFPN
+```
+
+第二种是增加如下代码到对应配置中，这种方式的好处是不需要改动代码
+
+```python
+custom_imports = dict(
+    imports=['mmdet.models.necks.augfpn.py'],
+    allow_failed_imports=False)
+```
+
+### 3 修改配置
+
+```python
+neck=dict(
+    type='AugFPN',
+    in_channels=[256, 512, 1024, 2048],
+    out_channels=256,
+    num_outs=5)
+```
+
+关于自定义模型其余相关细节例如实现新的骨架网络，头部网络、损失函数，以及运行时训练配置例如定义新的优化器、使用梯度裁剪、定制训练调度策略和钩子等，请参考文档 [自定义模型](tutorials/customize_models.md) 和 [自定义运行时训练配置](tutorials/customize_runtime.md)。
+
+## 准备配置文件
+
+第三步是准备训练配置所需要的配置文件。假设你打算基于 cityscapes 数据集，在 Cascade Mask R-CNN R50 中新增 `AugFPN` 模块，同时增加 `Rotate` 或者 `Translate` 数据增强策略，假设你的配置文件位于 `configs/cityscapes/` 目录下，并且取名为 `cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py`，则配置信息如下：
+
+```python
+# 继承 base 配置，然后进行针对性修改
+_base_ = [
+    '../_base_/models/cascade_mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py'
+]
+
+model = dict(
+    # 设置为 None，表示不加载 ImageNet 预训练权重，
+    # 后续可以设置 `load_from` 参数用来加载 COCO 预训练权重
+    backbone=dict(init_cfg=None),
+    pretrained=None,
+    # 使用新增的 `AugFPN` 模块代替默认的 `FPN`
+    neck=dict(
+        type='AugFPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    # 我们也需要将 num_classes 从 80 修改为 8 来匹配 cityscapes 数据集标注
+    # 这个修改包括 `bbox_head` 和 `mask_head`.
+    roi_head=dict(
+        bbox_head=[
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                # 将 COCO 类别修改为 cityscapes 类别
+                num_classes=8,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                # 将 COCO 类别修改为 cityscapes 类别
+                num_classes=8,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                # 将 COCO 类别修改为 cityscapes 类别
+                num_classes=8,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ],
+        mask_head=dict(
+            type='FCNMaskHead',
+            num_convs=4,
+            in_channels=256,
+            conv_out_channels=256,
+            # 将 COCO 类别修改为 cityscapes 类别
+            num_classes=8,
+            loss_mask=dict(
+                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))))
+
+# 覆写 `train_pipeline`，然后新增 `AutoAugment` 训练配置
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='AutoAugment',
+        policies=[
+            [dict(
+                 type='Rotate',
+                 level=5,
+                 img_fill_val=(124, 116, 104),
+                 prob=0.5,
+                 scale=1)
+            ],
+            [dict(type='Rotate', level=7, img_fill_val=(124, 116, 104)),
+             dict(
+                 type='Translate',
+                 level=5,
+                 prob=0.5,
+                 img_fill_val=(124, 116, 104))
+            ],
+        ]),
+    dict(
+        type='Resize', img_scale=[(2048, 800), (2048, 1024)], keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+
+# 设置每张显卡的批处理大小，同时设置新的训练 pipeline
+data = dict(
+    samples_per_gpu=1,
+    workers_per_gpu=3,
+    # 用新的训练 pipeline 配置覆写 pipeline
+    train=dict(dataset=dict(pipeline=train_pipeline)))
+
+# 设置优化器
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# 设置定制的学习率策略
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[8])
+runner = dict(type='EpochBasedRunner', max_epochs=10)
+
+# 我们采用 COCO 预训练过的 Cascade Mask R-CNN R50 模型权重作为初始化权重，可以得到更加稳定的性能
+load_from = 'http://download.openmmlab.com/mmdetection/v2.0/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco/cascade_mask_rcnn_r50_fpn_1x_coco_20200203-9d4dcb24.pth'
+```
+
+## 训练新模型
+
+为了能够使用新增配置来训练模型，你可以运行如下命令：
+
+```shell
+python tools/train.py configs/cityscapes/cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py
+```
+
+如果想了解更多用法，可以参考 [例子1](1_exist_data_model.md)。
+
+## 测试和推理
+
+为了能够测试训练好的模型，你可以运行如下命令：
+
+```shell
+python tools/test.py configs/cityscapes/cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py work_dirs/cascade_mask_rcnn_r50_augfpn_autoaug_10e_cityscapes.py/latest.pth --eval bbox segm
+```
+
+如果想了解更多用法，可以参考 [例子1](1_exist_data_model.md)。
diff --git a/docs/zh_cn/Makefile b/docs/zh_cn/Makefile
new file mode 100644
index 0000000..d4bb2cb
--- /dev/null
+++ b/docs/zh_cn/Makefile
@@ -0,0 +1,20 @@
+# Minimal makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line, and also
+# from the environment for the first two.
+SPHINXOPTS    ?=
+SPHINXBUILD   ?= sphinx-build
+SOURCEDIR     = .
+BUILDDIR      = _build
+
+# Put it first so that "make" without argument is like "make help".
+help:
+	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
+
+.PHONY: help Makefile
+
+# Catch-all target: route all unknown targets to Sphinx using the new
+# "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
+%: Makefile
+	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
diff --git a/docs/zh_cn/_static/css/readthedocs.css b/docs/zh_cn/_static/css/readthedocs.css
new file mode 100644
index 0000000..57ed0ad
--- /dev/null
+++ b/docs/zh_cn/_static/css/readthedocs.css
@@ -0,0 +1,6 @@
+.header-logo {
+    background-image: url("../image/mmdet-logo.png");
+    background-size: 156px 40px;
+    height: 40px;
+    width: 156px;
+}
diff --git a/docs/zh_cn/_static/image/mmdet-logo.png b/docs/zh_cn/_static/image/mmdet-logo.png
new file mode 100644
index 0000000..58e2b5e
Binary files /dev/null and b/docs/zh_cn/_static/image/mmdet-logo.png differ
diff --git a/docs/zh_cn/api.rst b/docs/zh_cn/api.rst
new file mode 100644
index 0000000..c75a467
--- /dev/null
+++ b/docs/zh_cn/api.rst
@@ -0,0 +1,108 @@
+mmdet.apis
+--------------
+.. automodule:: mmdet.apis
+    :members:
+
+mmdet.core
+--------------
+
+anchor
+^^^^^^^^^^
+.. automodule:: mmdet.core.anchor
+    :members:
+
+bbox
+^^^^^^^^^^
+.. automodule:: mmdet.core.bbox
+    :members:
+
+export
+^^^^^^^^^^
+.. automodule:: mmdet.core.export
+    :members:
+
+mask
+^^^^^^^^^^
+.. automodule:: mmdet.core.mask
+    :members:
+
+evaluation
+^^^^^^^^^^
+.. automodule:: mmdet.core.evaluation
+    :members:
+
+post_processing
+^^^^^^^^^^^^^^^
+.. automodule:: mmdet.core.post_processing
+    :members:
+
+utils
+^^^^^^^^^^
+.. automodule:: mmdet.core.utils
+    :members:
+
+mmdet.datasets
+--------------
+
+datasets
+^^^^^^^^^^
+.. automodule:: mmdet.datasets
+    :members:
+
+pipelines
+^^^^^^^^^^
+.. automodule:: mmdet.datasets.pipelines
+    :members:
+
+samplers
+^^^^^^^^^^
+.. automodule:: mmdet.datasets.samplers
+    :members:
+
+api_wrappers
+^^^^^^^^^^
+.. automodule:: mmdet.datasets.api_wrappers
+    :members:
+
+mmdet.models
+--------------
+
+detectors
+^^^^^^^^^^
+.. automodule:: mmdet.models.detectors
+    :members:
+
+backbones
+^^^^^^^^^^
+.. automodule:: mmdet.models.backbones
+    :members:
+
+necks
+^^^^^^^^^^^^
+.. automodule:: mmdet.models.necks
+    :members:
+
+dense_heads
+^^^^^^^^^^^^
+.. automodule:: mmdet.models.dense_heads
+    :members:
+
+roi_heads
+^^^^^^^^^^
+.. automodule:: mmdet.models.roi_heads
+    :members:
+
+losses
+^^^^^^^^^^
+.. automodule:: mmdet.models.losses
+    :members:
+
+utils
+^^^^^^^^^^
+.. automodule:: mmdet.models.utils
+    :members:
+
+mmdet.utils
+--------------
+.. automodule::mmdet.utils
+    :members:
diff --git a/docs/zh_cn/article.md b/docs/zh_cn/article.md
new file mode 100644
index 0000000..9cd6fb6
--- /dev/null
+++ b/docs/zh_cn/article.md
@@ -0,0 +1,53 @@
+## 中文解读文案汇总
+
+### 1 官方解读文案
+
+#### 1.1 框架解读
+
+- **[轻松掌握 MMDetection 整体构建流程(一)](https://zhuanlan.zhihu.com/p/337375549)**
+- **[轻松掌握 MMDetection 整体构建流程(二)](https://zhuanlan.zhihu.com/p/341954021)**
+- **[轻松掌握 MMDetection 中 Head 流程](https://zhuanlan.zhihu.com/p/343433169)**
+
+#### 1.2 算法解读
+
+- **[轻松掌握 MMDetection 中常用算法(一)：RetinaNet 及配置详解](https://zhuanlan.zhihu.com/p/346198300)**
+- **[轻松掌握 MMDetection 中常用算法(二)：Faster R-CNN|Mask R-CNN](https://zhuanlan.zhihu.com/p/349807581)**
+- [轻松掌握 MMDetection 中常用算法(三)：FCOS](https://zhuanlan.zhihu.com/p/358056615)
+- [轻松掌握 MMDetection 中常用算法(四)：ATSS](https://zhuanlan.zhihu.com/p/358125611)
+- [轻松掌握 MMDetection 中常用算法(五)：Cascade R-CNN](https://zhuanlan.zhihu.com/p/360952172)
+- [轻松掌握 MMDetection 中常用算法(六)：YOLOF](https://zhuanlan.zhihu.com/p/370758213)
+- [轻松掌握 MMDetection 中常用算法(七)：CenterNet](https://zhuanlan.zhihu.com/p/374891478)
+- [轻松掌握 MMDetection 中常用算法(八)：YOLACT](https://zhuanlan.zhihu.com/p/376347955)
+- [轻松掌握 MMDetection 中常用算法(九)：AutoAssign](https://zhuanlan.zhihu.com/p/378581552)
+- [YOLOX 在 MMDetection 中复现全流程解析](https://zhuanlan.zhihu.com/p/398545304)
+- [喂喂喂！你可以减重了！小模型 - MMDetection 新增SSDLite 、 MobileNetV2YOLOV3 两大经典算法](https://zhuanlan.zhihu.com/p/402781143)
+
+#### 1.3 工具解读
+
+- [OpenMMLab 中混合精度训练 AMP 的正确打开方式](https://zhuanlan.zhihu.com/p/375224982)
+- [小白都能看懂！手把手教你使用混淆矩阵分析目标检测](https://zhuanlan.zhihu.com/p/443499860)
+- [MMDetection 图像缩放 Resize 详细说明 OpenMMLab](https://zhuanlan.zhihu.com/p/381117525)
+- [拿什么拯救我的 4G 显卡](https://zhuanlan.zhihu.com/p/430123077)
+- [MMDet居然能用MMCls的Backbone？论配置文件的打开方式](https://zhuanlan.zhihu.com/p/436865195)
+
+#### 1.4 知乎问答
+
+- [COCO数据集上1x模式下为什么不采用多尺度训练?](https://www.zhihu.com/question/462170786/answer/1915119662)
+- [MMDetection中SOTA论文源码中将训练过程中BN层的eval打开?](https://www.zhihu.com/question/471189603/answer/2195540892)
+- [基于PyTorch的MMDetection中训练的随机性来自何处？](https://www.zhihu.com/question/453511684/answer/1839683634)
+- [单阶段、双阶段、anchor-based、anchor-free 这四者之间有什么联系吗？](https://www.zhihu.com/question/428972054/answer/1619925296)
+- [目标检测的深度学习方法，有推荐的书籍或资料吗？](https://www.zhihu.com/question/391577080/answer/1612593817)
+- [大佬们，刚入学研究生，想入门目标检测，有什么学习路线可以入门的？](https://www.zhihu.com/question/343768934/answer/1612580715)
+- [目标检测领域还有什么可以做的？](https://www.zhihu.com/question/280703314/answer/1627885518)
+- [如何看待Transformer在CV上的应用前景，未来有可能替代CNN吗？](https://www.zhihu.com/question/437495132/answer/1686380553)
+- [MMDetection如何学习源码？](https://www.zhihu.com/question/451585041/answer/1832498963)
+- [如何具体上手实现目标检测呢？](https://www.zhihu.com/question/341401981/answer/1848561187)
+
+#### 1.5 其他
+
+- **[不得不知的 MMDetection 学习路线(个人经验版)](https://zhuanlan.zhihu.com/p/369826931)**
+- [OpenMMLab 社区专访之 YOLOX 复现篇](https://zhuanlan.zhihu.com/p/405913343)
+
+### 2 社区解读文案
+
+- [手把手带你实现经典检测网络 Mask R-CNN 的推理](https://zhuanlan.zhihu.com/p/414082071)
diff --git a/docs/zh_cn/compatibility.md b/docs/zh_cn/compatibility.md
new file mode 100644
index 0000000..e9ebdd9
--- /dev/null
+++ b/docs/zh_cn/compatibility.md
@@ -0,0 +1,177 @@
+# MMDetection v2.x 兼容性说明
+
+## MMDetection 2.25.0
+
+为了加入 Mask2Former 实例分割模型，对 Mask2Former 的配置文件进行了重命名 [PR #7571](https://github.com/open-mmlab/mmdetection/pull/7571)：
+
+<table align="center">
+    <thead>
+        <tr align='center'>
+            <td>在 v2.25.0 之前</td>
+            <td>v2.25.0 及之后</td>
+        </tr>
+    </thead>
+    <tbody><tr valign='top'>
+    <th>
+
+```
+'mask2former_xxx_coco.py' 代表全景分割的配置文件
+```
+
+</th>
+    <th>
+
+```
+'mask2former_xxx_coco.py' 代表实例分割的配置文件
+'mask2former_xxx_coco-panoptic.py' 代表全景分割的配置文件
+```
+
+</th></tr>
+  </tbody></table>
+
+## MMDetection 2.21.0
+
+为了支持 CPU 训练，MMCV 中进行批处理的 scatter 的代码逻辑已经被修改。我们推荐使用 MMCV v1.4.4 或更高版本，
+更多信息请参考 [MMCV PR #1621](https://github.com/open-mmlab/mmcv/pull/1621).
+
+## MMDetection 2.18.1
+
+### MMCV compatibility
+
+为了修复 BaseTransformerLayer 中的权重引用问题, MultiheadAttention 中 batch first 的逻辑有所改变。
+我们推荐使用 MMCV v1.3.17 或更高版本。 更多信息请参考 [MMCV PR #1418](https://github.com/open-mmlab/mmcv/pull/1418) 。
+
+## MMDetection 2.18.0
+
+### DIIHead 兼容性
+
+为了支持 QueryInst，在 DIIHead 的返回元组中加入了 attn_feats。
+
+## MMDetection v2.14.0
+
+### MMCV 版本
+
+为了修复 EvalHook 优先级过低的问题，MMCV v1.3.8 中所有 hook 的优先级都重新进行了调整，因此 MMDetection v2.14.0 需要依赖最新的 MMCV v1.3.8 版本。 相关信息请参考[PR #1120](https://github.com/open-mmlab/mmcv/pull/1120) ，相关问题请参考[#5343](https://github.com/open-mmlab/mmdetection/issues/5343) 。
+
+### SSD 兼容性
+
+在 v2.14.0 中，为了使 SSD 能够被更灵活地使用，[PR #5291](https://github.com/open-mmlab/mmdetection/pull/5291) 重构了 SSD 的 backbone、neck 和 head。用户可以使用 tools/model_converters/upgrade_ssd_version.py 转换旧版本训练的模型。
+
+```shell
+python tools/model_converters/upgrade_ssd_version.py ${OLD_MODEL_PATH} ${NEW_MODEL_PATH}
+
+```
+
+- OLD_MODEL_PATH：旧版 SSD 模型的路径。
+- NEW_MODEL_PATH：保存转换后模型权重的路径。
+
+## MMDetection v2.12.0
+
+在 v2.12.0 到 v2.18.0（或以上）版本的这段时间，为了提升通用性和便捷性，MMDetection 正在进行大规模重构。在升级到 v2.12.0 后 MMDetection 不可避免地带来了一些 BC Breaking，包括 MMCV 的版本依赖、模型初始化方式、模型 registry 和 mask AP 的评估。
+
+### MMCV 版本
+
+MMDetection v2.12.0 依赖 MMCV v1.3.3 中新增加的功能，包括：使用 `BaseModule` 统一参数初始化，模型 registry，以及[Deformable DETR](https://arxiv.org/abs/2010.04159) 中的 `MultiScaleDeformableAttn` CUDA 算子。
+注意，尽管 MMCV v1.3.2 已经包含了 MMDet 所需的功能，但是存在一些已知的问题。我们建议用户跳过 MMCV v1.3.2 使用 v1.3.3 版本。
+
+### 统一模型初始化
+
+为了统一 OpenMMLab 项目中的参数初始化方式，MMCV 新增加了 `BaseModule` 类，使用 `init_cfg` 参数对模块进行统一且灵活的初始化配置管理。
+现在用户需要在训练脚本中显式调用 `model.init_weights()` 来初始化模型（例如 [这行代码](https://github.com/open-mmlab/mmdetection/blob/master/tools/train.py#L162) ，在这之前则是在 detector 中进行处理的。
+**下游项目必须相应地更新模型初始化方式才能使用 MMDetection v2.12.0**。请参阅 [PR #4750](https://github.com/open-mmlab/mmdetection/pull/4750) 了解详情。
+
+### 统一模型 registry
+
+为了能够使用在其他 OpenMMLab 项目中实现的 backbone，MMDetection v2.12.0 继承了在 MMCV (#760) 中创建的模型 registry。
+这样，只要 OpenMMLab 项目实现了某个 backbone，并且该项目也使用 MMCV 中的 registry，那么用户只需修改配置即可在 MMDetection 中使用该 backbone，不再需要将代码复制到 MMDetection 中。 更多详细信息，请参阅 [PR #5059](https://github.com/open-mmlab/mmdetection/pull/5059) 。
+
+### Mask AP 评估
+
+在 [PR #4898](https://github.com/open-mmlab/mmdetection/pull/4898) 和 v2.12.0 之前，对小、中、大目标的 mask AP 的评估是基于其边界框区域而不是真正的 mask 区域。
+这导致 `APs` 和 `APm` 变得更高但 `APl` 变得更低，但是不会影响整体的 mask AP。 [PR #4898](https://github.com/open-mmlab/mmdetection/pull/4898) 删除了 mask AP 计算中的 `bbox` ，改为使用 mask 区域。
+新的计算方式不会影响整体的 mask AP 评估，与 [Detectron2](https://github.com/facebookresearch/detectron2/)一致。
+
+## 与 MMDetection v1.x 的兼容性
+
+MMDetection v2.0 经过了大规模重构并解决了许多遗留问题。 MMDetection v2.0 不兼容 v1.x 版本，在这两个版本中使用相同的模型权重运行推理会产生不同的结果。 因此，MMDetection v2.0 重新对所有模型进行了 benchmark，并在 model zoo 中提供了新模型的权重和训练记录。
+
+新旧版本的主要的区别有四方面：坐标系、代码库约定、训练超参和模块设计。
+
+### 坐标系
+
+新坐标系与 [Detectron2](https://github.com/facebookresearch/detectron2/) 一致，
+将最左上角的像素的中心视为坐标原点 (0, 0) 而不是最左上角像素的左上角。 因此 COCO 边界框和分割标注中的坐标被解析为范围 `[0，width]` 和 `[0，height]` 中的坐标。 这个修改影响了所有与 bbox 及像素选择相关的计算，变得更加自然且更加准确。
+
+- 在新坐标系中，左上角和右下角为 (x1, y1) (x2, y2) 的框的宽度及高度计算公式为 `width = x2 - x1` 和 `height = y2 - y1`。
+  在 MMDetection v1.x 和之前的版本中，高度和宽度都多了 `+ 1` 的操作。
+  本次修改包括三部分：
+
+  1. box 回归中的检测框变换以及编码/解码。
+  2. IoU 计算。这会影响 ground truth 和检测框之间的匹配以及 NMS 。但对兼容性的影响可以忽略不计。
+  3. Box 的角点坐标为浮点型，不再取整。这能使得检测结果更为准确，也使得检测框和 RoI 的最小尺寸不再为 1，但影响很小。
+
+- Anchor 的中心与特征图的网格点对齐，类型变为 float。
+  在 MMDetection v1.x 和之前的版本中，anchors 是 `int` 类型且没有居中对齐。
+  这会影响 RPN 中的 Anchor 生成和所有基于 Anchor 的方法。
+
+- ROIAlign 更好地与图像坐标系对齐。新的实现来自 [Detectron2](https://github.com/facebookresearch/detectron2/tree/master/detectron2/layers/csrc/ROIAlign) 。
+  当 RoI 用于提取 RoI 特征时，与 MMDetection v1.x 相比默认情况下相差半个像素。
+  能够通过设置 `aligned=False` 而不是 `aligned=True` 来维持旧版本的设置。
+
+- Mask 的裁剪和粘贴更准确。
+
+  1. 我们使用新的 RoIAlign 来提取 mask 目标。 在 MMDetection v1.x 中，bounding box 在提取 mask 目标之前被取整，裁剪过程是 numpy 实现的。 而在新版本中，裁剪的边界框不经过取整直接输入 RoIAlign。 此实现大大加快了训练速度（每次迭代约加速 0.1 秒，1x schedule 训练 Mask R50 时加速约 2 小时）并且理论上会更准确。
+  2. 在 MMDetection v2.0 中，修改后的 `paste_mask()` 函数应该比之前版本更准确。 此更改参考了 [Detectron2](https://github.com/facebookresearch/detectron2/blob/master/detectron2/structures/masks.py) 中的修改，可以将 COCO 上的 mask AP 提高约 0.5%。
+
+### 代码库约定
+
+- MMDetection v2.0 更改了类别标签的顺序，减少了回归和 mask 分支里的无用参数并使得顺序更加自然（没有 +1 和 -1）。
+  这会影响模型的所有分类层，使其输出的类别标签顺序发生改变。回归分支和 mask head 的最后一层不再为 K 个类别保留 K+1 个通道，类别顺序与分类分支一致。
+
+  - 在 MMDetection v2.0 中，标签 “K” 表示背景，标签 \[0, K-1\] 对应于 K = num_categories 个对象类别。
+
+  - 在 MMDetection v1.x 及之前的版本中，标签 “0” 表示背景，标签 \[1, K\] 对应 K 个类别。
+
+  - **注意**：softmax RPN 的类顺序在 version\<=2.4.0 中仍然和 1.x 中的一样，而 sigmoid RPN 不受影响。从 MMDetection v2.5.0 开始，所有 head 中的类顺序是统一的。
+
+- 不使用 R-CNN 中的低质量匹配。在 MMDetection v1.x 和之前的版本中，`max_iou_assigner` 会在 RPN 和 R-CNN 训练时给每个 ground truth 匹配低质量框。我们发现这会导致最佳的 GT 框不会被分配给某些边界框，
+  因此，在MMDetection v2.0 的 R-CNN 训练中默认不允许低质量匹配。这有时可能会稍微改善 box AP（约为 0.1%）。
+
+- 单独的宽高比例系数。在 MMDetection v1.x 和以前的版本中，`keep_ratio=True` 时比例系数是单个浮点数，这并不准确，因为宽度和高度的比例系数会有一定的差异。 MMDetection v2.0 对宽度和高度使用单独的比例系数，对 AP 的提升约为 0.1%。
+
+- 修改了 config 文件名称的规范。 由于 model zoo 中模型不断增多， MMDetection v2.0 采用新的命名规则：
+
+  ```shell
+  [model]_(model setting)_[backbone]_[neck]_(norm setting)_(misc)_(gpu x batch)_[schedule]_[dataset].py
+  ```
+
+  其中 (`misc`) 包括 DCN 和 GCBlock 等。更多详细信息在 [配置文件说明文档](config.md) 中说明
+
+- MMDetection v2.0 使用新的 ResNet Caffe backbone 来减少加载预训练模型时的警告。新 backbone 中的大部分权重与以前的相同，但没有 `conv.bias`，且它们使用不同的 `img_norm_cfg`。因此，新的 backbone 不会报 `unexpected keys` 的警告。
+
+### 训练超参
+
+训练超参的调整不会影响模型的兼容性，但会略微提高性能。主要有：
+
+- 通过设置 `nms_post=1000` 和 `max_num=1000`，将 nms 之后的 proposal 数量从 2000 更改为 1000。使 mask AP 和 bbox AP 提高了约 0.2%。
+
+- Mask R-CNN、Faster R-CNN 和 RetinaNet 的默认回归损失从 smooth L1 损失更改为 L1 损失，使得 box AP 整体上都有所提升（约 0.6%）。但是，将 L1-loss 用在 Cascade R-CNN 和 HTC 等其他方法上并不能提高性能，因此我们保留这些方法的原始设置。
+
+- 为简单起见，RoIAlign 层的 `sampling_ratio` 设置为 0。略微提升了 AP（约 0.2% 绝对值）。
+
+- 为了提升训练速度，默认设置在训练过程中不再使用梯度裁剪。大多数模型的性能不会受到影响。对于某些模型（例如 RepPoints），我们依旧使用梯度裁剪来稳定训练过程从而获得更好的性能。
+
+- 因为不再默认使用梯度裁剪，默认 warmup 比率从 1/3 更改为 0.001，以使模型训练预热更加平缓。不过我们重新进行基准测试时发现这种影响可以忽略不计。
+
+### 将模型从 v1.x 升级至 v2.0
+
+用户可以使用脚本 `tools/model_converters/upgrade_model_version.py` 来将 MMDetection 1.x 训练的模型转换为 MMDetection v2.0。转换后的模型可以在 MMDetection v2.0 中运行，但性能略有下降（小于 1% AP）。
+详细信息可以在 `configs/legacy` 中找到。
+
+## pycocotools 兼容性
+
+`mmpycocotools` 是 OpenMMLab 维护的 `pycocotools` 的复刻版，适用于 MMDetection 和 Detectron2。
+在 [PR #4939](https://github.com/open-mmlab/mmdetection/pull/4939) 之前，由于 `pycocotools` 和 `mmpycocotool` 具有相同的包名，如果用户已经安装了 `pyccocotools`（在相同环境下先安装了 Detectron2 ），那么 MMDetection 的安装过程会跳过安装 `mmpycocotool`。 导致 MMDetection 缺少 `mmpycocotools` 而报错。
+但如果在 Detectron2 之前安装 MMDetection，则可以在相同的环境下工作。
+[PR #4939](https://github.com/open-mmlab/mmdetection/pull/4939) 弃用 mmpycocotools，使用官方 pycocotools。
+在 [PR #4939](https://github.com/open-mmlab/mmdetection/pull/4939) 之后，用户能够在相同环境下安装 MMDetection 和 Detectron2，不再需要关注安装顺序。
diff --git a/docs/zh_cn/conf.py b/docs/zh_cn/conf.py
new file mode 100644
index 0000000..1bb57a4
--- /dev/null
+++ b/docs/zh_cn/conf.py
@@ -0,0 +1,118 @@
+# Configuration file for the Sphinx documentation builder.
+#
+# This file only contains a selection of the most common options. For a full
+# list see the documentation:
+# https://www.sphinx-doc.org/en/master/usage/configuration.html
+
+# -- Path setup --------------------------------------------------------------
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#
+import os
+import subprocess
+import sys
+
+import pytorch_sphinx_theme
+
+sys.path.insert(0, os.path.abspath('../../'))
+
+# -- Project information -----------------------------------------------------
+
+project = 'MMDetection'
+copyright = '2018-2021, OpenMMLab'
+author = 'MMDetection Authors'
+version_file = '../../mmdet/version.py'
+
+
+def get_version():
+    with open(version_file, 'r') as f:
+        exec(compile(f.read(), version_file, 'exec'))
+    return locals()['__version__']
+
+
+# The full version, including alpha/beta/rc tags
+release = get_version()
+
+# -- General configuration ---------------------------------------------------
+
+# Add any Sphinx extension module names here, as strings. They can be
+# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
+# ones.
+extensions = [
+    'sphinx.ext.autodoc',
+    'sphinx.ext.napoleon',
+    'sphinx.ext.viewcode',
+    'myst_parser',
+    'sphinx_markdown_tables',
+    'sphinx_copybutton',
+]
+
+myst_enable_extensions = ['colon_fence']
+myst_heading_anchors = 3
+
+autodoc_mock_imports = [
+    'matplotlib', 'pycocotools', 'terminaltables', 'mmdet.version', 'mmcv.ops'
+]
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix(es) of source filenames.
+# You can specify multiple suffix as a list of string:
+#
+source_suffix = {
+    '.rst': 'restructuredtext',
+    '.md': 'markdown',
+}
+
+# The master toctree document.
+master_doc = 'index'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+# This pattern also affects html_static_path and html_extra_path.
+exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+
+# -- Options for HTML output -------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+#
+# html_theme = 'sphinx_rtd_theme'
+html_theme = 'pytorch_sphinx_theme'
+html_theme_path = [pytorch_sphinx_theme.get_html_theme_path()]
+
+html_theme_options = {
+    'menu': [
+        {
+            'name': 'GitHub',
+            'url': 'https://github.com/open-mmlab/mmdetection'
+        },
+    ],
+    # Specify the language of shared menu
+    'menu_lang':
+    'cn',
+}
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+html_css_files = ['css/readthedocs.css']
+
+language = 'zh_CN'
+
+# -- Extension configuration -------------------------------------------------
+# Ignore >>> when copying code
+copybutton_prompt_text = r'>>> |\.\.\. '
+copybutton_prompt_is_regexp = True
+
+
+def builder_inited_handler(app):
+    subprocess.run(['./stat.py'])
+
+
+def setup(app):
+    app.connect('builder-inited', builder_inited_handler)
diff --git a/docs/zh_cn/conventions.md b/docs/zh_cn/conventions.md
new file mode 100644
index 0000000..acbb21e
--- /dev/null
+++ b/docs/zh_cn/conventions.md
@@ -0,0 +1,75 @@
+# 默认约定
+
+如果你想把 MMDetection 修改为自己的项目，请遵循下面的约定。
+
+## 损失
+
+在 MMDetection 中，`model(**data)` 的返回值是一个字典，包含着所有的损失和评价指标，他们将会由 `model(**data)` 返回。
+
+例如，在 bbox head 中，
+
+```python
+class BBoxHead(nn.Module):
+    ...
+    def loss(self, ...):
+        losses = dict()
+        # 分类损失
+        losses['loss_cls'] = self.loss_cls(...)
+        # 分类准确率
+        losses['acc'] = accuracy(...)
+        # 边界框损失
+        losses['loss_bbox'] = self.loss_bbox(...)
+        return losses
+```
+
+`'bbox_head.loss()'` 在模型 forward 阶段会被调用。返回的字典中包含了 `'loss_bbox'`,`'loss_cls'`,`'acc'`。只有 `'loss_bbox'`, `'loss_cls'` 会被用于反向传播，`'acc'` 只会被作为评价指标来监控训练过程。
+
+我们默认，只有那些键的名称中包含 `'loss'` 的值会被用于反向传播。这个行为可以通过修改 `BaseDetector.train_step()` 来改变。
+
+## 空 proposals
+
+在 MMDetection 中，我们为两阶段方法中空 proposals 的情况增加了特殊处理和单元测试。我们同时需要处理整个 batch 和单一图片中空 proposals 的情况。例如，在 CascadeRoIHead 中，
+
+```python
+# 简单的测试
+...
+
+# 在整个 batch中 都没有 proposals
+if rois.shape[0] == 0:
+    bbox_results = [[
+        np.zeros((0, 5), dtype=np.float32)
+        for _ in range(self.bbox_head[-1].num_classes)
+    ]] * num_imgs
+    if self.with_mask:
+        mask_classes = self.mask_head[-1].num_classes
+        segm_results = [[[] for _ in range(mask_classes)]
+                        for _ in range(num_imgs)]
+        results = list(zip(bbox_results, segm_results))
+    else:
+        results = bbox_results
+    return results
+...
+
+# 在单张图片中没有 proposals
+for i in range(self.num_stages):
+    ...
+    if i < self.num_stages - 1:
+          for j in range(num_imgs):
+                   # 处理空 proposals
+                   if rois[j].shape[0] > 0:
+                       bbox_label = cls_score[j][:, :-1].argmax(dim=1)
+                       refine_roi = self.bbox_head[i].regress_by_class(
+                            rois[j], bbox_label[j], bbox_pred[j], img_metas[j])
+                       refine_roi_list.append(refine_roi)
+```
+
+如果你有自定义的 `RoIHead`, 你可以参考上面的方法来处理空 proposals 的情况。
+
+## 全景分割数据集
+
+在 MMDetection 中，我们支持了 COCO 全景分割数据集 `CocoPanopticDataset`。对于它的实现，我们在这里声明一些默认约定。
+
+1. 在 mmdet\<=2.16.0 时，语义分割标注中的前景和背景标签范围与 MMDetection 中的默认规定有所不同。标签 `0` 代表 `VOID` 标签。
+   从 mmdet=2.17.0 开始，为了和框的类别标注保持一致，语义分割标注的类别标签也改为从 `0` 开始，标签 `255` 代表 `VOID` 类。
+   为了达成这一目标，我们在流程 `Pad` 里支持了设置 `seg` 的填充值的功能。
+2. 在评估中，全景分割结果必须是一个与原图大小相同的图。结果图中每个像素的值有如此形式：`instance_id * INSTANCE_OFFSET + category_id`。
diff --git a/docs/zh_cn/device/npu.md b/docs/zh_cn/device/npu.md
new file mode 100644
index 0000000..b332b4f
--- /dev/null
+++ b/docs/zh_cn/device/npu.md
@@ -0,0 +1,54 @@
+# NPU (华为 昇腾)
+
+## 使用方法
+
+请参考 [MMCV 的安装文档](https://mmcv.readthedocs.io/en/latest/get_started/build.html#build-mmcv-full-on-ascend-npu-machine) 来安装 NPU 版本的 MMCV。
+
+以下展示单机八卡场景的运行指令:
+
+```shell
+bash tools/dist_train.sh configs/ssd/ssd300_coco.py 8
+```
+
+以下展示单机单卡下的运行指令:
+
+```shell
+python tools/train.py configs/ssd/ssd300_coco.py
+```
+
+## 模型验证结果
+
+|        Model         | box AP | mask AP | Config                                                                                                                        | Download                                                                                                     |
+| :------------------: | :----: | :-----: | :---------------------------------------------------------------------------------------------------------------------------- | :----------------------------------------------------------------------------------------------------------- |
+|     [ssd300](<>)     |  25.6  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd/ssd300_fp16_coco.py)                               | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/ssd300_coco.log.json)                              |
+|     [ssd512](<>)     |  29.4  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd/ssd512_fp16_coco.py)                               | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/ssd512_coco.log.json)                              |
+| [ssdlite-mbv2\*](<>) |  20.2  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd/ssdlite_mobilenetv2_scratch_600e_coco.py)          | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/ssdlite_mobilenetv2_scratch_600e_coco.log.json)    |
+| [retinanet-r18](<>)  |  31.8  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/retinanet_r18_fpn_1x8_1x_coco.py)            | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/retinanet_r18_fpn_1x8_1x_coco.log.json)            |
+| [retinanet-r50](<>)  |  36.6  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/retinanet_r50_fpn_fp16_1x_coco.py)           | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/retinanet_r50_fpn_1x_coco.log.json)                |
+|   [yolov3-608](<>)   |  34.7  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolo/yolov3_d53_fp16_mstrain-608_273e_coco.py)         | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/yolov3_d53_fp16_mstrain-608_273e_coco.log.json)    |
+|  [yolox-s\*\*](<>)   |  39.9  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolox/yolox_s_8x8_300e_coco.py)                        | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/yolox_s_8x8_300e_coco.log.json)                    |
+| [centernet-r18](<>)  |  26.1  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/centernet/centernet_resnet18_140e_coco.py)             | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/centernet_resnet18_140e_coco.log.json)             |
+|   [fcos-r50\*](<>)   |  36.1  |   ---   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/fcos/fcos_r50_caffe_fpn_gn-head_fp16_1x_bs8x8_coco.py) | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/fcos_r50_caffe_fpn_gn-head_1x_coco_bs8x8.log.json) |
+|   [solov2-r50](<>)   |  ---   |  34.7   | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/solov2/solov2_r50_fpn_1x_coco.py)                      | [log](https://download.openmmlab.com/mmdetection/v2.0/npu/solov2_r50_fpn_1x_coco.log.json)                   |
+
+**注意:**
+
+- 如果没有特别标记，NPU 上的结果与使用 FP32 的 GPU 上的结果结果相同。
+- (\*) 这些模型在 NPU 上的结果与 GPU 上的混合精度训练结果一致，但低于 FP32 的结果。这种情况主要与模型本身在混合精度训练中的特点有关，
+  用户可以自行调整超参数来获得更高精度。
+- (\*\*) GPU 上 yolox-s 在混合精度下的精度为 40.1 低于readme中 40.5 的水平;默认情况下，yolox-s 启用 `persister_woker=True`，但这个参数
+  目前在NPU上存在一些bug，会导致在最后几个epoch由于资源耗尽报错退出，对整体精度影响有限可以忽略。
+
+## Ascend加速模块验证结果
+
+优化方案简介：
+
+1. 修改循环计算为一次整体计算，目的是减少下发指令数量。
+2. 修改索引计算为掩码计算，原因是SIMD架构芯片擅长处理连续数据计算。
+
+|           Model            |                                                          Config                                                           | v100 iter time |       910A iter time       |
+| :------------------------: | :-----------------------------------------------------------------------------------------------------------------------: | :------------: | :------------------------: |
+|    [ascend-ssd300](<>)     |          [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd/ascend_ssd300_fp16_coco.py)           |  0.165s/iter   | 0.383s/iter -> 0.13s/iter  |
+| [ascend-retinanet-r18](<>) | [config](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/ascend_retinanet_r18_fpn_1x8_1x_coco.py) |  0.567s/iter   | 0.780s/iter -> 0.420s/iter |
+
+**以上模型结果由华为昇腾团队提供**
diff --git a/docs/zh_cn/faq.md b/docs/zh_cn/faq.md
new file mode 100644
index 0000000..8f9bcf8
--- /dev/null
+++ b/docs/zh_cn/faq.md
@@ -0,0 +1,162 @@
+# 常见问题解答
+
+我们在这里列出了使用时的一些常见问题及其相应的解决方案。 如果您发现有一些问题被遗漏，请随时提 PR 丰富这个列表。 如果您无法在此获得帮助，请使用 [issue模板](https://github.com/open-mmlab/mmdetection/blob/master/.github/ISSUE_TEMPLATE/error-report.md/)创建问题，但是请在模板中填写所有必填信息，这有助于我们更快定位问题。
+
+## MMCV 安装相关
+
+- MMCV 与 MMDetection 的兼容问题: "ConvWS is already registered in conv layer"; "AssertionError: MMCV==xxx is used but incompatible. Please install mmcv>=xxx, \<=xxx."
+
+  请按 [安装说明](https://mmdetection.readthedocs.io/zh_CN/latest/get_started.html#installation) 为你的 MMDetection 安装正确版本的 MMCV 。
+
+- "No module named 'mmcv.ops'"; "No module named 'mmcv.\_ext'".
+
+  原因是安装了 `mmcv` 而不是 `mmcv-full`。
+
+  1. `pip uninstall mmcv` 卸载安装的 `mmcv`
+
+  2. 安装 `mmcv-full` 根据 [安装说明](https://mmcv.readthedocs.io/zh/latest/#installation)。
+
+## PyTorch/CUDA 环境相关
+
+- "RTX 30 series card fails when building MMCV or MMDet"
+
+  1. 临时解决方案为使用命令 `MMCV_WITH_OPS=1 MMCV_CUDA_ARGS='-gencode=arch=compute_80,code=sm_80' pip install -e .` 进行编译。 常见报错信息为 `nvcc fatal : Unsupported gpu architecture 'compute_86'` 意思是你的编译器不支持 sm_86 架构(包括英伟达 30 系列的显卡)的优化，至 CUDA toolkit 11.0 依旧未支持. 这个命令是通过增加宏 `MMCV_CUDA_ARGS='-gencode=arch=compute_80,code=sm_80` 让 nvcc 编译器为英伟达 30 系列显卡进行 `sm_80` 的优化，虽然这有可能会无法发挥出显卡所有性能。
+
+  2. 有开发者已经在 [pytorch/pytorch#47585](https://github.com/pytorch/pytorch/pull/47585) 更新了 PyTorch 默认的编译 flag， 但是我们对此并没有进行测试。
+
+- "invalid device function" or "no kernel image is available for execution".
+
+  1. 检查您正常安装了 CUDA runtime (一般在`/usr/local/`)，或者使用 `nvcc --version` 检查本地版本，有时安装 PyTorch 会顺带安装一个 CUDA runtime，并且实际优先使用 conda 环境中的版本，你可以使用 `conda list cudatoolkit` 查看其版本。
+
+  2. 编译 extension 的 CUDA Toolkit 版本与运行时的 CUDA Toolkit 版本是否相符，
+
+     - 如果您从源码自己编译的，使用 `python mmdet/utils/collect_env.py` 检查编译编译 extension 的 CUDA Toolkit 版本，然后使用 `conda list cudatoolkit` 检查当前 conda 环境是否有 CUDA Toolkit，若有检查版本是否匹配， 如不匹配，更换 conda 环境的 CUDA Toolkit，或者使用匹配的 CUDA Toolkit 中的 nvcc 编译即可，如环境中无 CUDA Toolkit，可以使用 `nvcc -V`。
+
+       等命令查看当前使用的 CUDA runtime。
+
+     - 如果您是通过 pip 下载的预编译好的版本，请确保与当前 CUDA runtime 一致。
+
+  3. 运行 `python mmdet/utils/collect_env.py` 检查是否为正确的 GPU 架构编译的 PyTorch， torchvision， 与 MMCV。 你或许需要设置 `TORCH_CUDA_ARCH_LIST` 来重新安装 MMCV，可以参考 [GPU 架构表](https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#gpu-feature-list),
+     例如， 运行 `TORCH_CUDA_ARCH_LIST=7.0 pip install mmcv-full` 为 Volta GPU 编译 MMCV。这种架构不匹配的问题一般会出现在使用一些旧型号的 GPU 时候出现， 例如， Tesla K80。
+
+- "undefined symbol" or "cannot open xxx.so".
+
+  1. 如果这些 symbol 属于 CUDA/C++ (如 libcudart.so 或者 GLIBCXX)，使用 `python mmdet/utils/collect_env.py`检查 CUDA/GCC runtime 与编译 MMCV 的 CUDA 版本是否相同。
+  2. 如果这些 symbols 属于 PyTorch，(例如, symbols containing caffe, aten, and TH), 检查当前 Pytorch 版本是否与编译 MMCV 的版本一致。
+  3. 运行 `python mmdet/utils/collect_env.py` 检查 PyTorch， torchvision， MMCV 等的编译环境与运行环境一致。
+
+- setuptools.sandbox.UnpickleableException: DistutilsSetupError("each element of 'ext_modules' option must be an Extension instance or 2-tuple")
+
+  1. 如果你在使用 miniconda 而不是 anaconda，检查是否正确的安装了 Cython 如 [#3379](https://github.com/open-mmlab/mmdetection/issues/3379).
+  2. 检查环境中的 `setuptools`, `Cython`, and `PyTorch` 相互之间版本是否匹配。
+
+- "Segmentation fault".
+
+  1. 检查 GCC 的版本，通常是因为 PyTorch 版本与 GCC 版本不匹配 （例如 GCC \< 4.9 )，我们推荐用户使用 GCC 5.4，我们也不推荐使用 GCC 5.5， 因为有反馈 GCC 5.5 会导致 "segmentation fault" 并且切换到 GCC 5.4 就可以解决问题。
+
+  2. 检查是否正确安装了 CUDA 版本的 PyTorch 。
+
+     ```shell
+     python -c 'import torch; print(torch.cuda.is_available())'
+     ```
+
+     是否返回True。
+
+  3. 如果 `torch` 的安装是正确的，检查是否正确编译了 MMCV。
+
+     ```shell
+     python -c 'import mmcv; import mmcv.ops'
+     ```
+
+  4. 如果 MMCV 与 PyTorch 都被正确安装了，则使用 `ipdb`, `pdb` 设置断点，直接查找哪一部分的代码导致了 `segmentation fault`。
+
+## Training 相关
+
+- "Loss goes Nan"
+
+  1. 检查数据的标注是否正常， 长或宽为 0 的框可能会导致回归 loss 变为 nan，一些小尺寸（宽度或高度小于 1）的框在数据增强（例如，instaboost）后也会导致此问题。 因此，可以检查标注并过滤掉那些特别小甚至面积为 0 的框，并关闭一些可能会导致 0 面积框出现数据增强。
+  2. 降低学习率：由于某些原因，例如 batch size 大小的变化， 导致当前学习率可能太大。 您可以降低为可以稳定训练模型的值。
+  3. 延长 warm up 的时间：一些模型在训练初始时对学习率很敏感，您可以把 `warmup_iters` 从 500 更改为 1000 或 2000。
+  4. 添加 gradient clipping: 一些模型需要梯度裁剪来稳定训练过程。 默认的 `grad_clip` 是 `None`,  你可以在 config 设置 `optimizer_config=dict(_delete_=True, grad_clip=dict(max_norm=35, norm_type=2))`  如果你的 config 没有继承任何包含 `optimizer_config=dict(grad_clip=None)`,  你可以直接设置`optimizer_config=dict(grad_clip=dict(max_norm=35, norm_type=2))`.
+
+- "GPU out of memory"
+
+  1. 存在大量 ground truth boxes 或者大量 anchor 的场景，可能在 assigner 会 OOM。 您可以在 assigner 的配置中设置 `gpu_assign_thr=N`，这样当超过 N 个 GT boxes 时，assigner 会通过 CPU 计算 IOU。
+
+  2. 在 backbone 中设置 `with_cp=True`。 这使用 PyTorch 中的 `sublinear strategy` 来降低 backbone 占用的 GPU 显存。
+
+  3. 使用 `config/fp16` 中的示例尝试混合精度训练。`loss_scale` 可能需要针对不同模型进行调整。
+
+  4. 你也可以尝试使用 `AvoidCUDAOOM` 来避免该问题。首先它将尝试调用 `torch.cuda.empty_cache()`。如果失败，将会尝试把输入类型转换到 FP16。如果仍然失败，将会把输入从 GPUs 转换到 CPUs 进行计算。这里提供了两个使用的例子：
+
+     ```python
+     from mmdet.utils import AvoidCUDAOOM
+
+     output = AvoidCUDAOOM.retry_if_cuda_oom(some_function)(input1, input2)
+     ```
+
+     你也可也使用 `AvoidCUDAOOM` 作为装饰器让代码遇到 OOM 的时候继续运行：
+
+     ```python
+     from mmdet.utils import AvoidCUDAOOM
+
+     @AvoidCUDAOOM.retry_if_cuda_oom
+     def function(*args, **kwargs):
+         ...
+         return xxx
+     ```
+
+- "RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one"
+
+  1. 这个错误出现在存在参数没有在 forward 中使用，容易在 DDP 中运行不同分支时发生。
+  2. 你可以在 config 设置 `find_unused_parameters = True` 进行训练 (会降低训练速度)。
+  3. 你也可以通过在 config 中的 `optimizer_config` 里设置 `detect_anomalous_params=True` 查找哪些参数没有用到，但是需要 MMCV 的版本 >= 1.4.1。
+
+- 训练中保存最好模型
+
+  可以通过配置 `evaluation = dict(save_best=‘auto’)`开启。在 auto 参数情况下会根据返回的验证结果中的第一个 key 作为选择最优模型的依据，你也可以直接设置评估结果中的 key 来手动设置，例如 `evaluation = dict(save_best=‘mAP’)`。
+
+- 在 Resume 训练中使用 `ExpMomentumEMAHook`
+
+  如果在训练中使用了 `ExpMomentumEMAHook`，那么 resume 时候不能仅仅通过命令行参数 `--resume-from` 或 `--cfg-options resume_from` 实现恢复模型参数功能例如 `python tools/train.py configs/yolox/yolox_s_8x8_300e_coco.py --resume-from ./work_dir/yolox_s_8x8_300e_coco/epoch_x.pth`。以 `yolox_s` 算法为例，由于 `ExpMomentumEMAHook` 需要重新加载权重，你可以通过如下做法实现：
+
+  ```python
+  # 直接打开 configs/yolox/yolox_s_8x8_300e_coco.py 修改所有 resume_from 字段
+  resume_from=./work_dir/yolox_s_8x8_300e_coco/epoch_x.pth
+  custom_hooks=[...
+      dict(
+          type='ExpMomentumEMAHook',
+          resume_from=./work_dir/yolox_s_8x8_300e_coco/epoch_x.pth,
+          momentum=0.0001,
+          priority=49)
+      ]
+  ```
+
+## Evaluation 相关
+
+- 使用 COCO Dataset 的测评接口时, 测评结果中 AP 或者 AR = -1
+  1. 根据COCO数据集的定义，一张图像中的中等物体与小物体面积的阈值分别为 9216（96\*96）与 1024（32\*32）。
+  2. 如果在某个区间没有检测框 AP 与 AR 认定为 -1.
+
+## Model 相关
+
+- **ResNet style 参数说明**
+
+  ResNet style 可选参数允许 `pytorch` 和 `caffe`，其差别在于 Bottleneck 模块。Bottleneck 是 `1x1-3x3-1x1` 堆叠结构，在 `caffe` 模式模式下 stride=2 参数放置在第一个 `1x1` 卷积处，而 `pyorch` 模式下 stride=2 放在第二个 `3x3` 卷积处。一个简单示例如下：
+
+  ```python
+  if self.style == 'pytorch':
+        self.conv1_stride = 1
+        self.conv2_stride = stride
+  else:
+        self.conv1_stride = stride
+        self.conv2_stride = 1
+  ```
+
+- **ResNeXt 参数说明**
+
+  ResNeXt 来自论文 [`Aggregated Residual Transformations for Deep Neural Networks`](https://arxiv.org/abs/1611.05431). 其引入分组卷积，并且通过变量基数来控制组的数量达到精度和复杂度的平衡，其有两个超参 `baseWidth` 和 `cardinality `来控制内部 Bottleneck 模块的基本宽度和分组数参数。以 MMDetection 中配置名为 `mask_rcnn_x101_64x4d_fpn_mstrain-poly_3x_coco.py` 为例，其中 `mask_rcnn` 代表算法采用 Mask R-CNN，`x101` 代表骨架网络采用 ResNeXt-101，`64x4d`代表 Bottleneck 一共分成 64 组，每组的基本宽度是 4。
+
+- **骨架网络 eval 模式说明**
+
+  因为检测模型通常比较大且输入图片分辨率很高，这会导致检测模型的 batch 很小，通常是 2，这会使得 BatchNorm 在训练过程计算的统计量方差非常大，不如主干网络预训练时得到的统计量稳定，因此在训练是一般都会使用 `norm_eval=True` 模式，直接使用预训练主干网络中的 BatchNorm 统计量，少数使用大 batch 的算法是 `norm_eval=False` 模式，例如 NASFPN。对于没有 ImageNet 预训练的骨架网络，如果 batch 比较小，可以考虑使用 `SyncBN`。
diff --git a/docs/zh_cn/get_started.md b/docs/zh_cn/get_started.md
new file mode 100644
index 0000000..40d123a
--- /dev/null
+++ b/docs/zh_cn/get_started.md
@@ -0,0 +1,264 @@
+## 依赖
+
+- Linux 和 macOS （Windows 理论上支持）
+- Python 3.7 +
+- PyTorch 1.3+
+- CUDA 9.2+ （如果基于 PyTorch 源码安装，也能够支持 CUDA 9.0）
+- GCC 5+
+- [MMCV](https://mmcv.readthedocs.io/en/latest/#installation)
+
+MMDetection 和 MMCV 版本兼容性如下所示，需要安装正确的 MMCV 版本以避免安装出现问题。
+
+| MMDetection 版本 |         MMCV 版本          |
+| :--------------: | :------------------------: |
+|      master      | mmcv-full>=1.3.17, \<1.8.0 |
+|      2.28.2      | mmcv-full>=1.3.17, \<1.8.0 |
+|      2.28.1      | mmcv-full>=1.3.17, \<1.8.0 |
+|      2.28.0      | mmcv-full>=1.3.17, \<1.8.0 |
+|      2.27.0      | mmcv-full>=1.3.17, \<1.8.0 |
+|      2.26.0      | mmcv-full>=1.3.17, \<1.8.0 |
+|      2.25.3      | mmcv-full>=1.3.17, \<1.7.0 |
+|      2.25.2      | mmcv-full>=1.3.17, \<1.7.0 |
+|      2.25.1      | mmcv-full>=1.3.17, \<1.6.0 |
+|      2.25.0      | mmcv-full>=1.3.17, \<1.6.0 |
+|      2.24.1      | mmcv-full>=1.3.17, \<1.6.0 |
+|      2.24.0      | mmcv-full>=1.3.17, \<1.6.0 |
+|      2.23.0      | mmcv-full>=1.3.17, \<1.5.0 |
+|      2.22.0      | mmcv-full>=1.3.17, \<1.5.0 |
+|      2.21.0      | mmcv-full>=1.3.17, \<1.5.0 |
+|      2.20.0      | mmcv-full>=1.3.17, \<1.5.0 |
+|      2.19.1      | mmcv-full>=1.3.17, \<1.5.0 |
+|      2.19.0      | mmcv-full>=1.3.17, \<1.5.0 |
+|      2.18.1      | mmcv-full>=1.3.17, \<1.4.0 |
+|      2.18.0      | mmcv-full>=1.3.14, \<1.4.0 |
+|      2.17.0      | mmcv-full>=1.3.14, \<1.4.0 |
+|      2.16.0      | mmcv-full>=1.3.8, \<1.4.0  |
+|      2.15.1      | mmcv-full>=1.3.8, \<1.4.0  |
+|      2.15.0      | mmcv-full>=1.3.8, \<1.4.0  |
+|      2.14.0      | mmcv-full>=1.3.8, \<1.4.0  |
+|      2.13.0      | mmcv-full>=1.3.3, \<1.4.0  |
+|      2.12.0      | mmcv-full>=1.3.3, \<1.4.0  |
+|      2.11.0      | mmcv-full>=1.2.4, \<1.4.0  |
+|      2.10.0      | mmcv-full>=1.2.4, \<1.4.0  |
+|      2.9.0       | mmcv-full>=1.2.4, \<1.4.0  |
+|      2.8.0       | mmcv-full>=1.2.4, \<1.4.0  |
+|      2.7.0       | mmcv-full>=1.1.5, \<1.4.0  |
+|      2.6.0       | mmcv-full>=1.1.5, \<1.4.0  |
+|      2.5.0       | mmcv-full>=1.1.5, \<1.4.0  |
+|      2.4.0       | mmcv-full>=1.1.1, \<1.4.0  |
+|      2.3.0       |      mmcv-full==1.0.5      |
+|     2.3.0rc0     |      mmcv-full>=1.0.2      |
+|      2.2.1       |        mmcv==0.6.2         |
+|      2.2.0       |        mmcv==0.6.2         |
+|      2.1.0       |   mmcv>=0.5.9, \<=0.6.1    |
+|      2.0.0       |   mmcv>=0.5.1, \<=0.5.8    |
+
+\*\*注意：\*\*如果已经安装了 mmcv，首先需要使用 `pip uninstall mmcv` 卸载已安装的 mmcv，如果同时安装了 mmcv 和 mmcv-full，将会报 `ModuleNotFoundError` 错误。
+
+## 安装流程
+
+### 从零开始设置脚本
+
+假设当前已经成功安装 CUDA 10.1，这里提供了一个完整的基于 conda 安装 MMDetection 的脚本。您可以参考下一节中的分步安装说明。
+
+```shell
+conda create -n openmmlab python=3.7 pytorch==1.6.0 cudatoolkit=10.1 torchvision -c pytorch -y
+conda activate openmmlab
+pip install openmim
+mim install mmcv-full
+git clone https://github.com/open-mmlab/mmdetection.git
+cd mmdetection
+pip install -r requirements/build.txt
+pip install -v -e .
+```
+
+### 准备环境
+
+1. 使用 conda 新建虚拟环境，并进入该虚拟环境；
+
+   ```shell
+   conda create -n open-mmlab python=3.7 -y
+   conda activate open-mmlab
+   ```
+
+2. 基于 [PyTorch 官网](https://pytorch.org/)安装 PyTorch 和 torchvision，例如：
+
+   ```shell
+   conda install pytorch torchvision -c pytorch
+   ```
+
+   **注意**：需要确保 CUDA 的编译版本和运行版本匹配。可以在 [PyTorch 官网](https://pytorch.org/)查看预编译包所支持的 CUDA 版本。
+
+   `例 1` 例如在 `/usr/local/cuda` 下安装了 CUDA 10.1， 并想安装 PyTorch 1.5，则需要安装支持 CUDA 10.1 的预构建 PyTorch：
+
+   ```shell
+   conda install pytorch cudatoolkit=10.1 torchvision -c pytorch
+   ```
+
+   `例 2` 例如在 `/usr/local/cuda` 下安装了 CUDA 9.2， 并想安装 PyTorch 1.3.1，则需要安装支持 CUDA 9.2 的预构建 PyTorch：
+
+   ```shell
+   conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch
+   ```
+
+   如果不是安装预构建的包，而是从源码中构建 PyTorch，则可以使用更多的 CUDA 版本，例如 CUDA 9.0。
+
+### 安装 MMDetection
+
+我们建议使用 [MIM](https://github.com/open-mmlab/mim) 来安装 MMDetection：
+
+```shell
+pip install openmim
+mim install mmdet
+```
+
+MIM 能够自动地安装 OpenMMLab 的项目以及对应的依赖包。
+
+或者，可以手动安装 MMDetection：
+
+1. 安装 mmcv-full，我们建议使用预构建包来安装：
+
+   ```shell
+   pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/{cu_version}/{torch_version}/index.html
+   ```
+
+   需要把命令行中的 `{cu_version}` 和 `{torch_version}` 替换成对应的版本。例如：在 CUDA 11 和 PyTorch 1.7.0 的环境下，可以使用下面命令安装最新版本的 MMCV：
+
+   ```shell
+   pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html
+   ```
+
+   请参考 [MMCV](https://mmcv.readthedocs.io/en/latest/#installation) 获取不同版本的 MMCV 所兼容的的不同的 PyTorch 和 CUDA 版本。同时，也可以通过以下命令行从源码编译 MMCV：
+
+   ```shell
+   git clone https://github.com/open-mmlab/mmcv.git
+   cd mmcv
+   MMCV_WITH_OPS=1 pip install -e .  # 安装好 mmcv-full
+   cd ..
+   ```
+
+   或者，可以直接使用命令行安装：
+
+   ```shell
+   pip install mmcv-full
+   ```
+
+   PyTorch 在 1.x.0 和 1.x.1 之间通常是兼容的，故 mmcv-full 只提供 1.x.0 的编译包。如果你的 PyTorch 版本是 1.x.1，你可以放心地安装在 1.x.0 版本编译的 mmcv-full。
+
+   ```
+   # 我们可以忽略 PyTorch 的小版本号
+   pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7/index.html
+   ```
+
+2. 安装 MMDetection：
+
+   你可以直接通过如下命令从 pip 安装使用 mmdetection:
+
+   ```shell
+   pip install mmdet
+   ```
+
+   或者从 git 仓库编译源码
+
+   ```shell
+   git clone https://github.com/open-mmlab/mmdetection.git
+   cd mmdetection
+   pip install -r requirements/build.txt
+   pip install -v -e .  # or "python setup.py develop"
+   ```
+
+3. 安装额外的依赖以使用 Instaboost, 全景分割, 或者 LVIS 数据集
+
+   ```shell
+   # 安装 instaboost 依赖
+   pip install instaboostfast
+   # 安装全景分割依赖
+   pip install git+https://github.com/cocodataset/panopticapi.git
+   # 安装 LVIS 数据集依赖
+   pip install git+https://github.com/lvis-dataset/lvis-api.git
+   # 安装 albumentations 依赖
+   pip install -r requirements/albu.txt
+   ```
+
+**注意：**
+
+(1) 按照上述说明，MMDetection 安装在 `dev` 模式下，因此在本地对代码做的任何修改都会生效，无需重新安装；
+
+(2) 如果希望使用 `opencv-python-headless` 而不是 `opencv-python`， 可以在安装 MMCV 之前安装；
+
+(3) 一些安装依赖是可以选择的。例如只需要安装最低运行要求的版本，则可以使用 `pip install -v -e .` 命令。如果希望使用可选择的像 `albumentations` 和 `imagecorruptions` 这种依赖项，可以使用 `pip install -r requirements/optional.txt` 进行手动安装，或者在使用 `pip` 时指定所需的附加功能（例如 `pip install -v -e .[optional]`），支持附加功能的有效键值包括 `all`、`tests`、`build` 以及 `optional` 。
+
+(4) 如果希望使用 `albumentations`，我们建议使用 `pip install -r requirements/albu.txt` 或者 `pip install -U albumentations --no-binary qudida,albumentations` 进行安装。 如果简单地使用 `pip install albumentations>=0.3.2` 进行安装，则会同时安装 `opencv-python-headless`（即便已经安装了 `opencv-python` 也会再次安装）。我们建议在安装 `albumentations` 后检查环境，以确保没有同时安装 `opencv-python` 和 `opencv-python-headless`，因为同时安装可能会导致一些问题。更多细节请参考[官方文档](https://albumentations.ai/docs/getting_started/installation/#note-on-opencv-dependencies)。
+
+### 只在 CPU 安装
+
+我们的代码能够建立在只使用 CPU 的环境（CUDA 不可用）。
+
+在 CPU 模式下，可以进行模型训练（需要 MMCV 版本 >= 1.4.4)、测试或者推理，然而以下功能将在 CPU 模式下不能使用：
+
+- Deformable Convolution
+- Modulated Deformable Convolution
+- ROI pooling
+- Deformable ROI pooling
+- CARAFE: Content-Aware ReAssembly of FEatures
+- SyncBatchNorm
+- CrissCrossAttention: Criss-Cross Attention
+- MaskedConv2d
+- Temporal Interlace Shift
+- nms_cuda
+- sigmoid_focal_loss_cuda
+- bbox_overlaps
+
+因此，如果尝试使用包含上述操作的模型进行训练/测试/推理，将会报错。下表列出了由于依赖上述算子而无法在 CPU 上运行的相关模型：
+
+|                          操作                           |                                           模型                                           |
+| :-----------------------------------------------------: | :--------------------------------------------------------------------------------------: |
+| Deformable Convolution/Modulated Deformable Convolution | DCN、Guided Anchoring、RepPoints、CentripetalNet、VFNet、CascadeRPN、NAS-FCOS、DetectoRS |
+|                      MaskedConv2d                       |                                     Guided Anchoring                                     |
+|                         CARAFE                          |                                          CARAFE                                          |
+|                      SyncBatchNorm                      |                                         ResNeSt                                          |
+
+### 另一种选择： Docker 镜像
+
+我们提供了 [Dockerfile](https://github.com/open-mmlab/mmdetection/blob/master/docker/Dockerfile) 来生成镜像，请确保 [docker](https://docs.docker.com/engine/install/) 的版本 >= 19.03。
+
+```shell
+# 基于 PyTorch 1.6, CUDA 10.1 生成镜像
+docker build -t mmdetection docker/
+```
+
+运行命令：
+
+```shell
+docker run --gpus all --shm-size=8g -it -v {DATA_DIR}:/mmdetection/data mmdetection
+```
+
+### 使用多个 MMDetection 版本进行开发
+
+训练和测试的脚本已经在 PYTHONPATH 中进行了修改，以确保脚本使用当前目录中的 MMDetection。
+
+要使环境中安装默认的 MMDetection 而不是当前正在在使用的，可以删除出现在相关脚本中的代码：
+
+```shell
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH
+```
+
+## 验证
+
+为了验证是否正确安装了 MMDetection 和所需的环境，我们可以运行示例的 Python 代码来初始化检测器并推理一个演示图像：
+
+```python
+from mmdet.apis import init_detector, inference_detector
+
+config_file = 'configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+# 从 model zoo 下载 checkpoint 并放在 `checkpoints/` 文件下
+# 网址为: http://download.openmmlab.com/mmdetection/v2.0/faster_rcnn/faster_rcnn_r50_fpn_1x_coco/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth
+checkpoint_file = 'checkpoints/faster_rcnn_r50_fpn_1x_coco_20200130-047c8118.pth'
+device = 'cuda:0'
+# 初始化检测器
+model = init_detector(config_file, checkpoint_file, device=device)
+# 推理演示图像
+inference_detector(model, 'demo/demo.jpg')
+```
+
+如果成功安装 MMDetection，则上面的代码可以完整地运行。
diff --git a/docs/zh_cn/index.rst b/docs/zh_cn/index.rst
new file mode 100644
index 0000000..872606c
--- /dev/null
+++ b/docs/zh_cn/index.rst
@@ -0,0 +1,62 @@
+Welcome to MMDetection's documentation!
+=======================================
+
+.. toctree::
+   :maxdepth: 2
+   :caption: 开始你的第一步
+
+   get_started.md
+   model_zoo.md
+   article.md
+
+.. toctree::
+   :maxdepth: 2
+   :caption: 快速启动
+
+   1_exist_data_model.md
+   2_new_data_model.md
+   3_exist_data_new_model.md
+
+.. toctree::
+   :maxdepth: 2
+   :caption: 教程
+
+   tutorials/index.rst
+
+.. toctree::
+   :maxdepth: 2
+   :caption: 实用工具与脚本
+
+   useful_tools.md
+
+.. toctree::
+   :maxdepth: 2
+   :caption: 说明
+
+   conventions.md
+   compatibility.md
+   faq.md
+
+.. toctree::
+   :caption: 语言切换
+
+   switch_language.md
+
+.. toctree::
+   :maxdepth: 1
+   :caption: 接口文档（英文）
+
+   api.rst
+
+.. toctree::
+   :maxdepth: 1
+   :caption: 设备支持
+
+   device/npu.md
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`search`
diff --git a/docs/zh_cn/make.bat b/docs/zh_cn/make.bat
new file mode 100644
index 0000000..922152e
--- /dev/null
+++ b/docs/zh_cn/make.bat
@@ -0,0 +1,35 @@
+@ECHO OFF
+
+pushd %~dp0
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set SOURCEDIR=.
+set BUILDDIR=_build
+
+if "%1" == "" goto help
+
+%SPHINXBUILD% >NUL 2>NUL
+if errorlevel 9009 (
+	echo.
+	echo.The 'sphinx-build' command was not found. Make sure you have Sphinx
+	echo.installed, then set the SPHINXBUILD environment variable to point
+	echo.to the full path of the 'sphinx-build' executable. Alternatively you
+	echo.may add the Sphinx directory to PATH.
+	echo.
+	echo.If you don't have Sphinx installed, grab it from
+	echo.http://sphinx-doc.org/
+	exit /b 1
+)
+
+%SPHINXBUILD% -M %1 %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+goto end
+
+:help
+%SPHINXBUILD% -M help %SOURCEDIR% %BUILDDIR% %SPHINXOPTS% %O%
+
+:end
+popd
diff --git a/docs/zh_cn/model_zoo.md b/docs/zh_cn/model_zoo.md
new file mode 100644
index 0000000..84f5727
--- /dev/null
+++ b/docs/zh_cn/model_zoo.md
@@ -0,0 +1,337 @@
+# 模型库
+
+## 镜像地址
+
+从 MMDetection V2.0 起，我们只通过阿里云维护模型库。V1.x 版本的模型已经弃用。
+
+## 共同设置
+
+- 所有模型都是在 `coco_2017_train` 上训练，在 `coco_2017_val` 上测试。
+- 我们使用分布式训练。
+- 所有 pytorch-style 的 ImageNet 预训练主干网络来自 PyTorch 的模型库，caffe-style 的预训练主干网络来自 detectron2 最新开源的模型。
+- 为了与其他代码库公平比较，文档中所写的 GPU 内存是8个 GPU 的 `torch.cuda.max_memory_allocated()` 的最大值，此值通常小于 nvidia-smi 显示的值。
+- 我们以网络 forward 和后处理的时间加和作为推理时间，不包含数据加载时间。所有结果通过 [benchmark.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/benchmark.py) 脚本计算所得。该脚本会计算推理 2000 张图像的平均时间。
+
+## ImageNet 预训练模型
+
+通过 ImageNet 分类任务预训练的主干网络进行初始化是很常见的操作。所有预训练模型的链接都可以在 [open_mmlab](https://github.com/open-mmlab/mmcv/blob/master/mmcv/model_zoo/open_mmlab.json) 中找到。根据 `img_norm_cfg` 和原始权重，我们可以将所有 ImageNet 预训练模型分为以下几种情况：
+
+- TorchVision：torchvision 模型权重，包含 ResNet50, ResNet101。`img_norm_cfg` 为 `dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)`。
+- Pycls：[pycls](https://github.com/facebookresearch/pycls) 模型权重，包含 RegNetX。`img_norm_cfg` 为 `dict(   mean=[103.530, 116.280, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)`。
+- MSRA styles：[MSRA](https://github.com/KaimingHe/deep-residual-networks) 模型权重，包含 ResNet50_Caffe，ResNet101_Caffe。`img_norm_cfg` 为 `dict(   mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)`。
+- Caffe2 styles：现阶段只包含 ResNext101_32x8d。`img_norm_cfg` 为 `dict(mean=[103.530, 116.280, 123.675], std=[57.375, 57.120, 58.395], to_rgb=False)`。
+- Other styles: SSD 的 `img_norm_cfg` 为 `dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)`，YOLOv3 的 `img_norm_cfg` 为 `dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)`。
+
+MMdetection 常用到的主干网络细节如下表所示：
+
+| 模型             | 来源        | 链接                                                                                                                                                                                                | 描述                                                                                                                                                                                                                       |
+| ---------------- | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| ResNet50         | TorchVision | [torchvision 中的 ResNet-50](https://download.pytorch.org/models/resnet50-19c8e357.pth)                                                                                                             | 来自 [torchvision 中的 ResNet-50](https://download.pytorch.org/models/resnet50-19c8e357.pth)。                                                                                                                             |
+| ResNet101        | TorchVision | [torchvision 中的 ResNet-101](https://download.pytorch.org/models/resnet101-5d3b4d8f.pth)                                                                                                           | 来自 [torchvision 中的 ResNet-101](https://download.pytorch.org/models/resnet101-5d3b4d8f.pth)。                                                                                                                           |
+| RegNetX          | Pycls       | [RegNetX_3.2gf](https://download.openmmlab.com/pretrain/third_party/regnetx_3.2gf-c2599b0f.pth)，[RegNetX_800mf](https://download.openmmlab.com/pretrain/third_party/regnetx_800mf-1f4be4c7.pth) 等 | 来自 [pycls](https://github.com/facebookresearch/pycls)。                                                                                                                                                                  |
+| ResNet50_Caffe   | MSRA        | [MSRA 中的 ResNet-50](https://download.openmmlab.com/pretrain/third_party/resnet50_caffe-788b5fa3.pth)                                                                                              | 由 [Detectron2 中的 R-50.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-50.pkl) 转化的副本。原始权重文件来自 [MSRA 中的原始 ResNet-50](https://github.com/KaimingHe/deep-residual-networks)。    |
+| ResNet101_Caffe  | MSRA        | [MSRA 中的 ResNet-101](https://download.openmmlab.com/pretrain/third_party/resnet101_caffe-3ad79236.pth)                                                                                            | 由 [Detectron2 中的 R-101.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-101.pkl) 转化的副本。原始权重文件来自 [MSRA 中的原始 ResNet-101](https://github.com/KaimingHe/deep-residual-networks)。 |
+| ResNext101_32x8d | Caffe2      | [Caffe2 ResNext101_32x8d](https://download.openmmlab.com/pretrain/third_party/resnext101_32x8d-1516f1aa.pth)                                                                                        | 由 [Detectron2 中的 X-101-32x8d.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/FAIR/X-101-32x8d.pkl) 转化的副本。原始 ResNeXt-101-32x8d 由 FB 使用 Caffe2 训练。                                        |
+
+## Baselines
+
+### RPN
+
+请参考 [RPN](https://github.com/open-mmlab/mmdetection/blob/master/configs/rpn)。
+
+### Faster R-CNN
+
+请参考 [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn)。
+
+### Mask R-CNN
+
+请参考 [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn)。
+
+### Fast R-CNN (使用提前计算的 proposals)
+
+请参考 [Fast R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/fast_rcnn)。
+
+### RetinaNet
+
+请参考 [RetinaNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet)。
+
+### Cascade R-CNN and Cascade Mask R-CNN
+
+请参考 [Cascade R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/cascade_rcnn)。
+
+### Hybrid Task Cascade (HTC)
+
+请参考 [HTC](https://github.com/open-mmlab/mmdetection/blob/master/configs/htc)。
+
+### SSD
+
+请参考 [SSD](https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd)。
+
+### Group Normalization (GN)
+
+请参考 [Group Normalization](https://github.com/open-mmlab/mmdetection/blob/master/configs/gn)。
+
+### Weight Standardization
+
+请参考 [Weight Standardization](https://github.com/open-mmlab/mmdetection/blob/master/configs/gn+ws)。
+
+### Deformable Convolution v2
+
+请参考 [Deformable Convolutional Networks](https://github.com/open-mmlab/mmdetection/blob/master/configs/dcn)。
+
+### CARAFE: Content-Aware ReAssembly of FEatures
+
+请参考 [CARAFE](https://github.com/open-mmlab/mmdetection/blob/master/configs/carafe)。
+
+### Instaboost
+
+请参考 [Instaboost](https://github.com/open-mmlab/mmdetection/blob/master/configs/instaboost)。
+
+### Libra R-CNN
+
+请参考 [Libra R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/libra_rcnn)。
+
+### Guided Anchoring
+
+请参考 [Guided Anchoring](https://github.com/open-mmlab/mmdetection/blob/master/configs/guided_anchoring)。
+
+### FCOS
+
+请参考 [FCOS](https://github.com/open-mmlab/mmdetection/blob/master/configs/fcos)。
+
+### FoveaBox
+
+请参考 [FoveaBox](https://github.com/open-mmlab/mmdetection/blob/master/configs/foveabox)。
+
+### RepPoints
+
+请参考 [RepPoints](https://github.com/open-mmlab/mmdetection/blob/master/configs/reppoints)。
+
+### FreeAnchor
+
+请参考 [FreeAnchor](https://github.com/open-mmlab/mmdetection/blob/master/configs/free_anchor)。
+
+### Grid R-CNN (plus)
+
+请参考 [Grid R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/grid_rcnn)。
+
+### GHM
+
+请参考 [GHM](https://github.com/open-mmlab/mmdetection/blob/master/configs/ghm)。
+
+### GCNet
+
+请参考 [GCNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/gcnet)。
+
+### HRNet
+
+请参考 [HRNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/hrnet)。
+
+### Mask Scoring R-CNN
+
+请参考 [Mask Scoring R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/ms_rcnn)。
+
+### Train from Scratch
+
+请参考 [Rethinking ImageNet Pre-training](https://github.com/open-mmlab/mmdetection/blob/master/configs/scratch)。
+
+### NAS-FPN
+
+请参考 [NAS-FPN](https://github.com/open-mmlab/mmdetection/blob/master/configs/nas_fpn)。
+
+### ATSS
+
+请参考 [ATSS](https://github.com/open-mmlab/mmdetection/blob/master/configs/atss)。
+
+### FSAF
+
+请参考 [FSAF](https://github.com/open-mmlab/mmdetection/blob/master/configs/fsaf)。
+
+### RegNetX
+
+请参考 [RegNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/regnet)。
+
+### Res2Net
+
+请参考 [Res2Net](https://github.com/open-mmlab/mmdetection/blob/master/configs/res2net)。
+
+### GRoIE
+
+请参考 [GRoIE](https://github.com/open-mmlab/mmdetection/blob/master/configs/groie)。
+
+### Dynamic R-CNN
+
+请参考 [Dynamic R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/dynamic_rcnn)。
+
+### PointRend
+
+请参考 [PointRend](https://github.com/open-mmlab/mmdetection/blob/master/configs/point_rend)。
+
+### DetectoRS
+
+请参考 [DetectoRS](https://github.com/open-mmlab/mmdetection/blob/master/configs/detectors)。
+
+### Generalized Focal Loss
+
+请参考 [Generalized Focal Loss](https://github.com/open-mmlab/mmdetection/blob/master/configs/gfl)。
+
+### CornerNet
+
+请参考 [CornerNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/cornernet)。
+
+### YOLOv3
+
+请参考 [YOLOv3](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolo)。
+
+### PAA
+
+请参考 [PAA](https://github.com/open-mmlab/mmdetection/blob/master/configs/paa)。
+
+### SABL
+
+请参考 [SABL](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl)。
+
+### CentripetalNet
+
+请参考 [CentripetalNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/centripetalnet)。
+
+### ResNeSt
+
+请参考 [ResNeSt](https://github.com/open-mmlab/mmdetection/blob/master/configs/resnest)。
+
+### DETR
+
+请参考 [DETR](https://github.com/open-mmlab/mmdetection/blob/master/configs/detr)。
+
+### Deformable DETR
+
+请参考 [Deformable DETR](https://github.com/open-mmlab/mmdetection/blob/master/configs/deformable_detr)。
+
+### AutoAssign
+
+请参考 [AutoAssign](https://github.com/open-mmlab/mmdetection/blob/master/configs/autoassign)。
+
+### YOLOF
+
+请参考 [YOLOF](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolof)。
+
+### Seesaw Loss
+
+请参考 [Seesaw Loss](https://github.com/open-mmlab/mmdetection/blob/master/configs/seesaw_loss)。
+
+### CenterNet
+
+请参考 [CenterNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/centernet)。
+
+### YOLOX
+
+请参考 [YOLOX](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolox)。
+
+### PVT
+
+请参考 [PVT](https://github.com/open-mmlab/mmdetection/blob/master/configs/pvt)。
+
+### SOLO
+
+请参考 [SOLO](https://github.com/open-mmlab/mmdetection/blob/master/configs/solo)。
+
+### QueryInst
+
+请参考 [QueryInst](https://github.com/open-mmlab/mmdetection/blob/master/configs/queryinst)。
+
+### RF-Next
+
+请参考 [RF-Next](https://github.com/open-mmlab/mmdetection/blob/master/configs/rfnext).
+
+### Other datasets
+
+我们还在 [PASCAL VOC](https://github.com/open-mmlab/mmdetection/blob/master/configs/pascal_voc)，[Cityscapes](https://github.com/open-mmlab/mmdetection/blob/master/configs/cityscapes) 和 [WIDER FACE](https://github.com/open-mmlab/mmdetection/blob/master/configs/wider_face) 上对一些方法进行了基准测试。
+
+### Pre-trained Models
+
+我们还通过多尺度训练和更长的训练策略来训练用 ResNet-50 和 [RegNetX-3.2G](https://github.com/open-mmlab/mmdetection/blob/master/configs/regnet) 作为主干网络的 [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn) 和 [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn)。这些模型可以作为下游任务的预训练模型。
+
+## 速度基准
+
+### 训练速度基准
+
+我们提供 [analyze_logs.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/analyze_logs.py) 来得到训练中每一次迭代的平均时间。示例请参考 [Log Analysis](https://mmdetection.readthedocs.io/en/latest/useful_tools.html#log-analysis)。
+
+我们与其他流行框架的 Mask R-CNN 训练速度进行比较（数据是从 [detectron2](https://github.com/facebookresearch/detectron2/blob/master/docs/notes/benchmarks.md/) 复制而来）。在 mmdetection 中，我们使用 [mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py) 进行基准测试。它与 detectron2 的 [mask_rcnn_R_50_FPN_noaug_1x.yaml](https://github.com/facebookresearch/detectron2/blob/master/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml) 设置完全一样。同时，我们还提供了[模型权重](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug_compare_20200518-10127928.pth)和[训练 log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug_20200518_105755.log.json) 作为参考。为了跳过 GPU 预热时间，吞吐量按照100-500次迭代之间的平均吞吐量来计算。
+
+| 框架                                                                                   | 吞吐量 (img/s) |
+| -------------------------------------------------------------------------------------- | -------------- |
+| [Detectron2](https://github.com/facebookresearch/detectron2)                           | 62             |
+| [MMDetection](https://github.com/open-mmlab/mmdetection)                               | 61             |
+| [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/)          | 53             |
+| [tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) | 50             |
+| [simpledet](https://github.com/TuSimple/simpledet/)                                    | 39             |
+| [Detectron](https://github.com/facebookresearch/Detectron)                             | 19             |
+| [matterport/Mask_RCNN](https://github.com/matterport/Mask_RCNN/)                       | 14             |
+
+### 推理时间基准
+
+我们提供 [benchmark.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/benchmark.py) 对推理时间进行基准测试。此脚本将推理 2000 张图片并计算忽略前 5 次推理的平均推理时间。可以通过设置 `LOG-INTERVAL` 来改变 log 输出间隔（默认为 50）。
+
+```shell
+python tools/benchmark.py ${CONFIG} ${CHECKPOINT} [--log-interval $[LOG-INTERVAL]] [--fuse-conv-bn]
+```
+
+模型库中，所有模型在基准测量推理时间时都没设置 `fuse-conv-bn`, 此设置可以使推理时间更短。
+
+## 与 Detectron2 对比
+
+我们在速度和精度方面对 mmdetection 和 [Detectron2](https://github.com/facebookresearch/detectron2.git) 进行对比。对比所使用的 detectron2 的 commit id 为 [185c27e](https://github.com/facebookresearch/detectron2/tree/185c27e4b4d2d4c68b5627b3765420c6d7f5a659)(30/4/2020)。
+为了公平对比，我们所有的实验都在同一机器下进行。
+
+### 硬件
+
+- 8 NVIDIA Tesla V100 (32G) GPUs
+- Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
+
+### 软件环境
+
+- Python 3.7
+- PyTorch 1.4
+- CUDA 10.1
+- CUDNN 7.6.03
+- NCCL 2.4.08
+
+### 精度
+
+| 模型                                                                                                                                   | 训练策略 | Detectron2                                                                                                                             | mmdetection | 下载                                                                                                                                                                                                                                                                                                                                                             |
+| -------------------------------------------------------------------------------------------------------------------------------------- | -------- | -------------------------------------------------------------------------------------------------------------------------------------- | ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
+| [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py) | 1x       | [37.9](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml)                 | 38.0        | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-5324cff8.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco_20200429_234554.log.json)             |
+| [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py)  | 1x       | [38.6 & 35.2](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml) | 38.8 & 35.4 | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco-dbecf295.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco_20200430_054239.log.json) |
+| [Retinanet](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py)        | 1x       | [36.5](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml)                   | 37.0        | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/retinanet_r50_caffe_fpn_mstrain_1x_coco/retinanet_r50_caffe_fpn_mstrain_1x_coco-586977a0.pth) \| [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/retinanet_r50_caffe_fpn_mstrain_1x_coco/retinanet_r50_caffe_fpn_mstrain_1x_coco_20200430_014748.log.json)                     |
+
+### 训练速度
+
+训练速度使用 s/iter 来度量。结果越低越好。
+
+| 模型         | Detectron2 | mmdetection |
+| ------------ | ---------- | ----------- |
+| Faster R-CNN | 0.210      | 0.216       |
+| Mask R-CNN   | 0.261      | 0.265       |
+| Retinanet    | 0.200      | 0.205       |
+
+### 推理速度
+
+推理速度通过单张 GPU 下的 fps(img/s) 来度量，越高越好。
+为了与 Detectron2 保持一致，我们所写的推理时间除去了数据加载时间。
+对于 Mask RCNN，我们去除了后处理中 RLE 编码的时间。
+我们在括号中给出了官方给出的速度。由于硬件差异，官方给出的速度会比我们所测试得到的速度快一些。
+
+| 模型         | Detectron2  | mmdetection |
+| ------------ | ----------- | ----------- |
+| Faster R-CNN | 25.6 (26.3) | 22.2        |
+| Mask R-CNN   | 22.5 (23.3) | 19.6        |
+| Retinanet    | 17.8 (18.2) | 20.6        |
+
+### 训练内存
+
+| 模型         | Detectron2 | mmdetection |
+| ------------ | ---------- | ----------- |
+| Faster R-CNN | 3.0        | 3.8         |
+| Mask R-CNN   | 3.4        | 3.9         |
+| Retinanet    | 3.9        | 3.4         |
diff --git a/docs/zh_cn/projects.md b/docs/zh_cn/projects.md
new file mode 100644
index 0000000..6b9d300
--- /dev/null
+++ b/docs/zh_cn/projects.md
@@ -0,0 +1,48 @@
+# 基于 MMDetection 的项目
+
+有许多开源项目都是基于 MMDetection 搭建的，我们在这里列举一部分作为样例，展示如何基于 MMDetection 搭建您自己的项目。
+由于这个页面列举的项目并不完全，我们欢迎社区提交 Pull Request 来更新这个文档。
+
+## MMDetection 的拓展项目
+
+一些项目拓展了 MMDetection 的边界，如将 MMDetection 拓展支持 3D 检测或者将 MMDetection 用于部署。
+它们展示了 MMDetection 的许多可能性，所以我们在这里也列举一些。
+
+- [OTEDetection](https://github.com/opencv/mmdetection): OpenVINO training extensions for object detection.
+- [MMDetection3d](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
+
+## 研究项目
+
+同样有许多研究论文是基于 MMDetection 进行的。许多论文都发表在了顶级的会议或期刊上，或者对社区产生了深远的影响。
+为了向社区提供一个可以参考的论文列表，帮助大家开发或者比较新的前沿算法，我们在这里也遵循会议的时间顺序列举了一些论文。
+MMDetection 中已经支持的算法不在此列。
+
+- Involution: Inverting the Inherence of Convolution for Visual Recognition, CVPR21. [\[paper\]](https://arxiv.org/abs/2103.06255)[\[github\]](https://github.com/d-li14/involution)
+- Multiple Instance Active Learning for Object Detection, CVPR 2021. [\[paper\]](https://openaccess.thecvf.com/content/CVPR2021/papers/Yuan_Multiple_Instance_Active_Learning_for_Object_Detection_CVPR_2021_paper.pdf)[\[github\]](https://github.com/yuantn/MI-AOD)
+- Adaptive Class Suppression Loss for Long-Tail Object Detection, CVPR 2021. [\[paper\]](https://arxiv.org/abs/2104.00885)[\[github\]](https://github.com/CASIA-IVA-Lab/ACSL)
+- Generalizable Pedestrian Detection: The Elephant In The Room, CVPR2021. [\[paper\]](https://arxiv.org/abs/2003.08799)[\[github\]](https://github.com/hasanirtiza/Pedestron)
+- Group Fisher Pruning for Practical Network Compression, ICML2021. [\[paper\]](https://github.com/jshilong/FisherPruning/blob/main/resources/paper.pdf)[\[github\]](https://github.com/jshilong/FisherPruning)
+- Overcoming Classifier Imbalance for Long-tail Object Detection with Balanced Group Softmax, CVPR2020. [\[paper\]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Li_Overcoming_Classifier_Imbalance_for_Long-Tail_Object_Detection_With_Balanced_Group_CVPR_2020_paper.pdf)[\[github\]](https://github.com/FishYuLi/BalancedGroupSoftmax)
+- Coherent Reconstruction of Multiple Humans from a Single Image, CVPR2020. [\[paper\]](https://jiangwenpl.github.io/multiperson/)[\[github\]](https://github.com/JiangWenPL/multiperson)
+- Look-into-Object: Self-supervised Structure Modeling for Object Recognition, CVPR 2020. [\[paper\]](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhou_Look-Into-Object_Self-Supervised_Structure_Modeling_for_Object_Recognition_CVPR_2020_paper.pdf)[\[github\]](https://github.com/JDAI-CV/LIO)
+- Video Panoptic Segmentation, CVPR2020. [\[paper\]](https://arxiv.org/abs/2006.11339)[\[github\]](https://github.com/mcahny/vps)
+- D2Det: Towards High Quality Object Detection and Instance Segmentation, CVPR2020. [\[paper\]](http://openaccess.thecvf.com/content_CVPR_2020/html/Cao_D2Det_Towards_High_Quality_Object_Detection_and_Instance_Segmentation_CVPR_2020_paper.html)[\[github\]](https://github.com/JialeCao001/D2Det)
+- CentripetalNet: Pursuing High-quality Keypoint Pairs for Object Detection, CVPR2020. [\[paper\]](https://arxiv.org/abs/2003.09119)[\[github\]](https://github.com/KiveeDong/CentripetalNet)
+- Learning a Unified Sample Weighting Network for Object Detection, CVPR 2020. [\[paper\]](http://openaccess.thecvf.com/content_CVPR_2020/html/Cai_Learning_a_Unified_Sample_Weighting_Network_for_Object_Detection_CVPR_2020_paper.html)[\[github\]](https://github.com/caiqi/sample-weighting-network)
+- Scale-equalizing Pyramid Convolution for Object Detection, CVPR2020. [\[paper\]](https://arxiv.org/abs/2005.03101) [\[github\]](https://github.com/jshilong/SEPC)
+- Revisiting the Sibling Head in Object Detector, CVPR2020. [\[paper\]](https://arxiv.org/abs/2003.07540)[\[github\]](https://github.com/Sense-X/TSD)
+- PolarMask: Single Shot Instance Segmentation with Polar Representation, CVPR2020. [\[paper\]](https://arxiv.org/abs/1909.13226)[\[github\]](https://github.com/xieenze/PolarMask)
+- Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection, CVPR2020. [\[paper\]](https://arxiv.org/abs/2003.11818)[\[github\]](https://github.com/ggjy/HitDet.pytorch)
+- ZeroQ: A Novel Zero Shot Quantization Framework, CVPR2020. [\[paper\]](https://arxiv.org/abs/2001.00281)[\[github\]](https://github.com/amirgholami/ZeroQ)
+- CBNet: A Novel Composite Backbone Network Architecture for Object Detection, AAAI2020. [\[paper\]](https://aaai.org/Papers/AAAI/2020GB/AAAI-LiuY.1833.pdf)[\[github\]](https://github.com/VDIGPKU/CBNet)
+- RDSNet: A New Deep Architecture for Reciprocal Object Detection and Instance Segmentation, AAAI2020. [\[paper\]](https://arxiv.org/abs/1912.05070)[\[github\]](https://github.com/wangsr126/RDSNet)
+- Training-Time-Friendly Network for Real-Time Object Detection, AAAI2020. [\[paper\]](https://arxiv.org/abs/1909.00700)[\[github\]](https://github.com/ZJULearning/ttfnet)
+- Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution, NeurIPS 2019. [\[paper\]](https://arxiv.org/abs/1909.06720)[\[github\]](https://github.com/thangvubk/Cascade-RPN)
+- Reasoning R-CNN: Unifying Adaptive Global Reasoning into Large-scale Object Detection, CVPR2019. [\[paper\]](http://openaccess.thecvf.com/content_CVPR_2019/papers/Xu_Reasoning-RCNN_Unifying_Adaptive_Global_Reasoning_Into_Large-Scale_Object_Detection_CVPR_2019_paper.pdf)[\[github\]](https://github.com/chanyn/Reasoning-RCNN)
+- Learning RoI Transformer for Oriented Object Detection in Aerial Images, CVPR2019. [\[paper\]](https://arxiv.org/abs/1812.00155)[\[github\]](https://github.com/dingjiansw101/AerialDetection)
+- SOLO: Segmenting Objects by Locations. [\[paper\]](https://arxiv.org/abs/1912.04488)[\[github\]](https://github.com/WXinlong/SOLO)
+- SOLOv2: Dynamic, Faster and Stronger. [\[paper\]](https://arxiv.org/abs/2003.10152)[\[github\]](https://github.com/WXinlong/SOLO)
+- Dense Peppoints: Representing Visual Objects with Dense Point Sets. [\[paper\]](https://arxiv.org/abs/1912.11473)[\[github\]](https://github.com/justimyhxu/Dense-RepPoints)
+- IterDet: Iterative Scheme for Object Detection in Crowded Environments. [\[paper\]](https://arxiv.org/abs/2005.05708)[\[github\]](https://github.com/saic-vul/iterdet)
+- Cross-Iteration Batch Normalization. [\[paper\]](https://arxiv.org/abs/2002.05712)[\[github\]](https://github.com/Howal/Cross-iterationBatchNorm)
+- A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection, NeurIPS2020 [\[paper\]](https://arxiv.org/abs/2009.13592)[\[github\]](https://github.com/kemaloksuz/aLRPLoss)
diff --git a/docs/zh_cn/robustness_benchmarking.md b/docs/zh_cn/robustness_benchmarking.md
new file mode 100644
index 0000000..28a6759
--- /dev/null
+++ b/docs/zh_cn/robustness_benchmarking.md
@@ -0,0 +1,109 @@
+# 检测器鲁棒性检查
+
+## 介绍
+
+我们提供了在 [Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming](https://arxiv.org/abs/1907.07484) 中定义的「图像损坏基准测试」上测试目标检测和实例分割模型的工具。
+此页面提供了如何使用该基准测试的基本教程。
+
+```latex
+@article{michaelis2019winter,
+  title={Benchmarking Robustness in Object Detection:
+    Autonomous Driving when Winter is Coming},
+  author={Michaelis, Claudio and Mitzkus, Benjamin and
+    Geirhos, Robert and Rusak, Evgenia and
+    Bringmann, Oliver and Ecker, Alexander S. and
+    Bethge, Matthias and Brendel, Wieland},
+  journal={arXiv:1907.07484},
+  year={2019}
+}
+```
+
+![image corruption example](../resources/corruptions_sev_3.png)
+
+## 关于基准测试
+
+要将结果提交到基准测试，请访问[基准测试主页](https://github.com/bethgelab/robust-detection-benchmark)
+
+基准测试是仿照 [imagenet-c 基准测试](https://github.com/hendrycks/robustness)，由 Dan Hendrycks 和 Thomas Dietterich 在[Benchmarking Neural Network Robustness to Common Corruptions and Perturbations](https://arxiv.org/abs/1903.12261)(ICLR 2019)中发表。
+
+图像损坏变换功能包含在此库中，但可以使用以下方法单独安装：
+
+```shell
+pip install imagecorruptions
+```
+
+与 imagenet-c 相比，我们必须进行一些更改以处理任意大小的图像和灰度图像。
+我们还修改了“运动模糊”和“雪”损坏，以解除对于 linux 特定库的依赖，
+否则必须单独安装这些库。有关详细信息，请参阅 [imagecorruptions](https://github.com/bethgelab/imagecorruptions)。
+
+## 使用预训练模型进行推理
+
+我们提供了一个测试脚本来评估模型在基准测试中提供的各种损坏变换组合下的性能。
+
+### 在数据集上测试
+
+- [x] 单张 GPU 测试
+- [ ] 多张 GPU 测试
+- [ ] 可视化检测结果
+
+您可以使用以下命令在基准测试中使用 15 种损坏变换来测试模型性能。
+
+```shell
+# single-gpu testing
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}]
+```
+
+也可以选择其它不同类型的损坏变换。
+
+```shell
+# noise
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions noise
+
+# blur
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions blur
+
+# wetaher
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions weather
+
+# digital
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions digital
+```
+
+或者使用一组自定义的损坏变换，例如：
+
+```shell
+# gaussian noise, zoom blur and snow
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --corruptions gaussian_noise zoom_blur snow
+```
+
+最后，我们也可以选择施加在图像上的损坏变换的严重程度。
+严重程度从 1 到 5 逐级增强，0 表示不对图像施加损坏变换，即原始图像数据。
+
+```shell
+# severity 1
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 1
+
+# severities 0,2,4
+python tools/analysis_tools/test_robustness.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--eval ${EVAL_METRICS}] --severities 0 2 4
+```
+
+## 模型测试结果
+
+下表是各模型在 COCO 2017val 上的测试结果。
+
+|        Model        |      Backbone       |  Style  | Lr schd | box AP clean | box AP corr. | box % | mask AP clean | mask AP corr. | mask % |
+| :-----------------: | :-----------------: | :-----: | :-----: | :----------: | :----------: | :---: | :-----------: | :-----------: | :----: |
+|    Faster R-CNN     |      R-50-FPN       | pytorch |   1x    |     36.3     |     18.2     | 50.2  |       -       |       -       |   -    |
+|    Faster R-CNN     |      R-101-FPN      | pytorch |   1x    |     38.5     |     20.9     | 54.2  |       -       |       -       |   -    |
+|    Faster R-CNN     |   X-101-32x4d-FPN   | pytorch |   1x    |     40.1     |     22.3     | 55.5  |       -       |       -       |   -    |
+|    Faster R-CNN     |   X-101-64x4d-FPN   | pytorch |   1x    |     41.3     |     23.4     | 56.6  |       -       |       -       |   -    |
+|    Faster R-CNN     |    R-50-FPN-DCN     | pytorch |   1x    |     40.0     |     22.4     | 56.1  |       -       |       -       |   -    |
+|    Faster R-CNN     | X-101-32x4d-FPN-DCN | pytorch |   1x    |     43.4     |     26.7     | 61.6  |       -       |       -       |   -    |
+|     Mask R-CNN      |      R-50-FPN       | pytorch |   1x    |     37.3     |     18.7     | 50.1  |     34.2      |     16.8      |  49.1  |
+|     Mask R-CNN      |    R-50-FPN-DCN     | pytorch |   1x    |     41.1     |     23.3     | 56.7  |     37.2      |     20.7      |  55.7  |
+|    Cascade R-CNN    |      R-50-FPN       | pytorch |   1x    |     40.4     |     20.1     | 49.7  |       -       |       -       |   -    |
+| Cascade Mask R-CNN  |      R-50-FPN       | pytorch |   1x    |     41.2     |     20.7     | 50.2  |     35.7      |     17.6      |  49.3  |
+|      RetinaNet      |      R-50-FPN       | pytorch |   1x    |     35.6     |     17.8     | 50.1  |       -       |       -       |   -    |
+| Hybrid Task Cascade | X-101-64x4d-FPN-DCN | pytorch |   1x    |     50.6     |     32.7     | 64.7  |     43.8      |     28.1      |  64.0  |
+
+由于对图像的损坏变换存在随机性，测试结果可能略有不同。
diff --git a/docs/zh_cn/stat.py b/docs/zh_cn/stat.py
new file mode 100755
index 0000000..9625c62
--- /dev/null
+++ b/docs/zh_cn/stat.py
@@ -0,0 +1,64 @@
+#!/usr/bin/env python
+import functools as func
+import glob
+import os.path as osp
+import re
+
+import numpy as np
+
+url_prefix = 'https://github.com/open-mmlab/mmdetection/blob/master/'
+
+files = sorted(glob.glob('../configs/*/README.md'))
+
+stats = []
+titles = []
+num_ckpts = 0
+
+for f in files:
+    url = osp.dirname(f.replace('../', url_prefix))
+
+    with open(f, 'r') as content_file:
+        content = content_file.read()
+
+    title = content.split('\n')[0].replace('# ', '').strip()
+    ckpts = set(x.lower().strip()
+                for x in re.findall(r'\[model\]\((https?.*)\)', content))
+
+    if len(ckpts) == 0:
+        continue
+
+    _papertype = [x for x in re.findall(r'\[([A-Z]+)\]', content)]
+    assert len(_papertype) > 0
+    papertype = _papertype[0]
+
+    paper = set([(papertype, title)])
+
+    titles.append(title)
+    num_ckpts += len(ckpts)
+
+    statsmsg = f"""
+\t* [{papertype}] [{title}]({url}) ({len(ckpts)} ckpts)
+"""
+    stats.append((paper, ckpts, statsmsg))
+
+allpapers = func.reduce(lambda a, b: a.union(b), [p for p, _, _ in stats])
+msglist = '\n'.join(x for _, _, x in stats)
+
+papertypes, papercounts = np.unique([t for t, _ in allpapers],
+                                    return_counts=True)
+countstr = '\n'.join(
+    [f'   - {t}: {c}' for t, c in zip(papertypes, papercounts)])
+
+modelzoo = f"""
+# Model Zoo Statistics
+
+* Number of papers: {len(set(titles))}
+{countstr}
+
+* Number of checkpoints: {num_ckpts}
+
+{msglist}
+"""
+
+with open('modelzoo_statistics.md', 'w') as f:
+    f.write(modelzoo)
diff --git a/docs/zh_cn/switch_language.md b/docs/zh_cn/switch_language.md
new file mode 100644
index 0000000..b2c4ad9
--- /dev/null
+++ b/docs/zh_cn/switch_language.md
@@ -0,0 +1,3 @@
+## <a href='https://mmdetection.readthedocs.io/en/latest/'>English</a>
+
+## <a href='https://mmdetection.readthedocs.io/zh_CN/latest/'>简体中文</a>
diff --git a/docs/zh_cn/tutorials/config.md b/docs/zh_cn/tutorials/config.md
new file mode 100644
index 0000000..34ef58b
--- /dev/null
+++ b/docs/zh_cn/tutorials/config.md
@@ -0,0 +1,528 @@
+# 教程 1: 学习配置文件
+
+我们在配置文件中支持了继承和模块化，这便于进行各种实验。如果需要检查配置文件，可以通过运行 `python tools/misc/print_config.py /PATH/TO/CONFIG` 来查看完整的配置。
+
+## 通过脚本参数修改配置
+
+当运行 `tools/train.py` 和 `tools/test.py` 时，可以通过 `--cfg-options` 来修改配置文件。
+
+- 更新字典链中的配置
+
+  可以按照原始配置文件中的 dict 键顺序地指定配置预选项。例如，使用 `--cfg-options model.backbone.norm_eval=False` 将模型主干网络中的所有 BN 模块都改为 `train` 模式。
+
+- 更新配置列表中的键
+
+  在配置文件里，一些字典型的配置被包含在列表中。例如，数据训练流程 `data.train.pipeline` 通常是一个列表，比如 `[dict(type='LoadImageFromFile'), ...]`。如果需要将 `'LoadImageFromFile'` 改成 `'LoadImageFromWebcam'`，需要写成下述形式： `--cfg-options data.train.pipeline.0.type=LoadImageFromWebcam`。
+
+- 更新列表或元组的值
+
+  如果要更新的值是列表或元组。例如，配置文件通常设置 `workflow=[('train', 1)]`，如果需要改变这个键，可以通过 `--cfg-options workflow="[(train,1),(val,1)]"` 来重新设置。需要注意，引号 " 是支持列表或元组数据类型所必需的，并且在指定值的引号内**不允许**有空格。
+
+## 配置文件结构
+
+在 `config/_base_` 文件夹下有 4 个基本组件类型，分别是：数据集(dataset)，模型(model)，训练策略(schedule)和运行时的默认设置(default runtime)。许多方法，例如 Faster R-CNN、Mask R-CNN、Cascade R-CNN、RPN、SSD 能够很容易地构建出来。由 `_base_` 下的组件组成的配置，被我们称为 _原始配置(primitive)_。
+
+对于同一文件夹下的所有配置，推荐**只有一个**对应的**原始配置**文件。所有其他的配置文件都应该继承自这个**原始配置**文件。这样就能保证配置文件的最大继承深度为 3。
+
+为了便于理解，我们建议贡献者继承现有方法。例如，如果在 Faster R-CNN 的基础上做了一些修改，用户首先可以通过指定 `_base_ = ../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py` 来继承基础的 Faster R-CNN 结构，然后修改配置文件中的必要参数以完成继承。
+
+如果你在构建一个与任何现有方法不共享结构的全新方法，那么可以在 `configs` 文件夹下创建一个新的例如 `xxx_rcnn` 文件夹。更多细节请参考 [MMCV](https://mmcv.readthedocs.io/en/latest/understand_mmcv/config.html) 文档。
+
+## 配置文件名称风格
+
+我们遵循以下样式来命名配置文件。建议贡献者遵循相同的风格。
+
+```
+{model}_[model setting]_{backbone}_{neck}_[norm setting]_[misc]_[gpu x batch_per_gpu]_{schedule}_{dataset}
+```
+
+`{xxx}` 是被要求的文件 `[yyy]` 是可选的。
+
+- `{model}`： 模型种类，例如 `faster_rcnn`, `mask_rcnn` 等。
+- `[model setting]`： 特定的模型，例如 `htc` 中的`without_semantic`， `reppoints` 中的 `moment` 等。
+- `{backbone}`： 主干网络种类例如 `r50` (ResNet-50), `x101` (ResNeXt-101) 等。
+- `{neck}`：  Neck 模型的种类包括 `fpn`, `pafpn`, `nasfpn`, `c4 ` 等。
+- `[norm_setting]`： 默认使用 `bn` (Batch Normalization)，其他指定可以有 `gn` (Group Normalization)， `syncbn` (Synchronized Batch Normalization) 等。
+  `gn-head`/`gn-neck` 表示 GN 仅应用于网络的 Head 或 Neck， `gn-all` 表示 GN 用于整个模型， 例如主干网络、Neck 和 Head。
+- `[misc]`： 模型中各式各样的设置/插件，例如 `dconv`、 `gcb`、 `attention`、`albu`、 `mstrain` 等。
+- `[gpu x batch_per_gpu]`：GPU 数量和每个 GPU 的样本数，默认使用 `8x2`。
+- `{schedule}`： 训练方案，选项是 `1x`、 `2x`、 `20e` 等。`1x`  和 `2x` 分别代表 12 epoch 和 24 epoch，`20e` 在级联模型中使用，表示 20 epoch。对于 `1x`/`2x`，初始学习率在第 8/16 和第 11/22 epoch 衰减 10 倍；对于 `20e` ，初始学习率在第 16 和第 19 epoch 衰减 10 倍。
+- `{dataset}`：数据集，例如 `coco`、 `cityscapes`、 `voc_0712`、 `wider_face` 等。
+
+## 弃用的 train_cfg/test_cfg
+
+`train_cfg` 和 `test_cfg` 在配置文件中已弃用，请在模型配置中指定它们。原始配置结构如下：
+
+```python
+# 已经弃用的形式
+model = dict(
+    type=...,
+    ...
+)
+train_cfg=dict(...)
+test_cfg=dict(...)
+```
+
+推荐的配置结构如下：
+
+```python
+# 推荐的形式
+model = dict(
+    type=...,
+    ...
+train_cfg=dict(...),
+          test_cfg=dict(...),
+)
+```
+
+## Mask R-CNN 配置文件示例
+
+为了帮助用户对 MMDetection 检测系统中的完整配置和模块有一个基本的了解，我们对使用 ResNet50 和 FPN 的 Mask R-CNN 的配置文件进行简要注释说明。更详细的用法和各个模块对应的替代方案，请参考 API 文档。
+
+```python
+model = dict(
+    type='MaskRCNN',  # 检测器(detector)名称
+    backbone=dict(  # 主干网络的配置文件
+        type='ResNet',  # 主干网络的类别，可用选项请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/backbones/resnet.py#L308
+        depth=50,  # 主干网络的深度，对于 ResNet 和 ResNext 通常设置为 50 或 101。
+        num_stages=4,  # 主干网络状态(stages)的数目，这些状态产生的特征图作为后续的 head 的输入。
+        out_indices=(0, 1, 2, 3),  # 每个状态产生的特征图输出的索引。
+        frozen_stages=1,  # 第一个状态的权重被冻结
+        norm_cfg=dict(  # 归一化层(norm layer)的配置项。
+            type='BN',  # 归一化层的类别，通常是 BN 或 GN。
+            requires_grad=True),  # 是否训练归一化里的 gamma 和 beta。
+        norm_eval=True,  # 是否冻结 BN 里的统计项。
+        style='pytorch',  # 主干网络的风格，'pytorch' 意思是步长为2的层为 3x3 卷积， 'caffe' 意思是步长为2的层为 1x1 卷积。
+        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),  # 加载通过 ImageNet 预训练的模型
+    neck=dict(
+        type='FPN',  # 检测器的 neck 是 FPN，我们同样支持 'NASFPN', 'PAFPN' 等，更多细节可以参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/necks/fpn.py#L10。
+        in_channels=[256, 512, 1024, 2048],  # 输入通道数，这与主干网络的输出通道一致
+        out_channels=256,  # 金字塔特征图每一层的输出通道
+        num_outs=5),  # 输出的范围(scales)
+    rpn_head=dict(
+        type='RPNHead',  # RPN_head 的类型是 'RPNHead', 我们也支持 'GARPNHead' 等，更多细节可以参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/rpn_head.py#L12。
+        in_channels=256,  # 每个输入特征图的输入通道，这与 neck 的输出通道一致。
+        feat_channels=256,  # head 卷积层的特征通道。
+        anchor_generator=dict(  # 锚点(Anchor)生成器的配置。
+            type='AnchorGenerator',  # 大多是方法使用 AnchorGenerator 作为锚点生成器, SSD 检测器使用 `SSDAnchorGenerator`。更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/anchor/anchor_generator.py#L10。
+            scales=[8],  # 锚点的基本比例，特征图某一位置的锚点面积为 scale * base_sizes
+            ratios=[0.5, 1.0, 2.0],  # 高度和宽度之间的比率。
+            strides=[4, 8, 16, 32, 64]),  # 锚生成器的步幅。这与 FPN 特征步幅一致。 如果未设置 base_sizes，则当前步幅值将被视为 base_sizes。
+        bbox_coder=dict(  # 在训练和测试期间对框进行编码和解码。
+            type='DeltaXYWHBBoxCoder',  # 框编码器的类别，'DeltaXYWHBBoxCoder' 是最常用的，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py#L9。
+            target_means=[0.0, 0.0, 0.0, 0.0],  # 用于编码和解码框的目标均值
+            target_stds=[1.0, 1.0, 1.0, 1.0]),  # 用于编码和解码框的标准差
+        loss_cls=dict(  # 分类分支的损失函数配置
+            type='CrossEntropyLoss',  # 分类分支的损失类型，我们也支持 FocalLoss 等。
+            use_sigmoid=True,  # RPN通常进行二分类，所以通常使用sigmoid函数。
+            los_weight=1.0),  # 分类分支的损失权重。
+        loss_bbox=dict(  # 回归分支的损失函数配置。
+            type='L1Loss',  # 损失类型，我们还支持许多 IoU Losses 和 Smooth L1-loss 等，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/smooth_l1_loss.py#L56。
+            loss_weight=1.0)),  # 回归分支的损失权重。
+    roi_head=dict(  # RoIHead 封装了两步(two-stage)/级联(cascade)检测器的第二步。
+        type='StandardRoIHead',  # RoI head 的类型，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/standard_roi_head.py#L10。
+        bbox_roi_extractor=dict(  # 用于 bbox 回归的 RoI 特征提取器。
+            type='SingleRoIExtractor',  # RoI 特征提取器的类型，大多数方法使用  SingleRoIExtractor，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/roi_extractors/single_level.py#L10。
+            roi_layer=dict(  # RoI 层的配置
+                type='RoIAlign',  # RoI 层的类别, 也支持 DeformRoIPoolingPack 和 ModulatedDeformRoIPoolingPack，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/ops/roi_align/roi_align.py#L79。
+                output_size=7,  # 特征图的输出大小。
+                sampling_ratio=0),  # 提取 RoI 特征时的采样率。0 表示自适应比率。
+            out_channels=256,  # 提取特征的输出通道。
+            featmap_strides=[4, 8, 16, 32]),  # 多尺度特征图的步幅，应该与主干的架构保持一致。
+        bbox_head=dict(  # RoIHead 中 box head 的配置.
+            type='Shared2FCBBoxHead',  # bbox head 的类别，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py#L177。
+            in_channels=256,  # bbox head 的输入通道。 这与 roi_extractor 中的 out_channels 一致。
+            fc_out_channels=1024,  # FC 层的输出特征通道。
+            roi_feat_size=7,  # 候选区域(Region of Interest)特征的大小。
+            num_classes=80,  # 分类的类别数量。
+            bbox_coder=dict(  # 第二阶段使用的框编码器。
+                type='DeltaXYWHBBoxCoder',  # 框编码器的类别，大多数情况使用 'DeltaXYWHBBoxCoder'。
+                target_means=[0.0, 0.0, 0.0, 0.0],  # 用于编码和解码框的均值
+                target_stds=[0.1, 0.1, 0.2, 0.2]),  # 编码和解码的标准差。因为框更准确，所以值更小，常规设置时 [0.1, 0.1, 0.2, 0.2]。
+            reg_class_agnostic=False,  # 回归是否与类别无关。
+            loss_cls=dict(  # 分类分支的损失函数配置
+                type='CrossEntropyLoss',  # 分类分支的损失类型，我们也支持 FocalLoss 等。
+                use_sigmoid=False,  # 是否使用 sigmoid。
+                loss_weight=1.0),  # 分类分支的损失权重。
+            loss_bbox=dict(  # 回归分支的损失函数配置。
+                type='L1Loss',  # 损失类型，我们还支持许多 IoU Losses 和 Smooth L1-loss 等。
+                loss_weight=1.0)),  # 回归分支的损失权重。
+        mask_roi_extractor=dict(  # 用于 mask 生成的 RoI 特征提取器。
+            type='SingleRoIExtractor',  # RoI 特征提取器的类型，大多数方法使用 SingleRoIExtractor。
+            roi_layer=dict(  # 提取实例分割特征的 RoI 层配置
+                type='RoIAlign',  # RoI 层的类型，也支持 DeformRoIPoolingPack 和 ModulatedDeformRoIPoolingPack。
+                output_size=14,  # 特征图的输出大小。
+                sampling_ratio=0),  # 提取 RoI 特征时的采样率。
+            out_channels=256,  # 提取特征的输出通道。
+            featmap_strides=[4, 8, 16, 32]),  # 多尺度特征图的步幅。
+        mask_head=dict(  # mask 预测 head 模型
+            type='FCNMaskHead',  # mask head 的类型，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py#L21。
+            num_convs=4,  # mask head 中的卷积层数
+            in_channels=256,  # 输入通道，应与 mask roi extractor 的输出通道一致。
+            conv_out_channels=256,  # 卷积层的输出通道。
+            num_classes=80,  # 要分割的类别数。
+            loss_mask=dict(  # mask 分支的损失函数配置。
+                type='CrossEntropyLoss',  # 用于分割的损失类型。
+                use_mask=True,  # 是否只在正确的类中训练 mask。
+                loss_weight=1.0))),  # mask 分支的损失权重.
+    train_cfg = dict(  # rpn 和 rcnn 训练超参数的配置
+        rpn=dict(  # rpn 的训练配置
+            assigner=dict(  # 分配器(assigner)的配置
+                type='MaxIoUAssigner',  # 分配器的类型，MaxIoUAssigner 用于许多常见的检测器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10。
+                pos_iou_thr=0.7,  # IoU >= 0.7(阈值) 被视为正样本。
+                neg_iou_thr=0.3,  # IoU < 0.3(阈值) 被视为负样本。
+                min_pos_iou=0.3,  # 将框作为正样本的最小 IoU 阈值。
+                match_low_quality=True,  # 是否匹配低质量的框(更多细节见 API 文档).
+                ignore_iof_thr=-1),  # 忽略 bbox 的 IoF 阈值。
+            sampler=dict(  # 正/负采样器(sampler)的配置
+                type='RandomSampler',  # 采样器类型，还支持 PseudoSampler 和其他采样器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8。
+                num=256,  # 样本数量。
+                pos_fraction=0.5,  # 正样本占总样本的比例。
+                neg_pos_ub=-1,  # 基于正样本数量的负样本上限。
+                add_gt_as_proposals=False),  # 采样后是否添加 GT 作为 proposal。
+            allowed_border=-1,  # 填充有效锚点后允许的边框。
+            pos_weight=-1,  # 训练期间正样本的权重。
+            debug=False),  # 是否设置调试(debug)模式
+        rpn_proposal=dict(  # 在训练期间生成 proposals 的配置
+            nms_across_levels=False,  # 是否对跨层的 box 做 NMS。仅适用于 `GARPNHead` ，naive rpn 不支持 nms cross levels。
+            nms_pre=2000,  # NMS 前的 box 数
+            nms_post=1000,  # NMS 要保留的 box 的数量，只在 GARPNHead 中起作用。
+            max_per_img=1000,  # NMS 后要保留的 box 数量。
+            nms=dict( # NMS 的配置
+                type='nms',  # NMS 的类别
+                iou_threshold=0.7 # NMS 的阈值
+                ),
+            min_bbox_size=0),  # 允许的最小 box 尺寸
+        rcnn=dict(  # roi head 的配置。
+            assigner=dict(  # 第二阶段分配器的配置，这与 rpn 中的不同
+                type='MaxIoUAssigner',  # 分配器的类型，MaxIoUAssigner 目前用于所有 roi_heads。更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/assigners/max_iou_assigner.py#L10。
+                pos_iou_thr=0.5,  # IoU >= 0.5(阈值)被认为是正样本。
+                neg_iou_thr=0.5,  # IoU < 0.5(阈值)被认为是负样本。
+                min_pos_iou=0.5,  # 将 box 作为正样本的最小 IoU 阈值
+                match_low_quality=False,  # 是否匹配低质量下的 box(有关更多详细信息，请参阅 API 文档)。
+                ignore_iof_thr=-1),  # 忽略 bbox 的 IoF 阈值
+            sampler=dict(
+                type='RandomSampler',  #采样器的类型，还支持 PseudoSampler 和其他采样器，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/bbox/samplers/random_sampler.py#L8。
+                num=512,  # 样本数量
+                pos_fraction=0.25,  # 正样本占总样本的比例。.
+                neg_pos_ub=-1,  # 基于正样本数量的负样本上限。.
+                add_gt_as_proposals=True
+            ),  # 采样后是否添加 GT 作为 proposal。
+            mask_size=28,  # mask 的大小
+            pos_weight=-1,  # 训练期间正样本的权重。
+            debug=False)),  # 是否设置调试模式。
+    test_cfg = dict(  # 用于测试 rpn 和 rcnn 超参数的配置
+        rpn=dict(  # 测试阶段生成 proposals 的配置
+            nms_across_levels=False,  # 是否对跨层的 box 做 NMS。仅适用于`GARPNHead`，naive rpn 不支持做 NMS cross levels。
+            nms_pre=1000,  # NMS 前的 box 数
+            nms_post=1000,  # NMS 要保留的 box 的数量，只在`GARPNHead`中起作用。
+            max_per_img=1000,  # NMS 后要保留的 box 数量
+            nms=dict( # NMS 的配置
+                type='nms',  # NMS 的类型
+                iou_threshold=0.7 # NMS 阈值
+                ),
+            min_bbox_size=0),  # box 允许的最小尺寸
+        rcnn=dict(  # roi heads 的配置
+            score_thr=0.05,  # bbox 的分数阈值
+            nms=dict(  # 第二步的 NMS 配置
+                type='nms',  # NMS 的类型
+                iou_thr=0.5),  # NMS 的阈值
+            max_per_img=100,  # 每张图像的最大检测次数
+            mask_thr_binary=0.5)))  # mask 预处的阈值
+
+dataset_type = 'CocoDataset'  # 数据集类型，这将被用来定义数据集。
+data_root = 'data/coco/'  # 数据的根路径。
+img_norm_cfg = dict(  # 图像归一化配置，用来归一化输入的图像。
+    mean=[123.675, 116.28, 103.53],  # 预训练里用于预训练主干网络模型的平均值。
+    std=[58.395, 57.12, 57.375],  # 预训练里用于预训练主干网络模型的标准差。
+    to_rgb=True
+)  #  预训练里用于预训练主干网络的图像的通道顺序。
+train_pipeline = [  # 训练流程
+    dict(type='LoadImageFromFile'),  # 第 1 个流程，从文件路径里加载图像。
+    dict(
+        type='LoadAnnotations',  # 第 2 个流程，对于当前图像，加载它的注释信息。
+        with_bbox=True,  # 是否使用标注框(bounding box)， 目标检测需要设置为 True。
+        with_mask=True,  # 是否使用 instance mask，实例分割需要设置为 True。
+        poly2mask=False),  # 是否将 polygon mask 转化为 instance mask, 设置为 False 以加速和节省内存。
+    dict(
+        type='Resize',  # 变化图像和其注释大小的数据增广的流程。
+        img_scale=(1333, 800),  # 图像的最大规模。
+        keep_ratio=True
+    ),  # 是否保持图像的长宽比。
+    dict(
+        type='RandomFlip',  #  翻转图像和其注释大小的数据增广的流程。
+        flip_ratio=0.5),  # 翻转图像的概率。
+    dict(
+        type='Normalize',  # 归一化当前图像的数据增广的流程。
+        mean=[123.675, 116.28, 103.53],  # 这些键与 img_norm_cfg 一致，因为 img_norm_cfg 被
+        std=[58.395, 57.12, 57.375],     # 用作参数。
+        to_rgb=True),
+    dict(
+        type='Pad',  # 填充当前图像到指定大小的数据增广的流程。
+        size_divisor=32),  # 填充图像可以被当前值整除。
+    dict(type='DefaultFormatBundle'),  # 流程里收集数据的默认格式捆。
+    dict(
+        type='Collect',  # 决定数据中哪些键应该传递给检测器的流程
+        keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),  # 第 1 个流程，从文件路径里加载图像。
+    dict(
+        type='MultiScaleFlipAug',  # 封装测试时数据增广(test time augmentations)。
+        img_scale=(1333, 800),  # 决定测试时可改变图像的最大规模。用于改变图像大小的流程。
+        flip=False,  # 测试时是否翻转图像。
+        transforms=[
+            dict(type='Resize',  # 使用改变图像大小的数据增广。
+                 keep_ratio=True),  # 是否保持宽和高的比例，这里的图像比例设置将覆盖上面的图像规模大小的设置。
+            dict(type='RandomFlip'),  # 考虑到 RandomFlip 已经被添加到流程里，当 flip=False 时它将不被使用。
+            dict(
+                type='Normalize',  #  归一化配置项，值来自 img_norm_cfg。
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(
+                type='Pad',  # 将配置传递给可被 32 整除的图像。
+                size_divisor=32),
+            dict(
+                type='ImageToTensor',  # 将图像转为张量
+                keys=['img']),
+            dict(
+                type='Collect',  # 收集测试时必须的键的收集流程。
+                keys=['img'])
+        ])
+]
+data = dict(
+    samples_per_gpu=2,  # 单个 GPU 的 Batch size
+    workers_per_gpu=2,  # 单个 GPU 分配的数据加载线程数
+    train=dict(  # 训练数据集配置
+        type='CocoDataset',  # 数据集的类别, 更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/coco.py#L19。
+        ann_file='data/coco/annotations/instances_train2017.json',  # 注释文件路径
+        img_prefix='data/coco/train2017/',  # 图片路径前缀
+        pipeline=[  # 流程, 这是由之前创建的 train_pipeline 传递的。
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='LoadAnnotations',
+                with_bbox=True,
+                with_mask=True,
+                poly2mask=False),
+            dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+            dict(type='RandomFlip', flip_ratio=0.5),
+            dict(
+                type='Normalize',
+                mean=[123.675, 116.28, 103.53],
+                std=[58.395, 57.12, 57.375],
+                to_rgb=True),
+            dict(type='Pad', size_divisor=32),
+            dict(type='DefaultFormatBundle'),
+            dict(
+                type='Collect',
+                keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks'])
+        ]),
+    val=dict(  # 验证数据集的配置
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[  # 由之前创建的 test_pipeline 传递的流程。
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ]),
+    test=dict(  # 测试数据集配置，修改测试开发/测试(test-dev/test)提交的 ann_file
+        type='CocoDataset',
+        ann_file='data/coco/annotations/instances_val2017.json',
+        img_prefix='data/coco/val2017/',
+        pipeline=[  # 由之前创建的 test_pipeline 传递的流程。
+            dict(type='LoadImageFromFile'),
+            dict(
+                type='MultiScaleFlipAug',
+                img_scale=(1333, 800),
+                flip=False,
+                transforms=[
+                    dict(type='Resize', keep_ratio=True),
+                    dict(type='RandomFlip'),
+                    dict(
+                        type='Normalize',
+                        mean=[123.675, 116.28, 103.53],
+                        std=[58.395, 57.12, 57.375],
+                        to_rgb=True),
+                    dict(type='Pad', size_divisor=32),
+                    dict(type='ImageToTensor', keys=['img']),
+                    dict(type='Collect', keys=['img'])
+                ])
+        ],
+        samples_per_gpu=2  # 单个 GPU 测试时的 Batch size
+    ))
+evaluation = dict(  # evaluation hook 的配置，更多细节请参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/evaluation/eval_hooks.py#L7。
+    interval=1,  # 验证的间隔。
+    metric=['bbox', 'segm'])  # 验证期间使用的指标。
+optimizer = dict(  # 用于构建优化器的配置文件。支持 PyTorch 中的所有优化器，同时它们的参数与 PyTorch 里的优化器参数一致。
+    type='SGD',  # 优化器种类，更多细节可参考 https://github.com/open-mmlab/mmdetection/blob/master/mmdet/core/optimizer/default_constructor.py#L13。
+    lr=0.02,  # 优化器的学习率，参数的使用细节请参照对应的 PyTorch 文档。
+    momentum=0.9,  # 动量(Momentum)
+    weight_decay=0.0001)  # SGD 的衰减权重(weight decay)。
+optimizer_config = dict(  # optimizer hook 的配置文件，执行细节请参考 https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/optimizer.py#L8。
+    grad_clip=None)  # 大多数方法不使用梯度限制(grad_clip)。
+lr_config = dict(  # 学习率调整配置，用于注册 LrUpdater hook。
+    policy='step',  # 调度流程(scheduler)的策略，也支持 CosineAnnealing, Cyclic, 等。请从 https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/lr_updater.py#L9 参考 LrUpdater 的细节。
+    warmup='linear',  # 预热(warmup)策略，也支持 `exp` 和 `constant`。
+    warmup_iters=500,  # 预热的迭代次数
+    warmup_ratio=
+    0.001,  # 用于热身的起始学习率的比率
+    step=[8, 11])  # 衰减学习率的起止回合数
+runner = dict(
+    type='EpochBasedRunner',  # 将使用的 runner 的类别 (例如 IterBasedRunner 或 EpochBasedRunner)。
+    max_epochs=12) # runner 总回合数， 对于 IterBasedRunner 使用 `max_iters`
+checkpoint_config = dict(  # Checkpoint hook 的配置文件。执行时请参考 https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/hooks/checkpoint.py。
+    interval=1)  # 保存的间隔是 1。
+log_config = dict(  # register logger hook 的配置文件。
+    interval=50,  # 打印日志的间隔
+    hooks=[ # 训练期间执行的钩子
+        dict(type='TextLoggerHook', by_epoch=False),
+        dict(type='TensorboardLoggerHook', by_epoch=False),
+        dict(type='MMDetWandbHook', by_epoch=False, # 还支持 Wandb 记录器，它需要安装 `wandb`。
+             init_kwargs={'entity': "OpenMMLab", # 用于登录wandb的实体
+                          'project': "MMDet", # WandB中的项目名称
+                          'config': cfg_dict}), # 检查 https://docs.wandb.ai/ref/python/init 以获取更多初始化参数
+    ])  # 用于记录训练过程的记录器(logger)。
+
+dist_params = dict(backend='nccl')  # 用于设置分布式训练的参数，端口也同样可被设置。
+log_level = 'INFO'  # 日志的级别。
+load_from = None  # 从一个给定路径里加载模型作为预训练模型，它并不会消耗训练时间。
+resume_from = None  # 从给定路径里恢复检查点(checkpoints)，训练模式将从检查点保存的轮次开始恢复训练。
+workflow = [('train', 1)]  # runner 的工作流程，[('train', 1)] 表示只有一个工作流且工作流仅执行一次。根据 total_epochs 工作流训练 12个回合。
+work_dir = 'work_dir'  # 用于保存当前实验的模型检查点和日志的目录。
+```
+
+## 常问问题 (FAQ)
+
+### 忽略基础配置文件里的部分内容
+
+有时，您也许会设置 `_delete_=True` 去忽略基础配置文件里的一些域内容。 您也许可以参照 [mmcv](https://mmcv.readthedocs.io/en/latest/understand_mmcv/config.html#inherit-from-base-config-with-ignored-fields) 来获得一些简单的指导。
+
+在 MMDetection里，例如为了改变  Mask R-CNN 的主干网络的某些内容：
+
+```python
+model = dict(
+    type='MaskRCNN',
+    pretrained='torchvision://resnet50',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch'),
+    neck=dict(...),
+    rpn_head=dict(...),
+    roi_head=dict(...))
+```
+
+基础配置的 `Mask R-CNN` 使用 `ResNet-50`，在需要将主干网络改成 `HRNet` 的时候，因为 `HRNet` 和 `ResNet` 中有不同的字段，需要使用 `_delete_=True` 将新的键去替换 `backbone` 域内所有老的键。
+
+```python
+_base_ = '../mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    pretrained='open-mmlab://msra/hrnetv2_w32',
+    backbone=dict(
+        _delete_=True,
+        type='HRNet',
+        extra=dict(
+            stage1=dict(
+                num_modules=1,
+                num_branches=1,
+                block='BOTTLENECK',
+                num_blocks=(4, ),
+                num_channels=(64, )),
+            stage2=dict(
+                num_modules=1,
+                num_branches=2,
+                block='BASIC',
+                num_blocks=(4, 4),
+                num_channels=(32, 64)),
+            stage3=dict(
+                num_modules=4,
+                num_branches=3,
+                block='BASIC',
+                num_blocks=(4, 4, 4),
+                num_channels=(32, 64, 128)),
+            stage4=dict(
+                num_modules=3,
+                num_branches=4,
+                block='BASIC',
+                num_blocks=(4, 4, 4, 4),
+                num_channels=(32, 64, 128, 256)))),
+    neck=dict(...))
+```
+
+### 使用配置文件里的中间变量
+
+配置文件里会使用一些中间变量，例如数据集里的 `train_pipeline`/`test_pipeline`。我们在定义新的 `train_pipeline`/`test_pipeline` 之后，需要将它们传递到 `data` 里。例如，我们想在训练或测试时，改变 Mask R-CNN 的多尺度策略 (multi scale strategy)，`train_pipeline`/`test_pipeline` 是我们想要修改的中间变量。
+
+```python
+_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1333, 640), (1333, 672), (1333, 704), (1333, 736),
+                   (1333, 768), (1333, 800)],
+        multiscale_mode="value",
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+data = dict(
+    train=dict(pipeline=train_pipeline),
+    val=dict(pipeline=test_pipeline),
+    test=dict(pipeline=test_pipeline))
+```
+
+我们首先定义新的 `train_pipeline`/`test_pipeline` 然后传递到 `data` 里。
+
+同样的，如果我们想从 `SyncBN` 切换到 `BN` 或者 `MMSyncBN`，我们需要修改配置文件里的每一个  `norm_cfg`。
+
+```python
+_base_ = './mask_rcnn_r50_fpn_1x_coco.py'
+norm_cfg = dict(type='BN', requires_grad=True)
+model = dict(
+    backbone=dict(norm_cfg=norm_cfg),
+    neck=dict(norm_cfg=norm_cfg),
+    ...)
+```
diff --git a/docs/zh_cn/tutorials/customize_dataset.md b/docs/zh_cn/tutorials/customize_dataset.md
new file mode 100644
index 0000000..8468e40
--- /dev/null
+++ b/docs/zh_cn/tutorials/customize_dataset.md
@@ -0,0 +1,456 @@
+# 教程 2: 自定义数据集
+
+## 支持新的数据格式
+
+为了支持新的数据格式，可以选择将数据转换成现成的格式（COCO 或者 PASCAL）或将其转换成中间格式。当然也可以选择以离线的形式（在训练之前使用脚本转换）或者在线的形式（实现一个新的 dataset 在训练中进行转换）来转换数据。
+
+在 MMDetection 中，建议将数据转换成 COCO 格式并以离线的方式进行，因此在完成数据转换后只需修改配置文件中的标注数据的路径和类别即可。
+
+### 将新的数据格式转换为现有的数据格式
+
+最简单的方法就是将你的数据集转换成现有的数据格式（COCO 或者 PASCAL VOC）
+
+COCO 格式的 json 标注文件有如下必要的字段：
+
+```python
+'images': [
+    {
+        'file_name': 'COCO_val2014_000000001268.jpg',
+        'height': 427,
+        'width': 640,
+        'id': 1268
+    },
+    ...
+],
+
+'annotations': [
+    {
+        'segmentation': [[192.81,
+            247.09,
+            ...
+            219.03,
+            249.06]],  # 如果有 mask 标签
+        'area': 1035.749,
+        'iscrowd': 0,
+        'image_id': 1268,
+        'bbox': [192.81, 224.8, 74.73, 33.43],
+        'category_id': 16,
+        'id': 42986
+    },
+    ...
+],
+
+'categories': [
+    {'id': 0, 'name': 'car'},
+ ]
+```
+
+在 json 文件中有三个必要的键：
+
+- `images`: 包含多个图片以及它们的信息的数组，例如 `file_name`、`height`、`width` 和 `id`。
+- `annotations`: 包含多个实例标注信息的数组。
+- `categories`: 包含多个类别名字和 ID 的数组。
+
+在数据预处理之后，使用现有的数据格式来训练自定义的新数据集有如下两步（以 COCO 为例）：
+
+1. 为自定义数据集修改配置文件。
+2. 检查自定义数据集的标注。
+
+这里我们举一个例子来展示上面的两个步骤，这个例子使用包括 5 个类别的 COCO 格式的数据集来训练一个现有的 Cascade Mask R-CNN R50-FPN 检测器
+
+#### 1. 为自定义数据集修改配置文件
+
+配置文件的修改涉及两个方面：
+
+1. `data` 部分。需要在 `data.train`、`data.val` 和 `data.test` 中添加 `classes`。
+2. `model` 部分中的 `num_classes`。需要将默认值（COCO 数据集中为 80）修改为自定义数据集中的类别数。
+
+`configs/my_custom_config.py` 内容如下：
+
+```python
+
+# 新的配置来自基础的配置以更好地说明需要修改的地方
+_base_ = './cascade_mask_rcnn_r50_fpn_1x_coco.py'
+
+# 1. 数据集设定
+dataset_type = 'CocoDataset'
+classes = ('a', 'b', 'c', 'd', 'e')
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        # 将类别名字添加至 `classes` 字段中
+        classes=classes,
+        ann_file='path/to/your/train/annotation_data',
+        img_prefix='path/to/your/train/image_data'),
+    val=dict(
+        type=dataset_type,
+        # 将类别名字添加至 `classes` 字段中
+        classes=classes,
+        ann_file='path/to/your/val/annotation_data',
+        img_prefix='path/to/your/val/image_data'),
+    test=dict(
+        type=dataset_type,
+        # 将类别名字添加至 `classes` 字段中
+        classes=classes,
+        ann_file='path/to/your/test/annotation_data',
+        img_prefix='path/to/your/test/image_data'))
+
+# 2. 模型设置
+
+# 将所有的 `num_classes` 默认值修改为5（原来为80）
+model = dict(
+    roi_head=dict(
+        bbox_head=[
+            dict(
+                type='Shared2FCBBoxHead',
+                # 将所有的 `num_classes` 默认值修改为 5（原来为 80）
+                num_classes=5),
+            dict(
+                type='Shared2FCBBoxHead',
+                # 将所有的 `num_classes` 默认值修改为 5（原来为 80）
+                num_classes=5),
+            dict(
+                type='Shared2FCBBoxHead',
+                # 将所有的 `num_classes` 默认值修改为 5（原来为 80）
+                num_classes=5)],
+    # 将所有的 `num_classes` 默认值修改为 5（原来为 80）
+    mask_head=dict(num_classes=5)))
+```
+
+#### 2. 检查自定义数据集的标注
+
+假设你自己的数据集是 COCO 格式，那么需要保证数据的标注没有问题：
+
+1. 标注文件中 `categories` 的长度要与配置中的 `classes` 元组长度相匹配，它们都表示有几类。（如例子中有 5 个类别）
+2. 配置文件中 `classes` 字段应与标注文件里 `categories` 下的 `name` 有相同的元素且顺序一致。MMDetection 会自动将 `categories` 中不连续的 `id` 映射成连续的索引，因此 `categories` 下的 `name`的字符串顺序会影响标签的索引。同时，配置文件中的 `classes` 的字符串顺序也会影响到预测框可视化时的标签。
+3. `annotations` 中的 `category_id` 必须是有效的值。比如所有 `category_id` 的值都应该属于 `categories` 中的 `id`。
+
+下面是一个有效标注的例子：
+
+```python
+
+'annotations': [
+    {
+        'segmentation': [[192.81,
+            247.09,
+            ...
+            219.03,
+            249.06]],  #如果有 mask 标签。
+        'area': 1035.749,
+        'iscrowd': 0,
+        'image_id': 1268,
+        'bbox': [192.81, 224.8, 74.73, 33.43],
+        'category_id': 16,
+        'id': 42986
+    },
+    ...
+],
+
+# MMDetection 会自动将 `categories` 中不连续的 `id` 映射成连续的索引。
+'categories': [
+    {'id': 1, 'name': 'a'}, {'id': 3, 'name': 'b'}, {'id': 4, 'name': 'c'}, {'id': 16, 'name': 'd'}, {'id': 17, 'name': 'e'},
+ ]
+```
+
+我们使用这种方式来支持 CityScapes 数据集。脚本在[cityscapes.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/dataset_converters/cityscapes.py) 并且我们提供了微调的[configs](https://github.com/open-mmlab/mmdetection/blob/master/configs/cityscapes).
+
+**注意**
+
+1. 对于实例分割数据集, **MMDetection 目前只支持评估 COCO 格式的 mask AP**.
+2. 推荐训练之前进行离线转换，这样就可以继续使用 `CocoDataset` 且只需修改标注文件的路径以及训练的种类。
+
+### 调整新的数据格式为中间格式
+
+如果不想将标注格式转换为 COCO 或者 PASCAL 格式也是可行的。实际上，我们定义了一种简单的标注格式并且与所有现有的数据格式兼容，也能进行离线或者在线转换。
+
+数据集的标注是包含多个字典（dict）的列表，每个字典（dict）都与一张图片对应。测试时需要用到 `filename`（相对路径）、`width` 和 `height` 三个字段；训练时则额外需要 `ann`。`ann` 也是至少包含了两个字段的字典：`bboxes` 和 `labels`，它们都是 numpy array。有些数据集可能会提供如：crowd/difficult/ignored bboxes 标注，那么我们使用 `bboxes_ignore` 以及 `labels_ignore` 来包含它们。
+
+下面给出一个例子。
+
+```python
+
+[
+    {
+        'filename': 'a.jpg',
+        'width': 1280,
+        'height': 720,
+        'ann': {
+            'bboxes': <np.ndarray, float32> (n, 4),
+            'labels': <np.ndarray, int64> (n, ),
+            'bboxes_ignore': <np.ndarray, float32> (k, 4),
+            'labels_ignore': <np.ndarray, int64> (k, ) （可选字段）
+        }
+    },
+    ...
+]
+```
+
+有两种方法处理自定义数据。
+
+- 在线转换（online conversion）
+
+  可以新写一个继承自 `CustomDataset` 的 Dataset 类，并重写 `load_annotations(self, ann_file)` 以及 `get_ann_info(self, idx)` 这两个方法，正如[CocoDataset](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/coco.py)与[VOCDataset](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/voc.py).
+
+- 离线转换（offline conversion）
+
+  可以将标注格式转换为上述的任意格式并将其保存为 pickle 或者 json 文件，例如[pascal_voc.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/dataset_converters/pascal_voc.py)。
+  然后使用`CustomDataset`。
+
+### 自定义数据集的例子：
+
+假设文本文件中表示的是一种全新的标注格式。边界框的标注信息保存在 `annotation.txt` 中，内容如下：
+
+```
+#
+000001.jpg
+1280 720
+2
+10 20 40 60 1
+20 40 50 60 2
+#
+000002.jpg
+1280 720
+3
+50 20 40 60 2
+20 40 30 45 2
+30 40 50 60 3
+```
+
+我们可以在 `mmdet/datasets/my_dataset.py` 中创建一个新的 dataset 用以加载数据。
+
+```python
+import mmcv
+import numpy as np
+
+from .builder import DATASETS
+from .custom import CustomDataset
+
+
+@DATASETS.register_module()
+class MyDataset(CustomDataset):
+
+    CLASSES = ('person', 'bicycle', 'car', 'motorcycle')
+
+    def load_annotations(self, ann_file):
+        ann_list = mmcv.list_from_file(ann_file)
+
+        data_infos = []
+        for i, ann_line in enumerate(ann_list):
+            if ann_line != '#':
+                continue
+
+            img_shape = ann_list[i + 2].split(' ')
+            width = int(img_shape[0])
+            height = int(img_shape[1])
+            bbox_number = int(ann_list[i + 3])
+
+            anns = ann_line.split(' ')
+            bboxes = []
+            labels = []
+            for anns in ann_list[i + 4:i + 4 + bbox_number]:
+                bboxes.append([float(ann) for ann in anns[:4]])
+                labels.append(int(anns[4]))
+
+            data_infos.append(
+                dict(
+                    filename=ann_list[i + 1],
+                    width=width,
+                    height=height,
+                    ann=dict(
+                        bboxes=np.array(bboxes).astype(np.float32),
+                        labels=np.array(labels).astype(np.int64))
+                ))
+
+        return data_infos
+
+    def get_ann_info(self, idx):
+        return self.data_infos[idx]['ann']
+
+```
+
+配置文件中，可以使用 `MyDataset` 进行如下修改
+
+```python
+dataset_A_train = dict(
+    type='MyDataset',
+    ann_file = 'image_list.txt',
+    pipeline=train_pipeline
+)
+```
+
+## 使用 dataset 包装器自定义数据集
+
+MMDetection 也支持非常多的数据集包装器（wrapper）来混合数据集或在训练时修改数据集的分布。
+最近 MMDetection 支持如下三种数据集包装：
+
+- `RepeatDataset`：将整个数据集简单地重复。
+- `ClassBalancedDataset`：以类别均衡的方式重复数据集。
+- `ConcatDataset`：合并数据集。
+
+### 重复数据集（Repeat dataset）
+
+使用 `RepeatDataset` 包装器来重复数据集。例如，假设原始数据集为 `Dataset_A`，重复它过后，其配置如下：
+
+```python
+dataset_A_train = dict(
+        type='RepeatDataset',
+        times=N,
+        dataset=dict(  # Dataset_A 的原始配置信息
+            type='Dataset_A',
+            ...
+            pipeline=train_pipeline
+        )
+    )
+```
+
+### 类别均衡数据集（Class balanced dataset）
+
+使用 `ClassBalancedDataset` 作为包装器在类别的出现的频率上重复数据集。数据集需要实例化 `self.get_cat_ids(idx)` 函数以支持 `ClassBalancedDataset`。
+比如，以 `oversample_thr=1e-3` 来重复数据集 `Dataset_A`，其配置如下：
+
+```python
+dataset_A_train = dict(
+        type='ClassBalancedDataset',
+        oversample_thr=1e-3,
+        dataset=dict(  # Dataset_A 的原始配置信息
+            type='Dataset_A',
+            ...
+            pipeline=train_pipeline
+        )
+    )
+```
+
+更多细节请参考[源码](../../mmdet/datasets/dataset_wrappers.py)。
+
+### 合并数据集（Concatenate dataset）
+
+合并数据集有三种方法：
+
+1. 如果要合并的数据集类型一致但有多个的标注文件，那么可以使用如下配置将其合并。
+
+   ```python
+   dataset_A_train = dict(
+       type='Dataset_A',
+       ann_file = ['anno_file_1', 'anno_file_2'],
+       pipeline=train_pipeline
+   )
+   ```
+
+   如果合并的数据集适用于测试或者评估，那么这种方式支持每个数据集分开进行评估。如果想要将合并的数据集作为整体用于评估，那么可以像如下一样设置 `separate_eval=False`。
+
+   ```python
+   dataset_A_train = dict(
+       type='Dataset_A',
+       ann_file = ['anno_file_1', 'anno_file_2'],
+       separate_eval=False,
+       pipeline=train_pipeline
+   )
+   ```
+
+2. 如果想要合并的是不同数据集，那么可以使用如下配置。
+
+   ```python
+   dataset_A_val = dict()
+   dataset_B_val = dict()
+
+   data = dict(
+       imgs_per_gpu=2,
+       workers_per_gpu=2,
+       train=dataset_A_train,
+       val=dict(
+           type='ConcatDataset',
+           datasets=[dataset_A_val, dataset_B_val],
+           separate_eval=False))
+   ```
+
+   只需设置 `separate_eval=False`，用户就可以将所有的数据集作为一个整体来评估。
+
+**注意**
+
+1. 在做评估时，`separate_eval=False` 选项是假设数据集使用了 `self.data_infos`。因此COCO数据集不支持此项操作，因为COCO数据集在做评估时并不是所有都依赖 `self.data_infos`。组合不同类型的数据集并将其作为一个整体来评估，这种做法没有得到测试，也不建议这样做。
+
+2. 因为不支持评估 `ClassBalancedDataset` 和 `RepeatDataset`，所以也不支持评估它们的组合。
+
+一个更复杂的例子则是分别将 `Dataset_A` 和 `Dataset_B` 重复N和M次，然后进行如下合并。
+
+```python
+dataset_A_train = dict(
+    type='RepeatDataset',
+    times=N,
+    dataset=dict(
+        type='Dataset_A',
+        ...
+        pipeline=train_pipeline
+    )
+)
+dataset_A_val = dict(
+    ...
+    pipeline=test_pipeline
+)
+dataset_A_test = dict(
+    ...
+    pipeline=test_pipeline
+)
+dataset_B_train = dict(
+    type='RepeatDataset',
+    times=M,
+    dataset=dict(
+        type='Dataset_B',
+        ...
+        pipeline=train_pipeline
+    )
+)
+data = dict(
+    imgs_per_gpu=2,
+    workers_per_gpu=2,
+    train = [
+        dataset_A_train,
+        dataset_B_train
+    ],
+    val = dataset_A_val,
+    test = dataset_A_test
+)
+
+```
+
+## 修改数据集的类别
+
+根据现有数据集的类型，我们可以修改它们的类别名称来训练其标注的子集。
+例如，如果只想训练当前数据集中的三个类别，那么就可以修改数据集的类别元组。
+数据集就会自动屏蔽掉其他类别的真实框。
+
+```python
+classes = ('person', 'bicycle', 'car')
+data = dict(
+    train=dict(classes=classes),
+    val=dict(classes=classes),
+    test=dict(classes=classes))
+```
+
+MMDetection V2.0 也支持从文件中读取类别名称，这种方式在实际应用中很常见。
+假设存在文件 `classes.txt`，其包含了如下的类别名称。
+
+```
+person
+bicycle
+car
+```
+
+用户可以将类别设置成文件路径，数据集就会自动将其加载并转换成一个列表。
+
+```python
+classes = 'path/to/classes.txt'
+data = dict(
+    train=dict(classes=classes),
+    val=dict(classes=classes),
+    test=dict(classes=classes))
+```
+
+**注意**
+
+- 在 MMDetection v2.5.0 之前，如果类别为集合时数据集将自动过滤掉不包含 GT 的图片，且没办法通过修改配置将其关闭。这是一种不可取的行为而且会引起混淆，因为当类别不是集合时数据集只有在 `filter_empty_gt=True` 以及 `test_mode=False` 的情况下才会过滤掉不包含 GT 的图片。在 MMDetection v2.5.0 之后，我们将图片的过滤以及类别的修改进行解耦，如，数据集只有在 `filter_empty_gt=True` 和 `test_mode=False` 的情况下才会过滤掉不包含 GT 的图片，无论类别是否为集合。设置类别只会影响用于训练的标注类别，用户可以自行决定是否过滤不包含 GT 的图片。
+- 因为中间格式只有框的标签并不包含类别的名字，所以使用 `CustomDataset` 时用户不能通过修改配置来过滤不含 GT 的图片。但是可以通过离线的方式来解决。
+- 当设置数据集中的 `classes` 时，记得修改 `num_classes`。从 v2.9.0 (PR#4508) 之后，我们实现了[NumClassCheckHook](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/datasets/utils.py)来检查类别数是否一致。
+- 我们在未来将会重构设置数据集类别以及数据集过滤的特性，使其更加地方便用户使用。
diff --git a/docs/zh_cn/tutorials/customize_losses.md b/docs/zh_cn/tutorials/customize_losses.md
new file mode 100644
index 0000000..f721e77
--- /dev/null
+++ b/docs/zh_cn/tutorials/customize_losses.md
@@ -0,0 +1,125 @@
+# 教程 6: 自定义损失函数
+
+MMDetection 为用户提供了不同的损失函数。但是默认的配置可能无法适应不同的数据和模型，所以用户可能会希望修改某一个损失函数来适应新的情况。
+
+本教程首先详细的解释计算损失的过程然后给出一些关于如何修改每一个步骤的指导。对损失的修改可以被分为微调和加权。
+
+## 一个损失的计算过程
+
+给定输入（包括预测和目标，以及权重），损失函数会把输入的张量映射到最后的损失标量。映射过程可以分为下面五个步骤：
+
+1. 设置采样方法为对正负样本进行采样。
+
+2. 通过损失核函数获取**元素**或者**样本**损失。
+
+3. 通过权重张量来给损失**逐元素**权重。
+
+4. 把损失张量归纳为一个**标量**。
+
+5. 用一个**张量**给当前损失一个权重。
+
+## 设置采样方法（步骤 1）
+
+对于一些损失函数，需要采样策略来避免正负样本之间的不平衡。
+
+例如，在RPN head中使用`CrossEntropyLoss`时，我们需要在`train_cfg`中设置`RandomSampler`
+
+```python
+train_cfg=dict(
+    rpn=dict(
+        sampler=dict(
+            type='RandomSampler',
+            num=256,
+            pos_fraction=0.5,
+            neg_pos_ub=-1,
+            add_gt_as_proposals=False))
+```
+
+对于其他一些具有正负样本平衡机制的损失，例如 Focal Loss、GHMC 和 QualityFocalLoss，不再需要进行采样。
+
+## 微调损失
+
+微调一个损失主要与步骤 2，4，5 有关，大部分的修改可以在配置文件中指定。这里我们用 [Focal Loss (FL)](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/losses/focal_loss.py) 作为例子。
+下面的代码分别是构建 FL 的方法和它的配置文件，他们是一一对应的。
+
+```python
+@LOSSES.register_module()
+class FocalLoss(nn.Module):
+
+    def __init__(self,
+                 use_sigmoid=True,
+                 gamma=2.0,
+                 alpha=0.25,
+                 reduction='mean',
+                 loss_weight=1.0):
+```
+
+```python
+loss_cls=dict(
+    type='FocalLoss',
+    use_sigmoid=True,
+    gamma=2.0,
+    alpha=0.25,
+    loss_weight=1.0)
+```
+
+### 微调超参数（步骤2）
+
+`gamma` 和 `beta` 是 Focal Loss 中的两个超参数。如果我们想把 `gamma` 的值设为 1.5，把 `alpha` 的值设为 0.5，我们可以在配置文件中按照如下指定：
+
+```python
+loss_cls=dict(
+    type='FocalLoss',
+    use_sigmoid=True,
+    gamma=1.5,
+    alpha=0.5,
+    loss_weight=1.0)
+```
+
+### 微调归纳方式（步骤4）
+
+Focal Loss 默认的归纳方式是 `mean`。如果我们想把归纳方式从 `mean` 改成 `sum`，我们可以在配置文件中按照如下指定：
+
+```python
+loss_cls=dict(
+    type='FocalLoss',
+    use_sigmoid=True,
+    gamma=2.0,
+    alpha=0.25,
+    loss_weight=1.0,
+    reduction='sum')
+```
+
+### 微调损失权重（步骤5）
+
+这里的损失权重是一个标量，他用来控制多任务学习中不同损失的重要程度，例如，分类损失和回归损失。如果我们想把分类损失的权重设为 0.5，我们可以在配置文件中如下指定：
+
+```python
+loss_cls=dict(
+    type='FocalLoss',
+    use_sigmoid=True,
+    gamma=2.0,
+    alpha=0.25,
+    loss_weight=0.5)
+```
+
+## 加权损失（步骤3）
+
+加权损失就是我们逐元素修改损失权重。更具体来说，我们给损失张量乘以一个与他有相同形状的权重张量。所以，损失中不同的元素可以被赋予不同的比例，所以这里叫做逐元素。损失的权重在不同模型中变化很大，而且与上下文相关，但是总的来说主要有两种损失权重：分类损失的 `label_weights` 和边界框的 `bbox_weights`。你可以在相应的头中的 `get_target` 方法中找到他们。这里我们使用 [ATSSHead](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/atss_head.py#L530) 作为一个例子。它继承了 [AnchorHead](https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/anchor_head.py)，但是我们重写它的
+`get_targets` 方法来产生不同的 `label_weights` 和 `bbox_weights`。
+
+```
+class ATSSHead(AnchorHead):
+
+    ...
+
+    def get_targets(self,
+                    anchor_list,
+                    valid_flag_list,
+                    gt_bboxes_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None,
+                    gt_labels_list=None,
+                    label_channels=1,
+                    unmap_outputs=True):
+```
diff --git a/docs/zh_cn/tutorials/customize_models.md b/docs/zh_cn/tutorials/customize_models.md
new file mode 100644
index 0000000..b29254a
--- /dev/null
+++ b/docs/zh_cn/tutorials/customize_models.md
@@ -0,0 +1,359 @@
+# 教程 4: 自定义模型
+
+我们简单地把模型的各个组件分为五类：
+
+- 主干网络 (backbone)：通常是一个用来提取特征图 (feature map) 的全卷积网络 (FCN network)，例如：ResNet, MobileNet。
+- Neck：主干网络和 Head 之间的连接部分，例如：FPN, PAFPN。
+- Head：用于具体任务的组件，例如：边界框预测和掩码预测。
+- 区域提取器 (roi extractor)：从特征图中提取 RoI 特征，例如：RoI Align。
+- 损失 (loss)：在 Head 组件中用于计算损失的部分，例如：FocalLoss, L1Loss, GHMLoss.
+
+## 开发新的组件
+
+### 添加一个新的主干网络
+
+这里，我们以 MobileNet 为例来展示如何开发新组件。
+
+#### 1. 定义一个新的主干网络（以 MobileNet 为例）
+
+新建一个文件 `mmdet/models/backbones/mobilenet.py`
+
+```python
+import torch.nn as nn
+
+from ..builder import BACKBONES
+
+
+@BACKBONES.register_module()
+class MobileNet(nn.Module):
+
+    def __init__(self, arg1, arg2):
+        pass
+
+    def forward(self, x):  # should return a tuple
+        pass
+```
+
+#### 2. 导入该模块
+
+你可以添加下述代码到 `mmdet/models/backbones/__init__.py`
+
+```python
+from .mobilenet import MobileNet
+```
+
+或添加：
+
+```python
+custom_imports = dict(
+    imports=['mmdet.models.backbones.mobilenet'],
+    allow_failed_imports=False)
+```
+
+到配置文件以避免原始代码被修改。
+
+#### 3. 在你的配置文件中使用该主干网络
+
+```python
+model = dict(
+    ...
+    backbone=dict(
+        type='MobileNet',
+        arg1=xxx,
+        arg2=xxx),
+    ...
+```
+
+### 添加新的 Neck
+
+#### 1. 定义一个 Neck（以 PAFPN 为例）
+
+新建一个文件 `mmdet/models/necks/pafpn.py`
+
+```python
+from ..builder import NECKS
+
+@NECKS.register_module()
+class PAFPN(nn.Module):
+
+    def __init__(self,
+                in_channels,
+                out_channels,
+                num_outs,
+                start_level=0,
+                end_level=-1,
+                add_extra_convs=False):
+        pass
+
+    def forward(self, inputs):
+        # implementation is ignored
+        pass
+```
+
+#### 2. 导入该模块
+
+你可以添加下述代码到 `mmdet/models/necks/__init__.py`
+
+```python
+from .pafpn import PAFPN
+```
+
+或添加：
+
+```python
+custom_imports = dict(
+    imports=['mmdet.models.necks.pafpn.py'],
+    allow_failed_imports=False)
+```
+
+到配置文件以避免原始代码被修改。
+
+#### 3. 修改配置文件
+
+```python
+neck=dict(
+    type='PAFPN',
+    in_channels=[256, 512, 1024, 2048],
+    out_channels=256,
+    num_outs=5)
+```
+
+### 添加新的 Head
+
+我们以 [Double Head R-CNN](https://arxiv.org/abs/1904.06493) 为例来展示如何添加一个新的 Head。
+
+首先，添加一个新的 bbox head 到 `mmdet/models/roi_heads/bbox_heads/double_bbox_head.py`。
+Double Head R-CNN 在目标检测上实现了一个新的 bbox head。为了实现 bbox head，我们需要使用如下的新模块中三个函数。
+
+```python
+from mmdet.models.builder import HEADS
+from .bbox_head import BBoxHead
+
+@HEADS.register_module()
+class DoubleConvFCBBoxHead(BBoxHead):
+    r"""Bbox head used in Double-Head R-CNN
+
+                                      /-> cls
+                  /-> shared convs ->
+                                      \-> reg
+    roi features
+                                      /-> cls
+                  \-> shared fc    ->
+                                      \-> reg
+    """  # noqa: W605
+
+    def __init__(self,
+                 num_convs=0,
+                 num_fcs=0,
+                 conv_out_channels=1024,
+                 fc_out_channels=1024,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 **kwargs):
+        kwargs.setdefault('with_avg_pool', True)
+        super(DoubleConvFCBBoxHead, self).__init__(**kwargs)
+
+
+    def forward(self, x_cls, x_reg):
+
+```
+
+然后，如有必要，实现一个新的 bbox head。我们打算从 `StandardRoIHead` 来继承新的 `DoubleHeadRoIHead`。我们可以发现 `StandardRoIHead` 已经实现了下述函数。
+
+```python
+import torch
+
+from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
+from ..builder import HEADS, build_head, build_roi_extractor
+from .base_roi_head import BaseRoIHead
+from .test_mixins import BBoxTestMixin, MaskTestMixin
+
+
+@HEADS.register_module()
+class StandardRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin):
+    """Simplest base roi head including one bbox head and one mask head.
+    """
+
+    def init_assigner_sampler(self):
+
+    def init_bbox_head(self, bbox_roi_extractor, bbox_head):
+
+    def init_mask_head(self, mask_roi_extractor, mask_head):
+
+
+    def forward_dummy(self, x, proposals):
+
+
+    def forward_train(self,
+                      x,
+                      img_metas,
+                      proposal_list,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None,
+                      gt_masks=None):
+
+    def _bbox_forward(self, x, rois):
+
+    def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
+                            img_metas):
+
+    def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,
+                            img_metas):
+
+    def _mask_forward(self, x, rois=None, pos_inds=None, bbox_feats=None):
+
+
+    def simple_test(self,
+                    x,
+                    proposal_list,
+                    img_metas,
+                    proposals=None,
+                    rescale=False):
+        """Test without augmentation."""
+
+```
+
+Double Head 的修改主要在 bbox_forward 的逻辑中，且它从 `StandardRoIHead` 中继承了其他逻辑。在 `mmdet/models/roi_heads/double_roi_head.py` 中，我们用下述代码实现新的 bbox head：
+
+```python
+from ..builder import HEADS
+from .standard_roi_head import StandardRoIHead
+
+
+@HEADS.register_module()
+class DoubleHeadRoIHead(StandardRoIHead):
+    """RoI head for Double Head RCNN
+
+    https://arxiv.org/abs/1904.06493
+    """
+
+    def __init__(self, reg_roi_scale_factor, **kwargs):
+        super(DoubleHeadRoIHead, self).__init__(**kwargs)
+        self.reg_roi_scale_factor = reg_roi_scale_factor
+
+    def _bbox_forward(self, x, rois):
+        bbox_cls_feats = self.bbox_roi_extractor(
+            x[:self.bbox_roi_extractor.num_inputs], rois)
+        bbox_reg_feats = self.bbox_roi_extractor(
+            x[:self.bbox_roi_extractor.num_inputs],
+            rois,
+            roi_scale_factor=self.reg_roi_scale_factor)
+        if self.with_shared_head:
+            bbox_cls_feats = self.shared_head(bbox_cls_feats)
+            bbox_reg_feats = self.shared_head(bbox_reg_feats)
+        cls_score, bbox_pred = self.bbox_head(bbox_cls_feats, bbox_reg_feats)
+
+        bbox_results = dict(
+            cls_score=cls_score,
+            bbox_pred=bbox_pred,
+            bbox_feats=bbox_cls_feats)
+        return bbox_results
+```
+
+最终，用户需要把该模块添加到 `mmdet/models/bbox_heads/__init__.py` 和 `mmdet/models/roi_heads/__init__.py` 以使相关的注册表可以找到并加载他们。
+
+或者，用户可以添加：
+
+```python
+custom_imports=dict(
+    imports=['mmdet.models.roi_heads.double_roi_head', 'mmdet.models.bbox_heads.double_bbox_head'])
+```
+
+到配置文件并实现相同的目的。
+
+Double Head R-CNN 的配置文件如下：
+
+```python
+_base_ = '../faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+model = dict(
+    roi_head=dict(
+        type='DoubleHeadRoIHead',
+        reg_roi_scale_factor=1.3,
+        bbox_head=dict(
+            _delete_=True,
+            type='DoubleConvFCBBoxHead',
+            num_convs=4,
+            num_fcs=2,
+            in_channels=256,
+            conv_out_channels=1024,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=80,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=2.0),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=2.0))))
+
+```
+
+从 MMDetection 2.0 版本起，配置系统支持继承配置以使用户可以专注于修改。
+Double Head R-CNN 主要使用了一个新的 DoubleHeadRoIHead 和一个新的 `DoubleConvFCBBoxHead`，参数需要根据每个模块的 `__init__` 函数来设置。
+
+### 添加新的损失
+
+假设你想添加一个新的损失 `MyLoss` 用于边界框回归。
+为了添加一个新的损失函数，用户需要在 `mmdet/models/losses/my_loss.py` 中实现。
+装饰器 `weighted_loss` 可以使损失每个部分加权。
+
+```python
+import torch
+import torch.nn as nn
+
+from ..builder import LOSSES
+from .utils import weighted_loss
+
+@weighted_loss
+def my_loss(pred, target):
+    assert pred.size() == target.size() and target.numel() > 0
+    loss = torch.abs(pred - target)
+    return loss
+
+@LOSSES.register_module()
+class MyLoss(nn.Module):
+
+    def __init__(self, reduction='mean', loss_weight=1.0):
+        super(MyLoss, self).__init__()
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None):
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        loss_bbox = self.loss_weight * my_loss(
+            pred, target, weight, reduction=reduction, avg_factor=avg_factor)
+        return loss_bbox
+```
+
+然后，用户需要把它加到 `mmdet/models/losses/__init__.py`。
+
+```python
+from .my_loss import MyLoss, my_loss
+
+```
+
+或者，你可以添加：
+
+```python
+custom_imports=dict(
+    imports=['mmdet.models.losses.my_loss'])
+```
+
+到配置文件来实现相同的目的。
+
+如使用，请修改 `loss_xxx` 字段。
+因为 MyLoss 是用于回归的，你需要在 Head 中修改 `loss_xxx` 字段。
+
+```python
+loss_bbox=dict(type='MyLoss', loss_weight=1.0))
+```
diff --git a/docs/zh_cn/tutorials/customize_runtime.md b/docs/zh_cn/tutorials/customize_runtime.md
new file mode 100644
index 0000000..8d998c3
--- /dev/null
+++ b/docs/zh_cn/tutorials/customize_runtime.md
@@ -0,0 +1 @@
+# 教程 5: 自定义训练配置
diff --git a/docs/zh_cn/tutorials/data_pipeline.md b/docs/zh_cn/tutorials/data_pipeline.md
new file mode 100644
index 0000000..2fd7f8f
--- /dev/null
+++ b/docs/zh_cn/tutorials/data_pipeline.md
@@ -0,0 +1,190 @@
+# 教程 3: 自定义数据预处理流程
+
+## 数据流程的设计
+
+按照惯例，我们使用 `Dataset` 和 `DataLoader` 进行多进程的数据加载。`Dataset` 返回字典类型的数据，数据内容为模型 `forward` 方法的各个参数。由于在目标检测中，输入的图像数据具有不同的大小，我们在 `MMCV` 里引入一个新的 `DataContainer` 类去收集和分发不同大小的输入数据。更多细节请参考[这里](https://github.com/open-mmlab/mmcv/blob/master/mmcv/parallel/data_container.py)。
+
+数据的准备流程和数据集是解耦的。通常一个数据集定义了如何处理标注数据（annotations）信息，而一个数据流程定义了准备一个数据字典的所有步骤。一个流程包括一系列的操作，每个操作都把一个字典作为输入，然后再输出一个新的字典给下一个变换操作。
+
+我们在下图展示了一个经典的数据处理流程。蓝色块是数据处理操作，随着数据流程的处理，每个操作都可以在结果字典中加入新的键（标记为绿色）或更新现有的键（标记为橙色）。
+
+![pipeline figure](../../../resources/data_pipeline.png)
+
+这些操作可以分为数据加载（data loading）、预处理（pre-processing）、格式变化（formatting）和测试时数据增强（test-time augmentation）。
+
+下面的例子是 `Faster R-CNN` 的一个流程：
+
+```python
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+train_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True),
+    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile'),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1333, 800),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ])
+]
+```
+
+对于每个操作，我们列出它添加、更新、移除的相关字典域 (dict fields)：
+
+### 数据加载 Data loading
+
+`LoadImageFromFile`
+
+- 增加：img, img_shape, ori_shape
+
+`LoadAnnotations`
+
+- 增加：gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg, bbox_fields, mask_fields
+
+`LoadProposals`
+
+- 增加：proposals
+
+### 预处理 Pre-processing
+
+`Resize`
+
+- 增加：scale, scale_idx, pad_shape, scale_factor, keep_ratio
+- 更新：img, img_shape, \*bbox_fields, \*mask_fields, \*seg_fields
+
+`RandomFlip`
+
+- 增加：flip
+- 更新：img, \*bbox_fields, \*mask_fields, \*seg_fields
+
+`Pad`
+
+- 增加：pad_fixed_size, pad_size_divisor
+- 更新：img, pad_shape, \*mask_fields, \*seg_fields
+
+`RandomCrop`
+
+- 更新：img, pad_shape, gt_bboxes, gt_labels, gt_masks, \*bbox_fields
+
+`Normalize`
+
+- 增加：img_norm_cfg
+- 更新：img
+
+`SegRescale`
+
+- 更新：gt_semantic_seg
+
+`PhotoMetricDistortion`
+
+- 更新：img
+
+`Expand`
+
+- 更新：img, gt_bboxes
+
+`MinIoURandomCrop`
+
+- 更新：img, gt_bboxes, gt_labels
+
+`Corrupt`
+
+- 更新：img
+
+### 格式 Formatting
+
+`ToTensor`
+
+- 更新：由 `keys` 指定
+
+`ImageToTensor`
+
+- 更新：由 `keys` 指定
+
+`Transpose`
+
+- 更新：由 `keys` 指定
+
+`ToDataContainer`
+
+- 更新：由 `keys` 指定
+
+`DefaultFormatBundle`
+
+- 更新：img, proposals, gt_bboxes, gt_bboxes_ignore, gt_labels, gt_masks, gt_semantic_seg
+
+`Collect`
+
+- 增加：img_metas（img_metas 的键（key）被 `meta_keys` 指定)
+- 移除：除了 `keys` 指定的键（key）之外的所有其他的键（key）
+
+### 测试时数据增强 Test time augmentation
+
+`MultiScaleFlipAug`
+
+## 拓展和使用自定义的流程
+
+1. 在任意文件里写一个新的流程，例如在 `my_pipeline.py`，它以一个字典作为输入并且输出一个字典：
+
+   ```python
+   import random
+   from mmdet.datasets import PIPELINES
+
+
+   @PIPELINES.register_module()
+   class MyTransform:
+       """Add your transform
+
+       Args:
+           p (float): Probability of shifts. Default 0.5.
+       """
+
+       def __init__(self, p=0.5):
+           self.p = p
+
+       def __call__(self, results):
+           if random.random() > self.p:
+               results['dummy'] = True
+           return results
+   ```
+
+2. 在配置文件里调用并使用你写的数据处理流程，需要确保你的训练脚本能够正确导入新增模块：
+
+   ```python
+   custom_imports = dict(imports=['path.to.my_pipeline'], allow_failed_imports=False)
+
+   img_norm_cfg = dict(
+       mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+   train_pipeline = [
+       dict(type='LoadImageFromFile'),
+       dict(type='LoadAnnotations', with_bbox=True),
+       dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+       dict(type='RandomFlip', flip_ratio=0.5),
+       dict(type='Normalize', **img_norm_cfg),
+       dict(type='Pad', size_divisor=32),
+       dict(type='MyTransform', p=0.2),
+       dict(type='DefaultFormatBundle'),
+       dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
+   ]
+   ```
+
+3. 可视化数据增强处理流程的结果
+
+   如果想要可视化数据增强处理流程的结果，可以使用 `tools/misc/browse_dataset.py` 直观
+   地浏览检测数据集（图像和标注信息），或将图像保存到指定目录。
+   使用方法请参考[日志分析](../useful_tools.md)
diff --git a/docs/zh_cn/tutorials/finetune.md b/docs/zh_cn/tutorials/finetune.md
new file mode 100644
index 0000000..349660e
--- /dev/null
+++ b/docs/zh_cn/tutorials/finetune.md
@@ -0,0 +1,87 @@
+# 教程 7: 模型微调
+
+在 COCO 数据集上预训练的检测器可以作为其他数据集（例如 CityScapes 和 KITTI 数据集）优质的预训练模型。
+本教程将指导用户如何把 [ModelZoo](../model_zoo.md) 中提供的模型用于其他数据集中并使得当前所训练的模型获得更好性能。
+
+以下是在新数据集中微调模型需要的两个步骤。
+
+- 按 [教程2：自定义数据集的方法](customize_dataset.md) 中的方法对新数据集添加支持中的方法对新数据集添加支持
+- 按照本教程中所讨论方法，修改配置信息
+
+接下来将会以 Cityscapes Dataset 上的微调过程作为例子，具体讲述用户需要在配置中修改的五个部分。
+
+## 继承基础配置
+
+为了减轻编写整个配置的负担并减少漏洞的数量， MMDetection V2.0 支持从多个现有配置中继承配置信息。微调 MaskRCNN 模型的时候，新的配置信息需要使用从 `_base_/models/mask_rcnn_r50_fpn.py`中继承的配置信息来构建模型的基本结构。当使用 Cityscapes 数据集时，新的配置信息可以简便地从`_base_/datasets/cityscapes_instance.py`中继承。对于训练过程的运行设置部分，新配置需要从 `_base_/default_runtime.py`中继承。这些配置文件`configs`的目录下，用户可以选择全部内容的重新编写而不是使用继承方法。
+
+```python
+_base_ = [
+    '../_base_/models/mask_rcnn_r50_fpn.py',
+    '../_base_/datasets/cityscapes_instance.py', '../_base_/default_runtime.py'
+]
+```
+
+## Head 的修改
+
+接下来新的配置还需要根据新数据集的类别数量对 Head 进行修改。只需要对 roi_head 中的 `num_classes`进行修改。修改后除了最后的预测模型的 Head 之外，预训练模型的权重的大部分都会被重新使用。
+
+```python
+model = dict(
+    pretrained=None,
+    roi_head=dict(
+        bbox_head=dict(
+            type='Shared2FCBBoxHead',
+            in_channels=256,
+            fc_out_channels=1024,
+            roi_feat_size=7,
+            num_classes=8,
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[0., 0., 0., 0.],
+                target_stds=[0.1, 0.1, 0.2, 0.2]),
+            reg_class_agnostic=False,
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0)),
+        mask_head=dict(
+            type='FCNMaskHead',
+            num_convs=4,
+            in_channels=256,
+            conv_out_channels=256,
+            num_classes=8,
+            loss_mask=dict(
+                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))))
+```
+
+## 数据集的修改
+
+用户可能还需要准备数据集并编写有关数据集的配置。目前 MMDetection V2.0 的配置文件已经支持 VOC、WIDER FACE、COCO 和 Cityscapes Dataset 的数据集信息。
+
+## 训练策略的修改
+
+微调超参数与默认的训练策略不同。它通常需要更小的学习率和更少的训练回合。
+
+```python
+# 优化器
+# batch size 为 8 时的 lr 配置
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+optimizer_config = dict(grad_clip=None)
+# 学习策略
+lr_config = dict(
+    policy='step',
+    warmup='linear',
+    warmup_iters=500,
+    warmup_ratio=0.001,
+    step=[7])
+# lr_config 中的 max_epochs 和 step 需要针对自定义数据集进行专门调整
+runner = dict(max_epochs=8)
+log_config = dict(interval=100)
+```
+
+## 使用预训练模型
+
+如果要使用预训练模型时，可以在 `load_from` 中查阅新的配置信息，用户需要在训练开始之前下载好需要的模型权重，从而避免在训练过程中浪费了宝贵时间。
+
+```python
+load_from = 'https://download.openmmlab.com/mmdetection/v2.0/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_3x_coco_bbox_mAP-0.408__segm_mAP-0.37_20200504_163245-42aa3d00.pth'  # noqa
+```
diff --git a/docs/zh_cn/tutorials/how_to.md b/docs/zh_cn/tutorials/how_to.md
new file mode 100644
index 0000000..3587d32
--- /dev/null
+++ b/docs/zh_cn/tutorials/how_to.md
@@ -0,0 +1,203 @@
+# 教程 11: How to xxx
+
+本教程收集了任何如何使用 MMDetection 进行 xxx 的答案。 如果您遇到有关`如何做`的问题及答案，请随时更新此文档！
+
+## 使用 MMClassification 的骨干网络
+
+MMDet、MMCls、MMSeg 中的模型注册表都继承自 MMCV 中的根注册表，允许这些存储库直接使用彼此已经实现的模块。 因此用户可以在 MMDetection 中使用来自 MMClassification 的骨干网络，而无需实现MMClassification 中已经存在的网络。
+
+### 使用在 MMClassification 中实现的骨干网络
+
+假设想将 `MobileNetV3-small` 作为 `RetinaNet` 的骨干网络，则配置文件如下。
+
+```python
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+# please install mmcls>=0.20.0
+# import mmcls.models to trigger register_module in mmcls
+custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
+pretrained = 'https://download.openmmlab.com/mmclassification/v0/mobilenet_v3/convert/mobilenet_v3_small-8427ecf0.pth'
+model = dict(
+    backbone=dict(
+        _delete_=True, # 将 _base_ 中关于 backbone 的字段删除
+        type='mmcls.MobileNetV3', # 使用 mmcls 中的 MobileNetV3
+        arch='small',
+        out_indices=(3, 8, 11), # 修改 out_indices
+        init_cfg=dict(
+            type='Pretrained',
+            checkpoint=pretrained,
+            prefix='backbone.')), # MMCls 中骨干网络的预训练权重含义 prefix='backbone.'，为了正常加载权重，需要把这个 prefix 去掉。
+    # 修改 in_channels
+    neck=dict(in_channels=[24, 48, 96], start_level=0))
+```
+
+### 通过 MMClassification 使用 TIMM 中实现的骨干网络
+
+由于 MMClassification 提供了 Py**T**orch **Im**age **M**odels (`timm`) 骨干网络的封装，用户也可以通过 MMClassification 直接使用 `timm` 中的骨干网络。假设想将 [`EfficientNet-B1`](https://github.com/open-mmlab/mmdetection/blob/master/configs/timm_example/retinanet_timm_efficientnet_b1_fpn_1x_coco.py) 作为 `RetinaNet` 的骨干网络，则配置文件如下。
+
+```python
+# https://github.com/open-mmlab/mmdetection/blob/master/configs/timm_example/retinanet_timm_efficientnet_b1_fpn_1x_coco.py
+_base_ = [
+    '../_base_/models/retinanet_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+
+# please install mmcls>=0.20.0
+# import mmcls.models to trigger register_module in mmcls
+custom_imports = dict(imports=['mmcls.models'], allow_failed_imports=False)
+model = dict(
+    backbone=dict(
+        _delete_=True, # 将 _base_ 中关于 backbone 的字段删除
+        type='mmcls.TIMMBackbone', # 使用 mmcls 中 timm 骨干网络
+        model_name='efficientnet_b1',
+        features_only=True,
+        pretrained=True,
+        out_indices=(1, 2, 3, 4)), # 修改 out_indices
+    neck=dict(in_channels=[24, 40, 112, 320])) # 修改 in_channels
+
+optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+```
+
+`type='mmcls.TIMMBackbone'` 表示在 MMDetection 中使用 MMClassification 中的 `TIMMBackbone` 类，并且使用的模型为` EfficientNet-B1`，其中 `mmcls` 表示 MMClassification 库，而 `TIMMBackbone ` 表示 MMClassification 中实现的 TIMMBackbone 包装器。
+
+关于层次注册器的具体原理可以参考 [MMCV 文档](https://github.com/open-mmlab/mmcv/blob/master/docs/zh_cn/understand_mmcv/registry.md#%E6%B3%A8%E5%86%8C%E5%99%A8%E5%B1%82%E7%BB%93%E6%9E%84)，关于如何使用 MMClassification 中的其他 backbone，可以参考 [MMClassification 文档](https://github.com/open-mmlab/mmclassification/blob/master/docs/zh_CN/tutorials/config.md)。
+
+## 使用马赛克数据增强
+
+如果你想在训练中使用 `Mosaic`，那么请确保你同时使用 `MultiImageMixDataset`。以 `Faster R-CNN` 算法为例，你可以通过如下做法实现：
+
+```python
+# 直接打开 configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py ,增添如下字段
+data_root = 'data/coco/'
+dataset_type = 'CocoDataset'
+img_scale=(1333, 800)​
+img_norm_cfg = dict(
+    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
+
+train_pipeline = [
+    dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+    dict(
+        type='RandomAffine',
+        scaling_ratio_range=(0.1, 2),
+        border=(-img_scale[0] // 2, -img_scale[1] // 2)), # 图像经过马赛克处理后会放大4倍，所以我们使用仿射变换来恢复图像的大小。
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='Normalize', **img_norm_cfg),
+    dict(type='Pad', size_divisor=32),
+    dict(type='DefaultFormatBundle'),
+    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+]
+
+train_dataset = dict(
+    _delete_ = True, # 删除不必要的设置
+    type='MultiImageMixDataset',
+    dataset=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/instances_train2017.json',
+        img_prefix=data_root + 'train2017/',
+        pipeline=[
+            dict(type='LoadImageFromFile'),
+            dict(type='LoadAnnotations', with_bbox=True)
+        ],
+        filter_empty_gt=False,
+    ),
+    pipeline=train_pipeline
+    )
+​
+data = dict(
+    train=train_dataset
+    )
+```
+
+## 在配置文件中冻结骨干网络后在训练中解冻骨干网络
+
+如果你在配置文件中已经冻结了骨干网络并希望在几个训练周期后解冻它，你可以通过 hook 来实现这个功能。以用 ResNet 为骨干网络的 Faster R-CNN 为例，你可以冻结一个骨干网络的一个层并在配置文件中添加如下 `custom_hooks`:
+
+```python
+_base_ = [
+    '../_base_/models/faster_rcnn_r50_fpn.py',
+    '../_base_/datasets/coco_detection.py',
+    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
+]
+model = dict(
+    # freeze one stage of the backbone network.
+    backbone=dict(frozen_stages=1),
+)
+custom_hooks = [dict(type="UnfreezeBackboneEpochBasedHook", unfreeze_epoch=1)]
+```
+
+同时在 `mmdet/core/hook/unfreeze_backbone_epoch_based_hook.py` 当中书写 `UnfreezeBackboneEpochBasedHook` 类
+
+```python
+from mmcv.parallel import is_module_wrapper
+from mmcv.runner.hooks import HOOKS, Hook
+
+
+@HOOKS.register_module()
+class UnfreezeBackboneEpochBasedHook(Hook):
+    """Unfreeze backbone network Hook.
+
+    Args:
+        unfreeze_epoch (int): The epoch unfreezing the backbone network.
+    """
+
+    def __init__(self, unfreeze_epoch=1):
+        self.unfreeze_epoch = unfreeze_epoch
+
+    def before_train_epoch(self, runner):
+        # Unfreeze the backbone network.
+        # Only valid for resnet.
+        if runner.epoch == self.unfreeze_epoch:
+            model = runner.model
+            if is_module_wrapper(model):
+                model = model.module
+            backbone = model.backbone
+            if backbone.frozen_stages >= 0:
+                if backbone.deep_stem:
+                    backbone.stem.train()
+                    for param in backbone.stem.parameters():
+                        param.requires_grad = True
+                else:
+                    backbone.norm1.train()
+                    for m in [backbone.conv1, backbone.norm1]:
+                        for param in m.parameters():
+                            param.requires_grad = True
+
+            for i in range(1, backbone.frozen_stages + 1):
+                m = getattr(backbone, f'layer{i}')
+                m.train()
+                for param in m.parameters():
+                    param.requires_grad = True
+```
+
+## 获得新的骨干网络的通道数
+
+如果你想获得一个新骨干网络的通道数，你可以单独构建这个骨干网络并输入一个伪造的图片来获取每一个阶段的输出。
+
+以 `ResNet` 为例：
+
+```python
+from mmdet.models import ResNet
+import torch
+self = ResNet(depth=18)
+self.eval()
+inputs = torch.rand(1, 3, 32, 32)
+level_outputs = self.forward(inputs)
+for level_out in level_outputs:
+    print(tuple(level_out.shape))
+
+```
+
+以上脚本的输出为:
+
+```python
+(1, 64, 8, 8)
+(1, 128, 4, 4)
+(1, 256, 2, 2)
+(1, 512, 1, 1)
+```
+
+用户可以通过将脚本中的 `ResNet(depth=18)` 替换为自己的骨干网络配置来得到新的骨干网络的通道数。
diff --git a/docs/zh_cn/tutorials/index.rst b/docs/zh_cn/tutorials/index.rst
new file mode 100644
index 0000000..eaf4907
--- /dev/null
+++ b/docs/zh_cn/tutorials/index.rst
@@ -0,0 +1,14 @@
+.. toctree::
+   :maxdepth: 2
+
+   config.md
+   customize_dataset.md
+   data_pipeline.md
+   customize_models.md
+   customize_runtime.md
+   customize_losses.md
+   finetune.md
+   pytorch2onnx.md
+   onnx2tensorrt.md
+   init_cfg.md
+   how_to.md
diff --git a/docs/zh_cn/tutorials/init_cfg.md b/docs/zh_cn/tutorials/init_cfg.md
new file mode 100644
index 0000000..f6f5968
--- /dev/null
+++ b/docs/zh_cn/tutorials/init_cfg.md
@@ -0,0 +1,161 @@
+# 教程 10: 权重初始化
+
+在训练过程中，适当的初始化策略有利于加快训练速度或获得更⾼的性能。 [MMCV](https://github.com/open-mmlab/mmcv/blob/master/mmcv/cnn/utils/weight_init.py) 提供了一些常⽤的初始化模块的⽅法，如 `nn.Conv2d`。 MMdetection 中的模型初始化主要使⽤ `init_cfg`。⽤⼾可以通过以下两个步骤来初始化模型：
+
+1. 在 `model_cfg` 中为模型或其组件定义 `init_cfg`，但⼦组件的 `init_cfg` 优先级更⾼，会覆盖⽗模块的 `init_cfg` 。
+2. 像往常一样构建模型，然后显式调⽤ `model.init_weights()` ⽅法，此时模型参数将会被按照配置文件写法进行初始化。
+
+MMdetection 初始化工作流的高层 API 调用流程是：
+
+model_cfg(init_cfg) -> build_from_cfg -> model -> init_weight() -> initialize(self, self.init_cfg) -> children's init_weight()
+
+### 描述
+
+它的数据类型是 dict 或者 list\[dict\]，包含了下列键值:
+
+- `type` (str)，包含 `INTIALIZERS` 中的初始化器名称，后面跟着初始化器的参数。
+- `layer`（str 或 list\[str\]），包含 Pytorch 或 MMCV 中基本层的名称，以及将被初始化的可学习参数，例如 `'Conv2d'`，`'DeformConv2d'`。
+- `override` (dict 或 list\[dict\])，包含不继承⾃ `BaseModule` 且其初始化配置与 `layer` 键中的其他层不同的⼦模块。 `type` 中定义的初始化器将适⽤于 `layer` 中定义的所有层，因此如果⼦模块不是 `BaseModule` 的派⽣类但可以与 `layer` 中的层相同的⽅式初始化，则不需要使⽤ `override`。`override` 包含了：
+  - `type` 后跟初始化器的参数；
+  - `name` 用以指⽰将被初始化的⼦模块。
+
+### 初始化参数
+
+从 `mmcv.runner.BaseModule` 或 `mmdet.models` 继承一个新模型。这里我们用 FooModel 来举个例子。
+
+```python
+import torch.nn as nn
+from mmcv.runner import BaseModule
+
+class FooModel(BaseModule)
+	def __init__(self,
+                 arg1,
+                 arg2,
+                 init_cfg=None):
+    	super(FooModel, self).__init__(init_cfg)
+		...
+```
+
+- 直接在代码中使⽤ `init_cfg` 初始化模型
+
+  ```python
+  import torch.nn as nn
+  from mmcv.runner import BaseModule
+  # or directly inherit mmdet models
+
+  class FooModel(BaseModule)
+  	def __init__(self,
+                  arg1,
+                  arg2,
+                  init_cfg=XXX):
+    		super(FooModel, self).__init__(init_cfg)
+    	    ...
+  ```
+
+- 在 `mmcv.Sequential` 或 `mmcv.ModuleList` 代码中直接使⽤ `init_cfg` 初始化模型
+
+  ```python
+  from mmcv.runner import BaseModule, ModuleList
+
+  class FooModel(BaseModule)
+  	def __init__(self,
+                	arg1,
+                	arg2,
+                	init_cfg=None):
+    		super(FooModel, self).__init__(init_cfg)
+        	...
+        	self.conv1 = ModuleList(init_cfg=XXX)
+  ```
+
+- 使⽤配置⽂件中的 `init_cfg` 初始化模型
+
+  ```python
+  model = dict(
+  	...
+    	model = dict(
+        	type='FooModel',
+        	arg1=XXX,
+        	arg2=XXX,
+        	init_cfg=XXX),
+            ...
+  ```
+
+### init_cfg 的使用
+
+1. 用 `layer` 键初始化模型
+
+   如果我们只定义了 `layer`, 它只会在 `layer` 键中初始化网络层。
+
+   注意： `layer` 键对应的值是 Pytorch 的带有 weights 和 bias 属性的类名（因此不⽀持 `MultiheadAttention` 层）。
+
+- 定义⽤于初始化具有相同配置的模块的 `layer` 键。
+
+  ```python
+  init_cfg = dict(type='Constant', layer=['Conv1d', 'Conv2d', 'Linear'], val=1)
+  # ⽤相同的配置初始化整个模块
+  ```
+
+- 定义⽤于初始化具有不同配置的层的 `layer` 键。
+
+  ```python
+  init_cfg = [dict(type='Constant', layer='Conv1d', val=1),
+              dict(type='Constant', layer='Conv2d', val=2),
+              dict(type='Constant', layer='Linear', val=3)]
+  # nn.Conv1d 将被初始化为 dict(type='Constant', val=1)
+  # nn.Conv2d 将被初始化为 dict(type='Constant', val=2)
+  # nn.Linear 将被初始化为 dict(type='Constant', val=3)
+  ```
+
+2. 使⽤ `override` 键初始化模型
+
+- 当使⽤属性名初始化某些特定部分时，我们可以使⽤ `override` 键， `override` 中的值将忽略 init_cfg 中的值。
+
+  ```python
+  # layers：
+  # self.feat = nn.Conv1d(3, 1, 3)
+  # self.reg = nn.Conv2d(3, 3, 3)
+  # self.cls = nn.Linear(1,2)
+
+  init_cfg = dict(type='Constant',
+                  layer=['Conv1d','Conv2d'], val=1, bias=2,
+                  override=dict(type='Constant', name='reg', val=3, bias=4))
+  # self.feat and self.cls 将被初始化为 dict(type='Constant', val=1, bias=2)
+  # 叫 'reg' 的模块将被初始化为 dict(type='Constant', val=3, bias=4)
+  ```
+
+- 如果 init_cfg 中的 `layer` 为 None，则只会初始化 override 中有 name 的⼦模块，⽽ override 中的 type 和其他参数可以省略。
+
+  ```python
+  # layers：
+  # self.feat = nn.Conv1d(3, 1, 3)
+  # self.reg = nn.Conv2d(3, 3, 3)
+  # self.cls = nn.Linear(1,2)
+
+  init_cfg = dict(type='Constant', val=1, bias=2, 	override=dict(name='reg'))
+
+  # self.feat and self.cls 将被 Pytorch 初始化
+  # 叫 'reg' 的模块将被 dict(type='Constant', val=1, bias=2) 初始化
+  ```
+
+- 如果我们不定义 `layer` 或 `override` 键，它不会初始化任何东西。
+
+- 无效的使用
+
+  ```python
+  # override 没有 name 键的话是无效的
+  init_cfg = dict(type='Constant', layer=['Conv1d','Conv2d'], val=1, bias=2,
+              	override=dict(type='Constant', val=3, bias=4))
+
+  # override 有 name 键和其他参数但是没有 type 键也是无效的
+  init_cfg = dict(type='Constant', layer=['Conv1d','Conv2d'], val=1, bias=2,
+                  override=dict(name='reg', val=3, bias=4))
+  ```
+
+3. 使⽤预训练模型初始化模型
+
+   ```python
+   init_cfg = dict(type='Pretrained',
+                checkpoint='torchvision://resnet50')
+   ```
+
+更多细节可以参考 [MMCV](https://mmcv.readthedocs.io/en/latest/cnn.html#weight-initialization) 的文档和 MMCV [PR #780](https://github.com/open-mmlab/mmcv/pull/780)
diff --git a/docs/zh_cn/tutorials/onnx2tensorrt.md b/docs/zh_cn/tutorials/onnx2tensorrt.md
new file mode 100644
index 0000000..678a131
--- /dev/null
+++ b/docs/zh_cn/tutorials/onnx2tensorrt.md
@@ -0,0 +1,106 @@
+# 教程 9: ONNX 到 TensorRT 的模型转换（实验性支持）
+
+> ## [尝试使用新的 MMDeploy 来部署你的模型](https://mmdeploy.readthedocs.io/)
+
+<!-- TOC -->
+
+- [教程 9: ONNX 到 TensorRT 的模型转换（实验性支持）](#%E6%95%99%E7%A8%8B-9-onnx-%E5%88%B0-tensorrt-%E7%9A%84%E6%A8%A1%E5%9E%8B%E8%BD%AC%E6%8D%A2%E5%AE%9E%E9%AA%8C%E6%80%A7%E6%94%AF%E6%8C%81)
+  - [如何将模型从 ONNX 转换为 TensorRT](#%E5%A6%82%E4%BD%95%E5%B0%86%E6%A8%A1%E5%9E%8B%E4%BB%8E-onnx-%E8%BD%AC%E6%8D%A2%E4%B8%BA-tensorrt)
+    - [先决条件](#%E5%85%88%E5%86%B3%E6%9D%A1%E4%BB%B6)
+    - [用法](#%E7%94%A8%E6%B3%95)
+  - [如何评估导出的模型](#%E5%A6%82%E4%BD%95%E8%AF%84%E4%BC%B0%E5%AF%BC%E5%87%BA%E7%9A%84%E6%A8%A1%E5%9E%8B)
+  - [支持转换为 TensorRT 的模型列表](#%E6%94%AF%E6%8C%81%E8%BD%AC%E6%8D%A2%E4%B8%BA-tensorrt-%E7%9A%84%E6%A8%A1%E5%9E%8B%E5%88%97%E8%A1%A8)
+  - [提醒](#%E6%8F%90%E9%86%92)
+  - [常见问题](#%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98)
+
+<!-- TOC -->
+
+## 如何将模型从 ONNX 转换为 TensorRT
+
+### 先决条件
+
+1. 请参考 [get_started.md](https://mmdetection.readthedocs.io/en/latest/get_started.html) 从源码安装 MMCV 和 MMDetection。
+2. 请参考 [ONNXRuntime in mmcv](https://mmcv.readthedocs.io/en/latest/deployment/onnxruntime_op.html) 和 [TensorRT plugin in mmcv](https://github.com/open-mmlab/mmcv/blob/master/docs/en/deployment/tensorrt_plugin.md/) 安装支持 ONNXRuntime 自定义操作和 TensorRT 插件的 `mmcv-full`。
+3. 使用工具 [pytorch2onnx](https://mmdetection.readthedocs.io/en/latest/tutorials/pytorch2onnx.html) 将模型从 PyTorch 转换为 ONNX。
+
+### 用法
+
+```bash
+python tools/deployment/onnx2tensorrt.py \
+    ${CONFIG} \
+    ${MODEL} \
+    --trt-file ${TRT_FILE} \
+    --input-img ${INPUT_IMAGE_PATH} \
+    --shape ${INPUT_IMAGE_SHAPE} \
+    --min-shape ${MIN_IMAGE_SHAPE} \
+    --max-shape ${MAX_IMAGE_SHAPE} \
+    --workspace-size {WORKSPACE_SIZE} \
+    --show \
+    --verify \
+```
+
+所有参数的说明：
+
+- `config`: 模型配置文件的路径。
+- `model`: ONNX 模型文件的路径。
+- `--trt-file`: 输出 TensorRT 引擎文件的路径。如果未指定，它将被设置为 `tmp.trt`。
+- `--input-img`: 用于追踪和转换的输入图像的路径。默认情况下，它将设置为 `demo/demo.jpg`。
+- `--shape`: 模型输入的高度和宽度。如果未指定，它将设置为 `400 600`。
+- `--min-shape`: 模型输入的最小高度和宽度。如果未指定，它将被设置为与 `--shape` 相同。
+- `--max-shape`: 模型输入的最大高度和宽度。如果未指定，它将被设置为与 `--shape` 相同。
+- `--workspace-size`: 构建 TensorRT 引擎所需的 GPU 工作空间大小（以 GiB 为单位）。如果未指定，它将设置为 `1` GiB。
+- `--show`: 确定是否显示模型的输出。如果未指定，它将设置为 `False`。
+- `--verify`: 确定是否在 ONNXRuntime 和 TensorRT 之间验证模型的正确性。如果未指定，它将设置为 `False`。
+- `--verbose`: 确定是否打印日志消息。它对调试很有用。如果未指定，它将设置为 `False`。
+
+例子:
+
+```bash
+python tools/deployment/onnx2tensorrt.py \
+    configs/retinanet/retinanet_r50_fpn_1x_coco.py \
+    checkpoints/retinanet_r50_fpn_1x_coco.onnx \
+    --trt-file checkpoints/retinanet_r50_fpn_1x_coco.trt \
+    --input-img demo/demo.jpg \
+    --shape 400 600 \
+    --show \
+    --verify \
+```
+
+## 如何评估导出的模型
+
+我们准备了一个工具 `tools/deplopyment/test.py` 来评估 TensorRT 模型。
+
+请参阅以下链接以获取更多信息。
+
+- [如何评估导出的模型](pytorch2onnx.md#how-to-evaluate-the-exported-models)
+- [结果和模型](pytorch2onnx.md#results-and-models)
+
+## 支持转换为 TensorRT 的模型列表
+
+下表列出了确定可转换为 TensorRT 的模型。
+
+|       Model        |                              Config                              | Dynamic Shape | Batch Inference | Note |
+| :----------------: | :--------------------------------------------------------------: | :-----------: | :-------------: | :--: |
+|        SSD         |                   `configs/ssd/ssd300_coco.py`                   |       Y       |        Y        |      |
+|        FSAF        |              `configs/fsaf/fsaf_r50_fpn_1x_coco.py`              |       Y       |        Y        |      |
+|        FCOS        |         `configs/fcos/fcos_r50_caffe_fpn_4x4_1x_coco.py`         |       Y       |        Y        |      |
+|       YOLOv3       |        `configs/yolo/yolov3_d53_mstrain-608_273e_coco.py`        |       Y       |        Y        |      |
+|     RetinaNet      |         `configs/retinanet/retinanet_r50_fpn_1x_coco.py`         |       Y       |        Y        |      |
+|    Faster R-CNN    |       `configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py`       |       Y       |        Y        |      |
+|   Cascade R-CNN    |      `configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py`      |       Y       |        Y        |      |
+|     Mask R-CNN     |         `configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py`         |       Y       |        Y        |      |
+| Cascade Mask R-CNN |   `configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py`    |       Y       |        Y        |      |
+|     PointRend      | `configs/point_rend/point_rend_r50_caffe_fpn_mstrain_1x_coco.py` |       Y       |        Y        |      |
+
+注意:
+
+- *以上所有模型通过 Pytorch==1.6.0, onnx==1.7.0 与 TensorRT-7.2.1.6.Ubuntu-16.04.x86_64-gnu.cuda-10.2.cudnn8.0 测试*
+
+## 提醒
+
+- 如果您在上面列出的模型中遇到任何问题，请创建 issue，我们会尽快处理。对于未包含在列表中的模型，由于资源有限，我们可能无法在此提供太多帮助。请尝试深入挖掘并自行调试。
+- 由于此功能是实验性的，并且可能会快速更改，因此请始终尝试使用最新的 `mmcv` 和 `mmdetecion`。
+
+## 常见问题
+
+- 空
diff --git a/docs/zh_cn/tutorials/pytorch2onnx.md b/docs/zh_cn/tutorials/pytorch2onnx.md
new file mode 100644
index 0000000..93a647e
--- /dev/null
+++ b/docs/zh_cn/tutorials/pytorch2onnx.md
@@ -0,0 +1,3 @@
+# 教程 8: Pytorch 到 ONNX 的模型转换（实验性支持）
+
+> ## [尝试使用新的 MMDeploy 來部署你的模型](https://mmdeploy.readthedocs.io/)
diff --git a/docs/zh_cn/useful_tools.md b/docs/zh_cn/useful_tools.md
new file mode 100644
index 0000000..922164c
--- /dev/null
+++ b/docs/zh_cn/useful_tools.md
@@ -0,0 +1 @@
+## 日志分析
diff --git a/exp_note.md b/exp_note.md
new file mode 100644
index 0000000..3677702
--- /dev/null
+++ b/exp_note.md
@@ -0,0 +1,93 @@
+
+
+
+
+# 注意事项
+* 在rstar上，启动程序的方法是：`CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 --use_env object_detecion_qat/QAT_multi_GPU.py`
+    * 其中 CUDA_VISIBLE_DEVICES=0,1 表示可见的GPU序号为0,1。
+    * --nproc_per_node=2 表示在一个机器上跑2张卡。
+    * --use_env 表示从环境中加载distibuted配置。
+    * 主要的程序就是QAT_multi_GPU.py。
+    * 该指令在teql文件夹内执行。
+* 启动程序前务必确保config.py文件内的文件路径是否正确；如data_path、output_dir等。目前默认使用的config为/workspace/code/Quant/teql/mqbconfig/fp32/coco_fastrcnn_res50_fpn_fp32_config.yaml
+* object_detecion_qat/QAT_multi_GPU.py内有管道arg，附上了对应的描述。默认即可。后续再从.sh脚本出发。
+* /workspace/code/Quant/teql/global_placeholder.py 内的“常值”变量需要主动改掉，否则一些路径会报错。目前看来是必须得加载backbone的预训练模型的。
+
+
+# 杂项
+* model_type:  
+
+  4. 容器需要跑的实验：                    turn  device
+    1. COCO
+      1. RetinaNet18                                 20G+
+        1. LSQ w4a4                        0r   d0  tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w4a4.sh
+        2. LSQ w2a2                        0r   d1  tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w2a2.sh
+        3. LSQ HQOD w4a4                   0r   d2  tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HQOD_retinanet_18_coco_w4a4.sh
+        4. LSQ HQOD w2a2                   0r   d3  tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HQOD_retinanet_18_coco_w2a2.sh
+      2. RetinaNet50                                 40G+
+        1. LSQ w4a4                        0r   d4  tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_retinanet_50_coco_w4a4.sh
+        2. LSQ w2a2                        0r   d5  tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_retinanet_50_coco_w2a2.sh
+        3. LSQ HQOD w4a4                   0r   d6  tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_HQOD_retinanet_50_coco_w4a4.sh
+        4. LSQ HQOD w2a2                   2r   d0  tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_HQOD_retinanet_50_coco_w2a2.sh
+      3. YOLOX_s_LSQ                                 40G+
+        1. LSQ w4a4                        1r   d0  tools/starter_scripts/yolox_s/qat/coco/dist_lsq_yolox_s_coco_w4a4.sh
+        2. LSQ w2a2                        1r   d1  tools/starter_scripts/yolox_s/qat/coco/dist_lsq_yolox_s_coco_w2a2.sh
+        3. LSQ HQOD w4a4                   1r   d2  tools/starter_scripts/yolox_s/qat/coco/dist_lsq_HQOD_yolox_s_coco_w4a4.sh
+        4. LSQ HQOD w2a2                   1r   d3  tools/starter_scripts/yolox_s/qat/coco/dist_lsq_HQOD_yolox_s_coco_w2a2.sh
+      4. RetinaNet18_AQD                             20G+
+        1. fp32 pretrain                   0r   d7  tools/starter_scripts/retinanet_18/dist_train_retinanet_18_coco_AQD.sh
+        2. LSQ HQOD w2a2                   1r   d4  tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w2a2_HQOD_AQD.sh
+        3. LSQ HQOD w4a4                   1r   d5  tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w4a4_HQOD_AQD.sh
+      5. ATSS50                                      40G+
+        1. TQT w4a4                        1r   d6  tools/starter_scripts/atss_50/qat/coco/dist_tqt_atss_50_coco_w4a4.sh
+        2. TQT w2a2                        2r   d1  tools/starter_scripts/atss_50/qat/coco/dist_tqt_atss_50_coco_w2a2.sh
+        3. TQT HQOD w4a4                   1r   d7  tools/starter_scripts/atss_50/qat/coco/dist_tqt_HQOD_atss_50_coco_w4a4.sh
+        4. TQT HQOD w2a2                   2r   d2  tools/starter_scripts/atss_50/qat/coco/dist_tqt_HQOD_atss_50_coco_w2a2.sh
+      6. ATSS50                                      40G+
+        1. LSQ w4a4                        2r   d3  tools/starter_scripts/atss_50/qat/coco/dist_lsq_atss_50_coco_w4a4.sh
+        2. LSQ w2a2                        2r   d4  tools/starter_scripts/atss_50/qat/coco/dist_lsq_atss_50_coco_w2a2.sh
+        3. LSQ HQOD w4a4                   2r   d5  tools/starter_scripts/atss_50/qat/coco/dist_lsq_HQOD_atss_50_coco_w4a4.sh
+        4. LSQ HQOD w2a2                   2r   d6  tools/starter_scripts/atss_50/qat/coco/dist_lsq_HQOD_atss_50_coco_w2a2.sh
+
+检查一遍pretrain是否加载了  AQD的要提前设置一下 √
+batchsize √
+device √
+
+注意一下，要把master_port设置一下 √
+
+
+commands_file=tools/starter_scripts/experiment1.txt tmux_s_name=hqod1 bash tools/starter_scripts/main.sh
+
+commands_file=tools/starter_scripts/experiment2.txt tmux_s_name=hqod2 bash tools/starter_scripts/main.sh
+
+commands_file=tools/starter_scripts/experiment3.txt tmux_s_name=hqod3 bash tools/starter_scripts/main.sh
+
+commands_file=tools/starter_scripts/experiment41.txt tmux_s_name=hqod41 bash tools/starter_scripts/main.sh
+
+
+
+
+
+commands_file=tools/starter_scripts/experiment52.txt tmux_s_name=hqod52 bash tools/starter_scripts/main.sh
+
+commands_file=tools/starter_scripts/experiment53.txt tmux_s_name=hqod53 bash tools/starter_scripts/main.sh
+
+commands_file=tools/starter_scripts/experiment54.txt tmux_s_name=hqod54 bash tools/starter_scripts/main.sh
+
+commands_file=tools/starter_scripts/experiment55.txt tmux_s_name=hqod55 bash tools/starter_scripts/main.sh
+
+commands_file=tools/starter_scripts/experiment61.txt tmux_s_name=hqod61 bash tools/starter_scripts/main.sh
+: 记得换文件
+commands_file=tools/starter_scripts/experiment62.txt tmux_s_name=hqod62 bash tools/starter_scripts/main.sh
+
+
+commands_file=tools/starter_scripts/experiment71.txt tmux_s_name=hqod71 bash tools/starter_scripts/main.sh
+
+commands_file=tools/starter_scripts/experiment72.txt tmux_s_name=hqod72 bash tools/starter_scripts/main.sh
+
+<!-- commands_file=tools/starter_scripts/experiment73.txt tmux_s_name=hqod73 bash tools/starter_scripts/main.sh -->
+
+
+
+
+
diff --git a/fuser_method_mappings.py b/fuser_method_mappings.py
new file mode 100644
index 0000000..ae87726
--- /dev/null
+++ b/fuser_method_mappings.py
@@ -0,0 +1,61 @@
+from typing import Optional, Type
+
+import torch
+import torch.nn as nn
+from torch.quantization.fx.fusion_patterns import ConvBNReLUFusion, ModuleReLUFusion
+from torch.quantization.fx.quantization_types import QuantizerCls
+from torch.fx.graph import Node
+
+import mqbench.nn as qnn
+import mqbench.nn.intrinsic as qnni
+import mqbench.nn.intrinsic.qat as qnniqat
+from mqbench.utils.fusion import fuse_deconv_bn_eval
+from mqbench.nn.modules import FrozenBatchNorm2d
+
+
+fuse_custom_config_dict = {
+    "additional_fuser_method_mapping": {
+        (torch.nn.Linear, torch.nn.BatchNorm1d): fuse_linear_bn,
+        (torch.nn.ConvTranspose2d, torch.nn.BatchNorm2d): fuse_deconv_bn,
+        (torch.nn.ConvTranspose2d, torch.nn.BatchNorm2d, torch.nn.ReLU): fuse_deconv_bn_relu,
+        (torch.nn.ConvTranspose2d, torch.nn.ReLU): qnni.ConvTransposeReLU2d,
+        (nn.Conv2d, FrozenBatchNorm2d, nn.ReLU): fuse_conv_freezebn_relu,
+        (nn.Conv2d, FrozenBatchNorm2d): fuse_conv_freezebn,
+        (nn.ConvTranspose2d, FrozenBatchNorm2d, nn.ReLU): fuse_deconv_freezebn_relu,
+        (nn.ConvTranspose2d, FrozenBatchNorm2d): fuse_deconv_freezebn,
+    },
+    "additional_fusion_pattern": {  # 似乎这些都是torch官方的定义
+        (torch.nn.BatchNorm1d, torch.nn.Linear):
+        ConvBNReLUFusion,
+        (torch.nn.BatchNorm2d, torch.nn.ConvTranspose2d):
+        ConvBNReLUFusion,
+        (torch.nn.ReLU, torch.nn.ConvTranspose2d):
+        ConvBNReLUFusion,
+        (torch.nn.ReLU, (torch.nn.BatchNorm2d, torch.nn.ConvTranspose2d)):
+        ConvBNReLUFusion,
+        (torch.nn.functional.relu, torch.nn.ConvTranspose2d):
+        ConvBNReLUFusion,
+        (torch.nn.functional.relu, (torch.nn.BatchNorm2d, torch.nn.ConvTranspose2d)):
+        ConvBNReLUFusion,
+        (torch.nn.ReLU, (FrozenBatchNorm2d, torch.nn.Conv2d)):
+        ConvFreezebnReLUFusion,
+        (FrozenBatchNorm2d, torch.nn.Conv2d):
+        ConvFreezebnReLUFusion,
+        (torch.nn.ReLU, (FrozenBatchNorm2d, torch.nn.ConvTranspose2d)):
+        ConvFreezebnReLUFusion,
+        (FrozenBatchNorm2d, torch.nn.ConvTranspose2d):
+        ConvFreezebnReLUFusion,
+    },
+    "additional_qat_module_mappings": {
+        nn.ConvTranspose2d: qnn.qat.ConvTranspose2d,
+        qnni.LinearBn1d: qnniqat.LinearBn1d,
+        qnni.ConvTransposeBn2d: qnniqat.ConvTransposeBn2d,
+        qnni.ConvTransposeReLU2d: qnniqat.ConvTransposeReLU2d,
+        qnni.ConvTransposeBnReLU2d: qnniqat.ConvTransposeBnReLU2d,
+        qnni.ConvFreezebn2d: qnniqat.ConvFreezebn2d,
+        qnni.ConvFreezebnReLU2d: qnniqat.ConvFreezebnReLU2d,
+        qnni.ConvTransposeFreezebn2d: qnniqat.ConvTransposeFreezebn2d,
+        qnni.ConvTransposeFreezebnReLU2d: qnniqat.ConvTransposeFreezebnReLU2d,
+        nn.Embedding: qnn.qat.Embedding,
+    },
+}
diff --git a/global_placeholder.py b/global_placeholder.py
new file mode 100644
index 0000000..99ae5c6
--- /dev/null
+++ b/global_placeholder.py
@@ -0,0 +1,72 @@
+
+#--------- 常值
+# res18_pretrained_path = '/workspace/share/pretrained/resnet18_imagenet.pth'
+# res34_pretrained_path = '/workspace/share/pretrained/resnet34_imagenet.pth'
+# res50_pretrained_path = '/workspace/share/pretrained/resnet50_imagenet.pth'
+# mbnv2_pretrained_path = '/workspace/share/pretrained/mobilenetv2_imagenet.pth'
+# vgg16_pretrained_path = '/workspace/share/pretrained/vgg16_imagenet.pth'
+
+# ssd300_pretrained_path = '/workspace/share/pretrained/nvidia_ssdpyt_amp_200703.pt'
+#------------ 等着被初始化
+quant_bit = None
+quant_algorithm = None
+model_type = None
+num_classes = None
+pretrained_flag = None
+mybuff_flag = 0
+qloss_flag = False
+fold_bn_flag = False
+aqd_mode = 0
+
+def modify_quant_bit(bit_setting):
+    global quant_bit
+    print(f'\nModify the global bit hyparam as {bit_setting}')
+    quant_bit = bit_setting
+    
+def modify_quant_algorithm(algorithm_setting):
+    global quant_algorithm
+    print(f'\nModify the global algorithm as {algorithm_setting}')
+    quant_algorithm = algorithm_setting
+    
+def modify_model_type(model_type_setting):
+    global model_type
+    print(f'\nModify the global model type as {model_type_setting}')
+    model_type = model_type_setting
+    
+def modify_buff_flag(my_buff_setting):
+    global mybuff_flag
+    if my_buff_setting == 0:
+        text = 'naive'
+    elif my_buff_setting == 1:
+        text = 'hqod'
+    elif my_buff_setting == 2:
+        text = 'hardet'
+    else:
+        raise NotImplementedError
+    print(f'\nModify the global buff flag as {my_buff_setting}：{text}')
+    mybuff_flag = my_buff_setting
+
+def modify_qloss_flag(qloss_setting):
+    global qloss_flag
+    print(f'\nModify the global qloss flag as {qloss_setting}')
+    qloss_flag = qloss_setting
+
+def modify_num_classes(num_classes_setting):
+    global num_classes
+    print(f'\nModify the global class num as {num_classes_setting}')
+    num_classes = num_classes_setting
+    
+def modify_pretrained_flag(pretrained_flag_setting):
+    global pretrained_flag
+    print(f'\nModify the global pretrained flag as {pretrained_flag_setting}')
+    pretrained_flag = pretrained_flag_setting  
+      
+def modify_fold_bn_flag(fold_bn_flag_setting):
+    global fold_bn_flag
+    print(f'\nModify the global fold bn flag as {fold_bn_flag_setting}')
+    fold_bn_flag = fold_bn_flag_setting
+    
+def modify_AQD_mode(AQD_mode_setting):
+    global aqd_mode
+    print(f'\nModify the global AQD mode flag as {AQD_mode_setting}')
+    aqd_mode = AQD_mode_setting  
\ No newline at end of file
diff --git a/mmdet/__init__.py b/mmdet/__init__.py
new file mode 100644
index 0000000..4df16af
--- /dev/null
+++ b/mmdet/__init__.py
@@ -0,0 +1,29 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+
+from .version import __version__, short_version
+
+
+def digit_version(version_str):
+    digit_version = []
+    for x in version_str.split('.'):
+        if x.isdigit():
+            digit_version.append(int(x))
+        elif x.find('rc') != -1:
+            patch_version = x.split('rc')
+            digit_version.append(int(patch_version[0]) - 1)
+            digit_version.append(int(patch_version[1]))
+    return digit_version
+
+
+mmcv_minimum_version = '1.3.17'
+mmcv_maximum_version = '1.8.0'
+mmcv_version = digit_version(mmcv.__version__)
+
+
+assert (mmcv_version >= digit_version(mmcv_minimum_version)
+        and mmcv_version <= digit_version(mmcv_maximum_version)), \
+    f'MMCV=={mmcv.__version__} is used but incompatible. ' \
+    f'Please install mmcv>={mmcv_minimum_version}, <={mmcv_maximum_version}.'
+
+__all__ = ['__version__', 'short_version']
diff --git a/mmdet/apis/__init__.py b/mmdet/apis/__init__.py
new file mode 100644
index 0000000..3821d9c
--- /dev/null
+++ b/mmdet/apis/__init__.py
@@ -0,0 +1,12 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .inference import (async_inference_detector, inference_detector,
+                        init_detector, show_result_pyplot)
+from .test import multi_gpu_test, single_gpu_test
+from .train import (get_root_logger, init_random_seed, set_random_seed,
+                    train_detector, qat_detector)
+
+__all__ = [
+    'get_root_logger', 'set_random_seed', 'train_detector', 'qat_detector', 'init_detector',
+    'async_inference_detector', 'inference_detector', 'show_result_pyplot',
+    'multi_gpu_test', 'single_gpu_test', 'init_random_seed'
+]
diff --git a/mmdet/apis/inference.py b/mmdet/apis/inference.py
new file mode 100644
index 0000000..f0858a7
--- /dev/null
+++ b/mmdet/apis/inference.py
@@ -0,0 +1,257 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+from pathlib import Path
+
+import mmcv
+import numpy as np
+import torch
+from mmcv.ops import RoIPool
+from mmcv.parallel import collate, scatter
+from mmcv.runner import load_checkpoint
+
+from mmdet.core import get_classes
+from mmdet.datasets import replace_ImageToTensor
+from mmdet.datasets.pipelines import Compose
+from mmdet.models import build_detector
+
+
+def init_detector(config, checkpoint=None, device='cuda:0', cfg_options=None):
+    """Initialize a detector from config file.
+
+    Args:
+        config (str, :obj:`Path`, or :obj:`mmcv.Config`): Config file path,
+            :obj:`Path`, or the config object.
+        checkpoint (str, optional): Checkpoint path. If left as None, the model
+            will not load any weights.
+        cfg_options (dict): Options to override some settings in the used
+            config.
+
+    Returns:
+        nn.Module: The constructed detector.
+    """
+    if isinstance(config, (str, Path)):
+        config = mmcv.Config.fromfile(config)
+    elif not isinstance(config, mmcv.Config):
+        raise TypeError('config must be a filename or Config object, '
+                        f'but got {type(config)}')
+    if cfg_options is not None:
+        config.merge_from_dict(cfg_options)
+    if 'pretrained' in config.model:
+        config.model.pretrained = None
+    elif 'init_cfg' in config.model.backbone:
+        config.model.backbone.init_cfg = None
+    config.model.train_cfg = None
+    model = build_detector(config.model, test_cfg=config.get('test_cfg'))
+    if checkpoint is not None:
+        checkpoint = load_checkpoint(model, checkpoint, map_location='cpu')
+        if 'CLASSES' in checkpoint.get('meta', {}):
+            model.CLASSES = checkpoint['meta']['CLASSES']
+        else:
+            warnings.simplefilter('once')
+            warnings.warn('Class names are not saved in the checkpoint\'s '
+                          'meta data, use COCO classes by default.')
+            model.CLASSES = get_classes('coco')
+    model.cfg = config  # save the config in the model for convenience
+    model.to(device)
+    model.eval()
+
+    if device == 'npu':
+        from mmcv.device.npu import NPUDataParallel
+        model = NPUDataParallel(model)
+        model.cfg = config
+
+    return model
+
+
+class LoadImage:
+    """Deprecated.
+
+    A simple pipeline to load image.
+    """
+
+    def __call__(self, results):
+        """Call function to load images into results.
+
+        Args:
+            results (dict): A result dict contains the file name
+                of the image to be read.
+        Returns:
+            dict: ``results`` will be returned containing loaded image.
+        """
+        warnings.simplefilter('once')
+        warnings.warn('`LoadImage` is deprecated and will be removed in '
+                      'future releases. You may use `LoadImageFromWebcam` '
+                      'from `mmdet.datasets.pipelines.` instead.')
+        if isinstance(results['img'], str):
+            results['filename'] = results['img']
+            results['ori_filename'] = results['img']
+        else:
+            results['filename'] = None
+            results['ori_filename'] = None
+        img = mmcv.imread(results['img'])
+        results['img'] = img
+        results['img_fields'] = ['img']
+        results['img_shape'] = img.shape
+        results['ori_shape'] = img.shape
+        return results
+
+
+def inference_detector(model, imgs):
+    """Inference image(s) with the detector.
+
+    Args:
+        model (nn.Module): The loaded detector.
+        imgs (str/ndarray or list[str/ndarray] or tuple[str/ndarray]):
+           Either image files or loaded images.
+
+    Returns:
+        If imgs is a list or tuple, the same length list type results
+        will be returned, otherwise return the detection results directly.
+    """
+
+    if isinstance(imgs, (list, tuple)):
+        is_batch = True
+    else:
+        imgs = [imgs]
+        is_batch = False
+
+    cfg = model.cfg
+    device = next(model.parameters()).device  # model device
+
+    if isinstance(imgs[0], np.ndarray):
+        cfg = cfg.copy()
+        # set loading pipeline type
+        cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam'
+
+    cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
+    test_pipeline = Compose(cfg.data.test.pipeline)
+
+    datas = []
+    for img in imgs:
+        # prepare data
+        if isinstance(img, np.ndarray):
+            # directly add img
+            data = dict(img=img)
+        else:
+            # add information into dict
+            data = dict(img_info=dict(filename=img), img_prefix=None)
+        # build the data pipeline
+        data = test_pipeline(data)
+        datas.append(data)
+
+    data = collate(datas, samples_per_gpu=len(imgs))
+    # just get the actual data from DataContainer
+    data['img_metas'] = [img_metas.data[0] for img_metas in data['img_metas']]
+    data['img'] = [img.data[0] for img in data['img']]
+    if next(model.parameters()).is_cuda:
+        # scatter to specified GPU
+        data = scatter(data, [device])[0]
+    else:
+        for m in model.modules():
+            assert not isinstance(
+                m, RoIPool
+            ), 'CPU inference with RoIPool is not supported currently.'
+
+    # forward the model
+    with torch.no_grad():
+        results = model(return_loss=False, rescale=True, **data)
+
+    if not is_batch:
+        return results[0]
+    else:
+        return results
+
+
+async def async_inference_detector(model, imgs):
+    """Async inference image(s) with the detector.
+
+    Args:
+        model (nn.Module): The loaded detector.
+        img (str | ndarray): Either image files or loaded images.
+
+    Returns:
+        Awaitable detection results.
+    """
+    if not isinstance(imgs, (list, tuple)):
+        imgs = [imgs]
+
+    cfg = model.cfg
+    device = next(model.parameters()).device  # model device
+
+    if isinstance(imgs[0], np.ndarray):
+        cfg = cfg.copy()
+        # set loading pipeline type
+        cfg.data.test.pipeline[0].type = 'LoadImageFromWebcam'
+
+    cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
+    test_pipeline = Compose(cfg.data.test.pipeline)
+
+    datas = []
+    for img in imgs:
+        # prepare data
+        if isinstance(img, np.ndarray):
+            # directly add img
+            data = dict(img=img)
+        else:
+            # add information into dict
+            data = dict(img_info=dict(filename=img), img_prefix=None)
+        # build the data pipeline
+        data = test_pipeline(data)
+        datas.append(data)
+
+    data = collate(datas, samples_per_gpu=len(imgs))
+    # just get the actual data from DataContainer
+    data['img_metas'] = [img_metas.data[0] for img_metas in data['img_metas']]
+    data['img'] = [img.data[0] for img in data['img']]
+    if next(model.parameters()).is_cuda:
+        # scatter to specified GPU
+        data = scatter(data, [device])[0]
+    else:
+        for m in model.modules():
+            assert not isinstance(
+                m, RoIPool
+            ), 'CPU inference with RoIPool is not supported currently.'
+
+    # We don't restore `torch.is_grad_enabled()` value during concurrent
+    # inference since execution can overlap
+    torch.set_grad_enabled(False)
+    results = await model.aforward_test(rescale=True, **data)
+    return results
+
+
+def show_result_pyplot(model,
+                       img,
+                       result,
+                       score_thr=0.3,
+                       title='result',
+                       wait_time=0,
+                       palette=None,
+                       out_file=None):
+    """Visualize the detection results on the image.
+
+    Args:
+        model (nn.Module): The loaded detector.
+        img (str or np.ndarray): Image filename or loaded image.
+        result (tuple[list] or list): The detection result, can be either
+            (bbox, segm) or just bbox.
+        score_thr (float): The threshold to visualize the bboxes and masks.
+        title (str): Title of the pyplot figure.
+        wait_time (float): Value of waitKey param. Default: 0.
+        palette (str or tuple(int) or :obj:`Color`): Color.
+            The tuple of color should be in BGR order.
+        out_file (str or None): The path to write the image.
+            Default: None.
+    """
+    if hasattr(model, 'module'):
+        model = model.module
+    model.show_result(
+        img,
+        result,
+        score_thr=score_thr,
+        show=True,
+        wait_time=wait_time,
+        win_name=title,
+        bbox_color=palette,
+        text_color=(200, 200, 200),
+        mask_color=palette,
+        out_file=out_file)
diff --git a/mmdet/apis/test.py b/mmdet/apis/test.py
new file mode 100644
index 0000000..973d362
--- /dev/null
+++ b/mmdet/apis/test.py
@@ -0,0 +1,209 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import pickle
+import shutil
+import tempfile
+import time
+
+import mmcv
+import torch
+import torch.distributed as dist
+from mmcv.image import tensor2imgs
+from mmcv.runner import get_dist_info
+
+from mmdet.core import encode_mask_results
+
+
+def single_gpu_test(model,
+                    data_loader,
+                    show=False,
+                    out_dir=None,
+                    show_score_thr=0.3):
+    model.eval()
+    results = []
+    dataset = data_loader.dataset
+    PALETTE = getattr(dataset, 'PALETTE', None)
+    prog_bar = mmcv.ProgressBar(len(dataset))
+    for i, data in enumerate(data_loader):
+        with torch.no_grad():
+            result = model(return_loss=False, rescale=True, **data)
+
+        batch_size = len(result)
+        if show or out_dir:
+            if batch_size == 1 and isinstance(data['img'][0], torch.Tensor):
+                img_tensor = data['img'][0]
+            else:
+                img_tensor = data['img'][0].data[0]
+            img_metas = data['img_metas'][0].data[0]
+            imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
+            assert len(imgs) == len(img_metas)
+
+            for i, (img, img_meta) in enumerate(zip(imgs, img_metas)):
+                h, w, _ = img_meta['img_shape']
+                img_show = img[:h, :w, :]
+
+                ori_h, ori_w = img_meta['ori_shape'][:-1]
+                img_show = mmcv.imresize(img_show, (ori_w, ori_h))
+
+                if out_dir:
+                    out_file = osp.join(out_dir, img_meta['ori_filename'])
+                else:
+                    out_file = None
+
+                model.module.show_result(
+                    img_show,
+                    result[i],
+                    bbox_color=PALETTE,
+                    text_color=PALETTE,
+                    mask_color=PALETTE,
+                    show=show,
+                    out_file=out_file,
+                    score_thr=show_score_thr)
+
+        # encode mask results
+        if isinstance(result[0], tuple):
+            result = [(bbox_results, encode_mask_results(mask_results))
+                      for bbox_results, mask_results in result]
+        # This logic is only used in panoptic segmentation test.
+        elif isinstance(result[0], dict) and 'ins_results' in result[0]:
+            for j in range(len(result)):
+                bbox_results, mask_results = result[j]['ins_results']
+                result[j]['ins_results'] = (bbox_results,
+                                            encode_mask_results(mask_results))
+
+        results.extend(result)
+
+        for _ in range(batch_size):
+            prog_bar.update()
+    return results
+
+
+def multi_gpu_test(model, data_loader, tmpdir=None, gpu_collect=False):
+    """Test model with multiple gpus.
+
+    This method tests model with multiple gpus and collects the results
+    under two different modes: gpu and cpu modes. By setting 'gpu_collect=True'
+    it encodes results to gpu tensors and use gpu communication for results
+    collection. On cpu mode it saves the results on different gpus to 'tmpdir'
+    and collects them by the rank 0 worker.
+
+    Args:
+        model (nn.Module): Model to be tested.
+        data_loader (nn.Dataloader): Pytorch data loader.
+        tmpdir (str): Path of directory to save the temporary results from
+            different gpus under cpu mode.
+        gpu_collect (bool): Option to use either gpu or cpu to collect results.
+
+    Returns:
+        list: The prediction results.
+    """
+    model.eval()
+    results = []
+    dataset = data_loader.dataset
+    rank, world_size = get_dist_info()
+    if rank == 0:
+        prog_bar = mmcv.ProgressBar(len(dataset))
+    time.sleep(2)  # This line can prevent deadlock problem in some cases.
+    for i, data in enumerate(data_loader):
+        with torch.no_grad():
+            result = model(return_loss=False, rescale=True, **data)
+            # encode mask results
+            if isinstance(result[0], tuple):
+                result = [(bbox_results, encode_mask_results(mask_results))
+                          for bbox_results, mask_results in result]
+            # This logic is only used in panoptic segmentation test.
+            elif isinstance(result[0], dict) and 'ins_results' in result[0]:
+                for j in range(len(result)):
+                    bbox_results, mask_results = result[j]['ins_results']
+                    result[j]['ins_results'] = (
+                        bbox_results, encode_mask_results(mask_results))
+
+        results.extend(result)
+
+        if rank == 0:
+            batch_size = len(result)
+            for _ in range(batch_size * world_size):
+                prog_bar.update()
+
+    # collect results from all ranks
+    if gpu_collect:
+        results = collect_results_gpu(results, len(dataset))
+    else:
+        results = collect_results_cpu(results, len(dataset), tmpdir)
+    return results
+
+
+def collect_results_cpu(result_part, size, tmpdir=None):
+    rank, world_size = get_dist_info()
+    # create a tmp dir if it is not specified
+    if tmpdir is None:
+        MAX_LEN = 512
+        # 32 is whitespace
+        dir_tensor = torch.full((MAX_LEN, ),
+                                32,
+                                dtype=torch.uint8,
+                                device='cuda')
+        if rank == 0:
+            mmcv.mkdir_or_exist('.dist_test')
+            tmpdir = tempfile.mkdtemp(dir='.dist_test')
+            tmpdir = torch.tensor(
+                bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
+            dir_tensor[:len(tmpdir)] = tmpdir
+        dist.broadcast(dir_tensor, 0)
+        tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
+    else:
+        mmcv.mkdir_or_exist(tmpdir)
+    # dump the part result to the dir
+    mmcv.dump(result_part, osp.join(tmpdir, f'part_{rank}.pkl'))
+    dist.barrier()
+    # collect all parts
+    if rank != 0:
+        return None
+    else:
+        # load results of all parts from tmp dir
+        part_list = []
+        for i in range(world_size):
+            part_file = osp.join(tmpdir, f'part_{i}.pkl')
+            part_list.append(mmcv.load(part_file))
+        # sort the results
+        ordered_results = []
+        for res in zip(*part_list):
+            ordered_results.extend(list(res))
+        # the dataloader may pad some samples
+        ordered_results = ordered_results[:size]
+        # remove tmp dir
+        shutil.rmtree(tmpdir)
+        return ordered_results
+
+
+def collect_results_gpu(result_part, size):
+    rank, world_size = get_dist_info()
+    # dump result part to tensor with pickle
+    part_tensor = torch.tensor(
+        bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
+    # gather all result part tensor shape
+    shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
+    shape_list = [shape_tensor.clone() for _ in range(world_size)]
+    dist.all_gather(shape_list, shape_tensor)
+    # padding result part tensor to max length
+    shape_max = torch.tensor(shape_list).max()
+    part_send = torch.zeros(shape_max, dtype=torch.uint8, device='cuda')
+    part_send[:shape_tensor[0]] = part_tensor
+    part_recv_list = [
+        part_tensor.new_zeros(shape_max) for _ in range(world_size)
+    ]
+    # gather all result part
+    dist.all_gather(part_recv_list, part_send)
+
+    if rank == 0:
+        part_list = []
+        for recv, shape in zip(part_recv_list, shape_list):
+            part_list.append(
+                pickle.loads(recv[:shape[0]].cpu().numpy().tobytes()))
+        # sort the results
+        ordered_results = []
+        for res in zip(*part_list):
+            ordered_results.extend(list(res))
+        # the dataloader may pad some samples
+        ordered_results = ordered_results[:size]
+        return ordered_results
diff --git a/mmdet/apis/train.py b/mmdet/apis/train.py
new file mode 100644
index 0000000..e15bcbf
--- /dev/null
+++ b/mmdet/apis/train.py
@@ -0,0 +1,482 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import random
+
+import numpy as np
+import torch
+import torch.distributed as dist
+from mmcv.runner import (DistSamplerSeedHook, EpochBasedRunner, load_checkpoint,
+                         Fp16OptimizerHook, OptimizerHook, build_runner,
+                         get_dist_info)
+from mmdet.core import DistEvalHook, EvalHook, build_optimizer
+from mmdet.datasets import (build_dataloader, build_dataset,
+                            replace_ImageToTensor)
+from mmdet.utils import (build_ddp, build_dp, compat_cfg,
+                         find_latest_checkpoint, get_root_logger)
+from mmdet.apis.test import single_gpu_test
+from torch.fx import Tracer
+import torch.fx as fx
+from mqb_general_process import *
+from mqbench.utils.state import *
+
+import mmcv
+import torch
+import torch.distributed as dist
+from mmcv.image import tensor2imgs
+from mmcv.runner import get_dist_info
+
+from mmdet.core import encode_mask_results
+from copy import deepcopy
+
+def init_random_seed(seed=None, device='cuda'):
+    """Initialize random seed.
+
+    If the seed is not set, the seed will be automatically randomized,
+    and then broadcast to all processes to prevent some potential bugs.
+
+    Args:
+        seed (int, Optional): The seed. Default to None.
+        device (str): The device where the seed will be put on.
+            Default to 'cuda'.
+
+    Returns:
+        int: Seed to be used.
+    """
+    if seed is not None:
+        return seed
+
+    # Make sure all ranks share the same random seed to prevent
+    # some potential bugs. Please refer to
+    # https://github.com/open-mmlab/mmdetection/issues/6339
+    rank, world_size = get_dist_info()
+    seed = np.random.randint(2**31)
+    if world_size == 1:
+        return seed
+
+    if rank == 0:
+        random_num = torch.tensor(seed, dtype=torch.int32, device=device)
+    else:
+        random_num = torch.tensor(0, dtype=torch.int32, device=device)
+    dist.broadcast(random_num, src=0)
+    return random_num.item()
+
+
+def set_random_seed(seed, deterministic=False):
+    """Set random seed.
+
+    Args:
+        seed (int): Seed to be used.
+        deterministic (bool): Whether to set the deterministic option for
+            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
+            to True and `torch.backends.cudnn.benchmark` to False.
+            Default: False.
+    """
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    if deterministic:
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.benchmark = False
+
+
+def auto_scale_lr(cfg, distributed, logger):
+    """Automatically scaling LR according to GPU number and sample per GPU.
+
+    Args:
+        cfg (config): Training config.
+        distributed (bool): Using distributed or not.
+        logger (logging.Logger): Logger.
+    """
+    # Get flag from config
+    if ('auto_scale_lr' not in cfg) or \
+            (not cfg.auto_scale_lr.get('enable', False)):
+        logger.info('Automatic scaling of learning rate (LR)'
+                    ' has been disabled.')
+        return
+
+    # Get base batch size from config
+    base_batch_size = cfg.auto_scale_lr.get('base_batch_size', None)
+    if base_batch_size is None:
+        return
+
+    # Get gpu number
+    if distributed:
+        _, world_size = get_dist_info()
+        num_gpus = len(range(world_size))
+    else:
+        num_gpus = len(cfg.gpu_ids)
+
+    # calculate the batch size
+    samples_per_gpu = cfg.data.train_dataloader.samples_per_gpu
+    batch_size = num_gpus * samples_per_gpu
+    logger.info(f'Training with {num_gpus} GPU(s) with {samples_per_gpu} '
+                f'samples per GPU. The total batch size is {batch_size}.')
+
+    if batch_size != base_batch_size:
+        # scale LR with
+        # [linear scaling rule](https://arxiv.org/abs/1706.02677)
+        scaled_lr = (batch_size / base_batch_size) * cfg.optimizer.lr
+        logger.info('LR has been automatically scaled '
+                    f'from {cfg.optimizer.lr} to {scaled_lr}')
+        cfg.optimizer.lr = scaled_lr
+    else:
+        logger.info('The batch size match the '
+                    f'base batch size: {base_batch_size}, '
+                    f'will not scaling the LR ({cfg.optimizer.lr}).')
+
+def calibrate(model, data_loader, cali_num):  # TODO 这里给修改成达到一定次数后就break；然后只用eval_data
+    model.eval()
+    if isinstance(data_loader, list):
+        dataset = data_loader[0].dataset
+        data_loader = data_loader[0]
+    else:
+        dataset = data_loader.dataset
+    PALETTE = getattr(dataset, 'PALETTE', None)
+    
+    rank, world_size = get_dist_info()
+    
+    if rank == 0:
+        prog_bar = mmcv.ProgressBar(cali_num)
+    # prog_bar = mmcv.ProgressBar(cali_num)
+    steped_num = 0
+    for i, data in enumerate(data_loader):
+        with torch.no_grad():
+            data['img'] = data['img'].data
+            data['img_metas'] = data['img_metas'].data
+            data.pop('gt_bboxes')
+            data.pop('gt_labels')
+            result = model(return_loss=False, rescale=True, **data)
+        batch_size = len(result)
+        steped_num += batch_size * world_size
+        dist.barrier()
+        if rank == 0:
+            for _ in range(batch_size * world_size):
+                prog_bar.update()
+        if steped_num >= cali_num:
+            logger.info(f'Truly calibrate num {steped_num}')
+            break
+
+
+def qat_detector(model,
+                   dataset,
+                   cfg,
+                   quant_config,
+                   distributed=False,
+                   validate=False,
+                   timestamp=None,
+                   meta=None):
+
+    cfg = compat_cfg(cfg)
+    logger = get_root_logger(log_level=cfg.log_level)
+
+    # prepare data loaders
+    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
+
+    runner_type = 'EpochBasedRunner' if 'runner' not in cfg else cfg.runner[
+        'type']
+
+    train_dataloader_default_args = dict(
+        samples_per_gpu=2,
+        workers_per_gpu=2,
+        # `num_gpus` will be ignored if distributed
+        num_gpus=len(cfg.gpu_ids),
+        dist=distributed,
+        seed=cfg.seed,
+        runner_type=runner_type,
+        persistent_workers=False)
+
+    train_loader_cfg = {
+        **train_dataloader_default_args,
+        **cfg.data.get('train_dataloader', {})
+    }
+    
+
+    data_loaders = [build_dataloader(ds, **train_loader_cfg, pin_memory=True) for ds in dataset]
+    
+    # cali loader的设置
+    cali_loader_cfg = deepcopy(train_loader_cfg)
+    cali_loader_cfg['samples_per_gpu'] = 1
+    cali_loader_cfg['workers_per_gpu'] = 1
+    cali_data_loader = [build_dataloader(ds, **cali_loader_cfg, pin_memory=True) for ds in dataset][0]
+    
+    resume_from = None
+    if cfg.resume_from is None and cfg.get('auto_resume'):
+        resume_from = find_latest_checkpoint(cfg.work_dir)
+    if resume_from is not None:
+        cfg.resume_from = resume_from
+
+    elif cfg.load_from:  # 这个应该可以解决所谓的quant baseline问题
+        checkpoint = load_checkpoint(model, cfg.load_from, map_location='cpu')  # TODO 测一下这样的evaluate对不对
+    else:
+        checkpoint = {}
+    # old versions did not save class info in checkpoints, this walkaround is
+    # for backward compatibility
+    if 'CLASSES' in checkpoint.get('meta', {}):
+        model.CLASSES = checkpoint['meta']['CLASSES']
+    else:
+        model.CLASSES = dataset[0].CLASSES
+        
+        
+    # print('\nGet FakeQuant model\n')
+    # model.backbone = get_quantize_model(model.backbone, quant_config, cfg.trace_config.backbone_detail)  # QAT时，这个需要eval还是train
+    # model.neck = get_quantize_model(model.neck, quant_config, cfg.trace_config.neck_detail)  # QAT时，这个需要eval还是train
+    # temp = get_quantize_model(model.bbox_head, quant_config, cfg.trace_config.bbox_head_detail)  # QAT时，这个需要eval还是train
+    # model.bbox_head.forward = temp.forward  # 太傻蛋勒
+    # model.bbox_head = temp
+    model.train()  # prepare 前一定得是train模式
+    model = make_qmodel_for_mmd(model, quant_config, cfg.trace_config)
+    
+    if not hasattr(cfg, 'tune_from'):
+        cfg.tune_from = False
+    if cfg.tune_from:  # 加载进行tune
+        _ = load_checkpoint(model, cfg.tune_from, map_location='cpu')
+    
+    # put model on gpus
+    if distributed:
+        find_unused_parameters = cfg.get('find_unused_parameters', False)
+        # Sets the `find_unused_parameters` parameter in
+        # torch.nn.parallel.DistributedDataParallel
+        model = build_ddp(  # 细节不给看
+            model,
+            cfg.device,
+            device_ids=[int(os.environ['LOCAL_RANK'])],
+            broadcast_buffers=False,
+            find_unused_parameters=find_unused_parameters)
+    else:
+        model = build_dp(model, cfg.device, device_ids=cfg.gpu_ids)
+
+    # build optimizer
+    auto_scale_lr(cfg, distributed, logger)
+    optimizer = build_optimizer(model, cfg.optimizer)
+
+    runner = build_runner(
+        cfg.runner,
+        default_args=dict(
+            model=model,
+            optimizer=optimizer,
+            work_dir=cfg.work_dir,
+            logger=logger,
+            meta=meta))
+
+    # an ugly workaround to make .log and .log.json filenames the same
+    runner.timestamp = timestamp
+
+    # fp16 setting
+    fp16_cfg = cfg.get('fp16', None)
+    if fp16_cfg is None and cfg.get('device', None) == 'npu':
+        fp16_cfg = dict(loss_scale='dynamic')
+    if fp16_cfg is not None:
+        optimizer_config = Fp16OptimizerHook(
+            **cfg.optimizer_config, **fp16_cfg, distributed=distributed)
+    elif distributed and 'type' not in cfg.optimizer_config:
+        optimizer_config = OptimizerHook(**cfg.optimizer_config)
+    else:
+        optimizer_config = cfg.optimizer_config
+
+    # register hooks
+    runner.register_training_hooks(
+        cfg.lr_config,
+        optimizer_config,
+        cfg.checkpoint_config,
+        cfg.log_config,
+        cfg.get('momentum_config', None),
+        custom_hooks_config=cfg.get('custom_hooks', None))
+
+    if distributed:
+        if isinstance(runner, EpochBasedRunner):
+            runner.register_hook(DistSamplerSeedHook())
+
+    # register eval hooks
+    if validate:
+        val_dataloader_default_args = dict(
+            samples_per_gpu=1,
+            workers_per_gpu=2,
+            dist=distributed,
+            shuffle=False,
+            persistent_workers=False)
+
+        val_dataloader_args = {
+            **val_dataloader_default_args,
+            **cfg.data.get('val_dataloader', {})
+        }
+        # Support batch_size > 1 in validation
+
+        if val_dataloader_args['samples_per_gpu'] > 1:
+            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
+            cfg.data.val.pipeline = replace_ImageToTensor(
+                cfg.data.val.pipeline)
+        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
+
+        val_dataloader = build_dataloader(val_dataset, **val_dataloader_args)
+        eval_cfg = cfg.get('evaluation', {})
+        eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
+        eval_hook = DistEvalHook if distributed else EvalHook
+        # In this PR (https://github.com/open-mmlab/mmcv/pull/1193), the
+        # priority of IterTimerHook has been modified from 'NORMAL' to 'LOW'.
+        runner.register_hook(
+            eval_hook(val_dataloader, **eval_cfg), priority='LOW')
+
+    # cali_data_loader = deepcopy(val_dataloader)  # TODO 这里改成train的
+    if not cfg.resume_from and not cfg.tune_from:
+        print('\nCalibrating\n')
+        enable_calibration(model.module.backbone)
+        enable_calibration(model.module.neck)
+        model_general_architecture = cfg.trace_config.get('model_general_architecture', None)
+        if model_general_architecture == 'FasterRCNN':
+            enable_calibration(model.module.rpn_head)
+            enable_calibration(model.module.roi_head.bbox_head)
+        else:
+            enable_calibration(model.module.bbox_head)
+        
+        calibrate(model, cali_data_loader
+                #   , 1
+                  , quant_config.quantize.cali_batchnum
+                  )  # NOTE 训练集的data确实全带container
+    # 清除cali loader，释放内存
+    del cali_data_loader
+    
+    enable_quantization(model.module.backbone)
+    enable_quantization(model.module.neck)
+    model_general_architecture = cfg.trace_config.get('model_general_architecture', None)
+    if model_general_architecture == 'FasterRCNN':
+        enable_quantization(model.module.rpn_head)
+        enable_quantization(model.module.roi_head.bbox_head)
+    else:
+        enable_quantization(model.module.bbox_head)
+        
+    
+    if cfg.resume_from:
+        runner.resume(cfg.resume_from)
+    runner.run(data_loaders, cfg.workflow)
+    
+def train_detector(model,
+                   dataset,
+                   cfg,
+                   distributed=False,
+                   validate=False,
+                   timestamp=None,
+                   meta=None):
+
+    cfg = compat_cfg(cfg)
+    logger = get_root_logger(log_level=cfg.log_level)
+
+    # prepare data loaders
+    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
+
+    runner_type = 'EpochBasedRunner' if 'runner' not in cfg else cfg.runner[
+        'type']
+
+    train_dataloader_default_args = dict(
+        samples_per_gpu=2,
+        workers_per_gpu=2,
+        # `num_gpus` will be ignored if distributed
+        num_gpus=len(cfg.gpu_ids),
+        dist=distributed,
+        seed=cfg.seed,
+        runner_type=runner_type,
+        persistent_workers=False)
+
+    train_loader_cfg = {
+        **train_dataloader_default_args,
+        **cfg.data.get('train_dataloader', {})
+    }
+
+    data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]
+
+    # put model on gpus
+    if distributed:
+        find_unused_parameters = cfg.get('find_unused_parameters', False)
+        # Sets the `find_unused_parameters` parameter in
+        # torch.nn.parallel.DistributedDataParallel
+        model = build_ddp(  # 细节不给看
+            model,
+            cfg.device,
+            device_ids=[int(os.environ['LOCAL_RANK'])],
+            broadcast_buffers=False,
+            find_unused_parameters=find_unused_parameters)
+    else:
+        model = build_dp(model, cfg.device, device_ids=cfg.gpu_ids)
+
+    # build optimizer
+    auto_scale_lr(cfg, distributed, logger)
+    optimizer = build_optimizer(model, cfg.optimizer)
+
+    runner = build_runner(
+        cfg.runner,
+        default_args=dict(
+            model=model,
+            optimizer=optimizer,
+            work_dir=cfg.work_dir,
+            logger=logger,
+            meta=meta))
+
+    # an ugly workaround to make .log and .log.json filenames the same
+    runner.timestamp = timestamp
+
+    # fp16 setting
+    fp16_cfg = cfg.get('fp16', None)
+    if fp16_cfg is None and cfg.get('device', None) == 'npu':
+        fp16_cfg = dict(loss_scale='dynamic')
+    if fp16_cfg is not None:
+        optimizer_config = Fp16OptimizerHook(
+            **cfg.optimizer_config, **fp16_cfg, distributed=distributed)
+    elif distributed and 'type' not in cfg.optimizer_config:
+        optimizer_config = OptimizerHook(**cfg.optimizer_config)
+    else:
+        optimizer_config = cfg.optimizer_config
+
+    # register hooks
+    runner.register_training_hooks(
+        cfg.lr_config,
+        optimizer_config,
+        cfg.checkpoint_config,
+        cfg.log_config,
+        cfg.get('momentum_config', None),
+        custom_hooks_config=cfg.get('custom_hooks', None))
+
+    if distributed:
+        if isinstance(runner, EpochBasedRunner):
+            runner.register_hook(DistSamplerSeedHook())
+
+    # register eval hooks
+    if validate:
+        val_dataloader_default_args = dict(
+            samples_per_gpu=1,
+            workers_per_gpu=2,
+            dist=distributed,
+            shuffle=False,
+            persistent_workers=False)
+
+        val_dataloader_args = {
+            **val_dataloader_default_args,
+            **cfg.data.get('val_dataloader', {})
+        }
+        # Support batch_size > 1 in validation
+
+        if val_dataloader_args['samples_per_gpu'] > 1:
+            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
+            cfg.data.val.pipeline = replace_ImageToTensor(
+                cfg.data.val.pipeline)
+        val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
+
+        val_dataloader = build_dataloader(val_dataset, **val_dataloader_args)
+        eval_cfg = cfg.get('evaluation', {})
+        eval_cfg['by_epoch'] = cfg.runner['type'] != 'IterBasedRunner'
+        eval_hook = DistEvalHook if distributed else EvalHook
+        # In this PR (https://github.com/open-mmlab/mmcv/pull/1193), the
+        # priority of IterTimerHook has been modified from 'NORMAL' to 'LOW'.
+        runner.register_hook(
+            eval_hook(val_dataloader, **eval_cfg), priority='LOW')
+
+    resume_from = None
+    if cfg.resume_from is None and cfg.get('auto_resume'):
+        resume_from = find_latest_checkpoint(cfg.work_dir)
+    if resume_from is not None:
+        cfg.resume_from = resume_from
+
+    if cfg.resume_from:
+        runner.resume(cfg.resume_from)
+    elif cfg.load_from:
+        runner.load_checkpoint(cfg.load_from)
+    runner.run(data_loaders, cfg.workflow)
diff --git a/mmdet/core/__init__.py b/mmdet/core/__init__.py
new file mode 100644
index 0000000..2a62038
--- /dev/null
+++ b/mmdet/core/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .anchor import *  # noqa: F401, F403
+from .bbox import *  # noqa: F401, F403
+from .data_structures import *  # noqa: F401, F403
+from .evaluation import *  # noqa: F401, F403
+from .hook import *  # noqa: F401, F403
+from .mask import *  # noqa: F401, F403
+from .optimizers import *  # noqa: F401, F403
+from .post_processing import *  # noqa: F401, F403
+from .utils import *  # noqa: F401, F403
diff --git a/mmdet/core/anchor/__init__.py b/mmdet/core/anchor/__init__.py
new file mode 100644
index 0000000..fcc7e4a
--- /dev/null
+++ b/mmdet/core/anchor/__init__.py
@@ -0,0 +1,14 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .anchor_generator import (AnchorGenerator, LegacyAnchorGenerator,
+                               YOLOAnchorGenerator)
+from .builder import (ANCHOR_GENERATORS, PRIOR_GENERATORS,
+                      build_anchor_generator, build_prior_generator)
+from .point_generator import MlvlPointGenerator, PointGenerator
+from .utils import anchor_inside_flags, calc_region, images_to_levels
+
+__all__ = [
+    'AnchorGenerator', 'LegacyAnchorGenerator', 'anchor_inside_flags',
+    'PointGenerator', 'images_to_levels', 'calc_region',
+    'build_anchor_generator', 'ANCHOR_GENERATORS', 'YOLOAnchorGenerator',
+    'build_prior_generator', 'PRIOR_GENERATORS', 'MlvlPointGenerator'
+]
diff --git a/mmdet/core/anchor/anchor_generator.py b/mmdet/core/anchor/anchor_generator.py
new file mode 100644
index 0000000..20886fb
--- /dev/null
+++ b/mmdet/core/anchor/anchor_generator.py
@@ -0,0 +1,866 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import mmcv
+import numpy as np
+import torch
+from torch.nn.modules.utils import _pair
+
+from .builder import PRIOR_GENERATORS
+
+
+@PRIOR_GENERATORS.register_module()
+class AnchorGenerator:
+    """Standard anchor generator for 2D anchor-based detectors.
+
+    Args:
+        strides (list[int] | list[tuple[int, int]]): Strides of anchors
+            in multiple feature levels in order (w, h).
+        ratios (list[float]): The list of ratios between the height and width
+            of anchors in a single level.
+        scales (list[int] | None): Anchor scales for anchors in a single level.
+            It cannot be set at the same time if `octave_base_scale` and
+            `scales_per_octave` are set.
+        base_sizes (list[int] | None): The basic sizes
+            of anchors in multiple levels.
+            If None is given, strides will be used as base_sizes.
+            (If strides are non square, the shortest stride is taken.)
+        scale_major (bool): Whether to multiply scales first when generating
+            base anchors. If true, the anchors in the same row will have the
+            same scales. By default it is True in V2.0
+        octave_base_scale (int): The base scale of octave.
+        scales_per_octave (int): Number of scales for each octave.
+            `octave_base_scale` and `scales_per_octave` are usually used in
+            retinanet and the `scales` should be None when they are set.
+        centers (list[tuple[float, float]] | None): The centers of the anchor
+            relative to the feature grid center in multiple feature levels.
+            By default it is set to be None and not used. If a list of tuple of
+            float is given, they will be used to shift the centers of anchors.
+        center_offset (float): The offset of center in proportion to anchors'
+            width and height. By default it is 0 in V2.0.
+
+    Examples:
+        >>> from mmdet.core import AnchorGenerator
+        >>> self = AnchorGenerator([16], [1.], [1.], [9])
+        >>> all_anchors = self.grid_priors([(2, 2)], device='cpu')
+        >>> print(all_anchors)
+        [tensor([[-4.5000, -4.5000,  4.5000,  4.5000],
+                [11.5000, -4.5000, 20.5000,  4.5000],
+                [-4.5000, 11.5000,  4.5000, 20.5000],
+                [11.5000, 11.5000, 20.5000, 20.5000]])]
+        >>> self = AnchorGenerator([16, 32], [1.], [1.], [9, 18])
+        >>> all_anchors = self.grid_priors([(2, 2), (1, 1)], device='cpu')
+        >>> print(all_anchors)
+        [tensor([[-4.5000, -4.5000,  4.5000,  4.5000],
+                [11.5000, -4.5000, 20.5000,  4.5000],
+                [-4.5000, 11.5000,  4.5000, 20.5000],
+                [11.5000, 11.5000, 20.5000, 20.5000]]), \
+        tensor([[-9., -9., 9., 9.]])]
+    """
+
+    def __init__(self,
+                 strides,
+                 ratios,
+                 scales=None,
+                 base_sizes=None,
+                 scale_major=True,
+                 octave_base_scale=None,
+                 scales_per_octave=None,
+                 centers=None,
+                 center_offset=0.):
+        # check center and center_offset
+        if center_offset != 0:
+            assert centers is None, 'center cannot be set when center_offset' \
+                                    f'!=0, {centers} is given.'
+        if not (0 <= center_offset <= 1):
+            raise ValueError('center_offset should be in range [0, 1], '
+                             f'{center_offset} is given.')
+        if centers is not None:
+            assert len(centers) == len(strides), \
+                'The number of strides should be the same as centers, got ' \
+                f'{strides} and {centers}'
+
+        # calculate base sizes of anchors
+        self.strides = [_pair(stride) for stride in strides]
+        self.base_sizes = [min(stride) for stride in self.strides
+                           ] if base_sizes is None else base_sizes
+        assert len(self.base_sizes) == len(self.strides), \
+            'The number of strides should be the same as base sizes, got ' \
+            f'{self.strides} and {self.base_sizes}'
+
+        # calculate scales of anchors
+        assert ((octave_base_scale is not None
+                 and scales_per_octave is not None) ^ (scales is not None)), \
+            'scales and octave_base_scale with scales_per_octave cannot' \
+            ' be set at the same time'
+        if scales is not None:
+            self.scales = torch.Tensor(scales)
+        elif octave_base_scale is not None and scales_per_octave is not None:
+            octave_scales = np.array(
+                [2**(i / scales_per_octave) for i in range(scales_per_octave)])
+            scales = octave_scales * octave_base_scale
+            self.scales = torch.Tensor(scales)
+        else:
+            raise ValueError('Either scales or octave_base_scale with '
+                             'scales_per_octave should be set')
+
+        self.octave_base_scale = octave_base_scale
+        self.scales_per_octave = scales_per_octave
+        self.ratios = torch.Tensor(ratios)
+        self.scale_major = scale_major
+        self.centers = centers
+        self.center_offset = center_offset
+        self.base_anchors = self.gen_base_anchors()
+
+    @property
+    def num_base_anchors(self):
+        """list[int]: total number of base anchors in a feature grid"""
+        return self.num_base_priors
+
+    @property
+    def num_base_priors(self):
+        """list[int]: The number of priors (anchors) at a point
+        on the feature grid"""
+        return [base_anchors.size(0) for base_anchors in self.base_anchors]
+
+    @property
+    def num_levels(self):
+        """int: number of feature levels that the generator will be applied"""
+        return len(self.strides)
+
+    def gen_base_anchors(self):
+        """Generate base anchors.
+
+        Returns:
+            list(torch.Tensor): Base anchors of a feature grid in multiple \
+                feature levels.
+        """
+        multi_level_base_anchors = []
+        for i, base_size in enumerate(self.base_sizes):
+            center = None
+            if self.centers is not None:
+                center = self.centers[i]
+            multi_level_base_anchors.append(
+                self.gen_single_level_base_anchors(
+                    base_size,
+                    scales=self.scales,
+                    ratios=self.ratios,
+                    center=center))
+        return multi_level_base_anchors
+
+    def gen_single_level_base_anchors(self,
+                                      base_size,
+                                      scales,
+                                      ratios,
+                                      center=None):
+        """Generate base anchors of a single level.
+
+        Args:
+            base_size (int | float): Basic size of an anchor.
+            scales (torch.Tensor): Scales of the anchor.
+            ratios (torch.Tensor): The ratio between between the height
+                and width of anchors in a single level.
+            center (tuple[float], optional): The center of the base anchor
+                related to a single feature grid. Defaults to None.
+
+        Returns:
+            torch.Tensor: Anchors in a single-level feature maps.
+        """
+        w = base_size
+        h = base_size
+        if center is None:
+            x_center = self.center_offset * w
+            y_center = self.center_offset * h
+        else:
+            x_center, y_center = center
+
+        h_ratios = torch.sqrt(ratios)
+        w_ratios = 1 / h_ratios
+        if self.scale_major:
+            ws = (w * w_ratios[:, None] * scales[None, :]).view(-1)
+            hs = (h * h_ratios[:, None] * scales[None, :]).view(-1)
+        else:
+            ws = (w * scales[:, None] * w_ratios[None, :]).view(-1)
+            hs = (h * scales[:, None] * h_ratios[None, :]).view(-1)
+
+        # use float anchor and the anchor's center is aligned with the
+        # pixel center
+        base_anchors = [
+            x_center - 0.5 * ws, y_center - 0.5 * hs, x_center + 0.5 * ws,
+            y_center + 0.5 * hs
+        ]
+        base_anchors = torch.stack(base_anchors, dim=-1)
+
+        return base_anchors
+
+    def _meshgrid(self, x, y, row_major=True):
+        """Generate mesh grid of x and y.
+
+        Args:
+            x (torch.Tensor): Grids of x dimension.
+            y (torch.Tensor): Grids of y dimension.
+            row_major (bool, optional): Whether to return y grids first.
+                Defaults to True.
+
+        Returns:
+            tuple[torch.Tensor]: The mesh grids of x and y.
+        """
+        # use shape instead of len to keep tracing while exporting to onnx
+        xx = x.repeat(y.shape[0])
+        yy = y.view(-1, 1).repeat(1, x.shape[0]).view(-1)
+        if row_major:
+            return xx, yy
+        else:
+            return yy, xx
+
+    def grid_priors(self, featmap_sizes, dtype=torch.float32, device='cuda'):
+        """Generate grid anchors in multiple feature levels.
+
+        Args:
+            featmap_sizes (list[tuple]): List of feature map sizes in
+                multiple feature levels.
+            dtype (:obj:`torch.dtype`): Dtype of priors.
+                Default: torch.float32.
+            device (str): The device where the anchors will be put on.
+
+        Return:
+            list[torch.Tensor]: Anchors in multiple feature levels. \
+                The sizes of each tensor should be [N, 4], where \
+                N = width * height * num_base_anchors, width and height \
+                are the sizes of the corresponding feature level, \
+                num_base_anchors is the number of anchors for that level.
+        """
+        assert self.num_levels == len(featmap_sizes)
+        multi_level_anchors = []
+        for i in range(self.num_levels):
+            anchors = self.single_level_grid_priors(
+                featmap_sizes[i], level_idx=i, dtype=dtype, device=device)
+            multi_level_anchors.append(anchors)
+        return multi_level_anchors
+
+    def single_level_grid_priors(self,
+                                 featmap_size,
+                                 level_idx,
+                                 dtype=torch.float32,
+                                 device='cuda'):
+        """Generate grid anchors of a single level.
+
+        Note:
+            This function is usually called by method ``self.grid_priors``.
+
+        Args:
+            featmap_size (tuple[int]): Size of the feature maps.
+            level_idx (int): The index of corresponding feature map level.
+            dtype (obj:`torch.dtype`): Date type of points.Defaults to
+                ``torch.float32``.
+            device (str, optional): The device the tensor will be put on.
+                Defaults to 'cuda'.
+
+        Returns:
+            torch.Tensor: Anchors in the overall feature maps.
+        """
+
+        base_anchors = self.base_anchors[level_idx].to(device).to(dtype)
+        feat_h, feat_w = featmap_size
+        stride_w, stride_h = self.strides[level_idx]
+        # First create Range with the default dtype, than convert to
+        # target `dtype` for onnx exporting.
+        shift_x = torch.arange(0, feat_w, device=device).to(dtype) * stride_w
+        shift_y = torch.arange(0, feat_h, device=device).to(dtype) * stride_h
+
+        shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
+        shifts = torch.stack([shift_xx, shift_yy, shift_xx, shift_yy], dim=-1)
+        # first feat_w elements correspond to the first row of shifts
+        # add A anchors (1, A, 4) to K shifts (K, 1, 4) to get
+        # shifted anchors (K, A, 4), reshape to (K*A, 4)
+
+        all_anchors = base_anchors[None, :, :] + shifts[:, None, :]
+        all_anchors = all_anchors.view(-1, 4)
+        # first A rows correspond to A anchors of (0, 0) in feature map,
+        # then (0, 1), (0, 2), ...
+        return all_anchors
+
+    def sparse_priors(self,
+                      prior_idxs,
+                      featmap_size,
+                      level_idx,
+                      dtype=torch.float32,
+                      device='cuda'):
+        """Generate sparse anchors according to the ``prior_idxs``.
+
+        Args:
+            prior_idxs (Tensor): The index of corresponding anchors
+                in the feature map.
+            featmap_size (tuple[int]): feature map size arrange as (h, w).
+            level_idx (int): The level index of corresponding feature
+                map.
+            dtype (obj:`torch.dtype`): Date type of points.Defaults to
+                ``torch.float32``.
+            device (obj:`torch.device`): The device where the points is
+                located.
+        Returns:
+            Tensor: Anchor with shape (N, 4), N should be equal to
+                the length of ``prior_idxs``.
+        """
+
+        height, width = featmap_size
+        num_base_anchors = self.num_base_anchors[level_idx]
+        base_anchor_id = prior_idxs % num_base_anchors
+        x = (prior_idxs //
+             num_base_anchors) % width * self.strides[level_idx][0]
+        y = (prior_idxs // width //
+             num_base_anchors) % height * self.strides[level_idx][1]
+        priors = torch.stack([x, y, x, y], 1).to(dtype).to(device) + \
+            self.base_anchors[level_idx][base_anchor_id, :].to(device)
+
+        return priors
+
+    def grid_anchors(self, featmap_sizes, device='cuda'):
+        """Generate grid anchors in multiple feature levels.
+
+        Args:
+            featmap_sizes (list[tuple]): List of feature map sizes in
+                multiple feature levels.
+            device (str): Device where the anchors will be put on.
+
+        Return:
+            list[torch.Tensor]: Anchors in multiple feature levels. \
+                The sizes of each tensor should be [N, 4], where \
+                N = width * height * num_base_anchors, width and height \
+                are the sizes of the corresponding feature level, \
+                num_base_anchors is the number of anchors for that level.
+        """
+        warnings.warn('``grid_anchors`` would be deprecated soon. '
+                      'Please use ``grid_priors`` ')
+
+        assert self.num_levels == len(featmap_sizes)
+        multi_level_anchors = []
+        for i in range(self.num_levels):
+            anchors = self.single_level_grid_anchors(
+                self.base_anchors[i].to(device),
+                featmap_sizes[i],
+                self.strides[i],
+                device=device)
+            multi_level_anchors.append(anchors)
+        return multi_level_anchors
+
+    def single_level_grid_anchors(self,
+                                  base_anchors,
+                                  featmap_size,
+                                  stride=(16, 16),
+                                  device='cuda'):
+        """Generate grid anchors of a single level.
+
+        Note:
+            This function is usually called by method ``self.grid_anchors``.
+
+        Args:
+            base_anchors (torch.Tensor): The base anchors of a feature grid.
+            featmap_size (tuple[int]): Size of the feature maps.
+            stride (tuple[int], optional): Stride of the feature map in order
+                (w, h). Defaults to (16, 16).
+            device (str, optional): Device the tensor will be put on.
+                Defaults to 'cuda'.
+
+        Returns:
+            torch.Tensor: Anchors in the overall feature maps.
+        """
+
+        warnings.warn(
+            '``single_level_grid_anchors`` would be deprecated soon. '
+            'Please use ``single_level_grid_priors`` ')
+
+        # keep featmap_size as Tensor instead of int, so that we
+        # can convert to ONNX correctly
+        feat_h, feat_w = featmap_size
+        shift_x = torch.arange(0, feat_w, device=device) * stride[0]
+        shift_y = torch.arange(0, feat_h, device=device) * stride[1]
+
+        shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
+        shifts = torch.stack([shift_xx, shift_yy, shift_xx, shift_yy], dim=-1)
+        shifts = shifts.type_as(base_anchors)
+        # first feat_w elements correspond to the first row of shifts
+        # add A anchors (1, A, 4) to K shifts (K, 1, 4) to get
+        # shifted anchors (K, A, 4), reshape to (K*A, 4)
+
+        all_anchors = base_anchors[None, :, :] + shifts[:, None, :]
+        all_anchors = all_anchors.view(-1, 4)
+        # first A rows correspond to A anchors of (0, 0) in feature map,
+        # then (0, 1), (0, 2), ...
+        return all_anchors
+
+    def valid_flags(self, featmap_sizes, pad_shape, device='cuda'):
+        """Generate valid flags of anchors in multiple feature levels.
+
+        Args:
+            featmap_sizes (list(tuple)): List of feature map sizes in
+                multiple feature levels.
+            pad_shape (tuple): The padded shape of the image.
+            device (str): Device where the anchors will be put on.
+
+        Return:
+            list(torch.Tensor): Valid flags of anchors in multiple levels.
+        """
+        assert self.num_levels == len(featmap_sizes)
+        multi_level_flags = []
+        for i in range(self.num_levels):
+            anchor_stride = self.strides[i]
+            feat_h, feat_w = featmap_sizes[i]
+            h, w = pad_shape[:2]
+            valid_feat_h = min(int(np.ceil(h / anchor_stride[1])), feat_h)
+            valid_feat_w = min(int(np.ceil(w / anchor_stride[0])), feat_w)
+            flags = self.single_level_valid_flags((feat_h, feat_w),
+                                                  (valid_feat_h, valid_feat_w),
+                                                  self.num_base_anchors[i],
+                                                  device=device)
+            multi_level_flags.append(flags)
+        return multi_level_flags
+
+    def single_level_valid_flags(self,
+                                 featmap_size,
+                                 valid_size,
+                                 num_base_anchors,
+                                 device='cuda'):
+        """Generate the valid flags of anchor in a single feature map.
+
+        Args:
+            featmap_size (tuple[int]): The size of feature maps, arrange
+                as (h, w).
+            valid_size (tuple[int]): The valid size of the feature maps.
+            num_base_anchors (int): The number of base anchors.
+            device (str, optional): Device where the flags will be put on.
+                Defaults to 'cuda'.
+
+        Returns:
+            torch.Tensor: The valid flags of each anchor in a single level \
+                feature map.
+        """
+        feat_h, feat_w = featmap_size
+        valid_h, valid_w = valid_size
+        assert valid_h <= feat_h and valid_w <= feat_w
+        valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device)
+        valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device)
+        valid_x[:valid_w] = 1
+        valid_y[:valid_h] = 1
+        valid_xx, valid_yy = self._meshgrid(valid_x, valid_y)
+        valid = valid_xx & valid_yy
+        valid = valid[:, None].expand(valid.size(0),
+                                      num_base_anchors).contiguous().view(-1)
+        return valid
+
+    def __repr__(self):
+        """str: a string that describes the module"""
+        indent_str = '    '
+        repr_str = self.__class__.__name__ + '(\n'
+        repr_str += f'{indent_str}strides={self.strides},\n'
+        repr_str += f'{indent_str}ratios={self.ratios},\n'
+        repr_str += f'{indent_str}scales={self.scales},\n'
+        repr_str += f'{indent_str}base_sizes={self.base_sizes},\n'
+        repr_str += f'{indent_str}scale_major={self.scale_major},\n'
+        repr_str += f'{indent_str}octave_base_scale='
+        repr_str += f'{self.octave_base_scale},\n'
+        repr_str += f'{indent_str}scales_per_octave='
+        repr_str += f'{self.scales_per_octave},\n'
+        repr_str += f'{indent_str}num_levels={self.num_levels}\n'
+        repr_str += f'{indent_str}centers={self.centers},\n'
+        repr_str += f'{indent_str}center_offset={self.center_offset})'
+        return repr_str
+
+
+@PRIOR_GENERATORS.register_module()
+class SSDAnchorGenerator(AnchorGenerator):
+    """Anchor generator for SSD.
+
+    Args:
+        strides (list[int]  | list[tuple[int, int]]): Strides of anchors
+            in multiple feature levels.
+        ratios (list[float]): The list of ratios between the height and width
+            of anchors in a single level.
+        min_sizes (list[float]): The list of minimum anchor sizes on each
+            level.
+        max_sizes (list[float]): The list of maximum anchor sizes on each
+            level.
+        basesize_ratio_range (tuple(float)): Ratio range of anchors. Being
+            used when not setting min_sizes and max_sizes.
+        input_size (int): Size of feature map, 300 for SSD300, 512 for
+            SSD512. Being used when not setting min_sizes and max_sizes.
+        scale_major (bool): Whether to multiply scales first when generating
+            base anchors. If true, the anchors in the same row will have the
+            same scales. It is always set to be False in SSD.
+    """
+
+    def __init__(self,
+                 strides,
+                 ratios,
+                 min_sizes=None,
+                 max_sizes=None,
+                 basesize_ratio_range=(0.15, 0.9),
+                 input_size=300,
+                 scale_major=True):
+        assert len(strides) == len(ratios)
+        assert not (min_sizes is None) ^ (max_sizes is None)
+        self.strides = [_pair(stride) for stride in strides]
+        self.centers = [(stride[0] / 2., stride[1] / 2.)
+                        for stride in self.strides]
+
+        if min_sizes is None and max_sizes is None:
+            # use hard code to generate SSD anchors
+            self.input_size = input_size
+            assert mmcv.is_tuple_of(basesize_ratio_range, float)
+            self.basesize_ratio_range = basesize_ratio_range
+            # calculate anchor ratios and sizes
+            min_ratio, max_ratio = basesize_ratio_range
+            min_ratio = int(min_ratio * 100)
+            max_ratio = int(max_ratio * 100)
+            step = int(np.floor(max_ratio - min_ratio) / (self.num_levels - 2))
+            min_sizes = []
+            max_sizes = []
+            for ratio in range(int(min_ratio), int(max_ratio) + 1, step):
+                min_sizes.append(int(self.input_size * ratio / 100))
+                max_sizes.append(int(self.input_size * (ratio + step) / 100))
+            if self.input_size == 300:
+                if basesize_ratio_range[0] == 0.15:  # SSD300 COCO
+                    min_sizes.insert(0, int(self.input_size * 7 / 100))
+                    max_sizes.insert(0, int(self.input_size * 15 / 100))
+                elif basesize_ratio_range[0] == 0.2:  # SSD300 VOC
+                    min_sizes.insert(0, int(self.input_size * 10 / 100))
+                    max_sizes.insert(0, int(self.input_size * 20 / 100))
+                else:
+                    raise ValueError(
+                        'basesize_ratio_range[0] should be either 0.15'
+                        'or 0.2 when input_size is 300, got '
+                        f'{basesize_ratio_range[0]}.')
+            elif self.input_size == 512:
+                if basesize_ratio_range[0] == 0.1:  # SSD512 COCO
+                    min_sizes.insert(0, int(self.input_size * 4 / 100))
+                    max_sizes.insert(0, int(self.input_size * 10 / 100))
+                elif basesize_ratio_range[0] == 0.15:  # SSD512 VOC
+                    min_sizes.insert(0, int(self.input_size * 7 / 100))
+                    max_sizes.insert(0, int(self.input_size * 15 / 100))
+                else:
+                    raise ValueError(
+                        'When not setting min_sizes and max_sizes,'
+                        'basesize_ratio_range[0] should be either 0.1'
+                        'or 0.15 when input_size is 512, got'
+                        f' {basesize_ratio_range[0]}.')
+            else:
+                raise ValueError(
+                    'Only support 300 or 512 in SSDAnchorGenerator when '
+                    'not setting min_sizes and max_sizes, '
+                    f'got {self.input_size}.')
+
+        assert len(min_sizes) == len(max_sizes) == len(strides)
+
+        anchor_ratios = []
+        anchor_scales = []
+        for k in range(len(self.strides)):
+            scales = [1., np.sqrt(max_sizes[k] / min_sizes[k])]
+            anchor_ratio = [1.]
+            for r in ratios[k]:
+                anchor_ratio += [1 / r, r]  # 4 or 6 ratio
+            anchor_ratios.append(torch.Tensor(anchor_ratio))
+            anchor_scales.append(torch.Tensor(scales))
+
+        self.base_sizes = min_sizes
+        self.scales = anchor_scales
+        self.ratios = anchor_ratios
+        self.scale_major = scale_major
+        self.center_offset = 0
+        self.base_anchors = self.gen_base_anchors()
+
+    def gen_base_anchors(self):
+        """Generate base anchors.
+
+        Returns:
+            list(torch.Tensor): Base anchors of a feature grid in multiple \
+                feature levels.
+        """
+        multi_level_base_anchors = []
+        for i, base_size in enumerate(self.base_sizes):
+            base_anchors = self.gen_single_level_base_anchors(
+                base_size,
+                scales=self.scales[i],
+                ratios=self.ratios[i],
+                center=self.centers[i])
+            indices = list(range(len(self.ratios[i])))
+            indices.insert(1, len(indices))
+            base_anchors = torch.index_select(base_anchors, 0,
+                                              torch.LongTensor(indices))
+            multi_level_base_anchors.append(base_anchors)
+        return multi_level_base_anchors
+
+    def __repr__(self):
+        """str: a string that describes the module"""
+        indent_str = '    '
+        repr_str = self.__class__.__name__ + '(\n'
+        repr_str += f'{indent_str}strides={self.strides},\n'
+        repr_str += f'{indent_str}scales={self.scales},\n'
+        repr_str += f'{indent_str}scale_major={self.scale_major},\n'
+        repr_str += f'{indent_str}input_size={self.input_size},\n'
+        repr_str += f'{indent_str}scales={self.scales},\n'
+        repr_str += f'{indent_str}ratios={self.ratios},\n'
+        repr_str += f'{indent_str}num_levels={self.num_levels},\n'
+        repr_str += f'{indent_str}base_sizes={self.base_sizes},\n'
+        repr_str += f'{indent_str}basesize_ratio_range='
+        repr_str += f'{self.basesize_ratio_range})'
+        return repr_str
+
+
+@PRIOR_GENERATORS.register_module()
+class LegacyAnchorGenerator(AnchorGenerator):
+    """Legacy anchor generator used in MMDetection V1.x.
+
+    Note:
+        Difference to the V2.0 anchor generator:
+
+        1. The center offset of V1.x anchors are set to be 0.5 rather than 0.
+        2. The width/height are minused by 1 when calculating the anchors' \
+            centers and corners to meet the V1.x coordinate system.
+        3. The anchors' corners are quantized.
+
+    Args:
+        strides (list[int] | list[tuple[int]]): Strides of anchors
+            in multiple feature levels.
+        ratios (list[float]): The list of ratios between the height and width
+            of anchors in a single level.
+        scales (list[int] | None): Anchor scales for anchors in a single level.
+            It cannot be set at the same time if `octave_base_scale` and
+            `scales_per_octave` are set.
+        base_sizes (list[int]): The basic sizes of anchors in multiple levels.
+            If None is given, strides will be used to generate base_sizes.
+        scale_major (bool): Whether to multiply scales first when generating
+            base anchors. If true, the anchors in the same row will have the
+            same scales. By default it is True in V2.0
+        octave_base_scale (int): The base scale of octave.
+        scales_per_octave (int): Number of scales for each octave.
+            `octave_base_scale` and `scales_per_octave` are usually used in
+            retinanet and the `scales` should be None when they are set.
+        centers (list[tuple[float, float]] | None): The centers of the anchor
+            relative to the feature grid center in multiple feature levels.
+            By default it is set to be None and not used. It a list of float
+            is given, this list will be used to shift the centers of anchors.
+        center_offset (float): The offset of center in proportion to anchors'
+            width and height. By default it is 0.5 in V2.0 but it should be 0.5
+            in v1.x models.
+
+    Examples:
+        >>> from mmdet.core import LegacyAnchorGenerator
+        >>> self = LegacyAnchorGenerator(
+        >>>     [16], [1.], [1.], [9], center_offset=0.5)
+        >>> all_anchors = self.grid_anchors(((2, 2),), device='cpu')
+        >>> print(all_anchors)
+        [tensor([[ 0.,  0.,  8.,  8.],
+                [16.,  0., 24.,  8.],
+                [ 0., 16.,  8., 24.],
+                [16., 16., 24., 24.]])]
+    """
+
+    def gen_single_level_base_anchors(self,
+                                      base_size,
+                                      scales,
+                                      ratios,
+                                      center=None):
+        """Generate base anchors of a single level.
+
+        Note:
+            The width/height of anchors are minused by 1 when calculating \
+                the centers and corners to meet the V1.x coordinate system.
+
+        Args:
+            base_size (int | float): Basic size of an anchor.
+            scales (torch.Tensor): Scales of the anchor.
+            ratios (torch.Tensor): The ratio between between the height.
+                and width of anchors in a single level.
+            center (tuple[float], optional): The center of the base anchor
+                related to a single feature grid. Defaults to None.
+
+        Returns:
+            torch.Tensor: Anchors in a single-level feature map.
+        """
+        w = base_size
+        h = base_size
+        if center is None:
+            x_center = self.center_offset * (w - 1)
+            y_center = self.center_offset * (h - 1)
+        else:
+            x_center, y_center = center
+
+        h_ratios = torch.sqrt(ratios)
+        w_ratios = 1 / h_ratios
+        if self.scale_major:
+            ws = (w * w_ratios[:, None] * scales[None, :]).view(-1)
+            hs = (h * h_ratios[:, None] * scales[None, :]).view(-1)
+        else:
+            ws = (w * scales[:, None] * w_ratios[None, :]).view(-1)
+            hs = (h * scales[:, None] * h_ratios[None, :]).view(-1)
+
+        # use float anchor and the anchor's center is aligned with the
+        # pixel center
+        base_anchors = [
+            x_center - 0.5 * (ws - 1), y_center - 0.5 * (hs - 1),
+            x_center + 0.5 * (ws - 1), y_center + 0.5 * (hs - 1)
+        ]
+        base_anchors = torch.stack(base_anchors, dim=-1).round()
+
+        return base_anchors
+
+
+@PRIOR_GENERATORS.register_module()
+class LegacySSDAnchorGenerator(SSDAnchorGenerator, LegacyAnchorGenerator):
+    """Legacy anchor generator used in MMDetection V1.x.
+
+    The difference between `LegacySSDAnchorGenerator` and `SSDAnchorGenerator`
+    can be found in `LegacyAnchorGenerator`.
+    """
+
+    def __init__(self,
+                 strides,
+                 ratios,
+                 basesize_ratio_range,
+                 input_size=300,
+                 scale_major=True):
+        super(LegacySSDAnchorGenerator, self).__init__(
+            strides=strides,
+            ratios=ratios,
+            basesize_ratio_range=basesize_ratio_range,
+            input_size=input_size,
+            scale_major=scale_major)
+        self.centers = [((stride - 1) / 2., (stride - 1) / 2.)
+                        for stride in strides]
+        self.base_anchors = self.gen_base_anchors()
+
+
+@PRIOR_GENERATORS.register_module()
+class YOLOAnchorGenerator(AnchorGenerator):
+    """Anchor generator for YOLO.
+
+    Args:
+        strides (list[int] | list[tuple[int, int]]): Strides of anchors
+            in multiple feature levels.
+        base_sizes (list[list[tuple[int, int]]]): The basic sizes
+            of anchors in multiple levels.
+    """
+
+    def __init__(self, strides, base_sizes):
+        self.strides = [_pair(stride) for stride in strides]
+        self.centers = [(stride[0] / 2., stride[1] / 2.)
+                        for stride in self.strides]
+        self.base_sizes = []
+        num_anchor_per_level = len(base_sizes[0])
+        for base_sizes_per_level in base_sizes:
+            assert num_anchor_per_level == len(base_sizes_per_level)
+            self.base_sizes.append(
+                [_pair(base_size) for base_size in base_sizes_per_level])
+        self.base_anchors = self.gen_base_anchors()
+
+    @property
+    def num_levels(self):
+        """int: number of feature levels that the generator will be applied"""
+        return len(self.base_sizes)
+
+    def gen_base_anchors(self):
+        """Generate base anchors.
+
+        Returns:
+            list(torch.Tensor): Base anchors of a feature grid in multiple \
+                feature levels.
+        """
+        multi_level_base_anchors = []
+        for i, base_sizes_per_level in enumerate(self.base_sizes):
+            center = None
+            if self.centers is not None:
+                center = self.centers[i]
+            multi_level_base_anchors.append(
+                self.gen_single_level_base_anchors(base_sizes_per_level,
+                                                   center))
+        return multi_level_base_anchors
+
+    def gen_single_level_base_anchors(self, base_sizes_per_level, center=None):
+        """Generate base anchors of a single level.
+
+        Args:
+            base_sizes_per_level (list[tuple[int, int]]): Basic sizes of
+                anchors.
+            center (tuple[float], optional): The center of the base anchor
+                related to a single feature grid. Defaults to None.
+
+        Returns:
+            torch.Tensor: Anchors in a single-level feature maps.
+        """
+        x_center, y_center = center
+        base_anchors = []
+        for base_size in base_sizes_per_level:
+            w, h = base_size
+
+            # use float anchor and the anchor's center is aligned with the
+            # pixel center
+            base_anchor = torch.Tensor([
+                x_center - 0.5 * w, y_center - 0.5 * h, x_center + 0.5 * w,
+                y_center + 0.5 * h
+            ])
+            base_anchors.append(base_anchor)
+        base_anchors = torch.stack(base_anchors, dim=0)
+
+        return base_anchors
+
+    def responsible_flags(self, featmap_sizes, gt_bboxes, device='cuda'):
+        """Generate responsible anchor flags of grid cells in multiple scales.
+
+        Args:
+            featmap_sizes (list(tuple)): List of feature map sizes in multiple
+                feature levels.
+            gt_bboxes (Tensor): Ground truth boxes, shape (n, 4).
+            device (str): Device where the anchors will be put on.
+
+        Return:
+            list(torch.Tensor): responsible flags of anchors in multiple level
+        """
+        assert self.num_levels == len(featmap_sizes)
+        multi_level_responsible_flags = []
+        for i in range(self.num_levels):
+            anchor_stride = self.strides[i]
+            flags = self.single_level_responsible_flags(
+                featmap_sizes[i],
+                gt_bboxes,
+                anchor_stride,
+                self.num_base_anchors[i],
+                device=device)
+            multi_level_responsible_flags.append(flags)
+        return multi_level_responsible_flags
+
+    def single_level_responsible_flags(self,
+                                       featmap_size,
+                                       gt_bboxes,
+                                       stride,
+                                       num_base_anchors,
+                                       device='cuda'):
+        """Generate the responsible flags of anchor in a single feature map.
+
+        Args:
+            featmap_size (tuple[int]): The size of feature maps.
+            gt_bboxes (Tensor): Ground truth boxes, shape (n, 4).
+            stride (tuple(int)): stride of current level
+            num_base_anchors (int): The number of base anchors.
+            device (str, optional): Device where the flags will be put on.
+                Defaults to 'cuda'.
+
+        Returns:
+            torch.Tensor: The valid flags of each anchor in a single level \
+                feature map.
+        """
+        feat_h, feat_w = featmap_size
+        gt_bboxes_cx = ((gt_bboxes[:, 0] + gt_bboxes[:, 2]) * 0.5).to(device)
+        gt_bboxes_cy = ((gt_bboxes[:, 1] + gt_bboxes[:, 3]) * 0.5).to(device)
+        gt_bboxes_grid_x = torch.floor(gt_bboxes_cx / stride[0]).long()
+        gt_bboxes_grid_y = torch.floor(gt_bboxes_cy / stride[1]).long()
+
+        # row major indexing
+        gt_bboxes_grid_idx = gt_bboxes_grid_y * feat_w + gt_bboxes_grid_x
+
+        responsible_grid = torch.zeros(
+            feat_h * feat_w, dtype=torch.uint8, device=device)
+        responsible_grid[gt_bboxes_grid_idx] = 1
+
+        responsible_grid = responsible_grid[:, None].expand(
+            responsible_grid.size(0), num_base_anchors).contiguous().view(-1)
+        return responsible_grid
diff --git a/mmdet/core/anchor/builder.py b/mmdet/core/anchor/builder.py
new file mode 100644
index 0000000..ddb25ad
--- /dev/null
+++ b/mmdet/core/anchor/builder.py
@@ -0,0 +1,19 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+from mmcv.utils import Registry, build_from_cfg
+
+PRIOR_GENERATORS = Registry('Generator for anchors and points')
+
+ANCHOR_GENERATORS = PRIOR_GENERATORS
+
+
+def build_prior_generator(cfg, default_args=None):
+    return build_from_cfg(cfg, PRIOR_GENERATORS, default_args)
+
+
+def build_anchor_generator(cfg, default_args=None):
+    warnings.warn(
+        '``build_anchor_generator`` would be deprecated soon, please use '
+        '``build_prior_generator`` ')
+    return build_prior_generator(cfg, default_args=default_args)
diff --git a/mmdet/core/anchor/point_generator.py b/mmdet/core/anchor/point_generator.py
new file mode 100644
index 0000000..cc9c388
--- /dev/null
+++ b/mmdet/core/anchor/point_generator.py
@@ -0,0 +1,263 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+from torch.nn.modules.utils import _pair
+
+from .builder import PRIOR_GENERATORS
+
+
+@PRIOR_GENERATORS.register_module()
+class PointGenerator:
+
+    def _meshgrid(self, x, y, row_major=True):
+        xx = x.repeat(len(y))
+        yy = y.view(-1, 1).repeat(1, len(x)).view(-1)
+        if row_major:
+            return xx, yy
+        else:
+            return yy, xx
+
+    def grid_points(self, featmap_size, stride=16, device='cuda'):
+        feat_h, feat_w = featmap_size
+        shift_x = torch.arange(0., feat_w, device=device) * stride
+        shift_y = torch.arange(0., feat_h, device=device) * stride
+        shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
+        stride = shift_x.new_full((shift_xx.shape[0], ), stride)
+        shifts = torch.stack([shift_xx, shift_yy, stride], dim=-1)
+        all_points = shifts.to(device)
+        return all_points
+
+    def valid_flags(self, featmap_size, valid_size, device='cuda'):
+        feat_h, feat_w = featmap_size
+        valid_h, valid_w = valid_size
+        assert valid_h <= feat_h and valid_w <= feat_w
+        valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device)
+        valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device)
+        valid_x[:valid_w] = 1
+        valid_y[:valid_h] = 1
+        valid_xx, valid_yy = self._meshgrid(valid_x, valid_y)
+        valid = valid_xx & valid_yy
+        return valid
+
+
+@PRIOR_GENERATORS.register_module()
+class MlvlPointGenerator:
+    """Standard points generator for multi-level (Mlvl) feature maps in 2D
+    points-based detectors.
+
+    Args:
+        strides (list[int] | list[tuple[int, int]]): Strides of anchors
+            in multiple feature levels in order (w, h).
+        offset (float): The offset of points, the value is normalized with
+            corresponding stride. Defaults to 0.5.
+    """
+
+    def __init__(self, strides, offset=0.5):
+        self.strides = [_pair(stride) for stride in strides]
+        self.offset = offset
+
+    @property
+    def num_levels(self):
+        """int: number of feature levels that the generator will be applied"""
+        return len(self.strides)
+
+    @property
+    def num_base_priors(self):
+        """list[int]: The number of priors (points) at a point
+        on the feature grid"""
+        return [1 for _ in range(len(self.strides))]
+
+    def _meshgrid(self, x, y, row_major=True):
+        yy, xx = torch.meshgrid(y, x)
+        if row_major:
+            # warning .flatten() would cause error in ONNX exporting
+            # have to use reshape here
+            return xx.reshape(-1), yy.reshape(-1)
+
+        else:
+            return yy.reshape(-1), xx.reshape(-1)
+
+    def grid_priors(self,
+                    featmap_sizes,
+                    dtype=torch.float32,
+                    device='cuda',
+                    with_stride=False):
+        """Generate grid points of multiple feature levels.
+
+        Args:
+            featmap_sizes (list[tuple]): List of feature map sizes in
+                multiple feature levels, each size arrange as
+                as (h, w).
+            dtype (:obj:`dtype`): Dtype of priors. Default: torch.float32.
+            device (str): The device where the anchors will be put on.
+            with_stride (bool): Whether to concatenate the stride to
+                the last dimension of points.
+
+        Return:
+            list[torch.Tensor]: Points of  multiple feature levels.
+            The sizes of each tensor should be (N, 2) when with stride is
+            ``False``, where N = width * height, width and height
+            are the sizes of the corresponding feature level,
+            and the last dimension 2 represent (coord_x, coord_y),
+            otherwise the shape should be (N, 4),
+            and the last dimension 4 represent
+            (coord_x, coord_y, stride_w, stride_h).
+        """
+
+        assert self.num_levels == len(featmap_sizes)
+        multi_level_priors = []
+        for i in range(self.num_levels):
+            priors = self.single_level_grid_priors(
+                featmap_sizes[i],
+                level_idx=i,
+                dtype=dtype,
+                device=device,
+                with_stride=with_stride)
+            multi_level_priors.append(priors)
+        return multi_level_priors
+
+    def single_level_grid_priors(self,
+                                 featmap_size,
+                                 level_idx,
+                                 dtype=torch.float32,
+                                 device='cuda',
+                                 with_stride=False):
+        """Generate grid Points of a single level.
+
+        Note:
+            This function is usually called by method ``self.grid_priors``.
+
+        Args:
+            featmap_size (tuple[int]): Size of the feature maps, arrange as
+                (h, w).
+            level_idx (int): The index of corresponding feature map level.
+            dtype (:obj:`dtype`): Dtype of priors. Default: torch.float32.
+            device (str, optional): The device the tensor will be put on.
+                Defaults to 'cuda'.
+            with_stride (bool): Concatenate the stride to the last dimension
+                of points.
+
+        Return:
+            Tensor: Points of single feature levels.
+            The shape of tensor should be (N, 2) when with stride is
+            ``False``, where N = width * height, width and height
+            are the sizes of the corresponding feature level,
+            and the last dimension 2 represent (coord_x, coord_y),
+            otherwise the shape should be (N, 4),
+            and the last dimension 4 represent
+            (coord_x, coord_y, stride_w, stride_h).
+        """
+        feat_h, feat_w = featmap_size
+        stride_w, stride_h = self.strides[level_idx]
+        shift_x = (torch.arange(0, feat_w, device=device) +
+                   self.offset) * stride_w
+        # keep featmap_size as Tensor instead of int, so that we
+        # can convert to ONNX correctly
+        shift_x = shift_x.to(dtype)
+
+        shift_y = (torch.arange(0, feat_h, device=device) +
+                   self.offset) * stride_h
+        # keep featmap_size as Tensor instead of int, so that we
+        # can convert to ONNX correctly
+        shift_y = shift_y.to(dtype)
+        shift_xx, shift_yy = self._meshgrid(shift_x, shift_y)
+        if not with_stride:
+            shifts = torch.stack([shift_xx, shift_yy], dim=-1)
+        else:
+            # use `shape[0]` instead of `len(shift_xx)` for ONNX export
+            stride_w = shift_xx.new_full((shift_xx.shape[0], ),
+                                         stride_w).to(dtype)
+            stride_h = shift_xx.new_full((shift_yy.shape[0], ),
+                                         stride_h).to(dtype)
+            shifts = torch.stack([shift_xx, shift_yy, stride_w, stride_h],
+                                 dim=-1)
+        all_points = shifts.to(device)
+        return all_points
+
+    def valid_flags(self, featmap_sizes, pad_shape, device='cuda'):
+        """Generate valid flags of points of multiple feature levels.
+
+        Args:
+            featmap_sizes (list(tuple)): List of feature map sizes in
+                multiple feature levels, each size arrange as
+                as (h, w).
+            pad_shape (tuple(int)): The padded shape of the image,
+                 arrange as (h, w).
+            device (str): The device where the anchors will be put on.
+
+        Return:
+            list(torch.Tensor): Valid flags of points of multiple levels.
+        """
+        assert self.num_levels == len(featmap_sizes)
+        multi_level_flags = []
+        for i in range(self.num_levels):
+            point_stride = self.strides[i]
+            feat_h, feat_w = featmap_sizes[i]
+            h, w = pad_shape[:2]
+            valid_feat_h = min(int(np.ceil(h / point_stride[1])), feat_h)
+            valid_feat_w = min(int(np.ceil(w / point_stride[0])), feat_w)
+            flags = self.single_level_valid_flags((feat_h, feat_w),
+                                                  (valid_feat_h, valid_feat_w),
+                                                  device=device)
+            multi_level_flags.append(flags)
+        return multi_level_flags
+
+    def single_level_valid_flags(self,
+                                 featmap_size,
+                                 valid_size,
+                                 device='cuda'):
+        """Generate the valid flags of points of a single feature map.
+
+        Args:
+            featmap_size (tuple[int]): The size of feature maps, arrange as
+                as (h, w).
+            valid_size (tuple[int]): The valid size of the feature maps.
+                The size arrange as as (h, w).
+            device (str, optional): The device where the flags will be put on.
+                Defaults to 'cuda'.
+
+        Returns:
+            torch.Tensor: The valid flags of each points in a single level \
+                feature map.
+        """
+        feat_h, feat_w = featmap_size
+        valid_h, valid_w = valid_size
+        assert valid_h <= feat_h and valid_w <= feat_w
+        valid_x = torch.zeros(feat_w, dtype=torch.bool, device=device)
+        valid_y = torch.zeros(feat_h, dtype=torch.bool, device=device)
+        valid_x[:valid_w] = 1
+        valid_y[:valid_h] = 1
+        valid_xx, valid_yy = self._meshgrid(valid_x, valid_y)
+        valid = valid_xx & valid_yy
+        return valid
+
+    def sparse_priors(self,
+                      prior_idxs,
+                      featmap_size,
+                      level_idx,
+                      dtype=torch.float32,
+                      device='cuda'):
+        """Generate sparse points according to the ``prior_idxs``.
+
+        Args:
+            prior_idxs (Tensor): The index of corresponding anchors
+                in the feature map.
+            featmap_size (tuple[int]): feature map size arrange as (w, h).
+            level_idx (int): The level index of corresponding feature
+                map.
+            dtype (obj:`torch.dtype`): Date type of points. Defaults to
+                ``torch.float32``.
+            device (obj:`torch.device`): The device where the points is
+                located.
+        Returns:
+            Tensor: Anchor with shape (N, 2), N should be equal to
+            the length of ``prior_idxs``. And last dimension
+            2 represent (coord_x, coord_y).
+        """
+        height, width = featmap_size
+        x = (prior_idxs % width + self.offset) * self.strides[level_idx][0]
+        y = ((prior_idxs // width) % height +
+             self.offset) * self.strides[level_idx][1]
+        prioris = torch.stack([x, y], 1).to(dtype)
+        prioris = prioris.to(device)
+        return prioris
diff --git a/mmdet/core/anchor/utils.py b/mmdet/core/anchor/utils.py
new file mode 100644
index 0000000..c2f2024
--- /dev/null
+++ b/mmdet/core/anchor/utils.py
@@ -0,0 +1,72 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+
+def images_to_levels(target, num_levels):
+    """Convert targets by image to targets by feature level.
+
+    [target_img0, target_img1] -> [target_level0, target_level1, ...]
+    """
+    target = torch.stack(target, 0)
+    level_targets = []
+    start = 0
+    for n in num_levels:
+        end = start + n
+        # level_targets.append(target[:, start:end].squeeze(0))
+        level_targets.append(target[:, start:end])
+        start = end
+    return level_targets
+
+
+def anchor_inside_flags(flat_anchors,
+                        valid_flags,
+                        img_shape,
+                        allowed_border=0):
+    """Check whether the anchors are inside the border.
+
+    Args:
+        flat_anchors (torch.Tensor): Flatten anchors, shape (n, 4).
+        valid_flags (torch.Tensor): An existing valid flags of anchors.
+        img_shape (tuple(int)): Shape of current image.
+        allowed_border (int, optional): The border to allow the valid anchor.
+            Defaults to 0.
+
+    Returns:
+        torch.Tensor: Flags indicating whether the anchors are inside a \
+            valid range.
+    """
+    img_h, img_w = img_shape[:2]
+    if allowed_border >= 0:
+        inside_flags = valid_flags & \
+            (flat_anchors[:, 0] >= -allowed_border) & \
+            (flat_anchors[:, 1] >= -allowed_border) & \
+            (flat_anchors[:, 2] < img_w + allowed_border) & \
+            (flat_anchors[:, 3] < img_h + allowed_border)
+    else:
+        inside_flags = valid_flags
+    return inside_flags
+
+
+def calc_region(bbox, ratio, featmap_size=None):
+    """Calculate a proportional bbox region.
+
+    The bbox center are fixed and the new h' and w' is h * ratio and w * ratio.
+
+    Args:
+        bbox (Tensor): Bboxes to calculate regions, shape (n, 4).
+        ratio (float): Ratio of the output region.
+        featmap_size (tuple): Feature map size used for clipping the boundary.
+
+    Returns:
+        tuple: x1, y1, x2, y2
+    """
+    x1 = torch.round((1 - ratio) * bbox[0] + ratio * bbox[2]).long()
+    y1 = torch.round((1 - ratio) * bbox[1] + ratio * bbox[3]).long()
+    x2 = torch.round(ratio * bbox[0] + (1 - ratio) * bbox[2]).long()
+    y2 = torch.round(ratio * bbox[1] + (1 - ratio) * bbox[3]).long()
+    if featmap_size is not None:
+        x1 = x1.clamp(min=0, max=featmap_size[1])
+        y1 = y1.clamp(min=0, max=featmap_size[0])
+        x2 = x2.clamp(min=0, max=featmap_size[1])
+        y2 = y2.clamp(min=0, max=featmap_size[0])
+    return (x1, y1, x2, y2)
diff --git a/mmdet/core/bbox/__init__.py b/mmdet/core/bbox/__init__.py
new file mode 100644
index 0000000..371eba1
--- /dev/null
+++ b/mmdet/core/bbox/__init__.py
@@ -0,0 +1,28 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .assigners import (AssignResult, BaseAssigner, CenterRegionAssigner,
+                        MaxIoUAssigner, RegionAssigner)
+from .builder import build_assigner, build_bbox_coder, build_sampler
+from .coder import (BaseBBoxCoder, DeltaXYWHBBoxCoder, DistancePointBBoxCoder,
+                    PseudoBBoxCoder, TBLRBBoxCoder)
+from .iou_calculators import BboxOverlaps2D, bbox_overlaps
+from .samplers import (BaseSampler, CombinedSampler,
+                       InstanceBalancedPosSampler, IoUBalancedNegSampler,
+                       OHEMSampler, PseudoSampler, RandomSampler,
+                       SamplingResult, ScoreHLRSampler)
+from .transforms import (bbox2distance, bbox2result, bbox2roi,
+                         bbox_cxcywh_to_xyxy, bbox_flip, bbox_mapping,
+                         bbox_mapping_back, bbox_rescale, bbox_xyxy_to_cxcywh,
+                         distance2bbox, find_inside_bboxes, roi2bbox)
+
+__all__ = [
+    'bbox_overlaps', 'BboxOverlaps2D', 'BaseAssigner', 'MaxIoUAssigner',
+    'AssignResult', 'BaseSampler', 'PseudoSampler', 'RandomSampler',
+    'InstanceBalancedPosSampler', 'IoUBalancedNegSampler', 'CombinedSampler',
+    'OHEMSampler', 'SamplingResult', 'ScoreHLRSampler', 'build_assigner',
+    'build_sampler', 'bbox_flip', 'bbox_mapping', 'bbox_mapping_back',
+    'bbox2roi', 'roi2bbox', 'bbox2result', 'distance2bbox', 'bbox2distance',
+    'build_bbox_coder', 'BaseBBoxCoder', 'PseudoBBoxCoder',
+    'DeltaXYWHBBoxCoder', 'TBLRBBoxCoder', 'DistancePointBBoxCoder',
+    'CenterRegionAssigner', 'bbox_rescale', 'bbox_cxcywh_to_xyxy',
+    'bbox_xyxy_to_cxcywh', 'RegionAssigner', 'find_inside_bboxes'
+]
diff --git a/mmdet/core/bbox/assigners/__init__.py b/mmdet/core/bbox/assigners/__init__.py
new file mode 100644
index 0000000..d6480a7
--- /dev/null
+++ b/mmdet/core/bbox/assigners/__init__.py
@@ -0,0 +1,25 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .approx_max_iou_assigner import ApproxMaxIoUAssigner
+from .ascend_assign_result import AscendAssignResult
+from .ascend_max_iou_assigner import AscendMaxIoUAssigner
+from .assign_result import AssignResult
+from .atss_assigner import ATSSAssigner
+from .base_assigner import BaseAssigner
+from .center_region_assigner import CenterRegionAssigner
+from .grid_assigner import GridAssigner
+from .hungarian_assigner import HungarianAssigner
+from .mask_hungarian_assigner import MaskHungarianAssigner
+from .max_iou_assigner import MaxIoUAssigner
+from .point_assigner import PointAssigner
+from .region_assigner import RegionAssigner
+from .sim_ota_assigner import SimOTAAssigner
+from .task_aligned_assigner import TaskAlignedAssigner
+from .uniform_assigner import UniformAssigner
+
+__all__ = [
+    'BaseAssigner', 'MaxIoUAssigner', 'ApproxMaxIoUAssigner', 'AssignResult',
+    'PointAssigner', 'ATSSAssigner', 'CenterRegionAssigner', 'GridAssigner',
+    'HungarianAssigner', 'RegionAssigner', 'UniformAssigner', 'SimOTAAssigner',
+    'TaskAlignedAssigner', 'MaskHungarianAssigner', 'AscendAssignResult',
+    'AscendMaxIoUAssigner'
+]
diff --git a/mmdet/core/bbox/assigners/approx_max_iou_assigner.py b/mmdet/core/bbox/assigners/approx_max_iou_assigner.py
new file mode 100644
index 0000000..304d09c
--- /dev/null
+++ b/mmdet/core/bbox/assigners/approx_max_iou_assigner.py
@@ -0,0 +1,146 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from ..builder import BBOX_ASSIGNERS
+from ..iou_calculators import build_iou_calculator
+from .max_iou_assigner import MaxIoUAssigner
+
+
+@BBOX_ASSIGNERS.register_module()
+class ApproxMaxIoUAssigner(MaxIoUAssigner):
+    """Assign a corresponding gt bbox or background to each bbox.
+
+    Each proposals will be assigned with an integer indicating the ground truth
+     index. (semi-positive index: gt label (0-based), -1: background)
+
+    - -1: negative sample, no assigned gt
+    - semi-positive integer: positive sample, index (0-based) of assigned gt
+
+    Args:
+        pos_iou_thr (float): IoU threshold for positive bboxes.
+        neg_iou_thr (float or tuple): IoU threshold for negative bboxes.
+        min_pos_iou (float): Minimum iou for a bbox to be considered as a
+            positive bbox. Positive samples can have smaller IoU than
+            pos_iou_thr due to the 4th step (assign max IoU sample to each gt).
+        gt_max_assign_all (bool): Whether to assign all bboxes with the same
+            highest overlap with some gt to that gt.
+        ignore_iof_thr (float): IoF threshold for ignoring bboxes (if
+            `gt_bboxes_ignore` is specified). Negative values mean not
+            ignoring any bboxes.
+        ignore_wrt_candidates (bool): Whether to compute the iof between
+            `bboxes` and `gt_bboxes_ignore`, or the contrary.
+        match_low_quality (bool): Whether to allow quality matches. This is
+            usually allowed for RPN and single stage detectors, but not allowed
+            in the second stage.
+        gpu_assign_thr (int): The upper bound of the number of GT for GPU
+            assign. When the number of gt is above this threshold, will assign
+            on CPU device. Negative values mean not assign on CPU.
+    """
+
+    def __init__(self,
+                 pos_iou_thr,
+                 neg_iou_thr,
+                 min_pos_iou=.0,
+                 gt_max_assign_all=True,
+                 ignore_iof_thr=-1,
+                 ignore_wrt_candidates=True,
+                 match_low_quality=True,
+                 gpu_assign_thr=-1,
+                 iou_calculator=dict(type='BboxOverlaps2D')):
+        self.pos_iou_thr = pos_iou_thr
+        self.neg_iou_thr = neg_iou_thr
+        self.min_pos_iou = min_pos_iou
+        self.gt_max_assign_all = gt_max_assign_all
+        self.ignore_iof_thr = ignore_iof_thr
+        self.ignore_wrt_candidates = ignore_wrt_candidates
+        self.gpu_assign_thr = gpu_assign_thr
+        self.match_low_quality = match_low_quality
+        self.iou_calculator = build_iou_calculator(iou_calculator)
+
+    def assign(self,
+               approxs,
+               squares,
+               approxs_per_octave,
+               gt_bboxes,
+               gt_bboxes_ignore=None,
+               gt_labels=None):
+        """Assign gt to approxs.
+
+        This method assign a gt bbox to each group of approxs (bboxes),
+        each group of approxs is represent by a base approx (bbox) and
+        will be assigned with -1, or a semi-positive number.
+        background_label (-1) means negative sample,
+        semi-positive number is the index (0-based) of assigned gt.
+        The assignment is done in following steps, the order matters.
+
+        1. assign every bbox to background_label (-1)
+        2. use the max IoU of each group of approxs to assign
+        2. assign proposals whose iou with all gts < neg_iou_thr to background
+        3. for each bbox, if the iou with its nearest gt >= pos_iou_thr,
+           assign it to that bbox
+        4. for each gt bbox, assign its nearest proposals (may be more than
+           one) to itself
+
+        Args:
+            approxs (Tensor): Bounding boxes to be assigned,
+                shape(approxs_per_octave*n, 4).
+            squares (Tensor): Base Bounding boxes to be assigned,
+                shape(n, 4).
+            approxs_per_octave (int): number of approxs per octave
+            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
+            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
+                labelled as `ignored`, e.g., crowd boxes in COCO.
+            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
+
+        Returns:
+            :obj:`AssignResult`: The assign result.
+        """
+        num_squares = squares.size(0)
+        num_gts = gt_bboxes.size(0)
+
+        if num_squares == 0 or num_gts == 0:
+            # No predictions and/or truth, return empty assignment
+            overlaps = approxs.new(num_gts, num_squares)
+            assign_result = self.assign_wrt_overlaps(overlaps, gt_labels)
+            return assign_result
+
+        # re-organize anchors by approxs_per_octave x num_squares
+        approxs = torch.transpose(
+            approxs.view(num_squares, approxs_per_octave, 4), 0,
+            1).contiguous().view(-1, 4)
+        assign_on_cpu = True if (self.gpu_assign_thr > 0) and (
+            num_gts > self.gpu_assign_thr) else False
+        # compute overlap and assign gt on CPU when number of GT is large
+        if assign_on_cpu:
+            device = approxs.device
+            approxs = approxs.cpu()
+            gt_bboxes = gt_bboxes.cpu()
+            if gt_bboxes_ignore is not None:
+                gt_bboxes_ignore = gt_bboxes_ignore.cpu()
+            if gt_labels is not None:
+                gt_labels = gt_labels.cpu()
+        all_overlaps = self.iou_calculator(approxs, gt_bboxes)
+
+        overlaps, _ = all_overlaps.view(approxs_per_octave, num_squares,
+                                        num_gts).max(dim=0)
+        overlaps = torch.transpose(overlaps, 0, 1)
+
+        if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None
+                and gt_bboxes_ignore.numel() > 0 and squares.numel() > 0):
+            if self.ignore_wrt_candidates:
+                ignore_overlaps = self.iou_calculator(
+                    squares, gt_bboxes_ignore, mode='iof')
+                ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
+            else:
+                ignore_overlaps = self.iou_calculator(
+                    gt_bboxes_ignore, squares, mode='iof')
+                ignore_max_overlaps, _ = ignore_overlaps.max(dim=0)
+            overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1
+
+        assign_result = self.assign_wrt_overlaps(overlaps, gt_labels)
+        if assign_on_cpu:
+            assign_result.gt_inds = assign_result.gt_inds.to(device)
+            assign_result.max_overlaps = assign_result.max_overlaps.to(device)
+            if assign_result.labels is not None:
+                assign_result.labels = assign_result.labels.to(device)
+        return assign_result
diff --git a/mmdet/core/bbox/assigners/ascend_assign_result.py b/mmdet/core/bbox/assigners/ascend_assign_result.py
new file mode 100644
index 0000000..03d33c2
--- /dev/null
+++ b/mmdet/core/bbox/assigners/ascend_assign_result.py
@@ -0,0 +1,34 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmdet.utils import util_mixins
+
+
+class AscendAssignResult(util_mixins.NiceRepr):
+    """Stores ascend assignments between predicted and truth boxes.
+
+    Arguments:
+        batch_num_gts (list[int]): the number of truth boxes considered.
+        batch_pos_mask (IntTensor): Positive samples mask in all images.
+        batch_neg_mask (IntTensor): Negative samples mask in all images.
+        batch_max_overlaps (FloatTensor): The max overlaps of all bboxes
+            and ground truth boxes.
+        batch_anchor_gt_indes(None | LongTensor): The assigned truth
+            box index of all anchors.
+        batch_anchor_gt_labels(None | LongTensor): The gt labels
+            of all anchors
+    """
+
+    def __init__(self,
+                 batch_num_gts,
+                 batch_pos_mask,
+                 batch_neg_mask,
+                 batch_max_overlaps,
+                 batch_anchor_gt_indes=None,
+                 batch_anchor_gt_labels=None):
+        self.batch_num_gts = batch_num_gts
+        self.batch_pos_mask = batch_pos_mask
+        self.batch_neg_mask = batch_neg_mask
+        self.batch_max_overlaps = batch_max_overlaps
+        self.batch_anchor_gt_indes = batch_anchor_gt_indes
+        self.batch_anchor_gt_labels = batch_anchor_gt_labels
+        # Interface for possible user-defined properties
+        self._extra_properties = {}
diff --git a/mmdet/core/bbox/assigners/ascend_max_iou_assigner.py b/mmdet/core/bbox/assigners/ascend_max_iou_assigner.py
new file mode 100644
index 0000000..f8f528a
--- /dev/null
+++ b/mmdet/core/bbox/assigners/ascend_max_iou_assigner.py
@@ -0,0 +1,178 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from ....utils import masked_fill
+from ..builder import BBOX_ASSIGNERS
+from ..iou_calculators import build_iou_calculator
+from .ascend_assign_result import AscendAssignResult
+from .base_assigner import BaseAssigner
+
+
+@BBOX_ASSIGNERS.register_module()
+class AscendMaxIoUAssigner(BaseAssigner):
+    """Assign a corresponding gt bbox or background to each bbox.
+
+    Each proposals will be assigned with `-1`, or a semi-positive integer
+    indicating the ground truth index.
+
+    - -1: negative sample, no assigned gt
+    - semi-positive integer: positive sample, index (0-based) of assigned gt
+
+    Args:
+        pos_iou_thr (float): IoU threshold for positive bboxes.
+        neg_iou_thr (float or tuple): IoU threshold for negative bboxes.
+        min_pos_iou (float): Minimum iou for a bbox to be considered as a
+            positive bbox. Positive samples can have smaller IoU than
+            pos_iou_thr due to the 4th step (assign max IoU sample to each gt).
+            `min_pos_iou` is set to avoid assigning bboxes that have extremely
+            small iou with GT as positive samples. It brings about 0.3 mAP
+            improvements in 1x schedule but does not affect the performance of
+            3x schedule. More comparisons can be found in
+            `PR #7464 <https://github.com/open-mmlab/mmdetection/pull/7464>`_.
+        gt_max_assign_all (bool): Whether to assign all bboxes with the same
+            highest overlap with some gt to that gt.
+        ignore_iof_thr (float): IoF threshold for ignoring bboxes (if
+            `gt_bboxes_ignore` is specified). Negative values mean not
+            ignoring any bboxes.
+        ignore_wrt_candidates (bool): Whether to compute the iof between
+            `bboxes` and `gt_bboxes_ignore`, or the contrary.
+        match_low_quality (bool): Whether to allow low quality matches. This is
+            usually allowed for RPN and single stage detectors, but not allowed
+            in the second stage. Details are demonstrated in Step 4.
+        gpu_assign_thr (int): The upper bound of the number of GT for GPU
+            assign. When the number of gt is above this threshold, will assign
+            on CPU device. Negative values mean not assign on CPU.
+    """
+
+    def __init__(self,
+                 pos_iou_thr,
+                 neg_iou_thr,
+                 min_pos_iou=.0,
+                 gt_max_assign_all=True,
+                 ignore_iof_thr=-1,
+                 ignore_wrt_candidates=True,
+                 match_low_quality=True,
+                 gpu_assign_thr=-1,
+                 iou_calculator=dict(type='BboxOverlaps2D')):
+        self.pos_iou_thr = pos_iou_thr
+        self.neg_iou_thr = neg_iou_thr
+        self.min_pos_iou = min_pos_iou
+        self.gt_max_assign_all = gt_max_assign_all
+        self.ignore_iof_thr = ignore_iof_thr
+        self.ignore_wrt_candidates = ignore_wrt_candidates
+        self.gpu_assign_thr = gpu_assign_thr
+        self.match_low_quality = match_low_quality
+        self.iou_calculator = build_iou_calculator(iou_calculator)
+
+    def assign(self,
+               batch_bboxes,
+               batch_gt_bboxes,
+               batch_gt_bboxes_ignore=None,
+               batch_gt_labels=None,
+               batch_bboxes_ignore_mask=None,
+               batch_num_gts=None):
+        """Assign gt to bboxes.
+
+        Args:
+            batch_bboxes (Tensor): Bounding boxes to be assigned,
+                shape(b, n, 4).
+            batch_gt_bboxes (Tensor): Ground truth boxes,
+                shape (b, k, 4).
+            batch_gt_bboxes_ignore (Tensor, optional): Ground truth
+                bboxes that are labelled as `ignored`,
+                e.g., crowd boxes in COCO.
+            batch_gt_labels (Tensor, optional): Label of gt_bboxes,
+                shape (b, k, ).
+            batch_bboxes_ignore_mask: (b, n)
+            batch_num_gts:(b, )
+        Returns:
+            :obj:`AssignResult`: The assign result.
+        """
+        batch_overlaps = self.iou_calculator(batch_gt_bboxes, batch_bboxes)
+        batch_overlaps = masked_fill(
+            batch_overlaps,
+            batch_bboxes_ignore_mask.unsqueeze(1).float(),
+            -1,
+            neg=True)
+        if self.ignore_iof_thr > 0 and batch_gt_bboxes_ignore is not None:
+            if self.ignore_wrt_candidates:
+                batch_ignore_overlaps = self.iou_calculator(
+                    batch_bboxes, batch_gt_bboxes_ignore, mode='iof')
+                batch_ignore_overlaps = masked_fill(batch_ignore_overlaps,
+                                                    batch_bboxes_ignore_mask,
+                                                    -1)
+                batch_ignore_max_overlaps, _ = batch_ignore_overlaps.max(dim=2)
+            else:
+                batch_ignore_overlaps = self.iou_calculator(
+                    batch_gt_bboxes_ignore, batch_bboxes, mode='iof')
+                batch_ignore_overlaps = masked_fill(batch_ignore_overlaps,
+                                                    batch_bboxes_ignore_mask,
+                                                    -1)
+                batch_ignore_max_overlaps, _ = \
+                    batch_ignore_overlaps.max(dim=1)
+            batch_ignore_mask = \
+                batch_ignore_max_overlaps > self.ignore_iof_thr
+            batch_overlaps = masked_fill(batch_overlaps, batch_ignore_mask, -1)
+        batch_assign_result = self.batch_assign_wrt_overlaps(
+            batch_overlaps, batch_gt_labels, batch_num_gts)
+        return batch_assign_result
+
+    def batch_assign_wrt_overlaps(self,
+                                  batch_overlaps,
+                                  batch_gt_labels=None,
+                                  batch_num_gts=None):
+        num_images, num_gts, num_bboxes = batch_overlaps.size()
+        batch_max_overlaps, batch_argmax_overlaps = batch_overlaps.max(dim=1)
+        if isinstance(self.neg_iou_thr, float):
+            batch_neg_mask = \
+                ((batch_max_overlaps >= 0)
+                 & (batch_max_overlaps < self.neg_iou_thr)).int()
+        elif isinstance(self.neg_iou_thr, tuple):
+            assert len(self.neg_iou_thr) == 2
+            batch_neg_mask = \
+                ((batch_max_overlaps >= self.neg_iou_thr[0])
+                 & (batch_max_overlaps < self.neg_iou_thr[1])).int()
+        else:
+            batch_neg_mask = torch.zeros(
+                batch_max_overlaps.size(),
+                dtype=torch.int,
+                device=batch_max_overlaps.device)
+        batch_pos_mask = (batch_max_overlaps >= self.pos_iou_thr).int()
+        if self.match_low_quality:
+            batch_gt_max_overlaps, batch_gt_argmax_overlaps = \
+                batch_overlaps.max(dim=2)
+            batch_index_bool = (batch_gt_max_overlaps >= self.min_pos_iou) & \
+                               (batch_gt_max_overlaps > 0)
+            if self.gt_max_assign_all:
+                pos_inds_low_quality = \
+                    (batch_overlaps == batch_gt_max_overlaps.unsqueeze(2)) & \
+                    batch_index_bool.unsqueeze(2)
+                for i in range(num_gts):
+                    pos_inds_low_quality_gt = pos_inds_low_quality[:, i, :]
+                    batch_argmax_overlaps[pos_inds_low_quality_gt] = i
+                    batch_pos_mask[pos_inds_low_quality_gt] = 1
+            else:
+                index_temp = torch.arange(
+                    0, num_gts, device=batch_max_overlaps.device)
+                for index_image in range(num_images):
+                    gt_argmax_overlaps = batch_gt_argmax_overlaps[index_image]
+                    index_bool = batch_index_bool[index_image]
+                    pos_inds_low_quality = gt_argmax_overlaps[index_bool]
+                    batch_argmax_overlaps[index_image][pos_inds_low_quality] \
+                        = index_temp[index_bool]
+                    batch_pos_mask[index_image][pos_inds_low_quality] = 1
+        batch_neg_mask = batch_neg_mask * (1 - batch_pos_mask)
+        if batch_gt_labels is not None:
+            batch_anchor_gt_labels = torch.zeros((num_images, num_bboxes),
+                                                 dtype=batch_gt_labels.dtype,
+                                                 device=batch_gt_labels.device)
+            for index_image in range(num_images):
+                batch_anchor_gt_labels[index_image] = torch.index_select(
+                    batch_gt_labels[index_image], 0,
+                    batch_argmax_overlaps[index_image])
+        else:
+            batch_anchor_gt_labels = None
+        return AscendAssignResult(batch_num_gts, batch_pos_mask,
+                                  batch_neg_mask, batch_max_overlaps,
+                                  batch_argmax_overlaps,
+                                  batch_anchor_gt_labels)
diff --git a/mmdet/core/bbox/assigners/assign_result.py b/mmdet/core/bbox/assigners/assign_result.py
new file mode 100644
index 0000000..488010b
--- /dev/null
+++ b/mmdet/core/bbox/assigners/assign_result.py
@@ -0,0 +1,206 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdet.utils import util_mixins
+
+
+class AssignResult(util_mixins.NiceRepr):
+    """Stores assignments between predicted and truth boxes.
+
+    Attributes:
+        num_gts (int): the number of truth boxes considered when computing this
+            assignment
+
+        gt_inds (LongTensor): for each predicted box indicates the 1-based
+            index of the assigned truth box. 0 means unassigned and -1 means
+            ignore.
+
+        max_overlaps (FloatTensor): the iou between the predicted box and its
+            assigned truth box.
+
+        labels (None | LongTensor): If specified, for each predicted box
+            indicates the category label of the assigned truth box.
+
+    Example:
+        >>> # An assign result between 4 predicted boxes and 9 true boxes
+        >>> # where only two boxes were assigned.
+        >>> num_gts = 9
+        >>> max_overlaps = torch.LongTensor([0, .5, .9, 0])
+        >>> gt_inds = torch.LongTensor([-1, 1, 2, 0])
+        >>> labels = torch.LongTensor([0, 3, 4, 0])
+        >>> self = AssignResult(num_gts, gt_inds, max_overlaps, labels)
+        >>> print(str(self))  # xdoctest: +IGNORE_WANT
+        <AssignResult(num_gts=9, gt_inds.shape=(4,), max_overlaps.shape=(4,),
+                      labels.shape=(4,))>
+        >>> # Force addition of gt labels (when adding gt as proposals)
+        >>> new_labels = torch.LongTensor([3, 4, 5])
+        >>> self.add_gt_(new_labels)
+        >>> print(str(self))  # xdoctest: +IGNORE_WANT
+        <AssignResult(num_gts=9, gt_inds.shape=(7,), max_overlaps.shape=(7,),
+                      labels.shape=(7,))>
+    """
+
+    def __init__(self, num_gts, gt_inds, max_overlaps, labels=None):
+        self.num_gts = num_gts
+        self.gt_inds = gt_inds
+        self.max_overlaps = max_overlaps
+        self.labels = labels
+        # Interface for possible user-defined properties
+        self._extra_properties = {}
+
+    @property
+    def num_preds(self):
+        """int: the number of predictions in this assignment"""
+        return len(self.gt_inds)
+
+    def set_extra_property(self, key, value):
+        """Set user-defined new property."""
+        assert key not in self.info
+        self._extra_properties[key] = value
+
+    def get_extra_property(self, key):
+        """Get user-defined property."""
+        return self._extra_properties.get(key, None)
+
+    @property
+    def info(self):
+        """dict: a dictionary of info about the object"""
+        basic_info = {
+            'num_gts': self.num_gts,
+            'num_preds': self.num_preds,
+            'gt_inds': self.gt_inds,
+            'max_overlaps': self.max_overlaps,
+            'labels': self.labels,
+        }
+        basic_info.update(self._extra_properties)
+        return basic_info
+
+    def __nice__(self):
+        """str: a "nice" summary string describing this assign result"""
+        parts = []
+        parts.append(f'num_gts={self.num_gts!r}')
+        if self.gt_inds is None:
+            parts.append(f'gt_inds={self.gt_inds!r}')
+        else:
+            parts.append(f'gt_inds.shape={tuple(self.gt_inds.shape)!r}')
+        if self.max_overlaps is None:
+            parts.append(f'max_overlaps={self.max_overlaps!r}')
+        else:
+            parts.append('max_overlaps.shape='
+                         f'{tuple(self.max_overlaps.shape)!r}')
+        if self.labels is None:
+            parts.append(f'labels={self.labels!r}')
+        else:
+            parts.append(f'labels.shape={tuple(self.labels.shape)!r}')
+        return ', '.join(parts)
+
+    @classmethod
+    def random(cls, **kwargs):
+        """Create random AssignResult for tests or debugging.
+
+        Args:
+            num_preds: number of predicted boxes
+            num_gts: number of true boxes
+            p_ignore (float): probability of a predicted box assigned to an
+                ignored truth
+            p_assigned (float): probability of a predicted box not being
+                assigned
+            p_use_label (float | bool): with labels or not
+            rng (None | int | numpy.random.RandomState): seed or state
+
+        Returns:
+            :obj:`AssignResult`: Randomly generated assign results.
+
+        Example:
+            >>> from mmdet.core.bbox.assigners.assign_result import *  # NOQA
+            >>> self = AssignResult.random()
+            >>> print(self.info)
+        """
+        from mmdet.core.bbox import demodata
+        rng = demodata.ensure_rng(kwargs.get('rng', None))
+
+        num_gts = kwargs.get('num_gts', None)
+        num_preds = kwargs.get('num_preds', None)
+        p_ignore = kwargs.get('p_ignore', 0.3)
+        p_assigned = kwargs.get('p_assigned', 0.7)
+        p_use_label = kwargs.get('p_use_label', 0.5)
+        num_classes = kwargs.get('p_use_label', 3)
+
+        if num_gts is None:
+            num_gts = rng.randint(0, 8)
+        if num_preds is None:
+            num_preds = rng.randint(0, 16)
+
+        if num_gts == 0:
+            max_overlaps = torch.zeros(num_preds, dtype=torch.float32)
+            gt_inds = torch.zeros(num_preds, dtype=torch.int64)
+            if p_use_label is True or p_use_label < rng.rand():
+                labels = torch.zeros(num_preds, dtype=torch.int64)
+            else:
+                labels = None
+        else:
+            import numpy as np
+
+            # Create an overlap for each predicted box
+            max_overlaps = torch.from_numpy(rng.rand(num_preds))
+
+            # Construct gt_inds for each predicted box
+            is_assigned = torch.from_numpy(rng.rand(num_preds) < p_assigned)
+            # maximum number of assignments constraints
+            n_assigned = min(num_preds, min(num_gts, is_assigned.sum()))
+
+            assigned_idxs = np.where(is_assigned)[0]
+            rng.shuffle(assigned_idxs)
+            assigned_idxs = assigned_idxs[0:n_assigned]
+            assigned_idxs.sort()
+
+            is_assigned[:] = 0
+            is_assigned[assigned_idxs] = True
+
+            is_ignore = torch.from_numpy(
+                rng.rand(num_preds) < p_ignore) & is_assigned
+
+            gt_inds = torch.zeros(num_preds, dtype=torch.int64)
+
+            true_idxs = np.arange(num_gts)
+            rng.shuffle(true_idxs)
+            true_idxs = torch.from_numpy(true_idxs)
+            gt_inds[is_assigned] = true_idxs[:n_assigned].long()
+
+            gt_inds = torch.from_numpy(
+                rng.randint(1, num_gts + 1, size=num_preds))
+            gt_inds[is_ignore] = -1
+            gt_inds[~is_assigned] = 0
+            max_overlaps[~is_assigned] = 0
+
+            if p_use_label is True or p_use_label < rng.rand():
+                if num_classes == 0:
+                    labels = torch.zeros(num_preds, dtype=torch.int64)
+                else:
+                    labels = torch.from_numpy(
+                        # remind that we set FG labels to [0, num_class-1]
+                        # since mmdet v2.0
+                        # BG cat_id: num_class
+                        rng.randint(0, num_classes, size=num_preds))
+                    labels[~is_assigned] = 0
+            else:
+                labels = None
+
+        self = cls(num_gts, gt_inds, max_overlaps, labels)
+        return self
+
+    def add_gt_(self, gt_labels):
+        """Add ground truth as assigned results.
+
+        Args:
+            gt_labels (torch.Tensor): Labels of gt boxes
+        """
+        self_inds = torch.arange(
+            1, len(gt_labels) + 1, dtype=torch.long, device=gt_labels.device)
+        self.gt_inds = torch.cat([self_inds, self.gt_inds])
+
+        self.max_overlaps = torch.cat(
+            [self.max_overlaps.new_ones(len(gt_labels)), self.max_overlaps])
+
+        if self.labels is not None:
+            self.labels = torch.cat([gt_labels, self.labels])
diff --git a/mmdet/core/bbox/assigners/atss_assigner.py b/mmdet/core/bbox/assigners/atss_assigner.py
new file mode 100644
index 0000000..79c8281
--- /dev/null
+++ b/mmdet/core/bbox/assigners/atss_assigner.py
@@ -0,0 +1,234 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch
+
+from ..builder import BBOX_ASSIGNERS
+from ..iou_calculators import build_iou_calculator
+from .assign_result import AssignResult
+from .base_assigner import BaseAssigner
+
+
+@BBOX_ASSIGNERS.register_module()
+class ATSSAssigner(BaseAssigner):
+    """Assign a corresponding gt bbox or background to each bbox.
+
+    Each proposals will be assigned with `0` or a positive integer
+    indicating the ground truth index.
+
+    - 0: negative sample, no assigned gt
+    - positive integer: positive sample, index (1-based) of assigned gt
+
+    If ``alpha`` is not None, it means that the dynamic cost
+    ATSSAssigner is adopted, which is currently only used in the DDOD.
+
+    Args:
+        topk (float): number of bbox selected in each level
+    """
+
+    def __init__(self,
+                 topk,
+                 alpha=None,
+                 iou_calculator=dict(type='BboxOverlaps2D'),
+                 ignore_iof_thr=-1):
+        self.topk = topk
+        self.alpha = alpha
+        self.iou_calculator = build_iou_calculator(iou_calculator)
+        self.ignore_iof_thr = ignore_iof_thr
+
+    """Assign a corresponding gt bbox or background to each bbox.
+
+    Args:
+        topk (int): number of bbox selected in each level.
+        alpha (float): param of cost rate for each proposal only in DDOD.
+            Default None.
+        iou_calculator (dict): builder of IoU calculator.
+            Default dict(type='BboxOverlaps2D').
+        ignore_iof_thr (int): whether ignore max overlaps or not.
+            Default -1 (1 or -1).
+    """
+
+    # https://github.com/sfzhang15/ATSS/blob/master/atss_core/modeling/rpn/atss/loss.py
+    def assign(self,
+               bboxes,
+               num_level_bboxes,
+               gt_bboxes,
+               gt_bboxes_ignore=None,
+               gt_labels=None,
+               cls_scores=None,
+               bbox_preds=None):
+        """Assign gt to bboxes.
+
+        The assignment is done in following steps
+
+        1. compute iou between all bbox (bbox of all pyramid levels) and gt
+        2. compute center distance between all bbox and gt
+        3. on each pyramid level, for each gt, select k bbox whose center
+           are closest to the gt center, so we total select k*l bbox as
+           candidates for each gt
+        4. get corresponding iou for the these candidates, and compute the
+           mean and std, set mean + std as the iou threshold
+        5. select these candidates whose iou are greater than or equal to
+           the threshold as positive
+        6. limit the positive sample's center in gt
+
+        If ``alpha`` is not None, and ``cls_scores`` and `bbox_preds`
+        are not None, the overlaps calculation in the first step
+        will also include dynamic cost, which is currently only used in
+        the DDOD.
+
+        Args:
+            bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4).
+            num_level_bboxes (List): num of bboxes in each level
+            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
+            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
+                labelled as `ignored`, e.g., crowd boxes in COCO. Default None.
+            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
+            cls_scores (list[Tensor]): Classification scores for all scale
+                levels, each is a 4D-tensor, the channels number is
+                num_base_priors * num_classes. Default None.
+            bbox_preds (list[Tensor]): Box energies / deltas for all scale
+                levels, each is a 4D-tensor, the channels number is
+                num_base_priors * 4. Default None.
+
+        Returns:
+            :obj:`AssignResult`: The assign result.
+        """
+        INF = 100000000
+        bboxes = bboxes[:, :4]
+        num_gt, num_bboxes = gt_bboxes.size(0), bboxes.size(0)
+
+        message = 'Invalid alpha parameter because cls_scores or ' \
+                  'bbox_preds are None. If you want to use the ' \
+                  'cost-based ATSSAssigner,  please set cls_scores, ' \
+                  'bbox_preds and self.alpha at the same time. '
+
+        if self.alpha is None:
+            # ATSSAssigner
+            overlaps = self.iou_calculator(bboxes, gt_bboxes)
+            if cls_scores is not None or bbox_preds is not None:
+                warnings.warn(message)
+        else:
+            # Dynamic cost ATSSAssigner in DDOD
+            assert cls_scores is not None and bbox_preds is not None, message
+
+            # compute cls cost for bbox and GT
+            cls_cost = torch.sigmoid(cls_scores[:, gt_labels])
+
+            # compute iou between all bbox and gt
+            overlaps = self.iou_calculator(bbox_preds, gt_bboxes)
+
+            # make sure that we are in element-wise multiplication
+            assert cls_cost.shape == overlaps.shape
+
+            # overlaps is actually a cost matrix
+            overlaps = cls_cost**(1 - self.alpha) * overlaps**self.alpha
+
+        # assign 0 by default
+        assigned_gt_inds = overlaps.new_full((num_bboxes, ),
+                                             0,
+                                             dtype=torch.long)
+
+        if num_gt == 0 or num_bboxes == 0:
+            # No ground truth or boxes, return empty assignment
+            max_overlaps = overlaps.new_zeros((num_bboxes, ))
+            if num_gt == 0:
+                # No truth, assign everything to background
+                assigned_gt_inds[:] = 0
+            if gt_labels is None:
+                assigned_labels = None
+            else:
+                assigned_labels = overlaps.new_full((num_bboxes, ),
+                                                    -1,
+                                                    dtype=torch.long)
+            return AssignResult(
+                num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)
+
+        # compute center distance between all bbox and gt
+        gt_cx = (gt_bboxes[:, 0] + gt_bboxes[:, 2]) / 2.0
+        gt_cy = (gt_bboxes[:, 1] + gt_bboxes[:, 3]) / 2.0
+        gt_points = torch.stack((gt_cx, gt_cy), dim=1)
+
+        bboxes_cx = (bboxes[:, 0] + bboxes[:, 2]) / 2.0
+        bboxes_cy = (bboxes[:, 1] + bboxes[:, 3]) / 2.0
+        bboxes_points = torch.stack((bboxes_cx, bboxes_cy), dim=1)
+
+        distances = (bboxes_points[:, None, :] -
+                     gt_points[None, :, :]).pow(2).sum(-1).sqrt()
+
+        if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None
+                and gt_bboxes_ignore.numel() > 0 and bboxes.numel() > 0):
+            ignore_overlaps = self.iou_calculator(
+                bboxes, gt_bboxes_ignore, mode='iof')
+            ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
+            ignore_idxs = ignore_max_overlaps > self.ignore_iof_thr
+            distances[ignore_idxs, :] = INF
+            assigned_gt_inds[ignore_idxs] = -1
+
+        # Selecting candidates based on the center distance
+        candidate_idxs = []
+        start_idx = 0
+        for level, bboxes_per_level in enumerate(num_level_bboxes):
+            # on each pyramid level, for each gt,
+            # select k bbox whose center are closest to the gt center
+            end_idx = start_idx + bboxes_per_level
+            distances_per_level = distances[start_idx:end_idx, :]
+            selectable_k = min(self.topk, bboxes_per_level)
+
+            _, topk_idxs_per_level = distances_per_level.topk(
+                selectable_k, dim=0, largest=False)
+            candidate_idxs.append(topk_idxs_per_level + start_idx)
+            start_idx = end_idx
+        candidate_idxs = torch.cat(candidate_idxs, dim=0)
+
+        # get corresponding iou for the these candidates, and compute the
+        # mean and std, set mean + std as the iou threshold
+        candidate_overlaps = overlaps[candidate_idxs, torch.arange(num_gt)]
+        overlaps_mean_per_gt = candidate_overlaps.mean(0)
+        overlaps_std_per_gt = candidate_overlaps.std(0)
+        overlaps_thr_per_gt = overlaps_mean_per_gt + overlaps_std_per_gt
+
+        is_pos = candidate_overlaps >= overlaps_thr_per_gt[None, :]
+
+        # limit the positive sample's center in gt
+        for gt_idx in range(num_gt):
+            candidate_idxs[:, gt_idx] += gt_idx * num_bboxes
+        ep_bboxes_cx = bboxes_cx.view(1, -1).expand(
+            num_gt, num_bboxes).contiguous().view(-1)
+        ep_bboxes_cy = bboxes_cy.view(1, -1).expand(
+            num_gt, num_bboxes).contiguous().view(-1)
+        candidate_idxs = candidate_idxs.view(-1)
+
+        # calculate the left, top, right, bottom distance between positive
+        # bbox center and gt side
+        l_ = ep_bboxes_cx[candidate_idxs].view(-1, num_gt) - gt_bboxes[:, 0]
+        t_ = ep_bboxes_cy[candidate_idxs].view(-1, num_gt) - gt_bboxes[:, 1]
+        r_ = gt_bboxes[:, 2] - ep_bboxes_cx[candidate_idxs].view(-1, num_gt)
+        b_ = gt_bboxes[:, 3] - ep_bboxes_cy[candidate_idxs].view(-1, num_gt)
+        is_in_gts = torch.stack([l_, t_, r_, b_], dim=1).min(dim=1)[0] > 0.01
+
+        is_pos = is_pos & is_in_gts
+
+        # if an anchor box is assigned to multiple gts,
+        # the one with the highest IoU will be selected.
+        overlaps_inf = torch.full_like(overlaps,
+                                       -INF).t().contiguous().view(-1)
+        index = candidate_idxs.view(-1)[is_pos.view(-1)]
+        overlaps_inf[index] = overlaps.t().contiguous().view(-1)[index]
+        overlaps_inf = overlaps_inf.view(num_gt, -1).t()
+
+        max_overlaps, argmax_overlaps = overlaps_inf.max(dim=1)
+        assigned_gt_inds[
+            max_overlaps != -INF] = argmax_overlaps[max_overlaps != -INF] + 1
+
+        if gt_labels is not None:
+            assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)
+            pos_inds = torch.nonzero(
+                assigned_gt_inds > 0, as_tuple=False).squeeze()
+            if pos_inds.numel() > 0:
+                assigned_labels[pos_inds] = gt_labels[
+                    assigned_gt_inds[pos_inds] - 1]
+        else:
+            assigned_labels = None
+        return AssignResult(
+            num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)
diff --git a/mmdet/core/bbox/assigners/base_assigner.py b/mmdet/core/bbox/assigners/base_assigner.py
new file mode 100644
index 0000000..3c2d597
--- /dev/null
+++ b/mmdet/core/bbox/assigners/base_assigner.py
@@ -0,0 +1,10 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from abc import ABCMeta, abstractmethod
+
+
+class BaseAssigner(metaclass=ABCMeta):
+    """Base assigner that assigns boxes to ground truth boxes."""
+
+    @abstractmethod
+    def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
+        """Assign boxes to either a ground truth boxes or a negative boxes."""
diff --git a/mmdet/core/bbox/assigners/center_region_assigner.py b/mmdet/core/bbox/assigners/center_region_assigner.py
new file mode 100644
index 0000000..86e7859
--- /dev/null
+++ b/mmdet/core/bbox/assigners/center_region_assigner.py
@@ -0,0 +1,336 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from ..builder import BBOX_ASSIGNERS
+from ..iou_calculators import build_iou_calculator
+from .assign_result import AssignResult
+from .base_assigner import BaseAssigner
+
+
+def scale_boxes(bboxes, scale):
+    """Expand an array of boxes by a given scale.
+
+    Args:
+        bboxes (Tensor): Shape (m, 4)
+        scale (float): The scale factor of bboxes
+
+    Returns:
+        (Tensor): Shape (m, 4). Scaled bboxes
+    """
+    assert bboxes.size(1) == 4
+    w_half = (bboxes[:, 2] - bboxes[:, 0]) * .5
+    h_half = (bboxes[:, 3] - bboxes[:, 1]) * .5
+    x_c = (bboxes[:, 2] + bboxes[:, 0]) * .5
+    y_c = (bboxes[:, 3] + bboxes[:, 1]) * .5
+
+    w_half *= scale
+    h_half *= scale
+
+    boxes_scaled = torch.zeros_like(bboxes)
+    boxes_scaled[:, 0] = x_c - w_half
+    boxes_scaled[:, 2] = x_c + w_half
+    boxes_scaled[:, 1] = y_c - h_half
+    boxes_scaled[:, 3] = y_c + h_half
+    return boxes_scaled
+
+
+def is_located_in(points, bboxes):
+    """Are points located in bboxes.
+
+    Args:
+      points (Tensor): Points, shape: (m, 2).
+      bboxes (Tensor): Bounding boxes, shape: (n, 4).
+
+    Return:
+      Tensor: Flags indicating if points are located in bboxes, shape: (m, n).
+    """
+    assert points.size(1) == 2
+    assert bboxes.size(1) == 4
+    return (points[:, 0].unsqueeze(1) > bboxes[:, 0].unsqueeze(0)) & \
+           (points[:, 0].unsqueeze(1) < bboxes[:, 2].unsqueeze(0)) & \
+           (points[:, 1].unsqueeze(1) > bboxes[:, 1].unsqueeze(0)) & \
+           (points[:, 1].unsqueeze(1) < bboxes[:, 3].unsqueeze(0))
+
+
+def bboxes_area(bboxes):
+    """Compute the area of an array of bboxes.
+
+    Args:
+        bboxes (Tensor): The coordinates ox bboxes. Shape: (m, 4)
+
+    Returns:
+        Tensor: Area of the bboxes. Shape: (m, )
+    """
+    assert bboxes.size(1) == 4
+    w = (bboxes[:, 2] - bboxes[:, 0])
+    h = (bboxes[:, 3] - bboxes[:, 1])
+    areas = w * h
+    return areas
+
+
+@BBOX_ASSIGNERS.register_module()
+class CenterRegionAssigner(BaseAssigner):
+    """Assign pixels at the center region of a bbox as positive.
+
+    Each proposals will be assigned with `-1`, `0`, or a positive integer
+    indicating the ground truth index.
+    - -1: negative samples
+    - semi-positive numbers: positive sample, index (0-based) of assigned gt
+
+    Args:
+        pos_scale (float): Threshold within which pixels are
+          labelled as positive.
+        neg_scale (float): Threshold above which pixels are
+          labelled as positive.
+        min_pos_iof (float): Minimum iof of a pixel with a gt to be
+          labelled as positive. Default: 1e-2
+        ignore_gt_scale (float): Threshold within which the pixels
+          are ignored when the gt is labelled as shadowed. Default: 0.5
+        foreground_dominate (bool): If True, the bbox will be assigned as
+          positive when a gt's kernel region overlaps with another's shadowed
+          (ignored) region, otherwise it is set as ignored. Default to False.
+    """
+
+    def __init__(self,
+                 pos_scale,
+                 neg_scale,
+                 min_pos_iof=1e-2,
+                 ignore_gt_scale=0.5,
+                 foreground_dominate=False,
+                 iou_calculator=dict(type='BboxOverlaps2D')):
+        self.pos_scale = pos_scale
+        self.neg_scale = neg_scale
+        self.min_pos_iof = min_pos_iof
+        self.ignore_gt_scale = ignore_gt_scale
+        self.foreground_dominate = foreground_dominate
+        self.iou_calculator = build_iou_calculator(iou_calculator)
+
+    def get_gt_priorities(self, gt_bboxes):
+        """Get gt priorities according to their areas.
+
+        Smaller gt has higher priority.
+
+        Args:
+            gt_bboxes (Tensor): Ground truth boxes, shape (k, 4).
+
+        Returns:
+            Tensor: The priority of gts so that gts with larger priority is \
+              more likely to be assigned. Shape (k, )
+        """
+        gt_areas = bboxes_area(gt_bboxes)
+        # Rank all gt bbox areas. Smaller objects has larger priority
+        _, sort_idx = gt_areas.sort(descending=True)
+        sort_idx = sort_idx.argsort()
+        return sort_idx
+
+    def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
+        """Assign gt to bboxes.
+
+        This method assigns gts to every bbox (proposal/anchor), each bbox \
+        will be assigned with -1, or a semi-positive number. -1 means \
+        negative sample, semi-positive number is the index (0-based) of \
+        assigned gt.
+
+        Args:
+            bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4).
+            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
+            gt_bboxes_ignore (tensor, optional): Ground truth bboxes that are
+              labelled as `ignored`, e.g., crowd boxes in COCO.
+            gt_labels (tensor, optional): Label of gt_bboxes, shape (num_gts,).
+
+        Returns:
+            :obj:`AssignResult`: The assigned result. Note that \
+              shadowed_labels of shape (N, 2) is also added as an \
+              `assign_result` attribute. `shadowed_labels` is a tensor \
+              composed of N pairs of anchor_ind, class_label], where N \
+              is the number of anchors that lie in the outer region of a \
+              gt, anchor_ind is the shadowed anchor index and class_label \
+              is the shadowed class label.
+
+        Example:
+            >>> self = CenterRegionAssigner(0.2, 0.2)
+            >>> bboxes = torch.Tensor([[0, 0, 10, 10], [10, 10, 20, 20]])
+            >>> gt_bboxes = torch.Tensor([[0, 0, 10, 10]])
+            >>> assign_result = self.assign(bboxes, gt_bboxes)
+            >>> expected_gt_inds = torch.LongTensor([1, 0])
+            >>> assert torch.all(assign_result.gt_inds == expected_gt_inds)
+        """
+        # There are in total 5 steps in the pixel assignment
+        # 1. Find core (the center region, say inner 0.2)
+        #     and shadow (the relatively ourter part, say inner 0.2-0.5)
+        #     regions of every gt.
+        # 2. Find all prior bboxes that lie in gt_core and gt_shadow regions
+        # 3. Assign prior bboxes in gt_core with a one-hot id of the gt in
+        #      the image.
+        #    3.1. For overlapping objects, the prior bboxes in gt_core is
+        #           assigned with the object with smallest area
+        # 4. Assign prior bboxes with class label according to its gt id.
+        #    4.1. Assign -1 to prior bboxes lying in shadowed gts
+        #    4.2. Assign positive prior boxes with the corresponding label
+        # 5. Find pixels lying in the shadow of an object and assign them with
+        #      background label, but set the loss weight of its corresponding
+        #      gt to zero.
+        assert bboxes.size(1) == 4, 'bboxes must have size of 4'
+        # 1. Find core positive and shadow region of every gt
+        gt_core = scale_boxes(gt_bboxes, self.pos_scale)
+        gt_shadow = scale_boxes(gt_bboxes, self.neg_scale)
+
+        # 2. Find prior bboxes that lie in gt_core and gt_shadow regions
+        bbox_centers = (bboxes[:, 2:4] + bboxes[:, 0:2]) / 2
+        # The center points lie within the gt boxes
+        is_bbox_in_gt = is_located_in(bbox_centers, gt_bboxes)
+        # Only calculate bbox and gt_core IoF. This enables small prior bboxes
+        #   to match large gts
+        bbox_and_gt_core_overlaps = self.iou_calculator(
+            bboxes, gt_core, mode='iof')
+        # The center point of effective priors should be within the gt box
+        is_bbox_in_gt_core = is_bbox_in_gt & (
+            bbox_and_gt_core_overlaps > self.min_pos_iof)  # shape (n, k)
+
+        is_bbox_in_gt_shadow = (
+            self.iou_calculator(bboxes, gt_shadow, mode='iof') >
+            self.min_pos_iof)
+        # Rule out center effective positive pixels
+        is_bbox_in_gt_shadow &= (~is_bbox_in_gt_core)
+
+        num_gts, num_bboxes = gt_bboxes.size(0), bboxes.size(0)
+        if num_gts == 0 or num_bboxes == 0:
+            # If no gts exist, assign all pixels to negative
+            assigned_gt_ids = \
+                is_bbox_in_gt_core.new_zeros((num_bboxes,),
+                                             dtype=torch.long)
+            pixels_in_gt_shadow = assigned_gt_ids.new_empty((0, 2))
+        else:
+            # Step 3: assign a one-hot gt id to each pixel, and smaller objects
+            #    have high priority to assign the pixel.
+            sort_idx = self.get_gt_priorities(gt_bboxes)
+            assigned_gt_ids, pixels_in_gt_shadow = \
+                self.assign_one_hot_gt_indices(is_bbox_in_gt_core,
+                                               is_bbox_in_gt_shadow,
+                                               gt_priority=sort_idx)
+
+        if gt_bboxes_ignore is not None and gt_bboxes_ignore.numel() > 0:
+            # No ground truth or boxes, return empty assignment
+            gt_bboxes_ignore = scale_boxes(
+                gt_bboxes_ignore, scale=self.ignore_gt_scale)
+            is_bbox_in_ignored_gts = is_located_in(bbox_centers,
+                                                   gt_bboxes_ignore)
+            is_bbox_in_ignored_gts = is_bbox_in_ignored_gts.any(dim=1)
+            assigned_gt_ids[is_bbox_in_ignored_gts] = -1
+
+        # 4. Assign prior bboxes with class label according to its gt id.
+        assigned_labels = None
+        shadowed_pixel_labels = None
+        if gt_labels is not None:
+            # Default assigned label is the background (-1)
+            assigned_labels = assigned_gt_ids.new_full((num_bboxes, ), -1)
+            pos_inds = torch.nonzero(
+                assigned_gt_ids > 0, as_tuple=False).squeeze()
+            if pos_inds.numel() > 0:
+                assigned_labels[pos_inds] = gt_labels[assigned_gt_ids[pos_inds]
+                                                      - 1]
+            # 5. Find pixels lying in the shadow of an object
+            shadowed_pixel_labels = pixels_in_gt_shadow.clone()
+            if pixels_in_gt_shadow.numel() > 0:
+                pixel_idx, gt_idx =\
+                    pixels_in_gt_shadow[:, 0], pixels_in_gt_shadow[:, 1]
+                assert (assigned_gt_ids[pixel_idx] != gt_idx).all(), \
+                    'Some pixels are dually assigned to ignore and gt!'
+                shadowed_pixel_labels[:, 1] = gt_labels[gt_idx - 1]
+                override = (
+                    assigned_labels[pixel_idx] == shadowed_pixel_labels[:, 1])
+                if self.foreground_dominate:
+                    # When a pixel is both positive and shadowed, set it as pos
+                    shadowed_pixel_labels = shadowed_pixel_labels[~override]
+                else:
+                    # When a pixel is both pos and shadowed, set it as shadowed
+                    assigned_labels[pixel_idx[override]] = -1
+                    assigned_gt_ids[pixel_idx[override]] = 0
+
+        assign_result = AssignResult(
+            num_gts, assigned_gt_ids, None, labels=assigned_labels)
+        # Add shadowed_labels as assign_result property. Shape: (num_shadow, 2)
+        assign_result.set_extra_property('shadowed_labels',
+                                         shadowed_pixel_labels)
+        return assign_result
+
+    def assign_one_hot_gt_indices(self,
+                                  is_bbox_in_gt_core,
+                                  is_bbox_in_gt_shadow,
+                                  gt_priority=None):
+        """Assign only one gt index to each prior box.
+
+        Gts with large gt_priority are more likely to be assigned.
+
+        Args:
+            is_bbox_in_gt_core (Tensor): Bool tensor indicating the bbox center
+              is in the core area of a gt (e.g. 0-0.2).
+              Shape: (num_prior, num_gt).
+            is_bbox_in_gt_shadow (Tensor): Bool tensor indicating the bbox
+              center is in the shadowed area of a gt (e.g. 0.2-0.5).
+              Shape: (num_prior, num_gt).
+            gt_priority (Tensor): Priorities of gts. The gt with a higher
+              priority is more likely to be assigned to the bbox when the bbox
+              match with multiple gts. Shape: (num_gt, ).
+
+        Returns:
+            tuple: Returns (assigned_gt_inds, shadowed_gt_inds).
+
+                - assigned_gt_inds: The assigned gt index of each prior bbox \
+                    (i.e. index from 1 to num_gts). Shape: (num_prior, ).
+                - shadowed_gt_inds: shadowed gt indices. It is a tensor of \
+                    shape (num_ignore, 2) with first column being the \
+                    shadowed prior bbox indices and the second column the \
+                    shadowed gt indices (1-based).
+        """
+        num_bboxes, num_gts = is_bbox_in_gt_core.shape
+
+        if gt_priority is None:
+            gt_priority = torch.arange(
+                num_gts, device=is_bbox_in_gt_core.device)
+        assert gt_priority.size(0) == num_gts
+        # The bigger gt_priority, the more preferable to be assigned
+        # The assigned inds are by default 0 (background)
+        assigned_gt_inds = is_bbox_in_gt_core.new_zeros((num_bboxes, ),
+                                                        dtype=torch.long)
+        # Shadowed bboxes are assigned to be background. But the corresponding
+        #   label is ignored during loss calculation, which is done through
+        #   shadowed_gt_inds
+        shadowed_gt_inds = torch.nonzero(is_bbox_in_gt_shadow, as_tuple=False)
+        if is_bbox_in_gt_core.sum() == 0:  # No gt match
+            shadowed_gt_inds[:, 1] += 1  # 1-based. For consistency issue
+            return assigned_gt_inds, shadowed_gt_inds
+
+        # The priority of each prior box and gt pair. If one prior box is
+        #  matched bo multiple gts. Only the pair with the highest priority
+        #  is saved
+        pair_priority = is_bbox_in_gt_core.new_full((num_bboxes, num_gts),
+                                                    -1,
+                                                    dtype=torch.long)
+
+        # Each bbox could match with multiple gts.
+        # The following codes deal with this situation
+        # Matched  bboxes (to any gt). Shape: (num_pos_anchor, )
+        inds_of_match = torch.any(is_bbox_in_gt_core, dim=1)
+        # The matched gt index of each positive bbox. Length >= num_pos_anchor
+        #   , since one bbox could match multiple gts
+        matched_bbox_gt_inds = torch.nonzero(
+            is_bbox_in_gt_core, as_tuple=False)[:, 1]
+        # Assign priority to each bbox-gt pair.
+        pair_priority[is_bbox_in_gt_core] = gt_priority[matched_bbox_gt_inds]
+        _, argmax_priority = pair_priority[inds_of_match].max(dim=1)
+        assigned_gt_inds[inds_of_match] = argmax_priority + 1  # 1-based
+        # Zero-out the assigned anchor box to filter the shadowed gt indices
+        is_bbox_in_gt_core[inds_of_match, argmax_priority] = 0
+        # Concat the shadowed indices due to overlapping with that out side of
+        #   effective scale. shape: (total_num_ignore, 2)
+        shadowed_gt_inds = torch.cat(
+            (shadowed_gt_inds, torch.nonzero(
+                is_bbox_in_gt_core, as_tuple=False)),
+            dim=0)
+        # `is_bbox_in_gt_core` should be changed back to keep arguments intact.
+        is_bbox_in_gt_core[inds_of_match, argmax_priority] = 1
+        # 1-based shadowed gt indices, to be consistent with `assigned_gt_inds`
+        if shadowed_gt_inds.numel() > 0:
+            shadowed_gt_inds[:, 1] += 1
+        return assigned_gt_inds, shadowed_gt_inds
diff --git a/mmdet/core/bbox/assigners/grid_assigner.py b/mmdet/core/bbox/assigners/grid_assigner.py
new file mode 100644
index 0000000..a0c814e
--- /dev/null
+++ b/mmdet/core/bbox/assigners/grid_assigner.py
@@ -0,0 +1,156 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from ..builder import BBOX_ASSIGNERS
+from ..iou_calculators import build_iou_calculator
+from .assign_result import AssignResult
+from .base_assigner import BaseAssigner
+
+
+@BBOX_ASSIGNERS.register_module()
+class GridAssigner(BaseAssigner):
+    """Assign a corresponding gt bbox or background to each bbox.
+
+    Each proposals will be assigned with `-1`, `0`, or a positive integer
+    indicating the ground truth index.
+
+    - -1: don't care
+    - 0: negative sample, no assigned gt
+    - positive integer: positive sample, index (1-based) of assigned gt
+
+    Args:
+        pos_iou_thr (float): IoU threshold for positive bboxes.
+        neg_iou_thr (float or tuple): IoU threshold for negative bboxes.
+        min_pos_iou (float): Minimum iou for a bbox to be considered as a
+            positive bbox. Positive samples can have smaller IoU than
+            pos_iou_thr due to the 4th step (assign max IoU sample to each gt).
+        gt_max_assign_all (bool): Whether to assign all bboxes with the same
+            highest overlap with some gt to that gt.
+    """
+
+    def __init__(self,
+                 pos_iou_thr,
+                 neg_iou_thr,
+                 min_pos_iou=.0,
+                 gt_max_assign_all=True,
+                 iou_calculator=dict(type='BboxOverlaps2D')):
+        self.pos_iou_thr = pos_iou_thr
+        self.neg_iou_thr = neg_iou_thr
+        self.min_pos_iou = min_pos_iou
+        self.gt_max_assign_all = gt_max_assign_all
+        self.iou_calculator = build_iou_calculator(iou_calculator)
+
+    def assign(self, bboxes, box_responsible_flags, gt_bboxes, gt_labels=None):
+        """Assign gt to bboxes. The process is very much like the max iou
+        assigner, except that positive samples are constrained within the cell
+        that the gt boxes fell in.
+
+        This method assign a gt bbox to every bbox (proposal/anchor), each bbox
+        will be assigned with -1, 0, or a positive number. -1 means don't care,
+        0 means negative sample, positive number is the index (1-based) of
+        assigned gt.
+        The assignment is done in following steps, the order matters.
+
+        1. assign every bbox to -1
+        2. assign proposals whose iou with all gts <= neg_iou_thr to 0
+        3. for each bbox within a cell, if the iou with its nearest gt >
+            pos_iou_thr and the center of that gt falls inside the cell,
+            assign it to that bbox
+        4. for each gt bbox, assign its nearest proposals within the cell the
+            gt bbox falls in to itself.
+
+        Args:
+            bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4).
+            box_responsible_flags (Tensor): flag to indicate whether box is
+                responsible for prediction, shape(n, )
+            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
+            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
+
+        Returns:
+            :obj:`AssignResult`: The assign result.
+        """
+        num_gts, num_bboxes = gt_bboxes.size(0), bboxes.size(0)
+
+        # compute iou between all gt and bboxes
+        overlaps = self.iou_calculator(gt_bboxes, bboxes)
+
+        # 1. assign -1 by default
+        assigned_gt_inds = overlaps.new_full((num_bboxes, ),
+                                             -1,
+                                             dtype=torch.long)
+
+        if num_gts == 0 or num_bboxes == 0:
+            # No ground truth or boxes, return empty assignment
+            max_overlaps = overlaps.new_zeros((num_bboxes, ))
+            if num_gts == 0:
+                # No truth, assign everything to background
+                assigned_gt_inds[:] = 0
+            if gt_labels is None:
+                assigned_labels = None
+            else:
+                assigned_labels = overlaps.new_full((num_bboxes, ),
+                                                    -1,
+                                                    dtype=torch.long)
+            return AssignResult(
+                num_gts,
+                assigned_gt_inds,
+                max_overlaps,
+                labels=assigned_labels)
+
+        # 2. assign negative: below
+        # for each anchor, which gt best overlaps with it
+        # for each anchor, the max iou of all gts
+        # shape of max_overlaps == argmax_overlaps == num_bboxes
+        max_overlaps, argmax_overlaps = overlaps.max(dim=0)
+
+        if isinstance(self.neg_iou_thr, float):
+            assigned_gt_inds[(max_overlaps >= 0)
+                             & (max_overlaps <= self.neg_iou_thr)] = 0
+        elif isinstance(self.neg_iou_thr, (tuple, list)):
+            assert len(self.neg_iou_thr) == 2
+            assigned_gt_inds[(max_overlaps > self.neg_iou_thr[0])
+                             & (max_overlaps <= self.neg_iou_thr[1])] = 0
+
+        # 3. assign positive: falls into responsible cell and above
+        # positive IOU threshold, the order matters.
+        # the prior condition of comparison is to filter out all
+        # unrelated anchors, i.e. not box_responsible_flags
+        overlaps[:, ~box_responsible_flags.type(torch.bool)] = -1.
+
+        # calculate max_overlaps again, but this time we only consider IOUs
+        # for anchors responsible for prediction
+        max_overlaps, argmax_overlaps = overlaps.max(dim=0)
+
+        # for each gt, which anchor best overlaps with it
+        # for each gt, the max iou of all proposals
+        # shape of gt_max_overlaps == gt_argmax_overlaps == num_gts
+        gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1)
+
+        pos_inds = (max_overlaps >
+                    self.pos_iou_thr) & box_responsible_flags.type(torch.bool)
+        assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1
+
+        # 4. assign positive to max overlapped anchors within responsible cell
+        for i in range(num_gts):
+            if gt_max_overlaps[i] > self.min_pos_iou:
+                if self.gt_max_assign_all:
+                    max_iou_inds = (overlaps[i, :] == gt_max_overlaps[i]) & \
+                         box_responsible_flags.type(torch.bool)
+                    assigned_gt_inds[max_iou_inds] = i + 1
+                elif box_responsible_flags[gt_argmax_overlaps[i]]:
+                    assigned_gt_inds[gt_argmax_overlaps[i]] = i + 1
+
+        # assign labels of positive anchors
+        if gt_labels is not None:
+            assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)
+            pos_inds = torch.nonzero(
+                assigned_gt_inds > 0, as_tuple=False).squeeze()
+            if pos_inds.numel() > 0:
+                assigned_labels[pos_inds] = gt_labels[
+                    assigned_gt_inds[pos_inds] - 1]
+
+        else:
+            assigned_labels = None
+
+        return AssignResult(
+            num_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels)
diff --git a/mmdet/core/bbox/assigners/hungarian_assigner.py b/mmdet/core/bbox/assigners/hungarian_assigner.py
new file mode 100644
index 0000000..435612a
--- /dev/null
+++ b/mmdet/core/bbox/assigners/hungarian_assigner.py
@@ -0,0 +1,139 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from scipy.optimize import linear_sum_assignment
+
+from ..builder import BBOX_ASSIGNERS
+from ..match_costs import build_match_cost
+from ..transforms import bbox_cxcywh_to_xyxy
+from .assign_result import AssignResult
+from .base_assigner import BaseAssigner
+
+
+@BBOX_ASSIGNERS.register_module()
+class HungarianAssigner(BaseAssigner):
+    """Computes one-to-one matching between predictions and ground truth.
+
+    This class computes an assignment between the targets and the predictions
+    based on the costs. The costs are weighted sum of three components:
+    classification cost, regression L1 cost and regression iou cost. The
+    targets don't include the no_object, so generally there are more
+    predictions than targets. After the one-to-one matching, the un-matched
+    are treated as backgrounds. Thus each query prediction will be assigned
+    with `0` or a positive integer indicating the ground truth index:
+
+    - 0: negative sample, no assigned gt
+    - positive integer: positive sample, index (1-based) of assigned gt
+
+    Args:
+        cls_weight (int | float, optional): The scale factor for classification
+            cost. Default 1.0.
+        bbox_weight (int | float, optional): The scale factor for regression
+            L1 cost. Default 1.0.
+        iou_weight (int | float, optional): The scale factor for regression
+            iou cost. Default 1.0.
+        iou_calculator (dict | optional): The config for the iou calculation.
+            Default type `BboxOverlaps2D`.
+        iou_mode (str | optional): "iou" (intersection over union), "iof"
+                (intersection over foreground), or "giou" (generalized
+                intersection over union). Default "giou".
+    """
+
+    def __init__(self,
+                 cls_cost=dict(type='ClassificationCost', weight=1.),
+                 reg_cost=dict(type='BBoxL1Cost', weight=1.0),
+                 iou_cost=dict(type='IoUCost', iou_mode='giou', weight=1.0)):
+        self.cls_cost = build_match_cost(cls_cost)
+        self.reg_cost = build_match_cost(reg_cost)
+        self.iou_cost = build_match_cost(iou_cost)
+
+    def assign(self,
+               bbox_pred,
+               cls_pred,
+               gt_bboxes,
+               gt_labels,
+               img_meta,
+               gt_bboxes_ignore=None,
+               eps=1e-7):
+        """Computes one-to-one matching based on the weighted costs.
+
+        This method assign each query prediction to a ground truth or
+        background. The `assigned_gt_inds` with -1 means don't care,
+        0 means negative sample, and positive number is the index (1-based)
+        of assigned gt.
+        The assignment is done in the following steps, the order matters.
+
+        1. assign every prediction to -1
+        2. compute the weighted costs
+        3. do Hungarian matching on CPU based on the costs
+        4. assign all to 0 (background) first, then for each matched pair
+           between predictions and gts, treat this prediction as foreground
+           and assign the corresponding gt index (plus 1) to it.
+
+        Args:
+            bbox_pred (Tensor): Predicted boxes with normalized coordinates
+                (cx, cy, w, h), which are all in range [0, 1]. Shape
+                [num_query, 4].
+            cls_pred (Tensor): Predicted classification logits, shape
+                [num_query, num_class].
+            gt_bboxes (Tensor): Ground truth boxes with unnormalized
+                coordinates (x1, y1, x2, y2). Shape [num_gt, 4].
+            gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).
+            img_meta (dict): Meta information for current image.
+            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
+                labelled as `ignored`. Default None.
+            eps (int | float, optional): A value added to the denominator for
+                numerical stability. Default 1e-7.
+
+        Returns:
+            :obj:`AssignResult`: The assigned result.
+        """
+        assert gt_bboxes_ignore is None, \
+            'Only case when gt_bboxes_ignore is None is supported.'
+        num_gts, num_bboxes = gt_bboxes.size(0), bbox_pred.size(0)
+
+        # 1. assign -1 by default
+        assigned_gt_inds = bbox_pred.new_full((num_bboxes, ),
+                                              -1,
+                                              dtype=torch.long)
+        assigned_labels = bbox_pred.new_full((num_bboxes, ),
+                                             -1,
+                                             dtype=torch.long)
+        if num_gts == 0 or num_bboxes == 0:
+            # No ground truth or boxes, return empty assignment
+            if num_gts == 0:
+                # No ground truth, assign all to background
+                assigned_gt_inds[:] = 0
+            return AssignResult(
+                num_gts, assigned_gt_inds, None, labels=assigned_labels)
+        img_h, img_w, _ = img_meta['img_shape']
+        factor = gt_bboxes.new_tensor([img_w, img_h, img_w,
+                                       img_h]).unsqueeze(0)
+
+        # 2. compute the weighted costs
+        # classification and bboxcost.
+        cls_cost = self.cls_cost(cls_pred, gt_labels)
+        # regression L1 cost
+        normalize_gt_bboxes = gt_bboxes / factor
+        reg_cost = self.reg_cost(bbox_pred, normalize_gt_bboxes)
+        # regression iou cost, defaultly giou is used in official DETR.
+        bboxes = bbox_cxcywh_to_xyxy(bbox_pred) * factor
+        iou_cost = self.iou_cost(bboxes, gt_bboxes)
+        # weighted sum of above three costs
+        cost = cls_cost + reg_cost + iou_cost
+
+        # 3. do Hungarian matching on CPU using linear_sum_assignment
+        cost = cost.detach().cpu()
+        matched_row_inds, matched_col_inds = linear_sum_assignment(cost)
+        matched_row_inds = torch.from_numpy(matched_row_inds).to(
+            bbox_pred.device)
+        matched_col_inds = torch.from_numpy(matched_col_inds).to(
+            bbox_pred.device)
+
+        # 4. assign backgrounds and foregrounds
+        # assign all indices to backgrounds first
+        assigned_gt_inds[:] = 0
+        # assign foregrounds based on matching results
+        assigned_gt_inds[matched_row_inds] = matched_col_inds + 1
+        assigned_labels[matched_row_inds] = gt_labels[matched_col_inds]
+        return AssignResult(
+            num_gts, assigned_gt_inds, None, labels=assigned_labels)
diff --git a/mmdet/core/bbox/assigners/mask_hungarian_assigner.py b/mmdet/core/bbox/assigners/mask_hungarian_assigner.py
new file mode 100644
index 0000000..d83def1
--- /dev/null
+++ b/mmdet/core/bbox/assigners/mask_hungarian_assigner.py
@@ -0,0 +1,125 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from scipy.optimize import linear_sum_assignment
+
+from mmdet.core.bbox.builder import BBOX_ASSIGNERS
+from mmdet.core.bbox.match_costs.builder import build_match_cost
+from .assign_result import AssignResult
+from .base_assigner import BaseAssigner
+
+
+@BBOX_ASSIGNERS.register_module()
+class MaskHungarianAssigner(BaseAssigner):
+    """Computes one-to-one matching between predictions and ground truth for
+    mask.
+
+    This class computes an assignment between the targets and the predictions
+    based on the costs. The costs are weighted sum of three components:
+    classification cost, mask focal cost and mask dice cost. The
+    targets don't include the no_object, so generally there are more
+    predictions than targets. After the one-to-one matching, the un-matched
+    are treated as backgrounds. Thus each query prediction will be assigned
+    with `0` or a positive integer indicating the ground truth index:
+
+    - 0: negative sample, no assigned gt
+    - positive integer: positive sample, index (1-based) of assigned gt
+
+    Args:
+        cls_cost (:obj:`mmcv.ConfigDict` | dict): Classification cost config.
+        mask_cost (:obj:`mmcv.ConfigDict` | dict): Mask cost config.
+        dice_cost (:obj:`mmcv.ConfigDict` | dict): Dice cost config.
+    """
+
+    def __init__(self,
+                 cls_cost=dict(type='ClassificationCost', weight=1.0),
+                 mask_cost=dict(
+                     type='FocalLossCost', weight=1.0, binary_input=True),
+                 dice_cost=dict(type='DiceCost', weight=1.0)):
+        self.cls_cost = build_match_cost(cls_cost)
+        self.mask_cost = build_match_cost(mask_cost)
+        self.dice_cost = build_match_cost(dice_cost)
+
+    def assign(self,
+               cls_pred,
+               mask_pred,
+               gt_labels,
+               gt_mask,
+               img_meta,
+               gt_bboxes_ignore=None,
+               eps=1e-7):
+        """Computes one-to-one matching based on the weighted costs.
+
+        Args:
+            cls_pred (Tensor | None): Class prediction in shape
+                (num_query, cls_out_channels).
+            mask_pred (Tensor): Mask prediction in shape (num_query, H, W).
+            gt_labels (Tensor): Label of 'gt_mask'in shape = (num_gt, ).
+            gt_mask (Tensor): Ground truth mask in shape = (num_gt, H, W).
+            img_meta (dict): Meta information for current image.
+            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
+                labelled as `ignored`. Default None.
+            eps (int | float, optional): A value added to the denominator for
+                numerical stability. Default 1e-7.
+
+        Returns:
+            :obj:`AssignResult`: The assigned result.
+        """
+        assert gt_bboxes_ignore is None, \
+            'Only case when gt_bboxes_ignore is None is supported.'
+        # K-Net sometimes passes cls_pred=None to this assigner.
+        # So we should use the shape of mask_pred
+        num_gt, num_query = gt_labels.shape[0], mask_pred.shape[0]
+
+        # 1. assign -1 by default
+        assigned_gt_inds = mask_pred.new_full((num_query, ),
+                                              -1,
+                                              dtype=torch.long)
+        assigned_labels = mask_pred.new_full((num_query, ),
+                                             -1,
+                                             dtype=torch.long)
+        if num_gt == 0 or num_query == 0:
+            # No ground truth or boxes, return empty assignment
+            if num_gt == 0:
+                # No ground truth, assign all to background
+                assigned_gt_inds[:] = 0
+            return AssignResult(
+                num_gt, assigned_gt_inds, None, labels=assigned_labels)
+
+        # 2. compute the weighted costs
+        # classification and maskcost.
+        if self.cls_cost.weight != 0 and cls_pred is not None:
+            cls_cost = self.cls_cost(cls_pred, gt_labels)
+        else:
+            cls_cost = 0
+
+        if self.mask_cost.weight != 0:
+            # mask_pred shape = [num_query, h, w]
+            # gt_mask shape = [num_gt, h, w]
+            # mask_cost shape = [num_query, num_gt]
+            mask_cost = self.mask_cost(mask_pred, gt_mask)
+        else:
+            mask_cost = 0
+
+        if self.dice_cost.weight != 0:
+            dice_cost = self.dice_cost(mask_pred, gt_mask)
+        else:
+            dice_cost = 0
+        cost = cls_cost + mask_cost + dice_cost
+
+        # 3. do Hungarian matching on CPU using linear_sum_assignment
+        cost = cost.detach().cpu()
+
+        matched_row_inds, matched_col_inds = linear_sum_assignment(cost)
+        matched_row_inds = torch.from_numpy(matched_row_inds).to(
+            mask_pred.device)
+        matched_col_inds = torch.from_numpy(matched_col_inds).to(
+            mask_pred.device)
+
+        # 4. assign backgrounds and foregrounds
+        # assign all indices to backgrounds first
+        assigned_gt_inds[:] = 0
+        # assign foregrounds based on matching results
+        assigned_gt_inds[matched_row_inds] = matched_col_inds + 1
+        assigned_labels[matched_row_inds] = gt_labels[matched_col_inds]
+        return AssignResult(
+            num_gt, assigned_gt_inds, None, labels=assigned_labels)
diff --git a/mmdet/core/bbox/assigners/max_iou_assigner.py b/mmdet/core/bbox/assigners/max_iou_assigner.py
new file mode 100644
index 0000000..676421f
--- /dev/null
+++ b/mmdet/core/bbox/assigners/max_iou_assigner.py
@@ -0,0 +1,218 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from ..builder import BBOX_ASSIGNERS
+from ..iou_calculators import build_iou_calculator
+from .assign_result import AssignResult
+from .base_assigner import BaseAssigner
+
+
+@BBOX_ASSIGNERS.register_module()
+class MaxIoUAssigner(BaseAssigner):
+    """Assign a corresponding gt bbox or background to each bbox.
+
+    Each proposals will be assigned with `-1`, or a semi-positive integer
+    indicating the ground truth index.
+
+    - -1: negative sample, no assigned gt
+    - semi-positive integer: positive sample, index (0-based) of assigned gt
+
+    Args:
+        pos_iou_thr (float): IoU threshold for positive bboxes.
+        neg_iou_thr (float or tuple): IoU threshold for negative bboxes.
+        min_pos_iou (float): Minimum iou for a bbox to be considered as a
+            positive bbox. Positive samples can have smaller IoU than
+            pos_iou_thr due to the 4th step (assign max IoU sample to each gt).
+            `min_pos_iou` is set to avoid assigning bboxes that have extremely
+            small iou with GT as positive samples. It brings about 0.3 mAP
+            improvements in 1x schedule but does not affect the performance of
+            3x schedule. More comparisons can be found in
+            `PR #7464 <https://github.com/open-mmlab/mmdetection/pull/7464>`_.
+        gt_max_assign_all (bool): Whether to assign all bboxes with the same
+            highest overlap with some gt to that gt.
+        ignore_iof_thr (float): IoF threshold for ignoring bboxes (if
+            `gt_bboxes_ignore` is specified). Negative values mean not
+            ignoring any bboxes.
+        ignore_wrt_candidates (bool): Whether to compute the iof between
+            `bboxes` and `gt_bboxes_ignore`, or the contrary.
+        match_low_quality (bool): Whether to allow low quality matches. This is
+            usually allowed for RPN and single stage detectors, but not allowed
+            in the second stage. Details are demonstrated in Step 4.
+        gpu_assign_thr (int): The upper bound of the number of GT for GPU
+            assign. When the number of gt is above this threshold, will assign
+            on CPU device. Negative values mean not assign on CPU.
+    """
+
+    def __init__(self,
+                 pos_iou_thr,
+                 neg_iou_thr,
+                 min_pos_iou=.0,
+                 gt_max_assign_all=True,
+                 ignore_iof_thr=-1,
+                 ignore_wrt_candidates=True,
+                 match_low_quality=True,
+                 gpu_assign_thr=-1,
+                 iou_calculator=dict(type='BboxOverlaps2D')):
+        self.pos_iou_thr = pos_iou_thr
+        self.neg_iou_thr = neg_iou_thr
+        self.min_pos_iou = min_pos_iou
+        self.gt_max_assign_all = gt_max_assign_all
+        self.ignore_iof_thr = ignore_iof_thr
+        self.ignore_wrt_candidates = ignore_wrt_candidates
+        self.gpu_assign_thr = gpu_assign_thr
+        self.match_low_quality = match_low_quality
+        self.iou_calculator = build_iou_calculator(iou_calculator)
+
+    def assign(self, bboxes, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
+        """Assign gt to bboxes.
+
+        This method assign a gt bbox to every bbox (proposal/anchor), each bbox
+        will be assigned with -1, or a semi-positive number. -1 means negative
+        sample, semi-positive number is the index (0-based) of assigned gt.
+        The assignment is done in following steps, the order matters.
+
+        1. assign every bbox to the background
+        2. assign proposals whose iou with all gts < neg_iou_thr to 0
+        3. for each bbox, if the iou with its nearest gt >= pos_iou_thr,
+           assign it to that bbox
+        4. for each gt bbox, assign its nearest proposals (may be more than
+           one) to itself
+
+        Args:
+            bboxes (Tensor): Bounding boxes to be assigned, shape(n, 4).
+            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
+            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
+                labelled as `ignored`, e.g., crowd boxes in COCO.
+            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
+
+        Returns:
+            :obj:`AssignResult`: The assign result.
+
+        Example:
+            >>> self = MaxIoUAssigner(0.5, 0.5)
+            >>> bboxes = torch.Tensor([[0, 0, 10, 10], [10, 10, 20, 20]])
+            >>> gt_bboxes = torch.Tensor([[0, 0, 10, 9]])
+            >>> assign_result = self.assign(bboxes, gt_bboxes)
+            >>> expected_gt_inds = torch.LongTensor([1, 0])
+            >>> assert torch.all(assign_result.gt_inds == expected_gt_inds)
+        """
+        assign_on_cpu = True if (self.gpu_assign_thr > 0) and (
+            gt_bboxes.shape[0] > self.gpu_assign_thr) else False
+        # compute overlap and assign gt on CPU when number of GT is large
+        if assign_on_cpu:
+            device = bboxes.device
+            bboxes = bboxes.cpu()
+            gt_bboxes = gt_bboxes.cpu()
+            if gt_bboxes_ignore is not None:
+                gt_bboxes_ignore = gt_bboxes_ignore.cpu()
+            if gt_labels is not None:
+                gt_labels = gt_labels.cpu()
+
+        overlaps = self.iou_calculator(gt_bboxes, bboxes)
+
+        if (self.ignore_iof_thr > 0 and gt_bboxes_ignore is not None
+                and gt_bboxes_ignore.numel() > 0 and bboxes.numel() > 0):
+            if self.ignore_wrt_candidates:
+                ignore_overlaps = self.iou_calculator(
+                    bboxes, gt_bboxes_ignore, mode='iof')
+                ignore_max_overlaps, _ = ignore_overlaps.max(dim=1)
+            else:
+                ignore_overlaps = self.iou_calculator(
+                    gt_bboxes_ignore, bboxes, mode='iof')
+                ignore_max_overlaps, _ = ignore_overlaps.max(dim=0)
+            overlaps[:, ignore_max_overlaps > self.ignore_iof_thr] = -1
+
+        assign_result = self.assign_wrt_overlaps(overlaps, gt_labels)
+        if assign_on_cpu:
+            assign_result.gt_inds = assign_result.gt_inds.to(device)
+            assign_result.max_overlaps = assign_result.max_overlaps.to(device)
+            if assign_result.labels is not None:
+                assign_result.labels = assign_result.labels.to(device)
+        return assign_result
+
+    def assign_wrt_overlaps(self, overlaps, gt_labels=None):
+        """Assign w.r.t. the overlaps of bboxes with gts.
+
+        Args:
+            overlaps (Tensor): Overlaps between k gt_bboxes and n bboxes,
+                shape(k, n).
+            gt_labels (Tensor, optional): Labels of k gt_bboxes, shape (k, ).
+
+        Returns:
+            :obj:`AssignResult`: The assign result.
+        """
+        num_gts, num_bboxes = overlaps.size(0), overlaps.size(1)
+
+        # 1. assign -1 by default
+        assigned_gt_inds = overlaps.new_full((num_bboxes, ),
+                                             -1,
+                                             dtype=torch.long)
+
+        if num_gts == 0 or num_bboxes == 0:
+            # No ground truth or boxes, return empty assignment
+            max_overlaps = overlaps.new_zeros((num_bboxes, ))
+            if num_gts == 0:
+                # No truth, assign everything to background
+                assigned_gt_inds[:] = 0
+            if gt_labels is None:
+                assigned_labels = None
+            else:
+                assigned_labels = overlaps.new_full((num_bboxes, ),
+                                                    -1,
+                                                    dtype=torch.long)
+            return AssignResult(
+                num_gts,
+                assigned_gt_inds,
+                max_overlaps,
+                labels=assigned_labels)
+
+        # for each anchor, which gt best overlaps with it
+        # for each anchor, the max iou of all gts
+        max_overlaps, argmax_overlaps = overlaps.max(dim=0)
+        # for each gt, which anchor best overlaps with it
+        # for each gt, the max iou of all proposals
+        gt_max_overlaps, gt_argmax_overlaps = overlaps.max(dim=1)
+
+        # 2. assign negative: below
+        # the negative inds are set to be 0
+        if isinstance(self.neg_iou_thr, float):
+            assigned_gt_inds[(max_overlaps >= 0)
+                             & (max_overlaps < self.neg_iou_thr)] = 0
+        elif isinstance(self.neg_iou_thr, tuple):
+            assert len(self.neg_iou_thr) == 2
+            assigned_gt_inds[(max_overlaps >= self.neg_iou_thr[0])
+                             & (max_overlaps < self.neg_iou_thr[1])] = 0
+
+        # 3. assign positive: above positive IoU threshold
+        pos_inds = max_overlaps >= self.pos_iou_thr
+        assigned_gt_inds[pos_inds] = argmax_overlaps[pos_inds] + 1
+
+        if self.match_low_quality:
+            # Low-quality matching will overwrite the assigned_gt_inds assigned
+            # in Step 3. Thus, the assigned gt might not be the best one for
+            # prediction.
+            # For example, if bbox A has 0.9 and 0.8 iou with GT bbox 1 & 2,
+            # bbox 1 will be assigned as the best target for bbox A in step 3.
+            # However, if GT bbox 2's gt_argmax_overlaps = A, bbox A's
+            # assigned_gt_inds will be overwritten to be bbox 2.
+            # This might be the reason that it is not used in ROI Heads.
+            for i in range(num_gts):
+                if gt_max_overlaps[i] >= self.min_pos_iou:
+                    if self.gt_max_assign_all:
+                        max_iou_inds = overlaps[i, :] == gt_max_overlaps[i]
+                        assigned_gt_inds[max_iou_inds] = i + 1
+                    else:
+                        assigned_gt_inds[gt_argmax_overlaps[i]] = i + 1
+
+        if gt_labels is not None:
+            assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)
+            pos_inds = torch.nonzero(
+                assigned_gt_inds > 0, as_tuple=False).squeeze()
+            if pos_inds.numel() > 0:
+                assigned_labels[pos_inds] = gt_labels[
+                    assigned_gt_inds[pos_inds] - 1]
+        else:
+            assigned_labels = None
+
+        return AssignResult(
+            num_gts, assigned_gt_inds, max_overlaps, labels=assigned_labels)
diff --git a/mmdet/core/bbox/assigners/point_assigner.py b/mmdet/core/bbox/assigners/point_assigner.py
new file mode 100644
index 0000000..b0dc224
--- /dev/null
+++ b/mmdet/core/bbox/assigners/point_assigner.py
@@ -0,0 +1,134 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from ..builder import BBOX_ASSIGNERS
+from .assign_result import AssignResult
+from .base_assigner import BaseAssigner
+
+
+@BBOX_ASSIGNERS.register_module()
+class PointAssigner(BaseAssigner):
+    """Assign a corresponding gt bbox or background to each point.
+
+    Each proposals will be assigned with `0`, or a positive integer
+    indicating the ground truth index.
+
+    - 0: negative sample, no assigned gt
+    - positive integer: positive sample, index (1-based) of assigned gt
+    """
+
+    def __init__(self, scale=4, pos_num=3):
+        self.scale = scale
+        self.pos_num = pos_num
+
+    def assign(self, points, gt_bboxes, gt_bboxes_ignore=None, gt_labels=None):
+        """Assign gt to points.
+
+        This method assign a gt bbox to every points set, each points set
+        will be assigned with  the background_label (-1), or a label number.
+        -1 is background, and semi-positive number is the index (0-based) of
+        assigned gt.
+        The assignment is done in following steps, the order matters.
+
+        1. assign every points to the background_label (-1)
+        2. A point is assigned to some gt bbox if
+            (i) the point is within the k closest points to the gt bbox
+            (ii) the distance between this point and the gt is smaller than
+                other gt bboxes
+
+        Args:
+            points (Tensor): points to be assigned, shape(n, 3) while last
+                dimension stands for (x, y, stride).
+            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
+            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
+                labelled as `ignored`, e.g., crowd boxes in COCO.
+                NOTE: currently unused.
+            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
+
+        Returns:
+            :obj:`AssignResult`: The assign result.
+        """
+        num_points = points.shape[0]
+        num_gts = gt_bboxes.shape[0]
+
+        if num_gts == 0 or num_points == 0:
+            # If no truth assign everything to the background
+            assigned_gt_inds = points.new_full((num_points, ),
+                                               0,
+                                               dtype=torch.long)
+            if gt_labels is None:
+                assigned_labels = None
+            else:
+                assigned_labels = points.new_full((num_points, ),
+                                                  -1,
+                                                  dtype=torch.long)
+            return AssignResult(
+                num_gts, assigned_gt_inds, None, labels=assigned_labels)
+
+        points_xy = points[:, :2]
+        points_stride = points[:, 2]
+        points_lvl = torch.log2(
+            points_stride).int()  # [3...,4...,5...,6...,7...]
+        lvl_min, lvl_max = points_lvl.min(), points_lvl.max()
+
+        # assign gt box
+        gt_bboxes_xy = (gt_bboxes[:, :2] + gt_bboxes[:, 2:]) / 2
+        gt_bboxes_wh = (gt_bboxes[:, 2:] - gt_bboxes[:, :2]).clamp(min=1e-6)
+        scale = self.scale
+        gt_bboxes_lvl = ((torch.log2(gt_bboxes_wh[:, 0] / scale) +
+                          torch.log2(gt_bboxes_wh[:, 1] / scale)) / 2).int()
+        gt_bboxes_lvl = torch.clamp(gt_bboxes_lvl, min=lvl_min, max=lvl_max)
+
+        # stores the assigned gt index of each point
+        assigned_gt_inds = points.new_zeros((num_points, ), dtype=torch.long)
+        # stores the assigned gt dist (to this point) of each point
+        assigned_gt_dist = points.new_full((num_points, ), float('inf'))
+        points_range = torch.arange(points.shape[0])
+
+        for idx in range(num_gts):
+            gt_lvl = gt_bboxes_lvl[idx]
+            # get the index of points in this level
+            lvl_idx = gt_lvl == points_lvl
+            points_index = points_range[lvl_idx]
+            # get the points in this level
+            lvl_points = points_xy[lvl_idx, :]
+            # get the center point of gt
+            gt_point = gt_bboxes_xy[[idx], :]
+            # get width and height of gt
+            gt_wh = gt_bboxes_wh[[idx], :]
+            # compute the distance between gt center and
+            #   all points in this level
+            points_gt_dist = ((lvl_points - gt_point) / gt_wh).norm(dim=1)
+            # find the nearest k points to gt center in this level
+            min_dist, min_dist_index = torch.topk(
+                points_gt_dist, self.pos_num, largest=False)
+            # the index of nearest k points to gt center in this level
+            min_dist_points_index = points_index[min_dist_index]
+            # The less_than_recorded_index stores the index
+            #   of min_dist that is less then the assigned_gt_dist. Where
+            #   assigned_gt_dist stores the dist from previous assigned gt
+            #   (if exist) to each point.
+            less_than_recorded_index = min_dist < assigned_gt_dist[
+                min_dist_points_index]
+            # The min_dist_points_index stores the index of points satisfy:
+            #   (1) it is k nearest to current gt center in this level.
+            #   (2) it is closer to current gt center than other gt center.
+            min_dist_points_index = min_dist_points_index[
+                less_than_recorded_index]
+            # assign the result
+            assigned_gt_inds[min_dist_points_index] = idx + 1
+            assigned_gt_dist[min_dist_points_index] = min_dist[
+                less_than_recorded_index]
+
+        if gt_labels is not None:
+            assigned_labels = assigned_gt_inds.new_full((num_points, ), -1)
+            pos_inds = torch.nonzero(
+                assigned_gt_inds > 0, as_tuple=False).squeeze()
+            if pos_inds.numel() > 0:
+                assigned_labels[pos_inds] = gt_labels[
+                    assigned_gt_inds[pos_inds] - 1]
+        else:
+            assigned_labels = None
+
+        return AssignResult(
+            num_gts, assigned_gt_inds, None, labels=assigned_labels)
diff --git a/mmdet/core/bbox/assigners/region_assigner.py b/mmdet/core/bbox/assigners/region_assigner.py
new file mode 100644
index 0000000..1833b89
--- /dev/null
+++ b/mmdet/core/bbox/assigners/region_assigner.py
@@ -0,0 +1,222 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdet.core import anchor_inside_flags
+from ..builder import BBOX_ASSIGNERS
+from .assign_result import AssignResult
+from .base_assigner import BaseAssigner
+
+
+def calc_region(bbox, ratio, stride, featmap_size=None):
+    """Calculate region of the box defined by the ratio, the ratio is from the
+    center of the box to every edge."""
+    # project bbox on the feature
+    f_bbox = bbox / stride
+    x1 = torch.round((1 - ratio) * f_bbox[0] + ratio * f_bbox[2])
+    y1 = torch.round((1 - ratio) * f_bbox[1] + ratio * f_bbox[3])
+    x2 = torch.round(ratio * f_bbox[0] + (1 - ratio) * f_bbox[2])
+    y2 = torch.round(ratio * f_bbox[1] + (1 - ratio) * f_bbox[3])
+    if featmap_size is not None:
+        x1 = x1.clamp(min=0, max=featmap_size[1])
+        y1 = y1.clamp(min=0, max=featmap_size[0])
+        x2 = x2.clamp(min=0, max=featmap_size[1])
+        y2 = y2.clamp(min=0, max=featmap_size[0])
+    return (x1, y1, x2, y2)
+
+
+def anchor_ctr_inside_region_flags(anchors, stride, region):
+    """Get the flag indicate whether anchor centers are inside regions."""
+    x1, y1, x2, y2 = region
+    f_anchors = anchors / stride
+    x = (f_anchors[:, 0] + f_anchors[:, 2]) * 0.5
+    y = (f_anchors[:, 1] + f_anchors[:, 3]) * 0.5
+    flags = (x >= x1) & (x <= x2) & (y >= y1) & (y <= y2)
+    return flags
+
+
+@BBOX_ASSIGNERS.register_module()
+class RegionAssigner(BaseAssigner):
+    """Assign a corresponding gt bbox or background to each bbox.
+
+    Each proposals will be assigned with `-1`, `0`, or a positive integer
+    indicating the ground truth index.
+
+    - -1: don't care
+    - 0: negative sample, no assigned gt
+    - positive integer: positive sample, index (1-based) of assigned gt
+
+    Args:
+        center_ratio: ratio of the region in the center of the bbox to
+            define positive sample.
+        ignore_ratio: ratio of the region to define ignore samples.
+    """
+
+    def __init__(self, center_ratio=0.2, ignore_ratio=0.5):
+        self.center_ratio = center_ratio
+        self.ignore_ratio = ignore_ratio
+
+    def assign(self,
+               mlvl_anchors,
+               mlvl_valid_flags,
+               gt_bboxes,
+               img_meta,
+               featmap_sizes,
+               anchor_scale,
+               anchor_strides,
+               gt_bboxes_ignore=None,
+               gt_labels=None,
+               allowed_border=0):
+        """Assign gt to anchors.
+
+        This method assign a gt bbox to every bbox (proposal/anchor), each bbox
+        will be assigned with -1, 0, or a positive number. -1 means don't care,
+        0 means negative sample, positive number is the index (1-based) of
+        assigned gt.
+
+        The assignment is done in following steps, and the order matters.
+
+        1. Assign every anchor to 0 (negative)
+        2. (For each gt_bboxes) Compute ignore flags based on ignore_region
+           then assign -1 to anchors w.r.t. ignore flags
+        3. (For each gt_bboxes) Compute pos flags based on center_region then
+           assign gt_bboxes to anchors w.r.t. pos flags
+        4. (For each gt_bboxes) Compute ignore flags based on adjacent anchor
+           level then assign -1 to anchors w.r.t. ignore flags
+        5. Assign anchor outside of image to -1
+
+        Args:
+            mlvl_anchors (list[Tensor]): Multi level anchors.
+            mlvl_valid_flags (list[Tensor]): Multi level valid flags.
+            gt_bboxes (Tensor): Ground truth bboxes of image
+            img_meta (dict): Meta info of image.
+            featmap_sizes (list[Tensor]): Feature mapsize each level
+            anchor_scale (int): Scale of the anchor.
+            anchor_strides (list[int]): Stride of the anchor.
+            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
+            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
+                labelled as `ignored`, e.g., crowd boxes in COCO.
+            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
+            allowed_border (int, optional): The border to allow the valid
+                anchor. Defaults to 0.
+
+        Returns:
+            :obj:`AssignResult`: The assign result.
+        """
+        if gt_bboxes_ignore is not None:
+            raise NotImplementedError
+
+        num_gts = gt_bboxes.shape[0]
+        num_bboxes = sum(x.shape[0] for x in mlvl_anchors)
+
+        if num_gts == 0 or num_bboxes == 0:
+            # No ground truth or boxes, return empty assignment
+            max_overlaps = gt_bboxes.new_zeros((num_bboxes, ))
+            assigned_gt_inds = gt_bboxes.new_zeros((num_bboxes, ),
+                                                   dtype=torch.long)
+            if gt_labels is None:
+                assigned_labels = None
+            else:
+                assigned_labels = gt_bboxes.new_full((num_bboxes, ),
+                                                     -1,
+                                                     dtype=torch.long)
+            return AssignResult(
+                num_gts,
+                assigned_gt_inds,
+                max_overlaps,
+                labels=assigned_labels)
+
+        num_lvls = len(mlvl_anchors)
+        r1 = (1 - self.center_ratio) / 2
+        r2 = (1 - self.ignore_ratio) / 2
+
+        scale = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) *
+                           (gt_bboxes[:, 3] - gt_bboxes[:, 1]))
+        min_anchor_size = scale.new_full(
+            (1, ), float(anchor_scale * anchor_strides[0]))
+        target_lvls = torch.floor(
+            torch.log2(scale) - torch.log2(min_anchor_size) + 0.5)
+        target_lvls = target_lvls.clamp(min=0, max=num_lvls - 1).long()
+
+        # 1. assign 0 (negative) by default
+        mlvl_assigned_gt_inds = []
+        mlvl_ignore_flags = []
+        for lvl in range(num_lvls):
+            h, w = featmap_sizes[lvl]
+            assert h * w == mlvl_anchors[lvl].shape[0]
+            assigned_gt_inds = gt_bboxes.new_full((h * w, ),
+                                                  0,
+                                                  dtype=torch.long)
+            ignore_flags = torch.zeros_like(assigned_gt_inds)
+            mlvl_assigned_gt_inds.append(assigned_gt_inds)
+            mlvl_ignore_flags.append(ignore_flags)
+
+        for gt_id in range(num_gts):
+            lvl = target_lvls[gt_id].item()
+            featmap_size = featmap_sizes[lvl]
+            stride = anchor_strides[lvl]
+            anchors = mlvl_anchors[lvl]
+            gt_bbox = gt_bboxes[gt_id, :4]
+
+            # Compute regions
+            ignore_region = calc_region(gt_bbox, r2, stride, featmap_size)
+            ctr_region = calc_region(gt_bbox, r1, stride, featmap_size)
+
+            # 2. Assign -1 to ignore flags
+            ignore_flags = anchor_ctr_inside_region_flags(
+                anchors, stride, ignore_region)
+            mlvl_assigned_gt_inds[lvl][ignore_flags] = -1
+
+            # 3. Assign gt_bboxes to pos flags
+            pos_flags = anchor_ctr_inside_region_flags(anchors, stride,
+                                                       ctr_region)
+            mlvl_assigned_gt_inds[lvl][pos_flags] = gt_id + 1
+
+            # 4. Assign -1 to ignore adjacent lvl
+            if lvl > 0:
+                d_lvl = lvl - 1
+                d_anchors = mlvl_anchors[d_lvl]
+                d_featmap_size = featmap_sizes[d_lvl]
+                d_stride = anchor_strides[d_lvl]
+                d_ignore_region = calc_region(gt_bbox, r2, d_stride,
+                                              d_featmap_size)
+                ignore_flags = anchor_ctr_inside_region_flags(
+                    d_anchors, d_stride, d_ignore_region)
+                mlvl_ignore_flags[d_lvl][ignore_flags] = 1
+            if lvl < num_lvls - 1:
+                u_lvl = lvl + 1
+                u_anchors = mlvl_anchors[u_lvl]
+                u_featmap_size = featmap_sizes[u_lvl]
+                u_stride = anchor_strides[u_lvl]
+                u_ignore_region = calc_region(gt_bbox, r2, u_stride,
+                                              u_featmap_size)
+                ignore_flags = anchor_ctr_inside_region_flags(
+                    u_anchors, u_stride, u_ignore_region)
+                mlvl_ignore_flags[u_lvl][ignore_flags] = 1
+
+        # 4. (cont.) Assign -1 to ignore adjacent lvl
+        for lvl in range(num_lvls):
+            ignore_flags = mlvl_ignore_flags[lvl]
+            mlvl_assigned_gt_inds[lvl][ignore_flags] = -1
+
+        # 5. Assign -1 to anchor outside of image
+        flat_assigned_gt_inds = torch.cat(mlvl_assigned_gt_inds)
+        flat_anchors = torch.cat(mlvl_anchors)
+        flat_valid_flags = torch.cat(mlvl_valid_flags)
+        assert (flat_assigned_gt_inds.shape[0] == flat_anchors.shape[0] ==
+                flat_valid_flags.shape[0])
+        inside_flags = anchor_inside_flags(flat_anchors, flat_valid_flags,
+                                           img_meta['img_shape'],
+                                           allowed_border)
+        outside_flags = ~inside_flags
+        flat_assigned_gt_inds[outside_flags] = -1
+
+        if gt_labels is not None:
+            assigned_labels = torch.zeros_like(flat_assigned_gt_inds)
+            pos_flags = assigned_gt_inds > 0
+            assigned_labels[pos_flags] = gt_labels[
+                flat_assigned_gt_inds[pos_flags] - 1]
+        else:
+            assigned_labels = None
+
+        return AssignResult(
+            num_gts, flat_assigned_gt_inds, None, labels=assigned_labels)
diff --git a/mmdet/core/bbox/assigners/sim_ota_assigner.py b/mmdet/core/bbox/assigners/sim_ota_assigner.py
new file mode 100644
index 0000000..58bfef4
--- /dev/null
+++ b/mmdet/core/bbox/assigners/sim_ota_assigner.py
@@ -0,0 +1,257 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch
+import torch.nn.functional as F
+
+from ..builder import BBOX_ASSIGNERS
+from ..iou_calculators import bbox_overlaps
+from .assign_result import AssignResult
+from .base_assigner import BaseAssigner
+
+
+@BBOX_ASSIGNERS.register_module()
+class SimOTAAssigner(BaseAssigner):
+    """Computes matching between predictions and ground truth.
+
+    Args:
+        center_radius (int | float, optional): Ground truth center size
+            to judge whether a prior is in center. Default 2.5.
+        candidate_topk (int, optional): The candidate top-k which used to
+            get top-k ious to calculate dynamic-k. Default 10.
+        iou_weight (int | float, optional): The scale factor for regression
+            iou cost. Default 3.0.
+        cls_weight (int | float, optional): The scale factor for classification
+            cost. Default 1.0.
+    """
+
+    def __init__(self,
+                 center_radius=2.5,
+                 candidate_topk=10,
+                 iou_weight=3.0,
+                 cls_weight=1.0):
+        self.center_radius = center_radius
+        self.candidate_topk = candidate_topk
+        self.iou_weight = iou_weight
+        self.cls_weight = cls_weight
+
+    def assign(self,
+               pred_scores,
+               priors,
+               decoded_bboxes,
+               gt_bboxes,
+               gt_labels,
+               gt_bboxes_ignore=None,
+               eps=1e-7):
+        """Assign gt to priors using SimOTA. It will switch to CPU mode when
+        GPU is out of memory.
+        Args:
+            pred_scores (Tensor): Classification scores of one image,
+                a 2D-Tensor with shape [num_priors, num_classes]
+            priors (Tensor): All priors of one image, a 2D-Tensor with shape
+                [num_priors, 4] in [cx, xy, stride_w, stride_y] format.
+            decoded_bboxes (Tensor): Predicted bboxes, a 2D-Tensor with shape
+                [num_priors, 4] in [tl_x, tl_y, br_x, br_y] format.
+            gt_bboxes (Tensor): Ground truth bboxes of one image, a 2D-Tensor
+                with shape [num_gts, 4] in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (Tensor): Ground truth labels of one image, a Tensor
+                with shape [num_gts].
+            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
+                labelled as `ignored`, e.g., crowd boxes in COCO.
+            eps (float): A value added to the denominator for numerical
+                stability. Default 1e-7.
+        Returns:
+            assign_result (obj:`AssignResult`): The assigned result.
+        """
+        try:
+            assign_result = self._assign(pred_scores, priors, decoded_bboxes,
+                                         gt_bboxes, gt_labels,
+                                         gt_bboxes_ignore, eps)
+            return assign_result
+        except RuntimeError:
+            origin_device = pred_scores.device
+            warnings.warn('OOM RuntimeError is raised due to the huge memory '
+                          'cost during label assignment. CPU mode is applied '
+                          'in this batch. If you want to avoid this issue, '
+                          'try to reduce the batch size or image size.')
+            torch.cuda.empty_cache()
+
+            pred_scores = pred_scores.cpu()
+            priors = priors.cpu()
+            decoded_bboxes = decoded_bboxes.cpu()
+            gt_bboxes = gt_bboxes.cpu().float()
+            gt_labels = gt_labels.cpu()
+
+            assign_result = self._assign(pred_scores, priors, decoded_bboxes,
+                                         gt_bboxes, gt_labels,
+                                         gt_bboxes_ignore, eps)
+            assign_result.gt_inds = assign_result.gt_inds.to(origin_device)
+            assign_result.max_overlaps = assign_result.max_overlaps.to(
+                origin_device)
+            assign_result.labels = assign_result.labels.to(origin_device)
+
+            return assign_result
+
+    def _assign(self,
+                pred_scores,
+                priors,
+                decoded_bboxes,
+                gt_bboxes,
+                gt_labels,
+                gt_bboxes_ignore=None,
+                eps=1e-7):
+        """Assign gt to priors using SimOTA.
+        Args:
+            pred_scores (Tensor): Classification scores of one image,
+                a 2D-Tensor with shape [num_priors, num_classes]
+            priors (Tensor): All priors of one image, a 2D-Tensor with shape
+                [num_priors, 4] in [cx, xy, stride_w, stride_y] format.
+            decoded_bboxes (Tensor): Predicted bboxes, a 2D-Tensor with shape
+                [num_priors, 4] in [tl_x, tl_y, br_x, br_y] format.
+            gt_bboxes (Tensor): Ground truth bboxes of one image, a 2D-Tensor
+                with shape [num_gts, 4] in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (Tensor): Ground truth labels of one image, a Tensor
+                with shape [num_gts].
+            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
+                labelled as `ignored`, e.g., crowd boxes in COCO.
+            eps (float): A value added to the denominator for numerical
+                stability. Default 1e-7.
+        Returns:
+            :obj:`AssignResult`: The assigned result.
+        """
+        INF = 100000.0
+        num_gt = gt_bboxes.size(0)
+        num_bboxes = decoded_bboxes.size(0)
+
+        # assign 0 by default
+        assigned_gt_inds = decoded_bboxes.new_full((num_bboxes, ),
+                                                   0,
+                                                   dtype=torch.long)
+        valid_mask, is_in_boxes_and_center = self.get_in_gt_and_in_center_info(
+            priors, gt_bboxes)
+        valid_decoded_bbox = decoded_bboxes[valid_mask]
+        valid_pred_scores = pred_scores[valid_mask]
+        num_valid = valid_decoded_bbox.size(0)
+
+        if num_gt == 0 or num_bboxes == 0 or num_valid == 0:
+            # No ground truth or boxes, return empty assignment
+            max_overlaps = decoded_bboxes.new_zeros((num_bboxes, ))
+            if num_gt == 0:
+                # No truth, assign everything to background
+                assigned_gt_inds[:] = 0
+            if gt_labels is None:
+                assigned_labels = None
+            else:
+                assigned_labels = decoded_bboxes.new_full((num_bboxes, ),
+                                                          -1,
+                                                          dtype=torch.long)
+            return AssignResult(
+                num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)
+
+        pairwise_ious = bbox_overlaps(valid_decoded_bbox, gt_bboxes)
+        iou_cost = -torch.log(pairwise_ious + eps)
+
+        gt_onehot_label = (
+            F.one_hot(gt_labels.to(torch.int64),
+                      pred_scores.shape[-1]).float().unsqueeze(0).repeat(
+                          num_valid, 1, 1))
+
+        valid_pred_scores = valid_pred_scores.unsqueeze(1).repeat(1, num_gt, 1)
+        cls_cost = (
+            F.binary_cross_entropy(
+                valid_pred_scores.to(dtype=torch.float32).sqrt_(),
+                gt_onehot_label,
+                reduction='none',
+            ).sum(-1).to(dtype=valid_pred_scores.dtype))
+
+        cost_matrix = (
+            cls_cost * self.cls_weight + iou_cost * self.iou_weight +
+            (~is_in_boxes_and_center) * INF)
+
+        matched_pred_ious, matched_gt_inds = \
+            self.dynamic_k_matching(
+                cost_matrix, pairwise_ious, num_gt, valid_mask)
+
+        # convert to AssignResult format
+        assigned_gt_inds[valid_mask] = matched_gt_inds + 1
+        assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)
+        assigned_labels[valid_mask] = gt_labels[matched_gt_inds].long()
+        max_overlaps = assigned_gt_inds.new_full((num_bboxes, ),
+                                                 -INF,
+                                                 dtype=torch.float32)
+        max_overlaps[valid_mask] = matched_pred_ious
+        return AssignResult(
+            num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)
+
+    def get_in_gt_and_in_center_info(self, priors, gt_bboxes):
+        num_gt = gt_bboxes.size(0)
+
+        repeated_x = priors[:, 0].unsqueeze(1).repeat(1, num_gt)
+        repeated_y = priors[:, 1].unsqueeze(1).repeat(1, num_gt)
+        repeated_stride_x = priors[:, 2].unsqueeze(1).repeat(1, num_gt)
+        repeated_stride_y = priors[:, 3].unsqueeze(1).repeat(1, num_gt)
+
+        # is prior centers in gt bboxes, shape: [n_prior, n_gt]
+        l_ = repeated_x - gt_bboxes[:, 0]
+        t_ = repeated_y - gt_bboxes[:, 1]
+        r_ = gt_bboxes[:, 2] - repeated_x
+        b_ = gt_bboxes[:, 3] - repeated_y
+
+        deltas = torch.stack([l_, t_, r_, b_], dim=1)
+        is_in_gts = deltas.min(dim=1).values > 0
+        is_in_gts_all = is_in_gts.sum(dim=1) > 0
+
+        # is prior centers in gt centers
+        gt_cxs = (gt_bboxes[:, 0] + gt_bboxes[:, 2]) / 2.0
+        gt_cys = (gt_bboxes[:, 1] + gt_bboxes[:, 3]) / 2.0
+        ct_box_l = gt_cxs - self.center_radius * repeated_stride_x
+        ct_box_t = gt_cys - self.center_radius * repeated_stride_y
+        ct_box_r = gt_cxs + self.center_radius * repeated_stride_x
+        ct_box_b = gt_cys + self.center_radius * repeated_stride_y
+
+        cl_ = repeated_x - ct_box_l
+        ct_ = repeated_y - ct_box_t
+        cr_ = ct_box_r - repeated_x
+        cb_ = ct_box_b - repeated_y
+
+        ct_deltas = torch.stack([cl_, ct_, cr_, cb_], dim=1)
+        is_in_cts = ct_deltas.min(dim=1).values > 0
+        is_in_cts_all = is_in_cts.sum(dim=1) > 0
+
+        # in boxes or in centers, shape: [num_priors]
+        is_in_gts_or_centers = is_in_gts_all | is_in_cts_all
+
+        # both in boxes and centers, shape: [num_fg, num_gt]
+        is_in_boxes_and_centers = (
+            is_in_gts[is_in_gts_or_centers, :]
+            & is_in_cts[is_in_gts_or_centers, :])
+        return is_in_gts_or_centers, is_in_boxes_and_centers
+
+    def dynamic_k_matching(self, cost, pairwise_ious, num_gt, valid_mask):
+        matching_matrix = torch.zeros_like(cost, dtype=torch.uint8)
+        # select candidate topk ious for dynamic-k calculation
+        candidate_topk = min(self.candidate_topk, pairwise_ious.size(0))
+        topk_ious, _ = torch.topk(pairwise_ious, candidate_topk, dim=0)
+        # calculate dynamic k for each gt
+        dynamic_ks = torch.clamp(topk_ious.sum(0).int(), min=1)
+        for gt_idx in range(num_gt):
+            _, pos_idx = torch.topk(
+                cost[:, gt_idx], k=dynamic_ks[gt_idx], largest=False)
+            matching_matrix[:, gt_idx][pos_idx] = 1
+
+        del topk_ious, dynamic_ks, pos_idx
+
+        prior_match_gt_mask = matching_matrix.sum(1) > 1
+        if prior_match_gt_mask.sum() > 0:
+            cost_min, cost_argmin = torch.min(
+                cost[prior_match_gt_mask, :], dim=1)
+            matching_matrix[prior_match_gt_mask, :] *= 0
+            matching_matrix[prior_match_gt_mask, cost_argmin] = 1
+        # get foreground mask inside box and center prior
+        fg_mask_inboxes = matching_matrix.sum(1) > 0
+        valid_mask[valid_mask.clone()] = fg_mask_inboxes
+
+        matched_gt_inds = matching_matrix[fg_mask_inboxes, :].argmax(1)
+        matched_pred_ious = (matching_matrix *
+                             pairwise_ious).sum(1)[fg_mask_inboxes]
+        return matched_pred_ious, matched_gt_inds
diff --git a/mmdet/core/bbox/assigners/task_aligned_assigner.py b/mmdet/core/bbox/assigners/task_aligned_assigner.py
new file mode 100644
index 0000000..1872de4
--- /dev/null
+++ b/mmdet/core/bbox/assigners/task_aligned_assigner.py
@@ -0,0 +1,151 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from ..builder import BBOX_ASSIGNERS
+from ..iou_calculators import build_iou_calculator
+from .assign_result import AssignResult
+from .base_assigner import BaseAssigner
+
+INF = 100000000
+
+
+@BBOX_ASSIGNERS.register_module()
+class TaskAlignedAssigner(BaseAssigner):
+    """Task aligned assigner used in the paper:
+    `TOOD: Task-aligned One-stage Object Detection.
+    <https://arxiv.org/abs/2108.07755>`_.
+
+    Assign a corresponding gt bbox or background to each predicted bbox.
+    Each bbox will be assigned with `0` or a positive integer
+    indicating the ground truth index.
+
+    - 0: negative sample, no assigned gt
+    - positive integer: positive sample, index (1-based) of assigned gt
+
+    Args:
+        topk (int): number of bbox selected in each level
+        iou_calculator (dict): Config dict for iou calculator.
+            Default: dict(type='BboxOverlaps2D')
+    """
+
+    def __init__(self, topk, iou_calculator=dict(type='BboxOverlaps2D')):
+        assert topk >= 1
+        self.topk = topk
+        self.iou_calculator = build_iou_calculator(iou_calculator)
+
+    def assign(self,
+               pred_scores,
+               decode_bboxes,
+               anchors,
+               gt_bboxes,
+               gt_bboxes_ignore=None,
+               gt_labels=None,
+               alpha=1,
+               beta=6):
+        """Assign gt to bboxes.
+
+        The assignment is done in following steps
+
+        1. compute alignment metric between all bbox (bbox of all pyramid
+           levels) and gt
+        2. select top-k bbox as candidates for each gt
+        3. limit the positive sample's center in gt (because the anchor-free
+           detector only can predict positive distance)
+
+
+        Args:
+            pred_scores (Tensor): predicted class probability,
+                shape(n, num_classes)
+            decode_bboxes (Tensor): predicted bounding boxes, shape(n, 4)
+            anchors (Tensor): pre-defined anchors, shape(n, 4).
+            gt_bboxes (Tensor): Groundtruth boxes, shape (k, 4).
+            gt_bboxes_ignore (Tensor, optional): Ground truth bboxes that are
+                labelled as `ignored`, e.g., crowd boxes in COCO.
+            gt_labels (Tensor, optional): Label of gt_bboxes, shape (k, ).
+
+        Returns:
+            :obj:`TaskAlignedAssignResult`: The assign result.
+        """
+        anchors = anchors[:, :4]
+        num_gt, num_bboxes = gt_bboxes.size(0), anchors.size(0)
+        # compute alignment metric between all bbox and gt
+        overlaps = self.iou_calculator(decode_bboxes, gt_bboxes).detach()
+        bbox_scores = pred_scores[:, gt_labels].detach()
+        # assign 0 by default
+        assigned_gt_inds = anchors.new_full((num_bboxes, ),
+                                            0,
+                                            dtype=torch.long)
+        assign_metrics = anchors.new_zeros((num_bboxes, ))
+
+        if num_gt == 0 or num_bboxes == 0:
+            # No ground truth or boxes, return empty assignment
+            max_overlaps = anchors.new_zeros((num_bboxes, ))
+            if num_gt == 0:
+                # No gt boxes, assign everything to background
+                assigned_gt_inds[:] = 0
+            if gt_labels is None:
+                assigned_labels = None
+            else:
+                assigned_labels = anchors.new_full((num_bboxes, ),
+                                                   -1,
+                                                   dtype=torch.long)
+            assign_result = AssignResult(
+                num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)
+            assign_result.assign_metrics = assign_metrics
+            return assign_result
+
+        # select top-k bboxes as candidates for each gt
+        alignment_metrics = bbox_scores**alpha * overlaps**beta
+        topk = min(self.topk, alignment_metrics.size(0))
+        _, candidate_idxs = alignment_metrics.topk(topk, dim=0, largest=True)
+        candidate_metrics = alignment_metrics[candidate_idxs,
+                                              torch.arange(num_gt)]
+        is_pos = candidate_metrics > 0
+
+        # limit the positive sample's center in gt
+        anchors_cx = (anchors[:, 0] + anchors[:, 2]) / 2.0
+        anchors_cy = (anchors[:, 1] + anchors[:, 3]) / 2.0
+        for gt_idx in range(num_gt):
+            candidate_idxs[:, gt_idx] += gt_idx * num_bboxes
+        ep_anchors_cx = anchors_cx.view(1, -1).expand(
+            num_gt, num_bboxes).contiguous().view(-1)
+        ep_anchors_cy = anchors_cy.view(1, -1).expand(
+            num_gt, num_bboxes).contiguous().view(-1)
+        candidate_idxs = candidate_idxs.view(-1)
+
+        # calculate the left, top, right, bottom distance between positive
+        # bbox center and gt side
+        l_ = ep_anchors_cx[candidate_idxs].view(-1, num_gt) - gt_bboxes[:, 0]
+        t_ = ep_anchors_cy[candidate_idxs].view(-1, num_gt) - gt_bboxes[:, 1]
+        r_ = gt_bboxes[:, 2] - ep_anchors_cx[candidate_idxs].view(-1, num_gt)
+        b_ = gt_bboxes[:, 3] - ep_anchors_cy[candidate_idxs].view(-1, num_gt)
+        is_in_gts = torch.stack([l_, t_, r_, b_], dim=1).min(dim=1)[0] > 0.01
+        is_pos = is_pos & is_in_gts
+
+        # if an anchor box is assigned to multiple gts,
+        # the one with the highest iou will be selected.
+        overlaps_inf = torch.full_like(overlaps,
+                                       -INF).t().contiguous().view(-1)
+        index = candidate_idxs.view(-1)[is_pos.view(-1)]
+        overlaps_inf[index] = overlaps.t().contiguous().view(-1)[index]
+        overlaps_inf = overlaps_inf.view(num_gt, -1).t()
+
+        max_overlaps, argmax_overlaps = overlaps_inf.max(dim=1)
+        assigned_gt_inds[
+            max_overlaps != -INF] = argmax_overlaps[max_overlaps != -INF] + 1
+        assign_metrics[max_overlaps != -INF] = alignment_metrics[
+            max_overlaps != -INF, argmax_overlaps[max_overlaps != -INF]]
+
+        if gt_labels is not None:
+            assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)
+            pos_inds = torch.nonzero(
+                assigned_gt_inds > 0, as_tuple=False).squeeze()
+            if pos_inds.numel() > 0:
+                assigned_labels[pos_inds] = gt_labels[
+                    assigned_gt_inds[pos_inds] - 1]
+        else:
+            assigned_labels = None
+        assign_result = AssignResult(
+            num_gt, assigned_gt_inds, max_overlaps, labels=assigned_labels)
+        assign_result.assign_metrics = assign_metrics
+        return assign_result
diff --git a/mmdet/core/bbox/assigners/uniform_assigner.py b/mmdet/core/bbox/assigners/uniform_assigner.py
new file mode 100644
index 0000000..70294fc
--- /dev/null
+++ b/mmdet/core/bbox/assigners/uniform_assigner.py
@@ -0,0 +1,135 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from ..builder import BBOX_ASSIGNERS
+from ..iou_calculators import build_iou_calculator
+from ..transforms import bbox_xyxy_to_cxcywh
+from .assign_result import AssignResult
+from .base_assigner import BaseAssigner
+
+
+@BBOX_ASSIGNERS.register_module()
+class UniformAssigner(BaseAssigner):
+    """Uniform Matching between the anchors and gt boxes, which can achieve
+    balance in positive anchors, and gt_bboxes_ignore was not considered for
+    now.
+
+    Args:
+        pos_ignore_thr (float): the threshold to ignore positive anchors
+        neg_ignore_thr (float): the threshold to ignore negative anchors
+        match_times(int): Number of positive anchors for each gt box.
+           Default 4.
+        iou_calculator (dict): iou_calculator config
+    """
+
+    def __init__(self,
+                 pos_ignore_thr,
+                 neg_ignore_thr,
+                 match_times=4,
+                 iou_calculator=dict(type='BboxOverlaps2D')):
+        self.match_times = match_times
+        self.pos_ignore_thr = pos_ignore_thr
+        self.neg_ignore_thr = neg_ignore_thr
+        self.iou_calculator = build_iou_calculator(iou_calculator)
+
+    def assign(self,
+               bbox_pred,
+               anchor,
+               gt_bboxes,
+               gt_bboxes_ignore=None,
+               gt_labels=None):
+        num_gts, num_bboxes = gt_bboxes.size(0), bbox_pred.size(0)
+
+        # 1. assign -1 by default
+        assigned_gt_inds = bbox_pred.new_full((num_bboxes, ),
+                                              0,
+                                              dtype=torch.long)
+        assigned_labels = bbox_pred.new_full((num_bboxes, ),
+                                             -1,
+                                             dtype=torch.long)
+        if num_gts == 0 or num_bboxes == 0:
+            # No ground truth or boxes, return empty assignment
+            if num_gts == 0:
+                # No ground truth, assign all to background
+                assigned_gt_inds[:] = 0
+            assign_result = AssignResult(
+                num_gts, assigned_gt_inds, None, labels=assigned_labels)
+            assign_result.set_extra_property(
+                'pos_idx', bbox_pred.new_empty(0, dtype=torch.bool))
+            assign_result.set_extra_property('pos_predicted_boxes',
+                                             bbox_pred.new_empty((0, 4)))
+            assign_result.set_extra_property('target_boxes',
+                                             bbox_pred.new_empty((0, 4)))
+            return assign_result
+
+        # 2. Compute the L1 cost between boxes
+        # Note that we use anchors and predict boxes both
+        cost_bbox = torch.cdist(
+            bbox_xyxy_to_cxcywh(bbox_pred),
+            bbox_xyxy_to_cxcywh(gt_bboxes),
+            p=1)
+        cost_bbox_anchors = torch.cdist(
+            bbox_xyxy_to_cxcywh(anchor), bbox_xyxy_to_cxcywh(gt_bboxes), p=1)
+
+        # We found that topk function has different results in cpu and
+        # cuda mode. In order to ensure consistency with the source code,
+        # we also use cpu mode.
+        # TODO: Check whether the performance of cpu and cuda are the same.
+        C = cost_bbox.cpu()
+        C1 = cost_bbox_anchors.cpu()
+
+        # self.match_times x n
+        index = torch.topk(
+            C,  # c=b,n,x c[i]=n,x
+            k=self.match_times,
+            dim=0,
+            largest=False)[1]
+
+        # self.match_times x n
+        index1 = torch.topk(C1, k=self.match_times, dim=0, largest=False)[1]
+        # (self.match_times*2) x n
+        indexes = torch.cat((index, index1),
+                            dim=1).reshape(-1).to(bbox_pred.device)
+
+        pred_overlaps = self.iou_calculator(bbox_pred, gt_bboxes)
+        anchor_overlaps = self.iou_calculator(anchor, gt_bboxes)
+        pred_max_overlaps, _ = pred_overlaps.max(dim=1)
+        anchor_max_overlaps, _ = anchor_overlaps.max(dim=0)
+
+        # 3. Compute the ignore indexes use gt_bboxes and predict boxes
+        ignore_idx = pred_max_overlaps > self.neg_ignore_thr
+        assigned_gt_inds[ignore_idx] = -1
+
+        # 4. Compute the ignore indexes of positive sample use anchors
+        # and predict boxes
+        pos_gt_index = torch.arange(
+            0, C1.size(1),
+            device=bbox_pred.device).repeat(self.match_times * 2)
+        pos_ious = anchor_overlaps[indexes, pos_gt_index]
+        pos_ignore_idx = pos_ious < self.pos_ignore_thr
+
+        pos_gt_index_with_ignore = pos_gt_index + 1
+        pos_gt_index_with_ignore[pos_ignore_idx] = -1
+        assigned_gt_inds[indexes] = pos_gt_index_with_ignore
+
+        if gt_labels is not None:
+            assigned_labels = assigned_gt_inds.new_full((num_bboxes, ), -1)
+            pos_inds = torch.nonzero(
+                assigned_gt_inds > 0, as_tuple=False).squeeze()
+            if pos_inds.numel() > 0:
+                assigned_labels[pos_inds] = gt_labels[
+                    assigned_gt_inds[pos_inds] - 1]
+        else:
+            assigned_labels = None
+
+        assign_result = AssignResult(
+            num_gts,
+            assigned_gt_inds,
+            anchor_max_overlaps,
+            labels=assigned_labels)
+        assign_result.set_extra_property('pos_idx', ~pos_ignore_idx)
+        assign_result.set_extra_property('pos_predicted_boxes',
+                                         bbox_pred[indexes])
+        assign_result.set_extra_property('target_boxes',
+                                         gt_bboxes[pos_gt_index])
+        return assign_result
diff --git a/mmdet/core/bbox/builder.py b/mmdet/core/bbox/builder.py
new file mode 100644
index 0000000..9cfa055
--- /dev/null
+++ b/mmdet/core/bbox/builder.py
@@ -0,0 +1,21 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.utils import Registry, build_from_cfg
+
+BBOX_ASSIGNERS = Registry('bbox_assigner')
+BBOX_SAMPLERS = Registry('bbox_sampler')
+BBOX_CODERS = Registry('bbox_coder')
+
+
+def build_assigner(cfg, **default_args):
+    """Builder of box assigner."""
+    return build_from_cfg(cfg, BBOX_ASSIGNERS, default_args)
+
+
+def build_sampler(cfg, **default_args):
+    """Builder of box sampler."""
+    return build_from_cfg(cfg, BBOX_SAMPLERS, default_args)
+
+
+def build_bbox_coder(cfg, **default_args):
+    """Builder of box coder."""
+    return build_from_cfg(cfg, BBOX_CODERS, default_args)
diff --git a/mmdet/core/bbox/coder/__init__.py b/mmdet/core/bbox/coder/__init__.py
new file mode 100644
index 0000000..e12fd64
--- /dev/null
+++ b/mmdet/core/bbox/coder/__init__.py
@@ -0,0 +1,15 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .base_bbox_coder import BaseBBoxCoder
+from .bucketing_bbox_coder import BucketingBBoxCoder
+from .delta_xywh_bbox_coder import DeltaXYWHBBoxCoder
+from .distance_point_bbox_coder import DistancePointBBoxCoder
+from .legacy_delta_xywh_bbox_coder import LegacyDeltaXYWHBBoxCoder
+from .pseudo_bbox_coder import PseudoBBoxCoder
+from .tblr_bbox_coder import TBLRBBoxCoder
+from .yolo_bbox_coder import YOLOBBoxCoder
+
+__all__ = [
+    'BaseBBoxCoder', 'PseudoBBoxCoder', 'DeltaXYWHBBoxCoder',
+    'LegacyDeltaXYWHBBoxCoder', 'TBLRBBoxCoder', 'YOLOBBoxCoder',
+    'BucketingBBoxCoder', 'DistancePointBBoxCoder'
+]
diff --git a/mmdet/core/bbox/coder/base_bbox_coder.py b/mmdet/core/bbox/coder/base_bbox_coder.py
new file mode 100644
index 0000000..a7ed041
--- /dev/null
+++ b/mmdet/core/bbox/coder/base_bbox_coder.py
@@ -0,0 +1,18 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from abc import ABCMeta, abstractmethod
+
+
+class BaseBBoxCoder(metaclass=ABCMeta):
+    """Base bounding box coder."""
+
+    def __init__(self, **kwargs):
+        pass
+
+    @abstractmethod
+    def encode(self, bboxes, gt_bboxes):
+        """Encode deltas between bboxes and ground truth boxes."""
+
+    @abstractmethod
+    def decode(self, bboxes, bboxes_pred):
+        """Decode the predicted bboxes according to prediction and base
+        boxes."""
diff --git a/mmdet/core/bbox/coder/bucketing_bbox_coder.py b/mmdet/core/bbox/coder/bucketing_bbox_coder.py
new file mode 100644
index 0000000..4be0ada
--- /dev/null
+++ b/mmdet/core/bbox/coder/bucketing_bbox_coder.py
@@ -0,0 +1,351 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from ..builder import BBOX_CODERS
+from ..transforms import bbox_rescale
+from .base_bbox_coder import BaseBBoxCoder
+
+
+@BBOX_CODERS.register_module()
+class BucketingBBoxCoder(BaseBBoxCoder):
+    """Bucketing BBox Coder for Side-Aware Boundary Localization (SABL).
+
+    Boundary Localization with Bucketing and Bucketing Guided Rescoring
+    are implemented here.
+
+    Please refer to https://arxiv.org/abs/1912.04260 for more details.
+
+    Args:
+        num_buckets (int): Number of buckets.
+        scale_factor (int): Scale factor of proposals to generate buckets.
+        offset_topk (int): Topk buckets are used to generate
+             bucket fine regression targets. Defaults to 2.
+        offset_upperbound (float): Offset upperbound to generate
+             bucket fine regression targets.
+             To avoid too large offset displacements. Defaults to 1.0.
+        cls_ignore_neighbor (bool): Ignore second nearest bucket or Not.
+             Defaults to True.
+        clip_border (bool, optional): Whether clip the objects outside the
+            border of the image. Defaults to True.
+    """
+
+    def __init__(self,
+                 num_buckets,
+                 scale_factor,
+                 offset_topk=2,
+                 offset_upperbound=1.0,
+                 cls_ignore_neighbor=True,
+                 clip_border=True):
+        super(BucketingBBoxCoder, self).__init__()
+        self.num_buckets = num_buckets
+        self.scale_factor = scale_factor
+        self.offset_topk = offset_topk
+        self.offset_upperbound = offset_upperbound
+        self.cls_ignore_neighbor = cls_ignore_neighbor
+        self.clip_border = clip_border
+
+    def encode(self, bboxes, gt_bboxes):
+        """Get bucketing estimation and fine regression targets during
+        training.
+
+        Args:
+            bboxes (torch.Tensor): source boxes, e.g., object proposals.
+            gt_bboxes (torch.Tensor): target of the transformation, e.g.,
+                ground truth boxes.
+
+        Returns:
+           encoded_bboxes(tuple[Tensor]): bucketing estimation
+            and fine regression targets and weights
+        """
+
+        assert bboxes.size(0) == gt_bboxes.size(0)
+        assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
+        encoded_bboxes = bbox2bucket(bboxes, gt_bboxes, self.num_buckets,
+                                     self.scale_factor, self.offset_topk,
+                                     self.offset_upperbound,
+                                     self.cls_ignore_neighbor)
+        return encoded_bboxes
+
+    def decode(self, bboxes, pred_bboxes, max_shape=None):
+        """Apply transformation `pred_bboxes` to `boxes`.
+        Args:
+            boxes (torch.Tensor): Basic boxes.
+            pred_bboxes (torch.Tensor): Predictions for bucketing estimation
+                and fine regression
+            max_shape (tuple[int], optional): Maximum shape of boxes.
+                Defaults to None.
+
+        Returns:
+            torch.Tensor: Decoded boxes.
+        """
+        assert len(pred_bboxes) == 2
+        cls_preds, offset_preds = pred_bboxes
+        assert cls_preds.size(0) == bboxes.size(0) and offset_preds.size(
+            0) == bboxes.size(0)
+        decoded_bboxes = bucket2bbox(bboxes, cls_preds, offset_preds,
+                                     self.num_buckets, self.scale_factor,
+                                     max_shape, self.clip_border)
+
+        return decoded_bboxes
+
+
+@mmcv.jit(coderize=True)
+def generat_buckets(proposals, num_buckets, scale_factor=1.0):
+    """Generate buckets w.r.t bucket number and scale factor of proposals.
+
+    Args:
+        proposals (Tensor): Shape (n, 4)
+        num_buckets (int): Number of buckets.
+        scale_factor (float): Scale factor to rescale proposals.
+
+    Returns:
+        tuple[Tensor]: (bucket_w, bucket_h, l_buckets, r_buckets,
+         t_buckets, d_buckets)
+
+            - bucket_w: Width of buckets on x-axis. Shape (n, ).
+            - bucket_h: Height of buckets on y-axis. Shape (n, ).
+            - l_buckets: Left buckets. Shape (n, ceil(side_num/2)).
+            - r_buckets: Right buckets. Shape (n, ceil(side_num/2)).
+            - t_buckets: Top buckets. Shape (n, ceil(side_num/2)).
+            - d_buckets: Down buckets. Shape (n, ceil(side_num/2)).
+    """
+    proposals = bbox_rescale(proposals, scale_factor)
+
+    # number of buckets in each side
+    side_num = int(np.ceil(num_buckets / 2.0))
+    pw = proposals[..., 2] - proposals[..., 0]
+    ph = proposals[..., 3] - proposals[..., 1]
+    px1 = proposals[..., 0]
+    py1 = proposals[..., 1]
+    px2 = proposals[..., 2]
+    py2 = proposals[..., 3]
+
+    bucket_w = pw / num_buckets
+    bucket_h = ph / num_buckets
+
+    # left buckets
+    l_buckets = px1[:, None] + (0.5 + torch.arange(
+        0, side_num).to(proposals).float())[None, :] * bucket_w[:, None]
+    # right buckets
+    r_buckets = px2[:, None] - (0.5 + torch.arange(
+        0, side_num).to(proposals).float())[None, :] * bucket_w[:, None]
+    # top buckets
+    t_buckets = py1[:, None] + (0.5 + torch.arange(
+        0, side_num).to(proposals).float())[None, :] * bucket_h[:, None]
+    # down buckets
+    d_buckets = py2[:, None] - (0.5 + torch.arange(
+        0, side_num).to(proposals).float())[None, :] * bucket_h[:, None]
+    return bucket_w, bucket_h, l_buckets, r_buckets, t_buckets, d_buckets
+
+
+@mmcv.jit(coderize=True)
+def bbox2bucket(proposals,
+                gt,
+                num_buckets,
+                scale_factor,
+                offset_topk=2,
+                offset_upperbound=1.0,
+                cls_ignore_neighbor=True):
+    """Generate buckets estimation and fine regression targets.
+
+    Args:
+        proposals (Tensor): Shape (n, 4)
+        gt (Tensor): Shape (n, 4)
+        num_buckets (int): Number of buckets.
+        scale_factor (float): Scale factor to rescale proposals.
+        offset_topk (int): Topk buckets are used to generate
+             bucket fine regression targets. Defaults to 2.
+        offset_upperbound (float): Offset allowance to generate
+             bucket fine regression targets.
+             To avoid too large offset displacements. Defaults to 1.0.
+        cls_ignore_neighbor (bool): Ignore second nearest bucket or Not.
+             Defaults to True.
+
+    Returns:
+        tuple[Tensor]: (offsets, offsets_weights, bucket_labels, cls_weights).
+
+            - offsets: Fine regression targets. \
+                Shape (n, num_buckets*2).
+            - offsets_weights: Fine regression weights. \
+                Shape (n, num_buckets*2).
+            - bucket_labels: Bucketing estimation labels. \
+                Shape (n, num_buckets*2).
+            - cls_weights: Bucketing estimation weights. \
+                Shape (n, num_buckets*2).
+    """
+    assert proposals.size() == gt.size()
+
+    # generate buckets
+    proposals = proposals.float()
+    gt = gt.float()
+    (bucket_w, bucket_h, l_buckets, r_buckets, t_buckets,
+     d_buckets) = generat_buckets(proposals, num_buckets, scale_factor)
+
+    gx1 = gt[..., 0]
+    gy1 = gt[..., 1]
+    gx2 = gt[..., 2]
+    gy2 = gt[..., 3]
+
+    # generate offset targets and weights
+    # offsets from buckets to gts
+    l_offsets = (l_buckets - gx1[:, None]) / bucket_w[:, None]
+    r_offsets = (r_buckets - gx2[:, None]) / bucket_w[:, None]
+    t_offsets = (t_buckets - gy1[:, None]) / bucket_h[:, None]
+    d_offsets = (d_buckets - gy2[:, None]) / bucket_h[:, None]
+
+    # select top-k nearest buckets
+    l_topk, l_label = l_offsets.abs().topk(
+        offset_topk, dim=1, largest=False, sorted=True)
+    r_topk, r_label = r_offsets.abs().topk(
+        offset_topk, dim=1, largest=False, sorted=True)
+    t_topk, t_label = t_offsets.abs().topk(
+        offset_topk, dim=1, largest=False, sorted=True)
+    d_topk, d_label = d_offsets.abs().topk(
+        offset_topk, dim=1, largest=False, sorted=True)
+
+    offset_l_weights = l_offsets.new_zeros(l_offsets.size())
+    offset_r_weights = r_offsets.new_zeros(r_offsets.size())
+    offset_t_weights = t_offsets.new_zeros(t_offsets.size())
+    offset_d_weights = d_offsets.new_zeros(d_offsets.size())
+    inds = torch.arange(0, proposals.size(0)).to(proposals).long()
+
+    # generate offset weights of top-k nearest buckets
+    for k in range(offset_topk):
+        if k >= 1:
+            offset_l_weights[inds, l_label[:,
+                                           k]] = (l_topk[:, k] <
+                                                  offset_upperbound).float()
+            offset_r_weights[inds, r_label[:,
+                                           k]] = (r_topk[:, k] <
+                                                  offset_upperbound).float()
+            offset_t_weights[inds, t_label[:,
+                                           k]] = (t_topk[:, k] <
+                                                  offset_upperbound).float()
+            offset_d_weights[inds, d_label[:,
+                                           k]] = (d_topk[:, k] <
+                                                  offset_upperbound).float()
+        else:
+            offset_l_weights[inds, l_label[:, k]] = 1.0
+            offset_r_weights[inds, r_label[:, k]] = 1.0
+            offset_t_weights[inds, t_label[:, k]] = 1.0
+            offset_d_weights[inds, d_label[:, k]] = 1.0
+
+    offsets = torch.cat([l_offsets, r_offsets, t_offsets, d_offsets], dim=-1)
+    offsets_weights = torch.cat([
+        offset_l_weights, offset_r_weights, offset_t_weights, offset_d_weights
+    ],
+                                dim=-1)
+
+    # generate bucket labels and weight
+    side_num = int(np.ceil(num_buckets / 2.0))
+    labels = torch.stack(
+        [l_label[:, 0], r_label[:, 0], t_label[:, 0], d_label[:, 0]], dim=-1)
+
+    batch_size = labels.size(0)
+    bucket_labels = F.one_hot(labels.view(-1), side_num).view(batch_size,
+                                                              -1).float()
+    bucket_cls_l_weights = (l_offsets.abs() < 1).float()
+    bucket_cls_r_weights = (r_offsets.abs() < 1).float()
+    bucket_cls_t_weights = (t_offsets.abs() < 1).float()
+    bucket_cls_d_weights = (d_offsets.abs() < 1).float()
+    bucket_cls_weights = torch.cat([
+        bucket_cls_l_weights, bucket_cls_r_weights, bucket_cls_t_weights,
+        bucket_cls_d_weights
+    ],
+                                   dim=-1)
+    # ignore second nearest buckets for cls if necessary
+    if cls_ignore_neighbor:
+        bucket_cls_weights = (~((bucket_cls_weights == 1) &
+                                (bucket_labels == 0))).float()
+    else:
+        bucket_cls_weights[:] = 1.0
+    return offsets, offsets_weights, bucket_labels, bucket_cls_weights
+
+
+@mmcv.jit(coderize=True)
+def bucket2bbox(proposals,
+                cls_preds,
+                offset_preds,
+                num_buckets,
+                scale_factor=1.0,
+                max_shape=None,
+                clip_border=True):
+    """Apply bucketing estimation (cls preds) and fine regression (offset
+    preds) to generate det bboxes.
+
+    Args:
+        proposals (Tensor): Boxes to be transformed. Shape (n, 4)
+        cls_preds (Tensor): bucketing estimation. Shape (n, num_buckets*2).
+        offset_preds (Tensor): fine regression. Shape (n, num_buckets*2).
+        num_buckets (int): Number of buckets.
+        scale_factor (float): Scale factor to rescale proposals.
+        max_shape (tuple[int, int]): Maximum bounds for boxes. specifies (H, W)
+        clip_border (bool, optional): Whether clip the objects outside the
+            border of the image. Defaults to True.
+
+    Returns:
+        tuple[Tensor]: (bboxes, loc_confidence).
+
+            - bboxes: predicted bboxes. Shape (n, 4)
+            - loc_confidence: localization confidence of predicted bboxes.
+                Shape (n,).
+    """
+
+    side_num = int(np.ceil(num_buckets / 2.0))
+    cls_preds = cls_preds.view(-1, side_num)
+    offset_preds = offset_preds.view(-1, side_num)
+
+    scores = F.softmax(cls_preds, dim=1)
+    score_topk, score_label = scores.topk(2, dim=1, largest=True, sorted=True)
+
+    rescaled_proposals = bbox_rescale(proposals, scale_factor)
+
+    pw = rescaled_proposals[..., 2] - rescaled_proposals[..., 0]
+    ph = rescaled_proposals[..., 3] - rescaled_proposals[..., 1]
+    px1 = rescaled_proposals[..., 0]
+    py1 = rescaled_proposals[..., 1]
+    px2 = rescaled_proposals[..., 2]
+    py2 = rescaled_proposals[..., 3]
+
+    bucket_w = pw / num_buckets
+    bucket_h = ph / num_buckets
+
+    score_inds_l = score_label[0::4, 0]
+    score_inds_r = score_label[1::4, 0]
+    score_inds_t = score_label[2::4, 0]
+    score_inds_d = score_label[3::4, 0]
+    l_buckets = px1 + (0.5 + score_inds_l.float()) * bucket_w
+    r_buckets = px2 - (0.5 + score_inds_r.float()) * bucket_w
+    t_buckets = py1 + (0.5 + score_inds_t.float()) * bucket_h
+    d_buckets = py2 - (0.5 + score_inds_d.float()) * bucket_h
+
+    offsets = offset_preds.view(-1, 4, side_num)
+    inds = torch.arange(proposals.size(0)).to(proposals).long()
+    l_offsets = offsets[:, 0, :][inds, score_inds_l]
+    r_offsets = offsets[:, 1, :][inds, score_inds_r]
+    t_offsets = offsets[:, 2, :][inds, score_inds_t]
+    d_offsets = offsets[:, 3, :][inds, score_inds_d]
+
+    x1 = l_buckets - l_offsets * bucket_w
+    x2 = r_buckets - r_offsets * bucket_w
+    y1 = t_buckets - t_offsets * bucket_h
+    y2 = d_buckets - d_offsets * bucket_h
+
+    if clip_border and max_shape is not None:
+        x1 = x1.clamp(min=0, max=max_shape[1] - 1)
+        y1 = y1.clamp(min=0, max=max_shape[0] - 1)
+        x2 = x2.clamp(min=0, max=max_shape[1] - 1)
+        y2 = y2.clamp(min=0, max=max_shape[0] - 1)
+    bboxes = torch.cat([x1[:, None], y1[:, None], x2[:, None], y2[:, None]],
+                       dim=-1)
+
+    # bucketing guided rescoring
+    loc_confidence = score_topk[:, 0]
+    top2_neighbor_inds = (score_label[:, 0] - score_label[:, 1]).abs() == 1
+    loc_confidence += score_topk[:, 1] * top2_neighbor_inds.float()
+    loc_confidence = loc_confidence.view(-1, 4).mean(dim=1)
+
+    return bboxes, loc_confidence
diff --git a/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py b/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py
new file mode 100644
index 0000000..a7f1c62
--- /dev/null
+++ b/mmdet/core/bbox/coder/delta_xywh_bbox_coder.py
@@ -0,0 +1,392 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import mmcv
+import numpy as np
+import torch
+
+from ..builder import BBOX_CODERS
+from .base_bbox_coder import BaseBBoxCoder
+
+
+@BBOX_CODERS.register_module()
+class DeltaXYWHBBoxCoder(BaseBBoxCoder):
+    """Delta XYWH BBox coder.
+
+    Following the practice in `R-CNN <https://arxiv.org/abs/1311.2524>`_,
+    this coder encodes bbox (x1, y1, x2, y2) into delta (dx, dy, dw, dh) and
+    decodes delta (dx, dy, dw, dh) back to original bbox (x1, y1, x2, y2).
+
+    Args:
+        target_means (Sequence[float]): Denormalizing means of target for
+            delta coordinates
+        target_stds (Sequence[float]): Denormalizing standard deviation of
+            target for delta coordinates
+        clip_border (bool, optional): Whether clip the objects outside the
+            border of the image. Defaults to True.
+        add_ctr_clamp (bool): Whether to add center clamp, when added, the
+            predicted box is clamped is its center is too far away from
+            the original anchor's center. Only used by YOLOF. Default False.
+        ctr_clamp (int): the maximum pixel shift to clamp. Only used by YOLOF.
+            Default 32.
+    """
+
+    def __init__(self,
+                 target_means=(0., 0., 0., 0.),
+                 target_stds=(1., 1., 1., 1.),
+                 clip_border=True,
+                 add_ctr_clamp=False,
+                 ctr_clamp=32):
+        super(BaseBBoxCoder, self).__init__()
+        self.means = target_means
+        self.stds = target_stds
+        self.clip_border = clip_border
+        self.add_ctr_clamp = add_ctr_clamp
+        self.ctr_clamp = ctr_clamp
+
+    def encode(self, bboxes, gt_bboxes):
+        """Get box regression transformation deltas that can be used to
+        transform the ``bboxes`` into the ``gt_bboxes``.
+
+        Args:
+            bboxes (torch.Tensor): Source boxes, e.g., object proposals.
+            gt_bboxes (torch.Tensor): Target of the transformation, e.g.,
+                ground-truth boxes.
+
+        Returns:
+            torch.Tensor: Box transformation deltas
+        """
+
+        assert bboxes.size(0) == gt_bboxes.size(0)
+        assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
+        encoded_bboxes = bbox2delta(bboxes, gt_bboxes, self.means, self.stds)
+        return encoded_bboxes
+
+    def decode(self,
+               bboxes,
+               pred_bboxes,
+               max_shape=None,
+               wh_ratio_clip=16 / 1000):
+        """Apply transformation `pred_bboxes` to `boxes`.
+
+        Args:
+            bboxes (torch.Tensor): Basic boxes. Shape (B, N, 4) or (N, 4)
+            pred_bboxes (Tensor): Encoded offsets with respect to each roi.
+               Has shape (B, N, num_classes * 4) or (B, N, 4) or
+               (N, num_classes * 4) or (N, 4). Note N = num_anchors * W * H
+               when rois is a grid of anchors.Offset encoding follows [1]_.
+            max_shape (Sequence[int] or torch.Tensor or Sequence[
+               Sequence[int]],optional): Maximum bounds for boxes, specifies
+               (H, W, C) or (H, W). If bboxes shape is (B, N, 4), then
+               the max_shape should be a Sequence[Sequence[int]]
+               and the length of max_shape should also be B.
+            wh_ratio_clip (float, optional): The allowed ratio between
+                width and height.
+
+        Returns:
+            torch.Tensor: Decoded boxes.
+        """
+
+        assert pred_bboxes.size(0) == bboxes.size(0)
+        if pred_bboxes.ndim == 3:
+            assert pred_bboxes.size(1) == bboxes.size(1)
+
+        if pred_bboxes.ndim == 2 and not torch.onnx.is_in_onnx_export():
+            # single image decode
+            decoded_bboxes = delta2bbox(bboxes, pred_bboxes, self.means,
+                                        self.stds, max_shape, wh_ratio_clip,
+                                        self.clip_border, self.add_ctr_clamp,
+                                        self.ctr_clamp)
+        else:
+            if pred_bboxes.ndim == 3 and not torch.onnx.is_in_onnx_export():
+                warnings.warn(
+                    'DeprecationWarning: onnx_delta2bbox is deprecated '
+                    'in the case of batch decoding and non-ONNX, '
+                    'please use “delta2bbox” instead. In order to improve '
+                    'the decoding speed, the batch function will no '
+                    'longer be supported. ')
+            decoded_bboxes = onnx_delta2bbox(bboxes, pred_bboxes, self.means,
+                                             self.stds, max_shape,
+                                             wh_ratio_clip, self.clip_border,
+                                             self.add_ctr_clamp,
+                                             self.ctr_clamp)
+
+        return decoded_bboxes
+
+
+@mmcv.jit(coderize=True)
+def bbox2delta(proposals, gt, means=(0., 0., 0., 0.), stds=(1., 1., 1., 1.)):
+    """Compute deltas of proposals w.r.t. gt.
+
+    We usually compute the deltas of x, y, w, h of proposals w.r.t ground
+    truth bboxes to get regression target.
+    This is the inverse function of :func:`delta2bbox`.
+
+    Args:
+        proposals (Tensor): Boxes to be transformed, shape (N, ..., 4)
+        gt (Tensor): Gt bboxes to be used as base, shape (N, ..., 4)
+        means (Sequence[float]): Denormalizing means for delta coordinates
+        stds (Sequence[float]): Denormalizing standard deviation for delta
+            coordinates
+
+    Returns:
+        Tensor: deltas with shape (N, 4), where columns represent dx, dy,
+            dw, dh.
+    """
+    assert proposals.size() == gt.size()
+
+    proposals = proposals.float()
+    gt = gt.float()
+    px = (proposals[..., 0] + proposals[..., 2]) * 0.5
+    py = (proposals[..., 1] + proposals[..., 3]) * 0.5
+    pw = proposals[..., 2] - proposals[..., 0]
+    ph = proposals[..., 3] - proposals[..., 1]
+
+    gx = (gt[..., 0] + gt[..., 2]) * 0.5
+    gy = (gt[..., 1] + gt[..., 3]) * 0.5
+    gw = gt[..., 2] - gt[..., 0]
+    gh = gt[..., 3] - gt[..., 1]
+
+    dx = (gx - px) / pw
+    dy = (gy - py) / ph
+    dw = torch.log(gw / pw)
+    dh = torch.log(gh / ph)
+    deltas = torch.stack([dx, dy, dw, dh], dim=-1)
+
+    means = deltas.new_tensor(means).unsqueeze(0)
+    stds = deltas.new_tensor(stds).unsqueeze(0)
+    deltas = deltas.sub_(means).div_(stds)
+
+    return deltas
+
+
+@mmcv.jit(coderize=True)
+def delta2bbox(rois,
+               deltas,
+               means=(0., 0., 0., 0.),
+               stds=(1., 1., 1., 1.),
+               max_shape=None,
+               wh_ratio_clip=16 / 1000,
+               clip_border=True,
+               add_ctr_clamp=False,
+               ctr_clamp=32):
+    """Apply deltas to shift/scale base boxes.
+
+    Typically the rois are anchor or proposed bounding boxes and the deltas are
+    network outputs used to shift/scale those boxes.
+    This is the inverse function of :func:`bbox2delta`.
+
+    Args:
+        rois (Tensor): Boxes to be transformed. Has shape (N, 4).
+        deltas (Tensor): Encoded offsets relative to each roi.
+            Has shape (N, num_classes * 4) or (N, 4). Note
+            N = num_base_anchors * W * H, when rois is a grid of
+            anchors. Offset encoding follows [1]_.
+        means (Sequence[float]): Denormalizing means for delta coordinates.
+            Default (0., 0., 0., 0.).
+        stds (Sequence[float]): Denormalizing standard deviation for delta
+            coordinates. Default (1., 1., 1., 1.).
+        max_shape (tuple[int, int]): Maximum bounds for boxes, specifies
+           (H, W). Default None.
+        wh_ratio_clip (float): Maximum aspect ratio for boxes. Default
+            16 / 1000.
+        clip_border (bool, optional): Whether clip the objects outside the
+            border of the image. Default True.
+        add_ctr_clamp (bool): Whether to add center clamp. When set to True,
+            the center of the prediction bounding box will be clamped to
+            avoid being too far away from the center of the anchor.
+            Only used by YOLOF. Default False.
+        ctr_clamp (int): the maximum pixel shift to clamp. Only used by YOLOF.
+            Default 32.
+
+    Returns:
+        Tensor: Boxes with shape (N, num_classes * 4) or (N, 4), where 4
+           represent tl_x, tl_y, br_x, br_y.
+
+    References:
+        .. [1] https://arxiv.org/abs/1311.2524
+
+    Example:
+        >>> rois = torch.Tensor([[ 0.,  0.,  1.,  1.],
+        >>>                      [ 0.,  0.,  1.,  1.],
+        >>>                      [ 0.,  0.,  1.,  1.],
+        >>>                      [ 5.,  5.,  5.,  5.]])
+        >>> deltas = torch.Tensor([[  0.,   0.,   0.,   0.],
+        >>>                        [  1.,   1.,   1.,   1.],
+        >>>                        [  0.,   0.,   2.,  -1.],
+        >>>                        [ 0.7, -1.9, -0.5,  0.3]])
+        >>> delta2bbox(rois, deltas, max_shape=(32, 32, 3))
+        tensor([[0.0000, 0.0000, 1.0000, 1.0000],
+                [0.1409, 0.1409, 2.8591, 2.8591],
+                [0.0000, 0.3161, 4.1945, 0.6839],
+                [5.0000, 5.0000, 5.0000, 5.0000]])
+    """
+    num_bboxes, num_classes = deltas.size(0), deltas.size(1) // 4
+    if num_bboxes == 0:
+        return deltas
+
+    deltas = deltas.reshape(-1, 4)
+
+    means = deltas.new_tensor(means).view(1, -1)
+    stds = deltas.new_tensor(stds).view(1, -1)
+    denorm_deltas = deltas * stds + means
+
+    dxy = denorm_deltas[:, :2]
+    dwh = denorm_deltas[:, 2:]
+
+    # Compute width/height of each roi
+    rois_ = rois.repeat(1, num_classes).reshape(-1, 4)
+    pxy = ((rois_[:, :2] + rois_[:, 2:]) * 0.5)
+    pwh = (rois_[:, 2:] - rois_[:, :2])
+
+    dxy_wh = pwh * dxy
+
+    max_ratio = np.abs(np.log(wh_ratio_clip))
+    if add_ctr_clamp:
+        dxy_wh = torch.clamp(dxy_wh, max=ctr_clamp, min=-ctr_clamp)
+        dwh = torch.clamp(dwh, max=max_ratio)
+    else:
+        dwh = dwh.clamp(min=-max_ratio, max=max_ratio)
+
+    gxy = pxy + dxy_wh
+    gwh = pwh * dwh.exp()
+    x1y1 = gxy - (gwh * 0.5)
+    x2y2 = gxy + (gwh * 0.5)
+    bboxes = torch.cat([x1y1, x2y2], dim=-1)
+    if clip_border and max_shape is not None:
+        bboxes[..., 0::2].clamp_(min=0, max=max_shape[1])
+        bboxes[..., 1::2].clamp_(min=0, max=max_shape[0])
+    bboxes = bboxes.reshape(num_bboxes, -1)
+    return bboxes
+
+
+def onnx_delta2bbox(rois,
+                    deltas,
+                    means=(0., 0., 0., 0.),
+                    stds=(1., 1., 1., 1.),
+                    max_shape=None,
+                    wh_ratio_clip=16 / 1000,
+                    clip_border=True,
+                    add_ctr_clamp=False,
+                    ctr_clamp=32):
+    """Apply deltas to shift/scale base boxes.
+
+    Typically the rois are anchor or proposed bounding boxes and the deltas are
+    network outputs used to shift/scale those boxes.
+    This is the inverse function of :func:`bbox2delta`.
+
+    Args:
+        rois (Tensor): Boxes to be transformed. Has shape (N, 4) or (B, N, 4)
+        deltas (Tensor): Encoded offsets with respect to each roi.
+            Has shape (B, N, num_classes * 4) or (B, N, 4) or
+            (N, num_classes * 4) or (N, 4). Note N = num_anchors * W * H
+            when rois is a grid of anchors.Offset encoding follows [1]_.
+        means (Sequence[float]): Denormalizing means for delta coordinates.
+            Default (0., 0., 0., 0.).
+        stds (Sequence[float]): Denormalizing standard deviation for delta
+            coordinates. Default (1., 1., 1., 1.).
+        max_shape (Sequence[int] or torch.Tensor or Sequence[
+            Sequence[int]],optional): Maximum bounds for boxes, specifies
+            (H, W, C) or (H, W). If rois shape is (B, N, 4), then
+            the max_shape should be a Sequence[Sequence[int]]
+            and the length of max_shape should also be B. Default None.
+        wh_ratio_clip (float): Maximum aspect ratio for boxes.
+            Default 16 / 1000.
+        clip_border (bool, optional): Whether clip the objects outside the
+            border of the image. Default True.
+        add_ctr_clamp (bool): Whether to add center clamp, when added, the
+            predicted box is clamped is its center is too far away from
+            the original anchor's center. Only used by YOLOF. Default False.
+        ctr_clamp (int): the maximum pixel shift to clamp. Only used by YOLOF.
+            Default 32.
+
+    Returns:
+        Tensor: Boxes with shape (B, N, num_classes * 4) or (B, N, 4) or
+           (N, num_classes * 4) or (N, 4), where 4 represent
+           tl_x, tl_y, br_x, br_y.
+
+    References:
+        .. [1] https://arxiv.org/abs/1311.2524
+
+    Example:
+        >>> rois = torch.Tensor([[ 0.,  0.,  1.,  1.],
+        >>>                      [ 0.,  0.,  1.,  1.],
+        >>>                      [ 0.,  0.,  1.,  1.],
+        >>>                      [ 5.,  5.,  5.,  5.]])
+        >>> deltas = torch.Tensor([[  0.,   0.,   0.,   0.],
+        >>>                        [  1.,   1.,   1.,   1.],
+        >>>                        [  0.,   0.,   2.,  -1.],
+        >>>                        [ 0.7, -1.9, -0.5,  0.3]])
+        >>> delta2bbox(rois, deltas, max_shape=(32, 32, 3))
+        tensor([[0.0000, 0.0000, 1.0000, 1.0000],
+                [0.1409, 0.1409, 2.8591, 2.8591],
+                [0.0000, 0.3161, 4.1945, 0.6839],
+                [5.0000, 5.0000, 5.0000, 5.0000]])
+    """
+    means = deltas.new_tensor(means).view(1,
+                                          -1).repeat(1,
+                                                     deltas.size(-1) // 4)
+    stds = deltas.new_tensor(stds).view(1, -1).repeat(1, deltas.size(-1) // 4)
+    denorm_deltas = deltas * stds + means
+    dx = denorm_deltas[..., 0::4]
+    dy = denorm_deltas[..., 1::4]
+    dw = denorm_deltas[..., 2::4]
+    dh = denorm_deltas[..., 3::4]
+
+    x1, y1 = rois[..., 0], rois[..., 1]
+    x2, y2 = rois[..., 2], rois[..., 3]
+    # Compute center of each roi
+    px = ((x1 + x2) * 0.5).unsqueeze(-1).expand_as(dx)
+    py = ((y1 + y2) * 0.5).unsqueeze(-1).expand_as(dy)
+    # Compute width/height of each roi
+    pw = (x2 - x1).unsqueeze(-1).expand_as(dw)
+    ph = (y2 - y1).unsqueeze(-1).expand_as(dh)
+
+    dx_width = pw * dx
+    dy_height = ph * dy
+
+    max_ratio = np.abs(np.log(wh_ratio_clip))
+    if add_ctr_clamp:
+        dx_width = torch.clamp(dx_width, max=ctr_clamp, min=-ctr_clamp)
+        dy_height = torch.clamp(dy_height, max=ctr_clamp, min=-ctr_clamp)
+        dw = torch.clamp(dw, max=max_ratio)
+        dh = torch.clamp(dh, max=max_ratio)
+    else:
+        dw = dw.clamp(min=-max_ratio, max=max_ratio)
+        dh = dh.clamp(min=-max_ratio, max=max_ratio)
+    # Use exp(network energy) to enlarge/shrink each roi
+    gw = pw * dw.exp()
+    gh = ph * dh.exp()
+    # Use network energy to shift the center of each roi
+    gx = px + dx_width
+    gy = py + dy_height
+    # Convert center-xy/width/height to top-left, bottom-right
+    x1 = gx - gw * 0.5
+    y1 = gy - gh * 0.5
+    x2 = gx + gw * 0.5
+    y2 = gy + gh * 0.5
+
+    bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view(deltas.size())
+
+    if clip_border and max_shape is not None:
+        # clip bboxes with dynamic `min` and `max` for onnx
+        if torch.onnx.is_in_onnx_export():
+            from mmdet.core.export import dynamic_clip_for_onnx
+            x1, y1, x2, y2 = dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape)
+            bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view(deltas.size())
+            return bboxes
+        if not isinstance(max_shape, torch.Tensor):
+            max_shape = x1.new_tensor(max_shape)
+        max_shape = max_shape[..., :2].type_as(x1)
+        if max_shape.ndim == 2:
+            assert bboxes.ndim == 3
+            assert max_shape.size(0) == bboxes.size(0)
+
+        min_xy = x1.new_tensor(0)
+        max_xy = torch.cat(
+            [max_shape] * (deltas.size(-1) // 2),
+            dim=-1).flip(-1).unsqueeze(-2)
+        bboxes = torch.where(bboxes < min_xy, min_xy, bboxes)
+        bboxes = torch.where(bboxes > max_xy, max_xy, bboxes)
+
+    return bboxes
diff --git a/mmdet/core/bbox/coder/distance_point_bbox_coder.py b/mmdet/core/bbox/coder/distance_point_bbox_coder.py
new file mode 100644
index 0000000..9f308a8
--- /dev/null
+++ b/mmdet/core/bbox/coder/distance_point_bbox_coder.py
@@ -0,0 +1,63 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import BBOX_CODERS
+from ..transforms import bbox2distance, distance2bbox
+from .base_bbox_coder import BaseBBoxCoder
+
+
+@BBOX_CODERS.register_module()
+class DistancePointBBoxCoder(BaseBBoxCoder):
+    """Distance Point BBox coder.
+
+    This coder encodes gt bboxes (x1, y1, x2, y2) into (top, bottom, left,
+    right) and decode it back to the original.
+
+    Args:
+        clip_border (bool, optional): Whether clip the objects outside the
+            border of the image. Defaults to True.
+    """
+
+    def __init__(self, clip_border=True):
+        super(BaseBBoxCoder, self).__init__()
+        self.clip_border = clip_border
+
+    def encode(self, points, gt_bboxes, max_dis=None, eps=0.1):
+        """Encode bounding box to distances.
+
+        Args:
+            points (Tensor): Shape (N, 2), The format is [x, y].
+            gt_bboxes (Tensor): Shape (N, 4), The format is "xyxy"
+            max_dis (float): Upper bound of the distance. Default None.
+            eps (float): a small value to ensure target < max_dis, instead <=.
+                Default 0.1.
+
+        Returns:
+            Tensor: Box transformation deltas. The shape is (N, 4).
+        """
+        assert points.size(0) == gt_bboxes.size(0)
+        assert points.size(-1) == 2
+        assert gt_bboxes.size(-1) == 4
+        return bbox2distance(points, gt_bboxes, max_dis, eps)
+
+    def decode(self, points, pred_bboxes, max_shape=None):
+        """Decode distance prediction to bounding box.
+
+        Args:
+            points (Tensor): Shape (B, N, 2) or (N, 2).
+            pred_bboxes (Tensor): Distance from the given point to 4
+                boundaries (left, top, right, bottom). Shape (B, N, 4)
+                or (N, 4)
+            max_shape (Sequence[int] or torch.Tensor or Sequence[
+                Sequence[int]],optional): Maximum bounds for boxes, specifies
+                (H, W, C) or (H, W). If priors shape is (B, N, 4), then
+                the max_shape should be a Sequence[Sequence[int]],
+                and the length of max_shape should also be B.
+                Default None.
+        Returns:
+            Tensor: Boxes with shape (N, 4) or (B, N, 4)
+        """
+        assert points.size(0) == pred_bboxes.size(0)
+        assert points.size(-1) == 2
+        assert pred_bboxes.size(-1) == 4
+        if self.clip_border is False:
+            max_shape = None
+        return distance2bbox(points, pred_bboxes, max_shape)
diff --git a/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py b/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py
new file mode 100644
index 0000000..7fa348b
--- /dev/null
+++ b/mmdet/core/bbox/coder/legacy_delta_xywh_bbox_coder.py
@@ -0,0 +1,216 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import numpy as np
+import torch
+
+from ..builder import BBOX_CODERS
+from .base_bbox_coder import BaseBBoxCoder
+
+
+@BBOX_CODERS.register_module()
+class LegacyDeltaXYWHBBoxCoder(BaseBBoxCoder):
+    """Legacy Delta XYWH BBox coder used in MMDet V1.x.
+
+    Following the practice in R-CNN [1]_, this coder encodes bbox (x1, y1, x2,
+    y2) into delta (dx, dy, dw, dh) and decodes delta (dx, dy, dw, dh)
+    back to original bbox (x1, y1, x2, y2).
+
+    Note:
+        The main difference between :class`LegacyDeltaXYWHBBoxCoder` and
+        :class:`DeltaXYWHBBoxCoder` is whether ``+ 1`` is used during width and
+        height calculation. We suggest to only use this coder when testing with
+        MMDet V1.x models.
+
+    References:
+        .. [1] https://arxiv.org/abs/1311.2524
+
+    Args:
+        target_means (Sequence[float]): denormalizing means of target for
+            delta coordinates
+        target_stds (Sequence[float]): denormalizing standard deviation of
+            target for delta coordinates
+    """
+
+    def __init__(self,
+                 target_means=(0., 0., 0., 0.),
+                 target_stds=(1., 1., 1., 1.)):
+        super(BaseBBoxCoder, self).__init__()
+        self.means = target_means
+        self.stds = target_stds
+
+    def encode(self, bboxes, gt_bboxes):
+        """Get box regression transformation deltas that can be used to
+        transform the ``bboxes`` into the ``gt_bboxes``.
+
+        Args:
+            bboxes (torch.Tensor): source boxes, e.g., object proposals.
+            gt_bboxes (torch.Tensor): target of the transformation, e.g.,
+                ground-truth boxes.
+
+        Returns:
+            torch.Tensor: Box transformation deltas
+        """
+        assert bboxes.size(0) == gt_bboxes.size(0)
+        assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
+        encoded_bboxes = legacy_bbox2delta(bboxes, gt_bboxes, self.means,
+                                           self.stds)
+        return encoded_bboxes
+
+    def decode(self,
+               bboxes,
+               pred_bboxes,
+               max_shape=None,
+               wh_ratio_clip=16 / 1000):
+        """Apply transformation `pred_bboxes` to `boxes`.
+
+        Args:
+            boxes (torch.Tensor): Basic boxes.
+            pred_bboxes (torch.Tensor): Encoded boxes with shape
+            max_shape (tuple[int], optional): Maximum shape of boxes.
+                Defaults to None.
+            wh_ratio_clip (float, optional): The allowed ratio between
+                width and height.
+
+        Returns:
+            torch.Tensor: Decoded boxes.
+        """
+        assert pred_bboxes.size(0) == bboxes.size(0)
+        decoded_bboxes = legacy_delta2bbox(bboxes, pred_bboxes, self.means,
+                                           self.stds, max_shape, wh_ratio_clip)
+
+        return decoded_bboxes
+
+
+@mmcv.jit(coderize=True)
+def legacy_bbox2delta(proposals,
+                      gt,
+                      means=(0., 0., 0., 0.),
+                      stds=(1., 1., 1., 1.)):
+    """Compute deltas of proposals w.r.t. gt in the MMDet V1.x manner.
+
+    We usually compute the deltas of x, y, w, h of proposals w.r.t ground
+    truth bboxes to get regression target.
+    This is the inverse function of `delta2bbox()`
+
+    Args:
+        proposals (Tensor): Boxes to be transformed, shape (N, ..., 4)
+        gt (Tensor): Gt bboxes to be used as base, shape (N, ..., 4)
+        means (Sequence[float]): Denormalizing means for delta coordinates
+        stds (Sequence[float]): Denormalizing standard deviation for delta
+            coordinates
+
+    Returns:
+        Tensor: deltas with shape (N, 4), where columns represent dx, dy,
+            dw, dh.
+    """
+    assert proposals.size() == gt.size()
+
+    proposals = proposals.float()
+    gt = gt.float()
+    px = (proposals[..., 0] + proposals[..., 2]) * 0.5
+    py = (proposals[..., 1] + proposals[..., 3]) * 0.5
+    pw = proposals[..., 2] - proposals[..., 0] + 1.0
+    ph = proposals[..., 3] - proposals[..., 1] + 1.0
+
+    gx = (gt[..., 0] + gt[..., 2]) * 0.5
+    gy = (gt[..., 1] + gt[..., 3]) * 0.5
+    gw = gt[..., 2] - gt[..., 0] + 1.0
+    gh = gt[..., 3] - gt[..., 1] + 1.0
+
+    dx = (gx - px) / pw
+    dy = (gy - py) / ph
+    dw = torch.log(gw / pw)
+    dh = torch.log(gh / ph)
+    deltas = torch.stack([dx, dy, dw, dh], dim=-1)
+
+    means = deltas.new_tensor(means).unsqueeze(0)
+    stds = deltas.new_tensor(stds).unsqueeze(0)
+    deltas = deltas.sub_(means).div_(stds)
+
+    return deltas
+
+
+@mmcv.jit(coderize=True)
+def legacy_delta2bbox(rois,
+                      deltas,
+                      means=(0., 0., 0., 0.),
+                      stds=(1., 1., 1., 1.),
+                      max_shape=None,
+                      wh_ratio_clip=16 / 1000):
+    """Apply deltas to shift/scale base boxes in the MMDet V1.x manner.
+
+    Typically the rois are anchor or proposed bounding boxes and the deltas are
+    network outputs used to shift/scale those boxes.
+    This is the inverse function of `bbox2delta()`
+
+    Args:
+        rois (Tensor): Boxes to be transformed. Has shape (N, 4)
+        deltas (Tensor): Encoded offsets with respect to each roi.
+            Has shape (N, 4 * num_classes). Note N = num_anchors * W * H when
+            rois is a grid of anchors. Offset encoding follows [1]_.
+        means (Sequence[float]): Denormalizing means for delta coordinates
+        stds (Sequence[float]): Denormalizing standard deviation for delta
+            coordinates
+        max_shape (tuple[int, int]): Maximum bounds for boxes. specifies (H, W)
+        wh_ratio_clip (float): Maximum aspect ratio for boxes.
+
+    Returns:
+        Tensor: Boxes with shape (N, 4), where columns represent
+            tl_x, tl_y, br_x, br_y.
+
+    References:
+        .. [1] https://arxiv.org/abs/1311.2524
+
+    Example:
+        >>> rois = torch.Tensor([[ 0.,  0.,  1.,  1.],
+        >>>                      [ 0.,  0.,  1.,  1.],
+        >>>                      [ 0.,  0.,  1.,  1.],
+        >>>                      [ 5.,  5.,  5.,  5.]])
+        >>> deltas = torch.Tensor([[  0.,   0.,   0.,   0.],
+        >>>                        [  1.,   1.,   1.,   1.],
+        >>>                        [  0.,   0.,   2.,  -1.],
+        >>>                        [ 0.7, -1.9, -0.5,  0.3]])
+        >>> legacy_delta2bbox(rois, deltas, max_shape=(32, 32))
+        tensor([[0.0000, 0.0000, 1.5000, 1.5000],
+                [0.0000, 0.0000, 5.2183, 5.2183],
+                [0.0000, 0.1321, 7.8891, 0.8679],
+                [5.3967, 2.4251, 6.0033, 3.7749]])
+    """
+    means = deltas.new_tensor(means).repeat(1, deltas.size(1) // 4)
+    stds = deltas.new_tensor(stds).repeat(1, deltas.size(1) // 4)
+    denorm_deltas = deltas * stds + means
+    dx = denorm_deltas[:, 0::4]
+    dy = denorm_deltas[:, 1::4]
+    dw = denorm_deltas[:, 2::4]
+    dh = denorm_deltas[:, 3::4]
+    max_ratio = np.abs(np.log(wh_ratio_clip))
+    dw = dw.clamp(min=-max_ratio, max=max_ratio)
+    dh = dh.clamp(min=-max_ratio, max=max_ratio)
+    # Compute center of each roi
+    px = ((rois[:, 0] + rois[:, 2]) * 0.5).unsqueeze(1).expand_as(dx)
+    py = ((rois[:, 1] + rois[:, 3]) * 0.5).unsqueeze(1).expand_as(dy)
+    # Compute width/height of each roi
+    pw = (rois[:, 2] - rois[:, 0] + 1.0).unsqueeze(1).expand_as(dw)
+    ph = (rois[:, 3] - rois[:, 1] + 1.0).unsqueeze(1).expand_as(dh)
+    # Use exp(network energy) to enlarge/shrink each roi
+    gw = pw * dw.exp()
+    gh = ph * dh.exp()
+    # Use network energy to shift the center of each roi
+    gx = px + pw * dx
+    gy = py + ph * dy
+    # Convert center-xy/width/height to top-left, bottom-right
+
+    # The true legacy box coder should +- 0.5 here.
+    # However, current implementation improves the performance when testing
+    # the models trained in MMDetection 1.X (~0.5 bbox AP, 0.2 mask AP)
+    x1 = gx - gw * 0.5
+    y1 = gy - gh * 0.5
+    x2 = gx + gw * 0.5
+    y2 = gy + gh * 0.5
+    if max_shape is not None:
+        x1 = x1.clamp(min=0, max=max_shape[1] - 1)
+        y1 = y1.clamp(min=0, max=max_shape[0] - 1)
+        x2 = x2.clamp(min=0, max=max_shape[1] - 1)
+        y2 = y2.clamp(min=0, max=max_shape[0] - 1)
+    bboxes = torch.stack([x1, y1, x2, y2], dim=-1).view_as(deltas)
+    return bboxes
diff --git a/mmdet/core/bbox/coder/pseudo_bbox_coder.py b/mmdet/core/bbox/coder/pseudo_bbox_coder.py
new file mode 100644
index 0000000..fe71f36
--- /dev/null
+++ b/mmdet/core/bbox/coder/pseudo_bbox_coder.py
@@ -0,0 +1,19 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import BBOX_CODERS
+from .base_bbox_coder import BaseBBoxCoder
+
+
+@BBOX_CODERS.register_module()
+class PseudoBBoxCoder(BaseBBoxCoder):
+    """Pseudo bounding box coder."""
+
+    def __init__(self, **kwargs):
+        super(BaseBBoxCoder, self).__init__(**kwargs)
+
+    def encode(self, bboxes, gt_bboxes):
+        """torch.Tensor: return the given ``bboxes``"""
+        return gt_bboxes
+
+    def decode(self, bboxes, pred_bboxes):
+        """torch.Tensor: return the given ``pred_bboxes``"""
+        return pred_bboxes
diff --git a/mmdet/core/bbox/coder/tblr_bbox_coder.py b/mmdet/core/bbox/coder/tblr_bbox_coder.py
new file mode 100644
index 0000000..cb42066
--- /dev/null
+++ b/mmdet/core/bbox/coder/tblr_bbox_coder.py
@@ -0,0 +1,206 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from ..builder import BBOX_CODERS
+from .base_bbox_coder import BaseBBoxCoder
+
+
+@BBOX_CODERS.register_module()
+class TBLRBBoxCoder(BaseBBoxCoder):
+    """TBLR BBox coder.
+
+    Following the practice in `FSAF <https://arxiv.org/abs/1903.00621>`_,
+    this coder encodes gt bboxes (x1, y1, x2, y2) into (top, bottom, left,
+    right) and decode it back to the original.
+
+    Args:
+        normalizer (list | float): Normalization factor to be
+          divided with when coding the coordinates. If it is a list, it should
+          have length of 4 indicating normalization factor in tblr dims.
+          Otherwise it is a unified float factor for all dims. Default: 4.0
+        clip_border (bool, optional): Whether clip the objects outside the
+            border of the image. Defaults to True.
+    """
+
+    def __init__(self, normalizer=4.0, clip_border=True):
+        super(BaseBBoxCoder, self).__init__()
+        self.normalizer = normalizer
+        self.clip_border = clip_border
+
+    def encode(self, bboxes, gt_bboxes):
+        """Get box regression transformation deltas that can be used to
+        transform the ``bboxes`` into the ``gt_bboxes`` in the (top, left,
+        bottom, right) order.
+
+        Args:
+            bboxes (torch.Tensor): source boxes, e.g., object proposals.
+            gt_bboxes (torch.Tensor): target of the transformation, e.g.,
+                ground truth boxes.
+
+        Returns:
+            torch.Tensor: Box transformation deltas
+        """
+        assert bboxes.size(0) == gt_bboxes.size(0)
+        assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
+        encoded_bboxes = bboxes2tblr(
+            bboxes, gt_bboxes, normalizer=self.normalizer)
+        return encoded_bboxes
+
+    def decode(self, bboxes, pred_bboxes, max_shape=None):
+        """Apply transformation `pred_bboxes` to `boxes`.
+
+        Args:
+            bboxes (torch.Tensor): Basic boxes.Shape (B, N, 4) or (N, 4)
+            pred_bboxes (torch.Tensor): Encoded boxes with shape
+               (B, N, 4) or (N, 4)
+            max_shape (Sequence[int] or torch.Tensor or Sequence[
+               Sequence[int]],optional): Maximum bounds for boxes, specifies
+               (H, W, C) or (H, W). If bboxes shape is (B, N, 4), then
+               the max_shape should be a Sequence[Sequence[int]]
+               and the length of max_shape should also be B.
+
+        Returns:
+            torch.Tensor: Decoded boxes.
+        """
+        decoded_bboxes = tblr2bboxes(
+            bboxes,
+            pred_bboxes,
+            normalizer=self.normalizer,
+            max_shape=max_shape,
+            clip_border=self.clip_border)
+
+        return decoded_bboxes
+
+
+@mmcv.jit(coderize=True)
+def bboxes2tblr(priors, gts, normalizer=4.0, normalize_by_wh=True):
+    """Encode ground truth boxes to tblr coordinate.
+
+    It first convert the gt coordinate to tblr format,
+     (top, bottom, left, right), relative to prior box centers.
+     The tblr coordinate may be normalized by the side length of prior bboxes
+     if `normalize_by_wh` is specified as True, and it is then normalized by
+     the `normalizer` factor.
+
+    Args:
+        priors (Tensor): Prior boxes in point form
+            Shape: (num_proposals,4).
+        gts (Tensor): Coords of ground truth for each prior in point-form
+            Shape: (num_proposals, 4).
+        normalizer (Sequence[float] | float): normalization parameter of
+            encoded boxes. If it is a list, it has to have length = 4.
+            Default: 4.0
+        normalize_by_wh (bool): Whether to normalize tblr coordinate by the
+            side length (wh) of prior bboxes.
+
+    Return:
+        encoded boxes (Tensor), Shape: (num_proposals, 4)
+    """
+
+    # dist b/t match center and prior's center
+    if not isinstance(normalizer, float):
+        normalizer = torch.tensor(normalizer, device=priors.device)
+        assert len(normalizer) == 4, 'Normalizer must have length = 4'
+    assert priors.size(0) == gts.size(0)
+    prior_centers = (priors[:, 0:2] + priors[:, 2:4]) / 2
+    xmin, ymin, xmax, ymax = gts.split(1, dim=1)
+    top = prior_centers[:, 1].unsqueeze(1) - ymin
+    bottom = ymax - prior_centers[:, 1].unsqueeze(1)
+    left = prior_centers[:, 0].unsqueeze(1) - xmin
+    right = xmax - prior_centers[:, 0].unsqueeze(1)
+    loc = torch.cat((top, bottom, left, right), dim=1)
+    if normalize_by_wh:
+        # Normalize tblr by anchor width and height
+        wh = priors[:, 2:4] - priors[:, 0:2]
+        w, h = torch.split(wh, 1, dim=1)
+        loc[:, :2] /= h  # tb is normalized by h
+        loc[:, 2:] /= w  # lr is normalized by w
+    # Normalize tblr by the given normalization factor
+    return loc / normalizer
+
+
+@mmcv.jit(coderize=True)
+def tblr2bboxes(priors,
+                tblr,
+                normalizer=4.0,
+                normalize_by_wh=True,
+                max_shape=None,
+                clip_border=True):
+    """Decode tblr outputs to prediction boxes.
+
+    The process includes 3 steps: 1) De-normalize tblr coordinates by
+    multiplying it with `normalizer`; 2) De-normalize tblr coordinates by the
+    prior bbox width and height if `normalize_by_wh` is `True`; 3) Convert
+    tblr (top, bottom, left, right) pair relative to the center of priors back
+    to (xmin, ymin, xmax, ymax) coordinate.
+
+    Args:
+        priors (Tensor): Prior boxes in point form (x0, y0, x1, y1)
+          Shape: (N,4) or (B, N, 4).
+        tblr (Tensor): Coords of network output in tblr form
+          Shape: (N, 4) or (B, N, 4).
+        normalizer (Sequence[float] | float): Normalization parameter of
+          encoded boxes. By list, it represents the normalization factors at
+          tblr dims. By float, it is the unified normalization factor at all
+          dims. Default: 4.0
+        normalize_by_wh (bool): Whether the tblr coordinates have been
+          normalized by the side length (wh) of prior bboxes.
+        max_shape (Sequence[int] or torch.Tensor or Sequence[
+            Sequence[int]],optional): Maximum bounds for boxes, specifies
+            (H, W, C) or (H, W). If priors shape is (B, N, 4), then
+            the max_shape should be a Sequence[Sequence[int]]
+            and the length of max_shape should also be B.
+        clip_border (bool, optional): Whether clip the objects outside the
+            border of the image. Defaults to True.
+
+    Return:
+        encoded boxes (Tensor): Boxes with shape (N, 4) or (B, N, 4)
+    """
+    if not isinstance(normalizer, float):
+        normalizer = torch.tensor(normalizer, device=priors.device)
+        assert len(normalizer) == 4, 'Normalizer must have length = 4'
+    assert priors.size(0) == tblr.size(0)
+    if priors.ndim == 3:
+        assert priors.size(1) == tblr.size(1)
+
+    loc_decode = tblr * normalizer
+    prior_centers = (priors[..., 0:2] + priors[..., 2:4]) / 2
+    if normalize_by_wh:
+        wh = priors[..., 2:4] - priors[..., 0:2]
+        w, h = torch.split(wh, 1, dim=-1)
+        # Inplace operation with slice would failed for exporting to ONNX
+        th = h * loc_decode[..., :2]  # tb
+        tw = w * loc_decode[..., 2:]  # lr
+        loc_decode = torch.cat([th, tw], dim=-1)
+    # Cannot be exported using onnx when loc_decode.split(1, dim=-1)
+    top, bottom, left, right = loc_decode.split((1, 1, 1, 1), dim=-1)
+    xmin = prior_centers[..., 0].unsqueeze(-1) - left
+    xmax = prior_centers[..., 0].unsqueeze(-1) + right
+    ymin = prior_centers[..., 1].unsqueeze(-1) - top
+    ymax = prior_centers[..., 1].unsqueeze(-1) + bottom
+
+    bboxes = torch.cat((xmin, ymin, xmax, ymax), dim=-1)
+
+    if clip_border and max_shape is not None:
+        # clip bboxes with dynamic `min` and `max` for onnx
+        if torch.onnx.is_in_onnx_export():
+            from mmdet.core.export import dynamic_clip_for_onnx
+            xmin, ymin, xmax, ymax = dynamic_clip_for_onnx(
+                xmin, ymin, xmax, ymax, max_shape)
+            bboxes = torch.cat([xmin, ymin, xmax, ymax], dim=-1)
+            return bboxes
+        if not isinstance(max_shape, torch.Tensor):
+            max_shape = priors.new_tensor(max_shape)
+        max_shape = max_shape[..., :2].type_as(priors)
+        if max_shape.ndim == 2:
+            assert bboxes.ndim == 3
+            assert max_shape.size(0) == bboxes.size(0)
+
+        min_xy = priors.new_tensor(0)
+        max_xy = torch.cat([max_shape, max_shape],
+                           dim=-1).flip(-1).unsqueeze(-2)
+        bboxes = torch.where(bboxes < min_xy, min_xy, bboxes)
+        bboxes = torch.where(bboxes > max_xy, max_xy, bboxes)
+
+    return bboxes
diff --git a/mmdet/core/bbox/coder/yolo_bbox_coder.py b/mmdet/core/bbox/coder/yolo_bbox_coder.py
new file mode 100644
index 0000000..2852eca
--- /dev/null
+++ b/mmdet/core/bbox/coder/yolo_bbox_coder.py
@@ -0,0 +1,83 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from ..builder import BBOX_CODERS
+from .base_bbox_coder import BaseBBoxCoder
+
+
+@BBOX_CODERS.register_module()
+class YOLOBBoxCoder(BaseBBoxCoder):
+    """YOLO BBox coder.
+
+    Following `YOLO <https://arxiv.org/abs/1506.02640>`_, this coder divide
+    image into grids, and encode bbox (x1, y1, x2, y2) into (cx, cy, dw, dh).
+    cx, cy in [0., 1.], denotes relative center position w.r.t the center of
+    bboxes. dw, dh are the same as :obj:`DeltaXYWHBBoxCoder`.
+
+    Args:
+        eps (float): Min value of cx, cy when encoding.
+    """
+
+    def __init__(self, eps=1e-6):
+        super(BaseBBoxCoder, self).__init__()
+        self.eps = eps
+
+    @mmcv.jit(coderize=True)
+    def encode(self, bboxes, gt_bboxes, stride):
+        """Get box regression transformation deltas that can be used to
+        transform the ``bboxes`` into the ``gt_bboxes``.
+
+        Args:
+            bboxes (torch.Tensor): Source boxes, e.g., anchors.
+            gt_bboxes (torch.Tensor): Target of the transformation, e.g.,
+                ground-truth boxes.
+            stride (torch.Tensor | int): Stride of bboxes.
+
+        Returns:
+            torch.Tensor: Box transformation deltas
+        """
+
+        assert bboxes.size(0) == gt_bboxes.size(0)
+        assert bboxes.size(-1) == gt_bboxes.size(-1) == 4
+        x_center_gt = (gt_bboxes[..., 0] + gt_bboxes[..., 2]) * 0.5
+        y_center_gt = (gt_bboxes[..., 1] + gt_bboxes[..., 3]) * 0.5
+        w_gt = gt_bboxes[..., 2] - gt_bboxes[..., 0]
+        h_gt = gt_bboxes[..., 3] - gt_bboxes[..., 1]
+        x_center = (bboxes[..., 0] + bboxes[..., 2]) * 0.5
+        y_center = (bboxes[..., 1] + bboxes[..., 3]) * 0.5
+        w = bboxes[..., 2] - bboxes[..., 0]
+        h = bboxes[..., 3] - bboxes[..., 1]
+        w_target = torch.log((w_gt / w).clamp(min=self.eps))
+        h_target = torch.log((h_gt / h).clamp(min=self.eps))
+        x_center_target = ((x_center_gt - x_center) / stride + 0.5).clamp(
+            self.eps, 1 - self.eps)
+        y_center_target = ((y_center_gt - y_center) / stride + 0.5).clamp(
+            self.eps, 1 - self.eps)
+        encoded_bboxes = torch.stack(
+            [x_center_target, y_center_target, w_target, h_target], dim=-1)
+        return encoded_bboxes
+
+    @mmcv.jit(coderize=True)
+    def decode(self, bboxes, pred_bboxes, stride):
+        """Apply transformation `pred_bboxes` to `boxes`.
+
+        Args:
+            boxes (torch.Tensor): Basic boxes, e.g. anchors.
+            pred_bboxes (torch.Tensor): Encoded boxes with shape
+            stride (torch.Tensor | int): Strides of bboxes.
+
+        Returns:
+            torch.Tensor: Decoded boxes.
+        """
+        assert pred_bboxes.size(-1) == bboxes.size(-1) == 4
+        xy_centers = (bboxes[..., :2] + bboxes[..., 2:]) * 0.5 + (
+            pred_bboxes[..., :2] - 0.5) * stride
+        whs = (bboxes[..., 2:] -
+               bboxes[..., :2]) * 0.5 * pred_bboxes[..., 2:].exp()
+        decoded_bboxes = torch.stack(
+            (xy_centers[..., 0] - whs[..., 0], xy_centers[..., 1] -
+             whs[..., 1], xy_centers[..., 0] + whs[..., 0],
+             xy_centers[..., 1] + whs[..., 1]),
+            dim=-1)
+        return decoded_bboxes
diff --git a/mmdet/core/bbox/demodata.py b/mmdet/core/bbox/demodata.py
new file mode 100644
index 0000000..eb24b34
--- /dev/null
+++ b/mmdet/core/bbox/demodata.py
@@ -0,0 +1,42 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+
+from mmdet.utils.util_random import ensure_rng
+
+
+def random_boxes(num=1, scale=1, rng=None):
+    """Simple version of ``kwimage.Boxes.random``
+
+    Returns:
+        Tensor: shape (n, 4) in x1, y1, x2, y2 format.
+
+    References:
+        https://gitlab.kitware.com/computer-vision/kwimage/blob/master/kwimage/structs/boxes.py#L1390
+
+    Example:
+        >>> num = 3
+        >>> scale = 512
+        >>> rng = 0
+        >>> boxes = random_boxes(num, scale, rng)
+        >>> print(boxes)
+        tensor([[280.9925, 278.9802, 308.6148, 366.1769],
+                [216.9113, 330.6978, 224.0446, 456.5878],
+                [405.3632, 196.3221, 493.3953, 270.7942]])
+    """
+    rng = ensure_rng(rng)
+
+    tlbr = rng.rand(num, 4).astype(np.float32)
+
+    tl_x = np.minimum(tlbr[:, 0], tlbr[:, 2])
+    tl_y = np.minimum(tlbr[:, 1], tlbr[:, 3])
+    br_x = np.maximum(tlbr[:, 0], tlbr[:, 2])
+    br_y = np.maximum(tlbr[:, 1], tlbr[:, 3])
+
+    tlbr[:, 0] = tl_x * scale
+    tlbr[:, 1] = tl_y * scale
+    tlbr[:, 2] = br_x * scale
+    tlbr[:, 3] = br_y * scale
+
+    boxes = torch.from_numpy(tlbr)
+    return boxes
diff --git a/mmdet/core/bbox/iou_calculators/__init__.py b/mmdet/core/bbox/iou_calculators/__init__.py
new file mode 100644
index 0000000..04ba925
--- /dev/null
+++ b/mmdet/core/bbox/iou_calculators/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .builder import build_iou_calculator
+from .iou2d_calculator import BboxOverlaps2D, bbox_overlaps
+
+__all__ = ['build_iou_calculator', 'BboxOverlaps2D', 'bbox_overlaps']
diff --git a/mmdet/core/bbox/iou_calculators/builder.py b/mmdet/core/bbox/iou_calculators/builder.py
new file mode 100644
index 0000000..378ee26
--- /dev/null
+++ b/mmdet/core/bbox/iou_calculators/builder.py
@@ -0,0 +1,9 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.utils import Registry, build_from_cfg
+
+IOU_CALCULATORS = Registry('IoU calculator')
+
+
+def build_iou_calculator(cfg, default_args=None):
+    """Builder of IoU calculator."""
+    return build_from_cfg(cfg, IOU_CALCULATORS, default_args)
diff --git a/mmdet/core/bbox/iou_calculators/iou2d_calculator.py b/mmdet/core/bbox/iou_calculators/iou2d_calculator.py
new file mode 100644
index 0000000..b71a555
--- /dev/null
+++ b/mmdet/core/bbox/iou_calculators/iou2d_calculator.py
@@ -0,0 +1,260 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from .builder import IOU_CALCULATORS
+
+
+def cast_tensor_type(x, scale=1., dtype=None):
+    if dtype == 'fp16':
+        # scale is for preventing overflows
+        x = (x / scale).half()
+    return x
+
+
+def fp16_clamp(x, min=None, max=None):
+    if not x.is_cuda and x.dtype == torch.float16:
+        # clamp for cpu float16, tensor fp16 has no clamp implementation
+        return x.float().clamp(min, max).half()
+
+    return x.clamp(min, max)
+
+
+@IOU_CALCULATORS.register_module()
+class BboxOverlaps2D:
+    """2D Overlaps (e.g. IoUs, GIoUs) Calculator."""
+
+    def __init__(self, scale=1., dtype=None):
+        self.scale = scale
+        self.dtype = dtype
+
+    def __call__(self, bboxes1, bboxes2, mode='iou', is_aligned=False):
+        """Calculate IoU between 2D bboxes.
+
+        Args:
+            bboxes1 (Tensor): bboxes have shape (m, 4) in <x1, y1, x2, y2>
+                format, or shape (m, 5) in <x1, y1, x2, y2, score> format.
+            bboxes2 (Tensor): bboxes have shape (n, 4) in <x1, y1, x2, y2>
+                format, shape (n, 5) in <x1, y1, x2, y2, score> format, or be
+                empty.
+            mode (str): "iou" (intersection over union), "iof" (intersection
+                over foreground), or "giou" (generalized intersection over
+                union).
+            is_aligned (bool, optional): If True, then m and n must be equal.
+                Default False.
+
+        Returns:
+            Tensor: shape (m, n) if ``is_aligned `` is False else shape (m,)
+        """
+        assert bboxes1.size(-1) in [0, 4, 5]
+        assert bboxes2.size(-1) in [0, 4, 5]
+        if bboxes2.size(-1) == 5:
+            bboxes2 = bboxes2[..., :4]
+        if bboxes1.size(-1) == 5:
+            bboxes1 = bboxes1[..., :4]
+
+        if self.dtype == 'fp16':
+            # change tensor type to save cpu and cuda memory and keep speed
+            bboxes1 = cast_tensor_type(bboxes1, self.scale, self.dtype)
+            bboxes2 = cast_tensor_type(bboxes2, self.scale, self.dtype)
+            overlaps = bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)
+            if not overlaps.is_cuda and overlaps.dtype == torch.float16:
+                # resume cpu float32
+                overlaps = overlaps.float()
+            return overlaps
+
+        return bbox_overlaps(bboxes1, bboxes2, mode, is_aligned)
+
+    def __repr__(self):
+        """str: a string describing the module"""
+        repr_str = self.__class__.__name__ + f'(' \
+            f'scale={self.scale}, dtype={self.dtype})'
+        return repr_str
+
+
+def bbox_overlaps(bboxes1, bboxes2, mode='iou', is_aligned=False, eps=1e-6):
+    """Calculate overlap between two set of bboxes.
+
+    FP16 Contributed by https://github.com/open-mmlab/mmdetection/pull/4889
+    Note:
+        Assume bboxes1 is M x 4, bboxes2 is N x 4, when mode is 'iou',
+        there are some new generated variable when calculating IOU
+        using bbox_overlaps function:
+
+        1) is_aligned is False
+            area1: M x 1
+            area2: N x 1
+            lt: M x N x 2
+            rb: M x N x 2
+            wh: M x N x 2
+            overlap: M x N x 1
+            union: M x N x 1
+            ious: M x N x 1
+
+            Total memory:
+                S = (9 x N x M + N + M) * 4 Byte,
+
+            When using FP16, we can reduce:
+                R = (9 x N x M + N + M) * 4 / 2 Byte
+                R large than (N + M) * 4 * 2 is always true when N and M >= 1.
+                Obviously, N + M <= N * M < 3 * N * M, when N >=2 and M >=2,
+                           N + 1 < 3 * N, when N or M is 1.
+
+            Given M = 40 (ground truth), N = 400000 (three anchor boxes
+            in per grid, FPN, R-CNNs),
+                R = 275 MB (one times)
+
+            A special case (dense detection), M = 512 (ground truth),
+                R = 3516 MB = 3.43 GB
+
+            When the batch size is B, reduce:
+                B x R
+
+            Therefore, CUDA memory runs out frequently.
+
+            Experiments on GeForce RTX 2080Ti (11019 MiB):
+
+            |   dtype   |   M   |   N   |   Use    |   Real   |   Ideal   |
+            |:----:|:----:|:----:|:----:|:----:|:----:|
+            |   FP32   |   512 | 400000 | 8020 MiB |   --   |   --   |
+            |   FP16   |   512 | 400000 |   4504 MiB | 3516 MiB | 3516 MiB |
+            |   FP32   |   40 | 400000 |   1540 MiB |   --   |   --   |
+            |   FP16   |   40 | 400000 |   1264 MiB |   276MiB   | 275 MiB |
+
+        2) is_aligned is True
+            area1: N x 1
+            area2: N x 1
+            lt: N x 2
+            rb: N x 2
+            wh: N x 2
+            overlap: N x 1
+            union: N x 1
+            ious: N x 1
+
+            Total memory:
+                S = 11 x N * 4 Byte
+
+            When using FP16, we can reduce:
+                R = 11 x N * 4 / 2 Byte
+
+        So do the 'giou' (large than 'iou').
+
+        Time-wise, FP16 is generally faster than FP32.
+
+        When gpu_assign_thr is not -1, it takes more time on cpu
+        but not reduce memory.
+        There, we can reduce half the memory and keep the speed.
+
+    If ``is_aligned`` is ``False``, then calculate the overlaps between each
+    bbox of bboxes1 and bboxes2, otherwise the overlaps between each aligned
+    pair of bboxes1 and bboxes2.
+
+    Args:
+        bboxes1 (Tensor): shape (B, m, 4) in <x1, y1, x2, y2> format or empty.
+        bboxes2 (Tensor): shape (B, n, 4) in <x1, y1, x2, y2> format or empty.
+            B indicates the batch dim, in shape (B1, B2, ..., Bn).
+            If ``is_aligned`` is ``True``, then m and n must be equal.
+        mode (str): "iou" (intersection over union), "iof" (intersection over
+            foreground) or "giou" (generalized intersection over union).
+            Default "iou".
+        is_aligned (bool, optional): If True, then m and n must be equal.
+            Default False.
+        eps (float, optional): A value added to the denominator for numerical
+            stability. Default 1e-6.
+
+    Returns:
+        Tensor: shape (m, n) if ``is_aligned`` is False else shape (m,)
+
+    Example:
+        >>> bboxes1 = torch.FloatTensor([
+        >>>     [0, 0, 10, 10],
+        >>>     [10, 10, 20, 20],
+        >>>     [32, 32, 38, 42],
+        >>> ])
+        >>> bboxes2 = torch.FloatTensor([
+        >>>     [0, 0, 10, 20],
+        >>>     [0, 10, 10, 19],
+        >>>     [10, 10, 20, 20],
+        >>> ])
+        >>> overlaps = bbox_overlaps(bboxes1, bboxes2)
+        >>> assert overlaps.shape == (3, 3)
+        >>> overlaps = bbox_overlaps(bboxes1, bboxes2, is_aligned=True)
+        >>> assert overlaps.shape == (3, )
+
+    Example:
+        >>> empty = torch.empty(0, 4)
+        >>> nonempty = torch.FloatTensor([[0, 0, 10, 9]])
+        >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1)
+        >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0)
+        >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0)
+    """
+
+    assert mode in ['iou', 'iof', 'giou'], f'Unsupported mode {mode}'
+    # Either the boxes are empty or the length of boxes' last dimension is 4
+    assert (bboxes1.size(-1) == 4 or bboxes1.size(0) == 0)
+    assert (bboxes2.size(-1) == 4 or bboxes2.size(0) == 0)
+
+    # Batch dim must be the same
+    # Batch dim: (B1, B2, ... Bn)
+    assert bboxes1.shape[:-2] == bboxes2.shape[:-2]
+    batch_shape = bboxes1.shape[:-2]
+
+    rows = bboxes1.size(-2)
+    cols = bboxes2.size(-2)
+    if is_aligned:
+        assert rows == cols
+
+    if rows * cols == 0:
+        if is_aligned:
+            return bboxes1.new(batch_shape + (rows, ))
+        else:
+            return bboxes1.new(batch_shape + (rows, cols))
+
+    area1 = (bboxes1[..., 2] - bboxes1[..., 0]) * (
+        bboxes1[..., 3] - bboxes1[..., 1])
+    area2 = (bboxes2[..., 2] - bboxes2[..., 0]) * (
+        bboxes2[..., 3] - bboxes2[..., 1])
+
+    if is_aligned:
+        lt = torch.max(bboxes1[..., :2], bboxes2[..., :2])  # [B, rows, 2]
+        rb = torch.min(bboxes1[..., 2:], bboxes2[..., 2:])  # [B, rows, 2]
+
+        wh = fp16_clamp(rb - lt, min=0)
+        overlap = wh[..., 0] * wh[..., 1]
+
+        if mode in ['iou', 'giou']:
+            union = area1 + area2 - overlap
+        else:
+            union = area1
+        if mode == 'giou':
+            enclosed_lt = torch.min(bboxes1[..., :2], bboxes2[..., :2])
+            enclosed_rb = torch.max(bboxes1[..., 2:], bboxes2[..., 2:])
+    else:
+        lt = torch.max(bboxes1[..., :, None, :2],
+                       bboxes2[..., None, :, :2])  # [B, rows, cols, 2]
+        rb = torch.min(bboxes1[..., :, None, 2:],
+                       bboxes2[..., None, :, 2:])  # [B, rows, cols, 2]
+
+        wh = fp16_clamp(rb - lt, min=0)
+        overlap = wh[..., 0] * wh[..., 1]
+
+        if mode in ['iou', 'giou']:
+            union = area1[..., None] + area2[..., None, :] - overlap
+        else:
+            union = area1[..., None]
+        if mode == 'giou':
+            enclosed_lt = torch.min(bboxes1[..., :, None, :2],
+                                    bboxes2[..., None, :, :2])
+            enclosed_rb = torch.max(bboxes1[..., :, None, 2:],
+                                    bboxes2[..., None, :, 2:])
+
+    eps = union.new_tensor([eps])
+    union = torch.max(union, eps)
+    ious = overlap / union
+    if mode in ['iou', 'iof']:
+        return ious
+    # calculate gious
+    enclose_wh = fp16_clamp(enclosed_rb - enclosed_lt, min=0)
+    enclose_area = enclose_wh[..., 0] * enclose_wh[..., 1]
+    enclose_area = torch.max(enclose_area, eps)
+    gious = ious - (enclose_area - union) / enclose_area
+    return gious
diff --git a/mmdet/core/bbox/match_costs/__init__.py b/mmdet/core/bbox/match_costs/__init__.py
new file mode 100644
index 0000000..1b63679
--- /dev/null
+++ b/mmdet/core/bbox/match_costs/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .builder import build_match_cost
+from .match_cost import (BBoxL1Cost, ClassificationCost, CrossEntropyLossCost,
+                         DiceCost, FocalLossCost, IoUCost)
+
+__all__ = [
+    'build_match_cost', 'ClassificationCost', 'BBoxL1Cost', 'IoUCost',
+    'FocalLossCost', 'DiceCost', 'CrossEntropyLossCost'
+]
diff --git a/mmdet/core/bbox/match_costs/builder.py b/mmdet/core/bbox/match_costs/builder.py
new file mode 100644
index 0000000..ea086ad
--- /dev/null
+++ b/mmdet/core/bbox/match_costs/builder.py
@@ -0,0 +1,9 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.utils import Registry, build_from_cfg
+
+MATCH_COST = Registry('Match Cost')
+
+
+def build_match_cost(cfg, default_args=None):
+    """Builder of IoU calculator."""
+    return build_from_cfg(cfg, MATCH_COST, default_args)
diff --git a/mmdet/core/bbox/match_costs/match_cost.py b/mmdet/core/bbox/match_costs/match_cost.py
new file mode 100644
index 0000000..4342b02
--- /dev/null
+++ b/mmdet/core/bbox/match_costs/match_cost.py
@@ -0,0 +1,359 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn.functional as F
+
+from mmdet.core.bbox.iou_calculators import bbox_overlaps
+from mmdet.core.bbox.transforms import bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh
+from .builder import MATCH_COST
+
+
+@MATCH_COST.register_module()
+class BBoxL1Cost:
+    """BBoxL1Cost.
+
+     Args:
+         weight (int | float, optional): loss_weight
+         box_format (str, optional): 'xyxy' for DETR, 'xywh' for Sparse_RCNN
+
+     Examples:
+         >>> from mmdet.core.bbox.match_costs.match_cost import BBoxL1Cost
+         >>> import torch
+         >>> self = BBoxL1Cost()
+         >>> bbox_pred = torch.rand(1, 4)
+         >>> gt_bboxes= torch.FloatTensor([[0, 0, 2, 4], [1, 2, 3, 4]])
+         >>> factor = torch.tensor([10, 8, 10, 8])
+         >>> self(bbox_pred, gt_bboxes, factor)
+         tensor([[1.6172, 1.6422]])
+    """
+
+    def __init__(self, weight=1., box_format='xyxy'):
+        self.weight = weight
+        assert box_format in ['xyxy', 'xywh']
+        self.box_format = box_format
+
+    def __call__(self, bbox_pred, gt_bboxes):
+        """
+        Args:
+            bbox_pred (Tensor): Predicted boxes with normalized coordinates
+                (cx, cy, w, h), which are all in range [0, 1]. Shape
+                (num_query, 4).
+            gt_bboxes (Tensor): Ground truth boxes with normalized
+                coordinates (x1, y1, x2, y2). Shape (num_gt, 4).
+
+        Returns:
+            torch.Tensor: bbox_cost value with weight
+        """
+        if self.box_format == 'xywh':
+            gt_bboxes = bbox_xyxy_to_cxcywh(gt_bboxes)
+        elif self.box_format == 'xyxy':
+            bbox_pred = bbox_cxcywh_to_xyxy(bbox_pred)
+        bbox_cost = torch.cdist(bbox_pred, gt_bboxes, p=1)
+        return bbox_cost * self.weight
+
+
+@MATCH_COST.register_module()
+class FocalLossCost:
+    """FocalLossCost.
+
+     Args:
+         weight (int | float, optional): loss_weight
+         alpha (int | float, optional): focal_loss alpha
+         gamma (int | float, optional): focal_loss gamma
+         eps (float, optional): default 1e-12
+         binary_input (bool, optional): Whether the input is binary,
+            default False.
+
+     Examples:
+         >>> from mmdet.core.bbox.match_costs.match_cost import FocalLossCost
+         >>> import torch
+         >>> self = FocalLossCost()
+         >>> cls_pred = torch.rand(4, 3)
+         >>> gt_labels = torch.tensor([0, 1, 2])
+         >>> factor = torch.tensor([10, 8, 10, 8])
+         >>> self(cls_pred, gt_labels)
+         tensor([[-0.3236, -0.3364, -0.2699],
+                [-0.3439, -0.3209, -0.4807],
+                [-0.4099, -0.3795, -0.2929],
+                [-0.1950, -0.1207, -0.2626]])
+    """
+
+    def __init__(self,
+                 weight=1.,
+                 alpha=0.25,
+                 gamma=2,
+                 eps=1e-12,
+                 binary_input=False):
+        self.weight = weight
+        self.alpha = alpha
+        self.gamma = gamma
+        self.eps = eps
+        self.binary_input = binary_input
+
+    def _focal_loss_cost(self, cls_pred, gt_labels):
+        """
+        Args:
+            cls_pred (Tensor): Predicted classification logits, shape
+                (num_query, num_class).
+            gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).
+
+        Returns:
+            torch.Tensor: cls_cost value with weight
+        """
+        cls_pred = cls_pred.sigmoid()
+        neg_cost = -(1 - cls_pred + self.eps).log() * (
+            1 - self.alpha) * cls_pred.pow(self.gamma)
+        pos_cost = -(cls_pred + self.eps).log() * self.alpha * (
+            1 - cls_pred).pow(self.gamma)
+
+        cls_cost = pos_cost[:, gt_labels] - neg_cost[:, gt_labels]
+        return cls_cost * self.weight
+
+    def _mask_focal_loss_cost(self, cls_pred, gt_labels):
+        """
+        Args:
+            cls_pred (Tensor): Predicted classfication logits
+                in shape (num_query, d1, ..., dn), dtype=torch.float32.
+            gt_labels (Tensor): Ground truth in shape (num_gt, d1, ..., dn),
+                dtype=torch.long. Labels should be binary.
+
+        Returns:
+            Tensor: Focal cost matrix with weight in shape\
+                (num_query, num_gt).
+        """
+        cls_pred = cls_pred.flatten(1)
+        gt_labels = gt_labels.flatten(1).float()
+        n = cls_pred.shape[1]
+        cls_pred = cls_pred.sigmoid()
+        neg_cost = -(1 - cls_pred + self.eps).log() * (
+            1 - self.alpha) * cls_pred.pow(self.gamma)
+        pos_cost = -(cls_pred + self.eps).log() * self.alpha * (
+            1 - cls_pred).pow(self.gamma)
+
+        cls_cost = torch.einsum('nc,mc->nm', pos_cost, gt_labels) + \
+            torch.einsum('nc,mc->nm', neg_cost, (1 - gt_labels))
+        return cls_cost / n * self.weight
+
+    def __call__(self, cls_pred, gt_labels):
+        """
+        Args:
+            cls_pred (Tensor): Predicted classfication logits.
+            gt_labels (Tensor)): Labels.
+
+        Returns:
+            Tensor: Focal cost matrix with weight in shape\
+                (num_query, num_gt).
+        """
+        if self.binary_input:
+            return self._mask_focal_loss_cost(cls_pred, gt_labels)
+        else:
+            return self._focal_loss_cost(cls_pred, gt_labels)
+
+
+@MATCH_COST.register_module()
+class ClassificationCost:
+    """ClsSoftmaxCost.
+
+     Args:
+         weight (int | float, optional): loss_weight
+
+     Examples:
+         >>> from mmdet.core.bbox.match_costs.match_cost import \
+         ... ClassificationCost
+         >>> import torch
+         >>> self = ClassificationCost()
+         >>> cls_pred = torch.rand(4, 3)
+         >>> gt_labels = torch.tensor([0, 1, 2])
+         >>> factor = torch.tensor([10, 8, 10, 8])
+         >>> self(cls_pred, gt_labels)
+         tensor([[-0.3430, -0.3525, -0.3045],
+                [-0.3077, -0.2931, -0.3992],
+                [-0.3664, -0.3455, -0.2881],
+                [-0.3343, -0.2701, -0.3956]])
+    """
+
+    def __init__(self, weight=1.):
+        self.weight = weight
+
+    def __call__(self, cls_pred, gt_labels):
+        """
+        Args:
+            cls_pred (Tensor): Predicted classification logits, shape
+                (num_query, num_class).
+            gt_labels (Tensor): Label of `gt_bboxes`, shape (num_gt,).
+
+        Returns:
+            torch.Tensor: cls_cost value with weight
+        """
+        # Following the official DETR repo, contrary to the loss that
+        # NLL is used, we approximate it in 1 - cls_score[gt_label].
+        # The 1 is a constant that doesn't change the matching,
+        # so it can be omitted.
+        cls_score = cls_pred.softmax(-1)
+        cls_cost = -cls_score[:, gt_labels]
+        return cls_cost * self.weight
+
+
+@MATCH_COST.register_module()
+class IoUCost:
+    """IoUCost.
+
+     Args:
+         iou_mode (str, optional): iou mode such as 'iou' | 'giou'
+         weight (int | float, optional): loss weight
+
+     Examples:
+         >>> from mmdet.core.bbox.match_costs.match_cost import IoUCost
+         >>> import torch
+         >>> self = IoUCost()
+         >>> bboxes = torch.FloatTensor([[1,1, 2, 2], [2, 2, 3, 4]])
+         >>> gt_bboxes = torch.FloatTensor([[0, 0, 2, 4], [1, 2, 3, 4]])
+         >>> self(bboxes, gt_bboxes)
+         tensor([[-0.1250,  0.1667],
+                [ 0.1667, -0.5000]])
+    """
+
+    def __init__(self, iou_mode='giou', weight=1.):
+        self.weight = weight
+        self.iou_mode = iou_mode
+
+    def __call__(self, bboxes, gt_bboxes):
+        """
+        Args:
+            bboxes (Tensor): Predicted boxes with unnormalized coordinates
+                (x1, y1, x2, y2). Shape (num_query, 4).
+            gt_bboxes (Tensor): Ground truth boxes with unnormalized
+                coordinates (x1, y1, x2, y2). Shape (num_gt, 4).
+
+        Returns:
+            torch.Tensor: iou_cost value with weight
+        """
+        # overlaps: [num_bboxes, num_gt]
+        overlaps = bbox_overlaps(
+            bboxes, gt_bboxes, mode=self.iou_mode, is_aligned=False)
+        # The 1 is a constant that doesn't change the matching, so omitted.
+        iou_cost = -overlaps
+        return iou_cost * self.weight
+
+
+@MATCH_COST.register_module()
+class DiceCost:
+    """Cost of mask assignments based on dice losses.
+
+    Args:
+        weight (int | float, optional): loss_weight. Defaults to 1.
+        pred_act (bool, optional): Whether to apply sigmoid to mask_pred.
+            Defaults to False.
+        eps (float, optional): default 1e-12.
+        naive_dice (bool, optional): If True, use the naive dice loss
+            in which the power of the number in the denominator is
+            the first power. If Flase, use the second power that
+            is adopted by K-Net and SOLO.
+            Defaults to True.
+    """
+
+    def __init__(self, weight=1., pred_act=False, eps=1e-3, naive_dice=True):
+        self.weight = weight
+        self.pred_act = pred_act
+        self.eps = eps
+        self.naive_dice = naive_dice
+
+    def binary_mask_dice_loss(self, mask_preds, gt_masks):
+        """
+        Args:
+            mask_preds (Tensor): Mask prediction in shape (num_query, *).
+            gt_masks (Tensor): Ground truth in shape (num_gt, *)
+                store 0 or 1, 0 for negative class and 1 for
+                positive class.
+
+        Returns:
+            Tensor: Dice cost matrix in shape (num_query, num_gt).
+        """
+        mask_preds = mask_preds.flatten(1)
+        gt_masks = gt_masks.flatten(1).float()
+        numerator = 2 * torch.einsum('nc,mc->nm', mask_preds, gt_masks)
+        if self.naive_dice:
+            denominator = mask_preds.sum(-1)[:, None] + \
+                gt_masks.sum(-1)[None, :]
+        else:
+            denominator = mask_preds.pow(2).sum(1)[:, None] + \
+                gt_masks.pow(2).sum(1)[None, :]
+        loss = 1 - (numerator + self.eps) / (denominator + self.eps)
+        return loss
+
+    def __call__(self, mask_preds, gt_masks):
+        """
+        Args:
+            mask_preds (Tensor): Mask prediction logits in shape (num_query, *)
+            gt_masks (Tensor): Ground truth in shape (num_gt, *)
+
+        Returns:
+            Tensor: Dice cost matrix with weight in shape (num_query, num_gt).
+        """
+        if self.pred_act:
+            mask_preds = mask_preds.sigmoid()
+        dice_cost = self.binary_mask_dice_loss(mask_preds, gt_masks)
+        return dice_cost * self.weight
+
+
+@MATCH_COST.register_module()
+class CrossEntropyLossCost:
+    """CrossEntropyLossCost.
+
+    Args:
+        weight (int | float, optional): loss weight. Defaults to 1.
+        use_sigmoid (bool, optional): Whether the prediction uses sigmoid
+                of softmax. Defaults to True.
+    Examples:
+         >>> from mmdet.core.bbox.match_costs import CrossEntropyLossCost
+         >>> import torch
+         >>> bce = CrossEntropyLossCost(use_sigmoid=True)
+         >>> cls_pred = torch.tensor([[7.6, 1.2], [-1.3, 10]])
+         >>> gt_labels = torch.tensor([[1, 1], [1, 0]])
+         >>> print(bce(cls_pred, gt_labels))
+    """
+
+    def __init__(self, weight=1., use_sigmoid=True):
+        assert use_sigmoid, 'use_sigmoid = False is not supported yet.'
+        self.weight = weight
+        self.use_sigmoid = use_sigmoid
+
+    def _binary_cross_entropy(self, cls_pred, gt_labels):
+        """
+        Args:
+            cls_pred (Tensor): The prediction with shape (num_query, 1, *) or
+                (num_query, *).
+            gt_labels (Tensor): The learning label of prediction with
+                shape (num_gt, *).
+
+        Returns:
+            Tensor: Cross entropy cost matrix in shape (num_query, num_gt).
+        """
+        cls_pred = cls_pred.flatten(1).float()
+        gt_labels = gt_labels.flatten(1).float()
+        n = cls_pred.shape[1]
+        pos = F.binary_cross_entropy_with_logits(
+            cls_pred, torch.ones_like(cls_pred), reduction='none')
+        neg = F.binary_cross_entropy_with_logits(
+            cls_pred, torch.zeros_like(cls_pred), reduction='none')
+        cls_cost = torch.einsum('nc,mc->nm', pos, gt_labels) + \
+            torch.einsum('nc,mc->nm', neg, 1 - gt_labels)
+        cls_cost = cls_cost / n
+
+        return cls_cost
+
+    def __call__(self, cls_pred, gt_labels):
+        """
+        Args:
+            cls_pred (Tensor): Predicted classification logits.
+            gt_labels (Tensor): Labels.
+
+        Returns:
+            Tensor: Cross entropy cost matrix with weight in
+                shape (num_query, num_gt).
+        """
+        if self.use_sigmoid:
+            cls_cost = self._binary_cross_entropy(cls_pred, gt_labels)
+        else:
+            raise NotImplementedError
+
+        return cls_cost * self.weight
diff --git a/mmdet/core/bbox/samplers/__init__.py b/mmdet/core/bbox/samplers/__init__.py
new file mode 100644
index 0000000..f58505b
--- /dev/null
+++ b/mmdet/core/bbox/samplers/__init__.py
@@ -0,0 +1,19 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .base_sampler import BaseSampler
+from .combined_sampler import CombinedSampler
+from .instance_balanced_pos_sampler import InstanceBalancedPosSampler
+from .iou_balanced_neg_sampler import IoUBalancedNegSampler
+from .mask_pseudo_sampler import MaskPseudoSampler
+from .mask_sampling_result import MaskSamplingResult
+from .ohem_sampler import OHEMSampler
+from .pseudo_sampler import PseudoSampler
+from .random_sampler import RandomSampler
+from .sampling_result import SamplingResult
+from .score_hlr_sampler import ScoreHLRSampler
+
+__all__ = [
+    'BaseSampler', 'PseudoSampler', 'RandomSampler',
+    'InstanceBalancedPosSampler', 'IoUBalancedNegSampler', 'CombinedSampler',
+    'OHEMSampler', 'SamplingResult', 'ScoreHLRSampler', 'MaskPseudoSampler',
+    'MaskSamplingResult'
+]
diff --git a/mmdet/core/bbox/samplers/base_sampler.py b/mmdet/core/bbox/samplers/base_sampler.py
new file mode 100644
index 0000000..bd15c7c
--- /dev/null
+++ b/mmdet/core/bbox/samplers/base_sampler.py
@@ -0,0 +1,102 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from abc import ABCMeta, abstractmethod
+
+import torch
+
+from .sampling_result import SamplingResult
+
+
+class BaseSampler(metaclass=ABCMeta):
+    """Base class of samplers."""
+
+    def __init__(self,
+                 num,
+                 pos_fraction,
+                 neg_pos_ub=-1,
+                 add_gt_as_proposals=True,
+                 **kwargs):
+        self.num = num
+        self.pos_fraction = pos_fraction
+        self.neg_pos_ub = neg_pos_ub
+        self.add_gt_as_proposals = add_gt_as_proposals
+        self.pos_sampler = self
+        self.neg_sampler = self
+
+    @abstractmethod
+    def _sample_pos(self, assign_result, num_expected, **kwargs):
+        """Sample positive samples."""
+        pass
+
+    @abstractmethod
+    def _sample_neg(self, assign_result, num_expected, **kwargs):
+        """Sample negative samples."""
+        pass
+
+    def sample(self,
+               assign_result,
+               bboxes,
+               gt_bboxes,
+               gt_labels=None,
+               **kwargs):
+        """Sample positive and negative bboxes.
+
+        This is a simple implementation of bbox sampling given candidates,
+        assigning results and ground truth bboxes.
+
+        Args:
+            assign_result (:obj:`AssignResult`): Bbox assigning results.
+            bboxes (Tensor): Boxes to be sampled from.
+            gt_bboxes (Tensor): Ground truth bboxes.
+            gt_labels (Tensor, optional): Class labels of ground truth bboxes.
+
+        Returns:
+            :obj:`SamplingResult`: Sampling result.
+
+        Example:
+            >>> from mmdet.core.bbox import RandomSampler
+            >>> from mmdet.core.bbox import AssignResult
+            >>> from mmdet.core.bbox.demodata import ensure_rng, random_boxes
+            >>> rng = ensure_rng(None)
+            >>> assign_result = AssignResult.random(rng=rng)
+            >>> bboxes = random_boxes(assign_result.num_preds, rng=rng)
+            >>> gt_bboxes = random_boxes(assign_result.num_gts, rng=rng)
+            >>> gt_labels = None
+            >>> self = RandomSampler(num=32, pos_fraction=0.5, neg_pos_ub=-1,
+            >>>                      add_gt_as_proposals=False)
+            >>> self = self.sample(assign_result, bboxes, gt_bboxes, gt_labels)
+        """
+        if len(bboxes.shape) < 2:
+            bboxes = bboxes[None, :]
+
+        bboxes = bboxes[:, :4]
+
+        gt_flags = bboxes.new_zeros((bboxes.shape[0], ), dtype=torch.uint8)
+        if self.add_gt_as_proposals and len(gt_bboxes) > 0:
+            if gt_labels is None:
+                raise ValueError(
+                    'gt_labels must be given when add_gt_as_proposals is True')
+            bboxes = torch.cat([gt_bboxes, bboxes], dim=0)
+            assign_result.add_gt_(gt_labels)
+            gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8)
+            gt_flags = torch.cat([gt_ones, gt_flags])
+
+        num_expected_pos = int(self.num * self.pos_fraction)
+        pos_inds = self.pos_sampler._sample_pos(
+            assign_result, num_expected_pos, bboxes=bboxes, **kwargs)
+        # We found that sampled indices have duplicated items occasionally.
+        # (may be a bug of PyTorch)
+        pos_inds = pos_inds.unique()
+        num_sampled_pos = pos_inds.numel()
+        num_expected_neg = self.num - num_sampled_pos
+        if self.neg_pos_ub >= 0:
+            _pos = max(1, num_sampled_pos)
+            neg_upper_bound = int(self.neg_pos_ub * _pos)
+            if num_expected_neg > neg_upper_bound:
+                num_expected_neg = neg_upper_bound
+        neg_inds = self.neg_sampler._sample_neg(
+            assign_result, num_expected_neg, bboxes=bboxes, **kwargs)
+        neg_inds = neg_inds.unique()
+
+        sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes,
+                                         assign_result, gt_flags)
+        return sampling_result
diff --git a/mmdet/core/bbox/samplers/combined_sampler.py b/mmdet/core/bbox/samplers/combined_sampler.py
new file mode 100644
index 0000000..4f6d86f
--- /dev/null
+++ b/mmdet/core/bbox/samplers/combined_sampler.py
@@ -0,0 +1,21 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import BBOX_SAMPLERS, build_sampler
+from .base_sampler import BaseSampler
+
+
+@BBOX_SAMPLERS.register_module()
+class CombinedSampler(BaseSampler):
+    """A sampler that combines positive sampler and negative sampler."""
+
+    def __init__(self, pos_sampler, neg_sampler, **kwargs):
+        super(CombinedSampler, self).__init__(**kwargs)
+        self.pos_sampler = build_sampler(pos_sampler, **kwargs)
+        self.neg_sampler = build_sampler(neg_sampler, **kwargs)
+
+    def _sample_pos(self, **kwargs):
+        """Sample positive samples."""
+        raise NotImplementedError
+
+    def _sample_neg(self, **kwargs):
+        """Sample negative samples."""
+        raise NotImplementedError
diff --git a/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py b/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py
new file mode 100644
index 0000000..5e0d9cc
--- /dev/null
+++ b/mmdet/core/bbox/samplers/instance_balanced_pos_sampler.py
@@ -0,0 +1,56 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+
+from ..builder import BBOX_SAMPLERS
+from .random_sampler import RandomSampler
+
+
+@BBOX_SAMPLERS.register_module()
+class InstanceBalancedPosSampler(RandomSampler):
+    """Instance balanced sampler that samples equal number of positive samples
+    for each instance."""
+
+    def _sample_pos(self, assign_result, num_expected, **kwargs):
+        """Sample positive boxes.
+
+        Args:
+            assign_result (:obj:`AssignResult`): The assigned results of boxes.
+            num_expected (int): The number of expected positive samples
+
+        Returns:
+            Tensor or ndarray: sampled indices.
+        """
+        pos_inds = torch.nonzero(assign_result.gt_inds > 0, as_tuple=False)
+        if pos_inds.numel() != 0:
+            pos_inds = pos_inds.squeeze(1)
+        if pos_inds.numel() <= num_expected:
+            return pos_inds
+        else:
+            unique_gt_inds = assign_result.gt_inds[pos_inds].unique()
+            num_gts = len(unique_gt_inds)
+            num_per_gt = int(round(num_expected / float(num_gts)) + 1)
+            sampled_inds = []
+            for i in unique_gt_inds:
+                inds = torch.nonzero(
+                    assign_result.gt_inds == i.item(), as_tuple=False)
+                if inds.numel() != 0:
+                    inds = inds.squeeze(1)
+                else:
+                    continue
+                if len(inds) > num_per_gt:
+                    inds = self.random_choice(inds, num_per_gt)
+                sampled_inds.append(inds)
+            sampled_inds = torch.cat(sampled_inds)
+            if len(sampled_inds) < num_expected:
+                num_extra = num_expected - len(sampled_inds)
+                extra_inds = np.array(
+                    list(set(pos_inds.cpu()) - set(sampled_inds.cpu())))
+                if len(extra_inds) > num_extra:
+                    extra_inds = self.random_choice(extra_inds, num_extra)
+                extra_inds = torch.from_numpy(extra_inds).to(
+                    assign_result.gt_inds.device).long()
+                sampled_inds = torch.cat([sampled_inds, extra_inds])
+            elif len(sampled_inds) > num_expected:
+                sampled_inds = self.random_choice(sampled_inds, num_expected)
+            return sampled_inds
diff --git a/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py b/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py
new file mode 100644
index 0000000..56e2874
--- /dev/null
+++ b/mmdet/core/bbox/samplers/iou_balanced_neg_sampler.py
@@ -0,0 +1,158 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+
+from ..builder import BBOX_SAMPLERS
+from .random_sampler import RandomSampler
+
+
+@BBOX_SAMPLERS.register_module()
+class IoUBalancedNegSampler(RandomSampler):
+    """IoU Balanced Sampling.
+
+    arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019)
+
+    Sampling proposals according to their IoU. `floor_fraction` of needed RoIs
+    are sampled from proposals whose IoU are lower than `floor_thr` randomly.
+    The others are sampled from proposals whose IoU are higher than
+    `floor_thr`. These proposals are sampled from some bins evenly, which are
+    split by `num_bins` via IoU evenly.
+
+    Args:
+        num (int): number of proposals.
+        pos_fraction (float): fraction of positive proposals.
+        floor_thr (float): threshold (minimum) IoU for IoU balanced sampling,
+            set to -1 if all using IoU balanced sampling.
+        floor_fraction (float): sampling fraction of proposals under floor_thr.
+        num_bins (int): number of bins in IoU balanced sampling.
+    """
+
+    def __init__(self,
+                 num,
+                 pos_fraction,
+                 floor_thr=-1,
+                 floor_fraction=0,
+                 num_bins=3,
+                 **kwargs):
+        super(IoUBalancedNegSampler, self).__init__(num, pos_fraction,
+                                                    **kwargs)
+        assert floor_thr >= 0 or floor_thr == -1
+        assert 0 <= floor_fraction <= 1
+        assert num_bins >= 1
+
+        self.floor_thr = floor_thr
+        self.floor_fraction = floor_fraction
+        self.num_bins = num_bins
+
+    def sample_via_interval(self, max_overlaps, full_set, num_expected):
+        """Sample according to the iou interval.
+
+        Args:
+            max_overlaps (torch.Tensor): IoU between bounding boxes and ground
+                truth boxes.
+            full_set (set(int)): A full set of indices of boxes。
+            num_expected (int): Number of expected samples。
+
+        Returns:
+            np.ndarray: Indices  of samples
+        """
+        max_iou = max_overlaps.max()
+        iou_interval = (max_iou - self.floor_thr) / self.num_bins
+        per_num_expected = int(num_expected / self.num_bins)
+
+        sampled_inds = []
+        for i in range(self.num_bins):
+            start_iou = self.floor_thr + i * iou_interval
+            end_iou = self.floor_thr + (i + 1) * iou_interval
+            tmp_set = set(
+                np.where(
+                    np.logical_and(max_overlaps >= start_iou,
+                                   max_overlaps < end_iou))[0])
+            tmp_inds = list(tmp_set & full_set)
+            if len(tmp_inds) > per_num_expected:
+                tmp_sampled_set = self.random_choice(tmp_inds,
+                                                     per_num_expected)
+            else:
+                tmp_sampled_set = np.array(tmp_inds, dtype=np.int)
+            sampled_inds.append(tmp_sampled_set)
+
+        sampled_inds = np.concatenate(sampled_inds)
+        if len(sampled_inds) < num_expected:
+            num_extra = num_expected - len(sampled_inds)
+            extra_inds = np.array(list(full_set - set(sampled_inds)))
+            if len(extra_inds) > num_extra:
+                extra_inds = self.random_choice(extra_inds, num_extra)
+            sampled_inds = np.concatenate([sampled_inds, extra_inds])
+
+        return sampled_inds
+
+    def _sample_neg(self, assign_result, num_expected, **kwargs):
+        """Sample negative boxes.
+
+        Args:
+            assign_result (:obj:`AssignResult`): The assigned results of boxes.
+            num_expected (int): The number of expected negative samples
+
+        Returns:
+            Tensor or ndarray: sampled indices.
+        """
+        neg_inds = torch.nonzero(assign_result.gt_inds == 0, as_tuple=False)
+        if neg_inds.numel() != 0:
+            neg_inds = neg_inds.squeeze(1)
+        if len(neg_inds) <= num_expected:
+            return neg_inds
+        else:
+            max_overlaps = assign_result.max_overlaps.cpu().numpy()
+            # balance sampling for negative samples
+            neg_set = set(neg_inds.cpu().numpy())
+
+            if self.floor_thr > 0:
+                floor_set = set(
+                    np.where(
+                        np.logical_and(max_overlaps >= 0,
+                                       max_overlaps < self.floor_thr))[0])
+                iou_sampling_set = set(
+                    np.where(max_overlaps >= self.floor_thr)[0])
+            elif self.floor_thr == 0:
+                floor_set = set(np.where(max_overlaps == 0)[0])
+                iou_sampling_set = set(
+                    np.where(max_overlaps > self.floor_thr)[0])
+            else:
+                floor_set = set()
+                iou_sampling_set = set(
+                    np.where(max_overlaps > self.floor_thr)[0])
+                # for sampling interval calculation
+                self.floor_thr = 0
+
+            floor_neg_inds = list(floor_set & neg_set)
+            iou_sampling_neg_inds = list(iou_sampling_set & neg_set)
+            num_expected_iou_sampling = int(num_expected *
+                                            (1 - self.floor_fraction))
+            if len(iou_sampling_neg_inds) > num_expected_iou_sampling:
+                if self.num_bins >= 2:
+                    iou_sampled_inds = self.sample_via_interval(
+                        max_overlaps, set(iou_sampling_neg_inds),
+                        num_expected_iou_sampling)
+                else:
+                    iou_sampled_inds = self.random_choice(
+                        iou_sampling_neg_inds, num_expected_iou_sampling)
+            else:
+                iou_sampled_inds = np.array(
+                    iou_sampling_neg_inds, dtype=np.int)
+            num_expected_floor = num_expected - len(iou_sampled_inds)
+            if len(floor_neg_inds) > num_expected_floor:
+                sampled_floor_inds = self.random_choice(
+                    floor_neg_inds, num_expected_floor)
+            else:
+                sampled_floor_inds = np.array(floor_neg_inds, dtype=np.int)
+            sampled_inds = np.concatenate(
+                (sampled_floor_inds, iou_sampled_inds))
+            if len(sampled_inds) < num_expected:
+                num_extra = num_expected - len(sampled_inds)
+                extra_inds = np.array(list(neg_set - set(sampled_inds)))
+                if len(extra_inds) > num_extra:
+                    extra_inds = self.random_choice(extra_inds, num_extra)
+                sampled_inds = np.concatenate((sampled_inds, extra_inds))
+            sampled_inds = torch.from_numpy(sampled_inds).long().to(
+                assign_result.gt_inds.device)
+            return sampled_inds
diff --git a/mmdet/core/bbox/samplers/mask_pseudo_sampler.py b/mmdet/core/bbox/samplers/mask_pseudo_sampler.py
new file mode 100644
index 0000000..b5f6965
--- /dev/null
+++ b/mmdet/core/bbox/samplers/mask_pseudo_sampler.py
@@ -0,0 +1,44 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""copy from
+https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py."""
+
+import torch
+
+from mmdet.core.bbox.builder import BBOX_SAMPLERS
+from .base_sampler import BaseSampler
+from .mask_sampling_result import MaskSamplingResult
+
+
+@BBOX_SAMPLERS.register_module()
+class MaskPseudoSampler(BaseSampler):
+    """A pseudo sampler that does not do sampling actually."""
+
+    def __init__(self, **kwargs):
+        pass
+
+    def _sample_pos(self, **kwargs):
+        """Sample positive samples."""
+        raise NotImplementedError
+
+    def _sample_neg(self, **kwargs):
+        """Sample negative samples."""
+        raise NotImplementedError
+
+    def sample(self, assign_result, masks, gt_masks, **kwargs):
+        """Directly returns the positive and negative indices  of samples.
+
+        Args:
+            assign_result (:obj:`AssignResult`): Assigned results
+            masks (torch.Tensor): Bounding boxes
+            gt_masks (torch.Tensor): Ground truth boxes
+        Returns:
+            :obj:`SamplingResult`: sampler results
+        """
+        pos_inds = torch.nonzero(
+            assign_result.gt_inds > 0, as_tuple=False).squeeze(-1).unique()
+        neg_inds = torch.nonzero(
+            assign_result.gt_inds == 0, as_tuple=False).squeeze(-1).unique()
+        gt_flags = masks.new_zeros(masks.shape[0], dtype=torch.uint8)
+        sampling_result = MaskSamplingResult(pos_inds, neg_inds, masks,
+                                             gt_masks, assign_result, gt_flags)
+        return sampling_result
diff --git a/mmdet/core/bbox/samplers/mask_sampling_result.py b/mmdet/core/bbox/samplers/mask_sampling_result.py
new file mode 100644
index 0000000..3d10943
--- /dev/null
+++ b/mmdet/core/bbox/samplers/mask_sampling_result.py
@@ -0,0 +1,60 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""copy from
+https://github.com/ZwwWayne/K-Net/blob/main/knet/det/mask_pseudo_sampler.py."""
+
+import torch
+
+from .sampling_result import SamplingResult
+
+
+class MaskSamplingResult(SamplingResult):
+    """Mask sampling result."""
+
+    def __init__(self, pos_inds, neg_inds, masks, gt_masks, assign_result,
+                 gt_flags):
+        self.pos_inds = pos_inds
+        self.neg_inds = neg_inds
+        self.pos_masks = masks[pos_inds]
+        self.neg_masks = masks[neg_inds]
+        self.pos_is_gt = gt_flags[pos_inds]
+
+        self.num_gts = gt_masks.shape[0]
+        self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1
+
+        if gt_masks.numel() == 0:
+            # hack for index error case
+            assert self.pos_assigned_gt_inds.numel() == 0
+            self.pos_gt_masks = torch.empty_like(gt_masks)
+        else:
+            self.pos_gt_masks = gt_masks[self.pos_assigned_gt_inds, :]
+
+        if assign_result.labels is not None:
+            self.pos_gt_labels = assign_result.labels[pos_inds]
+        else:
+            self.pos_gt_labels = None
+
+    @property
+    def masks(self):
+        """torch.Tensor: concatenated positive and negative boxes"""
+        return torch.cat([self.pos_masks, self.neg_masks])
+
+    def __nice__(self):
+        data = self.info.copy()
+        data['pos_masks'] = data.pop('pos_masks').shape
+        data['neg_masks'] = data.pop('neg_masks').shape
+        parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())]
+        body = '    ' + ',\n    '.join(parts)
+        return '{\n' + body + '\n}'
+
+    @property
+    def info(self):
+        """Returns a dictionary of info about the object."""
+        return {
+            'pos_inds': self.pos_inds,
+            'neg_inds': self.neg_inds,
+            'pos_masks': self.pos_masks,
+            'neg_masks': self.neg_masks,
+            'pos_is_gt': self.pos_is_gt,
+            'num_gts': self.num_gts,
+            'pos_assigned_gt_inds': self.pos_assigned_gt_inds,
+        }
diff --git a/mmdet/core/bbox/samplers/ohem_sampler.py b/mmdet/core/bbox/samplers/ohem_sampler.py
new file mode 100644
index 0000000..7eb0666
--- /dev/null
+++ b/mmdet/core/bbox/samplers/ohem_sampler.py
@@ -0,0 +1,111 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from ..builder import BBOX_SAMPLERS
+from ..transforms import bbox2roi
+from .base_sampler import BaseSampler
+
+
+@BBOX_SAMPLERS.register_module()
+class OHEMSampler(BaseSampler):
+    r"""Online Hard Example Mining Sampler described in `Training Region-based
+    Object Detectors with Online Hard Example Mining
+    <https://arxiv.org/abs/1604.03540>`_.
+    """
+
+    def __init__(self,
+                 num,
+                 pos_fraction,
+                 context,
+                 neg_pos_ub=-1,
+                 add_gt_as_proposals=True,
+                 loss_key='loss_cls',
+                 **kwargs):
+        super(OHEMSampler, self).__init__(num, pos_fraction, neg_pos_ub,
+                                          add_gt_as_proposals)
+        self.context = context
+        if not hasattr(self.context, 'num_stages'):
+            self.bbox_head = self.context.bbox_head
+        else:
+            self.bbox_head = self.context.bbox_head[self.context.current_stage]
+
+        self.loss_key = loss_key
+
+    def hard_mining(self, inds, num_expected, bboxes, labels, feats):
+        with torch.no_grad():
+            rois = bbox2roi([bboxes])
+            if not hasattr(self.context, 'num_stages'):
+                bbox_results = self.context._bbox_forward(feats, rois)
+            else:
+                bbox_results = self.context._bbox_forward(
+                    self.context.current_stage, feats, rois)
+            cls_score = bbox_results['cls_score']
+            loss = self.bbox_head.loss(
+                cls_score=cls_score,
+                bbox_pred=None,
+                rois=rois,
+                labels=labels,
+                label_weights=cls_score.new_ones(cls_score.size(0)),
+                bbox_targets=None,
+                bbox_weights=None,
+                reduction_override='none')[self.loss_key]
+            _, topk_loss_inds = loss.topk(num_expected)
+        return inds[topk_loss_inds]
+
+    def _sample_pos(self,
+                    assign_result,
+                    num_expected,
+                    bboxes=None,
+                    feats=None,
+                    **kwargs):
+        """Sample positive boxes.
+
+        Args:
+            assign_result (:obj:`AssignResult`): Assigned results
+            num_expected (int): Number of expected positive samples
+            bboxes (torch.Tensor, optional): Boxes. Defaults to None.
+            feats (list[torch.Tensor], optional): Multi-level features.
+                Defaults to None.
+
+        Returns:
+            torch.Tensor: Indices  of positive samples
+        """
+        # Sample some hard positive samples
+        pos_inds = torch.nonzero(assign_result.gt_inds > 0, as_tuple=False)
+        if pos_inds.numel() != 0:
+            pos_inds = pos_inds.squeeze(1)
+        if pos_inds.numel() <= num_expected:
+            return pos_inds
+        else:
+            return self.hard_mining(pos_inds, num_expected, bboxes[pos_inds],
+                                    assign_result.labels[pos_inds], feats)
+
+    def _sample_neg(self,
+                    assign_result,
+                    num_expected,
+                    bboxes=None,
+                    feats=None,
+                    **kwargs):
+        """Sample negative boxes.
+
+        Args:
+            assign_result (:obj:`AssignResult`): Assigned results
+            num_expected (int): Number of expected negative samples
+            bboxes (torch.Tensor, optional): Boxes. Defaults to None.
+            feats (list[torch.Tensor], optional): Multi-level features.
+                Defaults to None.
+
+        Returns:
+            torch.Tensor: Indices  of negative samples
+        """
+        # Sample some hard negative samples
+        neg_inds = torch.nonzero(assign_result.gt_inds == 0, as_tuple=False)
+        if neg_inds.numel() != 0:
+            neg_inds = neg_inds.squeeze(1)
+        if len(neg_inds) <= num_expected:
+            return neg_inds
+        else:
+            neg_labels = assign_result.labels.new_empty(
+                neg_inds.size(0)).fill_(self.bbox_head.num_classes)
+            return self.hard_mining(neg_inds, num_expected, bboxes[neg_inds],
+                                    neg_labels, feats)
diff --git a/mmdet/core/bbox/samplers/pseudo_sampler.py b/mmdet/core/bbox/samplers/pseudo_sampler.py
new file mode 100644
index 0000000..dccd6ed
--- /dev/null
+++ b/mmdet/core/bbox/samplers/pseudo_sampler.py
@@ -0,0 +1,42 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from ..builder import BBOX_SAMPLERS
+from .base_sampler import BaseSampler
+from .sampling_result import SamplingResult
+
+
+@BBOX_SAMPLERS.register_module()
+class PseudoSampler(BaseSampler):
+    """A pseudo sampler that does not do sampling actually."""
+
+    def __init__(self, **kwargs):
+        pass
+
+    def _sample_pos(self, **kwargs):
+        """Sample positive samples."""
+        raise NotImplementedError
+
+    def _sample_neg(self, **kwargs):
+        """Sample negative samples."""
+        raise NotImplementedError
+
+    def sample(self, assign_result, bboxes, gt_bboxes, *args, **kwargs):
+        """Directly returns the positive and negative indices  of samples.
+
+        Args:
+            assign_result (:obj:`AssignResult`): Assigned results
+            bboxes (torch.Tensor): Bounding boxes
+            gt_bboxes (torch.Tensor): Ground truth boxes
+
+        Returns:
+            :obj:`SamplingResult`: sampler results
+        """
+        pos_inds = torch.nonzero(
+            assign_result.gt_inds > 0, as_tuple=False).squeeze(-1).unique()
+        neg_inds = torch.nonzero(
+            assign_result.gt_inds == 0, as_tuple=False).squeeze(-1).unique()
+        gt_flags = bboxes.new_zeros(bboxes.shape[0], dtype=torch.uint8)
+        sampling_result = SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes,
+                                         assign_result, gt_flags)  # TODO 看到这里
+        return sampling_result
diff --git a/mmdet/core/bbox/samplers/random_sampler.py b/mmdet/core/bbox/samplers/random_sampler.py
new file mode 100644
index 0000000..8d3effc
--- /dev/null
+++ b/mmdet/core/bbox/samplers/random_sampler.py
@@ -0,0 +1,82 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from ..builder import BBOX_SAMPLERS
+from .base_sampler import BaseSampler
+
+
+@BBOX_SAMPLERS.register_module()
+class RandomSampler(BaseSampler):
+    """Random sampler.
+
+    Args:
+        num (int): Number of samples
+        pos_fraction (float): Fraction of positive samples
+        neg_pos_ub (int, optional): Upper bound number of negative and
+            positive samples. Defaults to -1.
+        add_gt_as_proposals (bool, optional): Whether to add ground truth
+            boxes as proposals. Defaults to True.
+    """
+
+    def __init__(self,
+                 num,
+                 pos_fraction,
+                 neg_pos_ub=-1,
+                 add_gt_as_proposals=True,
+                 **kwargs):
+        from mmdet.core.bbox import demodata
+        super(RandomSampler, self).__init__(num, pos_fraction, neg_pos_ub,
+                                            add_gt_as_proposals)
+        self.rng = demodata.ensure_rng(kwargs.get('rng', None))
+
+    def random_choice(self, gallery, num):
+        """Random select some elements from the gallery.
+
+        If `gallery` is a Tensor, the returned indices will be a Tensor;
+        If `gallery` is a ndarray or list, the returned indices will be a
+        ndarray.
+
+        Args:
+            gallery (Tensor | ndarray | list): indices pool.
+            num (int): expected sample num.
+
+        Returns:
+            Tensor or ndarray: sampled indices.
+        """
+        assert len(gallery) >= num
+
+        is_tensor = isinstance(gallery, torch.Tensor)
+        if not is_tensor:
+            if torch.cuda.is_available():
+                device = torch.cuda.current_device()
+            else:
+                device = 'cpu'
+            gallery = torch.tensor(gallery, dtype=torch.long, device=device)
+        # This is a temporary fix. We can revert the following code
+        # when PyTorch fixes the abnormal return of torch.randperm.
+        # See: https://github.com/open-mmlab/mmdetection/pull/5014
+        perm = torch.randperm(gallery.numel())[:num].to(device=gallery.device)
+        rand_inds = gallery[perm]
+        if not is_tensor:
+            rand_inds = rand_inds.cpu().numpy()
+        return rand_inds
+
+    def _sample_pos(self, assign_result, num_expected, **kwargs):
+        """Randomly sample some positive samples."""
+        pos_inds = torch.nonzero(assign_result.gt_inds > 0, as_tuple=False)
+        if pos_inds.numel() != 0:
+            pos_inds = pos_inds.squeeze(1)
+        if pos_inds.numel() <= num_expected:
+            return pos_inds
+        else:
+            return self.random_choice(pos_inds, num_expected)
+
+    def _sample_neg(self, assign_result, num_expected, **kwargs):
+        """Randomly sample some negative samples."""
+        neg_inds = torch.nonzero(assign_result.gt_inds == 0, as_tuple=False)
+        if neg_inds.numel() != 0:
+            neg_inds = neg_inds.squeeze(1)
+        if len(neg_inds) <= num_expected:
+            return neg_inds
+        else:
+            return self.random_choice(neg_inds, num_expected)
diff --git a/mmdet/core/bbox/samplers/sampling_result.py b/mmdet/core/bbox/samplers/sampling_result.py
new file mode 100644
index 0000000..11a02c5
--- /dev/null
+++ b/mmdet/core/bbox/samplers/sampling_result.py
@@ -0,0 +1,153 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdet.utils import util_mixins
+
+
+class SamplingResult(util_mixins.NiceRepr):
+    """Bbox sampling result.
+
+    Example:
+        >>> # xdoctest: +IGNORE_WANT
+        >>> from mmdet.core.bbox.samplers.sampling_result import *  # NOQA
+        >>> self = SamplingResult.random(rng=10)
+        >>> print(f'self = {self}')
+        self = <SamplingResult({
+            'neg_bboxes': torch.Size([12, 4]),
+            'neg_inds': tensor([ 0,  1,  2,  4,  5,  6,  7,  8,  9, 10, 11, 12]),
+            'num_gts': 4,
+            'pos_assigned_gt_inds': tensor([], dtype=torch.int64),
+            'pos_bboxes': torch.Size([0, 4]),
+            'pos_inds': tensor([], dtype=torch.int64),
+            'pos_is_gt': tensor([], dtype=torch.uint8)
+        })>
+    """
+
+    def __init__(self, pos_inds, neg_inds, bboxes, gt_bboxes, assign_result,
+                 gt_flags):
+        self.pos_inds = pos_inds
+        self.neg_inds = neg_inds
+        self.pos_bboxes = bboxes[pos_inds]
+        self.neg_bboxes = bboxes[neg_inds]
+        self.pos_is_gt = gt_flags[pos_inds]
+
+        self.num_gts = gt_bboxes.shape[0]
+        self.pos_assigned_gt_inds = assign_result.gt_inds[pos_inds] - 1
+
+        if gt_bboxes.numel() == 0:
+            # hack for index error case
+            assert self.pos_assigned_gt_inds.numel() == 0
+            self.pos_gt_bboxes = torch.empty_like(gt_bboxes).view(-1, 4)
+        else:
+            if len(gt_bboxes.shape) < 2:
+                gt_bboxes = gt_bboxes.view(-1, 4)
+
+            self.pos_gt_bboxes = gt_bboxes[self.pos_assigned_gt_inds.long(), :]
+
+        if assign_result.labels is not None:
+            self.pos_gt_labels = assign_result.labels[pos_inds]
+        else:
+            self.pos_gt_labels = None
+
+    @property
+    def bboxes(self):
+        """torch.Tensor: concatenated positive and negative boxes"""
+        return torch.cat([self.pos_bboxes, self.neg_bboxes])
+
+    def to(self, device):
+        """Change the device of the data inplace.
+
+        Example:
+            >>> self = SamplingResult.random()
+            >>> print(f'self = {self.to(None)}')
+            >>> # xdoctest: +REQUIRES(--gpu)
+            >>> print(f'self = {self.to(0)}')
+        """
+        _dict = self.__dict__
+        for key, value in _dict.items():
+            if isinstance(value, torch.Tensor):
+                _dict[key] = value.to(device)
+        return self
+
+    def __nice__(self):
+        data = self.info.copy()
+        data['pos_bboxes'] = data.pop('pos_bboxes').shape
+        data['neg_bboxes'] = data.pop('neg_bboxes').shape
+        parts = [f"'{k}': {v!r}" for k, v in sorted(data.items())]
+        body = '    ' + ',\n    '.join(parts)
+        return '{\n' + body + '\n}'
+
+    @property
+    def info(self):
+        """Returns a dictionary of info about the object."""
+        return {
+            'pos_inds': self.pos_inds,
+            'neg_inds': self.neg_inds,
+            'pos_bboxes': self.pos_bboxes,
+            'neg_bboxes': self.neg_bboxes,
+            'pos_is_gt': self.pos_is_gt,
+            'num_gts': self.num_gts,
+            'pos_assigned_gt_inds': self.pos_assigned_gt_inds,
+        }
+
+    @classmethod
+    def random(cls, rng=None, **kwargs):
+        """
+        Args:
+            rng (None | int | numpy.random.RandomState): seed or state.
+            kwargs (keyword arguments):
+                - num_preds: number of predicted boxes
+                - num_gts: number of true boxes
+                - p_ignore (float): probability of a predicted box assigned to \
+                    an ignored truth.
+                - p_assigned (float): probability of a predicted box not being \
+                    assigned.
+                - p_use_label (float | bool): with labels or not.
+
+        Returns:
+            :obj:`SamplingResult`: Randomly generated sampling result.
+
+        Example:
+            >>> from mmdet.core.bbox.samplers.sampling_result import *  # NOQA
+            >>> self = SamplingResult.random()
+            >>> print(self.__dict__)
+        """
+        from mmdet.core.bbox import demodata
+        from mmdet.core.bbox.assigners.assign_result import AssignResult
+        from mmdet.core.bbox.samplers.random_sampler import RandomSampler
+        rng = demodata.ensure_rng(rng)
+
+        # make probabilistic?
+        num = 32
+        pos_fraction = 0.5
+        neg_pos_ub = -1
+
+        assign_result = AssignResult.random(rng=rng, **kwargs)
+
+        # Note we could just compute an assignment
+        bboxes = demodata.random_boxes(assign_result.num_preds, rng=rng)
+        gt_bboxes = demodata.random_boxes(assign_result.num_gts, rng=rng)
+
+        if rng.rand() > 0.2:
+            # sometimes algorithms squeeze their data, be robust to that
+            gt_bboxes = gt_bboxes.squeeze()
+            bboxes = bboxes.squeeze()
+
+        if assign_result.labels is None:
+            gt_labels = None
+        else:
+            gt_labels = None  # todo
+
+        if gt_labels is None:
+            add_gt_as_proposals = False
+        else:
+            add_gt_as_proposals = True  # make probabilistic?
+
+        sampler = RandomSampler(
+            num,
+            pos_fraction,
+            neg_pos_ub=neg_pos_ub,
+            add_gt_as_proposals=add_gt_as_proposals,
+            rng=rng)
+        self = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)
+        return self
diff --git a/mmdet/core/bbox/samplers/score_hlr_sampler.py b/mmdet/core/bbox/samplers/score_hlr_sampler.py
new file mode 100644
index 0000000..f4be9b8
--- /dev/null
+++ b/mmdet/core/bbox/samplers/score_hlr_sampler.py
@@ -0,0 +1,265 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from mmcv.ops import nms_match
+
+from ..builder import BBOX_SAMPLERS
+from ..transforms import bbox2roi
+from .base_sampler import BaseSampler
+from .sampling_result import SamplingResult
+
+
+@BBOX_SAMPLERS.register_module()
+class ScoreHLRSampler(BaseSampler):
+    r"""Importance-based Sample Reweighting (ISR_N), described in `Prime Sample
+    Attention in Object Detection <https://arxiv.org/abs/1904.04821>`_.
+
+    Score hierarchical local rank (HLR) differentiates with RandomSampler in
+    negative part. It firstly computes Score-HLR in a two-step way,
+    then linearly maps score hlr to the loss weights.
+
+    Args:
+        num (int): Total number of sampled RoIs.
+        pos_fraction (float): Fraction of positive samples.
+        context (:class:`BaseRoIHead`): RoI head that the sampler belongs to.
+        neg_pos_ub (int): Upper bound of the ratio of num negative to num
+            positive, -1 means no upper bound.
+        add_gt_as_proposals (bool): Whether to add ground truth as proposals.
+        k (float): Power of the non-linear mapping.
+        bias (float): Shift of the non-linear mapping.
+        score_thr (float): Minimum score that a negative sample is to be
+            considered as valid bbox.
+    """
+
+    def __init__(self,
+                 num,
+                 pos_fraction,
+                 context,
+                 neg_pos_ub=-1,
+                 add_gt_as_proposals=True,
+                 k=0.5,
+                 bias=0,
+                 score_thr=0.05,
+                 iou_thr=0.5,
+                 **kwargs):
+        super().__init__(num, pos_fraction, neg_pos_ub, add_gt_as_proposals)
+        self.k = k
+        self.bias = bias
+        self.score_thr = score_thr
+        self.iou_thr = iou_thr
+        self.context = context
+        # context of cascade detectors is a list, so distinguish them here.
+        if not hasattr(context, 'num_stages'):
+            self.bbox_roi_extractor = context.bbox_roi_extractor
+            self.bbox_head = context.bbox_head
+            self.with_shared_head = context.with_shared_head
+            if self.with_shared_head:
+                self.shared_head = context.shared_head
+        else:
+            self.bbox_roi_extractor = context.bbox_roi_extractor[
+                context.current_stage]
+            self.bbox_head = context.bbox_head[context.current_stage]
+
+    @staticmethod
+    def random_choice(gallery, num):
+        """Randomly select some elements from the gallery.
+
+        If `gallery` is a Tensor, the returned indices will be a Tensor;
+        If `gallery` is a ndarray or list, the returned indices will be a
+        ndarray.
+
+        Args:
+            gallery (Tensor | ndarray | list): indices pool.
+            num (int): expected sample num.
+
+        Returns:
+            Tensor or ndarray: sampled indices.
+        """
+        assert len(gallery) >= num
+
+        is_tensor = isinstance(gallery, torch.Tensor)
+        if not is_tensor:
+            if torch.cuda.is_available():
+                device = torch.cuda.current_device()
+            else:
+                device = 'cpu'
+            gallery = torch.tensor(gallery, dtype=torch.long, device=device)
+        perm = torch.randperm(gallery.numel(), device=gallery.device)[:num]
+        rand_inds = gallery[perm]
+        if not is_tensor:
+            rand_inds = rand_inds.cpu().numpy()
+        return rand_inds
+
+    def _sample_pos(self, assign_result, num_expected, **kwargs):
+        """Randomly sample some positive samples."""
+        pos_inds = torch.nonzero(assign_result.gt_inds > 0).flatten()
+        if pos_inds.numel() <= num_expected:
+            return pos_inds
+        else:
+            return self.random_choice(pos_inds, num_expected)
+
+    def _sample_neg(self,
+                    assign_result,
+                    num_expected,
+                    bboxes,
+                    feats=None,
+                    img_meta=None,
+                    **kwargs):
+        """Sample negative samples.
+
+        Score-HLR sampler is done in the following steps:
+        1. Take the maximum positive score prediction of each negative samples
+            as s_i.
+        2. Filter out negative samples whose s_i <= score_thr, the left samples
+            are called valid samples.
+        3. Use NMS-Match to divide valid samples into different groups,
+            samples in the same group will greatly overlap with each other
+        4. Rank the matched samples in two-steps to get Score-HLR.
+            (1) In the same group, rank samples with their scores.
+            (2) In the same score rank across different groups,
+                rank samples with their scores again.
+        5. Linearly map Score-HLR to the final label weights.
+
+        Args:
+            assign_result (:obj:`AssignResult`): result of assigner.
+            num_expected (int): Expected number of samples.
+            bboxes (Tensor): bbox to be sampled.
+            feats (Tensor): Features come from FPN.
+            img_meta (dict): Meta information dictionary.
+        """
+        neg_inds = torch.nonzero(assign_result.gt_inds == 0).flatten()
+        num_neg = neg_inds.size(0)
+        if num_neg == 0:
+            return neg_inds, None
+        with torch.no_grad():
+            neg_bboxes = bboxes[neg_inds]
+            neg_rois = bbox2roi([neg_bboxes])
+            bbox_result = self.context._bbox_forward(feats, neg_rois)
+            cls_score, bbox_pred = bbox_result['cls_score'], bbox_result[
+                'bbox_pred']
+
+            ori_loss = self.bbox_head.loss(
+                cls_score=cls_score,
+                bbox_pred=None,
+                rois=None,
+                labels=neg_inds.new_full((num_neg, ),
+                                         self.bbox_head.num_classes),
+                label_weights=cls_score.new_ones(num_neg),
+                bbox_targets=None,
+                bbox_weights=None,
+                reduction_override='none')['loss_cls']
+
+            # filter out samples with the max score lower than score_thr
+            max_score, argmax_score = cls_score.softmax(-1)[:, :-1].max(-1)
+            valid_inds = (max_score > self.score_thr).nonzero().view(-1)
+            invalid_inds = (max_score <= self.score_thr).nonzero().view(-1)
+            num_valid = valid_inds.size(0)
+            num_invalid = invalid_inds.size(0)
+
+            num_expected = min(num_neg, num_expected)
+            num_hlr = min(num_valid, num_expected)
+            num_rand = num_expected - num_hlr
+            if num_valid > 0:
+                valid_rois = neg_rois[valid_inds]
+                valid_max_score = max_score[valid_inds]
+                valid_argmax_score = argmax_score[valid_inds]
+                valid_bbox_pred = bbox_pred[valid_inds]
+
+                # valid_bbox_pred shape: [num_valid, #num_classes, 4]
+                valid_bbox_pred = valid_bbox_pred.view(
+                    valid_bbox_pred.size(0), -1, 4)
+                selected_bbox_pred = valid_bbox_pred[range(num_valid),
+                                                     valid_argmax_score]
+                pred_bboxes = self.bbox_head.bbox_coder.decode(
+                    valid_rois[:, 1:], selected_bbox_pred)
+                pred_bboxes_with_score = torch.cat(
+                    [pred_bboxes, valid_max_score[:, None]], -1)
+                group = nms_match(pred_bboxes_with_score, self.iou_thr)
+
+                # imp: importance
+                imp = cls_score.new_zeros(num_valid)
+                for g in group:
+                    g_score = valid_max_score[g]
+                    # g_score has already sorted
+                    rank = g_score.new_tensor(range(g_score.size(0)))
+                    imp[g] = num_valid - rank + g_score
+                _, imp_rank_inds = imp.sort(descending=True)
+                _, imp_rank = imp_rank_inds.sort()
+                hlr_inds = imp_rank_inds[:num_expected]
+
+                if num_rand > 0:
+                    rand_inds = torch.randperm(num_invalid)[:num_rand]
+                    select_inds = torch.cat(
+                        [valid_inds[hlr_inds], invalid_inds[rand_inds]])
+                else:
+                    select_inds = valid_inds[hlr_inds]
+
+                neg_label_weights = cls_score.new_ones(num_expected)
+
+                up_bound = max(num_expected, num_valid)
+                imp_weights = (up_bound -
+                               imp_rank[hlr_inds].float()) / up_bound
+                neg_label_weights[:num_hlr] = imp_weights
+                neg_label_weights[num_hlr:] = imp_weights.min()
+                neg_label_weights = (self.bias +
+                                     (1 - self.bias) * neg_label_weights).pow(
+                                         self.k)
+                ori_selected_loss = ori_loss[select_inds]
+                new_loss = ori_selected_loss * neg_label_weights
+                norm_ratio = ori_selected_loss.sum() / new_loss.sum()
+                neg_label_weights *= norm_ratio
+            else:
+                neg_label_weights = cls_score.new_ones(num_expected)
+                select_inds = torch.randperm(num_neg)[:num_expected]
+
+            return neg_inds[select_inds], neg_label_weights
+
+    def sample(self,
+               assign_result,
+               bboxes,
+               gt_bboxes,
+               gt_labels=None,
+               img_meta=None,
+               **kwargs):
+        """Sample positive and negative bboxes.
+
+        This is a simple implementation of bbox sampling given candidates,
+        assigning results and ground truth bboxes.
+
+        Args:
+            assign_result (:obj:`AssignResult`): Bbox assigning results.
+            bboxes (Tensor): Boxes to be sampled from.
+            gt_bboxes (Tensor): Ground truth bboxes.
+            gt_labels (Tensor, optional): Class labels of ground truth bboxes.
+
+        Returns:
+            tuple[:obj:`SamplingResult`, Tensor]: Sampling result and negative
+                label weights.
+        """
+        bboxes = bboxes[:, :4]
+
+        gt_flags = bboxes.new_zeros((bboxes.shape[0], ), dtype=torch.uint8)
+        if self.add_gt_as_proposals:
+            bboxes = torch.cat([gt_bboxes, bboxes], dim=0)
+            assign_result.add_gt_(gt_labels)
+            gt_ones = bboxes.new_ones(gt_bboxes.shape[0], dtype=torch.uint8)
+            gt_flags = torch.cat([gt_ones, gt_flags])
+
+        num_expected_pos = int(self.num * self.pos_fraction)
+        pos_inds = self.pos_sampler._sample_pos(
+            assign_result, num_expected_pos, bboxes=bboxes, **kwargs)
+        num_sampled_pos = pos_inds.numel()
+        num_expected_neg = self.num - num_sampled_pos
+        if self.neg_pos_ub >= 0:
+            _pos = max(1, num_sampled_pos)
+            neg_upper_bound = int(self.neg_pos_ub * _pos)
+            if num_expected_neg > neg_upper_bound:
+                num_expected_neg = neg_upper_bound
+        neg_inds, neg_label_weights = self.neg_sampler._sample_neg(
+            assign_result,
+            num_expected_neg,
+            bboxes,
+            img_meta=img_meta,
+            **kwargs)
+
+        return SamplingResult(pos_inds, neg_inds, bboxes, gt_bboxes,
+                              assign_result, gt_flags), neg_label_weights
diff --git a/mmdet/core/bbox/transforms.py b/mmdet/core/bbox/transforms.py
new file mode 100644
index 0000000..6d72076
--- /dev/null
+++ b/mmdet/core/bbox/transforms.py
@@ -0,0 +1,270 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+
+
+def find_inside_bboxes(bboxes, img_h, img_w):
+    """Find bboxes as long as a part of bboxes is inside the image.
+
+    Args:
+        bboxes (Tensor): Shape (N, 4).
+        img_h (int): Image height.
+        img_w (int): Image width.
+
+    Returns:
+        Tensor: Index of the remaining bboxes.
+    """
+    inside_inds = (bboxes[:, 0] < img_w) & (bboxes[:, 2] > 0) \
+        & (bboxes[:, 1] < img_h) & (bboxes[:, 3] > 0)
+    return inside_inds
+
+
+def bbox_flip(bboxes, img_shape, direction='horizontal'):
+    """Flip bboxes horizontally or vertically.
+
+    Args:
+        bboxes (Tensor): Shape (..., 4*k)
+        img_shape (tuple): Image shape.
+        direction (str): Flip direction, options are "horizontal", "vertical",
+            "diagonal". Default: "horizontal"
+
+    Returns:
+        Tensor: Flipped bboxes.
+    """
+    assert bboxes.shape[-1] % 4 == 0
+    assert direction in ['horizontal', 'vertical', 'diagonal']
+    flipped = bboxes.clone()
+    if direction == 'horizontal':
+        flipped[..., 0::4] = img_shape[1] - bboxes[..., 2::4]
+        flipped[..., 2::4] = img_shape[1] - bboxes[..., 0::4]
+    elif direction == 'vertical':
+        flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4]
+        flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4]
+    else:
+        flipped[..., 0::4] = img_shape[1] - bboxes[..., 2::4]
+        flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4]
+        flipped[..., 2::4] = img_shape[1] - bboxes[..., 0::4]
+        flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4]
+    return flipped
+
+
+def bbox_mapping(bboxes,
+                 img_shape,
+                 scale_factor,
+                 flip,
+                 flip_direction='horizontal'):
+    """Map bboxes from the original image scale to testing scale."""
+    new_bboxes = bboxes * bboxes.new_tensor(scale_factor)
+    if flip:
+        new_bboxes = bbox_flip(new_bboxes, img_shape, flip_direction)
+    return new_bboxes
+
+
+def bbox_mapping_back(bboxes,
+                      img_shape,
+                      scale_factor,
+                      flip,
+                      flip_direction='horizontal'):
+    """Map bboxes from testing scale to original image scale."""
+    new_bboxes = bbox_flip(bboxes, img_shape,
+                           flip_direction) if flip else bboxes
+    new_bboxes = new_bboxes.view(-1, 4) / new_bboxes.new_tensor(scale_factor)
+    return new_bboxes.view(bboxes.shape)
+
+
+def bbox2roi(bbox_list):
+    """Convert a list of bboxes to roi format.
+
+    Args:
+        bbox_list (list[Tensor]): a list of bboxes corresponding to a batch
+            of images.
+
+    Returns:
+        Tensor: shape (n, 5), [batch_ind, x1, y1, x2, y2]
+    """
+    rois_list = []
+    for img_id, bboxes in enumerate(bbox_list):
+        if bboxes.size(0) > 0:
+            img_inds = bboxes.new_full((bboxes.size(0), 1), img_id)
+            rois = torch.cat([img_inds, bboxes[:, :4]], dim=-1)
+        else:
+            rois = bboxes.new_zeros((0, 5))
+        rois_list.append(rois)
+    rois = torch.cat(rois_list, 0)
+    return rois
+
+
+def roi2bbox(rois):
+    """Convert rois to bounding box format.
+
+    Args:
+        rois (torch.Tensor): RoIs with the shape (n, 5) where the first
+            column indicates batch id of each RoI.
+
+    Returns:
+        list[torch.Tensor]: Converted boxes of corresponding rois.
+    """
+    bbox_list = []
+    img_ids = torch.unique(rois[:, 0].cpu(), sorted=True)
+    for img_id in img_ids:
+        inds = (rois[:, 0] == img_id.item())
+        bbox = rois[inds, 1:]
+        bbox_list.append(bbox)
+    return bbox_list
+
+
+def bbox2result(bboxes, labels, num_classes):
+    """Convert detection results to a list of numpy arrays.
+
+    Args:
+        bboxes (torch.Tensor | np.ndarray): shape (n, 5)
+        labels (torch.Tensor | np.ndarray): shape (n, )
+        num_classes (int): class number, including background class
+
+    Returns:
+        list(ndarray): bbox results of each class
+    """
+    if bboxes.shape[0] == 0:
+        return [np.zeros((0, 5), dtype=np.float32) for i in range(num_classes)]
+    else:
+        if isinstance(bboxes, torch.Tensor):
+            bboxes = bboxes.detach().cpu().numpy()
+            labels = labels.detach().cpu().numpy()
+        return [bboxes[labels == i, :] for i in range(num_classes)]
+
+
+def distance2bbox(points, distance, max_shape=None):
+    """Decode distance prediction to bounding box.
+
+    Args:
+        points (Tensor): Shape (B, N, 2) or (N, 2).
+        distance (Tensor): Distance from the given point to 4
+            boundaries (left, top, right, bottom). Shape (B, N, 4) or (N, 4)
+        max_shape (Sequence[int] or torch.Tensor or Sequence[
+            Sequence[int]],optional): Maximum bounds for boxes, specifies
+            (H, W, C) or (H, W). If priors shape is (B, N, 4), then
+            the max_shape should be a Sequence[Sequence[int]]
+            and the length of max_shape should also be B.
+
+    Returns:
+        Tensor: Boxes with shape (N, 4) or (B, N, 4)
+    """
+
+    x1 = points[..., 0] - distance[..., 0]
+    y1 = points[..., 1] - distance[..., 1]
+    x2 = points[..., 0] + distance[..., 2]
+    y2 = points[..., 1] + distance[..., 3]
+
+    bboxes = torch.stack([x1, y1, x2, y2], -1)
+
+    if max_shape is not None:
+        if bboxes.dim() == 2 and not torch.onnx.is_in_onnx_export():
+            # speed up
+            bboxes[:, 0::2].clamp_(min=0, max=max_shape[1])
+            bboxes[:, 1::2].clamp_(min=0, max=max_shape[0])
+            return bboxes
+
+        # clip bboxes with dynamic `min` and `max` for onnx
+        if torch.onnx.is_in_onnx_export():
+            from mmdet.core.export import dynamic_clip_for_onnx
+            x1, y1, x2, y2 = dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape)
+            bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
+            return bboxes
+        if not isinstance(max_shape, torch.Tensor):
+            max_shape = x1.new_tensor(max_shape)
+        max_shape = max_shape[..., :2].type_as(x1)
+        if max_shape.ndim == 2:
+            assert bboxes.ndim == 3
+            assert max_shape.size(0) == bboxes.size(0)
+
+        min_xy = x1.new_tensor(0)
+        max_xy = torch.cat([max_shape, max_shape],
+                           dim=-1).flip(-1).unsqueeze(-2)
+        bboxes = torch.where(bboxes < min_xy, min_xy, bboxes)
+        bboxes = torch.where(bboxes > max_xy, max_xy, bboxes)
+
+    return bboxes
+
+
+def bbox2distance(points, bbox, max_dis=None, eps=0.1):
+    """Decode bounding box based on distances.
+
+    Args:
+        points (Tensor): Shape (n, 2), [x, y].
+        bbox (Tensor): Shape (n, 4), "xyxy" format
+        max_dis (float): Upper bound of the distance.
+        eps (float): a small value to ensure target < max_dis, instead <=
+
+    Returns:
+        Tensor: Decoded distances.
+    """
+    left = points[:, 0] - bbox[:, 0]
+    top = points[:, 1] - bbox[:, 1]
+    right = bbox[:, 2] - points[:, 0]
+    bottom = bbox[:, 3] - points[:, 1]
+    if max_dis is not None:
+        left = left.clamp(min=0, max=max_dis - eps)
+        top = top.clamp(min=0, max=max_dis - eps)
+        right = right.clamp(min=0, max=max_dis - eps)
+        bottom = bottom.clamp(min=0, max=max_dis - eps)
+    return torch.stack([left, top, right, bottom], -1)
+
+
+def bbox_rescale(bboxes, scale_factor=1.0):
+    """Rescale bounding box w.r.t. scale_factor.
+
+    Args:
+        bboxes (Tensor): Shape (n, 4) for bboxes or (n, 5) for rois
+        scale_factor (float): rescale factor
+
+    Returns:
+        Tensor: Rescaled bboxes.
+    """
+    if bboxes.size(1) == 5:
+        bboxes_ = bboxes[:, 1:]
+        inds_ = bboxes[:, 0]
+    else:
+        bboxes_ = bboxes
+    cx = (bboxes_[:, 0] + bboxes_[:, 2]) * 0.5
+    cy = (bboxes_[:, 1] + bboxes_[:, 3]) * 0.5
+    w = bboxes_[:, 2] - bboxes_[:, 0]
+    h = bboxes_[:, 3] - bboxes_[:, 1]
+    w = w * scale_factor
+    h = h * scale_factor
+    x1 = cx - 0.5 * w
+    x2 = cx + 0.5 * w
+    y1 = cy - 0.5 * h
+    y2 = cy + 0.5 * h
+    if bboxes.size(1) == 5:
+        rescaled_bboxes = torch.stack([inds_, x1, y1, x2, y2], dim=-1)
+    else:
+        rescaled_bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
+    return rescaled_bboxes
+
+
+def bbox_cxcywh_to_xyxy(bbox):
+    """Convert bbox coordinates from (cx, cy, w, h) to (x1, y1, x2, y2).
+
+    Args:
+        bbox (Tensor): Shape (n, 4) for bboxes.
+
+    Returns:
+        Tensor: Converted bboxes.
+    """
+    cx, cy, w, h = bbox.split((1, 1, 1, 1), dim=-1)
+    bbox_new = [(cx - 0.5 * w), (cy - 0.5 * h), (cx + 0.5 * w), (cy + 0.5 * h)]
+    return torch.cat(bbox_new, dim=-1)
+
+
+def bbox_xyxy_to_cxcywh(bbox):
+    """Convert bbox coordinates from (x1, y1, x2, y2) to (cx, cy, w, h).
+
+    Args:
+        bbox (Tensor): Shape (n, 4) for bboxes.
+
+    Returns:
+        Tensor: Converted bboxes.
+    """
+    x1, y1, x2, y2 = bbox.split((1, 1, 1, 1), dim=-1)
+    bbox_new = [(x1 + x2) / 2, (y1 + y2) / 2, (x2 - x1), (y2 - y1)]
+    return torch.cat(bbox_new, dim=-1)
diff --git a/mmdet/core/data_structures/__init__.py b/mmdet/core/data_structures/__init__.py
new file mode 100644
index 0000000..11ab96c
--- /dev/null
+++ b/mmdet/core/data_structures/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .general_data import GeneralData
+from .instance_data import InstanceData
+
+__all__ = ['GeneralData', 'InstanceData']
diff --git a/mmdet/core/data_structures/general_data.py b/mmdet/core/data_structures/general_data.py
new file mode 100644
index 0000000..978fdfd
--- /dev/null
+++ b/mmdet/core/data_structures/general_data.py
@@ -0,0 +1,336 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import numpy as np
+import torch
+
+from mmdet.utils.util_mixins import NiceRepr
+
+
+class GeneralData(NiceRepr):
+    """A general data structure of OpenMMlab.
+
+    A data structure that stores the meta information,
+    the annotations of the images or the model predictions,
+    which can be used in communication between components.
+
+    The attributes in `GeneralData` are divided into two parts,
+    the `meta_info_fields` and the `data_fields` respectively.
+
+        - `meta_info_fields`: Usually contains the
+          information about the image such as filename,
+          image_shape, pad_shape, etc. All attributes in
+          it are immutable once set,
+          but the user can add new meta information with
+          `set_meta_info` function, all information can be accessed
+          with methods `meta_info_keys`, `meta_info_values`,
+          `meta_info_items`.
+
+        - `data_fields`: Annotations or model predictions are
+          stored. The attributes can be accessed or modified by
+          dict-like or object-like operations, such as
+          `.` , `[]`, `in`, `del`, `pop(str)` `get(str)`, `keys()`,
+          `values()`, `items()`. Users can also apply tensor-like methods
+          to all obj:`torch.Tensor` in the `data_fileds`,
+          such as `.cuda()`, `.cpu()`, `.numpy()`, `device`, `.to()`
+          `.detach()`, `.numpy()`
+
+    Args:
+        meta_info (dict, optional): A dict contains the meta information
+            of single image. such as `img_shape`, `scale_factor`, etc.
+            Default: None.
+        data (dict, optional): A dict contains annotations of single image or
+            model predictions. Default: None.
+
+    Examples:
+        >>> from mmdet.core import GeneralData
+        >>> img_meta = dict(img_shape=(800, 1196, 3), pad_shape=(800, 1216, 3))
+        >>> instance_data = GeneralData(meta_info=img_meta)
+        >>> img_shape in instance_data
+        True
+        >>> instance_data.det_labels = torch.LongTensor([0, 1, 2, 3])
+        >>> instance_data["det_scores"] = torch.Tensor([0.01, 0.1, 0.2, 0.3])
+        >>> print(results)
+        <GeneralData(
+
+          META INFORMATION
+        img_shape: (800, 1196, 3)
+        pad_shape: (800, 1216, 3)
+
+          DATA FIELDS
+        shape of det_labels: torch.Size([4])
+        shape of det_scores: torch.Size([4])
+
+        ) at 0x7f84acd10f90>
+        >>> instance_data.det_scores
+        tensor([0.0100, 0.1000, 0.2000, 0.3000])
+        >>> instance_data.det_labels
+        tensor([0, 1, 2, 3])
+        >>> instance_data['det_labels']
+        tensor([0, 1, 2, 3])
+        >>> 'det_labels' in instance_data
+        True
+        >>> instance_data.img_shape
+        (800, 1196, 3)
+        >>> 'det_scores' in instance_data
+        True
+        >>> del instance_data.det_scores
+        >>> 'det_scores' in instance_data
+        False
+        >>> det_labels = instance_data.pop('det_labels', None)
+        >>> det_labels
+        tensor([0, 1, 2, 3])
+        >>> 'det_labels' in instance_data
+        >>> False
+    """
+
+    def __init__(self, meta_info=None, data=None):
+
+        self._meta_info_fields = set()
+        self._data_fields = set()
+
+        if meta_info is not None:
+            self.set_meta_info(meta_info=meta_info)
+        if data is not None:
+            self.set_data(data)
+
+    def set_meta_info(self, meta_info):
+        """Add meta information.
+
+        Args:
+            meta_info (dict): A dict contains the meta information
+                of image. such as `img_shape`, `scale_factor`, etc.
+                Default: None.
+        """
+        assert isinstance(meta_info,
+                          dict), f'meta should be a `dict` but get {meta_info}'
+        meta = copy.deepcopy(meta_info)
+        for k, v in meta.items():
+            # should be consistent with original meta_info
+            if k in self._meta_info_fields:
+                ori_value = getattr(self, k)
+                if isinstance(ori_value, (torch.Tensor, np.ndarray)):
+                    if (ori_value == v).all():
+                        continue
+                    else:
+                        raise KeyError(
+                            f'img_meta_info {k} has been set as '
+                            f'{getattr(self, k)} before, which is immutable ')
+                elif ori_value == v:
+                    continue
+                else:
+                    raise KeyError(
+                        f'img_meta_info {k} has been set as '
+                        f'{getattr(self, k)} before, which is immutable ')
+            else:
+                self._meta_info_fields.add(k)
+                self.__dict__[k] = v
+
+    def set_data(self, data):
+        """Update a dict to `data_fields`.
+
+        Args:
+            data (dict): A dict contains annotations of image or
+                model predictions. Default: None.
+        """
+        assert isinstance(data,
+                          dict), f'meta should be a `dict` but get {data}'
+        for k, v in data.items():
+            self.__setattr__(k, v)
+
+    def new(self, meta_info=None, data=None):
+        """Return a new results with same image meta information.
+
+        Args:
+            meta_info (dict, optional): A dict contains the meta information
+                of image. such as `img_shape`, `scale_factor`, etc.
+                Default: None.
+            data (dict, optional): A dict contains annotations of image or
+                model predictions. Default: None.
+        """
+        new_data = self.__class__()
+        new_data.set_meta_info(dict(self.meta_info_items()))
+        if meta_info is not None:
+            new_data.set_meta_info(meta_info)
+        if data is not None:
+            new_data.set_data(data)
+        return new_data
+
+    def keys(self):
+        """
+        Returns:
+            list: Contains all keys in data_fields.
+        """
+        return [key for key in self._data_fields]
+
+    def meta_info_keys(self):
+        """
+        Returns:
+            list: Contains all keys in meta_info_fields.
+        """
+        return [key for key in self._meta_info_fields]
+
+    def values(self):
+        """
+        Returns:
+            list: Contains all values in data_fields.
+        """
+        return [getattr(self, k) for k in self.keys()]
+
+    def meta_info_values(self):
+        """
+        Returns:
+            list: Contains all values in meta_info_fields.
+        """
+        return [getattr(self, k) for k in self.meta_info_keys()]
+
+    def items(self):
+        for k in self.keys():
+            yield (k, getattr(self, k))
+
+    def meta_info_items(self):
+        for k in self.meta_info_keys():
+            yield (k, getattr(self, k))
+
+    def __setattr__(self, name, val):
+        if name in ('_meta_info_fields', '_data_fields'):
+            if not hasattr(self, name):
+                super().__setattr__(name, val)
+            else:
+                raise AttributeError(
+                    f'{name} has been used as a '
+                    f'private attribute, which is immutable. ')
+        else:
+            if name in self._meta_info_fields:
+                raise AttributeError(f'`{name}` is used in meta information,'
+                                     f'which is immutable')
+
+            self._data_fields.add(name)
+            super().__setattr__(name, val)
+
+    def __delattr__(self, item):
+
+        if item in ('_meta_info_fields', '_data_fields'):
+            raise AttributeError(f'{item} has been used as a '
+                                 f'private attribute, which is immutable. ')
+
+        if item in self._meta_info_fields:
+            raise KeyError(f'{item} is used in meta information, '
+                           f'which is immutable.')
+        super().__delattr__(item)
+        if item in self._data_fields:
+            self._data_fields.remove(item)
+
+    # dict-like methods
+    __setitem__ = __setattr__
+    __delitem__ = __delattr__
+
+    def __getitem__(self, name):
+        return getattr(self, name)
+
+    def get(self, *args):
+        assert len(args) < 3, '`get` get more than 2 arguments'
+        return self.__dict__.get(*args)
+
+    def pop(self, *args):
+        assert len(args) < 3, '`pop` get more than 2 arguments'
+        name = args[0]
+        if name in self._meta_info_fields:
+            raise KeyError(f'{name} is a key in meta information, '
+                           f'which is immutable')
+
+        if args[0] in self._data_fields:
+            self._data_fields.remove(args[0])
+            return self.__dict__.pop(*args)
+
+        # with default value
+        elif len(args) == 2:
+            return args[1]
+        else:
+            raise KeyError(f'{args[0]}')
+
+    def __contains__(self, item):
+        return item in self._data_fields or \
+                    item in self._meta_info_fields
+
+    # Tensor-like methods
+    def to(self, *args, **kwargs):
+        """Apply same name function to all tensors in data_fields."""
+        new_data = self.new()
+        for k, v in self.items():
+            if hasattr(v, 'to'):
+                v = v.to(*args, **kwargs)
+            new_data[k] = v
+        return new_data
+
+    # Tensor-like methods
+    def cpu(self):
+        """Apply same name function to all tensors in data_fields."""
+        new_data = self.new()
+        for k, v in self.items():
+            if isinstance(v, torch.Tensor):
+                v = v.cpu()
+            new_data[k] = v
+        return new_data
+
+    # Tensor-like methods
+    def npu(self):
+        """Apply same name function to all tensors in data_fields."""
+        new_data = self.new()
+        for k, v in self.items():
+            if isinstance(v, torch.Tensor):
+                v = v.npu()
+            new_data[k] = v
+        return new_data
+
+    # Tensor-like methods
+    def mlu(self):
+        """Apply same name function to all tensors in data_fields."""
+        new_data = self.new()
+        for k, v in self.items():
+            if isinstance(v, torch.Tensor):
+                v = v.mlu()
+            new_data[k] = v
+        return new_data
+
+    # Tensor-like methods
+    def cuda(self):
+        """Apply same name function to all tensors in data_fields."""
+        new_data = self.new()
+        for k, v in self.items():
+            if isinstance(v, torch.Tensor):
+                v = v.cuda()
+            new_data[k] = v
+        return new_data
+
+    # Tensor-like methods
+    def detach(self):
+        """Apply same name function to all tensors in data_fields."""
+        new_data = self.new()
+        for k, v in self.items():
+            if isinstance(v, torch.Tensor):
+                v = v.detach()
+            new_data[k] = v
+        return new_data
+
+    # Tensor-like methods
+    def numpy(self):
+        """Apply same name function to all tensors in data_fields."""
+        new_data = self.new()
+        for k, v in self.items():
+            if isinstance(v, torch.Tensor):
+                v = v.detach().cpu().numpy()
+            new_data[k] = v
+        return new_data
+
+    def __nice__(self):
+        repr = '\n \n  META INFORMATION \n'
+        for k, v in self.meta_info_items():
+            repr += f'{k}: {v} \n'
+        repr += '\n   DATA FIELDS \n'
+        for k, v in self.items():
+            if isinstance(v, (torch.Tensor, np.ndarray)):
+                repr += f'shape of {k}: {v.shape} \n'
+            else:
+                repr += f'{k}: {v} \n'
+        return repr + '\n'
diff --git a/mmdet/core/data_structures/instance_data.py b/mmdet/core/data_structures/instance_data.py
new file mode 100644
index 0000000..eef2065
--- /dev/null
+++ b/mmdet/core/data_structures/instance_data.py
@@ -0,0 +1,188 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import itertools
+
+import numpy as np
+import torch
+
+from .general_data import GeneralData
+
+
+class InstanceData(GeneralData):
+    """Data structure for instance-level annnotations or predictions.
+
+    Subclass of :class:`GeneralData`. All value in `data_fields`
+    should have the same length. This design refer to
+    https://github.com/facebookresearch/detectron2/blob/master/detectron2/structures/instances.py # noqa E501
+
+    Examples:
+        >>> from mmdet.core import InstanceData
+        >>> import numpy as np
+        >>> img_meta = dict(img_shape=(800, 1196, 3), pad_shape=(800, 1216, 3))
+        >>> results = InstanceData(img_meta)
+        >>> img_shape in results
+        True
+        >>> results.det_labels = torch.LongTensor([0, 1, 2, 3])
+        >>> results["det_scores"] = torch.Tensor([0.01, 0.7, 0.6, 0.3])
+        >>> results["det_masks"] = np.ndarray(4, 2, 2)
+        >>> len(results)
+        4
+        >>> print(resutls)
+        <InstanceData(
+
+            META INFORMATION
+        pad_shape: (800, 1216, 3)
+        img_shape: (800, 1196, 3)
+
+            PREDICTIONS
+        shape of det_labels: torch.Size([4])
+        shape of det_masks: (4, 2, 2)
+        shape of det_scores: torch.Size([4])
+
+        ) at 0x7fe26b5ca990>
+        >>> sorted_results = results[results.det_scores.sort().indices]
+        >>> sorted_results.det_scores
+        tensor([0.0100, 0.3000, 0.6000, 0.7000])
+        >>> sorted_results.det_labels
+        tensor([0, 3, 2, 1])
+        >>> print(results[results.scores > 0.5])
+        <InstanceData(
+
+            META INFORMATION
+        pad_shape: (800, 1216, 3)
+        img_shape: (800, 1196, 3)
+
+            PREDICTIONS
+        shape of det_labels: torch.Size([2])
+        shape of det_masks: (2, 2, 2)
+        shape of det_scores: torch.Size([2])
+
+        ) at 0x7fe26b6d7790>
+        >>> results[results.det_scores > 0.5].det_labels
+        tensor([1, 2])
+        >>> results[results.det_scores > 0.5].det_scores
+        tensor([0.7000, 0.6000])
+    """
+
+    def __setattr__(self, name, value):
+
+        if name in ('_meta_info_fields', '_data_fields'):
+            if not hasattr(self, name):
+                super().__setattr__(name, value)
+            else:
+                raise AttributeError(
+                    f'{name} has been used as a '
+                    f'private attribute, which is immutable. ')
+
+        else:
+            assert isinstance(value, (torch.Tensor, np.ndarray, list)), \
+                f'Can set {type(value)}, only support' \
+                f' {(torch.Tensor, np.ndarray, list)}'
+
+            if self._data_fields:
+                assert len(value) == len(self), f'the length of ' \
+                                             f'values {len(value)} is ' \
+                                             f'not consistent with' \
+                                             f' the length ' \
+                                             f'of this :obj:`InstanceData` ' \
+                                             f'{len(self)} '
+            super().__setattr__(name, value)
+
+    def __getitem__(self, item):
+        """
+        Args:
+            item (str, obj:`slice`,
+                obj`torch.LongTensor`, obj:`torch.BoolTensor`):
+                get the corresponding values according to item.
+
+        Returns:
+            obj:`InstanceData`: Corresponding values.
+        """
+        assert len(self), ' This is a empty instance'
+
+        assert isinstance(
+            item, (str, slice, int, torch.LongTensor, torch.BoolTensor))
+
+        if isinstance(item, str):
+            return getattr(self, item)
+
+        if type(item) == int:
+            if item >= len(self) or item < -len(self):
+                raise IndexError(f'Index {item} out of range!')
+            else:
+                # keep the dimension
+                item = slice(item, None, len(self))
+
+        new_data = self.new()
+        if isinstance(item, (torch.Tensor)):
+            assert item.dim() == 1, 'Only support to get the' \
+                                 ' values along the first dimension.'
+            if isinstance(item, torch.BoolTensor):
+                assert len(item) == len(self), f'The shape of the' \
+                                               f' input(BoolTensor)) ' \
+                                               f'{len(item)} ' \
+                                               f' does not match the shape ' \
+                                               f'of the indexed tensor ' \
+                                               f'in results_filed ' \
+                                               f'{len(self)} at ' \
+                                               f'first dimension. '
+
+            for k, v in self.items():
+                if isinstance(v, torch.Tensor):
+                    new_data[k] = v[item]
+                elif isinstance(v, np.ndarray):
+                    new_data[k] = v[item.cpu().numpy()]
+                elif isinstance(v, list):
+                    r_list = []
+                    # convert to indexes from boolTensor
+                    if isinstance(item, torch.BoolTensor):
+                        indexes = torch.nonzero(item).view(-1)
+                    else:
+                        indexes = item
+                    for index in indexes:
+                        r_list.append(v[index])
+                    new_data[k] = r_list
+        else:
+            # item is a slice
+            for k, v in self.items():
+                new_data[k] = v[item]
+        return new_data
+
+    @staticmethod
+    def cat(instances_list):
+        """Concat the predictions of all :obj:`InstanceData` in the list.
+
+        Args:
+            instances_list (list[:obj:`InstanceData`]): A list
+                of :obj:`InstanceData`.
+
+        Returns:
+            obj:`InstanceData`
+        """
+        assert all(
+            isinstance(results, InstanceData) for results in instances_list)
+        assert len(instances_list) > 0
+        if len(instances_list) == 1:
+            return instances_list[0]
+
+        new_data = instances_list[0].new()
+        for k in instances_list[0]._data_fields:
+            values = [results[k] for results in instances_list]
+            v0 = values[0]
+            if isinstance(v0, torch.Tensor):
+                values = torch.cat(values, dim=0)
+            elif isinstance(v0, np.ndarray):
+                values = np.concatenate(values, axis=0)
+            elif isinstance(v0, list):
+                values = list(itertools.chain(*values))
+            else:
+                raise ValueError(
+                    f'Can not concat the {k} which is a {type(v0)}')
+            new_data[k] = values
+        return new_data
+
+    def __len__(self):
+        if len(self._data_fields):
+            for v in self.values():
+                return len(v)
+        else:
+            raise AssertionError('This is an empty `InstanceData`.')
diff --git a/mmdet/core/evaluation/__init__.py b/mmdet/core/evaluation/__init__.py
new file mode 100644
index 0000000..2b488a7
--- /dev/null
+++ b/mmdet/core/evaluation/__init__.py
@@ -0,0 +1,21 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .class_names import (cityscapes_classes, coco_classes, dataset_aliases,
+                          get_classes, imagenet_det_classes,
+                          imagenet_vid_classes, objects365v1_classes,
+                          objects365v2_classes, oid_challenge_classes,
+                          oid_v6_classes, voc_classes)
+from .eval_hooks import DistEvalHook, EvalHook
+from .mean_ap import average_precision, eval_map, print_map_summary
+from .panoptic_utils import INSTANCE_OFFSET
+from .recall import (eval_recalls, plot_iou_recall, plot_num_recall,
+                     print_recall_summary)
+
+__all__ = [
+    'voc_classes', 'imagenet_det_classes', 'imagenet_vid_classes',
+    'coco_classes', 'cityscapes_classes', 'dataset_aliases', 'get_classes',
+    'DistEvalHook', 'EvalHook', 'average_precision', 'eval_map',
+    'print_map_summary', 'eval_recalls', 'print_recall_summary',
+    'plot_num_recall', 'plot_iou_recall', 'oid_v6_classes',
+    'oid_challenge_classes', 'objects365v1_classes', 'objects365v2_classes',
+    'INSTANCE_OFFSET'
+]
diff --git a/mmdet/core/evaluation/bbox_overlaps.py b/mmdet/core/evaluation/bbox_overlaps.py
new file mode 100644
index 0000000..5d6eb82
--- /dev/null
+++ b/mmdet/core/evaluation/bbox_overlaps.py
@@ -0,0 +1,65 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+
+
+def bbox_overlaps(bboxes1,
+                  bboxes2,
+                  mode='iou',
+                  eps=1e-6,
+                  use_legacy_coordinate=False):
+    """Calculate the ious between each bbox of bboxes1 and bboxes2.
+
+    Args:
+        bboxes1 (ndarray): Shape (n, 4)
+        bboxes2 (ndarray): Shape (k, 4)
+        mode (str): IOU (intersection over union) or IOF (intersection
+            over foreground)
+        use_legacy_coordinate (bool): Whether to use coordinate system in
+            mmdet v1.x. which means width, height should be
+            calculated as 'x2 - x1 + 1` and 'y2 - y1 + 1' respectively.
+            Note when function is used in `VOCDataset`, it should be
+            True to align with the official implementation
+            `http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCdevkit_18-May-2011.tar`
+            Default: False.
+
+    Returns:
+        ious (ndarray): Shape (n, k)
+    """
+
+    assert mode in ['iou', 'iof']
+    if not use_legacy_coordinate:
+        extra_length = 0.
+    else:
+        extra_length = 1.
+    bboxes1 = bboxes1.astype(np.float32)
+    bboxes2 = bboxes2.astype(np.float32)
+    rows = bboxes1.shape[0]
+    cols = bboxes2.shape[0]
+    ious = np.zeros((rows, cols), dtype=np.float32)
+    if rows * cols == 0:
+        return ious
+    exchange = False
+    if bboxes1.shape[0] > bboxes2.shape[0]:
+        bboxes1, bboxes2 = bboxes2, bboxes1
+        ious = np.zeros((cols, rows), dtype=np.float32)
+        exchange = True
+    area1 = (bboxes1[:, 2] - bboxes1[:, 0] + extra_length) * (
+        bboxes1[:, 3] - bboxes1[:, 1] + extra_length)
+    area2 = (bboxes2[:, 2] - bboxes2[:, 0] + extra_length) * (
+        bboxes2[:, 3] - bboxes2[:, 1] + extra_length)
+    for i in range(bboxes1.shape[0]):
+        x_start = np.maximum(bboxes1[i, 0], bboxes2[:, 0])
+        y_start = np.maximum(bboxes1[i, 1], bboxes2[:, 1])
+        x_end = np.minimum(bboxes1[i, 2], bboxes2[:, 2])
+        y_end = np.minimum(bboxes1[i, 3], bboxes2[:, 3])
+        overlap = np.maximum(x_end - x_start + extra_length, 0) * np.maximum(
+            y_end - y_start + extra_length, 0)
+        if mode == 'iou':
+            union = area1[i] + area2 - overlap
+        else:
+            union = area1[i] if not exchange else area2
+        union = np.maximum(union, eps)
+        ious[i, :] = overlap / union
+    if exchange:
+        ious = ious.T
+    return ious
diff --git a/mmdet/core/evaluation/class_names.py b/mmdet/core/evaluation/class_names.py
new file mode 100644
index 0000000..c015c5d
--- /dev/null
+++ b/mmdet/core/evaluation/class_names.py
@@ -0,0 +1,476 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+
+
+def wider_face_classes():
+    return ['face']
+
+
+def voc_classes():
+    return [
+        'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat',
+        'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person',
+        'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor'
+    ]
+
+
+def imagenet_det_classes():
+    return [
+        'accordion', 'airplane', 'ant', 'antelope', 'apple', 'armadillo',
+        'artichoke', 'axe', 'baby_bed', 'backpack', 'bagel', 'balance_beam',
+        'banana', 'band_aid', 'banjo', 'baseball', 'basketball', 'bathing_cap',
+        'beaker', 'bear', 'bee', 'bell_pepper', 'bench', 'bicycle', 'binder',
+        'bird', 'bookshelf', 'bow_tie', 'bow', 'bowl', 'brassiere', 'burrito',
+        'bus', 'butterfly', 'camel', 'can_opener', 'car', 'cart', 'cattle',
+        'cello', 'centipede', 'chain_saw', 'chair', 'chime', 'cocktail_shaker',
+        'coffee_maker', 'computer_keyboard', 'computer_mouse', 'corkscrew',
+        'cream', 'croquet_ball', 'crutch', 'cucumber', 'cup_or_mug', 'diaper',
+        'digital_clock', 'dishwasher', 'dog', 'domestic_cat', 'dragonfly',
+        'drum', 'dumbbell', 'electric_fan', 'elephant', 'face_powder', 'fig',
+        'filing_cabinet', 'flower_pot', 'flute', 'fox', 'french_horn', 'frog',
+        'frying_pan', 'giant_panda', 'goldfish', 'golf_ball', 'golfcart',
+        'guacamole', 'guitar', 'hair_dryer', 'hair_spray', 'hamburger',
+        'hammer', 'hamster', 'harmonica', 'harp', 'hat_with_a_wide_brim',
+        'head_cabbage', 'helmet', 'hippopotamus', 'horizontal_bar', 'horse',
+        'hotdog', 'iPod', 'isopod', 'jellyfish', 'koala_bear', 'ladle',
+        'ladybug', 'lamp', 'laptop', 'lemon', 'lion', 'lipstick', 'lizard',
+        'lobster', 'maillot', 'maraca', 'microphone', 'microwave', 'milk_can',
+        'miniskirt', 'monkey', 'motorcycle', 'mushroom', 'nail', 'neck_brace',
+        'oboe', 'orange', 'otter', 'pencil_box', 'pencil_sharpener', 'perfume',
+        'person', 'piano', 'pineapple', 'ping-pong_ball', 'pitcher', 'pizza',
+        'plastic_bag', 'plate_rack', 'pomegranate', 'popsicle', 'porcupine',
+        'power_drill', 'pretzel', 'printer', 'puck', 'punching_bag', 'purse',
+        'rabbit', 'racket', 'ray', 'red_panda', 'refrigerator',
+        'remote_control', 'rubber_eraser', 'rugby_ball', 'ruler',
+        'salt_or_pepper_shaker', 'saxophone', 'scorpion', 'screwdriver',
+        'seal', 'sheep', 'ski', 'skunk', 'snail', 'snake', 'snowmobile',
+        'snowplow', 'soap_dispenser', 'soccer_ball', 'sofa', 'spatula',
+        'squirrel', 'starfish', 'stethoscope', 'stove', 'strainer',
+        'strawberry', 'stretcher', 'sunglasses', 'swimming_trunks', 'swine',
+        'syringe', 'table', 'tape_player', 'tennis_ball', 'tick', 'tie',
+        'tiger', 'toaster', 'traffic_light', 'train', 'trombone', 'trumpet',
+        'turtle', 'tv_or_monitor', 'unicycle', 'vacuum', 'violin',
+        'volleyball', 'waffle_iron', 'washer', 'water_bottle', 'watercraft',
+        'whale', 'wine_bottle', 'zebra'
+    ]
+
+
+def imagenet_vid_classes():
+    return [
+        'airplane', 'antelope', 'bear', 'bicycle', 'bird', 'bus', 'car',
+        'cattle', 'dog', 'domestic_cat', 'elephant', 'fox', 'giant_panda',
+        'hamster', 'horse', 'lion', 'lizard', 'monkey', 'motorcycle', 'rabbit',
+        'red_panda', 'sheep', 'snake', 'squirrel', 'tiger', 'train', 'turtle',
+        'watercraft', 'whale', 'zebra'
+    ]
+
+
+def coco_classes():
+    return [
+        'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
+        'truck', 'boat', 'traffic_light', 'fire_hydrant', 'stop_sign',
+        'parking_meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
+        'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
+        'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
+        'sports_ball', 'kite', 'baseball_bat', 'baseball_glove', 'skateboard',
+        'surfboard', 'tennis_racket', 'bottle', 'wine_glass', 'cup', 'fork',
+        'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
+        'broccoli', 'carrot', 'hot_dog', 'pizza', 'donut', 'cake', 'chair',
+        'couch', 'potted_plant', 'bed', 'dining_table', 'toilet', 'tv',
+        'laptop', 'mouse', 'remote', 'keyboard', 'cell_phone', 'microwave',
+        'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
+        'scissors', 'teddy_bear', 'hair_drier', 'toothbrush'
+    ]
+
+
+def cityscapes_classes():
+    return [
+        'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
+        'bicycle'
+    ]
+
+
+def oid_challenge_classes():
+    return [
+        'Footwear', 'Jeans', 'House', 'Tree', 'Woman', 'Man', 'Land vehicle',
+        'Person', 'Wheel', 'Bus', 'Human face', 'Bird', 'Dress', 'Girl',
+        'Vehicle', 'Building', 'Cat', 'Car', 'Belt', 'Elephant', 'Dessert',
+        'Butterfly', 'Train', 'Guitar', 'Poster', 'Book', 'Boy', 'Bee',
+        'Flower', 'Window', 'Hat', 'Human head', 'Dog', 'Human arm', 'Drink',
+        'Human mouth', 'Human hair', 'Human nose', 'Human hand', 'Table',
+        'Marine invertebrates', 'Fish', 'Sculpture', 'Rose', 'Street light',
+        'Glasses', 'Fountain', 'Skyscraper', 'Swimwear', 'Brassiere', 'Drum',
+        'Duck', 'Countertop', 'Furniture', 'Ball', 'Human leg', 'Boat',
+        'Balloon', 'Bicycle helmet', 'Goggles', 'Door', 'Human eye', 'Shirt',
+        'Toy', 'Teddy bear', 'Pasta', 'Tomato', 'Human ear',
+        'Vehicle registration plate', 'Microphone', 'Musical keyboard',
+        'Tower', 'Houseplant', 'Flowerpot', 'Fruit', 'Vegetable',
+        'Musical instrument', 'Suit', 'Motorcycle', 'Bagel', 'French fries',
+        'Hamburger', 'Chair', 'Salt and pepper shakers', 'Snail', 'Airplane',
+        'Horse', 'Laptop', 'Computer keyboard', 'Football helmet', 'Cocktail',
+        'Juice', 'Tie', 'Computer monitor', 'Human beard', 'Bottle',
+        'Saxophone', 'Lemon', 'Mouse', 'Sock', 'Cowboy hat', 'Sun hat',
+        'Football', 'Porch', 'Sunglasses', 'Lobster', 'Crab', 'Picture frame',
+        'Van', 'Crocodile', 'Surfboard', 'Shorts', 'Helicopter', 'Helmet',
+        'Sports uniform', 'Taxi', 'Swan', 'Goose', 'Coat', 'Jacket', 'Handbag',
+        'Flag', 'Skateboard', 'Television', 'Tire', 'Spoon', 'Palm tree',
+        'Stairs', 'Salad', 'Castle', 'Oven', 'Microwave oven', 'Wine',
+        'Ceiling fan', 'Mechanical fan', 'Cattle', 'Truck', 'Box', 'Ambulance',
+        'Desk', 'Wine glass', 'Reptile', 'Tank', 'Traffic light', 'Billboard',
+        'Tent', 'Insect', 'Spider', 'Treadmill', 'Cupboard', 'Shelf',
+        'Seat belt', 'Human foot', 'Bicycle', 'Bicycle wheel', 'Couch',
+        'Bookcase', 'Fedora', 'Backpack', 'Bench', 'Oyster',
+        'Moths and butterflies', 'Lavender', 'Waffle', 'Fork', 'Animal',
+        'Accordion', 'Mobile phone', 'Plate', 'Coffee cup', 'Saucer',
+        'Platter', 'Dagger', 'Knife', 'Bull', 'Tortoise', 'Sea turtle', 'Deer',
+        'Weapon', 'Apple', 'Ski', 'Taco', 'Traffic sign', 'Beer', 'Necklace',
+        'Sunflower', 'Piano', 'Organ', 'Harpsichord', 'Bed', 'Cabinetry',
+        'Nightstand', 'Curtain', 'Chest of drawers', 'Drawer', 'Parrot',
+        'Sandal', 'High heels', 'Tableware', 'Cart', 'Mushroom', 'Kite',
+        'Missile', 'Seafood', 'Camera', 'Paper towel', 'Toilet paper',
+        'Sombrero', 'Radish', 'Lighthouse', 'Segway', 'Pig', 'Watercraft',
+        'Golf cart', 'studio couch', 'Dolphin', 'Whale', 'Earrings', 'Otter',
+        'Sea lion', 'Whiteboard', 'Monkey', 'Gondola', 'Zebra',
+        'Baseball glove', 'Scarf', 'Adhesive tape', 'Trousers', 'Scoreboard',
+        'Lily', 'Carnivore', 'Power plugs and sockets', 'Office building',
+        'Sandwich', 'Swimming pool', 'Headphones', 'Tin can', 'Crown', 'Doll',
+        'Cake', 'Frog', 'Beetle', 'Ant', 'Gas stove', 'Canoe', 'Falcon',
+        'Blue jay', 'Egg', 'Fire hydrant', 'Raccoon', 'Muffin', 'Wall clock',
+        'Coffee', 'Mug', 'Tea', 'Bear', 'Waste container', 'Home appliance',
+        'Candle', 'Lion', 'Mirror', 'Starfish', 'Marine mammal', 'Wheelchair',
+        'Umbrella', 'Alpaca', 'Violin', 'Cello', 'Brown bear', 'Canary', 'Bat',
+        'Ruler', 'Plastic bag', 'Penguin', 'Watermelon', 'Harbor seal', 'Pen',
+        'Pumpkin', 'Harp', 'Kitchen appliance', 'Roller skates', 'Bust',
+        'Coffee table', 'Tennis ball', 'Tennis racket', 'Ladder', 'Boot',
+        'Bowl', 'Stop sign', 'Volleyball', 'Eagle', 'Paddle', 'Chicken',
+        'Skull', 'Lamp', 'Beehive', 'Maple', 'Sink', 'Goldfish', 'Tripod',
+        'Coconut', 'Bidet', 'Tap', 'Bathroom cabinet', 'Toilet',
+        'Filing cabinet', 'Pretzel', 'Table tennis racket', 'Bronze sculpture',
+        'Rocket', 'Mouse', 'Hamster', 'Lizard', 'Lifejacket', 'Goat',
+        'Washing machine', 'Trumpet', 'Horn', 'Trombone', 'Sheep',
+        'Tablet computer', 'Pillow', 'Kitchen & dining room table',
+        'Parachute', 'Raven', 'Glove', 'Loveseat', 'Christmas tree',
+        'Shellfish', 'Rifle', 'Shotgun', 'Sushi', 'Sparrow', 'Bread',
+        'Toaster', 'Watch', 'Asparagus', 'Artichoke', 'Suitcase', 'Antelope',
+        'Broccoli', 'Ice cream', 'Racket', 'Banana', 'Cookie', 'Cucumber',
+        'Dragonfly', 'Lynx', 'Caterpillar', 'Light bulb', 'Office supplies',
+        'Miniskirt', 'Skirt', 'Fireplace', 'Potato', 'Light switch',
+        'Croissant', 'Cabbage', 'Ladybug', 'Handgun', 'Luggage and bags',
+        'Window blind', 'Snowboard', 'Baseball bat', 'Digital clock',
+        'Serving tray', 'Infant bed', 'Sofa bed', 'Guacamole', 'Fox', 'Pizza',
+        'Snowplow', 'Jet ski', 'Refrigerator', 'Lantern', 'Convenience store',
+        'Sword', 'Rugby ball', 'Owl', 'Ostrich', 'Pancake', 'Strawberry',
+        'Carrot', 'Tart', 'Dice', 'Turkey', 'Rabbit', 'Invertebrate', 'Vase',
+        'Stool', 'Swim cap', 'Shower', 'Clock', 'Jellyfish', 'Aircraft',
+        'Chopsticks', 'Orange', 'Snake', 'Sewing machine', 'Kangaroo', 'Mixer',
+        'Food processor', 'Shrimp', 'Towel', 'Porcupine', 'Jaguar', 'Cannon',
+        'Limousine', 'Mule', 'Squirrel', 'Kitchen knife', 'Tiara', 'Tiger',
+        'Bow and arrow', 'Candy', 'Rhinoceros', 'Shark', 'Cricket ball',
+        'Doughnut', 'Plumbing fixture', 'Camel', 'Polar bear', 'Coin',
+        'Printer', 'Blender', 'Giraffe', 'Billiard table', 'Kettle',
+        'Dinosaur', 'Pineapple', 'Zucchini', 'Jug', 'Barge', 'Teapot',
+        'Golf ball', 'Binoculars', 'Scissors', 'Hot dog', 'Door handle',
+        'Seahorse', 'Bathtub', 'Leopard', 'Centipede', 'Grapefruit', 'Snowman',
+        'Cheetah', 'Alarm clock', 'Grape', 'Wrench', 'Wok', 'Bell pepper',
+        'Cake stand', 'Barrel', 'Woodpecker', 'Flute', 'Corded phone',
+        'Willow', 'Punching bag', 'Pomegranate', 'Telephone', 'Pear',
+        'Common fig', 'Bench', 'Wood-burning stove', 'Burrito', 'Nail',
+        'Turtle', 'Submarine sandwich', 'Drinking straw', 'Peach', 'Popcorn',
+        'Frying pan', 'Picnic basket', 'Honeycomb', 'Envelope', 'Mango',
+        'Cutting board', 'Pitcher', 'Stationary bicycle', 'Dumbbell',
+        'Personal care', 'Dog bed', 'Snowmobile', 'Oboe', 'Briefcase',
+        'Squash', 'Tick', 'Slow cooker', 'Coffeemaker', 'Measuring cup',
+        'Crutch', 'Stretcher', 'Screwdriver', 'Flashlight', 'Spatula',
+        'Pressure cooker', 'Ring binder', 'Beaker', 'Torch', 'Winter melon'
+    ]
+
+
+def oid_v6_classes():
+    return [
+        'Tortoise', 'Container', 'Magpie', 'Sea turtle', 'Football',
+        'Ambulance', 'Ladder', 'Toothbrush', 'Syringe', 'Sink', 'Toy',
+        'Organ (Musical Instrument)', 'Cassette deck', 'Apple', 'Human eye',
+        'Cosmetics', 'Paddle', 'Snowman', 'Beer', 'Chopsticks', 'Human beard',
+        'Bird', 'Parking meter', 'Traffic light', 'Croissant', 'Cucumber',
+        'Radish', 'Towel', 'Doll', 'Skull', 'Washing machine', 'Glove', 'Tick',
+        'Belt', 'Sunglasses', 'Banjo', 'Cart', 'Ball', 'Backpack', 'Bicycle',
+        'Home appliance', 'Centipede', 'Boat', 'Surfboard', 'Boot',
+        'Headphones', 'Hot dog', 'Shorts', 'Fast food', 'Bus', 'Boy',
+        'Screwdriver', 'Bicycle wheel', 'Barge', 'Laptop', 'Miniskirt',
+        'Drill (Tool)', 'Dress', 'Bear', 'Waffle', 'Pancake', 'Brown bear',
+        'Woodpecker', 'Blue jay', 'Pretzel', 'Bagel', 'Tower', 'Teapot',
+        'Person', 'Bow and arrow', 'Swimwear', 'Beehive', 'Brassiere', 'Bee',
+        'Bat (Animal)', 'Starfish', 'Popcorn', 'Burrito', 'Chainsaw',
+        'Balloon', 'Wrench', 'Tent', 'Vehicle registration plate', 'Lantern',
+        'Toaster', 'Flashlight', 'Billboard', 'Tiara', 'Limousine', 'Necklace',
+        'Carnivore', 'Scissors', 'Stairs', 'Computer keyboard', 'Printer',
+        'Traffic sign', 'Chair', 'Shirt', 'Poster', 'Cheese', 'Sock',
+        'Fire hydrant', 'Land vehicle', 'Earrings', 'Tie', 'Watercraft',
+        'Cabinetry', 'Suitcase', 'Muffin', 'Bidet', 'Snack', 'Snowmobile',
+        'Clock', 'Medical equipment', 'Cattle', 'Cello', 'Jet ski', 'Camel',
+        'Coat', 'Suit', 'Desk', 'Cat', 'Bronze sculpture', 'Juice', 'Gondola',
+        'Beetle', 'Cannon', 'Computer mouse', 'Cookie', 'Office building',
+        'Fountain', 'Coin', 'Calculator', 'Cocktail', 'Computer monitor',
+        'Box', 'Stapler', 'Christmas tree', 'Cowboy hat', 'Hiking equipment',
+        'Studio couch', 'Drum', 'Dessert', 'Wine rack', 'Drink', 'Zucchini',
+        'Ladle', 'Human mouth', 'Dairy Product', 'Dice', 'Oven', 'Dinosaur',
+        'Ratchet (Device)', 'Couch', 'Cricket ball', 'Winter melon', 'Spatula',
+        'Whiteboard', 'Pencil sharpener', 'Door', 'Hat', 'Shower', 'Eraser',
+        'Fedora', 'Guacamole', 'Dagger', 'Scarf', 'Dolphin', 'Sombrero',
+        'Tin can', 'Mug', 'Tap', 'Harbor seal', 'Stretcher', 'Can opener',
+        'Goggles', 'Human body', 'Roller skates', 'Coffee cup',
+        'Cutting board', 'Blender', 'Plumbing fixture', 'Stop sign',
+        'Office supplies', 'Volleyball (Ball)', 'Vase', 'Slow cooker',
+        'Wardrobe', 'Coffee', 'Whisk', 'Paper towel', 'Personal care', 'Food',
+        'Sun hat', 'Tree house', 'Flying disc', 'Skirt', 'Gas stove',
+        'Salt and pepper shakers', 'Mechanical fan', 'Face powder', 'Fax',
+        'Fruit', 'French fries', 'Nightstand', 'Barrel', 'Kite', 'Tart',
+        'Treadmill', 'Fox', 'Flag', 'French horn', 'Window blind',
+        'Human foot', 'Golf cart', 'Jacket', 'Egg (Food)', 'Street light',
+        'Guitar', 'Pillow', 'Human leg', 'Isopod', 'Grape', 'Human ear',
+        'Power plugs and sockets', 'Panda', 'Giraffe', 'Woman', 'Door handle',
+        'Rhinoceros', 'Bathtub', 'Goldfish', 'Houseplant', 'Goat',
+        'Baseball bat', 'Baseball glove', 'Mixing bowl',
+        'Marine invertebrates', 'Kitchen utensil', 'Light switch', 'House',
+        'Horse', 'Stationary bicycle', 'Hammer', 'Ceiling fan', 'Sofa bed',
+        'Adhesive tape', 'Harp', 'Sandal', 'Bicycle helmet', 'Saucer',
+        'Harpsichord', 'Human hair', 'Heater', 'Harmonica', 'Hamster',
+        'Curtain', 'Bed', 'Kettle', 'Fireplace', 'Scale', 'Drinking straw',
+        'Insect', 'Hair dryer', 'Kitchenware', 'Indoor rower', 'Invertebrate',
+        'Food processor', 'Bookcase', 'Refrigerator', 'Wood-burning stove',
+        'Punching bag', 'Common fig', 'Cocktail shaker', 'Jaguar (Animal)',
+        'Golf ball', 'Fashion accessory', 'Alarm clock', 'Filing cabinet',
+        'Artichoke', 'Table', 'Tableware', 'Kangaroo', 'Koala', 'Knife',
+        'Bottle', 'Bottle opener', 'Lynx', 'Lavender (Plant)', 'Lighthouse',
+        'Dumbbell', 'Human head', 'Bowl', 'Humidifier', 'Porch', 'Lizard',
+        'Billiard table', 'Mammal', 'Mouse', 'Motorcycle',
+        'Musical instrument', 'Swim cap', 'Frying pan', 'Snowplow',
+        'Bathroom cabinet', 'Missile', 'Bust', 'Man', 'Waffle iron', 'Milk',
+        'Ring binder', 'Plate', 'Mobile phone', 'Baked goods', 'Mushroom',
+        'Crutch', 'Pitcher (Container)', 'Mirror', 'Personal flotation device',
+        'Table tennis racket', 'Pencil case', 'Musical keyboard', 'Scoreboard',
+        'Briefcase', 'Kitchen knife', 'Nail (Construction)', 'Tennis ball',
+        'Plastic bag', 'Oboe', 'Chest of drawers', 'Ostrich', 'Piano', 'Girl',
+        'Plant', 'Potato', 'Hair spray', 'Sports equipment', 'Pasta',
+        'Penguin', 'Pumpkin', 'Pear', 'Infant bed', 'Polar bear', 'Mixer',
+        'Cupboard', 'Jacuzzi', 'Pizza', 'Digital clock', 'Pig', 'Reptile',
+        'Rifle', 'Lipstick', 'Skateboard', 'Raven', 'High heels', 'Red panda',
+        'Rose', 'Rabbit', 'Sculpture', 'Saxophone', 'Shotgun', 'Seafood',
+        'Submarine sandwich', 'Snowboard', 'Sword', 'Picture frame', 'Sushi',
+        'Loveseat', 'Ski', 'Squirrel', 'Tripod', 'Stethoscope', 'Submarine',
+        'Scorpion', 'Segway', 'Training bench', 'Snake', 'Coffee table',
+        'Skyscraper', 'Sheep', 'Television', 'Trombone', 'Tea', 'Tank', 'Taco',
+        'Telephone', 'Torch', 'Tiger', 'Strawberry', 'Trumpet', 'Tree',
+        'Tomato', 'Train', 'Tool', 'Picnic basket', 'Cooking spray',
+        'Trousers', 'Bowling equipment', 'Football helmet', 'Truck',
+        'Measuring cup', 'Coffeemaker', 'Violin', 'Vehicle', 'Handbag',
+        'Paper cutter', 'Wine', 'Weapon', 'Wheel', 'Worm', 'Wok', 'Whale',
+        'Zebra', 'Auto part', 'Jug', 'Pizza cutter', 'Cream', 'Monkey', 'Lion',
+        'Bread', 'Platter', 'Chicken', 'Eagle', 'Helicopter', 'Owl', 'Duck',
+        'Turtle', 'Hippopotamus', 'Crocodile', 'Toilet', 'Toilet paper',
+        'Squid', 'Clothing', 'Footwear', 'Lemon', 'Spider', 'Deer', 'Frog',
+        'Banana', 'Rocket', 'Wine glass', 'Countertop', 'Tablet computer',
+        'Waste container', 'Swimming pool', 'Dog', 'Book', 'Elephant', 'Shark',
+        'Candle', 'Leopard', 'Axe', 'Hand dryer', 'Soap dispenser',
+        'Porcupine', 'Flower', 'Canary', 'Cheetah', 'Palm tree', 'Hamburger',
+        'Maple', 'Building', 'Fish', 'Lobster', 'Garden Asparagus',
+        'Furniture', 'Hedgehog', 'Airplane', 'Spoon', 'Otter', 'Bull',
+        'Oyster', 'Horizontal bar', 'Convenience store', 'Bomb', 'Bench',
+        'Ice cream', 'Caterpillar', 'Butterfly', 'Parachute', 'Orange',
+        'Antelope', 'Beaker', 'Moths and butterflies', 'Window', 'Closet',
+        'Castle', 'Jellyfish', 'Goose', 'Mule', 'Swan', 'Peach', 'Coconut',
+        'Seat belt', 'Raccoon', 'Chisel', 'Fork', 'Lamp', 'Camera',
+        'Squash (Plant)', 'Racket', 'Human face', 'Human arm', 'Vegetable',
+        'Diaper', 'Unicycle', 'Falcon', 'Chime', 'Snail', 'Shellfish',
+        'Cabbage', 'Carrot', 'Mango', 'Jeans', 'Flowerpot', 'Pineapple',
+        'Drawer', 'Stool', 'Envelope', 'Cake', 'Dragonfly', 'Common sunflower',
+        'Microwave oven', 'Honeycomb', 'Marine mammal', 'Sea lion', 'Ladybug',
+        'Shelf', 'Watch', 'Candy', 'Salad', 'Parrot', 'Handgun', 'Sparrow',
+        'Van', 'Grinder', 'Spice rack', 'Light bulb', 'Corded phone',
+        'Sports uniform', 'Tennis racket', 'Wall clock', 'Serving tray',
+        'Kitchen & dining room table', 'Dog bed', 'Cake stand',
+        'Cat furniture', 'Bathroom accessory', 'Facial tissue holder',
+        'Pressure cooker', 'Kitchen appliance', 'Tire', 'Ruler',
+        'Luggage and bags', 'Microphone', 'Broccoli', 'Umbrella', 'Pastry',
+        'Grapefruit', 'Band-aid', 'Animal', 'Bell pepper', 'Turkey', 'Lily',
+        'Pomegranate', 'Doughnut', 'Glasses', 'Human nose', 'Pen', 'Ant',
+        'Car', 'Aircraft', 'Human hand', 'Skunk', 'Teddy bear', 'Watermelon',
+        'Cantaloupe', 'Dishwasher', 'Flute', 'Balance beam', 'Sandwich',
+        'Shrimp', 'Sewing machine', 'Binoculars', 'Rays and skates', 'Ipod',
+        'Accordion', 'Willow', 'Crab', 'Crown', 'Seahorse', 'Perfume',
+        'Alpaca', 'Taxi', 'Canoe', 'Remote control', 'Wheelchair',
+        'Rugby ball', 'Armadillo', 'Maracas', 'Helmet'
+    ]
+
+
+def objects365v1_classes():
+    return [
+        'person', 'sneakers', 'chair', 'hat', 'lamp', 'bottle',
+        'cabinet/shelf', 'cup', 'car', 'glasses', 'picture/frame', 'desk',
+        'handbag', 'street lights', 'book', 'plate', 'helmet', 'leather shoes',
+        'pillow', 'glove', 'potted plant', 'bracelet', 'flower', 'tv',
+        'storage box', 'vase', 'bench', 'wine glass', 'boots', 'bowl',
+        'dining table', 'umbrella', 'boat', 'flag', 'speaker', 'trash bin/can',
+        'stool', 'backpack', 'couch', 'belt', 'carpet', 'basket',
+        'towel/napkin', 'slippers', 'barrel/bucket', 'coffee table', 'suv',
+        'toy', 'tie', 'bed', 'traffic light', 'pen/pencil', 'microphone',
+        'sandals', 'canned', 'necklace', 'mirror', 'faucet', 'bicycle',
+        'bread', 'high heels', 'ring', 'van', 'watch', 'sink', 'horse', 'fish',
+        'apple', 'camera', 'candle', 'teddy bear', 'cake', 'motorcycle',
+        'wild bird', 'laptop', 'knife', 'traffic sign', 'cell phone', 'paddle',
+        'truck', 'cow', 'power outlet', 'clock', 'drum', 'fork', 'bus',
+        'hanger', 'nightstand', 'pot/pan', 'sheep', 'guitar', 'traffic cone',
+        'tea pot', 'keyboard', 'tripod', 'hockey', 'fan', 'dog', 'spoon',
+        'blackboard/whiteboard', 'balloon', 'air conditioner', 'cymbal',
+        'mouse', 'telephone', 'pickup truck', 'orange', 'banana', 'airplane',
+        'luggage', 'skis', 'soccer', 'trolley', 'oven', 'remote',
+        'baseball glove', 'paper towel', 'refrigerator', 'train', 'tomato',
+        'machinery vehicle', 'tent', 'shampoo/shower gel', 'head phone',
+        'lantern', 'donut', 'cleaning products', 'sailboat', 'tangerine',
+        'pizza', 'kite', 'computer box', 'elephant', 'toiletries', 'gas stove',
+        'broccoli', 'toilet', 'stroller', 'shovel', 'baseball bat',
+        'microwave', 'skateboard', 'surfboard', 'surveillance camera', 'gun',
+        'life saver', 'cat', 'lemon', 'liquid soap', 'zebra', 'duck',
+        'sports car', 'giraffe', 'pumpkin', 'piano', 'stop sign', 'radiator',
+        'converter', 'tissue ', 'carrot', 'washing machine', 'vent', 'cookies',
+        'cutting/chopping board', 'tennis racket', 'candy',
+        'skating and skiing shoes', 'scissors', 'folder', 'baseball',
+        'strawberry', 'bow tie', 'pigeon', 'pepper', 'coffee machine',
+        'bathtub', 'snowboard', 'suitcase', 'grapes', 'ladder', 'pear',
+        'american football', 'basketball', 'potato', 'paint brush', 'printer',
+        'billiards', 'fire hydrant', 'goose', 'projector', 'sausage',
+        'fire extinguisher', 'extension cord', 'facial mask', 'tennis ball',
+        'chopsticks', 'electronic stove and gas stove', 'pie', 'frisbee',
+        'kettle', 'hamburger', 'golf club', 'cucumber', 'clutch', 'blender',
+        'tong', 'slide', 'hot dog', 'toothbrush', 'facial cleanser', 'mango',
+        'deer', 'egg', 'violin', 'marker', 'ship', 'chicken', 'onion',
+        'ice cream', 'tape', 'wheelchair', 'plum', 'bar soap', 'scale',
+        'watermelon', 'cabbage', 'router/modem', 'golf ball', 'pine apple',
+        'crane', 'fire truck', 'peach', 'cello', 'notepaper', 'tricycle',
+        'toaster', 'helicopter', 'green beans', 'brush', 'carriage', 'cigar',
+        'earphone', 'penguin', 'hurdle', 'swing', 'radio', 'CD',
+        'parking meter', 'swan', 'garlic', 'french fries', 'horn', 'avocado',
+        'saxophone', 'trumpet', 'sandwich', 'cue', 'kiwi fruit', 'bear',
+        'fishing rod', 'cherry', 'tablet', 'green vegetables', 'nuts', 'corn',
+        'key', 'screwdriver', 'globe', 'broom', 'pliers', 'volleyball',
+        'hammer', 'eggplant', 'trophy', 'dates', 'board eraser', 'rice',
+        'tape measure/ruler', 'dumbbell', 'hamimelon', 'stapler', 'camel',
+        'lettuce', 'goldfish', 'meat balls', 'medal', 'toothpaste', 'antelope',
+        'shrimp', 'rickshaw', 'trombone', 'pomegranate', 'coconut',
+        'jellyfish', 'mushroom', 'calculator', 'treadmill', 'butterfly',
+        'egg tart', 'cheese', 'pig', 'pomelo', 'race car', 'rice cooker',
+        'tuba', 'crosswalk sign', 'papaya', 'hair drier', 'green onion',
+        'chips', 'dolphin', 'sushi', 'urinal', 'donkey', 'electric drill',
+        'spring rolls', 'tortoise/turtle', 'parrot', 'flute', 'measuring cup',
+        'shark', 'steak', 'poker card', 'binoculars', 'llama', 'radish',
+        'noodles', 'yak', 'mop', 'crab', 'microscope', 'barbell', 'bread/bun',
+        'baozi', 'lion', 'red cabbage', 'polar bear', 'lighter', 'seal',
+        'mangosteen', 'comb', 'eraser', 'pitaya', 'scallop', 'pencil case',
+        'saw', 'table tennis paddle', 'okra', 'starfish', 'eagle', 'monkey',
+        'durian', 'game board', 'rabbit', 'french horn', 'ambulance',
+        'asparagus', 'hoverboard', 'pasta', 'target', 'hotair balloon',
+        'chainsaw', 'lobster', 'iron', 'flashlight'
+    ]
+
+
+def objects365v2_classes():
+    return [
+        'Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp',
+        'Glasses', 'Bottle', 'Desk', 'Cup', 'Street Lights', 'Cabinet/shelf',
+        'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet',
+        'Book', 'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower',
+        'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag', 'Pillow', 'Boots',
+        'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt',
+        'Moniter/TV', 'Backpack', 'Umbrella', 'Traffic Light', 'Speaker',
+        'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle', 'Stool',
+        'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Bakset', 'Drum',
+        'Pen/Pencil', 'Bus', 'Wild Bird', 'High Heels', 'Motorcycle', 'Guitar',
+        'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
+        'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy',
+        'Candle', 'Sailboat', 'Laptop', 'Awning', 'Bed', 'Faucet', 'Tent',
+        'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner',
+        'Knife', 'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork',
+        'Traffic Sign', 'Ballon', 'Tripod', 'Dog', 'Spoon', 'Clock', 'Pot',
+        'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger',
+        'Blackboard/Whiteboard', 'Napkin', 'Other Fish', 'Orange/Tangerine',
+        'Toiletry', 'Keyboard', 'Tomato', 'Lantern', 'Machinery Vehicle',
+        'Fan', 'Green Vegetables', 'Banana', 'Baseball Glove', 'Airplane',
+        'Mouse', 'Train', 'Pumpkin', 'Soccer', 'Skiboard', 'Luggage',
+        'Nightstand', 'Tea pot', 'Telephone', 'Trolley', 'Head Phone',
+        'Sports Car', 'Stop Sign', 'Dessert', 'Scooter', 'Stroller', 'Crane',
+        'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck', 'Baseball Bat',
+        'Surveillance Camera', 'Cat', 'Jug', 'Broccoli', 'Piano', 'Pizza',
+        'Elephant', 'Skateboard', 'Surfboard', 'Gun',
+        'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot',
+        'Toilet', 'Kite', 'Strawberry', 'Other Balls', 'Shovel', 'Pepper',
+        'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
+        'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board',
+        'Coffee Table', 'Side Table', 'Scissors', 'Marker', 'Pie', 'Ladder',
+        'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball',
+        'Zebra', 'Grape', 'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin',
+        'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck', 'Billards',
+        'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase',
+        'Cucumber', 'Cigar/Cigarette ', 'Paint Brush', 'Pear', 'Heavy Truck',
+        'Hamburger', 'Extractor', 'Extention Cord', 'Tong', 'Tennis Racket',
+        'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis',
+        'Ship', 'Swing', 'Coffee Machine', 'Slide', 'Carriage', 'Onion',
+        'Green beans', 'Projector', 'Frisbee',
+        'Washing Machine/Drying Machine', 'Chicken', 'Printer', 'Watermelon',
+        'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hotair ballon',
+        'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog',
+        'Blender', 'Peach', 'Rice', 'Wallet/Purse', 'Volleyball', 'Deer',
+        'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple',
+        'Golf Ball', 'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle',
+        'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin', 'Megaphone',
+        'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion',
+        'Sandwich', 'Nuts', 'Speed Limit Sign', 'Induction Cooker', 'Broom',
+        'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
+        'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese',
+        'Notepaper', 'Cherry', 'Pliers', 'CD', 'Pasta', 'Hammer', 'Cue',
+        'Avocado', 'Hamimelon', 'Flask', 'Mushroon', 'Screwdriver', 'Soap',
+        'Recorder', 'Bear', 'Eggplant', 'Board Eraser', 'Coconut',
+        'Tape Measur/ Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips', 'Steak',
+        'Crosswalk Sign', 'Stapler', 'Campel', 'Formula 1 ', 'Pomegranate',
+        'Dishwasher', 'Crab', 'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba',
+        'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal', 'Buttefly',
+        'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill',
+        'Hair Dryer', 'Egg tart', 'Jellyfish', 'Treadmill', 'Lighter',
+        'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target',
+        'French', 'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak',
+        'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell', 'Scallop',
+        'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Teniis paddle',
+        'Cosmetics Brush/Eyeliner Pencil', 'Chainsaw', 'Eraser', 'Lobster',
+        'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling',
+        'Table Tennis '
+    ]
+
+
+dataset_aliases = {
+    'voc': ['voc', 'pascal_voc', 'voc07', 'voc12'],
+    'imagenet_det': ['det', 'imagenet_det', 'ilsvrc_det'],
+    'imagenet_vid': ['vid', 'imagenet_vid', 'ilsvrc_vid'],
+    'coco': ['coco', 'mscoco', 'ms_coco'],
+    'wider_face': ['WIDERFaceDataset', 'wider_face', 'WIDERFace'],
+    'cityscapes': ['cityscapes'],
+    'oid_challenge': ['oid_challenge', 'openimages_challenge'],
+    'oid_v6': ['oid_v6', 'openimages_v6'],
+    'objects365v1': ['objects365v1', 'obj365v1'],
+    'objects365v2': ['objects365v2', 'obj365v2']
+}
+
+
+def get_classes(dataset):
+    """Get class names of a dataset."""
+    alias2name = {}
+    for name, aliases in dataset_aliases.items():
+        for alias in aliases:
+            alias2name[alias] = name
+
+    if mmcv.is_str(dataset):
+        if dataset in alias2name:
+            labels = eval(alias2name[dataset] + '_classes()')
+        else:
+            raise ValueError(f'Unrecognized dataset: {dataset}')
+    else:
+        raise TypeError(f'dataset must a str, but got {type(dataset)}')
+    return labels
diff --git a/mmdet/core/evaluation/eval_hooks.py b/mmdet/core/evaluation/eval_hooks.py
new file mode 100644
index 0000000..2d56df4
--- /dev/null
+++ b/mmdet/core/evaluation/eval_hooks.py
@@ -0,0 +1,189 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import bisect
+import os.path as osp
+
+import mmcv
+import torch.distributed as dist
+from mmcv.runner import DistEvalHook as BaseDistEvalHook
+from mmcv.runner import EvalHook as BaseEvalHook
+from torch.nn.modules.batchnorm import _BatchNorm
+
+
+def _calc_dynamic_intervals(start_interval, dynamic_interval_list):
+    assert mmcv.is_list_of(dynamic_interval_list, tuple)
+
+    dynamic_milestones = [0]
+    dynamic_milestones.extend(
+        [dynamic_interval[0] for dynamic_interval in dynamic_interval_list])
+    dynamic_intervals = [start_interval]
+    dynamic_intervals.extend(
+        [dynamic_interval[1] for dynamic_interval in dynamic_interval_list])
+    return dynamic_milestones, dynamic_intervals
+
+
+class EvalHook(BaseEvalHook):
+
+    def __init__(self, *args, dynamic_intervals=None, **kwargs):
+        super(EvalHook, self).__init__(*args, **kwargs)
+        self.latest_results = None
+
+        self.use_dynamic_intervals = dynamic_intervals is not None
+        if self.use_dynamic_intervals:
+            self.dynamic_milestones, self.dynamic_intervals = \
+                _calc_dynamic_intervals(self.interval, dynamic_intervals)
+
+    def _decide_interval(self, runner):
+        if self.use_dynamic_intervals:
+            progress = runner.epoch if self.by_epoch else runner.iter
+            step = bisect.bisect(self.dynamic_milestones, (progress + 1))
+            # Dynamically modify the evaluation interval
+            self.interval = self.dynamic_intervals[step - 1]
+
+    def before_train_epoch(self, runner):
+        """Evaluate the model only at the start of training by epoch."""
+        self._decide_interval(runner)
+        super().before_train_epoch(runner)
+
+    def before_train_iter(self, runner):
+        self._decide_interval(runner)
+        super().before_train_iter(runner)
+
+    def _do_evaluate(self, runner):
+        """perform evaluation and save ckpt."""
+        if not self._should_evaluate(runner):
+            return
+
+        from mmdet.apis import single_gpu_test
+
+        # Changed results to self.results so that MMDetWandbHook can access
+        # the evaluation results and log them to wandb.
+        results = single_gpu_test(runner.model, self.dataloader, show=False)
+        self.latest_results = results
+        runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
+        key_score = self.evaluate(runner, results)
+        # the key_score may be `None` so it needs to skip the action to save
+        # the best checkpoint
+        if self.save_best and key_score:
+            self._save_ckpt(runner, key_score)
+
+
+# Note: Considering that MMCV's EvalHook updated its interface in V1.3.16,
+# in order to avoid strong version dependency, we did not directly
+# inherit EvalHook but BaseDistEvalHook.
+class DistEvalHook(BaseDistEvalHook):
+
+    def __init__(self, *args, dynamic_intervals=None, **kwargs):
+        super(DistEvalHook, self).__init__(*args, **kwargs)
+        self.latest_results = None
+
+        self.use_dynamic_intervals = dynamic_intervals is not None
+        if self.use_dynamic_intervals:
+            self.dynamic_milestones, self.dynamic_intervals = \
+                _calc_dynamic_intervals(self.interval, dynamic_intervals)
+
+    def _decide_interval(self, runner):
+        if self.use_dynamic_intervals:
+            progress = runner.epoch if self.by_epoch else runner.iter
+            step = bisect.bisect(self.dynamic_milestones, (progress + 1))
+            # Dynamically modify the evaluation interval
+            self.interval = self.dynamic_intervals[step - 1]
+
+    def before_train_epoch(self, runner):
+        """Evaluate the model only at the start of training by epoch."""
+        self._decide_interval(runner)
+        super().before_train_epoch(runner)
+
+    def before_train_iter(self, runner):
+        self._decide_interval(runner)
+        super().before_train_iter(runner)
+
+    def _do_evaluate(self, runner):
+        """perform evaluation and save ckpt."""
+        # Synchronization of BatchNorm's buffer (running_mean
+        # and running_var) is not supported in the DDP of pytorch,
+        # which may cause the inconsistent performance of models in
+        # different ranks, so we broadcast BatchNorm's buffers
+        # of rank 0 to other ranks to avoid this.
+        if self.broadcast_bn_buffer:
+            model = runner.model
+            for name, module in model.named_modules():
+                if isinstance(module,
+                              _BatchNorm) and module.track_running_stats:
+                    dist.broadcast(module.running_var, 0)
+                    dist.broadcast(module.running_mean, 0)
+
+        if not self._should_evaluate(runner):
+            return
+
+        tmpdir = self.tmpdir
+        if tmpdir is None:
+            tmpdir = osp.join(runner.work_dir, '.eval_hook')
+
+        from mmdet.apis import multi_gpu_test
+
+        # Changed results to self.results so that MMDetWandbHook can access
+        # the evaluation results and log them to wandb.
+        results = multi_gpu_test(
+            runner.model,
+            self.dataloader,
+            tmpdir=tmpdir,
+            gpu_collect=self.gpu_collect)
+        self.latest_results = results
+        if runner.rank == 0:
+            print('\n')
+            runner.log_buffer.output['eval_iter_num'] = len(self.dataloader)
+            key_score = self.evaluate(runner, results)
+
+            # the key_score may be `None` so it needs to skip
+            # the action to save the best checkpoint
+            if self.save_best and key_score:
+                big_flag = self._save_ckpt(runner, key_score)
+                if big_flag:
+                    result_path = self.best_ckpt_path[:-4] + '.pkl'  # 上一步已经更新了best_ckpt_path了
+                    print(f'\nwriting results to {result_path}')
+                    mmcv.dump(results, result_path)
+
+    def _save_ckpt(self, runner, key_score):
+        """Save the best checkpoint.
+
+        It will compare the score according to the compare function, write
+        related information (best score, best checkpoint path) and save the
+        best checkpoint into ``work_dir``.
+        """
+        if self.by_epoch:
+            current = f'epoch_{runner.epoch + 1}'
+            cur_type, cur_time = 'epoch', runner.epoch + 1
+        else:
+            current = f'iter_{runner.iter + 1}'
+            cur_type, cur_time = 'iter', runner.iter + 1
+
+        best_score = runner.meta['hook_msgs'].get(
+            'best_score', self.init_value_map[self.rule])
+        if self.compare_func(key_score, best_score):
+            best_score = key_score
+            runner.meta['hook_msgs']['best_score'] = best_score
+
+            if self.best_ckpt_path and self.file_client.isfile(
+                    self.best_ckpt_path):
+                self.file_client.remove(self.best_ckpt_path)
+                runner.logger.info(
+                    f'The previous best checkpoint {self.best_ckpt_path} was '
+                    'removed')
+
+            best_ckpt_name = f'best_{self.key_indicator}_{current}.pth'
+            self.best_ckpt_path = self.file_client.join_path(
+                self.out_dir, best_ckpt_name)
+            runner.meta['hook_msgs']['best_ckpt'] = self.best_ckpt_path
+
+            runner.save_checkpoint(
+                self.out_dir,
+                filename_tmpl=best_ckpt_name,
+                create_symlink=False)
+            runner.logger.info(
+                f'Now best checkpoint is saved as {best_ckpt_name}.')
+            runner.logger.info(
+                f'Best {self.key_indicator} is {best_score:0.4f} '
+                f'at {cur_time} {cur_type}.')
+            return True
+        return False
+            
diff --git a/mmdet/core/evaluation/mean_ap.py b/mmdet/core/evaluation/mean_ap.py
new file mode 100644
index 0000000..9568912
--- /dev/null
+++ b/mmdet/core/evaluation/mean_ap.py
@@ -0,0 +1,782 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from multiprocessing import Pool
+
+import mmcv
+import numpy as np
+from mmcv.utils import print_log
+from terminaltables import AsciiTable
+
+from .bbox_overlaps import bbox_overlaps
+from .class_names import get_classes
+
+
+def average_precision(recalls, precisions, mode='area'):
+    """Calculate average precision (for single or multiple scales).
+
+    Args:
+        recalls (ndarray): shape (num_scales, num_dets) or (num_dets, )
+        precisions (ndarray): shape (num_scales, num_dets) or (num_dets, )
+        mode (str): 'area' or '11points', 'area' means calculating the area
+            under precision-recall curve, '11points' means calculating
+            the average precision of recalls at [0, 0.1, ..., 1]
+
+    Returns:
+        float or ndarray: calculated average precision
+    """
+    no_scale = False
+    if recalls.ndim == 1:
+        no_scale = True
+        recalls = recalls[np.newaxis, :]
+        precisions = precisions[np.newaxis, :]
+    assert recalls.shape == precisions.shape and recalls.ndim == 2
+    num_scales = recalls.shape[0]
+    ap = np.zeros(num_scales, dtype=np.float32)
+    if mode == 'area':
+        zeros = np.zeros((num_scales, 1), dtype=recalls.dtype)
+        ones = np.ones((num_scales, 1), dtype=recalls.dtype)
+        mrec = np.hstack((zeros, recalls, ones))
+        mpre = np.hstack((zeros, precisions, zeros))
+        for i in range(mpre.shape[1] - 1, 0, -1):
+            mpre[:, i - 1] = np.maximum(mpre[:, i - 1], mpre[:, i])
+        for i in range(num_scales):
+            ind = np.where(mrec[i, 1:] != mrec[i, :-1])[0]
+            ap[i] = np.sum(
+                (mrec[i, ind + 1] - mrec[i, ind]) * mpre[i, ind + 1])
+    elif mode == '11points':
+        for i in range(num_scales):
+            for thr in np.arange(0, 1 + 1e-3, 0.1):
+                precs = precisions[i, recalls[i, :] >= thr]
+                prec = precs.max() if precs.size > 0 else 0
+                ap[i] += prec
+        ap /= 11
+    else:
+        raise ValueError(
+            'Unrecognized mode, only "area" and "11points" are supported')
+    if no_scale:
+        ap = ap[0]
+    return ap
+
+
+def tpfp_imagenet(det_bboxes,
+                  gt_bboxes,
+                  gt_bboxes_ignore=None,
+                  default_iou_thr=0.5,
+                  area_ranges=None,
+                  use_legacy_coordinate=False,
+                  **kwargs):
+    """Check if detected bboxes are true positive or false positive.
+
+    Args:
+        det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5).
+        gt_bboxes (ndarray): GT bboxes of this image, of shape (n, 4).
+        gt_bboxes_ignore (ndarray): Ignored gt bboxes of this image,
+            of shape (k, 4). Default: None
+        default_iou_thr (float): IoU threshold to be considered as matched for
+            medium and large bboxes (small ones have special rules).
+            Default: 0.5.
+        area_ranges (list[tuple] | None): Range of bbox areas to be evaluated,
+            in the format [(min1, max1), (min2, max2), ...]. Default: None.
+        use_legacy_coordinate (bool): Whether to use coordinate system in
+            mmdet v1.x. which means width, height should be
+            calculated as 'x2 - x1 + 1` and 'y2 - y1 + 1' respectively.
+            Default: False.
+
+    Returns:
+        tuple[np.ndarray]: (tp, fp) whose elements are 0 and 1. The shape of
+        each array is (num_scales, m).
+    """
+
+    if not use_legacy_coordinate:
+        extra_length = 0.
+    else:
+        extra_length = 1.
+
+    # an indicator of ignored gts
+    gt_ignore_inds = np.concatenate(
+        (np.zeros(gt_bboxes.shape[0],
+                  dtype=bool), np.ones(gt_bboxes_ignore.shape[0], dtype=bool)))
+    # stack gt_bboxes and gt_bboxes_ignore for convenience
+    gt_bboxes = np.vstack((gt_bboxes, gt_bboxes_ignore))
+
+    num_dets = det_bboxes.shape[0]
+    num_gts = gt_bboxes.shape[0]
+    if area_ranges is None:
+        area_ranges = [(None, None)]
+    num_scales = len(area_ranges)
+    # tp and fp are of shape (num_scales, num_gts), each row is tp or fp
+    # of a certain scale.
+    tp = np.zeros((num_scales, num_dets), dtype=np.float32)
+    fp = np.zeros((num_scales, num_dets), dtype=np.float32)
+    if gt_bboxes.shape[0] == 0:
+        if area_ranges == [(None, None)]:
+            fp[...] = 1
+        else:
+            det_areas = (
+                det_bboxes[:, 2] - det_bboxes[:, 0] + extra_length) * (
+                    det_bboxes[:, 3] - det_bboxes[:, 1] + extra_length)
+            for i, (min_area, max_area) in enumerate(area_ranges):
+                fp[i, (det_areas >= min_area) & (det_areas < max_area)] = 1
+        return tp, fp
+    ious = bbox_overlaps(
+        det_bboxes, gt_bboxes - 1, use_legacy_coordinate=use_legacy_coordinate)
+    gt_w = gt_bboxes[:, 2] - gt_bboxes[:, 0] + extra_length
+    gt_h = gt_bboxes[:, 3] - gt_bboxes[:, 1] + extra_length
+    iou_thrs = np.minimum((gt_w * gt_h) / ((gt_w + 10.0) * (gt_h + 10.0)),
+                          default_iou_thr)
+    # sort all detections by scores in descending order
+    sort_inds = np.argsort(-det_bboxes[:, -1])
+    for k, (min_area, max_area) in enumerate(area_ranges):
+        gt_covered = np.zeros(num_gts, dtype=bool)
+        # if no area range is specified, gt_area_ignore is all False
+        if min_area is None:
+            gt_area_ignore = np.zeros_like(gt_ignore_inds, dtype=bool)
+        else:
+            gt_areas = gt_w * gt_h
+            gt_area_ignore = (gt_areas < min_area) | (gt_areas >= max_area)
+        for i in sort_inds:
+            max_iou = -1
+            matched_gt = -1
+            # find best overlapped available gt
+            for j in range(num_gts):
+                # different from PASCAL VOC: allow finding other gts if the
+                # best overlapped ones are already matched by other det bboxes
+                if gt_covered[j]:
+                    continue
+                elif ious[i, j] >= iou_thrs[j] and ious[i, j] > max_iou:
+                    max_iou = ious[i, j]
+                    matched_gt = j
+            # there are 4 cases for a det bbox:
+            # 1. it matches a gt, tp = 1, fp = 0
+            # 2. it matches an ignored gt, tp = 0, fp = 0
+            # 3. it matches no gt and within area range, tp = 0, fp = 1
+            # 4. it matches no gt but is beyond area range, tp = 0, fp = 0
+            if matched_gt >= 0:
+                gt_covered[matched_gt] = 1
+                if not (gt_ignore_inds[matched_gt]
+                        or gt_area_ignore[matched_gt]):
+                    tp[k, i] = 1
+            elif min_area is None:
+                fp[k, i] = 1
+            else:
+                bbox = det_bboxes[i, :4]
+                area = (bbox[2] - bbox[0] + extra_length) * (
+                    bbox[3] - bbox[1] + extra_length)
+                if area >= min_area and area < max_area:
+                    fp[k, i] = 1
+    return tp, fp
+
+
+def tpfp_default(det_bboxes,
+                 gt_bboxes,
+                 gt_bboxes_ignore=None,
+                 iou_thr=0.5,
+                 area_ranges=None,
+                 use_legacy_coordinate=False,
+                 **kwargs):
+    """Check if detected bboxes are true positive or false positive.
+
+    Args:
+        det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5).
+        gt_bboxes (ndarray): GT bboxes of this image, of shape (n, 4).
+        gt_bboxes_ignore (ndarray): Ignored gt bboxes of this image,
+            of shape (k, 4). Default: None
+        iou_thr (float): IoU threshold to be considered as matched.
+            Default: 0.5.
+        area_ranges (list[tuple] | None): Range of bbox areas to be
+            evaluated, in the format [(min1, max1), (min2, max2), ...].
+            Default: None.
+        use_legacy_coordinate (bool): Whether to use coordinate system in
+            mmdet v1.x. which means width, height should be
+            calculated as 'x2 - x1 + 1` and 'y2 - y1 + 1' respectively.
+            Default: False.
+
+    Returns:
+        tuple[np.ndarray]: (tp, fp) whose elements are 0 and 1. The shape of
+        each array is (num_scales, m).
+    """
+
+    if not use_legacy_coordinate:
+        extra_length = 0.
+    else:
+        extra_length = 1.
+
+    # an indicator of ignored gts
+    gt_ignore_inds = np.concatenate(
+        (np.zeros(gt_bboxes.shape[0],
+                  dtype=bool), np.ones(gt_bboxes_ignore.shape[0], dtype=bool)))
+    # stack gt_bboxes and gt_bboxes_ignore for convenience
+    gt_bboxes = np.vstack((gt_bboxes, gt_bboxes_ignore))
+
+    num_dets = det_bboxes.shape[0]
+    num_gts = gt_bboxes.shape[0]
+    if area_ranges is None:
+        area_ranges = [(None, None)]
+    num_scales = len(area_ranges)
+    # tp and fp are of shape (num_scales, num_gts), each row is tp or fp of
+    # a certain scale
+    tp = np.zeros((num_scales, num_dets), dtype=np.float32)
+    fp = np.zeros((num_scales, num_dets), dtype=np.float32)
+
+    # if there is no gt bboxes in this image, then all det bboxes
+    # within area range are false positives
+    if gt_bboxes.shape[0] == 0:
+        if area_ranges == [(None, None)]:
+            fp[...] = 1
+        else:
+            det_areas = (
+                det_bboxes[:, 2] - det_bboxes[:, 0] + extra_length) * (
+                    det_bboxes[:, 3] - det_bboxes[:, 1] + extra_length)
+            for i, (min_area, max_area) in enumerate(area_ranges):
+                fp[i, (det_areas >= min_area) & (det_areas < max_area)] = 1
+        return tp, fp
+
+    ious = bbox_overlaps(
+        det_bboxes, gt_bboxes, use_legacy_coordinate=use_legacy_coordinate)
+    # for each det, the max iou with all gts
+    ious_max = ious.max(axis=1)
+    # for each det, which gt overlaps most with it
+    ious_argmax = ious.argmax(axis=1)
+    # sort all dets in descending order by scores
+    sort_inds = np.argsort(-det_bboxes[:, -1])
+    for k, (min_area, max_area) in enumerate(area_ranges):
+        gt_covered = np.zeros(num_gts, dtype=bool)
+        # if no area range is specified, gt_area_ignore is all False
+        if min_area is None:
+            gt_area_ignore = np.zeros_like(gt_ignore_inds, dtype=bool)
+        else:
+            gt_areas = (gt_bboxes[:, 2] - gt_bboxes[:, 0] + extra_length) * (
+                gt_bboxes[:, 3] - gt_bboxes[:, 1] + extra_length)
+            gt_area_ignore = (gt_areas < min_area) | (gt_areas >= max_area)
+        for i in sort_inds:
+            if ious_max[i] >= iou_thr:
+                matched_gt = ious_argmax[i]
+                if not (gt_ignore_inds[matched_gt]
+                        or gt_area_ignore[matched_gt]):
+                    if not gt_covered[matched_gt]:
+                        gt_covered[matched_gt] = True
+                        tp[k, i] = 1
+                    else:
+                        fp[k, i] = 1
+                # otherwise ignore this detected bbox, tp = 0, fp = 0
+            elif min_area is None:
+                fp[k, i] = 1
+            else:
+                bbox = det_bboxes[i, :4]
+                area = (bbox[2] - bbox[0] + extra_length) * (
+                    bbox[3] - bbox[1] + extra_length)
+                if area >= min_area and area < max_area:
+                    fp[k, i] = 1
+    return tp, fp
+
+
+def tpfp_openimages(det_bboxes,
+                    gt_bboxes,
+                    gt_bboxes_ignore=None,
+                    iou_thr=0.5,
+                    area_ranges=None,
+                    use_legacy_coordinate=False,
+                    gt_bboxes_group_of=None,
+                    use_group_of=True,
+                    ioa_thr=0.5,
+                    **kwargs):
+    """Check if detected bboxes are true positive or false positive.
+
+    Args:
+        det_bbox (ndarray): Detected bboxes of this image, of shape (m, 5).
+        gt_bboxes (ndarray): GT bboxes of this image, of shape (n, 4).
+        gt_bboxes_ignore (ndarray): Ignored gt bboxes of this image,
+            of shape (k, 4). Default: None
+        iou_thr (float): IoU threshold to be considered as matched.
+            Default: 0.5.
+        area_ranges (list[tuple] | None): Range of bbox areas to be
+            evaluated, in the format [(min1, max1), (min2, max2), ...].
+            Default: None.
+        use_legacy_coordinate (bool): Whether to use coordinate system in
+            mmdet v1.x. which means width, height should be
+            calculated as 'x2 - x1 + 1` and 'y2 - y1 + 1' respectively.
+            Default: False.
+        gt_bboxes_group_of (ndarray): GT group_of of this image, of shape
+            (k, 1). Default: None
+        use_group_of (bool): Whether to use group of when calculate TP and FP,
+            which only used in OpenImages evaluation. Default: True.
+        ioa_thr (float | None): IoA threshold to be considered as matched,
+            which only used in OpenImages evaluation. Default: 0.5.
+
+    Returns:
+        tuple[np.ndarray]: Returns a tuple (tp, fp, det_bboxes), where
+        (tp, fp) whose elements are 0 and 1. The shape of each array is
+        (num_scales, m). (det_bboxes) whose will filter those are not
+        matched by group of gts when processing Open Images evaluation.
+        The shape is (num_scales, m).
+    """
+
+    if not use_legacy_coordinate:
+        extra_length = 0.
+    else:
+        extra_length = 1.
+
+    # an indicator of ignored gts
+    gt_ignore_inds = np.concatenate(
+        (np.zeros(gt_bboxes.shape[0],
+                  dtype=bool), np.ones(gt_bboxes_ignore.shape[0], dtype=bool)))
+    # stack gt_bboxes and gt_bboxes_ignore for convenience
+    gt_bboxes = np.vstack((gt_bboxes, gt_bboxes_ignore))
+
+    num_dets = det_bboxes.shape[0]
+    num_gts = gt_bboxes.shape[0]
+    if area_ranges is None:
+        area_ranges = [(None, None)]
+    num_scales = len(area_ranges)
+    # tp and fp are of shape (num_scales, num_gts), each row is tp or fp of
+    # a certain scale
+    tp = np.zeros((num_scales, num_dets), dtype=np.float32)
+    fp = np.zeros((num_scales, num_dets), dtype=np.float32)
+
+    # if there is no gt bboxes in this image, then all det bboxes
+    # within area range are false positives
+    if gt_bboxes.shape[0] == 0:
+        if area_ranges == [(None, None)]:
+            fp[...] = 1
+        else:
+            det_areas = (
+                det_bboxes[:, 2] - det_bboxes[:, 0] + extra_length) * (
+                    det_bboxes[:, 3] - det_bboxes[:, 1] + extra_length)
+            for i, (min_area, max_area) in enumerate(area_ranges):
+                fp[i, (det_areas >= min_area) & (det_areas < max_area)] = 1
+        return tp, fp, det_bboxes
+
+    if gt_bboxes_group_of is not None and use_group_of:
+        # if handle group-of boxes, divided gt boxes into two parts:
+        # non-group-of and group-of.Then calculate ious and ioas through
+        # non-group-of group-of gts respectively. This only used in
+        # OpenImages evaluation.
+        assert gt_bboxes_group_of.shape[0] == gt_bboxes.shape[0]
+        non_group_gt_bboxes = gt_bboxes[~gt_bboxes_group_of]
+        group_gt_bboxes = gt_bboxes[gt_bboxes_group_of]
+        num_gts_group = group_gt_bboxes.shape[0]
+        ious = bbox_overlaps(det_bboxes, non_group_gt_bboxes)
+        ioas = bbox_overlaps(det_bboxes, group_gt_bboxes, mode='iof')
+    else:
+        # if not consider group-of boxes, only calculate ious through gt boxes
+        ious = bbox_overlaps(
+            det_bboxes, gt_bboxes, use_legacy_coordinate=use_legacy_coordinate)
+        ioas = None
+
+    if ious.shape[1] > 0:
+        # for each det, the max iou with all gts
+        ious_max = ious.max(axis=1)
+        # for each det, which gt overlaps most with it
+        ious_argmax = ious.argmax(axis=1)
+        # sort all dets in descending order by scores
+        sort_inds = np.argsort(-det_bboxes[:, -1])
+        for k, (min_area, max_area) in enumerate(area_ranges):
+            gt_covered = np.zeros(num_gts, dtype=bool)
+            # if no area range is specified, gt_area_ignore is all False
+            if min_area is None:
+                gt_area_ignore = np.zeros_like(gt_ignore_inds, dtype=bool)
+            else:
+                gt_areas = (
+                    gt_bboxes[:, 2] - gt_bboxes[:, 0] + extra_length) * (
+                        gt_bboxes[:, 3] - gt_bboxes[:, 1] + extra_length)
+                gt_area_ignore = (gt_areas < min_area) | (gt_areas >= max_area)
+            for i in sort_inds:
+                if ious_max[i] >= iou_thr:
+                    matched_gt = ious_argmax[i]
+                    if not (gt_ignore_inds[matched_gt]
+                            or gt_area_ignore[matched_gt]):
+                        if not gt_covered[matched_gt]:
+                            gt_covered[matched_gt] = True
+                            tp[k, i] = 1
+                        else:
+                            fp[k, i] = 1
+                    # otherwise ignore this detected bbox, tp = 0, fp = 0
+                elif min_area is None:
+                    fp[k, i] = 1
+                else:
+                    bbox = det_bboxes[i, :4]
+                    area = (bbox[2] - bbox[0] + extra_length) * (
+                        bbox[3] - bbox[1] + extra_length)
+                    if area >= min_area and area < max_area:
+                        fp[k, i] = 1
+    else:
+        # if there is no no-group-of gt bboxes in this image,
+        # then all det bboxes within area range are false positives.
+        # Only used in OpenImages evaluation.
+        if area_ranges == [(None, None)]:
+            fp[...] = 1
+        else:
+            det_areas = (
+                det_bboxes[:, 2] - det_bboxes[:, 0] + extra_length) * (
+                    det_bboxes[:, 3] - det_bboxes[:, 1] + extra_length)
+            for i, (min_area, max_area) in enumerate(area_ranges):
+                fp[i, (det_areas >= min_area) & (det_areas < max_area)] = 1
+
+    if ioas is None or ioas.shape[1] <= 0:
+        return tp, fp, det_bboxes
+    else:
+        # The evaluation of group-of TP and FP are done in two stages:
+        # 1. All detections are first matched to non group-of boxes; true
+        #    positives are determined.
+        # 2. Detections that are determined as false positives are matched
+        #    against group-of boxes and calculated group-of TP and FP.
+        # Only used in OpenImages evaluation.
+        det_bboxes_group = np.zeros(
+            (num_scales, ioas.shape[1], det_bboxes.shape[1]), dtype=float)
+        match_group_of = np.zeros((num_scales, num_dets), dtype=bool)
+        tp_group = np.zeros((num_scales, num_gts_group), dtype=np.float32)
+        ioas_max = ioas.max(axis=1)
+        # for each det, which gt overlaps most with it
+        ioas_argmax = ioas.argmax(axis=1)
+        # sort all dets in descending order by scores
+        sort_inds = np.argsort(-det_bboxes[:, -1])
+        for k, (min_area, max_area) in enumerate(area_ranges):
+            box_is_covered = tp[k]
+            # if no area range is specified, gt_area_ignore is all False
+            if min_area is None:
+                gt_area_ignore = np.zeros_like(gt_ignore_inds, dtype=bool)
+            else:
+                gt_areas = (gt_bboxes[:, 2] - gt_bboxes[:, 0]) * (
+                    gt_bboxes[:, 3] - gt_bboxes[:, 1])
+                gt_area_ignore = (gt_areas < min_area) | (gt_areas >= max_area)
+            for i in sort_inds:
+                matched_gt = ioas_argmax[i]
+                if not box_is_covered[i]:
+                    if ioas_max[i] >= ioa_thr:
+                        if not (gt_ignore_inds[matched_gt]
+                                or gt_area_ignore[matched_gt]):
+                            if not tp_group[k, matched_gt]:
+                                tp_group[k, matched_gt] = 1
+                                match_group_of[k, i] = True
+                            else:
+                                match_group_of[k, i] = True
+
+                            if det_bboxes_group[k, matched_gt, -1] < \
+                                    det_bboxes[i, -1]:
+                                det_bboxes_group[k, matched_gt] = \
+                                    det_bboxes[i]
+
+        fp_group = (tp_group <= 0).astype(float)
+        tps = []
+        fps = []
+        # concatenate tp, fp, and det-boxes which not matched group of
+        # gt boxes and tp_group, fp_group, and det_bboxes_group which
+        # matched group of boxes respectively.
+        for i in range(num_scales):
+            tps.append(
+                np.concatenate((tp[i][~match_group_of[i]], tp_group[i])))
+            fps.append(
+                np.concatenate((fp[i][~match_group_of[i]], fp_group[i])))
+            det_bboxes = np.concatenate(
+                (det_bboxes[~match_group_of[i]], det_bboxes_group[i]))
+
+        tp = np.vstack(tps)
+        fp = np.vstack(fps)
+        return tp, fp, det_bboxes
+
+
+def get_cls_results(det_results, annotations, class_id):
+    """Get det results and gt information of a certain class.
+
+    Args:
+        det_results (list[list]): Same as `eval_map()`.
+        annotations (list[dict]): Same as `eval_map()`.
+        class_id (int): ID of a specific class.
+
+    Returns:
+        tuple[list[np.ndarray]]: detected bboxes, gt bboxes, ignored gt bboxes
+    """
+    cls_dets = [img_res[class_id] for img_res in det_results]
+    cls_gts = []
+    cls_gts_ignore = []
+    for ann in annotations:
+        gt_inds = ann['labels'] == class_id
+        cls_gts.append(ann['bboxes'][gt_inds, :])
+
+        if ann.get('labels_ignore', None) is not None:
+            ignore_inds = ann['labels_ignore'] == class_id
+            cls_gts_ignore.append(ann['bboxes_ignore'][ignore_inds, :])
+        else:
+            cls_gts_ignore.append(np.empty((0, 4), dtype=np.float32))
+
+    return cls_dets, cls_gts, cls_gts_ignore
+
+
+def get_cls_group_ofs(annotations, class_id):
+    """Get `gt_group_of` of a certain class, which is used in Open Images.
+
+    Args:
+        annotations (list[dict]): Same as `eval_map()`.
+        class_id (int): ID of a specific class.
+
+    Returns:
+        list[np.ndarray]: `gt_group_of` of a certain class.
+    """
+    gt_group_ofs = []
+    for ann in annotations:
+        gt_inds = ann['labels'] == class_id
+        if ann.get('gt_is_group_ofs', None) is not None:
+            gt_group_ofs.append(ann['gt_is_group_ofs'][gt_inds])
+        else:
+            gt_group_ofs.append(np.empty((0, 1), dtype=bool))
+
+    return gt_group_ofs
+
+
+def eval_map(det_results,
+             annotations,
+             scale_ranges=None,
+             iou_thr=0.5,
+             ioa_thr=None,
+             dataset=None,
+             logger=None,
+             tpfp_fn=None,
+             nproc=4,
+             use_legacy_coordinate=False,
+             use_group_of=False):
+    """Evaluate mAP of a dataset.
+
+    Args:
+        det_results (list[list]): [[cls1_det, cls2_det, ...], ...].
+            The outer list indicates images, and the inner list indicates
+            per-class detected bboxes.
+        annotations (list[dict]): Ground truth annotations where each item of
+            the list indicates an image. Keys of annotations are:
+
+            - `bboxes`: numpy array of shape (n, 4)
+            - `labels`: numpy array of shape (n, )
+            - `bboxes_ignore` (optional): numpy array of shape (k, 4)
+            - `labels_ignore` (optional): numpy array of shape (k, )
+        scale_ranges (list[tuple] | None): Range of scales to be evaluated,
+            in the format [(min1, max1), (min2, max2), ...]. A range of
+            (32, 64) means the area range between (32**2, 64**2).
+            Default: None.
+        iou_thr (float): IoU threshold to be considered as matched.
+            Default: 0.5.
+        ioa_thr (float | None): IoA threshold to be considered as matched,
+            which only used in OpenImages evaluation. Default: None.
+        dataset (list[str] | str | None): Dataset name or dataset classes,
+            there are minor differences in metrics for different datasets, e.g.
+            "voc07", "imagenet_det", etc. Default: None.
+        logger (logging.Logger | str | None): The way to print the mAP
+            summary. See `mmcv.utils.print_log()` for details. Default: None.
+        tpfp_fn (callable | None): The function used to determine true/
+            false positives. If None, :func:`tpfp_default` is used as default
+            unless dataset is 'det' or 'vid' (:func:`tpfp_imagenet` in this
+            case). If it is given as a function, then this function is used
+            to evaluate tp & fp. Default None.
+        nproc (int): Processes used for computing TP and FP.
+            Default: 4.
+        use_legacy_coordinate (bool): Whether to use coordinate system in
+            mmdet v1.x. which means width, height should be
+            calculated as 'x2 - x1 + 1` and 'y2 - y1 + 1' respectively.
+            Default: False.
+        use_group_of (bool): Whether to use group of when calculate TP and FP,
+            which only used in OpenImages evaluation. Default: False.
+
+    Returns:
+        tuple: (mAP, [dict, dict, ...])
+    """
+    assert len(det_results) == len(annotations)
+    if not use_legacy_coordinate:
+        extra_length = 0.
+    else:
+        extra_length = 1.
+
+    num_imgs = len(det_results)
+    num_scales = len(scale_ranges) if scale_ranges is not None else 1
+    num_classes = len(det_results[0])  # positive class num
+    area_ranges = ([(rg[0]**2, rg[1]**2) for rg in scale_ranges]
+                   if scale_ranges is not None else None)
+
+    # There is no need to use multi processes to process
+    # when num_imgs = 1 .
+    if num_imgs > 1:
+        assert nproc > 0, 'nproc must be at least one.'
+        nproc = min(nproc, num_imgs)
+        pool = Pool(nproc)
+
+    eval_results = []
+    for i in range(num_classes):
+        # get gt and det bboxes of this class
+        cls_dets, cls_gts, cls_gts_ignore = get_cls_results(
+            det_results, annotations, i)
+        # choose proper function according to datasets to compute tp and fp
+        if tpfp_fn is None:
+            if dataset in ['det', 'vid']:
+                tpfp_fn = tpfp_imagenet
+            elif dataset in ['oid_challenge', 'oid_v6'] \
+                    or use_group_of is True:
+                tpfp_fn = tpfp_openimages
+            else:
+                tpfp_fn = tpfp_default
+        if not callable(tpfp_fn):
+            raise ValueError(
+                f'tpfp_fn has to be a function or None, but got {tpfp_fn}')
+
+        if num_imgs > 1:
+            # compute tp and fp for each image with multiple processes
+            args = []
+            if use_group_of:
+                # used in Open Images Dataset evaluation
+                gt_group_ofs = get_cls_group_ofs(annotations, i)
+                args.append(gt_group_ofs)
+                args.append([use_group_of for _ in range(num_imgs)])
+            if ioa_thr is not None:
+                args.append([ioa_thr for _ in range(num_imgs)])
+
+            tpfp = pool.starmap(
+                tpfp_fn,
+                zip(cls_dets, cls_gts, cls_gts_ignore,
+                    [iou_thr for _ in range(num_imgs)],
+                    [area_ranges for _ in range(num_imgs)],
+                    [use_legacy_coordinate for _ in range(num_imgs)], *args))
+        else:
+            tpfp = tpfp_fn(
+                cls_dets[0],
+                cls_gts[0],
+                cls_gts_ignore[0],
+                iou_thr,
+                area_ranges,
+                use_legacy_coordinate,
+                gt_bboxes_group_of=(get_cls_group_ofs(annotations, i)[0]
+                                    if use_group_of else None),
+                use_group_of=use_group_of,
+                ioa_thr=ioa_thr)
+            tpfp = [tpfp]
+
+        if use_group_of:
+            tp, fp, cls_dets = tuple(zip(*tpfp))
+        else:
+            tp, fp = tuple(zip(*tpfp))
+        # calculate gt number of each scale
+        # ignored gts or gts beyond the specific scale are not counted
+        num_gts = np.zeros(num_scales, dtype=int)
+        for j, bbox in enumerate(cls_gts):
+            if area_ranges is None:
+                num_gts[0] += bbox.shape[0]
+            else:
+                gt_areas = (bbox[:, 2] - bbox[:, 0] + extra_length) * (
+                    bbox[:, 3] - bbox[:, 1] + extra_length)
+                for k, (min_area, max_area) in enumerate(area_ranges):
+                    num_gts[k] += np.sum((gt_areas >= min_area)
+                                         & (gt_areas < max_area))
+        # sort all det bboxes by score, also sort tp and fp
+        cls_dets = np.vstack(cls_dets)
+        num_dets = cls_dets.shape[0]
+        sort_inds = np.argsort(-cls_dets[:, -1])
+        tp = np.hstack(tp)[:, sort_inds]
+        fp = np.hstack(fp)[:, sort_inds]
+        # calculate recall and precision with tp and fp
+        tp = np.cumsum(tp, axis=1)
+        fp = np.cumsum(fp, axis=1)
+        eps = np.finfo(np.float32).eps
+        recalls = tp / np.maximum(num_gts[:, np.newaxis], eps)
+        precisions = tp / np.maximum((tp + fp), eps)
+        # calculate AP
+        if scale_ranges is None:
+            recalls = recalls[0, :]
+            precisions = precisions[0, :]
+            num_gts = num_gts.item()
+        mode = 'area' if dataset != 'voc07' else '11points'
+        ap = average_precision(recalls, precisions, mode)
+        eval_results.append({
+            'num_gts': num_gts,
+            'num_dets': num_dets,
+            'recall': recalls,
+            'precision': precisions,
+            'ap': ap
+        })
+
+    if num_imgs > 1:
+        pool.close()
+
+    if scale_ranges is not None:
+        # shape (num_classes, num_scales)
+        all_ap = np.vstack([cls_result['ap'] for cls_result in eval_results])
+        all_num_gts = np.vstack(
+            [cls_result['num_gts'] for cls_result in eval_results])
+        mean_ap = []
+        for i in range(num_scales):
+            if np.any(all_num_gts[:, i] > 0):
+                mean_ap.append(all_ap[all_num_gts[:, i] > 0, i].mean())
+            else:
+                mean_ap.append(0.0)
+    else:
+        aps = []
+        for cls_result in eval_results:
+            if cls_result['num_gts'] > 0:
+                aps.append(cls_result['ap'])
+        mean_ap = np.array(aps).mean().item() if aps else 0.0
+
+    print_map_summary(
+        mean_ap, eval_results, dataset, area_ranges, logger=logger)
+
+    return mean_ap, eval_results
+
+
+def print_map_summary(mean_ap,
+                      results,
+                      dataset=None,
+                      scale_ranges=None,
+                      logger=None):
+    """Print mAP and results of each class.
+
+    A table will be printed to show the gts/dets/recall/AP of each class and
+    the mAP.
+
+    Args:
+        mean_ap (float): Calculated from `eval_map()`.
+        results (list[dict]): Calculated from `eval_map()`.
+        dataset (list[str] | str | None): Dataset name or dataset classes.
+        scale_ranges (list[tuple] | None): Range of scales to be evaluated.
+        logger (logging.Logger | str | None): The way to print the mAP
+            summary. See `mmcv.utils.print_log()` for details. Default: None.
+    """
+
+    if logger == 'silent':
+        return
+
+    if isinstance(results[0]['ap'], np.ndarray):
+        num_scales = len(results[0]['ap'])
+    else:
+        num_scales = 1
+
+    if scale_ranges is not None:
+        assert len(scale_ranges) == num_scales
+
+    num_classes = len(results)
+
+    recalls = np.zeros((num_scales, num_classes), dtype=np.float32)
+    aps = np.zeros((num_scales, num_classes), dtype=np.float32)
+    num_gts = np.zeros((num_scales, num_classes), dtype=int)
+    for i, cls_result in enumerate(results):
+        if cls_result['recall'].size > 0:
+            recalls[:, i] = np.array(cls_result['recall'], ndmin=2)[:, -1]
+        aps[:, i] = cls_result['ap']
+        num_gts[:, i] = cls_result['num_gts']
+
+    if dataset is None:
+        label_names = [str(i) for i in range(num_classes)]
+    elif mmcv.is_str(dataset):
+        label_names = get_classes(dataset)
+    else:
+        label_names = dataset
+
+    if not isinstance(mean_ap, list):
+        mean_ap = [mean_ap]
+
+    header = ['class', 'gts', 'dets', 'recall', 'ap']
+    for i in range(num_scales):
+        if scale_ranges is not None:
+            print_log(f'Scale range {scale_ranges[i]}', logger=logger)
+        table_data = [header]
+        for j in range(num_classes):
+            row_data = [
+                label_names[j], num_gts[i, j], results[j]['num_dets'],
+                f'{recalls[i, j]:.3f}', f'{aps[i, j]:.3f}'
+            ]
+            table_data.append(row_data)
+        table_data.append(['mAP', '', '', '', f'{mean_ap[i]:.3f}'])
+        table = AsciiTable(table_data)
+        table.inner_footing_row_border = True
+        print_log('\n' + table.table, logger=logger)
diff --git a/mmdet/core/evaluation/panoptic_utils.py b/mmdet/core/evaluation/panoptic_utils.py
new file mode 100644
index 0000000..10c9ad9
--- /dev/null
+++ b/mmdet/core/evaluation/panoptic_utils.py
@@ -0,0 +1,6 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# A custom value to distinguish instance ID and category ID; need to
+# be greater than the number of categories.
+# For a pixel in the panoptic result map:
+#   pan_id = ins_id * INSTANCE_OFFSET + cat_id
+INSTANCE_OFFSET = 1000
diff --git a/mmdet/core/evaluation/recall.py b/mmdet/core/evaluation/recall.py
new file mode 100644
index 0000000..82b3c90
--- /dev/null
+++ b/mmdet/core/evaluation/recall.py
@@ -0,0 +1,197 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from collections.abc import Sequence
+
+import numpy as np
+from mmcv.utils import print_log
+from terminaltables import AsciiTable
+
+from .bbox_overlaps import bbox_overlaps
+
+
+def _recalls(all_ious, proposal_nums, thrs):
+
+    img_num = all_ious.shape[0]
+    total_gt_num = sum([ious.shape[0] for ious in all_ious])
+
+    _ious = np.zeros((proposal_nums.size, total_gt_num), dtype=np.float32)
+    for k, proposal_num in enumerate(proposal_nums):
+        tmp_ious = np.zeros(0)
+        for i in range(img_num):
+            ious = all_ious[i][:, :proposal_num].copy()
+            gt_ious = np.zeros((ious.shape[0]))
+            if ious.size == 0:
+                tmp_ious = np.hstack((tmp_ious, gt_ious))
+                continue
+            for j in range(ious.shape[0]):
+                gt_max_overlaps = ious.argmax(axis=1)
+                max_ious = ious[np.arange(0, ious.shape[0]), gt_max_overlaps]
+                gt_idx = max_ious.argmax()
+                gt_ious[j] = max_ious[gt_idx]
+                box_idx = gt_max_overlaps[gt_idx]
+                ious[gt_idx, :] = -1
+                ious[:, box_idx] = -1
+            tmp_ious = np.hstack((tmp_ious, gt_ious))
+        _ious[k, :] = tmp_ious
+
+    _ious = np.fliplr(np.sort(_ious, axis=1))
+    recalls = np.zeros((proposal_nums.size, thrs.size))
+    for i, thr in enumerate(thrs):
+        recalls[:, i] = (_ious >= thr).sum(axis=1) / float(total_gt_num)
+
+    return recalls
+
+
+def set_recall_param(proposal_nums, iou_thrs):
+    """Check proposal_nums and iou_thrs and set correct format."""
+    if isinstance(proposal_nums, Sequence):
+        _proposal_nums = np.array(proposal_nums)
+    elif isinstance(proposal_nums, int):
+        _proposal_nums = np.array([proposal_nums])
+    else:
+        _proposal_nums = proposal_nums
+
+    if iou_thrs is None:
+        _iou_thrs = np.array([0.5])
+    elif isinstance(iou_thrs, Sequence):
+        _iou_thrs = np.array(iou_thrs)
+    elif isinstance(iou_thrs, float):
+        _iou_thrs = np.array([iou_thrs])
+    else:
+        _iou_thrs = iou_thrs
+
+    return _proposal_nums, _iou_thrs
+
+
+def eval_recalls(gts,
+                 proposals,
+                 proposal_nums=None,
+                 iou_thrs=0.5,
+                 logger=None,
+                 use_legacy_coordinate=False):
+    """Calculate recalls.
+
+    Args:
+        gts (list[ndarray]): a list of arrays of shape (n, 4)
+        proposals (list[ndarray]): a list of arrays of shape (k, 4) or (k, 5)
+        proposal_nums (int | Sequence[int]): Top N proposals to be evaluated.
+        iou_thrs (float | Sequence[float]): IoU thresholds. Default: 0.5.
+        logger (logging.Logger | str | None): The way to print the recall
+            summary. See `mmcv.utils.print_log()` for details. Default: None.
+        use_legacy_coordinate (bool): Whether use coordinate system
+            in mmdet v1.x. "1" was added to both height and width
+            which means w, h should be
+            computed as 'x2 - x1 + 1` and 'y2 - y1 + 1'. Default: False.
+
+
+    Returns:
+        ndarray: recalls of different ious and proposal nums
+    """
+
+    img_num = len(gts)
+    assert img_num == len(proposals)
+    proposal_nums, iou_thrs = set_recall_param(proposal_nums, iou_thrs)
+    all_ious = []
+    for i in range(img_num):
+        if proposals[i].ndim == 2 and proposals[i].shape[1] == 5:
+            scores = proposals[i][:, 4]
+            sort_idx = np.argsort(scores)[::-1]
+            img_proposal = proposals[i][sort_idx, :]
+        else:
+            img_proposal = proposals[i]
+        prop_num = min(img_proposal.shape[0], proposal_nums[-1])
+        if gts[i] is None or gts[i].shape[0] == 0:
+            ious = np.zeros((0, img_proposal.shape[0]), dtype=np.float32)
+        else:
+            ious = bbox_overlaps(
+                gts[i],
+                img_proposal[:prop_num, :4],
+                use_legacy_coordinate=use_legacy_coordinate)
+        all_ious.append(ious)
+    all_ious = np.array(all_ious)
+    recalls = _recalls(all_ious, proposal_nums, iou_thrs)
+
+    print_recall_summary(recalls, proposal_nums, iou_thrs, logger=logger)
+    return recalls
+
+
+def print_recall_summary(recalls,
+                         proposal_nums,
+                         iou_thrs,
+                         row_idxs=None,
+                         col_idxs=None,
+                         logger=None):
+    """Print recalls in a table.
+
+    Args:
+        recalls (ndarray): calculated from `bbox_recalls`
+        proposal_nums (ndarray or list): top N proposals
+        iou_thrs (ndarray or list): iou thresholds
+        row_idxs (ndarray): which rows(proposal nums) to print
+        col_idxs (ndarray): which cols(iou thresholds) to print
+        logger (logging.Logger | str | None): The way to print the recall
+            summary. See `mmcv.utils.print_log()` for details. Default: None.
+    """
+    proposal_nums = np.array(proposal_nums, dtype=np.int32)
+    iou_thrs = np.array(iou_thrs)
+    if row_idxs is None:
+        row_idxs = np.arange(proposal_nums.size)
+    if col_idxs is None:
+        col_idxs = np.arange(iou_thrs.size)
+    row_header = [''] + iou_thrs[col_idxs].tolist()
+    table_data = [row_header]
+    for i, num in enumerate(proposal_nums[row_idxs]):
+        row = [f'{val:.3f}' for val in recalls[row_idxs[i], col_idxs].tolist()]
+        row.insert(0, num)
+        table_data.append(row)
+    table = AsciiTable(table_data)
+    print_log('\n' + table.table, logger=logger)
+
+
+def plot_num_recall(recalls, proposal_nums):
+    """Plot Proposal_num-Recalls curve.
+
+    Args:
+        recalls(ndarray or list): shape (k,)
+        proposal_nums(ndarray or list): same shape as `recalls`
+    """
+    if isinstance(proposal_nums, np.ndarray):
+        _proposal_nums = proposal_nums.tolist()
+    else:
+        _proposal_nums = proposal_nums
+    if isinstance(recalls, np.ndarray):
+        _recalls = recalls.tolist()
+    else:
+        _recalls = recalls
+
+    import matplotlib.pyplot as plt
+    f = plt.figure()
+    plt.plot([0] + _proposal_nums, [0] + _recalls)
+    plt.xlabel('Proposal num')
+    plt.ylabel('Recall')
+    plt.axis([0, proposal_nums.max(), 0, 1])
+    f.show()
+
+
+def plot_iou_recall(recalls, iou_thrs):
+    """Plot IoU-Recalls curve.
+
+    Args:
+        recalls(ndarray or list): shape (k,)
+        iou_thrs(ndarray or list): same shape as `recalls`
+    """
+    if isinstance(iou_thrs, np.ndarray):
+        _iou_thrs = iou_thrs.tolist()
+    else:
+        _iou_thrs = iou_thrs
+    if isinstance(recalls, np.ndarray):
+        _recalls = recalls.tolist()
+    else:
+        _recalls = recalls
+
+    import matplotlib.pyplot as plt
+    f = plt.figure()
+    plt.plot(_iou_thrs + [1.0], _recalls + [0.])
+    plt.xlabel('IoU')
+    plt.ylabel('Recall')
+    plt.axis([iou_thrs.min(), 1, 0, 1])
+    f.show()
diff --git a/mmdet/core/export/__init__.py b/mmdet/core/export/__init__.py
new file mode 100644
index 0000000..a8179c9
--- /dev/null
+++ b/mmdet/core/export/__init__.py
@@ -0,0 +1,12 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .onnx_helper import (add_dummy_nms_for_onnx, dynamic_clip_for_onnx,
+                          get_k_for_topk)
+from .pytorch2onnx import (build_model_from_cfg,
+                           generate_inputs_and_wrap_model,
+                           preprocess_example_input)
+
+__all__ = [
+    'build_model_from_cfg', 'generate_inputs_and_wrap_model',
+    'preprocess_example_input', 'get_k_for_topk', 'add_dummy_nms_for_onnx',
+    'dynamic_clip_for_onnx'
+]
diff --git a/mmdet/core/export/model_wrappers.py b/mmdet/core/export/model_wrappers.py
new file mode 100644
index 0000000..c7be2df
--- /dev/null
+++ b/mmdet/core/export/model_wrappers.py
@@ -0,0 +1,183 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import warnings
+
+import numpy as np
+import torch
+
+from mmdet.core import bbox2result
+from mmdet.models import BaseDetector
+
+
+class DeployBaseDetector(BaseDetector):
+    """DeployBaseDetector."""
+
+    def __init__(self, class_names, device_id):
+        super(DeployBaseDetector, self).__init__()
+        self.CLASSES = class_names
+        self.device_id = device_id
+
+    def simple_test(self, img, img_metas, **kwargs):
+        raise NotImplementedError('This method is not implemented.')
+
+    def aug_test(self, imgs, img_metas, **kwargs):
+        raise NotImplementedError('This method is not implemented.')
+
+    def extract_feat(self, imgs):
+        raise NotImplementedError('This method is not implemented.')
+
+    def forward_train(self, imgs, img_metas, **kwargs):
+        raise NotImplementedError('This method is not implemented.')
+
+    def val_step(self, data, optimizer):
+        raise NotImplementedError('This method is not implemented.')
+
+    def train_step(self, data, optimizer):
+        raise NotImplementedError('This method is not implemented.')
+
+    def forward_test(self, *, img, img_metas, **kwargs):
+        raise NotImplementedError('This method is not implemented.')
+
+    def async_simple_test(self, img, img_metas, **kwargs):
+        raise NotImplementedError('This method is not implemented.')
+
+    def forward(self, img, img_metas, return_loss=True, **kwargs):
+        outputs = self.forward_test(img, img_metas, **kwargs)
+        batch_dets, batch_labels = outputs[:2]
+        batch_masks = outputs[2] if len(outputs) == 3 else None
+        batch_size = img[0].shape[0]
+        img_metas = img_metas[0]
+        results = []
+        rescale = kwargs.get('rescale', True)
+        for i in range(batch_size):
+            dets, labels = batch_dets[i], batch_labels[i]
+            if rescale:
+                scale_factor = img_metas[i]['scale_factor']
+
+                if isinstance(scale_factor, (list, tuple, np.ndarray)):
+                    assert len(scale_factor) == 4
+                    scale_factor = np.array(scale_factor)[None, :]  # [1,4]
+                dets[:, :4] /= scale_factor
+
+            if 'border' in img_metas[i]:
+                # offset pixel of the top-left corners between original image
+                # and padded/enlarged image, 'border' is used when exporting
+                # CornerNet and CentripetalNet to onnx
+                x_off = img_metas[i]['border'][2]
+                y_off = img_metas[i]['border'][0]
+                dets[:, [0, 2]] -= x_off
+                dets[:, [1, 3]] -= y_off
+                dets[:, :4] *= (dets[:, :4] > 0).astype(dets.dtype)
+
+            dets_results = bbox2result(dets, labels, len(self.CLASSES))
+
+            if batch_masks is not None:
+                masks = batch_masks[i]
+                img_h, img_w = img_metas[i]['img_shape'][:2]
+                ori_h, ori_w = img_metas[i]['ori_shape'][:2]
+                masks = masks[:, :img_h, :img_w]
+                if rescale:
+                    masks = masks.astype(np.float32)
+                    masks = torch.from_numpy(masks)
+                    masks = torch.nn.functional.interpolate(
+                        masks.unsqueeze(0), size=(ori_h, ori_w))
+                    masks = masks.squeeze(0).detach().numpy()
+                if masks.dtype != bool:
+                    masks = masks >= 0.5
+                segms_results = [[] for _ in range(len(self.CLASSES))]
+                for j in range(len(dets)):
+                    segms_results[labels[j]].append(masks[j])
+                results.append((dets_results, segms_results))
+            else:
+                results.append(dets_results)
+        return results
+
+
+class ONNXRuntimeDetector(DeployBaseDetector):
+    """Wrapper for detector's inference with ONNXRuntime."""
+
+    def __init__(self, onnx_file, class_names, device_id):
+        super(ONNXRuntimeDetector, self).__init__(class_names, device_id)
+        import onnxruntime as ort
+
+        # get the custom op path
+        ort_custom_op_path = ''
+        try:
+            from mmcv.ops import get_onnxruntime_op_path
+            ort_custom_op_path = get_onnxruntime_op_path()
+        except (ImportError, ModuleNotFoundError):
+            warnings.warn('If input model has custom op from mmcv, \
+                you may have to build mmcv with ONNXRuntime from source.')
+        session_options = ort.SessionOptions()
+        # register custom op for onnxruntime
+        if osp.exists(ort_custom_op_path):
+            session_options.register_custom_ops_library(ort_custom_op_path)
+        sess = ort.InferenceSession(onnx_file, session_options)
+        providers = ['CPUExecutionProvider']
+        options = [{}]
+        is_cuda_available = ort.get_device() == 'GPU'
+        if is_cuda_available:
+            providers.insert(0, 'CUDAExecutionProvider')
+            options.insert(0, {'device_id': device_id})
+
+        sess.set_providers(providers, options)
+
+        self.sess = sess
+        self.io_binding = sess.io_binding()
+        self.output_names = [_.name for _ in sess.get_outputs()]
+        self.is_cuda_available = is_cuda_available
+
+    def forward_test(self, imgs, img_metas, **kwargs):
+        input_data = imgs[0]
+        # set io binding for inputs/outputs
+        device_type = 'cuda' if self.is_cuda_available else 'cpu'
+        if not self.is_cuda_available:
+            input_data = input_data.cpu()
+        self.io_binding.bind_input(
+            name='input',
+            device_type=device_type,
+            device_id=self.device_id,
+            element_type=np.float32,
+            shape=input_data.shape,
+            buffer_ptr=input_data.data_ptr())
+
+        for name in self.output_names:
+            self.io_binding.bind_output(name)
+        # run session to get outputs
+        self.sess.run_with_iobinding(self.io_binding)
+        ort_outputs = self.io_binding.copy_outputs_to_cpu()
+        return ort_outputs
+
+
+class TensorRTDetector(DeployBaseDetector):
+    """Wrapper for detector's inference with TensorRT."""
+
+    def __init__(self, engine_file, class_names, device_id, output_names=None):
+        super(TensorRTDetector, self).__init__(class_names, device_id)
+        warnings.warn('`output_names` is deprecated and will be removed in '
+                      'future releases.')
+        from mmcv.tensorrt import TRTWraper, load_tensorrt_plugin
+        try:
+            load_tensorrt_plugin()
+        except (ImportError, ModuleNotFoundError):
+            warnings.warn('If input model has custom op from mmcv, \
+                you may have to build mmcv with TensorRT from source.')
+
+        output_names = ['dets', 'labels']
+        model = TRTWraper(engine_file, ['input'], output_names)
+        with_masks = False
+        # if TensorRT has totally 4 inputs/outputs, then
+        # the detector should have `mask` output.
+        if len(model.engine) == 4:
+            model.output_names = output_names + ['masks']
+            with_masks = True
+        self.model = model
+        self.with_masks = with_masks
+
+    def forward_test(self, imgs, img_metas, **kwargs):
+        input_data = imgs[0].contiguous()
+        with torch.cuda.device(self.device_id), torch.no_grad():
+            outputs = self.model({'input': input_data})
+            outputs = [outputs[name] for name in self.model.output_names]
+        outputs = [out.detach().cpu().numpy() for out in outputs]
+        return outputs
diff --git a/mmdet/core/export/onnx_helper.py b/mmdet/core/export/onnx_helper.py
new file mode 100644
index 0000000..9f6b9a0
--- /dev/null
+++ b/mmdet/core/export/onnx_helper.py
@@ -0,0 +1,223 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+
+import torch
+
+
+def dynamic_clip_for_onnx(x1, y1, x2, y2, max_shape):
+    """Clip boxes dynamically for onnx.
+
+    Since torch.clamp cannot have dynamic `min` and `max`, we scale the
+      boxes by 1/max_shape and clamp in the range [0, 1].
+
+    Args:
+        x1 (Tensor): The x1 for bounding boxes.
+        y1 (Tensor): The y1 for bounding boxes.
+        x2 (Tensor): The x2 for bounding boxes.
+        y2 (Tensor): The y2 for bounding boxes.
+        max_shape (Tensor or torch.Size): The (H,W) of original image.
+    Returns:
+        tuple(Tensor): The clipped x1, y1, x2, y2.
+    """
+    assert isinstance(
+        max_shape,
+        torch.Tensor), '`max_shape` should be tensor of (h,w) for onnx'
+
+    # scale by 1/max_shape
+    x1 = x1 / max_shape[1]
+    y1 = y1 / max_shape[0]
+    x2 = x2 / max_shape[1]
+    y2 = y2 / max_shape[0]
+
+    # clamp [0, 1]
+    x1 = torch.clamp(x1, 0, 1)
+    y1 = torch.clamp(y1, 0, 1)
+    x2 = torch.clamp(x2, 0, 1)
+    y2 = torch.clamp(y2, 0, 1)
+
+    # scale back
+    x1 = x1 * max_shape[1]
+    y1 = y1 * max_shape[0]
+    x2 = x2 * max_shape[1]
+    y2 = y2 * max_shape[0]
+    return x1, y1, x2, y2
+
+
+def get_k_for_topk(k, size):
+    """Get k of TopK for onnx exporting.
+
+    The K of TopK in TensorRT should not be a Tensor, while in ONNX Runtime
+      it could be a Tensor.Due to dynamic shape feature, we have to decide
+      whether to do TopK and what K it should be while exporting to ONNX.
+    If returned K is less than zero, it means we do not have to do
+      TopK operation.
+
+    Args:
+        k (int or Tensor): The set k value for nms from config file.
+        size (Tensor or torch.Size): The number of elements of \
+            TopK's input tensor
+    Returns:
+        tuple: (int or Tensor): The final K for TopK.
+    """
+    ret_k = -1
+    if k <= 0 or size <= 0:
+        return ret_k
+    if torch.onnx.is_in_onnx_export():
+        is_trt_backend = os.environ.get('ONNX_BACKEND') == 'MMCVTensorRT'
+        if is_trt_backend:
+            # TensorRT does not support dynamic K with TopK op
+            if 0 < k < size:
+                ret_k = k
+        else:
+            # Always keep topk op for dynamic input in onnx for ONNX Runtime
+            ret_k = torch.where(k < size, k, size)
+    elif k < size:
+        ret_k = k
+    else:
+        # ret_k is -1
+        pass
+    return ret_k
+
+
+def add_dummy_nms_for_onnx(boxes,
+                           scores,
+                           max_output_boxes_per_class=1000,
+                           iou_threshold=0.5,
+                           score_threshold=0.05,
+                           pre_top_k=-1,
+                           after_top_k=-1,
+                           labels=None):
+    """Create a dummy onnx::NonMaxSuppression op while exporting to ONNX.
+
+    This function helps exporting to onnx with batch and multiclass NMS op.
+    It only supports class-agnostic detection results. That is, the scores
+    is of shape (N, num_bboxes, num_classes) and the boxes is of shape
+    (N, num_boxes, 4).
+
+    Args:
+        boxes (Tensor): The bounding boxes of shape [N, num_boxes, 4]
+        scores (Tensor): The detection scores of shape
+            [N, num_boxes, num_classes]
+        max_output_boxes_per_class (int): Maximum number of output
+            boxes per class of nms. Defaults to 1000.
+        iou_threshold (float): IOU threshold of nms. Defaults to 0.5
+        score_threshold (float): score threshold of nms.
+            Defaults to 0.05.
+        pre_top_k (bool): Number of top K boxes to keep before nms.
+            Defaults to -1.
+        after_top_k (int): Number of top K boxes to keep after nms.
+            Defaults to -1.
+        labels (Tensor, optional): It not None, explicit labels would be used.
+            Otherwise, labels would be automatically generated using
+            num_classed. Defaults to None.
+
+    Returns:
+        tuple[Tensor, Tensor]: dets of shape [N, num_det, 5]
+            and class labels of shape [N, num_det].
+    """
+    max_output_boxes_per_class = torch.LongTensor([max_output_boxes_per_class])
+    iou_threshold = torch.tensor([iou_threshold], dtype=torch.float32)
+    score_threshold = torch.tensor([score_threshold], dtype=torch.float32)
+    batch_size = scores.shape[0]
+    num_class = scores.shape[2]
+
+    nms_pre = torch.tensor(pre_top_k, device=scores.device, dtype=torch.long)
+    nms_pre = get_k_for_topk(nms_pre, boxes.shape[1])
+
+    if nms_pre > 0:
+        max_scores, _ = scores.max(-1)
+        _, topk_inds = max_scores.topk(nms_pre)
+        batch_inds = torch.arange(batch_size).view(
+            -1, 1).expand_as(topk_inds).long()
+        # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501
+        transformed_inds = boxes.shape[1] * batch_inds + topk_inds
+        boxes = boxes.reshape(-1, 4)[transformed_inds, :].reshape(
+            batch_size, -1, 4)
+        scores = scores.reshape(-1, num_class)[transformed_inds, :].reshape(
+            batch_size, -1, num_class)
+        if labels is not None:
+            labels = labels.reshape(-1, 1)[transformed_inds].reshape(
+                batch_size, -1)
+
+    scores = scores.permute(0, 2, 1)
+    num_box = boxes.shape[1]
+    # turn off tracing to create a dummy output of nms
+    state = torch._C._get_tracing_state()
+    # dummy indices of nms's output
+    num_fake_det = 2
+    batch_inds = torch.randint(batch_size, (num_fake_det, 1))
+    cls_inds = torch.randint(num_class, (num_fake_det, 1))
+    box_inds = torch.randint(num_box, (num_fake_det, 1))
+    indices = torch.cat([batch_inds, cls_inds, box_inds], dim=1)
+    output = indices
+    setattr(DummyONNXNMSop, 'output', output)
+
+    # open tracing
+    torch._C._set_tracing_state(state)
+    selected_indices = DummyONNXNMSop.apply(boxes, scores,
+                                            max_output_boxes_per_class,
+                                            iou_threshold, score_threshold)
+
+    batch_inds, cls_inds = selected_indices[:, 0], selected_indices[:, 1]
+    box_inds = selected_indices[:, 2]
+    if labels is None:
+        labels = torch.arange(num_class, dtype=torch.long).to(scores.device)
+        labels = labels.view(1, num_class, 1).expand_as(scores)
+    scores = scores.reshape(-1, 1)
+    boxes = boxes.reshape(batch_size, -1).repeat(1, num_class).reshape(-1, 4)
+    pos_inds = (num_class * batch_inds + cls_inds) * num_box + box_inds
+    mask = scores.new_zeros(scores.shape)
+    # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501
+    # PyTorch style code: mask[batch_inds, box_inds] += 1
+    mask[pos_inds, :] += 1
+    scores = scores * mask
+    boxes = boxes * mask
+
+    scores = scores.reshape(batch_size, -1)
+    boxes = boxes.reshape(batch_size, -1, 4)
+    labels = labels.reshape(batch_size, -1)
+
+    nms_after = torch.tensor(
+        after_top_k, device=scores.device, dtype=torch.long)
+    nms_after = get_k_for_topk(nms_after, num_box * num_class)
+
+    if nms_after > 0:
+        _, topk_inds = scores.topk(nms_after)
+        batch_inds = torch.arange(batch_size).view(-1, 1).expand_as(topk_inds)
+        # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501
+        transformed_inds = scores.shape[1] * batch_inds + topk_inds
+        scores = scores.reshape(-1, 1)[transformed_inds, :].reshape(
+            batch_size, -1)
+        boxes = boxes.reshape(-1, 4)[transformed_inds, :].reshape(
+            batch_size, -1, 4)
+        labels = labels.reshape(-1, 1)[transformed_inds, :].reshape(
+            batch_size, -1)
+
+    scores = scores.unsqueeze(2)
+    dets = torch.cat([boxes, scores], dim=2)
+    return dets, labels
+
+
+class DummyONNXNMSop(torch.autograd.Function):
+    """DummyONNXNMSop.
+
+    This class is only for creating onnx::NonMaxSuppression.
+    """
+
+    @staticmethod
+    def forward(ctx, boxes, scores, max_output_boxes_per_class, iou_threshold,
+                score_threshold):
+
+        return DummyONNXNMSop.output
+
+    @staticmethod
+    def symbolic(g, boxes, scores, max_output_boxes_per_class, iou_threshold,
+                 score_threshold):
+        return g.op(
+            'NonMaxSuppression',
+            boxes,
+            scores,
+            max_output_boxes_per_class,
+            iou_threshold,
+            score_threshold,
+            outputs=1)
diff --git a/mmdet/core/export/pytorch2onnx.py b/mmdet/core/export/pytorch2onnx.py
new file mode 100644
index 0000000..b8261ee
--- /dev/null
+++ b/mmdet/core/export/pytorch2onnx.py
@@ -0,0 +1,159 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from functools import partial
+
+import mmcv
+import numpy as np
+import torch
+from mmcv.runner import load_checkpoint
+
+
+def generate_inputs_and_wrap_model(config_path,
+                                   checkpoint_path,
+                                   input_config,
+                                   cfg_options=None):
+    """Prepare sample input and wrap model for ONNX export.
+
+    The ONNX export API only accept args, and all inputs should be
+    torch.Tensor or corresponding types (such as tuple of tensor).
+    So we should call this function before exporting. This function will:
+
+    1. generate corresponding inputs which are used to execute the model.
+    2. Wrap the model's forward function.
+
+    For example, the MMDet models' forward function has a parameter
+    ``return_loss:bool``. As we want to set it as False while export API
+    supports neither bool type or kwargs. So we have to replace the forward
+    method like ``model.forward = partial(model.forward, return_loss=False)``.
+
+    Args:
+        config_path (str): the OpenMMLab config for the model we want to
+            export to ONNX
+        checkpoint_path (str): Path to the corresponding checkpoint
+        input_config (dict): the exactly data in this dict depends on the
+            framework. For MMSeg, we can just declare the input shape,
+            and generate the dummy data accordingly. However, for MMDet,
+            we may pass the real img path, or the NMS will return None
+            as there is no legal bbox.
+
+    Returns:
+        tuple: (model, tensor_data) wrapped model which can be called by
+            ``model(*tensor_data)`` and a list of inputs which are used to
+            execute the model while exporting.
+    """
+
+    model = build_model_from_cfg(
+        config_path, checkpoint_path, cfg_options=cfg_options)
+    one_img, one_meta = preprocess_example_input(input_config)
+    tensor_data = [one_img]
+    model.forward = partial(
+        model.forward, img_metas=[[one_meta]], return_loss=False)
+
+    # pytorch has some bug in pytorch1.3, we have to fix it
+    # by replacing these existing op
+    opset_version = 11
+    # put the import within the function thus it will not cause import error
+    # when not using this function
+    try:
+        from mmcv.onnx.symbolic import register_extra_symbolics
+    except ModuleNotFoundError:
+        raise NotImplementedError('please update mmcv to version>=v1.0.4')
+    register_extra_symbolics(opset_version)
+
+    return model, tensor_data
+
+
+def build_model_from_cfg(config_path, checkpoint_path, cfg_options=None):
+    """Build a model from config and load the given checkpoint.
+
+    Args:
+        config_path (str): the OpenMMLab config for the model we want to
+            export to ONNX
+        checkpoint_path (str): Path to the corresponding checkpoint
+
+    Returns:
+        torch.nn.Module: the built model
+    """
+    from mmdet.models import build_detector
+
+    cfg = mmcv.Config.fromfile(config_path)
+    if cfg_options is not None:
+        cfg.merge_from_dict(cfg_options)
+    # set cudnn_benchmark
+    if cfg.get('cudnn_benchmark', False):
+        torch.backends.cudnn.benchmark = True
+    cfg.model.pretrained = None
+    cfg.data.test.test_mode = True
+
+    # build the model
+    cfg.model.train_cfg = None
+    model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
+    checkpoint = load_checkpoint(model, checkpoint_path, map_location='cpu')
+    if 'CLASSES' in checkpoint.get('meta', {}):
+        model.CLASSES = checkpoint['meta']['CLASSES']
+    else:
+        from mmdet.datasets import DATASETS
+        dataset = DATASETS.get(cfg.data.test['type'])
+        assert (dataset is not None)
+        model.CLASSES = dataset.CLASSES
+    model.cpu().eval()
+    return model
+
+
+def preprocess_example_input(input_config):
+    """Prepare an example input image for ``generate_inputs_and_wrap_model``.
+
+    Args:
+        input_config (dict): customized config describing the example input.
+
+    Returns:
+        tuple: (one_img, one_meta), tensor of the example input image and \
+            meta information for the example input image.
+
+    Examples:
+        >>> from mmdet.core.export import preprocess_example_input
+        >>> input_config = {
+        >>>         'input_shape': (1,3,224,224),
+        >>>         'input_path': 'demo/demo.jpg',
+        >>>         'normalize_cfg': {
+        >>>             'mean': (123.675, 116.28, 103.53),
+        >>>             'std': (58.395, 57.12, 57.375)
+        >>>             }
+        >>>         }
+        >>> one_img, one_meta = preprocess_example_input(input_config)
+        >>> print(one_img.shape)
+        torch.Size([1, 3, 224, 224])
+        >>> print(one_meta)
+        {'img_shape': (224, 224, 3),
+        'ori_shape': (224, 224, 3),
+        'pad_shape': (224, 224, 3),
+        'filename': '<demo>.png',
+        'scale_factor': 1.0,
+        'flip': False}
+    """
+    input_path = input_config['input_path']
+    input_shape = input_config['input_shape']
+    one_img = mmcv.imread(input_path)
+    one_img = mmcv.imresize(one_img, input_shape[2:][::-1])
+    show_img = one_img.copy()
+    if 'normalize_cfg' in input_config.keys():
+        normalize_cfg = input_config['normalize_cfg']
+        mean = np.array(normalize_cfg['mean'], dtype=np.float32)
+        std = np.array(normalize_cfg['std'], dtype=np.float32)
+        to_rgb = normalize_cfg.get('to_rgb', True)
+        one_img = mmcv.imnormalize(one_img, mean, std, to_rgb=to_rgb)
+    one_img = one_img.transpose(2, 0, 1)
+    one_img = torch.from_numpy(one_img).unsqueeze(0).float().requires_grad_(
+        True)
+    (_, C, H, W) = input_shape
+    one_meta = {
+        'img_shape': (H, W, C),
+        'ori_shape': (H, W, C),
+        'pad_shape': (H, W, C),
+        'filename': '<demo>.png',
+        'scale_factor': np.ones(4, dtype=np.float32),
+        'flip': False,
+        'show_img': show_img,
+        'flip_direction': None
+    }
+
+    return one_img, one_meta
diff --git a/mmdet/core/hook/__init__.py b/mmdet/core/hook/__init__.py
new file mode 100644
index 0000000..7b9ac9f
--- /dev/null
+++ b/mmdet/core/hook/__init__.py
@@ -0,0 +1,17 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .checkloss_hook import CheckInvalidLossHook
+from .ema import ExpMomentumEMAHook, LinearMomentumEMAHook
+from .memory_profiler_hook import MemoryProfilerHook
+from .set_epoch_info_hook import SetEpochInfoHook
+from .sync_norm_hook import SyncNormHook
+from .sync_random_size_hook import SyncRandomSizeHook
+from .wandblogger_hook import MMDetWandbHook
+from .yolox_lrupdater_hook import YOLOXLrUpdaterHook
+from .yolox_mode_switch_hook import YOLOXModeSwitchHook
+
+__all__ = [
+    'SyncRandomSizeHook', 'YOLOXModeSwitchHook', 'SyncNormHook',
+    'ExpMomentumEMAHook', 'LinearMomentumEMAHook', 'YOLOXLrUpdaterHook',
+    'CheckInvalidLossHook', 'SetEpochInfoHook', 'MemoryProfilerHook',
+    'MMDetWandbHook'
+]
diff --git a/mmdet/core/hook/checkloss_hook.py b/mmdet/core/hook/checkloss_hook.py
new file mode 100644
index 0000000..754e61b
--- /dev/null
+++ b/mmdet/core/hook/checkloss_hook.py
@@ -0,0 +1,24 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from mmcv.runner.hooks import HOOKS, Hook
+
+
+@HOOKS.register_module()
+class CheckInvalidLossHook(Hook):
+    """Check invalid loss hook.
+
+    This hook will regularly check whether the loss is valid
+    during training.
+
+    Args:
+        interval (int): Checking interval (every k iterations).
+            Default: 50.
+    """
+
+    def __init__(self, interval=50):
+        self.interval = interval
+
+    def after_train_iter(self, runner):
+        if self.every_n_iters(runner, self.interval):
+            assert torch.isfinite(runner.outputs['loss']), \
+                runner.logger.info('loss become infinite or NaN!')
diff --git a/mmdet/core/hook/ema.py b/mmdet/core/hook/ema.py
new file mode 100644
index 0000000..ff7bfba
--- /dev/null
+++ b/mmdet/core/hook/ema.py
@@ -0,0 +1,130 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+from mmcv.parallel import is_module_wrapper
+from mmcv.runner.hooks import HOOKS, Hook
+
+
+class BaseEMAHook(Hook):
+    """Exponential Moving Average Hook.
+
+    Use Exponential Moving Average on all parameters of model in training
+    process. All parameters have a ema backup, which update by the formula
+    as below. EMAHook takes priority over EvalHook and CheckpointHook. Note,
+    the original model parameters are actually saved in ema field after train.
+
+    Args:
+        momentum (float): The momentum used for updating ema parameter.
+            Ema's parameter are updated with the formula:
+           `ema_param = (1-momentum) * ema_param + momentum * cur_param`.
+            Defaults to 0.0002.
+        skip_buffers (bool): Whether to skip the model buffers, such as
+            batchnorm running stats (running_mean, running_var), it does not
+            perform the ema operation. Default to False.
+        interval (int): Update ema parameter every interval iteration.
+            Defaults to 1.
+        resume_from (str, optional): The checkpoint path. Defaults to None.
+        momentum_fun (func, optional): The function to change momentum
+            during early iteration (also warmup) to help early training.
+            It uses `momentum` as a constant. Defaults to None.
+    """
+
+    def __init__(self,
+                 momentum=0.0002,
+                 interval=1,
+                 skip_buffers=False,
+                 resume_from=None,
+                 momentum_fun=None):
+        assert 0 < momentum < 1
+        self.momentum = momentum
+        self.skip_buffers = skip_buffers
+        self.interval = interval
+        self.checkpoint = resume_from
+        self.momentum_fun = momentum_fun
+
+    def before_run(self, runner):
+        """To resume model with it's ema parameters more friendly.
+
+        Register ema parameter as ``named_buffer`` to model.
+        """
+        model = runner.model
+        if is_module_wrapper(model):
+            model = model.module
+        self.param_ema_buffer = {}
+        if self.skip_buffers:
+            self.model_parameters = dict(model.named_parameters())
+        else:
+            self.model_parameters = model.state_dict()
+        for name, value in self.model_parameters.items():
+            # "." is not allowed in module's buffer name
+            buffer_name = f"ema_{name.replace('.', '_')}"
+            self.param_ema_buffer[name] = buffer_name
+            model.register_buffer(buffer_name, value.data.clone())
+        self.model_buffers = dict(model.named_buffers())
+        if self.checkpoint is not None:
+            runner.resume(self.checkpoint)
+
+    def get_momentum(self, runner):
+        return self.momentum_fun(runner.iter) if self.momentum_fun else \
+                        self.momentum
+
+    def after_train_iter(self, runner):
+        """Update ema parameter every self.interval iterations."""
+        if (runner.iter + 1) % self.interval != 0:
+            return
+        momentum = self.get_momentum(runner)
+        for name, parameter in self.model_parameters.items():
+            # exclude num_tracking
+            if parameter.dtype.is_floating_point:
+                buffer_name = self.param_ema_buffer[name]
+                buffer_parameter = self.model_buffers[buffer_name]
+                buffer_parameter.mul_(1 - momentum).add_(
+                    parameter.data, alpha=momentum)
+
+    def after_train_epoch(self, runner):
+        """We load parameter values from ema backup to model before the
+        EvalHook."""
+        self._swap_ema_parameters()
+
+    def before_train_epoch(self, runner):
+        """We recover model's parameter from ema backup after last epoch's
+        EvalHook."""
+        self._swap_ema_parameters()
+
+    def _swap_ema_parameters(self):
+        """Swap the parameter of model with parameter in ema_buffer."""
+        for name, value in self.model_parameters.items():
+            temp = value.data.clone()
+            ema_buffer = self.model_buffers[self.param_ema_buffer[name]]
+            value.data.copy_(ema_buffer.data)
+            ema_buffer.data.copy_(temp)
+
+
+@HOOKS.register_module()
+class ExpMomentumEMAHook(BaseEMAHook):
+    """EMAHook using exponential momentum strategy.
+
+    Args:
+        total_iter (int): The total number of iterations of EMA momentum.
+           Defaults to 2000.
+    """
+
+    def __init__(self, total_iter=2000, **kwargs):
+        super(ExpMomentumEMAHook, self).__init__(**kwargs)
+        self.momentum_fun = lambda x: (1 - self.momentum) * math.exp(-(
+            1 + x) / total_iter) + self.momentum
+
+
+@HOOKS.register_module()
+class LinearMomentumEMAHook(BaseEMAHook):
+    """EMAHook using linear momentum strategy.
+
+    Args:
+        warm_up (int): During first warm_up steps, we may use smaller decay
+            to update ema parameters more slowly. Defaults to 100.
+    """
+
+    def __init__(self, warm_up=100, **kwargs):
+        super(LinearMomentumEMAHook, self).__init__(**kwargs)
+        self.momentum_fun = lambda x: min(self.momentum**self.interval,
+                                          (1 + x) / (warm_up + x))
diff --git a/mmdet/core/hook/memory_profiler_hook.py b/mmdet/core/hook/memory_profiler_hook.py
new file mode 100644
index 0000000..a473061
--- /dev/null
+++ b/mmdet/core/hook/memory_profiler_hook.py
@@ -0,0 +1,55 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.runner.hooks import HOOKS, Hook
+
+
+@HOOKS.register_module()
+class MemoryProfilerHook(Hook):
+    """Memory profiler hook recording memory information including virtual
+    memory, swap memory, and the memory of the current process.
+
+    Args:
+        interval (int): Checking interval (every k iterations).
+            Default: 50.
+    """
+
+    def __init__(self, interval=50):
+        try:
+            from psutil import swap_memory, virtual_memory
+            self._swap_memory = swap_memory
+            self._virtual_memory = virtual_memory
+        except ImportError:
+            raise ImportError('psutil is not installed, please install it by: '
+                              'pip install psutil')
+
+        try:
+            from memory_profiler import memory_usage
+            self._memory_usage = memory_usage
+        except ImportError:
+            raise ImportError(
+                'memory_profiler is not installed, please install it by: '
+                'pip install memory_profiler')
+
+        self.interval = interval
+
+    def after_iter(self, runner):
+        if self.every_n_iters(runner, self.interval):
+            # in Byte
+            virtual_memory = self._virtual_memory()
+            swap_memory = self._swap_memory()
+            # in MB
+            process_memory = self._memory_usage()[0]
+            factor = 1024 * 1024
+            runner.logger.info(
+                'Memory information '
+                'available_memory: '
+                f'{round(virtual_memory.available / factor)} MB, '
+                'used_memory: '
+                f'{round(virtual_memory.used / factor)} MB, '
+                f'memory_utilization: {virtual_memory.percent} %, '
+                'available_swap_memory: '
+                f'{round((swap_memory.total - swap_memory.used) / factor)}'
+                ' MB, '
+                f'used_swap_memory: {round(swap_memory.used / factor)} MB, '
+                f'swap_memory_utilization: {swap_memory.percent} %, '
+                'current_process_memory: '
+                f'{round(process_memory)} MB')
diff --git a/mmdet/core/hook/set_epoch_info_hook.py b/mmdet/core/hook/set_epoch_info_hook.py
new file mode 100644
index 0000000..c2b134c
--- /dev/null
+++ b/mmdet/core/hook/set_epoch_info_hook.py
@@ -0,0 +1,15 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.parallel import is_module_wrapper
+from mmcv.runner import HOOKS, Hook
+
+
+@HOOKS.register_module()
+class SetEpochInfoHook(Hook):
+    """Set runner's epoch information to the model."""
+
+    def before_train_epoch(self, runner):
+        epoch = runner.epoch
+        model = runner.model
+        if is_module_wrapper(model):
+            model = model.module
+        model.set_epoch(epoch)
diff --git a/mmdet/core/hook/sync_norm_hook.py b/mmdet/core/hook/sync_norm_hook.py
new file mode 100644
index 0000000..82931ce
--- /dev/null
+++ b/mmdet/core/hook/sync_norm_hook.py
@@ -0,0 +1,52 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from collections import OrderedDict
+
+from mmcv.runner import get_dist_info
+from mmcv.runner.hooks import HOOKS, Hook
+from torch import nn
+
+from ..utils.dist_utils import all_reduce_dict
+
+
+def get_norm_states(module):
+    async_norm_states = OrderedDict()
+    for name, child in module.named_modules():
+        if isinstance(child, nn.modules.batchnorm._NormBase):
+            for k, v in child.state_dict().items():
+                async_norm_states['.'.join([name, k])] = v
+    return async_norm_states
+
+
+@HOOKS.register_module()
+class SyncNormHook(Hook):
+    """Synchronize Norm states after training epoch, currently used in YOLOX.
+
+    Args:
+        num_last_epochs (int): The number of latter epochs in the end of the
+            training to switch to synchronizing norm interval. Default: 15.
+        interval (int): Synchronizing norm interval. Default: 1.
+    """
+
+    def __init__(self, num_last_epochs=15, interval=1):
+        self.interval = interval
+        self.num_last_epochs = num_last_epochs
+
+    def before_train_epoch(self, runner):
+        epoch = runner.epoch
+        if (epoch + 1) == runner.max_epochs - self.num_last_epochs:
+            # Synchronize norm every epoch.
+            self.interval = 1
+
+    def after_train_epoch(self, runner):
+        """Synchronizing norm."""
+        epoch = runner.epoch
+        module = runner.model
+        if (epoch + 1) % self.interval == 0:
+            _, world_size = get_dist_info()
+            if world_size == 1:
+                return
+            norm_states = get_norm_states(module)
+            if len(norm_states) == 0:
+                return
+            norm_states = all_reduce_dict(norm_states, op='mean')
+            module.load_state_dict(norm_states, strict=False)
diff --git a/mmdet/core/hook/sync_random_size_hook.py b/mmdet/core/hook/sync_random_size_hook.py
new file mode 100644
index 0000000..6d7e96c
--- /dev/null
+++ b/mmdet/core/hook/sync_random_size_hook.py
@@ -0,0 +1,72 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import random
+import warnings
+
+import torch
+from mmcv.runner import get_dist_info
+from mmcv.runner.hooks import HOOKS, Hook
+from torch import distributed as dist
+
+
+@HOOKS.register_module()
+class SyncRandomSizeHook(Hook):
+    """Change and synchronize the random image size across ranks.
+    SyncRandomSizeHook is deprecated, please use Resize pipeline to achieve
+    similar functions. Such as `dict(type='Resize', img_scale=[(448, 448),
+    (832, 832)], multiscale_mode='range', keep_ratio=True)`.
+
+    Note: Due to the multi-process dataloader, its behavior is different
+    from YOLOX's official implementation, the official is to change the
+    size every fixed iteration interval and what we achieved is a fixed
+    epoch interval.
+
+    Args:
+        ratio_range (tuple[int]): Random ratio range. It will be multiplied
+            by 32, and then change the dataset output image size.
+            Default: (14, 26).
+        img_scale (tuple[int]): Size of input image. Default: (640, 640).
+        interval (int): The epoch interval of change image size. Default: 1.
+        device (torch.device | str): device for returned tensors.
+            Default: 'cuda'.
+    """
+
+    def __init__(self,
+                 ratio_range=(14, 26),
+                 img_scale=(640, 640),
+                 interval=1,
+                 device='cuda'):
+        warnings.warn('DeprecationWarning: SyncRandomSizeHook is deprecated. '
+                      'Please use Resize pipeline to achieve similar '
+                      'functions. Due to the multi-process dataloader, '
+                      'its behavior is different from YOLOX\'s official '
+                      'implementation, the official is to change the size '
+                      'every fixed iteration interval and what we achieved '
+                      'is a fixed epoch interval.')
+        self.rank, world_size = get_dist_info()
+        self.is_distributed = world_size > 1
+        self.ratio_range = ratio_range
+        self.img_scale = img_scale
+        self.interval = interval
+        self.device = device
+
+    def after_train_epoch(self, runner):
+        """Change the dataset output image size."""
+        if self.ratio_range is not None and (runner.epoch +
+                                             1) % self.interval == 0:
+            # Due to DDP and DP get the device behavior inconsistent,
+            # so we did not get the device from runner.model.
+            tensor = torch.LongTensor(2).to(self.device)
+
+            if self.rank == 0:
+                size_factor = self.img_scale[1] * 1. / self.img_scale[0]
+                size = random.randint(*self.ratio_range)
+                size = (int(32 * size), 32 * int(size * size_factor))
+                tensor[0] = size[0]
+                tensor[1] = size[1]
+
+            if self.is_distributed:
+                dist.barrier()
+                dist.broadcast(tensor, 0)
+
+            runner.data_loader.dataset.update_dynamic_scale(
+                (tensor[0].item(), tensor[1].item()))
diff --git a/mmdet/core/hook/wandblogger_hook.py b/mmdet/core/hook/wandblogger_hook.py
new file mode 100644
index 0000000..7bf252f
--- /dev/null
+++ b/mmdet/core/hook/wandblogger_hook.py
@@ -0,0 +1,593 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import importlib
+import os.path as osp
+import sys
+import warnings
+
+import mmcv
+import numpy as np
+import pycocotools.mask as mask_util
+from mmcv.runner import HOOKS
+from mmcv.runner.dist_utils import master_only
+from mmcv.runner.hooks.checkpoint import CheckpointHook
+from mmcv.runner.hooks.logger.wandb import WandbLoggerHook
+from mmcv.utils import digit_version
+
+from mmdet.core import DistEvalHook, EvalHook
+from mmdet.core.mask.structures import polygon_to_bitmap
+
+
+@HOOKS.register_module()
+class MMDetWandbHook(WandbLoggerHook):
+    """Enhanced Wandb logger hook for MMDetection.
+
+    Comparing with the :cls:`mmcv.runner.WandbLoggerHook`, this hook can not
+    only automatically log all the metrics but also log the following extra
+    information - saves model checkpoints as W&B Artifact, and
+    logs model prediction as interactive W&B Tables.
+
+    - Metrics: The MMDetWandbHook will automatically log training
+        and validation metrics along with system metrics (CPU/GPU).
+
+    - Checkpointing: If `log_checkpoint` is True, the checkpoint saved at
+        every checkpoint interval will be saved as W&B Artifacts.
+        This depends on the : class:`mmcv.runner.CheckpointHook` whose priority
+        is higher than this hook. Please refer to
+        https://docs.wandb.ai/guides/artifacts/model-versioning
+        to learn more about model versioning with W&B Artifacts.
+
+    - Checkpoint Metadata: If evaluation results are available for a given
+        checkpoint artifact, it will have a metadata associated with it.
+        The metadata contains the evaluation metrics computed on validation
+        data with that checkpoint along with the current epoch. It depends
+        on `EvalHook` whose priority is more than MMDetWandbHook.
+
+    - Evaluation: At every evaluation interval, the `MMDetWandbHook` logs the
+        model prediction as interactive W&B Tables. The number of samples
+        logged is given by `num_eval_images`. Currently, the `MMDetWandbHook`
+        logs the predicted bounding boxes along with the ground truth at every
+        evaluation interval. This depends on the `EvalHook` whose priority is
+        more than `MMDetWandbHook`. Also note that the data is just logged once
+        and subsequent evaluation tables uses reference to the logged data
+        to save memory usage. Please refer to
+        https://docs.wandb.ai/guides/data-vis to learn more about W&B Tables.
+
+    For more details check out W&B's MMDetection docs:
+    https://docs.wandb.ai/guides/integrations/mmdetection
+
+    ```
+    Example:
+        log_config = dict(
+            ...
+            hooks=[
+                ...,
+                dict(type='MMDetWandbHook',
+                     init_kwargs={
+                         'entity': "YOUR_ENTITY",
+                         'project': "YOUR_PROJECT_NAME"
+                     },
+                     interval=50,
+                     log_checkpoint=True,
+                     log_checkpoint_metadata=True,
+                     num_eval_images=100,
+                     bbox_score_thr=0.3)
+            ])
+    ```
+
+    Args:
+        init_kwargs (dict): A dict passed to wandb.init to initialize
+            a W&B run. Please refer to https://docs.wandb.ai/ref/python/init
+            for possible key-value pairs.
+        interval (int): Logging interval (every k iterations). Defaults to 50.
+        log_checkpoint (bool): Save the checkpoint at every checkpoint interval
+            as W&B Artifacts. Use this for model versioning where each version
+            is a checkpoint. Defaults to False.
+        log_checkpoint_metadata (bool): Log the evaluation metrics computed
+            on the validation data with the checkpoint, along with current
+            epoch as a metadata to that checkpoint.
+            Defaults to True.
+        num_eval_images (int): The number of validation images to be logged.
+            If zero, the evaluation won't be logged. Defaults to 100.
+        bbox_score_thr (float): Threshold for bounding box scores.
+            Defaults to 0.3.
+    """
+
+    def __init__(self,
+                 init_kwargs=None,
+                 interval=50,
+                 log_checkpoint=False,
+                 log_checkpoint_metadata=False,
+                 num_eval_images=100,
+                 bbox_score_thr=0.3,
+                 **kwargs):
+        super(MMDetWandbHook, self).__init__(init_kwargs, interval, **kwargs)
+
+        self.log_checkpoint = log_checkpoint
+        self.log_checkpoint_metadata = (
+            log_checkpoint and log_checkpoint_metadata)
+        self.num_eval_images = num_eval_images
+        self.bbox_score_thr = bbox_score_thr
+        self.log_evaluation = (num_eval_images > 0)
+        self.ckpt_hook: CheckpointHook = None
+        self.eval_hook: EvalHook = None
+
+    def import_wandb(self):
+        try:
+            import wandb
+            from wandb import init  # noqa
+
+            # Fix ResourceWarning when calling wandb.log in wandb v0.12.10.
+            # https://github.com/wandb/client/issues/2837
+            if digit_version(wandb.__version__) < digit_version('0.12.10'):
+                warnings.warn(
+                    f'The current wandb {wandb.__version__} is '
+                    f'lower than v0.12.10 will cause ResourceWarning '
+                    f'when calling wandb.log, Please run '
+                    f'"pip install --upgrade wandb"')
+
+        except ImportError:
+            raise ImportError(
+                'Please run "pip install "wandb>=0.12.10"" to install wandb')
+        self.wandb = wandb
+
+    @master_only
+    def before_run(self, runner):
+        super(MMDetWandbHook, self).before_run(runner)
+
+        # Save and Log config.
+        if runner.meta is not None and runner.meta.get('exp_name',
+                                                       None) is not None:
+            src_cfg_path = osp.join(runner.work_dir,
+                                    runner.meta.get('exp_name', None))
+            if osp.exists(src_cfg_path):
+                self.wandb.save(src_cfg_path, base_path=runner.work_dir)
+                self._update_wandb_config(runner)
+        else:
+            runner.logger.warning('No meta information found in the runner. ')
+
+        # Inspect CheckpointHook and EvalHook
+        for hook in runner.hooks:
+            if isinstance(hook, CheckpointHook):
+                self.ckpt_hook = hook
+            if isinstance(hook, (EvalHook, DistEvalHook)):
+                self.eval_hook = hook
+
+        # Check conditions to log checkpoint
+        if self.log_checkpoint:
+            if self.ckpt_hook is None:
+                self.log_checkpoint = False
+                self.log_checkpoint_metadata = False
+                runner.logger.warning(
+                    'To log checkpoint in MMDetWandbHook, `CheckpointHook` is'
+                    'required, please check hooks in the runner.')
+            else:
+                self.ckpt_interval = self.ckpt_hook.interval
+
+        # Check conditions to log evaluation
+        if self.log_evaluation or self.log_checkpoint_metadata:
+            if self.eval_hook is None:
+                self.log_evaluation = False
+                self.log_checkpoint_metadata = False
+                runner.logger.warning(
+                    'To log evaluation or checkpoint metadata in '
+                    'MMDetWandbHook, `EvalHook` or `DistEvalHook` in mmdet '
+                    'is required, please check whether the validation '
+                    'is enabled.')
+            else:
+                self.eval_interval = self.eval_hook.interval
+                self.val_dataset = self.eval_hook.dataloader.dataset
+                # Determine the number of samples to be logged.
+                if self.num_eval_images > len(self.val_dataset):
+                    self.num_eval_images = len(self.val_dataset)
+                    runner.logger.warning(
+                        f'The num_eval_images ({self.num_eval_images}) is '
+                        'greater than the total number of validation samples '
+                        f'({len(self.val_dataset)}). The complete validation '
+                        'dataset will be logged.')
+
+        # Check conditions to log checkpoint metadata
+        if self.log_checkpoint_metadata:
+            assert self.ckpt_interval % self.eval_interval == 0, \
+                'To log checkpoint metadata in MMDetWandbHook, the interval ' \
+                f'of checkpoint saving ({self.ckpt_interval}) should be ' \
+                'divisible by the interval of evaluation ' \
+                f'({self.eval_interval}).'
+
+        # Initialize evaluation table
+        if self.log_evaluation:
+            # Initialize data table
+            self._init_data_table()
+            # Add data to the data table
+            self._add_ground_truth(runner)
+            # Log ground truth data
+            self._log_data_table()
+
+    @master_only
+    def after_train_epoch(self, runner):
+        super(MMDetWandbHook, self).after_train_epoch(runner)
+
+        if not self.by_epoch:
+            return
+
+        # Log checkpoint and metadata.
+        if (self.log_checkpoint
+                and self.every_n_epochs(runner, self.ckpt_interval)
+                or (self.ckpt_hook.save_last and self.is_last_epoch(runner))):
+            if self.log_checkpoint_metadata and self.eval_hook:
+                metadata = {
+                    'epoch': runner.epoch + 1,
+                    **self._get_eval_results()
+                }
+            else:
+                metadata = None
+            aliases = [f'epoch_{runner.epoch + 1}', 'latest']
+            model_path = osp.join(self.ckpt_hook.out_dir,
+                                  f'epoch_{runner.epoch + 1}.pth')
+            self._log_ckpt_as_artifact(model_path, aliases, metadata)
+
+        # Save prediction table
+        if self.log_evaluation and self.eval_hook._should_evaluate(runner):
+            results = self.eval_hook.latest_results
+            # Initialize evaluation table
+            self._init_pred_table()
+            # Log predictions
+            self._log_predictions(results)
+            # Log the table
+            self._log_eval_table(runner.epoch + 1)
+
+    # for the reason of this double-layered structure, refer to
+    # https://github.com/open-mmlab/mmdetection/issues/8145#issuecomment-1345343076
+    def after_train_iter(self, runner):
+        if self.get_mode(runner) == 'train':
+            # An ugly patch. The iter-based eval hook will call the
+            # `after_train_iter` method of all logger hooks before evaluation.
+            # Use this trick to skip that call.
+            # Don't call super method at first, it will clear the log_buffer
+            return super(MMDetWandbHook, self).after_train_iter(runner)
+        else:
+            super(MMDetWandbHook, self).after_train_iter(runner)
+        self._after_train_iter(runner)
+
+    @master_only
+    def _after_train_iter(self, runner):
+        if self.by_epoch:
+            return
+
+        # Save checkpoint and metadata
+        if (self.log_checkpoint
+                and self.every_n_iters(runner, self.ckpt_interval)
+                or (self.ckpt_hook.save_last and self.is_last_iter(runner))):
+            if self.log_checkpoint_metadata and self.eval_hook:
+                metadata = {
+                    'iter': runner.iter + 1,
+                    **self._get_eval_results()
+                }
+            else:
+                metadata = None
+            aliases = [f'iter_{runner.iter + 1}', 'latest']
+            model_path = osp.join(self.ckpt_hook.out_dir,
+                                  f'iter_{runner.iter + 1}.pth')
+            self._log_ckpt_as_artifact(model_path, aliases, metadata)
+
+        # Save prediction table
+        if self.log_evaluation and self.eval_hook._should_evaluate(runner):
+            results = self.eval_hook.latest_results
+            # Initialize evaluation table
+            self._init_pred_table()
+            # Log predictions
+            self._log_predictions(results)
+            # Log the table
+            self._log_eval_table(runner.iter + 1)
+
+    @master_only
+    def after_run(self, runner):
+        self.wandb.finish()
+
+    def _update_wandb_config(self, runner):
+        """Update wandb config."""
+        # Import the config file.
+        sys.path.append(runner.work_dir)
+        config_filename = runner.meta['exp_name'][:-3]
+        configs = importlib.import_module(config_filename)
+        # Prepare a nested dict of config variables.
+        config_keys = [key for key in dir(configs) if not key.startswith('__')]
+        config_dict = {key: getattr(configs, key) for key in config_keys}
+        # Update the W&B config.
+        self.wandb.config.update(config_dict)
+
+    def _log_ckpt_as_artifact(self, model_path, aliases, metadata=None):
+        """Log model checkpoint as  W&B Artifact.
+
+        Args:
+            model_path (str): Path of the checkpoint to log.
+            aliases (list): List of the aliases associated with this artifact.
+            metadata (dict, optional): Metadata associated with this artifact.
+        """
+        model_artifact = self.wandb.Artifact(
+            f'run_{self.wandb.run.id}_model', type='model', metadata=metadata)
+        model_artifact.add_file(model_path)
+        self.wandb.log_artifact(model_artifact, aliases=aliases)
+
+    def _get_eval_results(self):
+        """Get model evaluation results."""
+        results = self.eval_hook.latest_results
+        eval_results = self.val_dataset.evaluate(
+            results, logger='silent', **self.eval_hook.eval_kwargs)
+        return eval_results
+
+    def _init_data_table(self):
+        """Initialize the W&B Tables for validation data."""
+        columns = ['image_name', 'image']
+        self.data_table = self.wandb.Table(columns=columns)
+
+    def _init_pred_table(self):
+        """Initialize the W&B Tables for model evaluation."""
+        columns = ['image_name', 'ground_truth', 'prediction']
+        self.eval_table = self.wandb.Table(columns=columns)
+
+    def _add_ground_truth(self, runner):
+        # Get image loading pipeline
+        from mmdet.datasets.pipelines import LoadImageFromFile
+        img_loader = None
+        for t in self.val_dataset.pipeline.transforms:
+            if isinstance(t, LoadImageFromFile):
+                img_loader = t
+
+        if img_loader is None:
+            self.log_evaluation = False
+            runner.logger.warning(
+                'LoadImageFromFile is required to add images '
+                'to W&B Tables.')
+            return
+
+        # Select the images to be logged.
+        self.eval_image_indexs = np.arange(len(self.val_dataset))
+        # Set seed so that same validation set is logged each time.
+        np.random.seed(42)
+        np.random.shuffle(self.eval_image_indexs)
+        self.eval_image_indexs = self.eval_image_indexs[:self.num_eval_images]
+
+        CLASSES = self.val_dataset.CLASSES
+        self.class_id_to_label = {
+            id + 1: name
+            for id, name in enumerate(CLASSES)
+        }
+        self.class_set = self.wandb.Classes([{
+            'id': id,
+            'name': name
+        } for id, name in self.class_id_to_label.items()])
+
+        img_prefix = self.val_dataset.img_prefix
+
+        for idx in self.eval_image_indexs:
+            img_info = self.val_dataset.data_infos[idx]
+            image_name = img_info.get('filename', f'img_{idx}')
+            img_height, img_width = img_info['height'], img_info['width']
+
+            img_meta = img_loader(
+                dict(img_info=img_info, img_prefix=img_prefix))
+
+            # Get image and convert from BGR to RGB
+            image = mmcv.bgr2rgb(img_meta['img'])
+
+            data_ann = self.val_dataset.get_ann_info(idx)
+            bboxes = data_ann['bboxes']
+            labels = data_ann['labels']
+            masks = data_ann.get('masks', None)
+
+            # Get dict of bounding boxes to be logged.
+            assert len(bboxes) == len(labels)
+            wandb_boxes = self._get_wandb_bboxes(bboxes, labels)
+
+            # Get dict of masks to be logged.
+            if masks is not None:
+                wandb_masks = self._get_wandb_masks(
+                    masks,
+                    labels,
+                    is_poly_mask=True,
+                    height=img_height,
+                    width=img_width)
+            else:
+                wandb_masks = None
+            # TODO: Panoramic segmentation visualization.
+
+            # Log a row to the data table.
+            self.data_table.add_data(
+                image_name,
+                self.wandb.Image(
+                    image,
+                    boxes=wandb_boxes,
+                    masks=wandb_masks,
+                    classes=self.class_set))
+
+    def _log_predictions(self, results):
+        table_idxs = self.data_table_ref.get_index()
+        assert len(table_idxs) == len(self.eval_image_indexs)
+
+        for ndx, eval_image_index in enumerate(self.eval_image_indexs):
+            # Get the result
+            result = results[eval_image_index]
+            if isinstance(result, tuple):
+                bbox_result, segm_result = result
+                if isinstance(segm_result, tuple):
+                    segm_result = segm_result[0]  # ms rcnn
+            else:
+                bbox_result, segm_result = result, None
+            assert len(bbox_result) == len(self.class_id_to_label)
+
+            # Get labels
+            bboxes = np.vstack(bbox_result)
+            labels = [
+                np.full(bbox.shape[0], i, dtype=np.int32)
+                for i, bbox in enumerate(bbox_result)
+            ]
+            labels = np.concatenate(labels)
+
+            # Get segmentation mask if available.
+            segms = None
+            if segm_result is not None and len(labels) > 0:
+                segms = mmcv.concat_list(segm_result)
+                segms = mask_util.decode(segms)
+                segms = segms.transpose(2, 0, 1)
+                assert len(segms) == len(labels)
+            # TODO: Panoramic segmentation visualization.
+
+            # Remove bounding boxes and masks with score lower than threshold.
+            if self.bbox_score_thr > 0:
+                assert bboxes is not None and bboxes.shape[1] == 5
+                scores = bboxes[:, -1]
+                inds = scores > self.bbox_score_thr
+                bboxes = bboxes[inds, :]
+                labels = labels[inds]
+                if segms is not None:
+                    segms = segms[inds, ...]
+
+            # Get dict of bounding boxes to be logged.
+            wandb_boxes = self._get_wandb_bboxes(bboxes, labels, log_gt=False)
+            # Get dict of masks to be logged.
+            if segms is not None:
+                wandb_masks = self._get_wandb_masks(segms, labels)
+            else:
+                wandb_masks = None
+
+            # Log a row to the eval table.
+            self.eval_table.add_data(
+                self.data_table_ref.data[ndx][0],
+                self.data_table_ref.data[ndx][1],
+                self.wandb.Image(
+                    self.data_table_ref.data[ndx][1],
+                    boxes=wandb_boxes,
+                    masks=wandb_masks,
+                    classes=self.class_set))
+
+    def _get_wandb_bboxes(self, bboxes, labels, log_gt=True):
+        """Get list of structured dict for logging bounding boxes to W&B.
+
+        Args:
+            bboxes (list): List of bounding box coordinates in
+                        (minX, minY, maxX, maxY) format.
+            labels (int): List of label ids.
+            log_gt (bool): Whether to log ground truth or prediction boxes.
+
+        Returns:
+            Dictionary of bounding boxes to be logged.
+        """
+        wandb_boxes = {}
+
+        box_data = []
+        for bbox, label in zip(bboxes, labels):
+            if not isinstance(label, int):
+                label = int(label)
+            label = label + 1
+
+            if len(bbox) == 5:
+                confidence = float(bbox[4])
+                class_name = self.class_id_to_label[label]
+                box_caption = f'{class_name} {confidence:.2f}'
+            else:
+                box_caption = str(self.class_id_to_label[label])
+
+            position = dict(
+                minX=int(bbox[0]),
+                minY=int(bbox[1]),
+                maxX=int(bbox[2]),
+                maxY=int(bbox[3]))
+
+            box_data.append({
+                'position': position,
+                'class_id': label,
+                'box_caption': box_caption,
+                'domain': 'pixel'
+            })
+
+        wandb_bbox_dict = {
+            'box_data': box_data,
+            'class_labels': self.class_id_to_label
+        }
+
+        if log_gt:
+            wandb_boxes['ground_truth'] = wandb_bbox_dict
+        else:
+            wandb_boxes['predictions'] = wandb_bbox_dict
+
+        return wandb_boxes
+
+    def _get_wandb_masks(self,
+                         masks,
+                         labels,
+                         is_poly_mask=False,
+                         height=None,
+                         width=None):
+        """Get list of structured dict for logging masks to W&B.
+
+        Args:
+            masks (list): List of masks.
+            labels (int): List of label ids.
+            is_poly_mask (bool): Whether the mask is polygonal or not.
+                This is true for CocoDataset.
+            height (int): Height of the image.
+            width (int): Width of the image.
+
+        Returns:
+            Dictionary of masks to be logged.
+        """
+        mask_label_dict = dict()
+        for mask, label in zip(masks, labels):
+            label = label + 1
+            # Get bitmap mask from polygon.
+            if is_poly_mask:
+                if height is not None and width is not None:
+                    mask = polygon_to_bitmap(mask, height, width)
+            # Create composite masks for each class.
+            if label not in mask_label_dict.keys():
+                mask_label_dict[label] = mask
+            else:
+                mask_label_dict[label] = np.logical_or(mask_label_dict[label],
+                                                       mask)
+
+        wandb_masks = dict()
+        for key, value in mask_label_dict.items():
+            # Create mask for that class.
+            value = value.astype(np.uint8)
+            value[value > 0] = key
+
+            # Create dict of masks for logging.
+            class_name = self.class_id_to_label[key]
+            wandb_masks[class_name] = {
+                'mask_data': value,
+                'class_labels': self.class_id_to_label
+            }
+
+        return wandb_masks
+
+    def _log_data_table(self):
+        """Log the W&B Tables for validation data as artifact and calls
+        `use_artifact` on it so that the evaluation table can use the reference
+        of already uploaded images.
+
+        This allows the data to be uploaded just once.
+        """
+        data_artifact = self.wandb.Artifact('val', type='dataset')
+        data_artifact.add(self.data_table, 'val_data')
+
+        if not self.wandb.run.offline:
+            self.wandb.run.use_artifact(data_artifact)
+            data_artifact.wait()
+            self.data_table_ref = data_artifact.get('val_data')
+        else:
+            self.data_table_ref = self.data_table
+
+    def _log_eval_table(self, idx):
+        """Log the W&B Tables for model evaluation.
+
+        The table will be logged multiple times creating new version. Use this
+        to compare models at different intervals interactively.
+        """
+        pred_artifact = self.wandb.Artifact(
+            f'run_{self.wandb.run.id}_pred', type='evaluation')
+        pred_artifact.add(self.eval_table, 'eval_data')
+        if self.by_epoch:
+            aliases = ['latest', f'epoch_{idx}']
+        else:
+            aliases = ['latest', f'iter_{idx}']
+        self.wandb.run.log_artifact(pred_artifact, aliases=aliases)
diff --git a/mmdet/core/hook/yolox_lrupdater_hook.py b/mmdet/core/hook/yolox_lrupdater_hook.py
new file mode 100644
index 0000000..ecb028e
--- /dev/null
+++ b/mmdet/core/hook/yolox_lrupdater_hook.py
@@ -0,0 +1,67 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.runner.hooks import HOOKS
+from mmcv.runner.hooks.lr_updater import (CosineAnnealingLrUpdaterHook,
+                                          annealing_cos)
+
+
+@HOOKS.register_module()
+class YOLOXLrUpdaterHook(CosineAnnealingLrUpdaterHook):
+    """YOLOX learning rate scheme.
+
+    There are two main differences between YOLOXLrUpdaterHook
+    and CosineAnnealingLrUpdaterHook.
+
+       1. When the current running epoch is greater than
+           `max_epoch-last_epoch`, a fixed learning rate will be used
+       2. The exp warmup scheme is different with LrUpdaterHook in MMCV
+
+    Args:
+        num_last_epochs (int): The number of epochs with a fixed learning rate
+           before the end of the training.
+    """
+
+    def __init__(self, num_last_epochs, **kwargs):
+        self.num_last_epochs = num_last_epochs
+        super(YOLOXLrUpdaterHook, self).__init__(**kwargs)
+
+    def get_warmup_lr(self, cur_iters):
+
+        def _get_warmup_lr(cur_iters, regular_lr):
+            # exp warmup scheme
+            k = self.warmup_ratio * pow(
+                (cur_iters + 1) / float(self.warmup_iters), 2)
+            warmup_lr = [_lr * k for _lr in regular_lr]
+            return warmup_lr
+
+        if isinstance(self.base_lr, dict):
+            lr_groups = {}
+            for key, base_lr in self.base_lr.items():
+                lr_groups[key] = _get_warmup_lr(cur_iters, base_lr)
+            return lr_groups
+        else:
+            return _get_warmup_lr(cur_iters, self.base_lr)
+
+    def get_lr(self, runner, base_lr):
+        last_iter = len(runner.data_loader) * self.num_last_epochs
+
+        if self.by_epoch:
+            progress = runner.epoch
+            max_progress = runner.max_epochs
+        else:
+            progress = runner.iter
+            max_progress = runner.max_iters
+
+        progress += 1
+
+        if self.min_lr_ratio is not None:
+            target_lr = base_lr * self.min_lr_ratio
+        else:
+            target_lr = self.min_lr
+
+        if progress >= max_progress - last_iter:
+            # fixed learning rate
+            return target_lr
+        else:
+            return annealing_cos(
+                base_lr, target_lr, (progress - self.warmup_iters) /
+                (max_progress - self.warmup_iters - last_iter))
diff --git a/mmdet/core/hook/yolox_mode_switch_hook.py b/mmdet/core/hook/yolox_mode_switch_hook.py
new file mode 100644
index 0000000..10834e6
--- /dev/null
+++ b/mmdet/core/hook/yolox_mode_switch_hook.py
@@ -0,0 +1,52 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.parallel import is_module_wrapper
+from mmcv.runner.hooks import HOOKS, Hook
+
+
+@HOOKS.register_module()
+class YOLOXModeSwitchHook(Hook):
+    """Switch the mode of YOLOX during training.
+
+    This hook turns off the mosaic and mixup data augmentation and switches
+    to use L1 loss in bbox_head.
+
+    Args:
+        num_last_epochs (int): The number of latter epochs in the end of the
+            training to close the data augmentation and switch to L1 loss.
+            Default: 15.
+       skip_type_keys (list[str], optional): Sequence of type string to be
+            skip pipeline. Default: ('Mosaic', 'RandomAffine', 'MixUp')
+    """
+
+    def __init__(self,
+                 num_last_epochs=15,
+                 skip_type_keys=('Mosaic', 'RandomAffine', 'MixUp')):
+        self.num_last_epochs = num_last_epochs
+        self.skip_type_keys = skip_type_keys
+        self._restart_dataloader = False
+
+    def before_train_epoch(self, runner):
+        """Close mosaic and mixup augmentation and switches to use L1 loss."""
+        epoch = runner.epoch
+        train_loader = runner.data_loader
+        model = runner.model
+        if is_module_wrapper(model):
+            model = model.module
+        if (epoch + 1) == runner.max_epochs - self.num_last_epochs:
+            runner.logger.info('No mosaic and mixup aug now!')
+            # The dataset pipeline cannot be updated when persistent_workers
+            # is True, so we need to force the dataloader's multi-process
+            # restart. This is a very hacky approach.
+            train_loader.dataset.update_skip_type_keys(self.skip_type_keys)
+            if hasattr(train_loader, 'persistent_workers'
+                       ) and train_loader.persistent_workers is True:
+                train_loader._DataLoader__initialized = False
+                train_loader._iterator = None
+                self._restart_dataloader = True
+            runner.logger.info('Add additional L1 loss now!')
+            model.bbox_head.use_l1 = True
+        else:
+            # Once the restart is complete, we need to restore
+            # the initialization flag.
+            if self._restart_dataloader:
+                train_loader._DataLoader__initialized = True
diff --git a/mmdet/core/mask/__init__.py b/mmdet/core/mask/__init__.py
new file mode 100644
index 0000000..644a9b1
--- /dev/null
+++ b/mmdet/core/mask/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .mask_target import mask_target
+from .structures import BaseInstanceMasks, BitmapMasks, PolygonMasks
+from .utils import encode_mask_results, mask2bbox, split_combined_polys
+
+__all__ = [
+    'split_combined_polys', 'mask_target', 'BaseInstanceMasks', 'BitmapMasks',
+    'PolygonMasks', 'encode_mask_results', 'mask2bbox'
+]
diff --git a/mmdet/core/mask/mask_target.py b/mmdet/core/mask/mask_target.py
new file mode 100644
index 0000000..273e767
--- /dev/null
+++ b/mmdet/core/mask/mask_target.py
@@ -0,0 +1,127 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+from torch.nn.modules.utils import _pair
+
+
+def mask_target(pos_proposals_list, pos_assigned_gt_inds_list, gt_masks_list,
+                cfg):
+    """Compute mask target for positive proposals in multiple images.
+
+    Args:
+        pos_proposals_list (list[Tensor]): Positive proposals in multiple
+            images.
+        pos_assigned_gt_inds_list (list[Tensor]): Assigned GT indices for each
+            positive proposals.
+        gt_masks_list (list[:obj:`BaseInstanceMasks`]): Ground truth masks of
+            each image.
+        cfg (dict): Config dict that specifies the mask size.
+
+    Returns:
+        list[Tensor]: Mask target of each image.
+
+    Example:
+        >>> import mmcv
+        >>> import mmdet
+        >>> from mmdet.core.mask import BitmapMasks
+        >>> from mmdet.core.mask.mask_target import *
+        >>> H, W = 17, 18
+        >>> cfg = mmcv.Config({'mask_size': (13, 14)})
+        >>> rng = np.random.RandomState(0)
+        >>> # Positive proposals (tl_x, tl_y, br_x, br_y) for each image
+        >>> pos_proposals_list = [
+        >>>     torch.Tensor([
+        >>>         [ 7.2425,  5.5929, 13.9414, 14.9541],
+        >>>         [ 7.3241,  3.6170, 16.3850, 15.3102],
+        >>>     ]),
+        >>>     torch.Tensor([
+        >>>         [ 4.8448, 6.4010, 7.0314, 9.7681],
+        >>>         [ 5.9790, 2.6989, 7.4416, 4.8580],
+        >>>         [ 0.0000, 0.0000, 0.1398, 9.8232],
+        >>>     ]),
+        >>> ]
+        >>> # Corresponding class index for each proposal for each image
+        >>> pos_assigned_gt_inds_list = [
+        >>>     torch.LongTensor([7, 0]),
+        >>>     torch.LongTensor([5, 4, 1]),
+        >>> ]
+        >>> # Ground truth mask for each true object for each image
+        >>> gt_masks_list = [
+        >>>     BitmapMasks(rng.rand(8, H, W), height=H, width=W),
+        >>>     BitmapMasks(rng.rand(6, H, W), height=H, width=W),
+        >>> ]
+        >>> mask_targets = mask_target(
+        >>>     pos_proposals_list, pos_assigned_gt_inds_list,
+        >>>     gt_masks_list, cfg)
+        >>> assert mask_targets.shape == (5,) + cfg['mask_size']
+    """
+    cfg_list = [cfg for _ in range(len(pos_proposals_list))]
+    mask_targets = map(mask_target_single, pos_proposals_list,
+                       pos_assigned_gt_inds_list, gt_masks_list, cfg_list)
+    mask_targets = list(mask_targets)
+    if len(mask_targets) > 0:
+        mask_targets = torch.cat(mask_targets)
+    return mask_targets
+
+
+def mask_target_single(pos_proposals, pos_assigned_gt_inds, gt_masks, cfg):
+    """Compute mask target for each positive proposal in the image.
+
+    Args:
+        pos_proposals (Tensor): Positive proposals.
+        pos_assigned_gt_inds (Tensor): Assigned GT inds of positive proposals.
+        gt_masks (:obj:`BaseInstanceMasks`): GT masks in the format of Bitmap
+            or Polygon.
+        cfg (dict): Config dict that indicate the mask size.
+
+    Returns:
+        Tensor: Mask target of each positive proposals in the image.
+
+    Example:
+        >>> import mmcv
+        >>> import mmdet
+        >>> from mmdet.core.mask import BitmapMasks
+        >>> from mmdet.core.mask.mask_target import *  # NOQA
+        >>> H, W = 32, 32
+        >>> cfg = mmcv.Config({'mask_size': (7, 11)})
+        >>> rng = np.random.RandomState(0)
+        >>> # Masks for each ground truth box (relative to the image)
+        >>> gt_masks_data = rng.rand(3, H, W)
+        >>> gt_masks = BitmapMasks(gt_masks_data, height=H, width=W)
+        >>> # Predicted positive boxes in one image
+        >>> pos_proposals = torch.FloatTensor([
+        >>>     [ 16.2,   5.5, 19.9, 20.9],
+        >>>     [ 17.3,  13.6, 19.3, 19.3],
+        >>>     [ 14.8,  16.4, 17.0, 23.7],
+        >>>     [  0.0,   0.0, 16.0, 16.0],
+        >>>     [  4.0,   0.0, 20.0, 16.0],
+        >>> ])
+        >>> # For each predicted proposal, its assignment to a gt mask
+        >>> pos_assigned_gt_inds = torch.LongTensor([0, 1, 2, 1, 1])
+        >>> mask_targets = mask_target_single(
+        >>>     pos_proposals, pos_assigned_gt_inds, gt_masks, cfg)
+        >>> assert mask_targets.shape == (5,) + cfg['mask_size']
+    """
+    device = pos_proposals.device
+    mask_size = _pair(cfg.mask_size)
+    binarize = not cfg.get('soft_mask_target', False)
+    num_pos = pos_proposals.size(0)
+    if num_pos > 0:
+        proposals_np = pos_proposals.cpu().numpy()
+        maxh, maxw = gt_masks.height, gt_masks.width
+        proposals_np[:, [0, 2]] = np.clip(proposals_np[:, [0, 2]], 0, maxw)
+        proposals_np[:, [1, 3]] = np.clip(proposals_np[:, [1, 3]], 0, maxh)
+        pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy()
+
+        mask_targets = gt_masks.crop_and_resize(
+            proposals_np,
+            mask_size,
+            device=device,
+            inds=pos_assigned_gt_inds,
+            binarize=binarize).to_ndarray()
+
+        mask_targets = torch.from_numpy(mask_targets).float().to(device)
+    else:
+        mask_targets = pos_proposals.new_zeros((0, ) + mask_size)
+
+    return mask_targets
diff --git a/mmdet/core/mask/structures.py b/mmdet/core/mask/structures.py
new file mode 100644
index 0000000..7e730dc
--- /dev/null
+++ b/mmdet/core/mask/structures.py
@@ -0,0 +1,1102 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from abc import ABCMeta, abstractmethod
+
+import cv2
+import mmcv
+import numpy as np
+import pycocotools.mask as maskUtils
+import torch
+from mmcv.ops.roi_align import roi_align
+
+
+class BaseInstanceMasks(metaclass=ABCMeta):
+    """Base class for instance masks."""
+
+    @abstractmethod
+    def rescale(self, scale, interpolation='nearest'):
+        """Rescale masks as large as possible while keeping the aspect ratio.
+        For details can refer to `mmcv.imrescale`.
+
+        Args:
+            scale (tuple[int]): The maximum size (h, w) of rescaled mask.
+            interpolation (str): Same as :func:`mmcv.imrescale`.
+
+        Returns:
+            BaseInstanceMasks: The rescaled masks.
+        """
+
+    @abstractmethod
+    def resize(self, out_shape, interpolation='nearest'):
+        """Resize masks to the given out_shape.
+
+        Args:
+            out_shape: Target (h, w) of resized mask.
+            interpolation (str): See :func:`mmcv.imresize`.
+
+        Returns:
+            BaseInstanceMasks: The resized masks.
+        """
+
+    @abstractmethod
+    def flip(self, flip_direction='horizontal'):
+        """Flip masks alone the given direction.
+
+        Args:
+            flip_direction (str): Either 'horizontal' or 'vertical'.
+
+        Returns:
+            BaseInstanceMasks: The flipped masks.
+        """
+
+    @abstractmethod
+    def pad(self, out_shape, pad_val):
+        """Pad masks to the given size of (h, w).
+
+        Args:
+            out_shape (tuple[int]): Target (h, w) of padded mask.
+            pad_val (int): The padded value.
+
+        Returns:
+            BaseInstanceMasks: The padded masks.
+        """
+
+    @abstractmethod
+    def crop(self, bbox):
+        """Crop each mask by the given bbox.
+
+        Args:
+            bbox (ndarray): Bbox in format [x1, y1, x2, y2], shape (4, ).
+
+        Return:
+            BaseInstanceMasks: The cropped masks.
+        """
+
+    @abstractmethod
+    def crop_and_resize(self,
+                        bboxes,
+                        out_shape,
+                        inds,
+                        device,
+                        interpolation='bilinear',
+                        binarize=True):
+        """Crop and resize masks by the given bboxes.
+
+        This function is mainly used in mask targets computation.
+        It firstly align mask to bboxes by assigned_inds, then crop mask by the
+        assigned bbox and resize to the size of (mask_h, mask_w)
+
+        Args:
+            bboxes (Tensor): Bboxes in format [x1, y1, x2, y2], shape (N, 4)
+            out_shape (tuple[int]): Target (h, w) of resized mask
+            inds (ndarray): Indexes to assign masks to each bbox,
+                shape (N,) and values should be between [0, num_masks - 1].
+            device (str): Device of bboxes
+            interpolation (str): See `mmcv.imresize`
+            binarize (bool): if True fractional values are rounded to 0 or 1
+                after the resize operation. if False and unsupported an error
+                will be raised. Defaults to True.
+
+        Return:
+            BaseInstanceMasks: the cropped and resized masks.
+        """
+
+    @abstractmethod
+    def expand(self, expanded_h, expanded_w, top, left):
+        """see :class:`Expand`."""
+
+    @property
+    @abstractmethod
+    def areas(self):
+        """ndarray: areas of each instance."""
+
+    @abstractmethod
+    def to_ndarray(self):
+        """Convert masks to the format of ndarray.
+
+        Return:
+            ndarray: Converted masks in the format of ndarray.
+        """
+
+    @abstractmethod
+    def to_tensor(self, dtype, device):
+        """Convert masks to the format of Tensor.
+
+        Args:
+            dtype (str): Dtype of converted mask.
+            device (torch.device): Device of converted masks.
+
+        Returns:
+            Tensor: Converted masks in the format of Tensor.
+        """
+
+    @abstractmethod
+    def translate(self,
+                  out_shape,
+                  offset,
+                  direction='horizontal',
+                  fill_val=0,
+                  interpolation='bilinear'):
+        """Translate the masks.
+
+        Args:
+            out_shape (tuple[int]): Shape for output mask, format (h, w).
+            offset (int | float): The offset for translate.
+            direction (str): The translate direction, either "horizontal"
+                or "vertical".
+            fill_val (int | float): Border value. Default 0.
+            interpolation (str): Same as :func:`mmcv.imtranslate`.
+
+        Returns:
+            Translated masks.
+        """
+
+    def shear(self,
+              out_shape,
+              magnitude,
+              direction='horizontal',
+              border_value=0,
+              interpolation='bilinear'):
+        """Shear the masks.
+
+        Args:
+            out_shape (tuple[int]): Shape for output mask, format (h, w).
+            magnitude (int | float): The magnitude used for shear.
+            direction (str): The shear direction, either "horizontal"
+                or "vertical".
+            border_value (int | tuple[int]): Value used in case of a
+                constant border. Default 0.
+            interpolation (str): Same as in :func:`mmcv.imshear`.
+
+        Returns:
+            ndarray: Sheared masks.
+        """
+
+    @abstractmethod
+    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
+        """Rotate the masks.
+
+        Args:
+            out_shape (tuple[int]): Shape for output mask, format (h, w).
+            angle (int | float): Rotation angle in degrees. Positive values
+                mean counter-clockwise rotation.
+            center (tuple[float], optional): Center point (w, h) of the
+                rotation in source image. If not specified, the center of
+                the image will be used.
+            scale (int | float): Isotropic scale factor.
+            fill_val (int | float): Border value. Default 0 for masks.
+
+        Returns:
+            Rotated masks.
+        """
+
+
+class BitmapMasks(BaseInstanceMasks):
+    """This class represents masks in the form of bitmaps.
+
+    Args:
+        masks (ndarray): ndarray of masks in shape (N, H, W), where N is
+            the number of objects.
+        height (int): height of masks
+        width (int): width of masks
+
+    Example:
+        >>> from mmdet.core.mask.structures import *  # NOQA
+        >>> num_masks, H, W = 3, 32, 32
+        >>> rng = np.random.RandomState(0)
+        >>> masks = (rng.rand(num_masks, H, W) > 0.1).astype(np.int)
+        >>> self = BitmapMasks(masks, height=H, width=W)
+
+        >>> # demo crop_and_resize
+        >>> num_boxes = 5
+        >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes)
+        >>> out_shape = (14, 14)
+        >>> inds = torch.randint(0, len(self), size=(num_boxes,))
+        >>> device = 'cpu'
+        >>> interpolation = 'bilinear'
+        >>> new = self.crop_and_resize(
+        ...     bboxes, out_shape, inds, device, interpolation)
+        >>> assert len(new) == num_boxes
+        >>> assert new.height, new.width == out_shape
+    """
+
+    def __init__(self, masks, height, width):
+        self.height = height
+        self.width = width
+        if len(masks) == 0:
+            self.masks = np.empty((0, self.height, self.width), dtype=np.uint8)
+        else:
+            assert isinstance(masks, (list, np.ndarray))
+            if isinstance(masks, list):
+                assert isinstance(masks[0], np.ndarray)
+                assert masks[0].ndim == 2  # (H, W)
+            else:
+                assert masks.ndim == 3  # (N, H, W)
+
+            self.masks = np.stack(masks).reshape(-1, height, width)
+            assert self.masks.shape[1] == self.height
+            assert self.masks.shape[2] == self.width
+
+    def __getitem__(self, index):
+        """Index the BitmapMask.
+
+        Args:
+            index (int | ndarray): Indices in the format of integer or ndarray.
+
+        Returns:
+            :obj:`BitmapMasks`: Indexed bitmap masks.
+        """
+        masks = self.masks[index].reshape(-1, self.height, self.width)
+        return BitmapMasks(masks, self.height, self.width)
+
+    def __iter__(self):
+        return iter(self.masks)
+
+    def __repr__(self):
+        s = self.__class__.__name__ + '('
+        s += f'num_masks={len(self.masks)}, '
+        s += f'height={self.height}, '
+        s += f'width={self.width})'
+        return s
+
+    def __len__(self):
+        """Number of masks."""
+        return len(self.masks)
+
+    def rescale(self, scale, interpolation='nearest'):
+        """See :func:`BaseInstanceMasks.rescale`."""
+        if len(self.masks) == 0:
+            new_w, new_h = mmcv.rescale_size((self.width, self.height), scale)
+            rescaled_masks = np.empty((0, new_h, new_w), dtype=np.uint8)
+        else:
+            rescaled_masks = np.stack([
+                mmcv.imrescale(mask, scale, interpolation=interpolation)
+                for mask in self.masks
+            ])
+        height, width = rescaled_masks.shape[1:]
+        return BitmapMasks(rescaled_masks, height, width)
+
+    def resize(self, out_shape, interpolation='nearest'):
+        """See :func:`BaseInstanceMasks.resize`."""
+        if len(self.masks) == 0:
+            resized_masks = np.empty((0, *out_shape), dtype=np.uint8)
+        else:
+            resized_masks = np.stack([
+                mmcv.imresize(
+                    mask, out_shape[::-1], interpolation=interpolation)
+                for mask in self.masks
+            ])
+        return BitmapMasks(resized_masks, *out_shape)
+
+    def flip(self, flip_direction='horizontal'):
+        """See :func:`BaseInstanceMasks.flip`."""
+        assert flip_direction in ('horizontal', 'vertical', 'diagonal')
+
+        if len(self.masks) == 0:
+            flipped_masks = self.masks
+        else:
+            flipped_masks = np.stack([
+                mmcv.imflip(mask, direction=flip_direction)
+                for mask in self.masks
+            ])
+        return BitmapMasks(flipped_masks, self.height, self.width)
+
+    def pad(self, out_shape, pad_val=0):
+        """See :func:`BaseInstanceMasks.pad`."""
+        if len(self.masks) == 0:
+            padded_masks = np.empty((0, *out_shape), dtype=np.uint8)
+        else:
+            padded_masks = np.stack([
+                mmcv.impad(mask, shape=out_shape, pad_val=pad_val)
+                for mask in self.masks
+            ])
+        return BitmapMasks(padded_masks, *out_shape)
+
+    def crop(self, bbox):
+        """See :func:`BaseInstanceMasks.crop`."""
+        assert isinstance(bbox, np.ndarray)
+        assert bbox.ndim == 1
+
+        # clip the boundary
+        bbox = bbox.copy()
+        bbox[0::2] = np.clip(bbox[0::2], 0, self.width)
+        bbox[1::2] = np.clip(bbox[1::2], 0, self.height)
+        x1, y1, x2, y2 = bbox
+        w = np.maximum(x2 - x1, 1)
+        h = np.maximum(y2 - y1, 1)
+
+        if len(self.masks) == 0:
+            cropped_masks = np.empty((0, h, w), dtype=np.uint8)
+        else:
+            cropped_masks = self.masks[:, y1:y1 + h, x1:x1 + w]
+        return BitmapMasks(cropped_masks, h, w)
+
+    def crop_and_resize(self,
+                        bboxes,
+                        out_shape,
+                        inds,
+                        device='cpu',
+                        interpolation='bilinear',
+                        binarize=True):
+        """See :func:`BaseInstanceMasks.crop_and_resize`."""
+        if len(self.masks) == 0:
+            empty_masks = np.empty((0, *out_shape), dtype=np.uint8)
+            return BitmapMasks(empty_masks, *out_shape)
+
+        # convert bboxes to tensor
+        if isinstance(bboxes, np.ndarray):
+            bboxes = torch.from_numpy(bboxes).to(device=device)
+        if isinstance(inds, np.ndarray):
+            inds = torch.from_numpy(inds).to(device=device)
+
+        num_bbox = bboxes.shape[0]
+        fake_inds = torch.arange(
+            num_bbox, device=device).to(dtype=bboxes.dtype)[:, None]
+        rois = torch.cat([fake_inds, bboxes], dim=1)  # Nx5
+        rois = rois.to(device=device)
+        if num_bbox > 0:
+            gt_masks_th = torch.from_numpy(self.masks).to(device).index_select(
+                0, inds).to(dtype=rois.dtype)
+            targets = roi_align(gt_masks_th[:, None, :, :], rois, out_shape,
+                                1.0, 0, 'avg', True).squeeze(1)
+            if binarize:
+                resized_masks = (targets >= 0.5).cpu().numpy()
+            else:
+                resized_masks = targets.cpu().numpy()
+        else:
+            resized_masks = []
+        return BitmapMasks(resized_masks, *out_shape)
+
+    def expand(self, expanded_h, expanded_w, top, left):
+        """See :func:`BaseInstanceMasks.expand`."""
+        if len(self.masks) == 0:
+            expanded_mask = np.empty((0, expanded_h, expanded_w),
+                                     dtype=np.uint8)
+        else:
+            expanded_mask = np.zeros((len(self), expanded_h, expanded_w),
+                                     dtype=np.uint8)
+            expanded_mask[:, top:top + self.height,
+                          left:left + self.width] = self.masks
+        return BitmapMasks(expanded_mask, expanded_h, expanded_w)
+
+    def translate(self,
+                  out_shape,
+                  offset,
+                  direction='horizontal',
+                  fill_val=0,
+                  interpolation='bilinear'):
+        """Translate the BitmapMasks.
+
+        Args:
+            out_shape (tuple[int]): Shape for output mask, format (h, w).
+            offset (int | float): The offset for translate.
+            direction (str): The translate direction, either "horizontal"
+                or "vertical".
+            fill_val (int | float): Border value. Default 0 for masks.
+            interpolation (str): Same as :func:`mmcv.imtranslate`.
+
+        Returns:
+            BitmapMasks: Translated BitmapMasks.
+
+        Example:
+            >>> from mmdet.core.mask.structures import BitmapMasks
+            >>> self = BitmapMasks.random(dtype=np.uint8)
+            >>> out_shape = (32, 32)
+            >>> offset = 4
+            >>> direction = 'horizontal'
+            >>> fill_val = 0
+            >>> interpolation = 'bilinear'
+            >>> # Note, There seem to be issues when:
+            >>> # * out_shape is different than self's shape
+            >>> # * the mask dtype is not supported by cv2.AffineWarp
+            >>> new = self.translate(out_shape, offset, direction, fill_val,
+            >>>                      interpolation)
+            >>> assert len(new) == len(self)
+            >>> assert new.height, new.width == out_shape
+        """
+        if len(self.masks) == 0:
+            translated_masks = np.empty((0, *out_shape), dtype=np.uint8)
+        else:
+            translated_masks = mmcv.imtranslate(
+                self.masks.transpose((1, 2, 0)),
+                offset,
+                direction,
+                border_value=fill_val,
+                interpolation=interpolation)
+            if translated_masks.ndim == 2:
+                translated_masks = translated_masks[:, :, None]
+            translated_masks = translated_masks.transpose(
+                (2, 0, 1)).astype(self.masks.dtype)
+        return BitmapMasks(translated_masks, *out_shape)
+
+    def shear(self,
+              out_shape,
+              magnitude,
+              direction='horizontal',
+              border_value=0,
+              interpolation='bilinear'):
+        """Shear the BitmapMasks.
+
+        Args:
+            out_shape (tuple[int]): Shape for output mask, format (h, w).
+            magnitude (int | float): The magnitude used for shear.
+            direction (str): The shear direction, either "horizontal"
+                or "vertical".
+            border_value (int | tuple[int]): Value used in case of a
+                constant border.
+            interpolation (str): Same as in :func:`mmcv.imshear`.
+
+        Returns:
+            BitmapMasks: The sheared masks.
+        """
+        if len(self.masks) == 0:
+            sheared_masks = np.empty((0, *out_shape), dtype=np.uint8)
+        else:
+            sheared_masks = mmcv.imshear(
+                self.masks.transpose((1, 2, 0)),
+                magnitude,
+                direction,
+                border_value=border_value,
+                interpolation=interpolation)
+            if sheared_masks.ndim == 2:
+                sheared_masks = sheared_masks[:, :, None]
+            sheared_masks = sheared_masks.transpose(
+                (2, 0, 1)).astype(self.masks.dtype)
+        return BitmapMasks(sheared_masks, *out_shape)
+
+    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
+        """Rotate the BitmapMasks.
+
+        Args:
+            out_shape (tuple[int]): Shape for output mask, format (h, w).
+            angle (int | float): Rotation angle in degrees. Positive values
+                mean counter-clockwise rotation.
+            center (tuple[float], optional): Center point (w, h) of the
+                rotation in source image. If not specified, the center of
+                the image will be used.
+            scale (int | float): Isotropic scale factor.
+            fill_val (int | float): Border value. Default 0 for masks.
+
+        Returns:
+            BitmapMasks: Rotated BitmapMasks.
+        """
+        if len(self.masks) == 0:
+            rotated_masks = np.empty((0, *out_shape), dtype=self.masks.dtype)
+        else:
+            rotated_masks = mmcv.imrotate(
+                self.masks.transpose((1, 2, 0)),
+                angle,
+                center=center,
+                scale=scale,
+                border_value=fill_val)
+            if rotated_masks.ndim == 2:
+                # case when only one mask, (h, w)
+                rotated_masks = rotated_masks[:, :, None]  # (h, w, 1)
+            rotated_masks = rotated_masks.transpose(
+                (2, 0, 1)).astype(self.masks.dtype)
+        return BitmapMasks(rotated_masks, *out_shape)
+
+    @property
+    def areas(self):
+        """See :py:attr:`BaseInstanceMasks.areas`."""
+        return self.masks.sum((1, 2))
+
+    def to_ndarray(self):
+        """See :func:`BaseInstanceMasks.to_ndarray`."""
+        return self.masks
+
+    def to_tensor(self, dtype, device):
+        """See :func:`BaseInstanceMasks.to_tensor`."""
+        return torch.tensor(self.masks, dtype=dtype, device=device)
+
+    @classmethod
+    def random(cls,
+               num_masks=3,
+               height=32,
+               width=32,
+               dtype=np.uint8,
+               rng=None):
+        """Generate random bitmap masks for demo / testing purposes.
+
+        Example:
+            >>> from mmdet.core.mask.structures import BitmapMasks
+            >>> self = BitmapMasks.random()
+            >>> print('self = {}'.format(self))
+            self = BitmapMasks(num_masks=3, height=32, width=32)
+        """
+        from mmdet.utils.util_random import ensure_rng
+        rng = ensure_rng(rng)
+        masks = (rng.rand(num_masks, height, width) > 0.1).astype(dtype)
+        self = cls(masks, height=height, width=width)
+        return self
+
+    def get_bboxes(self):
+        num_masks = len(self)
+        boxes = np.zeros((num_masks, 4), dtype=np.float32)
+        x_any = self.masks.any(axis=1)
+        y_any = self.masks.any(axis=2)
+        for idx in range(num_masks):
+            x = np.where(x_any[idx, :])[0]
+            y = np.where(y_any[idx, :])[0]
+            if len(x) > 0 and len(y) > 0:
+                # use +1 for x_max and y_max so that the right and bottom
+                # boundary of instance masks are fully included by the box
+                boxes[idx, :] = np.array([x[0], y[0], x[-1] + 1, y[-1] + 1],
+                                         dtype=np.float32)
+        return boxes
+
+
+class PolygonMasks(BaseInstanceMasks):
+    """This class represents masks in the form of polygons.
+
+    Polygons is a list of three levels. The first level of the list
+    corresponds to objects, the second level to the polys that compose the
+    object, the third level to the poly coordinates
+
+    Args:
+        masks (list[list[ndarray]]): The first level of the list
+            corresponds to objects, the second level to the polys that
+            compose the object, the third level to the poly coordinates
+        height (int): height of masks
+        width (int): width of masks
+
+    Example:
+        >>> from mmdet.core.mask.structures import *  # NOQA
+        >>> masks = [
+        >>>     [ np.array([0, 0, 10, 0, 10, 10., 0, 10, 0, 0]) ]
+        >>> ]
+        >>> height, width = 16, 16
+        >>> self = PolygonMasks(masks, height, width)
+
+        >>> # demo translate
+        >>> new = self.translate((16, 16), 4., direction='horizontal')
+        >>> assert np.all(new.masks[0][0][1::2] == masks[0][0][1::2])
+        >>> assert np.all(new.masks[0][0][0::2] == masks[0][0][0::2] + 4)
+
+        >>> # demo crop_and_resize
+        >>> num_boxes = 3
+        >>> bboxes = np.array([[0, 0, 30, 10.0]] * num_boxes)
+        >>> out_shape = (16, 16)
+        >>> inds = torch.randint(0, len(self), size=(num_boxes,))
+        >>> device = 'cpu'
+        >>> interpolation = 'bilinear'
+        >>> new = self.crop_and_resize(
+        ...     bboxes, out_shape, inds, device, interpolation)
+        >>> assert len(new) == num_boxes
+        >>> assert new.height, new.width == out_shape
+    """
+
+    def __init__(self, masks, height, width):
+        assert isinstance(masks, list)
+        if len(masks) > 0:
+            assert isinstance(masks[0], list)
+            assert isinstance(masks[0][0], np.ndarray)
+
+        self.height = height
+        self.width = width
+        self.masks = masks
+
+    def __getitem__(self, index):
+        """Index the polygon masks.
+
+        Args:
+            index (ndarray | List): The indices.
+
+        Returns:
+            :obj:`PolygonMasks`: The indexed polygon masks.
+        """
+        if isinstance(index, np.ndarray):
+            index = index.tolist()
+        if isinstance(index, list):
+            masks = [self.masks[i] for i in index]
+        else:
+            try:
+                masks = self.masks[index]
+            except Exception:
+                raise ValueError(
+                    f'Unsupported input of type {type(index)} for indexing!')
+        if len(masks) and isinstance(masks[0], np.ndarray):
+            masks = [masks]  # ensure a list of three levels
+        return PolygonMasks(masks, self.height, self.width)
+
+    def __iter__(self):
+        return iter(self.masks)
+
+    def __repr__(self):
+        s = self.__class__.__name__ + '('
+        s += f'num_masks={len(self.masks)}, '
+        s += f'height={self.height}, '
+        s += f'width={self.width})'
+        return s
+
+    def __len__(self):
+        """Number of masks."""
+        return len(self.masks)
+
+    def rescale(self, scale, interpolation=None):
+        """see :func:`BaseInstanceMasks.rescale`"""
+        new_w, new_h = mmcv.rescale_size((self.width, self.height), scale)
+        if len(self.masks) == 0:
+            rescaled_masks = PolygonMasks([], new_h, new_w)
+        else:
+            rescaled_masks = self.resize((new_h, new_w))
+        return rescaled_masks
+
+    def resize(self, out_shape, interpolation=None):
+        """see :func:`BaseInstanceMasks.resize`"""
+        if len(self.masks) == 0:
+            resized_masks = PolygonMasks([], *out_shape)
+        else:
+            h_scale = out_shape[0] / self.height
+            w_scale = out_shape[1] / self.width
+            resized_masks = []
+            for poly_per_obj in self.masks:
+                resized_poly = []
+                for p in poly_per_obj:
+                    p = p.copy()
+                    p[0::2] = p[0::2] * w_scale
+                    p[1::2] = p[1::2] * h_scale
+                    resized_poly.append(p)
+                resized_masks.append(resized_poly)
+            resized_masks = PolygonMasks(resized_masks, *out_shape)
+        return resized_masks
+
+    def flip(self, flip_direction='horizontal'):
+        """see :func:`BaseInstanceMasks.flip`"""
+        assert flip_direction in ('horizontal', 'vertical', 'diagonal')
+        if len(self.masks) == 0:
+            flipped_masks = PolygonMasks([], self.height, self.width)
+        else:
+            flipped_masks = []
+            for poly_per_obj in self.masks:
+                flipped_poly_per_obj = []
+                for p in poly_per_obj:
+                    p = p.copy()
+                    if flip_direction == 'horizontal':
+                        p[0::2] = self.width - p[0::2]
+                    elif flip_direction == 'vertical':
+                        p[1::2] = self.height - p[1::2]
+                    else:
+                        p[0::2] = self.width - p[0::2]
+                        p[1::2] = self.height - p[1::2]
+                    flipped_poly_per_obj.append(p)
+                flipped_masks.append(flipped_poly_per_obj)
+            flipped_masks = PolygonMasks(flipped_masks, self.height,
+                                         self.width)
+        return flipped_masks
+
+    def crop(self, bbox):
+        """see :func:`BaseInstanceMasks.crop`"""
+        assert isinstance(bbox, np.ndarray)
+        assert bbox.ndim == 1
+
+        # clip the boundary
+        bbox = bbox.copy()
+        bbox[0::2] = np.clip(bbox[0::2], 0, self.width)
+        bbox[1::2] = np.clip(bbox[1::2], 0, self.height)
+        x1, y1, x2, y2 = bbox
+        w = np.maximum(x2 - x1, 1)
+        h = np.maximum(y2 - y1, 1)
+
+        if len(self.masks) == 0:
+            cropped_masks = PolygonMasks([], h, w)
+        else:
+            cropped_masks = []
+            for poly_per_obj in self.masks:
+                cropped_poly_per_obj = []
+                for p in poly_per_obj:
+                    # pycocotools will clip the boundary
+                    p = p.copy()
+                    p[0::2] = p[0::2] - bbox[0]
+                    p[1::2] = p[1::2] - bbox[1]
+                    cropped_poly_per_obj.append(p)
+                cropped_masks.append(cropped_poly_per_obj)
+            cropped_masks = PolygonMasks(cropped_masks, h, w)
+        return cropped_masks
+
+    def pad(self, out_shape, pad_val=0):
+        """padding has no effect on polygons`"""
+        return PolygonMasks(self.masks, *out_shape)
+
+    def expand(self, *args, **kwargs):
+        """TODO: Add expand for polygon"""
+        raise NotImplementedError
+
+    def crop_and_resize(self,
+                        bboxes,
+                        out_shape,
+                        inds,
+                        device='cpu',
+                        interpolation='bilinear',
+                        binarize=True):
+        """see :func:`BaseInstanceMasks.crop_and_resize`"""
+        out_h, out_w = out_shape
+        if len(self.masks) == 0:
+            return PolygonMasks([], out_h, out_w)
+
+        if not binarize:
+            raise ValueError('Polygons are always binary, '
+                             'setting binarize=False is unsupported')
+
+        resized_masks = []
+        for i in range(len(bboxes)):
+            mask = self.masks[inds[i]]
+            bbox = bboxes[i, :]
+            x1, y1, x2, y2 = bbox
+            w = np.maximum(x2 - x1, 1)
+            h = np.maximum(y2 - y1, 1)
+            h_scale = out_h / max(h, 0.1)  # avoid too large scale
+            w_scale = out_w / max(w, 0.1)
+
+            resized_mask = []
+            for p in mask:
+                p = p.copy()
+                # crop
+                # pycocotools will clip the boundary
+                p[0::2] = p[0::2] - bbox[0]
+                p[1::2] = p[1::2] - bbox[1]
+
+                # resize
+                p[0::2] = p[0::2] * w_scale
+                p[1::2] = p[1::2] * h_scale
+                resized_mask.append(p)
+            resized_masks.append(resized_mask)
+        return PolygonMasks(resized_masks, *out_shape)
+
+    def translate(self,
+                  out_shape,
+                  offset,
+                  direction='horizontal',
+                  fill_val=None,
+                  interpolation=None):
+        """Translate the PolygonMasks.
+
+        Example:
+            >>> self = PolygonMasks.random(dtype=np.int)
+            >>> out_shape = (self.height, self.width)
+            >>> new = self.translate(out_shape, 4., direction='horizontal')
+            >>> assert np.all(new.masks[0][0][1::2] == self.masks[0][0][1::2])
+            >>> assert np.all(new.masks[0][0][0::2] == self.masks[0][0][0::2] + 4)  # noqa: E501
+        """
+        assert fill_val is None or fill_val == 0, 'Here fill_val is not '\
+            f'used, and defaultly should be None or 0. got {fill_val}.'
+        if len(self.masks) == 0:
+            translated_masks = PolygonMasks([], *out_shape)
+        else:
+            translated_masks = []
+            for poly_per_obj in self.masks:
+                translated_poly_per_obj = []
+                for p in poly_per_obj:
+                    p = p.copy()
+                    if direction == 'horizontal':
+                        p[0::2] = np.clip(p[0::2] + offset, 0, out_shape[1])
+                    elif direction == 'vertical':
+                        p[1::2] = np.clip(p[1::2] + offset, 0, out_shape[0])
+                    translated_poly_per_obj.append(p)
+                translated_masks.append(translated_poly_per_obj)
+            translated_masks = PolygonMasks(translated_masks, *out_shape)
+        return translated_masks
+
+    def shear(self,
+              out_shape,
+              magnitude,
+              direction='horizontal',
+              border_value=0,
+              interpolation='bilinear'):
+        """See :func:`BaseInstanceMasks.shear`."""
+        if len(self.masks) == 0:
+            sheared_masks = PolygonMasks([], *out_shape)
+        else:
+            sheared_masks = []
+            if direction == 'horizontal':
+                shear_matrix = np.stack([[1, magnitude],
+                                         [0, 1]]).astype(np.float32)
+            elif direction == 'vertical':
+                shear_matrix = np.stack([[1, 0], [magnitude,
+                                                  1]]).astype(np.float32)
+            for poly_per_obj in self.masks:
+                sheared_poly = []
+                for p in poly_per_obj:
+                    p = np.stack([p[0::2], p[1::2]], axis=0)  # [2, n]
+                    new_coords = np.matmul(shear_matrix, p)  # [2, n]
+                    new_coords[0, :] = np.clip(new_coords[0, :], 0,
+                                               out_shape[1])
+                    new_coords[1, :] = np.clip(new_coords[1, :], 0,
+                                               out_shape[0])
+                    sheared_poly.append(
+                        new_coords.transpose((1, 0)).reshape(-1))
+                sheared_masks.append(sheared_poly)
+            sheared_masks = PolygonMasks(sheared_masks, *out_shape)
+        return sheared_masks
+
+    def rotate(self, out_shape, angle, center=None, scale=1.0, fill_val=0):
+        """See :func:`BaseInstanceMasks.rotate`."""
+        if len(self.masks) == 0:
+            rotated_masks = PolygonMasks([], *out_shape)
+        else:
+            rotated_masks = []
+            rotate_matrix = cv2.getRotationMatrix2D(center, -angle, scale)
+            for poly_per_obj in self.masks:
+                rotated_poly = []
+                for p in poly_per_obj:
+                    p = p.copy()
+                    coords = np.stack([p[0::2], p[1::2]], axis=1)  # [n, 2]
+                    # pad 1 to convert from format [x, y] to homogeneous
+                    # coordinates format [x, y, 1]
+                    coords = np.concatenate(
+                        (coords, np.ones((coords.shape[0], 1), coords.dtype)),
+                        axis=1)  # [n, 3]
+                    rotated_coords = np.matmul(
+                        rotate_matrix[None, :, :],
+                        coords[:, :, None])[..., 0]  # [n, 2, 1] -> [n, 2]
+                    rotated_coords[:, 0] = np.clip(rotated_coords[:, 0], 0,
+                                                   out_shape[1])
+                    rotated_coords[:, 1] = np.clip(rotated_coords[:, 1], 0,
+                                                   out_shape[0])
+                    rotated_poly.append(rotated_coords.reshape(-1))
+                rotated_masks.append(rotated_poly)
+            rotated_masks = PolygonMasks(rotated_masks, *out_shape)
+        return rotated_masks
+
+    def to_bitmap(self):
+        """convert polygon masks to bitmap masks."""
+        bitmap_masks = self.to_ndarray()
+        return BitmapMasks(bitmap_masks, self.height, self.width)
+
+    @property
+    def areas(self):
+        """Compute areas of masks.
+
+        This func is modified from `detectron2
+        <https://github.com/facebookresearch/detectron2/blob/ffff8acc35ea88ad1cb1806ab0f00b4c1c5dbfd9/detectron2/structures/masks.py#L387>`_.
+        The function only works with Polygons using the shoelace formula.
+
+        Return:
+            ndarray: areas of each instance
+        """  # noqa: W501
+        area = []
+        for polygons_per_obj in self.masks:
+            area_per_obj = 0
+            for p in polygons_per_obj:
+                area_per_obj += self._polygon_area(p[0::2], p[1::2])
+            area.append(area_per_obj)
+        return np.asarray(area)
+
+    def _polygon_area(self, x, y):
+        """Compute the area of a component of a polygon.
+
+        Using the shoelace formula:
+        https://stackoverflow.com/questions/24467972/calculate-area-of-polygon-given-x-y-coordinates
+
+        Args:
+            x (ndarray): x coordinates of the component
+            y (ndarray): y coordinates of the component
+
+        Return:
+            float: the are of the component
+        """  # noqa: 501
+        return 0.5 * np.abs(
+            np.dot(x, np.roll(y, 1)) - np.dot(y, np.roll(x, 1)))
+
+    def to_ndarray(self):
+        """Convert masks to the format of ndarray."""
+        if len(self.masks) == 0:
+            return np.empty((0, self.height, self.width), dtype=np.uint8)
+        bitmap_masks = []
+        for poly_per_obj in self.masks:
+            bitmap_masks.append(
+                polygon_to_bitmap(poly_per_obj, self.height, self.width))
+        return np.stack(bitmap_masks)
+
+    def to_tensor(self, dtype, device):
+        """See :func:`BaseInstanceMasks.to_tensor`."""
+        if len(self.masks) == 0:
+            return torch.empty((0, self.height, self.width),
+                               dtype=dtype,
+                               device=device)
+        ndarray_masks = self.to_ndarray()
+        return torch.tensor(ndarray_masks, dtype=dtype, device=device)
+
+    @classmethod
+    def random(cls,
+               num_masks=3,
+               height=32,
+               width=32,
+               n_verts=5,
+               dtype=np.float32,
+               rng=None):
+        """Generate random polygon masks for demo / testing purposes.
+
+        Adapted from [1]_
+
+        References:
+            .. [1] https://gitlab.kitware.com/computer-vision/kwimage/-/blob/928cae35ca8/kwimage/structs/polygon.py#L379  # noqa: E501
+
+        Example:
+            >>> from mmdet.core.mask.structures import PolygonMasks
+            >>> self = PolygonMasks.random()
+            >>> print('self = {}'.format(self))
+        """
+        from mmdet.utils.util_random import ensure_rng
+        rng = ensure_rng(rng)
+
+        def _gen_polygon(n, irregularity, spikeyness):
+            """Creates the polygon by sampling points on a circle around the
+            centre.  Random noise is added by varying the angular spacing
+            between sequential points, and by varying the radial distance of
+            each point from the centre.
+
+            Based on original code by Mike Ounsworth
+
+            Args:
+                n (int): number of vertices
+                irregularity (float): [0,1] indicating how much variance there
+                    is in the angular spacing of vertices. [0,1] will map to
+                    [0, 2pi/numberOfVerts]
+                spikeyness (float): [0,1] indicating how much variance there is
+                    in each vertex from the circle of radius aveRadius. [0,1]
+                    will map to [0, aveRadius]
+
+            Returns:
+                a list of vertices, in CCW order.
+            """
+            from scipy.stats import truncnorm
+
+            # Generate around the unit circle
+            cx, cy = (0.0, 0.0)
+            radius = 1
+
+            tau = np.pi * 2
+
+            irregularity = np.clip(irregularity, 0, 1) * 2 * np.pi / n
+            spikeyness = np.clip(spikeyness, 1e-9, 1)
+
+            # generate n angle steps
+            lower = (tau / n) - irregularity
+            upper = (tau / n) + irregularity
+            angle_steps = rng.uniform(lower, upper, n)
+
+            # normalize the steps so that point 0 and point n+1 are the same
+            k = angle_steps.sum() / (2 * np.pi)
+            angles = (angle_steps / k).cumsum() + rng.uniform(0, tau)
+
+            # Convert high and low values to be wrt the standard normal range
+            # https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.truncnorm.html
+            low = 0
+            high = 2 * radius
+            mean = radius
+            std = spikeyness
+            a = (low - mean) / std
+            b = (high - mean) / std
+            tnorm = truncnorm(a=a, b=b, loc=mean, scale=std)
+
+            # now generate the points
+            radii = tnorm.rvs(n, random_state=rng)
+            x_pts = cx + radii * np.cos(angles)
+            y_pts = cy + radii * np.sin(angles)
+
+            points = np.hstack([x_pts[:, None], y_pts[:, None]])
+
+            # Scale to 0-1 space
+            points = points - points.min(axis=0)
+            points = points / points.max(axis=0)
+
+            # Randomly place within 0-1 space
+            points = points * (rng.rand() * .8 + .2)
+            min_pt = points.min(axis=0)
+            max_pt = points.max(axis=0)
+
+            high = (1 - max_pt)
+            low = (0 - min_pt)
+            offset = (rng.rand(2) * (high - low)) + low
+            points = points + offset
+            return points
+
+        def _order_vertices(verts):
+            """
+            References:
+                https://stackoverflow.com/questions/1709283/how-can-i-sort-a-coordinate-list-for-a-rectangle-counterclockwise
+            """
+            mlat = verts.T[0].sum() / len(verts)
+            mlng = verts.T[1].sum() / len(verts)
+
+            tau = np.pi * 2
+            angle = (np.arctan2(mlat - verts.T[0], verts.T[1] - mlng) +
+                     tau) % tau
+            sortx = angle.argsort()
+            verts = verts.take(sortx, axis=0)
+            return verts
+
+        # Generate a random exterior for each requested mask
+        masks = []
+        for _ in range(num_masks):
+            exterior = _order_vertices(_gen_polygon(n_verts, 0.9, 0.9))
+            exterior = (exterior * [(width, height)]).astype(dtype)
+            masks.append([exterior.ravel()])
+
+        self = cls(masks, height, width)
+        return self
+
+    def get_bboxes(self):
+        num_masks = len(self)
+        boxes = np.zeros((num_masks, 4), dtype=np.float32)
+        for idx, poly_per_obj in enumerate(self.masks):
+            # simply use a number that is big enough for comparison with
+            # coordinates
+            xy_min = np.array([self.width * 2, self.height * 2],
+                              dtype=np.float32)
+            xy_max = np.zeros(2, dtype=np.float32)
+            for p in poly_per_obj:
+                xy = np.array(p).reshape(-1, 2).astype(np.float32)
+                xy_min = np.minimum(xy_min, np.min(xy, axis=0))
+                xy_max = np.maximum(xy_max, np.max(xy, axis=0))
+            boxes[idx, :2] = xy_min
+            boxes[idx, 2:] = xy_max
+
+        return boxes
+
+
+def polygon_to_bitmap(polygons, height, width):
+    """Convert masks from the form of polygons to bitmaps.
+
+    Args:
+        polygons (list[ndarray]): masks in polygon representation
+        height (int): mask height
+        width (int): mask width
+
+    Return:
+        ndarray: the converted masks in bitmap representation
+    """
+    rles = maskUtils.frPyObjects(polygons, height, width)
+    rle = maskUtils.merge(rles)
+    bitmap_mask = maskUtils.decode(rle).astype(bool)
+    return bitmap_mask
+
+
+def bitmap_to_polygon(bitmap):
+    """Convert masks from the form of bitmaps to polygons.
+
+    Args:
+        bitmap (ndarray): masks in bitmap representation.
+
+    Return:
+        list[ndarray]: the converted mask in polygon representation.
+        bool: whether the mask has holes.
+    """
+    bitmap = np.ascontiguousarray(bitmap).astype(np.uint8)
+    # cv2.RETR_CCOMP: retrieves all of the contours and organizes them
+    #   into a two-level hierarchy. At the top level, there are external
+    #   boundaries of the components. At the second level, there are
+    #   boundaries of the holes. If there is another contour inside a hole
+    #   of a connected component, it is still put at the top level.
+    # cv2.CHAIN_APPROX_NONE: stores absolutely all the contour points.
+    outs = cv2.findContours(bitmap, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
+    contours = outs[-2]
+    hierarchy = outs[-1]
+    if hierarchy is None:
+        return [], False
+    # hierarchy[i]: 4 elements, for the indexes of next, previous,
+    # parent, or nested contours. If there is no corresponding contour,
+    # it will be -1.
+    with_hole = (hierarchy.reshape(-1, 4)[:, 3] >= 0).any()
+    contours = [c.reshape(-1, 2) for c in contours]
+    return contours, with_hole
diff --git a/mmdet/core/mask/utils.py b/mmdet/core/mask/utils.py
new file mode 100644
index 0000000..90544b3
--- /dev/null
+++ b/mmdet/core/mask/utils.py
@@ -0,0 +1,89 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import numpy as np
+import pycocotools.mask as mask_util
+import torch
+
+
+def split_combined_polys(polys, poly_lens, polys_per_mask):
+    """Split the combined 1-D polys into masks.
+
+    A mask is represented as a list of polys, and a poly is represented as
+    a 1-D array. In dataset, all masks are concatenated into a single 1-D
+    tensor. Here we need to split the tensor into original representations.
+
+    Args:
+        polys (list): a list (length = image num) of 1-D tensors
+        poly_lens (list): a list (length = image num) of poly length
+        polys_per_mask (list): a list (length = image num) of poly number
+            of each mask
+
+    Returns:
+        list: a list (length = image num) of list (length = mask num) of \
+            list (length = poly num) of numpy array.
+    """
+    mask_polys_list = []
+    for img_id in range(len(polys)):
+        polys_single = polys[img_id]
+        polys_lens_single = poly_lens[img_id].tolist()
+        polys_per_mask_single = polys_per_mask[img_id].tolist()
+
+        split_polys = mmcv.slice_list(polys_single, polys_lens_single)
+        mask_polys = mmcv.slice_list(split_polys, polys_per_mask_single)
+        mask_polys_list.append(mask_polys)
+    return mask_polys_list
+
+
+# TODO: move this function to more proper place
+def encode_mask_results(mask_results):
+    """Encode bitmap mask to RLE code.
+
+    Args:
+        mask_results (list | tuple[list]): bitmap mask results.
+            In mask scoring rcnn, mask_results is a tuple of (segm_results,
+            segm_cls_score).
+
+    Returns:
+        list | tuple: RLE encoded mask.
+    """
+    if isinstance(mask_results, tuple):  # mask scoring
+        cls_segms, cls_mask_scores = mask_results
+    else:
+        cls_segms = mask_results
+    num_classes = len(cls_segms)
+    encoded_mask_results = [[] for _ in range(num_classes)]
+    for i in range(len(cls_segms)):
+        for cls_segm in cls_segms[i]:
+            encoded_mask_results[i].append(
+                mask_util.encode(
+                    np.array(
+                        cls_segm[:, :, np.newaxis], order='F',
+                        dtype='uint8'))[0])  # encoded with RLE
+    if isinstance(mask_results, tuple):
+        return encoded_mask_results, cls_mask_scores
+    else:
+        return encoded_mask_results
+
+
+def mask2bbox(masks):
+    """Obtain tight bounding boxes of binary masks.
+
+    Args:
+        masks (Tensor): Binary mask of shape (n, h, w).
+
+    Returns:
+        Tensor: Bboxe with shape (n, 4) of \
+            positive region in binary mask.
+    """
+    N = masks.shape[0]
+    bboxes = masks.new_zeros((N, 4), dtype=torch.float32)
+    x_any = torch.any(masks, dim=1)
+    y_any = torch.any(masks, dim=2)
+    for i in range(N):
+        x = torch.where(x_any[i, :])[0]
+        y = torch.where(y_any[i, :])[0]
+        if len(x) > 0 and len(y) > 0:
+            bboxes[i, :] = bboxes.new_tensor(
+                [x[0], y[0], x[-1] + 1, y[-1] + 1])
+
+    return bboxes
diff --git a/mmdet/core/optimizers/__init__.py b/mmdet/core/optimizers/__init__.py
new file mode 100644
index 0000000..e867d07
--- /dev/null
+++ b/mmdet/core/optimizers/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .builder import OPTIMIZER_BUILDERS, build_optimizer
+from .layer_decay_optimizer_constructor import \
+    LearningRateDecayOptimizerConstructor
+
+__all__ = [
+    'LearningRateDecayOptimizerConstructor', 'OPTIMIZER_BUILDERS',
+    'build_optimizer'
+]
diff --git a/mmdet/core/optimizers/builder.py b/mmdet/core/optimizers/builder.py
new file mode 100644
index 0000000..406dd9b
--- /dev/null
+++ b/mmdet/core/optimizers/builder.py
@@ -0,0 +1,33 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+from mmcv.runner.optimizer import OPTIMIZER_BUILDERS as MMCV_OPTIMIZER_BUILDERS
+from mmcv.utils import Registry, build_from_cfg
+
+OPTIMIZER_BUILDERS = Registry(
+    'optimizer builder', parent=MMCV_OPTIMIZER_BUILDERS)
+
+
+def build_optimizer_constructor(cfg):
+    constructor_type = cfg.get('type')
+    if constructor_type in OPTIMIZER_BUILDERS:
+        return build_from_cfg(cfg, OPTIMIZER_BUILDERS)
+    elif constructor_type in MMCV_OPTIMIZER_BUILDERS:
+        return build_from_cfg(cfg, MMCV_OPTIMIZER_BUILDERS)
+    else:
+        raise KeyError(f'{constructor_type} is not registered '
+                       'in the optimizer builder registry.')
+
+
+def build_optimizer(model, cfg):
+    optimizer_cfg = copy.deepcopy(cfg)
+    constructor_type = optimizer_cfg.pop('constructor',
+                                         'DefaultOptimizerConstructor')
+    paramwise_cfg = optimizer_cfg.pop('paramwise_cfg', None)
+    optim_constructor = build_optimizer_constructor(
+        dict(
+            type=constructor_type,
+            optimizer_cfg=optimizer_cfg,
+            paramwise_cfg=paramwise_cfg))
+    optimizer = optim_constructor(model)
+    return optimizer
diff --git a/mmdet/core/optimizers/layer_decay_optimizer_constructor.py b/mmdet/core/optimizers/layer_decay_optimizer_constructor.py
new file mode 100644
index 0000000..1bc3469
--- /dev/null
+++ b/mmdet/core/optimizers/layer_decay_optimizer_constructor.py
@@ -0,0 +1,154 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import json
+
+from mmcv.runner import DefaultOptimizerConstructor, get_dist_info
+
+from mmdet.utils import get_root_logger
+from .builder import OPTIMIZER_BUILDERS
+
+
+def get_layer_id_for_convnext(var_name, max_layer_id):
+    """Get the layer id to set the different learning rates in ``layer_wise``
+    decay_type.
+
+    Args:
+        var_name (str): The key of the model.
+        max_layer_id (int): Maximum layer id.
+
+    Returns:
+        int: The id number corresponding to different learning rate in
+        ``LearningRateDecayOptimizerConstructor``.
+    """
+
+    if var_name in ('backbone.cls_token', 'backbone.mask_token',
+                    'backbone.pos_embed'):
+        return 0
+    elif var_name.startswith('backbone.downsample_layers'):
+        stage_id = int(var_name.split('.')[2])
+        if stage_id == 0:
+            layer_id = 0
+        elif stage_id == 1:
+            layer_id = 2
+        elif stage_id == 2:
+            layer_id = 3
+        elif stage_id == 3:
+            layer_id = max_layer_id
+        return layer_id
+    elif var_name.startswith('backbone.stages'):
+        stage_id = int(var_name.split('.')[2])
+        block_id = int(var_name.split('.')[3])
+        if stage_id == 0:
+            layer_id = 1
+        elif stage_id == 1:
+            layer_id = 2
+        elif stage_id == 2:
+            layer_id = 3 + block_id // 3
+        elif stage_id == 3:
+            layer_id = max_layer_id
+        return layer_id
+    else:
+        return max_layer_id + 1
+
+
+def get_stage_id_for_convnext(var_name, max_stage_id):
+    """Get the stage id to set the different learning rates in ``stage_wise``
+    decay_type.
+
+    Args:
+        var_name (str): The key of the model.
+        max_stage_id (int): Maximum stage id.
+
+    Returns:
+        int: The id number corresponding to different learning rate in
+        ``LearningRateDecayOptimizerConstructor``.
+    """
+
+    if var_name in ('backbone.cls_token', 'backbone.mask_token',
+                    'backbone.pos_embed'):
+        return 0
+    elif var_name.startswith('backbone.downsample_layers'):
+        return 0
+    elif var_name.startswith('backbone.stages'):
+        stage_id = int(var_name.split('.')[2])
+        return stage_id + 1
+    else:
+        return max_stage_id - 1
+
+
+@OPTIMIZER_BUILDERS.register_module()
+class LearningRateDecayOptimizerConstructor(DefaultOptimizerConstructor):
+    # Different learning rates are set for different layers of backbone.
+    # Note: Currently, this optimizer constructor is built for ConvNeXt.
+
+    def add_params(self, params, module, **kwargs):
+        """Add all parameters of module to the params list.
+
+        The parameters of the given module will be added to the list of param
+        groups, with specific rules defined by paramwise_cfg.
+
+        Args:
+            params (list[dict]): A list of param groups, it will be modified
+                in place.
+            module (nn.Module): The module to be added.
+        """
+        logger = get_root_logger()
+
+        parameter_groups = {}
+        logger.info(f'self.paramwise_cfg is {self.paramwise_cfg}')
+        num_layers = self.paramwise_cfg.get('num_layers') + 2
+        decay_rate = self.paramwise_cfg.get('decay_rate')
+        decay_type = self.paramwise_cfg.get('decay_type', 'layer_wise')
+        logger.info('Build LearningRateDecayOptimizerConstructor  '
+                    f'{decay_type} {decay_rate} - {num_layers}')
+        weight_decay = self.base_wd
+        for name, param in module.named_parameters():
+            if not param.requires_grad:
+                continue  # frozen weights
+            if len(param.shape) == 1 or name.endswith('.bias') or name in (
+                    'pos_embed', 'cls_token'):
+                group_name = 'no_decay'
+                this_weight_decay = 0.
+            else:
+                group_name = 'decay'
+                this_weight_decay = weight_decay
+            if 'layer_wise' in decay_type:
+                if 'ConvNeXt' in module.backbone.__class__.__name__:
+                    layer_id = get_layer_id_for_convnext(
+                        name, self.paramwise_cfg.get('num_layers'))
+                    logger.info(f'set param {name} as id {layer_id}')
+                else:
+                    raise NotImplementedError()
+            elif decay_type == 'stage_wise':
+                if 'ConvNeXt' in module.backbone.__class__.__name__:
+                    layer_id = get_stage_id_for_convnext(name, num_layers)
+                    logger.info(f'set param {name} as id {layer_id}')
+                else:
+                    raise NotImplementedError()
+            group_name = f'layer_{layer_id}_{group_name}'
+
+            if group_name not in parameter_groups:
+                scale = decay_rate**(num_layers - layer_id - 1)
+
+                parameter_groups[group_name] = {
+                    'weight_decay': this_weight_decay,
+                    'params': [],
+                    'param_names': [],
+                    'lr_scale': scale,
+                    'group_name': group_name,
+                    'lr': scale * self.base_lr,
+                }
+
+            parameter_groups[group_name]['params'].append(param)
+            parameter_groups[group_name]['param_names'].append(name)
+        rank, _ = get_dist_info()
+        if rank == 0:
+            to_display = {}
+            for key in parameter_groups:
+                to_display[key] = {
+                    'param_names': parameter_groups[key]['param_names'],
+                    'lr_scale': parameter_groups[key]['lr_scale'],
+                    'lr': parameter_groups[key]['lr'],
+                    'weight_decay': parameter_groups[key]['weight_decay'],
+                }
+            logger.info(f'Param groups = {json.dumps(to_display, indent=2)}')
+        params.extend(parameter_groups.values())
diff --git a/mmdet/core/post_processing/__init__.py b/mmdet/core/post_processing/__init__.py
new file mode 100644
index 0000000..00376bd
--- /dev/null
+++ b/mmdet/core/post_processing/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .bbox_nms import fast_nms, multiclass_nms
+from .matrix_nms import mask_matrix_nms
+from .merge_augs import (merge_aug_bboxes, merge_aug_masks,
+                         merge_aug_proposals, merge_aug_scores)
+
+__all__ = [
+    'multiclass_nms', 'merge_aug_proposals', 'merge_aug_bboxes',
+    'merge_aug_scores', 'merge_aug_masks', 'mask_matrix_nms', 'fast_nms'
+]
diff --git a/mmdet/core/post_processing/bbox_nms.py b/mmdet/core/post_processing/bbox_nms.py
new file mode 100644
index 0000000..4fcf57b
--- /dev/null
+++ b/mmdet/core/post_processing/bbox_nms.py
@@ -0,0 +1,171 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from mmcv.ops.nms import batched_nms
+
+from mmdet.core.bbox.iou_calculators import bbox_overlaps
+
+
+def multiclass_nms(multi_bboxes,
+                   multi_scores,
+                   score_thr,
+                   nms_cfg,
+                   max_num=-1,
+                   score_factors=None,
+                   return_inds=False):
+    """NMS for multi-class bboxes.
+
+    Args:
+        multi_bboxes (Tensor): shape (n, #class*4) or (n, 4)
+        multi_scores (Tensor): shape (n, #class), where the last column
+            contains scores of the background class, but this will be ignored.
+        score_thr (float): bbox threshold, bboxes with scores lower than it
+            will not be considered.
+        nms_cfg (dict): a dict that contains the arguments of nms operations
+        max_num (int, optional): if there are more than max_num bboxes after
+            NMS, only top max_num will be kept. Default to -1.
+        score_factors (Tensor, optional): The factors multiplied to scores
+            before applying NMS. Default to None.
+        return_inds (bool, optional): Whether return the indices of kept
+            bboxes. Default to False.
+
+    Returns:
+        tuple: (dets, labels, indices (optional)), tensors of shape (k, 5),
+            (k), and (k). Dets are boxes with scores. Labels are 0-based.
+    """
+    num_classes = multi_scores.size(1) - 1
+    # exclude background category
+    if multi_bboxes.shape[1] > 4:
+        bboxes = multi_bboxes.view(multi_scores.size(0), -1, 4)
+    else:
+        bboxes = multi_bboxes[:, None].expand(
+            multi_scores.size(0), num_classes, 4)
+
+    scores = multi_scores[:, :-1]
+
+    labels = torch.arange(num_classes, dtype=torch.long, device=scores.device)
+    labels = labels.view(1, -1).expand_as(scores)
+
+    bboxes = bboxes.reshape(-1, 4)
+    scores = scores.reshape(-1)
+    labels = labels.reshape(-1)
+
+    if not torch.onnx.is_in_onnx_export():
+        # NonZero not supported  in TensorRT
+        # remove low scoring boxes
+        valid_mask = scores > score_thr
+    # multiply score_factor after threshold to preserve more bboxes, improve
+    # mAP by 1% for YOLOv3
+    if score_factors is not None:
+        # expand the shape to match original shape of score
+        score_factors = score_factors.view(-1, 1).expand(
+            multi_scores.size(0), num_classes)
+        score_factors = score_factors.reshape(-1)
+        scores = scores * score_factors
+
+    if not torch.onnx.is_in_onnx_export():
+        # NonZero not supported  in TensorRT
+        inds = valid_mask.nonzero(as_tuple=False).squeeze(1)
+        bboxes, scores, labels = bboxes[inds], scores[inds], labels[inds]
+    else:
+        # TensorRT NMS plugin has invalid output filled with -1
+        # add dummy data to make detection output correct.
+        bboxes = torch.cat([bboxes, bboxes.new_zeros(1, 4)], dim=0)
+        scores = torch.cat([scores, scores.new_zeros(1)], dim=0)
+        labels = torch.cat([labels, labels.new_zeros(1)], dim=0)
+
+    if bboxes.numel() == 0:
+        if torch.onnx.is_in_onnx_export():
+            raise RuntimeError('[ONNX Error] Can not record NMS '
+                               'as it has not been executed this time')
+        dets = torch.cat([bboxes, scores[:, None]], -1)
+        if return_inds:
+            return dets, labels, inds
+        else:
+            return dets, labels
+
+    dets, keep = batched_nms(bboxes, scores, labels, nms_cfg)
+
+    if max_num > 0:
+        dets = dets[:max_num]
+        keep = keep[:max_num]
+
+    if return_inds:
+        return dets, labels[keep], inds[keep]
+    else:
+        return dets, labels[keep]
+
+
+def fast_nms(multi_bboxes,
+             multi_scores,
+             multi_coeffs,
+             score_thr,
+             iou_thr,
+             top_k,
+             max_num=-1):
+    """Fast NMS in `YOLACT <https://arxiv.org/abs/1904.02689>`_.
+
+    Fast NMS allows already-removed detections to suppress other detections so
+    that every instance can be decided to be kept or discarded in parallel,
+    which is not possible in traditional NMS. This relaxation allows us to
+    implement Fast NMS entirely in standard GPU-accelerated matrix operations.
+
+    Args:
+        multi_bboxes (Tensor): shape (n, #class*4) or (n, 4)
+        multi_scores (Tensor): shape (n, #class+1), where the last column
+            contains scores of the background class, but this will be ignored.
+        multi_coeffs (Tensor): shape (n, #class*coeffs_dim).
+        score_thr (float): bbox threshold, bboxes with scores lower than it
+            will not be considered.
+        iou_thr (float): IoU threshold to be considered as conflicted.
+        top_k (int): if there are more than top_k bboxes before NMS,
+            only top top_k will be kept.
+        max_num (int): if there are more than max_num bboxes after NMS,
+            only top max_num will be kept. If -1, keep all the bboxes.
+            Default: -1.
+
+    Returns:
+        tuple: (dets, labels, coefficients), tensors of shape (k, 5), (k, 1),
+            and (k, coeffs_dim). Dets are boxes with scores.
+            Labels are 0-based.
+    """
+
+    scores = multi_scores[:, :-1].t()  # [#class, n]
+    scores, idx = scores.sort(1, descending=True)
+
+    idx = idx[:, :top_k].contiguous()
+    scores = scores[:, :top_k]  # [#class, topk]
+    num_classes, num_dets = idx.size()
+    boxes = multi_bboxes[idx.view(-1), :].view(num_classes, num_dets, 4)
+    coeffs = multi_coeffs[idx.view(-1), :].view(num_classes, num_dets, -1)
+
+    iou = bbox_overlaps(boxes, boxes)  # [#class, topk, topk]
+    iou.triu_(diagonal=1)
+    iou_max, _ = iou.max(dim=1)
+
+    # Now just filter out the ones higher than the threshold
+    keep = iou_max <= iou_thr
+
+    # Second thresholding introduces 0.2 mAP gain at negligible time cost
+    keep *= scores > score_thr
+
+    # Assign each kept detection to its corresponding class
+    classes = torch.arange(
+        num_classes, device=boxes.device)[:, None].expand_as(keep)
+    classes = classes[keep]
+
+    boxes = boxes[keep]
+    coeffs = coeffs[keep]
+    scores = scores[keep]
+
+    # Only keep the top max_num highest scores across all classes
+    scores, idx = scores.sort(0, descending=True)
+    if max_num > 0:
+        idx = idx[:max_num]
+        scores = scores[:max_num]
+
+    classes = classes[idx]
+    boxes = boxes[idx]
+    coeffs = coeffs[idx]
+
+    cls_dets = torch.cat([boxes, scores[:, None]], dim=1)
+    return cls_dets, classes, coeffs
diff --git a/mmdet/core/post_processing/matrix_nms.py b/mmdet/core/post_processing/matrix_nms.py
new file mode 100644
index 0000000..9dc8c4f
--- /dev/null
+++ b/mmdet/core/post_processing/matrix_nms.py
@@ -0,0 +1,121 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+
+def mask_matrix_nms(masks,
+                    labels,
+                    scores,
+                    filter_thr=-1,
+                    nms_pre=-1,
+                    max_num=-1,
+                    kernel='gaussian',
+                    sigma=2.0,
+                    mask_area=None):
+    """Matrix NMS for multi-class masks.
+
+    Args:
+        masks (Tensor): Has shape (num_instances, h, w)
+        labels (Tensor): Labels of corresponding masks,
+            has shape (num_instances,).
+        scores (Tensor): Mask scores of corresponding masks,
+            has shape (num_instances).
+        filter_thr (float): Score threshold to filter the masks
+            after matrix nms. Default: -1, which means do not
+            use filter_thr.
+        nms_pre (int): The max number of instances to do the matrix nms.
+            Default: -1, which means do not use nms_pre.
+        max_num (int, optional): If there are more than max_num masks after
+            matrix, only top max_num will be kept. Default: -1, which means
+            do not use max_num.
+        kernel (str): 'linear' or 'gaussian'.
+        sigma (float): std in gaussian method.
+        mask_area (Tensor): The sum of seg_masks.
+
+    Returns:
+        tuple(Tensor): Processed mask results.
+
+            - scores (Tensor): Updated scores, has shape (n,).
+            - labels (Tensor): Remained labels, has shape (n,).
+            - masks (Tensor): Remained masks, has shape (n, w, h).
+            - keep_inds (Tensor): The indices number of
+                the remaining mask in the input mask, has shape (n,).
+    """
+    assert len(labels) == len(masks) == len(scores)
+    if len(labels) == 0:
+        return scores.new_zeros(0), labels.new_zeros(0), masks.new_zeros(
+            0, *masks.shape[-2:]), labels.new_zeros(0)
+    if mask_area is None:
+        mask_area = masks.sum((1, 2)).float()
+    else:
+        assert len(masks) == len(mask_area)
+
+    # sort and keep top nms_pre
+    scores, sort_inds = torch.sort(scores, descending=True)
+
+    keep_inds = sort_inds
+    if nms_pre > 0 and len(sort_inds) > nms_pre:
+        sort_inds = sort_inds[:nms_pre]
+        keep_inds = keep_inds[:nms_pre]
+        scores = scores[:nms_pre]
+    masks = masks[sort_inds]
+    mask_area = mask_area[sort_inds]
+    labels = labels[sort_inds]
+
+    num_masks = len(labels)
+    flatten_masks = masks.reshape(num_masks, -1).float()
+    # inter.
+    inter_matrix = torch.mm(flatten_masks, flatten_masks.transpose(1, 0))
+    expanded_mask_area = mask_area.expand(num_masks, num_masks)
+    # Upper triangle iou matrix.
+    iou_matrix = (inter_matrix /
+                  (expanded_mask_area + expanded_mask_area.transpose(1, 0) -
+                   inter_matrix)).triu(diagonal=1)
+    # label_specific matrix.
+    expanded_labels = labels.expand(num_masks, num_masks)
+    # Upper triangle label matrix.
+    label_matrix = (expanded_labels == expanded_labels.transpose(
+        1, 0)).triu(diagonal=1)
+
+    # IoU compensation
+    compensate_iou, _ = (iou_matrix * label_matrix).max(0)
+    compensate_iou = compensate_iou.expand(num_masks,
+                                           num_masks).transpose(1, 0)
+
+    # IoU decay
+    decay_iou = iou_matrix * label_matrix
+
+    # Calculate the decay_coefficient
+    if kernel == 'gaussian':
+        decay_matrix = torch.exp(-1 * sigma * (decay_iou**2))
+        compensate_matrix = torch.exp(-1 * sigma * (compensate_iou**2))
+        decay_coefficient, _ = (decay_matrix / compensate_matrix).min(0)
+    elif kernel == 'linear':
+        decay_matrix = (1 - decay_iou) / (1 - compensate_iou)
+        decay_coefficient, _ = decay_matrix.min(0)
+    else:
+        raise NotImplementedError(
+            f'{kernel} kernel is not supported in matrix nms!')
+    # update the score.
+    scores = scores * decay_coefficient
+
+    if filter_thr > 0:
+        keep = scores >= filter_thr
+        keep_inds = keep_inds[keep]
+        if not keep.any():
+            return scores.new_zeros(0), labels.new_zeros(0), masks.new_zeros(
+                0, *masks.shape[-2:]), labels.new_zeros(0)
+        masks = masks[keep]
+        scores = scores[keep]
+        labels = labels[keep]
+
+    # sort and keep top max_num
+    scores, sort_inds = torch.sort(scores, descending=True)
+    keep_inds = keep_inds[sort_inds]
+    if max_num > 0 and len(sort_inds) > max_num:
+        sort_inds = sort_inds[:max_num]
+        keep_inds = keep_inds[:max_num]
+        scores = scores[:max_num]
+    masks = masks[sort_inds]
+    labels = labels[sort_inds]
+
+    return scores, labels, masks, keep_inds
diff --git a/mmdet/core/post_processing/merge_augs.py b/mmdet/core/post_processing/merge_augs.py
new file mode 100644
index 0000000..2ac4603
--- /dev/null
+++ b/mmdet/core/post_processing/merge_augs.py
@@ -0,0 +1,154 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import warnings
+
+import numpy as np
+import torch
+from mmcv import ConfigDict
+from mmcv.ops import nms
+
+from ..bbox import bbox_mapping_back
+
+
+def merge_aug_proposals(aug_proposals, img_metas, cfg):
+    """Merge augmented proposals (multiscale, flip, etc.)
+
+    Args:
+        aug_proposals (list[Tensor]): proposals from different testing
+            schemes, shape (n, 5). Note that they are not rescaled to the
+            original image size.
+
+        img_metas (list[dict]): list of image info dict where each dict has:
+            'img_shape', 'scale_factor', 'flip', and may also contain
+            'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+            For details on the values of these keys see
+            `mmdet/datasets/pipelines/formatting.py:Collect`.
+
+        cfg (dict): rpn test config.
+
+    Returns:
+        Tensor: shape (n, 4), proposals corresponding to original image scale.
+    """
+
+    cfg = copy.deepcopy(cfg)
+
+    # deprecate arguments warning
+    if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg:
+        warnings.warn(
+            'In rpn_proposal or test_cfg, '
+            'nms_thr has been moved to a dict named nms as '
+            'iou_threshold, max_num has been renamed as max_per_img, '
+            'name of original arguments and the way to specify '
+            'iou_threshold of NMS will be deprecated.')
+    if 'nms' not in cfg:
+        cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr))
+    if 'max_num' in cfg:
+        if 'max_per_img' in cfg:
+            assert cfg.max_num == cfg.max_per_img, f'You set max_num and ' \
+                f'max_per_img at the same time, but get {cfg.max_num} ' \
+                f'and {cfg.max_per_img} respectively' \
+                f'Please delete max_num which will be deprecated.'
+        else:
+            cfg.max_per_img = cfg.max_num
+    if 'nms_thr' in cfg:
+        assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set ' \
+            f'iou_threshold in nms and ' \
+            f'nms_thr at the same time, but get ' \
+            f'{cfg.nms.iou_threshold} and {cfg.nms_thr}' \
+            f' respectively. Please delete the nms_thr ' \
+            f'which will be deprecated.'
+
+    recovered_proposals = []
+    for proposals, img_info in zip(aug_proposals, img_metas):
+        img_shape = img_info['img_shape']
+        scale_factor = img_info['scale_factor']
+        flip = img_info['flip']
+        flip_direction = img_info['flip_direction']
+        _proposals = proposals.clone()
+        _proposals[:, :4] = bbox_mapping_back(_proposals[:, :4], img_shape,
+                                              scale_factor, flip,
+                                              flip_direction)
+        recovered_proposals.append(_proposals)
+    aug_proposals = torch.cat(recovered_proposals, dim=0)
+    merged_proposals, _ = nms(aug_proposals[:, :4].contiguous(),
+                              aug_proposals[:, -1].contiguous(),
+                              cfg.nms.iou_threshold)
+    scores = merged_proposals[:, 4]
+    _, order = scores.sort(0, descending=True)
+    num = min(cfg.max_per_img, merged_proposals.shape[0])
+    order = order[:num]
+    merged_proposals = merged_proposals[order, :]
+    return merged_proposals
+
+
+def merge_aug_bboxes(aug_bboxes, aug_scores, img_metas, rcnn_test_cfg):
+    """Merge augmented detection bboxes and scores.
+
+    Args:
+        aug_bboxes (list[Tensor]): shape (n, 4*#class)
+        aug_scores (list[Tensor] or None): shape (n, #class)
+        img_shapes (list[Tensor]): shape (3, ).
+        rcnn_test_cfg (dict): rcnn test config.
+
+    Returns:
+        tuple: (bboxes, scores)
+    """
+    recovered_bboxes = []
+    for bboxes, img_info in zip(aug_bboxes, img_metas):
+        img_shape = img_info[0]['img_shape']
+        scale_factor = img_info[0]['scale_factor']
+        flip = img_info[0]['flip']
+        flip_direction = img_info[0]['flip_direction']
+        bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip,
+                                   flip_direction)
+        recovered_bboxes.append(bboxes)
+    bboxes = torch.stack(recovered_bboxes).mean(dim=0)
+    if aug_scores is None:
+        return bboxes
+    else:
+        scores = torch.stack(aug_scores).mean(dim=0)
+        return bboxes, scores
+
+
+def merge_aug_scores(aug_scores):
+    """Merge augmented bbox scores."""
+    if isinstance(aug_scores[0], torch.Tensor):
+        return torch.mean(torch.stack(aug_scores), dim=0)
+    else:
+        return np.mean(aug_scores, axis=0)
+
+
+def merge_aug_masks(aug_masks, img_metas, rcnn_test_cfg, weights=None):
+    """Merge augmented mask prediction.
+
+    Args:
+        aug_masks (list[ndarray]): shape (n, #class, h, w)
+        img_shapes (list[ndarray]): shape (3, ).
+        rcnn_test_cfg (dict): rcnn test config.
+
+    Returns:
+        tuple: (bboxes, scores)
+    """
+    recovered_masks = []
+    for mask, img_info in zip(aug_masks, img_metas):
+        flip = img_info[0]['flip']
+        if flip:
+            flip_direction = img_info[0]['flip_direction']
+            if flip_direction == 'horizontal':
+                mask = mask[:, :, :, ::-1]
+            elif flip_direction == 'vertical':
+                mask = mask[:, :, ::-1, :]
+            elif flip_direction == 'diagonal':
+                mask = mask[:, :, :, ::-1]
+                mask = mask[:, :, ::-1, :]
+            else:
+                raise ValueError(
+                    f"Invalid flipping direction '{flip_direction}'")
+        recovered_masks.append(mask)
+
+    if weights is None:
+        merged_masks = np.mean(recovered_masks, axis=0)
+    else:
+        merged_masks = np.average(
+            np.array(recovered_masks), axis=0, weights=np.array(weights))
+    return merged_masks
diff --git a/mmdet/core/utils/__init__.py b/mmdet/core/utils/__init__.py
new file mode 100644
index 0000000..3f0d070
--- /dev/null
+++ b/mmdet/core/utils/__init__.py
@@ -0,0 +1,13 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .dist_utils import (DistOptimizerHook, all_reduce_dict, allreduce_grads,
+                         reduce_mean, sync_random_seed)
+from .misc import (center_of_mass, filter_scores_and_topk, flip_tensor,
+                   generate_coordinate, mask2ndarray, multi_apply,
+                   select_single_mlvl, unmap)
+
+__all__ = [
+    'allreduce_grads', 'DistOptimizerHook', 'reduce_mean', 'multi_apply',
+    'unmap', 'mask2ndarray', 'flip_tensor', 'all_reduce_dict',
+    'center_of_mass', 'generate_coordinate', 'select_single_mlvl',
+    'filter_scores_and_topk', 'sync_random_seed'
+]
diff --git a/mmdet/core/utils/dist_utils.py b/mmdet/core/utils/dist_utils.py
new file mode 100644
index 0000000..8760774
--- /dev/null
+++ b/mmdet/core/utils/dist_utils.py
@@ -0,0 +1,193 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import functools
+import pickle
+import warnings
+from collections import OrderedDict
+
+import numpy as np
+import torch
+import torch.distributed as dist
+from mmcv.runner import OptimizerHook, get_dist_info
+from torch._utils import (_flatten_dense_tensors, _take_tensors,
+                          _unflatten_dense_tensors)
+
+
+def _allreduce_coalesced(tensors, world_size, bucket_size_mb=-1):
+    if bucket_size_mb > 0:
+        bucket_size_bytes = bucket_size_mb * 1024 * 1024
+        buckets = _take_tensors(tensors, bucket_size_bytes)
+    else:
+        buckets = OrderedDict()
+        for tensor in tensors:
+            tp = tensor.type()
+            if tp not in buckets:
+                buckets[tp] = []
+            buckets[tp].append(tensor)
+        buckets = buckets.values()
+
+    for bucket in buckets:
+        flat_tensors = _flatten_dense_tensors(bucket)
+        dist.all_reduce(flat_tensors)
+        flat_tensors.div_(world_size)
+        for tensor, synced in zip(
+                bucket, _unflatten_dense_tensors(flat_tensors, bucket)):
+            tensor.copy_(synced)
+
+
+def allreduce_grads(params, coalesce=True, bucket_size_mb=-1):
+    """Allreduce gradients.
+
+    Args:
+        params (list[torch.Parameters]): List of parameters of a model
+        coalesce (bool, optional): Whether allreduce parameters as a whole.
+            Defaults to True.
+        bucket_size_mb (int, optional): Size of bucket, the unit is MB.
+            Defaults to -1.
+    """
+    grads = [
+        param.grad.data for param in params
+        if param.requires_grad and param.grad is not None
+    ]
+    world_size = dist.get_world_size()
+    if coalesce:
+        _allreduce_coalesced(grads, world_size, bucket_size_mb)
+    else:
+        for tensor in grads:
+            dist.all_reduce(tensor.div_(world_size))
+
+
+class DistOptimizerHook(OptimizerHook):
+    """Deprecated optimizer hook for distributed training."""
+
+    def __init__(self, *args, **kwargs):
+        warnings.warn('"DistOptimizerHook" is deprecated, please switch to'
+                      '"mmcv.runner.OptimizerHook".')
+        super().__init__(*args, **kwargs)
+
+
+def reduce_mean(tensor):
+    """"Obtain the mean of tensor on different GPUs."""
+    if not (dist.is_available() and dist.is_initialized()):
+        return tensor
+    tensor = tensor.clone()
+    dist.all_reduce(tensor.div_(dist.get_world_size()), op=dist.ReduceOp.SUM)
+    return tensor
+
+
+def obj2tensor(pyobj, device='cuda'):
+    """Serialize picklable python object to tensor."""
+    storage = torch.ByteStorage.from_buffer(pickle.dumps(pyobj))
+    return torch.ByteTensor(storage).to(device=device)
+
+
+def tensor2obj(tensor):
+    """Deserialize tensor to picklable python object."""
+    return pickle.loads(tensor.cpu().numpy().tobytes())
+
+
+@functools.lru_cache()
+def _get_global_gloo_group():
+    """Return a process group based on gloo backend, containing all the ranks
+    The result is cached."""
+    if dist.get_backend() == 'nccl':
+        return dist.new_group(backend='gloo')
+    else:
+        return dist.group.WORLD
+
+
+def all_reduce_dict(py_dict, op='sum', group=None, to_float=True):
+    """Apply all reduce function for python dict object.
+
+    The code is modified from https://github.com/Megvii-
+    BaseDetection/YOLOX/blob/main/yolox/utils/allreduce_norm.py.
+
+    NOTE: make sure that py_dict in different ranks has the same keys and
+    the values should be in the same shape. Currently only supports
+    nccl backend.
+
+    Args:
+        py_dict (dict): Dict to be applied all reduce op.
+        op (str): Operator, could be 'sum' or 'mean'. Default: 'sum'
+        group (:obj:`torch.distributed.group`, optional): Distributed group,
+            Default: None.
+        to_float (bool): Whether to convert all values of dict to float.
+            Default: True.
+
+    Returns:
+        OrderedDict: reduced python dict object.
+    """
+    warnings.warn(
+        'group` is deprecated. Currently only supports NCCL backend.')
+    _, world_size = get_dist_info()
+    if world_size == 1:
+        return py_dict
+
+    # all reduce logic across different devices.
+    py_key = list(py_dict.keys())
+    if not isinstance(py_dict, OrderedDict):
+        py_key_tensor = obj2tensor(py_key)
+        dist.broadcast(py_key_tensor, src=0)
+        py_key = tensor2obj(py_key_tensor)
+
+    tensor_shapes = [py_dict[k].shape for k in py_key]
+    tensor_numels = [py_dict[k].numel() for k in py_key]
+
+    if to_float:
+        warnings.warn('Note: the "to_float" is True, you need to '
+                      'ensure that the behavior is reasonable.')
+        flatten_tensor = torch.cat(
+            [py_dict[k].flatten().float() for k in py_key])
+    else:
+        flatten_tensor = torch.cat([py_dict[k].flatten() for k in py_key])
+
+    dist.all_reduce(flatten_tensor, op=dist.ReduceOp.SUM)
+    if op == 'mean':
+        flatten_tensor /= world_size
+
+    split_tensors = [
+        x.reshape(shape) for x, shape in zip(
+            torch.split(flatten_tensor, tensor_numels), tensor_shapes)
+    ]
+    out_dict = {k: v for k, v in zip(py_key, split_tensors)}
+    if isinstance(py_dict, OrderedDict):
+        out_dict = OrderedDict(out_dict)
+    return out_dict
+
+
+def sync_random_seed(seed=None, device='cuda'):
+    """Make sure different ranks share the same seed.
+
+    All workers must call this function, otherwise it will deadlock.
+    This method is generally used in `DistributedSampler`,
+    because the seed should be identical across all processes
+    in the distributed group.
+
+    In distributed sampling, different ranks should sample non-overlapped
+    data in the dataset. Therefore, this function is used to make sure that
+    each rank shuffles the data indices in the same order based
+    on the same seed. Then different ranks could use different indices
+    to select non-overlapped data from the same data list.
+
+    Args:
+        seed (int, Optional): The seed. Default to None.
+        device (str): The device where the seed will be put on.
+            Default to 'cuda'.
+
+    Returns:
+        int: Seed to be used.
+    """
+    if seed is None:
+        seed = np.random.randint(2**31)
+    assert isinstance(seed, int)
+
+    rank, world_size = get_dist_info()
+
+    if world_size == 1:
+        return seed
+
+    if rank == 0:
+        random_num = torch.tensor(seed, dtype=torch.int32, device=device)
+    else:
+        random_num = torch.tensor(0, dtype=torch.int32, device=device)
+    dist.broadcast(random_num, src=0)
+    return random_num.item()
diff --git a/mmdet/core/utils/misc.py b/mmdet/core/utils/misc.py
new file mode 100644
index 0000000..14cb745
--- /dev/null
+++ b/mmdet/core/utils/misc.py
@@ -0,0 +1,208 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from functools import partial
+
+import numpy as np
+import torch
+from six.moves import map, zip
+
+from ..mask.structures import BitmapMasks, PolygonMasks
+
+
+def multi_apply(func, *args, **kwargs):
+    """Apply function to a list of arguments.
+
+    Note:
+        This function applies the ``func`` to multiple inputs and
+        map the multiple outputs of the ``func`` into different
+        list. Each list contains the same type of outputs corresponding
+        to different inputs.
+
+    Args:
+        func (Function): A function that will be applied to a list of
+            arguments
+
+    Returns:
+        tuple(list): A tuple containing multiple list, each list contains \
+            a kind of returned results by the function
+    """
+    pfunc = partial(func, **kwargs) if kwargs else func
+    map_results = map(pfunc, *args)
+    return tuple(map(list, zip(*map_results)))
+
+
+def unmap(data, count, inds, fill=0):
+    """Unmap a subset of item (data) back to the original set of items (of size
+    count)"""
+    if data.dim() == 1:
+        ret = data.new_full((count, ), fill)
+        ret[inds.type(torch.bool)] = data
+    else:
+        new_size = (count, ) + data.size()[1:]
+        ret = data.new_full(new_size, fill)
+        ret[inds.type(torch.bool), :] = data
+    return ret
+
+
+def mask2ndarray(mask):
+    """Convert Mask to ndarray..
+
+    Args:
+        mask (:obj:`BitmapMasks` or :obj:`PolygonMasks` or
+        torch.Tensor or np.ndarray): The mask to be converted.
+
+    Returns:
+        np.ndarray: Ndarray mask of shape (n, h, w) that has been converted
+    """
+    if isinstance(mask, (BitmapMasks, PolygonMasks)):
+        mask = mask.to_ndarray()
+    elif isinstance(mask, torch.Tensor):
+        mask = mask.detach().cpu().numpy()
+    elif not isinstance(mask, np.ndarray):
+        raise TypeError(f'Unsupported {type(mask)} data type')
+    return mask
+
+
+def flip_tensor(src_tensor, flip_direction):
+    """flip tensor base on flip_direction.
+
+    Args:
+        src_tensor (Tensor): input feature map, shape (B, C, H, W).
+        flip_direction (str): The flipping direction. Options are
+          'horizontal', 'vertical', 'diagonal'.
+
+    Returns:
+        out_tensor (Tensor): Flipped tensor.
+    """
+    assert src_tensor.ndim == 4
+    valid_directions = ['horizontal', 'vertical', 'diagonal']
+    assert flip_direction in valid_directions
+    if flip_direction == 'horizontal':
+        out_tensor = torch.flip(src_tensor, [3])
+    elif flip_direction == 'vertical':
+        out_tensor = torch.flip(src_tensor, [2])
+    else:
+        out_tensor = torch.flip(src_tensor, [2, 3])
+    return out_tensor
+
+
+def select_single_mlvl(mlvl_tensors, batch_id, detach=True):
+    """Extract a multi-scale single image tensor from a multi-scale batch
+    tensor based on batch index.
+
+    Note: The default value of detach is True, because the proposal gradient
+    needs to be detached during the training of the two-stage model. E.g
+    Cascade Mask R-CNN.
+
+    Args:
+        mlvl_tensors (list[Tensor]): Batch tensor for all scale levels,
+           each is a 4D-tensor.
+        batch_id (int): Batch index.
+        detach (bool): Whether detach gradient. Default True.
+
+    Returns:
+        list[Tensor]: Multi-scale single image tensor.
+    """
+    assert isinstance(mlvl_tensors, (list, tuple))
+    num_levels = len(mlvl_tensors)
+
+    if detach:
+        mlvl_tensor_list = [
+            mlvl_tensors[i][batch_id].detach() for i in range(num_levels)
+        ]
+    else:
+        mlvl_tensor_list = [
+            mlvl_tensors[i][batch_id] for i in range(num_levels)
+        ]
+    return mlvl_tensor_list
+
+
+def filter_scores_and_topk(scores, score_thr, topk, results=None):
+    """Filter results using score threshold and topk candidates.
+
+    Args:
+        scores (Tensor): The scores, shape (num_bboxes, K).
+        score_thr (float): The score filter threshold.
+        topk (int): The number of topk candidates.
+        results (dict or list or Tensor, Optional): The results to
+           which the filtering rule is to be applied. The shape
+           of each item is (num_bboxes, N).
+
+    Returns:
+        tuple: Filtered results
+
+            - scores (Tensor): The scores after being filtered, \
+                shape (num_bboxes_filtered, ).
+            - labels (Tensor): The class labels, shape \
+                (num_bboxes_filtered, ).
+            - anchor_idxs (Tensor): The anchor indexes, shape \
+                (num_bboxes_filtered, ).
+            - filtered_results (dict or list or Tensor, Optional): \
+                The filtered results. The shape of each item is \
+                (num_bboxes_filtered, N).
+    """
+    valid_mask = scores > score_thr
+    scores = scores[valid_mask]
+    valid_idxs = torch.nonzero(valid_mask)
+
+    num_topk = min(topk, valid_idxs.size(0))
+    # torch.sort is actually faster than .topk (at least on GPUs)
+    scores, idxs = scores.sort(descending=True)
+    scores = scores[:num_topk]
+    topk_idxs = valid_idxs[idxs[:num_topk]]
+    keep_idxs, labels = topk_idxs.unbind(dim=1)
+
+    filtered_results = None
+    if results is not None:
+        if isinstance(results, dict):
+            filtered_results = {k: v[keep_idxs] for k, v in results.items()}
+        elif isinstance(results, list):
+            filtered_results = [result[keep_idxs] for result in results]
+        elif isinstance(results, torch.Tensor):
+            filtered_results = results[keep_idxs]
+        else:
+            raise NotImplementedError(f'Only supports dict or list or Tensor, '
+                                      f'but get {type(results)}.')
+    return scores, labels, keep_idxs, filtered_results
+
+
+def center_of_mass(mask, esp=1e-6):
+    """Calculate the centroid coordinates of the mask.
+
+    Args:
+        mask (Tensor): The mask to be calculated, shape (h, w).
+        esp (float): Avoid dividing by zero. Default: 1e-6.
+
+    Returns:
+        tuple[Tensor]: the coordinates of the center point of the mask.
+
+            - center_h (Tensor): the center point of the height.
+            - center_w (Tensor): the center point of the width.
+    """
+    h, w = mask.shape
+    grid_h = torch.arange(h, device=mask.device)[:, None]
+    grid_w = torch.arange(w, device=mask.device)
+    normalizer = mask.sum().float().clamp(min=esp)
+    center_h = (mask * grid_h).sum() / normalizer
+    center_w = (mask * grid_w).sum() / normalizer
+    return center_h, center_w
+
+
+def generate_coordinate(featmap_sizes, device='cuda'):
+    """Generate the coordinate.
+
+    Args:
+        featmap_sizes (tuple): The feature to be calculated,
+            of shape (N, C, W, H).
+        device (str): The device where the feature will be put on.
+    Returns:
+        coord_feat (Tensor): The coordinate feature, of shape (N, 2, W, H).
+    """
+
+    x_range = torch.linspace(-1, 1, featmap_sizes[-1], device=device)
+    y_range = torch.linspace(-1, 1, featmap_sizes[-2], device=device)
+    y, x = torch.meshgrid(y_range, x_range)
+    y = y.expand([featmap_sizes[0], 1, -1, -1])
+    x = x.expand([featmap_sizes[0], 1, -1, -1])
+    coord_feat = torch.cat([x, y], 1)
+
+    return coord_feat
diff --git a/mmdet/core/visualization/__init__.py b/mmdet/core/visualization/__init__.py
new file mode 100644
index 0000000..2eb17c4
--- /dev/null
+++ b/mmdet/core/visualization/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .image import (color_val_matplotlib, imshow_det_bboxes,
+                    imshow_gt_det_bboxes)
+from .palette import get_palette, palette_val
+
+__all__ = [
+    'imshow_det_bboxes', 'imshow_gt_det_bboxes', 'color_val_matplotlib',
+    'palette_val', 'get_palette'
+]
diff --git a/mmdet/core/visualization/image.py b/mmdet/core/visualization/image.py
new file mode 100644
index 0000000..63eae8a
--- /dev/null
+++ b/mmdet/core/visualization/image.py
@@ -0,0 +1,563 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import sys
+
+import cv2
+import matplotlib.pyplot as plt
+import mmcv
+import numpy as np
+import pycocotools.mask as mask_util
+from matplotlib.collections import PatchCollection
+from matplotlib.patches import Polygon
+
+from mmdet.core.evaluation.panoptic_utils import INSTANCE_OFFSET
+from ..mask.structures import bitmap_to_polygon
+from ..utils import mask2ndarray
+from .palette import get_palette, palette_val
+
+__all__ = [
+    'color_val_matplotlib', 'draw_masks', 'draw_bboxes', 'draw_labels',
+    'imshow_det_bboxes', 'imshow_gt_det_bboxes'
+]
+
+EPS = 1e-2
+
+
+def color_val_matplotlib(color):
+    """Convert various input in BGR order to normalized RGB matplotlib color
+    tuples.
+
+    Args:
+        color (:obj`Color` | str | tuple | int | ndarray): Color inputs.
+
+    Returns:
+        tuple[float]: A tuple of 3 normalized floats indicating RGB channels.
+    """
+    color = mmcv.color_val(color)
+    color = [color / 255 for color in color[::-1]]
+    return tuple(color)
+
+
+def _get_adaptive_scales(areas, min_area=800, max_area=30000):
+    """Get adaptive scales according to areas.
+
+    The scale range is [0.5, 1.0]. When the area is less than
+    ``'min_area'``, the scale is 0.5 while the area is larger than
+    ``'max_area'``, the scale is 1.0.
+
+    Args:
+        areas (ndarray): The areas of bboxes or masks with the
+            shape of (n, ).
+        min_area (int): Lower bound areas for adaptive scales.
+            Default: 800.
+        max_area (int): Upper bound areas for adaptive scales.
+            Default: 30000.
+
+    Returns:
+        ndarray: The adaotive scales with the shape of (n, ).
+    """
+    scales = 0.5 + (areas - min_area) / (max_area - min_area)
+    scales = np.clip(scales, 0.5, 1.0)
+    return scales
+
+
+def _get_bias_color(base, max_dist=30):
+    """Get different colors for each masks.
+
+    Get different colors for each masks by adding a bias
+    color to the base category color.
+    Args:
+        base (ndarray): The base category color with the shape
+            of (3, ).
+        max_dist (int): The max distance of bias. Default: 30.
+
+    Returns:
+        ndarray: The new color for a mask with the shape of (3, ).
+    """
+    new_color = base + np.random.randint(
+        low=-max_dist, high=max_dist + 1, size=3)
+    return np.clip(new_color, 0, 255, new_color)
+
+
+def draw_bboxes(ax, bboxes, color='g', alpha=0.8, thickness=2):
+    """Draw bounding boxes on the axes.
+
+    Args:
+        ax (matplotlib.Axes): The input axes.
+        bboxes (ndarray): The input bounding boxes with the shape
+            of (n, 4).
+        color (list[tuple] | matplotlib.color): the colors for each
+            bounding boxes.
+        alpha (float): Transparency of bounding boxes. Default: 0.8.
+        thickness (int): Thickness of lines. Default: 2.
+
+    Returns:
+        matplotlib.Axes: The result axes.
+    """
+    polygons = []
+    for i, bbox in enumerate(bboxes):
+        bbox_int = bbox.astype(np.int32)
+        poly = [[bbox_int[0], bbox_int[1]], [bbox_int[0], bbox_int[3]],
+                [bbox_int[2], bbox_int[3]], [bbox_int[2], bbox_int[1]]]
+        np_poly = np.array(poly).reshape((4, 2))
+        polygons.append(Polygon(np_poly))
+    p = PatchCollection(
+        polygons,
+        facecolor='none',
+        edgecolors=color,
+        linewidths=thickness,
+        alpha=alpha)
+    ax.add_collection(p)
+
+    return ax
+
+
+def draw_labels(ax,
+                labels,
+                positions,
+                scores=None,
+                class_names=None,
+                color='w',
+                font_size=8,
+                scales=None,
+                horizontal_alignment='left'):
+    """Draw labels on the axes.
+
+    Args:
+        ax (matplotlib.Axes): The input axes.
+        labels (ndarray): The labels with the shape of (n, ).
+        positions (ndarray): The positions to draw each labels.
+        scores (ndarray): The scores for each labels.
+        class_names (list[str]): The class names.
+        color (list[tuple] | matplotlib.color): The colors for labels.
+        font_size (int): Font size of texts. Default: 8.
+        scales (list[float]): Scales of texts. Default: None.
+        horizontal_alignment (str): The horizontal alignment method of
+            texts. Default: 'left'.
+
+    Returns:
+        matplotlib.Axes: The result axes.
+    """
+    for i, (pos, label) in enumerate(zip(positions, labels)):
+        label_text = class_names[
+            label] if class_names is not None else f'class {label}'
+        if scores is not None:
+            label_text += f'|{scores[i]:.02f}'
+        text_color = color[i] if isinstance(color, list) else color
+
+        font_size_mask = font_size if scales is None else font_size * scales[i]
+        ax.text(
+            pos[0],
+            pos[1],
+            f'{label_text}',
+            bbox={
+                'facecolor': 'black',
+                'alpha': 0.8,
+                'pad': 0.7,
+                'edgecolor': 'none'
+            },
+            color=text_color,
+            fontsize=font_size_mask,
+            verticalalignment='top',
+            horizontalalignment=horizontal_alignment)
+
+    return ax
+
+
+def draw_masks(ax, img, masks, color=None, with_edge=True, alpha=0.8):
+    """Draw masks on the image and their edges on the axes.
+
+    Args:
+        ax (matplotlib.Axes): The input axes.
+        img (ndarray): The image with the shape of (3, h, w).
+        masks (ndarray): The masks with the shape of (n, h, w).
+        color (ndarray): The colors for each masks with the shape
+            of (n, 3).
+        with_edge (bool): Whether to draw edges. Default: True.
+        alpha (float): Transparency of bounding boxes. Default: 0.8.
+
+    Returns:
+        matplotlib.Axes: The result axes.
+        ndarray: The result image.
+    """
+    taken_colors = set([0, 0, 0])
+    if color is None:
+        random_colors = np.random.randint(0, 255, (masks.size(0), 3))
+        color = [tuple(c) for c in random_colors]
+        color = np.array(color, dtype=np.uint8)
+    polygons = []
+    for i, mask in enumerate(masks):
+        if with_edge:
+            contours, _ = bitmap_to_polygon(mask)
+            polygons += [Polygon(c) for c in contours]
+
+        color_mask = color[i]
+        while tuple(color_mask) in taken_colors:
+            color_mask = _get_bias_color(color_mask)
+        taken_colors.add(tuple(color_mask))
+
+        mask = mask.astype(bool)
+        img[mask] = img[mask] * (1 - alpha) + color_mask * alpha
+
+    p = PatchCollection(
+        polygons, facecolor='none', edgecolors='w', linewidths=1, alpha=0.8)
+    ax.add_collection(p)
+
+    return ax, img
+
+
+def imshow_det_bboxes(img,
+                      bboxes=None,
+                      labels=None,
+                      segms=None,
+                      class_names=None,
+                      score_thr=0,
+                      bbox_color='green',
+                      text_color='green',
+                      mask_color=None,
+                      thickness=2,
+                      font_size=8,
+                      win_name='',
+                      show=True,
+                      wait_time=0,
+                      out_file=None):
+    """Draw bboxes and class labels (with scores) on an image.
+
+    Args:
+        img (str | ndarray): The image to be displayed.
+        bboxes (ndarray): Bounding boxes (with scores), shaped (n, 4) or
+            (n, 5).
+        labels (ndarray): Labels of bboxes.
+        segms (ndarray | None): Masks, shaped (n,h,w) or None.
+        class_names (list[str]): Names of each classes.
+        score_thr (float): Minimum score of bboxes to be shown. Default: 0.
+        bbox_color (list[tuple] | tuple | str | None): Colors of bbox lines.
+           If a single color is given, it will be applied to all classes.
+           The tuple of color should be in RGB order. Default: 'green'.
+        text_color (list[tuple] | tuple | str | None): Colors of texts.
+           If a single color is given, it will be applied to all classes.
+           The tuple of color should be in RGB order. Default: 'green'.
+        mask_color (list[tuple] | tuple | str | None, optional): Colors of
+           masks. If a single color is given, it will be applied to all
+           classes. The tuple of color should be in RGB order.
+           Default: None.
+        thickness (int): Thickness of lines. Default: 2.
+        font_size (int): Font size of texts. Default: 13.
+        show (bool): Whether to show the image. Default: True.
+        win_name (str): The window name. Default: ''.
+        wait_time (float): Value of waitKey param. Default: 0.
+        out_file (str, optional): The filename to write the image.
+            Default: None.
+
+    Returns:
+        ndarray: The image with bboxes drawn on it.
+    """
+    assert bboxes is None or bboxes.ndim == 2, \
+        f' bboxes ndim should be 2, but its ndim is {bboxes.ndim}.'
+    assert labels.ndim == 1, \
+        f' labels ndim should be 1, but its ndim is {labels.ndim}.'
+    assert bboxes is None or bboxes.shape[1] == 4 or bboxes.shape[1] == 5, \
+        f' bboxes.shape[1] should be 4 or 5, but its {bboxes.shape[1]}.'
+    assert bboxes is None or bboxes.shape[0] <= labels.shape[0], \
+        'labels.shape[0] should not be less than bboxes.shape[0].'
+    assert segms is None or segms.shape[0] == labels.shape[0], \
+        'segms.shape[0] and labels.shape[0] should have the same length.'
+    assert segms is not None or bboxes is not None, \
+        'segms and bboxes should not be None at the same time.'
+
+    img = mmcv.imread(img).astype(np.uint8)
+
+    if score_thr > 0:
+        assert bboxes is not None and bboxes.shape[1] == 5
+        scores = bboxes[:, -1]
+        inds = scores > score_thr
+        bboxes = bboxes[inds, :]
+        labels = labels[inds]
+        if segms is not None:
+            segms = segms[inds, ...]
+
+    img = mmcv.bgr2rgb(img)
+    width, height = img.shape[1], img.shape[0]
+    img = np.ascontiguousarray(img)
+
+    fig = plt.figure(win_name, frameon=False)
+    plt.title(win_name)
+    canvas = fig.canvas
+    dpi = fig.get_dpi()
+    # add a small EPS to avoid precision lost due to matplotlib's truncation
+    # (https://github.com/matplotlib/matplotlib/issues/15363)
+    fig.set_size_inches((width + EPS) / dpi, (height + EPS) / dpi)
+
+    # remove white edges by set subplot margin
+    plt.subplots_adjust(left=0, right=1, bottom=0, top=1)
+    ax = plt.gca()
+    ax.axis('off')
+
+    max_label = int(max(labels) if len(labels) > 0 else 0)
+    text_palette = palette_val(get_palette(text_color, max_label + 1))
+    text_colors = [text_palette[label] for label in labels]
+
+    num_bboxes = 0
+    if bboxes is not None:
+        num_bboxes = bboxes.shape[0]
+        bbox_palette = palette_val(get_palette(bbox_color, max_label + 1))
+        colors = [bbox_palette[label] for label in labels[:num_bboxes]]
+        draw_bboxes(ax, bboxes, colors, alpha=0.8, thickness=thickness)
+
+        horizontal_alignment = 'left'
+        positions = bboxes[:, :2].astype(np.int32) + thickness
+        areas = (bboxes[:, 3] - bboxes[:, 1]) * (bboxes[:, 2] - bboxes[:, 0])
+        scales = _get_adaptive_scales(areas)
+        scores = bboxes[:, 4] if bboxes.shape[1] == 5 else None
+        draw_labels(
+            ax,
+            labels[:num_bboxes],
+            positions,
+            scores=scores,
+            class_names=class_names,
+            color=text_colors,
+            font_size=font_size,
+            scales=scales,
+            horizontal_alignment=horizontal_alignment)
+
+    if segms is not None:
+        mask_palette = get_palette(mask_color, max_label + 1)
+        colors = [mask_palette[label] for label in labels]
+        colors = np.array(colors, dtype=np.uint8)
+        draw_masks(ax, img, segms, colors, with_edge=True)
+
+        if num_bboxes < segms.shape[0]:
+            segms = segms[num_bboxes:]
+            horizontal_alignment = 'center'
+            areas = []
+            positions = []
+            for mask in segms:
+                _, _, stats, centroids = cv2.connectedComponentsWithStats(
+                    mask.astype(np.uint8), connectivity=8)
+                largest_id = np.argmax(stats[1:, -1]) + 1
+                positions.append(centroids[largest_id])
+                areas.append(stats[largest_id, -1])
+            areas = np.stack(areas, axis=0)
+            scales = _get_adaptive_scales(areas)
+            draw_labels(
+                ax,
+                labels[num_bboxes:],
+                positions,
+                class_names=class_names,
+                color=text_colors,
+                font_size=font_size,
+                scales=scales,
+                horizontal_alignment=horizontal_alignment)
+
+    plt.imshow(img)
+
+    stream, _ = canvas.print_to_buffer()
+    buffer = np.frombuffer(stream, dtype='uint8')
+    if sys.platform == 'darwin':
+        width, height = canvas.get_width_height(physical=True)
+    img_rgba = buffer.reshape(height, width, 4)
+    rgb, alpha = np.split(img_rgba, [3], axis=2)
+    img = rgb.astype('uint8')
+    img = mmcv.rgb2bgr(img)
+
+    if show:
+        # We do not use cv2 for display because in some cases, opencv will
+        # conflict with Qt, it will output a warning: Current thread
+        # is not the object's thread. You can refer to
+        # https://github.com/opencv/opencv-python/issues/46 for details
+        if wait_time == 0:
+            plt.show()
+        else:
+            plt.show(block=False)
+            plt.pause(wait_time)
+    if out_file is not None:
+        mmcv.imwrite(img, out_file)
+
+    plt.close()
+
+    return img
+
+
+def imshow_gt_det_bboxes(img,
+                         annotation,
+                         result,
+                         class_names=None,
+                         score_thr=0,
+                         gt_bbox_color=(61, 102, 255),
+                         gt_text_color=(200, 200, 200),
+                         gt_mask_color=(61, 102, 255),
+                         det_bbox_color=(241, 101, 72),
+                         det_text_color=(200, 200, 200),
+                         det_mask_color=(241, 101, 72),
+                         thickness=2,
+                         font_size=13,
+                         win_name='',
+                         show=True,
+                         wait_time=0,
+                         out_file=None,
+                         overlay_gt_pred=True):
+    """General visualization GT and result function.
+
+    Args:
+      img (str | ndarray): The image to be displayed.
+      annotation (dict): Ground truth annotations where contain keys of
+          'gt_bboxes' and 'gt_labels' or 'gt_masks'.
+      result (tuple[list] | list): The detection result, can be either
+          (bbox, segm) or just bbox.
+      class_names (list[str]): Names of each classes.
+      score_thr (float): Minimum score of bboxes to be shown. Default: 0.
+      gt_bbox_color (list[tuple] | tuple | str | None): Colors of bbox lines.
+          If a single color is given, it will be applied to all classes.
+          The tuple of color should be in RGB order. Default: (61, 102, 255).
+      gt_text_color (list[tuple] | tuple | str | None): Colors of texts.
+          If a single color is given, it will be applied to all classes.
+          The tuple of color should be in RGB order. Default: (200, 200, 200).
+      gt_mask_color (list[tuple] | tuple | str | None, optional): Colors of
+          masks. If a single color is given, it will be applied to all classes.
+          The tuple of color should be in RGB order. Default: (61, 102, 255).
+      det_bbox_color (list[tuple] | tuple | str | None):Colors of bbox lines.
+          If a single color is given, it will be applied to all classes.
+          The tuple of color should be in RGB order. Default: (241, 101, 72).
+      det_text_color (list[tuple] | tuple | str | None):Colors of texts.
+          If a single color is given, it will be applied to all classes.
+          The tuple of color should be in RGB order. Default: (200, 200, 200).
+      det_mask_color (list[tuple] | tuple | str | None, optional): Color of
+          masks. If a single color is given, it will be applied to all classes.
+          The tuple of color should be in RGB order. Default: (241, 101, 72).
+      thickness (int): Thickness of lines. Default: 2.
+      font_size (int): Font size of texts. Default: 13.
+      win_name (str): The window name. Default: ''.
+      show (bool): Whether to show the image. Default: True.
+      wait_time (float): Value of waitKey param. Default: 0.
+      out_file (str, optional): The filename to write the image.
+          Default: None.
+      overlay_gt_pred (bool): Whether to plot gts and predictions on the
+       same image. If False, predictions and gts will be plotted on two same
+       image which will be concatenated in vertical direction. The image
+       above is drawn with gt, and the image below is drawn with the
+       prediction result. Default: True.
+
+    Returns:
+        ndarray: The image with bboxes or masks drawn on it.
+    """
+    assert 'gt_bboxes' in annotation
+    assert 'gt_labels' in annotation
+    assert isinstance(result, (tuple, list, dict)), 'Expected ' \
+        f'tuple or list or dict, but get {type(result)}'
+
+    gt_bboxes = annotation['gt_bboxes']
+    gt_labels = annotation['gt_labels']
+    gt_masks = annotation.get('gt_masks', None)
+    if gt_masks is not None:
+        gt_masks = mask2ndarray(gt_masks)
+
+    gt_seg = annotation.get('gt_semantic_seg', None)
+    if gt_seg is not None:
+        pad_value = 255  # the padding value of gt_seg
+        sem_labels = np.unique(gt_seg)
+        all_labels = np.concatenate((gt_labels, sem_labels), axis=0)
+        all_labels, counts = np.unique(all_labels, return_counts=True)
+        stuff_labels = all_labels[np.logical_and(counts < 2,
+                                                 all_labels != pad_value)]
+        stuff_masks = gt_seg[None] == stuff_labels[:, None, None]
+        gt_labels = np.concatenate((gt_labels, stuff_labels), axis=0)
+        gt_masks = np.concatenate((gt_masks, stuff_masks.astype(np.uint8)),
+                                  axis=0)
+        # If you need to show the bounding boxes,
+        # please comment the following line
+        # gt_bboxes = None
+
+    img = mmcv.imread(img)
+
+    img_with_gt = imshow_det_bboxes(
+        img,
+        gt_bboxes,
+        gt_labels,
+        gt_masks,
+        class_names=class_names,
+        bbox_color=gt_bbox_color,
+        text_color=gt_text_color,
+        mask_color=gt_mask_color,
+        thickness=thickness,
+        font_size=font_size,
+        win_name=win_name,
+        show=False)
+
+    if not isinstance(result, dict):
+        if isinstance(result, tuple):
+            bbox_result, segm_result = result
+            if isinstance(segm_result, tuple):
+                segm_result = segm_result[0]  # ms rcnn
+        else:
+            bbox_result, segm_result = result, None
+
+        bboxes = np.vstack(bbox_result)
+        labels = [
+            np.full(bbox.shape[0], i, dtype=np.int32)
+            for i, bbox in enumerate(bbox_result)
+        ]
+        labels = np.concatenate(labels)
+
+        segms = None
+        if segm_result is not None and len(labels) > 0:  # non empty
+            segms = mmcv.concat_list(segm_result)
+            segms = mask_util.decode(segms)
+            segms = segms.transpose(2, 0, 1)
+    else:
+        assert class_names is not None, 'We need to know the number ' \
+                                        'of classes.'
+        VOID = len(class_names)
+        bboxes = None
+        pan_results = result['pan_results']
+        # keep objects ahead
+        ids = np.unique(pan_results)[::-1]
+        legal_indices = ids != VOID
+        ids = ids[legal_indices]
+        labels = np.array([id % INSTANCE_OFFSET for id in ids], dtype=np.int64)
+        segms = (pan_results[None] == ids[:, None, None])
+
+    if overlay_gt_pred:
+        img = imshow_det_bboxes(
+            img_with_gt,
+            bboxes,
+            labels,
+            segms=segms,
+            class_names=class_names,
+            score_thr=score_thr,
+            bbox_color=det_bbox_color,
+            text_color=det_text_color,
+            mask_color=det_mask_color,
+            thickness=thickness,
+            font_size=font_size,
+            win_name=win_name,
+            show=show,
+            wait_time=wait_time,
+            out_file=out_file)
+    else:
+        img_with_det = imshow_det_bboxes(
+            img,
+            bboxes,
+            labels,
+            segms=segms,
+            class_names=class_names,
+            score_thr=score_thr,
+            bbox_color=det_bbox_color,
+            text_color=det_text_color,
+            mask_color=det_mask_color,
+            thickness=thickness,
+            font_size=font_size,
+            win_name=win_name,
+            show=False)
+        img = np.concatenate([img_with_gt, img_with_det], axis=0)
+
+        plt.imshow(img)
+        if show:
+            if wait_time == 0:
+                plt.show()
+            else:
+                plt.show(block=False)
+                plt.pause(wait_time)
+        if out_file is not None:
+            mmcv.imwrite(img, out_file)
+        plt.close()
+
+    return img
diff --git a/mmdet/core/visualization/palette.py b/mmdet/core/visualization/palette.py
new file mode 100644
index 0000000..11692cd
--- /dev/null
+++ b/mmdet/core/visualization/palette.py
@@ -0,0 +1,63 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import numpy as np
+
+
+def palette_val(palette):
+    """Convert palette to matplotlib palette.
+
+    Args:
+        palette List[tuple]: A list of color tuples.
+
+    Returns:
+        List[tuple[float]]: A list of RGB matplotlib color tuples.
+    """
+    new_palette = []
+    for color in palette:
+        color = [c / 255 for c in color]
+        new_palette.append(tuple(color))
+    return new_palette
+
+
+def get_palette(palette, num_classes):
+    """Get palette from various inputs.
+
+    Args:
+        palette (list[tuple] | str | tuple | :obj:`Color`): palette inputs.
+        num_classes (int): the number of classes.
+
+    Returns:
+        list[tuple[int]]: A list of color tuples.
+    """
+    assert isinstance(num_classes, int)
+
+    if isinstance(palette, list):
+        dataset_palette = palette
+    elif isinstance(palette, tuple):
+        dataset_palette = [palette] * num_classes
+    elif palette == 'random' or palette is None:
+        state = np.random.get_state()
+        # random color
+        np.random.seed(42)
+        palette = np.random.randint(0, 256, size=(num_classes, 3))
+        np.random.set_state(state)
+        dataset_palette = [tuple(c) for c in palette]
+    elif palette == 'coco':
+        from mmdet.datasets import CocoDataset, CocoPanopticDataset
+        dataset_palette = CocoDataset.PALETTE
+        if len(dataset_palette) < num_classes:
+            dataset_palette = CocoPanopticDataset.PALETTE
+    elif palette == 'citys':
+        from mmdet.datasets import CityscapesDataset
+        dataset_palette = CityscapesDataset.PALETTE
+    elif palette == 'voc':
+        from mmdet.datasets import VOCDataset
+        dataset_palette = VOCDataset.PALETTE
+    elif mmcv.is_str(palette):
+        dataset_palette = [mmcv.color_val(palette)[::-1]] * num_classes
+    else:
+        raise TypeError(f'Invalid type for palette: {type(palette)}')
+
+    assert len(dataset_palette) >= num_classes, \
+        'The length of palette should not be less than `num_classes`.'
+    return dataset_palette
diff --git a/mmdet/datasets/__init__.py b/mmdet/datasets/__init__.py
new file mode 100644
index 0000000..46c49fd
--- /dev/null
+++ b/mmdet/datasets/__init__.py
@@ -0,0 +1,31 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .builder import DATASETS, PIPELINES, build_dataloader, build_dataset
+from .cityscapes import CityscapesDataset
+from .coco import CocoDataset
+from .coco_occluded import OccludedSeparatedCocoDataset
+from .coco_panoptic import CocoPanopticDataset
+from .custom import CustomDataset
+from .dataset_wrappers import (ClassBalancedDataset, ConcatDataset,
+                               MultiImageMixDataset, RepeatDataset)
+from .deepfashion import DeepFashionDataset
+from .lvis import LVISDataset, LVISV1Dataset, LVISV05Dataset
+from .objects365 import Objects365V1Dataset, Objects365V2Dataset
+from .openimages import OpenImagesChallengeDataset, OpenImagesDataset
+from .samplers import DistributedGroupSampler, DistributedSampler, GroupSampler
+from .utils import (NumClassCheckHook, get_loading_pipeline,
+                    replace_ImageToTensor)
+from .voc import VOCDataset
+from .wider_face import WIDERFaceDataset
+from .xml_style import XMLDataset
+
+__all__ = [
+    'CustomDataset', 'XMLDataset', 'CocoDataset', 'DeepFashionDataset',
+    'VOCDataset', 'CityscapesDataset', 'LVISDataset', 'LVISV05Dataset',
+    'LVISV1Dataset', 'GroupSampler', 'DistributedGroupSampler',
+    'DistributedSampler', 'build_dataloader', 'ConcatDataset', 'RepeatDataset',
+    'ClassBalancedDataset', 'WIDERFaceDataset', 'DATASETS', 'PIPELINES',
+    'build_dataset', 'replace_ImageToTensor', 'get_loading_pipeline',
+    'NumClassCheckHook', 'CocoPanopticDataset', 'MultiImageMixDataset',
+    'OpenImagesDataset', 'OpenImagesChallengeDataset', 'Objects365V1Dataset',
+    'Objects365V2Dataset', 'OccludedSeparatedCocoDataset'
+]
diff --git a/mmdet/datasets/api_wrappers/__init__.py b/mmdet/datasets/api_wrappers/__init__.py
new file mode 100644
index 0000000..af85575
--- /dev/null
+++ b/mmdet/datasets/api_wrappers/__init__.py
@@ -0,0 +1,7 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .coco_api import COCO, COCOeval
+from .panoptic_evaluation import pq_compute_multi_core, pq_compute_single_core
+
+__all__ = [
+    'COCO', 'COCOeval', 'pq_compute_multi_core', 'pq_compute_single_core'
+]
diff --git a/mmdet/datasets/api_wrappers/coco_api.py b/mmdet/datasets/api_wrappers/coco_api.py
new file mode 100644
index 0000000..eef6341
--- /dev/null
+++ b/mmdet/datasets/api_wrappers/coco_api.py
@@ -0,0 +1,47 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# This file add snake case alias for coco api
+
+import warnings
+
+import pycocotools
+from pycocotools.coco import COCO as _COCO
+from pycocotools.cocoeval import COCOeval as _COCOeval
+
+
+class COCO(_COCO):
+    """This class is almost the same as official pycocotools package.
+
+    It implements some snake case function aliases. So that the COCO class has
+    the same interface as LVIS class.
+    """
+
+    def __init__(self, annotation_file=None):
+        if getattr(pycocotools, '__version__', '0') >= '12.0.2':
+            warnings.warn(
+                'mmpycocotools is deprecated. Please install official pycocotools by "pip install pycocotools"',  # noqa: E501
+                UserWarning)
+        super().__init__(annotation_file=annotation_file)
+        self.img_ann_map = self.imgToAnns
+        self.cat_img_map = self.catToImgs
+
+    def get_ann_ids(self, img_ids=[], cat_ids=[], area_rng=[], iscrowd=None):
+        return self.getAnnIds(img_ids, cat_ids, area_rng, iscrowd)
+
+    def get_cat_ids(self, cat_names=[], sup_names=[], cat_ids=[]):
+        return self.getCatIds(cat_names, sup_names, cat_ids)
+
+    def get_img_ids(self, img_ids=[], cat_ids=[]):
+        return self.getImgIds(img_ids, cat_ids)
+
+    def load_anns(self, ids):
+        return self.loadAnns(ids)
+
+    def load_cats(self, ids):
+        return self.loadCats(ids)
+
+    def load_imgs(self, ids):
+        return self.loadImgs(ids)
+
+
+# just for the ease of import
+COCOeval = _COCOeval
diff --git a/mmdet/datasets/api_wrappers/panoptic_evaluation.py b/mmdet/datasets/api_wrappers/panoptic_evaluation.py
new file mode 100644
index 0000000..55f57bf
--- /dev/null
+++ b/mmdet/datasets/api_wrappers/panoptic_evaluation.py
@@ -0,0 +1,228 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+
+# Copyright (c) 2018, Alexander Kirillov
+# This file supports `file_client` for `panopticapi`,
+# the source code is copied from `panopticapi`,
+# only the way to load the gt images is modified.
+import multiprocessing
+import os
+
+import mmcv
+import numpy as np
+
+try:
+    from panopticapi.evaluation import OFFSET, VOID, PQStat
+    from panopticapi.utils import rgb2id
+except ImportError:
+    PQStat = None
+    rgb2id = None
+    VOID = 0
+    OFFSET = 256 * 256 * 256
+
+
+def pq_compute_single_core(proc_id,
+                           annotation_set,
+                           gt_folder,
+                           pred_folder,
+                           categories,
+                           file_client=None,
+                           print_log=False):
+    """The single core function to evaluate the metric of Panoptic
+    Segmentation.
+
+    Same as the function with the same name in `panopticapi`. Only the function
+    to load the images is changed to use the file client.
+
+    Args:
+        proc_id (int): The id of the mini process.
+        gt_folder (str): The path of the ground truth images.
+        pred_folder (str): The path of the prediction images.
+        categories (str): The categories of the dataset.
+        file_client (object): The file client of the dataset. If None,
+            the backend will be set to `disk`.
+        print_log (bool): Whether to print the log. Defaults to False.
+    """
+    if PQStat is None:
+        raise RuntimeError(
+            'panopticapi is not installed, please install it by: '
+            'pip install git+https://github.com/cocodataset/'
+            'panopticapi.git.')
+
+    if file_client is None:
+        file_client_args = dict(backend='disk')
+        file_client = mmcv.FileClient(**file_client_args)
+
+    pq_stat = PQStat()
+
+    idx = 0
+    for gt_ann, pred_ann in annotation_set:
+        if print_log and idx % 100 == 0:
+            print('Core: {}, {} from {} images processed'.format(
+                proc_id, idx, len(annotation_set)))
+        idx += 1
+        # The gt images can be on the local disk or `ceph`, so we use
+        # file_client here.
+        img_bytes = file_client.get(
+            os.path.join(gt_folder, gt_ann['file_name']))
+        pan_gt = mmcv.imfrombytes(img_bytes, flag='color', channel_order='rgb')
+        pan_gt = rgb2id(pan_gt)
+
+        # The predictions can only be on the local dist now.
+        pan_pred = mmcv.imread(
+            os.path.join(pred_folder, pred_ann['file_name']),
+            flag='color',
+            channel_order='rgb')
+        pan_pred = rgb2id(pan_pred)
+
+        gt_segms = {el['id']: el for el in gt_ann['segments_info']}
+        pred_segms = {el['id']: el for el in pred_ann['segments_info']}
+
+        # predicted segments area calculation + prediction sanity checks
+        pred_labels_set = set(el['id'] for el in pred_ann['segments_info'])
+        labels, labels_cnt = np.unique(pan_pred, return_counts=True)
+        for label, label_cnt in zip(labels, labels_cnt):
+            if label not in pred_segms:
+                if label == VOID:
+                    continue
+                raise KeyError(
+                    'In the image with ID {} segment with ID {} is '
+                    'presented in PNG and not presented in JSON.'.format(
+                        gt_ann['image_id'], label))
+            pred_segms[label]['area'] = label_cnt
+            pred_labels_set.remove(label)
+            if pred_segms[label]['category_id'] not in categories:
+                raise KeyError(
+                    'In the image with ID {} segment with ID {} has '
+                    'unknown category_id {}.'.format(
+                        gt_ann['image_id'], label,
+                        pred_segms[label]['category_id']))
+        if len(pred_labels_set) != 0:
+            raise KeyError(
+                'In the image with ID {} the following segment IDs {} '
+                'are presented in JSON and not presented in PNG.'.format(
+                    gt_ann['image_id'], list(pred_labels_set)))
+
+        # confusion matrix calculation
+        pan_gt_pred = pan_gt.astype(np.uint64) * OFFSET + pan_pred.astype(
+            np.uint64)
+        gt_pred_map = {}
+        labels, labels_cnt = np.unique(pan_gt_pred, return_counts=True)
+        for label, intersection in zip(labels, labels_cnt):
+            gt_id = label // OFFSET
+            pred_id = label % OFFSET
+            gt_pred_map[(gt_id, pred_id)] = intersection
+
+        # count all matched pairs
+        gt_matched = set()
+        pred_matched = set()
+        for label_tuple, intersection in gt_pred_map.items():
+            gt_label, pred_label = label_tuple
+            if gt_label not in gt_segms:
+                continue
+            if pred_label not in pred_segms:
+                continue
+            if gt_segms[gt_label]['iscrowd'] == 1:
+                continue
+            if gt_segms[gt_label]['category_id'] != pred_segms[pred_label][
+                    'category_id']:
+                continue
+
+            union = pred_segms[pred_label]['area'] + gt_segms[gt_label][
+                'area'] - intersection - gt_pred_map.get((VOID, pred_label), 0)
+            iou = intersection / union
+            if iou > 0.5:
+                pq_stat[gt_segms[gt_label]['category_id']].tp += 1
+                pq_stat[gt_segms[gt_label]['category_id']].iou += iou
+                gt_matched.add(gt_label)
+                pred_matched.add(pred_label)
+
+        # count false positives
+        crowd_labels_dict = {}
+        for gt_label, gt_info in gt_segms.items():
+            if gt_label in gt_matched:
+                continue
+            # crowd segments are ignored
+            if gt_info['iscrowd'] == 1:
+                crowd_labels_dict[gt_info['category_id']] = gt_label
+                continue
+            pq_stat[gt_info['category_id']].fn += 1
+
+        # count false positives
+        for pred_label, pred_info in pred_segms.items():
+            if pred_label in pred_matched:
+                continue
+            # intersection of the segment with VOID
+            intersection = gt_pred_map.get((VOID, pred_label), 0)
+            # plus intersection with corresponding CROWD region if it exists
+            if pred_info['category_id'] in crowd_labels_dict:
+                intersection += gt_pred_map.get(
+                    (crowd_labels_dict[pred_info['category_id']], pred_label),
+                    0)
+            # predicted segment is ignored if more than half of
+            # the segment correspond to VOID and CROWD regions
+            if intersection / pred_info['area'] > 0.5:
+                continue
+            pq_stat[pred_info['category_id']].fp += 1
+
+    if print_log:
+        print('Core: {}, all {} images processed'.format(
+            proc_id, len(annotation_set)))
+    return pq_stat
+
+
+def pq_compute_multi_core(matched_annotations_list,
+                          gt_folder,
+                          pred_folder,
+                          categories,
+                          file_client=None,
+                          nproc=32):
+    """Evaluate the metrics of Panoptic Segmentation with multithreading.
+
+    Same as the function with the same name in `panopticapi`.
+
+    Args:
+        matched_annotations_list (list): The matched annotation list. Each
+            element is a tuple of annotations of the same image with the
+            format (gt_anns, pred_anns).
+        gt_folder (str): The path of the ground truth images.
+        pred_folder (str): The path of the prediction images.
+        categories (str): The categories of the dataset.
+        file_client (object): The file client of the dataset. If None,
+            the backend will be set to `disk`.
+        nproc (int): Number of processes for panoptic quality computing.
+            Defaults to 32. When `nproc` exceeds the number of cpu cores,
+            the number of cpu cores is used.
+    """
+    if PQStat is None:
+        raise RuntimeError(
+            'panopticapi is not installed, please install it by: '
+            'pip install git+https://github.com/cocodataset/'
+            'panopticapi.git.')
+
+    if file_client is None:
+        file_client_args = dict(backend='disk')
+        file_client = mmcv.FileClient(**file_client_args)
+
+    cpu_num = min(nproc, multiprocessing.cpu_count())
+
+    annotations_split = np.array_split(matched_annotations_list, cpu_num)
+    print('Number of cores: {}, images per core: {}'.format(
+        cpu_num, len(annotations_split[0])))
+    workers = multiprocessing.Pool(processes=cpu_num)
+    processes = []
+    for proc_id, annotation_set in enumerate(annotations_split):
+        p = workers.apply_async(pq_compute_single_core,
+                                (proc_id, annotation_set, gt_folder,
+                                 pred_folder, categories, file_client))
+        processes.append(p)
+
+    # Close the process pool, otherwise it will lead to memory
+    # leaking problems.
+    workers.close()
+    workers.join()
+
+    pq_stat = PQStat()
+    for p in processes:
+        pq_stat += p.get()
+
+    return pq_stat
diff --git a/mmdet/datasets/builder.py b/mmdet/datasets/builder.py
new file mode 100644
index 0000000..1936296
--- /dev/null
+++ b/mmdet/datasets/builder.py
@@ -0,0 +1,215 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import platform
+import random
+import warnings
+from functools import partial
+
+import numpy as np
+import torch
+from mmcv.parallel import collate
+from mmcv.runner import get_dist_info
+from mmcv.utils import TORCH_VERSION, Registry, build_from_cfg, digit_version
+from torch.utils.data import DataLoader
+
+from .samplers import (ClassAwareSampler, DistributedGroupSampler,
+                       DistributedSampler, GroupSampler, InfiniteBatchSampler,
+                       InfiniteGroupBatchSampler)
+
+if platform.system() != 'Windows':
+    # https://github.com/pytorch/pytorch/issues/973
+    import resource
+    rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
+    base_soft_limit = rlimit[0]
+    hard_limit = rlimit[1]
+    soft_limit = min(max(4096, base_soft_limit), hard_limit)
+    resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))
+
+DATASETS = Registry('dataset')
+PIPELINES = Registry('pipeline')
+
+
+def _concat_dataset(cfg, default_args=None):
+    from .dataset_wrappers import ConcatDataset
+    ann_files = cfg['ann_file']
+    img_prefixes = cfg.get('img_prefix', None)
+    seg_prefixes = cfg.get('seg_prefix', None)
+    proposal_files = cfg.get('proposal_file', None)
+    separate_eval = cfg.get('separate_eval', True)
+
+    datasets = []
+    num_dset = len(ann_files)
+    for i in range(num_dset):
+        data_cfg = copy.deepcopy(cfg)
+        # pop 'separate_eval' since it is not a valid key for common datasets.
+        if 'separate_eval' in data_cfg:
+            data_cfg.pop('separate_eval')
+        data_cfg['ann_file'] = ann_files[i]
+        if isinstance(img_prefixes, (list, tuple)):
+            data_cfg['img_prefix'] = img_prefixes[i]
+        if isinstance(seg_prefixes, (list, tuple)):
+            data_cfg['seg_prefix'] = seg_prefixes[i]
+        if isinstance(proposal_files, (list, tuple)):
+            data_cfg['proposal_file'] = proposal_files[i]
+        datasets.append(build_dataset(data_cfg, default_args))
+
+    return ConcatDataset(datasets, separate_eval)
+
+
+def build_dataset(cfg, default_args=None):
+    from .dataset_wrappers import (ClassBalancedDataset, ConcatDataset,
+                                   MultiImageMixDataset, RepeatDataset)
+    if isinstance(cfg, (list, tuple)):
+        dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
+    elif cfg['type'] == 'ConcatDataset':
+        dataset = ConcatDataset(
+            [build_dataset(c, default_args) for c in cfg['datasets']],
+            cfg.get('separate_eval', True))
+    elif cfg['type'] == 'RepeatDataset':
+        dataset = RepeatDataset(
+            build_dataset(cfg['dataset'], default_args), cfg['times'])
+    elif cfg['type'] == 'ClassBalancedDataset':
+        dataset = ClassBalancedDataset(
+            build_dataset(cfg['dataset'], default_args), cfg['oversample_thr'])
+    elif cfg['type'] == 'MultiImageMixDataset':
+        cp_cfg = copy.deepcopy(cfg)
+        cp_cfg['dataset'] = build_dataset(cp_cfg['dataset'])
+        cp_cfg.pop('type')
+        dataset = MultiImageMixDataset(**cp_cfg)
+    elif isinstance(cfg.get('ann_file'), (list, tuple)):
+        dataset = _concat_dataset(cfg, default_args)
+    else:
+        dataset = build_from_cfg(cfg, DATASETS, default_args)
+
+    return dataset
+
+
+def build_dataloader(dataset,
+                     samples_per_gpu,
+                     workers_per_gpu,
+                     num_gpus=1,
+                     dist=True,
+                     shuffle=True,
+                     seed=None,
+                     runner_type='EpochBasedRunner',
+                     persistent_workers=False,
+                     class_aware_sampler=None,
+                     **kwargs):
+    """Build PyTorch DataLoader.
+
+    In distributed training, each GPU/process has a dataloader.
+    In non-distributed training, there is only one dataloader for all GPUs.
+
+    Args:
+        dataset (Dataset): A PyTorch dataset.
+        samples_per_gpu (int): Number of training samples on each GPU, i.e.,
+            batch size of each GPU.
+        workers_per_gpu (int): How many subprocesses to use for data loading
+            for each GPU.
+        num_gpus (int): Number of GPUs. Only used in non-distributed training.
+        dist (bool): Distributed training/test or not. Default: True.
+        shuffle (bool): Whether to shuffle the data at every epoch.
+            Default: True.
+        seed (int, Optional): Seed to be used. Default: None.
+        runner_type (str): Type of runner. Default: `EpochBasedRunner`
+        persistent_workers (bool): If True, the data loader will not shutdown
+            the worker processes after a dataset has been consumed once.
+            This allows to maintain the workers `Dataset` instances alive.
+            This argument is only valid when PyTorch>=1.7.0. Default: False.
+        class_aware_sampler (dict): Whether to use `ClassAwareSampler`
+            during training. Default: None.
+        kwargs: any keyword argument to be used to initialize DataLoader
+
+    Returns:
+        DataLoader: A PyTorch dataloader.
+    """
+    rank, world_size = get_dist_info()
+
+    if dist:
+        # When model is :obj:`DistributedDataParallel`,
+        # `batch_size` of :obj:`dataloader` is the
+        # number of training samples on each GPU.
+        batch_size = samples_per_gpu
+        num_workers = workers_per_gpu
+    else:
+        # When model is obj:`DataParallel`
+        # the batch size is samples on all the GPUS
+        batch_size = num_gpus * samples_per_gpu
+        num_workers = num_gpus * workers_per_gpu
+
+    if runner_type == 'IterBasedRunner':
+        # this is a batch sampler, which can yield
+        # a mini-batch indices each time.
+        # it can be used in both `DataParallel` and
+        # `DistributedDataParallel`
+        if shuffle:
+            batch_sampler = InfiniteGroupBatchSampler(
+                dataset, batch_size, world_size, rank, seed=seed)
+        else:
+            batch_sampler = InfiniteBatchSampler(
+                dataset,
+                batch_size,
+                world_size,
+                rank,
+                seed=seed,
+                shuffle=False)
+        batch_size = 1
+        sampler = None
+    else:
+        if class_aware_sampler is not None:
+            # ClassAwareSampler can be used in both distributed and
+            # non-distributed training.
+            num_sample_class = class_aware_sampler.get('num_sample_class', 1)
+            sampler = ClassAwareSampler(
+                dataset,
+                samples_per_gpu,
+                world_size,
+                rank,
+                seed=seed,
+                num_sample_class=num_sample_class)
+        elif dist:
+            # DistributedGroupSampler will definitely shuffle the data to
+            # satisfy that images on each GPU are in the same group
+            if shuffle:
+                sampler = DistributedGroupSampler(
+                    dataset, samples_per_gpu, world_size, rank, seed=seed)
+            else:
+                sampler = DistributedSampler(
+                    dataset, world_size, rank, shuffle=False, seed=seed)
+        else:
+            sampler = GroupSampler(dataset,
+                                   samples_per_gpu) if shuffle else None
+        batch_sampler = None
+
+    init_fn = partial(
+        worker_init_fn, num_workers=num_workers, rank=rank,
+        seed=seed) if seed is not None else None
+
+    if (TORCH_VERSION != 'parrots'
+            and digit_version(TORCH_VERSION) >= digit_version('1.7.0')):
+        kwargs['persistent_workers'] = persistent_workers
+    elif persistent_workers is True:
+        warnings.warn('persistent_workers is invalid because your pytorch '
+                      'version is lower than 1.7.0')
+
+    data_loader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        sampler=sampler,
+        num_workers=num_workers,
+        batch_sampler=batch_sampler,
+        collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
+        pin_memory=kwargs.pop('pin_memory', False),
+        worker_init_fn=init_fn,
+        **kwargs)
+
+    return data_loader
+
+
+def worker_init_fn(worker_id, num_workers, rank, seed):
+    # The seed of each worker equals to
+    # num_worker * rank + worker_id + user_seed
+    worker_seed = num_workers * rank + worker_id + seed
+    np.random.seed(worker_seed)
+    random.seed(worker_seed)
+    torch.manual_seed(worker_seed)
diff --git a/mmdet/datasets/cityscapes.py b/mmdet/datasets/cityscapes.py
new file mode 100644
index 0000000..c998d12
--- /dev/null
+++ b/mmdet/datasets/cityscapes.py
@@ -0,0 +1,339 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# Modified from https://github.com/facebookresearch/detectron2/blob/master/detectron2/data/datasets/cityscapes.py # noqa
+# and https://github.com/mcordts/cityscapesScripts/blob/master/cityscapesscripts/evaluation/evalInstanceLevelSemanticLabeling.py # noqa
+
+import glob
+import os
+import os.path as osp
+import tempfile
+from collections import OrderedDict
+
+import mmcv
+import numpy as np
+import pycocotools.mask as maskUtils
+from mmcv.utils import print_log
+
+from .builder import DATASETS
+from .coco import CocoDataset
+
+
+@DATASETS.register_module()
+class CityscapesDataset(CocoDataset):
+
+    CLASSES = ('person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle',
+               'bicycle')
+
+    PALETTE = [(220, 20, 60), (255, 0, 0), (0, 0, 142), (0, 0, 70),
+               (0, 60, 100), (0, 80, 100), (0, 0, 230), (119, 11, 32)]
+
+    def _filter_imgs(self, min_size=32):
+        """Filter images too small or without ground truths."""
+        valid_inds = []
+        # obtain images that contain annotation
+        ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())
+        # obtain images that contain annotations of the required categories
+        ids_in_cat = set()
+        for i, class_id in enumerate(self.cat_ids):
+            ids_in_cat |= set(self.coco.cat_img_map[class_id])
+        # merge the image id sets of the two conditions and use the merged set
+        # to filter out images if self.filter_empty_gt=True
+        ids_in_cat &= ids_with_ann
+
+        valid_img_ids = []
+        for i, img_info in enumerate(self.data_infos):
+            img_id = img_info['id']
+            ann_ids = self.coco.getAnnIds(imgIds=[img_id])
+            ann_info = self.coco.loadAnns(ann_ids)
+            all_iscrowd = all([_['iscrowd'] for _ in ann_info])
+            if self.filter_empty_gt and (self.img_ids[i] not in ids_in_cat
+                                         or all_iscrowd):
+                continue
+            if min(img_info['width'], img_info['height']) >= min_size:
+                valid_inds.append(i)
+                valid_img_ids.append(img_id)
+        self.img_ids = valid_img_ids
+        return valid_inds
+
+    def _parse_ann_info(self, img_info, ann_info):
+        """Parse bbox and mask annotation.
+
+        Args:
+            img_info (dict): Image info of an image.
+            ann_info (list[dict]): Annotation info of an image.
+
+        Returns:
+            dict: A dict containing the following keys: bboxes, \
+                bboxes_ignore, labels, masks, seg_map. \
+                "masks" are already decoded into binary masks.
+        """
+        gt_bboxes = []
+        gt_labels = []
+        gt_bboxes_ignore = []
+        gt_masks_ann = []
+
+        for i, ann in enumerate(ann_info):
+            if ann.get('ignore', False):
+                continue
+            x1, y1, w, h = ann['bbox']
+            if ann['area'] <= 0 or w < 1 or h < 1:
+                continue
+            if ann['category_id'] not in self.cat_ids:
+                continue
+            bbox = [x1, y1, x1 + w, y1 + h]
+            if ann.get('iscrowd', False):
+                gt_bboxes_ignore.append(bbox)
+            else:
+                gt_bboxes.append(bbox)
+                gt_labels.append(self.cat2label[ann['category_id']])
+                gt_masks_ann.append(ann['segmentation'])
+
+        if gt_bboxes:
+            gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
+            gt_labels = np.array(gt_labels, dtype=np.int64)
+        else:
+            gt_bboxes = np.zeros((0, 4), dtype=np.float32)
+            gt_labels = np.array([], dtype=np.int64)
+
+        if gt_bboxes_ignore:
+            gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
+        else:
+            gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
+
+        ann = dict(
+            bboxes=gt_bboxes,
+            labels=gt_labels,
+            bboxes_ignore=gt_bboxes_ignore,
+            masks=gt_masks_ann,
+            seg_map=img_info['segm_file'])
+
+        return ann
+
+    def results2txt(self, results, outfile_prefix):
+        """Dump the detection results to a txt file.
+
+        Args:
+            results (list[list | tuple]): Testing results of the
+                dataset.
+            outfile_prefix (str): The filename prefix of the json files.
+                If the prefix is "somepath/xxx",
+                the txt files will be named "somepath/xxx.txt".
+
+        Returns:
+            list[str]: Result txt files which contains corresponding \
+                instance segmentation images.
+        """
+        try:
+            import cityscapesscripts.helpers.labels as CSLabels
+        except ImportError:
+            raise ImportError('Please run "pip install citscapesscripts" to '
+                              'install cityscapesscripts first.')
+        result_files = []
+        os.makedirs(outfile_prefix, exist_ok=True)
+        prog_bar = mmcv.ProgressBar(len(self))
+        for idx in range(len(self)):
+            result = results[idx]
+            filename = self.data_infos[idx]['filename']
+            basename = osp.splitext(osp.basename(filename))[0]
+            pred_txt = osp.join(outfile_prefix, basename + '_pred.txt')
+
+            bbox_result, segm_result = result
+            bboxes = np.vstack(bbox_result)
+            # segm results
+            if isinstance(segm_result, tuple):
+                # Some detectors use different scores for bbox and mask,
+                # like Mask Scoring R-CNN. Score of segm will be used instead
+                # of bbox score.
+                segms = mmcv.concat_list(segm_result[0])
+                mask_score = segm_result[1]
+            else:
+                # use bbox score for mask score
+                segms = mmcv.concat_list(segm_result)
+                mask_score = [bbox[-1] for bbox in bboxes]
+            labels = [
+                np.full(bbox.shape[0], i, dtype=np.int32)
+                for i, bbox in enumerate(bbox_result)
+            ]
+            labels = np.concatenate(labels)
+
+            assert len(bboxes) == len(segms) == len(labels)
+            num_instances = len(bboxes)
+            prog_bar.update()
+            with open(pred_txt, 'w') as fout:
+                for i in range(num_instances):
+                    pred_class = labels[i]
+                    classes = self.CLASSES[pred_class]
+                    class_id = CSLabels.name2label[classes].id
+                    score = mask_score[i]
+                    mask = maskUtils.decode(segms[i]).astype(np.uint8)
+                    png_filename = osp.join(outfile_prefix,
+                                            basename + f'_{i}_{classes}.png')
+                    mmcv.imwrite(mask, png_filename)
+                    fout.write(f'{osp.basename(png_filename)} {class_id} '
+                               f'{score}\n')
+            result_files.append(pred_txt)
+
+        return result_files
+
+    def format_results(self, results, txtfile_prefix=None):
+        """Format the results to txt (standard format for Cityscapes
+        evaluation).
+
+        Args:
+            results (list): Testing results of the dataset.
+            txtfile_prefix (str | None): The prefix of txt files. It includes
+                the file path and the prefix of filename, e.g., "a/b/prefix".
+                If not specified, a temp file will be created. Default: None.
+
+        Returns:
+            tuple: (result_files, tmp_dir), result_files is a dict containing \
+                the json filepaths, tmp_dir is the temporal directory created \
+                for saving txt/png files when txtfile_prefix is not specified.
+        """
+        assert isinstance(results, list), 'results must be a list'
+        assert len(results) == len(self), (
+            'The length of results is not equal to the dataset len: {} != {}'.
+            format(len(results), len(self)))
+
+        assert isinstance(results, list), 'results must be a list'
+        assert len(results) == len(self), (
+            'The length of results is not equal to the dataset len: {} != {}'.
+            format(len(results), len(self)))
+
+        if txtfile_prefix is None:
+            tmp_dir = tempfile.TemporaryDirectory()
+            txtfile_prefix = osp.join(tmp_dir.name, 'results')
+        else:
+            tmp_dir = None
+        result_files = self.results2txt(results, txtfile_prefix)
+
+        return result_files, tmp_dir
+
+    def evaluate(self,
+                 results,
+                 metric='bbox',
+                 logger=None,
+                 outfile_prefix=None,
+                 classwise=False,
+                 proposal_nums=(100, 300, 1000),
+                 iou_thrs=np.arange(0.5, 0.96, 0.05)):
+        """Evaluation in Cityscapes/COCO protocol.
+
+        Args:
+            results (list[list | tuple]): Testing results of the dataset.
+            metric (str | list[str]): Metrics to be evaluated. Options are
+                'bbox', 'segm', 'proposal', 'proposal_fast'.
+            logger (logging.Logger | str | None): Logger used for printing
+                related information during evaluation. Default: None.
+            outfile_prefix (str | None): The prefix of output file. It includes
+                the file path and the prefix of filename, e.g., "a/b/prefix".
+                If results are evaluated with COCO protocol, it would be the
+                prefix of output json file. For example, the metric is 'bbox'
+                and 'segm', then json files would be "a/b/prefix.bbox.json" and
+                "a/b/prefix.segm.json".
+                If results are evaluated with cityscapes protocol, it would be
+                the prefix of output txt/png files. The output files would be
+                png images under folder "a/b/prefix/xxx/" and the file name of
+                images would be written into a txt file
+                "a/b/prefix/xxx_pred.txt", where "xxx" is the video name of
+                cityscapes. If not specified, a temp file will be created.
+                Default: None.
+            classwise (bool): Whether to evaluating the AP for each class.
+            proposal_nums (Sequence[int]): Proposal number used for evaluating
+                recalls, such as recall@100, recall@1000.
+                Default: (100, 300, 1000).
+            iou_thrs (Sequence[float]): IoU threshold used for evaluating
+                recalls. If set to a list, the average recall of all IoUs will
+                also be computed. Default: 0.5.
+
+        Returns:
+            dict[str, float]: COCO style evaluation metric or cityscapes mAP \
+                and AP@50.
+        """
+        eval_results = dict()
+
+        metrics = metric.copy() if isinstance(metric, list) else [metric]
+
+        if 'cityscapes' in metrics:
+            eval_results.update(
+                self._evaluate_cityscapes(results, outfile_prefix, logger))
+            metrics.remove('cityscapes')
+
+        # left metrics are all coco metric
+        if len(metrics) > 0:
+            # create CocoDataset with CityscapesDataset annotation
+            self_coco = CocoDataset(self.ann_file, self.pipeline.transforms,
+                                    None, self.data_root, self.img_prefix,
+                                    self.seg_prefix, self.seg_suffix,
+                                    self.proposal_file, self.test_mode,
+                                    self.filter_empty_gt)
+            # TODO: remove this in the future
+            # reload annotations of correct class
+            self_coco.CLASSES = self.CLASSES
+            self_coco.data_infos = self_coco.load_annotations(self.ann_file)
+            eval_results.update(
+                self_coco.evaluate(results, metrics, logger, outfile_prefix,
+                                   classwise, proposal_nums, iou_thrs))
+
+        return eval_results
+
+    def _evaluate_cityscapes(self, results, txtfile_prefix, logger):
+        """Evaluation in Cityscapes protocol.
+
+        Args:
+            results (list): Testing results of the dataset.
+            txtfile_prefix (str | None): The prefix of output txt file
+            logger (logging.Logger | str | None): Logger used for printing
+                related information during evaluation. Default: None.
+
+        Returns:
+            dict[str: float]: Cityscapes evaluation results, contains 'mAP' \
+                and 'AP@50'.
+        """
+
+        try:
+            import cityscapesscripts.evaluation.evalInstanceLevelSemanticLabeling as CSEval  # noqa
+        except ImportError:
+            raise ImportError('Please run "pip install citscapesscripts" to '
+                              'install cityscapesscripts first.')
+        msg = 'Evaluating in Cityscapes style'
+        if logger is None:
+            msg = '\n' + msg
+        print_log(msg, logger=logger)
+
+        result_files, tmp_dir = self.format_results(results, txtfile_prefix)
+
+        if tmp_dir is None:
+            result_dir = osp.join(txtfile_prefix, 'results')
+        else:
+            result_dir = osp.join(tmp_dir.name, 'results')
+
+        eval_results = OrderedDict()
+        print_log(f'Evaluating results under {result_dir} ...', logger=logger)
+
+        # set global states in cityscapes evaluation API
+        CSEval.args.cityscapesPath = os.path.join(self.img_prefix, '../..')
+        CSEval.args.predictionPath = os.path.abspath(result_dir)
+        CSEval.args.predictionWalk = None
+        CSEval.args.JSONOutput = False
+        CSEval.args.colorized = False
+        CSEval.args.gtInstancesFile = os.path.join(result_dir,
+                                                   'gtInstances.json')
+        CSEval.args.groundTruthSearch = os.path.join(
+            self.img_prefix.replace('leftImg8bit', 'gtFine'),
+            '*/*_gtFine_instanceIds.png')
+
+        groundTruthImgList = glob.glob(CSEval.args.groundTruthSearch)
+        assert len(groundTruthImgList), 'Cannot find ground truth images' \
+            f' in {CSEval.args.groundTruthSearch}.'
+        predictionImgList = []
+        for gt in groundTruthImgList:
+            predictionImgList.append(CSEval.getPrediction(gt, CSEval.args))
+        CSEval_results = CSEval.evaluateImgLists(predictionImgList,
+                                                 groundTruthImgList,
+                                                 CSEval.args)['averages']
+
+        eval_results['mAP'] = CSEval_results['allAp']
+        eval_results['AP@50'] = CSEval_results['allAp50%']
+        if tmp_dir is not None:
+            tmp_dir.cleanup()
+        return eval_results
diff --git a/mmdet/datasets/coco.py b/mmdet/datasets/coco.py
new file mode 100644
index 0000000..d20a121
--- /dev/null
+++ b/mmdet/datasets/coco.py
@@ -0,0 +1,649 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import contextlib
+import io
+import itertools
+import logging
+import os.path as osp
+import tempfile
+import warnings
+from collections import OrderedDict
+
+import mmcv
+import numpy as np
+from mmcv.utils import print_log
+from terminaltables import AsciiTable
+
+from mmdet.core import eval_recalls
+from .api_wrappers import COCO, COCOeval
+from .builder import DATASETS
+from .custom import CustomDataset
+
+
+@DATASETS.register_module()
+class CocoDataset(CustomDataset):
+
+    CLASSES = ('person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
+               'train', 'truck', 'boat', 'traffic light', 'fire hydrant',
+               'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog',
+               'horse', 'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe',
+               'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+               'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat',
+               'baseball glove', 'skateboard', 'surfboard', 'tennis racket',
+               'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl',
+               'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
+               'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+               'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop',
+               'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
+               'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock',
+               'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush')
+
+    PALETTE = [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230),
+               (106, 0, 228), (0, 60, 100), (0, 80, 100), (0, 0, 70),
+               (0, 0, 192), (250, 170, 30), (100, 170, 30), (220, 220, 0),
+               (175, 116, 175), (250, 0, 30), (165, 42, 42), (255, 77, 255),
+               (0, 226, 252), (182, 182, 255), (0, 82, 0), (120, 166, 157),
+               (110, 76, 0), (174, 57, 255), (199, 100, 0), (72, 0, 118),
+               (255, 179, 240), (0, 125, 92), (209, 0, 151), (188, 208, 182),
+               (0, 220, 176), (255, 99, 164), (92, 0, 73), (133, 129, 255),
+               (78, 180, 255), (0, 228, 0), (174, 255, 243), (45, 89, 255),
+               (134, 134, 103), (145, 148, 174), (255, 208, 186),
+               (197, 226, 255), (171, 134, 1), (109, 63, 54), (207, 138, 255),
+               (151, 0, 95), (9, 80, 61), (84, 105, 51), (74, 65, 105),
+               (166, 196, 102), (208, 195, 210), (255, 109, 65), (0, 143, 149),
+               (179, 0, 194), (209, 99, 106), (5, 121, 0), (227, 255, 205),
+               (147, 186, 208), (153, 69, 1), (3, 95, 161), (163, 255, 0),
+               (119, 0, 170), (0, 182, 199), (0, 165, 120), (183, 130, 88),
+               (95, 32, 0), (130, 114, 135), (110, 129, 133), (166, 74, 118),
+               (219, 142, 185), (79, 210, 114), (178, 90, 62), (65, 70, 15),
+               (127, 167, 115), (59, 105, 106), (142, 108, 45), (196, 172, 0),
+               (95, 54, 80), (128, 76, 255), (201, 57, 1), (246, 0, 122),
+               (191, 162, 208)]
+
+    def load_annotations(self, ann_file):
+        """Load annotation from COCO style annotation file.
+
+        Args:
+            ann_file (str): Path of annotation file.
+
+        Returns:
+            list[dict]: Annotation info from COCO api.
+        """
+
+        self.coco = COCO(ann_file)
+        # The order of returned `cat_ids` will not
+        # change with the order of the CLASSES
+        self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
+
+        self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
+        self.img_ids = self.coco.get_img_ids()
+        data_infos = []
+        total_ann_ids = []
+        for i in self.img_ids:
+            info = self.coco.load_imgs([i])[0]
+            info['filename'] = info['file_name']
+            data_infos.append(info)
+            ann_ids = self.coco.get_ann_ids(img_ids=[i])
+            total_ann_ids.extend(ann_ids)
+        assert len(set(total_ann_ids)) == len(
+            total_ann_ids), f"Annotation ids in '{ann_file}' are not unique!"
+        return data_infos
+
+    def get_ann_info(self, idx):
+        """Get COCO annotation by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            dict: Annotation info of specified index.
+        """
+
+        img_id = self.data_infos[idx]['id']
+        ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
+        ann_info = self.coco.load_anns(ann_ids)
+        return self._parse_ann_info(self.data_infos[idx], ann_info)
+
+    def get_cat_ids(self, idx):
+        """Get COCO category ids by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            list[int]: All categories in the image of specified index.
+        """
+
+        img_id = self.data_infos[idx]['id']
+        ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
+        ann_info = self.coco.load_anns(ann_ids)
+        return [ann['category_id'] for ann in ann_info]
+
+    def _filter_imgs(self, min_size=32):
+        """Filter images too small or without ground truths."""
+        valid_inds = []
+        # obtain images that contain annotation
+        ids_with_ann = set(_['image_id'] for _ in self.coco.anns.values())
+        # obtain images that contain annotations of the required categories
+        ids_in_cat = set()
+        for i, class_id in enumerate(self.cat_ids):
+            ids_in_cat |= set(self.coco.cat_img_map[class_id])
+        # merge the image id sets of the two conditions and use the merged set
+        # to filter out images if self.filter_empty_gt=True
+        ids_in_cat &= ids_with_ann
+
+        valid_img_ids = []
+        for i, img_info in enumerate(self.data_infos):
+            img_id = self.img_ids[i]
+            if self.filter_empty_gt and img_id not in ids_in_cat:
+                continue
+            if min(img_info['width'], img_info['height']) >= min_size:
+                valid_inds.append(i)
+                valid_img_ids.append(img_id)
+        self.img_ids = valid_img_ids
+        return valid_inds
+
+    def _parse_ann_info(self, img_info, ann_info):
+        """Parse bbox and mask annotation.
+
+        Args:
+            ann_info (list[dict]): Annotation info of an image.
+            with_mask (bool): Whether to parse mask annotations.
+
+        Returns:
+            dict: A dict containing the following keys: bboxes, bboxes_ignore,\
+                labels, masks, seg_map. "masks" are raw annotations and not \
+                decoded into binary masks.
+        """
+        gt_bboxes = []
+        gt_labels = []
+        gt_bboxes_ignore = []
+        gt_masks_ann = []
+        for i, ann in enumerate(ann_info):
+            if ann.get('ignore', False):
+                continue
+            x1, y1, w, h = ann['bbox']
+            inter_w = max(0, min(x1 + w, img_info['width']) - max(x1, 0))
+            inter_h = max(0, min(y1 + h, img_info['height']) - max(y1, 0))
+            if inter_w * inter_h == 0:
+                continue
+            if ann['area'] <= 0 or w < 1 or h < 1:
+                continue
+            if ann['category_id'] not in self.cat_ids:
+                continue
+            bbox = [x1, y1, x1 + w, y1 + h]
+            if ann.get('iscrowd', False):
+                gt_bboxes_ignore.append(bbox)
+            else:
+                gt_bboxes.append(bbox)
+                gt_labels.append(self.cat2label[ann['category_id']])
+                gt_masks_ann.append(ann.get('segmentation', None))
+
+        if gt_bboxes:
+            gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
+            gt_labels = np.array(gt_labels, dtype=np.int64)
+        else:
+            gt_bboxes = np.zeros((0, 4), dtype=np.float32)
+            gt_labels = np.array([], dtype=np.int64)
+
+        if gt_bboxes_ignore:
+            gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
+        else:
+            gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
+
+        seg_map = img_info['filename'].rsplit('.', 1)[0] + self.seg_suffix
+
+        ann = dict(
+            bboxes=gt_bboxes,
+            labels=gt_labels,
+            bboxes_ignore=gt_bboxes_ignore,
+            masks=gt_masks_ann,
+            seg_map=seg_map)
+
+        return ann
+
+    def xyxy2xywh(self, bbox):
+        """Convert ``xyxy`` style bounding boxes to ``xywh`` style for COCO
+        evaluation.
+
+        Args:
+            bbox (numpy.ndarray): The bounding boxes, shape (4, ), in
+                ``xyxy`` order.
+
+        Returns:
+            list[float]: The converted bounding boxes, in ``xywh`` order.
+        """
+
+        _bbox = bbox.tolist()
+        return [
+            _bbox[0],
+            _bbox[1],
+            _bbox[2] - _bbox[0],
+            _bbox[3] - _bbox[1],
+        ]
+
+    def _proposal2json(self, results):
+        """Convert proposal results to COCO json style."""
+        json_results = []
+        for idx in range(len(self)):
+            img_id = self.img_ids[idx]
+            bboxes = results[idx]
+            for i in range(bboxes.shape[0]):
+                data = dict()
+                data['image_id'] = img_id
+                data['bbox'] = self.xyxy2xywh(bboxes[i])
+                data['score'] = float(bboxes[i][4])
+                data['category_id'] = 1
+                json_results.append(data)
+        return json_results
+
+    def _det2json(self, results):
+        """Convert detection results to COCO json style."""
+        json_results = []
+        for idx in range(len(self)):
+            img_id = self.img_ids[idx]
+            result = results[idx]
+            for label in range(len(result)):
+                bboxes = result[label]
+                for i in range(bboxes.shape[0]):
+                    data = dict()
+                    data['image_id'] = img_id
+                    data['bbox'] = self.xyxy2xywh(bboxes[i])
+                    data['score'] = float(bboxes[i][4])
+                    data['category_id'] = self.cat_ids[label]
+                    json_results.append(data)
+        return json_results
+
+    def _segm2json(self, results):
+        """Convert instance segmentation results to COCO json style."""
+        bbox_json_results = []
+        segm_json_results = []
+        for idx in range(len(self)):
+            img_id = self.img_ids[idx]
+            det, seg = results[idx]
+            for label in range(len(det)):
+                # bbox results
+                bboxes = det[label]
+                for i in range(bboxes.shape[0]):
+                    data = dict()
+                    data['image_id'] = img_id
+                    data['bbox'] = self.xyxy2xywh(bboxes[i])
+                    data['score'] = float(bboxes[i][4])
+                    data['category_id'] = self.cat_ids[label]
+                    bbox_json_results.append(data)
+
+                # segm results
+                # some detectors use different scores for bbox and mask
+                if isinstance(seg, tuple):
+                    segms = seg[0][label]
+                    mask_score = seg[1][label]
+                else:
+                    segms = seg[label]
+                    mask_score = [bbox[4] for bbox in bboxes]
+                for i in range(bboxes.shape[0]):
+                    data = dict()
+                    data['image_id'] = img_id
+                    data['bbox'] = self.xyxy2xywh(bboxes[i])
+                    data['score'] = float(mask_score[i])
+                    data['category_id'] = self.cat_ids[label]
+                    if isinstance(segms[i]['counts'], bytes):
+                        segms[i]['counts'] = segms[i]['counts'].decode()
+                    data['segmentation'] = segms[i]
+                    segm_json_results.append(data)
+        return bbox_json_results, segm_json_results
+
+    def results2json(self, results, outfile_prefix):
+        """Dump the detection results to a COCO style json file.
+
+        There are 3 types of results: proposals, bbox predictions, mask
+        predictions, and they have different data types. This method will
+        automatically recognize the type, and dump them to json files.
+
+        Args:
+            results (list[list | tuple | ndarray]): Testing results of the
+                dataset.
+            outfile_prefix (str): The filename prefix of the json files. If the
+                prefix is "somepath/xxx", the json files will be named
+                "somepath/xxx.bbox.json", "somepath/xxx.segm.json",
+                "somepath/xxx.proposal.json".
+
+        Returns:
+            dict[str: str]: Possible keys are "bbox", "segm", "proposal", and \
+                values are corresponding filenames.
+        """
+        result_files = dict()
+        if isinstance(results[0], list):
+            json_results = self._det2json(results)
+            result_files['bbox'] = f'{outfile_prefix}.bbox.json'
+            result_files['proposal'] = f'{outfile_prefix}.bbox.json'
+            mmcv.dump(json_results, result_files['bbox'])
+        elif isinstance(results[0], tuple):
+            json_results = self._segm2json(results)
+            result_files['bbox'] = f'{outfile_prefix}.bbox.json'
+            result_files['proposal'] = f'{outfile_prefix}.bbox.json'
+            result_files['segm'] = f'{outfile_prefix}.segm.json'
+            mmcv.dump(json_results[0], result_files['bbox'])
+            mmcv.dump(json_results[1], result_files['segm'])
+        elif isinstance(results[0], np.ndarray):
+            json_results = self._proposal2json(results)
+            result_files['proposal'] = f'{outfile_prefix}.proposal.json'
+            mmcv.dump(json_results, result_files['proposal'])
+        else:
+            raise TypeError('invalid type of results')
+        return result_files
+
+    def fast_eval_recall(self, results, proposal_nums, iou_thrs, logger=None):
+        gt_bboxes = []
+        for i in range(len(self.img_ids)):
+            ann_ids = self.coco.get_ann_ids(img_ids=self.img_ids[i])
+            ann_info = self.coco.load_anns(ann_ids)
+            if len(ann_info) == 0:
+                gt_bboxes.append(np.zeros((0, 4)))
+                continue
+            bboxes = []
+            for ann in ann_info:
+                if ann.get('ignore', False) or ann['iscrowd']:
+                    continue
+                x1, y1, w, h = ann['bbox']
+                bboxes.append([x1, y1, x1 + w, y1 + h])
+            bboxes = np.array(bboxes, dtype=np.float32)
+            if bboxes.shape[0] == 0:
+                bboxes = np.zeros((0, 4))
+            gt_bboxes.append(bboxes)
+
+        recalls = eval_recalls(
+            gt_bboxes, results, proposal_nums, iou_thrs, logger=logger)
+        ar = recalls.mean(axis=1)
+        return ar
+
+    def format_results(self, results, jsonfile_prefix=None, **kwargs):
+        """Format the results to json (standard format for COCO evaluation).
+
+        Args:
+            results (list[tuple | numpy.ndarray]): Testing results of the
+                dataset.
+            jsonfile_prefix (str | None): The prefix of json files. It includes
+                the file path and the prefix of filename, e.g., "a/b/prefix".
+                If not specified, a temp file will be created. Default: None.
+
+        Returns:
+            tuple: (result_files, tmp_dir), result_files is a dict containing \
+                the json filepaths, tmp_dir is the temporal directory created \
+                for saving json files when jsonfile_prefix is not specified.
+        """
+        assert isinstance(results, list), 'results must be a list'
+        assert len(results) == len(self), (
+            'The length of results is not equal to the dataset len: {} != {}'.
+            format(len(results), len(self)))
+
+        if jsonfile_prefix is None:
+            tmp_dir = tempfile.TemporaryDirectory()
+            jsonfile_prefix = osp.join(tmp_dir.name, 'results')
+        else:
+            tmp_dir = None
+        result_files = self.results2json(results, jsonfile_prefix)
+        return result_files, tmp_dir
+
+    def evaluate_det_segm(self,
+                          results,
+                          result_files,
+                          coco_gt,
+                          metrics,
+                          logger=None,
+                          classwise=False,
+                          proposal_nums=(100, 300, 1000),
+                          iou_thrs=None,
+                          metric_items=None):
+        """Instance segmentation and object detection evaluation in COCO
+        protocol.
+
+        Args:
+            results (list[list | tuple | dict]): Testing results of the
+                dataset.
+            result_files (dict[str, str]): a dict contains json file path.
+            coco_gt (COCO): COCO API object with ground truth annotation.
+            metric (str | list[str]): Metrics to be evaluated. Options are
+                'bbox', 'segm', 'proposal', 'proposal_fast'.
+            logger (logging.Logger | str | None): Logger used for printing
+                related information during evaluation. Default: None.
+            classwise (bool): Whether to evaluating the AP for each class.
+            proposal_nums (Sequence[int]): Proposal number used for evaluating
+                recalls, such as recall@100, recall@1000.
+                Default: (100, 300, 1000).
+            iou_thrs (Sequence[float], optional): IoU threshold used for
+                evaluating recalls/mAPs. If set to a list, the average of all
+                IoUs will also be computed. If not specified, [0.50, 0.55,
+                0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95] will be used.
+                Default: None.
+            metric_items (list[str] | str, optional): Metric items that will
+                be returned. If not specified, ``['AR@100', 'AR@300',
+                'AR@1000', 'AR_s@1000', 'AR_m@1000', 'AR_l@1000' ]`` will be
+                used when ``metric=='proposal'``, ``['mAP', 'mAP_50', 'mAP_75',
+                'mAP_s', 'mAP_m', 'mAP_l']`` will be used when
+                ``metric=='bbox' or metric=='segm'``.
+
+        Returns:
+            dict[str, float]: COCO style evaluation metric.
+        """
+        if iou_thrs is None:
+            iou_thrs = np.linspace(
+                .5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True)
+        if metric_items is not None:
+            if not isinstance(metric_items, list):
+                metric_items = [metric_items]
+
+        eval_results = OrderedDict()
+        for metric in metrics:
+            msg = f'Evaluating {metric}...'
+            if logger is None:
+                msg = '\n' + msg
+            print_log(msg, logger=logger)
+
+            if metric == 'proposal_fast':
+                if isinstance(results[0], tuple):
+                    raise KeyError('proposal_fast is not supported for '
+                                   'instance segmentation result.')
+                ar = self.fast_eval_recall(
+                    results, proposal_nums, iou_thrs, logger='silent')
+                log_msg = []
+                for i, num in enumerate(proposal_nums):
+                    eval_results[f'AR@{num}'] = ar[i]
+                    log_msg.append(f'\nAR@{num}\t{ar[i]:.4f}')
+                log_msg = ''.join(log_msg)
+                print_log(log_msg, logger=logger)
+                continue
+
+            iou_type = 'bbox' if metric == 'proposal' else metric
+            if metric not in result_files:
+                raise KeyError(f'{metric} is not in results')
+            try:
+                predictions = mmcv.load(result_files[metric])
+                if iou_type == 'segm':
+                    # Refer to https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocotools/coco.py#L331  # noqa
+                    # When evaluating mask AP, if the results contain bbox,
+                    # cocoapi will use the box area instead of the mask area
+                    # for calculating the instance area. Though the overall AP
+                    # is not affected, this leads to different
+                    # small/medium/large mask AP results.
+                    for x in predictions:
+                        x.pop('bbox')
+                    warnings.simplefilter('once')
+                    warnings.warn(
+                        'The key "bbox" is deleted for more accurate mask AP '
+                        'of small/medium/large instances since v2.12.0. This '
+                        'does not change the overall mAP calculation.',
+                        UserWarning)
+                coco_det = coco_gt.loadRes(predictions)
+            except IndexError:
+                print_log(
+                    'The testing results of the whole dataset is empty.',
+                    logger=logger,
+                    level=logging.ERROR)
+                break
+
+            cocoEval = COCOeval(coco_gt, coco_det, iou_type)
+            cocoEval.params.catIds = self.cat_ids
+            cocoEval.params.imgIds = self.img_ids
+            cocoEval.params.maxDets = list(proposal_nums)
+            cocoEval.params.iouThrs = iou_thrs
+            # mapping of cocoEval.stats
+            coco_metric_names = {
+                'mAP': 0,
+                'mAP_50': 1,
+                'mAP_75': 2,
+                'mAP_s': 3,
+                'mAP_m': 4,
+                'mAP_l': 5,
+                'AR@100': 6,
+                'AR@300': 7,
+                'AR@1000': 8,
+                'AR_s@1000': 9,
+                'AR_m@1000': 10,
+                'AR_l@1000': 11
+            }
+            if metric_items is not None:
+                for metric_item in metric_items:
+                    if metric_item not in coco_metric_names:
+                        raise KeyError(
+                            f'metric item {metric_item} is not supported')
+
+            if metric == 'proposal':
+                cocoEval.params.useCats = 0
+                cocoEval.evaluate()
+                cocoEval.accumulate()
+
+                # Save coco summarize print information to logger
+                redirect_string = io.StringIO()
+                with contextlib.redirect_stdout(redirect_string):
+                    cocoEval.summarize()
+                print_log('\n' + redirect_string.getvalue(), logger=logger)
+
+                if metric_items is None:
+                    metric_items = [
+                        'AR@100', 'AR@300', 'AR@1000', 'AR_s@1000',
+                        'AR_m@1000', 'AR_l@1000'
+                    ]
+
+                for item in metric_items:
+                    val = float(
+                        f'{cocoEval.stats[coco_metric_names[item]]:.4f}')
+                    eval_results[item] = val
+            else:
+                cocoEval.evaluate()
+                cocoEval.accumulate()
+
+                # Save coco summarize print information to logger
+                redirect_string = io.StringIO()
+                with contextlib.redirect_stdout(redirect_string):
+                    cocoEval.summarize()
+                print_log('\n' + redirect_string.getvalue(), logger=logger)
+
+                if classwise:  # Compute per-category AP
+                    # Compute per-category AP
+                    # from https://github.com/facebookresearch/detectron2/
+                    precisions = cocoEval.eval['precision']
+                    # precision: (iou, recall, cls, area range, max dets)
+                    assert len(self.cat_ids) == precisions.shape[2]
+
+                    results_per_category = []
+                    for idx, catId in enumerate(self.cat_ids):
+                        # area range index 0: all area ranges
+                        # max dets index -1: typically 100 per image
+                        nm = self.coco.loadCats(catId)[0]
+                        precision = precisions[:, :, idx, 0, -1]
+                        precision = precision[precision > -1]
+                        if precision.size:
+                            ap = np.mean(precision)
+                        else:
+                            ap = float('nan')
+                        results_per_category.append(
+                            (f'{nm["name"]}', f'{float(ap):0.3f}'))
+
+                    num_columns = min(6, len(results_per_category) * 2)
+                    results_flatten = list(
+                        itertools.chain(*results_per_category))
+                    headers = ['category', 'AP'] * (num_columns // 2)
+                    results_2d = itertools.zip_longest(*[
+                        results_flatten[i::num_columns]
+                        for i in range(num_columns)
+                    ])
+                    table_data = [headers]
+                    table_data += [result for result in results_2d]
+                    table = AsciiTable(table_data)
+                    print_log('\n' + table.table, logger=logger)
+
+                if metric_items is None:
+                    metric_items = [
+                        'mAP', 'mAP_50', 'mAP_75', 'mAP_s', 'mAP_m', 'mAP_l'
+                    ]
+
+                for metric_item in metric_items:
+                    key = f'{metric}_{metric_item}'
+                    val = float(
+                        f'{cocoEval.stats[coco_metric_names[metric_item]]:.4f}'
+                    )
+                    eval_results[key] = val
+                ap = cocoEval.stats[:6]
+                eval_results[f'{metric}_mAP_copypaste'] = (
+                    f'{ap[0]:.4f} {ap[1]:.4f} {ap[2]:.4f} {ap[3]:.4f} '
+                    f'{ap[4]:.4f} {ap[5]:.4f}')
+
+        return eval_results
+
+    def evaluate(self,
+                 results,
+                 metric='bbox',
+                 logger=None,
+                 jsonfile_prefix=None,
+                 classwise=False,
+                 proposal_nums=(100, 300, 1000),
+                 iou_thrs=None,
+                 metric_items=None):
+        """Evaluation in COCO protocol.
+
+        Args:
+            results (list[list | tuple]): Testing results of the dataset.
+            metric (str | list[str]): Metrics to be evaluated. Options are
+                'bbox', 'segm', 'proposal', 'proposal_fast'.
+            logger (logging.Logger | str | None): Logger used for printing
+                related information during evaluation. Default: None.
+            jsonfile_prefix (str | None): The prefix of json files. It includes
+                the file path and the prefix of filename, e.g., "a/b/prefix".
+                If not specified, a temp file will be created. Default: None.
+            classwise (bool): Whether to evaluating the AP for each class.
+            proposal_nums (Sequence[int]): Proposal number used for evaluating
+                recalls, such as recall@100, recall@1000.
+                Default: (100, 300, 1000).
+            iou_thrs (Sequence[float], optional): IoU threshold used for
+                evaluating recalls/mAPs. If set to a list, the average of all
+                IoUs will also be computed. If not specified, [0.50, 0.55,
+                0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95] will be used.
+                Default: None.
+            metric_items (list[str] | str, optional): Metric items that will
+                be returned. If not specified, ``['AR@100', 'AR@300',
+                'AR@1000', 'AR_s@1000', 'AR_m@1000', 'AR_l@1000' ]`` will be
+                used when ``metric=='proposal'``, ``['mAP', 'mAP_50', 'mAP_75',
+                'mAP_s', 'mAP_m', 'mAP_l']`` will be used when
+                ``metric=='bbox' or metric=='segm'``.
+
+        Returns:
+            dict[str, float]: COCO style evaluation metric.
+        """
+
+        metrics = metric if isinstance(metric, list) else [metric]
+        allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast']
+        for metric in metrics:
+            if metric not in allowed_metrics:
+                raise KeyError(f'metric {metric} is not supported')
+
+        coco_gt = self.coco
+        self.cat_ids = coco_gt.get_cat_ids(cat_names=self.CLASSES)
+
+        result_files, tmp_dir = self.format_results(results, jsonfile_prefix)
+        eval_results = self.evaluate_det_segm(results, result_files, coco_gt,
+                                              metrics, logger, classwise,
+                                              proposal_nums, iou_thrs,
+                                              metric_items)
+
+        if tmp_dir is not None:
+            tmp_dir.cleanup()
+        return eval_results
diff --git a/mmdet/datasets/coco_occluded.py b/mmdet/datasets/coco_occluded.py
new file mode 100644
index 0000000..96e439a
--- /dev/null
+++ b/mmdet/datasets/coco_occluded.py
@@ -0,0 +1,219 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+
+import mmcv
+import numpy as np
+from mmcv.fileio import load
+from mmcv.utils import print_log
+from pycocotools import mask as coco_mask
+from terminaltables import AsciiTable
+
+from .builder import DATASETS
+from .coco import CocoDataset
+
+
+@DATASETS.register_module()
+class OccludedSeparatedCocoDataset(CocoDataset):
+    """COCO dataset with evaluation on separated and occluded masks which
+    presented in paper `A Tri-Layer Plugin to Improve Occluded Detection.
+
+    <https://arxiv.org/abs/2210.10046>`_.
+
+    Separated COCO and Occluded COCO are automatically generated subsets of
+    COCO val dataset, collecting separated objects and partially occluded
+    objects for a large variety of categories. In this way, we define
+    occlusion into two major categories: separated and partially occluded.
+
+    - Separation: target object segmentation mask is separated into distinct
+      regions by the occluder.
+    - Partial Occlusion: target object is partially occluded but the
+      segmentation mask is connected.
+
+    These two new scalable real-image datasets are to benchmark a model's
+    capability to detect occluded objects of 80 common categories.
+
+    Please cite the paper if you use this dataset:
+
+    @article{zhan2022triocc,
+        title={A Tri-Layer Plugin to Improve Occluded Detection},
+        author={Zhan, Guanqi and Xie, Weidi and Zisserman, Andrew},
+        journal={British Machine Vision Conference},
+        year={2022}
+    }
+
+    Args:
+        occluded_ann (str): Path to the occluded coco annotation file.
+        separated_ann (str): Path to the separated coco annotation file.
+    """  # noqa
+
+    def __init__(
+            self,
+            *args,
+            occluded_ann='https://www.robots.ox.ac.uk/~vgg/research/tpod/datasets/occluded_coco.pkl',  # noqa
+            separated_ann='https://www.robots.ox.ac.uk/~vgg/research/tpod/datasets/separated_coco.pkl',  # noqa
+            **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # load from local file
+        if osp.isfile(occluded_ann) and not osp.isabs(occluded_ann):
+            occluded_ann = osp.join(self.data_root, occluded_ann)
+        if osp.isfile(separated_ann) and not osp.isabs(separated_ann):
+            separated_ann = osp.join(self.data_root, separated_ann)
+
+        self.occluded_ann = load(occluded_ann)
+        self.separated_ann = load(separated_ann)
+
+    def evaluate(self,
+                 results,
+                 metric=[],
+                 score_thr=0.3,
+                 iou_thr=0.75,
+                 **kwargs):
+        """Occluded and separated mask evaluation in COCO protocol.
+
+        Args:
+            results (list[tuple]): Testing results of the dataset.
+            metric (str | list[str]): Metrics to be evaluated. Options are
+                'bbox', 'segm', 'proposal', 'proposal_fast'. Defaults to [].
+            score_thr (float): Score threshold of the detection masks.
+                Defaults to 0.3.
+            iou_thr (float): IoU threshold for the recall calculation.
+                Defaults to 0.75.
+        Returns:
+            dict[str, float]: The recall of occluded and separated masks and
+            COCO style evaluation metric.
+        """
+        coco_metric_res = super().evaluate(results, metric=metric, **kwargs)
+        eval_res = self.evaluate_occluded_separated(results, score_thr,
+                                                    iou_thr)
+        coco_metric_res.update(eval_res)
+        return coco_metric_res
+
+    def evaluate_occluded_separated(self,
+                                    results,
+                                    score_thr=0.3,
+                                    iou_thr=0.75):
+        """Compute the recall of occluded and separated masks.
+
+        Args:
+            results (list[tuple]): Testing results of the dataset.
+            score_thr (float): Score threshold of the detection masks.
+                Defaults to 0.3.
+            iou_thr (float): IoU threshold for the recall calculation.
+                Defaults to 0.75.
+        Returns:
+            dict[str, float]: The recall of occluded and separated masks.
+        """
+        dict_det = {}
+        print_log('processing detection results...')
+        prog_bar = mmcv.ProgressBar(len(results))
+        for i in range(len(results)):
+            cur_img_name = self.data_infos[i]['filename']
+            if cur_img_name not in dict_det.keys():
+                dict_det[cur_img_name] = []
+            for cat_id in range(len(results[i][1])):
+                assert len(results[i][1][cat_id]) == len(results[i][0][cat_id])
+                for instance_id in range(len(results[i][1][cat_id])):
+                    cur_binary_mask = coco_mask.decode(
+                        results[i][1][cat_id][instance_id])
+                    cur_det_bbox = results[i][0][cat_id][instance_id][:4]
+                    dict_det[cur_img_name].append([
+                        results[i][0][cat_id][instance_id][4],
+                        self.CLASSES[cat_id], cur_binary_mask, cur_det_bbox
+                    ])
+            dict_det[cur_img_name].sort(
+                key=lambda x: (-x[0], x[3][0], x[3][1])
+            )  # rank by confidence from high to low, avoid same confidence
+            prog_bar.update()
+        print_log('\ncomputing occluded mask recall...')
+        occluded_correct_num, occluded_recall = self.compute_recall(
+            dict_det,
+            gt_ann=self.occluded_ann,
+            score_thr=score_thr,
+            iou_thr=iou_thr,
+            is_occ=True)
+        print_log(f'\nCOCO occluded mask recall: {occluded_recall:.2f}%')
+        print_log(f'COCO occluded mask success num: {occluded_correct_num}')
+        print_log('computing separated mask recall...')
+        separated_correct_num, separated_recall = self.compute_recall(
+            dict_det,
+            gt_ann=self.separated_ann,
+            score_thr=score_thr,
+            iou_thr=iou_thr,
+            is_occ=False)
+        print_log(f'\nCOCO separated mask recall: {separated_recall:.2f}%')
+        print_log(f'COCO separated mask success num: {separated_correct_num}')
+        table_data = [
+            ['mask type', 'recall', 'num correct'],
+            ['occluded', f'{occluded_recall:.2f}%', occluded_correct_num],
+            ['separated', f'{separated_recall:.2f}%', separated_correct_num]
+        ]
+        table = AsciiTable(table_data)
+        print_log('\n' + table.table)
+        return dict(
+            occluded_recall=occluded_recall, separated_recall=separated_recall)
+
+    def compute_recall(self,
+                       result_dict,
+                       gt_ann,
+                       score_thr=0.3,
+                       iou_thr=0.75,
+                       is_occ=True):
+        """Compute the recall of occluded or separated masks.
+
+        Args:
+            results (list[tuple]): Testing results of the dataset.
+            gt_ann (list): Occluded or separated coco annotations.
+            score_thr (float): Score threshold of the detection masks.
+                Defaults to 0.3.
+            iou_thr (float): IoU threshold for the recall calculation.
+                Defaults to 0.75.
+            is_occ (bool): Whether the annotation is occluded mask.
+                Defaults to True.
+        Returns:
+            tuple: number of correct masks and the recall.
+        """
+        correct = 0
+        prog_bar = mmcv.ProgressBar(len(gt_ann))
+        for iter_i in range(len(gt_ann)):
+            cur_item = gt_ann[iter_i]
+            cur_img_name = cur_item[0]
+            cur_gt_bbox = cur_item[3]
+            if is_occ:
+                cur_gt_bbox = [
+                    cur_gt_bbox[0], cur_gt_bbox[1],
+                    cur_gt_bbox[0] + cur_gt_bbox[2],
+                    cur_gt_bbox[1] + cur_gt_bbox[3]
+                ]
+            cur_gt_class = cur_item[1]
+            cur_gt_mask = coco_mask.decode(cur_item[4])
+
+            assert cur_img_name in result_dict.keys()
+            cur_detections = result_dict[cur_img_name]
+
+            correct_flag = False
+            for i in range(len(cur_detections)):
+                cur_det_confidence = cur_detections[i][0]
+                if cur_det_confidence < score_thr:
+                    break
+                cur_det_class = cur_detections[i][1]
+                if cur_det_class != cur_gt_class:
+                    continue
+                cur_det_mask = cur_detections[i][2]
+                cur_iou = self.mask_iou(cur_det_mask, cur_gt_mask)
+                if cur_iou >= iou_thr:
+                    correct_flag = True
+                    break
+            if correct_flag:
+                correct += 1
+            prog_bar.update()
+        recall = correct / len(gt_ann) * 100
+        return correct, recall
+
+    def mask_iou(self, mask1, mask2):
+        """Compute IoU between two masks."""
+        mask1_area = np.count_nonzero(mask1 == 1)
+        mask2_area = np.count_nonzero(mask2 == 1)
+        intersection = np.count_nonzero(np.logical_and(mask1 == 1, mask2 == 1))
+        iou = intersection / (mask1_area + mask2_area - intersection)
+        return iou
diff --git a/mmdet/datasets/coco_panoptic.py b/mmdet/datasets/coco_panoptic.py
new file mode 100644
index 0000000..53ef594
--- /dev/null
+++ b/mmdet/datasets/coco_panoptic.py
@@ -0,0 +1,692 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import itertools
+import os
+from collections import defaultdict
+
+import mmcv
+import numpy as np
+from mmcv.utils import print_log
+from terminaltables import AsciiTable
+
+from mmdet.core import INSTANCE_OFFSET
+from .api_wrappers import COCO, pq_compute_multi_core
+from .builder import DATASETS
+from .coco import CocoDataset
+
+try:
+    import panopticapi
+    from panopticapi.evaluation import VOID
+    from panopticapi.utils import id2rgb
+except ImportError:
+    panopticapi = None
+    id2rgb = None
+    VOID = None
+
+__all__ = ['CocoPanopticDataset']
+
+
+class COCOPanoptic(COCO):
+    """This wrapper is for loading the panoptic style annotation file.
+
+    The format is shown in the CocoPanopticDataset class.
+
+    Args:
+        annotation_file (str): Path of annotation file.
+    """
+
+    def __init__(self, annotation_file=None):
+        if panopticapi is None:
+            raise RuntimeError(
+                'panopticapi is not installed, please install it by: '
+                'pip install git+https://github.com/cocodataset/'
+                'panopticapi.git.')
+
+        super(COCOPanoptic, self).__init__(annotation_file)
+
+    def createIndex(self):
+        # create index
+        print('creating index...')
+        # anns stores 'segment_id -> annotation'
+        anns, cats, imgs = {}, {}, {}
+        img_to_anns, cat_to_imgs = defaultdict(list), defaultdict(list)
+        if 'annotations' in self.dataset:
+            for ann, img_info in zip(self.dataset['annotations'],
+                                     self.dataset['images']):
+                img_info['segm_file'] = ann['file_name']
+                for seg_ann in ann['segments_info']:
+                    # to match with instance.json
+                    seg_ann['image_id'] = ann['image_id']
+                    seg_ann['height'] = img_info['height']
+                    seg_ann['width'] = img_info['width']
+                    img_to_anns[ann['image_id']].append(seg_ann)
+                    # segment_id is not unique in coco dataset orz...
+                    if seg_ann['id'] in anns.keys():
+                        anns[seg_ann['id']].append(seg_ann)
+                    else:
+                        anns[seg_ann['id']] = [seg_ann]
+
+        if 'images' in self.dataset:
+            for img in self.dataset['images']:
+                imgs[img['id']] = img
+
+        if 'categories' in self.dataset:
+            for cat in self.dataset['categories']:
+                cats[cat['id']] = cat
+
+        if 'annotations' in self.dataset and 'categories' in self.dataset:
+            for ann in self.dataset['annotations']:
+                for seg_ann in ann['segments_info']:
+                    cat_to_imgs[seg_ann['category_id']].append(ann['image_id'])
+
+        print('index created!')
+
+        self.anns = anns
+        self.imgToAnns = img_to_anns
+        self.catToImgs = cat_to_imgs
+        self.imgs = imgs
+        self.cats = cats
+
+    def load_anns(self, ids=[]):
+        """Load anns with the specified ids.
+
+        self.anns is a list of annotation lists instead of a
+        list of annotations.
+
+        Args:
+            ids (int array): integer ids specifying anns
+
+        Returns:
+            anns (object array): loaded ann objects
+        """
+        anns = []
+
+        if hasattr(ids, '__iter__') and hasattr(ids, '__len__'):
+            # self.anns is a list of annotation lists instead of
+            # a list of annotations
+            for id in ids:
+                anns += self.anns[id]
+            return anns
+        elif type(ids) == int:
+            return self.anns[ids]
+
+
+@DATASETS.register_module()
+class CocoPanopticDataset(CocoDataset):
+    """Coco dataset for Panoptic segmentation.
+
+    The annotation format is shown as follows. The `ann` field is optional
+    for testing.
+
+    .. code-block:: none
+
+        [
+            {
+                'filename': f'{image_id:012}.png',
+                'image_id':9
+                'segments_info': {
+                    [
+                        {
+                            'id': 8345037, (segment_id in panoptic png,
+                                            convert from rgb)
+                            'category_id': 51,
+                            'iscrowd': 0,
+                            'bbox': (x1, y1, w, h),
+                            'area': 24315,
+                            'segmentation': list,(coded mask)
+                        },
+                        ...
+                    }
+                }
+            },
+            ...
+        ]
+
+    Args:
+        ann_file (str): Panoptic segmentation annotation file path.
+        pipeline (list[dict]): Processing pipeline.
+        ins_ann_file (str): Instance segmentation annotation file path.
+            Defaults to None.
+        classes (str | Sequence[str], optional): Specify classes to load.
+            If is None, ``cls.CLASSES`` will be used. Defaults to None.
+        data_root (str, optional): Data root for ``ann_file``,
+            ``ins_ann_file`` ``img_prefix``, ``seg_prefix``, ``proposal_file``
+            if specified. Defaults to None.
+        img_prefix (str, optional): Prefix of path to images. Defaults to ''.
+        seg_prefix (str, optional): Prefix of path to segmentation files.
+            Defaults to None.
+        proposal_file (str, optional): Path to proposal file. Defaults to None.
+        test_mode (bool, optional): If set True, annotation will not be loaded.
+            Defaults to False.
+        filter_empty_gt (bool, optional): If set true, images without bounding
+            boxes of the dataset's classes will be filtered out. This option
+            only works when `test_mode=False`, i.e., we never filter images
+            during tests. Defaults to True.
+        file_client_args (:obj:`mmcv.ConfigDict` | dict): file client args.
+            Defaults to dict(backend='disk').
+    """
+    CLASSES = [
+        'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
+        ' truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
+        'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
+        'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
+        'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
+        'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
+        'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
+        'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
+        'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
+        'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
+        'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
+        'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
+        'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner',
+        'blanket', 'bridge', 'cardboard', 'counter', 'curtain', 'door-stuff',
+        'floor-wood', 'flower', 'fruit', 'gravel', 'house', 'light',
+        'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield',
+        'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow',
+        'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile',
+        'wall-wood', 'water-other', 'window-blind', 'window-other',
+        'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged',
+        'cabinet-merged', 'table-merged', 'floor-other-merged',
+        'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged',
+        'paper-merged', 'food-other-merged', 'building-other-merged',
+        'rock-merged', 'wall-other-merged', 'rug-merged'
+    ]
+    THING_CLASSES = [
+        'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
+        'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
+        'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
+        'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
+        'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
+        'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
+        'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
+        'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
+        'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
+        'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
+        'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
+        'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
+        'scissors', 'teddy bear', 'hair drier', 'toothbrush'
+    ]
+    STUFF_CLASSES = [
+        'banner', 'blanket', 'bridge', 'cardboard', 'counter', 'curtain',
+        'door-stuff', 'floor-wood', 'flower', 'fruit', 'gravel', 'house',
+        'light', 'mirror-stuff', 'net', 'pillow', 'platform', 'playingfield',
+        'railroad', 'river', 'road', 'roof', 'sand', 'sea', 'shelf', 'snow',
+        'stairs', 'tent', 'towel', 'wall-brick', 'wall-stone', 'wall-tile',
+        'wall-wood', 'water-other', 'window-blind', 'window-other',
+        'tree-merged', 'fence-merged', 'ceiling-merged', 'sky-other-merged',
+        'cabinet-merged', 'table-merged', 'floor-other-merged',
+        'pavement-merged', 'mountain-merged', 'grass-merged', 'dirt-merged',
+        'paper-merged', 'food-other-merged', 'building-other-merged',
+        'rock-merged', 'wall-other-merged', 'rug-merged'
+    ]
+
+    PALETTE = [(220, 20, 60), (119, 11, 32), (0, 0, 142), (0, 0, 230),
+               (106, 0, 228), (0, 60, 100), (0, 80, 100), (0, 0, 70),
+               (0, 0, 192), (250, 170, 30), (100, 170, 30), (220, 220, 0),
+               (175, 116, 175), (250, 0, 30), (165, 42, 42), (255, 77, 255),
+               (0, 226, 252), (182, 182, 255), (0, 82, 0), (120, 166, 157),
+               (110, 76, 0), (174, 57, 255), (199, 100, 0), (72, 0, 118),
+               (255, 179, 240), (0, 125, 92), (209, 0, 151), (188, 208, 182),
+               (0, 220, 176), (255, 99, 164), (92, 0, 73), (133, 129, 255),
+               (78, 180, 255), (0, 228, 0), (174, 255, 243), (45, 89, 255),
+               (134, 134, 103), (145, 148, 174), (255, 208, 186),
+               (197, 226, 255), (171, 134, 1), (109, 63, 54), (207, 138, 255),
+               (151, 0, 95), (9, 80, 61), (84, 105, 51), (74, 65, 105),
+               (166, 196, 102), (208, 195, 210), (255, 109, 65), (0, 143, 149),
+               (179, 0, 194), (209, 99, 106), (5, 121, 0), (227, 255, 205),
+               (147, 186, 208), (153, 69, 1), (3, 95, 161), (163, 255, 0),
+               (119, 0, 170), (0, 182, 199), (0, 165, 120), (183, 130, 88),
+               (95, 32, 0), (130, 114, 135), (110, 129, 133), (166, 74, 118),
+               (219, 142, 185), (79, 210, 114), (178, 90, 62), (65, 70, 15),
+               (127, 167, 115), (59, 105, 106), (142, 108, 45), (196, 172, 0),
+               (95, 54, 80), (128, 76, 255), (201, 57, 1), (246, 0, 122),
+               (191, 162, 208), (255, 255, 128), (147, 211, 203),
+               (150, 100, 100), (168, 171, 172), (146, 112, 198),
+               (210, 170, 100), (92, 136, 89), (218, 88, 184), (241, 129, 0),
+               (217, 17, 255), (124, 74, 181), (70, 70, 70), (255, 228, 255),
+               (154, 208, 0), (193, 0, 92), (76, 91, 113), (255, 180, 195),
+               (106, 154, 176),
+               (230, 150, 140), (60, 143, 255), (128, 64, 128), (92, 82, 55),
+               (254, 212, 124), (73, 77, 174), (255, 160, 98), (255, 255, 255),
+               (104, 84, 109), (169, 164, 131), (225, 199, 255), (137, 54, 74),
+               (135, 158, 223), (7, 246, 231), (107, 255, 200), (58, 41, 149),
+               (183, 121, 142), (255, 73, 97), (107, 142, 35), (190, 153, 153),
+               (146, 139, 141),
+               (70, 130, 180), (134, 199, 156), (209, 226, 140), (96, 36, 108),
+               (96, 96, 96), (64, 170, 64), (152, 251, 152), (208, 229, 228),
+               (206, 186, 171), (152, 161, 64), (116, 112, 0), (0, 114, 143),
+               (102, 102, 156), (250, 141, 255)]
+
+    def __init__(self,
+                 ann_file,
+                 pipeline,
+                 ins_ann_file=None,
+                 classes=None,
+                 data_root=None,
+                 img_prefix='',
+                 seg_prefix=None,
+                 proposal_file=None,
+                 test_mode=False,
+                 filter_empty_gt=True,
+                 file_client_args=dict(backend='disk')):
+        super().__init__(
+            ann_file,
+            pipeline,
+            classes=classes,
+            data_root=data_root,
+            img_prefix=img_prefix,
+            seg_prefix=seg_prefix,
+            proposal_file=proposal_file,
+            test_mode=test_mode,
+            filter_empty_gt=filter_empty_gt,
+            file_client_args=file_client_args)
+        self.ins_ann_file = ins_ann_file
+
+    def load_annotations(self, ann_file):
+        """Load annotation from COCO Panoptic style annotation file.
+
+        Args:
+            ann_file (str): Path of annotation file.
+
+        Returns:
+            list[dict]: Annotation info from COCO api.
+        """
+        self.coco = COCOPanoptic(ann_file)
+        self.cat_ids = self.coco.get_cat_ids()
+        self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
+        self.categories = self.coco.cats
+        self.img_ids = self.coco.get_img_ids()
+        data_infos = []
+        for i in self.img_ids:
+            info = self.coco.load_imgs([i])[0]
+            info['filename'] = info['file_name']
+            info['segm_file'] = info['filename'].replace('jpg', 'png')
+            data_infos.append(info)
+        return data_infos
+
+    def get_ann_info(self, idx):
+        """Get COCO annotation by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            dict: Annotation info of specified index.
+        """
+        img_id = self.data_infos[idx]['id']
+        ann_ids = self.coco.get_ann_ids(img_ids=[img_id])
+        ann_info = self.coco.load_anns(ann_ids)
+        # filter out unmatched images
+        ann_info = [i for i in ann_info if i['image_id'] == img_id]
+        return self._parse_ann_info(self.data_infos[idx], ann_info)
+
+    def _parse_ann_info(self, img_info, ann_info):
+        """Parse annotations and load panoptic ground truths.
+
+        Args:
+            img_info (int): Image info of an image.
+            ann_info (list[dict]): Annotation info of an image.
+
+        Returns:
+            dict: A dict containing the following keys: bboxes, bboxes_ignore,
+                labels, masks, seg_map.
+        """
+        gt_bboxes = []
+        gt_labels = []
+        gt_bboxes_ignore = []
+        gt_mask_infos = []
+
+        for i, ann in enumerate(ann_info):
+            x1, y1, w, h = ann['bbox']
+            if ann['area'] <= 0 or w < 1 or h < 1:
+                continue
+            bbox = [x1, y1, x1 + w, y1 + h]
+
+            category_id = ann['category_id']
+            contiguous_cat_id = self.cat2label[category_id]
+
+            is_thing = self.coco.load_cats(ids=category_id)[0]['isthing']
+            if is_thing:
+                is_crowd = ann.get('iscrowd', False)
+                if not is_crowd:
+                    gt_bboxes.append(bbox)
+                    gt_labels.append(contiguous_cat_id)
+                else:
+                    gt_bboxes_ignore.append(bbox)
+                    is_thing = False
+
+            mask_info = {
+                'id': ann['id'],
+                'category': contiguous_cat_id,
+                'is_thing': is_thing
+            }
+            gt_mask_infos.append(mask_info)
+
+        if gt_bboxes:
+            gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
+            gt_labels = np.array(gt_labels, dtype=np.int64)
+        else:
+            gt_bboxes = np.zeros((0, 4), dtype=np.float32)
+            gt_labels = np.array([], dtype=np.int64)
+
+        if gt_bboxes_ignore:
+            gt_bboxes_ignore = np.array(gt_bboxes_ignore, dtype=np.float32)
+        else:
+            gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
+
+        ann = dict(
+            bboxes=gt_bboxes,
+            labels=gt_labels,
+            bboxes_ignore=gt_bboxes_ignore,
+            masks=gt_mask_infos,
+            seg_map=img_info['segm_file'])
+
+        return ann
+
+    def _filter_imgs(self, min_size=32):
+        """Filter images too small or without ground truths."""
+        ids_with_ann = []
+        # check whether images have legal thing annotations.
+        for lists in self.coco.anns.values():
+            for item in lists:
+                category_id = item['category_id']
+                is_thing = self.coco.load_cats(ids=category_id)[0]['isthing']
+                if not is_thing:
+                    continue
+                ids_with_ann.append(item['image_id'])
+        ids_with_ann = set(ids_with_ann)
+
+        valid_inds = []
+        valid_img_ids = []
+        for i, img_info in enumerate(self.data_infos):
+            img_id = self.img_ids[i]
+            if self.filter_empty_gt and img_id not in ids_with_ann:
+                continue
+            if min(img_info['width'], img_info['height']) >= min_size:
+                valid_inds.append(i)
+                valid_img_ids.append(img_id)
+        self.img_ids = valid_img_ids
+        return valid_inds
+
+    def _pan2json(self, results, outfile_prefix):
+        """Convert panoptic results to COCO panoptic json style."""
+        label2cat = dict((v, k) for (k, v) in self.cat2label.items())
+        pred_annotations = []
+        outdir = os.path.join(os.path.dirname(outfile_prefix), 'panoptic')
+
+        for idx in range(len(self)):
+            img_id = self.img_ids[idx]
+            segm_file = self.data_infos[idx]['segm_file']
+            pan = results[idx]
+
+            pan_labels = np.unique(pan)
+            segm_info = []
+            for pan_label in pan_labels:
+                sem_label = pan_label % INSTANCE_OFFSET
+                # We reserve the length of self.CLASSES for VOID label
+                if sem_label == len(self.CLASSES):
+                    continue
+                # convert sem_label to json label
+                cat_id = label2cat[sem_label]
+                is_thing = self.categories[cat_id]['isthing']
+                mask = pan == pan_label
+                area = mask.sum()
+                segm_info.append({
+                    'id': int(pan_label),
+                    'category_id': cat_id,
+                    'isthing': is_thing,
+                    'area': int(area)
+                })
+            # evaluation script uses 0 for VOID label.
+            pan[pan % INSTANCE_OFFSET == len(self.CLASSES)] = VOID
+            pan = id2rgb(pan).astype(np.uint8)
+            mmcv.imwrite(pan[:, :, ::-1], os.path.join(outdir, segm_file))
+            record = {
+                'image_id': img_id,
+                'segments_info': segm_info,
+                'file_name': segm_file
+            }
+            pred_annotations.append(record)
+        pan_json_results = dict(annotations=pred_annotations)
+        return pan_json_results
+
+    def results2json(self, results, outfile_prefix):
+        """Dump the results to a COCO style json file.
+
+        There are 4 types of results: proposals, bbox predictions, mask
+        predictions, panoptic segmentation predictions, and they have
+        different data types. This method will automatically recognize
+        the type, and dump them to json files.
+
+        .. code-block:: none
+
+            [
+                {
+                    'pan_results': np.array, # shape (h, w)
+                    # ins_results which includes bboxes and RLE encoded masks
+                    # is optional.
+                    'ins_results': (list[np.array], list[list[str]])
+                },
+                ...
+            ]
+
+        Args:
+            results (list[dict]): Testing results of the dataset.
+            outfile_prefix (str): The filename prefix of the json files. If the
+                prefix is "somepath/xxx", the json files will be named
+                "somepath/xxx.panoptic.json", "somepath/xxx.bbox.json",
+                "somepath/xxx.segm.json"
+
+        Returns:
+            dict[str: str]: Possible keys are "panoptic", "bbox", "segm", \
+                "proposal", and values are corresponding filenames.
+        """
+        result_files = dict()
+        # panoptic segmentation results
+        if 'pan_results' in results[0]:
+            pan_results = [result['pan_results'] for result in results]
+            pan_json_results = self._pan2json(pan_results, outfile_prefix)
+            result_files['panoptic'] = f'{outfile_prefix}.panoptic.json'
+            mmcv.dump(pan_json_results, result_files['panoptic'])
+
+        # instance segmentation results
+        if 'ins_results' in results[0]:
+            ins_results = [result['ins_results'] for result in results]
+            bbox_json_results, segm_json_results = self._segm2json(ins_results)
+            result_files['bbox'] = f'{outfile_prefix}.bbox.json'
+            result_files['proposal'] = f'{outfile_prefix}.bbox.json'
+            result_files['segm'] = f'{outfile_prefix}.segm.json'
+            mmcv.dump(bbox_json_results, result_files['bbox'])
+            mmcv.dump(segm_json_results, result_files['segm'])
+
+        return result_files
+
+    def evaluate_pan_json(self,
+                          result_files,
+                          outfile_prefix,
+                          logger=None,
+                          classwise=False,
+                          nproc=32):
+        """Evaluate PQ according to the panoptic results json file."""
+        imgs = self.coco.imgs
+        gt_json = self.coco.img_ann_map  # image to annotations
+        gt_json = [{
+            'image_id': k,
+            'segments_info': v,
+            'file_name': imgs[k]['segm_file']
+        } for k, v in gt_json.items()]
+        pred_json = mmcv.load(result_files['panoptic'])
+        pred_json = dict(
+            (el['image_id'], el) for el in pred_json['annotations'])
+
+        # match the gt_anns and pred_anns in the same image
+        matched_annotations_list = []
+        for gt_ann in gt_json:
+            img_id = gt_ann['image_id']
+            if img_id not in pred_json.keys():
+                raise Exception('no prediction for the image'
+                                ' with id: {}'.format(img_id))
+            matched_annotations_list.append((gt_ann, pred_json[img_id]))
+
+        gt_folder = self.seg_prefix
+        pred_folder = os.path.join(os.path.dirname(outfile_prefix), 'panoptic')
+
+        pq_stat = pq_compute_multi_core(
+            matched_annotations_list,
+            gt_folder,
+            pred_folder,
+            self.categories,
+            self.file_client,
+            nproc=nproc)
+
+        metrics = [('All', None), ('Things', True), ('Stuff', False)]
+        pq_results = {}
+
+        for name, isthing in metrics:
+            pq_results[name], classwise_results = pq_stat.pq_average(
+                self.categories, isthing=isthing)
+            if name == 'All':
+                pq_results['classwise'] = classwise_results
+
+        classwise_results = None
+        if classwise:
+            classwise_results = {
+                k: v
+                for k, v in zip(self.CLASSES, pq_results['classwise'].values())
+            }
+        print_panoptic_table(pq_results, classwise_results, logger=logger)
+        results = parse_pq_results(pq_results)
+        results['PQ_copypaste'] = (
+            f'{results["PQ"]:.3f} {results["SQ"]:.3f} '
+            f'{results["RQ"]:.3f} '
+            f'{results["PQ_th"]:.3f} {results["SQ_th"]:.3f} '
+            f'{results["RQ_th"]:.3f} '
+            f'{results["PQ_st"]:.3f} {results["SQ_st"]:.3f} '
+            f'{results["RQ_st"]:.3f}')
+
+        return results
+
+    def evaluate(self,
+                 results,
+                 metric='PQ',
+                 logger=None,
+                 jsonfile_prefix=None,
+                 classwise=False,
+                 nproc=32,
+                 **kwargs):
+        """Evaluation in COCO Panoptic protocol.
+
+        Args:
+            results (list[dict]): Testing results of the dataset.
+            metric (str | list[str]): Metrics to be evaluated. 'PQ', 'bbox',
+                'segm', 'proposal' are supported. 'pq' will be regarded as 'PQ.
+            logger (logging.Logger | str | None): Logger used for printing
+                related information during evaluation. Default: None.
+            jsonfile_prefix (str | None): The prefix of json files. It includes
+                the file path and the prefix of filename, e.g., "a/b/prefix".
+                If not specified, a temp file will be created. Default: None.
+            classwise (bool): Whether to print classwise evaluation results.
+                Default: False.
+            nproc (int): Number of processes for panoptic quality computing.
+                Defaults to 32. When `nproc` exceeds the number of cpu cores,
+                the number of cpu cores is used.
+
+        Returns:
+            dict[str, float]: COCO Panoptic style evaluation metric.
+        """
+        metrics = metric if isinstance(metric, list) else [metric]
+        # Compatible with lowercase 'pq'
+        metrics = ['PQ' if metric == 'pq' else metric for metric in metrics]
+        allowed_metrics = ['PQ', 'bbox', 'segm', 'proposal']
+        for metric in metrics:
+            if metric not in allowed_metrics:
+                raise KeyError(f'metric {metric} is not supported')
+
+        result_files, tmp_dir = self.format_results(results, jsonfile_prefix)
+        eval_results = {}
+
+        outfile_prefix = os.path.join(tmp_dir.name, 'results') \
+            if tmp_dir is not None else jsonfile_prefix
+        if 'PQ' in metrics:
+            eval_pan_results = self.evaluate_pan_json(
+                result_files, outfile_prefix, logger, classwise, nproc=nproc)
+
+            eval_results.update(eval_pan_results)
+            metrics.remove('PQ')
+
+        if (('bbox' in metrics) or ('segm' in metrics)
+                or ('proposal' in metrics)):
+
+            assert 'ins_results' in results[0], 'instance segmentation' \
+                'results are absent from results'
+
+            assert self.ins_ann_file is not None, 'Annotation '\
+                'file for instance segmentation or object detection ' \
+                'shuold not be None'
+
+            coco_gt = COCO(self.ins_ann_file)
+            panoptic_cat_ids = self.cat_ids
+            self.cat_ids = coco_gt.get_cat_ids(cat_names=self.THING_CLASSES)
+
+            eval_ins_results = self.evaluate_det_segm(results, result_files,
+                                                      coco_gt, metrics, logger,
+                                                      classwise, **kwargs)
+            self.cat_ids = panoptic_cat_ids
+            eval_results.update(eval_ins_results)
+
+        if tmp_dir is not None:
+            tmp_dir.cleanup()
+        return eval_results
+
+
+def parse_pq_results(pq_results):
+    """Parse the Panoptic Quality results."""
+    result = dict()
+    result['PQ'] = 100 * pq_results['All']['pq']
+    result['SQ'] = 100 * pq_results['All']['sq']
+    result['RQ'] = 100 * pq_results['All']['rq']
+    result['PQ_th'] = 100 * pq_results['Things']['pq']
+    result['SQ_th'] = 100 * pq_results['Things']['sq']
+    result['RQ_th'] = 100 * pq_results['Things']['rq']
+    result['PQ_st'] = 100 * pq_results['Stuff']['pq']
+    result['SQ_st'] = 100 * pq_results['Stuff']['sq']
+    result['RQ_st'] = 100 * pq_results['Stuff']['rq']
+    return result
+
+
+def print_panoptic_table(pq_results, classwise_results=None, logger=None):
+    """Print the panoptic evaluation results table.
+
+    Args:
+        pq_results(dict): The Panoptic Quality results.
+        classwise_results(dict | None): The classwise Panoptic Quality results.
+            The keys are class names and the values are metrics.
+        logger (logging.Logger | str | None): Logger used for printing
+            related information during evaluation. Default: None.
+    """
+
+    headers = ['', 'PQ', 'SQ', 'RQ', 'categories']
+    data = [headers]
+    for name in ['All', 'Things', 'Stuff']:
+        numbers = [
+            f'{(pq_results[name][k] * 100):0.3f}' for k in ['pq', 'sq', 'rq']
+        ]
+        row = [name] + numbers + [pq_results[name]['n']]
+        data.append(row)
+    table = AsciiTable(data)
+    print_log('Panoptic Evaluation Results:\n' + table.table, logger=logger)
+
+    if classwise_results is not None:
+        class_metrics = [(name, ) + tuple(f'{(metrics[k] * 100):0.3f}'
+                                          for k in ['pq', 'sq', 'rq'])
+                         for name, metrics in classwise_results.items()]
+        num_columns = min(8, len(class_metrics) * 4)
+        results_flatten = list(itertools.chain(*class_metrics))
+        headers = ['category', 'PQ', 'SQ', 'RQ'] * (num_columns // 4)
+        results_2d = itertools.zip_longest(
+            *[results_flatten[i::num_columns] for i in range(num_columns)])
+        data = [headers]
+        data += [result for result in results_2d]
+        table = AsciiTable(data)
+        print_log(
+            'Classwise Panoptic Evaluation Results:\n' + table.table,
+            logger=logger)
diff --git a/mmdet/datasets/custom.py b/mmdet/datasets/custom.py
new file mode 100644
index 0000000..3b97685
--- /dev/null
+++ b/mmdet/datasets/custom.py
@@ -0,0 +1,412 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import warnings
+from collections import OrderedDict
+
+import mmcv
+import numpy as np
+from mmcv.utils import print_log
+from terminaltables import AsciiTable
+from torch.utils.data import Dataset
+
+from mmdet.core import eval_map, eval_recalls
+from .builder import DATASETS
+from .pipelines import Compose
+
+
+@DATASETS.register_module()
+class CustomDataset(Dataset):
+    """Custom dataset for detection.
+
+    The annotation format is shown as follows. The `ann` field is optional for
+    testing.
+
+    .. code-block:: none
+
+        [
+            {
+                'filename': 'a.jpg',
+                'width': 1280,
+                'height': 720,
+                'ann': {
+                    'bboxes': <np.ndarray> (n, 4) in (x1, y1, x2, y2) order.
+                    'labels': <np.ndarray> (n, ),
+                    'bboxes_ignore': <np.ndarray> (k, 4), (optional field)
+                    'labels_ignore': <np.ndarray> (k, 4) (optional field)
+                }
+            },
+            ...
+        ]
+
+    Args:
+        ann_file (str): Annotation file path.
+        pipeline (list[dict]): Processing pipeline.
+        classes (str | Sequence[str], optional): Specify classes to load.
+            If is None, ``cls.CLASSES`` will be used. Default: None.
+        data_root (str, optional): Data root for ``ann_file``,
+            ``img_prefix``, ``seg_prefix``, ``proposal_file`` if specified.
+        test_mode (bool, optional): If set True, annotation will not be loaded.
+        filter_empty_gt (bool, optional): If set true, images without bounding
+            boxes of the dataset's classes will be filtered out. This option
+            only works when `test_mode=False`, i.e., we never filter images
+            during tests.
+    """
+
+    CLASSES = None
+
+    PALETTE = None
+
+    def __init__(self,
+                 ann_file,
+                 pipeline,
+                 classes=None,
+                 data_root=None,
+                 img_prefix='',
+                 seg_prefix=None,
+                 seg_suffix='.png',
+                 proposal_file=None,
+                 test_mode=False,
+                 filter_empty_gt=True,
+                 file_client_args=dict(backend='disk')):
+        self.ann_file = ann_file
+        self.data_root = data_root
+        self.img_prefix = img_prefix
+        self.seg_prefix = seg_prefix
+        self.seg_suffix = seg_suffix
+        self.proposal_file = proposal_file
+        self.test_mode = test_mode
+        self.filter_empty_gt = filter_empty_gt
+        self.file_client = mmcv.FileClient(**file_client_args)
+        self.CLASSES = self.get_classes(classes)
+
+        # join paths if data_root is specified
+        if self.data_root is not None:
+            if not osp.isabs(self.ann_file):
+                self.ann_file = osp.join(self.data_root, self.ann_file)
+            if not (self.img_prefix is None or osp.isabs(self.img_prefix)):
+                self.img_prefix = osp.join(self.data_root, self.img_prefix)
+            if not (self.seg_prefix is None or osp.isabs(self.seg_prefix)):
+                self.seg_prefix = osp.join(self.data_root, self.seg_prefix)
+            if not (self.proposal_file is None
+                    or osp.isabs(self.proposal_file)):
+                self.proposal_file = osp.join(self.data_root,
+                                              self.proposal_file)
+        # load annotations (and proposals)
+        if hasattr(self.file_client, 'get_local_path'):
+            with self.file_client.get_local_path(self.ann_file) as local_path:
+                self.data_infos = self.load_annotations(local_path)
+        else:
+            warnings.warn(
+                'The used MMCV version does not have get_local_path. '
+                f'We treat the {self.ann_file} as local paths and it '
+                'might cause errors if the path is not a local path. '
+                'Please use MMCV>= 1.3.16 if you meet errors.')
+            self.data_infos = self.load_annotations(self.ann_file)
+
+        if self.proposal_file is not None:
+            if hasattr(self.file_client, 'get_local_path'):
+                with self.file_client.get_local_path(
+                        self.proposal_file) as local_path:
+                    self.proposals = self.load_proposals(local_path)
+            else:
+                warnings.warn(
+                    'The used MMCV version does not have get_local_path. '
+                    f'We treat the {self.ann_file} as local paths and it '
+                    'might cause errors if the path is not a local path. '
+                    'Please use MMCV>= 1.3.16 if you meet errors.')
+                self.proposals = self.load_proposals(self.proposal_file)
+        else:
+            self.proposals = None
+
+        # filter images too small and containing no annotations
+        if not test_mode:
+            valid_inds = self._filter_imgs()
+            self.data_infos = [self.data_infos[i] for i in valid_inds]
+            if self.proposals is not None:
+                self.proposals = [self.proposals[i] for i in valid_inds]
+            # set group flag for the sampler
+            self._set_group_flag()
+
+        # processing pipeline
+        self.pipeline = Compose(pipeline)
+
+    def __len__(self):
+        """Total number of samples of data."""
+        return len(self.data_infos)
+
+    def load_annotations(self, ann_file):
+        """Load annotation from annotation file."""
+        return mmcv.load(ann_file)
+
+    def load_proposals(self, proposal_file):
+        """Load proposal from proposal file."""
+        return mmcv.load(proposal_file)
+
+    def get_ann_info(self, idx):
+        """Get annotation by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            dict: Annotation info of specified index.
+        """
+
+        return self.data_infos[idx]['ann']
+
+    def get_cat_ids(self, idx):
+        """Get category ids by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            list[int]: All categories in the image of specified index.
+        """
+
+        return self.data_infos[idx]['ann']['labels'].astype(np.int).tolist()
+
+    def pre_pipeline(self, results):
+        """Prepare results dict for pipeline."""
+        results['img_prefix'] = self.img_prefix
+        results['seg_prefix'] = self.seg_prefix
+        results['proposal_file'] = self.proposal_file
+        results['bbox_fields'] = []
+        results['mask_fields'] = []
+        results['seg_fields'] = []
+
+    def _filter_imgs(self, min_size=32):
+        """Filter images too small."""
+        if self.filter_empty_gt:
+            warnings.warn(
+                'CustomDataset does not support filtering empty gt images.')
+        valid_inds = []
+        for i, img_info in enumerate(self.data_infos):
+            if min(img_info['width'], img_info['height']) >= min_size:
+                valid_inds.append(i)
+        return valid_inds
+
+    def _set_group_flag(self):
+        """Set flag according to image aspect ratio.
+
+        Images with aspect ratio greater than 1 will be set as group 1,
+        otherwise group 0.
+        """
+        self.flag = np.zeros(len(self), dtype=np.uint8)
+        for i in range(len(self)):
+            img_info = self.data_infos[i]
+            if img_info['width'] / img_info['height'] > 1:
+                self.flag[i] = 1
+
+    def _rand_another(self, idx):
+        """Get another random index from the same group as the given index."""
+        pool = np.where(self.flag == self.flag[idx])[0]
+        return np.random.choice(pool)
+
+    def __getitem__(self, idx):
+        """Get training/test data after pipeline.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            dict: Training/test data (with annotation if `test_mode` is set \
+                True).
+        """
+
+        if self.test_mode:
+            return self.prepare_test_img(idx)
+        while True:
+            data = self.prepare_train_img(idx)
+            if data is None:
+                idx = self._rand_another(idx)
+                continue
+            return data
+
+    def prepare_train_img(self, idx):
+        """Get training data and annotations after pipeline.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            dict: Training data and annotation after pipeline with new keys \
+                introduced by pipeline.
+        """
+
+        img_info = self.data_infos[idx]
+        ann_info = self.get_ann_info(idx)
+        results = dict(img_info=img_info, ann_info=ann_info)
+        if self.proposals is not None:
+            results['proposals'] = self.proposals[idx]
+        self.pre_pipeline(results)
+        return self.pipeline(results)
+
+    def prepare_test_img(self, idx):
+        """Get testing data after pipeline.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            dict: Testing data after pipeline with new keys introduced by \
+                pipeline.
+        """
+
+        img_info = self.data_infos[idx]
+        results = dict(img_info=img_info)
+        if self.proposals is not None:
+            results['proposals'] = self.proposals[idx]
+        self.pre_pipeline(results)
+        return self.pipeline(results)
+
+    @classmethod
+    def get_classes(cls, classes=None):
+        """Get class names of current dataset.
+
+        Args:
+            classes (Sequence[str] | str | None): If classes is None, use
+                default CLASSES defined by builtin dataset. If classes is a
+                string, take it as a file name. The file contains the name of
+                classes where each line contains one class name. If classes is
+                a tuple or list, override the CLASSES defined by the dataset.
+
+        Returns:
+            tuple[str] or list[str]: Names of categories of the dataset.
+        """
+        if classes is None:
+            return cls.CLASSES
+
+        if isinstance(classes, str):
+            # take it as a file path
+            class_names = mmcv.list_from_file(classes)
+        elif isinstance(classes, (tuple, list)):
+            class_names = classes
+        else:
+            raise ValueError(f'Unsupported type {type(classes)} of classes.')
+
+        return class_names
+
+    def get_cat2imgs(self):
+        """Get a dict with class as key and img_ids as values, which will be
+        used in :class:`ClassAwareSampler`.
+
+        Returns:
+            dict[list]: A dict of per-label image list,
+            the item of the dict indicates a label index,
+            corresponds to the image index that contains the label.
+        """
+        if self.CLASSES is None:
+            raise ValueError('self.CLASSES can not be None')
+        # sort the label index
+        cat2imgs = {i: [] for i in range(len(self.CLASSES))}
+        for i in range(len(self)):
+            cat_ids = set(self.get_cat_ids(i))
+            for cat in cat_ids:
+                cat2imgs[cat].append(i)
+        return cat2imgs
+
+    def format_results(self, results, **kwargs):
+        """Place holder to format result to dataset specific output."""
+
+    def evaluate(self,
+                 results,
+                 metric='mAP',
+                 logger=None,
+                 proposal_nums=(100, 300, 1000),
+                 iou_thr=0.5,
+                 scale_ranges=None):
+        """Evaluate the dataset.
+
+        Args:
+            results (list): Testing results of the dataset.
+            metric (str | list[str]): Metrics to be evaluated.
+            logger (logging.Logger | None | str): Logger used for printing
+                related information during evaluation. Default: None.
+            proposal_nums (Sequence[int]): Proposal number used for evaluating
+                recalls, such as recall@100, recall@1000.
+                Default: (100, 300, 1000).
+            iou_thr (float | list[float]): IoU threshold. Default: 0.5.
+            scale_ranges (list[tuple] | None): Scale ranges for evaluating mAP.
+                Default: None.
+        """
+
+        if not isinstance(metric, str):
+            assert len(metric) == 1
+            metric = metric[0]
+        allowed_metrics = ['mAP', 'recall']
+        if metric not in allowed_metrics:
+            raise KeyError(f'metric {metric} is not supported')
+        annotations = [self.get_ann_info(i) for i in range(len(self))]
+        eval_results = OrderedDict()
+        iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr
+        if metric == 'mAP':
+            assert isinstance(iou_thrs, list)
+            mean_aps = []
+            for iou_thr in iou_thrs:
+                print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}')
+                mean_ap, _ = eval_map(
+                    results,
+                    annotations,
+                    scale_ranges=scale_ranges,
+                    iou_thr=iou_thr,
+                    dataset=self.CLASSES,
+                    logger=logger)
+                mean_aps.append(mean_ap)
+                eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3)
+            eval_results['mAP'] = sum(mean_aps) / len(mean_aps)
+        elif metric == 'recall':
+            gt_bboxes = [ann['bboxes'] for ann in annotations]
+            recalls = eval_recalls(
+                gt_bboxes, results, proposal_nums, iou_thr, logger=logger)
+            for i, num in enumerate(proposal_nums):
+                for j, iou in enumerate(iou_thrs):
+                    eval_results[f'recall@{num}@{iou}'] = recalls[i, j]
+            if recalls.shape[1] > 1:
+                ar = recalls.mean(axis=1)
+                for i, num in enumerate(proposal_nums):
+                    eval_results[f'AR@{num}'] = ar[i]
+        return eval_results
+
+    def __repr__(self):
+        """Print the number of instance number."""
+        dataset_type = 'Test' if self.test_mode else 'Train'
+        result = (f'\n{self.__class__.__name__} {dataset_type} dataset '
+                  f'with number of images {len(self)}, '
+                  f'and instance counts: \n')
+        if self.CLASSES is None:
+            result += 'Category names are not provided. \n'
+            return result
+        instance_count = np.zeros(len(self.CLASSES) + 1).astype(int)
+        # count the instance number in each image
+        for idx in range(len(self)):
+            label = self.get_ann_info(idx)['labels']
+            unique, counts = np.unique(label, return_counts=True)
+            if len(unique) > 0:
+                # add the occurrence number to each class
+                instance_count[unique] += counts
+            else:
+                # background is the last index
+                instance_count[-1] += 1
+        # create a table with category count
+        table_data = [['category', 'count'] * 5]
+        row_data = []
+        for cls, count in enumerate(instance_count):
+            if cls < len(self.CLASSES):
+                row_data += [f'{cls} [{self.CLASSES[cls]}]', f'{count}']
+            else:
+                # add the background number
+                row_data += ['-1 background', f'{count}']
+            if len(row_data) == 10:
+                table_data.append(row_data)
+                row_data = []
+        if len(row_data) >= 2:
+            if row_data[-1] == '0':
+                row_data = row_data[:-2]
+            if len(row_data) >= 2:
+                table_data.append([])
+                table_data.append(row_data)
+
+        table = AsciiTable(table_data)
+        result += table.table
+        return result
diff --git a/mmdet/datasets/dataset_wrappers.py b/mmdet/datasets/dataset_wrappers.py
new file mode 100644
index 0000000..d6ceffb
--- /dev/null
+++ b/mmdet/datasets/dataset_wrappers.py
@@ -0,0 +1,456 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import bisect
+import collections
+import copy
+import math
+from collections import defaultdict
+
+import numpy as np
+from mmcv.utils import build_from_cfg, print_log
+from torch.utils.data.dataset import ConcatDataset as _ConcatDataset
+
+from .builder import DATASETS, PIPELINES
+from .coco import CocoDataset
+
+
+@DATASETS.register_module()
+class ConcatDataset(_ConcatDataset):
+    """A wrapper of concatenated dataset.
+
+    Same as :obj:`torch.utils.data.dataset.ConcatDataset`, but
+    concat the group flag for image aspect ratio.
+
+    Args:
+        datasets (list[:obj:`Dataset`]): A list of datasets.
+        separate_eval (bool): Whether to evaluate the results
+            separately if it is used as validation dataset.
+            Defaults to True.
+    """
+
+    def __init__(self, datasets, separate_eval=True):
+        super(ConcatDataset, self).__init__(datasets)
+        self.CLASSES = datasets[0].CLASSES
+        self.PALETTE = getattr(datasets[0], 'PALETTE', None)
+        self.separate_eval = separate_eval
+        if not separate_eval:
+            if any([isinstance(ds, CocoDataset) for ds in datasets]):
+                raise NotImplementedError(
+                    'Evaluating concatenated CocoDataset as a whole is not'
+                    ' supported! Please set "separate_eval=True"')
+            elif len(set([type(ds) for ds in datasets])) != 1:
+                raise NotImplementedError(
+                    'All the datasets should have same types')
+
+        if hasattr(datasets[0], 'flag'):
+            flags = []
+            for i in range(0, len(datasets)):
+                flags.append(datasets[i].flag)
+            self.flag = np.concatenate(flags)
+
+    def get_cat_ids(self, idx):
+        """Get category ids of concatenated dataset by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            list[int]: All categories in the image of specified index.
+        """
+
+        if idx < 0:
+            if -idx > len(self):
+                raise ValueError(
+                    'absolute value of index should not exceed dataset length')
+            idx = len(self) + idx
+        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
+        if dataset_idx == 0:
+            sample_idx = idx
+        else:
+            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
+        return self.datasets[dataset_idx].get_cat_ids(sample_idx)
+
+    def get_ann_info(self, idx):
+        """Get annotation of concatenated dataset by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            dict: Annotation info of specified index.
+        """
+
+        if idx < 0:
+            if -idx > len(self):
+                raise ValueError(
+                    'absolute value of index should not exceed dataset length')
+            idx = len(self) + idx
+        dataset_idx = bisect.bisect_right(self.cumulative_sizes, idx)
+        if dataset_idx == 0:
+            sample_idx = idx
+        else:
+            sample_idx = idx - self.cumulative_sizes[dataset_idx - 1]
+        return self.datasets[dataset_idx].get_ann_info(sample_idx)
+
+    def evaluate(self, results, logger=None, **kwargs):
+        """Evaluate the results.
+
+        Args:
+            results (list[list | tuple]): Testing results of the dataset.
+            logger (logging.Logger | str | None): Logger used for printing
+                related information during evaluation. Default: None.
+
+        Returns:
+            dict[str: float]: AP results of the total dataset or each separate
+            dataset if `self.separate_eval=True`.
+        """
+        assert len(results) == self.cumulative_sizes[-1], \
+            ('Dataset and results have different sizes: '
+             f'{self.cumulative_sizes[-1]} v.s. {len(results)}')
+
+        # Check whether all the datasets support evaluation
+        for dataset in self.datasets:
+            assert hasattr(dataset, 'evaluate'), \
+                f'{type(dataset)} does not implement evaluate function'
+
+        if self.separate_eval:
+            dataset_idx = -1
+            total_eval_results = dict()
+            for size, dataset in zip(self.cumulative_sizes, self.datasets):
+                start_idx = 0 if dataset_idx == -1 else \
+                    self.cumulative_sizes[dataset_idx]
+                end_idx = self.cumulative_sizes[dataset_idx + 1]
+
+                results_per_dataset = results[start_idx:end_idx]
+                print_log(
+                    f'\nEvaluating {dataset.ann_file} with '
+                    f'{len(results_per_dataset)} images now',
+                    logger=logger)
+
+                eval_results_per_dataset = dataset.evaluate(
+                    results_per_dataset, logger=logger, **kwargs)
+                dataset_idx += 1
+                for k, v in eval_results_per_dataset.items():
+                    total_eval_results.update({f'{dataset_idx}_{k}': v})
+
+            return total_eval_results
+        elif any([isinstance(ds, CocoDataset) for ds in self.datasets]):
+            raise NotImplementedError(
+                'Evaluating concatenated CocoDataset as a whole is not'
+                ' supported! Please set "separate_eval=True"')
+        elif len(set([type(ds) for ds in self.datasets])) != 1:
+            raise NotImplementedError(
+                'All the datasets should have same types')
+        else:
+            original_data_infos = self.datasets[0].data_infos
+            self.datasets[0].data_infos = sum(
+                [dataset.data_infos for dataset in self.datasets], [])
+            eval_results = self.datasets[0].evaluate(
+                results, logger=logger, **kwargs)
+            self.datasets[0].data_infos = original_data_infos
+            return eval_results
+
+
+@DATASETS.register_module()
+class RepeatDataset:
+    """A wrapper of repeated dataset.
+
+    The length of repeated dataset will be `times` larger than the original
+    dataset. This is useful when the data loading time is long but the dataset
+    is small. Using RepeatDataset can reduce the data loading time between
+    epochs.
+
+    Args:
+        dataset (:obj:`Dataset`): The dataset to be repeated.
+        times (int): Repeat times.
+    """
+
+    def __init__(self, dataset, times):
+        self.dataset = dataset
+        self.times = times
+        self.CLASSES = dataset.CLASSES
+        self.PALETTE = getattr(dataset, 'PALETTE', None)
+        if hasattr(self.dataset, 'flag'):
+            self.flag = np.tile(self.dataset.flag, times)
+
+        self._ori_len = len(self.dataset)
+
+    def __getitem__(self, idx):
+        return self.dataset[idx % self._ori_len]
+
+    def get_cat_ids(self, idx):
+        """Get category ids of repeat dataset by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            list[int]: All categories in the image of specified index.
+        """
+
+        return self.dataset.get_cat_ids(idx % self._ori_len)
+
+    def get_ann_info(self, idx):
+        """Get annotation of repeat dataset by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            dict: Annotation info of specified index.
+        """
+
+        return self.dataset.get_ann_info(idx % self._ori_len)
+
+    def __len__(self):
+        """Length after repetition."""
+        return self.times * self._ori_len
+
+
+# Modified from https://github.com/facebookresearch/detectron2/blob/41d475b75a230221e21d9cac5d69655e3415e3a4/detectron2/data/samplers/distributed_sampler.py#L57 # noqa
+@DATASETS.register_module()
+class ClassBalancedDataset:
+    """A wrapper of repeated dataset with repeat factor.
+
+    Suitable for training on class imbalanced datasets like LVIS. Following
+    the sampling strategy in the `paper <https://arxiv.org/abs/1908.03195>`_,
+    in each epoch, an image may appear multiple times based on its
+    "repeat factor".
+    The repeat factor for an image is a function of the frequency the rarest
+    category labeled in that image. The "frequency of category c" in [0, 1]
+    is defined by the fraction of images in the training set (without repeats)
+    in which category c appears.
+    The dataset needs to instantiate :func:`self.get_cat_ids` to support
+    ClassBalancedDataset.
+
+    The repeat factor is computed as followed.
+
+    1. For each category c, compute the fraction # of images
+       that contain it: :math:`f(c)`
+    2. For each category c, compute the category-level repeat factor:
+       :math:`r(c) = max(1, sqrt(t/f(c)))`
+    3. For each image I, compute the image-level repeat factor:
+       :math:`r(I) = max_{c in I} r(c)`
+
+    Args:
+        dataset (:obj:`CustomDataset`): The dataset to be repeated.
+        oversample_thr (float): frequency threshold below which data is
+            repeated. For categories with ``f_c >= oversample_thr``, there is
+            no oversampling. For categories with ``f_c < oversample_thr``, the
+            degree of oversampling following the square-root inverse frequency
+            heuristic above.
+        filter_empty_gt (bool, optional): If set true, images without bounding
+            boxes will not be oversampled. Otherwise, they will be categorized
+            as the pure background class and involved into the oversampling.
+            Default: True.
+    """
+
+    def __init__(self, dataset, oversample_thr, filter_empty_gt=True):
+        self.dataset = dataset
+        self.oversample_thr = oversample_thr
+        self.filter_empty_gt = filter_empty_gt
+        self.CLASSES = dataset.CLASSES
+        self.PALETTE = getattr(dataset, 'PALETTE', None)
+
+        repeat_factors = self._get_repeat_factors(dataset, oversample_thr)
+        repeat_indices = []
+        for dataset_idx, repeat_factor in enumerate(repeat_factors):
+            repeat_indices.extend([dataset_idx] * math.ceil(repeat_factor))
+        self.repeat_indices = repeat_indices
+
+        flags = []
+        if hasattr(self.dataset, 'flag'):
+            for flag, repeat_factor in zip(self.dataset.flag, repeat_factors):
+                flags.extend([flag] * int(math.ceil(repeat_factor)))
+            assert len(flags) == len(repeat_indices)
+        self.flag = np.asarray(flags, dtype=np.uint8)
+
+    def _get_repeat_factors(self, dataset, repeat_thr):
+        """Get repeat factor for each images in the dataset.
+
+        Args:
+            dataset (:obj:`CustomDataset`): The dataset
+            repeat_thr (float): The threshold of frequency. If an image
+                contains the categories whose frequency below the threshold,
+                it would be repeated.
+
+        Returns:
+            list[float]: The repeat factors for each images in the dataset.
+        """
+
+        # 1. For each category c, compute the fraction # of images
+        #   that contain it: f(c)
+        category_freq = defaultdict(int)
+        num_images = len(dataset)
+        for idx in range(num_images):
+            cat_ids = set(self.dataset.get_cat_ids(idx))
+            if len(cat_ids) == 0 and not self.filter_empty_gt:
+                cat_ids = set([len(self.CLASSES)])
+            for cat_id in cat_ids:
+                category_freq[cat_id] += 1
+        for k, v in category_freq.items():
+            category_freq[k] = v / num_images
+
+        # 2. For each category c, compute the category-level repeat factor:
+        #    r(c) = max(1, sqrt(t/f(c)))
+        category_repeat = {
+            cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq))
+            for cat_id, cat_freq in category_freq.items()
+        }
+
+        # 3. For each image I, compute the image-level repeat factor:
+        #    r(I) = max_{c in I} r(c)
+        repeat_factors = []
+        for idx in range(num_images):
+            cat_ids = set(self.dataset.get_cat_ids(idx))
+            if len(cat_ids) == 0 and not self.filter_empty_gt:
+                cat_ids = set([len(self.CLASSES)])
+            repeat_factor = 1
+            if len(cat_ids) > 0:
+                repeat_factor = max(
+                    {category_repeat[cat_id]
+                     for cat_id in cat_ids})
+            repeat_factors.append(repeat_factor)
+
+        return repeat_factors
+
+    def __getitem__(self, idx):
+        ori_index = self.repeat_indices[idx]
+        return self.dataset[ori_index]
+
+    def get_ann_info(self, idx):
+        """Get annotation of dataset by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            dict: Annotation info of specified index.
+        """
+        ori_index = self.repeat_indices[idx]
+        return self.dataset.get_ann_info(ori_index)
+
+    def __len__(self):
+        """Length after repetition."""
+        return len(self.repeat_indices)
+
+
+@DATASETS.register_module()
+class MultiImageMixDataset:
+    """A wrapper of multiple images mixed dataset.
+
+    Suitable for training on multiple images mixed data augmentation like
+    mosaic and mixup. For the augmentation pipeline of mixed image data,
+    the `get_indexes` method needs to be provided to obtain the image
+    indexes, and you can set `skip_flags` to change the pipeline running
+    process. At the same time, we provide the `dynamic_scale` parameter
+    to dynamically change the output image size.
+
+    Args:
+        dataset (:obj:`CustomDataset`): The dataset to be mixed.
+        pipeline (Sequence[dict]): Sequence of transform object or
+            config dict to be composed.
+        dynamic_scale (tuple[int], optional): The image scale can be changed
+            dynamically. Default to None. It is deprecated.
+        skip_type_keys (list[str], optional): Sequence of type string to
+            be skip pipeline. Default to None.
+        max_refetch (int): The maximum number of retry iterations for getting
+            valid results from the pipeline. If the number of iterations is
+            greater than `max_refetch`, but results is still None, then the
+            iteration is terminated and raise the error. Default: 15.
+    """
+
+    def __init__(self,
+                 dataset,
+                 pipeline,
+                 dynamic_scale=None,
+                 skip_type_keys=None,
+                 max_refetch=15):
+        if dynamic_scale is not None:
+            raise RuntimeError(
+                'dynamic_scale is deprecated. Please use Resize pipeline '
+                'to achieve similar functions')
+        assert isinstance(pipeline, collections.abc.Sequence)
+        if skip_type_keys is not None:
+            assert all([
+                isinstance(skip_type_key, str)
+                for skip_type_key in skip_type_keys
+            ])
+        self._skip_type_keys = skip_type_keys
+
+        self.pipeline = []
+        self.pipeline_types = []
+        for transform in pipeline:
+            if isinstance(transform, dict):
+                self.pipeline_types.append(transform['type'])
+                transform = build_from_cfg(transform, PIPELINES)
+                self.pipeline.append(transform)
+            else:
+                raise TypeError('pipeline must be a dict')
+
+        self.dataset = dataset
+        self.CLASSES = dataset.CLASSES
+        self.PALETTE = getattr(dataset, 'PALETTE', None)
+        if hasattr(self.dataset, 'flag'):
+            self.flag = dataset.flag
+        self.num_samples = len(dataset)
+        self.max_refetch = max_refetch
+
+    def __len__(self):
+        return self.num_samples
+
+    def __getitem__(self, idx):
+        results = copy.deepcopy(self.dataset[idx])
+        for (transform, transform_type) in zip(self.pipeline,
+                                               self.pipeline_types):
+            if self._skip_type_keys is not None and \
+                    transform_type in self._skip_type_keys:
+                continue
+
+            if hasattr(transform, 'get_indexes'):
+                for i in range(self.max_refetch):
+                    # Make sure the results passed the loading pipeline
+                    # of the original dataset is not None.
+                    indexes = transform.get_indexes(self.dataset)
+                    if not isinstance(indexes, collections.abc.Sequence):
+                        indexes = [indexes]
+                    mix_results = [
+                        copy.deepcopy(self.dataset[index]) for index in indexes
+                    ]
+                    if None not in mix_results:
+                        results['mix_results'] = mix_results
+                        break
+                else:
+                    raise RuntimeError(
+                        'The loading pipeline of the original dataset'
+                        ' always return None. Please check the correctness '
+                        'of the dataset and its pipeline.')
+
+            for i in range(self.max_refetch):
+                # To confirm the results passed the training pipeline
+                # of the wrapper is not None.
+                updated_results = transform(copy.deepcopy(results))
+                if updated_results is not None:
+                    results = updated_results
+                    break
+            else:
+                raise RuntimeError(
+                    'The training pipeline of the dataset wrapper'
+                    ' always return None.Please check the correctness '
+                    'of the dataset and its pipeline.')
+
+            if 'mix_results' in results:
+                results.pop('mix_results')
+
+        return results
+
+    def update_skip_type_keys(self, skip_type_keys):
+        """Update skip_type_keys. It is called by an external hook.
+
+        Args:
+            skip_type_keys (list[str], optional): Sequence of type
+                string to be skip pipeline.
+        """
+        assert all([
+            isinstance(skip_type_key, str) for skip_type_key in skip_type_keys
+        ])
+        self._skip_type_keys = skip_type_keys
diff --git a/mmdet/datasets/deepfashion.py b/mmdet/datasets/deepfashion.py
new file mode 100644
index 0000000..609f809
--- /dev/null
+++ b/mmdet/datasets/deepfashion.py
@@ -0,0 +1,16 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .builder import DATASETS
+from .coco import CocoDataset
+
+
+@DATASETS.register_module()
+class DeepFashionDataset(CocoDataset):
+
+    CLASSES = ('top', 'skirt', 'leggings', 'dress', 'outer', 'pants', 'bag',
+               'neckwear', 'headwear', 'eyeglass', 'belt', 'footwear', 'hair',
+               'skin', 'face')
+
+    PALETTE = [(0, 192, 64), (0, 64, 96), (128, 192, 192), (0, 64, 64),
+               (0, 192, 224), (0, 192, 192), (128, 192, 64), (0, 192, 96),
+               (128, 32, 192), (0, 0, 224), (0, 0, 64), (0, 160, 192),
+               (128, 0, 96), (128, 0, 192), (0, 32, 192)]
diff --git a/mmdet/datasets/lvis.py b/mmdet/datasets/lvis.py
new file mode 100644
index 0000000..5f6196e
--- /dev/null
+++ b/mmdet/datasets/lvis.py
@@ -0,0 +1,742 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import itertools
+import logging
+import os.path as osp
+import tempfile
+import warnings
+from collections import OrderedDict
+
+import numpy as np
+from mmcv.utils import print_log
+from terminaltables import AsciiTable
+
+from .builder import DATASETS
+from .coco import CocoDataset
+
+
+@DATASETS.register_module()
+class LVISV05Dataset(CocoDataset):
+
+    CLASSES = (
+        'acorn', 'aerosol_can', 'air_conditioner', 'airplane', 'alarm_clock',
+        'alcohol', 'alligator', 'almond', 'ambulance', 'amplifier', 'anklet',
+        'antenna', 'apple', 'apple_juice', 'applesauce', 'apricot', 'apron',
+        'aquarium', 'armband', 'armchair', 'armoire', 'armor', 'artichoke',
+        'trash_can', 'ashtray', 'asparagus', 'atomizer', 'avocado', 'award',
+        'awning', 'ax', 'baby_buggy', 'basketball_backboard', 'backpack',
+        'handbag', 'suitcase', 'bagel', 'bagpipe', 'baguet', 'bait', 'ball',
+        'ballet_skirt', 'balloon', 'bamboo', 'banana', 'Band_Aid', 'bandage',
+        'bandanna', 'banjo', 'banner', 'barbell', 'barge', 'barrel',
+        'barrette', 'barrow', 'baseball_base', 'baseball', 'baseball_bat',
+        'baseball_cap', 'baseball_glove', 'basket', 'basketball_hoop',
+        'basketball', 'bass_horn', 'bat_(animal)', 'bath_mat', 'bath_towel',
+        'bathrobe', 'bathtub', 'batter_(food)', 'battery', 'beachball', 'bead',
+        'beaker', 'bean_curd', 'beanbag', 'beanie', 'bear', 'bed',
+        'bedspread', 'cow', 'beef_(food)', 'beeper', 'beer_bottle', 'beer_can',
+        'beetle', 'bell', 'bell_pepper', 'belt', 'belt_buckle', 'bench',
+        'beret', 'bib', 'Bible', 'bicycle', 'visor', 'binder', 'binoculars',
+        'bird', 'birdfeeder', 'birdbath', 'birdcage', 'birdhouse',
+        'birthday_cake', 'birthday_card', 'biscuit_(bread)', 'pirate_flag',
+        'black_sheep', 'blackboard', 'blanket', 'blazer', 'blender', 'blimp',
+        'blinker', 'blueberry', 'boar', 'gameboard', 'boat', 'bobbin',
+        'bobby_pin', 'boiled_egg', 'bolo_tie', 'deadbolt', 'bolt', 'bonnet',
+        'book', 'book_bag', 'bookcase', 'booklet', 'bookmark',
+        'boom_microphone', 'boot', 'bottle', 'bottle_opener', 'bouquet',
+        'bow_(weapon)', 'bow_(decorative_ribbons)', 'bow-tie', 'bowl',
+        'pipe_bowl', 'bowler_hat', 'bowling_ball', 'bowling_pin',
+        'boxing_glove', 'suspenders', 'bracelet', 'brass_plaque', 'brassiere',
+        'bread-bin', 'breechcloth', 'bridal_gown', 'briefcase',
+        'bristle_brush', 'broccoli', 'broach', 'broom', 'brownie',
+        'brussels_sprouts', 'bubble_gum', 'bucket', 'horse_buggy', 'bull',
+        'bulldog', 'bulldozer', 'bullet_train', 'bulletin_board',
+        'bulletproof_vest', 'bullhorn', 'corned_beef', 'bun', 'bunk_bed',
+        'buoy', 'burrito', 'bus_(vehicle)', 'business_card', 'butcher_knife',
+        'butter', 'butterfly', 'button', 'cab_(taxi)', 'cabana', 'cabin_car',
+        'cabinet', 'locker', 'cake', 'calculator', 'calendar', 'calf',
+        'camcorder', 'camel', 'camera', 'camera_lens', 'camper_(vehicle)',
+        'can', 'can_opener', 'candelabrum', 'candle', 'candle_holder',
+        'candy_bar', 'candy_cane', 'walking_cane', 'canister', 'cannon',
+        'canoe', 'cantaloup', 'canteen', 'cap_(headwear)', 'bottle_cap',
+        'cape', 'cappuccino', 'car_(automobile)', 'railcar_(part_of_a_train)',
+        'elevator_car', 'car_battery', 'identity_card', 'card', 'cardigan',
+        'cargo_ship', 'carnation', 'horse_carriage', 'carrot', 'tote_bag',
+        'cart', 'carton', 'cash_register', 'casserole', 'cassette', 'cast',
+        'cat', 'cauliflower', 'caviar', 'cayenne_(spice)', 'CD_player',
+        'celery', 'cellular_telephone', 'chain_mail', 'chair', 'chaise_longue',
+        'champagne', 'chandelier', 'chap', 'checkbook', 'checkerboard',
+        'cherry', 'chessboard', 'chest_of_drawers_(furniture)',
+        'chicken_(animal)', 'chicken_wire', 'chickpea', 'Chihuahua',
+        'chili_(vegetable)', 'chime', 'chinaware', 'crisp_(potato_chip)',
+        'poker_chip', 'chocolate_bar', 'chocolate_cake', 'chocolate_milk',
+        'chocolate_mousse', 'choker', 'chopping_board', 'chopstick',
+        'Christmas_tree', 'slide', 'cider', 'cigar_box', 'cigarette',
+        'cigarette_case', 'cistern', 'clarinet', 'clasp', 'cleansing_agent',
+        'clementine', 'clip', 'clipboard', 'clock', 'clock_tower',
+        'clothes_hamper', 'clothespin', 'clutch_bag', 'coaster', 'coat',
+        'coat_hanger', 'coatrack', 'cock', 'coconut', 'coffee_filter',
+        'coffee_maker', 'coffee_table', 'coffeepot', 'coil', 'coin',
+        'colander', 'coleslaw', 'coloring_material', 'combination_lock',
+        'pacifier', 'comic_book', 'computer_keyboard', 'concrete_mixer',
+        'cone', 'control', 'convertible_(automobile)', 'sofa_bed', 'cookie',
+        'cookie_jar', 'cooking_utensil', 'cooler_(for_food)',
+        'cork_(bottle_plug)', 'corkboard', 'corkscrew', 'edible_corn',
+        'cornbread', 'cornet', 'cornice', 'cornmeal', 'corset',
+        'romaine_lettuce', 'costume', 'cougar', 'coverall', 'cowbell',
+        'cowboy_hat', 'crab_(animal)', 'cracker', 'crape', 'crate', 'crayon',
+        'cream_pitcher', 'credit_card', 'crescent_roll', 'crib', 'crock_pot',
+        'crossbar', 'crouton', 'crow', 'crown', 'crucifix', 'cruise_ship',
+        'police_cruiser', 'crumb', 'crutch', 'cub_(animal)', 'cube',
+        'cucumber', 'cufflink', 'cup', 'trophy_cup', 'cupcake', 'hair_curler',
+        'curling_iron', 'curtain', 'cushion', 'custard', 'cutting_tool',
+        'cylinder', 'cymbal', 'dachshund', 'dagger', 'dartboard',
+        'date_(fruit)', 'deck_chair', 'deer', 'dental_floss', 'desk',
+        'detergent', 'diaper', 'diary', 'die', 'dinghy', 'dining_table', 'tux',
+        'dish', 'dish_antenna', 'dishrag', 'dishtowel', 'dishwasher',
+        'dishwasher_detergent', 'diskette', 'dispenser', 'Dixie_cup', 'dog',
+        'dog_collar', 'doll', 'dollar', 'dolphin', 'domestic_ass', 'eye_mask',
+        'doorbell', 'doorknob', 'doormat', 'doughnut', 'dove', 'dragonfly',
+        'drawer', 'underdrawers', 'dress', 'dress_hat', 'dress_suit',
+        'dresser', 'drill', 'drinking_fountain', 'drone', 'dropper',
+        'drum_(musical_instrument)', 'drumstick', 'duck', 'duckling',
+        'duct_tape', 'duffel_bag', 'dumbbell', 'dumpster', 'dustpan',
+        'Dutch_oven', 'eagle', 'earphone', 'earplug', 'earring', 'easel',
+        'eclair', 'eel', 'egg', 'egg_roll', 'egg_yolk', 'eggbeater',
+        'eggplant', 'electric_chair', 'refrigerator', 'elephant', 'elk',
+        'envelope', 'eraser', 'escargot', 'eyepatch', 'falcon', 'fan',
+        'faucet', 'fedora', 'ferret', 'Ferris_wheel', 'ferry', 'fig_(fruit)',
+        'fighter_jet', 'figurine', 'file_cabinet', 'file_(tool)', 'fire_alarm',
+        'fire_engine', 'fire_extinguisher', 'fire_hose', 'fireplace',
+        'fireplug', 'fish', 'fish_(food)', 'fishbowl', 'fishing_boat',
+        'fishing_rod', 'flag', 'flagpole', 'flamingo', 'flannel', 'flash',
+        'flashlight', 'fleece', 'flip-flop_(sandal)', 'flipper_(footwear)',
+        'flower_arrangement', 'flute_glass', 'foal', 'folding_chair',
+        'food_processor', 'football_(American)', 'football_helmet',
+        'footstool', 'fork', 'forklift', 'freight_car', 'French_toast',
+        'freshener', 'frisbee', 'frog', 'fruit_juice', 'fruit_salad',
+        'frying_pan', 'fudge', 'funnel', 'futon', 'gag', 'garbage',
+        'garbage_truck', 'garden_hose', 'gargle', 'gargoyle', 'garlic',
+        'gasmask', 'gazelle', 'gelatin', 'gemstone', 'giant_panda',
+        'gift_wrap', 'ginger', 'giraffe', 'cincture',
+        'glass_(drink_container)', 'globe', 'glove', 'goat', 'goggles',
+        'goldfish', 'golf_club', 'golfcart', 'gondola_(boat)', 'goose',
+        'gorilla', 'gourd', 'surgical_gown', 'grape', 'grasshopper', 'grater',
+        'gravestone', 'gravy_boat', 'green_bean', 'green_onion', 'griddle',
+        'grillroom', 'grinder_(tool)', 'grits', 'grizzly', 'grocery_bag',
+        'guacamole', 'guitar', 'gull', 'gun', 'hair_spray', 'hairbrush',
+        'hairnet', 'hairpin', 'ham', 'hamburger', 'hammer', 'hammock',
+        'hamper', 'hamster', 'hair_dryer', 'hand_glass', 'hand_towel',
+        'handcart', 'handcuff', 'handkerchief', 'handle', 'handsaw',
+        'hardback_book', 'harmonium', 'hat', 'hatbox', 'hatch', 'veil',
+        'headband', 'headboard', 'headlight', 'headscarf', 'headset',
+        'headstall_(for_horses)', 'hearing_aid', 'heart', 'heater',
+        'helicopter', 'helmet', 'heron', 'highchair', 'hinge', 'hippopotamus',
+        'hockey_stick', 'hog', 'home_plate_(baseball)', 'honey', 'fume_hood',
+        'hook', 'horse', 'hose', 'hot-air_balloon', 'hotplate', 'hot_sauce',
+        'hourglass', 'houseboat', 'hummingbird', 'hummus', 'polar_bear',
+        'icecream', 'popsicle', 'ice_maker', 'ice_pack', 'ice_skate',
+        'ice_tea', 'igniter', 'incense', 'inhaler', 'iPod',
+        'iron_(for_clothing)', 'ironing_board', 'jacket', 'jam', 'jean',
+        'jeep', 'jelly_bean', 'jersey', 'jet_plane', 'jewelry', 'joystick',
+        'jumpsuit', 'kayak', 'keg', 'kennel', 'kettle', 'key', 'keycard',
+        'kilt', 'kimono', 'kitchen_sink', 'kitchen_table', 'kite', 'kitten',
+        'kiwi_fruit', 'knee_pad', 'knife', 'knight_(chess_piece)',
+        'knitting_needle', 'knob', 'knocker_(on_a_door)', 'koala', 'lab_coat',
+        'ladder', 'ladle', 'ladybug', 'lamb_(animal)', 'lamb-chop', 'lamp',
+        'lamppost', 'lampshade', 'lantern', 'lanyard', 'laptop_computer',
+        'lasagna', 'latch', 'lawn_mower', 'leather', 'legging_(clothing)',
+        'Lego', 'lemon', 'lemonade', 'lettuce', 'license_plate', 'life_buoy',
+        'life_jacket', 'lightbulb', 'lightning_rod', 'lime', 'limousine',
+        'linen_paper', 'lion', 'lip_balm', 'lipstick', 'liquor', 'lizard',
+        'Loafer_(type_of_shoe)', 'log', 'lollipop', 'lotion',
+        'speaker_(stereo_equipment)', 'loveseat', 'machine_gun', 'magazine',
+        'magnet', 'mail_slot', 'mailbox_(at_home)', 'mallet', 'mammoth',
+        'mandarin_orange', 'manger', 'manhole', 'map', 'marker', 'martini',
+        'mascot', 'mashed_potato', 'masher', 'mask', 'mast',
+        'mat_(gym_equipment)', 'matchbox', 'mattress', 'measuring_cup',
+        'measuring_stick', 'meatball', 'medicine', 'melon', 'microphone',
+        'microscope', 'microwave_oven', 'milestone', 'milk', 'minivan',
+        'mint_candy', 'mirror', 'mitten', 'mixer_(kitchen_tool)', 'money',
+        'monitor_(computer_equipment) computer_monitor', 'monkey', 'motor',
+        'motor_scooter', 'motor_vehicle', 'motorboat', 'motorcycle',
+        'mound_(baseball)', 'mouse_(animal_rodent)',
+        'mouse_(computer_equipment)', 'mousepad', 'muffin', 'mug', 'mushroom',
+        'music_stool', 'musical_instrument', 'nailfile', 'nameplate', 'napkin',
+        'neckerchief', 'necklace', 'necktie', 'needle', 'nest', 'newsstand',
+        'nightshirt', 'nosebag_(for_animals)', 'noseband_(for_animals)',
+        'notebook', 'notepad', 'nut', 'nutcracker', 'oar', 'octopus_(food)',
+        'octopus_(animal)', 'oil_lamp', 'olive_oil', 'omelet', 'onion',
+        'orange_(fruit)', 'orange_juice', 'oregano', 'ostrich', 'ottoman',
+        'overalls_(clothing)', 'owl', 'packet', 'inkpad', 'pad', 'paddle',
+        'padlock', 'paintbox', 'paintbrush', 'painting', 'pajamas', 'palette',
+        'pan_(for_cooking)', 'pan_(metal_container)', 'pancake', 'pantyhose',
+        'papaya', 'paperclip', 'paper_plate', 'paper_towel', 'paperback_book',
+        'paperweight', 'parachute', 'parakeet', 'parasail_(sports)',
+        'parchment', 'parka', 'parking_meter', 'parrot',
+        'passenger_car_(part_of_a_train)', 'passenger_ship', 'passport',
+        'pastry', 'patty_(food)', 'pea_(food)', 'peach', 'peanut_butter',
+        'pear', 'peeler_(tool_for_fruit_and_vegetables)', 'pegboard',
+        'pelican', 'pen', 'pencil', 'pencil_box', 'pencil_sharpener',
+        'pendulum', 'penguin', 'pennant', 'penny_(coin)', 'pepper',
+        'pepper_mill', 'perfume', 'persimmon', 'baby', 'pet', 'petfood',
+        'pew_(church_bench)', 'phonebook', 'phonograph_record', 'piano',
+        'pickle', 'pickup_truck', 'pie', 'pigeon', 'piggy_bank', 'pillow',
+        'pin_(non_jewelry)', 'pineapple', 'pinecone', 'ping-pong_ball',
+        'pinwheel', 'tobacco_pipe', 'pipe', 'pistol', 'pita_(bread)',
+        'pitcher_(vessel_for_liquid)', 'pitchfork', 'pizza', 'place_mat',
+        'plate', 'platter', 'playing_card', 'playpen', 'pliers',
+        'plow_(farm_equipment)', 'pocket_watch', 'pocketknife',
+        'poker_(fire_stirring_tool)', 'pole', 'police_van', 'polo_shirt',
+        'poncho', 'pony', 'pool_table', 'pop_(soda)', 'portrait',
+        'postbox_(public)', 'postcard', 'poster', 'pot', 'flowerpot', 'potato',
+        'potholder', 'pottery', 'pouch', 'power_shovel', 'prawn', 'printer',
+        'projectile_(weapon)', 'projector', 'propeller', 'prune', 'pudding',
+        'puffer_(fish)', 'puffin', 'pug-dog', 'pumpkin', 'puncher', 'puppet',
+        'puppy', 'quesadilla', 'quiche', 'quilt', 'rabbit', 'race_car',
+        'racket', 'radar', 'radiator', 'radio_receiver', 'radish', 'raft',
+        'rag_doll', 'raincoat', 'ram_(animal)', 'raspberry', 'rat',
+        'razorblade', 'reamer_(juicer)', 'rearview_mirror', 'receipt',
+        'recliner', 'record_player', 'red_cabbage', 'reflector',
+        'remote_control', 'rhinoceros', 'rib_(food)', 'rifle', 'ring',
+        'river_boat', 'road_map', 'robe', 'rocking_chair', 'roller_skate',
+        'Rollerblade', 'rolling_pin', 'root_beer',
+        'router_(computer_equipment)', 'rubber_band', 'runner_(carpet)',
+        'plastic_bag', 'saddle_(on_an_animal)', 'saddle_blanket', 'saddlebag',
+        'safety_pin', 'sail', 'salad', 'salad_plate', 'salami',
+        'salmon_(fish)', 'salmon_(food)', 'salsa', 'saltshaker',
+        'sandal_(type_of_shoe)', 'sandwich', 'satchel', 'saucepan', 'saucer',
+        'sausage', 'sawhorse', 'saxophone', 'scale_(measuring_instrument)',
+        'scarecrow', 'scarf', 'school_bus', 'scissors', 'scoreboard',
+        'scrambled_eggs', 'scraper', 'scratcher', 'screwdriver',
+        'scrubbing_brush', 'sculpture', 'seabird', 'seahorse', 'seaplane',
+        'seashell', 'seedling', 'serving_dish', 'sewing_machine', 'shaker',
+        'shampoo', 'shark', 'sharpener', 'Sharpie', 'shaver_(electric)',
+        'shaving_cream', 'shawl', 'shears', 'sheep', 'shepherd_dog',
+        'sherbert', 'shield', 'shirt', 'shoe', 'shopping_bag', 'shopping_cart',
+        'short_pants', 'shot_glass', 'shoulder_bag', 'shovel', 'shower_head',
+        'shower_curtain', 'shredder_(for_paper)', 'sieve', 'signboard', 'silo',
+        'sink', 'skateboard', 'skewer', 'ski', 'ski_boot', 'ski_parka',
+        'ski_pole', 'skirt', 'sled', 'sleeping_bag', 'sling_(bandage)',
+        'slipper_(footwear)', 'smoothie', 'snake', 'snowboard', 'snowman',
+        'snowmobile', 'soap', 'soccer_ball', 'sock', 'soda_fountain',
+        'carbonated_water', 'sofa', 'softball', 'solar_array', 'sombrero',
+        'soup', 'soup_bowl', 'soupspoon', 'sour_cream', 'soya_milk',
+        'space_shuttle', 'sparkler_(fireworks)', 'spatula', 'spear',
+        'spectacles', 'spice_rack', 'spider', 'sponge', 'spoon', 'sportswear',
+        'spotlight', 'squirrel', 'stapler_(stapling_machine)', 'starfish',
+        'statue_(sculpture)', 'steak_(food)', 'steak_knife',
+        'steamer_(kitchen_appliance)', 'steering_wheel', 'stencil',
+        'stepladder', 'step_stool', 'stereo_(sound_system)', 'stew', 'stirrer',
+        'stirrup', 'stockings_(leg_wear)', 'stool', 'stop_sign', 'brake_light',
+        'stove', 'strainer', 'strap', 'straw_(for_drinking)', 'strawberry',
+        'street_sign', 'streetlight', 'string_cheese', 'stylus', 'subwoofer',
+        'sugar_bowl', 'sugarcane_(plant)', 'suit_(clothing)', 'sunflower',
+        'sunglasses', 'sunhat', 'sunscreen', 'surfboard', 'sushi', 'mop',
+        'sweat_pants', 'sweatband', 'sweater', 'sweatshirt', 'sweet_potato',
+        'swimsuit', 'sword', 'syringe', 'Tabasco_sauce', 'table-tennis_table',
+        'table', 'table_lamp', 'tablecloth', 'tachometer', 'taco', 'tag',
+        'taillight', 'tambourine', 'army_tank', 'tank_(storage_vessel)',
+        'tank_top_(clothing)', 'tape_(sticky_cloth_or_paper)', 'tape_measure',
+        'tapestry', 'tarp', 'tartan', 'tassel', 'tea_bag', 'teacup',
+        'teakettle', 'teapot', 'teddy_bear', 'telephone', 'telephone_booth',
+        'telephone_pole', 'telephoto_lens', 'television_camera',
+        'television_set', 'tennis_ball', 'tennis_racket', 'tequila',
+        'thermometer', 'thermos_bottle', 'thermostat', 'thimble', 'thread',
+        'thumbtack', 'tiara', 'tiger', 'tights_(clothing)', 'timer', 'tinfoil',
+        'tinsel', 'tissue_paper', 'toast_(food)', 'toaster', 'toaster_oven',
+        'toilet', 'toilet_tissue', 'tomato', 'tongs', 'toolbox', 'toothbrush',
+        'toothpaste', 'toothpick', 'cover', 'tortilla', 'tow_truck', 'towel',
+        'towel_rack', 'toy', 'tractor_(farm_equipment)', 'traffic_light',
+        'dirt_bike', 'trailer_truck', 'train_(railroad_vehicle)', 'trampoline',
+        'tray', 'tree_house', 'trench_coat', 'triangle_(musical_instrument)',
+        'tricycle', 'tripod', 'trousers', 'truck', 'truffle_(chocolate)',
+        'trunk', 'vat', 'turban', 'turkey_(bird)', 'turkey_(food)', 'turnip',
+        'turtle', 'turtleneck_(clothing)', 'typewriter', 'umbrella',
+        'underwear', 'unicycle', 'urinal', 'urn', 'vacuum_cleaner', 'valve',
+        'vase', 'vending_machine', 'vent', 'videotape', 'vinegar', 'violin',
+        'vodka', 'volleyball', 'vulture', 'waffle', 'waffle_iron', 'wagon',
+        'wagon_wheel', 'walking_stick', 'wall_clock', 'wall_socket', 'wallet',
+        'walrus', 'wardrobe', 'wasabi', 'automatic_washer', 'watch',
+        'water_bottle', 'water_cooler', 'water_faucet', 'water_filter',
+        'water_heater', 'water_jug', 'water_gun', 'water_scooter', 'water_ski',
+        'water_tower', 'watering_can', 'watermelon', 'weathervane', 'webcam',
+        'wedding_cake', 'wedding_ring', 'wet_suit', 'wheel', 'wheelchair',
+        'whipped_cream', 'whiskey', 'whistle', 'wick', 'wig', 'wind_chime',
+        'windmill', 'window_box_(for_plants)', 'windshield_wiper', 'windsock',
+        'wine_bottle', 'wine_bucket', 'wineglass', 'wing_chair',
+        'blinder_(for_horses)', 'wok', 'wolf', 'wooden_spoon', 'wreath',
+        'wrench', 'wristband', 'wristlet', 'yacht', 'yak', 'yogurt',
+        'yoke_(animal_equipment)', 'zebra', 'zucchini')
+
+    PALETTE = None
+
+    def load_annotations(self, ann_file):
+        """Load annotation from lvis style annotation file.
+
+        Args:
+            ann_file (str): Path of annotation file.
+
+        Returns:
+            list[dict]: Annotation info from LVIS api.
+        """
+
+        try:
+            import lvis
+            if getattr(lvis, '__version__', '0') >= '10.5.3':
+                warnings.warn(
+                    'mmlvis is deprecated, please install official lvis-api by "pip install git+https://github.com/lvis-dataset/lvis-api.git"',  # noqa: E501
+                    UserWarning)
+            from lvis import LVIS
+        except ImportError:
+            raise ImportError(
+                'Package lvis is not installed. Please run "pip install git+https://github.com/lvis-dataset/lvis-api.git".'  # noqa: E501
+            )
+        self.coco = LVIS(ann_file)
+        self.cat_ids = self.coco.get_cat_ids()
+        self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
+        self.img_ids = self.coco.get_img_ids()
+        data_infos = []
+        for i in self.img_ids:
+            info = self.coco.load_imgs([i])[0]
+            if info['file_name'].startswith('COCO'):
+                # Convert form the COCO 2014 file naming convention of
+                # COCO_[train/val/test]2014_000000000000.jpg to the 2017
+                # naming convention of 000000000000.jpg
+                # (LVIS v1 will fix this naming issue)
+                info['filename'] = info['file_name'][-16:]
+            else:
+                info['filename'] = info['file_name']
+            data_infos.append(info)
+        return data_infos
+
+    def evaluate(self,
+                 results,
+                 metric='bbox',
+                 logger=None,
+                 jsonfile_prefix=None,
+                 classwise=False,
+                 proposal_nums=(100, 300, 1000),
+                 iou_thrs=np.arange(0.5, 0.96, 0.05)):
+        """Evaluation in LVIS protocol.
+
+        Args:
+            results (list[list | tuple]): Testing results of the dataset.
+            metric (str | list[str]): Metrics to be evaluated. Options are
+                'bbox', 'segm', 'proposal', 'proposal_fast'.
+            logger (logging.Logger | str | None): Logger used for printing
+                related information during evaluation. Default: None.
+            jsonfile_prefix (str | None):
+            classwise (bool): Whether to evaluating the AP for each class.
+            proposal_nums (Sequence[int]): Proposal number used for evaluating
+                recalls, such as recall@100, recall@1000.
+                Default: (100, 300, 1000).
+            iou_thrs (Sequence[float]): IoU threshold used for evaluating
+                recalls. If set to a list, the average recall of all IoUs will
+                also be computed. Default: 0.5.
+
+        Returns:
+            dict[str, float]: LVIS style metrics.
+        """
+
+        try:
+            import lvis
+            if getattr(lvis, '__version__', '0') >= '10.5.3':
+                warnings.warn(
+                    'mmlvis is deprecated, please install official lvis-api by "pip install git+https://github.com/lvis-dataset/lvis-api.git"',  # noqa: E501
+                    UserWarning)
+            from lvis import LVISEval, LVISResults
+        except ImportError:
+            raise ImportError(
+                'Package lvis is not installed. Please run "pip install git+https://github.com/lvis-dataset/lvis-api.git".'  # noqa: E501
+            )
+        assert isinstance(results, list), 'results must be a list'
+        assert len(results) == len(self), (
+            'The length of results is not equal to the dataset len: {} != {}'.
+            format(len(results), len(self)))
+
+        metrics = metric if isinstance(metric, list) else [metric]
+        allowed_metrics = ['bbox', 'segm', 'proposal', 'proposal_fast']
+        for metric in metrics:
+            if metric not in allowed_metrics:
+                raise KeyError('metric {} is not supported'.format(metric))
+
+        if jsonfile_prefix is None:
+            tmp_dir = tempfile.TemporaryDirectory()
+            jsonfile_prefix = osp.join(tmp_dir.name, 'results')
+        else:
+            tmp_dir = None
+        result_files = self.results2json(results, jsonfile_prefix)
+
+        eval_results = OrderedDict()
+        # get original api
+        lvis_gt = self.coco
+        for metric in metrics:
+            msg = 'Evaluating {}...'.format(metric)
+            if logger is None:
+                msg = '\n' + msg
+            print_log(msg, logger=logger)
+
+            if metric == 'proposal_fast':
+                ar = self.fast_eval_recall(
+                    results, proposal_nums, iou_thrs, logger='silent')
+                log_msg = []
+                for i, num in enumerate(proposal_nums):
+                    eval_results['AR@{}'.format(num)] = ar[i]
+                    log_msg.append('\nAR@{}\t{:.4f}'.format(num, ar[i]))
+                log_msg = ''.join(log_msg)
+                print_log(log_msg, logger=logger)
+                continue
+
+            if metric not in result_files:
+                raise KeyError('{} is not in results'.format(metric))
+            try:
+                lvis_dt = LVISResults(lvis_gt, result_files[metric])
+            except IndexError:
+                print_log(
+                    'The testing results of the whole dataset is empty.',
+                    logger=logger,
+                    level=logging.ERROR)
+                break
+
+            iou_type = 'bbox' if metric == 'proposal' else metric
+            lvis_eval = LVISEval(lvis_gt, lvis_dt, iou_type)
+            lvis_eval.params.imgIds = self.img_ids
+            if metric == 'proposal':
+                lvis_eval.params.useCats = 0
+                lvis_eval.params.maxDets = list(proposal_nums)
+                lvis_eval.evaluate()
+                lvis_eval.accumulate()
+                lvis_eval.summarize()
+                for k, v in lvis_eval.get_results().items():
+                    if k.startswith('AR'):
+                        val = float('{:.4f}'.format(float(v)))
+                        eval_results[k] = val
+            else:
+                lvis_eval.evaluate()
+                lvis_eval.accumulate()
+                lvis_eval.summarize()
+                lvis_results = lvis_eval.get_results()
+                if classwise:  # Compute per-category AP
+                    # Compute per-category AP
+                    # from https://github.com/facebookresearch/detectron2/
+                    precisions = lvis_eval.eval['precision']
+                    # precision: (iou, recall, cls, area range, max dets)
+                    assert len(self.cat_ids) == precisions.shape[2]
+
+                    results_per_category = []
+                    for idx, catId in enumerate(self.cat_ids):
+                        # area range index 0: all area ranges
+                        # max dets index -1: typically 100 per image
+                        # the dimensions of precisions are
+                        # [num_thrs, num_recalls, num_cats, num_area_rngs]
+                        nm = self.coco.load_cats([catId])[0]
+                        precision = precisions[:, :, idx, 0]
+                        precision = precision[precision > -1]
+                        if precision.size:
+                            ap = np.mean(precision)
+                        else:
+                            ap = float('nan')
+                        results_per_category.append(
+                            (f'{nm["name"]}', f'{float(ap):0.3f}'))
+
+                    num_columns = min(6, len(results_per_category) * 2)
+                    results_flatten = list(
+                        itertools.chain(*results_per_category))
+                    headers = ['category', 'AP'] * (num_columns // 2)
+                    results_2d = itertools.zip_longest(*[
+                        results_flatten[i::num_columns]
+                        for i in range(num_columns)
+                    ])
+                    table_data = [headers]
+                    table_data += [result for result in results_2d]
+                    table = AsciiTable(table_data)
+                    print_log('\n' + table.table, logger=logger)
+
+                for k, v in lvis_results.items():
+                    if k.startswith('AP'):
+                        key = '{}_{}'.format(metric, k)
+                        val = float('{:.4f}'.format(float(v)))
+                        eval_results[key] = val
+                ap_summary = ' '.join([
+                    '{}:{:.4f}'.format(k, float(v))
+                    for k, v in lvis_results.items() if k.startswith('AP')
+                ])
+                eval_results['{}_mAP_copypaste'.format(metric)] = ap_summary
+            lvis_eval.print_results()
+        if tmp_dir is not None:
+            tmp_dir.cleanup()
+        return eval_results
+
+
+LVISDataset = LVISV05Dataset
+DATASETS.register_module(name='LVISDataset', module=LVISDataset)
+
+
+@DATASETS.register_module()
+class LVISV1Dataset(LVISDataset):
+
+    CLASSES = (
+        'aerosol_can', 'air_conditioner', 'airplane', 'alarm_clock', 'alcohol',
+        'alligator', 'almond', 'ambulance', 'amplifier', 'anklet', 'antenna',
+        'apple', 'applesauce', 'apricot', 'apron', 'aquarium',
+        'arctic_(type_of_shoe)', 'armband', 'armchair', 'armoire', 'armor',
+        'artichoke', 'trash_can', 'ashtray', 'asparagus', 'atomizer',
+        'avocado', 'award', 'awning', 'ax', 'baboon', 'baby_buggy',
+        'basketball_backboard', 'backpack', 'handbag', 'suitcase', 'bagel',
+        'bagpipe', 'baguet', 'bait', 'ball', 'ballet_skirt', 'balloon',
+        'bamboo', 'banana', 'Band_Aid', 'bandage', 'bandanna', 'banjo',
+        'banner', 'barbell', 'barge', 'barrel', 'barrette', 'barrow',
+        'baseball_base', 'baseball', 'baseball_bat', 'baseball_cap',
+        'baseball_glove', 'basket', 'basketball', 'bass_horn', 'bat_(animal)',
+        'bath_mat', 'bath_towel', 'bathrobe', 'bathtub', 'batter_(food)',
+        'battery', 'beachball', 'bead', 'bean_curd', 'beanbag', 'beanie',
+        'bear', 'bed', 'bedpan', 'bedspread', 'cow', 'beef_(food)', 'beeper',
+        'beer_bottle', 'beer_can', 'beetle', 'bell', 'bell_pepper', 'belt',
+        'belt_buckle', 'bench', 'beret', 'bib', 'Bible', 'bicycle', 'visor',
+        'billboard', 'binder', 'binoculars', 'bird', 'birdfeeder', 'birdbath',
+        'birdcage', 'birdhouse', 'birthday_cake', 'birthday_card',
+        'pirate_flag', 'black_sheep', 'blackberry', 'blackboard', 'blanket',
+        'blazer', 'blender', 'blimp', 'blinker', 'blouse', 'blueberry',
+        'gameboard', 'boat', 'bob', 'bobbin', 'bobby_pin', 'boiled_egg',
+        'bolo_tie', 'deadbolt', 'bolt', 'bonnet', 'book', 'bookcase',
+        'booklet', 'bookmark', 'boom_microphone', 'boot', 'bottle',
+        'bottle_opener', 'bouquet', 'bow_(weapon)', 'bow_(decorative_ribbons)',
+        'bow-tie', 'bowl', 'pipe_bowl', 'bowler_hat', 'bowling_ball', 'box',
+        'boxing_glove', 'suspenders', 'bracelet', 'brass_plaque', 'brassiere',
+        'bread-bin', 'bread', 'breechcloth', 'bridal_gown', 'briefcase',
+        'broccoli', 'broach', 'broom', 'brownie', 'brussels_sprouts',
+        'bubble_gum', 'bucket', 'horse_buggy', 'bull', 'bulldog', 'bulldozer',
+        'bullet_train', 'bulletin_board', 'bulletproof_vest', 'bullhorn',
+        'bun', 'bunk_bed', 'buoy', 'burrito', 'bus_(vehicle)', 'business_card',
+        'butter', 'butterfly', 'button', 'cab_(taxi)', 'cabana', 'cabin_car',
+        'cabinet', 'locker', 'cake', 'calculator', 'calendar', 'calf',
+        'camcorder', 'camel', 'camera', 'camera_lens', 'camper_(vehicle)',
+        'can', 'can_opener', 'candle', 'candle_holder', 'candy_bar',
+        'candy_cane', 'walking_cane', 'canister', 'canoe', 'cantaloup',
+        'canteen', 'cap_(headwear)', 'bottle_cap', 'cape', 'cappuccino',
+        'car_(automobile)', 'railcar_(part_of_a_train)', 'elevator_car',
+        'car_battery', 'identity_card', 'card', 'cardigan', 'cargo_ship',
+        'carnation', 'horse_carriage', 'carrot', 'tote_bag', 'cart', 'carton',
+        'cash_register', 'casserole', 'cassette', 'cast', 'cat', 'cauliflower',
+        'cayenne_(spice)', 'CD_player', 'celery', 'cellular_telephone',
+        'chain_mail', 'chair', 'chaise_longue', 'chalice', 'chandelier',
+        'chap', 'checkbook', 'checkerboard', 'cherry', 'chessboard',
+        'chicken_(animal)', 'chickpea', 'chili_(vegetable)', 'chime',
+        'chinaware', 'crisp_(potato_chip)', 'poker_chip', 'chocolate_bar',
+        'chocolate_cake', 'chocolate_milk', 'chocolate_mousse', 'choker',
+        'chopping_board', 'chopstick', 'Christmas_tree', 'slide', 'cider',
+        'cigar_box', 'cigarette', 'cigarette_case', 'cistern', 'clarinet',
+        'clasp', 'cleansing_agent', 'cleat_(for_securing_rope)', 'clementine',
+        'clip', 'clipboard', 'clippers_(for_plants)', 'cloak', 'clock',
+        'clock_tower', 'clothes_hamper', 'clothespin', 'clutch_bag', 'coaster',
+        'coat', 'coat_hanger', 'coatrack', 'cock', 'cockroach',
+        'cocoa_(beverage)', 'coconut', 'coffee_maker', 'coffee_table',
+        'coffeepot', 'coil', 'coin', 'colander', 'coleslaw',
+        'coloring_material', 'combination_lock', 'pacifier', 'comic_book',
+        'compass', 'computer_keyboard', 'condiment', 'cone', 'control',
+        'convertible_(automobile)', 'sofa_bed', 'cooker', 'cookie',
+        'cooking_utensil', 'cooler_(for_food)', 'cork_(bottle_plug)',
+        'corkboard', 'corkscrew', 'edible_corn', 'cornbread', 'cornet',
+        'cornice', 'cornmeal', 'corset', 'costume', 'cougar', 'coverall',
+        'cowbell', 'cowboy_hat', 'crab_(animal)', 'crabmeat', 'cracker',
+        'crape', 'crate', 'crayon', 'cream_pitcher', 'crescent_roll', 'crib',
+        'crock_pot', 'crossbar', 'crouton', 'crow', 'crowbar', 'crown',
+        'crucifix', 'cruise_ship', 'police_cruiser', 'crumb', 'crutch',
+        'cub_(animal)', 'cube', 'cucumber', 'cufflink', 'cup', 'trophy_cup',
+        'cupboard', 'cupcake', 'hair_curler', 'curling_iron', 'curtain',
+        'cushion', 'cylinder', 'cymbal', 'dagger', 'dalmatian', 'dartboard',
+        'date_(fruit)', 'deck_chair', 'deer', 'dental_floss', 'desk',
+        'detergent', 'diaper', 'diary', 'die', 'dinghy', 'dining_table', 'tux',
+        'dish', 'dish_antenna', 'dishrag', 'dishtowel', 'dishwasher',
+        'dishwasher_detergent', 'dispenser', 'diving_board', 'Dixie_cup',
+        'dog', 'dog_collar', 'doll', 'dollar', 'dollhouse', 'dolphin',
+        'domestic_ass', 'doorknob', 'doormat', 'doughnut', 'dove', 'dragonfly',
+        'drawer', 'underdrawers', 'dress', 'dress_hat', 'dress_suit',
+        'dresser', 'drill', 'drone', 'dropper', 'drum_(musical_instrument)',
+        'drumstick', 'duck', 'duckling', 'duct_tape', 'duffel_bag', 'dumbbell',
+        'dumpster', 'dustpan', 'eagle', 'earphone', 'earplug', 'earring',
+        'easel', 'eclair', 'eel', 'egg', 'egg_roll', 'egg_yolk', 'eggbeater',
+        'eggplant', 'electric_chair', 'refrigerator', 'elephant', 'elk',
+        'envelope', 'eraser', 'escargot', 'eyepatch', 'falcon', 'fan',
+        'faucet', 'fedora', 'ferret', 'Ferris_wheel', 'ferry', 'fig_(fruit)',
+        'fighter_jet', 'figurine', 'file_cabinet', 'file_(tool)', 'fire_alarm',
+        'fire_engine', 'fire_extinguisher', 'fire_hose', 'fireplace',
+        'fireplug', 'first-aid_kit', 'fish', 'fish_(food)', 'fishbowl',
+        'fishing_rod', 'flag', 'flagpole', 'flamingo', 'flannel', 'flap',
+        'flash', 'flashlight', 'fleece', 'flip-flop_(sandal)',
+        'flipper_(footwear)', 'flower_arrangement', 'flute_glass', 'foal',
+        'folding_chair', 'food_processor', 'football_(American)',
+        'football_helmet', 'footstool', 'fork', 'forklift', 'freight_car',
+        'French_toast', 'freshener', 'frisbee', 'frog', 'fruit_juice',
+        'frying_pan', 'fudge', 'funnel', 'futon', 'gag', 'garbage',
+        'garbage_truck', 'garden_hose', 'gargle', 'gargoyle', 'garlic',
+        'gasmask', 'gazelle', 'gelatin', 'gemstone', 'generator',
+        'giant_panda', 'gift_wrap', 'ginger', 'giraffe', 'cincture',
+        'glass_(drink_container)', 'globe', 'glove', 'goat', 'goggles',
+        'goldfish', 'golf_club', 'golfcart', 'gondola_(boat)', 'goose',
+        'gorilla', 'gourd', 'grape', 'grater', 'gravestone', 'gravy_boat',
+        'green_bean', 'green_onion', 'griddle', 'grill', 'grits', 'grizzly',
+        'grocery_bag', 'guitar', 'gull', 'gun', 'hairbrush', 'hairnet',
+        'hairpin', 'halter_top', 'ham', 'hamburger', 'hammer', 'hammock',
+        'hamper', 'hamster', 'hair_dryer', 'hand_glass', 'hand_towel',
+        'handcart', 'handcuff', 'handkerchief', 'handle', 'handsaw',
+        'hardback_book', 'harmonium', 'hat', 'hatbox', 'veil', 'headband',
+        'headboard', 'headlight', 'headscarf', 'headset',
+        'headstall_(for_horses)', 'heart', 'heater', 'helicopter', 'helmet',
+        'heron', 'highchair', 'hinge', 'hippopotamus', 'hockey_stick', 'hog',
+        'home_plate_(baseball)', 'honey', 'fume_hood', 'hook', 'hookah',
+        'hornet', 'horse', 'hose', 'hot-air_balloon', 'hotplate', 'hot_sauce',
+        'hourglass', 'houseboat', 'hummingbird', 'hummus', 'polar_bear',
+        'icecream', 'popsicle', 'ice_maker', 'ice_pack', 'ice_skate',
+        'igniter', 'inhaler', 'iPod', 'iron_(for_clothing)', 'ironing_board',
+        'jacket', 'jam', 'jar', 'jean', 'jeep', 'jelly_bean', 'jersey',
+        'jet_plane', 'jewel', 'jewelry', 'joystick', 'jumpsuit', 'kayak',
+        'keg', 'kennel', 'kettle', 'key', 'keycard', 'kilt', 'kimono',
+        'kitchen_sink', 'kitchen_table', 'kite', 'kitten', 'kiwi_fruit',
+        'knee_pad', 'knife', 'knitting_needle', 'knob', 'knocker_(on_a_door)',
+        'koala', 'lab_coat', 'ladder', 'ladle', 'ladybug', 'lamb_(animal)',
+        'lamb-chop', 'lamp', 'lamppost', 'lampshade', 'lantern', 'lanyard',
+        'laptop_computer', 'lasagna', 'latch', 'lawn_mower', 'leather',
+        'legging_(clothing)', 'Lego', 'legume', 'lemon', 'lemonade', 'lettuce',
+        'license_plate', 'life_buoy', 'life_jacket', 'lightbulb',
+        'lightning_rod', 'lime', 'limousine', 'lion', 'lip_balm', 'liquor',
+        'lizard', 'log', 'lollipop', 'speaker_(stereo_equipment)', 'loveseat',
+        'machine_gun', 'magazine', 'magnet', 'mail_slot', 'mailbox_(at_home)',
+        'mallard', 'mallet', 'mammoth', 'manatee', 'mandarin_orange', 'manger',
+        'manhole', 'map', 'marker', 'martini', 'mascot', 'mashed_potato',
+        'masher', 'mask', 'mast', 'mat_(gym_equipment)', 'matchbox',
+        'mattress', 'measuring_cup', 'measuring_stick', 'meatball', 'medicine',
+        'melon', 'microphone', 'microscope', 'microwave_oven', 'milestone',
+        'milk', 'milk_can', 'milkshake', 'minivan', 'mint_candy', 'mirror',
+        'mitten', 'mixer_(kitchen_tool)', 'money',
+        'monitor_(computer_equipment) computer_monitor', 'monkey', 'motor',
+        'motor_scooter', 'motor_vehicle', 'motorcycle', 'mound_(baseball)',
+        'mouse_(computer_equipment)', 'mousepad', 'muffin', 'mug', 'mushroom',
+        'music_stool', 'musical_instrument', 'nailfile', 'napkin',
+        'neckerchief', 'necklace', 'necktie', 'needle', 'nest', 'newspaper',
+        'newsstand', 'nightshirt', 'nosebag_(for_animals)',
+        'noseband_(for_animals)', 'notebook', 'notepad', 'nut', 'nutcracker',
+        'oar', 'octopus_(food)', 'octopus_(animal)', 'oil_lamp', 'olive_oil',
+        'omelet', 'onion', 'orange_(fruit)', 'orange_juice', 'ostrich',
+        'ottoman', 'oven', 'overalls_(clothing)', 'owl', 'packet', 'inkpad',
+        'pad', 'paddle', 'padlock', 'paintbrush', 'painting', 'pajamas',
+        'palette', 'pan_(for_cooking)', 'pan_(metal_container)', 'pancake',
+        'pantyhose', 'papaya', 'paper_plate', 'paper_towel', 'paperback_book',
+        'paperweight', 'parachute', 'parakeet', 'parasail_(sports)', 'parasol',
+        'parchment', 'parka', 'parking_meter', 'parrot',
+        'passenger_car_(part_of_a_train)', 'passenger_ship', 'passport',
+        'pastry', 'patty_(food)', 'pea_(food)', 'peach', 'peanut_butter',
+        'pear', 'peeler_(tool_for_fruit_and_vegetables)', 'wooden_leg',
+        'pegboard', 'pelican', 'pen', 'pencil', 'pencil_box',
+        'pencil_sharpener', 'pendulum', 'penguin', 'pennant', 'penny_(coin)',
+        'pepper', 'pepper_mill', 'perfume', 'persimmon', 'person', 'pet',
+        'pew_(church_bench)', 'phonebook', 'phonograph_record', 'piano',
+        'pickle', 'pickup_truck', 'pie', 'pigeon', 'piggy_bank', 'pillow',
+        'pin_(non_jewelry)', 'pineapple', 'pinecone', 'ping-pong_ball',
+        'pinwheel', 'tobacco_pipe', 'pipe', 'pistol', 'pita_(bread)',
+        'pitcher_(vessel_for_liquid)', 'pitchfork', 'pizza', 'place_mat',
+        'plate', 'platter', 'playpen', 'pliers', 'plow_(farm_equipment)',
+        'plume', 'pocket_watch', 'pocketknife', 'poker_(fire_stirring_tool)',
+        'pole', 'polo_shirt', 'poncho', 'pony', 'pool_table', 'pop_(soda)',
+        'postbox_(public)', 'postcard', 'poster', 'pot', 'flowerpot', 'potato',
+        'potholder', 'pottery', 'pouch', 'power_shovel', 'prawn', 'pretzel',
+        'printer', 'projectile_(weapon)', 'projector', 'propeller', 'prune',
+        'pudding', 'puffer_(fish)', 'puffin', 'pug-dog', 'pumpkin', 'puncher',
+        'puppet', 'puppy', 'quesadilla', 'quiche', 'quilt', 'rabbit',
+        'race_car', 'racket', 'radar', 'radiator', 'radio_receiver', 'radish',
+        'raft', 'rag_doll', 'raincoat', 'ram_(animal)', 'raspberry', 'rat',
+        'razorblade', 'reamer_(juicer)', 'rearview_mirror', 'receipt',
+        'recliner', 'record_player', 'reflector', 'remote_control',
+        'rhinoceros', 'rib_(food)', 'rifle', 'ring', 'river_boat', 'road_map',
+        'robe', 'rocking_chair', 'rodent', 'roller_skate', 'Rollerblade',
+        'rolling_pin', 'root_beer', 'router_(computer_equipment)',
+        'rubber_band', 'runner_(carpet)', 'plastic_bag',
+        'saddle_(on_an_animal)', 'saddle_blanket', 'saddlebag', 'safety_pin',
+        'sail', 'salad', 'salad_plate', 'salami', 'salmon_(fish)',
+        'salmon_(food)', 'salsa', 'saltshaker', 'sandal_(type_of_shoe)',
+        'sandwich', 'satchel', 'saucepan', 'saucer', 'sausage', 'sawhorse',
+        'saxophone', 'scale_(measuring_instrument)', 'scarecrow', 'scarf',
+        'school_bus', 'scissors', 'scoreboard', 'scraper', 'screwdriver',
+        'scrubbing_brush', 'sculpture', 'seabird', 'seahorse', 'seaplane',
+        'seashell', 'sewing_machine', 'shaker', 'shampoo', 'shark',
+        'sharpener', 'Sharpie', 'shaver_(electric)', 'shaving_cream', 'shawl',
+        'shears', 'sheep', 'shepherd_dog', 'sherbert', 'shield', 'shirt',
+        'shoe', 'shopping_bag', 'shopping_cart', 'short_pants', 'shot_glass',
+        'shoulder_bag', 'shovel', 'shower_head', 'shower_cap',
+        'shower_curtain', 'shredder_(for_paper)', 'signboard', 'silo', 'sink',
+        'skateboard', 'skewer', 'ski', 'ski_boot', 'ski_parka', 'ski_pole',
+        'skirt', 'skullcap', 'sled', 'sleeping_bag', 'sling_(bandage)',
+        'slipper_(footwear)', 'smoothie', 'snake', 'snowboard', 'snowman',
+        'snowmobile', 'soap', 'soccer_ball', 'sock', 'sofa', 'softball',
+        'solar_array', 'sombrero', 'soup', 'soup_bowl', 'soupspoon',
+        'sour_cream', 'soya_milk', 'space_shuttle', 'sparkler_(fireworks)',
+        'spatula', 'spear', 'spectacles', 'spice_rack', 'spider', 'crawfish',
+        'sponge', 'spoon', 'sportswear', 'spotlight', 'squid_(food)',
+        'squirrel', 'stagecoach', 'stapler_(stapling_machine)', 'starfish',
+        'statue_(sculpture)', 'steak_(food)', 'steak_knife', 'steering_wheel',
+        'stepladder', 'step_stool', 'stereo_(sound_system)', 'stew', 'stirrer',
+        'stirrup', 'stool', 'stop_sign', 'brake_light', 'stove', 'strainer',
+        'strap', 'straw_(for_drinking)', 'strawberry', 'street_sign',
+        'streetlight', 'string_cheese', 'stylus', 'subwoofer', 'sugar_bowl',
+        'sugarcane_(plant)', 'suit_(clothing)', 'sunflower', 'sunglasses',
+        'sunhat', 'surfboard', 'sushi', 'mop', 'sweat_pants', 'sweatband',
+        'sweater', 'sweatshirt', 'sweet_potato', 'swimsuit', 'sword',
+        'syringe', 'Tabasco_sauce', 'table-tennis_table', 'table',
+        'table_lamp', 'tablecloth', 'tachometer', 'taco', 'tag', 'taillight',
+        'tambourine', 'army_tank', 'tank_(storage_vessel)',
+        'tank_top_(clothing)', 'tape_(sticky_cloth_or_paper)', 'tape_measure',
+        'tapestry', 'tarp', 'tartan', 'tassel', 'tea_bag', 'teacup',
+        'teakettle', 'teapot', 'teddy_bear', 'telephone', 'telephone_booth',
+        'telephone_pole', 'telephoto_lens', 'television_camera',
+        'television_set', 'tennis_ball', 'tennis_racket', 'tequila',
+        'thermometer', 'thermos_bottle', 'thermostat', 'thimble', 'thread',
+        'thumbtack', 'tiara', 'tiger', 'tights_(clothing)', 'timer', 'tinfoil',
+        'tinsel', 'tissue_paper', 'toast_(food)', 'toaster', 'toaster_oven',
+        'toilet', 'toilet_tissue', 'tomato', 'tongs', 'toolbox', 'toothbrush',
+        'toothpaste', 'toothpick', 'cover', 'tortilla', 'tow_truck', 'towel',
+        'towel_rack', 'toy', 'tractor_(farm_equipment)', 'traffic_light',
+        'dirt_bike', 'trailer_truck', 'train_(railroad_vehicle)', 'trampoline',
+        'tray', 'trench_coat', 'triangle_(musical_instrument)', 'tricycle',
+        'tripod', 'trousers', 'truck', 'truffle_(chocolate)', 'trunk', 'vat',
+        'turban', 'turkey_(food)', 'turnip', 'turtle', 'turtleneck_(clothing)',
+        'typewriter', 'umbrella', 'underwear', 'unicycle', 'urinal', 'urn',
+        'vacuum_cleaner', 'vase', 'vending_machine', 'vent', 'vest',
+        'videotape', 'vinegar', 'violin', 'vodka', 'volleyball', 'vulture',
+        'waffle', 'waffle_iron', 'wagon', 'wagon_wheel', 'walking_stick',
+        'wall_clock', 'wall_socket', 'wallet', 'walrus', 'wardrobe',
+        'washbasin', 'automatic_washer', 'watch', 'water_bottle',
+        'water_cooler', 'water_faucet', 'water_heater', 'water_jug',
+        'water_gun', 'water_scooter', 'water_ski', 'water_tower',
+        'watering_can', 'watermelon', 'weathervane', 'webcam', 'wedding_cake',
+        'wedding_ring', 'wet_suit', 'wheel', 'wheelchair', 'whipped_cream',
+        'whistle', 'wig', 'wind_chime', 'windmill', 'window_box_(for_plants)',
+        'windshield_wiper', 'windsock', 'wine_bottle', 'wine_bucket',
+        'wineglass', 'blinder_(for_horses)', 'wok', 'wolf', 'wooden_spoon',
+        'wreath', 'wrench', 'wristband', 'wristlet', 'yacht', 'yogurt',
+        'yoke_(animal_equipment)', 'zebra', 'zucchini')
+
+    def load_annotations(self, ann_file):
+        try:
+            import lvis
+            if getattr(lvis, '__version__', '0') >= '10.5.3':
+                warnings.warn(
+                    'mmlvis is deprecated, please install official lvis-api by "pip install git+https://github.com/lvis-dataset/lvis-api.git"',  # noqa: E501
+                    UserWarning)
+            from lvis import LVIS
+        except ImportError:
+            raise ImportError(
+                'Package lvis is not installed. Please run "pip install git+https://github.com/lvis-dataset/lvis-api.git".'  # noqa: E501
+            )
+        self.coco = LVIS(ann_file)
+        self.cat_ids = self.coco.get_cat_ids()
+        self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
+        self.img_ids = self.coco.get_img_ids()
+        data_infos = []
+        for i in self.img_ids:
+            info = self.coco.load_imgs([i])[0]
+            # coco_url is used in LVISv1 instead of file_name
+            # e.g. http://images.cocodataset.org/train2017/000000391895.jpg
+            # train/val split in specified in url
+            info['filename'] = info['coco_url'].replace(
+                'http://images.cocodataset.org/', '')
+            data_infos.append(info)
+        return data_infos
diff --git a/mmdet/datasets/objects365.py b/mmdet/datasets/objects365.py
new file mode 100644
index 0000000..930f470
--- /dev/null
+++ b/mmdet/datasets/objects365.py
@@ -0,0 +1,232 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+
+from .api_wrappers import COCO
+from .builder import DATASETS
+from .coco import CocoDataset
+
+# images exist in annotations but not in image folder.
+objv2_ignore_list = [
+    osp.join('patch16', 'objects365_v2_00908726.jpg'),
+    osp.join('patch6', 'objects365_v1_00320532.jpg'),
+    osp.join('patch6', 'objects365_v1_00320534.jpg'),
+]
+
+
+@DATASETS.register_module()
+class Objects365V1Dataset(CocoDataset):
+    """Objects365 v1 dataset for detection."""
+    CLASSES = (
+        'person', 'sneakers', 'chair', 'hat', 'lamp', 'bottle',
+        'cabinet/shelf', 'cup', 'car', 'glasses', 'picture/frame', 'desk',
+        'handbag', 'street lights', 'book', 'plate', 'helmet', 'leather shoes',
+        'pillow', 'glove', 'potted plant', 'bracelet', 'flower', 'tv',
+        'storage box', 'vase', 'bench', 'wine glass', 'boots', 'bowl',
+        'dining table', 'umbrella', 'boat', 'flag', 'speaker', 'trash bin/can',
+        'stool', 'backpack', 'couch', 'belt', 'carpet', 'basket',
+        'towel/napkin', 'slippers', 'barrel/bucket', 'coffee table', 'suv',
+        'toy', 'tie', 'bed', 'traffic light', 'pen/pencil', 'microphone',
+        'sandals', 'canned', 'necklace', 'mirror', 'faucet', 'bicycle',
+        'bread', 'high heels', 'ring', 'van', 'watch', 'sink', 'horse', 'fish',
+        'apple', 'camera', 'candle', 'teddy bear', 'cake', 'motorcycle',
+        'wild bird', 'laptop', 'knife', 'traffic sign', 'cell phone', 'paddle',
+        'truck', 'cow', 'power outlet', 'clock', 'drum', 'fork', 'bus',
+        'hanger', 'nightstand', 'pot/pan', 'sheep', 'guitar', 'traffic cone',
+        'tea pot', 'keyboard', 'tripod', 'hockey', 'fan', 'dog', 'spoon',
+        'blackboard/whiteboard', 'balloon', 'air conditioner', 'cymbal',
+        'mouse', 'telephone', 'pickup truck', 'orange', 'banana', 'airplane',
+        'luggage', 'skis', 'soccer', 'trolley', 'oven', 'remote',
+        'baseball glove', 'paper towel', 'refrigerator', 'train', 'tomato',
+        'machinery vehicle', 'tent', 'shampoo/shower gel', 'head phone',
+        'lantern', 'donut', 'cleaning products', 'sailboat', 'tangerine',
+        'pizza', 'kite', 'computer box', 'elephant', 'toiletries', 'gas stove',
+        'broccoli', 'toilet', 'stroller', 'shovel', 'baseball bat',
+        'microwave', 'skateboard', 'surfboard', 'surveillance camera', 'gun',
+        'life saver', 'cat', 'lemon', 'liquid soap', 'zebra', 'duck',
+        'sports car', 'giraffe', 'pumpkin', 'piano', 'stop sign', 'radiator',
+        'converter', 'tissue ', 'carrot', 'washing machine', 'vent', 'cookies',
+        'cutting/chopping board', 'tennis racket', 'candy',
+        'skating and skiing shoes', 'scissors', 'folder', 'baseball',
+        'strawberry', 'bow tie', 'pigeon', 'pepper', 'coffee machine',
+        'bathtub', 'snowboard', 'suitcase', 'grapes', 'ladder', 'pear',
+        'american football', 'basketball', 'potato', 'paint brush', 'printer',
+        'billiards', 'fire hydrant', 'goose', 'projector', 'sausage',
+        'fire extinguisher', 'extension cord', 'facial mask', 'tennis ball',
+        'chopsticks', 'electronic stove and gas stove', 'pie', 'frisbee',
+        'kettle', 'hamburger', 'golf club', 'cucumber', 'clutch', 'blender',
+        'tong', 'slide', 'hot dog', 'toothbrush', 'facial cleanser', 'mango',
+        'deer', 'egg', 'violin', 'marker', 'ship', 'chicken', 'onion',
+        'ice cream', 'tape', 'wheelchair', 'plum', 'bar soap', 'scale',
+        'watermelon', 'cabbage', 'router/modem', 'golf ball', 'pine apple',
+        'crane', 'fire truck', 'peach', 'cello', 'notepaper', 'tricycle',
+        'toaster', 'helicopter', 'green beans', 'brush', 'carriage', 'cigar',
+        'earphone', 'penguin', 'hurdle', 'swing', 'radio', 'CD',
+        'parking meter', 'swan', 'garlic', 'french fries', 'horn', 'avocado',
+        'saxophone', 'trumpet', 'sandwich', 'cue', 'kiwi fruit', 'bear',
+        'fishing rod', 'cherry', 'tablet', 'green vegetables', 'nuts', 'corn',
+        'key', 'screwdriver', 'globe', 'broom', 'pliers', 'volleyball',
+        'hammer', 'eggplant', 'trophy', 'dates', 'board eraser', 'rice',
+        'tape measure/ruler', 'dumbbell', 'hamimelon', 'stapler', 'camel',
+        'lettuce', 'goldfish', 'meat balls', 'medal', 'toothpaste', 'antelope',
+        'shrimp', 'rickshaw', 'trombone', 'pomegranate', 'coconut',
+        'jellyfish', 'mushroom', 'calculator', 'treadmill', 'butterfly',
+        'egg tart', 'cheese', 'pig', 'pomelo', 'race car', 'rice cooker',
+        'tuba', 'crosswalk sign', 'papaya', 'hair drier', 'green onion',
+        'chips', 'dolphin', 'sushi', 'urinal', 'donkey', 'electric drill',
+        'spring rolls', 'tortoise/turtle', 'parrot', 'flute', 'measuring cup',
+        'shark', 'steak', 'poker card', 'binoculars', 'llama', 'radish',
+        'noodles', 'yak', 'mop', 'crab', 'microscope', 'barbell', 'bread/bun',
+        'baozi', 'lion', 'red cabbage', 'polar bear', 'lighter', 'seal',
+        'mangosteen', 'comb', 'eraser', 'pitaya', 'scallop', 'pencil case',
+        'saw', 'table tennis paddle', 'okra', 'starfish', 'eagle', 'monkey',
+        'durian', 'game board', 'rabbit', 'french horn', 'ambulance',
+        'asparagus', 'hoverboard', 'pasta', 'target', 'hotair balloon',
+        'chainsaw', 'lobster', 'iron', 'flashlight')
+
+    PALETTE = None
+
+    def load_annotations(self, ann_file):
+        """Load annotation from COCO style annotation file.
+
+        Args:
+            ann_file (str): Path of annotation file.
+
+        Returns:
+            list[dict]: Annotation info from COCO api.
+        """
+
+        self.coco = COCO(ann_file)
+        # 'categories' list in objects365_train.json and objects365_val.
+        # json is inconsistent, need sorted list(or dict) before get cat_ids.
+        cats = self.coco.cats
+        sorted_cats = {i: cats[i] for i in sorted(cats)}
+        self.coco.cats = sorted_cats
+        categories = self.coco.dataset['categories']
+        sorted_categories = sorted(categories, key=lambda i: i['id'])
+        self.coco.dataset['categories'] = sorted_categories
+        # The order of returned `cat_ids` will not
+        # change with the order of the CLASSES
+        self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
+
+        self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
+        self.img_ids = self.coco.get_img_ids()
+        data_infos = []
+        total_ann_ids = []
+        for i in self.img_ids:
+            info = self.coco.load_imgs([i])[0]
+            info['filename'] = info['file_name']
+            data_infos.append(info)
+            ann_ids = self.coco.get_ann_ids(img_ids=[i])
+            total_ann_ids.extend(ann_ids)
+        assert len(set(total_ann_ids)) == len(
+            total_ann_ids), f"Annotation ids in '{ann_file}' are not unique!"
+        return data_infos
+
+
+@DATASETS.register_module()
+class Objects365V2Dataset(CocoDataset):
+    """Objects365 v2 dataset for detection."""
+
+    CLASSES = (
+        'Person', 'Sneakers', 'Chair', 'Other Shoes', 'Hat', 'Car', 'Lamp',
+        'Glasses', 'Bottle', 'Desk', 'Cup', 'Street Lights', 'Cabinet/shelf',
+        'Handbag/Satchel', 'Bracelet', 'Plate', 'Picture/Frame', 'Helmet',
+        'Book', 'Gloves', 'Storage box', 'Boat', 'Leather Shoes', 'Flower',
+        'Bench', 'Potted Plant', 'Bowl/Basin', 'Flag', 'Pillow', 'Boots',
+        'Vase', 'Microphone', 'Necklace', 'Ring', 'SUV', 'Wine Glass', 'Belt',
+        'Moniter/TV', 'Backpack', 'Umbrella', 'Traffic Light', 'Speaker',
+        'Watch', 'Tie', 'Trash bin Can', 'Slippers', 'Bicycle', 'Stool',
+        'Barrel/bucket', 'Van', 'Couch', 'Sandals', 'Bakset', 'Drum',
+        'Pen/Pencil', 'Bus', 'Wild Bird', 'High Heels', 'Motorcycle', 'Guitar',
+        'Carpet', 'Cell Phone', 'Bread', 'Camera', 'Canned', 'Truck',
+        'Traffic cone', 'Cymbal', 'Lifesaver', 'Towel', 'Stuffed Toy',
+        'Candle', 'Sailboat', 'Laptop', 'Awning', 'Bed', 'Faucet', 'Tent',
+        'Horse', 'Mirror', 'Power outlet', 'Sink', 'Apple', 'Air Conditioner',
+        'Knife', 'Hockey Stick', 'Paddle', 'Pickup Truck', 'Fork',
+        'Traffic Sign', 'Ballon', 'Tripod', 'Dog', 'Spoon',
+        'Clock', 'Pot', 'Cow', 'Cake', 'Dinning Table', 'Sheep', 'Hanger',
+        'Blackboard/Whiteboard', 'Napkin', 'Other Fish', 'Orange/Tangerine',
+        'Toiletry', 'Keyboard', 'Tomato', 'Lantern',
+        'Machinery Vehicle', 'Fan', 'Green Vegetables', 'Banana',
+        'Baseball Glove', 'Airplane', 'Mouse', 'Train', 'Pumpkin', 'Soccer',
+        'Skiboard', 'Luggage', 'Nightstand', 'Tea pot', 'Telephone', 'Trolley',
+        'Head Phone', 'Sports Car', 'Stop Sign', 'Dessert', 'Scooter',
+        'Stroller', 'Crane', 'Remote', 'Refrigerator', 'Oven', 'Lemon', 'Duck',
+        'Baseball Bat', 'Surveillance Camera', 'Cat', 'Jug', 'Broccoli',
+        'Piano', 'Pizza', 'Elephant', 'Skateboard', 'Surfboard', 'Gun',
+        'Skating and Skiing shoes', 'Gas stove', 'Donut', 'Bow Tie', 'Carrot',
+        'Toilet', 'Kite', 'Strawberry', 'Other Balls', 'Shovel', 'Pepper',
+        'Computer Box', 'Toilet Paper', 'Cleaning Products', 'Chopsticks',
+        'Microwave', 'Pigeon', 'Baseball', 'Cutting/chopping Board',
+        'Coffee Table', 'Side Table', 'Scissors', 'Marker', 'Pie', 'Ladder',
+        'Snowboard', 'Cookies', 'Radiator', 'Fire Hydrant', 'Basketball',
+        'Zebra', 'Grape', 'Giraffe', 'Potato', 'Sausage', 'Tricycle', 'Violin',
+        'Egg', 'Fire Extinguisher', 'Candy', 'Fire Truck', 'Billards',
+        'Converter', 'Bathtub', 'Wheelchair', 'Golf Club', 'Briefcase',
+        'Cucumber', 'Cigar/Cigarette ', 'Paint Brush', 'Pear', 'Heavy Truck',
+        'Hamburger', 'Extractor', 'Extention Cord', 'Tong', 'Tennis Racket',
+        'Folder', 'American Football', 'earphone', 'Mask', 'Kettle', 'Tennis',
+        'Ship', 'Swing', 'Coffee Machine', 'Slide', 'Carriage', 'Onion',
+        'Green beans', 'Projector', 'Frisbee',
+        'Washing Machine/Drying Machine', 'Chicken', 'Printer', 'Watermelon',
+        'Saxophone', 'Tissue', 'Toothbrush', 'Ice cream', 'Hotair ballon',
+        'Cello', 'French Fries', 'Scale', 'Trophy', 'Cabbage', 'Hot dog',
+        'Blender', 'Peach', 'Rice', 'Wallet/Purse', 'Volleyball', 'Deer',
+        'Goose', 'Tape', 'Tablet', 'Cosmetics', 'Trumpet', 'Pineapple',
+        'Golf Ball', 'Ambulance', 'Parking meter', 'Mango', 'Key', 'Hurdle',
+        'Fishing Rod', 'Medal', 'Flute', 'Brush', 'Penguin', 'Megaphone',
+        'Corn', 'Lettuce', 'Garlic', 'Swan', 'Helicopter', 'Green Onion',
+        'Sandwich', 'Nuts', 'Speed Limit Sign', 'Induction Cooker', 'Broom',
+        'Trombone', 'Plum', 'Rickshaw', 'Goldfish', 'Kiwi fruit',
+        'Router/modem', 'Poker Card', 'Toaster', 'Shrimp', 'Sushi', 'Cheese',
+        'Notepaper', 'Cherry', 'Pliers', 'CD', 'Pasta', 'Hammer', 'Cue',
+        'Avocado', 'Hamimelon', 'Flask', 'Mushroon', 'Screwdriver', 'Soap',
+        'Recorder', 'Bear', 'Eggplant', 'Board Eraser', 'Coconut',
+        'Tape Measur/ Ruler', 'Pig', 'Showerhead', 'Globe', 'Chips', 'Steak',
+        'Crosswalk Sign', 'Stapler', 'Campel', 'Formula 1 ', 'Pomegranate',
+        'Dishwasher', 'Crab', 'Hoverboard', 'Meat ball', 'Rice Cooker', 'Tuba',
+        'Calculator', 'Papaya', 'Antelope', 'Parrot', 'Seal', 'Buttefly',
+        'Dumbbell', 'Donkey', 'Lion', 'Urinal', 'Dolphin', 'Electric Drill',
+        'Hair Dryer', 'Egg tart', 'Jellyfish', 'Treadmill', 'Lighter',
+        'Grapefruit', 'Game board', 'Mop', 'Radish', 'Baozi', 'Target',
+        'French', 'Spring Rolls', 'Monkey', 'Rabbit', 'Pencil Case', 'Yak',
+        'Red Cabbage', 'Binoculars', 'Asparagus', 'Barbell', 'Scallop',
+        'Noddles', 'Comb', 'Dumpling', 'Oyster', 'Table Teniis paddle',
+        'Cosmetics Brush/Eyeliner Pencil', 'Chainsaw', 'Eraser', 'Lobster',
+        'Durian', 'Okra', 'Lipstick', 'Cosmetics Mirror', 'Curling',
+        'Table Tennis ')
+
+    def load_annotations(self, ann_file):
+        """Load annotation from COCO style annotation file.
+
+        Args:
+            ann_file (str): Path of annotation file.
+
+        Returns:
+            list[dict]: Annotation info from COCO api.
+        """
+
+        self.coco = COCO(ann_file)
+        # The order of returned `cat_ids` will not
+        # change with the order of the CLASSES
+        self.cat_ids = self.coco.get_cat_ids(cat_names=self.CLASSES)
+
+        self.cat2label = {cat_id: i for i, cat_id in enumerate(self.cat_ids)}
+        self.img_ids = self.coco.get_img_ids()
+        data_infos = []
+        total_ann_ids = []
+        for i in self.img_ids:
+            info = self.coco.load_imgs([i])[0]
+            file_name = osp.join(
+                osp.split(osp.split(info['file_name'])[0])[-1],
+                osp.split(info['file_name'])[-1])
+            info['file_name'] = file_name
+            if info['file_name'] in objv2_ignore_list:
+                continue
+            info['filename'] = info['file_name']
+            data_infos.append(info)
+            ann_ids = self.coco.get_ann_ids(img_ids=[i])
+            total_ann_ids.extend(ann_ids)
+        assert len(set(total_ann_ids)) == len(
+            total_ann_ids), f"Annotation ids in '{ann_file}' are not unique!"
+        return data_infos
diff --git a/mmdet/datasets/openimages.py b/mmdet/datasets/openimages.py
new file mode 100644
index 0000000..1315349
--- /dev/null
+++ b/mmdet/datasets/openimages.py
@@ -0,0 +1,891 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import csv
+import json
+import os.path as osp
+import warnings
+from collections import OrderedDict, defaultdict
+
+import mmcv
+import numpy as np
+import torch.distributed as dist
+from mmcv.runner import get_dist_info
+from mmcv.utils import print_log
+
+from mmdet.core import eval_map
+from .builder import DATASETS
+from .custom import CustomDataset
+
+
+@DATASETS.register_module()
+class OpenImagesDataset(CustomDataset):
+    """Open Images dataset for detection.
+
+    Args:
+        ann_file (str): Annotation file path.
+        label_file (str): File path of the label description file that
+            maps the classes names in MID format to their short
+            descriptions.
+        image_level_ann_file (str): Image level annotation, which is used
+            in evaluation.
+        get_supercategory (bool): Whether to get parent class of the
+            current class. Default: True.
+        hierarchy_file (str): The file path of the class hierarchy.
+            Default: None.
+        get_metas (bool): Whether to get image metas in testing or
+            validation time. This should be `True` during evaluation.
+            Default: True. The OpenImages annotations do not have image
+            metas (width and height of the image), which will be used
+            during evaluation. We provide two ways to get image metas
+            in `OpenImagesDataset`:
+
+            - 1. `load from file`: Load image metas from pkl file, which
+              is suggested to use. We provided a script to get image metas:
+              `tools/misc/get_image_metas.py`, which need to run
+              this script before training/testing. Please refer to
+              `config/openimages/README.md` for more details.
+
+            - 2. `load from pipeline`, which will get image metas during
+              test time. However, this may reduce the inference speed,
+              especially when using distribution.
+
+        load_from_file (bool): Whether to get image metas from pkl file.
+        meta_file (str): File path to get image metas.
+        filter_labels (bool): Whether filter unannotated classes.
+            Default: True.
+        load_image_level_labels (bool): Whether load and consider image
+            level labels during evaluation. Default: True.
+        file_client_args (dict): Arguments to instantiate a FileClient.
+            See :class:`mmcv.fileio.FileClient` for details.
+            Defaults to ``dict(backend='disk')``.
+    """
+
+    def __init__(self,
+                 ann_file,
+                 label_file='',
+                 image_level_ann_file='',
+                 get_supercategory=True,
+                 hierarchy_file=None,
+                 get_metas=True,
+                 load_from_file=True,
+                 meta_file='',
+                 filter_labels=True,
+                 load_image_level_labels=True,
+                 file_client_args=dict(backend='disk'),
+                 **kwargs):
+        # may get error if use other file_client
+        self.file_client_args = file_client_args
+
+        self.cat2label = defaultdict(str)
+        self.index_dict = {}
+
+        # Although it will init file_client in `CustomDataset`,
+        # it needs to be init here.
+        file_client = mmcv.FileClient(**file_client_args)
+        # need get `index_dict` before load annotations
+        assert label_file.endswith('csv')
+        if hasattr(file_client, 'get_local_path'):
+            with file_client.get_local_path(label_file) as local_path:
+                class_names = self.get_classes_from_csv(local_path)
+        else:
+            class_names = self.get_classes_from_csv(label_file)
+        super(OpenImagesDataset, self).__init__(
+            ann_file=ann_file, file_client_args=file_client_args, **kwargs)
+        self.CLASSES = class_names
+        self.image_level_ann_file = image_level_ann_file
+        self.load_image_level_labels = load_image_level_labels
+        if get_supercategory is True:
+            assert hierarchy_file is not None
+            if self.__class__.__name__ == 'OpenImagesDataset':
+                assert hierarchy_file.endswith('json')
+            elif self.__class__.__name__ == 'OpenImagesChallengeDataset':
+                assert hierarchy_file.endswith('np')
+            else:
+                raise NotImplementedError
+            if hasattr(self.file_client, 'get_local_path'):
+                with self.file_client.get_local_path(
+                        hierarchy_file) as local_path:
+                    self.class_label_tree = self.get_relation_matrix(
+                        local_path)
+            else:
+                self.class_label_tree = self.get_relation_matrix(
+                    hierarchy_file)
+        self.get_supercategory = get_supercategory
+        self.get_metas = get_metas
+        self.load_from_file = load_from_file
+        self.meta_file = meta_file
+        if self.data_root is not None:
+            if not osp.isabs(self.meta_file):
+                self.meta_file = osp.join(self.data_root, self.meta_file)
+        self.filter_labels = filter_labels
+        self.rank, self.world_size = get_dist_info()
+        self.temp_img_metas = []
+        self.test_img_metas = []
+        self.test_img_shapes = []
+        self.load_from_pipeline = False if load_from_file else True
+
+    def get_classes_from_csv(self, label_file):
+        """Get classes name from file.
+
+        Args:
+            label_file (str): File path of the label description file that
+                maps the classes names in MID format to their short
+                descriptions.
+
+        Returns:
+            list[str]: Class name of OpenImages.
+        """
+
+        index_list = []
+        classes_names = []
+        with open(label_file, 'r') as f:
+            reader = csv.reader(f)
+            for line in reader:
+                self.cat2label[line[0]] = line[1]
+                classes_names.append(line[1])
+                index_list.append(line[0])
+        self.index_dict = {index: i for i, index in enumerate(index_list)}
+        return classes_names
+
+    def load_annotations(self, ann_file):
+        """Load annotation from annotation file.
+
+        Special described `self.data_infos` (defaultdict[list[dict]])
+        in this function: Annotations where item of the defaultdict
+        indicates an image, each of which has (n) dicts. Keys of dicts are:
+
+            - `bbox` (list): coordinates of the box, in normalized image
+              coordinates, of shape 4.
+            - `label` (int): the label id.
+            - `is_group_of` (bool):  Indicates that the box spans a group
+              of objects (e.g., a bed of flowers or a crowd of people).
+            - `is_occluded` (bool): Indicates that the object is occluded
+              by another object in the image.
+            - `is_truncated` (bool): Indicates that the object extends
+              beyond the boundary of the image.
+            - `is_depiction` (bool): Indicates that the object is a
+              depiction.
+            - `is_inside` (bool): Indicates a picture taken from the
+              inside of the object.
+
+        Args:
+            ann_file (str): CSV style annotation file path.
+
+        Returns:
+            list[dict]:  Data infos where each item of the list
+            indicates an image. Keys of annotations are:
+
+                - `img_id` (str): Image name.
+                - `filename` (str): Image name with suffix.
+        """
+        self.ann_infos = defaultdict(list)
+        data_infos = []
+        cp_filename = None
+        with open(ann_file, 'r') as f:
+            reader = csv.reader(f)
+            for i, line in enumerate(reader):
+                if i == 0:
+                    continue
+                img_id = line[0]
+                filename = f'{img_id}.jpg'
+                label_id = line[2]
+                assert label_id in self.index_dict
+                label = int(self.index_dict[label_id])
+                bbox = [
+                    float(line[4]),  # xmin
+                    float(line[6]),  # ymin
+                    float(line[5]),  # xmax
+                    float(line[7])  # ymax
+                ]
+                is_occluded = True if int(line[8]) == 1 else False
+                is_truncated = True if int(line[9]) == 1 else False
+                is_group_of = True if int(line[10]) == 1 else False
+                is_depiction = True if int(line[11]) == 1 else False
+                is_inside = True if int(line[12]) == 1 else False
+
+                self.ann_infos[img_id].append(
+                    dict(
+                        bbox=bbox,
+                        label=label,
+                        is_occluded=is_occluded,
+                        is_truncated=is_truncated,
+                        is_group_of=is_group_of,
+                        is_depiction=is_depiction,
+                        is_inside=is_inside))
+                if filename != cp_filename:
+                    data_infos.append(dict(img_id=img_id, filename=filename))
+                    cp_filename = filename
+        return data_infos
+
+    def get_ann_info(self, idx):
+        """Get OpenImages annotation by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            dict: Annotation info of specified index.
+        """
+        img_id = self.data_infos[idx]['img_id']
+        bboxes = []
+        labels = []
+        bboxes_ignore = []
+        labels_ignore = []
+        is_occludeds = []
+        is_truncateds = []
+        is_group_ofs = []
+        is_depictions = []
+        is_insides = []
+        for obj in self.ann_infos[img_id]:
+            label = int(obj['label'])
+            bbox = [
+                float(obj['bbox'][0]),
+                float(obj['bbox'][1]),
+                float(obj['bbox'][2]),
+                float(obj['bbox'][3])
+            ]
+            bboxes.append(bbox)
+            labels.append(label)
+
+            # Other parameters
+            is_occludeds.append(obj['is_occluded'])
+            is_truncateds.append(obj['is_truncated'])
+            is_group_ofs.append(obj['is_group_of'])
+            is_depictions.append(obj['is_depiction'])
+            is_insides.append(obj['is_inside'])
+        if not bboxes:
+            bboxes = np.zeros((0, 4))
+            labels = np.zeros((0, ))
+        else:
+            bboxes = np.array(bboxes)
+            labels = np.array(labels)
+        if not bboxes_ignore:
+            bboxes_ignore = np.zeros((0, 4))
+            labels_ignore = np.zeros((0, ))
+        else:
+            bboxes_ignore = np.array(bboxes_ignore)
+            labels_ignore = np.array(labels_ignore)
+
+        assert len(is_group_ofs) == len(labels) == len(bboxes)
+        gt_is_group_ofs = np.array(is_group_ofs, dtype=bool)
+
+        # These parameters is not used yet.
+        is_occludeds = np.array(is_occludeds, dtype=bool)
+        is_truncateds = np.array(is_truncateds, dtype=bool)
+        is_depictions = np.array(is_depictions, dtype=bool)
+        is_insides = np.array(is_insides, dtype=bool)
+
+        ann = dict(
+            bboxes=bboxes.astype(np.float32),
+            labels=labels.astype(np.int64),
+            bboxes_ignore=bboxes_ignore.astype(np.float32),
+            labels_ignore=labels_ignore.astype(np.int64),
+            gt_is_group_ofs=gt_is_group_ofs,
+            is_occludeds=is_occludeds,
+            is_truncateds=is_truncateds,
+            is_depictions=is_depictions,
+            is_insides=is_insides)
+
+        return ann
+
+    def get_meta_from_file(self, meta_file=''):
+        """Get image metas from pkl file."""
+        metas = mmcv.load(
+            meta_file,
+            file_format='pkl',
+            file_client_args=self.file_client_args)
+        assert len(metas) == len(self)
+        for i in range(len(metas)):
+            file_name = osp.split(metas[i]['filename'])[-1]
+            img_info = self.data_infos[i].get('img_info', None)
+            if img_info is not None:
+                assert file_name == osp.split(img_info['filename'])[-1]
+            else:
+                assert file_name == self.data_infos[i]['filename']
+            hw = metas[i]['ori_shape'][:2]
+            self.test_img_shapes.append(hw)
+
+    def get_meta_from_pipeline(self, results):
+        """Get image metas from pipeline."""
+        self.temp_img_metas.extend(results['img_metas'])
+        if dist.is_available() and self.world_size > 1:
+            from mmdet.apis.test import collect_results_cpu
+
+            self.test_img_metas = collect_results_cpu(self.temp_img_metas,
+                                                      len(self))
+        else:
+            self.test_img_metas = self.temp_img_metas
+
+    def get_img_shape(self, metas):
+        """Set images original shape into data_infos."""
+        assert len(metas) == len(self)
+        for i in range(len(metas)):
+            file_name = osp.split(metas[i].data['ori_filename'])[-1]
+            img_info = self.data_infos[i].get('img_info', None)
+            if img_info is not None:
+                assert file_name == osp.split(img_info['filename'])[-1]
+            else:
+                assert file_name == self.data_infos[i]['filename']
+            hw = metas[i].data['ori_shape'][:2]
+            self.test_img_shapes.append(hw)
+
+    def prepare_test_img(self, idx):
+        """Get testing data after pipeline."""
+        img_info = self.data_infos[idx]
+        results = dict(img_info=img_info)
+        if self.proposals is not None:
+            results['proposals'] = self.proposals[idx]
+        self.pre_pipeline(results)
+        results = self.pipeline(results)
+        if self.get_metas and self.load_from_pipeline:
+            self.get_meta_from_pipeline(results)
+        return results
+
+    def _filter_imgs(self, min_size=32):
+        """Filter images too small."""
+        if self.filter_empty_gt:
+            warnings.warn('OpenImageDatasets does not support '
+                          'filtering empty gt images.')
+        valid_inds = [i for i in range(len(self))]
+        return valid_inds
+
+    def _set_group_flag(self):
+        """Set flag according to image aspect ratio."""
+        self.flag = np.zeros(len(self), dtype=np.uint8)
+        # TODO: set flag without width and height
+
+    def get_relation_matrix(self, hierarchy_file):
+        """Get hierarchy for classes.
+
+        Args:
+            hierarchy_file (sty): File path to the hierarchy for classes.
+
+        Returns:
+            ndarray: The matrix of the corresponding relationship between
+            the parent class and the child class, of shape
+            (class_num, class_num).
+        """
+
+        if self.data_root is not None:
+            if not osp.isabs(hierarchy_file):
+                hierarchy_file = osp.join(self.data_root, hierarchy_file)
+        with open(hierarchy_file, 'r') as f:
+            hierarchy = json.load(f)
+        class_num = len(self.CLASSES)
+        class_label_tree = np.eye(class_num, class_num)
+        class_label_tree = self._convert_hierarchy_tree(
+            hierarchy, class_label_tree)
+        return class_label_tree
+
+    def _convert_hierarchy_tree(self,
+                                hierarchy_map,
+                                class_label_tree,
+                                parents=[],
+                                get_all_parents=True):
+        """Get matrix of the corresponding relationship between the parent
+        class and the child class.
+
+        Args:
+            hierarchy_map (dict): Including label name and corresponding
+                subcategory. Keys of dicts are:
+
+                - `LabeName` (str): Name of the label.
+                - `Subcategory` (dict | list): Corresponding subcategory(ies).
+            class_label_tree (ndarray): The matrix of the corresponding
+                relationship between the parent class and the child class,
+                of shape (class_num, class_num).
+            parents (list): Corresponding parent class.
+            get_all_parents (bool): Whether get all parent names.
+                Default: True
+
+        Returns:
+            ndarray: The matrix of the corresponding relationship between
+            the parent class and the child class, of shape
+            (class_num, class_num).
+        """
+
+        if 'Subcategory' in hierarchy_map:
+            for node in hierarchy_map['Subcategory']:
+                if 'LabelName' in node:
+                    children_name = node['LabelName']
+                    children_index = self.index_dict[children_name]
+                    children = [children_index]
+                else:
+                    continue
+                if len(parents) > 0:
+                    for parent_index in parents:
+                        if get_all_parents:
+                            children.append(parent_index)
+                        class_label_tree[children_index, parent_index] = 1
+
+                class_label_tree = self._convert_hierarchy_tree(
+                    node, class_label_tree, parents=children)
+
+        return class_label_tree
+
+    def add_supercategory_ann(self, annotations):
+        """Add parent classes of the corresponding class of the ground truth
+        bboxes."""
+        for i, ann in enumerate(annotations):
+            assert len(ann['labels']) == len(ann['bboxes']) == \
+                   len(ann['gt_is_group_ofs'])
+            gt_bboxes = []
+            gt_is_group_ofs = []
+            gt_labels = []
+            for j in range(len(ann['labels'])):
+                label = ann['labels'][j]
+                bbox = ann['bboxes'][j]
+                is_group = ann['gt_is_group_ofs'][j]
+                label = np.where(self.class_label_tree[label])[0]
+                if len(label) > 1:
+                    for k in range(len(label)):
+                        gt_bboxes.append(bbox)
+                        gt_is_group_ofs.append(is_group)
+                        gt_labels.append(label[k])
+                else:
+                    gt_bboxes.append(bbox)
+                    gt_is_group_ofs.append(is_group)
+                    gt_labels.append(label[0])
+            annotations[i] = dict(
+                bboxes=np.array(gt_bboxes).astype(np.float32),
+                labels=np.array(gt_labels).astype(np.int64),
+                bboxes_ignore=ann['bboxes_ignore'],
+                gt_is_group_ofs=np.array(gt_is_group_ofs).astype(bool))
+
+        return annotations
+
+    def process_results(self, det_results, annotations,
+                        image_level_annotations):
+        """Process results of the corresponding class of the detection bboxes.
+
+        Note: It will choose to do the following two processing according to
+        the parameters:
+
+        1. Whether to add parent classes of the corresponding class of the
+        detection bboxes.
+
+        2. Whether to ignore the classes that unannotated on that image.
+        """
+        if image_level_annotations is not None:
+            assert len(annotations) == \
+                   len(image_level_annotations) == \
+                   len(det_results)
+        else:
+            assert len(annotations) == len(det_results)
+        for i in range(len(det_results)):
+            results = copy.deepcopy(det_results[i])
+            valid_classes = np.where(
+                np.array([[bbox.shape[0]] for bbox in det_results[i]]) != 0)[0]
+            if image_level_annotations is not None:
+                labels = annotations[i]['labels']
+                image_level_labels = \
+                    image_level_annotations[i]['image_level_labels']
+                allowed_labeles = np.unique(
+                    np.append(labels, image_level_labels))
+            else:
+                allowed_labeles = np.unique(annotations[i]['labels'])
+
+            for valid_class in valid_classes:
+                det_cls = np.where(self.class_label_tree[valid_class])[0]
+                for index in det_cls:
+                    if index in allowed_labeles and \
+                            index != valid_class and \
+                            self.get_supercategory:
+                        det_results[i][index] = \
+                            np.concatenate((det_results[i][index],
+                                            results[valid_class]))
+                    elif index not in allowed_labeles and self.filter_labels:
+                        # Remove useless parts
+                        det_results[i][index] = np.empty(
+                            (0, 5)).astype(np.float32)
+        return det_results
+
+    def load_image_label_from_csv(self, image_level_ann_file):
+        """Load image level annotations from csv style ann_file.
+
+        Args:
+            image_level_ann_file (str): CSV style image level annotation
+                file path.
+
+        Returns:
+            defaultdict[list[dict]]: Annotations where item of the defaultdict
+            indicates an image, each of which has (n) dicts.
+            Keys of dicts are:
+
+                - `image_level_label` (int): Label id.
+                - `confidence` (float): Labels that are human-verified to be
+                  present in an image have confidence = 1 (positive labels).
+                  Labels that are human-verified to be absent from an image
+                  have confidence = 0 (negative labels). Machine-generated
+                  labels have fractional confidences, generally >= 0.5.
+                  The higher the confidence, the smaller the chance for
+                  the label to be a false positive.
+        """
+
+        item_lists = defaultdict(list)
+        with open(image_level_ann_file, 'r') as f:
+            reader = csv.reader(f)
+            for i, line in enumerate(reader):
+                if i == 0:
+                    continue
+                img_id = line[0]
+                item_lists[img_id].append(
+                    dict(
+                        image_level_label=int(self.index_dict[line[2]]),
+                        confidence=float(line[3])))
+        return item_lists
+
+    def get_image_level_ann(self, image_level_ann_file):
+        """Get OpenImages annotation by index.
+
+        Args:
+            image_level_ann_file (str): CSV style image level annotation
+                file path.
+
+        Returns:
+            dict: Annotation info of specified index.
+        """
+
+        if hasattr(self.file_client, 'get_local_path'):
+            with self.file_client.get_local_path(image_level_ann_file) \
+                    as local_path:
+                item_lists = self.load_image_label_from_csv(local_path)
+        else:
+            item_lists = self.load_image_label_from_csv(image_level_ann_file)
+        image_level_annotations = []
+        for i in range(len(self)):
+            img_info = self.data_infos[i].get('img_info', None)
+            if img_info is not None:
+                # for Open Images Challenges
+                img_id = osp.split(img_info['filename'])[-1][:-4]
+            else:
+                # for Open Images v6
+                img_id = self.data_infos[i]['img_id']
+            item_list = item_lists.get(img_id, None)
+            if item_list is not None:
+                image_level_labels = []
+                confidences = []
+                for obj in item_list:
+                    image_level_label = int(obj['image_level_label'])
+                    confidence = float(obj['confidence'])
+
+                    image_level_labels.append(image_level_label)
+                    confidences.append(confidence)
+
+                if not image_level_labels:
+                    image_level_labels = np.zeros((0, ))
+                    confidences = np.zeros((0, ))
+                else:
+                    image_level_labels = np.array(image_level_labels)
+                    confidences = np.array(confidences)
+            else:
+                image_level_labels = np.zeros((0, ))
+                confidences = np.zeros((0, ))
+            ann = dict(
+                image_level_labels=image_level_labels.astype(np.int64),
+                confidences=confidences.astype(np.float32))
+            image_level_annotations.append(ann)
+
+        return image_level_annotations
+
+    def denormalize_gt_bboxes(self, annotations):
+        """Convert ground truth bboxes from relative position to absolute
+        position.
+
+        Only used in evaluating time.
+        """
+        assert len(self.test_img_shapes) == len(annotations)
+        for i in range(len(annotations)):
+            h, w = self.test_img_shapes[i]
+            annotations[i]['bboxes'][:, 0::2] *= w
+            annotations[i]['bboxes'][:, 1::2] *= h
+        return annotations
+
+    def get_cat_ids(self, idx):
+        """Get category ids by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            list[int]: All categories in the image of specified index.
+        """
+        return self.get_ann_info(idx)['labels'].astype(np.int).tolist()
+
+    def evaluate(self,
+                 results,
+                 metric='mAP',
+                 logger=None,
+                 iou_thr=0.5,
+                 ioa_thr=0.5,
+                 scale_ranges=None,
+                 denorm_gt_bbox=True,
+                 use_group_of=True):
+        """Evaluate in OpenImages.
+
+        Args:
+            results (list[list | tuple]): Testing results of the dataset.
+            metric (str | list[str]): Metrics to be evaluated. Option is
+                 'mAP'. Default: 'mAP'.
+            logger (logging.Logger | str, optional): Logger used for printing
+                related information during evaluation. Default: None.
+            iou_thr (float | list[float]): IoU threshold. Default: 0.5.
+            ioa_thr (float | list[float]): IoA threshold. Default: 0.5.
+            scale_ranges (list[tuple], optional): Scale ranges for evaluating
+                mAP. If not specified, all bounding boxes would be included in
+                evaluation. Default: None
+            denorm_gt_bbox (bool): Whether to denorm ground truth bboxes from
+                relative position to absolute position. Default: True
+            use_group_of (bool): Whether consider group of groud truth bboxes
+                during evaluating. Default: True.
+
+        Returns:
+            dict[str, float]: AP metrics.
+        """
+
+        if not isinstance(metric, str):
+            assert len(metric) == 1
+            metric = metric[0]
+        allowed_metrics = ['mAP']
+        if metric not in allowed_metrics:
+            raise KeyError(f'metric {metric} is not supported')
+        annotations = [self.get_ann_info(i) for i in range(len(self))]
+
+        if self.load_image_level_labels:
+            image_level_annotations = \
+                self.get_image_level_ann(self.image_level_ann_file)
+        else:
+            image_level_annotations = None
+
+        # load metas from file
+        if self.get_metas and self.load_from_file:
+            assert self.meta_file.endswith(
+                'pkl'), 'File name must be pkl suffix'
+            self.get_meta_from_file(self.meta_file)
+        # load metas from pipeline
+        else:
+            self.get_img_shape(self.test_img_metas)
+
+        if len(self.test_img_shapes) > len(self):
+            self.test_img_shapes = self.test_img_shapes[:len(self)]
+
+        if denorm_gt_bbox:
+            annotations = self.denormalize_gt_bboxes(annotations)
+
+        # Reset test_image_metas, temp_image_metas and test_img_shapes
+        # to avoid potential error
+        self.temp_img_metas = []
+        self.test_img_shapes = []
+        self.test_img_metas = []
+        if self.get_supercategory:
+            annotations = self.add_supercategory_ann(annotations)
+
+        results = self.process_results(results, annotations,
+                                       image_level_annotations)
+        if use_group_of:
+            assert ioa_thr is not None, \
+                'ioa_thr must have value when using group_of in evaluation.'
+
+        eval_results = OrderedDict()
+        iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr
+        ioa_thrs = [ioa_thr] if isinstance(ioa_thr, float) or ioa_thr is None \
+            else ioa_thr
+
+        # get dataset type
+        if len(self.CLASSES) == 500:
+            ds_name = 'oid_challenge'
+        elif len(self.CLASSES) == 601:
+            ds_name = 'oid_v6'
+        else:
+            ds_name = self.CLASSES
+            warnings.warn('Cannot infer dataset type from the length of the '
+                          'classes. Set `oid_v6` as dataset type.')
+
+        if metric == 'mAP':
+            assert isinstance(iou_thrs, list) and isinstance(ioa_thrs, list)
+            assert len(ioa_thrs) == len(iou_thrs)
+            mean_aps = []
+            for iou_thr, ioa_thr in zip(iou_thrs, ioa_thrs):
+                print_log(f'\n{"-" * 15}iou_thr, ioa_thr: {iou_thr}, {ioa_thr}'
+                          f'{"-" * 15}')
+                mean_ap, _ = eval_map(
+                    results,
+                    annotations,
+                    scale_ranges=scale_ranges,
+                    iou_thr=iou_thr,
+                    ioa_thr=ioa_thr,
+                    dataset=ds_name,
+                    logger=logger,
+                    use_group_of=use_group_of)
+                mean_aps.append(mean_ap)
+                eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3)
+            eval_results['mAP'] = sum(mean_aps) / len(mean_aps)
+        return eval_results
+
+
+@DATASETS.register_module()
+class OpenImagesChallengeDataset(OpenImagesDataset):
+    """Open Images Challenge dataset for detection."""
+
+    def __init__(self, ann_file, **kwargs):
+        assert ann_file.endswith('txt')
+        super(OpenImagesChallengeDataset, self).__init__(
+            ann_file=ann_file, **kwargs)
+
+    def get_classes_from_csv(self, label_file):
+        """Get classes name from file.
+
+        Args:
+            label_file (str): File path of the label description file that
+                maps the classes names in MID format to their short
+                descriptions.
+
+        Returns:
+            list: Class name of OpenImages.
+        """
+
+        label_list = []
+        id_list = []
+        with open(label_file, 'r') as f:
+            reader = csv.reader(f)
+            for line in reader:
+                label_name = line[0]
+                label_id = int(line[2])
+
+                label_list.append(line[1])
+                id_list.append(label_id)
+                self.index_dict[label_name] = label_id - 1
+
+        indexes = np.argsort(id_list)
+        classes_names = []
+        for index in indexes:
+            classes_names.append(label_list[index])
+        return classes_names
+
+    def load_annotations(self, ann_file):
+        """Load annotation from annotation file."""
+        with open(ann_file) as f:
+            lines = f.readlines()
+        i = 0
+        ann_infos = []
+        while i < len(lines):
+            bboxes = []
+            labels = []
+            is_group_ofs = []
+            filename = lines[i].rstrip()
+            i += 2
+            img_gt_size = int(lines[i])
+            i += 1
+            for j in range(img_gt_size):
+                sp = lines[i + j].split()
+                bboxes.append(
+                    [float(sp[1]),
+                     float(sp[2]),
+                     float(sp[3]),
+                     float(sp[4])])
+                labels.append(int(sp[0]) - 1)  # labels begin from 1
+                is_group_ofs.append(True if int(sp[5]) == 1 else False)
+            i += img_gt_size
+
+            gt_bboxes = np.array(bboxes, dtype=np.float32)
+            gt_labels = np.array(labels, dtype=np.int64)
+            gt_bboxes_ignore = np.zeros((0, 4), dtype=np.float32)
+            gt_is_group_ofs = np.array(is_group_ofs, dtype=bool)
+
+            img_info = dict(filename=filename)
+            ann_info = dict(
+                bboxes=gt_bboxes,
+                labels=gt_labels,
+                bboxes_ignore=gt_bboxes_ignore,
+                gt_is_group_ofs=gt_is_group_ofs)
+            ann_infos.append(dict(img_info=img_info, ann_info=ann_info))
+
+        return ann_infos
+
+    def prepare_train_img(self, idx):
+        """Get training data and annotations after pipeline."""
+        ann_info = self.data_infos[idx]
+        results = dict(
+            img_info=ann_info['img_info'],
+            ann_info=ann_info['ann_info'],
+        )
+        if self.proposals is not None:
+            results['proposals'] = self.proposals[idx]
+        self.pre_pipeline(results)
+        return self.pipeline(results)
+
+    def prepare_test_img(self, idx):
+        """Get testing data after pipeline."""
+        ann_info = self.data_infos[idx]
+        results = dict(img_info=ann_info['img_info'])
+        if self.proposals is not None:
+            results['proposals'] = self.proposals[idx]
+        self.pre_pipeline(results)
+
+        results = self.pipeline(results)
+        if self.get_metas and self.load_from_pipeline:
+            self.get_meta_from_pipeline(results)
+        return results
+
+    def get_relation_matrix(self, hierarchy_file):
+        """Get hierarchy for classes.
+
+        Args:
+            hierarchy_file (str): File path to the hierarchy for classes.
+
+        Returns:
+            ndarray: The matrix of the corresponding
+            relationship between the parent class and the child class,
+            of shape (class_num, class_num).
+        """
+        class_label_tree = np.load(hierarchy_file, allow_pickle=True)
+        return class_label_tree[1:, 1:]
+
+    def get_ann_info(self, idx):
+        """Get OpenImages annotation by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            dict: Annotation info of specified index.
+        """
+        # avoid some potential error
+        data_infos = copy.deepcopy(self.data_infos[idx]['ann_info'])
+        return data_infos
+
+    def load_image_label_from_csv(self, image_level_ann_file):
+        """Load image level annotations from csv style ann_file.
+
+        Args:
+            image_level_ann_file (str): CSV style image level annotation
+                file path.
+
+        Returns:
+            defaultdict[list[dict]]: Annotations where item of the defaultdict
+            indicates an image, each of which has (n) dicts.
+            Keys of dicts are:
+
+                - `image_level_label` (int): of shape 1.
+                - `confidence` (float): of shape 1.
+        """
+
+        item_lists = defaultdict(list)
+        with open(image_level_ann_file, 'r') as f:
+            reader = csv.reader(f)
+            i = -1
+            for line in reader:
+                i += 1
+                if i == 0:
+                    continue
+                else:
+                    img_id = line[0]
+                    label_id = line[1]
+                    assert label_id in self.index_dict
+                    image_level_label = int(self.index_dict[label_id])
+                    confidence = float(line[2])
+                    item_lists[img_id].append(
+                        dict(
+                            image_level_label=image_level_label,
+                            confidence=confidence))
+        return item_lists
diff --git a/mmdet/datasets/pipelines/__init__.py b/mmdet/datasets/pipelines/__init__.py
new file mode 100644
index 0000000..8260da6
--- /dev/null
+++ b/mmdet/datasets/pipelines/__init__.py
@@ -0,0 +1,31 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .auto_augment import (AutoAugment, BrightnessTransform, ColorTransform,
+                           ContrastTransform, EqualizeTransform, Rotate, Shear,
+                           Translate)
+from .compose import Compose
+from .formatting import (Collect, DefaultFormatBundle, ImageToTensor,
+                         ToDataContainer, ToTensor, Transpose, to_tensor)
+from .instaboost import InstaBoost
+from .loading import (FilterAnnotations, LoadAnnotations, LoadImageFromFile,
+                      LoadImageFromWebcam, LoadMultiChannelImageFromFiles,
+                      LoadPanopticAnnotations, LoadProposals)
+from .test_time_aug import MultiScaleFlipAug
+from .transforms import (Albu, CopyPaste, CutOut, Expand, MinIoURandomCrop,
+                         MixUp, Mosaic, Normalize, Pad, PhotoMetricDistortion,
+                         RandomAffine, RandomCenterCropPad, RandomCrop,
+                         RandomFlip, RandomShift, Resize, SegRescale,
+                         YOLOXHSVRandomAug)
+
+__all__ = [
+    'Compose', 'to_tensor', 'ToTensor', 'ImageToTensor', 'ToDataContainer',
+    'Transpose', 'Collect', 'DefaultFormatBundle', 'LoadAnnotations',
+    'LoadImageFromFile', 'LoadImageFromWebcam', 'LoadPanopticAnnotations',
+    'LoadMultiChannelImageFromFiles', 'LoadProposals', 'FilterAnnotations',
+    'MultiScaleFlipAug', 'Resize', 'RandomFlip', 'Pad', 'RandomCrop',
+    'Normalize', 'SegRescale', 'MinIoURandomCrop', 'Expand',
+    'PhotoMetricDistortion', 'Albu', 'InstaBoost', 'RandomCenterCropPad',
+    'AutoAugment', 'CutOut', 'Shear', 'Rotate', 'ColorTransform',
+    'EqualizeTransform', 'BrightnessTransform', 'ContrastTransform',
+    'Translate', 'RandomShift', 'Mosaic', 'MixUp', 'RandomAffine',
+    'YOLOXHSVRandomAug', 'CopyPaste'
+]
diff --git a/mmdet/datasets/pipelines/auto_augment.py b/mmdet/datasets/pipelines/auto_augment.py
new file mode 100644
index 0000000..b0ff67d
--- /dev/null
+++ b/mmdet/datasets/pipelines/auto_augment.py
@@ -0,0 +1,894 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import cv2
+import mmcv
+import numpy as np
+
+from ..builder import PIPELINES
+from .compose import Compose
+
+_MAX_LEVEL = 10
+
+
+def level_to_value(level, max_value):
+    """Map from level to values based on max_value."""
+    return (level / _MAX_LEVEL) * max_value
+
+
+def enhance_level_to_value(level, a=1.8, b=0.1):
+    """Map from level to values."""
+    return (level / _MAX_LEVEL) * a + b
+
+
+def random_negative(value, random_negative_prob):
+    """Randomly negate value based on random_negative_prob."""
+    return -value if np.random.rand() < random_negative_prob else value
+
+
+def bbox2fields():
+    """The key correspondence from bboxes to labels, masks and
+    segmentations."""
+    bbox2label = {
+        'gt_bboxes': 'gt_labels',
+        'gt_bboxes_ignore': 'gt_labels_ignore'
+    }
+    bbox2mask = {
+        'gt_bboxes': 'gt_masks',
+        'gt_bboxes_ignore': 'gt_masks_ignore'
+    }
+    bbox2seg = {
+        'gt_bboxes': 'gt_semantic_seg',
+    }
+    return bbox2label, bbox2mask, bbox2seg
+
+
+@PIPELINES.register_module()
+class AutoAugment:
+    """Auto augmentation.
+
+    This data augmentation is proposed in `Learning Data Augmentation
+    Strategies for Object Detection <https://arxiv.org/pdf/1906.11172>`_.
+
+    TODO: Implement 'Shear', 'Sharpness' and 'Rotate' transforms
+
+    Args:
+        policies (list[list[dict]]): The policies of auto augmentation. Each
+            policy in ``policies`` is a specific augmentation policy, and is
+            composed by several augmentations (dict). When AutoAugment is
+            called, a random policy in ``policies`` will be selected to
+            augment images.
+
+    Examples:
+        >>> replace = (104, 116, 124)
+        >>> policies = [
+        >>>     [
+        >>>         dict(type='Sharpness', prob=0.0, level=8),
+        >>>         dict(
+        >>>             type='Shear',
+        >>>             prob=0.4,
+        >>>             level=0,
+        >>>             replace=replace,
+        >>>             axis='x')
+        >>>     ],
+        >>>     [
+        >>>         dict(
+        >>>             type='Rotate',
+        >>>             prob=0.6,
+        >>>             level=10,
+        >>>             replace=replace),
+        >>>         dict(type='Color', prob=1.0, level=6)
+        >>>     ]
+        >>> ]
+        >>> augmentation = AutoAugment(policies)
+        >>> img = np.ones(100, 100, 3)
+        >>> gt_bboxes = np.ones(10, 4)
+        >>> results = dict(img=img, gt_bboxes=gt_bboxes)
+        >>> results = augmentation(results)
+    """
+
+    def __init__(self, policies):
+        assert isinstance(policies, list) and len(policies) > 0, \
+            'Policies must be a non-empty list.'
+        for policy in policies:
+            assert isinstance(policy, list) and len(policy) > 0, \
+                'Each policy in policies must be a non-empty list.'
+            for augment in policy:
+                assert isinstance(augment, dict) and 'type' in augment, \
+                    'Each specific augmentation must be a dict with key' \
+                    ' "type".'
+
+        self.policies = copy.deepcopy(policies)
+        self.transforms = [Compose(policy) for policy in self.policies]
+
+    def __call__(self, results):
+        transform = np.random.choice(self.transforms)
+        return transform(results)
+
+    def __repr__(self):
+        return f'{self.__class__.__name__}(policies={self.policies})'
+
+
+@PIPELINES.register_module()
+class Shear:
+    """Apply Shear Transformation to image (and its corresponding bbox, mask,
+    segmentation).
+
+    Args:
+        level (int | float): The level should be in range [0,_MAX_LEVEL].
+        img_fill_val (int | float | tuple): The filled values for image border.
+            If float, the same fill value will be used for all the three
+            channels of image. If tuple, the should be 3 elements.
+        seg_ignore_label (int): The fill value used for segmentation map.
+            Note this value must equals ``ignore_label`` in ``semantic_head``
+            of the corresponding config. Default 255.
+        prob (float): The probability for performing Shear and should be in
+            range [0, 1].
+        direction (str): The direction for shear, either "horizontal"
+            or "vertical".
+        max_shear_magnitude (float): The maximum magnitude for Shear
+            transformation.
+        random_negative_prob (float): The probability that turns the
+                offset negative. Should be in range [0,1]
+        interpolation (str): Same as in :func:`mmcv.imshear`.
+    """
+
+    def __init__(self,
+                 level,
+                 img_fill_val=128,
+                 seg_ignore_label=255,
+                 prob=0.5,
+                 direction='horizontal',
+                 max_shear_magnitude=0.3,
+                 random_negative_prob=0.5,
+                 interpolation='bilinear'):
+        assert isinstance(level, (int, float)), 'The level must be type ' \
+            f'int or float, got {type(level)}.'
+        assert 0 <= level <= _MAX_LEVEL, 'The level should be in range ' \
+            f'[0,{_MAX_LEVEL}], got {level}.'
+        if isinstance(img_fill_val, (float, int)):
+            img_fill_val = tuple([float(img_fill_val)] * 3)
+        elif isinstance(img_fill_val, tuple):
+            assert len(img_fill_val) == 3, 'img_fill_val as tuple must ' \
+                f'have 3 elements. got {len(img_fill_val)}.'
+            img_fill_val = tuple([float(val) for val in img_fill_val])
+        else:
+            raise ValueError(
+                'img_fill_val must be float or tuple with 3 elements.')
+        assert np.all([0 <= val <= 255 for val in img_fill_val]), 'all ' \
+            'elements of img_fill_val should between range [0,255].' \
+            f'got {img_fill_val}.'
+        assert 0 <= prob <= 1.0, 'The probability of shear should be in ' \
+            f'range [0,1]. got {prob}.'
+        assert direction in ('horizontal', 'vertical'), 'direction must ' \
+            f'in be either "horizontal" or "vertical". got {direction}.'
+        assert isinstance(max_shear_magnitude, float), 'max_shear_magnitude ' \
+            f'should be type float. got {type(max_shear_magnitude)}.'
+        assert 0. <= max_shear_magnitude <= 1., 'Defaultly ' \
+            'max_shear_magnitude should be in range [0,1]. ' \
+            f'got {max_shear_magnitude}.'
+        self.level = level
+        self.magnitude = level_to_value(level, max_shear_magnitude)
+        self.img_fill_val = img_fill_val
+        self.seg_ignore_label = seg_ignore_label
+        self.prob = prob
+        self.direction = direction
+        self.max_shear_magnitude = max_shear_magnitude
+        self.random_negative_prob = random_negative_prob
+        self.interpolation = interpolation
+
+    def _shear_img(self,
+                   results,
+                   magnitude,
+                   direction='horizontal',
+                   interpolation='bilinear'):
+        """Shear the image.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+            magnitude (int | float): The magnitude used for shear.
+            direction (str): The direction for shear, either "horizontal"
+                or "vertical".
+            interpolation (str): Same as in :func:`mmcv.imshear`.
+        """
+        for key in results.get('img_fields', ['img']):
+            img = results[key]
+            img_sheared = mmcv.imshear(
+                img,
+                magnitude,
+                direction,
+                border_value=self.img_fill_val,
+                interpolation=interpolation)
+            results[key] = img_sheared.astype(img.dtype)
+            results['img_shape'] = results[key].shape
+
+    def _shear_bboxes(self, results, magnitude):
+        """Shear the bboxes."""
+        h, w, c = results['img_shape']
+        if self.direction == 'horizontal':
+            shear_matrix = np.stack([[1, magnitude],
+                                     [0, 1]]).astype(np.float32)  # [2, 2]
+        else:
+            shear_matrix = np.stack([[1, 0], [magnitude,
+                                              1]]).astype(np.float32)
+        for key in results.get('bbox_fields', []):
+            min_x, min_y, max_x, max_y = np.split(
+                results[key], results[key].shape[-1], axis=-1)
+            coordinates = np.stack([[min_x, min_y], [max_x, min_y],
+                                    [min_x, max_y],
+                                    [max_x, max_y]])  # [4, 2, nb_box, 1]
+            coordinates = coordinates[..., 0].transpose(
+                (2, 1, 0)).astype(np.float32)  # [nb_box, 2, 4]
+            new_coords = np.matmul(shear_matrix[None, :, :],
+                                   coordinates)  # [nb_box, 2, 4]
+            min_x = np.min(new_coords[:, 0, :], axis=-1)
+            min_y = np.min(new_coords[:, 1, :], axis=-1)
+            max_x = np.max(new_coords[:, 0, :], axis=-1)
+            max_y = np.max(new_coords[:, 1, :], axis=-1)
+            min_x = np.clip(min_x, a_min=0, a_max=w)
+            min_y = np.clip(min_y, a_min=0, a_max=h)
+            max_x = np.clip(max_x, a_min=min_x, a_max=w)
+            max_y = np.clip(max_y, a_min=min_y, a_max=h)
+            results[key] = np.stack([min_x, min_y, max_x, max_y],
+                                    axis=-1).astype(results[key].dtype)
+
+    def _shear_masks(self,
+                     results,
+                     magnitude,
+                     direction='horizontal',
+                     fill_val=0,
+                     interpolation='bilinear'):
+        """Shear the masks."""
+        h, w, c = results['img_shape']
+        for key in results.get('mask_fields', []):
+            masks = results[key]
+            results[key] = masks.shear((h, w),
+                                       magnitude,
+                                       direction,
+                                       border_value=fill_val,
+                                       interpolation=interpolation)
+
+    def _shear_seg(self,
+                   results,
+                   magnitude,
+                   direction='horizontal',
+                   fill_val=255,
+                   interpolation='bilinear'):
+        """Shear the segmentation maps."""
+        for key in results.get('seg_fields', []):
+            seg = results[key]
+            results[key] = mmcv.imshear(
+                seg,
+                magnitude,
+                direction,
+                border_value=fill_val,
+                interpolation=interpolation).astype(seg.dtype)
+
+    def _filter_invalid(self, results, min_bbox_size=0):
+        """Filter bboxes and corresponding masks too small after shear
+        augmentation."""
+        bbox2label, bbox2mask, _ = bbox2fields()
+        for key in results.get('bbox_fields', []):
+            bbox_w = results[key][:, 2] - results[key][:, 0]
+            bbox_h = results[key][:, 3] - results[key][:, 1]
+            valid_inds = (bbox_w > min_bbox_size) & (bbox_h > min_bbox_size)
+            valid_inds = np.nonzero(valid_inds)[0]
+            results[key] = results[key][valid_inds]
+            # label fields. e.g. gt_labels and gt_labels_ignore
+            label_key = bbox2label.get(key)
+            if label_key in results:
+                results[label_key] = results[label_key][valid_inds]
+            # mask fields, e.g. gt_masks and gt_masks_ignore
+            mask_key = bbox2mask.get(key)
+            if mask_key in results:
+                results[mask_key] = results[mask_key][valid_inds]
+
+    def __call__(self, results):
+        """Call function to shear images, bounding boxes, masks and semantic
+        segmentation maps.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Sheared results.
+        """
+        if np.random.rand() > self.prob:
+            return results
+        magnitude = random_negative(self.magnitude, self.random_negative_prob)
+        self._shear_img(results, magnitude, self.direction, self.interpolation)
+        self._shear_bboxes(results, magnitude)
+        # fill_val set to 0 for background of mask.
+        self._shear_masks(
+            results,
+            magnitude,
+            self.direction,
+            fill_val=0,
+            interpolation=self.interpolation)
+        self._shear_seg(
+            results,
+            magnitude,
+            self.direction,
+            fill_val=self.seg_ignore_label,
+            interpolation=self.interpolation)
+        self._filter_invalid(results)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(level={self.level}, '
+        repr_str += f'img_fill_val={self.img_fill_val}, '
+        repr_str += f'seg_ignore_label={self.seg_ignore_label}, '
+        repr_str += f'prob={self.prob}, '
+        repr_str += f'direction={self.direction}, '
+        repr_str += f'max_shear_magnitude={self.max_shear_magnitude}, '
+        repr_str += f'random_negative_prob={self.random_negative_prob}, '
+        repr_str += f'interpolation={self.interpolation})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class Rotate:
+    """Apply Rotate Transformation to image (and its corresponding bbox, mask,
+    segmentation).
+
+    Args:
+        level (int | float): The level should be in range (0,_MAX_LEVEL].
+        scale (int | float): Isotropic scale factor. Same in
+            ``mmcv.imrotate``.
+        center (int | float | tuple[float]): Center point (w, h) of the
+            rotation in the source image. If None, the center of the
+            image will be used. Same in ``mmcv.imrotate``.
+        img_fill_val (int | float | tuple): The fill value for image border.
+            If float, the same value will be used for all the three
+            channels of image. If tuple, the should be 3 elements (e.g.
+            equals the number of channels for image).
+        seg_ignore_label (int): The fill value used for segmentation map.
+            Note this value must equals ``ignore_label`` in ``semantic_head``
+            of the corresponding config. Default 255.
+        prob (float): The probability for perform transformation and
+            should be in range 0 to 1.
+        max_rotate_angle (int | float): The maximum angles for rotate
+            transformation.
+        random_negative_prob (float): The probability that turns the
+             offset negative.
+    """
+
+    def __init__(self,
+                 level,
+                 scale=1,
+                 center=None,
+                 img_fill_val=128,
+                 seg_ignore_label=255,
+                 prob=0.5,
+                 max_rotate_angle=30,
+                 random_negative_prob=0.5):
+        assert isinstance(level, (int, float)), \
+            f'The level must be type int or float. got {type(level)}.'
+        assert 0 <= level <= _MAX_LEVEL, \
+            f'The level should be in range (0,{_MAX_LEVEL}]. got {level}.'
+        assert isinstance(scale, (int, float)), \
+            f'The scale must be type int or float. got type {type(scale)}.'
+        if isinstance(center, (int, float)):
+            center = (center, center)
+        elif isinstance(center, tuple):
+            assert len(center) == 2, 'center with type tuple must have '\
+                f'2 elements. got {len(center)} elements.'
+        else:
+            assert center is None, 'center must be None or type int, '\
+                f'float or tuple, got type {type(center)}.'
+        if isinstance(img_fill_val, (float, int)):
+            img_fill_val = tuple([float(img_fill_val)] * 3)
+        elif isinstance(img_fill_val, tuple):
+            assert len(img_fill_val) == 3, 'img_fill_val as tuple must '\
+                f'have 3 elements. got {len(img_fill_val)}.'
+            img_fill_val = tuple([float(val) for val in img_fill_val])
+        else:
+            raise ValueError(
+                'img_fill_val must be float or tuple with 3 elements.')
+        assert np.all([0 <= val <= 255 for val in img_fill_val]), \
+            'all elements of img_fill_val should between range [0,255]. '\
+            f'got {img_fill_val}.'
+        assert 0 <= prob <= 1.0, 'The probability should be in range [0,1]. '\
+            f'got {prob}.'
+        assert isinstance(max_rotate_angle, (int, float)), 'max_rotate_angle '\
+            f'should be type int or float. got type {type(max_rotate_angle)}.'
+        self.level = level
+        self.scale = scale
+        # Rotation angle in degrees. Positive values mean
+        # clockwise rotation.
+        self.angle = level_to_value(level, max_rotate_angle)
+        self.center = center
+        self.img_fill_val = img_fill_val
+        self.seg_ignore_label = seg_ignore_label
+        self.prob = prob
+        self.max_rotate_angle = max_rotate_angle
+        self.random_negative_prob = random_negative_prob
+
+    def _rotate_img(self, results, angle, center=None, scale=1.0):
+        """Rotate the image.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+            angle (float): Rotation angle in degrees, positive values
+                mean clockwise rotation. Same in ``mmcv.imrotate``.
+            center (tuple[float], optional): Center point (w, h) of the
+                rotation. Same in ``mmcv.imrotate``.
+            scale (int | float): Isotropic scale factor. Same in
+                ``mmcv.imrotate``.
+        """
+        for key in results.get('img_fields', ['img']):
+            img = results[key].copy()
+            img_rotated = mmcv.imrotate(
+                img, angle, center, scale, border_value=self.img_fill_val)
+            results[key] = img_rotated.astype(img.dtype)
+            results['img_shape'] = results[key].shape
+
+    def _rotate_bboxes(self, results, rotate_matrix):
+        """Rotate the bboxes."""
+        h, w, c = results['img_shape']
+        for key in results.get('bbox_fields', []):
+            min_x, min_y, max_x, max_y = np.split(
+                results[key], results[key].shape[-1], axis=-1)
+            coordinates = np.stack([[min_x, min_y], [max_x, min_y],
+                                    [min_x, max_y],
+                                    [max_x, max_y]])  # [4, 2, nb_bbox, 1]
+            # pad 1 to convert from format [x, y] to homogeneous
+            # coordinates format [x, y, 1]
+            coordinates = np.concatenate(
+                (coordinates,
+                 np.ones((4, 1, coordinates.shape[2], 1), coordinates.dtype)),
+                axis=1)  # [4, 3, nb_bbox, 1]
+            coordinates = coordinates.transpose(
+                (2, 0, 1, 3))  # [nb_bbox, 4, 3, 1]
+            rotated_coords = np.matmul(rotate_matrix,
+                                       coordinates)  # [nb_bbox, 4, 2, 1]
+            rotated_coords = rotated_coords[..., 0]  # [nb_bbox, 4, 2]
+            min_x, min_y = np.min(
+                rotated_coords[:, :, 0], axis=1), np.min(
+                    rotated_coords[:, :, 1], axis=1)
+            max_x, max_y = np.max(
+                rotated_coords[:, :, 0], axis=1), np.max(
+                    rotated_coords[:, :, 1], axis=1)
+            min_x, min_y = np.clip(
+                min_x, a_min=0, a_max=w), np.clip(
+                    min_y, a_min=0, a_max=h)
+            max_x, max_y = np.clip(
+                max_x, a_min=min_x, a_max=w), np.clip(
+                    max_y, a_min=min_y, a_max=h)
+            results[key] = np.stack([min_x, min_y, max_x, max_y],
+                                    axis=-1).astype(results[key].dtype)
+
+    def _rotate_masks(self,
+                      results,
+                      angle,
+                      center=None,
+                      scale=1.0,
+                      fill_val=0):
+        """Rotate the masks."""
+        h, w, c = results['img_shape']
+        for key in results.get('mask_fields', []):
+            masks = results[key]
+            results[key] = masks.rotate((h, w), angle, center, scale, fill_val)
+
+    def _rotate_seg(self,
+                    results,
+                    angle,
+                    center=None,
+                    scale=1.0,
+                    fill_val=255):
+        """Rotate the segmentation map."""
+        for key in results.get('seg_fields', []):
+            seg = results[key].copy()
+            results[key] = mmcv.imrotate(
+                seg, angle, center, scale,
+                border_value=fill_val).astype(seg.dtype)
+
+    def _filter_invalid(self, results, min_bbox_size=0):
+        """Filter bboxes and corresponding masks too small after rotate
+        augmentation."""
+        bbox2label, bbox2mask, _ = bbox2fields()
+        for key in results.get('bbox_fields', []):
+            bbox_w = results[key][:, 2] - results[key][:, 0]
+            bbox_h = results[key][:, 3] - results[key][:, 1]
+            valid_inds = (bbox_w > min_bbox_size) & (bbox_h > min_bbox_size)
+            valid_inds = np.nonzero(valid_inds)[0]
+            results[key] = results[key][valid_inds]
+            # label fields. e.g. gt_labels and gt_labels_ignore
+            label_key = bbox2label.get(key)
+            if label_key in results:
+                results[label_key] = results[label_key][valid_inds]
+            # mask fields, e.g. gt_masks and gt_masks_ignore
+            mask_key = bbox2mask.get(key)
+            if mask_key in results:
+                results[mask_key] = results[mask_key][valid_inds]
+
+    def __call__(self, results):
+        """Call function to rotate images, bounding boxes, masks and semantic
+        segmentation maps.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Rotated results.
+        """
+        if np.random.rand() > self.prob:
+            return results
+        h, w = results['img'].shape[:2]
+        center = self.center
+        if center is None:
+            center = ((w - 1) * 0.5, (h - 1) * 0.5)
+        angle = random_negative(self.angle, self.random_negative_prob)
+        self._rotate_img(results, angle, center, self.scale)
+        rotate_matrix = cv2.getRotationMatrix2D(center, -angle, self.scale)
+        self._rotate_bboxes(results, rotate_matrix)
+        self._rotate_masks(results, angle, center, self.scale, fill_val=0)
+        self._rotate_seg(
+            results, angle, center, self.scale, fill_val=self.seg_ignore_label)
+        self._filter_invalid(results)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(level={self.level}, '
+        repr_str += f'scale={self.scale}, '
+        repr_str += f'center={self.center}, '
+        repr_str += f'img_fill_val={self.img_fill_val}, '
+        repr_str += f'seg_ignore_label={self.seg_ignore_label}, '
+        repr_str += f'prob={self.prob}, '
+        repr_str += f'max_rotate_angle={self.max_rotate_angle}, '
+        repr_str += f'random_negative_prob={self.random_negative_prob})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class Translate:
+    """Translate the images, bboxes, masks and segmentation maps horizontally
+    or vertically.
+
+    Args:
+        level (int | float): The level for Translate and should be in
+            range [0,_MAX_LEVEL].
+        prob (float): The probability for performing translation and
+            should be in range [0, 1].
+        img_fill_val (int | float | tuple): The filled value for image
+            border. If float, the same fill value will be used for all
+            the three channels of image. If tuple, the should be 3
+            elements (e.g. equals the number of channels for image).
+        seg_ignore_label (int): The fill value used for segmentation map.
+            Note this value must equals ``ignore_label`` in ``semantic_head``
+            of the corresponding config. Default 255.
+        direction (str): The translate direction, either "horizontal"
+            or "vertical".
+        max_translate_offset (int | float): The maximum pixel's offset for
+            Translate.
+        random_negative_prob (float): The probability that turns the
+            offset negative.
+        min_size (int | float): The minimum pixel for filtering
+            invalid bboxes after the translation.
+    """
+
+    def __init__(self,
+                 level,
+                 prob=0.5,
+                 img_fill_val=128,
+                 seg_ignore_label=255,
+                 direction='horizontal',
+                 max_translate_offset=250.,
+                 random_negative_prob=0.5,
+                 min_size=0):
+        assert isinstance(level, (int, float)), \
+            'The level must be type int or float.'
+        assert 0 <= level <= _MAX_LEVEL, \
+            'The level used for calculating Translate\'s offset should be ' \
+            'in range [0,_MAX_LEVEL]'
+        assert 0 <= prob <= 1.0, \
+            'The probability of translation should be in range [0, 1].'
+        if isinstance(img_fill_val, (float, int)):
+            img_fill_val = tuple([float(img_fill_val)] * 3)
+        elif isinstance(img_fill_val, tuple):
+            assert len(img_fill_val) == 3, \
+                'img_fill_val as tuple must have 3 elements.'
+            img_fill_val = tuple([float(val) for val in img_fill_val])
+        else:
+            raise ValueError('img_fill_val must be type float or tuple.')
+        assert np.all([0 <= val <= 255 for val in img_fill_val]), \
+            'all elements of img_fill_val should between range [0,255].'
+        assert direction in ('horizontal', 'vertical'), \
+            'direction should be "horizontal" or "vertical".'
+        assert isinstance(max_translate_offset, (int, float)), \
+            'The max_translate_offset must be type int or float.'
+        # the offset used for translation
+        self.offset = int(level_to_value(level, max_translate_offset))
+        self.level = level
+        self.prob = prob
+        self.img_fill_val = img_fill_val
+        self.seg_ignore_label = seg_ignore_label
+        self.direction = direction
+        self.max_translate_offset = max_translate_offset
+        self.random_negative_prob = random_negative_prob
+        self.min_size = min_size
+
+    def _translate_img(self, results, offset, direction='horizontal'):
+        """Translate the image.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+            offset (int | float): The offset for translate.
+            direction (str): The translate direction, either "horizontal"
+                or "vertical".
+        """
+        for key in results.get('img_fields', ['img']):
+            img = results[key].copy()
+            results[key] = mmcv.imtranslate(
+                img, offset, direction, self.img_fill_val).astype(img.dtype)
+            results['img_shape'] = results[key].shape
+
+    def _translate_bboxes(self, results, offset):
+        """Shift bboxes horizontally or vertically, according to offset."""
+        h, w, c = results['img_shape']
+        for key in results.get('bbox_fields', []):
+            min_x, min_y, max_x, max_y = np.split(
+                results[key], results[key].shape[-1], axis=-1)
+            if self.direction == 'horizontal':
+                min_x = np.maximum(0, min_x + offset)
+                max_x = np.minimum(w, max_x + offset)
+            elif self.direction == 'vertical':
+                min_y = np.maximum(0, min_y + offset)
+                max_y = np.minimum(h, max_y + offset)
+
+            # the boxes translated outside of image will be filtered along with
+            # the corresponding masks, by invoking ``_filter_invalid``.
+            results[key] = np.concatenate([min_x, min_y, max_x, max_y],
+                                          axis=-1)
+
+    def _translate_masks(self,
+                         results,
+                         offset,
+                         direction='horizontal',
+                         fill_val=0):
+        """Translate masks horizontally or vertically."""
+        h, w, c = results['img_shape']
+        for key in results.get('mask_fields', []):
+            masks = results[key]
+            results[key] = masks.translate((h, w), offset, direction, fill_val)
+
+    def _translate_seg(self,
+                       results,
+                       offset,
+                       direction='horizontal',
+                       fill_val=255):
+        """Translate segmentation maps horizontally or vertically."""
+        for key in results.get('seg_fields', []):
+            seg = results[key].copy()
+            results[key] = mmcv.imtranslate(seg, offset, direction,
+                                            fill_val).astype(seg.dtype)
+
+    def _filter_invalid(self, results, min_size=0):
+        """Filter bboxes and masks too small or translated out of image."""
+        bbox2label, bbox2mask, _ = bbox2fields()
+        for key in results.get('bbox_fields', []):
+            bbox_w = results[key][:, 2] - results[key][:, 0]
+            bbox_h = results[key][:, 3] - results[key][:, 1]
+            valid_inds = (bbox_w > min_size) & (bbox_h > min_size)
+            valid_inds = np.nonzero(valid_inds)[0]
+            results[key] = results[key][valid_inds]
+            # label fields. e.g. gt_labels and gt_labels_ignore
+            label_key = bbox2label.get(key)
+            if label_key in results:
+                results[label_key] = results[label_key][valid_inds]
+            # mask fields, e.g. gt_masks and gt_masks_ignore
+            mask_key = bbox2mask.get(key)
+            if mask_key in results:
+                results[mask_key] = results[mask_key][valid_inds]
+        return results
+
+    def __call__(self, results):
+        """Call function to translate images, bounding boxes, masks and
+        semantic segmentation maps.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Translated results.
+        """
+        if np.random.rand() > self.prob:
+            return results
+        offset = random_negative(self.offset, self.random_negative_prob)
+        self._translate_img(results, offset, self.direction)
+        self._translate_bboxes(results, offset)
+        # fill_val defaultly 0 for BitmapMasks and None for PolygonMasks.
+        self._translate_masks(results, offset, self.direction)
+        # fill_val set to ``seg_ignore_label`` for the ignored value
+        # of segmentation map.
+        self._translate_seg(
+            results, offset, self.direction, fill_val=self.seg_ignore_label)
+        self._filter_invalid(results, min_size=self.min_size)
+        return results
+
+
+@PIPELINES.register_module()
+class ColorTransform:
+    """Apply Color transformation to image. The bboxes, masks, and
+    segmentations are not modified.
+
+    Args:
+        level (int | float): Should be in range [0,_MAX_LEVEL].
+        prob (float): The probability for performing Color transformation.
+    """
+
+    def __init__(self, level, prob=0.5):
+        assert isinstance(level, (int, float)), \
+            'The level must be type int or float.'
+        assert 0 <= level <= _MAX_LEVEL, \
+            'The level should be in range [0,_MAX_LEVEL].'
+        assert 0 <= prob <= 1.0, \
+            'The probability should be in range [0,1].'
+        self.level = level
+        self.prob = prob
+        self.factor = enhance_level_to_value(level)
+
+    def _adjust_color_img(self, results, factor=1.0):
+        """Apply Color transformation to image."""
+        for key in results.get('img_fields', ['img']):
+            # NOTE defaultly the image should be BGR format
+            img = results[key]
+            results[key] = mmcv.adjust_color(img, factor).astype(img.dtype)
+
+    def __call__(self, results):
+        """Call function for Color transformation.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Colored results.
+        """
+        if np.random.rand() > self.prob:
+            return results
+        self._adjust_color_img(results, self.factor)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(level={self.level}, '
+        repr_str += f'prob={self.prob})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class EqualizeTransform:
+    """Apply Equalize transformation to image. The bboxes, masks and
+    segmentations are not modified.
+
+    Args:
+        prob (float): The probability for performing Equalize transformation.
+    """
+
+    def __init__(self, prob=0.5):
+        assert 0 <= prob <= 1.0, \
+            'The probability should be in range [0,1].'
+        self.prob = prob
+
+    def _imequalize(self, results):
+        """Equalizes the histogram of one image."""
+        for key in results.get('img_fields', ['img']):
+            img = results[key]
+            results[key] = mmcv.imequalize(img).astype(img.dtype)
+
+    def __call__(self, results):
+        """Call function for Equalize transformation.
+
+        Args:
+            results (dict): Results dict from loading pipeline.
+
+        Returns:
+            dict: Results after the transformation.
+        """
+        if np.random.rand() > self.prob:
+            return results
+        self._imequalize(results)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(prob={self.prob})'
+
+
+@PIPELINES.register_module()
+class BrightnessTransform:
+    """Apply Brightness transformation to image. The bboxes, masks and
+    segmentations are not modified.
+
+    Args:
+        level (int | float): Should be in range [0,_MAX_LEVEL].
+        prob (float): The probability for performing Brightness transformation.
+    """
+
+    def __init__(self, level, prob=0.5):
+        assert isinstance(level, (int, float)), \
+            'The level must be type int or float.'
+        assert 0 <= level <= _MAX_LEVEL, \
+            'The level should be in range [0,_MAX_LEVEL].'
+        assert 0 <= prob <= 1.0, \
+            'The probability should be in range [0,1].'
+        self.level = level
+        self.prob = prob
+        self.factor = enhance_level_to_value(level)
+
+    def _adjust_brightness_img(self, results, factor=1.0):
+        """Adjust the brightness of image."""
+        for key in results.get('img_fields', ['img']):
+            img = results[key]
+            results[key] = mmcv.adjust_brightness(img,
+                                                  factor).astype(img.dtype)
+
+    def __call__(self, results):
+        """Call function for Brightness transformation.
+
+        Args:
+            results (dict): Results dict from loading pipeline.
+
+        Returns:
+            dict: Results after the transformation.
+        """
+        if np.random.rand() > self.prob:
+            return results
+        self._adjust_brightness_img(results, self.factor)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(level={self.level}, '
+        repr_str += f'prob={self.prob})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class ContrastTransform:
+    """Apply Contrast transformation to image. The bboxes, masks and
+    segmentations are not modified.
+
+    Args:
+        level (int | float): Should be in range [0,_MAX_LEVEL].
+        prob (float): The probability for performing Contrast transformation.
+    """
+
+    def __init__(self, level, prob=0.5):
+        assert isinstance(level, (int, float)), \
+            'The level must be type int or float.'
+        assert 0 <= level <= _MAX_LEVEL, \
+            'The level should be in range [0,_MAX_LEVEL].'
+        assert 0 <= prob <= 1.0, \
+            'The probability should be in range [0,1].'
+        self.level = level
+        self.prob = prob
+        self.factor = enhance_level_to_value(level)
+
+    def _adjust_contrast_img(self, results, factor=1.0):
+        """Adjust the image contrast."""
+        for key in results.get('img_fields', ['img']):
+            img = results[key]
+            results[key] = mmcv.adjust_contrast(img, factor).astype(img.dtype)
+
+    def __call__(self, results):
+        """Call function for Contrast transformation.
+
+        Args:
+            results (dict): Results dict from loading pipeline.
+
+        Returns:
+            dict: Results after the transformation.
+        """
+        if np.random.rand() > self.prob:
+            return results
+        self._adjust_contrast_img(results, self.factor)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(level={self.level}, '
+        repr_str += f'prob={self.prob})'
+        return repr_str
diff --git a/mmdet/datasets/pipelines/compose.py b/mmdet/datasets/pipelines/compose.py
new file mode 100644
index 0000000..d759220
--- /dev/null
+++ b/mmdet/datasets/pipelines/compose.py
@@ -0,0 +1,55 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import collections
+
+from mmcv.utils import build_from_cfg
+
+from ..builder import PIPELINES
+
+
+@PIPELINES.register_module()
+class Compose:
+    """Compose multiple transforms sequentially.
+
+    Args:
+        transforms (Sequence[dict | callable]): Sequence of transform object or
+            config dict to be composed.
+    """
+
+    def __init__(self, transforms):
+        assert isinstance(transforms, collections.abc.Sequence)
+        self.transforms = []
+        for transform in transforms:
+            if isinstance(transform, dict):
+                transform = build_from_cfg(transform, PIPELINES)
+                self.transforms.append(transform)
+            elif callable(transform):
+                self.transforms.append(transform)
+            else:
+                raise TypeError('transform must be callable or a dict')
+
+    def __call__(self, data):
+        """Call function to apply transforms sequentially.
+
+        Args:
+            data (dict): A result dict contains the data to transform.
+
+        Returns:
+           dict: Transformed data.
+        """
+
+        for t in self.transforms:
+            data = t(data)
+            if data is None:
+                return None
+        return data
+
+    def __repr__(self):
+        format_string = self.__class__.__name__ + '('
+        for t in self.transforms:
+            str_ = t.__repr__()
+            if 'Compose(' in str_:
+                str_ = str_.replace('\n', '\n    ')
+            format_string += '\n'
+            format_string += f'    {str_}'
+        format_string += '\n)'
+        return format_string
diff --git a/mmdet/datasets/pipelines/formating.py b/mmdet/datasets/pipelines/formating.py
new file mode 100644
index 0000000..3b3e45a
--- /dev/null
+++ b/mmdet/datasets/pipelines/formating.py
@@ -0,0 +1,9 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# flake8: noqa
+import warnings
+
+from .formatting import *
+
+warnings.warn('DeprecationWarning: mmdet.datasets.pipelines.formating will be '
+              'deprecated, please replace it with '
+              'mmdet.datasets.pipelines.formatting.')
diff --git a/mmdet/datasets/pipelines/formatting.py b/mmdet/datasets/pipelines/formatting.py
new file mode 100644
index 0000000..2e07f38
--- /dev/null
+++ b/mmdet/datasets/pipelines/formatting.py
@@ -0,0 +1,403 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from collections.abc import Sequence
+
+import mmcv
+import numpy as np
+import torch
+from mmcv.parallel import DataContainer as DC
+
+from ..builder import PIPELINES
+
+
+def to_tensor(data):
+    """Convert objects of various python types to :obj:`torch.Tensor`.
+
+    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
+    :class:`Sequence`, :class:`int` and :class:`float`.
+
+    Args:
+        data (torch.Tensor | numpy.ndarray | Sequence | int | float): Data to
+            be converted.
+    """
+
+    if isinstance(data, torch.Tensor):
+        return data
+    elif isinstance(data, np.ndarray):
+        return torch.from_numpy(data)
+    elif isinstance(data, Sequence) and not mmcv.is_str(data):
+        return torch.tensor(data)
+    elif isinstance(data, int):
+        return torch.LongTensor([data])
+    elif isinstance(data, float):
+        return torch.FloatTensor([data])
+    else:
+        raise TypeError(f'type {type(data)} cannot be converted to tensor.')
+
+
+@PIPELINES.register_module()
+class ToTensor:
+    """Convert some results to :obj:`torch.Tensor` by given keys.
+
+    Args:
+        keys (Sequence[str]): Keys that need to be converted to Tensor.
+    """
+
+    def __init__(self, keys):
+        self.keys = keys
+
+    def __call__(self, results):
+        """Call function to convert data in results to :obj:`torch.Tensor`.
+
+        Args:
+            results (dict): Result dict contains the data to convert.
+
+        Returns:
+            dict: The result dict contains the data converted
+                to :obj:`torch.Tensor`.
+        """
+        for key in self.keys:
+            results[key] = to_tensor(results[key])
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + f'(keys={self.keys})'
+
+
+@PIPELINES.register_module()
+class ImageToTensor:
+    """Convert image to :obj:`torch.Tensor` by given keys.
+
+    The dimension order of input image is (H, W, C). The pipeline will convert
+    it to (C, H, W). If only 2 dimension (H, W) is given, the output would be
+    (1, H, W).
+
+    Args:
+        keys (Sequence[str]): Key of images to be converted to Tensor.
+    """
+
+    def __init__(self, keys):
+        self.keys = keys
+
+    def __call__(self, results):
+        """Call function to convert image in results to :obj:`torch.Tensor` and
+        permute the channel order.
+
+        Args:
+            results (dict): Result dict contains the image data to convert.
+
+        Returns:
+            dict: The result dict contains the image converted
+                to :obj:`torch.Tensor` and permuted to (C, H, W) order.
+        """
+        for key in self.keys:
+            img = results[key]
+            if len(img.shape) < 3:
+                img = np.expand_dims(img, -1)
+            results[key] = to_tensor(img).permute(2, 0, 1).contiguous()
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + f'(keys={self.keys})'
+
+
+@PIPELINES.register_module()
+class Transpose:
+    """Transpose some results by given keys.
+
+    Args:
+        keys (Sequence[str]): Keys of results to be transposed.
+        order (Sequence[int]): Order of transpose.
+    """
+
+    def __init__(self, keys, order):
+        self.keys = keys
+        self.order = order
+
+    def __call__(self, results):
+        """Call function to transpose the channel order of data in results.
+
+        Args:
+            results (dict): Result dict contains the data to transpose.
+
+        Returns:
+            dict: The result dict contains the data transposed to \
+                ``self.order``.
+        """
+        for key in self.keys:
+            results[key] = results[key].transpose(self.order)
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + \
+               f'(keys={self.keys}, order={self.order})'
+
+
+@PIPELINES.register_module()
+class ToDataContainer:
+    """Convert results to :obj:`mmcv.DataContainer` by given fields.
+
+    Args:
+        fields (Sequence[dict]): Each field is a dict like
+            ``dict(key='xxx', **kwargs)``. The ``key`` in result will
+            be converted to :obj:`mmcv.DataContainer` with ``**kwargs``.
+            Default: ``(dict(key='img', stack=True), dict(key='gt_bboxes'),
+            dict(key='gt_labels'))``.
+    """
+
+    def __init__(self,
+                 fields=(dict(key='img', stack=True), dict(key='gt_bboxes'),
+                         dict(key='gt_labels'))):
+        self.fields = fields
+
+    def __call__(self, results):
+        """Call function to convert data in results to
+        :obj:`mmcv.DataContainer`.
+
+        Args:
+            results (dict): Result dict contains the data to convert.
+
+        Returns:
+            dict: The result dict contains the data converted to \
+                :obj:`mmcv.DataContainer`.
+        """
+
+        for field in self.fields:
+            field = field.copy()
+            key = field.pop('key')
+            results[key] = DC(results[key], **field)
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + f'(fields={self.fields})'
+
+
+@PIPELINES.register_module()
+class DefaultFormatBundle:
+    """Default formatting bundle.
+
+    It simplifies the pipeline of formatting common fields, including "img",
+    "proposals", "gt_bboxes", "gt_labels", "gt_masks" and "gt_semantic_seg".
+    These fields are formatted as follows.
+
+    - img: (1)transpose & to tensor, (2)to DataContainer (stack=True)
+    - proposals: (1)to tensor, (2)to DataContainer
+    - gt_bboxes: (1)to tensor, (2)to DataContainer
+    - gt_bboxes_ignore: (1)to tensor, (2)to DataContainer
+    - gt_labels: (1)to tensor, (2)to DataContainer
+    - gt_masks: (1)to tensor, (2)to DataContainer (cpu_only=True)
+    - gt_semantic_seg: (1)unsqueeze dim-0 (2)to tensor, \
+                       (3)to DataContainer (stack=True)
+
+    Args:
+        img_to_float (bool): Whether to force the image to be converted to
+            float type. Default: True.
+        pad_val (dict): A dict for padding value in batch collating,
+            the default value is `dict(img=0, masks=0, seg=255)`.
+            Without this argument, the padding value of "gt_semantic_seg"
+            will be set to 0 by default, which should be 255.
+    """
+
+    def __init__(self,
+                 img_to_float=True,
+                 pad_val=dict(img=0, masks=0, seg=255)):
+        self.img_to_float = img_to_float
+        self.pad_val = pad_val
+
+    def __call__(self, results):
+        """Call function to transform and format common fields in results.
+
+        Args:
+            results (dict): Result dict contains the data to convert.
+
+        Returns:
+            dict: The result dict contains the data that is formatted with \
+                default bundle.
+        """
+
+        if 'img' in results:
+            img = results['img']
+            if self.img_to_float is True and img.dtype == np.uint8:
+                # Normally, image is of uint8 type without normalization.
+                # At this time, it needs to be forced to be converted to
+                # flot32, otherwise the model training and inference
+                # will be wrong. Only used for YOLOX currently .
+                img = img.astype(np.float32)
+            # add default meta keys
+            results = self._add_default_meta_keys(results)
+            if len(img.shape) < 3:
+                img = np.expand_dims(img, -1)
+            # To improve the computational speed by by 3-5 times, apply:
+            # If image is not contiguous, use
+            # `numpy.transpose()` followed by `numpy.ascontiguousarray()`
+            # If image is already contiguous, use
+            # `torch.permute()` followed by `torch.contiguous()`
+            # Refer to https://github.com/open-mmlab/mmdetection/pull/9533
+            # for more details
+            if not img.flags.c_contiguous:
+                img = np.ascontiguousarray(img.transpose(2, 0, 1))
+                img = to_tensor(img)
+            else:
+                img = to_tensor(img).permute(2, 0, 1).contiguous()
+            results['img'] = DC(
+                img, padding_value=self.pad_val['img'], stack=True)
+        for key in ['proposals', 'gt_bboxes', 'gt_bboxes_ignore', 'gt_labels']:
+            if key not in results:
+                continue
+            results[key] = DC(to_tensor(results[key]))
+        if 'gt_masks' in results:
+            results['gt_masks'] = DC(
+                results['gt_masks'],
+                padding_value=self.pad_val['masks'],
+                cpu_only=True)
+        if 'gt_semantic_seg' in results:
+            results['gt_semantic_seg'] = DC(
+                to_tensor(results['gt_semantic_seg'][None, ...]),
+                padding_value=self.pad_val['seg'],
+                stack=True)
+        return results
+
+    def _add_default_meta_keys(self, results):
+        """Add default meta keys.
+
+        We set default meta keys including `pad_shape`, `scale_factor` and
+        `img_norm_cfg` to avoid the case where no `Resize`, `Normalize` and
+        `Pad` are implemented during the whole pipeline.
+
+        Args:
+            results (dict): Result dict contains the data to convert.
+
+        Returns:
+            results (dict): Updated result dict contains the data to convert.
+        """
+        img = results['img']
+        results.setdefault('pad_shape', img.shape)
+        results.setdefault('scale_factor', 1.0)
+        num_channels = 1 if len(img.shape) < 3 else img.shape[2]
+        results.setdefault(
+            'img_norm_cfg',
+            dict(
+                mean=np.zeros(num_channels, dtype=np.float32),
+                std=np.ones(num_channels, dtype=np.float32),
+                to_rgb=False))
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + \
+               f'(img_to_float={self.img_to_float})'
+
+
+@PIPELINES.register_module()
+class Collect:
+    """Collect data from the loader relevant to the specific task.
+
+    This is usually the last stage of the data loader pipeline. Typically keys
+    is set to some subset of "img", "proposals", "gt_bboxes",
+    "gt_bboxes_ignore", "gt_labels", and/or "gt_masks".
+
+    The "img_meta" item is always populated.  The contents of the "img_meta"
+    dictionary depends on "meta_keys". By default this includes:
+
+        - "img_shape": shape of the image input to the network as a tuple \
+            (h, w, c).  Note that images may be zero padded on the \
+            bottom/right if the batch tensor is larger than this shape.
+
+        - "scale_factor": a float indicating the preprocessing scale
+
+        - "flip": a boolean indicating if image flip transform was used
+
+        - "filename": path to the image file
+
+        - "ori_shape": original shape of the image as a tuple (h, w, c)
+
+        - "pad_shape": image shape after padding
+
+        - "img_norm_cfg": a dict of normalization information:
+
+            - mean - per channel mean subtraction
+            - std - per channel std divisor
+            - to_rgb - bool indicating if bgr was converted to rgb
+
+    Args:
+        keys (Sequence[str]): Keys of results to be collected in ``data``.
+        meta_keys (Sequence[str], optional): Meta keys to be converted to
+            ``mmcv.DataContainer`` and collected in ``data[img_metas]``.
+            Default: ``('filename', 'ori_filename', 'ori_shape', 'img_shape',
+            'pad_shape', 'scale_factor', 'flip', 'flip_direction',
+            'img_norm_cfg')``
+    """
+
+    def __init__(self,
+                 keys,
+                 meta_keys=('filename', 'ori_filename', 'ori_shape',
+                            'img_shape', 'pad_shape', 'scale_factor', 'flip',
+                            'flip_direction', 'img_norm_cfg')):
+        self.keys = keys
+        self.meta_keys = meta_keys
+
+    def __call__(self, results):
+        """Call function to collect keys in results. The keys in ``meta_keys``
+        will be converted to :obj:mmcv.DataContainer.
+
+        Args:
+            results (dict): Result dict contains the data to collect.
+
+        Returns:
+            dict: The result dict contains the following keys
+
+                - keys in``self.keys``
+                - ``img_metas``
+        """
+
+        data = {}
+        img_meta = {}
+        for key in self.meta_keys:
+            img_meta[key] = results[key]
+        data['img_metas'] = DC(img_meta, cpu_only=True)
+        for key in self.keys:
+            data[key] = results[key]
+        return data
+
+    def __repr__(self):
+        return self.__class__.__name__ + \
+               f'(keys={self.keys}, meta_keys={self.meta_keys})'
+
+
+@PIPELINES.register_module()
+class WrapFieldsToLists:
+    """Wrap fields of the data dictionary into lists for evaluation.
+
+    This class can be used as a last step of a test or validation
+    pipeline for single image evaluation or inference.
+
+    Example:
+        >>> test_pipeline = [
+        >>>    dict(type='LoadImageFromFile'),
+        >>>    dict(type='Normalize',
+                    mean=[123.675, 116.28, 103.53],
+                    std=[58.395, 57.12, 57.375],
+                    to_rgb=True),
+        >>>    dict(type='Pad', size_divisor=32),
+        >>>    dict(type='ImageToTensor', keys=['img']),
+        >>>    dict(type='Collect', keys=['img']),
+        >>>    dict(type='WrapFieldsToLists')
+        >>> ]
+    """
+
+    def __call__(self, results):
+        """Call function to wrap fields into lists.
+
+        Args:
+            results (dict): Result dict contains the data to wrap.
+
+        Returns:
+            dict: The result dict where value of ``self.keys`` are wrapped \
+                into list.
+        """
+
+        # Wrap dict fields into lists
+        for key, val in results.items():
+            results[key] = [val]
+        return results
+
+    def __repr__(self):
+        return f'{self.__class__.__name__}()'
diff --git a/mmdet/datasets/pipelines/instaboost.py b/mmdet/datasets/pipelines/instaboost.py
new file mode 100644
index 0000000..ca10c4c
--- /dev/null
+++ b/mmdet/datasets/pipelines/instaboost.py
@@ -0,0 +1,118 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+
+from ..builder import PIPELINES
+
+
+@PIPELINES.register_module()
+class InstaBoost:
+    r"""Data augmentation method in `InstaBoost: Boosting Instance
+    Segmentation Via Probability Map Guided Copy-Pasting
+    <https://arxiv.org/abs/1908.07801>`_.
+
+    Refer to https://github.com/GothicAi/Instaboost for implementation details.
+
+    Args:
+        action_candidate (tuple): Action candidates. "normal", "horizontal", \
+            "vertical", "skip" are supported. Default: ('normal', \
+            'horizontal', 'skip').
+        action_prob (tuple): Corresponding action probabilities. Should be \
+            the same length as action_candidate. Default: (1, 0, 0).
+        scale (tuple): (min scale, max scale). Default: (0.8, 1.2).
+        dx (int): The maximum x-axis shift will be (instance width) / dx.
+            Default 15.
+        dy (int): The maximum y-axis shift will be (instance height) / dy.
+            Default 15.
+        theta (tuple): (min rotation degree, max rotation degree). \
+            Default: (-1, 1).
+        color_prob (float): Probability of images for color augmentation.
+            Default 0.5.
+        heatmap_flag (bool): Whether to use heatmap guided. Default False.
+        aug_ratio (float): Probability of applying this transformation. \
+            Default 0.5.
+    """
+
+    def __init__(self,
+                 action_candidate=('normal', 'horizontal', 'skip'),
+                 action_prob=(1, 0, 0),
+                 scale=(0.8, 1.2),
+                 dx=15,
+                 dy=15,
+                 theta=(-1, 1),
+                 color_prob=0.5,
+                 hflag=False,
+                 aug_ratio=0.5):
+        try:
+            import instaboostfast as instaboost
+        except ImportError:
+            raise ImportError(
+                'Please run "pip install instaboostfast" '
+                'to install instaboostfast first for instaboost augmentation.')
+        self.cfg = instaboost.InstaBoostConfig(action_candidate, action_prob,
+                                               scale, dx, dy, theta,
+                                               color_prob, hflag)
+        self.aug_ratio = aug_ratio
+
+    def _load_anns(self, results):
+        labels = results['ann_info']['labels']
+        masks = results['ann_info']['masks']
+        bboxes = results['ann_info']['bboxes']
+        n = len(labels)
+
+        anns = []
+        for i in range(n):
+            label = labels[i]
+            bbox = bboxes[i]
+            mask = masks[i]
+            x1, y1, x2, y2 = bbox
+            # assert (x2 - x1) >= 1 and (y2 - y1) >= 1
+            bbox = [x1, y1, x2 - x1, y2 - y1]
+            anns.append({
+                'category_id': label,
+                'segmentation': mask,
+                'bbox': bbox
+            })
+
+        return anns
+
+    def _parse_anns(self, results, anns, img):
+        gt_bboxes = []
+        gt_labels = []
+        gt_masks_ann = []
+        for ann in anns:
+            x1, y1, w, h = ann['bbox']
+            # TODO: more essential bug need to be fixed in instaboost
+            if w <= 0 or h <= 0:
+                continue
+            bbox = [x1, y1, x1 + w, y1 + h]
+            gt_bboxes.append(bbox)
+            gt_labels.append(ann['category_id'])
+            gt_masks_ann.append(ann['segmentation'])
+        gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
+        gt_labels = np.array(gt_labels, dtype=np.int64)
+        results['ann_info']['labels'] = gt_labels
+        results['ann_info']['bboxes'] = gt_bboxes
+        results['ann_info']['masks'] = gt_masks_ann
+        results['img'] = img
+        return results
+
+    def __call__(self, results):
+        img = results['img']
+        ori_type = img.dtype
+        anns = self._load_anns(results)
+        if np.random.choice([0, 1], p=[1 - self.aug_ratio, self.aug_ratio]):
+            try:
+                import instaboostfast as instaboost
+            except ImportError:
+                raise ImportError('Please run "pip install instaboostfast" '
+                                  'to install instaboostfast first.')
+            anns, img = instaboost.get_new_data(
+                anns, img.astype(np.uint8), self.cfg, background=None)
+
+        results = self._parse_anns(results, anns, img.astype(ori_type))
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(cfg={self.cfg}, aug_ratio={self.aug_ratio})'
+        return repr_str
diff --git a/mmdet/datasets/pipelines/loading.py b/mmdet/datasets/pipelines/loading.py
new file mode 100644
index 0000000..8af8cf3
--- /dev/null
+++ b/mmdet/datasets/pipelines/loading.py
@@ -0,0 +1,645 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+
+import mmcv
+import numpy as np
+import pycocotools.mask as maskUtils
+
+from mmdet.core import BitmapMasks, PolygonMasks
+from ..builder import PIPELINES
+
+try:
+    from panopticapi.utils import rgb2id
+except ImportError:
+    rgb2id = None
+
+
+@PIPELINES.register_module()
+class LoadImageFromFile:
+    """Load an image from file.
+
+    Required keys are "img_prefix" and "img_info" (a dict that must contain the
+    key "filename"). Added or updated keys are "filename", "img", "img_shape",
+    "ori_shape" (same as `img_shape`), "pad_shape" (same as `img_shape`),
+    "scale_factor" (1.0) and "img_norm_cfg" (means=0 and stds=1).
+
+    Args:
+        to_float32 (bool): Whether to convert the loaded image to a float32
+            numpy array. If set to False, the loaded image is an uint8 array.
+            Defaults to False.
+        color_type (str): The flag argument for :func:`mmcv.imfrombytes`.
+            Defaults to 'color'.
+        file_client_args (dict): Arguments to instantiate a FileClient.
+            See :class:`mmcv.fileio.FileClient` for details.
+            Defaults to ``dict(backend='disk')``.
+    """
+
+    def __init__(self,
+                 to_float32=False,
+                 color_type='color',
+                 channel_order='bgr',
+                 file_client_args=dict(backend='disk')):
+        self.to_float32 = to_float32
+        self.color_type = color_type
+        self.channel_order = channel_order
+        self.file_client_args = file_client_args.copy()
+        self.file_client = None
+
+    def __call__(self, results):
+        """Call functions to load image and get image meta information.
+
+        Args:
+            results (dict): Result dict from :obj:`mmdet.CustomDataset`.
+
+        Returns:
+            dict: The dict contains loaded image and meta information.
+        """
+
+        if self.file_client is None:
+            self.file_client = mmcv.FileClient(**self.file_client_args)
+
+        if results['img_prefix'] is not None:
+            filename = osp.join(results['img_prefix'],
+                                results['img_info']['filename'])
+        else:
+            filename = results['img_info']['filename']
+
+        img_bytes = self.file_client.get(filename)
+        img = mmcv.imfrombytes(
+            img_bytes, flag=self.color_type, channel_order=self.channel_order)
+        if self.to_float32:
+            img = img.astype(np.float32)
+
+        results['filename'] = filename
+        results['ori_filename'] = results['img_info']['filename']
+        results['img'] = img
+        results['img_shape'] = img.shape
+        results['ori_shape'] = img.shape
+        results['img_fields'] = ['img']
+        return results
+
+    def __repr__(self):
+        repr_str = (f'{self.__class__.__name__}('
+                    f'to_float32={self.to_float32}, '
+                    f"color_type='{self.color_type}', "
+                    f"channel_order='{self.channel_order}', "
+                    f'file_client_args={self.file_client_args})')
+        return repr_str
+
+
+@PIPELINES.register_module()
+class LoadImageFromWebcam(LoadImageFromFile):
+    """Load an image from webcam.
+
+    Similar with :obj:`LoadImageFromFile`, but the image read from webcam is in
+    ``results['img']``.
+    """
+
+    def __call__(self, results):
+        """Call functions to add image meta information.
+
+        Args:
+            results (dict): Result dict with Webcam read image in
+                ``results['img']``.
+
+        Returns:
+            dict: The dict contains loaded image and meta information.
+        """
+
+        img = results['img']
+        if self.to_float32:
+            img = img.astype(np.float32)
+
+        results['filename'] = None
+        results['ori_filename'] = None
+        results['img'] = img
+        results['img_shape'] = img.shape
+        results['ori_shape'] = img.shape
+        results['img_fields'] = ['img']
+        return results
+
+
+@PIPELINES.register_module()
+class LoadMultiChannelImageFromFiles:
+    """Load multi-channel images from a list of separate channel files.
+
+    Required keys are "img_prefix" and "img_info" (a dict that must contain the
+    key "filename", which is expected to be a list of filenames).
+    Added or updated keys are "filename", "img", "img_shape",
+    "ori_shape" (same as `img_shape`), "pad_shape" (same as `img_shape`),
+    "scale_factor" (1.0) and "img_norm_cfg" (means=0 and stds=1).
+
+    Args:
+        to_float32 (bool): Whether to convert the loaded image to a float32
+            numpy array. If set to False, the loaded image is an uint8 array.
+            Defaults to False.
+        color_type (str): The flag argument for :func:`mmcv.imfrombytes`.
+            Defaults to 'color'.
+        file_client_args (dict): Arguments to instantiate a FileClient.
+            See :class:`mmcv.fileio.FileClient` for details.
+            Defaults to ``dict(backend='disk')``.
+    """
+
+    def __init__(self,
+                 to_float32=False,
+                 color_type='unchanged',
+                 file_client_args=dict(backend='disk')):
+        self.to_float32 = to_float32
+        self.color_type = color_type
+        self.file_client_args = file_client_args.copy()
+        self.file_client = None
+
+    def __call__(self, results):
+        """Call functions to load multiple images and get images meta
+        information.
+
+        Args:
+            results (dict): Result dict from :obj:`mmdet.CustomDataset`.
+
+        Returns:
+            dict: The dict contains loaded images and meta information.
+        """
+
+        if self.file_client is None:
+            self.file_client = mmcv.FileClient(**self.file_client_args)
+
+        if results['img_prefix'] is not None:
+            filename = [
+                osp.join(results['img_prefix'], fname)
+                for fname in results['img_info']['filename']
+            ]
+        else:
+            filename = results['img_info']['filename']
+
+        img = []
+        for name in filename:
+            img_bytes = self.file_client.get(name)
+            img.append(mmcv.imfrombytes(img_bytes, flag=self.color_type))
+        img = np.stack(img, axis=-1)
+        if self.to_float32:
+            img = img.astype(np.float32)
+
+        results['filename'] = filename
+        results['ori_filename'] = results['img_info']['filename']
+        results['img'] = img
+        results['img_shape'] = img.shape
+        results['ori_shape'] = img.shape
+        # Set initial values for default meta_keys
+        results['pad_shape'] = img.shape
+        results['scale_factor'] = 1.0
+        num_channels = 1 if len(img.shape) < 3 else img.shape[2]
+        results['img_norm_cfg'] = dict(
+            mean=np.zeros(num_channels, dtype=np.float32),
+            std=np.ones(num_channels, dtype=np.float32),
+            to_rgb=False)
+        return results
+
+    def __repr__(self):
+        repr_str = (f'{self.__class__.__name__}('
+                    f'to_float32={self.to_float32}, '
+                    f"color_type='{self.color_type}', "
+                    f'file_client_args={self.file_client_args})')
+        return repr_str
+
+
+@PIPELINES.register_module()
+class LoadAnnotations:
+    """Load multiple types of annotations.
+
+    Args:
+        with_bbox (bool): Whether to parse and load the bbox annotation.
+             Default: True.
+        with_label (bool): Whether to parse and load the label annotation.
+            Default: True.
+        with_mask (bool): Whether to parse and load the mask annotation.
+             Default: False.
+        with_seg (bool): Whether to parse and load the semantic segmentation
+            annotation. Default: False.
+        poly2mask (bool): Whether to convert the instance masks from polygons
+            to bitmaps. Default: True.
+        denorm_bbox (bool): Whether to convert bbox from relative value to
+            absolute value. Only used in OpenImage Dataset.
+            Default: False.
+        file_client_args (dict): Arguments to instantiate a FileClient.
+            See :class:`mmcv.fileio.FileClient` for details.
+            Defaults to ``dict(backend='disk')``.
+    """
+
+    def __init__(self,
+                 with_bbox=True,
+                 with_label=True,
+                 with_mask=False,
+                 with_seg=False,
+                 poly2mask=True,
+                 denorm_bbox=False,
+                 file_client_args=dict(backend='disk')):
+        self.with_bbox = with_bbox
+        self.with_label = with_label
+        self.with_mask = with_mask
+        self.with_seg = with_seg
+        self.poly2mask = poly2mask
+        self.denorm_bbox = denorm_bbox
+        self.file_client_args = file_client_args.copy()
+        self.file_client = None
+
+    def _load_bboxes(self, results):
+        """Private function to load bounding box annotations.
+
+        Args:
+            results (dict): Result dict from :obj:`mmdet.CustomDataset`.
+
+        Returns:
+            dict: The dict contains loaded bounding box annotations.
+        """
+
+        ann_info = results['ann_info']
+        results['gt_bboxes'] = ann_info['bboxes'].copy()
+
+        if self.denorm_bbox:
+            bbox_num = results['gt_bboxes'].shape[0]
+            if bbox_num != 0:
+                h, w = results['img_shape'][:2]
+                results['gt_bboxes'][:, 0::2] *= w
+                results['gt_bboxes'][:, 1::2] *= h
+
+        gt_bboxes_ignore = ann_info.get('bboxes_ignore', None)
+        if gt_bboxes_ignore is not None:
+            results['gt_bboxes_ignore'] = gt_bboxes_ignore.copy()
+            results['bbox_fields'].append('gt_bboxes_ignore')
+        results['bbox_fields'].append('gt_bboxes')
+
+        gt_is_group_ofs = ann_info.get('gt_is_group_ofs', None)
+        if gt_is_group_ofs is not None:
+            results['gt_is_group_ofs'] = gt_is_group_ofs.copy()
+
+        return results
+
+    def _load_labels(self, results):
+        """Private function to load label annotations.
+
+        Args:
+            results (dict): Result dict from :obj:`mmdet.CustomDataset`.
+
+        Returns:
+            dict: The dict contains loaded label annotations.
+        """
+
+        results['gt_labels'] = results['ann_info']['labels'].copy()
+        return results
+
+    def _poly2mask(self, mask_ann, img_h, img_w):
+        """Private function to convert masks represented with polygon to
+        bitmaps.
+
+        Args:
+            mask_ann (list | dict): Polygon mask annotation input.
+            img_h (int): The height of output mask.
+            img_w (int): The width of output mask.
+
+        Returns:
+            numpy.ndarray: The decode bitmap mask of shape (img_h, img_w).
+        """
+
+        if isinstance(mask_ann, list):
+            # polygon -- a single object might consist of multiple parts
+            # we merge all parts into one mask rle code
+            rles = maskUtils.frPyObjects(mask_ann, img_h, img_w)
+            rle = maskUtils.merge(rles)
+        elif isinstance(mask_ann['counts'], list):
+            # uncompressed RLE
+            rle = maskUtils.frPyObjects(mask_ann, img_h, img_w)
+        else:
+            # rle
+            rle = mask_ann
+        mask = maskUtils.decode(rle)
+        return mask
+
+    def process_polygons(self, polygons):
+        """Convert polygons to list of ndarray and filter invalid polygons.
+
+        Args:
+            polygons (list[list]): Polygons of one instance.
+
+        Returns:
+            list[numpy.ndarray]: Processed polygons.
+        """
+
+        polygons = [np.array(p) for p in polygons]
+        valid_polygons = []
+        for polygon in polygons:
+            if len(polygon) % 2 == 0 and len(polygon) >= 6:
+                valid_polygons.append(polygon)
+        return valid_polygons
+
+    def _load_masks(self, results):
+        """Private function to load mask annotations.
+
+        Args:
+            results (dict): Result dict from :obj:`mmdet.CustomDataset`.
+
+        Returns:
+            dict: The dict contains loaded mask annotations.
+                If ``self.poly2mask`` is set ``True``, `gt_mask` will contain
+                :obj:`PolygonMasks`. Otherwise, :obj:`BitmapMasks` is used.
+        """
+
+        h, w = results['img_info']['height'], results['img_info']['width']
+        gt_masks = results['ann_info']['masks']
+        if self.poly2mask:
+            gt_masks = BitmapMasks(
+                [self._poly2mask(mask, h, w) for mask in gt_masks], h, w)
+        else:
+            gt_masks = PolygonMasks(
+                [self.process_polygons(polygons) for polygons in gt_masks], h,
+                w)
+        results['gt_masks'] = gt_masks
+        results['mask_fields'].append('gt_masks')
+        return results
+
+    def _load_semantic_seg(self, results):
+        """Private function to load semantic segmentation annotations.
+
+        Args:
+            results (dict): Result dict from :obj:`dataset`.
+
+        Returns:
+            dict: The dict contains loaded semantic segmentation annotations.
+        """
+
+        if self.file_client is None:
+            self.file_client = mmcv.FileClient(**self.file_client_args)
+
+        filename = osp.join(results['seg_prefix'],
+                            results['ann_info']['seg_map'])
+        img_bytes = self.file_client.get(filename)
+        results['gt_semantic_seg'] = mmcv.imfrombytes(
+            img_bytes, flag='unchanged').squeeze()
+        results['seg_fields'].append('gt_semantic_seg')
+        return results
+
+    def __call__(self, results):
+        """Call function to load multiple types annotations.
+
+        Args:
+            results (dict): Result dict from :obj:`mmdet.CustomDataset`.
+
+        Returns:
+            dict: The dict contains loaded bounding box, label, mask and
+                semantic segmentation annotations.
+        """
+
+        if self.with_bbox:
+            results = self._load_bboxes(results)
+            if results is None:
+                return None
+        if self.with_label:
+            results = self._load_labels(results)
+        if self.with_mask:
+            results = self._load_masks(results)
+        if self.with_seg:
+            results = self._load_semantic_seg(results)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(with_bbox={self.with_bbox}, '
+        repr_str += f'with_label={self.with_label}, '
+        repr_str += f'with_mask={self.with_mask}, '
+        repr_str += f'with_seg={self.with_seg}, '
+        repr_str += f'poly2mask={self.poly2mask}, '
+        repr_str += f'file_client_args={self.file_client_args})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class LoadPanopticAnnotations(LoadAnnotations):
+    """Load multiple types of panoptic annotations.
+
+    Args:
+        with_bbox (bool): Whether to parse and load the bbox annotation.
+             Default: True.
+        with_label (bool): Whether to parse and load the label annotation.
+            Default: True.
+        with_mask (bool): Whether to parse and load the mask annotation.
+             Default: True.
+        with_seg (bool): Whether to parse and load the semantic segmentation
+            annotation. Default: True.
+        file_client_args (dict): Arguments to instantiate a FileClient.
+            See :class:`mmcv.fileio.FileClient` for details.
+            Defaults to ``dict(backend='disk')``.
+    """
+
+    def __init__(self,
+                 with_bbox=True,
+                 with_label=True,
+                 with_mask=True,
+                 with_seg=True,
+                 file_client_args=dict(backend='disk')):
+        if rgb2id is None:
+            raise RuntimeError(
+                'panopticapi is not installed, please install it by: '
+                'pip install git+https://github.com/cocodataset/'
+                'panopticapi.git.')
+
+        super(LoadPanopticAnnotations, self).__init__(
+            with_bbox=with_bbox,
+            with_label=with_label,
+            with_mask=with_mask,
+            with_seg=with_seg,
+            poly2mask=True,
+            denorm_bbox=False,
+            file_client_args=file_client_args)
+
+    def _load_masks_and_semantic_segs(self, results):
+        """Private function to load mask and semantic segmentation annotations.
+
+        In gt_semantic_seg, the foreground label is from `0` to
+        `num_things - 1`, the background label is from `num_things` to
+        `num_things + num_stuff - 1`, 255 means the ignored label (`VOID`).
+
+        Args:
+            results (dict): Result dict from :obj:`mmdet.CustomDataset`.
+
+        Returns:
+            dict: The dict contains loaded mask and semantic segmentation
+                annotations. `BitmapMasks` is used for mask annotations.
+        """
+
+        if self.file_client is None:
+            self.file_client = mmcv.FileClient(**self.file_client_args)
+
+        filename = osp.join(results['seg_prefix'],
+                            results['ann_info']['seg_map'])
+        img_bytes = self.file_client.get(filename)
+        pan_png = mmcv.imfrombytes(
+            img_bytes, flag='color', channel_order='rgb').squeeze()
+        pan_png = rgb2id(pan_png)
+
+        gt_masks = []
+        gt_seg = np.zeros_like(pan_png) + 255  # 255 as ignore
+
+        for mask_info in results['ann_info']['masks']:
+            mask = (pan_png == mask_info['id'])
+            gt_seg = np.where(mask, mask_info['category'], gt_seg)
+
+            # The legal thing masks
+            if mask_info.get('is_thing'):
+                gt_masks.append(mask.astype(np.uint8))
+
+        if self.with_mask:
+            h, w = results['img_info']['height'], results['img_info']['width']
+            gt_masks = BitmapMasks(gt_masks, h, w)
+            results['gt_masks'] = gt_masks
+            results['mask_fields'].append('gt_masks')
+
+        if self.with_seg:
+            results['gt_semantic_seg'] = gt_seg
+            results['seg_fields'].append('gt_semantic_seg')
+        return results
+
+    def __call__(self, results):
+        """Call function to load multiple types panoptic annotations.
+
+        Args:
+            results (dict): Result dict from :obj:`mmdet.CustomDataset`.
+
+        Returns:
+            dict: The dict contains loaded bounding box, label, mask and
+                semantic segmentation annotations.
+        """
+
+        if self.with_bbox:
+            results = self._load_bboxes(results)
+            if results is None:
+                return None
+        if self.with_label:
+            results = self._load_labels(results)
+        if self.with_mask or self.with_seg:
+            # The tasks completed by '_load_masks' and '_load_semantic_segs'
+            # in LoadAnnotations are merged to one function.
+            results = self._load_masks_and_semantic_segs(results)
+
+        return results
+
+
+@PIPELINES.register_module()
+class LoadProposals:
+    """Load proposal pipeline.
+
+    Required key is "proposals". Updated keys are "proposals", "bbox_fields".
+
+    Args:
+        num_max_proposals (int, optional): Maximum number of proposals to load.
+            If not specified, all proposals will be loaded.
+    """
+
+    def __init__(self, num_max_proposals=None):
+        self.num_max_proposals = num_max_proposals
+
+    def __call__(self, results):
+        """Call function to load proposals from file.
+
+        Args:
+            results (dict): Result dict from :obj:`mmdet.CustomDataset`.
+
+        Returns:
+            dict: The dict contains loaded proposal annotations.
+        """
+
+        proposals = results['proposals']
+        if proposals.shape[1] not in (4, 5):
+            raise AssertionError(
+                'proposals should have shapes (n, 4) or (n, 5), '
+                f'but found {proposals.shape}')
+        proposals = proposals[:, :4]
+
+        if self.num_max_proposals is not None:
+            proposals = proposals[:self.num_max_proposals]
+
+        if len(proposals) == 0:
+            proposals = np.array([[0, 0, 0, 0]], dtype=np.float32)
+        results['proposals'] = proposals
+        results['bbox_fields'].append('proposals')
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + \
+            f'(num_max_proposals={self.num_max_proposals})'
+
+
+@PIPELINES.register_module()
+class FilterAnnotations:
+    """Filter invalid annotations.
+
+    Args:
+        min_gt_bbox_wh (tuple[float]): Minimum width and height of ground truth
+            boxes. Default: (1., 1.)
+        min_gt_mask_area (int): Minimum foreground area of ground truth masks.
+            Default: 1
+        by_box (bool): Filter instances with bounding boxes not meeting the
+            min_gt_bbox_wh threshold. Default: True
+        by_mask (bool): Filter instances with masks not meeting
+            min_gt_mask_area threshold. Default: False
+        keep_empty (bool): Whether to return None when it
+            becomes an empty bbox after filtering. Default: True
+    """
+
+    def __init__(self,
+                 min_gt_bbox_wh=(1., 1.),
+                 min_gt_mask_area=1,
+                 by_box=True,
+                 by_mask=False,
+                 keep_empty=True):
+        # TODO: add more filter options
+        assert by_box or by_mask
+        self.min_gt_bbox_wh = min_gt_bbox_wh
+        self.min_gt_mask_area = min_gt_mask_area
+        self.by_box = by_box
+        self.by_mask = by_mask
+        self.keep_empty = keep_empty
+
+    def __call__(self, results):
+        if self.by_box:
+            assert 'gt_bboxes' in results
+            gt_bboxes = results['gt_bboxes']
+            instance_num = gt_bboxes.shape[0]
+        if self.by_mask:
+            assert 'gt_masks' in results
+            gt_masks = results['gt_masks']
+            instance_num = len(gt_masks)
+
+        if instance_num == 0:
+            return results
+
+        tests = []
+        if self.by_box:
+            w = gt_bboxes[:, 2] - gt_bboxes[:, 0]
+            h = gt_bboxes[:, 3] - gt_bboxes[:, 1]
+            tests.append((w > self.min_gt_bbox_wh[0])
+                         & (h > self.min_gt_bbox_wh[1]))
+        if self.by_mask:
+            gt_masks = results['gt_masks']
+            tests.append(gt_masks.areas >= self.min_gt_mask_area)
+
+        keep = tests[0]
+        for t in tests[1:]:
+            keep = keep & t
+
+        keep = keep.nonzero()[0]
+
+        keys = ('gt_bboxes', 'gt_labels', 'gt_masks')
+        for key in keys:
+            if key in results:
+                results[key] = results[key][keep]
+        if keep.size == 0:
+            if self.keep_empty:
+                return None
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + \
+            f'(min_gt_bbox_wh={self.min_gt_bbox_wh},' \
+            f'min_gt_mask_area={self.min_gt_mask_area},' \
+            f'by_box={self.by_box},' \
+            f'by_mask={self.by_mask},' \
+            f'always_keep={self.always_keep})'
diff --git a/mmdet/datasets/pipelines/test_time_aug.py b/mmdet/datasets/pipelines/test_time_aug.py
new file mode 100644
index 0000000..5f1ab7b
--- /dev/null
+++ b/mmdet/datasets/pipelines/test_time_aug.py
@@ -0,0 +1,121 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import mmcv
+
+from ..builder import PIPELINES
+from .compose import Compose
+
+
+@PIPELINES.register_module()
+class MultiScaleFlipAug:
+    """Test-time augmentation with multiple scales and flipping.
+
+    An example configuration is as followed:
+
+    .. code-block::
+
+        img_scale=[(1333, 400), (1333, 800)],
+        flip=True,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+            dict(type='Normalize', **img_norm_cfg),
+            dict(type='Pad', size_divisor=32),
+            dict(type='ImageToTensor', keys=['img']),
+            dict(type='Collect', keys=['img']),
+        ]
+
+    After MultiScaleFLipAug with above configuration, the results are wrapped
+    into lists of the same length as followed:
+
+    .. code-block::
+
+        dict(
+            img=[...],
+            img_shape=[...],
+            scale=[(1333, 400), (1333, 400), (1333, 800), (1333, 800)]
+            flip=[False, True, False, True]
+            ...
+        )
+
+    Args:
+        transforms (list[dict]): Transforms to apply in each augmentation.
+        img_scale (tuple | list[tuple] | None): Images scales for resizing.
+        scale_factor (float | list[float] | None): Scale factors for resizing.
+        flip (bool): Whether apply flip augmentation. Default: False.
+        flip_direction (str | list[str]): Flip augmentation directions,
+            options are "horizontal", "vertical" and "diagonal". If
+            flip_direction is a list, multiple flip augmentations will be
+            applied. It has no effect when flip == False. Default:
+            "horizontal".
+    """
+
+    def __init__(self,
+                 transforms,
+                 img_scale=None,
+                 scale_factor=None,
+                 flip=False,
+                 flip_direction='horizontal'):
+        self.transforms = Compose(transforms)
+        assert (img_scale is None) ^ (scale_factor is None), (
+            'Must have but only one variable can be set')
+        if img_scale is not None:
+            self.img_scale = img_scale if isinstance(img_scale,
+                                                     list) else [img_scale]
+            self.scale_key = 'scale'
+            assert mmcv.is_list_of(self.img_scale, tuple)
+        else:
+            self.img_scale = scale_factor if isinstance(
+                scale_factor, list) else [scale_factor]
+            self.scale_key = 'scale_factor'
+
+        self.flip = flip
+        self.flip_direction = flip_direction if isinstance(
+            flip_direction, list) else [flip_direction]
+        assert mmcv.is_list_of(self.flip_direction, str)
+        if not self.flip and self.flip_direction != ['horizontal']:
+            warnings.warn(
+                'flip_direction has no effect when flip is set to False')
+        if (self.flip
+                and not any([t['type'] == 'RandomFlip' for t in transforms])):
+            warnings.warn(
+                'flip has no effect when RandomFlip is not in transforms')
+
+    def __call__(self, results):
+        """Call function to apply test time augment transforms on results.
+
+        Args:
+            results (dict): Result dict contains the data to transform.
+
+        Returns:
+           dict[str: list]: The augmented data, where each value is wrapped
+               into a list.
+        """
+
+        aug_data = []
+        flip_args = [(False, None)]
+        if self.flip:
+            flip_args += [(True, direction)
+                          for direction in self.flip_direction]
+        for scale in self.img_scale:
+            for flip, direction in flip_args:
+                _results = results.copy()
+                _results[self.scale_key] = scale
+                _results['flip'] = flip
+                _results['flip_direction'] = direction
+                data = self.transforms(_results)
+                aug_data.append(data)
+        # list of dict to dict of list
+        aug_data_dict = {key: [] for key in aug_data[0]}
+        for data in aug_data:
+            for key, val in data.items():
+                aug_data_dict[key].append(val)
+        return aug_data_dict
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(transforms={self.transforms}, '
+        repr_str += f'img_scale={self.img_scale}, flip={self.flip}, '
+        repr_str += f'flip_direction={self.flip_direction})'
+        return repr_str
diff --git a/mmdet/datasets/pipelines/transforms.py b/mmdet/datasets/pipelines/transforms.py
new file mode 100644
index 0000000..4c9ef72
--- /dev/null
+++ b/mmdet/datasets/pipelines/transforms.py
@@ -0,0 +1,2968 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import inspect
+import math
+import warnings
+
+import cv2
+import mmcv
+import numpy as np
+from numpy import random
+
+from mmdet.core import BitmapMasks, PolygonMasks, find_inside_bboxes
+from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
+from mmdet.utils import log_img_scale
+from ..builder import PIPELINES
+
+try:
+    from imagecorruptions import corrupt
+except ImportError:
+    corrupt = None
+
+try:
+    import albumentations
+    from albumentations import Compose
+except ImportError:
+    albumentations = None
+    Compose = None
+
+
+@PIPELINES.register_module()
+class Resize:
+    """Resize images & bbox & mask.
+
+    This transform resizes the input image to some scale. Bboxes and masks are
+    then resized with the same scale factor. If the input dict contains the key
+    "scale", then the scale in the input dict is used, otherwise the specified
+    scale in the init method is used. If the input dict contains the key
+    "scale_factor" (if MultiScaleFlipAug does not give img_scale but
+    scale_factor), the actual scale will be computed by image shape and
+    scale_factor.
+
+    `img_scale` can either be a tuple (single-scale) or a list of tuple
+    (multi-scale). There are 3 multiscale modes:
+
+    - ``ratio_range is not None``: randomly sample a ratio from the ratio \
+      range and multiply it with the image scale.
+    - ``ratio_range is None`` and ``multiscale_mode == "range"``: randomly \
+      sample a scale from the multiscale range.
+    - ``ratio_range is None`` and ``multiscale_mode == "value"``: randomly \
+      sample a scale from multiple scales.
+
+    Args:
+        img_scale (tuple or list[tuple]): Images scales for resizing.
+        multiscale_mode (str): Either "range" or "value".
+        ratio_range (tuple[float]): (min_ratio, max_ratio)
+        keep_ratio (bool): Whether to keep the aspect ratio when resizing the
+            image.
+        bbox_clip_border (bool, optional): Whether to clip the objects outside
+            the border of the image. In some dataset like MOT17, the gt bboxes
+            are allowed to cross the border of images. Therefore, we don't
+            need to clip the gt bboxes in these cases. Defaults to True.
+        backend (str): Image resize backend, choices are 'cv2' and 'pillow'.
+            These two backends generates slightly different results. Defaults
+            to 'cv2'.
+        interpolation (str): Interpolation method, accepted values are
+            "nearest", "bilinear", "bicubic", "area", "lanczos" for 'cv2'
+            backend, "nearest", "bilinear" for 'pillow' backend.
+        override (bool, optional): Whether to override `scale` and
+            `scale_factor` so as to call resize twice. Default False. If True,
+            after the first resizing, the existed `scale` and `scale_factor`
+            will be ignored so the second resizing can be allowed.
+            This option is a work-around for multiple times of resize in DETR.
+            Defaults to False.
+    """
+
+    def __init__(self,
+                 img_scale=None,
+                 multiscale_mode='range',
+                 ratio_range=None,
+                 keep_ratio=True,
+                 bbox_clip_border=True,
+                 backend='cv2',
+                 interpolation='bilinear',
+                 override=False):
+        if img_scale is None:
+            self.img_scale = None
+        else:
+            if isinstance(img_scale, list):
+                self.img_scale = img_scale
+            else:
+                self.img_scale = [img_scale]
+            assert mmcv.is_list_of(self.img_scale, tuple)
+
+        if ratio_range is not None:
+            # mode 1: given a scale and a range of image ratio
+            assert len(self.img_scale) == 1
+        else:
+            # mode 2: given multiple scales or a range of scales
+            assert multiscale_mode in ['value', 'range']
+
+        self.backend = backend
+        self.multiscale_mode = multiscale_mode
+        self.ratio_range = ratio_range
+        self.keep_ratio = keep_ratio
+        # TODO: refactor the override option in Resize
+        self.interpolation = interpolation
+        self.override = override
+        self.bbox_clip_border = bbox_clip_border
+
+    @staticmethod
+    def random_select(img_scales):
+        """Randomly select an img_scale from given candidates.
+
+        Args:
+            img_scales (list[tuple]): Images scales for selection.
+
+        Returns:
+            (tuple, int): Returns a tuple ``(img_scale, scale_dix)``, \
+                where ``img_scale`` is the selected image scale and \
+                ``scale_idx`` is the selected index in the given candidates.
+        """
+
+        assert mmcv.is_list_of(img_scales, tuple)
+        scale_idx = np.random.randint(len(img_scales))
+        img_scale = img_scales[scale_idx]
+        return img_scale, scale_idx
+
+    @staticmethod
+    def random_sample(img_scales):
+        """Randomly sample an img_scale when ``multiscale_mode=='range'``.
+
+        Args:
+            img_scales (list[tuple]): Images scale range for sampling.
+                There must be two tuples in img_scales, which specify the lower
+                and upper bound of image scales.
+
+        Returns:
+            (tuple, None): Returns a tuple ``(img_scale, None)``, where \
+                ``img_scale`` is sampled scale and None is just a placeholder \
+                to be consistent with :func:`random_select`.
+        """
+
+        assert mmcv.is_list_of(img_scales, tuple) and len(img_scales) == 2
+        img_scale_long = [max(s) for s in img_scales]
+        img_scale_short = [min(s) for s in img_scales]
+        long_edge = np.random.randint(
+            min(img_scale_long),
+            max(img_scale_long) + 1)
+        short_edge = np.random.randint(
+            min(img_scale_short),
+            max(img_scale_short) + 1)
+        img_scale = (long_edge, short_edge)
+        return img_scale, None
+
+    @staticmethod
+    def random_sample_ratio(img_scale, ratio_range):
+        """Randomly sample an img_scale when ``ratio_range`` is specified.
+
+        A ratio will be randomly sampled from the range specified by
+        ``ratio_range``. Then it would be multiplied with ``img_scale`` to
+        generate sampled scale.
+
+        Args:
+            img_scale (tuple): Images scale base to multiply with ratio.
+            ratio_range (tuple[float]): The minimum and maximum ratio to scale
+                the ``img_scale``.
+
+        Returns:
+            (tuple, None): Returns a tuple ``(scale, None)``, where \
+                ``scale`` is sampled ratio multiplied with ``img_scale`` and \
+                None is just a placeholder to be consistent with \
+                :func:`random_select`.
+        """
+
+        assert isinstance(img_scale, tuple) and len(img_scale) == 2
+        min_ratio, max_ratio = ratio_range
+        assert min_ratio <= max_ratio
+        ratio = np.random.random_sample() * (max_ratio - min_ratio) + min_ratio
+        scale = int(img_scale[0] * ratio), int(img_scale[1] * ratio)
+        return scale, None
+
+    def _random_scale(self, results):
+        """Randomly sample an img_scale according to ``ratio_range`` and
+        ``multiscale_mode``.
+
+        If ``ratio_range`` is specified, a ratio will be sampled and be
+        multiplied with ``img_scale``.
+        If multiple scales are specified by ``img_scale``, a scale will be
+        sampled according to ``multiscale_mode``.
+        Otherwise, single scale will be used.
+
+        Args:
+            results (dict): Result dict from :obj:`dataset`.
+
+        Returns:
+            dict: Two new keys 'scale` and 'scale_idx` are added into \
+                ``results``, which would be used by subsequent pipelines.
+        """
+
+        if self.ratio_range is not None:
+            scale, scale_idx = self.random_sample_ratio(
+                self.img_scale[0], self.ratio_range)
+        elif len(self.img_scale) == 1:
+            scale, scale_idx = self.img_scale[0], 0
+        elif self.multiscale_mode == 'range':
+            scale, scale_idx = self.random_sample(self.img_scale)
+        elif self.multiscale_mode == 'value':
+            scale, scale_idx = self.random_select(self.img_scale)
+        else:
+            raise NotImplementedError
+
+        results['scale'] = scale
+        results['scale_idx'] = scale_idx
+
+    def _resize_img(self, results):
+        """Resize images with ``results['scale']``."""
+        for key in results.get('img_fields', ['img']):
+            if self.keep_ratio:
+                img, scale_factor = mmcv.imrescale(
+                    results[key],
+                    results['scale'],
+                    return_scale=True,
+                    interpolation=self.interpolation,
+                    backend=self.backend)
+                # the w_scale and h_scale has minor difference
+                # a real fix should be done in the mmcv.imrescale in the future
+                new_h, new_w = img.shape[:2]
+                h, w = results[key].shape[:2]
+                w_scale = new_w / w
+                h_scale = new_h / h
+            else:
+                img, w_scale, h_scale = mmcv.imresize(
+                    results[key],
+                    results['scale'],
+                    return_scale=True,
+                    interpolation=self.interpolation,
+                    backend=self.backend)
+            results[key] = img
+
+            scale_factor = np.array([w_scale, h_scale, w_scale, h_scale],
+                                    dtype=np.float32)
+            results['img_shape'] = img.shape
+            # in case that there is no padding
+            results['pad_shape'] = img.shape
+            results['scale_factor'] = scale_factor
+            results['keep_ratio'] = self.keep_ratio
+
+    def _resize_bboxes(self, results):
+        """Resize bounding boxes with ``results['scale_factor']``."""
+        for key in results.get('bbox_fields', []):
+            bboxes = results[key] * results['scale_factor']
+            if self.bbox_clip_border:
+                img_shape = results['img_shape']
+                bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1])
+                bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0])
+            results[key] = bboxes
+
+    def _resize_masks(self, results):
+        """Resize masks with ``results['scale']``"""
+        for key in results.get('mask_fields', []):
+            if results[key] is None:
+                continue
+            if self.keep_ratio:
+                results[key] = results[key].rescale(results['scale'])
+            else:
+                results[key] = results[key].resize(results['img_shape'][:2])
+
+    def _resize_seg(self, results):
+        """Resize semantic segmentation map with ``results['scale']``."""
+        for key in results.get('seg_fields', []):
+            if self.keep_ratio:
+                gt_seg = mmcv.imrescale(
+                    results[key],
+                    results['scale'],
+                    interpolation='nearest',
+                    backend=self.backend)
+            else:
+                gt_seg = mmcv.imresize(
+                    results[key],
+                    results['scale'],
+                    interpolation='nearest',
+                    backend=self.backend)
+            results[key] = gt_seg
+
+    def __call__(self, results):
+        """Call function to resize images, bounding boxes, masks, semantic
+        segmentation map.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Resized results, 'img_shape', 'pad_shape', 'scale_factor', \
+                'keep_ratio' keys are added into result dict.
+        """
+
+        if 'scale' not in results:
+            if 'scale_factor' in results:
+                img_shape = results['img'].shape[:2]
+                scale_factor = results['scale_factor']
+                assert isinstance(scale_factor, float)
+                results['scale'] = tuple(
+                    [int(x * scale_factor) for x in img_shape][::-1])
+            else:
+                self._random_scale(results)
+        else:
+            if not self.override:
+                assert 'scale_factor' not in results, (
+                    'scale and scale_factor cannot be both set.')
+            else:
+                results.pop('scale')
+                if 'scale_factor' in results:
+                    results.pop('scale_factor')
+                self._random_scale(results)
+
+        self._resize_img(results)
+        self._resize_bboxes(results)
+        self._resize_masks(results)
+        self._resize_seg(results)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(img_scale={self.img_scale}, '
+        repr_str += f'multiscale_mode={self.multiscale_mode}, '
+        repr_str += f'ratio_range={self.ratio_range}, '
+        repr_str += f'keep_ratio={self.keep_ratio}, '
+        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class RandomFlip:
+    """Flip the image & bbox & mask.
+
+    If the input dict contains the key "flip", then the flag will be used,
+    otherwise it will be randomly decided by a ratio specified in the init
+    method.
+
+    When random flip is enabled, ``flip_ratio``/``direction`` can either be a
+    float/string or tuple of float/string. There are 3 flip modes:
+
+    - ``flip_ratio`` is float, ``direction`` is string: the image will be
+        ``direction``ly flipped with probability of ``flip_ratio`` .
+        E.g., ``flip_ratio=0.5``, ``direction='horizontal'``,
+        then image will be horizontally flipped with probability of 0.5.
+    - ``flip_ratio`` is float, ``direction`` is list of string: the image will
+        be ``direction[i]``ly flipped with probability of
+        ``flip_ratio/len(direction)``.
+        E.g., ``flip_ratio=0.5``, ``direction=['horizontal', 'vertical']``,
+        then image will be horizontally flipped with probability of 0.25,
+        vertically with probability of 0.25.
+    - ``flip_ratio`` is list of float, ``direction`` is list of string:
+        given ``len(flip_ratio) == len(direction)``, the image will
+        be ``direction[i]``ly flipped with probability of ``flip_ratio[i]``.
+        E.g., ``flip_ratio=[0.3, 0.5]``, ``direction=['horizontal',
+        'vertical']``, then image will be horizontally flipped with probability
+        of 0.3, vertically with probability of 0.5.
+
+    Args:
+        flip_ratio (float | list[float], optional): The flipping probability.
+            Default: None.
+        direction(str | list[str], optional): The flipping direction. Options
+            are 'horizontal', 'vertical', 'diagonal'. Default: 'horizontal'.
+            If input is a list, the length must equal ``flip_ratio``. Each
+            element in ``flip_ratio`` indicates the flip probability of
+            corresponding direction.
+    """
+
+    def __init__(self, flip_ratio=None, direction='horizontal'):
+        if isinstance(flip_ratio, list):
+            assert mmcv.is_list_of(flip_ratio, float)
+            assert 0 <= sum(flip_ratio) <= 1
+        elif isinstance(flip_ratio, float):
+            assert 0 <= flip_ratio <= 1
+        elif flip_ratio is None:
+            pass
+        else:
+            raise ValueError('flip_ratios must be None, float, '
+                             'or list of float')
+        self.flip_ratio = flip_ratio
+
+        valid_directions = ['horizontal', 'vertical', 'diagonal']
+        if isinstance(direction, str):
+            assert direction in valid_directions
+        elif isinstance(direction, list):
+            assert mmcv.is_list_of(direction, str)
+            assert set(direction).issubset(set(valid_directions))
+        else:
+            raise ValueError('direction must be either str or list of str')
+        self.direction = direction
+
+        if isinstance(flip_ratio, list):
+            assert len(self.flip_ratio) == len(self.direction)
+
+    def bbox_flip(self, bboxes, img_shape, direction):
+        """Flip bboxes horizontally.
+
+        Args:
+            bboxes (numpy.ndarray): Bounding boxes, shape (..., 4*k)
+            img_shape (tuple[int]): Image shape (height, width)
+            direction (str): Flip direction. Options are 'horizontal',
+                'vertical'.
+
+        Returns:
+            numpy.ndarray: Flipped bounding boxes.
+        """
+
+        assert bboxes.shape[-1] % 4 == 0
+        flipped = bboxes.copy()
+        if direction == 'horizontal':
+            w = img_shape[1]
+            flipped[..., 0::4] = w - bboxes[..., 2::4]
+            flipped[..., 2::4] = w - bboxes[..., 0::4]
+        elif direction == 'vertical':
+            h = img_shape[0]
+            flipped[..., 1::4] = h - bboxes[..., 3::4]
+            flipped[..., 3::4] = h - bboxes[..., 1::4]
+        elif direction == 'diagonal':
+            w = img_shape[1]
+            h = img_shape[0]
+            flipped[..., 0::4] = w - bboxes[..., 2::4]
+            flipped[..., 1::4] = h - bboxes[..., 3::4]
+            flipped[..., 2::4] = w - bboxes[..., 0::4]
+            flipped[..., 3::4] = h - bboxes[..., 1::4]
+        else:
+            raise ValueError(f"Invalid flipping direction '{direction}'")
+        return flipped
+
+    def __call__(self, results):
+        """Call function to flip bounding boxes, masks, semantic segmentation
+        maps.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Flipped results, 'flip', 'flip_direction' keys are added \
+                into result dict.
+        """
+
+        if 'flip' not in results:
+            if isinstance(self.direction, list):
+                # None means non-flip
+                direction_list = self.direction + [None]
+            else:
+                # None means non-flip
+                direction_list = [self.direction, None]
+
+            if isinstance(self.flip_ratio, list):
+                non_flip_ratio = 1 - sum(self.flip_ratio)
+                flip_ratio_list = self.flip_ratio + [non_flip_ratio]
+            else:
+                non_flip_ratio = 1 - self.flip_ratio
+                # exclude non-flip
+                single_ratio = self.flip_ratio / (len(direction_list) - 1)
+                flip_ratio_list = [single_ratio] * (len(direction_list) -
+                                                    1) + [non_flip_ratio]
+
+            cur_dir = np.random.choice(direction_list, p=flip_ratio_list)
+
+            results['flip'] = cur_dir is not None
+        if 'flip_direction' not in results:
+            results['flip_direction'] = cur_dir
+        if results['flip']:
+            # flip image
+            for key in results.get('img_fields', ['img']):
+                results[key] = mmcv.imflip(
+                    results[key], direction=results['flip_direction'])
+            # flip bboxes
+            for key in results.get('bbox_fields', []):
+                results[key] = self.bbox_flip(results[key],
+                                              results['img_shape'],
+                                              results['flip_direction'])
+            # flip masks
+            for key in results.get('mask_fields', []):
+                results[key] = results[key].flip(results['flip_direction'])
+
+            # flip segs
+            for key in results.get('seg_fields', []):
+                results[key] = mmcv.imflip(
+                    results[key], direction=results['flip_direction'])
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + f'(flip_ratio={self.flip_ratio})'
+
+
+@PIPELINES.register_module()
+class RandomShift:
+    """Shift the image and box given shift pixels and probability.
+
+    Args:
+        shift_ratio (float): Probability of shifts. Default 0.5.
+        max_shift_px (int): The max pixels for shifting. Default 32.
+        filter_thr_px (int): The width and height threshold for filtering.
+            The bbox and the rest of the targets below the width and
+            height threshold will be filtered. Default 1.
+    """
+
+    def __init__(self, shift_ratio=0.5, max_shift_px=32, filter_thr_px=1):
+        assert 0 <= shift_ratio <= 1
+        assert max_shift_px >= 0
+        self.shift_ratio = shift_ratio
+        self.max_shift_px = max_shift_px
+        self.filter_thr_px = int(filter_thr_px)
+        # The key correspondence from bboxes to labels.
+        self.bbox2label = {
+            'gt_bboxes': 'gt_labels',
+            'gt_bboxes_ignore': 'gt_labels_ignore'
+        }
+
+    def __call__(self, results):
+        """Call function to random shift images, bounding boxes.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Shift results.
+        """
+        if random.random() < self.shift_ratio:
+            img_shape = results['img'].shape[:2]
+
+            random_shift_x = random.randint(-self.max_shift_px,
+                                            self.max_shift_px)
+            random_shift_y = random.randint(-self.max_shift_px,
+                                            self.max_shift_px)
+            new_x = max(0, random_shift_x)
+            ori_x = max(0, -random_shift_x)
+            new_y = max(0, random_shift_y)
+            ori_y = max(0, -random_shift_y)
+
+            # TODO: support mask and semantic segmentation maps.
+            for key in results.get('bbox_fields', []):
+                bboxes = results[key].copy()
+                bboxes[..., 0::2] += random_shift_x
+                bboxes[..., 1::2] += random_shift_y
+
+                # clip border
+                bboxes[..., 0::2] = np.clip(bboxes[..., 0::2], 0, img_shape[1])
+                bboxes[..., 1::2] = np.clip(bboxes[..., 1::2], 0, img_shape[0])
+
+                # remove invalid bboxes
+                bbox_w = bboxes[..., 2] - bboxes[..., 0]
+                bbox_h = bboxes[..., 3] - bboxes[..., 1]
+                valid_inds = (bbox_w > self.filter_thr_px) & (
+                    bbox_h > self.filter_thr_px)
+                # If the shift does not contain any gt-bbox area, skip this
+                # image.
+                if key == 'gt_bboxes' and not valid_inds.any():
+                    return results
+                bboxes = bboxes[valid_inds]
+                results[key] = bboxes
+
+                # label fields. e.g. gt_labels and gt_labels_ignore
+                label_key = self.bbox2label.get(key)
+                if label_key in results:
+                    results[label_key] = results[label_key][valid_inds]
+
+            for key in results.get('img_fields', ['img']):
+                img = results[key]
+                new_img = np.zeros_like(img)
+                img_h, img_w = img.shape[:2]
+                new_h = img_h - np.abs(random_shift_y)
+                new_w = img_w - np.abs(random_shift_x)
+                new_img[new_y:new_y + new_h, new_x:new_x + new_w] \
+                    = img[ori_y:ori_y + new_h, ori_x:ori_x + new_w]
+                results[key] = new_img
+
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(max_shift_px={self.max_shift_px}, '
+        return repr_str
+
+
+@PIPELINES.register_module()
+class Pad:
+    """Pad the image & masks & segmentation map.
+
+    There are two padding modes: (1) pad to a fixed size and (2) pad to the
+    minimum size that is divisible by some number.
+    Added keys are "pad_shape", "pad_fixed_size", "pad_size_divisor",
+
+    Args:
+        size (tuple, optional): Fixed padding size.
+        size_divisor (int, optional): The divisor of padded size.
+        pad_to_square (bool): Whether to pad the image into a square.
+            Currently only used for YOLOX. Default: False.
+        pad_val (dict, optional): A dict for padding value, the default
+            value is `dict(img=0, masks=0, seg=255)`.
+    """
+
+    def __init__(self,
+                 size=None,
+                 size_divisor=None,
+                 pad_to_square=False,
+                 pad_val=dict(img=0, masks=0, seg=255)):
+        self.size = size
+        self.size_divisor = size_divisor
+        if isinstance(pad_val, float) or isinstance(pad_val, int):
+            warnings.warn(
+                'pad_val of float type is deprecated now, '
+                f'please use pad_val=dict(img={pad_val}, '
+                f'masks={pad_val}, seg=255) instead.', DeprecationWarning)
+            pad_val = dict(img=pad_val, masks=pad_val, seg=255)
+        assert isinstance(pad_val, dict)
+        self.pad_val = pad_val
+        self.pad_to_square = pad_to_square
+
+        if pad_to_square:
+            assert size is None and size_divisor is None, \
+                'The size and size_divisor must be None ' \
+                'when pad2square is True'
+        else:
+            assert size is not None or size_divisor is not None, \
+                'only one of size and size_divisor should be valid'
+            assert size is None or size_divisor is None
+
+    def _pad_img(self, results):
+        """Pad images according to ``self.size``."""
+        pad_val = self.pad_val.get('img', 0)
+        for key in results.get('img_fields', ['img']):
+            if self.pad_to_square:
+                max_size = max(results[key].shape[:2])
+                self.size = (max_size, max_size)
+            if self.size is not None:
+                padded_img = mmcv.impad(
+                    results[key], shape=self.size, pad_val=pad_val)
+            elif self.size_divisor is not None:
+                padded_img = mmcv.impad_to_multiple(
+                    results[key], self.size_divisor, pad_val=pad_val)
+            results[key] = padded_img
+        results['pad_shape'] = padded_img.shape
+        results['pad_fixed_size'] = self.size
+        results['pad_size_divisor'] = self.size_divisor
+
+    def _pad_masks(self, results):
+        """Pad masks according to ``results['pad_shape']``."""
+        pad_shape = results['pad_shape'][:2]
+        pad_val = self.pad_val.get('masks', 0)
+        for key in results.get('mask_fields', []):
+            results[key] = results[key].pad(pad_shape, pad_val=pad_val)
+
+    def _pad_seg(self, results):
+        """Pad semantic segmentation map according to
+        ``results['pad_shape']``."""
+        pad_val = self.pad_val.get('seg', 255)
+        for key in results.get('seg_fields', []):
+            results[key] = mmcv.impad(
+                results[key], shape=results['pad_shape'][:2], pad_val=pad_val)
+
+    def __call__(self, results):
+        """Call function to pad images, masks, semantic segmentation maps.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Updated result dict.
+        """
+        self._pad_img(results)
+        self._pad_masks(results)
+        self._pad_seg(results)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(size={self.size}, '
+        repr_str += f'size_divisor={self.size_divisor}, '
+        repr_str += f'pad_to_square={self.pad_to_square}, '
+        repr_str += f'pad_val={self.pad_val})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class Normalize:
+    """Normalize the image.
+
+    Added key is "img_norm_cfg".
+
+    Args:
+        mean (sequence): Mean values of 3 channels.
+        std (sequence): Std values of 3 channels.
+        to_rgb (bool): Whether to convert the image from BGR to RGB,
+            default is true.
+    """
+
+    def __init__(self, mean, std, to_rgb=True):
+        self.mean = np.array(mean, dtype=np.float32)
+        self.std = np.array(std, dtype=np.float32)
+        self.to_rgb = to_rgb
+
+    def __call__(self, results):
+        """Call function to normalize images.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Normalized results, 'img_norm_cfg' key is added into
+                result dict.
+        """
+        for key in results.get('img_fields', ['img']):
+            results[key] = mmcv.imnormalize(results[key], self.mean, self.std,
+                                            self.to_rgb)
+        results['img_norm_cfg'] = dict(
+            mean=self.mean, std=self.std, to_rgb=self.to_rgb)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(mean={self.mean}, std={self.std}, to_rgb={self.to_rgb})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class RandomCrop:
+    """Random crop the image & bboxes & masks.
+
+    The absolute `crop_size` is sampled based on `crop_type` and `image_size`,
+    then the cropped results are generated.
+
+    Args:
+        crop_size (tuple): The relative ratio or absolute pixels of
+            height and width.
+        crop_type (str, optional): one of "relative_range", "relative",
+            "absolute", "absolute_range". "relative" randomly crops
+            (h * crop_size[0], w * crop_size[1]) part from an input of size
+            (h, w). "relative_range" uniformly samples relative crop size from
+            range [crop_size[0], 1] and [crop_size[1], 1] for height and width
+            respectively. "absolute" crops from an input with absolute size
+            (crop_size[0], crop_size[1]). "absolute_range" uniformly samples
+            crop_h in range [crop_size[0], min(h, crop_size[1])] and crop_w
+            in range [crop_size[0], min(w, crop_size[1])]. Default "absolute".
+        allow_negative_crop (bool, optional): Whether to allow a crop that does
+            not contain any bbox area. Default False.
+        recompute_bbox (bool, optional): Whether to re-compute the boxes based
+            on cropped instance masks. Default False.
+        bbox_clip_border (bool, optional): Whether clip the objects outside
+            the border of the image. Defaults to True.
+
+    Note:
+        - If the image is smaller than the absolute crop size, return the
+            original image.
+        - The keys for bboxes, labels and masks must be aligned. That is,
+          `gt_bboxes` corresponds to `gt_labels` and `gt_masks`, and
+          `gt_bboxes_ignore` corresponds to `gt_labels_ignore` and
+          `gt_masks_ignore`.
+        - If the crop does not contain any gt-bbox region and
+          `allow_negative_crop` is set to False, skip this image.
+    """
+
+    def __init__(self,
+                 crop_size,
+                 crop_type='absolute',
+                 allow_negative_crop=False,
+                 recompute_bbox=False,
+                 bbox_clip_border=True):
+        if crop_type not in [
+                'relative_range', 'relative', 'absolute', 'absolute_range'
+        ]:
+            raise ValueError(f'Invalid crop_type {crop_type}.')
+        if crop_type in ['absolute', 'absolute_range']:
+            assert crop_size[0] > 0 and crop_size[1] > 0
+            assert isinstance(crop_size[0], int) and isinstance(
+                crop_size[1], int)
+        else:
+            assert 0 < crop_size[0] <= 1 and 0 < crop_size[1] <= 1
+        self.crop_size = crop_size
+        self.crop_type = crop_type
+        self.allow_negative_crop = allow_negative_crop
+        self.bbox_clip_border = bbox_clip_border
+        self.recompute_bbox = recompute_bbox
+        # The key correspondence from bboxes to labels and masks.
+        self.bbox2label = {
+            'gt_bboxes': 'gt_labels',
+            'gt_bboxes_ignore': 'gt_labels_ignore'
+        }
+        self.bbox2mask = {
+            'gt_bboxes': 'gt_masks',
+            'gt_bboxes_ignore': 'gt_masks_ignore'
+        }
+
+    def _crop_data(self, results, crop_size, allow_negative_crop):
+        """Function to randomly crop images, bounding boxes, masks, semantic
+        segmentation maps.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+            crop_size (tuple): Expected absolute size after cropping, (h, w).
+            allow_negative_crop (bool): Whether to allow a crop that does not
+                contain any bbox area. Default to False.
+
+        Returns:
+            dict: Randomly cropped results, 'img_shape' key in result dict is
+                updated according to crop size.
+        """
+        assert crop_size[0] > 0 and crop_size[1] > 0
+        for key in results.get('img_fields', ['img']):
+            img = results[key]
+            margin_h = max(img.shape[0] - crop_size[0], 0)
+            margin_w = max(img.shape[1] - crop_size[1], 0)
+            offset_h = np.random.randint(0, margin_h + 1)
+            offset_w = np.random.randint(0, margin_w + 1)
+            crop_y1, crop_y2 = offset_h, offset_h + crop_size[0]
+            crop_x1, crop_x2 = offset_w, offset_w + crop_size[1]
+
+            # crop the image
+            img = img[crop_y1:crop_y2, crop_x1:crop_x2, ...]
+            img_shape = img.shape
+            results[key] = img
+        results['img_shape'] = img_shape
+
+        # crop bboxes accordingly and clip to the image boundary
+        for key in results.get('bbox_fields', []):
+            # e.g. gt_bboxes and gt_bboxes_ignore
+            bbox_offset = np.array([offset_w, offset_h, offset_w, offset_h],
+                                   dtype=np.float32)
+            bboxes = results[key] - bbox_offset
+            if self.bbox_clip_border:
+                bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, img_shape[1])
+                bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, img_shape[0])
+            valid_inds = (bboxes[:, 2] > bboxes[:, 0]) & (
+                bboxes[:, 3] > bboxes[:, 1])
+            # If the crop does not contain any gt-bbox area and
+            # allow_negative_crop is False, skip this image.
+            if (key == 'gt_bboxes' and not valid_inds.any()
+                    and not allow_negative_crop):
+                return None
+            results[key] = bboxes[valid_inds, :]
+            # label fields. e.g. gt_labels and gt_labels_ignore
+            label_key = self.bbox2label.get(key)
+            if label_key in results:
+                results[label_key] = results[label_key][valid_inds]
+
+            # mask fields, e.g. gt_masks and gt_masks_ignore
+            mask_key = self.bbox2mask.get(key)
+            if mask_key in results:
+                results[mask_key] = results[mask_key][
+                    valid_inds.nonzero()[0]].crop(
+                        np.asarray([crop_x1, crop_y1, crop_x2, crop_y2]))
+                if self.recompute_bbox:
+                    results[key] = results[mask_key].get_bboxes()
+
+        # crop semantic seg
+        for key in results.get('seg_fields', []):
+            results[key] = results[key][crop_y1:crop_y2, crop_x1:crop_x2]
+
+        return results
+
+    def _get_crop_size(self, image_size):
+        """Randomly generates the absolute crop size based on `crop_type` and
+        `image_size`.
+
+        Args:
+            image_size (tuple): (h, w).
+
+        Returns:
+            crop_size (tuple): (crop_h, crop_w) in absolute pixels.
+        """
+        h, w = image_size
+        if self.crop_type == 'absolute':
+            return (min(self.crop_size[0], h), min(self.crop_size[1], w))
+        elif self.crop_type == 'absolute_range':
+            assert self.crop_size[0] <= self.crop_size[1]
+            crop_h = np.random.randint(
+                min(h, self.crop_size[0]),
+                min(h, self.crop_size[1]) + 1)
+            crop_w = np.random.randint(
+                min(w, self.crop_size[0]),
+                min(w, self.crop_size[1]) + 1)
+            return crop_h, crop_w
+        elif self.crop_type == 'relative':
+            crop_h, crop_w = self.crop_size
+            return int(h * crop_h + 0.5), int(w * crop_w + 0.5)
+        elif self.crop_type == 'relative_range':
+            crop_size = np.asarray(self.crop_size, dtype=np.float32)
+            crop_h, crop_w = crop_size + np.random.rand(2) * (1 - crop_size)
+            return int(h * crop_h + 0.5), int(w * crop_w + 0.5)
+
+    def __call__(self, results):
+        """Call function to randomly crop images, bounding boxes, masks,
+        semantic segmentation maps.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Randomly cropped results, 'img_shape' key in result dict is
+                updated according to crop size.
+        """
+        image_size = results['img'].shape[:2]
+        crop_size = self._get_crop_size(image_size)
+        results = self._crop_data(results, crop_size, self.allow_negative_crop)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(crop_size={self.crop_size}, '
+        repr_str += f'crop_type={self.crop_type}, '
+        repr_str += f'allow_negative_crop={self.allow_negative_crop}, '
+        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class SegRescale:
+    """Rescale semantic segmentation maps.
+
+    Args:
+        scale_factor (float): The scale factor of the final output.
+        backend (str): Image rescale backend, choices are 'cv2' and 'pillow'.
+            These two backends generates slightly different results. Defaults
+            to 'cv2'.
+    """
+
+    def __init__(self, scale_factor=1, backend='cv2'):
+        self.scale_factor = scale_factor
+        self.backend = backend
+
+    def __call__(self, results):
+        """Call function to scale the semantic segmentation map.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Result dict with semantic segmentation map scaled.
+        """
+
+        for key in results.get('seg_fields', []):
+            if self.scale_factor != 1:
+                results[key] = mmcv.imrescale(
+                    results[key],
+                    self.scale_factor,
+                    interpolation='nearest',
+                    backend=self.backend)
+        return results
+
+    def __repr__(self):
+        return self.__class__.__name__ + f'(scale_factor={self.scale_factor})'
+
+
+@PIPELINES.register_module()
+class PhotoMetricDistortion:
+    """Apply photometric distortion to image sequentially, every transformation
+    is applied with a probability of 0.5. The position of random contrast is in
+    second or second to last.
+
+    1. random brightness
+    2. random contrast (mode 0)
+    3. convert color from BGR to HSV
+    4. random saturation
+    5. random hue
+    6. convert color from HSV to BGR
+    7. random contrast (mode 1)
+    8. randomly swap channels
+
+    Args:
+        brightness_delta (int): delta of brightness.
+        contrast_range (tuple): range of contrast.
+        saturation_range (tuple): range of saturation.
+        hue_delta (int): delta of hue.
+    """
+
+    def __init__(self,
+                 brightness_delta=32,
+                 contrast_range=(0.5, 1.5),
+                 saturation_range=(0.5, 1.5),
+                 hue_delta=18):
+        self.brightness_delta = brightness_delta
+        self.contrast_lower, self.contrast_upper = contrast_range
+        self.saturation_lower, self.saturation_upper = saturation_range
+        self.hue_delta = hue_delta
+
+    def __call__(self, results):
+        """Call function to perform photometric distortion on images.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Result dict with images distorted.
+        """
+
+        if 'img_fields' in results:
+            assert results['img_fields'] == ['img'], \
+                'Only single img_fields is allowed'
+        img = results['img']
+        img = img.astype(np.float32)
+        # random brightness
+        if random.randint(2):
+            delta = random.uniform(-self.brightness_delta,
+                                   self.brightness_delta)
+            img += delta
+
+        # mode == 0 --> do random contrast first
+        # mode == 1 --> do random contrast last
+        mode = random.randint(2)
+        if mode == 1:
+            if random.randint(2):
+                alpha = random.uniform(self.contrast_lower,
+                                       self.contrast_upper)
+                img *= alpha
+
+        # convert color from BGR to HSV
+        img = mmcv.bgr2hsv(img)
+
+        # random saturation
+        if random.randint(2):
+            img[..., 1] *= random.uniform(self.saturation_lower,
+                                          self.saturation_upper)
+
+        # random hue
+        if random.randint(2):
+            img[..., 0] += random.uniform(-self.hue_delta, self.hue_delta)
+            img[..., 0][img[..., 0] > 360] -= 360
+            img[..., 0][img[..., 0] < 0] += 360
+
+        # convert color from HSV to BGR
+        img = mmcv.hsv2bgr(img)
+
+        # random contrast
+        if mode == 0:
+            if random.randint(2):
+                alpha = random.uniform(self.contrast_lower,
+                                       self.contrast_upper)
+                img *= alpha
+
+        # randomly swap channels
+        if random.randint(2):
+            img = img[..., random.permutation(3)]
+
+        results['img'] = img
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(\nbrightness_delta={self.brightness_delta},\n'
+        repr_str += 'contrast_range='
+        repr_str += f'{(self.contrast_lower, self.contrast_upper)},\n'
+        repr_str += 'saturation_range='
+        repr_str += f'{(self.saturation_lower, self.saturation_upper)},\n'
+        repr_str += f'hue_delta={self.hue_delta})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class Expand:
+    """Random expand the image & bboxes.
+
+    Randomly place the original image on a canvas of 'ratio' x original image
+    size filled with mean values. The ratio is in the range of ratio_range.
+
+    Args:
+        mean (tuple): mean value of dataset.
+        to_rgb (bool): if need to convert the order of mean to align with RGB.
+        ratio_range (tuple): range of expand ratio.
+        prob (float): probability of applying this transformation
+    """
+
+    def __init__(self,
+                 mean=(0, 0, 0),
+                 to_rgb=True,
+                 ratio_range=(1, 4),
+                 seg_ignore_label=None,
+                 prob=0.5):
+        self.to_rgb = to_rgb
+        self.ratio_range = ratio_range
+        if to_rgb:
+            self.mean = mean[::-1]
+        else:
+            self.mean = mean
+        self.min_ratio, self.max_ratio = ratio_range
+        self.seg_ignore_label = seg_ignore_label
+        self.prob = prob
+
+    def __call__(self, results):
+        """Call function to expand images, bounding boxes.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Result dict with images, bounding boxes expanded
+        """
+
+        if random.uniform(0, 1) > self.prob:
+            return results
+
+        if 'img_fields' in results:
+            assert results['img_fields'] == ['img'], \
+                'Only single img_fields is allowed'
+        img = results['img']
+
+        h, w, c = img.shape
+        ratio = random.uniform(self.min_ratio, self.max_ratio)
+        # speedup expand when meets large image
+        if np.all(self.mean == self.mean[0]):
+            expand_img = np.empty((int(h * ratio), int(w * ratio), c),
+                                  img.dtype)
+            expand_img.fill(self.mean[0])
+        else:
+            expand_img = np.full((int(h * ratio), int(w * ratio), c),
+                                 self.mean,
+                                 dtype=img.dtype)
+        left = int(random.uniform(0, w * ratio - w))
+        top = int(random.uniform(0, h * ratio - h))
+        expand_img[top:top + h, left:left + w] = img
+
+        results['img'] = expand_img
+        # expand bboxes
+        for key in results.get('bbox_fields', []):
+            results[key] = results[key] + np.tile(
+                (left, top), 2).astype(results[key].dtype)
+
+        # expand masks
+        for key in results.get('mask_fields', []):
+            results[key] = results[key].expand(
+                int(h * ratio), int(w * ratio), top, left)
+
+        # expand segs
+        for key in results.get('seg_fields', []):
+            gt_seg = results[key]
+            expand_gt_seg = np.full((int(h * ratio), int(w * ratio)),
+                                    self.seg_ignore_label,
+                                    dtype=gt_seg.dtype)
+            expand_gt_seg[top:top + h, left:left + w] = gt_seg
+            results[key] = expand_gt_seg
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(mean={self.mean}, to_rgb={self.to_rgb}, '
+        repr_str += f'ratio_range={self.ratio_range}, '
+        repr_str += f'seg_ignore_label={self.seg_ignore_label})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class MinIoURandomCrop:
+    """Random crop the image & bboxes, the cropped patches have minimum IoU
+    requirement with original image & bboxes, the IoU threshold is randomly
+    selected from min_ious.
+
+    Args:
+        min_ious (tuple): minimum IoU threshold for all intersections with
+        bounding boxes
+        min_crop_size (float): minimum crop's size (i.e. h,w := a*h, a*w,
+        where a >= min_crop_size).
+        bbox_clip_border (bool, optional): Whether clip the objects outside
+            the border of the image. Defaults to True.
+
+    Note:
+        The keys for bboxes, labels and masks should be paired. That is, \
+        `gt_bboxes` corresponds to `gt_labels` and `gt_masks`, and \
+        `gt_bboxes_ignore` to `gt_labels_ignore` and `gt_masks_ignore`.
+    """
+
+    def __init__(self,
+                 min_ious=(0.1, 0.3, 0.5, 0.7, 0.9),
+                 min_crop_size=0.3,
+                 bbox_clip_border=True):
+        # 1: return ori img
+        self.min_ious = min_ious
+        self.sample_mode = (1, *min_ious, 0)
+        self.min_crop_size = min_crop_size
+        self.bbox_clip_border = bbox_clip_border
+        self.bbox2label = {
+            'gt_bboxes': 'gt_labels',
+            'gt_bboxes_ignore': 'gt_labels_ignore'
+        }
+        self.bbox2mask = {
+            'gt_bboxes': 'gt_masks',
+            'gt_bboxes_ignore': 'gt_masks_ignore'
+        }
+
+    def __call__(self, results):
+        """Call function to crop images and bounding boxes with minimum IoU
+        constraint.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Result dict with images and bounding boxes cropped, \
+                'img_shape' key is updated.
+        """
+
+        if 'img_fields' in results:
+            assert results['img_fields'] == ['img'], \
+                'Only single img_fields is allowed'
+        img = results['img']
+        assert 'bbox_fields' in results
+        boxes = [results[key] for key in results['bbox_fields']]
+        boxes = np.concatenate(boxes, 0)
+        h, w, c = img.shape
+        while True:
+            mode = random.choice(self.sample_mode)
+            self.mode = mode
+            if mode == 1:
+                return results
+
+            min_iou = mode
+            for i in range(50):
+                new_w = random.uniform(self.min_crop_size * w, w)
+                new_h = random.uniform(self.min_crop_size * h, h)
+
+                # h / w in [0.5, 2]
+                if new_h / new_w < 0.5 or new_h / new_w > 2:
+                    continue
+
+                left = random.uniform(w - new_w)
+                top = random.uniform(h - new_h)
+
+                patch = np.array(
+                    (int(left), int(top), int(left + new_w), int(top + new_h)))
+                # Line or point crop is not allowed
+                if patch[2] == patch[0] or patch[3] == patch[1]:
+                    continue
+                overlaps = bbox_overlaps(
+                    patch.reshape(-1, 4), boxes.reshape(-1, 4)).reshape(-1)
+                if len(overlaps) > 0 and overlaps.min() < min_iou:
+                    continue
+
+                # center of boxes should inside the crop img
+                # only adjust boxes and instance masks when the gt is not empty
+                if len(overlaps) > 0:
+                    # adjust boxes
+                    def is_center_of_bboxes_in_patch(boxes, patch):
+                        center = (boxes[:, :2] + boxes[:, 2:]) / 2
+                        mask = ((center[:, 0] > patch[0]) *
+                                (center[:, 1] > patch[1]) *
+                                (center[:, 0] < patch[2]) *
+                                (center[:, 1] < patch[3]))
+                        return mask
+
+                    mask = is_center_of_bboxes_in_patch(boxes, patch)
+                    if not mask.any():
+                        continue
+                    for key in results.get('bbox_fields', []):
+                        boxes = results[key].copy()
+                        mask = is_center_of_bboxes_in_patch(boxes, patch)
+                        boxes = boxes[mask]
+                        if self.bbox_clip_border:
+                            boxes[:, 2:] = boxes[:, 2:].clip(max=patch[2:])
+                            boxes[:, :2] = boxes[:, :2].clip(min=patch[:2])
+                        boxes -= np.tile(patch[:2], 2)
+
+                        results[key] = boxes
+                        # labels
+                        label_key = self.bbox2label.get(key)
+                        if label_key in results:
+                            results[label_key] = results[label_key][mask]
+
+                        # mask fields
+                        mask_key = self.bbox2mask.get(key)
+                        if mask_key in results:
+                            results[mask_key] = results[mask_key][
+                                mask.nonzero()[0]].crop(patch)
+                # adjust the img no matter whether the gt is empty before crop
+                img = img[patch[1]:patch[3], patch[0]:patch[2]]
+                results['img'] = img
+                results['img_shape'] = img.shape
+
+                # seg fields
+                for key in results.get('seg_fields', []):
+                    results[key] = results[key][patch[1]:patch[3],
+                                                patch[0]:patch[2]]
+                return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(min_ious={self.min_ious}, '
+        repr_str += f'min_crop_size={self.min_crop_size}, '
+        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class Corrupt:
+    """Corruption augmentation.
+
+    Corruption transforms implemented based on
+    `imagecorruptions <https://github.com/bethgelab/imagecorruptions>`_.
+
+    Args:
+        corruption (str): Corruption name.
+        severity (int, optional): The severity of corruption. Default: 1.
+    """
+
+    def __init__(self, corruption, severity=1):
+        self.corruption = corruption
+        self.severity = severity
+
+    def __call__(self, results):
+        """Call function to corrupt image.
+
+        Args:
+            results (dict): Result dict from loading pipeline.
+
+        Returns:
+            dict: Result dict with images corrupted.
+        """
+
+        if corrupt is None:
+            raise RuntimeError('imagecorruptions is not installed')
+        if 'img_fields' in results:
+            assert results['img_fields'] == ['img'], \
+                'Only single img_fields is allowed'
+        results['img'] = corrupt(
+            results['img'].astype(np.uint8),
+            corruption_name=self.corruption,
+            severity=self.severity)
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(corruption={self.corruption}, '
+        repr_str += f'severity={self.severity})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class Albu:
+    """Albumentation augmentation.
+
+    Adds custom transformations from Albumentations library.
+    Please, visit `https://albumentations.readthedocs.io`
+    to get more information.
+
+    An example of ``transforms`` is as followed:
+
+    .. code-block::
+
+        [
+            dict(
+                type='ShiftScaleRotate',
+                shift_limit=0.0625,
+                scale_limit=0.0,
+                rotate_limit=0,
+                interpolation=1,
+                p=0.5),
+            dict(
+                type='RandomBrightnessContrast',
+                brightness_limit=[0.1, 0.3],
+                contrast_limit=[0.1, 0.3],
+                p=0.2),
+            dict(type='ChannelShuffle', p=0.1),
+            dict(
+                type='OneOf',
+                transforms=[
+                    dict(type='Blur', blur_limit=3, p=1.0),
+                    dict(type='MedianBlur', blur_limit=3, p=1.0)
+                ],
+                p=0.1),
+        ]
+
+    Args:
+        transforms (list[dict]): A list of albu transformations
+        bbox_params (dict): Bbox_params for albumentation `Compose`
+        keymap (dict): Contains {'input key':'albumentation-style key'}
+        skip_img_without_anno (bool): Whether to skip the image if no ann left
+            after aug
+    """
+
+    def __init__(self,
+                 transforms,
+                 bbox_params=None,
+                 keymap=None,
+                 update_pad_shape=False,
+                 skip_img_without_anno=False):
+        if Compose is None:
+            raise RuntimeError('albumentations is not installed')
+
+        # Args will be modified later, copying it will be safer
+        transforms = copy.deepcopy(transforms)
+        if bbox_params is not None:
+            bbox_params = copy.deepcopy(bbox_params)
+        if keymap is not None:
+            keymap = copy.deepcopy(keymap)
+        self.transforms = transforms
+        self.filter_lost_elements = False
+        self.update_pad_shape = update_pad_shape
+        self.skip_img_without_anno = skip_img_without_anno
+
+        # A simple workaround to remove masks without boxes
+        if (isinstance(bbox_params, dict) and 'label_fields' in bbox_params
+                and 'filter_lost_elements' in bbox_params):
+            self.filter_lost_elements = True
+            self.origin_label_fields = bbox_params['label_fields']
+            bbox_params['label_fields'] = ['idx_mapper']
+            del bbox_params['filter_lost_elements']
+
+        self.bbox_params = (
+            self.albu_builder(bbox_params) if bbox_params else None)
+        self.aug = Compose([self.albu_builder(t) for t in self.transforms],
+                           bbox_params=self.bbox_params)
+
+        if not keymap:
+            self.keymap_to_albu = {
+                'img': 'image',
+                'gt_masks': 'masks',
+                'gt_bboxes': 'bboxes'
+            }
+        else:
+            self.keymap_to_albu = keymap
+        self.keymap_back = {v: k for k, v in self.keymap_to_albu.items()}
+
+    def albu_builder(self, cfg):
+        """Import a module from albumentations.
+
+        It inherits some of :func:`build_from_cfg` logic.
+
+        Args:
+            cfg (dict): Config dict. It should at least contain the key "type".
+
+        Returns:
+            obj: The constructed object.
+        """
+
+        assert isinstance(cfg, dict) and 'type' in cfg
+        args = cfg.copy()
+
+        obj_type = args.pop('type')
+        if mmcv.is_str(obj_type):
+            if albumentations is None:
+                raise RuntimeError('albumentations is not installed')
+            obj_cls = getattr(albumentations, obj_type)
+        elif inspect.isclass(obj_type):
+            obj_cls = obj_type
+        else:
+            raise TypeError(
+                f'type must be a str or valid type, but got {type(obj_type)}')
+
+        if 'transforms' in args:
+            args['transforms'] = [
+                self.albu_builder(transform)
+                for transform in args['transforms']
+            ]
+
+        return obj_cls(**args)
+
+    @staticmethod
+    def mapper(d, keymap):
+        """Dictionary mapper. Renames keys according to keymap provided.
+
+        Args:
+            d (dict): old dict
+            keymap (dict): {'old_key':'new_key'}
+        Returns:
+            dict: new dict.
+        """
+
+        updated_dict = {}
+        for k, v in zip(d.keys(), d.values()):
+            new_k = keymap.get(k, k)
+            updated_dict[new_k] = d[k]
+        return updated_dict
+
+    def __call__(self, results):
+        # dict to albumentations format
+        results = self.mapper(results, self.keymap_to_albu)
+        # TODO: add bbox_fields
+        if 'bboxes' in results:
+            # to list of boxes
+            if isinstance(results['bboxes'], np.ndarray):
+                results['bboxes'] = [x for x in results['bboxes']]
+            # add pseudo-field for filtration
+            if self.filter_lost_elements:
+                results['idx_mapper'] = np.arange(len(results['bboxes']))
+
+        # TODO: Support mask structure in albu
+        if 'masks' in results:
+            if isinstance(results['masks'], PolygonMasks):
+                raise NotImplementedError(
+                    'Albu only supports BitMap masks now')
+            ori_masks = results['masks']
+            if albumentations.__version__ < '0.5':
+                results['masks'] = results['masks'].masks
+            else:
+                results['masks'] = [mask for mask in results['masks'].masks]
+
+        results = self.aug(**results)
+
+        if 'bboxes' in results:
+            if isinstance(results['bboxes'], list):
+                results['bboxes'] = np.array(
+                    results['bboxes'], dtype=np.float32)
+            results['bboxes'] = results['bboxes'].reshape(-1, 4)
+
+            # filter label_fields
+            if self.filter_lost_elements:
+
+                for label in self.origin_label_fields:
+                    results[label] = np.array(
+                        [results[label][i] for i in results['idx_mapper']])
+                if 'masks' in results:
+                    results['masks'] = np.array(
+                        [results['masks'][i] for i in results['idx_mapper']])
+                    results['masks'] = ori_masks.__class__(
+                        results['masks'], results['image'].shape[0],
+                        results['image'].shape[1])
+
+                if (not len(results['idx_mapper'])
+                        and self.skip_img_without_anno):
+                    return None
+
+        if 'gt_labels' in results:
+            if isinstance(results['gt_labels'], list):
+                results['gt_labels'] = np.array(results['gt_labels'])
+            results['gt_labels'] = results['gt_labels'].astype(np.int64)
+
+        # back to the original format
+        results = self.mapper(results, self.keymap_back)
+
+        # update final shape
+        if self.update_pad_shape:
+            results['pad_shape'] = results['img'].shape
+
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__ + f'(transforms={self.transforms})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class RandomCenterCropPad:
+    """Random center crop and random around padding for CornerNet.
+
+    This operation generates randomly cropped image from the original image and
+    pads it simultaneously. Different from :class:`RandomCrop`, the output
+    shape may not equal to ``crop_size`` strictly. We choose a random value
+    from ``ratios`` and the output shape could be larger or smaller than
+    ``crop_size``. The padding operation is also different from :class:`Pad`,
+    here we use around padding instead of right-bottom padding.
+
+    The relation between output image (padding image) and original image:
+
+    .. code:: text
+
+                        output image
+
+               +----------------------------+
+               |          padded area       |
+        +------|----------------------------|----------+
+        |      |         cropped area       |          |
+        |      |         +---------------+  |          |
+        |      |         |    .   center |  |          | original image
+        |      |         |        range  |  |          |
+        |      |         +---------------+  |          |
+        +------|----------------------------|----------+
+               |          padded area       |
+               +----------------------------+
+
+    There are 5 main areas in the figure:
+
+    - output image: output image of this operation, also called padding
+      image in following instruction.
+    - original image: input image of this operation.
+    - padded area: non-intersect area of output image and original image.
+    - cropped area: the overlap of output image and original image.
+    - center range: a smaller area where random center chosen from.
+      center range is computed by ``border`` and original image's shape
+      to avoid our random center is too close to original image's border.
+
+    Also this operation act differently in train and test mode, the summary
+    pipeline is listed below.
+
+    Train pipeline:
+
+    1. Choose a ``random_ratio`` from ``ratios``, the shape of padding image
+       will be ``random_ratio * crop_size``.
+    2. Choose a ``random_center`` in center range.
+    3. Generate padding image with center matches the ``random_center``.
+    4. Initialize the padding image with pixel value equals to ``mean``.
+    5. Copy the cropped area to padding image.
+    6. Refine annotations.
+
+    Test pipeline:
+
+    1. Compute output shape according to ``test_pad_mode``.
+    2. Generate padding image with center matches the original image
+       center.
+    3. Initialize the padding image with pixel value equals to ``mean``.
+    4. Copy the ``cropped area`` to padding image.
+
+    Args:
+        crop_size (tuple | None): expected size after crop, final size will
+            computed according to ratio. Requires (h, w) in train mode, and
+            None in test mode.
+        ratios (tuple): random select a ratio from tuple and crop image to
+            (crop_size[0] * ratio) * (crop_size[1] * ratio).
+            Only available in train mode.
+        border (int): max distance from center select area to image border.
+            Only available in train mode.
+        mean (sequence): Mean values of 3 channels.
+        std (sequence): Std values of 3 channels.
+        to_rgb (bool): Whether to convert the image from BGR to RGB.
+        test_mode (bool): whether involve random variables in transform.
+            In train mode, crop_size is fixed, center coords and ratio is
+            random selected from predefined lists. In test mode, crop_size
+            is image's original shape, center coords and ratio is fixed.
+        test_pad_mode (tuple): padding method and padding shape value, only
+            available in test mode. Default is using 'logical_or' with
+            127 as padding shape value.
+
+            - 'logical_or': final_shape = input_shape | padding_shape_value
+            - 'size_divisor': final_shape = int(
+              ceil(input_shape / padding_shape_value) * padding_shape_value)
+        test_pad_add_pix (int): Extra padding pixel in test mode. Default 0.
+        bbox_clip_border (bool, optional): Whether clip the objects outside
+            the border of the image. Defaults to True.
+    """
+
+    def __init__(self,
+                 crop_size=None,
+                 ratios=(0.9, 1.0, 1.1),
+                 border=128,
+                 mean=None,
+                 std=None,
+                 to_rgb=None,
+                 test_mode=False,
+                 test_pad_mode=('logical_or', 127),
+                 test_pad_add_pix=0,
+                 bbox_clip_border=True):
+        if test_mode:
+            assert crop_size is None, 'crop_size must be None in test mode'
+            assert ratios is None, 'ratios must be None in test mode'
+            assert border is None, 'border must be None in test mode'
+            assert isinstance(test_pad_mode, (list, tuple))
+            assert test_pad_mode[0] in ['logical_or', 'size_divisor']
+        else:
+            assert isinstance(crop_size, (list, tuple))
+            assert crop_size[0] > 0 and crop_size[1] > 0, (
+                'crop_size must > 0 in train mode')
+            assert isinstance(ratios, (list, tuple))
+            assert test_pad_mode is None, (
+                'test_pad_mode must be None in train mode')
+
+        self.crop_size = crop_size
+        self.ratios = ratios
+        self.border = border
+        # We do not set default value to mean, std and to_rgb because these
+        # hyper-parameters are easy to forget but could affect the performance.
+        # Please use the same setting as Normalize for performance assurance.
+        assert mean is not None and std is not None and to_rgb is not None
+        self.to_rgb = to_rgb
+        self.input_mean = mean
+        self.input_std = std
+        if to_rgb:
+            self.mean = mean[::-1]
+            self.std = std[::-1]
+        else:
+            self.mean = mean
+            self.std = std
+        self.test_mode = test_mode
+        self.test_pad_mode = test_pad_mode
+        self.test_pad_add_pix = test_pad_add_pix
+        self.bbox_clip_border = bbox_clip_border
+
+    def _get_border(self, border, size):
+        """Get final border for the target size.
+
+        This function generates a ``final_border`` according to image's shape.
+        The area between ``final_border`` and ``size - final_border`` is the
+        ``center range``. We randomly choose center from the ``center range``
+        to avoid our random center is too close to original image's border.
+        Also ``center range`` should be larger than 0.
+
+        Args:
+            border (int): The initial border, default is 128.
+            size (int): The width or height of original image.
+        Returns:
+            int: The final border.
+        """
+        k = 2 * border / size
+        i = pow(2, np.ceil(np.log2(np.ceil(k))) + (k == int(k)))
+        return border // i
+
+    def _filter_boxes(self, patch, boxes):
+        """Check whether the center of each box is in the patch.
+
+        Args:
+            patch (list[int]): The cropped area, [left, top, right, bottom].
+            boxes (numpy array, (N x 4)): Ground truth boxes.
+
+        Returns:
+            mask (numpy array, (N,)): Each box is inside or outside the patch.
+        """
+        center = (boxes[:, :2] + boxes[:, 2:]) / 2
+        mask = (center[:, 0] > patch[0]) * (center[:, 1] > patch[1]) * (
+            center[:, 0] < patch[2]) * (
+                center[:, 1] < patch[3])
+        return mask
+
+    def _crop_image_and_paste(self, image, center, size):
+        """Crop image with a given center and size, then paste the cropped
+        image to a blank image with two centers align.
+
+        This function is equivalent to generating a blank image with ``size``
+        as its shape. Then cover it on the original image with two centers (
+        the center of blank image and the random center of original image)
+        aligned. The overlap area is paste from the original image and the
+        outside area is filled with ``mean pixel``.
+
+        Args:
+            image (np array, H x W x C): Original image.
+            center (list[int]): Target crop center coord.
+            size (list[int]): Target crop size. [target_h, target_w]
+
+        Returns:
+            cropped_img (np array, target_h x target_w x C): Cropped image.
+            border (np array, 4): The distance of four border of
+                ``cropped_img`` to the original image area, [top, bottom,
+                left, right]
+            patch (list[int]): The cropped area, [left, top, right, bottom].
+        """
+        center_y, center_x = center
+        target_h, target_w = size
+        img_h, img_w, img_c = image.shape
+
+        x0 = max(0, center_x - target_w // 2)
+        x1 = min(center_x + target_w // 2, img_w)
+        y0 = max(0, center_y - target_h // 2)
+        y1 = min(center_y + target_h // 2, img_h)
+        patch = np.array((int(x0), int(y0), int(x1), int(y1)))
+
+        left, right = center_x - x0, x1 - center_x
+        top, bottom = center_y - y0, y1 - center_y
+
+        cropped_center_y, cropped_center_x = target_h // 2, target_w // 2
+        cropped_img = np.zeros((target_h, target_w, img_c), dtype=image.dtype)
+        for i in range(img_c):
+            cropped_img[:, :, i] += self.mean[i]
+        y_slice = slice(cropped_center_y - top, cropped_center_y + bottom)
+        x_slice = slice(cropped_center_x - left, cropped_center_x + right)
+        cropped_img[y_slice, x_slice, :] = image[y0:y1, x0:x1, :]
+
+        border = np.array([
+            cropped_center_y - top, cropped_center_y + bottom,
+            cropped_center_x - left, cropped_center_x + right
+        ],
+                          dtype=np.float32)
+
+        return cropped_img, border, patch
+
+    def _train_aug(self, results):
+        """Random crop and around padding the original image.
+
+        Args:
+            results (dict): Image infomations in the augment pipeline.
+
+        Returns:
+            results (dict): The updated dict.
+        """
+        img = results['img']
+        h, w, c = img.shape
+        boxes = results['gt_bboxes']
+        while True:
+            scale = random.choice(self.ratios)
+            new_h = int(self.crop_size[0] * scale)
+            new_w = int(self.crop_size[1] * scale)
+            h_border = self._get_border(self.border, h)
+            w_border = self._get_border(self.border, w)
+
+            for i in range(50):
+                center_x = random.randint(low=w_border, high=w - w_border)
+                center_y = random.randint(low=h_border, high=h - h_border)
+
+                cropped_img, border, patch = self._crop_image_and_paste(
+                    img, [center_y, center_x], [new_h, new_w])
+
+                mask = self._filter_boxes(patch, boxes)
+                # if image do not have valid bbox, any crop patch is valid.
+                if not mask.any() and len(boxes) > 0:
+                    continue
+
+                results['img'] = cropped_img
+                results['img_shape'] = cropped_img.shape
+                results['pad_shape'] = cropped_img.shape
+
+                x0, y0, x1, y1 = patch
+
+                left_w, top_h = center_x - x0, center_y - y0
+                cropped_center_x, cropped_center_y = new_w // 2, new_h // 2
+
+                # crop bboxes accordingly and clip to the image boundary
+                for key in results.get('bbox_fields', []):
+                    mask = self._filter_boxes(patch, results[key])
+                    bboxes = results[key][mask]
+                    bboxes[:, 0:4:2] += cropped_center_x - left_w - x0
+                    bboxes[:, 1:4:2] += cropped_center_y - top_h - y0
+                    if self.bbox_clip_border:
+                        bboxes[:, 0:4:2] = np.clip(bboxes[:, 0:4:2], 0, new_w)
+                        bboxes[:, 1:4:2] = np.clip(bboxes[:, 1:4:2], 0, new_h)
+                    keep = (bboxes[:, 2] > bboxes[:, 0]) & (
+                        bboxes[:, 3] > bboxes[:, 1])
+                    bboxes = bboxes[keep]
+                    results[key] = bboxes
+                    if key in ['gt_bboxes']:
+                        if 'gt_labels' in results:
+                            labels = results['gt_labels'][mask]
+                            labels = labels[keep]
+                            results['gt_labels'] = labels
+                        if 'gt_masks' in results:
+                            raise NotImplementedError(
+                                'RandomCenterCropPad only supports bbox.')
+
+                # crop semantic seg
+                for key in results.get('seg_fields', []):
+                    raise NotImplementedError(
+                        'RandomCenterCropPad only supports bbox.')
+                return results
+
+    def _test_aug(self, results):
+        """Around padding the original image without cropping.
+
+        The padding mode and value are from ``test_pad_mode``.
+
+        Args:
+            results (dict): Image infomations in the augment pipeline.
+
+        Returns:
+            results (dict): The updated dict.
+        """
+        img = results['img']
+        h, w, c = img.shape
+        results['img_shape'] = img.shape
+        if self.test_pad_mode[0] in ['logical_or']:
+            # self.test_pad_add_pix is only used for centernet
+            target_h = (h | self.test_pad_mode[1]) + self.test_pad_add_pix
+            target_w = (w | self.test_pad_mode[1]) + self.test_pad_add_pix
+        elif self.test_pad_mode[0] in ['size_divisor']:
+            divisor = self.test_pad_mode[1]
+            target_h = int(np.ceil(h / divisor)) * divisor
+            target_w = int(np.ceil(w / divisor)) * divisor
+        else:
+            raise NotImplementedError(
+                'RandomCenterCropPad only support two testing pad mode:'
+                'logical-or and size_divisor.')
+
+        cropped_img, border, _ = self._crop_image_and_paste(
+            img, [h // 2, w // 2], [target_h, target_w])
+        results['img'] = cropped_img
+        results['pad_shape'] = cropped_img.shape
+        results['border'] = border
+        return results
+
+    def __call__(self, results):
+        img = results['img']
+        assert img.dtype == np.float32, (
+            'RandomCenterCropPad needs the input image of dtype np.float32,'
+            ' please set "to_float32=True" in "LoadImageFromFile" pipeline')
+        h, w, c = img.shape
+        assert c == len(self.mean)
+        if self.test_mode:
+            return self._test_aug(results)
+        else:
+            return self._train_aug(results)
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(crop_size={self.crop_size}, '
+        repr_str += f'ratios={self.ratios}, '
+        repr_str += f'border={self.border}, '
+        repr_str += f'mean={self.input_mean}, '
+        repr_str += f'std={self.input_std}, '
+        repr_str += f'to_rgb={self.to_rgb}, '
+        repr_str += f'test_mode={self.test_mode}, '
+        repr_str += f'test_pad_mode={self.test_pad_mode}, '
+        repr_str += f'bbox_clip_border={self.bbox_clip_border})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class CutOut:
+    """CutOut operation.
+
+    Randomly drop some regions of image used in
+    `Cutout <https://arxiv.org/abs/1708.04552>`_.
+
+    Args:
+        n_holes (int | tuple[int, int]): Number of regions to be dropped.
+            If it is given as a list, number of holes will be randomly
+            selected from the closed interval [`n_holes[0]`, `n_holes[1]`].
+        cutout_shape (tuple[int, int] | list[tuple[int, int]]): The candidate
+            shape of dropped regions. It can be `tuple[int, int]` to use a
+            fixed cutout shape, or `list[tuple[int, int]]` to randomly choose
+            shape from the list.
+        cutout_ratio (tuple[float, float] | list[tuple[float, float]]): The
+            candidate ratio of dropped regions. It can be `tuple[float, float]`
+            to use a fixed ratio or `list[tuple[float, float]]` to randomly
+            choose ratio from the list. Please note that `cutout_shape`
+            and `cutout_ratio` cannot be both given at the same time.
+        fill_in (tuple[float, float, float] | tuple[int, int, int]): The value
+            of pixel to fill in the dropped regions. Default: (0, 0, 0).
+    """
+
+    def __init__(self,
+                 n_holes,
+                 cutout_shape=None,
+                 cutout_ratio=None,
+                 fill_in=(0, 0, 0)):
+
+        assert (cutout_shape is None) ^ (cutout_ratio is None), \
+            'Either cutout_shape or cutout_ratio should be specified.'
+        assert (isinstance(cutout_shape, (list, tuple))
+                or isinstance(cutout_ratio, (list, tuple)))
+        if isinstance(n_holes, tuple):
+            assert len(n_holes) == 2 and 0 <= n_holes[0] < n_holes[1]
+        else:
+            n_holes = (n_holes, n_holes)
+        self.n_holes = n_holes
+        self.fill_in = fill_in
+        self.with_ratio = cutout_ratio is not None
+        self.candidates = cutout_ratio if self.with_ratio else cutout_shape
+        if not isinstance(self.candidates, list):
+            self.candidates = [self.candidates]
+
+    def __call__(self, results):
+        """Call function to drop some regions of image."""
+        h, w, c = results['img'].shape
+        n_holes = np.random.randint(self.n_holes[0], self.n_holes[1] + 1)
+        for _ in range(n_holes):
+            x1 = np.random.randint(0, w)
+            y1 = np.random.randint(0, h)
+            index = np.random.randint(0, len(self.candidates))
+            if not self.with_ratio:
+                cutout_w, cutout_h = self.candidates[index]
+            else:
+                cutout_w = int(self.candidates[index][0] * w)
+                cutout_h = int(self.candidates[index][1] * h)
+
+            x2 = np.clip(x1 + cutout_w, 0, w)
+            y2 = np.clip(y1 + cutout_h, 0, h)
+            results['img'][y1:y2, x1:x2, :] = self.fill_in
+
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(n_holes={self.n_holes}, '
+        repr_str += (f'cutout_ratio={self.candidates}, ' if self.with_ratio
+                     else f'cutout_shape={self.candidates}, ')
+        repr_str += f'fill_in={self.fill_in})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class Mosaic:
+    """Mosaic augmentation.
+
+    Given 4 images, mosaic transform combines them into
+    one output image. The output image is composed of the parts from each sub-
+    image.
+
+    .. code:: text
+
+                        mosaic transform
+                           center_x
+                +------------------------------+
+                |       pad        |  pad      |
+                |      +-----------+           |
+                |      |           |           |
+                |      |  image1   |--------+  |
+                |      |           |        |  |
+                |      |           | image2 |  |
+     center_y   |----+-------------+-----------|
+                |    |   cropped   |           |
+                |pad |   image3    |  image4   |
+                |    |             |           |
+                +----|-------------+-----------+
+                     |             |
+                     +-------------+
+
+     The mosaic transform steps are as follows:
+
+         1. Choose the mosaic center as the intersections of 4 images
+         2. Get the left top image according to the index, and randomly
+            sample another 3 images from the custom dataset.
+         3. Sub image will be cropped if image is larger than mosaic patch
+
+    Args:
+        img_scale (Sequence[int]): Image size after mosaic pipeline of single
+            image. The shape order should be (height, width).
+            Default to (640, 640).
+        center_ratio_range (Sequence[float]): Center ratio range of mosaic
+            output. Default to (0.5, 1.5).
+        min_bbox_size (int | float): The minimum pixel for filtering
+            invalid bboxes after the mosaic pipeline. Default to 0.
+        bbox_clip_border (bool, optional): Whether to clip the objects outside
+            the border of the image. In some dataset like MOT17, the gt bboxes
+            are allowed to cross the border of images. Therefore, we don't
+            need to clip the gt bboxes in these cases. Defaults to True.
+        skip_filter (bool): Whether to skip filtering rules. If it
+            is True, the filter rule will not be applied, and the
+            `min_bbox_size` is invalid. Default to True.
+        pad_val (int): Pad value. Default to 114.
+        prob (float): Probability of applying this transformation.
+            Default to 1.0.
+    """
+
+    def __init__(self,
+                 img_scale=(640, 640),
+                 center_ratio_range=(0.5, 1.5),
+                 min_bbox_size=0,
+                 bbox_clip_border=True,
+                 skip_filter=True,
+                 pad_val=114,
+                 prob=1.0):
+        assert isinstance(img_scale, tuple)
+        assert 0 <= prob <= 1.0, 'The probability should be in range [0,1]. '\
+            f'got {prob}.'
+
+        log_img_scale(img_scale, skip_square=True)
+        self.img_scale = img_scale
+        self.center_ratio_range = center_ratio_range
+        self.min_bbox_size = min_bbox_size
+        self.bbox_clip_border = bbox_clip_border
+        self.skip_filter = skip_filter
+        self.pad_val = pad_val
+        self.prob = prob
+
+    def __call__(self, results):
+        """Call function to make a mosaic of image.
+
+        Args:
+            results (dict): Result dict.
+
+        Returns:
+            dict: Result dict with mosaic transformed.
+        """
+
+        if random.uniform(0, 1) > self.prob:
+            return results
+
+        results = self._mosaic_transform(results)
+        return results
+
+    def get_indexes(self, dataset):
+        """Call function to collect indexes.
+
+        Args:
+            dataset (:obj:`MultiImageMixDataset`): The dataset.
+
+        Returns:
+            list: indexes.
+        """
+
+        indexes = [random.randint(0, len(dataset)) for _ in range(3)]
+        return indexes
+
+    def _mosaic_transform(self, results):
+        """Mosaic transform function.
+
+        Args:
+            results (dict): Result dict.
+
+        Returns:
+            dict: Updated result dict.
+        """
+
+        assert 'mix_results' in results
+        mosaic_labels = []
+        mosaic_bboxes = []
+        if len(results['img'].shape) == 3:
+            mosaic_img = np.full(
+                (int(self.img_scale[0] * 2), int(self.img_scale[1] * 2), 3),
+                self.pad_val,
+                dtype=results['img'].dtype)
+        else:
+            mosaic_img = np.full(
+                (int(self.img_scale[0] * 2), int(self.img_scale[1] * 2)),
+                self.pad_val,
+                dtype=results['img'].dtype)
+
+        # mosaic center x, y
+        center_x = int(
+            random.uniform(*self.center_ratio_range) * self.img_scale[1])
+        center_y = int(
+            random.uniform(*self.center_ratio_range) * self.img_scale[0])
+        center_position = (center_x, center_y)
+
+        loc_strs = ('top_left', 'top_right', 'bottom_left', 'bottom_right')
+        for i, loc in enumerate(loc_strs):
+            if loc == 'top_left':
+                results_patch = copy.deepcopy(results)
+            else:
+                results_patch = copy.deepcopy(results['mix_results'][i - 1])
+
+            img_i = results_patch['img']
+            h_i, w_i = img_i.shape[:2]
+            # keep_ratio resize
+            scale_ratio_i = min(self.img_scale[0] / h_i,
+                                self.img_scale[1] / w_i)
+            img_i = mmcv.imresize(
+                img_i, (int(w_i * scale_ratio_i), int(h_i * scale_ratio_i)))
+
+            # compute the combine parameters
+            paste_coord, crop_coord = self._mosaic_combine(
+                loc, center_position, img_i.shape[:2][::-1])
+            x1_p, y1_p, x2_p, y2_p = paste_coord
+            x1_c, y1_c, x2_c, y2_c = crop_coord
+
+            # crop and paste image
+            mosaic_img[y1_p:y2_p, x1_p:x2_p] = img_i[y1_c:y2_c, x1_c:x2_c]
+
+            # adjust coordinate
+            gt_bboxes_i = results_patch['gt_bboxes']
+            gt_labels_i = results_patch['gt_labels']
+
+            if gt_bboxes_i.shape[0] > 0:
+                padw = x1_p - x1_c
+                padh = y1_p - y1_c
+                gt_bboxes_i[:, 0::2] = \
+                    scale_ratio_i * gt_bboxes_i[:, 0::2] + padw
+                gt_bboxes_i[:, 1::2] = \
+                    scale_ratio_i * gt_bboxes_i[:, 1::2] + padh
+
+            mosaic_bboxes.append(gt_bboxes_i)
+            mosaic_labels.append(gt_labels_i)
+
+        if len(mosaic_labels) > 0:
+            mosaic_bboxes = np.concatenate(mosaic_bboxes, 0)
+            mosaic_labels = np.concatenate(mosaic_labels, 0)
+
+            if self.bbox_clip_border:
+                mosaic_bboxes[:, 0::2] = np.clip(mosaic_bboxes[:, 0::2], 0,
+                                                 2 * self.img_scale[1])
+                mosaic_bboxes[:, 1::2] = np.clip(mosaic_bboxes[:, 1::2], 0,
+                                                 2 * self.img_scale[0])
+
+            if not self.skip_filter:
+                mosaic_bboxes, mosaic_labels = \
+                    self._filter_box_candidates(mosaic_bboxes, mosaic_labels)
+
+        # remove outside bboxes
+        inside_inds = find_inside_bboxes(mosaic_bboxes, 2 * self.img_scale[0],
+                                         2 * self.img_scale[1])
+        mosaic_bboxes = mosaic_bboxes[inside_inds]
+        mosaic_labels = mosaic_labels[inside_inds]
+
+        results['img'] = mosaic_img
+        results['img_shape'] = mosaic_img.shape
+        results['gt_bboxes'] = mosaic_bboxes
+        results['gt_labels'] = mosaic_labels
+
+        return results
+
+    def _mosaic_combine(self, loc, center_position_xy, img_shape_wh):
+        """Calculate global coordinate of mosaic image and local coordinate of
+        cropped sub-image.
+
+        Args:
+            loc (str): Index for the sub-image, loc in ('top_left',
+              'top_right', 'bottom_left', 'bottom_right').
+            center_position_xy (Sequence[float]): Mixing center for 4 images,
+                (x, y).
+            img_shape_wh (Sequence[int]): Width and height of sub-image
+
+        Returns:
+            tuple[tuple[float]]: Corresponding coordinate of pasting and
+                cropping
+                - paste_coord (tuple): paste corner coordinate in mosaic image.
+                - crop_coord (tuple): crop corner coordinate in mosaic image.
+        """
+        assert loc in ('top_left', 'top_right', 'bottom_left', 'bottom_right')
+        if loc == 'top_left':
+            # index0 to top left part of image
+            x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
+                             max(center_position_xy[1] - img_shape_wh[1], 0), \
+                             center_position_xy[0], \
+                             center_position_xy[1]
+            crop_coord = img_shape_wh[0] - (x2 - x1), img_shape_wh[1] - (
+                y2 - y1), img_shape_wh[0], img_shape_wh[1]
+
+        elif loc == 'top_right':
+            # index1 to top right part of image
+            x1, y1, x2, y2 = center_position_xy[0], \
+                             max(center_position_xy[1] - img_shape_wh[1], 0), \
+                             min(center_position_xy[0] + img_shape_wh[0],
+                                 self.img_scale[1] * 2), \
+                             center_position_xy[1]
+            crop_coord = 0, img_shape_wh[1] - (y2 - y1), min(
+                img_shape_wh[0], x2 - x1), img_shape_wh[1]
+
+        elif loc == 'bottom_left':
+            # index2 to bottom left part of image
+            x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
+                             center_position_xy[1], \
+                             center_position_xy[0], \
+                             min(self.img_scale[0] * 2, center_position_xy[1] +
+                                 img_shape_wh[1])
+            crop_coord = img_shape_wh[0] - (x2 - x1), 0, img_shape_wh[0], min(
+                y2 - y1, img_shape_wh[1])
+
+        else:
+            # index3 to bottom right part of image
+            x1, y1, x2, y2 = center_position_xy[0], \
+                             center_position_xy[1], \
+                             min(center_position_xy[0] + img_shape_wh[0],
+                                 self.img_scale[1] * 2), \
+                             min(self.img_scale[0] * 2, center_position_xy[1] +
+                                 img_shape_wh[1])
+            crop_coord = 0, 0, min(img_shape_wh[0],
+                                   x2 - x1), min(y2 - y1, img_shape_wh[1])
+
+        paste_coord = x1, y1, x2, y2
+        return paste_coord, crop_coord
+
+    def _filter_box_candidates(self, bboxes, labels):
+        """Filter out bboxes too small after Mosaic."""
+        bbox_w = bboxes[:, 2] - bboxes[:, 0]
+        bbox_h = bboxes[:, 3] - bboxes[:, 1]
+        valid_inds = (bbox_w > self.min_bbox_size) & \
+                     (bbox_h > self.min_bbox_size)
+        valid_inds = np.nonzero(valid_inds)[0]
+        return bboxes[valid_inds], labels[valid_inds]
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'img_scale={self.img_scale}, '
+        repr_str += f'center_ratio_range={self.center_ratio_range}, '
+        repr_str += f'pad_val={self.pad_val}, '
+        repr_str += f'min_bbox_size={self.min_bbox_size}, '
+        repr_str += f'skip_filter={self.skip_filter})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class MixUp:
+    """MixUp data augmentation.
+
+    .. code:: text
+
+                         mixup transform
+                +------------------------------+
+                | mixup image   |              |
+                |      +--------|--------+     |
+                |      |        |        |     |
+                |---------------+        |     |
+                |      |                 |     |
+                |      |      image      |     |
+                |      |                 |     |
+                |      |                 |     |
+                |      |-----------------+     |
+                |             pad              |
+                +------------------------------+
+
+     The mixup transform steps are as follows:
+
+        1. Another random image is picked by dataset and embedded in
+           the top left patch(after padding and resizing)
+        2. The target of mixup transform is the weighted average of mixup
+           image and origin image.
+
+    Args:
+        img_scale (Sequence[int]): Image output size after mixup pipeline.
+            The shape order should be (height, width). Default: (640, 640).
+        ratio_range (Sequence[float]): Scale ratio of mixup image.
+            Default: (0.5, 1.5).
+        flip_ratio (float): Horizontal flip ratio of mixup image.
+            Default: 0.5.
+        pad_val (int): Pad value. Default: 114.
+        max_iters (int): The maximum number of iterations. If the number of
+            iterations is greater than `max_iters`, but gt_bbox is still
+            empty, then the iteration is terminated. Default: 15.
+        min_bbox_size (float): Width and height threshold to filter bboxes.
+            If the height or width of a box is smaller than this value, it
+            will be removed. Default: 5.
+        min_area_ratio (float): Threshold of area ratio between
+            original bboxes and wrapped bboxes. If smaller than this value,
+            the box will be removed. Default: 0.2.
+        max_aspect_ratio (float): Aspect ratio of width and height
+            threshold to filter bboxes. If max(h/w, w/h) larger than this
+            value, the box will be removed. Default: 20.
+        bbox_clip_border (bool, optional): Whether to clip the objects outside
+            the border of the image. In some dataset like MOT17, the gt bboxes
+            are allowed to cross the border of images. Therefore, we don't
+            need to clip the gt bboxes in these cases. Defaults to True.
+        skip_filter (bool): Whether to skip filtering rules. If it
+            is True, the filter rule will not be applied, and the
+            `min_bbox_size` and `min_area_ratio` and `max_aspect_ratio`
+            is invalid. Default to True.
+    """
+
+    def __init__(self,
+                 img_scale=(640, 640),
+                 ratio_range=(0.5, 1.5),
+                 flip_ratio=0.5,
+                 pad_val=114,
+                 max_iters=15,
+                 min_bbox_size=5,
+                 min_area_ratio=0.2,
+                 max_aspect_ratio=20,
+                 bbox_clip_border=True,
+                 skip_filter=True):
+        assert isinstance(img_scale, tuple)
+        log_img_scale(img_scale, skip_square=True)
+        self.dynamic_scale = img_scale
+        self.ratio_range = ratio_range
+        self.flip_ratio = flip_ratio
+        self.pad_val = pad_val
+        self.max_iters = max_iters
+        self.min_bbox_size = min_bbox_size
+        self.min_area_ratio = min_area_ratio
+        self.max_aspect_ratio = max_aspect_ratio
+        self.bbox_clip_border = bbox_clip_border
+        self.skip_filter = skip_filter
+
+    def __call__(self, results):
+        """Call function to make a mixup of image.
+
+        Args:
+            results (dict): Result dict.
+
+        Returns:
+            dict: Result dict with mixup transformed.
+        """
+
+        results = self._mixup_transform(results)
+        return results
+
+    def get_indexes(self, dataset):
+        """Call function to collect indexes.
+
+        Args:
+            dataset (:obj:`MultiImageMixDataset`): The dataset.
+
+        Returns:
+            list: indexes.
+        """
+
+        for i in range(self.max_iters):
+            index = random.randint(0, len(dataset))
+            gt_bboxes_i = dataset.get_ann_info(index)['bboxes']
+            if len(gt_bboxes_i) != 0:
+                break
+
+        return index
+
+    def _mixup_transform(self, results):
+        """MixUp transform function.
+
+        Args:
+            results (dict): Result dict.
+
+        Returns:
+            dict: Updated result dict.
+        """
+
+        assert 'mix_results' in results
+        assert len(
+            results['mix_results']) == 1, 'MixUp only support 2 images now !'
+
+        if results['mix_results'][0]['gt_bboxes'].shape[0] == 0:
+            # empty bbox
+            return results
+
+        retrieve_results = results['mix_results'][0]
+        retrieve_img = retrieve_results['img']
+
+        jit_factor = random.uniform(*self.ratio_range)
+        is_filp = random.uniform(0, 1) < self.flip_ratio
+
+        if len(retrieve_img.shape) == 3:
+            out_img = np.ones(
+                (self.dynamic_scale[0], self.dynamic_scale[1], 3),
+                dtype=retrieve_img.dtype) * self.pad_val
+        else:
+            out_img = np.ones(
+                self.dynamic_scale, dtype=retrieve_img.dtype) * self.pad_val
+
+        # 1. keep_ratio resize
+        scale_ratio = min(self.dynamic_scale[0] / retrieve_img.shape[0],
+                          self.dynamic_scale[1] / retrieve_img.shape[1])
+        retrieve_img = mmcv.imresize(
+            retrieve_img, (int(retrieve_img.shape[1] * scale_ratio),
+                           int(retrieve_img.shape[0] * scale_ratio)))
+
+        # 2. paste
+        out_img[:retrieve_img.shape[0], :retrieve_img.shape[1]] = retrieve_img
+
+        # 3. scale jit
+        scale_ratio *= jit_factor
+        out_img = mmcv.imresize(out_img, (int(out_img.shape[1] * jit_factor),
+                                          int(out_img.shape[0] * jit_factor)))
+
+        # 4. flip
+        if is_filp:
+            out_img = out_img[:, ::-1, :]
+
+        # 5. random crop
+        ori_img = results['img']
+        origin_h, origin_w = out_img.shape[:2]
+        target_h, target_w = ori_img.shape[:2]
+        padded_img = np.zeros(
+            (max(origin_h, target_h), max(origin_w,
+                                          target_w), 3)).astype(np.uint8)
+        padded_img[:origin_h, :origin_w] = out_img
+
+        x_offset, y_offset = 0, 0
+        if padded_img.shape[0] > target_h:
+            y_offset = random.randint(0, padded_img.shape[0] - target_h)
+        if padded_img.shape[1] > target_w:
+            x_offset = random.randint(0, padded_img.shape[1] - target_w)
+        padded_cropped_img = padded_img[y_offset:y_offset + target_h,
+                                        x_offset:x_offset + target_w]
+
+        # 6. adjust bbox
+        retrieve_gt_bboxes = retrieve_results['gt_bboxes']
+        retrieve_gt_bboxes[:, 0::2] = retrieve_gt_bboxes[:, 0::2] * scale_ratio
+        retrieve_gt_bboxes[:, 1::2] = retrieve_gt_bboxes[:, 1::2] * scale_ratio
+        if self.bbox_clip_border:
+            retrieve_gt_bboxes[:, 0::2] = np.clip(retrieve_gt_bboxes[:, 0::2],
+                                                  0, origin_w)
+            retrieve_gt_bboxes[:, 1::2] = np.clip(retrieve_gt_bboxes[:, 1::2],
+                                                  0, origin_h)
+
+        if is_filp:
+            retrieve_gt_bboxes[:, 0::2] = (
+                origin_w - retrieve_gt_bboxes[:, 0::2][:, ::-1])
+
+        # 7. filter
+        cp_retrieve_gt_bboxes = retrieve_gt_bboxes.copy()
+        cp_retrieve_gt_bboxes[:, 0::2] = \
+            cp_retrieve_gt_bboxes[:, 0::2] - x_offset
+        cp_retrieve_gt_bboxes[:, 1::2] = \
+            cp_retrieve_gt_bboxes[:, 1::2] - y_offset
+        if self.bbox_clip_border:
+            cp_retrieve_gt_bboxes[:, 0::2] = np.clip(
+                cp_retrieve_gt_bboxes[:, 0::2], 0, target_w)
+            cp_retrieve_gt_bboxes[:, 1::2] = np.clip(
+                cp_retrieve_gt_bboxes[:, 1::2], 0, target_h)
+
+        # 8. mix up
+        ori_img = ori_img.astype(np.float32)
+        mixup_img = 0.5 * ori_img + 0.5 * padded_cropped_img.astype(np.float32)
+
+        retrieve_gt_labels = retrieve_results['gt_labels']
+        if not self.skip_filter:
+            keep_list = self._filter_box_candidates(retrieve_gt_bboxes.T,
+                                                    cp_retrieve_gt_bboxes.T)
+
+            retrieve_gt_labels = retrieve_gt_labels[keep_list]
+            cp_retrieve_gt_bboxes = cp_retrieve_gt_bboxes[keep_list]
+
+        mixup_gt_bboxes = np.concatenate(
+            (results['gt_bboxes'], cp_retrieve_gt_bboxes), axis=0)
+        mixup_gt_labels = np.concatenate(
+            (results['gt_labels'], retrieve_gt_labels), axis=0)
+
+        # remove outside bbox
+        inside_inds = find_inside_bboxes(mixup_gt_bboxes, target_h, target_w)
+        mixup_gt_bboxes = mixup_gt_bboxes[inside_inds]
+        mixup_gt_labels = mixup_gt_labels[inside_inds]
+
+        results['img'] = mixup_img.astype(np.uint8)
+        results['img_shape'] = mixup_img.shape
+        results['gt_bboxes'] = mixup_gt_bboxes
+        results['gt_labels'] = mixup_gt_labels
+
+        return results
+
+    def _filter_box_candidates(self, bbox1, bbox2):
+        """Compute candidate boxes which include following 5 things:
+
+        bbox1 before augment, bbox2 after augment, min_bbox_size (pixels),
+        min_area_ratio, max_aspect_ratio.
+        """
+
+        w1, h1 = bbox1[2] - bbox1[0], bbox1[3] - bbox1[1]
+        w2, h2 = bbox2[2] - bbox2[0], bbox2[3] - bbox2[1]
+        ar = np.maximum(w2 / (h2 + 1e-16), h2 / (w2 + 1e-16))
+        return ((w2 > self.min_bbox_size)
+                & (h2 > self.min_bbox_size)
+                & (w2 * h2 / (w1 * h1 + 1e-16) > self.min_area_ratio)
+                & (ar < self.max_aspect_ratio))
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'dynamic_scale={self.dynamic_scale}, '
+        repr_str += f'ratio_range={self.ratio_range}, '
+        repr_str += f'flip_ratio={self.flip_ratio}, '
+        repr_str += f'pad_val={self.pad_val}, '
+        repr_str += f'max_iters={self.max_iters}, '
+        repr_str += f'min_bbox_size={self.min_bbox_size}, '
+        repr_str += f'min_area_ratio={self.min_area_ratio}, '
+        repr_str += f'max_aspect_ratio={self.max_aspect_ratio}, '
+        repr_str += f'skip_filter={self.skip_filter})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class RandomAffine:
+    """Random affine transform data augmentation.
+
+    This operation randomly generates affine transform matrix which including
+    rotation, translation, shear and scaling transforms.
+
+    Args:
+        max_rotate_degree (float): Maximum degrees of rotation transform.
+            Default: 10.
+        max_translate_ratio (float): Maximum ratio of translation.
+            Default: 0.1.
+        scaling_ratio_range (tuple[float]): Min and max ratio of
+            scaling transform. Default: (0.5, 1.5).
+        max_shear_degree (float): Maximum degrees of shear
+            transform. Default: 2.
+        border (tuple[int]): Distance from height and width sides of input
+            image to adjust output shape. Only used in mosaic dataset.
+            Default: (0, 0).
+        border_val (tuple[int]): Border padding values of 3 channels.
+            Default: (114, 114, 114).
+        min_bbox_size (float): Width and height threshold to filter bboxes.
+            If the height or width of a box is smaller than this value, it
+            will be removed. Default: 2.
+        min_area_ratio (float): Threshold of area ratio between
+            original bboxes and wrapped bboxes. If smaller than this value,
+            the box will be removed. Default: 0.2.
+        max_aspect_ratio (float): Aspect ratio of width and height
+            threshold to filter bboxes. If max(h/w, w/h) larger than this
+            value, the box will be removed.
+        bbox_clip_border (bool, optional): Whether to clip the objects outside
+            the border of the image. In some dataset like MOT17, the gt bboxes
+            are allowed to cross the border of images. Therefore, we don't
+            need to clip the gt bboxes in these cases. Defaults to True.
+        skip_filter (bool): Whether to skip filtering rules. If it
+            is True, the filter rule will not be applied, and the
+            `min_bbox_size` and `min_area_ratio` and `max_aspect_ratio`
+            is invalid. Default to True.
+    """
+
+    def __init__(self,
+                 max_rotate_degree=10.0,
+                 max_translate_ratio=0.1,
+                 scaling_ratio_range=(0.5, 1.5),
+                 max_shear_degree=2.0,
+                 border=(0, 0),
+                 border_val=(114, 114, 114),
+                 min_bbox_size=2,
+                 min_area_ratio=0.2,
+                 max_aspect_ratio=20,
+                 bbox_clip_border=True,
+                 skip_filter=True):
+        assert 0 <= max_translate_ratio <= 1
+        assert scaling_ratio_range[0] <= scaling_ratio_range[1]
+        assert scaling_ratio_range[0] > 0
+        self.max_rotate_degree = max_rotate_degree
+        self.max_translate_ratio = max_translate_ratio
+        self.scaling_ratio_range = scaling_ratio_range
+        self.max_shear_degree = max_shear_degree
+        self.border = border
+        self.border_val = border_val
+        self.min_bbox_size = min_bbox_size
+        self.min_area_ratio = min_area_ratio
+        self.max_aspect_ratio = max_aspect_ratio
+        self.bbox_clip_border = bbox_clip_border
+        self.skip_filter = skip_filter
+
+    def __call__(self, results):
+        img = results['img']
+        height = img.shape[0] + self.border[0] * 2
+        width = img.shape[1] + self.border[1] * 2
+
+        # Rotation
+        rotation_degree = random.uniform(-self.max_rotate_degree,
+                                         self.max_rotate_degree)
+        rotation_matrix = self._get_rotation_matrix(rotation_degree)
+
+        # Scaling
+        scaling_ratio = random.uniform(self.scaling_ratio_range[0],
+                                       self.scaling_ratio_range[1])
+        scaling_matrix = self._get_scaling_matrix(scaling_ratio)
+
+        # Shear
+        x_degree = random.uniform(-self.max_shear_degree,
+                                  self.max_shear_degree)
+        y_degree = random.uniform(-self.max_shear_degree,
+                                  self.max_shear_degree)
+        shear_matrix = self._get_shear_matrix(x_degree, y_degree)
+
+        # Translation
+        trans_x = random.uniform(-self.max_translate_ratio,
+                                 self.max_translate_ratio) * width
+        trans_y = random.uniform(-self.max_translate_ratio,
+                                 self.max_translate_ratio) * height
+        translate_matrix = self._get_translation_matrix(trans_x, trans_y)
+
+        warp_matrix = (
+            translate_matrix @ shear_matrix @ rotation_matrix @ scaling_matrix)
+
+        img = cv2.warpPerspective(
+            img,
+            warp_matrix,
+            dsize=(width, height),
+            borderValue=self.border_val)
+        results['img'] = img
+        results['img_shape'] = img.shape
+
+        for key in results.get('bbox_fields', []):
+            bboxes = results[key]
+            num_bboxes = len(bboxes)
+            if num_bboxes:
+                # homogeneous coordinates
+                xs = bboxes[:, [0, 0, 2, 2]].reshape(num_bboxes * 4)
+                ys = bboxes[:, [1, 3, 3, 1]].reshape(num_bboxes * 4)
+                ones = np.ones_like(xs)
+                points = np.vstack([xs, ys, ones])
+
+                warp_points = warp_matrix @ points
+                warp_points = warp_points[:2] / warp_points[2]
+                xs = warp_points[0].reshape(num_bboxes, 4)
+                ys = warp_points[1].reshape(num_bboxes, 4)
+
+                warp_bboxes = np.vstack(
+                    (xs.min(1), ys.min(1), xs.max(1), ys.max(1))).T
+
+                if self.bbox_clip_border:
+                    warp_bboxes[:, [0, 2]] = \
+                        warp_bboxes[:, [0, 2]].clip(0, width)
+                    warp_bboxes[:, [1, 3]] = \
+                        warp_bboxes[:, [1, 3]].clip(0, height)
+
+                # remove outside bbox
+                valid_index = find_inside_bboxes(warp_bboxes, height, width)
+                if not self.skip_filter:
+                    # filter bboxes
+                    filter_index = self.filter_gt_bboxes(
+                        bboxes * scaling_ratio, warp_bboxes)
+                    valid_index = valid_index & filter_index
+
+                results[key] = warp_bboxes[valid_index]
+                if key in ['gt_bboxes']:
+                    if 'gt_labels' in results:
+                        results['gt_labels'] = results['gt_labels'][
+                            valid_index]
+
+                if 'gt_masks' in results:
+                    raise NotImplementedError(
+                        'RandomAffine only supports bbox.')
+        return results
+
+    def filter_gt_bboxes(self, origin_bboxes, wrapped_bboxes):
+        origin_w = origin_bboxes[:, 2] - origin_bboxes[:, 0]
+        origin_h = origin_bboxes[:, 3] - origin_bboxes[:, 1]
+        wrapped_w = wrapped_bboxes[:, 2] - wrapped_bboxes[:, 0]
+        wrapped_h = wrapped_bboxes[:, 3] - wrapped_bboxes[:, 1]
+        aspect_ratio = np.maximum(wrapped_w / (wrapped_h + 1e-16),
+                                  wrapped_h / (wrapped_w + 1e-16))
+
+        wh_valid_idx = (wrapped_w > self.min_bbox_size) & \
+                       (wrapped_h > self.min_bbox_size)
+        area_valid_idx = wrapped_w * wrapped_h / (origin_w * origin_h +
+                                                  1e-16) > self.min_area_ratio
+        aspect_ratio_valid_idx = aspect_ratio < self.max_aspect_ratio
+        return wh_valid_idx & area_valid_idx & aspect_ratio_valid_idx
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(max_rotate_degree={self.max_rotate_degree}, '
+        repr_str += f'max_translate_ratio={self.max_translate_ratio}, '
+        repr_str += f'scaling_ratio={self.scaling_ratio_range}, '
+        repr_str += f'max_shear_degree={self.max_shear_degree}, '
+        repr_str += f'border={self.border}, '
+        repr_str += f'border_val={self.border_val}, '
+        repr_str += f'min_bbox_size={self.min_bbox_size}, '
+        repr_str += f'min_area_ratio={self.min_area_ratio}, '
+        repr_str += f'max_aspect_ratio={self.max_aspect_ratio}, '
+        repr_str += f'skip_filter={self.skip_filter})'
+        return repr_str
+
+    @staticmethod
+    def _get_rotation_matrix(rotate_degrees):
+        radian = math.radians(rotate_degrees)
+        rotation_matrix = np.array(
+            [[np.cos(radian), -np.sin(radian), 0.],
+             [np.sin(radian), np.cos(radian), 0.], [0., 0., 1.]],
+            dtype=np.float32)
+        return rotation_matrix
+
+    @staticmethod
+    def _get_scaling_matrix(scale_ratio):
+        scaling_matrix = np.array(
+            [[scale_ratio, 0., 0.], [0., scale_ratio, 0.], [0., 0., 1.]],
+            dtype=np.float32)
+        return scaling_matrix
+
+    @staticmethod
+    def _get_share_matrix(scale_ratio):
+        scaling_matrix = np.array(
+            [[scale_ratio, 0., 0.], [0., scale_ratio, 0.], [0., 0., 1.]],
+            dtype=np.float32)
+        return scaling_matrix
+
+    @staticmethod
+    def _get_shear_matrix(x_shear_degrees, y_shear_degrees):
+        x_radian = math.radians(x_shear_degrees)
+        y_radian = math.radians(y_shear_degrees)
+        shear_matrix = np.array([[1, np.tan(x_radian), 0.],
+                                 [np.tan(y_radian), 1, 0.], [0., 0., 1.]],
+                                dtype=np.float32)
+        return shear_matrix
+
+    @staticmethod
+    def _get_translation_matrix(x, y):
+        translation_matrix = np.array([[1, 0., x], [0., 1, y], [0., 0., 1.]],
+                                      dtype=np.float32)
+        return translation_matrix
+
+
+@PIPELINES.register_module()
+class YOLOXHSVRandomAug:
+    """Apply HSV augmentation to image sequentially. It is referenced from
+    https://github.com/Megvii-
+    BaseDetection/YOLOX/blob/main/yolox/data/data_augment.py#L21.
+
+    Args:
+        hue_delta (int): delta of hue. Default: 5.
+        saturation_delta (int): delta of saturation. Default: 30.
+        value_delta (int): delat of value. Default: 30.
+    """
+
+    def __init__(self, hue_delta=5, saturation_delta=30, value_delta=30):
+        self.hue_delta = hue_delta
+        self.saturation_delta = saturation_delta
+        self.value_delta = value_delta
+
+    def __call__(self, results):
+        img = results['img']
+        hsv_gains = np.random.uniform(-1, 1, 3) * [
+            self.hue_delta, self.saturation_delta, self.value_delta
+        ]
+        # random selection of h, s, v
+        hsv_gains *= np.random.randint(0, 2, 3)
+        # prevent overflow
+        hsv_gains = hsv_gains.astype(np.int16)
+        img_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV).astype(np.int16)
+
+        img_hsv[..., 0] = (img_hsv[..., 0] + hsv_gains[0]) % 180
+        img_hsv[..., 1] = np.clip(img_hsv[..., 1] + hsv_gains[1], 0, 255)
+        img_hsv[..., 2] = np.clip(img_hsv[..., 2] + hsv_gains[2], 0, 255)
+        cv2.cvtColor(img_hsv.astype(img.dtype), cv2.COLOR_HSV2BGR, dst=img)
+
+        results['img'] = img
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'(hue_delta={self.hue_delta}, '
+        repr_str += f'saturation_delta={self.saturation_delta}, '
+        repr_str += f'value_delta={self.value_delta})'
+        return repr_str
+
+
+@PIPELINES.register_module()
+class CopyPaste:
+    """Simple Copy-Paste is a Strong Data Augmentation Method for Instance
+    Segmentation The simple copy-paste transform steps are as follows:
+
+    1. The destination image is already resized with aspect ratio kept,
+       cropped and padded.
+    2. Randomly select a source image, which is also already resized
+       with aspect ratio kept, cropped and padded in a similar way
+       as the destination image.
+    3. Randomly select some objects from the source image.
+    4. Paste these source objects to the destination image directly,
+       due to the source and destination image have the same size.
+    5. Update object masks of the destination image, for some origin objects
+       may be occluded.
+    6. Generate bboxes from the updated destination masks and
+       filter some objects which are totally occluded, and adjust bboxes
+       which are partly occluded.
+    7. Append selected source bboxes, masks, and labels.
+
+    Args:
+        max_num_pasted (int): The maximum number of pasted objects.
+            Default: 100.
+        bbox_occluded_thr (int): The threshold of occluded bbox.
+            Default: 10.
+        mask_occluded_thr (int): The threshold of occluded mask.
+            Default: 300.
+        selected (bool): Whether select objects or not. If select is False,
+            all objects of the source image will be pasted to the
+            destination image.
+            Default: True.
+    """
+
+    def __init__(
+        self,
+        max_num_pasted=100,
+        bbox_occluded_thr=10,
+        mask_occluded_thr=300,
+        selected=True,
+    ):
+        self.max_num_pasted = max_num_pasted
+        self.bbox_occluded_thr = bbox_occluded_thr
+        self.mask_occluded_thr = mask_occluded_thr
+        self.selected = selected
+        self.paste_by_box = False
+
+    def get_indexes(self, dataset):
+        """Call function to collect indexes.s.
+
+        Args:
+            dataset (:obj:`MultiImageMixDataset`): The dataset.
+        Returns:
+            list: Indexes.
+        """
+        return random.randint(0, len(dataset))
+
+    def gen_masks_from_bboxes(self, bboxes, img_shape):
+        """Generate gt_masks based on gt_bboxes.
+
+        Args:
+            bboxes (list): The bboxes's list.
+            img_shape (tuple): The shape of image.
+        Returns:
+            BitmapMasks
+        """
+        self.paste_by_box = True
+        img_h, img_w = img_shape[:2]
+        xmin, ymin = bboxes[:, 0:1], bboxes[:, 1:2]
+        xmax, ymax = bboxes[:, 2:3], bboxes[:, 3:4]
+        gt_masks = np.zeros((len(bboxes), img_h, img_w), dtype=np.uint8)
+        for i in range(len(bboxes)):
+            gt_masks[i,
+                     int(ymin[i]):int(ymax[i]),
+                     int(xmin[i]):int(xmax[i])] = 1
+        return BitmapMasks(gt_masks, img_h, img_w)
+
+    def get_gt_masks(self, results):
+        """Get gt_masks originally or generated based on bboxes.
+
+        If gt_masks is not contained in results,
+        it will be generated based on gt_bboxes.
+        Args:
+            results (dict): Result dict.
+        Returns:
+            BitmapMasks: gt_masks, originally or generated based on bboxes.
+        """
+        if results.get('gt_masks', None) is not None:
+            return results['gt_masks']
+        else:
+            return self.gen_masks_from_bboxes(
+                results.get('gt_bboxes', []), results['img'].shape)
+
+    def __call__(self, results):
+        """Call function to make a copy-paste of image.
+
+        Args:
+            results (dict): Result dict.
+        Returns:
+            dict: Result dict with copy-paste transformed.
+        """
+
+        assert 'mix_results' in results
+        num_images = len(results['mix_results'])
+        assert num_images == 1, \
+            f'CopyPaste only supports processing 2 images, got {num_images}'
+
+        # Get gt_masks originally or generated based on bboxes.
+        results['gt_masks'] = self.get_gt_masks(results)
+        # only one mix picture
+        results['mix_results'][0]['gt_masks'] = self.get_gt_masks(
+            results['mix_results'][0])
+
+        if self.selected:
+            selected_results = self._select_object(results['mix_results'][0])
+        else:
+            selected_results = results['mix_results'][0]
+        return self._copy_paste(results, selected_results)
+
+    def _select_object(self, results):
+        """Select some objects from the source results."""
+        bboxes = results['gt_bboxes']
+        labels = results['gt_labels']
+        masks = results['gt_masks']
+        max_num_pasted = min(bboxes.shape[0] + 1, self.max_num_pasted)
+        num_pasted = np.random.randint(0, max_num_pasted)
+        selected_inds = np.random.choice(
+            bboxes.shape[0], size=num_pasted, replace=False)
+
+        selected_bboxes = bboxes[selected_inds]
+        selected_labels = labels[selected_inds]
+        selected_masks = masks[selected_inds]
+
+        results['gt_bboxes'] = selected_bboxes
+        results['gt_labels'] = selected_labels
+        results['gt_masks'] = selected_masks
+        return results
+
+    def _copy_paste(self, dst_results, src_results):
+        """CopyPaste transform function.
+
+        Args:
+            dst_results (dict): Result dict of the destination image.
+            src_results (dict): Result dict of the source image.
+        Returns:
+            dict: Updated result dict.
+        """
+        dst_img = dst_results['img']
+        dst_bboxes = dst_results['gt_bboxes']
+        dst_labels = dst_results['gt_labels']
+        dst_masks = dst_results['gt_masks']
+
+        src_img = src_results['img']
+        src_bboxes = src_results['gt_bboxes']
+        src_labels = src_results['gt_labels']
+        src_masks = src_results['gt_masks']
+
+        if len(src_bboxes) == 0:
+            if self.paste_by_box:
+                dst_results.pop('gt_masks')
+            return dst_results
+
+        # update masks and generate bboxes from updated masks
+        composed_mask = np.where(np.any(src_masks.masks, axis=0), 1, 0)
+        updated_dst_masks = self.get_updated_masks(dst_masks, composed_mask)
+        updated_dst_bboxes = updated_dst_masks.get_bboxes()
+        assert len(updated_dst_bboxes) == len(updated_dst_masks)
+
+        # filter totally occluded objects
+        bboxes_inds = np.all(
+            np.abs(
+                (updated_dst_bboxes - dst_bboxes)) <= self.bbox_occluded_thr,
+            axis=-1)
+        masks_inds = updated_dst_masks.masks.sum(
+            axis=(1, 2)) > self.mask_occluded_thr
+        valid_inds = bboxes_inds | masks_inds
+
+        # Paste source objects to destination image directly
+        img = dst_img * (1 - composed_mask[..., np.newaxis]
+                         ) + src_img * composed_mask[..., np.newaxis]
+        bboxes = np.concatenate([updated_dst_bboxes[valid_inds], src_bboxes])
+        labels = np.concatenate([dst_labels[valid_inds], src_labels])
+        masks = np.concatenate(
+            [updated_dst_masks.masks[valid_inds], src_masks.masks])
+
+        dst_results['img'] = img
+        dst_results['gt_bboxes'] = bboxes
+        dst_results['gt_labels'] = labels
+        if self.paste_by_box:
+            dst_results.pop('gt_masks')
+        else:
+            dst_results['gt_masks'] = BitmapMasks(masks, masks.shape[1],
+                                                  masks.shape[2])
+
+        return dst_results
+
+    def get_updated_masks(self, masks, composed_mask):
+        assert masks.masks.shape[-2:] == composed_mask.shape[-2:], \
+            'Cannot compare two arrays of different size'
+        masks.masks = np.where(composed_mask, 0, masks.masks)
+        return masks
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__
+        repr_str += f'max_num_pasted={self.max_num_pasted}, '
+        repr_str += f'bbox_occluded_thr={self.bbox_occluded_thr}, '
+        repr_str += f'mask_occluded_thr={self.mask_occluded_thr}, '
+        repr_str += f'selected={self.selected}, '
+        return repr_str
diff --git a/mmdet/datasets/samplers/__init__.py b/mmdet/datasets/samplers/__init__.py
new file mode 100644
index 0000000..a4c7ea1
--- /dev/null
+++ b/mmdet/datasets/samplers/__init__.py
@@ -0,0 +1,10 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .class_aware_sampler import ClassAwareSampler
+from .distributed_sampler import DistributedSampler
+from .group_sampler import DistributedGroupSampler, GroupSampler
+from .infinite_sampler import InfiniteBatchSampler, InfiniteGroupBatchSampler
+
+__all__ = [
+    'DistributedSampler', 'DistributedGroupSampler', 'GroupSampler',
+    'InfiniteGroupBatchSampler', 'InfiniteBatchSampler', 'ClassAwareSampler'
+]
diff --git a/mmdet/datasets/samplers/class_aware_sampler.py b/mmdet/datasets/samplers/class_aware_sampler.py
new file mode 100644
index 0000000..c52708e
--- /dev/null
+++ b/mmdet/datasets/samplers/class_aware_sampler.py
@@ -0,0 +1,176 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+import torch
+from mmcv.runner import get_dist_info
+from torch.utils.data import Sampler
+
+from mmdet.core.utils import sync_random_seed
+
+
+class ClassAwareSampler(Sampler):
+    r"""Sampler that restricts data loading to the label of the dataset.
+
+    A class-aware sampling strategy to effectively tackle the
+    non-uniform class distribution. The length of the training data is
+    consistent with source data. Simple improvements based on `Relay
+    Backpropagation for Effective Learning of Deep Convolutional
+    Neural Networks <https://arxiv.org/abs/1512.05830>`_
+
+    The implementation logic is referred to
+    https://github.com/Sense-X/TSD/blob/master/mmdet/datasets/samplers/distributed_classaware_sampler.py
+
+    Args:
+        dataset: Dataset used for sampling.
+        samples_per_gpu (int): When model is :obj:`DistributedDataParallel`,
+            it is the number of training samples on each GPU.
+            When model is :obj:`DataParallel`, it is
+            `num_gpus * samples_per_gpu`.
+            Default : 1.
+        num_replicas (optional): Number of processes participating in
+            distributed training.
+        rank (optional): Rank of the current process within num_replicas.
+        seed (int, optional): random seed used to shuffle the sampler if
+            ``shuffle=True``. This number should be identical across all
+            processes in the distributed group. Default: 0.
+        num_sample_class (int): The number of samples taken from each
+            per-label list. Default: 1
+    """
+
+    def __init__(self,
+                 dataset,
+                 samples_per_gpu=1,
+                 num_replicas=None,
+                 rank=None,
+                 seed=0,
+                 num_sample_class=1):
+        _rank, _num_replicas = get_dist_info()
+        if num_replicas is None:
+            num_replicas = _num_replicas
+        if rank is None:
+            rank = _rank
+
+        self.dataset = dataset
+        self.num_replicas = num_replicas
+        self.samples_per_gpu = samples_per_gpu
+        self.rank = rank
+        self.epoch = 0
+        # Must be the same across all workers. If None, will use a
+        # random seed shared among workers
+        # (require synchronization among all workers)
+        self.seed = sync_random_seed(seed)
+
+        # The number of samples taken from each per-label list
+        assert num_sample_class > 0 and isinstance(num_sample_class, int)
+        self.num_sample_class = num_sample_class
+        # Get per-label image list from dataset
+        assert hasattr(dataset, 'get_cat2imgs'), \
+            'dataset must have `get_cat2imgs` function'
+        self.cat_dict = dataset.get_cat2imgs()
+
+        self.num_samples = int(
+            math.ceil(
+                len(self.dataset) * 1.0 / self.num_replicas /
+                self.samples_per_gpu)) * self.samples_per_gpu
+        self.total_size = self.num_samples * self.num_replicas
+
+        # get number of images containing each category
+        self.num_cat_imgs = [len(x) for x in self.cat_dict.values()]
+        # filter labels without images
+        self.valid_cat_inds = [
+            i for i, length in enumerate(self.num_cat_imgs) if length != 0
+        ]
+        self.num_classes = len(self.valid_cat_inds)
+
+    def __iter__(self):
+        # deterministically shuffle based on epoch
+        g = torch.Generator()
+        g.manual_seed(self.epoch + self.seed)
+
+        # initialize label list
+        label_iter_list = RandomCycleIter(self.valid_cat_inds, generator=g)
+        # initialize each per-label image list
+        data_iter_dict = dict()
+        for i in self.valid_cat_inds:
+            data_iter_dict[i] = RandomCycleIter(self.cat_dict[i], generator=g)
+
+        def gen_cat_img_inds(cls_list, data_dict, num_sample_cls):
+            """Traverse the categories and extract `num_sample_cls` image
+            indexes of the corresponding categories one by one."""
+            id_indices = []
+            for _ in range(len(cls_list)):
+                cls_idx = next(cls_list)
+                for _ in range(num_sample_cls):
+                    id = next(data_dict[cls_idx])
+                    id_indices.append(id)
+            return id_indices
+
+        # deterministically shuffle based on epoch
+        num_bins = int(
+            math.ceil(self.total_size * 1.0 / self.num_classes /
+                      self.num_sample_class))
+        indices = []
+        for i in range(num_bins):
+            indices += gen_cat_img_inds(label_iter_list, data_iter_dict,
+                                        self.num_sample_class)
+
+        # fix extra samples to make it evenly divisible
+        if len(indices) >= self.total_size:
+            indices = indices[:self.total_size]
+        else:
+            indices += indices[:(self.total_size - len(indices))]
+        assert len(indices) == self.total_size
+
+        # subsample
+        offset = self.num_samples * self.rank
+        indices = indices[offset:offset + self.num_samples]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
+
+    def __len__(self):
+        return self.num_samples
+
+    def set_epoch(self, epoch):
+        self.epoch = epoch
+
+
+class RandomCycleIter:
+    """Shuffle the list and do it again after the list have traversed.
+
+    The implementation logic is referred to
+    https://github.com/wutong16/DistributionBalancedLoss/blob/master/mllt/datasets/loader/sampler.py
+
+    Example:
+        >>> label_list = [0, 1, 2, 4, 5]
+        >>> g = torch.Generator()
+        >>> g.manual_seed(0)
+        >>> label_iter_list = RandomCycleIter(label_list, generator=g)
+        >>> index = next(label_iter_list)
+    Args:
+        data (list or ndarray): The data that needs to be shuffled.
+        generator: An torch.Generator object, which is used in setting the seed
+            for generating random numbers.
+    """  # noqa: W605
+
+    def __init__(self, data, generator=None):
+        self.data = data
+        self.length = len(data)
+        self.index = torch.randperm(self.length, generator=generator).numpy()
+        self.i = 0
+        self.generator = generator
+
+    def __iter__(self):
+        return self
+
+    def __len__(self):
+        return len(self.data)
+
+    def __next__(self):
+        if self.i == self.length:
+            self.index = torch.randperm(
+                self.length, generator=self.generator).numpy()
+            self.i = 0
+        idx = self.data[self.index[self.i]]
+        self.i += 1
+        return idx
diff --git a/mmdet/datasets/samplers/distributed_sampler.py b/mmdet/datasets/samplers/distributed_sampler.py
new file mode 100644
index 0000000..1bc8b7c
--- /dev/null
+++ b/mmdet/datasets/samplers/distributed_sampler.py
@@ -0,0 +1,54 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+import torch
+from torch.utils.data import DistributedSampler as _DistributedSampler
+
+from mmdet.core.utils import sync_random_seed
+from mmdet.utils import get_device
+
+
+class DistributedSampler(_DistributedSampler):
+
+    def __init__(self,
+                 dataset,
+                 num_replicas=None,
+                 rank=None,
+                 shuffle=True,
+                 seed=0):
+        super().__init__(
+            dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
+
+        # In distributed sampling, different ranks should sample
+        # non-overlapped data in the dataset. Therefore, this function
+        # is used to make sure that each rank shuffles the data indices
+        # in the same order based on the same seed. Then different ranks
+        # could use different indices to select non-overlapped data from the
+        # same data list.
+        device = get_device()
+        self.seed = sync_random_seed(seed, device)
+
+    def __iter__(self):
+        # deterministically shuffle based on epoch
+        if self.shuffle:
+            g = torch.Generator()
+            # When :attr:`shuffle=True`, this ensures all replicas
+            # use a different random ordering for each epoch.
+            # Otherwise, the next iteration of this sampler will
+            # yield the same ordering.
+            g.manual_seed(self.epoch + self.seed)
+            indices = torch.randperm(len(self.dataset), generator=g).tolist()
+        else:
+            indices = torch.arange(len(self.dataset)).tolist()
+
+        # add extra samples to make it evenly divisible
+        # in case that indices is shorter than half of total_size
+        indices = (indices *
+                   math.ceil(self.total_size / len(indices)))[:self.total_size]
+        assert len(indices) == self.total_size
+
+        # subsample
+        indices = indices[self.rank:self.total_size:self.num_replicas]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
diff --git a/mmdet/datasets/samplers/group_sampler.py b/mmdet/datasets/samplers/group_sampler.py
new file mode 100644
index 0000000..783d2b2
--- /dev/null
+++ b/mmdet/datasets/samplers/group_sampler.py
@@ -0,0 +1,148 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+import numpy as np
+import torch
+from mmcv.runner import get_dist_info
+from torch.utils.data import Sampler
+
+
+class GroupSampler(Sampler):
+
+    def __init__(self, dataset, samples_per_gpu=1):
+        assert hasattr(dataset, 'flag')
+        self.dataset = dataset
+        self.samples_per_gpu = samples_per_gpu
+        self.flag = dataset.flag.astype(np.int64)
+        self.group_sizes = np.bincount(self.flag)
+        self.num_samples = 0
+        for i, size in enumerate(self.group_sizes):
+            self.num_samples += int(np.ceil(
+                size / self.samples_per_gpu)) * self.samples_per_gpu
+
+    def __iter__(self):
+        indices = []
+        for i, size in enumerate(self.group_sizes):
+            if size == 0:
+                continue
+            indice = np.where(self.flag == i)[0]
+            assert len(indice) == size
+            np.random.shuffle(indice)
+            num_extra = int(np.ceil(size / self.samples_per_gpu)
+                            ) * self.samples_per_gpu - len(indice)
+            indice = np.concatenate(
+                [indice, np.random.choice(indice, num_extra)])
+            indices.append(indice)
+        indices = np.concatenate(indices)
+        indices = [
+            indices[i * self.samples_per_gpu:(i + 1) * self.samples_per_gpu]
+            for i in np.random.permutation(
+                range(len(indices) // self.samples_per_gpu))
+        ]
+        indices = np.concatenate(indices)
+        indices = indices.astype(np.int64).tolist()
+        assert len(indices) == self.num_samples
+        return iter(indices)
+
+    def __len__(self):
+        return self.num_samples
+
+
+class DistributedGroupSampler(Sampler):
+    """Sampler that restricts data loading to a subset of the dataset.
+
+    It is especially useful in conjunction with
+    :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
+    process can pass a DistributedSampler instance as a DataLoader sampler,
+    and load a subset of the original dataset that is exclusive to it.
+
+    .. note::
+        Dataset is assumed to be of constant size.
+
+    Arguments:
+        dataset: Dataset used for sampling.
+        num_replicas (optional): Number of processes participating in
+            distributed training.
+        rank (optional): Rank of the current process within num_replicas.
+        seed (int, optional): random seed used to shuffle the sampler if
+            ``shuffle=True``. This number should be identical across all
+            processes in the distributed group. Default: 0.
+    """
+
+    def __init__(self,
+                 dataset,
+                 samples_per_gpu=1,
+                 num_replicas=None,
+                 rank=None,
+                 seed=0):
+        _rank, _num_replicas = get_dist_info()
+        if num_replicas is None:
+            num_replicas = _num_replicas
+        if rank is None:
+            rank = _rank
+        self.dataset = dataset
+        self.samples_per_gpu = samples_per_gpu
+        self.num_replicas = num_replicas
+        self.rank = rank
+        self.epoch = 0
+        self.seed = seed if seed is not None else 0
+
+        assert hasattr(self.dataset, 'flag')
+        self.flag = self.dataset.flag
+        self.group_sizes = np.bincount(self.flag)
+
+        self.num_samples = 0
+        for i, j in enumerate(self.group_sizes):
+            self.num_samples += int(
+                math.ceil(self.group_sizes[i] * 1.0 / self.samples_per_gpu /
+                          self.num_replicas)) * self.samples_per_gpu
+        self.total_size = self.num_samples * self.num_replicas
+
+    def __iter__(self):
+        # deterministically shuffle based on epoch
+        g = torch.Generator()
+        g.manual_seed(self.epoch + self.seed)
+
+        indices = []
+        for i, size in enumerate(self.group_sizes):
+            if size > 0:
+                indice = np.where(self.flag == i)[0]
+                assert len(indice) == size
+                # add .numpy() to avoid bug when selecting indice in parrots.
+                # TODO: check whether torch.randperm() can be replaced by
+                # numpy.random.permutation().
+                indice = indice[list(
+                    torch.randperm(int(size), generator=g).numpy())].tolist()
+                extra = int(
+                    math.ceil(
+                        size * 1.0 / self.samples_per_gpu / self.num_replicas)
+                ) * self.samples_per_gpu * self.num_replicas - len(indice)
+                # pad indice
+                tmp = indice.copy()
+                for _ in range(extra // size):
+                    indice.extend(tmp)
+                indice.extend(tmp[:extra % size])
+                indices.extend(indice)
+
+        assert len(indices) == self.total_size
+
+        indices = [
+            indices[j] for i in list(
+                torch.randperm(
+                    len(indices) // self.samples_per_gpu, generator=g))
+            for j in range(i * self.samples_per_gpu, (i + 1) *
+                           self.samples_per_gpu)
+        ]
+
+        # subsample
+        offset = self.num_samples * self.rank
+        indices = indices[offset:offset + self.num_samples]
+        assert len(indices) == self.num_samples
+
+        return iter(indices)
+
+    def __len__(self):
+        return self.num_samples
+
+    def set_epoch(self, epoch):
+        self.epoch = epoch
diff --git a/mmdet/datasets/samplers/infinite_sampler.py b/mmdet/datasets/samplers/infinite_sampler.py
new file mode 100644
index 0000000..d42487e
--- /dev/null
+++ b/mmdet/datasets/samplers/infinite_sampler.py
@@ -0,0 +1,186 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import itertools
+
+import numpy as np
+import torch
+from mmcv.runner import get_dist_info
+from torch.utils.data.sampler import Sampler
+
+from mmdet.core.utils import sync_random_seed
+
+
+class InfiniteGroupBatchSampler(Sampler):
+    """Similar to `BatchSampler` warping a `GroupSampler. It is designed for
+    iteration-based runners like `IterBasedRunner` and yields a mini-batch
+    indices each time, all indices in a batch should be in the same group.
+
+    The implementation logic is referred to
+    https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/samplers/grouped_batch_sampler.py
+
+    Args:
+        dataset (object): The dataset.
+        batch_size (int): When model is :obj:`DistributedDataParallel`,
+            it is the number of training samples on each GPU.
+            When model is :obj:`DataParallel`, it is
+            `num_gpus * samples_per_gpu`.
+            Default : 1.
+        world_size (int, optional): Number of processes participating in
+            distributed training. Default: None.
+        rank (int, optional): Rank of current process. Default: None.
+        seed (int): Random seed. Default: 0.
+        shuffle (bool): Whether shuffle the indices of a dummy `epoch`, it
+            should be noted that `shuffle` can not guarantee that you can
+            generate sequential indices because it need to ensure
+            that all indices in a batch is in a group. Default: True.
+    """  # noqa: W605
+
+    def __init__(self,
+                 dataset,
+                 batch_size=1,
+                 world_size=None,
+                 rank=None,
+                 seed=0,
+                 shuffle=True):
+        _rank, _world_size = get_dist_info()
+        if world_size is None:
+            world_size = _world_size
+        if rank is None:
+            rank = _rank
+        self.rank = rank
+        self.world_size = world_size
+        self.dataset = dataset
+        self.batch_size = batch_size
+        # In distributed sampling, different ranks should sample
+        # non-overlapped data in the dataset. Therefore, this function
+        # is used to make sure that each rank shuffles the data indices
+        # in the same order based on the same seed. Then different ranks
+        # could use different indices to select non-overlapped data from the
+        # same data list.
+        self.seed = sync_random_seed(seed)
+        self.shuffle = shuffle
+
+        assert hasattr(self.dataset, 'flag')
+        self.flag = self.dataset.flag
+        self.group_sizes = np.bincount(self.flag)
+        # buffer used to save indices of each group
+        self.buffer_per_group = {k: [] for k in range(len(self.group_sizes))}
+
+        self.size = len(dataset)
+        self.indices = self._indices_of_rank()
+
+    def _infinite_indices(self):
+        """Infinitely yield a sequence of indices."""
+        g = torch.Generator()
+        g.manual_seed(self.seed)
+        while True:
+            if self.shuffle:
+                yield from torch.randperm(self.size, generator=g).tolist()
+
+            else:
+                yield from torch.arange(self.size).tolist()
+
+    def _indices_of_rank(self):
+        """Slice the infinite indices by rank."""
+        yield from itertools.islice(self._infinite_indices(), self.rank, None,
+                                    self.world_size)
+
+    def __iter__(self):
+        # once batch size is reached, yield the indices
+        for idx in self.indices:
+            flag = self.flag[idx]
+            group_buffer = self.buffer_per_group[flag]
+            group_buffer.append(idx)
+            if len(group_buffer) == self.batch_size:
+                yield group_buffer[:]
+                del group_buffer[:]
+
+    def __len__(self):
+        """Length of base dataset."""
+        return self.size
+
+    def set_epoch(self, epoch):
+        """Not supported in `IterationBased` runner."""
+        raise NotImplementedError
+
+
+class InfiniteBatchSampler(Sampler):
+    """Similar to `BatchSampler` warping a `DistributedSampler. It is designed
+    iteration-based runners like `IterBasedRunner` and yields a mini-batch
+    indices each time.
+
+    The implementation logic is referred to
+    https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/samplers/grouped_batch_sampler.py
+
+    Args:
+        dataset (object): The dataset.
+        batch_size (int): When model is :obj:`DistributedDataParallel`,
+            it is the number of training samples on each GPU,
+            When model is :obj:`DataParallel`, it is
+            `num_gpus * samples_per_gpu`.
+            Default : 1.
+        world_size (int, optional): Number of processes participating in
+            distributed training. Default: None.
+        rank (int, optional): Rank of current process. Default: None.
+        seed (int): Random seed. Default: 0.
+        shuffle (bool): Whether shuffle the dataset or not. Default: True.
+    """  # noqa: W605
+
+    def __init__(self,
+                 dataset,
+                 batch_size=1,
+                 world_size=None,
+                 rank=None,
+                 seed=0,
+                 shuffle=True):
+        _rank, _world_size = get_dist_info()
+        if world_size is None:
+            world_size = _world_size
+        if rank is None:
+            rank = _rank
+        self.rank = rank
+        self.world_size = world_size
+        self.dataset = dataset
+        self.batch_size = batch_size
+        # In distributed sampling, different ranks should sample
+        # non-overlapped data in the dataset. Therefore, this function
+        # is used to make sure that each rank shuffles the data indices
+        # in the same order based on the same seed. Then different ranks
+        # could use different indices to select non-overlapped data from the
+        # same data list.
+        self.seed = sync_random_seed(seed)
+        self.shuffle = shuffle
+        self.size = len(dataset)
+        self.indices = self._indices_of_rank()
+
+    def _infinite_indices(self):
+        """Infinitely yield a sequence of indices."""
+        g = torch.Generator()
+        g.manual_seed(self.seed)
+        while True:
+            if self.shuffle:
+                yield from torch.randperm(self.size, generator=g).tolist()
+
+            else:
+                yield from torch.arange(self.size).tolist()
+
+    def _indices_of_rank(self):
+        """Slice the infinite indices by rank."""
+        yield from itertools.islice(self._infinite_indices(), self.rank, None,
+                                    self.world_size)
+
+    def __iter__(self):
+        # once batch size is reached, yield the indices
+        batch_buffer = []
+        for idx in self.indices:
+            batch_buffer.append(idx)
+            if len(batch_buffer) == self.batch_size:
+                yield batch_buffer
+                batch_buffer = []
+
+    def __len__(self):
+        """Length of base dataset."""
+        return self.size
+
+    def set_epoch(self, epoch):
+        """Not supported in `IterationBased` runner."""
+        raise NotImplementedError
diff --git a/mmdet/datasets/utils.py b/mmdet/datasets/utils.py
new file mode 100644
index 0000000..17ec73b
--- /dev/null
+++ b/mmdet/datasets/utils.py
@@ -0,0 +1,166 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import warnings
+
+from mmcv.cnn import VGG
+from mmcv.runner.hooks import HOOKS, Hook
+
+from mmdet.datasets.builder import PIPELINES
+from mmdet.datasets.pipelines import (LoadAnnotations, LoadImageFromFile,
+                                      LoadPanopticAnnotations)
+from mmdet.models.dense_heads import GARPNHead, RPNHead
+from mmdet.models.roi_heads.mask_heads import FusedSemanticHead
+from torch.fx.graph_module import GraphModule
+
+def replace_ImageToTensor(pipelines):
+    """Replace the ImageToTensor transform in a data pipeline to
+    DefaultFormatBundle, which is normally useful in batch inference.
+
+    Args:
+        pipelines (list[dict]): Data pipeline configs.
+
+    Returns:
+        list: The new pipeline list with all ImageToTensor replaced by
+            DefaultFormatBundle.
+
+    Examples:
+        >>> pipelines = [
+        ...    dict(type='LoadImageFromFile'),
+        ...    dict(
+        ...        type='MultiScaleFlipAug',
+        ...        img_scale=(1333, 800),
+        ...        flip=False,
+        ...        transforms=[
+        ...            dict(type='Resize', keep_ratio=True),
+        ...            dict(type='RandomFlip'),
+        ...            dict(type='Normalize', mean=[0, 0, 0], std=[1, 1, 1]),
+        ...            dict(type='Pad', size_divisor=32),
+        ...            dict(type='ImageToTensor', keys=['img']),
+        ...            dict(type='Collect', keys=['img']),
+        ...        ])
+        ...    ]
+        >>> expected_pipelines = [
+        ...    dict(type='LoadImageFromFile'),
+        ...    dict(
+        ...        type='MultiScaleFlipAug',
+        ...        img_scale=(1333, 800),
+        ...        flip=False,
+        ...        transforms=[
+        ...            dict(type='Resize', keep_ratio=True),
+        ...            dict(type='RandomFlip'),
+        ...            dict(type='Normalize', mean=[0, 0, 0], std=[1, 1, 1]),
+        ...            dict(type='Pad', size_divisor=32),
+        ...            dict(type='DefaultFormatBundle'),
+        ...            dict(type='Collect', keys=['img']),
+        ...        ])
+        ...    ]
+        >>> assert expected_pipelines == replace_ImageToTensor(pipelines)
+    """
+    pipelines = copy.deepcopy(pipelines)
+    for i, pipeline in enumerate(pipelines):
+        if pipeline['type'] == 'MultiScaleFlipAug':
+            assert 'transforms' in pipeline
+            pipeline['transforms'] = replace_ImageToTensor(
+                pipeline['transforms'])
+        elif pipeline['type'] == 'ImageToTensor':
+            warnings.warn(
+                '"ImageToTensor" pipeline is replaced by '
+                '"DefaultFormatBundle" for batch inference. It is '
+                'recommended to manually replace it in the test '
+                'data pipeline in your config file.', UserWarning)
+            pipelines[i] = {'type': 'DefaultFormatBundle'}
+    return pipelines
+
+
+def get_loading_pipeline(pipeline):
+    """Only keep loading image and annotations related configuration.
+
+    Args:
+        pipeline (list[dict]): Data pipeline configs.
+
+    Returns:
+        list[dict]: The new pipeline list with only keep
+            loading image and annotations related configuration.
+
+    Examples:
+        >>> pipelines = [
+        ...    dict(type='LoadImageFromFile'),
+        ...    dict(type='LoadAnnotations', with_bbox=True),
+        ...    dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+        ...    dict(type='RandomFlip', flip_ratio=0.5),
+        ...    dict(type='Normalize', **img_norm_cfg),
+        ...    dict(type='Pad', size_divisor=32),
+        ...    dict(type='DefaultFormatBundle'),
+        ...    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+        ...    ]
+        >>> expected_pipelines = [
+        ...    dict(type='LoadImageFromFile'),
+        ...    dict(type='LoadAnnotations', with_bbox=True)
+        ...    ]
+        >>> assert expected_pipelines ==\
+        ...        get_loading_pipeline(pipelines)
+    """
+    loading_pipeline_cfg = []
+    for cfg in pipeline:
+        obj_cls = PIPELINES.get(cfg['type'])
+        # TODO：use more elegant way to distinguish loading modules
+        if obj_cls is not None and obj_cls in (LoadImageFromFile,
+                                               LoadAnnotations,
+                                               LoadPanopticAnnotations):
+            loading_pipeline_cfg.append(cfg)
+    assert len(loading_pipeline_cfg) == 2, \
+        'The data pipeline in your config file must include ' \
+        'loading image and annotations related pipeline.'
+    return loading_pipeline_cfg
+
+
+@HOOKS.register_module()
+class NumClassCheckHook(Hook):
+
+    def _check_head(self, runner):
+        """Check whether the `num_classes` in head matches the length of
+        `CLASSES` in `dataset`.
+
+        Args:
+            runner (obj:`EpochBasedRunner`): Epoch based Runner.
+        """
+        model = runner.model
+        dataset = runner.data_loader.dataset
+        if dataset.CLASSES is None:
+            runner.logger.warning(
+                f'Please set `CLASSES` '
+                f'in the {dataset.__class__.__name__} and'
+                f'check if it is consistent with the `num_classes` '
+                f'of head')
+        else:
+            assert type(dataset.CLASSES) is not str, \
+                (f'`CLASSES` in {dataset.__class__.__name__}'
+                 f'should be a tuple of str.'
+                 f'Add comma if number of classes is 1 as '
+                 f'CLASSES = ({dataset.CLASSES},)')
+            for name, module in model.named_modules():
+                if hasattr(module, 'num_classes') and not isinstance(
+                        module, (RPNHead, VGG, FusedSemanticHead, GARPNHead, GraphModule)):
+                    assert module.num_classes == len(dataset.CLASSES), \
+                        (f'The `num_classes` ({module.num_classes}) in '
+                         f'{module.__class__.__name__} of '
+                         f'{model.__class__.__name__} does not matches '
+                         f'the length of `CLASSES` '
+                         f'{len(dataset.CLASSES)}) in '
+                         f'{dataset.__class__.__name__}')
+
+    def before_train_epoch(self, runner):
+        """Check whether the training dataset is compatible with head.
+
+        Args:
+            runner (obj:`EpochBasedRunner`): Epoch based Runner.
+        """
+        self._check_head(runner)
+
+    def before_val_epoch(self, runner):
+        """Check whether the dataset in val epoch is compatible with head.
+
+        Args:
+            runner (obj:`EpochBasedRunner`): Epoch based Runner.
+        """
+        self._check_head(runner)
diff --git a/mmdet/datasets/voc.py b/mmdet/datasets/voc.py
new file mode 100644
index 0000000..0a3ea7a
--- /dev/null
+++ b/mmdet/datasets/voc.py
@@ -0,0 +1,112 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from collections import OrderedDict
+
+from mmcv.utils import print_log
+
+from mmdet.core import eval_map, eval_recalls
+from .builder import DATASETS
+from .xml_style import XMLDataset
+
+
+@DATASETS.register_module()
+class VOCDataset(XMLDataset):
+
+    CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
+               'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse',
+               'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train',
+               'tvmonitor')
+
+    PALETTE = [(106, 0, 228), (119, 11, 32), (165, 42, 42), (0, 0, 192),
+               (197, 226, 255), (0, 60, 100), (0, 0, 142), (255, 77, 255),
+               (153, 69, 1), (120, 166, 157), (0, 182, 199), (0, 226, 252),
+               (182, 182, 255), (0, 0, 230), (220, 20, 60), (163, 255, 0),
+               (0, 82, 0), (3, 95, 161), (0, 80, 100), (183, 130, 88)]
+
+    def __init__(self, **kwargs):
+        super(VOCDataset, self).__init__(**kwargs)
+        if 'VOC2007' in self.img_prefix:
+            self.year = 2007
+        elif 'VOC2012' in self.img_prefix:
+            self.year = 2012
+        else:
+            raise ValueError('Cannot infer dataset year from img_prefix')
+
+    def evaluate(self,
+                 results,
+                 metric='mAP',
+                 logger=None,
+                 proposal_nums=(100, 300, 1000),
+                 iou_thr=0.5,
+                 scale_ranges=None):
+        """Evaluate in VOC protocol.
+
+        Args:
+            results (list[list | tuple]): Testing results of the dataset.
+            metric (str | list[str]): Metrics to be evaluated. Options are
+                'mAP', 'recall'.
+            logger (logging.Logger | str, optional): Logger used for printing
+                related information during evaluation. Default: None.
+            proposal_nums (Sequence[int]): Proposal number used for evaluating
+                recalls, such as recall@100, recall@1000.
+                Default: (100, 300, 1000).
+            iou_thr (float | list[float]): IoU threshold. Default: 0.5.
+            scale_ranges (list[tuple], optional): Scale ranges for evaluating
+                mAP. If not specified, all bounding boxes would be included in
+                evaluation. Default: None.
+
+        Returns:
+            dict[str, float]: AP/recall metrics.
+        """
+
+        if not isinstance(metric, str):
+            assert len(metric) == 1
+            metric = metric[0]
+        allowed_metrics = ['mAP', 'recall']
+        if metric not in allowed_metrics:
+            raise KeyError(f'metric {metric} is not supported')
+        annotations = [self.get_ann_info(i) for i in range(len(self))]
+        eval_results = OrderedDict()
+        iou_thrs = [iou_thr] if isinstance(iou_thr, float) else iou_thr
+        if metric == 'mAP':
+            assert isinstance(iou_thrs, list)
+            if self.year == 2007:
+                ds_name = 'voc07'
+            else:
+                ds_name = self.CLASSES
+            mean_aps = []
+            for iou_thr in iou_thrs:
+                print_log(f'\n{"-" * 15}iou_thr: {iou_thr}{"-" * 15}')
+                # Follow the official implementation,
+                # http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCdevkit_18-May-2011.tar
+                # we should use the legacy coordinate system in mmdet 1.x,
+                # which means w, h should be computed as 'x2 - x1 + 1` and
+                # `y2 - y1 + 1`
+                mean_ap, _ = eval_map(
+                    results,
+                    annotations,
+                    scale_ranges=None,
+                    iou_thr=iou_thr,
+                    dataset=ds_name,
+                    logger=logger,
+                    use_legacy_coordinate=True)
+                mean_aps.append(mean_ap)
+                eval_results[f'AP{int(iou_thr * 100):02d}'] = round(mean_ap, 3)
+            eval_results['mAP'] = sum(mean_aps) / len(mean_aps)
+            eval_results.move_to_end('mAP', last=False)
+        elif metric == 'recall':
+            gt_bboxes = [ann['bboxes'] for ann in annotations]
+            recalls = eval_recalls(
+                gt_bboxes,
+                results,
+                proposal_nums,
+                iou_thrs,
+                logger=logger,
+                use_legacy_coordinate=True)
+            for i, num in enumerate(proposal_nums):
+                for j, iou_thr in enumerate(iou_thrs):
+                    eval_results[f'recall@{num}@{iou_thr}'] = recalls[i, j]
+            if recalls.shape[1] > 1:
+                ar = recalls.mean(axis=1)
+                for i, num in enumerate(proposal_nums):
+                    eval_results[f'AR@{num}'] = ar[i]
+        return eval_results
diff --git a/mmdet/datasets/wider_face.py b/mmdet/datasets/wider_face.py
new file mode 100644
index 0000000..85a5fdc
--- /dev/null
+++ b/mmdet/datasets/wider_face.py
@@ -0,0 +1,54 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import xml.etree.ElementTree as ET
+
+import mmcv
+
+from .builder import DATASETS
+from .xml_style import XMLDataset
+
+
+@DATASETS.register_module()
+class WIDERFaceDataset(XMLDataset):
+    """Reader for the WIDER Face dataset in PASCAL VOC format.
+
+    Conversion scripts can be found in
+    https://github.com/sovrasov/wider-face-pascal-voc-annotations
+    """
+    CLASSES = ('face', )
+
+    PALETTE = [(0, 255, 0)]
+
+    def __init__(self, **kwargs):
+        super(WIDERFaceDataset, self).__init__(**kwargs)
+
+    def load_annotations(self, ann_file):
+        """Load annotation from WIDERFace XML style annotation file.
+
+        Args:
+            ann_file (str): Path of XML file.
+
+        Returns:
+            list[dict]: Annotation info from XML file.
+        """
+
+        data_infos = []
+        img_ids = mmcv.list_from_file(ann_file)
+        for img_id in img_ids:
+            filename = f'{img_id}.jpg'
+            xml_path = osp.join(self.img_prefix, 'Annotations',
+                                f'{img_id}.xml')
+            tree = ET.parse(xml_path)
+            root = tree.getroot()
+            size = root.find('size')
+            width = int(size.find('width').text)
+            height = int(size.find('height').text)
+            folder = root.find('folder').text
+            data_infos.append(
+                dict(
+                    id=img_id,
+                    filename=osp.join(folder, filename),
+                    width=width,
+                    height=height))
+
+        return data_infos
diff --git a/mmdet/datasets/xml_style.py b/mmdet/datasets/xml_style.py
new file mode 100644
index 0000000..039d5d7
--- /dev/null
+++ b/mmdet/datasets/xml_style.py
@@ -0,0 +1,178 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import xml.etree.ElementTree as ET
+
+import mmcv
+import numpy as np
+from PIL import Image
+
+from .builder import DATASETS
+from .custom import CustomDataset
+
+
+@DATASETS.register_module()
+class XMLDataset(CustomDataset):
+    """XML dataset for detection.
+
+    Args:
+        min_size (int | float, optional): The minimum size of bounding
+            boxes in the images. If the size of a bounding box is less than
+            ``min_size``, it would be add to ignored field.
+        img_subdir (str): Subdir where images are stored. Default: JPEGImages.
+        ann_subdir (str): Subdir where annotations are. Default: Annotations.
+    """
+
+    def __init__(self,
+                 min_size=None,
+                 img_subdir='JPEGImages',
+                 ann_subdir='Annotations',
+                 **kwargs):
+        assert self.CLASSES or kwargs.get(
+            'classes', None), 'CLASSES in `XMLDataset` can not be None.'
+        self.img_subdir = img_subdir
+        self.ann_subdir = ann_subdir
+        super(XMLDataset, self).__init__(**kwargs)
+        self.cat2label = {cat: i for i, cat in enumerate(self.CLASSES)}
+        self.min_size = min_size
+
+    def load_annotations(self, ann_file):
+        """Load annotation from XML style ann_file.
+
+        Args:
+            ann_file (str): Path of XML file.
+
+        Returns:
+            list[dict]: Annotation info from XML file.
+        """
+
+        data_infos = []
+        img_ids = mmcv.list_from_file(ann_file)
+        for img_id in img_ids:
+            filename = osp.join(self.img_subdir, f'{img_id}.jpg')
+            xml_path = osp.join(self.img_prefix, self.ann_subdir,
+                                f'{img_id}.xml')
+            tree = ET.parse(xml_path)
+            root = tree.getroot()
+            size = root.find('size')
+            if size is not None:
+                width = int(size.find('width').text)
+                height = int(size.find('height').text)
+            else:
+                img_path = osp.join(self.img_prefix, filename)
+                img = Image.open(img_path)
+                width, height = img.size
+            data_infos.append(
+                dict(id=img_id, filename=filename, width=width, height=height))
+
+        return data_infos
+
+    def _filter_imgs(self, min_size=32):
+        """Filter images too small or without annotation."""
+        valid_inds = []
+        for i, img_info in enumerate(self.data_infos):
+            if min(img_info['width'], img_info['height']) < min_size:
+                continue
+            if self.filter_empty_gt:
+                img_id = img_info['id']
+                xml_path = osp.join(self.img_prefix, self.ann_subdir,
+                                    f'{img_id}.xml')
+                tree = ET.parse(xml_path)
+                root = tree.getroot()
+                for obj in root.findall('object'):
+                    name = obj.find('name').text
+                    if name in self.CLASSES:
+                        valid_inds.append(i)
+                        break
+            else:
+                valid_inds.append(i)
+        return valid_inds
+
+    def get_ann_info(self, idx):
+        """Get annotation from XML file by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            dict: Annotation info of specified index.
+        """
+
+        img_id = self.data_infos[idx]['id']
+        xml_path = osp.join(self.img_prefix, self.ann_subdir, f'{img_id}.xml')
+        tree = ET.parse(xml_path)
+        root = tree.getroot()
+        bboxes = []
+        labels = []
+        bboxes_ignore = []
+        labels_ignore = []
+        for obj in root.findall('object'):
+            name = obj.find('name').text
+            if name not in self.CLASSES:
+                continue
+            label = self.cat2label[name]
+            difficult = obj.find('difficult')
+            difficult = 0 if difficult is None else int(difficult.text)
+            bnd_box = obj.find('bndbox')
+            # TODO: check whether it is necessary to use int
+            # Coordinates may be float type
+            bbox = [
+                int(float(bnd_box.find('xmin').text)),
+                int(float(bnd_box.find('ymin').text)),
+                int(float(bnd_box.find('xmax').text)),
+                int(float(bnd_box.find('ymax').text))
+            ]
+            ignore = False
+            if self.min_size:
+                assert not self.test_mode
+                w = bbox[2] - bbox[0]
+                h = bbox[3] - bbox[1]
+                if w < self.min_size or h < self.min_size:
+                    ignore = True
+            if difficult or ignore:
+                bboxes_ignore.append(bbox)
+                labels_ignore.append(label)
+            else:
+                bboxes.append(bbox)
+                labels.append(label)
+        if not bboxes:
+            bboxes = np.zeros((0, 4))
+            labels = np.zeros((0, ))
+        else:
+            bboxes = np.array(bboxes, ndmin=2) - 1
+            labels = np.array(labels)
+        if not bboxes_ignore:
+            bboxes_ignore = np.zeros((0, 4))
+            labels_ignore = np.zeros((0, ))
+        else:
+            bboxes_ignore = np.array(bboxes_ignore, ndmin=2) - 1
+            labels_ignore = np.array(labels_ignore)
+        ann = dict(
+            bboxes=bboxes.astype(np.float32),
+            labels=labels.astype(np.int64),
+            bboxes_ignore=bboxes_ignore.astype(np.float32),
+            labels_ignore=labels_ignore.astype(np.int64))
+        return ann
+
+    def get_cat_ids(self, idx):
+        """Get category ids in XML file by index.
+
+        Args:
+            idx (int): Index of data.
+
+        Returns:
+            list[int]: All categories in the image of specified index.
+        """
+
+        cat_ids = []
+        img_id = self.data_infos[idx]['id']
+        xml_path = osp.join(self.img_prefix, self.ann_subdir, f'{img_id}.xml')
+        tree = ET.parse(xml_path)
+        root = tree.getroot()
+        for obj in root.findall('object'):
+            name = obj.find('name').text
+            if name not in self.CLASSES:
+                continue
+            label = self.cat2label[name]
+            cat_ids.append(label)
+
+        return cat_ids
diff --git a/mmdet/models/__init__.py b/mmdet/models/__init__.py
new file mode 100644
index 0000000..12efb01
--- /dev/null
+++ b/mmdet/models/__init__.py
@@ -0,0 +1,19 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .backbones import *  # noqa: F401,F403
+from .builder import (BACKBONES, DETECTORS, HEADS, LOSSES, NECKS,
+                      ROI_EXTRACTORS, SHARED_HEADS, build_backbone,
+                      build_detector, build_head, build_loss, build_neck,
+                      build_roi_extractor, build_shared_head)
+from .dense_heads import *  # noqa: F401,F403
+from .detectors import *  # noqa: F401,F403
+from .losses import *  # noqa: F401,F403
+from .necks import *  # noqa: F401,F403
+from .plugins import *  # noqa: F401,F403
+from .roi_heads import *  # noqa: F401,F403
+from .seg_heads import *  # noqa: F401,F403
+
+__all__ = [
+    'BACKBONES', 'NECKS', 'ROI_EXTRACTORS', 'SHARED_HEADS', 'HEADS', 'LOSSES',
+    'DETECTORS', 'build_backbone', 'build_neck', 'build_roi_extractor',
+    'build_shared_head', 'build_head', 'build_loss', 'build_detector'
+]
diff --git a/mmdet/models/backbones/__init__.py b/mmdet/models/backbones/__init__.py
new file mode 100644
index 0000000..91b50d2
--- /dev/null
+++ b/mmdet/models/backbones/__init__.py
@@ -0,0 +1,26 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .csp_darknet import CSPDarknet
+from .darknet import Darknet
+from .detectors_resnet import DetectoRS_ResNet
+from .detectors_resnext import DetectoRS_ResNeXt
+from .efficientnet import EfficientNet
+from .hourglass import HourglassNet
+from .hrnet import HRNet
+from .mobilenet_v2 import MobileNetV2
+from .pvt import PyramidVisionTransformer, PyramidVisionTransformerV2
+from .regnet import RegNet
+from .res2net import Res2Net
+from .resnest import ResNeSt
+from .resnet import ResNet, ResNetV1d
+from .resnext import ResNeXt
+from .ssd_vgg import SSDVGG
+from .swin import SwinTransformer
+from .trident_resnet import TridentResNet
+
+__all__ = [
+    'RegNet', 'ResNet', 'ResNetV1d', 'ResNeXt', 'SSDVGG', 'HRNet',
+    'MobileNetV2', 'Res2Net', 'HourglassNet', 'DetectoRS_ResNet',
+    'DetectoRS_ResNeXt', 'Darknet', 'ResNeSt', 'TridentResNet', 'CSPDarknet',
+    'SwinTransformer', 'PyramidVisionTransformer',
+    'PyramidVisionTransformerV2', 'EfficientNet'
+]
diff --git a/mmdet/models/backbones/csp_darknet.py b/mmdet/models/backbones/csp_darknet.py
new file mode 100644
index 0000000..2bbf396
--- /dev/null
+++ b/mmdet/models/backbones/csp_darknet.py
@@ -0,0 +1,284 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
+from mmcv.runner import BaseModule
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from ..builder import BACKBONES
+from ..utils import CSPLayer
+
+
+class Focus(nn.Module):
+    """Focus width and height information into channel space.
+
+    Args:
+        in_channels (int): The input channels of this Module.
+        out_channels (int): The output channels of this Module.
+        kernel_size (int): The kernel size of the convolution. Default: 1
+        stride (int): The stride of the convolution. Default: 1
+        conv_cfg (dict): Config dict for convolution layer. Default: None,
+            which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN', momentum=0.03, eps=0.001).
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='Swish').
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=1,
+                 stride=1,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+                 act_cfg=dict(type='Swish')):
+        super().__init__()
+        self.conv = ConvModule(
+            in_channels * 4,
+            out_channels,
+            kernel_size,
+            stride,
+            padding=(kernel_size - 1) // 2,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+
+    def forward(self, x):
+        # shape of x (b,c,w,h) -> y(b,4c,w/2,h/2)
+        patch_top_left = x[..., ::2, ::2]
+        patch_top_right = x[..., ::2, 1::2]
+        patch_bot_left = x[..., 1::2, ::2]
+        patch_bot_right = x[..., 1::2, 1::2]
+        x = torch.cat(
+            (
+                patch_top_left,
+                patch_bot_left,
+                patch_top_right,
+                patch_bot_right,
+            ),
+            dim=1,
+        )
+        return self.conv(x)
+
+
+class SPPBottleneck(BaseModule):
+    """Spatial pyramid pooling layer used in YOLOv3-SPP.
+
+    Args:
+        in_channels (int): The input channels of this Module.
+        out_channels (int): The output channels of this Module.
+        kernel_sizes (tuple[int]): Sequential of kernel sizes of pooling
+            layers. Default: (5, 9, 13).
+        conv_cfg (dict): Config dict for convolution layer. Default: None,
+            which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN').
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='Swish').
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_sizes=(5, 9, 13),
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+                 act_cfg=dict(type='Swish'),
+                 init_cfg=None):
+        super().__init__(init_cfg)
+        mid_channels = in_channels // 2
+        self.conv1 = ConvModule(
+            in_channels,
+            mid_channels,
+            1,
+            stride=1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        self.poolings = nn.ModuleList([
+            nn.MaxPool2d(kernel_size=ks, stride=1, padding=ks // 2)
+            for ks in kernel_sizes
+        ])
+        conv2_channels = mid_channels * (len(kernel_sizes) + 1)
+        self.conv2 = ConvModule(
+            conv2_channels,
+            out_channels,
+            1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = torch.cat([x] + [pooling(x) for pooling in self.poolings], dim=1)
+        x = self.conv2(x)
+        return x
+
+
+@BACKBONES.register_module()
+class CSPDarknet(BaseModule):
+    """CSP-Darknet backbone used in YOLOv5 and YOLOX.
+
+    Args:
+        arch (str): Architecture of CSP-Darknet, from {P5, P6}.
+            Default: P5.
+        deepen_factor (float): Depth multiplier, multiply number of
+            blocks in CSP layer by this amount. Default: 1.0.
+        widen_factor (float): Width multiplier, multiply number of
+            channels in each layer by this amount. Default: 1.0.
+        out_indices (Sequence[int]): Output from which stages.
+            Default: (2, 3, 4).
+        frozen_stages (int): Stages to be frozen (stop grad and set eval
+            mode). -1 means not freezing any parameters. Default: -1.
+        use_depthwise (bool): Whether to use depthwise separable convolution.
+            Default: False.
+        arch_ovewrite(list): Overwrite default arch settings. Default: None.
+        spp_kernal_sizes: (tuple[int]): Sequential of kernel sizes of SPP
+            layers. Default: (5, 9, 13).
+        conv_cfg (dict): Config dict for convolution layer. Default: None.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+            Default: dict(type='BN', requires_grad=True).
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='LeakyReLU', negative_slope=0.1).
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+    Example:
+        >>> from mmdet.models import CSPDarknet
+        >>> import torch
+        >>> self = CSPDarknet(depth=53)
+        >>> self.eval()
+        >>> inputs = torch.rand(1, 3, 416, 416)
+        >>> level_outputs = self.forward(inputs)
+        >>> for level_out in level_outputs:
+        ...     print(tuple(level_out.shape))
+        ...
+        (1, 256, 52, 52)
+        (1, 512, 26, 26)
+        (1, 1024, 13, 13)
+    """
+    # From left to right:
+    # in_channels, out_channels, num_blocks, add_identity, use_spp
+    arch_settings = {
+        'P5': [[64, 128, 3, True, False], [128, 256, 9, True, False],
+               [256, 512, 9, True, False], [512, 1024, 3, False, True]],
+        'P6': [[64, 128, 3, True, False], [128, 256, 9, True, False],
+               [256, 512, 9, True, False], [512, 768, 3, True, False],
+               [768, 1024, 3, False, True]]
+    }
+
+    def __init__(self,
+                 arch='P5',
+                 deepen_factor=1.0,
+                 widen_factor=1.0,
+                 out_indices=(2, 3, 4),
+                 frozen_stages=-1,
+                 use_depthwise=False,
+                 arch_ovewrite=None,
+                 spp_kernal_sizes=(5, 9, 13),
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+                 act_cfg=dict(type='Swish'),
+                 norm_eval=False,
+                 init_cfg=dict(
+                     type='Kaiming',
+                     layer='Conv2d',
+                     a=math.sqrt(5),
+                     distribution='uniform',
+                     mode='fan_in',
+                     nonlinearity='leaky_relu')):
+        super().__init__(init_cfg)
+        arch_setting = self.arch_settings[arch]
+        if arch_ovewrite:
+            arch_setting = arch_ovewrite
+        assert set(out_indices).issubset(
+            i for i in range(len(arch_setting) + 1))
+        if frozen_stages not in range(-1, len(arch_setting) + 1):
+            raise ValueError('frozen_stages must be in range(-1, '
+                             'len(arch_setting) + 1). But received '
+                             f'{frozen_stages}')
+
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+        self.use_depthwise = use_depthwise
+        self.norm_eval = norm_eval
+        conv = DepthwiseSeparableConvModule if use_depthwise else ConvModule
+
+        self.stem = Focus(
+            3,
+            int(arch_setting[0][0] * widen_factor),
+            kernel_size=3,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        self.layers = ['stem']
+
+        for i, (in_channels, out_channels, num_blocks, add_identity,
+                use_spp) in enumerate(arch_setting):
+            in_channels = int(in_channels * widen_factor)
+            out_channels = int(out_channels * widen_factor)
+            num_blocks = max(round(num_blocks * deepen_factor), 1)
+            stage = []
+            conv_layer = conv(
+                in_channels,
+                out_channels,
+                3,
+                stride=2,
+                padding=1,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg)
+            stage.append(conv_layer)
+            if use_spp:
+                spp = SPPBottleneck(
+                    out_channels,
+                    out_channels,
+                    kernel_sizes=spp_kernal_sizes,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg)
+                stage.append(spp)
+            csp_layer = CSPLayer(
+                out_channels,
+                out_channels,
+                num_blocks=num_blocks,
+                add_identity=add_identity,
+                use_depthwise=use_depthwise,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg)
+            stage.append(csp_layer)
+            self.add_module(f'stage{i + 1}', nn.Sequential(*stage))
+            self.layers.append(f'stage{i + 1}')
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            for i in range(self.frozen_stages + 1):
+                m = getattr(self, self.layers[i])
+                m.eval()
+                for param in m.parameters():
+                    param.requires_grad = False
+
+    def train(self, mode=True):
+        super(CSPDarknet, self).train(mode)
+        self._freeze_stages()
+        if mode and self.norm_eval:
+            for m in self.modules():
+                if isinstance(m, _BatchNorm):
+                    m.eval()
+
+    def forward(self, x):
+        outs = []
+        for i, layer_name in enumerate(self.layers):
+            layer = getattr(self, layer_name)
+            x = layer(x)
+            if i in self.out_indices:
+                outs.append(x)
+        return tuple(outs)
diff --git a/mmdet/models/backbones/darknet.py b/mmdet/models/backbones/darknet.py
new file mode 100644
index 0000000..adfb115
--- /dev/null
+++ b/mmdet/models/backbones/darknet.py
@@ -0,0 +1,213 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# Copyright (c) 2019 Western Digital Corporation or its affiliates.
+
+import warnings
+
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from ..builder import BACKBONES
+
+
+class ResBlock(BaseModule):
+    """The basic residual block used in Darknet. Each ResBlock consists of two
+    ConvModules and the input is added to the final output. Each ConvModule is
+    composed of Conv, BN, and LeakyReLU. In YoloV3 paper, the first convLayer
+    has half of the number of the filters as much as the second convLayer. The
+    first convLayer has filter size of 1x1 and the second one has the filter
+    size of 3x3.
+
+    Args:
+        in_channels (int): The input channels. Must be even.
+        conv_cfg (dict): Config dict for convolution layer. Default: None.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+            Default: dict(type='BN', requires_grad=True)
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='LeakyReLU', negative_slope=0.1).
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 in_channels,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
+                 init_cfg=None):
+        super(ResBlock, self).__init__(init_cfg)
+        assert in_channels % 2 == 0  # ensure the in_channels is even
+        half_in_channels = in_channels // 2
+
+        # shortcut
+        cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
+
+        self.conv1 = ConvModule(in_channels, half_in_channels, 1, **cfg)
+        self.conv2 = ConvModule(
+            half_in_channels, in_channels, 3, padding=1, **cfg)
+
+    def forward(self, x):
+        residual = x
+        out = self.conv1(x)
+        out = self.conv2(out)
+        out = out + residual
+
+        return out
+
+
+@BACKBONES.register_module()
+class Darknet(BaseModule):
+    """Darknet backbone.
+
+    Args:
+        depth (int): Depth of Darknet. Currently only support 53.
+        out_indices (Sequence[int]): Output from which stages.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters. Default: -1.
+        conv_cfg (dict): Config dict for convolution layer. Default: None.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+            Default: dict(type='BN', requires_grad=True)
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='LeakyReLU', negative_slope=0.1).
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only.
+        pretrained (str, optional): model pretrained path. Default: None
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+
+    Example:
+        >>> from mmdet.models import Darknet
+        >>> import torch
+        >>> self = Darknet(depth=53)
+        >>> self.eval()
+        >>> inputs = torch.rand(1, 3, 416, 416)
+        >>> level_outputs = self.forward(inputs)
+        >>> for level_out in level_outputs:
+        ...     print(tuple(level_out.shape))
+        ...
+        (1, 256, 52, 52)
+        (1, 512, 26, 26)
+        (1, 1024, 13, 13)
+    """
+
+    # Dict(depth: (layers, channels))
+    arch_settings = {
+        53: ((1, 2, 8, 8, 4), ((32, 64), (64, 128), (128, 256), (256, 512),
+                               (512, 1024)))
+    }
+
+    def __init__(self,
+                 depth=53,
+                 out_indices=(3, 4, 5),
+                 frozen_stages=-1,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
+                 norm_eval=True,
+                 pretrained=None,
+                 init_cfg=None):
+        super(Darknet, self).__init__(init_cfg)
+        if depth not in self.arch_settings:
+            raise KeyError(f'invalid depth {depth} for darknet')
+
+        self.depth = depth
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+        self.layers, self.channels = self.arch_settings[depth]
+
+        cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
+
+        self.conv1 = ConvModule(3, 32, 3, padding=1, **cfg)
+
+        self.cr_blocks = ['conv1']
+        for i, n_layers in enumerate(self.layers):
+            layer_name = f'conv_res_block{i + 1}'
+            in_c, out_c = self.channels[i]
+            self.add_module(
+                layer_name,
+                self.make_conv_res_block(in_c, out_c, n_layers, **cfg))
+            self.cr_blocks.append(layer_name)
+
+        self.norm_eval = norm_eval
+
+        assert not (init_cfg and pretrained), \
+            'init_cfg and pretrained cannot be specified at the same time'
+        if isinstance(pretrained, str):
+            warnings.warn('DeprecationWarning: pretrained is deprecated, '
+                          'please use "init_cfg" instead')
+            self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+        elif pretrained is None:
+            if init_cfg is None:
+                self.init_cfg = [
+                    dict(type='Kaiming', layer='Conv2d'),
+                    dict(
+                        type='Constant',
+                        val=1,
+                        layer=['_BatchNorm', 'GroupNorm'])
+                ]
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+    def forward(self, x):
+        outs = []
+        for i, layer_name in enumerate(self.cr_blocks):
+            cr_block = getattr(self, layer_name)
+            x = cr_block(x)
+            if i in self.out_indices:
+                outs.append(x)
+
+        return tuple(outs)
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            for i in range(self.frozen_stages):
+                m = getattr(self, self.cr_blocks[i])
+                m.eval()
+                for param in m.parameters():
+                    param.requires_grad = False
+
+    def train(self, mode=True):
+        super(Darknet, self).train(mode)
+        self._freeze_stages()
+        if mode and self.norm_eval:
+            for m in self.modules():
+                if isinstance(m, _BatchNorm):
+                    m.eval()
+
+    @staticmethod
+    def make_conv_res_block(in_channels,
+                            out_channels,
+                            res_repeat,
+                            conv_cfg=None,
+                            norm_cfg=dict(type='BN', requires_grad=True),
+                            act_cfg=dict(type='LeakyReLU',
+                                         negative_slope=0.1)):
+        """In Darknet backbone, ConvLayer is usually followed by ResBlock. This
+        function will make that. The Conv layers always have 3x3 filters with
+        stride=2. The number of the filters in Conv layer is the same as the
+        out channels of the ResBlock.
+
+        Args:
+            in_channels (int): The number of input channels.
+            out_channels (int): The number of output channels.
+            res_repeat (int): The number of ResBlocks.
+            conv_cfg (dict): Config dict for convolution layer. Default: None.
+            norm_cfg (dict): Dictionary to construct and config norm layer.
+                Default: dict(type='BN', requires_grad=True)
+            act_cfg (dict): Config dict for activation layer.
+                Default: dict(type='LeakyReLU', negative_slope=0.1).
+        """
+
+        cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
+
+        model = nn.Sequential()
+        model.add_module(
+            'conv',
+            ConvModule(
+                in_channels, out_channels, 3, stride=2, padding=1, **cfg))
+        for idx in range(res_repeat):
+            model.add_module('res{}'.format(idx),
+                             ResBlock(out_channels, **cfg))
+        return model
diff --git a/mmdet/models/backbones/detectors_resnet.py b/mmdet/models/backbones/detectors_resnet.py
new file mode 100644
index 0000000..a3c0d40
--- /dev/null
+++ b/mmdet/models/backbones/detectors_resnet.py
@@ -0,0 +1,353 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+from mmcv.cnn import (build_conv_layer, build_norm_layer, constant_init,
+                      kaiming_init)
+from mmcv.runner import Sequential, load_checkpoint
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from mmdet.utils import get_root_logger
+from ..builder import BACKBONES
+from .resnet import BasicBlock
+from .resnet import Bottleneck as _Bottleneck
+from .resnet import ResNet
+
+
+class Bottleneck(_Bottleneck):
+    r"""Bottleneck for the ResNet backbone in `DetectoRS
+    <https://arxiv.org/pdf/2006.02334.pdf>`_.
+
+    This bottleneck allows the users to specify whether to use
+    SAC (Switchable Atrous Convolution) and RFP (Recursive Feature Pyramid).
+
+    Args:
+         inplanes (int): The number of input channels.
+         planes (int): The number of output channels before expansion.
+         rfp_inplanes (int, optional): The number of channels from RFP.
+             Default: None. If specified, an additional conv layer will be
+             added for ``rfp_feat``. Otherwise, the structure is the same as
+             base class.
+         sac (dict, optional): Dictionary to construct SAC. Default: None.
+         init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+    expansion = 4
+
+    def __init__(self,
+                 inplanes,
+                 planes,
+                 rfp_inplanes=None,
+                 sac=None,
+                 init_cfg=None,
+                 **kwargs):
+        super(Bottleneck, self).__init__(
+            inplanes, planes, init_cfg=init_cfg, **kwargs)
+
+        assert sac is None or isinstance(sac, dict)
+        self.sac = sac
+        self.with_sac = sac is not None
+        if self.with_sac:
+            self.conv2 = build_conv_layer(
+                self.sac,
+                planes,
+                planes,
+                kernel_size=3,
+                stride=self.conv2_stride,
+                padding=self.dilation,
+                dilation=self.dilation,
+                bias=False)
+
+        self.rfp_inplanes = rfp_inplanes
+        if self.rfp_inplanes:
+            self.rfp_conv = build_conv_layer(
+                None,
+                self.rfp_inplanes,
+                planes * self.expansion,
+                1,
+                stride=1,
+                bias=True)
+            if init_cfg is None:
+                self.init_cfg = dict(
+                    type='Constant', val=0, override=dict(name='rfp_conv'))
+
+    def rfp_forward(self, x, rfp_feat):
+        """The forward function that also takes the RFP features as input."""
+
+        def _inner_forward(x):
+            identity = x
+
+            out = self.conv1(x)
+            out = self.norm1(out)
+            out = self.relu(out)
+
+            if self.with_plugins:
+                out = self.forward_plugin(out, self.after_conv1_plugin_names)
+
+            out = self.conv2(out)
+            out = self.norm2(out)
+            out = self.relu(out)
+
+            if self.with_plugins:
+                out = self.forward_plugin(out, self.after_conv2_plugin_names)
+
+            out = self.conv3(out)
+            out = self.norm3(out)
+
+            if self.with_plugins:
+                out = self.forward_plugin(out, self.after_conv3_plugin_names)
+
+            if self.downsample is not None:
+                identity = self.downsample(x)
+
+            out += identity
+
+            return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        if self.rfp_inplanes:
+            rfp_feat = self.rfp_conv(rfp_feat)
+            out = out + rfp_feat
+
+        out = self.relu(out)
+
+        return out
+
+
+class ResLayer(Sequential):
+    """ResLayer to build ResNet style backbone for RPF in detectoRS.
+
+    The difference between this module and base class is that we pass
+    ``rfp_inplanes`` to the first block.
+
+    Args:
+        block (nn.Module): block used to build ResLayer.
+        inplanes (int): inplanes of block.
+        planes (int): planes of block.
+        num_blocks (int): number of blocks.
+        stride (int): stride of the first block. Default: 1
+        avg_down (bool): Use AvgPool instead of stride conv when
+            downsampling in the bottleneck. Default: False
+        conv_cfg (dict): dictionary to construct and config conv layer.
+            Default: None
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: dict(type='BN')
+        downsample_first (bool): Downsample at the first block or last block.
+            False for Hourglass, True for ResNet. Default: True
+        rfp_inplanes (int, optional): The number of channels from RFP.
+            Default: None. If specified, an additional conv layer will be
+            added for ``rfp_feat``. Otherwise, the structure is the same as
+            base class.
+    """
+
+    def __init__(self,
+                 block,
+                 inplanes,
+                 planes,
+                 num_blocks,
+                 stride=1,
+                 avg_down=False,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 downsample_first=True,
+                 rfp_inplanes=None,
+                 **kwargs):
+        self.block = block
+        assert downsample_first, f'downsample_first={downsample_first} is ' \
+                                 'not supported in DetectoRS'
+
+        downsample = None
+        if stride != 1 or inplanes != planes * block.expansion:
+            downsample = []
+            conv_stride = stride
+            if avg_down and stride != 1:
+                conv_stride = 1
+                downsample.append(
+                    nn.AvgPool2d(
+                        kernel_size=stride,
+                        stride=stride,
+                        ceil_mode=True,
+                        count_include_pad=False))
+            downsample.extend([
+                build_conv_layer(
+                    conv_cfg,
+                    inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=conv_stride,
+                    bias=False),
+                build_norm_layer(norm_cfg, planes * block.expansion)[1]
+            ])
+            downsample = nn.Sequential(*downsample)
+
+        layers = []
+        layers.append(
+            block(
+                inplanes=inplanes,
+                planes=planes,
+                stride=stride,
+                downsample=downsample,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                rfp_inplanes=rfp_inplanes,
+                **kwargs))
+        inplanes = planes * block.expansion
+        for _ in range(1, num_blocks):
+            layers.append(
+                block(
+                    inplanes=inplanes,
+                    planes=planes,
+                    stride=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    **kwargs))
+
+        super(ResLayer, self).__init__(*layers)
+
+
+@BACKBONES.register_module()
+class DetectoRS_ResNet(ResNet):
+    """ResNet backbone for DetectoRS.
+
+    Args:
+        sac (dict, optional): Dictionary to construct SAC (Switchable Atrous
+            Convolution). Default: None.
+        stage_with_sac (list): Which stage to use sac. Default: (False, False,
+            False, False).
+        rfp_inplanes (int, optional): The number of channels from RFP.
+            Default: None. If specified, an additional conv layer will be
+            added for ``rfp_feat``. Otherwise, the structure is the same as
+            base class.
+        output_img (bool): If ``True``, the input image will be inserted into
+            the starting position of output. Default: False.
+    """
+
+    arch_settings = {
+        50: (Bottleneck, (3, 4, 6, 3)),
+        101: (Bottleneck, (3, 4, 23, 3)),
+        152: (Bottleneck, (3, 8, 36, 3))
+    }
+
+    def __init__(self,
+                 sac=None,
+                 stage_with_sac=(False, False, False, False),
+                 rfp_inplanes=None,
+                 output_img=False,
+                 pretrained=None,
+                 init_cfg=None,
+                 **kwargs):
+        assert not (init_cfg and pretrained), \
+            'init_cfg and pretrained cannot be specified at the same time'
+        self.pretrained = pretrained
+        if init_cfg is not None:
+            assert isinstance(init_cfg, dict), \
+                f'init_cfg must be a dict, but got {type(init_cfg)}'
+            if 'type' in init_cfg:
+                assert init_cfg.get('type') == 'Pretrained', \
+                    'Only can initialize module by loading a pretrained model'
+            else:
+                raise KeyError('`init_cfg` must contain the key "type"')
+            self.pretrained = init_cfg.get('checkpoint')
+        self.sac = sac
+        self.stage_with_sac = stage_with_sac
+        self.rfp_inplanes = rfp_inplanes
+        self.output_img = output_img
+        super(DetectoRS_ResNet, self).__init__(**kwargs)
+
+        self.inplanes = self.stem_channels
+        self.res_layers = []
+        for i, num_blocks in enumerate(self.stage_blocks):
+            stride = self.strides[i]
+            dilation = self.dilations[i]
+            dcn = self.dcn if self.stage_with_dcn[i] else None
+            sac = self.sac if self.stage_with_sac[i] else None
+            if self.plugins is not None:
+                stage_plugins = self.make_stage_plugins(self.plugins, i)
+            else:
+                stage_plugins = None
+            planes = self.base_channels * 2**i
+            res_layer = self.make_res_layer(
+                block=self.block,
+                inplanes=self.inplanes,
+                planes=planes,
+                num_blocks=num_blocks,
+                stride=stride,
+                dilation=dilation,
+                style=self.style,
+                avg_down=self.avg_down,
+                with_cp=self.with_cp,
+                conv_cfg=self.conv_cfg,
+                norm_cfg=self.norm_cfg,
+                dcn=dcn,
+                sac=sac,
+                rfp_inplanes=rfp_inplanes if i > 0 else None,
+                plugins=stage_plugins)
+            self.inplanes = planes * self.block.expansion
+            layer_name = f'layer{i + 1}'
+            self.add_module(layer_name, res_layer)
+            self.res_layers.append(layer_name)
+
+        self._freeze_stages()
+
+    # In order to be properly initialized by RFP
+    def init_weights(self):
+        # Calling this method will cause parameter initialization exception
+        # super(DetectoRS_ResNet, self).init_weights()
+
+        if isinstance(self.pretrained, str):
+            logger = get_root_logger()
+            load_checkpoint(self, self.pretrained, strict=False, logger=logger)
+        elif self.pretrained is None:
+            for m in self.modules():
+                if isinstance(m, nn.Conv2d):
+                    kaiming_init(m)
+                elif isinstance(m, (_BatchNorm, nn.GroupNorm)):
+                    constant_init(m, 1)
+
+            if self.dcn is not None:
+                for m in self.modules():
+                    if isinstance(m, Bottleneck) and hasattr(
+                            m.conv2, 'conv_offset'):
+                        constant_init(m.conv2.conv_offset, 0)
+
+            if self.zero_init_residual:
+                for m in self.modules():
+                    if isinstance(m, Bottleneck):
+                        constant_init(m.norm3, 0)
+                    elif isinstance(m, BasicBlock):
+                        constant_init(m.norm2, 0)
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+    def make_res_layer(self, **kwargs):
+        """Pack all blocks in a stage into a ``ResLayer`` for DetectoRS."""
+        return ResLayer(**kwargs)
+
+    def forward(self, x):
+        """Forward function."""
+        outs = list(super(DetectoRS_ResNet, self).forward(x))
+        if self.output_img:
+            outs.insert(0, x)
+        return tuple(outs)
+
+    def rfp_forward(self, x, rfp_feats):
+        """Forward function for RFP."""
+        if self.deep_stem:
+            x = self.stem(x)
+        else:
+            x = self.conv1(x)
+            x = self.norm1(x)
+            x = self.relu(x)
+        x = self.maxpool(x)
+        outs = []
+        for i, layer_name in enumerate(self.res_layers):
+            res_layer = getattr(self, layer_name)
+            rfp_feat = rfp_feats[i] if i > 0 else None
+            for layer in res_layer:
+                x = layer.rfp_forward(x, rfp_feat)
+            if i in self.out_indices:
+                outs.append(x)
+        return tuple(outs)
diff --git a/mmdet/models/backbones/detectors_resnext.py b/mmdet/models/backbones/detectors_resnext.py
new file mode 100644
index 0000000..5e8b20a
--- /dev/null
+++ b/mmdet/models/backbones/detectors_resnext.py
@@ -0,0 +1,123 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+from mmcv.cnn import build_conv_layer, build_norm_layer
+
+from ..builder import BACKBONES
+from .detectors_resnet import Bottleneck as _Bottleneck
+from .detectors_resnet import DetectoRS_ResNet
+
+
+class Bottleneck(_Bottleneck):
+    expansion = 4
+
+    def __init__(self,
+                 inplanes,
+                 planes,
+                 groups=1,
+                 base_width=4,
+                 base_channels=64,
+                 **kwargs):
+        """Bottleneck block for ResNeXt.
+
+        If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
+        it is "caffe", the stride-two layer is the first 1x1 conv layer.
+        """
+        super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
+
+        if groups == 1:
+            width = self.planes
+        else:
+            width = math.floor(self.planes *
+                               (base_width / base_channels)) * groups
+
+        self.norm1_name, norm1 = build_norm_layer(
+            self.norm_cfg, width, postfix=1)
+        self.norm2_name, norm2 = build_norm_layer(
+            self.norm_cfg, width, postfix=2)
+        self.norm3_name, norm3 = build_norm_layer(
+            self.norm_cfg, self.planes * self.expansion, postfix=3)
+
+        self.conv1 = build_conv_layer(
+            self.conv_cfg,
+            self.inplanes,
+            width,
+            kernel_size=1,
+            stride=self.conv1_stride,
+            bias=False)
+        self.add_module(self.norm1_name, norm1)
+        fallback_on_stride = False
+        self.with_modulated_dcn = False
+        if self.with_dcn:
+            fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
+        if self.with_sac:
+            self.conv2 = build_conv_layer(
+                self.sac,
+                width,
+                width,
+                kernel_size=3,
+                stride=self.conv2_stride,
+                padding=self.dilation,
+                dilation=self.dilation,
+                groups=groups,
+                bias=False)
+        elif not self.with_dcn or fallback_on_stride:
+            self.conv2 = build_conv_layer(
+                self.conv_cfg,
+                width,
+                width,
+                kernel_size=3,
+                stride=self.conv2_stride,
+                padding=self.dilation,
+                dilation=self.dilation,
+                groups=groups,
+                bias=False)
+        else:
+            assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
+            self.conv2 = build_conv_layer(
+                self.dcn,
+                width,
+                width,
+                kernel_size=3,
+                stride=self.conv2_stride,
+                padding=self.dilation,
+                dilation=self.dilation,
+                groups=groups,
+                bias=False)
+
+        self.add_module(self.norm2_name, norm2)
+        self.conv3 = build_conv_layer(
+            self.conv_cfg,
+            width,
+            self.planes * self.expansion,
+            kernel_size=1,
+            bias=False)
+        self.add_module(self.norm3_name, norm3)
+
+
+@BACKBONES.register_module()
+class DetectoRS_ResNeXt(DetectoRS_ResNet):
+    """ResNeXt backbone for DetectoRS.
+
+    Args:
+        groups (int): The number of groups in ResNeXt.
+        base_width (int): The base width of ResNeXt.
+    """
+
+    arch_settings = {
+        50: (Bottleneck, (3, 4, 6, 3)),
+        101: (Bottleneck, (3, 4, 23, 3)),
+        152: (Bottleneck, (3, 8, 36, 3))
+    }
+
+    def __init__(self, groups=1, base_width=4, **kwargs):
+        self.groups = groups
+        self.base_width = base_width
+        super(DetectoRS_ResNeXt, self).__init__(**kwargs)
+
+    def make_res_layer(self, **kwargs):
+        return super().make_res_layer(
+            groups=self.groups,
+            base_width=self.base_width,
+            base_channels=self.base_channels,
+            **kwargs)
diff --git a/mmdet/models/backbones/efficientnet.py b/mmdet/models/backbones/efficientnet.py
new file mode 100644
index 0000000..7ee3595
--- /dev/null
+++ b/mmdet/models/backbones/efficientnet.py
@@ -0,0 +1,417 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import math
+from functools import partial
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+from mmcv.cnn.bricks import ConvModule, DropPath
+from mmcv.runner import BaseModule, Sequential
+
+from ..builder import BACKBONES
+from ..utils import InvertedResidual, SELayer, make_divisible
+
+
+class EdgeResidual(BaseModule):
+    """Edge Residual Block.
+
+    Args:
+        in_channels (int): The input channels of this module.
+        out_channels (int): The output channels of this module.
+        mid_channels (int): The input channels of the second convolution.
+        kernel_size (int): The kernel size of the first convolution.
+            Defaults to 3.
+        stride (int): The stride of the first convolution. Defaults to 1.
+        se_cfg (dict, optional): Config dict for se layer. Defaults to None,
+            which means no se layer.
+        with_residual (bool): Use residual connection. Defaults to True.
+        conv_cfg (dict, optional): Config dict for convolution layer.
+            Defaults to None, which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Defaults to ``dict(type='BN')``.
+        act_cfg (dict): Config dict for activation layer.
+            Defaults to ``dict(type='ReLU')``.
+        drop_path_rate (float): stochastic depth rate. Defaults to 0.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Defaults to False.
+        init_cfg (dict | list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 mid_channels,
+                 kernel_size=3,
+                 stride=1,
+                 se_cfg=None,
+                 with_residual=True,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 drop_path_rate=0.,
+                 with_cp=False,
+                 init_cfg=None,
+                 **kwargs):
+        super(EdgeResidual, self).__init__(init_cfg=init_cfg)
+        assert stride in [1, 2]
+        self.with_cp = with_cp
+        self.drop_path = DropPath(
+            drop_path_rate) if drop_path_rate > 0 else nn.Identity()
+        self.with_se = se_cfg is not None
+        self.with_residual = (
+            stride == 1 and in_channels == out_channels and with_residual)
+
+        if self.with_se:
+            assert isinstance(se_cfg, dict)
+
+        self.conv1 = ConvModule(
+            in_channels=in_channels,
+            out_channels=mid_channels,
+            kernel_size=kernel_size,
+            stride=1,
+            padding=kernel_size // 2,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+
+        if self.with_se:
+            self.se = SELayer(**se_cfg)
+
+        self.conv2 = ConvModule(
+            in_channels=mid_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            stride=stride,
+            padding=0,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=None)
+
+    def forward(self, x):
+
+        def _inner_forward(x):
+            out = x
+            out = self.conv1(out)
+
+            if self.with_se:
+                out = self.se(out)
+
+            out = self.conv2(out)
+
+            if self.with_residual:
+                return x + self.drop_path(out)
+            else:
+                return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        return out
+
+
+def model_scaling(layer_setting, arch_setting):
+    """Scaling operation to the layer's parameters according to the
+    arch_setting."""
+    # scale width
+    new_layer_setting = copy.deepcopy(layer_setting)
+    for layer_cfg in new_layer_setting:
+        for block_cfg in layer_cfg:
+            block_cfg[1] = make_divisible(block_cfg[1] * arch_setting[0], 8)
+
+    # scale depth
+    split_layer_setting = [new_layer_setting[0]]
+    for layer_cfg in new_layer_setting[1:-1]:
+        tmp_index = [0]
+        for i in range(len(layer_cfg) - 1):
+            if layer_cfg[i + 1][1] != layer_cfg[i][1]:
+                tmp_index.append(i + 1)
+        tmp_index.append(len(layer_cfg))
+        for i in range(len(tmp_index) - 1):
+            split_layer_setting.append(layer_cfg[tmp_index[i]:tmp_index[i +
+                                                                        1]])
+    split_layer_setting.append(new_layer_setting[-1])
+
+    num_of_layers = [len(layer_cfg) for layer_cfg in split_layer_setting[1:-1]]
+    new_layers = [
+        int(math.ceil(arch_setting[1] * num)) for num in num_of_layers
+    ]
+
+    merge_layer_setting = [split_layer_setting[0]]
+    for i, layer_cfg in enumerate(split_layer_setting[1:-1]):
+        if new_layers[i] <= num_of_layers[i]:
+            tmp_layer_cfg = layer_cfg[:new_layers[i]]
+        else:
+            tmp_layer_cfg = copy.deepcopy(layer_cfg) + [layer_cfg[-1]] * (
+                new_layers[i] - num_of_layers[i])
+        if tmp_layer_cfg[0][3] == 1 and i != 0:
+            merge_layer_setting[-1] += tmp_layer_cfg.copy()
+        else:
+            merge_layer_setting.append(tmp_layer_cfg.copy())
+    merge_layer_setting.append(split_layer_setting[-1])
+
+    return merge_layer_setting
+
+
+@BACKBONES.register_module()
+class EfficientNet(BaseModule):
+    """EfficientNet backbone.
+
+    Args:
+        arch (str): Architecture of efficientnet. Defaults to b0.
+        out_indices (Sequence[int]): Output from which stages.
+            Defaults to (6, ).
+        frozen_stages (int): Stages to be frozen (all param fixed).
+            Defaults to 0, which means not freezing any parameters.
+        conv_cfg (dict): Config dict for convolution layer.
+            Defaults to None, which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Defaults to dict(type='BN').
+        act_cfg (dict): Config dict for activation layer.
+            Defaults to dict(type='Swish').
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Defaults to False.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Defaults to False.
+    """
+
+    # Parameters to build layers.
+    # 'b' represents the architecture of normal EfficientNet family includes
+    # 'b0', 'b1', 'b2', 'b3', 'b4', 'b5', 'b6', 'b7', 'b8'.
+    # 'e' represents the architecture of EfficientNet-EdgeTPU including 'es',
+    # 'em', 'el'.
+    # 6 parameters are needed to construct a layer, From left to right:
+    # - kernel_size: The kernel size of the block
+    # - out_channel: The number of out_channels of the block
+    # - se_ratio: The sequeeze ratio of SELayer.
+    # - stride: The stride of the block
+    # - expand_ratio: The expand_ratio of the mid_channels
+    # - block_type: -1: Not a block, 0: InvertedResidual, 1: EdgeResidual
+    layer_settings = {
+        'b': [[[3, 32, 0, 2, 0, -1]],
+              [[3, 16, 4, 1, 1, 0]],
+              [[3, 24, 4, 2, 6, 0],
+               [3, 24, 4, 1, 6, 0]],
+              [[5, 40, 4, 2, 6, 0],
+               [5, 40, 4, 1, 6, 0]],
+              [[3, 80, 4, 2, 6, 0],
+               [3, 80, 4, 1, 6, 0],
+               [3, 80, 4, 1, 6, 0],
+               [5, 112, 4, 1, 6, 0],
+               [5, 112, 4, 1, 6, 0],
+               [5, 112, 4, 1, 6, 0]],
+              [[5, 192, 4, 2, 6, 0],
+               [5, 192, 4, 1, 6, 0],
+               [5, 192, 4, 1, 6, 0],
+               [5, 192, 4, 1, 6, 0],
+               [3, 320, 4, 1, 6, 0]],
+              [[1, 1280, 0, 1, 0, -1]]
+              ],
+        'e': [[[3, 32, 0, 2, 0, -1]],
+              [[3, 24, 0, 1, 3, 1]],
+              [[3, 32, 0, 2, 8, 1],
+               [3, 32, 0, 1, 8, 1]],
+              [[3, 48, 0, 2, 8, 1],
+               [3, 48, 0, 1, 8, 1],
+               [3, 48, 0, 1, 8, 1],
+               [3, 48, 0, 1, 8, 1]],
+              [[5, 96, 0, 2, 8, 0],
+               [5, 96, 0, 1, 8, 0],
+               [5, 96, 0, 1, 8, 0],
+               [5, 96, 0, 1, 8, 0],
+               [5, 96, 0, 1, 8, 0],
+               [5, 144, 0, 1, 8, 0],
+               [5, 144, 0, 1, 8, 0],
+               [5, 144, 0, 1, 8, 0],
+               [5, 144, 0, 1, 8, 0]],
+              [[5, 192, 0, 2, 8, 0],
+               [5, 192, 0, 1, 8, 0]],
+              [[1, 1280, 0, 1, 0, -1]]
+              ]
+    }  # yapf: disable
+
+    # Parameters to build different kinds of architecture.
+    # From left to right: scaling factor for width, scaling factor for depth,
+    # resolution.
+    arch_settings = {
+        'b0': (1.0, 1.0, 224),
+        'b1': (1.0, 1.1, 240),
+        'b2': (1.1, 1.2, 260),
+        'b3': (1.2, 1.4, 300),
+        'b4': (1.4, 1.8, 380),
+        'b5': (1.6, 2.2, 456),
+        'b6': (1.8, 2.6, 528),
+        'b7': (2.0, 3.1, 600),
+        'b8': (2.2, 3.6, 672),
+        'es': (1.0, 1.0, 224),
+        'em': (1.0, 1.1, 240),
+        'el': (1.2, 1.4, 300)
+    }
+
+    def __init__(self,
+                 arch='b0',
+                 drop_path_rate=0.,
+                 out_indices=(6, ),
+                 frozen_stages=0,
+                 conv_cfg=dict(type='Conv2dAdaptivePadding'),
+                 norm_cfg=dict(type='BN', eps=1e-3),
+                 act_cfg=dict(type='Swish'),
+                 norm_eval=False,
+                 with_cp=False,
+                 init_cfg=[
+                     dict(type='Kaiming', layer='Conv2d'),
+                     dict(
+                         type='Constant',
+                         layer=['_BatchNorm', 'GroupNorm'],
+                         val=1)
+                 ]):
+        super(EfficientNet, self).__init__(init_cfg)
+        assert arch in self.arch_settings, \
+            f'"{arch}" is not one of the arch_settings ' \
+            f'({", ".join(self.arch_settings.keys())})'
+        self.arch_setting = self.arch_settings[arch]
+        self.layer_setting = self.layer_settings[arch[:1]]
+        for index in out_indices:
+            if index not in range(0, len(self.layer_setting)):
+                raise ValueError('the item in out_indices must in '
+                                 f'range(0, {len(self.layer_setting)}). '
+                                 f'But received {index}')
+
+        if frozen_stages not in range(len(self.layer_setting) + 1):
+            raise ValueError('frozen_stages must be in range(0, '
+                             f'{len(self.layer_setting) + 1}). '
+                             f'But received {frozen_stages}')
+        self.drop_path_rate = drop_path_rate
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+        self.norm_eval = norm_eval
+        self.with_cp = with_cp
+
+        self.layer_setting = model_scaling(self.layer_setting,
+                                           self.arch_setting)
+        block_cfg_0 = self.layer_setting[0][0]
+        block_cfg_last = self.layer_setting[-1][0]
+        self.in_channels = make_divisible(block_cfg_0[1], 8)
+        self.out_channels = block_cfg_last[1]
+        self.layers = nn.ModuleList()
+        self.layers.append(
+            ConvModule(
+                in_channels=3,
+                out_channels=self.in_channels,
+                kernel_size=block_cfg_0[0],
+                stride=block_cfg_0[3],
+                padding=block_cfg_0[0] // 2,
+                conv_cfg=self.conv_cfg,
+                norm_cfg=self.norm_cfg,
+                act_cfg=self.act_cfg))
+        self.make_layer()
+        # Avoid building unused layers in mmdetection.
+        if len(self.layers) < max(self.out_indices) + 1:
+            self.layers.append(
+                ConvModule(
+                    in_channels=self.in_channels,
+                    out_channels=self.out_channels,
+                    kernel_size=block_cfg_last[0],
+                    stride=block_cfg_last[3],
+                    padding=block_cfg_last[0] // 2,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    act_cfg=self.act_cfg))
+
+    def make_layer(self):
+        # Without the first and the final conv block.
+        layer_setting = self.layer_setting[1:-1]
+
+        total_num_blocks = sum([len(x) for x in layer_setting])
+        block_idx = 0
+        dpr = [
+            x.item()
+            for x in torch.linspace(0, self.drop_path_rate, total_num_blocks)
+        ]  # stochastic depth decay rule
+
+        for i, layer_cfg in enumerate(layer_setting):
+            # Avoid building unused layers in mmdetection.
+            if i > max(self.out_indices) - 1:
+                break
+            layer = []
+            for i, block_cfg in enumerate(layer_cfg):
+                (kernel_size, out_channels, se_ratio, stride, expand_ratio,
+                 block_type) = block_cfg
+
+                mid_channels = int(self.in_channels * expand_ratio)
+                out_channels = make_divisible(out_channels, 8)
+                if se_ratio <= 0:
+                    se_cfg = None
+                else:
+                    # In mmdetection, the `divisor` is deleted to align
+                    # the logic of SELayer with mmcls.
+                    se_cfg = dict(
+                        channels=mid_channels,
+                        ratio=expand_ratio * se_ratio,
+                        act_cfg=(self.act_cfg, dict(type='Sigmoid')))
+                if block_type == 1:  # edge tpu
+                    if i > 0 and expand_ratio == 3:
+                        with_residual = False
+                        expand_ratio = 4
+                    else:
+                        with_residual = True
+                    mid_channels = int(self.in_channels * expand_ratio)
+                    if se_cfg is not None:
+                        # In mmdetection, the `divisor` is deleted to align
+                        # the logic of SELayer with mmcls.
+                        se_cfg = dict(
+                            channels=mid_channels,
+                            ratio=se_ratio * expand_ratio,
+                            act_cfg=(self.act_cfg, dict(type='Sigmoid')))
+                    block = partial(EdgeResidual, with_residual=with_residual)
+                else:
+                    block = InvertedResidual
+                layer.append(
+                    block(
+                        in_channels=self.in_channels,
+                        out_channels=out_channels,
+                        mid_channels=mid_channels,
+                        kernel_size=kernel_size,
+                        stride=stride,
+                        se_cfg=se_cfg,
+                        conv_cfg=self.conv_cfg,
+                        norm_cfg=self.norm_cfg,
+                        act_cfg=self.act_cfg,
+                        drop_path_rate=dpr[block_idx],
+                        with_cp=self.with_cp,
+                        # In mmdetection, `with_expand_conv` is set to align
+                        # the logic of InvertedResidual with mmcls.
+                        with_expand_conv=(mid_channels != self.in_channels)))
+                self.in_channels = out_channels
+                block_idx += 1
+            self.layers.append(Sequential(*layer))
+
+    def forward(self, x):
+        outs = []
+        for i, layer in enumerate(self.layers):
+            x = layer(x)
+            if i in self.out_indices:
+                outs.append(x)
+
+        return tuple(outs)
+
+    def _freeze_stages(self):
+        for i in range(self.frozen_stages):
+            m = self.layers[i]
+            m.eval()
+            for param in m.parameters():
+                param.requires_grad = False
+
+    def train(self, mode=True):
+        super(EfficientNet, self).train(mode)
+        self._freeze_stages()
+        if mode and self.norm_eval:
+            for m in self.modules():
+                if isinstance(m, nn.BatchNorm2d):
+                    m.eval()
diff --git a/mmdet/models/backbones/hourglass.py b/mmdet/models/backbones/hourglass.py
new file mode 100644
index 0000000..f0dfb43
--- /dev/null
+++ b/mmdet/models/backbones/hourglass.py
@@ -0,0 +1,222 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule
+
+from ..builder import BACKBONES
+from ..utils import ResLayer
+from .resnet import BasicBlock
+
+
+class HourglassModule(BaseModule):
+    """Hourglass Module for HourglassNet backbone.
+
+    Generate module recursively and use BasicBlock as the base unit.
+
+    Args:
+        depth (int): Depth of current HourglassModule.
+        stage_channels (list[int]): Feature channels of sub-modules in current
+            and follow-up HourglassModule.
+        stage_blocks (list[int]): Number of sub-modules stacked in current and
+            follow-up HourglassModule.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+        upsample_cfg (dict, optional): Config dict for interpolate layer.
+            Default: `dict(mode='nearest')`
+    """
+
+    def __init__(self,
+                 depth,
+                 stage_channels,
+                 stage_blocks,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 init_cfg=None,
+                 upsample_cfg=dict(mode='nearest')):
+        super(HourglassModule, self).__init__(init_cfg)
+
+        self.depth = depth
+
+        cur_block = stage_blocks[0]
+        next_block = stage_blocks[1]
+
+        cur_channel = stage_channels[0]
+        next_channel = stage_channels[1]
+
+        self.up1 = ResLayer(
+            BasicBlock, cur_channel, cur_channel, cur_block, norm_cfg=norm_cfg)
+
+        self.low1 = ResLayer(
+            BasicBlock,
+            cur_channel,
+            next_channel,
+            cur_block,
+            stride=2,
+            norm_cfg=norm_cfg)
+
+        if self.depth > 1:
+            self.low2 = HourglassModule(depth - 1, stage_channels[1:],
+                                        stage_blocks[1:])
+        else:
+            self.low2 = ResLayer(
+                BasicBlock,
+                next_channel,
+                next_channel,
+                next_block,
+                norm_cfg=norm_cfg)
+
+        self.low3 = ResLayer(
+            BasicBlock,
+            next_channel,
+            cur_channel,
+            cur_block,
+            norm_cfg=norm_cfg,
+            downsample_first=False)
+
+        self.up2 = F.interpolate
+        self.upsample_cfg = upsample_cfg
+
+    def forward(self, x):
+        """Forward function."""
+        up1 = self.up1(x)
+        low1 = self.low1(x)
+        low2 = self.low2(low1)
+        low3 = self.low3(low2)
+        # Fixing `scale factor` (e.g. 2) is common for upsampling, but
+        # in some cases the spatial size is mismatched and error will arise.
+        if 'scale_factor' in self.upsample_cfg:
+            up2 = self.up2(low3, **self.upsample_cfg)
+        else:
+            shape = up1.shape[2:]
+            up2 = self.up2(low3, size=shape, **self.upsample_cfg)
+        return up1 + up2
+
+
+@BACKBONES.register_module()
+class HourglassNet(BaseModule):
+    """HourglassNet backbone.
+
+    Stacked Hourglass Networks for Human Pose Estimation.
+    More details can be found in the `paper
+    <https://arxiv.org/abs/1603.06937>`_ .
+
+    Args:
+        downsample_times (int): Downsample times in a HourglassModule.
+        num_stacks (int): Number of HourglassModule modules stacked,
+            1 for Hourglass-52, 2 for Hourglass-104.
+        stage_channels (list[int]): Feature channel of each sub-module in a
+            HourglassModule.
+        stage_blocks (list[int]): Number of sub-modules stacked in a
+            HourglassModule.
+        feat_channel (int): Feature channel of conv after a HourglassModule.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+        pretrained (str, optional): model pretrained path. Default: None
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+
+    Example:
+        >>> from mmdet.models import HourglassNet
+        >>> import torch
+        >>> self = HourglassNet()
+        >>> self.eval()
+        >>> inputs = torch.rand(1, 3, 511, 511)
+        >>> level_outputs = self.forward(inputs)
+        >>> for level_output in level_outputs:
+        ...     print(tuple(level_output.shape))
+        (1, 256, 128, 128)
+        (1, 256, 128, 128)
+    """
+
+    def __init__(self,
+                 downsample_times=5,
+                 num_stacks=2,
+                 stage_channels=(256, 256, 384, 384, 384, 512),
+                 stage_blocks=(2, 2, 2, 2, 2, 4),
+                 feat_channel=256,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 pretrained=None,
+                 init_cfg=None):
+        assert init_cfg is None, 'To prevent abnormal initialization ' \
+                                 'behavior, init_cfg is not allowed to be set'
+        super(HourglassNet, self).__init__(init_cfg)
+
+        self.num_stacks = num_stacks
+        assert self.num_stacks >= 1
+        assert len(stage_channels) == len(stage_blocks)
+        assert len(stage_channels) > downsample_times
+
+        cur_channel = stage_channels[0]
+
+        self.stem = nn.Sequential(
+            ConvModule(
+                3, cur_channel // 2, 7, padding=3, stride=2,
+                norm_cfg=norm_cfg),
+            ResLayer(
+                BasicBlock,
+                cur_channel // 2,
+                cur_channel,
+                1,
+                stride=2,
+                norm_cfg=norm_cfg))
+
+        self.hourglass_modules = nn.ModuleList([
+            HourglassModule(downsample_times, stage_channels, stage_blocks)
+            for _ in range(num_stacks)
+        ])
+
+        self.inters = ResLayer(
+            BasicBlock,
+            cur_channel,
+            cur_channel,
+            num_stacks - 1,
+            norm_cfg=norm_cfg)
+
+        self.conv1x1s = nn.ModuleList([
+            ConvModule(
+                cur_channel, cur_channel, 1, norm_cfg=norm_cfg, act_cfg=None)
+            for _ in range(num_stacks - 1)
+        ])
+
+        self.out_convs = nn.ModuleList([
+            ConvModule(
+                cur_channel, feat_channel, 3, padding=1, norm_cfg=norm_cfg)
+            for _ in range(num_stacks)
+        ])
+
+        self.remap_convs = nn.ModuleList([
+            ConvModule(
+                feat_channel, cur_channel, 1, norm_cfg=norm_cfg, act_cfg=None)
+            for _ in range(num_stacks - 1)
+        ])
+
+        self.relu = nn.ReLU(inplace=True)
+
+    def init_weights(self):
+        """Init module weights."""
+        # Training Centripetal Model needs to reset parameters for Conv2d
+        super(HourglassNet, self).init_weights()
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                m.reset_parameters()
+
+    def forward(self, x):
+        """Forward function."""
+        inter_feat = self.stem(x)
+        out_feats = []
+
+        for ind in range(self.num_stacks):
+            single_hourglass = self.hourglass_modules[ind]
+            out_conv = self.out_convs[ind]
+
+            hourglass_feat = single_hourglass(inter_feat)
+            out_feat = out_conv(hourglass_feat)
+            out_feats.append(out_feat)
+
+            if ind < self.num_stacks - 1:
+                inter_feat = self.conv1x1s[ind](
+                    inter_feat) + self.remap_convs[ind](
+                        out_feat)
+                inter_feat = self.inters[ind](self.relu(inter_feat))
+
+        return out_feats
diff --git a/mmdet/models/backbones/hrnet.py b/mmdet/models/backbones/hrnet.py
new file mode 100644
index 0000000..06c210a
--- /dev/null
+++ b/mmdet/models/backbones/hrnet.py
@@ -0,0 +1,589 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch.nn as nn
+from mmcv.cnn import build_conv_layer, build_norm_layer
+from mmcv.runner import BaseModule, ModuleList, Sequential
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from ..builder import BACKBONES
+from .resnet import BasicBlock, Bottleneck
+
+
+class HRModule(BaseModule):
+    """High-Resolution Module for HRNet.
+
+    In this module, every branch has 4 BasicBlocks/Bottlenecks. Fusion/Exchange
+    is in this module.
+    """
+
+    def __init__(self,
+                 num_branches,
+                 blocks,
+                 num_blocks,
+                 in_channels,
+                 num_channels,
+                 multiscale_output=True,
+                 with_cp=False,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 block_init_cfg=None,
+                 init_cfg=None):
+        super(HRModule, self).__init__(init_cfg)
+        self.block_init_cfg = block_init_cfg
+        self._check_branches(num_branches, num_blocks, in_channels,
+                             num_channels)
+
+        self.in_channels = in_channels
+        self.num_branches = num_branches
+
+        self.multiscale_output = multiscale_output
+        self.norm_cfg = norm_cfg
+        self.conv_cfg = conv_cfg
+        self.with_cp = with_cp
+        self.branches = self._make_branches(num_branches, blocks, num_blocks,
+                                            num_channels)
+        self.fuse_layers = self._make_fuse_layers()
+        self.relu = nn.ReLU(inplace=False)
+
+    def _check_branches(self, num_branches, num_blocks, in_channels,
+                        num_channels):
+        if num_branches != len(num_blocks):
+            error_msg = f'NUM_BRANCHES({num_branches}) ' \
+                        f'!= NUM_BLOCKS({len(num_blocks)})'
+            raise ValueError(error_msg)
+
+        if num_branches != len(num_channels):
+            error_msg = f'NUM_BRANCHES({num_branches}) ' \
+                        f'!= NUM_CHANNELS({len(num_channels)})'
+            raise ValueError(error_msg)
+
+        if num_branches != len(in_channels):
+            error_msg = f'NUM_BRANCHES({num_branches}) ' \
+                        f'!= NUM_INCHANNELS({len(in_channels)})'
+            raise ValueError(error_msg)
+
+    def _make_one_branch(self,
+                         branch_index,
+                         block,
+                         num_blocks,
+                         num_channels,
+                         stride=1):
+        downsample = None
+        if stride != 1 or \
+                self.in_channels[branch_index] != \
+                num_channels[branch_index] * block.expansion:
+            downsample = nn.Sequential(
+                build_conv_layer(
+                    self.conv_cfg,
+                    self.in_channels[branch_index],
+                    num_channels[branch_index] * block.expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=False),
+                build_norm_layer(self.norm_cfg, num_channels[branch_index] *
+                                 block.expansion)[1])
+
+        layers = []
+        layers.append(
+            block(
+                self.in_channels[branch_index],
+                num_channels[branch_index],
+                stride,
+                downsample=downsample,
+                with_cp=self.with_cp,
+                norm_cfg=self.norm_cfg,
+                conv_cfg=self.conv_cfg,
+                init_cfg=self.block_init_cfg))
+        self.in_channels[branch_index] = \
+            num_channels[branch_index] * block.expansion
+        for i in range(1, num_blocks[branch_index]):
+            layers.append(
+                block(
+                    self.in_channels[branch_index],
+                    num_channels[branch_index],
+                    with_cp=self.with_cp,
+                    norm_cfg=self.norm_cfg,
+                    conv_cfg=self.conv_cfg,
+                    init_cfg=self.block_init_cfg))
+
+        return Sequential(*layers)
+
+    def _make_branches(self, num_branches, block, num_blocks, num_channels):
+        branches = []
+
+        for i in range(num_branches):
+            branches.append(
+                self._make_one_branch(i, block, num_blocks, num_channels))
+
+        return ModuleList(branches)
+
+    def _make_fuse_layers(self):
+        if self.num_branches == 1:
+            return None
+
+        num_branches = self.num_branches
+        in_channels = self.in_channels
+        fuse_layers = []
+        num_out_branches = num_branches if self.multiscale_output else 1
+        for i in range(num_out_branches):
+            fuse_layer = []
+            for j in range(num_branches):
+                if j > i:
+                    fuse_layer.append(
+                        nn.Sequential(
+                            build_conv_layer(
+                                self.conv_cfg,
+                                in_channels[j],
+                                in_channels[i],
+                                kernel_size=1,
+                                stride=1,
+                                padding=0,
+                                bias=False),
+                            build_norm_layer(self.norm_cfg, in_channels[i])[1],
+                            nn.Upsample(
+                                scale_factor=2**(j - i), mode='nearest')))
+                elif j == i:
+                    fuse_layer.append(None)
+                else:
+                    conv_downsamples = []
+                    for k in range(i - j):
+                        if k == i - j - 1:
+                            conv_downsamples.append(
+                                nn.Sequential(
+                                    build_conv_layer(
+                                        self.conv_cfg,
+                                        in_channels[j],
+                                        in_channels[i],
+                                        kernel_size=3,
+                                        stride=2,
+                                        padding=1,
+                                        bias=False),
+                                    build_norm_layer(self.norm_cfg,
+                                                     in_channels[i])[1]))
+                        else:
+                            conv_downsamples.append(
+                                nn.Sequential(
+                                    build_conv_layer(
+                                        self.conv_cfg,
+                                        in_channels[j],
+                                        in_channels[j],
+                                        kernel_size=3,
+                                        stride=2,
+                                        padding=1,
+                                        bias=False),
+                                    build_norm_layer(self.norm_cfg,
+                                                     in_channels[j])[1],
+                                    nn.ReLU(inplace=False)))
+                    fuse_layer.append(nn.Sequential(*conv_downsamples))
+            fuse_layers.append(nn.ModuleList(fuse_layer))
+
+        return nn.ModuleList(fuse_layers)
+
+    def forward(self, x):
+        """Forward function."""
+        if self.num_branches == 1:
+            return [self.branches[0](x[0])]
+
+        for i in range(self.num_branches):
+            x[i] = self.branches[i](x[i])
+
+        x_fuse = []
+        for i in range(len(self.fuse_layers)):
+            y = 0
+            for j in range(self.num_branches):
+                if i == j:
+                    y += x[j]
+                else:
+                    y += self.fuse_layers[i][j](x[j])
+            x_fuse.append(self.relu(y))
+        return x_fuse
+
+
+@BACKBONES.register_module()
+class HRNet(BaseModule):
+    """HRNet backbone.
+
+    `High-Resolution Representations for Labeling Pixels and Regions
+    arXiv: <https://arxiv.org/abs/1904.04514>`_.
+
+    Args:
+        extra (dict): Detailed configuration for each stage of HRNet.
+            There must be 4 stages, the configuration for each stage must have
+            5 keys:
+
+                - num_modules(int): The number of HRModule in this stage.
+                - num_branches(int): The number of branches in the HRModule.
+                - block(str): The type of convolution block.
+                - num_blocks(tuple): The number of blocks in each branch.
+                    The length must be equal to num_branches.
+                - num_channels(tuple): The number of channels in each branch.
+                    The length must be equal to num_branches.
+        in_channels (int): Number of input image channels. Default: 3.
+        conv_cfg (dict): Dictionary to construct and config conv layer.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: True.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+        zero_init_residual (bool): Whether to use zero init for last norm layer
+            in resblocks to let them behave as identity. Default: False.
+        multiscale_output (bool): Whether to output multi-level features
+            produced by multiple branches. If False, only the first level
+            feature will be output. Default: True.
+        pretrained (str, optional): Model pretrained path. Default: None.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+
+    Example:
+        >>> from mmdet.models import HRNet
+        >>> import torch
+        >>> extra = dict(
+        >>>     stage1=dict(
+        >>>         num_modules=1,
+        >>>         num_branches=1,
+        >>>         block='BOTTLENECK',
+        >>>         num_blocks=(4, ),
+        >>>         num_channels=(64, )),
+        >>>     stage2=dict(
+        >>>         num_modules=1,
+        >>>         num_branches=2,
+        >>>         block='BASIC',
+        >>>         num_blocks=(4, 4),
+        >>>         num_channels=(32, 64)),
+        >>>     stage3=dict(
+        >>>         num_modules=4,
+        >>>         num_branches=3,
+        >>>         block='BASIC',
+        >>>         num_blocks=(4, 4, 4),
+        >>>         num_channels=(32, 64, 128)),
+        >>>     stage4=dict(
+        >>>         num_modules=3,
+        >>>         num_branches=4,
+        >>>         block='BASIC',
+        >>>         num_blocks=(4, 4, 4, 4),
+        >>>         num_channels=(32, 64, 128, 256)))
+        >>> self = HRNet(extra, in_channels=1)
+        >>> self.eval()
+        >>> inputs = torch.rand(1, 1, 32, 32)
+        >>> level_outputs = self.forward(inputs)
+        >>> for level_out in level_outputs:
+        ...     print(tuple(level_out.shape))
+        (1, 32, 8, 8)
+        (1, 64, 4, 4)
+        (1, 128, 2, 2)
+        (1, 256, 1, 1)
+    """
+
+    blocks_dict = {'BASIC': BasicBlock, 'BOTTLENECK': Bottleneck}
+
+    def __init__(self,
+                 extra,
+                 in_channels=3,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 norm_eval=True,
+                 with_cp=False,
+                 zero_init_residual=False,
+                 multiscale_output=True,
+                 pretrained=None,
+                 init_cfg=None):
+        super(HRNet, self).__init__(init_cfg)
+
+        self.pretrained = pretrained
+        assert not (init_cfg and pretrained), \
+            'init_cfg and pretrained cannot be specified at the same time'
+        if isinstance(pretrained, str):
+            warnings.warn('DeprecationWarning: pretrained is deprecated, '
+                          'please use "init_cfg" instead')
+            self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+        elif pretrained is None:
+            if init_cfg is None:
+                self.init_cfg = [
+                    dict(type='Kaiming', layer='Conv2d'),
+                    dict(
+                        type='Constant',
+                        val=1,
+                        layer=['_BatchNorm', 'GroupNorm'])
+                ]
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+        # Assert configurations of 4 stages are in extra
+        assert 'stage1' in extra and 'stage2' in extra \
+               and 'stage3' in extra and 'stage4' in extra
+        # Assert whether the length of `num_blocks` and `num_channels` are
+        # equal to `num_branches`
+        for i in range(4):
+            cfg = extra[f'stage{i + 1}']
+            assert len(cfg['num_blocks']) == cfg['num_branches'] and \
+                   len(cfg['num_channels']) == cfg['num_branches']
+
+        self.extra = extra
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.norm_eval = norm_eval
+        self.with_cp = with_cp
+        self.zero_init_residual = zero_init_residual
+
+        # stem net
+        self.norm1_name, norm1 = build_norm_layer(self.norm_cfg, 64, postfix=1)
+        self.norm2_name, norm2 = build_norm_layer(self.norm_cfg, 64, postfix=2)
+
+        self.conv1 = build_conv_layer(
+            self.conv_cfg,
+            in_channels,
+            64,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            bias=False)
+
+        self.add_module(self.norm1_name, norm1)
+        self.conv2 = build_conv_layer(
+            self.conv_cfg,
+            64,
+            64,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            bias=False)
+
+        self.add_module(self.norm2_name, norm2)
+        self.relu = nn.ReLU(inplace=True)
+
+        # stage 1
+        self.stage1_cfg = self.extra['stage1']
+        num_channels = self.stage1_cfg['num_channels'][0]
+        block_type = self.stage1_cfg['block']
+        num_blocks = self.stage1_cfg['num_blocks'][0]
+
+        block = self.blocks_dict[block_type]
+        stage1_out_channels = num_channels * block.expansion
+        self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
+
+        # stage 2
+        self.stage2_cfg = self.extra['stage2']
+        num_channels = self.stage2_cfg['num_channels']
+        block_type = self.stage2_cfg['block']
+
+        block = self.blocks_dict[block_type]
+        num_channels = [channel * block.expansion for channel in num_channels]
+        self.transition1 = self._make_transition_layer([stage1_out_channels],
+                                                       num_channels)
+        self.stage2, pre_stage_channels = self._make_stage(
+            self.stage2_cfg, num_channels)
+
+        # stage 3
+        self.stage3_cfg = self.extra['stage3']
+        num_channels = self.stage3_cfg['num_channels']
+        block_type = self.stage3_cfg['block']
+
+        block = self.blocks_dict[block_type]
+        num_channels = [channel * block.expansion for channel in num_channels]
+        self.transition2 = self._make_transition_layer(pre_stage_channels,
+                                                       num_channels)
+        self.stage3, pre_stage_channels = self._make_stage(
+            self.stage3_cfg, num_channels)
+
+        # stage 4
+        self.stage4_cfg = self.extra['stage4']
+        num_channels = self.stage4_cfg['num_channels']
+        block_type = self.stage4_cfg['block']
+
+        block = self.blocks_dict[block_type]
+        num_channels = [channel * block.expansion for channel in num_channels]
+        self.transition3 = self._make_transition_layer(pre_stage_channels,
+                                                       num_channels)
+        self.stage4, pre_stage_channels = self._make_stage(
+            self.stage4_cfg, num_channels, multiscale_output=multiscale_output)
+
+    @property
+    def norm1(self):
+        """nn.Module: the normalization layer named "norm1" """
+        return getattr(self, self.norm1_name)
+
+    @property
+    def norm2(self):
+        """nn.Module: the normalization layer named "norm2" """
+        return getattr(self, self.norm2_name)
+
+    def _make_transition_layer(self, num_channels_pre_layer,
+                               num_channels_cur_layer):
+        num_branches_cur = len(num_channels_cur_layer)
+        num_branches_pre = len(num_channels_pre_layer)
+
+        transition_layers = []
+        for i in range(num_branches_cur):
+            if i < num_branches_pre:
+                if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
+                    transition_layers.append(
+                        nn.Sequential(
+                            build_conv_layer(
+                                self.conv_cfg,
+                                num_channels_pre_layer[i],
+                                num_channels_cur_layer[i],
+                                kernel_size=3,
+                                stride=1,
+                                padding=1,
+                                bias=False),
+                            build_norm_layer(self.norm_cfg,
+                                             num_channels_cur_layer[i])[1],
+                            nn.ReLU(inplace=True)))
+                else:
+                    transition_layers.append(None)
+            else:
+                conv_downsamples = []
+                for j in range(i + 1 - num_branches_pre):
+                    in_channels = num_channels_pre_layer[-1]
+                    out_channels = num_channels_cur_layer[i] \
+                        if j == i - num_branches_pre else in_channels
+                    conv_downsamples.append(
+                        nn.Sequential(
+                            build_conv_layer(
+                                self.conv_cfg,
+                                in_channels,
+                                out_channels,
+                                kernel_size=3,
+                                stride=2,
+                                padding=1,
+                                bias=False),
+                            build_norm_layer(self.norm_cfg, out_channels)[1],
+                            nn.ReLU(inplace=True)))
+                transition_layers.append(nn.Sequential(*conv_downsamples))
+
+        return nn.ModuleList(transition_layers)
+
+    def _make_layer(self, block, inplanes, planes, blocks, stride=1):
+        downsample = None
+        if stride != 1 or inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                build_conv_layer(
+                    self.conv_cfg,
+                    inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=stride,
+                    bias=False),
+                build_norm_layer(self.norm_cfg, planes * block.expansion)[1])
+
+        layers = []
+        block_init_cfg = None
+        if self.pretrained is None and not hasattr(
+                self, 'init_cfg') and self.zero_init_residual:
+            if block is BasicBlock:
+                block_init_cfg = dict(
+                    type='Constant', val=0, override=dict(name='norm2'))
+            elif block is Bottleneck:
+                block_init_cfg = dict(
+                    type='Constant', val=0, override=dict(name='norm3'))
+        layers.append(
+            block(
+                inplanes,
+                planes,
+                stride,
+                downsample=downsample,
+                with_cp=self.with_cp,
+                norm_cfg=self.norm_cfg,
+                conv_cfg=self.conv_cfg,
+                init_cfg=block_init_cfg,
+            ))
+        inplanes = planes * block.expansion
+        for i in range(1, blocks):
+            layers.append(
+                block(
+                    inplanes,
+                    planes,
+                    with_cp=self.with_cp,
+                    norm_cfg=self.norm_cfg,
+                    conv_cfg=self.conv_cfg,
+                    init_cfg=block_init_cfg))
+
+        return Sequential(*layers)
+
+    def _make_stage(self, layer_config, in_channels, multiscale_output=True):
+        num_modules = layer_config['num_modules']
+        num_branches = layer_config['num_branches']
+        num_blocks = layer_config['num_blocks']
+        num_channels = layer_config['num_channels']
+        block = self.blocks_dict[layer_config['block']]
+
+        hr_modules = []
+        block_init_cfg = None
+        if self.pretrained is None and not hasattr(
+                self, 'init_cfg') and self.zero_init_residual:
+            if block is BasicBlock:
+                block_init_cfg = dict(
+                    type='Constant', val=0, override=dict(name='norm2'))
+            elif block is Bottleneck:
+                block_init_cfg = dict(
+                    type='Constant', val=0, override=dict(name='norm3'))
+
+        for i in range(num_modules):
+            # multi_scale_output is only used for the last module
+            if not multiscale_output and i == num_modules - 1:
+                reset_multiscale_output = False
+            else:
+                reset_multiscale_output = True
+
+            hr_modules.append(
+                HRModule(
+                    num_branches,
+                    block,
+                    num_blocks,
+                    in_channels,
+                    num_channels,
+                    reset_multiscale_output,
+                    with_cp=self.with_cp,
+                    norm_cfg=self.norm_cfg,
+                    conv_cfg=self.conv_cfg,
+                    block_init_cfg=block_init_cfg))
+
+        return Sequential(*hr_modules), in_channels
+
+    def forward(self, x):
+        """Forward function."""
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.relu(x)
+        x = self.conv2(x)
+        x = self.norm2(x)
+        x = self.relu(x)
+        x = self.layer1(x)
+
+        x_list = []
+        for i in range(self.stage2_cfg['num_branches']):
+            if self.transition1[i] is not None:
+                x_list.append(self.transition1[i](x))
+            else:
+                x_list.append(x)
+        y_list = self.stage2(x_list)
+
+        x_list = []
+        for i in range(self.stage3_cfg['num_branches']):
+            if self.transition2[i] is not None:
+                x_list.append(self.transition2[i](y_list[-1]))
+            else:
+                x_list.append(y_list[i])
+        y_list = self.stage3(x_list)
+
+        x_list = []
+        for i in range(self.stage4_cfg['num_branches']):
+            if self.transition3[i] is not None:
+                x_list.append(self.transition3[i](y_list[-1]))
+            else:
+                x_list.append(y_list[i])
+        y_list = self.stage4(x_list)
+
+        return y_list
+
+    def train(self, mode=True):
+        """Convert the model into training mode will keeping the normalization
+        layer freezed."""
+        super(HRNet, self).train(mode)
+        if mode and self.norm_eval:
+            for m in self.modules():
+                # trick: eval have effect on BatchNorm only
+                if isinstance(m, _BatchNorm):
+                    m.eval()
diff --git a/mmdet/models/backbones/mobilenet_v2.py b/mmdet/models/backbones/mobilenet_v2.py
new file mode 100644
index 0000000..8c6fcfa
--- /dev/null
+++ b/mmdet/models/backbones/mobilenet_v2.py
@@ -0,0 +1,197 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from ..builder import BACKBONES
+from ..utils import InvertedResidual, make_divisible
+
+
+@BACKBONES.register_module()
+class MobileNetV2(BaseModule):
+    """MobileNetV2 backbone.
+
+    Args:
+        widen_factor (float): Width multiplier, multiply number of
+            channels in each layer by this amount. Default: 1.0.
+        out_indices (Sequence[int], optional): Output from which stages.
+            Default: (1, 2, 4, 7).
+        frozen_stages (int): Stages to be frozen (all param fixed).
+            Default: -1, which means not freezing any parameters.
+        conv_cfg (dict, optional): Config dict for convolution layer.
+            Default: None, which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN').
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='ReLU6').
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only. Default: False.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+        pretrained (str, optional): model pretrained path. Default: None
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    # Parameters to build layers. 4 parameters are needed to construct a
+    # layer, from left to right: expand_ratio, channel, num_blocks, stride.
+    arch_settings = [[1, 16, 1, 1], [6, 24, 2, 2], [6, 32, 3, 2],
+                     [6, 64, 4, 2], [6, 96, 3, 1], [6, 160, 3, 2],
+                     [6, 320, 1, 1]]
+
+    def __init__(self,
+                 widen_factor=1.,
+                 out_indices=(1, 2, 4, 7),
+                 frozen_stages=-1,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU6'),
+                 norm_eval=False,
+                 with_cp=False,
+                 pretrained=None,
+                 init_cfg=None):
+        super(MobileNetV2, self).__init__(init_cfg)
+
+        self.pretrained = pretrained
+        assert not (init_cfg and pretrained), \
+            'init_cfg and pretrained cannot be specified at the same time'
+        if isinstance(pretrained, str):
+            warnings.warn('DeprecationWarning: pretrained is deprecated, '
+                          'please use "init_cfg" instead')
+            self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+        elif pretrained is None:
+            if init_cfg is None:
+                self.init_cfg = [
+                    dict(type='Kaiming', layer='Conv2d'),
+                    dict(
+                        type='Constant',
+                        val=1,
+                        layer=['_BatchNorm', 'GroupNorm'])
+                ]
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+        self.widen_factor = widen_factor
+        self.out_indices = out_indices
+        if not set(out_indices).issubset(set(range(0, 8))):
+            raise ValueError('out_indices must be a subset of range'
+                             f'(0, 8). But received {out_indices}')
+
+        if frozen_stages not in range(-1, 8):
+            raise ValueError('frozen_stages must be in range(-1, 8). '
+                             f'But received {frozen_stages}')
+        self.out_indices = out_indices
+        self.frozen_stages = frozen_stages
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+        self.norm_eval = norm_eval
+        self.with_cp = with_cp
+
+        self.in_channels = make_divisible(32 * widen_factor, 8)
+
+        self.conv1 = ConvModule(
+            in_channels=3,
+            out_channels=self.in_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            conv_cfg=self.conv_cfg,
+            norm_cfg=self.norm_cfg,
+            act_cfg=self.act_cfg)
+
+        self.layers = []
+
+        for i, layer_cfg in enumerate(self.arch_settings):
+            expand_ratio, channel, num_blocks, stride = layer_cfg
+            out_channels = make_divisible(channel * widen_factor, 8)
+            inverted_res_layer = self.make_layer(
+                out_channels=out_channels,
+                num_blocks=num_blocks,
+                stride=stride,
+                expand_ratio=expand_ratio)
+            layer_name = f'layer{i + 1}'
+            self.add_module(layer_name, inverted_res_layer)
+            self.layers.append(layer_name)
+
+        if widen_factor > 1.0:
+            self.out_channel = int(1280 * widen_factor)
+        else:
+            self.out_channel = 1280
+
+        layer = ConvModule(
+            in_channels=self.in_channels,
+            out_channels=self.out_channel,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            conv_cfg=self.conv_cfg,
+            norm_cfg=self.norm_cfg,
+            act_cfg=self.act_cfg)
+        self.add_module('conv2', layer)
+        self.layers.append('conv2')
+
+    def make_layer(self, out_channels, num_blocks, stride, expand_ratio):
+        """Stack InvertedResidual blocks to build a layer for MobileNetV2.
+
+        Args:
+            out_channels (int): out_channels of block.
+            num_blocks (int): number of blocks.
+            stride (int): stride of the first block. Default: 1
+            expand_ratio (int): Expand the number of channels of the
+                hidden layer in InvertedResidual by this ratio. Default: 6.
+        """
+        layers = []
+        for i in range(num_blocks):
+            if i >= 1:
+                stride = 1
+            layers.append(
+                InvertedResidual(
+                    self.in_channels,
+                    out_channels,
+                    mid_channels=int(round(self.in_channels * expand_ratio)),
+                    stride=stride,
+                    with_expand_conv=expand_ratio != 1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    act_cfg=self.act_cfg,
+                    with_cp=self.with_cp))
+            self.in_channels = out_channels
+
+        return nn.Sequential(*layers)
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            for param in self.conv1.parameters():
+                param.requires_grad = False
+        for i in range(1, self.frozen_stages + 1):
+            layer = getattr(self, f'layer{i}')
+            layer.eval()
+            for param in layer.parameters():
+                param.requires_grad = False
+
+    def forward(self, x):
+        """Forward function."""
+        x = self.conv1(x)
+        outs = []
+        for i, layer_name in enumerate(self.layers):
+            layer = getattr(self, layer_name)
+            x = layer(x)
+            if i in self.out_indices:
+                outs.append(x)
+        return tuple(outs)
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep normalization layer
+        frozen."""
+        super(MobileNetV2, self).train(mode)
+        self._freeze_stages()
+        if mode and self.norm_eval:
+            for m in self.modules():
+                # trick: eval have effect on BatchNorm only
+                if isinstance(m, _BatchNorm):
+                    m.eval()
diff --git a/mmdet/models/backbones/pvt.py b/mmdet/models/backbones/pvt.py
new file mode 100644
index 0000000..8b7d5d5
--- /dev/null
+++ b/mmdet/models/backbones/pvt.py
@@ -0,0 +1,591 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+import warnings
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import (Conv2d, build_activation_layer, build_norm_layer,
+                      constant_init, normal_init, trunc_normal_init)
+from mmcv.cnn.bricks.drop import build_dropout
+from mmcv.cnn.bricks.transformer import MultiheadAttention
+from mmcv.cnn.utils.weight_init import trunc_normal_
+from mmcv.runner import (BaseModule, ModuleList, Sequential, _load_checkpoint,
+                         load_state_dict)
+from torch.nn.modules.utils import _pair as to_2tuple
+
+from ...utils import get_root_logger
+from ..builder import BACKBONES
+from ..utils import PatchEmbed, nchw_to_nlc, nlc_to_nchw, pvt_convert
+
+
+class MixFFN(BaseModule):
+    """An implementation of MixFFN of PVT.
+
+    The differences between MixFFN & FFN:
+        1. Use 1X1 Conv to replace Linear layer.
+        2. Introduce 3X3 Depth-wise Conv to encode positional information.
+
+    Args:
+        embed_dims (int): The feature dimension. Same as
+            `MultiheadAttention`.
+        feedforward_channels (int): The hidden dimension of FFNs.
+        act_cfg (dict, optional): The activation config for FFNs.
+            Default: dict(type='GELU').
+        ffn_drop (float, optional): Probability of an element to be
+            zeroed in FFN. Default 0.0.
+        dropout_layer (obj:`ConfigDict`): The dropout_layer used
+            when adding the shortcut.
+            Default: None.
+        use_conv (bool): If True, add 3x3 DWConv between two Linear layers.
+            Defaults: False.
+        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
+            Default: None.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 feedforward_channels,
+                 act_cfg=dict(type='GELU'),
+                 ffn_drop=0.,
+                 dropout_layer=None,
+                 use_conv=False,
+                 init_cfg=None):
+        super(MixFFN, self).__init__(init_cfg=init_cfg)
+
+        self.embed_dims = embed_dims
+        self.feedforward_channels = feedforward_channels
+        self.act_cfg = act_cfg
+        activate = build_activation_layer(act_cfg)
+
+        in_channels = embed_dims
+        fc1 = Conv2d(
+            in_channels=in_channels,
+            out_channels=feedforward_channels,
+            kernel_size=1,
+            stride=1,
+            bias=True)
+        if use_conv:
+            # 3x3 depth wise conv to provide positional encode information
+            dw_conv = Conv2d(
+                in_channels=feedforward_channels,
+                out_channels=feedforward_channels,
+                kernel_size=3,
+                stride=1,
+                padding=(3 - 1) // 2,
+                bias=True,
+                groups=feedforward_channels)
+        fc2 = Conv2d(
+            in_channels=feedforward_channels,
+            out_channels=in_channels,
+            kernel_size=1,
+            stride=1,
+            bias=True)
+        drop = nn.Dropout(ffn_drop)
+        layers = [fc1, activate, drop, fc2, drop]
+        if use_conv:
+            layers.insert(1, dw_conv)
+        self.layers = Sequential(*layers)
+        self.dropout_layer = build_dropout(
+            dropout_layer) if dropout_layer else torch.nn.Identity()
+
+    def forward(self, x, hw_shape, identity=None):
+        out = nlc_to_nchw(x, hw_shape)
+        out = self.layers(out)
+        out = nchw_to_nlc(out)
+        if identity is None:
+            identity = x
+        return identity + self.dropout_layer(out)
+
+
+class SpatialReductionAttention(MultiheadAttention):
+    """An implementation of Spatial Reduction Attention of PVT.
+
+    This module is modified from MultiheadAttention which is a module from
+    mmcv.cnn.bricks.transformer.
+
+    Args:
+        embed_dims (int): The embedding dimension.
+        num_heads (int): Parallel attention heads.
+        attn_drop (float): A Dropout layer on attn_output_weights.
+            Default: 0.0.
+        proj_drop (float): A Dropout layer after `nn.MultiheadAttention`.
+            Default: 0.0.
+        dropout_layer (obj:`ConfigDict`): The dropout_layer used
+            when adding the shortcut. Default: None.
+        batch_first (bool): Key, Query and Value are shape of
+            (batch, n, embed_dim)
+            or (n, batch, embed_dim). Default: False.
+        qkv_bias (bool): enable bias for qkv if True. Default: True.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='LN').
+        sr_ratio (int): The ratio of spatial reduction of Spatial Reduction
+            Attention of PVT. Default: 1.
+        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
+            Default: None.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 attn_drop=0.,
+                 proj_drop=0.,
+                 dropout_layer=None,
+                 batch_first=True,
+                 qkv_bias=True,
+                 norm_cfg=dict(type='LN'),
+                 sr_ratio=1,
+                 init_cfg=None):
+        super().__init__(
+            embed_dims,
+            num_heads,
+            attn_drop,
+            proj_drop,
+            batch_first=batch_first,
+            dropout_layer=dropout_layer,
+            bias=qkv_bias,
+            init_cfg=init_cfg)
+
+        self.sr_ratio = sr_ratio
+        if sr_ratio > 1:
+            self.sr = Conv2d(
+                in_channels=embed_dims,
+                out_channels=embed_dims,
+                kernel_size=sr_ratio,
+                stride=sr_ratio)
+            # The ret[0] of build_norm_layer is norm name.
+            self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
+
+        # handle the BC-breaking from https://github.com/open-mmlab/mmcv/pull/1418 # noqa
+        from mmdet import digit_version, mmcv_version
+        if mmcv_version < digit_version('1.3.17'):
+            warnings.warn('The legacy version of forward function in'
+                          'SpatialReductionAttention is deprecated in'
+                          'mmcv>=1.3.17 and will no longer support in the'
+                          'future. Please upgrade your mmcv.')
+            self.forward = self.legacy_forward
+
+    def forward(self, x, hw_shape, identity=None):
+
+        x_q = x
+        if self.sr_ratio > 1:
+            x_kv = nlc_to_nchw(x, hw_shape)
+            x_kv = self.sr(x_kv)
+            x_kv = nchw_to_nlc(x_kv)
+            x_kv = self.norm(x_kv)
+        else:
+            x_kv = x
+
+        if identity is None:
+            identity = x_q
+
+        # Because the dataflow('key', 'query', 'value') of
+        # ``torch.nn.MultiheadAttention`` is (num_query, batch,
+        # embed_dims), We should adjust the shape of dataflow from
+        # batch_first (batch, num_query, embed_dims) to num_query_first
+        # (num_query ,batch, embed_dims), and recover ``attn_output``
+        # from num_query_first to batch_first.
+        if self.batch_first:
+            x_q = x_q.transpose(0, 1)
+            x_kv = x_kv.transpose(0, 1)
+
+        out = self.attn(query=x_q, key=x_kv, value=x_kv)[0]
+
+        if self.batch_first:
+            out = out.transpose(0, 1)
+
+        return identity + self.dropout_layer(self.proj_drop(out))
+
+    def legacy_forward(self, x, hw_shape, identity=None):
+        """multi head attention forward in mmcv version < 1.3.17."""
+        x_q = x
+        if self.sr_ratio > 1:
+            x_kv = nlc_to_nchw(x, hw_shape)
+            x_kv = self.sr(x_kv)
+            x_kv = nchw_to_nlc(x_kv)
+            x_kv = self.norm(x_kv)
+        else:
+            x_kv = x
+
+        if identity is None:
+            identity = x_q
+
+        out = self.attn(query=x_q, key=x_kv, value=x_kv)[0]
+
+        return identity + self.dropout_layer(self.proj_drop(out))
+
+
+class PVTEncoderLayer(BaseModule):
+    """Implements one encoder layer in PVT.
+
+    Args:
+        embed_dims (int): The feature dimension.
+        num_heads (int): Parallel attention heads.
+        feedforward_channels (int): The hidden dimension for FFNs.
+        drop_rate (float): Probability of an element to be zeroed.
+            after the feed forward layer. Default: 0.0.
+        attn_drop_rate (float): The drop out rate for attention layer.
+            Default: 0.0.
+        drop_path_rate (float): stochastic depth rate. Default: 0.0.
+        qkv_bias (bool): enable bias for qkv if True.
+            Default: True.
+        act_cfg (dict): The activation config for FFNs.
+            Default: dict(type='GELU').
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='LN').
+        sr_ratio (int): The ratio of spatial reduction of Spatial Reduction
+            Attention of PVT. Default: 1.
+        use_conv_ffn (bool): If True, use Convolutional FFN to replace FFN.
+            Default: False.
+        init_cfg (dict, optional): Initialization config dict.
+            Default: None.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 feedforward_channels,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.,
+                 qkv_bias=True,
+                 act_cfg=dict(type='GELU'),
+                 norm_cfg=dict(type='LN'),
+                 sr_ratio=1,
+                 use_conv_ffn=False,
+                 init_cfg=None):
+        super(PVTEncoderLayer, self).__init__(init_cfg=init_cfg)
+
+        # The ret[0] of build_norm_layer is norm name.
+        self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]
+
+        self.attn = SpatialReductionAttention(
+            embed_dims=embed_dims,
+            num_heads=num_heads,
+            attn_drop=attn_drop_rate,
+            proj_drop=drop_rate,
+            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
+            qkv_bias=qkv_bias,
+            norm_cfg=norm_cfg,
+            sr_ratio=sr_ratio)
+
+        # The ret[0] of build_norm_layer is norm name.
+        self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]
+
+        self.ffn = MixFFN(
+            embed_dims=embed_dims,
+            feedforward_channels=feedforward_channels,
+            ffn_drop=drop_rate,
+            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
+            use_conv=use_conv_ffn,
+            act_cfg=act_cfg)
+
+    def forward(self, x, hw_shape):
+        x = self.attn(self.norm1(x), hw_shape, identity=x)
+        x = self.ffn(self.norm2(x), hw_shape, identity=x)
+
+        return x
+
+
+class AbsolutePositionEmbedding(BaseModule):
+    """An implementation of the absolute position embedding in PVT.
+
+    Args:
+        pos_shape (int): The shape of the absolute position embedding.
+        pos_dim (int): The dimension of the absolute position embedding.
+        drop_rate (float): Probability of an element to be zeroed.
+            Default: 0.0.
+    """
+
+    def __init__(self, pos_shape, pos_dim, drop_rate=0., init_cfg=None):
+        super().__init__(init_cfg=init_cfg)
+
+        if isinstance(pos_shape, int):
+            pos_shape = to_2tuple(pos_shape)
+        elif isinstance(pos_shape, tuple):
+            if len(pos_shape) == 1:
+                pos_shape = to_2tuple(pos_shape[0])
+            assert len(pos_shape) == 2, \
+                f'The size of image should have length 1 or 2, ' \
+                f'but got {len(pos_shape)}'
+        self.pos_shape = pos_shape
+        self.pos_dim = pos_dim
+
+        self.pos_embed = nn.Parameter(
+            torch.zeros(1, pos_shape[0] * pos_shape[1], pos_dim))
+        self.drop = nn.Dropout(p=drop_rate)
+
+    def init_weights(self):
+        trunc_normal_(self.pos_embed, std=0.02)
+
+    def resize_pos_embed(self, pos_embed, input_shape, mode='bilinear'):
+        """Resize pos_embed weights.
+
+        Resize pos_embed using bilinear interpolate method.
+
+        Args:
+            pos_embed (torch.Tensor): Position embedding weights.
+            input_shape (tuple): Tuple for (downsampled input image height,
+                downsampled input image width).
+            mode (str): Algorithm used for upsampling:
+                ``'nearest'`` | ``'linear'`` | ``'bilinear'`` | ``'bicubic'`` |
+                ``'trilinear'``. Default: ``'bilinear'``.
+
+        Return:
+            torch.Tensor: The resized pos_embed of shape [B, L_new, C].
+        """
+        assert pos_embed.ndim == 3, 'shape of pos_embed must be [B, L, C]'
+        pos_h, pos_w = self.pos_shape
+        pos_embed_weight = pos_embed[:, (-1 * pos_h * pos_w):]
+        pos_embed_weight = pos_embed_weight.reshape(
+            1, pos_h, pos_w, self.pos_dim).permute(0, 3, 1, 2).contiguous()
+        pos_embed_weight = F.interpolate(
+            pos_embed_weight, size=input_shape, mode=mode)
+        pos_embed_weight = torch.flatten(pos_embed_weight,
+                                         2).transpose(1, 2).contiguous()
+        pos_embed = pos_embed_weight
+
+        return pos_embed
+
+    def forward(self, x, hw_shape, mode='bilinear'):
+        pos_embed = self.resize_pos_embed(self.pos_embed, hw_shape, mode)
+        return self.drop(x + pos_embed)
+
+
+@BACKBONES.register_module()
+class PyramidVisionTransformer(BaseModule):
+    """Pyramid Vision Transformer (PVT)
+
+    Implementation of `Pyramid Vision Transformer: A Versatile Backbone for
+    Dense Prediction without Convolutions
+    <https://arxiv.org/pdf/2102.12122.pdf>`_.
+
+    Args:
+        pretrain_img_size (int | tuple[int]): The size of input image when
+            pretrain. Defaults: 224.
+        in_channels (int): Number of input channels. Default: 3.
+        embed_dims (int): Embedding dimension. Default: 64.
+        num_stags (int): The num of stages. Default: 4.
+        num_layers (Sequence[int]): The layer number of each transformer encode
+            layer. Default: [3, 4, 6, 3].
+        num_heads (Sequence[int]): The attention heads of each transformer
+            encode layer. Default: [1, 2, 5, 8].
+        patch_sizes (Sequence[int]): The patch_size of each patch embedding.
+            Default: [4, 2, 2, 2].
+        strides (Sequence[int]): The stride of each patch embedding.
+            Default: [4, 2, 2, 2].
+        paddings (Sequence[int]): The padding of each patch embedding.
+            Default: [0, 0, 0, 0].
+        sr_ratios (Sequence[int]): The spatial reduction rate of each
+            transformer encode layer. Default: [8, 4, 2, 1].
+        out_indices (Sequence[int] | int): Output from which stages.
+            Default: (0, 1, 2, 3).
+        mlp_ratios (Sequence[int]): The ratio of the mlp hidden dim to the
+            embedding dim of each transformer encode layer.
+            Default: [8, 8, 4, 4].
+        qkv_bias (bool): Enable bias for qkv if True. Default: True.
+        drop_rate (float): Probability of an element to be zeroed.
+            Default 0.0.
+        attn_drop_rate (float): The drop out rate for attention layer.
+            Default 0.0.
+        drop_path_rate (float): stochastic depth rate. Default 0.1.
+        use_abs_pos_embed (bool): If True, add absolute position embedding to
+            the patch embedding. Defaults: True.
+        use_conv_ffn (bool): If True, use Convolutional FFN to replace FFN.
+            Default: False.
+        act_cfg (dict): The activation config for FFNs.
+            Default: dict(type='GELU').
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='LN').
+        pretrained (str, optional): model pretrained path. Default: None.
+        convert_weights (bool): The flag indicates whether the
+            pre-trained model is from the original repo. We may need
+            to convert some keys to make it compatible.
+            Default: True.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+    """
+
+    def __init__(self,
+                 pretrain_img_size=224,
+                 in_channels=3,
+                 embed_dims=64,
+                 num_stages=4,
+                 num_layers=[3, 4, 6, 3],
+                 num_heads=[1, 2, 5, 8],
+                 patch_sizes=[4, 2, 2, 2],
+                 strides=[4, 2, 2, 2],
+                 paddings=[0, 0, 0, 0],
+                 sr_ratios=[8, 4, 2, 1],
+                 out_indices=(0, 1, 2, 3),
+                 mlp_ratios=[8, 8, 4, 4],
+                 qkv_bias=True,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.1,
+                 use_abs_pos_embed=True,
+                 norm_after_stage=False,
+                 use_conv_ffn=False,
+                 act_cfg=dict(type='GELU'),
+                 norm_cfg=dict(type='LN', eps=1e-6),
+                 pretrained=None,
+                 convert_weights=True,
+                 init_cfg=None):
+        super().__init__(init_cfg=init_cfg)
+
+        self.convert_weights = convert_weights
+        if isinstance(pretrain_img_size, int):
+            pretrain_img_size = to_2tuple(pretrain_img_size)
+        elif isinstance(pretrain_img_size, tuple):
+            if len(pretrain_img_size) == 1:
+                pretrain_img_size = to_2tuple(pretrain_img_size[0])
+            assert len(pretrain_img_size) == 2, \
+                f'The size of image should have length 1 or 2, ' \
+                f'but got {len(pretrain_img_size)}'
+
+        assert not (init_cfg and pretrained), \
+            'init_cfg and pretrained cannot be setting at the same time'
+        if isinstance(pretrained, str):
+            warnings.warn('DeprecationWarning: pretrained is deprecated, '
+                          'please use "init_cfg" instead')
+            self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+        elif pretrained is None:
+            self.init_cfg = init_cfg
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+        self.embed_dims = embed_dims
+
+        self.num_stages = num_stages
+        self.num_layers = num_layers
+        self.num_heads = num_heads
+        self.patch_sizes = patch_sizes
+        self.strides = strides
+        self.sr_ratios = sr_ratios
+        assert num_stages == len(num_layers) == len(num_heads) \
+               == len(patch_sizes) == len(strides) == len(sr_ratios)
+
+        self.out_indices = out_indices
+        assert max(out_indices) < self.num_stages
+        self.pretrained = pretrained
+
+        # transformer encoder
+        dpr = [
+            x.item()
+            for x in torch.linspace(0, drop_path_rate, sum(num_layers))
+        ]  # stochastic num_layer decay rule
+
+        cur = 0
+        self.layers = ModuleList()
+        for i, num_layer in enumerate(num_layers):
+            embed_dims_i = embed_dims * num_heads[i]
+            patch_embed = PatchEmbed(
+                in_channels=in_channels,
+                embed_dims=embed_dims_i,
+                kernel_size=patch_sizes[i],
+                stride=strides[i],
+                padding=paddings[i],
+                bias=True,
+                norm_cfg=norm_cfg)
+
+            layers = ModuleList()
+            if use_abs_pos_embed:
+                pos_shape = pretrain_img_size // np.prod(patch_sizes[:i + 1])
+                pos_embed = AbsolutePositionEmbedding(
+                    pos_shape=pos_shape,
+                    pos_dim=embed_dims_i,
+                    drop_rate=drop_rate)
+                layers.append(pos_embed)
+            layers.extend([
+                PVTEncoderLayer(
+                    embed_dims=embed_dims_i,
+                    num_heads=num_heads[i],
+                    feedforward_channels=mlp_ratios[i] * embed_dims_i,
+                    drop_rate=drop_rate,
+                    attn_drop_rate=attn_drop_rate,
+                    drop_path_rate=dpr[cur + idx],
+                    qkv_bias=qkv_bias,
+                    act_cfg=act_cfg,
+                    norm_cfg=norm_cfg,
+                    sr_ratio=sr_ratios[i],
+                    use_conv_ffn=use_conv_ffn) for idx in range(num_layer)
+            ])
+            in_channels = embed_dims_i
+            # The ret[0] of build_norm_layer is norm name.
+            if norm_after_stage:
+                norm = build_norm_layer(norm_cfg, embed_dims_i)[1]
+            else:
+                norm = nn.Identity()
+            self.layers.append(ModuleList([patch_embed, layers, norm]))
+            cur += num_layer
+
+    def init_weights(self):
+        logger = get_root_logger()
+        if self.init_cfg is None:
+            logger.warn(f'No pre-trained weights for '
+                        f'{self.__class__.__name__}, '
+                        f'training start from scratch')
+            for m in self.modules():
+                if isinstance(m, nn.Linear):
+                    trunc_normal_init(m, std=.02, bias=0.)
+                elif isinstance(m, nn.LayerNorm):
+                    constant_init(m, 1.0)
+                elif isinstance(m, nn.Conv2d):
+                    fan_out = m.kernel_size[0] * m.kernel_size[
+                        1] * m.out_channels
+                    fan_out //= m.groups
+                    normal_init(m, 0, math.sqrt(2.0 / fan_out))
+                elif isinstance(m, AbsolutePositionEmbedding):
+                    m.init_weights()
+        else:
+            assert 'checkpoint' in self.init_cfg, f'Only support ' \
+                                                  f'specify `Pretrained` in ' \
+                                                  f'`init_cfg` in ' \
+                                                  f'{self.__class__.__name__} '
+            checkpoint = _load_checkpoint(
+                self.init_cfg.checkpoint, logger=logger, map_location='cpu')
+            logger.warn(f'Load pre-trained model for '
+                        f'{self.__class__.__name__} from original repo')
+            if 'state_dict' in checkpoint:
+                state_dict = checkpoint['state_dict']
+            elif 'model' in checkpoint:
+                state_dict = checkpoint['model']
+            else:
+                state_dict = checkpoint
+            if self.convert_weights:
+                # Because pvt backbones are not supported by mmcls,
+                # so we need to convert pre-trained weights to match this
+                # implementation.
+                state_dict = pvt_convert(state_dict)
+            load_state_dict(self, state_dict, strict=False, logger=logger)
+
+    def forward(self, x):
+        outs = []
+
+        for i, layer in enumerate(self.layers):
+            x, hw_shape = layer[0](x)
+
+            for block in layer[1]:
+                x = block(x, hw_shape)
+            x = layer[2](x)
+            x = nlc_to_nchw(x, hw_shape)
+            if i in self.out_indices:
+                outs.append(x)
+
+        return outs
+
+
+@BACKBONES.register_module()
+class PyramidVisionTransformerV2(PyramidVisionTransformer):
+    """Implementation of `PVTv2: Improved Baselines with Pyramid Vision
+    Transformer <https://arxiv.org/pdf/2106.13797.pdf>`_."""
+
+    def __init__(self, **kwargs):
+        super(PyramidVisionTransformerV2, self).__init__(
+            patch_sizes=[7, 3, 3, 3],
+            paddings=[3, 1, 1, 1],
+            use_abs_pos_embed=False,
+            norm_after_stage=True,
+            use_conv_ffn=True,
+            **kwargs)
diff --git a/mmdet/models/backbones/regnet.py b/mmdet/models/backbones/regnet.py
new file mode 100644
index 0000000..63adc3c
--- /dev/null
+++ b/mmdet/models/backbones/regnet.py
@@ -0,0 +1,356 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import numpy as np
+import torch.nn as nn
+from mmcv.cnn import build_conv_layer, build_norm_layer
+
+from ..builder import BACKBONES
+from .resnet import ResNet
+from .resnext import Bottleneck
+
+
+@BACKBONES.register_module()
+class RegNet(ResNet):
+    """RegNet backbone.
+
+    More details can be found in `paper <https://arxiv.org/abs/2003.13678>`_ .
+
+    Args:
+        arch (dict): The parameter of RegNets.
+
+            - w0 (int): initial width
+            - wa (float): slope of width
+            - wm (float): quantization parameter to quantize the width
+            - depth (int): depth of the backbone
+            - group_w (int): width of group
+            - bot_mul (float): bottleneck ratio, i.e. expansion of bottleneck.
+        strides (Sequence[int]): Strides of the first block of each stage.
+        base_channels (int): Base channels after stem layer.
+        in_channels (int): Number of input image channels. Default: 3.
+        dilations (Sequence[int]): Dilation of each stage.
+        out_indices (Sequence[int]): Output from which stages.
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer.
+        frozen_stages (int): Stages to be frozen (all param fixed). -1 means
+            not freezing any parameters.
+        norm_cfg (dict): dictionary to construct and config norm layer.
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed.
+        zero_init_residual (bool): whether to use zero init for last norm layer
+            in resblocks to let them behave as identity.
+        pretrained (str, optional): model pretrained path. Default: None
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+
+    Example:
+        >>> from mmdet.models import RegNet
+        >>> import torch
+        >>> self = RegNet(
+                arch=dict(
+                    w0=88,
+                    wa=26.31,
+                    wm=2.25,
+                    group_w=48,
+                    depth=25,
+                    bot_mul=1.0))
+        >>> self.eval()
+        >>> inputs = torch.rand(1, 3, 32, 32)
+        >>> level_outputs = self.forward(inputs)
+        >>> for level_out in level_outputs:
+        ...     print(tuple(level_out.shape))
+        (1, 96, 8, 8)
+        (1, 192, 4, 4)
+        (1, 432, 2, 2)
+        (1, 1008, 1, 1)
+    """
+    arch_settings = {
+        'regnetx_400mf':
+        dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22, bot_mul=1.0),
+        'regnetx_800mf':
+        dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16, bot_mul=1.0),
+        'regnetx_1.6gf':
+        dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18, bot_mul=1.0),
+        'regnetx_3.2gf':
+        dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25, bot_mul=1.0),
+        'regnetx_4.0gf':
+        dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23, bot_mul=1.0),
+        'regnetx_6.4gf':
+        dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17, bot_mul=1.0),
+        'regnetx_8.0gf':
+        dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23, bot_mul=1.0),
+        'regnetx_12gf':
+        dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19, bot_mul=1.0),
+    }
+
+    def __init__(self,
+                 arch,
+                 in_channels=3,
+                 stem_channels=32,
+                 base_channels=32,
+                 strides=(2, 2, 2, 2),
+                 dilations=(1, 1, 1, 1),
+                 out_indices=(0, 1, 2, 3),
+                 style='pytorch',
+                 deep_stem=False,
+                 avg_down=False,
+                 frozen_stages=-1,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 norm_eval=True,
+                 dcn=None,
+                 stage_with_dcn=(False, False, False, False),
+                 plugins=None,
+                 with_cp=False,
+                 zero_init_residual=True,
+                 pretrained=None,
+                 init_cfg=None):
+        super(ResNet, self).__init__(init_cfg)
+
+        # Generate RegNet parameters first
+        if isinstance(arch, str):
+            assert arch in self.arch_settings, \
+                f'"arch": "{arch}" is not one of the' \
+                ' arch_settings'
+            arch = self.arch_settings[arch]
+        elif not isinstance(arch, dict):
+            raise ValueError('Expect "arch" to be either a string '
+                             f'or a dict, got {type(arch)}')
+
+        widths, num_stages = self.generate_regnet(
+            arch['w0'],
+            arch['wa'],
+            arch['wm'],
+            arch['depth'],
+        )
+        # Convert to per stage format
+        stage_widths, stage_blocks = self.get_stages_from_blocks(widths)
+        # Generate group widths and bot muls
+        group_widths = [arch['group_w'] for _ in range(num_stages)]
+        self.bottleneck_ratio = [arch['bot_mul'] for _ in range(num_stages)]
+        # Adjust the compatibility of stage_widths and group_widths
+        stage_widths, group_widths = self.adjust_width_group(
+            stage_widths, self.bottleneck_ratio, group_widths)
+
+        # Group params by stage
+        self.stage_widths = stage_widths
+        self.group_widths = group_widths
+        self.depth = sum(stage_blocks)
+        self.stem_channels = stem_channels
+        self.base_channels = base_channels
+        self.num_stages = num_stages
+        assert num_stages >= 1 and num_stages <= 4
+        self.strides = strides
+        self.dilations = dilations
+        assert len(strides) == len(dilations) == num_stages
+        self.out_indices = out_indices
+        assert max(out_indices) < num_stages
+        self.style = style
+        self.deep_stem = deep_stem
+        self.avg_down = avg_down
+        self.frozen_stages = frozen_stages
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.with_cp = with_cp
+        self.norm_eval = norm_eval
+        self.dcn = dcn
+        self.stage_with_dcn = stage_with_dcn
+        if dcn is not None:
+            assert len(stage_with_dcn) == num_stages
+        self.plugins = plugins
+        self.zero_init_residual = zero_init_residual
+        self.block = Bottleneck
+        expansion_bak = self.block.expansion
+        self.block.expansion = 1
+        self.stage_blocks = stage_blocks[:num_stages]
+
+        self._make_stem_layer(in_channels, stem_channels)
+
+        block_init_cfg = None
+        assert not (init_cfg and pretrained), \
+            'init_cfg and pretrained cannot be specified at the same time'
+        if isinstance(pretrained, str):
+            warnings.warn('DeprecationWarning: pretrained is deprecated, '
+                          'please use "init_cfg" instead')
+            self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+        elif pretrained is None:
+            if init_cfg is None:
+                self.init_cfg = [
+                    dict(type='Kaiming', layer='Conv2d'),
+                    dict(
+                        type='Constant',
+                        val=1,
+                        layer=['_BatchNorm', 'GroupNorm'])
+                ]
+                if self.zero_init_residual:
+                    block_init_cfg = dict(
+                        type='Constant', val=0, override=dict(name='norm3'))
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+        self.inplanes = stem_channels
+        self.res_layers = []
+        for i, num_blocks in enumerate(self.stage_blocks):
+            stride = self.strides[i]
+            dilation = self.dilations[i]
+            group_width = self.group_widths[i]
+            width = int(round(self.stage_widths[i] * self.bottleneck_ratio[i]))
+            stage_groups = width // group_width
+
+            dcn = self.dcn if self.stage_with_dcn[i] else None
+            if self.plugins is not None:
+                stage_plugins = self.make_stage_plugins(self.plugins, i)
+            else:
+                stage_plugins = None
+
+            res_layer = self.make_res_layer(
+                block=self.block,
+                inplanes=self.inplanes,
+                planes=self.stage_widths[i],
+                num_blocks=num_blocks,
+                stride=stride,
+                dilation=dilation,
+                style=self.style,
+                avg_down=self.avg_down,
+                with_cp=self.with_cp,
+                conv_cfg=self.conv_cfg,
+                norm_cfg=self.norm_cfg,
+                dcn=dcn,
+                plugins=stage_plugins,
+                groups=stage_groups,
+                base_width=group_width,
+                base_channels=self.stage_widths[i],
+                init_cfg=block_init_cfg)
+            self.inplanes = self.stage_widths[i]
+            layer_name = f'layer{i + 1}'
+            self.add_module(layer_name, res_layer)
+            self.res_layers.append(layer_name)
+
+        self._freeze_stages()
+
+        self.feat_dim = stage_widths[-1]
+        self.block.expansion = expansion_bak
+
+    def _make_stem_layer(self, in_channels, base_channels):
+        self.conv1 = build_conv_layer(
+            self.conv_cfg,
+            in_channels,
+            base_channels,
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            bias=False)
+        self.norm1_name, norm1 = build_norm_layer(
+            self.norm_cfg, base_channels, postfix=1)
+        self.add_module(self.norm1_name, norm1)
+        self.relu = nn.ReLU(inplace=True)
+
+    def generate_regnet(self,
+                        initial_width,
+                        width_slope,
+                        width_parameter,
+                        depth,
+                        divisor=8):
+        """Generates per block width from RegNet parameters.
+
+        Args:
+            initial_width ([int]): Initial width of the backbone
+            width_slope ([float]): Slope of the quantized linear function
+            width_parameter ([int]): Parameter used to quantize the width.
+            depth ([int]): Depth of the backbone.
+            divisor (int, optional): The divisor of channels. Defaults to 8.
+
+        Returns:
+            list, int: return a list of widths of each stage and the number \
+                of stages
+        """
+        assert width_slope >= 0
+        assert initial_width > 0
+        assert width_parameter > 1
+        assert initial_width % divisor == 0
+        widths_cont = np.arange(depth) * width_slope + initial_width
+        ks = np.round(
+            np.log(widths_cont / initial_width) / np.log(width_parameter))
+        widths = initial_width * np.power(width_parameter, ks)
+        widths = np.round(np.divide(widths, divisor)) * divisor
+        num_stages = len(np.unique(widths))
+        widths, widths_cont = widths.astype(int).tolist(), widths_cont.tolist()
+        return widths, num_stages
+
+    @staticmethod
+    def quantize_float(number, divisor):
+        """Converts a float to closest non-zero int divisible by divisor.
+
+        Args:
+            number (int): Original number to be quantized.
+            divisor (int): Divisor used to quantize the number.
+
+        Returns:
+            int: quantized number that is divisible by devisor.
+        """
+        return int(round(number / divisor) * divisor)
+
+    def adjust_width_group(self, widths, bottleneck_ratio, groups):
+        """Adjusts the compatibility of widths and groups.
+
+        Args:
+            widths (list[int]): Width of each stage.
+            bottleneck_ratio (float): Bottleneck ratio.
+            groups (int): number of groups in each stage
+
+        Returns:
+            tuple(list): The adjusted widths and groups of each stage.
+        """
+        bottleneck_width = [
+            int(w * b) for w, b in zip(widths, bottleneck_ratio)
+        ]
+        groups = [min(g, w_bot) for g, w_bot in zip(groups, bottleneck_width)]
+        bottleneck_width = [
+            self.quantize_float(w_bot, g)
+            for w_bot, g in zip(bottleneck_width, groups)
+        ]
+        widths = [
+            int(w_bot / b)
+            for w_bot, b in zip(bottleneck_width, bottleneck_ratio)
+        ]
+        return widths, groups
+
+    def get_stages_from_blocks(self, widths):
+        """Gets widths/stage_blocks of network at each stage.
+
+        Args:
+            widths (list[int]): Width in each stage.
+
+        Returns:
+            tuple(list): width and depth of each stage
+        """
+        width_diff = [
+            width != width_prev
+            for width, width_prev in zip(widths + [0], [0] + widths)
+        ]
+        stage_widths = [
+            width for width, diff in zip(widths, width_diff[:-1]) if diff
+        ]
+        stage_blocks = np.diff([
+            depth for depth, diff in zip(range(len(width_diff)), width_diff)
+            if diff
+        ]).tolist()
+        return stage_widths, stage_blocks
+
+    def forward(self, x):
+        """Forward function."""
+        x = self.conv1(x)
+        x = self.norm1(x)
+        x = self.relu(x)
+
+        outs = []
+        for i, layer_name in enumerate(self.res_layers):
+            res_layer = getattr(self, layer_name)
+            x = res_layer(x)
+            if i in self.out_indices:
+                outs.append(x)
+        return tuple(outs)
diff --git a/mmdet/models/backbones/res2net.py b/mmdet/models/backbones/res2net.py
new file mode 100644
index 0000000..96afb2f
--- /dev/null
+++ b/mmdet/models/backbones/res2net.py
@@ -0,0 +1,327 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+import torch
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+from mmcv.cnn import build_conv_layer, build_norm_layer
+from mmcv.runner import Sequential
+
+from ..builder import BACKBONES
+from .resnet import Bottleneck as _Bottleneck
+from .resnet import ResNet
+
+
+class Bottle2neck(_Bottleneck):
+    expansion = 4
+
+    def __init__(self,
+                 inplanes,
+                 planes,
+                 scales=4,
+                 base_width=26,
+                 base_channels=64,
+                 stage_type='normal',
+                 **kwargs):
+        """Bottle2neck block for Res2Net.
+
+        If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
+        it is "caffe", the stride-two layer is the first 1x1 conv layer.
+        """
+        super(Bottle2neck, self).__init__(inplanes, planes, **kwargs)
+        assert scales > 1, 'Res2Net degenerates to ResNet when scales = 1.'
+        width = int(math.floor(self.planes * (base_width / base_channels)))
+
+        self.norm1_name, norm1 = build_norm_layer(
+            self.norm_cfg, width * scales, postfix=1)
+        self.norm3_name, norm3 = build_norm_layer(
+            self.norm_cfg, self.planes * self.expansion, postfix=3)
+
+        self.conv1 = build_conv_layer(
+            self.conv_cfg,
+            self.inplanes,
+            width * scales,
+            kernel_size=1,
+            stride=self.conv1_stride,
+            bias=False)
+        self.add_module(self.norm1_name, norm1)
+
+        if stage_type == 'stage' and self.conv2_stride != 1:
+            self.pool = nn.AvgPool2d(
+                kernel_size=3, stride=self.conv2_stride, padding=1)
+        convs = []
+        bns = []
+
+        fallback_on_stride = False
+        if self.with_dcn:
+            fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
+        if not self.with_dcn or fallback_on_stride:
+            for i in range(scales - 1):
+                convs.append(
+                    build_conv_layer(
+                        self.conv_cfg,
+                        width,
+                        width,
+                        kernel_size=3,
+                        stride=self.conv2_stride,
+                        padding=self.dilation,
+                        dilation=self.dilation,
+                        bias=False))
+                bns.append(
+                    build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1])
+            self.convs = nn.ModuleList(convs)
+            self.bns = nn.ModuleList(bns)
+        else:
+            assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
+            for i in range(scales - 1):
+                convs.append(
+                    build_conv_layer(
+                        self.dcn,
+                        width,
+                        width,
+                        kernel_size=3,
+                        stride=self.conv2_stride,
+                        padding=self.dilation,
+                        dilation=self.dilation,
+                        bias=False))
+                bns.append(
+                    build_norm_layer(self.norm_cfg, width, postfix=i + 1)[1])
+            self.convs = nn.ModuleList(convs)
+            self.bns = nn.ModuleList(bns)
+
+        self.conv3 = build_conv_layer(
+            self.conv_cfg,
+            width * scales,
+            self.planes * self.expansion,
+            kernel_size=1,
+            bias=False)
+        self.add_module(self.norm3_name, norm3)
+
+        self.stage_type = stage_type
+        self.scales = scales
+        self.width = width
+        delattr(self, 'conv2')
+        delattr(self, self.norm2_name)
+
+    def forward(self, x):
+        """Forward function."""
+
+        def _inner_forward(x):
+            identity = x
+
+            out = self.conv1(x)
+            out = self.norm1(out)
+            out = self.relu(out)
+
+            if self.with_plugins:
+                out = self.forward_plugin(out, self.after_conv1_plugin_names)
+
+            spx = torch.split(out, self.width, 1)
+            sp = self.convs[0](spx[0].contiguous())
+            sp = self.relu(self.bns[0](sp))
+            out = sp
+            for i in range(1, self.scales - 1):
+                if self.stage_type == 'stage':
+                    sp = spx[i]
+                else:
+                    sp = sp + spx[i]
+                sp = self.convs[i](sp.contiguous())
+                sp = self.relu(self.bns[i](sp))
+                out = torch.cat((out, sp), 1)
+
+            if self.stage_type == 'normal' or self.conv2_stride == 1:
+                out = torch.cat((out, spx[self.scales - 1]), 1)
+            elif self.stage_type == 'stage':
+                out = torch.cat((out, self.pool(spx[self.scales - 1])), 1)
+
+            if self.with_plugins:
+                out = self.forward_plugin(out, self.after_conv2_plugin_names)
+
+            out = self.conv3(out)
+            out = self.norm3(out)
+
+            if self.with_plugins:
+                out = self.forward_plugin(out, self.after_conv3_plugin_names)
+
+            if self.downsample is not None:
+                identity = self.downsample(x)
+
+            out += identity
+
+            return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        out = self.relu(out)
+
+        return out
+
+
+class Res2Layer(Sequential):
+    """Res2Layer to build Res2Net style backbone.
+
+    Args:
+        block (nn.Module): block used to build ResLayer.
+        inplanes (int): inplanes of block.
+        planes (int): planes of block.
+        num_blocks (int): number of blocks.
+        stride (int): stride of the first block. Default: 1
+        avg_down (bool): Use AvgPool instead of stride conv when
+            downsampling in the bottle2neck. Default: False
+        conv_cfg (dict): dictionary to construct and config conv layer.
+            Default: None
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: dict(type='BN')
+        scales (int): Scales used in Res2Net. Default: 4
+        base_width (int): Basic width of each scale. Default: 26
+    """
+
+    def __init__(self,
+                 block,
+                 inplanes,
+                 planes,
+                 num_blocks,
+                 stride=1,
+                 avg_down=True,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 scales=4,
+                 base_width=26,
+                 **kwargs):
+        self.block = block
+
+        downsample = None
+        if stride != 1 or inplanes != planes * block.expansion:
+            downsample = nn.Sequential(
+                nn.AvgPool2d(
+                    kernel_size=stride,
+                    stride=stride,
+                    ceil_mode=True,
+                    count_include_pad=False),
+                build_conv_layer(
+                    conv_cfg,
+                    inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=1,
+                    bias=False),
+                build_norm_layer(norm_cfg, planes * block.expansion)[1],
+            )
+
+        layers = []
+        layers.append(
+            block(
+                inplanes=inplanes,
+                planes=planes,
+                stride=stride,
+                downsample=downsample,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                scales=scales,
+                base_width=base_width,
+                stage_type='stage',
+                **kwargs))
+        inplanes = planes * block.expansion
+        for i in range(1, num_blocks):
+            layers.append(
+                block(
+                    inplanes=inplanes,
+                    planes=planes,
+                    stride=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    scales=scales,
+                    base_width=base_width,
+                    **kwargs))
+        super(Res2Layer, self).__init__(*layers)
+
+
+@BACKBONES.register_module()
+class Res2Net(ResNet):
+    """Res2Net backbone.
+
+    Args:
+        scales (int): Scales used in Res2Net. Default: 4
+        base_width (int): Basic width of each scale. Default: 26
+        depth (int): Depth of res2net, from {50, 101, 152}.
+        in_channels (int): Number of input image channels. Default: 3.
+        num_stages (int): Res2net stages. Default: 4.
+        strides (Sequence[int]): Strides of the first block of each stage.
+        dilations (Sequence[int]): Dilation of each stage.
+        out_indices (Sequence[int]): Output from which stages.
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer.
+        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv
+        avg_down (bool): Use AvgPool instead of stride conv when
+            downsampling in the bottle2neck.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only.
+        plugins (list[dict]): List of plugins for stages, each dict contains:
+
+            - cfg (dict, required): Cfg dict to build plugin.
+            - position (str, required): Position inside block to insert
+              plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'.
+            - stages (tuple[bool], optional): Stages to apply plugin, length
+              should be same as 'num_stages'.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed.
+        zero_init_residual (bool): Whether to use zero init for last norm layer
+            in resblocks to let them behave as identity.
+        pretrained (str, optional): model pretrained path. Default: None
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+
+    Example:
+        >>> from mmdet.models import Res2Net
+        >>> import torch
+        >>> self = Res2Net(depth=50, scales=4, base_width=26)
+        >>> self.eval()
+        >>> inputs = torch.rand(1, 3, 32, 32)
+        >>> level_outputs = self.forward(inputs)
+        >>> for level_out in level_outputs:
+        ...     print(tuple(level_out.shape))
+        (1, 256, 8, 8)
+        (1, 512, 4, 4)
+        (1, 1024, 2, 2)
+        (1, 2048, 1, 1)
+    """
+
+    arch_settings = {
+        50: (Bottle2neck, (3, 4, 6, 3)),
+        101: (Bottle2neck, (3, 4, 23, 3)),
+        152: (Bottle2neck, (3, 8, 36, 3))
+    }
+
+    def __init__(self,
+                 scales=4,
+                 base_width=26,
+                 style='pytorch',
+                 deep_stem=True,
+                 avg_down=True,
+                 pretrained=None,
+                 init_cfg=None,
+                 **kwargs):
+        self.scales = scales
+        self.base_width = base_width
+        super(Res2Net, self).__init__(
+            style='pytorch',
+            deep_stem=True,
+            avg_down=True,
+            pretrained=pretrained,
+            init_cfg=init_cfg,
+            **kwargs)
+
+    def make_res_layer(self, **kwargs):
+        return Res2Layer(
+            scales=self.scales,
+            base_width=self.base_width,
+            base_channels=self.base_channels,
+            **kwargs)
diff --git a/mmdet/models/backbones/resnest.py b/mmdet/models/backbones/resnest.py
new file mode 100644
index 0000000..69629b9
--- /dev/null
+++ b/mmdet/models/backbones/resnest.py
@@ -0,0 +1,322 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as cp
+from mmcv.cnn import build_conv_layer, build_norm_layer
+from mmcv.runner import BaseModule
+
+from ..builder import BACKBONES
+from ..utils import ResLayer
+from .resnet import Bottleneck as _Bottleneck
+from .resnet import ResNetV1d
+
+
+class RSoftmax(nn.Module):
+    """Radix Softmax module in ``SplitAttentionConv2d``.
+
+    Args:
+        radix (int): Radix of input.
+        groups (int): Groups of input.
+    """
+
+    def __init__(self, radix, groups):
+        super().__init__()
+        self.radix = radix
+        self.groups = groups
+
+    def forward(self, x):
+        batch = x.size(0)
+        if self.radix > 1:
+            x = x.view(batch, self.groups, self.radix, -1).transpose(1, 2)
+            x = F.softmax(x, dim=1)
+            x = x.reshape(batch, -1)
+        else:
+            x = torch.sigmoid(x)
+        return x
+
+
+class SplitAttentionConv2d(BaseModule):
+    """Split-Attention Conv2d in ResNeSt.
+
+    Args:
+        in_channels (int): Number of channels in the input feature map.
+        channels (int): Number of intermediate channels.
+        kernel_size (int | tuple[int]): Size of the convolution kernel.
+        stride (int | tuple[int]): Stride of the convolution.
+        padding (int | tuple[int]): Zero-padding added to both sides of
+        dilation (int | tuple[int]): Spacing between kernel elements.
+        groups (int): Number of blocked connections from input channels to
+            output channels.
+        groups (int): Same as nn.Conv2d.
+        radix (int): Radix of SpltAtConv2d. Default: 2
+        reduction_factor (int): Reduction factor of inter_channels. Default: 4.
+        conv_cfg (dict): Config dict for convolution layer. Default: None,
+            which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer. Default: None.
+        dcn (dict): Config dict for DCN. Default: None.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 in_channels,
+                 channels,
+                 kernel_size,
+                 stride=1,
+                 padding=0,
+                 dilation=1,
+                 groups=1,
+                 radix=2,
+                 reduction_factor=4,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 dcn=None,
+                 init_cfg=None):
+        super(SplitAttentionConv2d, self).__init__(init_cfg)
+        inter_channels = max(in_channels * radix // reduction_factor, 32)
+        self.radix = radix
+        self.groups = groups
+        self.channels = channels
+        self.with_dcn = dcn is not None
+        self.dcn = dcn
+        fallback_on_stride = False
+        if self.with_dcn:
+            fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
+        if self.with_dcn and not fallback_on_stride:
+            assert conv_cfg is None, 'conv_cfg must be None for DCN'
+            conv_cfg = dcn
+        self.conv = build_conv_layer(
+            conv_cfg,
+            in_channels,
+            channels * radix,
+            kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups * radix,
+            bias=False)
+        # To be consistent with original implementation, starting from 0
+        self.norm0_name, norm0 = build_norm_layer(
+            norm_cfg, channels * radix, postfix=0)
+        self.add_module(self.norm0_name, norm0)
+        self.relu = nn.ReLU(inplace=True)
+        self.fc1 = build_conv_layer(
+            None, channels, inter_channels, 1, groups=self.groups)
+        self.norm1_name, norm1 = build_norm_layer(
+            norm_cfg, inter_channels, postfix=1)
+        self.add_module(self.norm1_name, norm1)
+        self.fc2 = build_conv_layer(
+            None, inter_channels, channels * radix, 1, groups=self.groups)
+        self.rsoftmax = RSoftmax(radix, groups)
+
+    @property
+    def norm0(self):
+        """nn.Module: the normalization layer named "norm0" """
+        return getattr(self, self.norm0_name)
+
+    @property
+    def norm1(self):
+        """nn.Module: the normalization layer named "norm1" """
+        return getattr(self, self.norm1_name)
+
+    def forward(self, x):
+        x = self.conv(x)
+        x = self.norm0(x)
+        x = self.relu(x)
+
+        batch, rchannel = x.shape[:2]
+        batch = x.size(0)
+        if self.radix > 1:
+            splits = x.view(batch, self.radix, -1, *x.shape[2:])
+            gap = splits.sum(dim=1)
+        else:
+            gap = x
+        gap = F.adaptive_avg_pool2d(gap, 1)
+        gap = self.fc1(gap)
+
+        gap = self.norm1(gap)
+        gap = self.relu(gap)
+
+        atten = self.fc2(gap)
+        atten = self.rsoftmax(atten).view(batch, -1, 1, 1)
+
+        if self.radix > 1:
+            attens = atten.view(batch, self.radix, -1, *atten.shape[2:])
+            out = torch.sum(attens * splits, dim=1)
+        else:
+            out = atten * x
+        return out.contiguous()
+
+
+class Bottleneck(_Bottleneck):
+    """Bottleneck block for ResNeSt.
+
+    Args:
+        inplane (int): Input planes of this block.
+        planes (int): Middle planes of this block.
+        groups (int): Groups of conv2.
+        base_width (int): Base of width in terms of base channels. Default: 4.
+        base_channels (int): Base of channels for calculating width.
+            Default: 64.
+        radix (int): Radix of SpltAtConv2d. Default: 2
+        reduction_factor (int): Reduction factor of inter_channels in
+            SplitAttentionConv2d. Default: 4.
+        avg_down_stride (bool): Whether to use average pool for stride in
+            Bottleneck. Default: True.
+        kwargs (dict): Key word arguments for base class.
+    """
+    expansion = 4
+
+    def __init__(self,
+                 inplanes,
+                 planes,
+                 groups=1,
+                 base_width=4,
+                 base_channels=64,
+                 radix=2,
+                 reduction_factor=4,
+                 avg_down_stride=True,
+                 **kwargs):
+        """Bottleneck block for ResNeSt."""
+        super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
+
+        if groups == 1:
+            width = self.planes
+        else:
+            width = math.floor(self.planes *
+                               (base_width / base_channels)) * groups
+
+        self.avg_down_stride = avg_down_stride and self.conv2_stride > 1
+
+        self.norm1_name, norm1 = build_norm_layer(
+            self.norm_cfg, width, postfix=1)
+        self.norm3_name, norm3 = build_norm_layer(
+            self.norm_cfg, self.planes * self.expansion, postfix=3)
+
+        self.conv1 = build_conv_layer(
+            self.conv_cfg,
+            self.inplanes,
+            width,
+            kernel_size=1,
+            stride=self.conv1_stride,
+            bias=False)
+        self.add_module(self.norm1_name, norm1)
+        self.with_modulated_dcn = False
+        self.conv2 = SplitAttentionConv2d(
+            width,
+            width,
+            kernel_size=3,
+            stride=1 if self.avg_down_stride else self.conv2_stride,
+            padding=self.dilation,
+            dilation=self.dilation,
+            groups=groups,
+            radix=radix,
+            reduction_factor=reduction_factor,
+            conv_cfg=self.conv_cfg,
+            norm_cfg=self.norm_cfg,
+            dcn=self.dcn)
+        delattr(self, self.norm2_name)
+
+        if self.avg_down_stride:
+            self.avd_layer = nn.AvgPool2d(3, self.conv2_stride, padding=1)
+
+        self.conv3 = build_conv_layer(
+            self.conv_cfg,
+            width,
+            self.planes * self.expansion,
+            kernel_size=1,
+            bias=False)
+        self.add_module(self.norm3_name, norm3)
+
+    def forward(self, x):
+
+        def _inner_forward(x):
+            identity = x
+
+            out = self.conv1(x)
+            out = self.norm1(out)
+            out = self.relu(out)
+
+            if self.with_plugins:
+                out = self.forward_plugin(out, self.after_conv1_plugin_names)
+
+            out = self.conv2(out)
+
+            if self.avg_down_stride:
+                out = self.avd_layer(out)
+
+            if self.with_plugins:
+                out = self.forward_plugin(out, self.after_conv2_plugin_names)
+
+            out = self.conv3(out)
+            out = self.norm3(out)
+
+            if self.with_plugins:
+                out = self.forward_plugin(out, self.after_conv3_plugin_names)
+
+            if self.downsample is not None:
+                identity = self.downsample(x)
+
+            out += identity
+
+            return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        out = self.relu(out)
+
+        return out
+
+
+@BACKBONES.register_module()
+class ResNeSt(ResNetV1d):
+    """ResNeSt backbone.
+
+    Args:
+        groups (int): Number of groups of Bottleneck. Default: 1
+        base_width (int): Base width of Bottleneck. Default: 4
+        radix (int): Radix of SplitAttentionConv2d. Default: 2
+        reduction_factor (int): Reduction factor of inter_channels in
+            SplitAttentionConv2d. Default: 4.
+        avg_down_stride (bool): Whether to use average pool for stride in
+            Bottleneck. Default: True.
+        kwargs (dict): Keyword arguments for ResNet.
+    """
+
+    arch_settings = {
+        50: (Bottleneck, (3, 4, 6, 3)),
+        101: (Bottleneck, (3, 4, 23, 3)),
+        152: (Bottleneck, (3, 8, 36, 3)),
+        200: (Bottleneck, (3, 24, 36, 3))
+    }
+
+    def __init__(self,
+                 groups=1,
+                 base_width=4,
+                 radix=2,
+                 reduction_factor=4,
+                 avg_down_stride=True,
+                 **kwargs):
+        self.groups = groups
+        self.base_width = base_width
+        self.radix = radix
+        self.reduction_factor = reduction_factor
+        self.avg_down_stride = avg_down_stride
+        super(ResNeSt, self).__init__(**kwargs)
+
+    def make_res_layer(self, **kwargs):
+        """Pack all blocks in a stage into a ``ResLayer``."""
+        return ResLayer(
+            groups=self.groups,
+            base_width=self.base_width,
+            base_channels=self.base_channels,
+            radix=self.radix,
+            reduction_factor=self.reduction_factor,
+            avg_down_stride=self.avg_down_stride,
+            **kwargs)
diff --git a/mmdet/models/backbones/resnet.py b/mmdet/models/backbones/resnet.py
new file mode 100644
index 0000000..1eaaae6
--- /dev/null
+++ b/mmdet/models/backbones/resnet.py
@@ -0,0 +1,672 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+from mmcv.cnn import build_conv_layer, build_norm_layer, build_plugin_layer
+from mmcv.runner import BaseModule
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from ..builder import BACKBONES
+from ..utils import ResLayer
+
+
+class BasicBlock(BaseModule):
+    expansion = 1
+
+    def __init__(self,
+                 inplanes,
+                 planes,
+                 stride=1,
+                 dilation=1,
+                 downsample=None,
+                 style='pytorch',
+                 with_cp=False,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 dcn=None,
+                 plugins=None,
+                 init_cfg=None):
+        super(BasicBlock, self).__init__(init_cfg)
+        assert dcn is None, 'Not implemented yet.'
+        assert plugins is None, 'Not implemented yet.'
+
+        self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
+        self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
+
+        self.conv1 = build_conv_layer(
+            conv_cfg,
+            inplanes,
+            planes,
+            3,
+            stride=stride,
+            padding=dilation,
+            dilation=dilation,
+            bias=False)
+        self.add_module(self.norm1_name, norm1)
+        self.conv2 = build_conv_layer(
+            conv_cfg, planes, planes, 3, padding=1, bias=False)
+        self.add_module(self.norm2_name, norm2)
+
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+        self.dilation = dilation
+        self.with_cp = with_cp
+
+    @property
+    def norm1(self):
+        """nn.Module: normalization layer after the first convolution layer"""
+        return getattr(self, self.norm1_name)
+
+    @property
+    def norm2(self):
+        """nn.Module: normalization layer after the second convolution layer"""
+        return getattr(self, self.norm2_name)
+
+    def forward(self, x):
+        """Forward function."""
+
+        def _inner_forward(x):
+            identity = x
+
+            out = self.conv1(x)
+            out = self.norm1(out)
+            out = self.relu(out)
+
+            out = self.conv2(out)
+            out = self.norm2(out)
+
+            if self.downsample is not None:
+                identity = self.downsample(x)
+
+            out += identity
+
+            return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck(BaseModule):
+    expansion = 4
+
+    def __init__(self,
+                 inplanes,
+                 planes,
+                 stride=1,
+                 dilation=1,
+                 downsample=None,
+                 style='pytorch',
+                 with_cp=False,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 dcn=None,
+                 plugins=None,
+                 init_cfg=None):
+        """Bottleneck block for ResNet.
+
+        If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
+        it is "caffe", the stride-two layer is the first 1x1 conv layer.
+        """
+        super(Bottleneck, self).__init__(init_cfg)
+        assert style in ['pytorch', 'caffe']
+        assert dcn is None or isinstance(dcn, dict)
+        assert plugins is None or isinstance(plugins, list)
+        if plugins is not None:
+            allowed_position = ['after_conv1', 'after_conv2', 'after_conv3']
+            assert all(p['position'] in allowed_position for p in plugins)
+
+        self.inplanes = inplanes
+        self.planes = planes
+        self.stride = stride
+        self.dilation = dilation
+        self.style = style
+        self.with_cp = with_cp
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.dcn = dcn
+        self.with_dcn = dcn is not None
+        self.plugins = plugins
+        self.with_plugins = plugins is not None
+
+        if self.with_plugins:
+            # collect plugins for conv1/conv2/conv3
+            self.after_conv1_plugins = [
+                plugin['cfg'] for plugin in plugins
+                if plugin['position'] == 'after_conv1'
+            ]
+            self.after_conv2_plugins = [
+                plugin['cfg'] for plugin in plugins
+                if plugin['position'] == 'after_conv2'
+            ]
+            self.after_conv3_plugins = [
+                plugin['cfg'] for plugin in plugins
+                if plugin['position'] == 'after_conv3'
+            ]
+
+        if self.style == 'pytorch':
+            self.conv1_stride = 1
+            self.conv2_stride = stride
+        else:
+            self.conv1_stride = stride
+            self.conv2_stride = 1
+
+        self.norm1_name, norm1 = build_norm_layer(norm_cfg, planes, postfix=1)
+        self.norm2_name, norm2 = build_norm_layer(norm_cfg, planes, postfix=2)
+        self.norm3_name, norm3 = build_norm_layer(
+            norm_cfg, planes * self.expansion, postfix=3)
+
+        self.conv1 = build_conv_layer(
+            conv_cfg,
+            inplanes,
+            planes,
+            kernel_size=1,
+            stride=self.conv1_stride,
+            bias=False)
+        self.add_module(self.norm1_name, norm1)
+        fallback_on_stride = False
+        if self.with_dcn:
+            fallback_on_stride = dcn.pop('fallback_on_stride', False)
+        if not self.with_dcn or fallback_on_stride:
+            self.conv2 = build_conv_layer(
+                conv_cfg,
+                planes,
+                planes,
+                kernel_size=3,
+                stride=self.conv2_stride,
+                padding=dilation,
+                dilation=dilation,
+                bias=False)
+        else:
+            assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
+            self.conv2 = build_conv_layer(
+                dcn,
+                planes,
+                planes,
+                kernel_size=3,
+                stride=self.conv2_stride,
+                padding=dilation,
+                dilation=dilation,
+                bias=False)
+
+        self.add_module(self.norm2_name, norm2)
+        self.conv3 = build_conv_layer(
+            conv_cfg,
+            planes,
+            planes * self.expansion,
+            kernel_size=1,
+            bias=False)
+        self.add_module(self.norm3_name, norm3)
+
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+
+        if self.with_plugins:
+            self.after_conv1_plugin_names = self.make_block_plugins(
+                planes, self.after_conv1_plugins)
+            self.after_conv2_plugin_names = self.make_block_plugins(
+                planes, self.after_conv2_plugins)
+            self.after_conv3_plugin_names = self.make_block_plugins(
+                planes * self.expansion, self.after_conv3_plugins)
+
+    def make_block_plugins(self, in_channels, plugins):
+        """make plugins for block.
+
+        Args:
+            in_channels (int): Input channels of plugin.
+            plugins (list[dict]): List of plugins cfg to build.
+
+        Returns:
+            list[str]: List of the names of plugin.
+        """
+        assert isinstance(plugins, list)
+        plugin_names = []
+        for plugin in plugins:
+            plugin = plugin.copy()
+            name, layer = build_plugin_layer(
+                plugin,
+                in_channels=in_channels,
+                postfix=plugin.pop('postfix', ''))
+            assert not hasattr(self, name), f'duplicate plugin {name}'
+            self.add_module(name, layer)
+            plugin_names.append(name)
+        return plugin_names
+
+    def forward_plugin(self, x, plugin_names):
+        out = x
+        for name in plugin_names:
+            out = getattr(self, name)(out)
+        return out
+
+    @property
+    def norm1(self):
+        """nn.Module: normalization layer after the first convolution layer"""
+        return getattr(self, self.norm1_name)
+
+    @property
+    def norm2(self):
+        """nn.Module: normalization layer after the second convolution layer"""
+        return getattr(self, self.norm2_name)
+
+    @property
+    def norm3(self):
+        """nn.Module: normalization layer after the third convolution layer"""
+        return getattr(self, self.norm3_name)
+
+    def forward(self, x):
+        """Forward function."""
+
+        def _inner_forward(x):
+            identity = x
+            out = self.conv1(x)
+            out = self.norm1(out)
+            out = self.relu(out)
+
+            if self.with_plugins:
+                out = self.forward_plugin(out, self.after_conv1_plugin_names)
+
+            out = self.conv2(out)
+            out = self.norm2(out)
+            out = self.relu(out)
+
+            if self.with_plugins:
+                out = self.forward_plugin(out, self.after_conv2_plugin_names)
+
+            out = self.conv3(out)
+            out = self.norm3(out)
+
+            if self.with_plugins:
+                out = self.forward_plugin(out, self.after_conv3_plugin_names)
+
+            if self.downsample is not None:
+                identity = self.downsample(x)
+
+            out += identity
+
+            return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        out = self.relu(out)
+
+        return out
+
+
+@BACKBONES.register_module()
+class ResNet(BaseModule):
+    """ResNet backbone.
+
+    Args:
+        depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
+        stem_channels (int | None): Number of stem channels. If not specified,
+            it will be the same as `base_channels`. Default: None.
+        base_channels (int): Number of base channels of res layer. Default: 64.
+        in_channels (int): Number of input image channels. Default: 3.
+        num_stages (int): Resnet stages. Default: 4.
+        strides (Sequence[int]): Strides of the first block of each stage.
+        dilations (Sequence[int]): Dilation of each stage.
+        out_indices (Sequence[int]): Output from which stages.
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer.
+        deep_stem (bool): Replace 7x7 conv in input stem with 3 3x3 conv
+        avg_down (bool): Use AvgPool instead of stride conv when
+            downsampling in the bottleneck.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            -1 means not freezing any parameters.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only.
+        plugins (list[dict]): List of plugins for stages, each dict contains:
+
+            - cfg (dict, required): Cfg dict to build plugin.
+            - position (str, required): Position inside block to insert
+              plugin, options are 'after_conv1', 'after_conv2', 'after_conv3'.
+            - stages (tuple[bool], optional): Stages to apply plugin, length
+              should be same as 'num_stages'.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed.
+        zero_init_residual (bool): Whether to use zero init for last norm layer
+            in resblocks to let them behave as identity.
+        pretrained (str, optional): model pretrained path. Default: None
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+
+    Example:
+        >>> from mmdet.models import ResNet
+        >>> import torch
+        >>> self = ResNet(depth=18)
+        >>> self.eval()
+        >>> inputs = torch.rand(1, 3, 32, 32)
+        >>> level_outputs = self.forward(inputs)
+        >>> for level_out in level_outputs:
+        ...     print(tuple(level_out.shape))
+        (1, 64, 8, 8)
+        (1, 128, 4, 4)
+        (1, 256, 2, 2)
+        (1, 512, 1, 1)
+    """
+
+    arch_settings = {
+        18: (BasicBlock, (2, 2, 2, 2)),
+        34: (BasicBlock, (3, 4, 6, 3)),
+        50: (Bottleneck, (3, 4, 6, 3)),
+        101: (Bottleneck, (3, 4, 23, 3)),
+        152: (Bottleneck, (3, 8, 36, 3))
+    }
+
+    def __init__(self,
+                 depth,
+                 in_channels=3,
+                 stem_channels=None,
+                 base_channels=64,
+                 num_stages=4,
+                 strides=(1, 2, 2, 2),
+                 dilations=(1, 1, 1, 1),
+                 out_indices=(0, 1, 2, 3),
+                 style='pytorch',
+                 deep_stem=False,
+                 avg_down=False,
+                 frozen_stages=-1,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 norm_eval=True,
+                 dcn=None,
+                 stage_with_dcn=(False, False, False, False),
+                 plugins=None,
+                 with_cp=False,
+                 zero_init_residual=True,
+                 pretrained=None,
+                 init_cfg=None):
+        super(ResNet, self).__init__(init_cfg)
+        self.zero_init_residual = zero_init_residual
+        if depth not in self.arch_settings:
+            raise KeyError(f'invalid depth {depth} for resnet')
+
+        block_init_cfg = None
+        assert not (init_cfg and pretrained), \
+            'init_cfg and pretrained cannot be specified at the same time'
+        if isinstance(pretrained, str):
+            warnings.warn('DeprecationWarning: pretrained is deprecated, '
+                          'please use "init_cfg" instead')
+            self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+        elif pretrained is None:
+            if init_cfg is None:
+                self.init_cfg = [
+                    dict(type='Kaiming', layer='Conv2d'),
+                    dict(
+                        type='Constant',
+                        val=1,
+                        layer=['_BatchNorm', 'GroupNorm'])
+                ]
+                block = self.arch_settings[depth][0]
+                if self.zero_init_residual:
+                    if block is BasicBlock:
+                        block_init_cfg = dict(
+                            type='Constant',
+                            val=0,
+                            override=dict(name='norm2'))
+                    elif block is Bottleneck:
+                        block_init_cfg = dict(
+                            type='Constant',
+                            val=0,
+                            override=dict(name='norm3'))
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+        self.depth = depth
+        if stem_channels is None:
+            stem_channels = base_channels
+        self.stem_channels = stem_channels
+        self.base_channels = base_channels
+        self.num_stages = num_stages
+        assert num_stages >= 1 and num_stages <= 4
+        self.strides = strides
+        self.dilations = dilations
+        assert len(strides) == len(dilations) == num_stages
+        self.out_indices = out_indices
+        assert max(out_indices) < num_stages
+        self.style = style
+        self.deep_stem = deep_stem
+        self.avg_down = avg_down
+        self.frozen_stages = frozen_stages
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.with_cp = with_cp
+        self.norm_eval = norm_eval
+        self.dcn = dcn
+        self.stage_with_dcn = stage_with_dcn
+        if dcn is not None:
+            assert len(stage_with_dcn) == num_stages
+        self.plugins = plugins
+        self.block, stage_blocks = self.arch_settings[depth]
+        self.stage_blocks = stage_blocks[:num_stages]
+        self.inplanes = stem_channels
+
+        self._make_stem_layer(in_channels, stem_channels)
+
+        self.res_layers = []
+        for i, num_blocks in enumerate(self.stage_blocks):
+            stride = strides[i]
+            dilation = dilations[i]
+            dcn = self.dcn if self.stage_with_dcn[i] else None
+            if plugins is not None:
+                stage_plugins = self.make_stage_plugins(plugins, i)
+            else:
+                stage_plugins = None
+            planes = base_channels * 2**i
+            res_layer = self.make_res_layer(
+                block=self.block,
+                inplanes=self.inplanes,
+                planes=planes,
+                num_blocks=num_blocks,
+                stride=stride,
+                dilation=dilation,
+                style=self.style,
+                avg_down=self.avg_down,
+                with_cp=with_cp,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                dcn=dcn,
+                plugins=stage_plugins,
+                init_cfg=block_init_cfg)
+            self.inplanes = planes * self.block.expansion
+            layer_name = f'layer{i + 1}'
+            self.add_module(layer_name, res_layer)
+            self.res_layers.append(layer_name)
+
+        self._freeze_stages()
+
+        self.feat_dim = self.block.expansion * base_channels * 2**(
+            len(self.stage_blocks) - 1)
+
+    def make_stage_plugins(self, plugins, stage_idx):
+        """Make plugins for ResNet ``stage_idx`` th stage.
+
+        Currently we support to insert ``context_block``,
+        ``empirical_attention_block``, ``nonlocal_block`` into the backbone
+        like ResNet/ResNeXt. They could be inserted after conv1/conv2/conv3 of
+        Bottleneck.
+
+        An example of plugins format could be:
+
+        Examples:
+            >>> plugins=[
+            ...     dict(cfg=dict(type='xxx', arg1='xxx'),
+            ...          stages=(False, True, True, True),
+            ...          position='after_conv2'),
+            ...     dict(cfg=dict(type='yyy'),
+            ...          stages=(True, True, True, True),
+            ...          position='after_conv3'),
+            ...     dict(cfg=dict(type='zzz', postfix='1'),
+            ...          stages=(True, True, True, True),
+            ...          position='after_conv3'),
+            ...     dict(cfg=dict(type='zzz', postfix='2'),
+            ...          stages=(True, True, True, True),
+            ...          position='after_conv3')
+            ... ]
+            >>> self = ResNet(depth=18)
+            >>> stage_plugins = self.make_stage_plugins(plugins, 0)
+            >>> assert len(stage_plugins) == 3
+
+        Suppose ``stage_idx=0``, the structure of blocks in the stage would be:
+
+        .. code-block:: none
+
+            conv1-> conv2->conv3->yyy->zzz1->zzz2
+
+        Suppose 'stage_idx=1', the structure of blocks in the stage would be:
+
+        .. code-block:: none
+
+            conv1-> conv2->xxx->conv3->yyy->zzz1->zzz2
+
+        If stages is missing, the plugin would be applied to all stages.
+
+        Args:
+            plugins (list[dict]): List of plugins cfg to build. The postfix is
+                required if multiple same type plugins are inserted.
+            stage_idx (int): Index of stage to build
+
+        Returns:
+            list[dict]: Plugins for current stage
+        """
+        stage_plugins = []
+        for plugin in plugins:
+            plugin = plugin.copy()
+            stages = plugin.pop('stages', None)
+            assert stages is None or len(stages) == self.num_stages
+            # whether to insert plugin into current stage
+            if stages is None or stages[stage_idx]:
+                stage_plugins.append(plugin)
+
+        return stage_plugins
+
+    def make_res_layer(self, **kwargs):
+        """Pack all blocks in a stage into a ``ResLayer``."""
+        return ResLayer(**kwargs)
+
+    @property
+    def norm1(self):
+        """nn.Module: the normalization layer named "norm1" """
+        return getattr(self, self.norm1_name)
+
+    def _make_stem_layer(self, in_channels, stem_channels):
+        if self.deep_stem:
+            self.stem = nn.Sequential(
+                build_conv_layer(
+                    self.conv_cfg,
+                    in_channels,
+                    stem_channels // 2,
+                    kernel_size=3,
+                    stride=2,
+                    padding=1,
+                    bias=False),
+                build_norm_layer(self.norm_cfg, stem_channels // 2)[1],
+                nn.ReLU(inplace=True),
+                build_conv_layer(
+                    self.conv_cfg,
+                    stem_channels // 2,
+                    stem_channels // 2,
+                    kernel_size=3,
+                    stride=1,
+                    padding=1,
+                    bias=False),
+                build_norm_layer(self.norm_cfg, stem_channels // 2)[1],
+                nn.ReLU(inplace=True),
+                build_conv_layer(
+                    self.conv_cfg,
+                    stem_channels // 2,
+                    stem_channels,
+                    kernel_size=3,
+                    stride=1,
+                    padding=1,
+                    bias=False),
+                build_norm_layer(self.norm_cfg, stem_channels)[1],
+                nn.ReLU(inplace=True))
+        else:
+            self.conv1 = build_conv_layer(
+                self.conv_cfg,
+                in_channels,
+                stem_channels,
+                kernel_size=7,
+                stride=2,
+                padding=3,
+                bias=False)
+            self.norm1_name, norm1 = build_norm_layer(
+                self.norm_cfg, stem_channels, postfix=1)
+            self.add_module(self.norm1_name, norm1)
+            self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            if self.deep_stem:
+                self.stem.eval()
+                for param in self.stem.parameters():
+                    param.requires_grad = False
+            else:
+                self.norm1.eval()
+                for m in [self.conv1, self.norm1]:
+                    for param in m.parameters():
+                        param.requires_grad = False
+
+        for i in range(1, self.frozen_stages + 1):
+            m = getattr(self, f'layer{i}')
+            m.eval()
+            for param in m.parameters():
+                param.requires_grad = False
+
+    def forward(self, x):
+        """Forward function."""
+        if self.deep_stem:
+            x = self.stem(x)
+        else:
+            x = self.conv1(x)
+            x = self.norm1(x)
+            x = self.relu(x)
+        x = self.maxpool(x)
+        outs = []
+        for i, layer_name in enumerate(self.res_layers):
+            res_layer = getattr(self, layer_name)
+            x = res_layer(x)
+            if i in self.out_indices:
+                outs.append(x)
+        return tuple(outs)
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep normalization layer
+        freezed."""
+        super(ResNet, self).train(mode)
+        self._freeze_stages()
+        if mode and self.norm_eval:
+            for m in self.modules():
+                # trick: eval have effect on BatchNorm only
+                if isinstance(m, _BatchNorm):
+                    m.eval()
+
+
+@BACKBONES.register_module()
+class ResNetV1d(ResNet):
+    r"""ResNetV1d variant described in `Bag of Tricks
+    <https://arxiv.org/pdf/1812.01187.pdf>`_.
+
+    Compared with default ResNet(ResNetV1b), ResNetV1d replaces the 7x7 conv in
+    the input stem with three 3x3 convs. And in the downsampling block, a 2x2
+    avg_pool with stride 2 is added before conv, whose stride is changed to 1.
+    """
+
+    def __init__(self, **kwargs):
+        super(ResNetV1d, self).__init__(
+            deep_stem=True, avg_down=True, **kwargs)
diff --git a/mmdet/models/backbones/resnext.py b/mmdet/models/backbones/resnext.py
new file mode 100644
index 0000000..8675d7c
--- /dev/null
+++ b/mmdet/models/backbones/resnext.py
@@ -0,0 +1,154 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+from mmcv.cnn import build_conv_layer, build_norm_layer
+
+from ..builder import BACKBONES
+from ..utils import ResLayer
+from .resnet import Bottleneck as _Bottleneck
+from .resnet import ResNet
+
+
+class Bottleneck(_Bottleneck):
+    expansion = 4
+
+    def __init__(self,
+                 inplanes,
+                 planes,
+                 groups=1,
+                 base_width=4,
+                 base_channels=64,
+                 **kwargs):
+        """Bottleneck block for ResNeXt.
+
+        If style is "pytorch", the stride-two layer is the 3x3 conv layer, if
+        it is "caffe", the stride-two layer is the first 1x1 conv layer.
+        """
+        super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
+
+        if groups == 1:
+            width = self.planes
+        else:
+            width = math.floor(self.planes *
+                               (base_width / base_channels)) * groups
+
+        self.norm1_name, norm1 = build_norm_layer(
+            self.norm_cfg, width, postfix=1)
+        self.norm2_name, norm2 = build_norm_layer(
+            self.norm_cfg, width, postfix=2)
+        self.norm3_name, norm3 = build_norm_layer(
+            self.norm_cfg, self.planes * self.expansion, postfix=3)
+
+        self.conv1 = build_conv_layer(
+            self.conv_cfg,
+            self.inplanes,
+            width,
+            kernel_size=1,
+            stride=self.conv1_stride,
+            bias=False)
+        self.add_module(self.norm1_name, norm1)
+        fallback_on_stride = False
+        self.with_modulated_dcn = False
+        if self.with_dcn:
+            fallback_on_stride = self.dcn.pop('fallback_on_stride', False)
+        if not self.with_dcn or fallback_on_stride:
+            self.conv2 = build_conv_layer(
+                self.conv_cfg,
+                width,
+                width,
+                kernel_size=3,
+                stride=self.conv2_stride,
+                padding=self.dilation,
+                dilation=self.dilation,
+                groups=groups,
+                bias=False)
+        else:
+            assert self.conv_cfg is None, 'conv_cfg must be None for DCN'
+            self.conv2 = build_conv_layer(
+                self.dcn,
+                width,
+                width,
+                kernel_size=3,
+                stride=self.conv2_stride,
+                padding=self.dilation,
+                dilation=self.dilation,
+                groups=groups,
+                bias=False)
+
+        self.add_module(self.norm2_name, norm2)
+        self.conv3 = build_conv_layer(
+            self.conv_cfg,
+            width,
+            self.planes * self.expansion,
+            kernel_size=1,
+            bias=False)
+        self.add_module(self.norm3_name, norm3)
+
+        if self.with_plugins:
+            self._del_block_plugins(self.after_conv1_plugin_names +
+                                    self.after_conv2_plugin_names +
+                                    self.after_conv3_plugin_names)
+            self.after_conv1_plugin_names = self.make_block_plugins(
+                width, self.after_conv1_plugins)
+            self.after_conv2_plugin_names = self.make_block_plugins(
+                width, self.after_conv2_plugins)
+            self.after_conv3_plugin_names = self.make_block_plugins(
+                self.planes * self.expansion, self.after_conv3_plugins)
+
+    def _del_block_plugins(self, plugin_names):
+        """delete plugins for block if exist.
+
+        Args:
+            plugin_names (list[str]): List of plugins name to delete.
+        """
+        assert isinstance(plugin_names, list)
+        for plugin_name in plugin_names:
+            del self._modules[plugin_name]
+
+
+@BACKBONES.register_module()
+class ResNeXt(ResNet):
+    """ResNeXt backbone.
+
+    Args:
+        depth (int): Depth of resnet, from {18, 34, 50, 101, 152}.
+        in_channels (int): Number of input image channels. Default: 3.
+        num_stages (int): Resnet stages. Default: 4.
+        groups (int): Group of resnext.
+        base_width (int): Base width of resnext.
+        strides (Sequence[int]): Strides of the first block of each stage.
+        dilations (Sequence[int]): Dilation of each stage.
+        out_indices (Sequence[int]): Output from which stages.
+        style (str): `pytorch` or `caffe`. If set to "pytorch", the stride-two
+            layer is the 3x3 conv layer, otherwise the stride-two layer is
+            the first 1x1 conv layer.
+        frozen_stages (int): Stages to be frozen (all param fixed). -1 means
+            not freezing any parameters.
+        norm_cfg (dict): dictionary to construct and config norm layer.
+        norm_eval (bool): Whether to set norm layers to eval mode, namely,
+            freeze running stats (mean and var). Note: Effect on Batch Norm
+            and its variants only.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed.
+        zero_init_residual (bool): whether to use zero init for last norm layer
+            in resblocks to let them behave as identity.
+    """
+
+    arch_settings = {
+        50: (Bottleneck, (3, 4, 6, 3)),
+        101: (Bottleneck, (3, 4, 23, 3)),
+        152: (Bottleneck, (3, 8, 36, 3))
+    }
+
+    def __init__(self, groups=1, base_width=4, **kwargs):
+        self.groups = groups
+        self.base_width = base_width
+        super(ResNeXt, self).__init__(**kwargs)
+
+    def make_res_layer(self, **kwargs):
+        """Pack all blocks in a stage into a ``ResLayer``"""
+        return ResLayer(
+            groups=self.groups,
+            base_width=self.base_width,
+            base_channels=self.base_channels,
+            **kwargs)
diff --git a/mmdet/models/backbones/ssd_vgg.py b/mmdet/models/backbones/ssd_vgg.py
new file mode 100644
index 0000000..c15aeac
--- /dev/null
+++ b/mmdet/models/backbones/ssd_vgg.py
@@ -0,0 +1,128 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch.nn as nn
+from mmcv.cnn import VGG
+from mmcv.runner import BaseModule
+
+from ..builder import BACKBONES
+from ..necks import ssd_neck
+
+
+@BACKBONES.register_module()
+class SSDVGG(VGG, BaseModule):
+    """VGG Backbone network for single-shot-detection.
+
+    Args:
+        depth (int): Depth of vgg, from {11, 13, 16, 19}.
+        with_last_pool (bool): Whether to add a pooling layer at the last
+            of the model
+        ceil_mode (bool): When True, will use `ceil` instead of `floor`
+            to compute the output shape.
+        out_indices (Sequence[int]): Output from which stages.
+        out_feature_indices (Sequence[int]): Output from which feature map.
+        pretrained (str, optional): model pretrained path. Default: None
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+        input_size (int, optional): Deprecated argumment.
+            Width and height of input, from {300, 512}.
+        l2_norm_scale (float, optional) : Deprecated argumment.
+            L2 normalization layer init scale.
+
+    Example:
+        >>> self = SSDVGG(input_size=300, depth=11)
+        >>> self.eval()
+        >>> inputs = torch.rand(1, 3, 300, 300)
+        >>> level_outputs = self.forward(inputs)
+        >>> for level_out in level_outputs:
+        ...     print(tuple(level_out.shape))
+        (1, 1024, 19, 19)
+        (1, 512, 10, 10)
+        (1, 256, 5, 5)
+        (1, 256, 3, 3)
+        (1, 256, 1, 1)
+    """
+    extra_setting = {
+        300: (256, 'S', 512, 128, 'S', 256, 128, 256, 128, 256),
+        512: (256, 'S', 512, 128, 'S', 256, 128, 'S', 256, 128, 'S', 256, 128),
+    }
+
+    def __init__(self,
+                 depth,
+                 with_last_pool=False,
+                 ceil_mode=True,
+                 out_indices=(3, 4),
+                 out_feature_indices=(22, 34),
+                 pretrained=None,
+                 init_cfg=None,
+                 input_size=None,
+                 l2_norm_scale=None):
+        # TODO: in_channels for mmcv.VGG
+        super(SSDVGG, self).__init__(
+            depth,
+            with_last_pool=with_last_pool,
+            ceil_mode=ceil_mode,
+            out_indices=out_indices)
+
+        self.features.add_module(
+            str(len(self.features)),
+            nn.MaxPool2d(kernel_size=3, stride=1, padding=1))
+        self.features.add_module(
+            str(len(self.features)),
+            nn.Conv2d(512, 1024, kernel_size=3, padding=6, dilation=6))
+        self.features.add_module(
+            str(len(self.features)), nn.ReLU(inplace=True))
+        self.features.add_module(
+            str(len(self.features)), nn.Conv2d(1024, 1024, kernel_size=1))
+        self.features.add_module(
+            str(len(self.features)), nn.ReLU(inplace=True))
+        self.out_feature_indices = out_feature_indices
+
+        assert not (init_cfg and pretrained), \
+            'init_cfg and pretrained cannot be specified at the same time'
+
+        if init_cfg is not None:
+            self.init_cfg = init_cfg
+        elif isinstance(pretrained, str):
+            warnings.warn('DeprecationWarning: pretrained is deprecated, '
+                          'please use "init_cfg" instead')
+            self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+        elif pretrained is None:
+            self.init_cfg = [
+                dict(type='Kaiming', layer='Conv2d'),
+                dict(type='Constant', val=1, layer='BatchNorm2d'),
+                dict(type='Normal', std=0.01, layer='Linear'),
+            ]
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+        if input_size is not None:
+            warnings.warn('DeprecationWarning: input_size is deprecated')
+        if l2_norm_scale is not None:
+            warnings.warn('DeprecationWarning: l2_norm_scale in VGG is '
+                          'deprecated, it has been moved to SSDNeck.')
+
+    def init_weights(self, pretrained=None):
+        super(VGG, self).init_weights()
+
+    def forward(self, x):
+        """Forward function."""
+        outs = []
+        for i, layer in enumerate(self.features):
+            x = layer(x)
+            if i in self.out_feature_indices:
+                outs.append(x)
+
+        if len(outs) == 1:
+            return outs[0]
+        else:
+            return tuple(outs)
+
+
+class L2Norm(ssd_neck.L2Norm):
+
+    def __init__(self, **kwargs):
+        super(L2Norm, self).__init__(**kwargs)
+        warnings.warn('DeprecationWarning: L2Norm in ssd_vgg.py '
+                      'is deprecated, please use L2Norm in '
+                      'mmdet/models/necks/ssd_neck.py instead')
diff --git a/mmdet/models/backbones/swin.py b/mmdet/models/backbones/swin.py
new file mode 100644
index 0000000..b8eccfc
--- /dev/null
+++ b/mmdet/models/backbones/swin.py
@@ -0,0 +1,772 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+from collections import OrderedDict
+from copy import deepcopy
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as cp
+from mmcv.cnn import build_norm_layer, constant_init, trunc_normal_init
+from mmcv.cnn.bricks.transformer import FFN, build_dropout
+from mmcv.cnn.utils.weight_init import trunc_normal_
+from mmcv.runner import BaseModule, ModuleList, _load_checkpoint
+from mmcv.utils import to_2tuple
+
+from ...utils import get_root_logger
+from ..builder import BACKBONES
+from ..utils.ckpt_convert import swin_converter
+from ..utils.transformer import PatchEmbed, PatchMerging
+
+
+class WindowMSA(BaseModule):
+    """Window based multi-head self-attention (W-MSA) module with relative
+    position bias.
+
+    Args:
+        embed_dims (int): Number of input channels.
+        num_heads (int): Number of attention heads.
+        window_size (tuple[int]): The height and width of the window.
+        qkv_bias (bool, optional):  If True, add a learnable bias to q, k, v.
+            Default: True.
+        qk_scale (float | None, optional): Override default qk scale of
+            head_dim ** -0.5 if set. Default: None.
+        attn_drop_rate (float, optional): Dropout ratio of attention weight.
+            Default: 0.0
+        proj_drop_rate (float, optional): Dropout ratio of output. Default: 0.
+        init_cfg (dict | None, optional): The Config for initialization.
+            Default: None.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 window_size,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 attn_drop_rate=0.,
+                 proj_drop_rate=0.,
+                 init_cfg=None):
+
+        super().__init__()
+        self.embed_dims = embed_dims
+        self.window_size = window_size  # Wh, Ww
+        self.num_heads = num_heads
+        head_embed_dims = embed_dims // num_heads
+        self.scale = qk_scale or head_embed_dims**-0.5
+        self.init_cfg = init_cfg
+
+        # define a parameter table of relative position bias
+        self.relative_position_bias_table = nn.Parameter(
+            torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1),
+                        num_heads))  # 2*Wh-1 * 2*Ww-1, nH
+
+        # About 2x faster than original impl
+        Wh, Ww = self.window_size
+        rel_index_coords = self.double_step_seq(2 * Ww - 1, Wh, 1, Ww)
+        rel_position_index = rel_index_coords + rel_index_coords.T
+        rel_position_index = rel_position_index.flip(1).contiguous()
+        self.register_buffer('relative_position_index', rel_position_index)
+
+        self.qkv = nn.Linear(embed_dims, embed_dims * 3, bias=qkv_bias)
+        self.attn_drop = nn.Dropout(attn_drop_rate)
+        self.proj = nn.Linear(embed_dims, embed_dims)
+        self.proj_drop = nn.Dropout(proj_drop_rate)
+
+        self.softmax = nn.Softmax(dim=-1)
+
+    def init_weights(self):
+        trunc_normal_(self.relative_position_bias_table, std=0.02)
+
+    def forward(self, x, mask=None):
+        """
+        Args:
+
+            x (tensor): input features with shape of (num_windows*B, N, C)
+            mask (tensor | None, Optional): mask with shape of (num_windows,
+                Wh*Ww, Wh*Ww), value should be between (-inf, 0].
+        """
+        B, N, C = x.shape
+        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,
+                                  C // self.num_heads).permute(2, 0, 3, 1, 4)
+        # make torchscript happy (cannot use tensor as tuple)
+        q, k, v = qkv[0], qkv[1], qkv[2]
+
+        q = q * self.scale
+        attn = (q @ k.transpose(-2, -1))
+
+        relative_position_bias = self.relative_position_bias_table[
+            self.relative_position_index.view(-1)].view(
+                self.window_size[0] * self.window_size[1],
+                self.window_size[0] * self.window_size[1],
+                -1)  # Wh*Ww,Wh*Ww,nH
+        relative_position_bias = relative_position_bias.permute(
+            2, 0, 1).contiguous()  # nH, Wh*Ww, Wh*Ww
+        attn = attn + relative_position_bias.unsqueeze(0)
+
+        if mask is not None:
+            nW = mask.shape[0]
+            attn = attn.view(B // nW, nW, self.num_heads, N,
+                             N) + mask.unsqueeze(1).unsqueeze(0)
+            attn = attn.view(-1, self.num_heads, N, N)
+        attn = self.softmax(attn)
+
+        attn = self.attn_drop(attn)
+
+        x = (attn @ v).transpose(1, 2).reshape(B, N, C)
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+
+    @staticmethod
+    def double_step_seq(step1, len1, step2, len2):
+        seq1 = torch.arange(0, step1 * len1, step1)
+        seq2 = torch.arange(0, step2 * len2, step2)
+        return (seq1[:, None] + seq2[None, :]).reshape(1, -1)
+
+
+class ShiftWindowMSA(BaseModule):
+    """Shifted Window Multihead Self-Attention Module.
+
+    Args:
+        embed_dims (int): Number of input channels.
+        num_heads (int): Number of attention heads.
+        window_size (int): The height and width of the window.
+        shift_size (int, optional): The shift step of each window towards
+            right-bottom. If zero, act as regular window-msa. Defaults to 0.
+        qkv_bias (bool, optional): If True, add a learnable bias to q, k, v.
+            Default: True
+        qk_scale (float | None, optional): Override default qk scale of
+            head_dim ** -0.5 if set. Defaults: None.
+        attn_drop_rate (float, optional): Dropout ratio of attention weight.
+            Defaults: 0.
+        proj_drop_rate (float, optional): Dropout ratio of output.
+            Defaults: 0.
+        dropout_layer (dict, optional): The dropout_layer used before output.
+            Defaults: dict(type='DropPath', drop_prob=0.).
+        init_cfg (dict, optional): The extra config for initialization.
+            Default: None.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 window_size,
+                 shift_size=0,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 attn_drop_rate=0,
+                 proj_drop_rate=0,
+                 dropout_layer=dict(type='DropPath', drop_prob=0.),
+                 init_cfg=None):
+        super().__init__(init_cfg)
+
+        self.window_size = window_size
+        self.shift_size = shift_size
+        assert 0 <= self.shift_size < self.window_size
+
+        self.w_msa = WindowMSA(
+            embed_dims=embed_dims,
+            num_heads=num_heads,
+            window_size=to_2tuple(window_size),
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop_rate=attn_drop_rate,
+            proj_drop_rate=proj_drop_rate,
+            init_cfg=None)
+
+        self.drop = build_dropout(dropout_layer)
+
+    def forward(self, query, hw_shape):
+        B, L, C = query.shape
+        H, W = hw_shape
+        assert L == H * W, 'input feature has wrong size'
+        query = query.view(B, H, W, C)
+
+        # pad feature maps to multiples of window size
+        pad_r = (self.window_size - W % self.window_size) % self.window_size
+        pad_b = (self.window_size - H % self.window_size) % self.window_size
+        query = F.pad(query, (0, 0, 0, pad_r, 0, pad_b))
+        H_pad, W_pad = query.shape[1], query.shape[2]
+
+        # cyclic shift
+        if self.shift_size > 0:
+            shifted_query = torch.roll(
+                query,
+                shifts=(-self.shift_size, -self.shift_size),
+                dims=(1, 2))
+
+            # calculate attention mask for SW-MSA
+            img_mask = torch.zeros((1, H_pad, W_pad, 1), device=query.device)
+            h_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size,
+                              -self.shift_size), slice(-self.shift_size, None))
+            w_slices = (slice(0, -self.window_size),
+                        slice(-self.window_size,
+                              -self.shift_size), slice(-self.shift_size, None))
+            cnt = 0
+            for h in h_slices:
+                for w in w_slices:
+                    img_mask[:, h, w, :] = cnt
+                    cnt += 1
+
+            # nW, window_size, window_size, 1
+            mask_windows = self.window_partition(img_mask)
+            mask_windows = mask_windows.view(
+                -1, self.window_size * self.window_size)
+            attn_mask = mask_windows.unsqueeze(1) - mask_windows.unsqueeze(2)
+            attn_mask = attn_mask.masked_fill(attn_mask != 0,
+                                              float(-100.0)).masked_fill(
+                                                  attn_mask == 0, float(0.0))
+        else:
+            shifted_query = query
+            attn_mask = None
+
+        # nW*B, window_size, window_size, C
+        query_windows = self.window_partition(shifted_query)
+        # nW*B, window_size*window_size, C
+        query_windows = query_windows.view(-1, self.window_size**2, C)
+
+        # W-MSA/SW-MSA (nW*B, window_size*window_size, C)
+        attn_windows = self.w_msa(query_windows, mask=attn_mask)
+
+        # merge windows
+        attn_windows = attn_windows.view(-1, self.window_size,
+                                         self.window_size, C)
+
+        # B H' W' C
+        shifted_x = self.window_reverse(attn_windows, H_pad, W_pad)
+        # reverse cyclic shift
+        if self.shift_size > 0:
+            x = torch.roll(
+                shifted_x,
+                shifts=(self.shift_size, self.shift_size),
+                dims=(1, 2))
+        else:
+            x = shifted_x
+
+        if pad_r > 0 or pad_b:
+            x = x[:, :H, :W, :].contiguous()
+
+        x = x.view(B, H * W, C)
+
+        x = self.drop(x)
+        return x
+
+    def window_reverse(self, windows, H, W):
+        """
+        Args:
+            windows: (num_windows*B, window_size, window_size, C)
+            H (int): Height of image
+            W (int): Width of image
+        Returns:
+            x: (B, H, W, C)
+        """
+        window_size = self.window_size
+        B = int(windows.shape[0] / (H * W / window_size / window_size))
+        x = windows.view(B, H // window_size, W // window_size, window_size,
+                         window_size, -1)
+        x = x.permute(0, 1, 3, 2, 4, 5).contiguous().view(B, H, W, -1)
+        return x
+
+    def window_partition(self, x):
+        """
+        Args:
+            x: (B, H, W, C)
+        Returns:
+            windows: (num_windows*B, window_size, window_size, C)
+        """
+        B, H, W, C = x.shape
+        window_size = self.window_size
+        x = x.view(B, H // window_size, window_size, W // window_size,
+                   window_size, C)
+        windows = x.permute(0, 1, 3, 2, 4, 5).contiguous()
+        windows = windows.view(-1, window_size, window_size, C)
+        return windows
+
+
+class SwinBlock(BaseModule):
+    """"
+    Args:
+        embed_dims (int): The feature dimension.
+        num_heads (int): Parallel attention heads.
+        feedforward_channels (int): The hidden dimension for FFNs.
+        window_size (int, optional): The local window scale. Default: 7.
+        shift (bool, optional): whether to shift window or not. Default False.
+        qkv_bias (bool, optional): enable bias for qkv if True. Default: True.
+        qk_scale (float | None, optional): Override default qk scale of
+            head_dim ** -0.5 if set. Default: None.
+        drop_rate (float, optional): Dropout rate. Default: 0.
+        attn_drop_rate (float, optional): Attention dropout rate. Default: 0.
+        drop_path_rate (float, optional): Stochastic depth rate. Default: 0.
+        act_cfg (dict, optional): The config dict of activation function.
+            Default: dict(type='GELU').
+        norm_cfg (dict, optional): The config dict of normalization.
+            Default: dict(type='LN').
+        with_cp (bool, optional): Use checkpoint or not. Using checkpoint
+            will save some memory while slowing down the training speed.
+            Default: False.
+        init_cfg (dict | list | None, optional): The init config.
+            Default: None.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 feedforward_channels,
+                 window_size=7,
+                 shift=False,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.,
+                 act_cfg=dict(type='GELU'),
+                 norm_cfg=dict(type='LN'),
+                 with_cp=False,
+                 init_cfg=None):
+
+        super(SwinBlock, self).__init__()
+
+        self.init_cfg = init_cfg
+        self.with_cp = with_cp
+
+        self.norm1 = build_norm_layer(norm_cfg, embed_dims)[1]
+        self.attn = ShiftWindowMSA(
+            embed_dims=embed_dims,
+            num_heads=num_heads,
+            window_size=window_size,
+            shift_size=window_size // 2 if shift else 0,
+            qkv_bias=qkv_bias,
+            qk_scale=qk_scale,
+            attn_drop_rate=attn_drop_rate,
+            proj_drop_rate=drop_rate,
+            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
+            init_cfg=None)
+
+        self.norm2 = build_norm_layer(norm_cfg, embed_dims)[1]
+        self.ffn = FFN(
+            embed_dims=embed_dims,
+            feedforward_channels=feedforward_channels,
+            num_fcs=2,
+            ffn_drop=drop_rate,
+            dropout_layer=dict(type='DropPath', drop_prob=drop_path_rate),
+            act_cfg=act_cfg,
+            add_identity=True,
+            init_cfg=None)
+
+    def forward(self, x, hw_shape):
+
+        def _inner_forward(x):
+            identity = x
+            x = self.norm1(x)
+            x = self.attn(x, hw_shape)
+
+            x = x + identity
+
+            identity = x
+            x = self.norm2(x)
+            x = self.ffn(x, identity=identity)
+
+            return x
+
+        if self.with_cp and x.requires_grad:
+            x = cp.checkpoint(_inner_forward, x)
+        else:
+            x = _inner_forward(x)
+
+        return x
+
+
+class SwinBlockSequence(BaseModule):
+    """Implements one stage in Swin Transformer.
+
+    Args:
+        embed_dims (int): The feature dimension.
+        num_heads (int): Parallel attention heads.
+        feedforward_channels (int): The hidden dimension for FFNs.
+        depth (int): The number of blocks in this stage.
+        window_size (int, optional): The local window scale. Default: 7.
+        qkv_bias (bool, optional): enable bias for qkv if True. Default: True.
+        qk_scale (float | None, optional): Override default qk scale of
+            head_dim ** -0.5 if set. Default: None.
+        drop_rate (float, optional): Dropout rate. Default: 0.
+        attn_drop_rate (float, optional): Attention dropout rate. Default: 0.
+        drop_path_rate (float | list[float], optional): Stochastic depth
+            rate. Default: 0.
+        downsample (BaseModule | None, optional): The downsample operation
+            module. Default: None.
+        act_cfg (dict, optional): The config dict of activation function.
+            Default: dict(type='GELU').
+        norm_cfg (dict, optional): The config dict of normalization.
+            Default: dict(type='LN').
+        with_cp (bool, optional): Use checkpoint or not. Using checkpoint
+            will save some memory while slowing down the training speed.
+            Default: False.
+        init_cfg (dict | list | None, optional): The init config.
+            Default: None.
+    """
+
+    def __init__(self,
+                 embed_dims,
+                 num_heads,
+                 feedforward_channels,
+                 depth,
+                 window_size=7,
+                 qkv_bias=True,
+                 qk_scale=None,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.,
+                 downsample=None,
+                 act_cfg=dict(type='GELU'),
+                 norm_cfg=dict(type='LN'),
+                 with_cp=False,
+                 init_cfg=None):
+        super().__init__(init_cfg=init_cfg)
+
+        if isinstance(drop_path_rate, list):
+            drop_path_rates = drop_path_rate
+            assert len(drop_path_rates) == depth
+        else:
+            drop_path_rates = [deepcopy(drop_path_rate) for _ in range(depth)]
+
+        self.blocks = ModuleList()
+        for i in range(depth):
+            block = SwinBlock(
+                embed_dims=embed_dims,
+                num_heads=num_heads,
+                feedforward_channels=feedforward_channels,
+                window_size=window_size,
+                shift=False if i % 2 == 0 else True,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                drop_rate=drop_rate,
+                attn_drop_rate=attn_drop_rate,
+                drop_path_rate=drop_path_rates[i],
+                act_cfg=act_cfg,
+                norm_cfg=norm_cfg,
+                with_cp=with_cp,
+                init_cfg=None)
+            self.blocks.append(block)
+
+        self.downsample = downsample
+
+    def forward(self, x, hw_shape):
+        for block in self.blocks:
+            x = block(x, hw_shape)
+
+        if self.downsample:
+            x_down, down_hw_shape = self.downsample(x, hw_shape)
+            return x_down, down_hw_shape, x, hw_shape
+        else:
+            return x, hw_shape, x, hw_shape
+
+
+@BACKBONES.register_module()
+class SwinTransformer(BaseModule):
+    """ Swin Transformer
+    A PyTorch implement of : `Swin Transformer:
+    Hierarchical Vision Transformer using Shifted Windows`  -
+        https://arxiv.org/abs/2103.14030
+
+    Inspiration from
+    https://github.com/microsoft/Swin-Transformer
+
+    Args:
+        pretrain_img_size (int | tuple[int]): The size of input image when
+            pretrain. Defaults: 224.
+        in_channels (int): The num of input channels.
+            Defaults: 3.
+        embed_dims (int): The feature dimension. Default: 96.
+        patch_size (int | tuple[int]): Patch size. Default: 4.
+        window_size (int): Window size. Default: 7.
+        mlp_ratio (int | float): Ratio of mlp hidden dim to embedding dim.
+            Default: 4.
+        depths (tuple[int]): Depths of each Swin Transformer stage.
+            Default: (2, 2, 6, 2).
+        num_heads (tuple[int]): Parallel attention heads of each Swin
+            Transformer stage. Default: (3, 6, 12, 24).
+        strides (tuple[int]): The patch merging or patch embedding stride of
+            each Swin Transformer stage. (In swin, we set kernel size equal to
+            stride.) Default: (4, 2, 2, 2).
+        out_indices (tuple[int]): Output from which stages.
+            Default: (0, 1, 2, 3).
+        qkv_bias (bool, optional): If True, add a learnable bias to query, key,
+            value. Default: True
+        qk_scale (float | None, optional): Override default qk scale of
+            head_dim ** -0.5 if set. Default: None.
+        patch_norm (bool): If add a norm layer for patch embed and patch
+            merging. Default: True.
+        drop_rate (float): Dropout rate. Defaults: 0.
+        attn_drop_rate (float): Attention dropout rate. Default: 0.
+        drop_path_rate (float): Stochastic depth rate. Defaults: 0.1.
+        use_abs_pos_embed (bool): If True, add absolute position embedding to
+            the patch embedding. Defaults: False.
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='GELU').
+        norm_cfg (dict): Config dict for normalization layer at
+            output of backone. Defaults: dict(type='LN').
+        with_cp (bool, optional): Use checkpoint or not. Using checkpoint
+            will save some memory while slowing down the training speed.
+            Default: False.
+        pretrained (str, optional): model pretrained path. Default: None.
+        convert_weights (bool): The flag indicates whether the
+            pre-trained model is from the original repo. We may need
+            to convert some keys to make it compatible.
+            Default: False.
+        frozen_stages (int): Stages to be frozen (stop grad and set eval mode).
+            Default: -1 (-1 means not freezing any parameters).
+        init_cfg (dict, optional): The Config for initialization.
+            Defaults to None.
+    """
+
+    def __init__(self,
+                 pretrain_img_size=224,
+                 in_channels=3,
+                 embed_dims=96,
+                 patch_size=4,
+                 window_size=7,
+                 mlp_ratio=4,
+                 depths=(2, 2, 6, 2),
+                 num_heads=(3, 6, 12, 24),
+                 strides=(4, 2, 2, 2),
+                 out_indices=(0, 1, 2, 3),
+                 qkv_bias=True,
+                 qk_scale=None,
+                 patch_norm=True,
+                 drop_rate=0.,
+                 attn_drop_rate=0.,
+                 drop_path_rate=0.1,
+                 use_abs_pos_embed=False,
+                 act_cfg=dict(type='GELU'),
+                 norm_cfg=dict(type='LN'),
+                 with_cp=False,
+                 pretrained=None,
+                 convert_weights=False,
+                 frozen_stages=-1,
+                 init_cfg=None):
+        self.convert_weights = convert_weights
+        self.frozen_stages = frozen_stages
+        if isinstance(pretrain_img_size, int):
+            pretrain_img_size = to_2tuple(pretrain_img_size)
+        elif isinstance(pretrain_img_size, tuple):
+            if len(pretrain_img_size) == 1:
+                pretrain_img_size = to_2tuple(pretrain_img_size[0])
+            assert len(pretrain_img_size) == 2, \
+                f'The size of image should have length 1 or 2, ' \
+                f'but got {len(pretrain_img_size)}'
+
+        assert not (init_cfg and pretrained), \
+            'init_cfg and pretrained cannot be specified at the same time'
+        if isinstance(pretrained, str):
+            warnings.warn('DeprecationWarning: pretrained is deprecated, '
+                          'please use "init_cfg" instead')
+            self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+        elif pretrained is None:
+            self.init_cfg = init_cfg
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+        super(SwinTransformer, self).__init__(init_cfg=init_cfg)
+
+        num_layers = len(depths)
+        self.out_indices = out_indices
+        self.use_abs_pos_embed = use_abs_pos_embed
+
+        assert strides[0] == patch_size, 'Use non-overlapping patch embed.'
+
+        self.patch_embed = PatchEmbed(
+            in_channels=in_channels,
+            embed_dims=embed_dims,
+            conv_type='Conv2d',
+            kernel_size=patch_size,
+            stride=strides[0],
+            norm_cfg=norm_cfg if patch_norm else None,
+            init_cfg=None)
+
+        if self.use_abs_pos_embed:
+            patch_row = pretrain_img_size[0] // patch_size
+            patch_col = pretrain_img_size[1] // patch_size
+            self.absolute_pos_embed = nn.Parameter(
+                torch.zeros((1, embed_dims, patch_row, patch_col)))
+
+        self.drop_after_pos = nn.Dropout(p=drop_rate)
+
+        # set stochastic depth decay rule
+        total_depth = sum(depths)
+        dpr = [
+            x.item() for x in torch.linspace(0, drop_path_rate, total_depth)
+        ]
+
+        self.stages = ModuleList()
+        in_channels = embed_dims
+        for i in range(num_layers):
+            if i < num_layers - 1:
+                downsample = PatchMerging(
+                    in_channels=in_channels,
+                    out_channels=2 * in_channels,
+                    stride=strides[i + 1],
+                    norm_cfg=norm_cfg if patch_norm else None,
+                    init_cfg=None)
+            else:
+                downsample = None
+
+            stage = SwinBlockSequence(
+                embed_dims=in_channels,
+                num_heads=num_heads[i],
+                feedforward_channels=int(mlp_ratio * in_channels),
+                depth=depths[i],
+                window_size=window_size,
+                qkv_bias=qkv_bias,
+                qk_scale=qk_scale,
+                drop_rate=drop_rate,
+                attn_drop_rate=attn_drop_rate,
+                drop_path_rate=dpr[sum(depths[:i]):sum(depths[:i + 1])],
+                downsample=downsample,
+                act_cfg=act_cfg,
+                norm_cfg=norm_cfg,
+                with_cp=with_cp,
+                init_cfg=None)
+            self.stages.append(stage)
+            if downsample:
+                in_channels = downsample.out_channels
+
+        self.num_features = [int(embed_dims * 2**i) for i in range(num_layers)]
+        # Add a norm layer for each output
+        for i in out_indices:
+            layer = build_norm_layer(norm_cfg, self.num_features[i])[1]
+            layer_name = f'norm{i}'
+            self.add_module(layer_name, layer)
+
+    def train(self, mode=True):
+        """Convert the model into training mode while keep layers freezed."""
+        super(SwinTransformer, self).train(mode)
+        self._freeze_stages()
+
+    def _freeze_stages(self):
+        if self.frozen_stages >= 0:
+            self.patch_embed.eval()
+            for param in self.patch_embed.parameters():
+                param.requires_grad = False
+            if self.use_abs_pos_embed:
+                self.absolute_pos_embed.requires_grad = False
+            self.drop_after_pos.eval()
+
+        for i in range(1, self.frozen_stages + 1):
+
+            if (i - 1) in self.out_indices:
+                norm_layer = getattr(self, f'norm{i-1}')
+                norm_layer.eval()
+                for param in norm_layer.parameters():
+                    param.requires_grad = False
+
+            m = self.stages[i - 1]
+            m.eval()
+            for param in m.parameters():
+                param.requires_grad = False
+
+    def init_weights(self):
+        logger = get_root_logger()
+        if self.init_cfg is None:
+            logger.warn(f'No pre-trained weights for '
+                        f'{self.__class__.__name__}, '
+                        f'training start from scratch')
+            if self.use_abs_pos_embed:
+                trunc_normal_(self.absolute_pos_embed, std=0.02)
+            for m in self.modules():
+                if isinstance(m, nn.Linear):
+                    trunc_normal_init(m, std=.02, bias=0.)
+                elif isinstance(m, nn.LayerNorm):
+                    constant_init(m, 1.0)
+        else:
+            assert 'checkpoint' in self.init_cfg, f'Only support ' \
+                                                  f'specify `Pretrained` in ' \
+                                                  f'`init_cfg` in ' \
+                                                  f'{self.__class__.__name__} '
+            ckpt = _load_checkpoint(
+                self.init_cfg.checkpoint, logger=logger, map_location='cpu')
+            if 'state_dict' in ckpt:
+                _state_dict = ckpt['state_dict']
+            elif 'model' in ckpt:
+                _state_dict = ckpt['model']
+            else:
+                _state_dict = ckpt
+            if self.convert_weights:
+                # supported loading weight from original repo,
+                _state_dict = swin_converter(_state_dict)
+
+            state_dict = OrderedDict()
+            for k, v in _state_dict.items():
+                if k.startswith('backbone.'):
+                    state_dict[k[9:]] = v
+
+            # strip prefix of state_dict
+            if list(state_dict.keys())[0].startswith('module.'):
+                state_dict = {k[7:]: v for k, v in state_dict.items()}
+
+            # reshape absolute position embedding
+            if state_dict.get('absolute_pos_embed') is not None:
+                absolute_pos_embed = state_dict['absolute_pos_embed']
+                N1, L, C1 = absolute_pos_embed.size()
+                N2, C2, H, W = self.absolute_pos_embed.size()
+                if N1 != N2 or C1 != C2 or L != H * W:
+                    logger.warning('Error in loading absolute_pos_embed, pass')
+                else:
+                    state_dict['absolute_pos_embed'] = absolute_pos_embed.view(
+                        N2, H, W, C2).permute(0, 3, 1, 2).contiguous()
+
+            # interpolate position bias table if needed
+            relative_position_bias_table_keys = [
+                k for k in state_dict.keys()
+                if 'relative_position_bias_table' in k
+            ]
+            for table_key in relative_position_bias_table_keys:
+                table_pretrained = state_dict[table_key]
+                table_current = self.state_dict()[table_key]
+                L1, nH1 = table_pretrained.size()
+                L2, nH2 = table_current.size()
+                if nH1 != nH2:
+                    logger.warning(f'Error in loading {table_key}, pass')
+                elif L1 != L2:
+                    S1 = int(L1**0.5)
+                    S2 = int(L2**0.5)
+                    table_pretrained_resized = F.interpolate(
+                        table_pretrained.permute(1, 0).reshape(1, nH1, S1, S1),
+                        size=(S2, S2),
+                        mode='bicubic')
+                    state_dict[table_key] = table_pretrained_resized.view(
+                        nH2, L2).permute(1, 0).contiguous()
+
+            # load state_dict
+            self.load_state_dict(state_dict, False)
+
+    def forward(self, x):
+        x, hw_shape = self.patch_embed(x)
+
+        if self.use_abs_pos_embed:
+            h, w = self.absolute_pos_embed.shape[1:3]
+            if hw_shape[0] != h or hw_shape[1] != w:
+                absolute_pos_embed = F.interpolate(
+                    self.absolute_pos_embed,
+                    size=hw_shape,
+                    mode='bicubic',
+                    align_corners=False).flatten(2).transpose(1, 2)
+            else:
+                absolute_pos_embed = self.absolute_pos_embed.flatten(
+                    2).transpose(1, 2)
+            x = x + absolute_pos_embed
+        x = self.drop_after_pos(x)
+
+        outs = []
+        for i, stage in enumerate(self.stages):
+            x, hw_shape, out, out_hw_shape = stage(x, hw_shape)
+            if i in self.out_indices:
+                norm_layer = getattr(self, f'norm{i}')
+                out = norm_layer(out)
+                out = out.view(-1, *out_hw_shape,
+                               self.num_features[i]).permute(0, 3, 1,
+                                                             2).contiguous()
+                outs.append(out)
+
+        return outs
diff --git a/mmdet/models/backbones/trident_resnet.py b/mmdet/models/backbones/trident_resnet.py
new file mode 100644
index 0000000..013ba64
--- /dev/null
+++ b/mmdet/models/backbones/trident_resnet.py
@@ -0,0 +1,298 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.utils.checkpoint as cp
+from mmcv.cnn import build_conv_layer, build_norm_layer
+from mmcv.runner import BaseModule
+from torch.nn.modules.utils import _pair
+
+from mmdet.models.backbones.resnet import Bottleneck, ResNet
+from mmdet.models.builder import BACKBONES
+
+
+class TridentConv(BaseModule):
+    """Trident Convolution Module.
+
+    Args:
+        in_channels (int): Number of channels in input.
+        out_channels (int): Number of channels in output.
+        kernel_size (int): Size of convolution kernel.
+        stride (int, optional): Convolution stride. Default: 1.
+        trident_dilations (tuple[int, int, int], optional): Dilations of
+            different trident branch. Default: (1, 2, 3).
+        test_branch_idx (int, optional): In inference, all 3 branches will
+            be used if `test_branch_idx==-1`, otherwise only branch with
+            index `test_branch_idx` will be used. Default: 1.
+        bias (bool, optional): Whether to use bias in convolution or not.
+            Default: False.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size,
+                 stride=1,
+                 trident_dilations=(1, 2, 3),
+                 test_branch_idx=1,
+                 bias=False,
+                 init_cfg=None):
+        super(TridentConv, self).__init__(init_cfg)
+        self.num_branch = len(trident_dilations)
+        self.with_bias = bias
+        self.test_branch_idx = test_branch_idx
+        self.stride = _pair(stride)
+        self.kernel_size = _pair(kernel_size)
+        self.paddings = _pair(trident_dilations)
+        self.dilations = trident_dilations
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.bias = bias
+
+        self.weight = nn.Parameter(
+            torch.Tensor(out_channels, in_channels, *self.kernel_size))
+        if bias:
+            self.bias = nn.Parameter(torch.Tensor(out_channels))
+        else:
+            self.bias = None
+
+    def extra_repr(self):
+        tmpstr = f'in_channels={self.in_channels}'
+        tmpstr += f', out_channels={self.out_channels}'
+        tmpstr += f', kernel_size={self.kernel_size}'
+        tmpstr += f', num_branch={self.num_branch}'
+        tmpstr += f', test_branch_idx={self.test_branch_idx}'
+        tmpstr += f', stride={self.stride}'
+        tmpstr += f', paddings={self.paddings}'
+        tmpstr += f', dilations={self.dilations}'
+        tmpstr += f', bias={self.bias}'
+        return tmpstr
+
+    def forward(self, inputs):
+        if self.training or self.test_branch_idx == -1:
+            outputs = [
+                F.conv2d(input, self.weight, self.bias, self.stride, padding,
+                         dilation) for input, dilation, padding in zip(
+                             inputs, self.dilations, self.paddings)
+            ]
+        else:
+            assert len(inputs) == 1
+            outputs = [
+                F.conv2d(inputs[0], self.weight, self.bias, self.stride,
+                         self.paddings[self.test_branch_idx],
+                         self.dilations[self.test_branch_idx])
+            ]
+
+        return outputs
+
+
+# Since TridentNet is defined over ResNet50 and ResNet101, here we
+# only support TridentBottleneckBlock.
+class TridentBottleneck(Bottleneck):
+    """BottleBlock for TridentResNet.
+
+    Args:
+        trident_dilations (tuple[int, int, int]): Dilations of different
+            trident branch.
+        test_branch_idx (int): In inference, all 3 branches will be used
+            if `test_branch_idx==-1`, otherwise only branch with index
+            `test_branch_idx` will be used.
+        concat_output (bool): Whether to concat the output list to a Tensor.
+            `True` only in the last Block.
+    """
+
+    def __init__(self, trident_dilations, test_branch_idx, concat_output,
+                 **kwargs):
+
+        super(TridentBottleneck, self).__init__(**kwargs)
+        self.trident_dilations = trident_dilations
+        self.num_branch = len(trident_dilations)
+        self.concat_output = concat_output
+        self.test_branch_idx = test_branch_idx
+        self.conv2 = TridentConv(
+            self.planes,
+            self.planes,
+            kernel_size=3,
+            stride=self.conv2_stride,
+            bias=False,
+            trident_dilations=self.trident_dilations,
+            test_branch_idx=test_branch_idx,
+            init_cfg=dict(
+                type='Kaiming',
+                distribution='uniform',
+                mode='fan_in',
+                override=dict(name='conv2')))
+
+    def forward(self, x):
+
+        def _inner_forward(x):
+            num_branch = (
+                self.num_branch
+                if self.training or self.test_branch_idx == -1 else 1)
+            identity = x
+            if not isinstance(x, list):
+                x = (x, ) * num_branch
+                identity = x
+                if self.downsample is not None:
+                    identity = [self.downsample(b) for b in x]
+
+            out = [self.conv1(b) for b in x]
+            out = [self.norm1(b) for b in out]
+            out = [self.relu(b) for b in out]
+
+            if self.with_plugins:
+                for k in range(len(out)):
+                    out[k] = self.forward_plugin(out[k],
+                                                 self.after_conv1_plugin_names)
+
+            out = self.conv2(out)
+            out = [self.norm2(b) for b in out]
+            out = [self.relu(b) for b in out]
+            if self.with_plugins:
+                for k in range(len(out)):
+                    out[k] = self.forward_plugin(out[k],
+                                                 self.after_conv2_plugin_names)
+
+            out = [self.conv3(b) for b in out]
+            out = [self.norm3(b) for b in out]
+
+            if self.with_plugins:
+                for k in range(len(out)):
+                    out[k] = self.forward_plugin(out[k],
+                                                 self.after_conv3_plugin_names)
+
+            out = [
+                out_b + identity_b for out_b, identity_b in zip(out, identity)
+            ]
+            return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        out = [self.relu(b) for b in out]
+        if self.concat_output:
+            out = torch.cat(out, dim=0)
+        return out
+
+
+def make_trident_res_layer(block,
+                           inplanes,
+                           planes,
+                           num_blocks,
+                           stride=1,
+                           trident_dilations=(1, 2, 3),
+                           style='pytorch',
+                           with_cp=False,
+                           conv_cfg=None,
+                           norm_cfg=dict(type='BN'),
+                           dcn=None,
+                           plugins=None,
+                           test_branch_idx=-1):
+    """Build Trident Res Layers."""
+
+    downsample = None
+    if stride != 1 or inplanes != planes * block.expansion:
+        downsample = []
+        conv_stride = stride
+        downsample.extend([
+            build_conv_layer(
+                conv_cfg,
+                inplanes,
+                planes * block.expansion,
+                kernel_size=1,
+                stride=conv_stride,
+                bias=False),
+            build_norm_layer(norm_cfg, planes * block.expansion)[1]
+        ])
+        downsample = nn.Sequential(*downsample)
+
+    layers = []
+    for i in range(num_blocks):
+        layers.append(
+            block(
+                inplanes=inplanes,
+                planes=planes,
+                stride=stride if i == 0 else 1,
+                trident_dilations=trident_dilations,
+                downsample=downsample if i == 0 else None,
+                style=style,
+                with_cp=with_cp,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                dcn=dcn,
+                plugins=plugins,
+                test_branch_idx=test_branch_idx,
+                concat_output=True if i == num_blocks - 1 else False))
+        inplanes = planes * block.expansion
+    return nn.Sequential(*layers)
+
+
+@BACKBONES.register_module()
+class TridentResNet(ResNet):
+    """The stem layer, stage 1 and stage 2 in Trident ResNet are identical to
+    ResNet, while in stage 3, Trident BottleBlock is utilized to replace the
+    normal BottleBlock to yield trident output. Different branch shares the
+    convolution weight but uses different dilations to achieve multi-scale
+    output.
+
+                               / stage3(b0) \
+    x - stem - stage1 - stage2 - stage3(b1) - output
+                               \ stage3(b2) /
+
+    Args:
+        depth (int): Depth of resnet, from {50, 101, 152}.
+        num_branch (int): Number of branches in TridentNet.
+        test_branch_idx (int): In inference, all 3 branches will be used
+            if `test_branch_idx==-1`, otherwise only branch with index
+            `test_branch_idx` will be used.
+        trident_dilations (tuple[int]): Dilations of different trident branch.
+            len(trident_dilations) should be equal to num_branch.
+    """  # noqa
+
+    def __init__(self, depth, num_branch, test_branch_idx, trident_dilations,
+                 **kwargs):
+
+        assert num_branch == len(trident_dilations)
+        assert depth in (50, 101, 152)
+        super(TridentResNet, self).__init__(depth, **kwargs)
+        assert self.num_stages == 3
+        self.test_branch_idx = test_branch_idx
+        self.num_branch = num_branch
+
+        last_stage_idx = self.num_stages - 1
+        stride = self.strides[last_stage_idx]
+        dilation = trident_dilations
+        dcn = self.dcn if self.stage_with_dcn[last_stage_idx] else None
+        if self.plugins is not None:
+            stage_plugins = self.make_stage_plugins(self.plugins,
+                                                    last_stage_idx)
+        else:
+            stage_plugins = None
+        planes = self.base_channels * 2**last_stage_idx
+        res_layer = make_trident_res_layer(
+            TridentBottleneck,
+            inplanes=(self.block.expansion * self.base_channels *
+                      2**(last_stage_idx - 1)),
+            planes=planes,
+            num_blocks=self.stage_blocks[last_stage_idx],
+            stride=stride,
+            trident_dilations=dilation,
+            style=self.style,
+            with_cp=self.with_cp,
+            conv_cfg=self.conv_cfg,
+            norm_cfg=self.norm_cfg,
+            dcn=dcn,
+            plugins=stage_plugins,
+            test_branch_idx=self.test_branch_idx)
+
+        layer_name = f'layer{last_stage_idx + 1}'
+
+        self.__setattr__(layer_name, res_layer)
+        self.res_layers.pop(last_stage_idx)
+        self.res_layers.insert(last_stage_idx, layer_name)
+
+        self._freeze_stages()
diff --git a/mmdet/models/builder.py b/mmdet/models/builder.py
new file mode 100644
index 0000000..ace6209
--- /dev/null
+++ b/mmdet/models/builder.py
@@ -0,0 +1,59 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+from mmcv.cnn import MODELS as MMCV_MODELS
+from mmcv.utils import Registry
+
+MODELS = Registry('models', parent=MMCV_MODELS)
+
+BACKBONES = MODELS
+NECKS = MODELS
+ROI_EXTRACTORS = MODELS
+SHARED_HEADS = MODELS
+HEADS = MODELS
+LOSSES = MODELS
+DETECTORS = MODELS
+
+
+def build_backbone(cfg):
+    """Build backbone."""
+    return BACKBONES.build(cfg)
+
+
+def build_neck(cfg):
+    """Build neck."""
+    return NECKS.build(cfg)
+
+
+def build_roi_extractor(cfg):
+    """Build roi extractor."""
+    return ROI_EXTRACTORS.build(cfg)
+
+
+def build_shared_head(cfg):
+    """Build shared head."""
+    return SHARED_HEADS.build(cfg)
+
+
+def build_head(cfg):
+    """Build head."""
+    return HEADS.build(cfg)
+
+
+def build_loss(cfg):
+    """Build loss."""
+    return LOSSES.build(cfg)
+
+
+def build_detector(cfg, train_cfg=None, test_cfg=None):
+    """Build detector."""
+    if train_cfg is not None or test_cfg is not None:
+        warnings.warn(
+            'train_cfg and test_cfg is deprecated, '
+            'please specify them in model', UserWarning)
+    assert cfg.get('train_cfg') is None or train_cfg is None, \
+        'train_cfg specified in both outer field and model field '
+    assert cfg.get('test_cfg') is None or test_cfg is None, \
+        'test_cfg specified in both outer field and model field '
+    return DETECTORS.build(
+        cfg, default_args=dict(train_cfg=train_cfg, test_cfg=test_cfg))
diff --git a/mmdet/models/dense_heads/__init__.py b/mmdet/models/dense_heads/__init__.py
new file mode 100644
index 0000000..9c60ae1
--- /dev/null
+++ b/mmdet/models/dense_heads/__init__.py
@@ -0,0 +1,62 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .anchor_free_head import AnchorFreeHead
+from .anchor_head import AnchorHead
+from .ascend_anchor_head import AscendAnchorHead
+from .ascend_retina_head import AscendRetinaHead
+from .ascend_ssd_head import AscendSSDHead
+from .atss_head import ATSSHead
+from .autoassign_head import AutoAssignHead
+from .cascade_rpn_head import CascadeRPNHead, StageCascadeRPNHead
+from .centernet_head import CenterNetHead
+from .centripetal_head import CentripetalHead
+from .corner_head import CornerHead
+from .ddod_head import DDODHead
+from .deformable_detr_head import DeformableDETRHead
+from .detr_head import DETRHead
+from .embedding_rpn_head import EmbeddingRPNHead
+from .fcos_head import FCOSHead
+from .fovea_head import FoveaHead
+from .free_anchor_retina_head import FreeAnchorRetinaHead
+from .fsaf_head import FSAFHead
+from .ga_retina_head import GARetinaHead
+from .ga_rpn_head import GARPNHead
+from .gfl_head import GFLHead
+from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
+from .lad_head import LADHead
+from .ld_head import LDHead
+from .mask2former_head import Mask2FormerHead
+from .maskformer_head import MaskFormerHead
+from .nasfcos_head import NASFCOSHead
+from .paa_head import PAAHead
+from .pisa_retinanet_head import PISARetinaHead
+from .pisa_ssd_head import PISASSDHead
+from .reppoints_head import RepPointsHead
+from .retina_head import RetinaHead
+from .retina_sepbn_head import RetinaSepBNHead
+from .rpn_head import RPNHead
+from .sabl_retina_head import SABLRetinaHead
+from .solo_head import DecoupledSOLOHead, DecoupledSOLOLightHead, SOLOHead
+from .solov2_head import SOLOV2Head
+from .ssd_head import SSDHead
+from .tood_head import TOODHead
+from .vfnet_head import VFNetHead
+from .yolact_head import YOLACTHead, YOLACTProtonet, YOLACTSegmHead
+from .yolo_head import YOLOV3Head
+from .yolof_head import YOLOFHead
+from .yolox_head import YOLOXHead
+
+__all__ = [
+    'AnchorFreeHead', 'AnchorHead', 'GuidedAnchorHead', 'FeatureAdaption',
+    'RPNHead', 'GARPNHead', 'RetinaHead', 'RetinaSepBNHead', 'GARetinaHead',
+    'SSDHead', 'FCOSHead', 'RepPointsHead', 'FoveaHead',
+    'FreeAnchorRetinaHead', 'ATSSHead', 'FSAFHead', 'NASFCOSHead',
+    'PISARetinaHead', 'PISASSDHead', 'GFLHead', 'CornerHead', 'YOLACTHead',
+    'YOLACTSegmHead', 'YOLACTProtonet', 'YOLOV3Head', 'PAAHead',
+    'SABLRetinaHead', 'CentripetalHead', 'VFNetHead', 'StageCascadeRPNHead',
+    'CascadeRPNHead', 'EmbeddingRPNHead', 'LDHead', 'AutoAssignHead',
+    'DETRHead', 'YOLOFHead', 'DeformableDETRHead', 'SOLOHead',
+    'DecoupledSOLOHead', 'CenterNetHead', 'YOLOXHead',
+    'DecoupledSOLOLightHead', 'LADHead', 'TOODHead', 'MaskFormerHead',
+    'Mask2FormerHead', 'SOLOV2Head', 'DDODHead', 'AscendAnchorHead',
+    'AscendRetinaHead', 'AscendSSDHead'
+]
diff --git a/mmdet/models/dense_heads/anchor_free_head.py b/mmdet/models/dense_heads/anchor_free_head.py
new file mode 100644
index 0000000..b0460b9
--- /dev/null
+++ b/mmdet/models/dense_heads/anchor_free_head.py
@@ -0,0 +1,350 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+from abc import abstractmethod
+
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.runner import force_fp32
+
+from mmdet.core import build_bbox_coder, multi_apply
+from mmdet.core.anchor.point_generator import MlvlPointGenerator
+from ..builder import HEADS, build_loss
+from .base_dense_head import BaseDenseHead
+from .dense_test_mixins import BBoxTestMixin
+
+
+@HEADS.register_module()
+class AnchorFreeHead(BaseDenseHead, BBoxTestMixin):
+    """Anchor-free head (FCOS, Fovea, RepPoints, etc.).
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        feat_channels (int): Number of hidden channels. Used in child classes.
+        stacked_convs (int): Number of stacking convs of the head.
+        strides (tuple): Downsample factor of each feature map.
+        dcn_on_last_conv (bool): If true, use dcn in the last layer of
+            towers. Default: False.
+        conv_bias (bool | str): If specified as `auto`, it will be decided by
+            the norm_cfg. Bias of conv will be set as True if `norm_cfg` is
+            None, otherwise False. Default: "auto".
+        loss_cls (dict): Config of classification loss.
+        loss_bbox (dict): Config of localization loss.
+        bbox_coder (dict): Config of bbox coder. Defaults
+            'DistancePointBBoxCoder'.
+        conv_cfg (dict): Config dict for convolution layer. Default: None.
+        norm_cfg (dict): Config dict for normalization layer. Default: None.
+        train_cfg (dict): Training config of anchor head.
+        test_cfg (dict): Testing config of anchor head.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """  # noqa: W605
+
+    _version = 1
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 feat_channels=256,
+                 stacked_convs=4,
+                 strides=(4, 8, 16, 32, 64),
+                 dcn_on_last_conv=False,
+                 conv_bias='auto',
+                 loss_cls=dict(
+                     type='FocalLoss',
+                     use_sigmoid=True,
+                     gamma=2.0,
+                     alpha=0.25,
+                     loss_weight=1.0),
+                 loss_bbox=dict(type='IoULoss', loss_weight=1.0),
+                 bbox_coder=dict(type='DistancePointBBoxCoder'),
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 init_cfg=dict(
+                     type='Normal',
+                     layer='Conv2d',
+                     std=0.01,
+                     override=dict(
+                         type='Normal',
+                         name='conv_cls',
+                         std=0.01,
+                         bias_prob=0.01))):
+        super(AnchorFreeHead, self).__init__(init_cfg)
+        self.num_classes = num_classes
+        self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
+        if self.use_sigmoid_cls:
+            self.cls_out_channels = num_classes
+        else:
+            self.cls_out_channels = num_classes + 1
+        self.in_channels = in_channels
+        self.feat_channels = feat_channels
+        self.stacked_convs = stacked_convs
+        self.strides = strides
+        self.dcn_on_last_conv = dcn_on_last_conv
+        assert conv_bias == 'auto' or isinstance(conv_bias, bool)
+        self.conv_bias = conv_bias
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_bbox = build_loss(loss_bbox)
+        self.bbox_coder = build_bbox_coder(bbox_coder)
+
+        self.prior_generator = MlvlPointGenerator(strides)
+
+        # In order to keep a more general interface and be consistent with
+        # anchor_head. We can think of point like one anchor
+        self.num_base_priors = self.prior_generator.num_base_priors[0]
+
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.fp16_enabled = False
+
+        self._init_layers()
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self._init_cls_convs()
+        self._init_reg_convs()
+        self._init_predictor()
+
+    def _init_cls_convs(self):
+        """Initialize classification conv layers of the head."""
+        self.cls_convs = nn.ModuleList()
+        for i in range(self.stacked_convs):
+            chn = self.in_channels if i == 0 else self.feat_channels
+            if self.dcn_on_last_conv and i == self.stacked_convs - 1:
+                conv_cfg = dict(type='DCNv2')
+            else:
+                conv_cfg = self.conv_cfg
+            self.cls_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    bias=self.conv_bias))
+
+    def _init_reg_convs(self):
+        """Initialize bbox regression conv layers of the head."""
+        self.reg_convs = nn.ModuleList()
+        for i in range(self.stacked_convs):
+            chn = self.in_channels if i == 0 else self.feat_channels
+            if self.dcn_on_last_conv and i == self.stacked_convs - 1:
+                conv_cfg = dict(type='DCNv2')
+            else:
+                conv_cfg = self.conv_cfg
+            self.reg_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    bias=self.conv_bias))
+
+    def _init_predictor(self):
+        """Initialize predictor layers of the head."""
+        self.conv_cls = nn.Conv2d(
+            self.feat_channels, self.cls_out_channels, 3, padding=1)
+        self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
+
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
+                              missing_keys, unexpected_keys, error_msgs):
+        """Hack some keys of the model state dict so that can load checkpoints
+        of previous version."""
+        version = local_metadata.get('version', None)
+        if version is None:
+            # the key is different in early versions
+            # for example, 'fcos_cls' become 'conv_cls' now
+            bbox_head_keys = [
+                k for k in state_dict.keys() if k.startswith(prefix)
+            ]
+            ori_predictor_keys = []
+            new_predictor_keys = []
+            # e.g. 'fcos_cls' or 'fcos_reg'
+            for key in bbox_head_keys:
+                ori_predictor_keys.append(key)
+                key = key.split('.')
+                conv_name = None
+                if key[1].endswith('cls'):
+                    conv_name = 'conv_cls'
+                elif key[1].endswith('reg'):
+                    conv_name = 'conv_reg'
+                elif key[1].endswith('centerness'):
+                    conv_name = 'conv_centerness'
+                else:
+                    assert NotImplementedError
+                if conv_name is not None:
+                    key[1] = conv_name
+                    new_predictor_keys.append('.'.join(key))
+                else:
+                    ori_predictor_keys.pop(-1)
+            for i in range(len(new_predictor_keys)):
+                state_dict[new_predictor_keys[i]] = state_dict.pop(
+                    ori_predictor_keys[i])
+        super()._load_from_state_dict(state_dict, prefix, local_metadata,
+                                      strict, missing_keys, unexpected_keys,
+                                      error_msgs)
+
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple: Usually contain classification scores and bbox predictions.
+                cls_scores (list[Tensor]): Box scores for each scale level,
+                    each is a 4D-tensor, the channel number is
+                    num_points * num_classes.
+                bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                    level, each is a 4D-tensor, the channel number is
+                    num_points * 4.
+        """
+        return multi_apply(self.forward_single, feats)[:2]
+
+    def forward_single(self, x):
+        """Forward features of a single scale level.
+
+        Args:
+            x (Tensor): FPN feature maps of the specified stride.
+
+        Returns:
+            tuple: Scores for each class, bbox predictions, features
+                after classification and regression conv layers, some
+                models needs these features like FCOS.
+        """
+        cls_feat = x
+        reg_feat = x
+
+        for cls_layer in self.cls_convs:
+            cls_feat = cls_layer(cls_feat)
+        cls_score = self.conv_cls(cls_feat)
+
+        for reg_layer in self.reg_convs:
+            reg_feat = reg_layer(reg_feat)
+        bbox_pred = self.conv_reg(reg_feat)
+        return cls_score, bbox_pred, cls_feat, reg_feat
+
+    @abstractmethod
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute loss of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level,
+                each is a 4D-tensor, the channel number is
+                num_points * num_classes.
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level, each is a 4D-tensor, the channel number is
+                num_points * 4.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+        """
+
+        raise NotImplementedError
+
+    @abstractmethod
+    def get_targets(self, points, gt_bboxes_list, gt_labels_list):
+        """Compute regression, classification and centerness targets for points
+        in multiple images.
+
+        Args:
+            points (list[Tensor]): Points of each fpn level, each has shape
+                (num_points, 2).
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
+                each has shape (num_gt, 4).
+            gt_labels_list (list[Tensor]): Ground truth labels of each box,
+                each has shape (num_gt,).
+        """
+        raise NotImplementedError
+
+    def _get_points_single(self,
+                           featmap_size,
+                           stride,
+                           dtype,
+                           device,
+                           flatten=False):
+        """Get points of a single scale level.
+
+        This function will be deprecated soon.
+        """
+
+        warnings.warn(
+            '`_get_points_single` in `AnchorFreeHead` will be '
+            'deprecated soon, we support a multi level point generator now'
+            'you can get points of a single level feature map '
+            'with `self.prior_generator.single_level_grid_priors` ')
+
+        h, w = featmap_size
+        # First create Range with the default dtype, than convert to
+        # target `dtype` for onnx exporting.
+        x_range = torch.arange(w, device=device).to(dtype)
+        y_range = torch.arange(h, device=device).to(dtype)
+        y, x = torch.meshgrid(y_range, x_range)
+        if flatten:
+            y = y.flatten()
+            x = x.flatten()
+        return y, x
+
+    def get_points(self, featmap_sizes, dtype, device, flatten=False):
+        """Get points according to feature map sizes.
+
+        Args:
+            featmap_sizes (list[tuple]): Multi-level feature map sizes.
+            dtype (torch.dtype): Type of points.
+            device (torch.device): Device of points.
+
+        Returns:
+            tuple: points of each image.
+        """
+        warnings.warn(
+            '`get_points` in `AnchorFreeHead` will be '
+            'deprecated soon, we support a multi level point generator now'
+            'you can get points of all levels '
+            'with `self.prior_generator.grid_priors` ')
+
+        mlvl_points = []
+        for i in range(len(featmap_sizes)):
+            mlvl_points.append(
+                self._get_points_single(featmap_sizes[i], self.strides[i],
+                                        dtype, device, flatten))
+        return mlvl_points
+
+    def aug_test(self, feats, img_metas, rescale=False):
+        """Test function with test time augmentation.
+
+        Args:
+            feats (list[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains features for all images in the batch.
+            img_metas (list[list[dict]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch. each dict has image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[ndarray]: bbox results of each class
+        """
+        return self.aug_test_bboxes(feats, img_metas, rescale=rescale)
diff --git a/mmdet/models/dense_heads/anchor_head.py b/mmdet/models/dense_heads/anchor_head.py
new file mode 100644
index 0000000..6d0a49f
--- /dev/null
+++ b/mmdet/models/dense_heads/anchor_head.py
@@ -0,0 +1,763 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch
+import torch.nn as nn
+from mmcv.runner import force_fp32
+
+from mmdet.core import (anchor_inside_flags, build_assigner, build_bbox_coder,
+                        build_prior_generator, build_sampler, images_to_levels,
+                        multi_apply, unmap)
+from ..builder import HEADS, build_loss
+from .base_dense_head import BaseDenseHead
+from .dense_test_mixins import BBoxTestMixin
+from mmdet.core.bbox.iou_calculators import bbox_overlaps
+from my_equation import *
+import global_placeholder
+
+@HEADS.register_module()
+class AnchorHead(BaseDenseHead, BBoxTestMixin):
+    """Anchor-based head (RPN, RetinaNet, SSD, etc.).
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        feat_channels (int): Number of hidden channels. Used in child classes.
+        anchor_generator (dict): Config dict for anchor generator
+        bbox_coder (dict): Config of bounding box coder.
+        reg_decoded_bbox (bool): If true, the regression loss would be
+            applied directly on decoded bounding boxes, converting both
+            the predicted boxes and regression targets to absolute
+            coordinates format. Default False. It should be `True` when
+            using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
+        loss_cls (dict): Config of classification loss.
+        loss_bbox (dict): Config of localization loss.
+        train_cfg (dict): Training config of anchor head.
+        test_cfg (dict): Testing config of anchor head.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """  # noqa: W605
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 feat_channels=256,
+                 anchor_generator=dict(
+                     type='AnchorGenerator',
+                     scales=[8, 16, 32],
+                     ratios=[0.5, 1.0, 2.0],
+                     strides=[4, 8, 16, 32, 64]),
+                 bbox_coder=dict(
+                     type='DeltaXYWHBBoxCoder',
+                     clip_border=True,
+                     target_means=(.0, .0, .0, .0),
+                     target_stds=(1.0, 1.0, 1.0, 1.0)),
+                 reg_decoded_bbox=False,
+                 loss_cls=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
+                 loss_bbox=dict(
+                     type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
+                 train_cfg=None,
+                 test_cfg=None,
+                 init_cfg=dict(type='Normal', layer='Conv2d', std=0.01)):
+        super(AnchorHead, self).__init__(init_cfg)
+        self.in_channels = in_channels
+        self.num_classes = num_classes
+        self.feat_channels = feat_channels
+        self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
+        if self.use_sigmoid_cls:
+            self.cls_out_channels = num_classes
+        else:
+            self.cls_out_channels = num_classes + 1
+
+        if self.cls_out_channels <= 0:
+            raise ValueError(f'num_classes={num_classes} is too small')
+        self.reg_decoded_bbox = reg_decoded_bbox
+
+        self.bbox_coder = build_bbox_coder(bbox_coder)
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_bbox = build_loss(loss_bbox)
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+        if self.train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            if hasattr(self.train_cfg,
+                       'sampler') and self.train_cfg.sampler.type.split(
+                           '.')[-1] != 'PseudoSampler':
+                self.sampling = True
+                sampler_cfg = self.train_cfg.sampler
+                # avoid BC-breaking
+                if loss_cls['type'] in [
+                        'FocalLoss', 'GHMC', 'QualityFocalLoss'
+                ]:
+                    warnings.warn(
+                        'DeprecationWarning: Determining whether to sampling'
+                        'by loss type is deprecated, please delete sampler in'
+                        'your config when using `FocalLoss`, `GHMC`, '
+                        '`QualityFocalLoss` or other FocalLoss variant.')
+                    self.sampling = False
+                    sampler_cfg = dict(type='PseudoSampler')
+            else:
+                self.sampling = False
+                sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+        self.fp16_enabled = False
+
+        self.prior_generator = build_prior_generator(anchor_generator)
+
+        # Usually the numbers of anchors for each level are the same
+        # except SSD detectors. So it is an int in the most dense
+        # heads but a list of int in SSDHead
+        self.num_base_priors = self.prior_generator.num_base_priors[0]
+        self._init_layers()
+
+    @property
+    def num_anchors(self):
+        warnings.warn('DeprecationWarning: `num_anchors` is deprecated, '
+                      'for consistency or also use '
+                      '`num_base_priors` instead')
+        return self.prior_generator.num_base_priors[0]
+
+    @property
+    def anchor_generator(self):
+        warnings.warn('DeprecationWarning: anchor_generator is deprecated, '
+                      'please use "prior_generator" instead')
+        return self.prior_generator
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.conv_cls = nn.Conv2d(self.in_channels,
+                                  self.num_base_priors * self.cls_out_channels,
+                                  1)
+        self.conv_reg = nn.Conv2d(self.in_channels, self.num_base_priors * 4,
+                                  1)
+
+    def forward_single(self, x):
+        """Forward feature of a single scale level.
+
+        Args:
+            x (Tensor): Features of a single scale level.
+
+        Returns:
+            tuple:
+                cls_score (Tensor): Cls scores for a single scale level \
+                    the channels number is num_base_priors * num_classes.
+                bbox_pred (Tensor): Box energies / deltas for a single scale \
+                    level, the channels number is num_base_priors * 4.
+        """
+        cls_score = self.conv_cls(x)
+        bbox_pred = self.conv_reg(x)
+        return cls_score, bbox_pred
+
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple: A tuple of classification scores and bbox prediction.
+
+                - cls_scores (list[Tensor]): Classification scores for all \
+                    scale levels, each is a 4D-tensor, the channels number \
+                    is num_base_priors * num_classes.
+                - bbox_preds (list[Tensor]): Box energies / deltas for all \
+                    scale levels, each is a 4D-tensor, the channels number \
+                    is num_base_priors * 4.
+        """
+        return multi_apply(self.forward_single, feats)
+
+    def get_anchors(self, featmap_sizes, img_metas, device='cuda'):
+        """Get anchors according to feature map sizes.
+
+        Args:
+            featmap_sizes (list[tuple]): Multi-level feature map sizes.
+            img_metas (list[dict]): Image meta info.
+            device (torch.device | str): Device for returned tensors
+
+        Returns:
+            tuple:
+                anchor_list (list[Tensor]): Anchors of each image.
+                valid_flag_list (list[Tensor]): Valid flags of each image.
+        """
+        num_imgs = len(img_metas)
+
+        # since feature map sizes of all images are the same, we only compute
+        # anchors for one time
+        multi_level_anchors = self.prior_generator.grid_priors(
+            featmap_sizes, device=device)
+        anchor_list = [multi_level_anchors for _ in range(num_imgs)]
+
+        # for each image, we compute valid flags of multi level anchors
+        valid_flag_list = []
+        for img_id, img_meta in enumerate(img_metas):
+            multi_level_flags = self.prior_generator.valid_flags(
+                featmap_sizes, img_meta['pad_shape'], device)
+            valid_flag_list.append(multi_level_flags)
+
+        return anchor_list, valid_flag_list
+
+    def _get_targets_single(self,
+                            flat_anchors,
+                            valid_flags,
+                            gt_bboxes,
+                            gt_bboxes_ignore,
+                            gt_labels,
+                            img_meta,
+                            label_channels=1,
+                            unmap_outputs=True):
+        """Compute regression and classification targets for anchors in a
+        single image.
+
+        Args:
+            flat_anchors (Tensor): Multi-level anchors of the image, which are
+                concatenated into a single tensor of shape (num_anchors ,4)
+            valid_flags (Tensor): Multi level valid flags of the image,
+                which are concatenated into a single tensor of
+                    shape (num_anchors,).
+            gt_bboxes (Tensor): Ground truth bboxes of the image,
+                shape (num_gts, 4).
+            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4).
+            img_meta (dict): Meta info of the image.
+            gt_labels (Tensor): Ground truth labels of each box,
+                shape (num_gts,).
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+
+        Returns:
+            tuple:
+                labels_list (list[Tensor]): Labels of each level
+                label_weights_list (list[Tensor]): Label weights of each level
+                bbox_targets_list (list[Tensor]): BBox targets of each level
+                bbox_weights_list (list[Tensor]): BBox weights of each level
+                num_total_pos (int): Number of positive samples in all images
+                num_total_neg (int): Number of negative samples in all images
+        """
+        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
+                                           img_meta['img_shape'][:2],
+                                           self.train_cfg.allowed_border)
+        if not inside_flags.any():
+            return (None, ) * 7
+        # assign gt and sample anchors
+        anchors = flat_anchors[inside_flags, :]
+
+        assign_result = self.assigner.assign(
+            anchors, gt_bboxes, gt_bboxes_ignore,
+            None if self.sampling else gt_labels)
+        sampling_result = self.sampler.sample(assign_result, anchors,
+                                              gt_bboxes)
+
+        num_valid_anchors = anchors.shape[0]
+        bbox_targets = torch.zeros_like(anchors)
+        bbox_weights = torch.zeros_like(anchors)
+        labels = anchors.new_full((num_valid_anchors, ),
+                                  self.num_classes,
+                                  dtype=torch.long)
+        label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
+
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+        if len(pos_inds) > 0:
+            if not self.reg_decoded_bbox:
+                pos_bbox_targets = self.bbox_coder.encode(
+                    sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
+            else:
+                pos_bbox_targets = sampling_result.pos_gt_bboxes
+            bbox_targets[pos_inds, :] = pos_bbox_targets
+            bbox_weights[pos_inds, :] = 1.0
+            if gt_labels is None:
+                # Only rpn gives gt_labels as None
+                # Foreground is the first class since v2.5.0
+                labels[pos_inds] = 0
+            else:
+                labels[pos_inds] = gt_labels[
+                    sampling_result.pos_assigned_gt_inds]
+            if self.train_cfg.pos_weight <= 0:
+                label_weights[pos_inds] = 1.0
+            else:
+                label_weights[pos_inds] = self.train_cfg.pos_weight
+        if len(neg_inds) > 0:
+            label_weights[neg_inds] = 1.0
+
+        # map up to original set of anchors
+        if unmap_outputs:
+            num_total_anchors = flat_anchors.size(0)
+            labels = unmap(
+                labels, num_total_anchors, inside_flags,
+                fill=self.num_classes)  # fill bg label
+            label_weights = unmap(label_weights, num_total_anchors,
+                                  inside_flags)
+            bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
+            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
+
+        return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
+                neg_inds, sampling_result)
+
+    def get_targets(self,
+                    anchor_list,
+                    valid_flag_list,
+                    gt_bboxes_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None,
+                    gt_labels_list=None,
+                    label_channels=1,
+                    unmap_outputs=True,
+                    return_sampling_results=False):
+        """Compute regression and classification targets for anchors in
+        multiple images.
+
+        Args:
+            anchor_list (list[list[Tensor]]): Multi level anchors of each
+                image. The outer list indicates images, and the inner list
+                corresponds to feature levels of the image. Each element of
+                the inner list is a tensor of shape (num_anchors, 4).
+            valid_flag_list (list[list[Tensor]]): Multi level valid flags of
+                each image. The outer list indicates images, and the inner list
+                corresponds to feature levels of the image. Each element of
+                the inner list is a tensor of shape (num_anchors, )
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+            img_metas (list[dict]): Meta info of each image.
+            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
+                ignored.
+            gt_labels_list (list[Tensor]): Ground truth labels of each box.
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+
+        Returns:
+            tuple: Usually returns a tuple containing learning targets.
+
+                - labels_list (list[Tensor]): Labels of each level.
+                - label_weights_list (list[Tensor]): Label weights of each
+                  level.
+                - bbox_targets_list (list[Tensor]): BBox targets of each level.
+                - bbox_weights_list (list[Tensor]): BBox weights of each level.
+                - num_total_pos (int): Number of positive samples in all
+                  images.
+                - num_total_neg (int): Number of negative samples in all
+                  images.
+
+            additional_returns: This function enables user-defined returns from
+                `self._get_targets_single`. These returns are currently refined
+                to properties at each feature map (i.e. having HxW dimension).
+                The results will be concatenated after the end
+        """
+        num_imgs = len(img_metas)
+        assert len(anchor_list) == len(valid_flag_list) == num_imgs
+
+        # anchor number of multi levels
+        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+        # concat all level anchors to a single tensor
+        concat_anchor_list = []
+        concat_valid_flag_list = []
+        for i in range(num_imgs):
+            assert len(anchor_list[i]) == len(valid_flag_list[i])
+            concat_anchor_list.append(torch.cat(anchor_list[i]))
+            concat_valid_flag_list.append(torch.cat(valid_flag_list[i]))
+
+        # compute targets for each image
+        if gt_bboxes_ignore_list is None:
+            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
+        if gt_labels_list is None:
+            gt_labels_list = [None for _ in range(num_imgs)]
+        results = multi_apply(
+            self._get_targets_single,
+            concat_anchor_list,
+            concat_valid_flag_list,
+            gt_bboxes_list,
+            gt_bboxes_ignore_list,
+            gt_labels_list,
+            img_metas,
+            label_channels=label_channels,
+            unmap_outputs=unmap_outputs)
+        (all_labels, all_label_weights, all_bbox_targets, all_bbox_weights,
+         pos_inds_list, neg_inds_list, sampling_results_list) = results[:7]  # 这里会返回pos、neg inds
+        rest_results = list(results[7:])  # user-added return values
+        # no valid anchors
+        if any([labels is None for labels in all_labels]):
+            return None
+        # sampled anchors of all images
+        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
+        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
+        # split targets to a list w.r.t. multiple levels
+        labels_list = images_to_levels(all_labels, num_level_anchors)
+        label_weights_list = images_to_levels(all_label_weights,
+                                              num_level_anchors)
+        bbox_targets_list = images_to_levels(all_bbox_targets,
+                                             num_level_anchors)
+        bbox_weights_list = images_to_levels(all_bbox_weights,
+                                             num_level_anchors)
+        res = (labels_list, label_weights_list, bbox_targets_list,
+               bbox_weights_list, num_total_pos, num_total_neg)
+        if return_sampling_results:
+            res = res + (sampling_results_list, )
+        for i, r in enumerate(rest_results):  # user-added return values
+            rest_results[i] = images_to_levels(r, num_level_anchors)
+
+        return res + tuple(rest_results)
+
+    def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights,  # single指的是单分支；这里传进来就是一个batch的。
+                    bbox_targets, bbox_weights, cls_branch_factor, reg_branch_factor, num_total_samples):
+        """Compute loss of a single scale level.
+
+        Args:
+            cls_score (Tensor): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W).
+            bbox_pred (Tensor): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W).
+            anchors (Tensor): Box reference for each scale level with shape
+                (N, num_total_anchors, 4).
+            labels (Tensor): Labels of each anchors with shape
+                (N, num_total_anchors).
+            label_weights (Tensor): Label weights of each anchor with shape
+                (N, num_total_anchors)
+            bbox_targets (Tensor): BBox regression targets of each anchor
+                weight shape (N, num_total_anchors, 4).
+            bbox_weights (Tensor): BBox regression loss weights of each anchor
+                with shape (N, num_total_anchors, 4).
+            num_total_samples (int): If sampling, num total samples equal to
+                the number of total anchors; Otherwise, it is the number of
+                positive anchors.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        # classification loss
+        labels = labels.reshape(-1)
+        label_weights = label_weights.reshape(-1)
+        cls_score = cls_score.permute(0, 2, 3,
+                                      1).reshape(-1, self.cls_out_channels)
+        
+        bbox_targets = bbox_targets.reshape(-1, 4)
+        bbox_weights = bbox_weights.reshape(-1, 4)
+        
+        # 获取正负样本inds
+        pos_inds = torch.nonzero(bbox_weights.sum(dim=1), as_tuple=False).squeeze()
+        valid_inds = torch.nonzero(label_weights, as_tuple=False).squeeze()
+        if pos_inds.numel() == 1:
+            # 得升维
+            pos_inds = pos_inds.unsqueeze(dim=-1)
+        
+        loss_cls = self.loss_cls(
+            cls_score, labels, label_weights, reduction_override='none')  # 这个其实就是除以 num_total_samples
+        loss_cls = loss_cls.sum(dim=1)
+        # regression loss
+        # loss_cls = loss_cls / num_total_samples # note
+        
+        bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
+        if self.reg_decoded_bbox:
+            # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
+            # is applied directly on the decoded bounding boxes, it
+            # decodes the already encoded coordinates to absolute format.
+            raise NotImplementedError
+            anchors = anchors.reshape(-1, 4)
+            bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
+        loss_bbox = self.loss_bbox(
+            bbox_pred,
+            bbox_targets,
+            bbox_weights,
+            reduction_override='none'
+            )
+        
+        loss_bbox = loss_bbox.sum(dim=1)
+                
+        # loss_cls_pos = loss_cls_all[pos_inds]
+        # loss_cls_neg = topk_loss_cls_neg
+        
+        from mmdet.models.dense_heads import RetinaHead, RPNHead
+        if global_placeholder.mybuff_flag and not isinstance(self, RPNHead) and pos_inds.numel():
+            # 只在pos_indx有的情况下执行
+
+            if 'RetinaHead' in self.__class__.__name__:  # 
+            # if isinstance(self, RetinaHead):
+                conf_values = torch.sigmoid(cls_score)  # TODO 注意，这里是因为Retina要sigmoid！
+            else:
+                conf_values = F.softmax(cls_score, dim=1)  # TODO 注意，这里是因为Retina要sigmoid！
+            num_classes = conf_values.shape[-1]
+            if num_classes == 81 or num_classes == 21:
+                # 说明默认backbgound为81类 21类
+                pos_gtconf_idx = F.one_hot(labels, num_classes=num_classes)
+            else:
+                # 说明没有显式给出类
+                
+                num_classes = num_classes + 1
+                pos_gtconf_idx = F.one_hot(labels, num_classes=num_classes)
+                pos_gtconf_idx = pos_gtconf_idx[:, :-1]# 剔除最后一个背景类
+                
+            pos_gtconf_values, _ = (conf_values * pos_gtconf_idx).max(dim=1)  # TODO 想到一件事，是不是不能用GT来筛选正样本的结果？因为其实正样本也有错误的东西，所以就是得错
+            pos_gtconf_values = pos_gtconf_values[pos_inds]
+            
+            pos_conf_values, pos_conf_values_idx = conf_values.max(dim=1)  # TODO 好像真的是这个问题，正样本本来就是得对应到max的那个，不能经过GT筛选
+            pos_conf_values = pos_conf_values[pos_inds]
+            
+            anchors = anchors.reshape(-1, 4)
+            abs_bbox_pred = self.bbox_coder.decode(anchors, bbox_pred) # 直接解读
+            abs_bbox_targets = self.bbox_coder.decode(anchors, bbox_targets) # 直接解读
+            try:
+                pos_ious = bbox_overlaps(abs_bbox_pred[pos_inds], abs_bbox_targets[pos_inds], is_aligned=True)  # 这玩意得是ltrb坐标，好像就已经是了？？？
+            except IndexError as e:
+                print(f"inds.numel(): {pos_inds.numel()} \ninds.max(): {pos_inds.max()} \ninds.shape: {pos_inds.shape} \nabs_bbox_pred.shape: {abs_bbox_pred.shape}")
+                print(e)
+                exit()
+            if global_placeholder.mybuff_flag == 1:
+                # loss_bbox, loss_cls = loss_bbox * (1 + reg_branch_factor), loss_cls * (1 + cls_branch_factor)  # 确实这玩意应该是全局？？？
+                # loss_bbox, loss_cls = loss_bbox * (1 + cls_branch_factor), loss_cls * (1 + reg_branch_factor)  # 确实这玩意应该是全局？？？
+                # conf_values = F.softmax(cls_score, dim=1)
+                        
+                # cls_trade_off = (cls_branch_factor / (cls_branch_factor + reg_branch_factor)).detach() * 2
+                # reg_trade_off = (reg_branch_factor / (cls_branch_factor + reg_branch_factor)).detach() * 2
+                
+                # cls_trade_off = (cls_branch_factor / (cls_branch_factor + reg_branch_factor)).detach()
+                # reg_trade_off = (reg_branch_factor / (cls_branch_factor + reg_branch_factor)).detach()
+                
+                # cls_trade_off = torch.tanh(cls_branch_factor.mean()).detach()
+                # reg_trade_off = torch.tanh(reg_branch_factor.mean()).detach()
+                
+                # # cls_trade_off = 1+torch.exp(-cls_branch_factor)
+                # # reg_trade_off = 1+torch.exp(-reg_branch_factor)
+                
+                # # cls_trade_off = 1+torch.exp(-cls_branch_factor).detach()
+                # # reg_trade_off = 1+torch.exp(-reg_branch_factor).detach()
+                
+                
+                
+                # loss_cls = cls_trade_off * loss_cls
+                # loss_bbox = reg_trade_off * loss_bbox
+                 
+                # loss_cls = (1+cls_trade_off) * loss_cls  # 这更差了
+                # loss_bbox = (1+reg_trade_off) * loss_bbox
+                
+                # cls_right_mask = pos_gtconf_values == pos_conf_values
+                # cls_right_mask = torch.tensor(1.)
+                
+                
+                
+                
+                # eps = 2.220446049250313e-16# -10貌似可以，但是我狠一点
+                # # correlation = torch.pow(pos_conf_values+eps, 0.5) * torch.pow(ious+eps, 0.5) - eps
+                # correlation = torch.pow(pos_gtconf_values+eps, cls_trade_off) * torch.pow(pos_ious+eps, reg_trade_off) - eps
+                # indicator = torch.exp(1 - correlation)
+                # loss_bbox[pos_inds], loss_cls[pos_inds] = indicator * loss_bbox[pos_inds], indicator * loss_cls[pos_inds]
+                # loss_bbox[pos_inds] = ((1 + pos_ious) ** 0.8) * loss_bbox[pos_inds]
+                loss_bbox[pos_inds], loss_cls[pos_inds] = HQOD_loss(loss_bbox[pos_inds], loss_cls[pos_inds], conf_values[pos_inds], pos_gtconf_values, pos_conf_values, pos_ious, cls_branch_factor, reg_branch_factor, torch.tensor(1.))
+            elif global_placeholder.mybuff_flag == 2:
+                loss_bbox[pos_inds], loss_cls[pos_inds] = HarDet_loss(loss_bbox[pos_inds], loss_cls[pos_inds], conf_values[pos_inds], pos_gtconf_values, pos_conf_values, pos_ious, torch.tensor(1.))
+            
+        
+        
+        
+        loss_cls = loss_cls.sum() / num_total_samples
+        loss_bbox = loss_bbox.sum() / num_total_samples
+        
+        
+        # loss_bbox = loss_bbox / num_total_samples # note
+        return loss_cls, loss_bbox
+    
+    # def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights,
+    #                 bbox_targets, bbox_weights, num_total_samples):
+    #     """Compute loss of a single scale level.
+
+    #     Args:
+    #         cls_score (Tensor): Box scores for each scale level
+    #             Has shape (N, num_anchors * num_classes, H, W).
+    #         bbox_pred (Tensor): Box energies / deltas for each scale
+    #             level with shape (N, num_anchors * 4, H, W).
+    #         anchors (Tensor): Box reference for each scale level with shape
+    #             (N, num_total_anchors, 4).
+    #         labels (Tensor): Labels of each anchors with shape
+    #             (N, num_total_anchors).
+    #         label_weights (Tensor): Label weights of each anchor with shape
+    #             (N, num_total_anchors)
+    #         bbox_targets (Tensor): BBox regression targets of each anchor
+    #             weight shape (N, num_total_anchors, 4).
+    #         bbox_weights (Tensor): BBox regression loss weights of each anchor
+    #             with shape (N, num_total_anchors, 4).
+    #         num_total_samples (int): If sampling, num total samples equal to
+    #             the number of total anchors; Otherwise, it is the number of
+    #             positive anchors.
+
+    #     Returns:
+    #         dict[str, Tensor]: A dictionary of loss components.
+    #     """
+    #     # classification loss
+    #     labels = labels.reshape(-1)
+    #     label_weights = label_weights.reshape(-1)
+    #     cls_score = cls_score.permute(0, 2, 3,
+    #                                   1).reshape(-1, self.cls_out_channels)
+    #     loss_cls = self.loss_cls(
+    #         cls_score, labels, label_weights, avg_factor=num_total_samples)
+    #     # regression loss
+    #     bbox_targets = bbox_targets.reshape(-1, 4)
+    #     bbox_weights = bbox_weights.reshape(-1, 4)
+    #     bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
+    #     if self.reg_decoded_bbox:
+    #         # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
+    #         # is applied directly on the decoded bounding boxes, it
+    #         # decodes the already encoded coordinates to absolute format.
+    #         anchors = anchors.reshape(-1, 4)
+    #         bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
+    #     loss_bbox = self.loss_bbox(
+    #         bbox_pred,
+    #         bbox_targets,
+    #         bbox_weights,
+    #         avg_factor=num_total_samples)
+    #     return loss_cls, loss_bbox
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss. Default: None
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+
+        device = cls_scores[0].device
+
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels)
+        if cls_reg_targets is None:
+            return None
+        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
+         num_total_pos, num_total_neg) = cls_reg_targets
+        num_total_samples = (
+            num_total_pos + num_total_neg if self.sampling else num_total_pos)
+
+        # anchor number of multi levels
+        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+        # concat all level anchors and flags to a single tensor
+        concat_anchor_list = []
+        for i in range(len(anchor_list)):
+            concat_anchor_list.append(torch.cat(anchor_list[i]))
+        all_anchor_list = images_to_levels(concat_anchor_list,
+                                           num_level_anchors)
+
+
+        level_slicer = [len(num_level_anchors), 5, 4]  # [0]为level数；[1]为weight的个数；[2]为单level下的act个数
+        level_cls_factors = []
+        level_reg_factors = []
+        qloss_flag = global_placeholder.qloss_flag
+        
+        if False:
+            q_loss_total = []
+            cls_branch = []
+            reg_branch = []
+            for name, module in self.named_modules():
+                if hasattr(module, 'compute_qloss') and module.compute_qloss:
+                    # 挑出来量化器
+                # if 'fake_quant' in name.split('.')[-1]:
+                    # 说明是act quantizer
+                    if 'cls' in name:
+                        # 说明是cls分支的量化器
+                        # cls_branch.append(module.quantization_loss)
+                        if 'post_act' in name:
+                            # 说明是act量化器
+                            cls_branch.append([name, module.scale * 1.])
+                        else:
+                            cls_branch.append([name, module.scale * 2.])
+                            
+                    elif 'reg' in name:
+                        # 说明是reg分支的量化器
+                        # reg_branch.append(module.quantization_loss)
+                        if 'post_act' in name:
+                            # 说明是act量化器
+                            reg_branch.append([name, module.scale * 1.])
+                        else:
+                            reg_branch.append([name, module.scale * 2.])
+                        
+                    q_loss_total.append([name, module.scale])
+                    
+                # if hasattr(module, 'input'):
+                #     from plot_curve import save_distribution
+                #     title = 'input of ' + name + f' scale:{str(module.scale.data.cpu().numpy().round(5))}'
+                #     # if 'cls_convs_1_activate_1_post_act_fake_quantizer' in title:
+                #     #     save_distribution(module.input.cpu().numpy(), title)
+                #     save_distribution(module.input.cpu().numpy(), title)
+            
+            # 遍历level，整出每个level的factor
+            for it in range(level_slicer[0]):
+                cls_summation = 0
+                reg_summation = 0
+                tmp_cls_infos = cls_branch[level_slicer[1]+level_slicer[2]*it:level_slicer[1]+level_slicer[2]*(it+1)]
+                for info in tmp_cls_infos:
+                    cls_summation += info[1]
+                tmp_reg_infos = reg_branch[level_slicer[1]+level_slicer[2]*it:level_slicer[1]+level_slicer[2]*(it+1)]
+                for info in tmp_reg_infos:
+                    reg_summation += info[1]
+                
+                level_cls_factors.append(cls_summation)
+                level_reg_factors.append(reg_summation)
+        
+        if len(level_cls_factors) + len(level_reg_factors) == 0:
+            # 必须让list内有level个空list
+            level_cls_factors = [torch.tensor(1)] * len(num_level_anchors)
+            level_reg_factors = [torch.tensor(1)] * len(num_level_anchors)
+        losses_cls, losses_bbox = multi_apply(
+            self.loss_single,
+            cls_scores,
+            bbox_preds,
+            all_anchor_list,
+            labels_list,
+            label_weights_list,
+            bbox_targets_list,
+            bbox_weights_list,
+            level_cls_factors,
+            level_reg_factors,
+            num_total_samples=num_total_samples)
+        return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
+
+    def aug_test(self, feats, img_metas, rescale=False):
+        """Test function with test time augmentation.
+
+        Args:
+            feats (list[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains features for all images in the batch.
+            img_metas (list[list[dict]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch. each dict has image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
+                The first item is ``bboxes`` with shape (n, 5), where
+                5 represent (tl_x, tl_y, br_x, br_y, score).
+                The shape of the second tensor in the tuple is ``labels``
+                with shape (n,), The length of list should always be 1.
+        """
+        return self.aug_test_bboxes(feats, img_metas, rescale=rescale)
diff --git a/mmdet/models/dense_heads/ascend_anchor_head.py b/mmdet/models/dense_heads/ascend_anchor_head.py
new file mode 100644
index 0000000..7d100ba
--- /dev/null
+++ b/mmdet/models/dense_heads/ascend_anchor_head.py
@@ -0,0 +1,389 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from ...core.bbox.assigners import AscendMaxIoUAssigner
+from ...core.bbox.samplers import PseudoSampler
+from ...utils import (batch_images_to_levels, get_max_num_gt_division_factor,
+                      masked_fill)
+from ..builder import HEADS
+from .anchor_head import AnchorHead
+
+
+@HEADS.register_module()
+class AscendAnchorHead(AnchorHead):
+    """Ascend Anchor-based head (RetinaNet, SSD, etc.).
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        feat_channels (int): Number of hidden channels. Used in child classes.
+        anchor_generator (dict): Config dict for anchor generator
+        bbox_coder (dict): Config of bounding box coder.
+        reg_decoded_bbox (bool): If true, the regression loss would be
+            applied directly on decoded bounding boxes, converting both
+            the predicted boxes and regression targets to absolute
+            coordinates format. Default False. It should be `True` when
+            using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
+        loss_cls (dict): Config of classification loss.
+        loss_bbox (dict): Config of localization loss.
+        train_cfg (dict): Training config of anchor head.
+        test_cfg (dict): Testing config of anchor head.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """  # noqa: W605
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 feat_channels=256,
+                 anchor_generator=dict(
+                     type='AnchorGenerator',
+                     scales=[8, 16, 32],
+                     ratios=[0.5, 1.0, 2.0],
+                     strides=[4, 8, 16, 32, 64]),
+                 bbox_coder=dict(
+                     type='DeltaXYWHBBoxCoder',
+                     clip_border=True,
+                     target_means=(.0, .0, .0, .0),
+                     target_stds=(1.0, 1.0, 1.0, 1.0)),
+                 reg_decoded_bbox=False,
+                 loss_cls=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
+                 loss_bbox=dict(
+                     type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
+                 train_cfg=None,
+                 test_cfg=None,
+                 init_cfg=dict(type='Normal', layer='Conv2d', std=0.01)):
+        super(AscendAnchorHead, self).__init__(
+            num_classes=num_classes,
+            in_channels=in_channels,
+            feat_channels=feat_channels,
+            anchor_generator=anchor_generator,
+            bbox_coder=bbox_coder,
+            reg_decoded_bbox=reg_decoded_bbox,
+            loss_cls=loss_cls,
+            loss_bbox=loss_bbox,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            init_cfg=init_cfg)
+
+    def get_batch_gt_bboxes(self, gt_bboxes_list, num_images, gt_nums, device,
+                            max_gt_labels):
+        """Get ground truth bboxes of all image.
+
+        Args:
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+            num_images (int): The num of images.
+            gt_nums(list[int]): The ground truth bboxes num of each image.
+            device (torch.device | str): Device for returned tensors
+            max_gt_labels(int): The max ground truth bboxes num of all image.
+        Returns:
+            batch_gt_bboxes: (Tensor): Ground truth bboxes of all image.
+        """
+        # a static ground truth boxes.
+        # Save static gt. Related to Ascend. Helps improve performance
+        if not hasattr(self, 'batch_gt_bboxes'):
+            self.batch_gt_bboxes = {}
+        # a min anchor filled the excess anchor
+        if not hasattr(self, 'min_anchor'):
+            self.min_anchor = (-1354, -1344)
+        if gt_bboxes_list is None:
+            batch_gt_bboxes = None
+        else:
+            if self.batch_gt_bboxes.get(max_gt_labels) is None:
+                batch_gt_bboxes = torch.zeros((num_images, max_gt_labels, 4),
+                                              dtype=gt_bboxes_list[0].dtype,
+                                              device=device)
+                batch_gt_bboxes[:, :, :2] = self.min_anchor[0]
+                batch_gt_bboxes[:, :, 2:] = self.min_anchor[1]
+                self.batch_gt_bboxes[max_gt_labels] = batch_gt_bboxes.clone()
+            else:
+                batch_gt_bboxes = self.batch_gt_bboxes.get(
+                    max_gt_labels).clone()
+            for index_imgs, gt_bboxes in enumerate(gt_bboxes_list):
+                batch_gt_bboxes[index_imgs, :gt_nums[index_imgs]] = gt_bboxes
+        return batch_gt_bboxes
+
+    def get_batch_gt_bboxes_ignore(self, gt_bboxes_ignore_list, num_images,
+                                   gt_nums, device):
+        """Ground truth bboxes to be ignored of all image.
+
+        Args:
+            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
+                ignored.
+            num_images (int): The num of images.
+            gt_nums(list[int]): The ground truth bboxes num of each image.
+            device (torch.device | str): Device for returned tensors
+        Returns:
+            batch_gt_bboxes_ignore: (Tensor): Ground truth bboxes to be
+                ignored of all image.
+        """
+        # TODO: support gt_bboxes_ignore_list
+        if gt_bboxes_ignore_list is None:
+            batch_gt_bboxes_ignore = None
+        else:
+            raise RuntimeError('gt_bboxes_ignore not support yet')
+        return batch_gt_bboxes_ignore
+
+    def get_batch_gt_labels(self, gt_labels_list, num_images, gt_nums, device,
+                            max_gt_labels):
+        """Ground truth bboxes to be ignored of all image.
+
+        Args:
+            gt_labels_list (list[Tensor]): Ground truth labels.
+            num_images (int): The num of images.
+            gt_nums(list[int]): The ground truth bboxes num of each image.
+            device (torch.device | str): Device for returned tensors
+        Returns:
+            batch_gt_labels: (Tensor): Ground truth labels of all image.
+        """
+        if gt_labels_list is None:
+            batch_gt_labels = None
+        else:
+            batch_gt_labels = torch.zeros((num_images, max_gt_labels),
+                                          dtype=gt_labels_list[0].dtype,
+                                          device=device)
+            for index_imgs, gt_labels in enumerate(gt_labels_list):
+                batch_gt_labels[index_imgs, :gt_nums[index_imgs]] = gt_labels
+
+        return batch_gt_labels
+
+    def _get_targets_concat(self,
+                            batch_anchors,
+                            batch_valid_flags,
+                            batch_gt_bboxes,
+                            batch_gt_bboxes_ignore,
+                            batch_gt_labels,
+                            img_metas,
+                            label_channels=1,
+                            unmap_outputs=True):
+        """Compute regression and classification targets for anchors in all
+        images.
+
+        Args:
+            batch_anchors (Tensor): anchors of all image, which are
+                concatenated into a single tensor of
+                shape (num_imgs, num_anchors ,4).
+            batch_valid_flags (Tensor): valid flags of all image,
+                which are concatenated into a single tensor of
+                    shape (num_imgs, num_anchors,).
+            batch_gt_bboxes (Tensor): Ground truth bboxes of all image,
+                shape (num_imgs, max_gt_nums, 4).
+            batch_gt_bboxes_ignore (Tensor): Ground truth bboxes to be
+                ignored, shape (num_imgs, num_ignored_gts, 4).
+            batch_gt_labels (Tensor): Ground truth labels of each box,
+                shape (num_imgs, max_gt_nums,).
+            img_metas (list[dict]): Meta info of each image.
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+
+        Returns:
+            tuple:
+                batch_labels (Tensor): Labels of all level
+                batch_label_weights (Tensor): Label weights of all level
+                batch_bbox_targets (Tensor): BBox targets of all level
+                batch_bbox_weights (Tensor): BBox weights of all level
+                batch_pos_mask (Tensor): Positive samples mask in all images
+                batch_neg_mask (Tensor): Negative samples mask in all images
+                sampling_result (Sampling): The result of sampling,
+                    default: None.
+        """
+        num_imgs, num_anchors, _ = batch_anchors.size()
+        # assign gt and sample batch_anchors
+        assign_result = self.assigner.assign(
+            batch_anchors,
+            batch_gt_bboxes,
+            batch_gt_bboxes_ignore,
+            None if self.sampling else batch_gt_labels,
+            batch_bboxes_ignore_mask=batch_valid_flags)
+        # TODO: support sampling_result
+        sampling_result = None
+        batch_pos_mask = assign_result.batch_pos_mask
+        batch_neg_mask = assign_result.batch_neg_mask
+        batch_anchor_gt_indes = assign_result.batch_anchor_gt_indes
+        batch_anchor_gt_labels = assign_result.batch_anchor_gt_labels
+
+        batch_anchor_gt_bboxes = torch.zeros(
+            batch_anchors.size(),
+            dtype=batch_anchors.dtype,
+            device=batch_anchors.device)
+        for index_imgs in range(num_imgs):
+            batch_anchor_gt_bboxes[index_imgs] = torch.index_select(
+                batch_gt_bboxes[index_imgs], 0,
+                batch_anchor_gt_indes[index_imgs])
+
+        batch_bbox_targets = torch.zeros_like(batch_anchors)
+        batch_bbox_weights = torch.zeros_like(batch_anchors)
+        batch_labels = batch_anchors.new_full((num_imgs, num_anchors),
+                                              self.num_classes,
+                                              dtype=torch.int)
+        batch_label_weights = batch_anchors.new_zeros((num_imgs, num_anchors),
+                                                      dtype=torch.float)
+
+        if not self.reg_decoded_bbox:
+            batch_pos_bbox_targets = self.bbox_coder.encode(
+                batch_anchors, batch_anchor_gt_bboxes)
+        else:
+            batch_pos_bbox_targets = batch_anchor_gt_bboxes
+
+        batch_bbox_targets = masked_fill(batch_bbox_targets,
+                                         batch_pos_mask.unsqueeze(2),
+                                         batch_pos_bbox_targets)
+        batch_bbox_weights = masked_fill(batch_bbox_weights,
+                                         batch_pos_mask.unsqueeze(2), 1.0)
+        if batch_gt_labels is None:
+            batch_labels = masked_fill(batch_labels, batch_pos_mask, 0.0)
+        else:
+            batch_labels = masked_fill(batch_labels, batch_pos_mask,
+                                       batch_anchor_gt_labels)
+        if self.train_cfg.pos_weight <= 0:
+            batch_label_weights = masked_fill(batch_label_weights,
+                                              batch_pos_mask, 1.0)
+        else:
+            batch_label_weights = masked_fill(batch_label_weights,
+                                              batch_pos_mask,
+                                              self.train_cfg.pos_weight)
+        batch_label_weights = masked_fill(batch_label_weights, batch_neg_mask,
+                                          1.0)
+        return (batch_labels, batch_label_weights, batch_bbox_targets,
+                batch_bbox_weights, batch_pos_mask, batch_neg_mask,
+                sampling_result)
+
+    def get_targets(self,
+                    anchor_list,
+                    valid_flag_list,
+                    gt_bboxes_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None,
+                    gt_labels_list=None,
+                    label_channels=1,
+                    unmap_outputs=True,
+                    return_sampling_results=False,
+                    return_level=True):
+        """Compute regression and classification targets for anchors in
+        multiple images.
+
+        Args:
+            anchor_list (list[list[Tensor]]): Multi level anchors of each
+                image. The outer list indicates images, and the inner list
+                corresponds to feature levels of the image. Each element of
+                the inner list is a tensor of shape (num_anchors, 4).
+            valid_flag_list (list[list[Tensor]]): Multi level valid flags of
+                each image. The outer list indicates images, and the inner list
+                corresponds to feature levels of the image. Each element of
+                the inner list is a tensor of shape (num_anchors, )
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+            img_metas (list[dict]): Meta info of each image.
+            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
+                ignored.
+            gt_labels_list (list[Tensor]): Ground truth labels of each box.
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+            return_sampling_results (bool): Whether to return the result of
+                sample.
+            return_level (bool): Whether to map outputs back to the levels
+                of feature map sizes.
+        Returns:
+            tuple: Usually returns a tuple containing learning targets.
+
+                - labels_list (list[Tensor]): Labels of each level.
+                - label_weights_list (list[Tensor]): Label weights of each
+                  level.
+                - bbox_targets_list (list[Tensor]): BBox targets of each level.
+                - bbox_weights_list (list[Tensor]): BBox weights of each level.
+                - num_total_pos (int): Number of positive samples in all
+                  images.
+                - num_total_neg (int): Number of negative samples in all
+                  images.
+
+            additional_returns: This function enables user-defined returns from
+                `self._get_targets_single`. These returns are currently refined
+                to properties at each feature map (i.e. having HxW dimension).
+                The results will be concatenated after the end
+        """
+        assert gt_bboxes_ignore_list is None
+        assert unmap_outputs is True
+        assert return_sampling_results is False
+        assert self.train_cfg.allowed_border < 0
+        assert isinstance(self.assigner, AscendMaxIoUAssigner)
+        assert isinstance(self.sampler, PseudoSampler)
+        num_imgs = len(img_metas)
+        assert len(anchor_list) == len(valid_flag_list) == num_imgs
+
+        device = anchor_list[0][0].device
+        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+
+        batch_anchor_list = []
+        batch_valid_flag_list = []
+        for i in range(num_imgs):
+            assert len(anchor_list[i]) == len(valid_flag_list[i])
+            batch_anchor_list.append(torch.cat(anchor_list[i]))
+            batch_valid_flag_list.append(torch.cat(valid_flag_list[i]))
+        batch_anchors = torch.cat(
+            [torch.unsqueeze(anchor, 0) for anchor in batch_anchor_list], 0)
+        batch_valid_flags = torch.cat([
+            torch.unsqueeze(batch_valid_flag, 0)
+            for batch_valid_flag in batch_valid_flag_list
+        ], 0)
+
+        gt_nums = [len(gt_bbox) for gt_bbox in gt_bboxes_list]
+        max_gt_nums = get_max_num_gt_division_factor(gt_nums)
+        batch_gt_bboxes = self.get_batch_gt_bboxes(gt_bboxes_list, num_imgs,
+                                                   gt_nums, device,
+                                                   max_gt_nums)
+        batch_gt_bboxes_ignore = self.get_batch_gt_bboxes_ignore(
+            gt_bboxes_ignore_list, num_imgs, gt_nums, device)
+        batch_gt_labels = self.get_batch_gt_labels(gt_labels_list, num_imgs,
+                                                   gt_nums, device,
+                                                   max_gt_nums)
+
+        results = self._get_targets_concat(
+            batch_anchors,
+            batch_valid_flags,
+            batch_gt_bboxes,
+            batch_gt_bboxes_ignore,
+            batch_gt_labels,
+            img_metas,
+            label_channels=label_channels,
+            unmap_outputs=unmap_outputs)
+
+        (batch_labels, batch_label_weights, batch_bbox_targets,
+         batch_bbox_weights, batch_pos_mask, batch_neg_mask,
+         sampling_result) = results[:7]
+        rest_results = list(results[7:])  # user-added return values
+
+        # sampled anchors of all images
+        min_num = torch.ones((num_imgs, ),
+                             dtype=torch.long,
+                             device=batch_pos_mask.device)
+        num_total_pos = torch.sum(
+            torch.max(torch.sum(batch_pos_mask, dim=1), min_num))
+        num_total_neg = torch.sum(
+            torch.max(torch.sum(batch_neg_mask, dim=1), min_num))
+        if return_level is True:
+            labels_list = batch_images_to_levels(batch_labels,
+                                                 num_level_anchors)
+            label_weights_list = batch_images_to_levels(
+                batch_label_weights, num_level_anchors)
+            bbox_targets_list = batch_images_to_levels(batch_bbox_targets,
+                                                       num_level_anchors)
+            bbox_weights_list = batch_images_to_levels(batch_bbox_weights,
+                                                       num_level_anchors)
+            res = (labels_list, label_weights_list, bbox_targets_list,
+                   bbox_weights_list, num_total_pos, num_total_neg)
+            if return_sampling_results:
+                res = res + (sampling_result, )
+            for i, r in enumerate(rest_results):  # user-added return values
+                rest_results[i] = batch_images_to_levels(r, num_level_anchors)
+
+            return res + tuple(rest_results)
+        else:
+            res = (batch_labels, batch_label_weights, batch_bbox_targets,
+                   batch_bbox_weights, batch_pos_mask, batch_neg_mask,
+                   sampling_result, num_total_pos, num_total_neg,
+                   batch_anchors)
+            return res
diff --git a/mmdet/models/dense_heads/ascend_retina_head.py b/mmdet/models/dense_heads/ascend_retina_head.py
new file mode 100644
index 0000000..159fe75
--- /dev/null
+++ b/mmdet/models/dense_heads/ascend_retina_head.py
@@ -0,0 +1,115 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import HEADS
+from .ascend_anchor_head import AscendAnchorHead
+from .retina_head import RetinaHead
+
+
+@HEADS.register_module()
+class AscendRetinaHead(RetinaHead, AscendAnchorHead):
+    r"""An anchor-based head used in `RetinaNet
+    <https://arxiv.org/pdf/1708.02002.pdf>`_.
+
+    The head contains two subnetworks. The first classifies anchor boxes and
+    the second regresses deltas for the anchors.
+
+    Example:
+        >>> import torch
+        >>> self = RetinaHead(11, 7)
+        >>> x = torch.rand(1, 7, 32, 32)
+        >>> cls_score, bbox_pred = self.forward_single(x)
+        >>> # Each anchor predicts a score for each class except background
+        >>> cls_per_anchor = cls_score.shape[1] / self.num_anchors
+        >>> box_per_anchor = bbox_pred.shape[1] / self.num_anchors
+        >>> assert cls_per_anchor == (self.num_classes)
+        >>> assert box_per_anchor == 4
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 stacked_convs=4,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 anchor_generator=dict(
+                     type='AnchorGenerator',
+                     octave_base_scale=4,
+                     scales_per_octave=3,
+                     ratios=[0.5, 1.0, 2.0],
+                     strides=[8, 16, 32, 64, 128]),
+                 init_cfg=dict(
+                     type='Normal',
+                     layer='Conv2d',
+                     std=0.01,
+                     override=dict(
+                         type='Normal',
+                         name='retina_cls',
+                         std=0.01,
+                         bias_prob=0.01)),
+                 **kwargs):
+        super(AscendRetinaHead, self).__init__(
+            num_classes=num_classes,
+            in_channels=in_channels,
+            stacked_convs=stacked_convs,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            anchor_generator=anchor_generator,
+            init_cfg=init_cfg,
+            **kwargs)
+
+    def get_targets(self,
+                    anchor_list,
+                    valid_flag_list,
+                    gt_bboxes_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None,
+                    gt_labels_list=None,
+                    label_channels=1,
+                    unmap_outputs=True,
+                    return_sampling_results=False,
+                    return_level=True):
+        """Compute regression and classification targets for anchors in
+        multiple images.
+
+        Args:
+            anchor_list (list[list[Tensor]]): Multi level anchors of each
+                image. The outer list indicates images, and the inner list
+                corresponds to feature levels of the image. Each element of
+                the inner list is a tensor of shape (num_anchors, 4).
+            valid_flag_list (list[list[Tensor]]): Multi level valid flags of
+                each image. The outer list indicates images, and the inner list
+                corresponds to feature levels of the image. Each element of
+                the inner list is a tensor of shape (num_anchors, )
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+            img_metas (list[dict]): Meta info of each image.
+            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
+                ignored.
+            gt_labels_list (list[Tensor]): Ground truth labels of each box.
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+            return_sampling_results (bool): Whether to return the result of
+                sample.
+            return_level (bool): Whether to map outputs back to the levels
+                of feature map sizes.
+        Returns:
+            tuple: Usually returns a tuple containing learning targets.
+
+                - labels_list (list[Tensor]): Labels of each level.
+                - label_weights_list (list[Tensor]): Label weights of each
+                  level.
+                - bbox_targets_list (list[Tensor]): BBox targets of each level.
+                - bbox_weights_list (list[Tensor]): BBox weights of each level.
+                - num_total_pos (int): Number of positive samples in all
+                  images.
+                - num_total_neg (int): Number of negative samples in all
+                  images.
+
+            additional_returns: This function enables user-defined returns from
+                `self._get_targets_single`. These returns are currently refined
+                to properties at each feature map (i.e. having HxW dimension).
+                The results will be concatenated after the end
+        """
+        return AscendAnchorHead.get_targets(
+            self, anchor_list, valid_flag_list, gt_bboxes_list, img_metas,
+            gt_bboxes_ignore_list, gt_labels_list, label_channels,
+            unmap_outputs, return_sampling_results, return_level)
diff --git a/mmdet/models/dense_heads/ascend_ssd_head.py b/mmdet/models/dense_heads/ascend_ssd_head.py
new file mode 100644
index 0000000..9e326b4
--- /dev/null
+++ b/mmdet/models/dense_heads/ascend_ssd_head.py
@@ -0,0 +1,328 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn.functional as F
+from mmcv.runner import force_fp32
+
+from ..builder import HEADS
+from ..losses import smooth_l1_loss
+from .ascend_anchor_head import AscendAnchorHead
+from .ssd_head import SSDHead
+
+
+@HEADS.register_module()
+class AscendSSDHead(SSDHead, AscendAnchorHead):
+    """Ascend SSD head used in https://arxiv.org/abs/1512.02325.
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        stacked_convs (int): Number of conv layers in cls and reg tower.
+            Default: 0.
+        feat_channels (int): Number of hidden channels when stacked_convs
+            > 0. Default: 256.
+        use_depthwise (bool): Whether to use DepthwiseSeparableConv.
+            Default: False.
+        conv_cfg (dict): Dictionary to construct and config conv layer.
+            Default: None.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+            Default: None.
+        act_cfg (dict): Dictionary to construct and config activation layer.
+            Default: None.
+        anchor_generator (dict): Config dict for anchor generator
+        bbox_coder (dict): Config of bounding box coder.
+        reg_decoded_bbox (bool): If true, the regression loss would be
+            applied directly on decoded bounding boxes, converting both
+            the predicted boxes and regression targets to absolute
+            coordinates format. Default False. It should be `True` when
+            using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
+        train_cfg (dict): Training config of anchor head.
+        test_cfg (dict): Testing config of anchor head.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """  # noqa: W605
+
+    def __init__(self,
+                 num_classes=80,
+                 in_channels=(512, 1024, 512, 256, 256, 256),
+                 stacked_convs=0,
+                 feat_channels=256,
+                 use_depthwise=False,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 act_cfg=None,
+                 anchor_generator=dict(
+                     type='SSDAnchorGenerator',
+                     scale_major=False,
+                     input_size=300,
+                     strides=[8, 16, 32, 64, 100, 300],
+                     ratios=([2], [2, 3], [2, 3], [2, 3], [2], [2]),
+                     basesize_ratio_range=(0.1, 0.9)),
+                 bbox_coder=dict(
+                     type='DeltaXYWHBBoxCoder',
+                     clip_border=True,
+                     target_means=[.0, .0, .0, .0],
+                     target_stds=[1.0, 1.0, 1.0, 1.0],
+                 ),
+                 reg_decoded_bbox=False,
+                 train_cfg=None,
+                 test_cfg=None,
+                 init_cfg=dict(
+                     type='Xavier',
+                     layer='Conv2d',
+                     distribution='uniform',
+                     bias=0)):
+        super(AscendSSDHead, self).__init__(
+            num_classes=num_classes,
+            in_channels=in_channels,
+            stacked_convs=stacked_convs,
+            feat_channels=feat_channels,
+            use_depthwise=use_depthwise,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg,
+            anchor_generator=anchor_generator,
+            bbox_coder=bbox_coder,
+            reg_decoded_bbox=reg_decoded_bbox,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            init_cfg=init_cfg)
+        assert self.reg_decoded_bbox is False, \
+            'reg_decoded_bbox only support False now.'
+
+    def get_static_anchors(self, featmap_sizes, img_metas, device='cuda'):
+        """Get static anchors according to feature map sizes.
+
+        Args:
+            featmap_sizes (list[tuple]): Multi-level feature map sizes.
+            img_metas (list[dict]): Image meta info.
+            device (torch.device | str): Device for returned tensors
+
+        Returns:
+            tuple:
+                anchor_list (list[Tensor]): Anchors of each image.
+                valid_flag_list (list[Tensor]): Valid flags of each image.
+        """
+        if not hasattr(self, 'static_anchors') or \
+                not hasattr(self, 'static_valid_flags'):
+            static_anchors, static_valid_flags = self.get_anchors(
+                featmap_sizes, img_metas, device)
+            self.static_anchors = static_anchors
+            self.static_valid_flags = static_valid_flags
+        return self.static_anchors, self.static_valid_flags
+
+    def get_targets(self,
+                    anchor_list,
+                    valid_flag_list,
+                    gt_bboxes_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None,
+                    gt_labels_list=None,
+                    label_channels=1,
+                    unmap_outputs=True,
+                    return_sampling_results=False,
+                    return_level=True):
+        """Compute regression and classification targets for anchors in
+        multiple images.
+
+        Args:
+            anchor_list (list[list[Tensor]]): Multi level anchors of each
+                image. The outer list indicates images, and the inner list
+                corresponds to feature levels of the image. Each element of
+                the inner list is a tensor of shape (num_anchors, 4).
+            valid_flag_list (list[list[Tensor]]): Multi level valid flags of
+                each image. The outer list indicates images, and the inner list
+                corresponds to feature levels of the image. Each element of
+                the inner list is a tensor of shape (num_anchors, )
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+            img_metas (list[dict]): Meta info of each image.
+            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
+                ignored.
+            gt_labels_list (list[Tensor]): Ground truth labels of each box.
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+            return_sampling_results (bool): Whether to return the result of
+                sample.
+            return_level (bool): Whether to map outputs back to the levels
+                of feature map sizes.
+        Returns:
+            tuple: Usually returns a tuple containing learning targets.
+
+                - labels_list (list[Tensor]): Labels of each level.
+                - label_weights_list (list[Tensor]): Label weights of each
+                  level.
+                - bbox_targets_list (list[Tensor]): BBox targets of each level.
+                - bbox_weights_list (list[Tensor]): BBox weights of each level.
+                - num_total_pos (int): Number of positive samples in all
+                  images.
+                - num_total_neg (int): Number of negative samples in all
+                  images.
+
+            additional_returns: This function enables user-defined returns from
+                `self._get_targets_single`. These returns are currently refined
+                to properties at each feature map (i.e. having HxW dimension).
+                The results will be concatenated after the end
+        """
+        return AscendAnchorHead.get_targets(
+            self,
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes_list,
+            img_metas,
+            gt_bboxes_ignore_list,
+            gt_labels_list,
+            label_channels,
+            unmap_outputs,
+            return_sampling_results,
+            return_level,
+        )
+
+    def batch_loss(self, batch_cls_score, batch_bbox_pred, batch_anchor,
+                   batch_labels, batch_label_weights, batch_bbox_targets,
+                   batch_bbox_weights, batch_pos_mask, batch_neg_mask,
+                   num_total_samples):
+        """Compute loss of all images.
+
+        Args:
+            batch_cls_score (Tensor): Box scores for all image
+                Has shape (num_imgs, num_total_anchors, num_classes).
+            batch_bbox_pred (Tensor): Box energies / deltas for all image
+                level with shape (num_imgs, num_total_anchors, 4).
+            batch_anchor (Tensor): Box reference for all image with shape
+                (num_imgs, num_total_anchors, 4).
+            batch_labels (Tensor): Labels of all anchors with shape
+                (num_imgs, num_total_anchors,).
+            batch_label_weights (Tensor): Label weights of all anchor with
+                shape (num_imgs, num_total_anchors,)
+            batch_bbox_targets (Tensor): BBox regression targets of all anchor
+                weight shape (num_imgs, num_total_anchors, 4).
+            batch_bbox_weights (Tensor): BBox regression loss weights of
+                all anchor with shape (num_imgs, num_total_anchors, 4).
+            batch_pos_mask (Tensor): Positive samples mask in all images.
+            batch_neg_mask (Tensor): negative samples mask in all images.
+            num_total_samples (int): If sampling, num total samples equal to
+                the number of total anchors; Otherwise, it is the number of
+                positive anchors.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        num_images, num_anchors, _ = batch_anchor.size()
+
+        batch_loss_cls_all = F.cross_entropy(
+            batch_cls_score.view((-1, self.cls_out_channels)),
+            batch_labels.view(-1),
+            reduction='none').view(
+                batch_label_weights.size()) * batch_label_weights
+        # # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+        batch_num_pos_samples = torch.sum(batch_pos_mask, dim=1)
+        batch_num_neg_samples = \
+            self.train_cfg.neg_pos_ratio * batch_num_pos_samples
+
+        batch_num_neg_samples_max = torch.sum(batch_neg_mask, dim=1)
+        batch_num_neg_samples = torch.min(batch_num_neg_samples,
+                                          batch_num_neg_samples_max)
+
+        batch_topk_loss_cls_neg, _ = torch.topk(
+            batch_loss_cls_all * batch_neg_mask, k=num_anchors, dim=1)
+        batch_loss_cls_pos = torch.sum(
+            batch_loss_cls_all * batch_pos_mask, dim=1)
+
+        anchor_index = torch.arange(
+            end=num_anchors, dtype=torch.float,
+            device=batch_anchor.device).view((1, -1))
+        topk_loss_neg_mask = (anchor_index < batch_num_neg_samples.view(
+            -1, 1)).float()
+
+        batch_loss_cls_neg = torch.sum(
+            batch_topk_loss_cls_neg * topk_loss_neg_mask, dim=1)
+        loss_cls = \
+            (batch_loss_cls_pos + batch_loss_cls_neg) / num_total_samples
+
+        if self.reg_decoded_bbox:
+            # TODO: support self.reg_decoded_bbox is True
+            raise RuntimeError
+
+        loss_bbox_all = smooth_l1_loss(
+            batch_bbox_pred,
+            batch_bbox_targets,
+            batch_bbox_weights,
+            reduction='none',
+            beta=self.train_cfg.smoothl1_beta,
+            avg_factor=num_total_samples)
+        eps = torch.finfo(torch.float32).eps
+
+        sum_dim = (i for i in range(1, len(loss_bbox_all.size())))
+        loss_bbox = loss_bbox_all.sum(tuple(sum_dim)) / (
+            num_total_samples + eps)
+        return loss_cls[None], loss_bbox
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            gt_bboxes (list[Tensor]): each item are the truth boxes for each
+                image in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+
+        device = cls_scores[0].device
+
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=1,
+            unmap_outputs=True,
+            return_level=False)
+        if cls_reg_targets is None:
+            return None
+
+        (batch_labels, batch_label_weights, batch_bbox_targets,
+         batch_bbox_weights, batch_pos_mask, batch_neg_mask, sampling_result,
+         num_total_pos, num_total_neg, batch_anchors) = cls_reg_targets
+
+        num_imgs = len(img_metas)
+        batch_cls_score = torch.cat([
+            s.permute(0, 2, 3, 1).reshape(num_imgs, -1, self.cls_out_channels)
+            for s in cls_scores
+        ], 1)
+
+        batch_bbox_pred = torch.cat([
+            b.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4) for b in bbox_preds
+        ], -2)
+
+        batch_losses_cls, batch_losses_bbox = self.batch_loss(
+            batch_cls_score, batch_bbox_pred, batch_anchors, batch_labels,
+            batch_label_weights, batch_bbox_targets, batch_bbox_weights,
+            batch_pos_mask, batch_neg_mask, num_total_pos)
+        losses_cls = [
+            batch_losses_cls[:, index_imgs] for index_imgs in range(num_imgs)
+        ]
+        losses_bbox = [losses_bbox for losses_bbox in batch_losses_bbox]
+        return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
diff --git a/mmdet/models/dense_heads/atss_head.py b/mmdet/models/dense_heads/atss_head.py
new file mode 100644
index 0000000..c2edd57
--- /dev/null
+++ b/mmdet/models/dense_heads/atss_head.py
@@ -0,0 +1,765 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule, Scale
+from mmcv.runner import force_fp32
+
+from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler,
+                        images_to_levels, multi_apply, reduce_mean, unmap)
+from ..builder import HEADS, build_loss
+from .anchor_head import AnchorHead
+from mmdet.core.bbox.iou_calculators import bbox_overlaps
+from my_equation import *
+import global_placeholder
+
+@HEADS.register_module()
+class ATSSHead(AnchorHead):
+    """Bridging the Gap Between Anchor-based and Anchor-free Detection via
+    Adaptive Training Sample Selection.
+
+    ATSS head structure is similar with FCOS, however ATSS use anchor boxes
+    and assign label by Adaptive Training Sample Selection instead max-iou.
+
+    https://arxiv.org/abs/1912.02424
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 pred_kernel_size=3,
+                 stacked_convs=4,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
+                 reg_decoded_bbox=True,
+                 loss_centerness=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
+                 init_cfg=dict(
+                     type='Normal',
+                     layer='Conv2d',
+                     std=0.01,
+                     override=dict(
+                         type='Normal',
+                         name='atss_cls',
+                         std=0.01,
+                         bias_prob=0.01)),
+                 **kwargs):
+        self.pred_kernel_size = pred_kernel_size
+        self.stacked_convs = stacked_convs
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        super(ATSSHead, self).__init__(
+            num_classes,
+            in_channels,
+            reg_decoded_bbox=reg_decoded_bbox,
+            init_cfg=init_cfg,
+            **kwargs)
+
+        self.sampling = False
+        if self.train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            # SSD sampling=False so use PseudoSampler
+            sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+        self.loss_centerness = build_loss(loss_centerness)
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.relu = nn.ReLU(inplace=True)
+        self.cls_convs = nn.ModuleList()
+        self.reg_convs = nn.ModuleList()
+        for i in range(self.stacked_convs):
+            chn = self.in_channels if i == 0 else self.feat_channels
+            self.cls_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+            self.reg_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+        pred_pad_size = self.pred_kernel_size // 2
+        self.atss_cls = nn.Conv2d(
+            self.feat_channels,
+            self.num_anchors * self.cls_out_channels,
+            self.pred_kernel_size,
+            padding=pred_pad_size)
+        self.atss_reg = nn.Conv2d(
+            self.feat_channels,
+            self.num_base_priors * 4,
+            self.pred_kernel_size,
+            padding=pred_pad_size)
+        self.atss_centerness = nn.Conv2d(
+            self.feat_channels,
+            self.num_base_priors * 1,
+            self.pred_kernel_size,
+            padding=pred_pad_size)
+        self.scales = nn.ModuleList(
+            [Scale(1.0) for _ in self.prior_generator.strides])
+
+    # def forward(self, feats):
+    #     """Forward features from the upstream network.
+
+    #     Args:
+    #         feats (tuple[Tensor]): Features from the upstream network, each is
+    #             a 4D-tensor.
+
+    #     Returns:
+    #         tuple: Usually a tuple of classification scores and bbox prediction
+    #             cls_scores (list[Tensor]): Classification scores for all scale
+    #                 levels, each is a 4D-tensor, the channels number is
+    #                 num_anchors * num_classes.
+    #             bbox_preds (list[Tensor]): Box energies / deltas for all scale
+    #                 levels, each is a 4D-tensor, the channels number is
+    #                 num_anchors * 4.
+    #     """
+    #     return multi_apply(self.forward_single, feats, self.scales)
+    
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple: Usually a tuple of classification scores and bbox prediction
+                cls_scores (list[Tensor]): Classification scores for all scale
+                    levels, each is a 4D-tensor, the channels number is
+                    num_anchors * num_classes.
+                bbox_preds (list[Tensor]): Box energies / deltas for all scale
+                    levels, each is a 4D-tensor, the channels number is
+                    num_anchors * 4.
+        """
+        cls_scores = []
+        bbox_preds = []
+        centernesses = []
+        if hasattr(self,'in_num') and self.in_num == 5:
+            feats = [feats[0], feats[1], feats[2], feats[3], feats[4]]
+        elif hasattr(self,'in_num'):
+            raise NotImplementedError
+        else:
+            self.in_num = 5
+            feats = [feats[0], feats[1], feats[2], feats[3], feats[4]]
+        
+        for x, scale in zip(feats, self.scales):
+            cls_feat = x
+            reg_feat = x
+            for cls_conv in self.cls_convs:
+                cls_feat = cls_conv(cls_feat)
+            for reg_conv in self.reg_convs:
+                reg_feat = reg_conv(reg_feat)
+            cls_score = self.atss_cls(cls_feat)
+            # we just follow atss, not apply exp in bbox_pred
+            bbox_pred = scale(self.atss_reg(reg_feat)).float()
+            centerness = self.atss_centerness(reg_feat)
+            cls_scores.append(cls_score)
+            bbox_preds.append(bbox_pred)
+            centernesses.append(centerness)
+            
+        return cls_scores, bbox_preds, centernesses
+    
+
+    def forward_single(self, x, scale):
+        """Forward feature of a single scale level.
+
+        Args:
+            x (Tensor): Features of a single scale level.
+            scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
+                the bbox prediction.
+
+        Returns:
+            tuple:
+                cls_score (Tensor): Cls scores for a single scale level
+                    the channels number is num_anchors * num_classes.
+                bbox_pred (Tensor): Box energies / deltas for a single scale
+                    level, the channels number is num_anchors * 4.
+                centerness (Tensor): Centerness for a single scale level, the
+                    channel number is (N, num_anchors * 1, H, W).
+        """
+        cls_feat = x
+        reg_feat = x
+        for cls_conv in self.cls_convs:
+            cls_feat = cls_conv(cls_feat)
+        for reg_conv in self.reg_convs:
+            reg_feat = reg_conv(reg_feat)
+        cls_score = self.atss_cls(cls_feat)
+        # we just follow atss, not apply exp in bbox_pred
+        bbox_pred = scale(self.atss_reg(reg_feat)).float()
+        centerness = self.atss_centerness(reg_feat)
+        return cls_score, bbox_pred, centerness
+
+    def loss_single(self, anchors, cls_score, bbox_pred, centerness, labels,
+                    label_weights, bbox_targets, cls_branch_factor, reg_branch_factor, centerness_branch_factor, num_total_samples):
+        """Compute loss of a single scale level.
+
+        Args:
+            cls_score (Tensor): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W).
+            bbox_pred (Tensor): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W).
+            anchors (Tensor): Box reference for each scale level with shape
+                (N, num_total_anchors, 4).
+            labels (Tensor): Labels of each anchors with shape
+                (N, num_total_anchors).
+            label_weights (Tensor): Label weights of each anchor with shape
+                (N, num_total_anchors)
+            bbox_targets (Tensor): BBox regression targets of each anchor
+                weight shape (N, num_total_anchors, 4).
+            num_total_samples (int): Number os positive samples that is
+                reduced over all GPUs.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+
+        anchors = anchors.reshape(-1, 4)
+        cls_score = cls_score.permute(0, 2, 3, 1).reshape(
+            -1, self.cls_out_channels).contiguous()
+        bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
+        centerness = centerness.permute(0, 2, 3, 1).reshape(-1)
+        bbox_targets = bbox_targets.reshape(-1, 4)
+        labels = labels.reshape(-1)
+        label_weights = label_weights.reshape(-1)
+
+        # classification loss
+        loss_cls = self.loss_cls(
+            cls_score, labels, label_weights, reduction_override='none')
+        loss_cls = loss_cls.sum(dim=1)
+
+        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+        bg_class_ind = self.num_classes
+        pos_inds = ((labels >= 0)
+                    & (labels < bg_class_ind)).nonzero().squeeze(1)
+
+        if len(pos_inds) > 0:
+            pos_bbox_targets = bbox_targets[pos_inds]
+            pos_bbox_pred = bbox_pred[pos_inds]
+            pos_anchors = anchors[pos_inds]
+            pos_centerness = centerness[pos_inds]
+
+            centerness_targets = self.centerness_target(
+                pos_anchors, pos_bbox_targets)
+            pos_decode_bbox_pred = self.bbox_coder.decode(
+                pos_anchors, pos_bbox_pred)
+
+            # regression loss
+            loss_bbox = self.loss_bbox(
+                pos_decode_bbox_pred,
+                pos_bbox_targets,
+                weight=centerness_targets,
+                reduction_override='none')
+            # loss_bbox = loss_bbox.sum(dim=1)
+            
+
+            # centerness loss
+            loss_centerness = self.loss_centerness(
+                pos_centerness,
+                centerness_targets,
+                reduction_override='none')
+            # loss_centerness = loss_centerness.sum(dim=1)
+
+        else:
+            loss_bbox = bbox_pred.sum() * 0
+            loss_centerness = centerness.sum() * 0
+            centerness_targets = bbox_targets.new_tensor(0.)
+
+        if global_placeholder.mybuff_flag and pos_inds.numel():
+            # 只在pos_indx有的情况下执行
+
+            conf_values = torch.sigmoid(cls_score)  # TODO 注意，这里是因为Retina要sigmoid！
+            
+            num_classes = conf_values.shape[-1]
+            if num_classes == 81 or num_classes == 21:
+                # 说明默认backbgound为81类 21类
+                pos_gtconf_idx = F.one_hot(labels, num_classes=num_classes)
+            else:
+                # 说明没有显式给出类
+                
+                num_classes = num_classes + 1
+                pos_gtconf_idx = F.one_hot(labels, num_classes=num_classes)
+                pos_gtconf_idx = pos_gtconf_idx[:, :-1]# 剔除最后一个背景类
+                
+            pos_gtconf_values, _ = (conf_values * pos_gtconf_idx).max(dim=1)  # TODO 想到一件事，是不是不能用GT来筛选正样本的结果？因为其实正样本也有错误的东西，所以就是得错
+            pos_gtconf_values = pos_gtconf_values[pos_inds]
+            
+            pos_conf_values, pos_conf_values_idx = conf_values.max(dim=1)  # TODO 好像真的是这个问题，正样本本来就是得对应到max的那个，不能经过GT筛选
+            pos_conf_values = pos_conf_values[pos_inds]
+
+            try:
+                pos_ious = bbox_overlaps(pos_decode_bbox_pred, pos_bbox_targets, is_aligned=True)  # 这玩意得是ltrb坐标，好像就已经是了？？？
+            except IndexError as e:
+                print(f"inds.numel(): {pos_inds.numel()} \ninds.max(): {pos_inds.max()} \ninds.shape: {pos_inds.shape}")
+                print(e)
+                exit()
+            if global_placeholder.mybuff_flag == 1:
+                # loss_bbox, loss_cls = loss_bbox * (1 + reg_branch_factor), loss_cls * (1 + cls_branch_factor)  # 确实这玩意应该是全局？？？
+                # loss_bbox, loss_cls = loss_bbox * (1 + cls_branch_factor), loss_cls * (1 + reg_branch_factor)  # 确实这玩意应该是全局？？？
+                # conf_values = F.softmax(cls_score, dim=1)
+                        
+                # cls_trade_off = (cls_branch_factor / (cls_branch_factor + reg_branch_factor + centerness_branch_factor)).detach() * 3
+                # reg_trade_off = (reg_branch_factor / (cls_branch_factor + reg_branch_factor + centerness_branch_factor)).detach() * 3
+                # centerness_trade_off = (centerness_branch_factor / (cls_branch_factor + reg_branch_factor + centerness_branch_factor)).detach() * 3
+                
+                # # loss_cls = cls_trade_off * loss_cls
+                # # loss_bbox = reg_trade_off * loss_bbox
+                # loss_cls = cls_trade_off * loss_cls
+                # loss_bbox = reg_trade_off * loss_bbox
+                # loss_centerness = centerness_trade_off * loss_centerness
+                
+                loss_bbox, loss_cls[pos_inds] = HQOD_loss(loss_bbox, loss_cls[pos_inds], conf_values[pos_inds], pos_gtconf_values, pos_conf_values, pos_ious, cls_branch_factor, reg_branch_factor, torch.tensor(1.))
+            elif global_placeholder.mybuff_flag == 2:
+                loss_bbox, loss_cls[pos_inds] = HarDet_loss(loss_bbox, loss_cls[pos_inds], conf_values[pos_inds], pos_gtconf_values, pos_conf_values, pos_ious, torch.tensor(1.))
+            
+        
+
+
+        loss_cls = loss_cls.sum() / num_total_samples
+        loss_bbox = loss_bbox.sum() / 1.0
+        loss_centerness = loss_centerness.sum() / num_total_samples
+
+
+
+        return loss_cls, loss_bbox, loss_centerness, centerness_targets.sum()
+
+    # def loss_single(self, anchors, cls_score, bbox_pred, centerness, labels,
+    #                 label_weights, bbox_targets, level_cls_factor, level_reg_factor, level_centerness_factor, num_total_samples):
+    #     """Compute loss of a single scale level.
+
+    #     Args:
+    #         cls_score (Tensor): Box scores for each scale level
+    #             Has shape (N, num_anchors * num_classes, H, W).
+    #         bbox_pred (Tensor): Box energies / deltas for each scale
+    #             level with shape (N, num_anchors * 4, H, W).
+    #         anchors (Tensor): Box reference for each scale level with shape
+    #             (N, num_total_anchors, 4).
+    #         labels (Tensor): Labels of each anchors with shape
+    #             (N, num_total_anchors).
+    #         label_weights (Tensor): Label weights of each anchor with shape
+    #             (N, num_total_anchors)
+    #         bbox_targets (Tensor): BBox regression targets of each anchor
+    #             weight shape (N, num_total_anchors, 4).
+    #         num_total_samples (int): Number os positive samples that is
+    #             reduced over all GPUs.
+
+    #     Returns:
+    #         dict[str, Tensor]: A dictionary of loss components.
+    #     """
+
+    #     anchors = anchors.reshape(-1, 4)
+    #     cls_score = cls_score.permute(0, 2, 3, 1).reshape(
+    #         -1, self.cls_out_channels).contiguous()
+    #     bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
+    #     centerness = centerness.permute(0, 2, 3, 1).reshape(-1)
+    #     bbox_targets = bbox_targets.reshape(-1, 4)
+    #     labels = labels.reshape(-1)
+    #     label_weights = label_weights.reshape(-1)
+
+    #     # classification loss
+    #     loss_cls = self.loss_cls(
+    #         cls_score, labels, label_weights, avg_factor=num_total_samples)
+
+    #     # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+    #     bg_class_ind = self.num_classes
+    #     pos_inds = ((labels >= 0)
+    #                 & (labels < bg_class_ind)).nonzero().squeeze(1)
+
+    #     if len(pos_inds) > 0:
+    #         pos_bbox_targets = bbox_targets[pos_inds]
+    #         pos_bbox_pred = bbox_pred[pos_inds]
+    #         pos_anchors = anchors[pos_inds]
+    #         pos_centerness = centerness[pos_inds]
+
+    #         centerness_targets = self.centerness_target(
+    #             pos_anchors, pos_bbox_targets)
+    #         pos_decode_bbox_pred = self.bbox_coder.decode(
+    #             pos_anchors, pos_bbox_pred)
+
+    #         # regression loss
+    #         loss_bbox = self.loss_bbox(
+    #             pos_decode_bbox_pred,
+    #             pos_bbox_targets,
+    #             weight=centerness_targets,
+    #             avg_factor=1.0)
+
+    #         # centerness loss
+    #         loss_centerness = self.loss_centerness(
+    #             pos_centerness,
+    #             centerness_targets,
+    #             avg_factor=num_total_samples)
+
+    #     else:
+    #         loss_bbox = bbox_pred.sum() * 0
+    #         loss_centerness = centerness.sum() * 0
+    #         centerness_targets = bbox_targets.new_tensor(0.)
+
+    #     return loss_cls, loss_bbox, loss_centerness, centerness_targets.sum()
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             centernesses,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            centernesses (list[Tensor]): Centerness for each scale
+                level with shape (N, num_anchors * 1, H, W)
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (list[Tensor] | None): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+
+        device = cls_scores[0].device
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels)
+        if cls_reg_targets is None:
+            return None
+
+        (anchor_list, labels_list, label_weights_list, bbox_targets_list,
+         bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
+
+        num_total_samples = reduce_mean(
+            torch.tensor(num_total_pos, dtype=torch.float,
+                         device=device)).item()
+        num_total_samples = max(num_total_samples, 1.0)
+
+
+        level_slicer = [len(labels_list), 5, 4]  # [0]为level数；[1]为weight的个数；[2]为单level下的act个数
+        level_cls_factors = []
+        level_reg_factors = []
+        level_centerness_factors = []
+        
+        qloss_flag = global_placeholder.qloss_flag
+
+        if False:
+            q_loss_total = []
+            cls_branch = []
+            reg_branch = []
+            centerness_branch = []
+            for name, module in self.named_modules():
+                if hasattr(module, 'compute_qloss') and module.compute_qloss:
+                    # 挑出来量化器
+                    
+                    if 'cls' in name:
+                        # 说明是cls分支的量化器
+                        # cls_branch.append(module.quantization_loss)
+                        if 'post_act' in name:
+                            # 说明是act量化器
+                            cls_branch.append([name, module.scale * 1.])
+                        else:
+                            cls_branch.append([name, module.scale * 2.])
+                            
+                    elif 'reg' in name:
+                        # 说明是reg分支的量化器
+                        # reg_branch.append(module.quantization_loss)
+                        if 'post_act' in name:
+                            # 说明是act量化器
+                            reg_branch.append([name, module.scale * 1.])
+                        else:
+                            reg_branch.append([name, module.scale * 2.])        
+                            
+                    elif 'centerness' in name:
+                        # 说明是reg分支的量化器
+                        # reg_branch.append(module.quantization_loss)
+                        if 'post_act' in name:
+                            # 说明是act量化器
+                            centerness_branch.append([name, module.scale * 1.])
+                        else:
+                            centerness_branch.append([name, module.scale * 2.])
+                        
+                    q_loss_total.append([name, module.scale * 1])
+
+            # NOTE 不需要加item_post_act_quant的
+            # 由于centerness branch 的特殊性，所以得加上reg_branch分支的共用东西
+            centerness_branch = centerness_branch + reg_branch[1:]
+            
+            if level_slicer[0] != 5:
+                raise NotImplementedError
+            
+            for it in range(level_slicer[0]):
+                cls_summation = 0
+                reg_summation = 0
+                centerness_summation = 0
+                # tmp_cls_infos = cls_branch[0:level_slicer[1]] + cls_branch[level_slicer[1]+level_slicer[2]*it:level_slicer[1]+level_slicer[2]*(it+1)]
+                tmp_cls_infos = cls_branch[0:level_slicer[1]]
+                for info in tmp_cls_infos:
+                    cls_summation += info[1]
+                    
+                tmp_reg_infos = reg_branch[0:level_slicer[1]]
+                for info in tmp_reg_infos:
+                    reg_summation += info[1]
+                    
+                tmp_centerness_infos = centerness_branch[0:level_slicer[1]]
+                for info in tmp_centerness_infos:
+                    centerness_summation += info[1]
+            
+                level_cls_factors.append(cls_summation)
+                level_reg_factors.append(reg_summation)
+                level_centerness_factors.append(centerness_summation)
+        
+        if len(level_cls_factors) + len(level_reg_factors) == 0:
+            # 必须让list内有level个空list
+            level_cls_factors = [torch.tensor(1)] * len(labels_list)
+            level_reg_factors = [torch.tensor(1)] * len(labels_list)
+            level_centerness_factors = [torch.tensor(1)] * len(labels_list)
+
+
+        losses_cls, losses_bbox, loss_centerness,\
+            bbox_avg_factor = multi_apply(
+                self.loss_single,
+                anchor_list,
+                cls_scores,
+                bbox_preds,
+                centernesses,
+                labels_list,
+                label_weights_list,
+                bbox_targets_list,
+                level_cls_factors,
+                level_reg_factors,
+                level_centerness_factors,
+                num_total_samples=num_total_samples)
+
+        bbox_avg_factor = sum(bbox_avg_factor)
+        bbox_avg_factor = reduce_mean(bbox_avg_factor).clamp_(min=1).item()
+        losses_bbox = list(map(lambda x: x / bbox_avg_factor, losses_bbox))
+        return dict(
+            loss_cls=losses_cls,
+            loss_bbox=losses_bbox,
+            loss_centerness=loss_centerness)
+
+    def centerness_target(self, anchors, gts):
+        # only calculate pos centerness targets, otherwise there may be nan
+        anchors_cx = (anchors[:, 2] + anchors[:, 0]) / 2
+        anchors_cy = (anchors[:, 3] + anchors[:, 1]) / 2
+        l_ = anchors_cx - gts[:, 0]
+        t_ = anchors_cy - gts[:, 1]
+        r_ = gts[:, 2] - anchors_cx
+        b_ = gts[:, 3] - anchors_cy
+
+        left_right = torch.stack([l_, r_], dim=1)
+        top_bottom = torch.stack([t_, b_], dim=1)
+        centerness = torch.sqrt(
+            (left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) *
+            (top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0]))
+        assert not torch.isnan(centerness).any()
+        return centerness
+
+    def get_targets(self,
+                    anchor_list,
+                    valid_flag_list,
+                    gt_bboxes_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None,
+                    gt_labels_list=None,
+                    label_channels=1,
+                    unmap_outputs=True):
+        """Get targets for ATSS head.
+
+        This method is almost the same as `AnchorHead.get_targets()`. Besides
+        returning the targets as the parent method does, it also returns the
+        anchors as the first element of the returned tuple.
+        """
+        num_imgs = len(img_metas)
+        assert len(anchor_list) == len(valid_flag_list) == num_imgs
+
+        # anchor number of multi levels
+        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+        num_level_anchors_list = [num_level_anchors] * num_imgs
+
+        # concat all level anchors and flags to a single tensor
+        for i in range(num_imgs):
+            assert len(anchor_list[i]) == len(valid_flag_list[i])
+            anchor_list[i] = torch.cat(anchor_list[i])
+            valid_flag_list[i] = torch.cat(valid_flag_list[i])
+
+        # compute targets for each image
+        if gt_bboxes_ignore_list is None:
+            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
+        if gt_labels_list is None:
+            gt_labels_list = [None for _ in range(num_imgs)]
+        (all_anchors, all_labels, all_label_weights, all_bbox_targets,
+         all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
+             self._get_target_single,
+             anchor_list,
+             valid_flag_list,
+             num_level_anchors_list,
+             gt_bboxes_list,
+             gt_bboxes_ignore_list,
+             gt_labels_list,
+             img_metas,
+             label_channels=label_channels,
+             unmap_outputs=unmap_outputs)
+        # no valid anchors
+        if any([labels is None for labels in all_labels]):
+            return None
+        # sampled anchors of all images
+        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
+        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
+        # split targets to a list w.r.t. multiple levels
+        anchors_list = images_to_levels(all_anchors, num_level_anchors)
+        labels_list = images_to_levels(all_labels, num_level_anchors)
+        label_weights_list = images_to_levels(all_label_weights,
+                                              num_level_anchors)
+        bbox_targets_list = images_to_levels(all_bbox_targets,
+                                             num_level_anchors)
+        bbox_weights_list = images_to_levels(all_bbox_weights,
+                                             num_level_anchors)
+        return (anchors_list, labels_list, label_weights_list,
+                bbox_targets_list, bbox_weights_list, num_total_pos,
+                num_total_neg)
+
+    def _get_target_single(self,
+                           flat_anchors,
+                           valid_flags,
+                           num_level_anchors,
+                           gt_bboxes,
+                           gt_bboxes_ignore,
+                           gt_labels,
+                           img_meta,
+                           label_channels=1,
+                           unmap_outputs=True):
+        """Compute regression, classification targets for anchors in a single
+        image.
+
+        Args:
+            flat_anchors (Tensor): Multi-level anchors of the image, which are
+                concatenated into a single tensor of shape (num_anchors ,4)
+            valid_flags (Tensor): Multi level valid flags of the image,
+                which are concatenated into a single tensor of
+                    shape (num_anchors,).
+            num_level_anchors Tensor): Number of anchors of each scale level.
+            gt_bboxes (Tensor): Ground truth bboxes of the image,
+                shape (num_gts, 4).
+            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4).
+            gt_labels (Tensor): Ground truth labels of each box,
+                shape (num_gts,).
+            img_meta (dict): Meta info of the image.
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+
+        Returns:
+            tuple: N is the number of total anchors in the image.
+                labels (Tensor): Labels of all anchors in the image with shape
+                    (N,).
+                label_weights (Tensor): Label weights of all anchor in the
+                    image with shape (N,).
+                bbox_targets (Tensor): BBox targets of all anchors in the
+                    image with shape (N, 4).
+                bbox_weights (Tensor): BBox weights of all anchors in the
+                    image with shape (N, 4)
+                pos_inds (Tensor): Indices of positive anchor with shape
+                    (num_pos,).
+                neg_inds (Tensor): Indices of negative anchor with shape
+                    (num_neg,).
+        """
+        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
+                                           img_meta['img_shape'][:2],
+                                           self.train_cfg.allowed_border)
+        if not inside_flags.any():
+            return (None, ) * 7
+        # assign gt and sample anchors
+        anchors = flat_anchors[inside_flags, :]
+
+        num_level_anchors_inside = self.get_num_level_anchors_inside(
+            num_level_anchors, inside_flags)
+        assign_result = self.assigner.assign(anchors, num_level_anchors_inside,
+                                             gt_bboxes, gt_bboxes_ignore,
+                                             gt_labels)
+
+        sampling_result = self.sampler.sample(assign_result, anchors,
+                                              gt_bboxes)
+
+        num_valid_anchors = anchors.shape[0]
+        bbox_targets = torch.zeros_like(anchors)
+        bbox_weights = torch.zeros_like(anchors)
+        labels = anchors.new_full((num_valid_anchors, ),
+                                  self.num_classes,
+                                  dtype=torch.long)
+        label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
+
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+        if len(pos_inds) > 0:
+            if self.reg_decoded_bbox:
+                pos_bbox_targets = sampling_result.pos_gt_bboxes
+            else:
+                pos_bbox_targets = self.bbox_coder.encode(
+                    sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
+
+            bbox_targets[pos_inds, :] = pos_bbox_targets
+            bbox_weights[pos_inds, :] = 1.0
+            if gt_labels is None:
+                # Only rpn gives gt_labels as None
+                # Foreground is the first class since v2.5.0
+                labels[pos_inds] = 0
+            else:
+                labels[pos_inds] = gt_labels[
+                    sampling_result.pos_assigned_gt_inds]
+            if self.train_cfg.pos_weight <= 0:
+                label_weights[pos_inds] = 1.0
+            else:
+                label_weights[pos_inds] = self.train_cfg.pos_weight
+        if len(neg_inds) > 0:
+            label_weights[neg_inds] = 1.0
+
+        # map up to original set of anchors
+        if unmap_outputs:
+            num_total_anchors = flat_anchors.size(0)
+            anchors = unmap(anchors, num_total_anchors, inside_flags)
+            labels = unmap(
+                labels, num_total_anchors, inside_flags, fill=self.num_classes)
+            label_weights = unmap(label_weights, num_total_anchors,
+                                  inside_flags)
+            bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
+            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
+
+        return (anchors, labels, label_weights, bbox_targets, bbox_weights,
+                pos_inds, neg_inds)
+
+    def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):
+        split_inside_flags = torch.split(inside_flags, num_level_anchors)
+        num_level_anchors_inside = [
+            int(flags.sum()) for flags in split_inside_flags
+        ]
+        return num_level_anchors_inside
diff --git a/mmdet/models/dense_heads/autoassign_head.py b/mmdet/models/dense_heads/autoassign_head.py
new file mode 100644
index 0000000..446da24
--- /dev/null
+++ b/mmdet/models/dense_heads/autoassign_head.py
@@ -0,0 +1,527 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import bias_init_with_prob, normal_init
+from mmcv.runner import force_fp32
+
+from mmdet.core import multi_apply
+from mmdet.core.anchor.point_generator import MlvlPointGenerator
+from mmdet.core.bbox import bbox_overlaps
+from mmdet.models import HEADS
+from mmdet.models.dense_heads.atss_head import reduce_mean
+from mmdet.models.dense_heads.fcos_head import FCOSHead
+from mmdet.models.dense_heads.paa_head import levels_to_images
+
+EPS = 1e-12
+
+
+class CenterPrior(nn.Module):
+    """Center Weighting module to adjust the category-specific prior
+    distributions.
+
+    Args:
+        force_topk (bool): When no point falls into gt_bbox, forcibly
+            select the k points closest to the center to calculate
+            the center prior. Defaults to False.
+        topk (int): The number of points used to calculate the
+            center prior when no point falls in gt_bbox. Only work when
+            force_topk if True. Defaults to 9.
+        num_classes (int): The class number of dataset. Defaults to 80.
+        strides (tuple[int]): The stride of each input feature map. Defaults
+            to (8, 16, 32, 64, 128).
+    """
+
+    def __init__(self,
+                 force_topk=False,
+                 topk=9,
+                 num_classes=80,
+                 strides=(8, 16, 32, 64, 128)):
+        super(CenterPrior, self).__init__()
+        self.mean = nn.Parameter(torch.zeros(num_classes, 2))
+        self.sigma = nn.Parameter(torch.ones(num_classes, 2))
+        self.strides = strides
+        self.force_topk = force_topk
+        self.topk = topk
+
+    def forward(self, anchor_points_list, gt_bboxes, labels,
+                inside_gt_bbox_mask):
+        """Get the center prior of each point on the feature map for each
+        instance.
+
+        Args:
+            anchor_points_list (list[Tensor]): list of coordinate
+                of points on feature map. Each with shape
+                (num_points, 2).
+            gt_bboxes (Tensor): The gt_bboxes with shape of
+                (num_gt, 4).
+            labels (Tensor): The gt_labels with shape of (num_gt).
+            inside_gt_bbox_mask (Tensor): Tensor of bool type,
+                with shape of (num_points, num_gt), each
+                value is used to mark whether this point falls
+                within a certain gt.
+
+        Returns:
+            tuple(Tensor):
+
+                - center_prior_weights(Tensor): Float tensor with shape \
+                    of (num_points, num_gt). Each value represents \
+                    the center weighting coefficient.
+                - inside_gt_bbox_mask (Tensor): Tensor of bool type, \
+                    with shape of (num_points, num_gt), each \
+                    value is used to mark whether this point falls \
+                    within a certain gt or is the topk nearest points for \
+                    a specific gt_bbox.
+        """
+        inside_gt_bbox_mask = inside_gt_bbox_mask.clone()
+        num_gts = len(labels)
+        num_points = sum([len(item) for item in anchor_points_list])
+        if num_gts == 0:
+            return gt_bboxes.new_zeros(num_points,
+                                       num_gts), inside_gt_bbox_mask
+        center_prior_list = []
+        for slvl_points, stride in zip(anchor_points_list, self.strides):
+            # slvl_points: points from single level in FPN, has shape (h*w, 2)
+            # single_level_points has shape (h*w, num_gt, 2)
+            single_level_points = slvl_points[:, None, :].expand(
+                (slvl_points.size(0), len(gt_bboxes), 2))
+            gt_center_x = ((gt_bboxes[:, 0] + gt_bboxes[:, 2]) / 2)
+            gt_center_y = ((gt_bboxes[:, 1] + gt_bboxes[:, 3]) / 2)
+            gt_center = torch.stack((gt_center_x, gt_center_y), dim=1)
+            gt_center = gt_center[None]
+            # instance_center has shape (1, num_gt, 2)
+            instance_center = self.mean[labels][None]
+            # instance_sigma has shape (1, num_gt, 2)
+            instance_sigma = self.sigma[labels][None]
+            # distance has shape (num_points, num_gt, 2)
+            distance = (((single_level_points - gt_center) / float(stride) -
+                         instance_center)**2)
+            center_prior = torch.exp(-distance /
+                                     (2 * instance_sigma**2)).prod(dim=-1)
+            center_prior_list.append(center_prior)
+        center_prior_weights = torch.cat(center_prior_list, dim=0)
+
+        if self.force_topk:
+            gt_inds_no_points_inside = torch.nonzero(
+                inside_gt_bbox_mask.sum(0) == 0).reshape(-1)
+            if gt_inds_no_points_inside.numel():
+                topk_center_index = \
+                    center_prior_weights[:, gt_inds_no_points_inside].topk(
+                                                             self.topk,
+                                                             dim=0)[1]
+                temp_mask = inside_gt_bbox_mask[:, gt_inds_no_points_inside]
+                inside_gt_bbox_mask[:, gt_inds_no_points_inside] = \
+                    torch.scatter(temp_mask,
+                                  dim=0,
+                                  index=topk_center_index,
+                                  src=torch.ones_like(
+                                    topk_center_index,
+                                    dtype=torch.bool))
+
+        center_prior_weights[~inside_gt_bbox_mask] = 0
+        return center_prior_weights, inside_gt_bbox_mask
+
+
+@HEADS.register_module()
+class AutoAssignHead(FCOSHead):
+    """AutoAssignHead head used in AutoAssign.
+
+    More details can be found in the `paper
+    <https://arxiv.org/abs/2007.03496>`_ .
+
+    Args:
+        force_topk (bool): Used in center prior initialization to
+            handle extremely small gt. Default is False.
+        topk (int): The number of points used to calculate the
+            center prior when no point falls in gt_bbox. Only work when
+            force_topk if True. Defaults to 9.
+        pos_loss_weight (float): The loss weight of positive loss
+            and with default value 0.25.
+        neg_loss_weight (float): The loss weight of negative loss
+            and with default value 0.75.
+        center_loss_weight (float): The loss weight of center prior
+            loss and with default value 0.75.
+    """
+
+    def __init__(self,
+                 *args,
+                 force_topk=False,
+                 topk=9,
+                 pos_loss_weight=0.25,
+                 neg_loss_weight=0.75,
+                 center_loss_weight=0.75,
+                 **kwargs):
+        super().__init__(*args, conv_bias=True, **kwargs)
+        self.center_prior = CenterPrior(
+            force_topk=force_topk,
+            topk=topk,
+            num_classes=self.num_classes,
+            strides=self.strides)
+        self.pos_loss_weight = pos_loss_weight
+        self.neg_loss_weight = neg_loss_weight
+        self.center_loss_weight = center_loss_weight
+        self.prior_generator = MlvlPointGenerator(self.strides, offset=0)
+
+    def init_weights(self):
+        """Initialize weights of the head.
+
+        In particular, we have special initialization for classified conv's and
+        regression conv's bias
+        """
+
+        super(AutoAssignHead, self).init_weights()
+        bias_cls = bias_init_with_prob(0.02)
+        normal_init(self.conv_cls, std=0.01, bias=bias_cls)
+        normal_init(self.conv_reg, std=0.01, bias=4.0)
+
+    def forward_single(self, x, scale, stride):
+        """Forward features of a single scale level.
+
+        Args:
+            x (Tensor): FPN feature maps of the specified stride.
+            scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
+                the bbox prediction.
+            stride (int): The corresponding stride for feature maps, only
+                used to normalize the bbox prediction when self.norm_on_bbox
+                is True.
+
+        Returns:
+            tuple: scores for each class, bbox predictions and centerness \
+                predictions of input feature maps.
+        """
+        cls_score, bbox_pred, cls_feat, reg_feat = super(
+            FCOSHead, self).forward_single(x)
+        centerness = self.conv_centerness(reg_feat)
+        # scale the bbox_pred of different level
+        # float to avoid overflow when enabling FP16
+        bbox_pred = scale(bbox_pred).float()
+        # bbox_pred needed for gradient computation has been modified
+        # by F.relu(bbox_pred) when run with PyTorch 1.10. So replace
+        # F.relu(bbox_pred) with bbox_pred.clamp(min=0)
+        bbox_pred = bbox_pred.clamp(min=0)
+        bbox_pred *= stride
+        return cls_score, bbox_pred, centerness
+
+    def get_pos_loss_single(self, cls_score, objectness, reg_loss, gt_labels,
+                            center_prior_weights):
+        """Calculate the positive loss of all points in gt_bboxes.
+
+        Args:
+            cls_score (Tensor): All category scores for each point on
+                the feature map. The shape is (num_points, num_class).
+            objectness (Tensor): Foreground probability of all points,
+                has shape (num_points, 1).
+            reg_loss (Tensor): The regression loss of each gt_bbox and each
+                prediction box, has shape of (num_points, num_gt).
+            gt_labels (Tensor): The zeros based gt_labels of all gt
+                with shape of (num_gt,).
+            center_prior_weights (Tensor): Float tensor with shape
+                of (num_points, num_gt). Each value represents
+                the center weighting coefficient.
+
+        Returns:
+            tuple[Tensor]:
+
+                - pos_loss (Tensor): The positive loss of all points
+                  in the gt_bboxes.
+        """
+        # p_loc: localization confidence
+        p_loc = torch.exp(-reg_loss)
+        # p_cls: classification confidence
+        p_cls = (cls_score * objectness)[:, gt_labels]
+        # p_pos: joint confidence indicator
+        p_pos = p_cls * p_loc
+
+        # 3 is a hyper-parameter to control the contributions of high and
+        # low confidence locations towards positive losses.
+        confidence_weight = torch.exp(p_pos * 3)
+        p_pos_weight = (confidence_weight * center_prior_weights) / (
+            (confidence_weight * center_prior_weights).sum(
+                0, keepdim=True)).clamp(min=EPS)
+        reweighted_p_pos = (p_pos * p_pos_weight).sum(0)
+        pos_loss = F.binary_cross_entropy(
+            reweighted_p_pos,
+            torch.ones_like(reweighted_p_pos),
+            reduction='none')
+        pos_loss = pos_loss.sum() * self.pos_loss_weight
+        return pos_loss,
+
+    def get_neg_loss_single(self, cls_score, objectness, gt_labels, ious,
+                            inside_gt_bbox_mask):
+        """Calculate the negative loss of all points in feature map.
+
+        Args:
+            cls_score (Tensor): All category scores for each point on
+                the feature map. The shape is (num_points, num_class).
+            objectness (Tensor): Foreground probability of all points
+                and is shape of (num_points, 1).
+            gt_labels (Tensor): The zeros based label of all gt with shape of
+                (num_gt).
+            ious (Tensor): Float tensor with shape of (num_points, num_gt).
+                Each value represent the iou of pred_bbox and gt_bboxes.
+            inside_gt_bbox_mask (Tensor): Tensor of bool type,
+                with shape of (num_points, num_gt), each
+                value is used to mark whether this point falls
+                within a certain gt.
+
+        Returns:
+            tuple[Tensor]:
+
+                - neg_loss (Tensor): The negative loss of all points
+                  in the feature map.
+        """
+        num_gts = len(gt_labels)
+        joint_conf = (cls_score * objectness)
+        p_neg_weight = torch.ones_like(joint_conf)
+        if num_gts > 0:
+            # the order of dinmension would affect the value of
+            # p_neg_weight, we strictly follow the original
+            # implementation.
+            inside_gt_bbox_mask = inside_gt_bbox_mask.permute(1, 0)
+            ious = ious.permute(1, 0)
+
+            foreground_idxs = torch.nonzero(inside_gt_bbox_mask, as_tuple=True)
+            temp_weight = (1 / (1 - ious[foreground_idxs]).clamp_(EPS))
+
+            def normalize(x):
+                return (x - x.min() + EPS) / (x.max() - x.min() + EPS)
+
+            for instance_idx in range(num_gts):
+                idxs = foreground_idxs[0] == instance_idx
+                if idxs.any():
+                    temp_weight[idxs] = normalize(temp_weight[idxs])
+
+            p_neg_weight[foreground_idxs[1],
+                         gt_labels[foreground_idxs[0]]] = 1 - temp_weight
+
+        logits = (joint_conf * p_neg_weight)
+        neg_loss = (
+            logits**2 * F.binary_cross_entropy(
+                logits, torch.zeros_like(logits), reduction='none'))
+        neg_loss = neg_loss.sum() * self.neg_loss_weight
+        return neg_loss,
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'objectnesses'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             objectnesses,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute loss of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level,
+                each is a 4D-tensor, the channel number is
+                num_points * num_classes.
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level, each is a 4D-tensor, the channel number is
+                num_points * 4.
+            objectnesses (list[Tensor]): objectness for each scale level, each
+                is a 4D-tensor, the channel number is num_points * 1.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+
+        assert len(cls_scores) == len(bbox_preds) == len(objectnesses)
+        all_num_gt = sum([len(item) for item in gt_bboxes])
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        all_level_points = self.prior_generator.grid_priors(
+            featmap_sizes,
+            dtype=bbox_preds[0].dtype,
+            device=bbox_preds[0].device)
+        inside_gt_bbox_mask_list, bbox_targets_list = self.get_targets(
+            all_level_points, gt_bboxes)
+
+        center_prior_weight_list = []
+        temp_inside_gt_bbox_mask_list = []
+        for gt_bboxe, gt_label, inside_gt_bbox_mask in zip(
+                gt_bboxes, gt_labels, inside_gt_bbox_mask_list):
+            center_prior_weight, inside_gt_bbox_mask = \
+                self.center_prior(all_level_points, gt_bboxe, gt_label,
+                                  inside_gt_bbox_mask)
+            center_prior_weight_list.append(center_prior_weight)
+            temp_inside_gt_bbox_mask_list.append(inside_gt_bbox_mask)
+        inside_gt_bbox_mask_list = temp_inside_gt_bbox_mask_list
+        mlvl_points = torch.cat(all_level_points, dim=0)
+        bbox_preds = levels_to_images(bbox_preds)
+        cls_scores = levels_to_images(cls_scores)
+        objectnesses = levels_to_images(objectnesses)
+
+        reg_loss_list = []
+        ious_list = []
+        num_points = len(mlvl_points)
+
+        for bbox_pred, encoded_targets, inside_gt_bbox_mask in zip(
+                bbox_preds, bbox_targets_list, inside_gt_bbox_mask_list):
+            temp_num_gt = encoded_targets.size(1)
+            expand_mlvl_points = mlvl_points[:, None, :].expand(
+                num_points, temp_num_gt, 2).reshape(-1, 2)
+            encoded_targets = encoded_targets.reshape(-1, 4)
+            expand_bbox_pred = bbox_pred[:, None, :].expand(
+                num_points, temp_num_gt, 4).reshape(-1, 4)
+            decoded_bbox_preds = self.bbox_coder.decode(
+                expand_mlvl_points, expand_bbox_pred)
+            decoded_target_preds = self.bbox_coder.decode(
+                expand_mlvl_points, encoded_targets)
+            with torch.no_grad():
+                ious = bbox_overlaps(
+                    decoded_bbox_preds, decoded_target_preds, is_aligned=True)
+                ious = ious.reshape(num_points, temp_num_gt)
+                if temp_num_gt:
+                    ious = ious.max(
+                        dim=-1, keepdim=True).values.repeat(1, temp_num_gt)
+                else:
+                    ious = ious.new_zeros(num_points, temp_num_gt)
+                ious[~inside_gt_bbox_mask] = 0
+                ious_list.append(ious)
+            loss_bbox = self.loss_bbox(
+                decoded_bbox_preds,
+                decoded_target_preds,
+                weight=None,
+                reduction_override='none')
+            reg_loss_list.append(loss_bbox.reshape(num_points, temp_num_gt))
+
+        cls_scores = [item.sigmoid() for item in cls_scores]
+        objectnesses = [item.sigmoid() for item in objectnesses]
+        pos_loss_list, = multi_apply(self.get_pos_loss_single, cls_scores,
+                                     objectnesses, reg_loss_list, gt_labels,
+                                     center_prior_weight_list)
+        pos_avg_factor = reduce_mean(
+            bbox_pred.new_tensor(all_num_gt)).clamp_(min=1)
+        pos_loss = sum(pos_loss_list) / pos_avg_factor
+
+        neg_loss_list, = multi_apply(self.get_neg_loss_single, cls_scores,
+                                     objectnesses, gt_labels, ious_list,
+                                     inside_gt_bbox_mask_list)
+        neg_avg_factor = sum(item.data.sum()
+                             for item in center_prior_weight_list)
+        neg_avg_factor = reduce_mean(neg_avg_factor).clamp_(min=1)
+        neg_loss = sum(neg_loss_list) / neg_avg_factor
+
+        center_loss = []
+        for i in range(len(img_metas)):
+
+            if inside_gt_bbox_mask_list[i].any():
+                center_loss.append(
+                    len(gt_bboxes[i]) /
+                    center_prior_weight_list[i].sum().clamp_(min=EPS))
+            # when width or height of gt_bbox is smaller than stride of p3
+            else:
+                center_loss.append(center_prior_weight_list[i].sum() * 0)
+
+        center_loss = torch.stack(center_loss).mean() * self.center_loss_weight
+
+        # avoid dead lock in DDP
+        if all_num_gt == 0:
+            pos_loss = bbox_preds[0].sum() * 0
+            dummy_center_prior_loss = self.center_prior.mean.sum(
+            ) * 0 + self.center_prior.sigma.sum() * 0
+            center_loss = objectnesses[0].sum() * 0 + dummy_center_prior_loss
+
+        loss = dict(
+            loss_pos=pos_loss, loss_neg=neg_loss, loss_center=center_loss)
+
+        return loss
+
+    def get_targets(self, points, gt_bboxes_list):
+        """Compute regression targets and each point inside or outside gt_bbox
+        in multiple images.
+
+        Args:
+            points (list[Tensor]): Points of all fpn level, each has shape
+                (num_points, 2).
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
+                each has shape (num_gt, 4).
+
+        Returns:
+            tuple(list[Tensor]):
+
+                - inside_gt_bbox_mask_list (list[Tensor]): Each
+                  Tensor is with bool type and shape of
+                  (num_points, num_gt), each value
+                  is used to mark whether this point falls
+                  within a certain gt.
+                - concat_lvl_bbox_targets (list[Tensor]): BBox
+                  targets of each level. Each tensor has shape
+                  (num_points, num_gt, 4).
+        """
+
+        concat_points = torch.cat(points, dim=0)
+        # the number of points per img, per lvl
+        inside_gt_bbox_mask_list, bbox_targets_list = multi_apply(
+            self._get_target_single, gt_bboxes_list, points=concat_points)
+        return inside_gt_bbox_mask_list, bbox_targets_list
+
+    def _get_target_single(self, gt_bboxes, points):
+        """Compute regression targets and each point inside or outside gt_bbox
+        for a single image.
+
+        Args:
+            gt_bboxes (Tensor): gt_bbox of single image, has shape
+                (num_gt, 4).
+            points (Tensor): Points of all fpn level, has shape
+                (num_points, 2).
+
+        Returns:
+            tuple[Tensor]: Containing the following Tensors:
+
+                - inside_gt_bbox_mask (Tensor): Bool tensor with shape
+                  (num_points, num_gt), each value is used to mark
+                  whether this point falls within a certain gt.
+                - bbox_targets (Tensor): BBox targets of each points with
+                  each gt_bboxes, has shape (num_points, num_gt, 4).
+        """
+        num_points = points.size(0)
+        num_gts = gt_bboxes.size(0)
+        gt_bboxes = gt_bboxes[None].expand(num_points, num_gts, 4)
+        xs, ys = points[:, 0], points[:, 1]
+        xs = xs[:, None]
+        ys = ys[:, None]
+        left = xs - gt_bboxes[..., 0]
+        right = gt_bboxes[..., 2] - xs
+        top = ys - gt_bboxes[..., 1]
+        bottom = gt_bboxes[..., 3] - ys
+        bbox_targets = torch.stack((left, top, right, bottom), -1)
+        if num_gts:
+            inside_gt_bbox_mask = bbox_targets.min(-1)[0] > 0
+        else:
+            inside_gt_bbox_mask = bbox_targets.new_zeros((num_points, num_gts),
+                                                         dtype=torch.bool)
+
+        return inside_gt_bbox_mask, bbox_targets
+
+    def _get_points_single(self,
+                           featmap_size,
+                           stride,
+                           dtype,
+                           device,
+                           flatten=False):
+        """Almost the same as the implementation in fcos, we remove half stride
+        offset to align with the original implementation.
+
+        This function will be deprecated soon.
+        """
+        warnings.warn(
+            '`_get_points_single` in `AutoAssignHead` will be '
+            'deprecated soon, we support a multi level point generator now'
+            'you can get points of a single level feature map '
+            'with `self.prior_generator.single_level_grid_priors` ')
+        y, x = super(FCOSHead,
+                     self)._get_points_single(featmap_size, stride, dtype,
+                                              device)
+        points = torch.stack((x.reshape(-1) * stride, y.reshape(-1) * stride),
+                             dim=-1)
+        return points
diff --git a/mmdet/models/dense_heads/base_dense_head.py b/mmdet/models/dense_heads/base_dense_head.py
new file mode 100644
index 0000000..6ad07ba
--- /dev/null
+++ b/mmdet/models/dense_heads/base_dense_head.py
@@ -0,0 +1,527 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from abc import ABCMeta, abstractmethod
+
+import torch
+from mmcv.cnn.utils.weight_init import constant_init
+from mmcv.ops import batched_nms
+from mmcv.runner import BaseModule, force_fp32
+
+from mmdet.core.utils import filter_scores_and_topk, select_single_mlvl
+
+
+class BaseDenseHead(BaseModule, metaclass=ABCMeta):
+    """Base class for DenseHeads."""
+
+    def __init__(self, init_cfg=None):
+        super(BaseDenseHead, self).__init__(init_cfg)
+
+    def init_weights(self):
+        super(BaseDenseHead, self).init_weights()
+        # avoid init_cfg overwrite the initialization of `conv_offset`
+        for m in self.modules():
+            # DeformConv2dPack, ModulatedDeformConv2dPack
+            if hasattr(m, 'conv_offset'):
+                constant_init(m.conv_offset, 0)
+
+    @abstractmethod
+    def loss(self, **kwargs):
+        """Compute losses of the head."""
+        pass
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def get_bboxes(self,
+                   cls_scores,
+                   bbox_preds,
+                   score_factors=None,
+                   img_metas=None,
+                   cfg=None,
+                   rescale=False,
+                   with_nms=True,
+                   **kwargs):
+        """Transform network outputs of a batch into bbox results.
+
+        Note: When score_factors is not None, the cls_scores are
+        usually multiplied by it then obtain the real score used in NMS,
+        such as CenterNess in FCOS, IoU branch in ATSS.
+
+        Args:
+            cls_scores (list[Tensor]): Classification scores for all
+                scale levels, each is a 4D-tensor, has shape
+                (batch_size, num_priors * num_classes, H, W).
+            bbox_preds (list[Tensor]): Box energies / deltas for all
+                scale levels, each is a 4D-tensor, has shape
+                (batch_size, num_priors * 4, H, W).
+            score_factors (list[Tensor], Optional): Score factor for
+                all scale level, each is a 4D-tensor, has shape
+                (batch_size, num_priors * 1, H, W). Default None.
+            img_metas (list[dict], Optional): Image meta info. Default None.
+            cfg (mmcv.Config, Optional): Test / postprocessing configuration,
+                if None, test_cfg would be used.  Default None.
+            rescale (bool): If True, return boxes in original image space.
+                Default False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default True.
+
+        Returns:
+            list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple.
+                The first item is an (n, 5) tensor, where the first 4 columns
+                are bounding box positions (tl_x, tl_y, br_x, br_y) and the
+                5-th column is a score between 0 and 1. The second item is a
+                (n,) tensor where each item is the predicted class label of
+                the corresponding box.
+        """
+        assert len(cls_scores) == len(bbox_preds)
+
+        if score_factors is None:
+            # e.g. Retina, FreeAnchor, Foveabox, etc.
+            with_score_factors = False
+        else:
+            # e.g. FCOS, PAA, ATSS, AutoAssign, etc.
+            with_score_factors = True
+            assert len(cls_scores) == len(score_factors)
+
+        num_levels = len(cls_scores)
+
+        featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
+        mlvl_priors = self.prior_generator.grid_priors(
+            featmap_sizes,
+            dtype=cls_scores[0].dtype,
+            device=cls_scores[0].device)
+
+        result_list = []
+
+        for img_id in range(len(img_metas)):
+            img_meta = img_metas[img_id]
+            cls_score_list = select_single_mlvl(cls_scores, img_id)
+            bbox_pred_list = select_single_mlvl(bbox_preds, img_id)
+            if with_score_factors:
+                score_factor_list = select_single_mlvl(score_factors, img_id)
+            else:
+                score_factor_list = [None for _ in range(num_levels)]
+
+            results = self._get_bboxes_single(cls_score_list, bbox_pred_list,
+                                              score_factor_list, mlvl_priors,
+                                              img_meta, cfg, rescale, with_nms,
+                                              **kwargs)
+            result_list.append(results)
+        return result_list
+
+    def _get_bboxes_single(self,
+                           cls_score_list,
+                           bbox_pred_list,
+                           score_factor_list,
+                           mlvl_priors,
+                           img_meta,
+                           cfg,
+                           rescale=False,
+                           with_nms=True,
+                           **kwargs):
+        """Transform outputs of a single image into bbox predictions.
+
+        Args:
+            cls_score_list (list[Tensor]): Box scores from all scale
+                levels of a single image, each item has shape
+                (num_priors * num_classes, H, W).
+            bbox_pred_list (list[Tensor]): Box energies / deltas from
+                all scale levels of a single image, each item has shape
+                (num_priors * 4, H, W).
+            score_factor_list (list[Tensor]): Score factor from all scale
+                levels of a single image, each item has shape
+                (num_priors * 1, H, W).
+            mlvl_priors (list[Tensor]): Each element in the list is
+                the priors of a single level in feature pyramid. In all
+                anchor-based methods, it has shape (num_priors, 4). In
+                all anchor-free methods, it has shape (num_priors, 2)
+                when `with_stride=True`, otherwise it still has shape
+                (num_priors, 4).
+            img_meta (dict): Image meta info.
+            cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+
+        Returns:
+            tuple[Tensor]: Results of detected bboxes and labels. If with_nms
+                is False and mlvl_score_factor is None, return mlvl_bboxes and
+                mlvl_scores, else return mlvl_bboxes, mlvl_scores and
+                mlvl_score_factor. Usually with_nms is False is used for aug
+                test. If with_nms is True, then return the following format
+
+                - det_bboxes (Tensor): Predicted bboxes with shape \
+                    [num_bboxes, 5], where the first 4 columns are bounding \
+                    box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
+                    column are scores between 0 and 1.
+                - det_labels (Tensor): Predicted labels of the corresponding \
+                    box with shape [num_bboxes].
+        """
+        if score_factor_list[0] is None:
+            # e.g. Retina, FreeAnchor, etc.
+            with_score_factors = False
+        else:
+            # e.g. FCOS, PAA, ATSS, etc.
+            with_score_factors = True
+
+        cfg = self.test_cfg if cfg is None else cfg
+        img_shape = img_meta['img_shape']
+        nms_pre = cfg.get('nms_pre', -1)
+
+        mlvl_bboxes = []
+        mlvl_scores = []
+        mlvl_labels = []
+        if with_score_factors:
+            mlvl_score_factors = []
+        else:
+            mlvl_score_factors = None
+        for level_idx, (cls_score, bbox_pred, score_factor, priors) in \
+                enumerate(zip(cls_score_list, bbox_pred_list,
+                              score_factor_list, mlvl_priors)):
+
+            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
+
+            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
+            if with_score_factors:
+                score_factor = score_factor.permute(1, 2,
+                                                    0).reshape(-1).sigmoid()
+            cls_score = cls_score.permute(1, 2,
+                                          0).reshape(-1, self.cls_out_channels)
+            if self.use_sigmoid_cls:
+                scores = cls_score.sigmoid()
+            else:
+                # remind that we set FG labels to [0, num_class-1]
+                # since mmdet v2.0
+                # BG cat_id: num_class
+                scores = cls_score.softmax(-1)[:, :-1]
+
+            # After https://github.com/open-mmlab/mmdetection/pull/6268/,
+            # this operation keeps fewer bboxes under the same `nms_pre`.
+            # There is no difference in performance for most models. If you
+            # find a slight drop in performance, you can set a larger
+            # `nms_pre` than before.
+            results = filter_scores_and_topk(
+                scores, cfg.score_thr, nms_pre,
+                dict(bbox_pred=bbox_pred, priors=priors))
+            scores, labels, keep_idxs, filtered_results = results
+
+            bbox_pred = filtered_results['bbox_pred']
+            priors = filtered_results['priors']
+
+            if with_score_factors:
+                score_factor = score_factor[keep_idxs]
+
+            bboxes = self.bbox_coder.decode(
+                priors, bbox_pred, max_shape=img_shape)
+
+            mlvl_bboxes.append(bboxes)
+            mlvl_scores.append(scores)
+            mlvl_labels.append(labels)
+            if with_score_factors:
+                mlvl_score_factors.append(score_factor)
+
+        return self._bbox_post_process(mlvl_scores, mlvl_labels, mlvl_bboxes,
+                                       img_meta['scale_factor'], cfg, rescale,
+                                       with_nms, mlvl_score_factors, **kwargs)
+
+    def _bbox_post_process(self,
+                           mlvl_scores,
+                           mlvl_labels,
+                           mlvl_bboxes,
+                           scale_factor,
+                           cfg,
+                           rescale=False,
+                           with_nms=True,
+                           mlvl_score_factors=None,
+                           **kwargs):
+        """bbox post-processing method.
+
+        The boxes would be rescaled to the original image scale and do
+        the nms operation. Usually `with_nms` is False is used for aug test.
+
+        Args:
+            mlvl_scores (list[Tensor]): Box scores from all scale
+                levels of a single image, each item has shape
+                (num_bboxes, ).
+            mlvl_labels (list[Tensor]): Box class labels from all scale
+                levels of a single image, each item has shape
+                (num_bboxes, ).
+            mlvl_bboxes (list[Tensor]): Decoded bboxes from all scale
+                levels of a single image, each item has shape (num_bboxes, 4).
+            scale_factor (ndarray, optional): Scale factor of the image arange
+                as (w_scale, h_scale, w_scale, h_scale).
+            cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+            mlvl_score_factors (list[Tensor], optional): Score factor from
+                all scale levels of a single image, each item has shape
+                (num_bboxes, ). Default: None.
+
+        Returns:
+            tuple[Tensor]: Results of detected bboxes and labels. If with_nms
+                is False and mlvl_score_factor is None, return mlvl_bboxes and
+                mlvl_scores, else return mlvl_bboxes, mlvl_scores and
+                mlvl_score_factor. Usually with_nms is False is used for aug
+                test. If with_nms is True, then return the following format
+
+                - det_bboxes (Tensor): Predicted bboxes with shape \
+                    [num_bboxes, 5], where the first 4 columns are bounding \
+                    box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
+                    column are scores between 0 and 1.
+                - det_labels (Tensor): Predicted labels of the corresponding \
+                    box with shape [num_bboxes].
+        """
+        assert len(mlvl_scores) == len(mlvl_bboxes) == len(mlvl_labels)
+
+        mlvl_bboxes = torch.cat(mlvl_bboxes)
+        if rescale:
+            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
+        mlvl_scores = torch.cat(mlvl_scores)
+        mlvl_labels = torch.cat(mlvl_labels)
+
+        if mlvl_score_factors is not None:
+            # TODO： Add sqrt operation in order to be consistent with
+            #  the paper.
+            mlvl_score_factors = torch.cat(mlvl_score_factors)
+            mlvl_scores = mlvl_scores * mlvl_score_factors
+
+        if with_nms:
+            if mlvl_bboxes.numel() == 0:
+                det_bboxes = torch.cat([mlvl_bboxes, mlvl_scores[:, None]], -1)
+                return det_bboxes, mlvl_labels
+
+            det_bboxes, keep_idxs = batched_nms(mlvl_bboxes, mlvl_scores,
+                                                mlvl_labels, cfg.nms)
+            det_bboxes = det_bboxes[:cfg.max_per_img]
+            det_labels = mlvl_labels[keep_idxs][:cfg.max_per_img]
+            return det_bboxes, det_labels
+        else:
+            return mlvl_bboxes, mlvl_scores, mlvl_labels
+
+    def forward_train(self,
+                      x,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels=None,
+                      gt_bboxes_ignore=None,
+                      proposal_cfg=None,
+                      **kwargs):
+        """
+        Args:
+            x (list[Tensor]): Features from FPN.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes (Tensor): Ground truth bboxes of the image,
+                shape (num_gts, 4).
+            gt_labels (Tensor): Ground truth labels of each box,
+                shape (num_gts,).
+            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4).
+            proposal_cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used
+
+        Returns:
+            tuple:
+                losses: (dict[str, Tensor]): A dictionary of loss components.
+                proposal_list (list[Tensor]): Proposals of each image.
+        """
+        outs = self(x)
+        # if gt_labels is None:
+        #     loss_inputs = outs + (gt_bboxes, img_metas)
+        # else:
+        #     loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
+        loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
+        losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
+        if proposal_cfg is None:
+            return losses
+        else:
+            proposal_list = self.get_bboxes(
+                *outs, img_metas=img_metas, cfg=proposal_cfg)
+            return losses, proposal_list
+
+    def simple_test(self, feats, img_metas, rescale=False):
+        """Test function without test-time augmentation.
+
+        Args:
+            feats (tuple[torch.Tensor]): Multi-level features from the
+                upstream network, each is a 4D-tensor.
+            img_metas (list[dict]): List of image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
+                The first item is ``bboxes`` with shape (n, 5),
+                where 5 represent (tl_x, tl_y, br_x, br_y, score).
+                The shape of the second tensor in the tuple is ``labels``
+                with shape (n, ).
+        """
+        return self.simple_test_bboxes(feats, img_metas, rescale=rescale)  # TODO 这玩意跟谁绑定的。怎么绑定的，得清除重定向
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def onnx_export(self,
+                    cls_scores,
+                    bbox_preds,
+                    score_factors=None,
+                    img_metas=None,
+                    with_nms=True):
+        """Transform network output for a batch into bbox predictions.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                with shape (N, num_points * num_classes, H, W).
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_points * 4, H, W).
+            score_factors (list[Tensor]): score_factors for each s
+                cale level with shape (N, num_points * 1, H, W).
+                Default: None.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc. Default: None.
+            with_nms (bool): Whether apply nms to the bboxes. Default: True.
+
+        Returns:
+            tuple[Tensor, Tensor] | list[tuple]: When `with_nms` is True,
+            it is tuple[Tensor, Tensor], first tensor bboxes with shape
+            [N, num_det, 5], 5 arrange as (x1, y1, x2, y2, score)
+            and second element is class labels of shape [N, num_det].
+            When `with_nms` is False, first tensor is bboxes with
+            shape [N, num_det, 4], second tensor is raw score has
+            shape  [N, num_det, num_classes].
+        """
+        assert len(cls_scores) == len(bbox_preds)
+
+        num_levels = len(cls_scores)
+
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        mlvl_priors = self.prior_generator.grid_priors(
+            featmap_sizes,
+            dtype=bbox_preds[0].dtype,
+            device=bbox_preds[0].device)
+
+        mlvl_cls_scores = [cls_scores[i].detach() for i in range(num_levels)]
+        mlvl_bbox_preds = [bbox_preds[i].detach() for i in range(num_levels)]
+
+        assert len(
+            img_metas
+        ) == 1, 'Only support one input image while in exporting to ONNX'
+        img_shape = img_metas[0]['img_shape_for_onnx']
+
+        cfg = self.test_cfg
+        assert len(cls_scores) == len(bbox_preds) == len(mlvl_priors)
+        device = cls_scores[0].device
+        batch_size = cls_scores[0].shape[0]
+        # convert to tensor to keep tracing
+        nms_pre_tensor = torch.tensor(
+            cfg.get('nms_pre', -1), device=device, dtype=torch.long)
+
+        # e.g. Retina, FreeAnchor, etc.
+        if score_factors is None:
+            with_score_factors = False
+            mlvl_score_factor = [None for _ in range(num_levels)]
+        else:
+            # e.g. FCOS, PAA, ATSS, etc.
+            with_score_factors = True
+            mlvl_score_factor = [
+                score_factors[i].detach() for i in range(num_levels)
+            ]
+            mlvl_score_factors = []
+
+        mlvl_batch_bboxes = []
+        mlvl_scores = []
+
+        for cls_score, bbox_pred, score_factors, priors in zip(
+                mlvl_cls_scores, mlvl_bbox_preds, mlvl_score_factor,
+                mlvl_priors):
+            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
+
+            scores = cls_score.permute(0, 2, 3,
+                                       1).reshape(batch_size, -1,
+                                                  self.cls_out_channels)
+            if self.use_sigmoid_cls:
+                scores = scores.sigmoid()
+                nms_pre_score = scores
+            else:
+                scores = scores.softmax(-1)
+                nms_pre_score = scores
+
+            if with_score_factors:
+                score_factors = score_factors.permute(0, 2, 3, 1).reshape(
+                    batch_size, -1).sigmoid()
+            bbox_pred = bbox_pred.permute(0, 2, 3,
+                                          1).reshape(batch_size, -1, 4)
+            priors = priors.expand(batch_size, -1, priors.size(-1))
+            # Get top-k predictions
+            from mmdet.core.export import get_k_for_topk
+            nms_pre = get_k_for_topk(nms_pre_tensor, bbox_pred.shape[1])
+            if nms_pre > 0:
+
+                if with_score_factors:
+                    nms_pre_score = (nms_pre_score * score_factors[..., None])
+                else:
+                    nms_pre_score = nms_pre_score
+
+                # Get maximum scores for foreground classes.
+                if self.use_sigmoid_cls:
+                    max_scores, _ = nms_pre_score.max(-1)
+                else:
+                    # remind that we set FG labels to [0, num_class-1]
+                    # since mmdet v2.0
+                    # BG cat_id: num_class
+                    max_scores, _ = nms_pre_score[..., :-1].max(-1)
+                _, topk_inds = max_scores.topk(nms_pre)
+
+                batch_inds = torch.arange(
+                    batch_size, device=bbox_pred.device).view(
+                        -1, 1).expand_as(topk_inds).long()
+                # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501
+                transformed_inds = bbox_pred.shape[1] * batch_inds + topk_inds
+                priors = priors.reshape(
+                    -1, priors.size(-1))[transformed_inds, :].reshape(
+                        batch_size, -1, priors.size(-1))
+                bbox_pred = bbox_pred.reshape(-1,
+                                              4)[transformed_inds, :].reshape(
+                                                  batch_size, -1, 4)
+                scores = scores.reshape(
+                    -1, self.cls_out_channels)[transformed_inds, :].reshape(
+                        batch_size, -1, self.cls_out_channels)
+                if with_score_factors:
+                    score_factors = score_factors.reshape(
+                        -1, 1)[transformed_inds].reshape(batch_size, -1)
+
+            bboxes = self.bbox_coder.decode(
+                priors, bbox_pred, max_shape=img_shape)
+
+            mlvl_batch_bboxes.append(bboxes)
+            mlvl_scores.append(scores)
+            if with_score_factors:
+                mlvl_score_factors.append(score_factors)
+
+        batch_bboxes = torch.cat(mlvl_batch_bboxes, dim=1)
+        batch_scores = torch.cat(mlvl_scores, dim=1)
+        if with_score_factors:
+            batch_score_factors = torch.cat(mlvl_score_factors, dim=1)
+
+        # Replace multiclass_nms with ONNX::NonMaxSuppression in deployment
+
+        from mmdet.core.export import add_dummy_nms_for_onnx
+
+        if not self.use_sigmoid_cls:
+            batch_scores = batch_scores[..., :self.num_classes]
+
+        if with_score_factors:
+            batch_scores = batch_scores * (batch_score_factors.unsqueeze(2))
+
+        if with_nms:
+            max_output_boxes_per_class = cfg.nms.get(
+                'max_output_boxes_per_class', 200)
+            iou_threshold = cfg.nms.get('iou_threshold', 0.5)
+            score_threshold = cfg.score_thr
+            nms_pre = cfg.get('deploy_nms_pre', -1)
+            return add_dummy_nms_for_onnx(batch_bboxes, batch_scores,
+                                          max_output_boxes_per_class,
+                                          iou_threshold, score_threshold,
+                                          nms_pre, cfg.max_per_img)
+        else:
+            return batch_bboxes, batch_scores
diff --git a/mmdet/models/dense_heads/base_mask_head.py b/mmdet/models/dense_heads/base_mask_head.py
new file mode 100644
index 0000000..5eb94fb
--- /dev/null
+++ b/mmdet/models/dense_heads/base_mask_head.py
@@ -0,0 +1,116 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from abc import ABCMeta, abstractmethod
+
+from mmcv.runner import BaseModule
+
+
+class BaseMaskHead(BaseModule, metaclass=ABCMeta):
+    """Base class for mask heads used in One-Stage Instance Segmentation."""
+
+    def __init__(self, init_cfg):
+        super(BaseMaskHead, self).__init__(init_cfg)
+
+    @abstractmethod
+    def loss(self, **kwargs):
+        pass
+
+    @abstractmethod
+    def get_results(self, **kwargs):
+        """Get precessed :obj:`InstanceData` of multiple images."""
+        pass
+
+    def forward_train(self,
+                      x,
+                      gt_labels,
+                      gt_masks,
+                      img_metas,
+                      gt_bboxes=None,
+                      gt_bboxes_ignore=None,
+                      positive_infos=None,
+                      **kwargs):
+        """
+        Args:
+            x (list[Tensor] | tuple[Tensor]): Features from FPN.
+                Each has a shape (B, C, H, W).
+            gt_labels (list[Tensor]): Ground truth labels of all images.
+                each has a shape (num_gts,).
+            gt_masks (list[Tensor]) : Masks for each bbox, has a shape
+                (num_gts, h , w).
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes (list[Tensor]): Ground truth bboxes of the image,
+                each item has a shape (num_gts, 4).
+            gt_bboxes_ignore (list[Tensor], None): Ground truth bboxes to be
+                ignored, each item has a shape (num_ignored_gts, 4).
+            positive_infos (list[:obj:`InstanceData`], optional): Information
+                of positive samples. Used when the label assignment is
+                done outside the MaskHead, e.g., in BboxHead in
+                YOLACT or CondInst, etc. When the label assignment is done in
+                MaskHead, it would be None, like SOLO. All values
+                in it should have shape (num_positive_samples, *).
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        if positive_infos is None:
+            outs = self(x)
+        else:
+            outs = self(x, positive_infos)
+
+        assert isinstance(outs, tuple), 'Forward results should be a tuple, ' \
+                                        'even if only one item is returned'
+        loss = self.loss(
+            *outs,
+            gt_labels=gt_labels,
+            gt_masks=gt_masks,
+            img_metas=img_metas,
+            gt_bboxes=gt_bboxes,
+            gt_bboxes_ignore=gt_bboxes_ignore,
+            positive_infos=positive_infos,
+            **kwargs)
+        return loss
+
+    def simple_test(self,
+                    feats,
+                    img_metas,
+                    rescale=False,
+                    instances_list=None,
+                    **kwargs):
+        """Test function without test-time augmentation.
+
+        Args:
+            feats (tuple[torch.Tensor]): Multi-level features from the
+                upstream network, each is a 4D-tensor.
+            img_metas (list[dict]): List of image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+            instances_list (list[obj:`InstanceData`], optional): Detection
+                results of each image after the post process. Only exist
+                if there is a `bbox_head`, like `YOLACT`, `CondInst`, etc.
+
+        Returns:
+            list[obj:`InstanceData`]: Instance segmentation \
+                results of each image after the post process. \
+                Each item usually contains following keys. \
+
+                - scores (Tensor): Classification scores, has a shape
+                  (num_instance,)
+                - labels (Tensor): Has a shape (num_instances,).
+                - masks (Tensor): Processed mask results, has a
+                  shape (num_instances, h, w).
+        """
+        if instances_list is None:
+            outs = self(feats)
+        else:
+            outs = self(feats, instances_list=instances_list)
+        mask_inputs = outs + (img_metas, )
+        results_list = self.get_results(
+            *mask_inputs,
+            rescale=rescale,
+            instances_list=instances_list,
+            **kwargs)
+        return results_list
+
+    def onnx_export(self, img, img_metas):
+        raise NotImplementedError(f'{self.__class__.__name__} does '
+                                  f'not support ONNX EXPORT')
diff --git a/mmdet/models/dense_heads/cascade_rpn_head.py b/mmdet/models/dense_heads/cascade_rpn_head.py
new file mode 100644
index 0000000..69347e0
--- /dev/null
+++ b/mmdet/models/dense_heads/cascade_rpn_head.py
@@ -0,0 +1,801 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from __future__ import division
+import copy
+import warnings
+
+import torch
+import torch.nn as nn
+from mmcv import ConfigDict
+from mmcv.ops import DeformConv2d, batched_nms
+from mmcv.runner import BaseModule, ModuleList
+
+from mmdet.core import (RegionAssigner, build_assigner, build_sampler,
+                        images_to_levels, multi_apply)
+from mmdet.core.utils import select_single_mlvl
+from ..builder import HEADS, build_head
+from .base_dense_head import BaseDenseHead
+from .rpn_head import RPNHead
+
+
+class AdaptiveConv(BaseModule):
+    """AdaptiveConv used to adapt the sampling location with the anchors.
+
+    Args:
+        in_channels (int): Number of channels in the input image
+        out_channels (int): Number of channels produced by the convolution
+        kernel_size (int or tuple): Size of the conv kernel. Default: 3
+        stride (int or tuple, optional): Stride of the convolution. Default: 1
+        padding (int or tuple, optional): Zero-padding added to both sides of
+            the input. Default: 1
+        dilation (int or tuple, optional): Spacing between kernel elements.
+            Default: 3
+        groups (int, optional): Number of blocked connections from input
+            channels to output channels. Default: 1
+        bias (bool, optional): If set True, adds a learnable bias to the
+            output. Default: False.
+        type (str, optional): Type of adaptive conv, can be either 'offset'
+            (arbitrary anchors) or 'dilation' (uniform anchor).
+            Default: 'dilation'.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=3,
+                 stride=1,
+                 padding=1,
+                 dilation=3,
+                 groups=1,
+                 bias=False,
+                 type='dilation',
+                 init_cfg=dict(
+                     type='Normal', std=0.01, override=dict(name='conv'))):
+        super(AdaptiveConv, self).__init__(init_cfg)
+        assert type in ['offset', 'dilation']
+        self.adapt_type = type
+
+        assert kernel_size == 3, 'Adaptive conv only supports kernels 3'
+        if self.adapt_type == 'offset':
+            assert stride == 1 and padding == 1 and groups == 1, \
+                'Adaptive conv offset mode only supports padding: {1}, ' \
+                f'stride: {1}, groups: {1}'
+            self.conv = DeformConv2d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                padding=padding,
+                stride=stride,
+                groups=groups,
+                bias=bias)
+        else:
+            self.conv = nn.Conv2d(
+                in_channels,
+                out_channels,
+                kernel_size,
+                padding=dilation,
+                dilation=dilation)
+
+    def forward(self, x, offset):
+        """Forward function."""
+        if self.adapt_type == 'offset':
+            N, _, H, W = x.shape
+            assert offset is not None
+            assert H * W == offset.shape[1]
+            # reshape [N, NA, 18] to (N, 18, H, W)
+            offset = offset.permute(0, 2, 1).reshape(N, -1, H, W)
+            offset = offset.contiguous()
+            x = self.conv(x, offset)
+        else:
+            assert offset is None
+            x = self.conv(x)
+        return x
+
+
+@HEADS.register_module()
+class StageCascadeRPNHead(RPNHead):
+    """Stage of CascadeRPNHead.
+
+    Args:
+        in_channels (int): Number of channels in the input feature map.
+        anchor_generator (dict): anchor generator config.
+        adapt_cfg (dict): adaptation config.
+        bridged_feature (bool, optional): whether update rpn feature.
+            Default: False.
+        with_cls (bool, optional): whether use classification branch.
+            Default: True.
+        sampling (bool, optional): whether use sampling. Default: True.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 in_channels,
+                 anchor_generator=dict(
+                     type='AnchorGenerator',
+                     scales=[8],
+                     ratios=[1.0],
+                     strides=[4, 8, 16, 32, 64]),
+                 adapt_cfg=dict(type='dilation', dilation=3),
+                 bridged_feature=False,
+                 with_cls=True,
+                 sampling=True,
+                 init_cfg=None,
+                 **kwargs):
+        self.with_cls = with_cls
+        self.anchor_strides = anchor_generator['strides']
+        self.anchor_scales = anchor_generator['scales']
+        self.bridged_feature = bridged_feature
+        self.adapt_cfg = adapt_cfg
+        super(StageCascadeRPNHead, self).__init__(
+            in_channels,
+            anchor_generator=anchor_generator,
+            init_cfg=init_cfg,
+            **kwargs)
+
+        # override sampling and sampler
+        self.sampling = sampling
+        if self.train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            # use PseudoSampler when sampling is False
+            if self.sampling and hasattr(self.train_cfg, 'sampler'):
+                sampler_cfg = self.train_cfg.sampler
+            else:
+                sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+
+        if init_cfg is None:
+            self.init_cfg = dict(
+                type='Normal', std=0.01, override=[dict(name='rpn_reg')])
+            if self.with_cls:
+                self.init_cfg['override'].append(dict(name='rpn_cls'))
+
+    def _init_layers(self):
+        """Init layers of a CascadeRPN stage."""
+        self.rpn_conv = AdaptiveConv(self.in_channels, self.feat_channels,
+                                     **self.adapt_cfg)
+        if self.with_cls:
+            self.rpn_cls = nn.Conv2d(self.feat_channels,
+                                     self.num_anchors * self.cls_out_channels,
+                                     1)
+        self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_anchors * 4, 1)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward_single(self, x, offset):
+        """Forward function of single scale."""
+        bridged_x = x
+        x = self.relu(self.rpn_conv(x, offset))
+        if self.bridged_feature:
+            bridged_x = x  # update feature
+        cls_score = self.rpn_cls(x) if self.with_cls else None
+        bbox_pred = self.rpn_reg(x)
+        return bridged_x, cls_score, bbox_pred
+
+    def forward(self, feats, offset_list=None):
+        """Forward function."""
+        if offset_list is None:
+            offset_list = [None for _ in range(len(feats))]
+        return multi_apply(self.forward_single, feats, offset_list)
+
+    def _region_targets_single(self,
+                               anchors,
+                               valid_flags,
+                               gt_bboxes,
+                               gt_bboxes_ignore,
+                               gt_labels,
+                               img_meta,
+                               featmap_sizes,
+                               label_channels=1):
+        """Get anchor targets based on region for single level."""
+        assign_result = self.assigner.assign(
+            anchors,
+            valid_flags,
+            gt_bboxes,
+            img_meta,
+            featmap_sizes,
+            self.anchor_scales[0],
+            self.anchor_strides,
+            gt_bboxes_ignore=gt_bboxes_ignore,
+            gt_labels=None,
+            allowed_border=self.train_cfg.allowed_border)
+        flat_anchors = torch.cat(anchors)
+        sampling_result = self.sampler.sample(assign_result, flat_anchors,
+                                              gt_bboxes)
+
+        num_anchors = flat_anchors.shape[0]
+        bbox_targets = torch.zeros_like(flat_anchors)
+        bbox_weights = torch.zeros_like(flat_anchors)
+        labels = flat_anchors.new_zeros(num_anchors, dtype=torch.long)
+        label_weights = flat_anchors.new_zeros(num_anchors, dtype=torch.float)
+
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+        if len(pos_inds) > 0:
+            if not self.reg_decoded_bbox:
+                pos_bbox_targets = self.bbox_coder.encode(
+                    sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
+            else:
+                pos_bbox_targets = sampling_result.pos_gt_bboxes
+            bbox_targets[pos_inds, :] = pos_bbox_targets
+            bbox_weights[pos_inds, :] = 1.0
+            if gt_labels is None:
+                labels[pos_inds] = 1
+            else:
+                labels[pos_inds] = gt_labels[
+                    sampling_result.pos_assigned_gt_inds]
+            if self.train_cfg.pos_weight <= 0:
+                label_weights[pos_inds] = 1.0
+            else:
+                label_weights[pos_inds] = self.train_cfg.pos_weight
+        if len(neg_inds) > 0:
+            label_weights[neg_inds] = 1.0
+
+        return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
+                neg_inds)
+
+    def region_targets(self,
+                       anchor_list,
+                       valid_flag_list,
+                       gt_bboxes_list,
+                       img_metas,
+                       featmap_sizes,
+                       gt_bboxes_ignore_list=None,
+                       gt_labels_list=None,
+                       label_channels=1,
+                       unmap_outputs=True):
+        """See :func:`StageCascadeRPNHead.get_targets`."""
+        num_imgs = len(img_metas)
+        assert len(anchor_list) == len(valid_flag_list) == num_imgs
+
+        # anchor number of multi levels
+        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+
+        # compute targets for each image
+        if gt_bboxes_ignore_list is None:
+            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
+        if gt_labels_list is None:
+            gt_labels_list = [None for _ in range(num_imgs)]
+        (all_labels, all_label_weights, all_bbox_targets, all_bbox_weights,
+         pos_inds_list, neg_inds_list) = multi_apply(
+             self._region_targets_single,
+             anchor_list,
+             valid_flag_list,
+             gt_bboxes_list,
+             gt_bboxes_ignore_list,
+             gt_labels_list,
+             img_metas,
+             featmap_sizes=featmap_sizes,
+             label_channels=label_channels)
+        # no valid anchors
+        if any([labels is None for labels in all_labels]):
+            return None
+        # sampled anchors of all images
+        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
+        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
+        # split targets to a list w.r.t. multiple levels
+        labels_list = images_to_levels(all_labels, num_level_anchors)
+        label_weights_list = images_to_levels(all_label_weights,
+                                              num_level_anchors)
+        bbox_targets_list = images_to_levels(all_bbox_targets,
+                                             num_level_anchors)
+        bbox_weights_list = images_to_levels(all_bbox_weights,
+                                             num_level_anchors)
+        return (labels_list, label_weights_list, bbox_targets_list,
+                bbox_weights_list, num_total_pos, num_total_neg)
+
+    def get_targets(self,
+                    anchor_list,
+                    valid_flag_list,
+                    gt_bboxes,
+                    img_metas,
+                    featmap_sizes,
+                    gt_bboxes_ignore=None,
+                    label_channels=1):
+        """Compute regression and classification targets for anchors.
+
+        Args:
+            anchor_list (list[list]): Multi level anchors of each image.
+            valid_flag_list (list[list]): Multi level valid flags of each
+                image.
+            gt_bboxes (list[Tensor]): Ground truth bboxes of each image.
+            img_metas (list[dict]): Meta info of each image.
+            featmap_sizes (list[Tensor]): Feature mapsize each level
+            gt_bboxes_ignore (list[Tensor]): Ignore bboxes of each images
+            label_channels (int): Channel of label.
+
+        Returns:
+            cls_reg_targets (tuple)
+        """
+        if isinstance(self.assigner, RegionAssigner):
+            cls_reg_targets = self.region_targets(
+                anchor_list,
+                valid_flag_list,
+                gt_bboxes,
+                img_metas,
+                featmap_sizes,
+                gt_bboxes_ignore_list=gt_bboxes_ignore,
+                label_channels=label_channels)
+        else:
+            cls_reg_targets = super(StageCascadeRPNHead, self).get_targets(
+                anchor_list,
+                valid_flag_list,
+                gt_bboxes,
+                img_metas,
+                gt_bboxes_ignore_list=gt_bboxes_ignore,
+                label_channels=label_channels)
+        return cls_reg_targets
+
+    def anchor_offset(self, anchor_list, anchor_strides, featmap_sizes):
+        """ Get offset for deformable conv based on anchor shape
+        NOTE: currently support deformable kernel_size=3 and dilation=1
+
+        Args:
+            anchor_list (list[list[tensor])): [NI, NLVL, NA, 4] list of
+                multi-level anchors
+            anchor_strides (list[int]): anchor stride of each level
+
+        Returns:
+            offset_list (list[tensor]): [NLVL, NA, 2, 18]: offset of DeformConv
+                kernel.
+        """
+
+        def _shape_offset(anchors, stride, ks=3, dilation=1):
+            # currently support kernel_size=3 and dilation=1
+            assert ks == 3 and dilation == 1
+            pad = (ks - 1) // 2
+            idx = torch.arange(-pad, pad + 1, dtype=dtype, device=device)
+            yy, xx = torch.meshgrid(idx, idx)  # return order matters
+            xx = xx.reshape(-1)
+            yy = yy.reshape(-1)
+            w = (anchors[:, 2] - anchors[:, 0]) / stride
+            h = (anchors[:, 3] - anchors[:, 1]) / stride
+            w = w / (ks - 1) - dilation
+            h = h / (ks - 1) - dilation
+            offset_x = w[:, None] * xx  # (NA, ks**2)
+            offset_y = h[:, None] * yy  # (NA, ks**2)
+            return offset_x, offset_y
+
+        def _ctr_offset(anchors, stride, featmap_size):
+            feat_h, feat_w = featmap_size
+            assert len(anchors) == feat_h * feat_w
+
+            x = (anchors[:, 0] + anchors[:, 2]) * 0.5
+            y = (anchors[:, 1] + anchors[:, 3]) * 0.5
+            # compute centers on feature map
+            x = x / stride
+            y = y / stride
+            # compute predefine centers
+            xx = torch.arange(0, feat_w, device=anchors.device)
+            yy = torch.arange(0, feat_h, device=anchors.device)
+            yy, xx = torch.meshgrid(yy, xx)
+            xx = xx.reshape(-1).type_as(x)
+            yy = yy.reshape(-1).type_as(y)
+
+            offset_x = x - xx  # (NA, )
+            offset_y = y - yy  # (NA, )
+            return offset_x, offset_y
+
+        num_imgs = len(anchor_list)
+        num_lvls = len(anchor_list[0])
+        dtype = anchor_list[0][0].dtype
+        device = anchor_list[0][0].device
+        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+
+        offset_list = []
+        for i in range(num_imgs):
+            mlvl_offset = []
+            for lvl in range(num_lvls):
+                c_offset_x, c_offset_y = _ctr_offset(anchor_list[i][lvl],
+                                                     anchor_strides[lvl],
+                                                     featmap_sizes[lvl])
+                s_offset_x, s_offset_y = _shape_offset(anchor_list[i][lvl],
+                                                       anchor_strides[lvl])
+
+                # offset = ctr_offset + shape_offset
+                offset_x = s_offset_x + c_offset_x[:, None]
+                offset_y = s_offset_y + c_offset_y[:, None]
+
+                # offset order (y0, x0, y1, x2, .., y8, x8, y9, x9)
+                offset = torch.stack([offset_y, offset_x], dim=-1)
+                offset = offset.reshape(offset.size(0), -1)  # [NA, 2*ks**2]
+                mlvl_offset.append(offset)
+            offset_list.append(torch.cat(mlvl_offset))  # [totalNA, 2*ks**2]
+        offset_list = images_to_levels(offset_list, num_level_anchors)
+        return offset_list
+
+    def loss_single(self, cls_score, bbox_pred, anchors, labels, label_weights,
+                    bbox_targets, bbox_weights, num_total_samples):
+        """Loss function on single scale."""
+        # classification loss
+        if self.with_cls:
+            labels = labels.reshape(-1)
+            label_weights = label_weights.reshape(-1)
+            cls_score = cls_score.permute(0, 2, 3,
+                                          1).reshape(-1, self.cls_out_channels)
+            loss_cls = self.loss_cls(
+                cls_score, labels, label_weights, avg_factor=num_total_samples)
+        # regression loss
+        bbox_targets = bbox_targets.reshape(-1, 4)
+        bbox_weights = bbox_weights.reshape(-1, 4)
+        bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
+        if self.reg_decoded_bbox:
+            # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
+            # is applied directly on the decoded bounding boxes, it
+            # decodes the already encoded coordinates to absolute format.
+            anchors = anchors.reshape(-1, 4)
+            bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
+        loss_reg = self.loss_bbox(
+            bbox_pred,
+            bbox_targets,
+            bbox_weights,
+            avg_factor=num_total_samples)
+        if self.with_cls:
+            return loss_cls, loss_reg
+        return None, loss_reg
+
+    def loss(self,
+             anchor_list,
+             valid_flag_list,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            anchor_list (list[list]): Multi level anchors of each image.
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss. Default: None
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        featmap_sizes = [featmap.size()[-2:] for featmap in bbox_preds]
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            featmap_sizes,
+            gt_bboxes_ignore=gt_bboxes_ignore,
+            label_channels=label_channels)
+        if cls_reg_targets is None:
+            return None
+        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
+         num_total_pos, num_total_neg) = cls_reg_targets
+        if self.sampling:
+            num_total_samples = num_total_pos + num_total_neg
+        else:
+            # 200 is hard-coded average factor,
+            # which follows guided anchoring.
+            num_total_samples = sum([label.numel()
+                                     for label in labels_list]) / 200.0
+
+        # change per image, per level anchor_list to per_level, per_image
+        mlvl_anchor_list = list(zip(*anchor_list))
+        # concat mlvl_anchor_list
+        mlvl_anchor_list = [
+            torch.cat(anchors, dim=0) for anchors in mlvl_anchor_list
+        ]
+
+        losses = multi_apply(
+            self.loss_single,
+            cls_scores,
+            bbox_preds,
+            mlvl_anchor_list,
+            labels_list,
+            label_weights_list,
+            bbox_targets_list,
+            bbox_weights_list,
+            num_total_samples=num_total_samples)
+        if self.with_cls:
+            return dict(loss_rpn_cls=losses[0], loss_rpn_reg=losses[1])
+        return dict(loss_rpn_reg=losses[1])
+
+    def get_bboxes(self,
+                   anchor_list,
+                   cls_scores,
+                   bbox_preds,
+                   img_metas,
+                   cfg,
+                   rescale=False):
+        """Get proposal predict.
+
+        Args:
+            anchor_list (list[list]): Multi level anchors of each image.
+            cls_scores (list[Tensor]): Classification scores for all
+                scale levels, each is a 4D-tensor, has shape
+                (batch_size, num_priors * num_classes, H, W).
+            bbox_preds (list[Tensor]): Box energies / deltas for all
+                scale levels, each is a 4D-tensor, has shape
+                (batch_size, num_priors * 4, H, W).
+            img_metas (list[dict], Optional): Image meta info. Default None.
+            cfg (mmcv.Config, Optional): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+
+        Returns:
+            Tensor: Labeled boxes in shape (n, 5), where the first 4 columns
+                are bounding box positions (tl_x, tl_y, br_x, br_y) and the
+                5-th column is a score between 0 and 1.
+        """
+        assert len(cls_scores) == len(bbox_preds)
+
+        result_list = []
+        for img_id in range(len(img_metas)):
+            cls_score_list = select_single_mlvl(cls_scores, img_id)
+            bbox_pred_list = select_single_mlvl(bbox_preds, img_id)
+            img_shape = img_metas[img_id]['img_shape']
+            scale_factor = img_metas[img_id]['scale_factor']
+            proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list,
+                                                anchor_list[img_id], img_shape,
+                                                scale_factor, cfg, rescale)
+            result_list.append(proposals)
+        return result_list
+
+    def _get_bboxes_single(self,
+                           cls_scores,
+                           bbox_preds,
+                           mlvl_anchors,
+                           img_shape,
+                           scale_factor,
+                           cfg,
+                           rescale=False):
+        """Transform outputs of a single image into bbox predictions.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores from all scale
+                levels of a single image, each item has shape
+                (num_anchors * num_classes, H, W).
+            bbox_preds (list[Tensor]): Box energies / deltas from
+                all scale levels of a single image, each item has
+                shape (num_anchors * 4, H, W).
+            mlvl_anchors (list[Tensor]): Box reference from all scale
+                levels of a single image, each item has shape
+                (num_total_anchors, 4).
+            img_shape (tuple[int]): Shape of the input image,
+                (height, width, 3).
+            scale_factor (ndarray): Scale factor of the image arange as
+                (w_scale, h_scale, w_scale, h_scale).
+            cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+                Default False.
+
+        Returns:
+            Tensor: Labeled boxes in shape (n, 5), where the first 4 columns
+                are bounding box positions (tl_x, tl_y, br_x, br_y) and the
+                5-th column is a score between 0 and 1.
+        """
+        cfg = self.test_cfg if cfg is None else cfg
+        cfg = copy.deepcopy(cfg)
+        # bboxes from different level should be independent during NMS,
+        # level_ids are used as labels for batched NMS to separate them
+        level_ids = []
+        mlvl_scores = []
+        mlvl_bbox_preds = []
+        mlvl_valid_anchors = []
+        nms_pre = cfg.get('nms_pre', -1)
+        for idx in range(len(cls_scores)):
+            rpn_cls_score = cls_scores[idx]
+            rpn_bbox_pred = bbox_preds[idx]
+            assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
+            rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
+            if self.use_sigmoid_cls:
+                rpn_cls_score = rpn_cls_score.reshape(-1)
+                scores = rpn_cls_score.sigmoid()
+            else:
+                rpn_cls_score = rpn_cls_score.reshape(-1, 2)
+                # We set FG labels to [0, num_class-1] and BG label to
+                # num_class in RPN head since mmdet v2.5, which is unified to
+                # be consistent with other head since mmdet v2.0. In mmdet v2.0
+                # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head.
+                scores = rpn_cls_score.softmax(dim=1)[:, 0]
+            rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4)
+            anchors = mlvl_anchors[idx]
+
+            if 0 < nms_pre < scores.shape[0]:
+                # sort is faster than topk
+                # _, topk_inds = scores.topk(cfg.nms_pre)
+                ranked_scores, rank_inds = scores.sort(descending=True)
+                topk_inds = rank_inds[:nms_pre]
+                scores = ranked_scores[:nms_pre]
+                rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
+                anchors = anchors[topk_inds, :]
+            mlvl_scores.append(scores)
+            mlvl_bbox_preds.append(rpn_bbox_pred)
+            mlvl_valid_anchors.append(anchors)
+            level_ids.append(
+                scores.new_full((scores.size(0), ), idx, dtype=torch.long))
+
+        scores = torch.cat(mlvl_scores)
+        anchors = torch.cat(mlvl_valid_anchors)
+        rpn_bbox_pred = torch.cat(mlvl_bbox_preds)
+        proposals = self.bbox_coder.decode(
+            anchors, rpn_bbox_pred, max_shape=img_shape)
+        ids = torch.cat(level_ids)
+
+        if cfg.min_bbox_size >= 0:
+            w = proposals[:, 2] - proposals[:, 0]
+            h = proposals[:, 3] - proposals[:, 1]
+            valid_mask = (w > cfg.min_bbox_size) & (h > cfg.min_bbox_size)
+            if not valid_mask.all():
+                proposals = proposals[valid_mask]
+                scores = scores[valid_mask]
+                ids = ids[valid_mask]
+
+        # deprecate arguments warning
+        if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg:
+            warnings.warn(
+                'In rpn_proposal or test_cfg, '
+                'nms_thr has been moved to a dict named nms as '
+                'iou_threshold, max_num has been renamed as max_per_img, '
+                'name of original arguments and the way to specify '
+                'iou_threshold of NMS will be deprecated.')
+        if 'nms' not in cfg:
+            cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr))
+        if 'max_num' in cfg:
+            if 'max_per_img' in cfg:
+                assert cfg.max_num == cfg.max_per_img, f'You ' \
+                    f'set max_num and ' \
+                    f'max_per_img at the same time, but get {cfg.max_num} ' \
+                    f'and {cfg.max_per_img} respectively' \
+                    'Please delete max_num which will be deprecated.'
+            else:
+                cfg.max_per_img = cfg.max_num
+        if 'nms_thr' in cfg:
+            assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set' \
+                f' iou_threshold in nms and ' \
+                f'nms_thr at the same time, but get' \
+                f' {cfg.nms.iou_threshold} and {cfg.nms_thr}' \
+                f' respectively. Please delete the nms_thr ' \
+                f'which will be deprecated.'
+
+        if proposals.numel() > 0:
+            dets, _ = batched_nms(proposals, scores, ids, cfg.nms)
+        else:
+            return proposals.new_zeros(0, 5)
+
+        return dets[:cfg.max_per_img]
+
+    def refine_bboxes(self, anchor_list, bbox_preds, img_metas):
+        """Refine bboxes through stages."""
+        num_levels = len(bbox_preds)
+        new_anchor_list = []
+        for img_id in range(len(img_metas)):
+            mlvl_anchors = []
+            for i in range(num_levels):
+                bbox_pred = bbox_preds[i][img_id].detach()
+                bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
+                img_shape = img_metas[img_id]['img_shape']
+                bboxes = self.bbox_coder.decode(anchor_list[img_id][i],
+                                                bbox_pred, img_shape)
+                mlvl_anchors.append(bboxes)
+            new_anchor_list.append(mlvl_anchors)
+        return new_anchor_list
+
+
+@HEADS.register_module()
+class CascadeRPNHead(BaseDenseHead):
+    """The CascadeRPNHead will predict more accurate region proposals, which is
+    required for two-stage detectors (such as Fast/Faster R-CNN). CascadeRPN
+    consists of a sequence of RPNStage to progressively improve the accuracy of
+    the detected proposals.
+
+    More details can be found in ``https://arxiv.org/abs/1909.06720``.
+
+    Args:
+        num_stages (int): number of CascadeRPN stages.
+        stages (list[dict]): list of configs to build the stages.
+        train_cfg (list[dict]): list of configs at training time each stage.
+        test_cfg (dict): config at testing time.
+    """
+
+    def __init__(self, num_stages, stages, train_cfg, test_cfg, init_cfg=None):
+        super(CascadeRPNHead, self).__init__(init_cfg)
+        assert num_stages == len(stages)
+        self.num_stages = num_stages
+        # Be careful! Pretrained weights cannot be loaded when use
+        # nn.ModuleList
+        self.stages = ModuleList()
+        for i in range(len(stages)):
+            train_cfg_i = train_cfg[i] if train_cfg is not None else None
+            stages[i].update(train_cfg=train_cfg_i)
+            stages[i].update(test_cfg=test_cfg)
+            self.stages.append(build_head(stages[i]))
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+
+    def loss(self):
+        """loss() is implemented in StageCascadeRPNHead."""
+        pass
+
+    def get_bboxes(self):
+        """get_bboxes() is implemented in StageCascadeRPNHead."""
+        pass
+
+    def forward_train(self,
+                      x,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels=None,
+                      gt_bboxes_ignore=None,
+                      proposal_cfg=None):
+        """Forward train function."""
+        assert gt_labels is None, 'RPN does not require gt_labels'
+
+        featmap_sizes = [featmap.size()[-2:] for featmap in x]
+        device = x[0].device
+        anchor_list, valid_flag_list = self.stages[0].get_anchors(
+            featmap_sizes, img_metas, device=device)
+
+        losses = dict()
+
+        for i in range(self.num_stages):
+            stage = self.stages[i]
+
+            if stage.adapt_cfg['type'] == 'offset':
+                offset_list = stage.anchor_offset(anchor_list,
+                                                  stage.anchor_strides,
+                                                  featmap_sizes)
+            else:
+                offset_list = None
+            x, cls_score, bbox_pred = stage(x, offset_list)
+            rpn_loss_inputs = (anchor_list, valid_flag_list, cls_score,
+                               bbox_pred, gt_bboxes, img_metas)
+            stage_loss = stage.loss(*rpn_loss_inputs)
+            for name, value in stage_loss.items():
+                losses['s{}.{}'.format(i, name)] = value
+
+            # refine boxes
+            if i < self.num_stages - 1:
+                anchor_list = stage.refine_bboxes(anchor_list, bbox_pred,
+                                                  img_metas)
+        if proposal_cfg is None:
+            return losses
+        else:
+            proposal_list = self.stages[-1].get_bboxes(anchor_list, cls_score,
+                                                       bbox_pred, img_metas,
+                                                       self.test_cfg)
+            return losses, proposal_list
+
+    def simple_test_rpn(self, x, img_metas):
+        """Simple forward test function."""
+        featmap_sizes = [featmap.size()[-2:] for featmap in x]
+        device = x[0].device
+        anchor_list, _ = self.stages[0].get_anchors(
+            featmap_sizes, img_metas, device=device)
+
+        for i in range(self.num_stages):
+            stage = self.stages[i]
+            if stage.adapt_cfg['type'] == 'offset':
+                offset_list = stage.anchor_offset(anchor_list,
+                                                  stage.anchor_strides,
+                                                  featmap_sizes)
+            else:
+                offset_list = None
+            x, cls_score, bbox_pred = stage(x, offset_list)
+            if i < self.num_stages - 1:
+                anchor_list = stage.refine_bboxes(anchor_list, bbox_pred,
+                                                  img_metas)
+
+        proposal_list = self.stages[-1].get_bboxes(anchor_list, cls_score,
+                                                   bbox_pred, img_metas,
+                                                   self.test_cfg)
+        return proposal_list
+
+    def aug_test_rpn(self, x, img_metas):
+        """Augmented forward test function."""
+        raise NotImplementedError(
+            'CascadeRPNHead does not support test-time augmentation')
diff --git a/mmdet/models/dense_heads/centernet_head.py b/mmdet/models/dense_heads/centernet_head.py
new file mode 100644
index 0000000..b9d5d2f
--- /dev/null
+++ b/mmdet/models/dense_heads/centernet_head.py
@@ -0,0 +1,412 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.cnn import bias_init_with_prob, normal_init
+from mmcv.ops import batched_nms
+from mmcv.runner import force_fp32
+
+from mmdet.core import multi_apply
+from mmdet.models import HEADS, build_loss
+from mmdet.models.utils import gaussian_radius, gen_gaussian_target
+from ..utils.gaussian_target import (get_local_maximum, get_topk_from_heatmap,
+                                     transpose_and_gather_feat)
+from .base_dense_head import BaseDenseHead
+from .dense_test_mixins import BBoxTestMixin
+
+
+@HEADS.register_module()
+class CenterNetHead(BaseDenseHead, BBoxTestMixin):
+    """Objects as Points Head. CenterHead use center_point to indicate object's
+    position. Paper link <https://arxiv.org/abs/1904.07850>
+
+    Args:
+        in_channel (int): Number of channel in the input feature map.
+        feat_channel (int): Number of channel in the intermediate feature map.
+        num_classes (int): Number of categories excluding the background
+            category.
+        loss_center_heatmap (dict | None): Config of center heatmap loss.
+            Default: GaussianFocalLoss.
+        loss_wh (dict | None): Config of wh loss. Default: L1Loss.
+        loss_offset (dict | None): Config of offset loss. Default: L1Loss.
+        train_cfg (dict | None): Training config. Useless in CenterNet,
+            but we keep this variable for SingleStageDetector. Default: None.
+        test_cfg (dict | None): Testing config of CenterNet. Default: None.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 in_channel,
+                 feat_channel,
+                 num_classes,
+                 loss_center_heatmap=dict(
+                     type='GaussianFocalLoss', loss_weight=1.0),
+                 loss_wh=dict(type='L1Loss', loss_weight=0.1),
+                 loss_offset=dict(type='L1Loss', loss_weight=1.0),
+                 train_cfg=None,
+                 test_cfg=None,
+                 init_cfg=None):
+        super(CenterNetHead, self).__init__(init_cfg)
+        self.num_classes = num_classes
+        self.heatmap_head = self._build_head(in_channel, feat_channel,
+                                             num_classes)
+        self.wh_head = self._build_head(in_channel, feat_channel, 2)
+        self.offset_head = self._build_head(in_channel, feat_channel, 2)
+
+        self.loss_center_heatmap = build_loss(loss_center_heatmap)
+        self.loss_wh = build_loss(loss_wh)
+        self.loss_offset = build_loss(loss_offset)
+
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+        self.fp16_enabled = False
+
+    def _build_head(self, in_channel, feat_channel, out_channel):
+        """Build head for each branch."""
+        layer = nn.Sequential(
+            nn.Conv2d(in_channel, feat_channel, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(feat_channel, out_channel, kernel_size=1))
+        return layer
+
+    def init_weights(self):
+        """Initialize weights of the head."""
+        bias_init = bias_init_with_prob(0.1)
+        self.heatmap_head[-1].bias.data.fill_(bias_init)
+        for head in [self.wh_head, self.offset_head]:
+            for m in head.modules():
+                if isinstance(m, nn.Conv2d):
+                    normal_init(m, std=0.001)
+
+    def forward(self, feats):
+        """Forward features. Notice CenterNet head does not use FPN.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            center_heatmap_preds (List[Tensor]): center predict heatmaps for
+                all levels, the channels number is num_classes.
+            wh_preds (List[Tensor]): wh predicts for all levels, the channels
+                number is 2.
+            offset_preds (List[Tensor]): offset predicts for all levels, the
+               channels number is 2.
+        """
+        return multi_apply(self.forward_single, feats)
+
+    def forward_single(self, feat):
+        """Forward feature of a single level.
+
+        Args:
+            feat (Tensor): Feature of a single level.
+
+        Returns:
+            center_heatmap_pred (Tensor): center predict heatmaps, the
+               channels number is num_classes.
+            wh_pred (Tensor): wh predicts, the channels number is 2.
+            offset_pred (Tensor): offset predicts, the channels number is 2.
+        """
+        center_heatmap_pred = self.heatmap_head(feat).sigmoid()
+        wh_pred = self.wh_head(feat)
+        offset_pred = self.offset_head(feat)
+        return center_heatmap_pred, wh_pred, offset_pred
+
+    @force_fp32(apply_to=('center_heatmap_preds', 'wh_preds', 'offset_preds'))
+    def loss(self,
+             center_heatmap_preds,
+             wh_preds,
+             offset_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            center_heatmap_preds (list[Tensor]): center predict heatmaps for
+               all levels with shape (B, num_classes, H, W).
+            wh_preds (list[Tensor]): wh predicts for all levels with
+               shape (B, 2, H, W).
+            offset_preds (list[Tensor]): offset predicts for all levels
+               with shape (B, 2, H, W).
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss. Default: None
+
+        Returns:
+            dict[str, Tensor]: which has components below:
+                - loss_center_heatmap (Tensor): loss of center heatmap.
+                - loss_wh (Tensor): loss of hw heatmap
+                - loss_offset (Tensor): loss of offset heatmap.
+        """
+        assert len(center_heatmap_preds) == len(wh_preds) == len(
+            offset_preds) == 1
+        center_heatmap_pred = center_heatmap_preds[0]
+        wh_pred = wh_preds[0]
+        offset_pred = offset_preds[0]
+
+        target_result, avg_factor = self.get_targets(gt_bboxes, gt_labels,
+                                                     center_heatmap_pred.shape,
+                                                     img_metas[0]['pad_shape'])
+
+        center_heatmap_target = target_result['center_heatmap_target']
+        wh_target = target_result['wh_target']
+        offset_target = target_result['offset_target']
+        wh_offset_target_weight = target_result['wh_offset_target_weight']
+
+        # Since the channel of wh_target and offset_target is 2, the avg_factor
+        # of loss_center_heatmap is always 1/2 of loss_wh and loss_offset.
+        loss_center_heatmap = self.loss_center_heatmap(
+            center_heatmap_pred, center_heatmap_target, avg_factor=avg_factor)
+        loss_wh = self.loss_wh(
+            wh_pred,
+            wh_target,
+            wh_offset_target_weight,
+            avg_factor=avg_factor * 2)
+        loss_offset = self.loss_offset(
+            offset_pred,
+            offset_target,
+            wh_offset_target_weight,
+            avg_factor=avg_factor * 2)
+        return dict(
+            loss_center_heatmap=loss_center_heatmap,
+            loss_wh=loss_wh,
+            loss_offset=loss_offset)
+
+    def get_targets(self, gt_bboxes, gt_labels, feat_shape, img_shape):
+        """Compute regression and classification targets in multiple images.
+
+        Args:
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box.
+            feat_shape (list[int]): feature map shape with value [B, _, H, W]
+            img_shape (list[int]): image shape in [h, w] format.
+
+        Returns:
+            tuple[dict,float]: The float value is mean avg_factor, the dict has
+               components below:
+               - center_heatmap_target (Tensor): targets of center heatmap, \
+                   shape (B, num_classes, H, W).
+               - wh_target (Tensor): targets of wh predict, shape \
+                   (B, 2, H, W).
+               - offset_target (Tensor): targets of offset predict, shape \
+                   (B, 2, H, W).
+               - wh_offset_target_weight (Tensor): weights of wh and offset \
+                   predict, shape (B, 2, H, W).
+        """
+        img_h, img_w = img_shape[:2]
+        bs, _, feat_h, feat_w = feat_shape
+
+        width_ratio = float(feat_w / img_w)
+        height_ratio = float(feat_h / img_h)
+
+        center_heatmap_target = gt_bboxes[-1].new_zeros(
+            [bs, self.num_classes, feat_h, feat_w])
+        wh_target = gt_bboxes[-1].new_zeros([bs, 2, feat_h, feat_w])
+        offset_target = gt_bboxes[-1].new_zeros([bs, 2, feat_h, feat_w])
+        wh_offset_target_weight = gt_bboxes[-1].new_zeros(
+            [bs, 2, feat_h, feat_w])
+
+        for batch_id in range(bs):
+            gt_bbox = gt_bboxes[batch_id]
+            gt_label = gt_labels[batch_id]
+            center_x = (gt_bbox[:, [0]] + gt_bbox[:, [2]]) * width_ratio / 2
+            center_y = (gt_bbox[:, [1]] + gt_bbox[:, [3]]) * height_ratio / 2
+            gt_centers = torch.cat((center_x, center_y), dim=1)
+
+            for j, ct in enumerate(gt_centers):
+                ctx_int, cty_int = ct.int()
+                ctx, cty = ct
+                scale_box_h = (gt_bbox[j][3] - gt_bbox[j][1]) * height_ratio
+                scale_box_w = (gt_bbox[j][2] - gt_bbox[j][0]) * width_ratio
+                radius = gaussian_radius([scale_box_h, scale_box_w],
+                                         min_overlap=0.3)
+                radius = max(0, int(radius))
+                ind = gt_label[j]
+                gen_gaussian_target(center_heatmap_target[batch_id, ind],
+                                    [ctx_int, cty_int], radius)
+
+                wh_target[batch_id, 0, cty_int, ctx_int] = scale_box_w
+                wh_target[batch_id, 1, cty_int, ctx_int] = scale_box_h
+
+                offset_target[batch_id, 0, cty_int, ctx_int] = ctx - ctx_int
+                offset_target[batch_id, 1, cty_int, ctx_int] = cty - cty_int
+
+                wh_offset_target_weight[batch_id, :, cty_int, ctx_int] = 1
+
+        avg_factor = max(1, center_heatmap_target.eq(1).sum())
+        target_result = dict(
+            center_heatmap_target=center_heatmap_target,
+            wh_target=wh_target,
+            offset_target=offset_target,
+            wh_offset_target_weight=wh_offset_target_weight)
+        return target_result, avg_factor
+
+    @force_fp32(apply_to=('center_heatmap_preds', 'wh_preds', 'offset_preds'))
+    def get_bboxes(self,
+                   center_heatmap_preds,
+                   wh_preds,
+                   offset_preds,
+                   img_metas,
+                   rescale=True,
+                   with_nms=False):
+        """Transform network output for a batch into bbox predictions.
+
+        Args:
+            center_heatmap_preds (list[Tensor]): Center predict heatmaps for
+                all levels with shape (B, num_classes, H, W).
+            wh_preds (list[Tensor]): WH predicts for all levels with
+                shape (B, 2, H, W).
+            offset_preds (list[Tensor]): Offset predicts for all levels
+                with shape (B, 2, H, W).
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            rescale (bool): If True, return boxes in original image space.
+                Default: True.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: False.
+
+        Returns:
+            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
+                The first item is an (n, 5) tensor, where 5 represent
+                (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
+                The shape of the second tensor in the tuple is (n,), and
+                each element represents the class label of the corresponding
+                box.
+        """
+        assert len(center_heatmap_preds) == len(wh_preds) == len(
+            offset_preds) == 1
+        result_list = []
+        for img_id in range(len(img_metas)):
+            result_list.append(
+                self._get_bboxes_single(
+                    center_heatmap_preds[0][img_id:img_id + 1, ...],
+                    wh_preds[0][img_id:img_id + 1, ...],
+                    offset_preds[0][img_id:img_id + 1, ...],
+                    img_metas[img_id],
+                    rescale=rescale,
+                    with_nms=with_nms))
+        return result_list
+
+    def _get_bboxes_single(self,
+                           center_heatmap_pred,
+                           wh_pred,
+                           offset_pred,
+                           img_meta,
+                           rescale=False,
+                           with_nms=True):
+        """Transform outputs of a single image into bbox results.
+
+        Args:
+            center_heatmap_pred (Tensor): Center heatmap for current level with
+                shape (1, num_classes, H, W).
+            wh_pred (Tensor): WH heatmap for current level with shape
+                (1, num_classes, H, W).
+            offset_pred (Tensor): Offset for current level with shape
+                (1, corner_offset_channels, H, W).
+            img_meta (dict): Meta information of current image, e.g.,
+                image size, scaling factor, etc.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+
+        Returns:
+            tuple[Tensor, Tensor]: The first item is an (n, 5) tensor, where
+                5 represent (tl_x, tl_y, br_x, br_y, score) and the score
+                between 0 and 1. The shape of the second tensor in the tuple
+                is (n,), and each element represents the class label of the
+                corresponding box.
+        """
+        batch_det_bboxes, batch_labels = self.decode_heatmap(
+            center_heatmap_pred,
+            wh_pred,
+            offset_pred,
+            img_meta['batch_input_shape'],
+            k=self.test_cfg.topk,
+            kernel=self.test_cfg.local_maximum_kernel)
+
+        det_bboxes = batch_det_bboxes.view([-1, 5])
+        det_labels = batch_labels.view(-1)
+
+        batch_border = det_bboxes.new_tensor(img_meta['border'])[...,
+                                                                 [2, 0, 2, 0]]
+        det_bboxes[..., :4] -= batch_border
+
+        if rescale:
+            det_bboxes[..., :4] /= det_bboxes.new_tensor(
+                img_meta['scale_factor'])
+
+        if with_nms:
+            det_bboxes, det_labels = self._bboxes_nms(det_bboxes, det_labels,
+                                                      self.test_cfg)
+        return det_bboxes, det_labels
+
+    def decode_heatmap(self,
+                       center_heatmap_pred,
+                       wh_pred,
+                       offset_pred,
+                       img_shape,
+                       k=100,
+                       kernel=3):
+        """Transform outputs into detections raw bbox prediction.
+
+        Args:
+            center_heatmap_pred (Tensor): center predict heatmap,
+               shape (B, num_classes, H, W).
+            wh_pred (Tensor): wh predict, shape (B, 2, H, W).
+            offset_pred (Tensor): offset predict, shape (B, 2, H, W).
+            img_shape (list[int]): image shape in [h, w] format.
+            k (int): Get top k center keypoints from heatmap. Default 100.
+            kernel (int): Max pooling kernel for extract local maximum pixels.
+               Default 3.
+
+        Returns:
+            tuple[torch.Tensor]: Decoded output of CenterNetHead, containing
+               the following Tensors:
+
+              - batch_bboxes (Tensor): Coords of each box with shape (B, k, 5)
+              - batch_topk_labels (Tensor): Categories of each box with \
+                  shape (B, k)
+        """
+        height, width = center_heatmap_pred.shape[2:]
+        inp_h, inp_w = img_shape
+
+        center_heatmap_pred = get_local_maximum(
+            center_heatmap_pred, kernel=kernel)
+
+        *batch_dets, topk_ys, topk_xs = get_topk_from_heatmap(
+            center_heatmap_pred, k=k)
+        batch_scores, batch_index, batch_topk_labels = batch_dets
+
+        wh = transpose_and_gather_feat(wh_pred, batch_index)
+        offset = transpose_and_gather_feat(offset_pred, batch_index)
+        topk_xs = topk_xs + offset[..., 0]
+        topk_ys = topk_ys + offset[..., 1]
+        tl_x = (topk_xs - wh[..., 0] / 2) * (inp_w / width)
+        tl_y = (topk_ys - wh[..., 1] / 2) * (inp_h / height)
+        br_x = (topk_xs + wh[..., 0] / 2) * (inp_w / width)
+        br_y = (topk_ys + wh[..., 1] / 2) * (inp_h / height)
+
+        batch_bboxes = torch.stack([tl_x, tl_y, br_x, br_y], dim=2)
+        batch_bboxes = torch.cat((batch_bboxes, batch_scores[..., None]),
+                                 dim=-1)
+        return batch_bboxes, batch_topk_labels
+
+    def _bboxes_nms(self, bboxes, labels, cfg):
+        if labels.numel() > 0:
+            max_num = cfg.max_per_img
+            bboxes, keep = batched_nms(bboxes[:, :4], bboxes[:,
+                                                             -1].contiguous(),
+                                       labels, cfg.nms)
+            if max_num > 0:
+                bboxes = bboxes[:max_num]
+                labels = labels[keep][:max_num]
+
+        return bboxes, labels
diff --git a/mmdet/models/dense_heads/centripetal_head.py b/mmdet/models/dense_heads/centripetal_head.py
new file mode 100644
index 0000000..ebc721b
--- /dev/null
+++ b/mmdet/models/dense_heads/centripetal_head.py
@@ -0,0 +1,430 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+from mmcv.cnn import ConvModule, normal_init
+from mmcv.ops import DeformConv2d
+from mmcv.runner import force_fp32
+
+from mmdet.core import multi_apply
+from ..builder import HEADS, build_loss
+from .corner_head import CornerHead
+
+
+@HEADS.register_module()
+class CentripetalHead(CornerHead):
+    """Head of CentripetalNet: Pursuing High-quality Keypoint Pairs for Object
+    Detection.
+
+    CentripetalHead inherits from :class:`CornerHead`. It removes the
+    embedding branch and adds guiding shift and centripetal shift branches.
+    More details can be found in the `paper
+    <https://arxiv.org/abs/2003.09119>`_ .
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        num_feat_levels (int): Levels of feature from the previous module. 2
+            for HourglassNet-104 and 1 for HourglassNet-52. HourglassNet-104
+            outputs the final feature and intermediate supervision feature and
+            HourglassNet-52 only outputs the final feature. Default: 2.
+        corner_emb_channels (int): Channel of embedding vector. Default: 1.
+        train_cfg (dict | None): Training config. Useless in CornerHead,
+            but we keep this variable for SingleStageDetector. Default: None.
+        test_cfg (dict | None): Testing config of CornerHead. Default: None.
+        loss_heatmap (dict | None): Config of corner heatmap loss. Default:
+            GaussianFocalLoss.
+        loss_embedding (dict | None): Config of corner embedding loss. Default:
+            AssociativeEmbeddingLoss.
+        loss_offset (dict | None): Config of corner offset loss. Default:
+            SmoothL1Loss.
+        loss_guiding_shift (dict): Config of guiding shift loss. Default:
+            SmoothL1Loss.
+        loss_centripetal_shift (dict): Config of centripetal shift loss.
+            Default: SmoothL1Loss.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 *args,
+                 centripetal_shift_channels=2,
+                 guiding_shift_channels=2,
+                 feat_adaption_conv_kernel=3,
+                 loss_guiding_shift=dict(
+                     type='SmoothL1Loss', beta=1.0, loss_weight=0.05),
+                 loss_centripetal_shift=dict(
+                     type='SmoothL1Loss', beta=1.0, loss_weight=1),
+                 init_cfg=None,
+                 **kwargs):
+        assert init_cfg is None, 'To prevent abnormal initialization ' \
+                                 'behavior, init_cfg is not allowed to be set'
+        assert centripetal_shift_channels == 2, (
+            'CentripetalHead only support centripetal_shift_channels == 2')
+        self.centripetal_shift_channels = centripetal_shift_channels
+        assert guiding_shift_channels == 2, (
+            'CentripetalHead only support guiding_shift_channels == 2')
+        self.guiding_shift_channels = guiding_shift_channels
+        self.feat_adaption_conv_kernel = feat_adaption_conv_kernel
+        super(CentripetalHead, self).__init__(
+            *args, init_cfg=init_cfg, **kwargs)
+        self.loss_guiding_shift = build_loss(loss_guiding_shift)
+        self.loss_centripetal_shift = build_loss(loss_centripetal_shift)
+
+    def _init_centripetal_layers(self):
+        """Initialize centripetal layers.
+
+        Including feature adaption deform convs (feat_adaption), deform offset
+        prediction convs (dcn_off), guiding shift (guiding_shift) and
+        centripetal shift ( centripetal_shift). Each branch has two parts:
+        prefix `tl_` for top-left and `br_` for bottom-right.
+        """
+        self.tl_feat_adaption = nn.ModuleList()
+        self.br_feat_adaption = nn.ModuleList()
+        self.tl_dcn_offset = nn.ModuleList()
+        self.br_dcn_offset = nn.ModuleList()
+        self.tl_guiding_shift = nn.ModuleList()
+        self.br_guiding_shift = nn.ModuleList()
+        self.tl_centripetal_shift = nn.ModuleList()
+        self.br_centripetal_shift = nn.ModuleList()
+
+        for _ in range(self.num_feat_levels):
+            self.tl_feat_adaption.append(
+                DeformConv2d(self.in_channels, self.in_channels,
+                             self.feat_adaption_conv_kernel, 1, 1))
+            self.br_feat_adaption.append(
+                DeformConv2d(self.in_channels, self.in_channels,
+                             self.feat_adaption_conv_kernel, 1, 1))
+
+            self.tl_guiding_shift.append(
+                self._make_layers(
+                    out_channels=self.guiding_shift_channels,
+                    in_channels=self.in_channels))
+            self.br_guiding_shift.append(
+                self._make_layers(
+                    out_channels=self.guiding_shift_channels,
+                    in_channels=self.in_channels))
+
+            self.tl_dcn_offset.append(
+                ConvModule(
+                    self.guiding_shift_channels,
+                    self.feat_adaption_conv_kernel**2 *
+                    self.guiding_shift_channels,
+                    1,
+                    bias=False,
+                    act_cfg=None))
+            self.br_dcn_offset.append(
+                ConvModule(
+                    self.guiding_shift_channels,
+                    self.feat_adaption_conv_kernel**2 *
+                    self.guiding_shift_channels,
+                    1,
+                    bias=False,
+                    act_cfg=None))
+
+            self.tl_centripetal_shift.append(
+                self._make_layers(
+                    out_channels=self.centripetal_shift_channels,
+                    in_channels=self.in_channels))
+            self.br_centripetal_shift.append(
+                self._make_layers(
+                    out_channels=self.centripetal_shift_channels,
+                    in_channels=self.in_channels))
+
+    def _init_layers(self):
+        """Initialize layers for CentripetalHead.
+
+        Including two parts: CornerHead layers and CentripetalHead layers
+        """
+        super()._init_layers()  # using _init_layers in CornerHead
+        self._init_centripetal_layers()
+
+    def init_weights(self):
+        super(CentripetalHead, self).init_weights()
+        for i in range(self.num_feat_levels):
+            normal_init(self.tl_feat_adaption[i], std=0.01)
+            normal_init(self.br_feat_adaption[i], std=0.01)
+            normal_init(self.tl_dcn_offset[i].conv, std=0.1)
+            normal_init(self.br_dcn_offset[i].conv, std=0.1)
+            _ = [x.conv.reset_parameters() for x in self.tl_guiding_shift[i]]
+            _ = [x.conv.reset_parameters() for x in self.br_guiding_shift[i]]
+            _ = [
+                x.conv.reset_parameters() for x in self.tl_centripetal_shift[i]
+            ]
+            _ = [
+                x.conv.reset_parameters() for x in self.br_centripetal_shift[i]
+            ]
+
+    def forward_single(self, x, lvl_ind):
+        """Forward feature of a single level.
+
+        Args:
+            x (Tensor): Feature of a single level.
+            lvl_ind (int): Level index of current feature.
+
+        Returns:
+            tuple[Tensor]: A tuple of CentripetalHead's output for current
+            feature level. Containing the following Tensors:
+
+                - tl_heat (Tensor): Predicted top-left corner heatmap.
+                - br_heat (Tensor): Predicted bottom-right corner heatmap.
+                - tl_off (Tensor): Predicted top-left offset heatmap.
+                - br_off (Tensor): Predicted bottom-right offset heatmap.
+                - tl_guiding_shift (Tensor): Predicted top-left guiding shift
+                  heatmap.
+                - br_guiding_shift (Tensor): Predicted bottom-right guiding
+                  shift heatmap.
+                - tl_centripetal_shift (Tensor): Predicted top-left centripetal
+                  shift heatmap.
+                - br_centripetal_shift (Tensor): Predicted bottom-right
+                  centripetal shift heatmap.
+        """
+        tl_heat, br_heat, _, _, tl_off, br_off, tl_pool, br_pool = super(
+        ).forward_single(
+            x, lvl_ind, return_pool=True)
+
+        tl_guiding_shift = self.tl_guiding_shift[lvl_ind](tl_pool)
+        br_guiding_shift = self.br_guiding_shift[lvl_ind](br_pool)
+
+        tl_dcn_offset = self.tl_dcn_offset[lvl_ind](tl_guiding_shift.detach())
+        br_dcn_offset = self.br_dcn_offset[lvl_ind](br_guiding_shift.detach())
+
+        tl_feat_adaption = self.tl_feat_adaption[lvl_ind](tl_pool,
+                                                          tl_dcn_offset)
+        br_feat_adaption = self.br_feat_adaption[lvl_ind](br_pool,
+                                                          br_dcn_offset)
+
+        tl_centripetal_shift = self.tl_centripetal_shift[lvl_ind](
+            tl_feat_adaption)
+        br_centripetal_shift = self.br_centripetal_shift[lvl_ind](
+            br_feat_adaption)
+
+        result_list = [
+            tl_heat, br_heat, tl_off, br_off, tl_guiding_shift,
+            br_guiding_shift, tl_centripetal_shift, br_centripetal_shift
+        ]
+        return result_list
+
+    @force_fp32()
+    def loss(self,
+             tl_heats,
+             br_heats,
+             tl_offs,
+             br_offs,
+             tl_guiding_shifts,
+             br_guiding_shifts,
+             tl_centripetal_shifts,
+             br_centripetal_shifts,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            tl_heats (list[Tensor]): Top-left corner heatmaps for each level
+                with shape (N, num_classes, H, W).
+            br_heats (list[Tensor]): Bottom-right corner heatmaps for each
+                level with shape (N, num_classes, H, W).
+            tl_offs (list[Tensor]): Top-left corner offsets for each level
+                with shape (N, corner_offset_channels, H, W).
+            br_offs (list[Tensor]): Bottom-right corner offsets for each level
+                with shape (N, corner_offset_channels, H, W).
+            tl_guiding_shifts (list[Tensor]): Top-left guiding shifts for each
+                level with shape (N, guiding_shift_channels, H, W).
+            br_guiding_shifts (list[Tensor]): Bottom-right guiding shifts for
+                each level with shape (N, guiding_shift_channels, H, W).
+            tl_centripetal_shifts (list[Tensor]): Top-left centripetal shifts
+                for each level with shape (N, centripetal_shift_channels, H,
+                W).
+            br_centripetal_shifts (list[Tensor]): Bottom-right centripetal
+                shifts for each level with shape (N,
+                centripetal_shift_channels, H, W).
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [left, top, right, bottom] format.
+            gt_labels (list[Tensor]): Class indices corresponding to each box.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components. Containing the
+            following losses:
+
+                - det_loss (list[Tensor]): Corner keypoint losses of all
+                  feature levels.
+                - off_loss (list[Tensor]): Corner offset losses of all feature
+                  levels.
+                - guiding_loss (list[Tensor]): Guiding shift losses of all
+                  feature levels.
+                - centripetal_loss (list[Tensor]): Centripetal shift losses of
+                  all feature levels.
+        """
+        targets = self.get_targets(
+            gt_bboxes,
+            gt_labels,
+            tl_heats[-1].shape,
+            img_metas[0]['pad_shape'],
+            with_corner_emb=self.with_corner_emb,
+            with_guiding_shift=True,
+            with_centripetal_shift=True)
+        mlvl_targets = [targets for _ in range(self.num_feat_levels)]
+        [det_losses, off_losses, guiding_losses, centripetal_losses
+         ] = multi_apply(self.loss_single, tl_heats, br_heats, tl_offs,
+                         br_offs, tl_guiding_shifts, br_guiding_shifts,
+                         tl_centripetal_shifts, br_centripetal_shifts,
+                         mlvl_targets)
+        loss_dict = dict(
+            det_loss=det_losses,
+            off_loss=off_losses,
+            guiding_loss=guiding_losses,
+            centripetal_loss=centripetal_losses)
+        return loss_dict
+
+    def loss_single(self, tl_hmp, br_hmp, tl_off, br_off, tl_guiding_shift,
+                    br_guiding_shift, tl_centripetal_shift,
+                    br_centripetal_shift, targets):
+        """Compute losses for single level.
+
+        Args:
+            tl_hmp (Tensor): Top-left corner heatmap for current level with
+                shape (N, num_classes, H, W).
+            br_hmp (Tensor): Bottom-right corner heatmap for current level with
+                shape (N, num_classes, H, W).
+            tl_off (Tensor): Top-left corner offset for current level with
+                shape (N, corner_offset_channels, H, W).
+            br_off (Tensor): Bottom-right corner offset for current level with
+                shape (N, corner_offset_channels, H, W).
+            tl_guiding_shift (Tensor): Top-left guiding shift for current level
+                with shape (N, guiding_shift_channels, H, W).
+            br_guiding_shift (Tensor): Bottom-right guiding shift for current
+                level with shape (N, guiding_shift_channels, H, W).
+            tl_centripetal_shift (Tensor): Top-left centripetal shift for
+                current level with shape (N, centripetal_shift_channels, H, W).
+            br_centripetal_shift (Tensor): Bottom-right centripetal shift for
+                current level with shape (N, centripetal_shift_channels, H, W).
+            targets (dict): Corner target generated by `get_targets`.
+
+        Returns:
+            tuple[torch.Tensor]: Losses of the head's different branches
+            containing the following losses:
+
+                - det_loss (Tensor): Corner keypoint loss.
+                - off_loss (Tensor): Corner offset loss.
+                - guiding_loss (Tensor): Guiding shift loss.
+                - centripetal_loss (Tensor): Centripetal shift loss.
+        """
+        targets['corner_embedding'] = None
+
+        det_loss, _, _, off_loss = super().loss_single(tl_hmp, br_hmp, None,
+                                                       None, tl_off, br_off,
+                                                       targets)
+
+        gt_tl_guiding_shift = targets['topleft_guiding_shift']
+        gt_br_guiding_shift = targets['bottomright_guiding_shift']
+        gt_tl_centripetal_shift = targets['topleft_centripetal_shift']
+        gt_br_centripetal_shift = targets['bottomright_centripetal_shift']
+
+        gt_tl_heatmap = targets['topleft_heatmap']
+        gt_br_heatmap = targets['bottomright_heatmap']
+        # We only compute the offset loss at the real corner position.
+        # The value of real corner would be 1 in heatmap ground truth.
+        # The mask is computed in class agnostic mode and its shape is
+        # batch * 1 * width * height.
+        tl_mask = gt_tl_heatmap.eq(1).sum(1).gt(0).unsqueeze(1).type_as(
+            gt_tl_heatmap)
+        br_mask = gt_br_heatmap.eq(1).sum(1).gt(0).unsqueeze(1).type_as(
+            gt_br_heatmap)
+
+        # Guiding shift loss
+        tl_guiding_loss = self.loss_guiding_shift(
+            tl_guiding_shift,
+            gt_tl_guiding_shift,
+            tl_mask,
+            avg_factor=tl_mask.sum())
+        br_guiding_loss = self.loss_guiding_shift(
+            br_guiding_shift,
+            gt_br_guiding_shift,
+            br_mask,
+            avg_factor=br_mask.sum())
+        guiding_loss = (tl_guiding_loss + br_guiding_loss) / 2.0
+        # Centripetal shift loss
+        tl_centripetal_loss = self.loss_centripetal_shift(
+            tl_centripetal_shift,
+            gt_tl_centripetal_shift,
+            tl_mask,
+            avg_factor=tl_mask.sum())
+        br_centripetal_loss = self.loss_centripetal_shift(
+            br_centripetal_shift,
+            gt_br_centripetal_shift,
+            br_mask,
+            avg_factor=br_mask.sum())
+        centripetal_loss = (tl_centripetal_loss + br_centripetal_loss) / 2.0
+
+        return det_loss, off_loss, guiding_loss, centripetal_loss
+
+    @force_fp32()
+    def get_bboxes(self,
+                   tl_heats,
+                   br_heats,
+                   tl_offs,
+                   br_offs,
+                   tl_guiding_shifts,
+                   br_guiding_shifts,
+                   tl_centripetal_shifts,
+                   br_centripetal_shifts,
+                   img_metas,
+                   rescale=False,
+                   with_nms=True):
+        """Transform network output for a batch into bbox predictions.
+
+        Args:
+            tl_heats (list[Tensor]): Top-left corner heatmaps for each level
+                with shape (N, num_classes, H, W).
+            br_heats (list[Tensor]): Bottom-right corner heatmaps for each
+                level with shape (N, num_classes, H, W).
+            tl_offs (list[Tensor]): Top-left corner offsets for each level
+                with shape (N, corner_offset_channels, H, W).
+            br_offs (list[Tensor]): Bottom-right corner offsets for each level
+                with shape (N, corner_offset_channels, H, W).
+            tl_guiding_shifts (list[Tensor]): Top-left guiding shifts for each
+                level with shape (N, guiding_shift_channels, H, W). Useless in
+                this function, we keep this arg because it's the raw output
+                from CentripetalHead.
+            br_guiding_shifts (list[Tensor]): Bottom-right guiding shifts for
+                each level with shape (N, guiding_shift_channels, H, W).
+                Useless in this function, we keep this arg because it's the
+                raw output from CentripetalHead.
+            tl_centripetal_shifts (list[Tensor]): Top-left centripetal shifts
+                for each level with shape (N, centripetal_shift_channels, H,
+                W).
+            br_centripetal_shifts (list[Tensor]): Bottom-right centripetal
+                shifts for each level with shape (N,
+                centripetal_shift_channels, H, W).
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+        """
+        assert tl_heats[-1].shape[0] == br_heats[-1].shape[0] == len(img_metas)
+        result_list = []
+        for img_id in range(len(img_metas)):
+            result_list.append(
+                self._get_bboxes_single(
+                    tl_heats[-1][img_id:img_id + 1, :],
+                    br_heats[-1][img_id:img_id + 1, :],
+                    tl_offs[-1][img_id:img_id + 1, :],
+                    br_offs[-1][img_id:img_id + 1, :],
+                    img_metas[img_id],
+                    tl_emb=None,
+                    br_emb=None,
+                    tl_centripetal_shift=tl_centripetal_shifts[-1][
+                        img_id:img_id + 1, :],
+                    br_centripetal_shift=br_centripetal_shifts[-1][
+                        img_id:img_id + 1, :],
+                    rescale=rescale,
+                    with_nms=with_nms))
+
+        return result_list
diff --git a/mmdet/models/dense_heads/corner_head.py b/mmdet/models/dense_heads/corner_head.py
new file mode 100644
index 0000000..c6a2866
--- /dev/null
+++ b/mmdet/models/dense_heads/corner_head.py
@@ -0,0 +1,1086 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from logging import warning
+from math import ceil, log
+
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule, bias_init_with_prob
+from mmcv.ops import CornerPool, batched_nms
+from mmcv.runner import BaseModule, force_fp32
+
+from mmdet.core import multi_apply
+from ..builder import HEADS, build_loss
+from ..utils import gaussian_radius, gen_gaussian_target
+from ..utils.gaussian_target import (gather_feat, get_local_maximum,
+                                     get_topk_from_heatmap,
+                                     transpose_and_gather_feat)
+from .base_dense_head import BaseDenseHead
+from .dense_test_mixins import BBoxTestMixin
+
+
+class BiCornerPool(BaseModule):
+    """Bidirectional Corner Pooling Module (TopLeft, BottomRight, etc.)
+
+    Args:
+        in_channels (int): Input channels of module.
+        out_channels (int): Output channels of module.
+        feat_channels (int): Feature channels of module.
+        directions (list[str]): Directions of two CornerPools.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 in_channels,
+                 directions,
+                 feat_channels=128,
+                 out_channels=128,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 init_cfg=None):
+        super(BiCornerPool, self).__init__(init_cfg)
+        self.direction1_conv = ConvModule(
+            in_channels, feat_channels, 3, padding=1, norm_cfg=norm_cfg)
+        self.direction2_conv = ConvModule(
+            in_channels, feat_channels, 3, padding=1, norm_cfg=norm_cfg)
+
+        self.aftpool_conv = ConvModule(
+            feat_channels,
+            out_channels,
+            3,
+            padding=1,
+            norm_cfg=norm_cfg,
+            act_cfg=None)
+
+        self.conv1 = ConvModule(
+            in_channels, out_channels, 1, norm_cfg=norm_cfg, act_cfg=None)
+        self.conv2 = ConvModule(
+            in_channels, out_channels, 3, padding=1, norm_cfg=norm_cfg)
+
+        self.direction1_pool = CornerPool(directions[0])
+        self.direction2_pool = CornerPool(directions[1])
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        """Forward features from the upstream network.
+
+        Args:
+            x (tensor): Input feature of BiCornerPool.
+
+        Returns:
+            conv2 (tensor): Output feature of BiCornerPool.
+        """
+        direction1_conv = self.direction1_conv(x)
+        direction2_conv = self.direction2_conv(x)
+        direction1_feat = self.direction1_pool(direction1_conv)
+        direction2_feat = self.direction2_pool(direction2_conv)
+        aftpool_conv = self.aftpool_conv(direction1_feat + direction2_feat)
+        conv1 = self.conv1(x)
+        relu = self.relu(aftpool_conv + conv1)
+        conv2 = self.conv2(relu)
+        return conv2
+
+
+@HEADS.register_module()
+class CornerHead(BaseDenseHead, BBoxTestMixin):
+    """Head of CornerNet: Detecting Objects as Paired Keypoints.
+
+    Code is modified from the `official github repo
+    <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/
+    kp.py#L73>`_ .
+
+    More details can be found in the `paper
+    <https://arxiv.org/abs/1808.01244>`_ .
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        num_feat_levels (int): Levels of feature from the previous module. 2
+            for HourglassNet-104 and 1 for HourglassNet-52. Because
+            HourglassNet-104 outputs the final feature and intermediate
+            supervision feature and HourglassNet-52 only outputs the final
+            feature. Default: 2.
+        corner_emb_channels (int): Channel of embedding vector. Default: 1.
+        train_cfg (dict | None): Training config. Useless in CornerHead,
+            but we keep this variable for SingleStageDetector. Default: None.
+        test_cfg (dict | None): Testing config of CornerHead. Default: None.
+        loss_heatmap (dict | None): Config of corner heatmap loss. Default:
+            GaussianFocalLoss.
+        loss_embedding (dict | None): Config of corner embedding loss. Default:
+            AssociativeEmbeddingLoss.
+        loss_offset (dict | None): Config of corner offset loss. Default:
+            SmoothL1Loss.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 num_feat_levels=2,
+                 corner_emb_channels=1,
+                 train_cfg=None,
+                 test_cfg=None,
+                 loss_heatmap=dict(
+                     type='GaussianFocalLoss',
+                     alpha=2.0,
+                     gamma=4.0,
+                     loss_weight=1),
+                 loss_embedding=dict(
+                     type='AssociativeEmbeddingLoss',
+                     pull_weight=0.25,
+                     push_weight=0.25),
+                 loss_offset=dict(
+                     type='SmoothL1Loss', beta=1.0, loss_weight=1),
+                 init_cfg=None):
+        assert init_cfg is None, 'To prevent abnormal initialization ' \
+                                 'behavior, init_cfg is not allowed to be set'
+        super(CornerHead, self).__init__(init_cfg)
+        self.num_classes = num_classes
+        self.in_channels = in_channels
+        self.corner_emb_channels = corner_emb_channels
+        self.with_corner_emb = self.corner_emb_channels > 0
+        self.corner_offset_channels = 2
+        self.num_feat_levels = num_feat_levels
+        self.loss_heatmap = build_loss(
+            loss_heatmap) if loss_heatmap is not None else None
+        self.loss_embedding = build_loss(
+            loss_embedding) if loss_embedding is not None else None
+        self.loss_offset = build_loss(
+            loss_offset) if loss_offset is not None else None
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+
+        self.fp16_enabled = False
+        self._init_layers()
+
+    def _make_layers(self, out_channels, in_channels=256, feat_channels=256):
+        """Initialize conv sequential for CornerHead."""
+        return nn.Sequential(
+            ConvModule(in_channels, feat_channels, 3, padding=1),
+            ConvModule(
+                feat_channels, out_channels, 1, norm_cfg=None, act_cfg=None))
+
+    def _init_corner_kpt_layers(self):
+        """Initialize corner keypoint layers.
+
+        Including corner heatmap branch and corner offset branch. Each branch
+        has two parts: prefix `tl_` for top-left and `br_` for bottom-right.
+        """
+        self.tl_pool, self.br_pool = nn.ModuleList(), nn.ModuleList()
+        self.tl_heat, self.br_heat = nn.ModuleList(), nn.ModuleList()
+        self.tl_off, self.br_off = nn.ModuleList(), nn.ModuleList()
+
+        for _ in range(self.num_feat_levels):
+            self.tl_pool.append(
+                BiCornerPool(
+                    self.in_channels, ['top', 'left'],
+                    out_channels=self.in_channels))
+            self.br_pool.append(
+                BiCornerPool(
+                    self.in_channels, ['bottom', 'right'],
+                    out_channels=self.in_channels))
+
+            self.tl_heat.append(
+                self._make_layers(
+                    out_channels=self.num_classes,
+                    in_channels=self.in_channels))
+            self.br_heat.append(
+                self._make_layers(
+                    out_channels=self.num_classes,
+                    in_channels=self.in_channels))
+
+            self.tl_off.append(
+                self._make_layers(
+                    out_channels=self.corner_offset_channels,
+                    in_channels=self.in_channels))
+            self.br_off.append(
+                self._make_layers(
+                    out_channels=self.corner_offset_channels,
+                    in_channels=self.in_channels))
+
+    def _init_corner_emb_layers(self):
+        """Initialize corner embedding layers.
+
+        Only include corner embedding branch with two parts: prefix `tl_` for
+        top-left and `br_` for bottom-right.
+        """
+        self.tl_emb, self.br_emb = nn.ModuleList(), nn.ModuleList()
+
+        for _ in range(self.num_feat_levels):
+            self.tl_emb.append(
+                self._make_layers(
+                    out_channels=self.corner_emb_channels,
+                    in_channels=self.in_channels))
+            self.br_emb.append(
+                self._make_layers(
+                    out_channels=self.corner_emb_channels,
+                    in_channels=self.in_channels))
+
+    def _init_layers(self):
+        """Initialize layers for CornerHead.
+
+        Including two parts: corner keypoint layers and corner embedding layers
+        """
+        self._init_corner_kpt_layers()
+        if self.with_corner_emb:
+            self._init_corner_emb_layers()
+
+    def init_weights(self):
+        super(CornerHead, self).init_weights()
+        bias_init = bias_init_with_prob(0.1)
+        for i in range(self.num_feat_levels):
+            # The initialization of parameters are different between
+            # nn.Conv2d and ConvModule. Our experiments show that
+            # using the original initialization of nn.Conv2d increases
+            # the final mAP by about 0.2%
+            self.tl_heat[i][-1].conv.reset_parameters()
+            self.tl_heat[i][-1].conv.bias.data.fill_(bias_init)
+            self.br_heat[i][-1].conv.reset_parameters()
+            self.br_heat[i][-1].conv.bias.data.fill_(bias_init)
+            self.tl_off[i][-1].conv.reset_parameters()
+            self.br_off[i][-1].conv.reset_parameters()
+            if self.with_corner_emb:
+                self.tl_emb[i][-1].conv.reset_parameters()
+                self.br_emb[i][-1].conv.reset_parameters()
+
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple: Usually a tuple of corner heatmaps, offset heatmaps and
+            embedding heatmaps.
+                - tl_heats (list[Tensor]): Top-left corner heatmaps for all
+                  levels, each is a 4D-tensor, the channels number is
+                  num_classes.
+                - br_heats (list[Tensor]): Bottom-right corner heatmaps for all
+                  levels, each is a 4D-tensor, the channels number is
+                  num_classes.
+                - tl_embs (list[Tensor] | list[None]): Top-left embedding
+                  heatmaps for all levels, each is a 4D-tensor or None.
+                  If not None, the channels number is corner_emb_channels.
+                - br_embs (list[Tensor] | list[None]): Bottom-right embedding
+                  heatmaps for all levels, each is a 4D-tensor or None.
+                  If not None, the channels number is corner_emb_channels.
+                - tl_offs (list[Tensor]): Top-left offset heatmaps for all
+                  levels, each is a 4D-tensor. The channels number is
+                  corner_offset_channels.
+                - br_offs (list[Tensor]): Bottom-right offset heatmaps for all
+                  levels, each is a 4D-tensor. The channels number is
+                  corner_offset_channels.
+        """
+        lvl_ind = list(range(self.num_feat_levels))
+        return multi_apply(self.forward_single, feats, lvl_ind)
+
+    def forward_single(self, x, lvl_ind, return_pool=False):
+        """Forward feature of a single level.
+
+        Args:
+            x (Tensor): Feature of a single level.
+            lvl_ind (int): Level index of current feature.
+            return_pool (bool): Return corner pool feature or not.
+
+        Returns:
+            tuple[Tensor]: A tuple of CornerHead's output for current feature
+            level. Containing the following Tensors:
+
+                - tl_heat (Tensor): Predicted top-left corner heatmap.
+                - br_heat (Tensor): Predicted bottom-right corner heatmap.
+                - tl_emb (Tensor | None): Predicted top-left embedding heatmap.
+                  None for `self.with_corner_emb == False`.
+                - br_emb (Tensor | None): Predicted bottom-right embedding
+                  heatmap. None for `self.with_corner_emb == False`.
+                - tl_off (Tensor): Predicted top-left offset heatmap.
+                - br_off (Tensor): Predicted bottom-right offset heatmap.
+                - tl_pool (Tensor): Top-left corner pool feature. Not must
+                  have.
+                - br_pool (Tensor): Bottom-right corner pool feature. Not must
+                  have.
+        """
+        tl_pool = self.tl_pool[lvl_ind](x)
+        tl_heat = self.tl_heat[lvl_ind](tl_pool)
+        br_pool = self.br_pool[lvl_ind](x)
+        br_heat = self.br_heat[lvl_ind](br_pool)
+
+        tl_emb, br_emb = None, None
+        if self.with_corner_emb:
+            tl_emb = self.tl_emb[lvl_ind](tl_pool)
+            br_emb = self.br_emb[lvl_ind](br_pool)
+
+        tl_off = self.tl_off[lvl_ind](tl_pool)
+        br_off = self.br_off[lvl_ind](br_pool)
+
+        result_list = [tl_heat, br_heat, tl_emb, br_emb, tl_off, br_off]
+        if return_pool:
+            result_list.append(tl_pool)
+            result_list.append(br_pool)
+
+        return result_list
+
+    def get_targets(self,
+                    gt_bboxes,
+                    gt_labels,
+                    feat_shape,
+                    img_shape,
+                    with_corner_emb=False,
+                    with_guiding_shift=False,
+                    with_centripetal_shift=False):
+        """Generate corner targets.
+
+        Including corner heatmap, corner offset.
+
+        Optional: corner embedding, corner guiding shift, centripetal shift.
+
+        For CornerNet, we generate corner heatmap, corner offset and corner
+        embedding from this function.
+
+        For CentripetalNet, we generate corner heatmap, corner offset, guiding
+        shift and centripetal shift from this function.
+
+        Args:
+            gt_bboxes (list[Tensor]): Ground truth bboxes of each image, each
+                has shape (num_gt, 4).
+            gt_labels (list[Tensor]): Ground truth labels of each box, each has
+                shape (num_gt,).
+            feat_shape (list[int]): Shape of output feature,
+                [batch, channel, height, width].
+            img_shape (list[int]): Shape of input image,
+                [height, width, channel].
+            with_corner_emb (bool): Generate corner embedding target or not.
+                Default: False.
+            with_guiding_shift (bool): Generate guiding shift target or not.
+                Default: False.
+            with_centripetal_shift (bool): Generate centripetal shift target or
+                not. Default: False.
+
+        Returns:
+            dict: Ground truth of corner heatmap, corner offset, corner
+            embedding, guiding shift and centripetal shift. Containing the
+            following keys:
+
+                - topleft_heatmap (Tensor): Ground truth top-left corner
+                  heatmap.
+                - bottomright_heatmap (Tensor): Ground truth bottom-right
+                  corner heatmap.
+                - topleft_offset (Tensor): Ground truth top-left corner offset.
+                - bottomright_offset (Tensor): Ground truth bottom-right corner
+                  offset.
+                - corner_embedding (list[list[list[int]]]): Ground truth corner
+                  embedding. Not must have.
+                - topleft_guiding_shift (Tensor): Ground truth top-left corner
+                  guiding shift. Not must have.
+                - bottomright_guiding_shift (Tensor): Ground truth bottom-right
+                  corner guiding shift. Not must have.
+                - topleft_centripetal_shift (Tensor): Ground truth top-left
+                  corner centripetal shift. Not must have.
+                - bottomright_centripetal_shift (Tensor): Ground truth
+                  bottom-right corner centripetal shift. Not must have.
+        """
+        batch_size, _, height, width = feat_shape
+        img_h, img_w = img_shape[:2]
+
+        width_ratio = float(width / img_w)
+        height_ratio = float(height / img_h)
+
+        gt_tl_heatmap = gt_bboxes[-1].new_zeros(
+            [batch_size, self.num_classes, height, width])
+        gt_br_heatmap = gt_bboxes[-1].new_zeros(
+            [batch_size, self.num_classes, height, width])
+        gt_tl_offset = gt_bboxes[-1].new_zeros([batch_size, 2, height, width])
+        gt_br_offset = gt_bboxes[-1].new_zeros([batch_size, 2, height, width])
+
+        if with_corner_emb:
+            match = []
+
+        # Guiding shift is a kind of offset, from center to corner
+        if with_guiding_shift:
+            gt_tl_guiding_shift = gt_bboxes[-1].new_zeros(
+                [batch_size, 2, height, width])
+            gt_br_guiding_shift = gt_bboxes[-1].new_zeros(
+                [batch_size, 2, height, width])
+        # Centripetal shift is also a kind of offset, from center to corner
+        # and normalized by log.
+        if with_centripetal_shift:
+            gt_tl_centripetal_shift = gt_bboxes[-1].new_zeros(
+                [batch_size, 2, height, width])
+            gt_br_centripetal_shift = gt_bboxes[-1].new_zeros(
+                [batch_size, 2, height, width])
+
+        for batch_id in range(batch_size):
+            # Ground truth of corner embedding per image is a list of coord set
+            corner_match = []
+            for box_id in range(len(gt_labels[batch_id])):
+                left, top, right, bottom = gt_bboxes[batch_id][box_id]
+                center_x = (left + right) / 2.0
+                center_y = (top + bottom) / 2.0
+                label = gt_labels[batch_id][box_id]
+
+                # Use coords in the feature level to generate ground truth
+                scale_left = left * width_ratio
+                scale_right = right * width_ratio
+                scale_top = top * height_ratio
+                scale_bottom = bottom * height_ratio
+                scale_center_x = center_x * width_ratio
+                scale_center_y = center_y * height_ratio
+
+                # Int coords on feature map/ground truth tensor
+                left_idx = int(min(scale_left, width - 1))
+                right_idx = int(min(scale_right, width - 1))
+                top_idx = int(min(scale_top, height - 1))
+                bottom_idx = int(min(scale_bottom, height - 1))
+
+                # Generate gaussian heatmap
+                scale_box_width = ceil(scale_right - scale_left)
+                scale_box_height = ceil(scale_bottom - scale_top)
+                radius = gaussian_radius((scale_box_height, scale_box_width),
+                                         min_overlap=0.3)
+                radius = max(0, int(radius))
+                gt_tl_heatmap[batch_id, label] = gen_gaussian_target(
+                    gt_tl_heatmap[batch_id, label], [left_idx, top_idx],
+                    radius)
+                gt_br_heatmap[batch_id, label] = gen_gaussian_target(
+                    gt_br_heatmap[batch_id, label], [right_idx, bottom_idx],
+                    radius)
+
+                # Generate corner offset
+                left_offset = scale_left - left_idx
+                top_offset = scale_top - top_idx
+                right_offset = scale_right - right_idx
+                bottom_offset = scale_bottom - bottom_idx
+                gt_tl_offset[batch_id, 0, top_idx, left_idx] = left_offset
+                gt_tl_offset[batch_id, 1, top_idx, left_idx] = top_offset
+                gt_br_offset[batch_id, 0, bottom_idx, right_idx] = right_offset
+                gt_br_offset[batch_id, 1, bottom_idx,
+                             right_idx] = bottom_offset
+
+                # Generate corner embedding
+                if with_corner_emb:
+                    corner_match.append([[top_idx, left_idx],
+                                         [bottom_idx, right_idx]])
+                # Generate guiding shift
+                if with_guiding_shift:
+                    gt_tl_guiding_shift[batch_id, 0, top_idx,
+                                        left_idx] = scale_center_x - left_idx
+                    gt_tl_guiding_shift[batch_id, 1, top_idx,
+                                        left_idx] = scale_center_y - top_idx
+                    gt_br_guiding_shift[batch_id, 0, bottom_idx,
+                                        right_idx] = right_idx - scale_center_x
+                    gt_br_guiding_shift[
+                        batch_id, 1, bottom_idx,
+                        right_idx] = bottom_idx - scale_center_y
+                # Generate centripetal shift
+                if with_centripetal_shift:
+                    gt_tl_centripetal_shift[batch_id, 0, top_idx,
+                                            left_idx] = log(scale_center_x -
+                                                            scale_left)
+                    gt_tl_centripetal_shift[batch_id, 1, top_idx,
+                                            left_idx] = log(scale_center_y -
+                                                            scale_top)
+                    gt_br_centripetal_shift[batch_id, 0, bottom_idx,
+                                            right_idx] = log(scale_right -
+                                                             scale_center_x)
+                    gt_br_centripetal_shift[batch_id, 1, bottom_idx,
+                                            right_idx] = log(scale_bottom -
+                                                             scale_center_y)
+
+            if with_corner_emb:
+                match.append(corner_match)
+
+        target_result = dict(
+            topleft_heatmap=gt_tl_heatmap,
+            topleft_offset=gt_tl_offset,
+            bottomright_heatmap=gt_br_heatmap,
+            bottomright_offset=gt_br_offset)
+
+        if with_corner_emb:
+            target_result.update(corner_embedding=match)
+        if with_guiding_shift:
+            target_result.update(
+                topleft_guiding_shift=gt_tl_guiding_shift,
+                bottomright_guiding_shift=gt_br_guiding_shift)
+        if with_centripetal_shift:
+            target_result.update(
+                topleft_centripetal_shift=gt_tl_centripetal_shift,
+                bottomright_centripetal_shift=gt_br_centripetal_shift)
+
+        return target_result
+
+    @force_fp32()
+    def loss(self,
+             tl_heats,
+             br_heats,
+             tl_embs,
+             br_embs,
+             tl_offs,
+             br_offs,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            tl_heats (list[Tensor]): Top-left corner heatmaps for each level
+                with shape (N, num_classes, H, W).
+            br_heats (list[Tensor]): Bottom-right corner heatmaps for each
+                level with shape (N, num_classes, H, W).
+            tl_embs (list[Tensor]): Top-left corner embeddings for each level
+                with shape (N, corner_emb_channels, H, W).
+            br_embs (list[Tensor]): Bottom-right corner embeddings for each
+                level with shape (N, corner_emb_channels, H, W).
+            tl_offs (list[Tensor]): Top-left corner offsets for each level
+                with shape (N, corner_offset_channels, H, W).
+            br_offs (list[Tensor]): Bottom-right corner offsets for each level
+                with shape (N, corner_offset_channels, H, W).
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [left, top, right, bottom] format.
+            gt_labels (list[Tensor]): Class indices corresponding to each box.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components. Containing the
+            following losses:
+
+                - det_loss (list[Tensor]): Corner keypoint losses of all
+                  feature levels.
+                - pull_loss (list[Tensor]): Part one of AssociativeEmbedding
+                  losses of all feature levels.
+                - push_loss (list[Tensor]): Part two of AssociativeEmbedding
+                  losses of all feature levels.
+                - off_loss (list[Tensor]): Corner offset losses of all feature
+                  levels.
+        """
+        targets = self.get_targets(
+            gt_bboxes,
+            gt_labels,
+            tl_heats[-1].shape,
+            img_metas[0]['pad_shape'],
+            with_corner_emb=self.with_corner_emb)
+        mlvl_targets = [targets for _ in range(self.num_feat_levels)]
+        det_losses, pull_losses, push_losses, off_losses = multi_apply(
+            self.loss_single, tl_heats, br_heats, tl_embs, br_embs, tl_offs,
+            br_offs, mlvl_targets)
+        loss_dict = dict(det_loss=det_losses, off_loss=off_losses)
+        if self.with_corner_emb:
+            loss_dict.update(pull_loss=pull_losses, push_loss=push_losses)
+        return loss_dict
+
+    def loss_single(self, tl_hmp, br_hmp, tl_emb, br_emb, tl_off, br_off,
+                    targets):
+        """Compute losses for single level.
+
+        Args:
+            tl_hmp (Tensor): Top-left corner heatmap for current level with
+                shape (N, num_classes, H, W).
+            br_hmp (Tensor): Bottom-right corner heatmap for current level with
+                shape (N, num_classes, H, W).
+            tl_emb (Tensor): Top-left corner embedding for current level with
+                shape (N, corner_emb_channels, H, W).
+            br_emb (Tensor): Bottom-right corner embedding for current level
+                with shape (N, corner_emb_channels, H, W).
+            tl_off (Tensor): Top-left corner offset for current level with
+                shape (N, corner_offset_channels, H, W).
+            br_off (Tensor): Bottom-right corner offset for current level with
+                shape (N, corner_offset_channels, H, W).
+            targets (dict): Corner target generated by `get_targets`.
+
+        Returns:
+            tuple[torch.Tensor]: Losses of the head's different branches
+            containing the following losses:
+
+                - det_loss (Tensor): Corner keypoint loss.
+                - pull_loss (Tensor): Part one of AssociativeEmbedding loss.
+                - push_loss (Tensor): Part two of AssociativeEmbedding loss.
+                - off_loss (Tensor): Corner offset loss.
+        """
+        gt_tl_hmp = targets['topleft_heatmap']
+        gt_br_hmp = targets['bottomright_heatmap']
+        gt_tl_off = targets['topleft_offset']
+        gt_br_off = targets['bottomright_offset']
+        gt_embedding = targets['corner_embedding']
+
+        # Detection loss
+        tl_det_loss = self.loss_heatmap(
+            tl_hmp.sigmoid(),
+            gt_tl_hmp,
+            avg_factor=max(1,
+                           gt_tl_hmp.eq(1).sum()))
+        br_det_loss = self.loss_heatmap(
+            br_hmp.sigmoid(),
+            gt_br_hmp,
+            avg_factor=max(1,
+                           gt_br_hmp.eq(1).sum()))
+        det_loss = (tl_det_loss + br_det_loss) / 2.0
+
+        # AssociativeEmbedding loss
+        if self.with_corner_emb and self.loss_embedding is not None:
+            pull_loss, push_loss = self.loss_embedding(tl_emb, br_emb,
+                                                       gt_embedding)
+        else:
+            pull_loss, push_loss = None, None
+
+        # Offset loss
+        # We only compute the offset loss at the real corner position.
+        # The value of real corner would be 1 in heatmap ground truth.
+        # The mask is computed in class agnostic mode and its shape is
+        # batch * 1 * width * height.
+        tl_off_mask = gt_tl_hmp.eq(1).sum(1).gt(0).unsqueeze(1).type_as(
+            gt_tl_hmp)
+        br_off_mask = gt_br_hmp.eq(1).sum(1).gt(0).unsqueeze(1).type_as(
+            gt_br_hmp)
+        tl_off_loss = self.loss_offset(
+            tl_off,
+            gt_tl_off,
+            tl_off_mask,
+            avg_factor=max(1, tl_off_mask.sum()))
+        br_off_loss = self.loss_offset(
+            br_off,
+            gt_br_off,
+            br_off_mask,
+            avg_factor=max(1, br_off_mask.sum()))
+
+        off_loss = (tl_off_loss + br_off_loss) / 2.0
+
+        return det_loss, pull_loss, push_loss, off_loss
+
+    @force_fp32()
+    def get_bboxes(self,
+                   tl_heats,
+                   br_heats,
+                   tl_embs,
+                   br_embs,
+                   tl_offs,
+                   br_offs,
+                   img_metas,
+                   rescale=False,
+                   with_nms=True):
+        """Transform network output for a batch into bbox predictions.
+
+        Args:
+            tl_heats (list[Tensor]): Top-left corner heatmaps for each level
+                with shape (N, num_classes, H, W).
+            br_heats (list[Tensor]): Bottom-right corner heatmaps for each
+                level with shape (N, num_classes, H, W).
+            tl_embs (list[Tensor]): Top-left corner embeddings for each level
+                with shape (N, corner_emb_channels, H, W).
+            br_embs (list[Tensor]): Bottom-right corner embeddings for each
+                level with shape (N, corner_emb_channels, H, W).
+            tl_offs (list[Tensor]): Top-left corner offsets for each level
+                with shape (N, corner_offset_channels, H, W).
+            br_offs (list[Tensor]): Bottom-right corner offsets for each level
+                with shape (N, corner_offset_channels, H, W).
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+        """
+        assert tl_heats[-1].shape[0] == br_heats[-1].shape[0] == len(img_metas)
+        result_list = []
+        for img_id in range(len(img_metas)):
+            result_list.append(
+                self._get_bboxes_single(
+                    tl_heats[-1][img_id:img_id + 1, :],
+                    br_heats[-1][img_id:img_id + 1, :],
+                    tl_offs[-1][img_id:img_id + 1, :],
+                    br_offs[-1][img_id:img_id + 1, :],
+                    img_metas[img_id],
+                    tl_emb=tl_embs[-1][img_id:img_id + 1, :],
+                    br_emb=br_embs[-1][img_id:img_id + 1, :],
+                    rescale=rescale,
+                    with_nms=with_nms))
+
+        return result_list
+
+    def _get_bboxes_single(self,
+                           tl_heat,
+                           br_heat,
+                           tl_off,
+                           br_off,
+                           img_meta,
+                           tl_emb=None,
+                           br_emb=None,
+                           tl_centripetal_shift=None,
+                           br_centripetal_shift=None,
+                           rescale=False,
+                           with_nms=True):
+        """Transform outputs for a single batch item into bbox predictions.
+
+        Args:
+            tl_heat (Tensor): Top-left corner heatmap for current level with
+                shape (N, num_classes, H, W).
+            br_heat (Tensor): Bottom-right corner heatmap for current level
+                with shape (N, num_classes, H, W).
+            tl_off (Tensor): Top-left corner offset for current level with
+                shape (N, corner_offset_channels, H, W).
+            br_off (Tensor): Bottom-right corner offset for current level with
+                shape (N, corner_offset_channels, H, W).
+            img_meta (dict): Meta information of current image, e.g.,
+                image size, scaling factor, etc.
+            tl_emb (Tensor): Top-left corner embedding for current level with
+                shape (N, corner_emb_channels, H, W).
+            br_emb (Tensor): Bottom-right corner embedding for current level
+                with shape (N, corner_emb_channels, H, W).
+            tl_centripetal_shift: Top-left corner's centripetal shift for
+                current level with shape (N, 2, H, W).
+            br_centripetal_shift: Bottom-right corner's centripetal shift for
+                current level with shape (N, 2, H, W).
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+        """
+        if isinstance(img_meta, (list, tuple)):
+            img_meta = img_meta[0]
+
+        batch_bboxes, batch_scores, batch_clses = self.decode_heatmap(
+            tl_heat=tl_heat.sigmoid(),
+            br_heat=br_heat.sigmoid(),
+            tl_off=tl_off,
+            br_off=br_off,
+            tl_emb=tl_emb,
+            br_emb=br_emb,
+            tl_centripetal_shift=tl_centripetal_shift,
+            br_centripetal_shift=br_centripetal_shift,
+            img_meta=img_meta,
+            k=self.test_cfg.corner_topk,
+            kernel=self.test_cfg.local_maximum_kernel,
+            distance_threshold=self.test_cfg.distance_threshold)
+
+        if rescale:
+            batch_bboxes /= batch_bboxes.new_tensor(img_meta['scale_factor'])
+
+        bboxes = batch_bboxes.view([-1, 4])
+        scores = batch_scores.view(-1)
+        clses = batch_clses.view(-1)
+
+        detections = torch.cat([bboxes, scores.unsqueeze(-1)], -1)
+        keepinds = (detections[:, -1] > -0.1)
+        detections = detections[keepinds]
+        labels = clses[keepinds]
+
+        if with_nms:
+            detections, labels = self._bboxes_nms(detections, labels,
+                                                  self.test_cfg)
+
+        return detections, labels
+
+    def _bboxes_nms(self, bboxes, labels, cfg):
+        if 'nms_cfg' in cfg:
+            warning.warn('nms_cfg in test_cfg will be deprecated. '
+                         'Please rename it as nms')
+        if 'nms' not in cfg:
+            cfg.nms = cfg.nms_cfg
+
+        if labels.numel() > 0:
+            max_num = cfg.max_per_img
+            bboxes, keep = batched_nms(bboxes[:, :4], bboxes[:,
+                                                             -1].contiguous(),
+                                       labels, cfg.nms)
+            if max_num > 0:
+                bboxes = bboxes[:max_num]
+                labels = labels[keep][:max_num]
+
+        return bboxes, labels
+
+    def decode_heatmap(self,
+                       tl_heat,
+                       br_heat,
+                       tl_off,
+                       br_off,
+                       tl_emb=None,
+                       br_emb=None,
+                       tl_centripetal_shift=None,
+                       br_centripetal_shift=None,
+                       img_meta=None,
+                       k=100,
+                       kernel=3,
+                       distance_threshold=0.5,
+                       num_dets=1000):
+        """Transform outputs for a single batch item into raw bbox predictions.
+
+        Args:
+            tl_heat (Tensor): Top-left corner heatmap for current level with
+                shape (N, num_classes, H, W).
+            br_heat (Tensor): Bottom-right corner heatmap for current level
+                with shape (N, num_classes, H, W).
+            tl_off (Tensor): Top-left corner offset for current level with
+                shape (N, corner_offset_channels, H, W).
+            br_off (Tensor): Bottom-right corner offset for current level with
+                shape (N, corner_offset_channels, H, W).
+            tl_emb (Tensor | None): Top-left corner embedding for current
+                level with shape (N, corner_emb_channels, H, W).
+            br_emb (Tensor | None): Bottom-right corner embedding for current
+                level with shape (N, corner_emb_channels, H, W).
+            tl_centripetal_shift (Tensor | None): Top-left centripetal shift
+                for current level with shape (N, 2, H, W).
+            br_centripetal_shift (Tensor | None): Bottom-right centripetal
+                shift for current level with shape (N, 2, H, W).
+            img_meta (dict): Meta information of current image, e.g.,
+                image size, scaling factor, etc.
+            k (int): Get top k corner keypoints from heatmap.
+            kernel (int): Max pooling kernel for extract local maximum pixels.
+            distance_threshold (float): Distance threshold. Top-left and
+                bottom-right corner keypoints with feature distance less than
+                the threshold will be regarded as keypoints from same object.
+            num_dets (int): Num of raw boxes before doing nms.
+
+        Returns:
+            tuple[torch.Tensor]: Decoded output of CornerHead, containing the
+            following Tensors:
+
+            - bboxes (Tensor): Coords of each box.
+            - scores (Tensor): Scores of each box.
+            - clses (Tensor): Categories of each box.
+        """
+        with_embedding = tl_emb is not None and br_emb is not None
+        with_centripetal_shift = (
+            tl_centripetal_shift is not None
+            and br_centripetal_shift is not None)
+        assert with_embedding + with_centripetal_shift == 1
+        batch, _, height, width = tl_heat.size()
+        if torch.onnx.is_in_onnx_export():
+            inp_h, inp_w = img_meta['pad_shape_for_onnx'][:2]
+        else:
+            inp_h, inp_w, _ = img_meta['pad_shape']
+
+        # perform nms on heatmaps
+        tl_heat = get_local_maximum(tl_heat, kernel=kernel)
+        br_heat = get_local_maximum(br_heat, kernel=kernel)
+
+        tl_scores, tl_inds, tl_clses, tl_ys, tl_xs = get_topk_from_heatmap(
+            tl_heat, k=k)
+        br_scores, br_inds, br_clses, br_ys, br_xs = get_topk_from_heatmap(
+            br_heat, k=k)
+
+        # We use repeat instead of expand here because expand is a
+        # shallow-copy function. Thus it could cause unexpected testing result
+        # sometimes. Using expand will decrease about 10% mAP during testing
+        # compared to repeat.
+        tl_ys = tl_ys.view(batch, k, 1).repeat(1, 1, k)
+        tl_xs = tl_xs.view(batch, k, 1).repeat(1, 1, k)
+        br_ys = br_ys.view(batch, 1, k).repeat(1, k, 1)
+        br_xs = br_xs.view(batch, 1, k).repeat(1, k, 1)
+
+        tl_off = transpose_and_gather_feat(tl_off, tl_inds)
+        tl_off = tl_off.view(batch, k, 1, 2)
+        br_off = transpose_and_gather_feat(br_off, br_inds)
+        br_off = br_off.view(batch, 1, k, 2)
+
+        tl_xs = tl_xs + tl_off[..., 0]
+        tl_ys = tl_ys + tl_off[..., 1]
+        br_xs = br_xs + br_off[..., 0]
+        br_ys = br_ys + br_off[..., 1]
+
+        if with_centripetal_shift:
+            tl_centripetal_shift = transpose_and_gather_feat(
+                tl_centripetal_shift, tl_inds).view(batch, k, 1, 2).exp()
+            br_centripetal_shift = transpose_and_gather_feat(
+                br_centripetal_shift, br_inds).view(batch, 1, k, 2).exp()
+
+            tl_ctxs = tl_xs + tl_centripetal_shift[..., 0]
+            tl_ctys = tl_ys + tl_centripetal_shift[..., 1]
+            br_ctxs = br_xs - br_centripetal_shift[..., 0]
+            br_ctys = br_ys - br_centripetal_shift[..., 1]
+
+        # all possible boxes based on top k corners (ignoring class)
+        tl_xs *= (inp_w / width)
+        tl_ys *= (inp_h / height)
+        br_xs *= (inp_w / width)
+        br_ys *= (inp_h / height)
+
+        if with_centripetal_shift:
+            tl_ctxs *= (inp_w / width)
+            tl_ctys *= (inp_h / height)
+            br_ctxs *= (inp_w / width)
+            br_ctys *= (inp_h / height)
+
+        x_off, y_off = 0, 0  # no crop
+        if not torch.onnx.is_in_onnx_export():
+            # since `RandomCenterCropPad` is done on CPU with numpy and it's
+            # not dynamic traceable when exporting to ONNX, thus 'border'
+            # does not appears as key in 'img_meta'. As a tmp solution,
+            # we move this 'border' handle part to the postprocess after
+            # finished exporting to ONNX, which is handle in
+            # `mmdet/core/export/model_wrappers.py`. Though difference between
+            # pytorch and exported onnx model, it might be ignored since
+            # comparable performance is achieved between them (e.g. 40.4 vs
+            # 40.6 on COCO val2017, for CornerNet without test-time flip)
+            if 'border' in img_meta:
+                x_off = img_meta['border'][2]
+                y_off = img_meta['border'][0]
+
+        tl_xs -= x_off
+        tl_ys -= y_off
+        br_xs -= x_off
+        br_ys -= y_off
+
+        zeros = tl_xs.new_zeros(*tl_xs.size())
+        tl_xs = torch.where(tl_xs > 0.0, tl_xs, zeros)
+        tl_ys = torch.where(tl_ys > 0.0, tl_ys, zeros)
+        br_xs = torch.where(br_xs > 0.0, br_xs, zeros)
+        br_ys = torch.where(br_ys > 0.0, br_ys, zeros)
+
+        bboxes = torch.stack((tl_xs, tl_ys, br_xs, br_ys), dim=3)
+        area_bboxes = ((br_xs - tl_xs) * (br_ys - tl_ys)).abs()
+
+        if with_centripetal_shift:
+            tl_ctxs -= x_off
+            tl_ctys -= y_off
+            br_ctxs -= x_off
+            br_ctys -= y_off
+
+            tl_ctxs *= tl_ctxs.gt(0.0).type_as(tl_ctxs)
+            tl_ctys *= tl_ctys.gt(0.0).type_as(tl_ctys)
+            br_ctxs *= br_ctxs.gt(0.0).type_as(br_ctxs)
+            br_ctys *= br_ctys.gt(0.0).type_as(br_ctys)
+
+            ct_bboxes = torch.stack((tl_ctxs, tl_ctys, br_ctxs, br_ctys),
+                                    dim=3)
+            area_ct_bboxes = ((br_ctxs - tl_ctxs) * (br_ctys - tl_ctys)).abs()
+
+            rcentral = torch.zeros_like(ct_bboxes)
+            # magic nums from paper section 4.1
+            mu = torch.ones_like(area_bboxes) / 2.4
+            mu[area_bboxes > 3500] = 1 / 2.1  # large bbox have smaller mu
+
+            bboxes_center_x = (bboxes[..., 0] + bboxes[..., 2]) / 2
+            bboxes_center_y = (bboxes[..., 1] + bboxes[..., 3]) / 2
+            rcentral[..., 0] = bboxes_center_x - mu * (bboxes[..., 2] -
+                                                       bboxes[..., 0]) / 2
+            rcentral[..., 1] = bboxes_center_y - mu * (bboxes[..., 3] -
+                                                       bboxes[..., 1]) / 2
+            rcentral[..., 2] = bboxes_center_x + mu * (bboxes[..., 2] -
+                                                       bboxes[..., 0]) / 2
+            rcentral[..., 3] = bboxes_center_y + mu * (bboxes[..., 3] -
+                                                       bboxes[..., 1]) / 2
+            area_rcentral = ((rcentral[..., 2] - rcentral[..., 0]) *
+                             (rcentral[..., 3] - rcentral[..., 1])).abs()
+            dists = area_ct_bboxes / area_rcentral
+
+            tl_ctx_inds = (ct_bboxes[..., 0] <= rcentral[..., 0]) | (
+                ct_bboxes[..., 0] >= rcentral[..., 2])
+            tl_cty_inds = (ct_bboxes[..., 1] <= rcentral[..., 1]) | (
+                ct_bboxes[..., 1] >= rcentral[..., 3])
+            br_ctx_inds = (ct_bboxes[..., 2] <= rcentral[..., 0]) | (
+                ct_bboxes[..., 2] >= rcentral[..., 2])
+            br_cty_inds = (ct_bboxes[..., 3] <= rcentral[..., 1]) | (
+                ct_bboxes[..., 3] >= rcentral[..., 3])
+
+        if with_embedding:
+            tl_emb = transpose_and_gather_feat(tl_emb, tl_inds)
+            tl_emb = tl_emb.view(batch, k, 1)
+            br_emb = transpose_and_gather_feat(br_emb, br_inds)
+            br_emb = br_emb.view(batch, 1, k)
+            dists = torch.abs(tl_emb - br_emb)
+
+        tl_scores = tl_scores.view(batch, k, 1).repeat(1, 1, k)
+        br_scores = br_scores.view(batch, 1, k).repeat(1, k, 1)
+
+        scores = (tl_scores + br_scores) / 2  # scores for all possible boxes
+
+        # tl and br should have same class
+        tl_clses = tl_clses.view(batch, k, 1).repeat(1, 1, k)
+        br_clses = br_clses.view(batch, 1, k).repeat(1, k, 1)
+        cls_inds = (tl_clses != br_clses)
+
+        # reject boxes based on distances
+        dist_inds = dists > distance_threshold
+
+        # reject boxes based on widths and heights
+        width_inds = (br_xs <= tl_xs)
+        height_inds = (br_ys <= tl_ys)
+
+        # No use `scores[cls_inds]`, instead we use `torch.where` here.
+        # Since only 1-D indices with type 'tensor(bool)' are supported
+        # when exporting to ONNX, any other bool indices with more dimensions
+        # (e.g. 2-D bool tensor) as input parameter in node is invalid
+        negative_scores = -1 * torch.ones_like(scores)
+        scores = torch.where(cls_inds, negative_scores, scores)
+        scores = torch.where(width_inds, negative_scores, scores)
+        scores = torch.where(height_inds, negative_scores, scores)
+        scores = torch.where(dist_inds, negative_scores, scores)
+
+        if with_centripetal_shift:
+            scores[tl_ctx_inds] = -1
+            scores[tl_cty_inds] = -1
+            scores[br_ctx_inds] = -1
+            scores[br_cty_inds] = -1
+
+        scores = scores.view(batch, -1)
+        scores, inds = torch.topk(scores, num_dets)
+        scores = scores.unsqueeze(2)
+
+        bboxes = bboxes.view(batch, -1, 4)
+        bboxes = gather_feat(bboxes, inds)
+
+        clses = tl_clses.contiguous().view(batch, -1, 1)
+        clses = gather_feat(clses, inds).float()
+
+        return bboxes, scores, clses
+
+    def onnx_export(self,
+                    tl_heats,
+                    br_heats,
+                    tl_embs,
+                    br_embs,
+                    tl_offs,
+                    br_offs,
+                    img_metas,
+                    rescale=False,
+                    with_nms=True):
+        """Transform network output for a batch into bbox predictions.
+
+        Args:
+            tl_heats (list[Tensor]): Top-left corner heatmaps for each level
+                with shape (N, num_classes, H, W).
+            br_heats (list[Tensor]): Bottom-right corner heatmaps for each
+                level with shape (N, num_classes, H, W).
+            tl_embs (list[Tensor]): Top-left corner embeddings for each level
+                with shape (N, corner_emb_channels, H, W).
+            br_embs (list[Tensor]): Bottom-right corner embeddings for each
+                level with shape (N, corner_emb_channels, H, W).
+            tl_offs (list[Tensor]): Top-left corner offsets for each level
+                with shape (N, corner_offset_channels, H, W).
+            br_offs (list[Tensor]): Bottom-right corner offsets for each level
+                with shape (N, corner_offset_channels, H, W).
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+
+        Returns:
+            tuple[Tensor, Tensor]: First tensor bboxes with shape
+            [N, num_det, 5], 5 arrange as (x1, y1, x2, y2, score)
+            and second element is class labels of shape [N, num_det].
+        """
+        assert tl_heats[-1].shape[0] == br_heats[-1].shape[0] == len(
+            img_metas) == 1
+        result_list = []
+        for img_id in range(len(img_metas)):
+            result_list.append(
+                self._get_bboxes_single(
+                    tl_heats[-1][img_id:img_id + 1, :],
+                    br_heats[-1][img_id:img_id + 1, :],
+                    tl_offs[-1][img_id:img_id + 1, :],
+                    br_offs[-1][img_id:img_id + 1, :],
+                    img_metas[img_id],
+                    tl_emb=tl_embs[-1][img_id:img_id + 1, :],
+                    br_emb=br_embs[-1][img_id:img_id + 1, :],
+                    rescale=rescale,
+                    with_nms=with_nms))
+
+        detections, labels = result_list[0]
+        # batch_size 1 here, [1, num_det, 5], [1, num_det]
+        return detections.unsqueeze(0), labels.unsqueeze(0)
diff --git a/mmdet/models/dense_heads/ddod_head.py b/mmdet/models/dense_heads/ddod_head.py
new file mode 100644
index 0000000..b2ff223
--- /dev/null
+++ b/mmdet/models/dense_heads/ddod_head.py
@@ -0,0 +1,778 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
+from mmcv.runner import force_fp32
+
+from mmdet.core import (anchor_inside_flags, build_assigner, build_sampler,
+                        images_to_levels, multi_apply, reduce_mean, unmap)
+from mmdet.core.bbox import bbox_overlaps
+from ..builder import HEADS, build_loss
+from .anchor_head import AnchorHead
+
+EPS = 1e-12
+
+
+@HEADS.register_module()
+class DDODHead(AnchorHead):
+    """DDOD head decomposes conjunctions lying in most current one-stage
+    detectors via label assignment disentanglement, spatial feature
+    disentanglement, and pyramid supervision disentanglement.
+
+    https://arxiv.org/abs/2107.02963
+
+    Args:
+        num_classes (int): Number of categories excluding the
+            background category.
+        in_channels (int): Number of channels in the input feature map.
+        stacked_convs (int): The number of stacked Conv. Default: 4.
+        conv_cfg (dict): Conv config of ddod head. Default: None.
+        use_dcn (bool): Use dcn, Same as ATSS when False. Default: True.
+        norm_cfg (dict): Normal config of ddod head. Default:
+            dict(type='GN', num_groups=32, requires_grad=True).
+        loss_iou (dict): Config of IoU loss. Default:
+            dict(type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0).
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 stacked_convs=4,
+                 conv_cfg=None,
+                 use_dcn=True,
+                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
+                 loss_iou=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
+                 **kwargs):
+        self.stacked_convs = stacked_convs
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.use_dcn = use_dcn
+        super(DDODHead, self).__init__(num_classes, in_channels, **kwargs)
+
+        self.sampling = False
+        if self.train_cfg:
+            self.cls_assigner = build_assigner(self.train_cfg.assigner)
+            self.reg_assigner = build_assigner(self.train_cfg.reg_assigner)
+            sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+        self.loss_iou = build_loss(loss_iou)
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.relu = nn.ReLU(inplace=True)
+        self.cls_convs = nn.ModuleList()
+        self.reg_convs = nn.ModuleList()
+        for i in range(self.stacked_convs):
+            chn = self.in_channels if i == 0 else self.feat_channels
+            self.cls_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=dict(type='DCN', deform_groups=1)
+                    if i == 0 and self.use_dcn else self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+            self.reg_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=dict(type='DCN', deform_groups=1)
+                    if i == 0 and self.use_dcn else self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+        self.atss_cls = nn.Conv2d(
+            self.feat_channels,
+            self.num_base_priors * self.cls_out_channels,
+            3,
+            padding=1)
+        self.atss_reg = nn.Conv2d(
+            self.feat_channels, self.num_base_priors * 4, 3, padding=1)
+        self.atss_iou = nn.Conv2d(
+            self.feat_channels, self.num_base_priors * 1, 3, padding=1)
+        self.scales = nn.ModuleList(
+            [Scale(1.0) for _ in self.prior_generator.strides])
+
+        # we use the global list in loss
+        self.cls_num_pos_samples_per_level = [
+            0. for _ in range(len(self.prior_generator.strides))
+        ]
+        self.reg_num_pos_samples_per_level = [
+            0. for _ in range(len(self.prior_generator.strides))
+        ]
+
+    def init_weights(self):
+        """Initialize weights of the head."""
+        for m in self.cls_convs:
+            normal_init(m.conv, std=0.01)
+        for m in self.reg_convs:
+            normal_init(m.conv, std=0.01)
+        normal_init(self.atss_reg, std=0.01)
+        normal_init(self.atss_iou, std=0.01)
+        bias_cls = bias_init_with_prob(0.01)
+        normal_init(self.atss_cls, std=0.01, bias=bias_cls)
+
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple: Usually a tuple of classification scores and bbox prediction
+                cls_scores (list[Tensor]): Classification scores for all scale
+                    levels, each is a 4D-tensor, the channels number is
+                    num_base_priors * num_classes.
+                bbox_preds (list[Tensor]): Box energies / deltas for all scale
+                    levels, each is a 4D-tensor, the channels number is
+                    num_base_priors * 4.
+                iou_preds (list[Tensor]): IoU scores for all scale levels,
+                    each is a 4D-tensor, the channels number is
+                    num_base_priors * 1.
+        """
+        return multi_apply(self.forward_single, feats, self.scales)
+
+    def forward_single(self, x, scale):
+        """Forward feature of a single scale level.
+
+        Args:
+            x (Tensor): Features of a single scale level.
+            scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
+                the bbox prediction.
+
+        Returns:
+            tuple:
+                - cls_score (Tensor): Cls scores for a single scale level \
+                    the channels number is num_base_priors * num_classes.
+                - bbox_pred (Tensor): Box energies / deltas for a single \
+                    scale level, the channels number is num_base_priors * 4.
+                - iou_pred (Tensor): Iou for a single scale level, the \
+                    channel number is (N, num_base_priors * 1, H, W).
+        """
+        cls_feat = x
+        reg_feat = x
+        for cls_conv in self.cls_convs:
+            cls_feat = cls_conv(cls_feat)
+        for reg_conv in self.reg_convs:
+            reg_feat = reg_conv(reg_feat)
+        cls_score = self.atss_cls(cls_feat)
+        # we just follow atss, not apply exp in bbox_pred
+        bbox_pred = scale(self.atss_reg(reg_feat)).float()
+        iou_pred = self.atss_iou(reg_feat)
+        return cls_score, bbox_pred, iou_pred
+
+    def loss_cls_single(self, cls_score, labels, label_weights,
+                        reweight_factor, num_total_samples):
+        """Compute cls loss of a single scale level.
+
+        Args:
+            cls_score (Tensor): Box scores for each scale level
+                Has shape (N, num_base_priors * num_classes, H, W).
+            labels (Tensor): Labels of each anchors with shape
+                (N, num_total_anchors).
+            label_weights (Tensor): Label weights of each anchor with shape
+                (N, num_total_anchors)
+            reweight_factor (list[int]): Reweight factor for cls and reg
+                loss.
+            num_total_samples (int): Number of positive samples that is
+                reduced over all GPUs.
+
+        Returns:
+            tuple[Tensor]: A tuple of loss components.
+        """
+        cls_score = cls_score.permute(0, 2, 3, 1).reshape(
+            -1, self.cls_out_channels).contiguous()
+        labels = labels.reshape(-1)
+        label_weights = label_weights.reshape(-1)
+        loss_cls = self.loss_cls(
+            cls_score, labels, label_weights, avg_factor=num_total_samples)
+        return reweight_factor * loss_cls,
+
+    def loss_reg_single(self, anchors, bbox_pred, iou_pred, labels,
+                        label_weights, bbox_targets, bbox_weights,
+                        reweight_factor, num_total_samples):
+        """Compute reg loss of a single scale level.
+
+        Args:
+            anchors (Tensor): Box reference for each scale level with shape
+                (N, num_total_anchors, 4).
+            bbox_pred (Tensor): Box energies / deltas for each scale
+                level with shape (N, num_base_priors * 4, H, W).
+            iou_pred (Tensor): Iou for a single scale level, the
+                channel number is (N, num_base_priors * 1, H, W).
+            labels (Tensor): Labels of each anchors with shape
+                (N, num_total_anchors).
+            label_weights (Tensor): Label weights of each anchor with shape
+                (N, num_total_anchors)
+            bbox_targets (Tensor): BBox regression targets of each anchor
+                weight shape (N, num_total_anchors, 4).
+            bbox_weights (Tensor): BBox weights of all anchors in the
+                image with shape (N, 4)
+            reweight_factor (list[int]): Reweight factor for cls and reg
+                loss.
+            num_total_samples (int): Number of positive samples that is
+                reduced over all GPUs.
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        anchors = anchors.reshape(-1, 4)
+        bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
+        iou_pred = iou_pred.permute(0, 2, 3, 1).reshape(-1, )
+        bbox_targets = bbox_targets.reshape(-1, 4)
+        bbox_weights = bbox_weights.reshape(-1, 4)
+        labels = labels.reshape(-1)
+        label_weights = label_weights.reshape(-1)
+
+        iou_targets = label_weights.new_zeros(labels.shape)
+        iou_weights = label_weights.new_zeros(labels.shape)
+        iou_weights[(bbox_weights.sum(axis=1) > 0).nonzero(
+            as_tuple=False)] = 1.
+
+        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+        bg_class_ind = self.num_classes
+        pos_inds = ((labels >= 0)
+                    &
+                    (labels < bg_class_ind)).nonzero(as_tuple=False).squeeze(1)
+
+        if len(pos_inds) > 0:
+            pos_bbox_targets = bbox_targets[pos_inds]
+            pos_bbox_pred = bbox_pred[pos_inds]
+            pos_anchors = anchors[pos_inds]
+
+            pos_decode_bbox_pred = self.bbox_coder.decode(
+                pos_anchors, pos_bbox_pred)
+            pos_decode_bbox_targets = self.bbox_coder.decode(
+                pos_anchors, pos_bbox_targets)
+
+            # regression loss
+            loss_bbox = self.loss_bbox(
+                pos_decode_bbox_pred,
+                pos_decode_bbox_targets,
+                avg_factor=num_total_samples)
+
+            iou_targets[pos_inds] = bbox_overlaps(
+                pos_decode_bbox_pred.detach(),
+                pos_decode_bbox_targets,
+                is_aligned=True)
+            loss_iou = self.loss_iou(
+                iou_pred,
+                iou_targets,
+                iou_weights,
+                avg_factor=num_total_samples)
+        else:
+            loss_bbox = bbox_pred.sum() * 0
+            loss_iou = iou_pred.sum() * 0
+
+        return reweight_factor * loss_bbox, reweight_factor * loss_iou
+
+    def calc_reweight_factor(self, labels_list):
+        """Compute reweight_factor for regression and classification loss."""
+        # get pos samples for each level
+        bg_class_ind = self.num_classes
+        for ii, each_level_label in enumerate(labels_list):
+            pos_inds = ((each_level_label >= 0) &
+                        (each_level_label < bg_class_ind)).nonzero(
+                            as_tuple=False).squeeze(1)
+            self.cls_num_pos_samples_per_level[ii] += len(pos_inds)
+        # get reweight factor from 1 ~ 2 with bilinear interpolation
+        min_pos_samples = min(self.cls_num_pos_samples_per_level)
+        max_pos_samples = max(self.cls_num_pos_samples_per_level)
+        interval = 1. / (max_pos_samples - min_pos_samples + 1e-10)
+        reweight_factor_per_level = []
+        for pos_samples in self.cls_num_pos_samples_per_level:
+            factor = 2. - (pos_samples - min_pos_samples) * interval
+            reweight_factor_per_level.append(factor)
+        return reweight_factor_per_level
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             iou_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_base_priors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_base_priors * 4, H, W)
+            iou_preds (list[Tensor]): Score factor for all scale level,
+                each is a 4D-tensor, has shape (batch_size, 1, H, W).
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (list[Tensor] | None): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+
+        device = cls_scores[0].device
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+
+        # calculate common vars for cls and reg assigners at once
+        targets_com = self.process_predictions_and_anchors(
+            anchor_list, valid_flag_list, cls_scores, bbox_preds, img_metas,
+            gt_bboxes_ignore)
+        (anchor_list, valid_flag_list, num_level_anchors_list, cls_score_list,
+         bbox_pred_list, gt_bboxes_ignore_list) = targets_com
+
+        # classification branch assigner
+        cls_targets = self.get_cls_targets(
+            anchor_list,
+            valid_flag_list,
+            num_level_anchors_list,
+            cls_score_list,
+            bbox_pred_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore_list,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels)
+        if cls_targets is None:
+            return None
+
+        (cls_anchor_list, labels_list, label_weights_list, bbox_targets_list,
+         bbox_weights_list, num_total_pos, num_total_neg) = cls_targets
+
+        num_total_samples = reduce_mean(
+            torch.tensor(num_total_pos, dtype=torch.float,
+                         device=device)).item()
+        num_total_samples = max(num_total_samples, 1.0)
+
+        reweight_factor_per_level = self.calc_reweight_factor(labels_list)
+
+        cls_losses_cls, = multi_apply(
+            self.loss_cls_single,
+            cls_scores,
+            labels_list,
+            label_weights_list,
+            reweight_factor_per_level,
+            num_total_samples=num_total_samples)
+
+        # regression branch assigner
+        reg_targets = self.get_reg_targets(
+            anchor_list,
+            valid_flag_list,
+            num_level_anchors_list,
+            cls_score_list,
+            bbox_pred_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore_list,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels)
+        if reg_targets is None:
+            return None
+
+        (reg_anchor_list, labels_list, label_weights_list, bbox_targets_list,
+         bbox_weights_list, num_total_pos, num_total_neg) = reg_targets
+
+        num_total_samples = reduce_mean(
+            torch.tensor(num_total_pos, dtype=torch.float,
+                         device=device)).item()
+        num_total_samples = max(num_total_samples, 1.0)
+
+        reweight_factor_per_level = self.calc_reweight_factor(labels_list)
+
+        reg_losses_bbox, reg_losses_iou = multi_apply(
+            self.loss_reg_single,
+            reg_anchor_list,
+            bbox_preds,
+            iou_preds,
+            labels_list,
+            label_weights_list,
+            bbox_targets_list,
+            bbox_weights_list,
+            reweight_factor_per_level,
+            num_total_samples=num_total_samples)
+
+        return dict(
+            loss_cls=cls_losses_cls,
+            loss_bbox=reg_losses_bbox,
+            loss_iou=reg_losses_iou)
+
+    def process_predictions_and_anchors(self, anchor_list, valid_flag_list,
+                                        cls_scores, bbox_preds, img_metas,
+                                        gt_bboxes_ignore_list):
+        """Compute common vars for regression and classification targets.
+
+        Args:
+            anchor_list (list[Tensor]): anchors of each image.
+            valid_flag_list (list[Tensor]): Valid flags of each image.
+            cls_scores (list[Tensor]): Classification scores for all scale
+                levels, each is a 4D-tensor, the channels number is
+                num_base_priors * num_classes.
+            bbox_preds (list[Tensor]): Box energies / deltas for all scale
+                levels, each is a 4D-tensor, the channels number is
+                num_base_priors * 4.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore_list (list[Tensor] | None): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Return:
+            tuple[Tensor]: A tuple of common loss vars.
+        """
+        num_imgs = len(img_metas)
+        assert len(anchor_list) == len(valid_flag_list) == num_imgs
+
+        # anchor number of multi levels
+        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+        num_level_anchors_list = [num_level_anchors] * num_imgs
+
+        anchor_list_ = []
+        valid_flag_list_ = []
+        # concat all level anchors and flags to a single tensor
+        for i in range(num_imgs):
+            assert len(anchor_list[i]) == len(valid_flag_list[i])
+            anchor_list_.append(torch.cat(anchor_list[i]))
+            valid_flag_list_.append(torch.cat(valid_flag_list[i]))
+
+        # compute targets for each image
+        if gt_bboxes_ignore_list is None:
+            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
+
+        num_levels = len(cls_scores)
+        cls_score_list = []
+        bbox_pred_list = []
+
+        mlvl_cls_score_list = [
+            cls_score.permute(0, 2, 3, 1).reshape(
+                num_imgs, -1, self.num_base_priors * self.cls_out_channels)
+            for cls_score in cls_scores
+        ]
+        mlvl_bbox_pred_list = [
+            bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1,
+                                                  self.num_base_priors * 4)
+            for bbox_pred in bbox_preds
+        ]
+
+        for i in range(num_imgs):
+            mlvl_cls_tensor_list = [
+                mlvl_cls_score_list[j][i] for j in range(num_levels)
+            ]
+            mlvl_bbox_tensor_list = [
+                mlvl_bbox_pred_list[j][i] for j in range(num_levels)
+            ]
+            cat_mlvl_cls_score = torch.cat(mlvl_cls_tensor_list, dim=0)
+            cat_mlvl_bbox_pred = torch.cat(mlvl_bbox_tensor_list, dim=0)
+            cls_score_list.append(cat_mlvl_cls_score)
+            bbox_pred_list.append(cat_mlvl_bbox_pred)
+        return (anchor_list_, valid_flag_list_, num_level_anchors_list,
+                cls_score_list, bbox_pred_list, gt_bboxes_ignore_list)
+
+    def get_cls_targets(self,
+                        anchor_list,
+                        valid_flag_list,
+                        num_level_anchors_list,
+                        cls_score_list,
+                        bbox_pred_list,
+                        gt_bboxes_list,
+                        img_metas,
+                        gt_bboxes_ignore_list=None,
+                        gt_labels_list=None,
+                        label_channels=1,
+                        unmap_outputs=True):
+        """Get cls targets for DDOD head.
+
+        This method is almost the same as `AnchorHead.get_targets()`.
+        Besides returning the targets as the parent  method does,
+        it also returns the anchors as the first element of the
+        returned tuple.
+
+        Args:
+            anchor_list (list[Tensor]): anchors of each image.
+            valid_flag_list (list[Tensor]): Valid flags of each image.
+            num_level_anchors_list (list[Tensor]): Number of anchors of each
+                scale level of all image.
+            cls_score_list (list[Tensor]): Classification scores for all scale
+                levels, each is a 4D-tensor, the channels number is
+                num_base_priors * num_classes.
+            bbox_pred_list (list[Tensor]): Box energies / deltas for all scale
+                levels, each is a 4D-tensor, the channels number is
+                num_base_priors * 4.
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore_list (list[Tensor] | None): specify which bounding
+                boxes can be ignored when computing the loss.
+            gt_labels_list (list[Tensor]): class indices corresponding to
+                each box.
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+
+        Return:
+            tuple[Tensor]: A tuple of cls targets components.
+        """
+        (all_anchors, all_labels, all_label_weights, all_bbox_targets,
+         all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
+             self._get_target_single,
+             anchor_list,
+             valid_flag_list,
+             cls_score_list,
+             bbox_pred_list,
+             num_level_anchors_list,
+             gt_bboxes_list,
+             gt_bboxes_ignore_list,
+             gt_labels_list,
+             img_metas,
+             label_channels=label_channels,
+             unmap_outputs=unmap_outputs,
+             is_cls_assigner=True)
+        # no valid anchors
+        if any([labels is None for labels in all_labels]):
+            return None
+        # sampled anchors of all images
+        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
+        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
+        # split targets to a list w.r.t. multiple levels
+        anchors_list = images_to_levels(all_anchors, num_level_anchors_list[0])
+        labels_list = images_to_levels(all_labels, num_level_anchors_list[0])
+        label_weights_list = images_to_levels(all_label_weights,
+                                              num_level_anchors_list[0])
+        bbox_targets_list = images_to_levels(all_bbox_targets,
+                                             num_level_anchors_list[0])
+        bbox_weights_list = images_to_levels(all_bbox_weights,
+                                             num_level_anchors_list[0])
+        return (anchors_list, labels_list, label_weights_list,
+                bbox_targets_list, bbox_weights_list, num_total_pos,
+                num_total_neg)
+
+    def get_reg_targets(self,
+                        anchor_list,
+                        valid_flag_list,
+                        num_level_anchors_list,
+                        cls_score_list,
+                        bbox_pred_list,
+                        gt_bboxes_list,
+                        img_metas,
+                        gt_bboxes_ignore_list=None,
+                        gt_labels_list=None,
+                        label_channels=1,
+                        unmap_outputs=True):
+        """Get reg targets for DDOD head.
+
+        This method is almost the same as `AnchorHead.get_targets()` when
+        is_cls_assigner is False. Besides returning the targets as the parent
+        method does, it also returns the anchors as the first element of the
+        returned tuple.
+
+        Args:
+            anchor_list (list[Tensor]): anchors of each image.
+            valid_flag_list (list[Tensor]): Valid flags of each image.
+            num_level_anchors (int): Number of anchors of each scale level.
+            cls_scores (list[Tensor]): Classification scores for all scale
+                levels, each is a 4D-tensor, the channels number is
+                num_base_priors * num_classes.
+            bbox_preds (list[Tensor]): Box energies / deltas for all scale
+                levels, each is a 4D-tensor, the channels number is
+                num_base_priors * 4.
+            gt_labels_list (list[Tensor]): class indices corresponding to
+                each box.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore_list (list[Tensor] | None): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Return:
+            tuple[Tensor]: A tuple of reg targets components.
+        """
+        (all_anchors, all_labels, all_label_weights, all_bbox_targets,
+         all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
+             self._get_target_single,
+             anchor_list,
+             valid_flag_list,
+             cls_score_list,
+             bbox_pred_list,
+             num_level_anchors_list,
+             gt_bboxes_list,
+             gt_bboxes_ignore_list,
+             gt_labels_list,
+             img_metas,
+             label_channels=label_channels,
+             unmap_outputs=unmap_outputs,
+             is_cls_assigner=False)
+        # no valid anchors
+        if any([labels is None for labels in all_labels]):
+            return None
+        # sampled anchors of all images
+        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
+        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
+        # split targets to a list w.r.t. multiple levels
+        anchors_list = images_to_levels(all_anchors, num_level_anchors_list[0])
+        labels_list = images_to_levels(all_labels, num_level_anchors_list[0])
+        label_weights_list = images_to_levels(all_label_weights,
+                                              num_level_anchors_list[0])
+        bbox_targets_list = images_to_levels(all_bbox_targets,
+                                             num_level_anchors_list[0])
+        bbox_weights_list = images_to_levels(all_bbox_weights,
+                                             num_level_anchors_list[0])
+        return (anchors_list, labels_list, label_weights_list,
+                bbox_targets_list, bbox_weights_list, num_total_pos,
+                num_total_neg)
+
+    def _get_target_single(self,
+                           flat_anchors,
+                           valid_flags,
+                           cls_scores,
+                           bbox_preds,
+                           num_level_anchors,
+                           gt_bboxes,
+                           gt_bboxes_ignore,
+                           gt_labels,
+                           img_meta,
+                           label_channels=1,
+                           unmap_outputs=True,
+                           is_cls_assigner=True):
+        """Compute regression, classification targets for anchors in a single
+        image.
+
+        Args:
+            flat_anchors (Tensor): Multi-level anchors of the image,
+                which are concatenated into a single tensor of shape
+                (num_base_priors, 4).
+            valid_flags (Tensor): Multi level valid flags of the image,
+                which are concatenated into a single tensor of
+                shape (num_base_priors,).
+            cls_scores (Tensor): Classification scores for all scale
+                levels of the image.
+            bbox_preds (Tensor): Box energies / deltas for all scale
+                levels of the image.
+            num_level_anchors (list[int]): Number of anchors of each
+                scale level.
+            gt_bboxes (Tensor): Ground truth bboxes of the image,
+                shape (num_gts, 4).
+            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, ).
+            gt_labels (Tensor): Ground truth labels of each box,
+                shape (num_gts, ).
+            img_meta (dict): Meta info of the image.
+            label_channels (int): Channel of label. Default: 1.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors. Default: True.
+            is_cls_assigner (bool): Classification or regression.
+                Default: True.
+
+        Returns:
+            tuple: N is the number of total anchors in the image.
+                - labels (Tensor): Labels of all anchors in the image with \
+                    shape (N, ).
+                - label_weights (Tensor): Label weights of all anchor in the \
+                    image with shape (N, ).
+                - bbox_targets (Tensor): BBox targets of all anchors in the \
+                    image with shape (N, 4).
+                - bbox_weights (Tensor): BBox weights of all anchors in the \
+                    image with shape (N, 4)
+                - pos_inds (Tensor): Indices of positive anchor with shape \
+                    (num_pos, ).
+                - neg_inds (Tensor): Indices of negative anchor with shape \
+                    (num_neg, ).
+        """
+        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
+                                           img_meta['img_shape'][:2],
+                                           self.train_cfg.allowed_border)
+        if not inside_flags.any():
+            return (None, ) * 7
+        # assign gt and sample anchors
+        anchors = flat_anchors[inside_flags, :]
+
+        num_level_anchors_inside = self.get_num_level_anchors_inside(
+            num_level_anchors, inside_flags)
+        bbox_preds_valid = bbox_preds[inside_flags, :]
+        cls_scores_valid = cls_scores[inside_flags, :]
+
+        assigner = self.cls_assigner if is_cls_assigner else self.reg_assigner
+
+        # decode prediction out of assigner
+        bbox_preds_valid = self.bbox_coder.decode(anchors, bbox_preds_valid)
+        assign_result = assigner.assign(anchors, num_level_anchors_inside,
+                                        gt_bboxes, gt_bboxes_ignore, gt_labels,
+                                        cls_scores_valid, bbox_preds_valid)
+        sampling_result = self.sampler.sample(assign_result, anchors,
+                                              gt_bboxes)
+
+        num_valid_anchors = anchors.shape[0]
+        bbox_targets = torch.zeros_like(anchors)
+        bbox_weights = torch.zeros_like(anchors)
+        labels = anchors.new_full((num_valid_anchors, ),
+                                  self.num_classes,
+                                  dtype=torch.long)
+        label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
+
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+        if len(pos_inds) > 0:
+            if hasattr(self, 'bbox_coder'):
+                pos_bbox_targets = self.bbox_coder.encode(
+                    sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
+            else:
+                # used in VFNetHead
+                pos_bbox_targets = sampling_result.pos_gt_bboxes
+            bbox_targets[pos_inds, :] = pos_bbox_targets
+            bbox_weights[pos_inds, :] = 1.0
+            if gt_labels is None:
+                # Only rpn gives gt_labels as None
+                # Foreground is the first class since v2.5.0
+                labels[pos_inds] = 0
+            else:
+                labels[pos_inds] = gt_labels[
+                    sampling_result.pos_assigned_gt_inds]
+            if self.train_cfg.pos_weight <= 0:
+                label_weights[pos_inds] = 1.0
+            else:
+                label_weights[pos_inds] = self.train_cfg.pos_weight
+        if len(neg_inds) > 0:
+            label_weights[neg_inds] = 1.0
+
+        # map up to original set of anchors
+        if unmap_outputs:
+            num_total_anchors = flat_anchors.size(0)
+            anchors = unmap(anchors, num_total_anchors, inside_flags)
+            labels = unmap(
+                labels, num_total_anchors, inside_flags, fill=self.num_classes)
+            label_weights = unmap(label_weights, num_total_anchors,
+                                  inside_flags)
+            bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
+            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
+
+        return (anchors, labels, label_weights, bbox_targets, bbox_weights,
+                pos_inds, neg_inds)
+
+    def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):
+        """Get the anchors of each scale level inside.
+
+        Args:
+            num_level_anchors (list[int]): Number of anchors of each
+                scale level.
+            inside_flags (Tensor): Multi level inside flags of the image,
+                which are concatenated into a single tensor of
+                shape (num_base_priors,).
+
+        Returns:
+            list[int]: Number of anchors of each scale level inside.
+        """
+        split_inside_flags = torch.split(inside_flags, num_level_anchors)
+        num_level_anchors_inside = [
+            int(flags.sum()) for flags in split_inside_flags
+        ]
+        return num_level_anchors_inside
diff --git a/mmdet/models/dense_heads/deformable_detr_head.py b/mmdet/models/dense_heads/deformable_detr_head.py
new file mode 100644
index 0000000..31290db
--- /dev/null
+++ b/mmdet/models/dense_heads/deformable_detr_head.py
@@ -0,0 +1,318 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import Linear, bias_init_with_prob, constant_init
+from mmcv.runner import force_fp32
+
+from mmdet.core import multi_apply
+from mmdet.models.utils.transformer import inverse_sigmoid
+from ..builder import HEADS
+from .detr_head import DETRHead
+
+
+@HEADS.register_module()
+class DeformableDETRHead(DETRHead):
+    """Head of DeformDETR: Deformable DETR: Deformable Transformers for End-to-
+    End Object Detection.
+
+    Code is modified from the `official github repo
+    <https://github.com/fundamentalvision/Deformable-DETR>`_.
+
+    More details can be found in the `paper
+    <https://arxiv.org/abs/2010.04159>`_ .
+
+    Args:
+        with_box_refine (bool): Whether to refine the reference points
+            in the decoder. Defaults to False.
+        as_two_stage (bool) : Whether to generate the proposal from
+            the outputs of encoder.
+        transformer (obj:`ConfigDict`): ConfigDict is used for building
+            the Encoder and Decoder.
+    """
+
+    def __init__(self,
+                 *args,
+                 with_box_refine=False,
+                 as_two_stage=False,
+                 transformer=None,
+                 **kwargs):
+        self.with_box_refine = with_box_refine
+        self.as_two_stage = as_two_stage
+        if self.as_two_stage:
+            transformer['as_two_stage'] = self.as_two_stage
+
+        super(DeformableDETRHead, self).__init__(
+            *args, transformer=transformer, **kwargs)
+
+    def _init_layers(self):
+        """Initialize classification branch and regression branch of head."""
+
+        fc_cls = Linear(self.embed_dims, self.cls_out_channels)
+        reg_branch = []
+        for _ in range(self.num_reg_fcs):
+            reg_branch.append(Linear(self.embed_dims, self.embed_dims))
+            reg_branch.append(nn.ReLU())
+        reg_branch.append(Linear(self.embed_dims, 4))
+        reg_branch = nn.Sequential(*reg_branch)
+
+        def _get_clones(module, N):
+            return nn.ModuleList([copy.deepcopy(module) for i in range(N)])
+
+        # last reg_branch is used to generate proposal from
+        # encode feature map when as_two_stage is True.
+        num_pred = (self.transformer.decoder.num_layers + 1) if \
+            self.as_two_stage else self.transformer.decoder.num_layers
+
+        if self.with_box_refine:
+            self.cls_branches = _get_clones(fc_cls, num_pred)
+            self.reg_branches = _get_clones(reg_branch, num_pred)
+        else:
+
+            self.cls_branches = nn.ModuleList(
+                [fc_cls for _ in range(num_pred)])
+            self.reg_branches = nn.ModuleList(
+                [reg_branch for _ in range(num_pred)])
+
+        if not self.as_two_stage:
+            self.query_embedding = nn.Embedding(self.num_query,
+                                                self.embed_dims * 2)
+
+    def init_weights(self):
+        """Initialize weights of the DeformDETR head."""
+        self.transformer.init_weights()
+        if self.loss_cls.use_sigmoid:
+            bias_init = bias_init_with_prob(0.01)
+            for m in self.cls_branches:
+                nn.init.constant_(m.bias, bias_init)
+        for m in self.reg_branches:
+            constant_init(m[-1], 0, bias=0)
+        nn.init.constant_(self.reg_branches[0][-1].bias.data[2:], -2.0)
+        if self.as_two_stage:
+            for m in self.reg_branches:
+                nn.init.constant_(m[-1].bias.data[2:], 0.0)
+
+    def forward(self, mlvl_feats, img_metas):
+        """Forward function.
+
+        Args:
+            mlvl_feats (tuple[Tensor]): Features from the upstream
+                network, each is a 4D-tensor with shape
+                (N, C, H, W).
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            all_cls_scores (Tensor): Outputs from the classification head, \
+                shape [nb_dec, bs, num_query, cls_out_channels]. Note \
+                cls_out_channels should includes background.
+            all_bbox_preds (Tensor): Sigmoid outputs from the regression \
+                head with normalized coordinate format (cx, cy, w, h). \
+                Shape [nb_dec, bs, num_query, 4].
+            enc_outputs_class (Tensor): The score of each point on encode \
+                feature map, has shape (N, h*w, num_class). Only when \
+                as_two_stage is True it would be returned, otherwise \
+                `None` would be returned.
+            enc_outputs_coord (Tensor): The proposal generate from the \
+                encode feature map, has shape (N, h*w, 4). Only when \
+                as_two_stage is True it would be returned, otherwise \
+                `None` would be returned.
+        """
+
+        batch_size = mlvl_feats[0].size(0)
+        input_img_h, input_img_w = img_metas[0]['batch_input_shape']
+        img_masks = mlvl_feats[0].new_ones(
+            (batch_size, input_img_h, input_img_w))
+        for img_id in range(batch_size):
+            img_h, img_w, _ = img_metas[img_id]['img_shape']
+            img_masks[img_id, :img_h, :img_w] = 0
+
+        mlvl_masks = []
+        mlvl_positional_encodings = []
+        for feat in mlvl_feats:
+            mlvl_masks.append(
+                F.interpolate(img_masks[None],
+                              size=feat.shape[-2:]).to(torch.bool).squeeze(0))
+            mlvl_positional_encodings.append(
+                self.positional_encoding(mlvl_masks[-1]))
+
+        query_embeds = None
+        if not self.as_two_stage:
+            query_embeds = self.query_embedding.weight
+        hs, init_reference, inter_references, \
+            enc_outputs_class, enc_outputs_coord = self.transformer(
+                    mlvl_feats,
+                    mlvl_masks,
+                    query_embeds,
+                    mlvl_positional_encodings,
+                    reg_branches=self.reg_branches if self.with_box_refine else None,  # noqa:E501
+                    cls_branches=self.cls_branches if self.as_two_stage else None  # noqa:E501
+            )
+        hs = hs.permute(0, 2, 1, 3)
+        outputs_classes = []
+        outputs_coords = []
+
+        for lvl in range(hs.shape[0]):
+            if lvl == 0:
+                reference = init_reference
+            else:
+                reference = inter_references[lvl - 1]
+            reference = inverse_sigmoid(reference)
+            outputs_class = self.cls_branches[lvl](hs[lvl])
+            tmp = self.reg_branches[lvl](hs[lvl])
+            if reference.shape[-1] == 4:
+                tmp += reference
+            else:
+                assert reference.shape[-1] == 2
+                tmp[..., :2] += reference
+            outputs_coord = tmp.sigmoid()
+            outputs_classes.append(outputs_class)
+            outputs_coords.append(outputs_coord)
+
+        outputs_classes = torch.stack(outputs_classes)
+        outputs_coords = torch.stack(outputs_coords)
+        if self.as_two_stage:
+            return outputs_classes, outputs_coords, \
+                enc_outputs_class, \
+                enc_outputs_coord.sigmoid()
+        else:
+            return outputs_classes, outputs_coords, \
+                None, None
+
+    @force_fp32(apply_to=('all_cls_scores', 'all_bbox_preds'))
+    def loss(self,
+             all_cls_scores,
+             all_bbox_preds,
+             enc_cls_scores,
+             enc_bbox_preds,
+             gt_bboxes_list,
+             gt_labels_list,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """"Loss function.
+
+        Args:
+            all_cls_scores (Tensor): Classification score of all
+                decoder layers, has shape
+                [nb_dec, bs, num_query, cls_out_channels].
+            all_bbox_preds (Tensor): Sigmoid regression
+                outputs of all decode layers. Each is a 4D-tensor with
+                normalized coordinate format (cx, cy, w, h) and shape
+                [nb_dec, bs, num_query, 4].
+            enc_cls_scores (Tensor): Classification scores of
+                points on encode feature map , has shape
+                (N, h*w, num_classes). Only be passed when as_two_stage is
+                True, otherwise is None.
+            enc_bbox_preds (Tensor): Regression results of each points
+                on the encode feature map, has shape (N, h*w, 4). Only be
+                passed when as_two_stage is True, otherwise is None.
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image
+                with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels_list (list[Tensor]): Ground truth class indices for each
+                image with shape (num_gts, ).
+            img_metas (list[dict]): List of image meta information.
+            gt_bboxes_ignore (list[Tensor], optional): Bounding boxes
+                which can be ignored for each image. Default None.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        assert gt_bboxes_ignore is None, \
+            f'{self.__class__.__name__} only supports ' \
+            f'for gt_bboxes_ignore setting to None.'
+
+        num_dec_layers = len(all_cls_scores)
+        all_gt_bboxes_list = [gt_bboxes_list for _ in range(num_dec_layers)]
+        all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)]
+        all_gt_bboxes_ignore_list = [
+            gt_bboxes_ignore for _ in range(num_dec_layers)
+        ]
+        img_metas_list = [img_metas for _ in range(num_dec_layers)]
+
+        losses_cls, losses_bbox, losses_iou = multi_apply(
+            self.loss_single, all_cls_scores, all_bbox_preds,
+            all_gt_bboxes_list, all_gt_labels_list, img_metas_list,
+            all_gt_bboxes_ignore_list)
+
+        loss_dict = dict()
+        # loss of proposal generated from encode feature map.
+        if enc_cls_scores is not None:
+            binary_labels_list = [
+                torch.zeros_like(gt_labels_list[i])
+                for i in range(len(img_metas))
+            ]
+            enc_loss_cls, enc_losses_bbox, enc_losses_iou = \
+                self.loss_single(enc_cls_scores, enc_bbox_preds,
+                                 gt_bboxes_list, binary_labels_list,
+                                 img_metas, gt_bboxes_ignore)
+            loss_dict['enc_loss_cls'] = enc_loss_cls
+            loss_dict['enc_loss_bbox'] = enc_losses_bbox
+            loss_dict['enc_loss_iou'] = enc_losses_iou
+
+        # loss from the last decoder layer
+        loss_dict['loss_cls'] = losses_cls[-1]
+        loss_dict['loss_bbox'] = losses_bbox[-1]
+        loss_dict['loss_iou'] = losses_iou[-1]
+        # loss from other decoder layers
+        num_dec_layer = 0
+        for loss_cls_i, loss_bbox_i, loss_iou_i in zip(losses_cls[:-1],
+                                                       losses_bbox[:-1],
+                                                       losses_iou[:-1]):
+            loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i
+            loss_dict[f'd{num_dec_layer}.loss_bbox'] = loss_bbox_i
+            loss_dict[f'd{num_dec_layer}.loss_iou'] = loss_iou_i
+            num_dec_layer += 1
+        return loss_dict
+
+    @force_fp32(apply_to=('all_cls_scores', 'all_bbox_preds'))
+    def get_bboxes(self,
+                   all_cls_scores,
+                   all_bbox_preds,
+                   enc_cls_scores,
+                   enc_bbox_preds,
+                   img_metas,
+                   rescale=False):
+        """Transform network outputs for a batch into bbox predictions.
+
+        Args:
+            all_cls_scores (Tensor): Classification score of all
+                decoder layers, has shape
+                [nb_dec, bs, num_query, cls_out_channels].
+            all_bbox_preds (Tensor): Sigmoid regression
+                outputs of all decode layers. Each is a 4D-tensor with
+                normalized coordinate format (cx, cy, w, h) and shape
+                [nb_dec, bs, num_query, 4].
+            enc_cls_scores (Tensor): Classification scores of
+                points on encode feature map , has shape
+                (N, h*w, num_classes). Only be passed when as_two_stage is
+                True, otherwise is None.
+            enc_bbox_preds (Tensor): Regression results of each points
+                on the encode feature map, has shape (N, h*w, 4). Only be
+                passed when as_two_stage is True, otherwise is None.
+            img_metas (list[dict]): Meta information of each image.
+            rescale (bool, optional): If True, return boxes in original
+                image space. Default False.
+
+        Returns:
+            list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple. \
+                The first item is an (n, 5) tensor, where the first 4 columns \
+                are bounding box positions (tl_x, tl_y, br_x, br_y) and the \
+                5-th column is a score between 0 and 1. The second item is a \
+                (n,) tensor where each item is the predicted class label of \
+                the corresponding box.
+        """
+        cls_scores = all_cls_scores[-1]
+        bbox_preds = all_bbox_preds[-1]
+
+        result_list = []
+        for img_id in range(len(img_metas)):
+            cls_score = cls_scores[img_id]
+            bbox_pred = bbox_preds[img_id]
+            img_shape = img_metas[img_id]['img_shape']
+            scale_factor = img_metas[img_id]['scale_factor']
+            proposals = self._get_bboxes_single(cls_score, bbox_pred,
+                                                img_shape, scale_factor,
+                                                rescale)
+            result_list.append(proposals)
+        return result_list
diff --git a/mmdet/models/dense_heads/dense_test_mixins.py b/mmdet/models/dense_heads/dense_test_mixins.py
new file mode 100644
index 0000000..1b1e70e
--- /dev/null
+++ b/mmdet/models/dense_heads/dense_test_mixins.py
@@ -0,0 +1,206 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import sys
+from inspect import signature
+
+import torch
+from mmcv.ops import batched_nms
+
+from mmdet.core import bbox_mapping_back, merge_aug_proposals
+
+if sys.version_info >= (3, 7):
+    from mmdet.utils.contextmanagers import completed
+
+
+class BBoxTestMixin(object):
+    """Mixin class for testing det bboxes via DenseHead."""
+
+    def simple_test_bboxes(self, feats, img_metas, rescale=False):
+        """Test det bboxes without test-time augmentation, can be applied in
+        DenseHead except for ``RPNHead`` and its variants, e.g., ``GARPNHead``,
+        etc.
+
+        Args:
+            feats (tuple[torch.Tensor]): Multi-level features from the
+                upstream network, each is a 4D-tensor.
+            img_metas (list[dict]): List of image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
+                The first item is ``bboxes`` with shape (n, 5),
+                where 5 represent (tl_x, tl_y, br_x, br_y, score).
+                The shape of the second tensor in the tuple is ``labels``
+                with shape (n,)
+        """
+        outs = self.forward(feats)  # TODO 这怎么搞，这里会绑定原始的head
+        results_list = self.get_bboxes(
+            *outs, img_metas=img_metas, rescale=rescale)
+        return results_list
+
+    def aug_test_bboxes(self, feats, img_metas, rescale=False):
+        """Test det bboxes with test time augmentation, can be applied in
+        DenseHead except for ``RPNHead`` and its variants, e.g., ``GARPNHead``,
+        etc.
+
+        Args:
+            feats (list[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains features for all images in the batch.
+            img_metas (list[list[dict]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch. each dict has image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
+                The first item is ``bboxes`` with shape (n, 5),
+                where 5 represent (tl_x, tl_y, br_x, br_y, score).
+                The shape of the second tensor in the tuple is ``labels``
+                with shape (n,). The length of list should always be 1.
+        """
+        # check with_nms argument
+        gb_sig = signature(self.get_bboxes)
+        gb_args = [p.name for p in gb_sig.parameters.values()]
+        gbs_sig = signature(self._get_bboxes_single)
+        gbs_args = [p.name for p in gbs_sig.parameters.values()]
+        assert ('with_nms' in gb_args) and ('with_nms' in gbs_args), \
+            f'{self.__class__.__name__}' \
+            ' does not support test-time augmentation'
+
+        aug_bboxes = []
+        aug_scores = []
+        aug_labels = []
+        for x, img_meta in zip(feats, img_metas):
+            # only one image in the batch
+            outs = self.forward(x)
+            bbox_outputs = self.get_bboxes(
+                *outs,
+                img_metas=img_meta,
+                cfg=self.test_cfg,
+                rescale=False,
+                with_nms=False)[0]
+            aug_bboxes.append(bbox_outputs[0])
+            aug_scores.append(bbox_outputs[1])
+            if len(bbox_outputs) >= 3:
+                aug_labels.append(bbox_outputs[2])
+
+        # after merging, bboxes will be rescaled to the original image size
+        merged_bboxes, merged_scores = self.merge_aug_bboxes(
+            aug_bboxes, aug_scores, img_metas)
+        merged_labels = torch.cat(aug_labels, dim=0) if aug_labels else None
+
+        if merged_bboxes.numel() == 0:
+            det_bboxes = torch.cat([merged_bboxes, merged_scores[:, None]], -1)
+            return [
+                (det_bboxes, merged_labels),
+            ]
+
+        det_bboxes, keep_idxs = batched_nms(merged_bboxes, merged_scores,
+                                            merged_labels, self.test_cfg.nms)
+        det_bboxes = det_bboxes[:self.test_cfg.max_per_img]
+        det_labels = merged_labels[keep_idxs][:self.test_cfg.max_per_img]
+
+        if rescale:
+            _det_bboxes = det_bboxes
+        else:
+            _det_bboxes = det_bboxes.clone()
+            _det_bboxes[:, :4] *= det_bboxes.new_tensor(
+                img_metas[0][0]['scale_factor'])
+
+        return [
+            (_det_bboxes, det_labels),
+        ]
+
+    def simple_test_rpn(self, x, img_metas):
+        """Test without augmentation, only for ``RPNHead`` and its variants,
+        e.g., ``GARPNHead``, etc.
+
+        Args:
+            x (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+            img_metas (list[dict]): Meta info of each image.
+
+        Returns:
+            list[Tensor]: Proposals of each image, each item has shape (n, 5),
+                where 5 represent (tl_x, tl_y, br_x, br_y, score).
+        """
+        rpn_outs = self(x)
+        proposal_list = self.get_bboxes(*rpn_outs, img_metas=img_metas)
+        return proposal_list
+
+    def aug_test_rpn(self, feats, img_metas):
+        """Test with augmentation for only for ``RPNHead`` and its variants,
+        e.g., ``GARPNHead``, etc.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                        a 4D-tensor.
+            img_metas (list[dict]): Meta info of each image.
+
+        Returns:
+            list[Tensor]: Proposals of each image, each item has shape (n, 5),
+                where 5 represent (tl_x, tl_y, br_x, br_y, score).
+        """
+        samples_per_gpu = len(img_metas[0])
+        aug_proposals = [[] for _ in range(samples_per_gpu)]
+        for x, img_meta in zip(feats, img_metas):
+            proposal_list = self.simple_test_rpn(x, img_meta)
+            for i, proposals in enumerate(proposal_list):
+                aug_proposals[i].append(proposals)
+        # reorganize the order of 'img_metas' to match the dimensions
+        # of 'aug_proposals'
+        aug_img_metas = []
+        for i in range(samples_per_gpu):
+            aug_img_meta = []
+            for j in range(len(img_metas)):
+                aug_img_meta.append(img_metas[j][i])
+            aug_img_metas.append(aug_img_meta)
+        # after merging, proposals will be rescaled to the original image size
+        merged_proposals = [
+            merge_aug_proposals(proposals, aug_img_meta, self.test_cfg)
+            for proposals, aug_img_meta in zip(aug_proposals, aug_img_metas)
+        ]
+        return merged_proposals
+
+    if sys.version_info >= (3, 7):
+
+        async def async_simple_test_rpn(self, x, img_metas):
+            sleep_interval = self.test_cfg.pop('async_sleep_interval', 0.025)
+            async with completed(
+                    __name__, 'rpn_head_forward',
+                    sleep_interval=sleep_interval):
+                rpn_outs = self(x)
+
+            proposal_list = self.get_bboxes(*rpn_outs, img_metas=img_metas)
+            return proposal_list
+
+    def merge_aug_bboxes(self, aug_bboxes, aug_scores, img_metas):
+        """Merge augmented detection bboxes and scores.
+
+        Args:
+            aug_bboxes (list[Tensor]): shape (n, 4*#class)
+            aug_scores (list[Tensor] or None): shape (n, #class)
+            img_shapes (list[Tensor]): shape (3, ).
+
+        Returns:
+            tuple[Tensor]: ``bboxes`` with shape (n,4), where
+            4 represent (tl_x, tl_y, br_x, br_y)
+            and ``scores`` with shape (n,).
+        """
+        recovered_bboxes = []
+        for bboxes, img_info in zip(aug_bboxes, img_metas):
+            img_shape = img_info[0]['img_shape']
+            scale_factor = img_info[0]['scale_factor']
+            flip = img_info[0]['flip']
+            flip_direction = img_info[0]['flip_direction']
+            bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip,
+                                       flip_direction)
+            recovered_bboxes.append(bboxes)
+        bboxes = torch.cat(recovered_bboxes, dim=0)
+        if aug_scores is None:
+            return bboxes
+        else:
+            scores = torch.cat(aug_scores, dim=0)
+            return bboxes, scores
diff --git a/mmdet/models/dense_heads/detr_head.py b/mmdet/models/dense_heads/detr_head.py
new file mode 100644
index 0000000..de1913c
--- /dev/null
+++ b/mmdet/models/dense_heads/detr_head.py
@@ -0,0 +1,844 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import Conv2d, Linear, build_activation_layer
+from mmcv.cnn.bricks.transformer import FFN, build_positional_encoding
+from mmcv.runner import force_fp32
+
+from mmdet.core import (bbox_cxcywh_to_xyxy, bbox_xyxy_to_cxcywh,
+                        build_assigner, build_sampler, multi_apply,
+                        reduce_mean)
+from mmdet.models.utils import build_transformer
+from ..builder import HEADS, build_loss
+from .anchor_free_head import AnchorFreeHead
+
+
+@HEADS.register_module()
+class DETRHead(AnchorFreeHead):
+    """Implements the DETR transformer head.
+
+    See `paper: End-to-End Object Detection with Transformers
+    <https://arxiv.org/pdf/2005.12872>`_ for details.
+
+    Args:
+        num_classes (int): Number of categories excluding the background.
+        in_channels (int): Number of channels in the input feature map.
+        num_query (int): Number of query in Transformer.
+        num_reg_fcs (int, optional): Number of fully-connected layers used in
+            `FFN`, which is then used for the regression head. Default 2.
+        transformer (obj:`mmcv.ConfigDict`|dict): Config for transformer.
+            Default: None.
+        sync_cls_avg_factor (bool): Whether to sync the avg_factor of
+            all ranks. Default to False.
+        positional_encoding (obj:`mmcv.ConfigDict`|dict):
+            Config for position encoding.
+        loss_cls (obj:`mmcv.ConfigDict`|dict): Config of the
+            classification loss. Default `CrossEntropyLoss`.
+        loss_bbox (obj:`mmcv.ConfigDict`|dict): Config of the
+            regression loss. Default `L1Loss`.
+        loss_iou (obj:`mmcv.ConfigDict`|dict): Config of the
+            regression iou loss. Default `GIoULoss`.
+        tran_cfg (obj:`mmcv.ConfigDict`|dict): Training config of
+            transformer head.
+        test_cfg (obj:`mmcv.ConfigDict`|dict): Testing config of
+            transformer head.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    _version = 2
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 num_query=100,
+                 num_reg_fcs=2,
+                 transformer=None,
+                 sync_cls_avg_factor=False,
+                 positional_encoding=dict(
+                     type='SinePositionalEncoding',
+                     num_feats=128,
+                     normalize=True),
+                 loss_cls=dict(
+                     type='CrossEntropyLoss',
+                     bg_cls_weight=0.1,
+                     use_sigmoid=False,
+                     loss_weight=1.0,
+                     class_weight=1.0),
+                 loss_bbox=dict(type='L1Loss', loss_weight=5.0),
+                 loss_iou=dict(type='GIoULoss', loss_weight=2.0),
+                 train_cfg=dict(
+                     assigner=dict(
+                         type='HungarianAssigner',
+                         cls_cost=dict(type='ClassificationCost', weight=1.),
+                         reg_cost=dict(type='BBoxL1Cost', weight=5.0),
+                         iou_cost=dict(
+                             type='IoUCost', iou_mode='giou', weight=2.0))),
+                 test_cfg=dict(max_per_img=100),
+                 init_cfg=None,
+                 **kwargs):
+        # NOTE here use `AnchorFreeHead` instead of `TransformerHead`,
+        # since it brings inconvenience when the initialization of
+        # `AnchorFreeHead` is called.
+        super(AnchorFreeHead, self).__init__(init_cfg)
+        self.bg_cls_weight = 0
+        self.sync_cls_avg_factor = sync_cls_avg_factor
+        class_weight = loss_cls.get('class_weight', None)
+        if class_weight is not None and (self.__class__ is DETRHead):
+            assert isinstance(class_weight, float), 'Expected ' \
+                'class_weight to have type float. Found ' \
+                f'{type(class_weight)}.'
+            # NOTE following the official DETR rep0, bg_cls_weight means
+            # relative classification weight of the no-object class.
+            bg_cls_weight = loss_cls.get('bg_cls_weight', class_weight)
+            assert isinstance(bg_cls_weight, float), 'Expected ' \
+                'bg_cls_weight to have type float. Found ' \
+                f'{type(bg_cls_weight)}.'
+            class_weight = torch.ones(num_classes + 1) * class_weight
+            # set background class as the last indice
+            class_weight[num_classes] = bg_cls_weight
+            loss_cls.update({'class_weight': class_weight})
+            if 'bg_cls_weight' in loss_cls:
+                loss_cls.pop('bg_cls_weight')
+            self.bg_cls_weight = bg_cls_weight
+
+        if train_cfg:
+            assert 'assigner' in train_cfg, 'assigner should be provided '\
+                'when train_cfg is set.'
+            assigner = train_cfg['assigner']
+            assert loss_cls['loss_weight'] == assigner['cls_cost']['weight'], \
+                'The classification weight for loss and matcher should be' \
+                'exactly the same.'
+            assert loss_bbox['loss_weight'] == assigner['reg_cost'][
+                'weight'], 'The regression L1 weight for loss and matcher ' \
+                'should be exactly the same.'
+            assert loss_iou['loss_weight'] == assigner['iou_cost']['weight'], \
+                'The regression iou weight for loss and matcher should be' \
+                'exactly the same.'
+            self.assigner = build_assigner(assigner)
+            # DETR sampling=False, so use PseudoSampler
+            sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+        self.num_query = num_query
+        self.num_classes = num_classes
+        self.in_channels = in_channels
+        self.num_reg_fcs = num_reg_fcs
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+        self.fp16_enabled = False
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_bbox = build_loss(loss_bbox)
+        self.loss_iou = build_loss(loss_iou)
+
+        if self.loss_cls.use_sigmoid:
+            self.cls_out_channels = num_classes
+        else:
+            self.cls_out_channels = num_classes + 1
+        self.act_cfg = transformer.get('act_cfg',
+                                       dict(type='ReLU', inplace=True))
+        self.activate = build_activation_layer(self.act_cfg)
+        self.positional_encoding = build_positional_encoding(
+            positional_encoding)
+        self.transformer = build_transformer(transformer)
+        self.embed_dims = self.transformer.embed_dims
+        assert 'num_feats' in positional_encoding
+        num_feats = positional_encoding['num_feats']
+        assert num_feats * 2 == self.embed_dims, 'embed_dims should' \
+            f' be exactly 2 times of num_feats. Found {self.embed_dims}' \
+            f' and {num_feats}.'
+        self._init_layers()
+
+    def _init_layers(self):
+        """Initialize layers of the transformer head."""
+        self.input_proj = Conv2d(
+            self.in_channels, self.embed_dims, kernel_size=1)
+        self.fc_cls = Linear(self.embed_dims, self.cls_out_channels)
+        self.reg_ffn = FFN(
+            self.embed_dims,
+            self.embed_dims,
+            self.num_reg_fcs,
+            self.act_cfg,
+            dropout=0.0,
+            add_residual=False)
+        self.fc_reg = Linear(self.embed_dims, 4)
+        self.query_embedding = nn.Embedding(self.num_query, self.embed_dims)
+
+    def init_weights(self):
+        """Initialize weights of the transformer head."""
+        # The initialization for transformer is important
+        self.transformer.init_weights()
+
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
+                              missing_keys, unexpected_keys, error_msgs):
+        """load checkpoints."""
+        # NOTE here use `AnchorFreeHead` instead of `TransformerHead`,
+        # since `AnchorFreeHead._load_from_state_dict` should not be
+        # called here. Invoking the default `Module._load_from_state_dict`
+        # is enough.
+
+        # Names of some parameters in has been changed.
+        version = local_metadata.get('version', None)
+        if (version is None or version < 2) and self.__class__ is DETRHead:
+            convert_dict = {
+                '.self_attn.': '.attentions.0.',
+                '.ffn.': '.ffns.0.',
+                '.multihead_attn.': '.attentions.1.',
+                '.decoder.norm.': '.decoder.post_norm.'
+            }
+            state_dict_keys = list(state_dict.keys())
+            for k in state_dict_keys:
+                for ori_key, convert_key in convert_dict.items():
+                    if ori_key in k:
+                        convert_key = k.replace(ori_key, convert_key)
+                        state_dict[convert_key] = state_dict[k]
+                        del state_dict[k]
+
+        super(AnchorFreeHead,
+              self)._load_from_state_dict(state_dict, prefix, local_metadata,
+                                          strict, missing_keys,
+                                          unexpected_keys, error_msgs)
+
+    def forward(self, feats, img_metas):
+        """Forward function.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            tuple[list[Tensor], list[Tensor]]: Outputs for all scale levels.
+
+                - all_cls_scores_list (list[Tensor]): Classification scores \
+                    for each scale level. Each is a 4D-tensor with shape \
+                    [nb_dec, bs, num_query, cls_out_channels]. Note \
+                    `cls_out_channels` should includes background.
+                - all_bbox_preds_list (list[Tensor]): Sigmoid regression \
+                    outputs for each scale level. Each is a 4D-tensor with \
+                    normalized coordinate format (cx, cy, w, h) and shape \
+                    [nb_dec, bs, num_query, 4].
+        """
+        num_levels = len(feats)
+        img_metas_list = [img_metas for _ in range(num_levels)]
+        return multi_apply(self.forward_single, feats, img_metas_list)
+
+    def forward_single(self, x, img_metas):
+        """"Forward function for a single feature level.
+
+        Args:
+            x (Tensor): Input feature from backbone's single stage, shape
+                [bs, c, h, w].
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            all_cls_scores (Tensor): Outputs from the classification head,
+                shape [nb_dec, bs, num_query, cls_out_channels]. Note
+                cls_out_channels should includes background.
+            all_bbox_preds (Tensor): Sigmoid outputs from the regression
+                head with normalized coordinate format (cx, cy, w, h).
+                Shape [nb_dec, bs, num_query, 4].
+        """
+        # construct binary masks which used for the transformer.
+        # NOTE following the official DETR repo, non-zero values representing
+        # ignored positions, while zero values means valid positions.
+        batch_size = x.size(0)
+        input_img_h, input_img_w = img_metas[0]['batch_input_shape']
+        masks = x.new_ones((batch_size, input_img_h, input_img_w))
+        for img_id in range(batch_size):
+            img_h, img_w, _ = img_metas[img_id]['img_shape']
+            masks[img_id, :img_h, :img_w] = 0
+
+        x = self.input_proj(x)
+        # interpolate masks to have the same spatial shape with x
+        masks = F.interpolate(
+            masks.unsqueeze(1), size=x.shape[-2:]).to(torch.bool).squeeze(1)
+        # position encoding
+        pos_embed = self.positional_encoding(masks)  # [bs, embed_dim, h, w]
+        # outs_dec: [nb_dec, bs, num_query, embed_dim]
+        outs_dec, _ = self.transformer(x, masks, self.query_embedding.weight,
+                                       pos_embed)
+
+        all_cls_scores = self.fc_cls(outs_dec)
+        all_bbox_preds = self.fc_reg(self.activate(
+            self.reg_ffn(outs_dec))).sigmoid()
+        return all_cls_scores, all_bbox_preds
+
+    @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list'))
+    def loss(self,
+             all_cls_scores_list,
+             all_bbox_preds_list,
+             gt_bboxes_list,
+             gt_labels_list,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """"Loss function.
+
+        Only outputs from the last feature level are used for computing
+        losses by default.
+
+        Args:
+            all_cls_scores_list (list[Tensor]): Classification outputs
+                for each feature level. Each is a 4D-tensor with shape
+                [nb_dec, bs, num_query, cls_out_channels].
+            all_bbox_preds_list (list[Tensor]): Sigmoid regression
+                outputs for each feature level. Each is a 4D-tensor with
+                normalized coordinate format (cx, cy, w, h) and shape
+                [nb_dec, bs, num_query, 4].
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image
+                with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels_list (list[Tensor]): Ground truth class indices for each
+                image with shape (num_gts, ).
+            img_metas (list[dict]): List of image meta information.
+            gt_bboxes_ignore (list[Tensor], optional): Bounding boxes
+                which can be ignored for each image. Default None.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        # NOTE defaultly only the outputs from the last feature scale is used.
+        all_cls_scores = all_cls_scores_list[-1]
+        all_bbox_preds = all_bbox_preds_list[-1]
+        assert gt_bboxes_ignore is None, \
+            'Only supports for gt_bboxes_ignore setting to None.'
+
+        num_dec_layers = len(all_cls_scores)
+        all_gt_bboxes_list = [gt_bboxes_list for _ in range(num_dec_layers)]
+        all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)]
+        all_gt_bboxes_ignore_list = [
+            gt_bboxes_ignore for _ in range(num_dec_layers)
+        ]
+        img_metas_list = [img_metas for _ in range(num_dec_layers)]
+
+        losses_cls, losses_bbox, losses_iou = multi_apply(
+            self.loss_single, all_cls_scores, all_bbox_preds,
+            all_gt_bboxes_list, all_gt_labels_list, img_metas_list,
+            all_gt_bboxes_ignore_list)
+
+        loss_dict = dict()
+        # loss from the last decoder layer
+        loss_dict['loss_cls'] = losses_cls[-1]
+        loss_dict['loss_bbox'] = losses_bbox[-1]
+        loss_dict['loss_iou'] = losses_iou[-1]
+        # loss from other decoder layers
+        num_dec_layer = 0
+        for loss_cls_i, loss_bbox_i, loss_iou_i in zip(losses_cls[:-1],
+                                                       losses_bbox[:-1],
+                                                       losses_iou[:-1]):
+            loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i
+            loss_dict[f'd{num_dec_layer}.loss_bbox'] = loss_bbox_i
+            loss_dict[f'd{num_dec_layer}.loss_iou'] = loss_iou_i
+            num_dec_layer += 1
+        return loss_dict
+
+    def loss_single(self,
+                    cls_scores,
+                    bbox_preds,
+                    gt_bboxes_list,
+                    gt_labels_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None):
+        """"Loss function for outputs from a single decoder layer of a single
+        feature level.
+
+        Args:
+            cls_scores (Tensor): Box score logits from a single decoder layer
+                for all images. Shape [bs, num_query, cls_out_channels].
+            bbox_preds (Tensor): Sigmoid outputs from a single decoder layer
+                for all images, with normalized coordinate (cx, cy, w, h) and
+                shape [bs, num_query, 4].
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image
+                with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels_list (list[Tensor]): Ground truth class indices for each
+                image with shape (num_gts, ).
+            img_metas (list[dict]): List of image meta information.
+            gt_bboxes_ignore_list (list[Tensor], optional): Bounding
+                boxes which can be ignored for each image. Default None.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components for outputs from
+                a single decoder layer.
+        """
+        num_imgs = cls_scores.size(0)
+        cls_scores_list = [cls_scores[i] for i in range(num_imgs)]
+        bbox_preds_list = [bbox_preds[i] for i in range(num_imgs)]
+        cls_reg_targets = self.get_targets(cls_scores_list, bbox_preds_list,
+                                           gt_bboxes_list, gt_labels_list,
+                                           img_metas, gt_bboxes_ignore_list)
+        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
+         num_total_pos, num_total_neg) = cls_reg_targets
+        labels = torch.cat(labels_list, 0)
+        label_weights = torch.cat(label_weights_list, 0)
+        bbox_targets = torch.cat(bbox_targets_list, 0)
+        bbox_weights = torch.cat(bbox_weights_list, 0)
+
+        # classification loss
+        cls_scores = cls_scores.reshape(-1, self.cls_out_channels)
+        # construct weighted avg_factor to match with the official DETR repo
+        cls_avg_factor = num_total_pos * 1.0 + \
+            num_total_neg * self.bg_cls_weight
+        if self.sync_cls_avg_factor:
+            cls_avg_factor = reduce_mean(
+                cls_scores.new_tensor([cls_avg_factor]))
+        cls_avg_factor = max(cls_avg_factor, 1)
+
+        loss_cls = self.loss_cls(
+            cls_scores, labels, label_weights, avg_factor=cls_avg_factor)
+
+        # Compute the average number of gt boxes across all gpus, for
+        # normalization purposes
+        num_total_pos = loss_cls.new_tensor([num_total_pos])
+        num_total_pos = torch.clamp(reduce_mean(num_total_pos), min=1).item()
+
+        # construct factors used for rescale bboxes
+        factors = []
+        for img_meta, bbox_pred in zip(img_metas, bbox_preds):
+            img_h, img_w, _ = img_meta['img_shape']
+            factor = bbox_pred.new_tensor([img_w, img_h, img_w,
+                                           img_h]).unsqueeze(0).repeat(
+                                               bbox_pred.size(0), 1)
+            factors.append(factor)
+        factors = torch.cat(factors, 0)
+
+        # DETR regress the relative position of boxes (cxcywh) in the image,
+        # thus the learning target is normalized by the image size. So here
+        # we need to re-scale them for calculating IoU loss
+        bbox_preds = bbox_preds.reshape(-1, 4)
+        bboxes = bbox_cxcywh_to_xyxy(bbox_preds) * factors
+        bboxes_gt = bbox_cxcywh_to_xyxy(bbox_targets) * factors
+
+        # regression IoU loss, defaultly GIoU loss
+        loss_iou = self.loss_iou(
+            bboxes, bboxes_gt, bbox_weights, avg_factor=num_total_pos)
+
+        # regression L1 loss
+        loss_bbox = self.loss_bbox(
+            bbox_preds, bbox_targets, bbox_weights, avg_factor=num_total_pos)
+        return loss_cls, loss_bbox, loss_iou
+
+    def get_targets(self,
+                    cls_scores_list,
+                    bbox_preds_list,
+                    gt_bboxes_list,
+                    gt_labels_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None):
+        """"Compute regression and classification targets for a batch image.
+
+        Outputs from a single decoder layer of a single feature level are used.
+
+        Args:
+            cls_scores_list (list[Tensor]): Box score logits from a single
+                decoder layer for each image with shape [num_query,
+                cls_out_channels].
+            bbox_preds_list (list[Tensor]): Sigmoid outputs from a single
+                decoder layer for each image, with normalized coordinate
+                (cx, cy, w, h) and shape [num_query, 4].
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes for each image
+                with shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels_list (list[Tensor]): Ground truth class indices for each
+                image with shape (num_gts, ).
+            img_metas (list[dict]): List of image meta information.
+            gt_bboxes_ignore_list (list[Tensor], optional): Bounding
+                boxes which can be ignored for each image. Default None.
+
+        Returns:
+            tuple: a tuple containing the following targets.
+
+                - labels_list (list[Tensor]): Labels for all images.
+                - label_weights_list (list[Tensor]): Label weights for all \
+                    images.
+                - bbox_targets_list (list[Tensor]): BBox targets for all \
+                    images.
+                - bbox_weights_list (list[Tensor]): BBox weights for all \
+                    images.
+                - num_total_pos (int): Number of positive samples in all \
+                    images.
+                - num_total_neg (int): Number of negative samples in all \
+                    images.
+        """
+        assert gt_bboxes_ignore_list is None, \
+            'Only supports for gt_bboxes_ignore setting to None.'
+        num_imgs = len(cls_scores_list)
+        gt_bboxes_ignore_list = [
+            gt_bboxes_ignore_list for _ in range(num_imgs)
+        ]
+
+        (labels_list, label_weights_list, bbox_targets_list,
+         bbox_weights_list, pos_inds_list, neg_inds_list) = multi_apply(
+             self._get_target_single, cls_scores_list, bbox_preds_list,
+             gt_bboxes_list, gt_labels_list, img_metas, gt_bboxes_ignore_list)
+        num_total_pos = sum((inds.numel() for inds in pos_inds_list))
+        num_total_neg = sum((inds.numel() for inds in neg_inds_list))
+        return (labels_list, label_weights_list, bbox_targets_list,
+                bbox_weights_list, num_total_pos, num_total_neg)
+
+    def _get_target_single(self,
+                           cls_score,
+                           bbox_pred,
+                           gt_bboxes,
+                           gt_labels,
+                           img_meta,
+                           gt_bboxes_ignore=None):
+        """"Compute regression and classification targets for one image.
+
+        Outputs from a single decoder layer of a single feature level are used.
+
+        Args:
+            cls_score (Tensor): Box score logits from a single decoder layer
+                for one image. Shape [num_query, cls_out_channels].
+            bbox_pred (Tensor): Sigmoid outputs from a single decoder layer
+                for one image, with normalized coordinate (cx, cy, w, h) and
+                shape [num_query, 4].
+            gt_bboxes (Tensor): Ground truth bboxes for one image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (Tensor): Ground truth class indices for one image
+                with shape (num_gts, ).
+            img_meta (dict): Meta information for one image.
+            gt_bboxes_ignore (Tensor, optional): Bounding boxes
+                which can be ignored. Default None.
+
+        Returns:
+            tuple[Tensor]: a tuple containing the following for one image.
+
+                - labels (Tensor): Labels of each image.
+                - label_weights (Tensor]): Label weights of each image.
+                - bbox_targets (Tensor): BBox targets of each image.
+                - bbox_weights (Tensor): BBox weights of each image.
+                - pos_inds (Tensor): Sampled positive indices for each image.
+                - neg_inds (Tensor): Sampled negative indices for each image.
+        """
+
+        num_bboxes = bbox_pred.size(0)
+        # assigner and sampler
+        assign_result = self.assigner.assign(bbox_pred, cls_score, gt_bboxes,
+                                             gt_labels, img_meta,
+                                             gt_bboxes_ignore)
+        sampling_result = self.sampler.sample(assign_result, bbox_pred,
+                                              gt_bboxes)
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+
+        # label targets
+        labels = gt_bboxes.new_full((num_bboxes, ),
+                                    self.num_classes,
+                                    dtype=torch.long)
+        labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds]
+        label_weights = gt_bboxes.new_ones(num_bboxes)
+
+        # bbox targets
+        bbox_targets = torch.zeros_like(bbox_pred)
+        bbox_weights = torch.zeros_like(bbox_pred)
+        bbox_weights[pos_inds] = 1.0
+        img_h, img_w, _ = img_meta['img_shape']
+
+        # DETR regress the relative position of boxes (cxcywh) in the image.
+        # Thus the learning target should be normalized by the image size, also
+        # the box format should be converted from defaultly x1y1x2y2 to cxcywh.
+        factor = bbox_pred.new_tensor([img_w, img_h, img_w,
+                                       img_h]).unsqueeze(0)
+        pos_gt_bboxes_normalized = sampling_result.pos_gt_bboxes / factor
+        pos_gt_bboxes_targets = bbox_xyxy_to_cxcywh(pos_gt_bboxes_normalized)
+        bbox_targets[pos_inds] = pos_gt_bboxes_targets
+        return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
+                neg_inds)
+
+    # over-write because img_metas are needed as inputs for bbox_head.
+    def forward_train(self,
+                      x,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels=None,
+                      gt_bboxes_ignore=None,
+                      proposal_cfg=None,
+                      **kwargs):
+        """Forward function for training mode.
+
+        Args:
+            x (list[Tensor]): Features from backbone.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes (Tensor): Ground truth bboxes of the image,
+                shape (num_gts, 4).
+            gt_labels (Tensor): Ground truth labels of each box,
+                shape (num_gts,).
+            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4).
+            proposal_cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        assert proposal_cfg is None, '"proposal_cfg" must be None'
+        outs = self(x, img_metas)
+        if gt_labels is None:
+            loss_inputs = outs + (gt_bboxes, img_metas)
+        else:
+            loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
+        losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
+        return losses
+
+    @force_fp32(apply_to=('all_cls_scores_list', 'all_bbox_preds_list'))
+    def get_bboxes(self,
+                   all_cls_scores_list,
+                   all_bbox_preds_list,
+                   img_metas,
+                   rescale=False):
+        """Transform network outputs for a batch into bbox predictions.
+
+        Args:
+            all_cls_scores_list (list[Tensor]): Classification outputs
+                for each feature level. Each is a 4D-tensor with shape
+                [nb_dec, bs, num_query, cls_out_channels].
+            all_bbox_preds_list (list[Tensor]): Sigmoid regression
+                outputs for each feature level. Each is a 4D-tensor with
+                normalized coordinate format (cx, cy, w, h) and shape
+                [nb_dec, bs, num_query, 4].
+            img_metas (list[dict]): Meta information of each image.
+            rescale (bool, optional): If True, return boxes in original
+                image space. Default False.
+
+        Returns:
+            list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple. \
+                The first item is an (n, 5) tensor, where the first 4 columns \
+                are bounding box positions (tl_x, tl_y, br_x, br_y) and the \
+                5-th column is a score between 0 and 1. The second item is a \
+                (n,) tensor where each item is the predicted class label of \
+                the corresponding box.
+        """
+        # NOTE defaultly only using outputs from the last feature level,
+        # and only the outputs from the last decoder layer is used.
+        cls_scores = all_cls_scores_list[-1][-1]
+        bbox_preds = all_bbox_preds_list[-1][-1]
+
+        result_list = []
+        for img_id in range(len(img_metas)):
+            cls_score = cls_scores[img_id]
+            bbox_pred = bbox_preds[img_id]
+            img_shape = img_metas[img_id]['img_shape']
+            scale_factor = img_metas[img_id]['scale_factor']
+            proposals = self._get_bboxes_single(cls_score, bbox_pred,
+                                                img_shape, scale_factor,
+                                                rescale)
+            result_list.append(proposals)
+
+        return result_list
+
+    def _get_bboxes_single(self,
+                           cls_score,
+                           bbox_pred,
+                           img_shape,
+                           scale_factor,
+                           rescale=False):
+        """Transform outputs from the last decoder layer into bbox predictions
+        for each image.
+
+        Args:
+            cls_score (Tensor): Box score logits from the last decoder layer
+                for each image. Shape [num_query, cls_out_channels].
+            bbox_pred (Tensor): Sigmoid outputs from the last decoder layer
+                for each image, with coordinate format (cx, cy, w, h) and
+                shape [num_query, 4].
+            img_shape (tuple[int]): Shape of input image, (height, width, 3).
+            scale_factor (ndarray, optional): Scale factor of the image arange
+                as (w_scale, h_scale, w_scale, h_scale).
+            rescale (bool, optional): If True, return boxes in original image
+                space. Default False.
+
+        Returns:
+            tuple[Tensor]: Results of detected bboxes and labels.
+
+                - det_bboxes: Predicted bboxes with shape [num_query, 5], \
+                    where the first 4 columns are bounding box positions \
+                    (tl_x, tl_y, br_x, br_y) and the 5-th column are scores \
+                    between 0 and 1.
+                - det_labels: Predicted labels of the corresponding box with \
+                    shape [num_query].
+        """
+        assert len(cls_score) == len(bbox_pred)
+        max_per_img = self.test_cfg.get('max_per_img', self.num_query)
+        # exclude background
+        if self.loss_cls.use_sigmoid:
+            cls_score = cls_score.sigmoid()
+            scores, indexes = cls_score.view(-1).topk(max_per_img)
+            det_labels = indexes % self.num_classes
+            bbox_index = indexes // self.num_classes
+            bbox_pred = bbox_pred[bbox_index]
+        else:
+            scores, det_labels = F.softmax(cls_score, dim=-1)[..., :-1].max(-1)
+            scores, bbox_index = scores.topk(max_per_img)
+            bbox_pred = bbox_pred[bbox_index]
+            det_labels = det_labels[bbox_index]
+
+        det_bboxes = bbox_cxcywh_to_xyxy(bbox_pred)
+        det_bboxes[:, 0::2] = det_bboxes[:, 0::2] * img_shape[1]
+        det_bboxes[:, 1::2] = det_bboxes[:, 1::2] * img_shape[0]
+        det_bboxes[:, 0::2].clamp_(min=0, max=img_shape[1])
+        det_bboxes[:, 1::2].clamp_(min=0, max=img_shape[0])
+        if rescale:
+            det_bboxes /= det_bboxes.new_tensor(scale_factor)
+        det_bboxes = torch.cat((det_bboxes, scores.unsqueeze(1)), -1)
+
+        return det_bboxes, det_labels
+
+    def simple_test_bboxes(self, feats, img_metas, rescale=False):
+        """Test det bboxes without test-time augmentation.
+
+        Args:
+            feats (tuple[torch.Tensor]): Multi-level features from the
+                upstream network, each is a 4D-tensor.
+            img_metas (list[dict]): List of image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
+                The first item is ``bboxes`` with shape (n, 5),
+                where 5 represent (tl_x, tl_y, br_x, br_y, score).
+                The shape of the second tensor in the tuple is ``labels``
+                with shape (n,)
+        """
+        # forward of this head requires img_metas
+        outs = self.forward(feats, img_metas)
+        results_list = self.get_bboxes(*outs, img_metas, rescale=rescale)
+        return results_list
+
+    def forward_onnx(self, feats, img_metas):
+        """Forward function for exporting to ONNX.
+
+        Over-write `forward` because: `masks` is directly created with
+        zero (valid position tag) and has the same spatial size as `x`.
+        Thus the construction of `masks` is different from that in `forward`.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            tuple[list[Tensor], list[Tensor]]: Outputs for all scale levels.
+
+                - all_cls_scores_list (list[Tensor]): Classification scores \
+                    for each scale level. Each is a 4D-tensor with shape \
+                    [nb_dec, bs, num_query, cls_out_channels]. Note \
+                    `cls_out_channels` should includes background.
+                - all_bbox_preds_list (list[Tensor]): Sigmoid regression \
+                    outputs for each scale level. Each is a 4D-tensor with \
+                    normalized coordinate format (cx, cy, w, h) and shape \
+                    [nb_dec, bs, num_query, 4].
+        """
+        num_levels = len(feats)
+        img_metas_list = [img_metas for _ in range(num_levels)]
+        return multi_apply(self.forward_single_onnx, feats, img_metas_list)
+
+    def forward_single_onnx(self, x, img_metas):
+        """"Forward function for a single feature level with ONNX exportation.
+
+        Args:
+            x (Tensor): Input feature from backbone's single stage, shape
+                [bs, c, h, w].
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            all_cls_scores (Tensor): Outputs from the classification head,
+                shape [nb_dec, bs, num_query, cls_out_channels]. Note
+                cls_out_channels should includes background.
+            all_bbox_preds (Tensor): Sigmoid outputs from the regression
+                head with normalized coordinate format (cx, cy, w, h).
+                Shape [nb_dec, bs, num_query, 4].
+        """
+        # Note `img_shape` is not dynamically traceable to ONNX,
+        # since the related augmentation was done with numpy under
+        # CPU. Thus `masks` is directly created with zeros (valid tag)
+        # and the same spatial shape as `x`.
+        # The difference between torch and exported ONNX model may be
+        # ignored, since the same performance is achieved (e.g.
+        # 40.1 vs 40.1 for DETR)
+        batch_size = x.size(0)
+        h, w = x.size()[-2:]
+        masks = x.new_zeros((batch_size, h, w))  # [B,h,w]
+
+        x = self.input_proj(x)
+        # interpolate masks to have the same spatial shape with x
+        masks = F.interpolate(
+            masks.unsqueeze(1), size=x.shape[-2:]).to(torch.bool).squeeze(1)
+        pos_embed = self.positional_encoding(masks)
+        outs_dec, _ = self.transformer(x, masks, self.query_embedding.weight,
+                                       pos_embed)
+
+        all_cls_scores = self.fc_cls(outs_dec)
+        all_bbox_preds = self.fc_reg(self.activate(
+            self.reg_ffn(outs_dec))).sigmoid()
+        return all_cls_scores, all_bbox_preds
+
+    def onnx_export(self, all_cls_scores_list, all_bbox_preds_list, img_metas):
+        """Transform network outputs into bbox predictions, with ONNX
+        exportation.
+
+        Args:
+            all_cls_scores_list (list[Tensor]): Classification outputs
+                for each feature level. Each is a 4D-tensor with shape
+                [nb_dec, bs, num_query, cls_out_channels].
+            all_bbox_preds_list (list[Tensor]): Sigmoid regression
+                outputs for each feature level. Each is a 4D-tensor with
+                normalized coordinate format (cx, cy, w, h) and shape
+                [nb_dec, bs, num_query, 4].
+            img_metas (list[dict]): Meta information of each image.
+
+        Returns:
+            tuple[Tensor, Tensor]: dets of shape [N, num_det, 5]
+                and class labels of shape [N, num_det].
+        """
+        assert len(img_metas) == 1, \
+            'Only support one input image while in exporting to ONNX'
+
+        cls_scores = all_cls_scores_list[-1][-1]
+        bbox_preds = all_bbox_preds_list[-1][-1]
+
+        # Note `img_shape` is not dynamically traceable to ONNX,
+        # here `img_shape_for_onnx` (padded shape of image tensor)
+        # is used.
+        img_shape = img_metas[0]['img_shape_for_onnx']
+        max_per_img = self.test_cfg.get('max_per_img', self.num_query)
+        batch_size = cls_scores.size(0)
+        # `batch_index_offset` is used for the gather of concatenated tensor
+        batch_index_offset = torch.arange(batch_size).to(
+            cls_scores.device) * max_per_img
+        batch_index_offset = batch_index_offset.unsqueeze(1).expand(
+            batch_size, max_per_img)
+
+        # supports dynamical batch inference
+        if self.loss_cls.use_sigmoid:
+            cls_scores = cls_scores.sigmoid()
+            scores, indexes = cls_scores.view(batch_size, -1).topk(
+                max_per_img, dim=1)
+            det_labels = indexes % self.num_classes
+            bbox_index = indexes // self.num_classes
+            bbox_index = (bbox_index + batch_index_offset).view(-1)
+            bbox_preds = bbox_preds.view(-1, 4)[bbox_index]
+            bbox_preds = bbox_preds.view(batch_size, -1, 4)
+        else:
+            scores, det_labels = F.softmax(
+                cls_scores, dim=-1)[..., :-1].max(-1)
+            scores, bbox_index = scores.topk(max_per_img, dim=1)
+            bbox_index = (bbox_index + batch_index_offset).view(-1)
+            bbox_preds = bbox_preds.view(-1, 4)[bbox_index]
+            det_labels = det_labels.view(-1)[bbox_index]
+            bbox_preds = bbox_preds.view(batch_size, -1, 4)
+            det_labels = det_labels.view(batch_size, -1)
+
+        det_bboxes = bbox_cxcywh_to_xyxy(bbox_preds)
+        # use `img_shape_tensor` for dynamically exporting to ONNX
+        img_shape_tensor = img_shape.flip(0).repeat(2)  # [w,h,w,h]
+        img_shape_tensor = img_shape_tensor.unsqueeze(0).unsqueeze(0).expand(
+            batch_size, det_bboxes.size(1), 4)
+        det_bboxes = det_bboxes * img_shape_tensor
+        # dynamically clip bboxes
+        x1, y1, x2, y2 = det_bboxes.split((1, 1, 1, 1), dim=-1)
+        from mmdet.core.export import dynamic_clip_for_onnx
+        x1, y1, x2, y2 = dynamic_clip_for_onnx(x1, y1, x2, y2, img_shape)
+        det_bboxes = torch.cat([x1, y1, x2, y2], dim=-1)
+        det_bboxes = torch.cat((det_bboxes, scores.unsqueeze(-1)), -1)
+
+        return det_bboxes, det_labels
diff --git a/mmdet/models/dense_heads/embedding_rpn_head.py b/mmdet/models/dense_heads/embedding_rpn_head.py
new file mode 100644
index 0000000..22060b9
--- /dev/null
+++ b/mmdet/models/dense_heads/embedding_rpn_head.py
@@ -0,0 +1,116 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.runner import BaseModule
+
+from mmdet.models.builder import HEADS
+from ...core import bbox_cxcywh_to_xyxy
+
+
+@HEADS.register_module()
+class EmbeddingRPNHead(BaseModule):
+    """RPNHead in the `Sparse R-CNN <https://arxiv.org/abs/2011.12450>`_ .
+
+    Unlike traditional RPNHead, this module does not need FPN input, but just
+    decode `init_proposal_bboxes` and expand the first dimension of
+    `init_proposal_bboxes` and `init_proposal_features` to the batch_size.
+
+    Args:
+        num_proposals (int): Number of init_proposals. Default 100.
+        proposal_feature_channel (int): Channel number of
+            init_proposal_feature. Defaults to 256.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 num_proposals=100,
+                 proposal_feature_channel=256,
+                 init_cfg=None,
+                 **kwargs):
+        assert init_cfg is None, 'To prevent abnormal initialization ' \
+                                 'behavior, init_cfg is not allowed to be set'
+        super(EmbeddingRPNHead, self).__init__(init_cfg)
+        self.num_proposals = num_proposals
+        self.proposal_feature_channel = proposal_feature_channel
+        self._init_layers()
+
+    def _init_layers(self):
+        """Initialize a sparse set of proposal boxes and proposal features."""
+        self.init_proposal_bboxes = nn.Embedding(self.num_proposals, 4)
+        self.init_proposal_features = nn.Embedding(
+            self.num_proposals, self.proposal_feature_channel)
+
+    def init_weights(self):
+        """Initialize the init_proposal_bboxes as normalized.
+
+        [c_x, c_y, w, h], and we initialize it to the size of  the entire
+        image.
+        """
+        super(EmbeddingRPNHead, self).init_weights()
+        nn.init.constant_(self.init_proposal_bboxes.weight[:, :2], 0.5)
+        nn.init.constant_(self.init_proposal_bboxes.weight[:, 2:], 1)
+
+    def _decode_init_proposals(self, imgs, img_metas):
+        """Decode init_proposal_bboxes according to the size of images and
+        expand dimension of init_proposal_features to batch_size.
+
+        Args:
+            imgs (list[Tensor]): List of FPN features.
+            img_metas (list[dict]): List of meta-information of
+                images. Need the img_shape to decode the init_proposals.
+
+        Returns:
+            Tuple(Tensor):
+
+                - proposals (Tensor): Decoded proposal bboxes,
+                  has shape (batch_size, num_proposals, 4).
+                - init_proposal_features (Tensor): Expanded proposal
+                  features, has shape
+                  (batch_size, num_proposals, proposal_feature_channel).
+                - imgs_whwh (Tensor): Tensor with shape
+                  (batch_size, 4), the dimension means
+                  [img_width, img_height, img_width, img_height].
+        """
+        proposals = self.init_proposal_bboxes.weight.clone()
+        proposals = bbox_cxcywh_to_xyxy(proposals)
+        num_imgs = len(imgs[0])
+        imgs_whwh = []
+        for meta in img_metas:
+            h, w, _ = meta['img_shape']
+            imgs_whwh.append(imgs[0].new_tensor([[w, h, w, h]]))
+        imgs_whwh = torch.cat(imgs_whwh, dim=0)
+        imgs_whwh = imgs_whwh[:, None, :]
+
+        # imgs_whwh has shape (batch_size, 1, 4)
+        # The shape of proposals change from (num_proposals, 4)
+        # to (batch_size ,num_proposals, 4)
+        proposals = proposals * imgs_whwh
+
+        init_proposal_features = self.init_proposal_features.weight.clone()
+        init_proposal_features = init_proposal_features[None].expand(
+            num_imgs, *init_proposal_features.size())
+        return proposals, init_proposal_features, imgs_whwh
+
+    def forward_dummy(self, img, img_metas):
+        """Dummy forward function.
+
+        Used in flops calculation.
+        """
+        return self._decode_init_proposals(img, img_metas)
+
+    def forward_train(self, img, img_metas):
+        """Forward function in training stage."""
+        return self._decode_init_proposals(img, img_metas)
+
+    def simple_test_rpn(self, img, img_metas):
+        """Forward function in testing stage."""
+        return self._decode_init_proposals(img, img_metas)
+
+    def simple_test(self, img, img_metas):
+        """Forward function in testing stage."""
+        raise NotImplementedError
+
+    def aug_test_rpn(self, feats, img_metas):
+        raise NotImplementedError(
+            'EmbeddingRPNHead does not support test-time augmentation')
diff --git a/mmdet/models/dense_heads/fcos_head.py b/mmdet/models/dense_heads/fcos_head.py
new file mode 100644
index 0000000..d72fb56
--- /dev/null
+++ b/mmdet/models/dense_heads/fcos_head.py
@@ -0,0 +1,455 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch
+import torch.nn as nn
+from mmcv.cnn import Scale
+from mmcv.runner import force_fp32
+
+from mmdet.core import multi_apply, reduce_mean
+from ..builder import HEADS, build_loss
+from .anchor_free_head import AnchorFreeHead
+
+INF = 1e8
+
+
+@HEADS.register_module()
+class FCOSHead(AnchorFreeHead):
+    """Anchor-free head used in `FCOS <https://arxiv.org/abs/1904.01355>`_.
+
+    The FCOS head does not use anchor boxes. Instead bounding boxes are
+    predicted at each pixel and a centerness measure is used to suppress
+    low-quality predictions.
+    Here norm_on_bbox, centerness_on_reg, dcn_on_last_conv are training
+    tricks used in official repo, which will bring remarkable mAP gains
+    of up to 4.9. Please see https://github.com/tianzhi0549/FCOS for
+    more detail.
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        strides (list[int] | list[tuple[int, int]]): Strides of points
+            in multiple feature levels. Default: (4, 8, 16, 32, 64).
+        regress_ranges (tuple[tuple[int, int]]): Regress range of multiple
+            level points.
+        center_sampling (bool): If true, use center sampling. Default: False.
+        center_sample_radius (float): Radius of center sampling. Default: 1.5.
+        norm_on_bbox (bool): If true, normalize the regression targets
+            with FPN strides. Default: False.
+        centerness_on_reg (bool): If true, position centerness on the
+            regress branch. Please refer to https://github.com/tianzhi0549/FCOS/issues/89#issuecomment-516877042.
+            Default: False.
+        conv_bias (bool | str): If specified as `auto`, it will be decided by the
+            norm_cfg. Bias of conv will be set as True if `norm_cfg` is None, otherwise
+            False. Default: "auto".
+        loss_cls (dict): Config of classification loss.
+        loss_bbox (dict): Config of localization loss.
+        loss_centerness (dict): Config of centerness loss.
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: norm_cfg=dict(type='GN', num_groups=32, requires_grad=True).
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+
+    Example:
+        >>> self = FCOSHead(11, 7)
+        >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
+        >>> cls_score, bbox_pred, centerness = self.forward(feats)
+        >>> assert len(cls_score) == len(self.scales)
+    """  # noqa: E501
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 regress_ranges=((-1, 64), (64, 128), (128, 256), (256, 512),
+                                 (512, INF)),
+                 center_sampling=False,
+                 center_sample_radius=1.5,
+                 norm_on_bbox=False,
+                 centerness_on_reg=False,
+                 loss_cls=dict(
+                     type='FocalLoss',
+                     use_sigmoid=True,
+                     gamma=2.0,
+                     alpha=0.25,
+                     loss_weight=1.0),
+                 loss_bbox=dict(type='IoULoss', loss_weight=1.0),
+                 loss_centerness=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
+                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
+                 init_cfg=dict(
+                     type='Normal',
+                     layer='Conv2d',
+                     std=0.01,
+                     override=dict(
+                         type='Normal',
+                         name='conv_cls',
+                         std=0.01,
+                         bias_prob=0.01)),
+                 **kwargs):
+        self.regress_ranges = regress_ranges
+        self.center_sampling = center_sampling
+        self.center_sample_radius = center_sample_radius
+        self.norm_on_bbox = norm_on_bbox
+        self.centerness_on_reg = centerness_on_reg
+        super().__init__(
+            num_classes,
+            in_channels,
+            loss_cls=loss_cls,
+            loss_bbox=loss_bbox,
+            norm_cfg=norm_cfg,
+            init_cfg=init_cfg,
+            **kwargs)
+        self.loss_centerness = build_loss(loss_centerness)
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        super()._init_layers()
+        self.conv_centerness = nn.Conv2d(self.feat_channels, 1, 3, padding=1)
+        self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
+
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple:
+                cls_scores (list[Tensor]): Box scores for each scale level, \
+                    each is a 4D-tensor, the channel number is \
+                    num_points * num_classes.
+                bbox_preds (list[Tensor]): Box energies / deltas for each \
+                    scale level, each is a 4D-tensor, the channel number is \
+                    num_points * 4.
+                centernesses (list[Tensor]): centerness for each scale level, \
+                    each is a 4D-tensor, the channel number is num_points * 1.
+        """
+        return multi_apply(self.forward_single, feats, self.scales,
+                           self.strides)
+
+    def forward_single(self, x, scale, stride):
+        """Forward features of a single scale level.
+
+        Args:
+            x (Tensor): FPN feature maps of the specified stride.
+            scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
+                the bbox prediction.
+            stride (int): The corresponding stride for feature maps, only
+                used to normalize the bbox prediction when self.norm_on_bbox
+                is True.
+
+        Returns:
+            tuple: scores for each class, bbox predictions and centerness \
+                predictions of input feature maps.
+        """
+        cls_score, bbox_pred, cls_feat, reg_feat = super().forward_single(x)
+        if self.centerness_on_reg:
+            centerness = self.conv_centerness(reg_feat)
+        else:
+            centerness = self.conv_centerness(cls_feat)
+        # scale the bbox_pred of different level
+        # float to avoid overflow when enabling FP16
+        bbox_pred = scale(bbox_pred).float()
+        if self.norm_on_bbox:
+            # bbox_pred needed for gradient computation has been modified
+            # by F.relu(bbox_pred) when run with PyTorch 1.10. So replace
+            # F.relu(bbox_pred) with bbox_pred.clamp(min=0)
+            bbox_pred = bbox_pred.clamp(min=0)
+            if not self.training:
+                bbox_pred *= stride
+        else:
+            bbox_pred = bbox_pred.exp()
+        return cls_score, bbox_pred, centerness
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'centernesses'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             centernesses,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute loss of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level,
+                each is a 4D-tensor, the channel number is
+                num_points * num_classes.
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level, each is a 4D-tensor, the channel number is
+                num_points * 4.
+            centernesses (list[Tensor]): centerness for each scale level, each
+                is a 4D-tensor, the channel number is num_points * 1.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        assert len(cls_scores) == len(bbox_preds) == len(centernesses)
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        all_level_points = self.prior_generator.grid_priors(
+            featmap_sizes,
+            dtype=bbox_preds[0].dtype,
+            device=bbox_preds[0].device)
+        labels, bbox_targets = self.get_targets(all_level_points, gt_bboxes,
+                                                gt_labels)
+
+        num_imgs = cls_scores[0].size(0)
+        # flatten cls_scores, bbox_preds and centerness
+        flatten_cls_scores = [
+            cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels)
+            for cls_score in cls_scores
+        ]
+        flatten_bbox_preds = [
+            bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
+            for bbox_pred in bbox_preds
+        ]
+        flatten_centerness = [
+            centerness.permute(0, 2, 3, 1).reshape(-1)
+            for centerness in centernesses
+        ]
+        flatten_cls_scores = torch.cat(flatten_cls_scores)
+        flatten_bbox_preds = torch.cat(flatten_bbox_preds)
+        flatten_centerness = torch.cat(flatten_centerness)
+        flatten_labels = torch.cat(labels)
+        flatten_bbox_targets = torch.cat(bbox_targets)
+        # repeat points to align with bbox_preds
+        flatten_points = torch.cat(
+            [points.repeat(num_imgs, 1) for points in all_level_points])
+
+        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+        bg_class_ind = self.num_classes
+        pos_inds = ((flatten_labels >= 0)
+                    & (flatten_labels < bg_class_ind)).nonzero().reshape(-1)
+        num_pos = torch.tensor(
+            len(pos_inds), dtype=torch.float, device=bbox_preds[0].device)
+        num_pos = max(reduce_mean(num_pos), 1.0)
+        loss_cls = self.loss_cls(
+            flatten_cls_scores, flatten_labels, avg_factor=num_pos)
+
+        pos_bbox_preds = flatten_bbox_preds[pos_inds]
+        pos_centerness = flatten_centerness[pos_inds]
+        pos_bbox_targets = flatten_bbox_targets[pos_inds]
+        pos_centerness_targets = self.centerness_target(pos_bbox_targets)
+        # centerness weighted iou loss
+        centerness_denorm = max(
+            reduce_mean(pos_centerness_targets.sum().detach()), 1e-6)
+
+        if len(pos_inds) > 0:
+            pos_points = flatten_points[pos_inds]
+            pos_decoded_bbox_preds = self.bbox_coder.decode(
+                pos_points, pos_bbox_preds)
+            pos_decoded_target_preds = self.bbox_coder.decode(
+                pos_points, pos_bbox_targets)
+            loss_bbox = self.loss_bbox(
+                pos_decoded_bbox_preds,
+                pos_decoded_target_preds,
+                weight=pos_centerness_targets,
+                avg_factor=centerness_denorm)
+            loss_centerness = self.loss_centerness(
+                pos_centerness, pos_centerness_targets, avg_factor=num_pos)
+        else:
+            loss_bbox = pos_bbox_preds.sum()
+            loss_centerness = pos_centerness.sum()
+
+        return dict(
+            loss_cls=loss_cls,
+            loss_bbox=loss_bbox,
+            loss_centerness=loss_centerness)
+
+    def get_targets(self, points, gt_bboxes_list, gt_labels_list):
+        """Compute regression, classification and centerness targets for points
+        in multiple images.
+
+        Args:
+            points (list[Tensor]): Points of each fpn level, each has shape
+                (num_points, 2).
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
+                each has shape (num_gt, 4).
+            gt_labels_list (list[Tensor]): Ground truth labels of each box,
+                each has shape (num_gt,).
+
+        Returns:
+            tuple:
+                concat_lvl_labels (list[Tensor]): Labels of each level. \
+                concat_lvl_bbox_targets (list[Tensor]): BBox targets of each \
+                    level.
+        """
+        assert len(points) == len(self.regress_ranges)
+        num_levels = len(points)
+        # expand regress ranges to align with points
+        expanded_regress_ranges = [
+            points[i].new_tensor(self.regress_ranges[i])[None].expand_as(
+                points[i]) for i in range(num_levels)
+        ]
+        # concat all levels points and regress ranges
+        concat_regress_ranges = torch.cat(expanded_regress_ranges, dim=0)
+        concat_points = torch.cat(points, dim=0)
+
+        # the number of points per img, per lvl
+        num_points = [center.size(0) for center in points]
+
+        # get labels and bbox_targets of each image
+        labels_list, bbox_targets_list = multi_apply(
+            self._get_target_single,
+            gt_bboxes_list,
+            gt_labels_list,
+            points=concat_points,
+            regress_ranges=concat_regress_ranges,
+            num_points_per_lvl=num_points)
+
+        # split to per img, per level
+        labels_list = [labels.split(num_points, 0) for labels in labels_list]
+        bbox_targets_list = [
+            bbox_targets.split(num_points, 0)
+            for bbox_targets in bbox_targets_list
+        ]
+
+        # concat per level image
+        concat_lvl_labels = []
+        concat_lvl_bbox_targets = []
+        for i in range(num_levels):
+            concat_lvl_labels.append(
+                torch.cat([labels[i] for labels in labels_list]))
+            bbox_targets = torch.cat(
+                [bbox_targets[i] for bbox_targets in bbox_targets_list])
+            if self.norm_on_bbox:
+                bbox_targets = bbox_targets / self.strides[i]
+            concat_lvl_bbox_targets.append(bbox_targets)
+        return concat_lvl_labels, concat_lvl_bbox_targets
+
+    def _get_target_single(self, gt_bboxes, gt_labels, points, regress_ranges,
+                           num_points_per_lvl):
+        """Compute regression and classification targets for a single image."""
+        num_points = points.size(0)
+        num_gts = gt_labels.size(0)
+        if num_gts == 0:
+            return gt_labels.new_full((num_points,), self.num_classes), \
+                   gt_bboxes.new_zeros((num_points, 4))
+
+        areas = (gt_bboxes[:, 2] - gt_bboxes[:, 0]) * (
+            gt_bboxes[:, 3] - gt_bboxes[:, 1])
+        # TODO: figure out why these two are different
+        # areas = areas[None].expand(num_points, num_gts)
+        areas = areas[None].repeat(num_points, 1)
+        regress_ranges = regress_ranges[:, None, :].expand(
+            num_points, num_gts, 2)
+        gt_bboxes = gt_bboxes[None].expand(num_points, num_gts, 4)
+        xs, ys = points[:, 0], points[:, 1]
+        xs = xs[:, None].expand(num_points, num_gts)
+        ys = ys[:, None].expand(num_points, num_gts)
+
+        left = xs - gt_bboxes[..., 0]
+        right = gt_bboxes[..., 2] - xs
+        top = ys - gt_bboxes[..., 1]
+        bottom = gt_bboxes[..., 3] - ys
+        bbox_targets = torch.stack((left, top, right, bottom), -1)
+
+        if self.center_sampling:
+            # condition1: inside a `center bbox`
+            radius = self.center_sample_radius
+            center_xs = (gt_bboxes[..., 0] + gt_bboxes[..., 2]) / 2
+            center_ys = (gt_bboxes[..., 1] + gt_bboxes[..., 3]) / 2
+            center_gts = torch.zeros_like(gt_bboxes)
+            stride = center_xs.new_zeros(center_xs.shape)
+
+            # project the points on current lvl back to the `original` sizes
+            lvl_begin = 0
+            for lvl_idx, num_points_lvl in enumerate(num_points_per_lvl):
+                lvl_end = lvl_begin + num_points_lvl
+                stride[lvl_begin:lvl_end] = self.strides[lvl_idx] * radius
+                lvl_begin = lvl_end
+
+            x_mins = center_xs - stride
+            y_mins = center_ys - stride
+            x_maxs = center_xs + stride
+            y_maxs = center_ys + stride
+            center_gts[..., 0] = torch.where(x_mins > gt_bboxes[..., 0],
+                                             x_mins, gt_bboxes[..., 0])
+            center_gts[..., 1] = torch.where(y_mins > gt_bboxes[..., 1],
+                                             y_mins, gt_bboxes[..., 1])
+            center_gts[..., 2] = torch.where(x_maxs > gt_bboxes[..., 2],
+                                             gt_bboxes[..., 2], x_maxs)
+            center_gts[..., 3] = torch.where(y_maxs > gt_bboxes[..., 3],
+                                             gt_bboxes[..., 3], y_maxs)
+
+            cb_dist_left = xs - center_gts[..., 0]
+            cb_dist_right = center_gts[..., 2] - xs
+            cb_dist_top = ys - center_gts[..., 1]
+            cb_dist_bottom = center_gts[..., 3] - ys
+            center_bbox = torch.stack(
+                (cb_dist_left, cb_dist_top, cb_dist_right, cb_dist_bottom), -1)
+            inside_gt_bbox_mask = center_bbox.min(-1)[0] > 0
+        else:
+            # condition1: inside a gt bbox
+            inside_gt_bbox_mask = bbox_targets.min(-1)[0] > 0
+
+        # condition2: limit the regression range for each location
+        max_regress_distance = bbox_targets.max(-1)[0]
+        inside_regress_range = (
+            (max_regress_distance >= regress_ranges[..., 0])
+            & (max_regress_distance <= regress_ranges[..., 1]))
+
+        # if there are still more than one objects for a location,
+        # we choose the one with minimal area
+        areas[inside_gt_bbox_mask == 0] = INF
+        areas[inside_regress_range == 0] = INF
+        min_area, min_area_inds = areas.min(dim=1)
+
+        labels = gt_labels[min_area_inds]
+        labels[min_area == INF] = self.num_classes  # set as BG
+        bbox_targets = bbox_targets[range(num_points), min_area_inds]
+
+        return labels, bbox_targets
+
+    def centerness_target(self, pos_bbox_targets):
+        """Compute centerness targets.
+
+        Args:
+            pos_bbox_targets (Tensor): BBox targets of positive bboxes in shape
+                (num_pos, 4)
+
+        Returns:
+            Tensor: Centerness target.
+        """
+        # only calculate pos centerness targets, otherwise there may be nan
+        left_right = pos_bbox_targets[:, [0, 2]]
+        top_bottom = pos_bbox_targets[:, [1, 3]]
+        if len(left_right) == 0:
+            centerness_targets = left_right[..., 0]
+        else:
+            centerness_targets = (
+                left_right.min(dim=-1)[0] / left_right.max(dim=-1)[0]) * (
+                    top_bottom.min(dim=-1)[0] / top_bottom.max(dim=-1)[0])
+        return torch.sqrt(centerness_targets)
+
+    def _get_points_single(self,
+                           featmap_size,
+                           stride,
+                           dtype,
+                           device,
+                           flatten=False):
+        """Get points according to feature map size.
+
+        This function will be deprecated soon.
+        """
+        warnings.warn(
+            '`_get_points_single` in `FCOSHead` will be '
+            'deprecated soon, we support a multi level point generator now'
+            'you can get points of a single level feature map '
+            'with `self.prior_generator.single_level_grid_priors` ')
+
+        y, x = super()._get_points_single(featmap_size, stride, dtype, device)
+        points = torch.stack((x.reshape(-1) * stride, y.reshape(-1) * stride),
+                             dim=-1) + stride // 2
+        return points
diff --git a/mmdet/models/dense_heads/fovea_head.py b/mmdet/models/dense_heads/fovea_head.py
new file mode 100644
index 0000000..8be7fc9
--- /dev/null
+++ b/mmdet/models/dense_heads/fovea_head.py
@@ -0,0 +1,385 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.ops import DeformConv2d
+from mmcv.runner import BaseModule
+
+from mmdet.core import multi_apply
+from mmdet.core.utils import filter_scores_and_topk
+from ..builder import HEADS
+from .anchor_free_head import AnchorFreeHead
+
+INF = 1e8
+
+
+class FeatureAlign(BaseModule):
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=3,
+                 deform_groups=4,
+                 init_cfg=dict(
+                     type='Normal',
+                     layer='Conv2d',
+                     std=0.1,
+                     override=dict(
+                         type='Normal', name='conv_adaption', std=0.01))):
+        super(FeatureAlign, self).__init__(init_cfg)
+        offset_channels = kernel_size * kernel_size * 2
+        self.conv_offset = nn.Conv2d(
+            4, deform_groups * offset_channels, 1, bias=False)
+        self.conv_adaption = DeformConv2d(
+            in_channels,
+            out_channels,
+            kernel_size=kernel_size,
+            padding=(kernel_size - 1) // 2,
+            deform_groups=deform_groups)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x, shape):
+        offset = self.conv_offset(shape)
+        x = self.relu(self.conv_adaption(x, offset))
+        return x
+
+
+@HEADS.register_module()
+class FoveaHead(AnchorFreeHead):
+    """FoveaBox: Beyond Anchor-based Object Detector
+    https://arxiv.org/abs/1904.03797
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 base_edge_list=(16, 32, 64, 128, 256),
+                 scale_ranges=((8, 32), (16, 64), (32, 128), (64, 256), (128,
+                                                                         512)),
+                 sigma=0.4,
+                 with_deform=False,
+                 deform_groups=4,
+                 init_cfg=dict(
+                     type='Normal',
+                     layer='Conv2d',
+                     std=0.01,
+                     override=dict(
+                         type='Normal',
+                         name='conv_cls',
+                         std=0.01,
+                         bias_prob=0.01)),
+                 **kwargs):
+        self.base_edge_list = base_edge_list
+        self.scale_ranges = scale_ranges
+        self.sigma = sigma
+        self.with_deform = with_deform
+        self.deform_groups = deform_groups
+        super().__init__(num_classes, in_channels, init_cfg=init_cfg, **kwargs)
+
+    def _init_layers(self):
+        # box branch
+        super()._init_reg_convs()
+        self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
+
+        # cls branch
+        if not self.with_deform:
+            super()._init_cls_convs()
+            self.conv_cls = nn.Conv2d(
+                self.feat_channels, self.cls_out_channels, 3, padding=1)
+        else:
+            self.cls_convs = nn.ModuleList()
+            self.cls_convs.append(
+                ConvModule(
+                    self.feat_channels, (self.feat_channels * 4),
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    bias=self.norm_cfg is None))
+            self.cls_convs.append(
+                ConvModule((self.feat_channels * 4), (self.feat_channels * 4),
+                           1,
+                           stride=1,
+                           padding=0,
+                           conv_cfg=self.conv_cfg,
+                           norm_cfg=self.norm_cfg,
+                           bias=self.norm_cfg is None))
+            self.feature_adaption = FeatureAlign(
+                self.feat_channels,
+                self.feat_channels,
+                kernel_size=3,
+                deform_groups=self.deform_groups)
+            self.conv_cls = nn.Conv2d(
+                int(self.feat_channels * 4),
+                self.cls_out_channels,
+                3,
+                padding=1)
+
+    def forward_single(self, x):
+        cls_feat = x
+        reg_feat = x
+        for reg_layer in self.reg_convs:
+            reg_feat = reg_layer(reg_feat)
+        bbox_pred = self.conv_reg(reg_feat)
+        if self.with_deform:
+            cls_feat = self.feature_adaption(cls_feat, bbox_pred.exp())
+        for cls_layer in self.cls_convs:
+            cls_feat = cls_layer(cls_feat)
+        cls_score = self.conv_cls(cls_feat)
+        return cls_score, bbox_pred
+
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bbox_list,
+             gt_label_list,
+             img_metas,
+             gt_bboxes_ignore=None):
+        assert len(cls_scores) == len(bbox_preds)
+
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        points = self.prior_generator.grid_priors(
+            featmap_sizes,
+            dtype=bbox_preds[0].dtype,
+            device=bbox_preds[0].device)
+        num_imgs = cls_scores[0].size(0)
+        flatten_cls_scores = [
+            cls_score.permute(0, 2, 3, 1).reshape(-1, self.cls_out_channels)
+            for cls_score in cls_scores
+        ]
+        flatten_bbox_preds = [
+            bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
+            for bbox_pred in bbox_preds
+        ]
+        flatten_cls_scores = torch.cat(flatten_cls_scores)
+        flatten_bbox_preds = torch.cat(flatten_bbox_preds)
+        flatten_labels, flatten_bbox_targets = self.get_targets(
+            gt_bbox_list, gt_label_list, featmap_sizes, points)
+
+        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+        pos_inds = ((flatten_labels >= 0)
+                    & (flatten_labels < self.num_classes)).nonzero().view(-1)
+        num_pos = len(pos_inds)
+
+        loss_cls = self.loss_cls(
+            flatten_cls_scores, flatten_labels, avg_factor=num_pos + num_imgs)
+        if num_pos > 0:
+            pos_bbox_preds = flatten_bbox_preds[pos_inds]
+            pos_bbox_targets = flatten_bbox_targets[pos_inds]
+            pos_weights = pos_bbox_targets.new_zeros(
+                pos_bbox_targets.size()) + 1.0
+            loss_bbox = self.loss_bbox(
+                pos_bbox_preds,
+                pos_bbox_targets,
+                pos_weights,
+                avg_factor=num_pos)
+        else:
+            loss_bbox = torch.tensor(
+                0,
+                dtype=flatten_bbox_preds.dtype,
+                device=flatten_bbox_preds.device)
+        return dict(loss_cls=loss_cls, loss_bbox=loss_bbox)
+
+    def get_targets(self, gt_bbox_list, gt_label_list, featmap_sizes, points):
+        label_list, bbox_target_list = multi_apply(
+            self._get_target_single,
+            gt_bbox_list,
+            gt_label_list,
+            featmap_size_list=featmap_sizes,
+            point_list=points)
+        flatten_labels = [
+            torch.cat([
+                labels_level_img.flatten() for labels_level_img in labels_level
+            ]) for labels_level in zip(*label_list)
+        ]
+        flatten_bbox_targets = [
+            torch.cat([
+                bbox_targets_level_img.reshape(-1, 4)
+                for bbox_targets_level_img in bbox_targets_level
+            ]) for bbox_targets_level in zip(*bbox_target_list)
+        ]
+        flatten_labels = torch.cat(flatten_labels)
+        flatten_bbox_targets = torch.cat(flatten_bbox_targets)
+        return flatten_labels, flatten_bbox_targets
+
+    def _get_target_single(self,
+                           gt_bboxes_raw,
+                           gt_labels_raw,
+                           featmap_size_list=None,
+                           point_list=None):
+
+        gt_areas = torch.sqrt((gt_bboxes_raw[:, 2] - gt_bboxes_raw[:, 0]) *
+                              (gt_bboxes_raw[:, 3] - gt_bboxes_raw[:, 1]))
+        label_list = []
+        bbox_target_list = []
+        # for each pyramid, find the cls and box target
+        for base_len, (lower_bound, upper_bound), stride, featmap_size, \
+            points in zip(self.base_edge_list, self.scale_ranges,
+                          self.strides, featmap_size_list, point_list):
+            # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+            points = points.view(*featmap_size, 2)
+            x, y = points[..., 0], points[..., 1]
+            labels = gt_labels_raw.new_zeros(featmap_size) + self.num_classes
+            bbox_targets = gt_bboxes_raw.new(featmap_size[0], featmap_size[1],
+                                             4) + 1
+            # scale assignment
+            hit_indices = ((gt_areas >= lower_bound) &
+                           (gt_areas <= upper_bound)).nonzero().flatten()
+            if len(hit_indices) == 0:
+                label_list.append(labels)
+                bbox_target_list.append(torch.log(bbox_targets))
+                continue
+            _, hit_index_order = torch.sort(-gt_areas[hit_indices])
+            hit_indices = hit_indices[hit_index_order]
+            gt_bboxes = gt_bboxes_raw[hit_indices, :] / stride
+            gt_labels = gt_labels_raw[hit_indices]
+            half_w = 0.5 * (gt_bboxes[:, 2] - gt_bboxes[:, 0])
+            half_h = 0.5 * (gt_bboxes[:, 3] - gt_bboxes[:, 1])
+            # valid fovea area: left, right, top, down
+            pos_left = torch.ceil(
+                gt_bboxes[:, 0] + (1 - self.sigma) * half_w - 0.5).long(). \
+                clamp(0, featmap_size[1] - 1)
+            pos_right = torch.floor(
+                gt_bboxes[:, 0] + (1 + self.sigma) * half_w - 0.5).long(). \
+                clamp(0, featmap_size[1] - 1)
+            pos_top = torch.ceil(
+                gt_bboxes[:, 1] + (1 - self.sigma) * half_h - 0.5).long(). \
+                clamp(0, featmap_size[0] - 1)
+            pos_down = torch.floor(
+                gt_bboxes[:, 1] + (1 + self.sigma) * half_h - 0.5).long(). \
+                clamp(0, featmap_size[0] - 1)
+            for px1, py1, px2, py2, label, (gt_x1, gt_y1, gt_x2, gt_y2) in \
+                    zip(pos_left, pos_top, pos_right, pos_down, gt_labels,
+                        gt_bboxes_raw[hit_indices, :]):
+                labels[py1:py2 + 1, px1:px2 + 1] = label
+                bbox_targets[py1:py2 + 1, px1:px2 + 1, 0] = \
+                    (x[py1:py2 + 1, px1:px2 + 1] - gt_x1) / base_len
+                bbox_targets[py1:py2 + 1, px1:px2 + 1, 1] = \
+                    (y[py1:py2 + 1, px1:px2 + 1] - gt_y1) / base_len
+                bbox_targets[py1:py2 + 1, px1:px2 + 1, 2] = \
+                    (gt_x2 - x[py1:py2 + 1, px1:px2 + 1]) / base_len
+                bbox_targets[py1:py2 + 1, px1:px2 + 1, 3] = \
+                    (gt_y2 - y[py1:py2 + 1, px1:px2 + 1]) / base_len
+            bbox_targets = bbox_targets.clamp(min=1. / 16, max=16.)
+            label_list.append(labels)
+            bbox_target_list.append(torch.log(bbox_targets))
+        return label_list, bbox_target_list
+
+    # Same as base_dense_head/_get_bboxes_single except self._bbox_decode
+    def _get_bboxes_single(self,
+                           cls_score_list,
+                           bbox_pred_list,
+                           score_factor_list,
+                           mlvl_priors,
+                           img_meta,
+                           cfg,
+                           rescale=False,
+                           with_nms=True,
+                           **kwargs):
+        """Transform outputs of a single image into bbox predictions.
+
+        Args:
+            cls_score_list (list[Tensor]): Box scores from all scale
+                levels of a single image, each item has shape
+                (num_priors * num_classes, H, W).
+            bbox_pred_list (list[Tensor]): Box energies / deltas from
+                all scale levels of a single image, each item has shape
+                (num_priors * 4, H, W).
+            score_factor_list (list[Tensor]): Score factor from all scale
+                levels of a single image. Fovea head does not need this value.
+            mlvl_priors (list[Tensor]): Each element in the list is
+                the priors of a single level in feature pyramid, has shape
+                (num_priors, 2).
+            img_meta (dict): Image meta info.
+            cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+
+        Returns:
+            tuple[Tensor]: Results of detected bboxes and labels. If with_nms
+                is False and mlvl_score_factor is None, return mlvl_bboxes and
+                mlvl_scores, else return mlvl_bboxes, mlvl_scores and
+                mlvl_score_factor. Usually with_nms is False is used for aug
+                test. If with_nms is True, then return the following format
+
+                - det_bboxes (Tensor): Predicted bboxes with shape \
+                    [num_bboxes, 5], where the first 4 columns are bounding \
+                    box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
+                    column are scores between 0 and 1.
+                - det_labels (Tensor): Predicted labels of the corresponding \
+                    box with shape [num_bboxes].
+        """
+        cfg = self.test_cfg if cfg is None else cfg
+        assert len(cls_score_list) == len(bbox_pred_list)
+        img_shape = img_meta['img_shape']
+        nms_pre = cfg.get('nms_pre', -1)
+
+        mlvl_bboxes = []
+        mlvl_scores = []
+        mlvl_labels = []
+        for level_idx, (cls_score, bbox_pred, stride, base_len, priors) in \
+                enumerate(zip(cls_score_list, bbox_pred_list, self.strides,
+                              self.base_edge_list, mlvl_priors)):
+            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
+            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
+
+            scores = cls_score.permute(1, 2, 0).reshape(
+                -1, self.cls_out_channels).sigmoid()
+
+            # After https://github.com/open-mmlab/mmdetection/pull/6268/,
+            # this operation keeps fewer bboxes under the same `nms_pre`.
+            # There is no difference in performance for most models. If you
+            # find a slight drop in performance, you can set a larger
+            # `nms_pre` than before.
+            results = filter_scores_and_topk(
+                scores, cfg.score_thr, nms_pre,
+                dict(bbox_pred=bbox_pred, priors=priors))
+            scores, labels, _, filtered_results = results
+
+            bbox_pred = filtered_results['bbox_pred']
+            priors = filtered_results['priors']
+
+            bboxes = self._bbox_decode(priors, bbox_pred, base_len, img_shape)
+
+            mlvl_bboxes.append(bboxes)
+            mlvl_scores.append(scores)
+            mlvl_labels.append(labels)
+
+        return self._bbox_post_process(mlvl_scores, mlvl_labels, mlvl_bboxes,
+                                       img_meta['scale_factor'], cfg, rescale,
+                                       with_nms)
+
+    def _bbox_decode(self, priors, bbox_pred, base_len, max_shape):
+        bbox_pred = bbox_pred.exp()
+
+        y = priors[:, 1]
+        x = priors[:, 0]
+        x1 = (x - base_len * bbox_pred[:, 0]). \
+            clamp(min=0, max=max_shape[1] - 1)
+        y1 = (y - base_len * bbox_pred[:, 1]). \
+            clamp(min=0, max=max_shape[0] - 1)
+        x2 = (x + base_len * bbox_pred[:, 2]). \
+            clamp(min=0, max=max_shape[1] - 1)
+        y2 = (y + base_len * bbox_pred[:, 3]). \
+            clamp(min=0, max=max_shape[0] - 1)
+        decoded_bboxes = torch.stack([x1, y1, x2, y2], -1)
+        return decoded_bboxes
+
+    def _get_points_single(self, *args, **kwargs):
+        """Get points according to feature map size.
+
+        This function will be deprecated soon.
+        """
+        warnings.warn(
+            '`_get_points_single` in `FoveaHead` will be '
+            'deprecated soon, we support a multi level point generator now'
+            'you can get points of a single level feature map '
+            'with `self.prior_generator.single_level_grid_priors` ')
+        y, x = super()._get_points_single(*args, **kwargs)
+        return y + 0.5, x + 0.5
diff --git a/mmdet/models/dense_heads/free_anchor_retina_head.py b/mmdet/models/dense_heads/free_anchor_retina_head.py
new file mode 100644
index 0000000..3acd25e
--- /dev/null
+++ b/mmdet/models/dense_heads/free_anchor_retina_head.py
@@ -0,0 +1,272 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn.functional as F
+
+from mmdet.core import bbox_overlaps
+from ..builder import HEADS
+from .retina_head import RetinaHead
+
+EPS = 1e-12
+
+
+@HEADS.register_module()
+class FreeAnchorRetinaHead(RetinaHead):
+    """FreeAnchor RetinaHead used in https://arxiv.org/abs/1909.02466.
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        stacked_convs (int): Number of conv layers in cls and reg tower.
+            Default: 4.
+        conv_cfg (dict): dictionary to construct and config conv layer.
+            Default: None.
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: norm_cfg=dict(type='GN', num_groups=32,
+            requires_grad=True).
+        pre_anchor_topk (int): Number of boxes that be token in each bag.
+        bbox_thr (float): The threshold of the saturated linear function. It is
+            usually the same with the IoU threshold used in NMS.
+        gamma (float): Gamma parameter in focal loss.
+        alpha (float): Alpha parameter in focal loss.
+    """  # noqa: W605
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 stacked_convs=4,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 pre_anchor_topk=50,
+                 bbox_thr=0.6,
+                 gamma=2.0,
+                 alpha=0.5,
+                 **kwargs):
+        super(FreeAnchorRetinaHead,
+              self).__init__(num_classes, in_channels, stacked_convs, conv_cfg,
+                             norm_cfg, **kwargs)
+
+        self.pre_anchor_topk = pre_anchor_topk
+        self.bbox_thr = bbox_thr
+        self.gamma = gamma
+        self.alpha = alpha
+
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            gt_bboxes (list[Tensor]): each item are the truth boxes for each
+                image in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+        device = cls_scores[0].device
+        anchor_list, _ = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        anchors = [torch.cat(anchor) for anchor in anchor_list]
+
+        # concatenate each level
+        cls_scores = [
+            cls.permute(0, 2, 3,
+                        1).reshape(cls.size(0), -1, self.cls_out_channels)
+            for cls in cls_scores
+        ]
+        bbox_preds = [
+            bbox_pred.permute(0, 2, 3, 1).reshape(bbox_pred.size(0), -1, 4)
+            for bbox_pred in bbox_preds
+        ]
+        cls_scores = torch.cat(cls_scores, dim=1)
+        bbox_preds = torch.cat(bbox_preds, dim=1)
+
+        cls_prob = torch.sigmoid(cls_scores)
+        box_prob = []
+        num_pos = 0
+        positive_losses = []
+        for _, (anchors_, gt_labels_, gt_bboxes_, cls_prob_,
+                bbox_preds_) in enumerate(
+                    zip(anchors, gt_labels, gt_bboxes, cls_prob, bbox_preds)):
+
+            with torch.no_grad():
+                if len(gt_bboxes_) == 0:
+                    image_box_prob = torch.zeros(
+                        anchors_.size(0),
+                        self.cls_out_channels).type_as(bbox_preds_)
+                else:
+                    # box_localization: a_{j}^{loc}, shape: [j, 4]
+                    pred_boxes = self.bbox_coder.decode(anchors_, bbox_preds_)
+
+                    # object_box_iou: IoU_{ij}^{loc}, shape: [i, j]
+                    object_box_iou = bbox_overlaps(gt_bboxes_, pred_boxes)
+
+                    # object_box_prob: P{a_{j} -> b_{i}}, shape: [i, j]
+                    t1 = self.bbox_thr
+                    t2 = object_box_iou.max(
+                        dim=1, keepdim=True).values.clamp(min=t1 + 1e-12)
+                    object_box_prob = ((object_box_iou - t1) /
+                                       (t2 - t1)).clamp(
+                                           min=0, max=1)
+
+                    # object_cls_box_prob: P{a_{j} -> b_{i}}, shape: [i, c, j]
+                    num_obj = gt_labels_.size(0)
+                    indices = torch.stack([
+                        torch.arange(num_obj).type_as(gt_labels_), gt_labels_
+                    ],
+                                          dim=0)
+                    object_cls_box_prob = torch.sparse_coo_tensor(
+                        indices, object_box_prob)
+
+                    # image_box_iou: P{a_{j} \in A_{+}}, shape: [c, j]
+                    """
+                    from "start" to "end" implement:
+                    image_box_iou = torch.sparse.max(object_cls_box_prob,
+                                                     dim=0).t()
+
+                    """
+                    # start
+                    box_cls_prob = torch.sparse.sum(
+                        object_cls_box_prob, dim=0).to_dense()
+
+                    indices = torch.nonzero(box_cls_prob, as_tuple=False).t_()
+                    if indices.numel() == 0:
+                        image_box_prob = torch.zeros(
+                            anchors_.size(0),
+                            self.cls_out_channels).type_as(object_box_prob)
+                    else:
+                        nonzero_box_prob = torch.where(
+                            (gt_labels_.unsqueeze(dim=-1) == indices[0]),
+                            object_box_prob[:, indices[1]],
+                            torch.tensor([
+                                0
+                            ]).type_as(object_box_prob)).max(dim=0).values
+
+                        # upmap to shape [j, c]
+                        image_box_prob = torch.sparse_coo_tensor(
+                            indices.flip([0]),
+                            nonzero_box_prob,
+                            size=(anchors_.size(0),
+                                  self.cls_out_channels)).to_dense()
+                    # end
+
+                box_prob.append(image_box_prob)
+
+            # construct bags for objects
+            match_quality_matrix = bbox_overlaps(gt_bboxes_, anchors_)
+            _, matched = torch.topk(
+                match_quality_matrix,
+                self.pre_anchor_topk,
+                dim=1,
+                sorted=False)
+            del match_quality_matrix
+
+            # matched_cls_prob: P_{ij}^{cls}
+            matched_cls_prob = torch.gather(
+                cls_prob_[matched], 2,
+                gt_labels_.view(-1, 1, 1).repeat(1, self.pre_anchor_topk,
+                                                 1)).squeeze(2)
+
+            # matched_box_prob: P_{ij}^{loc}
+            matched_anchors = anchors_[matched]
+            matched_object_targets = self.bbox_coder.encode(
+                matched_anchors,
+                gt_bboxes_.unsqueeze(dim=1).expand_as(matched_anchors))
+            loss_bbox = self.loss_bbox(
+                bbox_preds_[matched],
+                matched_object_targets,
+                reduction_override='none').sum(-1)
+            matched_box_prob = torch.exp(-loss_bbox)
+
+            # positive_losses: {-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )}
+            num_pos += len(gt_bboxes_)
+            positive_losses.append(
+                self.positive_bag_loss(matched_cls_prob, matched_box_prob))
+        positive_loss = torch.cat(positive_losses).sum() / max(1, num_pos)
+
+        # box_prob: P{a_{j} \in A_{+}}
+        box_prob = torch.stack(box_prob, dim=0)
+
+        # negative_loss:
+        # \sum_{j}{ FL((1 - P{a_{j} \in A_{+}}) * (1 - P_{j}^{bg})) } / n||B||
+        negative_loss = self.negative_bag_loss(cls_prob, box_prob).sum() / max(
+            1, num_pos * self.pre_anchor_topk)
+
+        # avoid the absence of gradients in regression subnet
+        # when no ground-truth in a batch
+        if num_pos == 0:
+            positive_loss = bbox_preds.sum() * 0
+
+        losses = {
+            'positive_bag_loss': positive_loss,
+            'negative_bag_loss': negative_loss
+        }
+        return losses
+
+    def positive_bag_loss(self, matched_cls_prob, matched_box_prob):
+        """Compute positive bag loss.
+
+        :math:`-log( Mean-max(P_{ij}^{cls} * P_{ij}^{loc}) )`.
+
+        :math:`P_{ij}^{cls}`: matched_cls_prob, classification probability of matched samples.
+
+        :math:`P_{ij}^{loc}`: matched_box_prob, box probability of matched samples.
+
+        Args:
+            matched_cls_prob (Tensor): Classification probability of matched
+                samples in shape (num_gt, pre_anchor_topk).
+            matched_box_prob (Tensor): BBox probability of matched samples,
+                in shape (num_gt, pre_anchor_topk).
+
+        Returns:
+            Tensor: Positive bag loss in shape (num_gt,).
+        """  # noqa: E501, W605
+        # bag_prob = Mean-max(matched_prob)
+        matched_prob = matched_cls_prob * matched_box_prob
+        weight = 1 / torch.clamp(1 - matched_prob, 1e-12, None)
+        weight /= weight.sum(dim=1).unsqueeze(dim=-1)
+        bag_prob = (weight * matched_prob).sum(dim=1)
+        # positive_bag_loss = -self.alpha * log(bag_prob)
+        return self.alpha * F.binary_cross_entropy(
+            bag_prob, torch.ones_like(bag_prob), reduction='none')
+
+    def negative_bag_loss(self, cls_prob, box_prob):
+        """Compute negative bag loss.
+
+        :math:`FL((1 - P_{a_{j} \in A_{+}}) * (1 - P_{j}^{bg}))`.
+
+        :math:`P_{a_{j} \in A_{+}}`: Box_probability of matched samples.
+
+        :math:`P_{j}^{bg}`: Classification probability of negative samples.
+
+        Args:
+            cls_prob (Tensor): Classification probability, in shape
+                (num_img, num_anchors, num_classes).
+            box_prob (Tensor): Box probability, in shape
+                (num_img, num_anchors, num_classes).
+
+        Returns:
+            Tensor: Negative bag loss in shape (num_img, num_anchors, num_classes).
+        """  # noqa: E501, W605
+        prob = cls_prob * (1 - box_prob)
+        # There are some cases when neg_prob = 0.
+        # This will cause the neg_prob.log() to be inf without clamp.
+        prob = prob.clamp(min=EPS, max=1 - EPS)
+        negative_bag_loss = prob**self.gamma * F.binary_cross_entropy(
+            prob, torch.zeros_like(prob), reduction='none')
+        return (1 - self.alpha) * negative_bag_loss
diff --git a/mmdet/models/dense_heads/fsaf_head.py b/mmdet/models/dense_heads/fsaf_head.py
new file mode 100644
index 0000000..2d2b787
--- /dev/null
+++ b/mmdet/models/dense_heads/fsaf_head.py
@@ -0,0 +1,433 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+from mmcv.runner import force_fp32
+
+from mmdet.core import (anchor_inside_flags, images_to_levels, multi_apply,
+                        unmap)
+from ..builder import HEADS
+from ..losses.accuracy import accuracy
+from ..losses.utils import weight_reduce_loss
+from .retina_head import RetinaHead
+
+
+@HEADS.register_module()
+class FSAFHead(RetinaHead):
+    """Anchor-free head used in `FSAF <https://arxiv.org/abs/1903.00621>`_.
+
+    The head contains two subnetworks. The first classifies anchor boxes and
+    the second regresses deltas for the anchors (num_anchors is 1 for anchor-
+    free methods)
+
+    Args:
+        *args: Same as its base class in :class:`RetinaHead`
+        score_threshold (float, optional): The score_threshold to calculate
+            positive recall. If given, prediction scores lower than this value
+            is counted as incorrect prediction. Default to None.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+        **kwargs: Same as its base class in :class:`RetinaHead`
+
+    Example:
+        >>> import torch
+        >>> self = FSAFHead(11, 7)
+        >>> x = torch.rand(1, 7, 32, 32)
+        >>> cls_score, bbox_pred = self.forward_single(x)
+        >>> # Each anchor predicts a score for each class except background
+        >>> cls_per_anchor = cls_score.shape[1] / self.num_anchors
+        >>> box_per_anchor = bbox_pred.shape[1] / self.num_anchors
+        >>> assert cls_per_anchor == self.num_classes
+        >>> assert box_per_anchor == 4
+    """
+
+    def __init__(self, *args, score_threshold=None, init_cfg=None, **kwargs):
+        # The positive bias in self.retina_reg conv is to prevent predicted \
+        #  bbox with 0 area
+        if init_cfg is None:
+            init_cfg = dict(
+                type='Normal',
+                layer='Conv2d',
+                std=0.01,
+                override=[
+                    dict(
+                        type='Normal',
+                        name='retina_cls',
+                        std=0.01,
+                        bias_prob=0.01),
+                    dict(
+                        type='Normal', name='retina_reg', std=0.01, bias=0.25)
+                ])
+        super().__init__(*args, init_cfg=init_cfg, **kwargs)
+        self.score_threshold = score_threshold
+
+    def forward_single(self, x):
+        """Forward feature map of a single scale level.
+
+        Args:
+            x (Tensor): Feature map of a single scale level.
+
+        Returns:
+            tuple (Tensor):
+                cls_score (Tensor): Box scores for each scale level
+                    Has shape (N, num_points * num_classes, H, W).
+                bbox_pred (Tensor): Box energies / deltas for each scale
+                    level with shape (N, num_points * 4, H, W).
+        """
+        cls_score, bbox_pred = super().forward_single(x)
+        # relu: TBLR encoder only accepts positive bbox_pred
+        return cls_score, self.relu(bbox_pred)
+
+    def _get_targets_single(self,
+                            flat_anchors,
+                            valid_flags,
+                            gt_bboxes,
+                            gt_bboxes_ignore,
+                            gt_labels,
+                            img_meta,
+                            label_channels=1,
+                            unmap_outputs=True):
+        """Compute regression and classification targets for anchors in a
+        single image.
+
+        Most of the codes are the same with the base class
+          :obj: `AnchorHead`, except that it also collects and returns
+          the matched gt index in the image (from 0 to num_gt-1). If the
+          anchor bbox is not matched to any gt, the corresponding value in
+          pos_gt_inds is -1.
+        """
+        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
+                                           img_meta['img_shape'][:2],
+                                           self.train_cfg.allowed_border)
+        if not inside_flags.any():
+            return (None, ) * 7
+        # Assign gt and sample anchors
+        anchors = flat_anchors[inside_flags.type(torch.bool), :]
+        assign_result = self.assigner.assign(
+            anchors, gt_bboxes, gt_bboxes_ignore,
+            None if self.sampling else gt_labels)
+
+        sampling_result = self.sampler.sample(assign_result, anchors,
+                                              gt_bboxes)
+
+        num_valid_anchors = anchors.shape[0]
+        bbox_targets = torch.zeros_like(anchors)
+        bbox_weights = torch.zeros_like(anchors)
+        labels = anchors.new_full((num_valid_anchors, ),
+                                  self.num_classes,
+                                  dtype=torch.long)
+        label_weights = anchors.new_zeros((num_valid_anchors, label_channels),
+                                          dtype=torch.float)
+        pos_gt_inds = anchors.new_full((num_valid_anchors, ),
+                                       -1,
+                                       dtype=torch.long)
+
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+
+        if len(pos_inds) > 0:
+            if not self.reg_decoded_bbox:
+                pos_bbox_targets = self.bbox_coder.encode(
+                    sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
+            else:
+                # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
+                # is applied directly on the decoded bounding boxes, both
+                # the predicted boxes and regression targets should be with
+                # absolute coordinate format.
+                pos_bbox_targets = sampling_result.pos_gt_bboxes
+            bbox_targets[pos_inds, :] = pos_bbox_targets
+            bbox_weights[pos_inds, :] = 1.0
+            # The assigned gt_index for each anchor. (0-based)
+            pos_gt_inds[pos_inds] = sampling_result.pos_assigned_gt_inds
+            if gt_labels is None:
+                # Only rpn gives gt_labels as None
+                # Foreground is the first class
+                labels[pos_inds] = 0
+            else:
+                labels[pos_inds] = gt_labels[
+                    sampling_result.pos_assigned_gt_inds]
+            if self.train_cfg.pos_weight <= 0:
+                label_weights[pos_inds] = 1.0
+            else:
+                label_weights[pos_inds] = self.train_cfg.pos_weight
+
+        if len(neg_inds) > 0:
+            label_weights[neg_inds] = 1.0
+
+        # shadowed_labels is a tensor composed of tuples
+        #  (anchor_inds, class_label) that indicate those anchors lying in the
+        #  outer region of a gt or overlapped by another gt with a smaller
+        #  area.
+        #
+        # Therefore, only the shadowed labels are ignored for loss calculation.
+        # the key `shadowed_labels` is defined in :obj:`CenterRegionAssigner`
+        shadowed_labels = assign_result.get_extra_property('shadowed_labels')
+        if shadowed_labels is not None and shadowed_labels.numel():
+            if len(shadowed_labels.shape) == 2:
+                idx_, label_ = shadowed_labels[:, 0], shadowed_labels[:, 1]
+                assert (labels[idx_] != label_).all(), \
+                    'One label cannot be both positive and ignored'
+                label_weights[idx_, label_] = 0
+            else:
+                label_weights[shadowed_labels] = 0
+
+        # map up to original set of anchors
+        if unmap_outputs:
+            num_total_anchors = flat_anchors.size(0)
+            labels = unmap(labels, num_total_anchors, inside_flags)
+            label_weights = unmap(label_weights, num_total_anchors,
+                                  inside_flags)
+            bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
+            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
+            pos_gt_inds = unmap(
+                pos_gt_inds, num_total_anchors, inside_flags, fill=-1)
+
+        return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
+                neg_inds, sampling_result, pos_gt_inds)
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute loss of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_points * num_classes, H, W).
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_points * 4, H, W).
+            gt_bboxes (list[Tensor]): each item are the truth boxes for each
+                image in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        for i in range(len(bbox_preds)):  # loop over fpn level
+            # avoid 0 area of the predicted bbox
+            bbox_preds[i] = bbox_preds[i].clamp(min=1e-4)
+        # TODO: It may directly use the base-class loss function.
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+        batch_size = len(gt_bboxes)
+        device = cls_scores[0].device
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels)
+        if cls_reg_targets is None:
+            return None
+        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
+         num_total_pos, num_total_neg,
+         pos_assigned_gt_inds_list) = cls_reg_targets
+
+        num_gts = np.array(list(map(len, gt_labels)))
+        num_total_samples = (
+            num_total_pos + num_total_neg if self.sampling else num_total_pos)
+        # anchor number of multi levels
+        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+        # concat all level anchors and flags to a single tensor
+        concat_anchor_list = []
+        for i in range(len(anchor_list)):
+            concat_anchor_list.append(torch.cat(anchor_list[i]))
+        all_anchor_list = images_to_levels(concat_anchor_list,
+                                           num_level_anchors)
+        losses_cls, losses_bbox = multi_apply(
+            self.loss_single,
+            cls_scores,
+            bbox_preds,
+            all_anchor_list,
+            labels_list,
+            label_weights_list,
+            bbox_targets_list,
+            bbox_weights_list,
+            num_total_samples=num_total_samples)
+
+        # `pos_assigned_gt_inds_list` (length: fpn_levels) stores the assigned
+        # gt index of each anchor bbox in each fpn level.
+        cum_num_gts = list(np.cumsum(num_gts))  # length of batch_size
+        for i, assign in enumerate(pos_assigned_gt_inds_list):
+            # loop over fpn levels
+            for j in range(1, batch_size):
+                # loop over batch size
+                # Convert gt indices in each img to those in the batch
+                assign[j][assign[j] >= 0] += int(cum_num_gts[j - 1])
+            pos_assigned_gt_inds_list[i] = assign.flatten()
+            labels_list[i] = labels_list[i].flatten()
+        num_gts = sum(map(len, gt_labels))  # total number of gt in the batch
+        # The unique label index of each gt in the batch
+        label_sequence = torch.arange(num_gts, device=device)
+        # Collect the average loss of each gt in each level
+        with torch.no_grad():
+            loss_levels, = multi_apply(
+                self.collect_loss_level_single,
+                losses_cls,
+                losses_bbox,
+                pos_assigned_gt_inds_list,
+                labels_seq=label_sequence)
+            # Shape: (fpn_levels, num_gts). Loss of each gt at each fpn level
+            loss_levels = torch.stack(loss_levels, dim=0)
+            # Locate the best fpn level for loss back-propagation
+            if loss_levels.numel() == 0:  # zero gt
+                argmin = loss_levels.new_empty((num_gts, ), dtype=torch.long)
+            else:
+                _, argmin = loss_levels.min(dim=0)
+
+        # Reweight the loss of each (anchor, label) pair, so that only those
+        #  at the best gt level are back-propagated.
+        losses_cls, losses_bbox, pos_inds = multi_apply(
+            self.reweight_loss_single,
+            losses_cls,
+            losses_bbox,
+            pos_assigned_gt_inds_list,
+            labels_list,
+            list(range(len(losses_cls))),
+            min_levels=argmin)
+        num_pos = torch.cat(pos_inds, 0).sum().float()
+        pos_recall = self.calculate_pos_recall(cls_scores, labels_list,
+                                               pos_inds)
+
+        if num_pos == 0:  # No gt
+            avg_factor = num_pos + float(num_total_neg)
+        else:
+            avg_factor = num_pos
+        for i in range(len(losses_cls)):
+            losses_cls[i] /= avg_factor
+            losses_bbox[i] /= avg_factor
+        return dict(
+            loss_cls=losses_cls,
+            loss_bbox=losses_bbox,
+            num_pos=num_pos / batch_size,
+            pos_recall=pos_recall)
+
+    def calculate_pos_recall(self, cls_scores, labels_list, pos_inds):
+        """Calculate positive recall with score threshold.
+
+        Args:
+            cls_scores (list[Tensor]): Classification scores at all fpn levels.
+                Each tensor is in shape (N, num_classes * num_anchors, H, W)
+            labels_list (list[Tensor]): The label that each anchor is assigned
+                to. Shape (N * H * W * num_anchors, )
+            pos_inds (list[Tensor]): List of bool tensors indicating whether
+                the anchor is assigned to a positive label.
+                Shape (N * H * W * num_anchors, )
+
+        Returns:
+            Tensor: A single float number indicating the positive recall.
+        """
+        with torch.no_grad():
+            num_class = self.num_classes
+            scores = [
+                cls.permute(0, 2, 3, 1).reshape(-1, num_class)[pos]
+                for cls, pos in zip(cls_scores, pos_inds)
+            ]
+            labels = [
+                label.reshape(-1)[pos]
+                for label, pos in zip(labels_list, pos_inds)
+            ]
+            scores = torch.cat(scores, dim=0)
+            labels = torch.cat(labels, dim=0)
+            if self.use_sigmoid_cls:
+                scores = scores.sigmoid()
+            else:
+                scores = scores.softmax(dim=1)
+
+            return accuracy(scores, labels, thresh=self.score_threshold)
+
+    def collect_loss_level_single(self, cls_loss, reg_loss, assigned_gt_inds,
+                                  labels_seq):
+        """Get the average loss in each FPN level w.r.t. each gt label.
+
+        Args:
+            cls_loss (Tensor): Classification loss of each feature map pixel,
+              shape (num_anchor, num_class)
+            reg_loss (Tensor): Regression loss of each feature map pixel,
+              shape (num_anchor, 4)
+            assigned_gt_inds (Tensor): It indicates which gt the prior is
+              assigned to (0-based, -1: no assignment). shape (num_anchor),
+            labels_seq: The rank of labels. shape (num_gt)
+
+        Returns:
+            shape: (num_gt), average loss of each gt in this level
+        """
+        if len(reg_loss.shape) == 2:  # iou loss has shape (num_prior, 4)
+            reg_loss = reg_loss.sum(dim=-1)  # sum loss in tblr dims
+        if len(cls_loss.shape) == 2:
+            cls_loss = cls_loss.sum(dim=-1)  # sum loss in class dims
+        loss = cls_loss + reg_loss
+        assert loss.size(0) == assigned_gt_inds.size(0)
+        # Default loss value is 1e6 for a layer where no anchor is positive
+        #  to ensure it will not be chosen to back-propagate gradient
+        losses_ = loss.new_full(labels_seq.shape, 1e6)
+        for i, l in enumerate(labels_seq):
+            match = assigned_gt_inds == l
+            if match.any():
+                losses_[i] = loss[match].mean()
+        return losses_,
+
+    def reweight_loss_single(self, cls_loss, reg_loss, assigned_gt_inds,
+                             labels, level, min_levels):
+        """Reweight loss values at each level.
+
+        Reassign loss values at each level by masking those where the
+        pre-calculated loss is too large. Then return the reduced losses.
+
+        Args:
+            cls_loss (Tensor): Element-wise classification loss.
+              Shape: (num_anchors, num_classes)
+            reg_loss (Tensor): Element-wise regression loss.
+              Shape: (num_anchors, 4)
+            assigned_gt_inds (Tensor): The gt indices that each anchor bbox
+              is assigned to. -1 denotes a negative anchor, otherwise it is the
+              gt index (0-based). Shape: (num_anchors, ),
+            labels (Tensor): Label assigned to anchors. Shape: (num_anchors, ).
+            level (int): The current level index in the pyramid
+              (0-4 for RetinaNet)
+            min_levels (Tensor): The best-matching level for each gt.
+              Shape: (num_gts, ),
+
+        Returns:
+            tuple:
+                - cls_loss: Reduced corrected classification loss. Scalar.
+                - reg_loss: Reduced corrected regression loss. Scalar.
+                - pos_flags (Tensor): Corrected bool tensor indicating the
+                  final positive anchors. Shape: (num_anchors, ).
+        """
+        loc_weight = torch.ones_like(reg_loss)
+        cls_weight = torch.ones_like(cls_loss)
+        pos_flags = assigned_gt_inds >= 0  # positive pixel flag
+        pos_indices = torch.nonzero(pos_flags, as_tuple=False).flatten()
+
+        if pos_flags.any():  # pos pixels exist
+            pos_assigned_gt_inds = assigned_gt_inds[pos_flags]
+            zeroing_indices = (min_levels[pos_assigned_gt_inds] != level)
+            neg_indices = pos_indices[zeroing_indices]
+
+            if neg_indices.numel():
+                pos_flags[neg_indices] = 0
+                loc_weight[neg_indices] = 0
+                # Only the weight corresponding to the label is
+                #  zeroed out if not selected
+                zeroing_labels = labels[neg_indices]
+                assert (zeroing_labels >= 0).all()
+                cls_weight[neg_indices, zeroing_labels] = 0
+
+        # Weighted loss for both cls and reg loss
+        cls_loss = weight_reduce_loss(cls_loss, cls_weight, reduction='sum')
+        reg_loss = weight_reduce_loss(reg_loss, loc_weight, reduction='sum')
+
+        return cls_loss, reg_loss, pos_flags
diff --git a/mmdet/models/dense_heads/ga_retina_head.py b/mmdet/models/dense_heads/ga_retina_head.py
new file mode 100644
index 0000000..6d9e874
--- /dev/null
+++ b/mmdet/models/dense_heads/ga_retina_head.py
@@ -0,0 +1,113 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.ops import MaskedConv2d
+
+from ..builder import HEADS
+from .guided_anchor_head import FeatureAdaption, GuidedAnchorHead
+
+
+@HEADS.register_module()
+class GARetinaHead(GuidedAnchorHead):
+    """Guided-Anchor-based RetinaNet head."""
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 stacked_convs=4,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 init_cfg=None,
+                 **kwargs):
+        if init_cfg is None:
+            init_cfg = dict(
+                type='Normal',
+                layer='Conv2d',
+                std=0.01,
+                override=[
+                    dict(
+                        type='Normal',
+                        name='conv_loc',
+                        std=0.01,
+                        bias_prob=0.01),
+                    dict(
+                        type='Normal',
+                        name='retina_cls',
+                        std=0.01,
+                        bias_prob=0.01)
+                ])
+        self.stacked_convs = stacked_convs
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        super(GARetinaHead, self).__init__(
+            num_classes, in_channels, init_cfg=init_cfg, **kwargs)
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.relu = nn.ReLU(inplace=True)
+        self.cls_convs = nn.ModuleList()
+        self.reg_convs = nn.ModuleList()
+        for i in range(self.stacked_convs):
+            chn = self.in_channels if i == 0 else self.feat_channels
+            self.cls_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+            self.reg_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+
+        self.conv_loc = nn.Conv2d(self.feat_channels, 1, 1)
+        self.conv_shape = nn.Conv2d(self.feat_channels, self.num_anchors * 2,
+                                    1)
+        self.feature_adaption_cls = FeatureAdaption(
+            self.feat_channels,
+            self.feat_channels,
+            kernel_size=3,
+            deform_groups=self.deform_groups)
+        self.feature_adaption_reg = FeatureAdaption(
+            self.feat_channels,
+            self.feat_channels,
+            kernel_size=3,
+            deform_groups=self.deform_groups)
+        self.retina_cls = MaskedConv2d(
+            self.feat_channels,
+            self.num_base_priors * self.cls_out_channels,
+            3,
+            padding=1)
+        self.retina_reg = MaskedConv2d(
+            self.feat_channels, self.num_base_priors * 4, 3, padding=1)
+
+    def forward_single(self, x):
+        """Forward feature map of a single scale level."""
+        cls_feat = x
+        reg_feat = x
+        for cls_conv in self.cls_convs:
+            cls_feat = cls_conv(cls_feat)
+        for reg_conv in self.reg_convs:
+            reg_feat = reg_conv(reg_feat)
+
+        loc_pred = self.conv_loc(cls_feat)
+        shape_pred = self.conv_shape(reg_feat)
+
+        cls_feat = self.feature_adaption_cls(cls_feat, shape_pred)
+        reg_feat = self.feature_adaption_reg(reg_feat, shape_pred)
+
+        if not self.training:
+            mask = loc_pred.sigmoid()[0] >= self.loc_filter_thr
+        else:
+            mask = None
+        cls_score = self.retina_cls(cls_feat, mask)
+        bbox_pred = self.retina_reg(reg_feat, mask)
+        return cls_score, bbox_pred, shape_pred, loc_pred
diff --git a/mmdet/models/dense_heads/ga_rpn_head.py b/mmdet/models/dense_heads/ga_rpn_head.py
new file mode 100644
index 0000000..4123c8b
--- /dev/null
+++ b/mmdet/models/dense_heads/ga_rpn_head.py
@@ -0,0 +1,177 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import warnings
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv import ConfigDict
+from mmcv.ops import nms
+
+from ..builder import HEADS
+from .guided_anchor_head import GuidedAnchorHead
+
+
+@HEADS.register_module()
+class GARPNHead(GuidedAnchorHead):
+    """Guided-Anchor-based RPN head."""
+
+    def __init__(self,
+                 in_channels,
+                 init_cfg=dict(
+                     type='Normal',
+                     layer='Conv2d',
+                     std=0.01,
+                     override=dict(
+                         type='Normal',
+                         name='conv_loc',
+                         std=0.01,
+                         bias_prob=0.01)),
+                 **kwargs):
+        super(GARPNHead, self).__init__(
+            1, in_channels, init_cfg=init_cfg, **kwargs)
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.rpn_conv = nn.Conv2d(
+            self.in_channels, self.feat_channels, 3, padding=1)
+        super(GARPNHead, self)._init_layers()
+
+    def forward_single(self, x):
+        """Forward feature of a single scale level."""
+
+        x = self.rpn_conv(x)
+        x = F.relu(x, inplace=True)
+        (cls_score, bbox_pred, shape_pred,
+         loc_pred) = super(GARPNHead, self).forward_single(x)
+        return cls_score, bbox_pred, shape_pred, loc_pred
+
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             shape_preds,
+             loc_preds,
+             gt_bboxes,
+             img_metas,
+             gt_bboxes_ignore=None):
+        losses = super(GARPNHead, self).loss(
+            cls_scores,
+            bbox_preds,
+            shape_preds,
+            loc_preds,
+            gt_bboxes,
+            None,
+            img_metas,
+            gt_bboxes_ignore=gt_bboxes_ignore)
+        return dict(
+            loss_rpn_cls=losses['loss_cls'],
+            loss_rpn_bbox=losses['loss_bbox'],
+            loss_anchor_shape=losses['loss_shape'],
+            loss_anchor_loc=losses['loss_loc'])
+
+    def _get_bboxes_single(self,
+                           cls_scores,
+                           bbox_preds,
+                           mlvl_anchors,
+                           mlvl_masks,
+                           img_shape,
+                           scale_factor,
+                           cfg,
+                           rescale=False):
+        cfg = self.test_cfg if cfg is None else cfg
+
+        cfg = copy.deepcopy(cfg)
+
+        # deprecate arguments warning
+        if 'nms' not in cfg or 'max_num' in cfg or 'nms_thr' in cfg:
+            warnings.warn(
+                'In rpn_proposal or test_cfg, '
+                'nms_thr has been moved to a dict named nms as '
+                'iou_threshold, max_num has been renamed as max_per_img, '
+                'name of original arguments and the way to specify '
+                'iou_threshold of NMS will be deprecated.')
+        if 'nms' not in cfg:
+            cfg.nms = ConfigDict(dict(type='nms', iou_threshold=cfg.nms_thr))
+        if 'max_num' in cfg:
+            if 'max_per_img' in cfg:
+                assert cfg.max_num == cfg.max_per_img, f'You ' \
+                    f'set max_num and max_per_img at the same time, ' \
+                    f'but get {cfg.max_num} ' \
+                    f'and {cfg.max_per_img} respectively' \
+                    'Please delete max_num which will be deprecated.'
+            else:
+                cfg.max_per_img = cfg.max_num
+        if 'nms_thr' in cfg:
+            assert cfg.nms.iou_threshold == cfg.nms_thr, f'You set ' \
+                f'iou_threshold in nms and ' \
+                f'nms_thr at the same time, but get ' \
+                f'{cfg.nms.iou_threshold} and {cfg.nms_thr}' \
+                f' respectively. Please delete the ' \
+                f'nms_thr which will be deprecated.'
+
+        assert cfg.nms.get('type', 'nms') == 'nms', 'GARPNHead only support ' \
+            'naive nms.'
+
+        mlvl_proposals = []
+        for idx in range(len(cls_scores)):
+            rpn_cls_score = cls_scores[idx]
+            rpn_bbox_pred = bbox_preds[idx]
+            anchors = mlvl_anchors[idx]
+            mask = mlvl_masks[idx]
+            assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
+            # if no location is kept, end.
+            if mask.sum() == 0:
+                continue
+            rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
+            if self.use_sigmoid_cls:
+                rpn_cls_score = rpn_cls_score.reshape(-1)
+                scores = rpn_cls_score.sigmoid()
+            else:
+                rpn_cls_score = rpn_cls_score.reshape(-1, 2)
+                # remind that we set FG labels to [0, num_class-1]
+                # since mmdet v2.0
+                # BG cat_id: num_class
+                scores = rpn_cls_score.softmax(dim=1)[:, :-1]
+            # filter scores, bbox_pred w.r.t. mask.
+            # anchors are filtered in get_anchors() beforehand.
+            scores = scores[mask]
+            rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1,
+                                                                   4)[mask, :]
+            if scores.dim() == 0:
+                rpn_bbox_pred = rpn_bbox_pred.unsqueeze(0)
+                anchors = anchors.unsqueeze(0)
+                scores = scores.unsqueeze(0)
+            # filter anchors, bbox_pred, scores w.r.t. scores
+            if cfg.nms_pre > 0 and scores.shape[0] > cfg.nms_pre:
+                _, topk_inds = scores.topk(cfg.nms_pre)
+                rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
+                anchors = anchors[topk_inds, :]
+                scores = scores[topk_inds]
+            # get proposals w.r.t. anchors and rpn_bbox_pred
+            proposals = self.bbox_coder.decode(
+                anchors, rpn_bbox_pred, max_shape=img_shape)
+            # filter out too small bboxes
+            if cfg.min_bbox_size >= 0:
+                w = proposals[:, 2] - proposals[:, 0]
+                h = proposals[:, 3] - proposals[:, 1]
+                valid_mask = (w > cfg.min_bbox_size) & (h > cfg.min_bbox_size)
+                if not valid_mask.all():
+                    proposals = proposals[valid_mask]
+                    scores = scores[valid_mask]
+
+            # NMS in current level
+            proposals, _ = nms(proposals, scores, cfg.nms.iou_threshold)
+            proposals = proposals[:cfg.nms_post, :]
+            mlvl_proposals.append(proposals)
+        proposals = torch.cat(mlvl_proposals, 0)
+        if cfg.get('nms_across_levels', False):
+            # NMS across multi levels
+            proposals, _ = nms(proposals[:, :4], proposals[:, -1],
+                               cfg.nms.iou_threshold)
+            proposals = proposals[:cfg.max_per_img, :]
+        else:
+            scores = proposals[:, 4]
+            num = min(cfg.max_per_img, proposals.shape[0])
+            _, topk_inds = scores.topk(num)
+            proposals = proposals[topk_inds, :]
+        return proposals
diff --git a/mmdet/models/dense_heads/gfl_head.py b/mmdet/models/dense_heads/gfl_head.py
new file mode 100644
index 0000000..12eb89d
--- /dev/null
+++ b/mmdet/models/dense_heads/gfl_head.py
@@ -0,0 +1,648 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule, Scale
+from mmcv.runner import force_fp32
+
+from mmdet.core import (anchor_inside_flags, bbox_overlaps, build_assigner,
+                        build_sampler, images_to_levels, multi_apply,
+                        reduce_mean, unmap)
+from mmdet.core.utils import filter_scores_and_topk
+from ..builder import HEADS, build_loss
+from .anchor_head import AnchorHead
+
+
+class Integral(nn.Module):
+    """A fixed layer for calculating integral result from distribution.
+
+    This layer calculates the target location by :math: `sum{P(y_i) * y_i}`,
+    P(y_i) denotes the softmax vector that represents the discrete distribution
+    y_i denotes the discrete set, usually {0, 1, 2, ..., reg_max}
+
+    Args:
+        reg_max (int): The maximal value of the discrete set. Default: 16. You
+            may want to reset it according to your new dataset or related
+            settings.
+    """
+
+    def __init__(self, reg_max=16):
+        super(Integral, self).__init__()
+        self.reg_max = reg_max
+        self.register_buffer('project',
+                             torch.linspace(0, self.reg_max, self.reg_max + 1))
+
+    def forward(self, x):
+        """Forward feature from the regression head to get integral result of
+        bounding box location.
+
+        Args:
+            x (Tensor): Features of the regression head, shape (N, 4*(n+1)),
+                n is self.reg_max.
+
+        Returns:
+            x (Tensor): Integral result of box locations, i.e., distance
+                offsets from the box center in four directions, shape (N, 4).
+        """
+        x = F.softmax(x.reshape(-1, self.reg_max + 1), dim=1)
+        x = F.linear(x, self.project.type_as(x)).reshape(-1, 4)
+        return x
+
+
+@HEADS.register_module()
+class GFLHead(AnchorHead):
+    """Generalized Focal Loss: Learning Qualified and Distributed Bounding
+    Boxes for Dense Object Detection.
+
+    GFL head structure is similar with ATSS, however GFL uses
+    1) joint representation for classification and localization quality, and
+    2) flexible General distribution for bounding box locations,
+    which are supervised by
+    Quality Focal Loss (QFL) and Distribution Focal Loss (DFL), respectively
+
+    https://arxiv.org/abs/2006.04388
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        stacked_convs (int): Number of conv layers in cls and reg tower.
+            Default: 4.
+        conv_cfg (dict): dictionary to construct and config conv layer.
+            Default: None.
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: dict(type='GN', num_groups=32, requires_grad=True).
+        loss_qfl (dict): Config of Quality Focal Loss (QFL).
+        bbox_coder (dict): Config of bbox coder. Defaults
+            'DistancePointBBoxCoder'.
+        reg_max (int): Max value of integral set :math: `{0, ..., reg_max}`
+            in QFL setting. Default: 16.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    Example:
+        >>> self = GFLHead(11, 7)
+        >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
+        >>> cls_quality_score, bbox_pred = self.forward(feats)
+        >>> assert len(cls_quality_score) == len(self.scales)
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 stacked_convs=4,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
+                 loss_dfl=dict(type='DistributionFocalLoss', loss_weight=0.25),
+                 bbox_coder=dict(type='DistancePointBBoxCoder'),
+                 reg_max=16,
+                 init_cfg=dict(
+                     type='Normal',
+                     layer='Conv2d',
+                     std=0.01,
+                     override=dict(
+                         type='Normal',
+                         name='gfl_cls',
+                         std=0.01,
+                         bias_prob=0.01)),
+                 **kwargs):
+        self.stacked_convs = stacked_convs
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.reg_max = reg_max
+        super(GFLHead, self).__init__(
+            num_classes,
+            in_channels,
+            bbox_coder=bbox_coder,
+            init_cfg=init_cfg,
+            **kwargs)
+
+        self.sampling = False
+        if self.train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            # SSD sampling=False so use PseudoSampler
+            sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+
+        self.integral = Integral(self.reg_max)
+        self.loss_dfl = build_loss(loss_dfl)
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.relu = nn.ReLU(inplace=True)
+        self.cls_convs = nn.ModuleList()
+        self.reg_convs = nn.ModuleList()
+        for i in range(self.stacked_convs):
+            chn = self.in_channels if i == 0 else self.feat_channels
+            self.cls_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+            self.reg_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+        assert self.num_anchors == 1, 'anchor free version'
+        self.gfl_cls = nn.Conv2d(
+            self.feat_channels, self.cls_out_channels, 3, padding=1)
+        self.gfl_reg = nn.Conv2d(
+            self.feat_channels, 4 * (self.reg_max + 1), 3, padding=1)
+        self.scales = nn.ModuleList(
+            [Scale(1.0) for _ in self.prior_generator.strides])
+
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple: Usually a tuple of classification scores and bbox prediction
+                cls_scores (list[Tensor]): Classification and quality (IoU)
+                    joint scores for all scale levels, each is a 4D-tensor,
+                    the channel number is num_classes.
+                bbox_preds (list[Tensor]): Box distribution logits for all
+                    scale levels, each is a 4D-tensor, the channel number is
+                    4*(n+1), n is max value of integral set.
+        """
+        return multi_apply(self.forward_single, feats, self.scales)
+
+    def forward_single(self, x, scale):
+        """Forward feature of a single scale level.
+
+        Args:
+            x (Tensor): Features of a single scale level.
+            scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
+                the bbox prediction.
+
+        Returns:
+            tuple:
+                cls_score (Tensor): Cls and quality joint scores for a single
+                    scale level the channel number is num_classes.
+                bbox_pred (Tensor): Box distribution logits for a single scale
+                    level, the channel number is 4*(n+1), n is max value of
+                    integral set.
+        """
+        cls_feat = x
+        reg_feat = x
+        for cls_conv in self.cls_convs:
+            cls_feat = cls_conv(cls_feat)
+        for reg_conv in self.reg_convs:
+            reg_feat = reg_conv(reg_feat)
+        cls_score = self.gfl_cls(cls_feat)
+        bbox_pred = scale(self.gfl_reg(reg_feat)).float()
+        return cls_score, bbox_pred
+
+    def anchor_center(self, anchors):
+        """Get anchor centers from anchors.
+
+        Args:
+            anchors (Tensor): Anchor list with shape (N, 4), "xyxy" format.
+
+        Returns:
+            Tensor: Anchor centers with shape (N, 2), "xy" format.
+        """
+        anchors_cx = (anchors[..., 2] + anchors[..., 0]) / 2
+        anchors_cy = (anchors[..., 3] + anchors[..., 1]) / 2
+        return torch.stack([anchors_cx, anchors_cy], dim=-1)
+
+    def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights,
+                    bbox_targets, stride, num_total_samples):
+        """Compute loss of a single scale level.
+
+        Args:
+            anchors (Tensor): Box reference for each scale level with shape
+                (N, num_total_anchors, 4).
+            cls_score (Tensor): Cls and quality joint scores for each scale
+                level has shape (N, num_classes, H, W).
+            bbox_pred (Tensor): Box distribution logits for each scale
+                level with shape (N, 4*(n+1), H, W), n is max value of integral
+                set.
+            labels (Tensor): Labels of each anchors with shape
+                (N, num_total_anchors).
+            label_weights (Tensor): Label weights of each anchor with shape
+                (N, num_total_anchors)
+            bbox_targets (Tensor): BBox regression targets of each anchor
+                weight shape (N, num_total_anchors, 4).
+            stride (tuple): Stride in this scale level.
+            num_total_samples (int): Number of positive samples that is
+                reduced over all GPUs.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        assert stride[0] == stride[1], 'h stride is not equal to w stride!'
+        anchors = anchors.reshape(-1, 4)
+        cls_score = cls_score.permute(0, 2, 3,
+                                      1).reshape(-1, self.cls_out_channels)
+        bbox_pred = bbox_pred.permute(0, 2, 3,
+                                      1).reshape(-1, 4 * (self.reg_max + 1))
+        bbox_targets = bbox_targets.reshape(-1, 4)
+        labels = labels.reshape(-1)
+        label_weights = label_weights.reshape(-1)
+
+        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+        bg_class_ind = self.num_classes
+        pos_inds = ((labels >= 0)
+                    & (labels < bg_class_ind)).nonzero().squeeze(1)
+        score = label_weights.new_zeros(labels.shape)
+
+        if len(pos_inds) > 0:
+            pos_bbox_targets = bbox_targets[pos_inds]
+            pos_bbox_pred = bbox_pred[pos_inds]
+            pos_anchors = anchors[pos_inds]
+            pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0]
+
+            weight_targets = cls_score.detach().sigmoid()
+            weight_targets = weight_targets.max(dim=1)[0][pos_inds]
+            pos_bbox_pred_corners = self.integral(pos_bbox_pred)
+            pos_decode_bbox_pred = self.bbox_coder.decode(
+                pos_anchor_centers, pos_bbox_pred_corners)
+            pos_decode_bbox_targets = pos_bbox_targets / stride[0]
+            score[pos_inds] = bbox_overlaps(
+                pos_decode_bbox_pred.detach(),
+                pos_decode_bbox_targets,
+                is_aligned=True)
+            pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1)
+            target_corners = self.bbox_coder.encode(pos_anchor_centers,
+                                                    pos_decode_bbox_targets,
+                                                    self.reg_max).reshape(-1)
+
+            # regression loss
+            loss_bbox = self.loss_bbox(
+                pos_decode_bbox_pred,
+                pos_decode_bbox_targets,
+                weight=weight_targets,
+                avg_factor=1.0)
+
+            # dfl loss
+            loss_dfl = self.loss_dfl(
+                pred_corners,
+                target_corners,
+                weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
+                avg_factor=4.0)
+        else:
+            loss_bbox = bbox_pred.sum() * 0
+            loss_dfl = bbox_pred.sum() * 0
+            weight_targets = bbox_pred.new_tensor(0)
+
+        # cls (qfl) loss
+        loss_cls = self.loss_cls(
+            cls_score, (labels, score),
+            weight=label_weights,
+            avg_factor=num_total_samples)
+
+        return loss_cls, loss_bbox, loss_dfl, weight_targets.sum()
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Cls and quality scores for each scale
+                level has shape (N, num_classes, H, W).
+            bbox_preds (list[Tensor]): Box distribution logits for each scale
+                level with shape (N, 4*(n+1), H, W), n is max value of integral
+                set.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (list[Tensor] | None): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+
+        device = cls_scores[0].device
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels)
+        if cls_reg_targets is None:
+            return None
+
+        (anchor_list, labels_list, label_weights_list, bbox_targets_list,
+         bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
+
+        num_total_samples = reduce_mean(
+            torch.tensor(num_total_pos, dtype=torch.float,
+                         device=device)).item()
+        num_total_samples = max(num_total_samples, 1.0)
+
+        losses_cls, losses_bbox, losses_dfl,\
+            avg_factor = multi_apply(
+                self.loss_single,
+                anchor_list,
+                cls_scores,
+                bbox_preds,
+                labels_list,
+                label_weights_list,
+                bbox_targets_list,
+                self.prior_generator.strides,
+                num_total_samples=num_total_samples)
+
+        avg_factor = sum(avg_factor)
+        avg_factor = reduce_mean(avg_factor).clamp_(min=1).item()
+        losses_bbox = list(map(lambda x: x / avg_factor, losses_bbox))
+        losses_dfl = list(map(lambda x: x / avg_factor, losses_dfl))
+        return dict(
+            loss_cls=losses_cls, loss_bbox=losses_bbox, loss_dfl=losses_dfl)
+
+    def _get_bboxes_single(self,
+                           cls_score_list,
+                           bbox_pred_list,
+                           score_factor_list,
+                           mlvl_priors,
+                           img_meta,
+                           cfg,
+                           rescale=False,
+                           with_nms=True,
+                           **kwargs):
+        """Transform outputs of a single image into bbox predictions.
+
+        Args:
+            cls_score_list (list[Tensor]): Box scores from all scale
+                levels of a single image, each item has shape
+                (num_priors * num_classes, H, W).
+            bbox_pred_list (list[Tensor]): Box energies / deltas from
+                all scale levels of a single image, each item has shape
+                (num_priors * 4, H, W).
+            score_factor_list (list[Tensor]): Score factor from all scale
+                levels of a single image. GFL head does not need this value.
+            mlvl_priors (list[Tensor]): Each element in the list is
+                the priors of a single level in feature pyramid, has shape
+                (num_priors, 4).
+            img_meta (dict): Image meta info.
+            cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+
+        Returns:
+            tuple[Tensor]: Results of detected bboxes and labels. If with_nms
+                is False and mlvl_score_factor is None, return mlvl_bboxes and
+                mlvl_scores, else return mlvl_bboxes, mlvl_scores and
+                mlvl_score_factor. Usually with_nms is False is used for aug
+                test. If with_nms is True, then return the following format
+
+                - det_bboxes (Tensor): Predicted bboxes with shape \
+                    [num_bboxes, 5], where the first 4 columns are bounding \
+                    box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
+                    column are scores between 0 and 1.
+                - det_labels (Tensor): Predicted labels of the corresponding \
+                    box with shape [num_bboxes].
+        """
+        cfg = self.test_cfg if cfg is None else cfg
+        img_shape = img_meta['img_shape']
+        nms_pre = cfg.get('nms_pre', -1)
+
+        mlvl_bboxes = []
+        mlvl_scores = []
+        mlvl_labels = []
+        for level_idx, (cls_score, bbox_pred, stride, priors) in enumerate(
+                zip(cls_score_list, bbox_pred_list,
+                    self.prior_generator.strides, mlvl_priors)):
+            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
+            assert stride[0] == stride[1]
+
+            bbox_pred = bbox_pred.permute(1, 2, 0)
+            bbox_pred = self.integral(bbox_pred) * stride[0]
+
+            scores = cls_score.permute(1, 2, 0).reshape(
+                -1, self.cls_out_channels).sigmoid()
+
+            # After https://github.com/open-mmlab/mmdetection/pull/6268/,
+            # this operation keeps fewer bboxes under the same `nms_pre`.
+            # There is no difference in performance for most models. If you
+            # find a slight drop in performance, you can set a larger
+            # `nms_pre` than before.
+            results = filter_scores_and_topk(
+                scores, cfg.score_thr, nms_pre,
+                dict(bbox_pred=bbox_pred, priors=priors))
+            scores, labels, _, filtered_results = results
+
+            bbox_pred = filtered_results['bbox_pred']
+            priors = filtered_results['priors']
+
+            bboxes = self.bbox_coder.decode(
+                self.anchor_center(priors), bbox_pred, max_shape=img_shape)
+            mlvl_bboxes.append(bboxes)
+            mlvl_scores.append(scores)
+            mlvl_labels.append(labels)
+
+        return self._bbox_post_process(
+            mlvl_scores,
+            mlvl_labels,
+            mlvl_bboxes,
+            img_meta['scale_factor'],
+            cfg,
+            rescale=rescale,
+            with_nms=with_nms)
+
+    def get_targets(self,
+                    anchor_list,
+                    valid_flag_list,
+                    gt_bboxes_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None,
+                    gt_labels_list=None,
+                    label_channels=1,
+                    unmap_outputs=True):
+        """Get targets for GFL head.
+
+        This method is almost the same as `AnchorHead.get_targets()`. Besides
+        returning the targets as the parent method does, it also returns the
+        anchors as the first element of the returned tuple.
+        """
+        num_imgs = len(img_metas)
+        assert len(anchor_list) == len(valid_flag_list) == num_imgs
+
+        # anchor number of multi levels
+        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+        num_level_anchors_list = [num_level_anchors] * num_imgs
+
+        # concat all level anchors and flags to a single tensor
+        for i in range(num_imgs):
+            assert len(anchor_list[i]) == len(valid_flag_list[i])
+            anchor_list[i] = torch.cat(anchor_list[i])
+            valid_flag_list[i] = torch.cat(valid_flag_list[i])
+
+        # compute targets for each image
+        if gt_bboxes_ignore_list is None:
+            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
+        if gt_labels_list is None:
+            gt_labels_list = [None for _ in range(num_imgs)]
+        (all_anchors, all_labels, all_label_weights, all_bbox_targets,
+         all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
+             self._get_target_single,
+             anchor_list,
+             valid_flag_list,
+             num_level_anchors_list,
+             gt_bboxes_list,
+             gt_bboxes_ignore_list,
+             gt_labels_list,
+             img_metas,
+             label_channels=label_channels,
+             unmap_outputs=unmap_outputs)
+        # no valid anchors
+        if any([labels is None for labels in all_labels]):
+            return None
+        # sampled anchors of all images
+        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
+        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
+        # split targets to a list w.r.t. multiple levels
+        anchors_list = images_to_levels(all_anchors, num_level_anchors)
+        labels_list = images_to_levels(all_labels, num_level_anchors)
+        label_weights_list = images_to_levels(all_label_weights,
+                                              num_level_anchors)
+        bbox_targets_list = images_to_levels(all_bbox_targets,
+                                             num_level_anchors)
+        bbox_weights_list = images_to_levels(all_bbox_weights,
+                                             num_level_anchors)
+        return (anchors_list, labels_list, label_weights_list,
+                bbox_targets_list, bbox_weights_list, num_total_pos,
+                num_total_neg)
+
+    def _get_target_single(self,
+                           flat_anchors,
+                           valid_flags,
+                           num_level_anchors,
+                           gt_bboxes,
+                           gt_bboxes_ignore,
+                           gt_labels,
+                           img_meta,
+                           label_channels=1,
+                           unmap_outputs=True):
+        """Compute regression, classification targets for anchors in a single
+        image.
+
+        Args:
+            flat_anchors (Tensor): Multi-level anchors of the image, which are
+                concatenated into a single tensor of shape (num_anchors, 4)
+            valid_flags (Tensor): Multi level valid flags of the image,
+                which are concatenated into a single tensor of
+                    shape (num_anchors,).
+            num_level_anchors Tensor): Number of anchors of each scale level.
+            gt_bboxes (Tensor): Ground truth bboxes of the image,
+                shape (num_gts, 4).
+            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4).
+            gt_labels (Tensor): Ground truth labels of each box,
+                shape (num_gts,).
+            img_meta (dict): Meta info of the image.
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+
+        Returns:
+            tuple: N is the number of total anchors in the image.
+                anchors (Tensor): All anchors in the image with shape (N, 4).
+                labels (Tensor): Labels of all anchors in the image with shape
+                    (N,).
+                label_weights (Tensor): Label weights of all anchor in the
+                    image with shape (N,).
+                bbox_targets (Tensor): BBox targets of all anchors in the
+                    image with shape (N, 4).
+                bbox_weights (Tensor): BBox weights of all anchors in the
+                    image with shape (N, 4).
+                pos_inds (Tensor): Indices of positive anchor with shape
+                    (num_pos,).
+                neg_inds (Tensor): Indices of negative anchor with shape
+                    (num_neg,).
+        """
+        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
+                                           img_meta['img_shape'][:2],
+                                           self.train_cfg.allowed_border)
+        if not inside_flags.any():
+            return (None, ) * 7
+        # assign gt and sample anchors
+        anchors = flat_anchors[inside_flags, :]
+
+        num_level_anchors_inside = self.get_num_level_anchors_inside(
+            num_level_anchors, inside_flags)
+        assign_result = self.assigner.assign(anchors, num_level_anchors_inside,
+                                             gt_bboxes, gt_bboxes_ignore,
+                                             gt_labels)
+
+        sampling_result = self.sampler.sample(assign_result, anchors,
+                                              gt_bboxes)
+
+        num_valid_anchors = anchors.shape[0]
+        bbox_targets = torch.zeros_like(anchors)
+        bbox_weights = torch.zeros_like(anchors)
+        labels = anchors.new_full((num_valid_anchors, ),
+                                  self.num_classes,
+                                  dtype=torch.long)
+        label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
+
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+        if len(pos_inds) > 0:
+            pos_bbox_targets = sampling_result.pos_gt_bboxes
+            bbox_targets[pos_inds, :] = pos_bbox_targets
+            bbox_weights[pos_inds, :] = 1.0
+            if gt_labels is None:
+                # Only rpn gives gt_labels as None
+                # Foreground is the first class
+                labels[pos_inds] = 0
+            else:
+                labels[pos_inds] = gt_labels[
+                    sampling_result.pos_assigned_gt_inds]
+            if self.train_cfg.pos_weight <= 0:
+                label_weights[pos_inds] = 1.0
+            else:
+                label_weights[pos_inds] = self.train_cfg.pos_weight
+        if len(neg_inds) > 0:
+            label_weights[neg_inds] = 1.0
+
+        # map up to original set of anchors
+        if unmap_outputs:
+            num_total_anchors = flat_anchors.size(0)
+            anchors = unmap(anchors, num_total_anchors, inside_flags)
+            labels = unmap(
+                labels, num_total_anchors, inside_flags, fill=self.num_classes)
+            label_weights = unmap(label_weights, num_total_anchors,
+                                  inside_flags)
+            bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
+            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
+
+        return (anchors, labels, label_weights, bbox_targets, bbox_weights,
+                pos_inds, neg_inds)
+
+    def get_num_level_anchors_inside(self, num_level_anchors, inside_flags):
+        split_inside_flags = torch.split(inside_flags, num_level_anchors)
+        num_level_anchors_inside = [
+            int(flags.sum()) for flags in split_inside_flags
+        ]
+        return num_level_anchors_inside
diff --git a/mmdet/models/dense_heads/guided_anchor_head.py b/mmdet/models/dense_heads/guided_anchor_head.py
new file mode 100644
index 0000000..53e8cd8
--- /dev/null
+++ b/mmdet/models/dense_heads/guided_anchor_head.py
@@ -0,0 +1,868 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch
+import torch.nn as nn
+from mmcv.ops import DeformConv2d, MaskedConv2d
+from mmcv.runner import BaseModule, force_fp32
+
+from mmdet.core import (anchor_inside_flags, build_assigner, build_bbox_coder,
+                        build_prior_generator, build_sampler, calc_region,
+                        images_to_levels, multi_apply, multiclass_nms, unmap)
+from ..builder import HEADS, build_loss
+from .anchor_head import AnchorHead
+
+
+class FeatureAdaption(BaseModule):
+    """Feature Adaption Module.
+
+    Feature Adaption Module is implemented based on DCN v1.
+    It uses anchor shape prediction rather than feature map to
+    predict offsets of deform conv layer.
+
+    Args:
+        in_channels (int): Number of channels in the input feature map.
+        out_channels (int): Number of channels in the output feature map.
+        kernel_size (int): Deformable conv kernel size.
+        deform_groups (int): Deformable conv group size.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=3,
+                 deform_groups=4,
+                 init_cfg=dict(
+                     type='Normal',
+                     layer='Conv2d',
+                     std=0.1,
+                     override=dict(
+                         type='Normal', name='conv_adaption', std=0.01))):
+        super(FeatureAdaption, self).__init__(init_cfg)
+        offset_channels = kernel_size * kernel_size * 2
+        self.conv_offset = nn.Conv2d(
+            2, deform_groups * offset_channels, 1, bias=False)
+        self.conv_adaption = DeformConv2d(
+            in_channels,
+            out_channels,
+            kernel_size=kernel_size,
+            padding=(kernel_size - 1) // 2,
+            deform_groups=deform_groups)
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x, shape):
+        offset = self.conv_offset(shape.detach())
+        x = self.relu(self.conv_adaption(x, offset))
+        return x
+
+
+@HEADS.register_module()
+class GuidedAnchorHead(AnchorHead):
+    """Guided-Anchor-based head (GA-RPN, GA-RetinaNet, etc.).
+
+    This GuidedAnchorHead will predict high-quality feature guided
+    anchors and locations where anchors will be kept in inference.
+    There are mainly 3 categories of bounding-boxes.
+
+    - Sampled 9 pairs for target assignment. (approxes)
+    - The square boxes where the predicted anchors are based on. (squares)
+    - Guided anchors.
+
+    Please refer to https://arxiv.org/abs/1901.03278 for more details.
+
+    Args:
+        num_classes (int): Number of classes.
+        in_channels (int): Number of channels in the input feature map.
+        feat_channels (int): Number of hidden channels.
+        approx_anchor_generator (dict): Config dict for approx generator
+        square_anchor_generator (dict): Config dict for square generator
+        anchor_coder (dict): Config dict for anchor coder
+        bbox_coder (dict): Config dict for bbox coder
+        reg_decoded_bbox (bool): If true, the regression loss would be
+            applied directly on decoded bounding boxes, converting both
+            the predicted boxes and regression targets to absolute
+            coordinates format. Default False. It should be `True` when
+            using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
+        deform_groups: (int): Group number of DCN in
+            FeatureAdaption module.
+        loc_filter_thr (float): Threshold to filter out unconcerned regions.
+        loss_loc (dict): Config of location loss.
+        loss_shape (dict): Config of anchor shape loss.
+        loss_cls (dict): Config of classification loss.
+        loss_bbox (dict): Config of bbox regression loss.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(
+            self,
+            num_classes,
+            in_channels,
+            feat_channels=256,
+            approx_anchor_generator=dict(
+                type='AnchorGenerator',
+                octave_base_scale=8,
+                scales_per_octave=3,
+                ratios=[0.5, 1.0, 2.0],
+                strides=[4, 8, 16, 32, 64]),
+            square_anchor_generator=dict(
+                type='AnchorGenerator',
+                ratios=[1.0],
+                scales=[8],
+                strides=[4, 8, 16, 32, 64]),
+            anchor_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[.0, .0, .0, .0],
+                target_stds=[1.0, 1.0, 1.0, 1.0]
+            ),
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[.0, .0, .0, .0],
+                target_stds=[1.0, 1.0, 1.0, 1.0]
+            ),
+            reg_decoded_bbox=False,
+            deform_groups=4,
+            loc_filter_thr=0.01,
+            train_cfg=None,
+            test_cfg=None,
+            loss_loc=dict(
+                type='FocalLoss',
+                use_sigmoid=True,
+                gamma=2.0,
+                alpha=0.25,
+                loss_weight=1.0),
+            loss_shape=dict(type='BoundedIoULoss', beta=0.2, loss_weight=1.0),
+            loss_cls=dict(
+                type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+            loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                           loss_weight=1.0),
+            init_cfg=dict(type='Normal', layer='Conv2d', std=0.01,
+                          override=dict(type='Normal',
+                                        name='conv_loc',
+                                        std=0.01,
+                                        bias_prob=0.01))):  # yapf: disable
+        super(AnchorHead, self).__init__(init_cfg)
+        self.in_channels = in_channels
+        self.num_classes = num_classes
+        self.feat_channels = feat_channels
+        self.deform_groups = deform_groups
+        self.loc_filter_thr = loc_filter_thr
+
+        # build approx_anchor_generator and square_anchor_generator
+        assert (approx_anchor_generator['octave_base_scale'] ==
+                square_anchor_generator['scales'][0])
+        assert (approx_anchor_generator['strides'] ==
+                square_anchor_generator['strides'])
+        self.approx_anchor_generator = build_prior_generator(
+            approx_anchor_generator)
+        self.square_anchor_generator = build_prior_generator(
+            square_anchor_generator)
+        self.approxs_per_octave = self.approx_anchor_generator \
+            .num_base_priors[0]
+
+        self.reg_decoded_bbox = reg_decoded_bbox
+
+        # one anchor per location
+        self.num_base_priors = self.square_anchor_generator.num_base_priors[0]
+
+        self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
+        self.loc_focal_loss = loss_loc['type'] in ['FocalLoss']
+        self.sampling = loss_cls['type'] not in ['FocalLoss']
+        self.ga_sampling = train_cfg is not None and hasattr(
+            train_cfg, 'ga_sampler')
+        if self.use_sigmoid_cls:
+            self.cls_out_channels = self.num_classes
+        else:
+            self.cls_out_channels = self.num_classes + 1
+
+        # build bbox_coder
+        self.anchor_coder = build_bbox_coder(anchor_coder)
+        self.bbox_coder = build_bbox_coder(bbox_coder)
+
+        # build losses
+        self.loss_loc = build_loss(loss_loc)
+        self.loss_shape = build_loss(loss_shape)
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_bbox = build_loss(loss_bbox)
+
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+
+        if self.train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            # use PseudoSampler when sampling is False
+            if self.sampling and hasattr(self.train_cfg, 'sampler'):
+                sampler_cfg = self.train_cfg.sampler
+            else:
+                sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+
+            self.ga_assigner = build_assigner(self.train_cfg.ga_assigner)
+            if self.ga_sampling:
+                ga_sampler_cfg = self.train_cfg.ga_sampler
+            else:
+                ga_sampler_cfg = dict(type='PseudoSampler')
+            self.ga_sampler = build_sampler(ga_sampler_cfg, context=self)
+
+        self.fp16_enabled = False
+
+        self._init_layers()
+
+    @property
+    def num_anchors(self):
+        warnings.warn('DeprecationWarning: `num_anchors` is deprecated, '
+                      'please use "num_base_priors" instead')
+        return self.square_anchor_generator.num_base_priors[0]
+
+    def _init_layers(self):
+        self.relu = nn.ReLU(inplace=True)
+        self.conv_loc = nn.Conv2d(self.in_channels, 1, 1)
+        self.conv_shape = nn.Conv2d(self.in_channels, self.num_base_priors * 2,
+                                    1)
+        self.feature_adaption = FeatureAdaption(
+            self.in_channels,
+            self.feat_channels,
+            kernel_size=3,
+            deform_groups=self.deform_groups)
+        self.conv_cls = MaskedConv2d(
+            self.feat_channels, self.num_base_priors * self.cls_out_channels,
+            1)
+        self.conv_reg = MaskedConv2d(self.feat_channels,
+                                     self.num_base_priors * 4, 1)
+
+    def forward_single(self, x):
+        loc_pred = self.conv_loc(x)
+        shape_pred = self.conv_shape(x)
+        x = self.feature_adaption(x, shape_pred)
+        # masked conv is only used during inference for speed-up
+        if not self.training:
+            mask = loc_pred.sigmoid()[0] >= self.loc_filter_thr
+        else:
+            mask = None
+        cls_score = self.conv_cls(x, mask)
+        bbox_pred = self.conv_reg(x, mask)
+        return cls_score, bbox_pred, shape_pred, loc_pred
+
+    def forward(self, feats):
+        return multi_apply(self.forward_single, feats)
+
+    def get_sampled_approxs(self, featmap_sizes, img_metas, device='cuda'):
+        """Get sampled approxs and inside flags according to feature map sizes.
+
+        Args:
+            featmap_sizes (list[tuple]): Multi-level feature map sizes.
+            img_metas (list[dict]): Image meta info.
+            device (torch.device | str): device for returned tensors
+
+        Returns:
+            tuple: approxes of each image, inside flags of each image
+        """
+        num_imgs = len(img_metas)
+
+        # since feature map sizes of all images are the same, we only compute
+        # approxes for one time
+        multi_level_approxs = self.approx_anchor_generator.grid_priors(
+            featmap_sizes, device=device)
+        approxs_list = [multi_level_approxs for _ in range(num_imgs)]
+
+        # for each image, we compute inside flags of multi level approxes
+        inside_flag_list = []
+        for img_id, img_meta in enumerate(img_metas):
+            multi_level_flags = []
+            multi_level_approxs = approxs_list[img_id]
+
+            # obtain valid flags for each approx first
+            multi_level_approx_flags = self.approx_anchor_generator \
+                .valid_flags(featmap_sizes,
+                             img_meta['pad_shape'],
+                             device=device)
+
+            for i, flags in enumerate(multi_level_approx_flags):
+                approxs = multi_level_approxs[i]
+                inside_flags_list = []
+                for i in range(self.approxs_per_octave):
+                    split_valid_flags = flags[i::self.approxs_per_octave]
+                    split_approxs = approxs[i::self.approxs_per_octave, :]
+                    inside_flags = anchor_inside_flags(
+                        split_approxs, split_valid_flags,
+                        img_meta['img_shape'][:2],
+                        self.train_cfg.allowed_border)
+                    inside_flags_list.append(inside_flags)
+                # inside_flag for a position is true if any anchor in this
+                # position is true
+                inside_flags = (
+                    torch.stack(inside_flags_list, 0).sum(dim=0) > 0)
+                multi_level_flags.append(inside_flags)
+            inside_flag_list.append(multi_level_flags)
+        return approxs_list, inside_flag_list
+
+    def get_anchors(self,
+                    featmap_sizes,
+                    shape_preds,
+                    loc_preds,
+                    img_metas,
+                    use_loc_filter=False,
+                    device='cuda'):
+        """Get squares according to feature map sizes and guided anchors.
+
+        Args:
+            featmap_sizes (list[tuple]): Multi-level feature map sizes.
+            shape_preds (list[tensor]): Multi-level shape predictions.
+            loc_preds (list[tensor]): Multi-level location predictions.
+            img_metas (list[dict]): Image meta info.
+            use_loc_filter (bool): Use loc filter or not.
+            device (torch.device | str): device for returned tensors
+
+        Returns:
+            tuple: square approxs of each image, guided anchors of each image,
+                loc masks of each image
+        """
+        num_imgs = len(img_metas)
+        num_levels = len(featmap_sizes)
+
+        # since feature map sizes of all images are the same, we only compute
+        # squares for one time
+        multi_level_squares = self.square_anchor_generator.grid_priors(
+            featmap_sizes, device=device)
+        squares_list = [multi_level_squares for _ in range(num_imgs)]
+
+        # for each image, we compute multi level guided anchors
+        guided_anchors_list = []
+        loc_mask_list = []
+        for img_id, img_meta in enumerate(img_metas):
+            multi_level_guided_anchors = []
+            multi_level_loc_mask = []
+            for i in range(num_levels):
+                squares = squares_list[img_id][i]
+                shape_pred = shape_preds[i][img_id]
+                loc_pred = loc_preds[i][img_id]
+                guided_anchors, loc_mask = self._get_guided_anchors_single(
+                    squares,
+                    shape_pred,
+                    loc_pred,
+                    use_loc_filter=use_loc_filter)
+                multi_level_guided_anchors.append(guided_anchors)
+                multi_level_loc_mask.append(loc_mask)
+            guided_anchors_list.append(multi_level_guided_anchors)
+            loc_mask_list.append(multi_level_loc_mask)
+        return squares_list, guided_anchors_list, loc_mask_list
+
+    def _get_guided_anchors_single(self,
+                                   squares,
+                                   shape_pred,
+                                   loc_pred,
+                                   use_loc_filter=False):
+        """Get guided anchors and loc masks for a single level.
+
+        Args:
+            square (tensor): Squares of a single level.
+            shape_pred (tensor): Shape predictions of a single level.
+            loc_pred (tensor): Loc predictions of a single level.
+            use_loc_filter (list[tensor]): Use loc filter or not.
+
+        Returns:
+            tuple: guided anchors, location masks
+        """
+        # calculate location filtering mask
+        loc_pred = loc_pred.sigmoid().detach()
+        if use_loc_filter:
+            loc_mask = loc_pred >= self.loc_filter_thr
+        else:
+            loc_mask = loc_pred >= 0.0
+        mask = loc_mask.permute(1, 2, 0).expand(-1, -1, self.num_base_priors)
+        mask = mask.contiguous().view(-1)
+        # calculate guided anchors
+        squares = squares[mask]
+        anchor_deltas = shape_pred.permute(1, 2, 0).contiguous().view(
+            -1, 2).detach()[mask]
+        bbox_deltas = anchor_deltas.new_full(squares.size(), 0)
+        bbox_deltas[:, 2:] = anchor_deltas
+        guided_anchors = self.anchor_coder.decode(
+            squares, bbox_deltas, wh_ratio_clip=1e-6)
+        return guided_anchors, mask
+
+    def ga_loc_targets(self, gt_bboxes_list, featmap_sizes):
+        """Compute location targets for guided anchoring.
+
+        Each feature map is divided into positive, negative and ignore regions.
+        - positive regions: target 1, weight 1
+        - ignore regions: target 0, weight 0
+        - negative regions: target 0, weight 0.1
+
+        Args:
+            gt_bboxes_list (list[Tensor]): Gt bboxes of each image.
+            featmap_sizes (list[tuple]): Multi level sizes of each feature
+                maps.
+
+        Returns:
+            tuple
+        """
+        anchor_scale = self.approx_anchor_generator.octave_base_scale
+        anchor_strides = self.approx_anchor_generator.strides
+        # Currently only supports same stride in x and y direction.
+        for stride in anchor_strides:
+            assert (stride[0] == stride[1])
+        anchor_strides = [stride[0] for stride in anchor_strides]
+
+        center_ratio = self.train_cfg.center_ratio
+        ignore_ratio = self.train_cfg.ignore_ratio
+        img_per_gpu = len(gt_bboxes_list)
+        num_lvls = len(featmap_sizes)
+        r1 = (1 - center_ratio) / 2
+        r2 = (1 - ignore_ratio) / 2
+        all_loc_targets = []
+        all_loc_weights = []
+        all_ignore_map = []
+        for lvl_id in range(num_lvls):
+            h, w = featmap_sizes[lvl_id]
+            loc_targets = torch.zeros(
+                img_per_gpu,
+                1,
+                h,
+                w,
+                device=gt_bboxes_list[0].device,
+                dtype=torch.float32)
+            loc_weights = torch.full_like(loc_targets, -1)
+            ignore_map = torch.zeros_like(loc_targets)
+            all_loc_targets.append(loc_targets)
+            all_loc_weights.append(loc_weights)
+            all_ignore_map.append(ignore_map)
+        for img_id in range(img_per_gpu):
+            gt_bboxes = gt_bboxes_list[img_id]
+            scale = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) *
+                               (gt_bboxes[:, 3] - gt_bboxes[:, 1]))
+            min_anchor_size = scale.new_full(
+                (1, ), float(anchor_scale * anchor_strides[0]))
+            # assign gt bboxes to different feature levels w.r.t. their scales
+            target_lvls = torch.floor(
+                torch.log2(scale) - torch.log2(min_anchor_size) + 0.5)
+            target_lvls = target_lvls.clamp(min=0, max=num_lvls - 1).long()
+            for gt_id in range(gt_bboxes.size(0)):
+                lvl = target_lvls[gt_id].item()
+                # rescaled to corresponding feature map
+                gt_ = gt_bboxes[gt_id, :4] / anchor_strides[lvl]
+                # calculate ignore regions
+                ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region(
+                    gt_, r2, featmap_sizes[lvl])
+                # calculate positive (center) regions
+                ctr_x1, ctr_y1, ctr_x2, ctr_y2 = calc_region(
+                    gt_, r1, featmap_sizes[lvl])
+                all_loc_targets[lvl][img_id, 0, ctr_y1:ctr_y2 + 1,
+                                     ctr_x1:ctr_x2 + 1] = 1
+                all_loc_weights[lvl][img_id, 0, ignore_y1:ignore_y2 + 1,
+                                     ignore_x1:ignore_x2 + 1] = 0
+                all_loc_weights[lvl][img_id, 0, ctr_y1:ctr_y2 + 1,
+                                     ctr_x1:ctr_x2 + 1] = 1
+                # calculate ignore map on nearby low level feature
+                if lvl > 0:
+                    d_lvl = lvl - 1
+                    # rescaled to corresponding feature map
+                    gt_ = gt_bboxes[gt_id, :4] / anchor_strides[d_lvl]
+                    ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region(
+                        gt_, r2, featmap_sizes[d_lvl])
+                    all_ignore_map[d_lvl][img_id, 0, ignore_y1:ignore_y2 + 1,
+                                          ignore_x1:ignore_x2 + 1] = 1
+                # calculate ignore map on nearby high level feature
+                if lvl < num_lvls - 1:
+                    u_lvl = lvl + 1
+                    # rescaled to corresponding feature map
+                    gt_ = gt_bboxes[gt_id, :4] / anchor_strides[u_lvl]
+                    ignore_x1, ignore_y1, ignore_x2, ignore_y2 = calc_region(
+                        gt_, r2, featmap_sizes[u_lvl])
+                    all_ignore_map[u_lvl][img_id, 0, ignore_y1:ignore_y2 + 1,
+                                          ignore_x1:ignore_x2 + 1] = 1
+        for lvl_id in range(num_lvls):
+            # ignore negative regions w.r.t. ignore map
+            all_loc_weights[lvl_id][(all_loc_weights[lvl_id] < 0)
+                                    & (all_ignore_map[lvl_id] > 0)] = 0
+            # set negative regions with weight 0.1
+            all_loc_weights[lvl_id][all_loc_weights[lvl_id] < 0] = 0.1
+        # loc average factor to balance loss
+        loc_avg_factor = sum(
+            [t.size(0) * t.size(-1) * t.size(-2)
+             for t in all_loc_targets]) / 200
+        return all_loc_targets, all_loc_weights, loc_avg_factor
+
+    def _ga_shape_target_single(self,
+                                flat_approxs,
+                                inside_flags,
+                                flat_squares,
+                                gt_bboxes,
+                                gt_bboxes_ignore,
+                                img_meta,
+                                unmap_outputs=True):
+        """Compute guided anchoring targets.
+
+        This function returns sampled anchors and gt bboxes directly
+        rather than calculates regression targets.
+
+        Args:
+            flat_approxs (Tensor): flat approxs of a single image,
+                shape (n, 4)
+            inside_flags (Tensor): inside flags of a single image,
+                shape (n, ).
+            flat_squares (Tensor): flat squares of a single image,
+                shape (approxs_per_octave * n, 4)
+            gt_bboxes (Tensor): Ground truth bboxes of a single image.
+            img_meta (dict): Meta info of a single image.
+            approxs_per_octave (int): number of approxs per octave
+            cfg (dict): RPN train configs.
+            unmap_outputs (bool): unmap outputs or not.
+
+        Returns:
+            tuple
+        """
+        if not inside_flags.any():
+            return (None, ) * 5
+        # assign gt and sample anchors
+        expand_inside_flags = inside_flags[:, None].expand(
+            -1, self.approxs_per_octave).reshape(-1)
+        approxs = flat_approxs[expand_inside_flags, :]
+        squares = flat_squares[inside_flags, :]
+
+        assign_result = self.ga_assigner.assign(approxs, squares,
+                                                self.approxs_per_octave,
+                                                gt_bboxes, gt_bboxes_ignore)
+        sampling_result = self.ga_sampler.sample(assign_result, squares,
+                                                 gt_bboxes)
+
+        bbox_anchors = torch.zeros_like(squares)
+        bbox_gts = torch.zeros_like(squares)
+        bbox_weights = torch.zeros_like(squares)
+
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+        if len(pos_inds) > 0:
+            bbox_anchors[pos_inds, :] = sampling_result.pos_bboxes
+            bbox_gts[pos_inds, :] = sampling_result.pos_gt_bboxes
+            bbox_weights[pos_inds, :] = 1.0
+
+        # map up to original set of anchors
+        if unmap_outputs:
+            num_total_anchors = flat_squares.size(0)
+            bbox_anchors = unmap(bbox_anchors, num_total_anchors, inside_flags)
+            bbox_gts = unmap(bbox_gts, num_total_anchors, inside_flags)
+            bbox_weights = unmap(bbox_weights, num_total_anchors, inside_flags)
+
+        return (bbox_anchors, bbox_gts, bbox_weights, pos_inds, neg_inds)
+
+    def ga_shape_targets(self,
+                         approx_list,
+                         inside_flag_list,
+                         square_list,
+                         gt_bboxes_list,
+                         img_metas,
+                         gt_bboxes_ignore_list=None,
+                         unmap_outputs=True):
+        """Compute guided anchoring targets.
+
+        Args:
+            approx_list (list[list]): Multi level approxs of each image.
+            inside_flag_list (list[list]): Multi level inside flags of each
+                image.
+            square_list (list[list]): Multi level squares of each image.
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+            img_metas (list[dict]): Meta info of each image.
+            gt_bboxes_ignore_list (list[Tensor]): ignore list of gt bboxes.
+            unmap_outputs (bool): unmap outputs or not.
+
+        Returns:
+            tuple
+        """
+        num_imgs = len(img_metas)
+        assert len(approx_list) == len(inside_flag_list) == len(
+            square_list) == num_imgs
+        # anchor number of multi levels
+        num_level_squares = [squares.size(0) for squares in square_list[0]]
+        # concat all level anchors and flags to a single tensor
+        inside_flag_flat_list = []
+        approx_flat_list = []
+        square_flat_list = []
+        for i in range(num_imgs):
+            assert len(square_list[i]) == len(inside_flag_list[i])
+            inside_flag_flat_list.append(torch.cat(inside_flag_list[i]))
+            approx_flat_list.append(torch.cat(approx_list[i]))
+            square_flat_list.append(torch.cat(square_list[i]))
+
+        # compute targets for each image
+        if gt_bboxes_ignore_list is None:
+            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
+        (all_bbox_anchors, all_bbox_gts, all_bbox_weights, pos_inds_list,
+         neg_inds_list) = multi_apply(
+             self._ga_shape_target_single,
+             approx_flat_list,
+             inside_flag_flat_list,
+             square_flat_list,
+             gt_bboxes_list,
+             gt_bboxes_ignore_list,
+             img_metas,
+             unmap_outputs=unmap_outputs)
+        # no valid anchors
+        if any([bbox_anchors is None for bbox_anchors in all_bbox_anchors]):
+            return None
+        # sampled anchors of all images
+        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
+        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
+        # split targets to a list w.r.t. multiple levels
+        bbox_anchors_list = images_to_levels(all_bbox_anchors,
+                                             num_level_squares)
+        bbox_gts_list = images_to_levels(all_bbox_gts, num_level_squares)
+        bbox_weights_list = images_to_levels(all_bbox_weights,
+                                             num_level_squares)
+        return (bbox_anchors_list, bbox_gts_list, bbox_weights_list,
+                num_total_pos, num_total_neg)
+
+    def loss_shape_single(self, shape_pred, bbox_anchors, bbox_gts,
+                          anchor_weights, anchor_total_num):
+        shape_pred = shape_pred.permute(0, 2, 3, 1).contiguous().view(-1, 2)
+        bbox_anchors = bbox_anchors.contiguous().view(-1, 4)
+        bbox_gts = bbox_gts.contiguous().view(-1, 4)
+        anchor_weights = anchor_weights.contiguous().view(-1, 4)
+        bbox_deltas = bbox_anchors.new_full(bbox_anchors.size(), 0)
+        bbox_deltas[:, 2:] += shape_pred
+        # filter out negative samples to speed-up weighted_bounded_iou_loss
+        inds = torch.nonzero(
+            anchor_weights[:, 0] > 0, as_tuple=False).squeeze(1)
+        bbox_deltas_ = bbox_deltas[inds]
+        bbox_anchors_ = bbox_anchors[inds]
+        bbox_gts_ = bbox_gts[inds]
+        anchor_weights_ = anchor_weights[inds]
+        pred_anchors_ = self.anchor_coder.decode(
+            bbox_anchors_, bbox_deltas_, wh_ratio_clip=1e-6)
+        loss_shape = self.loss_shape(
+            pred_anchors_,
+            bbox_gts_,
+            anchor_weights_,
+            avg_factor=anchor_total_num)
+        return loss_shape
+
+    def loss_loc_single(self, loc_pred, loc_target, loc_weight,
+                        loc_avg_factor):
+        loss_loc = self.loss_loc(
+            loc_pred.reshape(-1, 1),
+            loc_target.reshape(-1).long(),
+            loc_weight.reshape(-1),
+            avg_factor=loc_avg_factor)
+        return loss_loc
+
+    @force_fp32(
+        apply_to=('cls_scores', 'bbox_preds', 'shape_preds', 'loc_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             shape_preds,
+             loc_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.approx_anchor_generator.num_levels
+
+        device = cls_scores[0].device
+
+        # get loc targets
+        loc_targets, loc_weights, loc_avg_factor = self.ga_loc_targets(
+            gt_bboxes, featmap_sizes)
+
+        # get sampled approxes
+        approxs_list, inside_flag_list = self.get_sampled_approxs(
+            featmap_sizes, img_metas, device=device)
+        # get squares and guided anchors
+        squares_list, guided_anchors_list, _ = self.get_anchors(
+            featmap_sizes, shape_preds, loc_preds, img_metas, device=device)
+
+        # get shape targets
+        shape_targets = self.ga_shape_targets(approxs_list, inside_flag_list,
+                                              squares_list, gt_bboxes,
+                                              img_metas)
+        if shape_targets is None:
+            return None
+        (bbox_anchors_list, bbox_gts_list, anchor_weights_list, anchor_fg_num,
+         anchor_bg_num) = shape_targets
+        anchor_total_num = (
+            anchor_fg_num if not self.ga_sampling else anchor_fg_num +
+            anchor_bg_num)
+
+        # get anchor targets
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+        cls_reg_targets = self.get_targets(
+            guided_anchors_list,
+            inside_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels)
+        if cls_reg_targets is None:
+            return None
+        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
+         num_total_pos, num_total_neg) = cls_reg_targets
+        num_total_samples = (
+            num_total_pos + num_total_neg if self.sampling else num_total_pos)
+
+        # anchor number of multi levels
+        num_level_anchors = [
+            anchors.size(0) for anchors in guided_anchors_list[0]
+        ]
+        # concat all level anchors to a single tensor
+        concat_anchor_list = []
+        for i in range(len(guided_anchors_list)):
+            concat_anchor_list.append(torch.cat(guided_anchors_list[i]))
+        all_anchor_list = images_to_levels(concat_anchor_list,
+                                           num_level_anchors)
+
+        # get classification and bbox regression losses
+        losses_cls, losses_bbox = multi_apply(
+            self.loss_single,
+            cls_scores,
+            bbox_preds,
+            all_anchor_list,
+            labels_list,
+            label_weights_list,
+            bbox_targets_list,
+            bbox_weights_list,
+            num_total_samples=num_total_samples)
+
+        # get anchor location loss
+        losses_loc = []
+        for i in range(len(loc_preds)):
+            loss_loc = self.loss_loc_single(
+                loc_preds[i],
+                loc_targets[i],
+                loc_weights[i],
+                loc_avg_factor=loc_avg_factor)
+            losses_loc.append(loss_loc)
+
+        # get anchor shape loss
+        losses_shape = []
+        for i in range(len(shape_preds)):
+            loss_shape = self.loss_shape_single(
+                shape_preds[i],
+                bbox_anchors_list[i],
+                bbox_gts_list[i],
+                anchor_weights_list[i],
+                anchor_total_num=anchor_total_num)
+            losses_shape.append(loss_shape)
+
+        return dict(
+            loss_cls=losses_cls,
+            loss_bbox=losses_bbox,
+            loss_shape=losses_shape,
+            loss_loc=losses_loc)
+
+    @force_fp32(
+        apply_to=('cls_scores', 'bbox_preds', 'shape_preds', 'loc_preds'))
+    def get_bboxes(self,
+                   cls_scores,
+                   bbox_preds,
+                   shape_preds,
+                   loc_preds,
+                   img_metas,
+                   cfg=None,
+                   rescale=False):
+        assert len(cls_scores) == len(bbox_preds) == len(shape_preds) == len(
+            loc_preds)
+        num_levels = len(cls_scores)
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        device = cls_scores[0].device
+        # get guided anchors
+        _, guided_anchors, loc_masks = self.get_anchors(
+            featmap_sizes,
+            shape_preds,
+            loc_preds,
+            img_metas,
+            use_loc_filter=not self.training,
+            device=device)
+        result_list = []
+        for img_id in range(len(img_metas)):
+            cls_score_list = [
+                cls_scores[i][img_id].detach() for i in range(num_levels)
+            ]
+            bbox_pred_list = [
+                bbox_preds[i][img_id].detach() for i in range(num_levels)
+            ]
+            guided_anchor_list = [
+                guided_anchors[img_id][i].detach() for i in range(num_levels)
+            ]
+            loc_mask_list = [
+                loc_masks[img_id][i].detach() for i in range(num_levels)
+            ]
+            img_shape = img_metas[img_id]['img_shape']
+            scale_factor = img_metas[img_id]['scale_factor']
+            proposals = self._get_bboxes_single(cls_score_list, bbox_pred_list,
+                                                guided_anchor_list,
+                                                loc_mask_list, img_shape,
+                                                scale_factor, cfg, rescale)
+            result_list.append(proposals)
+        return result_list
+
+    def _get_bboxes_single(self,
+                           cls_scores,
+                           bbox_preds,
+                           mlvl_anchors,
+                           mlvl_masks,
+                           img_shape,
+                           scale_factor,
+                           cfg,
+                           rescale=False):
+        cfg = self.test_cfg if cfg is None else cfg
+        assert len(cls_scores) == len(bbox_preds) == len(mlvl_anchors)
+        mlvl_bboxes = []
+        mlvl_scores = []
+        for cls_score, bbox_pred, anchors, mask in zip(cls_scores, bbox_preds,
+                                                       mlvl_anchors,
+                                                       mlvl_masks):
+            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
+            # if no location is kept, end.
+            if mask.sum() == 0:
+                continue
+            # reshape scores and bbox_pred
+            cls_score = cls_score.permute(1, 2,
+                                          0).reshape(-1, self.cls_out_channels)
+            if self.use_sigmoid_cls:
+                scores = cls_score.sigmoid()
+            else:
+                scores = cls_score.softmax(-1)
+            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
+            # filter scores, bbox_pred w.r.t. mask.
+            # anchors are filtered in get_anchors() beforehand.
+            scores = scores[mask, :]
+            bbox_pred = bbox_pred[mask, :]
+            if scores.dim() == 0:
+                anchors = anchors.unsqueeze(0)
+                scores = scores.unsqueeze(0)
+                bbox_pred = bbox_pred.unsqueeze(0)
+            # filter anchors, bbox_pred, scores w.r.t. scores
+            nms_pre = cfg.get('nms_pre', -1)
+            if nms_pre > 0 and scores.shape[0] > nms_pre:
+                if self.use_sigmoid_cls:
+                    max_scores, _ = scores.max(dim=1)
+                else:
+                    # remind that we set FG labels to [0, num_class-1]
+                    # since mmdet v2.0
+                    # BG cat_id: num_class
+                    max_scores, _ = scores[:, :-1].max(dim=1)
+                _, topk_inds = max_scores.topk(nms_pre)
+                anchors = anchors[topk_inds, :]
+                bbox_pred = bbox_pred[topk_inds, :]
+                scores = scores[topk_inds, :]
+            bboxes = self.bbox_coder.decode(
+                anchors, bbox_pred, max_shape=img_shape)
+            mlvl_bboxes.append(bboxes)
+            mlvl_scores.append(scores)
+        mlvl_bboxes = torch.cat(mlvl_bboxes)
+        if rescale:
+            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
+        mlvl_scores = torch.cat(mlvl_scores)
+        if self.use_sigmoid_cls:
+            # Add a dummy background class to the backend when using sigmoid
+            # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
+            # BG cat_id: num_class
+            padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
+            mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
+        # multi class NMS
+        det_bboxes, det_labels = multiclass_nms(mlvl_bboxes, mlvl_scores,
+                                                cfg.score_thr, cfg.nms,
+                                                cfg.max_per_img)
+        return det_bboxes, det_labels
diff --git a/mmdet/models/dense_heads/lad_head.py b/mmdet/models/dense_heads/lad_head.py
new file mode 100644
index 0000000..85273bc
--- /dev/null
+++ b/mmdet/models/dense_heads/lad_head.py
@@ -0,0 +1,232 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from mmcv.runner import force_fp32
+
+from mmdet.core import bbox_overlaps, multi_apply
+from ..builder import HEADS
+from .paa_head import PAAHead, levels_to_images
+
+
+@HEADS.register_module()
+class LADHead(PAAHead):
+    """Label Assignment Head from the paper: `Improving Object Detection by
+    Label Assignment Distillation <https://arxiv.org/pdf/2108.10520.pdf>`_"""
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds'))
+    def get_label_assignment(self,
+                             cls_scores,
+                             bbox_preds,
+                             iou_preds,
+                             gt_bboxes,
+                             gt_labels,
+                             img_metas,
+                             gt_bboxes_ignore=None):
+        """Get label assignment (from teacher).
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level.
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            iou_preds (list[Tensor]): iou_preds for each scale
+                level with shape (N, num_anchors * 1, H, W)
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
+                boxes can be ignored when are computing the loss.
+
+        Returns:
+            tuple: Returns a tuple containing label assignment variables.
+
+                - labels (Tensor): Labels of all anchors, each with
+                    shape (num_anchors,).
+                - labels_weight (Tensor): Label weights of all anchor.
+                    each with shape (num_anchors,).
+                - bboxes_target (Tensor): BBox targets of all anchors.
+                    each with shape (num_anchors, 4).
+                - bboxes_weight (Tensor): BBox weights of all anchors.
+                    each with shape (num_anchors, 4).
+                - pos_inds_flatten (Tensor): Contains all index of positive
+                    sample in all anchor.
+                - pos_anchors (Tensor): Positive anchors.
+                - num_pos (int): Number of positive anchors.
+        """
+
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+
+        device = cls_scores[0].device
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels,
+        )
+        (labels, labels_weight, bboxes_target, bboxes_weight, pos_inds,
+         pos_gt_index) = cls_reg_targets
+        cls_scores = levels_to_images(cls_scores)
+        cls_scores = [
+            item.reshape(-1, self.cls_out_channels) for item in cls_scores
+        ]
+        bbox_preds = levels_to_images(bbox_preds)
+        bbox_preds = [item.reshape(-1, 4) for item in bbox_preds]
+        pos_losses_list, = multi_apply(self.get_pos_loss, anchor_list,
+                                       cls_scores, bbox_preds, labels,
+                                       labels_weight, bboxes_target,
+                                       bboxes_weight, pos_inds)
+
+        with torch.no_grad():
+            reassign_labels, reassign_label_weight, \
+                reassign_bbox_weights, num_pos = multi_apply(
+                    self.paa_reassign,
+                    pos_losses_list,
+                    labels,
+                    labels_weight,
+                    bboxes_weight,
+                    pos_inds,
+                    pos_gt_index,
+                    anchor_list)
+            num_pos = sum(num_pos)
+        # convert all tensor list to a flatten tensor
+        labels = torch.cat(reassign_labels, 0).view(-1)
+        flatten_anchors = torch.cat(
+            [torch.cat(item, 0) for item in anchor_list])
+        labels_weight = torch.cat(reassign_label_weight, 0).view(-1)
+        bboxes_target = torch.cat(bboxes_target,
+                                  0).view(-1, bboxes_target[0].size(-1))
+
+        pos_inds_flatten = ((labels >= 0)
+                            &
+                            (labels < self.num_classes)).nonzero().reshape(-1)
+
+        if num_pos:
+            pos_anchors = flatten_anchors[pos_inds_flatten]
+        else:
+            pos_anchors = None
+
+        label_assignment_results = (labels, labels_weight, bboxes_target,
+                                    bboxes_weight, pos_inds_flatten,
+                                    pos_anchors, num_pos)
+        return label_assignment_results
+
+    def forward_train(self,
+                      x,
+                      label_assignment_results,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels=None,
+                      gt_bboxes_ignore=None,
+                      **kwargs):
+        """Forward train with the available label assignment (student receives
+        from teacher).
+
+        Args:
+            x (list[Tensor]): Features from FPN.
+            label_assignment_results (tuple): As the outputs defined in the
+                function `self.get_label_assignment`.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes (Tensor): Ground truth bboxes of the image,
+                shape (num_gts, 4).
+            gt_labels (Tensor): Ground truth labels of each box,
+                shape (num_gts,).
+            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4).
+
+        Returns:
+            losses: (dict[str, Tensor]): A dictionary of loss components.
+        """
+        outs = self(x)
+        if gt_labels is None:
+            loss_inputs = outs + (gt_bboxes, img_metas)
+        else:
+            loss_inputs = outs + (gt_bboxes, gt_labels, img_metas)
+        losses = self.loss(
+            *loss_inputs,
+            gt_bboxes_ignore=gt_bboxes_ignore,
+            label_assignment_results=label_assignment_results)
+        return losses
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             iou_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None,
+             label_assignment_results=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            iou_preds (list[Tensor]): iou_preds for each scale
+                level with shape (N, num_anchors * 1, H, W)
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
+                boxes can be ignored when are computing the loss.
+            label_assignment_results (tuple): As the outputs defined in the
+                function `self.get_label_assignment`.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss gmm_assignment.
+        """
+
+        (labels, labels_weight, bboxes_target, bboxes_weight, pos_inds_flatten,
+         pos_anchors, num_pos) = label_assignment_results
+
+        cls_scores = levels_to_images(cls_scores)
+        cls_scores = [
+            item.reshape(-1, self.cls_out_channels) for item in cls_scores
+        ]
+        bbox_preds = levels_to_images(bbox_preds)
+        bbox_preds = [item.reshape(-1, 4) for item in bbox_preds]
+        iou_preds = levels_to_images(iou_preds)
+        iou_preds = [item.reshape(-1, 1) for item in iou_preds]
+
+        # convert all tensor list to a flatten tensor
+        cls_scores = torch.cat(cls_scores, 0).view(-1, cls_scores[0].size(-1))
+        bbox_preds = torch.cat(bbox_preds, 0).view(-1, bbox_preds[0].size(-1))
+        iou_preds = torch.cat(iou_preds, 0).view(-1, iou_preds[0].size(-1))
+
+        losses_cls = self.loss_cls(
+            cls_scores,
+            labels,
+            labels_weight,
+            avg_factor=max(num_pos, len(img_metas)))  # avoid num_pos=0
+        if num_pos:
+            pos_bbox_pred = self.bbox_coder.decode(
+                pos_anchors, bbox_preds[pos_inds_flatten])
+            pos_bbox_target = bboxes_target[pos_inds_flatten]
+            iou_target = bbox_overlaps(
+                pos_bbox_pred.detach(), pos_bbox_target, is_aligned=True)
+            losses_iou = self.loss_centerness(
+                iou_preds[pos_inds_flatten],
+                iou_target.unsqueeze(-1),
+                avg_factor=num_pos)
+            losses_bbox = self.loss_bbox(
+                pos_bbox_pred, pos_bbox_target, avg_factor=num_pos)
+
+        else:
+            losses_iou = iou_preds.sum() * 0
+            losses_bbox = bbox_preds.sum() * 0
+
+        return dict(
+            loss_cls=losses_cls, loss_bbox=losses_bbox, loss_iou=losses_iou)
diff --git a/mmdet/models/dense_heads/ld_head.py b/mmdet/models/dense_heads/ld_head.py
new file mode 100644
index 0000000..c5a945f
--- /dev/null
+++ b/mmdet/models/dense_heads/ld_head.py
@@ -0,0 +1,261 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from mmcv.runner import force_fp32
+
+from mmdet.core import bbox_overlaps, multi_apply, reduce_mean
+from ..builder import HEADS, build_loss
+from .gfl_head import GFLHead
+
+
+@HEADS.register_module()
+class LDHead(GFLHead):
+    """Localization distillation Head. (Short description)
+
+    It utilizes the learned bbox distributions to transfer the localization
+    dark knowledge from teacher to student. Original paper: `Localization
+    Distillation for Object Detection. <https://arxiv.org/abs/2102.12252>`_
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        loss_ld (dict): Config of Localization Distillation Loss (LD),
+            T is the temperature for distillation.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 loss_ld=dict(
+                     type='LocalizationDistillationLoss',
+                     loss_weight=0.25,
+                     T=10),
+                 **kwargs):
+
+        super(LDHead, self).__init__(num_classes, in_channels, **kwargs)
+        self.loss_ld = build_loss(loss_ld)
+
+    def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights,
+                    bbox_targets, stride, soft_targets, num_total_samples):
+        """Compute loss of a single scale level.
+
+        Args:
+            anchors (Tensor): Box reference for each scale level with shape
+                (N, num_total_anchors, 4).
+            cls_score (Tensor): Cls and quality joint scores for each scale
+                level has shape (N, num_classes, H, W).
+            bbox_pred (Tensor): Box distribution logits for each scale
+                level with shape (N, 4*(n+1), H, W), n is max value of integral
+                set.
+            labels (Tensor): Labels of each anchors with shape
+                (N, num_total_anchors).
+            label_weights (Tensor): Label weights of each anchor with shape
+                (N, num_total_anchors)
+            bbox_targets (Tensor): BBox regression targets of each anchor
+                weight shape (N, num_total_anchors, 4).
+            stride (tuple): Stride in this scale level.
+            num_total_samples (int): Number of positive samples that is
+                reduced over all GPUs.
+
+        Returns:
+            dict[tuple, Tensor]: Loss components and weight targets.
+        """
+        assert stride[0] == stride[1], 'h stride is not equal to w stride!'
+        anchors = anchors.reshape(-1, 4)
+        cls_score = cls_score.permute(0, 2, 3,
+                                      1).reshape(-1, self.cls_out_channels)
+        bbox_pred = bbox_pred.permute(0, 2, 3,
+                                      1).reshape(-1, 4 * (self.reg_max + 1))
+        soft_targets = soft_targets.permute(0, 2, 3,
+                                            1).reshape(-1,
+                                                       4 * (self.reg_max + 1))
+
+        bbox_targets = bbox_targets.reshape(-1, 4)
+        labels = labels.reshape(-1)
+        label_weights = label_weights.reshape(-1)
+
+        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+        bg_class_ind = self.num_classes
+        pos_inds = ((labels >= 0)
+                    & (labels < bg_class_ind)).nonzero().squeeze(1)
+        score = label_weights.new_zeros(labels.shape)
+
+        if len(pos_inds) > 0:
+            pos_bbox_targets = bbox_targets[pos_inds]
+            pos_bbox_pred = bbox_pred[pos_inds]
+            pos_anchors = anchors[pos_inds]
+            pos_anchor_centers = self.anchor_center(pos_anchors) / stride[0]
+
+            weight_targets = cls_score.detach().sigmoid()
+            weight_targets = weight_targets.max(dim=1)[0][pos_inds]
+            pos_bbox_pred_corners = self.integral(pos_bbox_pred)
+            pos_decode_bbox_pred = self.bbox_coder.decode(
+                pos_anchor_centers, pos_bbox_pred_corners)
+            pos_decode_bbox_targets = pos_bbox_targets / stride[0]
+            score[pos_inds] = bbox_overlaps(
+                pos_decode_bbox_pred.detach(),
+                pos_decode_bbox_targets,
+                is_aligned=True)
+            pred_corners = pos_bbox_pred.reshape(-1, self.reg_max + 1)
+            pos_soft_targets = soft_targets[pos_inds]
+            soft_corners = pos_soft_targets.reshape(-1, self.reg_max + 1)
+
+            target_corners = self.bbox_coder.encode(pos_anchor_centers,
+                                                    pos_decode_bbox_targets,
+                                                    self.reg_max).reshape(-1)
+
+            # regression loss
+            loss_bbox = self.loss_bbox(
+                pos_decode_bbox_pred,
+                pos_decode_bbox_targets,
+                weight=weight_targets,
+                avg_factor=1.0)
+
+            # dfl loss
+            loss_dfl = self.loss_dfl(
+                pred_corners,
+                target_corners,
+                weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
+                avg_factor=4.0)
+
+            # ld loss
+            loss_ld = self.loss_ld(
+                pred_corners,
+                soft_corners,
+                weight=weight_targets[:, None].expand(-1, 4).reshape(-1),
+                avg_factor=4.0)
+
+        else:
+            loss_ld = bbox_pred.sum() * 0
+            loss_bbox = bbox_pred.sum() * 0
+            loss_dfl = bbox_pred.sum() * 0
+            weight_targets = bbox_pred.new_tensor(0)
+
+        # cls (qfl) loss
+        loss_cls = self.loss_cls(
+            cls_score, (labels, score),
+            weight=label_weights,
+            avg_factor=num_total_samples)
+
+        return loss_cls, loss_bbox, loss_dfl, loss_ld, weight_targets.sum()
+
+    def forward_train(self,
+                      x,
+                      out_teacher,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels=None,
+                      gt_bboxes_ignore=None,
+                      proposal_cfg=None,
+                      **kwargs):
+        """
+        Args:
+            x (list[Tensor]): Features from FPN.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes (Tensor): Ground truth bboxes of the image,
+                shape (num_gts, 4).
+            gt_labels (Tensor): Ground truth labels of each box,
+                shape (num_gts,).
+            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4).
+            proposal_cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used
+
+        Returns:
+            tuple[dict, list]: The loss components and proposals of each image.
+
+            - losses (dict[str, Tensor]): A dictionary of loss components.
+            - proposal_list (list[Tensor]): Proposals of each image.
+        """
+        outs = self(x)
+        soft_target = out_teacher[1]
+        if gt_labels is None:
+            loss_inputs = outs + (gt_bboxes, soft_target, img_metas)
+        else:
+            loss_inputs = outs + (gt_bboxes, gt_labels, soft_target, img_metas)
+        losses = self.loss(*loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
+        if proposal_cfg is None:
+            return losses
+        else:
+            proposal_list = self.get_bboxes(*outs, img_metas, cfg=proposal_cfg)
+            return losses, proposal_list
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             soft_target,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Cls and quality scores for each scale
+                level has shape (N, num_classes, H, W).
+            bbox_preds (list[Tensor]): Box distribution logits for each scale
+                level with shape (N, 4*(n+1), H, W), n is max value of integral
+                set.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (list[Tensor] | None): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+
+        device = cls_scores[0].device
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels)
+        if cls_reg_targets is None:
+            return None
+
+        (anchor_list, labels_list, label_weights_list, bbox_targets_list,
+         bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
+
+        num_total_samples = reduce_mean(
+            torch.tensor(num_total_pos, dtype=torch.float,
+                         device=device)).item()
+        num_total_samples = max(num_total_samples, 1.0)
+
+        losses_cls, losses_bbox, losses_dfl, losses_ld, \
+            avg_factor = multi_apply(
+                self.loss_single,
+                anchor_list,
+                cls_scores,
+                bbox_preds,
+                labels_list,
+                label_weights_list,
+                bbox_targets_list,
+                self.prior_generator.strides,
+                soft_target,
+                num_total_samples=num_total_samples)
+
+        avg_factor = sum(avg_factor) + 1e-6
+        avg_factor = reduce_mean(avg_factor).item()
+        losses_bbox = [x / avg_factor for x in losses_bbox]
+        losses_dfl = [x / avg_factor for x in losses_dfl]
+        return dict(
+            loss_cls=losses_cls,
+            loss_bbox=losses_bbox,
+            loss_dfl=losses_dfl,
+            loss_ld=losses_ld)
diff --git a/mmdet/models/dense_heads/mask2former_head.py b/mmdet/models/dense_heads/mask2former_head.py
new file mode 100644
index 0000000..59047bd
--- /dev/null
+++ b/mmdet/models/dense_heads/mask2former_head.py
@@ -0,0 +1,430 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import Conv2d, build_plugin_layer, caffe2_xavier_init
+from mmcv.cnn.bricks.transformer import (build_positional_encoding,
+                                         build_transformer_layer_sequence)
+from mmcv.ops import point_sample
+from mmcv.runner import ModuleList
+
+from mmdet.core import build_assigner, build_sampler, reduce_mean
+from mmdet.models.utils import get_uncertain_point_coords_with_randomness
+from ..builder import HEADS, build_loss
+from .anchor_free_head import AnchorFreeHead
+from .maskformer_head import MaskFormerHead
+
+
+@HEADS.register_module()
+class Mask2FormerHead(MaskFormerHead):
+    """Implements the Mask2Former head.
+
+    See `Masked-attention Mask Transformer for Universal Image
+    Segmentation <https://arxiv.org/pdf/2112.01527>`_ for details.
+
+    Args:
+        in_channels (list[int]): Number of channels in the input feature map.
+        feat_channels (int): Number of channels for features.
+        out_channels (int): Number of channels for output.
+        num_things_classes (int): Number of things.
+        num_stuff_classes (int): Number of stuff.
+        num_queries (int): Number of query in Transformer decoder.
+        pixel_decoder (:obj:`mmcv.ConfigDict` | dict): Config for pixel
+            decoder. Defaults to None.
+        enforce_decoder_input_project (bool, optional): Whether to add
+            a layer to change the embed_dim of tranformer encoder in
+            pixel decoder to the embed_dim of transformer decoder.
+            Defaults to False.
+        transformer_decoder (:obj:`mmcv.ConfigDict` | dict): Config for
+            transformer decoder. Defaults to None.
+        positional_encoding (:obj:`mmcv.ConfigDict` | dict): Config for
+            transformer decoder position encoding. Defaults to None.
+        loss_cls (:obj:`mmcv.ConfigDict` | dict): Config of the classification
+            loss. Defaults to None.
+        loss_mask (:obj:`mmcv.ConfigDict` | dict): Config of the mask loss.
+            Defaults to None.
+        loss_dice (:obj:`mmcv.ConfigDict` | dict): Config of the dice loss.
+            Defaults to None.
+        train_cfg (:obj:`mmcv.ConfigDict` | dict): Training config of
+            Mask2Former head.
+        test_cfg (:obj:`mmcv.ConfigDict` | dict): Testing config of
+            Mask2Former head.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Defaults to None.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 feat_channels,
+                 out_channels,
+                 num_things_classes=80,
+                 num_stuff_classes=53,
+                 num_queries=100,
+                 num_transformer_feat_level=3,
+                 pixel_decoder=None,
+                 enforce_decoder_input_project=False,
+                 transformer_decoder=None,
+                 positional_encoding=None,
+                 loss_cls=None,
+                 loss_mask=None,
+                 loss_dice=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 init_cfg=None,
+                 **kwargs):
+        super(AnchorFreeHead, self).__init__(init_cfg)
+        self.num_things_classes = num_things_classes
+        self.num_stuff_classes = num_stuff_classes
+        self.num_classes = self.num_things_classes + self.num_stuff_classes
+        self.num_queries = num_queries
+        self.num_transformer_feat_level = num_transformer_feat_level
+        self.num_heads = transformer_decoder.transformerlayers.\
+            attn_cfgs.num_heads
+        self.num_transformer_decoder_layers = transformer_decoder.num_layers
+        assert pixel_decoder.encoder.transformerlayers.\
+            attn_cfgs.num_levels == num_transformer_feat_level
+        pixel_decoder_ = copy.deepcopy(pixel_decoder)
+        pixel_decoder_.update(
+            in_channels=in_channels,
+            feat_channels=feat_channels,
+            out_channels=out_channels)
+        self.pixel_decoder = build_plugin_layer(pixel_decoder_)[1]
+        self.transformer_decoder = build_transformer_layer_sequence(
+            transformer_decoder)
+        self.decoder_embed_dims = self.transformer_decoder.embed_dims
+
+        self.decoder_input_projs = ModuleList()
+        # from low resolution to high resolution
+        for _ in range(num_transformer_feat_level):
+            if (self.decoder_embed_dims != feat_channels
+                    or enforce_decoder_input_project):
+                self.decoder_input_projs.append(
+                    Conv2d(
+                        feat_channels, self.decoder_embed_dims, kernel_size=1))
+            else:
+                self.decoder_input_projs.append(nn.Identity())
+        self.decoder_positional_encoding = build_positional_encoding(
+            positional_encoding)
+        self.query_embed = nn.Embedding(self.num_queries, feat_channels)
+        self.query_feat = nn.Embedding(self.num_queries, feat_channels)
+        # from low resolution to high resolution
+        self.level_embed = nn.Embedding(self.num_transformer_feat_level,
+                                        feat_channels)
+
+        self.cls_embed = nn.Linear(feat_channels, self.num_classes + 1)
+        self.mask_embed = nn.Sequential(
+            nn.Linear(feat_channels, feat_channels), nn.ReLU(inplace=True),
+            nn.Linear(feat_channels, feat_channels), nn.ReLU(inplace=True),
+            nn.Linear(feat_channels, out_channels))
+
+        self.test_cfg = test_cfg
+        self.train_cfg = train_cfg
+        if train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            self.sampler = build_sampler(self.train_cfg.sampler, context=self)
+            self.num_points = self.train_cfg.get('num_points', 12544)
+            self.oversample_ratio = self.train_cfg.get('oversample_ratio', 3.0)
+            self.importance_sample_ratio = self.train_cfg.get(
+                'importance_sample_ratio', 0.75)
+
+        self.class_weight = loss_cls.class_weight
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_mask = build_loss(loss_mask)
+        self.loss_dice = build_loss(loss_dice)
+
+    def init_weights(self):
+        for m in self.decoder_input_projs:
+            if isinstance(m, Conv2d):
+                caffe2_xavier_init(m, bias=0)
+
+        self.pixel_decoder.init_weights()
+
+        for p in self.transformer_decoder.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_normal_(p)
+
+    def _get_target_single(self, cls_score, mask_pred, gt_labels, gt_masks,
+                           img_metas):
+        """Compute classification and mask targets for one image.
+
+        Args:
+            cls_score (Tensor): Mask score logits from a single decoder layer
+                for one image. Shape (num_queries, cls_out_channels).
+            mask_pred (Tensor): Mask logits for a single decoder layer for one
+                image. Shape (num_queries, h, w).
+            gt_labels (Tensor): Ground truth class indices for one image with
+                shape (num_gts, ).
+            gt_masks (Tensor): Ground truth mask for each image, each with
+                shape (num_gts, h, w).
+            img_metas (dict): Image informtation.
+
+        Returns:
+            tuple[Tensor]: A tuple containing the following for one image.
+
+                - labels (Tensor): Labels of each image. \
+                    shape (num_queries, ).
+                - label_weights (Tensor): Label weights of each image. \
+                    shape (num_queries, ).
+                - mask_targets (Tensor): Mask targets of each image. \
+                    shape (num_queries, h, w).
+                - mask_weights (Tensor): Mask weights of each image. \
+                    shape (num_queries, ).
+                - pos_inds (Tensor): Sampled positive indices for each \
+                    image.
+                - neg_inds (Tensor): Sampled negative indices for each \
+                    image.
+        """
+        # sample points
+        num_queries = cls_score.shape[0]
+        num_gts = gt_labels.shape[0]
+
+        point_coords = torch.rand((1, self.num_points, 2),
+                                  device=cls_score.device)
+        # shape (num_queries, num_points)
+        mask_points_pred = point_sample(
+            mask_pred.unsqueeze(1), point_coords.repeat(num_queries, 1,
+                                                        1)).squeeze(1)
+        # shape (num_gts, num_points)
+        gt_points_masks = point_sample(
+            gt_masks.unsqueeze(1).float(), point_coords.repeat(num_gts, 1,
+                                                               1)).squeeze(1)
+
+        # assign and sample
+        assign_result = self.assigner.assign(cls_score, mask_points_pred,
+                                             gt_labels, gt_points_masks,
+                                             img_metas)
+        sampling_result = self.sampler.sample(assign_result, mask_pred,
+                                              gt_masks)
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+
+        # label target
+        labels = gt_labels.new_full((self.num_queries, ),
+                                    self.num_classes,
+                                    dtype=torch.long)
+        labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds]
+        label_weights = gt_labels.new_ones((self.num_queries, ))
+
+        # mask target
+        mask_targets = gt_masks[sampling_result.pos_assigned_gt_inds]
+        mask_weights = mask_pred.new_zeros((self.num_queries, ))
+        mask_weights[pos_inds] = 1.0
+
+        return (labels, label_weights, mask_targets, mask_weights, pos_inds,
+                neg_inds)
+
+    def loss_single(self, cls_scores, mask_preds, gt_labels_list,
+                    gt_masks_list, img_metas):
+        """Loss function for outputs from a single decoder layer.
+
+        Args:
+            cls_scores (Tensor): Mask score logits from a single decoder layer
+                for all images. Shape (batch_size, num_queries,
+                cls_out_channels). Note `cls_out_channels` should includes
+                background.
+            mask_preds (Tensor): Mask logits for a pixel decoder for all
+                images. Shape (batch_size, num_queries, h, w).
+            gt_labels_list (list[Tensor]): Ground truth class indices for each
+                image, each with shape (num_gts, ).
+            gt_masks_list (list[Tensor]): Ground truth mask for each image,
+                each with shape (num_gts, h, w).
+            img_metas (list[dict]): List of image meta information.
+
+        Returns:
+            tuple[Tensor]: Loss components for outputs from a single \
+                decoder layer.
+        """
+        num_imgs = cls_scores.size(0)
+        cls_scores_list = [cls_scores[i] for i in range(num_imgs)]
+        mask_preds_list = [mask_preds[i] for i in range(num_imgs)]
+        (labels_list, label_weights_list, mask_targets_list, mask_weights_list,
+         num_total_pos,
+         num_total_neg) = self.get_targets(cls_scores_list, mask_preds_list,
+                                           gt_labels_list, gt_masks_list,
+                                           img_metas)
+        # shape (batch_size, num_queries)
+        labels = torch.stack(labels_list, dim=0)
+        # shape (batch_size, num_queries)
+        label_weights = torch.stack(label_weights_list, dim=0)
+        # shape (num_total_gts, h, w)
+        mask_targets = torch.cat(mask_targets_list, dim=0)
+        # shape (batch_size, num_queries)
+        mask_weights = torch.stack(mask_weights_list, dim=0)
+
+        # classfication loss
+        # shape (batch_size * num_queries, )
+        cls_scores = cls_scores.flatten(0, 1)
+        labels = labels.flatten(0, 1)
+        label_weights = label_weights.flatten(0, 1)
+
+        class_weight = cls_scores.new_tensor(self.class_weight)
+        loss_cls = self.loss_cls(
+            cls_scores,
+            labels,
+            label_weights,
+            avg_factor=class_weight[labels].sum())
+
+        num_total_masks = reduce_mean(cls_scores.new_tensor([num_total_pos]))
+        num_total_masks = max(num_total_masks, 1)
+
+        # extract positive ones
+        # shape (batch_size, num_queries, h, w) -> (num_total_gts, h, w)
+        mask_preds = mask_preds[mask_weights > 0]
+
+        if mask_targets.shape[0] == 0:
+            # zero match
+            loss_dice = mask_preds.sum()
+            loss_mask = mask_preds.sum()
+            return loss_cls, loss_mask, loss_dice
+
+        with torch.no_grad():
+            points_coords = get_uncertain_point_coords_with_randomness(
+                mask_preds.unsqueeze(1), None, self.num_points,
+                self.oversample_ratio, self.importance_sample_ratio)
+            # shape (num_total_gts, h, w) -> (num_total_gts, num_points)
+            mask_point_targets = point_sample(
+                mask_targets.unsqueeze(1).float(), points_coords).squeeze(1)
+        # shape (num_queries, h, w) -> (num_queries, num_points)
+        mask_point_preds = point_sample(
+            mask_preds.unsqueeze(1), points_coords).squeeze(1)
+
+        # dice loss
+        loss_dice = self.loss_dice(
+            mask_point_preds, mask_point_targets, avg_factor=num_total_masks)
+
+        # mask loss
+        # shape (num_queries, num_points) -> (num_queries * num_points, )
+        mask_point_preds = mask_point_preds.reshape(-1)
+        # shape (num_total_gts, num_points) -> (num_total_gts * num_points, )
+        mask_point_targets = mask_point_targets.reshape(-1)
+        loss_mask = self.loss_mask(
+            mask_point_preds,
+            mask_point_targets,
+            avg_factor=num_total_masks * self.num_points)
+
+        return loss_cls, loss_mask, loss_dice
+
+    def forward_head(self, decoder_out, mask_feature, attn_mask_target_size):
+        """Forward for head part which is called after every decoder layer.
+
+        Args:
+            decoder_out (Tensor): in shape (num_queries, batch_size, c).
+            mask_feature (Tensor): in shape (batch_size, c, h, w).
+            attn_mask_target_size (tuple[int, int]): target attention
+                mask size.
+
+        Returns:
+            tuple: A tuple contain three elements.
+
+            - cls_pred (Tensor): Classification scores in shape \
+                (batch_size, num_queries, cls_out_channels). \
+                Note `cls_out_channels` should includes background.
+            - mask_pred (Tensor): Mask scores in shape \
+                (batch_size, num_queries,h, w).
+            - attn_mask (Tensor): Attention mask in shape \
+                (batch_size * num_heads, num_queries, h, w).
+        """
+        decoder_out = self.transformer_decoder.post_norm(decoder_out)
+        decoder_out = decoder_out.transpose(0, 1)
+        # shape (batch_size, num_queries, c)
+        cls_pred = self.cls_embed(decoder_out)
+        # shape (batch_size, num_queries, c)
+        mask_embed = self.mask_embed(decoder_out)
+        # shape (batch_size, num_queries, h, w)
+        mask_pred = torch.einsum('bqc,bchw->bqhw', mask_embed, mask_feature)
+        attn_mask = F.interpolate(
+            mask_pred,
+            attn_mask_target_size,
+            mode='bilinear',
+            align_corners=False)
+        # shape (batch_size, num_queries, h, w) ->
+        #   (batch_size * num_head, num_queries, h*w)
+        attn_mask = attn_mask.flatten(2).unsqueeze(1).repeat(
+            (1, self.num_heads, 1, 1)).flatten(0, 1)
+        attn_mask = attn_mask.sigmoid() < 0.5
+        attn_mask = attn_mask.detach()
+
+        return cls_pred, mask_pred, attn_mask
+
+    def forward(self, feats, img_metas):
+        """Forward function.
+
+        Args:
+            feats (list[Tensor]): Multi scale Features from the
+                upstream network, each is a 4D-tensor.
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            tuple: A tuple contains two elements.
+
+            - cls_pred_list (list[Tensor)]: Classification logits \
+                for each decoder layer. Each is a 3D-tensor with shape \
+                (batch_size, num_queries, cls_out_channels). \
+                Note `cls_out_channels` should includes background.
+            - mask_pred_list (list[Tensor]): Mask logits for each \
+                decoder layer. Each with shape (batch_size, num_queries, \
+                 h, w).
+        """
+        batch_size = len(img_metas)
+        mask_features, multi_scale_memorys = self.pixel_decoder(feats)
+        # multi_scale_memorys (from low resolution to high resolution)
+        decoder_inputs = []
+        decoder_positional_encodings = []
+        for i in range(self.num_transformer_feat_level):
+            decoder_input = self.decoder_input_projs[i](multi_scale_memorys[i])
+            # shape (batch_size, c, h, w) -> (h*w, batch_size, c)
+            decoder_input = decoder_input.flatten(2).permute(2, 0, 1)
+            level_embed = self.level_embed.weight[i].view(1, 1, -1)
+            decoder_input = decoder_input + level_embed
+            # shape (batch_size, c, h, w) -> (h*w, batch_size, c)
+            mask = decoder_input.new_zeros(
+                (batch_size, ) + multi_scale_memorys[i].shape[-2:],
+                dtype=torch.bool)
+            decoder_positional_encoding = self.decoder_positional_encoding(
+                mask)
+            decoder_positional_encoding = decoder_positional_encoding.flatten(
+                2).permute(2, 0, 1)
+            decoder_inputs.append(decoder_input)
+            decoder_positional_encodings.append(decoder_positional_encoding)
+        # shape (num_queries, c) -> (num_queries, batch_size, c)
+        query_feat = self.query_feat.weight.unsqueeze(1).repeat(
+            (1, batch_size, 1))
+        query_embed = self.query_embed.weight.unsqueeze(1).repeat(
+            (1, batch_size, 1))
+
+        cls_pred_list = []
+        mask_pred_list = []
+        cls_pred, mask_pred, attn_mask = self.forward_head(
+            query_feat, mask_features, multi_scale_memorys[0].shape[-2:])
+        cls_pred_list.append(cls_pred)
+        mask_pred_list.append(mask_pred)
+
+        for i in range(self.num_transformer_decoder_layers):
+            level_idx = i % self.num_transformer_feat_level
+            # if a mask is all True(all background), then set it all False.
+            attn_mask[torch.where(
+                attn_mask.sum(-1) == attn_mask.shape[-1])] = False
+
+            # cross_attn + self_attn
+            layer = self.transformer_decoder.layers[i]
+            attn_masks = [attn_mask, None]
+            query_feat = layer(
+                query=query_feat,
+                key=decoder_inputs[level_idx],
+                value=decoder_inputs[level_idx],
+                query_pos=query_embed,
+                key_pos=decoder_positional_encodings[level_idx],
+                attn_masks=attn_masks,
+                query_key_padding_mask=None,
+                # here we do not apply masking on padded region
+                key_padding_mask=None)
+            cls_pred, mask_pred, attn_mask = self.forward_head(
+                query_feat, mask_features, multi_scale_memorys[
+                    (i + 1) % self.num_transformer_feat_level].shape[-2:])
+
+            cls_pred_list.append(cls_pred)
+            mask_pred_list.append(mask_pred)
+
+        return cls_pred_list, mask_pred_list
diff --git a/mmdet/models/dense_heads/maskformer_head.py b/mmdet/models/dense_heads/maskformer_head.py
new file mode 100644
index 0000000..566dc07
--- /dev/null
+++ b/mmdet/models/dense_heads/maskformer_head.py
@@ -0,0 +1,556 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import Conv2d, build_plugin_layer, caffe2_xavier_init
+from mmcv.cnn.bricks.transformer import (build_positional_encoding,
+                                         build_transformer_layer_sequence)
+from mmcv.runner import force_fp32
+
+from mmdet.core import build_assigner, build_sampler, multi_apply, reduce_mean
+from mmdet.models.utils import preprocess_panoptic_gt
+from ..builder import HEADS, build_loss
+from .anchor_free_head import AnchorFreeHead
+
+
+@HEADS.register_module()
+class MaskFormerHead(AnchorFreeHead):
+    """Implements the MaskFormer head.
+
+    See `Per-Pixel Classification is Not All You Need for Semantic
+    Segmentation <https://arxiv.org/pdf/2107.06278>`_ for details.
+
+    Args:
+        in_channels (list[int]): Number of channels in the input feature map.
+        feat_channels (int): Number of channels for feature.
+        out_channels (int): Number of channels for output.
+        num_things_classes (int): Number of things.
+        num_stuff_classes (int): Number of stuff.
+        num_queries (int): Number of query in Transformer.
+        pixel_decoder (:obj:`mmcv.ConfigDict` | dict): Config for pixel
+            decoder. Defaults to None.
+        enforce_decoder_input_project (bool, optional): Whether to add a layer
+            to change the embed_dim of tranformer encoder in pixel decoder to
+            the embed_dim of transformer decoder. Defaults to False.
+        transformer_decoder (:obj:`mmcv.ConfigDict` | dict): Config for
+            transformer decoder. Defaults to None.
+        positional_encoding (:obj:`mmcv.ConfigDict` | dict): Config for
+            transformer decoder position encoding. Defaults to None.
+        loss_cls (:obj:`mmcv.ConfigDict` | dict): Config of the classification
+            loss. Defaults to `CrossEntropyLoss`.
+        loss_mask (:obj:`mmcv.ConfigDict` | dict): Config of the mask loss.
+            Defaults to `FocalLoss`.
+        loss_dice (:obj:`mmcv.ConfigDict` | dict): Config of the dice loss.
+            Defaults to `DiceLoss`.
+        train_cfg (:obj:`mmcv.ConfigDict` | dict): Training config of
+            Maskformer head.
+        test_cfg (:obj:`mmcv.ConfigDict` | dict): Testing config of Maskformer
+            head.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Defaults to None.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 feat_channels,
+                 out_channels,
+                 num_things_classes=80,
+                 num_stuff_classes=53,
+                 num_queries=100,
+                 pixel_decoder=None,
+                 enforce_decoder_input_project=False,
+                 transformer_decoder=None,
+                 positional_encoding=None,
+                 loss_cls=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=False,
+                     loss_weight=1.0,
+                     class_weight=[1.0] * 133 + [0.1]),
+                 loss_mask=dict(
+                     type='FocalLoss',
+                     use_sigmoid=True,
+                     gamma=2.0,
+                     alpha=0.25,
+                     loss_weight=20.0),
+                 loss_dice=dict(
+                     type='DiceLoss',
+                     use_sigmoid=True,
+                     activate=True,
+                     naive_dice=True,
+                     loss_weight=1.0),
+                 train_cfg=None,
+                 test_cfg=None,
+                 init_cfg=None,
+                 **kwargs):
+        super(AnchorFreeHead, self).__init__(init_cfg)
+        self.num_things_classes = num_things_classes
+        self.num_stuff_classes = num_stuff_classes
+        self.num_classes = self.num_things_classes + self.num_stuff_classes
+        self.num_queries = num_queries
+
+        pixel_decoder.update(
+            in_channels=in_channels,
+            feat_channels=feat_channels,
+            out_channels=out_channels)
+        self.pixel_decoder = build_plugin_layer(pixel_decoder)[1]
+        self.transformer_decoder = build_transformer_layer_sequence(
+            transformer_decoder)
+        self.decoder_embed_dims = self.transformer_decoder.embed_dims
+        pixel_decoder_type = pixel_decoder.get('type')
+        if pixel_decoder_type == 'PixelDecoder' and (
+                self.decoder_embed_dims != in_channels[-1]
+                or enforce_decoder_input_project):
+            self.decoder_input_proj = Conv2d(
+                in_channels[-1], self.decoder_embed_dims, kernel_size=1)
+        else:
+            self.decoder_input_proj = nn.Identity()
+        self.decoder_pe = build_positional_encoding(positional_encoding)
+        self.query_embed = nn.Embedding(self.num_queries, out_channels)
+
+        self.cls_embed = nn.Linear(feat_channels, self.num_classes + 1)
+        self.mask_embed = nn.Sequential(
+            nn.Linear(feat_channels, feat_channels), nn.ReLU(inplace=True),
+            nn.Linear(feat_channels, feat_channels), nn.ReLU(inplace=True),
+            nn.Linear(feat_channels, out_channels))
+
+        self.test_cfg = test_cfg
+        self.train_cfg = train_cfg
+        if train_cfg:
+            self.assigner = build_assigner(train_cfg.get('assigner', None))
+            self.sampler = build_sampler(
+                train_cfg.get('sampler', None), context=self)
+
+        self.class_weight = loss_cls.get('class_weight', None)
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_mask = build_loss(loss_mask)
+        self.loss_dice = build_loss(loss_dice)
+
+    def init_weights(self):
+        if isinstance(self.decoder_input_proj, Conv2d):
+            caffe2_xavier_init(self.decoder_input_proj, bias=0)
+
+        self.pixel_decoder.init_weights()
+
+        for p in self.transformer_decoder.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+
+    def preprocess_gt(self, gt_labels_list, gt_masks_list, gt_semantic_segs,
+                      img_metas):
+        """Preprocess the ground truth for all images.
+
+        Args:
+            gt_labels_list (list[Tensor]): Each is ground truth
+                labels of each bbox, with shape (num_gts, ).
+            gt_masks_list (list[BitmapMasks]): Each is ground truth
+                masks of each instances of a image, shape
+                (num_gts, h, w).
+            gt_semantic_seg (Tensor | None): Ground truth of semantic
+                segmentation with the shape (batch_size, n, h, w).
+                [0, num_thing_class - 1] means things,
+                [num_thing_class, num_class-1] means stuff,
+                255 means VOID. It's None when training instance segmentation.
+            img_metas (list[dict]): List of image meta information.
+
+        Returns:
+            tuple: a tuple containing the following targets.
+                - labels (list[Tensor]): Ground truth class indices\
+                    for all images. Each with shape (n, ), n is the sum of\
+                    number of stuff type and number of instance in a image.
+                - masks (list[Tensor]): Ground truth mask for each\
+                    image, each with shape (n, h, w).
+        """
+        num_things_list = [self.num_things_classes] * len(gt_labels_list)
+        num_stuff_list = [self.num_stuff_classes] * len(gt_labels_list)
+        if gt_semantic_segs is None:
+            gt_semantic_segs = [None] * len(gt_labels_list)
+
+        targets = multi_apply(preprocess_panoptic_gt, gt_labels_list,
+                              gt_masks_list, gt_semantic_segs, num_things_list,
+                              num_stuff_list, img_metas)
+        labels, masks = targets
+        return labels, masks
+
+    def get_targets(self, cls_scores_list, mask_preds_list, gt_labels_list,
+                    gt_masks_list, img_metas):
+        """Compute classification and mask targets for all images for a decoder
+        layer.
+
+        Args:
+            cls_scores_list (list[Tensor]): Mask score logits from a single
+                decoder layer for all images. Each with shape (num_queries,
+                cls_out_channels).
+            mask_preds_list (list[Tensor]): Mask logits from a single decoder
+                layer for all images. Each with shape (num_queries, h, w).
+            gt_labels_list (list[Tensor]): Ground truth class indices for all
+                images. Each with shape (n, ), n is the sum of number of stuff
+                type and number of instance in a image.
+            gt_masks_list (list[Tensor]): Ground truth mask for each image,
+                each with shape (n, h, w).
+            img_metas (list[dict]): List of image meta information.
+
+        Returns:
+            tuple[list[Tensor]]: a tuple containing the following targets.
+                - labels_list (list[Tensor]): Labels of all images.\
+                    Each with shape (num_queries, ).
+                - label_weights_list (list[Tensor]): Label weights\
+                    of all images. Each with shape (num_queries, ).
+                - mask_targets_list (list[Tensor]): Mask targets of\
+                    all images. Each with shape (num_queries, h, w).
+                - mask_weights_list (list[Tensor]): Mask weights of\
+                    all images. Each with shape (num_queries, ).
+                - num_total_pos (int): Number of positive samples in\
+                    all images.
+                - num_total_neg (int): Number of negative samples in\
+                    all images.
+        """
+        (labels_list, label_weights_list, mask_targets_list, mask_weights_list,
+         pos_inds_list,
+         neg_inds_list) = multi_apply(self._get_target_single, cls_scores_list,
+                                      mask_preds_list, gt_labels_list,
+                                      gt_masks_list, img_metas)
+
+        num_total_pos = sum((inds.numel() for inds in pos_inds_list))
+        num_total_neg = sum((inds.numel() for inds in neg_inds_list))
+        return (labels_list, label_weights_list, mask_targets_list,
+                mask_weights_list, num_total_pos, num_total_neg)
+
+    def _get_target_single(self, cls_score, mask_pred, gt_labels, gt_masks,
+                           img_metas):
+        """Compute classification and mask targets for one image.
+
+        Args:
+            cls_score (Tensor): Mask score logits from a single decoder layer
+                for one image. Shape (num_queries, cls_out_channels).
+            mask_pred (Tensor): Mask logits for a single decoder layer for one
+                image. Shape (num_queries, h, w).
+            gt_labels (Tensor): Ground truth class indices for one image with
+                shape (n, ). n is the sum of number of stuff type and number
+                of instance in a image.
+            gt_masks (Tensor): Ground truth mask for each image, each with
+                shape (n, h, w).
+            img_metas (dict): Image informtation.
+
+        Returns:
+            tuple[Tensor]: a tuple containing the following for one image.
+                - labels (Tensor): Labels of each image.
+                    shape (num_queries, ).
+                - label_weights (Tensor): Label weights of each image.
+                    shape (num_queries, ).
+                - mask_targets (Tensor): Mask targets of each image.
+                    shape (num_queries, h, w).
+                - mask_weights (Tensor): Mask weights of each image.
+                    shape (num_queries, ).
+                - pos_inds (Tensor): Sampled positive indices for each image.
+                - neg_inds (Tensor): Sampled negative indices for each image.
+        """
+        target_shape = mask_pred.shape[-2:]
+        if gt_masks.shape[0] > 0:
+            gt_masks_downsampled = F.interpolate(
+                gt_masks.unsqueeze(1).float(), target_shape,
+                mode='nearest').squeeze(1).long()
+        else:
+            gt_masks_downsampled = gt_masks
+
+        # assign and sample
+        assign_result = self.assigner.assign(cls_score, mask_pred, gt_labels,
+                                             gt_masks_downsampled, img_metas)
+        sampling_result = self.sampler.sample(assign_result, mask_pred,
+                                              gt_masks)
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+
+        # label target
+        labels = gt_labels.new_full((self.num_queries, ),
+                                    self.num_classes,
+                                    dtype=torch.long)
+        labels[pos_inds] = gt_labels[sampling_result.pos_assigned_gt_inds]
+        label_weights = gt_labels.new_ones(self.num_queries)
+
+        # mask target
+        mask_targets = gt_masks[sampling_result.pos_assigned_gt_inds]
+        mask_weights = mask_pred.new_zeros((self.num_queries, ))
+        mask_weights[pos_inds] = 1.0
+
+        return (labels, label_weights, mask_targets, mask_weights, pos_inds,
+                neg_inds)
+
+    @force_fp32(apply_to=('all_cls_scores', 'all_mask_preds'))
+    def loss(self, all_cls_scores, all_mask_preds, gt_labels_list,
+             gt_masks_list, img_metas):
+        """Loss function.
+
+        Args:
+            all_cls_scores (Tensor): Classification scores for all decoder
+                layers with shape (num_decoder, batch_size, num_queries,
+                cls_out_channels). Note `cls_out_channels` should includes
+                background.
+            all_mask_preds (Tensor): Mask scores for all decoder layers with
+                shape (num_decoder, batch_size, num_queries, h, w).
+            gt_labels_list (list[Tensor]): Ground truth class indices for each
+                image with shape (n, ). n is the sum of number of stuff type
+                and number of instance in a image.
+            gt_masks_list (list[Tensor]): Ground truth mask for each image with
+                shape (n, h, w).
+            img_metas (list[dict]): List of image meta information.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        num_dec_layers = len(all_cls_scores)
+        all_gt_labels_list = [gt_labels_list for _ in range(num_dec_layers)]
+        all_gt_masks_list = [gt_masks_list for _ in range(num_dec_layers)]
+        img_metas_list = [img_metas for _ in range(num_dec_layers)]
+        losses_cls, losses_mask, losses_dice = multi_apply(
+            self.loss_single, all_cls_scores, all_mask_preds,
+            all_gt_labels_list, all_gt_masks_list, img_metas_list)
+
+        loss_dict = dict()
+        # loss from the last decoder layer
+        loss_dict['loss_cls'] = losses_cls[-1]
+        loss_dict['loss_mask'] = losses_mask[-1]
+        loss_dict['loss_dice'] = losses_dice[-1]
+        # loss from other decoder layers
+        num_dec_layer = 0
+        for loss_cls_i, loss_mask_i, loss_dice_i in zip(
+                losses_cls[:-1], losses_mask[:-1], losses_dice[:-1]):
+            loss_dict[f'd{num_dec_layer}.loss_cls'] = loss_cls_i
+            loss_dict[f'd{num_dec_layer}.loss_mask'] = loss_mask_i
+            loss_dict[f'd{num_dec_layer}.loss_dice'] = loss_dice_i
+            num_dec_layer += 1
+        return loss_dict
+
+    def loss_single(self, cls_scores, mask_preds, gt_labels_list,
+                    gt_masks_list, img_metas):
+        """Loss function for outputs from a single decoder layer.
+
+        Args:
+            cls_scores (Tensor): Mask score logits from a single decoder layer
+                for all images. Shape (batch_size, num_queries,
+                cls_out_channels). Note `cls_out_channels` should includes
+                background.
+            mask_preds (Tensor): Mask logits for a pixel decoder for all
+                images. Shape (batch_size, num_queries, h, w).
+            gt_labels_list (list[Tensor]): Ground truth class indices for each
+                image, each with shape (n, ). n is the sum of number of stuff
+                types and number of instances in a image.
+            gt_masks_list (list[Tensor]): Ground truth mask for each image,
+                each with shape (n, h, w).
+            img_metas (list[dict]): List of image meta information.
+
+        Returns:
+            tuple[Tensor]: Loss components for outputs from a single decoder\
+                layer.
+        """
+        num_imgs = cls_scores.size(0)
+        cls_scores_list = [cls_scores[i] for i in range(num_imgs)]
+        mask_preds_list = [mask_preds[i] for i in range(num_imgs)]
+
+        (labels_list, label_weights_list, mask_targets_list, mask_weights_list,
+         num_total_pos,
+         num_total_neg) = self.get_targets(cls_scores_list, mask_preds_list,
+                                           gt_labels_list, gt_masks_list,
+                                           img_metas)
+        # shape (batch_size, num_queries)
+        labels = torch.stack(labels_list, dim=0)
+        # shape (batch_size, num_queries)
+        label_weights = torch.stack(label_weights_list, dim=0)
+        # shape (num_total_gts, h, w)
+        mask_targets = torch.cat(mask_targets_list, dim=0)
+        # shape (batch_size, num_queries)
+        mask_weights = torch.stack(mask_weights_list, dim=0)
+
+        # classfication loss
+        # shape (batch_size * num_queries, )
+        cls_scores = cls_scores.flatten(0, 1)
+        labels = labels.flatten(0, 1)
+        label_weights = label_weights.flatten(0, 1)
+
+        class_weight = cls_scores.new_tensor(self.class_weight)
+        loss_cls = self.loss_cls(
+            cls_scores,
+            labels,
+            label_weights,
+            avg_factor=class_weight[labels].sum())
+
+        num_total_masks = reduce_mean(cls_scores.new_tensor([num_total_pos]))
+        num_total_masks = max(num_total_masks, 1)
+
+        # extract positive ones
+        # shape (batch_size, num_queries, h, w) -> (num_total_gts, h, w)
+        mask_preds = mask_preds[mask_weights > 0]
+        target_shape = mask_targets.shape[-2:]
+
+        if mask_targets.shape[0] == 0:
+            # zero match
+            loss_dice = mask_preds.sum()
+            loss_mask = mask_preds.sum()
+            return loss_cls, loss_mask, loss_dice
+
+        # upsample to shape of target
+        # shape (num_total_gts, h, w)
+        mask_preds = F.interpolate(
+            mask_preds.unsqueeze(1),
+            target_shape,
+            mode='bilinear',
+            align_corners=False).squeeze(1)
+
+        # dice loss
+        loss_dice = self.loss_dice(
+            mask_preds, mask_targets, avg_factor=num_total_masks)
+
+        # mask loss
+        # FocalLoss support input of shape (n, num_class)
+        h, w = mask_preds.shape[-2:]
+        # shape (num_total_gts, h, w) -> (num_total_gts * h * w, 1)
+        mask_preds = mask_preds.reshape(-1, 1)
+        # shape (num_total_gts, h, w) -> (num_total_gts * h * w)
+        mask_targets = mask_targets.reshape(-1)
+        # target is (1 - mask_targets) !!!
+        loss_mask = self.loss_mask(
+            mask_preds, 1 - mask_targets, avg_factor=num_total_masks * h * w)
+
+        return loss_cls, loss_mask, loss_dice
+
+    def forward(self, feats, img_metas):
+        """Forward function.
+
+        Args:
+            feats (list[Tensor]): Features from the upstream network, each
+                is a 4D-tensor.
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            tuple: a tuple contains two elements.
+                - all_cls_scores (Tensor): Classification scores for each\
+                    scale level. Each is a 4D-tensor with shape\
+                    (num_decoder, batch_size, num_queries, cls_out_channels).\
+                    Note `cls_out_channels` should includes background.
+                - all_mask_preds (Tensor): Mask scores for each decoder\
+                    layer. Each with shape (num_decoder, batch_size,\
+                    num_queries, h, w).
+        """
+        batch_size = len(img_metas)
+        input_img_h, input_img_w = img_metas[0]['batch_input_shape']
+        padding_mask = feats[-1].new_ones(
+            (batch_size, input_img_h, input_img_w), dtype=torch.float32)
+        for i in range(batch_size):
+            img_h, img_w, _ = img_metas[i]['img_shape']
+            padding_mask[i, :img_h, :img_w] = 0
+        padding_mask = F.interpolate(
+            padding_mask.unsqueeze(1),
+            size=feats[-1].shape[-2:],
+            mode='nearest').to(torch.bool).squeeze(1)
+        # when backbone is swin, memory is output of last stage of swin.
+        # when backbone is r50, memory is output of tranformer encoder.
+        mask_features, memory = self.pixel_decoder(feats, img_metas)
+        pos_embed = self.decoder_pe(padding_mask)
+        memory = self.decoder_input_proj(memory)
+        # shape (batch_size, c, h, w) -> (h*w, batch_size, c)
+        memory = memory.flatten(2).permute(2, 0, 1)
+        pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
+        # shape (batch_size, h * w)
+        padding_mask = padding_mask.flatten(1)
+        # shape = (num_queries, embed_dims)
+        query_embed = self.query_embed.weight
+        # shape = (num_queries, batch_size, embed_dims)
+        query_embed = query_embed.unsqueeze(1).repeat(1, batch_size, 1)
+        target = torch.zeros_like(query_embed)
+        # shape (num_decoder, num_queries, batch_size, embed_dims)
+        out_dec = self.transformer_decoder(
+            query=target,
+            key=memory,
+            value=memory,
+            key_pos=pos_embed,
+            query_pos=query_embed,
+            key_padding_mask=padding_mask)
+        # shape (num_decoder, batch_size, num_queries, embed_dims)
+        out_dec = out_dec.transpose(1, 2)
+
+        # cls_scores
+        all_cls_scores = self.cls_embed(out_dec)
+
+        # mask_preds
+        mask_embed = self.mask_embed(out_dec)
+        all_mask_preds = torch.einsum('lbqc,bchw->lbqhw', mask_embed,
+                                      mask_features)
+
+        return all_cls_scores, all_mask_preds
+
+    def forward_train(self,
+                      feats,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_masks,
+                      gt_semantic_seg,
+                      gt_bboxes_ignore=None):
+        """Forward function for training mode.
+
+        Args:
+            feats (list[Tensor]): Multi-level features from the upstream
+                network, each is a 4D-tensor.
+            img_metas (list[Dict]): List of image information.
+            gt_bboxes (list[Tensor]): Each element is ground truth bboxes of
+                the image, shape (num_gts, 4). Not used here.
+            gt_labels (list[Tensor]): Each element is ground truth labels of
+                each box, shape (num_gts,).
+            gt_masks (list[BitmapMasks]): Each element is masks of instances
+                of a image, shape (num_gts, h, w).
+            gt_semantic_seg (list[tensor] | None): Each element is the ground
+                truth of semantic segmentation with the shape (N, H, W).
+                [0, num_thing_class - 1] means things,
+                [num_thing_class, num_class-1] means stuff,
+                255 means VOID. It's None when training instance segmentation.
+            gt_bboxes_ignore (list[Tensor]): Ground truth bboxes to be
+                ignored. Defaults to None.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        # not consider ignoring bboxes
+        assert gt_bboxes_ignore is None
+
+        # forward
+        all_cls_scores, all_mask_preds = self(feats, img_metas)
+
+        # preprocess ground truth
+        gt_labels, gt_masks = self.preprocess_gt(gt_labels, gt_masks,
+                                                 gt_semantic_seg, img_metas)
+
+        # loss
+        losses = self.loss(all_cls_scores, all_mask_preds, gt_labels, gt_masks,
+                           img_metas)
+
+        return losses
+
+    def simple_test(self, feats, img_metas, **kwargs):
+        """Test without augmentaton.
+
+        Args:
+            feats (list[Tensor]): Multi-level features from the
+                upstream network, each is a 4D-tensor.
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            tuple: A tuple contains two tensors.
+
+            - mask_cls_results (Tensor): Mask classification logits,\
+                shape (batch_size, num_queries, cls_out_channels).
+                Note `cls_out_channels` should includes background.
+            - mask_pred_results (Tensor): Mask logits, shape \
+                (batch_size, num_queries, h, w).
+        """
+        all_cls_scores, all_mask_preds = self(feats, img_metas)
+        mask_cls_results = all_cls_scores[-1]
+        mask_pred_results = all_mask_preds[-1]
+
+        # upsample masks
+        img_shape = img_metas[0]['batch_input_shape']
+        mask_pred_results = F.interpolate(
+            mask_pred_results,
+            size=(img_shape[0], img_shape[1]),
+            mode='bilinear',
+            align_corners=False)
+
+        return mask_cls_results, mask_pred_results
diff --git a/mmdet/models/dense_heads/nasfcos_head.py b/mmdet/models/dense_heads/nasfcos_head.py
new file mode 100644
index 0000000..380c912
--- /dev/null
+++ b/mmdet/models/dense_heads/nasfcos_head.py
@@ -0,0 +1,80 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import torch.nn as nn
+from mmcv.cnn import ConvModule, Scale
+
+from mmdet.models.dense_heads.fcos_head import FCOSHead
+from ..builder import HEADS
+
+
+@HEADS.register_module()
+class NASFCOSHead(FCOSHead):
+    """Anchor-free head used in `NASFCOS <https://arxiv.org/abs/1906.04423>`_.
+
+    It is quite similar with FCOS head, except for the searched structure of
+    classification branch and bbox regression branch, where a structure of
+    "dconv3x3, conv3x3, dconv3x3, conv1x1" is utilized instead.
+    """
+
+    def __init__(self, *args, init_cfg=None, **kwargs):
+        if init_cfg is None:
+            init_cfg = [
+                dict(type='Caffe2Xavier', layer=['ConvModule', 'Conv2d']),
+                dict(
+                    type='Normal',
+                    std=0.01,
+                    override=[
+                        dict(name='conv_reg'),
+                        dict(name='conv_centerness'),
+                        dict(
+                            name='conv_cls',
+                            type='Normal',
+                            std=0.01,
+                            bias_prob=0.01)
+                    ]),
+            ]
+        super(NASFCOSHead, self).__init__(*args, init_cfg=init_cfg, **kwargs)
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        dconv3x3_config = dict(
+            type='DCNv2',
+            kernel_size=3,
+            use_bias=True,
+            deform_groups=2,
+            padding=1)
+        conv3x3_config = dict(type='Conv', kernel_size=3, padding=1)
+        conv1x1_config = dict(type='Conv', kernel_size=1)
+
+        self.arch_config = [
+            dconv3x3_config, conv3x3_config, dconv3x3_config, conv1x1_config
+        ]
+        self.cls_convs = nn.ModuleList()
+        self.reg_convs = nn.ModuleList()
+        for i, op_ in enumerate(self.arch_config):
+            op = copy.deepcopy(op_)
+            chn = self.in_channels if i == 0 else self.feat_channels
+            assert isinstance(op, dict)
+            use_bias = op.pop('use_bias', False)
+            padding = op.pop('padding', 0)
+            kernel_size = op.pop('kernel_size')
+            module = ConvModule(
+                chn,
+                self.feat_channels,
+                kernel_size,
+                stride=1,
+                padding=padding,
+                norm_cfg=self.norm_cfg,
+                bias=use_bias,
+                conv_cfg=op)
+
+            self.cls_convs.append(copy.deepcopy(module))
+            self.reg_convs.append(copy.deepcopy(module))
+
+        self.conv_cls = nn.Conv2d(
+            self.feat_channels, self.cls_out_channels, 3, padding=1)
+        self.conv_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
+        self.conv_centerness = nn.Conv2d(self.feat_channels, 1, 3, padding=1)
+
+        self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
diff --git a/mmdet/models/dense_heads/paa_head.py b/mmdet/models/dense_heads/paa_head.py
new file mode 100644
index 0000000..d79b5b9
--- /dev/null
+++ b/mmdet/models/dense_heads/paa_head.py
@@ -0,0 +1,756 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+from mmcv.runner import force_fp32
+
+from mmdet.core import multi_apply, multiclass_nms
+from mmdet.core.bbox.iou_calculators import bbox_overlaps
+from mmdet.models import HEADS
+from mmdet.models.dense_heads import ATSSHead
+
+EPS = 1e-12
+try:
+    import sklearn.mixture as skm
+except ImportError:
+    skm = None
+
+
+def levels_to_images(mlvl_tensor):
+    """Concat multi-level feature maps by image.
+
+    [feature_level0, feature_level1...] -> [feature_image0, feature_image1...]
+    Convert the shape of each element in mlvl_tensor from (N, C, H, W) to
+    (N, H*W , C), then split the element to N elements with shape (H*W, C), and
+    concat elements in same image of all level along first dimension.
+
+    Args:
+        mlvl_tensor (list[torch.Tensor]): list of Tensor which collect from
+            corresponding level. Each element is of shape (N, C, H, W)
+
+    Returns:
+        list[torch.Tensor]: A list that contains N tensors and each tensor is
+            of shape (num_elements, C)
+    """
+    batch_size = mlvl_tensor[0].size(0)
+    batch_list = [[] for _ in range(batch_size)]
+    channels = mlvl_tensor[0].size(1)
+    for t in mlvl_tensor:
+        t = t.permute(0, 2, 3, 1)
+        t = t.view(batch_size, -1, channels).contiguous()
+        for img in range(batch_size):
+            batch_list[img].append(t[img])
+    return [torch.cat(item, 0) for item in batch_list]
+
+
+@HEADS.register_module()
+class PAAHead(ATSSHead):
+    """Head of PAAAssignment: Probabilistic Anchor Assignment with IoU
+    Prediction for Object Detection.
+
+    Code is modified from the `official github repo
+    <https://github.com/kkhoot/PAA/blob/master/paa_core
+    /modeling/rpn/paa/loss.py>`_.
+
+    More details can be found in the `paper
+    <https://arxiv.org/abs/2007.08103>`_ .
+
+    Args:
+        topk (int): Select topk samples with smallest loss in
+            each level.
+        score_voting (bool): Whether to use score voting in post-process.
+        covariance_type : String describing the type of covariance parameters
+            to be used in :class:`sklearn.mixture.GaussianMixture`.
+            It must be one of:
+
+            - 'full': each component has its own general covariance matrix
+            - 'tied': all components share the same general covariance matrix
+            - 'diag': each component has its own diagonal covariance matrix
+            - 'spherical': each component has its own single variance
+            Default: 'diag'. From 'full' to 'spherical', the gmm fitting
+            process is faster yet the performance could be influenced. For most
+            cases, 'diag' should be a good choice.
+    """
+
+    def __init__(self,
+                 *args,
+                 topk=9,
+                 score_voting=True,
+                 covariance_type='diag',
+                 **kwargs):
+        # topk used in paa reassign process
+        self.topk = topk
+        self.with_score_voting = score_voting
+        self.covariance_type = covariance_type
+        super(PAAHead, self).__init__(*args, **kwargs)
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'iou_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             iou_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            iou_preds (list[Tensor]): iou_preds for each scale
+                level with shape (N, num_anchors * 1, H, W)
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
+                boxes can be ignored when are computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss gmm_assignment.
+        """
+
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+
+        device = cls_scores[0].device
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels,
+        )
+        (labels, labels_weight, bboxes_target, bboxes_weight, pos_inds,
+         pos_gt_index) = cls_reg_targets
+        cls_scores = levels_to_images(cls_scores)
+        cls_scores = [
+            item.reshape(-1, self.cls_out_channels) for item in cls_scores
+        ]
+        bbox_preds = levels_to_images(bbox_preds)
+        bbox_preds = [item.reshape(-1, 4) for item in bbox_preds]
+        iou_preds = levels_to_images(iou_preds)
+        iou_preds = [item.reshape(-1, 1) for item in iou_preds]
+        pos_losses_list, = multi_apply(self.get_pos_loss, anchor_list,
+                                       cls_scores, bbox_preds, labels,
+                                       labels_weight, bboxes_target,
+                                       bboxes_weight, pos_inds)
+
+        with torch.no_grad():
+            reassign_labels, reassign_label_weight, \
+                reassign_bbox_weights, num_pos = multi_apply(
+                    self.paa_reassign,
+                    pos_losses_list,
+                    labels,
+                    labels_weight,
+                    bboxes_weight,
+                    pos_inds,
+                    pos_gt_index,
+                    anchor_list)
+            num_pos = sum(num_pos)
+        # convert all tensor list to a flatten tensor
+        cls_scores = torch.cat(cls_scores, 0).view(-1, cls_scores[0].size(-1))
+        bbox_preds = torch.cat(bbox_preds, 0).view(-1, bbox_preds[0].size(-1))
+        iou_preds = torch.cat(iou_preds, 0).view(-1, iou_preds[0].size(-1))
+        labels = torch.cat(reassign_labels, 0).view(-1)
+        flatten_anchors = torch.cat(
+            [torch.cat(item, 0) for item in anchor_list])
+        labels_weight = torch.cat(reassign_label_weight, 0).view(-1)
+        bboxes_target = torch.cat(bboxes_target,
+                                  0).view(-1, bboxes_target[0].size(-1))
+
+        pos_inds_flatten = ((labels >= 0)
+                            &
+                            (labels < self.num_classes)).nonzero().reshape(-1)
+
+        losses_cls = self.loss_cls(
+            cls_scores,
+            labels,
+            labels_weight,
+            avg_factor=max(num_pos, len(img_metas)))  # avoid num_pos=0
+        if num_pos:
+            pos_bbox_pred = self.bbox_coder.decode(
+                flatten_anchors[pos_inds_flatten],
+                bbox_preds[pos_inds_flatten])
+            pos_bbox_target = bboxes_target[pos_inds_flatten]
+            iou_target = bbox_overlaps(
+                pos_bbox_pred.detach(), pos_bbox_target, is_aligned=True)
+            losses_iou = self.loss_centerness(
+                iou_preds[pos_inds_flatten],
+                iou_target.unsqueeze(-1),
+                avg_factor=num_pos)
+            losses_bbox = self.loss_bbox(
+                pos_bbox_pred,
+                pos_bbox_target,
+                iou_target.clamp(min=EPS),
+                avg_factor=iou_target.sum())
+        else:
+            losses_iou = iou_preds.sum() * 0
+            losses_bbox = bbox_preds.sum() * 0
+
+        return dict(
+            loss_cls=losses_cls, loss_bbox=losses_bbox, loss_iou=losses_iou)
+
+    def get_pos_loss(self, anchors, cls_score, bbox_pred, label, label_weight,
+                     bbox_target, bbox_weight, pos_inds):
+        """Calculate loss of all potential positive samples obtained from first
+        match process.
+
+        Args:
+            anchors (list[Tensor]): Anchors of each scale.
+            cls_score (Tensor): Box scores of single image with shape
+                (num_anchors, num_classes)
+            bbox_pred (Tensor): Box energies / deltas of single image
+                with shape (num_anchors, 4)
+            label (Tensor): classification target of each anchor with
+                shape (num_anchors,)
+            label_weight (Tensor): Classification loss weight of each
+                anchor with shape (num_anchors).
+            bbox_target (dict): Regression target of each anchor with
+                shape (num_anchors, 4).
+            bbox_weight (Tensor): Bbox weight of each anchor with shape
+                (num_anchors, 4).
+            pos_inds (Tensor): Index of all positive samples got from
+                first assign process.
+
+        Returns:
+            Tensor: Losses of all positive samples in single image.
+        """
+        if not len(pos_inds):
+            return cls_score.new([]),
+        anchors_all_level = torch.cat(anchors, 0)
+        pos_scores = cls_score[pos_inds]
+        pos_bbox_pred = bbox_pred[pos_inds]
+        pos_label = label[pos_inds]
+        pos_label_weight = label_weight[pos_inds]
+        pos_bbox_target = bbox_target[pos_inds]
+        pos_bbox_weight = bbox_weight[pos_inds]
+        pos_anchors = anchors_all_level[pos_inds]
+        pos_bbox_pred = self.bbox_coder.decode(pos_anchors, pos_bbox_pred)
+
+        # to keep loss dimension
+        loss_cls = self.loss_cls(
+            pos_scores,
+            pos_label,
+            pos_label_weight,
+            avg_factor=1.0,
+            reduction_override='none')
+
+        loss_bbox = self.loss_bbox(
+            pos_bbox_pred,
+            pos_bbox_target,
+            pos_bbox_weight,
+            avg_factor=1.0,  # keep same loss weight before reassign
+            reduction_override='none')
+
+        loss_cls = loss_cls.sum(-1)
+        pos_loss = loss_bbox + loss_cls
+        return pos_loss,
+
+    def paa_reassign(self, pos_losses, label, label_weight, bbox_weight,
+                     pos_inds, pos_gt_inds, anchors):
+        """Fit loss to GMM distribution and separate positive, ignore, negative
+        samples again with GMM model.
+
+        Args:
+            pos_losses (Tensor): Losses of all positive samples in
+                single image.
+            label (Tensor): classification target of each anchor with
+                shape (num_anchors,)
+            label_weight (Tensor): Classification loss weight of each
+                anchor with shape (num_anchors).
+            bbox_weight (Tensor): Bbox weight of each anchor with shape
+                (num_anchors, 4).
+            pos_inds (Tensor): Index of all positive samples got from
+                first assign process.
+            pos_gt_inds (Tensor): Gt_index of all positive samples got
+                from first assign process.
+            anchors (list[Tensor]): Anchors of each scale.
+
+        Returns:
+            tuple: Usually returns a tuple containing learning targets.
+
+                - label (Tensor): classification target of each anchor after
+                  paa assign, with shape (num_anchors,)
+                - label_weight (Tensor): Classification loss weight of each
+                  anchor after paa assign, with shape (num_anchors).
+                - bbox_weight (Tensor): Bbox weight of each anchor with shape
+                  (num_anchors, 4).
+                - num_pos (int): The number of positive samples after paa
+                  assign.
+        """
+        if not len(pos_inds):
+            return label, label_weight, bbox_weight, 0
+        label = label.clone()
+        label_weight = label_weight.clone()
+        bbox_weight = bbox_weight.clone()
+        num_gt = pos_gt_inds.max() + 1
+        num_level = len(anchors)
+        num_anchors_each_level = [item.size(0) for item in anchors]
+        num_anchors_each_level.insert(0, 0)
+        inds_level_interval = np.cumsum(num_anchors_each_level)
+        pos_level_mask = []
+        for i in range(num_level):
+            mask = (pos_inds >= inds_level_interval[i]) & (
+                pos_inds < inds_level_interval[i + 1])
+            pos_level_mask.append(mask)
+        pos_inds_after_paa = [label.new_tensor([])]
+        ignore_inds_after_paa = [label.new_tensor([])]
+        for gt_ind in range(num_gt):
+            pos_inds_gmm = []
+            pos_loss_gmm = []
+            gt_mask = pos_gt_inds == gt_ind
+            for level in range(num_level):
+                level_mask = pos_level_mask[level]
+                level_gt_mask = level_mask & gt_mask
+                value, topk_inds = pos_losses[level_gt_mask].topk(
+                    min(level_gt_mask.sum(), self.topk), largest=False)
+                pos_inds_gmm.append(pos_inds[level_gt_mask][topk_inds])
+                pos_loss_gmm.append(value)
+            pos_inds_gmm = torch.cat(pos_inds_gmm)
+            pos_loss_gmm = torch.cat(pos_loss_gmm)
+            # fix gmm need at least two sample
+            if len(pos_inds_gmm) < 2:
+                continue
+            device = pos_inds_gmm.device
+            pos_loss_gmm, sort_inds = pos_loss_gmm.sort()
+            pos_inds_gmm = pos_inds_gmm[sort_inds]
+            pos_loss_gmm = pos_loss_gmm.view(-1, 1).cpu().numpy()
+            min_loss, max_loss = pos_loss_gmm.min(), pos_loss_gmm.max()
+            means_init = np.array([min_loss, max_loss]).reshape(2, 1)
+            weights_init = np.array([0.5, 0.5])
+            precisions_init = np.array([1.0, 1.0]).reshape(2, 1, 1)  # full
+            if self.covariance_type == 'spherical':
+                precisions_init = precisions_init.reshape(2)
+            elif self.covariance_type == 'diag':
+                precisions_init = precisions_init.reshape(2, 1)
+            elif self.covariance_type == 'tied':
+                precisions_init = np.array([[1.0]])
+            if skm is None:
+                raise ImportError('Please run "pip install sklearn" '
+                                  'to install sklearn first.')
+            gmm = skm.GaussianMixture(
+                2,
+                weights_init=weights_init,
+                means_init=means_init,
+                precisions_init=precisions_init,
+                covariance_type=self.covariance_type)
+            gmm.fit(pos_loss_gmm)
+            gmm_assignment = gmm.predict(pos_loss_gmm)
+            scores = gmm.score_samples(pos_loss_gmm)
+            gmm_assignment = torch.from_numpy(gmm_assignment).to(device)
+            scores = torch.from_numpy(scores).to(device)
+
+            pos_inds_temp, ignore_inds_temp = self.gmm_separation_scheme(
+                gmm_assignment, scores, pos_inds_gmm)
+            pos_inds_after_paa.append(pos_inds_temp)
+            ignore_inds_after_paa.append(ignore_inds_temp)
+
+        pos_inds_after_paa = torch.cat(pos_inds_after_paa)
+        ignore_inds_after_paa = torch.cat(ignore_inds_after_paa)
+        reassign_mask = (pos_inds.unsqueeze(1) != pos_inds_after_paa).all(1)
+        reassign_ids = pos_inds[reassign_mask]
+        label[reassign_ids] = self.num_classes
+        label_weight[ignore_inds_after_paa] = 0
+        bbox_weight[reassign_ids] = 0
+        num_pos = len(pos_inds_after_paa)
+        return label, label_weight, bbox_weight, num_pos
+
+    def gmm_separation_scheme(self, gmm_assignment, scores, pos_inds_gmm):
+        """A general separation scheme for gmm model.
+
+        It separates a GMM distribution of candidate samples into three
+        parts, 0 1 and uncertain areas, and you can implement other
+        separation schemes by rewriting this function.
+
+        Args:
+            gmm_assignment (Tensor): The prediction of GMM which is of shape
+                (num_samples,). The 0/1 value indicates the distribution
+                that each sample comes from.
+            scores (Tensor): The probability of sample coming from the
+                fit GMM distribution. The tensor is of shape (num_samples,).
+            pos_inds_gmm (Tensor): All the indexes of samples which are used
+                to fit GMM model. The tensor is of shape (num_samples,)
+
+        Returns:
+            tuple[Tensor]: The indices of positive and ignored samples.
+
+                - pos_inds_temp (Tensor): Indices of positive samples.
+                - ignore_inds_temp (Tensor): Indices of ignore samples.
+        """
+        # The implementation is (c) in Fig.3 in origin paper instead of (b).
+        # You can refer to issues such as
+        # https://github.com/kkhoot/PAA/issues/8 and
+        # https://github.com/kkhoot/PAA/issues/9.
+        fgs = gmm_assignment == 0
+        pos_inds_temp = fgs.new_tensor([], dtype=torch.long)
+        ignore_inds_temp = fgs.new_tensor([], dtype=torch.long)
+        if fgs.nonzero().numel():
+            _, pos_thr_ind = scores[fgs].topk(1)
+            pos_inds_temp = pos_inds_gmm[fgs][:pos_thr_ind + 1]
+            ignore_inds_temp = pos_inds_gmm.new_tensor([])
+        return pos_inds_temp, ignore_inds_temp
+
+    def get_targets(
+        self,
+        anchor_list,
+        valid_flag_list,
+        gt_bboxes_list,
+        img_metas,
+        gt_bboxes_ignore_list=None,
+        gt_labels_list=None,
+        label_channels=1,
+        unmap_outputs=True,
+    ):
+        """Get targets for PAA head.
+
+        This method is almost the same as `AnchorHead.get_targets()`. We direct
+        return the results from _get_targets_single instead map it to levels
+        by images_to_levels function.
+
+        Args:
+            anchor_list (list[list[Tensor]]): Multi level anchors of each
+                image. The outer list indicates images, and the inner list
+                corresponds to feature levels of the image. Each element of
+                the inner list is a tensor of shape (num_anchors, 4).
+            valid_flag_list (list[list[Tensor]]): Multi level valid flags of
+                each image. The outer list indicates images, and the inner list
+                corresponds to feature levels of the image. Each element of
+                the inner list is a tensor of shape (num_anchors, )
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+            img_metas (list[dict]): Meta info of each image.
+            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
+                ignored.
+            gt_labels_list (list[Tensor]): Ground truth labels of each box.
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+
+        Returns:
+            tuple: Usually returns a tuple containing learning targets.
+
+                - labels (list[Tensor]): Labels of all anchors, each with
+                    shape (num_anchors,).
+                - label_weights (list[Tensor]): Label weights of all anchor.
+                    each with shape (num_anchors,).
+                - bbox_targets (list[Tensor]): BBox targets of all anchors.
+                    each with shape (num_anchors, 4).
+                - bbox_weights (list[Tensor]): BBox weights of all anchors.
+                    each with shape (num_anchors, 4).
+                - pos_inds (list[Tensor]): Contains all index of positive
+                    sample in all anchor.
+                - gt_inds (list[Tensor]): Contains all gt_index of positive
+                    sample in all anchor.
+        """
+
+        num_imgs = len(img_metas)
+        assert len(anchor_list) == len(valid_flag_list) == num_imgs
+        concat_anchor_list = []
+        concat_valid_flag_list = []
+        for i in range(num_imgs):
+            assert len(anchor_list[i]) == len(valid_flag_list[i])
+            concat_anchor_list.append(torch.cat(anchor_list[i]))
+            concat_valid_flag_list.append(torch.cat(valid_flag_list[i]))
+
+        # compute targets for each image
+        if gt_bboxes_ignore_list is None:
+            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
+        if gt_labels_list is None:
+            gt_labels_list = [None for _ in range(num_imgs)]
+        results = multi_apply(
+            self._get_targets_single,
+            concat_anchor_list,
+            concat_valid_flag_list,
+            gt_bboxes_list,
+            gt_bboxes_ignore_list,
+            gt_labels_list,
+            img_metas,
+            label_channels=label_channels,
+            unmap_outputs=unmap_outputs)
+
+        (labels, label_weights, bbox_targets, bbox_weights, valid_pos_inds,
+         valid_neg_inds, sampling_result) = results
+
+        # Due to valid flag of anchors, we have to calculate the real pos_inds
+        # in origin anchor set.
+        pos_inds = []
+        for i, single_labels in enumerate(labels):
+            pos_mask = (0 <= single_labels) & (
+                single_labels < self.num_classes)
+            pos_inds.append(pos_mask.nonzero().view(-1))
+
+        gt_inds = [item.pos_assigned_gt_inds for item in sampling_result]
+        return (labels, label_weights, bbox_targets, bbox_weights, pos_inds,
+                gt_inds)
+
+    def _get_targets_single(self,
+                            flat_anchors,
+                            valid_flags,
+                            gt_bboxes,
+                            gt_bboxes_ignore,
+                            gt_labels,
+                            img_meta,
+                            label_channels=1,
+                            unmap_outputs=True):
+        """Compute regression and classification targets for anchors in a
+        single image.
+
+        This method is same as `AnchorHead._get_targets_single()`.
+        """
+        assert unmap_outputs, 'We must map outputs back to the original' \
+                              'set of anchors in PAAhead'
+        return super(ATSSHead, self)._get_targets_single(
+            flat_anchors,
+            valid_flags,
+            gt_bboxes,
+            gt_bboxes_ignore,
+            gt_labels,
+            img_meta,
+            label_channels=1,
+            unmap_outputs=True)
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def get_bboxes(self,
+                   cls_scores,
+                   bbox_preds,
+                   score_factors=None,
+                   img_metas=None,
+                   cfg=None,
+                   rescale=False,
+                   with_nms=True,
+                   **kwargs):
+        assert with_nms, 'PAA only supports "with_nms=True" now and it ' \
+                         'means PAAHead does not support ' \
+                         'test-time augmentation'
+        return super(ATSSHead, self).get_bboxes(cls_scores, bbox_preds,
+                                                score_factors, img_metas, cfg,
+                                                rescale, with_nms, **kwargs)
+
+    def _get_bboxes_single(self,
+                           cls_score_list,
+                           bbox_pred_list,
+                           score_factor_list,
+                           mlvl_priors,
+                           img_meta,
+                           cfg,
+                           rescale=False,
+                           with_nms=True,
+                           **kwargs):
+        """Transform outputs of a single image into bbox predictions.
+
+        Args:
+            cls_score_list (list[Tensor]): Box scores from all scale
+                levels of a single image, each item has shape
+                (num_priors * num_classes, H, W).
+            bbox_pred_list (list[Tensor]): Box energies / deltas from
+                all scale levels of a single image, each item has shape
+                (num_priors * 4, H, W).
+            score_factor_list (list[Tensor]): Score factors from all scale
+                levels of a single image, each item has shape
+                (num_priors * 1, H, W).
+            mlvl_priors (list[Tensor]): Each element in the list is
+                the priors of a single level in feature pyramid, has shape
+                (num_priors, 4).
+            img_meta (dict): Image meta info.
+            cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+
+        Returns:
+            tuple[Tensor]: Results of detected bboxes and labels. If with_nms
+                is False and mlvl_score_factor is None, return mlvl_bboxes and
+                mlvl_scores, else return mlvl_bboxes, mlvl_scores and
+                mlvl_score_factor. Usually with_nms is False is used for aug
+                test. If with_nms is True, then return the following format
+
+                - det_bboxes (Tensor): Predicted bboxes with shape \
+                    [num_bboxes, 5], where the first 4 columns are bounding \
+                    box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
+                    column are scores between 0 and 1.
+                - det_labels (Tensor): Predicted labels of the corresponding \
+                    box with shape [num_bboxes].
+        """
+        cfg = self.test_cfg if cfg is None else cfg
+        img_shape = img_meta['img_shape']
+        nms_pre = cfg.get('nms_pre', -1)
+
+        mlvl_bboxes = []
+        mlvl_scores = []
+        mlvl_score_factors = []
+        for level_idx, (cls_score, bbox_pred, score_factor, priors) in \
+                enumerate(zip(cls_score_list, bbox_pred_list,
+                              score_factor_list, mlvl_priors)):
+            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
+
+            scores = cls_score.permute(1, 2, 0).reshape(
+                -1, self.cls_out_channels).sigmoid()
+            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
+            score_factor = score_factor.permute(1, 2, 0).reshape(-1).sigmoid()
+
+            if 0 < nms_pre < scores.shape[0]:
+                max_scores, _ = (scores *
+                                 score_factor[:, None]).sqrt().max(dim=1)
+                _, topk_inds = max_scores.topk(nms_pre)
+                priors = priors[topk_inds, :]
+                bbox_pred = bbox_pred[topk_inds, :]
+                scores = scores[topk_inds, :]
+                score_factor = score_factor[topk_inds]
+
+            bboxes = self.bbox_coder.decode(
+                priors, bbox_pred, max_shape=img_shape)
+            mlvl_bboxes.append(bboxes)
+            mlvl_scores.append(scores)
+            mlvl_score_factors.append(score_factor)
+
+        return self._bbox_post_process(mlvl_scores, mlvl_bboxes,
+                                       img_meta['scale_factor'], cfg, rescale,
+                                       with_nms, mlvl_score_factors, **kwargs)
+
+    def _bbox_post_process(self,
+                           mlvl_scores,
+                           mlvl_bboxes,
+                           scale_factor,
+                           cfg,
+                           rescale=False,
+                           with_nms=True,
+                           mlvl_score_factors=None,
+                           **kwargs):
+        """bbox post-processing method.
+
+        The boxes would be rescaled to the original image scale and do
+        the nms operation. Usually with_nms is False is used for aug test.
+
+        Args:
+            mlvl_scores (list[Tensor]): Box scores from all scale
+                levels of a single image, each item has shape
+                (num_bboxes, num_class).
+            mlvl_bboxes (list[Tensor]): Decoded bboxes from all scale
+                levels of a single image, each item has shape (num_bboxes, 4).
+            scale_factor (ndarray, optional): Scale factor of the image arange
+                as (w_scale, h_scale, w_scale, h_scale).
+            cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+            mlvl_score_factors (list[Tensor], optional): Score factor from
+                all scale levels of a single image, each item has shape
+                (num_bboxes, ). Default: None.
+
+        Returns:
+            tuple[Tensor]: Results of detected bboxes and labels. If with_nms
+                is False and mlvl_score_factor is None, return mlvl_bboxes and
+                mlvl_scores, else return mlvl_bboxes, mlvl_scores and
+                mlvl_score_factor. Usually with_nms is False is used for aug
+                test. If with_nms is True, then return the following format
+
+                - det_bboxes (Tensor): Predicted bboxes with shape \
+                    [num_bboxes, 5], where the first 4 columns are bounding \
+                    box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
+                    column are scores between 0 and 1.
+                - det_labels (Tensor): Predicted labels of the corresponding \
+                    box with shape [num_bboxes].
+        """
+        mlvl_bboxes = torch.cat(mlvl_bboxes)
+        if rescale:
+            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
+        mlvl_scores = torch.cat(mlvl_scores)
+        # Add a dummy background class to the backend when using sigmoid
+        # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
+        # BG cat_id: num_class
+        padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
+        mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
+
+        mlvl_iou_preds = torch.cat(mlvl_score_factors)
+        mlvl_nms_scores = (mlvl_scores * mlvl_iou_preds[:, None]).sqrt()
+        det_bboxes, det_labels = multiclass_nms(
+            mlvl_bboxes,
+            mlvl_nms_scores,
+            cfg.score_thr,
+            cfg.nms,
+            cfg.max_per_img,
+            score_factors=None)
+        if self.with_score_voting and len(det_bboxes) > 0:
+            det_bboxes, det_labels = self.score_voting(det_bboxes, det_labels,
+                                                       mlvl_bboxes,
+                                                       mlvl_nms_scores,
+                                                       cfg.score_thr)
+
+        return det_bboxes, det_labels
+
+    def score_voting(self, det_bboxes, det_labels, mlvl_bboxes,
+                     mlvl_nms_scores, score_thr):
+        """Implementation of score voting method works on each remaining boxes
+        after NMS procedure.
+
+        Args:
+            det_bboxes (Tensor): Remaining boxes after NMS procedure,
+                with shape (k, 5), each dimension means
+                (x1, y1, x2, y2, score).
+            det_labels (Tensor): The label of remaining boxes, with shape
+                (k, 1),Labels are 0-based.
+            mlvl_bboxes (Tensor): All boxes before the NMS procedure,
+                with shape (num_anchors,4).
+            mlvl_nms_scores (Tensor): The scores of all boxes which is used
+                in the NMS procedure, with shape (num_anchors, num_class)
+            score_thr (float): The score threshold of bboxes.
+
+        Returns:
+            tuple: Usually returns a tuple containing voting results.
+
+                - det_bboxes_voted (Tensor): Remaining boxes after
+                    score voting procedure, with shape (k, 5), each
+                    dimension means (x1, y1, x2, y2, score).
+                - det_labels_voted (Tensor): Label of remaining bboxes
+                    after voting, with shape (num_anchors,).
+        """
+        candidate_mask = mlvl_nms_scores > score_thr
+        candidate_mask_nonzeros = candidate_mask.nonzero(as_tuple=False)
+        candidate_inds = candidate_mask_nonzeros[:, 0]
+        candidate_labels = candidate_mask_nonzeros[:, 1]
+        candidate_bboxes = mlvl_bboxes[candidate_inds]
+        candidate_scores = mlvl_nms_scores[candidate_mask]
+        det_bboxes_voted = []
+        det_labels_voted = []
+        for cls in range(self.cls_out_channels):
+            candidate_cls_mask = candidate_labels == cls
+            if not candidate_cls_mask.any():
+                continue
+            candidate_cls_scores = candidate_scores[candidate_cls_mask]
+            candidate_cls_bboxes = candidate_bboxes[candidate_cls_mask]
+            det_cls_mask = det_labels == cls
+            det_cls_bboxes = det_bboxes[det_cls_mask].view(
+                -1, det_bboxes.size(-1))
+            det_candidate_ious = bbox_overlaps(det_cls_bboxes[:, :4],
+                                               candidate_cls_bboxes)
+            for det_ind in range(len(det_cls_bboxes)):
+                single_det_ious = det_candidate_ious[det_ind]
+                pos_ious_mask = single_det_ious > 0.01
+                pos_ious = single_det_ious[pos_ious_mask]
+                pos_bboxes = candidate_cls_bboxes[pos_ious_mask]
+                pos_scores = candidate_cls_scores[pos_ious_mask]
+                pis = (torch.exp(-(1 - pos_ious)**2 / 0.025) *
+                       pos_scores)[:, None]
+                voted_box = torch.sum(
+                    pis * pos_bboxes, dim=0) / torch.sum(
+                        pis, dim=0)
+                voted_score = det_cls_bboxes[det_ind][-1:][None, :]
+                det_bboxes_voted.append(
+                    torch.cat((voted_box[None, :], voted_score), dim=1))
+                det_labels_voted.append(cls)
+
+        det_bboxes_voted = torch.cat(det_bboxes_voted, dim=0)
+        det_labels_voted = det_labels.new_tensor(det_labels_voted)
+        return det_bboxes_voted, det_labels_voted
diff --git a/mmdet/models/dense_heads/pisa_retinanet_head.py b/mmdet/models/dense_heads/pisa_retinanet_head.py
new file mode 100644
index 0000000..8654ef4
--- /dev/null
+++ b/mmdet/models/dense_heads/pisa_retinanet_head.py
@@ -0,0 +1,155 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from mmcv.runner import force_fp32
+
+from mmdet.core import images_to_levels
+from ..builder import HEADS
+from ..losses import carl_loss, isr_p
+from .retina_head import RetinaHead
+
+
+@HEADS.register_module()
+class PISARetinaHead(RetinaHead):
+    """PISA Retinanet Head.
+
+    The head owns the same structure with Retinanet Head, but differs in two
+        aspects:
+        1. Importance-based Sample Reweighting Positive (ISR-P) is applied to
+            change the positive loss weights.
+        2. Classification-aware regression loss is adopted as a third loss.
+    """
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            gt_bboxes (list[Tensor]): Ground truth bboxes of each image
+                with shape (num_obj, 4).
+            gt_labels (list[Tensor]): Ground truth labels of each image
+                with shape (num_obj, 4).
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (list[Tensor]): Ignored gt bboxes of each image.
+                Default: None.
+
+        Returns:
+            dict: Loss dict, comprise classification loss, regression loss and
+                carl loss.
+        """
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+
+        device = cls_scores[0].device
+
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels,
+            return_sampling_results=True)
+        if cls_reg_targets is None:
+            return None
+        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
+         num_total_pos, num_total_neg, sampling_results_list) = cls_reg_targets
+        num_total_samples = (
+            num_total_pos + num_total_neg if self.sampling else num_total_pos)
+
+        # anchor number of multi levels
+        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+        # concat all level anchors and flags to a single tensor
+        concat_anchor_list = []
+        for i in range(len(anchor_list)):
+            concat_anchor_list.append(torch.cat(anchor_list[i]))
+        all_anchor_list = images_to_levels(concat_anchor_list,
+                                           num_level_anchors)
+
+        num_imgs = len(img_metas)
+        flatten_cls_scores = [
+            cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1, label_channels)
+            for cls_score in cls_scores
+        ]
+        flatten_cls_scores = torch.cat(
+            flatten_cls_scores, dim=1).reshape(-1,
+                                               flatten_cls_scores[0].size(-1))
+        flatten_bbox_preds = [
+            bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4)
+            for bbox_pred in bbox_preds
+        ]
+        flatten_bbox_preds = torch.cat(
+            flatten_bbox_preds, dim=1).view(-1, flatten_bbox_preds[0].size(-1))
+        flatten_labels = torch.cat(labels_list, dim=1).reshape(-1)
+        flatten_label_weights = torch.cat(
+            label_weights_list, dim=1).reshape(-1)
+        flatten_anchors = torch.cat(all_anchor_list, dim=1).reshape(-1, 4)
+        flatten_bbox_targets = torch.cat(
+            bbox_targets_list, dim=1).reshape(-1, 4)
+        flatten_bbox_weights = torch.cat(
+            bbox_weights_list, dim=1).reshape(-1, 4)
+
+        # Apply ISR-P
+        isr_cfg = self.train_cfg.get('isr', None)
+        if isr_cfg is not None:
+            all_targets = (flatten_labels, flatten_label_weights,
+                           flatten_bbox_targets, flatten_bbox_weights)
+            with torch.no_grad():
+                all_targets = isr_p(
+                    flatten_cls_scores,
+                    flatten_bbox_preds,
+                    all_targets,
+                    flatten_anchors,
+                    sampling_results_list,
+                    bbox_coder=self.bbox_coder,
+                    loss_cls=self.loss_cls,
+                    num_class=self.num_classes,
+                    **self.train_cfg.isr)
+            (flatten_labels, flatten_label_weights, flatten_bbox_targets,
+             flatten_bbox_weights) = all_targets
+
+        # For convenience we compute loss once instead separating by fpn level,
+        # so that we don't need to separate the weights by level again.
+        # The result should be the same
+        losses_cls = self.loss_cls(
+            flatten_cls_scores,
+            flatten_labels,
+            flatten_label_weights,
+            avg_factor=num_total_samples)
+        losses_bbox = self.loss_bbox(
+            flatten_bbox_preds,
+            flatten_bbox_targets,
+            flatten_bbox_weights,
+            avg_factor=num_total_samples)
+        loss_dict = dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
+
+        # CARL Loss
+        carl_cfg = self.train_cfg.get('carl', None)
+        if carl_cfg is not None:
+            loss_carl = carl_loss(
+                flatten_cls_scores,
+                flatten_labels,
+                flatten_bbox_preds,
+                flatten_bbox_targets,
+                self.loss_bbox,
+                **self.train_cfg.carl,
+                avg_factor=num_total_pos,
+                sigmoid=True,
+                num_class=self.num_classes)
+            loss_dict.update(loss_carl)
+
+        return loss_dict
diff --git a/mmdet/models/dense_heads/pisa_ssd_head.py b/mmdet/models/dense_heads/pisa_ssd_head.py
new file mode 100644
index 0000000..86b67ab
--- /dev/null
+++ b/mmdet/models/dense_heads/pisa_ssd_head.py
@@ -0,0 +1,140 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdet.core import multi_apply
+from ..builder import HEADS
+from ..losses import CrossEntropyLoss, SmoothL1Loss, carl_loss, isr_p
+from .ssd_head import SSDHead
+
+
+# TODO: add loss evaluator for SSD
+@HEADS.register_module()
+class PISASSDHead(SSDHead):
+
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            gt_bboxes (list[Tensor]): Ground truth bboxes of each image
+                with shape (num_obj, 4).
+            gt_labels (list[Tensor]): Ground truth labels of each image
+                with shape (num_obj, 4).
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (list[Tensor]): Ignored gt bboxes of each image.
+                Default: None.
+
+        Returns:
+            dict: Loss dict, comprise classification loss regression loss and
+                carl loss.
+        """
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+
+        device = cls_scores[0].device
+
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=1,
+            unmap_outputs=False,
+            return_sampling_results=True)
+        if cls_reg_targets is None:
+            return None
+        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
+         num_total_pos, num_total_neg, sampling_results_list) = cls_reg_targets
+
+        num_images = len(img_metas)
+        all_cls_scores = torch.cat([
+            s.permute(0, 2, 3, 1).reshape(
+                num_images, -1, self.cls_out_channels) for s in cls_scores
+        ], 1)
+        all_labels = torch.cat(labels_list, -1).view(num_images, -1)
+        all_label_weights = torch.cat(label_weights_list,
+                                      -1).view(num_images, -1)
+        all_bbox_preds = torch.cat([
+            b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
+            for b in bbox_preds
+        ], -2)
+        all_bbox_targets = torch.cat(bbox_targets_list,
+                                     -2).view(num_images, -1, 4)
+        all_bbox_weights = torch.cat(bbox_weights_list,
+                                     -2).view(num_images, -1, 4)
+
+        # concat all level anchors to a single tensor
+        all_anchors = []
+        for i in range(num_images):
+            all_anchors.append(torch.cat(anchor_list[i]))
+
+        isr_cfg = self.train_cfg.get('isr', None)
+        all_targets = (all_labels.view(-1), all_label_weights.view(-1),
+                       all_bbox_targets.view(-1,
+                                             4), all_bbox_weights.view(-1, 4))
+        # apply ISR-P
+        if isr_cfg is not None:
+            all_targets = isr_p(
+                all_cls_scores.view(-1, all_cls_scores.size(-1)),
+                all_bbox_preds.view(-1, 4),
+                all_targets,
+                torch.cat(all_anchors),
+                sampling_results_list,
+                loss_cls=CrossEntropyLoss(),
+                bbox_coder=self.bbox_coder,
+                **self.train_cfg.isr,
+                num_class=self.num_classes)
+            (new_labels, new_label_weights, new_bbox_targets,
+             new_bbox_weights) = all_targets
+            all_labels = new_labels.view(all_labels.shape)
+            all_label_weights = new_label_weights.view(all_label_weights.shape)
+            all_bbox_targets = new_bbox_targets.view(all_bbox_targets.shape)
+            all_bbox_weights = new_bbox_weights.view(all_bbox_weights.shape)
+
+        # add CARL loss
+        carl_loss_cfg = self.train_cfg.get('carl', None)
+        if carl_loss_cfg is not None:
+            loss_carl = carl_loss(
+                all_cls_scores.view(-1, all_cls_scores.size(-1)),
+                all_targets[0],
+                all_bbox_preds.view(-1, 4),
+                all_targets[2],
+                SmoothL1Loss(beta=1.),
+                **self.train_cfg.carl,
+                avg_factor=num_total_pos,
+                num_class=self.num_classes)
+
+        # check NaN and Inf
+        assert torch.isfinite(all_cls_scores).all().item(), \
+            'classification scores become infinite or NaN!'
+        assert torch.isfinite(all_bbox_preds).all().item(), \
+            'bbox predications become infinite or NaN!'
+
+        losses_cls, losses_bbox = multi_apply(
+            self.loss_single,
+            all_cls_scores,
+            all_bbox_preds,
+            all_anchors,
+            all_labels,
+            all_label_weights,
+            all_bbox_targets,
+            all_bbox_weights,
+            num_total_samples=num_total_pos)
+        loss_dict = dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
+        if carl_loss_cfg is not None:
+            loss_dict.update(loss_carl)
+        return loss_dict
diff --git a/mmdet/models/dense_heads/reppoints_head.py b/mmdet/models/dense_heads/reppoints_head.py
new file mode 100644
index 0000000..f720414
--- /dev/null
+++ b/mmdet/models/dense_heads/reppoints_head.py
@@ -0,0 +1,764 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.ops import DeformConv2d
+
+from mmdet.core import (build_assigner, build_sampler, images_to_levels,
+                        multi_apply, unmap)
+from mmdet.core.anchor.point_generator import MlvlPointGenerator
+from mmdet.core.utils import filter_scores_and_topk
+from ..builder import HEADS, build_loss
+from .anchor_free_head import AnchorFreeHead
+
+
+@HEADS.register_module()
+class RepPointsHead(AnchorFreeHead):
+    """RepPoint head.
+
+    Args:
+        point_feat_channels (int): Number of channels of points features.
+        gradient_mul (float): The multiplier to gradients from
+            points refinement and recognition.
+        point_strides (Iterable): points strides.
+        point_base_scale (int): bbox scale for assigning labels.
+        loss_cls (dict): Config of classification loss.
+        loss_bbox_init (dict): Config of initial points loss.
+        loss_bbox_refine (dict): Config of points loss in refinement.
+        use_grid_points (bool): If we use bounding box representation, the
+        reppoints is represented as grid points on the bounding box.
+        center_init (bool): Whether to use center point assignment.
+        transform_method (str): The methods to transform RepPoints to bbox.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """  # noqa: W605
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 point_feat_channels=256,
+                 num_points=9,
+                 gradient_mul=0.1,
+                 point_strides=[8, 16, 32, 64, 128],
+                 point_base_scale=4,
+                 loss_cls=dict(
+                     type='FocalLoss',
+                     use_sigmoid=True,
+                     gamma=2.0,
+                     alpha=0.25,
+                     loss_weight=1.0),
+                 loss_bbox_init=dict(
+                     type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=0.5),
+                 loss_bbox_refine=dict(
+                     type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0),
+                 use_grid_points=False,
+                 center_init=True,
+                 transform_method='moment',
+                 moment_mul=0.01,
+                 init_cfg=dict(
+                     type='Normal',
+                     layer='Conv2d',
+                     std=0.01,
+                     override=dict(
+                         type='Normal',
+                         name='reppoints_cls_out',
+                         std=0.01,
+                         bias_prob=0.01)),
+                 **kwargs):
+        self.num_points = num_points
+        self.point_feat_channels = point_feat_channels
+        self.use_grid_points = use_grid_points
+        self.center_init = center_init
+
+        # we use deform conv to extract points features
+        self.dcn_kernel = int(np.sqrt(num_points))
+        self.dcn_pad = int((self.dcn_kernel - 1) / 2)
+        assert self.dcn_kernel * self.dcn_kernel == num_points, \
+            'The points number should be a square number.'
+        assert self.dcn_kernel % 2 == 1, \
+            'The points number should be an odd square number.'
+        dcn_base = np.arange(-self.dcn_pad,
+                             self.dcn_pad + 1).astype(np.float64)
+        dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
+        dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
+        dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
+            (-1))
+        self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
+
+        super().__init__(
+            num_classes,
+            in_channels,
+            loss_cls=loss_cls,
+            init_cfg=init_cfg,
+            **kwargs)
+
+        self.gradient_mul = gradient_mul
+        self.point_base_scale = point_base_scale
+        self.point_strides = point_strides
+        self.prior_generator = MlvlPointGenerator(
+            self.point_strides, offset=0.)
+
+        self.sampling = loss_cls['type'] not in ['FocalLoss']
+        if self.train_cfg:
+            self.init_assigner = build_assigner(self.train_cfg.init.assigner)
+            self.refine_assigner = build_assigner(
+                self.train_cfg.refine.assigner)
+            # use PseudoSampler when sampling is False
+            if self.sampling and hasattr(self.train_cfg, 'sampler'):
+                sampler_cfg = self.train_cfg.sampler
+            else:
+                sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+        self.transform_method = transform_method
+        if self.transform_method == 'moment':
+            self.moment_transfer = nn.Parameter(
+                data=torch.zeros(2), requires_grad=True)
+            self.moment_mul = moment_mul
+
+        self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
+        if self.use_sigmoid_cls:
+            self.cls_out_channels = self.num_classes
+        else:
+            self.cls_out_channels = self.num_classes + 1
+        self.loss_bbox_init = build_loss(loss_bbox_init)
+        self.loss_bbox_refine = build_loss(loss_bbox_refine)
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.relu = nn.ReLU(inplace=True)
+        self.cls_convs = nn.ModuleList()
+        self.reg_convs = nn.ModuleList()
+        for i in range(self.stacked_convs):
+            chn = self.in_channels if i == 0 else self.feat_channels
+            self.cls_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+            self.reg_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+        pts_out_dim = 4 if self.use_grid_points else 2 * self.num_points
+        self.reppoints_cls_conv = DeformConv2d(self.feat_channels,
+                                               self.point_feat_channels,
+                                               self.dcn_kernel, 1,
+                                               self.dcn_pad)
+        self.reppoints_cls_out = nn.Conv2d(self.point_feat_channels,
+                                           self.cls_out_channels, 1, 1, 0)
+        self.reppoints_pts_init_conv = nn.Conv2d(self.feat_channels,
+                                                 self.point_feat_channels, 3,
+                                                 1, 1)
+        self.reppoints_pts_init_out = nn.Conv2d(self.point_feat_channels,
+                                                pts_out_dim, 1, 1, 0)
+        self.reppoints_pts_refine_conv = DeformConv2d(self.feat_channels,
+                                                      self.point_feat_channels,
+                                                      self.dcn_kernel, 1,
+                                                      self.dcn_pad)
+        self.reppoints_pts_refine_out = nn.Conv2d(self.point_feat_channels,
+                                                  pts_out_dim, 1, 1, 0)
+
+    def points2bbox(self, pts, y_first=True):
+        """Converting the points set into bounding box.
+
+        :param pts: the input points sets (fields), each points
+            set (fields) is represented as 2n scalar.
+        :param y_first: if y_first=True, the point set is represented as
+            [y1, x1, y2, x2 ... yn, xn], otherwise the point set is
+            represented as [x1, y1, x2, y2 ... xn, yn].
+        :return: each points set is converting to a bbox [x1, y1, x2, y2].
+        """
+        pts_reshape = pts.view(pts.shape[0], -1, 2, *pts.shape[2:])
+        pts_y = pts_reshape[:, :, 0, ...] if y_first else pts_reshape[:, :, 1,
+                                                                      ...]
+        pts_x = pts_reshape[:, :, 1, ...] if y_first else pts_reshape[:, :, 0,
+                                                                      ...]
+        if self.transform_method == 'minmax':
+            bbox_left = pts_x.min(dim=1, keepdim=True)[0]
+            bbox_right = pts_x.max(dim=1, keepdim=True)[0]
+            bbox_up = pts_y.min(dim=1, keepdim=True)[0]
+            bbox_bottom = pts_y.max(dim=1, keepdim=True)[0]
+            bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom],
+                             dim=1)
+        elif self.transform_method == 'partial_minmax':
+            pts_y = pts_y[:, :4, ...]
+            pts_x = pts_x[:, :4, ...]
+            bbox_left = pts_x.min(dim=1, keepdim=True)[0]
+            bbox_right = pts_x.max(dim=1, keepdim=True)[0]
+            bbox_up = pts_y.min(dim=1, keepdim=True)[0]
+            bbox_bottom = pts_y.max(dim=1, keepdim=True)[0]
+            bbox = torch.cat([bbox_left, bbox_up, bbox_right, bbox_bottom],
+                             dim=1)
+        elif self.transform_method == 'moment':
+            pts_y_mean = pts_y.mean(dim=1, keepdim=True)
+            pts_x_mean = pts_x.mean(dim=1, keepdim=True)
+            pts_y_std = torch.std(pts_y - pts_y_mean, dim=1, keepdim=True)
+            pts_x_std = torch.std(pts_x - pts_x_mean, dim=1, keepdim=True)
+            moment_transfer = (self.moment_transfer * self.moment_mul) + (
+                self.moment_transfer.detach() * (1 - self.moment_mul))
+            moment_width_transfer = moment_transfer[0]
+            moment_height_transfer = moment_transfer[1]
+            half_width = pts_x_std * torch.exp(moment_width_transfer)
+            half_height = pts_y_std * torch.exp(moment_height_transfer)
+            bbox = torch.cat([
+                pts_x_mean - half_width, pts_y_mean - half_height,
+                pts_x_mean + half_width, pts_y_mean + half_height
+            ],
+                             dim=1)
+        else:
+            raise NotImplementedError
+        return bbox
+
+    def gen_grid_from_reg(self, reg, previous_boxes):
+        """Base on the previous bboxes and regression values, we compute the
+        regressed bboxes and generate the grids on the bboxes.
+
+        :param reg: the regression value to previous bboxes.
+        :param previous_boxes: previous bboxes.
+        :return: generate grids on the regressed bboxes.
+        """
+        b, _, h, w = reg.shape
+        bxy = (previous_boxes[:, :2, ...] + previous_boxes[:, 2:, ...]) / 2.
+        bwh = (previous_boxes[:, 2:, ...] -
+               previous_boxes[:, :2, ...]).clamp(min=1e-6)
+        grid_topleft = bxy + bwh * reg[:, :2, ...] - 0.5 * bwh * torch.exp(
+            reg[:, 2:, ...])
+        grid_wh = bwh * torch.exp(reg[:, 2:, ...])
+        grid_left = grid_topleft[:, [0], ...]
+        grid_top = grid_topleft[:, [1], ...]
+        grid_width = grid_wh[:, [0], ...]
+        grid_height = grid_wh[:, [1], ...]
+        intervel = torch.linspace(0., 1., self.dcn_kernel).view(
+            1, self.dcn_kernel, 1, 1).type_as(reg)
+        grid_x = grid_left + grid_width * intervel
+        grid_x = grid_x.unsqueeze(1).repeat(1, self.dcn_kernel, 1, 1, 1)
+        grid_x = grid_x.view(b, -1, h, w)
+        grid_y = grid_top + grid_height * intervel
+        grid_y = grid_y.unsqueeze(2).repeat(1, 1, self.dcn_kernel, 1, 1)
+        grid_y = grid_y.view(b, -1, h, w)
+        grid_yx = torch.stack([grid_y, grid_x], dim=2)
+        grid_yx = grid_yx.view(b, -1, h, w)
+        regressed_bbox = torch.cat([
+            grid_left, grid_top, grid_left + grid_width, grid_top + grid_height
+        ], 1)
+        return grid_yx, regressed_bbox
+
+    def forward(self, feats):
+        return multi_apply(self.forward_single, feats)
+
+    def forward_single(self, x):
+        """Forward feature map of a single FPN level."""
+        dcn_base_offset = self.dcn_base_offset.type_as(x)
+        # If we use center_init, the initial reppoints is from center points.
+        # If we use bounding bbox representation, the initial reppoints is
+        #   from regular grid placed on a pre-defined bbox.
+        if self.use_grid_points or not self.center_init:
+            scale = self.point_base_scale / 2
+            points_init = dcn_base_offset / dcn_base_offset.max() * scale
+            bbox_init = x.new_tensor([-scale, -scale, scale,
+                                      scale]).view(1, 4, 1, 1)
+        else:
+            points_init = 0
+        cls_feat = x
+        pts_feat = x
+        for cls_conv in self.cls_convs:
+            cls_feat = cls_conv(cls_feat)
+        for reg_conv in self.reg_convs:
+            pts_feat = reg_conv(pts_feat)
+        # initialize reppoints
+        pts_out_init = self.reppoints_pts_init_out(
+            self.relu(self.reppoints_pts_init_conv(pts_feat)))
+        if self.use_grid_points:
+            pts_out_init, bbox_out_init = self.gen_grid_from_reg(
+                pts_out_init, bbox_init.detach())
+        else:
+            pts_out_init = pts_out_init + points_init
+        # refine and classify reppoints
+        pts_out_init_grad_mul = (1 - self.gradient_mul) * pts_out_init.detach(
+        ) + self.gradient_mul * pts_out_init
+        dcn_offset = pts_out_init_grad_mul - dcn_base_offset
+        cls_out = self.reppoints_cls_out(
+            self.relu(self.reppoints_cls_conv(cls_feat, dcn_offset)))
+        pts_out_refine = self.reppoints_pts_refine_out(
+            self.relu(self.reppoints_pts_refine_conv(pts_feat, dcn_offset)))
+        if self.use_grid_points:
+            pts_out_refine, bbox_out_refine = self.gen_grid_from_reg(
+                pts_out_refine, bbox_out_init.detach())
+        else:
+            pts_out_refine = pts_out_refine + pts_out_init.detach()
+
+        if self.training:
+            return cls_out, pts_out_init, pts_out_refine
+        else:
+            return cls_out, self.points2bbox(pts_out_refine)
+
+    def get_points(self, featmap_sizes, img_metas, device):
+        """Get points according to feature map sizes.
+
+        Args:
+            featmap_sizes (list[tuple]): Multi-level feature map sizes.
+            img_metas (list[dict]): Image meta info.
+
+        Returns:
+            tuple: points of each image, valid flags of each image
+        """
+        num_imgs = len(img_metas)
+
+        # since feature map sizes of all images are the same, we only compute
+        # points center for one time
+        multi_level_points = self.prior_generator.grid_priors(
+            featmap_sizes, device=device, with_stride=True)
+        points_list = [[point.clone() for point in multi_level_points]
+                       for _ in range(num_imgs)]
+
+        # for each image, we compute valid flags of multi level grids
+        valid_flag_list = []
+        for img_id, img_meta in enumerate(img_metas):
+            multi_level_flags = self.prior_generator.valid_flags(
+                featmap_sizes, img_meta['pad_shape'])
+            valid_flag_list.append(multi_level_flags)
+
+        return points_list, valid_flag_list
+
+    def centers_to_bboxes(self, point_list):
+        """Get bboxes according to center points.
+
+        Only used in :class:`MaxIoUAssigner`.
+        """
+        bbox_list = []
+        for i_img, point in enumerate(point_list):
+            bbox = []
+            for i_lvl in range(len(self.point_strides)):
+                scale = self.point_base_scale * self.point_strides[i_lvl] * 0.5
+                bbox_shift = torch.Tensor([-scale, -scale, scale,
+                                           scale]).view(1, 4).type_as(point[0])
+                bbox_center = torch.cat(
+                    [point[i_lvl][:, :2], point[i_lvl][:, :2]], dim=1)
+                bbox.append(bbox_center + bbox_shift)
+            bbox_list.append(bbox)
+        return bbox_list
+
+    def offset_to_pts(self, center_list, pred_list):
+        """Change from point offset to point coordinate."""
+        pts_list = []
+        for i_lvl in range(len(self.point_strides)):
+            pts_lvl = []
+            for i_img in range(len(center_list)):
+                pts_center = center_list[i_img][i_lvl][:, :2].repeat(
+                    1, self.num_points)
+                pts_shift = pred_list[i_lvl][i_img]
+                yx_pts_shift = pts_shift.permute(1, 2, 0).view(
+                    -1, 2 * self.num_points)
+                y_pts_shift = yx_pts_shift[..., 0::2]
+                x_pts_shift = yx_pts_shift[..., 1::2]
+                xy_pts_shift = torch.stack([x_pts_shift, y_pts_shift], -1)
+                xy_pts_shift = xy_pts_shift.view(*yx_pts_shift.shape[:-1], -1)
+                pts = xy_pts_shift * self.point_strides[i_lvl] + pts_center
+                pts_lvl.append(pts)
+            pts_lvl = torch.stack(pts_lvl, 0)
+            pts_list.append(pts_lvl)
+        return pts_list
+
+    def _point_target_single(self,
+                             flat_proposals,
+                             valid_flags,
+                             gt_bboxes,
+                             gt_bboxes_ignore,
+                             gt_labels,
+                             stage='init',
+                             unmap_outputs=True):
+        inside_flags = valid_flags
+        if not inside_flags.any():
+            return (None, ) * 7
+        # assign gt and sample proposals
+        proposals = flat_proposals[inside_flags, :]
+
+        if stage == 'init':
+            assigner = self.init_assigner
+            pos_weight = self.train_cfg.init.pos_weight
+        else:
+            assigner = self.refine_assigner
+            pos_weight = self.train_cfg.refine.pos_weight
+        assign_result = assigner.assign(proposals, gt_bboxes, gt_bboxes_ignore,
+                                        None if self.sampling else gt_labels)
+        sampling_result = self.sampler.sample(assign_result, proposals,
+                                              gt_bboxes)
+
+        num_valid_proposals = proposals.shape[0]
+        bbox_gt = proposals.new_zeros([num_valid_proposals, 4])
+        pos_proposals = torch.zeros_like(proposals)
+        proposals_weights = proposals.new_zeros([num_valid_proposals, 4])
+        labels = proposals.new_full((num_valid_proposals, ),
+                                    self.num_classes,
+                                    dtype=torch.long)
+        label_weights = proposals.new_zeros(
+            num_valid_proposals, dtype=torch.float)
+
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+        if len(pos_inds) > 0:
+            pos_gt_bboxes = sampling_result.pos_gt_bboxes
+            bbox_gt[pos_inds, :] = pos_gt_bboxes
+            pos_proposals[pos_inds, :] = proposals[pos_inds, :]
+            proposals_weights[pos_inds, :] = 1.0
+            if gt_labels is None:
+                # Only rpn gives gt_labels as None
+                # Foreground is the first class
+                labels[pos_inds] = 0
+            else:
+                labels[pos_inds] = gt_labels[
+                    sampling_result.pos_assigned_gt_inds]
+            if pos_weight <= 0:
+                label_weights[pos_inds] = 1.0
+            else:
+                label_weights[pos_inds] = pos_weight
+        if len(neg_inds) > 0:
+            label_weights[neg_inds] = 1.0
+
+        # map up to original set of proposals
+        if unmap_outputs:
+            num_total_proposals = flat_proposals.size(0)
+            labels = unmap(labels, num_total_proposals, inside_flags)
+            label_weights = unmap(label_weights, num_total_proposals,
+                                  inside_flags)
+            bbox_gt = unmap(bbox_gt, num_total_proposals, inside_flags)
+            pos_proposals = unmap(pos_proposals, num_total_proposals,
+                                  inside_flags)
+            proposals_weights = unmap(proposals_weights, num_total_proposals,
+                                      inside_flags)
+
+        return (labels, label_weights, bbox_gt, pos_proposals,
+                proposals_weights, pos_inds, neg_inds)
+
+    def get_targets(self,
+                    proposals_list,
+                    valid_flag_list,
+                    gt_bboxes_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None,
+                    gt_labels_list=None,
+                    stage='init',
+                    label_channels=1,
+                    unmap_outputs=True):
+        """Compute corresponding GT box and classification targets for
+        proposals.
+
+        Args:
+            proposals_list (list[list]): Multi level points/bboxes of each
+                image.
+            valid_flag_list (list[list]): Multi level valid flags of each
+                image.
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+            img_metas (list[dict]): Meta info of each image.
+            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
+                ignored.
+            gt_bboxes_list (list[Tensor]): Ground truth labels of each box.
+            stage (str): `init` or `refine`. Generate target for init stage or
+                refine stage
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+
+        Returns:
+            tuple:
+                - labels_list (list[Tensor]): Labels of each level.
+                - label_weights_list (list[Tensor]): Label weights of each level.  # noqa: E501
+                - bbox_gt_list (list[Tensor]): Ground truth bbox of each level.
+                - proposal_list (list[Tensor]): Proposals(points/bboxes) of each level.  # noqa: E501
+                - proposal_weights_list (list[Tensor]): Proposal weights of each level.  # noqa: E501
+                - num_total_pos (int): Number of positive samples in all images.  # noqa: E501
+                - num_total_neg (int): Number of negative samples in all images.  # noqa: E501
+        """
+        assert stage in ['init', 'refine']
+        num_imgs = len(img_metas)
+        assert len(proposals_list) == len(valid_flag_list) == num_imgs
+
+        # points number of multi levels
+        num_level_proposals = [points.size(0) for points in proposals_list[0]]
+
+        # concat all level points and flags to a single tensor
+        for i in range(num_imgs):
+            assert len(proposals_list[i]) == len(valid_flag_list[i])
+            proposals_list[i] = torch.cat(proposals_list[i])
+            valid_flag_list[i] = torch.cat(valid_flag_list[i])
+
+        # compute targets for each image
+        if gt_bboxes_ignore_list is None:
+            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
+        if gt_labels_list is None:
+            gt_labels_list = [None for _ in range(num_imgs)]
+        (all_labels, all_label_weights, all_bbox_gt, all_proposals,
+         all_proposal_weights, pos_inds_list, neg_inds_list) = multi_apply(
+             self._point_target_single,
+             proposals_list,
+             valid_flag_list,
+             gt_bboxes_list,
+             gt_bboxes_ignore_list,
+             gt_labels_list,
+             stage=stage,
+             unmap_outputs=unmap_outputs)
+        # no valid points
+        if any([labels is None for labels in all_labels]):
+            return None
+        # sampled points of all images
+        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
+        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
+        labels_list = images_to_levels(all_labels, num_level_proposals)
+        label_weights_list = images_to_levels(all_label_weights,
+                                              num_level_proposals)
+        bbox_gt_list = images_to_levels(all_bbox_gt, num_level_proposals)
+        proposals_list = images_to_levels(all_proposals, num_level_proposals)
+        proposal_weights_list = images_to_levels(all_proposal_weights,
+                                                 num_level_proposals)
+        return (labels_list, label_weights_list, bbox_gt_list, proposals_list,
+                proposal_weights_list, num_total_pos, num_total_neg)
+
+    def loss_single(self, cls_score, pts_pred_init, pts_pred_refine, labels,
+                    label_weights, bbox_gt_init, bbox_weights_init,
+                    bbox_gt_refine, bbox_weights_refine, stride,
+                    num_total_samples_init, num_total_samples_refine):
+        # classification loss
+        labels = labels.reshape(-1)
+        label_weights = label_weights.reshape(-1)
+        cls_score = cls_score.permute(0, 2, 3,
+                                      1).reshape(-1, self.cls_out_channels)
+        cls_score = cls_score.contiguous()
+        loss_cls = self.loss_cls(
+            cls_score,
+            labels,
+            label_weights,
+            avg_factor=num_total_samples_refine)
+
+        # points loss
+        bbox_gt_init = bbox_gt_init.reshape(-1, 4)
+        bbox_weights_init = bbox_weights_init.reshape(-1, 4)
+        bbox_pred_init = self.points2bbox(
+            pts_pred_init.reshape(-1, 2 * self.num_points), y_first=False)
+        bbox_gt_refine = bbox_gt_refine.reshape(-1, 4)
+        bbox_weights_refine = bbox_weights_refine.reshape(-1, 4)
+        bbox_pred_refine = self.points2bbox(
+            pts_pred_refine.reshape(-1, 2 * self.num_points), y_first=False)
+        normalize_term = self.point_base_scale * stride
+        loss_pts_init = self.loss_bbox_init(
+            bbox_pred_init / normalize_term,
+            bbox_gt_init / normalize_term,
+            bbox_weights_init,
+            avg_factor=num_total_samples_init)
+        loss_pts_refine = self.loss_bbox_refine(
+            bbox_pred_refine / normalize_term,
+            bbox_gt_refine / normalize_term,
+            bbox_weights_refine,
+            avg_factor=num_total_samples_refine)
+        return loss_cls, loss_pts_init, loss_pts_refine
+
+    def loss(self,
+             cls_scores,
+             pts_preds_init,
+             pts_preds_refine,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        device = cls_scores[0].device
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+
+        # target for initial stage
+        center_list, valid_flag_list = self.get_points(featmap_sizes,
+                                                       img_metas, device)
+        pts_coordinate_preds_init = self.offset_to_pts(center_list,
+                                                       pts_preds_init)
+        if self.train_cfg.init.assigner['type'] == 'PointAssigner':
+            # Assign target for center list
+            candidate_list = center_list
+        else:
+            # transform center list to bbox list and
+            #   assign target for bbox list
+            bbox_list = self.centers_to_bboxes(center_list)
+            candidate_list = bbox_list
+        cls_reg_targets_init = self.get_targets(
+            candidate_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            stage='init',
+            label_channels=label_channels)
+        (*_, bbox_gt_list_init, candidate_list_init, bbox_weights_list_init,
+         num_total_pos_init, num_total_neg_init) = cls_reg_targets_init
+        num_total_samples_init = (
+            num_total_pos_init +
+            num_total_neg_init if self.sampling else num_total_pos_init)
+
+        # target for refinement stage
+        center_list, valid_flag_list = self.get_points(featmap_sizes,
+                                                       img_metas, device)
+        pts_coordinate_preds_refine = self.offset_to_pts(
+            center_list, pts_preds_refine)
+        bbox_list = []
+        for i_img, center in enumerate(center_list):
+            bbox = []
+            for i_lvl in range(len(pts_preds_refine)):
+                bbox_preds_init = self.points2bbox(
+                    pts_preds_init[i_lvl].detach())
+                bbox_shift = bbox_preds_init * self.point_strides[i_lvl]
+                bbox_center = torch.cat(
+                    [center[i_lvl][:, :2], center[i_lvl][:, :2]], dim=1)
+                bbox.append(bbox_center +
+                            bbox_shift[i_img].permute(1, 2, 0).reshape(-1, 4))
+            bbox_list.append(bbox)
+        cls_reg_targets_refine = self.get_targets(
+            bbox_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            stage='refine',
+            label_channels=label_channels)
+        (labels_list, label_weights_list, bbox_gt_list_refine,
+         candidate_list_refine, bbox_weights_list_refine, num_total_pos_refine,
+         num_total_neg_refine) = cls_reg_targets_refine
+        num_total_samples_refine = (
+            num_total_pos_refine +
+            num_total_neg_refine if self.sampling else num_total_pos_refine)
+
+        # compute loss
+        losses_cls, losses_pts_init, losses_pts_refine = multi_apply(
+            self.loss_single,
+            cls_scores,
+            pts_coordinate_preds_init,
+            pts_coordinate_preds_refine,
+            labels_list,
+            label_weights_list,
+            bbox_gt_list_init,
+            bbox_weights_list_init,
+            bbox_gt_list_refine,
+            bbox_weights_list_refine,
+            self.point_strides,
+            num_total_samples_init=num_total_samples_init,
+            num_total_samples_refine=num_total_samples_refine)
+        loss_dict_all = {
+            'loss_cls': losses_cls,
+            'loss_pts_init': losses_pts_init,
+            'loss_pts_refine': losses_pts_refine
+        }
+        return loss_dict_all
+
+    # Same as base_dense_head/_get_bboxes_single except self._bbox_decode
+    def _get_bboxes_single(self,
+                           cls_score_list,
+                           bbox_pred_list,
+                           score_factor_list,
+                           mlvl_priors,
+                           img_meta,
+                           cfg,
+                           rescale=False,
+                           with_nms=True,
+                           **kwargs):
+        """Transform outputs of a single image into bbox predictions.
+
+        Args:
+            cls_score_list (list[Tensor]): Box scores from all scale
+                levels of a single image, each item has shape
+                (num_priors * num_classes, H, W).
+            bbox_pred_list (list[Tensor]): Box energies / deltas from
+                all scale levels of a single image, each item has shape
+                (num_priors * 4, H, W).
+            score_factor_list (list[Tensor]): Score factor from all scale
+                levels of a single image. RepPoints head does not need
+                this value.
+            mlvl_priors (list[Tensor]): Each element in the list is
+                the priors of a single level in feature pyramid, has shape
+                (num_priors, 2).
+            img_meta (dict): Image meta info.
+            cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+
+        Returns:
+            tuple[Tensor]: Results of detected bboxes and labels. If with_nms
+                is False and mlvl_score_factor is None, return mlvl_bboxes and
+                mlvl_scores, else return mlvl_bboxes, mlvl_scores and
+                mlvl_score_factor. Usually with_nms is False is used for aug
+                test. If with_nms is True, then return the following format
+
+                - det_bboxes (Tensor): Predicted bboxes with shape \
+                    [num_bboxes, 5], where the first 4 columns are bounding \
+                    box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
+                    column are scores between 0 and 1.
+                - det_labels (Tensor): Predicted labels of the corresponding \
+                    box with shape [num_bboxes].
+        """
+        cfg = self.test_cfg if cfg is None else cfg
+        assert len(cls_score_list) == len(bbox_pred_list)
+        img_shape = img_meta['img_shape']
+        nms_pre = cfg.get('nms_pre', -1)
+
+        mlvl_bboxes = []
+        mlvl_scores = []
+        mlvl_labels = []
+        for level_idx, (cls_score, bbox_pred, priors) in enumerate(
+                zip(cls_score_list, bbox_pred_list, mlvl_priors)):
+            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
+            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
+
+            cls_score = cls_score.permute(1, 2,
+                                          0).reshape(-1, self.cls_out_channels)
+            if self.use_sigmoid_cls:
+                scores = cls_score.sigmoid()
+            else:
+                scores = cls_score.softmax(-1)[:, :-1]
+
+            # After https://github.com/open-mmlab/mmdetection/pull/6268/,
+            # this operation keeps fewer bboxes under the same `nms_pre`.
+            # There is no difference in performance for most models. If you
+            # find a slight drop in performance, you can set a larger
+            # `nms_pre` than before.
+            results = filter_scores_and_topk(
+                scores, cfg.score_thr, nms_pre,
+                dict(bbox_pred=bbox_pred, priors=priors))
+            scores, labels, _, filtered_results = results
+
+            bbox_pred = filtered_results['bbox_pred']
+            priors = filtered_results['priors']
+
+            bboxes = self._bbox_decode(priors, bbox_pred,
+                                       self.point_strides[level_idx],
+                                       img_shape)
+
+            mlvl_bboxes.append(bboxes)
+            mlvl_scores.append(scores)
+            mlvl_labels.append(labels)
+
+        return self._bbox_post_process(
+            mlvl_scores,
+            mlvl_labels,
+            mlvl_bboxes,
+            img_meta['scale_factor'],
+            cfg,
+            rescale=rescale,
+            with_nms=with_nms)
+
+    def _bbox_decode(self, points, bbox_pred, stride, max_shape):
+        bbox_pos_center = torch.cat([points[:, :2], points[:, :2]], dim=1)
+        bboxes = bbox_pred * stride + bbox_pos_center
+        x1 = bboxes[:, 0].clamp(min=0, max=max_shape[1])
+        y1 = bboxes[:, 1].clamp(min=0, max=max_shape[0])
+        x2 = bboxes[:, 2].clamp(min=0, max=max_shape[1])
+        y2 = bboxes[:, 3].clamp(min=0, max=max_shape[0])
+        decoded_bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
+        return decoded_bboxes
diff --git a/mmdet/models/dense_heads/retina_head.py b/mmdet/models/dense_heads/retina_head.py
new file mode 100644
index 0000000..bd04f73
--- /dev/null
+++ b/mmdet/models/dense_heads/retina_head.py
@@ -0,0 +1,191 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+from mmcv.cnn import build_norm_layer, ConvModule
+import global_placeholder
+from ..builder import HEADS
+from .anchor_head import AnchorHead
+
+class ModuleListDial(nn.ModuleList):
+    def __init__(self, modules=None):
+        super(ModuleListDial, self).__init__(modules)
+        self.cur_position = 0
+
+    def forward(self, x):
+        result = self[self.cur_position](x)
+        self.cur_position += 1
+        if self.cur_position >= len(self):
+            self.cur_position = 0
+        return result
+
+@HEADS.register_module()
+class RetinaHead(AnchorHead):
+    r"""An anchor-based head used in `RetinaNet
+    <https://arxiv.org/pdf/1708.02002.pdf>`_.
+
+    The head contains two subnetworks. The first classifies anchor boxes and
+    the second regresses deltas for the anchors.
+
+    Example:
+        >>> import torch
+        >>> self = RetinaHead(11, 7)
+        >>> x = torch.rand(1, 7, 32, 32)
+        >>> cls_score, bbox_pred = self.forward_single(x)
+        >>> # Each anchor predicts a score for each class except background
+        >>> cls_per_anchor = cls_score.shape[1] / self.num_anchors
+        >>> box_per_anchor = bbox_pred.shape[1] / self.num_anchors
+        >>> assert cls_per_anchor == (self.num_classes)
+        >>> assert box_per_anchor == 4
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 stacked_convs=4,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 anchor_generator=dict(
+                     type='AnchorGenerator',
+                     octave_base_scale=4,
+                     scales_per_octave=3,
+                     ratios=[0.5, 1.0, 2.0],
+                     strides=[8, 16, 32, 64, 128]),
+                 init_cfg=dict(
+                     type='Normal',
+                     layer='Conv2d',
+                     std=0.01,
+                     override=dict(
+                         type='Normal',
+                         name='retina_cls',
+                         std=0.01,
+                         bias_prob=0.01)),
+                 **kwargs):
+        self.stacked_convs = stacked_convs
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        super(RetinaHead, self).__init__(
+            num_classes,
+            in_channels,
+            anchor_generator=anchor_generator,
+            init_cfg=init_cfg,
+            **kwargs)
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.relu = nn.ReLU(inplace=True)
+        self.cls_convs = nn.ModuleList()
+        self.reg_convs = nn.ModuleList()
+        
+        if global_placeholder.aqd_mode != 0:
+            self.norm_cfg = dict(type='BN', requires_grad=True)
+            
+        for i in range(self.stacked_convs):
+            chn = self.in_channels if i == 0 else self.feat_channels
+            self.cls_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+            self.reg_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+            # if i == 0:
+            #     self.norm_cfg = dict(type='BN', requires_grad=True)
+            # else:
+            #     self.norm_cfg = None
+            # # self.norm_cfg = None
+            
+            if global_placeholder.aqd_mode != 0:
+                # 启动AQD模式
+                self.num_levels = global_placeholder.aqd_mode
+                
+                lega = self.cls_convs[-1].bn
+                self.cls_convs[-1].bn = ModuleListDial(
+                        [build_norm_layer(self.norm_cfg, self.feat_channels)[-1] for _ in range(self.num_levels)
+                    ])
+                del lega  # 删除，避免占内存（强迫症
+                
+                lega = self.reg_convs[-1].bn
+                self.reg_convs[-1].bn = ModuleListDial(
+                        [build_norm_layer(self.norm_cfg, self.feat_channels)[-1] for _ in range(self.num_levels)
+                    ])
+                del lega
+                
+        self.retina_cls = nn.Conv2d(
+            self.feat_channels,
+            self.num_base_priors * self.cls_out_channels,
+            3,
+            padding=1)
+        self.retina_reg = nn.Conv2d(
+            self.feat_channels, self.num_base_priors * 4, 3, padding=1)
+
+
+    def forward(self, feats):  # forward_single直接改成forward，应该不会有啥影响
+        """Forward feature of a single scale level.
+
+        Args:
+            x (Tensor): Features of a single scale level.
+
+        Returns:
+            tuple:
+                cls_score (Tensor): Cls scores for a single scale level
+                    the channels number is num_anchors * num_classes.
+                bbox_pred (Tensor): Box energies / deltas for a single scale
+                    level, the channels number is num_anchors * 4.
+        """
+        cls_scores = []
+        bbox_preds = []
+        if hasattr(self,'in_num') and self.in_num == 5:
+            feats = [feats[0], feats[1], feats[2], feats[3], feats[4]]
+        elif hasattr(self,'in_num'):
+            raise NotImplementedError
+        else:
+            self.in_num = 5
+            feats = [feats[0], feats[1], feats[2], feats[3], feats[4]]
+        
+        for x in feats:
+            cls_feat = x
+            reg_feat = x
+            for cls_conv in self.cls_convs:
+                cls_feat = cls_conv(cls_feat)
+            for reg_conv in self.reg_convs:
+                reg_feat = reg_conv(reg_feat)
+            cls_score = self.retina_cls(cls_feat)
+            bbox_pred = self.retina_reg(reg_feat)
+            cls_scores.append(cls_score)
+            bbox_preds.append(bbox_pred)
+            
+        return cls_scores, bbox_preds
+
+
+    def forward_single(self, x):
+        """Forward feature of a single scale level.
+
+        Args:
+            x (Tensor): Features of a single scale level.
+
+        Returns:
+            tuple:
+                cls_score (Tensor): Cls scores for a single scale level
+                    the channels number is num_anchors * num_classes.
+                bbox_pred (Tensor): Box energies / deltas for a single scale
+                    level, the channels number is num_anchors * 4.
+        """
+        cls_feat = x
+        reg_feat = x
+        for cls_conv in self.cls_convs:
+            cls_feat = cls_conv(cls_feat)
+        for reg_conv in self.reg_convs:
+            reg_feat = reg_conv(reg_feat)
+        cls_score = self.retina_cls(cls_feat)
+        bbox_pred = self.retina_reg(reg_feat)
+        return cls_score, bbox_pred
diff --git a/mmdet/models/dense_heads/retina_sepbn_head.py b/mmdet/models/dense_heads/retina_sepbn_head.py
new file mode 100644
index 0000000..b385c61
--- /dev/null
+++ b/mmdet/models/dense_heads/retina_sepbn_head.py
@@ -0,0 +1,118 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+from mmcv.cnn import ConvModule, bias_init_with_prob, normal_init
+
+from ..builder import HEADS
+from .anchor_head import AnchorHead
+
+
+@HEADS.register_module()
+class RetinaSepBNHead(AnchorHead):
+    """"RetinaHead with separate BN.
+
+    In RetinaHead, conv/norm layers are shared across different FPN levels,
+    while in RetinaSepBNHead, conv layers are shared across different FPN
+    levels, but BN layers are separated.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 num_ins,
+                 in_channels,
+                 stacked_convs=4,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 init_cfg=None,
+                 **kwargs):
+        assert init_cfg is None, 'To prevent abnormal initialization ' \
+                                 'behavior, init_cfg is not allowed to be set'
+        self.stacked_convs = stacked_convs
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.num_ins = num_ins
+        super(RetinaSepBNHead, self).__init__(
+            num_classes, in_channels, init_cfg=init_cfg, **kwargs)
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.relu = nn.ReLU(inplace=True)
+        self.cls_convs = nn.ModuleList()
+        self.reg_convs = nn.ModuleList()
+        for i in range(self.num_ins):
+            cls_convs = nn.ModuleList()
+            reg_convs = nn.ModuleList()
+            for i in range(self.stacked_convs):
+                chn = self.in_channels if i == 0 else self.feat_channels
+                cls_convs.append(
+                    ConvModule(
+                        chn,
+                        self.feat_channels,
+                        3,
+                        stride=1,
+                        padding=1,
+                        conv_cfg=self.conv_cfg,
+                        norm_cfg=self.norm_cfg))
+                reg_convs.append(
+                    ConvModule(
+                        chn,
+                        self.feat_channels,
+                        3,
+                        stride=1,
+                        padding=1,
+                        conv_cfg=self.conv_cfg,
+                        norm_cfg=self.norm_cfg))
+            self.cls_convs.append(cls_convs)
+            self.reg_convs.append(reg_convs)
+        for i in range(self.stacked_convs):
+            for j in range(1, self.num_ins):
+                self.cls_convs[j][i].conv = self.cls_convs[0][i].conv
+                self.reg_convs[j][i].conv = self.reg_convs[0][i].conv
+        self.retina_cls = nn.Conv2d(
+            self.feat_channels,
+            self.num_base_priors * self.cls_out_channels,
+            3,
+            padding=1)
+        self.retina_reg = nn.Conv2d(
+            self.feat_channels, self.num_base_priors * 4, 3, padding=1)
+
+    def init_weights(self):
+        """Initialize weights of the head."""
+        super(RetinaSepBNHead, self).init_weights()
+        for m in self.cls_convs[0]:
+            normal_init(m.conv, std=0.01)
+        for m in self.reg_convs[0]:
+            normal_init(m.conv, std=0.01)
+        bias_cls = bias_init_with_prob(0.01)
+        normal_init(self.retina_cls, std=0.01, bias=bias_cls)
+        normal_init(self.retina_reg, std=0.01)
+
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple: Usually a tuple of classification scores and bbox prediction
+                cls_scores (list[Tensor]): Classification scores for all scale
+                    levels, each is a 4D-tensor, the channels number is
+                    num_anchors * num_classes.
+                bbox_preds (list[Tensor]): Box energies / deltas for all scale
+                    levels, each is a 4D-tensor, the channels number is
+                    num_anchors * 4.
+        """
+        cls_scores = []
+        bbox_preds = []
+        for i, x in enumerate(feats):
+            cls_feat = feats[i]
+            reg_feat = feats[i]
+            for cls_conv in self.cls_convs[i]:
+                cls_feat = cls_conv(cls_feat)
+            for reg_conv in self.reg_convs[i]:
+                reg_feat = reg_conv(reg_feat)
+            cls_score = self.retina_cls(cls_feat)
+            bbox_pred = self.retina_reg(reg_feat)
+            cls_scores.append(cls_score)
+            bbox_preds.append(bbox_pred)
+        return cls_scores, bbox_preds
diff --git a/mmdet/models/dense_heads/rpn_head.py b/mmdet/models/dense_heads/rpn_head.py
new file mode 100644
index 0000000..a6577d6
--- /dev/null
+++ b/mmdet/models/dense_heads/rpn_head.py
@@ -0,0 +1,288 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+from mmcv.ops import batched_nms
+
+from ..builder import HEADS
+from .anchor_head import AnchorHead
+
+
+@HEADS.register_module()
+class RPNHead(AnchorHead):
+    """RPN head.
+
+    Args:
+        in_channels (int): Number of channels in the input feature map.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+        num_convs (int): Number of convolution layers in the head. Default 1.
+    """  # noqa: W605
+
+    def __init__(self,
+                 in_channels,
+                 init_cfg=dict(type='Normal', layer='Conv2d', std=0.01),
+                 num_convs=1,
+                 **kwargs):
+        self.num_convs = num_convs
+        super(RPNHead, self).__init__(
+            1, in_channels, init_cfg=init_cfg, **kwargs)
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        if self.num_convs > 1:
+            rpn_convs = []
+            for i in range(self.num_convs):
+                if i == 0:
+                    in_channels = self.in_channels
+                else:
+                    in_channels = self.feat_channels
+                # use ``inplace=False`` to avoid error: one of the variables
+                # needed for gradient computation has been modified by an
+                # inplace operation.
+                rpn_convs.append(
+                    ConvModule(
+                        in_channels,
+                        self.feat_channels,
+                        3,
+                        padding=1,
+                        inplace=False))
+            self.rpn_conv = nn.Sequential(*rpn_convs)
+        else:
+            self.rpn_conv = nn.Conv2d(
+                self.in_channels, self.feat_channels, 3, padding=1)
+        self.rpn_cls = nn.Conv2d(self.feat_channels,
+                                 self.num_base_priors * self.cls_out_channels,
+                                 1)
+        self.rpn_reg = nn.Conv2d(self.feat_channels, self.num_base_priors * 4,
+                                 1)
+
+    def forward_single(self, x):
+        """Forward feature map of a single scale level."""
+        x = self.rpn_conv(x)
+        x = F.relu(x, inplace=False)
+        rpn_cls_score = self.rpn_cls(x)
+        rpn_bbox_pred = self.rpn_reg(x)
+        return rpn_cls_score, rpn_bbox_pred
+    
+    
+    def forward(self, feats):
+        """Forward feature map of all scale levels."""
+        
+        rpn_cls_scores = []
+        rpn_bbox_preds = []
+        if hasattr(self,'in_num') and self.in_num == 5:
+            feats = [feats[0], feats[1], feats[2], feats[3], feats[4]]
+        elif hasattr(self,'in_num'):
+            raise NotImplementedError
+        else:
+            self.in_num = 5
+            feats = [feats[0], feats[1], feats[2], feats[3], feats[4]]
+        
+        for x in feats:
+            x = self.rpn_conv(x)
+            x = F.relu(x, inplace=False)
+            rpn_cls_score = self.rpn_cls(x)
+            rpn_bbox_pred = self.rpn_reg(x)
+            rpn_cls_scores.append(rpn_cls_score)
+            rpn_bbox_preds.append(rpn_bbox_pred)
+        return rpn_cls_scores, rpn_bbox_preds
+
+    # def loss(self,
+    #          cls_scores,
+    #          bbox_preds,
+    #          gt_bboxes,
+    #          img_metas,
+    #          gt_bboxes_ignore=None):
+    #     """Compute losses of the head.
+
+    #     Args:
+    #         cls_scores (list[Tensor]): Box scores for each scale level
+    #             Has shape (N, num_anchors * num_classes, H, W)
+    #         bbox_preds (list[Tensor]): Box energies / deltas for each scale
+    #             level with shape (N, num_anchors * 4, H, W)
+    #         gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+    #             shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+    #         img_metas (list[dict]): Meta information of each image, e.g.,
+    #             image size, scaling factor, etc.
+    #         gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+    #             boxes can be ignored when computing the loss.
+
+    #     Returns:
+    #         dict[str, Tensor]: A dictionary of loss components.
+    #     """
+    #     losses = super(RPNHead, self).loss(
+    #         cls_scores,
+    #         bbox_preds,
+    #         gt_bboxes,
+    #         None,
+    #         img_metas,
+    #         gt_bboxes_ignore=gt_bboxes_ignore)
+    #     return dict(
+    #         loss_rpn_cls=losses['loss_cls'], loss_rpn_bbox=losses['loss_bbox'])
+
+    def _get_bboxes_single(self,
+                           cls_score_list,
+                           bbox_pred_list,
+                           score_factor_list,
+                           mlvl_anchors,
+                           img_meta,
+                           cfg,
+                           rescale=False,
+                           with_nms=True,
+                           **kwargs):
+        """Transform outputs of a single image into bbox predictions.
+
+        Args:
+            cls_score_list (list[Tensor]): Box scores from all scale
+                levels of a single image, each item has shape
+                (num_anchors * num_classes, H, W).
+            bbox_pred_list (list[Tensor]): Box energies / deltas from
+                all scale levels of a single image, each item has
+                shape (num_anchors * 4, H, W).
+            score_factor_list (list[Tensor]): Score factor from all scale
+                levels of a single image. RPN head does not need this value.
+            mlvl_anchors (list[Tensor]): Anchors of all scale level
+                each item has shape (num_anchors, 4).
+            img_meta (dict): Image meta info.
+            cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+
+        Returns:
+            Tensor: Labeled boxes in shape (n, 5), where the first 4 columns
+                are bounding box positions (tl_x, tl_y, br_x, br_y) and the
+                5-th column is a score between 0 and 1.
+        """
+        cfg = self.test_cfg if cfg is None else cfg
+        cfg = copy.deepcopy(cfg)
+        img_shape = img_meta['img_shape']
+
+        # bboxes from different level should be independent during NMS,
+        # level_ids are used as labels for batched NMS to separate them
+        level_ids = []
+        mlvl_scores = []
+        mlvl_bbox_preds = []
+        mlvl_valid_anchors = []
+        nms_pre = cfg.get('nms_pre', -1)
+        for level_idx in range(len(cls_score_list)):
+            rpn_cls_score = cls_score_list[level_idx]
+            rpn_bbox_pred = bbox_pred_list[level_idx]
+            assert rpn_cls_score.size()[-2:] == rpn_bbox_pred.size()[-2:]
+            rpn_cls_score = rpn_cls_score.permute(1, 2, 0)
+            if self.use_sigmoid_cls:
+                rpn_cls_score = rpn_cls_score.reshape(-1)
+                scores = rpn_cls_score.sigmoid()
+            else:
+                rpn_cls_score = rpn_cls_score.reshape(-1, 2)
+                # We set FG labels to [0, num_class-1] and BG label to
+                # num_class in RPN head since mmdet v2.5, which is unified to
+                # be consistent with other head since mmdet v2.0. In mmdet v2.0
+                # to v2.4 we keep BG label as 0 and FG label as 1 in rpn head.
+                scores = rpn_cls_score.softmax(dim=1)[:, 0]
+            rpn_bbox_pred = rpn_bbox_pred.permute(1, 2, 0).reshape(-1, 4)
+
+            anchors = mlvl_anchors[level_idx]
+            if 0 < nms_pre < scores.shape[0]:
+                # sort is faster than topk
+                # _, topk_inds = scores.topk(cfg.nms_pre)
+                ranked_scores, rank_inds = scores.sort(descending=True)
+                topk_inds = rank_inds[:nms_pre]
+                scores = ranked_scores[:nms_pre]
+                rpn_bbox_pred = rpn_bbox_pred[topk_inds, :]
+                anchors = anchors[topk_inds, :]
+
+            mlvl_scores.append(scores)
+            mlvl_bbox_preds.append(rpn_bbox_pred)
+            mlvl_valid_anchors.append(anchors)
+            level_ids.append(
+                scores.new_full((scores.size(0), ),
+                                level_idx,
+                                dtype=torch.long))
+
+        return self._bbox_post_process(mlvl_scores, mlvl_bbox_preds,
+                                       mlvl_valid_anchors, level_ids, cfg,
+                                       img_shape)
+
+    def _bbox_post_process(self, mlvl_scores, mlvl_bboxes, mlvl_valid_anchors,
+                           level_ids, cfg, img_shape, **kwargs):
+        """bbox post-processing method.
+
+        Do the nms operation for bboxes in same level.
+
+        Args:
+            mlvl_scores (list[Tensor]): Box scores from all scale
+                levels of a single image, each item has shape
+                (num_bboxes, ).
+            mlvl_bboxes (list[Tensor]): Decoded bboxes from all scale
+                levels of a single image, each item has shape (num_bboxes, 4).
+            mlvl_valid_anchors (list[Tensor]): Anchors of all scale level
+                each item has shape (num_bboxes, 4).
+            level_ids (list[Tensor]): Indexes from all scale levels of a
+                single image, each item has shape (num_bboxes, ).
+            cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, `self.test_cfg` would be used.
+            img_shape (tuple(int)): The shape of model's input image.
+
+        Returns:
+            Tensor: Labeled boxes in shape (n, 5), where the first 4 columns
+                are bounding box positions (tl_x, tl_y, br_x, br_y) and the
+                5-th column is a score between 0 and 1.
+        """
+        scores = torch.cat(mlvl_scores)
+        anchors = torch.cat(mlvl_valid_anchors)
+        rpn_bbox_pred = torch.cat(mlvl_bboxes)
+        proposals = self.bbox_coder.decode(
+            anchors, rpn_bbox_pred, max_shape=img_shape)
+        ids = torch.cat(level_ids)
+
+        if cfg.min_bbox_size >= 0:
+            w = proposals[:, 2] - proposals[:, 0]
+            h = proposals[:, 3] - proposals[:, 1]
+            valid_mask = (w > cfg.min_bbox_size) & (h > cfg.min_bbox_size)
+            if not valid_mask.all():
+                proposals = proposals[valid_mask]
+                scores = scores[valid_mask]
+                ids = ids[valid_mask]
+
+        if proposals.numel() > 0:
+            dets, _ = batched_nms(proposals, scores, ids, cfg.nms)
+        else:
+            return proposals.new_zeros(0, 5)
+
+        return dets[:cfg.max_per_img]
+
+    def onnx_export(self, x, img_metas):
+        """Test without augmentation.
+
+        Args:
+            x (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+            img_metas (list[dict]): Meta info of each image.
+        Returns:
+            Tensor: dets of shape [N, num_det, 5].
+        """
+        cls_scores, bbox_preds = self(x)
+
+        assert len(cls_scores) == len(bbox_preds)
+
+        batch_bboxes, batch_scores = super(RPNHead, self).onnx_export(
+            cls_scores, bbox_preds, img_metas=img_metas, with_nms=False)
+        # Use ONNX::NonMaxSuppression in deployment
+        from mmdet.core.export import add_dummy_nms_for_onnx
+        cfg = copy.deepcopy(self.test_cfg)
+        score_threshold = cfg.nms.get('score_thr', 0.0)
+        nms_pre = cfg.get('deploy_nms_pre', -1)
+        # Different from the normal forward doing NMS level by level,
+        # we do NMS across all levels when exporting ONNX.
+        dets, _ = add_dummy_nms_for_onnx(batch_bboxes, batch_scores,
+                                         cfg.max_per_img,
+                                         cfg.nms.iou_threshold,
+                                         score_threshold, nms_pre,
+                                         cfg.max_per_img)
+        return dets
diff --git a/mmdet/models/dense_heads/sabl_retina_head.py b/mmdet/models/dense_heads/sabl_retina_head.py
new file mode 100644
index 0000000..4fede71
--- /dev/null
+++ b/mmdet/models/dense_heads/sabl_retina_head.py
@@ -0,0 +1,630 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import numpy as np
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.runner import force_fp32
+
+from mmdet.core import (build_assigner, build_bbox_coder,
+                        build_prior_generator, build_sampler, images_to_levels,
+                        multi_apply, unmap)
+from mmdet.core.utils import filter_scores_and_topk
+from ..builder import HEADS, build_loss
+from .base_dense_head import BaseDenseHead
+from .dense_test_mixins import BBoxTestMixin
+from .guided_anchor_head import GuidedAnchorHead
+
+
+@HEADS.register_module()
+class SABLRetinaHead(BaseDenseHead, BBoxTestMixin):
+    """Side-Aware Boundary Localization (SABL) for RetinaNet.
+
+    The anchor generation, assigning and sampling in SABLRetinaHead
+    are the same as GuidedAnchorHead for guided anchoring.
+
+    Please refer to https://arxiv.org/abs/1912.04260 for more details.
+
+    Args:
+        num_classes (int): Number of classes.
+        in_channels (int): Number of channels in the input feature map.
+        stacked_convs (int): Number of Convs for classification \
+            and regression branches. Defaults to 4.
+        feat_channels (int): Number of hidden channels. \
+            Defaults to 256.
+        approx_anchor_generator (dict): Config dict for approx generator.
+        square_anchor_generator (dict): Config dict for square generator.
+        conv_cfg (dict): Config dict for ConvModule. Defaults to None.
+        norm_cfg (dict): Config dict for Norm Layer. Defaults to None.
+        bbox_coder (dict): Config dict for bbox coder.
+        reg_decoded_bbox (bool): If true, the regression loss would be
+            applied directly on decoded bounding boxes, converting both
+            the predicted boxes and regression targets to absolute
+            coordinates format. Default False. It should be `True` when
+            using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
+        train_cfg (dict): Training config of SABLRetinaHead.
+        test_cfg (dict): Testing config of SABLRetinaHead.
+        loss_cls (dict): Config of classification loss.
+        loss_bbox_cls (dict): Config of classification loss for bbox branch.
+        loss_bbox_reg (dict): Config of regression loss for bbox branch.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 stacked_convs=4,
+                 feat_channels=256,
+                 approx_anchor_generator=dict(
+                     type='AnchorGenerator',
+                     octave_base_scale=4,
+                     scales_per_octave=3,
+                     ratios=[0.5, 1.0, 2.0],
+                     strides=[8, 16, 32, 64, 128]),
+                 square_anchor_generator=dict(
+                     type='AnchorGenerator',
+                     ratios=[1.0],
+                     scales=[4],
+                     strides=[8, 16, 32, 64, 128]),
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 bbox_coder=dict(
+                     type='BucketingBBoxCoder',
+                     num_buckets=14,
+                     scale_factor=3.0),
+                 reg_decoded_bbox=False,
+                 train_cfg=None,
+                 test_cfg=None,
+                 loss_cls=dict(
+                     type='FocalLoss',
+                     use_sigmoid=True,
+                     gamma=2.0,
+                     alpha=0.25,
+                     loss_weight=1.0),
+                 loss_bbox_cls=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.5),
+                 loss_bbox_reg=dict(
+                     type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.5),
+                 init_cfg=dict(
+                     type='Normal',
+                     layer='Conv2d',
+                     std=0.01,
+                     override=dict(
+                         type='Normal',
+                         name='retina_cls',
+                         std=0.01,
+                         bias_prob=0.01))):
+        super(SABLRetinaHead, self).__init__(init_cfg)
+        self.in_channels = in_channels
+        self.num_classes = num_classes
+        self.feat_channels = feat_channels
+        self.num_buckets = bbox_coder['num_buckets']
+        self.side_num = int(np.ceil(self.num_buckets / 2))
+
+        assert (approx_anchor_generator['octave_base_scale'] ==
+                square_anchor_generator['scales'][0])
+        assert (approx_anchor_generator['strides'] ==
+                square_anchor_generator['strides'])
+
+        self.approx_anchor_generator = build_prior_generator(
+            approx_anchor_generator)
+        self.square_anchor_generator = build_prior_generator(
+            square_anchor_generator)
+        self.approxs_per_octave = (
+            self.approx_anchor_generator.num_base_priors[0])
+
+        # one anchor per location
+        self.num_base_priors = self.square_anchor_generator.num_base_priors[0]
+
+        self.stacked_convs = stacked_convs
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+
+        self.reg_decoded_bbox = reg_decoded_bbox
+
+        self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
+        self.sampling = loss_cls['type'] not in [
+            'FocalLoss', 'GHMC', 'QualityFocalLoss'
+        ]
+        if self.use_sigmoid_cls:
+            self.cls_out_channels = num_classes
+        else:
+            self.cls_out_channels = num_classes + 1
+
+        self.bbox_coder = build_bbox_coder(bbox_coder)
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_bbox_cls = build_loss(loss_bbox_cls)
+        self.loss_bbox_reg = build_loss(loss_bbox_reg)
+
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+
+        if self.train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            # use PseudoSampler when sampling is False
+            if self.sampling and hasattr(self.train_cfg, 'sampler'):
+                sampler_cfg = self.train_cfg.sampler
+            else:
+                sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+
+        self.fp16_enabled = False
+        self._init_layers()
+
+    @property
+    def num_anchors(self):
+        warnings.warn('DeprecationWarning: `num_anchors` is deprecated, '
+                      'please use "num_base_priors" instead')
+        return self.square_anchor_generator.num_base_priors[0]
+
+    def _init_layers(self):
+        self.relu = nn.ReLU(inplace=True)
+        self.cls_convs = nn.ModuleList()
+        self.reg_convs = nn.ModuleList()
+        for i in range(self.stacked_convs):
+            chn = self.in_channels if i == 0 else self.feat_channels
+            self.cls_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+            self.reg_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+        self.retina_cls = nn.Conv2d(
+            self.feat_channels, self.cls_out_channels, 3, padding=1)
+        self.retina_bbox_reg = nn.Conv2d(
+            self.feat_channels, self.side_num * 4, 3, padding=1)
+        self.retina_bbox_cls = nn.Conv2d(
+            self.feat_channels, self.side_num * 4, 3, padding=1)
+
+    def forward_single(self, x):
+        cls_feat = x
+        reg_feat = x
+        for cls_conv in self.cls_convs:
+            cls_feat = cls_conv(cls_feat)
+        for reg_conv in self.reg_convs:
+            reg_feat = reg_conv(reg_feat)
+        cls_score = self.retina_cls(cls_feat)
+        bbox_cls_pred = self.retina_bbox_cls(reg_feat)
+        bbox_reg_pred = self.retina_bbox_reg(reg_feat)
+        bbox_pred = (bbox_cls_pred, bbox_reg_pred)
+        return cls_score, bbox_pred
+
+    def forward(self, feats):
+        return multi_apply(self.forward_single, feats)
+
+    def get_anchors(self, featmap_sizes, img_metas, device='cuda'):
+        """Get squares according to feature map sizes and guided anchors.
+
+        Args:
+            featmap_sizes (list[tuple]): Multi-level feature map sizes.
+            img_metas (list[dict]): Image meta info.
+            device (torch.device | str): device for returned tensors
+
+        Returns:
+            tuple: square approxs of each image
+        """
+        num_imgs = len(img_metas)
+
+        # since feature map sizes of all images are the same, we only compute
+        # squares for one time
+        multi_level_squares = self.square_anchor_generator.grid_priors(
+            featmap_sizes, device=device)
+        squares_list = [multi_level_squares for _ in range(num_imgs)]
+
+        return squares_list
+
+    def get_target(self,
+                   approx_list,
+                   inside_flag_list,
+                   square_list,
+                   gt_bboxes_list,
+                   img_metas,
+                   gt_bboxes_ignore_list=None,
+                   gt_labels_list=None,
+                   label_channels=None,
+                   sampling=True,
+                   unmap_outputs=True):
+        """Compute bucketing targets.
+        Args:
+            approx_list (list[list]): Multi level approxs of each image.
+            inside_flag_list (list[list]): Multi level inside flags of each
+                image.
+            square_list (list[list]): Multi level squares of each image.
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+            img_metas (list[dict]): Meta info of each image.
+            gt_bboxes_ignore_list (list[Tensor]): ignore list of gt bboxes.
+            gt_bboxes_list (list[Tensor]): Gt bboxes of each image.
+            label_channels (int): Channel of label.
+            sampling (bool): Sample Anchors or not.
+            unmap_outputs (bool): unmap outputs or not.
+
+        Returns:
+            tuple: Returns a tuple containing learning targets.
+
+                - labels_list (list[Tensor]): Labels of each level.
+                - label_weights_list (list[Tensor]): Label weights of each \
+                    level.
+                - bbox_cls_targets_list (list[Tensor]): BBox cls targets of \
+                    each level.
+                - bbox_cls_weights_list (list[Tensor]): BBox cls weights of \
+                    each level.
+                - bbox_reg_targets_list (list[Tensor]): BBox reg targets of \
+                    each level.
+                - bbox_reg_weights_list (list[Tensor]): BBox reg weights of \
+                    each level.
+                - num_total_pos (int): Number of positive samples in all \
+                    images.
+                - num_total_neg (int): Number of negative samples in all \
+                    images.
+        """
+        num_imgs = len(img_metas)
+        assert len(approx_list) == len(inside_flag_list) == len(
+            square_list) == num_imgs
+        # anchor number of multi levels
+        num_level_squares = [squares.size(0) for squares in square_list[0]]
+        # concat all level anchors and flags to a single tensor
+        inside_flag_flat_list = []
+        approx_flat_list = []
+        square_flat_list = []
+        for i in range(num_imgs):
+            assert len(square_list[i]) == len(inside_flag_list[i])
+            inside_flag_flat_list.append(torch.cat(inside_flag_list[i]))
+            approx_flat_list.append(torch.cat(approx_list[i]))
+            square_flat_list.append(torch.cat(square_list[i]))
+
+        # compute targets for each image
+        if gt_bboxes_ignore_list is None:
+            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
+        if gt_labels_list is None:
+            gt_labels_list = [None for _ in range(num_imgs)]
+        (all_labels, all_label_weights, all_bbox_cls_targets,
+         all_bbox_cls_weights, all_bbox_reg_targets, all_bbox_reg_weights,
+         pos_inds_list, neg_inds_list) = multi_apply(
+             self._get_target_single,
+             approx_flat_list,
+             inside_flag_flat_list,
+             square_flat_list,
+             gt_bboxes_list,
+             gt_bboxes_ignore_list,
+             gt_labels_list,
+             img_metas,
+             label_channels=label_channels,
+             sampling=sampling,
+             unmap_outputs=unmap_outputs)
+        # no valid anchors
+        if any([labels is None for labels in all_labels]):
+            return None
+        # sampled anchors of all images
+        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
+        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
+        # split targets to a list w.r.t. multiple levels
+        labels_list = images_to_levels(all_labels, num_level_squares)
+        label_weights_list = images_to_levels(all_label_weights,
+                                              num_level_squares)
+        bbox_cls_targets_list = images_to_levels(all_bbox_cls_targets,
+                                                 num_level_squares)
+        bbox_cls_weights_list = images_to_levels(all_bbox_cls_weights,
+                                                 num_level_squares)
+        bbox_reg_targets_list = images_to_levels(all_bbox_reg_targets,
+                                                 num_level_squares)
+        bbox_reg_weights_list = images_to_levels(all_bbox_reg_weights,
+                                                 num_level_squares)
+        return (labels_list, label_weights_list, bbox_cls_targets_list,
+                bbox_cls_weights_list, bbox_reg_targets_list,
+                bbox_reg_weights_list, num_total_pos, num_total_neg)
+
+    def _get_target_single(self,
+                           flat_approxs,
+                           inside_flags,
+                           flat_squares,
+                           gt_bboxes,
+                           gt_bboxes_ignore,
+                           gt_labels,
+                           img_meta,
+                           label_channels=None,
+                           sampling=True,
+                           unmap_outputs=True):
+        """Compute regression and classification targets for anchors in a
+        single image.
+
+        Args:
+            flat_approxs (Tensor): flat approxs of a single image,
+                shape (n, 4)
+            inside_flags (Tensor): inside flags of a single image,
+                shape (n, ).
+            flat_squares (Tensor): flat squares of a single image,
+                shape (approxs_per_octave * n, 4)
+            gt_bboxes (Tensor): Ground truth bboxes of a single image, \
+                shape (num_gts, 4).
+            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4).
+            gt_labels (Tensor): Ground truth labels of each box,
+                shape (num_gts,).
+            img_meta (dict): Meta info of the image.
+            label_channels (int): Channel of label.
+            sampling (bool): Sample Anchors or not.
+            unmap_outputs (bool): unmap outputs or not.
+
+        Returns:
+            tuple:
+
+                - labels_list (Tensor): Labels in a single image
+                - label_weights (Tensor): Label weights in a single image
+                - bbox_cls_targets (Tensor): BBox cls targets in a single image
+                - bbox_cls_weights (Tensor): BBox cls weights in a single image
+                - bbox_reg_targets (Tensor): BBox reg targets in a single image
+                - bbox_reg_weights (Tensor): BBox reg weights in a single image
+                - num_total_pos (int): Number of positive samples \
+                    in a single image
+                - num_total_neg (int): Number of negative samples \
+                    in a single image
+        """
+        if not inside_flags.any():
+            return (None, ) * 8
+        # assign gt and sample anchors
+        expand_inside_flags = inside_flags[:, None].expand(
+            -1, self.approxs_per_octave).reshape(-1)
+        approxs = flat_approxs[expand_inside_flags, :]
+        squares = flat_squares[inside_flags, :]
+
+        assign_result = self.assigner.assign(approxs, squares,
+                                             self.approxs_per_octave,
+                                             gt_bboxes, gt_bboxes_ignore)
+        sampling_result = self.sampler.sample(assign_result, squares,
+                                              gt_bboxes)
+
+        num_valid_squares = squares.shape[0]
+        bbox_cls_targets = squares.new_zeros(
+            (num_valid_squares, self.side_num * 4))
+        bbox_cls_weights = squares.new_zeros(
+            (num_valid_squares, self.side_num * 4))
+        bbox_reg_targets = squares.new_zeros(
+            (num_valid_squares, self.side_num * 4))
+        bbox_reg_weights = squares.new_zeros(
+            (num_valid_squares, self.side_num * 4))
+        labels = squares.new_full((num_valid_squares, ),
+                                  self.num_classes,
+                                  dtype=torch.long)
+        label_weights = squares.new_zeros(num_valid_squares, dtype=torch.float)
+
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+        if len(pos_inds) > 0:
+            (pos_bbox_reg_targets, pos_bbox_reg_weights, pos_bbox_cls_targets,
+             pos_bbox_cls_weights) = self.bbox_coder.encode(
+                 sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes)
+
+            bbox_cls_targets[pos_inds, :] = pos_bbox_cls_targets
+            bbox_reg_targets[pos_inds, :] = pos_bbox_reg_targets
+            bbox_cls_weights[pos_inds, :] = pos_bbox_cls_weights
+            bbox_reg_weights[pos_inds, :] = pos_bbox_reg_weights
+            if gt_labels is None:
+                # Only rpn gives gt_labels as None
+                # Foreground is the first class
+                labels[pos_inds] = 0
+            else:
+                labels[pos_inds] = gt_labels[
+                    sampling_result.pos_assigned_gt_inds]
+            if self.train_cfg.pos_weight <= 0:
+                label_weights[pos_inds] = 1.0
+            else:
+                label_weights[pos_inds] = self.train_cfg.pos_weight
+        if len(neg_inds) > 0:
+            label_weights[neg_inds] = 1.0
+
+        # map up to original set of anchors
+        if unmap_outputs:
+            num_total_anchors = flat_squares.size(0)
+            labels = unmap(
+                labels, num_total_anchors, inside_flags, fill=self.num_classes)
+            label_weights = unmap(label_weights, num_total_anchors,
+                                  inside_flags)
+            bbox_cls_targets = unmap(bbox_cls_targets, num_total_anchors,
+                                     inside_flags)
+            bbox_cls_weights = unmap(bbox_cls_weights, num_total_anchors,
+                                     inside_flags)
+            bbox_reg_targets = unmap(bbox_reg_targets, num_total_anchors,
+                                     inside_flags)
+            bbox_reg_weights = unmap(bbox_reg_weights, num_total_anchors,
+                                     inside_flags)
+        return (labels, label_weights, bbox_cls_targets, bbox_cls_weights,
+                bbox_reg_targets, bbox_reg_weights, pos_inds, neg_inds)
+
+    def loss_single(self, cls_score, bbox_pred, labels, label_weights,
+                    bbox_cls_targets, bbox_cls_weights, bbox_reg_targets,
+                    bbox_reg_weights, num_total_samples):
+        # classification loss
+        labels = labels.reshape(-1)
+        label_weights = label_weights.reshape(-1)
+        cls_score = cls_score.permute(0, 2, 3,
+                                      1).reshape(-1, self.cls_out_channels)
+        loss_cls = self.loss_cls(
+            cls_score, labels, label_weights, avg_factor=num_total_samples)
+        # regression loss
+        bbox_cls_targets = bbox_cls_targets.reshape(-1, self.side_num * 4)
+        bbox_cls_weights = bbox_cls_weights.reshape(-1, self.side_num * 4)
+        bbox_reg_targets = bbox_reg_targets.reshape(-1, self.side_num * 4)
+        bbox_reg_weights = bbox_reg_weights.reshape(-1, self.side_num * 4)
+        (bbox_cls_pred, bbox_reg_pred) = bbox_pred
+        bbox_cls_pred = bbox_cls_pred.permute(0, 2, 3, 1).reshape(
+            -1, self.side_num * 4)
+        bbox_reg_pred = bbox_reg_pred.permute(0, 2, 3, 1).reshape(
+            -1, self.side_num * 4)
+        loss_bbox_cls = self.loss_bbox_cls(
+            bbox_cls_pred,
+            bbox_cls_targets.long(),
+            bbox_cls_weights,
+            avg_factor=num_total_samples * 4 * self.side_num)
+        loss_bbox_reg = self.loss_bbox_reg(
+            bbox_reg_pred,
+            bbox_reg_targets,
+            bbox_reg_weights,
+            avg_factor=num_total_samples * 4 * self.bbox_coder.offset_topk)
+        return loss_cls, loss_bbox_cls, loss_bbox_reg
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.approx_anchor_generator.num_levels
+
+        device = cls_scores[0].device
+
+        # get sampled approxes
+        approxs_list, inside_flag_list = GuidedAnchorHead.get_sampled_approxs(
+            self, featmap_sizes, img_metas, device=device)
+
+        square_list = self.get_anchors(featmap_sizes, img_metas, device=device)
+
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+
+        cls_reg_targets = self.get_target(
+            approxs_list,
+            inside_flag_list,
+            square_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels,
+            sampling=self.sampling)
+        if cls_reg_targets is None:
+            return None
+        (labels_list, label_weights_list, bbox_cls_targets_list,
+         bbox_cls_weights_list, bbox_reg_targets_list, bbox_reg_weights_list,
+         num_total_pos, num_total_neg) = cls_reg_targets
+        num_total_samples = (
+            num_total_pos + num_total_neg if self.sampling else num_total_pos)
+        losses_cls, losses_bbox_cls, losses_bbox_reg = multi_apply(
+            self.loss_single,
+            cls_scores,
+            bbox_preds,
+            labels_list,
+            label_weights_list,
+            bbox_cls_targets_list,
+            bbox_cls_weights_list,
+            bbox_reg_targets_list,
+            bbox_reg_weights_list,
+            num_total_samples=num_total_samples)
+        return dict(
+            loss_cls=losses_cls,
+            loss_bbox_cls=losses_bbox_cls,
+            loss_bbox_reg=losses_bbox_reg)
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def get_bboxes(self,
+                   cls_scores,
+                   bbox_preds,
+                   img_metas,
+                   cfg=None,
+                   rescale=False):
+        assert len(cls_scores) == len(bbox_preds)
+        num_levels = len(cls_scores)
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+
+        device = cls_scores[0].device
+        mlvl_anchors = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        result_list = []
+        for img_id in range(len(img_metas)):
+            cls_score_list = [
+                cls_scores[i][img_id].detach() for i in range(num_levels)
+            ]
+            bbox_cls_pred_list = [
+                bbox_preds[i][0][img_id].detach() for i in range(num_levels)
+            ]
+            bbox_reg_pred_list = [
+                bbox_preds[i][1][img_id].detach() for i in range(num_levels)
+            ]
+            img_shape = img_metas[img_id]['img_shape']
+            scale_factor = img_metas[img_id]['scale_factor']
+            proposals = self._get_bboxes_single(
+                cls_score_list, bbox_cls_pred_list, bbox_reg_pred_list,
+                mlvl_anchors[img_id], img_shape, scale_factor, cfg, rescale)
+            result_list.append(proposals)
+        return result_list
+
+    def _get_bboxes_single(self,
+                           cls_scores,
+                           bbox_cls_preds,
+                           bbox_reg_preds,
+                           mlvl_anchors,
+                           img_shape,
+                           scale_factor,
+                           cfg,
+                           rescale=False):
+        cfg = self.test_cfg if cfg is None else cfg
+        nms_pre = cfg.get('nms_pre', -1)
+
+        mlvl_bboxes = []
+        mlvl_scores = []
+        mlvl_confids = []
+        mlvl_labels = []
+        assert len(cls_scores) == len(bbox_cls_preds) == len(
+            bbox_reg_preds) == len(mlvl_anchors)
+        for cls_score, bbox_cls_pred, bbox_reg_pred, anchors in zip(
+                cls_scores, bbox_cls_preds, bbox_reg_preds, mlvl_anchors):
+            assert cls_score.size()[-2:] == bbox_cls_pred.size(
+            )[-2:] == bbox_reg_pred.size()[-2::]
+            cls_score = cls_score.permute(1, 2,
+                                          0).reshape(-1, self.cls_out_channels)
+            if self.use_sigmoid_cls:
+                scores = cls_score.sigmoid()
+            else:
+                scores = cls_score.softmax(-1)[:, :-1]
+            bbox_cls_pred = bbox_cls_pred.permute(1, 2, 0).reshape(
+                -1, self.side_num * 4)
+            bbox_reg_pred = bbox_reg_pred.permute(1, 2, 0).reshape(
+                -1, self.side_num * 4)
+
+            # After https://github.com/open-mmlab/mmdetection/pull/6268/,
+            # this operation keeps fewer bboxes under the same `nms_pre`.
+            # There is no difference in performance for most models. If you
+            # find a slight drop in performance, you can set a larger
+            # `nms_pre` than before.
+            results = filter_scores_and_topk(
+                scores, cfg.score_thr, nms_pre,
+                dict(
+                    anchors=anchors,
+                    bbox_cls_pred=bbox_cls_pred,
+                    bbox_reg_pred=bbox_reg_pred))
+            scores, labels, _, filtered_results = results
+
+            anchors = filtered_results['anchors']
+            bbox_cls_pred = filtered_results['bbox_cls_pred']
+            bbox_reg_pred = filtered_results['bbox_reg_pred']
+
+            bbox_preds = [
+                bbox_cls_pred.contiguous(),
+                bbox_reg_pred.contiguous()
+            ]
+            bboxes, confids = self.bbox_coder.decode(
+                anchors.contiguous(), bbox_preds, max_shape=img_shape)
+
+            mlvl_bboxes.append(bboxes)
+            mlvl_scores.append(scores)
+            mlvl_confids.append(confids)
+            mlvl_labels.append(labels)
+        return self._bbox_post_process(mlvl_scores, mlvl_labels, mlvl_bboxes,
+                                       scale_factor, cfg, rescale, True,
+                                       mlvl_confids)
diff --git a/mmdet/models/dense_heads/solo_head.py b/mmdet/models/dense_heads/solo_head.py
new file mode 100644
index 0000000..e89aacb
--- /dev/null
+++ b/mmdet/models/dense_heads/solo_head.py
@@ -0,0 +1,1197 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+
+from mmdet.core import InstanceData, mask_matrix_nms, multi_apply
+from mmdet.core.utils import center_of_mass, generate_coordinate
+from mmdet.models.builder import HEADS, build_loss
+from mmdet.utils.misc import floordiv
+from .base_mask_head import BaseMaskHead
+
+
+@HEADS.register_module()
+class SOLOHead(BaseMaskHead):
+    """SOLO mask head used in `SOLO: Segmenting Objects by Locations.
+
+    <https://arxiv.org/abs/1912.04488>`_
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        feat_channels (int): Number of hidden channels. Used in child classes.
+            Default: 256.
+        stacked_convs (int): Number of stacking convs of the head.
+            Default: 4.
+        strides (tuple): Downsample factor of each feature map.
+        scale_ranges (tuple[tuple[int, int]]): Area range of multiple
+            level masks, in the format [(min1, max1), (min2, max2), ...].
+            A range of (16, 64) means the area range between (16, 64).
+        pos_scale (float): Constant scale factor to control the center region.
+        num_grids (list[int]): Divided image into a uniform grids, each
+            feature map has a different grid value. The number of output
+            channels is grid ** 2. Default: [40, 36, 24, 16, 12].
+        cls_down_index (int): The index of downsample operation in
+            classification branch. Default: 0.
+        loss_mask (dict): Config of mask loss.
+        loss_cls (dict): Config of classification loss.
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: norm_cfg=dict(type='GN', num_groups=32,
+                                   requires_grad=True).
+        train_cfg (dict): Training config of head.
+        test_cfg (dict): Testing config of head.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(
+        self,
+        num_classes,
+        in_channels,
+        feat_channels=256,
+        stacked_convs=4,
+        strides=(4, 8, 16, 32, 64),
+        scale_ranges=((8, 32), (16, 64), (32, 128), (64, 256), (128, 512)),
+        pos_scale=0.2,
+        num_grids=[40, 36, 24, 16, 12],
+        cls_down_index=0,
+        loss_mask=None,
+        loss_cls=None,
+        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
+        train_cfg=None,
+        test_cfg=None,
+        init_cfg=[
+            dict(type='Normal', layer='Conv2d', std=0.01),
+            dict(
+                type='Normal',
+                std=0.01,
+                bias_prob=0.01,
+                override=dict(name='conv_mask_list')),
+            dict(
+                type='Normal',
+                std=0.01,
+                bias_prob=0.01,
+                override=dict(name='conv_cls'))
+        ],
+    ):
+        super(SOLOHead, self).__init__(init_cfg)
+        self.num_classes = num_classes
+        self.cls_out_channels = self.num_classes
+        self.in_channels = in_channels
+        self.feat_channels = feat_channels
+        self.stacked_convs = stacked_convs
+        self.strides = strides
+        self.num_grids = num_grids
+        # number of FPN feats
+        self.num_levels = len(strides)
+        assert self.num_levels == len(scale_ranges) == len(num_grids)
+        self.scale_ranges = scale_ranges
+        self.pos_scale = pos_scale
+
+        self.cls_down_index = cls_down_index
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_mask = build_loss(loss_mask)
+        self.norm_cfg = norm_cfg
+        self.init_cfg = init_cfg
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+        self._init_layers()
+
+    def _init_layers(self):
+        self.mask_convs = nn.ModuleList()
+        self.cls_convs = nn.ModuleList()
+        for i in range(self.stacked_convs):
+            chn = self.in_channels + 2 if i == 0 else self.feat_channels
+            self.mask_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    norm_cfg=self.norm_cfg))
+            chn = self.in_channels if i == 0 else self.feat_channels
+            self.cls_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    norm_cfg=self.norm_cfg))
+        self.conv_mask_list = nn.ModuleList()
+        for num_grid in self.num_grids:
+            self.conv_mask_list.append(
+                nn.Conv2d(self.feat_channels, num_grid**2, 1))
+
+        self.conv_cls = nn.Conv2d(
+            self.feat_channels, self.cls_out_channels, 3, padding=1)
+
+    def resize_feats(self, feats):
+        """Downsample the first feat and upsample last feat in feats."""
+        out = []
+        for i in range(len(feats)):
+            if i == 0:
+                out.append(
+                    F.interpolate(
+                        feats[0],
+                        size=feats[i + 1].shape[-2:],
+                        mode='bilinear',
+                        align_corners=False))
+            elif i == len(feats) - 1:
+                out.append(
+                    F.interpolate(
+                        feats[i],
+                        size=feats[i - 1].shape[-2:],
+                        mode='bilinear',
+                        align_corners=False))
+            else:
+                out.append(feats[i])
+        return out
+
+    def forward(self, feats):
+        assert len(feats) == self.num_levels
+        feats = self.resize_feats(feats)
+        mlvl_mask_preds = []
+        mlvl_cls_preds = []
+        for i in range(self.num_levels):
+            x = feats[i]
+            mask_feat = x
+            cls_feat = x
+            # generate and concat the coordinate
+            coord_feat = generate_coordinate(mask_feat.size(),
+                                             mask_feat.device)
+            mask_feat = torch.cat([mask_feat, coord_feat], 1)
+
+            for mask_layer in (self.mask_convs):
+                mask_feat = mask_layer(mask_feat)
+
+            mask_feat = F.interpolate(
+                mask_feat, scale_factor=2, mode='bilinear')
+            mask_pred = self.conv_mask_list[i](mask_feat)
+
+            # cls branch
+            for j, cls_layer in enumerate(self.cls_convs):
+                if j == self.cls_down_index:
+                    num_grid = self.num_grids[i]
+                    cls_feat = F.interpolate(
+                        cls_feat, size=num_grid, mode='bilinear')
+                cls_feat = cls_layer(cls_feat)
+
+            cls_pred = self.conv_cls(cls_feat)
+
+            if not self.training:
+                feat_wh = feats[0].size()[-2:]
+                upsampled_size = (feat_wh[0] * 2, feat_wh[1] * 2)
+                mask_pred = F.interpolate(
+                    mask_pred.sigmoid(), size=upsampled_size, mode='bilinear')
+                cls_pred = cls_pred.sigmoid()
+                # get local maximum
+                local_max = F.max_pool2d(cls_pred, 2, stride=1, padding=1)
+                keep_mask = local_max[:, :, :-1, :-1] == cls_pred
+                cls_pred = cls_pred * keep_mask
+
+            mlvl_mask_preds.append(mask_pred)
+            mlvl_cls_preds.append(cls_pred)
+        return mlvl_mask_preds, mlvl_cls_preds
+
+    def loss(self,
+             mlvl_mask_preds,
+             mlvl_cls_preds,
+             gt_labels,
+             gt_masks,
+             img_metas,
+             gt_bboxes=None,
+             **kwargs):
+        """Calculate the loss of total batch.
+
+        Args:
+            mlvl_mask_preds (list[Tensor]): Multi-level mask prediction.
+                Each element in the list has shape
+                (batch_size, num_grids**2 ,h ,w).
+            mlvl_cls_preds (list[Tensor]): Multi-level scores. Each element
+                in the list has shape
+                (batch_size, num_classes, num_grids ,num_grids).
+            gt_labels (list[Tensor]): Labels of multiple images.
+            gt_masks (list[Tensor]): Ground truth masks of multiple images.
+                Each has shape (num_instances, h, w).
+            img_metas (list[dict]): Meta information of multiple images.
+            gt_bboxes (list[Tensor]): Ground truth bboxes of multiple
+                images. Default: None.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        num_levels = self.num_levels
+        num_imgs = len(gt_labels)
+
+        featmap_sizes = [featmap.size()[-2:] for featmap in mlvl_mask_preds]
+
+        # `BoolTensor` in `pos_masks` represent
+        # whether the corresponding point is
+        # positive
+        pos_mask_targets, labels, pos_masks = multi_apply(
+            self._get_targets_single,
+            gt_bboxes,
+            gt_labels,
+            gt_masks,
+            featmap_sizes=featmap_sizes)
+
+        # change from the outside list meaning multi images
+        # to the outside list meaning multi levels
+        mlvl_pos_mask_targets = [[] for _ in range(num_levels)]
+        mlvl_pos_mask_preds = [[] for _ in range(num_levels)]
+        mlvl_pos_masks = [[] for _ in range(num_levels)]
+        mlvl_labels = [[] for _ in range(num_levels)]
+        for img_id in range(num_imgs):
+            assert num_levels == len(pos_mask_targets[img_id])
+            for lvl in range(num_levels):
+                mlvl_pos_mask_targets[lvl].append(
+                    pos_mask_targets[img_id][lvl])
+                mlvl_pos_mask_preds[lvl].append(
+                    mlvl_mask_preds[lvl][img_id, pos_masks[img_id][lvl], ...])
+                mlvl_pos_masks[lvl].append(pos_masks[img_id][lvl].flatten())
+                mlvl_labels[lvl].append(labels[img_id][lvl].flatten())
+
+        # cat multiple image
+        temp_mlvl_cls_preds = []
+        for lvl in range(num_levels):
+            mlvl_pos_mask_targets[lvl] = torch.cat(
+                mlvl_pos_mask_targets[lvl], dim=0)
+            mlvl_pos_mask_preds[lvl] = torch.cat(
+                mlvl_pos_mask_preds[lvl], dim=0)
+            mlvl_pos_masks[lvl] = torch.cat(mlvl_pos_masks[lvl], dim=0)
+            mlvl_labels[lvl] = torch.cat(mlvl_labels[lvl], dim=0)
+            temp_mlvl_cls_preds.append(mlvl_cls_preds[lvl].permute(
+                0, 2, 3, 1).reshape(-1, self.cls_out_channels))
+
+        num_pos = sum(item.sum() for item in mlvl_pos_masks)
+        # dice loss
+        loss_mask = []
+        for pred, target in zip(mlvl_pos_mask_preds, mlvl_pos_mask_targets):
+            if pred.size()[0] == 0:
+                loss_mask.append(pred.sum().unsqueeze(0))
+                continue
+            loss_mask.append(
+                self.loss_mask(pred, target, reduction_override='none'))
+        if num_pos > 0:
+            loss_mask = torch.cat(loss_mask).sum() / num_pos
+        else:
+            loss_mask = torch.cat(loss_mask).mean()
+
+        flatten_labels = torch.cat(mlvl_labels)
+        flatten_cls_preds = torch.cat(temp_mlvl_cls_preds)
+        loss_cls = self.loss_cls(
+            flatten_cls_preds, flatten_labels, avg_factor=num_pos + 1)
+        return dict(loss_mask=loss_mask, loss_cls=loss_cls)
+
+    def _get_targets_single(self,
+                            gt_bboxes,
+                            gt_labels,
+                            gt_masks,
+                            featmap_sizes=None):
+        """Compute targets for predictions of single image.
+
+        Args:
+            gt_bboxes (Tensor): Ground truth bbox of each instance,
+                shape (num_gts, 4).
+            gt_labels (Tensor): Ground truth label of each instance,
+                shape (num_gts,).
+            gt_masks (Tensor): Ground truth mask of each instance,
+                shape (num_gts, h, w).
+            featmap_sizes (list[:obj:`torch.size`]): Size of each
+                feature map from feature pyramid, each element
+                means (feat_h, feat_w). Default: None.
+
+        Returns:
+            Tuple: Usually returns a tuple containing targets for predictions.
+
+                - mlvl_pos_mask_targets (list[Tensor]): Each element represent
+                  the binary mask targets for positive points in this
+                  level, has shape (num_pos, out_h, out_w).
+                - mlvl_labels (list[Tensor]): Each element is
+                  classification labels for all
+                  points in this level, has shape
+                  (num_grid, num_grid).
+                - mlvl_pos_masks (list[Tensor]): Each element is
+                  a `BoolTensor` to represent whether the
+                  corresponding point in single level
+                  is positive, has shape (num_grid **2).
+        """
+        device = gt_labels.device
+        gt_areas = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) *
+                              (gt_bboxes[:, 3] - gt_bboxes[:, 1]))
+
+        mlvl_pos_mask_targets = []
+        mlvl_labels = []
+        mlvl_pos_masks = []
+        for (lower_bound, upper_bound), stride, featmap_size, num_grid \
+                in zip(self.scale_ranges, self.strides,
+                       featmap_sizes, self.num_grids):
+
+            mask_target = torch.zeros(
+                [num_grid**2, featmap_size[0], featmap_size[1]],
+                dtype=torch.uint8,
+                device=device)
+            # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+            labels = torch.zeros([num_grid, num_grid],
+                                 dtype=torch.int64,
+                                 device=device) + self.num_classes
+            pos_mask = torch.zeros([num_grid**2],
+                                   dtype=torch.bool,
+                                   device=device)
+
+            gt_inds = ((gt_areas >= lower_bound) &
+                       (gt_areas <= upper_bound)).nonzero().flatten()
+            if len(gt_inds) == 0:
+                mlvl_pos_mask_targets.append(
+                    mask_target.new_zeros(0, featmap_size[0], featmap_size[1]))
+                mlvl_labels.append(labels)
+                mlvl_pos_masks.append(pos_mask)
+                continue
+            hit_gt_bboxes = gt_bboxes[gt_inds]
+            hit_gt_labels = gt_labels[gt_inds]
+            hit_gt_masks = gt_masks[gt_inds, ...]
+
+            pos_w_ranges = 0.5 * (hit_gt_bboxes[:, 2] -
+                                  hit_gt_bboxes[:, 0]) * self.pos_scale
+            pos_h_ranges = 0.5 * (hit_gt_bboxes[:, 3] -
+                                  hit_gt_bboxes[:, 1]) * self.pos_scale
+
+            # Make sure hit_gt_masks has a value
+            valid_mask_flags = hit_gt_masks.sum(dim=-1).sum(dim=-1) > 0
+            output_stride = stride / 2
+
+            for gt_mask, gt_label, pos_h_range, pos_w_range, \
+                valid_mask_flag in \
+                    zip(hit_gt_masks, hit_gt_labels, pos_h_ranges,
+                        pos_w_ranges, valid_mask_flags):
+                if not valid_mask_flag:
+                    continue
+                upsampled_size = (featmap_sizes[0][0] * 4,
+                                  featmap_sizes[0][1] * 4)
+                center_h, center_w = center_of_mass(gt_mask)
+
+                coord_w = int(
+                    floordiv((center_w / upsampled_size[1]), (1. / num_grid),
+                             rounding_mode='trunc'))
+                coord_h = int(
+                    floordiv((center_h / upsampled_size[0]), (1. / num_grid),
+                             rounding_mode='trunc'))
+
+                # left, top, right, down
+                top_box = max(
+                    0,
+                    int(
+                        floordiv(
+                            (center_h - pos_h_range) / upsampled_size[0],
+                            (1. / num_grid),
+                            rounding_mode='trunc')))
+                down_box = min(
+                    num_grid - 1,
+                    int(
+                        floordiv(
+                            (center_h + pos_h_range) / upsampled_size[0],
+                            (1. / num_grid),
+                            rounding_mode='trunc')))
+                left_box = max(
+                    0,
+                    int(
+                        floordiv(
+                            (center_w - pos_w_range) / upsampled_size[1],
+                            (1. / num_grid),
+                            rounding_mode='trunc')))
+                right_box = min(
+                    num_grid - 1,
+                    int(
+                        floordiv(
+                            (center_w + pos_w_range) / upsampled_size[1],
+                            (1. / num_grid),
+                            rounding_mode='trunc')))
+
+                top = max(top_box, coord_h - 1)
+                down = min(down_box, coord_h + 1)
+                left = max(coord_w - 1, left_box)
+                right = min(right_box, coord_w + 1)
+
+                labels[top:(down + 1), left:(right + 1)] = gt_label
+                # ins
+                gt_mask = np.uint8(gt_mask.cpu().numpy())
+                # Follow the original implementation, F.interpolate is
+                # different from cv2 and opencv
+                gt_mask = mmcv.imrescale(gt_mask, scale=1. / output_stride)
+                gt_mask = torch.from_numpy(gt_mask).to(device=device)
+
+                for i in range(top, down + 1):
+                    for j in range(left, right + 1):
+                        index = int(i * num_grid + j)
+                        mask_target[index, :gt_mask.shape[0], :gt_mask.
+                                    shape[1]] = gt_mask
+                        pos_mask[index] = True
+            mlvl_pos_mask_targets.append(mask_target[pos_mask])
+            mlvl_labels.append(labels)
+            mlvl_pos_masks.append(pos_mask)
+        return mlvl_pos_mask_targets, mlvl_labels, mlvl_pos_masks
+
+    def get_results(self, mlvl_mask_preds, mlvl_cls_scores, img_metas,
+                    **kwargs):
+        """Get multi-image mask results.
+
+        Args:
+            mlvl_mask_preds (list[Tensor]): Multi-level mask prediction.
+                Each element in the list has shape
+                (batch_size, num_grids**2 ,h ,w).
+            mlvl_cls_scores (list[Tensor]): Multi-level scores. Each element
+                in the list has shape
+                (batch_size, num_classes, num_grids ,num_grids).
+            img_metas (list[dict]): Meta information of all images.
+
+        Returns:
+            list[:obj:`InstanceData`]: Processed results of multiple
+            images.Each :obj:`InstanceData` usually contains
+            following keys.
+
+                - scores (Tensor): Classification scores, has shape
+                  (num_instance,).
+                - labels (Tensor): Has shape (num_instances,).
+                - masks (Tensor): Processed mask results, has
+                  shape (num_instances, h, w).
+        """
+        mlvl_cls_scores = [
+            item.permute(0, 2, 3, 1) for item in mlvl_cls_scores
+        ]
+        assert len(mlvl_mask_preds) == len(mlvl_cls_scores)
+        num_levels = len(mlvl_cls_scores)
+
+        results_list = []
+        for img_id in range(len(img_metas)):
+            cls_pred_list = [
+                mlvl_cls_scores[lvl][img_id].view(-1, self.cls_out_channels)
+                for lvl in range(num_levels)
+            ]
+            mask_pred_list = [
+                mlvl_mask_preds[lvl][img_id] for lvl in range(num_levels)
+            ]
+
+            cls_pred_list = torch.cat(cls_pred_list, dim=0)
+            mask_pred_list = torch.cat(mask_pred_list, dim=0)
+
+            results = self._get_results_single(
+                cls_pred_list, mask_pred_list, img_meta=img_metas[img_id])
+            results_list.append(results)
+
+        return results_list
+
+    def _get_results_single(self, cls_scores, mask_preds, img_meta, cfg=None):
+        """Get processed mask related results of single image.
+
+        Args:
+            cls_scores (Tensor): Classification score of all points
+                in single image, has shape (num_points, num_classes).
+            mask_preds (Tensor): Mask prediction of all points in
+                single image, has shape (num_points, feat_h, feat_w).
+            img_meta (dict): Meta information of corresponding image.
+            cfg (dict, optional): Config used in test phase.
+                Default: None.
+
+        Returns:
+            :obj:`InstanceData`: Processed results of single image.
+             it usually contains following keys.
+
+                - scores (Tensor): Classification scores, has shape
+                  (num_instance,).
+                - labels (Tensor): Has shape (num_instances,).
+                - masks (Tensor): Processed mask results, has
+                  shape (num_instances, h, w).
+        """
+
+        def empty_results(results, cls_scores):
+            """Generate a empty results."""
+            results.scores = cls_scores.new_ones(0)
+            results.masks = cls_scores.new_zeros(0, *results.ori_shape[:2])
+            results.labels = cls_scores.new_ones(0)
+            return results
+
+        cfg = self.test_cfg if cfg is None else cfg
+        assert len(cls_scores) == len(mask_preds)
+        results = InstanceData(img_meta)
+
+        featmap_size = mask_preds.size()[-2:]
+
+        img_shape = results.img_shape
+        ori_shape = results.ori_shape
+
+        h, w, _ = img_shape
+        upsampled_size = (featmap_size[0] * 4, featmap_size[1] * 4)
+
+        score_mask = (cls_scores > cfg.score_thr)
+        cls_scores = cls_scores[score_mask]
+        if len(cls_scores) == 0:
+            return empty_results(results, cls_scores)
+
+        inds = score_mask.nonzero()
+        cls_labels = inds[:, 1]
+
+        # Filter the mask mask with an area is smaller than
+        # stride of corresponding feature level
+        lvl_interval = cls_labels.new_tensor(self.num_grids).pow(2).cumsum(0)
+        strides = cls_scores.new_ones(lvl_interval[-1])
+        strides[:lvl_interval[0]] *= self.strides[0]
+        for lvl in range(1, self.num_levels):
+            strides[lvl_interval[lvl -
+                                 1]:lvl_interval[lvl]] *= self.strides[lvl]
+        strides = strides[inds[:, 0]]
+        mask_preds = mask_preds[inds[:, 0]]
+
+        masks = mask_preds > cfg.mask_thr
+        sum_masks = masks.sum((1, 2)).float()
+        keep = sum_masks > strides
+        if keep.sum() == 0:
+            return empty_results(results, cls_scores)
+        masks = masks[keep]
+        mask_preds = mask_preds[keep]
+        sum_masks = sum_masks[keep]
+        cls_scores = cls_scores[keep]
+        cls_labels = cls_labels[keep]
+
+        # maskness.
+        mask_scores = (mask_preds * masks).sum((1, 2)) / sum_masks
+        cls_scores *= mask_scores
+
+        scores, labels, _, keep_inds = mask_matrix_nms(
+            masks,
+            cls_labels,
+            cls_scores,
+            mask_area=sum_masks,
+            nms_pre=cfg.nms_pre,
+            max_num=cfg.max_per_img,
+            kernel=cfg.kernel,
+            sigma=cfg.sigma,
+            filter_thr=cfg.filter_thr)
+        mask_preds = mask_preds[keep_inds]
+        mask_preds = F.interpolate(
+            mask_preds.unsqueeze(0), size=upsampled_size,
+            mode='bilinear')[:, :, :h, :w]
+        mask_preds = F.interpolate(
+            mask_preds, size=ori_shape[:2], mode='bilinear').squeeze(0)
+        masks = mask_preds > cfg.mask_thr
+
+        results.masks = masks
+        results.labels = labels
+        results.scores = scores
+
+        return results
+
+
+@HEADS.register_module()
+class DecoupledSOLOHead(SOLOHead):
+    """Decoupled SOLO mask head used in `SOLO: Segmenting Objects by Locations.
+
+    <https://arxiv.org/abs/1912.04488>`_
+
+    Args:
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 *args,
+                 init_cfg=[
+                     dict(type='Normal', layer='Conv2d', std=0.01),
+                     dict(
+                         type='Normal',
+                         std=0.01,
+                         bias_prob=0.01,
+                         override=dict(name='conv_mask_list_x')),
+                     dict(
+                         type='Normal',
+                         std=0.01,
+                         bias_prob=0.01,
+                         override=dict(name='conv_mask_list_y')),
+                     dict(
+                         type='Normal',
+                         std=0.01,
+                         bias_prob=0.01,
+                         override=dict(name='conv_cls'))
+                 ],
+                 **kwargs):
+        super(DecoupledSOLOHead, self).__init__(
+            *args, init_cfg=init_cfg, **kwargs)
+
+    def _init_layers(self):
+        self.mask_convs_x = nn.ModuleList()
+        self.mask_convs_y = nn.ModuleList()
+        self.cls_convs = nn.ModuleList()
+
+        for i in range(self.stacked_convs):
+            chn = self.in_channels + 1 if i == 0 else self.feat_channels
+            self.mask_convs_x.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    norm_cfg=self.norm_cfg))
+            self.mask_convs_y.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    norm_cfg=self.norm_cfg))
+
+            chn = self.in_channels if i == 0 else self.feat_channels
+            self.cls_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    norm_cfg=self.norm_cfg))
+
+        self.conv_mask_list_x = nn.ModuleList()
+        self.conv_mask_list_y = nn.ModuleList()
+        for num_grid in self.num_grids:
+            self.conv_mask_list_x.append(
+                nn.Conv2d(self.feat_channels, num_grid, 3, padding=1))
+            self.conv_mask_list_y.append(
+                nn.Conv2d(self.feat_channels, num_grid, 3, padding=1))
+        self.conv_cls = nn.Conv2d(
+            self.feat_channels, self.cls_out_channels, 3, padding=1)
+
+    def forward(self, feats):
+        assert len(feats) == self.num_levels
+        feats = self.resize_feats(feats)
+        mask_preds_x = []
+        mask_preds_y = []
+        cls_preds = []
+        for i in range(self.num_levels):
+            x = feats[i]
+            mask_feat = x
+            cls_feat = x
+            # generate and concat the coordinate
+            coord_feat = generate_coordinate(mask_feat.size(),
+                                             mask_feat.device)
+            mask_feat_x = torch.cat([mask_feat, coord_feat[:, 0:1, ...]], 1)
+            mask_feat_y = torch.cat([mask_feat, coord_feat[:, 1:2, ...]], 1)
+
+            for mask_layer_x, mask_layer_y in \
+                    zip(self.mask_convs_x, self.mask_convs_y):
+                mask_feat_x = mask_layer_x(mask_feat_x)
+                mask_feat_y = mask_layer_y(mask_feat_y)
+
+            mask_feat_x = F.interpolate(
+                mask_feat_x, scale_factor=2, mode='bilinear')
+            mask_feat_y = F.interpolate(
+                mask_feat_y, scale_factor=2, mode='bilinear')
+
+            mask_pred_x = self.conv_mask_list_x[i](mask_feat_x)
+            mask_pred_y = self.conv_mask_list_y[i](mask_feat_y)
+
+            # cls branch
+            for j, cls_layer in enumerate(self.cls_convs):
+                if j == self.cls_down_index:
+                    num_grid = self.num_grids[i]
+                    cls_feat = F.interpolate(
+                        cls_feat, size=num_grid, mode='bilinear')
+                cls_feat = cls_layer(cls_feat)
+
+            cls_pred = self.conv_cls(cls_feat)
+
+            if not self.training:
+                feat_wh = feats[0].size()[-2:]
+                upsampled_size = (feat_wh[0] * 2, feat_wh[1] * 2)
+                mask_pred_x = F.interpolate(
+                    mask_pred_x.sigmoid(),
+                    size=upsampled_size,
+                    mode='bilinear')
+                mask_pred_y = F.interpolate(
+                    mask_pred_y.sigmoid(),
+                    size=upsampled_size,
+                    mode='bilinear')
+                cls_pred = cls_pred.sigmoid()
+                # get local maximum
+                local_max = F.max_pool2d(cls_pred, 2, stride=1, padding=1)
+                keep_mask = local_max[:, :, :-1, :-1] == cls_pred
+                cls_pred = cls_pred * keep_mask
+
+            mask_preds_x.append(mask_pred_x)
+            mask_preds_y.append(mask_pred_y)
+            cls_preds.append(cls_pred)
+        return mask_preds_x, mask_preds_y, cls_preds
+
+    def loss(self,
+             mlvl_mask_preds_x,
+             mlvl_mask_preds_y,
+             mlvl_cls_preds,
+             gt_labels,
+             gt_masks,
+             img_metas,
+             gt_bboxes=None,
+             **kwargs):
+        """Calculate the loss of total batch.
+
+        Args:
+            mlvl_mask_preds_x (list[Tensor]): Multi-level mask prediction
+                from x branch. Each element in the list has shape
+                (batch_size, num_grids ,h ,w).
+            mlvl_mask_preds_x (list[Tensor]): Multi-level mask prediction
+                from y branch. Each element in the list has shape
+                (batch_size, num_grids ,h ,w).
+            mlvl_cls_preds (list[Tensor]): Multi-level scores. Each element
+                in the list has shape
+                (batch_size, num_classes, num_grids ,num_grids).
+            gt_labels (list[Tensor]): Labels of multiple images.
+            gt_masks (list[Tensor]): Ground truth masks of multiple images.
+                Each has shape (num_instances, h, w).
+            img_metas (list[dict]): Meta information of multiple images.
+            gt_bboxes (list[Tensor]): Ground truth bboxes of multiple
+                images. Default: None.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        num_levels = self.num_levels
+        num_imgs = len(gt_labels)
+        featmap_sizes = [featmap.size()[-2:] for featmap in mlvl_mask_preds_x]
+
+        pos_mask_targets, labels, \
+            xy_pos_indexes = \
+            multi_apply(self._get_targets_single,
+                        gt_bboxes,
+                        gt_labels,
+                        gt_masks,
+                        featmap_sizes=featmap_sizes)
+
+        # change from the outside list meaning multi images
+        # to the outside list meaning multi levels
+        mlvl_pos_mask_targets = [[] for _ in range(num_levels)]
+        mlvl_pos_mask_preds_x = [[] for _ in range(num_levels)]
+        mlvl_pos_mask_preds_y = [[] for _ in range(num_levels)]
+        mlvl_labels = [[] for _ in range(num_levels)]
+        for img_id in range(num_imgs):
+
+            for lvl in range(num_levels):
+                mlvl_pos_mask_targets[lvl].append(
+                    pos_mask_targets[img_id][lvl])
+                mlvl_pos_mask_preds_x[lvl].append(
+                    mlvl_mask_preds_x[lvl][img_id,
+                                           xy_pos_indexes[img_id][lvl][:, 1]])
+                mlvl_pos_mask_preds_y[lvl].append(
+                    mlvl_mask_preds_y[lvl][img_id,
+                                           xy_pos_indexes[img_id][lvl][:, 0]])
+                mlvl_labels[lvl].append(labels[img_id][lvl].flatten())
+
+        # cat multiple image
+        temp_mlvl_cls_preds = []
+        for lvl in range(num_levels):
+            mlvl_pos_mask_targets[lvl] = torch.cat(
+                mlvl_pos_mask_targets[lvl], dim=0)
+            mlvl_pos_mask_preds_x[lvl] = torch.cat(
+                mlvl_pos_mask_preds_x[lvl], dim=0)
+            mlvl_pos_mask_preds_y[lvl] = torch.cat(
+                mlvl_pos_mask_preds_y[lvl], dim=0)
+            mlvl_labels[lvl] = torch.cat(mlvl_labels[lvl], dim=0)
+            temp_mlvl_cls_preds.append(mlvl_cls_preds[lvl].permute(
+                0, 2, 3, 1).reshape(-1, self.cls_out_channels))
+
+        num_pos = 0.
+        # dice loss
+        loss_mask = []
+        for pred_x, pred_y, target in \
+                zip(mlvl_pos_mask_preds_x,
+                    mlvl_pos_mask_preds_y, mlvl_pos_mask_targets):
+            num_masks = pred_x.size(0)
+            if num_masks == 0:
+                # make sure can get grad
+                loss_mask.append((pred_x.sum() + pred_y.sum()).unsqueeze(0))
+                continue
+            num_pos += num_masks
+            pred_mask = pred_y.sigmoid() * pred_x.sigmoid()
+            loss_mask.append(
+                self.loss_mask(pred_mask, target, reduction_override='none'))
+        if num_pos > 0:
+            loss_mask = torch.cat(loss_mask).sum() / num_pos
+        else:
+            loss_mask = torch.cat(loss_mask).mean()
+
+        # cate
+        flatten_labels = torch.cat(mlvl_labels)
+        flatten_cls_preds = torch.cat(temp_mlvl_cls_preds)
+
+        loss_cls = self.loss_cls(
+            flatten_cls_preds, flatten_labels, avg_factor=num_pos + 1)
+        return dict(loss_mask=loss_mask, loss_cls=loss_cls)
+
+    def _get_targets_single(self,
+                            gt_bboxes,
+                            gt_labels,
+                            gt_masks,
+                            featmap_sizes=None):
+        """Compute targets for predictions of single image.
+
+        Args:
+            gt_bboxes (Tensor): Ground truth bbox of each instance,
+                shape (num_gts, 4).
+            gt_labels (Tensor): Ground truth label of each instance,
+                shape (num_gts,).
+            gt_masks (Tensor): Ground truth mask of each instance,
+                shape (num_gts, h, w).
+            featmap_sizes (list[:obj:`torch.size`]): Size of each
+                feature map from feature pyramid, each element
+                means (feat_h, feat_w). Default: None.
+
+        Returns:
+            Tuple: Usually returns a tuple containing targets for predictions.
+
+                - mlvl_pos_mask_targets (list[Tensor]): Each element represent
+                  the binary mask targets for positive points in this
+                  level, has shape (num_pos, out_h, out_w).
+                - mlvl_labels (list[Tensor]): Each element is
+                  classification labels for all
+                  points in this level, has shape
+                  (num_grid, num_grid).
+                - mlvl_xy_pos_indexes (list[Tensor]): Each element
+                  in the list contains the index of positive samples in
+                  corresponding level, has shape (num_pos, 2), last
+                  dimension 2 present (index_x, index_y).
+        """
+        mlvl_pos_mask_targets, mlvl_labels, \
+            mlvl_pos_masks = \
+            super()._get_targets_single(gt_bboxes, gt_labels, gt_masks,
+                                        featmap_sizes=featmap_sizes)
+
+        mlvl_xy_pos_indexes = [(item - self.num_classes).nonzero()
+                               for item in mlvl_labels]
+
+        return mlvl_pos_mask_targets, mlvl_labels, mlvl_xy_pos_indexes
+
+    def get_results(self,
+                    mlvl_mask_preds_x,
+                    mlvl_mask_preds_y,
+                    mlvl_cls_scores,
+                    img_metas,
+                    rescale=None,
+                    **kwargs):
+        """Get multi-image mask results.
+
+        Args:
+            mlvl_mask_preds_x (list[Tensor]): Multi-level mask prediction
+                from x branch. Each element in the list has shape
+                (batch_size, num_grids ,h ,w).
+            mlvl_mask_preds_y (list[Tensor]): Multi-level mask prediction
+                from y branch. Each element in the list has shape
+                (batch_size, num_grids ,h ,w).
+            mlvl_cls_scores (list[Tensor]): Multi-level scores. Each element
+                in the list has shape
+                (batch_size, num_classes ,num_grids ,num_grids).
+            img_metas (list[dict]): Meta information of all images.
+
+        Returns:
+            list[:obj:`InstanceData`]: Processed results of multiple
+            images.Each :obj:`InstanceData` usually contains
+            following keys.
+
+                - scores (Tensor): Classification scores, has shape
+                  (num_instance,).
+                - labels (Tensor): Has shape (num_instances,).
+                - masks (Tensor): Processed mask results, has
+                  shape (num_instances, h, w).
+        """
+        mlvl_cls_scores = [
+            item.permute(0, 2, 3, 1) for item in mlvl_cls_scores
+        ]
+        assert len(mlvl_mask_preds_x) == len(mlvl_cls_scores)
+        num_levels = len(mlvl_cls_scores)
+
+        results_list = []
+        for img_id in range(len(img_metas)):
+            cls_pred_list = [
+                mlvl_cls_scores[i][img_id].view(
+                    -1, self.cls_out_channels).detach()
+                for i in range(num_levels)
+            ]
+            mask_pred_list_x = [
+                mlvl_mask_preds_x[i][img_id] for i in range(num_levels)
+            ]
+            mask_pred_list_y = [
+                mlvl_mask_preds_y[i][img_id] for i in range(num_levels)
+            ]
+
+            cls_pred_list = torch.cat(cls_pred_list, dim=0)
+            mask_pred_list_x = torch.cat(mask_pred_list_x, dim=0)
+            mask_pred_list_y = torch.cat(mask_pred_list_y, dim=0)
+
+            results = self._get_results_single(
+                cls_pred_list,
+                mask_pred_list_x,
+                mask_pred_list_y,
+                img_meta=img_metas[img_id],
+                cfg=self.test_cfg)
+            results_list.append(results)
+        return results_list
+
+    def _get_results_single(self, cls_scores, mask_preds_x, mask_preds_y,
+                            img_meta, cfg):
+        """Get processed mask related results of single image.
+
+        Args:
+            cls_scores (Tensor): Classification score of all points
+                in single image, has shape (num_points, num_classes).
+            mask_preds_x (Tensor): Mask prediction of x branch of
+                all points in single image, has shape
+                (sum_num_grids, feat_h, feat_w).
+            mask_preds_y (Tensor): Mask prediction of y branch of
+                all points in single image, has shape
+                (sum_num_grids, feat_h, feat_w).
+            img_meta (dict): Meta information of corresponding image.
+            cfg (dict): Config used in test phase.
+
+        Returns:
+            :obj:`InstanceData`: Processed results of single image.
+             it usually contains following keys.
+
+                - scores (Tensor): Classification scores, has shape
+                  (num_instance,).
+                - labels (Tensor): Has shape (num_instances,).
+                - masks (Tensor): Processed mask results, has
+                  shape (num_instances, h, w).
+        """
+
+        def empty_results(results, cls_scores):
+            """Generate a empty results."""
+            results.scores = cls_scores.new_ones(0)
+            results.masks = cls_scores.new_zeros(0, *results.ori_shape[:2])
+            results.labels = cls_scores.new_ones(0)
+            return results
+
+        cfg = self.test_cfg if cfg is None else cfg
+
+        results = InstanceData(img_meta)
+        img_shape = results.img_shape
+        ori_shape = results.ori_shape
+        h, w, _ = img_shape
+        featmap_size = mask_preds_x.size()[-2:]
+        upsampled_size = (featmap_size[0] * 4, featmap_size[1] * 4)
+
+        score_mask = (cls_scores > cfg.score_thr)
+        cls_scores = cls_scores[score_mask]
+        inds = score_mask.nonzero()
+        lvl_interval = inds.new_tensor(self.num_grids).pow(2).cumsum(0)
+        num_all_points = lvl_interval[-1]
+        lvl_start_index = inds.new_ones(num_all_points)
+        num_grids = inds.new_ones(num_all_points)
+        seg_size = inds.new_tensor(self.num_grids).cumsum(0)
+        mask_lvl_start_index = inds.new_ones(num_all_points)
+        strides = inds.new_ones(num_all_points)
+
+        lvl_start_index[:lvl_interval[0]] *= 0
+        mask_lvl_start_index[:lvl_interval[0]] *= 0
+        num_grids[:lvl_interval[0]] *= self.num_grids[0]
+        strides[:lvl_interval[0]] *= self.strides[0]
+
+        for lvl in range(1, self.num_levels):
+            lvl_start_index[lvl_interval[lvl - 1]:lvl_interval[lvl]] *= \
+                lvl_interval[lvl - 1]
+            mask_lvl_start_index[lvl_interval[lvl - 1]:lvl_interval[lvl]] *= \
+                seg_size[lvl - 1]
+            num_grids[lvl_interval[lvl - 1]:lvl_interval[lvl]] *= \
+                self.num_grids[lvl]
+            strides[lvl_interval[lvl - 1]:lvl_interval[lvl]] *= \
+                self.strides[lvl]
+
+        lvl_start_index = lvl_start_index[inds[:, 0]]
+        mask_lvl_start_index = mask_lvl_start_index[inds[:, 0]]
+        num_grids = num_grids[inds[:, 0]]
+        strides = strides[inds[:, 0]]
+
+        y_lvl_offset = (inds[:, 0] - lvl_start_index) // num_grids
+        x_lvl_offset = (inds[:, 0] - lvl_start_index) % num_grids
+        y_inds = mask_lvl_start_index + y_lvl_offset
+        x_inds = mask_lvl_start_index + x_lvl_offset
+
+        cls_labels = inds[:, 1]
+        mask_preds = mask_preds_x[x_inds, ...] * mask_preds_y[y_inds, ...]
+
+        masks = mask_preds > cfg.mask_thr
+        sum_masks = masks.sum((1, 2)).float()
+        keep = sum_masks > strides
+        if keep.sum() == 0:
+            return empty_results(results, cls_scores)
+
+        masks = masks[keep]
+        mask_preds = mask_preds[keep]
+        sum_masks = sum_masks[keep]
+        cls_scores = cls_scores[keep]
+        cls_labels = cls_labels[keep]
+
+        # maskness.
+        mask_scores = (mask_preds * masks).sum((1, 2)) / sum_masks
+        cls_scores *= mask_scores
+
+        scores, labels, _, keep_inds = mask_matrix_nms(
+            masks,
+            cls_labels,
+            cls_scores,
+            mask_area=sum_masks,
+            nms_pre=cfg.nms_pre,
+            max_num=cfg.max_per_img,
+            kernel=cfg.kernel,
+            sigma=cfg.sigma,
+            filter_thr=cfg.filter_thr)
+        mask_preds = mask_preds[keep_inds]
+        mask_preds = F.interpolate(
+            mask_preds.unsqueeze(0), size=upsampled_size,
+            mode='bilinear')[:, :, :h, :w]
+        mask_preds = F.interpolate(
+            mask_preds, size=ori_shape[:2], mode='bilinear').squeeze(0)
+        masks = mask_preds > cfg.mask_thr
+
+        results.masks = masks
+        results.labels = labels
+        results.scores = scores
+
+        return results
+
+
+@HEADS.register_module()
+class DecoupledSOLOLightHead(DecoupledSOLOHead):
+    """Decoupled Light SOLO mask head used in `SOLO: Segmenting Objects by
+    Locations <https://arxiv.org/abs/1912.04488>`_
+
+    Args:
+        with_dcn (bool): Whether use dcn in mask_convs and cls_convs,
+            default: False.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 *args,
+                 dcn_cfg=None,
+                 init_cfg=[
+                     dict(type='Normal', layer='Conv2d', std=0.01),
+                     dict(
+                         type='Normal',
+                         std=0.01,
+                         bias_prob=0.01,
+                         override=dict(name='conv_mask_list_x')),
+                     dict(
+                         type='Normal',
+                         std=0.01,
+                         bias_prob=0.01,
+                         override=dict(name='conv_mask_list_y')),
+                     dict(
+                         type='Normal',
+                         std=0.01,
+                         bias_prob=0.01,
+                         override=dict(name='conv_cls'))
+                 ],
+                 **kwargs):
+        assert dcn_cfg is None or isinstance(dcn_cfg, dict)
+        self.dcn_cfg = dcn_cfg
+        super(DecoupledSOLOLightHead, self).__init__(
+            *args, init_cfg=init_cfg, **kwargs)
+
+    def _init_layers(self):
+        self.mask_convs = nn.ModuleList()
+        self.cls_convs = nn.ModuleList()
+
+        for i in range(self.stacked_convs):
+            if self.dcn_cfg is not None\
+                    and i == self.stacked_convs - 1:
+                conv_cfg = self.dcn_cfg
+            else:
+                conv_cfg = None
+
+            chn = self.in_channels + 2 if i == 0 else self.feat_channels
+            self.mask_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=self.norm_cfg))
+
+            chn = self.in_channels if i == 0 else self.feat_channels
+            self.cls_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=self.norm_cfg))
+
+        self.conv_mask_list_x = nn.ModuleList()
+        self.conv_mask_list_y = nn.ModuleList()
+        for num_grid in self.num_grids:
+            self.conv_mask_list_x.append(
+                nn.Conv2d(self.feat_channels, num_grid, 3, padding=1))
+            self.conv_mask_list_y.append(
+                nn.Conv2d(self.feat_channels, num_grid, 3, padding=1))
+        self.conv_cls = nn.Conv2d(
+            self.feat_channels, self.cls_out_channels, 3, padding=1)
+
+    def forward(self, feats):
+        assert len(feats) == self.num_levels
+        feats = self.resize_feats(feats)
+        mask_preds_x = []
+        mask_preds_y = []
+        cls_preds = []
+        for i in range(self.num_levels):
+            x = feats[i]
+            mask_feat = x
+            cls_feat = x
+            # generate and concat the coordinate
+            coord_feat = generate_coordinate(mask_feat.size(),
+                                             mask_feat.device)
+            mask_feat = torch.cat([mask_feat, coord_feat], 1)
+
+            for mask_layer in self.mask_convs:
+                mask_feat = mask_layer(mask_feat)
+
+            mask_feat = F.interpolate(
+                mask_feat, scale_factor=2, mode='bilinear')
+
+            mask_pred_x = self.conv_mask_list_x[i](mask_feat)
+            mask_pred_y = self.conv_mask_list_y[i](mask_feat)
+
+            # cls branch
+            for j, cls_layer in enumerate(self.cls_convs):
+                if j == self.cls_down_index:
+                    num_grid = self.num_grids[i]
+                    cls_feat = F.interpolate(
+                        cls_feat, size=num_grid, mode='bilinear')
+                cls_feat = cls_layer(cls_feat)
+
+            cls_pred = self.conv_cls(cls_feat)
+
+            if not self.training:
+                feat_wh = feats[0].size()[-2:]
+                upsampled_size = (feat_wh[0] * 2, feat_wh[1] * 2)
+                mask_pred_x = F.interpolate(
+                    mask_pred_x.sigmoid(),
+                    size=upsampled_size,
+                    mode='bilinear')
+                mask_pred_y = F.interpolate(
+                    mask_pred_y.sigmoid(),
+                    size=upsampled_size,
+                    mode='bilinear')
+                cls_pred = cls_pred.sigmoid()
+                # get local maximum
+                local_max = F.max_pool2d(cls_pred, 2, stride=1, padding=1)
+                keep_mask = local_max[:, :, :-1, :-1] == cls_pred
+                cls_pred = cls_pred * keep_mask
+
+            mask_preds_x.append(mask_pred_x)
+            mask_preds_y.append(mask_pred_y)
+            cls_preds.append(cls_pred)
+        return mask_preds_x, mask_preds_y, cls_preds
diff --git a/mmdet/models/dense_heads/solov2_head.py b/mmdet/models/dense_heads/solov2_head.py
new file mode 100644
index 0000000..975306c
--- /dev/null
+++ b/mmdet/models/dense_heads/solov2_head.py
@@ -0,0 +1,766 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import mmcv
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule, auto_fp16, force_fp32
+
+from mmdet.core import InstanceData, mask_matrix_nms, multi_apply
+from mmdet.core.utils import center_of_mass, generate_coordinate
+from mmdet.models.builder import HEADS
+from mmdet.utils.misc import floordiv
+from .solo_head import SOLOHead
+
+
+class MaskFeatModule(BaseModule):
+    """SOLOv2 mask feature map branch used in `SOLOv2: Dynamic and Fast
+    Instance Segmentation. <https://arxiv.org/pdf/2003.10152>`_
+
+    Args:
+        in_channels (int): Number of channels in the input feature map.
+        feat_channels (int): Number of hidden channels of the mask feature
+             map branch.
+        start_level (int): The starting feature map level from RPN that
+             will be used to predict the mask feature map.
+        end_level (int): The ending feature map level from rpn that
+             will be used to predict the mask feature map.
+        out_channels (int): Number of output channels of the mask feature
+             map branch. This is the channel count of the mask
+             feature map that to be dynamically convolved with the predicted
+             kernel.
+        mask_stride (int): Downsample factor of the mask feature map output.
+            Default: 4.
+        conv_cfg (dict): Config dict for convolution layer. Default: None.
+        norm_cfg (dict): Config dict for normalization layer. Default: None.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 feat_channels,
+                 start_level,
+                 end_level,
+                 out_channels,
+                 mask_stride=4,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 init_cfg=[dict(type='Normal', layer='Conv2d', std=0.01)]):
+        super().__init__(init_cfg=init_cfg)
+
+        self.in_channels = in_channels
+        self.feat_channels = feat_channels
+        self.start_level = start_level
+        self.end_level = end_level
+        self.mask_stride = mask_stride
+        assert start_level >= 0 and end_level >= start_level
+        self.out_channels = out_channels
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self._init_layers()
+        self.fp16_enabled = False
+
+    def _init_layers(self):
+        self.convs_all_levels = nn.ModuleList()
+        for i in range(self.start_level, self.end_level + 1):
+            convs_per_level = nn.Sequential()
+            if i == 0:
+                convs_per_level.add_module(
+                    f'conv{i}',
+                    ConvModule(
+                        self.in_channels,
+                        self.feat_channels,
+                        3,
+                        padding=1,
+                        conv_cfg=self.conv_cfg,
+                        norm_cfg=self.norm_cfg,
+                        inplace=False))
+                self.convs_all_levels.append(convs_per_level)
+                continue
+
+            for j in range(i):
+                if j == 0:
+                    if i == self.end_level:
+                        chn = self.in_channels + 2
+                    else:
+                        chn = self.in_channels
+                    convs_per_level.add_module(
+                        f'conv{j}',
+                        ConvModule(
+                            chn,
+                            self.feat_channels,
+                            3,
+                            padding=1,
+                            conv_cfg=self.conv_cfg,
+                            norm_cfg=self.norm_cfg,
+                            inplace=False))
+                    convs_per_level.add_module(
+                        f'upsample{j}',
+                        nn.Upsample(
+                            scale_factor=2,
+                            mode='bilinear',
+                            align_corners=False))
+                    continue
+
+                convs_per_level.add_module(
+                    f'conv{j}',
+                    ConvModule(
+                        self.feat_channels,
+                        self.feat_channels,
+                        3,
+                        padding=1,
+                        conv_cfg=self.conv_cfg,
+                        norm_cfg=self.norm_cfg,
+                        inplace=False))
+                convs_per_level.add_module(
+                    f'upsample{j}',
+                    nn.Upsample(
+                        scale_factor=2, mode='bilinear', align_corners=False))
+
+            self.convs_all_levels.append(convs_per_level)
+
+        self.conv_pred = ConvModule(
+            self.feat_channels,
+            self.out_channels,
+            1,
+            padding=0,
+            conv_cfg=self.conv_cfg,
+            norm_cfg=self.norm_cfg)
+
+    @auto_fp16()
+    def forward(self, feats):
+        inputs = feats[self.start_level:self.end_level + 1]
+        assert len(inputs) == (self.end_level - self.start_level + 1)
+        feature_add_all_level = self.convs_all_levels[0](inputs[0])
+        for i in range(1, len(inputs)):
+            input_p = inputs[i]
+            if i == len(inputs) - 1:
+                coord_feat = generate_coordinate(input_p.size(),
+                                                 input_p.device)
+                input_p = torch.cat([input_p, coord_feat], 1)
+
+            # fix runtime error of "+=" inplace operation in PyTorch 1.10
+            feature_add_all_level = feature_add_all_level + \
+                self.convs_all_levels[i](input_p)
+
+        feature_pred = self.conv_pred(feature_add_all_level)
+        return feature_pred
+
+
+@HEADS.register_module()
+class SOLOV2Head(SOLOHead):
+    """SOLOv2 mask head used in `SOLOv2: Dynamic and Fast Instance
+    Segmentation. <https://arxiv.org/pdf/2003.10152>`_
+
+    Args:
+        mask_feature_head (dict): Config of SOLOv2MaskFeatHead.
+        dynamic_conv_size (int): Dynamic Conv kernel size. Default: 1.
+        dcn_cfg (dict): Dcn conv configurations in kernel_convs and cls_conv.
+            default: None.
+        dcn_apply_to_all_conv (bool): Whether to use dcn in every layer of
+            kernel_convs and cls_convs, or only the last layer. It shall be set
+            `True` for the normal version of SOLOv2 and `False` for the
+            light-weight version. default: True.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 *args,
+                 mask_feature_head,
+                 dynamic_conv_size=1,
+                 dcn_cfg=None,
+                 dcn_apply_to_all_conv=True,
+                 init_cfg=[
+                     dict(type='Normal', layer='Conv2d', std=0.01),
+                     dict(
+                         type='Normal',
+                         std=0.01,
+                         bias_prob=0.01,
+                         override=dict(name='conv_cls'))
+                 ],
+                 **kwargs):
+        assert dcn_cfg is None or isinstance(dcn_cfg, dict)
+        self.dcn_cfg = dcn_cfg
+        self.with_dcn = dcn_cfg is not None
+        self.dcn_apply_to_all_conv = dcn_apply_to_all_conv
+        self.dynamic_conv_size = dynamic_conv_size
+        mask_out_channels = mask_feature_head.get('out_channels')
+        self.kernel_out_channels = \
+            mask_out_channels * self.dynamic_conv_size * self.dynamic_conv_size
+
+        super().__init__(*args, init_cfg=init_cfg, **kwargs)
+
+        # update the in_channels of mask_feature_head
+        if mask_feature_head.get('in_channels', None) is not None:
+            if mask_feature_head.in_channels != self.in_channels:
+                warnings.warn('The `in_channels` of SOLOv2MaskFeatHead and '
+                              'SOLOv2Head should be same, changing '
+                              'mask_feature_head.in_channels to '
+                              f'{self.in_channels}')
+                mask_feature_head.update(in_channels=self.in_channels)
+        else:
+            mask_feature_head.update(in_channels=self.in_channels)
+
+        self.mask_feature_head = MaskFeatModule(**mask_feature_head)
+        self.mask_stride = self.mask_feature_head.mask_stride
+        self.fp16_enabled = False
+
+    def _init_layers(self):
+        self.cls_convs = nn.ModuleList()
+        self.kernel_convs = nn.ModuleList()
+        conv_cfg = None
+        for i in range(self.stacked_convs):
+            if self.with_dcn:
+                if self.dcn_apply_to_all_conv:
+                    conv_cfg = self.dcn_cfg
+                elif i == self.stacked_convs - 1:
+                    # light head
+                    conv_cfg = self.dcn_cfg
+
+            chn = self.in_channels + 2 if i == 0 else self.feat_channels
+            self.kernel_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    bias=self.norm_cfg is None))
+
+            chn = self.in_channels if i == 0 else self.feat_channels
+            self.cls_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    bias=self.norm_cfg is None))
+
+        self.conv_cls = nn.Conv2d(
+            self.feat_channels, self.cls_out_channels, 3, padding=1)
+
+        self.conv_kernel = nn.Conv2d(
+            self.feat_channels, self.kernel_out_channels, 3, padding=1)
+
+    @auto_fp16()
+    def forward(self, feats):
+        assert len(feats) == self.num_levels
+        mask_feats = self.mask_feature_head(feats)
+        feats = self.resize_feats(feats)
+        mlvl_kernel_preds = []
+        mlvl_cls_preds = []
+        for i in range(self.num_levels):
+            ins_kernel_feat = feats[i]
+            # ins branch
+            # concat coord
+            coord_feat = generate_coordinate(ins_kernel_feat.size(),
+                                             ins_kernel_feat.device)
+            ins_kernel_feat = torch.cat([ins_kernel_feat, coord_feat], 1)
+
+            # kernel branch
+            kernel_feat = ins_kernel_feat
+            kernel_feat = F.interpolate(
+                kernel_feat,
+                size=self.num_grids[i],
+                mode='bilinear',
+                align_corners=False)
+
+            cate_feat = kernel_feat[:, :-2, :, :]
+
+            kernel_feat = kernel_feat.contiguous()
+            for i, kernel_conv in enumerate(self.kernel_convs):
+                kernel_feat = kernel_conv(kernel_feat)
+            kernel_pred = self.conv_kernel(kernel_feat)
+
+            # cate branch
+            cate_feat = cate_feat.contiguous()
+            for i, cls_conv in enumerate(self.cls_convs):
+                cate_feat = cls_conv(cate_feat)
+            cate_pred = self.conv_cls(cate_feat)
+
+            mlvl_kernel_preds.append(kernel_pred)
+            mlvl_cls_preds.append(cate_pred)
+
+        return mlvl_kernel_preds, mlvl_cls_preds, mask_feats
+
+    def _get_targets_single(self,
+                            gt_bboxes,
+                            gt_labels,
+                            gt_masks,
+                            featmap_size=None):
+        """Compute targets for predictions of single image.
+
+        Args:
+            gt_bboxes (Tensor): Ground truth bbox of each instance,
+                shape (num_gts, 4).
+            gt_labels (Tensor): Ground truth label of each instance,
+                shape (num_gts,).
+            gt_masks (Tensor): Ground truth mask of each instance,
+                shape (num_gts, h, w).
+            featmap_sizes (:obj:`torch.size`): Size of UNified mask
+                feature map used to generate instance segmentation
+                masks by dynamic convolution, each element means
+                (feat_h, feat_w). Default: None.
+
+        Returns:
+            Tuple: Usually returns a tuple containing targets for predictions.
+
+                - mlvl_pos_mask_targets (list[Tensor]): Each element represent
+                  the binary mask targets for positive points in this
+                  level, has shape (num_pos, out_h, out_w).
+                - mlvl_labels (list[Tensor]): Each element is
+                  classification labels for all
+                  points in this level, has shape
+                  (num_grid, num_grid).
+                - mlvl_pos_masks  (list[Tensor]): Each element is
+                  a `BoolTensor` to represent whether the
+                  corresponding point in single level
+                  is positive, has shape (num_grid **2).
+                - mlvl_pos_indexes  (list[list]): Each element
+                  in the list contains the positive index in
+                  corresponding level, has shape (num_pos).
+        """
+
+        device = gt_labels.device
+        gt_areas = torch.sqrt((gt_bboxes[:, 2] - gt_bboxes[:, 0]) *
+                              (gt_bboxes[:, 3] - gt_bboxes[:, 1]))
+
+        mlvl_pos_mask_targets = []
+        mlvl_pos_indexes = []
+        mlvl_labels = []
+        mlvl_pos_masks = []
+        for (lower_bound, upper_bound), num_grid \
+                in zip(self.scale_ranges, self.num_grids):
+            mask_target = []
+            # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+            pos_index = []
+            labels = torch.zeros([num_grid, num_grid],
+                                 dtype=torch.int64,
+                                 device=device) + self.num_classes
+            pos_mask = torch.zeros([num_grid**2],
+                                   dtype=torch.bool,
+                                   device=device)
+
+            gt_inds = ((gt_areas >= lower_bound) &
+                       (gt_areas <= upper_bound)).nonzero().flatten()
+            if len(gt_inds) == 0:
+                mlvl_pos_mask_targets.append(
+                    torch.zeros([0, featmap_size[0], featmap_size[1]],
+                                dtype=torch.uint8,
+                                device=device))
+                mlvl_labels.append(labels)
+                mlvl_pos_masks.append(pos_mask)
+                mlvl_pos_indexes.append([])
+                continue
+            hit_gt_bboxes = gt_bboxes[gt_inds]
+            hit_gt_labels = gt_labels[gt_inds]
+            hit_gt_masks = gt_masks[gt_inds, ...]
+
+            pos_w_ranges = 0.5 * (hit_gt_bboxes[:, 2] -
+                                  hit_gt_bboxes[:, 0]) * self.pos_scale
+            pos_h_ranges = 0.5 * (hit_gt_bboxes[:, 3] -
+                                  hit_gt_bboxes[:, 1]) * self.pos_scale
+
+            # Make sure hit_gt_masks has a value
+            valid_mask_flags = hit_gt_masks.sum(dim=-1).sum(dim=-1) > 0
+
+            for gt_mask, gt_label, pos_h_range, pos_w_range, \
+                valid_mask_flag in \
+                    zip(hit_gt_masks, hit_gt_labels, pos_h_ranges,
+                        pos_w_ranges, valid_mask_flags):
+                if not valid_mask_flag:
+                    continue
+                upsampled_size = (featmap_size[0] * self.mask_stride,
+                                  featmap_size[1] * self.mask_stride)
+                center_h, center_w = center_of_mass(gt_mask)
+
+                coord_w = int(
+                    floordiv((center_w / upsampled_size[1]), (1. / num_grid),
+                             rounding_mode='trunc'))
+                coord_h = int(
+                    floordiv((center_h / upsampled_size[0]), (1. / num_grid),
+                             rounding_mode='trunc'))
+
+                # left, top, right, down
+                top_box = max(
+                    0,
+                    int(
+                        floordiv(
+                            (center_h - pos_h_range) / upsampled_size[0],
+                            (1. / num_grid),
+                            rounding_mode='trunc')))
+                down_box = min(
+                    num_grid - 1,
+                    int(
+                        floordiv(
+                            (center_h + pos_h_range) / upsampled_size[0],
+                            (1. / num_grid),
+                            rounding_mode='trunc')))
+                left_box = max(
+                    0,
+                    int(
+                        floordiv(
+                            (center_w - pos_w_range) / upsampled_size[1],
+                            (1. / num_grid),
+                            rounding_mode='trunc')))
+                right_box = min(
+                    num_grid - 1,
+                    int(
+                        floordiv(
+                            (center_w + pos_w_range) / upsampled_size[1],
+                            (1. / num_grid),
+                            rounding_mode='trunc')))
+
+                top = max(top_box, coord_h - 1)
+                down = min(down_box, coord_h + 1)
+                left = max(coord_w - 1, left_box)
+                right = min(right_box, coord_w + 1)
+
+                labels[top:(down + 1), left:(right + 1)] = gt_label
+                # ins
+                gt_mask = np.uint8(gt_mask.cpu().numpy())
+                # Follow the original implementation, F.interpolate is
+                # different from cv2 and opencv
+                gt_mask = mmcv.imrescale(gt_mask, scale=1. / self.mask_stride)
+                gt_mask = torch.from_numpy(gt_mask).to(device=device)
+
+                for i in range(top, down + 1):
+                    for j in range(left, right + 1):
+                        index = int(i * num_grid + j)
+                        this_mask_target = torch.zeros(
+                            [featmap_size[0], featmap_size[1]],
+                            dtype=torch.uint8,
+                            device=device)
+                        this_mask_target[:gt_mask.shape[0], :gt_mask.
+                                         shape[1]] = gt_mask
+                        mask_target.append(this_mask_target)
+                        pos_mask[index] = True
+                        pos_index.append(index)
+            if len(mask_target) == 0:
+                mask_target = torch.zeros(
+                    [0, featmap_size[0], featmap_size[1]],
+                    dtype=torch.uint8,
+                    device=device)
+            else:
+                mask_target = torch.stack(mask_target, 0)
+            mlvl_pos_mask_targets.append(mask_target)
+            mlvl_labels.append(labels)
+            mlvl_pos_masks.append(pos_mask)
+            mlvl_pos_indexes.append(pos_index)
+        return (mlvl_pos_mask_targets, mlvl_labels, mlvl_pos_masks,
+                mlvl_pos_indexes)
+
+    @force_fp32(apply_to=('mlvl_kernel_preds', 'mlvl_cls_preds', 'mask_feats'))
+    def loss(self,
+             mlvl_kernel_preds,
+             mlvl_cls_preds,
+             mask_feats,
+             gt_labels,
+             gt_masks,
+             img_metas,
+             gt_bboxes=None,
+             **kwargs):
+        """Calculate the loss of total batch.
+
+        Args:
+            mlvl_kernel_preds (list[Tensor]): Multi-level dynamic kernel
+                prediction. The kernel is used to generate instance
+                segmentation masks by dynamic convolution. Each element in the
+                list has shape
+                (batch_size, kernel_out_channels, num_grids, num_grids).
+            mlvl_cls_preds (list[Tensor]): Multi-level scores. Each element
+                in the list has shape
+                (batch_size, num_classes, num_grids, num_grids).
+            mask_feats (Tensor): Unified mask feature map used to generate
+                instance segmentation masks by dynamic convolution. Has shape
+                (batch_size, mask_out_channels, h, w).
+            gt_labels (list[Tensor]): Labels of multiple images.
+            gt_masks (list[Tensor]): Ground truth masks of multiple images.
+                Each has shape (num_instances, h, w).
+            img_metas (list[dict]): Meta information of multiple images.
+            gt_bboxes (list[Tensor]): Ground truth bboxes of multiple
+                images. Default: None.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        featmap_size = mask_feats.size()[-2:]
+
+        pos_mask_targets, labels, pos_masks, pos_indexes = multi_apply(
+            self._get_targets_single,
+            gt_bboxes,
+            gt_labels,
+            gt_masks,
+            featmap_size=featmap_size)
+
+        mlvl_mask_targets = [
+            torch.cat(lvl_mask_targets, 0)
+            for lvl_mask_targets in zip(*pos_mask_targets)
+        ]
+
+        mlvl_pos_kernel_preds = []
+        for lvl_kernel_preds, lvl_pos_indexes in zip(mlvl_kernel_preds,
+                                                     zip(*pos_indexes)):
+            lvl_pos_kernel_preds = []
+            for img_lvl_kernel_preds, img_lvl_pos_indexes in zip(
+                    lvl_kernel_preds, lvl_pos_indexes):
+                img_lvl_pos_kernel_preds = img_lvl_kernel_preds.view(
+                    img_lvl_kernel_preds.shape[0], -1)[:, img_lvl_pos_indexes]
+                lvl_pos_kernel_preds.append(img_lvl_pos_kernel_preds)
+            mlvl_pos_kernel_preds.append(lvl_pos_kernel_preds)
+
+        # make multilevel mlvl_mask_pred
+        mlvl_mask_preds = []
+        for lvl_pos_kernel_preds in mlvl_pos_kernel_preds:
+            lvl_mask_preds = []
+            for img_id, img_lvl_pos_kernel_pred in enumerate(
+                    lvl_pos_kernel_preds):
+                if img_lvl_pos_kernel_pred.size()[-1] == 0:
+                    continue
+                img_mask_feats = mask_feats[[img_id]]
+                h, w = img_mask_feats.shape[-2:]
+                num_kernel = img_lvl_pos_kernel_pred.shape[1]
+                img_lvl_mask_pred = F.conv2d(
+                    img_mask_feats,
+                    img_lvl_pos_kernel_pred.permute(1, 0).view(
+                        num_kernel, -1, self.dynamic_conv_size,
+                        self.dynamic_conv_size),
+                    stride=1).view(-1, h, w)
+                lvl_mask_preds.append(img_lvl_mask_pred)
+            if len(lvl_mask_preds) == 0:
+                lvl_mask_preds = None
+            else:
+                lvl_mask_preds = torch.cat(lvl_mask_preds, 0)
+            mlvl_mask_preds.append(lvl_mask_preds)
+        # dice loss
+        num_pos = 0
+        for img_pos_masks in pos_masks:
+            for lvl_img_pos_masks in img_pos_masks:
+                num_pos += lvl_img_pos_masks.count_nonzero()
+
+        loss_mask = []
+        for lvl_mask_preds, lvl_mask_targets in zip(mlvl_mask_preds,
+                                                    mlvl_mask_targets):
+            if lvl_mask_preds is None:
+                continue
+            loss_mask.append(
+                self.loss_mask(
+                    lvl_mask_preds,
+                    lvl_mask_targets,
+                    reduction_override='none'))
+        if num_pos > 0:
+            loss_mask = torch.cat(loss_mask).sum() / num_pos
+        else:
+            loss_mask = mask_feats.sum() * 0
+
+        # cate
+        flatten_labels = [
+            torch.cat(
+                [img_lvl_labels.flatten() for img_lvl_labels in lvl_labels])
+            for lvl_labels in zip(*labels)
+        ]
+        flatten_labels = torch.cat(flatten_labels)
+
+        flatten_cls_preds = [
+            lvl_cls_preds.permute(0, 2, 3, 1).reshape(-1, self.num_classes)
+            for lvl_cls_preds in mlvl_cls_preds
+        ]
+        flatten_cls_preds = torch.cat(flatten_cls_preds)
+
+        loss_cls = self.loss_cls(
+            flatten_cls_preds, flatten_labels, avg_factor=num_pos + 1)
+        return dict(loss_mask=loss_mask, loss_cls=loss_cls)
+
+    @force_fp32(
+        apply_to=('mlvl_kernel_preds', 'mlvl_cls_scores', 'mask_feats'))
+    def get_results(self, mlvl_kernel_preds, mlvl_cls_scores, mask_feats,
+                    img_metas, **kwargs):
+        """Get multi-image mask results.
+
+        Args:
+            mlvl_kernel_preds (list[Tensor]): Multi-level dynamic kernel
+                prediction. The kernel is used to generate instance
+                segmentation masks by dynamic convolution. Each element in the
+                list has shape
+                (batch_size, kernel_out_channels, num_grids, num_grids).
+            mlvl_cls_scores (list[Tensor]): Multi-level scores. Each element
+                in the list has shape
+                (batch_size, num_classes, num_grids, num_grids).
+            mask_feats (Tensor): Unified mask feature map used to generate
+                instance segmentation masks by dynamic convolution. Has shape
+                (batch_size, mask_out_channels, h, w).
+            img_metas (list[dict]): Meta information of all images.
+
+        Returns:
+            list[:obj:`InstanceData`]: Processed results of multiple
+            images.Each :obj:`InstanceData` usually contains
+            following keys.
+
+                - scores (Tensor): Classification scores, has shape
+                  (num_instance,).
+                - labels (Tensor): Has shape (num_instances,).
+                - masks (Tensor): Processed mask results, has
+                  shape (num_instances, h, w).
+        """
+        num_levels = len(mlvl_cls_scores)
+        assert len(mlvl_kernel_preds) == len(mlvl_cls_scores)
+
+        for lvl in range(num_levels):
+            cls_scores = mlvl_cls_scores[lvl]
+            cls_scores = cls_scores.sigmoid()
+            local_max = F.max_pool2d(cls_scores, 2, stride=1, padding=1)
+            keep_mask = local_max[:, :, :-1, :-1] == cls_scores
+            cls_scores = cls_scores * keep_mask
+            mlvl_cls_scores[lvl] = cls_scores.permute(0, 2, 3, 1)
+
+        result_list = []
+        for img_id in range(len(img_metas)):
+            img_cls_pred = [
+                mlvl_cls_scores[lvl][img_id].view(-1, self.cls_out_channels)
+                for lvl in range(num_levels)
+            ]
+            img_mask_feats = mask_feats[[img_id]]
+            img_kernel_pred = [
+                mlvl_kernel_preds[lvl][img_id].permute(1, 2, 0).view(
+                    -1, self.kernel_out_channels) for lvl in range(num_levels)
+            ]
+            img_cls_pred = torch.cat(img_cls_pred, dim=0)
+            img_kernel_pred = torch.cat(img_kernel_pred, dim=0)
+            result = self._get_results_single(
+                img_kernel_pred,
+                img_cls_pred,
+                img_mask_feats,
+                img_meta=img_metas[img_id])
+            result_list.append(result)
+        return result_list
+
+    def _get_results_single(self,
+                            kernel_preds,
+                            cls_scores,
+                            mask_feats,
+                            img_meta,
+                            cfg=None):
+        """Get processed mask related results of single image.
+
+        Args:
+            kernel_preds (Tensor): Dynamic kernel prediction of all points
+                in single image, has shape
+                (num_points, kernel_out_channels).
+            cls_scores (Tensor): Classification score of all points
+                in single image, has shape (num_points, num_classes).
+            mask_preds (Tensor): Mask prediction of all points in
+                single image, has shape (num_points, feat_h, feat_w).
+            img_meta (dict): Meta information of corresponding image.
+            cfg (dict, optional): Config used in test phase.
+                Default: None.
+
+        Returns:
+            :obj:`InstanceData`: Processed results of single image.
+             it usually contains following keys.
+                - scores (Tensor): Classification scores, has shape
+                  (num_instance,).
+                - labels (Tensor): Has shape (num_instances,).
+                - masks (Tensor): Processed mask results, has
+                  shape (num_instances, h, w).
+        """
+
+        def empty_results(results, cls_scores):
+            """Generate a empty results."""
+            results.scores = cls_scores.new_ones(0)
+            results.masks = cls_scores.new_zeros(0, *results.ori_shape[:2])
+            results.labels = cls_scores.new_ones(0)
+            return results
+
+        cfg = self.test_cfg if cfg is None else cfg
+        assert len(kernel_preds) == len(cls_scores)
+        results = InstanceData(img_meta)
+
+        featmap_size = mask_feats.size()[-2:]
+
+        img_shape = results.img_shape
+        ori_shape = results.ori_shape
+
+        # overall info
+        h, w, _ = img_shape
+        upsampled_size = (featmap_size[0] * self.mask_stride,
+                          featmap_size[1] * self.mask_stride)
+
+        # process.
+        score_mask = (cls_scores > cfg.score_thr)
+        cls_scores = cls_scores[score_mask]
+        if len(cls_scores) == 0:
+            return empty_results(results, cls_scores)
+
+        # cate_labels & kernel_preds
+        inds = score_mask.nonzero()
+        cls_labels = inds[:, 1]
+        kernel_preds = kernel_preds[inds[:, 0]]
+
+        # trans vector.
+        lvl_interval = cls_labels.new_tensor(self.num_grids).pow(2).cumsum(0)
+        strides = kernel_preds.new_ones(lvl_interval[-1])
+
+        strides[:lvl_interval[0]] *= self.strides[0]
+        for lvl in range(1, self.num_levels):
+            strides[lvl_interval[lvl -
+                                 1]:lvl_interval[lvl]] *= self.strides[lvl]
+        strides = strides[inds[:, 0]]
+
+        # mask encoding.
+        kernel_preds = kernel_preds.view(
+            kernel_preds.size(0), -1, self.dynamic_conv_size,
+            self.dynamic_conv_size)
+        mask_preds = F.conv2d(
+            mask_feats, kernel_preds, stride=1).squeeze(0).sigmoid()
+        # mask.
+        masks = mask_preds > cfg.mask_thr
+        sum_masks = masks.sum((1, 2)).float()
+        keep = sum_masks > strides
+        if keep.sum() == 0:
+            return empty_results(results, cls_scores)
+        masks = masks[keep]
+        mask_preds = mask_preds[keep]
+        sum_masks = sum_masks[keep]
+        cls_scores = cls_scores[keep]
+        cls_labels = cls_labels[keep]
+
+        # maskness.
+        mask_scores = (mask_preds * masks).sum((1, 2)) / sum_masks
+        cls_scores *= mask_scores
+
+        scores, labels, _, keep_inds = mask_matrix_nms(
+            masks,
+            cls_labels,
+            cls_scores,
+            mask_area=sum_masks,
+            nms_pre=cfg.nms_pre,
+            max_num=cfg.max_per_img,
+            kernel=cfg.kernel,
+            sigma=cfg.sigma,
+            filter_thr=cfg.filter_thr)
+        mask_preds = mask_preds[keep_inds]
+        mask_preds = F.interpolate(
+            mask_preds.unsqueeze(0),
+            size=upsampled_size,
+            mode='bilinear',
+            align_corners=False)[:, :, :h, :w]
+        mask_preds = F.interpolate(
+            mask_preds,
+            size=ori_shape[:2],
+            mode='bilinear',
+            align_corners=False).squeeze(0)
+        masks = mask_preds > cfg.mask_thr
+
+        results.masks = masks
+        results.labels = labels
+        results.scores = scores
+
+        return results
diff --git a/mmdet/models/dense_heads/ssd_head.py b/mmdet/models/dense_heads/ssd_head.py
new file mode 100644
index 0000000..3e1f2b6
--- /dev/null
+++ b/mmdet/models/dense_heads/ssd_head.py
@@ -0,0 +1,588 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
+from mmcv.runner import force_fp32
+
+from mmdet.core import (build_assigner, build_bbox_coder,
+                        build_prior_generator, build_sampler, multi_apply)
+from ..builder import HEADS
+from ..losses import smooth_l1_loss
+from .anchor_head import AnchorHead
+
+from mmdet.core.bbox.iou_calculators import bbox_overlaps
+from my_equation import *
+import global_placeholder
+
+# TODO: add loss evaluator for SSD
+@HEADS.register_module()
+class SSDHead(AnchorHead):
+    """SSD head used in https://arxiv.org/abs/1512.02325.
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        stacked_convs (int): Number of conv layers in cls and reg tower.
+            Default: 0.
+        feat_channels (int): Number of hidden channels when stacked_convs
+            > 0. Default: 256.
+        use_depthwise (bool): Whether to use DepthwiseSeparableConv.
+            Default: False.
+        conv_cfg (dict): Dictionary to construct and config conv layer.
+            Default: None.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+            Default: None.
+        act_cfg (dict): Dictionary to construct and config activation layer.
+            Default: None.
+        anchor_generator (dict): Config dict for anchor generator
+        bbox_coder (dict): Config of bounding box coder.
+        reg_decoded_bbox (bool): If true, the regression loss would be
+            applied directly on decoded bounding boxes, converting both
+            the predicted boxes and regression targets to absolute
+            coordinates format. Default False. It should be `True` when
+            using `IoULoss`, `GIoULoss`, or `DIoULoss` in the bbox head.
+        train_cfg (dict): Training config of anchor head.
+        test_cfg (dict): Testing config of anchor head.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """  # noqa: W605
+
+    def __init__(self,
+                 num_classes=80,
+                 in_channels=(512, 1024, 512, 256, 256, 256),
+                 stacked_convs=0,
+                 feat_channels=256,
+                 use_depthwise=False,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 act_cfg=None,
+                 anchor_generator=dict(
+                     type='SSDAnchorGenerator',
+                     scale_major=False,
+                     input_size=300,
+                     strides=[8, 16, 32, 64, 100, 300],
+                     ratios=([2], [2, 3], [2, 3], [2, 3], [2], [2]),
+                     basesize_ratio_range=(0.1, 0.9)),
+                 bbox_coder=dict(
+                     type='DeltaXYWHBBoxCoder',
+                     clip_border=True,
+                     target_means=[.0, .0, .0, .0],
+                     target_stds=[1.0, 1.0, 1.0, 1.0],
+                 ),
+                 reg_decoded_bbox=False,
+                 train_cfg=None,
+                 test_cfg=None,
+                 init_cfg=dict(
+                     type='Xavier',
+                     layer='Conv2d',
+                     distribution='uniform',
+                     bias=0)):
+        super(AnchorHead, self).__init__(init_cfg)
+        self.num_classes = num_classes
+        self.in_channels = in_channels
+        self.stacked_convs = stacked_convs
+        self.feat_channels = feat_channels
+        self.use_depthwise = use_depthwise
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+
+        self.cls_out_channels = num_classes + 1  # add background class
+        self.prior_generator = build_prior_generator(anchor_generator)
+
+        # Usually the numbers of anchors for each level are the same
+        # except SSD detectors. So it is an int in the most dense
+        # heads but a list of int in SSDHead
+        self.num_base_priors = self.prior_generator.num_base_priors
+
+        self._init_layers()
+
+        self.bbox_coder = build_bbox_coder(bbox_coder)
+        self.reg_decoded_bbox = reg_decoded_bbox
+        self.use_sigmoid_cls = False
+        self.cls_focal_loss = False
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+        # set sampling=False for archor_target
+        self.sampling = False
+        if self.train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            # SSD sampling=False so use PseudoSampler
+            sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+        self.fp16_enabled = False
+
+    @property
+    def num_anchors(self):
+        """
+        Returns:
+            list[int]: Number of base_anchors on each point of each level.
+        """
+        warnings.warn('DeprecationWarning: `num_anchors` is deprecated, '
+                      'please use "num_base_priors" instead')
+        return self.num_base_priors
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.cls_convs = nn.ModuleList()
+        self.reg_convs = nn.ModuleList()
+        # TODO: Use registry to choose ConvModule type
+        conv = DepthwiseSeparableConvModule \
+            if self.use_depthwise else ConvModule
+
+        for channel, num_base_priors in zip(self.in_channels,
+                                            self.num_base_priors):
+            cls_layers = []
+            reg_layers = []
+            in_channel = channel
+            # build stacked conv tower, not used in default ssd
+            for i in range(self.stacked_convs):
+                cls_layers.append(
+                    conv(
+                        in_channel,
+                        self.feat_channels,
+                        3,
+                        padding=1,
+                        conv_cfg=self.conv_cfg,
+                        norm_cfg=self.norm_cfg,
+                        act_cfg=self.act_cfg))
+                reg_layers.append(
+                    conv(
+                        in_channel,
+                        self.feat_channels,
+                        3,
+                        padding=1,
+                        conv_cfg=self.conv_cfg,
+                        norm_cfg=self.norm_cfg,
+                        act_cfg=self.act_cfg))
+                in_channel = self.feat_channels
+            # SSD-Lite head
+            if self.use_depthwise:
+                cls_layers.append(
+                    ConvModule(
+                        in_channel,
+                        in_channel,
+                        3,
+                        padding=1,
+                        groups=in_channel,
+                        conv_cfg=self.conv_cfg,
+                        norm_cfg=self.norm_cfg,
+                        act_cfg=self.act_cfg))
+                reg_layers.append(
+                    ConvModule(
+                        in_channel,
+                        in_channel,
+                        3,
+                        padding=1,
+                        groups=in_channel,
+                        conv_cfg=self.conv_cfg,
+                        norm_cfg=self.norm_cfg,
+                        act_cfg=self.act_cfg))
+            cls_layers.append(
+                nn.Conv2d(
+                    in_channel,
+                    num_base_priors * self.cls_out_channels,
+                    kernel_size=1 if self.use_depthwise else 3,
+                    padding=0 if self.use_depthwise else 1))
+            reg_layers.append(
+                nn.Conv2d(
+                    in_channel,
+                    num_base_priors * 4,
+                    kernel_size=1 if self.use_depthwise else 3,
+                    padding=0 if self.use_depthwise else 1))
+            self.cls_convs.append(nn.Sequential(*cls_layers))
+            self.reg_convs.append(nn.Sequential(*reg_layers))
+
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple:
+                cls_scores (list[Tensor]): Classification scores for all scale
+                    levels, each is a 4D-tensor, the channels number is
+                    num_anchors * num_classes.
+                bbox_preds (list[Tensor]): Box energies / deltas for all scale
+                    levels, each is a 4D-tensor, the channels number is
+                    num_anchors * 4.
+        """
+        cls_scores = []
+        bbox_preds = []
+        if hasattr(self,'in_num') and self.in_num == 6:
+            feats = [feats[0], feats[1], feats[2], feats[3], feats[4], feats[5]]
+        elif hasattr(self,'in_num'):
+            raise NotImplementedError
+        else:
+            self.in_num = 6
+            feats = [feats[0], feats[1], feats[2], feats[3], feats[4], feats[5]]
+        
+        for feat, reg_conv, cls_conv in zip(feats, self.reg_convs,
+                                            self.cls_convs):
+            cls_scores.append(cls_conv(feat))
+            bbox_preds.append(reg_conv(feat))
+        return cls_scores, bbox_preds
+
+    def loss_single(self, cls_score, bbox_pred, anchor, labels, label_weights,
+                    bbox_targets, bbox_weights, num_total_samples):
+        """Compute loss of a single image.
+
+        Args:
+            cls_score (Tensor): Box scores for eachimage
+                Has shape (num_total_anchors, num_classes).
+            bbox_pred (Tensor): Box energies / deltas for each image
+                level with shape (num_total_anchors, 4).
+            anchors (Tensor): Box reference for each scale level with shape
+                (num_total_anchors, 4).
+            labels (Tensor): Labels of each anchors with shape
+                (num_total_anchors,).
+            label_weights (Tensor): Label weights of each anchor with shape
+                (num_total_anchors,)
+            bbox_targets (Tensor): BBox regression targets of each anchor
+                weight shape (num_total_anchors, 4).
+            bbox_weights (Tensor): BBox regression loss weights of each anchor
+                with shape (num_total_anchors, 4).
+            num_total_samples (int): If sampling, num total samples equal to
+                the number of total anchors; Otherwise, it is the number of
+                positive anchors.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        if self.num_base_priors == [4, 6, 6, 6, 4, 4]:
+            # 说明是ssd
+            level_counter = [5776, 2166, 600, 150, 36, 4]  # 这个只出现在SSD300里，否则有问题
+            
+        elif self.num_base_priors == [6, 6, 6, 6, 6, 6]:
+            # 说明是ssdlite
+            level_counter = [6*20*20, 6*10*10, 6*5*5, 6*3*3, 6*2*2, 6*1*1]  # 这个只出现在SSDlite里，否则有问题
+        else:
+            raise NotImplementedError
+        # 实现level上的标记  编码第一个level为0；第二个level为1；第三个level为2
+        level_mapping = []
+        for it, temp in enumerate(level_counter):
+            temp_tensor = torch.zeros(temp,dtype=torch.uint8,device=labels.device) + it
+            level_mapping.append(temp_tensor)
+        level_mapping = torch.cat(level_mapping)
+            
+            
+        loss_cls_all = F.cross_entropy(
+            cls_score, labels, reduction='none') * label_weights
+        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+        pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero(
+            as_tuple=False).reshape(-1)
+        neg_inds = (labels == self.num_classes).nonzero(
+            as_tuple=False).view(-1)
+
+        num_pos_samples = pos_inds.size(0)
+        num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples
+        if num_neg_samples > neg_inds.size(0):
+            num_neg_samples = neg_inds.size(0)
+        topk_loss_cls_neg, topk_inds = loss_cls_all[neg_inds].topk(num_neg_samples)
+
+        if self.reg_decoded_bbox:
+            # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
+            # is applied directly on the decoded bounding boxes, it
+            # decodes the already encoded coordinates to absolute format.
+            bbox_pred = self.bbox_coder.decode(anchor, bbox_pred)
+
+        loss_bbox = smooth_l1_loss(
+            bbox_pred,
+            bbox_targets,
+            bbox_weights,
+            beta=self.train_cfg.smoothl1_beta,
+            reduction='none')  # 这里的处理（包括后面的），等价于这里的mean
+        
+        loss_bbox = loss_bbox.sum(dim=1)
+                
+        loss_cls_pos = loss_cls_all[pos_inds]
+        loss_cls_neg = topk_loss_cls_neg
+        pos_level_mapping = level_mapping[pos_inds]
+        neg_level_mapping = level_mapping[neg_inds][topk_inds]
+        
+        if global_placeholder.mybuff_flag:
+            
+            # # -----加上最小量化误差
+            # qloss_flag = global_placeholder.qloss_flag
+            # q_loss = torch.tensor(0)
+            # # if qloss_flag:
+            # q_loss_sum = []
+            # for name, module in self.named_modules():
+            #     if hasattr(module, 'compute_qloss') and module.compute_qloss:
+            #     # if 'fake_quant' in name.split('.')[-1]:
+            #         # 说明是act quantizer
+            #         q_loss_sum.append(module.quantization_loss)
+                
+            # q_loss = sum(q_loss_sum) / len(q_loss_sum)
+            
+            conf_values = F.softmax(cls_score, dim=1)
+            num_classes = conf_values.shape[-1]
+            pos_gtconf_idx = F.one_hot(labels, num_classes=num_classes)
+            pos_gtconf_values, _ = (conf_values * pos_gtconf_idx).max(dim=1)  # TODO 想到一件事，是不是不能用GT来筛选正样本的结果？因为其实正样本也有错误的东西，所以就是得错
+            pos_gtconf_values = pos_gtconf_values[pos_inds]
+            
+            pos_conf_values, pos_conf_values_idx = conf_values.max(dim=1)  # TODO 好像真的是这个问题，正样本本来就是得对应到max的那个，不能经过GT筛选
+            pos_conf_values = pos_conf_values[pos_inds]
+            
+            abs_bbox_pred = self.bbox_coder.decode(anchor, bbox_pred) # 直接解读
+            abs_bbox_targets = self.bbox_coder.decode(anchor, bbox_targets) # 直接解读
+            pos_ious = bbox_overlaps(abs_bbox_pred[pos_inds], abs_bbox_targets[pos_inds], is_aligned=True)  # 这玩意得是ltrb坐标，好像就已经是了？？？
+            if global_placeholder.mybuff_flag == 1:
+                
+                level_slicer = [6, 0, 0]  # [0]为level数；[1]为weight的个数；[2]为单level下的act个数
+                level_cls_factors = []
+                level_reg_factors = []
+                # level_obj_factors = []
+                qloss_flag = global_placeholder.qloss_flag
+                
+                if False:
+                    q_loss_total = []
+                    cls_branch = []
+                    reg_branch = []
+                    # obj_branch = []
+                    for name, module in self.named_modules():
+                        if hasattr(module, 'compute_qloss') and module.compute_qloss:
+                            # 挑出来量化器
+                            
+                            if 'cls' in name:
+                                # 说明是cls分支的量化器
+                                # cls_branch.append(module.quantization_loss)
+                                if 'post_act' in name:
+                                    # 说明是act量化器
+                                    cls_branch.append([name, module.scale * 1.])
+                                else:
+                                    cls_branch.append([name, module.scale * 2.])
+                                    
+                            elif 'reg' in name:
+                                # 说明是reg分支的量化器
+                                # reg_branch.append(module.quantization_loss)
+                                if 'post_act' in name:
+                                    # 说明是act量化器
+                                    reg_branch.append([name, module.scale * 1.])
+                                else:
+                                    reg_branch.append([name, module.scale * 2.])        
+                                    
+                            # elif 'obj' in name:
+                            #     # 说明是reg分支的量化器
+                            #     # reg_branch.append(module.quantization_loss)
+                            #     if 'post_act' in name:
+                            #         # 说明是act量化器
+                            #         obj_branch.append([name, module.scale * 1.])
+                            #     else:
+                            #         obj_branch.append([name, module.scale * 2.])
+                                
+                            q_loss_total.append([name, module.scale * 1])
+
+                    # NOTE 不需要加item_post_act_quant的
+                    # 由于obj branch 的特殊性，所以得加上reg_branch分支的共用东西
+                    # obj_branch = obj_branch + reg_branch[3:]
+                    
+                    # if level_slicer[0] != 3:
+                    #     raise NotImplementedError
+                    
+                    for it in range(level_slicer[0]):
+                        cls_summation = 0
+                        reg_summation = 0
+                        # obj_summation = 0
+                        tmp_cls_infos = [cls_branch[it]]
+                        for info in tmp_cls_infos:
+                            cls_summation += info[1]
+                            
+                        tmp_reg_infos = [reg_branch[it]]
+                        for info in tmp_reg_infos:
+                            reg_summation += info[1]
+                            
+                        # tmp_obj_infos = [obj_branch[it]] + obj_branch[3+it*2:3+(it+1)*2] + obj_branch[9+it*2:9+(it+1)*2]
+                        # for info in tmp_obj_infos:
+                        #     obj_summation += info[1]
+                    
+                        level_cls_factors.append(cls_summation)
+                        level_reg_factors.append(reg_summation)
+                        # level_obj_factors.append(obj_summation)
+                
+                
+                if len(level_cls_factors) + len(level_reg_factors) == 0:
+                    # 必须让list内有level个空list
+                    level_cls_factors = [torch.tensor(1)]
+                    level_reg_factors = [torch.tensor(1)]
+        
+
+                # # NOTE 编码第一个level为0 第二个为1 ......   
+                # for it, [cls_branch_factor, reg_branch_factor] in enumerate(zip(level_cls_factors, level_reg_factors)):
+                #     single_level_masks = (level_mapping == it)
+                #     single_pos_level_masks = (pos_level_mapping == it)
+                #     single_neg_level_masks = (neg_level_mapping == it)
+                    
+                #     cls_trade_off = (cls_branch_factor / (cls_branch_factor + reg_branch_factor)).detach() * 2
+                #     reg_trade_off = (reg_branch_factor / (cls_branch_factor + reg_branch_factor)).detach() * 2
+                #     # obj_trade_off = (obj_branch_factor / (cls_branch_factor + reg_branch_factor + obj_branch_factor)).detach() * 3
+                    
+                #     loss_cls_pos[single_pos_level_masks] = cls_trade_off * loss_cls_pos[single_pos_level_masks]
+                #     loss_cls_neg[single_neg_level_masks] = cls_trade_off * loss_cls_neg[single_neg_level_masks]
+                #     loss_bbox[single_level_masks] = reg_trade_off * loss_bbox[single_level_masks]
+                
+                loss_bbox[pos_inds], loss_cls_pos = HQOD_loss(loss_bbox[pos_inds], loss_cls_pos, conf_values[pos_inds], pos_gtconf_values, pos_conf_values, pos_ious, [torch.tensor(1)], [torch.tensor(1)], torch.tensor(1.))
+                
+                # loss_bbox[pos_inds], loss_cls_pos = HQOD_loss(loss_bbox[pos_inds], loss_cls_pos, conf_values[pos_inds], pos_gtconf_values, pos_conf_values, pos_ious, torch.tensor(1.))
+            elif global_placeholder.mybuff_flag == 2:
+                # 对比HarDet
+                loss_bbox[pos_inds], loss_cls_pos = HarDet_loss(loss_bbox[pos_inds], loss_cls_pos, conf_values[pos_inds], pos_gtconf_values, pos_conf_values, pos_ious, torch.tensor(1.))
+                
+                # loss_bbox[pos_inds], loss_cls_pos = HarDet_loss(loss_bbox[pos_inds], loss_cls_pos, conf_values[pos_inds], pos_gtconf_values, pos_conf_values, pos_ious, torch.tensor(1.))
+            else:
+                raise NotImplementedError
+        
+        
+        
+        loss_cls_pos_sum = loss_cls_pos.sum()
+        loss_cls_neg_sum = loss_cls_neg.sum()
+        
+        loss_cls = (loss_cls_pos_sum + loss_cls_neg_sum) / num_total_samples
+        loss_bbox_reduced = loss_bbox.sum() / num_total_samples
+        return loss_cls[None], loss_bbox_reduced
+
+
+    # def loss_single(self, cls_score, bbox_pred, anchor, labels, label_weights,
+    #                 bbox_targets, bbox_weights, num_total_samples):
+    #     """Compute loss of a single image.
+
+    #     Args:
+    #         cls_score (Tensor): Box scores for eachimage
+    #             Has shape (num_total_anchors, num_classes).
+    #         bbox_pred (Tensor): Box energies / deltas for each image
+    #             level with shape (num_total_anchors, 4).
+    #         anchors (Tensor): Box reference for each scale level with shape
+    #             (num_total_anchors, 4).
+    #         labels (Tensor): Labels of each anchors with shape
+    #             (num_total_anchors,).
+    #         label_weights (Tensor): Label weights of each anchor with shape
+    #             (num_total_anchors,)
+    #         bbox_targets (Tensor): BBox regression targets of each anchor
+    #             weight shape (num_total_anchors, 4).
+    #         bbox_weights (Tensor): BBox regression loss weights of each anchor
+    #             with shape (num_total_anchors, 4).
+    #         num_total_samples (int): If sampling, num total samples equal to
+    #             the number of total anchors; Otherwise, it is the number of
+    #             positive anchors.
+
+    #     Returns:
+    #         dict[str, Tensor]: A dictionary of loss components.
+    #     """
+
+    #     loss_cls_all = F.cross_entropy(
+    #         cls_score, labels, reduction='none') * label_weights
+    #     # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+    #     pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero(
+    #         as_tuple=False).reshape(-1)
+    #     neg_inds = (labels == self.num_classes).nonzero(
+    #         as_tuple=False).view(-1)
+
+    #     num_pos_samples = pos_inds.size(0)
+    #     num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples
+    #     if num_neg_samples > neg_inds.size(0):
+    #         num_neg_samples = neg_inds.size(0)
+    #     topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples)
+    #     loss_cls_pos = loss_cls_all[pos_inds].sum()
+    #     loss_cls_neg = topk_loss_cls_neg.sum()
+    #     loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples
+
+    #     if self.reg_decoded_bbox:
+    #         # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
+    #         # is applied directly on the decoded bounding boxes, it
+    #         # decodes the already encoded coordinates to absolute format.
+    #         bbox_pred = self.bbox_coder.decode(anchor, bbox_pred)
+
+    #     loss_bbox = smooth_l1_loss(
+    #         bbox_pred,
+    #         bbox_targets,
+    #         bbox_weights,
+    #         beta=self.train_cfg.smoothl1_beta,
+    #         avg_factor=num_total_samples)
+    #     return loss_cls[None], loss_bbox
+
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            gt_bboxes (list[Tensor]): each item are the truth boxes for each
+                image in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+
+        device = cls_scores[0].device
+
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=1,
+            unmap_outputs=True)
+        if cls_reg_targets is None:
+            return None
+        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
+         num_total_pos, num_total_neg) = cls_reg_targets
+
+        num_images = len(img_metas)
+        all_cls_scores = torch.cat([
+            s.permute(0, 2, 3, 1).reshape(
+                num_images, -1, self.cls_out_channels) for s in cls_scores
+        ], 1)
+        all_labels = torch.cat(labels_list, -1).view(num_images, -1)
+        all_label_weights = torch.cat(label_weights_list,
+                                      -1).view(num_images, -1)
+        all_bbox_preds = torch.cat([
+            b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
+            for b in bbox_preds
+        ], -2)
+        all_bbox_targets = torch.cat(bbox_targets_list,
+                                     -2).view(num_images, -1, 4)
+        all_bbox_weights = torch.cat(bbox_weights_list,
+                                     -2).view(num_images, -1, 4)
+
+        # concat all level anchors to a single tensor
+        all_anchors = []
+        for i in range(num_images):
+            all_anchors.append(torch.cat(anchor_list[i]))
+        # 这个multi apply的是batch，每张图片进行
+        losses_cls, losses_bbox = multi_apply(
+            self.loss_single,
+            all_cls_scores,
+            all_bbox_preds,
+            all_anchors,
+            all_labels,
+            all_label_weights,
+            all_bbox_targets,
+            all_bbox_weights,
+            num_total_samples=num_total_pos)
+        return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
diff --git a/mmdet/models/dense_heads/tood_head.py b/mmdet/models/dense_heads/tood_head.py
new file mode 100644
index 0000000..c64ebf7
--- /dev/null
+++ b/mmdet/models/dense_heads/tood_head.py
@@ -0,0 +1,778 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule, Scale, bias_init_with_prob, normal_init
+from mmcv.ops import deform_conv2d
+from mmcv.runner import force_fp32
+
+from mmdet.core import (anchor_inside_flags, build_assigner, distance2bbox,
+                        images_to_levels, multi_apply, reduce_mean, unmap)
+from mmdet.core.utils import filter_scores_and_topk
+from mmdet.models.utils import sigmoid_geometric_mean
+from ..builder import HEADS, build_loss
+from .atss_head import ATSSHead
+
+
+class TaskDecomposition(nn.Module):
+    """Task decomposition module in task-aligned predictor of TOOD.
+
+    Args:
+        feat_channels (int): Number of feature channels in TOOD head.
+        stacked_convs (int): Number of conv layers in TOOD head.
+        la_down_rate (int): Downsample rate of layer attention.
+        conv_cfg (dict): Config dict for convolution layer.
+        norm_cfg (dict): Config dict for normalization layer.
+    """
+
+    def __init__(self,
+                 feat_channels,
+                 stacked_convs,
+                 la_down_rate=8,
+                 conv_cfg=None,
+                 norm_cfg=None):
+        super(TaskDecomposition, self).__init__()
+        self.feat_channels = feat_channels
+        self.stacked_convs = stacked_convs
+        self.in_channels = self.feat_channels * self.stacked_convs
+        self.norm_cfg = norm_cfg
+        self.layer_attention = nn.Sequential(
+            nn.Conv2d(self.in_channels, self.in_channels // la_down_rate, 1),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(
+                self.in_channels // la_down_rate,
+                self.stacked_convs,
+                1,
+                padding=0), nn.Sigmoid())
+
+        self.reduction_conv = ConvModule(
+            self.in_channels,
+            self.feat_channels,
+            1,
+            stride=1,
+            padding=0,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            bias=norm_cfg is None)
+
+    def init_weights(self):
+        for m in self.layer_attention.modules():
+            if isinstance(m, nn.Conv2d):
+                normal_init(m, std=0.001)
+        normal_init(self.reduction_conv.conv, std=0.01)
+
+    def forward(self, feat, avg_feat=None):
+        b, c, h, w = feat.shape
+        if avg_feat is None:
+            avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))
+        weight = self.layer_attention(avg_feat)
+
+        # here we first compute the product between layer attention weight and
+        # conv weight, and then compute the convolution between new conv weight
+        # and feature map, in order to save memory and FLOPs.
+        conv_weight = weight.reshape(
+            b, 1, self.stacked_convs,
+            1) * self.reduction_conv.conv.weight.reshape(
+                1, self.feat_channels, self.stacked_convs, self.feat_channels)
+        conv_weight = conv_weight.reshape(b, self.feat_channels,
+                                          self.in_channels)
+        feat = feat.reshape(b, self.in_channels, h * w)
+        feat = torch.bmm(conv_weight, feat).reshape(b, self.feat_channels, h,
+                                                    w)
+        if self.norm_cfg is not None:
+            feat = self.reduction_conv.norm(feat)
+        feat = self.reduction_conv.activate(feat)
+
+        return feat
+
+
+@HEADS.register_module()
+class TOODHead(ATSSHead):
+    """TOODHead used in `TOOD: Task-aligned One-stage Object Detection.
+
+    <https://arxiv.org/abs/2108.07755>`_.
+
+    TOOD uses Task-aligned head (T-head) and is optimized by Task Alignment
+    Learning (TAL).
+
+    Args:
+        num_dcn (int): Number of deformable convolution in the head.
+            Default: 0.
+        anchor_type (str): If set to `anchor_free`, the head will use centers
+            to regress bboxes. If set to `anchor_based`, the head will
+            regress bboxes based on anchors. Default: `anchor_free`.
+        initial_loss_cls (dict): Config of initial loss.
+
+    Example:
+        >>> self = TOODHead(11, 7)
+        >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
+        >>> cls_score, bbox_pred = self.forward(feats)
+        >>> assert len(cls_score) == len(self.scales)
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 num_dcn=0,
+                 anchor_type='anchor_free',
+                 initial_loss_cls=dict(
+                     type='FocalLoss',
+                     use_sigmoid=True,
+                     activated=True,
+                     gamma=2.0,
+                     alpha=0.25,
+                     loss_weight=1.0),
+                 **kwargs):
+        assert anchor_type in ['anchor_free', 'anchor_based']
+        self.num_dcn = num_dcn
+        self.anchor_type = anchor_type
+        self.epoch = 0  # which would be update in SetEpochInfoHook!
+        super(TOODHead, self).__init__(num_classes, in_channels, **kwargs)
+
+        if self.train_cfg:
+            self.initial_epoch = self.train_cfg.initial_epoch
+            self.initial_assigner = build_assigner(
+                self.train_cfg.initial_assigner)
+            self.initial_loss_cls = build_loss(initial_loss_cls)
+            self.assigner = self.initial_assigner
+            self.alignment_assigner = build_assigner(self.train_cfg.assigner)
+            self.alpha = self.train_cfg.alpha
+            self.beta = self.train_cfg.beta
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.relu = nn.ReLU(inplace=True)
+        self.inter_convs = nn.ModuleList()
+        for i in range(self.stacked_convs):
+            if i < self.num_dcn:
+                conv_cfg = dict(type='DCNv2', deform_groups=4)
+            else:
+                conv_cfg = self.conv_cfg
+            chn = self.in_channels if i == 0 else self.feat_channels
+            self.inter_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=self.norm_cfg))
+
+        self.cls_decomp = TaskDecomposition(self.feat_channels,
+                                            self.stacked_convs,
+                                            self.stacked_convs * 8,
+                                            self.conv_cfg, self.norm_cfg)
+        self.reg_decomp = TaskDecomposition(self.feat_channels,
+                                            self.stacked_convs,
+                                            self.stacked_convs * 8,
+                                            self.conv_cfg, self.norm_cfg)
+
+        self.tood_cls = nn.Conv2d(
+            self.feat_channels,
+            self.num_base_priors * self.cls_out_channels,
+            3,
+            padding=1)
+        self.tood_reg = nn.Conv2d(
+            self.feat_channels, self.num_base_priors * 4, 3, padding=1)
+
+        self.cls_prob_module = nn.Sequential(
+            nn.Conv2d(self.feat_channels * self.stacked_convs,
+                      self.feat_channels // 4, 1), nn.ReLU(inplace=True),
+            nn.Conv2d(self.feat_channels // 4, 1, 3, padding=1))
+        self.reg_offset_module = nn.Sequential(
+            nn.Conv2d(self.feat_channels * self.stacked_convs,
+                      self.feat_channels // 4, 1), nn.ReLU(inplace=True),
+            nn.Conv2d(self.feat_channels // 4, 4 * 2, 3, padding=1))
+
+        self.scales = nn.ModuleList(
+            [Scale(1.0) for _ in self.prior_generator.strides])
+
+    def init_weights(self):
+        """Initialize weights of the head."""
+        bias_cls = bias_init_with_prob(0.01)
+        for m in self.inter_convs:
+            normal_init(m.conv, std=0.01)
+        for m in self.cls_prob_module:
+            if isinstance(m, nn.Conv2d):
+                normal_init(m, std=0.01)
+        for m in self.reg_offset_module:
+            if isinstance(m, nn.Conv2d):
+                normal_init(m, std=0.001)
+        normal_init(self.cls_prob_module[-1], std=0.01, bias=bias_cls)
+
+        self.cls_decomp.init_weights()
+        self.reg_decomp.init_weights()
+
+        normal_init(self.tood_cls, std=0.01, bias=bias_cls)
+        normal_init(self.tood_reg, std=0.01)
+
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple: Usually a tuple of classification scores and bbox prediction
+                cls_scores (list[Tensor]): Classification scores for all scale
+                    levels, each is a 4D-tensor, the channels number is
+                    num_anchors * num_classes.
+                bbox_preds (list[Tensor]): Decoded box for all scale levels,
+                    each is a 4D-tensor, the channels number is
+                    num_anchors * 4. In [tl_x, tl_y, br_x, br_y] format.
+        """
+        cls_scores = []
+        bbox_preds = []
+        for idx, (x, scale, stride) in enumerate(
+                zip(feats, self.scales, self.prior_generator.strides)):
+            b, c, h, w = x.shape
+            anchor = self.prior_generator.single_level_grid_priors(
+                (h, w), idx, device=x.device)
+            anchor = torch.cat([anchor for _ in range(b)])
+            # extract task interactive features
+            inter_feats = []
+            for inter_conv in self.inter_convs:
+                x = inter_conv(x)
+                inter_feats.append(x)
+            feat = torch.cat(inter_feats, 1)
+
+            # task decomposition
+            avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))
+            cls_feat = self.cls_decomp(feat, avg_feat)
+            reg_feat = self.reg_decomp(feat, avg_feat)
+
+            # cls prediction and alignment
+            cls_logits = self.tood_cls(cls_feat)
+            cls_prob = self.cls_prob_module(feat)
+            cls_score = sigmoid_geometric_mean(cls_logits, cls_prob)
+
+            # reg prediction and alignment
+            if self.anchor_type == 'anchor_free':
+                reg_dist = scale(self.tood_reg(reg_feat).exp()).float()
+                reg_dist = reg_dist.permute(0, 2, 3, 1).reshape(-1, 4)
+                reg_bbox = distance2bbox(
+                    self.anchor_center(anchor) / stride[0],
+                    reg_dist).reshape(b, h, w, 4).permute(0, 3, 1,
+                                                          2)  # (b, c, h, w)
+            elif self.anchor_type == 'anchor_based':
+                reg_dist = scale(self.tood_reg(reg_feat)).float()
+                reg_dist = reg_dist.permute(0, 2, 3, 1).reshape(-1, 4)
+                reg_bbox = self.bbox_coder.decode(anchor, reg_dist).reshape(
+                    b, h, w, 4).permute(0, 3, 1, 2) / stride[0]
+            else:
+                raise NotImplementedError(
+                    f'Unknown anchor type: {self.anchor_type}.'
+                    f'Please use `anchor_free` or `anchor_based`.')
+            reg_offset = self.reg_offset_module(feat)
+            bbox_pred = self.deform_sampling(reg_bbox.contiguous(),
+                                             reg_offset.contiguous())
+
+            # After deform_sampling, some boxes will become invalid (The
+            # left-top point is at the right or bottom of the right-bottom
+            # point), which will make the GIoULoss negative.
+            invalid_bbox_idx = (bbox_pred[:, [0]] > bbox_pred[:, [2]]) | \
+                               (bbox_pred[:, [1]] > bbox_pred[:, [3]])
+            invalid_bbox_idx = invalid_bbox_idx.expand_as(bbox_pred)
+            bbox_pred = torch.where(invalid_bbox_idx, reg_bbox, bbox_pred)
+
+            cls_scores.append(cls_score)
+            bbox_preds.append(bbox_pred)
+        return tuple(cls_scores), tuple(bbox_preds)
+
+    def deform_sampling(self, feat, offset):
+        """Sampling the feature x according to offset.
+
+        Args:
+            feat (Tensor): Feature
+            offset (Tensor): Spatial offset for feature sampling
+        """
+        # it is an equivalent implementation of bilinear interpolation
+        b, c, h, w = feat.shape
+        weight = feat.new_ones(c, 1, 1, 1)
+        y = deform_conv2d(feat, offset, weight, 1, 0, 1, c, c)
+        return y
+
+    def anchor_center(self, anchors):
+        """Get anchor centers from anchors.
+
+        Args:
+            anchors (Tensor): Anchor list with shape (N, 4), "xyxy" format.
+
+        Returns:
+            Tensor: Anchor centers with shape (N, 2), "xy" format.
+        """
+        anchors_cx = (anchors[:, 2] + anchors[:, 0]) / 2
+        anchors_cy = (anchors[:, 3] + anchors[:, 1]) / 2
+        return torch.stack([anchors_cx, anchors_cy], dim=-1)
+
+    def loss_single(self, anchors, cls_score, bbox_pred, labels, label_weights,
+                    bbox_targets, alignment_metrics, stride):
+        """Compute loss of a single scale level.
+
+        Args:
+            anchors (Tensor): Box reference for each scale level with shape
+                (N, num_total_anchors, 4).
+            cls_score (Tensor): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W).
+            bbox_pred (Tensor): Decoded bboxes for each scale
+                level with shape (N, num_anchors * 4, H, W).
+            labels (Tensor): Labels of each anchors with shape
+                (N, num_total_anchors).
+            label_weights (Tensor): Label weights of each anchor with shape
+                (N, num_total_anchors).
+            bbox_targets (Tensor): BBox regression targets of each anchor with
+                shape (N, num_total_anchors, 4).
+            alignment_metrics (Tensor): Alignment metrics with shape
+                (N, num_total_anchors).
+            stride (tuple[int]): Downsample stride of the feature map.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        assert stride[0] == stride[1], 'h stride is not equal to w stride!'
+        anchors = anchors.reshape(-1, 4)
+        cls_score = cls_score.permute(0, 2, 3, 1).reshape(
+            -1, self.cls_out_channels).contiguous()
+        bbox_pred = bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4)
+        bbox_targets = bbox_targets.reshape(-1, 4)
+        labels = labels.reshape(-1)
+        alignment_metrics = alignment_metrics.reshape(-1)
+        label_weights = label_weights.reshape(-1)
+        targets = labels if self.epoch < self.initial_epoch else (
+            labels, alignment_metrics)
+        cls_loss_func = self.initial_loss_cls \
+            if self.epoch < self.initial_epoch else self.loss_cls
+
+        loss_cls = cls_loss_func(
+            cls_score, targets, label_weights, avg_factor=1.0)
+
+        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+        bg_class_ind = self.num_classes
+        pos_inds = ((labels >= 0)
+                    & (labels < bg_class_ind)).nonzero().squeeze(1)
+
+        if len(pos_inds) > 0:
+            pos_bbox_targets = bbox_targets[pos_inds]
+            pos_bbox_pred = bbox_pred[pos_inds]
+            pos_anchors = anchors[pos_inds]
+
+            pos_decode_bbox_pred = pos_bbox_pred
+            pos_decode_bbox_targets = pos_bbox_targets / stride[0]
+
+            # regression loss
+            pos_bbox_weight = self.centerness_target(
+                pos_anchors, pos_bbox_targets
+            ) if self.epoch < self.initial_epoch else alignment_metrics[
+                pos_inds]
+
+            loss_bbox = self.loss_bbox(
+                pos_decode_bbox_pred,
+                pos_decode_bbox_targets,
+                weight=pos_bbox_weight,
+                avg_factor=1.0)
+        else:
+            loss_bbox = bbox_pred.sum() * 0
+            pos_bbox_weight = bbox_targets.new_tensor(0.)
+
+        return loss_cls, loss_bbox, alignment_metrics.sum(
+        ), pos_bbox_weight.sum()
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Decoded box for each scale
+                level with shape (N, num_anchors * 4, H, W) in
+                [tl_x, tl_y, br_x, br_y] format.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (list[Tensor] | None): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        num_imgs = len(img_metas)
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+
+        device = cls_scores[0].device
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+
+        flatten_cls_scores = torch.cat([
+            cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1,
+                                                  self.cls_out_channels)
+            for cls_score in cls_scores
+        ], 1)
+        flatten_bbox_preds = torch.cat([
+            bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4) * stride[0]
+            for bbox_pred, stride in zip(bbox_preds,
+                                         self.prior_generator.strides)
+        ], 1)
+
+        cls_reg_targets = self.get_targets(
+            flatten_cls_scores,
+            flatten_bbox_preds,
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels)
+        (anchor_list, labels_list, label_weights_list, bbox_targets_list,
+         alignment_metrics_list) = cls_reg_targets
+
+        losses_cls, losses_bbox,\
+            cls_avg_factors, bbox_avg_factors = multi_apply(
+                self.loss_single,
+                anchor_list,
+                cls_scores,
+                bbox_preds,
+                labels_list,
+                label_weights_list,
+                bbox_targets_list,
+                alignment_metrics_list,
+                self.prior_generator.strides)
+
+        cls_avg_factor = reduce_mean(sum(cls_avg_factors)).clamp_(min=1).item()
+        losses_cls = list(map(lambda x: x / cls_avg_factor, losses_cls))
+
+        bbox_avg_factor = reduce_mean(
+            sum(bbox_avg_factors)).clamp_(min=1).item()
+        losses_bbox = list(map(lambda x: x / bbox_avg_factor, losses_bbox))
+        return dict(loss_cls=losses_cls, loss_bbox=losses_bbox)
+
+    def _get_bboxes_single(self,
+                           cls_score_list,
+                           bbox_pred_list,
+                           score_factor_list,
+                           mlvl_priors,
+                           img_meta,
+                           cfg,
+                           rescale=False,
+                           with_nms=True,
+                           **kwargs):
+        """Transform outputs of a single image into bbox predictions.
+
+        Args:
+            cls_score_list (list[Tensor]): Box scores from all scale
+                levels of a single image, each item has shape
+                (num_priors * num_classes, H, W).
+            bbox_pred_list (list[Tensor]): Box energies / deltas from
+                all scale levels of a single image, each item has shape
+                (num_priors * 4, H, W).
+            score_factor_list (list[Tensor]): Score factor from all scale
+                levels of a single image, each item has shape
+                (num_priors * 1, H, W).
+            mlvl_priors (list[Tensor]): Each element in the list is
+                the priors of a single level in feature pyramid. In all
+                anchor-based methods, it has shape (num_priors, 4). In
+                all anchor-free methods, it has shape (num_priors, 2)
+                when `with_stride=True`, otherwise it still has shape
+                (num_priors, 4).
+            img_meta (dict): Image meta info.
+            cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+
+        Returns:
+            tuple[Tensor]: Results of detected bboxes and labels. If with_nms
+                is False and mlvl_score_factor is None, return mlvl_bboxes and
+                mlvl_scores, else return mlvl_bboxes, mlvl_scores and
+                mlvl_score_factor. Usually with_nms is False is used for aug
+                test. If with_nms is True, then return the following format
+
+                - det_bboxes (Tensor): Predicted bboxes with shape \
+                    [num_bboxes, 5], where the first 4 columns are bounding \
+                    box positions (tl_x, tl_y, br_x, br_y) and the 5-th \
+                    column are scores between 0 and 1.
+                - det_labels (Tensor): Predicted labels of the corresponding \
+                    box with shape [num_bboxes].
+        """
+
+        cfg = self.test_cfg if cfg is None else cfg
+        nms_pre = cfg.get('nms_pre', -1)
+
+        mlvl_bboxes = []
+        mlvl_scores = []
+        mlvl_labels = []
+        for cls_score, bbox_pred, priors, stride in zip(
+                cls_score_list, bbox_pred_list, mlvl_priors,
+                self.prior_generator.strides):
+
+            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
+
+            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4) * stride[0]
+            scores = cls_score.permute(1, 2,
+                                       0).reshape(-1, self.cls_out_channels)
+
+            # After https://github.com/open-mmlab/mmdetection/pull/6268/,
+            # this operation keeps fewer bboxes under the same `nms_pre`.
+            # There is no difference in performance for most models. If you
+            # find a slight drop in performance, you can set a larger
+            # `nms_pre` than before.
+            results = filter_scores_and_topk(
+                scores, cfg.score_thr, nms_pre,
+                dict(bbox_pred=bbox_pred, priors=priors))
+            scores, labels, keep_idxs, filtered_results = results
+
+            bboxes = filtered_results['bbox_pred']
+
+            mlvl_bboxes.append(bboxes)
+            mlvl_scores.append(scores)
+            mlvl_labels.append(labels)
+
+        return self._bbox_post_process(mlvl_scores, mlvl_labels, mlvl_bboxes,
+                                       img_meta['scale_factor'], cfg, rescale,
+                                       with_nms, None, **kwargs)
+
+    def get_targets(self,
+                    cls_scores,
+                    bbox_preds,
+                    anchor_list,
+                    valid_flag_list,
+                    gt_bboxes_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None,
+                    gt_labels_list=None,
+                    label_channels=1,
+                    unmap_outputs=True):
+        """Compute regression and classification targets for anchors in
+        multiple images.
+
+        Args:
+            cls_scores (Tensor): Classification predictions of images,
+                a 3D-Tensor with shape [num_imgs, num_priors, num_classes].
+            bbox_preds (Tensor): Decoded bboxes predictions of one image,
+                a 3D-Tensor with shape [num_imgs, num_priors, 4] in [tl_x,
+                tl_y, br_x, br_y] format.
+            anchor_list (list[list[Tensor]]): Multi level anchors of each
+                image. The outer list indicates images, and the inner list
+                corresponds to feature levels of the image. Each element of
+                the inner list is a tensor of shape (num_anchors, 4).
+            valid_flag_list (list[list[Tensor]]): Multi level valid flags of
+                each image. The outer list indicates images, and the inner list
+                corresponds to feature levels of the image. Each element of
+                the inner list is a tensor of shape (num_anchors, )
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+            img_metas (list[dict]): Meta info of each image.
+            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
+                ignored.
+            gt_labels_list (list[Tensor]): Ground truth labels of each box.
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+
+        Returns:
+            tuple: a tuple containing learning targets.
+
+                - anchors_list (list[list[Tensor]]): Anchors of each level.
+                - labels_list (list[Tensor]): Labels of each level.
+                - label_weights_list (list[Tensor]): Label weights of each
+                  level.
+                - bbox_targets_list (list[Tensor]): BBox targets of each level.
+                - norm_alignment_metrics_list (list[Tensor]): Normalized
+                  alignment metrics of each level.
+        """
+        num_imgs = len(img_metas)
+        assert len(anchor_list) == len(valid_flag_list) == num_imgs
+
+        # anchor number of multi levels
+        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+        num_level_anchors_list = [num_level_anchors] * num_imgs
+
+        # concat all level anchors and flags to a single tensor
+        for i in range(num_imgs):
+            assert len(anchor_list[i]) == len(valid_flag_list[i])
+            anchor_list[i] = torch.cat(anchor_list[i])
+            valid_flag_list[i] = torch.cat(valid_flag_list[i])
+
+        # compute targets for each image
+        if gt_bboxes_ignore_list is None:
+            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
+        if gt_labels_list is None:
+            gt_labels_list = [None for _ in range(num_imgs)]
+        # anchor_list: list(b * [-1, 4])
+
+        if self.epoch < self.initial_epoch:
+            (all_anchors, all_labels, all_label_weights, all_bbox_targets,
+             all_bbox_weights, pos_inds_list, neg_inds_list) = multi_apply(
+                 super()._get_target_single,
+                 anchor_list,
+                 valid_flag_list,
+                 num_level_anchors_list,
+                 gt_bboxes_list,
+                 gt_bboxes_ignore_list,
+                 gt_labels_list,
+                 img_metas,
+                 label_channels=label_channels,
+                 unmap_outputs=unmap_outputs)
+            all_assign_metrics = [
+                weight[..., 0] for weight in all_bbox_weights
+            ]
+        else:
+            (all_anchors, all_labels, all_label_weights, all_bbox_targets,
+             all_assign_metrics) = multi_apply(
+                 self._get_target_single,
+                 cls_scores,
+                 bbox_preds,
+                 anchor_list,
+                 valid_flag_list,
+                 gt_bboxes_list,
+                 gt_bboxes_ignore_list,
+                 gt_labels_list,
+                 img_metas,
+                 label_channels=label_channels,
+                 unmap_outputs=unmap_outputs)
+        # no valid anchors
+        if any([labels is None for labels in all_labels]):
+            return None
+
+        # split targets to a list w.r.t. multiple levels
+        anchors_list = images_to_levels(all_anchors, num_level_anchors)
+        labels_list = images_to_levels(all_labels, num_level_anchors)
+        label_weights_list = images_to_levels(all_label_weights,
+                                              num_level_anchors)
+        bbox_targets_list = images_to_levels(all_bbox_targets,
+                                             num_level_anchors)
+        norm_alignment_metrics_list = images_to_levels(all_assign_metrics,
+                                                       num_level_anchors)
+
+        return (anchors_list, labels_list, label_weights_list,
+                bbox_targets_list, norm_alignment_metrics_list)
+
+    def _get_target_single(self,
+                           cls_scores,
+                           bbox_preds,
+                           flat_anchors,
+                           valid_flags,
+                           gt_bboxes,
+                           gt_bboxes_ignore,
+                           gt_labels,
+                           img_meta,
+                           label_channels=1,
+                           unmap_outputs=True):
+        """Compute regression, classification targets for anchors in a single
+        image.
+
+        Args:
+            cls_scores (list(Tensor)): Box scores for each image.
+            bbox_preds (list(Tensor)): Box energies / deltas for each image.
+            flat_anchors (Tensor): Multi-level anchors of the image, which are
+                concatenated into a single tensor of shape (num_anchors ,4)
+            valid_flags (Tensor): Multi level valid flags of the image,
+                which are concatenated into a single tensor of
+                    shape (num_anchors,).
+            gt_bboxes (Tensor): Ground truth bboxes of the image,
+                shape (num_gts, 4).
+            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4).
+            gt_labels (Tensor): Ground truth labels of each box,
+                shape (num_gts,).
+            img_meta (dict): Meta info of the image.
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+
+        Returns:
+            tuple: N is the number of total anchors in the image.
+                anchors (Tensor): All anchors in the image with shape (N, 4).
+                labels (Tensor): Labels of all anchors in the image with shape
+                    (N,).
+                label_weights (Tensor): Label weights of all anchor in the
+                    image with shape (N,).
+                bbox_targets (Tensor): BBox targets of all anchors in the
+                    image with shape (N, 4).
+                norm_alignment_metrics (Tensor): Normalized alignment metrics
+                    of all priors in the image with shape (N,).
+        """
+        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
+                                           img_meta['img_shape'][:2],
+                                           self.train_cfg.allowed_border)
+        if not inside_flags.any():
+            return (None, ) * 7
+        # assign gt and sample anchors
+        anchors = flat_anchors[inside_flags, :]
+        assign_result = self.alignment_assigner.assign(
+            cls_scores[inside_flags, :], bbox_preds[inside_flags, :], anchors,
+            gt_bboxes, gt_bboxes_ignore, gt_labels, self.alpha, self.beta)
+        assign_ious = assign_result.max_overlaps
+        assign_metrics = assign_result.assign_metrics
+
+        sampling_result = self.sampler.sample(assign_result, anchors,
+                                              gt_bboxes)
+
+        num_valid_anchors = anchors.shape[0]
+        bbox_targets = torch.zeros_like(anchors)
+        labels = anchors.new_full((num_valid_anchors, ),
+                                  self.num_classes,
+                                  dtype=torch.long)
+        label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
+        norm_alignment_metrics = anchors.new_zeros(
+            num_valid_anchors, dtype=torch.float)
+
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+        if len(pos_inds) > 0:
+            # point-based
+            pos_bbox_targets = sampling_result.pos_gt_bboxes
+            bbox_targets[pos_inds, :] = pos_bbox_targets
+
+            if gt_labels is None:
+                # Only rpn gives gt_labels as None
+                # Foreground is the first class since v2.5.0
+                labels[pos_inds] = 0
+            else:
+                labels[pos_inds] = gt_labels[
+                    sampling_result.pos_assigned_gt_inds]
+            if self.train_cfg.pos_weight <= 0:
+                label_weights[pos_inds] = 1.0
+            else:
+                label_weights[pos_inds] = self.train_cfg.pos_weight
+        if len(neg_inds) > 0:
+            label_weights[neg_inds] = 1.0
+
+        class_assigned_gt_inds = torch.unique(
+            sampling_result.pos_assigned_gt_inds)
+        for gt_inds in class_assigned_gt_inds:
+            gt_class_inds = pos_inds[sampling_result.pos_assigned_gt_inds ==
+                                     gt_inds]
+            pos_alignment_metrics = assign_metrics[gt_class_inds]
+            pos_ious = assign_ious[gt_class_inds]
+            pos_norm_alignment_metrics = pos_alignment_metrics / (
+                pos_alignment_metrics.max() + 10e-8) * pos_ious.max()
+            norm_alignment_metrics[gt_class_inds] = pos_norm_alignment_metrics
+
+        # map up to original set of anchors
+        if unmap_outputs:
+            num_total_anchors = flat_anchors.size(0)
+            anchors = unmap(anchors, num_total_anchors, inside_flags)
+            labels = unmap(
+                labels, num_total_anchors, inside_flags, fill=self.num_classes)
+            label_weights = unmap(label_weights, num_total_anchors,
+                                  inside_flags)
+            bbox_targets = unmap(bbox_targets, num_total_anchors, inside_flags)
+            norm_alignment_metrics = unmap(norm_alignment_metrics,
+                                           num_total_anchors, inside_flags)
+        return (anchors, labels, label_weights, bbox_targets,
+                norm_alignment_metrics)
diff --git a/mmdet/models/dense_heads/vfnet_head.py b/mmdet/models/dense_heads/vfnet_head.py
new file mode 100644
index 0000000..ba285e2
--- /dev/null
+++ b/mmdet/models/dense_heads/vfnet_head.py
@@ -0,0 +1,740 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import numpy as np
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule, Scale
+from mmcv.ops import DeformConv2d
+from mmcv.runner import force_fp32
+
+from mmdet.core import (MlvlPointGenerator, bbox_overlaps, build_assigner,
+                        build_prior_generator, build_sampler, multi_apply,
+                        reduce_mean)
+from ..builder import HEADS, build_loss
+from .atss_head import ATSSHead
+from .fcos_head import FCOSHead
+
+INF = 1e8
+
+
+@HEADS.register_module()
+class VFNetHead(ATSSHead, FCOSHead):
+    """Head of `VarifocalNet (VFNet): An IoU-aware Dense Object
+    Detector.<https://arxiv.org/abs/2008.13367>`_.
+
+    The VFNet predicts IoU-aware classification scores which mix the
+    object presence confidence and object localization accuracy as the
+    detection score. It is built on the FCOS architecture and uses ATSS
+    for defining positive/negative training examples. The VFNet is trained
+    with Varifocal Loss and empolys star-shaped deformable convolution to
+    extract features for a bbox.
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        regress_ranges (tuple[tuple[int, int]]): Regress range of multiple
+            level points.
+        center_sampling (bool): If true, use center sampling. Default: False.
+        center_sample_radius (float): Radius of center sampling. Default: 1.5.
+        sync_num_pos (bool): If true, synchronize the number of positive
+            examples across GPUs. Default: True
+        gradient_mul (float): The multiplier to gradients from bbox refinement
+            and recognition. Default: 0.1.
+        bbox_norm_type (str): The bbox normalization type, 'reg_denom' or
+            'stride'. Default: reg_denom
+        loss_cls_fl (dict): Config of focal loss.
+        use_vfl (bool): If true, use varifocal loss for training.
+            Default: True.
+        loss_cls (dict): Config of varifocal loss.
+        loss_bbox (dict): Config of localization loss, GIoU Loss.
+        loss_bbox (dict): Config of localization refinement loss, GIoU Loss.
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: norm_cfg=dict(type='GN', num_groups=32,
+            requires_grad=True).
+        use_atss (bool): If true, use ATSS to define positive/negative
+            examples. Default: True.
+        anchor_generator (dict): Config of anchor generator for ATSS.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+
+    Example:
+        >>> self = VFNetHead(11, 7)
+        >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]
+        >>> cls_score, bbox_pred, bbox_pred_refine= self.forward(feats)
+        >>> assert len(cls_score) == len(self.scales)
+    """  # noqa: E501
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 regress_ranges=((-1, 64), (64, 128), (128, 256), (256, 512),
+                                 (512, INF)),
+                 center_sampling=False,
+                 center_sample_radius=1.5,
+                 sync_num_pos=True,
+                 gradient_mul=0.1,
+                 bbox_norm_type='reg_denom',
+                 loss_cls_fl=dict(
+                     type='FocalLoss',
+                     use_sigmoid=True,
+                     gamma=2.0,
+                     alpha=0.25,
+                     loss_weight=1.0),
+                 use_vfl=True,
+                 loss_cls=dict(
+                     type='VarifocalLoss',
+                     use_sigmoid=True,
+                     alpha=0.75,
+                     gamma=2.0,
+                     iou_weighted=True,
+                     loss_weight=1.0),
+                 loss_bbox=dict(type='GIoULoss', loss_weight=1.5),
+                 loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0),
+                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
+                 use_atss=True,
+                 reg_decoded_bbox=True,
+                 anchor_generator=dict(
+                     type='AnchorGenerator',
+                     ratios=[1.0],
+                     octave_base_scale=8,
+                     scales_per_octave=1,
+                     center_offset=0.0,
+                     strides=[8, 16, 32, 64, 128]),
+                 init_cfg=dict(
+                     type='Normal',
+                     layer='Conv2d',
+                     std=0.01,
+                     override=dict(
+                         type='Normal',
+                         name='vfnet_cls',
+                         std=0.01,
+                         bias_prob=0.01)),
+                 **kwargs):
+        # dcn base offsets, adapted from reppoints_head.py
+        self.num_dconv_points = 9
+        self.dcn_kernel = int(np.sqrt(self.num_dconv_points))
+        self.dcn_pad = int((self.dcn_kernel - 1) / 2)
+        dcn_base = np.arange(-self.dcn_pad,
+                             self.dcn_pad + 1).astype(np.float64)
+        dcn_base_y = np.repeat(dcn_base, self.dcn_kernel)
+        dcn_base_x = np.tile(dcn_base, self.dcn_kernel)
+        dcn_base_offset = np.stack([dcn_base_y, dcn_base_x], axis=1).reshape(
+            (-1))
+        self.dcn_base_offset = torch.tensor(dcn_base_offset).view(1, -1, 1, 1)
+
+        super(FCOSHead, self).__init__(
+            num_classes,
+            in_channels,
+            norm_cfg=norm_cfg,
+            init_cfg=init_cfg,
+            **kwargs)
+        self.regress_ranges = regress_ranges
+        self.reg_denoms = [
+            regress_range[-1] for regress_range in regress_ranges
+        ]
+        self.reg_denoms[-1] = self.reg_denoms[-2] * 2
+        self.center_sampling = center_sampling
+        self.center_sample_radius = center_sample_radius
+        self.sync_num_pos = sync_num_pos
+        self.bbox_norm_type = bbox_norm_type
+        self.gradient_mul = gradient_mul
+        self.use_vfl = use_vfl
+        if self.use_vfl:
+            self.loss_cls = build_loss(loss_cls)
+        else:
+            self.loss_cls = build_loss(loss_cls_fl)
+        self.loss_bbox = build_loss(loss_bbox)
+        self.loss_bbox_refine = build_loss(loss_bbox_refine)
+
+        # for getting ATSS targets
+        self.use_atss = use_atss
+        self.reg_decoded_bbox = reg_decoded_bbox
+        self.use_sigmoid_cls = loss_cls.get('use_sigmoid', False)
+
+        self.anchor_center_offset = anchor_generator['center_offset']
+
+        self.num_base_priors = self.prior_generator.num_base_priors[0]
+
+        self.sampling = False
+        if self.train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+        # only be used in `get_atss_targets` when `use_atss` is True
+        self.atss_prior_generator = build_prior_generator(anchor_generator)
+
+        self.fcos_prior_generator = MlvlPointGenerator(
+            anchor_generator['strides'],
+            self.anchor_center_offset if self.use_atss else 0.5)
+
+        # In order to reuse the `get_bboxes` in `BaseDenseHead.
+        # Only be used in testing phase.
+        self.prior_generator = self.fcos_prior_generator
+
+    @property
+    def num_anchors(self):
+        """
+        Returns:
+            int: Number of anchors on each point of feature map.
+        """
+        warnings.warn('DeprecationWarning: `num_anchors` is deprecated, '
+                      'please use "num_base_priors" instead')
+        return self.num_base_priors
+
+    @property
+    def anchor_generator(self):
+        warnings.warn('DeprecationWarning: anchor_generator is deprecated, '
+                      'please use "atss_prior_generator" instead')
+        return self.prior_generator
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        super(FCOSHead, self)._init_cls_convs()
+        super(FCOSHead, self)._init_reg_convs()
+        self.relu = nn.ReLU(inplace=True)
+        self.vfnet_reg_conv = ConvModule(
+            self.feat_channels,
+            self.feat_channels,
+            3,
+            stride=1,
+            padding=1,
+            conv_cfg=self.conv_cfg,
+            norm_cfg=self.norm_cfg,
+            bias=self.conv_bias)
+        self.vfnet_reg = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
+        self.scales = nn.ModuleList([Scale(1.0) for _ in self.strides])
+
+        self.vfnet_reg_refine_dconv = DeformConv2d(
+            self.feat_channels,
+            self.feat_channels,
+            self.dcn_kernel,
+            1,
+            padding=self.dcn_pad)
+        self.vfnet_reg_refine = nn.Conv2d(self.feat_channels, 4, 3, padding=1)
+        self.scales_refine = nn.ModuleList([Scale(1.0) for _ in self.strides])
+
+        self.vfnet_cls_dconv = DeformConv2d(
+            self.feat_channels,
+            self.feat_channels,
+            self.dcn_kernel,
+            1,
+            padding=self.dcn_pad)
+        self.vfnet_cls = nn.Conv2d(
+            self.feat_channels, self.cls_out_channels, 3, padding=1)
+
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple:
+                cls_scores (list[Tensor]): Box iou-aware scores for each scale
+                    level, each is a 4D-tensor, the channel number is
+                    num_points * num_classes.
+                bbox_preds (list[Tensor]): Box offsets for each
+                    scale level, each is a 4D-tensor, the channel number is
+                    num_points * 4.
+                bbox_preds_refine (list[Tensor]): Refined Box offsets for
+                    each scale level, each is a 4D-tensor, the channel
+                    number is num_points * 4.
+        """
+        return multi_apply(self.forward_single, feats, self.scales,
+                           self.scales_refine, self.strides, self.reg_denoms)
+
+    def forward_single(self, x, scale, scale_refine, stride, reg_denom):
+        """Forward features of a single scale level.
+
+        Args:
+            x (Tensor): FPN feature maps of the specified stride.
+            scale (:obj: `mmcv.cnn.Scale`): Learnable scale module to resize
+                the bbox prediction.
+            scale_refine (:obj: `mmcv.cnn.Scale`): Learnable scale module to
+                resize the refined bbox prediction.
+            stride (int): The corresponding stride for feature maps,
+                used to normalize the bbox prediction when
+                bbox_norm_type = 'stride'.
+            reg_denom (int): The corresponding regression range for feature
+                maps, only used to normalize the bbox prediction when
+                bbox_norm_type = 'reg_denom'.
+
+        Returns:
+            tuple: iou-aware cls scores for each box, bbox predictions and
+                refined bbox predictions of input feature maps.
+        """
+        cls_feat = x
+        reg_feat = x
+
+        for cls_layer in self.cls_convs:
+            cls_feat = cls_layer(cls_feat)
+
+        for reg_layer in self.reg_convs:
+            reg_feat = reg_layer(reg_feat)
+
+        # predict the bbox_pred of different level
+        reg_feat_init = self.vfnet_reg_conv(reg_feat)
+        if self.bbox_norm_type == 'reg_denom':
+            bbox_pred = scale(
+                self.vfnet_reg(reg_feat_init)).float().exp() * reg_denom
+        elif self.bbox_norm_type == 'stride':
+            bbox_pred = scale(
+                self.vfnet_reg(reg_feat_init)).float().exp() * stride
+        else:
+            raise NotImplementedError
+
+        # compute star deformable convolution offsets
+        # converting dcn_offset to reg_feat.dtype thus VFNet can be
+        # trained with FP16
+        dcn_offset = self.star_dcn_offset(bbox_pred, self.gradient_mul,
+                                          stride).to(reg_feat.dtype)
+
+        # refine the bbox_pred
+        reg_feat = self.relu(self.vfnet_reg_refine_dconv(reg_feat, dcn_offset))
+        bbox_pred_refine = scale_refine(
+            self.vfnet_reg_refine(reg_feat)).float().exp()
+        bbox_pred_refine = bbox_pred_refine * bbox_pred.detach()
+
+        # predict the iou-aware cls score
+        cls_feat = self.relu(self.vfnet_cls_dconv(cls_feat, dcn_offset))
+        cls_score = self.vfnet_cls(cls_feat)
+
+        if self.training:
+            return cls_score, bbox_pred, bbox_pred_refine
+        else:
+            return cls_score, bbox_pred_refine
+
+    def star_dcn_offset(self, bbox_pred, gradient_mul, stride):
+        """Compute the star deformable conv offsets.
+
+        Args:
+            bbox_pred (Tensor): Predicted bbox distance offsets (l, r, t, b).
+            gradient_mul (float): Gradient multiplier.
+            stride (int): The corresponding stride for feature maps,
+                used to project the bbox onto the feature map.
+
+        Returns:
+            dcn_offsets (Tensor): The offsets for deformable convolution.
+        """
+        dcn_base_offset = self.dcn_base_offset.type_as(bbox_pred)
+        bbox_pred_grad_mul = (1 - gradient_mul) * bbox_pred.detach() + \
+            gradient_mul * bbox_pred
+        # map to the feature map scale
+        bbox_pred_grad_mul = bbox_pred_grad_mul / stride
+        N, C, H, W = bbox_pred.size()
+
+        x1 = bbox_pred_grad_mul[:, 0, :, :]
+        y1 = bbox_pred_grad_mul[:, 1, :, :]
+        x2 = bbox_pred_grad_mul[:, 2, :, :]
+        y2 = bbox_pred_grad_mul[:, 3, :, :]
+        bbox_pred_grad_mul_offset = bbox_pred.new_zeros(
+            N, 2 * self.num_dconv_points, H, W)
+        bbox_pred_grad_mul_offset[:, 0, :, :] = -1.0 * y1  # -y1
+        bbox_pred_grad_mul_offset[:, 1, :, :] = -1.0 * x1  # -x1
+        bbox_pred_grad_mul_offset[:, 2, :, :] = -1.0 * y1  # -y1
+        bbox_pred_grad_mul_offset[:, 4, :, :] = -1.0 * y1  # -y1
+        bbox_pred_grad_mul_offset[:, 5, :, :] = x2  # x2
+        bbox_pred_grad_mul_offset[:, 7, :, :] = -1.0 * x1  # -x1
+        bbox_pred_grad_mul_offset[:, 11, :, :] = x2  # x2
+        bbox_pred_grad_mul_offset[:, 12, :, :] = y2  # y2
+        bbox_pred_grad_mul_offset[:, 13, :, :] = -1.0 * x1  # -x1
+        bbox_pred_grad_mul_offset[:, 14, :, :] = y2  # y2
+        bbox_pred_grad_mul_offset[:, 16, :, :] = y2  # y2
+        bbox_pred_grad_mul_offset[:, 17, :, :] = x2  # x2
+        dcn_offset = bbox_pred_grad_mul_offset - dcn_base_offset
+
+        return dcn_offset
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'bbox_preds_refine'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             bbox_preds_refine,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute loss of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box iou-aware scores for each scale
+                level, each is a 4D-tensor, the channel number is
+                num_points * num_classes.
+            bbox_preds (list[Tensor]): Box offsets for each
+                scale level, each is a 4D-tensor, the channel number is
+                num_points * 4.
+            bbox_preds_refine (list[Tensor]): Refined Box offsets for
+                each scale level, each is a 4D-tensor, the channel
+                number is num_points * 4.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+                Default: None.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        assert len(cls_scores) == len(bbox_preds) == len(bbox_preds_refine)
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        all_level_points = self.fcos_prior_generator.grid_priors(
+            featmap_sizes, bbox_preds[0].dtype, bbox_preds[0].device)
+        labels, label_weights, bbox_targets, bbox_weights = self.get_targets(
+            cls_scores, all_level_points, gt_bboxes, gt_labels, img_metas,
+            gt_bboxes_ignore)
+
+        num_imgs = cls_scores[0].size(0)
+        # flatten cls_scores, bbox_preds and bbox_preds_refine
+        flatten_cls_scores = [
+            cls_score.permute(0, 2, 3,
+                              1).reshape(-1,
+                                         self.cls_out_channels).contiguous()
+            for cls_score in cls_scores
+        ]
+        flatten_bbox_preds = [
+            bbox_pred.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
+            for bbox_pred in bbox_preds
+        ]
+        flatten_bbox_preds_refine = [
+            bbox_pred_refine.permute(0, 2, 3, 1).reshape(-1, 4).contiguous()
+            for bbox_pred_refine in bbox_preds_refine
+        ]
+        flatten_cls_scores = torch.cat(flatten_cls_scores)
+        flatten_bbox_preds = torch.cat(flatten_bbox_preds)
+        flatten_bbox_preds_refine = torch.cat(flatten_bbox_preds_refine)
+        flatten_labels = torch.cat(labels)
+        flatten_bbox_targets = torch.cat(bbox_targets)
+        # repeat points to align with bbox_preds
+        flatten_points = torch.cat(
+            [points.repeat(num_imgs, 1) for points in all_level_points])
+
+        # FG cat_id: [0, num_classes - 1], BG cat_id: num_classes
+        bg_class_ind = self.num_classes
+        pos_inds = torch.where(
+            ((flatten_labels >= 0) & (flatten_labels < bg_class_ind)) > 0)[0]
+        num_pos = len(pos_inds)
+
+        pos_bbox_preds = flatten_bbox_preds[pos_inds]
+        pos_bbox_preds_refine = flatten_bbox_preds_refine[pos_inds]
+        pos_labels = flatten_labels[pos_inds]
+
+        # sync num_pos across all gpus
+        if self.sync_num_pos:
+            num_pos_avg_per_gpu = reduce_mean(
+                pos_inds.new_tensor(num_pos).float()).item()
+            num_pos_avg_per_gpu = max(num_pos_avg_per_gpu, 1.0)
+        else:
+            num_pos_avg_per_gpu = num_pos
+
+        pos_bbox_targets = flatten_bbox_targets[pos_inds]
+        pos_points = flatten_points[pos_inds]
+
+        pos_decoded_bbox_preds = self.bbox_coder.decode(
+            pos_points, pos_bbox_preds)
+        pos_decoded_target_preds = self.bbox_coder.decode(
+            pos_points, pos_bbox_targets)
+        iou_targets_ini = bbox_overlaps(
+            pos_decoded_bbox_preds,
+            pos_decoded_target_preds.detach(),
+            is_aligned=True).clamp(min=1e-6)
+        bbox_weights_ini = iou_targets_ini.clone().detach()
+        bbox_avg_factor_ini = reduce_mean(
+            bbox_weights_ini.sum()).clamp_(min=1).item()
+
+        pos_decoded_bbox_preds_refine = \
+            self.bbox_coder.decode(pos_points, pos_bbox_preds_refine)
+        iou_targets_rf = bbox_overlaps(
+            pos_decoded_bbox_preds_refine,
+            pos_decoded_target_preds.detach(),
+            is_aligned=True).clamp(min=1e-6)
+        bbox_weights_rf = iou_targets_rf.clone().detach()
+        bbox_avg_factor_rf = reduce_mean(
+            bbox_weights_rf.sum()).clamp_(min=1).item()
+
+        if num_pos > 0:
+            loss_bbox = self.loss_bbox(
+                pos_decoded_bbox_preds,
+                pos_decoded_target_preds.detach(),
+                weight=bbox_weights_ini,
+                avg_factor=bbox_avg_factor_ini)
+
+            loss_bbox_refine = self.loss_bbox_refine(
+                pos_decoded_bbox_preds_refine,
+                pos_decoded_target_preds.detach(),
+                weight=bbox_weights_rf,
+                avg_factor=bbox_avg_factor_rf)
+
+            # build IoU-aware cls_score targets
+            if self.use_vfl:
+                pos_ious = iou_targets_rf.clone().detach()
+                cls_iou_targets = torch.zeros_like(flatten_cls_scores)
+                cls_iou_targets[pos_inds, pos_labels] = pos_ious
+        else:
+            loss_bbox = pos_bbox_preds.sum() * 0
+            loss_bbox_refine = pos_bbox_preds_refine.sum() * 0
+            if self.use_vfl:
+                cls_iou_targets = torch.zeros_like(flatten_cls_scores)
+
+        if self.use_vfl:
+            loss_cls = self.loss_cls(
+                flatten_cls_scores,
+                cls_iou_targets,
+                avg_factor=num_pos_avg_per_gpu)
+        else:
+            loss_cls = self.loss_cls(
+                flatten_cls_scores,
+                flatten_labels,
+                weight=label_weights,
+                avg_factor=num_pos_avg_per_gpu)
+
+        return dict(
+            loss_cls=loss_cls,
+            loss_bbox=loss_bbox,
+            loss_bbox_rf=loss_bbox_refine)
+
+    def get_targets(self, cls_scores, mlvl_points, gt_bboxes, gt_labels,
+                    img_metas, gt_bboxes_ignore):
+        """A wrapper for computing ATSS and FCOS targets for points in multiple
+        images.
+
+        Args:
+            cls_scores (list[Tensor]): Box iou-aware scores for each scale
+                level with shape (N, num_points * num_classes, H, W).
+            mlvl_points (list[Tensor]): Points of each fpn level, each has
+                shape (num_points, 2).
+            gt_bboxes (list[Tensor]): Ground truth bboxes of each image,
+                each has shape (num_gt, 4).
+            gt_labels (list[Tensor]): Ground truth labels of each box,
+                each has shape (num_gt,).
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4).
+
+        Returns:
+            tuple:
+                labels_list (list[Tensor]): Labels of each level.
+                label_weights (Tensor/None): Label weights of all levels.
+                bbox_targets_list (list[Tensor]): Regression targets of each
+                    level, (l, t, r, b).
+                bbox_weights (Tensor/None): Bbox weights of all levels.
+        """
+        if self.use_atss:
+            return self.get_atss_targets(cls_scores, mlvl_points, gt_bboxes,
+                                         gt_labels, img_metas,
+                                         gt_bboxes_ignore)
+        else:
+            self.norm_on_bbox = False
+            return self.get_fcos_targets(mlvl_points, gt_bboxes, gt_labels)
+
+    def _get_target_single(self, *args, **kwargs):
+        """Avoid ambiguity in multiple inheritance."""
+        if self.use_atss:
+            return ATSSHead._get_target_single(self, *args, **kwargs)
+        else:
+            return FCOSHead._get_target_single(self, *args, **kwargs)
+
+    def get_fcos_targets(self, points, gt_bboxes_list, gt_labels_list):
+        """Compute FCOS regression and classification targets for points in
+        multiple images.
+
+        Args:
+            points (list[Tensor]): Points of each fpn level, each has shape
+                (num_points, 2).
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image,
+                each has shape (num_gt, 4).
+            gt_labels_list (list[Tensor]): Ground truth labels of each box,
+                each has shape (num_gt,).
+
+        Returns:
+            tuple:
+                labels (list[Tensor]): Labels of each level.
+                label_weights: None, to be compatible with ATSS targets.
+                bbox_targets (list[Tensor]): BBox targets of each level.
+                bbox_weights: None, to be compatible with ATSS targets.
+        """
+        labels, bbox_targets = FCOSHead.get_targets(self, points,
+                                                    gt_bboxes_list,
+                                                    gt_labels_list)
+        label_weights = None
+        bbox_weights = None
+        return labels, label_weights, bbox_targets, bbox_weights
+
+    def get_anchors(self, featmap_sizes, img_metas, device='cuda'):
+        """Get anchors according to feature map sizes.
+
+        Args:
+            featmap_sizes (list[tuple]): Multi-level feature map sizes.
+            img_metas (list[dict]): Image meta info.
+            device (torch.device | str): Device for returned tensors
+
+        Returns:
+            tuple:
+                anchor_list (list[Tensor]): Anchors of each image.
+                valid_flag_list (list[Tensor]): Valid flags of each image.
+        """
+        num_imgs = len(img_metas)
+
+        # since feature map sizes of all images are the same, we only compute
+        # anchors for one time
+        multi_level_anchors = self.atss_prior_generator.grid_priors(
+            featmap_sizes, device=device)
+        anchor_list = [multi_level_anchors for _ in range(num_imgs)]
+
+        # for each image, we compute valid flags of multi level anchors
+        valid_flag_list = []
+        for img_id, img_meta in enumerate(img_metas):
+            multi_level_flags = self.atss_prior_generator.valid_flags(
+                featmap_sizes, img_meta['pad_shape'], device=device)
+            valid_flag_list.append(multi_level_flags)
+
+        return anchor_list, valid_flag_list
+
+    def get_atss_targets(self,
+                         cls_scores,
+                         mlvl_points,
+                         gt_bboxes,
+                         gt_labels,
+                         img_metas,
+                         gt_bboxes_ignore=None):
+        """A wrapper for computing ATSS targets for points in multiple images.
+
+        Args:
+            cls_scores (list[Tensor]): Box iou-aware scores for each scale
+                level with shape (N, num_points * num_classes, H, W).
+            mlvl_points (list[Tensor]): Points of each fpn level, each has
+                shape (num_points, 2).
+            gt_bboxes (list[Tensor]): Ground truth bboxes of each image,
+                each has shape (num_gt, 4).
+            gt_labels (list[Tensor]): Ground truth labels of each box,
+                each has shape (num_gt,).
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4). Default: None.
+
+        Returns:
+            tuple:
+                labels_list (list[Tensor]): Labels of each level.
+                label_weights (Tensor): Label weights of all levels.
+                bbox_targets_list (list[Tensor]): Regression targets of each
+                    level, (l, t, r, b).
+                bbox_weights (Tensor): Bbox weights of all levels.
+        """
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(
+            featmap_sizes
+        ) == self.atss_prior_generator.num_levels == \
+            self.fcos_prior_generator.num_levels
+
+        device = cls_scores[0].device
+
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+
+        cls_reg_targets = ATSSHead.get_targets(
+            self,
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels,
+            unmap_outputs=True)
+        if cls_reg_targets is None:
+            return None
+
+        (anchor_list, labels_list, label_weights_list, bbox_targets_list,
+         bbox_weights_list, num_total_pos, num_total_neg) = cls_reg_targets
+
+        bbox_targets_list = [
+            bbox_targets.reshape(-1, 4) for bbox_targets in bbox_targets_list
+        ]
+
+        num_imgs = len(img_metas)
+        # transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format
+        bbox_targets_list = self.transform_bbox_targets(
+            bbox_targets_list, mlvl_points, num_imgs)
+
+        labels_list = [labels.reshape(-1) for labels in labels_list]
+        label_weights_list = [
+            label_weights.reshape(-1) for label_weights in label_weights_list
+        ]
+        bbox_weights_list = [
+            bbox_weights.reshape(-1) for bbox_weights in bbox_weights_list
+        ]
+        label_weights = torch.cat(label_weights_list)
+        bbox_weights = torch.cat(bbox_weights_list)
+        return labels_list, label_weights, bbox_targets_list, bbox_weights
+
+    def transform_bbox_targets(self, decoded_bboxes, mlvl_points, num_imgs):
+        """Transform bbox_targets (x1, y1, x2, y2) into (l, t, r, b) format.
+
+        Args:
+            decoded_bboxes (list[Tensor]): Regression targets of each level,
+                in the form of (x1, y1, x2, y2).
+            mlvl_points (list[Tensor]): Points of each fpn level, each has
+                shape (num_points, 2).
+            num_imgs (int): the number of images in a batch.
+
+        Returns:
+            bbox_targets (list[Tensor]): Regression targets of each level in
+                the form of (l, t, r, b).
+        """
+        # TODO: Re-implemented in Class PointCoder
+        assert len(decoded_bboxes) == len(mlvl_points)
+        num_levels = len(decoded_bboxes)
+        mlvl_points = [points.repeat(num_imgs, 1) for points in mlvl_points]
+        bbox_targets = []
+        for i in range(num_levels):
+            bbox_target = self.bbox_coder.encode(mlvl_points[i],
+                                                 decoded_bboxes[i])
+            bbox_targets.append(bbox_target)
+
+        return bbox_targets
+
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
+                              missing_keys, unexpected_keys, error_msgs):
+        """Override the method in the parent class to avoid changing para's
+        name."""
+        pass
+
+    def _get_points_single(self,
+                           featmap_size,
+                           stride,
+                           dtype,
+                           device,
+                           flatten=False):
+        """Get points according to feature map size.
+
+        This function will be deprecated soon.
+        """
+
+        warnings.warn(
+            '`_get_points_single` in `VFNetHead` will be '
+            'deprecated soon, we support a multi level point generator now'
+            'you can get points of a single level feature map'
+            'with `self.fcos_prior_generator.single_level_grid_priors` ')
+
+        h, w = featmap_size
+        x_range = torch.arange(
+            0, w * stride, stride, dtype=dtype, device=device)
+        y_range = torch.arange(
+            0, h * stride, stride, dtype=dtype, device=device)
+        y, x = torch.meshgrid(y_range, x_range)
+        # to be compatible with anchor points in ATSS
+        if self.use_atss:
+            points = torch.stack(
+                (x.reshape(-1), y.reshape(-1)), dim=-1) + \
+                     stride * self.anchor_center_offset
+        else:
+            points = torch.stack(
+                (x.reshape(-1), y.reshape(-1)), dim=-1) + stride // 2
+        return points
diff --git a/mmdet/models/dense_heads/yolact_head.py b/mmdet/models/dense_heads/yolact_head.py
new file mode 100644
index 0000000..8f89a27
--- /dev/null
+++ b/mmdet/models/dense_heads/yolact_head.py
@@ -0,0 +1,1018 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule, ModuleList, force_fp32
+
+from mmdet.core import build_sampler, fast_nms, images_to_levels, multi_apply
+from mmdet.core.utils import select_single_mlvl
+from ..builder import HEADS, build_loss
+from .anchor_head import AnchorHead
+
+
+@HEADS.register_module()
+class YOLACTHead(AnchorHead):
+    """YOLACT box head used in https://arxiv.org/abs/1904.02689.
+
+    Note that YOLACT head is a light version of RetinaNet head.
+    Four differences are described as follows:
+
+    1. YOLACT box head has three-times fewer anchors.
+    2. YOLACT box head shares the convs for box and cls branches.
+    3. YOLACT box head uses OHEM instead of Focal loss.
+    4. YOLACT box head predicts a set of mask coefficients for each box.
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        anchor_generator (dict): Config dict for anchor generator
+        loss_cls (dict): Config of classification loss.
+        loss_bbox (dict): Config of localization loss.
+        num_head_convs (int): Number of the conv layers shared by
+            box and cls branches.
+        num_protos (int): Number of the mask coefficients.
+        use_ohem (bool): If true, ``loss_single_OHEM`` will be used for
+            cls loss calculation. If false, ``loss_single`` will be used.
+        conv_cfg (dict): Dictionary to construct and config conv layer.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 anchor_generator=dict(
+                     type='AnchorGenerator',
+                     octave_base_scale=3,
+                     scales_per_octave=1,
+                     ratios=[0.5, 1.0, 2.0],
+                     strides=[8, 16, 32, 64, 128]),
+                 loss_cls=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=False,
+                     reduction='none',
+                     loss_weight=1.0),
+                 loss_bbox=dict(
+                     type='SmoothL1Loss', beta=1.0, loss_weight=1.5),
+                 num_head_convs=1,
+                 num_protos=32,
+                 use_ohem=True,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 init_cfg=dict(
+                     type='Xavier',
+                     distribution='uniform',
+                     bias=0,
+                     layer='Conv2d'),
+                 **kwargs):
+        self.num_head_convs = num_head_convs
+        self.num_protos = num_protos
+        self.use_ohem = use_ohem
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        super(YOLACTHead, self).__init__(
+            num_classes,
+            in_channels,
+            loss_cls=loss_cls,
+            loss_bbox=loss_bbox,
+            anchor_generator=anchor_generator,
+            init_cfg=init_cfg,
+            **kwargs)
+        if self.use_ohem:
+            sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+            self.sampling = False
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.relu = nn.ReLU(inplace=True)
+        self.head_convs = ModuleList()
+        for i in range(self.num_head_convs):
+            chn = self.in_channels if i == 0 else self.feat_channels
+            self.head_convs.append(
+                ConvModule(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+        self.conv_cls = nn.Conv2d(
+            self.feat_channels,
+            self.num_base_priors * self.cls_out_channels,
+            3,
+            padding=1)
+        self.conv_reg = nn.Conv2d(
+            self.feat_channels, self.num_base_priors * 4, 3, padding=1)
+        self.conv_coeff = nn.Conv2d(
+            self.feat_channels,
+            self.num_base_priors * self.num_protos,
+            3,
+            padding=1)
+
+    def forward_single(self, x):
+        """Forward feature of a single scale level.
+
+        Args:
+            x (Tensor): Features of a single scale level.
+
+        Returns:
+            tuple:
+                cls_score (Tensor): Cls scores for a single scale level \
+                    the channels number is num_anchors * num_classes.
+                bbox_pred (Tensor): Box energies / deltas for a single scale \
+                    level, the channels number is num_anchors * 4.
+                coeff_pred (Tensor): Mask coefficients for a single scale \
+                    level, the channels number is num_anchors * num_protos.
+        """
+        for head_conv in self.head_convs:
+            x = head_conv(x)
+        cls_score = self.conv_cls(x)
+        bbox_pred = self.conv_reg(x)
+        coeff_pred = self.conv_coeff(x).tanh()
+        return cls_score, bbox_pred, coeff_pred
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """A combination of the func:``AnchorHead.loss`` and
+        func:``SSDHead.loss``.
+
+        When ``self.use_ohem == True``, it functions like ``SSDHead.loss``,
+        otherwise, it follows ``AnchorHead.loss``. Besides, it additionally
+        returns ``sampling_results``.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): Class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
+                boxes can be ignored when computing the loss. Default: None
+
+        Returns:
+            tuple:
+                dict[str, Tensor]: A dictionary of loss components.
+                List[:obj:``SamplingResult``]: Sampler results for each image.
+        """
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        assert len(featmap_sizes) == self.prior_generator.num_levels
+
+        device = cls_scores[0].device
+
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+        cls_reg_targets = self.get_targets(
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels,
+            unmap_outputs=not self.use_ohem,
+            return_sampling_results=True)
+        if cls_reg_targets is None:
+            return None
+        (labels_list, label_weights_list, bbox_targets_list, bbox_weights_list,
+         num_total_pos, num_total_neg, sampling_results) = cls_reg_targets
+
+        if self.use_ohem:
+            num_images = len(img_metas)
+            all_cls_scores = torch.cat([
+                s.permute(0, 2, 3, 1).reshape(
+                    num_images, -1, self.cls_out_channels) for s in cls_scores
+            ], 1)
+            all_labels = torch.cat(labels_list, -1).view(num_images, -1)
+            all_label_weights = torch.cat(label_weights_list,
+                                          -1).view(num_images, -1)
+            all_bbox_preds = torch.cat([
+                b.permute(0, 2, 3, 1).reshape(num_images, -1, 4)
+                for b in bbox_preds
+            ], -2)
+            all_bbox_targets = torch.cat(bbox_targets_list,
+                                         -2).view(num_images, -1, 4)
+            all_bbox_weights = torch.cat(bbox_weights_list,
+                                         -2).view(num_images, -1, 4)
+
+            # concat all level anchors to a single tensor
+            all_anchors = []
+            for i in range(num_images):
+                all_anchors.append(torch.cat(anchor_list[i]))
+
+            # check NaN and Inf
+            assert torch.isfinite(all_cls_scores).all().item(), \
+                'classification scores become infinite or NaN!'
+            assert torch.isfinite(all_bbox_preds).all().item(), \
+                'bbox predications become infinite or NaN!'
+
+            losses_cls, losses_bbox = multi_apply(
+                self.loss_single_OHEM,
+                all_cls_scores,
+                all_bbox_preds,
+                all_anchors,
+                all_labels,
+                all_label_weights,
+                all_bbox_targets,
+                all_bbox_weights,
+                num_total_samples=num_total_pos)
+        else:
+            num_total_samples = (
+                num_total_pos +
+                num_total_neg if self.sampling else num_total_pos)
+
+            # anchor number of multi levels
+            num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+            # concat all level anchors and flags to a single tensor
+            concat_anchor_list = []
+            for i in range(len(anchor_list)):
+                concat_anchor_list.append(torch.cat(anchor_list[i]))
+            all_anchor_list = images_to_levels(concat_anchor_list,
+                                               num_level_anchors)
+            losses_cls, losses_bbox = multi_apply(
+                self.loss_single,
+                cls_scores,
+                bbox_preds,
+                all_anchor_list,
+                labels_list,
+                label_weights_list,
+                bbox_targets_list,
+                bbox_weights_list,
+                num_total_samples=num_total_samples)
+
+        return dict(
+            loss_cls=losses_cls, loss_bbox=losses_bbox), sampling_results
+
+    def loss_single_OHEM(self, cls_score, bbox_pred, anchors, labels,
+                         label_weights, bbox_targets, bbox_weights,
+                         num_total_samples):
+        """"See func:``SSDHead.loss``."""
+        loss_cls_all = self.loss_cls(cls_score, labels, label_weights)
+
+        # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+        pos_inds = ((labels >= 0) & (labels < self.num_classes)).nonzero(
+            as_tuple=False).reshape(-1)
+        neg_inds = (labels == self.num_classes).nonzero(
+            as_tuple=False).view(-1)
+
+        num_pos_samples = pos_inds.size(0)
+        if num_pos_samples == 0:
+            num_neg_samples = neg_inds.size(0)
+        else:
+            num_neg_samples = self.train_cfg.neg_pos_ratio * num_pos_samples
+            if num_neg_samples > neg_inds.size(0):
+                num_neg_samples = neg_inds.size(0)
+        topk_loss_cls_neg, _ = loss_cls_all[neg_inds].topk(num_neg_samples)
+        loss_cls_pos = loss_cls_all[pos_inds].sum()
+        loss_cls_neg = topk_loss_cls_neg.sum()
+        loss_cls = (loss_cls_pos + loss_cls_neg) / num_total_samples
+        if self.reg_decoded_bbox:
+            # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
+            # is applied directly on the decoded bounding boxes, it
+            # decodes the already encoded coordinates to absolute format.
+            bbox_pred = self.bbox_coder.decode(anchors, bbox_pred)
+        loss_bbox = self.loss_bbox(
+            bbox_pred,
+            bbox_targets,
+            bbox_weights,
+            avg_factor=num_total_samples)
+        return loss_cls[None], loss_bbox
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'coeff_preds'))
+    def get_bboxes(self,
+                   cls_scores,
+                   bbox_preds,
+                   coeff_preds,
+                   img_metas,
+                   cfg=None,
+                   rescale=False):
+        """"Similar to func:``AnchorHead.get_bboxes``, but additionally
+        processes coeff_preds.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                with shape (N, num_anchors * num_classes, H, W)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (N, num_anchors * 4, H, W)
+            coeff_preds (list[Tensor]): Mask coefficients for each scale
+                level with shape (N, num_anchors * num_protos, H, W)
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            cfg (mmcv.Config | None): Test / postprocessing configuration,
+                if None, test_cfg would be used
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+
+        Returns:
+            list[tuple[Tensor, Tensor, Tensor]]: Each item in result_list is
+                a 3-tuple. The first item is an (n, 5) tensor, where the
+                first 4 columns are bounding box positions
+                (tl_x, tl_y, br_x, br_y) and the 5-th column is a score
+                between 0 and 1. The second item is an (n,) tensor where each
+                item is the predicted class label of the corresponding box.
+                The third item is an (n, num_protos) tensor where each item
+                is the predicted mask coefficients of instance inside the
+                corresponding box.
+        """
+        assert len(cls_scores) == len(bbox_preds)
+        num_levels = len(cls_scores)
+
+        device = cls_scores[0].device
+        featmap_sizes = [cls_scores[i].shape[-2:] for i in range(num_levels)]
+        mlvl_anchors = self.prior_generator.grid_priors(
+            featmap_sizes, device=device)
+
+        det_bboxes = []
+        det_labels = []
+        det_coeffs = []
+        for img_id in range(len(img_metas)):
+            cls_score_list = select_single_mlvl(cls_scores, img_id)
+            bbox_pred_list = select_single_mlvl(bbox_preds, img_id)
+            coeff_pred_list = select_single_mlvl(coeff_preds, img_id)
+            img_shape = img_metas[img_id]['img_shape']
+            scale_factor = img_metas[img_id]['scale_factor']
+            bbox_res = self._get_bboxes_single(cls_score_list, bbox_pred_list,
+                                               coeff_pred_list, mlvl_anchors,
+                                               img_shape, scale_factor, cfg,
+                                               rescale)
+            det_bboxes.append(bbox_res[0])
+            det_labels.append(bbox_res[1])
+            det_coeffs.append(bbox_res[2])
+        return det_bboxes, det_labels, det_coeffs
+
+    def _get_bboxes_single(self,
+                           cls_score_list,
+                           bbox_pred_list,
+                           coeff_preds_list,
+                           mlvl_anchors,
+                           img_shape,
+                           scale_factor,
+                           cfg,
+                           rescale=False):
+        """"Similar to func:``AnchorHead._get_bboxes_single``, but additionally
+        processes coeff_preds_list and uses fast NMS instead of traditional
+        NMS.
+
+        Args:
+            cls_score_list (list[Tensor]): Box scores for a single scale level
+                Has shape (num_anchors * num_classes, H, W).
+            bbox_pred_list (list[Tensor]): Box energies / deltas for a single
+                scale level with shape (num_anchors * 4, H, W).
+            coeff_preds_list (list[Tensor]): Mask coefficients for a single
+                scale level with shape (num_anchors * num_protos, H, W).
+            mlvl_anchors (list[Tensor]): Box reference for a single scale level
+                with shape (num_total_anchors, 4).
+            img_shape (tuple[int]): Shape of the input image,
+                (height, width, 3).
+            scale_factor (ndarray): Scale factor of the image arange as
+                (w_scale, h_scale, w_scale, h_scale).
+            cfg (mmcv.Config): Test / postprocessing configuration,
+                if None, test_cfg would be used.
+            rescale (bool): If True, return boxes in original image space.
+
+        Returns:
+            tuple[Tensor, Tensor, Tensor]: The first item is an (n, 5) tensor,
+                where the first 4 columns are bounding box positions
+                (tl_x, tl_y, br_x, br_y) and the 5-th column is a score between
+                0 and 1. The second item is an (n,) tensor where each item is
+                the predicted class label of the corresponding box. The third
+                item is an (n, num_protos) tensor where each item is the
+                predicted mask coefficients of instance inside the
+                corresponding box.
+        """
+        cfg = self.test_cfg if cfg is None else cfg
+        assert len(cls_score_list) == len(bbox_pred_list) == len(mlvl_anchors)
+        nms_pre = cfg.get('nms_pre', -1)
+        mlvl_bboxes = []
+        mlvl_scores = []
+        mlvl_coeffs = []
+        for cls_score, bbox_pred, coeff_pred, anchors in \
+                zip(cls_score_list, bbox_pred_list,
+                    coeff_preds_list, mlvl_anchors):
+            assert cls_score.size()[-2:] == bbox_pred.size()[-2:]
+            cls_score = cls_score.permute(1, 2,
+                                          0).reshape(-1, self.cls_out_channels)
+            if self.use_sigmoid_cls:
+                scores = cls_score.sigmoid()
+            else:
+                scores = cls_score.softmax(-1)
+            bbox_pred = bbox_pred.permute(1, 2, 0).reshape(-1, 4)
+            coeff_pred = coeff_pred.permute(1, 2,
+                                            0).reshape(-1, self.num_protos)
+
+            if 0 < nms_pre < scores.shape[0]:
+                # Get maximum scores for foreground classes.
+                if self.use_sigmoid_cls:
+                    max_scores, _ = scores.max(dim=1)
+                else:
+                    # remind that we set FG labels to [0, num_class-1]
+                    # since mmdet v2.0
+                    # BG cat_id: num_class
+                    max_scores, _ = scores[:, :-1].max(dim=1)
+                _, topk_inds = max_scores.topk(nms_pre)
+                anchors = anchors[topk_inds, :]
+                bbox_pred = bbox_pred[topk_inds, :]
+                scores = scores[topk_inds, :]
+                coeff_pred = coeff_pred[topk_inds, :]
+            bboxes = self.bbox_coder.decode(
+                anchors, bbox_pred, max_shape=img_shape)
+            mlvl_bboxes.append(bboxes)
+            mlvl_scores.append(scores)
+            mlvl_coeffs.append(coeff_pred)
+        mlvl_bboxes = torch.cat(mlvl_bboxes)
+        if rescale:
+            mlvl_bboxes /= mlvl_bboxes.new_tensor(scale_factor)
+        mlvl_scores = torch.cat(mlvl_scores)
+        mlvl_coeffs = torch.cat(mlvl_coeffs)
+        if self.use_sigmoid_cls:
+            # Add a dummy background class to the backend when using sigmoid
+            # remind that we set FG labels to [0, num_class-1] since mmdet v2.0
+            # BG cat_id: num_class
+            padding = mlvl_scores.new_zeros(mlvl_scores.shape[0], 1)
+            mlvl_scores = torch.cat([mlvl_scores, padding], dim=1)
+        det_bboxes, det_labels, det_coeffs = fast_nms(mlvl_bboxes, mlvl_scores,
+                                                      mlvl_coeffs,
+                                                      cfg.score_thr,
+                                                      cfg.iou_thr, cfg.top_k,
+                                                      cfg.max_per_img)
+        return det_bboxes, det_labels, det_coeffs
+
+
+@HEADS.register_module()
+class YOLACTSegmHead(BaseModule):
+    """YOLACT segmentation head used in https://arxiv.org/abs/1904.02689.
+
+    Apply a semantic segmentation loss on feature space using layers that are
+    only evaluated during training to increase performance with no speed
+    penalty.
+
+    Args:
+        in_channels (int): Number of channels in the input feature map.
+        num_classes (int): Number of categories excluding the background
+            category.
+        loss_segm (dict): Config of semantic segmentation loss.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels=256,
+                 loss_segm=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
+                 init_cfg=dict(
+                     type='Xavier',
+                     distribution='uniform',
+                     override=dict(name='segm_conv'))):
+        super(YOLACTSegmHead, self).__init__(init_cfg)
+        self.in_channels = in_channels
+        self.num_classes = num_classes
+        self.loss_segm = build_loss(loss_segm)
+        self._init_layers()
+        self.fp16_enabled = False
+
+    def _init_layers(self):
+        """Initialize layers of the head."""
+        self.segm_conv = nn.Conv2d(
+            self.in_channels, self.num_classes, kernel_size=1)
+
+    def forward(self, x):
+        """Forward feature from the upstream network.
+
+        Args:
+            x (Tensor): Feature from the upstream network, which is
+                a 4D-tensor.
+
+        Returns:
+            Tensor: Predicted semantic segmentation map with shape
+                (N, num_classes, H, W).
+        """
+        return self.segm_conv(x)
+
+    @force_fp32(apply_to=('segm_pred', ))
+    def loss(self, segm_pred, gt_masks, gt_labels):
+        """Compute loss of the head.
+
+        Args:
+            segm_pred (list[Tensor]): Predicted semantic segmentation map
+                with shape (N, num_classes, H, W).
+            gt_masks (list[Tensor]): Ground truth masks for each image with
+                the same shape of the input image.
+            gt_labels (list[Tensor]): Class indices corresponding to each box.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        loss_segm = []
+        num_imgs, num_classes, mask_h, mask_w = segm_pred.size()
+        for idx in range(num_imgs):
+            cur_segm_pred = segm_pred[idx]
+            cur_gt_masks = gt_masks[idx].float()
+            cur_gt_labels = gt_labels[idx]
+            segm_targets = self.get_targets(cur_segm_pred, cur_gt_masks,
+                                            cur_gt_labels)
+            if segm_targets is None:
+                loss = self.loss_segm(cur_segm_pred,
+                                      torch.zeros_like(cur_segm_pred),
+                                      torch.zeros_like(cur_segm_pred))
+            else:
+                loss = self.loss_segm(
+                    cur_segm_pred,
+                    segm_targets,
+                    avg_factor=num_imgs * mask_h * mask_w)
+            loss_segm.append(loss)
+        return dict(loss_segm=loss_segm)
+
+    def get_targets(self, segm_pred, gt_masks, gt_labels):
+        """Compute semantic segmentation targets for each image.
+
+        Args:
+            segm_pred (Tensor): Predicted semantic segmentation map
+                with shape (num_classes, H, W).
+            gt_masks (Tensor): Ground truth masks for each image with
+                the same shape of the input image.
+            gt_labels (Tensor): Class indices corresponding to each box.
+
+        Returns:
+            Tensor: Semantic segmentation targets with shape
+                (num_classes, H, W).
+        """
+        if gt_masks.size(0) == 0:
+            return None
+        num_classes, mask_h, mask_w = segm_pred.size()
+        with torch.no_grad():
+            downsampled_masks = F.interpolate(
+                gt_masks.unsqueeze(0), (mask_h, mask_w),
+                mode='bilinear',
+                align_corners=False).squeeze(0)
+            downsampled_masks = downsampled_masks.gt(0.5).float()
+            segm_targets = torch.zeros_like(segm_pred, requires_grad=False)
+            for obj_idx in range(downsampled_masks.size(0)):
+                segm_targets[gt_labels[obj_idx] - 1] = torch.max(
+                    segm_targets[gt_labels[obj_idx] - 1],
+                    downsampled_masks[obj_idx])
+            return segm_targets
+
+    def simple_test(self, feats, img_metas, rescale=False):
+        """Test function without test-time augmentation."""
+        raise NotImplementedError(
+            'simple_test of YOLACTSegmHead is not implemented '
+            'because this head is only evaluated during training')
+
+
+@HEADS.register_module()
+class YOLACTProtonet(BaseModule):
+    """YOLACT mask head used in https://arxiv.org/abs/1904.02689.
+
+    This head outputs the mask prototypes for YOLACT.
+
+    Args:
+        in_channels (int): Number of channels in the input feature map.
+        proto_channels (tuple[int]): Output channels of protonet convs.
+        proto_kernel_sizes (tuple[int]): Kernel sizes of protonet convs.
+        include_last_relu (Bool): If keep the last relu of protonet.
+        num_protos (int): Number of prototypes.
+        num_classes (int): Number of categories excluding the background
+            category.
+        loss_mask_weight (float): Reweight the mask loss by this factor.
+        max_masks_to_train (int): Maximum number of masks to train for
+            each image.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels=256,
+                 proto_channels=(256, 256, 256, None, 256, 32),
+                 proto_kernel_sizes=(3, 3, 3, -2, 3, 1),
+                 include_last_relu=True,
+                 num_protos=32,
+                 loss_mask_weight=1.0,
+                 max_masks_to_train=100,
+                 init_cfg=dict(
+                     type='Xavier',
+                     distribution='uniform',
+                     override=dict(name='protonet'))):
+        super(YOLACTProtonet, self).__init__(init_cfg)
+        self.in_channels = in_channels
+        self.proto_channels = proto_channels
+        self.proto_kernel_sizes = proto_kernel_sizes
+        self.include_last_relu = include_last_relu
+        self.protonet = self._init_layers()
+
+        self.loss_mask_weight = loss_mask_weight
+        self.num_protos = num_protos
+        self.num_classes = num_classes
+        self.max_masks_to_train = max_masks_to_train
+        self.fp16_enabled = False
+
+    def _init_layers(self):
+        """A helper function to take a config setting and turn it into a
+        network."""
+        # Possible patterns:
+        # ( 256, 3) -> conv
+        # ( 256,-2) -> deconv
+        # (None,-2) -> bilinear interpolate
+        in_channels = self.in_channels
+        protonets = ModuleList()
+        for num_channels, kernel_size in zip(self.proto_channels,
+                                             self.proto_kernel_sizes):
+            if kernel_size > 0:
+                layer = nn.Conv2d(
+                    in_channels,
+                    num_channels,
+                    kernel_size,
+                    padding=kernel_size // 2)
+            else:
+                if num_channels is None:
+                    layer = InterpolateModule(
+                        scale_factor=-kernel_size,
+                        mode='bilinear',
+                        align_corners=False)
+                else:
+                    layer = nn.ConvTranspose2d(
+                        in_channels,
+                        num_channels,
+                        -kernel_size,
+                        padding=kernel_size // 2)
+            protonets.append(layer)
+            protonets.append(nn.ReLU(inplace=True))
+            in_channels = num_channels if num_channels is not None \
+                else in_channels
+        if not self.include_last_relu:
+            protonets = protonets[:-1]
+        return nn.Sequential(*protonets)
+
+    def forward_dummy(self, x):
+        prototypes = self.protonet(x)
+        return prototypes
+
+    def forward(self, x, coeff_pred, bboxes, img_meta, sampling_results=None):
+        """Forward feature from the upstream network to get prototypes and
+        linearly combine the prototypes, using masks coefficients, into
+        instance masks. Finally, crop the instance masks with given bboxes.
+
+        Args:
+            x (Tensor): Feature from the upstream network, which is
+                a 4D-tensor.
+            coeff_pred (list[Tensor]): Mask coefficients for each scale
+                level with shape (N, num_anchors * num_protos, H, W).
+            bboxes (list[Tensor]): Box used for cropping with shape
+                (N, num_anchors * 4, H, W). During training, they are
+                ground truth boxes. During testing, they are predicted
+                boxes.
+            img_meta (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            sampling_results (List[:obj:``SamplingResult``]): Sampler results
+                for each image.
+
+        Returns:
+            list[Tensor]: Predicted instance segmentation masks.
+        """
+        prototypes = self.protonet(x)
+        prototypes = prototypes.permute(0, 2, 3, 1).contiguous()
+
+        num_imgs = x.size(0)
+
+        # The reason for not using self.training is that
+        # val workflow will have a dimension mismatch error.
+        # Note that this writing method is very tricky.
+        # Fix https://github.com/open-mmlab/mmdetection/issues/5978
+        is_train_or_val_workflow = (coeff_pred[0].dim() == 4)
+
+        # Train or val workflow
+        if is_train_or_val_workflow:
+            coeff_pred_list = []
+            for coeff_pred_per_level in coeff_pred:
+                coeff_pred_per_level = \
+                    coeff_pred_per_level.permute(
+                        0, 2, 3, 1).reshape(num_imgs, -1, self.num_protos)
+                coeff_pred_list.append(coeff_pred_per_level)
+            coeff_pred = torch.cat(coeff_pred_list, dim=1)
+
+        mask_pred_list = []
+        for idx in range(num_imgs):
+            cur_prototypes = prototypes[idx]
+            cur_coeff_pred = coeff_pred[idx]
+            cur_bboxes = bboxes[idx]
+            cur_img_meta = img_meta[idx]
+
+            # Testing state
+            if not is_train_or_val_workflow:
+                bboxes_for_cropping = cur_bboxes
+            else:
+                cur_sampling_results = sampling_results[idx]
+                pos_assigned_gt_inds = \
+                    cur_sampling_results.pos_assigned_gt_inds
+                bboxes_for_cropping = cur_bboxes[pos_assigned_gt_inds].clone()
+                pos_inds = cur_sampling_results.pos_inds
+                cur_coeff_pred = cur_coeff_pred[pos_inds]
+
+            # Linearly combine the prototypes with the mask coefficients
+            mask_pred = cur_prototypes @ cur_coeff_pred.t()
+            mask_pred = torch.sigmoid(mask_pred)
+
+            h, w = cur_img_meta['img_shape'][:2]
+            bboxes_for_cropping[:, 0] /= w
+            bboxes_for_cropping[:, 1] /= h
+            bboxes_for_cropping[:, 2] /= w
+            bboxes_for_cropping[:, 3] /= h
+
+            mask_pred = self.crop(mask_pred, bboxes_for_cropping)
+            mask_pred = mask_pred.permute(2, 0, 1).contiguous()
+            mask_pred_list.append(mask_pred)
+        return mask_pred_list
+
+    @force_fp32(apply_to=('mask_pred', ))
+    def loss(self, mask_pred, gt_masks, gt_bboxes, img_meta, sampling_results):
+        """Compute loss of the head.
+
+        Args:
+            mask_pred (list[Tensor]): Predicted prototypes with shape
+                (num_classes, H, W).
+            gt_masks (list[Tensor]): Ground truth masks for each image with
+                the same shape of the input image.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            img_meta (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            sampling_results (List[:obj:``SamplingResult``]): Sampler results
+                for each image.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        loss_mask = []
+        num_imgs = len(mask_pred)
+        total_pos = 0
+        for idx in range(num_imgs):
+            cur_mask_pred = mask_pred[idx]
+            cur_gt_masks = gt_masks[idx].float()
+            cur_gt_bboxes = gt_bboxes[idx]
+            cur_img_meta = img_meta[idx]
+            cur_sampling_results = sampling_results[idx]
+
+            pos_assigned_gt_inds = cur_sampling_results.pos_assigned_gt_inds
+            num_pos = pos_assigned_gt_inds.size(0)
+            # Since we're producing (near) full image masks,
+            # it'd take too much vram to backprop on every single mask.
+            # Thus we select only a subset.
+            if num_pos > self.max_masks_to_train:
+                perm = torch.randperm(num_pos)
+                select = perm[:self.max_masks_to_train]
+                cur_mask_pred = cur_mask_pred[select]
+                pos_assigned_gt_inds = pos_assigned_gt_inds[select]
+                num_pos = self.max_masks_to_train
+            total_pos += num_pos
+
+            gt_bboxes_for_reweight = cur_gt_bboxes[pos_assigned_gt_inds]
+
+            mask_targets = self.get_targets(cur_mask_pred, cur_gt_masks,
+                                            pos_assigned_gt_inds)
+            if num_pos == 0:
+                loss = cur_mask_pred.sum() * 0.
+            elif mask_targets is None:
+                loss = F.binary_cross_entropy(cur_mask_pred,
+                                              torch.zeros_like(cur_mask_pred),
+                                              torch.zeros_like(cur_mask_pred))
+            else:
+                cur_mask_pred = torch.clamp(cur_mask_pred, 0, 1)
+                loss = F.binary_cross_entropy(
+                    cur_mask_pred, mask_targets,
+                    reduction='none') * self.loss_mask_weight
+
+                h, w = cur_img_meta['img_shape'][:2]
+                gt_bboxes_width = (gt_bboxes_for_reweight[:, 2] -
+                                   gt_bboxes_for_reweight[:, 0]) / w
+                gt_bboxes_height = (gt_bboxes_for_reweight[:, 3] -
+                                    gt_bboxes_for_reweight[:, 1]) / h
+                loss = loss.mean(dim=(1,
+                                      2)) / gt_bboxes_width / gt_bboxes_height
+                loss = torch.sum(loss)
+            loss_mask.append(loss)
+
+        if total_pos == 0:
+            total_pos += 1  # avoid nan
+        loss_mask = [x / total_pos for x in loss_mask]
+
+        return dict(loss_mask=loss_mask)
+
+    def get_targets(self, mask_pred, gt_masks, pos_assigned_gt_inds):
+        """Compute instance segmentation targets for each image.
+
+        Args:
+            mask_pred (Tensor): Predicted prototypes with shape
+                (num_classes, H, W).
+            gt_masks (Tensor): Ground truth masks for each image with
+                the same shape of the input image.
+            pos_assigned_gt_inds (Tensor): GT indices of the corresponding
+                positive samples.
+        Returns:
+            Tensor: Instance segmentation targets with shape
+                (num_instances, H, W).
+        """
+        if gt_masks.size(0) == 0:
+            return None
+        mask_h, mask_w = mask_pred.shape[-2:]
+        gt_masks = F.interpolate(
+            gt_masks.unsqueeze(0), (mask_h, mask_w),
+            mode='bilinear',
+            align_corners=False).squeeze(0)
+        gt_masks = gt_masks.gt(0.5).float()
+        mask_targets = gt_masks[pos_assigned_gt_inds]
+        return mask_targets
+
+    def get_seg_masks(self, mask_pred, label_pred, img_meta, rescale):
+        """Resize, binarize, and format the instance mask predictions.
+
+        Args:
+            mask_pred (Tensor): shape (N, H, W).
+            label_pred (Tensor): shape (N, ).
+            img_meta (dict): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            rescale (bool): If rescale is False, then returned masks will
+                fit the scale of imgs[0].
+        Returns:
+            list[ndarray]: Mask predictions grouped by their predicted classes.
+        """
+        ori_shape = img_meta['ori_shape']
+        scale_factor = img_meta['scale_factor']
+        if rescale:
+            img_h, img_w = ori_shape[:2]
+        else:
+            img_h = np.round(ori_shape[0] * scale_factor[1]).astype(np.int32)
+            img_w = np.round(ori_shape[1] * scale_factor[0]).astype(np.int32)
+
+        cls_segms = [[] for _ in range(self.num_classes)]
+        if mask_pred.size(0) == 0:
+            return cls_segms
+
+        mask_pred = F.interpolate(
+            mask_pred.unsqueeze(0), (img_h, img_w),
+            mode='bilinear',
+            align_corners=False).squeeze(0) > 0.5
+        mask_pred = mask_pred.cpu().numpy().astype(np.uint8)
+
+        for m, l in zip(mask_pred, label_pred):
+            cls_segms[l].append(m)
+        return cls_segms
+
+    def crop(self, masks, boxes, padding=1):
+        """Crop predicted masks by zeroing out everything not in the predicted
+        bbox.
+
+        Args:
+            masks (Tensor): shape [H, W, N].
+            boxes (Tensor): bbox coords in relative point form with
+                shape [N, 4].
+
+        Return:
+            Tensor: The cropped masks.
+        """
+        h, w, n = masks.size()
+        x1, x2 = self.sanitize_coordinates(
+            boxes[:, 0], boxes[:, 2], w, padding, cast=False)
+        y1, y2 = self.sanitize_coordinates(
+            boxes[:, 1], boxes[:, 3], h, padding, cast=False)
+
+        rows = torch.arange(
+            w, device=masks.device, dtype=x1.dtype).view(1, -1,
+                                                         1).expand(h, w, n)
+        cols = torch.arange(
+            h, device=masks.device, dtype=x1.dtype).view(-1, 1,
+                                                         1).expand(h, w, n)
+
+        masks_left = rows >= x1.view(1, 1, -1)
+        masks_right = rows < x2.view(1, 1, -1)
+        masks_up = cols >= y1.view(1, 1, -1)
+        masks_down = cols < y2.view(1, 1, -1)
+
+        crop_mask = masks_left * masks_right * masks_up * masks_down
+
+        return masks * crop_mask.float()
+
+    def sanitize_coordinates(self, x1, x2, img_size, padding=0, cast=True):
+        """Sanitizes the input coordinates so that x1 < x2, x1 != x2, x1 >= 0,
+        and x2 <= image_size. Also converts from relative to absolute
+        coordinates and casts the results to long tensors.
+
+        Warning: this does things in-place behind the scenes so
+        copy if necessary.
+
+        Args:
+            _x1 (Tensor): shape (N, ).
+            _x2 (Tensor): shape (N, ).
+            img_size (int): Size of the input image.
+            padding (int): x1 >= padding, x2 <= image_size-padding.
+            cast (bool): If cast is false, the result won't be cast to longs.
+
+        Returns:
+            tuple:
+                x1 (Tensor): Sanitized _x1.
+                x2 (Tensor): Sanitized _x2.
+        """
+        x1 = x1 * img_size
+        x2 = x2 * img_size
+        if cast:
+            x1 = x1.long()
+            x2 = x2.long()
+        x1 = torch.min(x1, x2)
+        x2 = torch.max(x1, x2)
+        x1 = torch.clamp(x1 - padding, min=0)
+        x2 = torch.clamp(x2 + padding, max=img_size)
+        return x1, x2
+
+    def simple_test(self,
+                    feats,
+                    det_bboxes,
+                    det_labels,
+                    det_coeffs,
+                    img_metas,
+                    rescale=False):
+        """Test function without test-time augmentation.
+
+        Args:
+            feats (tuple[torch.Tensor]): Multi-level features from the
+               upstream network, each is a 4D-tensor.
+            det_bboxes (list[Tensor]): BBox results of each image. each
+               element is (n, 5) tensor, where 5 represent
+               (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
+            det_labels (list[Tensor]): BBox results of each image. each
+               element is (n, ) tensor, each element represents the class
+               label of the corresponding box.
+            det_coeffs (list[Tensor]): BBox coefficient of each image. each
+               element is (n, m) tensor, m is vector length.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[list]: encoded masks. The c-th item in the outer list
+                corresponds to the c-th class. Given the c-th outer list, the
+                i-th item in that inner list is the mask for the i-th box with
+                class label c.
+        """
+        num_imgs = len(img_metas)
+        scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
+        if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
+            segm_results = [[[] for _ in range(self.num_classes)]
+                            for _ in range(num_imgs)]
+        else:
+            # if det_bboxes is rescaled to the original image size, we need to
+            # rescale it back to the testing scale to obtain RoIs.
+            if rescale and not isinstance(scale_factors[0], float):
+                scale_factors = [
+                    torch.from_numpy(scale_factor).to(det_bboxes[0].device)
+                    for scale_factor in scale_factors
+                ]
+            _bboxes = [
+                det_bboxes[i][:, :4] *
+                scale_factors[i] if rescale else det_bboxes[i][:, :4]
+                for i in range(len(det_bboxes))
+            ]
+            mask_preds = self.forward(feats[0], det_coeffs, _bboxes, img_metas)
+            # apply mask post-processing to each image individually
+            segm_results = []
+            for i in range(num_imgs):
+                if det_bboxes[i].shape[0] == 0:
+                    segm_results.append([[] for _ in range(self.num_classes)])
+                else:
+                    segm_result = self.get_seg_masks(mask_preds[i],
+                                                     det_labels[i],
+                                                     img_metas[i], rescale)
+                    segm_results.append(segm_result)
+        return segm_results
+
+
+class InterpolateModule(BaseModule):
+    """This is a module version of F.interpolate.
+
+    Any arguments you give it just get passed along for the ride.
+    """
+
+    def __init__(self, *args, init_cfg=None, **kwargs):
+        super().__init__(init_cfg)
+
+        self.args = args
+        self.kwargs = kwargs
+
+    def forward(self, x):
+        """Forward features from the upstream network."""
+        return F.interpolate(x, *self.args, **self.kwargs)
diff --git a/mmdet/models/dense_heads/yolo_head.py b/mmdet/models/dense_heads/yolo_head.py
new file mode 100644
index 0000000..b446cb7
--- /dev/null
+++ b/mmdet/models/dense_heads/yolo_head.py
@@ -0,0 +1,621 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# Copyright (c) 2019 Western Digital Corporation or its affiliates.
+
+import warnings
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import (ConvModule, bias_init_with_prob, constant_init, is_norm,
+                      normal_init)
+from mmcv.runner import force_fp32
+
+from mmdet.core import (build_assigner, build_bbox_coder,
+                        build_prior_generator, build_sampler, images_to_levels,
+                        multi_apply, multiclass_nms)
+from ..builder import HEADS, build_loss
+from .base_dense_head import BaseDenseHead
+from .dense_test_mixins import BBoxTestMixin
+
+
+@HEADS.register_module()
+class YOLOV3Head(BaseDenseHead, BBoxTestMixin):
+    """YOLOV3Head Paper link: https://arxiv.org/abs/1804.02767.
+
+    Args:
+        num_classes (int): The number of object classes (w/o background)
+        in_channels (List[int]): Number of input channels per scale.
+        out_channels (List[int]): The number of output channels per scale
+            before the final 1x1 layer. Default: (1024, 512, 256).
+        anchor_generator (dict): Config dict for anchor generator
+        bbox_coder (dict): Config of bounding box coder.
+        featmap_strides (List[int]): The stride of each scale.
+            Should be in descending order. Default: (32, 16, 8).
+        one_hot_smoother (float): Set a non-zero value to enable label-smooth
+            Default: 0.
+        conv_cfg (dict): Config dict for convolution layer. Default: None.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+            Default: dict(type='BN', requires_grad=True)
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='LeakyReLU', negative_slope=0.1).
+        loss_cls (dict): Config of classification loss.
+        loss_conf (dict): Config of confidence loss.
+        loss_xy (dict): Config of xy coordinate loss.
+        loss_wh (dict): Config of wh coordinate loss.
+        train_cfg (dict): Training config of YOLOV3 head. Default: None.
+        test_cfg (dict): Testing config of YOLOV3 head. Default: None.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 out_channels=(1024, 512, 256),
+                 anchor_generator=dict(
+                     type='YOLOAnchorGenerator',
+                     base_sizes=[[(116, 90), (156, 198), (373, 326)],
+                                 [(30, 61), (62, 45), (59, 119)],
+                                 [(10, 13), (16, 30), (33, 23)]],
+                     strides=[32, 16, 8]),
+                 bbox_coder=dict(type='YOLOBBoxCoder'),
+                 featmap_strides=[32, 16, 8],
+                 one_hot_smoother=0.,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
+                 loss_cls=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
+                 loss_conf=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
+                 loss_xy=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
+                 loss_wh=dict(type='MSELoss', loss_weight=1.0),
+                 train_cfg=None,
+                 test_cfg=None,
+                 init_cfg=dict(
+                     type='Normal', std=0.01,
+                     override=dict(name='convs_pred'))):
+        super(YOLOV3Head, self).__init__(init_cfg)
+        # Check params
+        assert (len(in_channels) == len(out_channels) == len(featmap_strides))
+
+        self.num_classes = num_classes
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.featmap_strides = featmap_strides
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+        if self.train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            if hasattr(self.train_cfg, 'sampler'):
+                sampler_cfg = self.train_cfg.sampler
+            else:
+                sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+        self.fp16_enabled = False
+
+        self.one_hot_smoother = one_hot_smoother
+
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+
+        self.bbox_coder = build_bbox_coder(bbox_coder)
+
+        self.prior_generator = build_prior_generator(anchor_generator)
+
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_conf = build_loss(loss_conf)
+        self.loss_xy = build_loss(loss_xy)
+        self.loss_wh = build_loss(loss_wh)
+
+        self.num_base_priors = self.prior_generator.num_base_priors[0]
+        assert len(
+            self.prior_generator.num_base_priors) == len(featmap_strides)
+        self._init_layers()
+
+    @property
+    def anchor_generator(self):
+
+        warnings.warn('DeprecationWarning: `anchor_generator` is deprecated, '
+                      'please use "prior_generator" instead')
+        return self.prior_generator
+
+    @property
+    def num_anchors(self):
+        """
+        Returns:
+            int: Number of anchors on each point of feature map.
+        """
+        warnings.warn('DeprecationWarning: `num_anchors` is deprecated, '
+                      'please use "num_base_priors" instead')
+        return self.num_base_priors
+
+    @property
+    def num_levels(self):
+        return len(self.featmap_strides)
+
+    @property
+    def num_attrib(self):
+        """int: number of attributes in pred_map, bboxes (4) +
+        objectness (1) + num_classes"""
+
+        return 5 + self.num_classes
+
+    def _init_layers(self):
+        self.convs_bridge = nn.ModuleList()
+        self.convs_pred = nn.ModuleList()
+        for i in range(self.num_levels):
+            conv_bridge = ConvModule(
+                self.in_channels[i],
+                self.out_channels[i],
+                3,
+                padding=1,
+                conv_cfg=self.conv_cfg,
+                norm_cfg=self.norm_cfg,
+                act_cfg=self.act_cfg)
+            conv_pred = nn.Conv2d(self.out_channels[i],
+                                  self.num_base_priors * self.num_attrib, 1)
+
+            self.convs_bridge.append(conv_bridge)
+            self.convs_pred.append(conv_pred)
+
+    def init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                normal_init(m, mean=0, std=0.01)
+            if is_norm(m):
+                constant_init(m, 1)
+
+        # Use prior in model initialization to improve stability
+        for conv_pred, stride in zip(self.convs_pred, self.featmap_strides):
+            bias = conv_pred.bias.reshape(self.num_base_priors, -1)
+            # init objectness with prior of 8 objects per feature map
+            # refer to https://github.com/ultralytics/yolov3
+            nn.init.constant_(bias.data[:, 4],
+                              bias_init_with_prob(8 / (608 / stride)**2))
+            nn.init.constant_(bias.data[:, 5:], bias_init_with_prob(0.01))
+
+    def forward(self, feats):
+        """Forward features from the upstream network.
+
+        Args:
+            feats (tuple[Tensor]): Features from the upstream network, each is
+                a 4D-tensor.
+
+        Returns:
+            tuple[Tensor]: A tuple of multi-level predication map, each is a
+                4D-tensor of shape (batch_size, 5+num_classes, height, width).
+        """
+
+        assert len(feats) == self.num_levels
+        pred_maps = []
+        for i in range(self.num_levels):
+            x = feats[i]
+            x = self.convs_bridge[i](x)
+            pred_map = self.convs_pred[i](x)
+            pred_maps.append(pred_map)
+
+        return tuple(pred_maps),
+
+    @force_fp32(apply_to=('pred_maps', ))
+    def get_bboxes(self,
+                   pred_maps,
+                   img_metas,
+                   cfg=None,
+                   rescale=False,
+                   with_nms=True):
+        """Transform network output for a batch into bbox predictions. It has
+        been accelerated since PR #5991.
+
+        Args:
+            pred_maps (list[Tensor]): Raw predictions for a batch of images.
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            cfg (mmcv.Config | None): Test / postprocessing configuration,
+                if None, test_cfg would be used. Default: None.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default: True.
+
+        Returns:
+            list[tuple[Tensor, Tensor]]: Each item in result_list is 2-tuple.
+                The first item is an (n, 5) tensor, where 5 represent
+                (tl_x, tl_y, br_x, br_y, score) and the score between 0 and 1.
+                The shape of the second tensor in the tuple is (n,), and
+                each element represents the class label of the corresponding
+                box.
+        """
+        assert len(pred_maps) == self.num_levels
+        cfg = self.test_cfg if cfg is None else cfg
+        scale_factors = np.array(
+            [img_meta['scale_factor'] for img_meta in img_metas])
+
+        num_imgs = len(img_metas)
+        featmap_sizes = [pred_map.shape[-2:] for pred_map in pred_maps]
+
+        mlvl_anchors = self.prior_generator.grid_priors(
+            featmap_sizes, device=pred_maps[0].device)
+        flatten_preds = []
+        flatten_strides = []
+        for pred, stride in zip(pred_maps, self.featmap_strides):
+            pred = pred.permute(0, 2, 3, 1).reshape(num_imgs, -1,
+                                                    self.num_attrib)
+            pred[..., :2].sigmoid_()
+            flatten_preds.append(pred)
+            flatten_strides.append(
+                pred.new_tensor(stride).expand(pred.size(1)))
+
+        flatten_preds = torch.cat(flatten_preds, dim=1)
+        flatten_bbox_preds = flatten_preds[..., :4]
+        flatten_objectness = flatten_preds[..., 4].sigmoid()
+        flatten_cls_scores = flatten_preds[..., 5:].sigmoid()
+        flatten_anchors = torch.cat(mlvl_anchors)
+        flatten_strides = torch.cat(flatten_strides)
+        flatten_bboxes = self.bbox_coder.decode(flatten_anchors,
+                                                flatten_bbox_preds,
+                                                flatten_strides.unsqueeze(-1))
+
+        if with_nms and (flatten_objectness.size(0) == 0):
+            return torch.zeros((0, 5)), torch.zeros((0, ))
+
+        if rescale:
+            flatten_bboxes /= flatten_bboxes.new_tensor(
+                scale_factors).unsqueeze(1)
+
+        padding = flatten_bboxes.new_zeros(num_imgs, flatten_bboxes.shape[1],
+                                           1)
+        flatten_cls_scores = torch.cat([flatten_cls_scores, padding], dim=-1)
+
+        det_results = []
+        for (bboxes, scores, objectness) in zip(flatten_bboxes,
+                                                flatten_cls_scores,
+                                                flatten_objectness):
+            # Filtering out all predictions with conf < conf_thr
+            conf_thr = cfg.get('conf_thr', -1)
+            if conf_thr > 0:
+                conf_inds = objectness >= conf_thr
+                bboxes = bboxes[conf_inds, :]
+                scores = scores[conf_inds, :]
+                objectness = objectness[conf_inds]
+
+            det_bboxes, det_labels = multiclass_nms(
+                bboxes,
+                scores,
+                cfg.score_thr,
+                cfg.nms,
+                cfg.max_per_img,
+                score_factors=objectness)
+            det_results.append(tuple([det_bboxes, det_labels]))
+        return det_results
+
+    @force_fp32(apply_to=('pred_maps', ))
+    def loss(self,
+             pred_maps,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute loss of the head.
+
+        Args:
+            pred_maps (list[Tensor]): Prediction map for each scale level,
+                shape (N, num_anchors * num_attrib, H, W)
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        num_imgs = len(img_metas)
+        device = pred_maps[0][0].device
+
+        featmap_sizes = [
+            pred_maps[i].shape[-2:] for i in range(self.num_levels)
+        ]
+        mlvl_anchors = self.prior_generator.grid_priors(
+            featmap_sizes, device=device)
+        anchor_list = [mlvl_anchors for _ in range(num_imgs)]
+
+        responsible_flag_list = []
+        for img_id in range(len(img_metas)):
+            responsible_flag_list.append(
+                self.prior_generator.responsible_flags(featmap_sizes,
+                                                       gt_bboxes[img_id],
+                                                       device))
+
+        target_maps_list, neg_maps_list = self.get_targets(
+            anchor_list, responsible_flag_list, gt_bboxes, gt_labels)
+
+        losses_cls, losses_conf, losses_xy, losses_wh = multi_apply(
+            self.loss_single, pred_maps, target_maps_list, neg_maps_list)
+
+        return dict(
+            loss_cls=losses_cls,
+            loss_conf=losses_conf,
+            loss_xy=losses_xy,
+            loss_wh=losses_wh)
+
+    def loss_single(self, pred_map, target_map, neg_map):
+        """Compute loss of a single image from a batch.
+
+        Args:
+            pred_map (Tensor): Raw predictions for a single level.
+            target_map (Tensor): The Ground-Truth target for a single level.
+            neg_map (Tensor): The negative masks for a single level.
+
+        Returns:
+            tuple:
+                loss_cls (Tensor): Classification loss.
+                loss_conf (Tensor): Confidence loss.
+                loss_xy (Tensor): Regression loss of x, y coordinate.
+                loss_wh (Tensor): Regression loss of w, h coordinate.
+        """
+
+        num_imgs = len(pred_map)
+        pred_map = pred_map.permute(0, 2, 3,
+                                    1).reshape(num_imgs, -1, self.num_attrib)
+        neg_mask = neg_map.float()
+        pos_mask = target_map[..., 4]
+        pos_and_neg_mask = neg_mask + pos_mask
+        pos_mask = pos_mask.unsqueeze(dim=-1)
+        if torch.max(pos_and_neg_mask) > 1.:
+            warnings.warn('There is overlap between pos and neg sample.')
+            pos_and_neg_mask = pos_and_neg_mask.clamp(min=0., max=1.)
+
+        pred_xy = pred_map[..., :2]
+        pred_wh = pred_map[..., 2:4]
+        pred_conf = pred_map[..., 4]
+        pred_label = pred_map[..., 5:]
+
+        target_xy = target_map[..., :2]
+        target_wh = target_map[..., 2:4]
+        target_conf = target_map[..., 4]
+        target_label = target_map[..., 5:]
+
+        loss_cls = self.loss_cls(pred_label, target_label, weight=pos_mask)
+        loss_conf = self.loss_conf(
+            pred_conf, target_conf, weight=pos_and_neg_mask)
+        loss_xy = self.loss_xy(pred_xy, target_xy, weight=pos_mask)
+        loss_wh = self.loss_wh(pred_wh, target_wh, weight=pos_mask)
+
+        return loss_cls, loss_conf, loss_xy, loss_wh
+
+    def get_targets(self, anchor_list, responsible_flag_list, gt_bboxes_list,
+                    gt_labels_list):
+        """Compute target maps for anchors in multiple images.
+
+        Args:
+            anchor_list (list[list[Tensor]]): Multi level anchors of each
+                image. The outer list indicates images, and the inner list
+                corresponds to feature levels of the image. Each element of
+                the inner list is a tensor of shape (num_total_anchors, 4).
+            responsible_flag_list (list[list[Tensor]]): Multi level responsible
+                flags of each image. Each element is a tensor of shape
+                (num_total_anchors, )
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+            gt_labels_list (list[Tensor]): Ground truth labels of each box.
+
+        Returns:
+            tuple: Usually returns a tuple containing learning targets.
+                - target_map_list (list[Tensor]): Target map of each level.
+                - neg_map_list (list[Tensor]): Negative map of each level.
+        """
+        num_imgs = len(anchor_list)
+
+        # anchor number of multi levels
+        num_level_anchors = [anchors.size(0) for anchors in anchor_list[0]]
+
+        results = multi_apply(self._get_targets_single, anchor_list,
+                              responsible_flag_list, gt_bboxes_list,
+                              gt_labels_list)
+
+        all_target_maps, all_neg_maps = results
+        assert num_imgs == len(all_target_maps) == len(all_neg_maps)
+        target_maps_list = images_to_levels(all_target_maps, num_level_anchors)
+        neg_maps_list = images_to_levels(all_neg_maps, num_level_anchors)
+
+        return target_maps_list, neg_maps_list
+
+    def _get_targets_single(self, anchors, responsible_flags, gt_bboxes,
+                            gt_labels):
+        """Generate matching bounding box prior and converted GT.
+
+        Args:
+            anchors (list[Tensor]): Multi-level anchors of the image.
+            responsible_flags (list[Tensor]): Multi-level responsible flags of
+                anchors
+            gt_bboxes (Tensor): Ground truth bboxes of single image.
+            gt_labels (Tensor): Ground truth labels of single image.
+
+        Returns:
+            tuple:
+                target_map (Tensor): Predication target map of each
+                    scale level, shape (num_total_anchors,
+                    5+num_classes)
+                neg_map (Tensor): Negative map of each scale level,
+                    shape (num_total_anchors,)
+        """
+
+        anchor_strides = []
+        for i in range(len(anchors)):
+            anchor_strides.append(
+                torch.tensor(self.featmap_strides[i],
+                             device=gt_bboxes.device).repeat(len(anchors[i])))
+        concat_anchors = torch.cat(anchors)
+        concat_responsible_flags = torch.cat(responsible_flags)
+
+        anchor_strides = torch.cat(anchor_strides)
+        assert len(anchor_strides) == len(concat_anchors) == \
+               len(concat_responsible_flags)
+        assign_result = self.assigner.assign(concat_anchors,
+                                             concat_responsible_flags,
+                                             gt_bboxes)
+        sampling_result = self.sampler.sample(assign_result, concat_anchors,
+                                              gt_bboxes)
+
+        target_map = concat_anchors.new_zeros(
+            concat_anchors.size(0), self.num_attrib)
+
+        target_map[sampling_result.pos_inds, :4] = self.bbox_coder.encode(
+            sampling_result.pos_bboxes, sampling_result.pos_gt_bboxes,
+            anchor_strides[sampling_result.pos_inds])
+
+        target_map[sampling_result.pos_inds, 4] = 1
+
+        gt_labels_one_hot = F.one_hot(
+            gt_labels, num_classes=self.num_classes).float()
+        if self.one_hot_smoother != 0:  # label smooth
+            gt_labels_one_hot = gt_labels_one_hot * (
+                1 - self.one_hot_smoother
+            ) + self.one_hot_smoother / self.num_classes
+        target_map[sampling_result.pos_inds, 5:] = gt_labels_one_hot[
+            sampling_result.pos_assigned_gt_inds]
+
+        neg_map = concat_anchors.new_zeros(
+            concat_anchors.size(0), dtype=torch.uint8)
+        neg_map[sampling_result.neg_inds] = 1
+
+        return target_map, neg_map
+
+    def aug_test(self, feats, img_metas, rescale=False):
+        """Test function with test time augmentation.
+
+        Args:
+            feats (list[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains features for all images in the batch.
+            img_metas (list[list[dict]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch. each dict has image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[ndarray]: bbox results of each class
+        """
+        return self.aug_test_bboxes(feats, img_metas, rescale=rescale)
+
+    @force_fp32(apply_to=('pred_maps'))
+    def onnx_export(self, pred_maps, img_metas, with_nms=True):
+        num_levels = len(pred_maps)
+        pred_maps_list = [pred_maps[i].detach() for i in range(num_levels)]
+
+        cfg = self.test_cfg
+        assert len(pred_maps_list) == self.num_levels
+
+        device = pred_maps_list[0].device
+        batch_size = pred_maps_list[0].shape[0]
+
+        featmap_sizes = [
+            pred_maps_list[i].shape[-2:] for i in range(self.num_levels)
+        ]
+        mlvl_anchors = self.prior_generator.grid_priors(
+            featmap_sizes, device=device)
+        # convert to tensor to keep tracing
+        nms_pre_tensor = torch.tensor(
+            cfg.get('nms_pre', -1), device=device, dtype=torch.long)
+
+        multi_lvl_bboxes = []
+        multi_lvl_cls_scores = []
+        multi_lvl_conf_scores = []
+        for i in range(self.num_levels):
+            # get some key info for current scale
+            pred_map = pred_maps_list[i]
+            stride = self.featmap_strides[i]
+            # (b,h, w, num_anchors*num_attrib) ->
+            # (b,h*w*num_anchors, num_attrib)
+            pred_map = pred_map.permute(0, 2, 3,
+                                        1).reshape(batch_size, -1,
+                                                   self.num_attrib)
+            # Inplace operation like
+            # ```pred_map[..., :2] = \torch.sigmoid(pred_map[..., :2])```
+            # would create constant tensor when exporting to onnx
+            pred_map_conf = torch.sigmoid(pred_map[..., :2])
+            pred_map_rest = pred_map[..., 2:]
+            pred_map = torch.cat([pred_map_conf, pred_map_rest], dim=-1)
+            pred_map_boxes = pred_map[..., :4]
+            multi_lvl_anchor = mlvl_anchors[i]
+            multi_lvl_anchor = multi_lvl_anchor.expand_as(pred_map_boxes)
+            bbox_pred = self.bbox_coder.decode(multi_lvl_anchor,
+                                               pred_map_boxes, stride)
+            # conf and cls
+            conf_pred = torch.sigmoid(pred_map[..., 4])
+            cls_pred = torch.sigmoid(pred_map[..., 5:]).view(
+                batch_size, -1, self.num_classes)  # Cls pred one-hot.
+
+            # Get top-k prediction
+            from mmdet.core.export import get_k_for_topk
+            nms_pre = get_k_for_topk(nms_pre_tensor, bbox_pred.shape[1])
+            if nms_pre > 0:
+                _, topk_inds = conf_pred.topk(nms_pre)
+                batch_inds = torch.arange(batch_size).view(
+                    -1, 1).expand_as(topk_inds).long()
+                # Avoid onnx2tensorrt issue in https://github.com/NVIDIA/TensorRT/issues/1134 # noqa: E501
+                transformed_inds = (
+                    bbox_pred.shape[1] * batch_inds + topk_inds)
+                bbox_pred = bbox_pred.reshape(-1,
+                                              4)[transformed_inds, :].reshape(
+                                                  batch_size, -1, 4)
+                cls_pred = cls_pred.reshape(
+                    -1, self.num_classes)[transformed_inds, :].reshape(
+                        batch_size, -1, self.num_classes)
+                conf_pred = conf_pred.reshape(-1, 1)[transformed_inds].reshape(
+                    batch_size, -1)
+
+            # Save the result of current scale
+            multi_lvl_bboxes.append(bbox_pred)
+            multi_lvl_cls_scores.append(cls_pred)
+            multi_lvl_conf_scores.append(conf_pred)
+
+        # Merge the results of different scales together
+        batch_mlvl_bboxes = torch.cat(multi_lvl_bboxes, dim=1)
+        batch_mlvl_scores = torch.cat(multi_lvl_cls_scores, dim=1)
+        batch_mlvl_conf_scores = torch.cat(multi_lvl_conf_scores, dim=1)
+
+        # Replace multiclass_nms with ONNX::NonMaxSuppression in deployment
+        from mmdet.core.export import add_dummy_nms_for_onnx
+        conf_thr = cfg.get('conf_thr', -1)
+        score_thr = cfg.get('score_thr', -1)
+        # follow original pipeline of YOLOv3
+        if conf_thr > 0:
+            mask = (batch_mlvl_conf_scores >= conf_thr).float()
+            batch_mlvl_conf_scores *= mask
+        if score_thr > 0:
+            mask = (batch_mlvl_scores > score_thr).float()
+            batch_mlvl_scores *= mask
+        batch_mlvl_conf_scores = batch_mlvl_conf_scores.unsqueeze(2).expand_as(
+            batch_mlvl_scores)
+        batch_mlvl_scores = batch_mlvl_scores * batch_mlvl_conf_scores
+        if with_nms:
+            max_output_boxes_per_class = cfg.nms.get(
+                'max_output_boxes_per_class', 200)
+            iou_threshold = cfg.nms.get('iou_threshold', 0.5)
+            # keep aligned with original pipeline, improve
+            # mAP by 1% for YOLOv3 in ONNX
+            score_threshold = 0
+            nms_pre = cfg.get('deploy_nms_pre', -1)
+            return add_dummy_nms_for_onnx(
+                batch_mlvl_bboxes,
+                batch_mlvl_scores,
+                max_output_boxes_per_class,
+                iou_threshold,
+                score_threshold,
+                nms_pre,
+                cfg.max_per_img,
+            )
+        else:
+            return batch_mlvl_bboxes, batch_mlvl_scores
diff --git a/mmdet/models/dense_heads/yolof_head.py b/mmdet/models/dense_heads/yolof_head.py
new file mode 100644
index 0000000..1063524
--- /dev/null
+++ b/mmdet/models/dense_heads/yolof_head.py
@@ -0,0 +1,416 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.cnn import (ConvModule, bias_init_with_prob, constant_init, is_norm,
+                      normal_init)
+from mmcv.runner import force_fp32
+
+from mmdet.core import anchor_inside_flags, multi_apply, reduce_mean, unmap
+from ..builder import HEADS
+from .anchor_head import AnchorHead
+
+INF = 1e8
+
+
+def levels_to_images(mlvl_tensor):
+    """Concat multi-level feature maps by image.
+
+    [feature_level0, feature_level1...] -> [feature_image0, feature_image1...]
+    Convert the shape of each element in mlvl_tensor from (N, C, H, W) to
+    (N, H*W , C), then split the element to N elements with shape (H*W, C), and
+    concat elements in same image of all level along first dimension.
+
+    Args:
+        mlvl_tensor (list[torch.Tensor]): list of Tensor which collect from
+            corresponding level. Each element is of shape (N, C, H, W)
+
+    Returns:
+        list[torch.Tensor]: A list that contains N tensors and each tensor is
+            of shape (num_elements, C)
+    """
+    batch_size = mlvl_tensor[0].size(0)
+    batch_list = [[] for _ in range(batch_size)]
+    channels = mlvl_tensor[0].size(1)
+    for t in mlvl_tensor:
+        t = t.permute(0, 2, 3, 1)
+        t = t.view(batch_size, -1, channels).contiguous()
+        for img in range(batch_size):
+            batch_list[img].append(t[img])
+    return [torch.cat(item, 0) for item in batch_list]
+
+
+@HEADS.register_module()
+class YOLOFHead(AnchorHead):
+    """YOLOFHead Paper link: https://arxiv.org/abs/2103.09460.
+
+    Args:
+        num_classes (int): The number of object classes (w/o background)
+        in_channels (List[int]): The number of input channels per scale.
+        cls_num_convs (int): The number of convolutions of cls branch.
+           Default 2.
+        reg_num_convs (int): The number of convolutions of reg branch.
+           Default 4.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 num_cls_convs=2,
+                 num_reg_convs=4,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 **kwargs):
+        self.num_cls_convs = num_cls_convs
+        self.num_reg_convs = num_reg_convs
+        self.norm_cfg = norm_cfg
+        super(YOLOFHead, self).__init__(num_classes, in_channels, **kwargs)
+
+    def _init_layers(self):
+        cls_subnet = []
+        bbox_subnet = []
+        for i in range(self.num_cls_convs):
+            cls_subnet.append(
+                ConvModule(
+                    self.in_channels,
+                    self.in_channels,
+                    kernel_size=3,
+                    padding=1,
+                    norm_cfg=self.norm_cfg))
+        for i in range(self.num_reg_convs):
+            bbox_subnet.append(
+                ConvModule(
+                    self.in_channels,
+                    self.in_channels,
+                    kernel_size=3,
+                    padding=1,
+                    norm_cfg=self.norm_cfg))
+        self.cls_subnet = nn.Sequential(*cls_subnet)
+        self.bbox_subnet = nn.Sequential(*bbox_subnet)
+        self.cls_score = nn.Conv2d(
+            self.in_channels,
+            self.num_base_priors * self.num_classes,
+            kernel_size=3,
+            stride=1,
+            padding=1)
+        self.bbox_pred = nn.Conv2d(
+            self.in_channels,
+            self.num_base_priors * 4,
+            kernel_size=3,
+            stride=1,
+            padding=1)
+        self.object_pred = nn.Conv2d(
+            self.in_channels,
+            self.num_base_priors,
+            kernel_size=3,
+            stride=1,
+            padding=1)
+
+    def init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                normal_init(m, mean=0, std=0.01)
+            if is_norm(m):
+                constant_init(m, 1)
+
+        # Use prior in model initialization to improve stability
+        bias_cls = bias_init_with_prob(0.01)
+        torch.nn.init.constant_(self.cls_score.bias, bias_cls)
+
+    def forward_single(self, feature):
+        cls_score = self.cls_score(self.cls_subnet(feature))
+        N, _, H, W = cls_score.shape
+        cls_score = cls_score.view(N, -1, self.num_classes, H, W)
+
+        reg_feat = self.bbox_subnet(feature)
+        bbox_reg = self.bbox_pred(reg_feat)
+        objectness = self.object_pred(reg_feat)
+
+        # implicit objectness
+        objectness = objectness.view(N, -1, 1, H, W)
+        normalized_cls_score = cls_score + objectness - torch.log(
+            1. + torch.clamp(cls_score.exp(), max=INF) +
+            torch.clamp(objectness.exp(), max=INF))
+        normalized_cls_score = normalized_cls_score.view(N, -1, H, W)
+        return normalized_cls_score, bbox_reg
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute losses of the head.
+
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level
+                Has shape (batch, num_anchors * num_classes, h, w)
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level with shape (batch, num_anchors * 4, h, w)
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss. Default: None
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        assert len(cls_scores) == 1
+        assert self.prior_generator.num_levels == 1
+
+        device = cls_scores[0].device
+        featmap_sizes = [featmap.size()[-2:] for featmap in cls_scores]
+        anchor_list, valid_flag_list = self.get_anchors(
+            featmap_sizes, img_metas, device=device)
+
+        # The output level is always 1
+        anchor_list = [anchors[0] for anchors in anchor_list]
+        valid_flag_list = [valid_flags[0] for valid_flags in valid_flag_list]
+
+        cls_scores_list = levels_to_images(cls_scores)
+        bbox_preds_list = levels_to_images(bbox_preds)
+
+        label_channels = self.cls_out_channels if self.use_sigmoid_cls else 1
+        cls_reg_targets = self.get_targets(
+            cls_scores_list,
+            bbox_preds_list,
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes,
+            img_metas,
+            gt_bboxes_ignore_list=gt_bboxes_ignore,
+            gt_labels_list=gt_labels,
+            label_channels=label_channels)
+        if cls_reg_targets is None:
+            return None
+        (batch_labels, batch_label_weights, num_total_pos, num_total_neg,
+         batch_bbox_weights, batch_pos_predicted_boxes,
+         batch_target_boxes) = cls_reg_targets
+
+        flatten_labels = batch_labels.reshape(-1)
+        batch_label_weights = batch_label_weights.reshape(-1)
+        cls_score = cls_scores[0].permute(0, 2, 3,
+                                          1).reshape(-1, self.cls_out_channels)
+
+        num_total_samples = (num_total_pos +
+                             num_total_neg) if self.sampling else num_total_pos
+        num_total_samples = reduce_mean(
+            cls_score.new_tensor(num_total_samples)).clamp_(1.0).item()
+
+        # classification loss
+        loss_cls = self.loss_cls(
+            cls_score,
+            flatten_labels,
+            batch_label_weights,
+            avg_factor=num_total_samples)
+
+        # regression loss
+        if batch_pos_predicted_boxes.shape[0] == 0:
+            # no pos sample
+            loss_bbox = batch_pos_predicted_boxes.sum() * 0
+        else:
+            loss_bbox = self.loss_bbox(
+                batch_pos_predicted_boxes,
+                batch_target_boxes,
+                batch_bbox_weights.float(),
+                avg_factor=num_total_samples)
+
+        return dict(loss_cls=loss_cls, loss_bbox=loss_bbox)
+
+    def get_targets(self,
+                    cls_scores_list,
+                    bbox_preds_list,
+                    anchor_list,
+                    valid_flag_list,
+                    gt_bboxes_list,
+                    img_metas,
+                    gt_bboxes_ignore_list=None,
+                    gt_labels_list=None,
+                    label_channels=1,
+                    unmap_outputs=True):
+        """Compute regression and classification targets for anchors in
+        multiple images.
+
+        Args:
+            cls_scores_list (list[Tensor])： Classification scores of
+                each image. each is a 4D-tensor, the shape is
+                (h * w, num_anchors * num_classes).
+            bbox_preds_list (list[Tensor])： Bbox preds of each image.
+                each is a 4D-tensor, the shape is (h * w, num_anchors * 4).
+            anchor_list (list[Tensor]): Anchors of each image. Each element of
+                is a tensor of shape (h * w * num_anchors, 4).
+            valid_flag_list (list[Tensor]): Valid flags of each image. Each
+               element of is a tensor of shape (h * w * num_anchors, )
+            gt_bboxes_list (list[Tensor]): Ground truth bboxes of each image.
+            img_metas (list[dict]): Meta info of each image.
+            gt_bboxes_ignore_list (list[Tensor]): Ground truth bboxes to be
+                ignored.
+            gt_labels_list (list[Tensor]): Ground truth labels of each box.
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+
+        Returns:
+            tuple: Usually returns a tuple containing learning targets.
+
+                - batch_labels (Tensor): Label of all images. Each element \
+                    of is a tensor of shape (batch, h * w * num_anchors)
+                - batch_label_weights (Tensor): Label weights of all images \
+                    of is a tensor of shape (batch, h * w * num_anchors)
+                - num_total_pos (int): Number of positive samples in all \
+                    images.
+                - num_total_neg (int): Number of negative samples in all \
+                    images.
+            additional_returns: This function enables user-defined returns from
+                `self._get_targets_single`. These returns are currently refined
+                to properties at each feature map (i.e. having HxW dimension).
+                The results will be concatenated after the end
+        """
+        num_imgs = len(img_metas)
+        assert len(anchor_list) == len(valid_flag_list) == num_imgs
+
+        # compute targets for each image
+        if gt_bboxes_ignore_list is None:
+            gt_bboxes_ignore_list = [None for _ in range(num_imgs)]
+        if gt_labels_list is None:
+            gt_labels_list = [None for _ in range(num_imgs)]
+        results = multi_apply(
+            self._get_targets_single,
+            bbox_preds_list,
+            anchor_list,
+            valid_flag_list,
+            gt_bboxes_list,
+            gt_bboxes_ignore_list,
+            gt_labels_list,
+            img_metas,
+            label_channels=label_channels,
+            unmap_outputs=unmap_outputs)
+        (all_labels, all_label_weights, pos_inds_list, neg_inds_list,
+         sampling_results_list) = results[:5]
+        rest_results = list(results[5:])  # user-added return values
+        # no valid anchors
+        if any([labels is None for labels in all_labels]):
+            return None
+        # sampled anchors of all images
+        num_total_pos = sum([max(inds.numel(), 1) for inds in pos_inds_list])
+        num_total_neg = sum([max(inds.numel(), 1) for inds in neg_inds_list])
+
+        batch_labels = torch.stack(all_labels, 0)
+        batch_label_weights = torch.stack(all_label_weights, 0)
+
+        res = (batch_labels, batch_label_weights, num_total_pos, num_total_neg)
+        for i, rests in enumerate(rest_results):  # user-added return values
+            rest_results[i] = torch.cat(rests, 0)
+
+        return res + tuple(rest_results)
+
+    def _get_targets_single(self,
+                            bbox_preds,
+                            flat_anchors,
+                            valid_flags,
+                            gt_bboxes,
+                            gt_bboxes_ignore,
+                            gt_labels,
+                            img_meta,
+                            label_channels=1,
+                            unmap_outputs=True):
+        """Compute regression and classification targets for anchors in a
+        single image.
+
+        Args:
+            bbox_preds (Tensor): Bbox prediction of the image, which
+                shape is (h * w ,4)
+            flat_anchors (Tensor): Anchors of the image, which shape is
+                (h * w * num_anchors ,4)
+            valid_flags (Tensor): Valid flags of the image, which shape is
+                (h * w * num_anchors,).
+            gt_bboxes (Tensor): Ground truth bboxes of the image,
+                shape (num_gts, 4).
+            gt_bboxes_ignore (Tensor): Ground truth bboxes to be
+                ignored, shape (num_ignored_gts, 4).
+            img_meta (dict): Meta info of the image.
+            gt_labels (Tensor): Ground truth labels of each box,
+                shape (num_gts,).
+            label_channels (int): Channel of label.
+            unmap_outputs (bool): Whether to map outputs back to the original
+                set of anchors.
+
+        Returns:
+            tuple:
+                labels (Tensor): Labels of image, which shape is
+                    (h * w * num_anchors, ).
+                label_weights (Tensor): Label weights of image, which shape is
+                    (h * w * num_anchors, ).
+                pos_inds (Tensor): Pos index of image.
+                neg_inds (Tensor): Neg index of image.
+                sampling_result (obj:`SamplingResult`): Sampling result.
+                pos_bbox_weights (Tensor): The Weight of using to calculate
+                    the bbox branch loss, which shape is (num, ).
+                pos_predicted_boxes (Tensor): boxes predicted value of
+                    using to calculate the bbox branch loss, which shape is
+                    (num, 4).
+                pos_target_boxes (Tensor): boxes target value of
+                    using to calculate the bbox branch loss, which shape is
+                    (num, 4).
+        """
+        inside_flags = anchor_inside_flags(flat_anchors, valid_flags,
+                                           img_meta['img_shape'][:2],
+                                           self.train_cfg.allowed_border)
+        if not inside_flags.any():
+            return (None, ) * 8
+        # assign gt and sample anchors
+        anchors = flat_anchors[inside_flags, :]
+        bbox_preds = bbox_preds.reshape(-1, 4)
+        bbox_preds = bbox_preds[inside_flags, :]
+
+        # decoded bbox
+        decoder_bbox_preds = self.bbox_coder.decode(anchors, bbox_preds)
+        assign_result = self.assigner.assign(
+            decoder_bbox_preds, anchors, gt_bboxes, gt_bboxes_ignore,
+            None if self.sampling else gt_labels)
+
+        pos_bbox_weights = assign_result.get_extra_property('pos_idx')
+        pos_predicted_boxes = assign_result.get_extra_property(
+            'pos_predicted_boxes')
+        pos_target_boxes = assign_result.get_extra_property('target_boxes')
+
+        sampling_result = self.sampler.sample(assign_result, anchors,
+                                              gt_bboxes)
+        num_valid_anchors = anchors.shape[0]
+        labels = anchors.new_full((num_valid_anchors, ),
+                                  self.num_classes,
+                                  dtype=torch.long)
+        label_weights = anchors.new_zeros(num_valid_anchors, dtype=torch.float)
+
+        pos_inds = sampling_result.pos_inds
+        neg_inds = sampling_result.neg_inds
+        if len(pos_inds) > 0:
+            if gt_labels is None:
+                # Only rpn gives gt_labels as None
+                # Foreground is the first class since v2.5.0
+                labels[pos_inds] = 0
+            else:
+                labels[pos_inds] = gt_labels[
+                    sampling_result.pos_assigned_gt_inds]
+            if self.train_cfg.pos_weight <= 0:
+                label_weights[pos_inds] = 1.0
+            else:
+                label_weights[pos_inds] = self.train_cfg.pos_weight
+        if len(neg_inds) > 0:
+            label_weights[neg_inds] = 1.0
+
+        # map up to original set of anchors
+        if unmap_outputs:
+            num_total_anchors = flat_anchors.size(0)
+            labels = unmap(
+                labels, num_total_anchors, inside_flags,
+                fill=self.num_classes)  # fill bg label
+            label_weights = unmap(label_weights, num_total_anchors,
+                                  inside_flags)
+
+        return (labels, label_weights, pos_inds, neg_inds, sampling_result,
+                pos_bbox_weights, pos_predicted_boxes, pos_target_boxes)
diff --git a/mmdet/models/dense_heads/yolox_head.py b/mmdet/models/dense_heads/yolox_head.py
new file mode 100644
index 0000000..26d0c35
--- /dev/null
+++ b/mmdet/models/dense_heads/yolox_head.py
@@ -0,0 +1,796 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import (ConvModule, DepthwiseSeparableConvModule,
+                      bias_init_with_prob)
+from mmcv.ops.nms import batched_nms
+from mmcv.runner import force_fp32
+
+from mmdet.core import (MlvlPointGenerator, bbox_xyxy_to_cxcywh,
+                        build_assigner, build_sampler, multi_apply,
+                        reduce_mean)
+from ..builder import HEADS, build_loss
+from .base_dense_head import BaseDenseHead
+from .dense_test_mixins import BBoxTestMixin
+from mmdet.core.bbox.iou_calculators import bbox_overlaps
+from my_equation import *
+import global_placeholder
+
+@HEADS.register_module()
+class YOLOXHead(BaseDenseHead, BBoxTestMixin):
+    """YOLOXHead head used in `YOLOX <https://arxiv.org/abs/2107.08430>`_.
+
+    Args:
+        num_classes (int): Number of categories excluding the background
+            category.
+        in_channels (int): Number of channels in the input feature map.
+        feat_channels (int): Number of hidden channels in stacking convs.
+            Default: 256
+        stacked_convs (int): Number of stacking convs of the head.
+            Default: 2.
+        strides (tuple): Downsample factor of each feature map.
+        use_depthwise (bool): Whether to depthwise separable convolution in
+            blocks. Default: False
+        dcn_on_last_conv (bool): If true, use dcn in the last layer of
+            towers. Default: False.
+        conv_bias (bool | str): If specified as `auto`, it will be decided by
+            the norm_cfg. Bias of conv will be set as True if `norm_cfg` is
+            None, otherwise False. Default: "auto".
+        conv_cfg (dict): Config dict for convolution layer. Default: None.
+        norm_cfg (dict): Config dict for normalization layer. Default: None.
+        act_cfg (dict): Config dict for activation layer. Default: None.
+        loss_cls (dict): Config of classification loss.
+        loss_bbox (dict): Config of localization loss.
+        loss_obj (dict): Config of objectness loss.
+        loss_l1 (dict): Config of L1 loss.
+        train_cfg (dict): Training config of anchor head.
+        test_cfg (dict): Testing config of anchor head.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 in_channels,
+                 feat_channels=256,
+                 stacked_convs=2,
+                 strides=[8, 16, 32],
+                 use_depthwise=False,
+                 dcn_on_last_conv=False,
+                 conv_bias='auto',
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+                 act_cfg=dict(type='Swish'),
+                 loss_cls=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     reduction='sum',
+                     loss_weight=1.0),
+                 loss_bbox=dict(
+                     type='IoULoss',
+                     mode='square',
+                     eps=1e-16,
+                     reduction='sum',
+                     loss_weight=5.0),
+                 loss_obj=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     reduction='sum',
+                     loss_weight=1.0),
+                 loss_l1=dict(type='L1Loss', reduction='sum', loss_weight=1.0),
+                 train_cfg=None,
+                 test_cfg=None,
+                 init_cfg=dict(
+                     type='Kaiming',
+                     layer='Conv2d',
+                     a=math.sqrt(5),
+                     distribution='uniform',
+                     mode='fan_in',
+                     nonlinearity='leaky_relu')):
+
+        super().__init__(init_cfg=init_cfg)
+        self.num_classes = num_classes
+        self.cls_out_channels = num_classes
+        self.in_channels = in_channels
+        self.feat_channels = feat_channels
+        self.stacked_convs = stacked_convs
+        self.strides = strides
+        self.use_depthwise = use_depthwise
+        self.dcn_on_last_conv = dcn_on_last_conv
+        assert conv_bias == 'auto' or isinstance(conv_bias, bool)
+        self.conv_bias = conv_bias
+        self.use_sigmoid_cls = True
+
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_bbox = build_loss(loss_bbox)
+        self.loss_obj = build_loss(loss_obj)
+
+        self.use_l1 = False  # This flag will be modified by hooks.
+        self.loss_l1 = build_loss(loss_l1)
+
+        self.prior_generator = MlvlPointGenerator(strides, offset=0)
+
+        self.test_cfg = test_cfg
+        self.train_cfg = train_cfg
+
+        self.sampling = False
+        if self.train_cfg:
+            self.assigner = build_assigner(self.train_cfg.assigner)
+            # sampling=False so use PseudoSampler
+            sampler_cfg = dict(type='PseudoSampler')
+            self.sampler = build_sampler(sampler_cfg, context=self)
+
+        self.fp16_enabled = False
+        self._init_layers()
+
+    def _init_layers(self):
+        self.multi_level_cls_convs = nn.ModuleList()
+        self.multi_level_reg_convs = nn.ModuleList()
+        self.multi_level_conv_cls = nn.ModuleList()
+        self.multi_level_conv_reg = nn.ModuleList()
+        self.multi_level_conv_obj = nn.ModuleList()
+        for _ in self.strides:
+            self.multi_level_cls_convs.append(self._build_stacked_convs())
+            self.multi_level_reg_convs.append(self._build_stacked_convs())
+            conv_cls, conv_reg, conv_obj = self._build_predictor()
+            self.multi_level_conv_cls.append(conv_cls)
+            self.multi_level_conv_reg.append(conv_reg)
+            self.multi_level_conv_obj.append(conv_obj)
+
+    def _build_stacked_convs(self):
+        """Initialize conv layers of a single level head."""
+        conv = DepthwiseSeparableConvModule \
+            if self.use_depthwise else ConvModule
+        stacked_convs = []
+        for i in range(self.stacked_convs):
+            chn = self.in_channels if i == 0 else self.feat_channels
+            if self.dcn_on_last_conv and i == self.stacked_convs - 1:
+                conv_cfg = dict(type='DCNv2')
+            else:
+                conv_cfg = self.conv_cfg
+            stacked_convs.append(
+                conv(
+                    chn,
+                    self.feat_channels,
+                    3,
+                    stride=1,
+                    padding=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    act_cfg=self.act_cfg,
+                    bias=self.conv_bias))
+        return nn.Sequential(*stacked_convs)
+
+    def _build_predictor(self):
+        """Initialize predictor layers of a single level head."""
+        conv_cls = nn.Conv2d(self.feat_channels, self.cls_out_channels, 1)
+        conv_reg = nn.Conv2d(self.feat_channels, 4, 1)
+        conv_obj = nn.Conv2d(self.feat_channels, 1, 1)
+        return conv_cls, conv_reg, conv_obj
+
+    def init_weights(self):
+        super(YOLOXHead, self).init_weights()
+        # Use prior in model initialization to improve stability
+        bias_init = bias_init_with_prob(0.01)
+        for conv_cls, conv_obj in zip(self.multi_level_conv_cls,
+                                      self.multi_level_conv_obj):
+            conv_cls.bias.data.fill_(bias_init)
+            conv_obj.bias.data.fill_(bias_init)
+
+    def forward_single(self, x, cls_convs, reg_convs, conv_cls, conv_reg,
+                       conv_obj):
+        """Forward feature of a single scale level."""
+
+        cls_feat = cls_convs(x)
+        reg_feat = reg_convs(x)
+
+        cls_score = conv_cls(cls_feat)
+        bbox_pred = conv_reg(reg_feat)
+        objectness = conv_obj(reg_feat)
+
+        return cls_score, bbox_pred, objectness
+
+    def forward(self, feats):
+        """Forward feature of a single scale level."""
+        cls_scores = []
+        bbox_preds = []
+        objectnesses = []
+
+        if hasattr(self,'in_num') and self.in_num == 3:
+            feats = [feats[0], feats[1], feats[2]]
+        elif hasattr(self,'in_num'):
+            raise NotImplementedError
+        else:
+            self.in_num = 3
+            feats = [feats[0], feats[1], feats[2]]
+        
+        for x, cls_convs, reg_convs, conv_cls, conv_reg, conv_obj in zip(feats, self.multi_level_cls_convs, self.multi_level_reg_convs, self.multi_level_conv_cls, self.multi_level_conv_reg, self.multi_level_conv_obj):
+        # for i in range(in_num):
+        #     x, cls_convs, reg_convs, conv_cls, conv_reg, conv_obj = feats[i], self.multi_level_cls_convs[i], self.multi_level_reg_convs[i], self.multi_level_conv_cls[i], self.multi_level_conv_reg[i], self.multi_level_conv_obj[i]
+            cls_feat = cls_convs(x)
+            reg_feat = reg_convs(x)
+
+            cls_score = conv_cls(cls_feat)
+            bbox_pred = conv_reg(reg_feat)
+            objectness = conv_obj(reg_feat)
+            cls_scores.append(cls_score)
+            bbox_preds.append(bbox_pred)
+            objectnesses.append(objectness)
+            
+
+        return cls_scores, bbox_preds, objectnesses
+
+
+    # def forward(self, feats):
+    #     """Forward features from the upstream network.
+
+    #     Args:
+    #         feats (tuple[Tensor]): Features from the upstream network, each is
+    #             a 4D-tensor.
+    #     Returns:
+    #         tuple[Tensor]: A tuple of multi-level predication map, each is a
+    #             4D-tensor of shape (batch_size, 5+num_classes, height, width).
+    #     """
+
+    #     return multi_apply(self.forward_single, feats,
+    #                        self.multi_level_cls_convs,
+    #                        self.multi_level_reg_convs,
+    #                        self.multi_level_conv_cls,
+    #                        self.multi_level_conv_reg,
+    #                        self.multi_level_conv_obj)
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'objectnesses'))
+    def get_bboxes(self,
+                   cls_scores,
+                   bbox_preds,
+                   objectnesses,
+                   img_metas=None,
+                   cfg=None,
+                   rescale=False,
+                   with_nms=True):
+        """Transform network outputs of a batch into bbox results.
+        Args:
+            cls_scores (list[Tensor]): Classification scores for all
+                scale levels, each is a 4D-tensor, has shape
+                (batch_size, num_priors * num_classes, H, W).
+            bbox_preds (list[Tensor]): Box energies / deltas for all
+                scale levels, each is a 4D-tensor, has shape
+                (batch_size, num_priors * 4, H, W).
+            objectnesses (list[Tensor], Optional): Score factor for
+                all scale level, each is a 4D-tensor, has shape
+                (batch_size, 1, H, W).
+            img_metas (list[dict], Optional): Image meta info. Default None.
+            cfg (mmcv.Config, Optional): Test / postprocessing configuration,
+                if None, test_cfg would be used.  Default None.
+            rescale (bool): If True, return boxes in original image space.
+                Default False.
+            with_nms (bool): If True, do nms before return boxes.
+                Default True.
+        Returns:
+            list[list[Tensor, Tensor]]: Each item in result_list is 2-tuple.
+                The first item is an (n, 5) tensor, where the first 4 columns
+                are bounding box positions (tl_x, tl_y, br_x, br_y) and the
+                5-th column is a score between 0 and 1. The second item is a
+                (n,) tensor where each item is the predicted class label of
+                the corresponding box.
+        """
+        assert len(cls_scores) == len(bbox_preds) == len(objectnesses)
+        cfg = self.test_cfg if cfg is None else cfg
+        scale_factors = np.array(
+            [img_meta['scale_factor'] for img_meta in img_metas])
+
+        num_imgs = len(img_metas)
+        featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores]
+        mlvl_priors = self.prior_generator.grid_priors(
+            featmap_sizes,
+            dtype=cls_scores[0].dtype,
+            device=cls_scores[0].device,
+            with_stride=True)
+
+        # flatten cls_scores, bbox_preds and objectness
+        flatten_cls_scores = [
+            cls_score.permute(0, 2, 3, 1).reshape(num_imgs, -1,
+                                                  self.cls_out_channels)
+            for cls_score in cls_scores
+        ]
+        flatten_bbox_preds = [
+            bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4)
+            for bbox_pred in bbox_preds
+        ]
+        flatten_objectness = [
+            objectness.permute(0, 2, 3, 1).reshape(num_imgs, -1)
+            for objectness in objectnesses
+        ]
+
+        flatten_cls_scores = torch.cat(flatten_cls_scores, dim=1).sigmoid()
+        flatten_bbox_preds = torch.cat(flatten_bbox_preds, dim=1)
+        flatten_objectness = torch.cat(flatten_objectness, dim=1).sigmoid()
+        flatten_priors = torch.cat(mlvl_priors)
+
+        flatten_bboxes = self._bbox_decode(flatten_priors, flatten_bbox_preds)
+
+        if rescale:
+            flatten_bboxes[..., :4] /= flatten_bboxes.new_tensor(
+                scale_factors).unsqueeze(1)
+
+        result_list = []
+        for img_id in range(len(img_metas)):
+            cls_scores = flatten_cls_scores[img_id]
+            score_factor = flatten_objectness[img_id]
+            bboxes = flatten_bboxes[img_id]
+
+            result_list.append(
+                self._bboxes_nms(cls_scores, bboxes, score_factor, cfg))
+
+        return result_list
+
+    def _bbox_decode(self, priors, bbox_preds):
+        xys = (bbox_preds[..., :2] * priors[:, 2:]) + priors[:, :2]
+        whs = bbox_preds[..., 2:].exp() * priors[:, 2:]
+
+        tl_x = (xys[..., 0] - whs[..., 0] / 2)
+        tl_y = (xys[..., 1] - whs[..., 1] / 2)
+        br_x = (xys[..., 0] + whs[..., 0] / 2)
+        br_y = (xys[..., 1] + whs[..., 1] / 2)
+
+        decoded_bboxes = torch.stack([tl_x, tl_y, br_x, br_y], -1)
+        return decoded_bboxes
+
+    def _bboxes_nms(self, cls_scores, bboxes, score_factor, cfg):
+        max_scores, labels = torch.max(cls_scores, 1)
+        valid_mask = score_factor * max_scores >= cfg.score_thr
+
+        bboxes = bboxes[valid_mask]
+        scores = max_scores[valid_mask] * score_factor[valid_mask]
+        labels = labels[valid_mask]
+
+        if labels.numel() == 0:
+            return bboxes, labels
+        else:
+            dets, keep = batched_nms(bboxes, scores, labels, cfg.nms)
+            return dets, labels[keep]
+
+    # @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'objectnesses'))
+    # def loss(self,
+    #          cls_scores,
+    #          bbox_preds,
+    #          objectnesses,
+    #          gt_bboxes,
+    #          gt_labels,
+    #          img_metas,
+    #          gt_bboxes_ignore=None):
+    #     """Compute loss of the head.
+    #     Args:
+    #         cls_scores (list[Tensor]): Box scores for each scale level,
+    #             each is a 4D-tensor, the channel number is
+    #             num_priors * num_classes.
+    #         bbox_preds (list[Tensor]): Box energies / deltas for each scale
+    #             level, each is a 4D-tensor, the channel number is
+    #             num_priors * 4.
+    #         objectnesses (list[Tensor], Optional): Score factor for
+    #             all scale level, each is a 4D-tensor, has shape
+    #             (batch_size, 1, H, W).
+    #         gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+    #             shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+    #         gt_labels (list[Tensor]): class indices corresponding to each box
+    #         img_metas (list[dict]): Meta information of each image, e.g.,
+    #             image size, scaling factor, etc.
+    #         gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+    #             boxes can be ignored when computing the loss.
+    #     """
+    #     num_imgs = len(img_metas)
+    #     featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores]
+    #     mlvl_priors = self.prior_generator.grid_priors(
+    #         featmap_sizes,
+    #         dtype=cls_scores[0].dtype,
+    #         device=cls_scores[0].device,
+    #         with_stride=True)
+
+    #     flatten_cls_preds = [
+    #         cls_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1,
+    #                                              self.cls_out_channels)
+    #         for cls_pred in cls_scores
+    #     ]
+    #     flatten_bbox_preds = [
+    #         bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4)
+    #         for bbox_pred in bbox_preds
+    #     ]
+    #     flatten_objectness = [
+    #         objectness.permute(0, 2, 3, 1).reshape(num_imgs, -1)
+    #         for objectness in objectnesses
+    #     ]
+
+    #     flatten_cls_preds = torch.cat(flatten_cls_preds, dim=1)
+    #     flatten_bbox_preds = torch.cat(flatten_bbox_preds, dim=1)
+    #     flatten_objectness = torch.cat(flatten_objectness, dim=1)
+    #     flatten_priors = torch.cat(mlvl_priors)
+    #     flatten_bboxes = self._bbox_decode(flatten_priors, flatten_bbox_preds)
+
+    #     (pos_masks, cls_targets, obj_targets, bbox_targets, l1_targets,
+    #      num_fg_imgs) = multi_apply(
+    #          self._get_target_single, flatten_cls_preds.detach(),
+    #          flatten_objectness.detach(),
+    #          flatten_priors.unsqueeze(0).repeat(num_imgs, 1, 1),
+    #          flatten_bboxes.detach(), gt_bboxes, gt_labels)
+
+    #     # The experimental results show that ‘reduce_mean’ can improve
+    #     # performance on the COCO dataset.
+    #     num_pos = torch.tensor(
+    #         sum(num_fg_imgs),
+    #         dtype=torch.float,
+    #         device=flatten_cls_preds.device)
+    #     num_total_samples = max(reduce_mean(num_pos), 1.0)
+
+    #     pos_masks = torch.cat(pos_masks, 0)
+    #     cls_targets = torch.cat(cls_targets, 0)
+    #     obj_targets = torch.cat(obj_targets, 0)
+    #     bbox_targets = torch.cat(bbox_targets, 0)
+    #     if self.use_l1:
+    #         l1_targets = torch.cat(l1_targets, 0)
+
+    #     loss_bbox = self.loss_bbox(
+    #         flatten_bboxes.view(-1, 4)[pos_masks],
+    #         bbox_targets) / num_total_samples
+    #     loss_obj = self.loss_obj(flatten_objectness.view(-1, 1),
+    #                              obj_targets) / num_total_samples
+    #     loss_cls = self.loss_cls(
+    #         flatten_cls_preds.view(-1, self.num_classes)[pos_masks],
+    #         cls_targets) / num_total_samples
+
+    #     loss_dict = dict(
+    #         loss_cls=loss_cls, loss_bbox=loss_bbox, loss_obj=loss_obj)
+
+    #     if self.use_l1:
+    #         loss_l1 = self.loss_l1(
+    #             flatten_bbox_preds.view(-1, 4)[pos_masks],
+    #             l1_targets) / num_total_samples
+    #         loss_dict.update(loss_l1=loss_l1)
+
+    #     return loss_dict
+
+    @force_fp32(apply_to=('cls_scores', 'bbox_preds', 'objectnesses'))
+    def loss(self,
+             cls_scores,
+             bbox_preds,
+             objectnesses,
+             gt_bboxes,
+             gt_labels,
+             img_metas,
+             gt_bboxes_ignore=None):
+        """Compute loss of the head.
+        Args:
+            cls_scores (list[Tensor]): Box scores for each scale level,
+                each is a 4D-tensor, the channel number is
+                num_priors * num_classes.
+            bbox_preds (list[Tensor]): Box energies / deltas for each scale
+                level, each is a 4D-tensor, the channel number is
+                num_priors * 4.
+            objectnesses (list[Tensor], Optional): Score factor for
+                all scale level, each is a 4D-tensor, has shape
+                (batch_size, 1, H, W).
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            img_metas (list[dict]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+        """
+        num_imgs = len(img_metas)
+        featmap_sizes = [cls_score.shape[2:] for cls_score in cls_scores]
+        mlvl_priors = self.prior_generator.grid_priors(
+            featmap_sizes,
+            dtype=cls_scores[0].dtype,
+            device=cls_scores[0].device,
+            with_stride=True)
+
+        flatten_cls_preds = [
+            cls_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1,
+                                                 self.cls_out_channels)
+            for cls_pred in cls_scores
+        ]
+        flatten_bbox_preds = [
+            bbox_pred.permute(0, 2, 3, 1).reshape(num_imgs, -1, 4)
+            for bbox_pred in bbox_preds
+        ]
+        flatten_objectness = [
+            objectness.permute(0, 2, 3, 1).reshape(num_imgs, -1)
+            for objectness in objectnesses
+        ]
+
+        # 实现level上的标记  编码第一个level为0；第二个level为1；第三个level为2
+        level_mapping = []
+        for it, temp in enumerate(flatten_cls_preds):
+            temp_tensor = torch.zeros(temp.shape[:2],dtype=torch.uint8,device=flatten_cls_preds[0].device) + it
+            level_mapping.append(temp_tensor)
+        level_mapping = torch.cat(level_mapping, dim=1)
+            
+        flatten_cls_preds = torch.cat(flatten_cls_preds, dim=1)
+        flatten_bbox_preds = torch.cat(flatten_bbox_preds, dim=1)
+        flatten_objectness = torch.cat(flatten_objectness, dim=1)
+        flatten_priors = torch.cat(mlvl_priors)
+        flatten_bboxes = self._bbox_decode(flatten_priors, flatten_bbox_preds)
+
+        (pos_masks, cls_targets, obj_targets, bbox_targets, l1_targets,
+         num_fg_imgs) = multi_apply(
+             self._get_target_single, flatten_cls_preds.detach(),
+             flatten_objectness.detach(),
+             flatten_priors.unsqueeze(0).repeat(num_imgs, 1, 1),
+             flatten_bboxes.detach(), gt_bboxes, gt_labels)
+
+        # The experimental results show that ‘reduce_mean’ can improve
+        # performance on the COCO dataset.
+        num_pos = torch.tensor(
+            sum(num_fg_imgs),
+            dtype=torch.float,
+            device=flatten_cls_preds.device)
+        num_total_samples = max(reduce_mean(num_pos), 1.0)
+
+        pos_masks = torch.cat(pos_masks, 0)
+        cls_targets = torch.cat(cls_targets, 0)
+        obj_targets = torch.cat(obj_targets, 0)
+        bbox_targets = torch.cat(bbox_targets, 0)
+        if self.use_l1:
+            l1_targets = torch.cat(l1_targets, 0)
+        
+        # reshape同时映射pos
+        level_mapping = level_mapping.view(-1)
+        pos_level_mapping = level_mapping[pos_masks]
+        
+        loss_bbox = self.loss_bbox(
+            flatten_bboxes.view(-1, 4)[pos_masks],
+            bbox_targets, reduction_override='none')
+        loss_obj = self.loss_obj(flatten_objectness.view(-1, 1),
+                                 obj_targets, reduction_override='none')
+        loss_cls = self.loss_cls(
+            flatten_cls_preds.view(-1, self.num_classes)[pos_masks],
+            cls_targets, reduction_override='none')
+        
+        if self.use_l1:
+            loss_l1 = self.loss_l1(
+                flatten_bbox_preds.view(-1, 4)[pos_masks],
+                l1_targets, reduction_override='none')
+            loss_l1 = loss_l1.sum(dim=1)
+        
+        # loss_bbox = loss_bbox.sum(dim=1)
+        loss_cls = loss_cls.sum(dim=1)
+        loss_obj = loss_obj.sum(dim=1)
+
+        # 就是sigmoid
+        if global_placeholder.mybuff_flag:
+            # 只在pos_indx有的情况下执行
+                
+            matched_obj = torch.sigmoid(flatten_objectness.view(-1, 1)[pos_masks].view(-1))
+            conf_values = torch.sigmoid(flatten_cls_preds.view(-1, self.num_classes)[pos_masks]) 
+            
+            num_classes = conf_values.shape[-1]
+
+            # pos_gtconf_idx = cls_targets
+            _, temp_max_gt_idx = cls_targets.max(dim=1)
+            
+            num_classes = num_classes + 1
+            pos_gtconf_idx = F.one_hot(temp_max_gt_idx, num_classes=num_classes)
+            pos_gtconf_idx = pos_gtconf_idx[:, :-1]# 剔除最后一个背景类
+                
+            pos_gtconf_values, _ = (conf_values * pos_gtconf_idx).max(dim=1)  # TODO 想到一件事，是不是不能用GT来筛选正样本的结果？因为其实正样本也有错误的东西，所以就是得错
+            # pos_gtconf_values = pos_gtconf_values[pos_inds]
+            
+            pos_conf_values, pos_conf_values_idx = conf_values.max(dim=1)  # TODO 好像真的是这个问题，正样本本来就是得对应到max的那个，不能经过GT筛选
+            # pos_conf_values = pos_conf_values[pos_inds]
+            
+            # anchors = anchors.reshape(-1, 4)
+            abs_bbox_pred = flatten_bboxes.view(-1, 4)[pos_masks] # 直接解读
+            abs_bbox_targets = bbox_targets # 直接解读
+
+            pos_ious = bbox_overlaps(abs_bbox_pred, abs_bbox_targets, is_aligned=True)  # 这玩意得是ltrb坐标，好像就已经是了？？？
+
+            if global_placeholder.mybuff_flag == 1:
+                
+                level_slicer = [len(self.multi_level_cls_convs._modules), 5, 4]  # [0]为level数；[1]为weight的个数；[2]为单level下的act个数
+                level_cls_factors = []
+                level_reg_factors = []
+                level_obj_factors = []
+                qloss_flag = global_placeholder.qloss_flag
+                
+                if False:
+                    q_loss_total = []
+                    cls_branch = []
+                    reg_branch = []
+                    obj_branch = []
+                    for name, module in self.named_modules():
+                        if hasattr(module, 'compute_qloss') and module.compute_qloss:
+                            # 挑出来量化器
+                            
+                            if 'cls' in name:
+                                # 说明是cls分支的量化器
+                                # cls_branch.append(module.quantization_loss)
+                                if 'post_act' in name:
+                                    # 说明是act量化器
+                                    # cls_branch.append([name, module.scale * 1.])
+                                    pass
+                                else:
+                                    cls_branch.append([name, module.scale])
+                                    
+                            elif 'reg' in name:
+                                # 说明是reg分支的量化器
+                                # reg_branch.append(module.quantization_loss)
+                                if 'post_act' in name:
+                                    # 说明是act量化器
+                                    # reg_branch.append([name, module.scale * 1.])
+                                    pass
+                                else:
+                                    reg_branch.append([name, module.scale])        
+                                    
+                            elif 'obj' in name:
+                                # 说明是reg分支的量化器
+                                # reg_branch.append(module.quantization_loss)
+                                if 'post_act' in name:
+                                    # 说明是act量化器
+                                    # obj_branch.append([name, module.scale * 1.])
+                                    pass
+                                else:
+                                    obj_branch.append([name, module.scale])
+                                
+                            q_loss_total.append([name, module.scale * 1])
+                            # from plot_curve import save_distribution
+                            # title = 'input of ' + name + f' scale:{str(module.scale.data.cpu().numpy().round(5))}'
+                            # save_distribution(module.input.cpu().numpy(), title)
+
+                    # NOTE 不需要加item_post_act_quant的
+                    # 由于obj branch 的特殊性，所以得加上reg_branch分支的共用东西
+                    obj_branch = obj_branch + reg_branch[3:]
+                    
+                    if level_slicer[0] != 3:
+                        raise NotImplementedError
+                    
+                    for it in range(level_slicer[0]):
+                        cls_summation = 0
+                        reg_summation = 0
+                        obj_summation = 0
+                        # tmp_cls_infos = [cls_branch[it]] + cls_branch[3+it*2:3+(it+1)*2] + cls_branch[9+it*2:9+(it+1)*2]
+                        tmp_cls_infos = [cls_branch[it]] + cls_branch[3+it*2:3+(it+1)*2]
+                        for info in tmp_cls_infos:
+                            cls_summation += info[1]
+                            
+                        # tmp_reg_infos = [reg_branch[it]] + reg_branch[3+it*2:3+(it+1)*2] + reg_branch[9+it*2:9+(it+1)*2]
+                        tmp_reg_infos = [reg_branch[it]] + reg_branch[3+it*2:3+(it+1)*2]
+                        for info in tmp_reg_infos:
+                            reg_summation += info[1]
+                            
+                        # tmp_obj_infos = [obj_branch[it]] + obj_branch[3+it*2:3+(it+1)*2] + obj_branch[9+it*2:9+(it+1)*2]
+                        tmp_obj_infos = [obj_branch[it]] + obj_branch[3+it*2:3+(it+1)*2]
+                        for info in tmp_obj_infos:
+                            obj_summation += info[1]
+                    
+                        level_cls_factors.append(cls_summation)
+                        level_reg_factors.append(reg_summation)
+                        level_obj_factors.append(obj_summation)
+                
+                
+                if len(level_cls_factors) + len(level_reg_factors) + len(level_obj_factors) == 0:
+                    # 必须让list内有level个空list
+                    level_cls_factors = [torch.tensor(1)]
+                    level_reg_factors = [torch.tensor(1)]
+                    level_obj_factors = [torch.tensor(1)]
+                # NOTE 编码第一个level为0 第二个为1 ......   
+                # for it, [cls_branch_factor, reg_branch_factor, obj_branch_factor] in enumerate(zip(level_cls_factors, level_reg_factors, level_obj_factors)):
+                #     single_level_masks = (level_mapping == it)
+                #     single_pos_level_masks = (pos_level_mapping == it)
+                    
+                #     cls_trade_off = (cls_branch_factor / (cls_branch_factor + reg_branch_factor + obj_branch_factor)).detach() * 3
+                #     reg_trade_off = (reg_branch_factor / (cls_branch_factor + reg_branch_factor + obj_branch_factor)).detach() * 3
+                #     obj_trade_off = (obj_branch_factor / (cls_branch_factor + reg_branch_factor + obj_branch_factor)).detach() * 3
+                    
+                #     loss_cls[single_pos_level_masks] = cls_trade_off * loss_cls[single_pos_level_masks]
+                #     loss_bbox[single_pos_level_masks] = reg_trade_off * loss_bbox[single_pos_level_masks]
+                #     loss_obj[single_level_masks] = obj_trade_off * loss_obj[single_level_masks]
+                #     # if self.use_l1:  # 还是不该加这个
+                #     #     loss_l1[single_pos_level_masks] = reg_trade_off * loss_l1[single_pos_level_masks]
+                
+                
+                # # 再进行正样本上的harmonic加权. 这玩意是真真不行
+                # eps = 2.220446049250313e-16  # -10貌似可以，但是我狠一点
+                # # correlation = torch.pow(pos_gtconf_values+eps, 0.5) * torch.pow(pos_ious+eps, 0.5) - eps
+                # correlation = torch.pow(pos_gtconf_values * pos_ious * matched_obj+eps, 1/3) - eps
+                # harmonic_factor = torch.exp(1-correlation)
+                
+                # loss_bbox = loss_bbox * harmonic_factor
+                # loss_cls = loss_cls * harmonic_factor
+                # loss_obj[pos_masks] = loss_obj[pos_masks] * harmonic_factor
+                # if self.use_l1:
+                #     loss_l1 = loss_l1 * harmonic_factor
+                
+                loss_bbox, loss_cls = HQOD_loss(loss_bbox, loss_cls, conf_values, pos_gtconf_values, pos_conf_values, pos_ious, [torch.tensor(1)], [torch.tensor(1)], torch.tensor(1.))
+            elif global_placeholder.mybuff_flag == 2:
+                loss_bbox, loss_cls = HarDet_loss(loss_bbox, loss_cls, conf_values, pos_gtconf_values, pos_conf_values, pos_ious, torch.tensor(1.))
+            
+        
+        loss_cls = loss_cls.sum() / num_total_samples
+        loss_bbox = loss_bbox.sum() / num_total_samples
+        loss_obj = loss_obj.sum() / num_total_samples
+        
+        loss_dict = dict(
+            loss_cls=loss_cls, loss_bbox=loss_bbox, loss_obj=loss_obj)
+
+        if self.use_l1:
+            loss_l1 = loss_l1.sum() / num_total_samples
+            loss_dict.update(loss_l1=loss_l1)
+
+        return loss_dict
+    
+    @torch.no_grad()
+    def _get_target_single(self, cls_preds, objectness, priors, decoded_bboxes,
+                           gt_bboxes, gt_labels):
+        """Compute classification, regression, and objectness targets for
+        priors in a single image.
+        Args:
+            cls_preds (Tensor): Classification predictions of one image,
+                a 2D-Tensor with shape [num_priors, num_classes]
+            objectness (Tensor): Objectness predictions of one image,
+                a 1D-Tensor with shape [num_priors]
+            priors (Tensor): All priors of one image, a 2D-Tensor with shape
+                [num_priors, 4] in [cx, xy, stride_w, stride_y] format.
+            decoded_bboxes (Tensor): Decoded bboxes predictions of one image,
+                a 2D-Tensor with shape [num_priors, 4] in [tl_x, tl_y,
+                br_x, br_y] format.
+            gt_bboxes (Tensor): Ground truth bboxes of one image, a 2D-Tensor
+                with shape [num_gts, 4] in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (Tensor): Ground truth labels of one image, a Tensor
+                with shape [num_gts].
+        """
+
+        num_priors = priors.size(0)
+        num_gts = gt_labels.size(0)
+        gt_bboxes = gt_bboxes.to(decoded_bboxes.dtype)
+        # No target
+        if num_gts == 0:
+            cls_target = cls_preds.new_zeros((0, self.num_classes))
+            bbox_target = cls_preds.new_zeros((0, 4))
+            l1_target = cls_preds.new_zeros((0, 4))
+            obj_target = cls_preds.new_zeros((num_priors, 1))
+            foreground_mask = cls_preds.new_zeros(num_priors).bool()
+            return (foreground_mask, cls_target, obj_target, bbox_target,
+                    l1_target, 0)
+
+        # YOLOX uses center priors with 0.5 offset to assign targets,
+        # but use center priors without offset to regress bboxes.
+        offset_priors = torch.cat(
+            [priors[:, :2] + priors[:, 2:] * 0.5, priors[:, 2:]], dim=-1)
+
+        assign_result = self.assigner.assign(
+            cls_preds.sigmoid() * objectness.unsqueeze(1).sigmoid(),
+            offset_priors, decoded_bboxes, gt_bboxes, gt_labels)
+
+        sampling_result = self.sampler.sample(assign_result, priors, gt_bboxes)
+        pos_inds = sampling_result.pos_inds
+        num_pos_per_img = pos_inds.size(0)
+
+        pos_ious = assign_result.max_overlaps[pos_inds]
+        # IOU aware classification score
+        cls_target = F.one_hot(sampling_result.pos_gt_labels,
+                               self.num_classes) * pos_ious.unsqueeze(-1)
+        obj_target = torch.zeros_like(objectness).unsqueeze(-1)
+        obj_target[pos_inds] = 1
+        bbox_target = sampling_result.pos_gt_bboxes
+        l1_target = cls_preds.new_zeros((num_pos_per_img, 4))
+        if self.use_l1:
+            l1_target = self._get_l1_target(l1_target, bbox_target,
+                                            priors[pos_inds])
+        foreground_mask = torch.zeros_like(objectness).to(torch.bool)
+        foreground_mask[pos_inds] = 1
+        return (foreground_mask, cls_target, obj_target, bbox_target,
+                l1_target, num_pos_per_img)
+
+    def _get_l1_target(self, l1_target, gt_bboxes, priors, eps=1e-8):
+        """Convert gt bboxes to center offset and log width height."""
+        gt_cxcywh = bbox_xyxy_to_cxcywh(gt_bboxes)
+        l1_target[:, :2] = (gt_cxcywh[:, :2] - priors[:, :2]) / priors[:, 2:]
+        l1_target[:, 2:] = torch.log(gt_cxcywh[:, 2:] / priors[:, 2:] + eps)
+        return l1_target
diff --git a/mmdet/models/detectors/__init__.py b/mmdet/models/detectors/__init__.py
new file mode 100644
index 0000000..a0a89b8
--- /dev/null
+++ b/mmdet/models/detectors/__init__.py
@@ -0,0 +1,58 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .atss import ATSS
+from .autoassign import AutoAssign
+from .base import BaseDetector
+from .cascade_rcnn import CascadeRCNN
+from .centernet import CenterNet
+from .cornernet import CornerNet
+from .ddod import DDOD
+from .deformable_detr import DeformableDETR
+from .detr import DETR
+from .fast_rcnn import FastRCNN
+from .faster_rcnn import FasterRCNN
+from .fcos import FCOS
+from .fovea import FOVEA
+from .fsaf import FSAF
+from .gfl import GFL
+from .grid_rcnn import GridRCNN
+from .htc import HybridTaskCascade
+from .kd_one_stage import KnowledgeDistillationSingleStageDetector
+from .lad import LAD
+from .mask2former import Mask2Former
+from .mask_rcnn import MaskRCNN
+from .mask_scoring_rcnn import MaskScoringRCNN
+from .maskformer import MaskFormer
+from .nasfcos import NASFCOS
+from .paa import PAA
+from .panoptic_fpn import PanopticFPN
+from .panoptic_two_stage_segmentor import TwoStagePanopticSegmentor
+from .point_rend import PointRend
+from .queryinst import QueryInst
+from .reppoints_detector import RepPointsDetector
+from .retinanet import RetinaNet
+from .rpn import RPN
+from .scnet import SCNet
+from .single_stage import SingleStageDetector
+from .solo import SOLO
+from .solov2 import SOLOv2
+from .sparse_rcnn import SparseRCNN
+from .tood import TOOD
+from .trident_faster_rcnn import TridentFasterRCNN
+from .two_stage import TwoStageDetector
+from .vfnet import VFNet
+from .yolact import YOLACT
+from .yolo import YOLOV3
+from .yolof import YOLOF
+from .yolox import YOLOX
+
+__all__ = [
+    'ATSS', 'BaseDetector', 'SingleStageDetector', 'TwoStageDetector', 'RPN',
+    'KnowledgeDistillationSingleStageDetector', 'FastRCNN', 'FasterRCNN',
+    'MaskRCNN', 'CascadeRCNN', 'HybridTaskCascade', 'RetinaNet', 'FCOS',
+    'GridRCNN', 'MaskScoringRCNN', 'RepPointsDetector', 'FOVEA', 'FSAF',
+    'NASFCOS', 'PointRend', 'GFL', 'CornerNet', 'PAA', 'YOLOV3', 'YOLACT',
+    'VFNet', 'DETR', 'TridentFasterRCNN', 'SparseRCNN', 'SCNet', 'SOLO',
+    'SOLOv2', 'DeformableDETR', 'AutoAssign', 'YOLOF', 'CenterNet', 'YOLOX',
+    'TwoStagePanopticSegmentor', 'PanopticFPN', 'QueryInst', 'LAD', 'TOOD',
+    'MaskFormer', 'DDOD', 'Mask2Former'
+]
diff --git a/mmdet/models/detectors/atss.py b/mmdet/models/detectors/atss.py
new file mode 100644
index 0000000..00f1acd
--- /dev/null
+++ b/mmdet/models/detectors/atss.py
@@ -0,0 +1,19 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class ATSS(SingleStageDetector):
+    """Implementation of `ATSS <https://arxiv.org/abs/1912.02424>`_."""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(ATSS, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                   test_cfg, pretrained, init_cfg)
diff --git a/mmdet/models/detectors/autoassign.py b/mmdet/models/detectors/autoassign.py
new file mode 100644
index 0000000..30ab720
--- /dev/null
+++ b/mmdet/models/detectors/autoassign.py
@@ -0,0 +1,19 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class AutoAssign(SingleStageDetector):
+    """Implementation of `AutoAssign: Differentiable Label Assignment for Dense
+    Object Detection <https://arxiv.org/abs/2007.03496>`_."""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None):
+        super(AutoAssign, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                         test_cfg, pretrained)
diff --git a/mmdet/models/detectors/base.py b/mmdet/models/detectors/base.py
new file mode 100644
index 0000000..6412dd1
--- /dev/null
+++ b/mmdet/models/detectors/base.py
@@ -0,0 +1,407 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from abc import ABCMeta, abstractmethod
+from collections import OrderedDict
+
+import mmcv
+import numpy as np
+import torch
+import torch.distributed as dist
+from mmcv.runner import BaseModule, auto_fp16
+
+from mmdet.core.visualization import imshow_det_bboxes
+import global_placeholder
+
+class BaseDetector(BaseModule, metaclass=ABCMeta):
+    """Base class for detectors."""
+
+    def __init__(self, init_cfg=None):
+        super(BaseDetector, self).__init__(init_cfg)
+        self.fp16_enabled = False
+
+    @property
+    def with_neck(self):
+        """bool: whether the detector has a neck"""
+        return hasattr(self, 'neck') and self.neck is not None
+
+    # TODO: these properties need to be carefully handled
+    # for both single stage & two stage detectors
+    @property
+    def with_shared_head(self):
+        """bool: whether the detector has a shared head in the RoI Head"""
+        return hasattr(self, 'roi_head') and self.roi_head.with_shared_head
+
+    @property
+    def with_bbox(self):
+        """bool: whether the detector has a bbox head"""
+        return ((hasattr(self, 'roi_head') and self.roi_head.with_bbox)
+                or (hasattr(self, 'bbox_head') and self.bbox_head is not None))
+
+    @property
+    def with_mask(self):
+        """bool: whether the detector has a mask head"""
+        return ((hasattr(self, 'roi_head') and self.roi_head.with_mask)
+                or (hasattr(self, 'mask_head') and self.mask_head is not None))
+
+    @abstractmethod
+    def extract_feat(self, imgs):
+        """Extract features from images."""
+        pass
+
+    def extract_feats(self, imgs):
+        """Extract features from multiple images.
+
+        Args:
+            imgs (list[torch.Tensor]): A list of images. The images are
+                augmented from the same image but in different ways.
+
+        Returns:
+            list[torch.Tensor]: Features of different images
+        """
+        assert isinstance(imgs, list)
+        return [self.extract_feat(img) for img in imgs]
+
+    def forward_train(self, imgs, img_metas, **kwargs):
+        """
+        Args:
+            img (Tensor): of shape (N, C, H, W) encoding input images.
+                Typically these should be mean centered and std scaled.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys, see
+                :class:`mmdet.datasets.pipelines.Collect`.
+            kwargs (keyword arguments): Specific to concrete implementation.
+        """
+        # NOTE the batched image size information may be useful, e.g.
+        # in DETR, this is needed for the construction of masks, which is
+        # then used for the transformer_head.
+        batch_input_shape = tuple(imgs[0].size()[-2:])
+        for img_meta in img_metas:
+            img_meta['batch_input_shape'] = batch_input_shape
+
+    async def async_simple_test(self, img, img_metas, **kwargs):
+        raise NotImplementedError
+
+    @abstractmethod
+    def simple_test(self, img, img_metas, **kwargs):
+        pass
+
+    @abstractmethod
+    def aug_test(self, imgs, img_metas, **kwargs):
+        """Test function with test time augmentation."""
+        pass
+
+    async def aforward_test(self, *, img, img_metas, **kwargs):
+        for var, name in [(img, 'img'), (img_metas, 'img_metas')]:
+            if not isinstance(var, list):
+                raise TypeError(f'{name} must be a list, but got {type(var)}')
+
+        num_augs = len(img)
+        if num_augs != len(img_metas):
+            raise ValueError(f'num of augmentations ({len(img)}) '
+                             f'!= num of image metas ({len(img_metas)})')
+        # TODO: remove the restriction of samples_per_gpu == 1 when prepared
+        samples_per_gpu = img[0].size(0)
+        assert samples_per_gpu == 1
+
+        if num_augs == 1:
+            return await self.async_simple_test(img[0], img_metas[0], **kwargs)
+        else:
+            raise NotImplementedError
+
+    def forward_test(self, imgs, img_metas, **kwargs):
+        """
+        Args:
+            imgs (List[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains all images in the batch.
+            img_metas (List[List[dict]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch.
+        """
+        for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]:
+            if not isinstance(var, list):
+                raise TypeError(f'{name} must be a list, but got {type(var)}')
+
+        num_augs = len(imgs)
+        if num_augs != len(img_metas):
+            raise ValueError(f'num of augmentations ({len(imgs)}) '
+                             f'!= num of image meta ({len(img_metas)})')
+
+        # NOTE the batched image size information may be useful, e.g.
+        # in DETR, this is needed for the construction of masks, which is
+        # then used for the transformer_head.
+        for img, img_meta in zip(imgs, img_metas):
+            batch_size = len(img_meta)
+            for img_id in range(batch_size):
+                img_meta[img_id]['batch_input_shape'] = tuple(img.size()[-2:])
+
+        if num_augs == 1:
+            # proposals (List[List[Tensor]]): the outer list indicates
+            # test-time augs (multiscale, flip, etc.) and the inner list
+            # indicates images in a batch.
+            # The Tensor should have a shape Px4, where P is the number of
+            # proposals.
+            if 'proposals' in kwargs:
+                kwargs['proposals'] = kwargs['proposals'][0]
+            return self.simple_test(imgs[0], img_metas[0], **kwargs)
+        else:
+            assert imgs[0].size(0) == 1, 'aug test does not support ' \
+                                         'inference with batch size ' \
+                                         f'{imgs[0].size(0)}'
+            # TODO: support test augmentation for predefined proposals
+            assert 'proposals' not in kwargs
+            return self.aug_test(imgs, img_metas, **kwargs)
+
+    @auto_fp16(apply_to=('img', ))
+    def forward(self, img, img_metas, return_loss=True, **kwargs):  # NOTE 这个就是核心的，detector forward
+        """Calls either :func:`forward_train` or :func:`forward_test` depending
+        on whether ``return_loss`` is ``True``.
+
+        Note this setting will change the expected inputs. When
+        ``return_loss=True``, img and img_meta are single-nested (i.e. Tensor
+        and List[dict]), and when ``resturn_loss=False``, img and img_meta
+        should be double nested (i.e.  List[Tensor], List[List[dict]]), with
+        the outer list indicating test time augmentations.
+        """
+        if torch.onnx.is_in_onnx_export():
+            assert len(img_metas) == 1
+            return self.onnx_export(img[0], img_metas[0])
+
+        if return_loss:
+            
+            task_losses = self.forward_train(img, img_metas, **kwargs)
+        
+            # -----加上最小量化误差
+            qloss_flag = global_placeholder.qloss_flag
+            q_loss = torch.tensor(0)
+            if False:
+                q_loss_sum = []
+                for name, module in self.named_modules():
+                    if hasattr(module, 'compute_qloss') and module.compute_qloss:
+                        # if 'post_act' in name:
+                        #     # 说明是act量化器
+                        #     pass
+                        # else:
+                        #     # 说明是weight量化器
+                        #     q_loss_sum.append(module.quantization_loss)
+                        q_loss_sum.append(module.quantization_loss)
+                
+                # for name, module in self.backbone.named_modules():
+                #     if hasattr(module, 'compute_qloss') and module.compute_qloss:
+                #     # if 'fake_quant' in name.split('.')[-1]:
+                #         # 说明是act quantizer
+                #         q_loss_sum.append(module.quantization_loss)
+                # for name, module in self.neck.named_modules():
+                #     if hasattr(module, 'compute_qloss') and module.compute_qloss:
+                #     # if 'fake_quant' in name.split('.')[-1]:
+                #         # 说明是act quantizer
+                #         q_loss_sum.append(module.quantization_loss)
+                # for name, module in self.bbox_head.named_modules():
+                #     if hasattr(module, 'compute_qloss') and module.compute_qloss:
+                #     # if 'fake_quant' in name.split('.')[-1]:
+                #         # 说明是act quantizer
+                #         q_loss_sum.append(module.quantization_loss)
+
+
+                # 量化误差均值
+                qbit = float(global_placeholder.quant_bit)
+                # hyperparam = .01  # 对于纯act，这个不行
+                hyperparam = .001
+                q_loss = hyperparam * sum(q_loss_sum)
+                task_losses['q_loss'] = q_loss
+            
+            return task_losses
+        else:
+            return self.forward_test(img, img_metas, **kwargs)
+
+    def _parse_losses(self, losses):
+        """Parse the raw outputs (losses) of the network.
+
+        Args:
+            losses (dict): Raw output of the network, which usually contain
+                losses and other necessary information.
+
+        Returns:
+            tuple[Tensor, dict]: (loss, log_vars), loss is the loss tensor \
+                which may be a weighted sum of all losses, log_vars contains \
+                all the variables to be sent to the logger.
+        """
+        log_vars = OrderedDict()
+        for loss_name, loss_value in losses.items():
+            if isinstance(loss_value, torch.Tensor):
+                log_vars[loss_name] = loss_value.mean()
+            elif isinstance(loss_value, list):
+                log_vars[loss_name] = sum(_loss.mean() for _loss in loss_value)
+            else:
+                raise TypeError(
+                    f'{loss_name} is not a tensor or list of tensors')
+
+        loss = sum(_value for _key, _value in log_vars.items()
+                   if 'loss' in _key)
+
+        # If the loss_vars has different length, GPUs will wait infinitely
+        if dist.is_available() and dist.is_initialized():
+            log_var_length = torch.tensor(len(log_vars), device=loss.device)
+            dist.all_reduce(log_var_length)
+            message = (f'rank {dist.get_rank()}' +
+                       f' len(log_vars): {len(log_vars)}' + ' keys: ' +
+                       ','.join(log_vars.keys()))
+            assert log_var_length == len(log_vars) * dist.get_world_size(), \
+                'loss log variables are different across GPUs!\n' + message
+
+        log_vars['loss'] = loss
+        for loss_name, loss_value in log_vars.items():
+            # reduce loss when distributed training
+            if dist.is_available() and dist.is_initialized():
+                loss_value = loss_value.data.clone()
+                dist.all_reduce(loss_value.div_(dist.get_world_size()))
+            log_vars[loss_name] = loss_value.item()
+
+        return loss, log_vars
+
+    def train_step(self, data, optimizer):
+        """The iteration step during training.
+
+        This method defines an iteration step during training, except for the
+        back propagation and optimizer updating, which are done in an optimizer
+        hook. Note that in some complicated cases or models, the whole process
+        including back propagation and optimizer updating is also defined in
+        this method, such as GAN.
+
+        Args:
+            data (dict): The output of dataloader.
+            optimizer (:obj:`torch.optim.Optimizer` | dict): The optimizer of
+                runner is passed to ``train_step()``. This argument is unused
+                and reserved.
+
+        Returns:
+            dict: It should contain at least 3 keys: ``loss``, ``log_vars``, \
+                ``num_samples``.
+
+                - ``loss`` is a tensor for back propagation, which can be a
+                  weighted sum of multiple losses.
+                - ``log_vars`` contains all the variables to be sent to the
+                  logger.
+                - ``num_samples`` indicates the batch size (when the model is
+                  DDP, it means the batch size on each GPU), which is used for
+                  averaging the logs.
+        """
+        losses = self(**data)
+        loss, log_vars = self._parse_losses(losses)
+
+        outputs = dict(
+            loss=loss, log_vars=log_vars, num_samples=len(data['img_metas']))
+
+        return outputs
+
+    def val_step(self, data, optimizer=None):
+        """The iteration step during validation.
+
+        This method shares the same signature as :func:`train_step`, but used
+        during val epochs. Note that the evaluation after training epochs is
+        not implemented with this method, but an evaluation hook.
+        """
+        losses = self(**data)
+        loss, log_vars = self._parse_losses(losses)
+
+        log_vars_ = dict()
+        for loss_name, loss_value in log_vars.items():
+            k = loss_name + '_val'
+            log_vars_[k] = loss_value
+
+        outputs = dict(
+            loss=loss, log_vars=log_vars_, num_samples=len(data['img_metas']))
+
+        return outputs
+
+    def show_result(self,
+                    img,
+                    result,
+                    score_thr=0.3,
+                    bbox_color=(72, 101, 241),
+                    text_color=(72, 101, 241),
+                    mask_color=None,
+                    thickness=2,
+                    font_size=13,
+                    win_name='',
+                    show=False,
+                    wait_time=0,
+                    out_file=None):
+        """Draw `result` over `img`.
+
+        Args:
+            img (str or Tensor): The image to be displayed.
+            result (Tensor or tuple): The results to draw over `img`
+                bbox_result or (bbox_result, segm_result).
+            score_thr (float, optional): Minimum score of bboxes to be shown.
+                Default: 0.3.
+            bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
+               The tuple of color should be in BGR order. Default: 'green'
+            text_color (str or tuple(int) or :obj:`Color`):Color of texts.
+               The tuple of color should be in BGR order. Default: 'green'
+            mask_color (None or str or tuple(int) or :obj:`Color`):
+               Color of masks. The tuple of color should be in BGR order.
+               Default: None
+            thickness (int): Thickness of lines. Default: 2
+            font_size (int): Font size of texts. Default: 13
+            win_name (str): The window name. Default: ''
+            wait_time (float): Value of waitKey param.
+                Default: 0.
+            show (bool): Whether to show the image.
+                Default: False.
+            out_file (str or None): The filename to write the image.
+                Default: None.
+
+        Returns:
+            img (Tensor): Only if not `show` or `out_file`
+        """
+        img = mmcv.imread(img)
+        img = img.copy()
+        if isinstance(result, tuple):
+            bbox_result, segm_result = result
+            if isinstance(segm_result, tuple):
+                segm_result = segm_result[0]  # ms rcnn
+        else:
+            bbox_result, segm_result = result, None
+        bboxes = np.vstack(bbox_result)
+        labels = [
+            np.full(bbox.shape[0], i, dtype=np.int32)
+            for i, bbox in enumerate(bbox_result)
+        ]
+        labels = np.concatenate(labels)
+        # draw segmentation masks
+        segms = None
+        if segm_result is not None and len(labels) > 0:  # non empty
+            segms = mmcv.concat_list(segm_result)
+            if isinstance(segms[0], torch.Tensor):
+                segms = torch.stack(segms, dim=0).detach().cpu().numpy()
+            else:
+                segms = np.stack(segms, axis=0)
+        # if out_file specified, do not show image in window
+        if out_file is not None:
+            show = False
+        # draw bounding boxes
+        img = imshow_det_bboxes(
+            img,
+            bboxes,
+            labels,
+            segms,
+            class_names=self.CLASSES,
+            score_thr=score_thr,
+            bbox_color=bbox_color,
+            text_color=text_color,
+            mask_color=mask_color,
+            thickness=thickness,
+            font_size=font_size,
+            win_name=win_name,
+            show=show,
+            wait_time=wait_time,
+            out_file=out_file)
+
+        if not (show or out_file):
+            return img
+
+    def onnx_export(self, img, img_metas):
+        raise NotImplementedError(f'{self.__class__.__name__} does '
+                                  f'not support ONNX EXPORT')
diff --git a/mmdet/models/detectors/cascade_rcnn.py b/mmdet/models/detectors/cascade_rcnn.py
new file mode 100644
index 0000000..d8c7382
--- /dev/null
+++ b/mmdet/models/detectors/cascade_rcnn.py
@@ -0,0 +1,49 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .two_stage import TwoStageDetector
+
+
+@DETECTORS.register_module()
+class CascadeRCNN(TwoStageDetector):
+    r"""Implementation of `Cascade R-CNN: Delving into High Quality Object
+    Detection <https://arxiv.org/abs/1906.09756>`_"""
+
+    def __init__(self,
+                 backbone,
+                 neck=None,
+                 rpn_head=None,
+                 roi_head=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(CascadeRCNN, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            rpn_head=rpn_head,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained,
+            init_cfg=init_cfg)
+
+    def show_result(self, data, result, **kwargs):
+        """Show prediction results of the detector.
+
+        Args:
+            data (str or np.ndarray): Image filename or loaded image.
+            result (Tensor or tuple): The results to draw over `img`
+                bbox_result or (bbox_result, segm_result).
+
+        Returns:
+            np.ndarray: The image with bboxes drawn on it.
+        """
+        if self.with_mask:
+            ms_bbox_result, ms_segm_result = result
+            if isinstance(ms_bbox_result, dict):
+                result = (ms_bbox_result['ensemble'],
+                          ms_segm_result['ensemble'])
+        else:
+            if isinstance(result, dict):
+                result = result['ensemble']
+        return super(CascadeRCNN, self).show_result(data, result, **kwargs)
diff --git a/mmdet/models/detectors/centernet.py b/mmdet/models/detectors/centernet.py
new file mode 100644
index 0000000..e1e3fd3
--- /dev/null
+++ b/mmdet/models/detectors/centernet.py
@@ -0,0 +1,111 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdet.core import bbox2result
+from mmdet.models.builder import DETECTORS
+from ...core.utils import flip_tensor
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class CenterNet(SingleStageDetector):
+    """Implementation of CenterNet(Objects as Points)
+
+    <https://arxiv.org/abs/1904.07850>.
+    """
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(CenterNet, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                        test_cfg, pretrained, init_cfg)
+
+    def merge_aug_results(self, aug_results, with_nms):
+        """Merge augmented detection bboxes and score.
+
+        Args:
+            aug_results (list[list[Tensor]]): Det_bboxes and det_labels of each
+                image.
+            with_nms (bool): If True, do nms before return boxes.
+
+        Returns:
+            tuple: (out_bboxes, out_labels)
+        """
+        recovered_bboxes, aug_labels = [], []
+        for single_result in aug_results:
+            recovered_bboxes.append(single_result[0][0])
+            aug_labels.append(single_result[0][1])
+
+        bboxes = torch.cat(recovered_bboxes, dim=0).contiguous()
+        labels = torch.cat(aug_labels).contiguous()
+        if with_nms:
+            out_bboxes, out_labels = self.bbox_head._bboxes_nms(
+                bboxes, labels, self.bbox_head.test_cfg)
+        else:
+            out_bboxes, out_labels = bboxes, labels
+
+        return out_bboxes, out_labels
+
+    def aug_test(self, imgs, img_metas, rescale=True):
+        """Augment testing of CenterNet. Aug test must have flipped image pair,
+        and unlike CornerNet, it will perform an averaging operation on the
+        feature map instead of detecting bbox.
+
+        Args:
+            imgs (list[Tensor]): Augmented images.
+            img_metas (list[list[dict]]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            rescale (bool): If True, return boxes in original image space.
+                Default: True.
+
+        Note:
+            ``imgs`` must including flipped image pairs.
+
+        Returns:
+            list[list[np.ndarray]]: BBox results of each image and classes.
+                The outer list corresponds to each image. The inner list
+                corresponds to each class.
+        """
+        img_inds = list(range(len(imgs)))
+        assert img_metas[0][0]['flip'] + img_metas[1][0]['flip'], (
+            'aug test must have flipped image pair')
+        aug_results = []
+        for ind, flip_ind in zip(img_inds[0::2], img_inds[1::2]):
+            flip_direction = img_metas[flip_ind][0]['flip_direction']
+            img_pair = torch.cat([imgs[ind], imgs[flip_ind]])
+            x = self.extract_feat(img_pair)
+            center_heatmap_preds, wh_preds, offset_preds = self.bbox_head(x)
+            assert len(center_heatmap_preds) == len(wh_preds) == len(
+                offset_preds) == 1
+
+            # Feature map averaging
+            center_heatmap_preds[0] = (
+                center_heatmap_preds[0][0:1] +
+                flip_tensor(center_heatmap_preds[0][1:2], flip_direction)) / 2
+            wh_preds[0] = (wh_preds[0][0:1] +
+                           flip_tensor(wh_preds[0][1:2], flip_direction)) / 2
+
+            bbox_list = self.bbox_head.get_bboxes(
+                center_heatmap_preds,
+                wh_preds, [offset_preds[0][0:1]],
+                img_metas[ind],
+                rescale=rescale,
+                with_nms=False)
+            aug_results.append(bbox_list)
+
+        nms_cfg = self.bbox_head.test_cfg.get('nms_cfg', None)
+        if nms_cfg is None:
+            with_nms = False
+        else:
+            with_nms = True
+        bbox_list = [self.merge_aug_results(aug_results, with_nms)]
+        bbox_results = [
+            bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)
+            for det_bboxes, det_labels in bbox_list
+        ]
+        return bbox_results
diff --git a/mmdet/models/detectors/cornernet.py b/mmdet/models/detectors/cornernet.py
new file mode 100644
index 0000000..ce921cc
--- /dev/null
+++ b/mmdet/models/detectors/cornernet.py
@@ -0,0 +1,97 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdet.core import bbox2result, bbox_mapping_back
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class CornerNet(SingleStageDetector):
+    """CornerNet.
+
+    This detector is the implementation of the paper `CornerNet: Detecting
+    Objects as Paired Keypoints <https://arxiv.org/abs/1808.01244>`_ .
+    """
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(CornerNet, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                        test_cfg, pretrained, init_cfg)
+
+    def merge_aug_results(self, aug_results, img_metas):
+        """Merge augmented detection bboxes and score.
+
+        Args:
+            aug_results (list[list[Tensor]]): Det_bboxes and det_labels of each
+                image.
+            img_metas (list[list[dict]]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+
+        Returns:
+            tuple: (bboxes, labels)
+        """
+        recovered_bboxes, aug_labels = [], []
+        for bboxes_labels, img_info in zip(aug_results, img_metas):
+            img_shape = img_info[0]['img_shape']  # using shape before padding
+            scale_factor = img_info[0]['scale_factor']
+            flip = img_info[0]['flip']
+            bboxes, labels = bboxes_labels
+            bboxes, scores = bboxes[:, :4], bboxes[:, -1:]
+            bboxes = bbox_mapping_back(bboxes, img_shape, scale_factor, flip)
+            recovered_bboxes.append(torch.cat([bboxes, scores], dim=-1))
+            aug_labels.append(labels)
+
+        bboxes = torch.cat(recovered_bboxes, dim=0)
+        labels = torch.cat(aug_labels)
+
+        if bboxes.shape[0] > 0:
+            out_bboxes, out_labels = self.bbox_head._bboxes_nms(
+                bboxes, labels, self.bbox_head.test_cfg)
+        else:
+            out_bboxes, out_labels = bboxes, labels
+
+        return out_bboxes, out_labels
+
+    def aug_test(self, imgs, img_metas, rescale=False):
+        """Augment testing of CornerNet.
+
+        Args:
+            imgs (list[Tensor]): Augmented images.
+            img_metas (list[list[dict]]): Meta information of each image, e.g.,
+                image size, scaling factor, etc.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+
+        Note:
+            ``imgs`` must including flipped image pairs.
+
+        Returns:
+            list[list[np.ndarray]]: BBox results of each image and classes.
+                The outer list corresponds to each image. The inner list
+                corresponds to each class.
+        """
+        img_inds = list(range(len(imgs)))
+
+        assert img_metas[0][0]['flip'] + img_metas[1][0]['flip'], (
+            'aug test must have flipped image pair')
+        aug_results = []
+        for ind, flip_ind in zip(img_inds[0::2], img_inds[1::2]):
+            img_pair = torch.cat([imgs[ind], imgs[flip_ind]])
+            x = self.extract_feat(img_pair)
+            outs = self.bbox_head(x)
+            bbox_list = self.bbox_head.get_bboxes(
+                *outs, [img_metas[ind], img_metas[flip_ind]], False, False)
+            aug_results.append(bbox_list[0])
+            aug_results.append(bbox_list[1])
+
+        bboxes, labels = self.merge_aug_results(aug_results, img_metas)
+        bbox_results = bbox2result(bboxes, labels, self.bbox_head.num_classes)
+
+        return [bbox_results]
diff --git a/mmdet/models/detectors/ddod.py b/mmdet/models/detectors/ddod.py
new file mode 100644
index 0000000..2ae0a74
--- /dev/null
+++ b/mmdet/models/detectors/ddod.py
@@ -0,0 +1,19 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class DDOD(SingleStageDetector):
+    """Implementation of `DDOD <https://arxiv.org/pdf/2107.02963.pdf>`_."""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(DDOD, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                   test_cfg, pretrained, init_cfg)
diff --git a/mmdet/models/detectors/deformable_detr.py b/mmdet/models/detectors/deformable_detr.py
new file mode 100644
index 0000000..b1f1642
--- /dev/null
+++ b/mmdet/models/detectors/deformable_detr.py
@@ -0,0 +1,10 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .detr import DETR
+
+
+@DETECTORS.register_module()
+class DeformableDETR(DETR):
+
+    def __init__(self, *args, **kwargs):
+        super(DETR, self).__init__(*args, **kwargs)
diff --git a/mmdet/models/detectors/detr.py b/mmdet/models/detectors/detr.py
new file mode 100644
index 0000000..06d7691
--- /dev/null
+++ b/mmdet/models/detectors/detr.py
@@ -0,0 +1,70 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch
+
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class DETR(SingleStageDetector):
+    r"""Implementation of `DETR: End-to-End Object Detection with
+    Transformers <https://arxiv.org/pdf/2005.12872>`_"""
+
+    def __init__(self,
+                 backbone,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(DETR, self).__init__(backbone, None, bbox_head, train_cfg,
+                                   test_cfg, pretrained, init_cfg)
+
+    # over-write `forward_dummy` because:
+    # the forward of bbox_head requires img_metas
+    def forward_dummy(self, img):
+        """Used for computing network flops.
+
+        See `mmdetection/tools/analysis_tools/get_flops.py`
+        """
+        warnings.warn('Warning! MultiheadAttention in DETR does not '
+                      'support flops computation! Do not use the '
+                      'results in your papers!')
+
+        batch_size, _, height, width = img.shape
+        dummy_img_metas = [
+            dict(
+                batch_input_shape=(height, width),
+                img_shape=(height, width, 3)) for _ in range(batch_size)
+        ]
+        x = self.extract_feat(img)
+        outs = self.bbox_head(x, dummy_img_metas)
+        return outs
+
+    # over-write `onnx_export` because:
+    # (1) the forward of bbox_head requires img_metas
+    # (2) the different behavior (e.g. construction of `masks`) between
+    # torch and ONNX model, during the forward of bbox_head
+    def onnx_export(self, img, img_metas):
+        """Test function for exporting to ONNX, without test time augmentation.
+
+        Args:
+            img (torch.Tensor): input images.
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            tuple[Tensor, Tensor]: dets of shape [N, num_det, 5]
+                and class labels of shape [N, num_det].
+        """
+        x = self.extract_feat(img)
+        # forward of this head requires img_metas
+        outs = self.bbox_head.forward_onnx(x, img_metas)
+        # get shape as tensor
+        img_shape = torch._shape_as_tensor(img)[2:]
+        img_metas[0]['img_shape_for_onnx'] = img_shape
+
+        det_bboxes, det_labels = self.bbox_head.onnx_export(*outs, img_metas)
+
+        return det_bboxes, det_labels
diff --git a/mmdet/models/detectors/fast_rcnn.py b/mmdet/models/detectors/fast_rcnn.py
new file mode 100644
index 0000000..7aebe15
--- /dev/null
+++ b/mmdet/models/detectors/fast_rcnn.py
@@ -0,0 +1,55 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .two_stage import TwoStageDetector
+
+
+@DETECTORS.register_module()
+class FastRCNN(TwoStageDetector):
+    """Implementation of `Fast R-CNN <https://arxiv.org/abs/1504.08083>`_"""
+
+    def __init__(self,
+                 backbone,
+                 roi_head,
+                 train_cfg,
+                 test_cfg,
+                 neck=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(FastRCNN, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained,
+            init_cfg=init_cfg)
+
+    def forward_test(self, imgs, img_metas, proposals, **kwargs):
+        """
+        Args:
+            imgs (List[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains all images in the batch.
+            img_metas (List[List[dict]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch.
+            proposals (List[List[Tensor]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch. The Tensor should have a shape Px4, where
+                P is the number of proposals.
+        """
+        for var, name in [(imgs, 'imgs'), (img_metas, 'img_metas')]:
+            if not isinstance(var, list):
+                raise TypeError(f'{name} must be a list, but got {type(var)}')
+
+        num_augs = len(imgs)
+        if num_augs != len(img_metas):
+            raise ValueError(f'num of augmentations ({len(imgs)}) '
+                             f'!= num of image meta ({len(img_metas)})')
+
+        if num_augs == 1:
+            return self.simple_test(imgs[0], img_metas[0], proposals[0],
+                                    **kwargs)
+        else:
+            # TODO: support test-time augmentation
+            assert NotImplementedError
diff --git a/mmdet/models/detectors/faster_rcnn.py b/mmdet/models/detectors/faster_rcnn.py
new file mode 100644
index 0000000..70fb662
--- /dev/null
+++ b/mmdet/models/detectors/faster_rcnn.py
@@ -0,0 +1,27 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .two_stage import TwoStageDetector
+
+
+@DETECTORS.register_module()
+class FasterRCNN(TwoStageDetector):
+    """Implementation of `Faster R-CNN <https://arxiv.org/abs/1506.01497>`_"""
+
+    def __init__(self,
+                 backbone,
+                 rpn_head,
+                 roi_head,
+                 train_cfg,
+                 test_cfg,
+                 neck=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(FasterRCNN, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            rpn_head=rpn_head,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained,
+            init_cfg=init_cfg)
diff --git a/mmdet/models/detectors/fcos.py b/mmdet/models/detectors/fcos.py
new file mode 100644
index 0000000..d985bd0
--- /dev/null
+++ b/mmdet/models/detectors/fcos.py
@@ -0,0 +1,19 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class FCOS(SingleStageDetector):
+    """Implementation of `FCOS <https://arxiv.org/abs/1904.01355>`_"""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(FCOS, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                   test_cfg, pretrained, init_cfg)
diff --git a/mmdet/models/detectors/fovea.py b/mmdet/models/detectors/fovea.py
new file mode 100644
index 0000000..6fd908c
--- /dev/null
+++ b/mmdet/models/detectors/fovea.py
@@ -0,0 +1,19 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class FOVEA(SingleStageDetector):
+    """Implementation of `FoveaBox <https://arxiv.org/abs/1904.03797>`_"""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(FOVEA, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                    test_cfg, pretrained, init_cfg)
diff --git a/mmdet/models/detectors/fsaf.py b/mmdet/models/detectors/fsaf.py
new file mode 100644
index 0000000..81ed1bd
--- /dev/null
+++ b/mmdet/models/detectors/fsaf.py
@@ -0,0 +1,19 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class FSAF(SingleStageDetector):
+    """Implementation of `FSAF <https://arxiv.org/abs/1903.00621>`_"""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(FSAF, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                   test_cfg, pretrained, init_cfg)
diff --git a/mmdet/models/detectors/gfl.py b/mmdet/models/detectors/gfl.py
new file mode 100644
index 0000000..4628e2e
--- /dev/null
+++ b/mmdet/models/detectors/gfl.py
@@ -0,0 +1,18 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class GFL(SingleStageDetector):
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(GFL, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                  test_cfg, pretrained, init_cfg)
diff --git a/mmdet/models/detectors/grid_rcnn.py b/mmdet/models/detectors/grid_rcnn.py
new file mode 100644
index 0000000..bba7873
--- /dev/null
+++ b/mmdet/models/detectors/grid_rcnn.py
@@ -0,0 +1,32 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .two_stage import TwoStageDetector
+
+
+@DETECTORS.register_module()
+class GridRCNN(TwoStageDetector):
+    """Grid R-CNN.
+
+    This detector is the implementation of:
+    - Grid R-CNN (https://arxiv.org/abs/1811.12030)
+    - Grid R-CNN Plus: Faster and Better (https://arxiv.org/abs/1906.05688)
+    """
+
+    def __init__(self,
+                 backbone,
+                 rpn_head,
+                 roi_head,
+                 train_cfg,
+                 test_cfg,
+                 neck=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(GridRCNN, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            rpn_head=rpn_head,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained,
+            init_cfg=init_cfg)
diff --git a/mmdet/models/detectors/htc.py b/mmdet/models/detectors/htc.py
new file mode 100644
index 0000000..f7c9533
--- /dev/null
+++ b/mmdet/models/detectors/htc.py
@@ -0,0 +1,16 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .cascade_rcnn import CascadeRCNN
+
+
+@DETECTORS.register_module()
+class HybridTaskCascade(CascadeRCNN):
+    """Implementation of `HTC <https://arxiv.org/abs/1901.07518>`_"""
+
+    def __init__(self, **kwargs):
+        super(HybridTaskCascade, self).__init__(**kwargs)
+
+    @property
+    def with_semantic(self):
+        """bool: whether the detector has a semantic head"""
+        return self.roi_head.with_semantic
diff --git a/mmdet/models/detectors/kd_one_stage.py b/mmdet/models/detectors/kd_one_stage.py
new file mode 100644
index 0000000..fb66b51
--- /dev/null
+++ b/mmdet/models/detectors/kd_one_stage.py
@@ -0,0 +1,103 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from pathlib import Path
+
+import mmcv
+import torch
+from mmcv.runner import load_checkpoint
+
+from .. import build_detector
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class KnowledgeDistillationSingleStageDetector(SingleStageDetector):
+    r"""Implementation of `Distilling the Knowledge in a Neural Network.
+    <https://arxiv.org/abs/1503.02531>`_.
+
+    Args:
+        teacher_config (str | dict): Config file path
+            or the config object of teacher model.
+        teacher_ckpt (str, optional): Checkpoint path of teacher model.
+            If left as None, the model will not load any weights.
+    """
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 teacher_config,
+                 teacher_ckpt=None,
+                 eval_teacher=True,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None):
+        super().__init__(backbone, neck, bbox_head, train_cfg, test_cfg,
+                         pretrained)
+        self.eval_teacher = eval_teacher
+        # Build teacher model
+        if isinstance(teacher_config, (str, Path)):
+            teacher_config = mmcv.Config.fromfile(teacher_config)
+        self.teacher_model = build_detector(teacher_config['model'])
+        if teacher_ckpt is not None:
+            load_checkpoint(
+                self.teacher_model, teacher_ckpt, map_location='cpu')
+
+    def forward_train(self,
+                      img,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None):
+        """
+        Args:
+            img (Tensor): Input images of shape (N, C, H, W).
+                Typically these should be mean centered and std scaled.
+            img_metas (list[dict]): A List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                :class:`mmdet.datasets.pipelines.Collect`.
+            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
+                image in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): Class indices corresponding to each box
+            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
+                boxes can be ignored when computing the loss.
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        x = self.extract_feat(img)
+        with torch.no_grad():
+            teacher_x = self.teacher_model.extract_feat(img)
+            out_teacher = self.teacher_model.bbox_head(teacher_x)
+        losses = self.bbox_head.forward_train(x, out_teacher, img_metas,
+                                              gt_bboxes, gt_labels,
+                                              gt_bboxes_ignore)
+        return losses
+
+    def cuda(self, device=None):
+        """Since teacher_model is registered as a plain object, it is necessary
+        to put the teacher model to cuda when calling cuda function."""
+        self.teacher_model.cuda(device=device)
+        return super().cuda(device=device)
+
+    def train(self, mode=True):
+        """Set the same train mode for teacher and student model."""
+        if self.eval_teacher:
+            self.teacher_model.train(False)
+        else:
+            self.teacher_model.train(mode)
+        super().train(mode)
+
+    def __setattr__(self, name, value):
+        """Set attribute, i.e. self.name = value
+
+        This reloading prevent the teacher model from being registered as a
+        nn.Module. The teacher module is registered as a plain object, so that
+        the teacher parameters will not show up when calling
+        ``self.parameters``, ``self.modules``, ``self.children`` methods.
+        """
+        if name == 'teacher_model':
+            object.__setattr__(self, name, value)
+        else:
+            super().__setattr__(name, value)
diff --git a/mmdet/models/detectors/lad.py b/mmdet/models/detectors/lad.py
new file mode 100644
index 0000000..c6cc1e0
--- /dev/null
+++ b/mmdet/models/detectors/lad.py
@@ -0,0 +1,92 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.runner import load_checkpoint
+
+from ..builder import DETECTORS, build_backbone, build_head, build_neck
+from .kd_one_stage import KnowledgeDistillationSingleStageDetector
+
+
+@DETECTORS.register_module()
+class LAD(KnowledgeDistillationSingleStageDetector):
+    """Implementation of `LAD <https://arxiv.org/pdf/2108.10520.pdf>`_."""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 teacher_backbone,
+                 teacher_neck,
+                 teacher_bbox_head,
+                 teacher_ckpt,
+                 eval_teacher=True,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None):
+        super(KnowledgeDistillationSingleStageDetector,
+              self).__init__(backbone, neck, bbox_head, train_cfg, test_cfg,
+                             pretrained)
+        self.eval_teacher = eval_teacher
+        self.teacher_model = nn.Module()
+        self.teacher_model.backbone = build_backbone(teacher_backbone)
+        if teacher_neck is not None:
+            self.teacher_model.neck = build_neck(teacher_neck)
+        teacher_bbox_head.update(train_cfg=train_cfg)
+        teacher_bbox_head.update(test_cfg=test_cfg)
+        self.teacher_model.bbox_head = build_head(teacher_bbox_head)
+        if teacher_ckpt is not None:
+            load_checkpoint(
+                self.teacher_model, teacher_ckpt, map_location='cpu')
+
+    @property
+    def with_teacher_neck(self):
+        """bool: whether the detector has a teacher_neck"""
+        return hasattr(self.teacher_model, 'neck') and \
+            self.teacher_model.neck is not None
+
+    def extract_teacher_feat(self, img):
+        """Directly extract teacher features from the backbone+neck."""
+        x = self.teacher_model.backbone(img)
+        if self.with_teacher_neck:
+            x = self.teacher_model.neck(x)
+        return x
+
+    def forward_train(self,
+                      img,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None):
+        """
+        Args:
+            img (Tensor): Input images of shape (N, C, H, W).
+                Typically these should be mean centered and std scaled.
+            img_metas (list[dict]): A List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                :class:`mmdet.datasets.pipelines.Collect`.
+            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
+                image in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): Class indices corresponding to each box
+            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        # get label assignment from the teacher
+        with torch.no_grad():
+            x_teacher = self.extract_teacher_feat(img)
+            outs_teacher = self.teacher_model.bbox_head(x_teacher)
+            label_assignment_results = \
+                self.teacher_model.bbox_head.get_label_assignment(
+                    *outs_teacher, gt_bboxes, gt_labels, img_metas,
+                    gt_bboxes_ignore)
+
+        # the student use the label assignment from the teacher to learn
+        x = self.extract_feat(img)
+        losses = self.bbox_head.forward_train(x, label_assignment_results,
+                                              img_metas, gt_bboxes, gt_labels,
+                                              gt_bboxes_ignore)
+        return losses
diff --git a/mmdet/models/detectors/mask2former.py b/mmdet/models/detectors/mask2former.py
new file mode 100644
index 0000000..b9ad2ed
--- /dev/null
+++ b/mmdet/models/detectors/mask2former.py
@@ -0,0 +1,27 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .maskformer import MaskFormer
+
+
+@DETECTORS.register_module()
+class Mask2Former(MaskFormer):
+    r"""Implementation of `Masked-attention Mask
+    Transformer for Universal Image Segmentation
+    <https://arxiv.org/pdf/2112.01527>`_."""
+
+    def __init__(self,
+                 backbone,
+                 neck=None,
+                 panoptic_head=None,
+                 panoptic_fusion_head=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 init_cfg=None):
+        super().__init__(
+            backbone,
+            neck=neck,
+            panoptic_head=panoptic_head,
+            panoptic_fusion_head=panoptic_fusion_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            init_cfg=init_cfg)
diff --git a/mmdet/models/detectors/mask_rcnn.py b/mmdet/models/detectors/mask_rcnn.py
new file mode 100644
index 0000000..c68489f
--- /dev/null
+++ b/mmdet/models/detectors/mask_rcnn.py
@@ -0,0 +1,27 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .two_stage import TwoStageDetector
+
+
+@DETECTORS.register_module()
+class MaskRCNN(TwoStageDetector):
+    """Implementation of `Mask R-CNN <https://arxiv.org/abs/1703.06870>`_"""
+
+    def __init__(self,
+                 backbone,
+                 rpn_head,
+                 roi_head,
+                 train_cfg,
+                 test_cfg,
+                 neck=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(MaskRCNN, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            rpn_head=rpn_head,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained,
+            init_cfg=init_cfg)
diff --git a/mmdet/models/detectors/mask_scoring_rcnn.py b/mmdet/models/detectors/mask_scoring_rcnn.py
new file mode 100644
index 0000000..5f55656
--- /dev/null
+++ b/mmdet/models/detectors/mask_scoring_rcnn.py
@@ -0,0 +1,30 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .two_stage import TwoStageDetector
+
+
+@DETECTORS.register_module()
+class MaskScoringRCNN(TwoStageDetector):
+    """Mask Scoring RCNN.
+
+    https://arxiv.org/abs/1903.00241
+    """
+
+    def __init__(self,
+                 backbone,
+                 rpn_head,
+                 roi_head,
+                 train_cfg,
+                 test_cfg,
+                 neck=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(MaskScoringRCNN, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            rpn_head=rpn_head,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained,
+            init_cfg=init_cfg)
diff --git a/mmdet/models/detectors/maskformer.py b/mmdet/models/detectors/maskformer.py
new file mode 100644
index 0000000..3d251ad
--- /dev/null
+++ b/mmdet/models/detectors/maskformer.py
@@ -0,0 +1,258 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import mmcv
+import numpy as np
+
+from mmdet.core import INSTANCE_OFFSET, bbox2result
+from mmdet.core.visualization import imshow_det_bboxes
+from ..builder import DETECTORS, build_backbone, build_head, build_neck
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class MaskFormer(SingleStageDetector):
+    r"""Implementation of `Per-Pixel Classification is
+    NOT All You Need for Semantic Segmentation
+    <https://arxiv.org/pdf/2107.06278>`_."""
+
+    def __init__(self,
+                 backbone,
+                 neck=None,
+                 panoptic_head=None,
+                 panoptic_fusion_head=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 init_cfg=None):
+        super(SingleStageDetector, self).__init__(init_cfg=init_cfg)
+        self.backbone = build_backbone(backbone)
+        if neck is not None:
+            self.neck = build_neck(neck)
+
+        panoptic_head_ = copy.deepcopy(panoptic_head)
+        panoptic_head_.update(train_cfg=train_cfg)
+        panoptic_head_.update(test_cfg=test_cfg)
+        self.panoptic_head = build_head(panoptic_head_)
+
+        panoptic_fusion_head_ = copy.deepcopy(panoptic_fusion_head)
+        panoptic_fusion_head_.update(test_cfg=test_cfg)
+        self.panoptic_fusion_head = build_head(panoptic_fusion_head_)
+
+        self.num_things_classes = self.panoptic_head.num_things_classes
+        self.num_stuff_classes = self.panoptic_head.num_stuff_classes
+        self.num_classes = self.panoptic_head.num_classes
+
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+
+        # BaseDetector.show_result default for instance segmentation
+        if self.num_stuff_classes > 0:
+            self.show_result = self._show_pan_result
+
+    def forward_dummy(self, img, img_metas):
+        """Used for computing network flops. See
+        `mmdetection/tools/analysis_tools/get_flops.py`
+
+        Args:
+            img (Tensor): of shape (N, C, H, W) encoding input images.
+                Typically these should be mean centered and std scaled.
+            img_metas (list[Dict]): list of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `mmdet/datasets/pipelines/formatting.py:Collect`.
+        """
+        super(SingleStageDetector, self).forward_train(img, img_metas)
+        x = self.extract_feat(img)
+        outs = self.panoptic_head(x, img_metas)
+        return outs
+
+    def forward_train(self,
+                      img,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_masks,
+                      gt_semantic_seg=None,
+                      gt_bboxes_ignore=None,
+                      **kargs):
+        """
+        Args:
+            img (Tensor): of shape (N, C, H, W) encoding input images.
+                Typically these should be mean centered and std scaled.
+            img_metas (list[Dict]): list of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `mmdet/datasets/pipelines/formatting.py:Collect`.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box.
+            gt_masks (list[BitmapMasks]): true segmentation masks for each box
+                used if the architecture supports a segmentation task.
+            gt_semantic_seg (list[tensor]): semantic segmentation mask for
+                images for panoptic segmentation.
+                Defaults to None for instance segmentation.
+            gt_bboxes_ignore (list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+                Defaults to None.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        # add batch_input_shape in img_metas
+        super(SingleStageDetector, self).forward_train(img, img_metas)
+        x = self.extract_feat(img)
+        losses = self.panoptic_head.forward_train(x, img_metas, gt_bboxes,
+                                                  gt_labels, gt_masks,
+                                                  gt_semantic_seg,
+                                                  gt_bboxes_ignore)
+
+        return losses
+
+    def simple_test(self, imgs, img_metas, **kwargs):
+        """Test without augmentation.
+
+        Args:
+            imgs (Tensor): A batch of images.
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            list[dict[str, np.array | tuple[list]] | tuple[list]]:
+                Semantic segmentation results and panoptic segmentation \
+                results of each image for panoptic segmentation, or formatted \
+                bbox and mask results of each image for instance segmentation.
+
+            .. code-block:: none
+
+                [
+                    # panoptic segmentation
+                    {
+                        'pan_results': np.array, # shape = [h, w]
+                        'ins_results': tuple[list],
+                        # semantic segmentation results are not supported yet
+                        'sem_results': np.array
+                    },
+                    ...
+                ]
+
+            or
+
+            .. code-block:: none
+
+                [
+                    # instance segmentation
+                    (
+                        bboxes, # list[np.array]
+                        masks # list[list[np.array]]
+                    ),
+                    ...
+                ]
+        """
+        feats = self.extract_feat(imgs)
+        mask_cls_results, mask_pred_results = self.panoptic_head.simple_test(
+            feats, img_metas, **kwargs)
+        results = self.panoptic_fusion_head.simple_test(
+            mask_cls_results, mask_pred_results, img_metas, **kwargs)
+        for i in range(len(results)):
+            if 'pan_results' in results[i]:
+                results[i]['pan_results'] = results[i]['pan_results'].detach(
+                ).cpu().numpy()
+
+            if 'ins_results' in results[i]:
+                labels_per_image, bboxes, mask_pred_binary = results[i][
+                    'ins_results']
+                bbox_results = bbox2result(bboxes, labels_per_image,
+                                           self.num_things_classes)
+                mask_results = [[] for _ in range(self.num_things_classes)]
+                for j, label in enumerate(labels_per_image):
+                    mask = mask_pred_binary[j].detach().cpu().numpy()
+                    mask_results[label].append(mask)
+                results[i]['ins_results'] = bbox_results, mask_results
+
+            assert 'sem_results' not in results[i], 'segmantic segmentation '\
+                'results are not supported yet.'
+
+        if self.num_stuff_classes == 0:
+            results = [res['ins_results'] for res in results]
+
+        return results
+
+    def aug_test(self, imgs, img_metas, **kwargs):
+        raise NotImplementedError
+
+    def onnx_export(self, img, img_metas):
+        raise NotImplementedError
+
+    def _show_pan_result(self,
+                         img,
+                         result,
+                         score_thr=0.3,
+                         bbox_color=(72, 101, 241),
+                         text_color=(72, 101, 241),
+                         mask_color=None,
+                         thickness=2,
+                         font_size=13,
+                         win_name='',
+                         show=False,
+                         wait_time=0,
+                         out_file=None):
+        """Draw `panoptic result` over `img`.
+
+        Args:
+            img (str or Tensor): The image to be displayed.
+            result (dict): The results.
+
+            score_thr (float, optional): Minimum score of bboxes to be shown.
+                Default: 0.3.
+            bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
+               The tuple of color should be in BGR order. Default: 'green'.
+            text_color (str or tuple(int) or :obj:`Color`):Color of texts.
+               The tuple of color should be in BGR order. Default: 'green'.
+            mask_color (None or str or tuple(int) or :obj:`Color`):
+               Color of masks. The tuple of color should be in BGR order.
+               Default: None.
+            thickness (int): Thickness of lines. Default: 2.
+            font_size (int): Font size of texts. Default: 13.
+            win_name (str): The window name. Default: ''.
+            wait_time (float): Value of waitKey param.
+                Default: 0.
+            show (bool): Whether to show the image.
+                Default: False.
+            out_file (str or None): The filename to write the image.
+                Default: None.
+
+        Returns:
+            img (Tensor): Only if not `show` or `out_file`.
+        """
+        img = mmcv.imread(img)
+        img = img.copy()
+        pan_results = result['pan_results']
+        # keep objects ahead
+        ids = np.unique(pan_results)[::-1]
+        legal_indices = ids != self.num_classes  # for VOID label
+        ids = ids[legal_indices]
+        labels = np.array([id % INSTANCE_OFFSET for id in ids], dtype=np.int64)
+        segms = (pan_results[None] == ids[:, None, None])
+
+        # if out_file specified, do not show image in window
+        if out_file is not None:
+            show = False
+        # draw bounding boxes
+        img = imshow_det_bboxes(
+            img,
+            segms=segms,
+            labels=labels,
+            class_names=self.CLASSES,
+            bbox_color=bbox_color,
+            text_color=text_color,
+            mask_color=mask_color,
+            thickness=thickness,
+            font_size=font_size,
+            win_name=win_name,
+            show=show,
+            wait_time=wait_time,
+            out_file=out_file)
+
+        if not (show or out_file):
+            return img
diff --git a/mmdet/models/detectors/nasfcos.py b/mmdet/models/detectors/nasfcos.py
new file mode 100644
index 0000000..a34c228
--- /dev/null
+++ b/mmdet/models/detectors/nasfcos.py
@@ -0,0 +1,22 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class NASFCOS(SingleStageDetector):
+    """NAS-FCOS: Fast Neural Architecture Search for Object Detection.
+
+    https://arxiv.org/abs/1906.0442
+    """
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(NASFCOS, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                      test_cfg, pretrained, init_cfg)
diff --git a/mmdet/models/detectors/paa.py b/mmdet/models/detectors/paa.py
new file mode 100644
index 0000000..f5cb837
--- /dev/null
+++ b/mmdet/models/detectors/paa.py
@@ -0,0 +1,19 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class PAA(SingleStageDetector):
+    """Implementation of `PAA <https://arxiv.org/pdf/2007.08103.pdf>`_."""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(PAA, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                  test_cfg, pretrained, init_cfg)
diff --git a/mmdet/models/detectors/panoptic_fpn.py b/mmdet/models/detectors/panoptic_fpn.py
new file mode 100644
index 0000000..f8ac751
--- /dev/null
+++ b/mmdet/models/detectors/panoptic_fpn.py
@@ -0,0 +1,34 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .panoptic_two_stage_segmentor import TwoStagePanopticSegmentor
+
+
+@DETECTORS.register_module()
+class PanopticFPN(TwoStagePanopticSegmentor):
+    r"""Implementation of `Panoptic feature pyramid
+    networks <https://arxiv.org/pdf/1901.02446>`_"""
+
+    def __init__(
+            self,
+            backbone,
+            neck=None,
+            rpn_head=None,
+            roi_head=None,
+            train_cfg=None,
+            test_cfg=None,
+            pretrained=None,
+            init_cfg=None,
+            # for panoptic segmentation
+            semantic_head=None,
+            panoptic_fusion_head=None):
+        super(PanopticFPN, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            rpn_head=rpn_head,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained,
+            init_cfg=init_cfg,
+            semantic_head=semantic_head,
+            panoptic_fusion_head=panoptic_fusion_head)
diff --git a/mmdet/models/detectors/panoptic_two_stage_segmentor.py b/mmdet/models/detectors/panoptic_two_stage_segmentor.py
new file mode 100644
index 0000000..5ad49ba
--- /dev/null
+++ b/mmdet/models/detectors/panoptic_two_stage_segmentor.py
@@ -0,0 +1,279 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import numpy as np
+import torch
+
+from mmdet.core import INSTANCE_OFFSET, bbox2roi, multiclass_nms
+from mmdet.core.visualization import imshow_det_bboxes
+from ..builder import DETECTORS, build_head
+from ..roi_heads.mask_heads.fcn_mask_head import _do_paste_mask
+from .two_stage import TwoStageDetector
+
+
+@DETECTORS.register_module()
+class TwoStagePanopticSegmentor(TwoStageDetector):
+    """Base class of Two-stage Panoptic Segmentor.
+
+    As well as the components in TwoStageDetector, Panoptic Segmentor has extra
+    semantic_head and panoptic_fusion_head.
+    """
+
+    def __init__(
+            self,
+            backbone,
+            neck=None,
+            rpn_head=None,
+            roi_head=None,
+            train_cfg=None,
+            test_cfg=None,
+            pretrained=None,
+            init_cfg=None,
+            # for panoptic segmentation
+            semantic_head=None,
+            panoptic_fusion_head=None):
+        super(TwoStagePanopticSegmentor,
+              self).__init__(backbone, neck, rpn_head, roi_head, train_cfg,
+                             test_cfg, pretrained, init_cfg)
+        if semantic_head is not None:
+            self.semantic_head = build_head(semantic_head)
+        if panoptic_fusion_head is not None:
+            panoptic_cfg = test_cfg.panoptic if test_cfg is not None else None
+            panoptic_fusion_head_ = panoptic_fusion_head.deepcopy()
+            panoptic_fusion_head_.update(test_cfg=panoptic_cfg)
+            self.panoptic_fusion_head = build_head(panoptic_fusion_head_)
+
+            self.num_things_classes = self.panoptic_fusion_head.\
+                num_things_classes
+            self.num_stuff_classes = self.panoptic_fusion_head.\
+                num_stuff_classes
+            self.num_classes = self.panoptic_fusion_head.num_classes
+
+    @property
+    def with_semantic_head(self):
+        return hasattr(self,
+                       'semantic_head') and self.semantic_head is not None
+
+    @property
+    def with_panoptic_fusion_head(self):
+        return hasattr(self, 'panoptic_fusion_heads') and \
+               self.panoptic_fusion_head is not None
+
+    def forward_dummy(self, img):
+        """Used for computing network flops.
+
+        See `mmdetection/tools/get_flops.py`
+        """
+        raise NotImplementedError(
+            f'`forward_dummy` is not implemented in {self.__class__.__name__}')
+
+    def forward_train(self,
+                      img,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None,
+                      gt_masks=None,
+                      gt_semantic_seg=None,
+                      proposals=None,
+                      **kwargs):
+        x = self.extract_feat(img)
+        losses = dict()
+
+        # RPN forward and loss
+        if self.with_rpn:
+            proposal_cfg = self.train_cfg.get('rpn_proposal',
+                                              self.test_cfg.rpn)
+            rpn_losses, proposal_list = self.rpn_head.forward_train(
+                x,
+                img_metas,
+                gt_bboxes,
+                gt_labels=None,
+                gt_bboxes_ignore=gt_bboxes_ignore,
+                proposal_cfg=proposal_cfg)
+            losses.update(rpn_losses)
+        else:
+            proposal_list = proposals
+
+        roi_losses = self.roi_head.forward_train(x, img_metas, proposal_list,
+                                                 gt_bboxes, gt_labels,
+                                                 gt_bboxes_ignore, gt_masks,
+                                                 **kwargs)
+        losses.update(roi_losses)
+
+        semantic_loss = self.semantic_head.forward_train(x, gt_semantic_seg)
+        losses.update(semantic_loss)
+
+        return losses
+
+    def simple_test_mask(self,
+                         x,
+                         img_metas,
+                         det_bboxes,
+                         det_labels,
+                         rescale=False):
+        """Simple test for mask head without augmentation."""
+        img_shapes = tuple(meta['ori_shape']
+                           for meta in img_metas) if rescale else tuple(
+                               meta['pad_shape'] for meta in img_metas)
+        scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
+
+        if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
+            masks = []
+            for img_shape in img_shapes:
+                out_shape = (0, self.roi_head.bbox_head.num_classes) \
+                            + img_shape[:2]
+                masks.append(det_bboxes[0].new_zeros(out_shape))
+            mask_pred = det_bboxes[0].new_zeros((0, 80, 28, 28))
+            mask_results = dict(
+                masks=masks, mask_pred=mask_pred, mask_feats=None)
+            return mask_results
+
+        _bboxes = [det_bboxes[i][:, :4] for i in range(len(det_bboxes))]
+        if rescale:
+            if not isinstance(scale_factors[0], float):
+                scale_factors = [
+                    det_bboxes[0].new_tensor(scale_factor)
+                    for scale_factor in scale_factors
+                ]
+            _bboxes = [
+                _bboxes[i] * scale_factors[i] for i in range(len(_bboxes))
+            ]
+
+        mask_rois = bbox2roi(_bboxes)
+        mask_results = self.roi_head._mask_forward(x, mask_rois)
+        mask_pred = mask_results['mask_pred']
+        # split batch mask prediction back to each image
+        num_mask_roi_per_img = [len(det_bbox) for det_bbox in det_bboxes]
+        mask_preds = mask_pred.split(num_mask_roi_per_img, 0)
+
+        # resize the mask_preds to (K, H, W)
+        masks = []
+        for i in range(len(_bboxes)):
+            det_bbox = det_bboxes[i][:, :4]
+            det_label = det_labels[i]
+
+            mask_pred = mask_preds[i].sigmoid()
+
+            box_inds = torch.arange(mask_pred.shape[0])
+            mask_pred = mask_pred[box_inds, det_label][:, None]
+
+            img_h, img_w, _ = img_shapes[i]
+            mask_pred, _ = _do_paste_mask(
+                mask_pred, det_bbox, img_h, img_w, skip_empty=False)
+            masks.append(mask_pred)
+
+        mask_results['masks'] = masks
+
+        return mask_results
+
+    def simple_test(self, img, img_metas, proposals=None, rescale=False):
+        """Test without Augmentation."""
+        x = self.extract_feat(img)
+
+        if proposals is None:
+            proposal_list = self.rpn_head.simple_test_rpn(x, img_metas)
+        else:
+            proposal_list = proposals
+
+        bboxes, scores = self.roi_head.simple_test_bboxes(
+            x, img_metas, proposal_list, None, rescale=rescale)
+
+        pan_cfg = self.test_cfg.panoptic
+        # class-wise predictions
+        det_bboxes = []
+        det_labels = []
+        for bboxe, score in zip(bboxes, scores):
+            det_bbox, det_label = multiclass_nms(bboxe, score,
+                                                 pan_cfg.score_thr,
+                                                 pan_cfg.nms,
+                                                 pan_cfg.max_per_img)
+            det_bboxes.append(det_bbox)
+            det_labels.append(det_label)
+
+        mask_results = self.simple_test_mask(
+            x, img_metas, det_bboxes, det_labels, rescale=rescale)
+        masks = mask_results['masks']
+
+        seg_preds = self.semantic_head.simple_test(x, img_metas, rescale)
+
+        results = []
+        for i in range(len(det_bboxes)):
+            pan_results = self.panoptic_fusion_head.simple_test(
+                det_bboxes[i], det_labels[i], masks[i], seg_preds[i])
+            pan_results = pan_results.int().detach().cpu().numpy()
+            result = dict(pan_results=pan_results)
+            results.append(result)
+        return results
+
+    def show_result(self,
+                    img,
+                    result,
+                    score_thr=0.3,
+                    bbox_color=(72, 101, 241),
+                    text_color=(72, 101, 241),
+                    mask_color=None,
+                    thickness=2,
+                    font_size=13,
+                    win_name='',
+                    show=False,
+                    wait_time=0,
+                    out_file=None):
+        """Draw `result` over `img`.
+
+        Args:
+            img (str or Tensor): The image to be displayed.
+            result (dict): The results.
+
+            score_thr (float, optional): Minimum score of bboxes to be shown.
+                Default: 0.3.
+            bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
+               The tuple of color should be in BGR order. Default: 'green'.
+            text_color (str or tuple(int) or :obj:`Color`):Color of texts.
+               The tuple of color should be in BGR order. Default: 'green'.
+            mask_color (None or str or tuple(int) or :obj:`Color`):
+               Color of masks. The tuple of color should be in BGR order.
+               Default: None.
+            thickness (int): Thickness of lines. Default: 2.
+            font_size (int): Font size of texts. Default: 13.
+            win_name (str): The window name. Default: ''.
+            wait_time (float): Value of waitKey param.
+                Default: 0.
+            show (bool): Whether to show the image.
+                Default: False.
+            out_file (str or None): The filename to write the image.
+                Default: None.
+
+        Returns:
+            img (Tensor): Only if not `show` or `out_file`.
+        """
+        img = mmcv.imread(img)
+        img = img.copy()
+        pan_results = result['pan_results']
+        # keep objects ahead
+        ids = np.unique(pan_results)[::-1]
+        legal_indices = ids != self.num_classes  # for VOID label
+        ids = ids[legal_indices]
+        labels = np.array([id % INSTANCE_OFFSET for id in ids], dtype=np.int64)
+        segms = (pan_results[None] == ids[:, None, None])
+
+        # if out_file specified, do not show image in window
+        if out_file is not None:
+            show = False
+        # draw bounding boxes
+        img = imshow_det_bboxes(
+            img,
+            segms=segms,
+            labels=labels,
+            class_names=self.CLASSES,
+            bbox_color=bbox_color,
+            text_color=text_color,
+            mask_color=mask_color,
+            thickness=thickness,
+            font_size=font_size,
+            win_name=win_name,
+            show=show,
+            wait_time=wait_time,
+            out_file=out_file)
+
+        if not (show or out_file):
+            return img
diff --git a/mmdet/models/detectors/point_rend.py b/mmdet/models/detectors/point_rend.py
new file mode 100644
index 0000000..90eb4d4
--- /dev/null
+++ b/mmdet/models/detectors/point_rend.py
@@ -0,0 +1,32 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .two_stage import TwoStageDetector
+
+
+@DETECTORS.register_module()
+class PointRend(TwoStageDetector):
+    """PointRend: Image Segmentation as Rendering
+
+    This detector is the implementation of
+    `PointRend <https://arxiv.org/abs/1912.08193>`_.
+
+    """
+
+    def __init__(self,
+                 backbone,
+                 rpn_head,
+                 roi_head,
+                 train_cfg,
+                 test_cfg,
+                 neck=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(PointRend, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            rpn_head=rpn_head,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained,
+            init_cfg=init_cfg)
diff --git a/mmdet/models/detectors/queryinst.py b/mmdet/models/detectors/queryinst.py
new file mode 100644
index 0000000..5fc216c
--- /dev/null
+++ b/mmdet/models/detectors/queryinst.py
@@ -0,0 +1,28 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .sparse_rcnn import SparseRCNN
+
+
+@DETECTORS.register_module()
+class QueryInst(SparseRCNN):
+    r"""Implementation of
+    `Instances as Queries <http://arxiv.org/abs/2105.01928>`_"""
+
+    def __init__(self,
+                 backbone,
+                 rpn_head,
+                 roi_head,
+                 train_cfg,
+                 test_cfg,
+                 neck=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(QueryInst, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            rpn_head=rpn_head,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained,
+            init_cfg=init_cfg)
diff --git a/mmdet/models/detectors/reppoints_detector.py b/mmdet/models/detectors/reppoints_detector.py
new file mode 100644
index 0000000..f1986cd
--- /dev/null
+++ b/mmdet/models/detectors/reppoints_detector.py
@@ -0,0 +1,24 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class RepPointsDetector(SingleStageDetector):
+    """RepPoints: Point Set Representation for Object Detection.
+
+        This detector is the implementation of:
+        - RepPoints detector (https://arxiv.org/pdf/1904.11490)
+    """
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(RepPointsDetector,
+              self).__init__(backbone, neck, bbox_head, train_cfg, test_cfg,
+                             pretrained, init_cfg)
diff --git a/mmdet/models/detectors/retinanet.py b/mmdet/models/detectors/retinanet.py
new file mode 100644
index 0000000..c28545a
--- /dev/null
+++ b/mmdet/models/detectors/retinanet.py
@@ -0,0 +1,19 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class RetinaNet(SingleStageDetector):
+    """Implementation of `RetinaNet <https://arxiv.org/abs/1708.02002>`_"""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(RetinaNet, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                        test_cfg, pretrained, init_cfg)
diff --git a/mmdet/models/detectors/rpn.py b/mmdet/models/detectors/rpn.py
new file mode 100644
index 0000000..707e02b
--- /dev/null
+++ b/mmdet/models/detectors/rpn.py
@@ -0,0 +1,162 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+from inspect import signature
+
+import mmcv
+import torch
+from mmcv.image import tensor2imgs
+
+from mmdet.core import bbox_mapping
+from ..builder import DETECTORS, build_backbone, build_head, build_neck
+from .base import BaseDetector
+
+
+@DETECTORS.register_module()
+class RPN(BaseDetector):
+    """Implementation of Region Proposal Network."""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 rpn_head,
+                 train_cfg,
+                 test_cfg,
+                 pretrained=None,
+                 init_cfg=None):
+        super(RPN, self).__init__(init_cfg)
+        if pretrained:
+            warnings.warn('DeprecationWarning: pretrained is deprecated, '
+                          'please use "init_cfg" instead')
+            backbone.pretrained = pretrained
+        self.backbone = build_backbone(backbone)
+        self.neck = build_neck(neck) if neck is not None else None
+        rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None
+        rpn_head.update(train_cfg=rpn_train_cfg)
+        rpn_head.update(test_cfg=test_cfg.rpn)
+        self.rpn_head = build_head(rpn_head)
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+
+    def extract_feat(self, img):
+        """Extract features.
+
+        Args:
+            img (torch.Tensor): Image tensor with shape (n, c, h ,w).
+
+        Returns:
+            list[torch.Tensor]: Multi-level features that may have
+                different resolutions.
+        """
+        x = self.backbone(img)
+        if self.with_neck:
+            x = self.neck(x)
+        return x
+
+    def forward_dummy(self, img):
+        """Dummy forward function."""
+        x = self.extract_feat(img)
+        rpn_outs = self.rpn_head(x)
+        return rpn_outs
+
+    def forward_train(self,
+                      img,
+                      img_metas,
+                      gt_bboxes=None,
+                      gt_bboxes_ignore=None):
+        """
+        Args:
+            img (Tensor): Input images of shape (N, C, H, W).
+                Typically these should be mean centered and std scaled.
+            img_metas (list[dict]): A List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                :class:`mmdet.datasets.pipelines.Collect`.
+            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
+                image in [tl_x, tl_y, br_x, br_y] format.
+            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        if (isinstance(self.train_cfg.rpn, dict)
+                and self.train_cfg.rpn.get('debug', False)):
+            self.rpn_head.debug_imgs = tensor2imgs(img)
+
+        x = self.extract_feat(img)
+        losses = self.rpn_head.forward_train(x, img_metas, gt_bboxes, None,
+                                             gt_bboxes_ignore)
+        return losses
+
+    def simple_test(self, img, img_metas, rescale=False):
+        """Test function without test time augmentation.
+
+        Args:
+            imgs (list[torch.Tensor]): List of multiple images
+            img_metas (list[dict]): List of image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[np.ndarray]: proposals
+        """
+        x = self.extract_feat(img)
+        # get origin input shape to onnx dynamic input shape
+        if torch.onnx.is_in_onnx_export():
+            img_shape = torch._shape_as_tensor(img)[2:]
+            img_metas[0]['img_shape_for_onnx'] = img_shape
+        proposal_list = self.rpn_head.simple_test_rpn(x, img_metas)
+        if rescale:
+            for proposals, meta in zip(proposal_list, img_metas):
+                proposals[:, :4] /= proposals.new_tensor(meta['scale_factor'])
+        if torch.onnx.is_in_onnx_export():
+            return proposal_list
+
+        return [proposal.cpu().numpy() for proposal in proposal_list]
+
+    def aug_test(self, imgs, img_metas, rescale=False):
+        """Test function with test time augmentation.
+
+        Args:
+            imgs (list[torch.Tensor]): List of multiple images
+            img_metas (list[dict]): List of image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[np.ndarray]: proposals
+        """
+        proposal_list = self.rpn_head.aug_test_rpn(
+            self.extract_feats(imgs), img_metas)
+        if not rescale:
+            for proposals, img_meta in zip(proposal_list, img_metas[0]):
+                img_shape = img_meta['img_shape']
+                scale_factor = img_meta['scale_factor']
+                flip = img_meta['flip']
+                flip_direction = img_meta['flip_direction']
+                proposals[:, :4] = bbox_mapping(proposals[:, :4], img_shape,
+                                                scale_factor, flip,
+                                                flip_direction)
+        return [proposal.cpu().numpy() for proposal in proposal_list]
+
+    def show_result(self, data, result, top_k=20, **kwargs):
+        """Show RPN proposals on the image.
+
+        Args:
+            data (str or np.ndarray): Image filename or loaded image.
+            result (Tensor or tuple): The results to draw over `img`
+                bbox_result or (bbox_result, segm_result).
+            top_k (int): Plot the first k bboxes only
+               if set positive. Default: 20
+
+        Returns:
+            np.ndarray: The image with bboxes drawn on it.
+        """
+        if kwargs is not None:
+            kwargs['colors'] = 'green'
+            sig = signature(mmcv.imshow_bboxes)
+            for k in list(kwargs.keys()):
+                if k not in sig.parameters:
+                    kwargs.pop(k)
+        mmcv.imshow_bboxes(data, result, top_k=top_k, **kwargs)
diff --git a/mmdet/models/detectors/scnet.py b/mmdet/models/detectors/scnet.py
new file mode 100644
index 0000000..a361d81
--- /dev/null
+++ b/mmdet/models/detectors/scnet.py
@@ -0,0 +1,11 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .cascade_rcnn import CascadeRCNN
+
+
+@DETECTORS.register_module()
+class SCNet(CascadeRCNN):
+    """Implementation of `SCNet <https://arxiv.org/abs/2012.10150>`_"""
+
+    def __init__(self, **kwargs):
+        super(SCNet, self).__init__(**kwargs)
diff --git a/mmdet/models/detectors/single_stage.py b/mmdet/models/detectors/single_stage.py
new file mode 100644
index 0000000..62b49d9
--- /dev/null
+++ b/mmdet/models/detectors/single_stage.py
@@ -0,0 +1,171 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch
+
+from mmdet.core import bbox2result
+from ..builder import DETECTORS, build_backbone, build_head, build_neck
+from .base import BaseDetector
+
+
+@DETECTORS.register_module()
+class SingleStageDetector(BaseDetector):
+    """Base class for single-stage detectors.
+
+    Single-stage detectors directly and densely predict bounding boxes on the
+    output features of the backbone+neck.
+    """
+
+    def __init__(self,
+                 backbone,
+                 neck=None,
+                 bbox_head=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(SingleStageDetector, self).__init__(init_cfg)
+        if pretrained:
+            warnings.warn('DeprecationWarning: pretrained is deprecated, '
+                          'please use "init_cfg" instead')
+            backbone.pretrained = pretrained
+        self.backbone = build_backbone(backbone)
+        if neck is not None:
+            self.neck = build_neck(neck)
+        bbox_head.update(train_cfg=train_cfg)
+        bbox_head.update(test_cfg=test_cfg)
+        self.bbox_head = build_head(bbox_head)
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+
+    def extract_feat(self, img):
+        """Directly extract features from the backbone+neck."""
+        x = self.backbone(img)
+        if self.with_neck:
+            x = self.neck(x)
+        return x
+
+    def forward_dummy(self, img):
+        """Used for computing network flops.
+
+        See `mmdetection/tools/analysis_tools/get_flops.py`
+        """
+        x = self.extract_feat(img)
+        outs = self.bbox_head(x)
+        return outs
+
+    def forward_train(self,  # TODO 感觉还是可行的，需要看一下trace那边怎么传proxy
+                      img,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None):
+        """
+        Args:
+            img (Tensor): Input images of shape (N, C, H, W).
+                Typically these should be mean centered and std scaled.
+            img_metas (list[dict]): A List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                :class:`mmdet.datasets.pipelines.Collect`.
+            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
+                image in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): Class indices corresponding to each box
+            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        super(SingleStageDetector, self).forward_train(img, img_metas)
+        x = self.extract_feat(img)
+        losses = self.bbox_head.forward_train(x, img_metas, gt_bboxes,
+                                              gt_labels, gt_bboxes_ignore)
+        return losses
+
+    def simple_test(self, img, img_metas, rescale=False):
+        """Test function without test-time augmentation.
+
+        Args:
+            img (torch.Tensor): Images with shape (N, C, H, W).
+            img_metas (list[dict]): List of image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[list[np.ndarray]]: BBox results of each image and classes.
+                The outer list corresponds to each image. The inner list
+                corresponds to each class.
+        """
+        feat = self.extract_feat(img)
+        results_list = self.bbox_head.simple_test(
+            feat, img_metas, rescale=rescale)
+        bbox_results = [
+            bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)
+            for det_bboxes, det_labels in results_list
+        ]
+        return bbox_results
+
+    def aug_test(self, imgs, img_metas, rescale=False):
+        """Test function with test time augmentation.
+
+        Args:
+            imgs (list[Tensor]): the outer list indicates test-time
+                augmentations and inner Tensor should have a shape NxCxHxW,
+                which contains all images in the batch.
+            img_metas (list[list[dict]]): the outer list indicates test-time
+                augs (multiscale, flip, etc.) and the inner list indicates
+                images in a batch. each dict has image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[list[np.ndarray]]: BBox results of each image and classes.
+                The outer list corresponds to each image. The inner list
+                corresponds to each class.
+        """
+        assert hasattr(self.bbox_head, 'aug_test'), \
+            f'{self.bbox_head.__class__.__name__}' \
+            ' does not support test-time augmentation'
+
+        feats = self.extract_feats(imgs)
+        results_list = self.bbox_head.aug_test(
+            feats, img_metas, rescale=rescale)
+        bbox_results = [
+            bbox2result(det_bboxes, det_labels, self.bbox_head.num_classes)
+            for det_bboxes, det_labels in results_list
+        ]
+        return bbox_results
+
+    def onnx_export(self, img, img_metas, with_nms=True):
+        """Test function without test time augmentation.
+
+        Args:
+            img (torch.Tensor): input images.
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            tuple[Tensor, Tensor]: dets of shape [N, num_det, 5]
+                and class labels of shape [N, num_det].
+        """
+        x = self.extract_feat(img)
+        outs = self.bbox_head(x)
+        # get origin input shape to support onnx dynamic shape
+
+        # get shape as tensor
+        img_shape = torch._shape_as_tensor(img)[2:]
+        img_metas[0]['img_shape_for_onnx'] = img_shape
+        # get pad input shape to support onnx dynamic shape for exporting
+        # `CornerNet` and `CentripetalNet`, which 'pad_shape' is used
+        # for inference
+        img_metas[0]['pad_shape_for_onnx'] = img_shape
+
+        if len(outs) == 2:
+            # add dummy score_factor
+            outs = (*outs, None)
+        # TODO Can we change to `get_bboxes` when `onnx_export` fail
+        det_bboxes, det_labels = self.bbox_head.onnx_export(
+            *outs, img_metas, with_nms=with_nms)
+
+        return det_bboxes, det_labels
diff --git a/mmdet/models/detectors/single_stage_instance_seg.py b/mmdet/models/detectors/single_stage_instance_seg.py
new file mode 100644
index 0000000..239b669
--- /dev/null
+++ b/mmdet/models/detectors/single_stage_instance_seg.py
@@ -0,0 +1,363 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import warnings
+
+import mmcv
+import numpy as np
+import torch
+
+from mmdet.core.visualization.image import imshow_det_bboxes
+from ..builder import DETECTORS, build_backbone, build_head, build_neck
+from .base import BaseDetector
+
+INF = 1e8
+
+
+@DETECTORS.register_module()
+class SingleStageInstanceSegmentor(BaseDetector):
+    """Base class for single-stage instance segmentors."""
+
+    def __init__(self,
+                 backbone,
+                 neck=None,
+                 bbox_head=None,
+                 mask_head=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+
+        if pretrained:
+            warnings.warn('DeprecationWarning: pretrained is deprecated, '
+                          'please use "init_cfg" instead')
+            backbone.pretrained = pretrained
+        super(SingleStageInstanceSegmentor, self).__init__(init_cfg=init_cfg)
+        self.backbone = build_backbone(backbone)
+        if neck is not None:
+            self.neck = build_neck(neck)
+        else:
+            self.neck = None
+        if bbox_head is not None:
+            bbox_head.update(train_cfg=copy.deepcopy(train_cfg))
+            bbox_head.update(test_cfg=copy.deepcopy(test_cfg))
+            self.bbox_head = build_head(bbox_head)
+        else:
+            self.bbox_head = None
+
+        assert mask_head, f'`mask_head` must ' \
+                          f'be implemented in {self.__class__.__name__}'
+        mask_head.update(train_cfg=copy.deepcopy(train_cfg))
+        mask_head.update(test_cfg=copy.deepcopy(test_cfg))
+        self.mask_head = build_head(mask_head)
+
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+
+    def extract_feat(self, img):
+        """Directly extract features from the backbone and neck."""
+        x = self.backbone(img)
+        if self.with_neck:
+            x = self.neck(x)
+        return x
+
+    def forward_dummy(self, img):
+        """Used for computing network flops.
+
+        See `mmdetection/tools/analysis_tools/get_flops.py`
+        """
+        raise NotImplementedError(
+            f'`forward_dummy` is not implemented in {self.__class__.__name__}')
+
+    def forward_train(self,
+                      img,
+                      img_metas,
+                      gt_masks,
+                      gt_labels,
+                      gt_bboxes=None,
+                      gt_bboxes_ignore=None,
+                      **kwargs):
+        """
+        Args:
+            img (Tensor): Input images of shape (B, C, H, W).
+                Typically these should be mean centered and std scaled.
+            img_metas (list[dict]): A List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                :class:`mmdet.datasets.pipelines.Collect`.
+            gt_masks (list[:obj:`BitmapMasks`] | None) : The segmentation
+                masks for each box.
+            gt_labels (list[Tensor]): Class indices corresponding to each box
+            gt_bboxes (list[Tensor]): Each item is the truth boxes
+                of each image in [tl_x, tl_y, br_x, br_y] format.
+                Default: None.
+            gt_bboxes_ignore (list[Tensor] | None): Specify which bounding
+                boxes can be ignored when computing the loss.
+
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+
+        gt_masks = [
+            gt_mask.to_tensor(dtype=torch.bool, device=img.device)
+            for gt_mask in gt_masks
+        ]
+        x = self.extract_feat(img)
+        losses = dict()
+
+        # CondInst and YOLACT have bbox_head
+        if self.bbox_head:
+            # bbox_head_preds is a tuple
+            bbox_head_preds = self.bbox_head(x)
+            # positive_infos is a list of obj:`InstanceData`
+            # It contains the information about the positive samples
+            # CondInst, YOLACT
+            det_losses, positive_infos = self.bbox_head.loss(
+                *bbox_head_preds,
+                gt_bboxes=gt_bboxes,
+                gt_labels=gt_labels,
+                gt_masks=gt_masks,
+                img_metas=img_metas,
+                gt_bboxes_ignore=gt_bboxes_ignore,
+                **kwargs)
+            losses.update(det_losses)
+        else:
+            positive_infos = None
+
+        mask_loss = self.mask_head.forward_train(
+            x,
+            gt_labels,
+            gt_masks,
+            img_metas,
+            positive_infos=positive_infos,
+            gt_bboxes=gt_bboxes,
+            gt_bboxes_ignore=gt_bboxes_ignore,
+            **kwargs)
+        # avoid loss override
+        assert not set(mask_loss.keys()) & set(losses.keys())
+
+        losses.update(mask_loss)
+        return losses
+
+    def simple_test(self, img, img_metas, rescale=False):
+        """Test function without test-time augmentation.
+
+        Args:
+            img (torch.Tensor): Images with shape (B, C, H, W).
+            img_metas (list[dict]): List of image information.
+            rescale (bool, optional): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list(tuple): Formatted bbox and mask results of multiple \
+                images. The outer list corresponds to each image. \
+                Each tuple contains two type of results of single image:
+
+                - bbox_results (list[np.ndarray]): BBox results of
+                  single image. The list corresponds to each class.
+                  each ndarray has a shape (N, 5), N is the number of
+                  bboxes with this category, and last dimension
+                  5 arrange as (x1, y1, x2, y2, scores).
+                - mask_results (list[np.ndarray]): Mask results of
+                  single image. The list corresponds to each class.
+                  each ndarray has a shape (N, img_h, img_w), N
+                  is the number of masks with this category.
+        """
+        feat = self.extract_feat(img)
+        if self.bbox_head:
+            outs = self.bbox_head(feat)
+            # results_list is list[obj:`InstanceData`]
+            results_list = self.bbox_head.get_results(
+                *outs, img_metas=img_metas, cfg=self.test_cfg, rescale=rescale)
+        else:
+            results_list = None
+
+        results_list = self.mask_head.simple_test(
+            feat, img_metas, rescale=rescale, instances_list=results_list)
+
+        format_results_list = []
+        for results in results_list:
+            format_results_list.append(self.format_results(results))
+
+        return format_results_list
+
+    def format_results(self, results):
+        """Format the model predictions according to the interface with
+        dataset.
+
+        Args:
+            results (:obj:`InstanceData`): Processed
+                results of single images. Usually contains
+                following keys.
+
+                - scores (Tensor): Classification scores, has shape
+                  (num_instance,)
+                - labels (Tensor): Has shape (num_instances,).
+                - masks (Tensor): Processed mask results, has
+                  shape (num_instances, h, w).
+
+        Returns:
+            tuple: Formatted bbox and mask results.. It contains two items:
+
+                - bbox_results (list[np.ndarray]): BBox results of
+                  single image. The list corresponds to each class.
+                  each ndarray has a shape (N, 5), N is the number of
+                  bboxes with this category, and last dimension
+                  5 arrange as (x1, y1, x2, y2, scores).
+                - mask_results (list[np.ndarray]): Mask results of
+                  single image. The list corresponds to each class.
+                  each ndarray has shape (N, img_h, img_w), N
+                  is the number of masks with this category.
+        """
+        data_keys = results.keys()
+        assert 'scores' in data_keys
+        assert 'labels' in data_keys
+
+        assert 'masks' in data_keys, \
+            'results should contain ' \
+            'masks when format the results '
+        mask_results = [[] for _ in range(self.mask_head.num_classes)]
+
+        num_masks = len(results)
+
+        if num_masks == 0:
+            bbox_results = [
+                np.zeros((0, 5), dtype=np.float32)
+                for _ in range(self.mask_head.num_classes)
+            ]
+            return bbox_results, mask_results
+
+        labels = results.labels.detach().cpu().numpy()
+
+        if 'bboxes' not in results:
+            # create dummy bbox results to store the scores
+            results.bboxes = results.scores.new_zeros(len(results), 4)
+
+        det_bboxes = torch.cat([results.bboxes, results.scores[:, None]],
+                               dim=-1)
+        det_bboxes = det_bboxes.detach().cpu().numpy()
+        bbox_results = [
+            det_bboxes[labels == i, :]
+            for i in range(self.mask_head.num_classes)
+        ]
+
+        masks = results.masks.detach().cpu().numpy()
+
+        for idx in range(num_masks):
+            mask = masks[idx]
+            mask_results[labels[idx]].append(mask)
+
+        return bbox_results, mask_results
+
+    def aug_test(self, imgs, img_metas, rescale=False):
+        raise NotImplementedError
+
+    def show_result(self,
+                    img,
+                    result,
+                    score_thr=0.3,
+                    bbox_color=(72, 101, 241),
+                    text_color=(72, 101, 241),
+                    mask_color=None,
+                    thickness=2,
+                    font_size=13,
+                    win_name='',
+                    show=False,
+                    wait_time=0,
+                    out_file=None):
+        """Draw `result` over `img`.
+
+        Args:
+            img (str or Tensor): The image to be displayed.
+            result (tuple): Format bbox and mask results.
+                It contains two items:
+
+                - bbox_results (list[np.ndarray]): BBox results of
+                  single image. The list corresponds to each class.
+                  each ndarray has a shape (N, 5), N is the number of
+                  bboxes with this category, and last dimension
+                  5 arrange as (x1, y1, x2, y2, scores).
+                - mask_results (list[np.ndarray]): Mask results of
+                  single image. The list corresponds to each class.
+                  each ndarray has shape (N, img_h, img_w), N
+                  is the number of masks with this category.
+
+            score_thr (float, optional): Minimum score of bboxes to be shown.
+                Default: 0.3.
+            bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
+               The tuple of color should be in BGR order. Default: 'green'
+            text_color (str or tuple(int) or :obj:`Color`):Color of texts.
+               The tuple of color should be in BGR order. Default: 'green'
+            mask_color (None or str or tuple(int) or :obj:`Color`):
+               Color of masks. The tuple of color should be in BGR order.
+               Default: None
+            thickness (int): Thickness of lines. Default: 2
+            font_size (int): Font size of texts. Default: 13
+            win_name (str): The window name. Default: ''
+            wait_time (float): Value of waitKey param.
+                Default: 0.
+            show (bool): Whether to show the image.
+                Default: False.
+            out_file (str or None): The filename to write the image.
+                Default: None.
+
+        Returns:
+            img (Tensor): Only if not `show` or `out_file`
+        """
+
+        assert isinstance(result, tuple)
+        bbox_result, mask_result = result
+        bboxes = np.vstack(bbox_result)
+        img = mmcv.imread(img)
+        img = img.copy()
+        labels = [
+            np.full(bbox.shape[0], i, dtype=np.int32)
+            for i, bbox in enumerate(bbox_result)
+        ]
+        labels = np.concatenate(labels)
+        if len(labels) == 0:
+            bboxes = np.zeros([0, 5])
+            masks = np.zeros([0, 0, 0])
+        # draw segmentation masks
+        else:
+            masks = mmcv.concat_list(mask_result)
+
+            if isinstance(masks[0], torch.Tensor):
+                masks = torch.stack(masks, dim=0).detach().cpu().numpy()
+            else:
+                masks = np.stack(masks, axis=0)
+            # dummy bboxes
+            if bboxes[:, :4].sum() == 0:
+                num_masks = len(bboxes)
+                x_any = masks.any(axis=1)
+                y_any = masks.any(axis=2)
+                for idx in range(num_masks):
+                    x = np.where(x_any[idx, :])[0]
+                    y = np.where(y_any[idx, :])[0]
+                    if len(x) > 0 and len(y) > 0:
+                        bboxes[idx, :4] = np.array(
+                            [x[0], y[0], x[-1] + 1, y[-1] + 1],
+                            dtype=np.float32)
+        # if out_file specified, do not show image in window
+        if out_file is not None:
+            show = False
+        # draw bounding boxes
+        img = imshow_det_bboxes(
+            img,
+            bboxes,
+            labels,
+            masks,
+            class_names=self.CLASSES,
+            score_thr=score_thr,
+            bbox_color=bbox_color,
+            text_color=text_color,
+            mask_color=mask_color,
+            thickness=thickness,
+            font_size=font_size,
+            win_name=win_name,
+            show=show,
+            wait_time=wait_time,
+            out_file=out_file)
+
+        if not (show or out_file):
+            return img
diff --git a/mmdet/models/detectors/solo.py b/mmdet/models/detectors/solo.py
new file mode 100644
index 0000000..df6f6de
--- /dev/null
+++ b/mmdet/models/detectors/solo.py
@@ -0,0 +1,30 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage_instance_seg import SingleStageInstanceSegmentor
+
+
+@DETECTORS.register_module()
+class SOLO(SingleStageInstanceSegmentor):
+    """`SOLO: Segmenting Objects by Locations
+    <https://arxiv.org/abs/1912.04488>`_
+
+    """
+
+    def __init__(self,
+                 backbone,
+                 neck=None,
+                 bbox_head=None,
+                 mask_head=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 init_cfg=None,
+                 pretrained=None):
+        super().__init__(
+            backbone=backbone,
+            neck=neck,
+            bbox_head=bbox_head,
+            mask_head=mask_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            init_cfg=init_cfg,
+            pretrained=pretrained)
diff --git a/mmdet/models/detectors/solov2.py b/mmdet/models/detectors/solov2.py
new file mode 100644
index 0000000..711fcb4
--- /dev/null
+++ b/mmdet/models/detectors/solov2.py
@@ -0,0 +1,30 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage_instance_seg import SingleStageInstanceSegmentor
+
+
+@DETECTORS.register_module()
+class SOLOv2(SingleStageInstanceSegmentor):
+    """`SOLOv2: Dynamic and Fast Instance Segmentation
+    <https://arxiv.org/abs/2003.10152>`_
+
+    """
+
+    def __init__(self,
+                 backbone,
+                 neck=None,
+                 bbox_head=None,
+                 mask_head=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 init_cfg=None,
+                 pretrained=None):
+        super().__init__(
+            backbone=backbone,
+            neck=neck,
+            bbox_head=bbox_head,
+            mask_head=mask_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            init_cfg=init_cfg,
+            pretrained=pretrained)
diff --git a/mmdet/models/detectors/sparse_rcnn.py b/mmdet/models/detectors/sparse_rcnn.py
new file mode 100644
index 0000000..e90c2a5
--- /dev/null
+++ b/mmdet/models/detectors/sparse_rcnn.py
@@ -0,0 +1,111 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .two_stage import TwoStageDetector
+
+
+@DETECTORS.register_module()
+class SparseRCNN(TwoStageDetector):
+    r"""Implementation of `Sparse R-CNN: End-to-End Object Detection with
+    Learnable Proposals <https://arxiv.org/abs/2011.12450>`_"""
+
+    def __init__(self, *args, **kwargs):
+        super(SparseRCNN, self).__init__(*args, **kwargs)
+        assert self.with_rpn, 'Sparse R-CNN and QueryInst ' \
+            'do not support external proposals'
+
+    def forward_train(self,
+                      img,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None,
+                      gt_masks=None,
+                      proposals=None,
+                      **kwargs):
+        """Forward function of SparseR-CNN and QueryInst in train stage.
+
+        Args:
+            img (Tensor): of shape (N, C, H, W) encoding input images.
+                Typically these should be mean centered and std scaled.
+            img_metas (list[dict]): list of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                :class:`mmdet.datasets.pipelines.Collect`.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            gt_bboxes_ignore (None | list[Tensor): specify which bounding
+                boxes can be ignored when computing the loss.
+            gt_masks (List[Tensor], optional) : Segmentation masks for
+                each box. This is required to train QueryInst.
+            proposals (List[Tensor], optional): override rpn proposals with
+                custom proposals. Use when `with_rpn` is False.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+
+        assert proposals is None, 'Sparse R-CNN and QueryInst ' \
+            'do not support external proposals'
+
+        x = self.extract_feat(img)
+        proposal_boxes, proposal_features, imgs_whwh = \
+            self.rpn_head.forward_train(x, img_metas)
+        roi_losses = self.roi_head.forward_train(
+            x,
+            proposal_boxes,
+            proposal_features,
+            img_metas,
+            gt_bboxes,
+            gt_labels,
+            gt_bboxes_ignore=gt_bboxes_ignore,
+            gt_masks=gt_masks,
+            imgs_whwh=imgs_whwh)
+        return roi_losses
+
+    def simple_test(self, img, img_metas, rescale=False):
+        """Test function without test time augmentation.
+
+        Args:
+            imgs (list[torch.Tensor]): List of multiple images
+            img_metas (list[dict]): List of image information.
+            rescale (bool): Whether to rescale the results.
+                Defaults to False.
+
+        Returns:
+            list[list[np.ndarray]]: BBox results of each image and classes.
+                The outer list corresponds to each image. The inner list
+                corresponds to each class.
+        """
+        x = self.extract_feat(img)
+        proposal_boxes, proposal_features, imgs_whwh = \
+            self.rpn_head.simple_test_rpn(x, img_metas)
+        results = self.roi_head.simple_test(
+            x,
+            proposal_boxes,
+            proposal_features,
+            img_metas,
+            imgs_whwh=imgs_whwh,
+            rescale=rescale)
+        return results
+
+    def forward_dummy(self, img):
+        """Used for computing network flops.
+
+        See `mmdetection/tools/analysis_tools/get_flops.py`
+        """
+        # backbone
+        x = self.extract_feat(img)
+        # rpn
+        num_imgs = len(img)
+        dummy_img_metas = [
+            dict(img_shape=(800, 1333, 3)) for _ in range(num_imgs)
+        ]
+        proposal_boxes, proposal_features, imgs_whwh = \
+            self.rpn_head.simple_test_rpn(x, dummy_img_metas)
+        # roi_head
+        roi_outs = self.roi_head.forward_dummy(x, proposal_boxes,
+                                               proposal_features,
+                                               dummy_img_metas)
+        return roi_outs
diff --git a/mmdet/models/detectors/tood.py b/mmdet/models/detectors/tood.py
new file mode 100644
index 0000000..7dd18c3
--- /dev/null
+++ b/mmdet/models/detectors/tood.py
@@ -0,0 +1,23 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class TOOD(SingleStageDetector):
+    r"""Implementation of `TOOD: Task-aligned One-stage Object Detection.
+    <https://arxiv.org/abs/2108.07755>`_."""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(TOOD, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                   test_cfg, pretrained, init_cfg)
+
+    def set_epoch(self, epoch):
+        self.bbox_head.epoch = epoch
diff --git a/mmdet/models/detectors/trident_faster_rcnn.py b/mmdet/models/detectors/trident_faster_rcnn.py
new file mode 100644
index 0000000..fb26168
--- /dev/null
+++ b/mmdet/models/detectors/trident_faster_rcnn.py
@@ -0,0 +1,70 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .faster_rcnn import FasterRCNN
+
+
+@DETECTORS.register_module()
+class TridentFasterRCNN(FasterRCNN):
+    """Implementation of `TridentNet <https://arxiv.org/abs/1901.01892>`_"""
+
+    def __init__(self,
+                 backbone,
+                 rpn_head,
+                 roi_head,
+                 train_cfg,
+                 test_cfg,
+                 neck=None,
+                 pretrained=None,
+                 init_cfg=None):
+
+        super(TridentFasterRCNN, self).__init__(
+            backbone=backbone,
+            neck=neck,
+            rpn_head=rpn_head,
+            roi_head=roi_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained,
+            init_cfg=init_cfg)
+        assert self.backbone.num_branch == self.roi_head.num_branch
+        assert self.backbone.test_branch_idx == self.roi_head.test_branch_idx
+        self.num_branch = self.backbone.num_branch
+        self.test_branch_idx = self.backbone.test_branch_idx
+
+    def simple_test(self, img, img_metas, proposals=None, rescale=False):
+        """Test without augmentation."""
+        assert self.with_bbox, 'Bbox head must be implemented.'
+        x = self.extract_feat(img)
+        if proposals is None:
+            num_branch = (self.num_branch if self.test_branch_idx == -1 else 1)
+            trident_img_metas = img_metas * num_branch
+            proposal_list = self.rpn_head.simple_test_rpn(x, trident_img_metas)
+        else:
+            proposal_list = proposals
+        # TODO： Fix trident_img_metas undefined errors
+        #  when proposals is specified
+        return self.roi_head.simple_test(
+            x, proposal_list, trident_img_metas, rescale=rescale)
+
+    def aug_test(self, imgs, img_metas, rescale=False):
+        """Test with augmentations.
+
+        If rescale is False, then returned bboxes and masks will fit the scale
+        of imgs[0].
+        """
+        x = self.extract_feats(imgs)
+        num_branch = (self.num_branch if self.test_branch_idx == -1 else 1)
+        trident_img_metas = [img_metas * num_branch for img_metas in img_metas]
+        proposal_list = self.rpn_head.aug_test_rpn(x, trident_img_metas)
+        return self.roi_head.aug_test(
+            x, proposal_list, img_metas, rescale=rescale)
+
+    def forward_train(self, img, img_metas, gt_bboxes, gt_labels, **kwargs):
+        """make copies of img and gts to fit multi-branch."""
+        trident_gt_bboxes = tuple(gt_bboxes * self.num_branch)
+        trident_gt_labels = tuple(gt_labels * self.num_branch)
+        trident_img_metas = tuple(img_metas * self.num_branch)
+
+        return super(TridentFasterRCNN,
+                     self).forward_train(img, trident_img_metas,
+                                         trident_gt_bboxes, trident_gt_labels)
diff --git a/mmdet/models/detectors/two_stage.py b/mmdet/models/detectors/two_stage.py
new file mode 100644
index 0000000..ebc748f
--- /dev/null
+++ b/mmdet/models/detectors/two_stage.py
@@ -0,0 +1,213 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch
+
+from ..builder import DETECTORS, build_backbone, build_head, build_neck
+from .base import BaseDetector
+
+
+@DETECTORS.register_module()
+class TwoStageDetector(BaseDetector):
+    """Base class for two-stage detectors.
+
+    Two-stage detectors typically consisting of a region proposal network and a
+    task-specific regression head.
+    """
+
+    def __init__(self,
+                 backbone,
+                 neck=None,
+                 rpn_head=None,
+                 roi_head=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(TwoStageDetector, self).__init__(init_cfg)
+        if pretrained:
+            warnings.warn('DeprecationWarning: pretrained is deprecated, '
+                          'please use "init_cfg" instead')
+            backbone.pretrained = pretrained
+        self.backbone = build_backbone(backbone)
+
+        if neck is not None:
+            self.neck = build_neck(neck)
+
+        if rpn_head is not None:
+            rpn_train_cfg = train_cfg.rpn if train_cfg is not None else None
+            rpn_head_ = rpn_head.copy()
+            rpn_head_.update(train_cfg=rpn_train_cfg, test_cfg=test_cfg.rpn)
+            self.rpn_head = build_head(rpn_head_)
+
+        if roi_head is not None:
+            # update train and test cfg here for now
+            # TODO: refactor assigner & sampler
+            rcnn_train_cfg = train_cfg.rcnn if train_cfg is not None else None
+            roi_head.update(train_cfg=rcnn_train_cfg)
+            roi_head.update(test_cfg=test_cfg.rcnn)
+            roi_head.pretrained = pretrained
+            self.roi_head = build_head(roi_head)
+
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+
+    @property
+    def with_rpn(self):
+        """bool: whether the detector has RPN"""
+        return hasattr(self, 'rpn_head') and self.rpn_head is not None
+
+    @property
+    def with_roi_head(self):
+        """bool: whether the detector has a RoI head"""
+        return hasattr(self, 'roi_head') and self.roi_head is not None
+
+    def extract_feat(self, img):
+        """Directly extract features from the backbone+neck."""
+        x = self.backbone(img)
+        if self.with_neck:
+            x = self.neck(x)
+        return x
+
+    def forward_dummy(self, img):
+        """Used for computing network flops.
+
+        See `mmdetection/tools/analysis_tools/get_flops.py`
+        """
+        outs = ()
+        # backbone
+        x = self.extract_feat(img)
+        # rpn
+        if self.with_rpn:
+            rpn_outs = self.rpn_head(x)
+            outs = outs + (rpn_outs, )
+        proposals = torch.randn(1000, 4).to(img.device)
+        # roi_head
+        roi_outs = self.roi_head.forward_dummy(x, proposals)
+        outs = outs + (roi_outs, )
+        return outs
+
+    def forward_train(self,
+                      img,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None,
+                      gt_masks=None,
+                      proposals=None,
+                      **kwargs):
+        """
+        Args:
+            img (Tensor): of shape (N, C, H, W) encoding input images.
+                Typically these should be mean centered and std scaled.
+
+            img_metas (list[dict]): list of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `mmdet/datasets/pipelines/formatting.py:Collect`.
+
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+
+            gt_labels (list[Tensor]): class indices corresponding to each box
+
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+
+            gt_masks (None | Tensor) : true segmentation masks for each box
+                used if the architecture supports a segmentation task.
+
+            proposals : override rpn proposals with custom proposals. Use when
+                `with_rpn` is False.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        x = self.extract_feat(img)
+
+        losses = dict()
+
+        # RPN forward and loss
+        if self.with_rpn:
+            proposal_cfg = self.train_cfg.get('rpn_proposal',
+                                              self.test_cfg.rpn)
+            rpn_losses, proposal_list = self.rpn_head.forward_train(
+                x,
+                img_metas,
+                gt_bboxes,
+                gt_labels=None,
+                gt_bboxes_ignore=gt_bboxes_ignore,
+                proposal_cfg=proposal_cfg,
+                **kwargs)
+            rpn_losses = dict(
+                loss_rpn_cls=rpn_losses['loss_cls'], loss_rpn_bbox=rpn_losses['loss_bbox'])
+            losses.update(rpn_losses)
+        else:
+            proposal_list = proposals
+
+        roi_losses = self.roi_head.forward_train(x, img_metas, proposal_list,
+                                                 gt_bboxes, gt_labels,
+                                                 gt_bboxes_ignore, gt_masks,
+                                                 **kwargs)
+        losses.update(roi_losses)
+
+        return losses
+
+    async def async_simple_test(self,
+                                img,
+                                img_meta,
+                                proposals=None,
+                                rescale=False):
+        """Async test without augmentation."""
+        assert self.with_bbox, 'Bbox head must be implemented.'
+        x = self.extract_feat(img)
+
+        if proposals is None:
+            proposal_list = await self.rpn_head.async_simple_test_rpn(
+                x, img_meta)
+        else:
+            proposal_list = proposals
+
+        return await self.roi_head.async_simple_test(
+            x, proposal_list, img_meta, rescale=rescale)
+
+    def simple_test(self, img, img_metas, proposals=None, rescale=False):
+        """Test without augmentation."""
+
+        assert self.with_bbox, 'Bbox head must be implemented.'
+        x = self.extract_feat(img)
+        if proposals is None:
+            proposal_list = self.rpn_head.simple_test_rpn(x, img_metas)
+        else:
+            proposal_list = proposals
+
+        return self.roi_head.simple_test(
+            x, proposal_list, img_metas, rescale=rescale)
+
+    def aug_test(self, imgs, img_metas, rescale=False):
+        """Test with augmentations.
+
+        If rescale is False, then returned bboxes and masks will fit the scale
+        of imgs[0].
+        """
+        x = self.extract_feats(imgs)
+        proposal_list = self.rpn_head.aug_test_rpn(x, img_metas)
+        return self.roi_head.aug_test(
+            x, proposal_list, img_metas, rescale=rescale)
+
+    def onnx_export(self, img, img_metas):
+
+        img_shape = torch._shape_as_tensor(img)[2:]
+        img_metas[0]['img_shape_for_onnx'] = img_shape
+        x = self.extract_feat(img)
+        proposals = self.rpn_head.onnx_export(x, img_metas)
+        if hasattr(self.roi_head, 'onnx_export'):
+            return self.roi_head.onnx_export(x, proposals, img_metas)
+        else:
+            raise NotImplementedError(
+                f'{self.__class__.__name__} can not '
+                f'be exported to ONNX. Please refer to the '
+                f'list of supported models,'
+                f'https://mmdetection.readthedocs.io/en/latest/tutorials/pytorch2onnx.html#list-of-supported-models-exportable-to-onnx'  # noqa E501
+            )
diff --git a/mmdet/models/detectors/vfnet.py b/mmdet/models/detectors/vfnet.py
new file mode 100644
index 0000000..38ddcda
--- /dev/null
+++ b/mmdet/models/detectors/vfnet.py
@@ -0,0 +1,20 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class VFNet(SingleStageDetector):
+    """Implementation of `VarifocalNet
+    (VFNet).<https://arxiv.org/abs/2008.13367>`_"""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(VFNet, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                    test_cfg, pretrained, init_cfg)
diff --git a/mmdet/models/detectors/yolact.py b/mmdet/models/detectors/yolact.py
new file mode 100644
index 0000000..4ddea0b
--- /dev/null
+++ b/mmdet/models/detectors/yolact.py
@@ -0,0 +1,120 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdet.core import bbox2result
+from ..builder import DETECTORS, build_head
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class YOLACT(SingleStageDetector):
+    """Implementation of `YOLACT <https://arxiv.org/abs/1904.02689>`_"""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 segm_head,
+                 mask_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(YOLACT, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                     test_cfg, pretrained, init_cfg)
+        self.segm_head = build_head(segm_head)
+        self.mask_head = build_head(mask_head)
+
+    def forward_dummy(self, img):
+        """Used for computing network flops.
+
+        See `mmdetection/tools/analysis_tools/get_flops.py`
+        """
+        feat = self.extract_feat(img)
+        bbox_outs = self.bbox_head(feat)
+        prototypes = self.mask_head.forward_dummy(feat[0])
+        return (bbox_outs, prototypes)
+
+    def forward_train(self,
+                      img,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None,
+                      gt_masks=None):
+        """
+        Args:
+            img (Tensor): of shape (N, C, H, W) encoding input images.
+                Typically these should be mean centered and std scaled.
+            img_metas (list[dict]): list of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `mmdet/datasets/pipelines/formatting.py:Collect`.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+            gt_masks (None | Tensor) : true segmentation masks for each box
+                used if the architecture supports a segmentation task.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        # convert Bitmap mask or Polygon Mask to Tensor here
+        gt_masks = [
+            gt_mask.to_tensor(dtype=torch.uint8, device=img.device)
+            for gt_mask in gt_masks
+        ]
+
+        x = self.extract_feat(img)
+
+        cls_score, bbox_pred, coeff_pred = self.bbox_head(x)
+        bbox_head_loss_inputs = (cls_score, bbox_pred) + (gt_bboxes, gt_labels,
+                                                          img_metas)
+        losses, sampling_results = self.bbox_head.loss(
+            *bbox_head_loss_inputs, gt_bboxes_ignore=gt_bboxes_ignore)
+
+        segm_head_outs = self.segm_head(x[0])
+        loss_segm = self.segm_head.loss(segm_head_outs, gt_masks, gt_labels)
+        losses.update(loss_segm)
+
+        mask_pred = self.mask_head(x[0], coeff_pred, gt_bboxes, img_metas,
+                                   sampling_results)
+        loss_mask = self.mask_head.loss(mask_pred, gt_masks, gt_bboxes,
+                                        img_metas, sampling_results)
+        losses.update(loss_mask)
+
+        # check NaN and Inf
+        for loss_name in losses.keys():
+            assert torch.isfinite(torch.stack(losses[loss_name]))\
+                .all().item(), '{} becomes infinite or NaN!'\
+                .format(loss_name)
+
+        return losses
+
+    def simple_test(self, img, img_metas, rescale=False):
+        """Test function without test-time augmentation."""
+        feat = self.extract_feat(img)
+        det_bboxes, det_labels, det_coeffs = self.bbox_head.simple_test(
+            feat, img_metas, rescale=rescale)
+        bbox_results = [
+            bbox2result(det_bbox, det_label, self.bbox_head.num_classes)
+            for det_bbox, det_label in zip(det_bboxes, det_labels)
+        ]
+
+        segm_results = self.mask_head.simple_test(
+            feat,
+            det_bboxes,
+            det_labels,
+            det_coeffs,
+            img_metas,
+            rescale=rescale)
+
+        return list(zip(bbox_results, segm_results))
+
+    def aug_test(self, imgs, img_metas, rescale=False):
+        """Test with augmentations."""
+        raise NotImplementedError(
+            'YOLACT does not support test-time augmentation')
diff --git a/mmdet/models/detectors/yolo.py b/mmdet/models/detectors/yolo.py
new file mode 100644
index 0000000..0ccd417
--- /dev/null
+++ b/mmdet/models/detectors/yolo.py
@@ -0,0 +1,42 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# Copyright (c) 2019 Western Digital Corporation or its affiliates.
+import torch
+
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class YOLOV3(SingleStageDetector):
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(YOLOV3, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                     test_cfg, pretrained, init_cfg)
+
+    def onnx_export(self, img, img_metas):
+        """Test function for exporting to ONNX, without test time augmentation.
+
+        Args:
+            img (torch.Tensor): input images.
+            img_metas (list[dict]): List of image information.
+
+        Returns:
+            tuple[Tensor, Tensor]: dets of shape [N, num_det, 5]
+                and class labels of shape [N, num_det].
+        """
+        x = self.extract_feat(img)
+        outs = self.bbox_head.forward(x)
+        # get shape as tensor
+        img_shape = torch._shape_as_tensor(img)[2:]
+        img_metas[0]['img_shape_for_onnx'] = img_shape
+
+        det_bboxes, det_labels = self.bbox_head.onnx_export(*outs, img_metas)
+
+        return det_bboxes, det_labels
diff --git a/mmdet/models/detectors/yolof.py b/mmdet/models/detectors/yolof.py
new file mode 100644
index 0000000..2bc4f1a
--- /dev/null
+++ b/mmdet/models/detectors/yolof.py
@@ -0,0 +1,20 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class YOLOF(SingleStageDetector):
+    r"""Implementation of `You Only Look One-level Feature
+    <https://arxiv.org/abs/2103.09460>`_"""
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(YOLOF, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                    test_cfg, pretrained, init_cfg)
diff --git a/mmdet/models/detectors/yolox.py b/mmdet/models/detectors/yolox.py
new file mode 100644
index 0000000..34d51b1
--- /dev/null
+++ b/mmdet/models/detectors/yolox.py
@@ -0,0 +1,136 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import random
+
+import torch
+import torch.distributed as dist
+import torch.nn.functional as F
+from mmcv.runner import get_dist_info
+
+from ...utils import log_img_scale
+from ..builder import DETECTORS
+from .single_stage import SingleStageDetector
+
+
+@DETECTORS.register_module()
+class YOLOX(SingleStageDetector):
+    r"""Implementation of `YOLOX: Exceeding YOLO Series in 2021
+    <https://arxiv.org/abs/2107.08430>`_
+
+    Note: Considering the trade-off between training speed and accuracy,
+    multi-scale training is temporarily kept. More elegant implementation
+    will be adopted in the future.
+
+    Args:
+        backbone (nn.Module): The backbone module.
+        neck (nn.Module): The neck module.
+        bbox_head (nn.Module): The bbox head module.
+        train_cfg (obj:`ConfigDict`, optional): The training config
+            of YOLOX. Default: None.
+        test_cfg (obj:`ConfigDict`, optional): The testing config
+            of YOLOX. Default: None.
+        pretrained (str, optional): model pretrained path.
+            Default: None.
+        input_size (tuple): The model default input image size. The shape
+            order should be (height, width). Default: (640, 640).
+        size_multiplier (int): Image size multiplication factor.
+            Default: 32.
+        random_size_range (tuple): The multi-scale random range during
+            multi-scale training. The real training image size will
+            be multiplied by size_multiplier. Default: (15, 25).
+        random_size_interval (int): The iter interval of change
+            image size. Default: 10.
+        init_cfg (dict, optional): Initialization config dict.
+            Default: None.
+    """
+
+    def __init__(self,
+                 backbone,
+                 neck,
+                 bbox_head,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 input_size=(640, 640),
+                 size_multiplier=32,
+                 random_size_range=(15, 25),
+                 random_size_interval=10,
+                 init_cfg=None):
+        super(YOLOX, self).__init__(backbone, neck, bbox_head, train_cfg,
+                                    test_cfg, pretrained, init_cfg)
+        log_img_scale(input_size, skip_square=True)
+        self.rank, self.world_size = get_dist_info()
+        self._default_input_size = input_size
+        self._input_size = input_size
+        self._random_size_range = random_size_range
+        self._random_size_interval = random_size_interval
+        self._size_multiplier = size_multiplier
+        self._progress_in_iter = 0
+
+    def forward_train(self,
+                      img,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None):
+        """
+        Args:
+            img (Tensor): Input images of shape (N, C, H, W).
+                Typically these should be mean centered and std scaled.
+            img_metas (list[dict]): A List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                :class:`mmdet.datasets.pipelines.Collect`.
+            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
+                image in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): Class indices corresponding to each box
+            gt_bboxes_ignore (None | list[Tensor]): Specify which bounding
+                boxes can be ignored when computing the loss.
+        Returns:
+            dict[str, Tensor]: A dictionary of loss components.
+        """
+        # Multi-scale training
+        img, gt_bboxes = self._preprocess(img, gt_bboxes)
+
+        losses = super(YOLOX, self).forward_train(img, img_metas, gt_bboxes,
+                                                  gt_labels, gt_bboxes_ignore)
+
+        # random resizing
+        if (self._progress_in_iter + 1) % self._random_size_interval == 0:
+            self._input_size = self._random_resize(device=img.device)
+        self._progress_in_iter += 1
+
+        return losses
+
+    def _preprocess(self, img, gt_bboxes):
+        scale_y = self._input_size[0] / self._default_input_size[0]
+        scale_x = self._input_size[1] / self._default_input_size[1]
+        if scale_x != 1 or scale_y != 1:
+            img = F.interpolate(
+                img,
+                size=self._input_size,
+                mode='bilinear',
+                align_corners=False)
+            for gt_bbox in gt_bboxes:
+                gt_bbox[..., 0::2] = gt_bbox[..., 0::2] * scale_x
+                gt_bbox[..., 1::2] = gt_bbox[..., 1::2] * scale_y
+        return img, gt_bboxes
+
+    def _random_resize(self, device):
+        tensor = torch.LongTensor(2).to(device)
+
+        if self.rank == 0:
+            size = random.randint(*self._random_size_range)
+            aspect_ratio = float(
+                self._default_input_size[1]) / self._default_input_size[0]
+            size = (self._size_multiplier * size,
+                    self._size_multiplier * int(aspect_ratio * size))
+            tensor[0] = size[0]
+            tensor[1] = size[1]
+
+        if self.world_size > 1:
+            dist.barrier()
+            dist.broadcast(tensor, 0)
+
+        input_size = (tensor[0].item(), tensor[1].item())
+        return input_size
diff --git a/mmdet/models/losses/__init__.py b/mmdet/models/losses/__init__.py
new file mode 100644
index 0000000..068a54d
--- /dev/null
+++ b/mmdet/models/losses/__init__.py
@@ -0,0 +1,32 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .accuracy import Accuracy, accuracy
+from .ae_loss import AssociativeEmbeddingLoss
+from .balanced_l1_loss import BalancedL1Loss, balanced_l1_loss
+from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy,
+                                 cross_entropy, mask_cross_entropy)
+from .dice_loss import DiceLoss
+from .focal_loss import FocalLoss, sigmoid_focal_loss
+from .gaussian_focal_loss import GaussianFocalLoss
+from .gfocal_loss import DistributionFocalLoss, QualityFocalLoss
+from .ghm_loss import GHMC, GHMR
+from .iou_loss import (BoundedIoULoss, CIoULoss, DIoULoss, GIoULoss, IoULoss,
+                       bounded_iou_loss, iou_loss)
+from .kd_loss import KnowledgeDistillationKLDivLoss
+from .mse_loss import MSELoss, mse_loss
+from .pisa_loss import carl_loss, isr_p
+from .seesaw_loss import SeesawLoss
+from .smooth_l1_loss import L1Loss, SmoothL1Loss, l1_loss, smooth_l1_loss
+from .utils import reduce_loss, weight_reduce_loss, weighted_loss
+from .varifocal_loss import VarifocalLoss
+
+__all__ = [
+    'accuracy', 'Accuracy', 'cross_entropy', 'binary_cross_entropy',
+    'mask_cross_entropy', 'CrossEntropyLoss', 'sigmoid_focal_loss',
+    'FocalLoss', 'smooth_l1_loss', 'SmoothL1Loss', 'balanced_l1_loss',
+    'BalancedL1Loss', 'mse_loss', 'MSELoss', 'iou_loss', 'bounded_iou_loss',
+    'IoULoss', 'BoundedIoULoss', 'GIoULoss', 'DIoULoss', 'CIoULoss', 'GHMC',
+    'GHMR', 'reduce_loss', 'weight_reduce_loss', 'weighted_loss', 'L1Loss',
+    'l1_loss', 'isr_p', 'carl_loss', 'AssociativeEmbeddingLoss',
+    'GaussianFocalLoss', 'QualityFocalLoss', 'DistributionFocalLoss',
+    'VarifocalLoss', 'KnowledgeDistillationKLDivLoss', 'SeesawLoss', 'DiceLoss'
+]
diff --git a/mmdet/models/losses/accuracy.py b/mmdet/models/losses/accuracy.py
new file mode 100644
index 0000000..fe765a3
--- /dev/null
+++ b/mmdet/models/losses/accuracy.py
@@ -0,0 +1,79 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch.nn as nn
+
+
+@mmcv.jit(coderize=True)
+def accuracy(pred, target, topk=1, thresh=None):
+    """Calculate accuracy according to the prediction and target.
+
+    Args:
+        pred (torch.Tensor): The model prediction, shape (N, num_class)
+        target (torch.Tensor): The target of each prediction, shape (N, )
+        topk (int | tuple[int], optional): If the predictions in ``topk``
+            matches the target, the predictions will be regarded as
+            correct ones. Defaults to 1.
+        thresh (float, optional): If not None, predictions with scores under
+            this threshold are considered incorrect. Default to None.
+
+    Returns:
+        float | tuple[float]: If the input ``topk`` is a single integer,
+            the function will return a single float as accuracy. If
+            ``topk`` is a tuple containing multiple integers, the
+            function will return a tuple containing accuracies of
+            each ``topk`` number.
+    """
+    assert isinstance(topk, (int, tuple))
+    if isinstance(topk, int):
+        topk = (topk, )
+        return_single = True
+    else:
+        return_single = False
+
+    maxk = max(topk)
+    if pred.size(0) == 0:
+        accu = [pred.new_tensor(0.) for i in range(len(topk))]
+        return accu[0] if return_single else accu
+    assert pred.ndim == 2 and target.ndim == 1
+    assert pred.size(0) == target.size(0)
+    assert maxk <= pred.size(1), \
+        f'maxk {maxk} exceeds pred dimension {pred.size(1)}'
+    pred_value, pred_label = pred.topk(maxk, dim=1)
+    pred_label = pred_label.t()  # transpose to shape (maxk, N)
+    correct = pred_label.eq(target.view(1, -1).expand_as(pred_label))
+    if thresh is not None:
+        # Only prediction values larger than thresh are counted as correct
+        correct = correct & (pred_value > thresh).t()
+    res = []
+    for k in topk:
+        correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
+        res.append(correct_k.mul_(100.0 / pred.size(0)))
+    return res[0] if return_single else res
+
+
+class Accuracy(nn.Module):
+
+    def __init__(self, topk=(1, ), thresh=None):
+        """Module to calculate the accuracy.
+
+        Args:
+            topk (tuple, optional): The criterion used to calculate the
+                accuracy. Defaults to (1,).
+            thresh (float, optional): If not None, predictions with scores
+                under this threshold are considered incorrect. Default to None.
+        """
+        super().__init__()
+        self.topk = topk
+        self.thresh = thresh
+
+    def forward(self, pred, target):
+        """Forward function to calculate accuracy.
+
+        Args:
+            pred (torch.Tensor): Prediction of models.
+            target (torch.Tensor): Target for each prediction.
+
+        Returns:
+            tuple[float]: The accuracies under different topk criterions.
+        """
+        return accuracy(pred, target, self.topk, self.thresh)
diff --git a/mmdet/models/losses/ae_loss.py b/mmdet/models/losses/ae_loss.py
new file mode 100644
index 0000000..5c6da22
--- /dev/null
+++ b/mmdet/models/losses/ae_loss.py
@@ -0,0 +1,103 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..builder import LOSSES
+
+
+@mmcv.jit(derivate=True, coderize=True)
+def ae_loss_per_image(tl_preds, br_preds, match):
+    """Associative Embedding Loss in one image.
+
+    Associative Embedding Loss including two parts: pull loss and push loss.
+    Pull loss makes embedding vectors from same object closer to each other.
+    Push loss distinguish embedding vector from different objects, and makes
+        the gap between them is large enough.
+
+    During computing, usually there are 3 cases:
+        - no object in image: both pull loss and push loss will be 0.
+        - one object in image: push loss will be 0 and pull loss is computed
+            by the two corner of the only object.
+        - more than one objects in image: pull loss is computed by corner pairs
+            from each object, push loss is computed by each object with all
+            other objects. We use confusion matrix with 0 in diagonal to
+            compute the push loss.
+
+    Args:
+        tl_preds (tensor): Embedding feature map of left-top corner.
+        br_preds (tensor): Embedding feature map of bottim-right corner.
+        match (list): Downsampled coordinates pair of each ground truth box.
+    """
+
+    tl_list, br_list, me_list = [], [], []
+    if len(match) == 0:  # no object in image
+        pull_loss = tl_preds.sum() * 0.
+        push_loss = tl_preds.sum() * 0.
+    else:
+        for m in match:
+            [tl_y, tl_x], [br_y, br_x] = m
+            tl_e = tl_preds[:, tl_y, tl_x].view(-1, 1)
+            br_e = br_preds[:, br_y, br_x].view(-1, 1)
+            tl_list.append(tl_e)
+            br_list.append(br_e)
+            me_list.append((tl_e + br_e) / 2.0)
+
+        tl_list = torch.cat(tl_list)
+        br_list = torch.cat(br_list)
+        me_list = torch.cat(me_list)
+
+        assert tl_list.size() == br_list.size()
+
+        # N is object number in image, M is dimension of embedding vector
+        N, M = tl_list.size()
+
+        pull_loss = (tl_list - me_list).pow(2) + (br_list - me_list).pow(2)
+        pull_loss = pull_loss.sum() / N
+
+        margin = 1  # exp setting of CornerNet, details in section 3.3 of paper
+
+        # confusion matrix of push loss
+        conf_mat = me_list.expand((N, N, M)).permute(1, 0, 2) - me_list
+        conf_weight = 1 - torch.eye(N).type_as(me_list)
+        conf_mat = conf_weight * (margin - conf_mat.sum(-1).abs())
+
+        if N > 1:  # more than one object in current image
+            push_loss = F.relu(conf_mat).sum() / (N * (N - 1))
+        else:
+            push_loss = tl_preds.sum() * 0.
+
+    return pull_loss, push_loss
+
+
+@LOSSES.register_module()
+class AssociativeEmbeddingLoss(nn.Module):
+    """Associative Embedding Loss.
+
+    More details can be found in
+    `Associative Embedding <https://arxiv.org/abs/1611.05424>`_ and
+    `CornerNet <https://arxiv.org/abs/1808.01244>`_ .
+    Code is modified from `kp_utils.py <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/kp_utils.py#L180>`_  # noqa: E501
+
+    Args:
+        pull_weight (float): Loss weight for corners from same object.
+        push_weight (float): Loss weight for corners from different object.
+    """
+
+    def __init__(self, pull_weight=0.25, push_weight=0.25):
+        super(AssociativeEmbeddingLoss, self).__init__()
+        self.pull_weight = pull_weight
+        self.push_weight = push_weight
+
+    def forward(self, pred, target, match):
+        """Forward function."""
+        batch = pred.size(0)
+        pull_all, push_all = 0.0, 0.0
+        for i in range(batch):
+            pull, push = ae_loss_per_image(pred[i], target[i], match[i])
+
+            pull_all += self.pull_weight * pull
+            push_all += self.push_weight * push
+
+        return pull_all, push_all
diff --git a/mmdet/models/losses/balanced_l1_loss.py b/mmdet/models/losses/balanced_l1_loss.py
new file mode 100644
index 0000000..8500345
--- /dev/null
+++ b/mmdet/models/losses/balanced_l1_loss.py
@@ -0,0 +1,124 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import numpy as np
+import torch
+import torch.nn as nn
+
+from ..builder import LOSSES
+from .utils import weighted_loss
+
+
+@mmcv.jit(derivate=True, coderize=True)
+@weighted_loss
+def balanced_l1_loss(pred,
+                     target,
+                     beta=1.0,
+                     alpha=0.5,
+                     gamma=1.5,
+                     reduction='mean'):
+    """Calculate balanced L1 loss.
+
+    Please see the `Libra R-CNN <https://arxiv.org/pdf/1904.02701.pdf>`_
+
+    Args:
+        pred (torch.Tensor): The prediction with shape (N, 4).
+        target (torch.Tensor): The learning target of the prediction with
+            shape (N, 4).
+        beta (float): The loss is a piecewise function of prediction and target
+            and ``beta`` serves as a threshold for the difference between the
+            prediction and target. Defaults to 1.0.
+        alpha (float): The denominator ``alpha`` in the balanced L1 loss.
+            Defaults to 0.5.
+        gamma (float): The ``gamma`` in the balanced L1 loss.
+            Defaults to 1.5.
+        reduction (str, optional): The method that reduces the loss to a
+            scalar. Options are "none", "mean" and "sum".
+
+    Returns:
+        torch.Tensor: The calculated loss
+    """
+    assert beta > 0
+    if target.numel() == 0:
+        return pred.sum() * 0
+
+    assert pred.size() == target.size()
+
+    diff = torch.abs(pred - target)
+    b = np.e**(gamma / alpha) - 1
+    loss = torch.where(
+        diff < beta, alpha / b *
+        (b * diff + 1) * torch.log(b * diff / beta + 1) - alpha * diff,
+        gamma * diff + gamma / b - alpha * beta)
+
+    return loss
+
+
+@LOSSES.register_module()
+class BalancedL1Loss(nn.Module):
+    """Balanced L1 Loss.
+
+    arXiv: https://arxiv.org/pdf/1904.02701.pdf (CVPR 2019)
+
+    Args:
+        alpha (float): The denominator ``alpha`` in the balanced L1 loss.
+            Defaults to 0.5.
+        gamma (float): The ``gamma`` in the balanced L1 loss. Defaults to 1.5.
+        beta (float, optional): The loss is a piecewise function of prediction
+            and target. ``beta`` serves as a threshold for the difference
+            between the prediction and target. Defaults to 1.0.
+        reduction (str, optional): The method that reduces the loss to a
+            scalar. Options are "none", "mean" and "sum".
+        loss_weight (float, optional): The weight of the loss. Defaults to 1.0
+    """
+
+    def __init__(self,
+                 alpha=0.5,
+                 gamma=1.5,
+                 beta=1.0,
+                 reduction='mean',
+                 loss_weight=1.0):
+        super(BalancedL1Loss, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+        self.beta = beta
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None,
+                **kwargs):
+        """Forward function of loss.
+
+        Args:
+            pred (torch.Tensor): The prediction with shape (N, 4).
+            target (torch.Tensor): The learning target of the prediction with
+                shape (N, 4).
+            weight (torch.Tensor, optional): Sample-wise loss weight with
+                shape (N, ).
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Options are "none", "mean" and "sum".
+
+        Returns:
+            torch.Tensor: The calculated loss
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        loss_bbox = self.loss_weight * balanced_l1_loss(
+            pred,
+            target,
+            weight,
+            alpha=self.alpha,
+            gamma=self.gamma,
+            beta=self.beta,
+            reduction=reduction,
+            avg_factor=avg_factor,
+            **kwargs)
+        return loss_bbox
diff --git a/mmdet/models/losses/cross_entropy_loss.py b/mmdet/models/losses/cross_entropy_loss.py
new file mode 100644
index 0000000..41411fc
--- /dev/null
+++ b/mmdet/models/losses/cross_entropy_loss.py
@@ -0,0 +1,301 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..builder import LOSSES
+from .utils import weight_reduce_loss
+
+
+def cross_entropy(pred,
+                  label,
+                  weight=None,
+                  reduction='mean',
+                  avg_factor=None,
+                  class_weight=None,
+                  ignore_index=-100,
+                  avg_non_ignore=False):
+    """Calculate the CrossEntropy loss.
+
+    Args:
+        pred (torch.Tensor): The prediction with shape (N, C), C is the number
+            of classes.
+        label (torch.Tensor): The learning label of the prediction.
+        weight (torch.Tensor, optional): Sample-wise loss weight.
+        reduction (str, optional): The method used to reduce the loss.
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Defaults to None.
+        class_weight (list[float], optional): The weight for each class.
+        ignore_index (int | None): The label index to be ignored.
+            If None, it will be set to default value. Default: -100.
+        avg_non_ignore (bool): The flag decides to whether the loss is
+            only averaged over non-ignored targets. Default: False.
+
+    Returns:
+        torch.Tensor: The calculated loss
+    """
+    # The default value of ignore_index is the same as F.cross_entropy
+    ignore_index = -100 if ignore_index is None else ignore_index
+    # element-wise losses
+    loss = F.cross_entropy(
+        pred,
+        label,
+        weight=class_weight,
+        reduction='none',
+        ignore_index=ignore_index)
+
+    # average loss over non-ignored elements
+    # pytorch's official cross_entropy average loss over non-ignored elements
+    # refer to https://github.com/pytorch/pytorch/blob/56b43f4fec1f76953f15a627694d4bba34588969/torch/nn/functional.py#L2660  # noqa
+    if (avg_factor is None) and avg_non_ignore and reduction == 'mean':
+        avg_factor = label.numel() - (label == ignore_index).sum().item()
+
+    # apply weights and do the reduction
+    if weight is not None:
+        weight = weight.float()
+    loss = weight_reduce_loss(
+        loss, weight=weight, reduction=reduction, avg_factor=avg_factor)
+
+    return loss
+
+
+def _expand_onehot_labels(labels, label_weights, label_channels, ignore_index):
+    """Expand onehot labels to match the size of prediction."""
+    bin_labels = labels.new_full((labels.size(0), label_channels), 0)
+    valid_mask = (labels >= 0) & (labels != ignore_index)
+    inds = torch.nonzero(
+        valid_mask & (labels < label_channels), as_tuple=False)
+
+    if inds.numel() > 0:
+        bin_labels[inds, labels[inds]] = 1
+
+    valid_mask = valid_mask.view(-1, 1).expand(labels.size(0),
+                                               label_channels).float()
+    if label_weights is None:
+        bin_label_weights = valid_mask
+    else:
+        bin_label_weights = label_weights.view(-1, 1).repeat(1, label_channels)
+        bin_label_weights *= valid_mask
+
+    return bin_labels, bin_label_weights, valid_mask
+
+
+def binary_cross_entropy(pred,
+                         label,
+                         weight=None,
+                         reduction='mean',
+                         avg_factor=None,
+                         class_weight=None,
+                         ignore_index=-100,
+                         avg_non_ignore=False):
+    """Calculate the binary CrossEntropy loss.
+
+    Args:
+        pred (torch.Tensor): The prediction with shape (N, 1) or (N, ).
+            When the shape of pred is (N, 1), label will be expanded to
+            one-hot format, and when the shape of pred is (N, ), label
+            will not be expanded to one-hot format.
+        label (torch.Tensor): The learning label of the prediction,
+            with shape (N, ).
+        weight (torch.Tensor, optional): Sample-wise loss weight.
+        reduction (str, optional): The method used to reduce the loss.
+            Options are "none", "mean" and "sum".
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Defaults to None.
+        class_weight (list[float], optional): The weight for each class.
+        ignore_index (int | None): The label index to be ignored.
+            If None, it will be set to default value. Default: -100.
+        avg_non_ignore (bool): The flag decides to whether the loss is
+            only averaged over non-ignored targets. Default: False.
+
+    Returns:
+        torch.Tensor: The calculated loss.
+    """
+    # The default value of ignore_index is the same as F.cross_entropy
+    ignore_index = -100 if ignore_index is None else ignore_index
+
+    if pred.dim() != label.dim():
+        label, weight, valid_mask = _expand_onehot_labels(
+            label, weight, pred.size(-1), ignore_index)
+    else:
+        # should mask out the ignored elements
+        valid_mask = ((label >= 0) & (label != ignore_index)).float()
+        if weight is not None:
+            # The inplace writing method will have a mismatched broadcast
+            # shape error if the weight and valid_mask dimensions
+            # are inconsistent such as (B,N,1) and (B,N,C).
+            weight = weight * valid_mask
+        else:
+            weight = valid_mask
+
+    # average loss over non-ignored elements
+    if (avg_factor is None) and avg_non_ignore and reduction == 'mean':
+        avg_factor = valid_mask.sum().item()
+
+    # weighted element-wise losses
+    weight = weight.float()
+    loss = F.binary_cross_entropy_with_logits(
+        pred, label.float(), pos_weight=class_weight, reduction='none')
+    # do the reduction for the weighted loss
+    loss = weight_reduce_loss(
+        loss, weight, reduction=reduction, avg_factor=avg_factor)
+
+    return loss
+
+
+def mask_cross_entropy(pred,
+                       target,
+                       label,
+                       reduction='mean',
+                       avg_factor=None,
+                       class_weight=None,
+                       ignore_index=None,
+                       **kwargs):
+    """Calculate the CrossEntropy loss for masks.
+
+    Args:
+        pred (torch.Tensor): The prediction with shape (N, C, *), C is the
+            number of classes. The trailing * indicates arbitrary shape.
+        target (torch.Tensor): The learning label of the prediction.
+        label (torch.Tensor): ``label`` indicates the class label of the mask
+            corresponding object. This will be used to select the mask in the
+            of the class which the object belongs to when the mask prediction
+            if not class-agnostic.
+        reduction (str, optional): The method used to reduce the loss.
+            Options are "none", "mean" and "sum".
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Defaults to None.
+        class_weight (list[float], optional): The weight for each class.
+        ignore_index (None): Placeholder, to be consistent with other loss.
+            Default: None.
+
+    Returns:
+        torch.Tensor: The calculated loss
+
+    Example:
+        >>> N, C = 3, 11
+        >>> H, W = 2, 2
+        >>> pred = torch.randn(N, C, H, W) * 1000
+        >>> target = torch.rand(N, H, W)
+        >>> label = torch.randint(0, C, size=(N,))
+        >>> reduction = 'mean'
+        >>> avg_factor = None
+        >>> class_weights = None
+        >>> loss = mask_cross_entropy(pred, target, label, reduction,
+        >>>                           avg_factor, class_weights)
+        >>> assert loss.shape == (1,)
+    """
+    assert ignore_index is None, 'BCE loss does not support ignore_index'
+    # TODO: handle these two reserved arguments
+    assert reduction == 'mean' and avg_factor is None
+    num_rois = pred.size()[0]
+    inds = torch.arange(0, num_rois, dtype=torch.long, device=pred.device)
+    pred_slice = pred[inds, label].squeeze(1)
+    return F.binary_cross_entropy_with_logits(
+        pred_slice, target, weight=class_weight, reduction='mean')[None]
+
+
+@LOSSES.register_module()
+class CrossEntropyLoss(nn.Module):
+
+    def __init__(self,
+                 use_sigmoid=False,
+                 use_mask=False,
+                 reduction='mean',
+                 class_weight=None,
+                 ignore_index=None,
+                 loss_weight=1.0,
+                 avg_non_ignore=False):
+        """CrossEntropyLoss.
+
+        Args:
+            use_sigmoid (bool, optional): Whether the prediction uses sigmoid
+                of softmax. Defaults to False.
+            use_mask (bool, optional): Whether to use mask cross entropy loss.
+                Defaults to False.
+            reduction (str, optional): . Defaults to 'mean'.
+                Options are "none", "mean" and "sum".
+            class_weight (list[float], optional): Weight of each class.
+                Defaults to None.
+            ignore_index (int | None): The label index to be ignored.
+                Defaults to None.
+            loss_weight (float, optional): Weight of the loss. Defaults to 1.0.
+            avg_non_ignore (bool): The flag decides to whether the loss is
+                only averaged over non-ignored targets. Default: False.
+        """
+        super(CrossEntropyLoss, self).__init__()
+        assert (use_sigmoid is False) or (use_mask is False)
+        self.use_sigmoid = use_sigmoid
+        self.use_mask = use_mask
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+        self.class_weight = class_weight
+        self.ignore_index = ignore_index
+        self.avg_non_ignore = avg_non_ignore
+        if ((ignore_index is not None) and not self.avg_non_ignore
+                and self.reduction == 'mean'):
+            warnings.warn(
+                'Default ``avg_non_ignore`` is False, if you would like to '
+                'ignore the certain label and average loss over non-ignore '
+                'labels, which is the same with PyTorch official '
+                'cross_entropy, set ``avg_non_ignore=True``.')
+
+        if self.use_sigmoid:
+            self.cls_criterion = binary_cross_entropy
+        elif self.use_mask:
+            self.cls_criterion = mask_cross_entropy
+        else:
+            self.cls_criterion = cross_entropy
+
+    def extra_repr(self):
+        """Extra repr."""
+        s = f'avg_non_ignore={self.avg_non_ignore}'
+        return s
+
+    def forward(self,
+                cls_score,
+                label,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None,
+                ignore_index=None,
+                **kwargs):
+        """Forward function.
+
+        Args:
+            cls_score (torch.Tensor): The prediction.
+            label (torch.Tensor): The learning label of the prediction.
+            weight (torch.Tensor, optional): Sample-wise loss weight.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The method used to reduce the
+                loss. Options are "none", "mean" and "sum".
+            ignore_index (int | None): The label index to be ignored.
+                If not None, it will override the default value. Default: None.
+        Returns:
+            torch.Tensor: The calculated loss.
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        if ignore_index is None:
+            ignore_index = self.ignore_index
+
+        if self.class_weight is not None:
+            class_weight = cls_score.new_tensor(
+                self.class_weight, device=cls_score.device)
+        else:
+            class_weight = None
+        loss_cls = self.loss_weight * self.cls_criterion(
+            cls_score,
+            label,
+            weight,
+            class_weight=class_weight,
+            reduction=reduction,
+            avg_factor=avg_factor,
+            ignore_index=ignore_index,
+            avg_non_ignore=self.avg_non_ignore,
+            **kwargs)
+        return loss_cls
diff --git a/mmdet/models/losses/dice_loss.py b/mmdet/models/losses/dice_loss.py
new file mode 100644
index 0000000..585beea
--- /dev/null
+++ b/mmdet/models/losses/dice_loss.py
@@ -0,0 +1,146 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+
+from ..builder import LOSSES
+from .utils import weight_reduce_loss
+
+
+def dice_loss(pred,
+              target,
+              weight=None,
+              eps=1e-3,
+              reduction='mean',
+              naive_dice=False,
+              avg_factor=None):
+    """Calculate dice loss, there are two forms of dice loss is supported:
+
+        - the one proposed in `V-Net: Fully Convolutional Neural
+            Networks for Volumetric Medical Image Segmentation
+            <https://arxiv.org/abs/1606.04797>`_.
+        - the dice loss in which the power of the number in the
+            denominator is the first power instead of the second
+            power.
+
+    Args:
+        pred (torch.Tensor): The prediction, has a shape (n, *)
+        target (torch.Tensor): The learning label of the prediction,
+            shape (n, *), same shape of pred.
+        weight (torch.Tensor, optional): The weight of loss for each
+            prediction, has a shape (n,). Defaults to None.
+        eps (float): Avoid dividing by zero. Default: 1e-3.
+        reduction (str, optional): The method used to reduce the loss into
+            a scalar. Defaults to 'mean'.
+            Options are "none", "mean" and "sum".
+        naive_dice (bool, optional): If false, use the dice
+                loss defined in the V-Net paper, otherwise, use the
+                naive dice loss in which the power of the number in the
+                denominator is the first power instead of the second
+                power.Defaults to False.
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Defaults to None.
+    """
+
+    input = pred.flatten(1)
+    target = target.flatten(1).float()
+
+    a = torch.sum(input * target, 1)
+    if naive_dice:
+        b = torch.sum(input, 1)
+        c = torch.sum(target, 1)
+        d = (2 * a + eps) / (b + c + eps)
+    else:
+        b = torch.sum(input * input, 1) + eps
+        c = torch.sum(target * target, 1) + eps
+        d = (2 * a) / (b + c)
+
+    loss = 1 - d
+    if weight is not None:
+        assert weight.ndim == loss.ndim
+        assert len(weight) == len(pred)
+    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
+    return loss
+
+
+@LOSSES.register_module()
+class DiceLoss(nn.Module):
+
+    def __init__(self,
+                 use_sigmoid=True,
+                 activate=True,
+                 reduction='mean',
+                 naive_dice=False,
+                 loss_weight=1.0,
+                 eps=1e-3):
+        """Compute dice loss.
+
+        Args:
+            use_sigmoid (bool, optional): Whether to the prediction is
+                used for sigmoid or softmax. Defaults to True.
+            activate (bool): Whether to activate the predictions inside,
+                this will disable the inside sigmoid operation.
+                Defaults to True.
+            reduction (str, optional): The method used
+                to reduce the loss. Options are "none",
+                "mean" and "sum". Defaults to 'mean'.
+            naive_dice (bool, optional): If false, use the dice
+                loss defined in the V-Net paper, otherwise, use the
+                naive dice loss in which the power of the number in the
+                denominator is the first power instead of the second
+                power. Defaults to False.
+            loss_weight (float, optional): Weight of loss. Defaults to 1.0.
+            eps (float): Avoid dividing by zero. Defaults to 1e-3.
+        """
+
+        super(DiceLoss, self).__init__()
+        self.use_sigmoid = use_sigmoid
+        self.reduction = reduction
+        self.naive_dice = naive_dice
+        self.loss_weight = loss_weight
+        self.eps = eps
+        self.activate = activate
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                reduction_override=None,
+                avg_factor=None):
+        """Forward function.
+
+        Args:
+            pred (torch.Tensor): The prediction, has a shape (n, *).
+            target (torch.Tensor): The label of the prediction,
+                shape (n, *), same shape of pred.
+            weight (torch.Tensor, optional): The weight of loss for each
+                prediction, has a shape (n,). Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Options are "none", "mean" and "sum".
+
+        Returns:
+            torch.Tensor: The calculated loss
+        """
+
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+
+        if self.activate:
+            if self.use_sigmoid:
+                pred = pred.sigmoid()
+            else:
+                raise NotImplementedError
+
+        loss = self.loss_weight * dice_loss(
+            pred,
+            target,
+            weight,
+            eps=self.eps,
+            reduction=reduction,
+            naive_dice=self.naive_dice,
+            avg_factor=avg_factor)
+
+        return loss
diff --git a/mmdet/models/losses/focal_loss.py b/mmdet/models/losses/focal_loss.py
new file mode 100644
index 0000000..2858c19
--- /dev/null
+++ b/mmdet/models/losses/focal_loss.py
@@ -0,0 +1,244 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss
+
+from ..builder import LOSSES
+from .utils import weight_reduce_loss
+
+
+# This method is only for debugging
+def py_sigmoid_focal_loss(pred,
+                          target,
+                          weight=None,
+                          gamma=2.0,
+                          alpha=0.25,
+                          reduction='mean',
+                          avg_factor=None):
+    """PyTorch version of `Focal Loss <https://arxiv.org/abs/1708.02002>`_.
+
+    Args:
+        pred (torch.Tensor): The prediction with shape (N, C), C is the
+            number of classes
+        target (torch.Tensor): The learning label of the prediction.
+        weight (torch.Tensor, optional): Sample-wise loss weight.
+        gamma (float, optional): The gamma for calculating the modulating
+            factor. Defaults to 2.0.
+        alpha (float, optional): A balanced form for Focal Loss.
+            Defaults to 0.25.
+        reduction (str, optional): The method used to reduce the loss into
+            a scalar. Defaults to 'mean'.
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Defaults to None.
+    """
+    pred_sigmoid = pred.sigmoid()
+    target = target.type_as(pred)
+    pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target)
+    focal_weight = (alpha * target + (1 - alpha) *
+                    (1 - target)) * pt.pow(gamma)
+    loss = F.binary_cross_entropy_with_logits(
+        pred, target, reduction='none') * focal_weight
+    if weight is not None:
+        if weight.shape != loss.shape:
+            if weight.size(0) == loss.size(0):
+                # For most cases, weight is of shape (num_priors, ),
+                #  which means it does not have the second axis num_class
+                weight = weight.view(-1, 1)
+            else:
+                # Sometimes, weight per anchor per class is also needed. e.g.
+                #  in FSAF. But it may be flattened of shape
+                #  (num_priors x num_class, ), while loss is still of shape
+                #  (num_priors, num_class).
+                assert weight.numel() == loss.numel()
+                weight = weight.view(loss.size(0), -1)
+        assert weight.ndim == loss.ndim
+    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
+    return loss
+
+
+def py_focal_loss_with_prob(pred,
+                            target,
+                            weight=None,
+                            gamma=2.0,
+                            alpha=0.25,
+                            reduction='mean',
+                            avg_factor=None):
+    """PyTorch version of `Focal Loss <https://arxiv.org/abs/1708.02002>`_.
+    Different from `py_sigmoid_focal_loss`, this function accepts probability
+    as input.
+
+    Args:
+        pred (torch.Tensor): The prediction probability with shape (N, C),
+            C is the number of classes.
+        target (torch.Tensor): The learning label of the prediction.
+        weight (torch.Tensor, optional): Sample-wise loss weight.
+        gamma (float, optional): The gamma for calculating the modulating
+            factor. Defaults to 2.0.
+        alpha (float, optional): A balanced form for Focal Loss.
+            Defaults to 0.25.
+        reduction (str, optional): The method used to reduce the loss into
+            a scalar. Defaults to 'mean'.
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Defaults to None.
+    """
+    num_classes = pred.size(1)
+    target = F.one_hot(target, num_classes=num_classes + 1)
+    target = target[:, :num_classes]
+
+    target = target.type_as(pred)
+    pt = (1 - pred) * target + pred * (1 - target)
+    focal_weight = (alpha * target + (1 - alpha) *
+                    (1 - target)) * pt.pow(gamma)
+    loss = F.binary_cross_entropy(
+        pred, target, reduction='none') * focal_weight
+    if weight is not None:
+        if weight.shape != loss.shape:
+            if weight.size(0) == loss.size(0):
+                # For most cases, weight is of shape (num_priors, ),
+                #  which means it does not have the second axis num_class
+                weight = weight.view(-1, 1)
+            else:
+                # Sometimes, weight per anchor per class is also needed. e.g.
+                #  in FSAF. But it may be flattened of shape
+                #  (num_priors x num_class, ), while loss is still of shape
+                #  (num_priors, num_class).
+                assert weight.numel() == loss.numel()
+                weight = weight.view(loss.size(0), -1)
+        assert weight.ndim == loss.ndim
+    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
+    return loss
+
+
+def sigmoid_focal_loss(pred,
+                       target,
+                       weight=None,
+                       gamma=2.0,
+                       alpha=0.25,
+                       reduction='mean',
+                       avg_factor=None):
+    r"""A wrapper of cuda version `Focal Loss
+    <https://arxiv.org/abs/1708.02002>`_.
+
+    Args:
+        pred (torch.Tensor): The prediction with shape (N, C), C is the number
+            of classes.
+        target (torch.Tensor): The learning label of the prediction.
+        weight (torch.Tensor, optional): Sample-wise loss weight.
+        gamma (float, optional): The gamma for calculating the modulating
+            factor. Defaults to 2.0.
+        alpha (float, optional): A balanced form for Focal Loss.
+            Defaults to 0.25.
+        reduction (str, optional): The method used to reduce the loss into
+            a scalar. Defaults to 'mean'. Options are "none", "mean" and "sum".
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Defaults to None.
+    """
+    # Function.apply does not accept keyword arguments, so the decorator
+    # "weighted_loss" is not applicable
+    loss = _sigmoid_focal_loss(pred.contiguous(), target.contiguous(), gamma,
+                               alpha, None, 'none')
+    if weight is not None:
+        if weight.shape != loss.shape:
+            if weight.size(0) == loss.size(0):
+                # For most cases, weight is of shape (num_priors, ),
+                #  which means it does not have the second axis num_class
+                weight = weight.view(-1, 1)
+            else:
+                # Sometimes, weight per anchor per class is also needed. e.g.
+                #  in FSAF. But it may be flattened of shape
+                #  (num_priors x num_class, ), while loss is still of shape
+                #  (num_priors, num_class).
+                assert weight.numel() == loss.numel()
+                weight = weight.view(loss.size(0), -1)
+        assert weight.ndim == loss.ndim
+    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
+    return loss
+
+
+@LOSSES.register_module()
+class FocalLoss(nn.Module):
+
+    def __init__(self,
+                 use_sigmoid=True,
+                 gamma=2.0,
+                 alpha=0.25,
+                 reduction='mean',
+                 loss_weight=1.0,
+                 activated=False):
+        """`Focal Loss <https://arxiv.org/abs/1708.02002>`_
+
+        Args:
+            use_sigmoid (bool, optional): Whether to the prediction is
+                used for sigmoid or softmax. Defaults to True.
+            gamma (float, optional): The gamma for calculating the modulating
+                factor. Defaults to 2.0.
+            alpha (float, optional): A balanced form for Focal Loss.
+                Defaults to 0.25.
+            reduction (str, optional): The method used to reduce the loss into
+                a scalar. Defaults to 'mean'. Options are "none", "mean" and
+                "sum".
+            loss_weight (float, optional): Weight of loss. Defaults to 1.0.
+            activated (bool, optional): Whether the input is activated.
+                If True, it means the input has been activated and can be
+                treated as probabilities. Else, it should be treated as logits.
+                Defaults to False.
+        """
+        super(FocalLoss, self).__init__()
+        assert use_sigmoid is True, 'Only sigmoid focal loss supported now.'
+        self.use_sigmoid = use_sigmoid
+        self.gamma = gamma
+        self.alpha = alpha
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+        self.activated = activated
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None):
+        """Forward function.
+
+        Args:
+            pred (torch.Tensor): The prediction.
+            target (torch.Tensor): The learning label of the prediction.
+            weight (torch.Tensor, optional): The weight of loss for each
+                prediction. Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Options are "none", "mean" and "sum".
+
+        Returns:
+            torch.Tensor: The calculated loss
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        if self.use_sigmoid:
+            if self.activated:
+                calculate_loss_func = py_focal_loss_with_prob
+            else:
+                if torch.cuda.is_available() and pred.is_cuda:
+                    calculate_loss_func = sigmoid_focal_loss
+                else:
+                    num_classes = pred.size(1)
+                    target = F.one_hot(target, num_classes=num_classes + 1)
+                    target = target[:, :num_classes]
+                    calculate_loss_func = py_sigmoid_focal_loss
+
+            loss_cls = self.loss_weight * calculate_loss_func(
+                pred,
+                target,
+                weight,
+                gamma=self.gamma,
+                alpha=self.alpha,
+                reduction=reduction,
+                avg_factor=avg_factor)
+
+        else:
+            raise NotImplementedError
+        return loss_cls
diff --git a/mmdet/models/losses/gaussian_focal_loss.py b/mmdet/models/losses/gaussian_focal_loss.py
new file mode 100644
index 0000000..7abcb69
--- /dev/null
+++ b/mmdet/models/losses/gaussian_focal_loss.py
@@ -0,0 +1,92 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch.nn as nn
+
+from ..builder import LOSSES
+from .utils import weighted_loss
+
+
+@mmcv.jit(derivate=True, coderize=True)
+@weighted_loss
+def gaussian_focal_loss(pred, gaussian_target, alpha=2.0, gamma=4.0):
+    """`Focal Loss <https://arxiv.org/abs/1708.02002>`_ for targets in gaussian
+    distribution.
+
+    Args:
+        pred (torch.Tensor): The prediction.
+        gaussian_target (torch.Tensor): The learning target of the prediction
+            in gaussian distribution.
+        alpha (float, optional): A balanced form for Focal Loss.
+            Defaults to 2.0.
+        gamma (float, optional): The gamma for calculating the modulating
+            factor. Defaults to 4.0.
+    """
+    eps = 1e-12
+    pos_weights = gaussian_target.eq(1)
+    neg_weights = (1 - gaussian_target).pow(gamma)
+    pos_loss = -(pred + eps).log() * (1 - pred).pow(alpha) * pos_weights
+    neg_loss = -(1 - pred + eps).log() * pred.pow(alpha) * neg_weights
+    return pos_loss + neg_loss
+
+
+@LOSSES.register_module()
+class GaussianFocalLoss(nn.Module):
+    """GaussianFocalLoss is a variant of focal loss.
+
+    More details can be found in the `paper
+    <https://arxiv.org/abs/1808.01244>`_
+    Code is modified from `kp_utils.py
+    <https://github.com/princeton-vl/CornerNet/blob/master/models/py_utils/kp_utils.py#L152>`_  # noqa: E501
+    Please notice that the target in GaussianFocalLoss is a gaussian heatmap,
+    not 0/1 binary target.
+
+    Args:
+        alpha (float): Power of prediction.
+        gamma (float): Power of target for negative samples.
+        reduction (str): Options are "none", "mean" and "sum".
+        loss_weight (float): Loss weight of current loss.
+    """
+
+    def __init__(self,
+                 alpha=2.0,
+                 gamma=4.0,
+                 reduction='mean',
+                 loss_weight=1.0):
+        super(GaussianFocalLoss, self).__init__()
+        self.alpha = alpha
+        self.gamma = gamma
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None):
+        """Forward function.
+
+        Args:
+            pred (torch.Tensor): The prediction.
+            target (torch.Tensor): The learning target of the prediction
+                in gaussian distribution.
+            weight (torch.Tensor, optional): The weight of loss for each
+                prediction. Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Defaults to None.
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        loss_reg = self.loss_weight * gaussian_focal_loss(
+            pred,
+            target,
+            weight,
+            alpha=self.alpha,
+            gamma=self.gamma,
+            reduction=reduction,
+            avg_factor=avg_factor)
+        return loss_reg
diff --git a/mmdet/models/losses/gfocal_loss.py b/mmdet/models/losses/gfocal_loss.py
new file mode 100644
index 0000000..0e8d263
--- /dev/null
+++ b/mmdet/models/losses/gfocal_loss.py
@@ -0,0 +1,245 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..builder import LOSSES
+from .utils import weighted_loss
+
+
+@mmcv.jit(derivate=True, coderize=True)
+@weighted_loss
+def quality_focal_loss(pred, target, beta=2.0):
+    r"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning
+    Qualified and Distributed Bounding Boxes for Dense Object Detection
+    <https://arxiv.org/abs/2006.04388>`_.
+
+    Args:
+        pred (torch.Tensor): Predicted joint representation of classification
+            and quality (IoU) estimation with shape (N, C), C is the number of
+            classes.
+        target (tuple([torch.Tensor])): Target category label with shape (N,)
+            and target quality label with shape (N,).
+        beta (float): The beta parameter for calculating the modulating factor.
+            Defaults to 2.0.
+
+    Returns:
+        torch.Tensor: Loss tensor with shape (N,).
+    """
+    assert len(target) == 2, """target for QFL must be a tuple of two elements,
+        including category label and quality label, respectively"""
+    # label denotes the category id, score denotes the quality score
+    label, score = target
+
+    # negatives are supervised by 0 quality score
+    pred_sigmoid = pred.sigmoid()
+    scale_factor = pred_sigmoid
+    zerolabel = scale_factor.new_zeros(pred.shape)
+    loss = F.binary_cross_entropy_with_logits(
+        pred, zerolabel, reduction='none') * scale_factor.pow(beta)
+
+    # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+    bg_class_ind = pred.size(1)
+    pos = ((label >= 0) & (label < bg_class_ind)).nonzero().squeeze(1)
+    pos_label = label[pos].long()
+    # positives are supervised by bbox quality (IoU) score
+    scale_factor = score[pos] - pred_sigmoid[pos, pos_label]
+    loss[pos, pos_label] = F.binary_cross_entropy_with_logits(
+        pred[pos, pos_label], score[pos],
+        reduction='none') * scale_factor.abs().pow(beta)
+
+    loss = loss.sum(dim=1, keepdim=False)
+    return loss
+
+
+@weighted_loss
+def quality_focal_loss_with_prob(pred, target, beta=2.0):
+    r"""Quality Focal Loss (QFL) is from `Generalized Focal Loss: Learning
+    Qualified and Distributed Bounding Boxes for Dense Object Detection
+    <https://arxiv.org/abs/2006.04388>`_.
+    Different from `quality_focal_loss`, this function accepts probability
+    as input.
+
+    Args:
+        pred (torch.Tensor): Predicted joint representation of classification
+            and quality (IoU) estimation with shape (N, C), C is the number of
+            classes.
+        target (tuple([torch.Tensor])): Target category label with shape (N,)
+            and target quality label with shape (N,).
+        beta (float): The beta parameter for calculating the modulating factor.
+            Defaults to 2.0.
+
+    Returns:
+        torch.Tensor: Loss tensor with shape (N,).
+    """
+    assert len(target) == 2, """target for QFL must be a tuple of two elements,
+        including category label and quality label, respectively"""
+    # label denotes the category id, score denotes the quality score
+    label, score = target
+
+    # negatives are supervised by 0 quality score
+    pred_sigmoid = pred
+    scale_factor = pred_sigmoid
+    zerolabel = scale_factor.new_zeros(pred.shape)
+    loss = F.binary_cross_entropy(
+        pred, zerolabel, reduction='none') * scale_factor.pow(beta)
+
+    # FG cat_id: [0, num_classes -1], BG cat_id: num_classes
+    bg_class_ind = pred.size(1)
+    pos = ((label >= 0) & (label < bg_class_ind)).nonzero().squeeze(1)
+    pos_label = label[pos].long()
+    # positives are supervised by bbox quality (IoU) score
+    scale_factor = score[pos] - pred_sigmoid[pos, pos_label]
+    loss[pos, pos_label] = F.binary_cross_entropy(
+        pred[pos, pos_label], score[pos],
+        reduction='none') * scale_factor.abs().pow(beta)
+
+    loss = loss.sum(dim=1, keepdim=False)
+    return loss
+
+
+@mmcv.jit(derivate=True, coderize=True)
+@weighted_loss
+def distribution_focal_loss(pred, label):
+    r"""Distribution Focal Loss (DFL) is from `Generalized Focal Loss: Learning
+    Qualified and Distributed Bounding Boxes for Dense Object Detection
+    <https://arxiv.org/abs/2006.04388>`_.
+
+    Args:
+        pred (torch.Tensor): Predicted general distribution of bounding boxes
+            (before softmax) with shape (N, n+1), n is the max value of the
+            integral set `{0, ..., n}` in paper.
+        label (torch.Tensor): Target distance label for bounding boxes with
+            shape (N,).
+
+    Returns:
+        torch.Tensor: Loss tensor with shape (N,).
+    """
+    dis_left = label.long()
+    dis_right = dis_left + 1
+    weight_left = dis_right.float() - label
+    weight_right = label - dis_left.float()
+    loss = F.cross_entropy(pred, dis_left, reduction='none') * weight_left \
+        + F.cross_entropy(pred, dis_right, reduction='none') * weight_right
+    return loss
+
+
+@LOSSES.register_module()
+class QualityFocalLoss(nn.Module):
+    r"""Quality Focal Loss (QFL) is a variant of `Generalized Focal Loss:
+    Learning Qualified and Distributed Bounding Boxes for Dense Object
+    Detection <https://arxiv.org/abs/2006.04388>`_.
+
+    Args:
+        use_sigmoid (bool): Whether sigmoid operation is conducted in QFL.
+            Defaults to True.
+        beta (float): The beta parameter for calculating the modulating factor.
+            Defaults to 2.0.
+        reduction (str): Options are "none", "mean" and "sum".
+        loss_weight (float): Loss weight of current loss.
+        activated (bool, optional): Whether the input is activated.
+            If True, it means the input has been activated and can be
+            treated as probabilities. Else, it should be treated as logits.
+            Defaults to False.
+    """
+
+    def __init__(self,
+                 use_sigmoid=True,
+                 beta=2.0,
+                 reduction='mean',
+                 loss_weight=1.0,
+                 activated=False):
+        super(QualityFocalLoss, self).__init__()
+        assert use_sigmoid is True, 'Only sigmoid in QFL supported now.'
+        self.use_sigmoid = use_sigmoid
+        self.beta = beta
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+        self.activated = activated
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None):
+        """Forward function.
+
+        Args:
+            pred (torch.Tensor): Predicted joint representation of
+                classification and quality (IoU) estimation with shape (N, C),
+                C is the number of classes.
+            target (tuple([torch.Tensor])): Target category label with shape
+                (N,) and target quality label with shape (N,).
+            weight (torch.Tensor, optional): The weight of loss for each
+                prediction. Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Defaults to None.
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        if self.use_sigmoid:
+            if self.activated:
+                calculate_loss_func = quality_focal_loss_with_prob
+            else:
+                calculate_loss_func = quality_focal_loss
+            loss_cls = self.loss_weight * calculate_loss_func(
+                pred,
+                target,
+                weight,
+                beta=self.beta,
+                reduction=reduction,
+                avg_factor=avg_factor)
+        else:
+            raise NotImplementedError
+        return loss_cls
+
+
+@LOSSES.register_module()
+class DistributionFocalLoss(nn.Module):
+    r"""Distribution Focal Loss (DFL) is a variant of `Generalized Focal Loss:
+    Learning Qualified and Distributed Bounding Boxes for Dense Object
+    Detection <https://arxiv.org/abs/2006.04388>`_.
+
+    Args:
+        reduction (str): Options are `'none'`, `'mean'` and `'sum'`.
+        loss_weight (float): Loss weight of current loss.
+    """
+
+    def __init__(self, reduction='mean', loss_weight=1.0):
+        super(DistributionFocalLoss, self).__init__()
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None):
+        """Forward function.
+
+        Args:
+            pred (torch.Tensor): Predicted general distribution of bounding
+                boxes (before softmax) with shape (N, n+1), n is the max value
+                of the integral set `{0, ..., n}` in paper.
+            target (torch.Tensor): Target distance label for bounding boxes
+                with shape (N,).
+            weight (torch.Tensor, optional): The weight of loss for each
+                prediction. Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Defaults to None.
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        loss_cls = self.loss_weight * distribution_focal_loss(
+            pred, target, weight, reduction=reduction, avg_factor=avg_factor)
+        return loss_cls
diff --git a/mmdet/models/losses/ghm_loss.py b/mmdet/models/losses/ghm_loss.py
new file mode 100644
index 0000000..a4df9fe
--- /dev/null
+++ b/mmdet/models/losses/ghm_loss.py
@@ -0,0 +1,213 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..builder import LOSSES
+from .utils import weight_reduce_loss
+
+
+def _expand_onehot_labels(labels, label_weights, label_channels):
+    bin_labels = labels.new_full((labels.size(0), label_channels), 0)
+    inds = torch.nonzero(
+        (labels >= 0) & (labels < label_channels), as_tuple=False).squeeze()
+    if inds.numel() > 0:
+        bin_labels[inds, labels[inds]] = 1
+    bin_label_weights = label_weights.view(-1, 1).expand(
+        label_weights.size(0), label_channels)
+    return bin_labels, bin_label_weights
+
+
+# TODO: code refactoring to make it consistent with other losses
+@LOSSES.register_module()
+class GHMC(nn.Module):
+    """GHM Classification Loss.
+
+    Details of the theorem can be viewed in the paper
+    `Gradient Harmonized Single-stage Detector
+    <https://arxiv.org/abs/1811.05181>`_.
+
+    Args:
+        bins (int): Number of the unit regions for distribution calculation.
+        momentum (float): The parameter for moving average.
+        use_sigmoid (bool): Can only be true for BCE based loss now.
+        loss_weight (float): The weight of the total GHM-C loss.
+        reduction (str): Options are "none", "mean" and "sum".
+            Defaults to "mean"
+    """
+
+    def __init__(self,
+                 bins=10,
+                 momentum=0,
+                 use_sigmoid=True,
+                 loss_weight=1.0,
+                 reduction='mean'):
+        super(GHMC, self).__init__()
+        self.bins = bins
+        self.momentum = momentum
+        edges = torch.arange(bins + 1).float() / bins
+        self.register_buffer('edges', edges)
+        self.edges[-1] += 1e-6
+        if momentum > 0:
+            acc_sum = torch.zeros(bins)
+            self.register_buffer('acc_sum', acc_sum)
+        self.use_sigmoid = use_sigmoid
+        if not self.use_sigmoid:
+            raise NotImplementedError
+        self.loss_weight = loss_weight
+        self.reduction = reduction
+
+    def forward(self,
+                pred,
+                target,
+                label_weight,
+                reduction_override=None,
+                **kwargs):
+        """Calculate the GHM-C loss.
+
+        Args:
+            pred (float tensor of size [batch_num, class_num]):
+                The direct prediction of classification fc layer.
+            target (float tensor of size [batch_num, class_num]):
+                Binary class target for each sample.
+            label_weight (float tensor of size [batch_num, class_num]):
+                the value is 1 if the sample is valid and 0 if ignored.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Defaults to None.
+        Returns:
+            The gradient harmonized loss.
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        # the target should be binary class label
+        if pred.dim() != target.dim():
+            target, label_weight = _expand_onehot_labels(
+                target, label_weight, pred.size(-1))
+        target, label_weight = target.float(), label_weight.float()
+        edges = self.edges
+        mmt = self.momentum
+        weights = torch.zeros_like(pred)
+
+        # gradient length
+        g = torch.abs(pred.sigmoid().detach() - target)
+
+        valid = label_weight > 0
+        tot = max(valid.float().sum().item(), 1.0)
+        n = 0  # n valid bins
+        for i in range(self.bins):
+            inds = (g >= edges[i]) & (g < edges[i + 1]) & valid
+            num_in_bin = inds.sum().item()
+            if num_in_bin > 0:
+                if mmt > 0:
+                    self.acc_sum[i] = mmt * self.acc_sum[i] \
+                        + (1 - mmt) * num_in_bin
+                    weights[inds] = tot / self.acc_sum[i]
+                else:
+                    weights[inds] = tot / num_in_bin
+                n += 1
+        if n > 0:
+            weights = weights / n
+
+        loss = F.binary_cross_entropy_with_logits(
+            pred, target, reduction='none')
+        loss = weight_reduce_loss(
+            loss, weights, reduction=reduction, avg_factor=tot)
+        return loss * self.loss_weight
+
+
+# TODO: code refactoring to make it consistent with other losses
+@LOSSES.register_module()
+class GHMR(nn.Module):
+    """GHM Regression Loss.
+
+    Details of the theorem can be viewed in the paper
+    `Gradient Harmonized Single-stage Detector
+    <https://arxiv.org/abs/1811.05181>`_.
+
+    Args:
+        mu (float): The parameter for the Authentic Smooth L1 loss.
+        bins (int): Number of the unit regions for distribution calculation.
+        momentum (float): The parameter for moving average.
+        loss_weight (float): The weight of the total GHM-R loss.
+        reduction (str): Options are "none", "mean" and "sum".
+            Defaults to "mean"
+    """
+
+    def __init__(self,
+                 mu=0.02,
+                 bins=10,
+                 momentum=0,
+                 loss_weight=1.0,
+                 reduction='mean'):
+        super(GHMR, self).__init__()
+        self.mu = mu
+        self.bins = bins
+        edges = torch.arange(bins + 1).float() / bins
+        self.register_buffer('edges', edges)
+        self.edges[-1] = 1e3
+        self.momentum = momentum
+        if momentum > 0:
+            acc_sum = torch.zeros(bins)
+            self.register_buffer('acc_sum', acc_sum)
+        self.loss_weight = loss_weight
+        self.reduction = reduction
+
+    # TODO: support reduction parameter
+    def forward(self,
+                pred,
+                target,
+                label_weight,
+                avg_factor=None,
+                reduction_override=None):
+        """Calculate the GHM-R loss.
+
+        Args:
+            pred (float tensor of size [batch_num, 4 (* class_num)]):
+                The prediction of box regression layer. Channel number can be 4
+                or 4 * class_num depending on whether it is class-agnostic.
+            target (float tensor of size [batch_num, 4 (* class_num)]):
+                The target regression values with the same size of pred.
+            label_weight (float tensor of size [batch_num, 4 (* class_num)]):
+                The weight of each sample, 0 if ignored.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Defaults to None.
+        Returns:
+            The gradient harmonized loss.
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        mu = self.mu
+        edges = self.edges
+        mmt = self.momentum
+
+        # ASL1 loss
+        diff = pred - target
+        loss = torch.sqrt(diff * diff + mu * mu) - mu
+
+        # gradient length
+        g = torch.abs(diff / torch.sqrt(mu * mu + diff * diff)).detach()
+        weights = torch.zeros_like(g)
+
+        valid = label_weight > 0
+        tot = max(label_weight.float().sum().item(), 1.0)
+        n = 0  # n: valid bins
+        for i in range(self.bins):
+            inds = (g >= edges[i]) & (g < edges[i + 1]) & valid
+            num_in_bin = inds.sum().item()
+            if num_in_bin > 0:
+                n += 1
+                if mmt > 0:
+                    self.acc_sum[i] = mmt * self.acc_sum[i] \
+                        + (1 - mmt) * num_in_bin
+                    weights[inds] = tot / self.acc_sum[i]
+                else:
+                    weights[inds] = tot / num_in_bin
+        if n > 0:
+            weights /= n
+        loss = weight_reduce_loss(
+            loss, weights, reduction=reduction, avg_factor=tot)
+        return loss * self.loss_weight
diff --git a/mmdet/models/losses/iou_loss.py b/mmdet/models/losses/iou_loss.py
new file mode 100644
index 0000000..bf1ed04
--- /dev/null
+++ b/mmdet/models/losses/iou_loss.py
@@ -0,0 +1,474 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+import warnings
+
+import mmcv
+import torch
+import torch.nn as nn
+
+from mmdet.core import bbox_overlaps
+from ..builder import LOSSES
+from .utils import weighted_loss
+
+
+@mmcv.jit(derivate=True, coderize=True)
+@weighted_loss
+def iou_loss(pred, target, linear=False, mode='log', eps=1e-6):
+    """IoU loss.
+
+    Computing the IoU loss between a set of predicted bboxes and target bboxes.
+    The loss is calculated as negative log of IoU.
+
+    Args:
+        pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
+            shape (n, 4).
+        target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
+        linear (bool, optional): If True, use linear scale of loss instead of
+            log scale. Default: False.
+        mode (str): Loss scaling mode, including "linear", "square", and "log".
+            Default: 'log'
+        eps (float): Eps to avoid log(0).
+
+    Return:
+        torch.Tensor: Loss tensor.
+    """
+    assert mode in ['linear', 'square', 'log']
+    if linear:
+        mode = 'linear'
+        warnings.warn('DeprecationWarning: Setting "linear=True" in '
+                      'iou_loss is deprecated, please use "mode=`linear`" '
+                      'instead.')
+    ious = bbox_overlaps(pred, target, is_aligned=True).clamp(min=eps)
+    if mode == 'linear':
+        loss = 1 - ious
+    elif mode == 'square':
+        loss = 1 - ious**2
+    elif mode == 'log':
+        loss = -ious.log()
+    else:
+        raise NotImplementedError
+    return loss
+
+
+@mmcv.jit(derivate=True, coderize=True)
+@weighted_loss
+def bounded_iou_loss(pred, target, beta=0.2, eps=1e-3):
+    """BIoULoss.
+
+    This is an implementation of paper
+    `Improving Object Localization with Fitness NMS and Bounded IoU Loss.
+    <https://arxiv.org/abs/1711.00164>`_.
+
+    Args:
+        pred (torch.Tensor): Predicted bboxes.
+        target (torch.Tensor): Target bboxes.
+        beta (float): beta parameter in smoothl1.
+        eps (float): eps to avoid NaN.
+    """
+    pred_ctrx = (pred[:, 0] + pred[:, 2]) * 0.5
+    pred_ctry = (pred[:, 1] + pred[:, 3]) * 0.5
+    pred_w = pred[:, 2] - pred[:, 0]
+    pred_h = pred[:, 3] - pred[:, 1]
+    with torch.no_grad():
+        target_ctrx = (target[:, 0] + target[:, 2]) * 0.5
+        target_ctry = (target[:, 1] + target[:, 3]) * 0.5
+        target_w = target[:, 2] - target[:, 0]
+        target_h = target[:, 3] - target[:, 1]
+
+    dx = target_ctrx - pred_ctrx
+    dy = target_ctry - pred_ctry
+
+    loss_dx = 1 - torch.max(
+        (target_w - 2 * dx.abs()) /
+        (target_w + 2 * dx.abs() + eps), torch.zeros_like(dx))
+    loss_dy = 1 - torch.max(
+        (target_h - 2 * dy.abs()) /
+        (target_h + 2 * dy.abs() + eps), torch.zeros_like(dy))
+    loss_dw = 1 - torch.min(target_w / (pred_w + eps), pred_w /
+                            (target_w + eps))
+    loss_dh = 1 - torch.min(target_h / (pred_h + eps), pred_h /
+                            (target_h + eps))
+    # view(..., -1) does not work for empty tensor
+    loss_comb = torch.stack([loss_dx, loss_dy, loss_dw, loss_dh],
+                            dim=-1).flatten(1)
+
+    loss = torch.where(loss_comb < beta, 0.5 * loss_comb * loss_comb / beta,
+                       loss_comb - 0.5 * beta)
+    return loss
+
+
+@mmcv.jit(derivate=True, coderize=True)
+@weighted_loss
+def giou_loss(pred, target, eps=1e-7):
+    r"""`Generalized Intersection over Union: A Metric and A Loss for Bounding
+    Box Regression <https://arxiv.org/abs/1902.09630>`_.
+
+    Args:
+        pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
+            shape (n, 4).
+        target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
+        eps (float): Eps to avoid log(0).
+
+    Return:
+        Tensor: Loss tensor.
+    """
+    gious = bbox_overlaps(pred, target, mode='giou', is_aligned=True, eps=eps)
+    loss = 1 - gious
+    return loss
+
+
+@mmcv.jit(derivate=True, coderize=True)
+@weighted_loss
+def diou_loss(pred, target, eps=1e-7):
+    r"""`Implementation of Distance-IoU Loss: Faster and Better
+    Learning for Bounding Box Regression, https://arxiv.org/abs/1911.08287`_.
+
+    Code is modified from https://github.com/Zzh-tju/DIoU.
+
+    Args:
+        pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
+            shape (n, 4).
+        target (Tensor): Corresponding gt bboxes, shape (n, 4).
+        eps (float): Eps to avoid log(0).
+    Return:
+        Tensor: Loss tensor.
+    """
+    # overlap
+    lt = torch.max(pred[:, :2], target[:, :2])
+    rb = torch.min(pred[:, 2:], target[:, 2:])
+    wh = (rb - lt).clamp(min=0)
+    overlap = wh[:, 0] * wh[:, 1]
+
+    # union
+    ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
+    ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
+    union = ap + ag - overlap + eps
+
+    # IoU
+    ious = overlap / union
+
+    # enclose area
+    enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
+    enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
+    enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)
+
+    cw = enclose_wh[:, 0]
+    ch = enclose_wh[:, 1]
+
+    c2 = cw**2 + ch**2 + eps
+
+    b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
+    b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
+    b2_x1, b2_y1 = target[:, 0], target[:, 1]
+    b2_x2, b2_y2 = target[:, 2], target[:, 3]
+
+    left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
+    right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
+    rho2 = left + right
+
+    # DIoU
+    dious = ious - rho2 / c2
+    loss = 1 - dious
+    return loss
+
+
+@mmcv.jit(derivate=True, coderize=True)
+@weighted_loss
+def ciou_loss(pred, target, eps=1e-7):
+    r"""`Implementation of paper `Enhancing Geometric Factors into
+    Model Learning and Inference for Object Detection and Instance
+    Segmentation <https://arxiv.org/abs/2005.03572>`_.
+
+    Code is modified from https://github.com/Zzh-tju/CIoU.
+
+    Args:
+        pred (Tensor): Predicted bboxes of format (x1, y1, x2, y2),
+            shape (n, 4).
+        target (Tensor): Corresponding gt bboxes, shape (n, 4).
+        eps (float): Eps to avoid log(0).
+    Return:
+        Tensor: Loss tensor.
+    """
+    # overlap
+    lt = torch.max(pred[:, :2], target[:, :2])
+    rb = torch.min(pred[:, 2:], target[:, 2:])
+    wh = (rb - lt).clamp(min=0)
+    overlap = wh[:, 0] * wh[:, 1]
+
+    # union
+    ap = (pred[:, 2] - pred[:, 0]) * (pred[:, 3] - pred[:, 1])
+    ag = (target[:, 2] - target[:, 0]) * (target[:, 3] - target[:, 1])
+    union = ap + ag - overlap + eps
+
+    # IoU
+    ious = overlap / union
+
+    # enclose area
+    enclose_x1y1 = torch.min(pred[:, :2], target[:, :2])
+    enclose_x2y2 = torch.max(pred[:, 2:], target[:, 2:])
+    enclose_wh = (enclose_x2y2 - enclose_x1y1).clamp(min=0)
+
+    cw = enclose_wh[:, 0]
+    ch = enclose_wh[:, 1]
+
+    c2 = cw**2 + ch**2 + eps
+
+    b1_x1, b1_y1 = pred[:, 0], pred[:, 1]
+    b1_x2, b1_y2 = pred[:, 2], pred[:, 3]
+    b2_x1, b2_y1 = target[:, 0], target[:, 1]
+    b2_x2, b2_y2 = target[:, 2], target[:, 3]
+
+    w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
+    w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
+
+    left = ((b2_x1 + b2_x2) - (b1_x1 + b1_x2))**2 / 4
+    right = ((b2_y1 + b2_y2) - (b1_y1 + b1_y2))**2 / 4
+    rho2 = left + right
+
+    factor = 4 / math.pi**2
+    v = factor * torch.pow(torch.atan(w2 / h2) - torch.atan(w1 / h1), 2)
+
+    with torch.no_grad():
+        alpha = (ious > 0.5).float() * v / (1 - ious + v)
+
+    # CIoU
+    cious = ious - (rho2 / c2 + alpha * v)
+    loss = 1 - cious.clamp(min=-1.0, max=1.0)
+    return loss
+
+
+@LOSSES.register_module()
+class IoULoss(nn.Module):
+    """IoULoss.
+
+    Computing the IoU loss between a set of predicted bboxes and target bboxes.
+
+    Args:
+        linear (bool): If True, use linear scale of loss else determined
+            by mode. Default: False.
+        eps (float): Eps to avoid log(0).
+        reduction (str): Options are "none", "mean" and "sum".
+        loss_weight (float): Weight of loss.
+        mode (str): Loss scaling mode, including "linear", "square", and "log".
+            Default: 'log'
+    """
+
+    def __init__(self,
+                 linear=False,
+                 eps=1e-6,
+                 reduction='mean',
+                 loss_weight=1.0,
+                 mode='log'):
+        super(IoULoss, self).__init__()
+        assert mode in ['linear', 'square', 'log']
+        if linear:
+            mode = 'linear'
+            warnings.warn('DeprecationWarning: Setting "linear=True" in '
+                          'IOULoss is deprecated, please use "mode=`linear`" '
+                          'instead.')
+        self.mode = mode
+        self.linear = linear
+        self.eps = eps
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None,
+                **kwargs):
+        """Forward function.
+
+        Args:
+            pred (torch.Tensor): The prediction.
+            target (torch.Tensor): The learning target of the prediction.
+            weight (torch.Tensor, optional): The weight of loss for each
+                prediction. Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Defaults to None. Options are "none", "mean" and "sum".
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        if (weight is not None) and (not torch.any(weight > 0)) and (
+                reduction != 'none'):
+            if pred.dim() == weight.dim() + 1:
+                weight = weight.unsqueeze(1)
+            return (pred * weight).sum()  # 0
+        if weight is not None and weight.dim() > 1:
+            # TODO: remove this in the future
+            # reduce the weight of shape (n, 4) to (n,) to match the
+            # iou_loss of shape (n,)
+            assert weight.shape == pred.shape
+            weight = weight.mean(-1)
+        loss = self.loss_weight * iou_loss(
+            pred,
+            target,
+            weight,
+            mode=self.mode,
+            eps=self.eps,
+            reduction=reduction,
+            avg_factor=avg_factor,
+            **kwargs)
+        return loss
+
+
+@LOSSES.register_module()
+class BoundedIoULoss(nn.Module):
+
+    def __init__(self, beta=0.2, eps=1e-3, reduction='mean', loss_weight=1.0):
+        super(BoundedIoULoss, self).__init__()
+        self.beta = beta
+        self.eps = eps
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None,
+                **kwargs):
+        if weight is not None and not torch.any(weight > 0):
+            if pred.dim() == weight.dim() + 1:
+                weight = weight.unsqueeze(1)
+            return (pred * weight).sum()  # 0
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        loss = self.loss_weight * bounded_iou_loss(
+            pred,
+            target,
+            weight,
+            beta=self.beta,
+            eps=self.eps,
+            reduction=reduction,
+            avg_factor=avg_factor,
+            **kwargs)
+        return loss
+
+
+@LOSSES.register_module()
+class GIoULoss(nn.Module):
+
+    def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
+        super(GIoULoss, self).__init__()
+        self.eps = eps
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None,
+                **kwargs):
+        if weight is not None and not torch.any(weight > 0):
+            if pred.dim() == weight.dim() + 1:
+                weight = weight.unsqueeze(1)
+            return (pred * weight).sum()  # 0
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        if weight is not None and weight.dim() > 1:
+            # TODO: remove this in the future
+            # reduce the weight of shape (n, 4) to (n,) to match the
+            # giou_loss of shape (n,)
+            assert weight.shape == pred.shape
+            weight = weight.mean(-1)
+        loss = self.loss_weight * giou_loss(
+            pred,
+            target,
+            weight,
+            eps=self.eps,
+            reduction=reduction,
+            avg_factor=avg_factor,
+            **kwargs)
+        return loss
+
+
+@LOSSES.register_module()
+class DIoULoss(nn.Module):
+
+    def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
+        super(DIoULoss, self).__init__()
+        self.eps = eps
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None,
+                **kwargs):
+        if weight is not None and not torch.any(weight > 0):
+            if pred.dim() == weight.dim() + 1:
+                weight = weight.unsqueeze(1)
+            return (pred * weight).sum()  # 0
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        if weight is not None and weight.dim() > 1:
+            # TODO: remove this in the future
+            # reduce the weight of shape (n, 4) to (n,) to match the
+            # giou_loss of shape (n,)
+            assert weight.shape == pred.shape
+            weight = weight.mean(-1)
+        loss = self.loss_weight * diou_loss(
+            pred,
+            target,
+            weight,
+            eps=self.eps,
+            reduction=reduction,
+            avg_factor=avg_factor,
+            **kwargs)
+        return loss
+
+
+@LOSSES.register_module()
+class CIoULoss(nn.Module):
+
+    def __init__(self, eps=1e-6, reduction='mean', loss_weight=1.0):
+        super(CIoULoss, self).__init__()
+        self.eps = eps
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None,
+                **kwargs):
+        if weight is not None and not torch.any(weight > 0):
+            if pred.dim() == weight.dim() + 1:
+                weight = weight.unsqueeze(1)
+            return (pred * weight).sum()  # 0
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        if weight is not None and weight.dim() > 1:
+            # TODO: remove this in the future
+            # reduce the weight of shape (n, 4) to (n,) to match the
+            # giou_loss of shape (n,)
+            assert weight.shape == pred.shape
+            weight = weight.mean(-1)
+        loss = self.loss_weight * ciou_loss(
+            pred,
+            target,
+            weight,
+            eps=self.eps,
+            reduction=reduction,
+            avg_factor=avg_factor,
+            **kwargs)
+        return loss
diff --git a/mmdet/models/losses/kd_loss.py b/mmdet/models/losses/kd_loss.py
new file mode 100644
index 0000000..75c1935
--- /dev/null
+++ b/mmdet/models/losses/kd_loss.py
@@ -0,0 +1,88 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..builder import LOSSES
+from .utils import weighted_loss
+
+
+@mmcv.jit(derivate=True, coderize=True)
+@weighted_loss
+def knowledge_distillation_kl_div_loss(pred,
+                                       soft_label,
+                                       T,
+                                       detach_target=True):
+    r"""Loss function for knowledge distilling using KL divergence.
+
+    Args:
+        pred (Tensor): Predicted logits with shape (N, n + 1).
+        soft_label (Tensor): Target logits with shape (N, N + 1).
+        T (int): Temperature for distillation.
+        detach_target (bool): Remove soft_label from automatic differentiation
+
+    Returns:
+        torch.Tensor: Loss tensor with shape (N,).
+    """
+    assert pred.size() == soft_label.size()
+    target = F.softmax(soft_label / T, dim=1)
+    if detach_target:
+        target = target.detach()
+
+    kd_loss = F.kl_div(
+        F.log_softmax(pred / T, dim=1), target, reduction='none').mean(1) * (
+            T * T)
+
+    return kd_loss
+
+
+@LOSSES.register_module()
+class KnowledgeDistillationKLDivLoss(nn.Module):
+    """Loss function for knowledge distilling using KL divergence.
+
+    Args:
+        reduction (str): Options are `'none'`, `'mean'` and `'sum'`.
+        loss_weight (float): Loss weight of current loss.
+        T (int): Temperature for distillation.
+    """
+
+    def __init__(self, reduction='mean', loss_weight=1.0, T=10):
+        super(KnowledgeDistillationKLDivLoss, self).__init__()
+        assert T >= 1
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+        self.T = T
+
+    def forward(self,
+                pred,
+                soft_label,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None):
+        """Forward function.
+
+        Args:
+            pred (Tensor): Predicted logits with shape (N, n + 1).
+            soft_label (Tensor): Target logits with shape (N, N + 1).
+            weight (torch.Tensor, optional): The weight of loss for each
+                prediction. Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Defaults to None.
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+
+        loss_kd = self.loss_weight * knowledge_distillation_kl_div_loss(
+            pred,
+            soft_label,
+            weight,
+            reduction=reduction,
+            avg_factor=avg_factor,
+            T=self.T)
+
+        return loss_kd
diff --git a/mmdet/models/losses/mse_loss.py b/mmdet/models/losses/mse_loss.py
new file mode 100644
index 0000000..2ebd161
--- /dev/null
+++ b/mmdet/models/losses/mse_loss.py
@@ -0,0 +1,57 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..builder import LOSSES
+from .utils import weighted_loss
+
+
+@weighted_loss
+def mse_loss(pred, target):
+    """Wrapper of mse loss."""
+    return F.mse_loss(pred, target, reduction='none')
+
+
+@LOSSES.register_module()
+class MSELoss(nn.Module):
+    """MSELoss.
+
+    Args:
+        reduction (str, optional): The method that reduces the loss to a
+            scalar. Options are "none", "mean" and "sum".
+        loss_weight (float, optional): The weight of the loss. Defaults to 1.0
+    """
+
+    def __init__(self, reduction='mean', loss_weight=1.0):
+        super().__init__()
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None):
+        """Forward function of loss.
+
+        Args:
+            pred (torch.Tensor): The prediction.
+            target (torch.Tensor): The learning target of the prediction.
+            weight (torch.Tensor, optional): Weight of the loss for each
+                prediction. Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Defaults to None.
+
+        Returns:
+            torch.Tensor: The calculated loss
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        loss = self.loss_weight * mse_loss(
+            pred, target, weight, reduction=reduction, avg_factor=avg_factor)
+        return loss
diff --git a/mmdet/models/losses/pisa_loss.py b/mmdet/models/losses/pisa_loss.py
new file mode 100644
index 0000000..6afea0e
--- /dev/null
+++ b/mmdet/models/losses/pisa_loss.py
@@ -0,0 +1,184 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.core import bbox_overlaps
+
+
+@mmcv.jit(derivate=True, coderize=True)
+def isr_p(cls_score,
+          bbox_pred,
+          bbox_targets,
+          rois,
+          sampling_results,
+          loss_cls,
+          bbox_coder,
+          k=2,
+          bias=0,
+          num_class=80):
+    """Importance-based Sample Reweighting (ISR_P), positive part.
+
+    Args:
+        cls_score (Tensor): Predicted classification scores.
+        bbox_pred (Tensor): Predicted bbox deltas.
+        bbox_targets (tuple[Tensor]): A tuple of bbox targets, the are
+            labels, label_weights, bbox_targets, bbox_weights, respectively.
+        rois (Tensor): Anchors (single_stage) in shape (n, 4) or RoIs
+            (two_stage) in shape (n, 5).
+        sampling_results (obj): Sampling results.
+        loss_cls (func): Classification loss func of the head.
+        bbox_coder (obj): BBox coder of the head.
+        k (float): Power of the non-linear mapping.
+        bias (float): Shift of the non-linear mapping.
+        num_class (int): Number of classes, default: 80.
+
+    Return:
+        tuple([Tensor]): labels, imp_based_label_weights, bbox_targets,
+            bbox_target_weights
+    """
+
+    labels, label_weights, bbox_targets, bbox_weights = bbox_targets
+    pos_label_inds = ((labels >= 0) &
+                      (labels < num_class)).nonzero().reshape(-1)
+    pos_labels = labels[pos_label_inds]
+
+    # if no positive samples, return the original targets
+    num_pos = float(pos_label_inds.size(0))
+    if num_pos == 0:
+        return labels, label_weights, bbox_targets, bbox_weights
+
+    # merge pos_assigned_gt_inds of per image to a single tensor
+    gts = list()
+    last_max_gt = 0
+    for i in range(len(sampling_results)):
+        gt_i = sampling_results[i].pos_assigned_gt_inds
+        gts.append(gt_i + last_max_gt)
+        if len(gt_i) != 0:
+            last_max_gt = gt_i.max() + 1
+    gts = torch.cat(gts)
+    assert len(gts) == num_pos
+
+    cls_score = cls_score.detach()
+    bbox_pred = bbox_pred.detach()
+
+    # For single stage detectors, rois here indicate anchors, in shape (N, 4)
+    # For two stage detectors, rois are in shape (N, 5)
+    if rois.size(-1) == 5:
+        pos_rois = rois[pos_label_inds][:, 1:]
+    else:
+        pos_rois = rois[pos_label_inds]
+
+    if bbox_pred.size(-1) > 4:
+        bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4)
+        pos_delta_pred = bbox_pred[pos_label_inds, pos_labels].view(-1, 4)
+    else:
+        pos_delta_pred = bbox_pred[pos_label_inds].view(-1, 4)
+
+    # compute iou of the predicted bbox and the corresponding GT
+    pos_delta_target = bbox_targets[pos_label_inds].view(-1, 4)
+    pos_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_pred)
+    target_bbox_pred = bbox_coder.decode(pos_rois, pos_delta_target)
+    ious = bbox_overlaps(pos_bbox_pred, target_bbox_pred, is_aligned=True)
+
+    pos_imp_weights = label_weights[pos_label_inds]
+    # Two steps to compute IoU-HLR. Samples are first sorted by IoU locally,
+    # then sorted again within the same-rank group
+    max_l_num = pos_labels.bincount().max()
+    for label in pos_labels.unique():
+        l_inds = (pos_labels == label).nonzero().view(-1)
+        l_gts = gts[l_inds]
+        for t in l_gts.unique():
+            t_inds = l_inds[l_gts == t]
+            t_ious = ious[t_inds]
+            _, t_iou_rank_idx = t_ious.sort(descending=True)
+            _, t_iou_rank = t_iou_rank_idx.sort()
+            ious[t_inds] += max_l_num - t_iou_rank.float()
+        l_ious = ious[l_inds]
+        _, l_iou_rank_idx = l_ious.sort(descending=True)
+        _, l_iou_rank = l_iou_rank_idx.sort()  # IoU-HLR
+        # linearly map HLR to label weights
+        pos_imp_weights[l_inds] *= (max_l_num - l_iou_rank.float()) / max_l_num
+
+    pos_imp_weights = (bias + pos_imp_weights * (1 - bias)).pow(k)
+
+    # normalize to make the new weighted loss value equal to the original loss
+    pos_loss_cls = loss_cls(
+        cls_score[pos_label_inds], pos_labels, reduction_override='none')
+    if pos_loss_cls.dim() > 1:
+        ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds][:,
+                                                                        None]
+        new_pos_loss_cls = pos_loss_cls * pos_imp_weights[:, None]
+    else:
+        ori_pos_loss_cls = pos_loss_cls * label_weights[pos_label_inds]
+        new_pos_loss_cls = pos_loss_cls * pos_imp_weights
+    pos_loss_cls_ratio = ori_pos_loss_cls.sum() / new_pos_loss_cls.sum()
+    pos_imp_weights = pos_imp_weights * pos_loss_cls_ratio
+    label_weights[pos_label_inds] = pos_imp_weights
+
+    bbox_targets = labels, label_weights, bbox_targets, bbox_weights
+    return bbox_targets
+
+
+@mmcv.jit(derivate=True, coderize=True)
+def carl_loss(cls_score,
+              labels,
+              bbox_pred,
+              bbox_targets,
+              loss_bbox,
+              k=1,
+              bias=0.2,
+              avg_factor=None,
+              sigmoid=False,
+              num_class=80):
+    """Classification-Aware Regression Loss (CARL).
+
+    Args:
+        cls_score (Tensor): Predicted classification scores.
+        labels (Tensor): Targets of classification.
+        bbox_pred (Tensor): Predicted bbox deltas.
+        bbox_targets (Tensor): Target of bbox regression.
+        loss_bbox (func): Regression loss func of the head.
+        bbox_coder (obj): BBox coder of the head.
+        k (float): Power of the non-linear mapping.
+        bias (float): Shift of the non-linear mapping.
+        avg_factor (int): Average factor used in regression loss.
+        sigmoid (bool): Activation of the classification score.
+        num_class (int): Number of classes, default: 80.
+
+    Return:
+        dict: CARL loss dict.
+    """
+    pos_label_inds = ((labels >= 0) &
+                      (labels < num_class)).nonzero().reshape(-1)
+    if pos_label_inds.numel() == 0:
+        return dict(loss_carl=cls_score.sum()[None] * 0.)
+    pos_labels = labels[pos_label_inds]
+
+    # multiply pos_cls_score with the corresponding bbox weight
+    # and remain gradient
+    if sigmoid:
+        pos_cls_score = cls_score.sigmoid()[pos_label_inds, pos_labels]
+    else:
+        pos_cls_score = cls_score.softmax(-1)[pos_label_inds, pos_labels]
+    carl_loss_weights = (bias + (1 - bias) * pos_cls_score).pow(k)
+
+    # normalize carl_loss_weight to make its sum equal to num positive
+    num_pos = float(pos_cls_score.size(0))
+    weight_ratio = num_pos / carl_loss_weights.sum()
+    carl_loss_weights *= weight_ratio
+
+    if avg_factor is None:
+        avg_factor = bbox_targets.size(0)
+    # if is class agnostic, bbox pred is in shape (N, 4)
+    # otherwise, bbox pred is in shape (N, #classes, 4)
+    if bbox_pred.size(-1) > 4:
+        bbox_pred = bbox_pred.view(bbox_pred.size(0), -1, 4)
+        pos_bbox_preds = bbox_pred[pos_label_inds, pos_labels]
+    else:
+        pos_bbox_preds = bbox_pred[pos_label_inds]
+    ori_loss_reg = loss_bbox(
+        pos_bbox_preds,
+        bbox_targets[pos_label_inds],
+        reduction_override='none') / avg_factor
+    loss_carl = (ori_loss_reg * carl_loss_weights[:, None]).sum()
+    return dict(loss_carl=loss_carl[None])
diff --git a/mmdet/models/losses/seesaw_loss.py b/mmdet/models/losses/seesaw_loss.py
new file mode 100644
index 0000000..0104047
--- /dev/null
+++ b/mmdet/models/losses/seesaw_loss.py
@@ -0,0 +1,262 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..builder import LOSSES
+from .accuracy import accuracy
+from .cross_entropy_loss import cross_entropy
+from .utils import weight_reduce_loss
+
+
+def seesaw_ce_loss(cls_score,
+                   labels,
+                   label_weights,
+                   cum_samples,
+                   num_classes,
+                   p,
+                   q,
+                   eps,
+                   reduction='mean',
+                   avg_factor=None):
+    """Calculate the Seesaw CrossEntropy loss.
+
+    Args:
+        cls_score (torch.Tensor): The prediction with shape (N, C),
+             C is the number of classes.
+        labels (torch.Tensor): The learning label of the prediction.
+        label_weights (torch.Tensor): Sample-wise loss weight.
+        cum_samples (torch.Tensor): Cumulative samples for each category.
+        num_classes (int): The number of classes.
+        p (float): The ``p`` in the mitigation factor.
+        q (float): The ``q`` in the compenstation factor.
+        eps (float): The minimal value of divisor to smooth
+             the computation of compensation factor
+        reduction (str, optional): The method used to reduce the loss.
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Defaults to None.
+
+    Returns:
+        torch.Tensor: The calculated loss
+    """
+    assert cls_score.size(-1) == num_classes
+    assert len(cum_samples) == num_classes
+
+    onehot_labels = F.one_hot(labels, num_classes)
+    seesaw_weights = cls_score.new_ones(onehot_labels.size())
+
+    # mitigation factor
+    if p > 0:
+        sample_ratio_matrix = cum_samples[None, :].clamp(
+            min=1) / cum_samples[:, None].clamp(min=1)
+        index = (sample_ratio_matrix < 1.0).float()
+        sample_weights = sample_ratio_matrix.pow(p) * index + (1 - index)
+        mitigation_factor = sample_weights[labels.long(), :]
+        seesaw_weights = seesaw_weights * mitigation_factor
+
+    # compensation factor
+    if q > 0:
+        scores = F.softmax(cls_score.detach(), dim=1)
+        self_scores = scores[
+            torch.arange(0, len(scores)).to(scores.device).long(),
+            labels.long()]
+        score_matrix = scores / self_scores[:, None].clamp(min=eps)
+        index = (score_matrix > 1.0).float()
+        compensation_factor = score_matrix.pow(q) * index + (1 - index)
+        seesaw_weights = seesaw_weights * compensation_factor
+
+    cls_score = cls_score + (seesaw_weights.log() * (1 - onehot_labels))
+
+    loss = F.cross_entropy(cls_score, labels, weight=None, reduction='none')
+
+    if label_weights is not None:
+        label_weights = label_weights.float()
+    loss = weight_reduce_loss(
+        loss, weight=label_weights, reduction=reduction, avg_factor=avg_factor)
+    return loss
+
+
+@LOSSES.register_module()
+class SeesawLoss(nn.Module):
+    """
+    Seesaw Loss for Long-Tailed Instance Segmentation (CVPR 2021)
+    arXiv: https://arxiv.org/abs/2008.10032
+
+    Args:
+        use_sigmoid (bool, optional): Whether the prediction uses sigmoid
+             of softmax. Only False is supported.
+        p (float, optional): The ``p`` in the mitigation factor.
+             Defaults to 0.8.
+        q (float, optional): The ``q`` in the compenstation factor.
+             Defaults to 2.0.
+        num_classes (int, optional): The number of classes.
+             Default to 1203 for LVIS v1 dataset.
+        eps (float, optional): The minimal value of divisor to smooth
+             the computation of compensation factor
+        reduction (str, optional): The method that reduces the loss to a
+             scalar. Options are "none", "mean" and "sum".
+        loss_weight (float, optional): The weight of the loss. Defaults to 1.0
+        return_dict (bool, optional): Whether return the losses as a dict.
+             Default to True.
+    """
+
+    def __init__(self,
+                 use_sigmoid=False,
+                 p=0.8,
+                 q=2.0,
+                 num_classes=1203,
+                 eps=1e-2,
+                 reduction='mean',
+                 loss_weight=1.0,
+                 return_dict=True):
+        super(SeesawLoss, self).__init__()
+        assert not use_sigmoid
+        self.use_sigmoid = False
+        self.p = p
+        self.q = q
+        self.num_classes = num_classes
+        self.eps = eps
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+        self.return_dict = return_dict
+
+        # 0 for pos, 1 for neg
+        self.cls_criterion = seesaw_ce_loss
+
+        # cumulative samples for each category
+        self.register_buffer(
+            'cum_samples',
+            torch.zeros(self.num_classes + 1, dtype=torch.float))
+
+        # custom output channels of the classifier
+        self.custom_cls_channels = True
+        # custom activation of cls_score
+        self.custom_activation = True
+        # custom accuracy of the classsifier
+        self.custom_accuracy = True
+
+    def _split_cls_score(self, cls_score):
+        # split cls_score to cls_score_classes and cls_score_objectness
+        assert cls_score.size(-1) == self.num_classes + 2
+        cls_score_classes = cls_score[..., :-2]
+        cls_score_objectness = cls_score[..., -2:]
+        return cls_score_classes, cls_score_objectness
+
+    def get_cls_channels(self, num_classes):
+        """Get custom classification channels.
+
+        Args:
+            num_classes (int): The number of classes.
+
+        Returns:
+            int: The custom classification channels.
+        """
+        assert num_classes == self.num_classes
+        return num_classes + 2
+
+    def get_activation(self, cls_score):
+        """Get custom activation of cls_score.
+
+        Args:
+            cls_score (torch.Tensor): The prediction with shape (N, C + 2).
+
+        Returns:
+            torch.Tensor: The custom activation of cls_score with shape
+                 (N, C + 1).
+        """
+        cls_score_classes, cls_score_objectness = self._split_cls_score(
+            cls_score)
+        score_classes = F.softmax(cls_score_classes, dim=-1)
+        score_objectness = F.softmax(cls_score_objectness, dim=-1)
+        score_pos = score_objectness[..., [0]]
+        score_neg = score_objectness[..., [1]]
+        score_classes = score_classes * score_pos
+        scores = torch.cat([score_classes, score_neg], dim=-1)
+        return scores
+
+    def get_accuracy(self, cls_score, labels):
+        """Get custom accuracy w.r.t. cls_score and labels.
+
+        Args:
+            cls_score (torch.Tensor): The prediction with shape (N, C + 2).
+            labels (torch.Tensor): The learning label of the prediction.
+
+        Returns:
+            Dict [str, torch.Tensor]: The accuracy for objectness and classes,
+                 respectively.
+        """
+        pos_inds = labels < self.num_classes
+        obj_labels = (labels == self.num_classes).long()
+        cls_score_classes, cls_score_objectness = self._split_cls_score(
+            cls_score)
+        acc_objectness = accuracy(cls_score_objectness, obj_labels)
+        acc_classes = accuracy(cls_score_classes[pos_inds], labels[pos_inds])
+        acc = dict()
+        acc['acc_objectness'] = acc_objectness
+        acc['acc_classes'] = acc_classes
+        return acc
+
+    def forward(self,
+                cls_score,
+                labels,
+                label_weights=None,
+                avg_factor=None,
+                reduction_override=None):
+        """Forward function.
+
+        Args:
+            cls_score (torch.Tensor): The prediction with shape (N, C + 2).
+            labels (torch.Tensor): The learning label of the prediction.
+            label_weights (torch.Tensor, optional): Sample-wise loss weight.
+            avg_factor (int, optional): Average factor that is used to average
+                 the loss. Defaults to None.
+            reduction (str, optional): The method used to reduce the loss.
+                 Options are "none", "mean" and "sum".
+        Returns:
+            torch.Tensor | Dict [str, torch.Tensor]:
+                 if return_dict == False: The calculated loss |
+                 if return_dict == True: The dict of calculated losses
+                 for objectness and classes, respectively.
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        assert cls_score.size(-1) == self.num_classes + 2
+        pos_inds = labels < self.num_classes
+        # 0 for pos, 1 for neg
+        obj_labels = (labels == self.num_classes).long()
+
+        # accumulate the samples for each category
+        unique_labels = labels.unique()
+        for u_l in unique_labels:
+            inds_ = labels == u_l.item()
+            self.cum_samples[u_l] += inds_.sum()
+
+        if label_weights is not None:
+            label_weights = label_weights.float()
+        else:
+            label_weights = labels.new_ones(labels.size(), dtype=torch.float)
+
+        cls_score_classes, cls_score_objectness = self._split_cls_score(
+            cls_score)
+        # calculate loss_cls_classes (only need pos samples)
+        if pos_inds.sum() > 0:
+            loss_cls_classes = self.loss_weight * self.cls_criterion(
+                cls_score_classes[pos_inds], labels[pos_inds],
+                label_weights[pos_inds], self.cum_samples[:self.num_classes],
+                self.num_classes, self.p, self.q, self.eps, reduction,
+                avg_factor)
+        else:
+            loss_cls_classes = cls_score_classes[pos_inds].sum()
+        # calculate loss_cls_objectness
+        loss_cls_objectness = self.loss_weight * cross_entropy(
+            cls_score_objectness, obj_labels, label_weights, reduction,
+            avg_factor)
+
+        if self.return_dict:
+            loss_cls = dict()
+            loss_cls['loss_cls_objectness'] = loss_cls_objectness
+            loss_cls['loss_cls_classes'] = loss_cls_classes
+        else:
+            loss_cls = loss_cls_classes + loss_cls_objectness
+        return loss_cls
diff --git a/mmdet/models/losses/smooth_l1_loss.py b/mmdet/models/losses/smooth_l1_loss.py
new file mode 100644
index 0000000..5511746
--- /dev/null
+++ b/mmdet/models/losses/smooth_l1_loss.py
@@ -0,0 +1,146 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+import torch.nn as nn
+
+from ..builder import LOSSES
+from .utils import weighted_loss
+
+
+@mmcv.jit(derivate=True, coderize=True)
+@weighted_loss
+def smooth_l1_loss(pred, target, beta=1.0):
+    """Smooth L1 loss.
+
+    Args:
+        pred (torch.Tensor): The prediction.
+        target (torch.Tensor): The learning target of the prediction.
+        beta (float, optional): The threshold in the piecewise function.
+            Defaults to 1.0.
+
+    Returns:
+        torch.Tensor: Calculated loss
+    """
+    assert beta > 0
+    if target.numel() == 0:
+        return pred.sum() * 0
+
+    assert pred.size() == target.size()
+    diff = torch.abs(pred - target)
+    loss = torch.where(diff < beta, 0.5 * diff * diff / beta,
+                       diff - 0.5 * beta)
+    return loss
+
+
+@mmcv.jit(derivate=True, coderize=True)
+@weighted_loss
+def l1_loss(pred, target):
+    """L1 loss.
+
+    Args:
+        pred (torch.Tensor): The prediction.
+        target (torch.Tensor): The learning target of the prediction.
+
+    Returns:
+        torch.Tensor: Calculated loss
+    """
+    if target.numel() == 0:
+        return pred.sum() * 0
+
+    assert pred.size() == target.size()
+    loss = torch.abs(pred - target)
+    return loss
+
+
+@LOSSES.register_module()
+class SmoothL1Loss(nn.Module):
+    """Smooth L1 loss.
+
+    Args:
+        beta (float, optional): The threshold in the piecewise function.
+            Defaults to 1.0.
+        reduction (str, optional): The method to reduce the loss.
+            Options are "none", "mean" and "sum". Defaults to "mean".
+        loss_weight (float, optional): The weight of loss.
+    """
+
+    def __init__(self, beta=1.0, reduction='mean', loss_weight=1.0):
+        super(SmoothL1Loss, self).__init__()
+        self.beta = beta
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None,
+                **kwargs):
+        """Forward function.
+
+        Args:
+            pred (torch.Tensor): The prediction.
+            target (torch.Tensor): The learning target of the prediction.
+            weight (torch.Tensor, optional): The weight of loss for each
+                prediction. Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Defaults to None.
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        loss_bbox = self.loss_weight * smooth_l1_loss(
+            pred,
+            target,
+            weight,
+            beta=self.beta,
+            reduction=reduction,
+            avg_factor=avg_factor,
+            **kwargs)
+        return loss_bbox
+
+
+@LOSSES.register_module()
+class L1Loss(nn.Module):
+    """L1 loss.
+
+    Args:
+        reduction (str, optional): The method to reduce the loss.
+            Options are "none", "mean" and "sum".
+        loss_weight (float, optional): The weight of loss.
+    """
+
+    def __init__(self, reduction='mean', loss_weight=1.0):
+        super(L1Loss, self).__init__()
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None):
+        """Forward function.
+
+        Args:
+            pred (torch.Tensor): The prediction.
+            target (torch.Tensor): The learning target of the prediction.
+            weight (torch.Tensor, optional): The weight of loss for each
+                prediction. Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Defaults to None.
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        loss_bbox = self.loss_weight * l1_loss(
+            pred, target, weight, reduction=reduction, avg_factor=avg_factor)
+        return loss_bbox
diff --git a/mmdet/models/losses/utils.py b/mmdet/models/losses/utils.py
new file mode 100644
index 0000000..778237e
--- /dev/null
+++ b/mmdet/models/losses/utils.py
@@ -0,0 +1,105 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import functools
+
+import mmcv
+import torch
+import torch.nn.functional as F
+
+
+def reduce_loss(loss, reduction):
+    """Reduce loss as specified.
+
+    Args:
+        loss (Tensor): Elementwise loss tensor.
+        reduction (str): Options are "none", "mean" and "sum".
+
+    Return:
+        Tensor: Reduced loss tensor.
+    """
+    reduction_enum = F._Reduction.get_enum(reduction)
+    # none: 0, elementwise_mean:1, sum: 2
+    if reduction_enum == 0:
+        return loss
+    elif reduction_enum == 1:
+        return loss.mean()
+    elif reduction_enum == 2:
+        return loss.sum()
+
+
+@mmcv.jit(derivate=True, coderize=True)
+def weight_reduce_loss(loss, weight=None, reduction='mean', avg_factor=None):
+    """Apply element-wise weight and reduce loss.
+
+    Args:
+        loss (Tensor): Element-wise loss.
+        weight (Tensor): Element-wise weights.
+        reduction (str): Same as built-in losses of PyTorch.
+        avg_factor (float): Average factor when computing the mean of losses.
+
+    Returns:
+        Tensor: Processed loss values.
+    """
+    # if weight is specified, apply element-wise weight
+    if weight is not None:
+        loss = loss * weight
+
+    # if avg_factor is not specified, just reduce the loss
+    if avg_factor is None:
+        loss = reduce_loss(loss, reduction)
+    else:
+        # if reduction is mean, then average the loss by avg_factor
+        if reduction == 'mean':
+            # Avoid causing ZeroDivisionError when avg_factor is 0.0,
+            # i.e., all labels of an image belong to ignore index.
+            eps = torch.finfo(torch.float32).eps
+            loss = loss.sum() / (avg_factor + eps)
+        # if reduction is 'none', then do nothing, otherwise raise an error
+        elif reduction != 'none':
+            raise ValueError('avg_factor can not be used with reduction="sum"')
+    return loss
+
+
+def weighted_loss(loss_func):
+    """Create a weighted version of a given loss function.
+
+    To use this decorator, the loss function must have the signature like
+    `loss_func(pred, target, **kwargs)`. The function only needs to compute
+    element-wise loss without any reduction. This decorator will add weight
+    and reduction arguments to the function. The decorated function will have
+    the signature like `loss_func(pred, target, weight=None, reduction='mean',
+    avg_factor=None, **kwargs)`.
+
+    :Example:
+
+    >>> import torch
+    >>> @weighted_loss
+    >>> def l1_loss(pred, target):
+    >>>     return (pred - target).abs()
+
+    >>> pred = torch.Tensor([0, 2, 3])
+    >>> target = torch.Tensor([1, 1, 1])
+    >>> weight = torch.Tensor([1, 0, 1])
+
+    >>> l1_loss(pred, target)
+    tensor(1.3333)
+    >>> l1_loss(pred, target, weight)
+    tensor(1.)
+    >>> l1_loss(pred, target, reduction='none')
+    tensor([1., 1., 2.])
+    >>> l1_loss(pred, target, weight, avg_factor=2)
+    tensor(1.5000)
+    """
+
+    @functools.wraps(loss_func)
+    def wrapper(pred,
+                target,
+                weight=None,
+                reduction='mean',
+                avg_factor=None,
+                **kwargs):
+        # get element-wise loss
+        loss = loss_func(pred, target, **kwargs)
+        loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
+        return loss
+
+    return wrapper
diff --git a/mmdet/models/losses/varifocal_loss.py b/mmdet/models/losses/varifocal_loss.py
new file mode 100644
index 0000000..42f0eef
--- /dev/null
+++ b/mmdet/models/losses/varifocal_loss.py
@@ -0,0 +1,134 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..builder import LOSSES
+from .utils import weight_reduce_loss
+
+
+@mmcv.jit(derivate=True, coderize=True)
+def varifocal_loss(pred,
+                   target,
+                   weight=None,
+                   alpha=0.75,
+                   gamma=2.0,
+                   iou_weighted=True,
+                   reduction='mean',
+                   avg_factor=None):
+    """`Varifocal Loss <https://arxiv.org/abs/2008.13367>`_
+
+    Args:
+        pred (torch.Tensor): The prediction with shape (N, C), C is the
+            number of classes
+        target (torch.Tensor): The learning target of the iou-aware
+            classification score with shape (N, C), C is the number of classes.
+        weight (torch.Tensor, optional): The weight of loss for each
+            prediction. Defaults to None.
+        alpha (float, optional): A balance factor for the negative part of
+            Varifocal Loss, which is different from the alpha of Focal Loss.
+            Defaults to 0.75.
+        gamma (float, optional): The gamma for calculating the modulating
+            factor. Defaults to 2.0.
+        iou_weighted (bool, optional): Whether to weight the loss of the
+            positive example with the iou target. Defaults to True.
+        reduction (str, optional): The method used to reduce the loss into
+            a scalar. Defaults to 'mean'. Options are "none", "mean" and
+            "sum".
+        avg_factor (int, optional): Average factor that is used to average
+            the loss. Defaults to None.
+    """
+    # pred and target should be of the same size
+    assert pred.size() == target.size()
+    pred_sigmoid = pred.sigmoid()
+    target = target.type_as(pred)
+    if iou_weighted:
+        focal_weight = target * (target > 0.0).float() + \
+            alpha * (pred_sigmoid - target).abs().pow(gamma) * \
+            (target <= 0.0).float()
+    else:
+        focal_weight = (target > 0.0).float() + \
+            alpha * (pred_sigmoid - target).abs().pow(gamma) * \
+            (target <= 0.0).float()
+    loss = F.binary_cross_entropy_with_logits(
+        pred, target, reduction='none') * focal_weight
+    loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
+    return loss
+
+
+@LOSSES.register_module()
+class VarifocalLoss(nn.Module):
+
+    def __init__(self,
+                 use_sigmoid=True,
+                 alpha=0.75,
+                 gamma=2.0,
+                 iou_weighted=True,
+                 reduction='mean',
+                 loss_weight=1.0):
+        """`Varifocal Loss <https://arxiv.org/abs/2008.13367>`_
+
+        Args:
+            use_sigmoid (bool, optional): Whether the prediction is
+                used for sigmoid or softmax. Defaults to True.
+            alpha (float, optional): A balance factor for the negative part of
+                Varifocal Loss, which is different from the alpha of Focal
+                Loss. Defaults to 0.75.
+            gamma (float, optional): The gamma for calculating the modulating
+                factor. Defaults to 2.0.
+            iou_weighted (bool, optional): Whether to weight the loss of the
+                positive examples with the iou target. Defaults to True.
+            reduction (str, optional): The method used to reduce the loss into
+                a scalar. Defaults to 'mean'. Options are "none", "mean" and
+                "sum".
+            loss_weight (float, optional): Weight of loss. Defaults to 1.0.
+        """
+        super(VarifocalLoss, self).__init__()
+        assert use_sigmoid is True, \
+            'Only sigmoid varifocal loss supported now.'
+        assert alpha >= 0.0
+        self.use_sigmoid = use_sigmoid
+        self.alpha = alpha
+        self.gamma = gamma
+        self.iou_weighted = iou_weighted
+        self.reduction = reduction
+        self.loss_weight = loss_weight
+
+    def forward(self,
+                pred,
+                target,
+                weight=None,
+                avg_factor=None,
+                reduction_override=None):
+        """Forward function.
+
+        Args:
+            pred (torch.Tensor): The prediction.
+            target (torch.Tensor): The learning target of the prediction.
+            weight (torch.Tensor, optional): The weight of loss for each
+                prediction. Defaults to None.
+            avg_factor (int, optional): Average factor that is used to average
+                the loss. Defaults to None.
+            reduction_override (str, optional): The reduction method used to
+                override the original reduction method of the loss.
+                Options are "none", "mean" and "sum".
+
+        Returns:
+            torch.Tensor: The calculated loss
+        """
+        assert reduction_override in (None, 'none', 'mean', 'sum')
+        reduction = (
+            reduction_override if reduction_override else self.reduction)
+        if self.use_sigmoid:
+            loss_cls = self.loss_weight * varifocal_loss(
+                pred,
+                target,
+                weight,
+                alpha=self.alpha,
+                gamma=self.gamma,
+                iou_weighted=self.iou_weighted,
+                reduction=reduction,
+                avg_factor=avg_factor)
+        else:
+            raise NotImplementedError
+        return loss_cls
diff --git a/mmdet/models/necks/__init__.py b/mmdet/models/necks/__init__.py
new file mode 100644
index 0000000..6f2fa82
--- /dev/null
+++ b/mmdet/models/necks/__init__.py
@@ -0,0 +1,23 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .bfp import BFP
+from .channel_mapper import ChannelMapper
+from .ct_resnet_neck import CTResNetNeck
+from .dilated_encoder import DilatedEncoder
+from .dyhead import DyHead
+from .fpg import FPG
+from .fpn import FPN
+from .fpn_carafe import FPN_CARAFE
+from .hrfpn import HRFPN
+from .nas_fpn import NASFPN
+from .nasfcos_fpn import NASFCOS_FPN
+from .pafpn import PAFPN
+from .rfp import RFP
+from .ssd_neck import SSDNeck
+from .yolo_neck import YOLOV3Neck
+from .yolox_pafpn import YOLOXPAFPN
+
+__all__ = [
+    'FPN', 'BFP', 'ChannelMapper', 'HRFPN', 'NASFPN', 'FPN_CARAFE', 'PAFPN',
+    'NASFCOS_FPN', 'RFP', 'YOLOV3Neck', 'FPG', 'DilatedEncoder',
+    'CTResNetNeck', 'SSDNeck', 'YOLOXPAFPN', 'DyHead'
+]
diff --git a/mmdet/models/necks/bfp.py b/mmdet/models/necks/bfp.py
new file mode 100644
index 0000000..9fdfa03
--- /dev/null
+++ b/mmdet/models/necks/bfp.py
@@ -0,0 +1,102 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+from mmcv.cnn.bricks import NonLocal2d
+from mmcv.runner import BaseModule
+
+from ..builder import NECKS
+
+
+@NECKS.register_module()
+class BFP(BaseModule):
+    """BFP (Balanced Feature Pyramids)
+
+    BFP takes multi-level features as inputs and gather them into a single one,
+    then refine the gathered feature and scatter the refined results to
+    multi-level features. This module is used in Libra R-CNN (CVPR 2019), see
+    the paper `Libra R-CNN: Towards Balanced Learning for Object Detection
+    <https://arxiv.org/abs/1904.02701>`_ for details.
+
+    Args:
+        in_channels (int): Number of input channels (feature maps of all levels
+            should have the same channels).
+        num_levels (int): Number of input feature levels.
+        conv_cfg (dict): The config dict for convolution layers.
+        norm_cfg (dict): The config dict for normalization layers.
+        refine_level (int): Index of integration and refine level of BSF in
+            multi-level features from bottom to top.
+        refine_type (str): Type of the refine op, currently support
+            [None, 'conv', 'non_local'].
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 num_levels,
+                 refine_level=2,
+                 refine_type=None,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 init_cfg=dict(
+                     type='Xavier', layer='Conv2d', distribution='uniform')):
+        super(BFP, self).__init__(init_cfg)
+        assert refine_type in [None, 'conv', 'non_local']
+
+        self.in_channels = in_channels
+        self.num_levels = num_levels
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+
+        self.refine_level = refine_level
+        self.refine_type = refine_type
+        assert 0 <= self.refine_level < self.num_levels
+
+        if self.refine_type == 'conv':
+            self.refine = ConvModule(
+                self.in_channels,
+                self.in_channels,
+                3,
+                padding=1,
+                conv_cfg=self.conv_cfg,
+                norm_cfg=self.norm_cfg)
+        elif self.refine_type == 'non_local':
+            self.refine = NonLocal2d(
+                self.in_channels,
+                reduction=1,
+                use_scale=False,
+                conv_cfg=self.conv_cfg,
+                norm_cfg=self.norm_cfg)
+
+    def forward(self, inputs):
+        """Forward function."""
+        assert len(inputs) == self.num_levels
+
+        # step 1: gather multi-level features by resize and average
+        feats = []
+        gather_size = inputs[self.refine_level].size()[2:]
+        for i in range(self.num_levels):
+            if i < self.refine_level:
+                gathered = F.adaptive_max_pool2d(
+                    inputs[i], output_size=gather_size)
+            else:
+                gathered = F.interpolate(
+                    inputs[i], size=gather_size, mode='nearest')
+            feats.append(gathered)
+
+        bsf = sum(feats) / len(feats)
+
+        # step 2: refine gathered features
+        if self.refine_type is not None:
+            bsf = self.refine(bsf)
+
+        # step 3: scatter refined features to multi-levels by a residual path
+        outs = []
+        for i in range(self.num_levels):
+            out_size = inputs[i].size()[2:]
+            if i < self.refine_level:
+                residual = F.interpolate(bsf, size=out_size, mode='nearest')
+            else:
+                residual = F.adaptive_max_pool2d(bsf, output_size=out_size)
+            outs.append(residual + inputs[i])
+
+        return tuple(outs)
diff --git a/mmdet/models/necks/channel_mapper.py b/mmdet/models/necks/channel_mapper.py
new file mode 100644
index 0000000..774bdb1
--- /dev/null
+++ b/mmdet/models/necks/channel_mapper.py
@@ -0,0 +1,100 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule
+
+from ..builder import NECKS
+
+
+@NECKS.register_module()
+class ChannelMapper(BaseModule):
+    r"""Channel Mapper to reduce/increase channels of backbone features.
+
+    This is used to reduce/increase channels of backbone features.
+
+    Args:
+        in_channels (List[int]): Number of input channels per scale.
+        out_channels (int): Number of output channels (used at each scale).
+        kernel_size (int, optional): kernel_size for reducing channels (used
+            at each scale). Default: 3.
+        conv_cfg (dict, optional): Config dict for convolution layer.
+            Default: None.
+        norm_cfg (dict, optional): Config dict for normalization layer.
+            Default: None.
+        act_cfg (dict, optional): Config dict for activation layer in
+            ConvModule. Default: dict(type='ReLU').
+        num_outs (int, optional): Number of output feature maps. There
+            would be extra_convs when num_outs larger than the length
+            of in_channels.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    Example:
+        >>> import torch
+        >>> in_channels = [2, 3, 5, 7]
+        >>> scales = [340, 170, 84, 43]
+        >>> inputs = [torch.rand(1, c, s, s)
+        ...           for c, s in zip(in_channels, scales)]
+        >>> self = ChannelMapper(in_channels, 11, 3).eval()
+        >>> outputs = self.forward(inputs)
+        >>> for i in range(len(outputs)):
+        ...     print(f'outputs[{i}].shape = {outputs[i].shape}')
+        outputs[0].shape = torch.Size([1, 11, 340, 340])
+        outputs[1].shape = torch.Size([1, 11, 170, 170])
+        outputs[2].shape = torch.Size([1, 11, 84, 84])
+        outputs[3].shape = torch.Size([1, 11, 43, 43])
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=3,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 act_cfg=dict(type='ReLU'),
+                 num_outs=None,
+                 init_cfg=dict(
+                     type='Xavier', layer='Conv2d', distribution='uniform')):
+        super(ChannelMapper, self).__init__(init_cfg)
+        assert isinstance(in_channels, list)
+        self.extra_convs = None
+        if num_outs is None:
+            num_outs = len(in_channels)
+        self.convs = nn.ModuleList()
+        for in_channel in in_channels:
+            self.convs.append(
+                ConvModule(
+                    in_channel,
+                    out_channels,
+                    kernel_size,
+                    padding=(kernel_size - 1) // 2,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg))
+        if num_outs > len(in_channels):
+            self.extra_convs = nn.ModuleList()
+            for i in range(len(in_channels), num_outs):
+                if i == len(in_channels):
+                    in_channel = in_channels[-1]
+                else:
+                    in_channel = out_channels
+                self.extra_convs.append(
+                    ConvModule(
+                        in_channel,
+                        out_channels,
+                        3,
+                        stride=2,
+                        padding=1,
+                        conv_cfg=conv_cfg,
+                        norm_cfg=norm_cfg,
+                        act_cfg=act_cfg))
+
+    def forward(self, inputs):
+        """Forward function."""
+        assert len(inputs) == len(self.convs)
+        outs = [self.convs[i](inputs[i]) for i in range(len(inputs))]
+        if self.extra_convs:
+            for i in range(len(self.extra_convs)):
+                if i == 0:
+                    outs.append(self.extra_convs[0](inputs[-1]))
+                else:
+                    outs.append(self.extra_convs[i](outs[-1]))
+        return tuple(outs)
diff --git a/mmdet/models/necks/ct_resnet_neck.py b/mmdet/models/necks/ct_resnet_neck.py
new file mode 100644
index 0000000..40eb268
--- /dev/null
+++ b/mmdet/models/necks/ct_resnet_neck.py
@@ -0,0 +1,94 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule, auto_fp16
+
+from mmdet.models.builder import NECKS
+
+
+@NECKS.register_module()
+class CTResNetNeck(BaseModule):
+    """The neck used in `CenterNet <https://arxiv.org/abs/1904.07850>`_ for
+    object classification and box regression.
+
+    Args:
+         in_channel (int): Number of input channels.
+         num_deconv_filters (tuple[int]): Number of filters per stage.
+         num_deconv_kernels (tuple[int]): Number of kernels per stage.
+         use_dcn (bool): If True, use DCNv2. Default: True.
+         init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 in_channel,
+                 num_deconv_filters,
+                 num_deconv_kernels,
+                 use_dcn=True,
+                 init_cfg=None):
+        super(CTResNetNeck, self).__init__(init_cfg)
+        assert len(num_deconv_filters) == len(num_deconv_kernels)
+        self.fp16_enabled = False
+        self.use_dcn = use_dcn
+        self.in_channel = in_channel
+        self.deconv_layers = self._make_deconv_layer(num_deconv_filters,
+                                                     num_deconv_kernels)
+
+    def _make_deconv_layer(self, num_deconv_filters, num_deconv_kernels):
+        """use deconv layers to upsample backbone's output."""
+        layers = []
+        for i in range(len(num_deconv_filters)):
+            feat_channel = num_deconv_filters[i]
+            conv_module = ConvModule(
+                self.in_channel,
+                feat_channel,
+                3,
+                padding=1,
+                conv_cfg=dict(type='DCNv2') if self.use_dcn else None,
+                norm_cfg=dict(type='BN'))
+            layers.append(conv_module)
+            upsample_module = ConvModule(
+                feat_channel,
+                feat_channel,
+                num_deconv_kernels[i],
+                stride=2,
+                padding=1,
+                conv_cfg=dict(type='deconv'),
+                norm_cfg=dict(type='BN'))
+            layers.append(upsample_module)
+            self.in_channel = feat_channel
+
+        return nn.Sequential(*layers)
+
+    def init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.ConvTranspose2d):
+                # In order to be consistent with the source code,
+                # reset the ConvTranspose2d initialization parameters
+                m.reset_parameters()
+                # Simulated bilinear upsampling kernel
+                w = m.weight.data
+                f = math.ceil(w.size(2) / 2)
+                c = (2 * f - 1 - f % 2) / (2. * f)
+                for i in range(w.size(2)):
+                    for j in range(w.size(3)):
+                        w[0, 0, i, j] = \
+                            (1 - math.fabs(i / f - c)) * (
+                                    1 - math.fabs(j / f - c))
+                for c in range(1, w.size(0)):
+                    w[c, 0, :, :] = w[0, 0, :, :]
+            elif isinstance(m, nn.BatchNorm2d):
+                nn.init.constant_(m.weight, 1)
+                nn.init.constant_(m.bias, 0)
+            # self.use_dcn is False
+            elif not self.use_dcn and isinstance(m, nn.Conv2d):
+                # In order to be consistent with the source code,
+                # reset the Conv2d initialization parameters
+                m.reset_parameters()
+
+    @auto_fp16()
+    def forward(self, inputs):
+        assert isinstance(inputs, (list, tuple))
+        outs = self.deconv_layers(inputs[-1])
+        return outs,
diff --git a/mmdet/models/necks/dilated_encoder.py b/mmdet/models/necks/dilated_encoder.py
new file mode 100644
index 0000000..79a8f4b
--- /dev/null
+++ b/mmdet/models/necks/dilated_encoder.py
@@ -0,0 +1,109 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+from mmcv.cnn import (ConvModule, caffe2_xavier_init, constant_init, is_norm,
+                      normal_init)
+from torch.nn import BatchNorm2d
+
+from ..builder import NECKS
+
+
+class Bottleneck(nn.Module):
+    """Bottleneck block for DilatedEncoder used in `YOLOF.
+
+    <https://arxiv.org/abs/2103.09460>`.
+
+    The Bottleneck contains three ConvLayers and one residual connection.
+
+    Args:
+        in_channels (int): The number of input channels.
+        mid_channels (int): The number of middle output channels.
+        dilation (int): Dilation rate.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 mid_channels,
+                 dilation,
+                 norm_cfg=dict(type='BN', requires_grad=True)):
+        super(Bottleneck, self).__init__()
+        self.conv1 = ConvModule(
+            in_channels, mid_channels, 1, norm_cfg=norm_cfg)
+        self.conv2 = ConvModule(
+            mid_channels,
+            mid_channels,
+            3,
+            padding=dilation,
+            dilation=dilation,
+            norm_cfg=norm_cfg)
+        self.conv3 = ConvModule(
+            mid_channels, in_channels, 1, norm_cfg=norm_cfg)
+
+    def forward(self, x):
+        identity = x
+        out = self.conv1(x)
+        out = self.conv2(out)
+        out = self.conv3(out)
+        out = out + identity
+        return out
+
+
+@NECKS.register_module()
+class DilatedEncoder(nn.Module):
+    """Dilated Encoder for YOLOF <https://arxiv.org/abs/2103.09460>`.
+
+    This module contains two types of components:
+        - the original FPN lateral convolution layer and fpn convolution layer,
+              which are 1x1 conv + 3x3 conv
+        - the dilated residual block
+
+    Args:
+        in_channels (int): The number of input channels.
+        out_channels (int): The number of output channels.
+        block_mid_channels (int): The number of middle block output channels
+        num_residual_blocks (int): The number of residual blocks.
+        block_dilations (list): The list of residual blocks dilation.
+    """
+
+    def __init__(self, in_channels, out_channels, block_mid_channels,
+                 num_residual_blocks, block_dilations):
+        super(DilatedEncoder, self).__init__()
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.block_mid_channels = block_mid_channels
+        self.num_residual_blocks = num_residual_blocks
+        self.block_dilations = block_dilations
+        self._init_layers()
+
+    def _init_layers(self):
+        self.lateral_conv = nn.Conv2d(
+            self.in_channels, self.out_channels, kernel_size=1)
+        self.lateral_norm = BatchNorm2d(self.out_channels)
+        self.fpn_conv = nn.Conv2d(
+            self.out_channels, self.out_channels, kernel_size=3, padding=1)
+        self.fpn_norm = BatchNorm2d(self.out_channels)
+        encoder_blocks = []
+        for i in range(self.num_residual_blocks):
+            dilation = self.block_dilations[i]
+            encoder_blocks.append(
+                Bottleneck(
+                    self.out_channels,
+                    self.block_mid_channels,
+                    dilation=dilation))
+        self.dilated_encoder_blocks = nn.Sequential(*encoder_blocks)
+
+    def init_weights(self):
+        caffe2_xavier_init(self.lateral_conv)
+        caffe2_xavier_init(self.fpn_conv)
+        for m in [self.lateral_norm, self.fpn_norm]:
+            constant_init(m, 1)
+        for m in self.dilated_encoder_blocks.modules():
+            if isinstance(m, nn.Conv2d):
+                normal_init(m, mean=0, std=0.01)
+            if is_norm(m):
+                constant_init(m, 1)
+
+    def forward(self, feature):
+        out = self.lateral_norm(self.lateral_conv(feature[-1]))
+        out = self.fpn_norm(self.fpn_conv(out))
+        return self.dilated_encoder_blocks(out),
diff --git a/mmdet/models/necks/dyhead.py b/mmdet/models/necks/dyhead.py
new file mode 100644
index 0000000..649bb4c
--- /dev/null
+++ b/mmdet/models/necks/dyhead.py
@@ -0,0 +1,176 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import (build_activation_layer, build_norm_layer, constant_init,
+                      normal_init)
+from mmcv.ops.modulated_deform_conv import ModulatedDeformConv2d
+from mmcv.runner import BaseModule
+
+from ..builder import NECKS
+from ..utils import DyReLU
+
+# Reference:
+# https://github.com/microsoft/DynamicHead
+# https://github.com/jshilong/SEPC
+
+
+class DyDCNv2(nn.Module):
+    """ModulatedDeformConv2d with normalization layer used in DyHead.
+
+    This module cannot be configured with `conv_cfg=dict(type='DCNv2')`
+    because DyHead calculates offset and mask from middle-level feature.
+
+    Args:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+        stride (int | tuple[int], optional): Stride of the convolution.
+            Default: 1.
+        norm_cfg (dict, optional): Config dict for normalization layer.
+            Default: dict(type='GN', num_groups=16, requires_grad=True).
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 stride=1,
+                 norm_cfg=dict(type='GN', num_groups=16, requires_grad=True)):
+        super().__init__()
+        self.with_norm = norm_cfg is not None
+        bias = not self.with_norm
+        self.conv = ModulatedDeformConv2d(
+            in_channels, out_channels, 3, stride=stride, padding=1, bias=bias)
+        if self.with_norm:
+            self.norm = build_norm_layer(norm_cfg, out_channels)[1]
+
+    def forward(self, x, offset, mask):
+        """Forward function."""
+        x = self.conv(x.contiguous(), offset.contiguous(), mask)
+        if self.with_norm:
+            x = self.norm(x)
+        return x
+
+
+class DyHeadBlock(nn.Module):
+    """DyHead Block with three types of attention.
+
+    HSigmoid arguments in default act_cfg follow official code, not paper.
+    https://github.com/microsoft/DynamicHead/blob/master/dyhead/dyrelu.py
+
+    Args:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+        zero_init_offset (bool, optional): Whether to use zero init for
+            `spatial_conv_offset`. Default: True.
+        act_cfg (dict, optional): Config dict for the last activation layer of
+            scale-aware attention. Default: dict(type='HSigmoid', bias=3.0,
+            divisor=6.0).
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 zero_init_offset=True,
+                 act_cfg=dict(type='HSigmoid', bias=3.0, divisor=6.0)):
+        super().__init__()
+        self.zero_init_offset = zero_init_offset
+        # (offset_x, offset_y, mask) * kernel_size_y * kernel_size_x
+        self.offset_and_mask_dim = 3 * 3 * 3
+        self.offset_dim = 2 * 3 * 3
+
+        self.spatial_conv_high = DyDCNv2(in_channels, out_channels)
+        self.spatial_conv_mid = DyDCNv2(in_channels, out_channels)
+        self.spatial_conv_low = DyDCNv2(in_channels, out_channels, stride=2)
+        self.spatial_conv_offset = nn.Conv2d(
+            in_channels, self.offset_and_mask_dim, 3, padding=1)
+        self.scale_attn_module = nn.Sequential(
+            nn.AdaptiveAvgPool2d(1), nn.Conv2d(out_channels, 1, 1),
+            nn.ReLU(inplace=True), build_activation_layer(act_cfg))
+        self.task_attn_module = DyReLU(out_channels)
+        self._init_weights()
+
+    def _init_weights(self):
+        for m in self.modules():
+            if isinstance(m, nn.Conv2d):
+                normal_init(m, 0, 0.01)
+        if self.zero_init_offset:
+            constant_init(self.spatial_conv_offset, 0)
+
+    def forward(self, x):
+        """Forward function."""
+        outs = []
+        for level in range(len(x)):
+            # calculate offset and mask of DCNv2 from middle-level feature
+            offset_and_mask = self.spatial_conv_offset(x[level])
+            offset = offset_and_mask[:, :self.offset_dim, :, :]
+            mask = offset_and_mask[:, self.offset_dim:, :, :].sigmoid()
+
+            mid_feat = self.spatial_conv_mid(x[level], offset, mask)
+            sum_feat = mid_feat * self.scale_attn_module(mid_feat)
+            summed_levels = 1
+            if level > 0:
+                low_feat = self.spatial_conv_low(x[level - 1], offset, mask)
+                sum_feat = sum_feat + \
+                    low_feat * self.scale_attn_module(low_feat)
+                summed_levels += 1
+            if level < len(x) - 1:
+                # this upsample order is weird, but faster than natural order
+                # https://github.com/microsoft/DynamicHead/issues/25
+                high_feat = F.interpolate(
+                    self.spatial_conv_high(x[level + 1], offset, mask),
+                    size=x[level].shape[-2:],
+                    mode='bilinear',
+                    align_corners=True)
+                sum_feat = sum_feat + high_feat * \
+                    self.scale_attn_module(high_feat)
+                summed_levels += 1
+            outs.append(self.task_attn_module(sum_feat / summed_levels))
+
+        return outs
+
+
+@NECKS.register_module()
+class DyHead(BaseModule):
+    """DyHead neck consisting of multiple DyHead Blocks.
+
+    See `Dynamic Head: Unifying Object Detection Heads with Attentions
+    <https://arxiv.org/abs/2106.08322>`_ for details.
+
+    Args:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+        num_blocks (int, optional): Number of DyHead Blocks. Default: 6.
+        zero_init_offset (bool, optional): Whether to use zero init for
+            `spatial_conv_offset`. Default: True.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 num_blocks=6,
+                 zero_init_offset=True,
+                 init_cfg=None):
+        assert init_cfg is None, 'To prevent abnormal initialization ' \
+                                 'behavior, init_cfg is not allowed to be set'
+        super().__init__(init_cfg=init_cfg)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_blocks = num_blocks
+        self.zero_init_offset = zero_init_offset
+
+        dyhead_blocks = []
+        for i in range(num_blocks):
+            in_channels = self.in_channels if i == 0 else self.out_channels
+            dyhead_blocks.append(
+                DyHeadBlock(
+                    in_channels,
+                    self.out_channels,
+                    zero_init_offset=zero_init_offset))
+        self.dyhead_blocks = nn.Sequential(*dyhead_blocks)
+
+    def forward(self, inputs):
+        """Forward function."""
+        assert isinstance(inputs, (tuple, list))
+        outs = self.dyhead_blocks(inputs)
+        return tuple(outs)
diff --git a/mmdet/models/necks/fpg.py b/mmdet/models/necks/fpg.py
new file mode 100644
index 0000000..a6a2a12
--- /dev/null
+++ b/mmdet/models/necks/fpg.py
@@ -0,0 +1,406 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule
+
+from ..builder import NECKS
+
+
+class Transition(BaseModule):
+    """Base class for transition.
+
+    Args:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+    """
+
+    def __init__(self, in_channels, out_channels, init_cfg=None):
+        super().__init__(init_cfg)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+    def forward(x):
+        pass
+
+
+class UpInterpolationConv(Transition):
+    """A transition used for up-sampling.
+
+    Up-sample the input by interpolation then refines the feature by
+    a convolution layer.
+
+    Args:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+        scale_factor (int): Up-sampling factor. Default: 2.
+        mode (int): Interpolation mode. Default: nearest.
+        align_corners (bool): Whether align corners when interpolation.
+            Default: None.
+        kernel_size (int): Kernel size for the conv. Default: 3.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 scale_factor=2,
+                 mode='nearest',
+                 align_corners=None,
+                 kernel_size=3,
+                 init_cfg=None,
+                 **kwargs):
+        super().__init__(in_channels, out_channels, init_cfg)
+        self.mode = mode
+        self.scale_factor = scale_factor
+        self.align_corners = align_corners
+        self.conv = ConvModule(
+            in_channels,
+            out_channels,
+            kernel_size,
+            padding=(kernel_size - 1) // 2,
+            **kwargs)
+
+    def forward(self, x):
+        x = F.interpolate(
+            x,
+            scale_factor=self.scale_factor,
+            mode=self.mode,
+            align_corners=self.align_corners)
+        x = self.conv(x)
+        return x
+
+
+class LastConv(Transition):
+    """A transition used for refining the output of the last stage.
+
+    Args:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of output channels.
+        num_inputs (int): Number of inputs of the FPN features.
+        kernel_size (int): Kernel size for the conv. Default: 3.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 num_inputs,
+                 kernel_size=3,
+                 init_cfg=None,
+                 **kwargs):
+        super().__init__(in_channels, out_channels, init_cfg)
+        self.num_inputs = num_inputs
+        self.conv_out = ConvModule(
+            in_channels,
+            out_channels,
+            kernel_size,
+            padding=(kernel_size - 1) // 2,
+            **kwargs)
+
+    def forward(self, inputs):
+        assert len(inputs) == self.num_inputs
+        return self.conv_out(inputs[-1])
+
+
+@NECKS.register_module()
+class FPG(BaseModule):
+    """FPG.
+
+    Implementation of `Feature Pyramid Grids (FPG)
+    <https://arxiv.org/abs/2004.03580>`_.
+    This implementation only gives the basic structure stated in the paper.
+    But users can implement different type of transitions to fully explore the
+    the potential power of the structure of FPG.
+
+    Args:
+        in_channels (int): Number of input channels (feature maps of all levels
+            should have the same channels).
+        out_channels (int): Number of output channels (used at each scale)
+        num_outs (int): Number of output scales.
+        stack_times (int): The number of times the pyramid architecture will
+            be stacked.
+        paths (list[str]): Specify the path order of each stack level.
+            Each element in the list should be either 'bu' (bottom-up) or
+            'td' (top-down).
+        inter_channels (int): Number of inter channels.
+        same_up_trans (dict): Transition that goes down at the same stage.
+        same_down_trans (dict): Transition that goes up at the same stage.
+        across_lateral_trans (dict): Across-pathway same-stage
+        across_down_trans (dict): Across-pathway bottom-up connection.
+        across_up_trans (dict): Across-pathway top-down connection.
+        across_skip_trans (dict): Across-pathway skip connection.
+        output_trans (dict): Transition that trans the output of the
+            last stage.
+        start_level (int): Index of the start input backbone level used to
+            build the feature pyramid. Default: 0.
+        end_level (int): Index of the end input backbone level (exclusive) to
+            build the feature pyramid. Default: -1, which means the last level.
+        add_extra_convs (bool): It decides whether to add conv
+            layers on top of the original feature maps. Default to False.
+            If True, its actual mode is specified by `extra_convs_on_inputs`.
+        norm_cfg (dict): Config dict for normalization layer. Default: None.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    transition_types = {
+        'conv': ConvModule,
+        'interpolation_conv': UpInterpolationConv,
+        'last_conv': LastConv,
+    }
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 num_outs,
+                 stack_times,
+                 paths,
+                 inter_channels=None,
+                 same_down_trans=None,
+                 same_up_trans=dict(
+                     type='conv', kernel_size=3, stride=2, padding=1),
+                 across_lateral_trans=dict(type='conv', kernel_size=1),
+                 across_down_trans=dict(type='conv', kernel_size=3),
+                 across_up_trans=None,
+                 across_skip_trans=dict(type='identity'),
+                 output_trans=dict(type='last_conv', kernel_size=3),
+                 start_level=0,
+                 end_level=-1,
+                 add_extra_convs=False,
+                 norm_cfg=None,
+                 skip_inds=None,
+                 init_cfg=[
+                     dict(type='Caffe2Xavier', layer='Conv2d'),
+                     dict(
+                         type='Constant',
+                         layer=[
+                             '_BatchNorm', '_InstanceNorm', 'GroupNorm',
+                             'LayerNorm'
+                         ],
+                         val=1.0)
+                 ]):
+        super(FPG, self).__init__(init_cfg)
+        assert isinstance(in_channels, list)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_ins = len(in_channels)
+        self.num_outs = num_outs
+        if inter_channels is None:
+            self.inter_channels = [out_channels for _ in range(num_outs)]
+        elif isinstance(inter_channels, int):
+            self.inter_channels = [inter_channels for _ in range(num_outs)]
+        else:
+            assert isinstance(inter_channels, list)
+            assert len(inter_channels) == num_outs
+            self.inter_channels = inter_channels
+        self.stack_times = stack_times
+        self.paths = paths
+        assert isinstance(paths, list) and len(paths) == stack_times
+        for d in paths:
+            assert d in ('bu', 'td')
+
+        self.same_down_trans = same_down_trans
+        self.same_up_trans = same_up_trans
+        self.across_lateral_trans = across_lateral_trans
+        self.across_down_trans = across_down_trans
+        self.across_up_trans = across_up_trans
+        self.output_trans = output_trans
+        self.across_skip_trans = across_skip_trans
+
+        self.with_bias = norm_cfg is None
+        # skip inds must be specified if across skip trans is not None
+        if self.across_skip_trans is not None:
+            skip_inds is not None
+        self.skip_inds = skip_inds
+        assert len(self.skip_inds[0]) <= self.stack_times
+
+        if end_level == -1 or end_level == self.num_ins - 1:
+            self.backbone_end_level = self.num_ins
+            assert num_outs >= self.num_ins - start_level
+        else:
+            # if end_level is not the last level, no extra level is allowed
+            self.backbone_end_level = end_level + 1
+            assert end_level < self.num_ins
+            assert num_outs == end_level - start_level + 1
+        self.start_level = start_level
+        self.end_level = end_level
+        self.add_extra_convs = add_extra_convs
+
+        # build lateral 1x1 convs to reduce channels
+        self.lateral_convs = nn.ModuleList()
+        for i in range(self.start_level, self.backbone_end_level):
+            l_conv = nn.Conv2d(self.in_channels[i],
+                               self.inter_channels[i - self.start_level], 1)
+            self.lateral_convs.append(l_conv)
+
+        extra_levels = num_outs - self.backbone_end_level + self.start_level
+        self.extra_downsamples = nn.ModuleList()
+        for i in range(extra_levels):
+            if self.add_extra_convs:
+                fpn_idx = self.backbone_end_level - self.start_level + i
+                extra_conv = nn.Conv2d(
+                    self.inter_channels[fpn_idx - 1],
+                    self.inter_channels[fpn_idx],
+                    3,
+                    stride=2,
+                    padding=1)
+                self.extra_downsamples.append(extra_conv)
+            else:
+                self.extra_downsamples.append(nn.MaxPool2d(1, stride=2))
+
+        self.fpn_transitions = nn.ModuleList()  # stack times
+        for s in range(self.stack_times):
+            stage_trans = nn.ModuleList()  # num of feature levels
+            for i in range(self.num_outs):
+                # same, across_lateral, across_down, across_up
+                trans = nn.ModuleDict()
+                if s in self.skip_inds[i]:
+                    stage_trans.append(trans)
+                    continue
+                # build same-stage down trans (used in bottom-up paths)
+                if i == 0 or self.same_up_trans is None:
+                    same_up_trans = None
+                else:
+                    same_up_trans = self.build_trans(
+                        self.same_up_trans, self.inter_channels[i - 1],
+                        self.inter_channels[i])
+                trans['same_up'] = same_up_trans
+                # build same-stage up trans (used in top-down paths)
+                if i == self.num_outs - 1 or self.same_down_trans is None:
+                    same_down_trans = None
+                else:
+                    same_down_trans = self.build_trans(
+                        self.same_down_trans, self.inter_channels[i + 1],
+                        self.inter_channels[i])
+                trans['same_down'] = same_down_trans
+                # build across lateral trans
+                across_lateral_trans = self.build_trans(
+                    self.across_lateral_trans, self.inter_channels[i],
+                    self.inter_channels[i])
+                trans['across_lateral'] = across_lateral_trans
+                # build across down trans
+                if i == self.num_outs - 1 or self.across_down_trans is None:
+                    across_down_trans = None
+                else:
+                    across_down_trans = self.build_trans(
+                        self.across_down_trans, self.inter_channels[i + 1],
+                        self.inter_channels[i])
+                trans['across_down'] = across_down_trans
+                # build across up trans
+                if i == 0 or self.across_up_trans is None:
+                    across_up_trans = None
+                else:
+                    across_up_trans = self.build_trans(
+                        self.across_up_trans, self.inter_channels[i - 1],
+                        self.inter_channels[i])
+                trans['across_up'] = across_up_trans
+                if self.across_skip_trans is None:
+                    across_skip_trans = None
+                else:
+                    across_skip_trans = self.build_trans(
+                        self.across_skip_trans, self.inter_channels[i - 1],
+                        self.inter_channels[i])
+                trans['across_skip'] = across_skip_trans
+                # build across_skip trans
+                stage_trans.append(trans)
+            self.fpn_transitions.append(stage_trans)
+
+        self.output_transition = nn.ModuleList()  # output levels
+        for i in range(self.num_outs):
+            trans = self.build_trans(
+                self.output_trans,
+                self.inter_channels[i],
+                self.out_channels,
+                num_inputs=self.stack_times + 1)
+            self.output_transition.append(trans)
+
+        self.relu = nn.ReLU(inplace=True)
+
+    def build_trans(self, cfg, in_channels, out_channels, **extra_args):
+        cfg_ = cfg.copy()
+        trans_type = cfg_.pop('type')
+        trans_cls = self.transition_types[trans_type]
+        return trans_cls(in_channels, out_channels, **cfg_, **extra_args)
+
+    def fuse(self, fuse_dict):
+        out = None
+        for item in fuse_dict.values():
+            if item is not None:
+                if out is None:
+                    out = item
+                else:
+                    out = out + item
+        return out
+
+    def forward(self, inputs):
+        assert len(inputs) == len(self.in_channels)
+
+        # build all levels from original feature maps
+        feats = [
+            lateral_conv(inputs[i + self.start_level])
+            for i, lateral_conv in enumerate(self.lateral_convs)
+        ]
+        for downsample in self.extra_downsamples:
+            feats.append(downsample(feats[-1]))
+
+        outs = [feats]
+
+        for i in range(self.stack_times):
+            current_outs = outs[-1]
+            next_outs = []
+            direction = self.paths[i]
+            for j in range(self.num_outs):
+                if i in self.skip_inds[j]:
+                    next_outs.append(outs[-1][j])
+                    continue
+                # feature level
+                if direction == 'td':
+                    lvl = self.num_outs - j - 1
+                else:
+                    lvl = j
+                # get transitions
+                if direction == 'td':
+                    same_trans = self.fpn_transitions[i][lvl]['same_down']
+                else:
+                    same_trans = self.fpn_transitions[i][lvl]['same_up']
+                across_lateral_trans = self.fpn_transitions[i][lvl][
+                    'across_lateral']
+                across_down_trans = self.fpn_transitions[i][lvl]['across_down']
+                across_up_trans = self.fpn_transitions[i][lvl]['across_up']
+                across_skip_trans = self.fpn_transitions[i][lvl]['across_skip']
+                # init output
+                to_fuse = dict(
+                    same=None, lateral=None, across_up=None, across_down=None)
+                # same downsample/upsample
+                if same_trans is not None:
+                    to_fuse['same'] = same_trans(next_outs[-1])
+                # across lateral
+                if across_lateral_trans is not None:
+                    to_fuse['lateral'] = across_lateral_trans(
+                        current_outs[lvl])
+                # across downsample
+                if lvl > 0 and across_up_trans is not None:
+                    to_fuse['across_up'] = across_up_trans(current_outs[lvl -
+                                                                        1])
+                # across upsample
+                if (lvl < self.num_outs - 1 and across_down_trans is not None):
+                    to_fuse['across_down'] = across_down_trans(
+                        current_outs[lvl + 1])
+                if across_skip_trans is not None:
+                    to_fuse['across_skip'] = across_skip_trans(outs[0][lvl])
+                x = self.fuse(to_fuse)
+                next_outs.append(x)
+
+            if direction == 'td':
+                outs.append(next_outs[::-1])
+            else:
+                outs.append(next_outs)
+
+        # output trans
+        final_outs = []
+        for i in range(self.num_outs):
+            lvl_out_list = []
+            for s in range(len(outs)):
+                lvl_out_list.append(outs[s][i])
+            lvl_out = self.output_transition[i](lvl_out_list)
+            final_outs.append(lvl_out)
+
+        return final_outs
diff --git a/mmdet/models/necks/fpn.py b/mmdet/models/necks/fpn.py
new file mode 100644
index 0000000..10ab618
--- /dev/null
+++ b/mmdet/models/necks/fpn.py
@@ -0,0 +1,214 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule, auto_fp16
+
+from ..builder import NECKS
+
+
+@NECKS.register_module()
+class FPN(BaseModule):
+    r"""Feature Pyramid Network.
+
+    This is an implementation of paper `Feature Pyramid Networks for Object
+    Detection <https://arxiv.org/abs/1612.03144>`_.
+
+    Args:
+        in_channels (list[int]): Number of input channels per scale.
+        out_channels (int): Number of output channels (used at each scale).
+        num_outs (int): Number of output scales.
+        start_level (int): Index of the start input backbone level used to
+            build the feature pyramid. Default: 0.
+        end_level (int): Index of the end input backbone level (exclusive) to
+            build the feature pyramid. Default: -1, which means the last level.
+        add_extra_convs (bool | str): If bool, it decides whether to add conv
+            layers on top of the original feature maps. Default to False.
+            If True, it is equivalent to `add_extra_convs='on_input'`.
+            If str, it specifies the source feature map of the extra convs.
+            Only the following options are allowed
+
+            - 'on_input': Last feat map of neck inputs (i.e. backbone feature).
+            - 'on_lateral': Last feature map after lateral convs.
+            - 'on_output': The last output feature map after fpn convs.
+        relu_before_extra_convs (bool): Whether to apply relu before the extra
+            conv. Default: False.
+        no_norm_on_lateral (bool): Whether to apply norm on lateral.
+            Default: False.
+        conv_cfg (dict): Config dict for convolution layer. Default: None.
+        norm_cfg (dict): Config dict for normalization layer. Default: None.
+        act_cfg (dict): Config dict for activation layer in ConvModule.
+            Default: None.
+        upsample_cfg (dict): Config dict for interpolate layer.
+            Default: dict(mode='nearest').
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+
+    Example:
+        >>> import torch
+        >>> in_channels = [2, 3, 5, 7]
+        >>> scales = [340, 170, 84, 43]
+        >>> inputs = [torch.rand(1, c, s, s)
+        ...           for c, s in zip(in_channels, scales)]
+        >>> self = FPN(in_channels, 11, len(in_channels)).eval()
+        >>> outputs = self.forward(inputs)
+        >>> for i in range(len(outputs)):
+        ...     print(f'outputs[{i}].shape = {outputs[i].shape}')
+        outputs[0].shape = torch.Size([1, 11, 340, 340])
+        outputs[1].shape = torch.Size([1, 11, 170, 170])
+        outputs[2].shape = torch.Size([1, 11, 84, 84])
+        outputs[3].shape = torch.Size([1, 11, 43, 43])
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 num_outs,
+                 start_level=0,
+                 end_level=-1,
+                 add_extra_convs=False,
+                 relu_before_extra_convs=False,
+                 no_norm_on_lateral=False,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 act_cfg=None,
+                 upsample_cfg=dict(mode='nearest'),
+                 init_cfg=dict(
+                     type='Xavier', layer='Conv2d', distribution='uniform')):
+        super(FPN, self).__init__(init_cfg)
+        assert isinstance(in_channels, list)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_ins = len(in_channels)
+        self.num_outs = num_outs
+        self.relu_before_extra_convs = relu_before_extra_convs
+        self.no_norm_on_lateral = no_norm_on_lateral
+        self.fp16_enabled = False
+        self.upsample_cfg = upsample_cfg.copy()
+
+        if end_level == -1 or end_level == self.num_ins - 1:
+            self.backbone_end_level = self.num_ins
+            assert num_outs >= self.num_ins - start_level
+        else:
+            # if end_level is not the last level, no extra level is allowed
+            self.backbone_end_level = end_level + 1
+            assert end_level < self.num_ins
+            assert num_outs == end_level - start_level + 1
+        self.start_level = start_level
+        self.end_level = end_level
+        self.add_extra_convs = add_extra_convs
+        assert isinstance(add_extra_convs, (str, bool))
+        if isinstance(add_extra_convs, str):
+            # Extra_convs_source choices: 'on_input', 'on_lateral', 'on_output'
+            assert add_extra_convs in ('on_input', 'on_lateral', 'on_output')
+        elif add_extra_convs:  # True
+            self.add_extra_convs = 'on_input'
+
+        self.lateral_convs = nn.ModuleList()
+        self.fpn_convs = nn.ModuleList()
+
+        for i in range(self.start_level, self.backbone_end_level):
+            l_conv = ConvModule(
+                in_channels[i],
+                out_channels,
+                1,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg if not self.no_norm_on_lateral else None,
+                act_cfg=act_cfg,
+                inplace=False)
+            fpn_conv = ConvModule(
+                out_channels,
+                out_channels,
+                3,
+                padding=1,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg,
+                inplace=False)
+
+            self.lateral_convs.append(l_conv)
+            self.fpn_convs.append(fpn_conv)
+
+        # add extra conv layers (e.g., RetinaNet)
+        extra_levels = num_outs - self.backbone_end_level + self.start_level
+        if self.add_extra_convs and extra_levels >= 1:
+            for i in range(extra_levels):
+                if i == 0 and self.add_extra_convs == 'on_input':
+                    in_channels = self.in_channels[self.backbone_end_level - 1]
+                else:
+                    in_channels = out_channels
+                extra_fpn_conv = ConvModule(
+                    in_channels,
+                    out_channels,
+                    3,
+                    stride=2,
+                    padding=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg,
+                    inplace=False)
+                self.fpn_convs.append(extra_fpn_conv)
+
+    @auto_fp16()
+    def forward(self, inputs):
+        """Forward function."""
+
+        if hasattr(self,'in_num') and self.in_num == 4:
+            inputs = [inputs[0], inputs[1], inputs[2], inputs[3]]
+        elif hasattr(self,'in_num'):
+            raise NotImplementedError
+        else:
+            self.in_num = 4
+            inputs = [inputs[0], inputs[1], inputs[2], inputs[3]]
+        
+        # assert len(inputs) == len(self.in_channels)  # 
+
+        
+        # build laterals
+        laterals = [
+            lateral_conv(inputs[i + self.start_level])
+            for i, lateral_conv in enumerate(self.lateral_convs)
+        ]
+
+        # build top-down path
+        used_backbone_levels = len(laterals)
+        for i in range(used_backbone_levels - 1, 0, -1):
+            # In some cases, fixing `scale factor` (e.g. 2) is preferred, but
+            #  it cannot co-exist with `size` in `F.interpolate`.
+            if 'scale_factor' in self.upsample_cfg:
+                # fix runtime error of "+=" inplace operation in PyTorch 1.10
+                laterals[i - 1] = laterals[i - 1] + F.interpolate(
+                    laterals[i], **self.upsample_cfg)
+            else:
+                prev_shape = laterals[i - 1].shape[2:]
+                laterals[i - 1] = laterals[i - 1] + F.interpolate(
+                    laterals[i], size=prev_shape, **self.upsample_cfg)
+
+        # build outputs
+        # part 1: from original levels
+        outs = [
+            self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels)
+        ]
+        # part 2: add extra levels
+        if self.num_outs > len(outs):
+            # use max pool to get more levels on top of outputs
+            # (e.g., Faster R-CNN, Mask R-CNN)
+            if not self.add_extra_convs:
+                for i in range(self.num_outs - used_backbone_levels):
+                    outs.append(F.max_pool2d(outs[-1], 1, stride=2))
+            # add conv layers on top of original feature maps (RetinaNet)
+            else:
+                if self.add_extra_convs == 'on_input':
+                    extra_source = inputs[self.backbone_end_level - 1]
+                elif self.add_extra_convs == 'on_lateral':
+                    extra_source = laterals[-1]
+                elif self.add_extra_convs == 'on_output':
+                    extra_source = outs[-1]
+                else:
+                    raise NotImplementedError
+                outs.append(self.fpn_convs[used_backbone_levels](extra_source))
+                for i in range(used_backbone_levels + 1, self.num_outs):
+                    if self.relu_before_extra_convs:
+                        outs.append(self.fpn_convs[i](F.relu(outs[-1])))
+                    else:
+                        outs.append(self.fpn_convs[i](outs[-1]))
+        return tuple(outs)
diff --git a/mmdet/models/necks/fpn_carafe.py b/mmdet/models/necks/fpn_carafe.py
new file mode 100644
index 0000000..fdd91f3
--- /dev/null
+++ b/mmdet/models/necks/fpn_carafe.py
@@ -0,0 +1,275 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+from mmcv.cnn import ConvModule, build_upsample_layer, xavier_init
+from mmcv.ops.carafe import CARAFEPack
+from mmcv.runner import BaseModule, ModuleList
+
+from ..builder import NECKS
+
+
+@NECKS.register_module()
+class FPN_CARAFE(BaseModule):
+    """FPN_CARAFE is a more flexible implementation of FPN. It allows more
+    choice for upsample methods during the top-down pathway.
+
+    It can reproduce the performance of ICCV 2019 paper
+    CARAFE: Content-Aware ReAssembly of FEatures
+    Please refer to https://arxiv.org/abs/1905.02188 for more details.
+
+    Args:
+        in_channels (list[int]): Number of channels for each input feature map.
+        out_channels (int): Output channels of feature pyramids.
+        num_outs (int): Number of output stages.
+        start_level (int): Start level of feature pyramids.
+            (Default: 0)
+        end_level (int): End level of feature pyramids.
+            (Default: -1 indicates the last level).
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+        activate (str): Type of activation function in ConvModule
+            (Default: None indicates w/o activation).
+        order (dict): Order of components in ConvModule.
+        upsample (str): Type of upsample layer.
+        upsample_cfg (dict): Dictionary to construct and config upsample layer.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 num_outs,
+                 start_level=0,
+                 end_level=-1,
+                 norm_cfg=None,
+                 act_cfg=None,
+                 order=('conv', 'norm', 'act'),
+                 upsample_cfg=dict(
+                     type='carafe',
+                     up_kernel=5,
+                     up_group=1,
+                     encoder_kernel=3,
+                     encoder_dilation=1),
+                 init_cfg=None):
+        assert init_cfg is None, 'To prevent abnormal initialization ' \
+                                 'behavior, init_cfg is not allowed to be set'
+        super(FPN_CARAFE, self).__init__(init_cfg)
+        assert isinstance(in_channels, list)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_ins = len(in_channels)
+        self.num_outs = num_outs
+        self.norm_cfg = norm_cfg
+        self.act_cfg = act_cfg
+        self.with_bias = norm_cfg is None
+        self.upsample_cfg = upsample_cfg.copy()
+        self.upsample = self.upsample_cfg.get('type')
+        self.relu = nn.ReLU(inplace=False)
+
+        self.order = order
+        assert order in [('conv', 'norm', 'act'), ('act', 'conv', 'norm')]
+
+        assert self.upsample in [
+            'nearest', 'bilinear', 'deconv', 'pixel_shuffle', 'carafe', None
+        ]
+        if self.upsample in ['deconv', 'pixel_shuffle']:
+            assert hasattr(
+                self.upsample_cfg,
+                'upsample_kernel') and self.upsample_cfg.upsample_kernel > 0
+            self.upsample_kernel = self.upsample_cfg.pop('upsample_kernel')
+
+        if end_level == -1 or end_level == self.num_ins - 1:
+            self.backbone_end_level = self.num_ins
+            assert num_outs >= self.num_ins - start_level
+        else:
+            # if end_level is not the last level, no extra level is allowed
+            self.backbone_end_level = end_level + 1
+            assert end_level < self.num_ins
+            assert num_outs == end_level - start_level + 1
+        self.start_level = start_level
+        self.end_level = end_level
+
+        self.lateral_convs = ModuleList()
+        self.fpn_convs = ModuleList()
+        self.upsample_modules = ModuleList()
+
+        for i in range(self.start_level, self.backbone_end_level):
+            l_conv = ConvModule(
+                in_channels[i],
+                out_channels,
+                1,
+                norm_cfg=norm_cfg,
+                bias=self.with_bias,
+                act_cfg=act_cfg,
+                inplace=False,
+                order=self.order)
+            fpn_conv = ConvModule(
+                out_channels,
+                out_channels,
+                3,
+                padding=1,
+                norm_cfg=self.norm_cfg,
+                bias=self.with_bias,
+                act_cfg=act_cfg,
+                inplace=False,
+                order=self.order)
+            if i != self.backbone_end_level - 1:
+                upsample_cfg_ = self.upsample_cfg.copy()
+                if self.upsample == 'deconv':
+                    upsample_cfg_.update(
+                        in_channels=out_channels,
+                        out_channels=out_channels,
+                        kernel_size=self.upsample_kernel,
+                        stride=2,
+                        padding=(self.upsample_kernel - 1) // 2,
+                        output_padding=(self.upsample_kernel - 1) // 2)
+                elif self.upsample == 'pixel_shuffle':
+                    upsample_cfg_.update(
+                        in_channels=out_channels,
+                        out_channels=out_channels,
+                        scale_factor=2,
+                        upsample_kernel=self.upsample_kernel)
+                elif self.upsample == 'carafe':
+                    upsample_cfg_.update(channels=out_channels, scale_factor=2)
+                else:
+                    # suppress warnings
+                    align_corners = (None
+                                     if self.upsample == 'nearest' else False)
+                    upsample_cfg_.update(
+                        scale_factor=2,
+                        mode=self.upsample,
+                        align_corners=align_corners)
+                upsample_module = build_upsample_layer(upsample_cfg_)
+                self.upsample_modules.append(upsample_module)
+            self.lateral_convs.append(l_conv)
+            self.fpn_convs.append(fpn_conv)
+
+        # add extra conv layers (e.g., RetinaNet)
+        extra_out_levels = (
+            num_outs - self.backbone_end_level + self.start_level)
+        if extra_out_levels >= 1:
+            for i in range(extra_out_levels):
+                in_channels = (
+                    self.in_channels[self.backbone_end_level -
+                                     1] if i == 0 else out_channels)
+                extra_l_conv = ConvModule(
+                    in_channels,
+                    out_channels,
+                    3,
+                    stride=2,
+                    padding=1,
+                    norm_cfg=norm_cfg,
+                    bias=self.with_bias,
+                    act_cfg=act_cfg,
+                    inplace=False,
+                    order=self.order)
+                if self.upsample == 'deconv':
+                    upsampler_cfg_ = dict(
+                        in_channels=out_channels,
+                        out_channels=out_channels,
+                        kernel_size=self.upsample_kernel,
+                        stride=2,
+                        padding=(self.upsample_kernel - 1) // 2,
+                        output_padding=(self.upsample_kernel - 1) // 2)
+                elif self.upsample == 'pixel_shuffle':
+                    upsampler_cfg_ = dict(
+                        in_channels=out_channels,
+                        out_channels=out_channels,
+                        scale_factor=2,
+                        upsample_kernel=self.upsample_kernel)
+                elif self.upsample == 'carafe':
+                    upsampler_cfg_ = dict(
+                        channels=out_channels,
+                        scale_factor=2,
+                        **self.upsample_cfg)
+                else:
+                    # suppress warnings
+                    align_corners = (None
+                                     if self.upsample == 'nearest' else False)
+                    upsampler_cfg_ = dict(
+                        scale_factor=2,
+                        mode=self.upsample,
+                        align_corners=align_corners)
+                upsampler_cfg_['type'] = self.upsample
+                upsample_module = build_upsample_layer(upsampler_cfg_)
+                extra_fpn_conv = ConvModule(
+                    out_channels,
+                    out_channels,
+                    3,
+                    padding=1,
+                    norm_cfg=self.norm_cfg,
+                    bias=self.with_bias,
+                    act_cfg=act_cfg,
+                    inplace=False,
+                    order=self.order)
+                self.upsample_modules.append(upsample_module)
+                self.fpn_convs.append(extra_fpn_conv)
+                self.lateral_convs.append(extra_l_conv)
+
+    # default init_weights for conv(msra) and norm in ConvModule
+    def init_weights(self):
+        """Initialize the weights of module."""
+        super(FPN_CARAFE, self).init_weights()
+        for m in self.modules():
+            if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d)):
+                xavier_init(m, distribution='uniform')
+        for m in self.modules():
+            if isinstance(m, CARAFEPack):
+                m.init_weights()
+
+    def slice_as(self, src, dst):
+        """Slice ``src`` as ``dst``
+
+        Note:
+            ``src`` should have the same or larger size than ``dst``.
+
+        Args:
+            src (torch.Tensor): Tensors to be sliced.
+            dst (torch.Tensor): ``src`` will be sliced to have the same
+                size as ``dst``.
+
+        Returns:
+            torch.Tensor: Sliced tensor.
+        """
+        assert (src.size(2) >= dst.size(2)) and (src.size(3) >= dst.size(3))
+        if src.size(2) == dst.size(2) and src.size(3) == dst.size(3):
+            return src
+        else:
+            return src[:, :, :dst.size(2), :dst.size(3)]
+
+    def tensor_add(self, a, b):
+        """Add tensors ``a`` and ``b`` that might have different sizes."""
+        if a.size() == b.size():
+            c = a + b
+        else:
+            c = a + self.slice_as(b, a)
+        return c
+
+    def forward(self, inputs):
+        """Forward function."""
+        assert len(inputs) == len(self.in_channels)
+
+        # build laterals
+        laterals = []
+        for i, lateral_conv in enumerate(self.lateral_convs):
+            if i <= self.backbone_end_level - self.start_level:
+                input = inputs[min(i + self.start_level, len(inputs) - 1)]
+            else:
+                input = laterals[-1]
+            lateral = lateral_conv(input)
+            laterals.append(lateral)
+
+        # build top-down path
+        for i in range(len(laterals) - 1, 0, -1):
+            if self.upsample is not None:
+                upsample_feat = self.upsample_modules[i - 1](laterals[i])
+            else:
+                upsample_feat = laterals[i]
+            laterals[i - 1] = self.tensor_add(laterals[i - 1], upsample_feat)
+
+        # build outputs
+        num_conv_outs = len(self.fpn_convs)
+        outs = []
+        for i in range(num_conv_outs):
+            out = self.fpn_convs[i](laterals[i])
+            outs.append(out)
+        return tuple(outs)
diff --git a/mmdet/models/necks/hrfpn.py b/mmdet/models/necks/hrfpn.py
new file mode 100644
index 0000000..ca15be6
--- /dev/null
+++ b/mmdet/models/necks/hrfpn.py
@@ -0,0 +1,100 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule
+from torch.utils.checkpoint import checkpoint
+
+from ..builder import NECKS
+
+
+@NECKS.register_module()
+class HRFPN(BaseModule):
+    """HRFPN (High Resolution Feature Pyramids)
+
+    paper: `High-Resolution Representations for Labeling Pixels and Regions
+    <https://arxiv.org/abs/1904.04514>`_.
+
+    Args:
+        in_channels (list): number of channels for each branch.
+        out_channels (int): output channels of feature pyramids.
+        num_outs (int): number of output stages.
+        pooling_type (str): pooling for generating feature pyramids
+            from {MAX, AVG}.
+        conv_cfg (dict): dictionary to construct and config conv layer.
+        norm_cfg (dict): dictionary to construct and config norm layer.
+        with_cp  (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed.
+        stride (int): stride of 3x3 convolutional layers
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 num_outs=5,
+                 pooling_type='AVG',
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 with_cp=False,
+                 stride=1,
+                 init_cfg=dict(type='Caffe2Xavier', layer='Conv2d')):
+        super(HRFPN, self).__init__(init_cfg)
+        assert isinstance(in_channels, list)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_ins = len(in_channels)
+        self.num_outs = num_outs
+        self.with_cp = with_cp
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+
+        self.reduction_conv = ConvModule(
+            sum(in_channels),
+            out_channels,
+            kernel_size=1,
+            conv_cfg=self.conv_cfg,
+            act_cfg=None)
+
+        self.fpn_convs = nn.ModuleList()
+        for i in range(self.num_outs):
+            self.fpn_convs.append(
+                ConvModule(
+                    out_channels,
+                    out_channels,
+                    kernel_size=3,
+                    padding=1,
+                    stride=stride,
+                    conv_cfg=self.conv_cfg,
+                    act_cfg=None))
+
+        if pooling_type == 'MAX':
+            self.pooling = F.max_pool2d
+        else:
+            self.pooling = F.avg_pool2d
+
+    def forward(self, inputs):
+        """Forward function."""
+        assert len(inputs) == self.num_ins
+        outs = [inputs[0]]
+        for i in range(1, self.num_ins):
+            outs.append(
+                F.interpolate(inputs[i], scale_factor=2**i, mode='bilinear'))
+        out = torch.cat(outs, dim=1)
+        if out.requires_grad and self.with_cp:
+            out = checkpoint(self.reduction_conv, out)
+        else:
+            out = self.reduction_conv(out)
+        outs = [out]
+        for i in range(1, self.num_outs):
+            outs.append(self.pooling(out, kernel_size=2**i, stride=2**i))
+        outputs = []
+
+        for i in range(self.num_outs):
+            if outs[i].requires_grad and self.with_cp:
+                tmp_out = checkpoint(self.fpn_convs[i], outs[i])
+            else:
+                tmp_out = self.fpn_convs[i](outs[i])
+            outputs.append(tmp_out)
+        return tuple(outputs)
diff --git a/mmdet/models/necks/nas_fpn.py b/mmdet/models/necks/nas_fpn.py
new file mode 100644
index 0000000..710592e
--- /dev/null
+++ b/mmdet/models/necks/nas_fpn.py
@@ -0,0 +1,158 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.ops.merge_cells import GlobalPoolingCell, SumCell
+from mmcv.runner import BaseModule, ModuleList
+
+from ..builder import NECKS
+
+
+@NECKS.register_module()
+class NASFPN(BaseModule):
+    """NAS-FPN.
+
+    Implementation of `NAS-FPN: Learning Scalable Feature Pyramid Architecture
+    for Object Detection <https://arxiv.org/abs/1904.07392>`_
+
+    Args:
+        in_channels (List[int]): Number of input channels per scale.
+        out_channels (int): Number of output channels (used at each scale)
+        num_outs (int): Number of output scales.
+        stack_times (int): The number of times the pyramid architecture will
+            be stacked.
+        start_level (int): Index of the start input backbone level used to
+            build the feature pyramid. Default: 0.
+        end_level (int): Index of the end input backbone level (exclusive) to
+            build the feature pyramid. Default: -1, which means the last level.
+        add_extra_convs (bool): It decides whether to add conv
+            layers on top of the original feature maps. Default to False.
+            If True, its actual mode is specified by `extra_convs_on_inputs`.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 num_outs,
+                 stack_times,
+                 start_level=0,
+                 end_level=-1,
+                 add_extra_convs=False,
+                 norm_cfg=None,
+                 init_cfg=dict(type='Caffe2Xavier', layer='Conv2d')):
+        super(NASFPN, self).__init__(init_cfg)
+        assert isinstance(in_channels, list)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_ins = len(in_channels)  # num of input feature levels
+        self.num_outs = num_outs  # num of output feature levels
+        self.stack_times = stack_times
+        self.norm_cfg = norm_cfg
+
+        if end_level == -1 or end_level == self.num_ins - 1:
+            self.backbone_end_level = self.num_ins
+            assert num_outs >= self.num_ins - start_level
+        else:
+            # if end_level is not the last level, no extra level is allowed
+            self.backbone_end_level = end_level + 1
+            assert end_level < self.num_ins
+            assert num_outs == end_level - start_level + 1
+        self.start_level = start_level
+        self.end_level = end_level
+        self.add_extra_convs = add_extra_convs
+
+        # add lateral connections
+        self.lateral_convs = nn.ModuleList()
+        for i in range(self.start_level, self.backbone_end_level):
+            l_conv = ConvModule(
+                in_channels[i],
+                out_channels,
+                1,
+                norm_cfg=norm_cfg,
+                act_cfg=None)
+            self.lateral_convs.append(l_conv)
+
+        # add extra downsample layers (stride-2 pooling or conv)
+        extra_levels = num_outs - self.backbone_end_level + self.start_level
+        self.extra_downsamples = nn.ModuleList()
+        for i in range(extra_levels):
+            extra_conv = ConvModule(
+                out_channels, out_channels, 1, norm_cfg=norm_cfg, act_cfg=None)
+            self.extra_downsamples.append(
+                nn.Sequential(extra_conv, nn.MaxPool2d(2, 2)))
+
+        # add NAS FPN connections
+        self.fpn_stages = ModuleList()
+        for _ in range(self.stack_times):
+            stage = nn.ModuleDict()
+            # gp(p6, p4) -> p4_1
+            stage['gp_64_4'] = GlobalPoolingCell(
+                in_channels=out_channels,
+                out_channels=out_channels,
+                out_norm_cfg=norm_cfg)
+            # sum(p4_1, p4) -> p4_2
+            stage['sum_44_4'] = SumCell(
+                in_channels=out_channels,
+                out_channels=out_channels,
+                out_norm_cfg=norm_cfg)
+            # sum(p4_2, p3) -> p3_out
+            stage['sum_43_3'] = SumCell(
+                in_channels=out_channels,
+                out_channels=out_channels,
+                out_norm_cfg=norm_cfg)
+            # sum(p3_out, p4_2) -> p4_out
+            stage['sum_34_4'] = SumCell(
+                in_channels=out_channels,
+                out_channels=out_channels,
+                out_norm_cfg=norm_cfg)
+            # sum(p5, gp(p4_out, p3_out)) -> p5_out
+            stage['gp_43_5'] = GlobalPoolingCell(with_out_conv=False)
+            stage['sum_55_5'] = SumCell(
+                in_channels=out_channels,
+                out_channels=out_channels,
+                out_norm_cfg=norm_cfg)
+            # sum(p7, gp(p5_out, p4_2)) -> p7_out
+            stage['gp_54_7'] = GlobalPoolingCell(with_out_conv=False)
+            stage['sum_77_7'] = SumCell(
+                in_channels=out_channels,
+                out_channels=out_channels,
+                out_norm_cfg=norm_cfg)
+            # gp(p7_out, p5_out) -> p6_out
+            stage['gp_75_6'] = GlobalPoolingCell(
+                in_channels=out_channels,
+                out_channels=out_channels,
+                out_norm_cfg=norm_cfg)
+            self.fpn_stages.append(stage)
+
+    def forward(self, inputs):
+        """Forward function."""
+        # build P3-P5
+        feats = [
+            lateral_conv(inputs[i + self.start_level])
+            for i, lateral_conv in enumerate(self.lateral_convs)
+        ]
+        # build P6-P7 on top of P5
+        for downsample in self.extra_downsamples:
+            feats.append(downsample(feats[-1]))
+
+        p3, p4, p5, p6, p7 = feats
+
+        for stage in self.fpn_stages:
+            # gp(p6, p4) -> p4_1
+            p4_1 = stage['gp_64_4'](p6, p4, out_size=p4.shape[-2:])
+            # sum(p4_1, p4) -> p4_2
+            p4_2 = stage['sum_44_4'](p4_1, p4, out_size=p4.shape[-2:])
+            # sum(p4_2, p3) -> p3_out
+            p3 = stage['sum_43_3'](p4_2, p3, out_size=p3.shape[-2:])
+            # sum(p3_out, p4_2) -> p4_out
+            p4 = stage['sum_34_4'](p3, p4_2, out_size=p4.shape[-2:])
+            # sum(p5, gp(p4_out, p3_out)) -> p5_out
+            p5_tmp = stage['gp_43_5'](p4, p3, out_size=p5.shape[-2:])
+            p5 = stage['sum_55_5'](p5, p5_tmp, out_size=p5.shape[-2:])
+            # sum(p7, gp(p5_out, p4_2)) -> p7_out
+            p7_tmp = stage['gp_54_7'](p5, p4_2, out_size=p7.shape[-2:])
+            p7 = stage['sum_77_7'](p7, p7_tmp, out_size=p7.shape[-2:])
+            # gp(p7_out, p5_out) -> p6_out
+            p6 = stage['gp_75_6'](p7, p5, out_size=p6.shape[-2:])
+
+        return p3, p4, p5, p6, p7
diff --git a/mmdet/models/necks/nasfcos_fpn.py b/mmdet/models/necks/nasfcos_fpn.py
new file mode 100644
index 0000000..c4abfe7
--- /dev/null
+++ b/mmdet/models/necks/nasfcos_fpn.py
@@ -0,0 +1,170 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule, caffe2_xavier_init
+from mmcv.ops.merge_cells import ConcatCell
+from mmcv.runner import BaseModule
+
+from ..builder import NECKS
+
+
+@NECKS.register_module()
+class NASFCOS_FPN(BaseModule):
+    """FPN structure in NASFPN.
+
+    Implementation of paper `NAS-FCOS: Fast Neural Architecture Search for
+    Object Detection <https://arxiv.org/abs/1906.04423>`_
+
+    Args:
+        in_channels (List[int]): Number of input channels per scale.
+        out_channels (int): Number of output channels (used at each scale)
+        num_outs (int): Number of output scales.
+        start_level (int): Index of the start input backbone level used to
+            build the feature pyramid. Default: 0.
+        end_level (int): Index of the end input backbone level (exclusive) to
+            build the feature pyramid. Default: -1, which means the last level.
+        add_extra_convs (bool): It decides whether to add conv
+            layers on top of the original feature maps. Default to False.
+            If True, its actual mode is specified by `extra_convs_on_inputs`.
+        conv_cfg (dict): dictionary to construct and config conv layer.
+        norm_cfg (dict): dictionary to construct and config norm layer.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 num_outs,
+                 start_level=1,
+                 end_level=-1,
+                 add_extra_convs=False,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 init_cfg=None):
+        assert init_cfg is None, 'To prevent abnormal initialization ' \
+                                 'behavior, init_cfg is not allowed to be set'
+        super(NASFCOS_FPN, self).__init__(init_cfg)
+        assert isinstance(in_channels, list)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.num_ins = len(in_channels)
+        self.num_outs = num_outs
+        self.norm_cfg = norm_cfg
+        self.conv_cfg = conv_cfg
+
+        if end_level == -1 or end_level == self.num_ins - 1:
+            self.backbone_end_level = self.num_ins
+            assert num_outs >= self.num_ins - start_level
+        else:
+            # if end_level is not the last level, no extra level is allowed
+            self.backbone_end_level = end_level + 1
+            assert end_level < self.num_ins
+            assert num_outs == end_level - start_level + 1
+        self.start_level = start_level
+        self.end_level = end_level
+        self.add_extra_convs = add_extra_convs
+
+        self.adapt_convs = nn.ModuleList()
+        for i in range(self.start_level, self.backbone_end_level):
+            adapt_conv = ConvModule(
+                in_channels[i],
+                out_channels,
+                1,
+                stride=1,
+                padding=0,
+                bias=False,
+                norm_cfg=dict(type='BN'),
+                act_cfg=dict(type='ReLU', inplace=False))
+            self.adapt_convs.append(adapt_conv)
+
+        # C2 is omitted according to the paper
+        extra_levels = num_outs - self.backbone_end_level + self.start_level
+
+        def build_concat_cell(with_input1_conv, with_input2_conv):
+            cell_conv_cfg = dict(
+                kernel_size=1, padding=0, bias=False, groups=out_channels)
+            return ConcatCell(
+                in_channels=out_channels,
+                out_channels=out_channels,
+                with_out_conv=True,
+                out_conv_cfg=cell_conv_cfg,
+                out_norm_cfg=dict(type='BN'),
+                out_conv_order=('norm', 'act', 'conv'),
+                with_input1_conv=with_input1_conv,
+                with_input2_conv=with_input2_conv,
+                input_conv_cfg=conv_cfg,
+                input_norm_cfg=norm_cfg,
+                upsample_mode='nearest')
+
+        # Denote c3=f0, c4=f1, c5=f2 for convince
+        self.fpn = nn.ModuleDict()
+        self.fpn['c22_1'] = build_concat_cell(True, True)
+        self.fpn['c22_2'] = build_concat_cell(True, True)
+        self.fpn['c32'] = build_concat_cell(True, False)
+        self.fpn['c02'] = build_concat_cell(True, False)
+        self.fpn['c42'] = build_concat_cell(True, True)
+        self.fpn['c36'] = build_concat_cell(True, True)
+        self.fpn['c61'] = build_concat_cell(True, True)  # f9
+        self.extra_downsamples = nn.ModuleList()
+        for i in range(extra_levels):
+            extra_act_cfg = None if i == 0 \
+                else dict(type='ReLU', inplace=False)
+            self.extra_downsamples.append(
+                ConvModule(
+                    out_channels,
+                    out_channels,
+                    3,
+                    stride=2,
+                    padding=1,
+                    act_cfg=extra_act_cfg,
+                    order=('act', 'norm', 'conv')))
+
+    def forward(self, inputs):
+        """Forward function."""
+        feats = [
+            adapt_conv(inputs[i + self.start_level])
+            for i, adapt_conv in enumerate(self.adapt_convs)
+        ]
+
+        for (i, module_name) in enumerate(self.fpn):
+            idx_1, idx_2 = int(module_name[1]), int(module_name[2])
+            res = self.fpn[module_name](feats[idx_1], feats[idx_2])
+            feats.append(res)
+
+        ret = []
+        for (idx, input_idx) in zip([9, 8, 7], [1, 2, 3]):  # add P3, P4, P5
+            feats1, feats2 = feats[idx], feats[5]
+            feats2_resize = F.interpolate(
+                feats2,
+                size=feats1.size()[2:],
+                mode='bilinear',
+                align_corners=False)
+
+            feats_sum = feats1 + feats2_resize
+            ret.append(
+                F.interpolate(
+                    feats_sum,
+                    size=inputs[input_idx].size()[2:],
+                    mode='bilinear',
+                    align_corners=False))
+
+        for submodule in self.extra_downsamples:
+            ret.append(submodule(ret[-1]))
+
+        return tuple(ret)
+
+    def init_weights(self):
+        """Initialize the weights of module."""
+        super(NASFCOS_FPN, self).init_weights()
+        for module in self.fpn.values():
+            if hasattr(module, 'conv_out'):
+                caffe2_xavier_init(module.out_conv.conv)
+
+        for modules in [
+                self.adapt_convs.modules(),
+                self.extra_downsamples.modules()
+        ]:
+            for module in modules:
+                if isinstance(module, nn.Conv2d):
+                    caffe2_xavier_init(module)
diff --git a/mmdet/models/necks/pafpn.py b/mmdet/models/necks/pafpn.py
new file mode 100644
index 0000000..2edd348
--- /dev/null
+++ b/mmdet/models/necks/pafpn.py
@@ -0,0 +1,159 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+from mmcv.runner import auto_fp16
+
+from ..builder import NECKS
+from .fpn import FPN
+
+
+@NECKS.register_module()
+class PAFPN(FPN):
+    """Path Aggregation Network for Instance Segmentation.
+
+    This is an implementation of the `PAFPN in Path Aggregation Network
+    <https://arxiv.org/abs/1803.01534>`_.
+
+    Args:
+        in_channels (List[int]): Number of input channels per scale.
+        out_channels (int): Number of output channels (used at each scale)
+        num_outs (int): Number of output scales.
+        start_level (int): Index of the start input backbone level used to
+            build the feature pyramid. Default: 0.
+        end_level (int): Index of the end input backbone level (exclusive) to
+            build the feature pyramid. Default: -1, which means the last level.
+        add_extra_convs (bool | str): If bool, it decides whether to add conv
+            layers on top of the original feature maps. Default to False.
+            If True, it is equivalent to `add_extra_convs='on_input'`.
+            If str, it specifies the source feature map of the extra convs.
+            Only the following options are allowed
+
+            - 'on_input': Last feat map of neck inputs (i.e. backbone feature).
+            - 'on_lateral':  Last feature map after lateral convs.
+            - 'on_output': The last output feature map after fpn convs.
+        relu_before_extra_convs (bool): Whether to apply relu before the extra
+            conv. Default: False.
+        no_norm_on_lateral (bool): Whether to apply norm on lateral.
+            Default: False.
+        conv_cfg (dict): Config dict for convolution layer. Default: None.
+        norm_cfg (dict): Config dict for normalization layer. Default: None.
+        act_cfg (str): Config dict for activation layer in ConvModule.
+            Default: None.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 num_outs,
+                 start_level=0,
+                 end_level=-1,
+                 add_extra_convs=False,
+                 relu_before_extra_convs=False,
+                 no_norm_on_lateral=False,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 act_cfg=None,
+                 init_cfg=dict(
+                     type='Xavier', layer='Conv2d', distribution='uniform')):
+        super(PAFPN, self).__init__(
+            in_channels,
+            out_channels,
+            num_outs,
+            start_level,
+            end_level,
+            add_extra_convs,
+            relu_before_extra_convs,
+            no_norm_on_lateral,
+            conv_cfg,
+            norm_cfg,
+            act_cfg,
+            init_cfg=init_cfg)
+        # add extra bottom up pathway
+        self.downsample_convs = nn.ModuleList()
+        self.pafpn_convs = nn.ModuleList()
+        for i in range(self.start_level + 1, self.backbone_end_level):
+            d_conv = ConvModule(
+                out_channels,
+                out_channels,
+                3,
+                stride=2,
+                padding=1,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg,
+                inplace=False)
+            pafpn_conv = ConvModule(
+                out_channels,
+                out_channels,
+                3,
+                padding=1,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg,
+                inplace=False)
+            self.downsample_convs.append(d_conv)
+            self.pafpn_convs.append(pafpn_conv)
+
+    @auto_fp16()
+    def forward(self, inputs):
+        """Forward function."""
+        assert len(inputs) == len(self.in_channels)
+
+        # build laterals
+        laterals = [
+            lateral_conv(inputs[i + self.start_level])
+            for i, lateral_conv in enumerate(self.lateral_convs)
+        ]
+
+        # build top-down path
+        used_backbone_levels = len(laterals)
+        for i in range(used_backbone_levels - 1, 0, -1):
+            prev_shape = laterals[i - 1].shape[2:]
+            # fix runtime error of "+=" inplace operation in PyTorch 1.10
+            laterals[i - 1] = laterals[i - 1] + F.interpolate(
+                laterals[i], size=prev_shape, mode='nearest')
+
+        # build outputs
+        # part 1: from original levels
+        inter_outs = [
+            self.fpn_convs[i](laterals[i]) for i in range(used_backbone_levels)
+        ]
+
+        # part 2: add bottom-up path
+        for i in range(0, used_backbone_levels - 1):
+            inter_outs[i + 1] += self.downsample_convs[i](inter_outs[i])
+
+        outs = []
+        outs.append(inter_outs[0])
+        outs.extend([
+            self.pafpn_convs[i - 1](inter_outs[i])
+            for i in range(1, used_backbone_levels)
+        ])
+
+        # part 3: add extra levels
+        if self.num_outs > len(outs):
+            # use max pool to get more levels on top of outputs
+            # (e.g., Faster R-CNN, Mask R-CNN)
+            if not self.add_extra_convs:
+                for i in range(self.num_outs - used_backbone_levels):
+                    outs.append(F.max_pool2d(outs[-1], 1, stride=2))
+            # add conv layers on top of original feature maps (RetinaNet)
+            else:
+                if self.add_extra_convs == 'on_input':
+                    orig = inputs[self.backbone_end_level - 1]
+                    outs.append(self.fpn_convs[used_backbone_levels](orig))
+                elif self.add_extra_convs == 'on_lateral':
+                    outs.append(self.fpn_convs[used_backbone_levels](
+                        laterals[-1]))
+                elif self.add_extra_convs == 'on_output':
+                    outs.append(self.fpn_convs[used_backbone_levels](outs[-1]))
+                else:
+                    raise NotImplementedError
+                for i in range(used_backbone_levels + 1, self.num_outs):
+                    if self.relu_before_extra_convs:
+                        outs.append(self.fpn_convs[i](F.relu(outs[-1])))
+                    else:
+                        outs.append(self.fpn_convs[i](outs[-1]))
+        return tuple(outs)
diff --git a/mmdet/models/necks/rfp.py b/mmdet/models/necks/rfp.py
new file mode 100644
index 0000000..6976f4d
--- /dev/null
+++ b/mmdet/models/necks/rfp.py
@@ -0,0 +1,135 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import constant_init, xavier_init
+from mmcv.runner import BaseModule, ModuleList
+
+from ..builder import NECKS, build_backbone
+from .fpn import FPN
+
+
+class ASPP(BaseModule):
+    """ASPP (Atrous Spatial Pyramid Pooling)
+
+    This is an implementation of the ASPP module used in DetectoRS
+    (https://arxiv.org/pdf/2006.02334.pdf)
+
+    Args:
+        in_channels (int): Number of input channels.
+        out_channels (int): Number of channels produced by this module
+        dilations (tuple[int]): Dilations of the four branches.
+            Default: (1, 3, 6, 1)
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 dilations=(1, 3, 6, 1),
+                 init_cfg=dict(type='Kaiming', layer='Conv2d')):
+        super().__init__(init_cfg)
+        assert dilations[-1] == 1
+        self.aspp = nn.ModuleList()
+        for dilation in dilations:
+            kernel_size = 3 if dilation > 1 else 1
+            padding = dilation if dilation > 1 else 0
+            conv = nn.Conv2d(
+                in_channels,
+                out_channels,
+                kernel_size=kernel_size,
+                stride=1,
+                dilation=dilation,
+                padding=padding,
+                bias=True)
+            self.aspp.append(conv)
+        self.gap = nn.AdaptiveAvgPool2d(1)
+
+    def forward(self, x):
+        avg_x = self.gap(x)
+        out = []
+        for aspp_idx in range(len(self.aspp)):
+            inp = avg_x if (aspp_idx == len(self.aspp) - 1) else x
+            out.append(F.relu_(self.aspp[aspp_idx](inp)))
+        out[-1] = out[-1].expand_as(out[-2])
+        out = torch.cat(out, dim=1)
+        return out
+
+
+@NECKS.register_module()
+class RFP(FPN):
+    """RFP (Recursive Feature Pyramid)
+
+    This is an implementation of RFP in `DetectoRS
+    <https://arxiv.org/pdf/2006.02334.pdf>`_. Different from standard FPN, the
+    input of RFP should be multi level features along with origin input image
+    of backbone.
+
+    Args:
+        rfp_steps (int): Number of unrolled steps of RFP.
+        rfp_backbone (dict): Configuration of the backbone for RFP.
+        aspp_out_channels (int): Number of output channels of ASPP module.
+        aspp_dilations (tuple[int]): Dilation rates of four branches.
+            Default: (1, 3, 6, 1)
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 rfp_steps,
+                 rfp_backbone,
+                 aspp_out_channels,
+                 aspp_dilations=(1, 3, 6, 1),
+                 init_cfg=None,
+                 **kwargs):
+        assert init_cfg is None, 'To prevent abnormal initialization ' \
+                                 'behavior, init_cfg is not allowed to be set'
+        super().__init__(init_cfg=init_cfg, **kwargs)
+        self.rfp_steps = rfp_steps
+        # Be careful! Pretrained weights cannot be loaded when use
+        # nn.ModuleList
+        self.rfp_modules = ModuleList()
+        for rfp_idx in range(1, rfp_steps):
+            rfp_module = build_backbone(rfp_backbone)
+            self.rfp_modules.append(rfp_module)
+        self.rfp_aspp = ASPP(self.out_channels, aspp_out_channels,
+                             aspp_dilations)
+        self.rfp_weight = nn.Conv2d(
+            self.out_channels,
+            1,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            bias=True)
+
+    def init_weights(self):
+        # Avoid using super().init_weights(), which may alter the default
+        # initialization of the modules in self.rfp_modules that have missing
+        # keys in the pretrained checkpoint.
+        for convs in [self.lateral_convs, self.fpn_convs]:
+            for m in convs.modules():
+                if isinstance(m, nn.Conv2d):
+                    xavier_init(m, distribution='uniform')
+        for rfp_idx in range(self.rfp_steps - 1):
+            self.rfp_modules[rfp_idx].init_weights()
+        constant_init(self.rfp_weight, 0)
+
+    def forward(self, inputs):
+        inputs = list(inputs)
+        assert len(inputs) == len(self.in_channels) + 1  # +1 for input image
+        img = inputs.pop(0)
+        # FPN forward
+        x = super().forward(tuple(inputs))
+        for rfp_idx in range(self.rfp_steps - 1):
+            rfp_feats = [x[0]] + list(
+                self.rfp_aspp(x[i]) for i in range(1, len(x)))
+            x_idx = self.rfp_modules[rfp_idx].rfp_forward(img, rfp_feats)
+            # FPN forward
+            x_idx = super().forward(x_idx)
+            x_new = []
+            for ft_idx in range(len(x_idx)):
+                add_weight = torch.sigmoid(self.rfp_weight(x_idx[ft_idx]))
+                x_new.append(add_weight * x_idx[ft_idx] +
+                             (1 - add_weight) * x[ft_idx])
+            x = x_new
+        return x
diff --git a/mmdet/models/necks/ssd_neck.py b/mmdet/models/necks/ssd_neck.py
new file mode 100644
index 0000000..d8d9100
--- /dev/null
+++ b/mmdet/models/necks/ssd_neck.py
@@ -0,0 +1,139 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
+from mmcv.runner import BaseModule
+
+from ..builder import NECKS
+
+
+@NECKS.register_module()
+class SSDNeck(BaseModule):
+    """Extra layers of SSD backbone to generate multi-scale feature maps.
+
+    Args:
+        in_channels (Sequence[int]): Number of input channels per scale.
+        out_channels (Sequence[int]): Number of output channels per scale.
+        level_strides (Sequence[int]): Stride of 3x3 conv per level.
+        level_paddings (Sequence[int]): Padding size of 3x3 conv per level.
+        l2_norm_scale (float|None): L2 normalization layer init scale.
+            If None, not use L2 normalization on the first input feature.
+        last_kernel_size (int): Kernel size of the last conv layer.
+            Default: 3.
+        use_depthwise (bool): Whether to use DepthwiseSeparableConv.
+            Default: False.
+        conv_cfg (dict): Config dict for convolution layer. Default: None.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+            Default: None.
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='ReLU').
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 level_strides,
+                 level_paddings,
+                 l2_norm_scale=20.,
+                 last_kernel_size=3,
+                 use_depthwise=False,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 act_cfg=dict(type='ReLU'),
+                 init_cfg=[
+                     dict(
+                         type='Xavier', distribution='uniform',
+                         layer='Conv2d'),
+                     dict(type='Constant', val=1, layer='BatchNorm2d'),
+                 ]):
+        super(SSDNeck, self).__init__(init_cfg)
+        assert len(out_channels) > len(in_channels)
+        assert len(out_channels) - len(in_channels) == len(level_strides)
+        assert len(level_strides) == len(level_paddings)
+        assert in_channels == out_channels[:len(in_channels)]
+
+        if l2_norm_scale:
+            self.l2_norm = L2Norm(in_channels[0], l2_norm_scale)
+            self.init_cfg += [
+                dict(
+                    type='Constant',
+                    val=self.l2_norm.scale,
+                    override=dict(name='l2_norm'))
+            ]
+
+        self.extra_layers = nn.ModuleList()
+        extra_layer_channels = out_channels[len(in_channels):]
+        second_conv = DepthwiseSeparableConvModule if \
+            use_depthwise else ConvModule
+
+        for i, (out_channel, stride, padding) in enumerate(
+                zip(extra_layer_channels, level_strides, level_paddings)):
+            kernel_size = last_kernel_size \
+                if i == len(extra_layer_channels) - 1 else 3
+            per_lvl_convs = nn.Sequential(
+                ConvModule(
+                    out_channels[len(in_channels) - 1 + i],
+                    out_channel // 2,
+                    1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg),
+                second_conv(
+                    out_channel // 2,
+                    out_channel,
+                    kernel_size,
+                    stride=stride,
+                    padding=padding,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg))
+            self.extra_layers.append(per_lvl_convs)
+
+    def forward(self, inputs):
+        """Forward function."""
+
+        if hasattr(self,'in_num') and self.in_num == 2:
+            outs = [inputs[0], inputs[1]]
+        elif hasattr(self,'in_num') and self.in_num == 1:
+            outs = inputs[0]
+        elif hasattr(self,'in_num'):
+            raise NotImplementedError
+        else:
+            self.in_num = 2
+            outs = [inputs[0], inputs[1]]
+        
+        if hasattr(self, 'l2_norm'):
+            outs[0] = self.l2_norm(outs[0])
+
+        feat = outs[-1]
+        for layer in self.extra_layers:
+            feat = layer(feat)
+            outs.append(feat)
+        return tuple(outs)
+
+
+class L2Norm(nn.Module):
+
+    def __init__(self, n_dims, scale=20., eps=1e-10):
+        """L2 normalization layer.
+
+        Args:
+            n_dims (int): Number of dimensions to be normalized
+            scale (float, optional): Defaults to 20..
+            eps (float, optional): Used to avoid division by zero.
+                Defaults to 1e-10.
+        """
+        super(L2Norm, self).__init__()
+        self.n_dims = n_dims
+        self.weight = nn.Parameter(torch.Tensor(self.n_dims))
+        self.eps = eps
+        self.scale = scale
+
+    def forward(self, x):
+        """Forward function."""
+        # normalization layer convert to FP32 in FP16 training
+        x_float = x.float()
+        norm = x_float.pow(2).sum(1, keepdim=True).sqrt() + self.eps
+        return (self.weight[None, :, None, None].float().expand_as(x_float) *
+                x_float / norm).type_as(x)
diff --git a/mmdet/models/necks/yolo_neck.py b/mmdet/models/necks/yolo_neck.py
new file mode 100644
index 0000000..eb6e1b9
--- /dev/null
+++ b/mmdet/models/necks/yolo_neck.py
@@ -0,0 +1,148 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# Copyright (c) 2019 Western Digital Corporation or its affiliates.
+
+import torch
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule
+
+from ..builder import NECKS
+
+
+class DetectionBlock(BaseModule):
+    """Detection block in YOLO neck.
+
+    Let out_channels = n, the DetectionBlock contains:
+    Six ConvLayers, 1 Conv2D Layer and 1 YoloLayer.
+    The first 6 ConvLayers are formed the following way:
+        1x1xn, 3x3x2n, 1x1xn, 3x3x2n, 1x1xn, 3x3x2n.
+    The Conv2D layer is 1x1x255.
+    Some block will have branch after the fifth ConvLayer.
+    The input channel is arbitrary (in_channels)
+
+    Args:
+        in_channels (int): The number of input channels.
+        out_channels (int): The number of output channels.
+        conv_cfg (dict): Config dict for convolution layer. Default: None.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+            Default: dict(type='BN', requires_grad=True)
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='LeakyReLU', negative_slope=0.1).
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
+                 init_cfg=None):
+        super(DetectionBlock, self).__init__(init_cfg)
+        double_out_channels = out_channels * 2
+
+        # shortcut
+        cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
+        self.conv1 = ConvModule(in_channels, out_channels, 1, **cfg)
+        self.conv2 = ConvModule(
+            out_channels, double_out_channels, 3, padding=1, **cfg)
+        self.conv3 = ConvModule(double_out_channels, out_channels, 1, **cfg)
+        self.conv4 = ConvModule(
+            out_channels, double_out_channels, 3, padding=1, **cfg)
+        self.conv5 = ConvModule(double_out_channels, out_channels, 1, **cfg)
+
+    def forward(self, x):
+        tmp = self.conv1(x)
+        tmp = self.conv2(tmp)
+        tmp = self.conv3(tmp)
+        tmp = self.conv4(tmp)
+        out = self.conv5(tmp)
+        return out
+
+
+@NECKS.register_module()
+class YOLOV3Neck(BaseModule):
+    """The neck of YOLOV3.
+
+    It can be treated as a simplified version of FPN. It
+    will take the result from Darknet backbone and do some upsampling and
+    concatenation. It will finally output the detection result.
+
+    Note:
+        The input feats should be from top to bottom.
+            i.e., from high-lvl to low-lvl
+        But YOLOV3Neck will process them in reversed order.
+            i.e., from bottom (high-lvl) to top (low-lvl)
+
+    Args:
+        num_scales (int): The number of scales / stages.
+        in_channels (List[int]): The number of input channels per scale.
+        out_channels (List[int]): The number of output channels  per scale.
+        conv_cfg (dict, optional): Config dict for convolution layer.
+            Default: None.
+        norm_cfg (dict, optional): Dictionary to construct and config norm
+            layer. Default: dict(type='BN', requires_grad=True)
+        act_cfg (dict, optional): Config dict for activation layer.
+            Default: dict(type='LeakyReLU', negative_slope=0.1).
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 num_scales,
+                 in_channels,
+                 out_channels,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 act_cfg=dict(type='LeakyReLU', negative_slope=0.1),
+                 init_cfg=None):
+        super(YOLOV3Neck, self).__init__(init_cfg)
+        assert (num_scales == len(in_channels) == len(out_channels))
+        self.num_scales = num_scales
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        # shortcut
+        cfg = dict(conv_cfg=conv_cfg, norm_cfg=norm_cfg, act_cfg=act_cfg)
+
+        # To support arbitrary scales, the code looks awful, but it works.
+        # Better solution is welcomed.
+        self.detect1 = DetectionBlock(in_channels[0], out_channels[0], **cfg)
+        for i in range(1, self.num_scales):
+            in_c, out_c = self.in_channels[i], self.out_channels[i]
+            inter_c = out_channels[i - 1]
+            self.add_module(f'conv{i}', ConvModule(inter_c, out_c, 1, **cfg))
+            # in_c + out_c : High-lvl feats will be cat with low-lvl feats
+            self.add_module(f'detect{i+1}',
+                            DetectionBlock(in_c + out_c, out_c, **cfg))
+
+    def forward(self, feats):
+        # assert len(feats) == self.num_scales
+
+        if hasattr(self,'in_num') and self.in_num == 3:
+            feats = [feats[0], feats[1], feats[2]]
+        elif hasattr(self,'in_num'):
+            raise NotImplementedError
+        else:
+            self.in_num = 3
+            feats = [feats[0], feats[1], feats[2]]
+        
+        # processed from bottom (high-lvl) to top (low-lvl)
+        outs = []
+        out = self.detect1(feats[-1])
+        outs.append(out)
+
+        for i, x in enumerate(reversed(feats[:-1])):
+            conv = getattr(self, f'conv{i+1}')
+            tmp = conv(out)
+
+            # Cat with low-lvl feats
+            tmp = F.interpolate(tmp, scale_factor=2)
+            tmp = torch.cat((tmp, x), 1)
+
+            detect = getattr(self, f'detect{i+2}')
+            out = detect(tmp)
+            outs.append(out)
+
+        return tuple(outs)
diff --git a/mmdet/models/necks/yolox_pafpn.py b/mmdet/models/necks/yolox_pafpn.py
new file mode 100644
index 0000000..bb5493b
--- /dev/null
+++ b/mmdet/models/necks/yolox_pafpn.py
@@ -0,0 +1,165 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
+from mmcv.runner import BaseModule
+
+from ..builder import NECKS
+from ..utils import CSPLayer
+
+
+@NECKS.register_module()
+class YOLOXPAFPN(BaseModule):
+    """Path Aggregation Network used in YOLOX.
+
+    Args:
+        in_channels (List[int]): Number of input channels per scale.
+        out_channels (int): Number of output channels (used at each scale)
+        num_csp_blocks (int): Number of bottlenecks in CSPLayer. Default: 3
+        use_depthwise (bool): Whether to depthwise separable convolution in
+            blocks. Default: False
+        upsample_cfg (dict): Config dict for interpolate layer.
+            Default: `dict(scale_factor=2, mode='nearest')`
+        conv_cfg (dict, optional): Config dict for convolution layer.
+            Default: None, which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN')
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='Swish')
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 num_csp_blocks=3,
+                 use_depthwise=False,
+                 upsample_cfg=dict(scale_factor=2, mode='nearest'),
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+                 act_cfg=dict(type='Swish'),
+                 init_cfg=dict(
+                     type='Kaiming',
+                     layer='Conv2d',
+                     a=math.sqrt(5),
+                     distribution='uniform',
+                     mode='fan_in',
+                     nonlinearity='leaky_relu')):
+        super(YOLOXPAFPN, self).__init__(init_cfg)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+
+        conv = DepthwiseSeparableConvModule if use_depthwise else ConvModule
+
+        # build top-down blocks
+        self.upsample = nn.Upsample(**upsample_cfg)
+        self.reduce_layers = nn.ModuleList()
+        self.top_down_blocks = nn.ModuleList()
+        for idx in range(len(in_channels) - 1, 0, -1):
+            self.reduce_layers.append(
+                ConvModule(
+                    in_channels[idx],
+                    in_channels[idx - 1],
+                    1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg))
+            self.top_down_blocks.append(
+                CSPLayer(
+                    in_channels[idx - 1] * 2,
+                    in_channels[idx - 1],
+                    num_blocks=num_csp_blocks,
+                    add_identity=False,
+                    use_depthwise=use_depthwise,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg))
+
+        # build bottom-up blocks
+        self.downsamples = nn.ModuleList()
+        self.bottom_up_blocks = nn.ModuleList()
+        for idx in range(len(in_channels) - 1):
+            self.downsamples.append(
+                conv(
+                    in_channels[idx],
+                    in_channels[idx],
+                    3,
+                    stride=2,
+                    padding=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg))
+            self.bottom_up_blocks.append(
+                CSPLayer(
+                    in_channels[idx] * 2,
+                    in_channels[idx + 1],
+                    num_blocks=num_csp_blocks,
+                    add_identity=False,
+                    use_depthwise=use_depthwise,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg))
+
+        self.out_convs = nn.ModuleList()
+        for i in range(len(in_channels)):
+            self.out_convs.append(
+                ConvModule(
+                    in_channels[i],
+                    out_channels,
+                    1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    act_cfg=act_cfg))
+
+    def forward(self, inputs):
+        """
+        Args:
+            inputs (tuple[Tensor]): input features.
+
+        Returns:
+            tuple[Tensor]: YOLOXPAFPN features.
+        """
+
+        if hasattr(self,'in_num') and self.in_num == 3:
+            inputs = [inputs[0], inputs[1], inputs[2]]
+        elif hasattr(self,'in_num'):
+            raise NotImplementedError
+        else:
+            self.in_num = 3
+            inputs = [inputs[0], inputs[1], inputs[2]]
+        
+        assert len(inputs) == len(self.in_channels)
+
+        # top-down path
+        inner_outs = [inputs[-1]]
+        for idx in range(len(self.in_channels) - 1, 0, -1):
+            feat_heigh = inner_outs[0]
+            feat_low = inputs[idx - 1]
+            feat_heigh = self.reduce_layers[len(self.in_channels) - 1 - idx](
+                feat_heigh)
+            inner_outs[0] = feat_heigh
+
+            upsample_feat = self.upsample(feat_heigh)
+
+            inner_out = self.top_down_blocks[len(self.in_channels) - 1 - idx](
+                torch.cat([upsample_feat, feat_low], 1))
+            inner_outs.insert(0, inner_out)
+
+        # bottom-up path
+        outs = [inner_outs[0]]
+        for idx in range(len(self.in_channels) - 1):
+            feat_low = outs[-1]
+            feat_height = inner_outs[idx + 1]
+            downsample_feat = self.downsamples[idx](feat_low)
+            out = self.bottom_up_blocks[idx](
+                torch.cat([downsample_feat, feat_height], 1))
+            outs.append(out)
+
+        # out convs
+        for idx, conv in enumerate(self.out_convs):
+            outs[idx] = conv(outs[idx])
+
+        return tuple(outs)
diff --git a/mmdet/models/plugins/__init__.py b/mmdet/models/plugins/__init__.py
new file mode 100644
index 0000000..a455c07
--- /dev/null
+++ b/mmdet/models/plugins/__init__.py
@@ -0,0 +1,9 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .dropblock import DropBlock
+from .msdeformattn_pixel_decoder import MSDeformAttnPixelDecoder
+from .pixel_decoder import PixelDecoder, TransformerEncoderPixelDecoder
+
+__all__ = [
+    'DropBlock', 'PixelDecoder', 'TransformerEncoderPixelDecoder',
+    'MSDeformAttnPixelDecoder'
+]
diff --git a/mmdet/models/plugins/dropblock.py b/mmdet/models/plugins/dropblock.py
new file mode 100644
index 0000000..bb00ade
--- /dev/null
+++ b/mmdet/models/plugins/dropblock.py
@@ -0,0 +1,85 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import PLUGIN_LAYERS
+
+eps = 1e-6
+
+
+@PLUGIN_LAYERS.register_module()
+class DropBlock(nn.Module):
+    """Randomly drop some regions of feature maps.
+
+     Please refer to the method proposed in `DropBlock
+     <https://arxiv.org/abs/1810.12890>`_ for details.
+
+    Args:
+        drop_prob (float): The probability of dropping each block.
+        block_size (int): The size of dropped blocks.
+        warmup_iters (int): The drop probability will linearly increase
+            from `0` to `drop_prob` during the first `warmup_iters` iterations.
+            Default: 2000.
+    """
+
+    def __init__(self, drop_prob, block_size, warmup_iters=2000, **kwargs):
+        super(DropBlock, self).__init__()
+        assert block_size % 2 == 1
+        assert 0 < drop_prob <= 1
+        assert warmup_iters >= 0
+        self.drop_prob = drop_prob
+        self.block_size = block_size
+        self.warmup_iters = warmup_iters
+        self.iter_cnt = 0
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): Input feature map on which some areas will be randomly
+                dropped.
+
+        Returns:
+            Tensor: The tensor after DropBlock layer.
+        """
+        if not self.training:
+            return x
+        self.iter_cnt += 1
+        N, C, H, W = list(x.shape)
+        gamma = self._compute_gamma((H, W))
+        mask_shape = (N, C, H - self.block_size + 1, W - self.block_size + 1)
+        mask = torch.bernoulli(torch.full(mask_shape, gamma, device=x.device))
+
+        mask = F.pad(mask, [self.block_size // 2] * 4, value=0)
+        mask = F.max_pool2d(
+            input=mask,
+            stride=(1, 1),
+            kernel_size=(self.block_size, self.block_size),
+            padding=self.block_size // 2)
+        mask = 1 - mask
+        x = x * mask * mask.numel() / (eps + mask.sum())
+        return x
+
+    def _compute_gamma(self, feat_size):
+        """Compute the value of gamma according to paper. gamma is the
+        parameter of bernoulli distribution, which controls the number of
+        features to drop.
+
+        gamma = (drop_prob * fm_area) / (drop_area * keep_area)
+
+        Args:
+            feat_size (tuple[int, int]): The height and width of feature map.
+
+        Returns:
+            float: The value of gamma.
+        """
+        gamma = (self.drop_prob * feat_size[0] * feat_size[1])
+        gamma /= ((feat_size[0] - self.block_size + 1) *
+                  (feat_size[1] - self.block_size + 1))
+        gamma /= (self.block_size**2)
+        factor = (1.0 if self.iter_cnt > self.warmup_iters else self.iter_cnt /
+                  self.warmup_iters)
+        return gamma * factor
+
+    def extra_repr(self):
+        return (f'drop_prob={self.drop_prob}, block_size={self.block_size}, '
+                f'warmup_iters={self.warmup_iters}')
diff --git a/mmdet/models/plugins/msdeformattn_pixel_decoder.py b/mmdet/models/plugins/msdeformattn_pixel_decoder.py
new file mode 100644
index 0000000..d553582
--- /dev/null
+++ b/mmdet/models/plugins/msdeformattn_pixel_decoder.py
@@ -0,0 +1,269 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import (PLUGIN_LAYERS, Conv2d, ConvModule, caffe2_xavier_init,
+                      normal_init, xavier_init)
+from mmcv.cnn.bricks.transformer import (build_positional_encoding,
+                                         build_transformer_layer_sequence)
+from mmcv.runner import BaseModule, ModuleList
+
+from mmdet.core.anchor import MlvlPointGenerator
+from mmdet.models.utils.transformer import MultiScaleDeformableAttention
+
+
+@PLUGIN_LAYERS.register_module()
+class MSDeformAttnPixelDecoder(BaseModule):
+    """Pixel decoder with multi-scale deformable attention.
+
+    Args:
+        in_channels (list[int] | tuple[int]): Number of channels in the
+            input feature maps.
+        strides (list[int] | tuple[int]): Output strides of feature from
+            backbone.
+        feat_channels (int): Number of channels for feature.
+        out_channels (int): Number of channels for output.
+        num_outs (int): Number of output scales.
+        norm_cfg (:obj:`mmcv.ConfigDict` | dict): Config for normalization.
+            Defaults to dict(type='GN', num_groups=32).
+        act_cfg (:obj:`mmcv.ConfigDict` | dict): Config for activation.
+            Defaults to dict(type='ReLU').
+        encoder (:obj:`mmcv.ConfigDict` | dict): Config for transformer
+            encoder. Defaults to `DetrTransformerEncoder`.
+        positional_encoding (:obj:`mmcv.ConfigDict` | dict): Config for
+            transformer encoder position encoding. Defaults to
+            dict(type='SinePositionalEncoding', num_feats=128,
+            normalize=True).
+        init_cfg (:obj:`mmcv.ConfigDict` | dict): Initialization config dict.
+    """
+
+    def __init__(self,
+                 in_channels=[256, 512, 1024, 2048],
+                 strides=[4, 8, 16, 32],
+                 feat_channels=256,
+                 out_channels=256,
+                 num_outs=3,
+                 norm_cfg=dict(type='GN', num_groups=32),
+                 act_cfg=dict(type='ReLU'),
+                 encoder=dict(
+                     type='DetrTransformerEncoder',
+                     num_layers=6,
+                     transformerlayers=dict(
+                         type='BaseTransformerLayer',
+                         attn_cfgs=dict(
+                             type='MultiScaleDeformableAttention',
+                             embed_dims=256,
+                             num_heads=8,
+                             num_levels=3,
+                             num_points=4,
+                             im2col_step=64,
+                             dropout=0.0,
+                             batch_first=False,
+                             norm_cfg=None,
+                             init_cfg=None),
+                         feedforward_channels=1024,
+                         ffn_dropout=0.0,
+                         operation_order=('self_attn', 'norm', 'ffn', 'norm')),
+                     init_cfg=None),
+                 positional_encoding=dict(
+                     type='SinePositionalEncoding',
+                     num_feats=128,
+                     normalize=True),
+                 init_cfg=None):
+        super().__init__(init_cfg=init_cfg)
+        self.strides = strides
+        self.num_input_levels = len(in_channels)
+        self.num_encoder_levels = \
+            encoder.transformerlayers.attn_cfgs.num_levels
+        assert self.num_encoder_levels >= 1, \
+            'num_levels in attn_cfgs must be at least one'
+        input_conv_list = []
+        # from top to down (low to high resolution)
+        for i in range(self.num_input_levels - 1,
+                       self.num_input_levels - self.num_encoder_levels - 1,
+                       -1):
+            input_conv = ConvModule(
+                in_channels[i],
+                feat_channels,
+                kernel_size=1,
+                norm_cfg=norm_cfg,
+                act_cfg=None,
+                bias=True)
+            input_conv_list.append(input_conv)
+        self.input_convs = ModuleList(input_conv_list)
+
+        self.encoder = build_transformer_layer_sequence(encoder)
+        self.postional_encoding = build_positional_encoding(
+            positional_encoding)
+        # high resolution to low resolution
+        self.level_encoding = nn.Embedding(self.num_encoder_levels,
+                                           feat_channels)
+
+        # fpn-like structure
+        self.lateral_convs = ModuleList()
+        self.output_convs = ModuleList()
+        self.use_bias = norm_cfg is None
+        # from top to down (low to high resolution)
+        # fpn for the rest features that didn't pass in encoder
+        for i in range(self.num_input_levels - self.num_encoder_levels - 1, -1,
+                       -1):
+            lateral_conv = ConvModule(
+                in_channels[i],
+                feat_channels,
+                kernel_size=1,
+                bias=self.use_bias,
+                norm_cfg=norm_cfg,
+                act_cfg=None)
+            output_conv = ConvModule(
+                feat_channels,
+                feat_channels,
+                kernel_size=3,
+                stride=1,
+                padding=1,
+                bias=self.use_bias,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg)
+            self.lateral_convs.append(lateral_conv)
+            self.output_convs.append(output_conv)
+
+        self.mask_feature = Conv2d(
+            feat_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+        self.num_outs = num_outs
+        self.point_generator = MlvlPointGenerator(strides)
+
+    def init_weights(self):
+        """Initialize weights."""
+        for i in range(0, self.num_encoder_levels):
+            xavier_init(
+                self.input_convs[i].conv,
+                gain=1,
+                bias=0,
+                distribution='uniform')
+
+        for i in range(0, self.num_input_levels - self.num_encoder_levels):
+            caffe2_xavier_init(self.lateral_convs[i].conv, bias=0)
+            caffe2_xavier_init(self.output_convs[i].conv, bias=0)
+
+        caffe2_xavier_init(self.mask_feature, bias=0)
+
+        normal_init(self.level_encoding, mean=0, std=1)
+        for p in self.encoder.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_normal_(p)
+
+        # init_weights defined in MultiScaleDeformableAttention
+        for layer in self.encoder.layers:
+            for attn in layer.attentions:
+                if isinstance(attn, MultiScaleDeformableAttention):
+                    attn.init_weights()
+
+    def forward(self, feats):
+        """
+        Args:
+            feats (list[Tensor]): Feature maps of each level. Each has
+                shape of (batch_size, c, h, w).
+
+        Returns:
+            tuple: A tuple containing the following:
+
+            - mask_feature (Tensor): shape (batch_size, c, h, w).
+            - multi_scale_features (list[Tensor]): Multi scale \
+                    features, each in shape (batch_size, c, h, w).
+        """
+        # generate padding mask for each level, for each image
+        batch_size = feats[0].shape[0]
+        encoder_input_list = []
+        padding_mask_list = []
+        level_positional_encoding_list = []
+        spatial_shapes = []
+        reference_points_list = []
+        for i in range(self.num_encoder_levels):
+            level_idx = self.num_input_levels - i - 1
+            feat = feats[level_idx]
+            feat_projected = self.input_convs[i](feat)
+            h, w = feat.shape[-2:]
+
+            # no padding
+            padding_mask_resized = feat.new_zeros(
+                (batch_size, ) + feat.shape[-2:], dtype=torch.bool)
+            pos_embed = self.postional_encoding(padding_mask_resized)
+            level_embed = self.level_encoding.weight[i]
+            level_pos_embed = level_embed.view(1, -1, 1, 1) + pos_embed
+            # (h_i * w_i, 2)
+            reference_points = self.point_generator.single_level_grid_priors(
+                feat.shape[-2:], level_idx, device=feat.device)
+            # normalize
+            factor = feat.new_tensor([[w, h]]) * self.strides[level_idx]
+            reference_points = reference_points / factor
+
+            # shape (batch_size, c, h_i, w_i) -> (h_i * w_i, batch_size, c)
+            feat_projected = feat_projected.flatten(2).permute(2, 0, 1)
+            level_pos_embed = level_pos_embed.flatten(2).permute(2, 0, 1)
+            padding_mask_resized = padding_mask_resized.flatten(1)
+
+            encoder_input_list.append(feat_projected)
+            padding_mask_list.append(padding_mask_resized)
+            level_positional_encoding_list.append(level_pos_embed)
+            spatial_shapes.append(feat.shape[-2:])
+            reference_points_list.append(reference_points)
+        # shape (batch_size, total_num_query),
+        # total_num_query=sum([., h_i * w_i,.])
+        padding_masks = torch.cat(padding_mask_list, dim=1)
+        # shape (total_num_query, batch_size, c)
+        encoder_inputs = torch.cat(encoder_input_list, dim=0)
+        level_positional_encodings = torch.cat(
+            level_positional_encoding_list, dim=0)
+        device = encoder_inputs.device
+        # shape (num_encoder_levels, 2), from low
+        # resolution to high resolution
+        spatial_shapes = torch.as_tensor(
+            spatial_shapes, dtype=torch.long, device=device)
+        # shape (0, h_0*w_0, h_0*w_0+h_1*w_1, ...)
+        level_start_index = torch.cat((spatial_shapes.new_zeros(
+            (1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))
+        reference_points = torch.cat(reference_points_list, dim=0)
+        reference_points = reference_points[None, :, None].repeat(
+            batch_size, 1, self.num_encoder_levels, 1)
+        valid_radios = reference_points.new_ones(
+            (batch_size, self.num_encoder_levels, 2))
+        # shape (num_total_query, batch_size, c)
+        memory = self.encoder(
+            query=encoder_inputs,
+            key=None,
+            value=None,
+            query_pos=level_positional_encodings,
+            key_pos=None,
+            attn_masks=None,
+            key_padding_mask=None,
+            query_key_padding_mask=padding_masks,
+            spatial_shapes=spatial_shapes,
+            reference_points=reference_points,
+            level_start_index=level_start_index,
+            valid_radios=valid_radios)
+        # (num_total_query, batch_size, c) -> (batch_size, c, num_total_query)
+        memory = memory.permute(1, 2, 0)
+
+        # from low resolution to high resolution
+        num_query_per_level = [e[0] * e[1] for e in spatial_shapes]
+        outs = torch.split(memory, num_query_per_level, dim=-1)
+        outs = [
+            x.reshape(batch_size, -1, spatial_shapes[i][0],
+                      spatial_shapes[i][1]) for i, x in enumerate(outs)
+        ]
+
+        for i in range(self.num_input_levels - self.num_encoder_levels - 1, -1,
+                       -1):
+            x = feats[i]
+            cur_feat = self.lateral_convs[i](x)
+            y = cur_feat + F.interpolate(
+                outs[-1],
+                size=cur_feat.shape[-2:],
+                mode='bilinear',
+                align_corners=False)
+            y = self.output_convs[i](y)
+            outs.append(y)
+        multi_scale_features = outs[:self.num_outs]
+
+        mask_feature = self.mask_feature(outs[-1])
+        return mask_feature, multi_scale_features
diff --git a/mmdet/models/plugins/pixel_decoder.py b/mmdet/models/plugins/pixel_decoder.py
new file mode 100644
index 0000000..537a187
--- /dev/null
+++ b/mmdet/models/plugins/pixel_decoder.py
@@ -0,0 +1,243 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import PLUGIN_LAYERS, Conv2d, ConvModule, caffe2_xavier_init
+from mmcv.cnn.bricks.transformer import (build_positional_encoding,
+                                         build_transformer_layer_sequence)
+from mmcv.runner import BaseModule, ModuleList
+
+
+@PLUGIN_LAYERS.register_module()
+class PixelDecoder(BaseModule):
+    """Pixel decoder with a structure like fpn.
+
+    Args:
+        in_channels (list[int] | tuple[int]): Number of channels in the
+            input feature maps.
+        feat_channels (int): Number channels for feature.
+        out_channels (int): Number channels for output.
+        norm_cfg (:obj:`mmcv.ConfigDict` | dict): Config for normalization.
+            Defaults to dict(type='GN', num_groups=32).
+        act_cfg (:obj:`mmcv.ConfigDict` | dict): Config for activation.
+            Defaults to dict(type='ReLU').
+        encoder (:obj:`mmcv.ConfigDict` | dict): Config for transorformer
+            encoder.Defaults to None.
+        positional_encoding (:obj:`mmcv.ConfigDict` | dict): Config for
+            transformer encoder position encoding. Defaults to
+            dict(type='SinePositionalEncoding', num_feats=128,
+            normalize=True).
+        init_cfg (:obj:`mmcv.ConfigDict` | dict):  Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 in_channels,
+                 feat_channels,
+                 out_channels,
+                 norm_cfg=dict(type='GN', num_groups=32),
+                 act_cfg=dict(type='ReLU'),
+                 init_cfg=None):
+        super().__init__(init_cfg=init_cfg)
+        self.in_channels = in_channels
+        self.num_inputs = len(in_channels)
+        self.lateral_convs = ModuleList()
+        self.output_convs = ModuleList()
+        self.use_bias = norm_cfg is None
+        for i in range(0, self.num_inputs - 1):
+            lateral_conv = ConvModule(
+                in_channels[i],
+                feat_channels,
+                kernel_size=1,
+                bias=self.use_bias,
+                norm_cfg=norm_cfg,
+                act_cfg=None)
+            output_conv = ConvModule(
+                feat_channels,
+                feat_channels,
+                kernel_size=3,
+                stride=1,
+                padding=1,
+                bias=self.use_bias,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg)
+            self.lateral_convs.append(lateral_conv)
+            self.output_convs.append(output_conv)
+
+        self.last_feat_conv = ConvModule(
+            in_channels[-1],
+            feat_channels,
+            kernel_size=3,
+            padding=1,
+            stride=1,
+            bias=self.use_bias,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        self.mask_feature = Conv2d(
+            feat_channels, out_channels, kernel_size=3, stride=1, padding=1)
+
+    def init_weights(self):
+        """Initialize weights."""
+        for i in range(0, self.num_inputs - 2):
+            caffe2_xavier_init(self.lateral_convs[i].conv, bias=0)
+            caffe2_xavier_init(self.output_convs[i].conv, bias=0)
+
+        caffe2_xavier_init(self.mask_feature, bias=0)
+        caffe2_xavier_init(self.last_feat_conv, bias=0)
+
+    def forward(self, feats, img_metas):
+        """
+        Args:
+            feats (list[Tensor]): Feature maps of each level. Each has
+                shape of (batch_size, c, h, w).
+            img_metas (list[dict]): List of image information. Pass in
+                for creating more accurate padding mask. Not used here.
+
+        Returns:
+            tuple: a tuple containing the following:
+                - mask_feature (Tensor): Shape (batch_size, c, h, w).
+                - memory (Tensor): Output of last stage of backbone.\
+                        Shape (batch_size, c, h, w).
+        """
+        y = self.last_feat_conv(feats[-1])
+        for i in range(self.num_inputs - 2, -1, -1):
+            x = feats[i]
+            cur_feat = self.lateral_convs[i](x)
+            y = cur_feat + \
+                F.interpolate(y, size=cur_feat.shape[-2:], mode='nearest')
+            y = self.output_convs[i](y)
+
+        mask_feature = self.mask_feature(y)
+        memory = feats[-1]
+        return mask_feature, memory
+
+
+@PLUGIN_LAYERS.register_module()
+class TransformerEncoderPixelDecoder(PixelDecoder):
+    """Pixel decoder with transormer encoder inside.
+
+    Args:
+        in_channels (list[int] | tuple[int]): Number of channels in the
+            input feature maps.
+        feat_channels (int): Number channels for feature.
+        out_channels (int): Number channels for output.
+        norm_cfg (:obj:`mmcv.ConfigDict` | dict): Config for normalization.
+            Defaults to dict(type='GN', num_groups=32).
+        act_cfg (:obj:`mmcv.ConfigDict` | dict): Config for activation.
+            Defaults to dict(type='ReLU').
+        encoder (:obj:`mmcv.ConfigDict` | dict): Config for transorformer
+            encoder.Defaults to None.
+        positional_encoding (:obj:`mmcv.ConfigDict` | dict): Config for
+            transformer encoder position encoding. Defaults to
+            dict(type='SinePositionalEncoding', num_feats=128,
+            normalize=True).
+        init_cfg (:obj:`mmcv.ConfigDict` | dict):  Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 in_channels,
+                 feat_channels,
+                 out_channels,
+                 norm_cfg=dict(type='GN', num_groups=32),
+                 act_cfg=dict(type='ReLU'),
+                 encoder=None,
+                 positional_encoding=dict(
+                     type='SinePositionalEncoding',
+                     num_feats=128,
+                     normalize=True),
+                 init_cfg=None):
+        super(TransformerEncoderPixelDecoder, self).__init__(
+            in_channels,
+            feat_channels,
+            out_channels,
+            norm_cfg,
+            act_cfg,
+            init_cfg=init_cfg)
+        self.last_feat_conv = None
+
+        self.encoder = build_transformer_layer_sequence(encoder)
+        self.encoder_embed_dims = self.encoder.embed_dims
+        assert self.encoder_embed_dims == feat_channels, 'embed_dims({}) of ' \
+            'tranformer encoder must equal to feat_channels({})'.format(
+                feat_channels, self.encoder_embed_dims)
+        self.positional_encoding = build_positional_encoding(
+            positional_encoding)
+        self.encoder_in_proj = Conv2d(
+            in_channels[-1], feat_channels, kernel_size=1)
+        self.encoder_out_proj = ConvModule(
+            feat_channels,
+            feat_channels,
+            kernel_size=3,
+            stride=1,
+            padding=1,
+            bias=self.use_bias,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+
+    def init_weights(self):
+        """Initialize weights."""
+        for i in range(0, self.num_inputs - 2):
+            caffe2_xavier_init(self.lateral_convs[i].conv, bias=0)
+            caffe2_xavier_init(self.output_convs[i].conv, bias=0)
+
+        caffe2_xavier_init(self.mask_feature, bias=0)
+        caffe2_xavier_init(self.encoder_in_proj, bias=0)
+        caffe2_xavier_init(self.encoder_out_proj.conv, bias=0)
+
+        for p in self.encoder.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+
+    def forward(self, feats, img_metas):
+        """
+        Args:
+            feats (list[Tensor]): Feature maps of each level. Each has
+                shape of (batch_size, c, h, w).
+            img_metas (list[dict]): List of image information. Pass in
+                for creating more accurate padding mask.
+
+        Returns:
+            tuple: a tuple containing the following:
+                - mask_feature (Tensor): shape (batch_size, c, h, w).
+                - memory (Tensor): shape (batch_size, c, h, w).
+        """
+        feat_last = feats[-1]
+        bs, c, h, w = feat_last.shape
+        input_img_h, input_img_w = img_metas[0]['batch_input_shape']
+        padding_mask = feat_last.new_ones((bs, input_img_h, input_img_w),
+                                          dtype=torch.float32)
+        for i in range(bs):
+            img_h, img_w, _ = img_metas[i]['img_shape']
+            padding_mask[i, :img_h, :img_w] = 0
+        padding_mask = F.interpolate(
+            padding_mask.unsqueeze(1),
+            size=feat_last.shape[-2:],
+            mode='nearest').to(torch.bool).squeeze(1)
+
+        pos_embed = self.positional_encoding(padding_mask)
+        feat_last = self.encoder_in_proj(feat_last)
+        # (batch_size, c, h, w) -> (num_queries, batch_size, c)
+        feat_last = feat_last.flatten(2).permute(2, 0, 1)
+        pos_embed = pos_embed.flatten(2).permute(2, 0, 1)
+        # (batch_size, h, w) -> (batch_size, h*w)
+        padding_mask = padding_mask.flatten(1)
+        memory = self.encoder(
+            query=feat_last,
+            key=None,
+            value=None,
+            query_pos=pos_embed,
+            query_key_padding_mask=padding_mask)
+        # (num_queries, batch_size, c) -> (batch_size, c, h, w)
+        memory = memory.permute(1, 2, 0).view(bs, self.encoder_embed_dims, h,
+                                              w)
+        y = self.encoder_out_proj(memory)
+        for i in range(self.num_inputs - 2, -1, -1):
+            x = feats[i]
+            cur_feat = self.lateral_convs[i](x)
+            y = cur_feat + \
+                F.interpolate(y, size=cur_feat.shape[-2:], mode='nearest')
+            y = self.output_convs[i](y)
+
+        mask_feature = self.mask_feature(y)
+        return mask_feature, memory
diff --git a/mmdet/models/roi_heads/__init__.py b/mmdet/models/roi_heads/__init__.py
new file mode 100644
index 0000000..baae2a0
--- /dev/null
+++ b/mmdet/models/roi_heads/__init__.py
@@ -0,0 +1,37 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .base_roi_head import BaseRoIHead
+from .bbox_heads import (BBoxHead, ConvFCBBoxHead, DIIHead,
+                         DoubleConvFCBBoxHead, SABLHead, SCNetBBoxHead,
+                         Shared2FCBBoxHead, Shared4Conv1FCBBoxHead)
+from .cascade_roi_head import CascadeRoIHead
+from .double_roi_head import DoubleHeadRoIHead
+from .dynamic_roi_head import DynamicRoIHead
+from .grid_roi_head import GridRoIHead
+from .htc_roi_head import HybridTaskCascadeRoIHead
+from .mask_heads import (CoarseMaskHead, FCNMaskHead, FeatureRelayHead,
+                         FusedSemanticHead, GlobalContextHead, GridHead,
+                         HTCMaskHead, MaskIoUHead, MaskPointHead,
+                         SCNetMaskHead, SCNetSemanticHead)
+from .mask_scoring_roi_head import MaskScoringRoIHead
+from .pisa_roi_head import PISARoIHead
+from .point_rend_roi_head import PointRendRoIHead
+from .roi_extractors import (BaseRoIExtractor, GenericRoIExtractor,
+                             SingleRoIExtractor)
+from .scnet_roi_head import SCNetRoIHead
+from .shared_heads import ResLayer
+from .sparse_roi_head import SparseRoIHead
+from .standard_roi_head import StandardRoIHead
+from .trident_roi_head import TridentRoIHead
+
+__all__ = [
+    'BaseRoIHead', 'CascadeRoIHead', 'DoubleHeadRoIHead', 'MaskScoringRoIHead',
+    'HybridTaskCascadeRoIHead', 'GridRoIHead', 'ResLayer', 'BBoxHead',
+    'ConvFCBBoxHead', 'DIIHead', 'SABLHead', 'Shared2FCBBoxHead',
+    'StandardRoIHead', 'Shared4Conv1FCBBoxHead', 'DoubleConvFCBBoxHead',
+    'FCNMaskHead', 'HTCMaskHead', 'FusedSemanticHead', 'GridHead',
+    'MaskIoUHead', 'BaseRoIExtractor', 'GenericRoIExtractor',
+    'SingleRoIExtractor', 'PISARoIHead', 'PointRendRoIHead', 'MaskPointHead',
+    'CoarseMaskHead', 'DynamicRoIHead', 'SparseRoIHead', 'TridentRoIHead',
+    'SCNetRoIHead', 'SCNetMaskHead', 'SCNetSemanticHead', 'SCNetBBoxHead',
+    'FeatureRelayHead', 'GlobalContextHead'
+]
diff --git a/mmdet/models/roi_heads/base_roi_head.py b/mmdet/models/roi_heads/base_roi_head.py
new file mode 100644
index 0000000..4adbdef
--- /dev/null
+++ b/mmdet/models/roi_heads/base_roi_head.py
@@ -0,0 +1,103 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from abc import ABCMeta, abstractmethod
+
+from mmcv.runner import BaseModule
+
+from ..builder import build_shared_head
+
+
+class BaseRoIHead(BaseModule, metaclass=ABCMeta):
+    """Base class for RoIHeads."""
+
+    def __init__(self,
+                 bbox_roi_extractor=None,
+                 bbox_head=None,
+                 mask_roi_extractor=None,
+                 mask_head=None,
+                 shared_head=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(BaseRoIHead, self).__init__(init_cfg)
+        self.train_cfg = train_cfg
+        self.test_cfg = test_cfg
+        if shared_head is not None:
+            shared_head.pretrained = pretrained
+            self.shared_head = build_shared_head(shared_head)
+
+        if bbox_head is not None:
+            self.init_bbox_head(bbox_roi_extractor, bbox_head)
+
+        if mask_head is not None:
+            self.init_mask_head(mask_roi_extractor, mask_head)
+
+        self.init_assigner_sampler()
+
+    @property
+    def with_bbox(self):
+        """bool: whether the RoI head contains a `bbox_head`"""
+        return hasattr(self, 'bbox_head') and self.bbox_head is not None
+
+    @property
+    def with_mask(self):
+        """bool: whether the RoI head contains a `mask_head`"""
+        return hasattr(self, 'mask_head') and self.mask_head is not None
+
+    @property
+    def with_shared_head(self):
+        """bool: whether the RoI head contains a `shared_head`"""
+        return hasattr(self, 'shared_head') and self.shared_head is not None
+
+    @abstractmethod
+    def init_bbox_head(self):
+        """Initialize ``bbox_head``"""
+        pass
+
+    @abstractmethod
+    def init_mask_head(self):
+        """Initialize ``mask_head``"""
+        pass
+
+    @abstractmethod
+    def init_assigner_sampler(self):
+        """Initialize assigner and sampler."""
+        pass
+
+    @abstractmethod
+    def forward_train(self,
+                      x,
+                      img_meta,
+                      proposal_list,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None,
+                      gt_masks=None,
+                      **kwargs):
+        """Forward function during training."""
+
+    async def async_simple_test(self,
+                                x,
+                                proposal_list,
+                                img_metas,
+                                proposals=None,
+                                rescale=False,
+                                **kwargs):
+        """Asynchronized test function."""
+        raise NotImplementedError
+
+    def simple_test(self,
+                    x,
+                    proposal_list,
+                    img_meta,
+                    proposals=None,
+                    rescale=False,
+                    **kwargs):
+        """Test without augmentation."""
+
+    def aug_test(self, x, proposal_list, img_metas, rescale=False, **kwargs):
+        """Test with augmentations.
+
+        If rescale is False, then returned bboxes and masks will fit the scale
+        of imgs[0].
+        """
diff --git a/mmdet/models/roi_heads/bbox_heads/__init__.py b/mmdet/models/roi_heads/bbox_heads/__init__.py
new file mode 100644
index 0000000..d1207db
--- /dev/null
+++ b/mmdet/models/roi_heads/bbox_heads/__init__.py
@@ -0,0 +1,14 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .bbox_head import BBoxHead
+from .convfc_bbox_head import (ConvFCBBoxHead, Shared2FCBBoxHead,
+                               Shared4Conv1FCBBoxHead)
+from .dii_head import DIIHead
+from .double_bbox_head import DoubleConvFCBBoxHead
+from .sabl_head import SABLHead
+from .scnet_bbox_head import SCNetBBoxHead
+
+__all__ = [
+    'BBoxHead', 'ConvFCBBoxHead', 'Shared2FCBBoxHead',
+    'Shared4Conv1FCBBoxHead', 'DoubleConvFCBBoxHead', 'SABLHead', 'DIIHead',
+    'SCNetBBoxHead'
+]
diff --git a/mmdet/models/roi_heads/bbox_heads/bbox_head.py b/mmdet/models/roi_heads/bbox_heads/bbox_head.py
new file mode 100644
index 0000000..c26c82d
--- /dev/null
+++ b/mmdet/models/roi_heads/bbox_heads/bbox_head.py
@@ -0,0 +1,715 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.runner import BaseModule, auto_fp16, force_fp32
+from torch.nn.modules.utils import _pair
+
+from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
+from mmdet.models.builder import HEADS, build_loss
+from mmdet.models.losses import accuracy
+from mmdet.models.utils import build_linear_layer
+from mmdet.core.bbox.iou_calculators import bbox_overlaps
+from my_equation import *
+import global_placeholder
+
+
+@HEADS.register_module()
+class BBoxHead(BaseModule):
+    """Simplest RoI head, with only two fc layers for classification and
+    regression respectively."""
+
+    def __init__(self,
+                 with_avg_pool=False,
+                 with_cls=True,
+                 with_reg=True,
+                 roi_feat_size=7,
+                 in_channels=256,
+                 num_classes=80,
+                 bbox_coder=dict(
+                     type='DeltaXYWHBBoxCoder',
+                     clip_border=True,
+                     target_means=[0., 0., 0., 0.],
+                     target_stds=[0.1, 0.1, 0.2, 0.2]),
+                 reg_class_agnostic=False,
+                 reg_decoded_bbox=False,
+                 reg_predictor_cfg=dict(type='Linear'),
+                 cls_predictor_cfg=dict(type='Linear'),
+                 loss_cls=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=False,
+                     loss_weight=1.0),
+                 loss_bbox=dict(
+                     type='SmoothL1Loss', beta=1.0, loss_weight=1.0),
+                 init_cfg=None):
+        super(BBoxHead, self).__init__(init_cfg)
+        assert with_cls or with_reg
+        self.with_avg_pool = with_avg_pool
+        self.with_cls = with_cls
+        self.with_reg = with_reg
+        self.roi_feat_size = _pair(roi_feat_size)
+        self.roi_feat_area = self.roi_feat_size[0] * self.roi_feat_size[1]
+        self.in_channels = in_channels
+        self.num_classes = num_classes
+        self.reg_class_agnostic = reg_class_agnostic
+        self.reg_decoded_bbox = reg_decoded_bbox
+        self.reg_predictor_cfg = reg_predictor_cfg
+        self.cls_predictor_cfg = cls_predictor_cfg
+        self.fp16_enabled = False
+
+        self.bbox_coder = build_bbox_coder(bbox_coder)
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_bbox = build_loss(loss_bbox)
+
+        in_channels = self.in_channels
+        if self.with_avg_pool:
+            self.avg_pool = nn.AvgPool2d(self.roi_feat_size)
+        else:
+            in_channels *= self.roi_feat_area
+        if self.with_cls:
+            # need to add background class
+            if self.custom_cls_channels:
+                cls_channels = self.loss_cls.get_cls_channels(self.num_classes)
+            else:
+                cls_channels = num_classes + 1
+            self.fc_cls = build_linear_layer(
+                self.cls_predictor_cfg,
+                in_features=in_channels,
+                out_features=cls_channels)
+        if self.with_reg:
+            out_dim_reg = 4 if reg_class_agnostic else 4 * num_classes
+            self.fc_reg = build_linear_layer(
+                self.reg_predictor_cfg,
+                in_features=in_channels,
+                out_features=out_dim_reg)
+        self.debug_imgs = None
+        if init_cfg is None:
+            self.init_cfg = []
+            if self.with_cls:
+                self.init_cfg += [
+                    dict(
+                        type='Normal', std=0.01, override=dict(name='fc_cls'))
+                ]
+            if self.with_reg:
+                self.init_cfg += [
+                    dict(
+                        type='Normal', std=0.001, override=dict(name='fc_reg'))
+                ]
+
+    @property
+    def custom_cls_channels(self):
+        return getattr(self.loss_cls, 'custom_cls_channels', False)
+
+    @property
+    def custom_activation(self):
+        return getattr(self.loss_cls, 'custom_activation', False)
+
+    @property
+    def custom_accuracy(self):
+        return getattr(self.loss_cls, 'custom_accuracy', False)
+
+    @auto_fp16()
+    def forward(self, x):
+        if self.with_avg_pool:
+            if x.numel() > 0:
+                x = self.avg_pool(x)
+                x = x.view(x.size(0), -1)
+            else:
+                # avg_pool does not support empty tensor,
+                # so use torch.mean instead it
+                x = torch.mean(x, dim=(-1, -2))
+        cls_score = self.fc_cls(x) if self.with_cls else None
+        bbox_pred = self.fc_reg(x) if self.with_reg else None
+        return cls_score, bbox_pred
+
+    def _get_target_single(self, pos_bboxes, neg_bboxes, pos_gt_bboxes,
+                           pos_gt_labels, cfg):
+        """Calculate the ground truth for proposals in the single image
+        according to the sampling results.
+
+        Args:
+            pos_bboxes (Tensor): Contains all the positive boxes,
+                has shape (num_pos, 4), the last dimension 4
+                represents [tl_x, tl_y, br_x, br_y].
+            neg_bboxes (Tensor): Contains all the negative boxes,
+                has shape (num_neg, 4), the last dimension 4
+                represents [tl_x, tl_y, br_x, br_y].
+            pos_gt_bboxes (Tensor): Contains gt_boxes for
+                all positive samples, has shape (num_pos, 4),
+                the last dimension 4
+                represents [tl_x, tl_y, br_x, br_y].
+            pos_gt_labels (Tensor): Contains gt_labels for
+                all positive samples, has shape (num_pos, ).
+            cfg (obj:`ConfigDict`): `train_cfg` of R-CNN.
+
+        Returns:
+            Tuple[Tensor]: Ground truth for proposals
+            in a single image. Containing the following Tensors:
+
+                - labels(Tensor): Gt_labels for all proposals, has
+                  shape (num_proposals,).
+                - label_weights(Tensor): Labels_weights for all
+                  proposals, has shape (num_proposals,).
+                - bbox_targets(Tensor):Regression target for all
+                  proposals, has shape (num_proposals, 4), the
+                  last dimension 4 represents [tl_x, tl_y, br_x, br_y].
+                - bbox_weights(Tensor):Regression weights for all
+                  proposals, has shape (num_proposals, 4).
+        """
+        num_pos = pos_bboxes.size(0)
+        num_neg = neg_bboxes.size(0)
+        num_samples = num_pos + num_neg
+
+        # original implementation uses new_zeros since BG are set to be 0
+        # now use empty & fill because BG cat_id = num_classes,
+        # FG cat_id = [0, num_classes-1]
+        labels = pos_bboxes.new_full((num_samples, ),
+                                     self.num_classes,
+                                     dtype=torch.long)
+        label_weights = pos_bboxes.new_zeros(num_samples)
+        bbox_targets = pos_bboxes.new_zeros(num_samples, 4)
+        bbox_weights = pos_bboxes.new_zeros(num_samples, 4)
+        if num_pos > 0:
+            labels[:num_pos] = pos_gt_labels
+            pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight
+            label_weights[:num_pos] = pos_weight
+            if not self.reg_decoded_bbox:
+                pos_bbox_targets = self.bbox_coder.encode(
+                    pos_bboxes, pos_gt_bboxes)
+            else:
+                # When the regression loss (e.g. `IouLoss`, `GIouLoss`)
+                # is applied directly on the decoded bounding boxes, both
+                # the predicted boxes and regression targets should be with
+                # absolute coordinate format.
+                pos_bbox_targets = pos_gt_bboxes
+            bbox_targets[:num_pos, :] = pos_bbox_targets
+            bbox_weights[:num_pos, :] = 1
+        if num_neg > 0:
+            label_weights[-num_neg:] = 1.0
+
+        return labels, label_weights, bbox_targets, bbox_weights
+
+    def get_targets(self,
+                    sampling_results,
+                    gt_bboxes,
+                    gt_labels,
+                    rcnn_train_cfg,
+                    concat=True):
+        """Calculate the ground truth for all samples in a batch according to
+        the sampling_results.
+
+        Almost the same as the implementation in bbox_head, we passed
+        additional parameters pos_inds_list and neg_inds_list to
+        `_get_target_single` function.
+
+        Args:
+            sampling_results (List[obj:SamplingResults]): Assign results of
+                all images in a batch after sampling.
+            gt_bboxes (list[Tensor]): Gt_bboxes of all images in a batch,
+                each tensor has shape (num_gt, 4),  the last dimension 4
+                represents [tl_x, tl_y, br_x, br_y].
+            gt_labels (list[Tensor]): Gt_labels of all images in a batch,
+                each tensor has shape (num_gt,).
+            rcnn_train_cfg (obj:ConfigDict): `train_cfg` of RCNN.
+            concat (bool): Whether to concatenate the results of all
+                the images in a single batch.
+
+        Returns:
+            Tuple[Tensor]: Ground truth for proposals in a single image.
+            Containing the following list of Tensors:
+
+                - labels (list[Tensor],Tensor): Gt_labels for all
+                  proposals in a batch, each tensor in list has
+                  shape (num_proposals,) when `concat=False`, otherwise
+                  just a single tensor has shape (num_all_proposals,).
+                - label_weights (list[Tensor]): Labels_weights for
+                  all proposals in a batch, each tensor in list has
+                  shape (num_proposals,) when `concat=False`, otherwise
+                  just a single tensor has shape (num_all_proposals,).
+                - bbox_targets (list[Tensor],Tensor): Regression target
+                  for all proposals in a batch, each tensor in list
+                  has shape (num_proposals, 4) when `concat=False`,
+                  otherwise just a single tensor has shape
+                  (num_all_proposals, 4), the last dimension 4 represents
+                  [tl_x, tl_y, br_x, br_y].
+                - bbox_weights (list[tensor],Tensor): Regression weights for
+                  all proposals in a batch, each tensor in list has shape
+                  (num_proposals, 4) when `concat=False`, otherwise just a
+                  single tensor has shape (num_all_proposals, 4).
+        """
+        pos_bboxes_list = [res.pos_bboxes for res in sampling_results]
+        neg_bboxes_list = [res.neg_bboxes for res in sampling_results]
+        pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results]
+        pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results]
+        labels, label_weights, bbox_targets, bbox_weights = multi_apply(
+            self._get_target_single,
+            pos_bboxes_list,
+            neg_bboxes_list,
+            pos_gt_bboxes_list,
+            pos_gt_labels_list,
+            cfg=rcnn_train_cfg)
+
+        if concat:
+            labels = torch.cat(labels, 0)
+            label_weights = torch.cat(label_weights, 0)
+            bbox_targets = torch.cat(bbox_targets, 0)
+            bbox_weights = torch.cat(bbox_weights, 0)
+        return labels, label_weights, bbox_targets, bbox_weights
+
+    @force_fp32(apply_to=('cls_score', 'bbox_pred'))
+    def loss(self,
+             cls_score,
+             bbox_pred,
+             rois,
+             labels,
+             label_weights,
+             bbox_targets,
+             bbox_weights,
+             reduction_override=None):
+        losses = dict()  # TODO 这里得好整
+        if cls_score is not None:
+            avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.)
+            if cls_score.numel() > 0:
+                loss_cls_ = self.loss_cls(
+                    cls_score,
+                    labels,
+                    label_weights,
+                    # avg_factor=avg_factor,
+                    reduction_override='none')
+                # loss_cls_ = loss_cls_.sum(dim=1)
+                
+                if isinstance(loss_cls_, dict):
+                    losses.update(loss_cls_)
+                else:
+                    losses['loss_cls'] = loss_cls_
+                if self.custom_activation:
+                    acc_ = self.loss_cls.get_accuracy(cls_score, labels)
+                    losses.update(acc_)
+                else:
+                    losses['acc'] = accuracy(cls_score, labels)
+        if bbox_pred is not None:
+            bg_class_ind = self.num_classes
+            # 0~self.num_classes-1 are FG, self.num_classes is BG
+            pos_inds = (labels >= 0) & (labels < bg_class_ind)
+            # do not perform bounding box regression for BG anymore.
+            if pos_inds.any():
+                if self.reg_decoded_bbox:
+                    # When the regression loss (e.g. `IouLoss`,
+                    # `GIouLoss`, `DIouLoss`) is applied directly on
+                    # the decoded bounding boxes, it decodes the
+                    # already encoded coordinates to absolute format.
+                    raise NotImplementedError
+                    bbox_pred = self.bbox_coder.decode(rois[:, 1:], bbox_pred)
+                if self.reg_class_agnostic:
+                    pos_bbox_pred = bbox_pred.view(
+                        bbox_pred.size(0), 4)[pos_inds.type(torch.bool)]
+                else:
+                    pos_bbox_pred = bbox_pred.view(
+                        bbox_pred.size(0), -1,
+                        4)[pos_inds.type(torch.bool),
+                           labels[pos_inds.type(torch.bool)]]
+                losses['loss_bbox'] = self.loss_bbox(
+                    pos_bbox_pred,
+                    bbox_targets[pos_inds.type(torch.bool)],
+                    bbox_weights[pos_inds.type(torch.bool)],
+                    # avg_factor=bbox_targets.size(0),
+                    reduction_override='none')
+                
+                losses['loss_bbox'] = losses['loss_bbox'].sum(dim=1)
+                
+            else:
+                losses['loss_bbox'] = bbox_pred[pos_inds].sum()
+        
+        
+        
+        
+        from mmdet.models.dense_heads import RetinaHead, RPNHead
+        if global_placeholder.mybuff_flag and not isinstance(self, RPNHead) and pos_inds.any():
+            
+            if isinstance(self, RetinaHead):
+                conf_values = torch.sigmoid(cls_score)  # TODO 看一下FRCNN的cls是啥？
+            else:
+                conf_values = F.softmax(cls_score, dim=1)
+            num_classes = conf_values.shape[-1]
+            if num_classes == 81 or num_classes == 21:
+                # 说明默认backbgound为81类 21类
+                pos_gtconf_idx = F.one_hot(labels, num_classes=num_classes)
+            else:
+                # 说明没有显式给出类
+                
+                num_classes = num_classes + 1
+                pos_gtconf_idx = F.one_hot(labels, num_classes=num_classes)
+                pos_gtconf_idx = pos_gtconf_idx[:, :-1]# 剔除最后一个背景类
+                
+            pos_gtconf_values, _ = (conf_values * pos_gtconf_idx).max(dim=1)
+            pos_gtconf_values = pos_gtconf_values[pos_inds]
+            
+            pos_conf_values, pos_conf_values_idx = conf_values.max(dim=1)
+            pos_conf_values = pos_conf_values[pos_inds]
+            
+            anchors = rois[:, 1:][pos_inds]
+            abs_bbox_pred = self.bbox_coder.decode(anchors, pos_bbox_pred) # 直接解读
+            abs_bbox_targets = self.bbox_coder.decode(anchors, bbox_targets[pos_inds]) # 直接解读
+            pos_ious = bbox_overlaps(abs_bbox_pred, abs_bbox_targets, is_aligned=True)  # 这玩意得是ltrb坐标，好像就已经是了？？？
+            if global_placeholder.mybuff_flag == 1:
+                losses['loss_bbox'], losses['loss_cls'][pos_inds] = HQOD_loss(losses['loss_bbox'], losses['loss_cls'][pos_inds], conf_values[pos_inds], pos_gtconf_values, pos_conf_values, pos_ious, torch.tensor(1.))
+            elif global_placeholder.mybuff_flag == 2:
+                # HarDet
+                losses['loss_bbox'], losses['loss_cls'][pos_inds] = HarDet_loss(losses['loss_bbox'], losses['loss_cls'][pos_inds], conf_values[pos_inds], pos_gtconf_values, pos_conf_values, pos_ious, torch.tensor(1.))
+            else:
+                raise NotImplementedError
+            
+        
+        
+        
+        
+        loss_cls = losses['loss_cls'].sum() / avg_factor
+        loss_bbox = losses['loss_bbox'].sum() / bbox_targets.size(0)
+        
+        
+        return losses
+
+
+    # @force_fp32(apply_to=('cls_score', 'bbox_pred'))
+    # def loss(self,
+    #          cls_score,
+    #          bbox_pred,
+    #          rois,
+    #          labels,
+    #          label_weights,
+    #          bbox_targets,
+    #          bbox_weights,
+    #          reduction_override=None):
+    #     losses = dict()  # TODO 这里得好整
+    #     if cls_score is not None:
+    #         avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.)
+    #         if cls_score.numel() > 0:
+    #             loss_cls_ = self.loss_cls(
+    #                 cls_score,
+    #                 labels,
+    #                 label_weights,
+    #                 avg_factor=avg_factor,
+    #                 reduction_override=reduction_override)
+    #             if isinstance(loss_cls_, dict):
+    #                 losses.update(loss_cls_)
+    #             else:
+    #                 losses['loss_cls'] = loss_cls_
+    #             if self.custom_activation:
+    #                 acc_ = self.loss_cls.get_accuracy(cls_score, labels)
+    #                 losses.update(acc_)
+    #             else:
+    #                 losses['acc'] = accuracy(cls_score, labels)
+    #     if bbox_pred is not None:
+    #         bg_class_ind = self.num_classes
+    #         # 0~self.num_classes-1 are FG, self.num_classes is BG
+    #         pos_inds = (labels >= 0) & (labels < bg_class_ind)
+    #         # do not perform bounding box regression for BG anymore.
+    #         if pos_inds.any():
+    #             if self.reg_decoded_bbox:
+    #                 # When the regression loss (e.g. `IouLoss`,
+    #                 # `GIouLoss`, `DIouLoss`) is applied directly on
+    #                 # the decoded bounding boxes, it decodes the
+    #                 # already encoded coordinates to absolute format.
+    #                 bbox_pred = self.bbox_coder.decode(rois[:, 1:], bbox_pred)
+    #             if self.reg_class_agnostic:
+    #                 pos_bbox_pred = bbox_pred.view(
+    #                     bbox_pred.size(0), 4)[pos_inds.type(torch.bool)]
+    #             else:
+    #                 pos_bbox_pred = bbox_pred.view(
+    #                     bbox_pred.size(0), -1,
+    #                     4)[pos_inds.type(torch.bool),
+    #                        labels[pos_inds.type(torch.bool)]]
+    #             losses['loss_bbox'] = self.loss_bbox(
+    #                 pos_bbox_pred,
+    #                 bbox_targets[pos_inds.type(torch.bool)],
+    #                 bbox_weights[pos_inds.type(torch.bool)],
+    #                 avg_factor=bbox_targets.size(0),
+    #                 reduction_override=reduction_override)
+    #         else:
+    #             losses['loss_bbox'] = bbox_pred[pos_inds].sum()
+        
+        
+        
+        
+    #     return losses
+
+    @force_fp32(apply_to=('cls_score', 'bbox_pred'))
+    def get_bboxes(self,
+                   rois,
+                   cls_score,
+                   bbox_pred,
+                   img_shape,
+                   scale_factor,
+                   rescale=False,
+                   cfg=None):
+        """Transform network output for a batch into bbox predictions.
+
+        Args:
+            rois (Tensor): Boxes to be transformed. Has shape (num_boxes, 5).
+                last dimension 5 arrange as (batch_index, x1, y1, x2, y2).
+            cls_score (Tensor): Box scores, has shape
+                (num_boxes, num_classes + 1).
+            bbox_pred (Tensor, optional): Box energies / deltas.
+                has shape (num_boxes, num_classes * 4).
+            img_shape (Sequence[int], optional): Maximum bounds for boxes,
+                specifies (H, W, C) or (H, W).
+            scale_factor (ndarray): Scale factor of the
+               image arrange as (w_scale, h_scale, w_scale, h_scale).
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+            cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head. Default: None
+
+        Returns:
+            tuple[Tensor, Tensor]:
+                First tensor is `det_bboxes`, has the shape
+                (num_boxes, 5) and last
+                dimension 5 represent (tl_x, tl_y, br_x, br_y, score).
+                Second tensor is the labels with shape (num_boxes, ).
+        """
+
+        # some loss (Seesaw loss..) may have custom activation
+        if self.custom_cls_channels:
+            scores = self.loss_cls.get_activation(cls_score)
+        else:
+            scores = F.softmax(
+                cls_score, dim=-1) if cls_score is not None else None
+        # bbox_pred would be None in some detector when with_reg is False,
+        # e.g. Grid R-CNN.
+        if bbox_pred is not None:
+            bboxes = self.bbox_coder.decode(
+                rois[..., 1:], bbox_pred, max_shape=img_shape)
+        else:
+            bboxes = rois[:, 1:].clone()
+            if img_shape is not None:
+                bboxes[:, [0, 2]].clamp_(min=0, max=img_shape[1])
+                bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0])
+
+        if rescale and bboxes.size(0) > 0:
+            scale_factor = bboxes.new_tensor(scale_factor)
+            bboxes = (bboxes.view(bboxes.size(0), -1, 4) / scale_factor).view(
+                bboxes.size()[0], -1)
+
+        if cfg is None:
+            return bboxes, scores
+        else:
+            det_bboxes, det_labels = multiclass_nms(bboxes, scores,
+                                                    cfg.score_thr, cfg.nms,
+                                                    cfg.max_per_img)
+
+            return det_bboxes, det_labels
+
+    @force_fp32(apply_to=('bbox_preds', ))
+    def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas):
+        """Refine bboxes during training.
+
+        Args:
+            rois (Tensor): Shape (n*bs, 5), where n is image number per GPU,
+                and bs is the sampled RoIs per image. The first column is
+                the image id and the next 4 columns are x1, y1, x2, y2.
+            labels (Tensor): Shape (n*bs, ).
+            bbox_preds (Tensor): Shape (n*bs, 4) or (n*bs, 4*#class).
+            pos_is_gts (list[Tensor]): Flags indicating if each positive bbox
+                is a gt bbox.
+            img_metas (list[dict]): Meta info of each image.
+
+        Returns:
+            list[Tensor]: Refined bboxes of each image in a mini-batch.
+
+        Example:
+            >>> # xdoctest: +REQUIRES(module:kwarray)
+            >>> import kwarray
+            >>> import numpy as np
+            >>> from mmdet.core.bbox.demodata import random_boxes
+            >>> self = BBoxHead(reg_class_agnostic=True)
+            >>> n_roi = 2
+            >>> n_img = 4
+            >>> scale = 512
+            >>> rng = np.random.RandomState(0)
+            >>> img_metas = [{'img_shape': (scale, scale)}
+            ...              for _ in range(n_img)]
+            >>> # Create rois in the expected format
+            >>> roi_boxes = random_boxes(n_roi, scale=scale, rng=rng)
+            >>> img_ids = torch.randint(0, n_img, (n_roi,))
+            >>> img_ids = img_ids.float()
+            >>> rois = torch.cat([img_ids[:, None], roi_boxes], dim=1)
+            >>> # Create other args
+            >>> labels = torch.randint(0, 2, (n_roi,)).long()
+            >>> bbox_preds = random_boxes(n_roi, scale=scale, rng=rng)
+            >>> # For each image, pretend random positive boxes are gts
+            >>> is_label_pos = (labels.numpy() > 0).astype(np.int)
+            >>> lbl_per_img = kwarray.group_items(is_label_pos,
+            ...                                   img_ids.numpy())
+            >>> pos_per_img = [sum(lbl_per_img.get(gid, []))
+            ...                for gid in range(n_img)]
+            >>> pos_is_gts = [
+            >>>     torch.randint(0, 2, (npos,)).byte().sort(
+            >>>         descending=True)[0]
+            >>>     for npos in pos_per_img
+            >>> ]
+            >>> bboxes_list = self.refine_bboxes(rois, labels, bbox_preds,
+            >>>                    pos_is_gts, img_metas)
+            >>> print(bboxes_list)
+        """
+        img_ids = rois[:, 0].long().unique(sorted=True)
+        assert img_ids.numel() <= len(img_metas)
+
+        bboxes_list = []
+        for i in range(len(img_metas)):
+            inds = torch.nonzero(
+                rois[:, 0] == i, as_tuple=False).squeeze(dim=1)
+            num_rois = inds.numel()
+
+            bboxes_ = rois[inds, 1:]
+            label_ = labels[inds]
+            bbox_pred_ = bbox_preds[inds]
+            img_meta_ = img_metas[i]
+            pos_is_gts_ = pos_is_gts[i]
+
+            bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_,
+                                           img_meta_)
+
+            # filter gt bboxes
+            pos_keep = 1 - pos_is_gts_
+            keep_inds = pos_is_gts_.new_ones(num_rois)
+            keep_inds[:len(pos_is_gts_)] = pos_keep
+
+            bboxes_list.append(bboxes[keep_inds.type(torch.bool)])
+
+        return bboxes_list
+
+    @force_fp32(apply_to=('bbox_pred', ))
+    def regress_by_class(self, rois, label, bbox_pred, img_meta):
+        """Regress the bbox for the predicted class. Used in Cascade R-CNN.
+
+        Args:
+            rois (Tensor): Rois from `rpn_head` or last stage
+                `bbox_head`, has shape (num_proposals, 4) or
+                (num_proposals, 5).
+            label (Tensor): Only used when `self.reg_class_agnostic`
+                is False, has shape (num_proposals, ).
+            bbox_pred (Tensor): Regression prediction of
+                current stage `bbox_head`. When `self.reg_class_agnostic`
+                is False, it has shape (n, num_classes * 4), otherwise
+                it has shape (n, 4).
+            img_meta (dict): Image meta info.
+
+        Returns:
+            Tensor: Regressed bboxes, the same shape as input rois.
+        """
+
+        assert rois.size(1) == 4 or rois.size(1) == 5, repr(rois.shape)
+
+        if not self.reg_class_agnostic:
+            label = label * 4
+            inds = torch.stack((label, label + 1, label + 2, label + 3), 1)
+            bbox_pred = torch.gather(bbox_pred, 1, inds)
+        assert bbox_pred.size(1) == 4
+
+        max_shape = img_meta['img_shape']
+
+        if rois.size(1) == 4:
+            new_rois = self.bbox_coder.decode(
+                rois, bbox_pred, max_shape=max_shape)
+        else:
+            bboxes = self.bbox_coder.decode(
+                rois[:, 1:], bbox_pred, max_shape=max_shape)
+            new_rois = torch.cat((rois[:, [0]], bboxes), dim=1)
+
+        return new_rois
+
+    def onnx_export(self,
+                    rois,
+                    cls_score,
+                    bbox_pred,
+                    img_shape,
+                    cfg=None,
+                    **kwargs):
+        """Transform network output for a batch into bbox predictions.
+
+        Args:
+            rois (Tensor): Boxes to be transformed.
+                Has shape (B, num_boxes, 5)
+            cls_score (Tensor): Box scores. has shape
+                (B, num_boxes, num_classes + 1), 1 represent the background.
+            bbox_pred (Tensor, optional): Box energies / deltas for,
+                has shape (B, num_boxes, num_classes * 4) when.
+            img_shape (torch.Tensor): Shape of image.
+            cfg (obj:`ConfigDict`): `test_cfg` of Bbox Head. Default: None
+
+        Returns:
+            tuple[Tensor, Tensor]: dets of shape [N, num_det, 5]
+                and class labels of shape [N, num_det].
+        """
+
+        assert rois.ndim == 3, 'Only support export two stage ' \
+                               'model to ONNX ' \
+                               'with batch dimension. '
+        if self.custom_cls_channels:
+            scores = self.loss_cls.get_activation(cls_score)
+        else:
+            scores = F.softmax(
+                cls_score, dim=-1) if cls_score is not None else None
+
+        if bbox_pred is not None:
+            bboxes = self.bbox_coder.decode(
+                rois[..., 1:], bbox_pred, max_shape=img_shape)
+        else:
+            bboxes = rois[..., 1:].clone()
+            if img_shape is not None:
+                max_shape = bboxes.new_tensor(img_shape)[..., :2]
+                min_xy = bboxes.new_tensor(0)
+                max_xy = torch.cat(
+                    [max_shape] * 2, dim=-1).flip(-1).unsqueeze(-2)
+                bboxes = torch.where(bboxes < min_xy, min_xy, bboxes)
+                bboxes = torch.where(bboxes > max_xy, max_xy, bboxes)
+
+        # Replace multiclass_nms with ONNX::NonMaxSuppression in deployment
+        from mmdet.core.export import add_dummy_nms_for_onnx
+        max_output_boxes_per_class = cfg.nms.get('max_output_boxes_per_class',
+                                                 cfg.max_per_img)
+        iou_threshold = cfg.nms.get('iou_threshold', 0.5)
+        score_threshold = cfg.score_thr
+        nms_pre = cfg.get('deploy_nms_pre', -1)
+
+        scores = scores[..., :self.num_classes]
+        if self.reg_class_agnostic:
+            return add_dummy_nms_for_onnx(
+                bboxes,
+                scores,
+                max_output_boxes_per_class,
+                iou_threshold,
+                score_threshold,
+                pre_top_k=nms_pre,
+                after_top_k=cfg.max_per_img)
+        else:
+            batch_size = scores.shape[0]
+            labels = torch.arange(
+                self.num_classes, dtype=torch.long).to(scores.device)
+            labels = labels.view(1, 1, -1).expand_as(scores)
+            labels = labels.reshape(batch_size, -1)
+            scores = scores.reshape(batch_size, -1)
+            bboxes = bboxes.reshape(batch_size, -1, 4)
+
+            max_size = torch.max(img_shape)
+            # Offset bboxes of each class so that bboxes of different labels
+            #  do not overlap.
+            offsets = (labels * max_size + 1).unsqueeze(2)
+            bboxes_for_nms = bboxes + offsets
+
+            batch_dets, labels = add_dummy_nms_for_onnx(
+                bboxes_for_nms,
+                scores.unsqueeze(2),
+                max_output_boxes_per_class,
+                iou_threshold,
+                score_threshold,
+                pre_top_k=nms_pre,
+                after_top_k=cfg.max_per_img,
+                labels=labels)
+            # Offset the bboxes back after dummy nms.
+            offsets = (labels * max_size + 1).unsqueeze(2)
+            # Indexing + inplace operation fails with dynamic shape in ONNX
+            # original style: batch_dets[..., :4] -= offsets
+            bboxes, scores = batch_dets[..., 0:4], batch_dets[..., 4:5]
+            bboxes -= offsets
+            batch_dets = torch.cat([bboxes, scores], dim=2)
+            return batch_dets, labels
diff --git a/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py b/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py
new file mode 100644
index 0000000..2dca4db
--- /dev/null
+++ b/mmdet/models/roi_heads/bbox_heads/convfc_bbox_head.py
@@ -0,0 +1,239 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+
+from mmdet.models.builder import HEADS
+from mmdet.models.utils import build_linear_layer
+from .bbox_head import BBoxHead
+
+
+@HEADS.register_module()
+class ConvFCBBoxHead(BBoxHead):
+    r"""More general bbox head, with shared conv and fc layers and two optional
+    separated branches.
+
+    .. code-block:: none
+
+                                    /-> cls convs -> cls fcs -> cls
+        shared convs -> shared fcs
+                                    \-> reg convs -> reg fcs -> reg
+    """  # noqa: W605
+
+    def __init__(self,
+                 num_shared_convs=0,
+                 num_shared_fcs=0,
+                 num_cls_convs=0,
+                 num_cls_fcs=0,
+                 num_reg_convs=0,
+                 num_reg_fcs=0,
+                 conv_out_channels=256,
+                 fc_out_channels=1024,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 init_cfg=None,
+                 *args,
+                 **kwargs):
+        super(ConvFCBBoxHead, self).__init__(
+            *args, init_cfg=init_cfg, **kwargs)
+        assert (num_shared_convs + num_shared_fcs + num_cls_convs +
+                num_cls_fcs + num_reg_convs + num_reg_fcs > 0)
+        if num_cls_convs > 0 or num_reg_convs > 0:
+            assert num_shared_fcs == 0
+        if not self.with_cls:
+            assert num_cls_convs == 0 and num_cls_fcs == 0
+        if not self.with_reg:
+            assert num_reg_convs == 0 and num_reg_fcs == 0
+        self.num_shared_convs = num_shared_convs
+        self.num_shared_fcs = num_shared_fcs
+        self.num_cls_convs = num_cls_convs
+        self.num_cls_fcs = num_cls_fcs
+        self.num_reg_convs = num_reg_convs
+        self.num_reg_fcs = num_reg_fcs
+        self.conv_out_channels = conv_out_channels
+        self.fc_out_channels = fc_out_channels
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+
+        # add shared convs and fcs
+        self.shared_convs, self.shared_fcs, last_layer_dim = \
+            self._add_conv_fc_branch(
+                self.num_shared_convs, self.num_shared_fcs, self.in_channels,
+                True)
+        self.shared_out_channels = last_layer_dim
+
+        # add cls specific branch
+        self.cls_convs, self.cls_fcs, self.cls_last_dim = \
+            self._add_conv_fc_branch(
+                self.num_cls_convs, self.num_cls_fcs, self.shared_out_channels)
+
+        # add reg specific branch
+        self.reg_convs, self.reg_fcs, self.reg_last_dim = \
+            self._add_conv_fc_branch(
+                self.num_reg_convs, self.num_reg_fcs, self.shared_out_channels)
+
+        if self.num_shared_fcs == 0 and not self.with_avg_pool:
+            if self.num_cls_fcs == 0:
+                self.cls_last_dim *= self.roi_feat_area
+            if self.num_reg_fcs == 0:
+                self.reg_last_dim *= self.roi_feat_area
+
+        self.relu = nn.ReLU(inplace=True)
+        # reconstruct fc_cls and fc_reg since input channels are changed
+        if self.with_cls:
+            if self.custom_cls_channels:
+                cls_channels = self.loss_cls.get_cls_channels(self.num_classes)
+            else:
+                cls_channels = self.num_classes + 1
+            self.fc_cls = build_linear_layer(
+                self.cls_predictor_cfg,
+                in_features=self.cls_last_dim,
+                out_features=cls_channels)
+        if self.with_reg:
+            out_dim_reg = (4 if self.reg_class_agnostic else 4 *
+                           self.num_classes)
+            self.fc_reg = build_linear_layer(
+                self.reg_predictor_cfg,
+                in_features=self.reg_last_dim,
+                out_features=out_dim_reg)
+
+        if init_cfg is None:
+            # when init_cfg is None,
+            # It has been set to
+            # [[dict(type='Normal', std=0.01, override=dict(name='fc_cls'))],
+            #  [dict(type='Normal', std=0.001, override=dict(name='fc_reg'))]
+            # after `super(ConvFCBBoxHead, self).__init__()`
+            # we only need to append additional configuration
+            # for `shared_fcs`, `cls_fcs` and `reg_fcs`
+            self.init_cfg += [
+                dict(
+                    type='Xavier',
+                    distribution='uniform',
+                    override=[
+                        dict(name='shared_fcs'),
+                        dict(name='cls_fcs'),
+                        dict(name='reg_fcs')
+                    ])
+            ]
+
+    def _add_conv_fc_branch(self,
+                            num_branch_convs,
+                            num_branch_fcs,
+                            in_channels,
+                            is_shared=False):
+        """Add shared or separable branch.
+
+        convs -> avg pool (optional) -> fcs
+        """
+        last_layer_dim = in_channels
+        # add branch specific conv layers
+        branch_convs = nn.ModuleList()
+        if num_branch_convs > 0:
+            for i in range(num_branch_convs):
+                conv_in_channels = (
+                    last_layer_dim if i == 0 else self.conv_out_channels)
+                branch_convs.append(
+                    ConvModule(
+                        conv_in_channels,
+                        self.conv_out_channels,
+                        3,
+                        padding=1,
+                        conv_cfg=self.conv_cfg,
+                        norm_cfg=self.norm_cfg))
+            last_layer_dim = self.conv_out_channels
+        # add branch specific fc layers
+        branch_fcs = nn.ModuleList()
+        if num_branch_fcs > 0:
+            # for shared branch, only consider self.with_avg_pool
+            # for separated branches, also consider self.num_shared_fcs
+            if (is_shared
+                    or self.num_shared_fcs == 0) and not self.with_avg_pool:
+                last_layer_dim *= self.roi_feat_area
+            for i in range(num_branch_fcs):
+                fc_in_channels = (
+                    last_layer_dim if i == 0 else self.fc_out_channels)
+                branch_fcs.append(
+                    nn.Linear(fc_in_channels, self.fc_out_channels))
+            last_layer_dim = self.fc_out_channels
+        return branch_convs, branch_fcs, last_layer_dim
+
+    def forward(self, x, dim_setting=2):
+        # shared part
+        if self.num_shared_convs > 0:
+            for conv in self.shared_convs:
+                x = conv(x)
+
+        if self.num_shared_fcs > 0:
+            if self.with_avg_pool:
+                x = self.avg_pool(x)
+
+            x = x.flatten(1)
+
+            for fc in self.shared_fcs:
+                x = self.relu(fc(x))
+        # separate branches
+        x_cls = x
+        x_reg = x
+
+        for conv in self.cls_convs:
+            x_cls = conv(x_cls)
+            
+        
+        if dim_setting == 2:
+            # dim_setting == x_cls.dim()
+            # FasterRCNN
+            pass
+        else:
+            raise NotImplementedError
+        
+        
+        if dim_setting > 2:
+            if self.with_avg_pool:
+                x_cls = self.avg_pool(x_cls)
+            x_cls = x_cls.flatten(1)
+        for fc in self.cls_fcs:
+            x_cls = self.relu(fc(x_cls))
+
+        for conv in self.reg_convs:
+            x_reg = conv(x_reg)
+        if dim_setting > 2:
+            if self.with_avg_pool:
+                x_reg = self.avg_pool(x_reg)
+            x_reg = x_reg.flatten(1)
+        for fc in self.reg_fcs:
+            x_reg = self.relu(fc(x_reg))
+
+        cls_score = self.fc_cls(x_cls) if self.with_cls else None
+        bbox_pred = self.fc_reg(x_reg) if self.with_reg else None
+        return cls_score, bbox_pred
+
+
+@HEADS.register_module()
+class Shared2FCBBoxHead(ConvFCBBoxHead):
+
+    def __init__(self, fc_out_channels=1024, *args, **kwargs):
+        super(Shared2FCBBoxHead, self).__init__(
+            num_shared_convs=0,
+            num_shared_fcs=2,
+            num_cls_convs=0,
+            num_cls_fcs=0,
+            num_reg_convs=0,
+            num_reg_fcs=0,
+            fc_out_channels=fc_out_channels,
+            *args,
+            **kwargs)
+
+
+@HEADS.register_module()
+class Shared4Conv1FCBBoxHead(ConvFCBBoxHead):
+
+    def __init__(self, fc_out_channels=1024, *args, **kwargs):
+        super(Shared4Conv1FCBBoxHead, self).__init__(
+            num_shared_convs=4,
+            num_shared_fcs=1,
+            num_cls_convs=0,
+            num_cls_fcs=0,
+            num_reg_convs=0,
+            num_reg_fcs=0,
+            fc_out_channels=fc_out_channels,
+            *args,
+            **kwargs)
diff --git a/mmdet/models/roi_heads/bbox_heads/dii_head.py b/mmdet/models/roi_heads/bbox_heads/dii_head.py
new file mode 100644
index 0000000..3777f52
--- /dev/null
+++ b/mmdet/models/roi_heads/bbox_heads/dii_head.py
@@ -0,0 +1,426 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.cnn import (bias_init_with_prob, build_activation_layer,
+                      build_norm_layer)
+from mmcv.cnn.bricks.transformer import FFN, MultiheadAttention
+from mmcv.runner import auto_fp16, force_fp32
+
+from mmdet.core import multi_apply
+from mmdet.models.builder import HEADS, build_loss
+from mmdet.models.dense_heads.atss_head import reduce_mean
+from mmdet.models.losses import accuracy
+from mmdet.models.utils import build_transformer
+from .bbox_head import BBoxHead
+
+
+@HEADS.register_module()
+class DIIHead(BBoxHead):
+    r"""Dynamic Instance Interactive Head for `Sparse R-CNN: End-to-End Object
+    Detection with Learnable Proposals <https://arxiv.org/abs/2011.12450>`_
+
+    Args:
+        num_classes (int): Number of class in dataset.
+            Defaults to 80.
+        num_ffn_fcs (int): The number of fully-connected
+            layers in FFNs. Defaults to 2.
+        num_heads (int): The hidden dimension of FFNs.
+            Defaults to 8.
+        num_cls_fcs (int): The number of fully-connected
+            layers in classification subnet. Defaults to 1.
+        num_reg_fcs (int): The number of fully-connected
+            layers in regression subnet. Defaults to 3.
+        feedforward_channels (int): The hidden dimension
+            of FFNs. Defaults to 2048
+        in_channels (int): Hidden_channels of MultiheadAttention.
+            Defaults to 256.
+        dropout (float): Probability of drop the channel.
+            Defaults to 0.0
+        ffn_act_cfg (dict): The activation config for FFNs.
+        dynamic_conv_cfg (dict): The convolution config
+            for DynamicConv.
+        loss_iou (dict): The config for iou or giou loss.
+
+    """
+
+    def __init__(self,
+                 num_classes=80,
+                 num_ffn_fcs=2,
+                 num_heads=8,
+                 num_cls_fcs=1,
+                 num_reg_fcs=3,
+                 feedforward_channels=2048,
+                 in_channels=256,
+                 dropout=0.0,
+                 ffn_act_cfg=dict(type='ReLU', inplace=True),
+                 dynamic_conv_cfg=dict(
+                     type='DynamicConv',
+                     in_channels=256,
+                     feat_channels=64,
+                     out_channels=256,
+                     input_feat_shape=7,
+                     act_cfg=dict(type='ReLU', inplace=True),
+                     norm_cfg=dict(type='LN')),
+                 loss_iou=dict(type='GIoULoss', loss_weight=2.0),
+                 init_cfg=None,
+                 **kwargs):
+        assert init_cfg is None, 'To prevent abnormal initialization ' \
+                                 'behavior, init_cfg is not allowed to be set'
+        super(DIIHead, self).__init__(
+            num_classes=num_classes,
+            reg_decoded_bbox=True,
+            reg_class_agnostic=True,
+            init_cfg=init_cfg,
+            **kwargs)
+        self.loss_iou = build_loss(loss_iou)
+        self.in_channels = in_channels
+        self.fp16_enabled = False
+        self.attention = MultiheadAttention(in_channels, num_heads, dropout)
+        self.attention_norm = build_norm_layer(dict(type='LN'), in_channels)[1]
+
+        self.instance_interactive_conv = build_transformer(dynamic_conv_cfg)
+        self.instance_interactive_conv_dropout = nn.Dropout(dropout)
+        self.instance_interactive_conv_norm = build_norm_layer(
+            dict(type='LN'), in_channels)[1]
+
+        self.ffn = FFN(
+            in_channels,
+            feedforward_channels,
+            num_ffn_fcs,
+            act_cfg=ffn_act_cfg,
+            dropout=dropout)
+        self.ffn_norm = build_norm_layer(dict(type='LN'), in_channels)[1]
+
+        self.cls_fcs = nn.ModuleList()
+        for _ in range(num_cls_fcs):
+            self.cls_fcs.append(
+                nn.Linear(in_channels, in_channels, bias=False))
+            self.cls_fcs.append(
+                build_norm_layer(dict(type='LN'), in_channels)[1])
+            self.cls_fcs.append(
+                build_activation_layer(dict(type='ReLU', inplace=True)))
+
+        # over load the self.fc_cls in BBoxHead
+        if self.loss_cls.use_sigmoid:
+            self.fc_cls = nn.Linear(in_channels, self.num_classes)
+        else:
+            self.fc_cls = nn.Linear(in_channels, self.num_classes + 1)
+
+        self.reg_fcs = nn.ModuleList()
+        for _ in range(num_reg_fcs):
+            self.reg_fcs.append(
+                nn.Linear(in_channels, in_channels, bias=False))
+            self.reg_fcs.append(
+                build_norm_layer(dict(type='LN'), in_channels)[1])
+            self.reg_fcs.append(
+                build_activation_layer(dict(type='ReLU', inplace=True)))
+        # over load the self.fc_cls in BBoxHead
+        self.fc_reg = nn.Linear(in_channels, 4)
+
+        assert self.reg_class_agnostic, 'DIIHead only ' \
+            'suppport `reg_class_agnostic=True` '
+        assert self.reg_decoded_bbox, 'DIIHead only ' \
+            'suppport `reg_decoded_bbox=True`'
+
+    def init_weights(self):
+        """Use xavier initialization for all weight parameter and set
+        classification head bias as a specific value when use focal loss."""
+        super(DIIHead, self).init_weights()
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+            else:
+                # adopt the default initialization for
+                # the weight and bias of the layer norm
+                pass
+        if self.loss_cls.use_sigmoid:
+            bias_init = bias_init_with_prob(0.01)
+            nn.init.constant_(self.fc_cls.bias, bias_init)
+
+    @auto_fp16()
+    def forward(self, roi_feat, proposal_feat):
+        """Forward function of Dynamic Instance Interactive Head.
+
+        Args:
+            roi_feat (Tensor): Roi-pooling features with shape
+                (batch_size*num_proposals, feature_dimensions,
+                pooling_h , pooling_w).
+            proposal_feat (Tensor): Intermediate feature get from
+                diihead in last stage, has shape
+                (batch_size, num_proposals, feature_dimensions)
+
+          Returns:
+                tuple[Tensor]: Usually a tuple of classification scores
+                and bbox prediction and a intermediate feature.
+
+                    - cls_scores (Tensor): Classification scores for
+                      all proposals, has shape
+                      (batch_size, num_proposals, num_classes).
+                    - bbox_preds (Tensor): Box energies / deltas for
+                      all proposals, has shape
+                      (batch_size, num_proposals, 4).
+                    - obj_feat (Tensor): Object feature before classification
+                      and regression subnet, has shape
+                      (batch_size, num_proposal, feature_dimensions).
+        """
+        N, num_proposals = proposal_feat.shape[:2]
+
+        # Self attention
+        proposal_feat = proposal_feat.permute(1, 0, 2)
+        proposal_feat = self.attention_norm(self.attention(proposal_feat))
+        attn_feats = proposal_feat.permute(1, 0, 2)
+
+        # instance interactive
+        proposal_feat = attn_feats.reshape(-1, self.in_channels)
+        proposal_feat_iic = self.instance_interactive_conv(
+            proposal_feat, roi_feat)
+        proposal_feat = proposal_feat + self.instance_interactive_conv_dropout(
+            proposal_feat_iic)
+        obj_feat = self.instance_interactive_conv_norm(proposal_feat)
+
+        # FFN
+        obj_feat = self.ffn_norm(self.ffn(obj_feat))
+
+        cls_feat = obj_feat
+        reg_feat = obj_feat
+
+        for cls_layer in self.cls_fcs:
+            cls_feat = cls_layer(cls_feat)
+        for reg_layer in self.reg_fcs:
+            reg_feat = reg_layer(reg_feat)
+
+        cls_score = self.fc_cls(cls_feat).view(
+            N, num_proposals, self.num_classes
+            if self.loss_cls.use_sigmoid else self.num_classes + 1)
+        bbox_delta = self.fc_reg(reg_feat).view(N, num_proposals, 4)
+
+        return cls_score, bbox_delta, obj_feat.view(
+            N, num_proposals, self.in_channels), attn_feats
+
+    @force_fp32(apply_to=('cls_score', 'bbox_pred'))
+    def loss(self,
+             cls_score,
+             bbox_pred,
+             labels,
+             label_weights,
+             bbox_targets,
+             bbox_weights,
+             imgs_whwh=None,
+             reduction_override=None,
+             **kwargs):
+        """"Loss function of DIIHead, get loss of all images.
+
+        Args:
+            cls_score (Tensor): Classification prediction
+                results of all class, has shape
+                (batch_size * num_proposals_single_image, num_classes)
+            bbox_pred (Tensor): Regression prediction results,
+                has shape
+                (batch_size * num_proposals_single_image, 4), the last
+                dimension 4 represents [tl_x, tl_y, br_x, br_y].
+            labels (Tensor): Label of each proposals, has shape
+                (batch_size * num_proposals_single_image
+            label_weights (Tensor): Classification loss
+                weight of each proposals, has shape
+                (batch_size * num_proposals_single_image
+            bbox_targets (Tensor): Regression targets of each
+                proposals, has shape
+                (batch_size * num_proposals_single_image, 4),
+                the last dimension 4 represents
+                [tl_x, tl_y, br_x, br_y].
+            bbox_weights (Tensor): Regression loss weight of each
+                proposals's coordinate, has shape
+                (batch_size * num_proposals_single_image, 4),
+            imgs_whwh (Tensor): imgs_whwh (Tensor): Tensor with\
+                shape (batch_size, num_proposals, 4), the last
+                dimension means
+                [img_width,img_height, img_width, img_height].
+            reduction_override (str, optional): The reduction
+                method used to override the original reduction
+                method of the loss. Options are "none",
+                "mean" and "sum". Defaults to None,
+
+            Returns:
+                dict[str, Tensor]: Dictionary of loss components
+        """
+        losses = dict()
+        bg_class_ind = self.num_classes
+        # note in spare rcnn num_gt == num_pos
+        pos_inds = (labels >= 0) & (labels < bg_class_ind)
+        num_pos = pos_inds.sum().float()
+        avg_factor = reduce_mean(num_pos)
+        if cls_score is not None:
+            if cls_score.numel() > 0:
+                losses['loss_cls'] = self.loss_cls(
+                    cls_score,
+                    labels,
+                    label_weights,
+                    avg_factor=avg_factor,
+                    reduction_override=reduction_override)
+                losses['pos_acc'] = accuracy(cls_score[pos_inds],
+                                             labels[pos_inds])
+        if bbox_pred is not None:
+            # 0~self.num_classes-1 are FG, self.num_classes is BG
+            # do not perform bounding box regression for BG anymore.
+            if pos_inds.any():
+                pos_bbox_pred = bbox_pred.reshape(bbox_pred.size(0),
+                                                  4)[pos_inds.type(torch.bool)]
+                imgs_whwh = imgs_whwh.reshape(bbox_pred.size(0),
+                                              4)[pos_inds.type(torch.bool)]
+                losses['loss_bbox'] = self.loss_bbox(
+                    pos_bbox_pred / imgs_whwh,
+                    bbox_targets[pos_inds.type(torch.bool)] / imgs_whwh,
+                    bbox_weights[pos_inds.type(torch.bool)],
+                    avg_factor=avg_factor)
+                losses['loss_iou'] = self.loss_iou(
+                    pos_bbox_pred,
+                    bbox_targets[pos_inds.type(torch.bool)],
+                    bbox_weights[pos_inds.type(torch.bool)],
+                    avg_factor=avg_factor)
+            else:
+                losses['loss_bbox'] = bbox_pred.sum() * 0
+                losses['loss_iou'] = bbox_pred.sum() * 0
+        return losses
+
+    def _get_target_single(self, pos_inds, neg_inds, pos_bboxes, neg_bboxes,
+                           pos_gt_bboxes, pos_gt_labels, cfg):
+        """Calculate the ground truth for proposals in the single image
+        according to the sampling results.
+
+        Almost the same as the implementation in `bbox_head`,
+        we add pos_inds and neg_inds to select positive and
+        negative samples instead of selecting the first num_pos
+        as positive samples.
+
+        Args:
+            pos_inds (Tensor): The length is equal to the
+                positive sample numbers contain all index
+                of the positive sample in the origin proposal set.
+            neg_inds (Tensor): The length is equal to the
+                negative sample numbers contain all index
+                of the negative sample in the origin proposal set.
+            pos_bboxes (Tensor): Contains all the positive boxes,
+                has shape (num_pos, 4), the last dimension 4
+                represents [tl_x, tl_y, br_x, br_y].
+            neg_bboxes (Tensor): Contains all the negative boxes,
+                has shape (num_neg, 4), the last dimension 4
+                represents [tl_x, tl_y, br_x, br_y].
+            pos_gt_bboxes (Tensor): Contains gt_boxes for
+                all positive samples, has shape (num_pos, 4),
+                the last dimension 4
+                represents [tl_x, tl_y, br_x, br_y].
+            pos_gt_labels (Tensor): Contains gt_labels for
+                all positive samples, has shape (num_pos, ).
+            cfg (obj:`ConfigDict`): `train_cfg` of R-CNN.
+
+        Returns:
+            Tuple[Tensor]: Ground truth for proposals in a single image.
+            Containing the following Tensors:
+
+                - labels(Tensor): Gt_labels for all proposals, has
+                  shape (num_proposals,).
+                - label_weights(Tensor): Labels_weights for all proposals, has
+                  shape (num_proposals,).
+                - bbox_targets(Tensor):Regression target for all proposals, has
+                  shape (num_proposals, 4), the last dimension 4
+                  represents [tl_x, tl_y, br_x, br_y].
+                - bbox_weights(Tensor):Regression weights for all proposals,
+                  has shape (num_proposals, 4).
+        """
+        num_pos = pos_bboxes.size(0)
+        num_neg = neg_bboxes.size(0)
+        num_samples = num_pos + num_neg
+
+        # original implementation uses new_zeros since BG are set to be 0
+        # now use empty & fill because BG cat_id = num_classes,
+        # FG cat_id = [0, num_classes-1]
+        labels = pos_bboxes.new_full((num_samples, ),
+                                     self.num_classes,
+                                     dtype=torch.long)
+        label_weights = pos_bboxes.new_zeros(num_samples)
+        bbox_targets = pos_bboxes.new_zeros(num_samples, 4)
+        bbox_weights = pos_bboxes.new_zeros(num_samples, 4)
+        if num_pos > 0:
+            labels[pos_inds] = pos_gt_labels
+            pos_weight = 1.0 if cfg.pos_weight <= 0 else cfg.pos_weight
+            label_weights[pos_inds] = pos_weight
+            if not self.reg_decoded_bbox:
+                pos_bbox_targets = self.bbox_coder.encode(
+                    pos_bboxes, pos_gt_bboxes)
+            else:
+                pos_bbox_targets = pos_gt_bboxes
+            bbox_targets[pos_inds, :] = pos_bbox_targets
+            bbox_weights[pos_inds, :] = 1
+        if num_neg > 0:
+            label_weights[neg_inds] = 1.0
+
+        return labels, label_weights, bbox_targets, bbox_weights
+
+    def get_targets(self,
+                    sampling_results,
+                    gt_bboxes,
+                    gt_labels,
+                    rcnn_train_cfg,
+                    concat=True):
+        """Calculate the ground truth for all samples in a batch according to
+        the sampling_results.
+
+        Almost the same as the implementation in bbox_head, we passed
+        additional parameters pos_inds_list and neg_inds_list to
+        `_get_target_single` function.
+
+        Args:
+            sampling_results (List[obj:SamplingResults]): Assign results of
+                all images in a batch after sampling.
+            gt_bboxes (list[Tensor]): Gt_bboxes of all images in a batch,
+                each tensor has shape (num_gt, 4),  the last dimension 4
+                represents [tl_x, tl_y, br_x, br_y].
+            gt_labels (list[Tensor]): Gt_labels of all images in a batch,
+                each tensor has shape (num_gt,).
+            rcnn_train_cfg (obj:`ConfigDict`): `train_cfg` of RCNN.
+            concat (bool): Whether to concatenate the results of all
+                the images in a single batch.
+
+        Returns:
+            Tuple[Tensor]: Ground truth for proposals in a single image.
+            Containing the following list of Tensors:
+
+                - labels (list[Tensor],Tensor): Gt_labels for all
+                  proposals in a batch, each tensor in list has
+                  shape (num_proposals,) when `concat=False`, otherwise just
+                  a single tensor has shape (num_all_proposals,).
+                - label_weights (list[Tensor]): Labels_weights for
+                  all proposals in a batch, each tensor in list has shape
+                  (num_proposals,) when `concat=False`, otherwise just a
+                  single tensor has shape (num_all_proposals,).
+                - bbox_targets (list[Tensor],Tensor): Regression target
+                  for all proposals in a batch, each tensor in list has
+                  shape (num_proposals, 4) when `concat=False`, otherwise
+                  just a single tensor has shape (num_all_proposals, 4),
+                  the last dimension 4 represents [tl_x, tl_y, br_x, br_y].
+                - bbox_weights (list[tensor],Tensor): Regression weights for
+                  all proposals in a batch, each tensor in list has shape
+                  (num_proposals, 4) when `concat=False`, otherwise just a
+                  single tensor has shape (num_all_proposals, 4).
+        """
+        pos_inds_list = [res.pos_inds for res in sampling_results]
+        neg_inds_list = [res.neg_inds for res in sampling_results]
+        pos_bboxes_list = [res.pos_bboxes for res in sampling_results]
+        neg_bboxes_list = [res.neg_bboxes for res in sampling_results]
+        pos_gt_bboxes_list = [res.pos_gt_bboxes for res in sampling_results]
+        pos_gt_labels_list = [res.pos_gt_labels for res in sampling_results]
+        labels, label_weights, bbox_targets, bbox_weights = multi_apply(
+            self._get_target_single,
+            pos_inds_list,
+            neg_inds_list,
+            pos_bboxes_list,
+            neg_bboxes_list,
+            pos_gt_bboxes_list,
+            pos_gt_labels_list,
+            cfg=rcnn_train_cfg)
+        if concat:
+            labels = torch.cat(labels, 0)
+            label_weights = torch.cat(label_weights, 0)
+            bbox_targets = torch.cat(bbox_targets, 0)
+            bbox_weights = torch.cat(bbox_weights, 0)
+        return labels, label_weights, bbox_targets, bbox_weights
diff --git a/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py b/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py
new file mode 100644
index 0000000..2a38d59
--- /dev/null
+++ b/mmdet/models/roi_heads/bbox_heads/double_bbox_head.py
@@ -0,0 +1,178 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule, ModuleList
+
+from mmdet.models.backbones.resnet import Bottleneck
+from mmdet.models.builder import HEADS
+from .bbox_head import BBoxHead
+
+
+class BasicResBlock(BaseModule):
+    """Basic residual block.
+
+    This block is a little different from the block in the ResNet backbone.
+    The kernel size of conv1 is 1 in this block while 3 in ResNet BasicBlock.
+
+    Args:
+        in_channels (int): Channels of the input feature map.
+        out_channels (int): Channels of the output feature map.
+        conv_cfg (dict): The config dict for convolution layers.
+        norm_cfg (dict): The config dict for normalization layers.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 init_cfg=None):
+        super(BasicResBlock, self).__init__(init_cfg)
+
+        # main path
+        self.conv1 = ConvModule(
+            in_channels,
+            in_channels,
+            kernel_size=3,
+            padding=1,
+            bias=False,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg)
+        self.conv2 = ConvModule(
+            in_channels,
+            out_channels,
+            kernel_size=1,
+            bias=False,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=None)
+
+        # identity path
+        self.conv_identity = ConvModule(
+            in_channels,
+            out_channels,
+            kernel_size=1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=None)
+
+        self.relu = nn.ReLU(inplace=True)
+
+    def forward(self, x):
+        identity = x
+
+        x = self.conv1(x)
+        x = self.conv2(x)
+
+        identity = self.conv_identity(identity)
+        out = x + identity
+
+        out = self.relu(out)
+        return out
+
+
+@HEADS.register_module()
+class DoubleConvFCBBoxHead(BBoxHead):
+    r"""Bbox head used in Double-Head R-CNN
+
+    .. code-block:: none
+
+                                          /-> cls
+                      /-> shared convs ->
+                                          \-> reg
+        roi features
+                                          /-> cls
+                      \-> shared fc    ->
+                                          \-> reg
+    """  # noqa: W605
+
+    def __init__(self,
+                 num_convs=0,
+                 num_fcs=0,
+                 conv_out_channels=1024,
+                 fc_out_channels=1024,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 init_cfg=dict(
+                     type='Normal',
+                     override=[
+                         dict(type='Normal', name='fc_cls', std=0.01),
+                         dict(type='Normal', name='fc_reg', std=0.001),
+                         dict(
+                             type='Xavier',
+                             name='fc_branch',
+                             distribution='uniform')
+                     ]),
+                 **kwargs):
+        kwargs.setdefault('with_avg_pool', True)
+        super(DoubleConvFCBBoxHead, self).__init__(init_cfg=init_cfg, **kwargs)
+        assert self.with_avg_pool
+        assert num_convs > 0
+        assert num_fcs > 0
+        self.num_convs = num_convs
+        self.num_fcs = num_fcs
+        self.conv_out_channels = conv_out_channels
+        self.fc_out_channels = fc_out_channels
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+
+        # increase the channel of input features
+        self.res_block = BasicResBlock(self.in_channels,
+                                       self.conv_out_channels)
+
+        # add conv heads
+        self.conv_branch = self._add_conv_branch()
+        # add fc heads
+        self.fc_branch = self._add_fc_branch()
+
+        out_dim_reg = 4 if self.reg_class_agnostic else 4 * self.num_classes
+        self.fc_reg = nn.Linear(self.conv_out_channels, out_dim_reg)
+
+        self.fc_cls = nn.Linear(self.fc_out_channels, self.num_classes + 1)
+        self.relu = nn.ReLU(inplace=True)
+
+    def _add_conv_branch(self):
+        """Add the fc branch which consists of a sequential of conv layers."""
+        branch_convs = ModuleList()
+        for i in range(self.num_convs):
+            branch_convs.append(
+                Bottleneck(
+                    inplanes=self.conv_out_channels,
+                    planes=self.conv_out_channels // 4,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+        return branch_convs
+
+    def _add_fc_branch(self):
+        """Add the fc branch which consists of a sequential of fc layers."""
+        branch_fcs = ModuleList()
+        for i in range(self.num_fcs):
+            fc_in_channels = (
+                self.in_channels *
+                self.roi_feat_area if i == 0 else self.fc_out_channels)
+            branch_fcs.append(nn.Linear(fc_in_channels, self.fc_out_channels))
+        return branch_fcs
+
+    def forward(self, x_cls, x_reg):
+        # conv head
+        x_conv = self.res_block(x_reg)
+
+        for conv in self.conv_branch:
+            x_conv = conv(x_conv)
+
+        if self.with_avg_pool:
+            x_conv = self.avg_pool(x_conv)
+
+        x_conv = x_conv.view(x_conv.size(0), -1)
+        bbox_pred = self.fc_reg(x_conv)
+
+        # fc head
+        x_fc = x_cls.view(x_cls.size(0), -1)
+        for fc in self.fc_branch:
+            x_fc = self.relu(fc(x_fc))
+
+        cls_score = self.fc_cls(x_fc)
+
+        return cls_score, bbox_pred
diff --git a/mmdet/models/roi_heads/bbox_heads/sabl_head.py b/mmdet/models/roi_heads/bbox_heads/sabl_head.py
new file mode 100644
index 0000000..0ce986b
--- /dev/null
+++ b/mmdet/models/roi_heads/bbox_heads/sabl_head.py
@@ -0,0 +1,596 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule, force_fp32
+
+from mmdet.core import build_bbox_coder, multi_apply, multiclass_nms
+from mmdet.models.builder import HEADS, build_loss
+from mmdet.models.losses import accuracy
+
+
+@HEADS.register_module()
+class SABLHead(BaseModule):
+    """Side-Aware Boundary Localization (SABL) for RoI-Head.
+
+    Side-Aware features are extracted by conv layers
+    with an attention mechanism.
+    Boundary Localization with Bucketing and Bucketing Guided Rescoring
+    are implemented in BucketingBBoxCoder.
+
+    Please refer to https://arxiv.org/abs/1912.04260 for more details.
+
+    Args:
+        cls_in_channels (int): Input channels of cls RoI feature. \
+            Defaults to 256.
+        reg_in_channels (int): Input channels of reg RoI feature. \
+            Defaults to 256.
+        roi_feat_size (int): Size of RoI features. Defaults to 7.
+        reg_feat_up_ratio (int): Upsample ratio of reg features. \
+            Defaults to 2.
+        reg_pre_kernel (int): Kernel of 2D conv layers before \
+            attention pooling. Defaults to 3.
+        reg_post_kernel (int): Kernel of 1D conv layers after \
+            attention pooling. Defaults to 3.
+        reg_pre_num (int): Number of pre convs. Defaults to 2.
+        reg_post_num (int): Number of post convs. Defaults to 1.
+        num_classes (int): Number of classes in dataset. Defaults to 80.
+        cls_out_channels (int): Hidden channels in cls fcs. Defaults to 1024.
+        reg_offset_out_channels (int): Hidden and output channel \
+            of reg offset branch. Defaults to 256.
+        reg_cls_out_channels (int): Hidden and output channel \
+            of reg cls branch. Defaults to 256.
+        num_cls_fcs (int): Number of fcs for cls branch. Defaults to 1.
+        num_reg_fcs (int): Number of fcs for reg branch.. Defaults to 0.
+        reg_class_agnostic (bool): Class agnostic regression or not. \
+            Defaults to True.
+        norm_cfg (dict): Config of norm layers. Defaults to None.
+        bbox_coder (dict): Config of bbox coder. Defaults 'BucketingBBoxCoder'.
+        loss_cls (dict): Config of classification loss.
+        loss_bbox_cls (dict): Config of classification loss for bbox branch.
+        loss_bbox_reg (dict): Config of regression loss for bbox branch.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 num_classes,
+                 cls_in_channels=256,
+                 reg_in_channels=256,
+                 roi_feat_size=7,
+                 reg_feat_up_ratio=2,
+                 reg_pre_kernel=3,
+                 reg_post_kernel=3,
+                 reg_pre_num=2,
+                 reg_post_num=1,
+                 cls_out_channels=1024,
+                 reg_offset_out_channels=256,
+                 reg_cls_out_channels=256,
+                 num_cls_fcs=1,
+                 num_reg_fcs=0,
+                 reg_class_agnostic=True,
+                 norm_cfg=None,
+                 bbox_coder=dict(
+                     type='BucketingBBoxCoder',
+                     num_buckets=14,
+                     scale_factor=1.7),
+                 loss_cls=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=False,
+                     loss_weight=1.0),
+                 loss_bbox_cls=dict(
+                     type='CrossEntropyLoss',
+                     use_sigmoid=True,
+                     loss_weight=1.0),
+                 loss_bbox_reg=dict(
+                     type='SmoothL1Loss', beta=0.1, loss_weight=1.0),
+                 init_cfg=None):
+        super(SABLHead, self).__init__(init_cfg)
+        self.cls_in_channels = cls_in_channels
+        self.reg_in_channels = reg_in_channels
+        self.roi_feat_size = roi_feat_size
+        self.reg_feat_up_ratio = int(reg_feat_up_ratio)
+        self.num_buckets = bbox_coder['num_buckets']
+        assert self.reg_feat_up_ratio // 2 >= 1
+        self.up_reg_feat_size = roi_feat_size * self.reg_feat_up_ratio
+        assert self.up_reg_feat_size == bbox_coder['num_buckets']
+        self.reg_pre_kernel = reg_pre_kernel
+        self.reg_post_kernel = reg_post_kernel
+        self.reg_pre_num = reg_pre_num
+        self.reg_post_num = reg_post_num
+        self.num_classes = num_classes
+        self.cls_out_channels = cls_out_channels
+        self.reg_offset_out_channels = reg_offset_out_channels
+        self.reg_cls_out_channels = reg_cls_out_channels
+        self.num_cls_fcs = num_cls_fcs
+        self.num_reg_fcs = num_reg_fcs
+        self.reg_class_agnostic = reg_class_agnostic
+        assert self.reg_class_agnostic
+        self.norm_cfg = norm_cfg
+
+        self.bbox_coder = build_bbox_coder(bbox_coder)
+        self.loss_cls = build_loss(loss_cls)
+        self.loss_bbox_cls = build_loss(loss_bbox_cls)
+        self.loss_bbox_reg = build_loss(loss_bbox_reg)
+
+        self.cls_fcs = self._add_fc_branch(self.num_cls_fcs,
+                                           self.cls_in_channels,
+                                           self.roi_feat_size,
+                                           self.cls_out_channels)
+
+        self.side_num = int(np.ceil(self.num_buckets / 2))
+
+        if self.reg_feat_up_ratio > 1:
+            self.upsample_x = nn.ConvTranspose1d(
+                reg_in_channels,
+                reg_in_channels,
+                self.reg_feat_up_ratio,
+                stride=self.reg_feat_up_ratio)
+            self.upsample_y = nn.ConvTranspose1d(
+                reg_in_channels,
+                reg_in_channels,
+                self.reg_feat_up_ratio,
+                stride=self.reg_feat_up_ratio)
+
+        self.reg_pre_convs = nn.ModuleList()
+        for i in range(self.reg_pre_num):
+            reg_pre_conv = ConvModule(
+                reg_in_channels,
+                reg_in_channels,
+                kernel_size=reg_pre_kernel,
+                padding=reg_pre_kernel // 2,
+                norm_cfg=norm_cfg,
+                act_cfg=dict(type='ReLU'))
+            self.reg_pre_convs.append(reg_pre_conv)
+
+        self.reg_post_conv_xs = nn.ModuleList()
+        for i in range(self.reg_post_num):
+            reg_post_conv_x = ConvModule(
+                reg_in_channels,
+                reg_in_channels,
+                kernel_size=(1, reg_post_kernel),
+                padding=(0, reg_post_kernel // 2),
+                norm_cfg=norm_cfg,
+                act_cfg=dict(type='ReLU'))
+            self.reg_post_conv_xs.append(reg_post_conv_x)
+        self.reg_post_conv_ys = nn.ModuleList()
+        for i in range(self.reg_post_num):
+            reg_post_conv_y = ConvModule(
+                reg_in_channels,
+                reg_in_channels,
+                kernel_size=(reg_post_kernel, 1),
+                padding=(reg_post_kernel // 2, 0),
+                norm_cfg=norm_cfg,
+                act_cfg=dict(type='ReLU'))
+            self.reg_post_conv_ys.append(reg_post_conv_y)
+
+        self.reg_conv_att_x = nn.Conv2d(reg_in_channels, 1, 1)
+        self.reg_conv_att_y = nn.Conv2d(reg_in_channels, 1, 1)
+
+        self.fc_cls = nn.Linear(self.cls_out_channels, self.num_classes + 1)
+        self.relu = nn.ReLU(inplace=True)
+
+        self.reg_cls_fcs = self._add_fc_branch(self.num_reg_fcs,
+                                               self.reg_in_channels, 1,
+                                               self.reg_cls_out_channels)
+        self.reg_offset_fcs = self._add_fc_branch(self.num_reg_fcs,
+                                                  self.reg_in_channels, 1,
+                                                  self.reg_offset_out_channels)
+        self.fc_reg_cls = nn.Linear(self.reg_cls_out_channels, 1)
+        self.fc_reg_offset = nn.Linear(self.reg_offset_out_channels, 1)
+
+        if init_cfg is None:
+            self.init_cfg = [
+                dict(
+                    type='Xavier',
+                    layer='Linear',
+                    distribution='uniform',
+                    override=[
+                        dict(type='Normal', name='reg_conv_att_x', std=0.01),
+                        dict(type='Normal', name='reg_conv_att_y', std=0.01),
+                        dict(type='Normal', name='fc_reg_cls', std=0.01),
+                        dict(type='Normal', name='fc_cls', std=0.01),
+                        dict(type='Normal', name='fc_reg_offset', std=0.001)
+                    ])
+            ]
+            if self.reg_feat_up_ratio > 1:
+                self.init_cfg += [
+                    dict(
+                        type='Kaiming',
+                        distribution='normal',
+                        override=[
+                            dict(name='upsample_x'),
+                            dict(name='upsample_y')
+                        ])
+                ]
+
+    @property
+    def custom_cls_channels(self):
+        return getattr(self.loss_cls, 'custom_cls_channels', False)
+
+    @property
+    def custom_activation(self):
+        return getattr(self.loss_cls, 'custom_activation', False)
+
+    @property
+    def custom_accuracy(self):
+        return getattr(self.loss_cls, 'custom_accuracy', False)
+
+    def _add_fc_branch(self, num_branch_fcs, in_channels, roi_feat_size,
+                       fc_out_channels):
+        in_channels = in_channels * roi_feat_size * roi_feat_size
+        branch_fcs = nn.ModuleList()
+        for i in range(num_branch_fcs):
+            fc_in_channels = (in_channels if i == 0 else fc_out_channels)
+            branch_fcs.append(nn.Linear(fc_in_channels, fc_out_channels))
+        return branch_fcs
+
+    def cls_forward(self, cls_x):
+        cls_x = cls_x.view(cls_x.size(0), -1)
+        for fc in self.cls_fcs:
+            cls_x = self.relu(fc(cls_x))
+        cls_score = self.fc_cls(cls_x)
+        return cls_score
+
+    def attention_pool(self, reg_x):
+        """Extract direction-specific features fx and fy with attention
+        methanism."""
+        reg_fx = reg_x
+        reg_fy = reg_x
+        reg_fx_att = self.reg_conv_att_x(reg_fx).sigmoid()
+        reg_fy_att = self.reg_conv_att_y(reg_fy).sigmoid()
+        reg_fx_att = reg_fx_att / reg_fx_att.sum(dim=2).unsqueeze(2)
+        reg_fy_att = reg_fy_att / reg_fy_att.sum(dim=3).unsqueeze(3)
+        reg_fx = (reg_fx * reg_fx_att).sum(dim=2)
+        reg_fy = (reg_fy * reg_fy_att).sum(dim=3)
+        return reg_fx, reg_fy
+
+    def side_aware_feature_extractor(self, reg_x):
+        """Refine and extract side-aware features without split them."""
+        for reg_pre_conv in self.reg_pre_convs:
+            reg_x = reg_pre_conv(reg_x)
+        reg_fx, reg_fy = self.attention_pool(reg_x)
+
+        if self.reg_post_num > 0:
+            reg_fx = reg_fx.unsqueeze(2)
+            reg_fy = reg_fy.unsqueeze(3)
+            for i in range(self.reg_post_num):
+                reg_fx = self.reg_post_conv_xs[i](reg_fx)
+                reg_fy = self.reg_post_conv_ys[i](reg_fy)
+            reg_fx = reg_fx.squeeze(2)
+            reg_fy = reg_fy.squeeze(3)
+        if self.reg_feat_up_ratio > 1:
+            reg_fx = self.relu(self.upsample_x(reg_fx))
+            reg_fy = self.relu(self.upsample_y(reg_fy))
+        reg_fx = torch.transpose(reg_fx, 1, 2)
+        reg_fy = torch.transpose(reg_fy, 1, 2)
+        return reg_fx.contiguous(), reg_fy.contiguous()
+
+    def reg_pred(self, x, offset_fcs, cls_fcs):
+        """Predict bucketing estimation (cls_pred) and fine regression (offset
+        pred) with side-aware features."""
+        x_offset = x.view(-1, self.reg_in_channels)
+        x_cls = x.view(-1, self.reg_in_channels)
+
+        for fc in offset_fcs:
+            x_offset = self.relu(fc(x_offset))
+        for fc in cls_fcs:
+            x_cls = self.relu(fc(x_cls))
+        offset_pred = self.fc_reg_offset(x_offset)
+        cls_pred = self.fc_reg_cls(x_cls)
+
+        offset_pred = offset_pred.view(x.size(0), -1)
+        cls_pred = cls_pred.view(x.size(0), -1)
+
+        return offset_pred, cls_pred
+
+    def side_aware_split(self, feat):
+        """Split side-aware features aligned with orders of bucketing
+        targets."""
+        l_end = int(np.ceil(self.up_reg_feat_size / 2))
+        r_start = int(np.floor(self.up_reg_feat_size / 2))
+        feat_fl = feat[:, :l_end]
+        feat_fr = feat[:, r_start:].flip(dims=(1, ))
+        feat_fl = feat_fl.contiguous()
+        feat_fr = feat_fr.contiguous()
+        feat = torch.cat([feat_fl, feat_fr], dim=-1)
+        return feat
+
+    def bbox_pred_split(self, bbox_pred, num_proposals_per_img):
+        """Split batch bbox prediction back to each image."""
+        bucket_cls_preds, bucket_offset_preds = bbox_pred
+        bucket_cls_preds = bucket_cls_preds.split(num_proposals_per_img, 0)
+        bucket_offset_preds = bucket_offset_preds.split(
+            num_proposals_per_img, 0)
+        bbox_pred = tuple(zip(bucket_cls_preds, bucket_offset_preds))
+        return bbox_pred
+
+    def reg_forward(self, reg_x):
+        outs = self.side_aware_feature_extractor(reg_x)
+        edge_offset_preds = []
+        edge_cls_preds = []
+        reg_fx = outs[0]
+        reg_fy = outs[1]
+        offset_pred_x, cls_pred_x = self.reg_pred(reg_fx, self.reg_offset_fcs,
+                                                  self.reg_cls_fcs)
+        offset_pred_y, cls_pred_y = self.reg_pred(reg_fy, self.reg_offset_fcs,
+                                                  self.reg_cls_fcs)
+        offset_pred_x = self.side_aware_split(offset_pred_x)
+        offset_pred_y = self.side_aware_split(offset_pred_y)
+        cls_pred_x = self.side_aware_split(cls_pred_x)
+        cls_pred_y = self.side_aware_split(cls_pred_y)
+        edge_offset_preds = torch.cat([offset_pred_x, offset_pred_y], dim=-1)
+        edge_cls_preds = torch.cat([cls_pred_x, cls_pred_y], dim=-1)
+
+        return (edge_cls_preds, edge_offset_preds)
+
+    def forward(self, x):
+
+        bbox_pred = self.reg_forward(x)
+        cls_score = self.cls_forward(x)
+
+        return cls_score, bbox_pred
+
+    def get_targets(self, sampling_results, gt_bboxes, gt_labels,
+                    rcnn_train_cfg):
+        pos_proposals = [res.pos_bboxes for res in sampling_results]
+        neg_proposals = [res.neg_bboxes for res in sampling_results]
+        pos_gt_bboxes = [res.pos_gt_bboxes for res in sampling_results]
+        pos_gt_labels = [res.pos_gt_labels for res in sampling_results]
+        cls_reg_targets = self.bucket_target(pos_proposals, neg_proposals,
+                                             pos_gt_bboxes, pos_gt_labels,
+                                             rcnn_train_cfg)
+        (labels, label_weights, bucket_cls_targets, bucket_cls_weights,
+         bucket_offset_targets, bucket_offset_weights) = cls_reg_targets
+        return (labels, label_weights, (bucket_cls_targets,
+                                        bucket_offset_targets),
+                (bucket_cls_weights, bucket_offset_weights))
+
+    def bucket_target(self,
+                      pos_proposals_list,
+                      neg_proposals_list,
+                      pos_gt_bboxes_list,
+                      pos_gt_labels_list,
+                      rcnn_train_cfg,
+                      concat=True):
+        (labels, label_weights, bucket_cls_targets, bucket_cls_weights,
+         bucket_offset_targets, bucket_offset_weights) = multi_apply(
+             self._bucket_target_single,
+             pos_proposals_list,
+             neg_proposals_list,
+             pos_gt_bboxes_list,
+             pos_gt_labels_list,
+             cfg=rcnn_train_cfg)
+
+        if concat:
+            labels = torch.cat(labels, 0)
+            label_weights = torch.cat(label_weights, 0)
+            bucket_cls_targets = torch.cat(bucket_cls_targets, 0)
+            bucket_cls_weights = torch.cat(bucket_cls_weights, 0)
+            bucket_offset_targets = torch.cat(bucket_offset_targets, 0)
+            bucket_offset_weights = torch.cat(bucket_offset_weights, 0)
+        return (labels, label_weights, bucket_cls_targets, bucket_cls_weights,
+                bucket_offset_targets, bucket_offset_weights)
+
+    def _bucket_target_single(self, pos_proposals, neg_proposals,
+                              pos_gt_bboxes, pos_gt_labels, cfg):
+        """Compute bucketing estimation targets and fine regression targets for
+        a single image.
+
+        Args:
+            pos_proposals (Tensor): positive proposals of a single image,
+                 Shape (n_pos, 4)
+            neg_proposals (Tensor): negative proposals of a single image,
+                 Shape (n_neg, 4).
+            pos_gt_bboxes (Tensor): gt bboxes assigned to positive proposals
+                 of a single image, Shape (n_pos, 4).
+            pos_gt_labels (Tensor): gt labels assigned to positive proposals
+                 of a single image, Shape (n_pos, ).
+            cfg (dict): Config of calculating targets
+
+        Returns:
+            tuple:
+
+                - labels (Tensor): Labels in a single image. \
+                    Shape (n,).
+                - label_weights (Tensor): Label weights in a single image.\
+                    Shape (n,)
+                - bucket_cls_targets (Tensor): Bucket cls targets in \
+                    a single image. Shape (n, num_buckets*2).
+                - bucket_cls_weights (Tensor): Bucket cls weights in \
+                    a single image. Shape (n, num_buckets*2).
+                - bucket_offset_targets (Tensor): Bucket offset targets \
+                    in a single image. Shape (n, num_buckets*2).
+                - bucket_offset_targets (Tensor): Bucket offset weights \
+                    in a single image. Shape (n, num_buckets*2).
+        """
+        num_pos = pos_proposals.size(0)
+        num_neg = neg_proposals.size(0)
+        num_samples = num_pos + num_neg
+        labels = pos_gt_bboxes.new_full((num_samples, ),
+                                        self.num_classes,
+                                        dtype=torch.long)
+        label_weights = pos_proposals.new_zeros(num_samples)
+        bucket_cls_targets = pos_proposals.new_zeros(num_samples,
+                                                     4 * self.side_num)
+        bucket_cls_weights = pos_proposals.new_zeros(num_samples,
+                                                     4 * self.side_num)
+        bucket_offset_targets = pos_proposals.new_zeros(
+            num_samples, 4 * self.side_num)
+        bucket_offset_weights = pos_proposals.new_zeros(
+            num_samples, 4 * self.side_num)
+        if num_pos > 0:
+            labels[:num_pos] = pos_gt_labels
+            label_weights[:num_pos] = 1.0
+            (pos_bucket_offset_targets, pos_bucket_offset_weights,
+             pos_bucket_cls_targets,
+             pos_bucket_cls_weights) = self.bbox_coder.encode(
+                 pos_proposals, pos_gt_bboxes)
+            bucket_cls_targets[:num_pos, :] = pos_bucket_cls_targets
+            bucket_cls_weights[:num_pos, :] = pos_bucket_cls_weights
+            bucket_offset_targets[:num_pos, :] = pos_bucket_offset_targets
+            bucket_offset_weights[:num_pos, :] = pos_bucket_offset_weights
+        if num_neg > 0:
+            label_weights[-num_neg:] = 1.0
+        return (labels, label_weights, bucket_cls_targets, bucket_cls_weights,
+                bucket_offset_targets, bucket_offset_weights)
+
+    def loss(self,
+             cls_score,
+             bbox_pred,
+             rois,
+             labels,
+             label_weights,
+             bbox_targets,
+             bbox_weights,
+             reduction_override=None):
+        losses = dict()
+        if cls_score is not None:
+            avg_factor = max(torch.sum(label_weights > 0).float().item(), 1.)
+            losses['loss_cls'] = self.loss_cls(
+                cls_score,
+                labels,
+                label_weights,
+                avg_factor=avg_factor,
+                reduction_override=reduction_override)
+            losses['acc'] = accuracy(cls_score, labels)
+
+        if bbox_pred is not None:
+            bucket_cls_preds, bucket_offset_preds = bbox_pred
+            bucket_cls_targets, bucket_offset_targets = bbox_targets
+            bucket_cls_weights, bucket_offset_weights = bbox_weights
+            # edge cls
+            bucket_cls_preds = bucket_cls_preds.view(-1, self.side_num)
+            bucket_cls_targets = bucket_cls_targets.view(-1, self.side_num)
+            bucket_cls_weights = bucket_cls_weights.view(-1, self.side_num)
+            losses['loss_bbox_cls'] = self.loss_bbox_cls(
+                bucket_cls_preds,
+                bucket_cls_targets,
+                bucket_cls_weights,
+                avg_factor=bucket_cls_targets.size(0),
+                reduction_override=reduction_override)
+
+            losses['loss_bbox_reg'] = self.loss_bbox_reg(
+                bucket_offset_preds,
+                bucket_offset_targets,
+                bucket_offset_weights,
+                avg_factor=bucket_offset_targets.size(0),
+                reduction_override=reduction_override)
+
+        return losses
+
+    @force_fp32(apply_to=('cls_score', 'bbox_pred'))
+    def get_bboxes(self,
+                   rois,
+                   cls_score,
+                   bbox_pred,
+                   img_shape,
+                   scale_factor,
+                   rescale=False,
+                   cfg=None):
+        if isinstance(cls_score, list):
+            cls_score = sum(cls_score) / float(len(cls_score))
+        scores = F.softmax(cls_score, dim=1) if cls_score is not None else None
+
+        if bbox_pred is not None:
+            bboxes, confidences = self.bbox_coder.decode(
+                rois[:, 1:], bbox_pred, img_shape)
+        else:
+            bboxes = rois[:, 1:].clone()
+            confidences = None
+            if img_shape is not None:
+                bboxes[:, [0, 2]].clamp_(min=0, max=img_shape[1] - 1)
+                bboxes[:, [1, 3]].clamp_(min=0, max=img_shape[0] - 1)
+
+        if rescale and bboxes.size(0) > 0:
+            if isinstance(scale_factor, float):
+                bboxes /= scale_factor
+            else:
+                bboxes /= torch.from_numpy(scale_factor).to(bboxes.device)
+
+        if cfg is None:
+            return bboxes, scores
+        else:
+            det_bboxes, det_labels = multiclass_nms(
+                bboxes,
+                scores,
+                cfg.score_thr,
+                cfg.nms,
+                cfg.max_per_img,
+                score_factors=confidences)
+
+            return det_bboxes, det_labels
+
+    @force_fp32(apply_to=('bbox_preds', ))
+    def refine_bboxes(self, rois, labels, bbox_preds, pos_is_gts, img_metas):
+        """Refine bboxes during training.
+
+        Args:
+            rois (Tensor): Shape (n*bs, 5), where n is image number per GPU,
+                and bs is the sampled RoIs per image.
+            labels (Tensor): Shape (n*bs, ).
+            bbox_preds (list[Tensor]): Shape [(n*bs, num_buckets*2), \
+                (n*bs, num_buckets*2)].
+            pos_is_gts (list[Tensor]): Flags indicating if each positive bbox
+                is a gt bbox.
+            img_metas (list[dict]): Meta info of each image.
+
+        Returns:
+            list[Tensor]: Refined bboxes of each image in a mini-batch.
+        """
+        img_ids = rois[:, 0].long().unique(sorted=True)
+        assert img_ids.numel() == len(img_metas)
+
+        bboxes_list = []
+        for i in range(len(img_metas)):
+            inds = torch.nonzero(
+                rois[:, 0] == i, as_tuple=False).squeeze(dim=1)
+            num_rois = inds.numel()
+
+            bboxes_ = rois[inds, 1:]
+            label_ = labels[inds]
+            edge_cls_preds, edge_offset_preds = bbox_preds
+            edge_cls_preds_ = edge_cls_preds[inds]
+            edge_offset_preds_ = edge_offset_preds[inds]
+            bbox_pred_ = [edge_cls_preds_, edge_offset_preds_]
+            img_meta_ = img_metas[i]
+            pos_is_gts_ = pos_is_gts[i]
+
+            bboxes = self.regress_by_class(bboxes_, label_, bbox_pred_,
+                                           img_meta_)
+            # filter gt bboxes
+            pos_keep = 1 - pos_is_gts_
+            keep_inds = pos_is_gts_.new_ones(num_rois)
+            keep_inds[:len(pos_is_gts_)] = pos_keep
+
+            bboxes_list.append(bboxes[keep_inds.type(torch.bool)])
+
+        return bboxes_list
+
+    @force_fp32(apply_to=('bbox_pred', ))
+    def regress_by_class(self, rois, label, bbox_pred, img_meta):
+        """Regress the bbox for the predicted class. Used in Cascade R-CNN.
+
+        Args:
+            rois (Tensor): shape (n, 4) or (n, 5)
+            label (Tensor): shape (n, )
+            bbox_pred (list[Tensor]): shape [(n, num_buckets *2), \
+                (n, num_buckets *2)]
+            img_meta (dict): Image meta info.
+
+        Returns:
+            Tensor: Regressed bboxes, the same shape as input rois.
+        """
+        assert rois.size(1) == 4 or rois.size(1) == 5
+
+        if rois.size(1) == 4:
+            new_rois, _ = self.bbox_coder.decode(rois, bbox_pred,
+                                                 img_meta['img_shape'])
+        else:
+            bboxes, _ = self.bbox_coder.decode(rois[:, 1:], bbox_pred,
+                                               img_meta['img_shape'])
+            new_rois = torch.cat((rois[:, [0]], bboxes), dim=1)
+
+        return new_rois
diff --git a/mmdet/models/roi_heads/bbox_heads/scnet_bbox_head.py b/mmdet/models/roi_heads/bbox_heads/scnet_bbox_head.py
new file mode 100644
index 0000000..cf39ebe
--- /dev/null
+++ b/mmdet/models/roi_heads/bbox_heads/scnet_bbox_head.py
@@ -0,0 +1,77 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmdet.models.builder import HEADS
+from .convfc_bbox_head import ConvFCBBoxHead
+
+
+@HEADS.register_module()
+class SCNetBBoxHead(ConvFCBBoxHead):
+    """BBox head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
+
+    This inherits ``ConvFCBBoxHead`` with modified forward() function, allow us
+    to get intermediate shared feature.
+    """
+
+    def _forward_shared(self, x):
+        """Forward function for shared part."""
+        if self.num_shared_convs > 0:
+            for conv in self.shared_convs:
+                x = conv(x)
+
+        if self.num_shared_fcs > 0:
+            if self.with_avg_pool:
+                x = self.avg_pool(x)
+
+            x = x.flatten(1)
+
+            for fc in self.shared_fcs:
+                x = self.relu(fc(x))
+
+        return x
+
+    def _forward_cls_reg(self, x):
+        """Forward function for classification and regression parts."""
+        x_cls = x
+        x_reg = x
+
+        for conv in self.cls_convs:
+            x_cls = conv(x_cls)
+        if x_cls.dim() > 2:
+            if self.with_avg_pool:
+                x_cls = self.avg_pool(x_cls)
+            x_cls = x_cls.flatten(1)
+        for fc in self.cls_fcs:
+            x_cls = self.relu(fc(x_cls))
+
+        for conv in self.reg_convs:
+            x_reg = conv(x_reg)
+        if x_reg.dim() > 2:
+            if self.with_avg_pool:
+                x_reg = self.avg_pool(x_reg)
+            x_reg = x_reg.flatten(1)
+        for fc in self.reg_fcs:
+            x_reg = self.relu(fc(x_reg))
+
+        cls_score = self.fc_cls(x_cls) if self.with_cls else None
+        bbox_pred = self.fc_reg(x_reg) if self.with_reg else None
+
+        return cls_score, bbox_pred
+
+    def forward(self, x, return_shared_feat=False):
+        """Forward function.
+
+        Args:
+            x (Tensor): input features
+            return_shared_feat (bool): If True, return cls-reg-shared feature.
+
+        Return:
+            out (tuple[Tensor]): contain ``cls_score`` and ``bbox_pred``,
+                if  ``return_shared_feat`` is True, append ``x_shared`` to the
+                returned tuple.
+        """
+        x_shared = self._forward_shared(x)
+        out = self._forward_cls_reg(x_shared)
+
+        if return_shared_feat:
+            out += (x_shared, )
+
+        return out
diff --git a/mmdet/models/roi_heads/cascade_roi_head.py b/mmdet/models/roi_heads/cascade_roi_head.py
new file mode 100644
index 0000000..e17313f
--- /dev/null
+++ b/mmdet/models/roi_heads/cascade_roi_head.py
@@ -0,0 +1,631 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+import torch.nn as nn
+from mmcv.runner import ModuleList
+
+from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, build_assigner,
+                        build_sampler, merge_aug_bboxes, merge_aug_masks,
+                        multiclass_nms)
+from ..builder import HEADS, build_head, build_roi_extractor
+from .base_roi_head import BaseRoIHead
+from .test_mixins import BBoxTestMixin, MaskTestMixin
+
+
+@HEADS.register_module()
+class CascadeRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin):
+    """Cascade roi head including one bbox head and one mask head.
+
+    https://arxiv.org/abs/1712.00726
+    """
+
+    def __init__(self,
+                 num_stages,
+                 stage_loss_weights,
+                 bbox_roi_extractor=None,
+                 bbox_head=None,
+                 mask_roi_extractor=None,
+                 mask_head=None,
+                 shared_head=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        assert bbox_roi_extractor is not None
+        assert bbox_head is not None
+        assert shared_head is None, \
+            'Shared head is not supported in Cascade RCNN anymore'
+
+        self.num_stages = num_stages
+        self.stage_loss_weights = stage_loss_weights
+        super(CascadeRoIHead, self).__init__(
+            bbox_roi_extractor=bbox_roi_extractor,
+            bbox_head=bbox_head,
+            mask_roi_extractor=mask_roi_extractor,
+            mask_head=mask_head,
+            shared_head=shared_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained,
+            init_cfg=init_cfg)
+
+    def init_bbox_head(self, bbox_roi_extractor, bbox_head):
+        """Initialize box head and box roi extractor.
+
+        Args:
+            bbox_roi_extractor (dict): Config of box roi extractor.
+            bbox_head (dict): Config of box in box head.
+        """
+        self.bbox_roi_extractor = ModuleList()
+        self.bbox_head = ModuleList()
+        if not isinstance(bbox_roi_extractor, list):
+            bbox_roi_extractor = [
+                bbox_roi_extractor for _ in range(self.num_stages)
+            ]
+        if not isinstance(bbox_head, list):
+            bbox_head = [bbox_head for _ in range(self.num_stages)]
+        assert len(bbox_roi_extractor) == len(bbox_head) == self.num_stages
+        for roi_extractor, head in zip(bbox_roi_extractor, bbox_head):
+            self.bbox_roi_extractor.append(build_roi_extractor(roi_extractor))
+            self.bbox_head.append(build_head(head))
+
+    def init_mask_head(self, mask_roi_extractor, mask_head):
+        """Initialize mask head and mask roi extractor.
+
+        Args:
+            mask_roi_extractor (dict): Config of mask roi extractor.
+            mask_head (dict): Config of mask in mask head.
+        """
+        self.mask_head = nn.ModuleList()
+        if not isinstance(mask_head, list):
+            mask_head = [mask_head for _ in range(self.num_stages)]
+        assert len(mask_head) == self.num_stages
+        for head in mask_head:
+            self.mask_head.append(build_head(head))
+        if mask_roi_extractor is not None:
+            self.share_roi_extractor = False
+            self.mask_roi_extractor = ModuleList()
+            if not isinstance(mask_roi_extractor, list):
+                mask_roi_extractor = [
+                    mask_roi_extractor for _ in range(self.num_stages)
+                ]
+            assert len(mask_roi_extractor) == self.num_stages
+            for roi_extractor in mask_roi_extractor:
+                self.mask_roi_extractor.append(
+                    build_roi_extractor(roi_extractor))
+        else:
+            self.share_roi_extractor = True
+            self.mask_roi_extractor = self.bbox_roi_extractor
+
+    def init_assigner_sampler(self):
+        """Initialize assigner and sampler for each stage."""
+        self.bbox_assigner = []
+        self.bbox_sampler = []
+        if self.train_cfg is not None:
+            for idx, rcnn_train_cfg in enumerate(self.train_cfg):
+                self.bbox_assigner.append(
+                    build_assigner(rcnn_train_cfg.assigner))
+                self.current_stage = idx
+                self.bbox_sampler.append(
+                    build_sampler(rcnn_train_cfg.sampler, context=self))
+
+    def forward_dummy(self, x, proposals):
+        """Dummy forward function."""
+        # bbox head
+        outs = ()
+        rois = bbox2roi([proposals])
+        if self.with_bbox:
+            for i in range(self.num_stages):
+                bbox_results = self._bbox_forward(i, x, rois)
+                outs = outs + (bbox_results['cls_score'],
+                               bbox_results['bbox_pred'])
+        # mask heads
+        if self.with_mask:
+            mask_rois = rois[:100]
+            for i in range(self.num_stages):
+                mask_results = self._mask_forward(i, x, mask_rois)
+                outs = outs + (mask_results['mask_pred'], )
+        return outs
+
+    def _bbox_forward(self, stage, x, rois):
+        """Box head forward function used in both training and testing."""
+        bbox_roi_extractor = self.bbox_roi_extractor[stage]
+        bbox_head = self.bbox_head[stage]
+        bbox_feats = bbox_roi_extractor(x[:bbox_roi_extractor.num_inputs],
+                                        rois)
+        # do not support caffe_c4 model anymore
+        cls_score, bbox_pred = bbox_head(bbox_feats)
+
+        bbox_results = dict(
+            cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
+        return bbox_results
+
+    def _bbox_forward_train(self, stage, x, sampling_results, gt_bboxes,
+                            gt_labels, rcnn_train_cfg):
+        """Run forward function and calculate loss for box head in training."""
+        rois = bbox2roi([res.bboxes for res in sampling_results])
+        bbox_results = self._bbox_forward(stage, x, rois)
+        bbox_targets = self.bbox_head[stage].get_targets(
+            sampling_results, gt_bboxes, gt_labels, rcnn_train_cfg)
+        loss_bbox = self.bbox_head[stage].loss(bbox_results['cls_score'],
+                                               bbox_results['bbox_pred'], rois,
+                                               *bbox_targets)
+
+        bbox_results.update(
+            loss_bbox=loss_bbox, rois=rois, bbox_targets=bbox_targets)
+        return bbox_results
+
+    def _mask_forward(self, stage, x, rois):
+        """Mask head forward function used in both training and testing."""
+        mask_roi_extractor = self.mask_roi_extractor[stage]
+        mask_head = self.mask_head[stage]
+        mask_feats = mask_roi_extractor(x[:mask_roi_extractor.num_inputs],
+                                        rois)
+        # do not support caffe_c4 model anymore
+        mask_pred = mask_head(mask_feats)
+
+        mask_results = dict(mask_pred=mask_pred)
+        return mask_results
+
+    def _mask_forward_train(self,
+                            stage,
+                            x,
+                            sampling_results,
+                            gt_masks,
+                            rcnn_train_cfg,
+                            bbox_feats=None):
+        """Run forward function and calculate loss for mask head in
+        training."""
+        pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
+        mask_results = self._mask_forward(stage, x, pos_rois)
+
+        mask_targets = self.mask_head[stage].get_targets(
+            sampling_results, gt_masks, rcnn_train_cfg)
+        pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
+        loss_mask = self.mask_head[stage].loss(mask_results['mask_pred'],
+                                               mask_targets, pos_labels)
+
+        mask_results.update(loss_mask=loss_mask)
+        return mask_results
+
+    def forward_train(self,
+                      x,
+                      img_metas,
+                      proposal_list,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None,
+                      gt_masks=None):
+        """
+        Args:
+            x (list[Tensor]): list of multi-level img features.
+            img_metas (list[dict]): list of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `mmdet/datasets/pipelines/formatting.py:Collect`.
+            proposals (list[Tensors]): list of region proposals.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+            gt_masks (None | Tensor) : true segmentation masks for each box
+                used if the architecture supports a segmentation task.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        losses = dict()
+        for i in range(self.num_stages):
+            self.current_stage = i
+            rcnn_train_cfg = self.train_cfg[i]
+            lw = self.stage_loss_weights[i]
+
+            # assign gts and sample proposals
+            sampling_results = []
+            if self.with_bbox or self.with_mask:
+                bbox_assigner = self.bbox_assigner[i]
+                bbox_sampler = self.bbox_sampler[i]
+                num_imgs = len(img_metas)
+                if gt_bboxes_ignore is None:
+                    gt_bboxes_ignore = [None for _ in range(num_imgs)]
+
+                for j in range(num_imgs):
+                    assign_result = bbox_assigner.assign(
+                        proposal_list[j], gt_bboxes[j], gt_bboxes_ignore[j],
+                        gt_labels[j])
+                    sampling_result = bbox_sampler.sample(
+                        assign_result,
+                        proposal_list[j],
+                        gt_bboxes[j],
+                        gt_labels[j],
+                        feats=[lvl_feat[j][None] for lvl_feat in x])
+                    sampling_results.append(sampling_result)
+
+            # bbox head forward and loss
+            bbox_results = self._bbox_forward_train(i, x, sampling_results,
+                                                    gt_bboxes, gt_labels,
+                                                    rcnn_train_cfg)
+
+            for name, value in bbox_results['loss_bbox'].items():
+                losses[f's{i}.{name}'] = (
+                    value * lw if 'loss' in name else value)
+
+            # mask head forward and loss
+            if self.with_mask:
+                mask_results = self._mask_forward_train(
+                    i, x, sampling_results, gt_masks, rcnn_train_cfg,
+                    bbox_results['bbox_feats'])
+                for name, value in mask_results['loss_mask'].items():
+                    losses[f's{i}.{name}'] = (
+                        value * lw if 'loss' in name else value)
+
+            # refine bboxes
+            if i < self.num_stages - 1:
+                pos_is_gts = [res.pos_is_gt for res in sampling_results]
+                # bbox_targets is a tuple
+                roi_labels = bbox_results['bbox_targets'][0]
+                with torch.no_grad():
+                    cls_score = bbox_results['cls_score']
+                    if self.bbox_head[i].custom_activation:
+                        cls_score = self.bbox_head[i].loss_cls.get_activation(
+                            cls_score)
+
+                    # Empty proposal.
+                    if cls_score.numel() == 0:
+                        break
+
+                    roi_labels = torch.where(
+                        roi_labels == self.bbox_head[i].num_classes,
+                        cls_score[:, :-1].argmax(1), roi_labels)
+                    proposal_list = self.bbox_head[i].refine_bboxes(
+                        bbox_results['rois'], roi_labels,
+                        bbox_results['bbox_pred'], pos_is_gts, img_metas)
+
+        return losses
+
+    def simple_test(self, x, proposal_list, img_metas, rescale=False):
+        """Test without augmentation.
+
+        Args:
+            x (tuple[Tensor]): Features from upstream network. Each
+                has shape (batch_size, c, h, w).
+            proposal_list (list(Tensor)): Proposals from rpn head.
+                Each has shape (num_proposals, 5), last dimension
+                5 represent (x1, y1, x2, y2, score).
+            img_metas (list[dict]): Meta information of images.
+            rescale (bool): Whether to rescale the results to
+                the original image. Default: True.
+
+        Returns:
+            list[list[np.ndarray]] or list[tuple]: When no mask branch,
+            it is bbox results of each image and classes with type
+            `list[list[np.ndarray]]`. The outer list
+            corresponds to each image. The inner list
+            corresponds to each class. When the model has mask branch,
+            it contains bbox results and mask results.
+            The outer list corresponds to each image, and first element
+            of tuple is bbox results, second element is mask results.
+        """
+        assert self.with_bbox, 'Bbox head must be implemented.'
+        num_imgs = len(proposal_list)
+        img_shapes = tuple(meta['img_shape'] for meta in img_metas)
+        ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
+        scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
+
+        # "ms" in variable names means multi-stage
+        ms_bbox_result = {}
+        ms_segm_result = {}
+        ms_scores = []
+        rcnn_test_cfg = self.test_cfg
+
+        rois = bbox2roi(proposal_list)
+
+        if rois.shape[0] == 0:
+            # There is no proposal in the whole batch
+            bbox_results = [[
+                np.zeros((0, 5), dtype=np.float32)
+                for _ in range(self.bbox_head[-1].num_classes)
+            ]] * num_imgs
+
+            if self.with_mask:
+                mask_classes = self.mask_head[-1].num_classes
+                segm_results = [[[] for _ in range(mask_classes)]
+                                for _ in range(num_imgs)]
+                results = list(zip(bbox_results, segm_results))
+            else:
+                results = bbox_results
+
+            return results
+
+        for i in range(self.num_stages):
+            bbox_results = self._bbox_forward(i, x, rois)
+
+            # split batch bbox prediction back to each image
+            cls_score = bbox_results['cls_score']
+            bbox_pred = bbox_results['bbox_pred']
+            num_proposals_per_img = tuple(
+                len(proposals) for proposals in proposal_list)
+            rois = rois.split(num_proposals_per_img, 0)
+            cls_score = cls_score.split(num_proposals_per_img, 0)
+            if isinstance(bbox_pred, torch.Tensor):
+                bbox_pred = bbox_pred.split(num_proposals_per_img, 0)
+            else:
+                bbox_pred = self.bbox_head[i].bbox_pred_split(
+                    bbox_pred, num_proposals_per_img)
+            ms_scores.append(cls_score)
+
+            if i < self.num_stages - 1:
+                if self.bbox_head[i].custom_activation:
+                    cls_score = [
+                        self.bbox_head[i].loss_cls.get_activation(s)
+                        for s in cls_score
+                    ]
+                refine_rois_list = []
+                for j in range(num_imgs):
+                    if rois[j].shape[0] > 0:
+                        bbox_label = cls_score[j][:, :-1].argmax(dim=1)
+                        refined_rois = self.bbox_head[i].regress_by_class(
+                            rois[j], bbox_label, bbox_pred[j], img_metas[j])
+                        refine_rois_list.append(refined_rois)
+                rois = torch.cat(refine_rois_list)
+
+        # average scores of each image by stages
+        cls_score = [
+            sum([score[i] for score in ms_scores]) / float(len(ms_scores))
+            for i in range(num_imgs)
+        ]
+
+        # apply bbox post-processing to each image individually
+        det_bboxes = []
+        det_labels = []
+        for i in range(num_imgs):
+            det_bbox, det_label = self.bbox_head[-1].get_bboxes(
+                rois[i],
+                cls_score[i],
+                bbox_pred[i],
+                img_shapes[i],
+                scale_factors[i],
+                rescale=rescale,
+                cfg=rcnn_test_cfg)
+            det_bboxes.append(det_bbox)
+            det_labels.append(det_label)
+
+        bbox_results = [
+            bbox2result(det_bboxes[i], det_labels[i],
+                        self.bbox_head[-1].num_classes)
+            for i in range(num_imgs)
+        ]
+        ms_bbox_result['ensemble'] = bbox_results
+
+        if self.with_mask:
+            if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
+                mask_classes = self.mask_head[-1].num_classes
+                segm_results = [[[] for _ in range(mask_classes)]
+                                for _ in range(num_imgs)]
+            else:
+                if rescale and not isinstance(scale_factors[0], float):
+                    scale_factors = [
+                        torch.from_numpy(scale_factor).to(det_bboxes[0].device)
+                        for scale_factor in scale_factors
+                    ]
+                _bboxes = [
+                    det_bboxes[i][:, :4] *
+                    scale_factors[i] if rescale else det_bboxes[i][:, :4]
+                    for i in range(len(det_bboxes))
+                ]
+                mask_rois = bbox2roi(_bboxes)
+                num_mask_rois_per_img = tuple(
+                    _bbox.size(0) for _bbox in _bboxes)
+                aug_masks = []
+                for i in range(self.num_stages):
+                    mask_results = self._mask_forward(i, x, mask_rois)
+                    mask_pred = mask_results['mask_pred']
+                    # split batch mask prediction back to each image
+                    mask_pred = mask_pred.split(num_mask_rois_per_img, 0)
+                    aug_masks.append([
+                        m.sigmoid().cpu().detach().numpy() for m in mask_pred
+                    ])
+
+                # apply mask post-processing to each image individually
+                segm_results = []
+                for i in range(num_imgs):
+                    if det_bboxes[i].shape[0] == 0:
+                        segm_results.append(
+                            [[]
+                             for _ in range(self.mask_head[-1].num_classes)])
+                    else:
+                        aug_mask = [mask[i] for mask in aug_masks]
+                        merged_masks = merge_aug_masks(
+                            aug_mask, [[img_metas[i]]] * self.num_stages,
+                            rcnn_test_cfg)
+                        segm_result = self.mask_head[-1].get_seg_masks(
+                            merged_masks, _bboxes[i], det_labels[i],
+                            rcnn_test_cfg, ori_shapes[i], scale_factors[i],
+                            rescale)
+                        segm_results.append(segm_result)
+            ms_segm_result['ensemble'] = segm_results
+
+        if self.with_mask:
+            results = list(
+                zip(ms_bbox_result['ensemble'], ms_segm_result['ensemble']))
+        else:
+            results = ms_bbox_result['ensemble']
+
+        return results
+
+    def aug_test(self, features, proposal_list, img_metas, rescale=False):
+        """Test with augmentations.
+
+        If rescale is False, then returned bboxes and masks will fit the scale
+        of imgs[0].
+        """
+        rcnn_test_cfg = self.test_cfg
+        aug_bboxes = []
+        aug_scores = []
+        for x, img_meta in zip(features, img_metas):
+            # only one image in the batch
+            img_shape = img_meta[0]['img_shape']
+            scale_factor = img_meta[0]['scale_factor']
+            flip = img_meta[0]['flip']
+            flip_direction = img_meta[0]['flip_direction']
+
+            proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
+                                     scale_factor, flip, flip_direction)
+            # "ms" in variable names means multi-stage
+            ms_scores = []
+
+            rois = bbox2roi([proposals])
+
+            if rois.shape[0] == 0:
+                # There is no proposal in the single image
+                aug_bboxes.append(rois.new_zeros(0, 4))
+                aug_scores.append(rois.new_zeros(0, 1))
+                continue
+
+            for i in range(self.num_stages):
+                bbox_results = self._bbox_forward(i, x, rois)
+                ms_scores.append(bbox_results['cls_score'])
+
+                if i < self.num_stages - 1:
+                    cls_score = bbox_results['cls_score']
+                    if self.bbox_head[i].custom_activation:
+                        cls_score = self.bbox_head[i].loss_cls.get_activation(
+                            cls_score)
+                    bbox_label = cls_score[:, :-1].argmax(dim=1)
+                    rois = self.bbox_head[i].regress_by_class(
+                        rois, bbox_label, bbox_results['bbox_pred'],
+                        img_meta[0])
+
+            cls_score = sum(ms_scores) / float(len(ms_scores))
+            bboxes, scores = self.bbox_head[-1].get_bboxes(
+                rois,
+                cls_score,
+                bbox_results['bbox_pred'],
+                img_shape,
+                scale_factor,
+                rescale=False,
+                cfg=None)
+            aug_bboxes.append(bboxes)
+            aug_scores.append(scores)
+
+        # after merging, bboxes will be rescaled to the original image size
+        merged_bboxes, merged_scores = merge_aug_bboxes(
+            aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
+        det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
+                                                rcnn_test_cfg.score_thr,
+                                                rcnn_test_cfg.nms,
+                                                rcnn_test_cfg.max_per_img)
+
+        bbox_result = bbox2result(det_bboxes, det_labels,
+                                  self.bbox_head[-1].num_classes)
+
+        if self.with_mask:
+            if det_bboxes.shape[0] == 0:
+                segm_result = [[]
+                               for _ in range(self.mask_head[-1].num_classes)]
+            else:
+                aug_masks = []
+                aug_img_metas = []
+                for x, img_meta in zip(features, img_metas):
+                    img_shape = img_meta[0]['img_shape']
+                    scale_factor = img_meta[0]['scale_factor']
+                    flip = img_meta[0]['flip']
+                    flip_direction = img_meta[0]['flip_direction']
+                    _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
+                                           scale_factor, flip, flip_direction)
+                    mask_rois = bbox2roi([_bboxes])
+                    for i in range(self.num_stages):
+                        mask_results = self._mask_forward(i, x, mask_rois)
+                        aug_masks.append(
+                            mask_results['mask_pred'].sigmoid().cpu().numpy())
+                        aug_img_metas.append(img_meta)
+                merged_masks = merge_aug_masks(aug_masks, aug_img_metas,
+                                               self.test_cfg)
+
+                ori_shape = img_metas[0][0]['ori_shape']
+                dummy_scale_factor = np.ones(4)
+                segm_result = self.mask_head[-1].get_seg_masks(
+                    merged_masks,
+                    det_bboxes,
+                    det_labels,
+                    rcnn_test_cfg,
+                    ori_shape,
+                    scale_factor=dummy_scale_factor,
+                    rescale=False)
+            return [(bbox_result, segm_result)]
+        else:
+            return [bbox_result]
+
+    def onnx_export(self, x, proposals, img_metas):
+
+        assert self.with_bbox, 'Bbox head must be implemented.'
+        assert proposals.shape[0] == 1, 'Only support one input image ' \
+                                        'while in exporting to ONNX'
+        # remove the scores
+        rois = proposals[..., :-1]
+        batch_size = rois.shape[0]
+        num_proposals_per_img = rois.shape[1]
+        # Eliminate the batch dimension
+        rois = rois.view(-1, 4)
+
+        # add dummy batch index
+        rois = torch.cat([rois.new_zeros(rois.shape[0], 1), rois], dim=-1)
+
+        max_shape = img_metas[0]['img_shape_for_onnx']
+        ms_scores = []
+        rcnn_test_cfg = self.test_cfg
+
+        for i in range(self.num_stages):
+            bbox_results = self._bbox_forward(i, x, rois)
+
+            cls_score = bbox_results['cls_score']
+            bbox_pred = bbox_results['bbox_pred']
+            # Recover the batch dimension
+            rois = rois.reshape(batch_size, num_proposals_per_img,
+                                rois.size(-1))
+            cls_score = cls_score.reshape(batch_size, num_proposals_per_img,
+                                          cls_score.size(-1))
+            bbox_pred = bbox_pred.reshape(batch_size, num_proposals_per_img, 4)
+            ms_scores.append(cls_score)
+            if i < self.num_stages - 1:
+                assert self.bbox_head[i].reg_class_agnostic
+                new_rois = self.bbox_head[i].bbox_coder.decode(
+                    rois[..., 1:], bbox_pred, max_shape=max_shape)
+                rois = new_rois.reshape(-1, new_rois.shape[-1])
+                # add dummy batch index
+                rois = torch.cat([rois.new_zeros(rois.shape[0], 1), rois],
+                                 dim=-1)
+
+        cls_score = sum(ms_scores) / float(len(ms_scores))
+        bbox_pred = bbox_pred.reshape(batch_size, num_proposals_per_img, 4)
+        rois = rois.reshape(batch_size, num_proposals_per_img, -1)
+        det_bboxes, det_labels = self.bbox_head[-1].onnx_export(
+            rois, cls_score, bbox_pred, max_shape, cfg=rcnn_test_cfg)
+
+        if not self.with_mask:
+            return det_bboxes, det_labels
+        else:
+            batch_index = torch.arange(
+                det_bboxes.size(0),
+                device=det_bboxes.device).float().view(-1, 1, 1).expand(
+                    det_bboxes.size(0), det_bboxes.size(1), 1)
+            rois = det_bboxes[..., :4]
+            mask_rois = torch.cat([batch_index, rois], dim=-1)
+            mask_rois = mask_rois.view(-1, 5)
+            aug_masks = []
+            for i in range(self.num_stages):
+                mask_results = self._mask_forward(i, x, mask_rois)
+                mask_pred = mask_results['mask_pred']
+                aug_masks.append(mask_pred)
+            max_shape = img_metas[0]['img_shape_for_onnx']
+            # calculate the mean of masks from several stage
+            mask_pred = sum(aug_masks) / len(aug_masks)
+            segm_results = self.mask_head[-1].onnx_export(
+                mask_pred, rois.reshape(-1, 4), det_labels.reshape(-1),
+                self.test_cfg, max_shape)
+            segm_results = segm_results.reshape(batch_size,
+                                                det_bboxes.shape[1],
+                                                max_shape[0], max_shape[1])
+            return det_bboxes, det_labels, segm_results
diff --git a/mmdet/models/roi_heads/double_roi_head.py b/mmdet/models/roi_heads/double_roi_head.py
new file mode 100644
index 0000000..895b5d3
--- /dev/null
+++ b/mmdet/models/roi_heads/double_roi_head.py
@@ -0,0 +1,34 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from ..builder import HEADS
+from .standard_roi_head import StandardRoIHead
+
+
+@HEADS.register_module()
+class DoubleHeadRoIHead(StandardRoIHead):
+    """RoI head for Double Head RCNN.
+
+    https://arxiv.org/abs/1904.06493
+    """
+
+    def __init__(self, reg_roi_scale_factor, **kwargs):
+        super(DoubleHeadRoIHead, self).__init__(**kwargs)
+        self.reg_roi_scale_factor = reg_roi_scale_factor
+
+    def _bbox_forward(self, x, rois):
+        """Box head forward function used in both training and testing time."""
+        bbox_cls_feats = self.bbox_roi_extractor(
+            x[:self.bbox_roi_extractor.num_inputs], rois)
+        bbox_reg_feats = self.bbox_roi_extractor(
+            x[:self.bbox_roi_extractor.num_inputs],
+            rois,
+            roi_scale_factor=self.reg_roi_scale_factor)
+        if self.with_shared_head:
+            bbox_cls_feats = self.shared_head(bbox_cls_feats)
+            bbox_reg_feats = self.shared_head(bbox_reg_feats)
+        cls_score, bbox_pred = self.bbox_head(bbox_cls_feats, bbox_reg_feats)
+
+        bbox_results = dict(
+            cls_score=cls_score,
+            bbox_pred=bbox_pred,
+            bbox_feats=bbox_cls_feats)
+        return bbox_results
diff --git a/mmdet/models/roi_heads/dynamic_roi_head.py b/mmdet/models/roi_heads/dynamic_roi_head.py
new file mode 100644
index 0000000..4c2b6cd
--- /dev/null
+++ b/mmdet/models/roi_heads/dynamic_roi_head.py
@@ -0,0 +1,155 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+
+from mmdet.core import bbox2roi
+from mmdet.models.losses import SmoothL1Loss
+from ..builder import HEADS
+from .standard_roi_head import StandardRoIHead
+
+EPS = 1e-15
+
+
+@HEADS.register_module()
+class DynamicRoIHead(StandardRoIHead):
+    """RoI head for `Dynamic R-CNN <https://arxiv.org/abs/2004.06002>`_."""
+
+    def __init__(self, **kwargs):
+        super(DynamicRoIHead, self).__init__(**kwargs)
+        assert isinstance(self.bbox_head.loss_bbox, SmoothL1Loss)
+        # the IoU history of the past `update_iter_interval` iterations
+        self.iou_history = []
+        # the beta history of the past `update_iter_interval` iterations
+        self.beta_history = []
+
+    def forward_train(self,
+                      x,
+                      img_metas,
+                      proposal_list,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None,
+                      gt_masks=None):
+        """Forward function for training.
+
+        Args:
+            x (list[Tensor]): list of multi-level img features.
+
+            img_metas (list[dict]): list of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `mmdet/datasets/pipelines/formatting.py:Collect`.
+
+            proposals (list[Tensors]): list of region proposals.
+
+            gt_bboxes (list[Tensor]): each item are the truth boxes for each
+                image in [tl_x, tl_y, br_x, br_y] format.
+
+            gt_labels (list[Tensor]): class indices corresponding to each box
+
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+
+            gt_masks (None | Tensor) : true segmentation masks for each box
+                used if the architecture supports a segmentation task.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        # assign gts and sample proposals
+        if self.with_bbox or self.with_mask:
+            num_imgs = len(img_metas)
+            if gt_bboxes_ignore is None:
+                gt_bboxes_ignore = [None for _ in range(num_imgs)]
+            sampling_results = []
+            cur_iou = []
+            for i in range(num_imgs):
+                assign_result = self.bbox_assigner.assign(
+                    proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
+                    gt_labels[i])
+                sampling_result = self.bbox_sampler.sample(
+                    assign_result,
+                    proposal_list[i],
+                    gt_bboxes[i],
+                    gt_labels[i],
+                    feats=[lvl_feat[i][None] for lvl_feat in x])
+                # record the `iou_topk`-th largest IoU in an image
+                iou_topk = min(self.train_cfg.dynamic_rcnn.iou_topk,
+                               len(assign_result.max_overlaps))
+                ious, _ = torch.topk(assign_result.max_overlaps, iou_topk)
+                cur_iou.append(ious[-1].item())
+                sampling_results.append(sampling_result)
+            # average the current IoUs over images
+            cur_iou = np.mean(cur_iou)
+            self.iou_history.append(cur_iou)
+
+        losses = dict()
+        # bbox head forward and loss
+        if self.with_bbox:
+            bbox_results = self._bbox_forward_train(x, sampling_results,
+                                                    gt_bboxes, gt_labels,
+                                                    img_metas)
+            losses.update(bbox_results['loss_bbox'])
+
+        # mask head forward and loss
+        if self.with_mask:
+            mask_results = self._mask_forward_train(x, sampling_results,
+                                                    bbox_results['bbox_feats'],
+                                                    gt_masks, img_metas)
+            losses.update(mask_results['loss_mask'])
+
+        # update IoU threshold and SmoothL1 beta
+        update_iter_interval = self.train_cfg.dynamic_rcnn.update_iter_interval
+        if len(self.iou_history) % update_iter_interval == 0:
+            new_iou_thr, new_beta = self.update_hyperparameters()
+
+        return losses
+
+    def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
+                            img_metas):
+        num_imgs = len(img_metas)
+        rois = bbox2roi([res.bboxes for res in sampling_results])
+        bbox_results = self._bbox_forward(x, rois)
+
+        bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
+                                                  gt_labels, self.train_cfg)
+        # record the `beta_topk`-th smallest target
+        # `bbox_targets[2]` and `bbox_targets[3]` stand for bbox_targets
+        # and bbox_weights, respectively
+        pos_inds = bbox_targets[3][:, 0].nonzero().squeeze(1)
+        num_pos = len(pos_inds)
+        cur_target = bbox_targets[2][pos_inds, :2].abs().mean(dim=1)
+        beta_topk = min(self.train_cfg.dynamic_rcnn.beta_topk * num_imgs,
+                        num_pos)
+        cur_target = torch.kthvalue(cur_target, beta_topk)[0].item()
+        self.beta_history.append(cur_target)
+        loss_bbox = self.bbox_head.loss(bbox_results['cls_score'],
+                                        bbox_results['bbox_pred'], rois,
+                                        *bbox_targets)
+
+        bbox_results.update(loss_bbox=loss_bbox)
+        return bbox_results
+
+    def update_hyperparameters(self):
+        """Update hyperparameters like IoU thresholds for assigner and beta for
+        SmoothL1 loss based on the training statistics.
+
+        Returns:
+            tuple[float]: the updated ``iou_thr`` and ``beta``.
+        """
+        new_iou_thr = max(self.train_cfg.dynamic_rcnn.initial_iou,
+                          np.mean(self.iou_history))
+        self.iou_history = []
+        self.bbox_assigner.pos_iou_thr = new_iou_thr
+        self.bbox_assigner.neg_iou_thr = new_iou_thr
+        self.bbox_assigner.min_pos_iou = new_iou_thr
+        if (np.median(self.beta_history) < EPS):
+            # avoid 0 or too small value for new_beta
+            new_beta = self.bbox_head.loss_bbox.beta
+        else:
+            new_beta = min(self.train_cfg.dynamic_rcnn.initial_beta,
+                           np.median(self.beta_history))
+        self.beta_history = []
+        self.bbox_head.loss_bbox.beta = new_beta
+        return new_iou_thr, new_beta
diff --git a/mmdet/models/roi_heads/grid_roi_head.py b/mmdet/models/roi_heads/grid_roi_head.py
new file mode 100644
index 0000000..333f629
--- /dev/null
+++ b/mmdet/models/roi_heads/grid_roi_head.py
@@ -0,0 +1,170 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+
+from mmdet.core import bbox2result, bbox2roi
+from ..builder import HEADS, build_head, build_roi_extractor
+from .standard_roi_head import StandardRoIHead
+
+
+@HEADS.register_module()
+class GridRoIHead(StandardRoIHead):
+    """Grid roi head for Grid R-CNN.
+
+    https://arxiv.org/abs/1811.12030
+    """
+
+    def __init__(self, grid_roi_extractor, grid_head, **kwargs):
+        assert grid_head is not None
+        super(GridRoIHead, self).__init__(**kwargs)
+        if grid_roi_extractor is not None:
+            self.grid_roi_extractor = build_roi_extractor(grid_roi_extractor)
+            self.share_roi_extractor = False
+        else:
+            self.share_roi_extractor = True
+            self.grid_roi_extractor = self.bbox_roi_extractor
+        self.grid_head = build_head(grid_head)
+
+    def _random_jitter(self, sampling_results, img_metas, amplitude=0.15):
+        """Ramdom jitter positive proposals for training."""
+        for sampling_result, img_meta in zip(sampling_results, img_metas):
+            bboxes = sampling_result.pos_bboxes
+            random_offsets = bboxes.new_empty(bboxes.shape[0], 4).uniform_(
+                -amplitude, amplitude)
+            # before jittering
+            cxcy = (bboxes[:, 2:4] + bboxes[:, :2]) / 2
+            wh = (bboxes[:, 2:4] - bboxes[:, :2]).abs()
+            # after jittering
+            new_cxcy = cxcy + wh * random_offsets[:, :2]
+            new_wh = wh * (1 + random_offsets[:, 2:])
+            # xywh to xyxy
+            new_x1y1 = (new_cxcy - new_wh / 2)
+            new_x2y2 = (new_cxcy + new_wh / 2)
+            new_bboxes = torch.cat([new_x1y1, new_x2y2], dim=1)
+            # clip bboxes
+            max_shape = img_meta['img_shape']
+            if max_shape is not None:
+                new_bboxes[:, 0::2].clamp_(min=0, max=max_shape[1] - 1)
+                new_bboxes[:, 1::2].clamp_(min=0, max=max_shape[0] - 1)
+
+            sampling_result.pos_bboxes = new_bboxes
+        return sampling_results
+
+    def forward_dummy(self, x, proposals):
+        """Dummy forward function."""
+        # bbox head
+        outs = ()
+        rois = bbox2roi([proposals])
+        if self.with_bbox:
+            bbox_results = self._bbox_forward(x, rois)
+            outs = outs + (bbox_results['cls_score'],
+                           bbox_results['bbox_pred'])
+
+        # grid head
+        grid_rois = rois[:100]
+        grid_feats = self.grid_roi_extractor(
+            x[:self.grid_roi_extractor.num_inputs], grid_rois)
+        if self.with_shared_head:
+            grid_feats = self.shared_head(grid_feats)
+        grid_pred = self.grid_head(grid_feats)
+        outs = outs + (grid_pred, )
+
+        # mask head
+        if self.with_mask:
+            mask_rois = rois[:100]
+            mask_results = self._mask_forward(x, mask_rois)
+            outs = outs + (mask_results['mask_pred'], )
+        return outs
+
+    def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
+                            img_metas):
+        """Run forward function and calculate loss for box head in training."""
+        bbox_results = super(GridRoIHead,
+                             self)._bbox_forward_train(x, sampling_results,
+                                                       gt_bboxes, gt_labels,
+                                                       img_metas)
+
+        # Grid head forward and loss
+        sampling_results = self._random_jitter(sampling_results, img_metas)
+        pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
+
+        # GN in head does not support zero shape input
+        if pos_rois.shape[0] == 0:
+            return bbox_results
+
+        grid_feats = self.grid_roi_extractor(
+            x[:self.grid_roi_extractor.num_inputs], pos_rois)
+        if self.with_shared_head:
+            grid_feats = self.shared_head(grid_feats)
+        # Accelerate training
+        max_sample_num_grid = self.train_cfg.get('max_num_grid', 192)
+        sample_idx = torch.randperm(
+            grid_feats.shape[0])[:min(grid_feats.shape[0], max_sample_num_grid
+                                      )]
+        grid_feats = grid_feats[sample_idx]
+
+        grid_pred = self.grid_head(grid_feats)
+
+        grid_targets = self.grid_head.get_targets(sampling_results,
+                                                  self.train_cfg)
+        grid_targets = grid_targets[sample_idx]
+
+        loss_grid = self.grid_head.loss(grid_pred, grid_targets)
+
+        bbox_results['loss_bbox'].update(loss_grid)
+        return bbox_results
+
+    def simple_test(self,
+                    x,
+                    proposal_list,
+                    img_metas,
+                    proposals=None,
+                    rescale=False):
+        """Test without augmentation."""
+        assert self.with_bbox, 'Bbox head must be implemented.'
+
+        det_bboxes, det_labels = self.simple_test_bboxes(
+            x, img_metas, proposal_list, self.test_cfg, rescale=False)
+        # pack rois into bboxes
+        grid_rois = bbox2roi([det_bbox[:, :4] for det_bbox in det_bboxes])
+        if grid_rois.shape[0] != 0:
+            grid_feats = self.grid_roi_extractor(
+                x[:len(self.grid_roi_extractor.featmap_strides)], grid_rois)
+            self.grid_head.test_mode = True
+            grid_pred = self.grid_head(grid_feats)
+            # split batch grid head prediction back to each image
+            num_roi_per_img = tuple(len(det_bbox) for det_bbox in det_bboxes)
+            grid_pred = {
+                k: v.split(num_roi_per_img, 0)
+                for k, v in grid_pred.items()
+            }
+
+            # apply bbox post-processing to each image individually
+            bbox_results = []
+            num_imgs = len(det_bboxes)
+            for i in range(num_imgs):
+                if det_bboxes[i].shape[0] == 0:
+                    bbox_results.append([
+                        np.zeros((0, 5), dtype=np.float32)
+                        for _ in range(self.bbox_head.num_classes)
+                    ])
+                else:
+                    det_bbox = self.grid_head.get_bboxes(
+                        det_bboxes[i], grid_pred['fused'][i], [img_metas[i]])
+                    if rescale:
+                        det_bbox[:, :4] /= img_metas[i]['scale_factor']
+                    bbox_results.append(
+                        bbox2result(det_bbox, det_labels[i],
+                                    self.bbox_head.num_classes))
+        else:
+            bbox_results = [[
+                np.zeros((0, 5), dtype=np.float32)
+                for _ in range(self.bbox_head.num_classes)
+            ] for _ in range(len(det_bboxes))]
+
+        if not self.with_mask:
+            return bbox_results
+        else:
+            segm_results = self.simple_test_mask(
+                x, img_metas, det_bboxes, det_labels, rescale=rescale)
+            return list(zip(bbox_results, segm_results))
diff --git a/mmdet/models/roi_heads/htc_roi_head.py b/mmdet/models/roi_heads/htc_roi_head.py
new file mode 100644
index 0000000..86a6db1
--- /dev/null
+++ b/mmdet/models/roi_heads/htc_roi_head.py
@@ -0,0 +1,628 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes,
+                        merge_aug_masks, multiclass_nms)
+from ..builder import HEADS, build_head, build_roi_extractor
+from ..utils.brick_wrappers import adaptive_avg_pool2d
+from .cascade_roi_head import CascadeRoIHead
+
+
+@HEADS.register_module()
+class HybridTaskCascadeRoIHead(CascadeRoIHead):
+    """Hybrid task cascade roi head including one bbox head and one mask head.
+
+    https://arxiv.org/abs/1901.07518
+    """
+
+    def __init__(self,
+                 num_stages,
+                 stage_loss_weights,
+                 semantic_roi_extractor=None,
+                 semantic_head=None,
+                 semantic_fusion=('bbox', 'mask'),
+                 interleaved=True,
+                 mask_info_flow=True,
+                 **kwargs):
+        super(HybridTaskCascadeRoIHead,
+              self).__init__(num_stages, stage_loss_weights, **kwargs)
+        assert self.with_bbox
+        assert not self.with_shared_head  # shared head is not supported
+
+        if semantic_head is not None:
+            self.semantic_roi_extractor = build_roi_extractor(
+                semantic_roi_extractor)
+            self.semantic_head = build_head(semantic_head)
+
+        self.semantic_fusion = semantic_fusion
+        self.interleaved = interleaved
+        self.mask_info_flow = mask_info_flow
+
+    @property
+    def with_semantic(self):
+        """bool: whether the head has semantic head"""
+        if hasattr(self, 'semantic_head') and self.semantic_head is not None:
+            return True
+        else:
+            return False
+
+    def forward_dummy(self, x, proposals):
+        """Dummy forward function."""
+        outs = ()
+        # semantic head
+        if self.with_semantic:
+            _, semantic_feat = self.semantic_head(x)
+        else:
+            semantic_feat = None
+        # bbox heads
+        rois = bbox2roi([proposals])
+        for i in range(self.num_stages):
+            bbox_results = self._bbox_forward(
+                i, x, rois, semantic_feat=semantic_feat)
+            outs = outs + (bbox_results['cls_score'],
+                           bbox_results['bbox_pred'])
+        # mask heads
+        if self.with_mask:
+            mask_rois = rois[:100]
+            mask_roi_extractor = self.mask_roi_extractor[-1]
+            mask_feats = mask_roi_extractor(
+                x[:len(mask_roi_extractor.featmap_strides)], mask_rois)
+            if self.with_semantic and 'mask' in self.semantic_fusion:
+                mask_semantic_feat = self.semantic_roi_extractor(
+                    [semantic_feat], mask_rois)
+                mask_feats = mask_feats + mask_semantic_feat
+            last_feat = None
+            for i in range(self.num_stages):
+                mask_head = self.mask_head[i]
+                if self.mask_info_flow:
+                    mask_pred, last_feat = mask_head(mask_feats, last_feat)
+                else:
+                    mask_pred = mask_head(mask_feats)
+                outs = outs + (mask_pred, )
+        return outs
+
+    def _bbox_forward_train(self,
+                            stage,
+                            x,
+                            sampling_results,
+                            gt_bboxes,
+                            gt_labels,
+                            rcnn_train_cfg,
+                            semantic_feat=None):
+        """Run forward function and calculate loss for box head in training."""
+        bbox_head = self.bbox_head[stage]
+        rois = bbox2roi([res.bboxes for res in sampling_results])
+        bbox_results = self._bbox_forward(
+            stage, x, rois, semantic_feat=semantic_feat)
+
+        bbox_targets = bbox_head.get_targets(sampling_results, gt_bboxes,
+                                             gt_labels, rcnn_train_cfg)
+        loss_bbox = bbox_head.loss(bbox_results['cls_score'],
+                                   bbox_results['bbox_pred'], rois,
+                                   *bbox_targets)
+
+        bbox_results.update(
+            loss_bbox=loss_bbox,
+            rois=rois,
+            bbox_targets=bbox_targets,
+        )
+        return bbox_results
+
+    def _mask_forward_train(self,
+                            stage,
+                            x,
+                            sampling_results,
+                            gt_masks,
+                            rcnn_train_cfg,
+                            semantic_feat=None):
+        """Run forward function and calculate loss for mask head in
+        training."""
+        mask_roi_extractor = self.mask_roi_extractor[stage]
+        mask_head = self.mask_head[stage]
+        pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
+        mask_feats = mask_roi_extractor(x[:mask_roi_extractor.num_inputs],
+                                        pos_rois)
+
+        # semantic feature fusion
+        # element-wise sum for original features and pooled semantic features
+        if self.with_semantic and 'mask' in self.semantic_fusion:
+            mask_semantic_feat = self.semantic_roi_extractor([semantic_feat],
+                                                             pos_rois)
+            if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]:
+                mask_semantic_feat = F.adaptive_avg_pool2d(
+                    mask_semantic_feat, mask_feats.shape[-2:])
+            mask_feats = mask_feats + mask_semantic_feat
+
+        # mask information flow
+        # forward all previous mask heads to obtain last_feat, and fuse it
+        # with the normal mask feature
+        if self.mask_info_flow:
+            last_feat = None
+            for i in range(stage):
+                last_feat = self.mask_head[i](
+                    mask_feats, last_feat, return_logits=False)
+            mask_pred = mask_head(mask_feats, last_feat, return_feat=False)
+        else:
+            mask_pred = mask_head(mask_feats, return_feat=False)
+
+        mask_targets = mask_head.get_targets(sampling_results, gt_masks,
+                                             rcnn_train_cfg)
+        pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
+        loss_mask = mask_head.loss(mask_pred, mask_targets, pos_labels)
+
+        mask_results = dict(loss_mask=loss_mask)
+        return mask_results
+
+    def _bbox_forward(self, stage, x, rois, semantic_feat=None):
+        """Box head forward function used in both training and testing."""
+        bbox_roi_extractor = self.bbox_roi_extractor[stage]
+        bbox_head = self.bbox_head[stage]
+        bbox_feats = bbox_roi_extractor(
+            x[:len(bbox_roi_extractor.featmap_strides)], rois)
+        if self.with_semantic and 'bbox' in self.semantic_fusion:
+            bbox_semantic_feat = self.semantic_roi_extractor([semantic_feat],
+                                                             rois)
+            if bbox_semantic_feat.shape[-2:] != bbox_feats.shape[-2:]:
+                bbox_semantic_feat = adaptive_avg_pool2d(
+                    bbox_semantic_feat, bbox_feats.shape[-2:])
+            bbox_feats = bbox_feats + bbox_semantic_feat
+        cls_score, bbox_pred = bbox_head(bbox_feats)
+
+        bbox_results = dict(cls_score=cls_score, bbox_pred=bbox_pred)
+        return bbox_results
+
+    def _mask_forward_test(self, stage, x, bboxes, semantic_feat=None):
+        """Mask head forward function for testing."""
+        mask_roi_extractor = self.mask_roi_extractor[stage]
+        mask_head = self.mask_head[stage]
+        mask_rois = bbox2roi([bboxes])
+        mask_feats = mask_roi_extractor(
+            x[:len(mask_roi_extractor.featmap_strides)], mask_rois)
+        if self.with_semantic and 'mask' in self.semantic_fusion:
+            mask_semantic_feat = self.semantic_roi_extractor([semantic_feat],
+                                                             mask_rois)
+            if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]:
+                mask_semantic_feat = F.adaptive_avg_pool2d(
+                    mask_semantic_feat, mask_feats.shape[-2:])
+            mask_feats = mask_feats + mask_semantic_feat
+        if self.mask_info_flow:
+            last_feat = None
+            last_pred = None
+            for i in range(stage):
+                mask_pred, last_feat = self.mask_head[i](mask_feats, last_feat)
+                if last_pred is not None:
+                    mask_pred = mask_pred + last_pred
+                last_pred = mask_pred
+            mask_pred = mask_head(mask_feats, last_feat, return_feat=False)
+            if last_pred is not None:
+                mask_pred = mask_pred + last_pred
+        else:
+            mask_pred = mask_head(mask_feats)
+        return mask_pred
+
+    def forward_train(self,
+                      x,
+                      img_metas,
+                      proposal_list,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None,
+                      gt_masks=None,
+                      gt_semantic_seg=None):
+        """
+        Args:
+            x (list[Tensor]): list of multi-level img features.
+
+            img_metas (list[dict]): list of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `mmdet/datasets/pipelines/formatting.py:Collect`.
+
+            proposal_list (list[Tensors]): list of region proposals.
+
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+
+            gt_labels (list[Tensor]): class indices corresponding to each box
+
+            gt_bboxes_ignore (None, list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+
+            gt_masks (None, Tensor) : true segmentation masks for each box
+                used if the architecture supports a segmentation task.
+
+            gt_semantic_seg (None, list[Tensor]): semantic segmentation masks
+                used if the architecture supports semantic segmentation task.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        # semantic segmentation part
+        # 2 outputs: segmentation prediction and embedded features
+        losses = dict()
+        if self.with_semantic:
+            semantic_pred, semantic_feat = self.semantic_head(x)
+            loss_seg = self.semantic_head.loss(semantic_pred, gt_semantic_seg)
+            losses['loss_semantic_seg'] = loss_seg
+        else:
+            semantic_feat = None
+
+        for i in range(self.num_stages):
+            self.current_stage = i
+            rcnn_train_cfg = self.train_cfg[i]
+            lw = self.stage_loss_weights[i]
+
+            # assign gts and sample proposals
+            sampling_results = []
+            bbox_assigner = self.bbox_assigner[i]
+            bbox_sampler = self.bbox_sampler[i]
+            num_imgs = len(img_metas)
+            if gt_bboxes_ignore is None:
+                gt_bboxes_ignore = [None for _ in range(num_imgs)]
+
+            for j in range(num_imgs):
+                assign_result = bbox_assigner.assign(proposal_list[j],
+                                                     gt_bboxes[j],
+                                                     gt_bboxes_ignore[j],
+                                                     gt_labels[j])
+                sampling_result = bbox_sampler.sample(
+                    assign_result,
+                    proposal_list[j],
+                    gt_bboxes[j],
+                    gt_labels[j],
+                    feats=[lvl_feat[j][None] for lvl_feat in x])
+                sampling_results.append(sampling_result)
+
+            # bbox head forward and loss
+            bbox_results = \
+                self._bbox_forward_train(
+                    i, x, sampling_results, gt_bboxes, gt_labels,
+                    rcnn_train_cfg, semantic_feat)
+            roi_labels = bbox_results['bbox_targets'][0]
+
+            for name, value in bbox_results['loss_bbox'].items():
+                losses[f's{i}.{name}'] = (
+                    value * lw if 'loss' in name else value)
+
+            # mask head forward and loss
+            if self.with_mask:
+                # interleaved execution: use regressed bboxes by the box branch
+                # to train the mask branch
+                if self.interleaved:
+                    pos_is_gts = [res.pos_is_gt for res in sampling_results]
+                    with torch.no_grad():
+                        proposal_list = self.bbox_head[i].refine_bboxes(
+                            bbox_results['rois'], roi_labels,
+                            bbox_results['bbox_pred'], pos_is_gts, img_metas)
+                        # re-assign and sample 512 RoIs from 512 RoIs
+                        sampling_results = []
+                        for j in range(num_imgs):
+                            assign_result = bbox_assigner.assign(
+                                proposal_list[j], gt_bboxes[j],
+                                gt_bboxes_ignore[j], gt_labels[j])
+                            sampling_result = bbox_sampler.sample(
+                                assign_result,
+                                proposal_list[j],
+                                gt_bboxes[j],
+                                gt_labels[j],
+                                feats=[lvl_feat[j][None] for lvl_feat in x])
+                            sampling_results.append(sampling_result)
+                mask_results = self._mask_forward_train(
+                    i, x, sampling_results, gt_masks, rcnn_train_cfg,
+                    semantic_feat)
+                for name, value in mask_results['loss_mask'].items():
+                    losses[f's{i}.{name}'] = (
+                        value * lw if 'loss' in name else value)
+
+            # refine bboxes (same as Cascade R-CNN)
+            if i < self.num_stages - 1 and not self.interleaved:
+                pos_is_gts = [res.pos_is_gt for res in sampling_results]
+                with torch.no_grad():
+                    proposal_list = self.bbox_head[i].refine_bboxes(
+                        bbox_results['rois'], roi_labels,
+                        bbox_results['bbox_pred'], pos_is_gts, img_metas)
+
+        return losses
+
+    def simple_test(self, x, proposal_list, img_metas, rescale=False):
+        """Test without augmentation.
+
+        Args:
+            x (tuple[Tensor]): Features from upstream network. Each
+                has shape (batch_size, c, h, w).
+            proposal_list (list(Tensor)): Proposals from rpn head.
+                Each has shape (num_proposals, 5), last dimension
+                5 represent (x1, y1, x2, y2, score).
+            img_metas (list[dict]): Meta information of images.
+            rescale (bool): Whether to rescale the results to
+                the original image. Default: True.
+
+        Returns:
+            list[list[np.ndarray]] or list[tuple]: When no mask branch,
+            it is bbox results of each image and classes with type
+            `list[list[np.ndarray]]`. The outer list
+            corresponds to each image. The inner list
+            corresponds to each class. When the model has mask branch,
+            it contains bbox results and mask results.
+            The outer list corresponds to each image, and first element
+            of tuple is bbox results, second element is mask results.
+        """
+        if self.with_semantic:
+            _, semantic_feat = self.semantic_head(x)
+        else:
+            semantic_feat = None
+
+        num_imgs = len(proposal_list)
+        img_shapes = tuple(meta['img_shape'] for meta in img_metas)
+        ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
+        scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
+
+        # "ms" in variable names means multi-stage
+        ms_bbox_result = {}
+        ms_segm_result = {}
+        ms_scores = []
+        rcnn_test_cfg = self.test_cfg
+
+        rois = bbox2roi(proposal_list)
+
+        if rois.shape[0] == 0:
+            # There is no proposal in the whole batch
+            bbox_results = [[
+                np.zeros((0, 5), dtype=np.float32)
+                for _ in range(self.bbox_head[-1].num_classes)
+            ]] * num_imgs
+
+            if self.with_mask:
+                mask_classes = self.mask_head[-1].num_classes
+                segm_results = [[[] for _ in range(mask_classes)]
+                                for _ in range(num_imgs)]
+                results = list(zip(bbox_results, segm_results))
+            else:
+                results = bbox_results
+
+            return results
+
+        for i in range(self.num_stages):
+            bbox_head = self.bbox_head[i]
+            bbox_results = self._bbox_forward(
+                i, x, rois, semantic_feat=semantic_feat)
+            # split batch bbox prediction back to each image
+            cls_score = bbox_results['cls_score']
+            bbox_pred = bbox_results['bbox_pred']
+            num_proposals_per_img = tuple(len(p) for p in proposal_list)
+            rois = rois.split(num_proposals_per_img, 0)
+            cls_score = cls_score.split(num_proposals_per_img, 0)
+            bbox_pred = bbox_pred.split(num_proposals_per_img, 0)
+            ms_scores.append(cls_score)
+
+            if i < self.num_stages - 1:
+                refine_rois_list = []
+                for j in range(num_imgs):
+                    if rois[j].shape[0] > 0:
+                        bbox_label = cls_score[j][:, :-1].argmax(dim=1)
+                        refine_rois = bbox_head.regress_by_class(
+                            rois[j], bbox_label, bbox_pred[j], img_metas[j])
+                        refine_rois_list.append(refine_rois)
+                rois = torch.cat(refine_rois_list)
+
+        # average scores of each image by stages
+        cls_score = [
+            sum([score[i] for score in ms_scores]) / float(len(ms_scores))
+            for i in range(num_imgs)
+        ]
+
+        # apply bbox post-processing to each image individually
+        det_bboxes = []
+        det_labels = []
+        for i in range(num_imgs):
+            det_bbox, det_label = self.bbox_head[-1].get_bboxes(
+                rois[i],
+                cls_score[i],
+                bbox_pred[i],
+                img_shapes[i],
+                scale_factors[i],
+                rescale=rescale,
+                cfg=rcnn_test_cfg)
+            det_bboxes.append(det_bbox)
+            det_labels.append(det_label)
+        bbox_result = [
+            bbox2result(det_bboxes[i], det_labels[i],
+                        self.bbox_head[-1].num_classes)
+            for i in range(num_imgs)
+        ]
+        ms_bbox_result['ensemble'] = bbox_result
+
+        if self.with_mask:
+            if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
+                mask_classes = self.mask_head[-1].num_classes
+                segm_results = [[[] for _ in range(mask_classes)]
+                                for _ in range(num_imgs)]
+            else:
+                if rescale and not isinstance(scale_factors[0], float):
+                    scale_factors = [
+                        torch.from_numpy(scale_factor).to(det_bboxes[0].device)
+                        for scale_factor in scale_factors
+                    ]
+                _bboxes = [
+                    det_bboxes[i][:, :4] *
+                    scale_factors[i] if rescale else det_bboxes[i]
+                    for i in range(num_imgs)
+                ]
+                mask_rois = bbox2roi(_bboxes)
+                aug_masks = []
+                mask_roi_extractor = self.mask_roi_extractor[-1]
+                mask_feats = mask_roi_extractor(
+                    x[:len(mask_roi_extractor.featmap_strides)], mask_rois)
+                if self.with_semantic and 'mask' in self.semantic_fusion:
+                    mask_semantic_feat = self.semantic_roi_extractor(
+                        [semantic_feat], mask_rois)
+                    mask_feats = mask_feats + mask_semantic_feat
+                last_feat = None
+
+                num_bbox_per_img = tuple(len(_bbox) for _bbox in _bboxes)
+                for i in range(self.num_stages):
+                    mask_head = self.mask_head[i]
+                    if self.mask_info_flow:
+                        mask_pred, last_feat = mask_head(mask_feats, last_feat)
+                    else:
+                        mask_pred = mask_head(mask_feats)
+
+                    # split batch mask prediction back to each image
+                    mask_pred = mask_pred.split(num_bbox_per_img, 0)
+                    aug_masks.append(
+                        [mask.sigmoid().cpu().numpy() for mask in mask_pred])
+
+                # apply mask post-processing to each image individually
+                segm_results = []
+                for i in range(num_imgs):
+                    if det_bboxes[i].shape[0] == 0:
+                        segm_results.append(
+                            [[]
+                             for _ in range(self.mask_head[-1].num_classes)])
+                    else:
+                        aug_mask = [mask[i] for mask in aug_masks]
+                        merged_mask = merge_aug_masks(
+                            aug_mask, [[img_metas[i]]] * self.num_stages,
+                            rcnn_test_cfg)
+                        segm_result = self.mask_head[-1].get_seg_masks(
+                            merged_mask, _bboxes[i], det_labels[i],
+                            rcnn_test_cfg, ori_shapes[i], scale_factors[i],
+                            rescale)
+                        segm_results.append(segm_result)
+            ms_segm_result['ensemble'] = segm_results
+
+        if self.with_mask:
+            results = list(
+                zip(ms_bbox_result['ensemble'], ms_segm_result['ensemble']))
+        else:
+            results = ms_bbox_result['ensemble']
+
+        return results
+
+    def aug_test(self, img_feats, proposal_list, img_metas, rescale=False):
+        """Test with augmentations.
+
+        If rescale is False, then returned bboxes and masks will fit the scale
+        of imgs[0].
+        """
+        if self.with_semantic:
+            semantic_feats = [
+                self.semantic_head(feat)[1] for feat in img_feats
+            ]
+        else:
+            semantic_feats = [None] * len(img_metas)
+
+        rcnn_test_cfg = self.test_cfg
+        aug_bboxes = []
+        aug_scores = []
+        for x, img_meta, semantic in zip(img_feats, img_metas, semantic_feats):
+            # only one image in the batch
+            img_shape = img_meta[0]['img_shape']
+            scale_factor = img_meta[0]['scale_factor']
+            flip = img_meta[0]['flip']
+            flip_direction = img_meta[0]['flip_direction']
+
+            proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
+                                     scale_factor, flip, flip_direction)
+            # "ms" in variable names means multi-stage
+            ms_scores = []
+
+            rois = bbox2roi([proposals])
+
+            if rois.shape[0] == 0:
+                # There is no proposal in the single image
+                aug_bboxes.append(rois.new_zeros(0, 4))
+                aug_scores.append(rois.new_zeros(0, 1))
+                continue
+
+            for i in range(self.num_stages):
+                bbox_head = self.bbox_head[i]
+                bbox_results = self._bbox_forward(
+                    i, x, rois, semantic_feat=semantic)
+                ms_scores.append(bbox_results['cls_score'])
+
+                if i < self.num_stages - 1:
+                    bbox_label = bbox_results['cls_score'].argmax(dim=1)
+                    rois = bbox_head.regress_by_class(
+                        rois, bbox_label, bbox_results['bbox_pred'],
+                        img_meta[0])
+
+            cls_score = sum(ms_scores) / float(len(ms_scores))
+            bboxes, scores = self.bbox_head[-1].get_bboxes(
+                rois,
+                cls_score,
+                bbox_results['bbox_pred'],
+                img_shape,
+                scale_factor,
+                rescale=False,
+                cfg=None)
+            aug_bboxes.append(bboxes)
+            aug_scores.append(scores)
+
+        # after merging, bboxes will be rescaled to the original image size
+        merged_bboxes, merged_scores = merge_aug_bboxes(
+            aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
+        det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
+                                                rcnn_test_cfg.score_thr,
+                                                rcnn_test_cfg.nms,
+                                                rcnn_test_cfg.max_per_img)
+
+        bbox_result = bbox2result(det_bboxes, det_labels,
+                                  self.bbox_head[-1].num_classes)
+
+        if self.with_mask:
+            if det_bboxes.shape[0] == 0:
+                segm_result = [[]
+                               for _ in range(self.mask_head[-1].num_classes)]
+            else:
+                aug_masks = []
+                aug_img_metas = []
+                for x, img_meta, semantic in zip(img_feats, img_metas,
+                                                 semantic_feats):
+                    img_shape = img_meta[0]['img_shape']
+                    scale_factor = img_meta[0]['scale_factor']
+                    flip = img_meta[0]['flip']
+                    flip_direction = img_meta[0]['flip_direction']
+                    _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
+                                           scale_factor, flip, flip_direction)
+                    mask_rois = bbox2roi([_bboxes])
+                    mask_feats = self.mask_roi_extractor[-1](
+                        x[:len(self.mask_roi_extractor[-1].featmap_strides)],
+                        mask_rois)
+                    if self.with_semantic:
+                        semantic_feat = semantic
+                        mask_semantic_feat = self.semantic_roi_extractor(
+                            [semantic_feat], mask_rois)
+                        if mask_semantic_feat.shape[-2:] != mask_feats.shape[
+                                -2:]:
+                            mask_semantic_feat = F.adaptive_avg_pool2d(
+                                mask_semantic_feat, mask_feats.shape[-2:])
+                        mask_feats = mask_feats + mask_semantic_feat
+                    last_feat = None
+                    for i in range(self.num_stages):
+                        mask_head = self.mask_head[i]
+                        if self.mask_info_flow:
+                            mask_pred, last_feat = mask_head(
+                                mask_feats, last_feat)
+                        else:
+                            mask_pred = mask_head(mask_feats)
+                        aug_masks.append(mask_pred.sigmoid().cpu().numpy())
+                        aug_img_metas.append(img_meta)
+                merged_masks = merge_aug_masks(aug_masks, aug_img_metas,
+                                               self.test_cfg)
+
+                ori_shape = img_metas[0][0]['ori_shape']
+                segm_result = self.mask_head[-1].get_seg_masks(
+                    merged_masks,
+                    det_bboxes,
+                    det_labels,
+                    rcnn_test_cfg,
+                    ori_shape,
+                    scale_factor=1.0,
+                    rescale=False)
+            return [(bbox_result, segm_result)]
+        else:
+            return [bbox_result]
diff --git a/mmdet/models/roi_heads/mask_heads/__init__.py b/mmdet/models/roi_heads/mask_heads/__init__.py
new file mode 100644
index 0000000..48a5d42
--- /dev/null
+++ b/mmdet/models/roi_heads/mask_heads/__init__.py
@@ -0,0 +1,20 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .coarse_mask_head import CoarseMaskHead
+from .dynamic_mask_head import DynamicMaskHead
+from .fcn_mask_head import FCNMaskHead
+from .feature_relay_head import FeatureRelayHead
+from .fused_semantic_head import FusedSemanticHead
+from .global_context_head import GlobalContextHead
+from .grid_head import GridHead
+from .htc_mask_head import HTCMaskHead
+from .mask_point_head import MaskPointHead
+from .maskiou_head import MaskIoUHead
+from .scnet_mask_head import SCNetMaskHead
+from .scnet_semantic_head import SCNetSemanticHead
+
+__all__ = [
+    'FCNMaskHead', 'HTCMaskHead', 'FusedSemanticHead', 'GridHead',
+    'MaskIoUHead', 'CoarseMaskHead', 'MaskPointHead', 'SCNetMaskHead',
+    'SCNetSemanticHead', 'GlobalContextHead', 'FeatureRelayHead',
+    'DynamicMaskHead'
+]
diff --git a/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py b/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py
new file mode 100644
index 0000000..946254c
--- /dev/null
+++ b/mmdet/models/roi_heads/mask_heads/coarse_mask_head.py
@@ -0,0 +1,100 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.cnn import ConvModule, Linear
+from mmcv.runner import ModuleList, auto_fp16
+
+from mmdet.models.builder import HEADS
+from .fcn_mask_head import FCNMaskHead
+
+
+@HEADS.register_module()
+class CoarseMaskHead(FCNMaskHead):
+    """Coarse mask head used in PointRend.
+
+    Compared with standard ``FCNMaskHead``, ``CoarseMaskHead`` will downsample
+    the input feature map instead of upsample it.
+
+    Args:
+        num_convs (int): Number of conv layers in the head. Default: 0.
+        num_fcs (int): Number of fc layers in the head. Default: 2.
+        fc_out_channels (int): Number of output channels of fc layer.
+            Default: 1024.
+        downsample_factor (int): The factor that feature map is downsampled by.
+            Default: 2.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 num_convs=0,
+                 num_fcs=2,
+                 fc_out_channels=1024,
+                 downsample_factor=2,
+                 init_cfg=dict(
+                     type='Xavier',
+                     override=[
+                         dict(name='fcs'),
+                         dict(type='Constant', val=0.001, name='fc_logits')
+                     ]),
+                 *arg,
+                 **kwarg):
+        super(CoarseMaskHead, self).__init__(
+            *arg,
+            num_convs=num_convs,
+            upsample_cfg=dict(type=None),
+            init_cfg=None,
+            **kwarg)
+        self.init_cfg = init_cfg
+        self.num_fcs = num_fcs
+        assert self.num_fcs > 0
+        self.fc_out_channels = fc_out_channels
+        self.downsample_factor = downsample_factor
+        assert self.downsample_factor >= 1
+        # remove conv_logit
+        delattr(self, 'conv_logits')
+
+        if downsample_factor > 1:
+            downsample_in_channels = (
+                self.conv_out_channels
+                if self.num_convs > 0 else self.in_channels)
+            self.downsample_conv = ConvModule(
+                downsample_in_channels,
+                self.conv_out_channels,
+                kernel_size=downsample_factor,
+                stride=downsample_factor,
+                padding=0,
+                conv_cfg=self.conv_cfg,
+                norm_cfg=self.norm_cfg)
+        else:
+            self.downsample_conv = None
+
+        self.output_size = (self.roi_feat_size[0] // downsample_factor,
+                            self.roi_feat_size[1] // downsample_factor)
+        self.output_area = self.output_size[0] * self.output_size[1]
+
+        last_layer_dim = self.conv_out_channels * self.output_area
+
+        self.fcs = ModuleList()
+        for i in range(num_fcs):
+            fc_in_channels = (
+                last_layer_dim if i == 0 else self.fc_out_channels)
+            self.fcs.append(Linear(fc_in_channels, self.fc_out_channels))
+        last_layer_dim = self.fc_out_channels
+        output_channels = self.num_classes * self.output_area
+        self.fc_logits = Linear(last_layer_dim, output_channels)
+
+    def init_weights(self):
+        super(FCNMaskHead, self).init_weights()
+
+    @auto_fp16()
+    def forward(self, x):
+        for conv in self.convs:
+            x = conv(x)
+
+        if self.downsample_conv is not None:
+            x = self.downsample_conv(x)
+
+        x = x.flatten(1)
+        for fc in self.fcs:
+            x = self.relu(fc(x))
+        mask_pred = self.fc_logits(x).view(
+            x.size(0), self.num_classes, *self.output_size)
+        return mask_pred
diff --git a/mmdet/models/roi_heads/mask_heads/dynamic_mask_head.py b/mmdet/models/roi_heads/mask_heads/dynamic_mask_head.py
new file mode 100644
index 0000000..5bbe7ee
--- /dev/null
+++ b/mmdet/models/roi_heads/mask_heads/dynamic_mask_head.py
@@ -0,0 +1,147 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.runner import auto_fp16, force_fp32
+
+from mmdet.core import mask_target
+from mmdet.models.builder import HEADS
+from mmdet.models.dense_heads.atss_head import reduce_mean
+from mmdet.models.utils import build_transformer
+from .fcn_mask_head import FCNMaskHead
+
+
+@HEADS.register_module()
+class DynamicMaskHead(FCNMaskHead):
+    r"""Dynamic Mask Head for
+    `Instances as Queries <http://arxiv.org/abs/2105.01928>`_
+
+    Args:
+        num_convs (int): Number of convolution layer.
+            Defaults to 4.
+        roi_feat_size (int): The output size of RoI extractor,
+            Defaults to 14.
+        in_channels (int): Input feature channels.
+            Defaults to 256.
+        conv_kernel_size (int): Kernel size of convolution layers.
+            Defaults to 3.
+        conv_out_channels (int): Output channels of convolution layers.
+            Defaults to 256.
+        num_classes (int): Number of classes.
+            Defaults to 80
+        class_agnostic (int): Whether generate class agnostic prediction.
+            Defaults to False.
+        dropout (float): Probability of drop the channel.
+            Defaults to 0.0
+        upsample_cfg (dict): The config for upsample layer.
+        conv_cfg (dict): The convolution layer config.
+        norm_cfg (dict): The norm layer config.
+        dynamic_conv_cfg (dict): The dynamic convolution layer config.
+        loss_mask (dict): The config for mask loss.
+    """
+
+    def __init__(self,
+                 num_convs=4,
+                 roi_feat_size=14,
+                 in_channels=256,
+                 conv_kernel_size=3,
+                 conv_out_channels=256,
+                 num_classes=80,
+                 class_agnostic=False,
+                 upsample_cfg=dict(type='deconv', scale_factor=2),
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 dynamic_conv_cfg=dict(
+                     type='DynamicConv',
+                     in_channels=256,
+                     feat_channels=64,
+                     out_channels=256,
+                     input_feat_shape=14,
+                     with_proj=False,
+                     act_cfg=dict(type='ReLU', inplace=True),
+                     norm_cfg=dict(type='LN')),
+                 loss_mask=dict(type='DiceLoss', loss_weight=8.0),
+                 **kwargs):
+        super(DynamicMaskHead, self).__init__(
+            num_convs=num_convs,
+            roi_feat_size=roi_feat_size,
+            in_channels=in_channels,
+            conv_kernel_size=conv_kernel_size,
+            conv_out_channels=conv_out_channels,
+            num_classes=num_classes,
+            class_agnostic=class_agnostic,
+            upsample_cfg=upsample_cfg,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            loss_mask=loss_mask,
+            **kwargs)
+        assert class_agnostic is False, \
+            'DynamicMaskHead only support class_agnostic=False'
+        self.fp16_enabled = False
+
+        self.instance_interactive_conv = build_transformer(dynamic_conv_cfg)
+
+    def init_weights(self):
+        """Use xavier initialization for all weight parameter and set
+        classification head bias as a specific value when use focal loss."""
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+            nn.init.constant_(self.conv_logits.bias, 0.)
+
+    @auto_fp16()
+    def forward(self, roi_feat, proposal_feat):
+        """Forward function of DynamicMaskHead.
+
+        Args:
+            roi_feat (Tensor): Roi-pooling features with shape
+                (batch_size*num_proposals, feature_dimensions,
+                pooling_h , pooling_w).
+            proposal_feat (Tensor): Intermediate feature get from
+                diihead in last stage, has shape
+                (batch_size*num_proposals, feature_dimensions)
+
+          Returns:
+            mask_pred (Tensor): Predicted foreground masks with shape
+                (batch_size*num_proposals, num_classes,
+                                        pooling_h*2, pooling_w*2).
+        """
+
+        proposal_feat = proposal_feat.reshape(-1, self.in_channels)
+        proposal_feat_iic = self.instance_interactive_conv(
+            proposal_feat, roi_feat)
+
+        x = proposal_feat_iic.permute(0, 2, 1).reshape(roi_feat.size())
+
+        for conv in self.convs:
+            x = conv(x)
+        if self.upsample is not None:
+            x = self.upsample(x)
+            if self.upsample_method == 'deconv':
+                x = self.relu(x)
+        mask_pred = self.conv_logits(x)
+        return mask_pred
+
+    @force_fp32(apply_to=('mask_pred', ))
+    def loss(self, mask_pred, mask_targets, labels):
+        num_pos = labels.new_ones(labels.size()).float().sum()
+        avg_factor = torch.clamp(reduce_mean(num_pos), min=1.).item()
+        loss = dict()
+        if mask_pred.size(0) == 0:
+            loss_mask = mask_pred.sum()
+        else:
+            loss_mask = self.loss_mask(
+                mask_pred[torch.arange(num_pos).long(), labels, ...].sigmoid(),
+                mask_targets,
+                avg_factor=avg_factor)
+        loss['loss_mask'] = loss_mask
+        return loss
+
+    def get_targets(self, sampling_results, gt_masks, rcnn_train_cfg):
+
+        pos_proposals = [res.pos_bboxes for res in sampling_results]
+        pos_assigned_gt_inds = [
+            res.pos_assigned_gt_inds for res in sampling_results
+        ]
+        mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds,
+                                   gt_masks, rcnn_train_cfg)
+        return mask_targets
diff --git a/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py b/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py
new file mode 100644
index 0000000..355d882
--- /dev/null
+++ b/mmdet/models/roi_heads/mask_heads/fcn_mask_head.py
@@ -0,0 +1,412 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from warnings import warn
+
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule, build_conv_layer, build_upsample_layer
+from mmcv.ops.carafe import CARAFEPack
+from mmcv.runner import BaseModule, ModuleList, auto_fp16, force_fp32
+from torch.nn.modules.utils import _pair
+
+from mmdet.core import mask_target
+from mmdet.models.builder import HEADS, build_loss
+
+BYTES_PER_FLOAT = 4
+# TODO: This memory limit may be too much or too little. It would be better to
+# determine it based on available resources.
+GPU_MEM_LIMIT = 1024**3  # 1 GB memory limit
+
+
+@HEADS.register_module()
+class FCNMaskHead(BaseModule):
+
+    def __init__(self,
+                 num_convs=4,
+                 roi_feat_size=14,
+                 in_channels=256,
+                 conv_kernel_size=3,
+                 conv_out_channels=256,
+                 num_classes=80,
+                 class_agnostic=False,
+                 upsample_cfg=dict(type='deconv', scale_factor=2),
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 predictor_cfg=dict(type='Conv'),
+                 loss_mask=dict(
+                     type='CrossEntropyLoss', use_mask=True, loss_weight=1.0),
+                 init_cfg=None):
+        assert init_cfg is None, 'To prevent abnormal initialization ' \
+                                 'behavior, init_cfg is not allowed to be set'
+        super(FCNMaskHead, self).__init__(init_cfg)
+        self.upsample_cfg = upsample_cfg.copy()
+        if self.upsample_cfg['type'] not in [
+                None, 'deconv', 'nearest', 'bilinear', 'carafe'
+        ]:
+            raise ValueError(
+                f'Invalid upsample method {self.upsample_cfg["type"]}, '
+                'accepted methods are "deconv", "nearest", "bilinear", '
+                '"carafe"')
+        self.num_convs = num_convs
+        # WARN: roi_feat_size is reserved and not used
+        self.roi_feat_size = _pair(roi_feat_size)
+        self.in_channels = in_channels
+        self.conv_kernel_size = conv_kernel_size
+        self.conv_out_channels = conv_out_channels
+        self.upsample_method = self.upsample_cfg.get('type')
+        self.scale_factor = self.upsample_cfg.pop('scale_factor', None)
+        self.num_classes = num_classes
+        self.class_agnostic = class_agnostic
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.predictor_cfg = predictor_cfg
+        self.fp16_enabled = False
+        self.loss_mask = build_loss(loss_mask)
+
+        self.convs = ModuleList()
+        for i in range(self.num_convs):
+            in_channels = (
+                self.in_channels if i == 0 else self.conv_out_channels)
+            padding = (self.conv_kernel_size - 1) // 2
+            self.convs.append(
+                ConvModule(
+                    in_channels,
+                    self.conv_out_channels,
+                    self.conv_kernel_size,
+                    padding=padding,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg))
+        upsample_in_channels = (
+            self.conv_out_channels if self.num_convs > 0 else in_channels)
+        upsample_cfg_ = self.upsample_cfg.copy()
+        if self.upsample_method is None:
+            self.upsample = None
+        elif self.upsample_method == 'deconv':
+            upsample_cfg_.update(
+                in_channels=upsample_in_channels,
+                out_channels=self.conv_out_channels,
+                kernel_size=self.scale_factor,
+                stride=self.scale_factor)
+            self.upsample = build_upsample_layer(upsample_cfg_)
+        elif self.upsample_method == 'carafe':
+            upsample_cfg_.update(
+                channels=upsample_in_channels, scale_factor=self.scale_factor)
+            self.upsample = build_upsample_layer(upsample_cfg_)
+        else:
+            # suppress warnings
+            align_corners = (None
+                             if self.upsample_method == 'nearest' else False)
+            upsample_cfg_.update(
+                scale_factor=self.scale_factor,
+                mode=self.upsample_method,
+                align_corners=align_corners)
+            self.upsample = build_upsample_layer(upsample_cfg_)
+
+        out_channels = 1 if self.class_agnostic else self.num_classes
+        logits_in_channel = (
+            self.conv_out_channels
+            if self.upsample_method == 'deconv' else upsample_in_channels)
+        self.conv_logits = build_conv_layer(self.predictor_cfg,
+                                            logits_in_channel, out_channels, 1)
+        self.relu = nn.ReLU(inplace=True)
+        self.debug_imgs = None
+
+    def init_weights(self):
+        super(FCNMaskHead, self).init_weights()
+        for m in [self.upsample, self.conv_logits]:
+            if m is None:
+                continue
+            elif isinstance(m, CARAFEPack):
+                m.init_weights()
+            elif hasattr(m, 'weight') and hasattr(m, 'bias'):
+                nn.init.kaiming_normal_(
+                    m.weight, mode='fan_out', nonlinearity='relu')
+                nn.init.constant_(m.bias, 0)
+
+    @auto_fp16()
+    def forward(self, x):
+        for conv in self.convs:
+            x = conv(x)
+        if self.upsample is not None:
+            x = self.upsample(x)
+            if self.upsample_method == 'deconv':
+                x = self.relu(x)
+        mask_pred = self.conv_logits(x)
+        return mask_pred
+
+    def get_targets(self, sampling_results, gt_masks, rcnn_train_cfg):
+        pos_proposals = [res.pos_bboxes for res in sampling_results]
+        pos_assigned_gt_inds = [
+            res.pos_assigned_gt_inds for res in sampling_results
+        ]
+        mask_targets = mask_target(pos_proposals, pos_assigned_gt_inds,
+                                   gt_masks, rcnn_train_cfg)
+        return mask_targets
+
+    @force_fp32(apply_to=('mask_pred', ))
+    def loss(self, mask_pred, mask_targets, labels):
+        """
+        Example:
+            >>> from mmdet.models.roi_heads.mask_heads.fcn_mask_head import *  # NOQA
+            >>> N = 7  # N = number of extracted ROIs
+            >>> C, H, W = 11, 32, 32
+            >>> # Create example instance of FCN Mask Head.
+            >>> # There are lots of variations depending on the configuration
+            >>> self = FCNMaskHead(num_classes=C, num_convs=1)
+            >>> inputs = torch.rand(N, self.in_channels, H, W)
+            >>> mask_pred = self.forward(inputs)
+            >>> sf = self.scale_factor
+            >>> labels = torch.randint(0, C, size=(N,))
+            >>> # With the default properties the mask targets should indicate
+            >>> # a (potentially soft) single-class label
+            >>> mask_targets = torch.rand(N, H * sf, W * sf)
+            >>> loss = self.loss(mask_pred, mask_targets, labels)
+            >>> print('loss = {!r}'.format(loss))
+        """
+        loss = dict()
+        if mask_pred.size(0) == 0:
+            loss_mask = mask_pred.sum()
+        else:
+            if self.class_agnostic:
+                loss_mask = self.loss_mask(mask_pred, mask_targets,
+                                           torch.zeros_like(labels))
+            else:
+                loss_mask = self.loss_mask(mask_pred, mask_targets, labels)
+        loss['loss_mask'] = loss_mask
+        return loss
+
+    def get_seg_masks(self, mask_pred, det_bboxes, det_labels, rcnn_test_cfg,
+                      ori_shape, scale_factor, rescale):
+        """Get segmentation masks from mask_pred and bboxes.
+
+        Args:
+            mask_pred (Tensor or ndarray): shape (n, #class, h, w).
+                For single-scale testing, mask_pred is the direct output of
+                model, whose type is Tensor, while for multi-scale testing,
+                it will be converted to numpy array outside of this method.
+            det_bboxes (Tensor): shape (n, 4/5)
+            det_labels (Tensor): shape (n, )
+            rcnn_test_cfg (dict): rcnn testing config
+            ori_shape (Tuple): original image height and width, shape (2,)
+            scale_factor(ndarray | Tensor): If ``rescale is True``, box
+                coordinates are divided by this scale factor to fit
+                ``ori_shape``.
+            rescale (bool): If True, the resulting masks will be rescaled to
+                ``ori_shape``.
+
+        Returns:
+            list[list]: encoded masks. The c-th item in the outer list
+                corresponds to the c-th class. Given the c-th outer list, the
+                i-th item in that inner list is the mask for the i-th box with
+                class label c.
+
+        Example:
+            >>> import mmcv
+            >>> from mmdet.models.roi_heads.mask_heads.fcn_mask_head import *  # NOQA
+            >>> N = 7  # N = number of extracted ROIs
+            >>> C, H, W = 11, 32, 32
+            >>> # Create example instance of FCN Mask Head.
+            >>> self = FCNMaskHead(num_classes=C, num_convs=0)
+            >>> inputs = torch.rand(N, self.in_channels, H, W)
+            >>> mask_pred = self.forward(inputs)
+            >>> # Each input is associated with some bounding box
+            >>> det_bboxes = torch.Tensor([[1, 1, 42, 42 ]] * N)
+            >>> det_labels = torch.randint(0, C, size=(N,))
+            >>> rcnn_test_cfg = mmcv.Config({'mask_thr_binary': 0, })
+            >>> ori_shape = (H * 4, W * 4)
+            >>> scale_factor = torch.FloatTensor((1, 1))
+            >>> rescale = False
+            >>> # Encoded masks are a list for each category.
+            >>> encoded_masks = self.get_seg_masks(
+            >>>     mask_pred, det_bboxes, det_labels, rcnn_test_cfg, ori_shape,
+            >>>     scale_factor, rescale
+            >>> )
+            >>> assert len(encoded_masks) == C
+            >>> assert sum(list(map(len, encoded_masks))) == N
+        """
+        if isinstance(mask_pred, torch.Tensor):
+            mask_pred = mask_pred.sigmoid()
+        else:
+            # In AugTest, has been activated before
+            mask_pred = det_bboxes.new_tensor(mask_pred)
+
+        device = mask_pred.device
+        cls_segms = [[] for _ in range(self.num_classes)
+                     ]  # BG is not included in num_classes
+        bboxes = det_bboxes[:, :4]
+        labels = det_labels
+
+        # In most cases, scale_factor should have been
+        # converted to Tensor when rescale the bbox
+        if not isinstance(scale_factor, torch.Tensor):
+            if isinstance(scale_factor, float):
+                scale_factor = np.array([scale_factor] * 4)
+                warn('Scale_factor should be a Tensor or ndarray '
+                     'with shape (4,), float would be deprecated. ')
+            assert isinstance(scale_factor, np.ndarray)
+            scale_factor = torch.Tensor(scale_factor)
+
+        if rescale:
+            img_h, img_w = ori_shape[:2]
+            bboxes = bboxes / scale_factor.to(bboxes)
+        else:
+            w_scale, h_scale = scale_factor[0], scale_factor[1]
+            img_h = np.round(ori_shape[0] * h_scale.item()).astype(np.int32)
+            img_w = np.round(ori_shape[1] * w_scale.item()).astype(np.int32)
+
+        N = len(mask_pred)
+        # The actual implementation split the input into chunks,
+        # and paste them chunk by chunk.
+        if device.type == 'cpu':
+            # CPU is most efficient when they are pasted one by one with
+            # skip_empty=True, so that it performs minimal number of
+            # operations.
+            num_chunks = N
+        else:
+            # GPU benefits from parallelism for larger chunks,
+            # but may have memory issue
+            # the types of img_w and img_h are np.int32,
+            # when the image resolution is large,
+            # the calculation of num_chunks will overflow.
+            # so we need to change the types of img_w and img_h to int.
+            # See https://github.com/open-mmlab/mmdetection/pull/5191
+            num_chunks = int(
+                np.ceil(N * int(img_h) * int(img_w) * BYTES_PER_FLOAT /
+                        GPU_MEM_LIMIT))
+            assert (num_chunks <=
+                    N), 'Default GPU_MEM_LIMIT is too small; try increasing it'
+        chunks = torch.chunk(torch.arange(N, device=device), num_chunks)
+
+        threshold = rcnn_test_cfg.mask_thr_binary
+        im_mask = torch.zeros(
+            N,
+            img_h,
+            img_w,
+            device=device,
+            dtype=torch.bool if threshold >= 0 else torch.uint8)
+
+        if not self.class_agnostic:
+            mask_pred = mask_pred[range(N), labels][:, None]
+
+        for inds in chunks:
+            masks_chunk, spatial_inds = _do_paste_mask(
+                mask_pred[inds],
+                bboxes[inds],
+                img_h,
+                img_w,
+                skip_empty=device.type == 'cpu')
+
+            if threshold >= 0:
+                masks_chunk = (masks_chunk >= threshold).to(dtype=torch.bool)
+            else:
+                # for visualization and debugging
+                masks_chunk = (masks_chunk * 255).to(dtype=torch.uint8)
+
+            im_mask[(inds, ) + spatial_inds] = masks_chunk
+
+        for i in range(N):
+            cls_segms[labels[i]].append(im_mask[i].detach().cpu().numpy())
+        return cls_segms
+
+    def onnx_export(self, mask_pred, det_bboxes, det_labels, rcnn_test_cfg,
+                    ori_shape, **kwargs):
+        """Get segmentation masks from mask_pred and bboxes.
+
+        Args:
+            mask_pred (Tensor): shape (n, #class, h, w).
+            det_bboxes (Tensor): shape (n, 4/5)
+            det_labels (Tensor): shape (n, )
+            rcnn_test_cfg (dict): rcnn testing config
+            ori_shape (Tuple): original image height and width, shape (2,)
+
+        Returns:
+            Tensor: a mask of shape (N, img_h, img_w).
+        """
+
+        mask_pred = mask_pred.sigmoid()
+        bboxes = det_bboxes[:, :4]
+        labels = det_labels
+        # No need to consider rescale and scale_factor while exporting to ONNX
+        img_h, img_w = ori_shape[:2]
+        threshold = rcnn_test_cfg.mask_thr_binary
+        if not self.class_agnostic:
+            box_inds = torch.arange(mask_pred.shape[0])
+            mask_pred = mask_pred[box_inds, labels][:, None]
+        masks, _ = _do_paste_mask(
+            mask_pred, bboxes, img_h, img_w, skip_empty=False)
+        if threshold >= 0:
+            # should convert to float to avoid problems in TRT
+            masks = (masks >= threshold).to(dtype=torch.float)
+        return masks
+
+
+def _do_paste_mask(masks, boxes, img_h, img_w, skip_empty=True):
+    """Paste instance masks according to boxes.
+
+    This implementation is modified from
+    https://github.com/facebookresearch/detectron2/
+
+    Args:
+        masks (Tensor): N, 1, H, W
+        boxes (Tensor): N, 4
+        img_h (int): Height of the image to be pasted.
+        img_w (int): Width of the image to be pasted.
+        skip_empty (bool): Only paste masks within the region that
+            tightly bound all boxes, and returns the results this region only.
+            An important optimization for CPU.
+
+    Returns:
+        tuple: (Tensor, tuple). The first item is mask tensor, the second one
+            is the slice object.
+        If skip_empty == False, the whole image will be pasted. It will
+            return a mask of shape (N, img_h, img_w) and an empty tuple.
+        If skip_empty == True, only area around the mask will be pasted.
+            A mask of shape (N, h', w') and its start and end coordinates
+            in the original image will be returned.
+    """
+    # On GPU, paste all masks together (up to chunk size)
+    # by using the entire image to sample the masks
+    # Compared to pasting them one by one,
+    # this has more operations but is faster on COCO-scale dataset.
+    device = masks.device
+    if skip_empty:
+        x0_int, y0_int = torch.clamp(
+            boxes.min(dim=0).values.floor()[:2] - 1,
+            min=0).to(dtype=torch.int32)
+        x1_int = torch.clamp(
+            boxes[:, 2].max().ceil() + 1, max=img_w).to(dtype=torch.int32)
+        y1_int = torch.clamp(
+            boxes[:, 3].max().ceil() + 1, max=img_h).to(dtype=torch.int32)
+    else:
+        x0_int, y0_int = 0, 0
+        x1_int, y1_int = img_w, img_h
+    x0, y0, x1, y1 = torch.split(boxes, 1, dim=1)  # each is Nx1
+
+    N = masks.shape[0]
+
+    img_y = torch.arange(y0_int, y1_int, device=device).to(torch.float32) + 0.5
+    img_x = torch.arange(x0_int, x1_int, device=device).to(torch.float32) + 0.5
+    img_y = (img_y - y0) / (y1 - y0) * 2 - 1
+    img_x = (img_x - x0) / (x1 - x0) * 2 - 1
+    # img_x, img_y have shapes (N, w), (N, h)
+    # IsInf op is not supported with ONNX<=1.7.0
+    if not torch.onnx.is_in_onnx_export():
+        if torch.isinf(img_x).any():
+            inds = torch.where(torch.isinf(img_x))
+            img_x[inds] = 0
+        if torch.isinf(img_y).any():
+            inds = torch.where(torch.isinf(img_y))
+            img_y[inds] = 0
+
+    gx = img_x[:, None, :].expand(N, img_y.size(1), img_x.size(1))
+    gy = img_y[:, :, None].expand(N, img_y.size(1), img_x.size(1))
+    grid = torch.stack([gx, gy], dim=3)
+
+    img_masks = F.grid_sample(
+        masks.to(dtype=torch.float32), grid, align_corners=False)
+
+    if skip_empty:
+        return img_masks[:, 0], (slice(y0_int, y1_int), slice(x0_int, x1_int))
+    else:
+        return img_masks[:, 0], ()
diff --git a/mmdet/models/roi_heads/mask_heads/feature_relay_head.py b/mmdet/models/roi_heads/mask_heads/feature_relay_head.py
new file mode 100644
index 0000000..452f37a
--- /dev/null
+++ b/mmdet/models/roi_heads/mask_heads/feature_relay_head.py
@@ -0,0 +1,53 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+from mmcv.runner import BaseModule, auto_fp16
+
+from mmdet.models.builder import HEADS
+
+
+@HEADS.register_module()
+class FeatureRelayHead(BaseModule):
+    """Feature Relay Head used in `SCNet <https://arxiv.org/abs/2012.10150>`_.
+
+    Args:
+        in_channels (int, optional): number of input channels. Default: 256.
+        conv_out_channels (int, optional): number of output channels before
+            classification layer. Default: 256.
+        roi_feat_size (int, optional): roi feat size at box head. Default: 7.
+        scale_factor (int, optional): scale factor to match roi feat size
+            at mask head. Default: 2.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 in_channels=1024,
+                 out_conv_channels=256,
+                 roi_feat_size=7,
+                 scale_factor=2,
+                 init_cfg=dict(type='Kaiming', layer='Linear')):
+        super(FeatureRelayHead, self).__init__(init_cfg)
+        assert isinstance(roi_feat_size, int)
+
+        self.in_channels = in_channels
+        self.out_conv_channels = out_conv_channels
+        self.roi_feat_size = roi_feat_size
+        self.out_channels = (roi_feat_size**2) * out_conv_channels
+        self.scale_factor = scale_factor
+        self.fp16_enabled = False
+
+        self.fc = nn.Linear(self.in_channels, self.out_channels)
+        self.upsample = nn.Upsample(
+            scale_factor=scale_factor, mode='bilinear', align_corners=True)
+
+    @auto_fp16()
+    def forward(self, x):
+        """Forward function."""
+        N, in_C = x.shape
+        if N > 0:
+            out_C = self.out_conv_channels
+            out_HW = self.roi_feat_size
+            x = self.fc(x)
+            x = x.reshape(N, out_C, out_HW, out_HW)
+            x = self.upsample(x)
+            return x
+        return None
diff --git a/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py b/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py
new file mode 100644
index 0000000..c6eaa54
--- /dev/null
+++ b/mmdet/models/roi_heads/mask_heads/fused_semantic_head.py
@@ -0,0 +1,118 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule, auto_fp16, force_fp32
+
+from mmdet.models.builder import HEADS, build_loss
+
+
+@HEADS.register_module()
+class FusedSemanticHead(BaseModule):
+    r"""Multi-level fused semantic segmentation head.
+
+    .. code-block:: none
+
+        in_1 -> 1x1 conv ---
+                            |
+        in_2 -> 1x1 conv -- |
+                           ||
+        in_3 -> 1x1 conv - ||
+                          |||                  /-> 1x1 conv (mask prediction)
+        in_4 -> 1x1 conv -----> 3x3 convs (*4)
+                            |                  \-> 1x1 conv (feature)
+        in_5 -> 1x1 conv ---
+    """  # noqa: W605
+
+    def __init__(self,
+                 num_ins,
+                 fusion_level,
+                 num_convs=4,
+                 in_channels=256,
+                 conv_out_channels=256,
+                 num_classes=183,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 ignore_label=None,
+                 loss_weight=None,
+                 loss_seg=dict(
+                     type='CrossEntropyLoss',
+                     ignore_index=255,
+                     loss_weight=0.2),
+                 init_cfg=dict(
+                     type='Kaiming', override=dict(name='conv_logits'))):
+        super(FusedSemanticHead, self).__init__(init_cfg)
+        self.num_ins = num_ins
+        self.fusion_level = fusion_level
+        self.num_convs = num_convs
+        self.in_channels = in_channels
+        self.conv_out_channels = conv_out_channels
+        self.num_classes = num_classes
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.fp16_enabled = False
+
+        self.lateral_convs = nn.ModuleList()
+        for i in range(self.num_ins):
+            self.lateral_convs.append(
+                ConvModule(
+                    self.in_channels,
+                    self.in_channels,
+                    1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    inplace=False))
+
+        self.convs = nn.ModuleList()
+        for i in range(self.num_convs):
+            in_channels = self.in_channels if i == 0 else conv_out_channels
+            self.convs.append(
+                ConvModule(
+                    in_channels,
+                    conv_out_channels,
+                    3,
+                    padding=1,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg))
+        self.conv_embedding = ConvModule(
+            conv_out_channels,
+            conv_out_channels,
+            1,
+            conv_cfg=self.conv_cfg,
+            norm_cfg=self.norm_cfg)
+        self.conv_logits = nn.Conv2d(conv_out_channels, self.num_classes, 1)
+        if ignore_label:
+            loss_seg['ignore_index'] = ignore_label
+        if loss_weight:
+            loss_seg['loss_weight'] = loss_weight
+        if ignore_label or loss_weight:
+            warnings.warn('``ignore_label`` and ``loss_weight`` would be '
+                          'deprecated soon. Please set ``ingore_index`` and '
+                          '``loss_weight`` in ``loss_seg`` instead.')
+        self.criterion = build_loss(loss_seg)
+
+    @auto_fp16()
+    def forward(self, feats):
+        x = self.lateral_convs[self.fusion_level](feats[self.fusion_level])
+        fused_size = tuple(x.shape[-2:])
+        for i, feat in enumerate(feats):
+            if i != self.fusion_level:
+                feat = F.interpolate(
+                    feat, size=fused_size, mode='bilinear', align_corners=True)
+                # fix runtime error of "+=" inplace operation in PyTorch 1.10
+                x = x + self.lateral_convs[i](feat)
+
+        for i in range(self.num_convs):
+            x = self.convs[i](x)
+
+        mask_pred = self.conv_logits(x)
+        x = self.conv_embedding(x)
+        return mask_pred, x
+
+    @force_fp32(apply_to=('mask_pred', ))
+    def loss(self, mask_pred, labels):
+        labels = labels.squeeze(1).long()
+        loss_semantic_seg = self.criterion(mask_pred, labels)
+        return loss_semantic_seg
diff --git a/mmdet/models/roi_heads/mask_heads/global_context_head.py b/mmdet/models/roi_heads/mask_heads/global_context_head.py
new file mode 100644
index 0000000..af76a17
--- /dev/null
+++ b/mmdet/models/roi_heads/mask_heads/global_context_head.py
@@ -0,0 +1,101 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule, auto_fp16, force_fp32
+
+from mmdet.models.builder import HEADS
+from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
+
+
+@HEADS.register_module()
+class GlobalContextHead(BaseModule):
+    """Global context head used in `SCNet <https://arxiv.org/abs/2012.10150>`_.
+
+    Args:
+        num_convs (int, optional): number of convolutional layer in GlbCtxHead.
+            Default: 4.
+        in_channels (int, optional): number of input channels. Default: 256.
+        conv_out_channels (int, optional): number of output channels before
+            classification layer. Default: 256.
+        num_classes (int, optional): number of classes. Default: 80.
+        loss_weight (float, optional): global context loss weight. Default: 1.
+        conv_cfg (dict, optional): config to init conv layer. Default: None.
+        norm_cfg (dict, optional): config to init norm layer. Default: None.
+        conv_to_res (bool, optional): if True, 2 convs will be grouped into
+            1 `SimplifiedBasicBlock` using a skip connection. Default: False.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 num_convs=4,
+                 in_channels=256,
+                 conv_out_channels=256,
+                 num_classes=80,
+                 loss_weight=1.0,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 conv_to_res=False,
+                 init_cfg=dict(
+                     type='Normal', std=0.01, override=dict(name='fc'))):
+        super(GlobalContextHead, self).__init__(init_cfg)
+        self.num_convs = num_convs
+        self.in_channels = in_channels
+        self.conv_out_channels = conv_out_channels
+        self.num_classes = num_classes
+        self.loss_weight = loss_weight
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.conv_to_res = conv_to_res
+        self.fp16_enabled = False
+
+        if self.conv_to_res:
+            num_res_blocks = num_convs // 2
+            self.convs = ResLayer(
+                SimplifiedBasicBlock,
+                in_channels,
+                self.conv_out_channels,
+                num_res_blocks,
+                conv_cfg=self.conv_cfg,
+                norm_cfg=self.norm_cfg)
+            self.num_convs = num_res_blocks
+        else:
+            self.convs = nn.ModuleList()
+            for i in range(self.num_convs):
+                in_channels = self.in_channels if i == 0 else conv_out_channels
+                self.convs.append(
+                    ConvModule(
+                        in_channels,
+                        conv_out_channels,
+                        3,
+                        padding=1,
+                        conv_cfg=self.conv_cfg,
+                        norm_cfg=self.norm_cfg))
+
+        self.pool = nn.AdaptiveAvgPool2d(1)
+        self.fc = nn.Linear(conv_out_channels, num_classes)
+
+        self.criterion = nn.BCEWithLogitsLoss()
+
+    @auto_fp16()
+    def forward(self, feats):
+        """Forward function."""
+        x = feats[-1]
+        for i in range(self.num_convs):
+            x = self.convs[i](x)
+        x = self.pool(x)
+
+        # multi-class prediction
+        mc_pred = x.reshape(x.size(0), -1)
+        mc_pred = self.fc(mc_pred)
+
+        return mc_pred, x
+
+    @force_fp32(apply_to=('pred', ))
+    def loss(self, pred, labels):
+        """Loss function."""
+        labels = [lbl.unique() for lbl in labels]
+        targets = pred.new_zeros(pred.size())
+        for i, label in enumerate(labels):
+            targets[i, label] = 1.0
+        loss = self.loss_weight * self.criterion(pred, targets)
+        return loss
diff --git a/mmdet/models/roi_heads/mask_heads/grid_head.py b/mmdet/models/roi_heads/mask_heads/grid_head.py
new file mode 100644
index 0000000..0c0702d
--- /dev/null
+++ b/mmdet/models/roi_heads/mask_heads/grid_head.py
@@ -0,0 +1,363 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule
+
+from mmdet.models.builder import HEADS, build_loss
+
+
+@HEADS.register_module()
+class GridHead(BaseModule):
+
+    def __init__(self,
+                 grid_points=9,
+                 num_convs=8,
+                 roi_feat_size=14,
+                 in_channels=256,
+                 conv_kernel_size=3,
+                 point_feat_channels=64,
+                 deconv_kernel_size=4,
+                 class_agnostic=False,
+                 loss_grid=dict(
+                     type='CrossEntropyLoss', use_sigmoid=True,
+                     loss_weight=15),
+                 conv_cfg=None,
+                 norm_cfg=dict(type='GN', num_groups=36),
+                 init_cfg=[
+                     dict(type='Kaiming', layer=['Conv2d', 'Linear']),
+                     dict(
+                         type='Normal',
+                         layer='ConvTranspose2d',
+                         std=0.001,
+                         override=dict(
+                             type='Normal',
+                             name='deconv2',
+                             std=0.001,
+                             bias=-np.log(0.99 / 0.01)))
+                 ]):
+        super(GridHead, self).__init__(init_cfg)
+        self.grid_points = grid_points
+        self.num_convs = num_convs
+        self.roi_feat_size = roi_feat_size
+        self.in_channels = in_channels
+        self.conv_kernel_size = conv_kernel_size
+        self.point_feat_channels = point_feat_channels
+        self.conv_out_channels = self.point_feat_channels * self.grid_points
+        self.class_agnostic = class_agnostic
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        if isinstance(norm_cfg, dict) and norm_cfg['type'] == 'GN':
+            assert self.conv_out_channels % norm_cfg['num_groups'] == 0
+
+        assert self.grid_points >= 4
+        self.grid_size = int(np.sqrt(self.grid_points))
+        if self.grid_size * self.grid_size != self.grid_points:
+            raise ValueError('grid_points must be a square number')
+
+        # the predicted heatmap is half of whole_map_size
+        if not isinstance(self.roi_feat_size, int):
+            raise ValueError('Only square RoIs are supporeted in Grid R-CNN')
+        self.whole_map_size = self.roi_feat_size * 4
+
+        # compute point-wise sub-regions
+        self.sub_regions = self.calc_sub_regions()
+
+        self.convs = []
+        for i in range(self.num_convs):
+            in_channels = (
+                self.in_channels if i == 0 else self.conv_out_channels)
+            stride = 2 if i == 0 else 1
+            padding = (self.conv_kernel_size - 1) // 2
+            self.convs.append(
+                ConvModule(
+                    in_channels,
+                    self.conv_out_channels,
+                    self.conv_kernel_size,
+                    stride=stride,
+                    padding=padding,
+                    conv_cfg=self.conv_cfg,
+                    norm_cfg=self.norm_cfg,
+                    bias=True))
+        self.convs = nn.Sequential(*self.convs)
+
+        self.deconv1 = nn.ConvTranspose2d(
+            self.conv_out_channels,
+            self.conv_out_channels,
+            kernel_size=deconv_kernel_size,
+            stride=2,
+            padding=(deconv_kernel_size - 2) // 2,
+            groups=grid_points)
+        self.norm1 = nn.GroupNorm(grid_points, self.conv_out_channels)
+        self.deconv2 = nn.ConvTranspose2d(
+            self.conv_out_channels,
+            grid_points,
+            kernel_size=deconv_kernel_size,
+            stride=2,
+            padding=(deconv_kernel_size - 2) // 2,
+            groups=grid_points)
+
+        # find the 4-neighbor of each grid point
+        self.neighbor_points = []
+        grid_size = self.grid_size
+        for i in range(grid_size):  # i-th column
+            for j in range(grid_size):  # j-th row
+                neighbors = []
+                if i > 0:  # left: (i - 1, j)
+                    neighbors.append((i - 1) * grid_size + j)
+                if j > 0:  # up: (i, j - 1)
+                    neighbors.append(i * grid_size + j - 1)
+                if j < grid_size - 1:  # down: (i, j + 1)
+                    neighbors.append(i * grid_size + j + 1)
+                if i < grid_size - 1:  # right: (i + 1, j)
+                    neighbors.append((i + 1) * grid_size + j)
+                self.neighbor_points.append(tuple(neighbors))
+        # total edges in the grid
+        self.num_edges = sum([len(p) for p in self.neighbor_points])
+
+        self.forder_trans = nn.ModuleList()  # first-order feature transition
+        self.sorder_trans = nn.ModuleList()  # second-order feature transition
+        for neighbors in self.neighbor_points:
+            fo_trans = nn.ModuleList()
+            so_trans = nn.ModuleList()
+            for _ in range(len(neighbors)):
+                # each transition module consists of a 5x5 depth-wise conv and
+                # 1x1 conv.
+                fo_trans.append(
+                    nn.Sequential(
+                        nn.Conv2d(
+                            self.point_feat_channels,
+                            self.point_feat_channels,
+                            5,
+                            stride=1,
+                            padding=2,
+                            groups=self.point_feat_channels),
+                        nn.Conv2d(self.point_feat_channels,
+                                  self.point_feat_channels, 1)))
+                so_trans.append(
+                    nn.Sequential(
+                        nn.Conv2d(
+                            self.point_feat_channels,
+                            self.point_feat_channels,
+                            5,
+                            1,
+                            2,
+                            groups=self.point_feat_channels),
+                        nn.Conv2d(self.point_feat_channels,
+                                  self.point_feat_channels, 1)))
+            self.forder_trans.append(fo_trans)
+            self.sorder_trans.append(so_trans)
+
+        self.loss_grid = build_loss(loss_grid)
+
+    def forward(self, x):
+        assert x.shape[-1] == x.shape[-2] == self.roi_feat_size
+        # RoI feature transformation, downsample 2x
+        x = self.convs(x)
+
+        c = self.point_feat_channels
+        # first-order fusion
+        x_fo = [None for _ in range(self.grid_points)]
+        for i, points in enumerate(self.neighbor_points):
+            x_fo[i] = x[:, i * c:(i + 1) * c]
+            for j, point_idx in enumerate(points):
+                x_fo[i] = x_fo[i] + self.forder_trans[i][j](
+                    x[:, point_idx * c:(point_idx + 1) * c])
+
+        # second-order fusion
+        x_so = [None for _ in range(self.grid_points)]
+        for i, points in enumerate(self.neighbor_points):
+            x_so[i] = x[:, i * c:(i + 1) * c]
+            for j, point_idx in enumerate(points):
+                x_so[i] = x_so[i] + self.sorder_trans[i][j](x_fo[point_idx])
+
+        # predicted heatmap with fused features
+        x2 = torch.cat(x_so, dim=1)
+        x2 = self.deconv1(x2)
+        x2 = F.relu(self.norm1(x2), inplace=True)
+        heatmap = self.deconv2(x2)
+
+        # predicted heatmap with original features (applicable during training)
+        if self.training:
+            x1 = x
+            x1 = self.deconv1(x1)
+            x1 = F.relu(self.norm1(x1), inplace=True)
+            heatmap_unfused = self.deconv2(x1)
+        else:
+            heatmap_unfused = heatmap
+
+        return dict(fused=heatmap, unfused=heatmap_unfused)
+
+    def calc_sub_regions(self):
+        """Compute point specific representation regions.
+
+        See Grid R-CNN Plus (https://arxiv.org/abs/1906.05688) for details.
+        """
+        # to make it consistent with the original implementation, half_size
+        # is computed as 2 * quarter_size, which is smaller
+        half_size = self.whole_map_size // 4 * 2
+        sub_regions = []
+        for i in range(self.grid_points):
+            x_idx = i // self.grid_size
+            y_idx = i % self.grid_size
+            if x_idx == 0:
+                sub_x1 = 0
+            elif x_idx == self.grid_size - 1:
+                sub_x1 = half_size
+            else:
+                ratio = x_idx / (self.grid_size - 1) - 0.25
+                sub_x1 = max(int(ratio * self.whole_map_size), 0)
+
+            if y_idx == 0:
+                sub_y1 = 0
+            elif y_idx == self.grid_size - 1:
+                sub_y1 = half_size
+            else:
+                ratio = y_idx / (self.grid_size - 1) - 0.25
+                sub_y1 = max(int(ratio * self.whole_map_size), 0)
+            sub_regions.append(
+                (sub_x1, sub_y1, sub_x1 + half_size, sub_y1 + half_size))
+        return sub_regions
+
+    def get_targets(self, sampling_results, rcnn_train_cfg):
+        # mix all samples (across images) together.
+        pos_bboxes = torch.cat([res.pos_bboxes for res in sampling_results],
+                               dim=0).cpu()
+        pos_gt_bboxes = torch.cat(
+            [res.pos_gt_bboxes for res in sampling_results], dim=0).cpu()
+        assert pos_bboxes.shape == pos_gt_bboxes.shape
+
+        # expand pos_bboxes to 2x of original size
+        x1 = pos_bboxes[:, 0] - (pos_bboxes[:, 2] - pos_bboxes[:, 0]) / 2
+        y1 = pos_bboxes[:, 1] - (pos_bboxes[:, 3] - pos_bboxes[:, 1]) / 2
+        x2 = pos_bboxes[:, 2] + (pos_bboxes[:, 2] - pos_bboxes[:, 0]) / 2
+        y2 = pos_bboxes[:, 3] + (pos_bboxes[:, 3] - pos_bboxes[:, 1]) / 2
+        pos_bboxes = torch.stack([x1, y1, x2, y2], dim=-1)
+        pos_bbox_ws = (pos_bboxes[:, 2] - pos_bboxes[:, 0]).unsqueeze(-1)
+        pos_bbox_hs = (pos_bboxes[:, 3] - pos_bboxes[:, 1]).unsqueeze(-1)
+
+        num_rois = pos_bboxes.shape[0]
+        map_size = self.whole_map_size
+        # this is not the final target shape
+        targets = torch.zeros((num_rois, self.grid_points, map_size, map_size),
+                              dtype=torch.float)
+
+        # pre-compute interpolation factors for all grid points.
+        # the first item is the factor of x-dim, and the second is y-dim.
+        # for a 9-point grid, factors are like (1, 0), (0.5, 0.5), (0, 1)
+        factors = []
+        for j in range(self.grid_points):
+            x_idx = j // self.grid_size
+            y_idx = j % self.grid_size
+            factors.append((1 - x_idx / (self.grid_size - 1),
+                            1 - y_idx / (self.grid_size - 1)))
+
+        radius = rcnn_train_cfg.pos_radius
+        radius2 = radius**2
+        for i in range(num_rois):
+            # ignore small bboxes
+            if (pos_bbox_ws[i] <= self.grid_size
+                    or pos_bbox_hs[i] <= self.grid_size):
+                continue
+            # for each grid point, mark a small circle as positive
+            for j in range(self.grid_points):
+                factor_x, factor_y = factors[j]
+                gridpoint_x = factor_x * pos_gt_bboxes[i, 0] + (
+                    1 - factor_x) * pos_gt_bboxes[i, 2]
+                gridpoint_y = factor_y * pos_gt_bboxes[i, 1] + (
+                    1 - factor_y) * pos_gt_bboxes[i, 3]
+
+                cx = int((gridpoint_x - pos_bboxes[i, 0]) / pos_bbox_ws[i] *
+                         map_size)
+                cy = int((gridpoint_y - pos_bboxes[i, 1]) / pos_bbox_hs[i] *
+                         map_size)
+
+                for x in range(cx - radius, cx + radius + 1):
+                    for y in range(cy - radius, cy + radius + 1):
+                        if x >= 0 and x < map_size and y >= 0 and y < map_size:
+                            if (x - cx)**2 + (y - cy)**2 <= radius2:
+                                targets[i, j, y, x] = 1
+        # reduce the target heatmap size by a half
+        # proposed in Grid R-CNN Plus (https://arxiv.org/abs/1906.05688).
+        sub_targets = []
+        for i in range(self.grid_points):
+            sub_x1, sub_y1, sub_x2, sub_y2 = self.sub_regions[i]
+            sub_targets.append(targets[:, [i], sub_y1:sub_y2, sub_x1:sub_x2])
+        sub_targets = torch.cat(sub_targets, dim=1)
+        sub_targets = sub_targets.to(sampling_results[0].pos_bboxes.device)
+        return sub_targets
+
+    def loss(self, grid_pred, grid_targets):
+        loss_fused = self.loss_grid(grid_pred['fused'], grid_targets)
+        loss_unfused = self.loss_grid(grid_pred['unfused'], grid_targets)
+        loss_grid = loss_fused + loss_unfused
+        return dict(loss_grid=loss_grid)
+
+    def get_bboxes(self, det_bboxes, grid_pred, img_metas):
+        # TODO: refactoring
+        assert det_bboxes.shape[0] == grid_pred.shape[0]
+        det_bboxes = det_bboxes.cpu()
+        cls_scores = det_bboxes[:, [4]]
+        det_bboxes = det_bboxes[:, :4]
+        grid_pred = grid_pred.sigmoid().cpu()
+
+        R, c, h, w = grid_pred.shape
+        half_size = self.whole_map_size // 4 * 2
+        assert h == w == half_size
+        assert c == self.grid_points
+
+        # find the point with max scores in the half-sized heatmap
+        grid_pred = grid_pred.view(R * c, h * w)
+        pred_scores, pred_position = grid_pred.max(dim=1)
+        xs = pred_position % w
+        ys = pred_position // w
+
+        # get the position in the whole heatmap instead of half-sized heatmap
+        for i in range(self.grid_points):
+            xs[i::self.grid_points] += self.sub_regions[i][0]
+            ys[i::self.grid_points] += self.sub_regions[i][1]
+
+        # reshape to (num_rois, grid_points)
+        pred_scores, xs, ys = tuple(
+            map(lambda x: x.view(R, c), [pred_scores, xs, ys]))
+
+        # get expanded pos_bboxes
+        widths = (det_bboxes[:, 2] - det_bboxes[:, 0]).unsqueeze(-1)
+        heights = (det_bboxes[:, 3] - det_bboxes[:, 1]).unsqueeze(-1)
+        x1 = (det_bboxes[:, 0, None] - widths / 2)
+        y1 = (det_bboxes[:, 1, None] - heights / 2)
+        # map the grid point to the absolute coordinates
+        abs_xs = (xs.float() + 0.5) / w * widths + x1
+        abs_ys = (ys.float() + 0.5) / h * heights + y1
+
+        # get the grid points indices that fall on the bbox boundaries
+        x1_inds = [i for i in range(self.grid_size)]
+        y1_inds = [i * self.grid_size for i in range(self.grid_size)]
+        x2_inds = [
+            self.grid_points - self.grid_size + i
+            for i in range(self.grid_size)
+        ]
+        y2_inds = [(i + 1) * self.grid_size - 1 for i in range(self.grid_size)]
+
+        # voting of all grid points on some boundary
+        bboxes_x1 = (abs_xs[:, x1_inds] * pred_scores[:, x1_inds]).sum(
+            dim=1, keepdim=True) / (
+                pred_scores[:, x1_inds].sum(dim=1, keepdim=True))
+        bboxes_y1 = (abs_ys[:, y1_inds] * pred_scores[:, y1_inds]).sum(
+            dim=1, keepdim=True) / (
+                pred_scores[:, y1_inds].sum(dim=1, keepdim=True))
+        bboxes_x2 = (abs_xs[:, x2_inds] * pred_scores[:, x2_inds]).sum(
+            dim=1, keepdim=True) / (
+                pred_scores[:, x2_inds].sum(dim=1, keepdim=True))
+        bboxes_y2 = (abs_ys[:, y2_inds] * pred_scores[:, y2_inds]).sum(
+            dim=1, keepdim=True) / (
+                pred_scores[:, y2_inds].sum(dim=1, keepdim=True))
+
+        bbox_res = torch.cat(
+            [bboxes_x1, bboxes_y1, bboxes_x2, bboxes_y2, cls_scores], dim=1)
+        bbox_res[:, [0, 2]].clamp_(min=0, max=img_metas[0]['img_shape'][1])
+        bbox_res[:, [1, 3]].clamp_(min=0, max=img_metas[0]['img_shape'][0])
+
+        return bbox_res
diff --git a/mmdet/models/roi_heads/mask_heads/htc_mask_head.py b/mmdet/models/roi_heads/mask_heads/htc_mask_head.py
new file mode 100644
index 0000000..7ad8592
--- /dev/null
+++ b/mmdet/models/roi_heads/mask_heads/htc_mask_head.py
@@ -0,0 +1,39 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.cnn import ConvModule
+
+from mmdet.models.builder import HEADS
+from .fcn_mask_head import FCNMaskHead
+
+
+@HEADS.register_module()
+class HTCMaskHead(FCNMaskHead):
+
+    def __init__(self, with_conv_res=True, *args, **kwargs):
+        super(HTCMaskHead, self).__init__(*args, **kwargs)
+        self.with_conv_res = with_conv_res
+        if self.with_conv_res:
+            self.conv_res = ConvModule(
+                self.conv_out_channels,
+                self.conv_out_channels,
+                1,
+                conv_cfg=self.conv_cfg,
+                norm_cfg=self.norm_cfg)
+
+    def forward(self, x, res_feat=None, return_logits=True, return_feat=True):
+        if res_feat is not None:
+            assert self.with_conv_res
+            res_feat = self.conv_res(res_feat)
+            x = x + res_feat
+        for conv in self.convs:
+            x = conv(x)
+        res_feat = x
+        outs = []
+        if return_logits:
+            x = self.upsample(x)
+            if self.upsample_method == 'deconv':
+                x = self.relu(x)
+            mask_pred = self.conv_logits(x)
+            outs.append(mask_pred)
+        if return_feat:
+            outs.append(res_feat)
+        return outs if len(outs) > 1 else outs[0]
diff --git a/mmdet/models/roi_heads/mask_heads/mask_point_head.py b/mmdet/models/roi_heads/mask_heads/mask_point_head.py
new file mode 100644
index 0000000..c77c46d
--- /dev/null
+++ b/mmdet/models/roi_heads/mask_heads/mask_point_head.py
@@ -0,0 +1,253 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend/point_head/point_head.py  # noqa
+
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
+from mmcv.runner import BaseModule
+
+from mmdet.models.builder import HEADS, build_loss
+from mmdet.models.utils import (get_uncertain_point_coords_with_randomness,
+                                get_uncertainty)
+
+
+@HEADS.register_module()
+class MaskPointHead(BaseModule):
+    """A mask point head use in PointRend.
+
+    ``MaskPointHead`` use shared multi-layer perceptron (equivalent to
+    nn.Conv1d) to predict the logit of input points. The fine-grained feature
+    and coarse feature will be concatenate together for predication.
+
+    Args:
+        num_fcs (int): Number of fc layers in the head. Default: 3.
+        in_channels (int): Number of input channels. Default: 256.
+        fc_channels (int): Number of fc channels. Default: 256.
+        num_classes (int): Number of classes for logits. Default: 80.
+        class_agnostic (bool): Whether use class agnostic classification.
+            If so, the output channels of logits will be 1. Default: False.
+        coarse_pred_each_layer (bool): Whether concatenate coarse feature with
+            the output of each fc layer. Default: True.
+        conv_cfg (dict | None): Dictionary to construct and config conv layer.
+            Default: dict(type='Conv1d'))
+        norm_cfg (dict | None): Dictionary to construct and config norm layer.
+            Default: None.
+        loss_point (dict): Dictionary to construct and config loss layer of
+            point head. Default: dict(type='CrossEntropyLoss', use_mask=True,
+            loss_weight=1.0).
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 num_fcs=3,
+                 in_channels=256,
+                 fc_channels=256,
+                 class_agnostic=False,
+                 coarse_pred_each_layer=True,
+                 conv_cfg=dict(type='Conv1d'),
+                 norm_cfg=None,
+                 act_cfg=dict(type='ReLU'),
+                 loss_point=dict(
+                     type='CrossEntropyLoss', use_mask=True, loss_weight=1.0),
+                 init_cfg=dict(
+                     type='Normal', std=0.001,
+                     override=dict(name='fc_logits'))):
+        super().__init__(init_cfg)
+        self.num_fcs = num_fcs
+        self.in_channels = in_channels
+        self.fc_channels = fc_channels
+        self.num_classes = num_classes
+        self.class_agnostic = class_agnostic
+        self.coarse_pred_each_layer = coarse_pred_each_layer
+        self.conv_cfg = conv_cfg
+        self.norm_cfg = norm_cfg
+        self.loss_point = build_loss(loss_point)
+
+        fc_in_channels = in_channels + num_classes
+        self.fcs = nn.ModuleList()
+        for _ in range(num_fcs):
+            fc = ConvModule(
+                fc_in_channels,
+                fc_channels,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg)
+            self.fcs.append(fc)
+            fc_in_channels = fc_channels
+            fc_in_channels += num_classes if self.coarse_pred_each_layer else 0
+
+        out_channels = 1 if self.class_agnostic else self.num_classes
+        self.fc_logits = nn.Conv1d(
+            fc_in_channels, out_channels, kernel_size=1, stride=1, padding=0)
+
+    def forward(self, fine_grained_feats, coarse_feats):
+        """Classify each point base on fine grained and coarse feats.
+
+        Args:
+            fine_grained_feats (Tensor): Fine grained feature sampled from FPN,
+                shape (num_rois, in_channels, num_points).
+            coarse_feats (Tensor): Coarse feature sampled from CoarseMaskHead,
+                shape (num_rois, num_classes, num_points).
+
+        Returns:
+            Tensor: Point classification results,
+                shape (num_rois, num_class, num_points).
+        """
+
+        x = torch.cat([fine_grained_feats, coarse_feats], dim=1)
+        for fc in self.fcs:
+            x = fc(x)
+            if self.coarse_pred_each_layer:
+                x = torch.cat((x, coarse_feats), dim=1)
+        return self.fc_logits(x)
+
+    def get_targets(self, rois, rel_roi_points, sampling_results, gt_masks,
+                    cfg):
+        """Get training targets of MaskPointHead for all images.
+
+        Args:
+            rois (Tensor): Region of Interest, shape (num_rois, 5).
+            rel_roi_points: Points coordinates relative to RoI, shape
+                (num_rois, num_points, 2).
+            sampling_results (:obj:`SamplingResult`): Sampling result after
+                sampling and assignment.
+            gt_masks (Tensor) : Ground truth segmentation masks of
+                corresponding boxes, shape (num_rois, height, width).
+            cfg (dict): Training cfg.
+
+        Returns:
+            Tensor: Point target, shape (num_rois, num_points).
+        """
+
+        num_imgs = len(sampling_results)
+        rois_list = []
+        rel_roi_points_list = []
+        for batch_ind in range(num_imgs):
+            inds = (rois[:, 0] == batch_ind)
+            rois_list.append(rois[inds])
+            rel_roi_points_list.append(rel_roi_points[inds])
+        pos_assigned_gt_inds_list = [
+            res.pos_assigned_gt_inds for res in sampling_results
+        ]
+        cfg_list = [cfg for _ in range(num_imgs)]
+
+        point_targets = map(self._get_target_single, rois_list,
+                            rel_roi_points_list, pos_assigned_gt_inds_list,
+                            gt_masks, cfg_list)
+        point_targets = list(point_targets)
+
+        if len(point_targets) > 0:
+            point_targets = torch.cat(point_targets)
+
+        return point_targets
+
+    def _get_target_single(self, rois, rel_roi_points, pos_assigned_gt_inds,
+                           gt_masks, cfg):
+        """Get training target of MaskPointHead for each image."""
+        num_pos = rois.size(0)
+        num_points = cfg.num_points
+        if num_pos > 0:
+            gt_masks_th = (
+                gt_masks.to_tensor(rois.dtype, rois.device).index_select(
+                    0, pos_assigned_gt_inds))
+            gt_masks_th = gt_masks_th.unsqueeze(1)
+            rel_img_points = rel_roi_point_to_rel_img_point(
+                rois, rel_roi_points, gt_masks_th)
+            point_targets = point_sample(gt_masks_th,
+                                         rel_img_points).squeeze(1)
+        else:
+            point_targets = rois.new_zeros((0, num_points))
+        return point_targets
+
+    def loss(self, point_pred, point_targets, labels):
+        """Calculate loss for MaskPointHead.
+
+        Args:
+            point_pred (Tensor): Point predication result, shape
+                (num_rois, num_classes, num_points).
+            point_targets (Tensor): Point targets, shape (num_roi, num_points).
+            labels (Tensor): Class label of corresponding boxes,
+                shape (num_rois, )
+
+        Returns:
+            dict[str, Tensor]: a dictionary of point loss components
+        """
+
+        loss = dict()
+        if self.class_agnostic:
+            loss_point = self.loss_point(point_pred, point_targets,
+                                         torch.zeros_like(labels))
+        else:
+            loss_point = self.loss_point(point_pred, point_targets, labels)
+        loss['loss_point'] = loss_point
+        return loss
+
+    def get_roi_rel_points_train(self, mask_pred, labels, cfg):
+        """Get ``num_points`` most uncertain points with random points during
+        train.
+
+        Sample points in [0, 1] x [0, 1] coordinate space based on their
+        uncertainty. The uncertainties are calculated for each point using
+        '_get_uncertainty()' function that takes point's logit prediction as
+        input.
+
+        Args:
+            mask_pred (Tensor): A tensor of shape (num_rois, num_classes,
+                mask_height, mask_width) for class-specific or class-agnostic
+                prediction.
+            labels (list): The ground truth class for each instance.
+            cfg (dict): Training config of point head.
+
+        Returns:
+            point_coords (Tensor): A tensor of shape (num_rois, num_points, 2)
+                that contains the coordinates sampled points.
+        """
+        point_coords = get_uncertain_point_coords_with_randomness(
+            mask_pred, labels, cfg.num_points, cfg.oversample_ratio,
+            cfg.importance_sample_ratio)
+        return point_coords
+
+    def get_roi_rel_points_test(self, mask_pred, pred_label, cfg):
+        """Get ``num_points`` most uncertain points during test.
+
+        Args:
+            mask_pred (Tensor): A tensor of shape (num_rois, num_classes,
+                mask_height, mask_width) for class-specific or class-agnostic
+                prediction.
+            pred_label (list): The predication class for each instance.
+            cfg (dict): Testing config of point head.
+
+        Returns:
+            point_indices (Tensor): A tensor of shape (num_rois, num_points)
+                that contains indices from [0, mask_height x mask_width) of the
+                most uncertain points.
+            point_coords (Tensor): A tensor of shape (num_rois, num_points, 2)
+                that contains [0, 1] x [0, 1] normalized coordinates of the
+                most uncertain points from the [mask_height, mask_width] grid .
+        """
+        num_points = cfg.subdivision_num_points
+        uncertainty_map = get_uncertainty(mask_pred, pred_label)
+        num_rois, _, mask_height, mask_width = uncertainty_map.shape
+
+        # During ONNX exporting, the type of each elements of 'shape' is
+        # `Tensor(float)`, while it is `float` during PyTorch inference.
+        if isinstance(mask_height, torch.Tensor):
+            h_step = 1.0 / mask_height.float()
+            w_step = 1.0 / mask_width.float()
+        else:
+            h_step = 1.0 / mask_height
+            w_step = 1.0 / mask_width
+        # cast to int to avoid dynamic K for TopK op in ONNX
+        mask_size = int(mask_height * mask_width)
+        uncertainty_map = uncertainty_map.view(num_rois, mask_size)
+        num_points = min(mask_size, num_points)
+        point_indices = uncertainty_map.topk(num_points, dim=1)[1]
+        xs = w_step / 2.0 + (point_indices % mask_width).float() * w_step
+        ys = h_step / 2.0 + (point_indices // mask_width).float() * h_step
+        point_coords = torch.stack([xs, ys], dim=2)
+        return point_indices, point_coords
diff --git a/mmdet/models/roi_heads/mask_heads/maskiou_head.py b/mmdet/models/roi_heads/mask_heads/maskiou_head.py
new file mode 100644
index 0000000..a7ff7c7
--- /dev/null
+++ b/mmdet/models/roi_heads/mask_heads/maskiou_head.py
@@ -0,0 +1,183 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+import torch.nn as nn
+from mmcv.cnn import Conv2d, Linear, MaxPool2d
+from mmcv.runner import BaseModule, force_fp32
+from torch.nn.modules.utils import _pair
+
+from mmdet.models.builder import HEADS, build_loss
+
+
+@HEADS.register_module()
+class MaskIoUHead(BaseModule):
+    """Mask IoU Head.
+
+    This head predicts the IoU of predicted masks and corresponding gt masks.
+    """
+
+    def __init__(self,
+                 num_convs=4,
+                 num_fcs=2,
+                 roi_feat_size=14,
+                 in_channels=256,
+                 conv_out_channels=256,
+                 fc_out_channels=1024,
+                 num_classes=80,
+                 loss_iou=dict(type='MSELoss', loss_weight=0.5),
+                 init_cfg=[
+                     dict(type='Kaiming', override=dict(name='convs')),
+                     dict(type='Caffe2Xavier', override=dict(name='fcs')),
+                     dict(
+                         type='Normal',
+                         std=0.01,
+                         override=dict(name='fc_mask_iou'))
+                 ]):
+        super(MaskIoUHead, self).__init__(init_cfg)
+        self.in_channels = in_channels
+        self.conv_out_channels = conv_out_channels
+        self.fc_out_channels = fc_out_channels
+        self.num_classes = num_classes
+        self.fp16_enabled = False
+
+        self.convs = nn.ModuleList()
+        for i in range(num_convs):
+            if i == 0:
+                # concatenation of mask feature and mask prediction
+                in_channels = self.in_channels + 1
+            else:
+                in_channels = self.conv_out_channels
+            stride = 2 if i == num_convs - 1 else 1
+            self.convs.append(
+                Conv2d(
+                    in_channels,
+                    self.conv_out_channels,
+                    3,
+                    stride=stride,
+                    padding=1))
+
+        roi_feat_size = _pair(roi_feat_size)
+        pooled_area = (roi_feat_size[0] // 2) * (roi_feat_size[1] // 2)
+        self.fcs = nn.ModuleList()
+        for i in range(num_fcs):
+            in_channels = (
+                self.conv_out_channels *
+                pooled_area if i == 0 else self.fc_out_channels)
+            self.fcs.append(Linear(in_channels, self.fc_out_channels))
+
+        self.fc_mask_iou = Linear(self.fc_out_channels, self.num_classes)
+        self.relu = nn.ReLU()
+        self.max_pool = MaxPool2d(2, 2)
+        self.loss_iou = build_loss(loss_iou)
+
+    def forward(self, mask_feat, mask_pred):
+        mask_pred = mask_pred.sigmoid()
+        mask_pred_pooled = self.max_pool(mask_pred.unsqueeze(1))
+
+        x = torch.cat((mask_feat, mask_pred_pooled), 1)
+
+        for conv in self.convs:
+            x = self.relu(conv(x))
+        x = x.flatten(1)
+        for fc in self.fcs:
+            x = self.relu(fc(x))
+        mask_iou = self.fc_mask_iou(x)
+        return mask_iou
+
+    @force_fp32(apply_to=('mask_iou_pred', ))
+    def loss(self, mask_iou_pred, mask_iou_targets):
+        pos_inds = mask_iou_targets > 0
+        if pos_inds.sum() > 0:
+            loss_mask_iou = self.loss_iou(mask_iou_pred[pos_inds],
+                                          mask_iou_targets[pos_inds])
+        else:
+            loss_mask_iou = mask_iou_pred.sum() * 0
+        return dict(loss_mask_iou=loss_mask_iou)
+
+    @force_fp32(apply_to=('mask_pred', ))
+    def get_targets(self, sampling_results, gt_masks, mask_pred, mask_targets,
+                    rcnn_train_cfg):
+        """Compute target of mask IoU.
+
+        Mask IoU target is the IoU of the predicted mask (inside a bbox) and
+        the gt mask of corresponding gt mask (the whole instance).
+        The intersection area is computed inside the bbox, and the gt mask area
+        is computed with two steps, firstly we compute the gt area inside the
+        bbox, then divide it by the area ratio of gt area inside the bbox and
+        the gt area of the whole instance.
+
+        Args:
+            sampling_results (list[:obj:`SamplingResult`]): sampling results.
+            gt_masks (BitmapMask | PolygonMask): Gt masks (the whole instance)
+                of each image, with the same shape of the input image.
+            mask_pred (Tensor): Predicted masks of each positive proposal,
+                shape (num_pos, h, w).
+            mask_targets (Tensor): Gt mask of each positive proposal,
+                binary map of the shape (num_pos, h, w).
+            rcnn_train_cfg (dict): Training config for R-CNN part.
+
+        Returns:
+            Tensor: mask iou target (length == num positive).
+        """
+        pos_proposals = [res.pos_bboxes for res in sampling_results]
+        pos_assigned_gt_inds = [
+            res.pos_assigned_gt_inds for res in sampling_results
+        ]
+
+        # compute the area ratio of gt areas inside the proposals and
+        # the whole instance
+        area_ratios = map(self._get_area_ratio, pos_proposals,
+                          pos_assigned_gt_inds, gt_masks)
+        area_ratios = torch.cat(list(area_ratios))
+        assert mask_targets.size(0) == area_ratios.size(0)
+
+        mask_pred = (mask_pred > rcnn_train_cfg.mask_thr_binary).float()
+        mask_pred_areas = mask_pred.sum((-1, -2))
+
+        # mask_pred and mask_targets are binary maps
+        overlap_areas = (mask_pred * mask_targets).sum((-1, -2))
+
+        # compute the mask area of the whole instance
+        gt_full_areas = mask_targets.sum((-1, -2)) / (area_ratios + 1e-7)
+
+        mask_iou_targets = overlap_areas / (
+            mask_pred_areas + gt_full_areas - overlap_areas)
+        return mask_iou_targets
+
+    def _get_area_ratio(self, pos_proposals, pos_assigned_gt_inds, gt_masks):
+        """Compute area ratio of the gt mask inside the proposal and the gt
+        mask of the corresponding instance."""
+        num_pos = pos_proposals.size(0)
+        if num_pos > 0:
+            area_ratios = []
+            proposals_np = pos_proposals.cpu().numpy()
+            pos_assigned_gt_inds = pos_assigned_gt_inds.cpu().numpy()
+            # compute mask areas of gt instances (batch processing for speedup)
+            gt_instance_mask_area = gt_masks.areas
+            for i in range(num_pos):
+                gt_mask = gt_masks[pos_assigned_gt_inds[i]]
+
+                # crop the gt mask inside the proposal
+                bbox = proposals_np[i, :].astype(np.int32)
+                gt_mask_in_proposal = gt_mask.crop(bbox)
+
+                ratio = gt_mask_in_proposal.areas[0] / (
+                    gt_instance_mask_area[pos_assigned_gt_inds[i]] + 1e-7)
+                area_ratios.append(ratio)
+            area_ratios = torch.from_numpy(np.stack(area_ratios)).float().to(
+                pos_proposals.device)
+        else:
+            area_ratios = pos_proposals.new_zeros((0, ))
+        return area_ratios
+
+    @force_fp32(apply_to=('mask_iou_pred', ))
+    def get_mask_scores(self, mask_iou_pred, det_bboxes, det_labels):
+        """Get the mask scores.
+
+        mask_score = bbox_score * mask_iou
+        """
+        inds = range(det_labels.size(0))
+        mask_scores = mask_iou_pred[inds, det_labels] * det_bboxes[inds, -1]
+        mask_scores = mask_scores.cpu().numpy()
+        det_labels = det_labels.cpu().numpy()
+        return [mask_scores[det_labels == i] for i in range(self.num_classes)]
diff --git a/mmdet/models/roi_heads/mask_heads/scnet_mask_head.py b/mmdet/models/roi_heads/mask_heads/scnet_mask_head.py
new file mode 100644
index 0000000..ca62486
--- /dev/null
+++ b/mmdet/models/roi_heads/mask_heads/scnet_mask_head.py
@@ -0,0 +1,28 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmdet.models.builder import HEADS
+from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
+from .fcn_mask_head import FCNMaskHead
+
+
+@HEADS.register_module()
+class SCNetMaskHead(FCNMaskHead):
+    """Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
+
+    Args:
+        conv_to_res (bool, optional): if True, change the conv layers to
+            ``SimplifiedBasicBlock``.
+    """
+
+    def __init__(self, conv_to_res=True, **kwargs):
+        super(SCNetMaskHead, self).__init__(**kwargs)
+        self.conv_to_res = conv_to_res
+        if conv_to_res:
+            assert self.conv_kernel_size == 3
+            self.num_res_blocks = self.num_convs // 2
+            self.convs = ResLayer(
+                SimplifiedBasicBlock,
+                self.in_channels,
+                self.conv_out_channels,
+                self.num_res_blocks,
+                conv_cfg=self.conv_cfg,
+                norm_cfg=self.norm_cfg)
diff --git a/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py b/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py
new file mode 100644
index 0000000..2b8c5c3
--- /dev/null
+++ b/mmdet/models/roi_heads/mask_heads/scnet_semantic_head.py
@@ -0,0 +1,28 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmdet.models.builder import HEADS
+from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
+from .fused_semantic_head import FusedSemanticHead
+
+
+@HEADS.register_module()
+class SCNetSemanticHead(FusedSemanticHead):
+    """Mask head for `SCNet <https://arxiv.org/abs/2012.10150>`_.
+
+    Args:
+        conv_to_res (bool, optional): if True, change the conv layers to
+            ``SimplifiedBasicBlock``.
+    """
+
+    def __init__(self, conv_to_res=True, **kwargs):
+        super(SCNetSemanticHead, self).__init__(**kwargs)
+        self.conv_to_res = conv_to_res
+        if self.conv_to_res:
+            num_res_blocks = self.num_convs // 2
+            self.convs = ResLayer(
+                SimplifiedBasicBlock,
+                self.in_channels,
+                self.conv_out_channels,
+                num_res_blocks,
+                conv_cfg=self.conv_cfg,
+                norm_cfg=self.norm_cfg)
+            self.num_convs = num_res_blocks
diff --git a/mmdet/models/roi_heads/mask_scoring_roi_head.py b/mmdet/models/roi_heads/mask_scoring_roi_head.py
new file mode 100644
index 0000000..4617988
--- /dev/null
+++ b/mmdet/models/roi_heads/mask_scoring_roi_head.py
@@ -0,0 +1,113 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdet.core import bbox2roi
+from ..builder import HEADS, build_head
+from .standard_roi_head import StandardRoIHead
+
+
+@HEADS.register_module()
+class MaskScoringRoIHead(StandardRoIHead):
+    """Mask Scoring RoIHead for Mask Scoring RCNN.
+
+    https://arxiv.org/abs/1903.00241
+    """
+
+    def __init__(self, mask_iou_head, **kwargs):
+        assert mask_iou_head is not None
+        super(MaskScoringRoIHead, self).__init__(**kwargs)
+        self.mask_iou_head = build_head(mask_iou_head)
+
+    def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,
+                            img_metas):
+        """Run forward function and calculate loss for Mask head in
+        training."""
+        pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
+        mask_results = super(MaskScoringRoIHead,
+                             self)._mask_forward_train(x, sampling_results,
+                                                       bbox_feats, gt_masks,
+                                                       img_metas)
+        if mask_results['loss_mask'] is None:
+            return mask_results
+
+        # mask iou head forward and loss
+        pos_mask_pred = mask_results['mask_pred'][
+            range(mask_results['mask_pred'].size(0)), pos_labels]
+        mask_iou_pred = self.mask_iou_head(mask_results['mask_feats'],
+                                           pos_mask_pred)
+        pos_mask_iou_pred = mask_iou_pred[range(mask_iou_pred.size(0)),
+                                          pos_labels]
+
+        mask_iou_targets = self.mask_iou_head.get_targets(
+            sampling_results, gt_masks, pos_mask_pred,
+            mask_results['mask_targets'], self.train_cfg)
+        loss_mask_iou = self.mask_iou_head.loss(pos_mask_iou_pred,
+                                                mask_iou_targets)
+        mask_results['loss_mask'].update(loss_mask_iou)
+        return mask_results
+
+    def simple_test_mask(self,
+                         x,
+                         img_metas,
+                         det_bboxes,
+                         det_labels,
+                         rescale=False):
+        """Obtain mask prediction without augmentation."""
+        # image shapes of images in the batch
+        ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
+        scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
+
+        num_imgs = len(det_bboxes)
+        if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
+            num_classes = self.mask_head.num_classes
+            segm_results = [[[] for _ in range(num_classes)]
+                            for _ in range(num_imgs)]
+            mask_scores = [[[] for _ in range(num_classes)]
+                           for _ in range(num_imgs)]
+        else:
+            # if det_bboxes is rescaled to the original image size, we need to
+            # rescale it back to the testing scale to obtain RoIs.
+            if rescale and not isinstance(scale_factors[0], float):
+                scale_factors = [
+                    torch.from_numpy(scale_factor).to(det_bboxes[0].device)
+                    for scale_factor in scale_factors
+                ]
+            _bboxes = [
+                det_bboxes[i][:, :4] *
+                scale_factors[i] if rescale else det_bboxes[i]
+                for i in range(num_imgs)
+            ]
+            mask_rois = bbox2roi(_bboxes)
+            mask_results = self._mask_forward(x, mask_rois)
+            concat_det_labels = torch.cat(det_labels)
+            # get mask scores with mask iou head
+            mask_feats = mask_results['mask_feats']
+            mask_pred = mask_results['mask_pred']
+            mask_iou_pred = self.mask_iou_head(
+                mask_feats, mask_pred[range(concat_det_labels.size(0)),
+                                      concat_det_labels])
+            # split batch mask prediction back to each image
+            num_bboxes_per_img = tuple(len(_bbox) for _bbox in _bboxes)
+            mask_preds = mask_pred.split(num_bboxes_per_img, 0)
+            mask_iou_preds = mask_iou_pred.split(num_bboxes_per_img, 0)
+
+            # apply mask post-processing to each image individually
+            segm_results = []
+            mask_scores = []
+            for i in range(num_imgs):
+                if det_bboxes[i].shape[0] == 0:
+                    segm_results.append(
+                        [[] for _ in range(self.mask_head.num_classes)])
+                    mask_scores.append(
+                        [[] for _ in range(self.mask_head.num_classes)])
+                else:
+                    segm_result = self.mask_head.get_seg_masks(
+                        mask_preds[i], _bboxes[i], det_labels[i],
+                        self.test_cfg, ori_shapes[i], scale_factors[i],
+                        rescale)
+                    # get mask scores with mask iou head
+                    mask_score = self.mask_iou_head.get_mask_scores(
+                        mask_iou_preds[i], det_bboxes[i], det_labels[i])
+                    segm_results.append(segm_result)
+                    mask_scores.append(mask_score)
+        return list(zip(segm_results, mask_scores))
diff --git a/mmdet/models/roi_heads/pisa_roi_head.py b/mmdet/models/roi_heads/pisa_roi_head.py
new file mode 100644
index 0000000..92a5118
--- /dev/null
+++ b/mmdet/models/roi_heads/pisa_roi_head.py
@@ -0,0 +1,160 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmdet.core import bbox2roi
+from ..builder import HEADS
+from ..losses.pisa_loss import carl_loss, isr_p
+from .standard_roi_head import StandardRoIHead
+
+
+@HEADS.register_module()
+class PISARoIHead(StandardRoIHead):
+    r"""The RoI head for `Prime Sample Attention in Object Detection
+    <https://arxiv.org/abs/1904.04821>`_."""
+
+    def forward_train(self,
+                      x,
+                      img_metas,
+                      proposal_list,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None,
+                      gt_masks=None):
+        """Forward function for training.
+
+        Args:
+            x (list[Tensor]): List of multi-level img features.
+            img_metas (list[dict]): List of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `mmdet/datasets/pipelines/formatting.py:Collect`.
+            proposals (list[Tensors]): List of region proposals.
+            gt_bboxes (list[Tensor]): Each item are the truth boxes for each
+                image in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): Class indices corresponding to each box
+            gt_bboxes_ignore (list[Tensor], optional): Specify which bounding
+                boxes can be ignored when computing the loss.
+            gt_masks (None | Tensor) : True segmentation masks for each box
+                used if the architecture supports a segmentation task.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        # assign gts and sample proposals
+        if self.with_bbox or self.with_mask:
+            num_imgs = len(img_metas)
+            if gt_bboxes_ignore is None:
+                gt_bboxes_ignore = [None for _ in range(num_imgs)]
+            sampling_results = []
+            neg_label_weights = []
+            for i in range(num_imgs):
+                assign_result = self.bbox_assigner.assign(
+                    proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
+                    gt_labels[i])
+                sampling_result = self.bbox_sampler.sample(
+                    assign_result,
+                    proposal_list[i],
+                    gt_bboxes[i],
+                    gt_labels[i],
+                    feats=[lvl_feat[i][None] for lvl_feat in x])
+                # neg label weight is obtained by sampling when using ISR-N
+                neg_label_weight = None
+                if isinstance(sampling_result, tuple):
+                    sampling_result, neg_label_weight = sampling_result
+                sampling_results.append(sampling_result)
+                neg_label_weights.append(neg_label_weight)
+
+        losses = dict()
+        # bbox head forward and loss
+        if self.with_bbox:
+            bbox_results = self._bbox_forward_train(
+                x,
+                sampling_results,
+                gt_bboxes,
+                gt_labels,
+                img_metas,
+                neg_label_weights=neg_label_weights)
+            losses.update(bbox_results['loss_bbox'])
+
+        # mask head forward and loss
+        if self.with_mask:
+            mask_results = self._mask_forward_train(x, sampling_results,
+                                                    bbox_results['bbox_feats'],
+                                                    gt_masks, img_metas)
+            losses.update(mask_results['loss_mask'])
+
+        return losses
+
+    def _bbox_forward(self, x, rois):
+        """Box forward function used in both training and testing."""
+        # TODO: a more flexible way to decide which feature maps to use
+        bbox_feats = self.bbox_roi_extractor(
+            x[:self.bbox_roi_extractor.num_inputs], rois)
+        if self.with_shared_head:
+            bbox_feats = self.shared_head(bbox_feats)
+        cls_score, bbox_pred = self.bbox_head(bbox_feats)
+
+        bbox_results = dict(
+            cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
+        return bbox_results
+
+    def _bbox_forward_train(self,
+                            x,
+                            sampling_results,
+                            gt_bboxes,
+                            gt_labels,
+                            img_metas,
+                            neg_label_weights=None):
+        """Run forward function and calculate loss for box head in training."""
+        rois = bbox2roi([res.bboxes for res in sampling_results])
+
+        bbox_results = self._bbox_forward(x, rois)
+
+        bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
+                                                  gt_labels, self.train_cfg)
+
+        # neg_label_weights obtained by sampler is image-wise, mapping back to
+        # the corresponding location in label weights
+        if neg_label_weights[0] is not None:
+            label_weights = bbox_targets[1]
+            cur_num_rois = 0
+            for i in range(len(sampling_results)):
+                num_pos = sampling_results[i].pos_inds.size(0)
+                num_neg = sampling_results[i].neg_inds.size(0)
+                label_weights[cur_num_rois + num_pos:cur_num_rois + num_pos +
+                              num_neg] = neg_label_weights[i]
+                cur_num_rois += num_pos + num_neg
+
+        cls_score = bbox_results['cls_score']
+        bbox_pred = bbox_results['bbox_pred']
+
+        # Apply ISR-P
+        isr_cfg = self.train_cfg.get('isr', None)
+        if isr_cfg is not None:
+            bbox_targets = isr_p(
+                cls_score,
+                bbox_pred,
+                bbox_targets,
+                rois,
+                sampling_results,
+                self.bbox_head.loss_cls,
+                self.bbox_head.bbox_coder,
+                **isr_cfg,
+                num_class=self.bbox_head.num_classes)
+        loss_bbox = self.bbox_head.loss(cls_score, bbox_pred, rois,
+                                        *bbox_targets)
+
+        # Add CARL Loss
+        carl_cfg = self.train_cfg.get('carl', None)
+        if carl_cfg is not None:
+            loss_carl = carl_loss(
+                cls_score,
+                bbox_targets[0],
+                bbox_pred,
+                bbox_targets[2],
+                self.bbox_head.loss_bbox,
+                **carl_cfg,
+                num_class=self.bbox_head.num_classes)
+            loss_bbox.update(loss_carl)
+
+        bbox_results.update(loss_bbox=loss_bbox)
+        return bbox_results
diff --git a/mmdet/models/roi_heads/point_rend_roi_head.py b/mmdet/models/roi_heads/point_rend_roi_head.py
new file mode 100644
index 0000000..9f66779
--- /dev/null
+++ b/mmdet/models/roi_heads/point_rend_roi_head.py
@@ -0,0 +1,393 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+# Modified from https://github.com/facebookresearch/detectron2/tree/master/projects/PointRend  # noqa
+import os
+import warnings
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from mmcv.ops import point_sample, rel_roi_point_to_rel_img_point
+
+from mmdet.core import bbox2roi, bbox_mapping, merge_aug_masks
+from .. import builder
+from ..builder import HEADS
+from .standard_roi_head import StandardRoIHead
+
+
+@HEADS.register_module()
+class PointRendRoIHead(StandardRoIHead):
+    """`PointRend <https://arxiv.org/abs/1912.08193>`_."""
+
+    def __init__(self, point_head, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        assert self.with_bbox and self.with_mask
+        self.init_point_head(point_head)
+
+    def init_point_head(self, point_head):
+        """Initialize ``point_head``"""
+        self.point_head = builder.build_head(point_head)
+
+    def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,
+                            img_metas):
+        """Run forward function and calculate loss for mask head and point head
+        in training."""
+        mask_results = super()._mask_forward_train(x, sampling_results,
+                                                   bbox_feats, gt_masks,
+                                                   img_metas)
+        if mask_results['loss_mask'] is not None:
+            loss_point = self._mask_point_forward_train(
+                x, sampling_results, mask_results['mask_pred'], gt_masks,
+                img_metas)
+            mask_results['loss_mask'].update(loss_point)
+
+        return mask_results
+
+    def _mask_point_forward_train(self, x, sampling_results, mask_pred,
+                                  gt_masks, img_metas):
+        """Run forward function and calculate loss for point head in
+        training."""
+        pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
+        rel_roi_points = self.point_head.get_roi_rel_points_train(
+            mask_pred, pos_labels, cfg=self.train_cfg)
+        rois = bbox2roi([res.pos_bboxes for res in sampling_results])
+
+        fine_grained_point_feats = self._get_fine_grained_point_feats(
+            x, rois, rel_roi_points, img_metas)
+        coarse_point_feats = point_sample(mask_pred, rel_roi_points)
+        mask_point_pred = self.point_head(fine_grained_point_feats,
+                                          coarse_point_feats)
+        mask_point_target = self.point_head.get_targets(
+            rois, rel_roi_points, sampling_results, gt_masks, self.train_cfg)
+        loss_mask_point = self.point_head.loss(mask_point_pred,
+                                               mask_point_target, pos_labels)
+
+        return loss_mask_point
+
+    def _get_fine_grained_point_feats(self, x, rois, rel_roi_points,
+                                      img_metas):
+        """Sample fine grained feats from each level feature map and
+        concatenate them together.
+
+        Args:
+            x (tuple[Tensor]): Feature maps of all scale level.
+            rois (Tensor): shape (num_rois, 5).
+            rel_roi_points (Tensor): A tensor of shape (num_rois, num_points,
+                2) that contains [0, 1] x [0, 1] normalized coordinates of the
+                most uncertain points from the [mask_height, mask_width] grid.
+            img_metas (list[dict]): Image meta info.
+
+        Returns:
+            Tensor: The fine grained features for each points,
+                has shape (num_rois, feats_channels, num_points).
+        """
+        num_imgs = len(img_metas)
+        fine_grained_feats = []
+        for idx in range(self.mask_roi_extractor.num_inputs):
+            feats = x[idx]
+            spatial_scale = 1. / float(
+                self.mask_roi_extractor.featmap_strides[idx])
+            point_feats = []
+            for batch_ind in range(num_imgs):
+                # unravel batch dim
+                feat = feats[batch_ind].unsqueeze(0)
+                inds = (rois[:, 0].long() == batch_ind)
+                if inds.any():
+                    rel_img_points = rel_roi_point_to_rel_img_point(
+                        rois[inds], rel_roi_points[inds], feat.shape[2:],
+                        spatial_scale).unsqueeze(0)
+                    point_feat = point_sample(feat, rel_img_points)
+                    point_feat = point_feat.squeeze(0).transpose(0, 1)
+                    point_feats.append(point_feat)
+            fine_grained_feats.append(torch.cat(point_feats, dim=0))
+        return torch.cat(fine_grained_feats, dim=1)
+
+    def _mask_point_forward_test(self, x, rois, label_pred, mask_pred,
+                                 img_metas):
+        """Mask refining process with point head in testing.
+
+        Args:
+            x (tuple[Tensor]): Feature maps of all scale level.
+            rois (Tensor): shape (num_rois, 5).
+            label_pred (Tensor): The predication class for each rois.
+            mask_pred (Tensor): The predication coarse masks of
+                shape (num_rois, num_classes, small_size, small_size).
+            img_metas (list[dict]): Image meta info.
+
+        Returns:
+            Tensor: The refined masks of shape (num_rois, num_classes,
+                large_size, large_size).
+        """
+        refined_mask_pred = mask_pred.clone()
+        for subdivision_step in range(self.test_cfg.subdivision_steps):
+            refined_mask_pred = F.interpolate(
+                refined_mask_pred,
+                scale_factor=self.test_cfg.scale_factor,
+                mode='bilinear',
+                align_corners=False)
+            # If `subdivision_num_points` is larger or equal to the
+            # resolution of the next step, then we can skip this step
+            num_rois, channels, mask_height, mask_width = \
+                refined_mask_pred.shape
+            if (self.test_cfg.subdivision_num_points >=
+                    self.test_cfg.scale_factor**2 * mask_height * mask_width
+                    and
+                    subdivision_step < self.test_cfg.subdivision_steps - 1):
+                continue
+            point_indices, rel_roi_points = \
+                self.point_head.get_roi_rel_points_test(
+                    refined_mask_pred, label_pred, cfg=self.test_cfg)
+            fine_grained_point_feats = self._get_fine_grained_point_feats(
+                x, rois, rel_roi_points, img_metas)
+            coarse_point_feats = point_sample(mask_pred, rel_roi_points)
+            mask_point_pred = self.point_head(fine_grained_point_feats,
+                                              coarse_point_feats)
+
+            point_indices = point_indices.unsqueeze(1).expand(-1, channels, -1)
+            refined_mask_pred = refined_mask_pred.reshape(
+                num_rois, channels, mask_height * mask_width)
+            refined_mask_pred = refined_mask_pred.scatter_(
+                2, point_indices, mask_point_pred)
+            refined_mask_pred = refined_mask_pred.view(num_rois, channels,
+                                                       mask_height, mask_width)
+
+        return refined_mask_pred
+
+    def simple_test_mask(self,
+                         x,
+                         img_metas,
+                         det_bboxes,
+                         det_labels,
+                         rescale=False):
+        """Obtain mask prediction without augmentation."""
+        ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
+        scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
+
+        if isinstance(scale_factors[0], float):
+            warnings.warn(
+                'Scale factor in img_metas should be a '
+                'ndarray with shape (4,) '
+                'arrange as (factor_w, factor_h, factor_w, factor_h), '
+                'The scale_factor with float type has been deprecated. ')
+            scale_factors = np.array([scale_factors] * 4, dtype=np.float32)
+
+        num_imgs = len(det_bboxes)
+        if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
+            segm_results = [[[] for _ in range(self.mask_head.num_classes)]
+                            for _ in range(num_imgs)]
+        else:
+            # if det_bboxes is rescaled to the original image size, we need to
+            # rescale it back to the testing scale to obtain RoIs.
+            _bboxes = [det_bboxes[i][:, :4] for i in range(len(det_bboxes))]
+            if rescale:
+                scale_factors = [
+                    torch.from_numpy(scale_factor).to(det_bboxes[0].device)
+                    for scale_factor in scale_factors
+                ]
+                _bboxes = [
+                    _bboxes[i] * scale_factors[i] for i in range(len(_bboxes))
+                ]
+
+            mask_rois = bbox2roi(_bboxes)
+            mask_results = self._mask_forward(x, mask_rois)
+            # split batch mask prediction back to each image
+            mask_pred = mask_results['mask_pred']
+            num_mask_roi_per_img = [len(det_bbox) for det_bbox in det_bboxes]
+            mask_preds = mask_pred.split(num_mask_roi_per_img, 0)
+            mask_rois = mask_rois.split(num_mask_roi_per_img, 0)
+
+            # apply mask post-processing to each image individually
+            segm_results = []
+            for i in range(num_imgs):
+                if det_bboxes[i].shape[0] == 0:
+                    segm_results.append(
+                        [[] for _ in range(self.mask_head.num_classes)])
+                else:
+                    x_i = [xx[[i]] for xx in x]
+                    mask_rois_i = mask_rois[i]
+                    mask_rois_i[:, 0] = 0  # TODO: remove this hack
+                    mask_pred_i = self._mask_point_forward_test(
+                        x_i, mask_rois_i, det_labels[i], mask_preds[i],
+                        [img_metas])
+                    segm_result = self.mask_head.get_seg_masks(
+                        mask_pred_i, _bboxes[i], det_labels[i], self.test_cfg,
+                        ori_shapes[i], scale_factors[i], rescale)
+                    segm_results.append(segm_result)
+        return segm_results
+
+    def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
+        """Test for mask head with test time augmentation."""
+        if det_bboxes.shape[0] == 0:
+            segm_result = [[] for _ in range(self.mask_head.num_classes)]
+        else:
+            aug_masks = []
+            for x, img_meta in zip(feats, img_metas):
+                img_shape = img_meta[0]['img_shape']
+                scale_factor = img_meta[0]['scale_factor']
+                flip = img_meta[0]['flip']
+                _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
+                                       scale_factor, flip)
+                mask_rois = bbox2roi([_bboxes])
+                mask_results = self._mask_forward(x, mask_rois)
+                mask_results['mask_pred'] = self._mask_point_forward_test(
+                    x, mask_rois, det_labels, mask_results['mask_pred'],
+                    img_meta)
+                # convert to numpy array to save memory
+                aug_masks.append(
+                    mask_results['mask_pred'].sigmoid().cpu().numpy())
+            merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg)
+
+            ori_shape = img_metas[0][0]['ori_shape']
+            segm_result = self.mask_head.get_seg_masks(
+                merged_masks,
+                det_bboxes,
+                det_labels,
+                self.test_cfg,
+                ori_shape,
+                scale_factor=1.0,
+                rescale=False)
+        return segm_result
+
+    def _onnx_get_fine_grained_point_feats(self, x, rois, rel_roi_points):
+        """Export the process of sampling fine grained feats to onnx.
+
+        Args:
+            x (tuple[Tensor]): Feature maps of all scale level.
+            rois (Tensor): shape (num_rois, 5).
+            rel_roi_points (Tensor): A tensor of shape (num_rois, num_points,
+                2) that contains [0, 1] x [0, 1] normalized coordinates of the
+                most uncertain points from the [mask_height, mask_width] grid.
+
+        Returns:
+            Tensor: The fine grained features for each points,
+                has shape (num_rois, feats_channels, num_points).
+        """
+        batch_size = x[0].shape[0]
+        num_rois = rois.shape[0]
+        fine_grained_feats = []
+        for idx in range(self.mask_roi_extractor.num_inputs):
+            feats = x[idx]
+            spatial_scale = 1. / float(
+                self.mask_roi_extractor.featmap_strides[idx])
+
+            rel_img_points = rel_roi_point_to_rel_img_point(
+                rois, rel_roi_points, feats, spatial_scale)
+            channels = feats.shape[1]
+            num_points = rel_img_points.shape[1]
+            rel_img_points = rel_img_points.reshape(batch_size, -1, num_points,
+                                                    2)
+            point_feats = point_sample(feats, rel_img_points)
+            point_feats = point_feats.transpose(1, 2).reshape(
+                num_rois, channels, num_points)
+            fine_grained_feats.append(point_feats)
+        return torch.cat(fine_grained_feats, dim=1)
+
+    def _mask_point_onnx_export(self, x, rois, label_pred, mask_pred):
+        """Export mask refining process with point head to onnx.
+
+        Args:
+            x (tuple[Tensor]): Feature maps of all scale level.
+            rois (Tensor): shape (num_rois, 5).
+            label_pred (Tensor): The predication class for each rois.
+            mask_pred (Tensor): The predication coarse masks of
+                shape (num_rois, num_classes, small_size, small_size).
+
+        Returns:
+            Tensor: The refined masks of shape (num_rois, num_classes,
+                large_size, large_size).
+        """
+        refined_mask_pred = mask_pred.clone()
+        for subdivision_step in range(self.test_cfg.subdivision_steps):
+            refined_mask_pred = F.interpolate(
+                refined_mask_pred,
+                scale_factor=self.test_cfg.scale_factor,
+                mode='bilinear',
+                align_corners=False)
+            # If `subdivision_num_points` is larger or equal to the
+            # resolution of the next step, then we can skip this step
+            num_rois, channels, mask_height, mask_width = \
+                refined_mask_pred.shape
+            if (self.test_cfg.subdivision_num_points >=
+                    self.test_cfg.scale_factor**2 * mask_height * mask_width
+                    and
+                    subdivision_step < self.test_cfg.subdivision_steps - 1):
+                continue
+            point_indices, rel_roi_points = \
+                self.point_head.get_roi_rel_points_test(
+                    refined_mask_pred, label_pred, cfg=self.test_cfg)
+            fine_grained_point_feats = self._onnx_get_fine_grained_point_feats(
+                x, rois, rel_roi_points)
+            coarse_point_feats = point_sample(mask_pred, rel_roi_points)
+            mask_point_pred = self.point_head(fine_grained_point_feats,
+                                              coarse_point_feats)
+
+            point_indices = point_indices.unsqueeze(1).expand(-1, channels, -1)
+            refined_mask_pred = refined_mask_pred.reshape(
+                num_rois, channels, mask_height * mask_width)
+
+            is_trt_backend = os.environ.get('ONNX_BACKEND') == 'MMCVTensorRT'
+            # avoid ScatterElements op in ONNX for TensorRT
+            if is_trt_backend:
+                mask_shape = refined_mask_pred.shape
+                point_shape = point_indices.shape
+                inds_dim0 = torch.arange(point_shape[0]).reshape(
+                    point_shape[0], 1, 1).expand_as(point_indices)
+                inds_dim1 = torch.arange(point_shape[1]).reshape(
+                    1, point_shape[1], 1).expand_as(point_indices)
+                inds_1d = inds_dim0.reshape(
+                    -1) * mask_shape[1] * mask_shape[2] + inds_dim1.reshape(
+                        -1) * mask_shape[2] + point_indices.reshape(-1)
+                refined_mask_pred = refined_mask_pred.reshape(-1)
+                refined_mask_pred[inds_1d] = mask_point_pred.reshape(-1)
+                refined_mask_pred = refined_mask_pred.reshape(*mask_shape)
+            else:
+                refined_mask_pred = refined_mask_pred.scatter_(
+                    2, point_indices, mask_point_pred)
+
+            refined_mask_pred = refined_mask_pred.view(num_rois, channels,
+                                                       mask_height, mask_width)
+
+        return refined_mask_pred
+
+    def mask_onnx_export(self, x, img_metas, det_bboxes, det_labels, **kwargs):
+        """Export mask branch to onnx which supports batch inference.
+
+        Args:
+            x (tuple[Tensor]): Feature maps of all scale level.
+            img_metas (list[dict]): Image meta info.
+            det_bboxes (Tensor): Bboxes and corresponding scores.
+                has shape [N, num_bboxes, 5].
+            det_labels (Tensor): class labels of
+                shape [N, num_bboxes].
+
+        Returns:
+            Tensor: The segmentation results of shape [N, num_bboxes,
+                image_height, image_width].
+        """
+        if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
+            raise RuntimeError('[ONNX Error] Can not record MaskHead '
+                               'as it has not been executed this time')
+        batch_size = det_bboxes.size(0)
+        # if det_bboxes is rescaled to the original image size, we need to
+        # rescale it back to the testing scale to obtain RoIs.
+        det_bboxes = det_bboxes[..., :4]
+        batch_index = torch.arange(
+            det_bboxes.size(0), device=det_bboxes.device).float().view(
+                -1, 1, 1).expand(det_bboxes.size(0), det_bboxes.size(1), 1)
+        mask_rois = torch.cat([batch_index, det_bboxes], dim=-1)
+        mask_rois = mask_rois.view(-1, 5)
+        mask_results = self._mask_forward(x, mask_rois)
+        mask_pred = mask_results['mask_pred']
+        max_shape = img_metas[0]['img_shape_for_onnx']
+        num_det = det_bboxes.shape[1]
+        det_bboxes = det_bboxes.reshape(-1, 4)
+        det_labels = det_labels.reshape(-1)
+
+        mask_pred = self._mask_point_onnx_export(x, mask_rois, det_labels,
+                                                 mask_pred)
+
+        segm_results = self.mask_head.onnx_export(mask_pred, det_bboxes,
+                                                  det_labels, self.test_cfg,
+                                                  max_shape)
+        segm_results = segm_results.reshape(batch_size, num_det, max_shape[0],
+                                            max_shape[1])
+        return segm_results
diff --git a/mmdet/models/roi_heads/roi_extractors/__init__.py b/mmdet/models/roi_heads/roi_extractors/__init__.py
new file mode 100644
index 0000000..0f60214
--- /dev/null
+++ b/mmdet/models/roi_heads/roi_extractors/__init__.py
@@ -0,0 +1,6 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .base_roi_extractor import BaseRoIExtractor
+from .generic_roi_extractor import GenericRoIExtractor
+from .single_level_roi_extractor import SingleRoIExtractor
+
+__all__ = ['BaseRoIExtractor', 'SingleRoIExtractor', 'GenericRoIExtractor']
diff --git a/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py b/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py
new file mode 100644
index 0000000..8262975
--- /dev/null
+++ b/mmdet/models/roi_heads/roi_extractors/base_roi_extractor.py
@@ -0,0 +1,88 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from abc import ABCMeta, abstractmethod
+
+import torch
+import torch.nn as nn
+from mmcv import ops
+from mmcv.runner import BaseModule
+
+
+class BaseRoIExtractor(BaseModule, metaclass=ABCMeta):
+    """Base class for RoI extractor.
+
+    Args:
+        roi_layer (dict): Specify RoI layer type and arguments.
+        out_channels (int): Output channels of RoI layers.
+        featmap_strides (int): Strides of input feature maps.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 roi_layer,
+                 out_channels,
+                 featmap_strides,
+                 init_cfg=None):
+        super(BaseRoIExtractor, self).__init__(init_cfg)
+        self.roi_layers = self.build_roi_layers(roi_layer, featmap_strides)
+        self.out_channels = out_channels
+        self.featmap_strides = featmap_strides
+        self.fp16_enabled = False
+
+    @property
+    def num_inputs(self):
+        """int: Number of input feature maps."""
+        return len(self.featmap_strides)
+
+    def build_roi_layers(self, layer_cfg, featmap_strides):
+        """Build RoI operator to extract feature from each level feature map.
+
+        Args:
+            layer_cfg (dict): Dictionary to construct and config RoI layer
+                operation. Options are modules under ``mmcv/ops`` such as
+                ``RoIAlign``.
+            featmap_strides (List[int]): The stride of input feature map w.r.t
+                to the original image size, which would be used to scale RoI
+                coordinate (original image coordinate system) to feature
+                coordinate system.
+
+        Returns:
+            nn.ModuleList: The RoI extractor modules for each level feature
+                map.
+        """
+
+        cfg = layer_cfg.copy()
+        layer_type = cfg.pop('type')
+        assert hasattr(ops, layer_type)
+        layer_cls = getattr(ops, layer_type)
+        roi_layers = nn.ModuleList(
+            [layer_cls(spatial_scale=1 / s, **cfg) for s in featmap_strides])
+        return roi_layers
+
+    def roi_rescale(self, rois, scale_factor):
+        """Scale RoI coordinates by scale factor.
+
+        Args:
+            rois (torch.Tensor): RoI (Region of Interest), shape (n, 5)
+            scale_factor (float): Scale factor that RoI will be multiplied by.
+
+        Returns:
+            torch.Tensor: Scaled RoI.
+        """
+
+        cx = (rois[:, 1] + rois[:, 3]) * 0.5
+        cy = (rois[:, 2] + rois[:, 4]) * 0.5
+        w = rois[:, 3] - rois[:, 1]
+        h = rois[:, 4] - rois[:, 2]
+        new_w = w * scale_factor
+        new_h = h * scale_factor
+        x1 = cx - new_w * 0.5
+        x2 = cx + new_w * 0.5
+        y1 = cy - new_h * 0.5
+        y2 = cy + new_h * 0.5
+        new_rois = torch.stack((rois[:, 0], x1, y1, x2, y2), dim=-1)
+        return new_rois
+
+    @abstractmethod
+    def forward(self, feats, rois, roi_scale_factor=None):
+        pass
diff --git a/mmdet/models/roi_heads/roi_extractors/generic_roi_extractor.py b/mmdet/models/roi_heads/roi_extractors/generic_roi_extractor.py
new file mode 100644
index 0000000..89a9f89
--- /dev/null
+++ b/mmdet/models/roi_heads/roi_extractors/generic_roi_extractor.py
@@ -0,0 +1,84 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.cnn.bricks import build_plugin_layer
+from mmcv.runner import force_fp32
+
+from mmdet.models.builder import ROI_EXTRACTORS
+from .base_roi_extractor import BaseRoIExtractor
+
+
+@ROI_EXTRACTORS.register_module()
+class GenericRoIExtractor(BaseRoIExtractor):
+    """Extract RoI features from all level feature maps levels.
+
+    This is the implementation of `A novel Region of Interest Extraction Layer
+    for Instance Segmentation <https://arxiv.org/abs/2004.13665>`_.
+
+    Args:
+        aggregation (str): The method to aggregate multiple feature maps.
+            Options are 'sum', 'concat'. Default: 'sum'.
+        pre_cfg (dict | None): Specify pre-processing modules. Default: None.
+        post_cfg (dict | None): Specify post-processing modules. Default: None.
+        kwargs (keyword arguments): Arguments that are the same
+            as :class:`BaseRoIExtractor`.
+    """
+
+    def __init__(self,
+                 aggregation='sum',
+                 pre_cfg=None,
+                 post_cfg=None,
+                 **kwargs):
+        super(GenericRoIExtractor, self).__init__(**kwargs)
+
+        assert aggregation in ['sum', 'concat']
+
+        self.aggregation = aggregation
+        self.with_post = post_cfg is not None
+        self.with_pre = pre_cfg is not None
+        # build pre/post processing modules
+        if self.with_post:
+            self.post_module = build_plugin_layer(post_cfg, '_post_module')[1]
+        if self.with_pre:
+            self.pre_module = build_plugin_layer(pre_cfg, '_pre_module')[1]
+
+    @force_fp32(apply_to=('feats', ), out_fp16=True)
+    def forward(self, feats, rois, roi_scale_factor=None):
+        """Forward function."""
+        if len(feats) == 1:
+            return self.roi_layers[0](feats[0], rois)
+
+        out_size = self.roi_layers[0].output_size
+        num_levels = len(feats)
+        roi_feats = feats[0].new_zeros(
+            rois.size(0), self.out_channels, *out_size)
+
+        # some times rois is an empty tensor
+        if roi_feats.shape[0] == 0:
+            return roi_feats
+
+        if roi_scale_factor is not None:
+            rois = self.roi_rescale(rois, roi_scale_factor)
+
+        # mark the starting channels for concat mode
+        start_channels = 0
+        for i in range(num_levels):
+            roi_feats_t = self.roi_layers[i](feats[i], rois)
+            end_channels = start_channels + roi_feats_t.size(1)
+            if self.with_pre:
+                # apply pre-processing to a RoI extracted from each layer
+                roi_feats_t = self.pre_module(roi_feats_t)
+            if self.aggregation == 'sum':
+                # and sum them all
+                roi_feats = roi_feats + roi_feats_t
+            else:
+                # and concat them along channel dimension
+                roi_feats[:, start_channels:end_channels] = roi_feats_t
+            # update channels starting position
+            start_channels = end_channels
+        # check if concat channels match at the end
+        if self.aggregation == 'concat':
+            assert start_channels == self.out_channels
+
+        if self.with_post:
+            # apply post-processing before return the result
+            roi_feats = self.post_module(roi_feats)
+        return roi_feats
diff --git a/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py b/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py
new file mode 100644
index 0000000..dbc5aef
--- /dev/null
+++ b/mmdet/models/roi_heads/roi_extractors/single_level_roi_extractor.py
@@ -0,0 +1,112 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from mmcv.runner import force_fp32
+
+from mmdet.models.builder import ROI_EXTRACTORS
+from .base_roi_extractor import BaseRoIExtractor
+
+
+@ROI_EXTRACTORS.register_module()
+class SingleRoIExtractor(BaseRoIExtractor):
+    """Extract RoI features from a single level feature map.
+
+    If there are multiple input feature levels, each RoI is mapped to a level
+    according to its scale. The mapping rule is proposed in
+    `FPN <https://arxiv.org/abs/1612.03144>`_.
+
+    Args:
+        roi_layer (dict): Specify RoI layer type and arguments.
+        out_channels (int): Output channels of RoI layers.
+        featmap_strides (List[int]): Strides of input feature maps.
+        finest_scale (int): Scale threshold of mapping to level 0. Default: 56.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 roi_layer,
+                 out_channels,
+                 featmap_strides,
+                 finest_scale=56,
+                 init_cfg=None):
+        super(SingleRoIExtractor, self).__init__(roi_layer, out_channels,
+                                                 featmap_strides, init_cfg)
+        self.finest_scale = finest_scale
+
+    def map_roi_levels(self, rois, num_levels):
+        """Map rois to corresponding feature levels by scales.
+
+        - scale < finest_scale * 2: level 0
+        - finest_scale * 2 <= scale < finest_scale * 4: level 1
+        - finest_scale * 4 <= scale < finest_scale * 8: level 2
+        - scale >= finest_scale * 8: level 3
+
+        Args:
+            rois (Tensor): Input RoIs, shape (k, 5).
+            num_levels (int): Total level number.
+
+        Returns:
+            Tensor: Level index (0-based) of each RoI, shape (k, )
+        """
+        scale = torch.sqrt(
+            (rois[:, 3] - rois[:, 1]) * (rois[:, 4] - rois[:, 2]))
+        target_lvls = torch.floor(torch.log2(scale / self.finest_scale + 1e-6))
+        target_lvls = target_lvls.clamp(min=0, max=num_levels - 1).long()
+        return target_lvls
+
+    @force_fp32(apply_to=('feats', ), out_fp16=True)
+    def forward(self, feats, rois, roi_scale_factor=None):
+        """Forward function."""
+        out_size = self.roi_layers[0].output_size
+        num_levels = len(feats)
+        expand_dims = (-1, self.out_channels * out_size[0] * out_size[1])
+        if torch.onnx.is_in_onnx_export():
+            # Work around to export mask-rcnn to onnx
+            roi_feats = rois[:, :1].clone().detach()
+            roi_feats = roi_feats.expand(*expand_dims)
+            roi_feats = roi_feats.reshape(-1, self.out_channels, *out_size)
+            roi_feats = roi_feats * 0
+        else:
+            roi_feats = feats[0].new_zeros(
+                rois.size(0), self.out_channels, *out_size)
+
+        if num_levels == 1:
+            if len(rois) == 0:
+                return roi_feats
+            return self.roi_layers[0](feats[0], rois)
+
+        target_lvls = self.map_roi_levels(rois, num_levels)
+
+        if roi_scale_factor is not None:
+            rois = self.roi_rescale(rois, roi_scale_factor)
+
+        for i in range(num_levels):
+            mask = target_lvls == i
+            if torch.onnx.is_in_onnx_export():
+                # To keep all roi_align nodes exported to onnx
+                # and skip nonzero op
+                mask = mask.float().unsqueeze(-1)
+                # select target level rois and reset the rest rois to zero.
+                rois_i = rois.clone().detach()
+                rois_i = rois_i * mask
+                mask_exp = mask.expand(*expand_dims).reshape(roi_feats.shape)
+                roi_feats_t = self.roi_layers[i](feats[i], rois_i)
+                roi_feats_t = roi_feats_t * mask_exp
+                roi_feats = roi_feats + roi_feats_t
+                continue
+            inds = mask.nonzero(as_tuple=False).squeeze(1)
+            if inds.numel() > 0:
+                rois_ = rois[inds]
+                roi_feats_t = self.roi_layers[i](feats[i], rois_)
+                roi_feats[inds] = roi_feats_t
+            else:
+                # Sometimes some pyramid levels will not be used for RoI
+                # feature extraction and this will cause an incomplete
+                # computation graph in one GPU, which is different from those
+                # in other GPUs and will cause a hanging error.
+                # Therefore, we add it to ensure each feature pyramid is
+                # included in the computation graph to avoid runtime bugs.
+                roi_feats = roi_feats + sum(
+                    x.view(-1)[0]
+                    for x in self.parameters()) * 0. + feats[i].sum() * 0.
+        return roi_feats
diff --git a/mmdet/models/roi_heads/scnet_roi_head.py b/mmdet/models/roi_heads/scnet_roi_head.py
new file mode 100644
index 0000000..32f56aa
--- /dev/null
+++ b/mmdet/models/roi_heads/scnet_roi_head.py
@@ -0,0 +1,605 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes,
+                        merge_aug_masks, multiclass_nms)
+from ..builder import HEADS, build_head, build_roi_extractor
+from ..utils.brick_wrappers import adaptive_avg_pool2d
+from .cascade_roi_head import CascadeRoIHead
+
+
+@HEADS.register_module()
+class SCNetRoIHead(CascadeRoIHead):
+    """RoIHead for `SCNet <https://arxiv.org/abs/2012.10150>`_.
+
+    Args:
+        num_stages (int): number of cascade stages.
+        stage_loss_weights (list): loss weight of cascade stages.
+        semantic_roi_extractor (dict): config to init semantic roi extractor.
+        semantic_head (dict): config to init semantic head.
+        feat_relay_head (dict): config to init feature_relay_head.
+        glbctx_head (dict): config to init global context head.
+    """
+
+    def __init__(self,
+                 num_stages,
+                 stage_loss_weights,
+                 semantic_roi_extractor=None,
+                 semantic_head=None,
+                 feat_relay_head=None,
+                 glbctx_head=None,
+                 **kwargs):
+        super(SCNetRoIHead, self).__init__(num_stages, stage_loss_weights,
+                                           **kwargs)
+        assert self.with_bbox and self.with_mask
+        assert not self.with_shared_head  # shared head is not supported
+
+        if semantic_head is not None:
+            self.semantic_roi_extractor = build_roi_extractor(
+                semantic_roi_extractor)
+            self.semantic_head = build_head(semantic_head)
+
+        if feat_relay_head is not None:
+            self.feat_relay_head = build_head(feat_relay_head)
+
+        if glbctx_head is not None:
+            self.glbctx_head = build_head(glbctx_head)
+
+    def init_mask_head(self, mask_roi_extractor, mask_head):
+        """Initialize ``mask_head``"""
+        if mask_roi_extractor is not None:
+            self.mask_roi_extractor = build_roi_extractor(mask_roi_extractor)
+            self.mask_head = build_head(mask_head)
+
+    @property
+    def with_semantic(self):
+        """bool: whether the head has semantic head"""
+        return hasattr(self,
+                       'semantic_head') and self.semantic_head is not None
+
+    @property
+    def with_feat_relay(self):
+        """bool: whether the head has feature relay head"""
+        return (hasattr(self, 'feat_relay_head')
+                and self.feat_relay_head is not None)
+
+    @property
+    def with_glbctx(self):
+        """bool: whether the head has global context head"""
+        return hasattr(self, 'glbctx_head') and self.glbctx_head is not None
+
+    def _fuse_glbctx(self, roi_feats, glbctx_feat, rois):
+        """Fuse global context feats with roi feats."""
+        assert roi_feats.size(0) == rois.size(0)
+        img_inds = torch.unique(rois[:, 0].cpu(), sorted=True).long()
+        fused_feats = torch.zeros_like(roi_feats)
+        for img_id in img_inds:
+            inds = (rois[:, 0] == img_id.item())
+            fused_feats[inds] = roi_feats[inds] + glbctx_feat[img_id]
+        return fused_feats
+
+    def _slice_pos_feats(self, feats, sampling_results):
+        """Get features from pos rois."""
+        num_rois = [res.bboxes.size(0) for res in sampling_results]
+        num_pos_rois = [res.pos_bboxes.size(0) for res in sampling_results]
+        inds = torch.zeros(sum(num_rois), dtype=torch.bool)
+        start = 0
+        for i in range(len(num_rois)):
+            start = 0 if i == 0 else start + num_rois[i - 1]
+            stop = start + num_pos_rois[i]
+            inds[start:stop] = 1
+        sliced_feats = feats[inds]
+        return sliced_feats
+
+    def _bbox_forward(self,
+                      stage,
+                      x,
+                      rois,
+                      semantic_feat=None,
+                      glbctx_feat=None):
+        """Box head forward function used in both training and testing."""
+        bbox_roi_extractor = self.bbox_roi_extractor[stage]
+        bbox_head = self.bbox_head[stage]
+        bbox_feats = bbox_roi_extractor(
+            x[:len(bbox_roi_extractor.featmap_strides)], rois)
+        if self.with_semantic and semantic_feat is not None:
+            bbox_semantic_feat = self.semantic_roi_extractor([semantic_feat],
+                                                             rois)
+            if bbox_semantic_feat.shape[-2:] != bbox_feats.shape[-2:]:
+                bbox_semantic_feat = adaptive_avg_pool2d(
+                    bbox_semantic_feat, bbox_feats.shape[-2:])
+            bbox_feats = bbox_feats + bbox_semantic_feat
+        if self.with_glbctx and glbctx_feat is not None:
+            bbox_feats = self._fuse_glbctx(bbox_feats, glbctx_feat, rois)
+        cls_score, bbox_pred, relayed_feat = bbox_head(
+            bbox_feats, return_shared_feat=True)
+
+        bbox_results = dict(
+            cls_score=cls_score,
+            bbox_pred=bbox_pred,
+            relayed_feat=relayed_feat)
+        return bbox_results
+
+    def _mask_forward(self,
+                      x,
+                      rois,
+                      semantic_feat=None,
+                      glbctx_feat=None,
+                      relayed_feat=None):
+        """Mask head forward function used in both training and testing."""
+        mask_feats = self.mask_roi_extractor(
+            x[:self.mask_roi_extractor.num_inputs], rois)
+        if self.with_semantic and semantic_feat is not None:
+            mask_semantic_feat = self.semantic_roi_extractor([semantic_feat],
+                                                             rois)
+            if mask_semantic_feat.shape[-2:] != mask_feats.shape[-2:]:
+                mask_semantic_feat = F.adaptive_avg_pool2d(
+                    mask_semantic_feat, mask_feats.shape[-2:])
+            mask_feats = mask_feats + mask_semantic_feat
+        if self.with_glbctx and glbctx_feat is not None:
+            mask_feats = self._fuse_glbctx(mask_feats, glbctx_feat, rois)
+        if self.with_feat_relay and relayed_feat is not None:
+            mask_feats = mask_feats + relayed_feat
+        mask_pred = self.mask_head(mask_feats)
+        mask_results = dict(mask_pred=mask_pred)
+
+        return mask_results
+
+    def _bbox_forward_train(self,
+                            stage,
+                            x,
+                            sampling_results,
+                            gt_bboxes,
+                            gt_labels,
+                            rcnn_train_cfg,
+                            semantic_feat=None,
+                            glbctx_feat=None):
+        """Run forward function and calculate loss for box head in training."""
+        bbox_head = self.bbox_head[stage]
+        rois = bbox2roi([res.bboxes for res in sampling_results])
+        bbox_results = self._bbox_forward(
+            stage,
+            x,
+            rois,
+            semantic_feat=semantic_feat,
+            glbctx_feat=glbctx_feat)
+
+        bbox_targets = bbox_head.get_targets(sampling_results, gt_bboxes,
+                                             gt_labels, rcnn_train_cfg)
+        loss_bbox = bbox_head.loss(bbox_results['cls_score'],
+                                   bbox_results['bbox_pred'], rois,
+                                   *bbox_targets)
+
+        bbox_results.update(
+            loss_bbox=loss_bbox, rois=rois, bbox_targets=bbox_targets)
+        return bbox_results
+
+    def _mask_forward_train(self,
+                            x,
+                            sampling_results,
+                            gt_masks,
+                            rcnn_train_cfg,
+                            semantic_feat=None,
+                            glbctx_feat=None,
+                            relayed_feat=None):
+        """Run forward function and calculate loss for mask head in
+        training."""
+        pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
+        mask_results = self._mask_forward(
+            x,
+            pos_rois,
+            semantic_feat=semantic_feat,
+            glbctx_feat=glbctx_feat,
+            relayed_feat=relayed_feat)
+
+        mask_targets = self.mask_head.get_targets(sampling_results, gt_masks,
+                                                  rcnn_train_cfg)
+        pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
+        loss_mask = self.mask_head.loss(mask_results['mask_pred'],
+                                        mask_targets, pos_labels)
+
+        mask_results = loss_mask
+        return mask_results
+
+    def forward_train(self,
+                      x,
+                      img_metas,
+                      proposal_list,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None,
+                      gt_masks=None,
+                      gt_semantic_seg=None):
+        """
+        Args:
+            x (list[Tensor]): list of multi-level img features.
+            img_metas (list[dict]): list of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `mmdet/datasets/pipelines/formatting.py:Collect`.
+            proposal_list (list[Tensors]): list of region proposals.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            gt_bboxes_ignore (None, list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+            gt_masks (None, Tensor) : true segmentation masks for each box
+                used if the architecture supports a segmentation task.
+            gt_semantic_seg (None, list[Tensor]): semantic segmentation masks
+                used if the architecture supports semantic segmentation task.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        losses = dict()
+
+        # semantic segmentation branch
+        if self.with_semantic:
+            semantic_pred, semantic_feat = self.semantic_head(x)
+            loss_seg = self.semantic_head.loss(semantic_pred, gt_semantic_seg)
+            losses['loss_semantic_seg'] = loss_seg
+        else:
+            semantic_feat = None
+
+        # global context branch
+        if self.with_glbctx:
+            mc_pred, glbctx_feat = self.glbctx_head(x)
+            loss_glbctx = self.glbctx_head.loss(mc_pred, gt_labels)
+            losses['loss_glbctx'] = loss_glbctx
+        else:
+            glbctx_feat = None
+
+        for i in range(self.num_stages):
+            self.current_stage = i
+            rcnn_train_cfg = self.train_cfg[i]
+            lw = self.stage_loss_weights[i]
+
+            # assign gts and sample proposals
+            sampling_results = []
+            bbox_assigner = self.bbox_assigner[i]
+            bbox_sampler = self.bbox_sampler[i]
+            num_imgs = len(img_metas)
+            if gt_bboxes_ignore is None:
+                gt_bboxes_ignore = [None for _ in range(num_imgs)]
+
+            for j in range(num_imgs):
+                assign_result = bbox_assigner.assign(proposal_list[j],
+                                                     gt_bboxes[j],
+                                                     gt_bboxes_ignore[j],
+                                                     gt_labels[j])
+                sampling_result = bbox_sampler.sample(
+                    assign_result,
+                    proposal_list[j],
+                    gt_bboxes[j],
+                    gt_labels[j],
+                    feats=[lvl_feat[j][None] for lvl_feat in x])
+                sampling_results.append(sampling_result)
+
+            bbox_results = \
+                self._bbox_forward_train(
+                    i, x, sampling_results, gt_bboxes, gt_labels,
+                    rcnn_train_cfg, semantic_feat, glbctx_feat)
+            roi_labels = bbox_results['bbox_targets'][0]
+
+            for name, value in bbox_results['loss_bbox'].items():
+                losses[f's{i}.{name}'] = (
+                    value * lw if 'loss' in name else value)
+
+            # refine boxes
+            if i < self.num_stages - 1:
+                pos_is_gts = [res.pos_is_gt for res in sampling_results]
+                with torch.no_grad():
+                    proposal_list = self.bbox_head[i].refine_bboxes(
+                        bbox_results['rois'], roi_labels,
+                        bbox_results['bbox_pred'], pos_is_gts, img_metas)
+
+        if self.with_feat_relay:
+            relayed_feat = self._slice_pos_feats(bbox_results['relayed_feat'],
+                                                 sampling_results)
+            relayed_feat = self.feat_relay_head(relayed_feat)
+        else:
+            relayed_feat = None
+
+        mask_results = self._mask_forward_train(x, sampling_results, gt_masks,
+                                                rcnn_train_cfg, semantic_feat,
+                                                glbctx_feat, relayed_feat)
+        mask_lw = sum(self.stage_loss_weights)
+        losses['loss_mask'] = mask_lw * mask_results['loss_mask']
+
+        return losses
+
+    def simple_test(self, x, proposal_list, img_metas, rescale=False):
+        """Test without augmentation.
+
+        Args:
+            x (tuple[Tensor]): Features from upstream network. Each
+                has shape (batch_size, c, h, w).
+            proposal_list (list(Tensor)): Proposals from rpn head.
+                Each has shape (num_proposals, 5), last dimension
+                5 represent (x1, y1, x2, y2, score).
+            img_metas (list[dict]): Meta information of images.
+            rescale (bool): Whether to rescale the results to
+                the original image. Default: True.
+
+        Returns:
+            list[list[np.ndarray]] or list[tuple]: When no mask branch,
+            it is bbox results of each image and classes with type
+            `list[list[np.ndarray]]`. The outer list
+            corresponds to each image. The inner list
+            corresponds to each class. When the model has mask branch,
+            it contains bbox results and mask results.
+            The outer list corresponds to each image, and first element
+            of tuple is bbox results, second element is mask results.
+        """
+        if self.with_semantic:
+            _, semantic_feat = self.semantic_head(x)
+        else:
+            semantic_feat = None
+
+        if self.with_glbctx:
+            mc_pred, glbctx_feat = self.glbctx_head(x)
+        else:
+            glbctx_feat = None
+
+        num_imgs = len(proposal_list)
+        img_shapes = tuple(meta['img_shape'] for meta in img_metas)
+        ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
+        scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
+
+        # "ms" in variable names means multi-stage
+        ms_scores = []
+        rcnn_test_cfg = self.test_cfg
+
+        rois = bbox2roi(proposal_list)
+
+        if rois.shape[0] == 0:
+            # There is no proposal in the whole batch
+            bbox_results = [[
+                np.zeros((0, 5), dtype=np.float32)
+                for _ in range(self.bbox_head[-1].num_classes)
+            ]] * num_imgs
+
+            if self.with_mask:
+                mask_classes = self.mask_head.num_classes
+                segm_results = [[[] for _ in range(mask_classes)]
+                                for _ in range(num_imgs)]
+                results = list(zip(bbox_results, segm_results))
+            else:
+                results = bbox_results
+
+            return results
+
+        for i in range(self.num_stages):
+            bbox_head = self.bbox_head[i]
+            bbox_results = self._bbox_forward(
+                i,
+                x,
+                rois,
+                semantic_feat=semantic_feat,
+                glbctx_feat=glbctx_feat)
+            # split batch bbox prediction back to each image
+            cls_score = bbox_results['cls_score']
+            bbox_pred = bbox_results['bbox_pred']
+            num_proposals_per_img = tuple(len(p) for p in proposal_list)
+            rois = rois.split(num_proposals_per_img, 0)
+            cls_score = cls_score.split(num_proposals_per_img, 0)
+            bbox_pred = bbox_pred.split(num_proposals_per_img, 0)
+            ms_scores.append(cls_score)
+
+            if i < self.num_stages - 1:
+                refine_rois_list = []
+                for j in range(num_imgs):
+                    if rois[j].shape[0] > 0:
+                        bbox_label = cls_score[j][:, :-1].argmax(dim=1)
+                        refine_rois = bbox_head.regress_by_class(
+                            rois[j], bbox_label, bbox_pred[j], img_metas[j])
+                        refine_rois_list.append(refine_rois)
+                rois = torch.cat(refine_rois_list)
+
+        # average scores of each image by stages
+        cls_score = [
+            sum([score[i] for score in ms_scores]) / float(len(ms_scores))
+            for i in range(num_imgs)
+        ]
+
+        # apply bbox post-processing to each image individually
+        det_bboxes = []
+        det_labels = []
+        for i in range(num_imgs):
+            det_bbox, det_label = self.bbox_head[-1].get_bboxes(
+                rois[i],
+                cls_score[i],
+                bbox_pred[i],
+                img_shapes[i],
+                scale_factors[i],
+                rescale=rescale,
+                cfg=rcnn_test_cfg)
+            det_bboxes.append(det_bbox)
+            det_labels.append(det_label)
+        det_bbox_results = [
+            bbox2result(det_bboxes[i], det_labels[i],
+                        self.bbox_head[-1].num_classes)
+            for i in range(num_imgs)
+        ]
+
+        if self.with_mask:
+            if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
+                mask_classes = self.mask_head.num_classes
+                det_segm_results = [[[] for _ in range(mask_classes)]
+                                    for _ in range(num_imgs)]
+            else:
+                if rescale and not isinstance(scale_factors[0], float):
+                    scale_factors = [
+                        torch.from_numpy(scale_factor).to(det_bboxes[0].device)
+                        for scale_factor in scale_factors
+                    ]
+                _bboxes = [
+                    det_bboxes[i][:, :4] *
+                    scale_factors[i] if rescale else det_bboxes[i]
+                    for i in range(num_imgs)
+                ]
+                mask_rois = bbox2roi(_bboxes)
+
+                # get relay feature on mask_rois
+                bbox_results = self._bbox_forward(
+                    -1,
+                    x,
+                    mask_rois,
+                    semantic_feat=semantic_feat,
+                    glbctx_feat=glbctx_feat)
+                relayed_feat = bbox_results['relayed_feat']
+                relayed_feat = self.feat_relay_head(relayed_feat)
+
+                mask_results = self._mask_forward(
+                    x,
+                    mask_rois,
+                    semantic_feat=semantic_feat,
+                    glbctx_feat=glbctx_feat,
+                    relayed_feat=relayed_feat)
+                mask_pred = mask_results['mask_pred']
+
+                # split batch mask prediction back to each image
+                num_bbox_per_img = tuple(len(_bbox) for _bbox in _bboxes)
+                mask_preds = mask_pred.split(num_bbox_per_img, 0)
+
+                # apply mask post-processing to each image individually
+                det_segm_results = []
+                for i in range(num_imgs):
+                    if det_bboxes[i].shape[0] == 0:
+                        det_segm_results.append(
+                            [[] for _ in range(self.mask_head.num_classes)])
+                    else:
+                        segm_result = self.mask_head.get_seg_masks(
+                            mask_preds[i], _bboxes[i], det_labels[i],
+                            self.test_cfg, ori_shapes[i], scale_factors[i],
+                            rescale)
+                        det_segm_results.append(segm_result)
+
+        # return results
+        if self.with_mask:
+            return list(zip(det_bbox_results, det_segm_results))
+        else:
+            return det_bbox_results
+
+    def aug_test(self, img_feats, proposal_list, img_metas, rescale=False):
+        if self.with_semantic:
+            semantic_feats = [
+                self.semantic_head(feat)[1] for feat in img_feats
+            ]
+        else:
+            semantic_feats = [None] * len(img_metas)
+
+        if self.with_glbctx:
+            glbctx_feats = [self.glbctx_head(feat)[1] for feat in img_feats]
+        else:
+            glbctx_feats = [None] * len(img_metas)
+
+        rcnn_test_cfg = self.test_cfg
+        aug_bboxes = []
+        aug_scores = []
+        for x, img_meta, semantic_feat, glbctx_feat in zip(
+                img_feats, img_metas, semantic_feats, glbctx_feats):
+            # only one image in the batch
+            img_shape = img_meta[0]['img_shape']
+            scale_factor = img_meta[0]['scale_factor']
+            flip = img_meta[0]['flip']
+
+            proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
+                                     scale_factor, flip)
+            # "ms" in variable names means multi-stage
+            ms_scores = []
+
+            rois = bbox2roi([proposals])
+
+            if rois.shape[0] == 0:
+                # There is no proposal in the single image
+                aug_bboxes.append(rois.new_zeros(0, 4))
+                aug_scores.append(rois.new_zeros(0, 1))
+                continue
+
+            for i in range(self.num_stages):
+                bbox_head = self.bbox_head[i]
+                bbox_results = self._bbox_forward(
+                    i,
+                    x,
+                    rois,
+                    semantic_feat=semantic_feat,
+                    glbctx_feat=glbctx_feat)
+                ms_scores.append(bbox_results['cls_score'])
+                if i < self.num_stages - 1:
+                    bbox_label = bbox_results['cls_score'].argmax(dim=1)
+                    rois = bbox_head.regress_by_class(
+                        rois, bbox_label, bbox_results['bbox_pred'],
+                        img_meta[0])
+
+            cls_score = sum(ms_scores) / float(len(ms_scores))
+            bboxes, scores = self.bbox_head[-1].get_bboxes(
+                rois,
+                cls_score,
+                bbox_results['bbox_pred'],
+                img_shape,
+                scale_factor,
+                rescale=False,
+                cfg=None)
+            aug_bboxes.append(bboxes)
+            aug_scores.append(scores)
+
+        # after merging, bboxes will be rescaled to the original image size
+        merged_bboxes, merged_scores = merge_aug_bboxes(
+            aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
+        det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
+                                                rcnn_test_cfg.score_thr,
+                                                rcnn_test_cfg.nms,
+                                                rcnn_test_cfg.max_per_img)
+
+        det_bbox_results = bbox2result(det_bboxes, det_labels,
+                                       self.bbox_head[-1].num_classes)
+
+        if self.with_mask:
+            if det_bboxes.shape[0] == 0:
+                det_segm_results = [[]
+                                    for _ in range(self.mask_head.num_classes)]
+            else:
+                aug_masks = []
+                for x, img_meta, semantic_feat, glbctx_feat in zip(
+                        img_feats, img_metas, semantic_feats, glbctx_feats):
+                    img_shape = img_meta[0]['img_shape']
+                    scale_factor = img_meta[0]['scale_factor']
+                    flip = img_meta[0]['flip']
+                    _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
+                                           scale_factor, flip)
+                    mask_rois = bbox2roi([_bboxes])
+                    # get relay feature on mask_rois
+                    bbox_results = self._bbox_forward(
+                        -1,
+                        x,
+                        mask_rois,
+                        semantic_feat=semantic_feat,
+                        glbctx_feat=glbctx_feat)
+                    relayed_feat = bbox_results['relayed_feat']
+                    relayed_feat = self.feat_relay_head(relayed_feat)
+                    mask_results = self._mask_forward(
+                        x,
+                        mask_rois,
+                        semantic_feat=semantic_feat,
+                        glbctx_feat=glbctx_feat,
+                        relayed_feat=relayed_feat)
+                    mask_pred = mask_results['mask_pred']
+                    aug_masks.append(mask_pred.sigmoid().cpu().numpy())
+                merged_masks = merge_aug_masks(aug_masks, img_metas,
+                                               self.test_cfg)
+                ori_shape = img_metas[0][0]['ori_shape']
+                det_segm_results = self.mask_head.get_seg_masks(
+                    merged_masks,
+                    det_bboxes,
+                    det_labels,
+                    rcnn_test_cfg,
+                    ori_shape,
+                    scale_factor=1.0,
+                    rescale=False)
+            return [(det_bbox_results, det_segm_results)]
+        else:
+            return [det_bbox_results]
diff --git a/mmdet/models/roi_heads/shared_heads/__init__.py b/mmdet/models/roi_heads/shared_heads/__init__.py
new file mode 100644
index 0000000..d56636a
--- /dev/null
+++ b/mmdet/models/roi_heads/shared_heads/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .res_layer import ResLayer
+
+__all__ = ['ResLayer']
diff --git a/mmdet/models/roi_heads/shared_heads/res_layer.py b/mmdet/models/roi_heads/shared_heads/res_layer.py
new file mode 100644
index 0000000..bef00a0
--- /dev/null
+++ b/mmdet/models/roi_heads/shared_heads/res_layer.py
@@ -0,0 +1,80 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch.nn as nn
+from mmcv.runner import BaseModule, auto_fp16
+
+from mmdet.models.backbones import ResNet
+from mmdet.models.builder import SHARED_HEADS
+from mmdet.models.utils import ResLayer as _ResLayer
+
+
+@SHARED_HEADS.register_module()
+class ResLayer(BaseModule):
+
+    def __init__(self,
+                 depth,
+                 stage=3,
+                 stride=2,
+                 dilation=1,
+                 style='pytorch',
+                 norm_cfg=dict(type='BN', requires_grad=True),
+                 norm_eval=True,
+                 with_cp=False,
+                 dcn=None,
+                 pretrained=None,
+                 init_cfg=None):
+        super(ResLayer, self).__init__(init_cfg)
+
+        self.norm_eval = norm_eval
+        self.norm_cfg = norm_cfg
+        self.stage = stage
+        self.fp16_enabled = False
+        block, stage_blocks = ResNet.arch_settings[depth]
+        stage_block = stage_blocks[stage]
+        planes = 64 * 2**stage
+        inplanes = 64 * 2**(stage - 1) * block.expansion
+
+        res_layer = _ResLayer(
+            block,
+            inplanes,
+            planes,
+            stage_block,
+            stride=stride,
+            dilation=dilation,
+            style=style,
+            with_cp=with_cp,
+            norm_cfg=self.norm_cfg,
+            dcn=dcn)
+        self.add_module(f'layer{stage + 1}', res_layer)
+
+        assert not (init_cfg and pretrained), \
+            'init_cfg and pretrained cannot be specified at the same time'
+        if isinstance(pretrained, str):
+            warnings.warn('DeprecationWarning: pretrained is a deprecated, '
+                          'please use "init_cfg" instead')
+            self.init_cfg = dict(type='Pretrained', checkpoint=pretrained)
+        elif pretrained is None:
+            if init_cfg is None:
+                self.init_cfg = [
+                    dict(type='Kaiming', layer='Conv2d'),
+                    dict(
+                        type='Constant',
+                        val=1,
+                        layer=['_BatchNorm', 'GroupNorm'])
+                ]
+        else:
+            raise TypeError('pretrained must be a str or None')
+
+    @auto_fp16()
+    def forward(self, x):
+        res_layer = getattr(self, f'layer{self.stage + 1}')
+        out = res_layer(x)
+        return out
+
+    def train(self, mode=True):
+        super(ResLayer, self).train(mode)
+        if self.norm_eval:
+            for m in self.modules():
+                if isinstance(m, nn.BatchNorm2d):
+                    m.eval()
diff --git a/mmdet/models/roi_heads/sparse_roi_head.py b/mmdet/models/roi_heads/sparse_roi_head.py
new file mode 100644
index 0000000..2613469
--- /dev/null
+++ b/mmdet/models/roi_heads/sparse_roi_head.py
@@ -0,0 +1,424 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+
+from mmdet.core import bbox2result, bbox2roi, bbox_xyxy_to_cxcywh
+from mmdet.core.bbox.samplers import PseudoSampler
+from ..builder import HEADS
+from .cascade_roi_head import CascadeRoIHead
+
+
+@HEADS.register_module()
+class SparseRoIHead(CascadeRoIHead):
+    r"""The RoIHead for `Sparse R-CNN: End-to-End Object Detection with
+    Learnable Proposals <https://arxiv.org/abs/2011.12450>`_
+    and `Instances as Queries <http://arxiv.org/abs/2105.01928>`_
+
+    Args:
+        num_stages (int): Number of stage whole iterative process.
+            Defaults to 6.
+        stage_loss_weights (Tuple[float]): The loss
+            weight of each stage. By default all stages have
+            the same weight 1.
+        bbox_roi_extractor (dict): Config of box roi extractor.
+        mask_roi_extractor (dict): Config of mask roi extractor.
+        bbox_head (dict): Config of box head.
+        mask_head (dict): Config of mask head.
+        train_cfg (dict, optional): Configuration information in train stage.
+            Defaults to None.
+        test_cfg (dict, optional): Configuration information in test stage.
+            Defaults to None.
+        pretrained (str, optional): model pretrained path. Default: None
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+
+    """
+
+    def __init__(self,
+                 num_stages=6,
+                 stage_loss_weights=(1, 1, 1, 1, 1, 1),
+                 proposal_feature_channel=256,
+                 bbox_roi_extractor=dict(
+                     type='SingleRoIExtractor',
+                     roi_layer=dict(
+                         type='RoIAlign', output_size=7, sampling_ratio=2),
+                     out_channels=256,
+                     featmap_strides=[4, 8, 16, 32]),
+                 mask_roi_extractor=None,
+                 bbox_head=dict(
+                     type='DIIHead',
+                     num_classes=80,
+                     num_fcs=2,
+                     num_heads=8,
+                     num_cls_fcs=1,
+                     num_reg_fcs=3,
+                     feedforward_channels=2048,
+                     hidden_channels=256,
+                     dropout=0.0,
+                     roi_feat_size=7,
+                     ffn_act_cfg=dict(type='ReLU', inplace=True)),
+                 mask_head=None,
+                 train_cfg=None,
+                 test_cfg=None,
+                 pretrained=None,
+                 init_cfg=None):
+        assert bbox_roi_extractor is not None
+        assert bbox_head is not None
+        assert len(stage_loss_weights) == num_stages
+        self.num_stages = num_stages
+        self.stage_loss_weights = stage_loss_weights
+        self.proposal_feature_channel = proposal_feature_channel
+        super(SparseRoIHead, self).__init__(
+            num_stages,
+            stage_loss_weights,
+            bbox_roi_extractor=bbox_roi_extractor,
+            mask_roi_extractor=mask_roi_extractor,
+            bbox_head=bbox_head,
+            mask_head=mask_head,
+            train_cfg=train_cfg,
+            test_cfg=test_cfg,
+            pretrained=pretrained,
+            init_cfg=init_cfg)
+        # train_cfg would be None when run the test.py
+        if train_cfg is not None:
+            for stage in range(num_stages):
+                assert isinstance(self.bbox_sampler[stage], PseudoSampler), \
+                    'Sparse R-CNN and QueryInst only support `PseudoSampler`'
+
+    def _bbox_forward(self, stage, x, rois, object_feats, img_metas):
+        """Box head forward function used in both training and testing. Returns
+        all regression, classification results and a intermediate feature.
+
+        Args:
+            stage (int): The index of current stage in
+                iterative process.
+            x (List[Tensor]): List of FPN features
+            rois (Tensor): Rois in total batch. With shape (num_proposal, 5).
+                the last dimension 5 represents (img_index, x1, y1, x2, y2).
+            object_feats (Tensor): The object feature extracted from
+                the previous stage.
+            img_metas (dict): meta information of images.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of bbox head outputs,
+                Containing the following results:
+
+                    - cls_score (Tensor): The score of each class, has
+                      shape (batch_size, num_proposals, num_classes)
+                      when use focal loss or
+                      (batch_size, num_proposals, num_classes+1)
+                      otherwise.
+                    - decode_bbox_pred (Tensor): The regression results
+                      with shape (batch_size, num_proposal, 4).
+                      The last dimension 4 represents
+                      [tl_x, tl_y, br_x, br_y].
+                    - object_feats (Tensor): The object feature extracted
+                      from current stage
+                    - detach_cls_score_list (list[Tensor]): The detached
+                      classification results, length is batch_size, and
+                      each tensor has shape (num_proposal, num_classes).
+                    - detach_proposal_list (list[tensor]): The detached
+                      regression results, length is batch_size, and each
+                      tensor has shape (num_proposal, 4). The last
+                      dimension 4 represents [tl_x, tl_y, br_x, br_y].
+        """
+        num_imgs = len(img_metas)
+        bbox_roi_extractor = self.bbox_roi_extractor[stage]
+        bbox_head = self.bbox_head[stage]
+        bbox_feats = bbox_roi_extractor(x[:bbox_roi_extractor.num_inputs],
+                                        rois)
+        cls_score, bbox_pred, object_feats, attn_feats = bbox_head(
+            bbox_feats, object_feats)
+        proposal_list = self.bbox_head[stage].refine_bboxes(
+            rois,
+            rois.new_zeros(len(rois)),  # dummy arg
+            bbox_pred.view(-1, bbox_pred.size(-1)),
+            [rois.new_zeros(object_feats.size(1)) for _ in range(num_imgs)],
+            img_metas)
+        bbox_results = dict(
+            cls_score=cls_score,
+            decode_bbox_pred=torch.cat(proposal_list),
+            object_feats=object_feats,
+            attn_feats=attn_feats,
+            # detach then use it in label assign
+            detach_cls_score_list=[
+                cls_score[i].detach() for i in range(num_imgs)
+            ],
+            detach_proposal_list=[item.detach() for item in proposal_list])
+
+        return bbox_results
+
+    def _mask_forward(self, stage, x, rois, attn_feats):
+        """Mask head forward function used in both training and testing."""
+        mask_roi_extractor = self.mask_roi_extractor[stage]
+        mask_head = self.mask_head[stage]
+        mask_feats = mask_roi_extractor(x[:mask_roi_extractor.num_inputs],
+                                        rois)
+        # do not support caffe_c4 model anymore
+        mask_pred = mask_head(mask_feats, attn_feats)
+
+        mask_results = dict(mask_pred=mask_pred)
+        return mask_results
+
+    def _mask_forward_train(self, stage, x, attn_feats, sampling_results,
+                            gt_masks, rcnn_train_cfg):
+        """Run forward function and calculate loss for mask head in
+        training."""
+        pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
+        attn_feats = torch.cat([
+            feats[res.pos_inds]
+            for (feats, res) in zip(attn_feats, sampling_results)
+        ])
+        mask_results = self._mask_forward(stage, x, pos_rois, attn_feats)
+
+        mask_targets = self.mask_head[stage].get_targets(
+            sampling_results, gt_masks, rcnn_train_cfg)
+
+        pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
+
+        loss_mask = self.mask_head[stage].loss(mask_results['mask_pred'],
+                                               mask_targets, pos_labels)
+        mask_results.update(loss_mask)
+        return mask_results
+
+    def forward_train(self,
+                      x,
+                      proposal_boxes,
+                      proposal_features,
+                      img_metas,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None,
+                      imgs_whwh=None,
+                      gt_masks=None):
+        """Forward function in training stage.
+
+        Args:
+            x (list[Tensor]): list of multi-level img features.
+            proposals (Tensor): Decoded proposal bboxes, has shape
+                (batch_size, num_proposals, 4)
+            proposal_features (Tensor): Expanded proposal
+                features, has shape
+                (batch_size, num_proposals, proposal_feature_channel)
+            img_metas (list[dict]): list of image info dict where
+                each dict has: 'img_shape', 'scale_factor', 'flip',
+                and may also contain 'filename', 'ori_shape',
+                'pad_shape', and 'img_norm_cfg'. For details on the
+                values of these keys see
+                `mmdet/datasets/pipelines/formatting.py:Collect`.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+            imgs_whwh (Tensor): Tensor with shape (batch_size, 4),
+                    the dimension means
+                    [img_width,img_height, img_width, img_height].
+            gt_masks (None | Tensor) : true segmentation masks for each box
+                used if the architecture supports a segmentation task.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components of all stage.
+        """
+
+        num_imgs = len(img_metas)
+        num_proposals = proposal_boxes.size(1)
+        imgs_whwh = imgs_whwh.repeat(1, num_proposals, 1)
+        all_stage_bbox_results = []
+        proposal_list = [proposal_boxes[i] for i in range(len(proposal_boxes))]
+        object_feats = proposal_features
+        all_stage_loss = {}
+        for stage in range(self.num_stages):
+            rois = bbox2roi(proposal_list)
+            bbox_results = self._bbox_forward(stage, x, rois, object_feats,
+                                              img_metas)
+            all_stage_bbox_results.append(bbox_results)
+            if gt_bboxes_ignore is None:
+                # TODO support ignore
+                gt_bboxes_ignore = [None for _ in range(num_imgs)]
+            sampling_results = []
+            cls_pred_list = bbox_results['detach_cls_score_list']
+            proposal_list = bbox_results['detach_proposal_list']
+            for i in range(num_imgs):
+                normalize_bbox_ccwh = bbox_xyxy_to_cxcywh(proposal_list[i] /
+                                                          imgs_whwh[i])
+                assign_result = self.bbox_assigner[stage].assign(
+                    normalize_bbox_ccwh, cls_pred_list[i], gt_bboxes[i],
+                    gt_labels[i], img_metas[i])
+                sampling_result = self.bbox_sampler[stage].sample(
+                    assign_result, proposal_list[i], gt_bboxes[i])
+                sampling_results.append(sampling_result)
+            bbox_targets = self.bbox_head[stage].get_targets(
+                sampling_results, gt_bboxes, gt_labels, self.train_cfg[stage],
+                True)
+            cls_score = bbox_results['cls_score']
+            decode_bbox_pred = bbox_results['decode_bbox_pred']
+
+            single_stage_loss = self.bbox_head[stage].loss(
+                cls_score.view(-1, cls_score.size(-1)),
+                decode_bbox_pred.view(-1, 4),
+                *bbox_targets,
+                imgs_whwh=imgs_whwh)
+
+            if self.with_mask:
+                mask_results = self._mask_forward_train(
+                    stage, x, bbox_results['attn_feats'], sampling_results,
+                    gt_masks, self.train_cfg[stage])
+                single_stage_loss['loss_mask'] = mask_results['loss_mask']
+
+            for key, value in single_stage_loss.items():
+                all_stage_loss[f'stage{stage}_{key}'] = value * \
+                                    self.stage_loss_weights[stage]
+            object_feats = bbox_results['object_feats']
+
+        return all_stage_loss
+
+    def simple_test(self,
+                    x,
+                    proposal_boxes,
+                    proposal_features,
+                    img_metas,
+                    imgs_whwh,
+                    rescale=False):
+        """Test without augmentation.
+
+        Args:
+            x (list[Tensor]): list of multi-level img features.
+            proposal_boxes (Tensor): Decoded proposal bboxes, has shape
+                (batch_size, num_proposals, 4)
+            proposal_features (Tensor): Expanded proposal
+                features, has shape
+                (batch_size, num_proposals, proposal_feature_channel)
+            img_metas (dict): meta information of images.
+            imgs_whwh (Tensor): Tensor with shape (batch_size, 4),
+                    the dimension means
+                    [img_width,img_height, img_width, img_height].
+            rescale (bool): If True, return boxes in original image
+                space. Defaults to False.
+
+        Returns:
+            list[list[np.ndarray]] or list[tuple]: When no mask branch,
+            it is bbox results of each image and classes with type
+            `list[list[np.ndarray]]`. The outer list
+            corresponds to each image. The inner list
+            corresponds to each class. When the model has a mask branch,
+            it is a list[tuple] that contains bbox results and mask results.
+            The outer list corresponds to each image, and first element
+            of tuple is bbox results, second element is mask results.
+        """
+        assert self.with_bbox, 'Bbox head must be implemented.'
+        # Decode initial proposals
+        num_imgs = len(img_metas)
+        proposal_list = [proposal_boxes[i] for i in range(num_imgs)]
+        ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
+        scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
+
+        object_feats = proposal_features
+        if all([proposal.shape[0] == 0 for proposal in proposal_list]):
+            # There is no proposal in the whole batch
+            bbox_results = [[
+                np.zeros((0, 5), dtype=np.float32)
+                for i in range(self.bbox_head[-1].num_classes)
+            ]] * num_imgs
+            return bbox_results
+
+        for stage in range(self.num_stages):
+            rois = bbox2roi(proposal_list)
+            bbox_results = self._bbox_forward(stage, x, rois, object_feats,
+                                              img_metas)
+            object_feats = bbox_results['object_feats']
+            cls_score = bbox_results['cls_score']
+            proposal_list = bbox_results['detach_proposal_list']
+
+        if self.with_mask:
+            rois = bbox2roi(proposal_list)
+            mask_results = self._mask_forward(stage, x, rois,
+                                              bbox_results['attn_feats'])
+            mask_results['mask_pred'] = mask_results['mask_pred'].reshape(
+                num_imgs, -1, *mask_results['mask_pred'].size()[1:])
+
+        num_classes = self.bbox_head[-1].num_classes
+        det_bboxes = []
+        det_labels = []
+
+        if self.bbox_head[-1].loss_cls.use_sigmoid:
+            cls_score = cls_score.sigmoid()
+        else:
+            cls_score = cls_score.softmax(-1)[..., :-1]
+
+        for img_id in range(num_imgs):
+            cls_score_per_img = cls_score[img_id]
+            scores_per_img, topk_indices = cls_score_per_img.flatten(
+                0, 1).topk(
+                    self.test_cfg.max_per_img, sorted=False)
+            labels_per_img = topk_indices % num_classes
+            bbox_pred_per_img = proposal_list[img_id][topk_indices //
+                                                      num_classes]
+            if rescale:
+                scale_factor = img_metas[img_id]['scale_factor']
+                bbox_pred_per_img /= bbox_pred_per_img.new_tensor(scale_factor)
+            det_bboxes.append(
+                torch.cat([bbox_pred_per_img, scores_per_img[:, None]], dim=1))
+            det_labels.append(labels_per_img)
+
+        bbox_results = [
+            bbox2result(det_bboxes[i], det_labels[i], num_classes)
+            for i in range(num_imgs)
+        ]
+
+        if self.with_mask:
+            if rescale and not isinstance(scale_factors[0], float):
+                scale_factors = [
+                    torch.from_numpy(scale_factor).to(det_bboxes[0].device)
+                    for scale_factor in scale_factors
+                ]
+            _bboxes = [
+                det_bboxes[i][:, :4] *
+                scale_factors[i] if rescale else det_bboxes[i][:, :4]
+                for i in range(len(det_bboxes))
+            ]
+            segm_results = []
+            mask_pred = mask_results['mask_pred']
+            for img_id in range(num_imgs):
+                mask_pred_per_img = mask_pred[img_id].flatten(0,
+                                                              1)[topk_indices]
+                mask_pred_per_img = mask_pred_per_img[:, None, ...].repeat(
+                    1, num_classes, 1, 1)
+                segm_result = self.mask_head[-1].get_seg_masks(
+                    mask_pred_per_img, _bboxes[img_id], det_labels[img_id],
+                    self.test_cfg, ori_shapes[img_id], scale_factors[img_id],
+                    rescale)
+                segm_results.append(segm_result)
+
+        if self.with_mask:
+            results = list(zip(bbox_results, segm_results))
+        else:
+            results = bbox_results
+
+        return results
+
+    def aug_test(self, features, proposal_list, img_metas, rescale=False):
+        raise NotImplementedError(
+            'Sparse R-CNN and QueryInst does not support `aug_test`')
+
+    def forward_dummy(self, x, proposal_boxes, proposal_features, img_metas):
+        """Dummy forward function when do the flops computing."""
+        all_stage_bbox_results = []
+        proposal_list = [proposal_boxes[i] for i in range(len(proposal_boxes))]
+        object_feats = proposal_features
+        if self.with_bbox:
+            for stage in range(self.num_stages):
+                rois = bbox2roi(proposal_list)
+                bbox_results = self._bbox_forward(stage, x, rois, object_feats,
+                                                  img_metas)
+
+                all_stage_bbox_results.append((bbox_results, ))
+                proposal_list = bbox_results['detach_proposal_list']
+                object_feats = bbox_results['object_feats']
+
+                if self.with_mask:
+                    rois = bbox2roi(proposal_list)
+                    mask_results = self._mask_forward(
+                        stage, x, rois, bbox_results['attn_feats'])
+                    all_stage_bbox_results[-1] += (mask_results, )
+        return all_stage_bbox_results
diff --git a/mmdet/models/roi_heads/standard_roi_head.py b/mmdet/models/roi_heads/standard_roi_head.py
new file mode 100644
index 0000000..3fdd82a
--- /dev/null
+++ b/mmdet/models/roi_heads/standard_roi_head.py
@@ -0,0 +1,397 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdet.core import bbox2result, bbox2roi, build_assigner, build_sampler
+from ..builder import HEADS, build_head, build_roi_extractor
+from .base_roi_head import BaseRoIHead
+from .test_mixins import BBoxTestMixin, MaskTestMixin
+
+
+@HEADS.register_module()
+class StandardRoIHead(BaseRoIHead, BBoxTestMixin, MaskTestMixin):
+    """Simplest base roi head including one bbox head and one mask head."""
+
+    def init_assigner_sampler(self):
+        """Initialize assigner and sampler."""
+        self.bbox_assigner = None
+        self.bbox_sampler = None
+        if self.train_cfg:
+            self.bbox_assigner = build_assigner(self.train_cfg.assigner)
+            self.bbox_sampler = build_sampler(
+                self.train_cfg.sampler, context=self)
+
+    def init_bbox_head(self, bbox_roi_extractor, bbox_head):
+        """Initialize ``bbox_head``"""
+        self.bbox_roi_extractor = build_roi_extractor(bbox_roi_extractor)
+        self.bbox_head = build_head(bbox_head)
+
+    def init_mask_head(self, mask_roi_extractor, mask_head):
+        """Initialize ``mask_head``"""
+        if mask_roi_extractor is not None:
+            self.mask_roi_extractor = build_roi_extractor(mask_roi_extractor)
+            self.share_roi_extractor = False
+        else:
+            self.share_roi_extractor = True
+            self.mask_roi_extractor = self.bbox_roi_extractor
+        self.mask_head = build_head(mask_head)
+
+    def forward_dummy(self, x, proposals):
+        """Dummy forward function."""
+        # bbox head
+        outs = ()
+        rois = bbox2roi([proposals])
+        if self.with_bbox:
+            bbox_results = self._bbox_forward(x, rois)
+            outs = outs + (bbox_results['cls_score'],
+                           bbox_results['bbox_pred'])
+        # mask head
+        if self.with_mask:
+            mask_rois = rois[:100]
+            mask_results = self._mask_forward(x, mask_rois)
+            outs = outs + (mask_results['mask_pred'], )
+        return outs
+
+    def forward_train(self,
+                      x,
+                      img_metas,
+                      proposal_list,
+                      gt_bboxes,
+                      gt_labels,
+                      gt_bboxes_ignore=None,
+                      gt_masks=None,
+                      **kwargs):
+        """
+        Args:
+            x (list[Tensor]): list of multi-level img features.
+            img_metas (list[dict]): list of image info dict where each dict
+                has: 'img_shape', 'scale_factor', 'flip', and may also contain
+                'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+                For details on the values of these keys see
+                `mmdet/datasets/pipelines/formatting.py:Collect`.
+            proposals (list[Tensors]): list of region proposals.
+            gt_bboxes (list[Tensor]): Ground truth bboxes for each image with
+                shape (num_gts, 4) in [tl_x, tl_y, br_x, br_y] format.
+            gt_labels (list[Tensor]): class indices corresponding to each box
+            gt_bboxes_ignore (None | list[Tensor]): specify which bounding
+                boxes can be ignored when computing the loss.
+            gt_masks (None | Tensor) : true segmentation masks for each box
+                used if the architecture supports a segmentation task.
+
+        Returns:
+            dict[str, Tensor]: a dictionary of loss components
+        """
+        # assign gts and sample proposals
+        if self.with_bbox or self.with_mask:
+            num_imgs = len(img_metas)
+            if gt_bboxes_ignore is None:
+                gt_bboxes_ignore = [None for _ in range(num_imgs)]
+            sampling_results = []
+            for i in range(num_imgs):
+                assign_result = self.bbox_assigner.assign(
+                    proposal_list[i], gt_bboxes[i], gt_bboxes_ignore[i],
+                    gt_labels[i])
+                sampling_result = self.bbox_sampler.sample(
+                    assign_result,
+                    proposal_list[i],
+                    gt_bboxes[i],
+                    gt_labels[i],
+                    feats=[lvl_feat[i][None] for lvl_feat in x])
+                sampling_results.append(sampling_result)
+
+        losses = dict()
+        # bbox head forward and loss
+        if self.with_bbox:
+            bbox_results = self._bbox_forward_train(x, sampling_results,
+                                                    gt_bboxes, gt_labels,
+                                                    img_metas)
+            losses.update(bbox_results['loss_bbox'])
+
+        # mask head forward and loss
+        if self.with_mask:
+            mask_results = self._mask_forward_train(x, sampling_results,
+                                                    bbox_results['bbox_feats'],
+                                                    gt_masks, img_metas)
+            losses.update(mask_results['loss_mask'])
+
+        return losses
+
+    def _bbox_forward(self, x, rois):
+        """Box head forward function used in both training and testing."""
+        # TODO: a more flexible way to decide which feature maps to use
+        bbox_feats = self.bbox_roi_extractor(
+            x[:self.bbox_roi_extractor.num_inputs], rois)
+        if self.with_shared_head:
+            bbox_feats = self.shared_head(bbox_feats)
+        cls_score, bbox_pred = self.bbox_head(bbox_feats)
+
+        bbox_results = dict(
+            cls_score=cls_score, bbox_pred=bbox_pred, bbox_feats=bbox_feats)
+        return bbox_results
+
+    def _bbox_forward_train(self, x, sampling_results, gt_bboxes, gt_labels,
+                            img_metas):
+        """Run forward function and calculate loss for box head in training."""
+        rois = bbox2roi([res.bboxes for res in sampling_results])
+        bbox_results = self._bbox_forward(x, rois)
+
+        bbox_targets = self.bbox_head.get_targets(sampling_results, gt_bboxes,
+                                                  gt_labels, self.train_cfg)
+        loss_bbox = self.bbox_head.loss(bbox_results['cls_score'],
+                                        bbox_results['bbox_pred'], rois,
+                                        *bbox_targets)
+
+        bbox_results.update(loss_bbox=loss_bbox)
+        return bbox_results
+
+    def _mask_forward_train(self, x, sampling_results, bbox_feats, gt_masks,
+                            img_metas):
+        """Run forward function and calculate loss for mask head in
+        training."""
+        if not self.share_roi_extractor:
+            pos_rois = bbox2roi([res.pos_bboxes for res in sampling_results])
+            mask_results = self._mask_forward(x, pos_rois)
+        else:
+            pos_inds = []
+            device = bbox_feats.device
+            for res in sampling_results:
+                pos_inds.append(
+                    torch.ones(
+                        res.pos_bboxes.shape[0],
+                        device=device,
+                        dtype=torch.uint8))
+                pos_inds.append(
+                    torch.zeros(
+                        res.neg_bboxes.shape[0],
+                        device=device,
+                        dtype=torch.uint8))
+            pos_inds = torch.cat(pos_inds)
+
+            mask_results = self._mask_forward(
+                x, pos_inds=pos_inds, bbox_feats=bbox_feats)
+
+        mask_targets = self.mask_head.get_targets(sampling_results, gt_masks,
+                                                  self.train_cfg)
+        pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
+        loss_mask = self.mask_head.loss(mask_results['mask_pred'],
+                                        mask_targets, pos_labels)
+
+        mask_results.update(loss_mask=loss_mask, mask_targets=mask_targets)
+        return mask_results
+
+    def _mask_forward(self, x, rois=None, pos_inds=None, bbox_feats=None):
+        """Mask head forward function used in both training and testing."""
+        assert ((rois is not None) ^
+                (pos_inds is not None and bbox_feats is not None))
+        if rois is not None:
+            mask_feats = self.mask_roi_extractor(
+                x[:self.mask_roi_extractor.num_inputs], rois)
+            if self.with_shared_head:
+                mask_feats = self.shared_head(mask_feats)
+        else:
+            assert bbox_feats is not None
+            mask_feats = bbox_feats[pos_inds]
+
+        mask_pred = self.mask_head(mask_feats)
+        mask_results = dict(mask_pred=mask_pred, mask_feats=mask_feats)
+        return mask_results
+
+    async def async_simple_test(self,
+                                x,
+                                proposal_list,
+                                img_metas,
+                                proposals=None,
+                                rescale=False):
+        """Async test without augmentation."""
+        assert self.with_bbox, 'Bbox head must be implemented.'
+
+        det_bboxes, det_labels = await self.async_test_bboxes(
+            x, img_metas, proposal_list, self.test_cfg, rescale=rescale)
+        bbox_results = bbox2result(det_bboxes, det_labels,
+                                   self.bbox_head.num_classes)
+        if not self.with_mask:
+            return bbox_results
+        else:
+            segm_results = await self.async_test_mask(
+                x,
+                img_metas,
+                det_bboxes,
+                det_labels,
+                rescale=rescale,
+                mask_test_cfg=self.test_cfg.get('mask'))
+            return bbox_results, segm_results
+
+    def simple_test(self,
+                    x,
+                    proposal_list,
+                    img_metas,
+                    proposals=None,
+                    rescale=False):
+        """Test without augmentation.
+
+        Args:
+            x (tuple[Tensor]): Features from upstream network. Each
+                has shape (batch_size, c, h, w).
+            proposal_list (list(Tensor)): Proposals from rpn head.
+                Each has shape (num_proposals, 5), last dimension
+                5 represent (x1, y1, x2, y2, score).
+            img_metas (list[dict]): Meta information of images.
+            rescale (bool): Whether to rescale the results to
+                the original image. Default: True.
+
+        Returns:
+            list[list[np.ndarray]] or list[tuple]: When no mask branch,
+            it is bbox results of each image and classes with type
+            `list[list[np.ndarray]]`. The outer list
+            corresponds to each image. The inner list
+            corresponds to each class. When the model has mask branch,
+            it contains bbox results and mask results.
+            The outer list corresponds to each image, and first element
+            of tuple is bbox results, second element is mask results.
+        """
+        assert self.with_bbox, 'Bbox head must be implemented.'
+
+        det_bboxes, det_labels = self.simple_test_bboxes(
+            x, img_metas, proposal_list, self.test_cfg, rescale=rescale)
+
+        bbox_results = [
+            bbox2result(det_bboxes[i], det_labels[i],
+                        self.bbox_head.num_classes)
+            for i in range(len(det_bboxes))
+        ]
+
+        if not self.with_mask:
+            return bbox_results
+        else:
+            segm_results = self.simple_test_mask(
+                x, img_metas, det_bboxes, det_labels, rescale=rescale)
+            return list(zip(bbox_results, segm_results))
+
+    def aug_test(self, x, proposal_list, img_metas, rescale=False):
+        """Test with augmentations.
+
+        If rescale is False, then returned bboxes and masks will fit the scale
+        of imgs[0].
+        """
+        det_bboxes, det_labels = self.aug_test_bboxes(x, img_metas,
+                                                      proposal_list,
+                                                      self.test_cfg)
+        if rescale:
+            _det_bboxes = det_bboxes
+        else:
+            _det_bboxes = det_bboxes.clone()
+            _det_bboxes[:, :4] *= det_bboxes.new_tensor(
+                img_metas[0][0]['scale_factor'])
+        bbox_results = bbox2result(_det_bboxes, det_labels,
+                                   self.bbox_head.num_classes)
+
+        # det_bboxes always keep the original scale
+        if self.with_mask:
+            segm_results = self.aug_test_mask(x, img_metas, det_bboxes,
+                                              det_labels)
+            return [(bbox_results, segm_results)]
+        else:
+            return [bbox_results]
+
+    def onnx_export(self, x, proposals, img_metas, rescale=False):
+        """Test without augmentation."""
+        assert self.with_bbox, 'Bbox head must be implemented.'
+        det_bboxes, det_labels = self.bbox_onnx_export(
+            x, img_metas, proposals, self.test_cfg, rescale=rescale)
+
+        if not self.with_mask:
+            return det_bboxes, det_labels
+        else:
+            segm_results = self.mask_onnx_export(
+                x, img_metas, det_bboxes, det_labels, rescale=rescale)
+            return det_bboxes, det_labels, segm_results
+
+    def mask_onnx_export(self, x, img_metas, det_bboxes, det_labels, **kwargs):
+        """Export mask branch to onnx which supports batch inference.
+
+        Args:
+            x (tuple[Tensor]): Feature maps of all scale level.
+            img_metas (list[dict]): Image meta info.
+            det_bboxes (Tensor): Bboxes and corresponding scores.
+                has shape [N, num_bboxes, 5].
+            det_labels (Tensor): class labels of
+                shape [N, num_bboxes].
+
+        Returns:
+            Tensor: The segmentation results of shape [N, num_bboxes,
+                image_height, image_width].
+        """
+        # image shapes of images in the batch
+
+        if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
+            raise RuntimeError('[ONNX Error] Can not record MaskHead '
+                               'as it has not been executed this time')
+        batch_size = det_bboxes.size(0)
+        # if det_bboxes is rescaled to the original image size, we need to
+        # rescale it back to the testing scale to obtain RoIs.
+        det_bboxes = det_bboxes[..., :4]
+        batch_index = torch.arange(
+            det_bboxes.size(0), device=det_bboxes.device).float().view(
+                -1, 1, 1).expand(det_bboxes.size(0), det_bboxes.size(1), 1)
+        mask_rois = torch.cat([batch_index, det_bboxes], dim=-1)
+        mask_rois = mask_rois.view(-1, 5)
+        mask_results = self._mask_forward(x, mask_rois)
+        mask_pred = mask_results['mask_pred']
+        max_shape = img_metas[0]['img_shape_for_onnx']
+        num_det = det_bboxes.shape[1]
+        det_bboxes = det_bboxes.reshape(-1, 4)
+        det_labels = det_labels.reshape(-1)
+        segm_results = self.mask_head.onnx_export(mask_pred, det_bboxes,
+                                                  det_labels, self.test_cfg,
+                                                  max_shape)
+        segm_results = segm_results.reshape(batch_size, num_det, max_shape[0],
+                                            max_shape[1])
+        return segm_results
+
+    def bbox_onnx_export(self, x, img_metas, proposals, rcnn_test_cfg,
+                         **kwargs):
+        """Export bbox branch to onnx which supports batch inference.
+
+        Args:
+            x (tuple[Tensor]): Feature maps of all scale level.
+            img_metas (list[dict]): Image meta info.
+            proposals (Tensor): Region proposals with
+                batch dimension, has shape [N, num_bboxes, 5].
+            rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of R-CNN.
+
+        Returns:
+            tuple[Tensor, Tensor]: bboxes of shape [N, num_bboxes, 5]
+                and class labels of shape [N, num_bboxes].
+        """
+        # get origin input shape to support onnx dynamic input shape
+        assert len(
+            img_metas
+        ) == 1, 'Only support one input image while in exporting to ONNX'
+        img_shapes = img_metas[0]['img_shape_for_onnx']
+
+        rois = proposals
+
+        batch_index = torch.arange(
+            rois.size(0), device=rois.device).float().view(-1, 1, 1).expand(
+                rois.size(0), rois.size(1), 1)
+
+        rois = torch.cat([batch_index, rois[..., :4]], dim=-1)
+        batch_size = rois.shape[0]
+        num_proposals_per_img = rois.shape[1]
+
+        # Eliminate the batch dimension
+        rois = rois.view(-1, 5)
+        bbox_results = self._bbox_forward(x, rois)
+        cls_score = bbox_results['cls_score']
+        bbox_pred = bbox_results['bbox_pred']
+
+        # Recover the batch dimension
+        rois = rois.reshape(batch_size, num_proposals_per_img, rois.size(-1))
+        cls_score = cls_score.reshape(batch_size, num_proposals_per_img,
+                                      cls_score.size(-1))
+
+        bbox_pred = bbox_pred.reshape(batch_size, num_proposals_per_img,
+                                      bbox_pred.size(-1))
+        det_bboxes, det_labels = self.bbox_head.onnx_export(
+            rois, cls_score, bbox_pred, img_shapes, cfg=rcnn_test_cfg)
+
+        return det_bboxes, det_labels
diff --git a/mmdet/models/roi_heads/test_mixins.py b/mmdet/models/roi_heads/test_mixins.py
new file mode 100644
index 0000000..ae6e79a
--- /dev/null
+++ b/mmdet/models/roi_heads/test_mixins.py
@@ -0,0 +1,311 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import sys
+import warnings
+
+import numpy as np
+import torch
+
+from mmdet.core import (bbox2roi, bbox_mapping, merge_aug_bboxes,
+                        merge_aug_masks, multiclass_nms)
+
+if sys.version_info >= (3, 7):
+    from mmdet.utils.contextmanagers import completed
+
+
+class BBoxTestMixin:
+
+    if sys.version_info >= (3, 7):
+
+        async def async_test_bboxes(self,
+                                    x,
+                                    img_metas,
+                                    proposals,
+                                    rcnn_test_cfg,
+                                    rescale=False,
+                                    **kwargs):
+            """Asynchronized test for box head without augmentation."""
+            rois = bbox2roi(proposals)
+            roi_feats = self.bbox_roi_extractor(
+                x[:len(self.bbox_roi_extractor.featmap_strides)], rois)
+            if self.with_shared_head:
+                roi_feats = self.shared_head(roi_feats)
+            sleep_interval = rcnn_test_cfg.get('async_sleep_interval', 0.017)
+
+            async with completed(
+                    __name__, 'bbox_head_forward',
+                    sleep_interval=sleep_interval):
+                cls_score, bbox_pred = self.bbox_head(roi_feats)
+
+            img_shape = img_metas[0]['img_shape']
+            scale_factor = img_metas[0]['scale_factor']
+            det_bboxes, det_labels = self.bbox_head.get_bboxes(
+                rois,
+                cls_score,
+                bbox_pred,
+                img_shape,
+                scale_factor,
+                rescale=rescale,
+                cfg=rcnn_test_cfg)
+            return det_bboxes, det_labels
+
+    def simple_test_bboxes(self,
+                           x,
+                           img_metas,
+                           proposals,
+                           rcnn_test_cfg,
+                           rescale=False):
+        """Test only det bboxes without augmentation.
+
+        Args:
+            x (tuple[Tensor]): Feature maps of all scale level.
+            img_metas (list[dict]): Image meta info.
+            proposals (List[Tensor]): Region proposals.
+            rcnn_test_cfg (obj:`ConfigDict`): `test_cfg` of R-CNN.
+            rescale (bool): If True, return boxes in original image space.
+                Default: False.
+
+        Returns:
+            tuple[list[Tensor], list[Tensor]]: The first list contains
+                the boxes of the corresponding image in a batch, each
+                tensor has the shape (num_boxes, 5) and last dimension
+                5 represent (tl_x, tl_y, br_x, br_y, score). Each Tensor
+                in the second list is the labels with shape (num_boxes, ).
+                The length of both lists should be equal to batch_size.
+        """
+
+        rois = bbox2roi(proposals)
+
+        if rois.shape[0] == 0:
+            batch_size = len(proposals)
+            det_bbox = rois.new_zeros(0, 5)
+            det_label = rois.new_zeros((0, ), dtype=torch.long)
+            if rcnn_test_cfg is None:
+                det_bbox = det_bbox[:, :4]
+                det_label = rois.new_zeros(
+                    (0, self.bbox_head.fc_cls.out_features))
+            # There is no proposal in the whole batch
+            return [det_bbox] * batch_size, [det_label] * batch_size
+
+        bbox_results = self._bbox_forward(x, rois)
+        img_shapes = tuple(meta['img_shape'] for meta in img_metas)
+        scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
+
+        # split batch bbox prediction back to each image
+        cls_score = bbox_results['cls_score']
+        bbox_pred = bbox_results['bbox_pred']
+        num_proposals_per_img = tuple(len(p) for p in proposals)
+        rois = rois.split(num_proposals_per_img, 0)
+        cls_score = cls_score.split(num_proposals_per_img, 0)
+
+        # some detector with_reg is False, bbox_pred will be None
+        if bbox_pred is not None:
+            # TODO move this to a sabl_roi_head
+            # the bbox prediction of some detectors like SABL is not Tensor
+            if isinstance(bbox_pred, torch.Tensor):
+                bbox_pred = bbox_pred.split(num_proposals_per_img, 0)
+            else:
+                bbox_pred = self.bbox_head.bbox_pred_split(
+                    bbox_pred, num_proposals_per_img)
+        else:
+            bbox_pred = (None, ) * len(proposals)
+
+        # apply bbox post-processing to each image individually
+        det_bboxes = []
+        det_labels = []
+        for i in range(len(proposals)):
+            if rois[i].shape[0] == 0:
+                # There is no proposal in the single image
+                det_bbox = rois[i].new_zeros(0, 5)
+                det_label = rois[i].new_zeros((0, ), dtype=torch.long)
+                if rcnn_test_cfg is None:
+                    det_bbox = det_bbox[:, :4]
+                    det_label = rois[i].new_zeros(
+                        (0, self.bbox_head.fc_cls.out_features))
+
+            else:
+                det_bbox, det_label = self.bbox_head.get_bboxes(
+                    rois[i],
+                    cls_score[i],
+                    bbox_pred[i],
+                    img_shapes[i],
+                    scale_factors[i],
+                    rescale=rescale,
+                    cfg=rcnn_test_cfg)
+            det_bboxes.append(det_bbox)
+            det_labels.append(det_label)
+        return det_bboxes, det_labels
+
+    def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg):
+        """Test det bboxes with test time augmentation."""
+        aug_bboxes = []
+        aug_scores = []
+        for x, img_meta in zip(feats, img_metas):
+            # only one image in the batch
+            img_shape = img_meta[0]['img_shape']
+            scale_factor = img_meta[0]['scale_factor']
+            flip = img_meta[0]['flip']
+            flip_direction = img_meta[0]['flip_direction']
+            # TODO more flexible
+            proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
+                                     scale_factor, flip, flip_direction)
+            rois = bbox2roi([proposals])
+            bbox_results = self._bbox_forward(x, rois)
+            bboxes, scores = self.bbox_head.get_bboxes(
+                rois,
+                bbox_results['cls_score'],
+                bbox_results['bbox_pred'],
+                img_shape,
+                scale_factor,
+                rescale=False,
+                cfg=None)
+            aug_bboxes.append(bboxes)
+            aug_scores.append(scores)
+        # after merging, bboxes will be rescaled to the original image size
+        merged_bboxes, merged_scores = merge_aug_bboxes(
+            aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
+        if merged_bboxes.shape[0] == 0:
+            # There is no proposal in the single image
+            det_bboxes = merged_bboxes.new_zeros(0, 5)
+            det_labels = merged_bboxes.new_zeros((0, ), dtype=torch.long)
+        else:
+            det_bboxes, det_labels = multiclass_nms(merged_bboxes,
+                                                    merged_scores,
+                                                    rcnn_test_cfg.score_thr,
+                                                    rcnn_test_cfg.nms,
+                                                    rcnn_test_cfg.max_per_img)
+        return det_bboxes, det_labels
+
+
+class MaskTestMixin:
+
+    if sys.version_info >= (3, 7):
+
+        async def async_test_mask(self,
+                                  x,
+                                  img_metas,
+                                  det_bboxes,
+                                  det_labels,
+                                  rescale=False,
+                                  mask_test_cfg=None):
+            """Asynchronized test for mask head without augmentation."""
+            # image shape of the first image in the batch (only one)
+            ori_shape = img_metas[0]['ori_shape']
+            scale_factor = img_metas[0]['scale_factor']
+            if det_bboxes.shape[0] == 0:
+                segm_result = [[] for _ in range(self.mask_head.num_classes)]
+            else:
+                if rescale and not isinstance(scale_factor,
+                                              (float, torch.Tensor)):
+                    scale_factor = det_bboxes.new_tensor(scale_factor)
+                _bboxes = (
+                    det_bboxes[:, :4] *
+                    scale_factor if rescale else det_bboxes)
+                mask_rois = bbox2roi([_bboxes])
+                mask_feats = self.mask_roi_extractor(
+                    x[:len(self.mask_roi_extractor.featmap_strides)],
+                    mask_rois)
+
+                if self.with_shared_head:
+                    mask_feats = self.shared_head(mask_feats)
+                if mask_test_cfg and mask_test_cfg.get('async_sleep_interval'):
+                    sleep_interval = mask_test_cfg['async_sleep_interval']
+                else:
+                    sleep_interval = 0.035
+                async with completed(
+                        __name__,
+                        'mask_head_forward',
+                        sleep_interval=sleep_interval):
+                    mask_pred = self.mask_head(mask_feats)
+                segm_result = self.mask_head.get_seg_masks(
+                    mask_pred, _bboxes, det_labels, self.test_cfg, ori_shape,
+                    scale_factor, rescale)
+            return segm_result
+
+    def simple_test_mask(self,
+                         x,
+                         img_metas,
+                         det_bboxes,
+                         det_labels,
+                         rescale=False):
+        """Simple test for mask head without augmentation."""
+        # image shapes of images in the batch
+        ori_shapes = tuple(meta['ori_shape'] for meta in img_metas)
+        scale_factors = tuple(meta['scale_factor'] for meta in img_metas)
+
+        if isinstance(scale_factors[0], float):
+            warnings.warn(
+                'Scale factor in img_metas should be a '
+                'ndarray with shape (4,) '
+                'arrange as (factor_w, factor_h, factor_w, factor_h), '
+                'The scale_factor with float type has been deprecated. ')
+            scale_factors = np.array([scale_factors] * 4, dtype=np.float32)
+
+        num_imgs = len(det_bboxes)
+        if all(det_bbox.shape[0] == 0 for det_bbox in det_bboxes):
+            segm_results = [[[] for _ in range(self.mask_head.num_classes)]
+                            for _ in range(num_imgs)]
+        else:
+            # if det_bboxes is rescaled to the original image size, we need to
+            # rescale it back to the testing scale to obtain RoIs.
+            if rescale:
+                scale_factors = [
+                    torch.from_numpy(scale_factor).to(det_bboxes[0].device)
+                    for scale_factor in scale_factors
+                ]
+            _bboxes = [
+                det_bboxes[i][:, :4] *
+                scale_factors[i] if rescale else det_bboxes[i][:, :4]
+                for i in range(len(det_bboxes))
+            ]
+            mask_rois = bbox2roi(_bboxes)
+            mask_results = self._mask_forward(x, mask_rois)
+            mask_pred = mask_results['mask_pred']
+            # split batch mask prediction back to each image
+            num_mask_roi_per_img = [len(det_bbox) for det_bbox in det_bboxes]
+            mask_preds = mask_pred.split(num_mask_roi_per_img, 0)
+
+            # apply mask post-processing to each image individually
+            segm_results = []
+            for i in range(num_imgs):
+                if det_bboxes[i].shape[0] == 0:
+                    segm_results.append(
+                        [[] for _ in range(self.mask_head.num_classes)])
+                else:
+                    segm_result = self.mask_head.get_seg_masks(
+                        mask_preds[i], _bboxes[i], det_labels[i],
+                        self.test_cfg, ori_shapes[i], scale_factors[i],
+                        rescale)
+                    segm_results.append(segm_result)
+        return segm_results
+
+    def aug_test_mask(self, feats, img_metas, det_bboxes, det_labels):
+        """Test for mask head with test time augmentation."""
+        if det_bboxes.shape[0] == 0:
+            segm_result = [[] for _ in range(self.mask_head.num_classes)]
+        else:
+            aug_masks = []
+            for x, img_meta in zip(feats, img_metas):
+                img_shape = img_meta[0]['img_shape']
+                scale_factor = img_meta[0]['scale_factor']
+                flip = img_meta[0]['flip']
+                flip_direction = img_meta[0]['flip_direction']
+                _bboxes = bbox_mapping(det_bboxes[:, :4], img_shape,
+                                       scale_factor, flip, flip_direction)
+                mask_rois = bbox2roi([_bboxes])
+                mask_results = self._mask_forward(x, mask_rois)
+                # convert to numpy array to save memory
+                aug_masks.append(
+                    mask_results['mask_pred'].sigmoid().cpu().numpy())
+            merged_masks = merge_aug_masks(aug_masks, img_metas, self.test_cfg)
+
+            ori_shape = img_metas[0][0]['ori_shape']
+            scale_factor = det_bboxes.new_ones(4)
+            segm_result = self.mask_head.get_seg_masks(
+                merged_masks,
+                det_bboxes,
+                det_labels,
+                self.test_cfg,
+                ori_shape,
+                scale_factor=scale_factor,
+                rescale=False)
+        return segm_result
diff --git a/mmdet/models/roi_heads/trident_roi_head.py b/mmdet/models/roi_heads/trident_roi_head.py
new file mode 100644
index 0000000..0975879
--- /dev/null
+++ b/mmdet/models/roi_heads/trident_roi_head.py
@@ -0,0 +1,120 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from mmcv.ops import batched_nms
+
+from mmdet.core import (bbox2result, bbox2roi, bbox_mapping, merge_aug_bboxes,
+                        multiclass_nms)
+from mmdet.models.roi_heads.standard_roi_head import StandardRoIHead
+from ..builder import HEADS
+
+
+@HEADS.register_module()
+class TridentRoIHead(StandardRoIHead):
+    """Trident roi head.
+
+    Args:
+        num_branch (int): Number of branches in TridentNet.
+        test_branch_idx (int): In inference, all 3 branches will be used
+            if `test_branch_idx==-1`, otherwise only branch with index
+            `test_branch_idx` will be used.
+    """
+
+    def __init__(self, num_branch, test_branch_idx, **kwargs):
+        self.num_branch = num_branch
+        self.test_branch_idx = test_branch_idx
+        super(TridentRoIHead, self).__init__(**kwargs)
+
+    def merge_trident_bboxes(self, trident_det_bboxes, trident_det_labels):
+        """Merge bbox predictions of each branch."""
+        if trident_det_bboxes.numel() == 0:
+            det_bboxes = trident_det_bboxes.new_zeros((0, 5))
+            det_labels = trident_det_bboxes.new_zeros((0, ), dtype=torch.long)
+        else:
+            nms_bboxes = trident_det_bboxes[:, :4]
+            nms_scores = trident_det_bboxes[:, 4].contiguous()
+            nms_inds = trident_det_labels
+            nms_cfg = self.test_cfg['nms']
+            det_bboxes, keep = batched_nms(nms_bboxes, nms_scores, nms_inds,
+                                           nms_cfg)
+            det_labels = trident_det_labels[keep]
+            if self.test_cfg['max_per_img'] > 0:
+                det_labels = det_labels[:self.test_cfg['max_per_img']]
+                det_bboxes = det_bboxes[:self.test_cfg['max_per_img']]
+
+        return det_bboxes, det_labels
+
+    def simple_test(self,
+                    x,
+                    proposal_list,
+                    img_metas,
+                    proposals=None,
+                    rescale=False):
+        """Test without augmentation as follows:
+
+        1. Compute prediction bbox and label per branch.
+        2. Merge predictions of each branch according to scores of
+           bboxes, i.e., bboxes with higher score are kept to give
+           top-k prediction.
+        """
+        assert self.with_bbox, 'Bbox head must be implemented.'
+        det_bboxes_list, det_labels_list = self.simple_test_bboxes(
+            x, img_metas, proposal_list, self.test_cfg, rescale=rescale)
+        num_branch = self.num_branch if self.test_branch_idx == -1 else 1
+        for _ in range(len(det_bboxes_list)):
+            if det_bboxes_list[_].shape[0] == 0:
+                det_bboxes_list[_] = det_bboxes_list[_].new_empty((0, 5))
+        det_bboxes, det_labels = [], []
+        for i in range(len(img_metas) // num_branch):
+            det_result = self.merge_trident_bboxes(
+                torch.cat(det_bboxes_list[i * num_branch:(i + 1) *
+                                          num_branch]),
+                torch.cat(det_labels_list[i * num_branch:(i + 1) *
+                                          num_branch]))
+            det_bboxes.append(det_result[0])
+            det_labels.append(det_result[1])
+
+        bbox_results = [
+            bbox2result(det_bboxes[i], det_labels[i],
+                        self.bbox_head.num_classes)
+            for i in range(len(det_bboxes))
+        ]
+        return bbox_results
+
+    def aug_test_bboxes(self, feats, img_metas, proposal_list, rcnn_test_cfg):
+        """Test det bboxes with test time augmentation."""
+        aug_bboxes = []
+        aug_scores = []
+        for x, img_meta in zip(feats, img_metas):
+            # only one image in the batch
+            img_shape = img_meta[0]['img_shape']
+            scale_factor = img_meta[0]['scale_factor']
+            flip = img_meta[0]['flip']
+            flip_direction = img_meta[0]['flip_direction']
+
+            trident_bboxes, trident_scores = [], []
+            for branch_idx in range(len(proposal_list)):
+                proposals = bbox_mapping(proposal_list[0][:, :4], img_shape,
+                                         scale_factor, flip, flip_direction)
+                rois = bbox2roi([proposals])
+                bbox_results = self._bbox_forward(x, rois)
+                bboxes, scores = self.bbox_head.get_bboxes(
+                    rois,
+                    bbox_results['cls_score'],
+                    bbox_results['bbox_pred'],
+                    img_shape,
+                    scale_factor,
+                    rescale=False,
+                    cfg=None)
+                trident_bboxes.append(bboxes)
+                trident_scores.append(scores)
+
+            aug_bboxes.append(torch.cat(trident_bboxes, 0))
+            aug_scores.append(torch.cat(trident_scores, 0))
+        # after merging, bboxes will be rescaled to the original image size
+        merged_bboxes, merged_scores = merge_aug_bboxes(
+            aug_bboxes, aug_scores, img_metas, rcnn_test_cfg)
+        det_bboxes, det_labels = multiclass_nms(merged_bboxes, merged_scores,
+                                                rcnn_test_cfg.score_thr,
+                                                rcnn_test_cfg.nms,
+                                                rcnn_test_cfg.max_per_img)
+        return det_bboxes, det_labels
diff --git a/mmdet/models/seg_heads/__init__.py b/mmdet/models/seg_heads/__init__.py
new file mode 100644
index 0000000..b489a90
--- /dev/null
+++ b/mmdet/models/seg_heads/__init__.py
@@ -0,0 +1,3 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .panoptic_fpn_head import PanopticFPNHead  # noqa: F401,F403
+from .panoptic_fusion_heads import *  # noqa: F401,F403
diff --git a/mmdet/models/seg_heads/base_semantic_head.py b/mmdet/models/seg_heads/base_semantic_head.py
new file mode 100644
index 0000000..2b6ca14
--- /dev/null
+++ b/mmdet/models/seg_heads/base_semantic_head.py
@@ -0,0 +1,86 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from abc import ABCMeta, abstractmethod
+
+import torch.nn.functional as F
+from mmcv.runner import BaseModule, force_fp32
+
+from ..builder import build_loss
+from ..utils import interpolate_as
+
+
+class BaseSemanticHead(BaseModule, metaclass=ABCMeta):
+    """Base module of Semantic Head.
+
+    Args:
+        num_classes (int): the number of classes.
+        init_cfg (dict): the initialization config.
+        loss_seg (dict): the loss of the semantic head.
+    """
+
+    def __init__(self,
+                 num_classes,
+                 init_cfg=None,
+                 loss_seg=dict(
+                     type='CrossEntropyLoss',
+                     ignore_index=255,
+                     loss_weight=1.0)):
+        super(BaseSemanticHead, self).__init__(init_cfg)
+        self.loss_seg = build_loss(loss_seg)
+        self.num_classes = num_classes
+
+    @force_fp32(apply_to=('seg_preds', ))
+    def loss(self, seg_preds, gt_semantic_seg):
+        """Get the loss of semantic head.
+
+        Args:
+            seg_preds (Tensor): The input logits with the shape (N, C, H, W).
+            gt_semantic_seg: The ground truth of semantic segmentation with
+                the shape (N, H, W).
+            label_bias: The starting number of the semantic label.
+                Default: 1.
+
+        Returns:
+            dict: the loss of semantic head.
+        """
+        if seg_preds.shape[-2:] != gt_semantic_seg.shape[-2:]:
+            seg_preds = interpolate_as(seg_preds, gt_semantic_seg)
+        seg_preds = seg_preds.permute((0, 2, 3, 1))
+
+        loss_seg = self.loss_seg(
+            seg_preds.reshape(-1, self.num_classes),  # => [NxHxW, C]
+            gt_semantic_seg.reshape(-1).long())
+        return dict(loss_seg=loss_seg)
+
+    @abstractmethod
+    def forward(self, x):
+        """Placeholder of forward function.
+
+        Returns:
+            dict[str, Tensor]: A dictionary, including features
+                and predicted scores. Required keys: 'seg_preds'
+                and 'feats'.
+        """
+        pass
+
+    def forward_train(self, x, gt_semantic_seg):
+        output = self.forward(x)
+        seg_preds = output['seg_preds']
+        return self.loss(seg_preds, gt_semantic_seg)
+
+    def simple_test(self, x, img_metas, rescale=False):
+        output = self.forward(x)
+        seg_preds = output['seg_preds']
+        seg_preds = F.interpolate(
+            seg_preds,
+            size=img_metas[0]['pad_shape'][:2],
+            mode='bilinear',
+            align_corners=False)
+
+        if rescale:
+            h, w, _ = img_metas[0]['img_shape']
+            seg_preds = seg_preds[:, :, :h, :w]
+
+            h, w, _ = img_metas[0]['ori_shape']
+            seg_preds = F.interpolate(
+                seg_preds, size=(h, w), mode='bilinear', align_corners=False)
+        return seg_preds
diff --git a/mmdet/models/seg_heads/panoptic_fpn_head.py b/mmdet/models/seg_heads/panoptic_fpn_head.py
new file mode 100644
index 0000000..f1df297
--- /dev/null
+++ b/mmdet/models/seg_heads/panoptic_fpn_head.py
@@ -0,0 +1,155 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+import torch
+import torch.nn as nn
+from mmcv.runner import ModuleList
+
+from ..builder import HEADS
+from ..utils import ConvUpsample
+from .base_semantic_head import BaseSemanticHead
+
+
+@HEADS.register_module()
+class PanopticFPNHead(BaseSemanticHead):
+    """PanopticFPNHead used in Panoptic FPN.
+
+    In this head, the number of output channels is ``num_stuff_classes
+    + 1``, including all stuff classes and one thing class. The stuff
+    classes will be reset from ``0`` to ``num_stuff_classes - 1``, the
+    thing classes will be merged to ``num_stuff_classes``-th channel.
+
+    Arg:
+        num_things_classes (int): Number of thing classes. Default: 80.
+        num_stuff_classes (int): Number of stuff classes. Default: 53.
+        num_classes (int): Number of classes, including all stuff
+            classes and one thing class. This argument is deprecated,
+            please use ``num_things_classes`` and ``num_stuff_classes``.
+            The module will automatically infer the num_classes by
+            ``num_stuff_classes + 1``.
+        in_channels (int): Number of channels in the input feature
+            map.
+        inner_channels (int): Number of channels in inner features.
+        start_level (int): The start level of the input features
+            used in PanopticFPN.
+        end_level (int): The end level of the used features, the
+            ``end_level``-th layer will not be used.
+        fg_range (tuple): Range of the foreground classes. It starts
+            from ``0`` to ``num_things_classes-1``. Deprecated, please use
+             ``num_things_classes`` directly.
+        bg_range (tuple): Range of the background classes. It starts
+            from ``num_things_classes`` to ``num_things_classes +
+            num_stuff_classes - 1``. Deprecated, please use
+            ``num_stuff_classes`` and ``num_things_classes`` directly.
+        conv_cfg (dict): Dictionary to construct and config
+            conv layer. Default: None.
+        norm_cfg (dict): Dictionary to construct and config norm layer.
+            Use ``GN`` by default.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+        loss_seg (dict): the loss of the semantic head.
+    """
+
+    def __init__(self,
+                 num_things_classes=80,
+                 num_stuff_classes=53,
+                 num_classes=None,
+                 in_channels=256,
+                 inner_channels=128,
+                 start_level=0,
+                 end_level=4,
+                 fg_range=None,
+                 bg_range=None,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
+                 init_cfg=None,
+                 loss_seg=dict(
+                     type='CrossEntropyLoss', ignore_index=-1,
+                     loss_weight=1.0)):
+        if num_classes is not None:
+            warnings.warn(
+                '`num_classes` is deprecated now, please set '
+                '`num_stuff_classes` directly, the `num_classes` will be '
+                'set to `num_stuff_classes + 1`')
+            # num_classes = num_stuff_classes + 1 for PanopticFPN.
+            assert num_classes == num_stuff_classes + 1
+        super(PanopticFPNHead, self).__init__(num_stuff_classes + 1, init_cfg,
+                                              loss_seg)
+        self.num_things_classes = num_things_classes
+        self.num_stuff_classes = num_stuff_classes
+        if fg_range is not None and bg_range is not None:
+            self.fg_range = fg_range
+            self.bg_range = bg_range
+            self.num_things_classes = fg_range[1] - fg_range[0] + 1
+            self.num_stuff_classes = bg_range[1] - bg_range[0] + 1
+            warnings.warn(
+                '`fg_range` and `bg_range` are deprecated now, '
+                f'please use `num_things_classes`={self.num_things_classes} '
+                f'and `num_stuff_classes`={self.num_stuff_classes} instead.')
+
+        # Used feature layers are [start_level, end_level)
+        self.start_level = start_level
+        self.end_level = end_level
+        self.num_stages = end_level - start_level
+        self.inner_channels = inner_channels
+
+        self.conv_upsample_layers = ModuleList()
+        for i in range(start_level, end_level):
+            self.conv_upsample_layers.append(
+                ConvUpsample(
+                    in_channels,
+                    inner_channels,
+                    num_layers=i if i > 0 else 1,
+                    num_upsample=i if i > 0 else 0,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                ))
+        self.conv_logits = nn.Conv2d(inner_channels, self.num_classes, 1)
+
+    def _set_things_to_void(self, gt_semantic_seg):
+        """Merge thing classes to one class.
+
+        In PanopticFPN, the background labels will be reset from `0` to
+        `self.num_stuff_classes-1`, the foreground labels will be merged to
+        `self.num_stuff_classes`-th channel.
+        """
+        gt_semantic_seg = gt_semantic_seg.int()
+        fg_mask = gt_semantic_seg < self.num_things_classes
+        bg_mask = (gt_semantic_seg >= self.num_things_classes) * (
+            gt_semantic_seg < self.num_things_classes + self.num_stuff_classes)
+
+        new_gt_seg = torch.clone(gt_semantic_seg)
+        new_gt_seg = torch.where(bg_mask,
+                                 gt_semantic_seg - self.num_things_classes,
+                                 new_gt_seg)
+        new_gt_seg = torch.where(fg_mask,
+                                 fg_mask.int() * self.num_stuff_classes,
+                                 new_gt_seg)
+        return new_gt_seg
+
+    def loss(self, seg_preds, gt_semantic_seg):
+        """The loss of PanopticFPN head.
+
+        Things classes will be merged to one class in PanopticFPN.
+        """
+        gt_semantic_seg = self._set_things_to_void(gt_semantic_seg)
+        return super().loss(seg_preds, gt_semantic_seg)
+
+    def init_weights(self):
+        super().init_weights()
+        nn.init.normal_(self.conv_logits.weight.data, 0, 0.01)
+        self.conv_logits.bias.data.zero_()
+
+    def forward(self, x):
+        # the number of subnets must be not more than
+        # the length of features.
+        assert self.num_stages <= len(x)
+
+        feats = []
+        for i, layer in enumerate(self.conv_upsample_layers):
+            f = layer(x[self.start_level + i])
+            feats.append(f)
+
+        feats = torch.sum(torch.stack(feats, dim=0), dim=0)
+        seg_preds = self.conv_logits(feats)
+        out = dict(seg_preds=seg_preds, feats=feats)
+        return out
diff --git a/mmdet/models/seg_heads/panoptic_fusion_heads/__init__.py b/mmdet/models/seg_heads/panoptic_fusion_heads/__init__.py
new file mode 100644
index 0000000..41625a6
--- /dev/null
+++ b/mmdet/models/seg_heads/panoptic_fusion_heads/__init__.py
@@ -0,0 +1,5 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .base_panoptic_fusion_head import \
+    BasePanopticFusionHead  # noqa: F401,F403
+from .heuristic_fusion_head import HeuristicFusionHead  # noqa: F401,F403
+from .maskformer_fusion_head import MaskFormerFusionHead  # noqa: F401,F403
diff --git a/mmdet/models/seg_heads/panoptic_fusion_heads/base_panoptic_fusion_head.py b/mmdet/models/seg_heads/panoptic_fusion_heads/base_panoptic_fusion_head.py
new file mode 100644
index 0000000..a38ac1c
--- /dev/null
+++ b/mmdet/models/seg_heads/panoptic_fusion_heads/base_panoptic_fusion_head.py
@@ -0,0 +1,48 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from abc import ABCMeta, abstractmethod
+
+from mmcv.runner import BaseModule
+
+from ...builder import build_loss
+
+
+class BasePanopticFusionHead(BaseModule, metaclass=ABCMeta):
+    """Base class for panoptic heads."""
+
+    def __init__(self,
+                 num_things_classes=80,
+                 num_stuff_classes=53,
+                 test_cfg=None,
+                 loss_panoptic=None,
+                 init_cfg=None,
+                 **kwargs):
+        super(BasePanopticFusionHead, self).__init__(init_cfg)
+        self.num_things_classes = num_things_classes
+        self.num_stuff_classes = num_stuff_classes
+        self.num_classes = num_things_classes + num_stuff_classes
+        self.test_cfg = test_cfg
+
+        if loss_panoptic:
+            self.loss_panoptic = build_loss(loss_panoptic)
+        else:
+            self.loss_panoptic = None
+
+    @property
+    def with_loss(self):
+        """bool: whether the panoptic head contains loss function."""
+        return self.loss_panoptic is not None
+
+    @abstractmethod
+    def forward_train(self, gt_masks=None, gt_semantic_seg=None, **kwargs):
+        """Forward function during training."""
+
+    @abstractmethod
+    def simple_test(self,
+                    img_metas,
+                    det_labels,
+                    mask_preds,
+                    seg_preds,
+                    det_bboxes,
+                    cfg=None,
+                    **kwargs):
+        """Test without augmentation."""
diff --git a/mmdet/models/seg_heads/panoptic_fusion_heads/heuristic_fusion_head.py b/mmdet/models/seg_heads/panoptic_fusion_heads/heuristic_fusion_head.py
new file mode 100644
index 0000000..06c1de2
--- /dev/null
+++ b/mmdet/models/seg_heads/panoptic_fusion_heads/heuristic_fusion_head.py
@@ -0,0 +1,126 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdet.core.evaluation.panoptic_utils import INSTANCE_OFFSET
+from mmdet.models.builder import HEADS
+from .base_panoptic_fusion_head import BasePanopticFusionHead
+
+
+@HEADS.register_module()
+class HeuristicFusionHead(BasePanopticFusionHead):
+    """Fusion Head with Heuristic method."""
+
+    def __init__(self,
+                 num_things_classes=80,
+                 num_stuff_classes=53,
+                 test_cfg=None,
+                 init_cfg=None,
+                 **kwargs):
+        super(HeuristicFusionHead,
+              self).__init__(num_things_classes, num_stuff_classes, test_cfg,
+                             None, init_cfg, **kwargs)
+
+    def forward_train(self, gt_masks=None, gt_semantic_seg=None, **kwargs):
+        """HeuristicFusionHead has no training loss."""
+        return dict()
+
+    def _lay_masks(self, bboxes, labels, masks, overlap_thr=0.5):
+        """Lay instance masks to a result map.
+
+        Args:
+            bboxes: The bboxes results, (K, 4).
+            labels: The labels of bboxes, (K, ).
+            masks: The instance masks, (K, H, W).
+            overlap_thr: Threshold to determine whether two masks overlap.
+                default: 0.5.
+
+        Returns:
+            Tensor: The result map, (H, W).
+        """
+        num_insts = bboxes.shape[0]
+        id_map = torch.zeros(
+            masks.shape[-2:], device=bboxes.device, dtype=torch.long)
+        if num_insts == 0:
+            return id_map, labels
+
+        scores, bboxes = bboxes[:, -1], bboxes[:, :4]
+
+        # Sort by score to use heuristic fusion
+        order = torch.argsort(-scores)
+        bboxes = bboxes[order]
+        labels = labels[order]
+        segm_masks = masks[order]
+
+        instance_id = 1
+        left_labels = []
+        for idx in range(bboxes.shape[0]):
+            _cls = labels[idx]
+            _mask = segm_masks[idx]
+            instance_id_map = torch.ones_like(
+                _mask, dtype=torch.long) * instance_id
+            area = _mask.sum()
+            if area == 0:
+                continue
+
+            pasted = id_map > 0
+            intersect = (_mask * pasted).sum()
+            if (intersect / (area + 1e-5)) > overlap_thr:
+                continue
+
+            _part = _mask * (~pasted)
+            id_map = torch.where(_part, instance_id_map, id_map)
+            left_labels.append(_cls)
+            instance_id += 1
+
+        if len(left_labels) > 0:
+            instance_labels = torch.stack(left_labels)
+        else:
+            instance_labels = bboxes.new_zeros((0, ), dtype=torch.long)
+        assert instance_id == (len(instance_labels) + 1)
+        return id_map, instance_labels
+
+    def simple_test(self, det_bboxes, det_labels, mask_preds, seg_preds,
+                    **kwargs):
+        """Fuse the results of instance and semantic segmentations.
+
+        Args:
+            det_bboxes: The bboxes results, (K, 4).
+            det_labels: The labels of bboxes, (K,).
+            mask_preds: The masks results, (K, H, W).
+            seg_preds: The semantic segmentation results,
+                (K, num_stuff + 1, H, W).
+
+        Returns:
+            Tensor : The panoptic segmentation result, (H, W).
+        """
+        mask_preds = mask_preds >= self.test_cfg.mask_thr_binary
+        id_map, labels = self._lay_masks(det_bboxes, det_labels, mask_preds,
+                                         self.test_cfg.mask_overlap)
+
+        seg_results = seg_preds.argmax(dim=0)
+        seg_results = seg_results + self.num_things_classes
+
+        pan_results = seg_results
+        instance_id = 1
+        for idx in range(det_labels.shape[0]):
+            _mask = id_map == (idx + 1)
+            if _mask.sum() == 0:
+                continue
+            _cls = labels[idx]
+            # simply trust detection
+            segment_id = _cls + instance_id * INSTANCE_OFFSET
+            pan_results[_mask] = segment_id
+            instance_id += 1
+
+        ids, counts = torch.unique(
+            pan_results % INSTANCE_OFFSET, return_counts=True)
+        stuff_ids = ids[ids >= self.num_things_classes]
+        stuff_counts = counts[ids >= self.num_things_classes]
+        ignore_stuff_ids = stuff_ids[
+            stuff_counts < self.test_cfg.stuff_area_limit]
+
+        assert pan_results.ndim == 2
+        pan_results[(pan_results.unsqueeze(2) == ignore_stuff_ids.reshape(
+            1, 1, -1)).any(dim=2)] = self.num_classes
+
+        return pan_results
diff --git a/mmdet/models/seg_heads/panoptic_fusion_heads/maskformer_fusion_head.py b/mmdet/models/seg_heads/panoptic_fusion_heads/maskformer_fusion_head.py
new file mode 100644
index 0000000..5b59ce4
--- /dev/null
+++ b/mmdet/models/seg_heads/panoptic_fusion_heads/maskformer_fusion_head.py
@@ -0,0 +1,241 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn.functional as F
+
+from mmdet.core.evaluation.panoptic_utils import INSTANCE_OFFSET
+from mmdet.core.mask import mask2bbox
+from mmdet.models.builder import HEADS
+from .base_panoptic_fusion_head import BasePanopticFusionHead
+
+
+@HEADS.register_module()
+class MaskFormerFusionHead(BasePanopticFusionHead):
+
+    def __init__(self,
+                 num_things_classes=80,
+                 num_stuff_classes=53,
+                 test_cfg=None,
+                 loss_panoptic=None,
+                 init_cfg=None,
+                 **kwargs):
+        super().__init__(num_things_classes, num_stuff_classes, test_cfg,
+                         loss_panoptic, init_cfg, **kwargs)
+
+    def forward_train(self, **kwargs):
+        """MaskFormerFusionHead has no training loss."""
+        return dict()
+
+    def panoptic_postprocess(self, mask_cls, mask_pred):
+        """Panoptic segmengation inference.
+
+        Args:
+            mask_cls (Tensor): Classfication outputs of shape
+                (num_queries, cls_out_channels) for a image.
+                Note `cls_out_channels` should includes
+                background.
+            mask_pred (Tensor): Mask outputs of shape
+                (num_queries, h, w) for a image.
+
+        Returns:
+            Tensor: Panoptic segment result of shape \
+                (h, w), each element in Tensor means: \
+                ``segment_id = _cls + instance_id * INSTANCE_OFFSET``.
+        """
+        object_mask_thr = self.test_cfg.get('object_mask_thr', 0.8)
+        iou_thr = self.test_cfg.get('iou_thr', 0.8)
+        filter_low_score = self.test_cfg.get('filter_low_score', False)
+
+        scores, labels = F.softmax(mask_cls, dim=-1).max(-1)
+        mask_pred = mask_pred.sigmoid()
+
+        keep = labels.ne(self.num_classes) & (scores > object_mask_thr)
+        cur_scores = scores[keep]
+        cur_classes = labels[keep]
+        cur_masks = mask_pred[keep]
+
+        cur_prob_masks = cur_scores.view(-1, 1, 1) * cur_masks
+
+        h, w = cur_masks.shape[-2:]
+        panoptic_seg = torch.full((h, w),
+                                  self.num_classes,
+                                  dtype=torch.int32,
+                                  device=cur_masks.device)
+        if cur_masks.shape[0] == 0:
+            # We didn't detect any mask :(
+            pass
+        else:
+            cur_mask_ids = cur_prob_masks.argmax(0)
+            instance_id = 1
+            for k in range(cur_classes.shape[0]):
+                pred_class = int(cur_classes[k].item())
+                isthing = pred_class < self.num_things_classes
+                mask = cur_mask_ids == k
+                mask_area = mask.sum().item()
+                original_area = (cur_masks[k] >= 0.5).sum().item()
+
+                if filter_low_score:
+                    mask = mask & (cur_masks[k] >= 0.5)
+
+                if mask_area > 0 and original_area > 0:
+                    if mask_area / original_area < iou_thr:
+                        continue
+
+                    if not isthing:
+                        # different stuff regions of same class will be
+                        # merged here, and stuff share the instance_id 0.
+                        panoptic_seg[mask] = pred_class
+                    else:
+                        panoptic_seg[mask] = (
+                            pred_class + instance_id * INSTANCE_OFFSET)
+                        instance_id += 1
+
+        return panoptic_seg
+
+    def semantic_postprocess(self, mask_cls, mask_pred):
+        """Semantic segmengation postprocess.
+
+        Args:
+            mask_cls (Tensor): Classfication outputs of shape
+                (num_queries, cls_out_channels) for a image.
+                Note `cls_out_channels` should includes
+                background.
+            mask_pred (Tensor): Mask outputs of shape
+                (num_queries, h, w) for a image.
+
+        Returns:
+            Tensor: Semantic segment result of shape \
+                (cls_out_channels, h, w).
+        """
+        # TODO add semantic segmentation result
+        raise NotImplementedError
+
+    def instance_postprocess(self, mask_cls, mask_pred):
+        """Instance segmengation postprocess.
+
+        Args:
+            mask_cls (Tensor): Classfication outputs of shape
+                (num_queries, cls_out_channels) for a image.
+                Note `cls_out_channels` should includes
+                background.
+            mask_pred (Tensor): Mask outputs of shape
+                (num_queries, h, w) for a image.
+
+        Returns:
+            tuple[Tensor]: Instance segmentation results.
+
+            - labels_per_image (Tensor): Predicted labels,\
+                shape (n, ).
+            - bboxes (Tensor): Bboxes and scores with shape (n, 5) of \
+                positive region in binary mask, the last column is scores.
+            - mask_pred_binary (Tensor): Instance masks of \
+                shape (n, h, w).
+        """
+        max_per_image = self.test_cfg.get('max_per_image', 100)
+        num_queries = mask_cls.shape[0]
+        # shape (num_queries, num_class)
+        scores = F.softmax(mask_cls, dim=-1)[:, :-1]
+        # shape (num_queries * num_class, )
+        labels = torch.arange(self.num_classes, device=mask_cls.device).\
+            unsqueeze(0).repeat(num_queries, 1).flatten(0, 1)
+        scores_per_image, top_indices = scores.flatten(0, 1).topk(
+            max_per_image, sorted=False)
+        labels_per_image = labels[top_indices]
+
+        query_indices = top_indices // self.num_classes
+        mask_pred = mask_pred[query_indices]
+
+        # extract things
+        is_thing = labels_per_image < self.num_things_classes
+        scores_per_image = scores_per_image[is_thing]
+        labels_per_image = labels_per_image[is_thing]
+        mask_pred = mask_pred[is_thing]
+
+        mask_pred_binary = (mask_pred > 0).float()
+        mask_scores_per_image = (mask_pred.sigmoid() *
+                                 mask_pred_binary).flatten(1).sum(1) / (
+                                     mask_pred_binary.flatten(1).sum(1) + 1e-6)
+        det_scores = scores_per_image * mask_scores_per_image
+        mask_pred_binary = mask_pred_binary.bool()
+        bboxes = mask2bbox(mask_pred_binary)
+        bboxes = torch.cat([bboxes, det_scores[:, None]], dim=-1)
+
+        return labels_per_image, bboxes, mask_pred_binary
+
+    def simple_test(self,
+                    mask_cls_results,
+                    mask_pred_results,
+                    img_metas,
+                    rescale=False,
+                    **kwargs):
+        """Test segment without test-time aumengtation.
+
+        Only the output of last decoder layers was used.
+
+        Args:
+            mask_cls_results (Tensor): Mask classification logits,
+                shape (batch_size, num_queries, cls_out_channels).
+                Note `cls_out_channels` should includes background.
+            mask_pred_results (Tensor): Mask logits, shape
+                (batch_size, num_queries, h, w).
+            img_metas (list[dict]): List of image information.
+            rescale (bool, optional): If True, return boxes in
+                original image space. Default False.
+
+        Returns:
+            list[dict[str, Tensor | tuple[Tensor]]]: Semantic segmentation \
+                results and panoptic segmentation results for each \
+                image.
+
+            .. code-block:: none
+
+                [
+                    {
+                        'pan_results': Tensor, # shape = [h, w]
+                        'ins_results': tuple[Tensor],
+                        # semantic segmentation results are not supported yet
+                        'sem_results': Tensor
+                    },
+                    ...
+                ]
+        """
+        panoptic_on = self.test_cfg.get('panoptic_on', True)
+        semantic_on = self.test_cfg.get('semantic_on', False)
+        instance_on = self.test_cfg.get('instance_on', False)
+        assert not semantic_on, 'segmantic segmentation '\
+            'results are not supported yet.'
+
+        results = []
+        for mask_cls_result, mask_pred_result, meta in zip(
+                mask_cls_results, mask_pred_results, img_metas):
+            # remove padding
+            img_height, img_width = meta['img_shape'][:2]
+            mask_pred_result = mask_pred_result[:, :img_height, :img_width]
+
+            if rescale:
+                # return result in original resolution
+                ori_height, ori_width = meta['ori_shape'][:2]
+                mask_pred_result = F.interpolate(
+                    mask_pred_result[:, None],
+                    size=(ori_height, ori_width),
+                    mode='bilinear',
+                    align_corners=False)[:, 0]
+
+            result = dict()
+            if panoptic_on:
+                pan_results = self.panoptic_postprocess(
+                    mask_cls_result, mask_pred_result)
+                result['pan_results'] = pan_results
+
+            if instance_on:
+                ins_results = self.instance_postprocess(
+                    mask_cls_result, mask_pred_result)
+                result['ins_results'] = ins_results
+
+            if semantic_on:
+                sem_results = self.semantic_postprocess(
+                    mask_cls_result, mask_pred_result)
+                result['sem_results'] = sem_results
+
+            results.append(result)
+
+        return results
diff --git a/mmdet/models/utils/__init__.py b/mmdet/models/utils/__init__.py
new file mode 100644
index 0000000..e74ba89
--- /dev/null
+++ b/mmdet/models/utils/__init__.py
@@ -0,0 +1,34 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .brick_wrappers import AdaptiveAvgPool2d, adaptive_avg_pool2d
+from .builder import build_linear_layer, build_transformer
+from .ckpt_convert import pvt_convert
+from .conv_upsample import ConvUpsample
+from .csp_layer import CSPLayer
+from .gaussian_target import gaussian_radius, gen_gaussian_target
+from .inverted_residual import InvertedResidual
+from .make_divisible import make_divisible
+from .misc import interpolate_as, sigmoid_geometric_mean
+from .normed_predictor import NormedConv2d, NormedLinear
+from .panoptic_gt_processing import preprocess_panoptic_gt
+from .point_sample import (get_uncertain_point_coords_with_randomness,
+                           get_uncertainty)
+from .positional_encoding import (LearnedPositionalEncoding,
+                                  SinePositionalEncoding)
+from .res_layer import ResLayer, SimplifiedBasicBlock
+from .se_layer import DyReLU, SELayer
+from .transformer import (DetrTransformerDecoder, DetrTransformerDecoderLayer,
+                          DynamicConv, PatchEmbed, Transformer, nchw_to_nlc,
+                          nlc_to_nchw)
+
+__all__ = [
+    'ResLayer', 'gaussian_radius', 'gen_gaussian_target',
+    'DetrTransformerDecoderLayer', 'DetrTransformerDecoder', 'Transformer',
+    'build_transformer', 'build_linear_layer', 'SinePositionalEncoding',
+    'LearnedPositionalEncoding', 'DynamicConv', 'SimplifiedBasicBlock',
+    'NormedLinear', 'NormedConv2d', 'make_divisible', 'InvertedResidual',
+    'SELayer', 'interpolate_as', 'ConvUpsample', 'CSPLayer',
+    'adaptive_avg_pool2d', 'AdaptiveAvgPool2d', 'PatchEmbed', 'nchw_to_nlc',
+    'nlc_to_nchw', 'pvt_convert', 'sigmoid_geometric_mean',
+    'preprocess_panoptic_gt', 'DyReLU',
+    'get_uncertain_point_coords_with_randomness', 'get_uncertainty'
+]
diff --git a/mmdet/models/utils/brick_wrappers.py b/mmdet/models/utils/brick_wrappers.py
new file mode 100644
index 0000000..fa0279a
--- /dev/null
+++ b/mmdet/models/utils/brick_wrappers.py
@@ -0,0 +1,51 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn.bricks.wrappers import NewEmptyTensorOp, obsolete_torch_version
+
+if torch.__version__ == 'parrots':
+    TORCH_VERSION = torch.__version__
+else:
+    # torch.__version__ could be 1.3.1+cu92, we only need the first two
+    # for comparison
+    TORCH_VERSION = tuple(int(x) for x in torch.__version__.split('.')[:2])
+
+
+def adaptive_avg_pool2d(input, output_size):
+    """Handle empty batch dimension to adaptive_avg_pool2d.
+
+    Args:
+        input (tensor): 4D tensor.
+        output_size (int, tuple[int,int]): the target output size.
+    """
+    if input.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)):
+        if isinstance(output_size, int):
+            output_size = [output_size, output_size]
+        output_size = [*input.shape[:2], *output_size]
+        empty = NewEmptyTensorOp.apply(input, output_size)
+        return empty
+    else:
+        return F.adaptive_avg_pool2d(input, output_size)
+
+
+class AdaptiveAvgPool2d(nn.AdaptiveAvgPool2d):
+    """Handle empty batch dimension to AdaptiveAvgPool2d."""
+
+    def forward(self, x):
+        # PyTorch 1.9 does not support empty tensor inference yet
+        if x.numel() == 0 and obsolete_torch_version(TORCH_VERSION, (1, 9)):
+            output_size = self.output_size
+            if isinstance(output_size, int):
+                output_size = [output_size, output_size]
+            else:
+                output_size = [
+                    v if v is not None else d
+                    for v, d in zip(output_size,
+                                    x.size()[-2:])
+                ]
+            output_size = [*x.shape[:2], *output_size]
+            empty = NewEmptyTensorOp.apply(x, output_size)
+            return empty
+
+        return super().forward(x)
diff --git a/mmdet/models/utils/builder.py b/mmdet/models/utils/builder.py
new file mode 100644
index 0000000..20fe7a6
--- /dev/null
+++ b/mmdet/models/utils/builder.py
@@ -0,0 +1,47 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+from mmcv.utils import Registry, build_from_cfg
+
+TRANSFORMER = Registry('Transformer')
+LINEAR_LAYERS = Registry('linear layers')
+
+
+def build_transformer(cfg, default_args=None):
+    """Builder for Transformer."""
+    return build_from_cfg(cfg, TRANSFORMER, default_args)
+
+
+LINEAR_LAYERS.register_module('Linear', module=nn.Linear)
+
+
+def build_linear_layer(cfg, *args, **kwargs):
+    """Build linear layer.
+    Args:
+        cfg (None or dict): The linear layer config, which should contain:
+            - type (str): Layer type.
+            - layer args: Args needed to instantiate an linear layer.
+        args (argument list): Arguments passed to the `__init__`
+            method of the corresponding linear layer.
+        kwargs (keyword arguments): Keyword arguments passed to the `__init__`
+            method of the corresponding linear layer.
+    Returns:
+        nn.Module: Created linear layer.
+    """
+    if cfg is None:
+        cfg_ = dict(type='Linear')
+    else:
+        if not isinstance(cfg, dict):
+            raise TypeError('cfg must be a dict')
+        if 'type' not in cfg:
+            raise KeyError('the cfg dict must contain the key "type"')
+        cfg_ = cfg.copy()
+
+    layer_type = cfg_.pop('type')
+    if layer_type not in LINEAR_LAYERS:
+        raise KeyError(f'Unrecognized linear type {layer_type}')
+    else:
+        linear_layer = LINEAR_LAYERS.get(layer_type)
+
+    layer = linear_layer(*args, **kwargs, **cfg_)
+
+    return layer
diff --git a/mmdet/models/utils/ckpt_convert.py b/mmdet/models/utils/ckpt_convert.py
new file mode 100644
index 0000000..4d660c4
--- /dev/null
+++ b/mmdet/models/utils/ckpt_convert.py
@@ -0,0 +1,137 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+
+# This script consists of several convert functions which
+# can modify the weights of model in original repo to be
+# pre-trained weights.
+
+from collections import OrderedDict
+
+import torch
+
+
+def pvt_convert(ckpt):
+    new_ckpt = OrderedDict()
+    # Process the concat between q linear weights and kv linear weights
+    use_abs_pos_embed = False
+    use_conv_ffn = False
+    for k in ckpt.keys():
+        if k.startswith('pos_embed'):
+            use_abs_pos_embed = True
+        if k.find('dwconv') >= 0:
+            use_conv_ffn = True
+    for k, v in ckpt.items():
+        if k.startswith('head'):
+            continue
+        if k.startswith('norm.'):
+            continue
+        if k.startswith('cls_token'):
+            continue
+        if k.startswith('pos_embed'):
+            stage_i = int(k.replace('pos_embed', ''))
+            new_k = k.replace(f'pos_embed{stage_i}',
+                              f'layers.{stage_i - 1}.1.0.pos_embed')
+            if stage_i == 4 and v.size(1) == 50:  # 1 (cls token) + 7 * 7
+                new_v = v[:, 1:, :]  # remove cls token
+            else:
+                new_v = v
+        elif k.startswith('patch_embed'):
+            stage_i = int(k.split('.')[0].replace('patch_embed', ''))
+            new_k = k.replace(f'patch_embed{stage_i}',
+                              f'layers.{stage_i - 1}.0')
+            new_v = v
+            if 'proj.' in new_k:
+                new_k = new_k.replace('proj.', 'projection.')
+        elif k.startswith('block'):
+            stage_i = int(k.split('.')[0].replace('block', ''))
+            layer_i = int(k.split('.')[1])
+            new_layer_i = layer_i + use_abs_pos_embed
+            new_k = k.replace(f'block{stage_i}.{layer_i}',
+                              f'layers.{stage_i - 1}.1.{new_layer_i}')
+            new_v = v
+            if 'attn.q.' in new_k:
+                sub_item_k = k.replace('q.', 'kv.')
+                new_k = new_k.replace('q.', 'attn.in_proj_')
+                new_v = torch.cat([v, ckpt[sub_item_k]], dim=0)
+            elif 'attn.kv.' in new_k:
+                continue
+            elif 'attn.proj.' in new_k:
+                new_k = new_k.replace('proj.', 'attn.out_proj.')
+            elif 'attn.sr.' in new_k:
+                new_k = new_k.replace('sr.', 'sr.')
+            elif 'mlp.' in new_k:
+                string = f'{new_k}-'
+                new_k = new_k.replace('mlp.', 'ffn.layers.')
+                if 'fc1.weight' in new_k or 'fc2.weight' in new_k:
+                    new_v = v.reshape((*v.shape, 1, 1))
+                new_k = new_k.replace('fc1.', '0.')
+                new_k = new_k.replace('dwconv.dwconv.', '1.')
+                if use_conv_ffn:
+                    new_k = new_k.replace('fc2.', '4.')
+                else:
+                    new_k = new_k.replace('fc2.', '3.')
+                string += f'{new_k} {v.shape}-{new_v.shape}'
+        elif k.startswith('norm'):
+            stage_i = int(k[4])
+            new_k = k.replace(f'norm{stage_i}', f'layers.{stage_i - 1}.2')
+            new_v = v
+        else:
+            new_k = k
+            new_v = v
+        new_ckpt[new_k] = new_v
+
+    return new_ckpt
+
+
+def swin_converter(ckpt):
+
+    new_ckpt = OrderedDict()
+
+    def correct_unfold_reduction_order(x):
+        out_channel, in_channel = x.shape
+        x = x.reshape(out_channel, 4, in_channel // 4)
+        x = x[:, [0, 2, 1, 3], :].transpose(1,
+                                            2).reshape(out_channel, in_channel)
+        return x
+
+    def correct_unfold_norm_order(x):
+        in_channel = x.shape[0]
+        x = x.reshape(4, in_channel // 4)
+        x = x[[0, 2, 1, 3], :].transpose(0, 1).reshape(in_channel)
+        return x
+
+    for k, v in ckpt.items():
+        if k.startswith('head'):
+            continue
+        elif k.startswith('layers'):
+            new_v = v
+            if 'attn.' in k:
+                new_k = k.replace('attn.', 'attn.w_msa.')
+            elif 'mlp.' in k:
+                if 'mlp.fc1.' in k:
+                    new_k = k.replace('mlp.fc1.', 'ffn.layers.0.0.')
+                elif 'mlp.fc2.' in k:
+                    new_k = k.replace('mlp.fc2.', 'ffn.layers.1.')
+                else:
+                    new_k = k.replace('mlp.', 'ffn.')
+            elif 'downsample' in k:
+                new_k = k
+                if 'reduction.' in k:
+                    new_v = correct_unfold_reduction_order(v)
+                elif 'norm.' in k:
+                    new_v = correct_unfold_norm_order(v)
+            else:
+                new_k = k
+            new_k = new_k.replace('layers', 'stages', 1)
+        elif k.startswith('patch_embed'):
+            new_v = v
+            if 'proj' in k:
+                new_k = k.replace('proj', 'projection')
+            else:
+                new_k = k
+        else:
+            new_v = v
+            new_k = k
+
+        new_ckpt['backbone.' + new_k] = new_v
+
+    return new_ckpt
diff --git a/mmdet/models/utils/conv_upsample.py b/mmdet/models/utils/conv_upsample.py
new file mode 100644
index 0000000..bb5ba76
--- /dev/null
+++ b/mmdet/models/utils/conv_upsample.py
@@ -0,0 +1,67 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn.functional as F
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule, ModuleList
+
+
+class ConvUpsample(BaseModule):
+    """ConvUpsample performs 2x upsampling after Conv.
+
+    There are several `ConvModule` layers. In the first few layers, upsampling
+    will be applied after each layer of convolution. The number of upsampling
+    must be no more than the number of ConvModule layers.
+
+    Args:
+        in_channels (int): Number of channels in the input feature map.
+        inner_channels (int): Number of channels produced by the convolution.
+        num_layers (int): Number of convolution layers.
+        num_upsample (int | optional): Number of upsampling layer. Must be no
+            more than num_layers. Upsampling will be applied after the first
+            ``num_upsample`` layers of convolution. Default: ``num_layers``.
+        conv_cfg (dict): Config dict for convolution layer. Default: None,
+            which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer. Default: None.
+        init_cfg (dict): Config dict for initialization. Default: None.
+        kwargs (key word augments): Other augments used in ConvModule.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 inner_channels,
+                 num_layers=1,
+                 num_upsample=None,
+                 conv_cfg=None,
+                 norm_cfg=None,
+                 init_cfg=None,
+                 **kwargs):
+        super(ConvUpsample, self).__init__(init_cfg)
+        if num_upsample is None:
+            num_upsample = num_layers
+        assert num_upsample <= num_layers, \
+            f'num_upsample({num_upsample})must be no more than ' \
+            f'num_layers({num_layers})'
+        self.num_layers = num_layers
+        self.num_upsample = num_upsample
+        self.conv = ModuleList()
+        for i in range(num_layers):
+            self.conv.append(
+                ConvModule(
+                    in_channels,
+                    inner_channels,
+                    3,
+                    padding=1,
+                    stride=1,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    **kwargs))
+            in_channels = inner_channels
+
+    def forward(self, x):
+        num_upsample = self.num_upsample
+        for i in range(self.num_layers):
+            x = self.conv[i](x)
+            if num_upsample > 0:
+                num_upsample -= 1
+                x = F.interpolate(
+                    x, scale_factor=2, mode='bilinear', align_corners=False)
+        return x
diff --git a/mmdet/models/utils/csp_layer.py b/mmdet/models/utils/csp_layer.py
new file mode 100644
index 0000000..5760b01
--- /dev/null
+++ b/mmdet/models/utils/csp_layer.py
@@ -0,0 +1,150 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
+from mmcv.runner import BaseModule
+
+
+class DarknetBottleneck(BaseModule):
+    """The basic bottleneck block used in Darknet.
+
+    Each ResBlock consists of two ConvModules and the input is added to the
+    final output. Each ConvModule is composed of Conv, BN, and LeakyReLU.
+    The first convLayer has filter size of 1x1 and the second one has the
+    filter size of 3x3.
+
+    Args:
+        in_channels (int): The input channels of this Module.
+        out_channels (int): The output channels of this Module.
+        expansion (int): The kernel size of the convolution. Default: 0.5
+        add_identity (bool): Whether to add identity to the out.
+            Default: True
+        use_depthwise (bool): Whether to use depthwise separable convolution.
+            Default: False
+        conv_cfg (dict): Config dict for convolution layer. Default: None,
+            which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN').
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='Swish').
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 expansion=0.5,
+                 add_identity=True,
+                 use_depthwise=False,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+                 act_cfg=dict(type='Swish'),
+                 init_cfg=None):
+        super().__init__(init_cfg)
+        hidden_channels = int(out_channels * expansion)
+        conv = DepthwiseSeparableConvModule if use_depthwise else ConvModule
+        self.conv1 = ConvModule(
+            in_channels,
+            hidden_channels,
+            1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        self.conv2 = conv(
+            hidden_channels,
+            out_channels,
+            3,
+            stride=1,
+            padding=1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        self.add_identity = \
+            add_identity and in_channels == out_channels
+
+    def forward(self, x):
+        identity = x
+        out = self.conv1(x)
+        out = self.conv2(out)
+
+        if self.add_identity:
+            return out + identity
+        else:
+            return out
+
+
+class CSPLayer(BaseModule):
+    """Cross Stage Partial Layer.
+
+    Args:
+        in_channels (int): The input channels of the CSP layer.
+        out_channels (int): The output channels of the CSP layer.
+        expand_ratio (float): Ratio to adjust the number of channels of the
+            hidden layer. Default: 0.5
+        num_blocks (int): Number of blocks. Default: 1
+        add_identity (bool): Whether to add identity in blocks.
+            Default: True
+        use_depthwise (bool): Whether to depthwise separable convolution in
+            blocks. Default: False
+        conv_cfg (dict, optional): Config dict for convolution layer.
+            Default: None, which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN')
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='Swish')
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 expand_ratio=0.5,
+                 num_blocks=1,
+                 add_identity=True,
+                 use_depthwise=False,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN', momentum=0.03, eps=0.001),
+                 act_cfg=dict(type='Swish'),
+                 init_cfg=None):
+        super().__init__(init_cfg)
+        mid_channels = int(out_channels * expand_ratio)
+        self.main_conv = ConvModule(
+            in_channels,
+            mid_channels,
+            1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        self.short_conv = ConvModule(
+            in_channels,
+            mid_channels,
+            1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+        self.final_conv = ConvModule(
+            2 * mid_channels,
+            out_channels,
+            1,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+
+        self.blocks = nn.Sequential(*[
+            DarknetBottleneck(
+                mid_channels,
+                mid_channels,
+                1.0,
+                add_identity,
+                use_depthwise,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg) for _ in range(num_blocks)
+        ])
+
+    def forward(self, x):
+        x_short = self.short_conv(x)
+
+        x_main = self.main_conv(x)
+        x_main = self.blocks(x_main)
+
+        x_final = torch.cat((x_main, x_short), dim=1)
+        return self.final_conv(x_final)
diff --git a/mmdet/models/utils/gaussian_target.py b/mmdet/models/utils/gaussian_target.py
new file mode 100644
index 0000000..9997d3b
--- /dev/null
+++ b/mmdet/models/utils/gaussian_target.py
@@ -0,0 +1,268 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from math import sqrt
+
+import torch
+import torch.nn.functional as F
+
+
+def gaussian2D(radius, sigma=1, dtype=torch.float32, device='cpu'):
+    """Generate 2D gaussian kernel.
+
+    Args:
+        radius (int): Radius of gaussian kernel.
+        sigma (int): Sigma of gaussian function. Default: 1.
+        dtype (torch.dtype): Dtype of gaussian tensor. Default: torch.float32.
+        device (str): Device of gaussian tensor. Default: 'cpu'.
+
+    Returns:
+        h (Tensor): Gaussian kernel with a
+            ``(2 * radius + 1) * (2 * radius + 1)`` shape.
+    """
+    x = torch.arange(
+        -radius, radius + 1, dtype=dtype, device=device).view(1, -1)
+    y = torch.arange(
+        -radius, radius + 1, dtype=dtype, device=device).view(-1, 1)
+
+    h = (-(x * x + y * y) / (2 * sigma * sigma)).exp()
+
+    h[h < torch.finfo(h.dtype).eps * h.max()] = 0
+    return h
+
+
+def gen_gaussian_target(heatmap, center, radius, k=1):
+    """Generate 2D gaussian heatmap.
+
+    Args:
+        heatmap (Tensor): Input heatmap, the gaussian kernel will cover on
+            it and maintain the max value.
+        center (list[int]): Coord of gaussian kernel's center.
+        radius (int): Radius of gaussian kernel.
+        k (int): Coefficient of gaussian kernel. Default: 1.
+
+    Returns:
+        out_heatmap (Tensor): Updated heatmap covered by gaussian kernel.
+    """
+    diameter = 2 * radius + 1
+    gaussian_kernel = gaussian2D(
+        radius, sigma=diameter / 6, dtype=heatmap.dtype, device=heatmap.device)
+
+    x, y = center
+
+    height, width = heatmap.shape[:2]
+
+    left, right = min(x, radius), min(width - x, radius + 1)
+    top, bottom = min(y, radius), min(height - y, radius + 1)
+
+    masked_heatmap = heatmap[y - top:y + bottom, x - left:x + right]
+    masked_gaussian = gaussian_kernel[radius - top:radius + bottom,
+                                      radius - left:radius + right]
+    out_heatmap = heatmap
+    torch.max(
+        masked_heatmap,
+        masked_gaussian * k,
+        out=out_heatmap[y - top:y + bottom, x - left:x + right])
+
+    return out_heatmap
+
+
+def gaussian_radius(det_size, min_overlap):
+    r"""Generate 2D gaussian radius.
+
+    This function is modified from the `official github repo
+    <https://github.com/princeton-vl/CornerNet-Lite/blob/master/core/sample/
+    utils.py#L65>`_.
+
+    Given ``min_overlap``, radius could computed by a quadratic equation
+    according to Vieta's formulas.
+
+    There are 3 cases for computing gaussian radius, details are following:
+
+    - Explanation of figure: ``lt`` and ``br`` indicates the left-top and
+      bottom-right corner of ground truth box. ``x`` indicates the
+      generated corner at the limited position when ``radius=r``.
+
+    - Case1: one corner is inside the gt box and the other is outside.
+
+    .. code:: text
+
+        |<   width   >|
+
+        lt-+----------+         -
+        |  |          |         ^
+        +--x----------+--+
+        |  |          |  |
+        |  |          |  |    height
+        |  | overlap  |  |
+        |  |          |  |
+        |  |          |  |      v
+        +--+---------br--+      -
+           |          |  |
+           +----------+--x
+
+    To ensure IoU of generated box and gt box is larger than ``min_overlap``:
+
+    .. math::
+        \cfrac{(w-r)*(h-r)}{w*h+(w+h)r-r^2} \ge {iou} \quad\Rightarrow\quad
+        {r^2-(w+h)r+\cfrac{1-iou}{1+iou}*w*h} \ge 0 \\
+        {a} = 1,\quad{b} = {-(w+h)},\quad{c} = {\cfrac{1-iou}{1+iou}*w*h} \\
+        {r} \le \cfrac{-b-\sqrt{b^2-4*a*c}}{2*a}
+
+    - Case2: both two corners are inside the gt box.
+
+    .. code:: text
+
+        |<   width   >|
+
+        lt-+----------+         -
+        |  |          |         ^
+        +--x-------+  |
+        |  |       |  |
+        |  |overlap|  |       height
+        |  |       |  |
+        |  +-------x--+
+        |          |  |         v
+        +----------+-br         -
+
+    To ensure IoU of generated box and gt box is larger than ``min_overlap``:
+
+    .. math::
+        \cfrac{(w-2*r)*(h-2*r)}{w*h} \ge {iou} \quad\Rightarrow\quad
+        {4r^2-2(w+h)r+(1-iou)*w*h} \ge 0 \\
+        {a} = 4,\quad {b} = {-2(w+h)},\quad {c} = {(1-iou)*w*h} \\
+        {r} \le \cfrac{-b-\sqrt{b^2-4*a*c}}{2*a}
+
+    - Case3: both two corners are outside the gt box.
+
+    .. code:: text
+
+           |<   width   >|
+
+        x--+----------------+
+        |  |                |
+        +-lt-------------+  |   -
+        |  |             |  |   ^
+        |  |             |  |
+        |  |   overlap   |  | height
+        |  |             |  |
+        |  |             |  |   v
+        |  +------------br--+   -
+        |                |  |
+        +----------------+--x
+
+    To ensure IoU of generated box and gt box is larger than ``min_overlap``:
+
+    .. math::
+        \cfrac{w*h}{(w+2*r)*(h+2*r)} \ge {iou} \quad\Rightarrow\quad
+        {4*iou*r^2+2*iou*(w+h)r+(iou-1)*w*h} \le 0 \\
+        {a} = {4*iou},\quad {b} = {2*iou*(w+h)},\quad {c} = {(iou-1)*w*h} \\
+        {r} \le \cfrac{-b+\sqrt{b^2-4*a*c}}{2*a}
+
+    Args:
+        det_size (list[int]): Shape of object.
+        min_overlap (float): Min IoU with ground truth for boxes generated by
+            keypoints inside the gaussian kernel.
+
+    Returns:
+        radius (int): Radius of gaussian kernel.
+    """
+    height, width = det_size
+
+    a1 = 1
+    b1 = (height + width)
+    c1 = width * height * (1 - min_overlap) / (1 + min_overlap)
+    sq1 = sqrt(b1**2 - 4 * a1 * c1)
+    r1 = (b1 - sq1) / (2 * a1)
+
+    a2 = 4
+    b2 = 2 * (height + width)
+    c2 = (1 - min_overlap) * width * height
+    sq2 = sqrt(b2**2 - 4 * a2 * c2)
+    r2 = (b2 - sq2) / (2 * a2)
+
+    a3 = 4 * min_overlap
+    b3 = -2 * min_overlap * (height + width)
+    c3 = (min_overlap - 1) * width * height
+    sq3 = sqrt(b3**2 - 4 * a3 * c3)
+    r3 = (b3 + sq3) / (2 * a3)
+    return min(r1, r2, r3)
+
+
+def get_local_maximum(heat, kernel=3):
+    """Extract local maximum pixel with given kernel.
+
+    Args:
+        heat (Tensor): Target heatmap.
+        kernel (int): Kernel size of max pooling. Default: 3.
+
+    Returns:
+        heat (Tensor): A heatmap where local maximum pixels maintain its
+            own value and other positions are 0.
+    """
+    pad = (kernel - 1) // 2
+    hmax = F.max_pool2d(heat, kernel, stride=1, padding=pad)
+    keep = (hmax == heat).float()
+    return heat * keep
+
+
+def get_topk_from_heatmap(scores, k=20):
+    """Get top k positions from heatmap.
+
+    Args:
+        scores (Tensor): Target heatmap with shape
+            [batch, num_classes, height, width].
+        k (int): Target number. Default: 20.
+
+    Returns:
+        tuple[torch.Tensor]: Scores, indexes, categories and coords of
+            topk keypoint. Containing following Tensors:
+
+        - topk_scores (Tensor): Max scores of each topk keypoint.
+        - topk_inds (Tensor): Indexes of each topk keypoint.
+        - topk_clses (Tensor): Categories of each topk keypoint.
+        - topk_ys (Tensor): Y-coord of each topk keypoint.
+        - topk_xs (Tensor): X-coord of each topk keypoint.
+    """
+    batch, _, height, width = scores.size()
+    topk_scores, topk_inds = torch.topk(scores.view(batch, -1), k)
+    topk_clses = topk_inds // (height * width)
+    topk_inds = topk_inds % (height * width)
+    topk_ys = topk_inds // width
+    topk_xs = (topk_inds % width).int().float()
+    return topk_scores, topk_inds, topk_clses, topk_ys, topk_xs
+
+
+def gather_feat(feat, ind, mask=None):
+    """Gather feature according to index.
+
+    Args:
+        feat (Tensor): Target feature map.
+        ind (Tensor): Target coord index.
+        mask (Tensor | None): Mask of feature map. Default: None.
+
+    Returns:
+        feat (Tensor): Gathered feature.
+    """
+    dim = feat.size(2)
+    ind = ind.unsqueeze(2).repeat(1, 1, dim)
+    feat = feat.gather(1, ind)
+    if mask is not None:
+        mask = mask.unsqueeze(2).expand_as(feat)
+        feat = feat[mask]
+        feat = feat.view(-1, dim)
+    return feat
+
+
+def transpose_and_gather_feat(feat, ind):
+    """Transpose and gather feature according to index.
+
+    Args:
+        feat (Tensor): Target feature map.
+        ind (Tensor): Target coord index.
+
+    Returns:
+        feat (Tensor): Transposed and gathered feature.
+    """
+    feat = feat.permute(0, 2, 3, 1).contiguous()
+    feat = feat.view(feat.size(0), -1, feat.size(3))
+    feat = gather_feat(feat, ind)
+    return feat
diff --git a/mmdet/models/utils/inverted_residual.py b/mmdet/models/utils/inverted_residual.py
new file mode 100644
index 0000000..1f241ae
--- /dev/null
+++ b/mmdet/models/utils/inverted_residual.py
@@ -0,0 +1,130 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch.nn as nn
+import torch.utils.checkpoint as cp
+from mmcv.cnn import ConvModule
+from mmcv.cnn.bricks import DropPath
+from mmcv.runner import BaseModule
+
+from .se_layer import SELayer
+
+
+class InvertedResidual(BaseModule):
+    """Inverted Residual Block.
+
+    Args:
+        in_channels (int): The input channels of this Module.
+        out_channels (int): The output channels of this Module.
+        mid_channels (int): The input channels of the depthwise convolution.
+        kernel_size (int): The kernel size of the depthwise convolution.
+            Default: 3.
+        stride (int): The stride of the depthwise convolution. Default: 1.
+        se_cfg (dict): Config dict for se layer. Default: None, which means no
+            se layer.
+        with_expand_conv (bool): Use expand conv or not. If set False,
+            mid_channels must be the same with in_channels.
+            Default: True.
+        conv_cfg (dict): Config dict for convolution layer. Default: None,
+            which means using conv2d.
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: dict(type='BN').
+        act_cfg (dict): Config dict for activation layer.
+            Default: dict(type='ReLU').
+        drop_path_rate (float): stochastic depth rate. Defaults to 0.
+        with_cp (bool): Use checkpoint or not. Using checkpoint will save some
+            memory while slowing down the training speed. Default: False.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+
+    Returns:
+        Tensor: The output tensor.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 mid_channels,
+                 kernel_size=3,
+                 stride=1,
+                 se_cfg=None,
+                 with_expand_conv=True,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 act_cfg=dict(type='ReLU'),
+                 drop_path_rate=0.,
+                 with_cp=False,
+                 init_cfg=None):
+        super(InvertedResidual, self).__init__(init_cfg)
+        self.with_res_shortcut = (stride == 1 and in_channels == out_channels)
+        assert stride in [1, 2], f'stride must in [1, 2]. ' \
+            f'But received {stride}.'
+        self.with_cp = with_cp
+        self.drop_path = DropPath(
+            drop_path_rate) if drop_path_rate > 0 else nn.Identity()
+        self.with_se = se_cfg is not None
+        self.with_expand_conv = with_expand_conv
+
+        if self.with_se:
+            assert isinstance(se_cfg, dict)
+        if not self.with_expand_conv:
+            assert mid_channels == in_channels
+
+        if self.with_expand_conv:
+            self.expand_conv = ConvModule(
+                in_channels=in_channels,
+                out_channels=mid_channels,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+                conv_cfg=conv_cfg,
+                norm_cfg=norm_cfg,
+                act_cfg=act_cfg)
+        self.depthwise_conv = ConvModule(
+            in_channels=mid_channels,
+            out_channels=mid_channels,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=kernel_size // 2,
+            groups=mid_channels,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=act_cfg)
+
+        if self.with_se:
+            self.se = SELayer(**se_cfg)
+
+        self.linear_conv = ConvModule(
+            in_channels=mid_channels,
+            out_channels=out_channels,
+            kernel_size=1,
+            stride=1,
+            padding=0,
+            conv_cfg=conv_cfg,
+            norm_cfg=norm_cfg,
+            act_cfg=None)
+
+    def forward(self, x):
+
+        def _inner_forward(x):
+            out = x
+
+            if self.with_expand_conv:
+                out = self.expand_conv(out)
+
+            out = self.depthwise_conv(out)
+
+            if self.with_se:
+                out = self.se(out)
+
+            out = self.linear_conv(out)
+
+            if self.with_res_shortcut:
+                return x + self.drop_path(out)
+            else:
+                return out
+
+        if self.with_cp and x.requires_grad:
+            out = cp.checkpoint(_inner_forward, x)
+        else:
+            out = _inner_forward(x)
+
+        return out
diff --git a/mmdet/models/utils/make_divisible.py b/mmdet/models/utils/make_divisible.py
new file mode 100644
index 0000000..ed42c2e
--- /dev/null
+++ b/mmdet/models/utils/make_divisible.py
@@ -0,0 +1,28 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+def make_divisible(value, divisor, min_value=None, min_ratio=0.9):
+    """Make divisible function.
+
+    This function rounds the channel number to the nearest value that can be
+    divisible by the divisor. It is taken from the original tf repo. It ensures
+    that all layers have a channel number that is divisible by divisor. It can
+    be seen here: https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py  # noqa
+
+    Args:
+        value (int): The original channel number.
+        divisor (int): The divisor to fully divide the channel number.
+        min_value (int): The minimum value of the output channel.
+            Default: None, means that the minimum value equal to the divisor.
+        min_ratio (float): The minimum ratio of the rounded channel number to
+            the original channel number. Default: 0.9.
+
+    Returns:
+        int: The modified output channel number.
+    """
+
+    if min_value is None:
+        min_value = divisor
+    new_value = max(min_value, int(value + divisor / 2) // divisor * divisor)
+    # Make sure that round down does not go down by more than (1-min_ratio).
+    if new_value < min_ratio * value:
+        new_value += divisor
+    return new_value
diff --git a/mmdet/models/utils/misc.py b/mmdet/models/utils/misc.py
new file mode 100644
index 0000000..8f9be9a
--- /dev/null
+++ b/mmdet/models/utils/misc.py
@@ -0,0 +1,72 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from torch.autograd import Function
+from torch.nn import functional as F
+
+
+class SigmoidGeometricMean(Function):
+    """Forward and backward function of geometric mean of two sigmoid
+    functions.
+
+    This implementation with analytical gradient function substitutes
+    the autograd function of (x.sigmoid() * y.sigmoid()).sqrt(). The
+    original implementation incurs none during gradient backprapagation
+    if both x and y are very small values.
+    """
+
+    @staticmethod
+    def forward(ctx, x, y):
+        x_sigmoid = x.sigmoid()
+        y_sigmoid = y.sigmoid()
+        z = (x_sigmoid * y_sigmoid).sqrt()
+        ctx.save_for_backward(x_sigmoid, y_sigmoid, z)
+        return z
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        x_sigmoid, y_sigmoid, z = ctx.saved_tensors
+        grad_x = grad_output * z * (1 - x_sigmoid) / 2
+        grad_y = grad_output * z * (1 - y_sigmoid) / 2
+        return grad_x, grad_y
+
+
+sigmoid_geometric_mean = SigmoidGeometricMean.apply
+
+
+def interpolate_as(source, target, mode='bilinear', align_corners=False):
+    """Interpolate the `source` to the shape of the `target`.
+
+    The `source` must be a Tensor, but the `target` can be a Tensor or a
+    np.ndarray with the shape (..., target_h, target_w).
+
+    Args:
+        source (Tensor): A 3D/4D Tensor with the shape (N, H, W) or
+            (N, C, H, W).
+        target (Tensor | np.ndarray): The interpolation target with the shape
+            (..., target_h, target_w).
+        mode (str): Algorithm used for interpolation. The options are the
+            same as those in F.interpolate(). Default: ``'bilinear'``.
+        align_corners (bool): The same as the argument in F.interpolate().
+
+    Returns:
+        Tensor: The interpolated source Tensor.
+    """
+    assert len(target.shape) >= 2
+
+    def _interpolate_as(source, target, mode='bilinear', align_corners=False):
+        """Interpolate the `source` (4D) to the shape of the `target`."""
+        target_h, target_w = target.shape[-2:]
+        source_h, source_w = source.shape[-2:]
+        if target_h != source_h or target_w != source_w:
+            source = F.interpolate(
+                source,
+                size=(target_h, target_w),
+                mode=mode,
+                align_corners=align_corners)
+        return source
+
+    if len(source.shape) == 3:
+        source = source[:, None, :, :]
+        source = _interpolate_as(source, target, mode, align_corners)
+        return source[:, 0, :, :]
+    else:
+        return _interpolate_as(source, target, mode, align_corners)
diff --git a/mmdet/models/utils/normed_predictor.py b/mmdet/models/utils/normed_predictor.py
new file mode 100644
index 0000000..f0eeef7
--- /dev/null
+++ b/mmdet/models/utils/normed_predictor.py
@@ -0,0 +1,88 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import CONV_LAYERS
+
+from .builder import LINEAR_LAYERS
+
+
+@LINEAR_LAYERS.register_module(name='NormedLinear')
+class NormedLinear(nn.Linear):
+    """Normalized Linear Layer.
+
+    Args:
+        tempeature (float, optional): Tempeature term. Default to 20.
+        power (int, optional): Power term. Default to 1.0.
+        eps (float, optional): The minimal value of divisor to
+             keep numerical stability. Default to 1e-6.
+    """
+
+    def __init__(self, *args, tempearture=20, power=1.0, eps=1e-6, **kwargs):
+        super(NormedLinear, self).__init__(*args, **kwargs)
+        self.tempearture = tempearture
+        self.power = power
+        self.eps = eps
+        self.init_weights()
+
+    def init_weights(self):
+        nn.init.normal_(self.weight, mean=0, std=0.01)
+        if self.bias is not None:
+            nn.init.constant_(self.bias, 0)
+
+    def forward(self, x):
+        weight_ = self.weight / (
+            self.weight.norm(dim=1, keepdim=True).pow(self.power) + self.eps)
+        x_ = x / (x.norm(dim=1, keepdim=True).pow(self.power) + self.eps)
+        x_ = x_ * self.tempearture
+
+        return F.linear(x_, weight_, self.bias)
+
+
+@CONV_LAYERS.register_module(name='NormedConv2d')
+class NormedConv2d(nn.Conv2d):
+    """Normalized Conv2d Layer.
+
+    Args:
+        tempeature (float, optional): Tempeature term. Default to 20.
+        power (int, optional): Power term. Default to 1.0.
+        eps (float, optional): The minimal value of divisor to
+             keep numerical stability. Default to 1e-6.
+        norm_over_kernel (bool, optional): Normalize over kernel.
+             Default to False.
+    """
+
+    def __init__(self,
+                 *args,
+                 tempearture=20,
+                 power=1.0,
+                 eps=1e-6,
+                 norm_over_kernel=False,
+                 **kwargs):
+        super(NormedConv2d, self).__init__(*args, **kwargs)
+        self.tempearture = tempearture
+        self.power = power
+        self.norm_over_kernel = norm_over_kernel
+        self.eps = eps
+
+    def forward(self, x):
+        if not self.norm_over_kernel:
+            weight_ = self.weight / (
+                self.weight.norm(dim=1, keepdim=True).pow(self.power) +
+                self.eps)
+        else:
+            weight_ = self.weight / (
+                self.weight.view(self.weight.size(0), -1).norm(
+                    dim=1, keepdim=True).pow(self.power)[..., None, None] +
+                self.eps)
+        x_ = x / (x.norm(dim=1, keepdim=True).pow(self.power) + self.eps)
+        x_ = x_ * self.tempearture
+
+        if hasattr(self, 'conv2d_forward'):
+            x_ = self.conv2d_forward(x_, weight_)
+        else:
+            if torch.__version__ >= '1.8':
+                x_ = self._conv_forward(x_, weight_, self.bias)
+            else:
+                x_ = self._conv_forward(x_, weight_)
+        return x_
diff --git a/mmdet/models/utils/panoptic_gt_processing.py b/mmdet/models/utils/panoptic_gt_processing.py
new file mode 100644
index 0000000..7685ac9
--- /dev/null
+++ b/mmdet/models/utils/panoptic_gt_processing.py
@@ -0,0 +1,68 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+
+def preprocess_panoptic_gt(gt_labels, gt_masks, gt_semantic_seg, num_things,
+                           num_stuff, img_metas):
+    """Preprocess the ground truth for a image.
+
+    Args:
+        gt_labels (Tensor): Ground truth labels of each bbox,
+            with shape (num_gts, ).
+        gt_masks (BitmapMasks): Ground truth masks of each instances
+            of a image, shape (num_gts, h, w).
+        gt_semantic_seg (Tensor | None): Ground truth of semantic
+            segmentation with the shape (1, h, w).
+            [0, num_thing_class - 1] means things,
+            [num_thing_class, num_class-1] means stuff,
+            255 means VOID. It's None when training instance segmentation.
+        img_metas (dict): List of image meta information.
+
+    Returns:
+        tuple: a tuple containing the following targets.
+
+            - labels (Tensor): Ground truth class indices for a
+                image, with shape (n, ), n is the sum of number
+                of stuff type and number of instance in a image.
+            - masks (Tensor): Ground truth mask for a image, with
+                shape (n, h, w). Contains stuff and things when training
+                panoptic segmentation, and things only when training
+                instance segmentation.
+    """
+    num_classes = num_things + num_stuff
+
+    things_masks = gt_masks.pad(img_metas['pad_shape'][:2], pad_val=0)\
+        .to_tensor(dtype=torch.bool, device=gt_labels.device)
+
+    if gt_semantic_seg is None:
+        masks = things_masks.long()
+        return gt_labels, masks
+
+    things_labels = gt_labels
+    gt_semantic_seg = gt_semantic_seg.squeeze(0)
+
+    semantic_labels = torch.unique(
+        gt_semantic_seg,
+        sorted=False,
+        return_inverse=False,
+        return_counts=False)
+    stuff_masks_list = []
+    stuff_labels_list = []
+    for label in semantic_labels:
+        if label < num_things or label >= num_classes:
+            continue
+        stuff_mask = gt_semantic_seg == label
+        stuff_masks_list.append(stuff_mask)
+        stuff_labels_list.append(label)
+
+    if len(stuff_masks_list) > 0:
+        stuff_masks = torch.stack(stuff_masks_list, dim=0)
+        stuff_labels = torch.stack(stuff_labels_list, dim=0)
+        labels = torch.cat([things_labels, stuff_labels], dim=0)
+        masks = torch.cat([things_masks, stuff_masks], dim=0)
+    else:
+        labels = things_labels
+        masks = things_masks
+
+    masks = masks.long()
+    return labels, masks
diff --git a/mmdet/models/utils/point_sample.py b/mmdet/models/utils/point_sample.py
new file mode 100644
index 0000000..c2c3cf9
--- /dev/null
+++ b/mmdet/models/utils/point_sample.py
@@ -0,0 +1,87 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from mmcv.ops import point_sample
+
+
+def get_uncertainty(mask_pred, labels):
+    """Estimate uncertainty based on pred logits.
+
+    We estimate uncertainty as L1 distance between 0.0 and the logits
+    prediction in 'mask_pred' for the foreground class in `classes`.
+
+    Args:
+        mask_pred (Tensor): mask predication logits, shape (num_rois,
+            num_classes, mask_height, mask_width).
+
+        labels (list[Tensor]): Either predicted or ground truth label for
+            each predicted mask, of length num_rois.
+
+    Returns:
+        scores (Tensor): Uncertainty scores with the most uncertain
+            locations having the highest uncertainty score,
+            shape (num_rois, 1, mask_height, mask_width)
+    """
+    if mask_pred.shape[1] == 1:
+        gt_class_logits = mask_pred.clone()
+    else:
+        inds = torch.arange(mask_pred.shape[0], device=mask_pred.device)
+        gt_class_logits = mask_pred[inds, labels].unsqueeze(1)
+    return -torch.abs(gt_class_logits)
+
+
+def get_uncertain_point_coords_with_randomness(mask_pred, labels, num_points,
+                                               oversample_ratio,
+                                               importance_sample_ratio):
+    """Get ``num_points`` most uncertain points with random points during
+    train.
+
+    Sample points in [0, 1] x [0, 1] coordinate space based on their
+    uncertainty. The uncertainties are calculated for each point using
+    'get_uncertainty()' function that takes point's logit prediction as
+    input.
+
+    Args:
+        mask_pred (Tensor): A tensor of shape (num_rois, num_classes,
+            mask_height, mask_width) for class-specific or class-agnostic
+            prediction.
+        labels (list): The ground truth class for each instance.
+        num_points (int): The number of points to sample.
+        oversample_ratio (int): Oversampling parameter.
+        importance_sample_ratio (float): Ratio of points that are sampled
+            via importnace sampling.
+
+    Returns:
+        point_coords (Tensor): A tensor of shape (num_rois, num_points, 2)
+            that contains the coordinates sampled points.
+    """
+    assert oversample_ratio >= 1
+    assert 0 <= importance_sample_ratio <= 1
+    batch_size = mask_pred.shape[0]
+    num_sampled = int(num_points * oversample_ratio)
+    point_coords = torch.rand(
+        batch_size, num_sampled, 2, device=mask_pred.device)
+    point_logits = point_sample(mask_pred, point_coords)
+    # It is crucial to calculate uncertainty based on the sampled
+    # prediction value for the points. Calculating uncertainties of the
+    # coarse predictions first and sampling them for points leads to
+    # incorrect results.  To illustrate this: assume uncertainty func(
+    # logits)=-abs(logits), a sampled point between two coarse
+    # predictions with -1 and 1 logits has 0 logits, and therefore 0
+    # uncertainty value. However, if we calculate uncertainties for the
+    # coarse predictions first, both will have -1 uncertainty,
+    # and sampled point will get -1 uncertainty.
+    point_uncertainties = get_uncertainty(point_logits, labels)
+    num_uncertain_points = int(importance_sample_ratio * num_points)
+    num_random_points = num_points - num_uncertain_points
+    idx = torch.topk(
+        point_uncertainties[:, 0, :], k=num_uncertain_points, dim=1)[1]
+    shift = num_sampled * torch.arange(
+        batch_size, dtype=torch.long, device=mask_pred.device)
+    idx += shift[:, None]
+    point_coords = point_coords.view(-1, 2)[idx.view(-1), :].view(
+        batch_size, num_uncertain_points, 2)
+    if num_random_points > 0:
+        rand_roi_coords = torch.rand(
+            batch_size, num_random_points, 2, device=mask_pred.device)
+        point_coords = torch.cat((point_coords, rand_roi_coords), dim=1)
+    return point_coords
diff --git a/mmdet/models/utils/positional_encoding.py b/mmdet/models/utils/positional_encoding.py
new file mode 100644
index 0000000..dd29cd6
--- /dev/null
+++ b/mmdet/models/utils/positional_encoding.py
@@ -0,0 +1,163 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+
+import torch
+import torch.nn as nn
+from mmcv.cnn.bricks.transformer import POSITIONAL_ENCODING
+from mmcv.runner import BaseModule
+
+
+@POSITIONAL_ENCODING.register_module()
+class SinePositionalEncoding(BaseModule):
+    """Position encoding with sine and cosine functions.
+
+    See `End-to-End Object Detection with Transformers
+    <https://arxiv.org/pdf/2005.12872>`_ for details.
+
+    Args:
+        num_feats (int): The feature dimension for each position
+            along x-axis or y-axis. Note the final returned dimension
+            for each position is 2 times of this value.
+        temperature (int, optional): The temperature used for scaling
+            the position embedding. Defaults to 10000.
+        normalize (bool, optional): Whether to normalize the position
+            embedding. Defaults to False.
+        scale (float, optional): A scale factor that scales the position
+            embedding. The scale will be used only when `normalize` is True.
+            Defaults to 2*pi.
+        eps (float, optional): A value added to the denominator for
+            numerical stability. Defaults to 1e-6.
+        offset (float): offset add to embed when do the normalization.
+            Defaults to 0.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 num_feats,
+                 temperature=10000,
+                 normalize=False,
+                 scale=2 * math.pi,
+                 eps=1e-6,
+                 offset=0.,
+                 init_cfg=None):
+        super(SinePositionalEncoding, self).__init__(init_cfg)
+        if normalize:
+            assert isinstance(scale, (float, int)), 'when normalize is set,' \
+                'scale should be provided and in float or int type, ' \
+                f'found {type(scale)}'
+        self.num_feats = num_feats
+        self.temperature = temperature
+        self.normalize = normalize
+        self.scale = scale
+        self.eps = eps
+        self.offset = offset
+
+    def forward(self, mask):
+        """Forward function for `SinePositionalEncoding`.
+
+        Args:
+            mask (Tensor): ByteTensor mask. Non-zero values representing
+                ignored positions, while zero values means valid positions
+                for this image. Shape [bs, h, w].
+
+        Returns:
+            pos (Tensor): Returned position embedding with shape
+                [bs, num_feats*2, h, w].
+        """
+        # For convenience of exporting to ONNX, it's required to convert
+        # `masks` from bool to int.
+        mask = mask.to(torch.int)
+        not_mask = 1 - mask  # logical_not
+        y_embed = not_mask.cumsum(1, dtype=torch.float32)
+        x_embed = not_mask.cumsum(2, dtype=torch.float32)
+        if self.normalize:
+            y_embed = (y_embed + self.offset) / \
+                      (y_embed[:, -1:, :] + self.eps) * self.scale
+            x_embed = (x_embed + self.offset) / \
+                      (x_embed[:, :, -1:] + self.eps) * self.scale
+        dim_t = torch.arange(
+            self.num_feats, dtype=torch.float32, device=mask.device)
+        dim_t = self.temperature**(2 * (dim_t // 2) / self.num_feats)
+        pos_x = x_embed[:, :, :, None] / dim_t
+        pos_y = y_embed[:, :, :, None] / dim_t
+        # use `view` instead of `flatten` for dynamically exporting to ONNX
+        B, H, W = mask.size()
+        pos_x = torch.stack(
+            (pos_x[:, :, :, 0::2].sin(), pos_x[:, :, :, 1::2].cos()),
+            dim=4).view(B, H, W, -1)
+        pos_y = torch.stack(
+            (pos_y[:, :, :, 0::2].sin(), pos_y[:, :, :, 1::2].cos()),
+            dim=4).view(B, H, W, -1)
+        pos = torch.cat((pos_y, pos_x), dim=3).permute(0, 3, 1, 2)
+        return pos
+
+    def __repr__(self):
+        """str: a string that describes the module"""
+        repr_str = self.__class__.__name__
+        repr_str += f'(num_feats={self.num_feats}, '
+        repr_str += f'temperature={self.temperature}, '
+        repr_str += f'normalize={self.normalize}, '
+        repr_str += f'scale={self.scale}, '
+        repr_str += f'eps={self.eps})'
+        return repr_str
+
+
+@POSITIONAL_ENCODING.register_module()
+class LearnedPositionalEncoding(BaseModule):
+    """Position embedding with learnable embedding weights.
+
+    Args:
+        num_feats (int): The feature dimension for each position
+            along x-axis or y-axis. The final returned dimension for
+            each position is 2 times of this value.
+        row_num_embed (int, optional): The dictionary size of row embeddings.
+            Default 50.
+        col_num_embed (int, optional): The dictionary size of col embeddings.
+            Default 50.
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+    """
+
+    def __init__(self,
+                 num_feats,
+                 row_num_embed=50,
+                 col_num_embed=50,
+                 init_cfg=dict(type='Uniform', layer='Embedding')):
+        super(LearnedPositionalEncoding, self).__init__(init_cfg)
+        self.row_embed = nn.Embedding(row_num_embed, num_feats)
+        self.col_embed = nn.Embedding(col_num_embed, num_feats)
+        self.num_feats = num_feats
+        self.row_num_embed = row_num_embed
+        self.col_num_embed = col_num_embed
+
+    def forward(self, mask):
+        """Forward function for `LearnedPositionalEncoding`.
+
+        Args:
+            mask (Tensor): ByteTensor mask. Non-zero values representing
+                ignored positions, while zero values means valid positions
+                for this image. Shape [bs, h, w].
+
+        Returns:
+            pos (Tensor): Returned position embedding with shape
+                [bs, num_feats*2, h, w].
+        """
+        h, w = mask.shape[-2:]
+        x = torch.arange(w, device=mask.device)
+        y = torch.arange(h, device=mask.device)
+        x_embed = self.col_embed(x)
+        y_embed = self.row_embed(y)
+        pos = torch.cat(
+            (x_embed.unsqueeze(0).repeat(h, 1, 1), y_embed.unsqueeze(1).repeat(
+                1, w, 1)),
+            dim=-1).permute(2, 0,
+                            1).unsqueeze(0).repeat(mask.shape[0], 1, 1, 1)
+        return pos
+
+    def __repr__(self):
+        """str: a string that describes the module"""
+        repr_str = self.__class__.__name__
+        repr_str += f'(num_feats={self.num_feats}, '
+        repr_str += f'row_num_embed={self.row_num_embed}, '
+        repr_str += f'col_num_embed={self.col_num_embed})'
+        return repr_str
diff --git a/mmdet/models/utils/res_layer.py b/mmdet/models/utils/res_layer.py
new file mode 100644
index 0000000..5c3e89f
--- /dev/null
+++ b/mmdet/models/utils/res_layer.py
@@ -0,0 +1,190 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.cnn import build_conv_layer, build_norm_layer
+from mmcv.runner import BaseModule, Sequential
+from torch import nn as nn
+
+
+class ResLayer(Sequential):
+    """ResLayer to build ResNet style backbone.
+
+    Args:
+        block (nn.Module): block used to build ResLayer.
+        inplanes (int): inplanes of block.
+        planes (int): planes of block.
+        num_blocks (int): number of blocks.
+        stride (int): stride of the first block. Default: 1
+        avg_down (bool): Use AvgPool instead of stride conv when
+            downsampling in the bottleneck. Default: False
+        conv_cfg (dict): dictionary to construct and config conv layer.
+            Default: None
+        norm_cfg (dict): dictionary to construct and config norm layer.
+            Default: dict(type='BN')
+        downsample_first (bool): Downsample at the first block or last block.
+            False for Hourglass, True for ResNet. Default: True
+    """
+
+    def __init__(self,
+                 block,
+                 inplanes,
+                 planes,
+                 num_blocks,
+                 stride=1,
+                 avg_down=False,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 downsample_first=True,
+                 **kwargs):
+        self.block = block
+
+        downsample = None
+        if stride != 1 or inplanes != planes * block.expansion:
+            downsample = []
+            conv_stride = stride
+            if avg_down:
+                conv_stride = 1
+                downsample.append(
+                    nn.AvgPool2d(
+                        kernel_size=stride,
+                        stride=stride,
+                        ceil_mode=True,
+                        count_include_pad=False))
+            downsample.extend([
+                build_conv_layer(
+                    conv_cfg,
+                    inplanes,
+                    planes * block.expansion,
+                    kernel_size=1,
+                    stride=conv_stride,
+                    bias=False),
+                build_norm_layer(norm_cfg, planes * block.expansion)[1]
+            ])
+            downsample = nn.Sequential(*downsample)
+
+        layers = []
+        if downsample_first:
+            layers.append(
+                block(
+                    inplanes=inplanes,
+                    planes=planes,
+                    stride=stride,
+                    downsample=downsample,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    **kwargs))
+            inplanes = planes * block.expansion
+            for _ in range(1, num_blocks):
+                layers.append(
+                    block(
+                        inplanes=inplanes,
+                        planes=planes,
+                        stride=1,
+                        conv_cfg=conv_cfg,
+                        norm_cfg=norm_cfg,
+                        **kwargs))
+
+        else:  # downsample_first=False is for HourglassModule
+            for _ in range(num_blocks - 1):
+                layers.append(
+                    block(
+                        inplanes=inplanes,
+                        planes=inplanes,
+                        stride=1,
+                        conv_cfg=conv_cfg,
+                        norm_cfg=norm_cfg,
+                        **kwargs))
+            layers.append(
+                block(
+                    inplanes=inplanes,
+                    planes=planes,
+                    stride=stride,
+                    downsample=downsample,
+                    conv_cfg=conv_cfg,
+                    norm_cfg=norm_cfg,
+                    **kwargs))
+        super(ResLayer, self).__init__(*layers)
+
+
+class SimplifiedBasicBlock(BaseModule):
+    """Simplified version of original basic residual block. This is used in
+    `SCNet <https://arxiv.org/abs/2012.10150>`_.
+
+    - Norm layer is now optional
+    - Last ReLU in forward function is removed
+    """
+    expansion = 1
+
+    def __init__(self,
+                 inplanes,
+                 planes,
+                 stride=1,
+                 dilation=1,
+                 downsample=None,
+                 style='pytorch',
+                 with_cp=False,
+                 conv_cfg=None,
+                 norm_cfg=dict(type='BN'),
+                 dcn=None,
+                 plugins=None,
+                 init_fg=None):
+        super(SimplifiedBasicBlock, self).__init__(init_fg)
+        assert dcn is None, 'Not implemented yet.'
+        assert plugins is None, 'Not implemented yet.'
+        assert not with_cp, 'Not implemented yet.'
+        self.with_norm = norm_cfg is not None
+        with_bias = True if norm_cfg is None else False
+        self.conv1 = build_conv_layer(
+            conv_cfg,
+            inplanes,
+            planes,
+            3,
+            stride=stride,
+            padding=dilation,
+            dilation=dilation,
+            bias=with_bias)
+        if self.with_norm:
+            self.norm1_name, norm1 = build_norm_layer(
+                norm_cfg, planes, postfix=1)
+            self.add_module(self.norm1_name, norm1)
+        self.conv2 = build_conv_layer(
+            conv_cfg, planes, planes, 3, padding=1, bias=with_bias)
+        if self.with_norm:
+            self.norm2_name, norm2 = build_norm_layer(
+                norm_cfg, planes, postfix=2)
+            self.add_module(self.norm2_name, norm2)
+
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+        self.dilation = dilation
+        self.with_cp = with_cp
+
+    @property
+    def norm1(self):
+        """nn.Module: normalization layer after the first convolution layer"""
+        return getattr(self, self.norm1_name) if self.with_norm else None
+
+    @property
+    def norm2(self):
+        """nn.Module: normalization layer after the second convolution layer"""
+        return getattr(self, self.norm2_name) if self.with_norm else None
+
+    def forward(self, x):
+        """Forward function."""
+
+        identity = x
+
+        out = self.conv1(x)
+        if self.with_norm:
+            out = self.norm1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        if self.with_norm:
+            out = self.norm2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+
+        return out
diff --git a/mmdet/models/utils/se_layer.py b/mmdet/models/utils/se_layer.py
new file mode 100644
index 0000000..a249210
--- /dev/null
+++ b/mmdet/models/utils/se_layer.py
@@ -0,0 +1,127 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+from mmcv.runner import BaseModule
+
+
+class SELayer(BaseModule):
+    """Squeeze-and-Excitation Module.
+
+    Args:
+        channels (int): The input (and output) channels of the SE layer.
+        ratio (int): Squeeze ratio in SELayer, the intermediate channel will be
+            ``int(channels/ratio)``. Default: 16.
+        conv_cfg (None or dict): Config dict for convolution layer.
+            Default: None, which means using conv2d.
+        act_cfg (dict or Sequence[dict]): Config dict for activation layer.
+            If act_cfg is a dict, two activation layers will be configurated
+            by this dict. If act_cfg is a sequence of dicts, the first
+            activation layer will be configurated by the first dict and the
+            second activation layer will be configurated by the second dict.
+            Default: (dict(type='ReLU'), dict(type='Sigmoid'))
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 channels,
+                 ratio=16,
+                 conv_cfg=None,
+                 act_cfg=(dict(type='ReLU'), dict(type='Sigmoid')),
+                 init_cfg=None):
+        super(SELayer, self).__init__(init_cfg)
+        if isinstance(act_cfg, dict):
+            act_cfg = (act_cfg, act_cfg)
+        assert len(act_cfg) == 2
+        assert mmcv.is_tuple_of(act_cfg, dict)
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.conv1 = ConvModule(
+            in_channels=channels,
+            out_channels=int(channels / ratio),
+            kernel_size=1,
+            stride=1,
+            conv_cfg=conv_cfg,
+            act_cfg=act_cfg[0])
+        self.conv2 = ConvModule(
+            in_channels=int(channels / ratio),
+            out_channels=channels,
+            kernel_size=1,
+            stride=1,
+            conv_cfg=conv_cfg,
+            act_cfg=act_cfg[1])
+
+    def forward(self, x):
+        out = self.global_avgpool(x)
+        out = self.conv1(out)
+        out = self.conv2(out)
+        return x * out
+
+
+class DyReLU(BaseModule):
+    """Dynamic ReLU (DyReLU) module.
+
+    See `Dynamic ReLU <https://arxiv.org/abs/2003.10027>`_ for details.
+    Current implementation is specialized for task-aware attention in DyHead.
+    HSigmoid arguments in default act_cfg follow DyHead official code.
+    https://github.com/microsoft/DynamicHead/blob/master/dyhead/dyrelu.py
+
+    Args:
+        channels (int): The input (and output) channels of DyReLU module.
+        ratio (int): Squeeze ratio in Squeeze-and-Excitation-like module,
+            the intermediate channel will be ``int(channels/ratio)``.
+            Default: 4.
+        conv_cfg (None or dict): Config dict for convolution layer.
+            Default: None, which means using conv2d.
+        act_cfg (dict or Sequence[dict]): Config dict for activation layer.
+            If act_cfg is a dict, two activation layers will be configurated
+            by this dict. If act_cfg is a sequence of dicts, the first
+            activation layer will be configurated by the first dict and the
+            second activation layer will be configurated by the second dict.
+            Default: (dict(type='ReLU'), dict(type='HSigmoid', bias=3.0,
+            divisor=6.0))
+        init_cfg (dict or list[dict], optional): Initialization config dict.
+            Default: None
+    """
+
+    def __init__(self,
+                 channels,
+                 ratio=4,
+                 conv_cfg=None,
+                 act_cfg=(dict(type='ReLU'),
+                          dict(type='HSigmoid', bias=3.0, divisor=6.0)),
+                 init_cfg=None):
+        super().__init__(init_cfg=init_cfg)
+        if isinstance(act_cfg, dict):
+            act_cfg = (act_cfg, act_cfg)
+        assert len(act_cfg) == 2
+        assert mmcv.is_tuple_of(act_cfg, dict)
+        self.channels = channels
+        self.expansion = 4  # for a1, b1, a2, b2
+        self.global_avgpool = nn.AdaptiveAvgPool2d(1)
+        self.conv1 = ConvModule(
+            in_channels=channels,
+            out_channels=int(channels / ratio),
+            kernel_size=1,
+            stride=1,
+            conv_cfg=conv_cfg,
+            act_cfg=act_cfg[0])
+        self.conv2 = ConvModule(
+            in_channels=int(channels / ratio),
+            out_channels=channels * self.expansion,
+            kernel_size=1,
+            stride=1,
+            conv_cfg=conv_cfg,
+            act_cfg=act_cfg[1])
+
+    def forward(self, x):
+        """Forward function."""
+        coeffs = self.global_avgpool(x)
+        coeffs = self.conv1(coeffs)
+        coeffs = self.conv2(coeffs) - 0.5  # value range: [-0.5, 0.5]
+        a1, b1, a2, b2 = torch.split(coeffs, self.channels, dim=1)
+        a1 = a1 * 2.0 + 1.0  # [-1.0, 1.0] + 1.0
+        a2 = a2 * 2.0  # [-1.0, 1.0]
+        out = torch.max(x * a1 + b1, x * a2 + b2)
+        return out
diff --git a/mmdet/models/utils/transformer.py b/mmdet/models/utils/transformer.py
new file mode 100644
index 0000000..3c390c8
--- /dev/null
+++ b/mmdet/models/utils/transformer.py
@@ -0,0 +1,1167 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import math
+import warnings
+from typing import Sequence
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from mmcv.cnn import (build_activation_layer, build_conv_layer,
+                      build_norm_layer, xavier_init)
+from mmcv.cnn.bricks.registry import (TRANSFORMER_LAYER,
+                                      TRANSFORMER_LAYER_SEQUENCE)
+from mmcv.cnn.bricks.transformer import (BaseTransformerLayer,
+                                         TransformerLayerSequence,
+                                         build_transformer_layer_sequence)
+from mmcv.runner.base_module import BaseModule
+from mmcv.utils import to_2tuple
+from torch.nn.init import normal_
+
+from mmdet.models.utils.builder import TRANSFORMER
+
+try:
+    from mmcv.ops.multi_scale_deform_attn import MultiScaleDeformableAttention
+
+except ImportError:
+    warnings.warn(
+        '`MultiScaleDeformableAttention` in MMCV has been moved to '
+        '`mmcv.ops.multi_scale_deform_attn`, please update your MMCV')
+    from mmcv.cnn.bricks.transformer import MultiScaleDeformableAttention
+
+
+def nlc_to_nchw(x, hw_shape):
+    """Convert [N, L, C] shape tensor to [N, C, H, W] shape tensor.
+
+    Args:
+        x (Tensor): The input tensor of shape [N, L, C] before conversion.
+        hw_shape (Sequence[int]): The height and width of output feature map.
+
+    Returns:
+        Tensor: The output tensor of shape [N, C, H, W] after conversion.
+    """
+    H, W = hw_shape
+    assert len(x.shape) == 3
+    B, L, C = x.shape
+    assert L == H * W, 'The seq_len does not match H, W'
+    return x.transpose(1, 2).reshape(B, C, H, W).contiguous()
+
+
+def nchw_to_nlc(x):
+    """Flatten [N, C, H, W] shape tensor to [N, L, C] shape tensor.
+
+    Args:
+        x (Tensor): The input tensor of shape [N, C, H, W] before conversion.
+
+    Returns:
+        Tensor: The output tensor of shape [N, L, C] after conversion.
+    """
+    assert len(x.shape) == 4
+    return x.flatten(2).transpose(1, 2).contiguous()
+
+
+class AdaptivePadding(nn.Module):
+    """Applies padding to input (if needed) so that input can get fully covered
+    by filter you specified. It support two modes "same" and "corner". The
+    "same" mode is same with "SAME" padding mode in TensorFlow, pad zero around
+    input. The "corner"  mode would pad zero to bottom right.
+
+    Args:
+        kernel_size (int | tuple): Size of the kernel:
+        stride (int | tuple): Stride of the filter. Default: 1:
+        dilation (int | tuple): Spacing between kernel elements.
+            Default: 1
+        padding (str): Support "same" and "corner", "corner" mode
+            would pad zero to bottom right, and "same" mode would
+            pad zero around input. Default: "corner".
+    Example:
+        >>> kernel_size = 16
+        >>> stride = 16
+        >>> dilation = 1
+        >>> input = torch.rand(1, 1, 15, 17)
+        >>> adap_pad = AdaptivePadding(
+        >>>     kernel_size=kernel_size,
+        >>>     stride=stride,
+        >>>     dilation=dilation,
+        >>>     padding="corner")
+        >>> out = adap_pad(input)
+        >>> assert (out.shape[2], out.shape[3]) == (16, 32)
+        >>> input = torch.rand(1, 1, 16, 17)
+        >>> out = adap_pad(input)
+        >>> assert (out.shape[2], out.shape[3]) == (16, 32)
+    """
+
+    def __init__(self, kernel_size=1, stride=1, dilation=1, padding='corner'):
+
+        super(AdaptivePadding, self).__init__()
+
+        assert padding in ('same', 'corner')
+
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        padding = to_2tuple(padding)
+        dilation = to_2tuple(dilation)
+
+        self.padding = padding
+        self.kernel_size = kernel_size
+        self.stride = stride
+        self.dilation = dilation
+
+    def get_pad_shape(self, input_shape):
+        input_h, input_w = input_shape
+        kernel_h, kernel_w = self.kernel_size
+        stride_h, stride_w = self.stride
+        output_h = math.ceil(input_h / stride_h)
+        output_w = math.ceil(input_w / stride_w)
+        pad_h = max((output_h - 1) * stride_h +
+                    (kernel_h - 1) * self.dilation[0] + 1 - input_h, 0)
+        pad_w = max((output_w - 1) * stride_w +
+                    (kernel_w - 1) * self.dilation[1] + 1 - input_w, 0)
+        return pad_h, pad_w
+
+    def forward(self, x):
+        pad_h, pad_w = self.get_pad_shape(x.size()[-2:])
+        if pad_h > 0 or pad_w > 0:
+            if self.padding == 'corner':
+                x = F.pad(x, [0, pad_w, 0, pad_h])
+            elif self.padding == 'same':
+                x = F.pad(x, [
+                    pad_w // 2, pad_w - pad_w // 2, pad_h // 2,
+                    pad_h - pad_h // 2
+                ])
+        return x
+
+
+class PatchEmbed(BaseModule):
+    """Image to Patch Embedding.
+
+    We use a conv layer to implement PatchEmbed.
+
+    Args:
+        in_channels (int): The num of input channels. Default: 3
+        embed_dims (int): The dimensions of embedding. Default: 768
+        conv_type (str): The config dict for embedding
+            conv layer type selection. Default: "Conv2d.
+        kernel_size (int): The kernel_size of embedding conv. Default: 16.
+        stride (int): The slide stride of embedding conv.
+            Default: None (Would be set as `kernel_size`).
+        padding (int | tuple | string ): The padding length of
+            embedding conv. When it is a string, it means the mode
+            of adaptive padding, support "same" and "corner" now.
+            Default: "corner".
+        dilation (int): The dilation rate of embedding conv. Default: 1.
+        bias (bool): Bias of embed conv. Default: True.
+        norm_cfg (dict, optional): Config dict for normalization layer.
+            Default: None.
+        input_size (int | tuple | None): The size of input, which will be
+            used to calculate the out size. Only work when `dynamic_size`
+            is False. Default: None.
+        init_cfg (`mmcv.ConfigDict`, optional): The Config for initialization.
+            Default: None.
+    """
+
+    def __init__(
+        self,
+        in_channels=3,
+        embed_dims=768,
+        conv_type='Conv2d',
+        kernel_size=16,
+        stride=16,
+        padding='corner',
+        dilation=1,
+        bias=True,
+        norm_cfg=None,
+        input_size=None,
+        init_cfg=None,
+    ):
+        super(PatchEmbed, self).__init__(init_cfg=init_cfg)
+
+        self.embed_dims = embed_dims
+        if stride is None:
+            stride = kernel_size
+
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        dilation = to_2tuple(dilation)
+
+        if isinstance(padding, str):
+            self.adap_padding = AdaptivePadding(
+                kernel_size=kernel_size,
+                stride=stride,
+                dilation=dilation,
+                padding=padding)
+            # disable the padding of conv
+            padding = 0
+        else:
+            self.adap_padding = None
+        padding = to_2tuple(padding)
+
+        self.projection = build_conv_layer(
+            dict(type=conv_type),
+            in_channels=in_channels,
+            out_channels=embed_dims,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias)
+
+        if norm_cfg is not None:
+            self.norm = build_norm_layer(norm_cfg, embed_dims)[1]
+        else:
+            self.norm = None
+
+        if input_size:
+            input_size = to_2tuple(input_size)
+            # `init_out_size` would be used outside to
+            # calculate the num_patches
+            # when `use_abs_pos_embed` outside
+            self.init_input_size = input_size
+            if self.adap_padding:
+                pad_h, pad_w = self.adap_padding.get_pad_shape(input_size)
+                input_h, input_w = input_size
+                input_h = input_h + pad_h
+                input_w = input_w + pad_w
+                input_size = (input_h, input_w)
+
+            # https://pytorch.org/docs/stable/generated/torch.nn.Conv2d.html
+            h_out = (input_size[0] + 2 * padding[0] - dilation[0] *
+                     (kernel_size[0] - 1) - 1) // stride[0] + 1
+            w_out = (input_size[1] + 2 * padding[1] - dilation[1] *
+                     (kernel_size[1] - 1) - 1) // stride[1] + 1
+            self.init_out_size = (h_out, w_out)
+        else:
+            self.init_input_size = None
+            self.init_out_size = None
+
+    def forward(self, x):
+        """
+        Args:
+            x (Tensor): Has shape (B, C, H, W). In most case, C is 3.
+
+        Returns:
+            tuple: Contains merged results and its spatial shape.
+
+                - x (Tensor): Has shape (B, out_h * out_w, embed_dims)
+                - out_size (tuple[int]): Spatial shape of x, arrange as
+                    (out_h, out_w).
+        """
+
+        if self.adap_padding:
+            x = self.adap_padding(x)
+
+        x = self.projection(x)
+        out_size = (x.shape[2], x.shape[3])
+        x = x.flatten(2).transpose(1, 2)
+        if self.norm is not None:
+            x = self.norm(x)
+        return x, out_size
+
+
+class PatchMerging(BaseModule):
+    """Merge patch feature map.
+
+    This layer groups feature map by kernel_size, and applies norm and linear
+    layers to the grouped feature map. Our implementation uses `nn.Unfold` to
+    merge patch, which is about 25% faster than original implementation.
+    Instead, we need to modify pretrained models for compatibility.
+
+    Args:
+        in_channels (int): The num of input channels.
+            to gets fully covered by filter and stride you specified..
+            Default: True.
+        out_channels (int): The num of output channels.
+        kernel_size (int | tuple, optional): the kernel size in the unfold
+            layer. Defaults to 2.
+        stride (int | tuple, optional): the stride of the sliding blocks in the
+            unfold layer. Default: None. (Would be set as `kernel_size`)
+        padding (int | tuple | string ): The padding length of
+            embedding conv. When it is a string, it means the mode
+            of adaptive padding, support "same" and "corner" now.
+            Default: "corner".
+        dilation (int | tuple, optional): dilation parameter in the unfold
+            layer. Default: 1.
+        bias (bool, optional): Whether to add bias in linear layer or not.
+            Defaults: False.
+        norm_cfg (dict, optional): Config dict for normalization layer.
+            Default: dict(type='LN').
+        init_cfg (dict, optional): The extra config for initialization.
+            Default: None.
+    """
+
+    def __init__(self,
+                 in_channels,
+                 out_channels,
+                 kernel_size=2,
+                 stride=None,
+                 padding='corner',
+                 dilation=1,
+                 bias=False,
+                 norm_cfg=dict(type='LN'),
+                 init_cfg=None):
+        super().__init__(init_cfg=init_cfg)
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        if stride:
+            stride = stride
+        else:
+            stride = kernel_size
+
+        kernel_size = to_2tuple(kernel_size)
+        stride = to_2tuple(stride)
+        dilation = to_2tuple(dilation)
+
+        if isinstance(padding, str):
+            self.adap_padding = AdaptivePadding(
+                kernel_size=kernel_size,
+                stride=stride,
+                dilation=dilation,
+                padding=padding)
+            # disable the padding of unfold
+            padding = 0
+        else:
+            self.adap_padding = None
+
+        padding = to_2tuple(padding)
+        self.sampler = nn.Unfold(
+            kernel_size=kernel_size,
+            dilation=dilation,
+            padding=padding,
+            stride=stride)
+
+        sample_dim = kernel_size[0] * kernel_size[1] * in_channels
+
+        if norm_cfg is not None:
+            self.norm = build_norm_layer(norm_cfg, sample_dim)[1]
+        else:
+            self.norm = None
+
+        self.reduction = nn.Linear(sample_dim, out_channels, bias=bias)
+
+    def forward(self, x, input_size):
+        """
+        Args:
+            x (Tensor): Has shape (B, H*W, C_in).
+            input_size (tuple[int]): The spatial shape of x, arrange as (H, W).
+                Default: None.
+
+        Returns:
+            tuple: Contains merged results and its spatial shape.
+
+                - x (Tensor): Has shape (B, Merged_H * Merged_W, C_out)
+                - out_size (tuple[int]): Spatial shape of x, arrange as
+                    (Merged_H, Merged_W).
+        """
+        B, L, C = x.shape
+        assert isinstance(input_size, Sequence), f'Expect ' \
+                                                 f'input_size is ' \
+                                                 f'`Sequence` ' \
+                                                 f'but get {input_size}'
+
+        H, W = input_size
+        assert L == H * W, 'input feature has wrong size'
+
+        x = x.view(B, H, W, C).permute([0, 3, 1, 2])  # B, C, H, W
+        # Use nn.Unfold to merge patch. About 25% faster than original method,
+        # but need to modify pretrained model for compatibility
+
+        if self.adap_padding:
+            x = self.adap_padding(x)
+            H, W = x.shape[-2:]
+
+        x = self.sampler(x)
+        # if kernel_size=2 and stride=2, x should has shape (B, 4*C, H/2*W/2)
+
+        out_h = (H + 2 * self.sampler.padding[0] - self.sampler.dilation[0] *
+                 (self.sampler.kernel_size[0] - 1) -
+                 1) // self.sampler.stride[0] + 1
+        out_w = (W + 2 * self.sampler.padding[1] - self.sampler.dilation[1] *
+                 (self.sampler.kernel_size[1] - 1) -
+                 1) // self.sampler.stride[1] + 1
+
+        output_size = (out_h, out_w)
+        x = x.transpose(1, 2)  # B, H/2*W/2, 4*C
+        x = self.norm(x) if self.norm else x
+        x = self.reduction(x)
+        return x, output_size
+
+
+def inverse_sigmoid(x, eps=1e-5):
+    """Inverse function of sigmoid.
+
+    Args:
+        x (Tensor): The tensor to do the
+            inverse.
+        eps (float): EPS avoid numerical
+            overflow. Defaults 1e-5.
+    Returns:
+        Tensor: The x has passed the inverse
+            function of sigmoid, has same
+            shape with input.
+    """
+    x = x.clamp(min=0, max=1)
+    x1 = x.clamp(min=eps)
+    x2 = (1 - x).clamp(min=eps)
+    return torch.log(x1 / x2)
+
+
+@TRANSFORMER_LAYER.register_module()
+class DetrTransformerDecoderLayer(BaseTransformerLayer):
+    """Implements decoder layer in DETR transformer.
+
+    Args:
+        attn_cfgs (list[`mmcv.ConfigDict`] | list[dict] | dict )):
+            Configs for self_attention or cross_attention, the order
+            should be consistent with it in `operation_order`. If it is
+            a dict, it would be expand to the number of attention in
+            `operation_order`.
+        feedforward_channels (int): The hidden dimension for FFNs.
+        ffn_dropout (float): Probability of an element to be zeroed
+            in ffn. Default 0.0.
+        operation_order (tuple[str]): The execution order of operation
+            in transformer. Such as ('self_attn', 'norm', 'ffn', 'norm').
+            Default：None
+        act_cfg (dict): The activation config for FFNs. Default: `LN`
+        norm_cfg (dict): Config dict for normalization layer.
+            Default: `LN`.
+        ffn_num_fcs (int): The number of fully-connected layers in FFNs.
+            Default：2.
+    """
+
+    def __init__(self,
+                 attn_cfgs,
+                 feedforward_channels,
+                 ffn_dropout=0.0,
+                 operation_order=None,
+                 act_cfg=dict(type='ReLU', inplace=True),
+                 norm_cfg=dict(type='LN'),
+                 ffn_num_fcs=2,
+                 **kwargs):
+        super(DetrTransformerDecoderLayer, self).__init__(
+            attn_cfgs=attn_cfgs,
+            feedforward_channels=feedforward_channels,
+            ffn_dropout=ffn_dropout,
+            operation_order=operation_order,
+            act_cfg=act_cfg,
+            norm_cfg=norm_cfg,
+            ffn_num_fcs=ffn_num_fcs,
+            **kwargs)
+        assert len(operation_order) == 6
+        assert set(operation_order) == set(
+            ['self_attn', 'norm', 'cross_attn', 'ffn'])
+
+
+@TRANSFORMER_LAYER_SEQUENCE.register_module()
+class DetrTransformerEncoder(TransformerLayerSequence):
+    """TransformerEncoder of DETR.
+
+    Args:
+        post_norm_cfg (dict): Config of last normalization layer. Default：
+            `LN`. Only used when `self.pre_norm` is `True`
+    """
+
+    def __init__(self, *args, post_norm_cfg=dict(type='LN'), **kwargs):
+        super(DetrTransformerEncoder, self).__init__(*args, **kwargs)
+        if post_norm_cfg is not None:
+            self.post_norm = build_norm_layer(
+                post_norm_cfg, self.embed_dims)[1] if self.pre_norm else None
+        else:
+            assert not self.pre_norm, f'Use prenorm in ' \
+                                      f'{self.__class__.__name__},' \
+                                      f'Please specify post_norm_cfg'
+            self.post_norm = None
+
+    def forward(self, *args, **kwargs):
+        """Forward function for `TransformerCoder`.
+
+        Returns:
+            Tensor: forwarded results with shape [num_query, bs, embed_dims].
+        """
+        x = super(DetrTransformerEncoder, self).forward(*args, **kwargs)
+        if self.post_norm is not None:
+            x = self.post_norm(x)
+        return x
+
+
+@TRANSFORMER_LAYER_SEQUENCE.register_module()
+class DetrTransformerDecoder(TransformerLayerSequence):
+    """Implements the decoder in DETR transformer.
+
+    Args:
+        return_intermediate (bool): Whether to return intermediate outputs.
+        post_norm_cfg (dict): Config of last normalization layer. Default：
+            `LN`.
+    """
+
+    def __init__(self,
+                 *args,
+                 post_norm_cfg=dict(type='LN'),
+                 return_intermediate=False,
+                 **kwargs):
+
+        super(DetrTransformerDecoder, self).__init__(*args, **kwargs)
+        self.return_intermediate = return_intermediate
+        if post_norm_cfg is not None:
+            self.post_norm = build_norm_layer(post_norm_cfg,
+                                              self.embed_dims)[1]
+        else:
+            self.post_norm = None
+
+    def forward(self, query, *args, **kwargs):
+        """Forward function for `TransformerDecoder`.
+
+        Args:
+            query (Tensor): Input query with shape
+                `(num_query, bs, embed_dims)`.
+
+        Returns:
+            Tensor: Results with shape [1, num_query, bs, embed_dims] when
+                return_intermediate is `False`, otherwise it has shape
+                [num_layers, num_query, bs, embed_dims].
+        """
+        if not self.return_intermediate:
+            x = super().forward(query, *args, **kwargs)
+            if self.post_norm:
+                x = self.post_norm(x)[None]
+            return x
+
+        intermediate = []
+        for layer in self.layers:
+            query = layer(query, *args, **kwargs)
+            if self.return_intermediate:
+                if self.post_norm is not None:
+                    intermediate.append(self.post_norm(query))
+                else:
+                    intermediate.append(query)
+        return torch.stack(intermediate)
+
+
+@TRANSFORMER.register_module()
+class Transformer(BaseModule):
+    """Implements the DETR transformer.
+
+    Following the official DETR implementation, this module copy-paste
+    from torch.nn.Transformer with modifications:
+
+        * positional encodings are passed in MultiheadAttention
+        * extra LN at the end of encoder is removed
+        * decoder returns a stack of activations from all decoding layers
+
+    See `paper: End-to-End Object Detection with Transformers
+    <https://arxiv.org/pdf/2005.12872>`_ for details.
+
+    Args:
+        encoder (`mmcv.ConfigDict` | Dict): Config of
+            TransformerEncoder. Defaults to None.
+        decoder ((`mmcv.ConfigDict` | Dict)): Config of
+            TransformerDecoder. Defaults to None
+        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
+            Defaults to None.
+    """
+
+    def __init__(self, encoder=None, decoder=None, init_cfg=None):
+        super(Transformer, self).__init__(init_cfg=init_cfg)
+        self.encoder = build_transformer_layer_sequence(encoder)
+        self.decoder = build_transformer_layer_sequence(decoder)
+        self.embed_dims = self.encoder.embed_dims
+
+    def init_weights(self):
+        # follow the official DETR to init parameters
+        for m in self.modules():
+            if hasattr(m, 'weight') and m.weight.dim() > 1:
+                xavier_init(m, distribution='uniform')
+        self._is_init = True
+
+    def forward(self, x, mask, query_embed, pos_embed):
+        """Forward function for `Transformer`.
+
+        Args:
+            x (Tensor): Input query with shape [bs, c, h, w] where
+                c = embed_dims.
+            mask (Tensor): The key_padding_mask used for encoder and decoder,
+                with shape [bs, h, w].
+            query_embed (Tensor): The query embedding for decoder, with shape
+                [num_query, c].
+            pos_embed (Tensor): The positional encoding for encoder and
+                decoder, with the same shape as `x`.
+
+        Returns:
+            tuple[Tensor]: results of decoder containing the following tensor.
+
+                - out_dec: Output from decoder. If return_intermediate_dec \
+                      is True output has shape [num_dec_layers, bs,
+                      num_query, embed_dims], else has shape [1, bs, \
+                      num_query, embed_dims].
+                - memory: Output results from encoder, with shape \
+                      [bs, embed_dims, h, w].
+        """
+        bs, c, h, w = x.shape
+        # use `view` instead of `flatten` for dynamically exporting to ONNX
+        x = x.view(bs, c, -1).permute(2, 0, 1)  # [bs, c, h, w] -> [h*w, bs, c]
+        pos_embed = pos_embed.view(bs, c, -1).permute(2, 0, 1)
+        query_embed = query_embed.unsqueeze(1).repeat(
+            1, bs, 1)  # [num_query, dim] -> [num_query, bs, dim]
+        mask = mask.view(bs, -1)  # [bs, h, w] -> [bs, h*w]
+        memory = self.encoder(
+            query=x,
+            key=None,
+            value=None,
+            query_pos=pos_embed,
+            query_key_padding_mask=mask)
+        target = torch.zeros_like(query_embed)
+        # out_dec: [num_layers, num_query, bs, dim]
+        out_dec = self.decoder(
+            query=target,
+            key=memory,
+            value=memory,
+            key_pos=pos_embed,
+            query_pos=query_embed,
+            key_padding_mask=mask)
+        out_dec = out_dec.transpose(1, 2)
+        memory = memory.permute(1, 2, 0).reshape(bs, c, h, w)
+        return out_dec, memory
+
+
+@TRANSFORMER_LAYER_SEQUENCE.register_module()
+class DeformableDetrTransformerDecoder(TransformerLayerSequence):
+    """Implements the decoder in DETR transformer.
+
+    Args:
+        return_intermediate (bool): Whether to return intermediate outputs.
+        coder_norm_cfg (dict): Config of last normalization layer. Default：
+            `LN`.
+    """
+
+    def __init__(self, *args, return_intermediate=False, **kwargs):
+
+        super(DeformableDetrTransformerDecoder, self).__init__(*args, **kwargs)
+        self.return_intermediate = return_intermediate
+
+    def forward(self,
+                query,
+                *args,
+                reference_points=None,
+                valid_ratios=None,
+                reg_branches=None,
+                **kwargs):
+        """Forward function for `TransformerDecoder`.
+
+        Args:
+            query (Tensor): Input query with shape
+                `(num_query, bs, embed_dims)`.
+            reference_points (Tensor): The reference
+                points of offset. has shape
+                (bs, num_query, 4) when as_two_stage,
+                otherwise has shape ((bs, num_query, 2).
+            valid_ratios (Tensor): The radios of valid
+                points on the feature map, has shape
+                (bs, num_levels, 2)
+            reg_branch: (obj:`nn.ModuleList`): Used for
+                refining the regression results. Only would
+                be passed when with_box_refine is True,
+                otherwise would be passed a `None`.
+
+        Returns:
+            Tensor: Results with shape [1, num_query, bs, embed_dims] when
+                return_intermediate is `False`, otherwise it has shape
+                [num_layers, num_query, bs, embed_dims].
+        """
+        output = query
+        intermediate = []
+        intermediate_reference_points = []
+        for lid, layer in enumerate(self.layers):
+            if reference_points.shape[-1] == 4:
+                reference_points_input = reference_points[:, :, None] * \
+                    torch.cat([valid_ratios, valid_ratios], -1)[:, None]
+            else:
+                assert reference_points.shape[-1] == 2
+                reference_points_input = reference_points[:, :, None] * \
+                    valid_ratios[:, None]
+            output = layer(
+                output,
+                *args,
+                reference_points=reference_points_input,
+                **kwargs)
+            output = output.permute(1, 0, 2)
+
+            if reg_branches is not None:
+                tmp = reg_branches[lid](output)
+                if reference_points.shape[-1] == 4:
+                    new_reference_points = tmp + inverse_sigmoid(
+                        reference_points)
+                    new_reference_points = new_reference_points.sigmoid()
+                else:
+                    assert reference_points.shape[-1] == 2
+                    new_reference_points = tmp
+                    new_reference_points[..., :2] = tmp[
+                        ..., :2] + inverse_sigmoid(reference_points)
+                    new_reference_points = new_reference_points.sigmoid()
+                reference_points = new_reference_points.detach()
+
+            output = output.permute(1, 0, 2)
+            if self.return_intermediate:
+                intermediate.append(output)
+                intermediate_reference_points.append(reference_points)
+
+        if self.return_intermediate:
+            return torch.stack(intermediate), torch.stack(
+                intermediate_reference_points)
+
+        return output, reference_points
+
+
+@TRANSFORMER.register_module()
+class DeformableDetrTransformer(Transformer):
+    """Implements the DeformableDETR transformer.
+
+    Args:
+        as_two_stage (bool): Generate query from encoder features.
+            Default: False.
+        num_feature_levels (int): Number of feature maps from FPN:
+            Default: 4.
+        two_stage_num_proposals (int): Number of proposals when set
+            `as_two_stage` as True. Default: 300.
+    """
+
+    def __init__(self,
+                 as_two_stage=False,
+                 num_feature_levels=4,
+                 two_stage_num_proposals=300,
+                 **kwargs):
+        super(DeformableDetrTransformer, self).__init__(**kwargs)
+        self.as_two_stage = as_two_stage
+        self.num_feature_levels = num_feature_levels
+        self.two_stage_num_proposals = two_stage_num_proposals
+        self.embed_dims = self.encoder.embed_dims
+        self.init_layers()
+
+    def init_layers(self):
+        """Initialize layers of the DeformableDetrTransformer."""
+        self.level_embeds = nn.Parameter(
+            torch.Tensor(self.num_feature_levels, self.embed_dims))
+
+        if self.as_two_stage:
+            self.enc_output = nn.Linear(self.embed_dims, self.embed_dims)
+            self.enc_output_norm = nn.LayerNorm(self.embed_dims)
+            self.pos_trans = nn.Linear(self.embed_dims * 2,
+                                       self.embed_dims * 2)
+            self.pos_trans_norm = nn.LayerNorm(self.embed_dims * 2)
+        else:
+            self.reference_points = nn.Linear(self.embed_dims, 2)
+
+    def init_weights(self):
+        """Initialize the transformer weights."""
+        for p in self.parameters():
+            if p.dim() > 1:
+                nn.init.xavier_uniform_(p)
+        for m in self.modules():
+            if isinstance(m, MultiScaleDeformableAttention):
+                m.init_weights()
+        if not self.as_two_stage:
+            xavier_init(self.reference_points, distribution='uniform', bias=0.)
+        normal_(self.level_embeds)
+
+    def gen_encoder_output_proposals(self, memory, memory_padding_mask,
+                                     spatial_shapes):
+        """Generate proposals from encoded memory.
+
+        Args:
+            memory (Tensor) : The output of encoder,
+                has shape (bs, num_key, embed_dim).  num_key is
+                equal the number of points on feature map from
+                all level.
+            memory_padding_mask (Tensor): Padding mask for memory.
+                has shape (bs, num_key).
+            spatial_shapes (Tensor): The shape of all feature maps.
+                has shape (num_level, 2).
+
+        Returns:
+            tuple: A tuple of feature map and bbox prediction.
+
+                - output_memory (Tensor): The input of decoder,  \
+                    has shape (bs, num_key, embed_dim).  num_key is \
+                    equal the number of points on feature map from \
+                    all levels.
+                - output_proposals (Tensor): The normalized proposal \
+                    after a inverse sigmoid, has shape \
+                    (bs, num_keys, 4).
+        """
+
+        N, S, C = memory.shape
+        proposals = []
+        _cur = 0
+        for lvl, (H, W) in enumerate(spatial_shapes):
+            mask_flatten_ = memory_padding_mask[:, _cur:(_cur + H * W)].view(
+                N, H, W, 1)
+            valid_H = torch.sum(~mask_flatten_[:, :, 0, 0], 1)
+            valid_W = torch.sum(~mask_flatten_[:, 0, :, 0], 1)
+
+            grid_y, grid_x = torch.meshgrid(
+                torch.linspace(
+                    0, H - 1, H, dtype=torch.float32, device=memory.device),
+                torch.linspace(
+                    0, W - 1, W, dtype=torch.float32, device=memory.device))
+            grid = torch.cat([grid_x.unsqueeze(-1), grid_y.unsqueeze(-1)], -1)
+
+            scale = torch.cat([valid_W.unsqueeze(-1),
+                               valid_H.unsqueeze(-1)], 1).view(N, 1, 1, 2)
+            grid = (grid.unsqueeze(0).expand(N, -1, -1, -1) + 0.5) / scale
+            wh = torch.ones_like(grid) * 0.05 * (2.0**lvl)
+            proposal = torch.cat((grid, wh), -1).view(N, -1, 4)
+            proposals.append(proposal)
+            _cur += (H * W)
+        output_proposals = torch.cat(proposals, 1)
+        output_proposals_valid = ((output_proposals > 0.01) &
+                                  (output_proposals < 0.99)).all(
+                                      -1, keepdim=True)
+        output_proposals = torch.log(output_proposals / (1 - output_proposals))
+        output_proposals = output_proposals.masked_fill(
+            memory_padding_mask.unsqueeze(-1), float('inf'))
+        output_proposals = output_proposals.masked_fill(
+            ~output_proposals_valid, float('inf'))
+
+        output_memory = memory
+        output_memory = output_memory.masked_fill(
+            memory_padding_mask.unsqueeze(-1), float(0))
+        output_memory = output_memory.masked_fill(~output_proposals_valid,
+                                                  float(0))
+        output_memory = self.enc_output_norm(self.enc_output(output_memory))
+        return output_memory, output_proposals
+
+    @staticmethod
+    def get_reference_points(spatial_shapes, valid_ratios, device):
+        """Get the reference points used in decoder.
+
+        Args:
+            spatial_shapes (Tensor): The shape of all
+                feature maps, has shape (num_level, 2).
+            valid_ratios (Tensor): The radios of valid
+                points on the feature map, has shape
+                (bs, num_levels, 2)
+            device (obj:`device`): The device where
+                reference_points should be.
+
+        Returns:
+            Tensor: reference points used in decoder, has \
+                shape (bs, num_keys, num_levels, 2).
+        """
+        reference_points_list = []
+        for lvl, (H, W) in enumerate(spatial_shapes):
+            #  TODO  check this 0.5
+            ref_y, ref_x = torch.meshgrid(
+                torch.linspace(
+                    0.5, H - 0.5, H, dtype=torch.float32, device=device),
+                torch.linspace(
+                    0.5, W - 0.5, W, dtype=torch.float32, device=device))
+            ref_y = ref_y.reshape(-1)[None] / (
+                valid_ratios[:, None, lvl, 1] * H)
+            ref_x = ref_x.reshape(-1)[None] / (
+                valid_ratios[:, None, lvl, 0] * W)
+            ref = torch.stack((ref_x, ref_y), -1)
+            reference_points_list.append(ref)
+        reference_points = torch.cat(reference_points_list, 1)
+        reference_points = reference_points[:, :, None] * valid_ratios[:, None]
+        return reference_points
+
+    def get_valid_ratio(self, mask):
+        """Get the valid radios of feature maps of all  level."""
+        _, H, W = mask.shape
+        valid_H = torch.sum(~mask[:, :, 0], 1)
+        valid_W = torch.sum(~mask[:, 0, :], 1)
+        valid_ratio_h = valid_H.float() / H
+        valid_ratio_w = valid_W.float() / W
+        valid_ratio = torch.stack([valid_ratio_w, valid_ratio_h], -1)
+        return valid_ratio
+
+    def get_proposal_pos_embed(self,
+                               proposals,
+                               num_pos_feats=128,
+                               temperature=10000):
+        """Get the position embedding of proposal."""
+        scale = 2 * math.pi
+        dim_t = torch.arange(
+            num_pos_feats, dtype=torch.float32, device=proposals.device)
+        dim_t = temperature**(2 * (dim_t // 2) / num_pos_feats)
+        # N, L, 4
+        proposals = proposals.sigmoid() * scale
+        # N, L, 4, 128
+        pos = proposals[:, :, :, None] / dim_t
+        # N, L, 4, 64, 2
+        pos = torch.stack((pos[:, :, :, 0::2].sin(), pos[:, :, :, 1::2].cos()),
+                          dim=4).flatten(2)
+        return pos
+
+    def forward(self,
+                mlvl_feats,
+                mlvl_masks,
+                query_embed,
+                mlvl_pos_embeds,
+                reg_branches=None,
+                cls_branches=None,
+                **kwargs):
+        """Forward function for `Transformer`.
+
+        Args:
+            mlvl_feats (list(Tensor)): Input queries from
+                different level. Each element has shape
+                [bs, embed_dims, h, w].
+            mlvl_masks (list(Tensor)): The key_padding_mask from
+                different level used for encoder and decoder,
+                each element has shape  [bs, h, w].
+            query_embed (Tensor): The query embedding for decoder,
+                with shape [num_query, c].
+            mlvl_pos_embeds (list(Tensor)): The positional encoding
+                of feats from different level, has the shape
+                 [bs, embed_dims, h, w].
+            reg_branches (obj:`nn.ModuleList`): Regression heads for
+                feature maps from each decoder layer. Only would
+                be passed when
+                `with_box_refine` is True. Default to None.
+            cls_branches (obj:`nn.ModuleList`): Classification heads
+                for feature maps from each decoder layer. Only would
+                 be passed when `as_two_stage`
+                 is True. Default to None.
+
+
+        Returns:
+            tuple[Tensor]: results of decoder containing the following tensor.
+
+                - inter_states: Outputs from decoder. If
+                    return_intermediate_dec is True output has shape \
+                      (num_dec_layers, bs, num_query, embed_dims), else has \
+                      shape (1, bs, num_query, embed_dims).
+                - init_reference_out: The initial value of reference \
+                    points, has shape (bs, num_queries, 4).
+                - inter_references_out: The internal value of reference \
+                    points in decoder, has shape \
+                    (num_dec_layers, bs,num_query, embed_dims)
+                - enc_outputs_class: The classification score of \
+                    proposals generated from \
+                    encoder's feature maps, has shape \
+                    (batch, h*w, num_classes). \
+                    Only would be returned when `as_two_stage` is True, \
+                    otherwise None.
+                - enc_outputs_coord_unact: The regression results \
+                    generated from encoder's feature maps., has shape \
+                    (batch, h*w, 4). Only would \
+                    be returned when `as_two_stage` is True, \
+                    otherwise None.
+        """
+        assert self.as_two_stage or query_embed is not None
+
+        feat_flatten = []
+        mask_flatten = []
+        lvl_pos_embed_flatten = []
+        spatial_shapes = []
+        for lvl, (feat, mask, pos_embed) in enumerate(
+                zip(mlvl_feats, mlvl_masks, mlvl_pos_embeds)):
+            bs, c, h, w = feat.shape
+            spatial_shape = (h, w)
+            spatial_shapes.append(spatial_shape)
+            feat = feat.flatten(2).transpose(1, 2)
+            mask = mask.flatten(1)
+            pos_embed = pos_embed.flatten(2).transpose(1, 2)
+            lvl_pos_embed = pos_embed + self.level_embeds[lvl].view(1, 1, -1)
+            lvl_pos_embed_flatten.append(lvl_pos_embed)
+            feat_flatten.append(feat)
+            mask_flatten.append(mask)
+        feat_flatten = torch.cat(feat_flatten, 1)
+        mask_flatten = torch.cat(mask_flatten, 1)
+        lvl_pos_embed_flatten = torch.cat(lvl_pos_embed_flatten, 1)
+        spatial_shapes = torch.as_tensor(
+            spatial_shapes, dtype=torch.long, device=feat_flatten.device)
+        level_start_index = torch.cat((spatial_shapes.new_zeros(
+            (1, )), spatial_shapes.prod(1).cumsum(0)[:-1]))
+        valid_ratios = torch.stack(
+            [self.get_valid_ratio(m) for m in mlvl_masks], 1)
+
+        reference_points = \
+            self.get_reference_points(spatial_shapes,
+                                      valid_ratios,
+                                      device=feat.device)
+
+        feat_flatten = feat_flatten.permute(1, 0, 2)  # (H*W, bs, embed_dims)
+        lvl_pos_embed_flatten = lvl_pos_embed_flatten.permute(
+            1, 0, 2)  # (H*W, bs, embed_dims)
+        memory = self.encoder(
+            query=feat_flatten,
+            key=None,
+            value=None,
+            query_pos=lvl_pos_embed_flatten,
+            query_key_padding_mask=mask_flatten,
+            spatial_shapes=spatial_shapes,
+            reference_points=reference_points,
+            level_start_index=level_start_index,
+            valid_ratios=valid_ratios,
+            **kwargs)
+
+        memory = memory.permute(1, 0, 2)
+        bs, _, c = memory.shape
+        if self.as_two_stage:
+            output_memory, output_proposals = \
+                self.gen_encoder_output_proposals(
+                    memory, mask_flatten, spatial_shapes)
+            enc_outputs_class = cls_branches[self.decoder.num_layers](
+                output_memory)
+            enc_outputs_coord_unact = \
+                reg_branches[
+                    self.decoder.num_layers](output_memory) + output_proposals
+
+            topk = self.two_stage_num_proposals
+            # We only use the first channel in enc_outputs_class as foreground,
+            # the other (num_classes - 1) channels are actually not used.
+            # Its targets are set to be 0s, which indicates the first
+            # class (foreground) because we use [0, num_classes - 1] to
+            # indicate class labels, background class is indicated by
+            # num_classes (similar convention in RPN).
+            # See https://github.com/open-mmlab/mmdetection/blob/master/mmdet/models/dense_heads/deformable_detr_head.py#L241 # noqa
+            # This follows the official implementation of Deformable DETR.
+            topk_proposals = torch.topk(
+                enc_outputs_class[..., 0], topk, dim=1)[1]
+            topk_coords_unact = torch.gather(
+                enc_outputs_coord_unact, 1,
+                topk_proposals.unsqueeze(-1).repeat(1, 1, 4))
+            topk_coords_unact = topk_coords_unact.detach()
+            reference_points = topk_coords_unact.sigmoid()
+            init_reference_out = reference_points
+            pos_trans_out = self.pos_trans_norm(
+                self.pos_trans(self.get_proposal_pos_embed(topk_coords_unact)))
+            query_pos, query = torch.split(pos_trans_out, c, dim=2)
+        else:
+            query_pos, query = torch.split(query_embed, c, dim=1)
+            query_pos = query_pos.unsqueeze(0).expand(bs, -1, -1)
+            query = query.unsqueeze(0).expand(bs, -1, -1)
+            reference_points = self.reference_points(query_pos).sigmoid()
+            init_reference_out = reference_points
+
+        # decoder
+        query = query.permute(1, 0, 2)
+        memory = memory.permute(1, 0, 2)
+        query_pos = query_pos.permute(1, 0, 2)
+        inter_states, inter_references = self.decoder(
+            query=query,
+            key=None,
+            value=memory,
+            query_pos=query_pos,
+            key_padding_mask=mask_flatten,
+            reference_points=reference_points,
+            spatial_shapes=spatial_shapes,
+            level_start_index=level_start_index,
+            valid_ratios=valid_ratios,
+            reg_branches=reg_branches,
+            **kwargs)
+
+        inter_references_out = inter_references
+        if self.as_two_stage:
+            return inter_states, init_reference_out,\
+                inter_references_out, enc_outputs_class,\
+                enc_outputs_coord_unact
+        return inter_states, init_reference_out, \
+            inter_references_out, None, None
+
+
+@TRANSFORMER.register_module()
+class DynamicConv(BaseModule):
+    """Implements Dynamic Convolution.
+
+    This module generate parameters for each sample and
+    use bmm to implement 1*1 convolution. Code is modified
+    from the `official github repo <https://github.com/PeizeSun/
+    SparseR-CNN/blob/main/projects/SparseRCNN/sparsercnn/head.py#L258>`_ .
+
+    Args:
+        in_channels (int): The input feature channel.
+            Defaults to 256.
+        feat_channels (int): The inner feature channel.
+            Defaults to 64.
+        out_channels (int, optional): The output feature channel.
+            When not specified, it will be set to `in_channels`
+            by default
+        input_feat_shape (int): The shape of input feature.
+            Defaults to 7.
+        with_proj (bool): Project two-dimentional feature to
+            one-dimentional feature. Default to True.
+        act_cfg (dict): The activation config for DynamicConv.
+        norm_cfg (dict): Config dict for normalization layer. Default
+            layer normalization.
+        init_cfg (obj:`mmcv.ConfigDict`): The Config for initialization.
+            Default: None.
+    """
+
+    def __init__(self,
+                 in_channels=256,
+                 feat_channels=64,
+                 out_channels=None,
+                 input_feat_shape=7,
+                 with_proj=True,
+                 act_cfg=dict(type='ReLU', inplace=True),
+                 norm_cfg=dict(type='LN'),
+                 init_cfg=None):
+        super(DynamicConv, self).__init__(init_cfg)
+        self.in_channels = in_channels
+        self.feat_channels = feat_channels
+        self.out_channels_raw = out_channels
+        self.input_feat_shape = input_feat_shape
+        self.with_proj = with_proj
+        self.act_cfg = act_cfg
+        self.norm_cfg = norm_cfg
+        self.out_channels = out_channels if out_channels else in_channels
+
+        self.num_params_in = self.in_channels * self.feat_channels
+        self.num_params_out = self.out_channels * self.feat_channels
+        self.dynamic_layer = nn.Linear(
+            self.in_channels, self.num_params_in + self.num_params_out)
+
+        self.norm_in = build_norm_layer(norm_cfg, self.feat_channels)[1]
+        self.norm_out = build_norm_layer(norm_cfg, self.out_channels)[1]
+
+        self.activation = build_activation_layer(act_cfg)
+
+        num_output = self.out_channels * input_feat_shape**2
+        if self.with_proj:
+            self.fc_layer = nn.Linear(num_output, self.out_channels)
+            self.fc_norm = build_norm_layer(norm_cfg, self.out_channels)[1]
+
+    def forward(self, param_feature, input_feature):
+        """Forward function for `DynamicConv`.
+
+        Args:
+            param_feature (Tensor): The feature can be used
+                to generate the parameter, has shape
+                (num_all_proposals, in_channels).
+            input_feature (Tensor): Feature that
+                interact with parameters, has shape
+                (num_all_proposals, in_channels, H, W).
+
+        Returns:
+            Tensor: The output feature has shape
+            (num_all_proposals, out_channels).
+        """
+        input_feature = input_feature.flatten(2).permute(2, 0, 1)
+
+        input_feature = input_feature.permute(1, 0, 2)
+        parameters = self.dynamic_layer(param_feature)
+
+        param_in = parameters[:, :self.num_params_in].view(
+            -1, self.in_channels, self.feat_channels)
+        param_out = parameters[:, -self.num_params_out:].view(
+            -1, self.feat_channels, self.out_channels)
+
+        # input_feature has shape (num_all_proposals, H*W, in_channels)
+        # param_in has shape (num_all_proposals, in_channels, feat_channels)
+        # feature has shape (num_all_proposals, H*W, feat_channels)
+        features = torch.bmm(input_feature, param_in)
+        features = self.norm_in(features)
+        features = self.activation(features)
+
+        # param_out has shape (batch_size, feat_channels, out_channels)
+        features = torch.bmm(features, param_out)
+        features = self.norm_out(features)
+        features = self.activation(features)
+
+        if self.with_proj:
+            features = features.flatten(1)
+            features = self.fc_layer(features)
+            features = self.fc_norm(features)
+            features = self.activation(features)
+
+        return features
diff --git a/mmdet/utils/__init__.py b/mmdet/utils/__init__.py
new file mode 100644
index 0000000..728fd9d
--- /dev/null
+++ b/mmdet/utils/__init__.py
@@ -0,0 +1,22 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .ascend_util import (batch_images_to_levels,
+                          get_max_num_gt_division_factor, masked_fill)
+from .collect_env import collect_env
+from .compat_config import compat_cfg, fast_compat_cfg
+from .logger import get_caller_name, get_root_logger, log_img_scale
+from .memory import AvoidCUDAOOM, AvoidOOM
+from .misc import find_latest_checkpoint, update_data_root
+from .replace_cfg_vals import replace_cfg_vals
+from .rfnext import rfnext_init_model
+from .setup_env import setup_multi_processes
+from .split_batch import split_batch
+from .util_distribution import build_ddp, build_dp, get_device
+
+__all__ = [
+    'get_root_logger', 'collect_env', 'find_latest_checkpoint',
+    'update_data_root', 'setup_multi_processes', 'get_caller_name',
+    'log_img_scale', 'compat_cfg', 'split_batch', 'build_ddp', 'build_dp',
+    'get_device', 'replace_cfg_vals', 'AvoidOOM', 'AvoidCUDAOOM',
+    'get_max_num_gt_division_factor', 'masked_fill', 'batch_images_to_levels',
+    'rfnext_init_model'
+]
diff --git a/mmdet/utils/ascend_util.py b/mmdet/utils/ascend_util.py
new file mode 100644
index 0000000..df90dec
--- /dev/null
+++ b/mmdet/utils/ascend_util.py
@@ -0,0 +1,69 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+
+def masked_fill(ori_tensor, mask, new_value, neg=False):
+    """The Value of ori_tensor is new_value, depending on mask.
+
+    Args:
+        ori_tensor (Tensor): Input tensor.
+        mask (Tensor): If select new_value.
+        new_value(Tensor | scalar): Value selected for ori_tensor.
+        neg (bool): If True, select ori_tensor. If False, select new_value.
+    Returns:
+        ori_tensor: (Tensor): The Value of ori_tensor is new_value,
+            depending on mask.
+    """
+    if mask is None:
+        return ori_tensor
+    else:
+        if neg:
+            return ori_tensor * mask + new_value * (1 - mask)
+        else:
+            return ori_tensor * (1 - mask) + new_value * mask
+
+
+def batch_images_to_levels(target, num_levels):
+    """Convert targets by image to targets by feature level.
+
+    [target_img0, target_img1] -> [target_level0, target_level1, ...]  or
+    target_imgs -> [target_level0, target_level1, ...]
+    Args:
+        target (Tensor | List[Tensor]): Tensor split to image levels.
+        num_levels (List[int]): Image levels num.
+    Returns:
+        level_targets: (Tensor): Tensor split by image levels.
+    """
+    if not isinstance(target, torch.Tensor):
+        target = torch.stack(target, 0)
+    level_targets = []
+    start = 0
+    for n in num_levels:
+        end = start + n
+        # level_targets.append(target[:, start:end].squeeze(0))
+        level_targets.append(target[:, start:end])
+        start = end
+    return level_targets
+
+
+def get_max_num_gt_division_factor(gt_nums,
+                                   min_num_gt=32,
+                                   max_num_gt=1024,
+                                   division_factor=2):
+    """Count max num of gt.
+
+    Args:
+        gt_nums (List[int]):  Ground truth bboxes num of images.
+        min_num_gt (int): Min num of ground truth bboxes.
+        max_num_gt (int): Max num of ground truth bboxes.
+        division_factor (int): Division factor of result.
+    Returns:
+        max_gt_nums_align: (int): max num of ground truth bboxes.
+    """
+    max_gt_nums = max(gt_nums)
+    max_gt_nums_align = min_num_gt
+    while max_gt_nums_align < max_gt_nums:
+        max_gt_nums_align *= division_factor
+    if max_gt_nums_align > max_num_gt:
+        raise RuntimeError
+    return max_gt_nums_align
diff --git a/mmdet/utils/collect_env.py b/mmdet/utils/collect_env.py
new file mode 100644
index 0000000..97e25c0
--- /dev/null
+++ b/mmdet/utils/collect_env.py
@@ -0,0 +1,17 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmcv.utils import collect_env as collect_base_env
+from mmcv.utils import get_git_hash
+
+import mmdet
+
+
+def collect_env():
+    """Collect the information of the running environments."""
+    env_info = collect_base_env()
+    env_info['MMDetection'] = mmdet.__version__ + '+' + get_git_hash()[:7]
+    return env_info
+
+
+if __name__ == '__main__':
+    for name, val in collect_env().items():
+        print(f'{name}: {val}')
diff --git a/mmdet/utils/compat_config.py b/mmdet/utils/compat_config.py
new file mode 100644
index 0000000..9c99180
--- /dev/null
+++ b/mmdet/utils/compat_config.py
@@ -0,0 +1,152 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import warnings
+
+from mmcv import ConfigDict
+
+
+def compat_cfg(cfg):
+    """This function would modify some filed to keep the compatibility of
+    config.
+
+    For example, it will move some args which will be deprecated to the correct
+    fields.
+    """
+    cfg = copy.deepcopy(cfg)
+    cfg = compat_imgs_per_gpu(cfg)
+    cfg = compat_loader_args(cfg)
+    cfg = compat_runner_args(cfg)
+    return cfg
+
+
+def fast_compat_cfg(cfg):
+    """This function would modify some filed to keep the compatibility of
+    config.
+
+    For example, it will move some args which will be deprecated to the correct
+    fields.
+    """
+    cfg = copy.deepcopy(cfg)
+    cfg = compat_imgs_per_gpu(cfg)
+    cfg = compat_loader_args(cfg)
+    # cfg = compat_runner_args(cfg)
+    return cfg
+
+def compat_runner_args(cfg):
+    if 'runner' not in cfg:
+        cfg.runner = ConfigDict({
+            'type': 'EpochBasedRunner',
+            'max_epochs': cfg.total_epochs
+        })
+        warnings.warn(
+            'config is now expected to have a `runner` section, '
+            'please set `runner` in your config.', UserWarning)
+    else:
+        if 'total_epochs' in cfg:
+            assert cfg.total_epochs == cfg.runner.max_epochs
+    return cfg
+
+
+def compat_imgs_per_gpu(cfg):
+    cfg = copy.deepcopy(cfg)
+    if 'imgs_per_gpu' in cfg.data:
+        warnings.warn('"imgs_per_gpu" is deprecated in MMDet V2.0. '
+                      'Please use "samples_per_gpu" instead')
+        if 'samples_per_gpu' in cfg.data:
+            warnings.warn(
+                f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and '
+                f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"'
+                f'={cfg.data.imgs_per_gpu} is used in this experiments')
+        else:
+            warnings.warn('Automatically set "samples_per_gpu"="imgs_per_gpu"='
+                          f'{cfg.data.imgs_per_gpu} in this experiments')
+        cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu
+    return cfg
+
+
+def compat_loader_args(cfg):
+    """Deprecated sample_per_gpu in cfg.data."""
+
+    cfg = copy.deepcopy(cfg)
+    if 'train_dataloader' not in cfg.data:
+        cfg.data['train_dataloader'] = ConfigDict()
+    if 'val_dataloader' not in cfg.data:
+        cfg.data['val_dataloader'] = ConfigDict()
+    if 'test_dataloader' not in cfg.data:
+        cfg.data['test_dataloader'] = ConfigDict()
+
+    # special process for train_dataloader
+    if 'samples_per_gpu' in cfg.data:
+
+        samples_per_gpu = cfg.data.pop('samples_per_gpu')
+        assert 'samples_per_gpu' not in \
+               cfg.data.train_dataloader, ('`samples_per_gpu` are set '
+                                           'in `data` field and ` '
+                                           'data.train_dataloader` '
+                                           'at the same time. '
+                                           'Please only set it in '
+                                           '`data.train_dataloader`. ')
+        cfg.data.train_dataloader['samples_per_gpu'] = samples_per_gpu
+
+    if 'persistent_workers' in cfg.data:
+
+        persistent_workers = cfg.data.pop('persistent_workers')
+        assert 'persistent_workers' not in \
+               cfg.data.train_dataloader, ('`persistent_workers` are set '
+                                           'in `data` field and ` '
+                                           'data.train_dataloader` '
+                                           'at the same time. '
+                                           'Please only set it in '
+                                           '`data.train_dataloader`. ')
+        cfg.data.train_dataloader['persistent_workers'] = persistent_workers
+
+    if 'workers_per_gpu' in cfg.data:
+
+        workers_per_gpu = cfg.data.pop('workers_per_gpu')
+        cfg.data.train_dataloader['workers_per_gpu'] = workers_per_gpu
+        cfg.data.val_dataloader['workers_per_gpu'] = workers_per_gpu
+        cfg.data.test_dataloader['workers_per_gpu'] = workers_per_gpu
+
+    # special process for val_dataloader
+    if 'samples_per_gpu' in cfg.data.val:
+        # keep default value of `sample_per_gpu` is 1
+        assert 'samples_per_gpu' not in \
+               cfg.data.val_dataloader, ('`samples_per_gpu` are set '
+                                         'in `data.val` field and ` '
+                                         'data.val_dataloader` at '
+                                         'the same time. '
+                                         'Please only set it in '
+                                         '`data.val_dataloader`. ')
+        cfg.data.val_dataloader['samples_per_gpu'] = \
+            cfg.data.val.pop('samples_per_gpu')
+    # special process for val_dataloader
+
+    # in case the test dataset is concatenated
+    if isinstance(cfg.data.test, dict):
+        if 'samples_per_gpu' in cfg.data.test:
+            assert 'samples_per_gpu' not in \
+                   cfg.data.test_dataloader, ('`samples_per_gpu` are set '
+                                              'in `data.test` field and ` '
+                                              'data.test_dataloader` '
+                                              'at the same time. '
+                                              'Please only set it in '
+                                              '`data.test_dataloader`. ')
+
+            cfg.data.test_dataloader['samples_per_gpu'] = \
+                cfg.data.test.pop('samples_per_gpu')
+
+    elif isinstance(cfg.data.test, list):
+        for ds_cfg in cfg.data.test:
+            if 'samples_per_gpu' in ds_cfg:
+                assert 'samples_per_gpu' not in \
+                       cfg.data.test_dataloader, ('`samples_per_gpu` are set '
+                                                  'in `data.test` field and ` '
+                                                  'data.test_dataloader` at'
+                                                  ' the same time. '
+                                                  'Please only set it in '
+                                                  '`data.test_dataloader`. ')
+        samples_per_gpu = max(
+            [ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test])
+        cfg.data.test_dataloader['samples_per_gpu'] = samples_per_gpu
+
+    return cfg
diff --git a/mmdet/utils/contextmanagers.py b/mmdet/utils/contextmanagers.py
new file mode 100644
index 0000000..fa12bfc
--- /dev/null
+++ b/mmdet/utils/contextmanagers.py
@@ -0,0 +1,122 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import asyncio
+import contextlib
+import logging
+import os
+import time
+from typing import List
+
+import torch
+
+logger = logging.getLogger(__name__)
+
+DEBUG_COMPLETED_TIME = bool(os.environ.get('DEBUG_COMPLETED_TIME', False))
+
+
+@contextlib.asynccontextmanager
+async def completed(trace_name='',
+                    name='',
+                    sleep_interval=0.05,
+                    streams: List[torch.cuda.Stream] = None):
+    """Async context manager that waits for work to complete on given CUDA
+    streams."""
+    if not torch.cuda.is_available():
+        yield
+        return
+
+    stream_before_context_switch = torch.cuda.current_stream()
+    if not streams:
+        streams = [stream_before_context_switch]
+    else:
+        streams = [s if s else stream_before_context_switch for s in streams]
+
+    end_events = [
+        torch.cuda.Event(enable_timing=DEBUG_COMPLETED_TIME) for _ in streams
+    ]
+
+    if DEBUG_COMPLETED_TIME:
+        start = torch.cuda.Event(enable_timing=True)
+        stream_before_context_switch.record_event(start)
+
+        cpu_start = time.monotonic()
+    logger.debug('%s %s starting, streams: %s', trace_name, name, streams)
+    grad_enabled_before = torch.is_grad_enabled()
+    try:
+        yield
+    finally:
+        current_stream = torch.cuda.current_stream()
+        assert current_stream == stream_before_context_switch
+
+        if DEBUG_COMPLETED_TIME:
+            cpu_end = time.monotonic()
+        for i, stream in enumerate(streams):
+            event = end_events[i]
+            stream.record_event(event)
+
+        grad_enabled_after = torch.is_grad_enabled()
+
+        # observed change of torch.is_grad_enabled() during concurrent run of
+        # async_test_bboxes code
+        assert (grad_enabled_before == grad_enabled_after
+                ), 'Unexpected is_grad_enabled() value change'
+
+        are_done = [e.query() for e in end_events]
+        logger.debug('%s %s completed: %s streams: %s', trace_name, name,
+                     are_done, streams)
+        with torch.cuda.stream(stream_before_context_switch):
+            while not all(are_done):
+                await asyncio.sleep(sleep_interval)
+                are_done = [e.query() for e in end_events]
+                logger.debug(
+                    '%s %s completed: %s streams: %s',
+                    trace_name,
+                    name,
+                    are_done,
+                    streams,
+                )
+
+        current_stream = torch.cuda.current_stream()
+        assert current_stream == stream_before_context_switch
+
+        if DEBUG_COMPLETED_TIME:
+            cpu_time = (cpu_end - cpu_start) * 1000
+            stream_times_ms = ''
+            for i, stream in enumerate(streams):
+                elapsed_time = start.elapsed_time(end_events[i])
+                stream_times_ms += f' {stream} {elapsed_time:.2f} ms'
+            logger.info('%s %s %.2f ms %s', trace_name, name, cpu_time,
+                        stream_times_ms)
+
+
+@contextlib.asynccontextmanager
+async def concurrent(streamqueue: asyncio.Queue,
+                     trace_name='concurrent',
+                     name='stream'):
+    """Run code concurrently in different streams.
+
+    :param streamqueue: asyncio.Queue instance.
+
+    Queue tasks define the pool of streams used for concurrent execution.
+    """
+    if not torch.cuda.is_available():
+        yield
+        return
+
+    initial_stream = torch.cuda.current_stream()
+
+    with torch.cuda.stream(initial_stream):
+        stream = await streamqueue.get()
+        assert isinstance(stream, torch.cuda.Stream)
+
+        try:
+            with torch.cuda.stream(stream):
+                logger.debug('%s %s is starting, stream: %s', trace_name, name,
+                             stream)
+                yield
+                current = torch.cuda.current_stream()
+                assert current == stream
+                logger.debug('%s %s has finished, stream: %s', trace_name,
+                             name, stream)
+        finally:
+            streamqueue.task_done()
+            streamqueue.put_nowait(stream)
diff --git a/mmdet/utils/logger.py b/mmdet/utils/logger.py
new file mode 100644
index 0000000..485f641
--- /dev/null
+++ b/mmdet/utils/logger.py
@@ -0,0 +1,65 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import inspect
+import logging
+
+from mmcv.utils import get_logger
+
+
+def get_root_logger(log_file=None, log_level=logging.INFO):
+    """Get root logger.
+
+    Args:
+        log_file (str, optional): File path of log. Defaults to None.
+        log_level (int, optional): The level of logger.
+            Defaults to logging.INFO.
+
+    Returns:
+        :obj:`logging.Logger`: The obtained logger
+    """
+    logger = get_logger(name='mmdet', log_file=log_file, log_level=log_level)
+
+    return logger
+
+
+def get_caller_name():
+    """Get name of caller method."""
+    # this_func_frame = inspect.stack()[0][0]  # i.e., get_caller_name
+    # callee_frame = inspect.stack()[1][0]  # e.g., log_img_scale
+    caller_frame = inspect.stack()[2][0]  # e.g., caller of log_img_scale
+    caller_method = caller_frame.f_code.co_name
+    try:
+        caller_class = caller_frame.f_locals['self'].__class__.__name__
+        return f'{caller_class}.{caller_method}'
+    except KeyError:  # caller is a function
+        return caller_method
+
+
+def log_img_scale(img_scale, shape_order='hw', skip_square=False):
+    """Log image size.
+
+    Args:
+        img_scale (tuple): Image size to be logged.
+        shape_order (str, optional): The order of image shape.
+            'hw' for (height, width) and 'wh' for (width, height).
+            Defaults to 'hw'.
+        skip_square (bool, optional): Whether to skip logging for square
+            img_scale. Defaults to False.
+
+    Returns:
+        bool: Whether to have done logging.
+    """
+    if shape_order == 'hw':
+        height, width = img_scale
+    elif shape_order == 'wh':
+        width, height = img_scale
+    else:
+        raise ValueError(f'Invalid shape_order {shape_order}.')
+
+    if skip_square and (height == width):
+        return False
+
+    logger = get_root_logger()
+    caller = get_caller_name()
+    logger.info(f'image shape: height={height}, width={width} in {caller}')
+
+    return True
diff --git a/mmdet/utils/memory.py b/mmdet/utils/memory.py
new file mode 100644
index 0000000..eb212bc
--- /dev/null
+++ b/mmdet/utils/memory.py
@@ -0,0 +1,213 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+from collections import abc
+from contextlib import contextmanager
+from functools import wraps
+
+import torch
+
+from mmdet.utils import get_root_logger
+
+
+def cast_tensor_type(inputs, src_type=None, dst_type=None):
+    """Recursively convert Tensor in inputs from ``src_type`` to ``dst_type``.
+
+    Args:
+        inputs: Inputs that to be casted.
+        src_type (torch.dtype | torch.device): Source type.
+        src_type (torch.dtype | torch.device): Destination type.
+
+    Returns:
+        The same type with inputs, but all contained Tensors have been cast.
+    """
+    assert dst_type is not None
+    if isinstance(inputs, torch.Tensor):
+        if isinstance(dst_type, torch.device):
+            # convert Tensor to dst_device
+            if hasattr(inputs, 'to') and \
+                    hasattr(inputs, 'device') and \
+                    (inputs.device == src_type or src_type is None):
+                return inputs.to(dst_type)
+            else:
+                return inputs
+        else:
+            # convert Tensor to dst_dtype
+            if hasattr(inputs, 'to') and \
+                    hasattr(inputs, 'dtype') and \
+                    (inputs.dtype == src_type or src_type is None):
+                return inputs.to(dst_type)
+            else:
+                return inputs
+        # we need to ensure that the type of inputs to be casted are the same
+        # as the argument `src_type`.
+    elif isinstance(inputs, abc.Mapping):
+        return type(inputs)({
+            k: cast_tensor_type(v, src_type=src_type, dst_type=dst_type)
+            for k, v in inputs.items()
+        })
+    elif isinstance(inputs, abc.Iterable):
+        return type(inputs)(
+            cast_tensor_type(item, src_type=src_type, dst_type=dst_type)
+            for item in inputs)
+    # TODO: Currently not supported
+    # elif isinstance(inputs, InstanceData):
+    #     for key, value in inputs.items():
+    #         inputs[key] = cast_tensor_type(
+    #             value, src_type=src_type, dst_type=dst_type)
+    #     return inputs
+    else:
+        return inputs
+
+
+@contextmanager
+def _ignore_torch_cuda_oom():
+    """A context which ignores CUDA OOM exception from pytorch.
+
+    Code is modified from
+    <https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/memory.py>  # noqa: E501
+    """
+    try:
+        yield
+    except RuntimeError as e:
+        # NOTE: the string may change?
+        if 'CUDA out of memory. ' in str(e):
+            pass
+        else:
+            raise
+
+
+class AvoidOOM:
+    """Try to convert inputs to FP16 and CPU if got a PyTorch's CUDA Out of
+    Memory error. It will do the following steps:
+
+        1. First retry after calling `torch.cuda.empty_cache()`.
+        2. If that still fails, it will then retry by converting inputs
+          to FP16.
+        3. If that still fails trying to convert inputs to CPUs.
+          In this case, it expects the function to dispatch to
+          CPU implementation.
+
+    Args:
+        to_cpu (bool): Whether to convert outputs to CPU if get an OOM
+            error. This will slow down the code significantly.
+            Defaults to True.
+        test (bool): Skip `_ignore_torch_cuda_oom` operate that can use
+            lightweight data in unit test, only used in
+            test unit. Defaults to False.
+
+    Examples:
+        >>> from mmdet.utils.memory import AvoidOOM
+        >>> AvoidCUDAOOM = AvoidOOM()
+        >>> output = AvoidOOM.retry_if_cuda_oom(
+        >>>     some_torch_function)(input1, input2)
+        >>> # To use as a decorator
+        >>> # from mmdet.utils import AvoidCUDAOOM
+        >>> @AvoidCUDAOOM.retry_if_cuda_oom
+        >>> def function(*args, **kwargs):
+        >>>     return None
+    ```
+
+    Note:
+        1. The output may be on CPU even if inputs are on GPU. Processing
+            on CPU will slow down the code significantly.
+        2. When converting inputs to CPU, it will only look at each argument
+            and check if it has `.device` and `.to` for conversion. Nested
+            structures of tensors are not supported.
+        3. Since the function might be called more than once, it has to be
+            stateless.
+    """
+
+    def __init__(self, to_cpu=True, test=False):
+        self.to_cpu = to_cpu
+        self.test = test
+
+    def retry_if_cuda_oom(self, func):
+        """Makes a function retry itself after encountering pytorch's CUDA OOM
+        error.
+
+        The implementation logic is referred to
+        https://github.com/facebookresearch/detectron2/blob/main/detectron2/utils/memory.py
+
+        Args:
+            func: a stateless callable that takes tensor-like objects
+                as arguments.
+        Returns:
+            func: a callable which retries `func` if OOM is encountered.
+        """  # noqa: W605
+
+        @wraps(func)
+        def wrapped(*args, **kwargs):
+
+            # raw function
+            if not self.test:
+                with _ignore_torch_cuda_oom():
+                    return func(*args, **kwargs)
+
+                # Clear cache and retry
+                torch.cuda.empty_cache()
+                with _ignore_torch_cuda_oom():
+                    return func(*args, **kwargs)
+
+            # get the type and device of first tensor
+            dtype, device = None, None
+            values = args + tuple(kwargs.values())
+            for value in values:
+                if isinstance(value, torch.Tensor):
+                    dtype = value.dtype
+                    device = value.device
+                    break
+            if dtype is None or device is None:
+                raise ValueError('There is no tensor in the inputs, '
+                                 'cannot get dtype and device.')
+
+            # Convert to FP16
+            fp16_args = cast_tensor_type(args, dst_type=torch.half)
+            fp16_kwargs = cast_tensor_type(kwargs, dst_type=torch.half)
+            logger = get_root_logger()
+            logger.warning(f'Attempting to copy inputs of {str(func)} '
+                           'to FP16 due to CUDA OOM')
+
+            # get input tensor type, the output type will same as
+            # the first parameter type.
+            with _ignore_torch_cuda_oom():
+                output = func(*fp16_args, **fp16_kwargs)
+                output = cast_tensor_type(
+                    output, src_type=torch.half, dst_type=dtype)
+                if not self.test:
+                    return output
+            logger.warning('Using FP16 still meet CUDA OOM')
+
+            # Try on CPU. This will slow down the code significantly,
+            # therefore print a notice.
+            if self.to_cpu:
+                logger.warning(f'Attempting to copy inputs of {str(func)} '
+                               'to CPU due to CUDA OOM')
+                cpu_device = torch.empty(0).device
+                cpu_args = cast_tensor_type(args, dst_type=cpu_device)
+                cpu_kwargs = cast_tensor_type(kwargs, dst_type=cpu_device)
+
+                # convert outputs to GPU
+                with _ignore_torch_cuda_oom():
+                    logger.warning(f'Convert outputs to GPU (device={device})')
+                    output = func(*cpu_args, **cpu_kwargs)
+                    output = cast_tensor_type(
+                        output, src_type=cpu_device, dst_type=device)
+                    return output
+
+                warnings.warn('Cannot convert output to GPU due to CUDA OOM, '
+                              'the output is now on CPU, which might cause '
+                              'errors if the output need to interact with GPU '
+                              'data in subsequent operations')
+                logger.warning('Cannot convert output to GPU due to '
+                               'CUDA OOM, the output is on CPU now.')
+
+                return func(*cpu_args, **cpu_kwargs)
+            else:
+                # may still get CUDA OOM error
+                return func(*args, **kwargs)
+
+        return wrapped
+
+
+# To use AvoidOOM as a decorator
+AvoidCUDAOOM = AvoidOOM()
diff --git a/mmdet/utils/misc.py b/mmdet/utils/misc.py
new file mode 100644
index 0000000..2017cbb
--- /dev/null
+++ b/mmdet/utils/misc.py
@@ -0,0 +1,89 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import glob
+import os
+import os.path as osp
+import warnings
+
+import mmcv
+import torch
+from mmcv.utils import TORCH_VERSION, digit_version, print_log
+
+
+def find_latest_checkpoint(path, suffix='pth'):
+    """Find the latest checkpoint from the working directory.
+
+    Args:
+        path(str): The path to find checkpoints.
+        suffix(str): File extension.
+            Defaults to pth.
+
+    Returns:
+        latest_path(str | None): File path of the latest checkpoint.
+    References:
+        .. [1] https://github.com/microsoft/SoftTeacher
+                  /blob/main/ssod/utils/patch.py
+    """
+    if not osp.exists(path):
+        warnings.warn('The path of checkpoints does not exist.')
+        return None
+    if osp.exists(osp.join(path, f'latest.{suffix}')):
+        return osp.join(path, f'latest.{suffix}')
+
+    checkpoints = glob.glob(osp.join(path, f'*.{suffix}'))
+    if len(checkpoints) == 0:
+        warnings.warn('There are no checkpoints in the path.')
+        return None
+    latest = -1
+    latest_path = None
+    for checkpoint in checkpoints:
+        count = int(osp.basename(checkpoint).split('_')[-1].split('.')[0])
+        if count > latest:
+            latest = count
+            latest_path = checkpoint
+    return latest_path
+
+
+def update_data_root(cfg, logger=None):
+    """Update data root according to env MMDET_DATASETS.
+
+    If set env MMDET_DATASETS, update cfg.data_root according to
+    MMDET_DATASETS. Otherwise, using cfg.data_root as default.
+
+    Args:
+        cfg (mmcv.Config): The model config need to modify
+        logger (logging.Logger | str | None): the way to print msg
+    """
+    assert isinstance(cfg, mmcv.Config), \
+        f'cfg got wrong type: {type(cfg)}, expected mmcv.Config'
+
+    if 'MMDET_DATASETS' in os.environ:
+        dst_root = os.environ['MMDET_DATASETS']
+        print_log(f'MMDET_DATASETS has been set to be {dst_root}.'
+                  f'Using {dst_root} as data root.')
+    else:
+        return
+
+    assert isinstance(cfg, mmcv.Config), \
+        f'cfg got wrong type: {type(cfg)}, expected mmcv.Config'
+
+    def update(cfg, src_str, dst_str):
+        for k, v in cfg.items():
+            if isinstance(v, mmcv.ConfigDict):
+                update(cfg[k], src_str, dst_str)
+            if isinstance(v, str) and src_str in v:
+                cfg[k] = v.replace(src_str, dst_str)
+
+    update(cfg.data, cfg.data_root, dst_root)
+    cfg.data_root = dst_root
+
+
+_torch_version_div_indexing = (
+    'parrots' not in TORCH_VERSION
+    and digit_version(TORCH_VERSION) >= digit_version('1.8'))
+
+
+def floordiv(dividend, divisor, rounding_mode='trunc'):
+    if _torch_version_div_indexing:
+        return torch.div(dividend, divisor, rounding_mode=rounding_mode)
+    else:
+        return dividend // divisor
diff --git a/mmdet/utils/profiling.py b/mmdet/utils/profiling.py
new file mode 100644
index 0000000..2f53f45
--- /dev/null
+++ b/mmdet/utils/profiling.py
@@ -0,0 +1,40 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import contextlib
+import sys
+import time
+
+import torch
+
+if sys.version_info >= (3, 7):
+
+    @contextlib.contextmanager
+    def profile_time(trace_name,
+                     name,
+                     enabled=True,
+                     stream=None,
+                     end_stream=None):
+        """Print time spent by CPU and GPU.
+
+        Useful as a temporary context manager to find sweet spots of code
+        suitable for async implementation.
+        """
+        if (not enabled) or not torch.cuda.is_available():
+            yield
+            return
+        stream = stream if stream else torch.cuda.current_stream()
+        end_stream = end_stream if end_stream else stream
+        start = torch.cuda.Event(enable_timing=True)
+        end = torch.cuda.Event(enable_timing=True)
+        stream.record_event(start)
+        try:
+            cpu_start = time.monotonic()
+            yield
+        finally:
+            cpu_end = time.monotonic()
+            end_stream.record_event(end)
+            end.synchronize()
+            cpu_time = (cpu_end - cpu_start) * 1000
+            gpu_time = start.elapsed_time(end)
+            msg = f'{trace_name} {name} cpu_time {cpu_time:.2f} ms '
+            msg += f'gpu_time {gpu_time:.2f} ms stream {stream}'
+            print(msg, end_stream)
diff --git a/mmdet/utils/replace_cfg_vals.py b/mmdet/utils/replace_cfg_vals.py
new file mode 100644
index 0000000..6ca301d
--- /dev/null
+++ b/mmdet/utils/replace_cfg_vals.py
@@ -0,0 +1,70 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import re
+
+from mmcv.utils import Config
+
+
+def replace_cfg_vals(ori_cfg):
+    """Replace the string "${key}" with the corresponding value.
+
+    Replace the "${key}" with the value of ori_cfg.key in the config. And
+    support replacing the chained ${key}. Such as, replace "${key0.key1}"
+    with the value of cfg.key0.key1. Code is modified from `vars.py
+    < https://github.com/microsoft/SoftTeacher/blob/main/ssod/utils/vars.py>`_  # noqa: E501
+
+    Args:
+        ori_cfg (mmcv.utils.config.Config):
+            The origin config with "${key}" generated from a file.
+
+    Returns:
+        updated_cfg [mmcv.utils.config.Config]:
+            The config with "${key}" replaced by the corresponding value.
+    """
+
+    def get_value(cfg, key):
+        for k in key.split('.'):
+            cfg = cfg[k]
+        return cfg
+
+    def replace_value(cfg):
+        if isinstance(cfg, dict):
+            return {key: replace_value(value) for key, value in cfg.items()}
+        elif isinstance(cfg, list):
+            return [replace_value(item) for item in cfg]
+        elif isinstance(cfg, tuple):
+            return tuple([replace_value(item) for item in cfg])
+        elif isinstance(cfg, str):
+            # the format of string cfg may be:
+            # 1) "${key}", which will be replaced with cfg.key directly
+            # 2) "xxx${key}xxx" or "xxx${key1}xxx${key2}xxx",
+            # which will be replaced with the string of the cfg.key
+            keys = pattern_key.findall(cfg)
+            values = [get_value(ori_cfg, key[2:-1]) for key in keys]
+            if len(keys) == 1 and keys[0] == cfg:
+                # the format of string cfg is "${key}"
+                cfg = values[0]
+            else:
+                for key, value in zip(keys, values):
+                    # the format of string cfg is
+                    # "xxx${key}xxx" or "xxx${key1}xxx${key2}xxx"
+                    assert not isinstance(value, (dict, list, tuple)), \
+                        f'for the format of string cfg is ' \
+                        f"'xxxxx${key}xxxxx' or 'xxx${key}xxx${key}xxx', " \
+                        f"the type of the value of '${key}' " \
+                        f'can not be dict, list, or tuple' \
+                        f'but you input {type(value)} in {cfg}'
+                    cfg = cfg.replace(key, str(value))
+            return cfg
+        else:
+            return cfg
+
+    # the pattern of string "${key}"
+    pattern_key = re.compile(r'\$\{[a-zA-Z\d_.]*\}')
+    # the type of ori_cfg._cfg_dict is mmcv.utils.config.ConfigDict
+    updated_cfg = Config(
+        replace_value(ori_cfg._cfg_dict), filename=ori_cfg.filename)
+    # replace the model with model_wrapper
+    if updated_cfg.get('model_wrapper', None) is not None:
+        updated_cfg.model = updated_cfg.model_wrapper
+        updated_cfg.pop('model_wrapper')
+    return updated_cfg
diff --git a/mmdet/utils/rfnext.py b/mmdet/utils/rfnext.py
new file mode 100644
index 0000000..568f3d3
--- /dev/null
+++ b/mmdet/utils/rfnext.py
@@ -0,0 +1,43 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+try:
+    from mmcv.cnn import RFSearchHook
+except ImportError:
+    RFSearchHook = None
+
+
+def rfnext_init_model(detector, cfg):
+    """Rcecptive field search via dilation rates.
+
+    Please refer to `RF-Next: Efficient Receptive Field
+    Search for Convolutional Neural Networks
+    <https://arxiv.org/abs/2206.06637>`_ for more details.
+
+    Args:
+        detector (nn.Module): The detector before initializing RF-Next.
+        cfg (mmcv.Config): The config for RF-Next.
+            If the RFSearchHook is defined in the cfg.custom_hooks,
+            the detector will be initialized for RF-Next.
+    """
+
+    if cfg.get('custom_hooks', None) is None:
+        return
+    custom_hook_types = [hook['type'] for hook in cfg.custom_hooks]
+    if 'RFSearchHook' not in custom_hook_types:
+        return
+
+    index = custom_hook_types.index('RFSearchHook')
+    rfsearch_cfg = cfg.custom_hooks[index]
+    assert rfsearch_cfg['type'] == 'RFSearchHook'
+
+    assert RFSearchHook is not None, 'Please install mmcv > 1.7.0'
+
+    # initlize a RFSearchHook
+    rfsearch_warp = RFSearchHook(
+        mode=rfsearch_cfg.get('mode', 'search'),
+        config=rfsearch_cfg.get('config', None),
+        rfstructure_file=rfsearch_cfg.get('rfstructure_file', None),
+        by_epoch=rfsearch_cfg.get('by_epoch', True),
+        verbose=rfsearch_cfg.get('verbose', True),
+    )
+    rfsearch_warp.init_model(detector)
+    rfsearch_cfg['rfstructure_file'] = None
diff --git a/mmdet/utils/setup_env.py b/mmdet/utils/setup_env.py
new file mode 100644
index 0000000..6637cf8
--- /dev/null
+++ b/mmdet/utils/setup_env.py
@@ -0,0 +1,53 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import platform
+import warnings
+
+import cv2
+import torch.multiprocessing as mp
+
+
+def setup_multi_processes(cfg):
+    """Setup multi-processing environment variables."""
+    # set multi-process start method as `fork` to speed up the training
+    if platform.system() != 'Windows':
+        mp_start_method = cfg.get('mp_start_method', 'fork')
+        current_method = mp.get_start_method(allow_none=True)
+        if current_method is not None and current_method != mp_start_method:
+            warnings.warn(
+                f'Multi-processing start method `{mp_start_method}` is '
+                f'different from the previous setting `{current_method}`.'
+                f'It will be force set to `{mp_start_method}`. You can change '
+                f'this behavior by changing `mp_start_method` in your config.')
+        mp.set_start_method(mp_start_method, force=True)
+
+    # disable opencv multithreading to avoid system being overloaded
+    opencv_num_threads = cfg.get('opencv_num_threads', 0)
+    cv2.setNumThreads(opencv_num_threads)
+
+    # setup OMP threads
+    # This code is referred from https://github.com/pytorch/pytorch/blob/master/torch/distributed/run.py  # noqa
+    workers_per_gpu = cfg.data.get('workers_per_gpu', 1)
+    if 'train_dataloader' in cfg.data:
+        workers_per_gpu = \
+            max(cfg.data.train_dataloader.get('workers_per_gpu', 1),
+                workers_per_gpu)
+
+    if 'OMP_NUM_THREADS' not in os.environ and workers_per_gpu > 1:
+        omp_num_threads = 1
+        warnings.warn(
+            f'Setting OMP_NUM_THREADS environment variable for each process '
+            f'to be {omp_num_threads} in default, to avoid your system being '
+            f'overloaded, please further tune the variable for optimal '
+            f'performance in your application as needed.')
+        os.environ['OMP_NUM_THREADS'] = str(omp_num_threads)
+
+    # setup MKL threads
+    if 'MKL_NUM_THREADS' not in os.environ and workers_per_gpu > 1:
+        mkl_num_threads = 1
+        warnings.warn(
+            f'Setting MKL_NUM_THREADS environment variable for each process '
+            f'to be {mkl_num_threads} in default, to avoid your system being '
+            f'overloaded, please further tune the variable for optimal '
+            f'performance in your application as needed.')
+        os.environ['MKL_NUM_THREADS'] = str(mkl_num_threads)
diff --git a/mmdet/utils/split_batch.py b/mmdet/utils/split_batch.py
new file mode 100644
index 0000000..0276fb3
--- /dev/null
+++ b/mmdet/utils/split_batch.py
@@ -0,0 +1,45 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+
+def split_batch(img, img_metas, kwargs):
+    """Split data_batch by tags.
+
+    Code is modified from
+    <https://github.com/microsoft/SoftTeacher/blob/main/ssod/utils/structure_utils.py> # noqa: E501
+
+    Args:
+        img (Tensor): of shape (N, C, H, W) encoding input images.
+            Typically these should be mean centered and std scaled.
+        img_metas (list[dict]): List of image info dict where each dict
+            has: 'img_shape', 'scale_factor', 'flip', and may also contain
+            'filename', 'ori_shape', 'pad_shape', and 'img_norm_cfg'.
+            For details on the values of these keys, see
+            :class:`mmdet.datasets.pipelines.Collect`.
+        kwargs (dict): Specific to concrete implementation.
+
+    Returns:
+        data_groups (dict): a dict that data_batch splited by tags,
+            such as 'sup', 'unsup_teacher', and 'unsup_student'.
+    """
+
+    # only stack img in the batch
+    def fuse_list(obj_list, obj):
+        return torch.stack(obj_list) if isinstance(obj,
+                                                   torch.Tensor) else obj_list
+
+    # select data with tag from data_batch
+    def select_group(data_batch, current_tag):
+        group_flag = [tag == current_tag for tag in data_batch['tag']]
+        return {
+            k: fuse_list([vv for vv, gf in zip(v, group_flag) if gf], v)
+            for k, v in data_batch.items()
+        }
+
+    kwargs.update({'img': img, 'img_metas': img_metas})
+    kwargs.update({'tag': [meta['tag'] for meta in img_metas]})
+    tags = list(set(kwargs['tag']))
+    data_groups = {tag: select_group(kwargs, tag) for tag in tags}
+    for tag, group in data_groups.items():
+        group.pop('tag')
+    return data_groups
diff --git a/mmdet/utils/util_distribution.py b/mmdet/utils/util_distribution.py
new file mode 100644
index 0000000..ba32cc9
--- /dev/null
+++ b/mmdet/utils/util_distribution.py
@@ -0,0 +1,92 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
+
+dp_factory = {'cuda': MMDataParallel, 'cpu': MMDataParallel}
+
+ddp_factory = {'cuda': MMDistributedDataParallel}
+
+
+def build_dp(model, device='cuda', dim=0, *args, **kwargs):
+    """build DataParallel module by device type.
+
+    if device is cuda, return a MMDataParallel model; if device is mlu,
+    return a MLUDataParallel model.
+
+    Args:
+        model (:class:`nn.Module`): model to be parallelized.
+        device (str): device type, cuda, cpu or mlu. Defaults to cuda.
+        dim (int): Dimension used to scatter the data. Defaults to 0.
+
+    Returns:
+        nn.Module: the model to be parallelized.
+    """
+    if device == 'npu':
+        from mmcv.device.npu import NPUDataParallel
+        dp_factory['npu'] = NPUDataParallel
+        torch.npu.set_device(kwargs['device_ids'][0])
+        torch.npu.set_compile_mode(jit_compile=False)
+        model = model.npu()
+    elif device == 'cuda':
+        model = model.cuda(kwargs['device_ids'][0])
+    elif device == 'mlu':
+        from mmcv.device.mlu import MLUDataParallel
+        dp_factory['mlu'] = MLUDataParallel
+        model = model.mlu()
+
+    return dp_factory[device](model, dim=dim, *args, **kwargs)
+
+
+def build_ddp(model, device='cuda', *args, **kwargs):
+    """Build DistributedDataParallel module by device type.
+
+    If device is cuda, return a MMDistributedDataParallel model;
+    if device is mlu, return a MLUDistributedDataParallel model.
+
+    Args:
+        model (:class:`nn.Module`): module to be parallelized.
+        device (str): device type, mlu or cuda.
+
+    Returns:
+        :class:`nn.Module`: the module to be parallelized
+
+    References:
+        .. [1] https://pytorch.org/docs/stable/generated/torch.nn.parallel.
+                     DistributedDataParallel.html
+    """
+    assert device in ['cuda', 'mlu',
+                      'npu'], 'Only available for cuda or mlu or npu devices.'
+    if device == 'npu':
+        from mmcv.device.npu import NPUDistributedDataParallel
+        torch.npu.set_compile_mode(jit_compile=False)
+        ddp_factory['npu'] = NPUDistributedDataParallel
+        model = model.npu()
+    elif device == 'cuda':
+        model = model.cuda()
+    elif device == 'mlu':
+        from mmcv.device.mlu import MLUDistributedDataParallel
+        ddp_factory['mlu'] = MLUDistributedDataParallel
+        model = model.mlu()
+
+    return ddp_factory[device](model, *args, **kwargs)
+
+
+def is_npu_available():
+    """Returns a bool indicating if NPU is currently available."""
+    return hasattr(torch, 'npu') and torch.npu.is_available()
+
+
+def is_mlu_available():
+    """Returns a bool indicating if MLU is currently available."""
+    return hasattr(torch, 'is_mlu_available') and torch.is_mlu_available()
+
+
+def get_device():
+    """Returns an available device, cpu, cuda or mlu."""
+    is_device_available = {
+        'npu': is_npu_available(),
+        'cuda': torch.cuda.is_available(),
+        'mlu': is_mlu_available()
+    }
+    device_list = [k for k, v in is_device_available.items() if v]
+    return device_list[0] if len(device_list) >= 1 else 'cpu'
diff --git a/mmdet/utils/util_mixins.py b/mmdet/utils/util_mixins.py
new file mode 100644
index 0000000..b83b661
--- /dev/null
+++ b/mmdet/utils/util_mixins.py
@@ -0,0 +1,105 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""This module defines the :class:`NiceRepr` mixin class, which defines a
+``__repr__`` and ``__str__`` method that only depend on a custom ``__nice__``
+method, which you must define. This means you only have to overload one
+function instead of two.  Furthermore, if the object defines a ``__len__``
+method, then the ``__nice__`` method defaults to something sensible, otherwise
+it is treated as abstract and raises ``NotImplementedError``.
+
+To use simply have your object inherit from :class:`NiceRepr`
+(multi-inheritance should be ok).
+
+This code was copied from the ubelt library: https://github.com/Erotemic/ubelt
+
+Example:
+    >>> # Objects that define __nice__ have a default __str__ and __repr__
+    >>> class Student(NiceRepr):
+    ...    def __init__(self, name):
+    ...        self.name = name
+    ...    def __nice__(self):
+    ...        return self.name
+    >>> s1 = Student('Alice')
+    >>> s2 = Student('Bob')
+    >>> print(f's1 = {s1}')
+    >>> print(f's2 = {s2}')
+    s1 = <Student(Alice)>
+    s2 = <Student(Bob)>
+
+Example:
+    >>> # Objects that define __len__ have a default __nice__
+    >>> class Group(NiceRepr):
+    ...    def __init__(self, data):
+    ...        self.data = data
+    ...    def __len__(self):
+    ...        return len(self.data)
+    >>> g = Group([1, 2, 3])
+    >>> print(f'g = {g}')
+    g = <Group(3)>
+"""
+import warnings
+
+
+class NiceRepr:
+    """Inherit from this class and define ``__nice__`` to "nicely" print your
+    objects.
+
+    Defines ``__str__`` and ``__repr__`` in terms of ``__nice__`` function
+    Classes that inherit from :class:`NiceRepr` should redefine ``__nice__``.
+    If the inheriting class has a ``__len__``, method then the default
+    ``__nice__`` method will return its length.
+
+    Example:
+        >>> class Foo(NiceRepr):
+        ...    def __nice__(self):
+        ...        return 'info'
+        >>> foo = Foo()
+        >>> assert str(foo) == '<Foo(info)>'
+        >>> assert repr(foo).startswith('<Foo(info) at ')
+
+    Example:
+        >>> class Bar(NiceRepr):
+        ...    pass
+        >>> bar = Bar()
+        >>> import pytest
+        >>> with pytest.warns(None) as record:
+        >>>     assert 'object at' in str(bar)
+        >>>     assert 'object at' in repr(bar)
+
+    Example:
+        >>> class Baz(NiceRepr):
+        ...    def __len__(self):
+        ...        return 5
+        >>> baz = Baz()
+        >>> assert str(baz) == '<Baz(5)>'
+    """
+
+    def __nice__(self):
+        """str: a "nice" summary string describing this module"""
+        if hasattr(self, '__len__'):
+            # It is a common pattern for objects to use __len__ in __nice__
+            # As a convenience we define a default __nice__ for these objects
+            return str(len(self))
+        else:
+            # In all other cases force the subclass to overload __nice__
+            raise NotImplementedError(
+                f'Define the __nice__ method for {self.__class__!r}')
+
+    def __repr__(self):
+        """str: the string of the module"""
+        try:
+            nice = self.__nice__()
+            classname = self.__class__.__name__
+            return f'<{classname}({nice}) at {hex(id(self))}>'
+        except NotImplementedError as ex:
+            warnings.warn(str(ex), category=RuntimeWarning)
+            return object.__repr__(self)
+
+    def __str__(self):
+        """str: the string of the module"""
+        try:
+            classname = self.__class__.__name__
+            nice = self.__nice__()
+            return f'<{classname}({nice})>'
+        except NotImplementedError as ex:
+            warnings.warn(str(ex), category=RuntimeWarning)
+            return object.__repr__(self)
diff --git a/mmdet/utils/util_random.py b/mmdet/utils/util_random.py
new file mode 100644
index 0000000..dc1ecb6
--- /dev/null
+++ b/mmdet/utils/util_random.py
@@ -0,0 +1,34 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Helpers for random number generators."""
+import numpy as np
+
+
+def ensure_rng(rng=None):
+    """Coerces input into a random number generator.
+
+    If the input is None, then a global random state is returned.
+
+    If the input is a numeric value, then that is used as a seed to construct a
+    random state. Otherwise the input is returned as-is.
+
+    Adapted from [1]_.
+
+    Args:
+        rng (int | numpy.random.RandomState | None):
+            if None, then defaults to the global rng. Otherwise this can be an
+            integer or a RandomState class
+    Returns:
+        (numpy.random.RandomState) : rng -
+            a numpy random number generator
+
+    References:
+        .. [1] https://gitlab.kitware.com/computer-vision/kwarray/blob/master/kwarray/util_random.py#L270  # noqa: E501
+    """
+
+    if rng is None:
+        rng = np.random.mtrand._rand
+    elif isinstance(rng, int):
+        rng = np.random.RandomState(rng)
+    else:
+        rng = rng
+    return rng
diff --git a/mmdet/version.py b/mmdet/version.py
new file mode 100644
index 0000000..fecd645
--- /dev/null
+++ b/mmdet/version.py
@@ -0,0 +1,19 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+
+__version__ = '2.28.2'
+short_version = __version__
+
+
+def parse_version_info(version_str):
+    version_info = []
+    for x in version_str.split('.'):
+        if x.isdigit():
+            version_info.append(int(x))
+        elif x.find('rc') != -1:
+            patch_version = x.split('rc')
+            version_info.append(int(patch_version[0]))
+            version_info.append(f'rc{patch_version[1]}')
+    return tuple(version_info)
+
+
+version_info = parse_version_info(__version__)
diff --git a/model-index.yml b/model-index.yml
new file mode 100644
index 0000000..587255b
--- /dev/null
+++ b/model-index.yml
@@ -0,0 +1,73 @@
+Import:
+  - configs/atss/metafile.yml
+  - configs/autoassign/metafile.yml
+  - configs/carafe/metafile.yml
+  - configs/cascade_rcnn/metafile.yml
+  - configs/cascade_rpn/metafile.yml
+  - configs/centernet/metafile.yml
+  - configs/centripetalnet/metafile.yml
+  - configs/cornernet/metafile.yml
+  - configs/convnext/metafile.yml
+  - configs/dcn/metafile.yml
+  - configs/dcnv2/metafile.yml
+  - configs/deformable_detr/metafile.yml
+  - configs/detectors/metafile.yml
+  - configs/detr/metafile.yml
+  - configs/double_heads/metafile.yml
+  - configs/dyhead/metafile.yml
+  - configs/dynamic_rcnn/metafile.yml
+  - configs/efficientnet/metafile.yml
+  - configs/empirical_attention/metafile.yml
+  - configs/faster_rcnn/metafile.yml
+  - configs/fcos/metafile.yml
+  - configs/foveabox/metafile.yml
+  - configs/fpg/metafile.yml
+  - configs/free_anchor/metafile.yml
+  - configs/fsaf/metafile.yml
+  - configs/gcnet/metafile.yml
+  - configs/gfl/metafile.yml
+  - configs/ghm/metafile.yml
+  - configs/gn/metafile.yml
+  - configs/gn+ws/metafile.yml
+  - configs/grid_rcnn/metafile.yml
+  - configs/groie/metafile.yml
+  - configs/guided_anchoring/metafile.yml
+  - configs/hrnet/metafile.yml
+  - configs/htc/metafile.yml
+  - configs/instaboost/metafile.yml
+  - configs/lad/metafile.yml
+  - configs/ld/metafile.yml
+  - configs/libra_rcnn/metafile.yml
+  - configs/mask_rcnn/metafile.yml
+  - configs/ms_rcnn/metafile.yml
+  - configs/nas_fcos/metafile.yml
+  - configs/nas_fpn/metafile.yml
+  - configs/openimages/metafile.yml
+  - configs/paa/metafile.yml
+  - configs/pafpn/metafile.yml
+  - configs/panoptic_fpn/metafile.yml
+  - configs/pvt/metafile.yml
+  - configs/pisa/metafile.yml
+  - configs/point_rend/metafile.yml
+  - configs/queryinst/metafile.yml
+  - configs/regnet/metafile.yml
+  - configs/reppoints/metafile.yml
+  - configs/res2net/metafile.yml
+  - configs/resnest/metafile.yml
+  - configs/retinanet/metafile.yml
+  - configs/sabl/metafile.yml
+  - configs/scnet/metafile.yml
+  - configs/scratch/metafile.yml
+  - configs/seesaw_loss/metafile.yml
+  - configs/sparse_rcnn/metafile.yml
+  - configs/solo/metafile.yml
+  - configs/ssd/metafile.yml
+  - configs/swin/metafile.yml
+  - configs/tridentnet/metafile.yml
+  - configs/tood/metafile.yml
+  - configs/vfnet/metafile.yml
+  - configs/yolact/metafile.yml
+  - configs/yolo/metafile.yml
+  - configs/yolof/metafile.yml
+  - configs/yolox/metafile.yml
+  - configs/rfnext/metafile.yml
diff --git a/mqb_general_process.py b/mqb_general_process.py
new file mode 100644
index 0000000..debb6dc
--- /dev/null
+++ b/mqb_general_process.py
@@ -0,0 +1,127 @@
+from mqbench.prepare_by_platform import prepare_by_platform, BackendType
+from mqbench.advanced_ptq import ptq_reconstruction
+from mqbench.convert_deploy import convert_deploy
+import os
+import yaml
+from easydict import EasyDict
+from mmdet.models.dense_heads import GARPNHead, RPNHead
+
+from shutil import copyfile
+import time
+import errno
+
+backend_dict = {
+    'Academic': BackendType.Academic,
+    'Tensorrt': BackendType.Tensorrt,
+    'SNPE': BackendType.SNPE,
+    'PPLW8A16': BackendType.PPLW8A16,
+    'NNIE': BackendType.NNIE,
+    'Vitis': BackendType.Vitis,
+    'ONNX_QNN': BackendType.ONNX_QNN,
+    'PPLCUDA': BackendType.PPLCUDA,
+}
+
+# def load_calibrate_data(train_loader, cali_batchsize):
+#     cali_data = []
+#     targets = []
+#     for i, batch in enumerate(train_loader):
+#         cali_data.append(batch[0])
+#         targets.append(batch[1])
+#         if i + 1 == cali_batchsize:
+#             break
+#     return cali_data, targets
+
+def get_quantize_model(model, config, structure_detail):
+    backend_type = BackendType.Academic if not hasattr(
+        config.quantize, 'backend') else backend_dict[config.quantize.backend]
+    extra_prepare_dict = {} if not hasattr(
+        config, 'extra_prepare_dict') else config.extra_prepare_dict
+    return prepare_by_platform(
+        model, backend_type, structure_detail, extra_prepare_dict)
+
+
+# def deploy(model, config):
+#     backend_type = BackendType.Academic if not hasattr(
+#         config.quantize, 'backend') else backend_dict[config.quantize.backend]
+#     output_path = './' if not hasattr(
+#         config.quantize, 'deploy') else config.quantize.deploy.output_path
+#     model_name = config.quantize.deploy.model_name
+#     deploy_to_qlinear = False if not hasattr(
+#         config.quantize.deploy, 'deploy_to_qlinear') else config.quantize.deploy.deploy_to_qlinear
+
+#     convert_deploy(model, backend_type, {
+#                    'input': [1, 3, 224, 224]}, output_path=output_path, model_name=model_name, deploy_to_qlinear=deploy_to_qlinear)
+
+
+def make_qmodel_for_mmd(model, quant_config, cfg):
+    print('\nGet FakeQuant model\n')
+    model.backbone = get_quantize_model(model.backbone, quant_config, cfg.backbone_detail)  # QAT时，这个需要eval还是train
+    model.neck = get_quantize_model(model.neck, quant_config, cfg.neck_detail)  # QAT时，这个需要eval还是train
+    model_general_architecture = cfg.get('model_general_architecture', None)
+    if model_general_architecture == 'FasterRCNN':
+        temp = get_quantize_model(model.rpn_head, quant_config, cfg.rpn_head_detail)  # QAT时，这个需要eval还是train
+        # temp.__class__ = model.rpn_head.__class__  # NOTE 无奈之举
+        model.rpn_head.forward = temp.forward  # 太傻蛋勒
+        model.rpn_head = temp
+        temp = get_quantize_model(model.roi_head.bbox_head, quant_config, cfg.roi_head_bbox_head_detail)  # QAT时，这个需要eval还是train
+        # temp.__class__ = model.roi_head.bbox_head.__class__
+        model.roi_head.bbox_head.forward = temp.forward  # 太傻蛋勒
+        model.roi_head.bbox_head = temp
+    else:
+        temp = get_quantize_model(model.bbox_head, quant_config, cfg.bbox_head_detail)  # QAT时，这个需要eval还是train
+        model.bbox_head.forward = temp.forward  # 太傻蛋勒
+        model.bbox_head = temp
+    
+    # TODO 把东西del掉？
+    return model
+
+def prepocess(config_path):
+    # TODO 写一个cp yaml操作，
+    config = parse_config(config_path)
+    # seed first
+    # 如果指定了保存文件地址，检查文件夹是否存在，若不存在，则创建
+    # if config.misc.output_dir:
+    #     mkdir(config.misc.output_dir)
+    #     config.misc.output_data_dir = os.path.join(config.misc.output_dir, 'data')
+    #     mkdir(config.misc.output_data_dir)
+    # seed_all(config.process.seed)
+    return config
+
+
+def parse_config(config_file):
+    with open(config_file) as f:
+        config = yaml.load(f, Loader=yaml.FullLoader)
+        cur_config = config
+        cur_path = config_file
+        while 'root' in cur_config:
+            root_path = os.path.dirname(cur_path)
+            cur_path = os.path.join(root_path, cur_config['root'])
+            with open(cur_path) as r:
+                root_config = yaml.load(r, Loader=yaml.FullLoader)
+                for k, v in root_config.items():
+                    if k not in config:
+                        config[k] = v
+                cur_config = root_config
+        # config = yaml.safe_load(f)
+    config = EasyDict(config)
+    return config
+
+def mkdir(path):
+    try:
+        os.makedirs(path)
+    except OSError as e:
+        if e.errno != errno.EEXIST:
+            raise
+        
+        
+def copy_config_file(config_file_path, dir):
+    # file_name = os.path.basename(config_file_path)
+    now_stamp = int(round(time.time()*1000))
+    now_time = time.strftime('%Y_%m_%d_%H_%M',time.localtime(now_stamp/1000))
+    new_file_name = f'config_{now_time}.yaml'
+    new_file_path = os.path.join(dir, new_file_name)
+    if not os.path.exists(dir):  # 判断路径是否存在
+        os.makedirs(dir)  # 创建文件夹
+    copyfile(config_file_path, new_file_path)
+    
+    
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/COCO/fasterrcnn/res50_fpn/quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq/COCO/fasterrcnn/res50_fpn/quant_config_w4a4.yaml
new file mode 100644
index 0000000..cf23c8a
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/COCO/fasterrcnn/res50_fpn/quant_config_w4a4.yaml
@@ -0,0 +1,73 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, roi_heads, rpn, single_head, 
+        anchor_generator, postprocess, assign_targets_to_anchors, compute_loss, box_coder, fg_bg_sampler, 
+        proposal_matcher, select_training_samples, box_roi_pool]  # 还得包括一些rpn、roi的子函数         
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: COCO
+    data_path: ../COCO
+    num_classes: 81
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: XXX
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.001
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00006
+    print_freq: 800
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: fasterrcnn_res50_fpn  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/COCO_FasterRCNN_Res50_FPN_bat16_usebase_w4a4
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/COCO/fasterrcnn/res50_fpn/quant_config_w8a8.yaml b/mqbconfig/_legacy/lsq/COCO/fasterrcnn/res50_fpn/quant_config_w8a8.yaml
new file mode 100644
index 0000000..da63bbb
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/COCO/fasterrcnn/res50_fpn/quant_config_w8a8.yaml
@@ -0,0 +1,73 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, roi_heads, rpn, single_head, 
+        anchor_generator, postprocess, assign_targets_to_anchors, compute_loss, box_coder, fg_bg_sampler, 
+        proposal_matcher, select_training_samples, box_roi_pool]  # 还得包括一些rpn、roi的子函数         
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: COCO
+    data_path: ../COCO
+    num_classes: 81
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: XXX
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.0008
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00006
+    print_freq: 800
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: fasterrcnn_res50_fpn  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/COCO_FasterRCNN_Res50_FPN_bat16_usebase_w8a8
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/COCO/retinanet/res50/quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq/COCO/retinanet/res50/quant_config_w4a4.yaml
new file mode 100644
index 0000000..588b50d
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/COCO/retinanet/res50/quant_config_w4a4.yaml
@@ -0,0 +1,72 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, anchor_generator, compute_loss, postprocess_detections]
+        # anchor_generator
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: COCO
+    data_path: ../COCO
+    num_classes: 81
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: 
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.004
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00008
+    print_freq: 100
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: retinanet_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/COCO_RetinaNet_Res50_bat16_usebase_w4a4
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/COCO/retinanet/res50/quant_config_w8a8.yaml b/mqbconfig/_legacy/lsq/COCO/retinanet/res50/quant_config_w8a8.yaml
new file mode 100644
index 0000000..7eab053
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/COCO/retinanet/res50/quant_config_w8a8.yaml
@@ -0,0 +1,72 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, anchor_generator, compute_loss, postprocess_detections]
+        # anchor_generator
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: COCO
+    data_path: ../COCO
+    num_classes: 81
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: 
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.004
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00008
+    print_freq: 100
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: retinanet_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/COCO_RetinaNet_Res50_bat16_usebase_w8a8
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/COCO/ssd300/ssd_quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq/COCO/ssd300/ssd_quant_config_w4a4.yaml
new file mode 100644
index 0000000..785665c
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/COCO/ssd300/ssd_quant_config_w4a4.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: COCO  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: ../COCO
+    num_classes: 81
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/COCO_SSD300_Res50_bat128/model-12.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 16
+    workers: 8
+    lr: 0.001
+    lr_steps: [5, 11]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 1600
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/COCO_ssd300_Res50_bat32_usebase_w4a4
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/COCO/ssd300/ssd_quant_config_w4a4_test.yaml b/mqbconfig/_legacy/lsq/COCO/ssd300/ssd_quant_config_w4a4_test.yaml
new file mode 100644
index 0000000..4a62a8b
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/COCO/ssd300/ssd_quant_config_w4a4_test.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: COCO  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: XXX
+    num_classes: 81
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline:
+    batch_size: 24
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.001
+    lr_steps: [4, 10, 15, 18]
+    lr_gamma: 0.25
+    momentum: 0.9
+    weight_decay: 0.000016
+    print_freq: 850
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/COCO_ssd300_Res50_bat72_usebase_w4a4
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/VOC0712/fasterrcnn/mbnv2/quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq/VOC0712/fasterrcnn/mbnv2/quant_config_w4a4.yaml
new file mode 100644
index 0000000..a988789
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/VOC0712/fasterrcnn/mbnv2/quant_config_w4a4.yaml
@@ -0,0 +1,73 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, roi_heads, rpn, single_head, 
+        anchor_generator, postprocess, assign_targets_to_anchors, compute_loss, box_coder, fg_bg_sampler, 
+        proposal_matcher, select_training_samples, box_roi_pool]  # 还得包括一些rpn、roi的子函数         
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_FasterRCNN_MBNv2_bat24/model-20.pth
+    batch_size: 5
+    start_epoch: 0
+    epochs: 18
+    workers: 8
+    lr: 0.001
+    lr_steps: [7, 14]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 1600
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: fasterrcnn_mbnv2  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/VOC_FasterRCNN_MBNv2_bat10_usebase_w4a4
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/VOC0712/fasterrcnn/mbnv2/quant_config_w8a8.yaml b/mqbconfig/_legacy/lsq/VOC0712/fasterrcnn/mbnv2/quant_config_w8a8.yaml
new file mode 100644
index 0000000..fcd9df9
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/VOC0712/fasterrcnn/mbnv2/quant_config_w8a8.yaml
@@ -0,0 +1,73 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, roi_heads, rpn, single_head,
+        anchor_generator, postprocess, assign_targets_to_anchors, compute_loss, box_coder, fg_bg_sampler, 
+        proposal_matcher, select_training_samples, box_roi_pool]  # 还得包括一些rpn、roi的子函数         
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: 
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.0008
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00006
+    print_freq: 800
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: fasterrcnn_mbnv2  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/VOC_FasterRCNN_MBNv2_bat16_usebase_w8a8
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/VOC0712/fasterrcnn/res50_fpn/quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq/VOC0712/fasterrcnn/res50_fpn/quant_config_w4a4.yaml
new file mode 100644
index 0000000..ac5a89f
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/VOC0712/fasterrcnn/res50_fpn/quant_config_w4a4.yaml
@@ -0,0 +1,73 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, roi_heads, rpn, single_head, 
+        anchor_generator, postprocess, assign_targets_to_anchors, compute_loss, box_coder, fg_bg_sampler, 
+        proposal_matcher, select_training_samples, box_roi_pool]  # 还得包括一些rpn、roi的子函数         
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: XXX
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.001
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00006
+    print_freq: 800
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: fasterrcnn_res50_fpn  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/VOC_FasterRCNN_Res50_FPN_bat16_usebase_w4a4
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/VOC0712/fasterrcnn/res50_fpn/quant_config_w8a8.yaml b/mqbconfig/_legacy/lsq/VOC0712/fasterrcnn/res50_fpn/quant_config_w8a8.yaml
new file mode 100644
index 0000000..60a4c30
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/VOC0712/fasterrcnn/res50_fpn/quant_config_w8a8.yaml
@@ -0,0 +1,73 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, roi_heads, rpn, single_head, 
+        anchor_generator, postprocess, assign_targets_to_anchors, compute_loss, box_coder, fg_bg_sampler, 
+        proposal_matcher, select_training_samples, box_roi_pool]  # 还得包括一些rpn、roi的子函数         
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: 
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.0008
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00006
+    print_freq: 800
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: fasterrcnn_res50_fpn  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/VOC_FasterRCNN_Res50_FPN_bat16_usebase_w8a8
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/VOC0712/retinanet/res18/quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq/VOC0712/retinanet/res18/quant_config_w4a4.yaml
new file mode 100644
index 0000000..0e1d9eb
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/VOC0712/retinanet/res18/quant_config_w4a4.yaml
@@ -0,0 +1,72 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, anchor_generator, compute_loss, postprocess_detections]
+        # anchor_generator
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_Retinanet_Res18_bat32/model-24.pth
+    batch_size: 8
+    start_epoch: 0
+    epochs: 20
+    workers: 8
+    lr: 0.001
+    lr_steps: [6, 13, 18]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 1600
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: retinanet_res18  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/VOC_RetinaNet_Res18_bat16_usebase_w4a4
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/VOC0712/retinanet/res18/quant_config_w8a8.yaml b/mqbconfig/_legacy/lsq/VOC0712/retinanet/res18/quant_config_w8a8.yaml
new file mode 100644
index 0000000..80aeb1f
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/VOC0712/retinanet/res18/quant_config_w8a8.yaml
@@ -0,0 +1,72 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, anchor_generator, compute_loss, postprocess_detections]
+        # anchor_generator
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: 
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.004
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00008
+    print_freq: 100
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: retinanet_res18  # retinanet/ssd300
+    output_dir: save_weights/QAT/VOC_RetinaNet_Res18_bat16_usebase_w8a8
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/VOC0712/retinanet/res50/quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq/VOC0712/retinanet/res50/quant_config_w4a4.yaml
new file mode 100644
index 0000000..a3fb521
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/VOC0712/retinanet/res50/quant_config_w4a4.yaml
@@ -0,0 +1,72 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, anchor_generator, compute_loss, postprocess_detections]
+        # anchor_generator
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: 
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.004
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00008
+    print_freq: 100
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: retinanet_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/VOC_RetinaNet_Res50_bat16_usebase_w4a4
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/VOC0712/retinanet/res50/quant_config_w8a8.yaml b/mqbconfig/_legacy/lsq/VOC0712/retinanet/res50/quant_config_w8a8.yaml
new file mode 100644
index 0000000..79107a7
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/VOC0712/retinanet/res50/quant_config_w8a8.yaml
@@ -0,0 +1,72 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, anchor_generator, compute_loss, postprocess_detections]
+        # anchor_generator
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: 
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.004
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00008
+    print_freq: 100
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: retinanet_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/VOC_RetinaNet_Res50_bat16_usebase_w8a8
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/VOC0712/ssd300/ssd_quant_config_w2a2.yaml b/mqbconfig/_legacy/lsq/VOC0712/ssd300/ssd_quant_config_w2a2.yaml
new file mode 100644
index 0000000..b7defe4
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/VOC0712/ssd300/ssd_quant_config_w2a2.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 2
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 2
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_SSD300_Res50_bat128/model-9.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 28
+    workers: 8
+    lr: 0.01
+    lr_steps: [14, 20, 25]  # 001 0001
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 850
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/VOC_ssd300_Res50_bat32_usebase_w2a2
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/VOC0712/ssd300/ssd_quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq/VOC0712/ssd300/ssd_quant_config_w4a4.yaml
new file mode 100644
index 0000000..001f806
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/VOC0712/ssd300/ssd_quant_config_w4a4.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_SSD300_Res50_bat128/model-9.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 16
+    workers: 8
+    lr: 0.001
+    lr_steps: [6, 13]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 850
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/VOC_ssd300_Res50_bat32_usebase_w4a4
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq/VOC0712/ssd300/ssd_quant_config_w8a8.yaml b/mqbconfig/_legacy/lsq/VOC0712/ssd300/ssd_quant_config_w8a8.yaml
new file mode 100644
index 0000000..4e0bbd3
--- /dev/null
+++ b/mqbconfig/_legacy/lsq/VOC0712/ssd300/ssd_quant_config_w8a8.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_SSD300_Res50_bat128/model-9.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 8
+    workers: 8
+    lr: 0.0001
+    lr_steps: [4]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 850
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/VOC_ssd300_Res50_bat32_usebase_w8a8
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/COCO/fasterrcnn/res50_fpn/quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq_mypro/COCO/fasterrcnn/res50_fpn/quant_config_w4a4.yaml
new file mode 100644
index 0000000..936d18f
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/COCO/fasterrcnn/res50_fpn/quant_config_w4a4.yaml
@@ -0,0 +1,73 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, roi_heads, rpn, single_head, 
+        anchor_generator, postprocess, assign_targets_to_anchors, compute_loss, box_coder, fg_bg_sampler, 
+        proposal_matcher, select_training_samples, box_roi_pool]  # 还得包括一些rpn、roi的子函数         
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: COCO
+    data_path: ../COCO
+    num_classes: 81
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: XXX
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.001
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00006
+    print_freq: 800
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: fasterrcnn_res50_fpn  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ_MyPro/COCO_FasterRCNN_Res50_FPN_bat16_usebase_w4a4
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/COCO/fasterrcnn/res50_fpn/quant_config_w8a8.yaml b/mqbconfig/_legacy/lsq_mypro/COCO/fasterrcnn/res50_fpn/quant_config_w8a8.yaml
new file mode 100644
index 0000000..ea06c82
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/COCO/fasterrcnn/res50_fpn/quant_config_w8a8.yaml
@@ -0,0 +1,73 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, roi_heads, rpn, single_head, 
+        anchor_generator, postprocess, assign_targets_to_anchors, compute_loss, box_coder, fg_bg_sampler, 
+        proposal_matcher, select_training_samples, box_roi_pool]  # 还得包括一些rpn、roi的子函数         
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: COCO
+    data_path: ../COCO
+    num_classes: 81
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: XXX
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.0008
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00006
+    print_freq: 800
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: fasterrcnn_res50_fpn  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/COCO_FasterRCNN_Res50_FPN_bat16_usebase_w8a8
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/COCO/retinanet/res50/quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq_mypro/COCO/retinanet/res50/quant_config_w4a4.yaml
new file mode 100644
index 0000000..2bf90c5
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/COCO/retinanet/res50/quant_config_w4a4.yaml
@@ -0,0 +1,72 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, anchor_generator, compute_loss, postprocess_detections]
+        # anchor_generator
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: COCO
+    data_path: ../COCO
+    num_classes: 81
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: 
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.004
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00008
+    print_freq: 100
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: retinanet_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/COCO_RetinaNet_Res50_bat16_usebase_w4a4
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/COCO/retinanet/res50/quant_config_w8a8.yaml b/mqbconfig/_legacy/lsq_mypro/COCO/retinanet/res50/quant_config_w8a8.yaml
new file mode 100644
index 0000000..c4d7e53
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/COCO/retinanet/res50/quant_config_w8a8.yaml
@@ -0,0 +1,72 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, anchor_generator, compute_loss, postprocess_detections]
+        # anchor_generator
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: COCO
+    data_path: ../COCO
+    num_classes: 81
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: 
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.004
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00008
+    print_freq: 100
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: retinanet_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/COCO_RetinaNet_Res50_bat16_usebase_w8a8
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/COCO/ssd300/ssd_quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq_mypro/COCO/ssd300/ssd_quant_config_w4a4.yaml
new file mode 100644
index 0000000..45683a3
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/COCO/ssd300/ssd_quant_config_w4a4.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: COCO  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: ../COCO
+    num_classes: 81
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/COCO_SSD300_Res50_bat128/model-12.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 16
+    workers: 8
+    lr: 0.001
+    lr_steps: [5, 11]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.00008
+    print_freq: 1600
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ_MyPro/COCO_ssd300_Res50_bat32_usebase_w4a4
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/COCO/ssd300/ssd_quant_config_w4a4_onlyQL.yaml b/mqbconfig/_legacy/lsq_mypro/COCO/ssd300/ssd_quant_config_w4a4_onlyQL.yaml
new file mode 100644
index 0000000..5fac7f8
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/COCO/ssd300/ssd_quant_config_w4a4_onlyQL.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: COCO  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: ../COCO
+    num_classes: 81
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/COCO_SSD300_Res50_bat128/model-12.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 16
+    workers: 8
+    lr: 0.001
+    lr_steps: [6, 13]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 1000
+    my_buff_flag: False
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ_MyPro/COCO_ssd300_Res50_bat72_usebase_w4a4_onlyQL
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/VOC0712/fasterrcnn/mbnv2/quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq_mypro/VOC0712/fasterrcnn/mbnv2/quant_config_w4a4.yaml
new file mode 100644
index 0000000..faeaefd
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/VOC0712/fasterrcnn/mbnv2/quant_config_w4a4.yaml
@@ -0,0 +1,73 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, roi_heads, rpn, single_head, 
+        anchor_generator, postprocess, assign_targets_to_anchors, compute_loss, box_coder, fg_bg_sampler, 
+        proposal_matcher, select_training_samples, box_roi_pool]  # 还得包括一些rpn、roi的子函数         
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_FasterRCNN_MBNv2_bat24/model-20.pth
+    batch_size: 5
+    start_epoch: 0
+    epochs: 18
+    workers: 8
+    lr: 0.001
+    lr_steps: [7, 14]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 1600
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: fasterrcnn_mbnv2  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ_MyPro/VOC_FasterRCNN_MBNv2_bat10_usebase_w4a4
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/VOC0712/fasterrcnn/mbnv2/quant_config_w8a8.yaml b/mqbconfig/_legacy/lsq_mypro/VOC0712/fasterrcnn/mbnv2/quant_config_w8a8.yaml
new file mode 100644
index 0000000..a7a7a16
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/VOC0712/fasterrcnn/mbnv2/quant_config_w8a8.yaml
@@ -0,0 +1,73 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, roi_heads, rpn, single_head, 
+        anchor_generator, postprocess, assign_targets_to_anchors, compute_loss, box_coder, fg_bg_sampler, 
+        proposal_matcher, select_training_samples, box_roi_pool]  # 还得包括一些rpn、roi的子函数         
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: XXX
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.0008
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00006
+    print_freq: 800
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: fasterrcnn_mbnv2  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/VOC_FasterRCNN_MBNv2_bat16_usebase_w8a8
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/VOC0712/fasterrcnn/res50_fpn/quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq_mypro/VOC0712/fasterrcnn/res50_fpn/quant_config_w4a4.yaml
new file mode 100644
index 0000000..11d0b4b
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/VOC0712/fasterrcnn/res50_fpn/quant_config_w4a4.yaml
@@ -0,0 +1,73 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, roi_heads, rpn, single_head, 
+        anchor_generator, postprocess, assign_targets_to_anchors, compute_loss, box_coder, fg_bg_sampler, 
+        proposal_matcher, select_training_samples, box_roi_pool]  # 还得包括一些rpn、roi的子函数         
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: XXX
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.001
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00006
+    print_freq: 800
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: fasterrcnn_res50_fpn  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/VOC_FasterRCNN_Res50_FPN_bat16_usebase_w4a4
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/VOC0712/fasterrcnn/res50_fpn/quant_config_w8a8.yaml b/mqbconfig/_legacy/lsq_mypro/VOC0712/fasterrcnn/res50_fpn/quant_config_w8a8.yaml
new file mode 100644
index 0000000..586763f
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/VOC0712/fasterrcnn/res50_fpn/quant_config_w8a8.yaml
@@ -0,0 +1,73 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, roi_heads, rpn, single_head, 
+        anchor_generator, postprocess, assign_targets_to_anchors, compute_loss, box_coder, fg_bg_sampler, 
+        proposal_matcher, select_training_samples, box_roi_pool]  # 还得包括一些rpn、roi的子函数         
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: XXX
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.0008
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00006
+    print_freq: 800
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: fasterrcnn_res50_fpn  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ/VOC_FasterRCNN_Res50_FPN_bat16_usebase_w8a8
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/VOC0712/retinanet/res18/quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq_mypro/VOC0712/retinanet/res18/quant_config_w4a4.yaml
new file mode 100644
index 0000000..4918eec
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/VOC0712/retinanet/res18/quant_config_w4a4.yaml
@@ -0,0 +1,72 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, anchor_generator, compute_loss, postprocess_detections]
+        # anchor_generator
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_Retinanet_Res18_bat32/model-29.pth
+    batch_size: 8
+    start_epoch: 0
+    epochs: 20
+    workers: 8
+    lr: 0.001
+    lr_steps: [6, 13, 18]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.00008
+    print_freq: 1600
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: retinanet_res18  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ_MyPro/VOC_RetinaNet_Res18_bat16_usebase_w4a4
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/VOC0712/retinanet/res18/quant_config_w8a8.yaml b/mqbconfig/_legacy/lsq_mypro/VOC0712/retinanet/res18/quant_config_w8a8.yaml
new file mode 100644
index 0000000..aa18c99
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/VOC0712/retinanet/res18/quant_config_w8a8.yaml
@@ -0,0 +1,72 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, anchor_generator, compute_loss, postprocess_detections]
+        # anchor_generator
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: 
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.004
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00008
+    print_freq: 100
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: retinanet_res18  # retinanet/ssd300
+    output_dir: save_weights/QAT/VOC_RetinaNet_Res18_bat16_usebase_w8a8
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/VOC0712/retinanet/res50/quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq_mypro/VOC0712/retinanet/res50/quant_config_w4a4.yaml
new file mode 100644
index 0000000..1d7cef7
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/VOC0712/retinanet/res50/quant_config_w4a4.yaml
@@ -0,0 +1,72 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, anchor_generator, compute_loss, postprocess_detections]
+        # anchor_generator
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: 
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.004
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00008
+    print_freq: 100
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: retinanet_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/VOC_RetinaNet_Res50_bat16_usebase_w4a4
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/VOC0712/retinanet/res50/quant_config_w8a8.yaml b/mqbconfig/_legacy/lsq_mypro/VOC0712/retinanet/res50/quant_config_w8a8.yaml
new file mode 100644
index 0000000..79107a7
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/VOC0712/retinanet/res50/quant_config_w8a8.yaml
@@ -0,0 +1,72 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [transform, anchor_generator, compute_loss, postprocess_detections]
+        # anchor_generator
+        # compute_loss
+        # postprocess_detections
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: 
+    batch_size: 8
+    start_epoch: 0
+    epochs: 25
+    workers: 8
+    lr: 0.004
+    lr_steps: [8, 14, 19, 22]
+    lr_gamma: 0.4
+    momentum: 0.9
+    weight_decay: 0.00008
+    print_freq: 100
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: retinanet_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/VOC_RetinaNet_Res50_bat16_usebase_w8a8
+    resume:  # 注意一下这个
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w2a2.yaml b/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w2a2.yaml
new file mode 100644
index 0000000..54f3250
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w2a2.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 2
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 2
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_SSD300_Res50_bat128/model-9.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 16
+    workers: 8
+    lr: 0.001
+    lr_steps: [6]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 850
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ_MyPro/VOC_ssd300_Res50_bat32_usebase_w4a4
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w4a4.yaml b/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w4a4.yaml
new file mode 100644
index 0000000..827a9de
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w4a4.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_SSD300_Res50_bat128/model-9.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 20
+    workers: 8
+    lr: 0.001
+    lr_steps: [6, 13, 18]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.00001
+    print_freq: 850
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ_MyPro/VOC_ssd300_Res50_bat32_usebase_w4a4
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w4a4_noQL.yaml b/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w4a4_noQL.yaml
new file mode 100644
index 0000000..f536630
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w4a4_noQL.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_SSD300_Res50_bat128/model-9.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 16
+    workers: 8
+    lr: 0.001
+    lr_steps: [6, 13]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 850
+    my_buff_flag: True
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ_MyPro/VOC_ssd300_Res50_bat32_usebase_w4a4_noQL
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w4a4_only_QL.yaml b/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w4a4_only_QL.yaml
new file mode 100644
index 0000000..c2ebc4a
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w4a4_only_QL.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_SSD300_Res50_bat128/model-9.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 16
+    workers: 8
+    lr: 0.001
+    lr_steps: [6, 13]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 850
+    my_buff_flag: False
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ_MyPro/VOC_ssd300_Res50_bat32_usebase_w4a4_only_QL
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w8a8.yaml b/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w8a8.yaml
new file mode 100644
index 0000000..550b59d
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w8a8.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_SSD300_Res50_bat128/model-9.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 8
+    workers: 8
+    lr: 0.0001
+    lr_steps: [7]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 850
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ_MyPro/VOC_ssd300_Res50_bat32_usebase_w8a8
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w8a8_brutal.yaml b/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w8a8_brutal.yaml
new file mode 100644
index 0000000..fff7c51
--- /dev/null
+++ b/mqbconfig/_legacy/lsq_mypro/VOC0712/ssd300/ssd_quant_config_w8a8_brutal.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_SSD300_Res50_bat128/model-9.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 8
+    workers: 8
+    lr: 0.001
+    lr_steps: [4]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 850
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/LSQ_MyPro/VOC_ssd300_Res50_bat32_usebase_w8a8
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/tqt/COCO/ssd300/ssd_quant_config_w4a4.yaml b/mqbconfig/_legacy/tqt/COCO/ssd300/ssd_quant_config_w4a4.yaml
new file mode 100644
index 0000000..edfdd02
--- /dev/null
+++ b/mqbconfig/_legacy/tqt/COCO/ssd300/ssd_quant_config_w4a4.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: COCO  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: ../COCO
+    num_classes: 81
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/COCO_SSD300_Res50_bat128/model-12.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 16
+    workers: 8
+    lr: 0.001
+    lr_steps: [5, 11]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 1600
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/TQT/COCO_ssd300_Res50_bat32_usebase_w4a4
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/tqt/VOC0712/retinanet/res18/quant_config_w4a4.yaml b/mqbconfig/_legacy/tqt/VOC0712/retinanet/res18/quant_config_w4a4.yaml
new file mode 100644
index 0000000..c2748c9
--- /dev/null
+++ b/mqbconfig/_legacy/tqt/VOC0712/retinanet/res18/quant_config_w4a4.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+    preserve_attr:
+        [transform, anchor_generator, compute_loss, postprocess_detections]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_Retinanet_Res18_bat32/model-29.pth
+    batch_size: 8
+    start_epoch: 0
+    epochs: 20
+    workers: 8
+    lr: 0.001
+    lr_steps: [6, 13, 18]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 1600
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: retinanet_res18  # retinanet/ssd300
+    output_dir: save_weights/QAT/TQT/VOC_RetinaNet_Res18_bat16_usebase_w4a4
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/tqt/VOC0712/ssd300/ssd_quant_config_w4a4.yaml b/mqbconfig/_legacy/tqt/VOC0712/ssd300/ssd_quant_config_w4a4.yaml
new file mode 100644
index 0000000..b340010
--- /dev/null
+++ b/mqbconfig/_legacy/tqt/VOC0712/ssd300/ssd_quant_config_w4a4.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_SSD300_Res50_bat128/model-9.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 16
+    workers: 8
+    lr: 0.001
+    lr_steps: [6, 12]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 850
+    my_buff_flag: False
+    qloss_flag: False
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/TQT/VOC_ssd300_Res50_bat32_usebase_w4a4
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/tqt/quant_config_mypro_w4a4e.yaml b/mqbconfig/_legacy/tqt/quant_config_mypro_w4a4e.yaml
new file mode 100644
index 0000000..1d2cba6
--- /dev/null
+++ b/mqbconfig/_legacy/tqt/quant_config_mypro_w4a4e.yaml
@@ -0,0 +1,31 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+
+training:
+    my_buff_flag: True
+    qloss_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/_legacy/tqt/quant_config_w4a4.yaml b/mqbconfig/_legacy/tqt/quant_config_w4a4.yaml
new file mode 100644
index 0000000..c4b769c
--- /dev/null
+++ b/mqbconfig/_legacy/tqt/quant_config_w4a4.yaml
@@ -0,0 +1,31 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+
+training:
+    my_buff_flag: False
+    qloss_flag: False
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/_legacy/tqt_mypro/COCO/ssd300/ssd_quant_config_w4a4.yaml b/mqbconfig/_legacy/tqt_mypro/COCO/ssd300/ssd_quant_config_w4a4.yaml
new file mode 100644
index 0000000..21a4b6a
--- /dev/null
+++ b/mqbconfig/_legacy/tqt_mypro/COCO/ssd300/ssd_quant_config_w4a4.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: COCO  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: ../COCO
+    num_classes: 81
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/COCO_SSD300_Res50_bat128/model-12.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 16
+    workers: 8
+    lr: 0.001
+    lr_steps: [5, 11]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 1600
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/TQT_MyPro/COCO_ssd300_Res50_bat32_usebase_w4a4
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/tqt_mypro/VOC0712/retinanet/res18/quant_config_w4a4.yaml b/mqbconfig/_legacy/tqt_mypro/VOC0712/retinanet/res18/quant_config_w4a4.yaml
new file mode 100644
index 0000000..74372f5
--- /dev/null
+++ b/mqbconfig/_legacy/tqt_mypro/VOC0712/retinanet/res18/quant_config_w4a4.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+    preserve_attr:
+        [transform, anchor_generator, compute_loss, postprocess_detections]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_Retinanet_Res18_bat32/model-29.pth
+    batch_size: 8
+    start_epoch: 0
+    epochs: 20
+    workers: 8
+    lr: 0.001
+    lr_steps: [6, 13, 18]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 1600
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: retinanet_res18  # retinanet/ssd300
+    output_dir: save_weights/QAT/TQT_MyPro/VOC_RetinaNet_Res18_bat16_usebase_w4a4
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/_legacy/tqt_mypro/VOC0712/ssd300/ssd_quant_config_w4a4.yaml b/mqbconfig/_legacy/tqt_mypro/VOC0712/ssd300/ssd_quant_config_w4a4.yaml
new file mode 100644
index 0000000..e565ba6
--- /dev/null
+++ b/mqbconfig/_legacy/tqt_mypro/VOC0712/ssd300/ssd_quant_config_w4a4.yaml
@@ -0,0 +1,69 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+    preserve_attr:
+        [compute_loss, postprocess]
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 64  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+# model:                    # architecture details
+#     type: resnet18        # model name
+#     kwargs:
+#         num_classes: 1000
+#     path: /path-of-pretrained
+dataset:
+    type: VOC0712  # VOC2012 VOC2007 VOC0712 COCO
+    data_path: /workspace/share/datasets/VOC
+    num_classes: 21
+    aspect_ratio_group_factor: 3
+    # path: /path-of-imagenet
+    # batch_size: 64
+    # num_workers: 4
+    # pin_memory: True
+    # input_size: 224
+    # test_resize: 256
+training:
+    device: cuda
+    use_baseline: save_weights/ORI/VOC_SSD300_Res50_bat128/model-9.pth
+    batch_size: 16
+    start_epoch: 0
+    epochs: 20
+    workers: 8
+    lr: 0.001
+    lr_steps: [6, 13, 18]
+    lr_gamma: 0.1
+    momentum: 0.9
+    weight_decay: 0.0001
+    print_freq: 850
+    my_buff_flag: True
+    qloss_flag: True
+    pretrained_flag: True
+
+misc:
+    model: ssd300_res50  # retinanet/ssd300
+    output_dir: save_weights/QAT/TQT_MyPro/VOC_ssd300_Res50_bat32_usebase_w4a4
+    resume: 
+    amp: False  # 混合精度训练
+dist:
+    world_size: 1
+    dist_url: env://
+
+process:
+    seed: 1005
\ No newline at end of file
diff --git a/mqbconfig/lsq/quant_config_hardet_w2a2.yaml b/mqbconfig/lsq/quant_config_hardet_w2a2.yaml
new file mode 100644
index 0000000..3b8d8a0
--- /dev/null
+++ b/mqbconfig/lsq/quant_config_hardet_w2a2.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 2
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 2
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+
+training:
+    my_buff_flag: 2
+    qloss_flag: True
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/lsq/quant_config_hardet_w4a4.yaml b/mqbconfig/lsq/quant_config_hardet_w4a4.yaml
new file mode 100644
index 0000000..a06d9ed
--- /dev/null
+++ b/mqbconfig/lsq/quant_config_hardet_w4a4.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+
+training:
+    my_buff_flag: 2
+    qloss_flag: False
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/lsq/quant_config_hardet_w8a8.yaml b/mqbconfig/lsq/quant_config_hardet_w8a8.yaml
new file mode 100644
index 0000000..d46b4be
--- /dev/null
+++ b/mqbconfig/lsq/quant_config_hardet_w8a8.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+
+training:
+    my_buff_flag: 2
+    qloss_flag: False
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/lsq/quant_config_mypro_w2a2.yaml b/mqbconfig/lsq/quant_config_mypro_w2a2.yaml
new file mode 100644
index 0000000..b51dc04
--- /dev/null
+++ b/mqbconfig/lsq/quant_config_mypro_w2a2.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 2
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 2
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+
+training:
+    my_buff_flag: 1  # 重新编码意思 0 为 false; 1 为 hqod; 2 为 hardet
+    qloss_flag: True
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/lsq/quant_config_mypro_w2a2_weight_loose.yaml b/mqbconfig/lsq/quant_config_mypro_w2a2_weight_loose.yaml
new file mode 100644
index 0000000..3484830
--- /dev/null
+++ b/mqbconfig/lsq/quant_config_mypro_w2a2_weight_loose.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: LSQObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 2
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 2
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+
+training:
+    my_buff_flag: 1  # 重新编码意思 0 为 false; 1 为 hqod; 2 为 hardet
+    qloss_flag: True
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/lsq/quant_config_mypro_w4a4.yaml b/mqbconfig/lsq/quant_config_mypro_w4a4.yaml
new file mode 100644
index 0000000..2b7e375
--- /dev/null
+++ b/mqbconfig/lsq/quant_config_mypro_w4a4.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+
+training:
+    my_buff_flag: 1  # 重新编码意思 0 为 false; 1 为 hqod; 2 为 hardet
+    qloss_flag: True
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/lsq/quant_config_mypro_w8a8.yaml b/mqbconfig/lsq/quant_config_mypro_w8a8.yaml
new file mode 100644
index 0000000..328b58b
--- /dev/null
+++ b/mqbconfig/lsq/quant_config_mypro_w8a8.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+
+training:
+    my_buff_flag: 1
+    qloss_flag: True
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/lsq/quant_config_w2a2.yaml b/mqbconfig/lsq/quant_config_w2a2.yaml
new file mode 100644
index 0000000..6d73e71
--- /dev/null
+++ b/mqbconfig/lsq/quant_config_w2a2.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 2
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False  # TODO 这有问题
+            pot_scale: False
+        a_qscheme:
+            bit: 2
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+
+training:
+    my_buff_flag: 0
+    qloss_flag: True
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/lsq/quant_config_w2a2_weight_loose.yaml b/mqbconfig/lsq/quant_config_w2a2_weight_loose.yaml
new file mode 100644
index 0000000..fde32d6
--- /dev/null
+++ b/mqbconfig/lsq/quant_config_w2a2_weight_loose.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: LSQObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 2
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False  # TODO 这有问题
+            pot_scale: False
+        a_qscheme:
+            bit: 2
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+
+training:
+    my_buff_flag: 0
+    qloss_flag: True
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/lsq/quant_config_w4a4.yaml b/mqbconfig/lsq/quant_config_w4a4.yaml
new file mode 100644
index 0000000..5bb1a62
--- /dev/null
+++ b/mqbconfig/lsq/quant_config_w4a4.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+
+training:
+    my_buff_flag: 0
+    qloss_flag: True
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/lsq/quant_config_w8a8.yaml b/mqbconfig/lsq/quant_config_w8a8.yaml
new file mode 100644
index 0000000..c66f1e2
--- /dev/null
+++ b/mqbconfig/lsq/quant_config_w8a8.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: LearnableFakeQuantize
+        a_fakequantize: LearnableFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: lsq
+
+training:
+    my_buff_flag: 0
+    qloss_flag: False
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/pure_hook.yaml b/mqbconfig/pure_hook.yaml
new file mode 100644
index 0000000..ab5b9dc
--- /dev/null
+++ b/mqbconfig/pure_hook.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: MinMaxObserver
+        a_observer: EMAMSEObserver
+        w_fakequantize: PureHooker
+        a_fakequantize: PureHooker
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: False
+
+quantize:
+    quantize_type: pure_hook # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: none
+
+training:
+    my_buff_flag: 0
+    qloss_flag: True
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/tqt/quant_config_hardet_w4a4.yaml b/mqbconfig/tqt/quant_config_hardet_w4a4.yaml
new file mode 100644
index 0000000..e0069ee
--- /dev/null
+++ b/mqbconfig/tqt/quant_config_hardet_w4a4.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+
+training:
+    my_buff_flag: 2
+    qloss_flag: False
+    pretrained_flag: True
+    fold_bn_flag: True
+misc:
+    resume: False
+
diff --git a/mqbconfig/tqt/quant_config_mypro_w2a2.yaml b/mqbconfig/tqt/quant_config_mypro_w2a2.yaml
new file mode 100644
index 0000000..60e60e7
--- /dev/null
+++ b/mqbconfig/tqt/quant_config_mypro_w2a2.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 2
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 2
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+
+training:
+    my_buff_flag: 1  # 重新编码意思 0 为 false; 1 为 hqod; 2 为 hardet
+    qloss_flag: True
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/tqt/quant_config_mypro_w4a4.yaml b/mqbconfig/tqt/quant_config_mypro_w4a4.yaml
new file mode 100644
index 0000000..6534c38
--- /dev/null
+++ b/mqbconfig/tqt/quant_config_mypro_w4a4.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+
+training:
+    my_buff_flag: 1  # 重新编码意思 0 为 false; 1 为 hqod; 2 为 hardet
+    qloss_flag: True
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/tqt/quant_config_mypro_w8a8.yaml b/mqbconfig/tqt/quant_config_mypro_w8a8.yaml
new file mode 100644
index 0000000..60e60e7
--- /dev/null
+++ b/mqbconfig/tqt/quant_config_mypro_w8a8.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 2
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 2
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+
+training:
+    my_buff_flag: 1  # 重新编码意思 0 为 false; 1 为 hqod; 2 为 hardet
+    qloss_flag: True
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/tqt/quant_config_w2a2.yaml b/mqbconfig/tqt/quant_config_w2a2.yaml
new file mode 100644
index 0000000..abd2c28
--- /dev/null
+++ b/mqbconfig/tqt/quant_config_w2a2.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 2
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 2
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+
+training:
+    my_buff_flag: 0
+    qloss_flag: False
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/tqt/quant_config_w4a4.yaml b/mqbconfig/tqt/quant_config_w4a4.yaml
new file mode 100644
index 0000000..93d70b6
--- /dev/null
+++ b/mqbconfig/tqt/quant_config_w4a4.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 4
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 4
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+
+training:
+    my_buff_flag: 0
+    qloss_flag: False
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbconfig/tqt/quant_config_w8a8.yaml b/mqbconfig/tqt/quant_config_w8a8.yaml
new file mode 100644
index 0000000..313d3f4
--- /dev/null
+++ b/mqbconfig/tqt/quant_config_w8a8.yaml
@@ -0,0 +1,32 @@
+extra_prepare_dict:
+    extra_qconfig_dict:
+        w_observer: PoTModeObserver
+        a_observer: PoTModeObserver
+        w_fakequantize: TqtFakeQuantize
+        a_fakequantize: TqtFakeQuantize
+        w_qscheme:
+            bit: 8
+            symmetry: True
+            sign: True  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+        a_qscheme:
+            bit: 8
+            symmetry: True
+            sign: False  # MQB就缺个这个，整蒙了
+            per_channel: False
+            pot_scale: True
+
+quantize:
+    quantize_type: naive_ptq # support naive_ptq or advanced_ptq
+    cali_batchnum: 256  # 越多越好？？似乎是的
+    quant_algorithm: tqt
+
+training:
+    my_buff_flag: 0
+    qloss_flag: False
+    pretrained_flag: True
+    fold_bn_flag: False
+misc:
+    resume: False
+
diff --git a/mqbench/__init__.py b/mqbench/__init__.py
new file mode 100644
index 0000000..fa9c4ec
--- /dev/null
+++ b/mqbench/__init__.py
@@ -0,0 +1 @@
+__version__ = '0.0.6'
diff --git a/mqbench/advanced_ptq.py b/mqbench/advanced_ptq.py
new file mode 100644
index 0000000..f9ede72
--- /dev/null
+++ b/mqbench/advanced_ptq.py
@@ -0,0 +1,676 @@
+import torch
+import torch.nn.intrinsic.qat as nniqat
+from torch.fx import GraphModule, Node
+from torch import fx, nn
+from torch.nn import Module
+
+USE_LINK = False
+USE_DDP = False
+
+__all__ = ['ptq_reconstruction']
+
+try:
+    import spring.linklink as link
+    if not link.is_initialized():
+        link.initialize()
+    USE_LINK = True
+except (ModuleNotFoundError, AssertionError):
+    import torch.distributed as dist
+    if torch.distributed.is_initialized():
+        USE_DDP = True
+
+import numpy as np
+from typing import List
+
+from mqbench.utils.logger import logger
+from mqbench.utils.hook import DataSaverHook, StopForwardException
+from mqbench.utils import deepcopy_graphmodule, deepcopy_mixedmodule, topology_order, getitem2node
+from mqbench.utils.utils import _fix_succ_recursivly
+from mqbench.utils.state import enable_quantization, disable_all
+import mqbench.nn.intrinsic.qat as qnniqat
+
+_ADAROUND_SUPPORT_TYPE = (torch.nn.Conv2d, torch.nn.Linear)
+_FUSED_TYPE = (nniqat.ConvBnReLU2d, nniqat.ConvBn2d, qnniqat.ConvFreezebn2d, qnniqat.ConvFreezebnReLU2d)
+_WEIGHTS_MODULE_TYPE = (torch.nn.Conv2d, torch.nn.Linear)
+
+def node2modules(name2modules, nodes):
+    modules = dict()
+    for node in nodes:
+        if node.target in name2modules:
+            modules[node] = name2modules[node.target]
+    return modules
+
+
+def qnode2fpnode(quant_modules, fp32_modules):
+    quant_named_nodes = {node.target: node for node in quant_modules}
+    fp32_named_nodes = {node.target: node for node in fp32_modules}
+    qnode2fpnode_dict = {quant_named_nodes[key]: fp32_named_nodes[key] for key in quant_named_nodes}
+    return qnode2fpnode_dict
+
+def layer_has_weights(nodes, modules):
+    has_weights = False
+    for node in nodes:
+        if node in modules:
+            if isinstance(modules[node], _WEIGHTS_MODULE_TYPE):
+                has_weights = True
+                break 
+    return has_weights
+
+
+def lp_loss(pred, tgt, p=2.0):
+    """
+    loss function measured in L_p Norm
+    """
+    return (pred - tgt).abs().pow(p).sum(1).mean()
+
+
+def to_device(data, device='cpu'):
+    if isinstance(data, torch.Tensor):
+        return data.to(device)
+    elif isinstance(data, dict):
+        for key in data:
+            data[key] = to_device(data[key], device)
+        return data
+    elif isinstance(data, list):
+        for idx, _ in enumerate(data):
+            data[idx] = to_device(data[idx], device)
+        return data
+    else:
+        return data
+
+
+def tensor_detach(data):
+    if isinstance(data, torch.Tensor):
+        return data.detach()
+    elif isinstance(data, dict):
+        for key in data:
+            data[key] = tensor_detach(data[key])
+        return data
+    elif isinstance(data, list):
+        data = [tensor_detach(dat) for dat in data]
+    else:
+        return data
+
+
+def save_inp_oup_data(model: GraphModule, inp_module: Module, oup_module: Module, cali_data: list, store_inp=True, store_oup=True,
+                      keep_gpu: bool = True):
+    """
+    Save input data and output data of a particular layer/block over calibration dataset.
+    :param fp_model: fp_model
+    :param quant_model: quant_model
+    :param cali_data: calibration data set
+    :param keep_gpu: put saved data on GPU for faster optimization
+    :return: input and output data
+    """
+    device = next(model.parameters()).device
+    if store_inp:
+        assert inp_module is not None
+        inp_saver = DataSaverHook(store_input=store_inp, store_output=False, stop_forward=(not store_oup))
+        inp_handle = inp_module.register_forward_hook(inp_saver)
+    if store_oup:
+        assert oup_module is not None
+        oup_saver = DataSaverHook(store_input=False, store_output=store_oup, stop_forward=True)
+        oup_handle = oup_module.register_forward_hook(oup_saver)
+    cached = ([], [])
+    with torch.no_grad():
+        for batch in cali_data:
+            try:
+                _ = model(to_device(batch, device))
+            except StopForwardException:
+                pass
+            if store_inp:
+                if keep_gpu:
+                    cached[0].append([tensor_detach(inp) for inp in inp_saver.input_store])
+                else:
+                    cached[0].append([to_device(tensor_detach(inp), 'cpu') for inp in inp_saver.input_store])  # tuple/list one
+            if store_oup:
+                if keep_gpu:
+                    cached[1].append(tensor_detach(oup_saver.output_store))
+                else:
+                    cached[1].append(to_device(tensor_detach(oup_saver.output_store), 'cpu'))
+    if store_inp:
+        inp_handle.remove()
+    if store_oup:
+        oup_handle.remove()
+    torch.cuda.empty_cache()
+    return cached
+
+
+class LinearTempDecay:
+    def __init__(self, t_max=10000, warm_up=0.2, start_b=20, end_b=2):
+        self.t_max = t_max
+        self.start_decay = warm_up * t_max
+        self.start_b = start_b
+        self.end_b = end_b
+
+    def __call__(self, t):
+        if t < self.start_decay:
+            return self.start_b
+        elif t > self.t_max:
+            return self.end_b
+        else:
+            rel_t = (t - self.start_decay) / (self.t_max - self.start_decay)
+            return self.end_b + (self.start_b - self.end_b) * max(0.0, (1 - rel_t))
+
+
+class CosineTempDecay:
+    def __init__(self, t_max=10000, warm_up=0.2, start_b=20, end_b=2):
+        self.t_max = t_max
+        self.start_decay = warm_up * t_max
+        self.start_b = start_b
+        self.end_b = end_b
+
+    def __call__(self, t):
+        if t < self.start_decay:
+            return self.start_b
+        elif t > self.t_max:
+            return self.end_b
+        else:
+            rel_t = (t - self.start_decay) / (self.t_max - self.start_decay)
+            return self.end_b + 0.5 * (self.start_b - self.end_b) * (1 + np.cos(rel_t * np.pi))
+
+
+class LossFunction:
+    r'''loss function to calculate mse reconstruction loss and relaxation loss
+    use some tempdecay to balance the two losses.
+    '''
+    def __init__(self,
+                 subgraph: Module,
+                 weight: float = 1.,
+                 max_count: int = 10000,
+                 b_range: tuple = (20, 2),
+                 warm_up: float = 0.0,
+                 p: float = 2.):
+
+        self.subgraph = subgraph
+        self.weight = weight
+        self.loss_start = max_count * warm_up
+        self.p = p
+
+        self.temp_decay = LinearTempDecay(max_count, warm_up=warm_up,
+                                          start_b=b_range[0], end_b=b_range[1])
+        self.count = 0
+
+    def __call__(self, pred, tgt):
+        """
+        Compute the total loss for adaptive rounding:
+        rec_loss is the quadratic output reconstruction loss, round_loss is
+        a regularization term to optimize the rounding policy
+
+        :param pred: output from quantized model
+        :param tgt: output from FP model
+        :return: total loss function
+        """
+        self.count += 1
+        rec_loss = lp_loss(pred, tgt, p=self.p)
+
+        b = self.temp_decay(self.count)
+        if self.count < self.loss_start:
+            round_loss = 0
+        else:
+            round_loss = 0
+            for layer in self.subgraph.modules():
+                if isinstance(layer, _ADAROUND_SUPPORT_TYPE):
+                    round_vals = layer.weight_fake_quant.rectified_sigmoid()
+                    round_loss += self.weight * (1 - ((round_vals - .5).abs() * 2).pow(b)).sum()
+
+        total_loss = rec_loss + round_loss
+        if self.count % 500 == 0:
+            logger.info('Total loss:\t{:.3f} (rec:{:.3f}, round:{:.3f})\tb={:.2f}\tcount={}'.format(
+                float(total_loss), float(rec_loss), float(round_loss), b, self.count))
+        return total_loss
+
+
+def _flatten_args(node):
+    flattned_args = []
+    if isinstance(node, dict):
+        for v in node.values():
+            flattned_args.extend(_flatten_args(v))
+    elif isinstance(node, tuple) or isinstance(node, list):
+        for n in node:
+            flattned_args.extend(_flatten_args(n))
+    else:
+        flattned_args.extend([node])
+    return flattned_args
+
+
+def find_used_times(nodes, target):
+    used = len([_node for _node in target.users if _node in nodes])    
+    return used
+
+
+
+
+def find_cur_node(layer_node_list):
+    node_list = []
+    used_later = []
+    for idx, node in enumerate(layer_node_list):
+        for _node in layer_node_list[idx + 1:]:
+            if node in _flatten_args(_node.args):
+                used_later.append(node)
+                break
+    not_used_later = [node for node in layer_node_list if node not in used_later]
+    single_branch = dict()
+    for node in not_used_later:
+        single_branch[node] = set([node])
+        q = [node]
+        while True:
+            now_args = sum([_flatten_args(_node.args) for _node in q], [])
+            p = [_node for _node in now_args if isinstance(_node, torch.fx.Node) and find_used_times(layer_node_list, _node) == 1]
+            single_branch[node] = single_branch[node].union(set(p))
+            if len(p) == 0:
+                break
+            else:
+                q = p
+    for node in layer_node_list:
+        if node.op == 'call_function' or node.op == 'call_method':
+            continue
+        if node not in used_later:
+            break
+    unwanted = set()
+    for key in single_branch:
+        if key is node:
+            continue 
+        else:
+            unwanted = unwanted.union(single_branch[key])
+    layer_node_list = [_node for _node in layer_node_list if _node not in unwanted]
+    for _node in layer_node_list:
+        node_list.append(_node)
+        if _node is node:
+            return node_list
+
+
+def subgraph_reconstruction(subgraph, cached_inps, cached_oups, config):
+    global USE_LINK
+    global USE_DDP
+    device = next(subgraph.parameters()).device
+    w_para, a_para = [], []
+    w_opt, w_scheduler = None, None
+    if hasattr(config, 'scale_lr'):
+        a_para = []
+    for name, layer in subgraph.named_modules():
+        if isinstance(layer, _ADAROUND_SUPPORT_TYPE):
+            weight_quantizer = layer.weight_fake_quant
+            # assert isinstance(weight_quantizer, adaround_quantizer) is True
+            weight_quantizer.init(layer.weight.data, config.round_mode)
+            w_para += [weight_quantizer.alpha]
+        if isinstance(layer, torch.quantization.FakeQuantizeBase) and 'post_act_fake_quantize' in name:
+            if hasattr(config, 'scale_lr'):
+                logger.info('learn the scale for {}'.format(name))
+                a_para += [layer.scale]
+            layer.prob = config.prob
+    if len(a_para) != 0:
+        a_opt = torch.optim.Adam(a_para, lr=config.scale_lr)
+        a_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(a_opt, T_max=config.max_count, eta_min=0.)
+    else:
+        a_opt, a_scheduler = None, None
+    w_opt = torch.optim.Adam(w_para)
+
+    loss_func = LossFunction(subgraph=subgraph, weight=config.weight, max_count=config.max_count, b_range=config.b_range,
+                             warm_up=config.warm_up)
+
+    if any([USE_DDP, USE_LINK]):
+        world_size = link.get_world_size() if USE_LINK else dist.get_world_size()
+    else:
+        world_size = 1
+
+    logger.info('The world size is {}.'.format(world_size))
+    '''start training'''
+    logger.info('start tuning by adaround')
+    if config.prob < 1.0:
+        # cache inps: drop x args x batch x data
+        sz = len(cached_inps[0][0])
+        num_args = len(cached_inps[0])
+    else:
+        # cache inps: args x batch x data
+        sz = len(cached_inps[0])
+        num_args = len(cached_inps)
+    for i in range(config.max_count):
+        idx = np.random.randint(0, sz)
+        cur_args = []
+        for a in range(num_args):
+            if config.prob < 1.0:
+                cur_inp = to_device(cached_inps[0][a][idx], device)
+                cur_sym = to_device(cached_inps[1][a][idx], device)
+                cur_inp = torch.where(torch.rand_like(cur_inp) < config.prob, cur_inp, cur_sym)
+            else:
+                cur_inp = to_device(cached_inps[a][idx], device)
+            cur_args.append(cur_inp)
+        cur_args = tuple(cur_args)
+        cur_out = to_device(cached_oups[idx], device)
+        if a_opt:
+            a_opt.zero_grad()
+        w_opt.zero_grad()
+        out_quant = subgraph(*cur_args)
+        err = loss_func(out_quant, cur_out)
+        err /= world_size
+        err.backward()
+        if world_size > 1:
+            for param in w_para:
+                if USE_LINK:
+                    link.allreduce(param.grad.data)
+                elif USE_DDP:
+                    dist.all_reduce(param.grad.data)
+        w_opt.step()
+        if a_opt:
+            a_opt.step()
+        if w_scheduler:
+            w_scheduler.step()
+        if a_scheduler:
+            a_scheduler.step()
+    torch.cuda.empty_cache()
+    for name, layer in subgraph.named_modules():        
+        if isinstance(layer, _FUSED_TYPE):
+            # We need to do bn fold simulation here.
+            weight_quantizer = layer.weight_fake_quant
+            scale_factor = layer.bn.weight / torch.sqrt(layer.bn.running_var + layer.bn.eps)
+            merged_rounded_weight = weight_quantizer.get_hard_value(
+                layer.weight.data * scale_factor.reshape([-1] + [1] * (len(layer.weight.shape) - 1)))
+            layer.weight.data = merged_rounded_weight / scale_factor.reshape([-1] + [1] * (len(merged_rounded_weight.shape) - 1))
+            weight_quantizer.adaround = False
+        elif isinstance(layer, _ADAROUND_SUPPORT_TYPE):
+            assert not hasattr(layer, 'bn'), 'Layer {} with type {} has BN ! Should not reach here.'.format(name, type(layer))
+            weight_quantizer = layer.weight_fake_quant
+            layer.weight.data = weight_quantizer.get_hard_value(layer.weight.data)
+            weight_quantizer.adaround = False
+        if isinstance(layer, torch.quantization.FakeQuantizeBase) and 'post_act_fake_quantize' in name:
+            layer.prob = 1.0   # recover to promise that drop activation quantization only occurs at reconstruction phase
+
+
+def extract_subgraph(orig_module: nn.Module, nodes: List[fx.Node], output: fx.Node, g2node: dict):
+    """
+    Given lists of nodes from an existing graph that represent a subgraph, returns a submodule that executes that subgraph.
+    """
+    new_graph = fx.Graph()
+    env = dict()
+    inp_lst = []
+    for node in nodes:
+        for arg in _flatten_args(node.args):
+            if isinstance(arg, torch.fx.Node):
+                if arg not in nodes and arg not in inp_lst:
+                    inp_lst.append(node)
+                    if node in g2node:
+                        arg_name = g2node[node].name
+                    else:
+                        arg_name = node.name
+                    new_node = new_graph.placeholder(arg_name)
+                    env[node] = new_node
+                    break
+    for node in nodes:
+        if node in inp_lst:
+            continue
+        if node in g2node:
+            node = g2node[node]
+        new_node = new_graph.node_copy(node, lambda x: env[x])
+        env[node] = new_node
+    # create this or there will not be return value
+    new_graph.output(env[output])
+    new_graph.lint()
+    return fx.GraphModule(orig_module, new_graph)
+
+def find_num_nodes(nodes):
+    num = 0
+    for node in nodes:
+        if isinstance(node, Node):
+            num += 1
+    return num
+
+
+# Recommend: log this to check if the layer is right. You can define your own layer manually or automatically like this
+# extract the linked-list/single-chain
+def extract_layer(node, fp32_modules):
+    layer_node_list = []
+    cur_node = node
+    is_next_block = False  # check whether stoped by a block
+    while True:
+        logger.debug('cur_node in layer is {}'.format(cur_node))
+        layer_node_list.append(cur_node)  # valid node here
+        stop = (len(cur_node.users) == 0)
+        for user in cur_node.users:
+            if user.target == 'update':
+                continue
+            if user.op == 'call_module' and isinstance(
+                    fp32_modules[user], _ADAROUND_SUPPORT_TYPE):
+                stop = True
+            # TODO: only short-cut here, consider more here
+            # TODO: can also use un/completed to check here.
+            if ('add' in user.name
+                    and user.op in ['call_function', 'call_method']):
+                stop = True
+            if user.op == 'output':
+                is_next_block, stop = True, True
+        if stop:
+            break
+        cur_node = list(cur_node.users.keys())[0]
+    if find_num_nodes(cur_node.users) > 1:
+        is_next_block = True
+    return layer_node_list, is_next_block
+
+
+# Recommend: log this to check if the block is right. You can define your own block manually or automatically like this
+# extract the block one such as short-cut
+def extract_block(input_nodes, fp32_modules, depth=0):
+    if depth > 2:
+        # stack 2 or 3 layers for no short-cut structure
+        return []
+    layer_node_list = []
+    is_block = False
+    cnt = dict()
+    q, p = [], []  # q records the completed node, p records the uncompleted nodes
+    cur_node = None
+    for input in input_nodes:
+        for user in input.users:
+            if user not in cnt:
+                cnt[user] = find_num_nodes(user.args)
+                if cnt[user] > 1:
+                    is_block = True
+                p.append(user)
+            cnt[user] -= 1
+            if cnt[user] == 0:
+                q.append(user)
+                p.remove(user)
+    while len(q) != 0:
+        cur_node = q.pop(0)  # valid node here
+        logger.debug('cur node is {}'.format(cur_node))
+        if cur_node.target == 'update':
+            continue
+        if len(p) == 0 and len(q) == 0:
+            break
+        layer_node_list.append(cur_node)
+        for user in cur_node.users:
+            if user not in cnt:
+                cnt[user] = find_num_nodes(user.args)
+                if cnt[user] > 1:
+                    is_block = True
+                p.append(user)
+            cnt[user] -= 1
+            if cnt[user] == 0:
+                q.append(user)
+                p.remove(user)
+        logger.debug('uncompleted nodes are {}'.format(p))
+    if not cur_node:
+        return layer_node_list
+    exp_nodes, is_next_block = extract_layer(cur_node, fp32_modules)
+    if is_block or is_next_block:
+        return layer_node_list + exp_nodes
+    else:
+        return layer_node_list + exp_nodes + extract_block(
+            [exp_nodes[-1]], fp32_modules, depth + 1)
+
+
+def ptq_reconstruction(model: GraphModule, cali_data: list, config: dict, graph_module_list: list = None):
+    r"""
+    Reconsturction for AdaRound, BRECQ, QDrop.
+    Basic optimization objective:
+
+    .. math::
+
+        \mathop{\arg\min}_{\mathbf{V}}\ \ || Wx-\tilde{W}x ||_F^2 + \lambda f_{reg}(\mathbf{V}),
+
+        \tilde{W}=s \cdot clip\left( \left\lfloor\dfrac{W}{s}\right\rfloor+h(\mathbf{V}), n, p \right)
+
+    where :math:`h(\mathbf{V}_{i,j})=clip(\sigma(\mathbf{V}_{i,j})(\zeta-\gamma)+\gamma, 0, 1)`, and :math:`f_{reg}(\mathbf{V})=\mathop{\sum}_{i,j}{1-|2h(\mathbf{V}_{i,j})-1|^\beta}`. By annealing on :math:`\beta`, the rounding mask can adapt freely in initial phase and converge to 0 or 1 in later phase.
+
+    Args:
+        model (torch.nn.Module): a prepared GraphModule to do PTQ
+        cali_data (List): a list of calibration tensor
+        config (dict): a config for PTQ reconstruction
+        graph_module_list (list): a list of model's children modules which need quantization. if this is used, the model is partial quantized; if not, the model is fully quantized.
+
+    >>> sample config : {
+            pattern: block (str, Available options are [layer, block].)
+            scale_lr: 4.0e-5 (learning rate for learning step size of activation)
+            warm_up: 0.2 (0.2 * max_count iters without regularization to floor or ceil)
+            weight: 0.01 (loss weight for regularization item)
+            max_count: 20000 (optimization iteration)
+            b_range: [20,2] (beta decaying range )
+            keep_gpu: True (calibration data restore in gpu or cpu)
+            round_mode: learned_hard_sigmoid (ways to reconstruct the weight, currently only support learned_hard_sigmoid)
+            prob: 0.5 (dropping probability of QDROP)
+        }
+
+    """
+    # assert model is on cuda
+    if not config.keep_gpu:
+        cali_data = [to_device(inp, 'cpu') for inp in cali_data]
+    '''set state first'''
+
+    fp32_model = model
+    fp32_model.eval()
+    if graph_module_list is None:
+        assert isinstance(fp32_model, torch.fx.GraphModule)
+        quant_model = deepcopy_graphmodule(model)
+        nodes = list(quant_model.graph.nodes)
+        g2node = getitem2node(quant_model)
+        fp32_modules = node2modules(dict(fp32_model.named_modules()), fp32_model.graph.nodes)
+        quant_modules = node2modules(dict(quant_model.named_modules()), quant_model.graph.nodes)
+        topology_order_by_node = topology_order(quant_model)
+    else:
+        quant_model = deepcopy_mixedmodule(model, graph_module_list)
+        nodes = []
+        g2node = dict()
+        fp32_modules = dict()
+        quant_modules = dict()
+        topology_order_by_node = {}
+        topo_cnt = 0
+        for mname in graph_module_list:
+            child = getattr(quant_model, mname)
+            assert isinstance(child, torch.fx.GraphModule)
+            nodes += list(child.graph.nodes)
+            g2node.update(getitem2node(child))
+        for mname in graph_module_list:
+            fp_child = getattr(fp32_model, mname)
+            q_child = getattr(quant_model, mname)
+            # note: the nodes we use is from the quant model, so build q_node2fp_module, rather than fp2fp.
+            fp_modules = node2modules(dict(fp_child.named_modules()), q_child.graph.nodes)
+            q_modules = node2modules(dict(q_child.named_modules()), q_child.graph.nodes)
+            fp32_modules.update(fp_modules)
+            quant_modules.update(q_modules)
+            child_topo = topology_order(q_child)
+            for k in child_topo:
+                child_topo[k] += topo_cnt
+            topology_order_by_node.update(child_topo)
+            topo_cnt += len(topology_order_by_node)
+    qnode2fpnode_dict = qnode2fpnode(quant_modules, fp32_modules)
+    quant_model.eval()
+    disable_all(fp32_model)
+    enable_quantization(quant_model)
+    torch.cuda.empty_cache()
+    checked_nodes = dict()
+    for node in nodes:
+        if 'exclude_node_prefix' in config:
+            cont = False
+            for prefix in config['exclude_node']:
+                if node.name.startswith(prefix):
+                    cont = True
+                    break
+            if cont:
+                logger.info(f'Exclude node {node}')
+                continue
+        if node in checked_nodes:
+            continue
+        if node.op == "call_module" and isinstance(quant_modules[node], _ADAROUND_SUPPORT_TYPE):
+            logger.info('prepare {} reconstruction for {}'.format(config.pattern, node))
+            if config.pattern == 'layer':
+                layer_node_list, _ = extract_layer(node, quant_modules)
+            elif config.pattern == 'block':
+                layer_node_list = extract_block(node.all_input_nodes, quant_modules)
+            else:
+                raise NotImplementedError
+            # if the update is not used in the block, remove it
+            if not all([n.target != 'update' for n in layer_node_list]):
+                remove_nodes = []
+                for idx, n in enumerate(layer_node_list):
+                    if n.target == 'update':
+                        src = n.args[0]
+                        remove = True
+                        for _idx in range(idx + 1, len(layer_node_list)):
+                            if src in _flatten_args(
+                                    layer_node_list[_idx].args):
+                                remove = False
+                                break
+                        if remove:
+                            remove_nodes.append(n)
+                layer_node_list = [n for n in layer_node_list if n not in remove_nodes]
+            missing_inputs = []
+            for _node in layer_node_list:
+                for arg in _flatten_args(_node.args):
+                    if isinstance(arg, torch.fx.Node):
+                        if arg not in layer_node_list and arg not in missing_inputs:
+                            missing_inputs.append(arg)
+            layer_node_list.extend(missing_inputs)
+            # replace getitem nodes into its source node
+            layer_node_list = [n if n not in g2node else g2node[n] for n in layer_node_list]
+            for _node in layer_node_list:
+                src = [arg for arg in _flatten_args(_node.args) if arg in g2node]
+                for arg in src:
+                    _node.args = _fix_succ_recursivly(_node.args, arg, g2node[arg])
+            layer_node_list = sorted(layer_node_list, key=lambda x: topology_order_by_node[x])
+            layer_node_list = find_cur_node(layer_node_list)
+            if layer_has_weights(layer_node_list, quant_modules):
+                pass
+            else:
+                continue
+            logger.info('the node list is below!')
+            logger.info(layer_node_list)
+            fp32_module = fp32_modules[qnode2fpnode_dict[layer_node_list[-1]]]
+            fp32_all_inps = []
+            quant_all_inps = []
+            fp32_final_oups = None
+            out_is_cached = False
+            for _node in layer_node_list:
+                if all([arg in layer_node_list for arg in _flatten_args(_node.args) if isinstance(arg, torch.fx.Node)]):
+                    continue
+                else:
+                    fp32_inp_module = fp32_modules[qnode2fpnode_dict[_node]]
+                    quant_module = quant_modules[_node]
+                    # fp32 inps: [out_b1, out_b2, ...]
+                    _, fp32_inps = save_inp_oup_data(fp32_model, None, fp32_inp_module, cali_data, 
+                                                     store_inp=False, store_oup=(config.prob < 1.0), keep_gpu=config.keep_gpu)
+                    _, fp32_oups = save_inp_oup_data(fp32_model, None, fp32_module, cali_data,
+                                                     store_inp=False, store_oup=(not out_is_cached), keep_gpu=config.keep_gpu)
+                    _, quant_inps = save_inp_oup_data(quant_model, None, quant_module, cali_data,
+                                                      store_inp=False, store_oup=True, keep_gpu=config.keep_gpu)
+                    fp32_all_inps.append(fp32_inps)
+                    quant_all_inps.append(quant_inps)
+                    if not out_is_cached:
+                        fp32_final_oups = fp32_oups
+                        out_is_cached = True
+            cached_inps = (quant_all_inps, fp32_all_inps) if config.prob < 1.0 else quant_all_inps
+            cached_oups = fp32_final_oups
+            quant_modules_by_name = dict()
+            for node in layer_node_list:
+                if node.op == 'call_module':
+                    quant_modules_by_name[node.target] = quant_modules[node]
+            subgraph = extract_subgraph(quant_modules_by_name, layer_node_list,
+                                        layer_node_list[-1], g2node)
+            logger.info(subgraph.code)
+            subgraph_reconstruction(subgraph, cached_inps, cached_oups, config)
+            for x in layer_node_list:
+                checked_nodes[x] = True
+    disable_all(quant_model)
+    for node in checked_nodes:
+        if node.op == 'call_module':
+            enable_quantization(quant_modules[node])
+            logger.info(f'set the node {node.target} in quant')
+    return quant_model
diff --git a/mqbench/convert_deploy.py b/mqbench/convert_deploy.py
new file mode 100644
index 0000000..c5fdb0b
--- /dev/null
+++ b/mqbench/convert_deploy.py
@@ -0,0 +1,184 @@
+import os.path as osp
+
+import torch
+from torch.fx import GraphModule
+
+import mqbench.custom_symbolic_opset  # noqa: F401
+import mqbench.fusion_method          # noqa: F401
+from mqbench.prepare_by_platform import BackendType
+from mqbench.utils import deepcopy_graphmodule
+from mqbench.utils.logger import logger
+from mqbench.utils.registry import (
+    BACKEND_DEPLOY_FUNCTION,
+    register_deploy_function,
+    FUSED_MODULE_CONVERT_FUNCTION
+)
+from mqbench.deploy import (
+    remove_fakequantize_and_collect_params_nnie,
+    remove_fakequantize_and_collect_params,
+    replace_fakequantize_and_collect_params_openvino,
+    remove_fakequantize_and_collect_params_tengine,
+    ONNXQLinearPass, ONNXQNNPass
+)
+
+__all__ = ['convert_deploy']
+
+@register_deploy_function(BackendType.Tengine_u8)
+@register_deploy_function(BackendType.PPLCUDA)
+@register_deploy_function(BackendType.ONNX_QNN)
+@register_deploy_function(BackendType.SNPE)
+@register_deploy_function(BackendType.PPLW8A16)
+@register_deploy_function(BackendType.Tensorrt)
+@register_deploy_function(BackendType.NNIE)
+@register_deploy_function(BackendType.Vitis)
+@register_deploy_function(BackendType.OPENVINO)
+def convert_merge_bn(model: GraphModule, **kwargs):
+    logger.info("Merge BN for deploy.")
+    nodes = list(model.graph.nodes)
+    modules = dict(model.named_modules())
+    for node in nodes:
+        if node.op == 'call_module':
+            if type(modules[node.target]) in FUSED_MODULE_CONVERT_FUNCTION:
+                FUSED_MODULE_CONVERT_FUNCTION[type(modules[node.target])](model, node)
+
+
+@register_deploy_function(BackendType.Academic_NLP)
+@register_deploy_function(BackendType.Tensorrt_NLP)
+@register_deploy_function(BackendType.Tengine_u8)
+@register_deploy_function(BackendType.PPLCUDA)
+@register_deploy_function(BackendType.ONNX_QNN)
+@register_deploy_function(BackendType.Academic)
+@register_deploy_function(BackendType.SNPE)
+@register_deploy_function(BackendType.PPLW8A16)
+@register_deploy_function(BackendType.Tensorrt)
+@register_deploy_function(BackendType.NNIE)
+@register_deploy_function(BackendType.Vitis)
+@register_deploy_function(BackendType.OPENVINO)
+def convert_onnx(model: GraphModule, input_shape_dict, dummy_input, onnx_model_path, **kwargs):
+    logger.info("Export to onnx.")
+    output_names = kwargs.get('output_names', [])
+    dynamic_axes = kwargs.get('dynamic_axes', {})
+    input_names = kwargs.get('input_names', [])
+    if dummy_input is None:
+        device = next(model.parameters()).device
+        dummy_input = {name: torch.rand(shape).to(device) for name, shape in input_shape_dict.items()}
+        input_names = list(dummy_input.keys())
+        dummy_input = tuple(dummy_input.values())
+    # Per-channel QuantizeLinear and DequantizeLinear is supported since opset 13
+    opset_version = 13 if kwargs.get('deploy_to_qlinear', False) else 11
+    with torch.no_grad():
+        try:
+            from torch.onnx.utils import ONNXCheckerError
+            try:
+                torch.onnx.export(model, dummy_input, onnx_model_path,
+                                  input_names=input_names,
+                                  output_names=output_names,
+                                  opset_version=opset_version,
+                                  dynamic_axes=dynamic_axes,
+                                  do_constant_folding=True,
+                                  custom_opsets={'' : opset_version})
+            except ONNXCheckerError:
+                pass
+        except ImportError:
+            torch.onnx.export(model, dummy_input, onnx_model_path,
+                              input_names=input_names,
+                              output_names=output_names,
+                              opset_version=opset_version,
+                              do_constant_folding=True,
+                              custom_opsets={'' : opset_version},
+                              enable_onnx_checker=False)
+
+
+@register_deploy_function(BackendType.Tensorrt)
+def convert_onnx_qlinear(model: GraphModule, onnx_model_path, model_name, **kwargs):
+    if kwargs.get('deploy_to_qlinear', False):
+        logger.info("Convert to ONNX QLinear.")
+        ONNXQLinearPass(onnx_model_path).run()
+
+
+@register_deploy_function(BackendType.NNIE)
+def deploy_qparams_nnie(model: GraphModule, onnx_model_path, model_name, **kwargs):
+    logger.info("Extract qparams for NNIE.")
+    remove_fakequantize_and_collect_params_nnie(onnx_model_path, model_name)
+
+
+@register_deploy_function(BackendType.OPENVINO)
+def deploy_qparams_openvino(model: GraphModule, onnx_model_path, model_name, **kwargs):
+    logger.info("Extract qparams for OPENVINO.")
+    replace_fakequantize_and_collect_params_openvino(onnx_model_path, model_name)
+
+
+@register_deploy_function(BackendType.Tensorrt)
+def deploy_qparams_tensorrt(model: GraphModule, onnx_model_path, model_name, **kwargs):
+    logger.info("Extract qparams for TensorRT.")
+    remove_fakequantize_and_collect_params(onnx_model_path, model_name, backend='tensorrt')
+
+
+@register_deploy_function(BackendType.Vitis)
+def deploy_qparams_vitis(model: GraphModule, onnx_model_path, model_name, **kwargs):
+    logger.info("Extract qparams for Vitis-DPU.")
+    remove_fakequantize_and_collect_params(onnx_model_path, model_name, backend='vitis')
+
+
+@register_deploy_function(BackendType.SNPE)
+def deploy_qparams_snpe(model: GraphModule, onnx_model_path, model_name, **kwargs):
+    logger.info("Extract qparams for SNPE.")
+    remove_fakequantize_and_collect_params(onnx_model_path, model_name, backend='snpe')
+
+
+@register_deploy_function(BackendType.PPLW8A16)
+def deploy_qparams_pplw8a16(model: GraphModule, onnx_model_path, model_name, **kwargs):
+    logger.info("Extract qparams for PPLW8A16.")
+    remove_fakequantize_and_collect_params(onnx_model_path, model_name, backend='ppl')
+
+
+@register_deploy_function(BackendType.ONNX_QNN)
+def deploy_qparams_tvm(model: GraphModule, onnx_model_path, model_name, **kwargs):
+    logger.info("Convert to ONNX QNN.")
+    ONNXQNNPass(onnx_model_path).run(model_name)
+
+
+@register_deploy_function(BackendType.PPLCUDA)
+def deploy_qparams_ppl_cuda(model: GraphModule, onnx_model_path, model_name, **kwargs):
+    logger.info("Extract qparams for PPL-CUDA.")
+    remove_fakequantize_and_collect_params(onnx_model_path, model_name, backend='ppl-cuda')
+
+@register_deploy_function(BackendType.Tengine_u8)
+def deploy_qparams_tengine(model: GraphModule, onnx_model_path, model_name, **kwargs):
+    logger.info("Extract qparams for Tengine.")
+    remove_fakequantize_and_collect_params_tengine(onnx_model_path, model_name)
+
+
+def convert_deploy(model: GraphModule, backend_type: BackendType,
+                   input_shape_dict=None, dummy_input=None, output_path='./',
+                   model_name='mqbench_qmodel', deploy_to_qlinear=False, **extra_kwargs):
+    r"""Convert model to onnx model and quantization params depends on backend.
+
+    Args:
+        model (GraphModule): GraphModule prepared qat module.
+        backend_type (BackendType): specific which backend should be converted to.
+        input_shape_dict (dict): keys are model input name(should be forward function
+                                 params name, values are list of tensor dims)
+        output_path (str, optional): path to save convert results. Defaults to './'.
+        model_name (str, optional): name of converted onnx model. Defaults to 'mqbench_qmodel'.
+
+    >>> note on input_shape_dict:
+        example: {'input_0': [1, 3, 224, 224]
+                'input_1': [1, 3, 112, 112]
+                }
+        while forward function signature is like:
+                def forward(self, input_0, input_1):
+                    pass
+    """
+    kwargs = {
+        'input_shape_dict': input_shape_dict,
+        'dummy_input': dummy_input,
+        'output_path': output_path,
+        'model_name': model_name,
+        'onnx_model_path': osp.join(output_path, '{}.onnx'.format(model_name)),
+        'deploy_to_qlinear': deploy_to_qlinear
+    }
+    kwargs.update(extra_kwargs)
+    deploy_model = deepcopy_graphmodule(model)
+    for convert_function in BACKEND_DEPLOY_FUNCTION[backend_type]:
+        convert_function(deploy_model, **kwargs)
diff --git a/mqbench/custom_quantizer/__init__.py b/mqbench/custom_quantizer/__init__.py
new file mode 100644
index 0000000..94646a0
--- /dev/null
+++ b/mqbench/custom_quantizer/__init__.py
@@ -0,0 +1,9 @@
+from .model_quantizer import ModelQuantizer
+from .academic_quantizer import AcademicQuantizer
+from .openvino_quantizer import OPENVINOQuantizer
+from .vitis_quantizer import VitisQuantizer
+from .total_int_quantizer import TotalINTQuantizer
+from .tensorrt_quantizer import TRTModelQuantizer, TensorrtNLPQuantizer
+from .tengine_u8_quantizer import TengineQuantizer
+from .onnx_qnn_quantizer import ONNXQNNQuantizer
+from .nlp_quantizer import AcademicNLPQuantizer
\ No newline at end of file
diff --git a/mqbench/custom_quantizer/academic_quantizer.py b/mqbench/custom_quantizer/academic_quantizer.py
new file mode 100644
index 0000000..9cb86c0
--- /dev/null
+++ b/mqbench/custom_quantizer/academic_quantizer.py
@@ -0,0 +1,466 @@
+import copy
+from collections import OrderedDict
+from distutils.log import warn
+from typing import List
+import global_placeholder
+import operator
+import mmcv
+import torch
+from torch.fx import GraphModule
+from torch.quantization import propagate_qconfig_
+from torch.quantization.fx.qconfig_utils import get_flattened_qconfig_dict
+import torch.nn.functional as F
+import torch.nn as nn
+from mqbench.utils import is_symmetric_quant, getitem2node
+from mqbench.utils.logger import logger
+from mqbench.utils.registry import register_model_quantizer
+from mqbench.prepare_by_platform import BackendType
+from mqbench.custom_quantizer import ModelQuantizer
+from mqbench.fake_quantize.tqt import TqtFakeQuantize
+from torch.quantization.quantize_fx import _fuse_fx
+import mqbench.nn.intrinsic as qnni 
+import mqbench.nn.intrinsic.qat as qnniqat
+import torch.nn.intrinsic as nni
+from torch.nn.parameter import Parameter
+
+@register_model_quantizer(BackendType.Academic)  # 装饰器，妙
+class AcademicQuantizer(ModelQuantizer):
+    """Academic setting mostly do not merge BN and leave the first and last layer to higher bits.
+    """
+
+    def __init__(self, extra_quantizer_dict, extra_fuse_dict):
+        super().__init__(extra_quantizer_dict, extra_fuse_dict)
+        self.io_module = {}
+        self.post_act_8bit_node_name = []
+        # self.additional_qat_module_mapping = {
+        #     # Intrinsic modules:
+        #     nni.ConvBn2d: qnniqat.ConvBn2d,
+        #     nni.ConvBnReLU2d: qnniqat.ConvBnReLU2d,
+        #     nni.ConvReLU2d: qnniqat.ConvReLU2d,
+        # }
+        
+
+    def prepare(self, model: GraphModule, qconfig, further_detail=dict(), testing=False):
+        if global_placeholder.fold_bn_flag:
+            # 进行bn fuse  TODO 验证一下，量化插入逻辑对不对；影响的optimi;   weight是成功插入了
+            model = _fuse_fx(model, self.extra_fuse_dict)
+        
+        specified_general_quantizers = further_detail.get('specified_general_quantizers', [])
+        last_8bit_module = further_detail.get('last_8bit_module', [])  # TODO 要主动给出I！！
+        self.exclude_module_name = further_detail.get('exclude_prefixes', [])  # 不进行量化
+        self.removed_quantizer_names = further_detail.get('removed_quantizer_names', [])  # 不进行量化
+        qloss_flag = further_detail.get('qloss_flag', False)
+    
+        self._get_io_module(model, last_8bit_module)  # XXX 找出真正意义的首node和尾node（可不止一个），存到self的dict中。理论上来说，就应该是实实在在的layer！！！！
+        self._get_post_act_8bit_node_name(model)  # 根据首尾layer！，找出各自前一个node!，存到self的dict中.这些node后会插入8bitact quant
+        model = self._weight_quant(model, qconfig, testing=testing)  # 利用io_module，为module layer插入 weight quantizer
+        model, node_to_quantize_output = self._insert_fake_quantize_for_act_quant(model, qconfig, specified_general_quantizers, testing=testing)  # 同时利用post_act ，插入act quantizer
+        if qloss_flag:
+            self.open_qloss(model)
+        # if global_placeholder.quant_algorithm == 'tqt':
+        #     logger.info(f'\nNow initialize type of TQT quantizers \n')
+        # 这里是针对pot scale形式的量化。关键是fold bn 后的bias就是pot scale形式的
+        self._set_quant_type(model, node_to_quantize_output)
+        return model
+
+    def open_qloss(self, model):
+        qloss_flag = global_placeholder.qloss_flag
+        if qloss_flag:
+            for name, module in model.named_modules():
+                if hasattr(module, 'compute_qloss'):
+                    # 说明是quantizer
+                    module.compute_qloss = True 
+                    # 111111111111111   and 'getitem' not in name
+                    
+                    # if 'post_act' in name:
+                    #     module.compute_qloss = True # 22222222222
+                    #     module.regular_margin = Parameter(torch.tensor([1.]))
+                        
+                    #     # # 说明是act量化器
+                    #     # module.identity = 2
+                    
+                    # # else:
+                    # #     # 说明是quantizer
+                    # #     module.compute_qloss = True # 22222222222
+                    # #     # 说明是weight量化器
+                    # #     # module.identity = 1
+                    # #     # module.compute_qloss = True
+                    # #     # module.regular_margin = Parameter(torch.tensor([1.]))
+                
+
+    def _weight_quant(self, model: GraphModule, qconfig, testing=False):  # 为每个layer标上qconfig
+        logger.info("Replace module to qat module.")
+        
+        wq_sign = qconfig.weight.p.keywords.pop('sign')
+        wq_bit = qconfig.weight.p.keywords.pop('bit')
+        wqconfig_8bit = copy.deepcopy(qconfig)
+        wq_symmetry = True if is_symmetric_quant(qconfig.weight.p.keywords['qscheme']) else False
+        wqconfig_8bit.weight.p.keywords['quant_min'] = -2 ** (8 - 1) if wq_symmetry else 0
+        wqconfig_8bit.weight.p.keywords['quant_max'] = 2 ** (8 - 1) - 1 if wq_symmetry else 2 ** 8 - 1
+        wqconfig_8bit.weight.p.keywords['dtype'] = torch.qint8 if wq_symmetry else torch.quint8
+        
+        for name, module in model.named_modules():  # XXX 原来GraphModule也储存着原先torch.nn
+            if name in self.io_module.keys():
+                logger.info("Set layer {} to 8 bit.".format(name))
+                module.qconfig = wqconfig_8bit
+        flattened_qconfig_dict = get_flattened_qconfig_dict({'': qconfig})
+        if not testing:
+            propagate_qconfig_(model, flattened_qconfig_dict)  # XXX 这是torch官方的函数，就是绑定qconfig。为所有的层或着叫节点绑定qconfig属性。
+        else:
+            warn('只量化首尾！！')
+        self._qat_swap_modules(model, self.additional_qat_module_mapping)  # 为layer插入weight quantizer
+        return model
+
+    @property
+    def function_type_to_quant_input(self) -> list:
+        return self.additional_function_type + [
+            # operator.add,
+            # operator.mul,
+            # torch.nn.functional.adaptive_avg_pool2d,
+            # torch.nn.functional.max_pool2d,
+            # torch.nn.functional.avg_pool2d,
+            # torch.flatten,
+            # 'mean',
+            # 'sum',
+            # # torch.nn.functional.interpolate,
+            
+            # mmcv.cnn.bricks.swish.Swish,
+            # mmcv.cnn.bricks.activation.Clamp,
+            # mmcv.cnn.bricks.hsigmoid.HSigmoid
+        ]
+
+    def _set_quant_type(self, model: GraphModule, tensor_type_set):
+        # tensor_type_set = self._find_act_quants(model) # 可以复用输入
+        params_type_set = self._find_weight_quants(model)
+        inputs_type_set = self._find_input_quants(model)
+        module_dict = dict(model.named_modules())
+        quantizer_prefix = "_post_act_fake_quantizer"
+
+        for node in tensor_type_set:
+            if isinstance(node.name, str) and (node.name + quantizer_prefix) in module_dict:
+                next_op = module_dict[node.name + quantizer_prefix]
+                if isinstance(next_op, TqtFakeQuantize):
+                    next_op.set_quant_type('tensor') # 就是指定act量化节点的类型
+                    logger.info(f'{node.name + quantizer_prefix} has been set to quant type <tensor>')
+        for node in params_type_set:
+            if isinstance(node.target, str) and node.target in module_dict:
+                op = module_dict[node.target]
+                if hasattr(op, 'weight_fake_quant'):
+                    if isinstance(op.weight_fake_quant, TqtFakeQuantize):
+                        op.weight_fake_quant.set_quant_type('param')
+                        logger.info(f'{node.target} has been set to quant type <param/weight>')
+                if hasattr(op, 'bias_fake_quant'):  # NOTE TODO 有趣，其实是可以给出bias_fake_quant。其实在本文，就是走academic quantization。
+                    if isinstance(op.bias_fake_quant, TqtFakeQuantize):
+                        op.bias_fake_quant.set_quant_type('param')
+                        logger.info(f'{node.target} has been set to quant type <param/bias>')
+        for node in inputs_type_set:
+            if isinstance(node.target, str) and node.target in module_dict:
+                next_op = module_dict[node.target]    
+                if isinstance(next_op, TqtFakeQuantize):
+                    next_op.set_quant_type('input')
+                    logger.info(f'{node.target} has been set to quant type <input>')
+    
+    def _find_input_quants(self, model) -> List:
+        node_need_to_quantize_weight = []
+        nodes = list(model.graph.nodes)
+        for node in nodes:
+            if node.op == 'placeholder' and node.all_input_nodes == []:
+                node_need_to_quantize_weight.append(list(node.users)[0])
+        return node_need_to_quantize_weight
+
+    def _find_weight_quants(self, model) -> List:
+        node_need_to_quantize_weight = []
+        nodes = list(model.graph.nodes)
+        module_dict = dict(model.named_modules())
+        for node in nodes:
+            if node.target in module_dict:
+                if hasattr(module_dict[node.target], 'weight_fake_quant') or hasattr(module_dict[node.target], 'bias_fake_quant'):
+                    node_need_to_quantize_weight.append(node)
+        return node_need_to_quantize_weight
+
+    @property
+    def module_type_to_quant_input(self) -> tuple:
+        return (  # 也就是说，带有weight quantizer的都是属于此
+            # Conv            # Conv
+            torch.nn.intrinsic.qat.modules.conv_fused.ConvBnReLU2d,
+            torch.nn.intrinsic.qat.modules.conv_fused.ConvReLU2d,
+            torch.nn.intrinsic.qat.modules.conv_fused.ConvBn2d,
+            torch.nn.intrinsic.qat.modules.linear_relu.LinearReLU,
+            torch.nn.qat.modules.conv.Conv2d,
+            qnniqat.ConvBnReLU2d,
+            qnniqat.ConvBn2d,
+            qnniqat.ConvReLU2d,
+            # Linear
+            torch.nn.qat.modules.linear.Linear,
+            # # Pooling
+            # torch.nn.modules.pooling.AvgPool2d,
+            # torch.nn.modules.pooling.AdaptiveAvgPool2d,
+            # torch.nn.modules.pooling.MaxPool2d,
+            
+            # mmcv.cnn.bricks.swish.Swish,
+            # mmcv.cnn.bricks.activation.Clamp,
+            # mmcv.cnn.bricks.hsigmoid.HSigmoid
+            
+        ) + self.additional_module_type
+
+    def _get_post_act_8bit_node_name(self, model):
+        for nodes in self.io_module.values():
+            for node in nodes:
+                for _arg in node.args:
+                    if isinstance(_arg, list):
+                        print('{} node 的输入比较多（反复使用）！'.format(node.name))
+                        for a_arg in _arg:
+                            if isinstance(a_arg, torch.fx.node.Node):
+                                self.post_act_8bit_node_name.append(a_arg.name)
+                    elif isinstance(_arg, torch.fx.node.Node):
+                        self.post_act_8bit_node_name.append(_arg.name)
+                    
+        # # 原写法
+        # for node in self.io_module.values():
+        #     for _arg in node.args:
+        #         if isinstance(_arg, list):
+        #             print('{} node 的输入比较多（反复使用）！'.format(node.name))
+        #             for a_arg in _arg:
+        #                 if isinstance(a_arg, torch.fx.node.Node):
+        #                     self.post_act_8bit_node_name.append(a_arg.name)
+        #         elif isinstance(_arg, torch.fx.node.Node):
+        #             self.post_act_8bit_node_name.append(_arg.name)
+
+
+    def _get_io_module(self, model, bit8_last_module_names):
+        
+        # 导入全局设置
+        # model_type = global_placeholder.model_type
+        # model_type = model_type.split('_')[0]  # 直取大类
+        # bit8_last_module_names = last_module_names[model_type]
+        # bit8_last_module_names = []  # dummy
+        
+        nodes = list(model.graph.nodes)
+        for node in nodes:
+            total_args = []
+            the_first_layer = False  # NOTE 这个first layer 找法其实也有点问题，因为palceholder node 的下一个node不一定是module
+            for _arg in node.args:
+                if isinstance(_arg, torch.fx.node.Node):
+                    if _arg.op == 'placeholder' and isinstance(node.target, str):
+                        the_first_layer = True
+                    total_args.append(_arg.name)
+            if the_first_layer:
+                self.io_module[node.target] = [node]  # 找到首，这倒没啥问题
+            
+            if node.target in bit8_last_module_names:
+                # 在想要保留成8bit的list里的话，则成功保存
+                if node.target in self.io_module.keys():
+                    # 如果已经创建过键值对了的话,添加新的相关node
+                    self.io_module[node.target].append(node)
+                else:
+                    # 如果还没有创建键值对
+                    self.io_module[node.target] = [node]
+                # bit8_last_module_names.remove(node.target)  # TODO 这样好像有问题？因为node是可重复的！
+            
+            
+            
+            continue
+            # 下面写得太冗余了！
+            if node.op == 'output':
+                for _arg in node.args[0]:  # XXX _arg还会出现多个，根据你模型定义了几个输出
+                    if isinstance(_arg, dict):
+                        for out in _arg.values():# 遍历一下
+                            if isinstance(out, list):
+                                for arg_node in out:
+                                    if arg_node.target in bit8_last_module_names:
+                                        # 在想要保留成8bit的list里的话，则成功保存
+                                        # 弹出
+                                        self.io_module[arg_node.target] = arg_node
+                                        bit8_last_module_names.remove(arg_node.target)
+                            elif out.target in bit8_last_module_names:
+                                # 在想要保留成8bit的list里的话，则成功保存
+                                # 弹出
+                                self.io_module[out.target] = out
+                                bit8_last_module_names.remove(out.target)
+                            else:
+                                raise NotImplementedError
+                    elif isinstance(_arg, list):
+                        pass
+                            
+                    
+                    # if isinstance(_arg, tuple):
+                    #     # 说明是更复杂的情况
+                    #     print('\n!!find complex output!!!!!接下来取最后一个输出来处理尾量化问题')
+                    #     if isinstance(_arg[-1], dict):
+                    #         for _value in _arg[-1].values():
+                    #             if isinstance(_value, list):
+                    #                 for it in _value:
+                    #                     self.io_module[it.target] = it
+                    #             elif isinstance(_value, torch.fx.node.Node):
+                    #                 self.io_module[_value.target] = _value
+                    #     else:
+                    #         raise NotImplementedError
+
+                    # elif isinstance(_arg, torch.fx.node.Node):
+                    #     self.io_module[_arg.target] = _arg  # XXX 准确地来说不应该叫module
+                    # else:
+                    #     raise NotImplementedError
+
+    def _find_act_quants(self, model: GraphModule) -> List:
+        nodes = list(model.graph.nodes)
+        modules = dict(model.named_modules())
+        node_need_to_quantize_output = []  # TODO 意思是输出量化？
+        g2node = getitem2node(model)  # TODO 这是干啥的
+        for node in nodes:  # 两个筛选条件，一个是用来确认该node是否是不允许量化，一个用来确认是否满足量化并整理（C or FC）其输入node
+            if ((node.op == "call_module" and node.target in self.exclude_module_name) or
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                 node.target in self.exclude_function_type) or
+                    node.name in self.exclude_node_name) and node.name not in self.additional_node_name:
+                logger.info("Exclude skip: {}".format(node.name))
+                continue
+            if (node.op == "call_module" and isinstance(modules[node.target], self.module_type_to_quant_input)) or \
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                    node.target in self.function_type_to_quant_input) or node.name in self.additional_node_name:  # XXX 是layer且属于module_type_to_quant_input、是函数且属于function_type_to_quant_input、name属于additional_node_name
+                input_node_list = self._flatten_args(node.args)  # XXX 将node 的输入node dict reorg成list 可以利用这个
+                # Means this is not Tensor + Tensor. 接下来检查，输入是否是存粹的node
+                if not all([isinstance(_node, torch.fx.node.Node) for _node in input_node_list]):
+                    continue
+                for _node in input_node_list:
+                    if self._is_implicit_merge(modules, (node, _node)):  # TODO 这个是拿来检验，父子关系是否为mul或add，会被fused？
+                        logger.info("Implicit merge: {} + {}".format(_node.name, node.name))
+                        continue
+                    if _node in g2node:
+                        _node = g2node[_node]
+                    node_need_to_quantize_output.append(_node)  # XXX 总结来说，就是找到需要前置插入量化节点的node，然后找到他父节点，在所有父节点后面插入act 量化节点
+        return node_need_to_quantize_output  # 意思就是Conv 或Linear之前肯定会有act 量化节点
+
+    def _insert_fake_quantize_for_act_quant(self, model: GraphModule, qconfig, specified_general_quantizers, testing=False):  # 在conv前插入
+        graph = model.graph
+        nodes = list(model.graph.nodes)
+        # self.exclude_node_name = ['backbone_fpn_extra_blocks_p7']  # p7的输入不进行量化！！是这个意思
+        quantizer_prefix = "_post_act_fake_quantizer"
+        node_to_quantize_output = self._find_act_quants(model)  # 找到那些输出act需要被量化的node TODO 这里有问题
+        node_to_quantize_output = OrderedDict.fromkeys(node_to_quantize_output).keys()
+
+        aq_sign = qconfig.activation.p.keywords.pop('sign')
+        aq_bit = qconfig.activation.p.keywords.pop('bit')
+        
+        # 先造8bit量化的config，因为尾保持8bit量化会需要这个. 8bit 对称 unsign量化
+        aqconfig_8bit = copy.deepcopy(qconfig.activation)
+        aq_symmetry = True if is_symmetric_quant(qconfig.activation.p.keywords['qscheme']) else False
+        aqconfig_8bit.p.keywords['quant_min'] = -2 ** (8 - 1) if (aq_symmetry and aq_sign) else 0
+        aqconfig_8bit.p.keywords['quant_max'] = 2 ** (8 - 1) - 1 if (aq_symmetry and aq_sign)  else 2 ** 8 - 1
+        aqconfig_8bit.p.keywords['dtype'] = torch.qint8 if (aq_symmetry and aq_sign)  else torch.quint8
+        # 再造8bit量化的特殊config，因为首保持8bit量化会需要这个. 8bit 对称 sign量化
+        aqconfig_8bit_special = copy.deepcopy(qconfig.activation)
+        aq_symmetry = True if is_symmetric_quant(qconfig.activation.p.keywords['qscheme']) else False
+        aqconfig_8bit_special.p.keywords['quant_min'] = -2 ** (8 - 1) if aq_symmetry else 0
+        aqconfig_8bit_special.p.keywords['quant_max'] = 2 ** (8 - 1) - 1 if aq_symmetry else 2 ** 8 - 1
+        aqconfig_8bit_special.p.keywords['dtype'] = torch.qint8 if aq_symmetry else torch.quint8
+        # 再造特殊config，因为一些非ReLU后面的quantizer若symmetric则置为sign。  同bit 对称 sign量化
+        aqconfig_special = copy.deepcopy(qconfig.activation)
+        aqconfig_special.p.keywords['quant_min'] = -2 ** (aq_bit - 1) if aq_symmetry else 0
+        aqconfig_special.p.keywords['quant_max'] = 2 ** (aq_bit - 1) - 1 if aq_symmetry else 2 ** aq_bit - 1
+        aqconfig_special.p.keywords['dtype'] = torch.qint8 if aq_symmetry else torch.quint8
+        
+        module_dict = dict(model.named_modules())
+        
+        for node in node_to_quantize_output:  # 开始遍历，插入act量化节点
+            quantizer_name = node.name + quantizer_prefix
+            
+            # 检查是否为需要跳过的quantizer。需要跳过。比如backbone输出其实已经被量化过了，那么neck的输入就不需要被量化。
+            if quantizer_name in self.removed_quantizer_names:
+                logger.info("Remove {} quantizer".format(quantizer_name))
+                continue
+            
+            if node.name in self.post_act_8bit_node_name:  # TODO 尾巴不应该是sign！也应该走下面那一套  都块函数化，然后现在的8bitconfig其实就是special情况
+                logger.info("Set {} post act quantize to 8 bit.".format(node.name))  # 确实说明的是，在该node后面加上fakequant用于act
+                
+                fake_quantizer = self._execute_act_quantizer(node, module_dict, quantizer_name, aqconfig_8bit, aqconfig_8bit_special, specified_general_quantizers)
+                
+                # # NOTE！因为共享头的输入会被插入多个量化器
+                # quantizer_name = node.name + quantizer_prefix
+                # logger.info("Insert act quant {}".format(quantizer_name))
+                # fake_quantizer = aqconfig_8bit()  # 直接生成量化器  NOTE 这玩意就是量化器，走公式的那种，是layer！
+                
+                # fake_quantizer.compute_qloss = True  # 置True，表示act的quantizer要计算qloss
+                # setattr(model, quantizer_name, fake_quantizer)  # 绑定layer到model中
+                # with graph.inserting_after(node):  # XXX 确实是在node后面插入 act fquantizer  但其实这个node就是act或者其他函数
+                #     inserted_node = graph.create_node("call_module", quantizer_name, (node,), {})  # 定义node
+                #     for _node in nodes:  #  遍历graph，想把原来接着的node的arg重定向到inserted_node上。
+                #         _node.args = self._fix_succ_recursivly(_node.args, node, inserted_node)
+            else:  
+                fake_quantizer = self._execute_act_quantizer(node, module_dict, quantizer_name, qconfig.activation, aqconfig_special, specified_general_quantizers)
+                
+                # fake_quantizer = None
+                # if (node.op == 'call_function' or node.op == 'call_method'):
+                #     target_module = None
+                # else:
+                #     target_module = module_dict[node.target]  # TODO  要判断一下是不是call function，然后解决        add、interp处的actquantizer一样的道理，mqbench的逻辑是兼容的
+                # quantizer_name = node.name + quantizer_prefix
+                # if ('quantizer' not in node._prev.name and  # 这个就能筛大部分的了
+                #     (isinstance(target_module, (nn.ReLU, nn.MaxPool2d)) or node.target in (F.relu, F.max_pool2d))):  # 如果说target module是relu、maxpool、那就遵守quantizer
+                #     fake_quantizer = qconfig.activation()
+                #     logger.info("Insert act quant {} with general config".format(quantizer_name))
+                # else:  # 如果为conv、bn、或者op=placeholder，
+                #     # 说明不是Relu后面的quantizer，要改为特制的quantizer    这个分支对应了mobilenetv2的情况\add、interp处的情况
+                #     fake_quantizer = aqconfig_special()
+                #     logger.info("Insert act quant {} with special config".format(quantizer_name))
+                    
+                # # 二次检查，把非ReLU后的actquantizer搞成若symmetric则sign
+                # fake_quantizer.compute_qloss = True  # 置True，表示act的quantizer要计算qloss
+                # setattr(model, quantizer_name, fake_quantizer)
+                # with graph.inserting_after(node):  # XXX 确实是在node后面插入 act fquantizer  但其实这个node就是act或者其他函数
+                #     inserted_node = graph.create_node("call_module", quantizer_name, (node,), {})  # 定义node
+                #     for _node in nodes:  #  遍历graph，想把原来接着的node的arg重定向到inserted_node上。
+                #         _node.args = self._fix_succ_recursivly(_node.args, node, inserted_node)
+            
+            
+            # fake_quantizer.compute_qloss = compute_qloss_flag  # 置False，表示act的quantizer不计算qloss；置true, 表示act的quantizer算qloss
+            setattr(model, quantizer_name, fake_quantizer)
+            with graph.inserting_after(node):  # XXX 确实是在node后面插入 act fquantizer  但其实这个node就是act或者其他函数
+                inserted_node = graph.create_node("call_module", quantizer_name, (node,), {})  # 定义node
+                for _node in nodes:  #  遍历老graph，想把原来接着的node的arg重定向到inserted_node上。  注意是老nodes集合，很妙！
+                    _node.args = self._fix_succ_recursivly(_node.args, node, inserted_node)
+    
+            # else:
+            #     warn('只量化首尾！！')
+
+        model.recompile()
+        model.graph.lint()
+        return model, node_to_quantize_output
+    def _execute_act_quantizer(self, node, module_dict, quantizer_name, config, special_config, specified_general_quantizers):
+        def is_node_names_have_word(in_nodes, word):
+            # 这里可能会有问题，因为有些relu node的名字可没有“relu”
+            for in_node in in_nodes:
+                if word in in_node.name:
+                    return True
+            return False
+        
+        fake_quantizer = None
+        if (node.op == 'call_function' or node.op == 'call_method' or node.op == 'placeholder'):
+            target_module = None
+        else:
+            target_module = module_dict[node.target]  # TODO  要判断一下是不是call function，然后解决        add、interp处的actquantizer一样的道理，mqbench的逻辑是兼容的
+        if (
+            (
+            quantizer_name in specified_general_quantizers 
+                )
+            or 
+            (
+                # not is_node_names_have_word(node.all_input_nodes, 'quantizer')  # 这个就能筛大部分的了  'quantizer' not in node._prev.name
+                # and 
+                (
+                    isinstance(target_module, (qnniqat.ConvReLU2d, qnniqat.ConvBnReLU2d, nn.intrinsic.qat.modules.conv_fused.ConvReLU2d, nn.intrinsic.qat.modules.conv_fused.ConvBnReLU2d, torch.nn.intrinsic.qat.modules.linear_relu.LinearReLU, nn.ReLU, nn.ReLU6, nn.MaxPool2d)) 
+                    or node.target in (F.relu, F.relu6, F.max_pool2d)
+                )
+                ) 
+            or 
+            (
+                is_node_names_have_word(node.all_input_nodes, 'relu') 
+                and 'flatten' in node.name
+                )):  # 如果说target module是relu、maxpool、那就遵守quantizer
+            
+            fake_quantizer = config()
+            logger.info("Insert act quant {} with general config".format(quantizer_name))
+        else:  # 如果为conv、bn、或者op=placeholder，
+            # 说明不是Relu或relu6后面的quantizer，要改为特制的quantizer    这个分支对应了mobilenetv2的情况\add、interp处的情况
+            fake_quantizer = special_config()  # special唯一的意义就是作为对称量化setting
+            logger.info("Insert act quant {} with special config".format(quantizer_name))
+        
+        return fake_quantizer
\ No newline at end of file
diff --git a/mqbench/custom_quantizer/model_quantizer.py b/mqbench/custom_quantizer/model_quantizer.py
new file mode 100644
index 0000000..640b788
--- /dev/null
+++ b/mqbench/custom_quantizer/model_quantizer.py
@@ -0,0 +1,275 @@
+import copy
+import operator
+from collections import OrderedDict
+from typing import (
+    List, Dict, Any, Callable
+)
+
+import torch
+from torch.fx import (
+    GraphModule
+)
+from torch.quantization import (
+    propagate_qconfig_,
+    swap_module
+)
+from torch.nn.intrinsic import (
+    _FusedModule
+)
+from torch.quantization.quantization_mappings import (
+    get_default_qat_module_mappings,
+    get_default_static_quant_module_mappings
+)
+from torch.quantization.utils import (
+    get_combined_dict
+)
+from torch.quantization.fx.qconfig_utils import (
+    get_flattened_qconfig_dict
+)
+from torch.quantization.quantize_fx import (
+    _fuse_fx
+)
+
+from mqbench.utils import getitem2node
+from mqbench.utils.logger import logger
+from mqbench.utils.registry import register_model_quantizer
+from mqbench.prepare_by_platform import BackendType
+
+
+@register_model_quantizer(BackendType.Tensorrt)
+@register_model_quantizer(BackendType.NNIE)
+class ModelQuantizer(object):
+    """General model quantizer class.
+    First, replace common float module to nn.qat.modules to make weight fake
+    quantized.
+    Second, insert activation fake quantize node before specific layers. Layer
+    type is defined in function_type_to_quant_input / module_type_to_quant_input.
+    We only quantize the inputs of layers and leave the output not quantized
+    since it is next layer's input.
+    """
+
+    def __init__(self, extra_quantizer_dict, extra_fuse_dict):
+        self.additional_function_type = extra_quantizer_dict.get('additional_function_type', [])  # XXX 这里似乎是说明如何加限制和定制化的
+        self.additional_module_type = extra_quantizer_dict.get('additional_module_type', ())
+        self.additional_fuser_method_mapping = extra_fuse_dict.get('additional_fuser_method_mapping', {})
+        self.additional_fusion_pattern = extra_fuse_dict.get('additional_fusion_pattern', {})
+        self.additional_qat_module_mapping = extra_fuse_dict.get('additional_qat_module_mapping', {})
+        self.additional_node_name = extra_quantizer_dict.get('additional_node_name', [])
+        self.exclude_module_name = extra_quantizer_dict.get('exclude_module_name', [])  # TODO 这个是用来定位首尾8bit吗
+        self.exclude_function_type = extra_quantizer_dict.get('exclude_function_type', [])
+        self.exclude_node_name = extra_quantizer_dict.get('exclude_node_name', [])
+        self.extra_fuse_dict = extra_fuse_dict
+
+    def prepare(self, model: GraphModule, qconfig):
+        model = _fuse_fx(model, self.extra_fuse_dict)
+        model = self._weight_quant(model, qconfig)
+        model = self._insert_fake_quantize_for_act_quant(model, qconfig)
+        return model
+
+    def _insert_fake_quantize_for_act_quant(
+            self,
+            model: GraphModule,
+            qconfig: Any):
+        graph = model.graph
+        nodes = list(model.graph.nodes)
+
+        quantizer_prefix = "_post_act_fake_quantizer"
+        node_to_quantize_output = self._find_act_quants(model)
+        node_to_quantize_output = OrderedDict.fromkeys(node_to_quantize_output).keys()
+
+        for node in node_to_quantize_output:
+            fake_quantizer = qconfig.activation()
+            quantizer_name = node.name + quantizer_prefix
+            setattr(model, quantizer_name, fake_quantizer)
+            logger.info("Insert act quant {}".format(quantizer_name))
+            with graph.inserting_after(node):
+                inserted_node = graph.create_node("call_module", quantizer_name, (node,), {})
+                for _node in nodes:
+                    _node.args = self._fix_succ_recursivly(_node.args, node, inserted_node)
+
+        model.recompile()
+        model.graph.lint()
+        return model
+
+    def _fix_succ_recursivly(self, args, target_node, inserted_node):  # 这是想判断每个node的输入arg是否异常（因为新加了node），然后重新调整、更新
+        # List / Tuple
+        if isinstance(args, (list, tuple)):
+            _tmp = list(args)
+            for _i, _arg in enumerate(args):  # 遍历args，找出arg_node是target的情况，说明该node的输入是target，然后得替换成输入是act quant。相当于act quant接管了该node的user
+                if _arg == target_node:
+                    _tmp[_i] = inserted_node
+                elif isinstance(_arg, tuple):
+                    _tmp[_i] = self._fix_succ_recursivly(_arg, target_node, inserted_node)
+                elif isinstance(_arg, list):
+                    _tmp[_i] = list(self._fix_succ_recursivly(_arg, target_node, inserted_node))
+                elif isinstance(_arg, dict):
+                    _tmp[_i] = self._fix_succ_recursivly(_arg, target_node, inserted_node)
+            return tuple(_tmp)
+        # Dict
+        elif isinstance(args, dict):
+            _tmp = {}
+            for k, v in args.items():
+                if v == target_node:
+                    _tmp[k] = inserted_node
+                elif not isinstance(v, torch.fx.node.Node):
+                    _tmp[k] = self._fix_succ_recursivly(v, target_node, inserted_node)
+                else:
+                    _tmp[k] = v
+            return _tmp
+        else:
+            raise NotImplementedError('{} can not be handled now.'.format(type(args)))
+
+    def _weight_quant(self, model: GraphModule, qconfig):
+        logger.info("Replace module to qat module.")
+        flattened_qconfig_dict = get_flattened_qconfig_dict({'': qconfig})
+        propagate_qconfig_(model, flattened_qconfig_dict)
+        self._qat_swap_modules(model, self.additional_qat_module_mapping)
+        return model
+
+    @property  # 这个意思是说，调用的时候当成属性调用就行，不像函数还带括号
+    def implicit_merge_patterns(self) -> list:
+        # Layers which do not need quantize among them.
+        # In reversed order!
+        return [
+            (operator.add, operator.mul)
+        ]
+
+    def _on_merge_chain(self, modules, pattern, pair, p_pos=0, v_pos=0):
+        if v_pos == len(pair):
+            return True
+        if p_pos == len(pattern):
+            return v_pos == len(pair)
+        node = pair[v_pos]
+        cur_pattern = pattern[p_pos]
+        # Means current node is matched.
+        if (node.op == "call_module" and type(modules[node.target]) == cur_pattern) or \
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                    node.target == cur_pattern):
+            # Means compairing pair.
+            if len(pattern) > p_pos and len(pair) > v_pos:
+                return self._on_merge_chain(modules, pattern, pair, p_pos + 1, v_pos + 1)
+            # Means compairing extra node.
+            matched = False
+            flatten_args = self._flatten_args(node.args)
+            for _arg in flatten_args:
+                extra_pair = (*pair, _arg)
+                if isinstance(_arg, torch.fx.node.Node) and \
+                        self._on_merge_chain(modules, pattern, extra_pair, p_pos + 1, v_pos + 1):
+                    matched = True
+            return matched
+        # Current node is not matched, skip to next.
+        else:
+            return self._on_merge_chain(modules, pattern, pair, p_pos + 1, v_pos)  # 额，回溯
+
+    def _is_implicit_merge(self, modules, pair):
+        for pattern in self.implicit_merge_patterns:
+            if self._on_merge_chain(modules, pattern, pair):
+                return True
+        return False
+
+    @property
+    def function_type_to_update_data_struct(self) -> list:
+        return [
+            'update'
+        ]
+
+    @property
+    def function_type_to_quant_input(self) -> list:
+        return [
+            operator.add,
+            operator.mul,
+            torch.nn.functional.adaptive_avg_pool2d,
+            torch.nn.functional.interpolate
+        ] + self.additional_function_type
+
+    @property
+    def module_type_to_quant_input(self) -> tuple:
+        return (
+            # Conv
+            torch.nn.intrinsic.qat.modules.conv_fused.ConvBnReLU2d,
+            torch.nn.intrinsic.qat.modules.conv_fused.ConvBn2d,
+            torch.nn.qat.modules.conv.Conv2d,
+            # ConvTranspose
+            torch.nn.ConvTranspose2d,
+            # Linear
+            torch.nn.qat.modules.linear.Linear,
+            # Pooling
+            torch.nn.modules.pooling.MaxPool2d,
+            torch.nn.modules.pooling.AvgPool2d,
+            torch.nn.modules.pooling.AdaptiveAvgPool2d,
+            # BN
+            torch.nn.BatchNorm2d,
+            # Prelu mostly do not merge.
+            torch.nn.PReLU,
+            # Upsample
+            torch.nn.Upsample
+        ) + self.additional_module_type
+
+    def _flatten_args(self, node):
+        flattned_args = []
+        if isinstance(node, dict):
+            for v in node.values():
+                flattned_args.extend(self._flatten_args(v))
+        elif isinstance(node, tuple) or isinstance(node, list):
+            for n in node:
+                flattned_args.extend(self._flatten_args(n))
+        else:
+            flattned_args.extend([node])
+        return flattned_args
+
+    def _find_act_quants(self, model: GraphModule) -> List:
+        nodes = list(model.graph.nodes)
+        modules = dict(model.named_modules())
+        node_need_to_quantize_output = []
+        g2node = getitem2node(model)
+        for node in nodes:
+            if ((node.op == "call_module" and node.target in self.exclude_module_name) or
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                 node.target in self.exclude_function_type) or
+                    node.name in self.exclude_node_name) and node.name not in self.additional_node_name:
+                logger.info("Exclude skip: {}".format(node.name))
+                continue
+            if (node.op == "call_module" and isinstance(modules[node.target], self.module_type_to_quant_input)) or \
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                    node.target in self.function_type_to_quant_input) or node.name in self.additional_node_name:
+                input_node_list = self._flatten_args(node.args)
+                # Means this is not Tensor + Tensor.
+                if not all([isinstance(_node, torch.fx.node.Node) for _node in input_node_list]):
+                    continue
+                for _node in input_node_list:
+                    if self._is_implicit_merge(modules, (node, _node)):
+                        logger.info("Implicit merge: {} + {}".format(_node.name, node.name))
+                        continue
+                    if _node in node_need_to_quantize_output:
+                        continue
+                    if _node in g2node:
+                        _node = g2node[_node]
+                    node_need_to_quantize_output.append(_node)
+        return node_need_to_quantize_output
+
+    def _qat_swap_modules(self, root: GraphModule, additional_qat_module_mapping: Dict[Callable, Callable]):
+        all_mappings = get_combined_dict(
+            get_default_qat_module_mappings(), additional_qat_module_mapping)
+        root = self._convert(root, all_mappings, inplace=True)  # TODO 这是在将layer转化成带weight quantizer的对应layer
+        return root
+
+    def _convert(self, module, mapping=None, inplace=False, scope=''):
+        if mapping is None:
+            mapping = get_default_static_quant_module_mappings()
+
+        if not inplace:
+            module = copy.deepcopy(module)
+        reassign = {}
+        for name, mod in module.named_children():
+            # fused modules are swapped as one unit
+            new_scope = "{}.{}".format(scope, name) if scope != '' else name
+            if new_scope in self.exclude_module_name:  # TODO 这里能避免某些layer被量化
+                logger.info("Skip quant layer: " + new_scope)
+                continue
+            if not isinstance(mod, _FusedModule):  # 嵌套，回溯，直到找到leaf，即子layer
+                self._convert(mod, mapping, True, new_scope)
+            reassign[name] = swap_module(mod, mapping, {})  # 这是官方的函数，将带有Qconfig的layer转成对应的量化型layer,同时加入weight quantizer（之前定义的），然后才返回对应的layer。若不是layer输入，则无返回
+        for key, value in reassign.items():
+            module._modules[key] = value  # 最后再完成替换
+        # logger.info(f"layer {scope} get weight quant")
+        return module
\ No newline at end of file
diff --git a/mqbench/custom_quantizer/nlp_quantizer.py b/mqbench/custom_quantizer/nlp_quantizer.py
new file mode 100644
index 0000000..69251f9
--- /dev/null
+++ b/mqbench/custom_quantizer/nlp_quantizer.py
@@ -0,0 +1,28 @@
+import torch
+
+from mqbench.utils.registry import register_model_quantizer
+from mqbench.prepare_by_platform import BackendType
+from mqbench.custom_quantizer import ModelQuantizer
+
+
+@register_model_quantizer(BackendType.Academic_NLP)
+class AcademicNLPQuantizer(ModelQuantizer):
+    """
+    NLP model quantizer for Academic settings. Should not de 8bit for
+    first / last layer.
+    We should uantize Linear / Embedding weights.
+    Linear / Matmul layer inputs(activations).
+    """
+    @property
+    def function_type_to_quant_input(self) -> list:
+        return [
+            # Matmul in MSA
+            torch.matmul
+        ] + self.additional_function_type
+
+    @property
+    def module_type_to_quant_input(self) -> tuple:
+        return (
+            # Linear
+            torch.nn.qat.modules.linear.Linear,
+        ) + self.additional_module_type
\ No newline at end of file
diff --git a/mqbench/custom_quantizer/onnx_qnn_quantizer.py b/mqbench/custom_quantizer/onnx_qnn_quantizer.py
new file mode 100644
index 0000000..c4469e4
--- /dev/null
+++ b/mqbench/custom_quantizer/onnx_qnn_quantizer.py
@@ -0,0 +1,99 @@
+import operator
+from typing import Dict, Callable, List
+
+import torch
+from torch.fx import GraphModule
+from torch.quantization.quantization_mappings import get_default_qat_module_mappings
+from torch.quantization.utils import get_combined_dict
+
+
+import mqbench.nn as qnn
+import mqbench.nn.intrinsic as qnni
+from mqbench.utils.registry import register_model_quantizer
+from mqbench.prepare_by_platform import BackendType
+from mqbench.custom_quantizer import ModelQuantizer
+
+
+@register_model_quantizer(BackendType.ONNX_QNN)
+class ONNXQNNQuantizer(ModelQuantizer):
+    """Quantize model according to TVM ONNX frontend.
+    """
+
+    def __init__(self, extra_quantizer_dict, extra_fuse_dict):
+        super().__init__(extra_quantizer_dict, extra_fuse_dict)
+
+    @property
+    def _relu_module_type(self):
+        return (torch.nn.ReLU, torch.nn.ReLU6)
+
+    @property
+    def _relu_function_type(self):
+        return (torch.nn.functional.relu, torch.nn.functional.relu6)
+
+    def _find_act_quants(self, model: GraphModule) -> List:
+        nodes = list(model.graph.nodes)
+        modules = dict(model.named_modules())
+        node_need_to_quantize_output = super()._find_act_quants(model)
+        for node in nodes:
+            if (node.op == "call_module" and node.target in self.exclude_module_name) or \
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                 node.target in self.exclude_function_type) or \
+                    node.name in self.exclude_node_name:
+                continue
+            if (node.op == "call_module" and isinstance(modules[node.target], self.module_type_to_quant_input)) or \
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                    node.target in self.function_type_to_quant_input):
+                # Add current node if not merge relu.
+                for next_node in node.users:
+                    if not ((next_node.op == 'call_function' and next_node.target in self._relu_function_type) or (
+                            next_node.op == 'call_module' and isinstance(modules[next_node.target], self._relu_module_type))):
+                        node_need_to_quantize_output.append(node)
+                    else:
+                        node_need_to_quantize_output.append(next_node)
+        return node_need_to_quantize_output
+
+    def _qat_swap_modules(self, root: GraphModule, additional_qat_module_mapping: Dict[Callable, Callable]):
+        all_mappings = get_combined_dict(
+            get_default_qat_module_mappings(), additional_qat_module_mapping)
+        # There is no QLinearFC in ONNX for now.
+        del(all_mappings[torch.nn.modules.linear.Linear])
+        del(all_mappings[torch.nn.intrinsic.modules.fused.LinearReLU])
+        del(all_mappings[qnni.modules.fused.LinearBn1d])
+        root = self._convert(root, all_mappings, inplace=True)
+        return root
+
+    @property
+    def function_type_to_quant_input(self) -> list:
+        return [
+            operator.add,
+            # TODO operator.mul,
+            # TODO torch.cat,
+            torch.nn.functional.adaptive_avg_pool2d
+            # sigmoid
+            # TODO torch.nn.functional.sigmoid
+        ]
+
+    @property
+    def module_type_to_quant_input(self) -> tuple:
+        return (
+            # Conv
+            torch.nn.intrinsic.qat.modules.conv_fused.ConvBnReLU2d,
+            torch.nn.intrinsic.qat.modules.conv_fused.ConvBn2d,
+            torch.nn.qat.Conv2d,
+            # Linear
+            torch.nn.qat.modules.linear.Linear,
+            qnn.intrinsic.qat.LinearBn1d,
+            # Pooling
+            torch.nn.modules.pooling.AvgPool2d,
+            torch.nn.modules.pooling.AdaptiveAvgPool2d,
+            # Prelu
+            # TODO torch.nn.PReLU,
+        )
+
+    @property
+    def implicit_merge_patterns(self) -> list:
+        # Layers which do not need quantize among them.
+        # In reversed order!
+        return [
+            (torch.nn.ReLU, operator.add)
+        ]
diff --git a/mqbench/custom_quantizer/openvino_quantizer.py b/mqbench/custom_quantizer/openvino_quantizer.py
new file mode 100644
index 0000000..1509b83
--- /dev/null
+++ b/mqbench/custom_quantizer/openvino_quantizer.py
@@ -0,0 +1,297 @@
+import copy
+import operator
+from collections import OrderedDict
+from typing import Any
+
+import torch
+from torch.fx import GraphModule
+from torch.quantization import propagate_qconfig_
+from torch.quantization.fx.qconfig_utils import get_flattened_qconfig_dict
+from torch.quantization.quantize_fx import _fuse_fx
+
+from mqbench.utils import is_symmetric_quant
+from mqbench.utils.logger import logger
+from mqbench.utils.registry import register_model_quantizer
+from mqbench.prepare_by_platform import BackendType
+from mqbench.custom_quantizer import ModelQuantizer
+
+
+@register_model_quantizer(BackendType.OPENVINO)
+class OPENVINOQuantizer(ModelQuantizer):
+    """OPENVINO type, activation is scaled to [0, 255] when qscheme is symmetric
+    """
+
+    def __init__(self, extra_quantizer_dict, extra_fuse_dict):
+        super().__init__(extra_quantizer_dict, extra_fuse_dict)
+        self.academic_mode = extra_quantizer_dict.get('academic_mode', False)
+
+    @property
+    def _passed_func_type(self):
+        academic_pass_type = (operator.getitem, getattr)
+        if self.academic_mode:
+            return academic_pass_type
+        else:
+            return academic_pass_type + (torch.cat, )
+
+    @property
+    def _passed_module_type(self):
+        return tuple()
+
+    @property
+    def _linear_module_node(self) -> tuple:
+        return (
+            torch.nn.qat.modules.conv.Conv2d,
+            torch.nn.intrinsic.qat.modules.conv_fused.ConvBnReLU2d,
+            torch.nn.intrinsic.qat.modules.conv_fused.ConvBnReLU1d,
+            torch.nn.intrinsic.qat.modules.conv_fused.ConvReLU2d,
+            torch.nn.intrinsic.qat.modules.conv_fused.ConvBn2d,
+            torch.nn.intrinsic.qat.modules.conv_fused.ConvBn1d,
+            torch.nn.qat.modules.conv.Conv2d,
+            torch.nn.qat.modules.linear.Linear,
+        )
+
+    @property
+    def _propagated_pattern(self) -> tuple:
+        prev_nodes_pattern = {
+            'func_type': (torch.nn.functional.max_pool2d, torch.flatten),
+            'module_type': (torch.nn.modules.pooling.MaxPool2d, torch.nn.modules.Flatten)
+        }
+
+        cur_nodes_pattern = {
+            'func_type': (torch.nn.functional.conv2d, torch.nn.functional.conv1d, torch.nn.functional.conv3d, torch.matmul),
+            'module_type': self._linear_module_node,
+        }
+
+        return (prev_nodes_pattern, cur_nodes_pattern)
+
+    @property
+    def function_type_to_quant_input(self) -> list:
+        return [
+            operator.add,
+            torch.nn.functional.adaptive_avg_pool2d,
+            torch.nn.functional.max_pool2d,
+            torch.nn.functional.avg_pool2d,
+            torch.flatten,
+            'mean',
+            'sum',
+            torch.nn.functional.interpolate,
+        ]
+
+    @property
+    def module_type_to_quant_input(self) -> tuple:
+        if self.academic_mode:
+            return (
+                # Conv
+                torch.nn.qat.modules.conv.Conv2d,
+                # Linear
+                torch.nn.qat.modules.linear.Linear,
+                # Pooling
+                torch.nn.modules.pooling.AvgPool2d,
+                torch.nn.modules.pooling.AdaptiveAvgPool2d,
+                torch.nn.modules.pooling.MaxPool2d,
+                # Prelu
+                # TODO torch.nn.PReLU,
+                torch.nn.modules.Upsample,
+            ) + self.additional_module_type
+        else:
+            return (
+                # Conv
+                torch.nn.intrinsic.qat.modules.conv_fused.ConvBnReLU2d,
+                torch.nn.intrinsic.qat.modules.conv_fused.ConvBnReLU1d,
+                torch.nn.intrinsic.qat.modules.conv_fused.ConvReLU2d,
+                torch.nn.intrinsic.qat.modules.conv_fused.ConvBn2d,
+                torch.nn.intrinsic.qat.modules.conv_fused.ConvBn1d,
+                torch.nn.qat.modules.conv.Conv2d,
+                # Linear
+                torch.nn.qat.modules.linear.Linear,
+                # Pooling
+                torch.nn.modules.pooling.AvgPool2d,
+                torch.nn.modules.pooling.AdaptiveAvgPool2d,
+                torch.nn.modules.pooling.MaxPool2d,
+                # Prelu
+                # TODO torch.nn.PReLU,
+                torch.nn.modules.Upsample,
+            ) + self.additional_module_type
+
+    @property
+    def module_type_to_quant_unsigned(self) -> tuple:
+        if self.academic_mode:
+            return (torch.nn.modules.ReLU, )
+        else:
+            return (
+                torch.nn.modules.ReLU,
+                torch.nn.intrinsic.qat.modules.conv_fused.ConvBnReLU2d,
+                torch.nn.intrinsic.qat.modules.conv_fused.ConvBnReLU1d,
+                torch.nn.intrinsic.qat.modules.conv_fused.ConvReLU2d,
+            )
+
+    @property
+    def function_type_maybe_unsigned(self) -> tuple:
+        return self.function_type_to_quant_input
+
+    @property
+    def function_type_to_quant_unsigned(self) -> tuple:
+        return (torch.nn.functional.relu, )
+
+    @property
+    def module_type_maybe_unsigned(self) -> tuple:
+        return (torch.nn.Upsample, torch.nn.modules.pooling.MaxPool2d, torch.nn.modules.pooling.AvgPool2d, torch.nn.modules.pooling.AdaptiveAvgPool2d)
+
+    def prepare(self, model: GraphModule, qconfig):
+        if not self.academic_mode:
+            model = _fuse_fx(model, self.extra_fuse_dict)
+        model = self._weight_quant(model, qconfig)
+        model = self._insert_fake_quantize_for_act_quant(model, qconfig)
+        return model
+
+    def _find_act_quants(self, model: GraphModule) -> list:
+        nodes = list(model.graph.nodes)
+        modules = dict(model.named_modules())
+        node_need_to_quantize_output = []
+
+        def quanlified_node(node):
+            return (node.op == "call_module" and isinstance(modules[node.target], self.module_type_to_quant_input)) or \
+                ((node.op == 'call_function' or node.op == 'call_method') and node.target in self.function_type_to_quant_input) or node.op == 'placeholder'
+
+        def passed_node(node):
+            return (node.op == 'call_function' and node.target in self._passed_func_type) or \
+                (node.op == 'call_module' and isinstance(modules[node.target], self._passed_module_type))
+
+        prev_nodes_pattern, cur_nodes_pattern = self._propagated_pattern
+
+        def node_in_pattern(node, pattern):
+            return ((node.op == 'call_function' or node.op == 'call_method') and node.target in pattern['func_type']) or \
+                (node.op == "call_module" and isinstance(modules[node.target], pattern['module_type'])) 
+
+        def propagated_pattern(prev_node, cur_node):
+            return node_in_pattern(prev_node, prev_nodes_pattern) and node_in_pattern(cur_node, cur_nodes_pattern)
+
+        for node in nodes:
+            if (node.op == "call_module" and node.target in self.exclude_module_name) or \
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                 node.target in self.exclude_function_type) or \
+                    node.name in self.exclude_node_name:
+                continue
+            if passed_node(node):
+                continue
+            if node.op == 'placeholder':
+                node_need_to_quantize_output.append(node)
+                continue
+            is_output = False
+            # last layer do not quantize
+            for next_node in node.users:
+                if next_node.op == 'output':
+                    is_output = True
+                    break
+            if is_output:
+                continue
+            # check propagated pattern
+            is_propagated_pattern = False
+            for next_node in node.users:
+                if propagated_pattern(node, next_node):
+                    is_propagated_pattern = True
+                    break
+            if is_propagated_pattern:
+                continue
+            for next_node in node.users:
+                if quanlified_node(next_node):
+                    node_need_to_quantize_output.append(node)
+                    break
+        return node_need_to_quantize_output
+
+    def _weight_quant(self, model: GraphModule, qconfig):
+        logger.info("Replace module to qat module.")
+        wqconfig_8bit = copy.deepcopy(qconfig)
+        wq_symmetry = True if is_symmetric_quant(qconfig.weight.p.keywords['qscheme']) else False
+        numbits = 8
+        logger.info('Now all weight quantizers will effectively use only 7 bits out of 8 bits. This resolves the overflow issue problem on AVX2 and AVX-512 machines.')
+        wqconfig_8bit.weight.p.keywords['quant_min'] = -2 ** (numbits - 2) if wq_symmetry else 0
+        wqconfig_8bit.weight.p.keywords['quant_max'] = 2 ** (numbits - 2) - 1 if wq_symmetry else 2 ** (numbits - 1) - 1
+        wqconfig_8bit.weight.p.keywords['factory_kwargs'] = {'not_calc_quant_min_max': True}
+        if self.academic_mode and wq_symmetry:
+            wqconfig_8bit.weight.p.keywords['quant_min'] = -2 ** (numbits - 2) + 1
+            wqconfig_8bit.weight.p.keywords['quant_max'] = 2 ** (numbits - 2) - 1
+        flattened_qconfig_dict = get_flattened_qconfig_dict({'': wqconfig_8bit})
+        propagate_qconfig_(model, flattened_qconfig_dict)
+        self._qat_swap_modules(model, self.additional_qat_module_mapping)
+        return model
+
+
+    def _insert_fake_quantize_for_act_quant(
+            self,
+            model: GraphModule,
+            qconfig: Any):
+        graph = model.graph
+        modules = dict(model.named_modules())
+        nodes = list(model.graph.nodes)
+
+        quantizer_postfix = "_post_act_fake_quantizer"
+        node_to_quantize_output = self._find_act_quants(model)
+        node_to_quantize_output = OrderedDict.fromkeys(node_to_quantize_output).keys()
+
+        aqconfig_8bit = copy.deepcopy(qconfig.activation)
+        aq_symmetry = True if is_symmetric_quant(qconfig.activation.p.keywords['qscheme']) else False
+        aqconfig_8bit.p.keywords['quant_min'] = 0
+        aqconfig_8bit.p.keywords['quant_max'] = 2 ** 8 - 1
+        aqconfig_8bit.p.keywords['factory_kwargs'] = {'not_calc_quant_min_max': True}
+
+        def maybe_unsigned(node):
+            return ((node.op == 'call_function' or node.op == 'call_method') and node.target in self.function_type_maybe_unsigned) or \
+                (node.op == "call_module" and isinstance(modules[node.target], self.module_type_maybe_unsigned))
+
+        def real_unsigned(node):
+            return ((node.op == 'call_function' or node.op == 'call_method') and node.target in self.function_type_to_quant_unsigned) or \
+                (node.op == "call_module" and isinstance(modules[node.target], self.module_type_to_quant_unsigned))
+
+        for node in node_to_quantize_output:
+            if aq_symmetry:
+                if real_unsigned(node):
+                    logger.info("Set {} post act quantize to 8 bit unsigned type.".format(node.name))
+                    fake_quantizer = aqconfig_8bit()
+                elif maybe_unsigned(node):
+                    is_unsigned = False
+                    # bfs to determin1e whether it should be set unsigned activation
+                    queue = [(node, -1)]
+                    bfs_result = dict()
+                    while len(queue) > 0:
+                        cur_node, level = queue.pop(0)
+                        for input_node in cur_node.args:
+                            if isinstance(input_node, torch.fx.node.Node):
+                                queue.append((input_node, level + 1))
+                        cur_node_is_unsigned = None
+                        if isinstance(cur_node.target, str) and cur_node.target.endswith(quantizer_postfix):
+                            last_fakequantize = getattr(model, cur_node.target)
+                            cur_node_is_unsigned = last_fakequantize.quant_min == 0
+                        elif real_unsigned(node):
+                            cur_node_is_unsigned = True
+
+                        if cur_node_is_unsigned is not None:
+                            if level not in bfs_result:
+                                if len(bfs_result) > 0:
+                                    break
+                                else:
+                                    bfs_result[level] = cur_node_is_unsigned
+                            else:
+                                bfs_result[level] = bfs_result[level] and cur_node_is_unsigned
+                    queue.clear()
+                    for key in bfs_result:
+                        is_unsigned = bfs_result[key]
+                        break
+                    fake_quantizer = aqconfig_8bit() if is_unsigned else qconfig.activation()
+                    if is_unsigned:
+                        logger.info("Set {} post act quantize to 8 bit unsigned type.".format(node.name))
+                else:
+                    fake_quantizer = qconfig.activation()
+            else:
+                fake_quantizer = qconfig.activation()
+            quantizer_name = node.name + quantizer_postfix
+            setattr(model, quantizer_name, fake_quantizer)
+            logger.info("Insert act quant {}".format(quantizer_name))
+            with graph.inserting_after(node):
+                inserted_node = graph.create_node("call_module", quantizer_name, (node,), {})
+                for _node in nodes:
+                    _node.args = self._fix_succ_recursivly(_node.args, node, inserted_node)
+
+        model.recompile()
+        model.graph.lint()
+        return model
\ No newline at end of file
diff --git a/mqbench/custom_quantizer/tengine_u8_quantizer.py b/mqbench/custom_quantizer/tengine_u8_quantizer.py
new file mode 100644
index 0000000..460b327
--- /dev/null
+++ b/mqbench/custom_quantizer/tengine_u8_quantizer.py
@@ -0,0 +1,79 @@
+import torch
+from torch.fx import GraphModule
+
+from mqbench.utils.registry import register_model_quantizer
+from mqbench.utils import getitem2node
+from mqbench.prepare_by_platform import BackendType
+from mqbench.custom_quantizer import ModelQuantizer
+
+
+@register_model_quantizer(BackendType.Tengine_u8)
+class TengineQuantizer(ModelQuantizer):
+    """
+    Tengine needs de-quantization parameters for output.
+
+    Parameters
+    ----------
+    ModelQuantizer : _type_
+        _description_
+    """
+    @property
+    def _passed_func_type(self):
+        return (
+            torch.flatten,
+        )
+
+    @property
+    def _passed_module_type(self):
+        # TODO: softmax
+        return ()
+
+    @property
+    def implicit_merge_patterns(self) -> list:
+        # Layers which do not need quantize among them.
+        # In reversed order!
+        return []
+
+    @property
+    def function_type_to_quant_input(self) -> list:
+        return [
+            torch.cat,
+            torch.nn.functional.hardswish,
+            torch.nn.functional.sigmoid
+        ] + super().function_type_to_quant_input
+
+    @property
+    def module_type_to_quant_input(self) -> tuple:
+        return (
+            torch.nn.Hardswish,
+            torch.nn.Sigmoid,
+        ) + super().module_type_to_quant_input
+
+    def _find_act_quants(self, model: GraphModule) -> list:
+        nodes = list(model.graph.nodes)
+        modules = dict(model.named_modules())
+        node_need_to_quantize_output = super()._find_act_quants(model)
+        g2node = getitem2node(model)
+        for node in nodes:
+            if (node.op == "call_module" and node.target in self.exclude_module_name) or \
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                 node.target in self.exclude_function_type) or \
+                    node.name in self.exclude_node_name:
+                continue
+            if (node.op == "call_module" and isinstance(modules[node.target], self.module_type_to_quant_input)) or \
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                    node.target in self.function_type_to_quant_input):
+                for next_node in node.users:
+                    if not ((next_node.op == 'call_function' and next_node.target in self._passed_func_type) or
+                            (next_node.op == 'call_module' and isinstance(modules[next_node.target], self._passed_module_type))):
+                        node_need_to_quantize_output.append(node)
+                    else:
+                        node_need_to_quantize_output.append(next_node)
+            elif node.op == "output":
+                for _arg in node.args:
+                    if isinstance(_arg, torch.fx.node.Node):
+                        if _arg.op == 'placeholder':
+                            continue
+                        node_need_to_quantize_output.append(_arg)
+        node_need_to_quantize_output = [node if node not in g2node else g2node[node] for node in node_need_to_quantize_output]
+        return node_need_to_quantize_output
diff --git a/mqbench/custom_quantizer/tensorrt_quantizer.py b/mqbench/custom_quantizer/tensorrt_quantizer.py
new file mode 100644
index 0000000..3dcb5b7
--- /dev/null
+++ b/mqbench/custom_quantizer/tensorrt_quantizer.py
@@ -0,0 +1,142 @@
+import operator
+from typing import List
+
+import torch
+from torch.fx import GraphModule
+
+import mqbench.nn.qat as qnnqat
+from mqbench.utils.logger import logger
+from mqbench.utils.registry import register_model_quantizer
+from mqbench.prepare_by_platform import BackendType
+from mqbench.custom_quantizer import ModelQuantizer
+
+
+class TRTModelQuantizer(ModelQuantizer):
+    """The different points of TRT quantizer are how to deal with add op
+    and the last layer.
+    """
+
+    def __init__(self, extra_quantizer_dict, extra_fuse_dict):
+        super().__init__(extra_quantizer_dict, extra_fuse_dict)
+
+    @property
+    def _merge_add_type(self):
+        return (torch.nn.Conv2d, torch.nn.Linear)
+
+    def _find_act_quants(self, model: GraphModule) -> set:
+        nodes = list(model.graph.nodes)
+        modules = dict(model.named_modules())
+        node_need_to_quantize_output = []
+        for node in nodes:
+            if ((node.op == "call_module" and node.target in self.exclude_module_name) or
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                 node.target in self.exclude_function_type) or
+                    node.name in self.exclude_node_name) and node.name not in self.additional_node_name:
+                continue
+            if (node.op == "call_module" and isinstance(modules[node.target], self.module_type_to_quant_input)) or \
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                    node.target in self.function_type_to_quant_input) or node.name in self.additional_node_name:
+                # Add will be merged with previous conv.
+                input_node_list = list(filter(lambda x: isinstance(x, torch.fx.node.Node),
+                                              self._flatten_args(node.args)))
+                if node.target is operator.add:
+                    merge_node = self._find_add_merge_node(model, input_node_list, node)
+                    if merge_node:
+                        input_node_list.remove(merge_node)
+                    node_need_to_quantize_output.extend(input_node_list)
+                else:
+                    for _node in input_node_list:
+                        if self._is_implicit_merge(modules, (node, _node)):
+                            continue
+                        if isinstance(_node, torch.fx.node.Node):
+                            node_need_to_quantize_output.append(_node)
+        return node_need_to_quantize_output
+
+    def _find_add_merge_node(self, model, input_node_list, node):
+        """Find the first input node which has only one successor from the last.
+        This kind of node can be merge with add.
+        """
+        input_node_list.reverse()
+        modules = dict(model.named_modules())
+        for input_node in input_node_list:
+            if input_node.op == 'call_module' and type(modules[input_node.target]) in self._merge_add_type:
+                succ = 0
+                for _node in list(model.graph.nodes):
+                    _node_input_list = self._flatten_args(_node.args)
+                    if input_node in _node_input_list:
+                        succ += 1
+                if succ == 1:
+                    return input_node
+        return None
+
+
+@register_model_quantizer(BackendType.Tensorrt_NLP)
+class TensorrtNLPQuantizer(ModelQuantizer):
+    """
+    NLP model quantizer for Tensorrt settings.
+    We should quantize Linear / Embedding weights.
+    Linear / Matmul / Add layer inputs(activations).
+    We notice embedding add(word + pos + token_type) is not quantized,
+    so we find and skiped.
+    Add in MSA(add mask) should not be quantized either, we skipped it
+    by implicit_merge.
+    """
+    @property
+    def implicit_merge_patterns(self) -> list:
+        # Layers which do not need quantize among them.
+        # In reversed order!
+        return [
+            (operator.add, operator.mul),
+            # Add in MSA block should not be quantized.
+            (operator.add, operator.truediv)
+        ]
+
+    @property
+    def function_type_to_quant_input(self) -> list:
+        return [
+            operator.add,
+            # Matmul in MSA
+            torch.matmul
+        ] + self.additional_function_type
+
+    @property
+    def module_type_to_quant_input(self) -> tuple:
+        return (
+            # Linear
+            torch.nn.qat.modules.linear.Linear,
+        ) + self.additional_module_type
+
+    def _find_act_quants(self, model: GraphModule) -> List:
+        nodes = list(model.graph.nodes)
+        modules = dict(model.named_modules())
+        node_need_to_quantize_output = []
+        for node in nodes:
+            if ((node.op == "call_module" and node.target in self.exclude_module_name) or
+                ((node.op == "call_function" or node.op == "all_method") and
+                 node.target in self.exclude_function_type) or
+                    node.name in self.exclude_node_name) and node.name not in self.additional_node_name:
+                logger.info("Exclude skip: {}".format(node.name))
+                continue
+            if (node.op == "call_module" and isinstance(modules[node.target], self.module_type_to_quant_input)) or \
+                ((node.op == "call_function" or node.op == "call_method") and
+                    node.target in self.function_type_to_quant_input) or node.name in self.additional_node_name:
+                input_node_list = self._flatten_args(node.args)
+                # Means this is not Tensor + Tensor.
+                if not all([isinstance(_node, torch.fx.node.Node) for _node in input_node_list]):
+                    continue
+                # Embedding Add and MSA mask Add should be skipped.
+                if node.op == "call_function" and node.target == operator.add and \
+                        self._is_skiped_add(node, modules, input_node_list):
+                    continue
+                for _node in input_node_list:
+                    if self._is_implicit_merge(modules, (node, _node)):
+                        logger.info("Implicit merge: {} + {}".format(_node.name, node.name))
+                        continue
+                    node_need_to_quantize_output.append(_node)
+        return node_need_to_quantize_output
+
+    def _is_skiped_add(self, node, modules, input_node_list):
+        for _node in input_node_list:
+            if _node.op == "call_module" and isinstance(modules[_node.target], (qnnqat.Embedding, torch.nn.Embedding)):
+                logger.info("Skip embedding add: {}".format(node.name))
+                return True
\ No newline at end of file
diff --git a/mqbench/custom_quantizer/total_int_quantizer.py b/mqbench/custom_quantizer/total_int_quantizer.py
new file mode 100644
index 0000000..05b45da
--- /dev/null
+++ b/mqbench/custom_quantizer/total_int_quantizer.py
@@ -0,0 +1,57 @@
+import torch
+from torch.fx import GraphModule
+
+from mqbench.utils.registry import register_model_quantizer
+from mqbench.prepare_by_platform import BackendType
+from mqbench.custom_quantizer import ModelQuantizer
+
+
+@register_model_quantizer(BackendType.PPLCUDA)
+@register_model_quantizer(BackendType.SNPE)
+@register_model_quantizer(BackendType.PPLW8A16)
+class TotalINTQuantizer(ModelQuantizer):
+    """There is only INT8 calculations in the model.
+    We quantize the input tensors and output tensors of all layers,
+    except those in _passed_func_type and _passed_module_type.
+    For example add + relu pattern, there is no need to insert fake
+    quantize node between them.
+    """
+
+    def __init__(self, extra_quantizer_dict, extra_fuse_dict):
+        super().__init__(extra_quantizer_dict, extra_fuse_dict)
+
+    @property
+    def _passed_func_type(self):
+        return (
+            torch.nn.functional.relu, 
+            torch.nn.functional.relu6,
+            torch.flatten
+        )
+
+    @property
+    def _passed_module_type(self):
+        return (
+            torch.nn.ReLU,
+            torch.nn.ReLU6
+        )
+
+    def _find_act_quants(self, model: GraphModule) -> list:
+        nodes = list(model.graph.nodes)
+        modules = dict(model.named_modules())
+        node_need_to_quantize_output = super()._find_act_quants(model)
+        for node in nodes:
+            if (node.op == "call_module" and node.target in self.exclude_module_name) or \
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                 node.target in self.exclude_function_type) or \
+                    node.name in self.exclude_node_name:
+                continue
+            if (node.op == "call_module" and isinstance(modules[node.target], self.module_type_to_quant_input)) or \
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                    node.target in self.function_type_to_quant_input):
+                for next_node in node.users:
+                    if not ((next_node.op == 'call_function' and next_node.target in self._passed_func_type) or
+                            (next_node.op == 'call_module' and isinstance(modules[next_node.target], self._passed_module_type))):
+                        node_need_to_quantize_output.append(node)
+                    else:
+                        node_need_to_quantize_output.append(next_node)
+        return node_need_to_quantize_output
\ No newline at end of file
diff --git a/mqbench/custom_quantizer/vitis_quantizer.py b/mqbench/custom_quantizer/vitis_quantizer.py
new file mode 100644
index 0000000..992082f
--- /dev/null
+++ b/mqbench/custom_quantizer/vitis_quantizer.py
@@ -0,0 +1,162 @@
+import operator
+from typing import List, NoReturn
+
+import torch
+import torch.nn.intrinsic as nni
+from torch.fx import GraphModule
+from torch.quantization.quantize_fx import _fuse_fx
+
+import mqbench.nn.intrinsic as qnni 
+import mqbench.nn.intrinsic.qat as qnniqat
+from mqbench.utils import getitem2node
+from mqbench.utils.logger import logger
+from mqbench.utils.registry import register_model_quantizer
+from mqbench.prepare_by_platform import BackendType
+from mqbench.fake_quantize.tqt import TqtFakeQuantize
+from mqbench.custom_quantizer.model_quantizer import ModelQuantizer
+
+
+@register_model_quantizer(BackendType.Vitis)
+class VitisQuantizer(ModelQuantizer):
+    """There is only INT8 calculations in the model.
+    We quantize the input tensors of all layers and the output tensors
+    of the last layers. We quantize every activations tensors and weight
+    tensors using this method. NOTE: the acti and weight have different 
+    quantize type.
+    """
+
+    def __init__(self, extra_quantizer_dict, extra_fuse_dict):
+        super().__init__(extra_quantizer_dict, extra_fuse_dict)
+        self.additional_qat_module_mapping = {
+            # Intrinsic modules:
+            nni.ConvBn2d: qnniqat.ConvBn2d,
+            nni.ConvBnReLU2d: qnniqat.ConvBnReLU2d,
+            nni.ConvReLU2d: qnniqat.ConvReLU2d,
+        }
+
+    @property
+    def module_type_to_quant_input(self) -> tuple:
+        return super().module_type_to_quant_input + (
+            torch.nn.Conv2d,
+            qnni.ConvBn2d, 
+            qnni.ConvReLU2d,
+            qnni.ConvBnReLU2d
+        )
+
+    @property
+    def module_type_to_quant_output(self) -> tuple:
+        return (
+            # Conv
+            torch.nn.intrinsic.qat.modules.conv_fused.ConvBnReLU2d,
+            torch.nn.intrinsic.qat.modules.conv_fused.ConvBn2d,
+            torch.nn.qat.modules.conv.Conv2d,
+            qnniqat.ConvBnReLU2d,
+            qnniqat.ConvBn2d,
+            qnniqat.ConvReLU2d,
+            # ConvTranspose
+            torch.nn.ConvTranspose2d,
+            # Linear
+            torch.nn.qat.modules.linear.Linear,
+            # Pooling
+            torch.nn.modules.pooling.AvgPool2d,
+            torch.nn.modules.pooling.AdaptiveAvgPool2d,
+            # BN
+            torch.nn.BatchNorm2d,
+            torch.nn.ReLU,
+            # Prelu mostly do not merge.
+            torch.nn.PReLU,
+            torch.nn.Upsample,
+        ) 
+
+
+    @property
+    def function_type_to_quant_output(self) -> List:
+        return [
+            operator.add,
+            operator.mul,
+            torch.cat,
+            torch.nn.functional.adaptive_avg_pool2d,
+            torch.nn.functional.avg_pool2d,
+            torch.nn.functional.relu,
+            torch.nn.functional.conv2d,
+            torch.nn.functional.linear,
+            torch.nn.functional.interpolate,
+        ] 
+
+    def prepare(self, model: GraphModule, qconfig):
+        model = _fuse_fx(model, self.extra_fuse_dict)
+        model = self._weight_quant(model, qconfig)  # 就是转换成nnqat module
+        model = self._insert_fake_quantize_for_act_quant(model, qconfig)  # 就是插入act量化节点
+        prepared = model
+        self._set_quant_type(prepared)
+        return prepared
+
+
+    def _find_act_quants(self, model: GraphModule) -> List:
+        nodes = list(model.graph.nodes)
+        modules = dict(model.named_modules())
+        if hasattr(self, 'node_need_to_quantize_output'):
+            return self.node_need_to_quantize_output
+        self.node_need_to_quantize_output = []
+        g2node = getitem2node(model)
+        for node in nodes:
+            if (node.op == "call_module" and node.target in self.exclude_module_name) or \
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                 node.target in self.exclude_function_type) or \
+                    node.name in self.exclude_node_name:
+                continue
+            if (node.op == "call_module" and isinstance(modules[node.target], self.module_type_to_quant_output)) or \
+                ((node.op == 'call_function' or node.op == 'call_method') and
+                    node.target in self.function_type_to_quant_output):
+                self.node_need_to_quantize_output.append(node)
+                logger.info(f'Add {node.name} to output quantize')
+        return self.node_need_to_quantize_output
+
+    def _find_input_quants(self, model) -> List:
+        node_need_to_quantize_weight = []
+        nodes = list(model.graph.nodes)
+        for node in nodes:
+            if node.op == 'placeholder' and node.all_input_nodes == []:
+                node_need_to_quantize_weight.append(list(node.users)[0])
+        return node_need_to_quantize_weight
+
+    def _find_weight_quants(self, model) -> List:
+        node_need_to_quantize_weight = []
+        nodes = list(model.graph.nodes)
+        module_dict = dict(model.named_modules())
+        for node in nodes:
+            if node.target in module_dict:
+                if hasattr(module_dict[node.target], 'weight_fake_quant') or hasattr(module_dict[node.target], 'bias_fake_quant'):
+                    node_need_to_quantize_weight.append(node)
+        return node_need_to_quantize_weight
+
+    def _set_quant_type(self, model: GraphModule) -> NoReturn:
+        tensor_type_set = self._find_act_quants(model)
+        params_type_set = self._find_weight_quants(model)
+        inputs_type_set = self._find_input_quants(model)
+        module_dict = dict(model.named_modules())
+        quantizer_prefix = "_post_act_fake_quantizer"
+
+        for node in tensor_type_set:
+            if isinstance(node.name, str) and (node.name + quantizer_prefix) in module_dict:
+                next_op = module_dict[node.name + quantizer_prefix]
+                if isinstance(next_op, TqtFakeQuantize):
+                    next_op.set_quant_type('tensor')
+                    logger.info(f'{node.target} has been set to quant type <tensor>')
+        for node in params_type_set:
+            if isinstance(node.target, str) and node.target in module_dict:
+                op = module_dict[node.target]
+                if hasattr(op, 'weight_fake_quant'):
+                    if isinstance(op.weight_fake_quant, TqtFakeQuantize):
+                        op.weight_fake_quant.set_quant_type('param')
+                        logger.info(f'{node.target} has been set to quant type <param/weight>')
+                if hasattr(op, 'bias_fake_quant'):
+                    if isinstance(op.bias_fake_quant, TqtFakeQuantize):
+                        op.bias_fake_quant.set_quant_type('param')
+                        logger.info(f'{node.target} has been set to quant type <param/bias>')
+        for node in inputs_type_set:
+            if isinstance(node.target, str) and node.target in module_dict:
+                next_op = module_dict[node.target]    
+                if isinstance(next_op, TqtFakeQuantize):
+                    next_op.set_quant_type('input')
+                    logger.info(f'{node.target} has been set to quant type <input>')
diff --git a/mqbench/custom_symbolic_opset.py b/mqbench/custom_symbolic_opset.py
new file mode 100644
index 0000000..6fcb1f2
--- /dev/null
+++ b/mqbench/custom_symbolic_opset.py
@@ -0,0 +1,23 @@
+from torch.onnx import register_custom_op_symbolic
+
+# Register symbolic op for torch.quantize_function op.
+
+def _fake_quantize_learnable_per_tensor_affine(g, x, scale, zero_point, quant_min, quant_max, grad_factor):
+    return g.op("::LearnablePerTensorAffine", x, scale, zero_point, quant_min, quant_max)
+
+
+register_custom_op_symbolic('::_fake_quantize_learnable_per_tensor_affine', _fake_quantize_learnable_per_tensor_affine, 11)
+
+
+def fake_quantize_per_channel_affine(g, x, scale, zero_point, ch_axis, quant_min, quant_max):
+    return g.op("::FixedPerChannelAffine", x, scale, zero_point, ch_axis, quant_min, quant_max)
+
+
+register_custom_op_symbolic('::fake_quantize_per_channel_affine', fake_quantize_per_channel_affine, 11)
+
+
+def fake_quantize_per_tensor_affine(g, x, scale, zero_point, quant_min, quant_max):
+    return g.op("::FixedPerTensorAffine", x, scale, zero_point, quant_min, quant_max)
+
+
+register_custom_op_symbolic('::fake_quantize_per_tensor_affine', fake_quantize_per_tensor_affine, 11)
\ No newline at end of file
diff --git a/mqbench/deploy/__init__.py b/mqbench/deploy/__init__.py
new file mode 100644
index 0000000..9aed37e
--- /dev/null
+++ b/mqbench/deploy/__init__.py
@@ -0,0 +1,6 @@
+from .deploy_linear import remove_fakequantize_and_collect_params
+from .deploy_nnie import remove_fakequantize_and_collect_params_nnie
+from .deploy_onnx_qlinear import ONNXQLinearPass
+from .deploy_onnx_qnn import ONNXQNNPass
+from .deploy_openvino import replace_fakequantize_and_collect_params_openvino
+from .deploy_tengine import remove_fakequantize_and_collect_params_tengine
diff --git a/mqbench/deploy/common.py b/mqbench/deploy/common.py
new file mode 100644
index 0000000..54dab48
--- /dev/null
+++ b/mqbench/deploy/common.py
@@ -0,0 +1,255 @@
+import copy
+import onnx
+import numpy as np
+from onnx import numpy_helper
+from onnx import TensorProto
+
+
+from mqbench.utils.logger import logger
+
+
+class ONNXGraph(object):
+    def __init__(self, onnx_model_path):
+        '''Describe onnx graph
+        args: 
+            input_map[tensor_name] = node which input is tensor_name
+            output_map[tensor_name] = node which output is tensor_name
+        '''
+        self.model = onnx.load(onnx_model_path)
+        self.graph = self.model.graph
+        self.initializer = {}
+        self.input_map = {}
+        self.output_map = {}
+        self.topologize_graph()
+        self.prepare_initializer()
+
+    def prepare_initializer(self):
+        self.initializer.clear()
+        for idx, init in enumerate(self.graph.initializer):
+            self.initializer[init.name] = (init, idx)
+
+    def get_constant(self, name):
+        for node in self.model.graph.node:
+            if node.op_type == 'Constant':
+                if node.output[0] == name:
+                    return numpy_helper.to_array(node.attribute[0].t).tolist()
+
+    def get_initializer(self, initializer_name):
+        return numpy_helper.to_array(self.initializer[initializer_name][0])
+
+    def set_initializer(self, initializer_name, value_tensor, raw=True):
+        idx = None
+        if initializer_name in self.initializer:
+            idx = self.initializer[initializer_name][1]
+        if raw:
+            initializer = numpy_helper.from_array(value_tensor)
+        else:
+            if value_tensor.dtype == np.float32:
+                data_type = TensorProto.FLOAT
+            if value_tensor.dtype == np.uint8:
+                data_type = TensorProto.UINT8
+            if value_tensor.dtype == np.int8:
+                data_type = TensorProto.INT8
+            initializer = onnx.helper.make_tensor(name=initializer_name,
+                                                  data_type=data_type,
+                                                  dims=[] if value_tensor.size == 1 else list(value_tensor.shape),
+                                                  vals=value_tensor,
+                                                  raw=False)
+        initializer.name = initializer_name
+        if idx is not None:
+            self.graph.initializer.remove(self.graph.initializer[idx])
+        self.graph.initializer.append(initializer)
+        self.prepare_initializer()
+
+    def topologize_graph(self):
+        self.input_map.clear()
+        self.output_map.clear()
+        for node in self.graph.node:
+            for output_name in node.output:
+                self.output_map[output_name] = node
+            for input_name in node.input:
+                if input_name not in self.input_map:
+                    self.input_map[input_name] = []
+                self.input_map[input_name].append(node)
+
+    def get_tensor_producer(self, output_name):
+        if output_name not in self.output_map:
+            return 'INPUT_TOKEN'
+        return self.output_map[output_name]
+
+    def get_tensor_consumer(self, input_name):
+        if input_name not in self.input_map:
+            return ['OUTPUT_TOKEN']
+        return self.input_map[input_name]
+
+    def save_onnx_model(self, model_path):
+        onnx.save(self.model, model_path)
+
+    def remove_node_purely(self, node):
+        self.graph.node.remove(node)
+
+    def insert_node_purely(self, node, idx=0):
+        self.graph.node.insert(idx, node)
+
+    def del_initializer(self, initializer_name):
+        if initializer_name in self.initializer:
+            del(self.initializer[initializer_name])
+
+    def optimize_model(self):
+        # Delete redundant nodes.
+        remove_node_list = []
+        for node in self.model.graph.node:
+            if len(node.input) == 0:
+                not_be_used = True
+                for output_name in node.output:
+                    if output_name in self.input_map:
+                        not_be_used = False
+                        break
+                if not_be_used:
+                    remove_node_list.append(node)
+        for node in remove_node_list:
+            self.remove_node_purely(node)
+        self.topologize_graph()
+        # Delete redundant initializers.
+        initializers = copy.deepcopy(self.initializer)
+        for initializer_name in initializers:
+            if initializer_name not in self.input_map:
+                self.del_initializer(initializer_name)
+        # Make node in topology order.
+        exist_input = [input_node.name for input_node in self.model.graph.input]
+        origin_node_num = len(self.model.graph.node)
+        finished_node_name = []
+        # O(n^2)
+        while len(finished_node_name) < origin_node_num:
+            node_detect = False
+            for i in range(origin_node_num):
+                node = self.model.graph.node[i]
+                all_inputs_exist = True
+                for input_name in node.input:
+                    if input_name not in exist_input and input_name not in self.initializer:
+                        all_inputs_exist = False
+                        break
+                if all_inputs_exist:
+                    if node.name not in finished_node_name:
+                        node_detect = True
+                        finished_node_name.append(node.name)
+                        self.model.graph.node.append(node)
+                        for output_name in node.output:
+                            exist_input.append(output_name)
+            assert node_detect, "Graph is illegel, error occured!"
+        for i in range(origin_node_num):
+            self.model.graph.node.remove(self.model.graph.node[0])
+
+    def set_opset_version(self, domain, version):
+        opset_info = copy.deepcopy(self.model.opset_import[0])
+        opset_info.domain = domain
+        opset_info.version = version
+        self.model.opset_import.insert(0, opset_info)
+
+
+class OnnxPreprocess(object):
+    def replace_resize_op_with_upsample(self, graph, out2node):
+        nodes_to_be_removed = []
+        idx = 0
+        while idx < len(graph.node):
+            node = graph.node[idx]
+            if node.op_type == 'Resize':
+                logger.info(f"Replace resize op: <{node.name}> with upsample.")
+                mode = 'nearest'
+                for attr in node.attribute:
+                    if attr.name == 'mode':
+                        mode = attr.s
+                upsample_node = onnx.helper.make_node('Upsample',
+                                                      name=node.name,
+                                                      inputs=[node.input[0], node.input[2]],
+                                                      outputs=node.output,
+                                                      mode=mode)
+                nodes_to_be_removed.append(node)
+                nodes_to_be_removed.extend(get_constant_inputs(node, out2node))
+                graph.node.insert(idx, upsample_node)
+                idx += 1
+            idx += 1
+        for node in nodes_to_be_removed:
+            graph.node.remove(node)
+        return
+
+    def remove_fake_pad_op(self, graph, name2data, inp2node, out2node):
+        nodes_to_be_removed = []
+        for idx, node in enumerate(graph.node):
+            if node.op_type == 'Pad':
+                pads = name2data[node.input[1]]
+                if all([x == 0 for x in pads]):
+                    logger.info(f"Remove pad op: <{node.name}>.")
+                    next_nodes = inp2node[node.output[0]]
+                    for next_node, idx in next_nodes:
+                        next_node.input[idx] = node.input[0]
+                    nodes_to_be_removed.append(node)
+                    nodes_to_be_removed.extend(get_constant_inputs(node, out2node))
+        for node in nodes_to_be_removed:
+            graph.node.remove(node)
+        return
+
+
+
+def update_inp2node_out2node(graph):
+    out2node = {}
+    inp2node = {}
+    for node in graph.node:
+        for out in node.output:
+            # suppose each node only has one output
+            out2node[out] = node
+        for idx, inp in enumerate(node.input):
+            # one node may have multiple inputs
+            if inp not in inp2node:
+                inp2node[inp] = []
+            inp2node[inp].append([node, idx])
+    return out2node, inp2node
+
+
+def prepare_data(graph):
+    params = {}
+    for init in graph.initializer:
+        params[init.name] = numpy_helper.to_array(init)
+    for node in graph.node:
+        if node.op_type == "Constant":
+            for attr in node.attribute:
+                if attr.name == "value":
+                    params[node.output[0]] = numpy_helper.to_array(attr.t)
+    return params
+
+
+def prepare_initializer(graph):
+    named_initializer = {}
+    for init in graph.initializer:
+        named_initializer[init.name] = init
+    return named_initializer
+
+
+def parse_attrs(node_attrs):
+    attrs = {}
+    for attr in node_attrs:
+        if attr.type == onnx.AttributeProto.AttributeType.INTS:
+            attrs[attr.name] = tuple(attr.ints)
+        elif attr.type == onnx.AttributeProto.AttributeType.INT:
+            attrs[attr.name] = attr.i
+        elif attr.type == onnx.AttributeProto.AttributeType.FLOATS:
+            attrs[attr.name] = tuple(attr.floats)
+        elif attr.type == onnx.AttributeProto.AttributeType.FLOAT:
+            attrs[attr.name] = attr.f
+        elif attr.type == onnx.AttributeProto.AttributeType.TENSOR:
+            attrs[attr.name] = numpy_helper.to_array(attr.t)
+        elif attr.type == onnx.AttributeProto.AttributeType.STRING:
+            attrs[attr.name] = str(attr.s)
+        elif attr.type == onnx.AttributeProto.AttributeType.STRINGS:
+            attrs[attr.name] = tuple([str(x) for x in attr.strings])
+        else:
+            raise Exception("ATTR Type [{}] Not Supported!".format(attr.type))
+    return attrs
+
+
+def get_constant_inputs(node, out2node):
+    node_list = []
+    for inp in node.input:
+        if inp in out2node and out2node[inp].op_type == 'Constant':
+            node_list.append(out2node[inp])
+    return node_list
diff --git a/mqbench/deploy/convert_xir.py b/mqbench/deploy/convert_xir.py
new file mode 100644
index 0000000..cbfb0e3
--- /dev/null
+++ b/mqbench/deploy/convert_xir.py
@@ -0,0 +1,1025 @@
+import argparse
+
+import onnx
+import numpy as np
+
+from onnx import numpy_helper
+
+from collections import namedtuple
+from typing import Any, Dict, Optional
+from functools import partial
+
+from nndct_shared.base import NNDCT_OP
+from nndct_shared.nndct_graph.base_tensor import Tensor
+from nndct_shared.utils import AddXopError
+from nndct_shared.compile.xgraph import XGraph
+from nndct_shared.compile.xop_creator import _Converter, _get_xir_attr_from_node, _pack 
+from pytorch_nndct.parse.op_dispatcher import OpCreator
+
+class ONNX_OP(object):
+    CONV2d = 'Conv'
+    RELU = 'Relu'
+    MAXPOOL = 'MaxPool'
+    ADD = 'Add'
+    GEMM = 'Gemm'
+    ADPTIVEAVGPOOL2D = 'GlobalAveragePool'
+    FLATTEN = 'Flatten'
+    INPUT = 'Input'
+    RESIZE = 'Resize'
+    CONCAT = 'Concat'
+
+
+class ONNX_PARAM(object):
+    WEIGHT = 'weight'
+    BIAS = 'bias'
+    ZEROPOINT = 'zero_point'
+    SCALE = 'scale'
+    ALL = [WEIGHT, BIAS, ZEROPOINT, SCALE]
+
+
+ONNX2NNDCT_CONVERTOR = {
+    ONNX_OP.CONV2d: NNDCT_OP.CONV2D,
+    ONNX_OP.RELU: NNDCT_OP.RELU,
+    ONNX_OP.MAXPOOL: NNDCT_OP.MAX_POOL,
+    ONNX_OP.ADD: NNDCT_OP.ADD,
+    ONNX_OP.GEMM: NNDCT_OP.DENSE,
+    ONNX_OP.ADPTIVEAVGPOOL2D: NNDCT_OP.ADAPTIVEAVGPOOL2D,
+    ONNX_OP.FLATTEN: NNDCT_OP.FLATTEN,
+    ONNX_OP.INPUT: NNDCT_OP.INPUT,
+    ONNX_OP.RESIZE: NNDCT_OP.RESIZE,
+    ONNX_OP.CONCAT: NNDCT_OP.CONCAT,
+}
+
+perchannel_fakequantizer = [
+    'FakeQuantizeLearnablePerchannelAffine', 'FixedPerChannelAffine',
+    'FakeQuantizeDSQPerchannel', 
+]
+pertensor_fakequantizer = [
+    'LearnablePerTensorAffine', 'FixedPerTensorAffine',
+    'FakeQuantizeDSQPertensor', 'FakeQuantizeTqtAffine'
+]
+output_fakequantizer = [
+    'LearnablePerTensorAffine', 'FakeQuantizeTqtAffine'
+]
+all_fakequantizer = perchannel_fakequantizer + pertensor_fakequantizer
+
+_filed = [
+    'name', 'shape', 'op_type', 'in_tensors', 'in_tensors_dim',
+    'in_tensors_layout', 'out_tensors_shape', 'out_name', 'op', 'params',
+    'attrs'
+]
+FakeNode = namedtuple(
+    'FakeNode',
+    _filed,
+)
+FakeNode.__new__.__defaults__ = (None, ) * len(_filed)
+
+
+def data_onnx_op(xgraph: XGraph, node, quant_config):
+    shape = node.out_tensors_shape
+
+    out_tensor = np.zeros(shape, dtype=np.float32)
+    attrs: Dict[str, Any] = {}
+    attrs["shape"] = shape
+    attrs["data_type"] = _Converter.to_xir_dtype(out_tensor.dtype)
+    xgraph.create_fixed_normal_op(node.name,
+                                  "data",
+                                  quant_config,
+                                  tensor=out_tensor,
+                                  attrs=attrs)
+
+
+def onnx_to_xir(onnx_op_type):
+    return partial(default_onnx_to_xop, onnx_op_type)
+
+
+def default_onnx_to_xop(onnx_op_type, xgraph, node, quant_config):
+    attrs = _get_xir_attr_from_node(node)
+
+    input_ops = {}
+    if node.attrs['has_bound_params']:
+        for param_name, param_tensor in node.params:
+            param = xgraph.get_op_by_name(param_name)
+            head = param_name.split('.')[-1].lower()[0]
+            if param:
+                input_ops['weights' if head == 'w' else 'bias'] = [param]
+
+    input_list = []
+    for input_name in node.in_tensors:
+        if node.attrs['has_bound_params'] and is_param_tensor(input_name):
+            continue
+        elif is_param_tensor(input_name):
+            input_op = xgraph.get_op_by_name(input_name)
+        else:
+            input_op = xgraph.get_op_by_name(input_name)
+        input_list.append(input_op)
+
+    input_ops["input"] = xgraph.create_input_fix_ops(input_list, node.name,
+                                                     quant_config)
+
+    xgraph.create_fixed_normal_op(node.out_name,
+                                  onnx_op_type,
+                                  quant_config,
+                                  attrs=attrs,
+                                  input_ops=input_ops)
+
+
+
+def resize(xgraph, node, quant_config):
+    attrs: Dict[str, Any] = {}
+    attrs["scale"] = node.attrs['scale']
+    attrs["align_corners"] = node.attrs['align_corners']
+    attrs["half_pixel_centers"] = node.attrs['half_pixel_centers']
+    attrs["mode"] = node.attrs['mode']
+    attrs["mode"] = {'nearest': "NEAREST"}.get(attrs["mode"].s.decode())
+    size = node.attrs['size']
+    if size[0] == 0 and size[1] == 0:
+        input_ops = {}
+        input_list = []
+        for input in node.in_tensors:
+            input_op = xgraph.get_op_by_name(input)
+            input_list.append(input_op)
+        input_ops["input"] = xgraph.create_input_fix_ops(input_list, node.name, quant_config)
+        xgraph.create_fixed_normal_op(
+            node.name, "resize", quant_config, attrs=attrs, input_ops=input_ops)
+    else:
+        sub_pack_op, pack_list = _pack(xgraph, node, "size", size, quant_config)
+        input_ops = {}
+        input_ops["size"] = [sub_pack_op]
+        input_list = [xgraph.get_op_by_name(node.in_tensors[0])]
+        input_ops["input"] = input_list
+        input_ops["input"] = [
+            op for op in input_ops["input"]
+            if op and op.get_name() not in [i.get_name() for i in pack_list]
+        ]
+        input_ops["input"] = xgraph.create_input_fix_ops(input_ops["input"], node.name, quant_config)
+        xgraph.create_fixed_normal_op(
+            node.out_name, "resize", quant_config, attrs=attrs, input_ops=input_ops)
+        node_need_to_be_clear = node.attrs['to_remove'] 
+        for n in node_need_to_be_clear:
+            xgraph.graph.remove_op(xgraph.get_op_by_name(n))   
+
+
+def avgpool(xgraph: XGraph, node, quant_config):
+    needScale = False
+    scale = 1.0
+    if node.attrs['kernel'] == [3, 3]:
+        needScale = True
+        scale = 9.0 * 7.0 / 64.0
+    elif node.attrs['kernel'] == [5, 5]:
+        needScale = True
+        scale = 25.0 * 10.0 / 256.0
+    elif node.attrs['kernel'] in [[6, 6], [3, 6], [6, 3]]:
+        needScale = True
+        scale = 36.0 * 7.0 / 256.0
+    elif node.attrs['kernel'] == [7, 7]:
+        needScale = True
+        scale = 49.0 * 21.0 / 1024.0
+    elif node.attrs['kernel'] == [14, 14]:
+        needScale = True
+        scale = 196.0 * 21.0 / 4096.0
+
+    if needScale:
+        attrs = node.attrs
+        input_ops = {}
+        input_ops["input"] = [xgraph.get_op_by_name(node.in_tensors[0])]
+        input_ops["input"] = xgraph.create_input_fix_ops(
+            input_ops["input"], node.name, quant_config)
+        xgraph.create_fixed_normal_op(node.name + '_pool',
+                                      "avgpool2d",
+                                      quant_config,
+                                      attrs=attrs,
+                                      input_ops=input_ops)
+
+        scale = [scale]
+        xgraph.create_fixed_const_op(name=node.name + "_scale",
+                                     data=np.array(scale, dtype=np.float32),
+                                     quant_info=quant_config)
+
+        input_ops = {}
+        input_ops["input"] = [
+            xgraph.get_op_by_name(node.name + '_pool'),
+            xgraph.get_op_by_name(node.name + "_scale")
+        ]
+        xgraph.create_fixed_normal_op(node.out_name,
+                                      "mul",
+                                      quant_config,
+                                      input_ops=input_ops)
+    else:
+        onnx_to_xir("avgpool2d")(xgraph, node, quant_config)
+
+
+def flatten(xgraph: XGraph, node, quant_config):
+
+    if node.in_tensors_dim[0] != 4 or node.in_tensors_layout[
+            0] == Tensor.Layout.NHWC:
+        onnx_to_xir("flatten")(xgraph, node, quant_config)
+    else:
+        attrs: Dict[str, Any] = {}
+        # NHWC -> NCHW
+        attrs["order"] = [0, 3, 1, 2]
+        input_ops = {}
+        input_ops["input"] = [xgraph.get_op_by_name(node.in_tensors[0])]
+        xgraph.create_fixed_normal_op(node.name + "_i0",
+                                      "transpose",
+                                      quant_config,
+                                      attrs=attrs,
+                                      input_ops=input_ops)
+
+        attrs = node.attrs
+
+        input_ops = {}
+        input_ops["input"] = [xgraph.get_op_by_name(node.name + "_i0")]
+
+        xgraph.create_fixed_normal_op(node.out_name,
+                                      "flatten",
+                                      quant_config,
+                                      attrs=attrs,
+                                      input_ops=input_ops)
+
+
+def dense(xgraph: XGraph, node, quant_config):
+    input_ops = {}
+    for param_name, param_tensor in node.params:
+        param = xgraph.get_op_by_name(param_name)
+        head = param_name.split('.')[-1].lower()[0]
+        if head == 'b':
+            input_ops['bias'] = [param]
+        else:
+            weights = xgraph.get_op_by_name(param_name)
+
+    input_list = []
+    for input in node.in_tensors:
+        input_op = xgraph.get_op_by_name(input)
+        input_list.append(input_op)
+    input_ops["input"] = xgraph.create_input_fix_ops(input_list, node.name,
+                                                     quant_config)
+    input_ops["input"].append(weights)
+
+    attrs: Dict[str, Any] = {}
+    attrs["transpose_a"] = False
+    attrs["transpose_b"] = True
+
+    xgraph.create_fixed_normal_op(node.out_name,
+                                  "matmul",
+                                  quant_config,
+                                  attrs=attrs,
+                                  input_ops=input_ops)
+
+
+def is_param_tensor(name):
+    # judge a instance is a parameter tensor or not
+    return name.split('.')[-1] in ONNX_PARAM.ALL if name else False
+
+
+ONNX2XIR_CONVERTOR = {
+    ONNX_OP.INPUT: data_onnx_op,
+    ONNX_OP.CONV2d: onnx_to_xir('conv2d'),
+    ONNX_OP.ADPTIVEAVGPOOL2D: avgpool,
+    ONNX_OP.MAXPOOL: onnx_to_xir('maxpool2d'),
+    ONNX_OP.RELU: onnx_to_xir('relu'),
+    ONNX_OP.ADD: onnx_to_xir('add'),
+    ONNX_OP.GEMM: dense,
+    ONNX_OP.FLATTEN: flatten,
+    ONNX_OP.RESIZE: resize,
+    ONNX_OP.CONCAT: onnx_to_xir('concat')
+}
+
+
+def update_inp2node_out2node(graph):
+    out2node = {}
+    inp2node = {}
+    for node in graph.node:
+        for out in node.output:
+            # suppose each node only has one output
+            out2node[out] = node
+        for idx, inp in enumerate(node.input):
+            # one node may have multiple inputs
+            if inp not in inp2node:
+                inp2node[inp] = []
+            inp2node[inp].append([node, idx])
+    return out2node, inp2node
+
+def prepare_data(graph):
+    params = {}
+    for init in graph.initializer:
+        params[init.name] = numpy_helper.to_array(init)
+    for node in graph.node:
+        if node.op_type == "Constant":
+            for attr in node.attribute:
+                if attr.name == "value":
+                    params[node.output[0]] = numpy_helper.to_array(attr.t)
+    return params
+
+def prepare_initializer(graph):
+    named_initializer = {}
+    for init in graph.initializer:
+        named_initializer[init.name] = init
+    return named_initializer
+
+def parse_attrs(node_attrs):
+    attrs = {}
+    for attr in node_attrs:
+        if attr.type == onnx.AttributeProto.AttributeType.INTS:
+            attrs[attr.name] = tuple(attr.ints)
+        elif attr.type == onnx.AttributeProto.AttributeType.INT:
+            attrs[attr.name] = attr.i
+        elif attr.type == onnx.AttributeProto.AttributeType.FLOATS:
+            attrs[attr.name] = tuple(attr.floats)
+        elif attr.type == onnx.AttributeProto.AttributeType.FLOAT:
+            attrs[attr.name] = attr.f
+        elif attr.type == onnx.AttributeProto.AttributeType.TENSOR:
+            attrs[attr.name] = numpy_helper.to_array(attr.t)
+        elif attr.type == onnx.AttributeProto.AttributeType.STRING:
+            attrs[attr.name] = str(attr.s)
+        elif attr.type == onnx.AttributeProto.AttributeType.STRINGS:
+            attrs[attr.name] = tuple([str(x) for x in attr.strings])
+        else:
+            raise Exception("ATTR Type [{}] Not Supported!".format(attr.type))
+    return attrs
+
+
+
+def get_constant_inputs(node, out2node):
+    node_list = []
+    for inp in node.input:
+        if inp in out2node and out2node[inp].op_type == 'Constant':
+            node_list.append(out2node[inp])
+    return node_list
+
+
+class XIR_process(object):
+
+    bias_fix_point, bias_bitwidth = 0, 8
+
+    def create_normal_nodes_from_onnx_graph(self, onnx_graph):
+        normal_nodes = []
+        for node in onnx_graph.node:
+            if node.op_type in ['Constant']:
+                continue
+            elif node.op_type == ONNX_OP.CONV2d:
+                node_attr = {'has_bound_params': True}
+                node_op = self.get_xop_of_conv2d(node)
+                input_weight_bias = self.get_unquant_input_weight_bias_of_node(
+                    node)
+                params = [(input_weight_bias['weight'],
+                           self.name2data[input_weight_bias['weight']])]
+                if input_weight_bias['bias']:
+                    params.append((input_weight_bias['bias'],
+                                   self.name2data[input_weight_bias['bias']]))
+
+                new_node = FakeNode(
+                    name=node.name,
+                    shape=self.name2shape[input_weight_bias['input']],
+                    op_type=node.op_type,
+                    out_tensors_shape=self.name2shape[node.name],
+                    op=node_op,
+                    params=params,
+                    in_tensors=[
+                        input_weight_bias[k] for k in input_weight_bias.keys() if input_weight_bias[k]
+                    ],
+                    attrs=node_attr,
+                    out_name=node.output[0])
+                normal_nodes.append(new_node)
+            elif node.op_type == ONNX_OP.GEMM:
+                node_attr = {'has_bound_params': True}
+                input_weight_bias = self.get_unquant_input_weight_bias_of_node(
+                    node)
+                params = [(input_weight_bias['weight'],
+                           self.name2data[input_weight_bias['weight']])]
+                if input_weight_bias['bias']:
+                    params.append((input_weight_bias['bias'],
+                                   self.name2data[input_weight_bias['bias']]))
+
+                inputs = [input_weight_bias['input']]
+
+                new_node = FakeNode(name=node.name,
+                                    op_type=node.op_type,
+                                    params=params,
+                                    in_tensors=inputs,
+                                    out_name=node.output[0])
+                normal_nodes.append(new_node)
+            elif node.op_type == ONNX_OP.MAXPOOL:
+                node_attr = {'has_bound_params': False}
+                node_op = self.get_xop_of_max_pool(node)
+                inputs = self.get_unquant_inputs(node)
+                input_weight_bias = self.get_unquant_input_weight_bias_of_node(
+                    node)
+                new_node = FakeNode(
+                    name=node.name,
+                    shape=self.name2shape[input_weight_bias['input']],
+                    op_type=node.op_type,
+                    out_tensors_shape=self.name2shape[node.name],
+                    op=node_op,
+                    in_tensors=inputs,
+                    attrs=node_attr,
+                    out_name=node.output[0])
+                normal_nodes.append(new_node)
+            elif node.op_type == ONNX_OP.RELU:
+                node_attr = {'has_bound_params': False}
+                node_op = self.get_xop_of_relu(node)
+                inputs = self.get_unquant_inputs(node)
+
+                new_node = FakeNode(
+                    name=node.name,
+                    op_type=node.op_type,
+                    out_tensors_shape=self.name2shape[node.name],
+                    op=node_op,
+                    in_tensors=inputs,
+                    attrs=node_attr,
+                    out_name=node.output[0])
+                normal_nodes.append(new_node)
+            elif node.op_type == ONNX_OP.ADD:
+                node_attr = {'has_bound_params': False}
+                inputs = self.get_unquant_inputs(node)
+                node_op = self.get_xop_of_add(node, inputs)
+
+                new_node = FakeNode(
+                    name=node.name,
+                    shape=self.name2shape[input_weight_bias['input']],
+                    op_type=node.op_type,
+                    out_tensors_shape=self.name2shape[node.name],
+                    op=node_op,
+                    in_tensors=inputs,
+                    attrs=node_attr,
+                    out_name=node.output[0])
+                normal_nodes.append(new_node)
+            elif node.op_type == ONNX_OP.ADPTIVEAVGPOOL2D:
+                node_op = self.get_xop_of_adaptive_avg_pool2d(node)
+                inputs = self.get_unquant_inputs(node)
+                attrs = {}
+                attrs['kernel'] = self.name2shape[inputs[0]][1:3]
+                attrs['stride'] = self.name2shape[inputs[0]][1:3]
+                attrs['count_include_pad'] = True
+                attrs['global'] = True
+                attrs['pad'] = [0, 0, 0, 0]
+                attrs['pad_mode'] = 'FLOOR'
+                attrs['has_bound_params'] = False
+                new_node = FakeNode(
+                    name=node.name,
+                    op_type=node.op_type,
+                    op=node_op,
+                    in_tensors=inputs,
+                    out_tensors_shape=self.name2shape[node.name],
+                    attrs=attrs,
+                    out_name=node.output[0])
+                normal_nodes.append(new_node)
+            elif node.op_type == ONNX_OP.FLATTEN:
+                inputs = self.get_unquant_inputs(node)
+                node_op = self.get_xop_of_flatten(node)
+                inputs_dim = [len(self.name2shape[i]) for i in inputs]
+                # IT IS A BUG OF THE XIR COMPILER for vitis-ai 1.4.1.978
+                # inputs_layout = [
+                #     Tensor.Layout.NHWC
+                #     if self.name2shape[i][-1] > 1 else Tensor.Layout.NCHW
+                #     for i in inputs
+                # ]
+                inputs_layout = [Tensor.Layout.NCHW]
+                start_axis = node.attribute[0].i
+                end_axis = inputs_dim[0] - 1
+                attrs = {}
+                attrs['start_axis'] = start_axis
+                attrs['end_axis'] = end_axis
+                attrs['has_bound_params'] = False
+                new_node = FakeNode(name=node.name,
+                                    op_type=node.op_type,
+                                    op=node_op,
+                                    in_tensors=inputs,
+                                    in_tensors_dim=inputs_dim,
+                                    in_tensors_layout=inputs_layout,
+                                    attrs=attrs,
+                                    out_name=node.output[0])
+                normal_nodes.append(new_node)
+            elif node.op_type == ONNX_OP.RESIZE:
+                node_op = self.get_xop_of_interpolate(node) 
+                inputs = self.get_unquant_inputs(node)
+                inputs_layout = [Tensor.Layout.NCHW]
+                size = numpy_helper.to_array(self.name2data[node.input[3]]) if len(node.input) == 4 else None 
+                scale = self.name2data[node.input[2]] if not size else None 
+                assert scale is None or (scale - scale.astype('int') == 0).all(), f'Only integer scales is supportted! The given scale is {scale}'
+                if not size:
+                    size = self.name2shape[node.name] 
+                    scale = np.array([1, 1]) 
+                attrs_dict = {} 
+                for a in node.attribute:
+                    attrs_dict[a.name] = a 
+                mode = attrs_dict['mode'] 
+                align_corners = True if attrs_dict['coordinate_transformation_mode'] == "align_corners" else False
+                half_pixel_centers = True if attrs_dict['coordinate_transformation_mode'] == 'pytorch_half_pixel' else False
+                if size and len(size) == 4:
+                    size = size[1:-1] 
+                attrs = {} 
+                attrs['scale'] = scale 
+                attrs['align_corners'] = align_corners 
+                attrs['half_pixel_centers'] = half_pixel_centers 
+                attrs['mode'] = mode 
+                attrs['size'] = size 
+                attrs['has_bound_params'] = False
+                if scale is not None:
+                    attrs['to_remove'] = [node.input[2]]
+                new_node = FakeNode(name=node.name,
+                                    op_type=node.op_type,
+                                    op=node_op,
+                                    in_tensors=inputs,
+                                    in_tensors_layout=inputs_layout,
+                                    out_tensors_shape=size,
+                                    attrs=attrs,
+                                    out_name=node.output[0])
+                normal_nodes.append(new_node)
+            elif node.op_type == ONNX_OP.CONCAT:
+                node_op = self.get_xop_of_concat(node)
+                inputs = self.get_unquant_inputs(node)
+                inputs_layout = [Tensor.Layout.NCHW]
+                size = self.name2shape[node.name] 
+                axis = node.attribute[0].i 
+                dim = axis
+                attrs = {'axis': dim}
+                attrs['has_bound_params'] = False
+                new_node = FakeNode(name=node.name,
+                                    op_type=node.op_type,
+                                    op=node_op,
+                                    in_tensors=inputs,
+                                    in_tensors_layout=inputs_layout,
+                                    out_tensors_shape=size,
+                                    attrs=attrs,
+                                    out_name=node.output[0])
+                normal_nodes.append(new_node)
+        return normal_nodes
+
+    def create_input_nodes_from_onnx_graph(self, onnx_graph, reshape=True):
+        input_nodes = []
+        for input_message in onnx_graph.input:
+            shape = self.get_dim_from_tensor_shape_message(input_message)
+            if reshape and len(shape) == 4:
+                shape = list(np.zeros(shape=shape).transpose(0, 2, 3, 1).shape)
+            node = FakeNode(
+                name=input_message.name,
+                shape=self.get_dim_from_tensor_shape_message(input_message),
+                op_type='Input',
+                out_tensors_shape=shape)
+
+            input_nodes.append(node)
+        return input_nodes
+
+    def get_xop_of_concat(self, node):
+        _n = namedtuple('_n', ['name', 'shape'])
+        inputs = self.get_unquant_inputs(node)
+        ts = [_n(i, self.name2shape[i]) for i in inputs]
+        axis = node.attribute[0].i 
+        dim = axis
+        return OpCreator(None).cat(ts, dim)
+
+    def get_xop_of_interpolate(self, node):
+        input = self.out2node[node.input[0]]
+        _n = namedtuple('_n', ['name', 'shape']) 
+        inputs = self.get_unquant_inputs(node)
+        input_node = Tensor(name=inputs[0], shape=self.name2shape[inputs[0]])
+        # self.graph._graph.node(get_full_name(self.graph.name, node.input[0])).out_tensors[0]
+        size = numpy_helper.to_array(node.input[3]) if len(node.input) == 4 else None 
+        scale_factor = self.name2data[node.input[2]].tolist() if not size else [1, 1]
+        if len(scale_factor) == 1:
+            scale_factor += scale_factor
+        elif len(scale_factor) == 4:
+            scale_factor = scale_factor[-2:]
+        if size and len(size) == 4:
+            size = size[1:-1]
+        attrs = node.attribute
+        attrs_dict = {} 
+        for a in attrs:
+            attrs_dict[a.name] = a 
+        mode = f"'{attrs_dict['mode'].s.decode()}'"
+        assert mode == "'nearest'", f'the interpolate {mode} is not supported' 
+        align_corners = True if attrs_dict['coordinate_transformation_mode'].s.decode() == "align_corners" else None 
+        recompute_scale_factor = None 
+        return OpCreator(None)._interpolate(input_node, size, scale_factor, mode, align_corners, recompute_scale_factor)
+
+    def get_xop_of_conv2d(self, node):
+        pre_op = self.out2node.get(node.input[0], None)
+        if pre_op and pre_op.op_type in all_fakequantizer:
+            if pre_op.input[0] in self.out2node:
+                pre_op = self.out2node[pre_op.input[0]]
+            elif pre_op.input[0] in self.input_nodes:
+                pre_op = FakeNode(name=pre_op.input[0])
+        input_shape = self.name2shape[pre_op.name] if pre_op else self.name2shape[node.input[0]]
+        input = np.zeros(input_shape)
+        weight = self.name2data[self.out2node[node.input[1]].input[0]]
+        if len(node.input) >= 3:
+            bias = node.input[2]
+        else:
+            bias = np.zeros(weight.shape[-1])
+        stride = list(node.attribute[4].ints)
+        padding = list(node.attribute[3].ints)
+        dilation = list(node.attribute[0].ints)
+        transposed = False
+        output_padding = None
+        groups = int(node.attribute[1].i)
+        return OpCreator(None)._convolution(input, weight, bias, stride,
+                                            padding, dilation, transposed,
+                                            output_padding, groups, None, None,
+                                            None)
+
+    def get_xop_of_adaptive_avg_pool2d(self, node):
+        output_shape = self.name2shape[node.name]
+        pre_op = self.out2node[node.input[0]]
+        if pre_op.op_type in all_fakequantizer:
+            if pre_op.input[0] in self.out2node:
+                pre_op = self.out2node[pre_op.input[0]]
+            elif pre_op.input[0] in self.input_nodes:
+                pre_op = FakeNode(name=pre_op.input[0])
+        input_shape = self.name2shape[pre_op.name]
+        return OpCreator(None).adaptive_avg_pool2d(np.zeros(input_shape),
+                                                   output_shape[1:3])
+
+    def get_xop_of_max_pool(self, node):
+        kernel_size = list(node.attribute[1].ints)
+        stride = list(node.attribute[3].ints)
+        padding = list(node.attribute[2].ints)
+        dilation = [1]
+        ceil_mode = int(node.attribute[0].i)
+        return OpCreator(None).max_pool2d(None, kernel_size, stride, padding,
+                                          dilation, ceil_mode)
+
+    def get_xop_of_relu(self, node):
+        return OpCreator(None).relu(None)
+
+    def get_xop_of_add(self, node, inputs):
+        input_tensor = Tensor(name=inputs[0],
+                              shape=self.name2shape[inputs[0]],
+                              dtype=np.dtype('int8'))
+        other_tensor = Tensor(name=inputs[1],
+                              shape=self.name2shape[inputs[1]],
+                              dtype=np.dtype('int8'))
+        return OpCreator(None).add(input_tensor, other_tensor)
+
+    def get_xop_of_flatten(self, node):
+        inputs = self.get_unquant_inputs(node)
+        inputs_dim = [len(self.name2shape[i]) for i in inputs]
+        start_axis = node.attribute[0].i
+        end_axis = inputs_dim[0] - 1
+        return OpCreator(None).flatten(inputs[0], start_axis, end_axis)
+
+    def shape_patch(self, graph, graph_without_quant, reshape=True):
+        inferred_model = onnx.shape_inference.infer_shapes(graph_without_quant)
+        value_info = inferred_model.graph.value_info
+        self.name2shape = {}
+        self.input_nodes = []
+        for out in value_info:
+            shape = self.get_dim_from_tensor_shape_message(out)
+            if reshape and len(shape) == 4:
+                shape = list(np.zeros(shape=shape).transpose(0, 2, 3, 1).shape)
+            self.name2shape[self.out2node[out.name].name] = shape
+            self.name2shape[out.name] = shape
+        for input_message in graph.graph.input:
+            shape = self.get_dim_from_tensor_shape_message(input_message)
+            if reshape and len(shape) == 4:
+                shape = list(np.zeros(shape=shape).transpose(0, 2, 3, 1).shape)
+            self.name2shape[input_message.name] = shape
+            self.input_nodes.append(input_message.name)
+        for output_message in graph.graph.output:
+            shape = self.get_dim_from_tensor_shape_message(output_message)
+            if reshape and len(shape) == 4:
+                shape = list(np.zeros(shape=shape).transpose(0, 2, 3, 1).shape)
+            if output_message.name in self.out2node:
+                pre_node = self.out2node[output_message.name]
+                if pre_node.op_type in all_fakequantizer:
+                    unquant_node = self.out2node[pre_node.input[0]]
+                    input = unquant_node.name
+                    input = unquant_node.output[0]
+                else:
+                    input = pre_node.name
+                    input = pre_node.output[0]
+                self.name2shape[input] = shape 
+                self.name2shape[self.out2node[input].name] = shape 
+            self.name2shape[output_message.name] = shape
+
+
+    def get_dim_from_tensor_shape_message(self, message):
+        return [v.dim_value for v in message.type.tensor_type.shape.dim]
+
+    def get_unquant_inputs(self, node):
+        inputs = []
+        for input in node.input:
+            if input in self.name2data:
+                inputs.append(input)
+            elif input in self.out2node and not self.out2node[
+                    input].op_type in all_fakequantizer:
+                inputs.append(self.out2node[input].output[0])
+            elif input in self.out2node and not self.out2node[
+                    input].op_type in output_fakequantizer and self.out2node[
+                        input].op_type in all_fakequantizer:
+                continue
+            elif input in self.out2node and self.out2node[
+                    input].op_type in output_fakequantizer:
+                input = self.out2node[input].input[0]
+                if input in self.out2node:
+                    pre_node = self.out2node[input]
+                    if pre_node.op_type in all_fakequantizer:
+                        unquant_node = self.out2node[pre_node.input[0]]
+                        input = unquant_node.name
+                        input = unquant_node.output[0]
+                    else:
+                        input = pre_node.name
+                        input = pre_node.output[0]
+                inputs.append(input)
+            elif input in self.inp2node and input not in self.out2node:
+                inputs.append(input)
+        return inputs
+
+    def get_unquant_input_weight_bias_of_node(self, node):
+        input_weight_and_bias = {'input': None, 'weight': None, 'bias': None}
+        for input in node.input:
+            type = None
+            if input in self.name2data:
+                type = input.split('.')[-1]
+            elif input in self.out2node and not self.out2node[
+                    input].op_type in all_fakequantizer:
+                type = 'input'
+                if input in self.out2node:
+                    pre_node = self.out2node[input]
+                    if pre_node.op_type in all_fakequantizer:
+                        unquant_node = self.out2node[pre_node.input[0]]
+                        input = unquant_node.name
+                        input = unquant_node.output[0]
+                    else:
+                        input = pre_node.name
+                        input = pre_node.output[0]
+            elif input in self.out2node and self.out2node[
+                    input].op_type in all_fakequantizer:
+                pre_node = self.out2node[input]
+                real_in = pre_node.input[0]
+                if real_in in self.name2data:
+                    type = real_in.split('.')[-1]
+                    input = real_in
+                elif real_in in self.inp2node:
+                    type = 'input'
+                    if real_in in self.out2node:
+                        input = self.out2node[real_in].name
+                        input = self.out2node[real_in].output[0]
+                    else:
+                        input = real_in
+            elif input in self.inp2node and input not in self.out2node:
+                type = 'input' 
+
+            if type in [ONNX_PARAM.BIAS, ONNX_PARAM.WEIGHT, 'input']:
+                input_weight_and_bias[type] = input
+        return input_weight_and_bias
+
+    def parse_bias_qparams_from_data(self, node, name2data):
+        tensor_name = node.name 
+        bit_width = 8
+        max_val = max(-1 * self.name2data[tensor_name].flatten().min(), self.name2data[tensor_name].flatten().max()) 
+        if max_val < 2 ** -10:
+            sign_shift = -1
+            val_shift = -bit_width
+        elif max_val == -1 * self.name2data[tensor_name].flatten().min():
+            sign_shift = 0
+            val_shift = -np.round(np.log2(max_val))
+        else:
+            sign_shift = 1
+            val_shift = -np.round(np.log2(max_val))
+        sym_fix = int(bit_width - sign_shift + val_shift - 1)
+        return tensor_name, sym_fix, bit_width
+
+    def get_quant_weight_loss(self, unquant_weight, amp, val_max):
+        real_weight = unquant_weight * amp 
+        real_weight = np.round(np.clip(real_weight, -val_max, val_max - 1))
+        real_weight /= amp 
+        return float(((real_weight - unquant_weight)**2).sum())
+
+    def parse_qparams(self, node, name2data):
+        tensor_name, scale, zero_point = node.input[:3]
+        scale, zero_point = name2data[scale], name2data[zero_point]
+        if len(node.input) > 3:
+            qmin, qmax = node.input[-2:]
+            qmin, qmax = name2data[qmin], name2data[qmax]
+        elif len(node.attribute) > 0:
+            qparams = parse_attrs(node.attribute)
+            qmin = qparams['quant_min']
+            qmax = qparams['quant_max']
+        else:
+            print(f'qmin and qmax are not found for <{node.name}>!')
+            raise ValueError('The name2data is corrputed.')
+
+        if abs(zero_point - 0).all() < 1e-5:
+            bit_width = np.log2(qmax - qmin + 1).astype(int)
+            final_scale = int(np.floor(np.log2(1 / scale)))
+        else:
+            print(f'<{node.name}> is a asym quant node, which is not support by Vitis Backend')
+        return tensor_name, final_scale, bit_width
+
+    def get_wbi_of_onnx_calib(self, graph, calib_graph):
+        self.biasname2wi = {}
+        for node in calib_graph.node:
+            if node.op_type in [ONNX_OP.CONV2d, ONNX_OP.GEMM]:
+                input_pre_fix = node.input[0]
+                weight_pre_fix = node.input[1]
+                if len(node.input) > 2:
+                    bias_fix = node.input[2]
+                    self.biasname2wi[bias_fix] = [weight_pre_fix, input_pre_fix]
+                else: 
+                    bias_fix = None 
+
+    def get_quant_config_of_onnx(self, graph, name):
+        qconfig = {}
+
+        output_config = {}
+        for node in graph.node:
+            if node.op_type in output_fakequantizer:
+                output_name, fix_point, bitwidth = self.parse_qparams(
+                    node, self.name2data)
+                output_config[output_name] = [bitwidth, fix_point]
+
+        param_config = {}
+        for param in graph.initializer:
+            ptype = param.name[param.name.rfind('.') + 1:]
+            data_type = param.data_type
+            if ptype in [ONNX_PARAM.WEIGHT
+                         ] or (ptype in [ONNX_PARAM.BIAS]
+                               and self.inp2node[param.name][0][0].op_type
+                               in all_fakequantizer):
+                _, fix_point, bitwidth = self.parse_qparams(
+                    self.inp2node[param.name][0][0], self.name2data)
+                param_config[param.name] = [bitwidth, fix_point]
+            elif ptype in [ONNX_PARAM.BIAS] and not self.inp2node[
+                    param.name][0][0].op_type in all_fakequantizer:
+                bitwidth = self.bias_bitwidth
+                _, fix_point, bitwidth = self.parse_bias_qparams_from_data(param, self.name2data)
+                param_config[param.name] = [bitwidth, fix_point]
+            elif ptype in [ONNX_PARAM.ZEROPOINT, ONNX_PARAM.SCALE]:
+                continue
+            else:
+                if param.name in self.inp2node:
+                    print(f'A data array named {param.name} with {numpy_helper.to_array(param)} has been used.')
+
+        output_config = {}
+        for node in graph.node:
+            if node.op_type in output_fakequantizer:
+                output_name, fix_point, bitwidth = self.parse_qparams(
+                    node, self.name2data)
+                output_config[output_name] = [bitwidth, fix_point]
+
+        input_config = {}
+
+        qconfig['param'] = param_config
+        qconfig['output'] = output_config
+        qconfig['input'] = input_config
+
+        return qconfig
+
+    def do_compile(self,
+                   onnx_graph_file,
+                   onnx_graph_file_cali,
+                   name=None,
+                   graph_attr_kwargs: Optional[Dict[str, Any]] = None,
+                   reshape=True) -> None:
+        # get the onnx graph
+        onnx_graph = onnx_graph_file.graph
+        # create a xgraph like do_compile
+        xgraph = XGraph(name if name else onnx_graph.name)
+        self.graph = xgraph
+
+        # build the inter-node via the in/out infomation
+        self.out2node, self.inp2node = update_inp2node_out2node(onnx_graph)
+
+        # add the extra args
+        if graph_attr_kwargs is not None:
+            for name, attr in graph_attr_kwargs.items():
+                xgraph.graph.set_attr(name, attr)
+
+        # get the weight/bias in the initializer
+        self.name2data = prepare_data(onnx_graph)
+        # get the input/output
+        self.out2node, self.inp2node = update_inp2node_out2node(onnx_graph)
+        # patch the wbi pairs 
+        self.get_wbi_of_onnx_calib(onnx_graph, onnx_graph_file_cali.graph)
+        # create weight/bias node <fix> via FakeQuantizeLearnablePerchannelAffine
+        quant_config_info = self.get_quant_config_of_onnx(onnx_graph, name)
+        # patch the shape
+        self.shape_patch(onnx_graph_file,
+                         onnx_graph_file_cali,
+                         reshape=reshape)
+
+        op_to_xir_op = ONNX2NNDCT_CONVERTOR
+
+        def get_op_of_initializer(name):
+            if get_type_of_initializer(name) in [
+                    ONNX_PARAM.ZEROPOINT, ONNX_PARAM.SCALE
+            ]:
+                return None
+            if name not in self.inp2node:
+                return None
+            node = self.inp2node[name][0][0]
+            if node.op_type in all_fakequantizer:
+                node = self.inp2node[node.output[0]][0][0]
+            ret_type = op_to_xir_op[
+                node.op_type] if node.op_type in op_to_xir_op.keys() else node.op_type
+            return ret_type
+
+        def get_type_of_initializer(name):
+            name = name[name.rfind('.') + 1:]
+            if name in ONNX_PARAM.ALL:
+                return name
+            else:
+                return None
+
+        implemented_op = [
+            ONNX2NNDCT_CONVERTOR[i] for i in ONNX2NNDCT_CONVERTOR.keys()
+        ]
+        implemented_op += all_fakequantizer
+
+
+        print('Essential data has been collected by the Vitis backend compiler.')
+        for node in onnx_graph.initializer:
+            op_type = get_op_of_initializer(node.name)
+            if op_type is None:
+                continue
+            param_type = get_type_of_initializer(node.name)
+            if op_type in [
+                    NNDCT_OP.BATCH_NORM, NNDCT_OP.BATCH_NORM1D,
+                    NNDCT_OP.BATCH_NORM3D
+            ]:
+                print('BN should have been merged in previous precess!')
+                raise Exception()
+            if xgraph.get_op_by_name(node.name):
+                continue
+            data = self.name2data[node.name]
+            if op_type == NNDCT_OP.CONV2D and param_type == ONNX_PARAM.WEIGHT:
+                # do weight reshape from oikk to okki
+                data = data.transpose(0, 2, 3, 1)
+                data = np.ascontiguousarray(data)
+            if op_type in implemented_op is False:
+                raise NotImplementedError(
+                    f'{op_type} has not been implemented.')
+
+            try:
+                xgraph.create_fixed_const_op(name=node.name,
+                                             data=data,
+                                             quant_info=quant_config_info)
+            except Exception as e:
+                raise AddXopError(node.name, 'const', str(e))
+
+        unknown_op_types = {
+            f"{node.op_type}({node.name})"
+            for node in onnx_graph.node
+            if node.op_type not in ONNX2NNDCT_CONVERTOR
+            and node.op_type not in all_fakequantizer + ['Constant']
+        }
+        if not unknown_op_types:
+            input_nodes = self.create_input_nodes_from_onnx_graph(
+                onnx_graph, reshape=reshape)
+            for node in input_nodes:
+                try:
+                    print(f'Trying to insert {node.name}<{node.op_type}> into the Vitis Call-Graph.')
+                    ONNX2XIR_CONVERTOR.get(node.op_type,
+                                           None)(xgraph, node,
+                                                 quant_config_info)
+                except Exception as e:
+                    raise AddXopError(node.name, node.op_type, str(e))
+            # before that need to insert input
+            normal_node = self.create_normal_nodes_from_onnx_graph(onnx_graph)
+            for node in normal_node:
+                if node.op_type in all_fakequantizer + ['Constant']:
+                    continue
+                try:
+                    ONNX2XIR_CONVERTOR.get(node.op_type,
+                                           None)(xgraph, node,
+                                                 quant_config_info)
+                except Exception as e:
+                    raise AddXopError(node.name, node.op_type, str(e))
+        else:
+            raise AddXopError(unknown_op_types)
+
+        return_ops = []
+        print('Trying to append <Output Nodes> into the Vitis Call-Graph.')
+        for out in onnx_graph.output:
+            if out.name in self.out2node:
+                pre_node = self.get_unquant_inputs(self.out2node[out.name])[0]
+                if xgraph.get_op_by_name(pre_node + '_fix'):
+                    return_ops.append(pre_node + '_fix')
+                else:
+                    return_ops.append(pre_node)
+
+        if return_ops:
+            xgraph.graph.set_attr("return_ops", return_ops)
+
+        if name:
+            if quant_config_info is None:
+                name += '_float'
+            else:
+                name += '_int'
+
+            xgraph.export_to_xmodel(name)
+            print('Finished exporting the Vitis Xmodel.')
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument("-Q", "--qmodel", help="onnx model with fake quantize nodes.", required=True)
+    parser.add_argument("-C", "--cmodel", help="onnx model without fake quantize nodes, or deploy model.", required=True)
+    parser.add_argument("-N", "--name", help="model name", required=True)
+    args = parser.parse_args()
+
+    xir_compiler = XIR_process()
+    qmodel = onnx.load(args.qmodel)
+    cmodel = onnx.load(args.cmodel)
+    xir_compiler.do_compile(qmodel, cmodel, args.name)
+    print('MQBench has converted the model into xmodel.')
diff --git a/mqbench/deploy/deploy_linear.py b/mqbench/deploy/deploy_linear.py
new file mode 100644
index 0000000..8dd4deb
--- /dev/null
+++ b/mqbench/deploy/deploy_linear.py
@@ -0,0 +1,257 @@
+import json
+import os
+
+
+import onnx
+import numpy as np
+from onnx import numpy_helper
+
+from mqbench.utils.logger import logger
+from mqbench.deploy.common import (
+    update_inp2node_out2node,
+    prepare_initializer,
+    prepare_data,
+    OnnxPreprocess,
+    get_constant_inputs,
+    parse_attrs
+)
+
+
+PERCHANNEL_FAKEQUANTIZER = ['FakeQuantizeLearnablePerchannelAffine', 
+                            'FixedPerChannelAffine',
+                            'FakeQuantizeDSQPerchannel']
+PERTENSOR_FAKEQUANTIZER = ['LearnablePerTensorAffine', 
+                           'FixedPerTensorAffine',
+                           'FakeQuantizeDSQPertensor',
+                           'FakeQuantizeTqtAffine']
+ALL_FAKEQUANTIZER = PERCHANNEL_FAKEQUANTIZER + PERTENSOR_FAKEQUANTIZER
+
+
+class LinearQuantizer_process(object):
+    # some method like dorefa need pre-compute weights
+    def weight_preprocess(self, target_tensor, out2node, inp2node, named_initializer):
+        def find_weight(tensor):
+            if tensor not in named_initializer:
+                _node = out2node[tensor]
+                for inp in _node.input:
+                    return find_weight(inp)
+            return tensor
+        weight = find_weight(target_tensor)
+
+        # TODO need more general method, like onnxruntime infer
+        data = numpy_helper.to_array(named_initializer[weight])
+        data = np.tanh(data)
+        data = data / (np.max(np.abs(data)) + 1e-5)
+        data = numpy_helper.from_array(data)
+        named_initializer[weight].raw_data = data.raw_data
+
+        redundant_nodes = []
+
+        def find_redundant_nodes(tensor):
+            if tensor == target_tensor:
+                return
+            nodes = inp2node[tensor]
+            for node, idx in nodes:
+                if node not in redundant_nodes:
+                    redundant_nodes.append(node)
+                    redundant_nodes.extend(get_constant_inputs(node, out2node))
+                find_redundant_nodes(node.output[0])
+        find_redundant_nodes(weight)
+        return weight, redundant_nodes
+
+    def deal_with_weight_fakequant(self, node, out2node, inp2node, named_initializer):
+        next_nodes = inp2node[node.output[0]]
+        assert len(next_nodes) == 1
+        next_node, idx = next_nodes[0]
+        assert next_node.op_type in ['Conv', 'Gemm', 'ConvTranspose']
+        redundant_nodes = []
+        if node.input[0] not in named_initializer:
+            node.input[0], redundant_nodes = \
+                self.weight_preprocess(node.input[0], out2node, inp2node, named_initializer)
+        next_node.input[idx] = node.input[0]
+        return redundant_nodes
+
+    def deal_with_activation_fakequant(self, node, inp2node):
+        next_nodes = inp2node[node.output[0]]
+        for next_node, idx in next_nodes:
+            next_node.input[idx] = node.input[0]
+        return
+
+    def parse_qparams(self, node, name2data):
+        tensor_name, scale, zero_point = node.input[:3]
+        scale, zero_point = name2data[scale], name2data[zero_point]
+        if len(node.input) > 3:
+            qmin, qmax = node.input[-2:]
+            qmin, qmax = name2data[qmin], name2data[qmax]
+        elif len(node.attribute) > 0:
+            qparams = parse_attrs(node.attribute)
+            qmin = qparams['quant_min']
+            qmax = qparams['quant_max']
+        else:
+            logger.info(f'qmin and qmax are not found for <{node.name}>!')
+        return tensor_name, scale, zero_point, qmin, qmax
+
+    def clip_weight(self, node, name2data, inp2node, named_initializer):
+        tensor_name, scale, zero_point, qmin, qmax = self.parse_qparams(node, name2data)
+        data = name2data[tensor_name]
+        clip_range_min = ((qmin - zero_point) * scale).astype(data.dtype)
+        clip_range_max = ((qmax - zero_point) * scale).astype(data.dtype)
+        if len(scale.shape) > 0 and scale.shape[0] > 1:
+            new_data = []
+            transposed = False
+            next_node = inp2node[node.output[0]]
+            if len(next_node) == 1 and next_node[0][0].op_type == 'ConvTranspose':
+                transposed = True
+                data = data.transpose(1, 0, 2, 3)
+            for c in range(data.shape[0]):
+                new_data.append(np.clip(data[c], clip_range_min[c], clip_range_max[c]))
+            new_data = np.array(new_data)
+            if transposed:
+                new_data = new_data.transpose(1, 0, 2, 3)
+            logger.info(f'Clip weights <{tensor_name}> to per-channel ranges.')
+        else:
+            new_data = np.clip(data, clip_range_min, clip_range_max)
+            logger.info(f'Clip weights <{tensor_name}> to range [{clip_range_min}, {clip_range_max}].')
+        new_data = numpy_helper.from_array(new_data)
+        named_initializer[tensor_name].raw_data = new_data.raw_data
+
+    def post_process_clip_ranges(self, clip_ranges, graph, inp2node):
+        def find_the_closest_clip_range(node):
+            if node.input[0] in clip_ranges:
+                return node.input[0]
+            elif node.op_type in ['Flatten', 'Resize'] and node.output[0] in inp2node:
+                return find_the_closest_clip_range(inp2node[node.output[0]][0][0])
+            else:
+                return None
+
+        for node in graph.node:
+            if node.op_type in ['Flatten', 'Resize']:
+                tensor_name = find_the_closest_clip_range(node)
+                if tensor_name:
+                    clip_ranges[node.input[0]] = clip_ranges[tensor_name]
+                    logger.info(f'Pass <{tensor_name}> clip range to <{node.name}> input <{node.input[0]}>.')
+        return clip_ranges
+
+    def remove_fakequantize_and_collect_params(self, onnx_path, model_name, backend):
+        model = onnx.load(onnx_path)
+        graph = model.graph
+        out2node, inp2node = update_inp2node_out2node(graph)
+        name2data = prepare_data(graph)
+        named_initializer = prepare_initializer(graph)
+
+        preprocess = OnnxPreprocess()
+        preprocess.remove_fake_pad_op(graph, name2data, inp2node, out2node)
+        out2node, inp2node = update_inp2node_out2node(graph)
+
+        clip_ranges = {}
+        nodes_to_be_removed = []
+        for node in graph.node:
+            if node.op_type in ALL_FAKEQUANTIZER:
+                nodes_to_be_removed.append(node)
+                nodes_to_be_removed.extend(get_constant_inputs(node, out2node))
+
+            if node.op_type in PERCHANNEL_FAKEQUANTIZER:
+                # fake quantize for weights, suppose per-channel quantize only for weight
+                redundant_nodes = self.deal_with_weight_fakequant(node, out2node, inp2node, named_initializer)
+                nodes_to_be_removed.extend(redundant_nodes)
+                self.clip_weight(node, name2data, inp2node, named_initializer)
+                if backend == 'ppl':
+                    tensor_name, scale, zero_point, qmin, qmax = self.parse_qparams(node, name2data)
+                    clip_ranges[tensor_name] = {'step': [float(x) for x in scale],
+                                                'zero_point': [int(x) for x in zero_point],
+                                                'min': [float(x) for x in scale * (qmin - zero_point)],
+                                                'max': [float(x) for x in scale * (qmax - zero_point)],
+                                                'bit': int(np.log2(qmax - qmin + 1)),
+                                                'type': "biased",
+                                                }
+                elif backend == 'vitis':
+                    logger.info("Vitis-DPU does not support per-channel quatization.")
+                    raise NotImplementedError("Vitis-DPU does not support per-channel quatization.")
+
+
+            elif node.op_type in PERTENSOR_FAKEQUANTIZER:
+                if node.output[0] not in inp2node:
+                    assert node.output[0] in [l.name for l in graph.output]
+                    inp2node[node.output[0]] = []
+                next_nodes = inp2node[node.output[0]]
+                if len(next_nodes) == 1 and next_nodes[0][1] == 1 and next_nodes[0][0].op_type in ['Gemm', 'Conv']:
+                    # fake quantize for weights
+                    redundant_nodes = self.deal_with_weight_fakequant(node, out2node, inp2node, named_initializer)
+                    tensor_name, scale, zero_point, qmin, qmax = self.parse_qparams(node, name2data)
+                    nodes_to_be_removed.extend(redundant_nodes)
+                    self.clip_weight(node, name2data, inp2node, named_initializer)
+                elif len(next_nodes) == 1 and next_nodes[0][1] == 2 and next_nodes[0][0].op_type in ['Gemm', 'Conv']:
+                    # fake quantize for bias 
+                    assert backend == 'vitis'
+                    redundant_nodes = self.deal_with_weight_fakequant(node, out2node, inp2node, named_initializer)
+                    tensor_name, scale, zero_point, qmin, qmax = self.parse_qparams(node, name2data)
+                    nodes_to_be_removed.extend(redundant_nodes)
+                    self.clip_weight(node, name2data, inp2node, named_initializer)
+                else:
+                    # fake quantize for activations
+                    self.deal_with_activation_fakequant(node, inp2node)
+                    tensor_name, scale, zero_point, qmin, qmax = self.parse_qparams(node, name2data)
+                    for out in graph.output:
+                        if out.name == node.output[0]:
+                            out.name = tensor_name
+
+                    if backend == 'tensorrt':
+                        clip_ranges[tensor_name] = float(scale * max(-qmin, qmax))
+                    elif backend == 'snpe':
+                        clip_ranges[tensor_name] = [
+                            {'bitwidth': int(np.log2(qmax - qmin + 1)),
+                             'min': float(scale * (qmin - zero_point)),
+                             'max': float(scale * (qmax - zero_point))}
+                        ]
+                if backend == 'ppl':
+                    clip_ranges[tensor_name] = {'step': float(scale),
+                                                'zero_point': int(zero_point),
+                                                'min': float(scale * (qmin - zero_point)),
+                                                'max': float(scale * (qmax - zero_point)),
+                                                'bit': int(np.log2(qmax - qmin + 1)),
+                                                'type': "biased",
+                                                }
+                elif backend == 'vitis':
+                    clip_ranges[tensor_name] = {'scale': float(scale)}
+                elif backend == 'ppl-cuda':
+                    clip_ranges[tensor_name] = float(max(-scale * (qmin - zero_point), scale * (qmax - zero_point)))
+
+        for node in nodes_to_be_removed:
+            graph.node.remove(node)
+        # delete initializer
+        out2node, inp2node = update_inp2node_out2node(graph)
+        named_initializer = prepare_initializer(graph)
+        for name, initial_data in named_initializer.items():
+            if name in (out2node.keys() | inp2node.keys()):
+                continue
+            graph.initializer.remove(initial_data)
+
+        clip_ranges = self.post_process_clip_ranges(clip_ranges, graph, inp2node)
+        if backend == 'tensorrt':
+            context = {"tensorrt": {"blob_range": clip_ranges}}
+        elif backend == 'snpe':
+            context = {'activation_encodings': clip_ranges, 'param_encodings': {}}
+        elif backend == 'ppl':
+            context = {"ppl": clip_ranges}
+        elif backend == 'vitis':
+            context = {'vitis': clip_ranges}
+        elif backend == 'ppl-cuda':
+            context = {'ppl-cuda': clip_ranges}
+        output_path = os.path.dirname(onnx_path)
+        context_filename = os.path.join(output_path, '{}_clip_ranges.json'.format(model_name))
+        with open(context_filename, 'w') as f:
+            json.dump(context, f, indent=4)
+        onnx_filename = os.path.join(output_path, '{}_deploy_model.onnx'.format(model_name))
+        onnx.save(model, onnx_filename)
+        if backend == 'ppl-cuda':
+            with open(context_filename, 'w') as f:
+                for k, v in clip_ranges.items():
+                    f.write('{}: {}\n'.format(k, v))
+        if backend == 'vitis':
+            logger.info(f"To finish xmodel converting process, call \
+                $ mqbench.deploy.convert_xir -Q {onnx_filename} -C {onnx_path} -N <name> \
+                    in the mqbench docker built from Dockerfile")
+        logger.info("Finish deploy process.")
+
+
+remove_fakequantize_and_collect_params = LinearQuantizer_process().remove_fakequantize_and_collect_params
diff --git a/mqbench/deploy/deploy_nnie.py b/mqbench/deploy/deploy_nnie.py
new file mode 100644
index 0000000..ea41f2b
--- /dev/null
+++ b/mqbench/deploy/deploy_nnie.py
@@ -0,0 +1,102 @@
+import json
+import os
+
+
+import onnx
+import numpy as np
+from onnx import numpy_helper
+
+from mqbench.utils.logger import logger
+from mqbench.deploy.common import (
+    update_inp2node_out2node,
+    prepare_initializer,
+    prepare_data,
+    OnnxPreprocess,
+    get_constant_inputs
+)
+
+
+class NNIE_process(object):
+    def gen_gfpq_param_file(self, graph, clip_val):
+        nnie_exclude_layer_type = ['Flatten', 'Relu', 'PRelu', 'Sigmoid', 'Reshape',
+                                   'Softmax', 'CaffeSoftmax', 'Clip', 'GlobalAveragePool', 'Mul']
+        interp_layer_cnt = 0
+        gfpq_param_dict = {}
+        for idx, node in enumerate(graph.node):
+            # We can not support NNIE group conv.
+            # Group conv need group-size input params.
+            if node.op_type == 'Conv' and node.attribute[1].i != 1:
+                continue
+
+            layer_input_tensor = []
+            for in_tensor in node.input:
+                if in_tensor in clip_val:
+                    clip_value = clip_val[in_tensor]
+                    layer_input_tensor.append(float(clip_value))
+                # Upsample layer only reserve one input.
+                if node.op_type in ['Upsample', 'DynamicUpsample']:
+                    break
+
+            if node.op_type not in nnie_exclude_layer_type and len(layer_input_tensor) > 0:
+                gfpq_param_dict[node.name] = layer_input_tensor
+
+            # Upsample ---> Upsample + Permute in NNIE.
+            if node.op_type in ['Upsample', 'DynamicUpsample']:
+                interp_layer_name = node.name
+                gfpq_param_dict[interp_layer_name + '_permute_' + str(interp_layer_cnt)] = gfpq_param_dict[interp_layer_name]
+                interp_layer_cnt += 1
+        return gfpq_param_dict
+
+    def remove_fakequantize_and_collect_params(self, onnx_path, model_name):
+        model = onnx.load(onnx_path)
+        graph = model.graph
+        out2node, inp2node = update_inp2node_out2node(graph)
+        name2data = prepare_data(graph)
+        named_initializer = prepare_initializer(graph)
+
+        preprocess = OnnxPreprocess()
+        preprocess.replace_resize_op_with_upsample(graph, out2node)
+        preprocess.remove_fake_pad_op(graph, name2data, inp2node, out2node)
+        out2node, inp2node = update_inp2node_out2node(graph)
+
+        nodes_to_be_removed = []
+        clip_ranges = {}
+        for node in graph.node:
+            if node.op_type == 'NNIEQuantize':
+                next_nodes = inp2node[node.output[0]]
+                if len(next_nodes) == 1 and next_nodes[0][1] == 1 and next_nodes[0][0].op_type in ['Gemm', 'Conv']:
+                    # fake quantize for weights
+                    next_node, idx = next_nodes[0]
+                    next_node.input[idx] = node.input[0]
+                    # clip weights
+                    tensor_name = node.input[0]
+                    data = name2data[tensor_name]
+                    clip_range = name2data[node.input[1]]
+                    new_data = np.clip(data, -clip_range, clip_range)
+                    new_data = numpy_helper.from_array(new_data)
+                    named_initializer[tensor_name].raw_data = new_data.raw_data
+                    logger.info(f'Clip weights {tensor_name} to range [{-clip_range}, {clip_range}].')
+                else:
+                    # fake quantize for activations
+                    clip_ranges[node.input[0]] = name2data[node.input[1]]
+                    for next_node, idx in next_nodes:
+                        next_node.input[idx] = node.input[0]
+
+                nodes_to_be_removed.append(node)
+                nodes_to_be_removed.extend(get_constant_inputs(node, out2node))
+
+        for node in nodes_to_be_removed:
+            graph.node.remove(node)
+
+        gfpq_param_dict = self.gen_gfpq_param_file(graph, clip_ranges)
+
+        output_path = os.path.dirname(onnx_path)
+        gfpq_param_file = os.path.join(output_path, '{}_gfpq_param_dict.json'.format(model_name))
+        with open(gfpq_param_file, 'w') as f:
+            json.dump({"nnie": {"gfpq_param_dict": gfpq_param_dict}}, f, indent=4)
+        onnx_filename = os.path.join(output_path, '{}_deploy_model.onnx'.format(model_name))
+        onnx.save(model, onnx_filename)
+        logger.info("Finish deploy process.")
+
+
+remove_fakequantize_and_collect_params_nnie = NNIE_process().remove_fakequantize_and_collect_params
\ No newline at end of file
diff --git a/mqbench/deploy/deploy_onnx_qlinear.py b/mqbench/deploy/deploy_onnx_qlinear.py
new file mode 100644
index 0000000..3c9d46f
--- /dev/null
+++ b/mqbench/deploy/deploy_onnx_qlinear.py
@@ -0,0 +1,146 @@
+import numpy as np
+import onnx
+import os
+
+from mqbench.utils.logger import logger
+from onnx import numpy_helper
+from .deploy_onnx_qnn import ONNXQNNPass, FAKE_QUANTIZE_OP
+from .common import parse_attrs, prepare_data, prepare_initializer
+
+
+
+class ONNXQLinearPass(ONNXQNNPass):
+    def __init__(self, onnx_model_path):
+        super(ONNXQLinearPass, self).__init__(onnx_model_path)
+        self.onnx_model_path = onnx_model_path
+
+    def parse_qparams(self, node, name2data):
+        tensor_name, scale, zero_point = node.input[:3]
+        scale, zero_point = name2data[scale], name2data[zero_point]
+        if len(node.input) > 3:
+            qmin, qmax = node.input[-2:]
+            qmin, qmax = name2data[qmin], name2data[qmax]
+        elif len(node.attribute) > 0:
+            qparams = parse_attrs(node.attribute)
+            qmin = qparams['quant_min']
+            qmax = qparams['quant_max']
+        else:
+            logger.info(f'qmin and qmax are not found for <{node.name}>!')
+        return tensor_name, scale, zero_point, qmin, qmax
+
+    def clip_weight(self, node, name2data, named_initializer):
+        tensor_name, scale, zero_point, qmin, qmax = self.parse_qparams(node, name2data)
+        data = name2data[tensor_name]
+        clip_range_min = (qmin - zero_point) * scale
+        clip_range_max = (qmax - zero_point) * scale
+        if scale.shape[0] > 1:
+            new_data = []
+            next_node = self.onnx_model.get_tensor_consumer(node.output[0])[0]
+            if next_node.op_type == 'ConvTranspose':
+                for c in range(data.shape[1]):
+                    new_data.append(np.clip(data[:, c], clip_range_min[c], clip_range_max[c]))
+            else:
+                for c in range(data.shape[0]):
+                    new_data.append(np.clip(data[c], clip_range_min[c], clip_range_max[c]))
+            new_data = np.array(new_data)
+            logger.info(f'Clip weights <{tensor_name}> to per-channel ranges.')
+        else:
+            new_data = np.clip(data, clip_range_min, clip_range_max)
+            logger.info(f'Clip weights <{tensor_name}> to range [{clip_range_min}, {clip_range_max}].')
+        new_data = numpy_helper.from_array(new_data)
+        named_initializer[tensor_name].raw_data = new_data.raw_data
+
+    def wrap_onnx_constant(self, data):
+        """warp onnx constant data to iterable numpy object
+
+        Args:
+            data (float or list): data from onnx.get_constant
+
+        Returns:
+            ndarray: iterable numpy array
+        """
+        if type(data) != list:
+            return np.array([data])
+        else:
+            return np.array(data)
+
+    def format_qlinear_dtype_pass(self):
+        name2data = prepare_data(self.onnx_model.graph)
+        named_initializer = prepare_initializer(self.onnx_model.graph)
+        for node in self.onnx_model.graph.node:
+            if node.op_type in FAKE_QUANTIZE_OP:
+                if node.op_type == 'FakeQuantizeLearnablePerchannelAffine':
+                    scale, zero_point = node.input[1], node.input[2]
+                    assert node.attribute[0].name == 'quant_max' and node.attribute[1].name == 'quant_min'
+                    qmax = node.attribute[0].i
+                    qmin = node.attribute[1].i
+                else:
+                    scale, zero_point, qmin, qmax = node.input[-4:]
+                    qmin = self.onnx_model.get_constant(qmin)
+                    qmax = self.onnx_model.get_constant(qmax)
+                assert qmax - qmin in (2 ** 8 - 1, 2 ** 8 - 2), "Only 8 bit quantization support deployment to ONNX."
+                # In onnx, quantize linear node value is within [-128, 127]. This step is to remove inconsistency for
+                # fake quantize node which clips to [-127, 127] by clipping its value to [-127 * scale, 127 * scale]
+                # in advance
+                # Notice: If the node is not a weight node, then the inconsistency between onnx model and pytorch 
+                # model persists.
+                if qmax - qmin == 2 ** 8 - 2 and node.op_type == 'FakeQuantizeLearnablePerchannelAffine':
+                    self.clip_weight(node, name2data, named_initializer)
+                # ? for model mixed constant and initializer
+                # scale
+                try:
+                    scale_proto = self.onnx_model.initializer[scale][0]
+                    if scale_proto.raw_data != b'':
+                        scale_data = self.onnx_model.get_initializer(scale)
+                        self.onnx_model.set_initializer(scale, scale_data.astype(np.float32), raw=False)
+                except KeyError:
+                    scale_data = self.wrap_onnx_constant(self.onnx_model.get_constant(scale))
+                    self.onnx_model.set_initializer(scale, scale_data.astype(np.float32), raw=False)
+                # zero_point
+                try:
+                    zero_point_data = self.onnx_model.get_initializer(zero_point)
+                except KeyError:
+                    zero_point_data = self.wrap_onnx_constant(self.onnx_model.get_constant(zero_point))
+                assert not np.any(zero_point_data != 0), "This pass is only supposed to be used with TensorRT Backend which" \
+                    "does not support asymmetric quantization."
+                if qmin == 0:
+                    self.onnx_model.set_initializer(zero_point, zero_point_data.astype(np.uint8), raw=False)
+                else:
+                    self.onnx_model.set_initializer(zero_point, zero_point_data.astype(np.int8), raw=False)
+
+    def replace_qlinear_layer_pass(self):
+        for node in self.onnx_model.graph.node:
+            if node.op_type in FAKE_QUANTIZE_OP:
+                kwargs = {}
+                if node.op_type == 'FakeQuantizeLearnablePerchannelAffine':
+                    next_node = self.onnx_model.get_tensor_consumer(node.output[0])[0]
+                    if next_node.op_type == 'ConvTranspose':
+                        kwargs['axis'] = 1
+                    else:
+                        kwargs['axis'] = 0
+                quantize_linear_node = onnx.helper.make_node("QuantizeLinear", node.input[:3],
+                                                             [node.name + '_quantized_out'], node.name + '_quantized', **kwargs)
+                dequantize_linear_node = onnx.helper.make_node("DequantizeLinear",
+                                                               [node.name + '_quantized_out'] +
+                                                               quantize_linear_node.input[1:3],
+                                                               node.output,
+                                                               node.name + '_dequantized', **kwargs)
+                self.onnx_model.insert_node_purely(quantize_linear_node)
+                self.onnx_model.insert_node_purely(dequantize_linear_node)
+                self.onnx_model.remove_node_purely(node)
+                self.onnx_model.topologize_graph()
+
+    def run(self):
+        self.onnx_model.topologize_graph()
+        self.format_qlinear_dtype_pass()
+        self.replace_qlinear_layer_pass()
+        self.onnx_model.optimize_model()
+        # Per-channel QuantizeLinear and DequantizeLinear is supported since opset 13
+        self.onnx_model.set_opset_version('', 13)
+        # This gives error with `axis` in QuantizeLinear node
+        try:
+            onnx.checker.check_model(self.onnx_model.model)
+        except onnx.checker.ValidationError as e:
+            logger.critical('The model is invalid: %s' % e)
+        output_dir = os.path.dirname(self.onnx_model_path)
+        self.onnx_model.save_onnx_model(os.path.join(output_dir, 'onnx_quantized_model.onnx'))
diff --git a/mqbench/deploy/deploy_onnx_qnn.py b/mqbench/deploy/deploy_onnx_qnn.py
new file mode 100644
index 0000000..d1ab85d
--- /dev/null
+++ b/mqbench/deploy/deploy_onnx_qnn.py
@@ -0,0 +1,284 @@
+import onnx
+import numpy as np
+
+from mqbench.utils.logger import logger
+from .common import ONNXGraph
+
+
+FAKE_QUANTIZE_OP = ['FakeQuantizeLearnablePerchannelAffine', 'FixedPerChannelAffine', 'FakeQuantizeDSQPerchannel',
+                    'LearnablePerTensorAffine', 'FixedPerTensorAffine', 'FakeQuantizeDSQPertensor']
+
+
+class ONNXQNNPass(object):
+    def __init__(self, onnx_model_path):
+        self.onnx_model = ONNXGraph(onnx_model_path)
+
+    @property
+    def qlinear_op_type(self):
+        return ['QuantizeLinear', 'QLinearConv', 'QLinearAdd', 'QLinearGemm', 'QLinearGlobalAveragePool',
+                'QLinearAveragePool', 'QLinearConcat']
+
+    @staticmethod
+    def attribute_to_kwarg(attribute):
+        '''
+        Convert attribute to kwarg format for use with onnx.helper.make_node.
+            :parameter attribute: attribute in AttributeProto format.
+            :return: attribute in {key: value} format.
+        '''
+        if (attribute.type == 0):
+            raise ValueError('attribute {} does not have type specified.'.format(attribute.name))
+
+        # Based on attribute type definitions from AttributeProto
+        # definition in https://github.com/onnx/onnx/blob/master/onnx/onnx.proto
+        if (attribute.type == 1):
+            value = attribute.f
+        elif (attribute.type == 2):
+            value = attribute.i
+        elif (attribute.type == 3):
+            value = attribute.s
+        elif (attribute.type == 4):
+            value = attribute.t
+        elif (attribute.type == 5):
+            value = attribute.g
+        elif (attribute.type == 6):
+            value = attribute.floats
+        elif (attribute.type == 7):
+            value = attribute.ints
+        elif (attribute.type == 8):
+            value = attribute.strings
+        elif (attribute.type == 9):
+            value = attribute.tensors
+        elif (attribute.type == 10):
+            value = attribute.graphs
+        else:
+            raise ValueError('attribute {} has unsupported type {}.'.format(attribute.name, attribute.type))
+
+        return {attribute.name: value}
+
+    def quantize_weight(self, weight_name, scale_name, zero_point_name):
+        weight = self.onnx_model.get_initializer(weight_name)
+        scale = self.onnx_model.get_initializer(scale_name)
+        zero_point = self.onnx_model.get_initializer(zero_point_name)
+        return ((weight / scale).round() + zero_point).astype(np.uint8)
+
+    def quantize_bias(self, bias, x_scale, w_scale):
+        x_scale = self.onnx_model.get_initializer(x_scale)
+        w_scale = self.onnx_model.get_initializer(w_scale)
+        bias = self.onnx_model.get_initializer(bias)
+        return (bias / (x_scale * w_scale)).astype(np.int32)
+
+    @property
+    def node_without_qparams(self):
+        return ['Flatten']
+
+    def replace_conv_gemm(self, node, idx, is_conv):
+        # Input scale
+        qlinear_conv_inputs = []
+        input_fake_quant_node = self.onnx_model.get_tensor_producer(node.input[0])
+        assert input_fake_quant_node.op_type in FAKE_QUANTIZE_OP
+        x_scale, x_zero_point = input_fake_quant_node.input[1], input_fake_quant_node.input[2]
+        # Output scale
+        qlinear_conv_output = node.output
+        y_scale, y_zero_point = self.get_node_output_qparams(node)
+        # Weight scale
+        weight_fake_quant_node = self.onnx_model.get_tensor_producer(node.input[1])
+        w_scale, w_zero_point = weight_fake_quant_node.input[1], weight_fake_quant_node.input[2]
+        weight_name = weight_fake_quant_node.input[0]
+        W = self.quantize_weight(weight_name, w_scale, w_zero_point)
+        self.onnx_model.set_initializer(weight_name, W)
+        qlinear_conv_inputs.extend([node.input[0], x_scale, x_zero_point,
+                                    weight_name, w_scale, w_zero_point,
+                                    y_scale, y_zero_point])
+        # Bias
+        if len(node.input) == 3:
+            bias_name = node.input[2]
+            B = self.quantize_bias(bias_name, x_scale, w_scale)
+            self.onnx_model.set_initializer(bias_name, B)
+            qlinear_conv_inputs.append(bias_name)
+        kwargs = {}
+        for attribute in node.attribute:
+            kwargs.update(ONNXQNNPass.attribute_to_kwarg(attribute))
+        node_type = "QLinearConv" if is_conv else "QLinearGemm"
+        qlinear_conv_node = onnx.helper.make_node(node_type, 
+                                                  qlinear_conv_inputs,
+                                                  qlinear_conv_output,
+                                                  node.name + '_quantized',
+                                                  **kwargs)
+        self.onnx_model.remove_node_purely(node)
+        self.onnx_model.remove_node_purely(weight_fake_quant_node)
+        self.onnx_model.insert_node_purely(qlinear_conv_node, idx)
+        self.onnx_model.topologize_graph()
+
+    def replace_add_to_qlinearadd(self, node, idx):
+        # First input
+        qlinear_add_input = []
+        qlinear_add_output = node.output
+        first_input_node = self.onnx_model.get_tensor_producer(node.input[0])
+        assert first_input_node.op_type in FAKE_QUANTIZE_OP
+        first_input_quantized = first_input_node.output[0]
+        first_scale = first_input_node.input[1]
+        first_zero_point = first_input_node.input[2]
+        # Second input
+        second_input_node = self.onnx_model.get_tensor_producer(node.input[1])
+        assert second_input_node.op_type in FAKE_QUANTIZE_OP
+        second_input_quantized = second_input_node.output[0]
+        second_scale = second_input_node.input[1]
+        second_zero_point = second_input_node.input[2]
+        # Output
+        output_scale, output_zero_point = self.get_node_output_qparams(node)
+        qlinear_add_input.extend([first_input_quantized, first_scale, first_zero_point,
+                                  second_input_quantized, second_scale, second_zero_point,
+                                  output_scale, output_zero_point])
+        kwargs = {}
+        for attribute in node.attribute:
+            kwargs.update(ONNXQNNPass.attribute_to_kwarg(attribute))
+        qlinear_add_node = onnx.helper.make_node("QLinearAdd", 
+                                                 qlinear_add_input,
+                                                 qlinear_add_output,
+                                                 node.name + '_quantized',
+                                                 domain='com.microsoft',
+                                                 **kwargs)
+        self.onnx_model.insert_node_purely(qlinear_add_node, idx)
+        self.onnx_model.remove_node_purely(node)
+        self.onnx_model.topologize_graph()
+
+    def replace_pool_to_qlinearpool(self, node, idx, is_global):
+        qlinear_pool_input = []
+        prev_node = self.onnx_model.get_tensor_producer(node.input[0])
+        assert prev_node.op_type in FAKE_QUANTIZE_OP
+        x_scale, x_zero_point = prev_node.input[1], prev_node.input[2]
+        y_scale, y_zero_point = self.get_node_output_qparams(node)
+        qlinear_pool_input.extend([node.input[0], x_scale, x_zero_point,
+                                   y_scale, y_zero_point])
+        kwargs = {}
+        for attribute in node.attribute:
+            kwargs.update(ONNXQNNPass.attribute_to_kwarg(attribute))
+        qlinear_add_output = node.output
+        node_type = "QLinearGlobalAveragePool" if is_global else "QLinearAveragePool"
+        qlinear_pool_node = onnx.helper.make_node(node_type, 
+                                                  qlinear_pool_input,
+                                                  qlinear_add_output,
+                                                  node.name + '_quantized',
+                                                  domain='com.microsoft',
+                                                  **kwargs)
+        self.onnx_model.insert_node_purely(qlinear_pool_node, idx)
+        self.onnx_model.remove_node_purely(node)
+        self.onnx_model.topologize_graph()
+
+    def get_node_output_qparams(self, node):
+        fake_quantize_node = self.onnx_model.get_tensor_consumer(node.output[0])[0]
+        while fake_quantize_node.op_type not in FAKE_QUANTIZE_OP:
+            assert fake_quantize_node.op_type in self.node_without_qparams
+            fake_quantize_node = self.onnx_model.get_tensor_consumer(fake_quantize_node.output[0])[0]
+        return fake_quantize_node.input[1], fake_quantize_node.input[2]
+
+    def replace_op_pass(self):
+        # Replace Conv / Gemm / Add / AvgPool / Concat / LeakyRelu.
+        for idx, node in enumerate(self.onnx_model.graph.node):
+            if node.op_type == 'Conv':
+                self.replace_conv_gemm(node, idx, is_conv=True)
+            if node.op_type == 'Gemm':
+                pass
+                # onnxruntime and tvm is not supported yet.
+                # self.replace_conv_gemm(node, idx, is_conv=False)
+            if node.op_type == 'Add':
+                self.replace_add_to_qlinearadd(node, idx)
+            if node.op_type == 'GlobalAveragePool':
+                self.replace_pool_to_qlinearpool(node, idx, is_global=True)
+            if node.op_type == 'AveragePool':
+                self.replace_pool_to_qlinearpool(node, idx, is_global=False)
+            # TODO
+            if node.op_type == 'Concat':
+                pass
+            if node.op_type == 'LeakyRelu':
+                pass
+
+    def replace_qlinear_layer_pass(self):
+        # Replace FakeQuantize
+        def search_and_replace_input(next_node, name, new_name):
+            for idx, _input_name in enumerate(next_node.input):
+                if _input_name == name:
+                    next_node.input[idx] = new_name
+
+        for node in self.onnx_model.graph.node:
+            if node.op_type in FAKE_QUANTIZE_OP:
+                prev_node = self.onnx_model.get_tensor_producer(node.input[0])
+                next_node_list = self.onnx_model.get_tensor_consumer(node.output[0])
+                quantize_node = None
+                dequantize_node = None
+                for next_node in next_node_list:
+                    if prev_node != 'INPUT_TOKEN' and prev_node.op_type in self.qlinear_op_type and \
+                            next_node != 'OUTPUT_TOKEN' and next_node.op_type in self.qlinear_op_type:
+                        search_and_replace_input(next_node, node.output[0], node.input[0])
+                    elif prev_node != 'INPUT_TOKEN' and prev_node.op_type in self.qlinear_op_type and \
+                            next_node == 'OUTPUT_TOKEN':
+                        if dequantize_node is None:
+                            output_value_info = [f'{node.output[0]}_DequantizeLinear']
+                            dequantize_node = onnx.helper.make_node("DequantizeLinear",
+                                                                    node.input[0:3],
+                                                                    output_value_info,
+                                                                    ('input' if prev_node == 'INPUT_TOKEN' else prev_node.name) + '_dequantized')
+                            self.onnx_model.insert_node_purely(dequantize_node)
+                    else:
+                        if quantize_node is None:
+                            output_value_info = [f'{node.output[0]}_QuantizeLinear']
+                            quantize_node = onnx.helper.make_node("QuantizeLinear",
+                                                                  node.input[0:3],
+                                                                  output_value_info,
+                                                                  ('input' if prev_node == 'INPUT_TOKEN' else prev_node.name) + '_quantized')
+                            self.onnx_model.insert_node_purely(quantize_node)
+                        search_and_replace_input(next_node, node.output[0], quantize_node.output[0])
+                self.onnx_model.remove_node_purely(node)
+                self.onnx_model.topologize_graph()
+
+    def merge_relu_pass(self):
+        for node in self.onnx_model.graph.node:
+            if node.op_type == 'Relu':
+                next_node = self.onnx_model.get_tensor_consumer(node.output[0])[0]
+                assert next_node.op_type in FAKE_QUANTIZE_OP
+                # Input idx2 is zero point.
+                self.onnx_model.set_initializer(next_node.input[2], np.array([0], dtype=np.uint8), raw=False)
+                self.onnx_model.remove_node_purely(node)
+                next_node.input[0] = node.input[0]
+            if node.op_type == 'Clip':
+                next_node = self.onnx_model.get_tensor_consumer(node.output[0])[0]
+                assert next_node.op_type in FAKE_QUANTIZE_OP
+                # Input idx2 is zero point.
+                scale = self.onnx_model.get_initializer(next_node.input[1])
+                scale = min(scale, 6.0 / 255)
+                self.onnx_model.set_initializer(next_node.input[1], np.array([scale], dtype=np.float32), raw=False)
+                self.onnx_model.set_initializer(next_node.input[2], np.array([0], dtype=np.uint8), raw=False)
+                self.onnx_model.remove_node_purely(node)
+                next_node.input[0] = node.input[0]
+        self.onnx_model.topologize_graph()
+
+    def format_qlinear_dtype_pass(self):
+        for node in self.onnx_model.graph.node:
+            if node.op_type in FAKE_QUANTIZE_OP:
+                scale, zero_point, qmin, qmax = node.input[1], node.input[2], node.input[3], node.input[4]
+                qmin = self.onnx_model.get_constant(qmin)
+                qmax = self.onnx_model.get_constant(qmax)
+                assert qmax - qmin == 2 ** 8 - 1, "Only 8 bit quantization support deploy to QNN."
+                scale_proto = self.onnx_model.initializer[scale][0]
+                if scale_proto.raw_data != b'' and scale_proto.dims[0] == 1:
+                    scale_data = self.onnx_model.get_initializer(scale)
+                    self.onnx_model.set_initializer(scale, scale_data.astype(np.float32), raw=False)
+                zero_point_proto = self.onnx_model.initializer[zero_point][0]
+                zero_point_data = self.onnx_model.get_initializer(zero_point)
+                # Align sym and asym scheme.
+                zero_point_data = (zero_point_data - qmin).reshape((1,))
+                self.onnx_model.set_initializer(zero_point, zero_point_data.astype(np.uint8), raw=False)
+
+    def run(self, model_name):
+        self.format_qlinear_dtype_pass()
+        self.merge_relu_pass()
+        self.replace_op_pass()
+        self.replace_qlinear_layer_pass()
+        self.onnx_model.optimize_model()
+        self.onnx_model.set_opset_version('com.microsoft', 1)
+
+        try:
+            onnx.checker.check_model(self.onnx_model.model)
+        except onnx.checker.ValidationError as e:
+            logger.critical('The model is invalid: %s' % e)
+        self.onnx_model.save_onnx_model('{}.onnx'.format(model_name))
diff --git a/mqbench/deploy/deploy_openvino.py b/mqbench/deploy/deploy_openvino.py
new file mode 100644
index 0000000..bec664c
--- /dev/null
+++ b/mqbench/deploy/deploy_openvino.py
@@ -0,0 +1,130 @@
+import os
+
+import onnx
+import numpy as np
+from onnx import numpy_helper
+
+from onnx import helper
+
+from mqbench.utils.logger import logger
+from mqbench.deploy.common import (
+    update_inp2node_out2node,
+    prepare_data,
+    OnnxPreprocess,
+    ONNXGraph,
+    get_constant_inputs,
+    parse_attrs
+)
+
+PERCHANNEL_FAKEQUANTIZER = ['FakeQuantizeLearnablePerchannelAffine', 
+                            'FixedPerChannelAffine',
+                            'FakeQuantizeDSQPerchannel']
+PERTENSOR_FAKEQUANTIZER = ['LearnablePerTensorAffine', 
+                           'FixedPerTensorAffine',
+                           'FakeQuantizeDSQPertensor',
+                           'FakeQuantizeTqtAffine']
+ALL_FAKEQUANTIZER = PERCHANNEL_FAKEQUANTIZER + PERTENSOR_FAKEQUANTIZER
+
+
+class OPENVINO_process(object):
+
+    def parse_qparams(self, node, name2data):
+        tensor_name, scale, zero_point = node.input[:3]
+        scale, zero_point = name2data[scale], name2data[zero_point]
+        if len(node.input) > 3:
+            qmin, qmax = node.input[-2:]
+            qmin, qmax = name2data[qmin], name2data[qmax]
+        elif len(node.attribute) > 0:
+            qparams = parse_attrs(node.attribute)
+            qmin = qparams['quant_min']
+            qmax = qparams['quant_max']
+        else:
+            logger.info(f'qmin and qmax are not found for <{node.name}>!')
+            qmax = qmin = None
+        return tensor_name, scale, zero_point, qmin, qmax
+
+    def replace_fakequantize_and_collect_params(self, onnx_path, model_name):
+        onnx_graph = ONNXGraph(onnx_path)
+        model = onnx_graph.model
+        graph = model.graph
+        out2node, inp2node = update_inp2node_out2node(graph)
+        name2data = prepare_data(graph)
+
+        preprocess = OnnxPreprocess()
+        preprocess.remove_fake_pad_op(graph, name2data, inp2node, out2node)
+        out2node, inp2node = update_inp2node_out2node(graph)
+
+        nodes_to_be_removed = []
+        node_defs = []
+        insert_initializer_names = set()
+        for node in graph.node:
+            if node.op_type in ALL_FAKEQUANTIZER:
+                nodes_to_be_removed.append(node)
+                nodes_to_be_removed.extend(get_constant_inputs(node, out2node))
+
+                tensor_name, scale, zero_point, qmin, qmax = self.parse_qparams(node, name2data)
+                qmax = int(qmax)
+                qmin = int(qmin)
+                levels = qmax - qmin + 1
+                # adjust weight levels
+                if levels == 128:
+                    levels = 256
+                    qmax = qmax * 2 + 1
+                    qmin = qmin * 2
+                output_name = node.output[0]
+                # Create a node (FakeQuantize)
+                fakeq_inputnames = [item % tensor_name for item in ['input_min_%s', 'input_max_%s', 'output_min_%s', 'output_max_%s']]
+                node_def = helper.make_node(
+                    'FakeQuantize',  # node name
+                    [tensor_name, *fakeq_inputnames],  # inputs
+                    [output_name],  # outputs
+                    levels=levels,  # Attributes
+                    domain="org.openvinotoolkit",
+                    name=node.name
+                )
+                node_defs.append(node_def)
+                scale = np.abs(np.asarray(scale, dtype=np.float64).reshape(-1))
+                zero_point = np.clip(np.asarray(np.round(zero_point), dtype=np.int32).reshape(-1), a_min=qmin, a_max=qmax)
+
+                qrange = float(qmax - qmin)
+                input_range = scale * qrange
+                input_high = (qmax - zero_point).astype(np.float64) * input_range / qrange
+                input_low = input_high - input_range
+                input_low_size = input_low.size
+
+                try:
+                    next_node = inp2node[node.output[0]][0][0]
+                    # node for save weights
+                    fake_node = out2node[next_node.input[1]]
+                    tensor = name2data[fake_node.input[0]]
+                    shape_length = len(tensor.shape)
+                    new_shape = [-1, ] + [1, ] * (shape_length - 1)
+                except Exception as e:
+                    new_shape = [-1, ]
+
+                if input_low_size != 1:
+                    input_low = input_low.reshape(*new_shape)
+                    input_high = input_high.reshape(*new_shape)
+                input_low = input_low.astype(np.float32)
+                input_high = input_high.astype(np.float32)
+                for initializer_name, value_tensor in zip(fakeq_inputnames, [input_low, input_high, input_low, input_high]):
+                    if initializer_name in insert_initializer_names:
+                        continue
+                    initializer = numpy_helper.from_array(value_tensor)
+                    initializer.name = initializer_name
+                    insert_initializer_names.add(initializer_name)
+                    graph.initializer.append(initializer)
+
+        for node in nodes_to_be_removed:
+            graph.node.remove(node)
+        graph.node.extend(node_defs)
+        onnx_graph.topologize_graph()
+        onnx_graph.prepare_initializer()
+        onnx_graph.optimize_model()
+        output_path = os.path.dirname(onnx_path)
+        onnx_filename = os.path.join(output_path, '{}_deploy_model.onnx'.format(model_name))
+        onnx.save(model, onnx_filename)
+        logger.info("Finish deploy process.")
+
+
+replace_fakequantize_and_collect_params_openvino = OPENVINO_process().replace_fakequantize_and_collect_params
\ No newline at end of file
diff --git a/mqbench/deploy/deploy_tengine.py b/mqbench/deploy/deploy_tengine.py
new file mode 100644
index 0000000..ab63ef2
--- /dev/null
+++ b/mqbench/deploy/deploy_tengine.py
@@ -0,0 +1,126 @@
+import os
+from collections import OrderedDict
+
+from ..utils.logger import logger
+from .deploy_linear import (
+    LinearQuantizer_process,
+    ALL_FAKEQUANTIZER,
+    PERCHANNEL_FAKEQUANTIZER,
+    PERTENSOR_FAKEQUANTIZER
+)
+from .common import (
+    update_inp2node_out2node,
+    prepare_initializer,
+    prepare_data,
+    OnnxPreprocess,
+    get_constant_inputs
+)
+
+import onnx
+from onnx import numpy_helper
+try:
+    from onnxsim import simplify
+except ModuleNotFoundError:
+    logger.warn('onnxsim not found, if you want to use deploy_tengine, please install it.')
+
+
+
+class Tengine_process(LinearQuantizer_process):
+
+    @staticmethod
+    def get_constant(node: onnx.NodeProto):
+        return numpy_helper.to_array(node.attribute[0].t).tolist()
+
+    def remove_fakequantize_and_collect_params(self, onnx_path, model_name):
+        model = onnx.load(onnx_path)
+        graph = model.graph
+        out2node, inp2node = update_inp2node_out2node(graph)
+        name2data = prepare_data(graph)
+        named_initializer = prepare_initializer(graph)
+
+        preprocess = OnnxPreprocess()
+        preprocess.remove_fake_pad_op(graph, name2data, inp2node, out2node)
+        out2node, inp2node = update_inp2node_out2node(graph)
+
+        quant_params = OrderedDict()
+        nodes_to_be_removed = []
+        for node in graph.node:
+            if node.op_type in ALL_FAKEQUANTIZER:
+                nodes_to_be_removed.append(node)
+                nodes_to_be_removed.extend(get_constant_inputs(node, out2node))
+
+            if node.op_type in PERCHANNEL_FAKEQUANTIZER:
+                # fake quantize for weights, suppose per-channel quantize only for weight
+                redundant_nodes = self.deal_with_weight_fakequant(node, out2node, inp2node, named_initializer)
+                nodes_to_be_removed.extend(redundant_nodes)
+                self.clip_weight(node, name2data, inp2node, named_initializer)
+            elif node.op_type in PERTENSOR_FAKEQUANTIZER:
+                if node.output[0] not in inp2node:
+                    assert node.output[0] in [x.name for x in graph.output]
+                    inp2node[node.output[0]] = []
+
+                next_nodes = inp2node[node.output[0]]
+                if len(next_nodes) == 1 and next_nodes[0][1] == 1 and next_nodes[0][0].op_type in ['Gemm', 'Conv']:
+                    # fake quantize for weights
+                    redundant_nodes = self.deal_with_weight_fakequant(node, out2node, inp2node, named_initializer)
+                    tensor_name, scale, zero_point, qmin, qmax = self.parse_qparams(node, name2data)
+                    nodes_to_be_removed.extend(redundant_nodes)
+                    self.clip_weight(node, name2data, inp2node, named_initializer)
+                else:
+                    # fake quantize for activations
+                    self.deal_with_activation_fakequant(node, inp2node)
+                    tensor_name, scale, zero_point, qmin, qmax = self.parse_qparams(node, name2data)
+                    for out in graph.output:
+                        if out.name == node.output[0]:
+                            out.name = tensor_name
+
+                    quant_params[tensor_name] = [
+                        float(scale),
+                        int(zero_point)
+                    ]
+
+                    # detect fusion for tengine graph
+                    # since tengine convert tool will optimize graph
+                    # by fusing conv+relu, conv+relu6
+                    # ref: https://github.com/OAID/Tengine/blob/cdb4ccf77c04a0a771ec6a43631b9d25acd2bae1/tools/convert_tool/utils/graph_optimizer/graph_opt.cpp#L941
+                    pre_node = out2node.get(tensor_name, None)
+                    if pre_node and pre_node.op_type in {"Clip", "ReLU"}:
+                        # suppose onnx version be 11
+                        # for relu6
+                        if pre_node.op_type == "Clip" and \
+                            not (self.get_constant(out2node[pre_node.input[1]]) == 0 and 
+                                 self.get_constant(out2node[pre_node.input[2]]) == 6):
+                            continue
+
+                        conv_node = out2node[pre_node.input[0]]
+                        if conv_node.op_type == "Conv":
+                            conv_tensor_name = conv_node.output[0]
+                            quant_params[conv_tensor_name] = quant_params[tensor_name]
+
+        for node in nodes_to_be_removed:
+            graph.node.remove(node)
+        named_initializer = prepare_initializer(graph)
+        for name, initial_data in named_initializer.items():
+            if name in (out2node.keys() | inp2node.keys()):
+                continue
+            graph.initializer.remove(initial_data)
+
+        # TODO: softmax
+        quant_params = self.post_process_clip_ranges(quant_params, graph, inp2node)
+        output_path = os.path.dirname(onnx_path)
+        context_filename = os.path.join(output_path, f"{model_name}_for_tengine.scale")
+        with open(context_filename, "w") as f:
+            for name, value in quant_params.items():
+                scale, zero_point = value
+                f.write(f"{name} {scale} {zero_point}\n")
+
+        model_opt, check = simplify(model)
+        assert check, "Simplified ONNX model could not be validated"
+
+        onnx_filename = os.path.join(output_path, f"{model_name}_for_tengine.onnx")
+        onnx.save(model_opt, onnx_filename)
+
+        logger.info("Finish deploy process.")
+
+
+remove_fakequantize_and_collect_params_tengine = Tengine_process().remove_fakequantize_and_collect_params
diff --git a/mqbench/fake_quantize/__init__.py b/mqbench/fake_quantize/__init__.py
new file mode 100644
index 0000000..20296c4
--- /dev/null
+++ b/mqbench/fake_quantize/__init__.py
@@ -0,0 +1,10 @@
+from .dorefa import DoReFaFakeQuantize
+from .dsq import DSQFakeQuantize
+from .fixed import FixedFakeQuantize
+from .lsq import LearnableFakeQuantize
+from .nnie import NNIEFakeQuantize
+from .pact import PACTFakeQuantize
+from .tqt import TqtFakeQuantize
+from .adaround_quantizer import AdaRoundFakeQuantize
+from .qdrop_quantizer import QDropFakeQuantize
+from .pure_hooker import PureHooker
\ No newline at end of file
diff --git a/mqbench/fake_quantize/adaround_quantizer.py b/mqbench/fake_quantize/adaround_quantizer.py
new file mode 100644
index 0000000..eb641c2
--- /dev/null
+++ b/mqbench/fake_quantize/adaround_quantizer.py
@@ -0,0 +1,135 @@
+import torch
+from torch.nn.parameter import Parameter
+
+from mqbench.fake_quantize.quantize_base import QuantizeBase, _version_under_1100 
+from mqbench.utils.hook import PerChannelLoadHook
+
+def _rectified_sigmoid(alpha, zeta, gamma):
+    """Function to generate rounding mask.
+
+    Args:
+        x (torch.Tensor):
+        zeta (torch.Tensor):
+        gamma (torch.Tensor):
+
+    Returns:
+        torch.Tensor:
+    """
+    return ((zeta - gamma) * torch.sigmoid(alpha) + gamma).clamp(0, 1)
+
+
+def adaround_forward(x, scale, zero_point, quant_min, quant_max, ch_axis, alpha, zeta, gamma, hard_value=False):
+    if ch_axis != -1:
+        new_shape = [1] * len(x.shape)
+        new_shape[ch_axis] = x.shape[ch_axis]
+        scale = scale.reshape(new_shape)
+        zero_point = zero_point.reshape(new_shape)
+    x = torch.floor(x / scale)
+    if hard_value:
+        x += (alpha >= 0).float()
+    else:
+        x += _rectified_sigmoid(alpha, zeta, gamma)
+    x += zero_point
+    x = torch.clamp(x, quant_min, quant_max)
+    x = (x - zero_point) * scale
+    return x
+
+
+class AdaRoundFakeQuantize(QuantizeBase):
+    """This is based on the fixedpointquantize. Because adaround only works at FC and Conv, there is an extra variables
+    to define the state and could only serve as weight quantizer.
+    self.adaround basicquantize (False) adaroundquantize(True)
+    """
+
+    def __init__(self, observer, **observer_kwargs):
+        super(AdaRoundFakeQuantize, self).__init__(observer, **observer_kwargs)
+        self.register_buffer('scale', torch.tensor([1.0], dtype=torch.float))
+        self.register_buffer('zero_point', torch.tensor([0], dtype=torch.int))
+        self.adaround = False
+        self.load_state_dict_hook = PerChannelLoadHook(self, hook_param=['scale', 'zero_point', 'alpha'])
+
+    def init(self, weight_tensor: torch.Tensor, round_mode='learned_hard_sigmoid', ):
+        self.adaround = True
+        self.observer_enabled[0] = 0
+        self.fake_quant_enabled[0] = 1
+        self.round_mode = round_mode
+
+        # self.soft_targets = False  # delete this
+        self.gamma, self.zeta = -0.1, 1.1
+        self.init_alpha(x=weight_tensor.data.clone())
+
+    def init_alpha(self, x: torch.Tensor):
+        if self.ch_axis != -1:
+            new_shape = [1] * len(x.shape)
+            new_shape[self.ch_axis] = x.shape[self.ch_axis]
+            scale = self.scale.data.reshape(new_shape)
+        else:
+            scale = self.scale.data
+        x_floor = torch.floor(x / scale)
+        if self.round_mode == 'learned_hard_sigmoid':
+            print('Init alpha to be FP32')
+            rest = (x / scale) - x_floor  # rest of rounding [0, 1)
+            alpha = -torch.log((self.zeta - self.gamma) / (rest - self.gamma) - 1)  # => sigmoid(alpha) = rest
+            self.alpha = Parameter(alpha)
+        else:
+            raise NotImplementedError
+
+    def rectified_sigmoid(self):
+        """Function to generate rounding mask.
+
+        Args:
+            x (torch.Tensor):
+            zeta (torch.Tensor):
+            gamma (torch.Tensor):
+
+        Returns:
+            torch.Tensor:
+        """
+        return ((self.zeta - self.gamma) * torch.sigmoid(self.alpha) + self.gamma).clamp(0, 1)
+
+    def get_hard_value(self, X):
+        X = adaround_forward(X, self.scale.data, self.zero_point.data.long(), self.quant_min,
+                             self.quant_max, self.ch_axis, self.alpha, self.zeta, self.gamma, hard_value=True)
+        return X
+
+    def forward(self, X):
+        if self.observer_enabled[0] == 1:
+            self.activation_post_process(X.detach())
+            _scale, _zero_point = self.calculate_qparams()
+            _scale, _zero_point = _scale.to(self.scale.device), _zero_point.to(self.zero_point.device)
+            if self.scale.shape != _scale.shape:
+                self.scale.resize_(_scale.shape)
+                self.zero_point.resize_(_zero_point.shape)
+            self.scale.copy_(_scale)
+            self.zero_point.copy_(_zero_point)
+
+        if self.fake_quant_enabled[0] == 1:
+            if not self.adaround:
+                if self.is_per_channel:
+                    X = torch.fake_quantize_per_channel_affine(
+                        X, self.scale,
+                        self.zero_point.long() if _version_under_1100 else self.zero_point,
+                        self.ch_axis, self.quant_min, self.quant_max)
+                else:
+                    X = torch.fake_quantize_per_tensor_affine(
+                        X, self.scale.item(), int(self.zero_point.item()),
+                        self.quant_min, self.quant_max)
+            else:
+                if not hasattr(self, 'alpha'):
+                    raise NotImplementedError
+                if self.round_mode == 'learned_hard_sigmoid':
+                    X = adaround_forward(X, self.scale.data, self.zero_point.data.long(), self.quant_min,
+                                         self.quant_max, self.ch_axis, self.alpha, self.zeta, self.gamma)
+                else:
+                    raise NotImplementedError
+        return X
+
+    @torch.jit.export
+    def extra_repr(self):
+        return 'fake_quant_enabled={}, observer_enabled={}, ' \
+               'quant_min={}, quant_max={}, dtype={}, qscheme={}, ch_axis={}, ' \
+               'scale={}, zero_point={}'.format(
+                   self.fake_quant_enabled, self.observer_enabled,
+                   self.quant_min, self.quant_max,
+                   self.dtype, self.qscheme, self.ch_axis, self.scale if self.ch_axis == -1 else 'List',
+                   self.zero_point if self.ch_axis == -1 else 'List')
diff --git a/mqbench/fake_quantize/dorefa.py b/mqbench/fake_quantize/dorefa.py
new file mode 100644
index 0000000..d570edd
--- /dev/null
+++ b/mqbench/fake_quantize/dorefa.py
@@ -0,0 +1,37 @@
+import torch
+
+from mqbench.fake_quantize.quantize_base import QuantizeBase
+
+
+_version_under_1100 = int(torch.__version__.split('.')[1]) < 10
+
+class DoReFaFakeQuantize(QuantizeBase):
+    def __init__(self, observer, **observer_kwargs):
+        super(DoReFaFakeQuantize, self).__init__(observer, **observer_kwargs)
+        self.register_buffer('scale', torch.tensor([1.0], dtype=torch.float))
+        self.register_buffer('zero_point', torch.tensor([0], dtype=torch.int))
+
+    def forward(self, X):
+        X = torch.tanh(X)
+        X = X.div(X.abs().max() + 1e-5)
+
+        if self.observer_enabled[0] == 1:
+            self.activation_post_process(X.detach())
+            _scale, _zero_point = self.activation_post_process.calculate_qparams()
+            _scale, _zero_point = _scale.to(self.scale.device), _zero_point.to(self.zero_point.device)
+            if self.scale.shape != _scale.shape:
+                self.scale.resize_(_scale.shape)
+                self.zero_point.resize_(_zero_point.shape)
+            self.scale.copy_(_scale)
+            self.zero_point.copy_(_zero_point)
+
+        if self.fake_quant_enabled[0] == 1:
+            if self.is_per_channel:
+                X = torch.fake_quantize_per_channel_affine(
+                    X, self.scale, 
+                    self.zero_point.long() if _version_under_1100 else self.zero_point,
+                    self.ch_axis, self.quant_min, self.quant_max)
+            else:
+                X = torch.fake_quantize_per_tensor_affine(
+                    X, self.scale.item(), int(self.zero_point.item()), self.quant_min, self.quant_max)
+        return X
\ No newline at end of file
diff --git a/mqbench/fake_quantize/dsq.py b/mqbench/fake_quantize/dsq.py
new file mode 100644
index 0000000..f087e33
--- /dev/null
+++ b/mqbench/fake_quantize/dsq.py
@@ -0,0 +1,132 @@
+import math
+
+import torch
+from torch.nn.parameter import Parameter
+
+from mqbench.fake_quantize.quantize_base import QuantizeBase
+from mqbench.utils import is_symmetric_quant, is_tracing_state
+from mqbench.utils.hook import PerChannelLoadHook
+
+
+def dsq_function_per_tensor(x, scale, zero_point, quant_min, quant_max, alpha):
+    tanh_scale = 1 / (1 - alpha)
+    tanh_k = math.log((tanh_scale + 1) / (tanh_scale - 1))
+
+    x = x / scale + zero_point
+    x = torch.clamp(x, quant_min, quant_max)
+    x = x.floor() + (tanh_scale * torch.tanh(tanh_k * (x - x.floor() - 0.5))) * 0.5 + 0.5  # 软
+    x = (x.round() - x).detach() + x  # 硬
+    x = (x - zero_point) * scale
+
+    return x
+
+
+def dsq_function_per_channel(x, scale, zero_point, quant_min, quant_max, ch_axis, alpha):
+
+    new_shape = [1] * len(x.shape)
+    new_shape[ch_axis] = x.shape[ch_axis]
+    scale = scale.reshape(new_shape)
+    zero_point = zero_point.reshape(new_shape)
+
+    tanh_scale = 1 / (1 - alpha)
+    tanh_k = math.log((tanh_scale + 1) / (tanh_scale - 1))
+
+    x = x / scale + zero_point
+    x = torch.clamp(x, quant_min, quant_max)
+    x = x.floor() + (tanh_scale * torch.tanh(tanh_k * (x - x.floor() - 0.5))) * 0.5 + 0.5
+    x = (x.round() - x).detach() + x
+    x = (x - zero_point) * scale
+
+    return x
+
+
+class DSQFakeQuantize(QuantizeBase):
+    def __init__(self, observer, alpha=0.4, **observer_kwargs):
+        super(DSQFakeQuantize, self).__init__(observer, **observer_kwargs)
+        self.scale = Parameter(torch.tensor([1.0]))# TODO 这玩意应该是可学习参数
+        self.register_buffer('zero_point', torch.tensor([0.]))
+        self.alpha = Parameter(torch.tensor([alpha]))  # 这个真有问题,得限制在0~0.5
+        # self.alpha = alpha
+        self.load_state_dict_hook = PerChannelLoadHook(self)
+        self.register_buffer('eps', torch.tensor([torch.finfo(torch.float32).eps]))
+        self.compute_qloss = True
+        
+        
+    def forward(self, X):
+        # if self.training:  # NOTE晕，怎么还能随时统计更新量化参数，得换掉
+        #     self.activation_post_process(X.detach())
+        #     _scale, _zero_point = self.activation_post_process.calculate_qparams()
+        #     _scale, _zero_point = _scale.to(self.scale.device), _zero_point.to(self.zero_point.device)
+        #     if self.scale.shape != _scale.shape:
+        #         self.scale.resize_(_scale.shape)
+        #         self.zero_point.resize_(_zero_point.shape)
+        #     self.scale.copy_(_scale)
+        #     self.zero_point.copy_(_zero_point.float())
+
+        if self.observer_enabled[0] == 1:
+            self.activation_post_process(X.detach())
+            _scale, _zero_point = self.activation_post_process.calculate_qparams()
+            _scale = _scale.to(self.scale.device)
+            _zero_point = _zero_point.to(self.zero_point.device)
+
+            if self.scale.shape != _scale.shape:
+                self.scale.resize_(_scale.shape)
+                self.zero_point.resize_(_zero_point.shape)
+
+            self.scale.data.copy_(_scale)
+            self.zero_point.data.copy_(_zero_point.float())
+        else:
+            self.scale.data.abs_()
+            self.scale.data.clamp_(min=self.eps.item(), max=1.0)
+            # 也得钳制一下alpha
+            self.alpha.data.abs_()
+            self.alpha.data.clamp_(min=self.eps.item(), max=0.5)
+        
+        X_old = X
+        
+        if self.fake_quant_enabled[0] == 1:  # NOTE 原代码确实是没有区分是否对称量化
+            if is_symmetric_quant(self.qscheme):
+                self.zero_point.data.zero_()
+            else:
+                self.zero_point.data.clamp_(self.quant_min, self.quant_max).float()
+            
+            if self.is_per_channel:
+                if is_tracing_state():
+                    X = FakeQuantizeDSQPerchannel.apply(
+                        X, self.scale, self.zero_point, self.quant_min, self.quant_max, self.ch_axis, self.alpha)
+                else:
+                    X = dsq_function_per_channel(
+                        X, self.scale, self.zero_point, self.quant_min, self.quant_max, self.ch_axis, self.alpha)
+            else:
+                if is_tracing_state():
+                    X = FakeQuantizeDSQPertensor.apply(
+                        X, self.scale, self.zero_point, self.quant_min, self.quant_max, self.alpha)
+                else:
+                    X = dsq_function_per_tensor(
+                        X, self.scale, self.zero_point, self.quant_min, self.quant_max, self.alpha)
+
+            # NOTE 算
+            if self.compute_qloss:
+                self.quantization_loss = (torch.norm(X_old - X, p="fro", dim=1) ** 2).mean()
+                
+        return X
+
+
+class FakeQuantizeDSQPerchannel(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, scale, zero_point, quant_min, quant_max, ch_axis, alpha):
+        return dsq_function_per_channel(x, scale, zero_point, quant_min, quant_max, ch_axis, alpha)
+
+    @staticmethod
+    def symbolic(g, x, scale, zero_point, quant_min, quant_max, ch_axis, alpha):
+        return g.op("::FakeQuantizeDSQPerchannel", x, scale, zero_point, quant_min_i=quant_min, quant_max_i=quant_max, alpha_f=alpha)
+
+
+class FakeQuantizeDSQPertensor(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, scale, zero_point, quant_min, quant_max, alpha):
+        return dsq_function_per_tensor(x, scale, zero_point, quant_min, quant_max, alpha)
+
+    @staticmethod
+    def symbolic(g, x, scale, zero_point, quant_min, quant_max, alpha):
+        return g.op("::FakeQuantizeDSQPertensor", x, scale, zero_point, quant_min_i=quant_min, quant_max_i=quant_max, alpha_f=alpha)
diff --git a/mqbench/fake_quantize/fixed.py b/mqbench/fake_quantize/fixed.py
new file mode 100644
index 0000000..4e2e463
--- /dev/null
+++ b/mqbench/fake_quantize/fixed.py
@@ -0,0 +1,87 @@
+import torch
+
+from mqbench.fake_quantize.quantize_base import QuantizeBase
+from mqbench.utils.hook import PerChannelLoadHook
+
+
+_version_under_1100 = int(torch.__version__.split('.')[1]) < 10
+
+class FixedFakeQuantize(QuantizeBase):  # XXX 原来量化和ob是写到一起的，通过flag来决定哪个会执行
+    """This is actually torch.quantization.FakeQuantize.
+    """
+    def __init__(self, observer, **observer_kwargs):
+        super(FixedFakeQuantize, self).__init__(observer, **observer_kwargs)
+        self.register_buffer('scale', torch.tensor([1.0], dtype=torch.float))  # 这里的定义，说明了其实没法被更新
+        self.register_buffer('zero_point', torch.tensor([0], dtype=torch.int))
+        self.load_state_dict_hook = PerChannelLoadHook(self)
+
+    def forward(self, X):  # XXX 也就是说，在ob阶段，每次使用ob后qparam都会更新！
+        if self.observer_enabled[0] == 1:  # 做Detach，就是避免这个也带进了梯度!
+            self.activation_post_process(X.detach())  # 这个activation post是ob类，module，所以整体就是在进行对应的forward
+            _scale, _zero_point = self.calculate_qparams()
+            _scale, _zero_point = _scale.to(self.scale.device), _zero_point.to(self.zero_point.device)
+            if self.scale.shape != _scale.shape:
+                self.scale.resize_(_scale.shape)
+                self.zero_point.resize_(_zero_point.shape)
+            self.scale.copy_(_scale)
+            self.zero_point.copy_(_zero_point)
+
+        if self.fake_quant_enabled[0] == 1:
+            if self.is_per_channel:
+                X = torch.fake_quantize_per_channel_affine(
+                    X, self.scale,
+                    self.zero_point.long() if _version_under_1100 else self.zero_point,
+                    self.ch_axis, self.quant_min, self.quant_max)
+            else:
+                X = torch.fake_quantize_per_tensor_affine(
+                    X, self.scale.item(), int(self.zero_point.item()),
+                    self.quant_min, self.quant_max)
+        return X
+
+    @torch.jit.export
+    def extra_repr(self):
+        return 'fake_quant_enabled={}, observer_enabled={}, ' \
+               'quant_min={}, quant_max={}, dtype={}, qscheme={}, ch_axis={}, ' \
+               'scale={}, zero_point={}'.format(
+                   self.fake_quant_enabled, self.observer_enabled,
+                   self.quant_min, self.quant_max,
+                   self.dtype, self.qscheme, self.ch_axis, self.scale if self.ch_axis == -1 else 'List', 
+                   self.zero_point if self.ch_axis == -1 else 'List')
+
+    def _save_to_state_dict(self, destination, prefix, keep_vars):
+        # We cannot currently register scalar values as buffers, so need to manually
+        # specify serialization here.
+        super(FixedFakeQuantize, self)._save_to_state_dict(destination, prefix, keep_vars)
+        destination[prefix + 'scale'] = self.scale
+        destination[prefix + 'zero_point'] = self.zero_point
+
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
+                              missing_keys, unexpected_keys, error_msgs):
+        # Removing this function throws an error that the the size of the loaded tensor does not match the original size
+        # i.e., These buffers start out with numel 0 and become numel 1 once they have their first forward pass.
+        local_state = ['scale', 'zero_point']
+        for name in local_state:
+            key = prefix + name
+            if key in state_dict:
+                val = state_dict[key]
+                # Custom handling to allow loading scale and zero_point
+                # of size N into uninitialized buffers of size 0. The
+                # buffers are resized here, and the values are copied in
+                # the default state_dict loading code of the parent.
+                if name == 'scale':
+                    self.scale.resize_(val.shape)
+                else:
+                    assert name == 'zero_point'
+                    self.zero_point.resize_(val.shape)
+                # For torchscript module we need to update the attributes here since we do not
+                # call the `_load_from_state_dict` function defined module.py
+                if torch.jit.is_scripting():
+                    if name == 'scale':
+                        self.scale.copy_(val)
+                    else:
+                        assert name == 'zero_point'
+                        self.zero_point.copy_(val)
+            elif strict:
+                missing_keys.append(key)
+        super(FixedFakeQuantize, self)._load_from_state_dict(state_dict, prefix, local_metadata, strict,
+                                                             missing_keys, unexpected_keys, error_msgs)
\ No newline at end of file
diff --git a/mqbench/fake_quantize/lsq.py b/mqbench/fake_quantize/lsq.py
new file mode 100644
index 0000000..5b2db68
--- /dev/null
+++ b/mqbench/fake_quantize/lsq.py
@@ -0,0 +1,164 @@
+import torch
+from torch.nn.parameter import Parameter
+
+from mqbench.fake_quantize.quantize_base import QuantizeBase
+from mqbench.utils import is_symmetric_quant, is_tracing_state
+from mqbench.utils.hook import PerChannelLoadHook
+import global_placeholder
+
+class LearnableFakeQuantize(QuantizeBase):
+    r""" This is an extension of the FakeQuantize module in fake_quantize.py, which
+    supports more generalized lower-bit quantization and support learning of the scale
+    and zero point parameters through backpropagation. For literature references,
+    please see the class _LearnableFakeQuantizePerTensorOp.
+    In addition to the attributes in the original FakeQuantize module, the _LearnableFakeQuantize
+    module also includes the following attributes to support quantization parameter learning.
+    """
+
+    def __init__(self, observer, scale=1., zero_point=0., use_grad_scaling=True, **observer_kwargs):
+        super(LearnableFakeQuantize, self).__init__(observer, **observer_kwargs)
+        self.use_grad_scaling = use_grad_scaling
+        self.scale = Parameter(torch.tensor([scale]))
+        self.register_buffer('zero_point', torch.tensor([zero_point]))  # NOTE 已改  这里就是不对劲，就应该是buffer，而且grad还会占用显存
+        self.register_buffer('eps', torch.tensor([torch.finfo(torch.float32).eps]))
+        # Check whether the module will load a state dict;
+        # Initialize the shape of per-channel 'scale' and 'zero-point' before copying values
+        self.load_state_dict_hook = PerChannelLoadHook(self)
+        # NOTE test
+        # self.register_buffer('quantization_loss', torch.tensor([0]))
+        self.compute_qloss = False
+
+
+    @torch.jit.export
+    def extra_repr(self):
+        return 'fake_quant_enabled={}, observer_enabled={}, ' \
+               'quant_min={}, quant_max={}, dtype={}, qscheme={}, ch_axis={}, ' \
+               'scale={}, zero_point={}'.format(
+                   self.fake_quant_enabled, self.observer_enabled,
+                   self.quant_min, self.quant_max,
+                   self.dtype, self.qscheme, self.ch_axis, self.scale if self.ch_axis == -1 else 'List[%s]' % str(self.scale.shape),
+                   self.zero_point if self.ch_axis == -1 else 'List')
+
+    def forward(self, X):
+        # Learnable fake quantize have to zero_point.float() to make it learnable.
+        if self.observer_enabled[0] == 1:
+            self.activation_post_process(X.detach())
+            _scale, _zero_point = self.activation_post_process.calculate_qparams()
+            _scale = _scale.to(self.scale.device)
+            _zero_point = _zero_point.to(self.zero_point.device)
+
+            if self.ch_axis != -1:
+                self.scale.data = torch.ones_like(_scale)
+                self.zero_point.data = torch.zeros_like(_zero_point.float())
+
+            self.scale.data.copy_(_scale)
+            self.zero_point.data.copy_(_zero_point.float())
+            
+            # if self.compute_qloss:
+            #     # 计算std，初始化margin
+            #     self.regular_margin.data.copy_(2 * X.std())
+        else:
+            # if self.compute_qloss:
+            #     # 计算std，初始化margin
+            #     self.regular_margin.data.abs_()  # 要求绝对化
+            self.scale.data.abs_()
+            self.scale.data.clamp_(min=self.eps.item())
+
+        # TODO 写能求最小化量化误差的代码
+        X_old = X
+        # if self.compute_qloss:
+        #     X = grad_scale(X, 1+self.scale.detach())
+
+
+        if self.fake_quant_enabled[0] == 1:
+            if is_symmetric_quant(self.qscheme):
+                self.zero_point.data.zero_()
+            else:
+                self.zero_point.data.clamp_(self.quant_min, self.quant_max).float()
+
+            if self.is_per_channel:
+                if self.use_grad_scaling:
+                    grad_factor = 1.0 / (X.numel() / X.shape[self.ch_axis] * self.quant_max) ** 0.5
+                else:
+                    grad_factor = 1.0
+                if is_tracing_state():
+                    X = FakeQuantizeLearnablePerchannelAffine.apply(
+                        X, self.scale, self.zero_point, self.ch_axis,
+                        self.quant_min, self.quant_max, grad_factor)
+                else:
+                    X = _fake_quantize_learnable_per_channel_affine_training(
+                        X, self.scale, self.zero_point, self.ch_axis,
+                        self.quant_min, self.quant_max, grad_factor)
+            else:
+                if self.use_grad_scaling:
+                    grad_factor = 1.0 / (X.numel() * self.quant_max) ** 0.5
+                else:
+                    grad_factor = 1.0
+                X = torch._fake_quantize_learnable_per_tensor_affine(  # 原装
+                    X, self.scale, self.zero_point,
+                    self.quant_min, self.quant_max, grad_factor)
+                # X = _fake_quantize_learnable_per_tensor_affine_training(X, self.scale, self.zero_point, self.quant_min, self.quant_max, grad_factor)
+            # NOTE 算
+            # self.input = X_old.detach()
+            
+            # if self.compute_qloss and hasattr(self, 'identity'):
+            # # #     # self.quantization_loss = (torch.norm(X_old - X, p="fro", dim=1) ** 2).mean()  # 这玩意也不行了
+                
+            # # #     # gap = (X_old - X_old.min())/(X_old.max() - X_old.min()) - (X - X.min())/(X.max() - X.min())
+            # # #     # gap = ((X_old - X) / self.scale + self.zero_point) / (self.quant_max - self.quant_min + 1)
+            # # #     scale = self.scale.detach()
+            # # #     zero_point = self.zero_point.detach()
+            # # #     # scale = grad_scale(scale, grad_factor)
+            # # #     # zero_point = grad_scale(zero_point, grad_factor)
+            # # #     gap = ((X_old - X) / scale)
+            # # #     # self.quantization_loss = (torch.norm(X_old - X, p="fro", dim=1) ** 2).mean()  # 这玩意也不行了
+            # # #     self.quantization_loss = (gap.abs()).mean()
+            # # #     # self.quantization_loss = (gap ** 2).mean()   # 拉爆了
+            # #     diff = torch.max(X_old.abs() - self.regular_margin)
+            # #     diff = torch.where(diff < 0., torch.zeros_like(diff), diff)
+            # #     self.quantization_loss = self.regular_margin + diff + 1/global_placeholder.quant_bit * self.scale.detach() * self.identity * X_old.std()
+            # #     # self.quantization_loss = self.regular_margin + diff
+                
+            #     # self.quantization_loss =1/global_placeholder.quant_bit * self.scale.detach() * self.identity * X.std()
+            #     scale = grad_scale(self.scale, grad_factor)
+                
+            #     self.quantization_loss =(1/global_placeholder.quant_bit * scale) ** 2
+
+
+        return X
+
+
+def _fake_quantize_learnable_per_tensor_affine_training(x, scale, zero_point, quant_min, quant_max, grad_factor):
+    zero_point = (zero_point.round() - zero_point).detach() + zero_point
+    scale = grad_scale(scale, grad_factor)
+    zero_point = grad_scale(zero_point, grad_factor)
+    x = x / scale + zero_point
+    x = (x.round() - x).detach() + x
+    x = torch.clamp(x, quant_min, quant_max)
+    return (x - zero_point) * scale
+
+def _fake_quantize_learnable_per_channel_affine_training(x, scale, zero_point, ch_axis, quant_min, quant_max, grad_factor):
+    zero_point = (zero_point.round() - zero_point).detach() + zero_point
+    new_shape = [1] * len(x.shape)
+    new_shape[ch_axis] = x.shape[ch_axis]
+    scale = grad_scale(scale, grad_factor).reshape(new_shape)
+    zero_point = grad_scale(zero_point, grad_factor).reshape(new_shape)
+    x = x / scale + zero_point
+    x = (x.round() - x).detach() + x
+    x = torch.clamp(x, quant_min, quant_max)
+    return (x - zero_point) * scale
+
+
+def grad_scale(t, scale):
+    return (t - (t * scale)).detach() + (t * scale)
+
+
+class FakeQuantizeLearnablePerchannelAffine(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, scale, zero_point, ch_axis, quant_min, quant_max, grad_factor):
+        return _fake_quantize_learnable_per_channel_affine_training(x, scale, zero_point, ch_axis,
+                                                                    quant_min, quant_max, grad_factor)
+
+    @staticmethod
+    def symbolic(g, x, scale, zero_point, ch_axis, quant_min, quant_max, grad_factor):
+        return g.op("::FakeQuantizeLearnablePerchannelAffine", x, scale, zero_point, quant_min_i=quant_min, quant_max_i=quant_max)
diff --git a/mqbench/fake_quantize/nnie.py b/mqbench/fake_quantize/nnie.py
new file mode 100644
index 0000000..d05c18d
--- /dev/null
+++ b/mqbench/fake_quantize/nnie.py
@@ -0,0 +1,44 @@
+import torch
+
+from mqbench.fake_quantize.quantize_base import QuantizeBase
+from mqbench.utils import no_jit_trace
+
+
+class NNIEFakeQuantize(QuantizeBase):
+    def __init__(self, observer, **observer_kwargs):
+        super(NNIEFakeQuantize, self).__init__(observer, **observer_kwargs)
+        self.register_buffer('data_max', torch.tensor(float('-inf')))
+
+    def forward(self, X):
+        with no_jit_trace():
+            if self.observer_enabled[0] == 1:
+                self.activation_post_process(X.detach())
+            data_max = torch.max(-self.activation_post_process.min_val, self.activation_post_process.max_val)
+            self.data_max = torch.max(data_max, self.data_max)
+        if self.fake_quant_enabled[0] == 1:
+            X = NNIEQuantizeFunc.apply(X, self.data_max)
+        return X
+
+
+class NNIEQuantizeFunc(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, data_max):
+        z = (16 * torch.log2(data_max.double())).round() - 127
+        x = x.double()
+        pos_idx = x > 2 ** ((z - 16) / 16)
+        neg_idx = x < - 2 ** ((z + 1 - 16) / 16)
+        zero_idx = (x >= - 2 ** ((z + 1 - 16) / 16)) & (x < 2 ** ((z - 16) / 16))
+        x[zero_idx] = 0
+        x[pos_idx] = 2 ** ((torch.clamp(torch.round(16 * torch.log2(x[pos_idx]) - z), 0, 127) + z) / 16)
+        x[neg_idx] = - 2 ** ((torch.clamp(torch.round(16 * torch.log2(-x[neg_idx]) - z), 1, 127) + z) / 16)
+        x = x.float()
+        return x
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        grad_input = grad_output
+        return grad_input, None
+
+    @staticmethod
+    def symbolic(g, x, data_max):
+        return g.op("::NNIEQuantize", x, data_max)
\ No newline at end of file
diff --git a/mqbench/fake_quantize/pact.py b/mqbench/fake_quantize/pact.py
new file mode 100644
index 0000000..181567c
--- /dev/null
+++ b/mqbench/fake_quantize/pact.py
@@ -0,0 +1,52 @@
+import torch
+from torch.nn.parameter import Parameter
+
+from mqbench.fake_quantize.quantize_base import QuantizeBase
+
+
+class PACTFakeQuantize(QuantizeBase):
+    def __init__(self, observer, alpha=6.0, **observer_kwargs):
+        super(PACTFakeQuantize, self).__init__(observer, **observer_kwargs)
+        self.alpha = Parameter(torch.tensor([alpha]))
+        if not self.is_symmetric_quant:
+            self.n_alpha = Parameter(torch.tensor([-alpha]))
+        self.register_buffer('scale', torch.tensor([1.0], dtype=torch.float))
+        self.register_buffer('zero_point', torch.tensor([0], dtype=torch.int))
+
+    @torch.jit.export
+    def extra_repr(self):
+        return 'fake_quant_enabled={}, observer_enabled={}, ' \
+               'quant_min={}, quant_max={}, dtype={}, qscheme={}, ch_axis={}, ' \
+               'alpha={}'.format(
+                   self.fake_quant_enabled, self.observer_enabled,
+                   self.quant_min, self.quant_max,
+                   self.dtype, self.qscheme, self.ch_axis, self.alpha)
+
+    def forward(self, X):
+        if self.observer_enabled[0] == 1:
+            self.activation_post_process(X.detach())
+            X = torch.where(X > self.alpha, self.alpha, X)
+            self.activation_post_process.max_val.data.fill_(self.alpha.data[0])
+            if X.min() < 0:
+                if self.is_symmetric_quant:
+                    X = torch.where(X < -self.alpha, -self.alpha, X)
+                    self.activation_post_process.min_val.data.fill_(-self.alpha[0].data)
+                else:
+                    X = torch.where(X < self.n_alpha, self.n_alpha, X)
+                    self.activation_post_process.min_val.data.fill_(self.n_alpha[0].data)
+            else:
+                self.activation_post_process.min_val.data.fill_(0.)
+
+            _scale, _zero_point = self.activation_post_process.calculate_qparams()
+            _scale, _zero_point = _scale.to(self.scale.device), _zero_point.to(self.zero_point.device)
+            if self.scale.shape != _scale.shape:
+                self.scale.resize_(_scale.shape)
+                self.zero_point.resize_(_zero_point.shape)
+            self.scale.copy_(_scale)
+            self.zero_point.copy_(_zero_point)
+
+        if self.fake_quant_enabled[0] == 1:
+            X = torch.fake_quantize_per_tensor_affine(
+                X, self.scale.item(), int(self.zero_point.item()), self.quant_min, self.quant_max)
+
+        return X
\ No newline at end of file
diff --git a/mqbench/fake_quantize/pure_hooker.py b/mqbench/fake_quantize/pure_hooker.py
new file mode 100644
index 0000000..b0a4e5c
--- /dev/null
+++ b/mqbench/fake_quantize/pure_hooker.py
@@ -0,0 +1,64 @@
+import torch
+from torch.nn.parameter import Parameter
+
+from mqbench.fake_quantize.quantize_base import QuantizeBase
+from mqbench.utils import is_symmetric_quant, is_tracing_state
+from mqbench.utils.hook import PerChannelLoadHook
+import global_placeholder
+
+class PureHooker(QuantizeBase):
+    r""" 
+    """
+
+    def __init__(self, observer, scale=1., zero_point=0., use_grad_scaling=True, **observer_kwargs):
+        super(PureHooker, self).__init__(observer, **observer_kwargs)
+        # self.regular_margin = Parameter(torch.tensor([1.]))
+        # self.use_grad_scaling = use_grad_scaling
+        # self.scale = Parameter(torch.tensor([scale]))
+        # self.register_buffer('zero_point', torch.tensor([zero_point]))  # NOTE 已改  这里就是不对劲，就应该是buffer，而且grad还会占用显存
+        # self.register_buffer('eps', torch.tensor([torch.finfo(torch.float32).eps]))
+        # # Check whether the module will load a state dict;
+        # # Initialize the shape of per-channel 'scale' and 'zero-point' before copying values
+        # self.load_state_dict_hook = PerChannelLoadHook(self)
+        # # NOTE test
+        # # self.register_buffer('quantization_loss', torch.tensor([0]))
+        self.compute_qloss = False
+        self.ema = 0
+
+
+    @torch.jit.export
+    def extra_repr(self):
+        return 'fake_quant_enabled={}, observer_enabled={}, ' \
+               'quant_min={}, quant_max={}, dtype={}, qscheme={}, ch_axis={}, ' \
+               'scale={}, zero_point={}'.format(
+                   self.fake_quant_enabled, self.observer_enabled,
+                   self.quant_min, self.quant_max,
+                   self.dtype, self.qscheme, self.ch_axis, self.scale if self.ch_axis == -1 else 'List[%s]' % str(self.scale.shape),
+                   self.zero_point if self.ch_axis == -1 else 'List')
+
+    def forward(self, X):
+        if self.observer_enabled[0] == 1:
+            
+            if self.compute_qloss:
+                # 计算std，初始化margin
+                if self.ema == 0:
+                    self.ema = 2 * X.std()
+                else:
+                    self.ema = 0.9 * 2 * X.std() + 0.1 * self.ema
+                self.regular_margin.data.copy_(self.ema)
+            
+        if self.fake_quant_enabled[0] == 1:
+        # Learnable fake quantize have to zero_point.float() to make it learnable.
+            if self.compute_qloss:
+                diff = torch.max(X.abs() - self.regular_margin)
+                diff = torch.where(diff < 0., torch.zeros_like(diff), diff)
+                self.quantization_loss = self.regular_margin + diff + X.std() ** 2  # 这样子一开始直接崩
+                # self.quantization_loss = self.regular_margin + diff
+        
+        return X
+
+
+
+def grad_scale(t, scale):
+    return (t - (t * scale)).detach() + (t * scale)
+
diff --git a/mqbench/fake_quantize/qdrop_quantizer.py b/mqbench/fake_quantize/qdrop_quantizer.py
new file mode 100644
index 0000000..3717aed
--- /dev/null
+++ b/mqbench/fake_quantize/qdrop_quantizer.py
@@ -0,0 +1,77 @@
+'''this is for activation quantizer in BRECQ and QDrop'''
+import torch
+from torch.nn.parameter import Parameter
+from mqbench.fake_quantize.quantize_base import QuantizeBase
+
+
+class QDropFakeQuantize(QuantizeBase):
+    """This is based on the fixedfakequantize.
+    And we wrap scale as parameter, where BRECQ and QDrop both learn the scale.
+    """
+
+    def __init__(self, observer, **observer_kwargs):
+        super(QDropFakeQuantize, self).__init__(observer, **observer_kwargs)
+        # self.register_buffer('scale', torch.tensor([1.0], dtype=torch.float))
+        self.scale = Parameter(torch.tensor([1.0], dtype=torch.float))
+        self.register_buffer('zero_point', torch.tensor([0], dtype=torch.int))
+        self.prob = 1.0  # 1.0 means no drop;
+
+    def forward(self, X):
+        if self.observer_enabled[0] == 1:
+            self.activation_post_process(X.detach())
+            _scale, _zero_point = self.calculate_qparams()
+            _scale, _zero_point = _scale.to(self.scale.device), _zero_point.to(self.zero_point.device)
+            if self.ch_axis != -1:
+                self.scale.data = torch.ones_like(_scale)
+                self.zero_point.resize_(_zero_point.shape)
+
+            self.scale.data.copy_(_scale)
+            self.zero_point.copy_(_zero_point)
+
+        if self.fake_quant_enabled[0] == 1:
+            x_orig = X
+            if self.is_per_channel:
+                X = _fake_quantize_learnable_per_channel_affine_training(
+                    X, self.scale, self.zero_point, self.ch_axis,
+                    self.quant_min, self.quant_max)
+            else:
+                X = _fake_quantize_learnable_per_tensor_affine_training(
+                    X, self.scale, self.zero_point, self.quant_min, self.quant_max)
+            if self.prob < 1.0:
+                x_prob = torch.where(torch.rand_like(X) < self.prob, X, x_orig)
+                return x_prob
+        return X
+
+    @torch.jit.export
+    def extra_repr(self):
+        return 'fake_quant_enabled={}, observer_enabled={}, ' \
+               'quant_min={}, quant_max={}, dtype={}, qscheme={}, ch_axis={}, ' \
+               'scale={}, zero_point={}'.format(
+                   self.fake_quant_enabled, self.observer_enabled,
+                   self.quant_min, self.quant_max,
+                   self.dtype, self.qscheme, self.ch_axis, self.scale.data if self.ch_axis == -1 else 'List',
+                   self.zero_point if self.ch_axis == -1 else 'List')
+
+
+def round_ste(x: torch.Tensor):
+    """
+    Implement Straight-Through Estimator for rounding operation.
+    """
+    return (x.round() - x).detach() + x
+
+
+def _fake_quantize_learnable_per_channel_affine_training(x, scale, zero_point, ch_axis, quant_min, quant_max):
+    new_shape = [1] * len(x.shape)
+    new_shape[ch_axis] = x.shape[ch_axis]
+    scale = scale.reshape(new_shape)
+    x_int = round_ste(x / scale) + zero_point
+    x_quant = torch.clamp(x_int, quant_min, quant_max)
+    x_dequant = (x_quant - zero_point) * scale
+    return x_dequant
+
+
+def _fake_quantize_learnable_per_tensor_affine_training(x, scale, zero_point, quant_min, quant_max):
+    x_int = round_ste(x / scale) + zero_point
+    x_quant = torch.clamp(x_int, quant_min, quant_max)
+    x_dequant = (x_quant - zero_point) * scale
+    return x_dequant
diff --git a/mqbench/fake_quantize/quantize_base.py b/mqbench/fake_quantize/quantize_base.py
new file mode 100644
index 0000000..8fe8fc1
--- /dev/null
+++ b/mqbench/fake_quantize/quantize_base.py
@@ -0,0 +1,49 @@
+import torch
+from torch.quantization import FakeQuantizeBase
+from torch.quantization.observer import MovingAverageMinMaxObserver
+from torch.quantization.fake_quantize import _is_per_channel, _is_per_tensor
+
+from mqbench.utils import is_symmetric_quant
+
+_version_under_1100 = int(torch.__version__.split('.')[1]) < 10
+
+class QuantizeBase(FakeQuantizeBase):
+    r""" This is an extension of the FakeQuantize module in fake_quantize.py, which
+    supports more generalized lower-bit quantization and support learning of the scale
+    and zero point parameters through backpropagation. For literature references,
+    please see the class _LearnableFakeQuantizePerTensorOp.
+    In addition to the attributes in the original FakeQuantize module, the _LearnableFakeQuantize
+    module also includes the following attributes to support quantization parameter learning.
+    """
+    def __init__(self, observer=MovingAverageMinMaxObserver, **observer_kwargs):
+        super().__init__()
+        self.activation_post_process = observer(**observer_kwargs)
+        self.dtype = self.activation_post_process.dtype
+        self.qscheme = self.activation_post_process.qscheme
+        self.quant_min = self.activation_post_process.quant_min
+        self.quant_max = self.activation_post_process.quant_max
+        assert self.quant_min <= self.quant_max, \
+            'quant_min must be less than or equal to quant_max'
+        self.pot_scale = self.activation_post_process.pot_scale
+        self.ch_axis = self.activation_post_process.ch_axis \
+            if hasattr(self.activation_post_process, 'ch_axis') else -1
+        assert _is_per_channel(self.qscheme) or \
+            _is_per_tensor(self.qscheme), \
+            'Only per channel and per tensor quantization are supported in fake quantize' + \
+            ' got qscheme: ' + str(self.qscheme)
+        self.is_per_channel = _is_per_channel(self.qscheme)
+        bitrange = torch.tensor(self.quant_max - self.quant_min + 1).double()
+        self.bitwidth = int(torch.log2(bitrange).item())
+        self.is_symmetric_quant = is_symmetric_quant(self.qscheme)
+
+    @torch.jit.export
+    def calculate_qparams(self):
+        return self.activation_post_process.calculate_qparams()
+
+    @torch.jit.export
+    def extra_repr(self):
+        return 'fake_quant_enabled={}, observer_enabled={}, ' \
+               'quant_min={}, quant_max={}, dtype={}, qscheme={}, ch_axis={}, '.format(
+                   self.fake_quant_enabled, self.observer_enabled,
+                   self.quant_min, self.quant_max,
+                   self.dtype, self.qscheme, self.ch_axis)
diff --git a/mqbench/fake_quantize/tqt.py b/mqbench/fake_quantize/tqt.py
new file mode 100644
index 0000000..1b4937c
--- /dev/null
+++ b/mqbench/fake_quantize/tqt.py
@@ -0,0 +1,126 @@
+import torch
+
+from torch.nn.parameter import Parameter
+from mqbench.fake_quantize.quantize_base import QuantizeBase
+from mqbench.utils import is_symmetric_quant
+
+
+class TqtFakeQuantize(QuantizeBase):
+    def __init__(self, observer, scale=1., zero_point=0., **observer_kwargs):
+        super(TqtFakeQuantize, self).__init__(observer, **observer_kwargs)
+        self.scale = Parameter(torch.tensor([scale]))
+        self.register_buffer('zero_point', torch.tensor([zero_point]))
+        self.register_buffer('eps', torch.tensor([torch.finfo(torch.float32).eps]))
+        self.quant_type = None
+        self.mth = None
+        self.compute_qloss = False
+
+    @torch.jit.export
+    def extra_repr(self):
+        return 'fake_quant_enabled={}, observer_enabled={}, ' \
+               'quant_min={}, quant_max={}, dtype={}, qscheme={}, ch_axis={}, ' \
+               'scale={}, zero_point={}'.format(
+                   self.fake_quant_enabled, self.observer_enabled,
+                   self.quant_min, self.quant_max,
+                   self.dtype, self.qscheme, self.ch_axis, self.scale if self.ch_axis == -1 else 'List',
+                   self.zero_point if self.ch_axis == -1 else 'List')
+
+    def forward(self, X):
+        # Learnable fake quantize have to zero_point.float() to make it learnable.
+        if self.observer_enabled[0] == 1:
+            self.activation_post_process(X.detach())
+            _scale, _zero_point = self.activation_post_process.calculate_qparams()
+            _scale = _scale.to(self.scale.device)
+            _zero_point = _zero_point.to(self.zero_point.device)
+
+            if self.ch_axis != -1:
+                self.scale.data = torch.ones_like(_scale)
+                self.zero_point.data = torch.zeros_like(_zero_point.float())
+
+            self.scale.data.copy_(_scale)
+            self.zero_point.data.copy_(_zero_point.float())
+        else:
+            self.scale.data.abs_()
+            self.scale.data.clamp_(min=self.eps.item())
+            
+        # TODO 写能求最小化量化误差的代码
+        X_old = X
+        
+        if self.fake_quant_enabled[0] == 1:
+            assert is_symmetric_quant(self.qscheme)
+            "TQT is a symmetric quantization FakeQuantize Op."
+            self.zero_point.data.zero_()
+            assert self.is_per_channel is False
+            "TQT is a per-tensor quantization FakeQuantize Op."
+            X = FakeQuantizeTqtAffine.apply(X, self.scale, self.zero_point, self.quant_min, self.quant_max, self.mth)  # 但是这里，虽然传勒zero point 但是没用到
+        
+            # NOTE 算
+            # if self.compute_qloss:
+            #     self.quantization_loss = (torch.norm(X_old - X, p="fro", dim=1) ** 2).mean()
+        return X
+
+    def set_quant_type(self, quant_type):
+        if quant_type in ['input', 'tensor', 'param']:
+            self.quant_type = quant_type
+            self.activation_post_process.set_quant_type(quant_type)
+            self.set_forward_method()
+        else:
+            raise ValueError(f'The quant type {quant_type} of TQTQuantizer is not right.')
+
+    def set_forward_method(self):
+        self.mth = torch.tensor(3) if self.quant_type == 'param' else torch.tensor(2)
+
+def _fake_quantize_tqt_affine_training(x, scale, zero_point, quant_min, quant_max, mth):
+    if scale < 2 ** -15:
+        max_scale = 0
+    else:
+        max_scale = 1 / scale
+        max_scale = torch.floor(max_scale.log2())
+    scale = 1 / (2 ** max_scale)
+    if mth == 3:
+        new_x = torch.clamp(scale_round(x / scale), quant_min, quant_max) * scale
+    elif mth == 2:
+        new_x = torch.clamp(x / scale, quant_min, quant_max)
+        new_x = scale_floor_ceil(new_x)
+        new_x *= scale
+    else:
+        raise ValueError(f'Invalid method {mth} encoding!')
+    return new_x
+
+
+def scale_round(t):
+    return (torch.round(t) - t).detach() + t
+
+def scale_floor_ceil(t):
+    return (torch.where((t < 0) & (t - t.floor() == 0.5), t.ceil(), t.round()) - t).detach() + t
+
+def _t(x, t):
+    return torch.tensor(x).type_as(t)
+
+class FakeQuantizeTqtAffine(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, x, scale, zero_point, quant_min, quant_max, mth):
+        qx = _fake_quantize_tqt_affine_training(x, scale, zero_point, quant_min, quant_max, mth)
+        ctx.save_for_backward(x, scale, _t(quant_min, x), _t(quant_max, x))
+        return qx
+
+    @staticmethod
+    def backward(ctx, grad_outputs):
+        x, s, qmin, qmax = ctx.saved_tensors
+        scaled_x = x / s
+        rounded_scaled_x = torch.where(
+            (scaled_x < 0) & (scaled_x - torch.floor(scaled_x) == 0.5),
+            torch.ceil(scaled_x), torch.round(scaled_x)
+        )
+
+        is_lt_min = rounded_scaled_x < qmin
+        is_gt_max = rounded_scaled_x > qmax
+        is_ge_min_and_le_max = ~is_lt_min & ~is_gt_max
+
+        grad_x = grad_outputs.clone()
+        grad_x = torch.where(is_ge_min_and_le_max, grad_x, 0 * grad_x)
+        return grad_x.to(grad_outputs.device), None, None, None, None, None
+
+    @staticmethod
+    def symbolic(g, x, scale, zero_point, quant_min, quant_max, mth):
+        return g.op("::FakeQuantizeTqtAffine", x, scale, zero_point, quant_min_i=quant_min, quant_max_i=quant_max)
diff --git a/mqbench/fuser_method_mappings.py b/mqbench/fuser_method_mappings.py
new file mode 100644
index 0000000..f3b4f37
--- /dev/null
+++ b/mqbench/fuser_method_mappings.py
@@ -0,0 +1,222 @@
+from typing import Optional, Type
+
+import torch
+import torch.nn as nn
+from torch.quantization.fx.fusion_patterns import ConvBNReLUFusion, ModuleReLUFusion
+from torch.quantization.fx.quantization_types import QuantizerCls
+from torch.fx.graph import Node
+
+import mqbench.nn as qnn
+import mqbench.nn.intrinsic as qnni
+import mqbench.nn.intrinsic.qat as qnniqat
+from mqbench.utils.fusion import fuse_deconv_bn_eval
+from mqbench.nn.modules import FrozenBatchNorm2d
+
+
+class ConvFreezebnReLUFusion(ConvBNReLUFusion):
+    def __init__(self, quantizer: QuantizerCls, node: Node):
+        super(ConvBNReLUFusion, self).__init__(quantizer, node)
+        self.relu_node = None
+        self.bn_node = None
+        if (node.op == 'call_function' and node.target is torch.nn.functional.relu) or \
+           (node.op == 'call_module' and type(quantizer.modules[node.target]) == torch.nn.ReLU):
+            self.relu_node = node
+            assert isinstance(node.args[0], Node)
+            node = node.args[0]
+        assert node.op == 'call_module'
+        if type(quantizer.modules[node.target]) in [torch.nn.BatchNorm1d, torch.nn.BatchNorm2d, torch.nn.BatchNorm3d, FrozenBatchNorm2d]:
+            self.bn_node = node
+            self.bn = quantizer.modules[self.bn_node.target]
+            assert isinstance(node.args[0], Node)
+            node = node.args[0]
+        assert node.op == 'call_module'
+        self.conv_node = node
+        self.conv = quantizer.modules[self.conv_node.target]
+
+
+def fuse_linear_bn(linear, bn):
+    r"""Given the linear and bn modules, fuses them and returns the fused module
+
+    Args:
+        conv: Module instance of type Linear
+        bn: Spatial BN instance that needs to be fused with the conv
+
+    Examples::
+
+        >>> m1 = nn.Linear(10, 20)
+        >>> b1 = nn.BatchNorm1d(20)
+        >>> m2 = fuse_linear_bn(m1, b1)
+    """
+    assert(linear.training == bn.training),\
+        "Linear and BN both must be in the same mode (train or eval)."
+
+    if linear.training:
+        assert bn.affine, 'Only support fusing BatchNorm1d with affine set to True'
+        assert bn.track_running_stats, 'Only support fusing BatchNorm1d with tracking_running_stats set to True'
+        return qnn.intrinsic.LinearBn1d(linear, bn)
+    else:
+        return nn.utils.fusion.fuse_linear_bn_eval(linear, bn)
+
+
+def fuse_deconv_bn(deconv, bn):
+    assert(deconv.training == bn.training),\
+        'DeConv and BN must be in the same mode (train or eval)'
+
+    if deconv.training:
+        assert bn.num_features == deconv.out_channels, 'Output channel of ConvTranspose2d must match num_features of BatchNorm2d'
+        assert bn.affine, 'Only support fusing BatchNorm2d with affine set to True'
+        assert bn.track_running_stats, 'Only support fusing BatchNorm2d with tracking_running_stats set to True'
+        return qnni.ConvTransposeBn2d(deconv, bn)
+    else:
+        return fuse_deconv_bn_eval(deconv, bn)
+
+
+def fuse_deconv_bn_relu(deconv, bn, relu):
+    assert(deconv.training == bn.training == relu.training),\
+        "DeConv and BN both must be in the same mode (train or eval)."
+
+    if deconv.training:
+        assert bn.num_features == deconv.out_channels, 'Output channel of ConvTranspose2d must match num_features of BatchNorm2d'
+        assert bn.affine, 'Only support fusing BatchNorm2d with affine set to True'
+        assert bn.track_running_stats, 'Only support fusing BatchNorm2d with tracking_running_stats set to True'
+        return qnni.ConvTransposeBnReLU2d(deconv, bn, relu)
+    else:
+        return qnni.ConvTransposeReLU2d(fuse_deconv_bn_eval(deconv, bn), relu)
+
+
+def fuse_conv_freezebn(conv, bn):
+    assert(bn.training is False), "Freezebn must be eval."
+
+    fused_module_class_map = {
+        nn.Conv2d: qnni.ConvFreezebn2d,
+    }
+
+    if conv.training:
+        assert bn.num_features == conv.out_channels, 'Output channel of Conv2d must match num_features of BatchNorm2d'
+        assert bn.affine, 'Only support fusing BatchNorm2d with affine set to True'
+        assert bn.track_running_stats, 'Only support fusing BatchNorm2d with tracking_running_stats set to True'
+        fused_module_class = fused_module_class_map.get((type(conv)), None)
+        return fused_module_class(conv, bn)
+    else:
+        return nn.utils.fuse_conv_bn_eval(conv, bn)
+
+
+def fuse_conv_freezebn_relu(conv, bn, relu):
+    assert(conv.training == relu.training and bn.training is False), "Conv and relu both must be in the same mode (train or eval) and bn must be eval."
+    fused_module : Optional[Type[nn.Sequential]] = None
+    if conv.training:
+        map_to_fused_module_train = {
+            nn.Conv2d: qnni.ConvFreezebnReLU2d,
+        }
+        assert bn.num_features == conv.out_channels, 'Output channel of Conv must match num_features of BatchNorm'
+        assert bn.affine, 'Only support fusing BatchNorm with affine set to True'
+        assert bn.track_running_stats, 'Only support fusing BatchNorm with tracking_running_stats set to True'
+        fused_module = map_to_fused_module_train.get(type(conv), None)
+        return fused_module(conv, bn, relu)
+    else:
+        map_to_fused_module_eval = {
+            nn.Conv2d: nn.intrinsic.ConvReLU2d,
+        }
+        fused_module = map_to_fused_module_eval.get(type(conv), None)
+        fused_conv = nn.utils.fusion.fuse_conv_bn_eval(conv, bn)
+        return fused_module(fused_conv, relu)
+
+
+def fuse_deconv_freezebn(deconv, bn):
+    assert(bn.training is False), "Freezebn must be eval."
+
+    if deconv.training:
+        assert bn.num_features == deconv.out_channels, 'Output channel of ConvTranspose2d must match num_features of BatchNorm2d'
+        assert bn.affine, 'Only support fusing BatchNorm2d with affine set to True'
+        assert bn.track_running_stats, 'Only support fusing BatchNorm2d with tracking_running_stats set to True'
+        return qnni.ConvTransposeFreezebn2d(deconv, bn)
+    else:
+        return fuse_deconv_bn_eval(deconv, bn)
+
+
+def fuse_deconv_freezebn_relu(deconv, bn, relu):
+    assert(deconv.training == relu.training and bn.training is False), "Conv and relu both must be in the same mode (train or eval) and bn must be eval."
+
+    if deconv.training:
+        assert bn.num_features == deconv.out_channels, 'Output channel of ConvTranspose2d must match num_features of BatchNorm2d'
+        assert bn.affine, 'Only support fusing BatchNorm2d with affine set to True'
+        assert bn.track_running_stats, 'Only support fusing BatchNorm2d with tracking_running_stats set to True'
+        return qnni.ConvTransposeFreezebnReLU2d(deconv, bn, relu)
+    else:
+        return qnni.ConvTransposeReLU2d(fuse_deconv_bn_eval(deconv, bn), relu)
+
+
+fuse_custom_config_dict = {
+    "additional_fuser_method_mapping": {
+        (torch.nn.Linear, torch.nn.BatchNorm1d): fuse_linear_bn,
+        (torch.nn.ConvTranspose2d, torch.nn.BatchNorm2d): fuse_deconv_bn,
+        (torch.nn.ConvTranspose2d, torch.nn.BatchNorm2d, torch.nn.ReLU): fuse_deconv_bn_relu,
+        (torch.nn.ConvTranspose2d, torch.nn.ReLU): qnni.ConvTransposeReLU2d,
+        (nn.Conv2d, FrozenBatchNorm2d, nn.ReLU): fuse_conv_freezebn_relu,
+        (nn.Conv2d, FrozenBatchNorm2d): fuse_conv_freezebn,
+        (nn.ConvTranspose2d, FrozenBatchNorm2d, nn.ReLU): fuse_deconv_freezebn_relu,
+        (nn.ConvTranspose2d, FrozenBatchNorm2d): fuse_deconv_freezebn,
+    },
+    "additional_fusion_pattern": {  # 似乎这些都是torch官方的定义
+        (torch.nn.BatchNorm1d, torch.nn.Linear):
+        ConvBNReLUFusion,
+        (torch.nn.BatchNorm2d, torch.nn.ConvTranspose2d):
+        ConvBNReLUFusion,
+        (torch.nn.ReLU, torch.nn.ConvTranspose2d):
+        ConvBNReLUFusion,
+        (torch.nn.ReLU, (torch.nn.BatchNorm2d, torch.nn.ConvTranspose2d)):
+        ConvBNReLUFusion,
+        (torch.nn.functional.relu, torch.nn.ConvTranspose2d):
+        ConvBNReLUFusion,
+        (torch.nn.functional.relu, (torch.nn.BatchNorm2d, torch.nn.ConvTranspose2d)):
+        ConvBNReLUFusion,
+        (torch.nn.ReLU, (FrozenBatchNorm2d, torch.nn.Conv2d)):
+        ConvFreezebnReLUFusion,
+        (FrozenBatchNorm2d, torch.nn.Conv2d):
+        ConvFreezebnReLUFusion,
+        (torch.nn.ReLU, (FrozenBatchNorm2d, torch.nn.ConvTranspose2d)):
+        ConvFreezebnReLUFusion,
+        (FrozenBatchNorm2d, torch.nn.ConvTranspose2d):
+        ConvFreezebnReLUFusion,
+    },
+    "additional_qat_module_mappings": {
+        nn.ConvTranspose2d: qnn.qat.ConvTranspose2d,
+        qnni.LinearBn1d: qnniqat.LinearBn1d,
+        qnni.ConvTransposeBn2d: qnniqat.ConvTransposeBn2d,
+        qnni.ConvTransposeReLU2d: qnniqat.ConvTransposeReLU2d,
+        qnni.ConvTransposeBnReLU2d: qnniqat.ConvTransposeBnReLU2d,
+        qnni.ConvFreezebn2d: qnniqat.ConvFreezebn2d,
+        qnni.ConvFreezebnReLU2d: qnniqat.ConvFreezebnReLU2d,
+        qnni.ConvTransposeFreezebn2d: qnniqat.ConvTransposeFreezebn2d,
+        qnni.ConvTransposeFreezebnReLU2d: qnniqat.ConvTransposeFreezebnReLU2d,
+        nn.Embedding: qnn.qat.Embedding,
+    },
+}
+
+
+def _sort_fusion_patterns(pats):
+    keys = []
+    for key in pats.keys():
+        if pats[key] is ModuleReLUFusion:
+            keys.append(key)
+    for key in keys:
+        pats.move_to_end(key)
+
+
+# Sinse additional_fuser_method_mapping will not be set because fuser.py:54
+# do not pass this dict.
+from torch.quantization.fuser_method_mappings import DEFAULT_OP_LIST_TO_FUSER_METHOD
+from torch.quantization.fx.pattern_utils import DEFAULT_FUSION_PATTERNS
+from torch.quantization.quantization_mappings import DEFAULT_QAT_MODULE_MAPPINGS
+
+DEFAULT_OP_LIST_TO_FUSER_METHOD.update(
+    fuse_custom_config_dict['additional_fuser_method_mapping'])
+DEFAULT_FUSION_PATTERNS.update(
+    fuse_custom_config_dict['additional_fusion_pattern'])
+# Make longer matched pattern prior.
+# i.e. Conv + BN + Relu should match ConvBnRelu before BNRelu.
+# Any thing registered in class ConvBNReLUFusion should be
+# proir than class ModuleReLUFusion.
+_sort_fusion_patterns(DEFAULT_FUSION_PATTERNS)
+DEFAULT_QAT_MODULE_MAPPINGS.update(
+    fuse_custom_config_dict['additional_qat_module_mappings'])
diff --git a/mqbench/fusion_method.py b/mqbench/fusion_method.py
new file mode 100644
index 0000000..5b5b6b0
--- /dev/null
+++ b/mqbench/fusion_method.py
@@ -0,0 +1,274 @@
+import torch
+import torch.nn.intrinsic.qat as nniqat
+from torch.nn.utils.fusion import fuse_conv_bn_eval, fuse_linear_bn_eval
+from torch.quantization.fx.utils import _parent_name
+
+import mqbench.nn.intrinsic as qnni
+import mqbench.nn.intrinsic.qat as qnniqat
+import mqbench.nn.qat as qnnqat
+from mqbench.utils.registry import register_convert_function
+from mqbench.fuser_method_mappings import fuse_deconv_bn_eval
+from mqbench.quantization.default_bias_fake_quant import bias_fake_quantizer
+
+
+@register_convert_function(qnni.LinearBn1d)
+def convert_qnni_linearbn(model, fused_node):
+    modules = dict(model.named_modules())
+    fused_module = modules[fused_node.target]
+    fused_linear = fuse_linear_bn_eval(fused_module[0], fused_module[1])
+    linear_parent_name, linear_name = _parent_name(fused_node.target)
+    setattr(modules[linear_parent_name], linear_name, fused_linear)
+
+
+@register_convert_function(qnniqat.LinearBn1d)
+def convert_qnniqat_linearbn(model, fused_node):
+    modules = dict(model.named_modules())
+    fused_module = modules[fused_node.target]
+    # Create a Linear from FusedModule.
+    linear = torch.nn.Linear(fused_module.in_features, fused_module.out_features, fused_module.bias is not None)
+    linear.weight = fused_module.weight
+    if fused_module.bias is not None:
+        linear.bias = fused_module.bias
+    # Merge Linear + BN
+    fused_linear = fuse_linear_bn_eval(linear.eval(), fused_module.bn)
+    # We need nn.qat.linear here to export weight quantize node.
+    linear.qconfig = fused_module.qconfig
+    linear = torch.nn.qat.Linear.from_float(linear)
+    # Attach weight fake quantize params.
+    linear.weight_fake_quant = fused_module.weight_fake_quant
+    linear_parent_name, linear_name = _parent_name(fused_node.target)
+    setattr(modules[linear_parent_name], linear_name, fused_linear)
+
+
+@register_convert_function(qnniqat.ConvFreezebn2d)
+@register_convert_function(nniqat.ConvBn2d)
+# @register_convert_function(nniqat.ConvBn3d)  # FIXME 为什么这个有报错 XXX 
+def convert_nniqat_convbn(model, fused_node):
+    """nniqat.ConvBn2d ----> nn.Conv2d ----> nniqat.Conv2d
+    """
+    fused_module_class_map = {
+        qnniqat.ConvFreezebn2d: torch.nn.Conv2d,
+        qnniqat.ConvFreezebnReLU2d: torch.nn.Conv2d,
+        nniqat.ConvBn2d: torch.nn.Conv2d,
+        nniqat.ConvBnReLU2d: torch.nn.Conv2d,
+        nniqat.ConvBn3d: torch.nn.Conv3d,
+        nniqat.ConvBnReLU3d: torch.nn.Conv3d,
+    }
+    fused_qat_module_class_map = {
+        torch.nn.Conv2d: torch.nn.qat.Conv2d,
+        torch.nn.Conv3d: torch.nn.qat.Conv3d,
+    }
+    modules = dict(model.named_modules())
+    fused_module = modules[fused_node.target]
+    # Create a Conv2d from FusedModule.
+    conv = fused_module_class_map[type(fused_module)](fused_module.in_channels, fused_module.out_channels,
+                                                      fused_module.kernel_size, fused_module.stride,
+                                                      fused_module.padding, fused_module.dilation,
+                                                      fused_module.groups, fused_module.bias is not None,
+                                                      fused_module.padding_mode)
+    conv.weight = fused_module.weight
+    if fused_module.bias is not None:
+        conv.bias = fused_module.bias
+    fused_conv = fuse_conv_bn_eval(conv.eval(), fused_module.bn)
+    # We need nn.qat.conv here to export weight quantize node.
+    fused_conv.qconfig = fused_module.qconfig
+    fused_conv = fused_qat_module_class_map[type(conv)].from_float(fused_conv)
+    # Attach weight fake quantize params.
+    fused_conv.weight_fake_quant = fused_module.weight_fake_quant
+    conv_parent_name, conv_name = _parent_name(fused_node.target)
+    setattr(modules[conv_parent_name], conv_name, fused_conv)
+
+
+@register_convert_function(qnniqat.ConvFreezebnReLU2d)
+@register_convert_function(nniqat.ConvBnReLU2d)
+# @register_convert_function(nniqat.ConvBnReLU3d)
+def convert_nniqat_convbnrelu(model, fused_node):    
+    convert_nniqat_convbn(model, fused_node)
+    modules = dict(model.named_modules())
+    fused_module = modules[fused_node.target]
+    # We need to Insert Relu after Merged conv.
+    conv_parent_name, conv_name = _parent_name(fused_node.target)
+    relu_name = 'relu'
+    # Maybe has another name, but we cannot know for now.
+    if not hasattr(modules[conv_parent_name], relu_name):
+        setattr(modules[conv_parent_name], relu_name, 
+                torch.nn.ReLU(inplace=True).train(fused_module.training))
+    # Update modules.
+    modules = dict(model.named_modules())
+    graph = model.graph
+    nodes = list(model.graph.nodes)
+    with graph.inserting_after(fused_node):
+        relu_node_name = relu_name if conv_parent_name == "" else "{}.{}".format(conv_parent_name, relu_name)
+        assert relu_node_name in modules and isinstance(modules[relu_node_name], torch.nn.ReLU)
+        inserted_node = graph.create_node("call_module", relu_node_name, (fused_node,), {})
+        for _node in nodes:
+            for i, _arg in enumerate(_node.args):
+                if _arg == fused_node:
+                    _tmp = list(_node.args)
+                    _tmp[i] = inserted_node
+                    _node.args = tuple(_tmp)
+    model.recompile()
+    model.graph.lint()
+
+
+@register_convert_function(qnni.ConvTransposeFreezebn2d)
+@register_convert_function(qnni.ConvTransposeBn2d)
+def convert_qnni_deconvbn(model, fused_node):
+    modules = dict(model.named_modules())
+    fused_module = modules[fused_node.target]
+    fused_module_deconv = fused_module[0]
+    fused_module_bn = fused_module[1]
+    # Create a ConvTranspose2d from FusedModule.
+    deconv = torch.nn.ConvTranspose2d(fused_module_deconv.in_channels, fused_module_deconv.out_channels, fused_module_deconv.kernel_size, 
+                                      stride=fused_module_deconv.stride, padding=fused_module_deconv.padding, output_padding=fused_module_deconv.output_padding,
+                                      groups=fused_module_deconv.groups, bias=fused_module_deconv.bias is not None, 
+                                      dilation=fused_module_deconv.dilation,
+                                      padding_mode=fused_module_deconv.padding_mode) 
+    deconv.weight = fused_module_deconv.weight
+    if fused_module_deconv.bias is not None:
+        deconv.bias = fused_module_deconv.bias
+    fused_deconv = fuse_deconv_bn_eval(deconv.eval(), fused_module_bn)
+    deconv_parent_name, deconv_name = _parent_name(fused_node.target)
+    setattr(modules[deconv_parent_name], deconv_name, fused_deconv)
+
+
+@register_convert_function(qnniqat.ConvTransposeFreezebn2d)
+@register_convert_function(qnniqat.ConvTransposeBn2d)
+def convert_qnniqat_deconvbn(model, fused_node):
+    modules = dict(model.named_modules())
+    fused_module = modules[fused_node.target]
+    # Create a ConvTranspose2d from FusedModule.
+    deconv = torch.nn.ConvTranspose2d(fused_module.in_channels, fused_module.out_channels, fused_module.kernel_size, 
+                                      stride=fused_module.stride, padding=fused_module.padding, output_padding=fused_module.output_padding,
+                                      groups=fused_module.groups, bias=fused_module.bias is not None, 
+                                      dilation=fused_module.dilation,
+                                      padding_mode=fused_module.padding_mode) 
+    deconv.weight = fused_module.weight
+    if fused_module.bias is not None:
+        deconv.bias = fused_module.bias
+    fused_deconv = fuse_deconv_bn_eval(deconv.eval(), fused_module.bn)
+    # We need nn.qat.conv here to export weight quantize node.
+    fused_deconv.qconfig = fused_module.qconfig
+    fused_deconv = qnnqat.ConvTranspose2d.from_float(fused_deconv)
+    # Attach weight fake quantize params.
+    fused_deconv.weight_fake_quant = fused_module.weight_fake_quant
+    deconv_parent_name, deconv_name = _parent_name(fused_node.target)
+    setattr(modules[deconv_parent_name], deconv_name, fused_deconv)
+
+
+@register_convert_function(qnni.ConvTransposeFreezebnReLU2d)
+@register_convert_function(qnni.ConvTransposeBnReLU2d)
+def convert_qnni_deconvbnrelu(model, fused_node):
+    convert_qnni_deconvbn(model, fused_node)
+    modules = dict(model.named_modules())
+    fused_module = modules[fused_node.target]
+    deconv_parent_name, deconv_name = _parent_name(fused_node.target)
+    relu_name = 'relu'
+    if not hasattr(modules[deconv_parent_name], relu_name):
+        setattr(modules[deconv_parent_name], relu_name, torch.nn.ReLU(inplace=True).train(fused_module.training))
+    # Update modules.
+    modules = dict(model.named_modules())
+    graph = model.graph
+    nodes = list(model.graph.nodes)
+    with graph.inserting_after(fused_node):
+        relu_node_name = relu_name if deconv_parent_name == "" else "{}.{}".format(deconv_parent_name, relu_name)
+        assert relu_node_name in modules and isinstance(modules[relu_node_name], torch.nn.ReLU)
+        inserted_node = graph.create_node("call_module", relu_node_name, (fused_node,), {})
+        for _node in nodes:
+            for i, _arg in enumerate(_node.args):
+                if _arg == fused_node:
+                    _tmp = list(_node.args)
+                    _tmp[i] = inserted_node
+                    _node.args = tuple(_tmp)
+    model.recompile()
+    model.graph.lint()
+
+
+@register_convert_function(qnniqat.ConvTransposeFreezebnReLU2d)
+@register_convert_function(qnniqat.ConvTransposeBnReLU2d)
+def convert_qnniqat_deconvbnrelu(model, fused_node):
+    convert_qnniqat_deconvbn(model, fused_node)
+    modules = dict(model.named_modules())
+    fused_module = modules[fused_node.target]
+    deconv_parent_name, deconv_name = _parent_name(fused_node.target)
+    relu_name = 'relu'
+    if not hasattr(modules[deconv_parent_name], relu_name):
+        setattr(modules[deconv_parent_name], relu_name, torch.nn.ReLU(inplace=True).train(fused_module.training))
+    # Update modules.
+    modules = dict(model.named_modules())
+    graph = model.graph
+    nodes = list(model.graph.nodes)
+    with graph.inserting_after(fused_node):
+        relu_node_name = relu_name if deconv_parent_name == "" else "{}.{}".format(deconv_parent_name, relu_name)
+        assert relu_node_name in modules and isinstance(modules[relu_node_name], torch.nn.ReLU)
+        inserted_node = graph.create_node("call_module", relu_node_name, (fused_node,), {})
+        for _node in nodes:
+            for i, _arg in enumerate(_node.args):
+                if _arg == fused_node:
+                    _tmp = list(_node.args)
+                    _tmp[i] = inserted_node
+                    _node.args = tuple(_tmp)
+    model.recompile()
+    model.graph.lint()
+
+
+@register_convert_function(qnniqat.ConvBn2d)
+def convert_qnniqat_convbn(model, fused_node):
+    """mqbench.nn.intrinsic.qat module add bias quant.
+    That is the difference between torch.nn.intrinsic.qat module.
+    """
+    modules = dict(model.named_modules())
+    fused_module = modules[fused_node.target]
+    # Create a Conv2d from FusedModule.
+    conv = torch.nn.Conv2d(fused_module.in_channels, fused_module.out_channels, fused_module.kernel_size, 
+                           fused_module.stride, fused_module.padding, fused_module.dilation,
+                           fused_module.groups, fused_module.bias is not None, fused_module.padding_mode)
+    conv.weight = fused_module.weight
+    if fused_module.bias is not None:
+        conv.bias = fused_module.bias
+    fused_conv = fuse_conv_bn_eval(conv.eval(), fused_module.bn)
+    # We need nn.qat.conv here to export weight quantize node.
+    fused_conv.qconfig = fused_module.qconfig
+    fused_conv = qnnqat.Conv2d.from_float(fused_conv)
+    # Attach weight fake quantize params.
+    fused_conv.weight_fake_quant = fused_module.weight_fake_quant
+    if hasattr(fused_module, 'bias_fake_quant'):
+        fused_conv.bias_fake_quant = fused_module.bias_fake_quant
+    else:
+        fused_conv.bias_fake_quant = bias_fake_quantizer()
+        fused_conv.bias_fake_quant.set_quant_type('param')
+    conv_parent_name, conv_name = _parent_name(fused_node.target)
+    setattr(modules[conv_parent_name], conv_name, fused_conv)
+
+
+@register_convert_function(qnniqat.ConvBnReLU2d)
+def convert_qnniqat_convbnrelu(model, fused_node):
+    """mqbench.nn.intrinsic.qat module add bias quant.
+    That is the difference between torch.nn.intrinsic.qat module.
+    """
+    convert_qnniqat_convbn(model, fused_node)
+    modules = dict(model.named_modules())
+    fused_module = modules[fused_node.target]
+    # We need to Insert Relu after Merged conv.
+    conv_parent_name, conv_name = _parent_name(fused_node.target)
+    relu_name = 'relu'
+    # Maybe has another name, but we cannot know for now.
+    if not hasattr(modules[conv_parent_name], relu_name):
+        setattr(modules[conv_parent_name], relu_name, 
+                torch.nn.ReLU(inplace=True).train(fused_module.training))
+    # Update modules.
+    modules = dict(model.named_modules())
+    graph = model.graph
+    nodes = list(model.graph.nodes)
+    with graph.inserting_after(fused_node):
+        relu_node_name = relu_name if conv_parent_name == "" else "{}.{}".format(conv_parent_name, relu_name)
+        assert relu_node_name in modules and isinstance(modules[relu_node_name], torch.nn.ReLU)
+        inserted_node = graph.create_node("call_module", relu_node_name, (fused_node,), {})
+        for _node in nodes:
+            for i, _arg in enumerate(_node.args):
+                if _arg == fused_node:
+                    _tmp = list(_node.args)
+                    _tmp[i] = inserted_node
+                    _node.args = tuple(_tmp)
+    model.recompile()
+    model.graph.lint()
\ No newline at end of file
diff --git a/mqbench/mix_precision/hessian_per_layer.py b/mqbench/mix_precision/hessian_per_layer.py
new file mode 100644
index 0000000..2b94a73
--- /dev/null
+++ b/mqbench/mix_precision/hessian_per_layer.py
@@ -0,0 +1,198 @@
+from typing import Dict
+
+import torch
+import numpy as np
+from pyhessian import hessian, hessian_vector_product, group_product, orthnormal, normalization
+
+
+class hessian_per_layer(hessian):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.first_order_grad_dict = {}
+        for name, mod in self.model.named_modules():
+            if isinstance(mod, (torch.nn.Conv2d, torch.nn.Linear)):
+                self.first_order_grad_dict[name] = mod.weight.grad + 0.
+
+    def layer_eigenvalues(self, maxIter=100, tol=1e-3) -> Dict:
+        """
+        compute the top_n eigenvalues in one model by layer.
+        """
+        device = self.device
+        max_eigenvalues_dict = {}
+
+        for name, mod in self.model.named_modules():
+            if isinstance(mod, (torch.nn.Conv2d, torch.nn.Linear)):
+                weight = mod.weight
+                eigenvectors = []
+                eigenvalue = None
+                v = [torch.randn(weight.size()).to(device)]
+                v = normalization(v)
+                first_order_grad = self.first_order_grad_dict[name]
+
+                for i in range(maxIter):
+                    v = orthnormal(v, eigenvectors)
+                    self.model.zero_grad()
+
+                    if self.full_dataset:
+                        tmp_eigenvalue, Hv = self.dataloader_hv_product(v)
+                    else:
+                        Hv = hessian_vector_product(first_order_grad, weight, v)
+                        tmp_eigenvalue = group_product(Hv, v).cpu().item()
+
+                    v = normalization(Hv)
+
+                    if eigenvalue is None:
+                        eigenvalue = tmp_eigenvalue
+                    else:
+                        if abs(eigenvalue - tmp_eigenvalue) / (abs(eigenvalue) + 1e-6) < tol:
+                            break
+                        else:
+                            eigenvalue = tmp_eigenvalue
+                max_eigenvalues_dict[name] = eigenvalue
+
+        return max_eigenvalues_dict
+
+    def layer_trace(self, maxIter=100, tol=1e-3) -> Dict:
+        """
+        Compute the trace of hessian in one model by layer.
+        """
+        device = self.device
+        trace_dict = {}
+        for name, mod in self.model.named_modules():
+            if isinstance(mod, (torch.nn.Conv2d, torch.nn.Linear)):
+                trace_vhv = []
+                trace = 0.
+                weight = mod.weight
+                first_order_grad = self.first_order_grad_dict[name]
+                for i in range(maxIter):
+                    self.model.zero_grad()
+                    v = torch.randint_like(weight, high=2, device=device)
+                    # generate Rademacher random variables
+                    v[v == 0] = -1
+                    v = [v]
+
+                    Hv = hessian_vector_product(first_order_grad, weight, v)
+                    trace_vhv.append(group_product(Hv, v).cpu().item())
+                    if abs(np.mean(trace_vhv) - trace) / (trace + 1e-6) < tol:
+                        break
+                    else:
+                        trace = np.mean(trace_vhv)
+                trace_dict[name] = trace
+
+        return trace_dict
+
+class FWDSaverHook:
+    """
+    Forward hook that stores the input and output of a layer/block
+    """
+    def __init__(self):
+        self.store_input = None 
+
+    def __call__(self, module, input_batch, output_batch):
+        output_batch.requires_grad_()
+        output_batch.retain_grad()
+        self.store_input = output_batch
+
+class BWDSaverHook:
+    """
+    Forward hook that stores the input and output of a layer/block
+    """
+    def __init__(self):
+        self.store_input = None 
+
+    def __call__(self, module, input_batch, output_batch):
+        self.store_input = output_batch
+
+class hessian_per_layer_acti(hessian):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.first_order_grad_dict = {}
+        data = kwargs['data']
+        self.layer_prepare(data[0], data[1])
+
+    def layer_prepare(self, data, target):
+        self.grad_savers = {}
+        self.acti_savers = {}
+        self.model.zero_grad()
+        for name, mod in self.model.named_modules():
+            if isinstance(mod, (torch.nn.Conv2d, torch.nn.Linear)):
+                self.grad_savers[name] = BWDSaverHook()
+                self.acti_savers[name] = FWDSaverHook()
+                mod.register_forward_hook(self.acti_savers[name])
+                mod.register_full_backward_hook(self.grad_savers[name])
+        los = torch.nn.CrossEntropyLoss()
+        loss = los(self.model(data.cuda()), target.cuda())
+        loss.backward(create_graph=True)
+        self.grad_dict = {key: self.grad_savers[key].store_input[0] + 0. for key in self.grad_savers}
+        self.acti_dict = {key: self.acti_savers[key].store_input for key in self.acti_savers}
+
+    def layer_eigenvalues(self, maxIter=100, tol=1e-3) -> Dict:
+        """
+        compute the top_n eigenvalues in one model by layer.
+        """
+        device = self.device
+        max_eigenvalues_dict = {}
+
+        for name, mod in self.model.named_modules():
+            if isinstance(mod, (torch.nn.Conv2d, torch.nn.Linear)):
+                acti = self.acti_dict[name]
+                max_eigenvalues_dict[name] = []
+                first_order_grad = self.grad_dict[name]
+                v = [torch.randn(acti.size()).to(device)]
+                v = normalization(v)
+                eigenvectors = []
+                eigenvalue = None
+                for i in range(maxIter):
+                    v = orthnormal(v, eigenvectors)
+                    self.model.zero_grad()
+
+                    actis = [self.acti_dict[name] for name in self.acti_dict] 
+                    grads = [self.grad_dict[name] for name in self.grad_dict]
+                    v = [torch.randn_like(a) for a in actis]
+                    Hv = hessian_vector_product(grads, actis, v)
+                    Hv = hessian_vector_product(first_order_grad, acti, v)
+                    tmp_eigenvalue = group_product(Hv, v).cpu().item()
+
+                    v = normalization(Hv)
+                    for i in range(len(Hv)):
+                        Hv[i] = None
+
+                    if eigenvalue is None:
+                        eigenvalue = tmp_eigenvalue
+                    else:
+                        if abs(eigenvalue - tmp_eigenvalue) / (abs(eigenvalue) + 1e-6) < tol:
+                            max_eigenvalues_dict[name] = eigenvalue
+                            break
+                        else:
+                            eigenvalue = tmp_eigenvalue
+                        max_eigenvalues_dict[name] = eigenvalue
+
+        return max_eigenvalues_dict
+
+    def layer_trace(self, maxIter=100, tol=1e-3) -> Dict:
+        """
+        Compute the trace of hessian in one model by layer.
+        """
+        device = self.device
+        trace_dict = {}
+        for name, mod in self.model.named_modules():
+            if isinstance(mod, (torch.nn.Conv2d, torch.nn.Linear)):
+                trace_vhv = []
+                trace = 0.
+                acti = self.acti_dict[name]
+                first_order_grad = self.grad_dict[name]
+                for i in range(maxIter):
+                    self.model.zero_grad()
+                    v = torch.randint_like(acti, high=2, device=device)
+                    # generate Rademacher random variables
+                    v[v == 0] = -1
+                    v = [v]
+                    Hv = hessian_vector_product(first_order_grad, acti, v)
+                    trace_vhv.append(group_product(Hv, v).cpu().item())
+                    if abs(np.mean(trace_vhv) - trace) / (trace + 1e-6) < tol:
+                        break
+                    else:
+                        trace = np.mean(trace_vhv)
+                    torch.cuda.empty_cache()
+                trace_dict[name] = trace
+        return trace_dict
diff --git a/mqbench/mix_precision/mix_precision.py b/mqbench/mix_precision/mix_precision.py
new file mode 100644
index 0000000..1a8fa96
--- /dev/null
+++ b/mqbench/mix_precision/mix_precision.py
@@ -0,0 +1,261 @@
+from typing import List, Tuple
+
+from torch.nn import Module
+
+from mqbench.mix_precision.hessian_per_layer import hessian_per_layer
+from mqbench.prepare_by_platform import BackendType, prepare_by_platform
+from mqbench.utils import is_symmetric_quant
+from mqbench.utils.logger import logger
+from mqbench.utils.state import disable_all
+
+
+def mixprecision_profiling(model: Module, quantized_model: Module, bitwidth_list: List, data: Tuple, criterion, algo='naive'):
+    """
+    Get layer sensitive index under a list of bitwidth.
+    A lot of algorithms can do the same thing.
+    HAWQ is the most useful one.
+    Naive is the most straight forward one.
+    """
+    layer_parameters_dict = model_size_analysis(model)
+    sensetive_dict = {}
+    if algo == 'hawq_eigen':
+        eigen_values_dict = hawq(model, data, criterion, type='eigenvalues')
+        # Do normalize.
+        for layer, eigen_val in eigen_values_dict.items():
+            eigen_values_dict[layer] = eigen_val / layer_parameters_dict[layer]
+        for name, max_eignevalues in eigen_values_dict.items():
+            logger.info("Layer {} with max eigen values: {}".format(name, max_eignevalues))
+        delta_w = get_delta_w(quantized_model, bitwidth_list)
+        for layer, max_eignevalues in eigen_values_dict.items():
+            # max_eigne_val: Float
+            # delta_w: List shape = bitwidth_list
+            sensetive_dict[layer] = max_eignevalues * delta_w[layer]
+    elif algo == 'hawq_trace':
+        trace_value_dict = hawq(model, data, criterion, type='trace')
+        # Do normalize.
+        for layer, trace in trace_value_dict.items():
+            trace_value_dict[layer] = trace / layer_parameters_dict[layer]
+        for name, trace in trace_value_dict.items():
+            logger.info("Layer {} with trace: {}".format(name, trace))
+        delta_w = get_delta_w(quantized_model, bitwidth_list)
+        for layer, trace in trace_value_dict.items():
+            # max_eigne_val: Float
+            # delta_w: List shape = bitwidth_list
+            sensetive_dict[layer] = trace * delta_w[layer]
+    elif algo == 'naive':
+        sensetive_dict = prec_degradation_by_layer(model, quantized_model, bitwidth_list, data, criterion)
+    else:
+        logger.info("Unknown algorithm!")
+    return sensetive_dict
+
+
+def get_delta_w(quantized_model: Module, bitwidth_list: List):
+    def get_new_qrange(bits, qscheme):
+        if is_symmetric_quant(qscheme):
+            return -2 ** (bits - 1), 2 ** (bits - 1) - 1
+        return 0, 2 ** bits - 1
+
+    def square_mean(ta, tb):
+        return torch.pow((ta - tb), 2.0).mean().detach().cpu().numpy()
+
+    delta_w = {}
+    for name, mod in quantized_model.named_modules():
+        logger.setLevel('CRITICAL')
+        disable_all(quantized_model)
+        logger.setLevel('INFO')
+        if hasattr(mod, 'weight_fake_quant'):
+            delta_w[name] = []
+            mod.weight_fake_quant.enable_observer()
+            mod.weight_fake_quant.enable_fake_quant()
+            for bits in bitwidth_list:
+                qscheme = mod.weight_fake_quant.activation_post_process.qscheme
+                new_quant_min, new_quant_max = get_new_qrange(bits, qscheme)
+                mod.weight_fake_quant.activation_post_process.quant_min = new_quant_min
+                mod.weight_fake_quant.activation_post_process.quant_max = new_quant_max
+                mod.weight_fake_quant.quant_min = new_quant_min
+                mod.weight_fake_quant.quant_max = new_quant_max
+                delta_w[name].append(square_mean(mod.weight, mod.weight_fake_quant(mod.weight)))
+            delta_w[name] = np.array(delta_w[name])
+            mod.weight_fake_quant.disable_observer()
+            mod.weight_fake_quant.disable_fake_quant()
+
+    return delta_w
+
+
+def model_size_analysis(model):
+    layer_parameters_dict = {}
+    for name, mod in model.named_modules():
+        if isinstance(mod, (torch.nn.Conv2d, torch.nn.Linear)):
+            layer_parameters_dict[name] = mod.weight.numel()
+    return layer_parameters_dict
+
+
+def model_latency_analysis(model):
+    pass
+
+
+def model_flops_analyze(model):
+    pass
+
+
+def mp_model_size(model: Module):
+    """
+    Calcualte model size in different bitwidth.
+    """
+    mp_size = 0
+    for mod in model.modules():
+        if hasattr(mod, 'weight_fake_quant'):
+            bitwidth = mod.weight_fake_quant.bitwidth
+            mp_size += mod.weight.numel() * bitwidth
+        elif hasattr(mod, 'weight'):
+            mp_size += mod.weight.numel() * 32
+    return mp_size / 8 / 1024 / 1024
+
+
+def prec_degradation_by_layer(model: Module, quantized_model: Module, bitwidth_list: List, data: Tuple, creterion):
+    """
+    Calculate degradation of each layer in different bitwidth.
+    """
+    def get_new_qrange(bits, qscheme):
+        if is_symmetric_quant(qscheme):
+            return -2 ** (bits - 1), 2 ** (bits - 1) - 1
+        return 0, 2 ** bits - 1
+
+    input_data, label_data = data
+    sensetive_dict = {}
+    output_data = model(input_data)
+    fp_loss = creterion(output_data, label_data)
+
+    for name, mod in quantized_model.named_modules():
+        logger.setLevel('CRITICAL')
+        disable_all(quantized_model)
+        logger.setLevel('INFO')
+        if hasattr(mod, 'weight_fake_quant'):
+            sensetive_dict[name] = []
+            mod.weight_fake_quant.enable_observer()
+            mod.weight_fake_quant.enable_fake_quant()
+            for bits in bitwidth_list:
+                qscheme = mod.weight_fake_quant.activation_post_process.qscheme
+                new_quant_min, new_quant_max = get_new_qrange(bits, qscheme)
+                mod.weight_fake_quant.activation_post_process.quant_min = new_quant_min
+                mod.weight_fake_quant.activation_post_process.quant_max = new_quant_max
+                mod.weight_fake_quant.quant_min = new_quant_min
+                mod.weight_fake_quant.quant_max = new_quant_max
+                with torch.no_grad():
+                    output_data = quantized_model(input_data)
+                loss = creterion(output_data, label_data)
+                sensetive_dict[name].append(loss)
+                logger.info("Layer {} under bit {} with sensetive {}".format(name, bits, loss - fp_loss))
+            mod.weight_fake_quant.disable_observer()
+            mod.weight_fake_quant.disable_fake_quant()
+
+    return sensetive_dict
+
+
+def hawq(model: Module, data: Tuple, criterion, type='trace'):
+    """
+    HAWQ layer sensetive indicator. Using extend PyHessian to calculate.
+    """
+    inputs, targets = data
+    hessian_comp = hessian_per_layer(model, criterion, data=(inputs, targets), cuda=True)
+    if type == 'eigenvalues':
+        return hessian_comp.layer_eigenvalues()
+    elif type == 'trace':
+        return hessian_comp.layer_trace()
+    else:
+        raise(NotImplementedError, "{} is not supported, only trace and eigenvalues.".format(type))
+
+
+def mixprecision_bit_selection(bitwidth_list, sensetive_dict, layer_parameters_dict, model_size_constraints, latency_constraints):
+    """
+    Resolute bitwidth by layer sensetive index / model size / accuracy.
+    """
+    # preato_frontier(model)
+    ILP_bit_selection(bitwidth_list, sensetive_dict, layer_parameters_dict, model_size_constraints, latency_constraints)
+
+
+def ILP_bit_selection(bitwidth_list, sensetive_dict, layer_parameters_dict, model_size_constraints: int, latency_constraints: int):
+    """
+    Bit selection process using ILP.
+    """
+    import pulp
+    from pulp import (GLPK_CMD, LpInteger, LpMinimize, LpStatus, LpVariable,
+                      value)
+
+    assert model_size_constraints or latency_constraints
+
+    prob = pulp.LpProblem("Min model size with best acc", LpMinimize)
+    variable = {}
+    for layer_name in sensetive_dict:
+        for bit in bitwidth_list:
+            variable[f"x_{layer_name}_{bit}"] = LpVariable(f"x_{layer_name}_{bit}", 0, 1, cat=LpInteger)
+
+    # Model acc constrains
+    senseitve_contrains = []
+    for name, params in layer_parameters_dict.items():
+        for idx, bit in enumerate(bitwidth_list):
+            senseitve_contrains.append(variable[f"x_{name}_{bit}"] * sensetive_dict[name][idx])
+    prob += sum(senseitve_contrains)
+
+    # Every Layer can only be assigned to one bitwidth.
+    for layer_name in sensetive_dict:
+        prob += sum([variable[f"x_{layer_name}_{bit}"] for bit in bitwidth_list]) == 1
+
+    # Model size constrains
+    total_size = []
+    for name, params in layer_parameters_dict.items():
+        for bit in bitwidth_list:
+            total_size.append(variable[f"x_{name}_{bit}"] * bit * params)
+    prob += sum(total_size) <= model_size_constraints * 8 * 1024 * 1024
+
+    status = prob.solve(GLPK_CMD(msg=1, options=["--tmlim", "10000", "--simplex"]))
+    LpStatus[status]
+    for layer_name in sensetive_dict:
+        for bit in bitwidth_list:
+            if value(variable[f"x_{layer_name}_{bit}"]) == 1:
+                logger.info("Layer {} with {} bits".format(layer_name, bit))
+    total_size = []
+    for name, params in layer_parameters_dict.items():
+        for bit in bitwidth_list:
+            total_size.append(value(variable[f"x_{name}_{bit}"]) * bit * params)
+    logger.info("Result model size {} MB.".format(sum(total_size) / 8 / 1024 / 1024))
+
+    senseitve_contrains = []
+    for name, params in layer_parameters_dict.items():
+        for idx, bit in enumerate(bitwidth_list):
+            senseitve_contrains.append(value(variable[f"x_{name}_{bit}"]) * sensetive_dict[name][idx])
+    logger.info("Result model sensetive is {}".format(sum(senseitve_contrains)))
+
+
+if __name__ == '__main__':
+    import numpy as np
+    import torch
+    import torchvision
+
+    model = torchvision.models.resnet18(pretrained=True).eval()
+
+    inputs = torch.rand(2, 3, 224, 224).cuda()
+    model = model.cuda()
+    with torch.no_grad():
+        targets = model(inputs)
+    targets = (targets == targets.max(dim=1, keepdim=True)[0]).to(dtype=torch.float32)
+
+    test_bitwidth_list = [2, 4, 8, 16]
+
+    quantized_model = prepare_by_platform(model, BackendType.Tensorrt)
+    layer_parameters_dict = model_size_analysis(model)
+    model_size = sum(list(layer_parameters_dict.values())) * 32 / 8 / 1024 / 1024
+    logger.info("FP model size: {:.2f} MB".format(model_size))
+    naive_sensetive_dict = mixprecision_profiling(model, quantized_model, test_bitwidth_list,
+                                                  data=(inputs, targets), criterion=torch.nn.CrossEntropyLoss(), algo='naive')
+    # maxeigen_sensetive_dict = mixprecision_profiling(model, quantized_model, test_bitwidth_list,
+    #                                                  data=(inputs, targets), criterion=torch.nn.CrossEntropyLoss(), algo='hawq_eigen')
+    # trace_sensetive_dict = mixprecision_profiling(model, quantized_model, test_bitwidth_list,
+    #                                               data=(inputs, targets), criterion=torch.nn.CrossEntropyLoss(), algo='hawq_trace')
+
+    mixprecision_bit_selection(test_bitwidth_list, 
+                               naive_sensetive_dict,
+                               # maxeigen_sensetive_dict,
+                               # trace_sensetive_dict,
+                               layer_parameters_dict,
+                               model_size_constraints=3, latency_constraints=None)
diff --git a/mqbench/nn/__init__.py b/mqbench/nn/__init__.py
new file mode 100644
index 0000000..9a8067b
--- /dev/null
+++ b/mqbench/nn/__init__.py
@@ -0,0 +1 @@
+from .modules import *
\ No newline at end of file
diff --git a/mqbench/nn/intrinsic/__init__.py b/mqbench/nn/intrinsic/__init__.py
new file mode 100644
index 0000000..9a8067b
--- /dev/null
+++ b/mqbench/nn/intrinsic/__init__.py
@@ -0,0 +1 @@
+from .modules import *
\ No newline at end of file
diff --git a/mqbench/nn/intrinsic/modules/__init__.py b/mqbench/nn/intrinsic/modules/__init__.py
new file mode 100644
index 0000000..7735e4e
--- /dev/null
+++ b/mqbench/nn/intrinsic/modules/__init__.py
@@ -0,0 +1 @@
+from .fused import LinearBn1d, ConvTransposeBn2d, ConvTransposeReLU2d, ConvTransposeBnReLU2d, ConvBnReLU2d, ConvBn2d, ConvReLU2d, ConvFreezebn2d, ConvFreezebnReLU2d, ConvTransposeFreezebn2d, ConvTransposeFreezebnReLU2d
diff --git a/mqbench/nn/intrinsic/modules/fused.py b/mqbench/nn/intrinsic/modules/fused.py
new file mode 100644
index 0000000..6faa73a
--- /dev/null
+++ b/mqbench/nn/intrinsic/modules/fused.py
@@ -0,0 +1,86 @@
+from torch.nn.intrinsic import _FusedModule
+from torch.nn import Linear, BatchNorm1d, BatchNorm2d, ReLU, ConvTranspose2d, Conv2d
+from mqbench.nn.modules import FrozenBatchNorm2d
+
+class LinearBn1d(_FusedModule):
+    r"""This is a sequential container which calls the Linear and Batch Norm 1d modules.
+    During quantization this will be replaced with the corresponding fused module."""
+    def __init__(self, linear, bn):
+        assert type(linear) == Linear and type(bn) == BatchNorm1d, \
+            'Incorrect types for input modules{}{}'.format(
+                type(linear), type(bn))
+        super().__init__(linear, bn) 
+
+class ConvTransposeBn2d(_FusedModule):
+    def __init__(self, deconv, bn):
+        assert type(deconv) == ConvTranspose2d and type(bn) == BatchNorm2d, \
+            'Incorrect types for input modules{}{}'.format(
+                type(deconv), type(bn))
+        super().__init__(deconv, bn)
+
+
+class ConvTransposeBnReLU2d(_FusedModule):
+    def __init__(self, deconv, bn, relu):
+        assert type(deconv) == ConvTranspose2d and type(bn) == BatchNorm2d and type(relu) == ReLU, \
+            'Incorrect types for input modules{}{}{}'.format(
+                type(deconv), type(bn), type(relu))
+        super().__init__(deconv, bn, relu)
+
+
+class ConvTransposeReLU2d(_FusedModule):
+    def __init__(self, deconv, relu):
+        assert type(deconv) == ConvTranspose2d and type(relu) == ReLU, \
+            'Incorrect types for input modules{}{}'.format(
+                type(deconv), type(relu))
+        super().__init__(deconv, relu)
+class ConvBn2d(_FusedModule):
+    def __init__(self, conv, bn):
+        assert type(conv) == Conv2d and type(bn) == BatchNorm2d, \
+            'Incorrect types for input modules{}{}'.format(
+                type(conv), type(bn))
+        super().__init__(conv, bn)
+
+
+class ConvBnReLU2d(_FusedModule):
+    def __init__(self, conv, bn, relu):
+        assert type(conv) == Conv2d and type(bn) == BatchNorm2d and type(relu) == ReLU, \
+            'Incorrect types for input modules{}{}{}'.format(
+                type(conv), type(bn), type(relu))
+        super().__init__(conv, bn, relu)
+
+
+class ConvReLU2d(_FusedModule):
+    def __init__(self, conv, relu):
+        assert type(conv) == Conv2d and type(relu) == ReLU, \
+            'Incorrect types for input modules{}{}'.format(
+                type(conv), type(relu))
+        super().__init__(conv, relu)
+
+
+class ConvFreezebn2d(_FusedModule):
+    def __init__(self, conv, bn):
+        assert type(conv) == Conv2d and type(bn) == FrozenBatchNorm2d, \
+            'Incorrect types for input modules{}{}'.format(
+                type(conv), type(bn))
+        super().__init__(conv, bn)
+
+class ConvFreezebnReLU2d(_FusedModule):
+    def __init__(self, conv, bn, relu):
+        assert type(conv) == Conv2d and type(bn) == FrozenBatchNorm2d and type(relu) == ReLU, \
+            'Incorrect types for input modules{}{}{}'.format(
+                type(conv), type(bn), type(relu))
+        super().__init__(conv, bn, relu)
+
+class ConvTransposeFreezebn2d(_FusedModule):
+    def __init__(self, deconv, bn):
+        assert type(deconv) == ConvTranspose2d and type(bn) == FrozenBatchNorm2d, \
+            'Incorrect types for input modules{}{}'.format(
+                type(deconv), type(bn))
+        super().__init__(deconv, bn)
+
+class ConvTransposeFreezebnReLU2d(_FusedModule):
+    def __init__(self, deconv, bn, relu):
+        assert type(deconv) == ConvTranspose2d and type(bn) == FrozenBatchNorm2d and type(relu) == ReLU, \
+            'Incorrect types for input modules{}{}{}'.format(
+                type(deconv), type(bn), type(relu))
+        super().__init__(deconv, bn, relu)
diff --git a/mqbench/nn/intrinsic/qat/__init__.py b/mqbench/nn/intrinsic/qat/__init__.py
new file mode 100644
index 0000000..9a8067b
--- /dev/null
+++ b/mqbench/nn/intrinsic/qat/__init__.py
@@ -0,0 +1 @@
+from .modules import *
\ No newline at end of file
diff --git a/mqbench/nn/intrinsic/qat/modules/__init__.py b/mqbench/nn/intrinsic/qat/modules/__init__.py
new file mode 100644
index 0000000..3118f9b
--- /dev/null
+++ b/mqbench/nn/intrinsic/qat/modules/__init__.py
@@ -0,0 +1,4 @@
+from .linear_fused import LinearBn1d
+from .deconv_fused import ConvTransposeBnReLU2d, ConvTransposeBn2d, ConvTransposeReLU2d
+from .conv_fused import ConvBnReLU2d, ConvBn2d, ConvReLU2d
+from .freezebn import ConvFreezebn2d, ConvFreezebnReLU2d, ConvTransposeFreezebn2d, ConvTransposeFreezebnReLU2d
diff --git a/mqbench/nn/intrinsic/qat/modules/conv_fused.py b/mqbench/nn/intrinsic/qat/modules/conv_fused.py
new file mode 100644
index 0000000..8e64e8f
--- /dev/null
+++ b/mqbench/nn/intrinsic/qat/modules/conv_fused.py
@@ -0,0 +1,411 @@
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.intrinsic as nni
+import torch.nn.functional as F
+from torch.nn import init
+from torch.nn.intrinsic import _FusedModule
+from torch.nn.parameter import Parameter
+from torch.nn.modules.utils import _pair
+
+from typing import TypeVar 
+
+
+import mqbench.nn.qat as qnnqat
+from mqbench.quantization.default_bias_fake_quant import bias_fake_quantizer
+
+_BN_CLASS_MAP = {
+    1: nn.BatchNorm1d,
+    2: nn.BatchNorm2d,
+    3: nn.BatchNorm3d,
+}
+
+MOD = TypeVar('MOD', bound=nn.modules.conv._ConvNd)
+
+
+class _ConvBnNd(nn.modules.conv._ConvNd, _FusedModule):
+
+    _version = 2
+    _FLOAT_MODULE = MOD
+
+    def __init__(self,
+                 # ConvNd args
+                 in_channels, out_channels, kernel_size, stride,
+                 padding, dilation, transposed, output_padding,
+                 groups,
+                 bias,
+                 padding_mode,
+                 # BatchNormNd args
+                 # num_features: out_channels
+                 eps=1e-05, momentum=0.1,
+                 # affine: True
+                 # track_running_stats: True
+                 # Args for this module
+                 freeze_bn=False,
+                 qconfig=None,
+                 dim=2):
+        nn.modules.conv._ConvNd.__init__(self, in_channels, out_channels, kernel_size,
+                                         stride, padding, dilation, transposed,
+                                         output_padding, groups, False, padding_mode)
+        assert qconfig, 'qconfig must be provided for QAT module'
+        self.qconfig = qconfig
+        self.freeze_bn = freeze_bn if self.training else True
+        self.bn = _BN_CLASS_MAP[dim](out_channels, eps, momentum, True, True)
+        self.weight_fake_quant = self.qconfig.weight()
+        if bias:
+            self.bias = Parameter(torch.empty(out_channels))
+        else:
+            self.register_parameter('bias', None)
+        # self.bias_fake_quant = bias_fake_quantizer()  # 这玩意调用的是tqt的
+        self.reset_bn_parameters()
+
+        # this needs to be called after reset_bn_parameters,
+        # as they modify the same state
+        if self.training:
+            if freeze_bn:
+                self.freeze_bn_stats()
+            else:
+                self.update_bn_stats()
+        else:
+            self.freeze_bn_stats()
+
+    def reset_running_stats(self):
+        self.bn.reset_running_stats()
+
+    def reset_bn_parameters(self):
+        self.bn.reset_running_stats()
+        init.uniform_(self.bn.weight)
+        init.zeros_(self.bn.bias)
+        # note: below is actully for conv, not BN
+        if self.bias is not None:
+            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / math.sqrt(fan_in)
+            init.uniform_(self.bias, -bound, bound)
+
+    def reset_parameters(self):
+        super(_ConvBnNd, self).reset_parameters()
+
+    def update_bn_stats(self):
+        self.freeze_bn = False
+        self.bn.training = True
+        return self
+
+    def freeze_bn_stats(self):
+        self.freeze_bn = True
+        self.bn.training = False
+        return self
+
+    def _forward(self, input):
+        assert self.bn.running_var is not None
+        running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
+        scale_factor = self.bn.weight / running_std
+        weight_shape = [1] * len(self.weight.shape)
+        weight_shape[0] = -1
+        bias_shape = [1] * len(self.weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = self.weight_fake_quant(self.weight * scale_factor.reshape(weight_shape))
+        # using zero bias here since the bias for original conv
+        # will be added later
+        if self.bias is not None:
+            zero_bias = torch.zeros_like(self.bias)
+            conv_bias = self.bias 
+        else:
+            zero_bias = torch.zeros(self.out_channels, device=scaled_weight.device)
+            conv_bias = torch.zeros_like(zero_bias, device=scaled_weight.device)
+        if self.bn.affine:
+            full_bias = (conv_bias - self.bn.running_mean) / running_std * self.bn.weight + self.bn.bias 
+        else:
+            full_bias = (conv_bias - self.bn.running_mean) / running_std 
+        # quant_bias = self.bias_fake_quant(full_bias)  # 我们这里就不进行量化了。因为终归由后端硬件支持来决定bias量化的方式
+        quant_bias = full_bias
+        conv_with_bias = self._conv_forward(input, scaled_weight, quant_bias)
+        conv_orig = (conv_with_bias - full_bias.reshape(bias_shape)) / scale_factor.reshape(bias_shape) + conv_bias.reshape(bias_shape)
+        conv = self.bn(conv_orig)
+        return conv
+
+    def extra_repr(self):
+        # TODO(jerryzh): extend
+        return super(_ConvBnNd, self).extra_repr()
+
+    def forward(self, input):
+        return self._forward(input)
+
+    def train(self, mode=True):
+        """
+        Batchnorm's training behavior is using the self.training flag. Prevent
+        changing it if BN is frozen. This makes sure that calling `model.train()`
+        on a model with a frozen BN will behave properly.
+        """
+        self.training = mode
+        if not self.freeze_bn:
+            for module in self.children():
+                module.train(mode)
+        return self
+
+    # ===== Serialization version history =====
+    #
+    # Version 1/None
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- gamma : Tensor
+    #   |--- beta : Tensor
+    #   |--- running_mean : Tensor
+    #   |--- running_var : Tensor
+    #   |--- num_batches_tracked : Tensor
+    #
+    # Version 2
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- bn : Module
+    #        |--- weight : Tensor (moved from v1.self.gamma)
+    #        |--- bias : Tensor (moved from v1.self.beta)
+    #        |--- running_mean : Tensor (moved from v1.self.running_mean)
+    #        |--- running_var : Tensor (moved from v1.self.running_var)
+    #        |--- num_batches_tracked : Tensor (moved from v1.self.num_batches_tracked)
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
+        version = local_metadata.get('version', None)
+        if version is None or version == 1:
+            # BN related parameters and buffers were moved into the BN module for v2
+            v2_to_v1_names = {
+                'bn.weight': 'gamma',
+                'bn.bias': 'beta',
+                'bn.running_mean': 'running_mean',
+                'bn.running_var': 'running_var',
+                'bn.num_batches_tracked': 'num_batches_tracked',
+            }
+            for v2_name, v1_name in v2_to_v1_names.items():
+                if prefix + v1_name in state_dict:
+                    state_dict[prefix + v2_name] = state_dict[prefix + v1_name]
+                    state_dict.pop(prefix + v1_name)
+                elif prefix + v2_name in state_dict:
+                    # there was a brief period where forward compatibility
+                    # for this module was broken (between
+                    # https://github.com/pytorch/pytorch/pull/38478
+                    # and https://github.com/pytorch/pytorch/pull/38820)
+                    # and modules emitted the v2 state_dict format while
+                    # specifying that version == 1. This patches the forward
+                    # compatibility issue by allowing the v2 style entries to
+                    # be used.
+                    pass
+                elif strict:
+                    missing_keys.append(prefix + v2_name)
+
+        super(_ConvBnNd, self)._load_from_state_dict(
+            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
+
+    @classmethod
+    def from_float(cls, mod):
+        r"""Create a qat module from a float module or qparams_dict
+
+            Args: `mod` a float module, either produced by torch.ao.quantization utilities
+            or directly from user
+        """
+        # The ignore is because _FLOAT_MODULE is a TypeVar here where the bound
+        # has no __name__ (code is fine though)
+        assert type(mod) == cls._FLOAT_MODULE, 'qat.' + cls.__name__ + '.from_float only works for ' + \
+            cls._FLOAT_MODULE.__name__  # type: ignore[attr-defined]
+        assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
+        assert mod.qconfig, 'Input float module must have a valid qconfig'
+        qconfig = mod.qconfig
+        conv, bn = mod[0], mod[1]
+        qat_convbn = cls(conv.in_channels, conv.out_channels, conv.kernel_size,
+                         conv.stride, conv.padding, conv.dilation,
+                         conv.groups, conv.bias is not None,
+                         conv.padding_mode,
+                         bn.eps, bn.momentum,
+                         False,
+                         qconfig)
+        qat_convbn.weight = conv.weight
+        qat_convbn.bias = conv.bias
+        qat_convbn.bn.weight = bn.weight
+        qat_convbn.bn.bias = bn.bias
+        qat_convbn.bn.running_mean = bn.running_mean
+        qat_convbn.bn.running_var = bn.running_var
+        # mypy error: Cannot determine type of 'num_batches_tracked'
+        qat_convbn.bn.num_batches_tracked = bn.num_batches_tracked  # type: ignore[has-type]
+        return qat_convbn
+
+    def to_float(self):
+        modules = []
+        cls = type(self)
+        conv = cls._FLOAT_CONV_MODULE(  # type: ignore[attr-defined]
+            self.in_channels,
+            self.out_channels,
+            self.kernel_size,
+            self.stride,
+            self.padding,
+            self.dilation,
+            self.groups,
+            self.bias is not None,
+            self.padding_mode)
+        conv.weight = torch.nn.Parameter(self.weight.detach())
+        if self.bias is not None:
+            conv.bias = torch.nn.Parameter(self.bias.detach())
+        modules.append(conv)
+
+        if cls._FLOAT_BN_MODULE:  # type: ignore[attr-defined]
+            bn = cls._FLOAT_BN_MODULE(  # type: ignore[attr-defined]
+                self.bn.num_features,
+                self.bn.eps,
+                self.bn.momentum,
+                self.bn.affine,
+                self.bn.track_running_stats)
+            bn.weight = Parameter(self.bn.weight.detach())
+            if self.bn.affine:
+                bn.bias = Parameter(self.bn.bias.detach())
+            modules.append(bn)
+
+        if cls._FLOAT_RELU_MODULE:  # type: ignore[attr-defined]
+            relu = cls._FLOAT_RELU_MODULE()  # type: ignore[attr-defined]
+            modules.append(relu)
+
+        result = cls._FLOAT_MODULE(*modules)  # type: ignore[operator]
+        result.train(self.training)
+        return result
+
+
+
+class ConvBn2d(_ConvBnNd, nn.Conv2d):
+    r"""
+    A ConvBn2d module is a module fused from Conv2d and BatchNorm2d,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv2d` and
+    :class:`torch.nn.BatchNorm2d`.
+
+    Similar to :class:`torch.nn.Conv2d`, with FakeQuantize modules initialized
+    to default.
+
+    Attributes:
+        freeze_bn:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_MODULE = nni.ConvBn2d
+    _FLOAT_CONV_MODULE = nn.Conv2d
+    _FLOAT_BN_MODULE = nn.BatchNorm2d
+    _FLOAT_RELU_MODULE = None
+
+    def __init__(self,
+                 # ConvNd args
+                 in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1,
+                 bias=None,
+                 padding_mode='zeros',
+                 # BatchNorm2d args
+                 # num_features: out_channels
+                 eps=1e-05, momentum=0.1,
+                 # affine: True
+                 # track_running_stats: True
+                 # Args for this module
+                 freeze_bn=False,
+                 qconfig=None):
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+        _ConvBnNd.__init__(self, in_channels, out_channels, kernel_size, stride,
+                           padding, dilation, False, _pair(0), groups, bias, padding_mode,
+                           eps, momentum, freeze_bn, qconfig, dim=2)
+
+class ConvBnReLU2d(ConvBn2d):
+    r"""
+    A ConvBnReLU2d module is a module fused from Conv2d, BatchNorm2d and ReLU,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+
+    We combined the interface of :class:`torch.nn.Conv2d` and
+    :class:`torch.nn.BatchNorm2d` and :class:`torch.nn.ReLU`.
+
+    Similar to `torch.nn.Conv2d`, with FakeQuantize modules initialized to
+    default.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    # base class defines _FLOAT_MODULE as "ConvBn2d"
+    _FLOAT_MODULE = nni.ConvBnReLU2d  # type: ignore[assignment]
+    _FLOAT_CONV_MODULE = nn.Conv2d
+    _FLOAT_BN_MODULE = nn.BatchNorm2d
+    _FLOAT_RELU_MODULE = nn.ReLU  # type: ignore[assignment]
+
+    def __init__(self,
+                 # Conv2d args
+                 in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1,
+                 bias=None,
+                 padding_mode='zeros',
+                 # BatchNorm2d args
+                 # num_features: out_channels
+                 eps=1e-05, momentum=0.1,
+                 # affine: True
+                 # track_running_stats: True
+                 # Args for this module
+                 freeze_bn=False,
+                 qconfig=None):
+        super(ConvBnReLU2d, self).__init__(in_channels, out_channels, kernel_size, stride,
+                                           padding, dilation, groups, bias,
+                                           padding_mode, eps, momentum,
+                                           freeze_bn,
+                                           qconfig)
+
+    def forward(self, input):
+        return F.relu(ConvBn2d._forward(self, input))
+
+    @classmethod
+    def from_float(cls, mod):
+        return super(ConvBnReLU2d, cls).from_float(mod)
+
+class ConvReLU2d(qnnqat.Conv2d, _FusedModule):
+    r"""A ConvReLU2d module is a fused module of Conv2d and ReLU, attached with
+    FakeQuantize modules for weight for
+    quantization aware training.
+
+    We combined the interface of :class:`~torch.nn.Conv2d` and
+    :class:`~torch.nn.BatchNorm2d`.
+
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+
+    """
+    _FLOAT_MODULE = nni.ConvReLU2d
+    _FLOAT_CONV_MODULE = nn.Conv2d
+    _FLOAT_BN_MODULE = None
+    _FLOAT_RELU_MODULE = nn.ReLU
+
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1,
+                 bias=True, padding_mode='zeros',
+                 qconfig=None):
+        super(ConvReLU2d, self).__init__(in_channels, out_channels, kernel_size,
+                                         stride=stride, padding=padding, dilation=dilation,
+                                         groups=groups, bias=bias, padding_mode=padding_mode,
+                                         qconfig=qconfig)
+        assert qconfig, 'qconfig must be provided for QAT module'
+        self.qconfig = qconfig
+        self.weight_fake_quant = self.qconfig.weight()
+
+    def forward(self, input):
+        return F.relu(
+            self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias))
+
+    @classmethod
+    def from_float(cls, mod):
+        assert type(mod) == cls._FLOAT_MODULE, 'qat.' + cls.__name__ + '.from_float only works for ' + cls._FLOAT_MODULE.__name__
+        assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
+        assert mod.qconfig, 'Input float module must have a valid qconfig'
+        if type(mod) == cls._FLOAT_MODULE:
+            mod = mod[0]
+        qconfig = mod.qconfig
+        qat_conv = cls(mod.in_channels, mod.out_channels, mod.kernel_size,
+                       stride=mod.stride, padding=mod.padding, dilation=mod.dilation,
+                       groups=mod.groups, bias=mod.bias is not None,
+                       padding_mode=mod.padding_mode, qconfig=qconfig)
+        qat_conv.weight = mod.weight
+        qat_conv.bias = mod.bias
+        return qat_conv
diff --git a/mqbench/nn/intrinsic/qat/modules/deconv_fused.py b/mqbench/nn/intrinsic/qat/modules/deconv_fused.py
new file mode 100644
index 0000000..c17570f
--- /dev/null
+++ b/mqbench/nn/intrinsic/qat/modules/deconv_fused.py
@@ -0,0 +1,392 @@
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import init
+from torch.nn.intrinsic import _FusedModule
+from torch.nn.parameter import Parameter
+from torch.nn.modules.utils import _pair, _single
+
+from typing import TypeVar
+
+import mqbench.nn.intrinsic as qnni
+import mqbench.nn.qat as qnnqat
+
+
+_BN_CLASS_MAP = {
+    1: nn.BatchNorm1d,
+    2: nn.BatchNorm2d,
+    3: nn.BatchNorm3d,
+}
+
+MOD = TypeVar('MOD', bound=nn.modules.conv._ConvTransposeNd)
+
+
+class _ConvTransposeBnNd(nn.modules.conv._ConvTransposeNd, _FusedModule):
+
+    _version = 2
+    _FLOAT_MODULE = MOD
+
+    def __init__(
+            self,
+            # ConvTransposeBnNd args
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            bias,
+            transposed,
+            padding,
+            output_padding,
+            groups,
+            dilation,
+            padding_mode,
+            # bn args
+            # BatchNormNd args
+            # num_features: out_channels
+            eps=1e-05,
+            momentum=0.1,
+            # affine: True
+            # track_running_stats: True
+            # Args for this module
+            freeze_bn=False,
+            qconfig=None,
+            dim=2):
+        kernel_size = _single(kernel_size)
+        stride = _single(stride)
+        padding = _single(padding)
+        dilation = _single(dilation)
+        output_padding = _single(output_padding)
+        nn.modules.conv._ConvTransposeNd.__init__(self, in_channels,
+                                                  out_channels, kernel_size,
+                                                  stride, padding, dilation,
+                                                  transposed, output_padding,
+                                                  groups, False, padding_mode)
+        assert qconfig, 'qconfig must be provided for a QAT module'
+        self.qconfig = qconfig
+        self.freeze_bn = freeze_bn if self.training else True
+        self.bn = _BN_CLASS_MAP[dim](out_channels, eps, momentum, True, True)
+        self.weight_fake_quant = self.qconfig.weight()
+        # ConvTranspose do per-channel quantize on output channel.
+        if self.weight_fake_quant.ch_axis != -1:
+            self.weight_fake_quant.ch_axis = 1
+            self.weight_fake_quant.activation_post_process.ch_axis = 1
+        if bias:
+            self.bias = Parameter(torch.Tensor(out_channels))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_bn_parameters()
+
+        if self.training:
+            if freeze_bn:
+                self.freeze_bn_stats()
+            else:
+                self.update_bn_stats()
+        else:
+            self.freeze_bn_stats()
+
+    def reset_running_stats(self):
+        self.bn.reset_running_stats()
+
+    def reset_bn_parameters(self):
+        self.bn.reset_running_stats()
+        init.uniform_(self.bn.weight)
+        init.zeros_(self.bn.bias)
+        # note: below is actully for conv, not BN
+        if self.bias is not None:
+            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / math.sqrt(fan_in)
+            init.uniform_(self.bias, -bound, bound)
+
+    def reset_parameters(self):
+        super(_ConvTransposeBnNd, self).reset_parameters()
+
+    def update_bn_stats(self):
+        self.freeze_bn = False
+        self.bn.training = True
+        return self
+
+    def freeze_bn_stats(self):
+        self.freeze_bn = True
+        self.bn.training = False
+        return self
+
+    def _forward(self, input):
+        assert self.bn.running_var is not None
+        running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
+        scale_factor = self.bn.weight / running_std
+        weight_shape = [1] * len(self.weight.shape)
+        weight_shape[1] = -1
+        bias_shape = [1] * len(self.weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = self.weight_fake_quant(self.weight * scale_factor.reshape(weight_shape))
+        # using zero bias here since the bias for original conv
+        # will be added later
+        if self.bias is not None:
+            zero_bias = torch.zeros_like(self.bias)
+        else:
+            zero_bias = torch.zeros(self.out_channels, device=scaled_weight.device)
+        deconv = self._convtransposed_forward(input, scaled_weight, zero_bias)
+        deconv_orig = deconv / scale_factor.reshape(bias_shape)
+        if self.bias is not None:
+            deconv_orig = deconv_orig + self.bias.reshape(bias_shape)
+        deconv = self.bn(deconv_orig)
+        return deconv
+
+    def _convtransposed_forward(self, x, w, b):
+        raise NotImplementedError(
+            'The sub-class must implement this function to forward in the needed dim-version!'
+        )
+
+    def extra_repr(self):
+        # TODO(jerryzh): extend
+        return super(_ConvTransposeBnNd, self).extra_repr()
+
+    def forward(self, input):
+        return self._forward(input)
+
+    def train(self, mode=True):
+        """
+        Batchnorm's training behavior is using the self.training flag. Prevent
+        changing it if BN is frozen. This makes sure that calling `model.train()`
+        on a model with a frozen BN will behave properly.
+        """
+        self.training = mode
+        if not self.freeze_bn:
+            for module in self.children():
+                module.train(mode)
+        return self
+
+    # ===== Serialization version history =====
+    #
+    # Version 1/None
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- gamma : Tensor
+    #   |--- beta : Tensor
+    #   |--- running_mean : Tensor
+    #   |--- running_var : Tensor
+    #   |--- num_batches_tracked : Tensor
+    #
+    # Version 2
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- bn : Module
+    #        |--- weight : Tensor (moved from v1.self.gamma)
+    #        |--- bias : Tensor (moved from v1.self.beta)
+    #        |--- running_mean : Tensor (moved from v1.self.running_mean)
+    #        |--- running_var : Tensor (moved from v1.self.running_var)
+    #        |--- num_batches_tracked : Tensor (moved from v1.self.num_batches_tracked)
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict,
+                              missing_keys, unexpected_keys, error_msgs):
+        version = local_metadata.get('version', None)
+        if version is None or version == 1:
+            # BN related parameters and buffers were moved into the BN module for v2
+            v2_to_v1_names = {
+                'bn.weight': 'gamma',
+                'bn.bias': 'beta',
+                'bn.running_mean': 'running_mean',
+                'bn.running_var': 'running_var',
+                'bn.num_batches_tracked': 'num_batches_tracked',
+            }
+            for v2_name, v1_name in v2_to_v1_names.items():
+                if prefix + v1_name in state_dict:
+                    state_dict[prefix + v2_name] = state_dict[prefix + v1_name]
+                    state_dict.pop(prefix + v1_name)
+                elif prefix + v2_name in state_dict:
+                    # there was a brief period where forward compatibility
+                    # for this module was broken (between
+                    # https://github.com/pytorch/pytorch/pull/38478
+                    # and https://github.com/pytorch/pytorch/pull/38820)
+                    # and modules emitted the v2 state_dict format while
+                    # specifying that version == 1. This patches the forward
+                    # compatibility issue by allowing the v2 style entries to
+                    # be used.
+                    pass
+                elif strict:
+                    missing_keys.append(prefix + v2_name)
+
+        super(_ConvTransposeBnNd,
+              self)._load_from_state_dict(state_dict, prefix, local_metadata,
+                                          strict, missing_keys,
+                                          unexpected_keys, error_msgs)
+
+    @classmethod
+    def from_float(cls, mod):
+        r"""Create a qat module from a float module or qparams_dict
+
+            Args: `mod` a float module, either produced by torch.quantization utilities
+            or directly from user
+        """
+        assert type(mod) == cls._FLOAT_MODULE, 'qat.' + cls.__name__ + '.from_float only works for ' + \
+            cls._FLOAT_MODULE.__name__
+        assert hasattr(
+            mod, 'qconfig'), 'Input float module must have qconfig defined'
+        assert mod.qconfig, 'Input float module must have a valid qconfig'
+        qconfig = mod.qconfig
+        deconv, bn = mod[0], mod[1]
+        qat_deconvbn = cls(deconv.in_channels, deconv.out_channels,
+                           deconv.kernel_size, deconv.stride, deconv.bias
+                           is not None, deconv.transposed, deconv.padding,
+                           deconv.output_padding, deconv.groups,
+                           deconv.dilation, deconv.padding_mode, bn.eps,
+                           bn.momentum, False, qconfig)
+        qat_deconvbn.weight = deconv.weight
+        qat_deconvbn.bias = deconv.bias
+        qat_deconvbn.bn.weight = bn.weight
+        qat_deconvbn.bn.bias = bn.bias
+        qat_deconvbn.bn.running_mean = bn.running_mean
+        qat_deconvbn.bn.running_var = bn.running_var
+        # mypy error: Cannot determine type of 'num_batches_tracked'
+        qat_deconvbn.bn.num_batches_tracked = bn.num_batches_tracked  # type: ignore[has-type]
+        return qat_deconvbn
+
+
+class ConvTransposeBn2d(_ConvTransposeBnNd, nn.ConvTranspose2d):
+    _FLOAT_MODULE = qnni.ConvTransposeBn2d
+
+    def __init__(
+            self,
+            # ConvTransposeBnNd args
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            bias=None,
+            transposed=True,
+            padding=0,
+            output_padding=0,
+            groups=1,
+            dilation=1,
+            padding_mode='zeros',
+            # bn args
+            # BatchNormNd args
+            # num_features: out_channels
+            eps=1e-05,
+            momentum=0.1,
+            # affine: True
+            # track_running_stats: True
+            # Args for this module
+            freeze_bn=False,
+            qconfig=None):
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+        _ConvTransposeBnNd.__init__(self, in_channels, out_channels,
+                                    kernel_size, stride, bias, transposed,
+                                    padding, output_padding, groups, dilation,
+                                    padding_mode, eps, momentum, freeze_bn,
+                                    qconfig)
+
+    def _convtransposed_forward(self, x, w, b):
+        output_padding = self._output_padding(x, None, self.stride,
+                                              self.padding, self.kernel_size,
+                                              self.dilation)
+        return F.conv_transpose2d(x, w, b, self.stride, self.padding,
+                                  output_padding, self.groups, self.dilation)
+
+
+class ConvTransposeBnReLU2d(ConvTransposeBn2d):
+    _FLOAT_MODULE = qnni.ConvTransposeBnReLU2d
+
+    def __init__(
+            self,
+            # ConvTransposeBnNd args
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            bias=None,
+            transposed=True,
+            padding=0,
+            output_padding=0,
+            groups=1,
+            dilation=1,
+            padding_mode='zeros',
+            # bn args
+            # BatchNormNd args
+            # num_features: out_channels
+            eps=1e-05,
+            momentum=0.1,
+            # affine: True
+            # track_running_stats: True
+            # Args for this module
+            freeze_bn=False,
+            qconfig=None):
+        # super(ConvTransposeBnReLU2d, self).__init__(in_channels, out_channels, kernel_size, stride,
+        #                                             padding, dilation, groups, bias,
+        #                                             padding_mode, eps, momentum,
+        #                                             freeze_bn,
+        #                                             qconfig)
+        super(ConvTransposeBnReLU2d,
+              self).__init__(in_channels,
+                             out_channels,
+                             kernel_size,
+                             stride=stride,
+                             bias=bias,
+                             transposed=transposed,
+                             padding=padding,
+                             output_padding=output_padding,
+                             groups=groups,
+                             dilation=dilation,
+                             padding_mode=padding_mode,
+                             eps=eps,
+                             momentum=momentum,
+                             freeze_bn=freeze_bn,
+                             qconfig=qconfig)
+
+    def forward(self, input):
+        return F.relu(ConvTransposeBn2d._forward(self, input))
+
+    @classmethod
+    def from_float(cls, mod):
+        return super(ConvTransposeBnReLU2d, cls).from_float(mod)
+
+
+class ConvTransposeReLU2d(qnnqat.ConvTranspose2d):
+    _FLOAT_MODULE = qnni.ConvTransposeReLU2d
+    _FLOAT_DECONV_MODULE = nn.ConvTranspose2d
+    _FLOAT_BN_MODULE = None
+    _FLOAT_RELU_MODULE = nn.ReLU
+
+    def __init__(
+            self,
+            # ConvTransposeBnNd args
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            bias=None,
+            transposed=True,
+            padding=0,
+            output_padding=0,
+            groups=1,
+            dilation=1,
+            padding_mode='zeros',
+            qconfig=None):
+
+        super(ConvTransposeReLU2d,
+              self).__init__(in_channels,
+                             out_channels,
+                             kernel_size,
+                             stride=stride,
+                             bias=bias,
+                             padding=padding,
+                             output_padding=output_padding,
+                             groups=groups,
+                             dilation=dilation,
+                             padding_mode=padding_mode,
+                             qconfig=qconfig)
+        assert qconfig, 'qconfig must be provided for QAT module'
+
+    def forward(self, input, output_size=None):
+        output_padding = self._output_padding(input, output_size, self.stride,
+                                              self.padding, self.kernel_size,
+                                              self.dilation)
+        return F.relu(F.conv_transpose2d(input, self.weight_fake_quant(self.weight),
+                                         self.bias, self.stride, self.padding, output_padding,
+                                         self.groups, self.dilation))
\ No newline at end of file
diff --git a/mqbench/nn/intrinsic/qat/modules/freezebn.py b/mqbench/nn/intrinsic/qat/modules/freezebn.py
new file mode 100644
index 0000000..1d2209c
--- /dev/null
+++ b/mqbench/nn/intrinsic/qat/modules/freezebn.py
@@ -0,0 +1,448 @@
+import math
+import torch
+import torch.nn as nn
+import torch.nn.intrinsic as nni
+import torch.nn.functional as F
+from torch.nn import init
+from torch.nn.modules.utils import _pair, _single
+from torch.nn.parameter import Parameter
+from typing import TypeVar
+import mqbench.nn.intrinsic as qnni
+from mqbench.nn.modules import FrozenBatchNorm2d
+from .deconv_fused import _ConvTransposeBnNd
+
+MOD = TypeVar('MOD', bound=nn.modules.conv._ConvNd)
+
+
+class _ConvFreezebnNd(nn.modules.conv._ConvNd, nni._FusedModule):
+
+    _version = 2
+    _FLOAT_MODULE = MOD
+
+    def __init__(self,
+                 # ConvNd args
+                 in_channels, out_channels, kernel_size, stride,
+                 padding, dilation, transposed, output_padding,
+                 groups,
+                 bias,
+                 padding_mode,
+                 # BatchNormNd args
+                 # num_features: out_channels
+                 eps=1e-05, momentum=0.1,
+                 # affine: True
+                 # track_running_stats: True
+                 # Args for this module
+                 freeze_bn=False,
+                 qconfig=None,
+                 dim=2):
+        nn.modules.conv._ConvNd.__init__(self, in_channels, out_channels, kernel_size,
+                                         stride, padding, dilation, transposed,
+                                         output_padding, groups, False, padding_mode)
+        assert qconfig, 'qconfig must be provided for QAT module'
+        self.qconfig = qconfig
+        self.freeze_bn = freeze_bn if self.training else True
+        self.bn = FrozenBatchNorm2d(out_channels, eps, momentum, True, True)
+        self.weight_fake_quant = self.qconfig.weight()
+        if bias:
+            self.bias = Parameter(torch.Tensor(out_channels))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_bn_parameters()
+
+        # this needs to be called after reset_bn_parameters,
+        # as they modify the same state
+        if self.training:
+            if freeze_bn:
+                self.freeze_bn_stats()
+            else:
+                self.update_bn_stats()
+        else:
+            self.freeze_bn_stats()
+
+    def reset_running_stats(self):
+        self.bn.reset_running_stats()
+
+    def reset_bn_parameters(self):
+        self.bn.reset_running_stats()
+        init.uniform_(self.bn.weight)
+        init.zeros_(self.bn.bias)
+        # note: below is actully for conv, not BN
+        if self.bias is not None:
+            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / math.sqrt(fan_in)
+            init.uniform_(self.bias, -bound, bound)
+
+    def reset_parameters(self):
+        super(_ConvFreezebnNd, self).reset_parameters()
+
+    def update_bn_stats(self):
+        self.freeze_bn = False
+        self.bn.training = True
+        return self
+
+    def freeze_bn_stats(self):
+        self.freeze_bn = True
+        self.bn.training = False
+        return self
+
+    def _forward(self, input):
+        assert isinstance(self.bn.running_var, torch.Tensor)
+        running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
+        scale_factor = self.bn.weight / running_std
+        weight_shape = [1] * len(self.weight.shape)
+        weight_shape[0] = -1
+        bias_shape = [1] * len(self.weight.shape)
+        bias_shape[1] = -1
+        scaled_weight = self.weight_fake_quant(self.weight * scale_factor.reshape(weight_shape))
+        # using zero bias here since the bias for original conv
+        # will be added later
+        if self.bias is not None:
+            zero_bias = torch.zeros_like(self.bias)
+        else:
+            zero_bias = torch.zeros(self.out_channels, device=scaled_weight.device)
+        conv = self._conv_forward(input, scaled_weight, zero_bias)
+        conv_orig = conv / scale_factor.reshape(bias_shape)
+        if self.bias is not None:
+            conv_orig = conv_orig + self.bias.reshape(bias_shape)
+        conv = self.bn(conv_orig)
+        return conv
+
+    def extra_repr(self):
+        # TODO(jerryzh): extend
+        return super(_ConvFreezebnNd, self).extra_repr()
+
+    def forward(self, input):
+        return self._forward(input)
+
+    def train(self, mode=True):
+        """
+        Batchnorm's training behavior is using the self.training flag. Prevent
+        changing it if BN is frozen. This makes sure that calling `model.train()`
+        on a model with a frozen BN will behave properly.
+        """
+        self.training = mode
+        if not self.freeze_bn:
+            for module in self.children():
+                module.train(mode)
+        return self
+
+    # ===== Serialization version history =====
+    #
+    # Version 1/None
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- gamma : Tensor
+    #   |--- beta : Tensor
+    #   |--- running_mean : Tensor
+    #   |--- running_var : Tensor
+    #   |--- num_batches_tracked : Tensor
+    #
+    # Version 2
+    #   self
+    #   |--- weight : Tensor
+    #   |--- bias : Tensor
+    #   |--- bn : Module
+    #        |--- weight : Tensor (moved from v1.self.gamma)
+    #        |--- bias : Tensor (moved from v1.self.beta)
+    #        |--- running_mean : Tensor (moved from v1.self.running_mean)
+    #        |--- running_var : Tensor (moved from v1.self.running_var)
+    #        |--- num_batches_tracked : Tensor (moved from v1.self.num_batches_tracked)
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
+        version = local_metadata.get('version', None)
+        if version is None or version == 1:
+            # BN related parameters and buffers were moved into the BN module for v2
+            v2_to_v1_names = {
+                'bn.weight': 'gamma',
+                'bn.bias': 'beta',
+                'bn.running_mean': 'running_mean',
+                'bn.running_var': 'running_var',
+                'bn.num_batches_tracked': 'num_batches_tracked',
+            }
+            for v2_name, v1_name in v2_to_v1_names.items():
+                if prefix + v1_name in state_dict:
+                    state_dict[prefix + v2_name] = state_dict[prefix + v1_name]
+                    state_dict.pop(prefix + v1_name)
+                elif prefix + v2_name in state_dict:
+                    # there was a brief period where forward compatibility
+                    # for this module was broken (between
+                    # https://github.com/pytorch/pytorch/pull/38478
+                    # and https://github.com/pytorch/pytorch/pull/38820)
+                    # and modules emitted the v2 state_dict format while
+                    # specifying that version == 1. This patches the forward
+                    # compatibility issue by allowing the v2 style entries to
+                    # be used.
+                    pass
+                elif strict:
+                    missing_keys.append(prefix + v2_name)
+
+        super(_ConvFreezebnNd, self)._load_from_state_dict(
+            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
+
+    @classmethod
+    def from_float(cls, mod):
+        r"""Create a qat module from a float module or qparams_dict
+            Args: `mod` a float module, either produced by torch.quantization utilities
+            or directly from user
+        """
+        assert type(mod) == cls._FLOAT_MODULE, 'qat.' + cls.__name__ + '.from_float only works for ' + \
+            cls._FLOAT_MODULE.__name__
+        assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
+        assert mod.qconfig, 'Input float module must have a valid qconfig'
+        qconfig = mod.qconfig
+        conv, bn = mod[0], mod[1]
+        qat_convbn = cls(conv.in_channels, conv.out_channels, conv.kernel_size,
+                         conv.stride, conv.padding, conv.dilation,
+                         conv.groups, conv.bias is not None,
+                         conv.padding_mode,
+                         bn.eps, bn.momentum,
+                         False,
+                         qconfig)
+        qat_convbn.weight = conv.weight
+        qat_convbn.bias = conv.bias
+        qat_convbn.bn.weight = bn.weight
+        qat_convbn.bn.bias = bn.bias
+        qat_convbn.bn.running_mean = bn.running_mean
+        qat_convbn.bn.running_var = bn.running_var
+        # mypy error: Cannot determine type of 'num_batches_tracked'
+        qat_convbn.bn.num_batches_tracked = bn.num_batches_tracked  # type: ignore[has-type]
+        return qat_convbn
+
+class ConvFreezebn2d(_ConvFreezebnNd, nn.Conv2d):
+    r"""
+    A ConvBn2d module is a module fused from Conv2d and BatchNorm2d,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+    We combined the interface of :class:`torch.nn.Conv2d` and
+    :class:`torch.nn.BatchNorm2d`.
+    Similar to :class:`torch.nn.Conv2d`, with FakeQuantize modules initialized
+    to default.
+    Attributes:
+        freeze_bn:
+        weight_fake_quant: fake quant module for weight
+    """
+    _FLOAT_MODULE = qnni.ConvFreezebn2d
+
+    def __init__(self,
+                 # ConvNd args
+                 in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1,
+                 bias=None,
+                 padding_mode='zeros',
+                 # BatchNorm2d args
+                 # num_features: out_channels
+                 eps=1e-05, momentum=0.1,
+                 # affine: True
+                 # track_running_stats: True
+                 # Args for this module
+                 freeze_bn=False,
+                 qconfig=None):
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+        _ConvFreezebnNd.__init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, False, _pair(0), groups, bias, padding_mode, eps, momentum, freeze_bn, qconfig, dim=2)
+
+class ConvFreezebnReLU2d(ConvFreezebn2d):
+    r"""
+    A ConvBnReLU2d module is a module fused from Conv2d, BatchNorm2d and ReLU,
+    attached with FakeQuantize modules for weight,
+    used in quantization aware training.
+    We combined the interface of :class:`torch.nn.Conv2d` and
+    :class:`torch.nn.BatchNorm2d` and :class:`torch.nn.ReLU`.
+    Similar to `torch.nn.Conv2d`, with FakeQuantize modules initialized to
+    default.
+    Attributes:
+        weight_fake_quant: fake quant module for weight
+    """
+    # base class defines _FLOAT_MODULE as "ConvBn2d"
+    _FLOAT_MODULE = qnni.ConvFreezebnReLU2d  # type: ignore[assignment]
+
+    def __init__(self,
+                 # Conv2d args
+                 in_channels, out_channels, kernel_size, stride=1,
+                 padding=0, dilation=1, groups=1,
+                 bias=None,
+                 padding_mode='zeros',
+                 # BatchNorm2d args
+                 # num_features: out_channels
+                 eps=1e-05, momentum=0.1,
+                 # affine: True
+                 # track_running_stats: True
+                 # Args for this module
+                 freeze_bn=False,
+                 qconfig=None):
+        super(ConvFreezebnReLU2d, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, padding_mode, eps, momentum, freeze_bn, qconfig)
+
+    def forward(self, input):
+        return F.relu(ConvFreezebn2d._forward(self, input))
+
+    @classmethod
+    def from_float(cls, mod):
+        return super(ConvFreezebnReLU2d, cls).from_float(mod)
+
+
+class _ConvTransposeFreezebnNd(_ConvTransposeBnNd):
+
+    _version = 2
+    _FLOAT_MODULE = MOD
+
+    def __init__(
+            self,
+            # ConvTransposeBnNd args
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride,
+            bias,
+            transposed,
+            padding,
+            output_padding,
+            groups,
+            dilation,
+            padding_mode,
+            # bn args
+            # BatchNormNd args
+            # num_features: out_channels
+            eps=1e-05,
+            momentum=0.1,
+            # affine: True
+            # track_running_stats: True
+            # Args for this module
+            freeze_bn=False,
+            qconfig=None,
+            dim=2):
+        kernel_size = _single(kernel_size)
+        stride = _single(stride)
+        padding = _single(padding)
+        dilation = _single(dilation)
+        output_padding = _single(output_padding)
+        nn.modules.conv._ConvTransposeNd.__init__(self, in_channels,
+                                                  out_channels, kernel_size,
+                                                  stride, padding, dilation,
+                                                  transposed, output_padding,
+                                                  groups, False, padding_mode)
+        assert qconfig, 'qconfig must be provided for a QAT module'
+        self.qconfig = qconfig
+        self.freeze_bn = freeze_bn if self.training else True
+        self.bn = FrozenBatchNorm2d(out_channels, eps, momentum, True, True)
+        self.weight_fake_quant = self.qconfig.weight()
+        # ConvTranspose do per-channel quantize on output channel.
+        if self.weight_fake_quant.ch_axis != -1:
+            self.weight_fake_quant.ch_axis = 1
+            self.weight_fake_quant.activation_post_process.ch_axis = 1
+        if bias:
+            self.bias = Parameter(torch.Tensor(out_channels))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_bn_parameters()
+
+        if self.training:
+            if freeze_bn:
+                self.freeze_bn_stats()
+            else:
+                self.update_bn_stats()
+        else:
+            self.freeze_bn_stats()
+
+
+class ConvTransposeFreezebn2d(_ConvTransposeFreezebnNd, nn.ConvTranspose2d):
+    _FLOAT_MODULE = qnni.ConvTransposeFreezebn2d
+
+    def __init__(
+            self,
+            # ConvTransposeBnNd args
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            bias=None,
+            transposed=True,
+            padding=0,
+            output_padding=0,
+            groups=1,
+            dilation=1,
+            padding_mode='zeros',
+            # bn args
+            # BatchNormNd args
+            # num_features: out_channels
+            eps=1e-05,
+            momentum=0.1,
+            # affine: True
+            # track_running_stats: True
+            # Args for this module
+            freeze_bn=False,
+            qconfig=None):
+        kernel_size = _pair(kernel_size)
+        stride = _pair(stride)
+        padding = _pair(padding)
+        dilation = _pair(dilation)
+        _ConvTransposeFreezebnNd.__init__(self, in_channels, out_channels,
+                                          kernel_size, stride, bias, transposed,
+                                          padding, output_padding, groups, dilation,
+                                          padding_mode, eps, momentum, freeze_bn,
+                                          qconfig)
+
+    def _convtransposed_forward(self, x, w, b):
+        output_padding = self._output_padding(x, None, self.stride,
+                                              self.padding, self.kernel_size,
+                                              self.dilation)
+        return F.conv_transpose2d(x, w, b, self.stride, self.padding,
+                                  output_padding, self.groups, self.dilation)
+
+
+class ConvTransposeFreezebnReLU2d(ConvTransposeFreezebn2d):
+    _FLOAT_MODULE = qnni.ConvTransposeFreezebnReLU2d
+
+    def __init__(
+            self,
+            # ConvTransposeBnNd args
+            in_channels,
+            out_channels,
+            kernel_size,
+            stride=1,
+            bias=None,
+            transposed=True,
+            padding=0,
+            output_padding=0,
+            groups=1,
+            dilation=1,
+            padding_mode='zeros',
+            # bn args
+            # BatchNormNd args
+            # num_features: out_channels
+            eps=1e-05,
+            momentum=0.1,
+            # affine: True
+            # track_running_stats: True
+            # Args for this module
+            freeze_bn=False,
+            qconfig=None):
+        # super(ConvTransposeBnReLU2d, self).__init__(in_channels, out_channels, kernel_size, stride,
+        #                                             padding, dilation, groups, bias,
+        #                                             padding_mode, eps, momentum,
+        #                                             freeze_bn,
+        #                                             qconfig)
+        super(ConvTransposeFreezebnReLU2d,
+              self).__init__(in_channels,
+                             out_channels,
+                             kernel_size,
+                             stride=stride,
+                             bias=bias,
+                             transposed=transposed,
+                             padding=padding,
+                             output_padding=output_padding,
+                             groups=groups,
+                             dilation=dilation,
+                             padding_mode=padding_mode,
+                             eps=eps,
+                             momentum=momentum,
+                             freeze_bn=freeze_bn,
+                             qconfig=qconfig)
+
+    def forward(self, input):
+        return F.relu(ConvTransposeFreezebn2d._forward(self, input))
+
+    @classmethod
+    def from_float(cls, mod):
+        return super(ConvTransposeFreezebnReLU2d, cls).from_float(mod)
diff --git a/mqbench/nn/intrinsic/qat/modules/linear_fused.py b/mqbench/nn/intrinsic/qat/modules/linear_fused.py
new file mode 100644
index 0000000..98dfb4e
--- /dev/null
+++ b/mqbench/nn/intrinsic/qat/modules/linear_fused.py
@@ -0,0 +1,178 @@
+import math
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn import init
+from torch.nn import Linear
+from torch.nn.intrinsic import _FusedModule
+from torch.nn.parameter import Parameter
+
+from mqbench.nn.intrinsic import LinearBn1d
+
+
+class LinearBn1d(Linear, _FusedModule):
+    _version = 2
+    _FLOAT_MODULE = LinearBn1d
+
+    def __init__(self,
+                 # ConvNd args
+                 in_features, out_features, bias,
+                 # BatchNormNd args
+                 # num_features: out_channels
+                 eps=1e-05, momentum=0.1,
+                 # affine: True
+                 # track_running_stats: True
+                 # Args for this module
+                 freeze_bn=False,
+                 qconfig=None):
+        Linear.__init__(self, in_features, out_features, False)
+        assert qconfig, 'qconfig must be provided for QAT module'
+        self.qconfig = qconfig
+        self.freeze_bn = freeze_bn if self.training else True
+        self.bn = nn.BatchNorm1d(out_features, eps, momentum, True, True)
+        self.weight_fake_quant = self.qconfig.weight()
+        if bias:
+            self.bias = Parameter(torch.empty(out_features))
+        else:
+            self.register_parameter('bias', None)
+        self.reset_bn_parameters()
+
+        # this needs to be called after reset_bn_parameters,
+        # as they modify the same state
+        if self.training:
+            if freeze_bn:
+                self.freeze_bn_stats()
+            else:
+                self.update_bn_stats()
+        else:
+            self.freeze_bn_stats()
+
+    def reset_running_stats(self):
+        self.bn.reset_running_stats()
+
+    def reset_bn_parameters(self):
+        self.bn.reset_running_stats()
+        init.uniform_(self.bn.weight)
+        init.zeros_(self.bn.bias)
+        # note: below is actully for Linear, not BN
+        if self.bias is not None:
+            fan_in, _ = init._calculate_fan_in_and_fan_out(self.weight)
+            bound = 1 / math.sqrt(fan_in)
+            init.uniform_(self.bias, -bound, bound)
+
+    def reset_parameters(self):
+        super(LinearBn1d, self).reset_parameters()
+
+    def update_bn_stats(self):
+        self.freeze_bn = False
+        self.bn.training = True
+        return self
+
+    def freeze_bn_stats(self):
+        self.freeze_bn = True
+        self.bn.training = False
+        return self 
+
+    def _forward(self, input):
+        assert self.bn.running_var is not None
+        running_std = torch.sqrt(self.bn.running_var + self.bn.eps)
+        # input.shape = (batch_size, in_features, *)
+        # scale_factor.shape = (out_feature, )
+        # self.weight.shape = (out_feature, in_feature, *)
+        # self.bias.shape = (out_feature, *)
+        # output.shape = (batch_size, out_feature, *)
+        if self.bn.affine:
+            scale_factor = self.bn.weight / running_std
+        else:
+            scale_factor = 1. / running_std
+        weight_shape = [1] * len(self.weight.shape)
+        weight_shape[0] = -1
+        bias_shape = [1] * len(input.shape)
+        bias_shape[1] = -1
+        scaled_weight = self.weight_fake_quant(self.weight * scale_factor.reshape(weight_shape))
+        # using zero bias here since the bias for original Linear
+        # will be added later
+        # Linear layer takes permuted input since the format is (batch_size, *, in_features)
+        linear_out = F.linear(input, scaled_weight)
+        linear_orig = linear_out / scale_factor.reshape(bias_shape)
+        if self.bias is not None:
+            linear_orig = linear_orig + self.bias.reshape(bias_shape)
+        linear_out = self.bn(linear_orig)
+        return linear_out
+
+    def extra_repr(self):
+        return super(LinearBn1d, self).extra_repr()
+
+    def forward(self, input):
+        return self._forward(input)
+
+    def train(self, mode=True):
+        """
+        Batchnorm's training behavior is using the self.training flag. Prevent
+        changing it if BN is frozen. This makes sure that calling `model.train()`
+        on a model with a frozen BN will behave properly.
+        """
+        self.training = mode
+        if not self.freeze_bn:
+            for module in self.children():
+                module.train(mode)
+        return self
+
+    def _load_from_state_dict(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs):
+        version = local_metadata.get('version', None)
+        if version is None or version == 1:
+            # BN related parameters and buffers were moved into the BN module for v2
+            v2_to_v1_names = {
+                'bn.weight': 'gamma',
+                'bn.bias': 'beta',
+                'bn.running_mean': 'running_mean',
+                'bn.running_var': 'running_var',
+                'bn.num_batches_tracked': 'num_batches_tracked',
+            }
+            for v2_name, v1_name in v2_to_v1_names.items():
+                if prefix + v1_name in state_dict:
+                    state_dict[prefix + v2_name] = state_dict[prefix + v1_name]
+                    state_dict.pop(prefix + v1_name)
+                elif prefix + v2_name in state_dict:
+                    # there was a brief period where forward compatibility
+                    # for this module was broken (between
+                    # https://github.com/pytorch/pytorch/pull/38478
+                    # and https://github.com/pytorch/pytorch/pull/38820)
+                    # and modules emitted the v2 state_dict format while
+                    # specifying that version == 1. This patches the forward
+                    # compatibility issue by allowing the v2 style entries to
+                    # be used.
+                    pass
+                elif strict:
+                    missing_keys.append(prefix + v2_name)
+
+        super(LinearBn1d, self)._load_from_state_dict(
+            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs)
+
+    @classmethod
+    def from_float(cls, mod):
+        r"""Create a qat module from a float module or qparams_dict
+
+            Args: `mod` a float module, either produced by torch.quantization utilities
+            or directly from user
+        """
+        assert type(mod) == cls._FLOAT_MODULE, 'qat.' + cls.__name__ + '.from_float only works for ' + \
+            cls._FLOAT_MODULE.__name__
+        assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
+        assert mod.qconfig, 'Input float module must have a valid qconfig'
+        qconfig = mod.qconfig
+        linear, bn = mod[0], mod[1]
+        qat_linearbn = cls(linear.in_features, linear.out_features, False,
+                           bn.eps, bn.momentum,
+                           False,
+                           qconfig)
+        qat_linearbn.weight = linear.weight
+        qat_linearbn.bias = linear.bias
+        qat_linearbn.bn.weight = bn.weight
+        qat_linearbn.bn.bias = bn.bias
+        qat_linearbn.bn.running_mean = bn.running_mean
+        qat_linearbn.bn.running_var = bn.running_var
+        # mypy error: Cannot determine type of 'num_batches_tracked'
+        qat_linearbn.bn.num_batches_tracked = bn.num_batches_tracked  # type: ignore[has-type]
+        return qat_linearbn
diff --git a/mqbench/nn/modules/__init__.py b/mqbench/nn/modules/__init__.py
new file mode 100644
index 0000000..7842583
--- /dev/null
+++ b/mqbench/nn/modules/__init__.py
@@ -0,0 +1 @@
+from .freezebn import FrozenBatchNorm2d
\ No newline at end of file
diff --git a/mqbench/nn/modules/freezebn.py b/mqbench/nn/modules/freezebn.py
new file mode 100644
index 0000000..222a418
--- /dev/null
+++ b/mqbench/nn/modules/freezebn.py
@@ -0,0 +1,12 @@
+import torch
+
+class FrozenBatchNorm2d(torch.nn.BatchNorm2d):
+    def __init__(self, *args, **kwargs):
+        super(FrozenBatchNorm2d, self).__init__(*args, **kwargs)
+        self.training = False
+
+    def train(self, mode=False):
+        self.training = False
+        for module in self.children():
+            module.train(False)
+        return self
\ No newline at end of file
diff --git a/mqbench/nn/qat/__init__.py b/mqbench/nn/qat/__init__.py
new file mode 100644
index 0000000..9a8067b
--- /dev/null
+++ b/mqbench/nn/qat/__init__.py
@@ -0,0 +1 @@
+from .modules import *
\ No newline at end of file
diff --git a/mqbench/nn/qat/modules/__init__.py b/mqbench/nn/qat/modules/__init__.py
new file mode 100644
index 0000000..c010902
--- /dev/null
+++ b/mqbench/nn/qat/modules/__init__.py
@@ -0,0 +1,4 @@
+from .linear import Linear
+from .deconv import ConvTranspose2d
+from .conv import Conv2d
+from .embedding import Embedding
\ No newline at end of file
diff --git a/mqbench/nn/qat/modules/conv.py b/mqbench/nn/qat/modules/conv.py
new file mode 100644
index 0000000..08a7e0e
--- /dev/null
+++ b/mqbench/nn/qat/modules/conv.py
@@ -0,0 +1,11 @@
+import torch.nn.qat.modules as nnqat
+
+from mqbench.quantization.default_bias_fake_quant import bias_fake_quantizer
+
+class Conv2d(nnqat.Conv2d):
+    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, padding_mode='zeros', qconfig=None, device=None, dtype=None):
+        super().__init__(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bias=bias, padding_mode=padding_mode, qconfig=qconfig) 
+        self.bias_fake_quant = bias_fake_quantizer()
+
+    def forward(self, input):
+        return self._conv_forward(input, self.weight_fake_quant(self.weight), self.bias_fake_quant(self.bias)) 
diff --git a/mqbench/nn/qat/modules/deconv.py b/mqbench/nn/qat/modules/deconv.py
new file mode 100644
index 0000000..ec7ef82
--- /dev/null
+++ b/mqbench/nn/qat/modules/deconv.py
@@ -0,0 +1,47 @@
+import torch.nn as nn 
+import torch.nn.functional as F
+
+from mqbench.nn.intrinsic import ConvTransposeReLU2d
+
+
+class ConvTranspose2d(nn.ConvTranspose2d):
+    _FLOAT_MODULE = nn.ConvTranspose2d
+
+    def __init__(self, in_channels, out_channels, kernel_size, 
+                 stride=1, padding=0, output_padding=0, 
+                 groups=1, bias=True, dilation=1,
+                 padding_mode='zeros', qconfig=None):
+        super().__init__(in_channels, out_channels, kernel_size, 
+                         stride=stride, padding=padding, output_padding=output_padding,
+                         groups=groups, bias=bias, dilation=dilation, padding_mode=padding_mode)
+        assert qconfig, 'qconfig must be provided for QAT module'
+        self.qconfig = qconfig 
+        self.weight_fake_quant = qconfig.weight()
+        # ConvTranspose do per-channel quantize on output channel.
+        if self.weight_fake_quant.ch_axis != -1:
+            self.weight_fake_quant.ch_axis = 1
+            self.weight_fake_quant.activation_post_process.ch_axis = 1
+
+    def forward(self, x, output_size=None):
+        output_padding = self._output_padding(
+            x, output_size, self.stride, self.padding, self.kernel_size, self.dilation
+        )
+        return F.conv_transpose2d(
+            x, self.weight_fake_quant(self.weight), self.bias, self.stride, self.padding,
+            output_padding, self.groups, self.dilation)
+
+    @classmethod
+    def from_float(cls, mod):
+        assert type(mod) == cls._FLOAT_MODULE, 'qat.' + cls.__name__ + '.from_float only works for ' + \
+            cls._FLOAT_MODULE.__name__ 
+        assert mod.qconfig, 'Input float module must have a valid qconfig'
+        if type(mod) == ConvTransposeReLU2d:
+            mod = mod[0]
+        qconfig = mod.qconfig
+        qat_deconv = cls(mod.in_channels, mod.out_channels, mod.kernel_size, 
+                         stride=mod.stride, padding=mod.padding, output_padding=mod.output_padding,
+                         groups=mod.groups, bias=mod.bias is not None, dilation=mod.dilation,
+                         padding_mode=mod.padding_mode, qconfig=qconfig) 
+        qat_deconv.weight = mod.weight
+        qat_deconv.bias = mod.bias
+        return qat_deconv
\ No newline at end of file
diff --git a/mqbench/nn/qat/modules/embedding.py b/mqbench/nn/qat/modules/embedding.py
new file mode 100644
index 0000000..2357e38
--- /dev/null
+++ b/mqbench/nn/qat/modules/embedding.py
@@ -0,0 +1,69 @@
+import torch
+from torch import Tensor
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class Embedding(nn.Embedding):
+    r"""
+    We release the restrict of scheme type.
+    TODO: Delete this module since this project support torch1.10.
+
+    An embedding bag module attached with FakeQuantize modules for weight,
+    used for quantization aware training.
+    We adopt the same interface as `torch.nn.Embedding`, please see
+    https://pytorch.org/docs/stable/generated/torch.nn.Embedding.html#torch.nn.Embedding
+    for documentation.
+    Similar to `torch.nn.Embedding`, with FakeQuantize modules initialized to
+    default.
+    Attributes:
+        weight: fake quant module for weight
+    """
+    _FLOAT_MODULE = nn.Embedding
+
+    def __init__(self, num_embeddings, embedding_dim, padding_idx=None,
+                 max_norm=None, norm_type=2.0, scale_grad_by_freq=False,
+                 sparse=False, _weight=None, device=None, dtype=None, qconfig=None) -> None:
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        super().__init__(num_embeddings, embedding_dim, padding_idx, max_norm,
+                         norm_type, scale_grad_by_freq, sparse, _weight,
+                         **factory_kwargs)
+        assert qconfig, 'qconfig must be provided for QAT module'
+
+        self.qconfig = qconfig
+        self.weight_fake_quant = qconfig.weight(factory_kwargs=factory_kwargs)
+        # Embedding do per-channel quantize on embedding channel.
+        if self.weight_fake_quant.ch_axis != -1:
+            self.weight_fake_quant.ch_axis = 1
+            self.weight_fake_quant.activation_post_process.ch_axis = 1
+
+    def forward(self, input) -> Tensor:
+        return F.embedding(input, self.weight_fake_quant(self.weight), self.padding_idx,
+                           self.max_norm, self.norm_type, self.scale_grad_by_freq,
+                           self.sparse)
+
+    @classmethod
+    def from_float(cls, mod):
+        r"""Create a qat module from a float module
+            Args: `mod` a float module, either produced by torch.ao.quantization utilities
+            or directly from user
+        """
+        assert type(mod) == cls._FLOAT_MODULE, ' qat.' + cls.__name__ + '.from_float only works for ' + \
+            cls._FLOAT_MODULE.__name__
+        assert hasattr(mod, 'qconfig'), 'Input float module must have qconfig defined'
+        assert mod.qconfig, 'Input float module must have a valid qconfig'
+
+        qconfig = mod.qconfig
+        qat_embedding_bag = cls(mod.num_embeddings, mod.embedding_dim, mod.padding_idx,
+                                mod.max_norm, mod.norm_type, mod.scale_grad_by_freq,
+                                mod.sparse, mod.weight, qconfig=qconfig)
+
+        return qat_embedding_bag
+
+    def to_float(self):
+        embedding_bag = torch.nn.Embedding(self.num_embeddings, self.embedding_dim, self.padding_idx,
+                                           self.max_norm, self.norm_type, self.scale_grad_by_freq,
+                                           self.sparse, None, self.device, self.dtype)
+        embedding_bag.weight = torch.nn.Parameter(self.weight.detach())
+        embedding_bag.train(self.training)
+        return embedding_bag
\ No newline at end of file
diff --git a/mqbench/nn/qat/modules/linear.py b/mqbench/nn/qat/modules/linear.py
new file mode 100644
index 0000000..1b57ed2
--- /dev/null
+++ b/mqbench/nn/qat/modules/linear.py
@@ -0,0 +1,13 @@
+import torch.nn.qat.modules as nnqat
+import torch.nn.functional as F
+
+
+class Linear(nnqat.Linear):
+    def __init__(self, in_features, out_features, bias=True, qconfig=None, device=None, dtype=None):
+        assert hasattr(qconfig, 'bias'), 'The qconfig should provide bias observer settings for the QAT module!'
+        super().__init__(in_features, out_features, bias=bias, qconfig=qconfig, device=device, dtype=dtype)
+        factory_kwargs = {'device': device, 'dtype': dtype}
+        self.bias_fake_quant = qconfig.bias(**factory_kwargs)
+
+    def forward(self, input):
+        return F.linear(input, self.weight_fake_quant(self.weight), self.bias_fake_quant(self.bias)) 
diff --git a/mqbench/observer.py b/mqbench/observer.py
new file mode 100644
index 0000000..7b42caf
--- /dev/null
+++ b/mqbench/observer.py
@@ -0,0 +1,734 @@
+import math
+from typing import Tuple
+
+import torch
+from torch.quantization.observer import _ObserverBase
+
+from mqbench.fake_quantize.quantize_base import _version_under_1100 
+from mqbench.utils import sync_tensor, pot_quantization, is_symmetric_quant
+from mqbench.utils.logger import logger
+from mqbench.utils.hook import PerChannelLoadHook
+import warnings
+
+class ObserverBase(_ObserverBase):
+    '''
+        Support per-tensor / per-channel.
+        dtype: quant min/max can be infered using dtype, we actually do not need this.
+        qscheme: quantization scheme
+        reduce_range: special for fbgemm to avoid overflow
+        quant_min: fix point value min
+        quant_max: fix point value max
+        ch_axis: per-channel axis or per-tensor(-1)
+        above is similiar to torch observer.
+        pot_scale: indecate wheather scale is power of two.
+    '''
+
+    min_val: torch.Tensor
+    max_val: torch.Tensor
+
+    def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine,
+                 reduce_range=False, quant_min=None, quant_max=None, ch_axis=-1, pot_scale=False,
+                 factory_kwargs=None):
+        # Since torch 1.10, function calculate_qmin_qmax is not a member function of observer,
+        # but import from utils. It is hard to control. We use try...except here.
+        stored_min, sotred_max = quant_min, quant_max
+        if quant_max is not None and quant_min is not None and (quant_max - quant_min + 1 > 256):
+            quant_min, quant_max = -128, 127
+        super(ObserverBase, self).__init__(dtype, qscheme, reduce_range, quant_min, quant_max)
+        self.quant_min = stored_min
+        self.quant_max = sotred_max
+        self.quant_min, self.quant_max = self._calculate_qmin_qmax()
+        self.ch_axis = ch_axis
+        self.pot_scale = pot_scale
+        self.register_buffer("min_val", torch.tensor(float("inf")))
+        self.register_buffer("max_val", torch.tensor(float("-inf")))
+        self.load_state_dict_hook = PerChannelLoadHook(self)
+
+    @torch.jit.export
+    def calculate_qparams(self) -> Tuple[torch.Tensor, torch.Tensor]:
+        r"""Calculates the quantization parameters."""
+        scale, zero_point = self._calculate_qparams(self.min_val, self.max_val)
+        scale.data = sync_tensor(scale).data
+        zero_point.data = sync_tensor(zero_point).data
+        if self.pot_scale:
+            scale = pot_quantization(scale)
+        return scale, zero_point
+    
+    @torch.jit.export
+    def _calculate_qparams(self, min_val: torch.Tensor, max_val: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:  # 从源码继承而来
+        r"""Calculates the quantization parameters, given min and max
+        value tensors. Works for both per tensor and per channel cases
+
+        Args:
+            min_val: Minimum values per channel
+            max_val: Maximum values per channel
+
+        Returns:
+            scales: Scales tensor of shape (#channels,)
+            zero_points: Zero points tensor of shape (#channels,)
+        """
+        if min_val.numel() == 0 or max_val.numel() == 0:
+            warnings.warn(
+                "must run observer before calling calculate_qparams.\
+                                    Returning default scale and zero point "
+            )
+            return torch.tensor([1.0]), torch.tensor([0])
+
+        if min_val.dim() == 0 or max_val.dim() == 0:
+            if min_val == float('inf') and max_val == float('-inf'):
+                warnings.warn(
+                    "must run observer before calling calculate_qparams.\
+                                        Returning default scale and zero point "
+                )
+                return torch.tensor([1.0]), torch.tensor([0])
+
+            assert min_val <= max_val, "min {} should be less than max {}".format(
+                min_val, max_val
+            )
+        else:
+            assert torch.all(min_val <= max_val), "min {} should be less than max {}".format(
+                min_val, max_val
+            )
+
+        quant_min, quant_max = self._calculate_qmin_qmax()
+        min_val_neg = torch.min(min_val, torch.zeros_like(min_val))
+        max_val_pos = torch.max(max_val, torch.zeros_like(max_val))
+
+        device = min_val_neg.device
+        scale = torch.ones(min_val_neg.size(), dtype=torch.float32, device=device)
+        zero_point = torch.zeros(min_val_neg.size(), dtype=torch.int64, device=device)
+
+        if self.qscheme == torch.per_tensor_symmetric or self.qscheme == torch.per_channel_symmetric:
+            max_val_pos = torch.max(-min_val_neg, max_val_pos)
+            
+            if self.dtype == torch.quint8:
+                # 非负对称量化
+                # if self.has_customized_qrange:
+                #     # When customized quantization range is used, down-rounded midpoint of the range is chosen.
+                #     zero_point = zero_point.new_full(zero_point.size(), (quant_min + quant_max) // 2)
+                # else:
+                #     zero_point = zero_point.new_full(zero_point.size(), 128)
+                scale = (max_val - 0.) / float(quant_max - quant_min)
+                scale = torch.where(scale > self.eps, scale, torch.ones_like(scale))
+                
+            elif self.dtype == torch.qint8:
+                # 对称量化
+                scale = max_val_pos / (float(quant_max - quant_min) / 2)  # 这玩意除2，意在
+                scale = torch.max(scale, self.eps)
+            else:
+                raise NotImplementedError
+        elif self.qscheme == torch.per_channel_affine_float_qparams:
+            scale = (max_val - min_val) / float(quant_max - quant_min)
+            scale = torch.where(scale > self.eps, scale, torch.ones_like(scale))
+            # We use the quantize function
+            # xq = Round(Xf * inv_scale + zero_point),
+            # setting zero_point to (-1 * min *inv_scale) we get
+            # Xq = Round((Xf - min) * inv_scale)
+            zero_point = -1 * min_val / scale
+        else:
+            scale = (max_val_pos - min_val_neg) / float(quant_max - quant_min)
+            scale = torch.max(scale, self.eps)
+            zero_point = quant_min - torch.round(min_val_neg / scale)
+            zero_point = torch.clamp(zero_point, quant_min, quant_max)
+
+        # For scalar values, cast them to Tensors of size 1 to keep the shape
+        # consistent with default values in FakeQuantize.
+        if len(scale.shape) == 0:
+            # TODO: switch to scale.item() after adding JIT support
+            scale = torch.tensor([float(scale)], dtype=scale.dtype, device=device)
+        if len(zero_point.shape) == 0:
+            # TODO: switch to zero_point.item() after adding JIT support
+            zero_point = torch.tensor([int(zero_point)], dtype=zero_point.dtype, device=device)
+            if self.qscheme == torch.per_channel_affine_float_qparams:
+                zero_point = torch.tensor([float(zero_point)], dtype=zero_point.dtype, device=device)
+
+        return scale, zero_point
+    
+    @torch.jit.export
+    def _calculate_qmin_qmax(self) -> Tuple[int, int]:
+        r"""Calculates actual qmin and qmax based on the quantization range,
+        observer datatype and if range is reduced.
+        """
+        if self.has_customized_qrange:
+            quant_min, quant_max = self.quant_min, self.quant_max
+        else:
+            # Fallback onto default 8-bit qmin and qmax calculation if dynamic range is not used.
+            if self.dtype == torch.qint8:
+                if self.reduce_range:
+                    quant_min, quant_max = -64, 63
+                else:
+                    quant_min, quant_max = -128, 127
+            elif self.dtype == torch.quint8:
+                if self.reduce_range:
+                    quant_min, quant_max = 0, 127
+                else:
+                    quant_min, quant_max = 0, 255
+            else:
+                quant_min, quant_max = 0, 15
+        return quant_min, quant_max
+
+    @torch.jit.export
+    def extra_repr(self):
+        return "min_val={}, max_val={} ch_axis={} pot={}".format(self.min_val if self.ch_axis == -1 else 'List',
+                                                                 self.max_val if self.ch_axis == -1 else 'List',
+                                                                 self.ch_axis, self.pot_scale)
+
+
+class MinMaxObserver(ObserverBase):
+    '''
+    Calculate minmax of whole calibration dataset.
+    '''
+
+    def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine,
+                 reduce_range=False, quant_min=None, quant_max=None, ch_axis=-1, pot_scale=False,
+                 factory_kwargs=None):
+        super(MinMaxObserver, self).__init__(dtype, qscheme, reduce_range, quant_min, quant_max,
+                                             ch_axis, pot_scale, factory_kwargs)
+
+    def forward(self, x_orig):
+        r"""Records the running minimum and maximum of ``x``."""
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.to(self.min_val.dtype)
+        if self.ch_axis == -1:
+            min_val_cur, max_val_cur = torch._aminmax(x)
+        else:
+            x_dim = x.size()
+            new_axis_list = [i for i in range(len(x_dim))]
+            new_axis_list[self.ch_axis] = 0
+            new_axis_list[0] = self.ch_axis
+            y = x.permute(new_axis_list)
+            y = torch.flatten(y, start_dim=1)
+            min_val_cur, max_val_cur = torch._aminmax(y, 1)
+        self.min_val = torch.min(self.min_val, min_val_cur)
+        self.max_val = torch.max(self.max_val, max_val_cur)
+
+        return x
+
+
+class MinMaxFloorObserver(ObserverBase):
+    '''
+    Calculate minmax of whole calibration dataset with floor but round.
+    '''
+
+    def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine,
+                 reduce_range=False, quant_min=None, quant_max=None, ch_axis=-1, pot_scale=False,
+                 factory_kwargs=None):
+        super(MinMaxFloorObserver, self).__init__(dtype, qscheme, reduce_range, quant_min, quant_max,
+                                                  ch_axis, pot_scale, factory_kwargs)
+        '''
+        The quant_type could be 'input', 'param', 'tensor', the co-responding
+        range is 1, 5, 5,
+        mth is 2, 3, 2
+        '''
+        self.quant_type = None
+
+
+    def forward(self, x_orig):
+        r"""Records the running minimum and maximum of ``x``."""
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.to(self.min_val.dtype)
+        if self.ch_axis == -1:
+            min_val_cur, max_val_cur = torch._aminmax(x)
+        else:
+            logger.warn('The per-tensor observer does not support per-channel min-max!')
+            min_val_cur, max_val_cur = torch._aminmax(x)
+
+        self.min_val = min_val_cur
+        self.max_val = max_val_cur
+        self._x = x
+        return x
+
+    def calculate_qparams(self):
+        if self.quant_type is None:
+            raise ValueError('You should set the observer type before forward!')
+        else:
+            scale_range = 1 if self.quant_type == 'input' else 5
+            mth = 3 if self.quant_type == 'param' else 2
+        scale, zero_point = self._calculate_qparams(self.min_val, self.max_val)
+        scale.data = scale.data * 0 + max(self.min_val / self.quant_min, self.max_val / self.quant_max)
+        if scale < 2 ** -15:
+            max_scale = 0
+        else:
+            max_scale = 1 / scale
+            max_scale = torch.floor(max_scale.log2())
+        min_loss = torch.tensor([float('inf')])
+        final_scale = max_scale
+        max_scale = int(max_scale)
+        for s in range(max_scale, max_scale + scale_range):
+            _s = 1 / 2 ** s
+            if mth == 3:
+                new_x = _s * torch.clamp(torch.round(self._x / _s), self.quant_min, self.quant_max)
+            elif mth == 2:
+                new_x = torch.clamp(self._x / _s, self.quant_min, self.quant_max)
+                new_x = torch.where((new_x < 0) & (new_x - new_x.floor() == 0.5), new_x.ceil(), new_x.round())
+                new_x *= _s
+            loss = ((new_x - self._x)**2).sum()
+            min_loss = min_loss.to(loss.device)
+            if loss < min_loss:
+                min_loss = loss
+                final_scale = s
+        final_scale = min(final_scale, 12)
+        scale = scale.data * 0 + 1 / (2 ** final_scale)
+        zero_point = torch.zeros_like(zero_point)
+        if not is_symmetric_quant(self.qscheme):
+            if self.min_val >= 0.:
+                zero_point = self.quant_min - torch.round(self.min_val / scale)
+        sync_tensor(scale)
+        sync_tensor(zero_point)
+        return scale, zero_point
+
+    def set_quant_type(self, qtype):
+        self.quant_type = qtype
+
+
+class EMAMinMaxObserver(ObserverBase):
+    """Moving average min/max among batches.
+    """
+
+    def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=False,
+                 quant_min=None, quant_max=None, ch_axis=-1, pot_scale=False, ema_ratio=0.9,
+                 factory_kwargs=None):
+        super(EMAMinMaxObserver, self).__init__(dtype, qscheme, reduce_range, quant_min, quant_max,
+                                                ch_axis, pot_scale, factory_kwargs)
+        self.ema_ratio = ema_ratio
+
+    def forward(self, x_orig):
+        r"""Records the running minimum and maximum of ``x``."""
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.to(self.min_val.dtype)
+        if self.ch_axis == -1:
+            min_val_cur, max_val_cur = torch._aminmax(x)
+        else:
+            x_dim = x.size()
+            new_axis_list = [i for i in range(len(x_dim))]  # noqa: C416
+            new_axis_list[self.ch_axis] = 0
+            new_axis_list[0] = self.ch_axis
+            y = x.permute(new_axis_list)
+            y = torch.flatten(y, start_dim=1)
+            min_val_cur, max_val_cur = torch._aminmax(y, 1)
+
+        if self.max_val.numel() <= 1 and self.max_val.isinf():
+            self.min_val = min_val_cur
+            self.max_val = max_val_cur
+        else:
+            self.min_val = self.min_val * self.ema_ratio + min_val_cur * (1.0 - self.ema_ratio)
+            self.max_val = self.max_val * self.ema_ratio + max_val_cur * (1.0 - self.ema_ratio)
+        return x
+
+
+class PoTModeObserver(ObserverBase):
+    r"""Records the most frequent Potscale of ``x``."""
+    """
+    Borrow from vitis
+    https://github.com/Xilinx/Vitis-AI/blob/master/tools/Vitis-AI-Quantizer/vai_q_pytorch/pytorch_binding/pytorch_nndct/quantization/torchquantizer.py
+    """
+
+    def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=False,
+                 quant_min=None, quant_max=None, ch_axis=-1, pot_scale=False, factory_kwargs=None):
+        super(PoTModeObserver, self).__init__(dtype, qscheme, reduce_range, quant_min, quant_max, ch_axis, pot_scale, factory_kwargs)
+        self.quant_type = None
+        self.counter = [0] * 20
+
+    def forward(self, x_orig):
+        r"""Records the running minimum and maximum of ``x``."""
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.to(self.min_val.dtype)
+        if self.ch_axis == -1:
+            min_val_cur, max_val_cur = torch._aminmax(x)
+        else:
+            logger.warn('The per-tensor observer does not support per-channel min-max!')
+            min_val_cur, max_val_cur = torch._aminmax(x)
+
+        self.min_val = min_val_cur
+        self.max_val = max_val_cur
+        self._x = x
+        return x
+
+    def calculate_qparams(self):
+        if self.quant_type is None:
+            raise ValueError('You should set the observer type before forward!')
+        else:
+            scale_range = 1 if self.quant_type == 'input' else 5
+            mth = 3 if self.quant_type == 'param' else 2
+        scale, zero_point = self._calculate_qparams(self.min_val, self.max_val)
+        if self.quant_min != 0:
+            
+            scale.data = scale.data * 0 + max(self.min_val / self.quant_min, self.max_val / self.quant_max)
+        else:
+            # 说明就是非负对称量化
+            scale.data = scale.data * 0 + self.max_val / self.quant_max
+            
+        if scale < 2 ** -15:
+            max_scale = 0
+        else:
+            max_scale = 1 / scale
+            max_scale = torch.floor(max_scale.log2())
+        min_loss = torch.tensor([float('inf')])
+        final_scale = max_scale
+        max_scale = int(max_scale)  # 这里出问题了
+        for s in range(max_scale, max_scale + scale_range):
+            _s = 1 / 2 ** s
+            if mth == 3:
+                new_x = _s * torch.clamp(torch.round(self._x / _s), self.quant_min, self.quant_max)
+            elif mth == 2:
+                new_x = torch.clamp(self._x / _s, self.quant_min, self.quant_max)
+                new_x = torch.where((new_x < 0) & (new_x - new_x.floor() == 0.5), new_x.ceil(), new_x.round())
+                new_x *= _s
+            loss = ((new_x - self._x)**2).sum()
+            min_loss = min_loss.to(loss.device)
+            if loss < min_loss:
+                min_loss = loss
+                final_scale = s
+        final_scale = min(final_scale, 12)
+        self.counter[final_scale + 7] += 1
+        final_scale = self.counter.index(max(self.counter)) - 7
+        scale = scale.data * 0 + 1 / (2 ** final_scale)
+        zero_point = torch.zeros_like(zero_point)
+        if not is_symmetric_quant(self.qscheme):
+            if self.min_val >= 0.:
+                zero_point = self.quant_min - torch.round(self.min_val / scale)
+        sync_tensor(scale)
+        sync_tensor(zero_point)
+        return scale, zero_point
+
+    def set_quant_type(self, qtype):
+        self.quant_type = qtype
+
+
+class EMAQuantileObserver(ObserverBase):
+    """Moving average quantile among batches.
+    """
+
+    def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=False,
+                 quant_min=None, quant_max=None, ch_axis=-1, pot_scale=False, ema_ratio=0.9,
+                 threshold=0.99999, bins=2048, factory_kwargs=None):
+        super(EMAQuantileObserver, self).__init__(dtype, qscheme, reduce_range, quant_min, quant_max,
+                                                  ch_axis, pot_scale, factory_kwargs)
+        assert self.ch_axis == -1, "Quantile observer only support in per-tensor scheme."
+        self.ema_ratio = ema_ratio
+        self.threshold = threshold
+        self.bins = bins
+
+    def forward(self, x_orig):
+        r"""Records the running minimum and maximum of ``x``."""
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.to(self.min_val.dtype)
+        min_val_cur, max_val_cur = torch._aminmax(x)
+        max_hist_range = torch.max(-min_val_cur, max_val_cur)
+        hist = torch.histc(torch.abs(x), bins=self.bins, min=0., max=max_hist_range)
+        cur_total = 0
+        clip_value = max_hist_range
+        for i, cnt in enumerate(hist):
+            if cur_total + cnt >= self.threshold * x.numel():
+                clip_value = (i + 0.5) * (max_hist_range / self.bins)
+                break
+            cur_total += cnt
+
+        if self.max_val.numel() <= 1 and self.max_val.isinf():
+            self.min_val = max(min_val_cur, -clip_value)
+            self.max_val = min(max_val_cur, clip_value)
+        else:
+            self.min_val = self.min_val * self.ema_ratio + max(min_val_cur, -clip_value) * (1.0 - self.ema_ratio)
+            self.max_val = self.max_val * self.ema_ratio + min(max_val_cur, clip_value) * (1.0 - self.ema_ratio)
+        return x
+
+
+class ClipStdObserver(ObserverBase):
+    """Clip std.
+    """
+
+    def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=False,
+                 quant_min=None, quant_max=None, ch_axis=-1, pot_scale=False, std_scale=2.6,
+                 factory_kwargs=None):
+        super(ClipStdObserver, self).__init__(dtype, qscheme, reduce_range, quant_min, quant_max,
+                                              ch_axis, pot_scale, factory_kwargs=None)
+        self.std_scale = std_scale
+
+    def forward(self, x_orig):
+        r"""Records the running minimum and maximum of ``x``."""
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.to(self.min_val.dtype)
+        if self.ch_axis == -1:
+            min_val_cur, max_val_cur = torch._aminmax(x)
+            mean = x.mean()
+            std = x.std()
+        else:
+            x_dim = x.size()
+            new_axis_list = [i for i in range(len(x_dim))]
+            new_axis_list[self.ch_axis] = 0
+            new_axis_list[0] = self.ch_axis
+            y = x.permute(new_axis_list)
+            y = torch.flatten(y, start_dim=1)
+            min_val_cur, max_val_cur = torch._aminmax(y, 1)
+            mean = y.mean(1)
+            std = y.std(1)
+
+        # using statistics to clip min and max
+        min_val = torch.minimum(mean - self.std_scale * std, min_val_cur)
+        max_val = torch.maximum(mean + self.std_scale * std, max_val_cur)
+
+        self.min_val = min_val
+        self.max_val = max_val
+
+        return x
+
+
+class LSQObserver(ObserverBase):
+    '''
+    LSQ observer.
+    '''
+
+    def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=False,
+                 quant_min=None, quant_max=None, ch_axis=-1, pot_scale=False, factory_kwargs=None):
+        super(LSQObserver, self).__init__(dtype, qscheme, reduce_range, quant_min, quant_max,
+                                          ch_axis, pot_scale, factory_kwargs)
+        self.tensor_norm = None
+
+    def forward(self, x_orig):
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.to(self.min_val.dtype)
+        if self.ch_axis == -1:
+            self.tensor_norm = x.abs().mean()
+            self.min_val, self.max_val = torch._aminmax(x)
+        else:
+            # compute channel-wise mean
+            x_dim = x.size()
+            new_axis_list = [i for i in range(len(x_dim))]
+            new_axis_list[self.ch_axis] = 0
+            new_axis_list[0] = self.ch_axis
+            y = x.permute(new_axis_list)
+            y = torch.flatten(y, start_dim=1)
+            self.tensor_norm = y.abs().mean(1)
+            self.min_val, self.max_val = torch._aminmax(y, 1)  # TODO 会运行到这吗
+
+        return x
+
+    def calculate_qparams(self):
+        scale = 2 * self.tensor_norm / math.sqrt(self.quant_max)
+        zero_point = torch.zeros_like(self.tensor_norm)
+        sync_tensor(scale)
+        sync_tensor(zero_point)
+        if self.pot_scale:
+            scale = pot_quantization(scale)
+        if not is_symmetric_quant(self.qscheme):
+            zero_point = self.quant_min - torch.round(self.min_val / scale)
+        return scale, zero_point
+
+
+class LSQPlusObserver(ObserverBase):
+    '''
+    LSQ+ observer.
+    '''
+
+    def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine, reduce_range=False,
+                 quant_min=None, quant_max=None, ch_axis=-1, pot_scale=False, factory_kwargs=None):
+
+        super(LSQPlusObserver, self).__init__(dtype, qscheme, reduce_range, quant_min, quant_max,
+                                              ch_axis, pot_scale, factory_kwargs)
+        self.mean = None
+        self.std = None
+
+    def forward(self, x_orig):
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.to(self.min_val.dtype)
+        if self.ch_axis == -1:
+            self.mean = x.mean()
+            self.std = x.std()
+            self.min_val, self.max_val = torch._aminmax(x)
+        else:
+            # compute channel-wise mean
+            x_dim = x.size()
+            new_axis_list = [i for i in range(len(x_dim))]  # noqa: C416
+            new_axis_list[self.ch_axis] = 0
+            new_axis_list[0] = self.ch_axis
+            y = x.permute(new_axis_list)
+            y = torch.flatten(y, start_dim=1)
+            self.mean = y.mean(1)
+            self.std = y.std(1)
+            self.min_val, self.max_val = torch._aminmax(y)
+
+        return x
+
+    def calculate_qparams(self):
+        scale = torch.maximum((self.mean - 3 * self.std).abs(),
+                              (self.mean + 3 * self.std).abs()) / (self.quant_max - self.quant_min + 1)
+        sync_tensor(scale)
+        sync_tensor(zero_point)
+        if self.pot_scale:
+            scale = pot_quantization(scale)
+        zero_point = torch.zeros_like(self.mean)
+        if not is_symmetric_quant(self.qscheme):
+            if self.min_val >= 0.:
+                zero_point = self.quant_min - torch.round(self.min_val / scale)
+        return scale, zero_point
+
+
+class MSEObserver(ObserverBase):
+    '''
+    Calculate mseobserver of whole calibration dataset.
+    '''
+
+    def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine,
+                 reduce_range=False, quant_min=None, quant_max=None, ch_axis=-1, pot_scale=False, p=2.0,
+                 factory_kwargs=None):
+        super(MSEObserver, self).__init__(dtype, qscheme, reduce_range, quant_min, quant_max,
+                                          ch_axis, pot_scale, factory_kwargs)
+        self.p = p
+
+    def lp_loss(self, pred, tgt, dim=None):
+        """
+        loss function measured in L_p Norm
+        """
+        return (pred - tgt).abs().pow(self.p).mean(dim) if dim else (pred - tgt).abs().pow(self.p).mean()
+
+
+    def mse(self, x: torch.Tensor, x_min: torch.Tensor, x_max: torch.Tensor, iter=80):
+        best_score = 1e+10
+        best_min, best_max = torch.tensor([1.0], dtype=torch.float), torch.tensor([1.0], dtype=torch.float)
+        best_min.copy_(x_min)
+        best_max.copy_(x_max)
+        for i in range(iter):
+            new_min = x_min * (1.0 - (i * 0.01))
+            new_max = x_max * (1.0 - (i * 0.01))
+            scale, zero_point = self._calculate_qparams(new_min, new_max)
+            x_q = torch.fake_quantize_per_tensor_affine(
+                x, scale.item(), int(zero_point.item()),
+                self.quant_min, self.quant_max)
+            score = self.lp_loss(x_q, x)
+            if score < best_score:
+                best_score = score
+                best_min, best_max = new_min, new_max
+        return best_min, best_max
+
+    def mse_perchannel(self, x: torch.Tensor, x_min: torch.Tensor, x_max: torch.Tensor, iter=80, ch_axis=0):
+        assert x_min.shape == x_max.shape
+        assert ch_axis >= 0, f'{ch_axis}'
+        best_score = 1e+10 * torch.ones_like(x_min)
+        best_min, best_max = x_min.clone(), x_max.clone()
+        reduce_dim = tuple([i for i in range(len(x.shape)) if i != ch_axis])
+        for i in range(iter):
+            new_min = x_min * (1.0 - (i * 0.01))
+            new_max = x_max * (1.0 - (i * 0.01))
+            scale, zero_point = self._calculate_qparams(new_min, new_max)
+            x_q = torch.fake_quantize_per_channel_affine(
+                x, scale, zero_point.long() if _version_under_1100 else zero_point, ch_axis, 
+                self.quant_min, self.quant_max)
+            score = self.lp_loss(x_q, x, reduce_dim)
+            update_idx = (score < best_score)
+            best_score[update_idx] = score[update_idx]
+            best_min[update_idx] = new_min[update_idx]
+            best_max[update_idx] = new_max[update_idx]
+        return best_min, best_max
+
+    def forward(self, x_orig):
+        r"""Records the running minimum and maximum of ``x``."""
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.clone().detach().to(self.min_val.dtype)
+        if self.ch_axis == -1:
+            min_val_cur, max_val_cur = torch._aminmax(x)
+            min_val_cur, max_val_cur = self.mse(x, min_val_cur, max_val_cur, iter=95)
+        else:
+            x_dim = x.size()
+            new_axis_list = [i for i in range(len(x_dim))]
+            new_axis_list[self.ch_axis] = 0
+            new_axis_list[0] = self.ch_axis
+            x_channel = x.permute(new_axis_list)
+            y = torch.flatten(x_channel, start_dim=1)
+            min_val_cur, max_val_cur = torch._aminmax(y, 1)
+            min_val_cur, max_val_cur = self.mse_perchannel(x, min_val_cur, max_val_cur, iter=80, ch_axis=self.ch_axis)
+
+        self.min_val = torch.min(self.min_val, min_val_cur)
+        self.max_val = torch.max(self.max_val, max_val_cur)
+        return x
+
+
+class EMAMSEObserver(ObserverBase):
+    '''
+    Calculate mseobserver of whole calibration dataset.
+    '''
+    def __init__(self, dtype=torch.quint8, qscheme=torch.per_tensor_affine,
+                 reduce_range=False, quant_min=None, quant_max=None, ch_axis=-1, pot_scale=False,
+                 p=2.0, ema_ratio=0.9, factory_kwargs=None):
+        super(EMAMSEObserver, self).__init__(dtype, qscheme, reduce_range, quant_min, quant_max,
+                                             ch_axis, pot_scale, factory_kwargs)
+        self.ema_ratio = ema_ratio
+        self.p = p
+
+    def lp_loss(self, pred, tgt, dim=None):
+        """
+        loss function measured in L_p Norm
+        """
+        return (pred - tgt).abs().pow(self.p).mean(dim) if dim else (pred - tgt).abs().pow(self.p).mean()
+
+    def mse(self, x: torch.Tensor, x_min: torch.Tensor, x_max: torch.Tensor, iter=80):
+        best_score = 1e+10
+        best_min, best_max = torch.tensor([1.0], dtype=torch.float), torch.tensor([1.0], dtype=torch.float)
+        best_min.copy_(x_min)
+        best_max.copy_(x_max)
+        for i in range(iter):
+            new_min = x_min * (1.0 - (i * 0.01))
+            new_max = x_max * (1.0 - (i * 0.01))
+            scale, zero_point = self._calculate_qparams(new_min, new_max)  # 算出s和z（都是tensor，妙）。对称量化下就是取绝对值max做为。
+            x_q = torch.fake_quantize_per_tensor_affine(
+                x, scale.item(), int(zero_point.item()),
+                self.quant_min, self.quant_max)
+            score = self.lp_loss(x_q, x)  # 和欧式不一样！这是灵魂是求均值
+            if score < best_score:
+                best_score = score
+                best_min, best_max = new_min, new_max
+        return best_min, best_max
+
+    def mse_perchannel(self, x: torch.Tensor, x_min: torch.Tensor, x_max: torch.Tensor, iter=80, ch_axis=0):
+        assert x_min.shape == x_max.shape
+        assert ch_axis >= 0, f'{ch_axis}'
+        best_score = 1e+10 * torch.ones_like(x_min)
+        best_min, best_max = x_min.clone(), x_max.clone()
+        reduce_dim = tuple([i for i in range(len(x.shape)) if i != ch_axis])
+        for i in range(iter):
+            new_min = x_min * (1.0 - (i * 0.01))
+            new_max = x_max * (1.0 - (i * 0.01))
+            scale, zero_point = self._calculate_qparams(new_min, new_max)
+            x_q = torch.fake_quantize_per_channel_affine(
+                x, scale, zero_point.long() if _version_under_1100 else zero_point, ch_axis, 
+                self.quant_min, self.quant_max)
+            score = self.lp_loss(x_q, x, reduce_dim)
+            update_idx = (score < best_score)
+            best_score[update_idx] = score[update_idx]
+            best_min[update_idx] = new_min[update_idx]
+            best_max[update_idx] = new_max[update_idx]
+        return best_min, best_max
+
+    def forward(self, x_orig):
+        r"""Records the running minimum and maximum of ``x``."""
+        if x_orig.numel() == 0:
+            return x_orig
+        x = x_orig.clone().detach().to(self.min_val.dtype)
+        if self.ch_axis == -1:
+            min_val_cur, max_val_cur = torch._aminmax(x)
+            min_val_cur, max_val_cur = self.mse(x, min_val_cur, max_val_cur, iter=95)   # 因为是求MSE呀，所以是从minmax两边开始搜索
+        else:
+            x_dim = x.size()
+            new_axis_list = [i for i in range(len(x_dim))]
+            new_axis_list[self.ch_axis] = 0
+            new_axis_list[0] = self.ch_axis
+            x_channel = x.permute(new_axis_list)
+            y = torch.flatten(x_channel, start_dim=1)
+            min_val_cur, max_val_cur = torch._aminmax(y, 1)
+            min_val_cur, max_val_cur = self.mse_perchannel(x, min_val_cur, max_val_cur, iter=80, ch_axis=self.ch_axis)
+
+        if self.max_val.numel() <= 1 and self.max_val.isinf():  # 初次，更新最值
+            self.min_val = min_val_cur
+            self.max_val = max_val_cur
+        else:  # 再次，通过滑动EMA型更新
+            self.min_val = self.min_val * self.ema_ratio + min_val_cur * (1.0 - self.ema_ratio)
+            self.max_val = self.max_val * self.ema_ratio + max_val_cur * (1.0 - self.ema_ratio)
+        return x
diff --git a/mqbench/prepare_by_platform.py b/mqbench/prepare_by_platform.py
new file mode 100644
index 0000000..f159b45
--- /dev/null
+++ b/mqbench/prepare_by_platform.py
@@ -0,0 +1,515 @@
+from copy import deepcopy
+from enum import Enum
+from typing import Any, Dict
+import types
+import inspect
+
+import torch
+from torch.fx import Tracer
+from torch.fx.graph_module import GraphModule
+from torch.quantization.quantize_fx import _swap_ff_with_fxff
+from torch.quantization import QConfig
+import mmcv
+import mmdet
+
+from mqbench.fake_quantize import (
+    LearnableFakeQuantize,
+    NNIEFakeQuantize,
+    FixedFakeQuantize,
+    DoReFaFakeQuantize,
+    DSQFakeQuantize,
+    PACTFakeQuantize,
+    TqtFakeQuantize,
+    AdaRoundFakeQuantize,
+    QDropFakeQuantize,
+    PureHooker
+)
+from mqbench.observer import (
+    ClipStdObserver,
+    LSQObserver,
+    MinMaxFloorObserver,
+    MinMaxObserver,
+    EMAMinMaxObserver,
+    PoTModeObserver,
+    EMAQuantileObserver,
+    MSEObserver,
+    EMAMSEObserver,
+)
+from mqbench.fuser_method_mappings import fuse_custom_config_dict
+from mqbench.utils.logger import logger
+from mqbench.utils.registry import DEFAULT_MODEL_QUANTIZER
+from mqbench.scheme import QuantizeScheme
+
+__all__ = ['prepare_by_platform']
+
+class BackendType(Enum):
+    Academic = 'Academic'
+    Tensorrt = 'Tensorrt'
+    SNPE = 'SNPE'
+    PPLW8A16 = 'PPLW8A16'
+    NNIE = 'NNIE'
+    Vitis = 'Vitis'
+    ONNX_QNN = 'ONNX_QNN'
+    PPLCUDA = 'PPLCUDA'
+    OPENVINO = 'OPENVINO'
+    Tengine_u8 = "Tengine_u8"
+    Tensorrt_NLP = "Tensorrt_NLP"
+    Academic_NLP = "Academic_NLP"
+
+
+ParamsTable = {
+    BackendType.Academic:   dict(qtype='affine'),    # noqa: E241
+    BackendType.NNIE:       dict(qtype='nnie',       # noqa: E241
+                                 # NNIE actually do not need w/a qscheme. We add for initialize observer only.
+                                 w_qscheme=QuantizeScheme(symmetry=True, per_channel=False, pot_scale=False, bit=8),
+                                 a_qscheme=QuantizeScheme(symmetry=True, per_channel=False, pot_scale=False, bit=8),
+                                 default_weight_quantize=NNIEFakeQuantize,
+                                 default_act_quantize=NNIEFakeQuantize,
+                                 default_weight_observer=MinMaxObserver,
+                                 default_act_observer=EMAMinMaxObserver),
+    BackendType.Tensorrt:   dict(qtype='affine',     # noqa: E241
+                                 w_qscheme=QuantizeScheme(symmetry=True, per_channel=True, pot_scale=False, bit=8, symmetric_range=True),
+                                 a_qscheme=QuantizeScheme(symmetry=True, per_channel=False, pot_scale=False, bit=8, symmetric_range=True),
+                                 default_weight_quantize=LearnableFakeQuantize,
+                                 default_act_quantize=LearnableFakeQuantize,
+                                 default_weight_observer=MinMaxObserver,
+                                 default_act_observer=EMAMinMaxObserver),
+    BackendType.OPENVINO:   dict(qtype='affine',     # noqa: E241
+                                 w_qscheme=QuantizeScheme(symmetry=True, per_channel=True, pot_scale=False, bit=8),
+                                 a_qscheme=QuantizeScheme(symmetry=True, per_channel=False, pot_scale=False, bit=8),
+                                 default_weight_quantize=LearnableFakeQuantize,
+                                 default_act_quantize=LearnableFakeQuantize,
+                                 default_weight_observer=MinMaxObserver,
+                                 default_act_observer=EMAMinMaxObserver),
+    BackendType.SNPE:       dict(qtype='affine',     # noqa: E241
+                                 w_qscheme=QuantizeScheme(symmetry=False, per_channel=False, pot_scale=False, bit=8),
+                                 a_qscheme=QuantizeScheme(symmetry=False, per_channel=False, pot_scale=False, bit=8),
+                                 default_weight_quantize=LearnableFakeQuantize,
+                                 default_act_quantize=LearnableFakeQuantize,
+                                 default_weight_observer=MinMaxObserver,
+                                 default_act_observer=EMAMinMaxObserver),
+    BackendType.PPLW8A16:   dict(qtype='affine',     # noqa: E241
+                                 w_qscheme=QuantizeScheme(symmetry=False, per_channel=False, pot_scale=False, bit=8),
+                                 a_qscheme=QuantizeScheme(symmetry=False, per_channel=False, pot_scale=False, bit=16),
+                                 default_weight_quantize=LearnableFakeQuantize,
+                                 default_act_quantize=LearnableFakeQuantize,
+                                 default_weight_observer=MinMaxObserver,
+                                 default_act_observer=EMAMinMaxObserver),
+    BackendType.Vitis:      dict(qtype='vitis',     # noqa: E241
+                                 w_qscheme=QuantizeScheme(symmetry=True, per_channel=False, pot_scale=True, bit=8),
+                                 a_qscheme=QuantizeScheme(symmetry=True, per_channel=False, pot_scale=True, bit=8),
+                                 default_weight_quantize=TqtFakeQuantize,
+                                 default_act_quantize=TqtFakeQuantize,
+                                 default_weight_observer=MinMaxFloorObserver,
+                                 default_act_observer=PoTModeObserver),
+    BackendType.ONNX_QNN:   dict(qtype='affine',     # noqa: E241
+                                 w_qscheme=QuantizeScheme(symmetry=False, per_channel=False, pot_scale=False, bit=8),
+                                 a_qscheme=QuantizeScheme(symmetry=False, per_channel=False, pot_scale=False, bit=8),
+                                 default_weight_quantize=LearnableFakeQuantize,
+                                 default_act_quantize=LearnableFakeQuantize,
+                                 default_weight_observer=MinMaxObserver,
+                                 default_act_observer=MinMaxObserver),
+    BackendType.PPLCUDA:    dict(qtype='affine',     # noqa: E241
+                                 w_qscheme=QuantizeScheme(symmetry=False, per_channel=True, pot_scale=False, bit=8),
+                                 a_qscheme=QuantizeScheme(symmetry=False, per_channel=False, pot_scale=False, bit=8),
+                                 default_weight_quantize=LearnableFakeQuantize,
+                                 default_act_quantize=LearnableFakeQuantize,
+                                 default_weight_observer=MinMaxObserver,
+                                 default_act_observer=MinMaxObserver),
+    BackendType.Tengine_u8: dict(qtype="affine",
+                                 w_qscheme=QuantizeScheme(symmetry=False, per_channel=False, pot_scale=False, bit=8),
+                                 a_qscheme=QuantizeScheme(symmetry=False, per_channel=False, pot_scale=False, bit=8),
+                                 default_weight_quantize=LearnableFakeQuantize,
+                                 default_act_quantize=LearnableFakeQuantize,
+                                 default_weight_observer=MinMaxObserver,
+                                 default_act_observer=EMAMinMaxObserver),
+}
+ParamsTable[BackendType.Tensorrt_NLP] = ParamsTable[BackendType.Tensorrt]
+ParamsTable[BackendType.Academic_NLP] = ParamsTable[BackendType.Academic]
+
+ObserverDict = {  # Obeser 映射到类头
+    'MinMaxObserver':           MinMaxObserver,                                    # noqa: E241
+    'EMAMinMaxObserver':        EMAMinMaxObserver,        # More general choice.   # noqa: E241
+    'MinMaxFloorObserver':      MinMaxFloorObserver,      # For Vitis HW           # noqa: E241
+    'PoTModeObserver':          PoTModeObserver,   # For Vitis HW           # noqa: E241
+    'EMAQuantileObserver':      EMAQuantileObserver,      # Quantile observer.     # noqa: E241
+    'ClipStdObserver':          ClipStdObserver,          # Usually used for DSQ.  # noqa: E241
+    'LSQObserver':              LSQObserver,              # Usually used for LSQ.  # noqa: E241
+    'MSEObserver':              MSEObserver,                                       # noqa: E241
+    'EMAMSEObserver':           EMAMSEObserver,                                    # noqa: E241
+}
+
+FakeQuantizeDict = {  # 量化器映射到类头
+    'FixedFakeQuantize': FixedFakeQuantize,      # Unlearnable scale/zeropoint  # noqa: E241
+    'LearnableFakeQuantize': LearnableFakeQuantize,  # Learnable scale/zeropoint    # noqa: E241
+    'NNIEFakeQuantize':      NNIEFakeQuantize,       # Quantize function for NNIE   # noqa: E241
+    'DoReFaFakeQuantize':    DoReFaFakeQuantize,     # Dorefa                       # noqa: E241
+    'DSQFakeQuantize':       DSQFakeQuantize,        # DSQ                          # noqa: E241
+    'PACTFakeQuantize':      PACTFakeQuantize,       # PACT                         # noqa: E241
+    'TqtFakeQuantize':       TqtFakeQuantize,        # TQT                          # noqa: E241
+    'AdaRoundFakeQuantize':  AdaRoundFakeQuantize,   # AdaRound                     # noqa: E241
+    'QDropFakeQuantize':     QDropFakeQuantize,      # BRECQ & QDrop                # noqa: E241
+    'PureHooker': PureHooker
+}
+
+
+def get_qconfig_by_platform(deploy_backend: BackendType, extra_qparams: Dict):  # 拉取默认的config，再修改成自定义的config
+    """
+
+    Args:
+        deploy_backend (BackendType):
+        extra_qparams (dict):
+
+    >>> extra params format: {
+            'w_observer': str, weight observer name,
+            'a_observer': str, activation observer name,
+            'w_fakequantize': str, weight fake quantize function name,
+            'w_fakeq_params": dict, params for weight quantize function,
+            'a_fakequantize': str, activation fake quantize function name,
+            'a_fakeq_params': dict, params for act quantize function,
+            if deploy_backend == BackendType.Academic keys below will be used:
+            'w_qscheme': {
+                'bit': bitwidth,
+                'symmetry': whether quantize scheme is symmetric,
+                'per_channel': whether quantize scheme is perchannel,
+                'pot_scale': whether scale is power of two.
+            }
+            'a_qscheme': {
+                same with w_qscheme.
+            }
+        }
+    """
+    w_observer = extra_qparams.get('w_observer', None)
+    if w_observer:
+        assert w_observer in ObserverDict, \
+            'Do not support observer name: {}'.format(w_observer)
+        w_observer = ObserverDict[w_observer]
+    a_observer = extra_qparams.get('a_observer', None)
+    if a_observer:
+        assert a_observer in ObserverDict, \
+            'Do not support observer name: {}'.format(a_observer)
+        a_observer = ObserverDict[a_observer]
+    w_fakequantize = extra_qparams.get('w_fakequantize', None)
+    if w_fakequantize:
+        assert w_fakequantize in FakeQuantizeDict, \
+            'Do not support fakequantize name: {}'.format(w_fakequantize)
+        w_fakequantize = FakeQuantizeDict[w_fakequantize]
+    a_fakequantize = extra_qparams.get('a_fakequantize', None)
+    if a_fakequantize:
+        assert a_fakequantize in FakeQuantizeDict, \
+            'Do not support fakequantize name: {}'.format(a_fakequantize)
+        a_fakequantize = FakeQuantizeDict[a_fakequantize]
+    backend_params = ParamsTable[deploy_backend]  # 拉取默认config
+
+    # NNIE backend must use NNIEFakeQuantize but leave observer adjustable.
+    if backend_params['qtype'] == 'nnie':
+        if not w_observer:
+            w_observer = backend_params['default_weight_observer']
+        if not a_observer:
+            a_observer = backend_params['default_act_observer']
+        w_qscheme = backend_params['w_qscheme']
+        a_qscheme = backend_params['a_qscheme']
+        w_config = backend_params['default_weight_quantize'].with_args(observer=w_observer,
+                                                                       **w_qscheme.to_observer_params())
+        a_config = backend_params['default_act_quantize'].with_args(observer=a_observer,
+                                                                    **a_qscheme.to_observer_params())
+        return QConfig(activation=a_config, weight=w_config)
+
+    # Academic setting should specific quant scheme in config.
+    if deploy_backend in [BackendType.Academic, BackendType.Academic_NLP]:
+        w_qscheme = QuantizeScheme(**extra_qparams['w_qscheme'])  # qscheme就是量化有关参数，bit 对称 POT
+        a_qscheme = QuantizeScheme(**extra_qparams['a_qscheme'])
+    else:
+        w_qscheme = extra_qparams.get('w_qscheme', None)
+        if w_qscheme is None:
+            w_qscheme = backend_params['w_qscheme']
+        else:
+            logger.info("Weight Quant Scheme is overrided!")
+            w_qscheme = QuantizeScheme(**w_qscheme)
+        a_qscheme = extra_qparams.get('a_qscheme', None)
+        if a_qscheme is None:
+            a_qscheme = backend_params['a_qscheme']
+        else:
+            logger.info("Activation Quant Scheme is overrided!")
+            a_qscheme = QuantizeScheme(**a_qscheme)
+
+    # Set extra args for observers.
+    w_observer_extra_args = extra_qparams.get('w_observer_extra_args', {})
+    a_observer_extra_args = extra_qparams.get('a_observer_extra_args', {})
+    w_qscheme.kwargs.update(w_observer_extra_args)
+    a_qscheme.kwargs.update(a_observer_extra_args)
+    # Get weight / act fake quantize function and params. And bias fake quantizer if needed(Vitis)
+    if not w_fakequantize:
+        w_fakequantize = backend_params['default_weight_quantize']
+    w_fakeq_params = extra_qparams.get('w_fakeq_params', {})  # TODO 这是干嘛的
+    if not a_fakequantize:
+        a_fakequantize = backend_params['default_act_quantize']
+    a_fakeq_params = extra_qparams.get('a_fakeq_params', {})
+    # Get default observer type.
+    if not w_observer:
+        w_observer = backend_params['default_weight_observer']
+    if not a_observer:
+        a_observer = backend_params['default_act_observer']
+
+    # Create qconfig.
+    # here, rewrited by with_args
+    w_qconfig = w_fakequantize.with_args(observer=w_observer, **w_fakeq_params, **w_qscheme.to_observer_params())  # TODO 这是干嘛的
+    a_qconfig = a_fakequantize.with_args(observer=a_observer, **a_fakeq_params, **a_qscheme.to_observer_params())
+    logger.info('Weight Qconfig:\n    FakeQuantize: {} Params: {}\n'
+                '    Oberver:      {} Params: {}'.format(w_fakequantize.__name__, w_fakeq_params,
+                                                         w_observer.__name__, str(w_qscheme)))
+    logger.info('Activation Qconfig:\n    FakeQuantize: {} Params: {}\n'
+                '    Oberver:      {} Params: {}'.format(a_fakequantize.__name__, a_fakeq_params,
+                                                         a_observer.__name__, str(a_qscheme)))
+    if backend_params['qtype'] == 'vitis':
+        logger.info('Bias Qconfig:\n    TqtFakeQuantize with MinMaxObserver')
+
+    return QConfig(activation=a_qconfig, weight=w_qconfig)  # TODO 这是啥意思
+
+
+class CustomedTracer(Tracer):
+    """
+    ``Tracer`` is the class that implements the symbolic tracing functionality
+    of ``torch.fx.symbolic_trace``. A call to ``symbolic_trace(m)`` is equivalent
+    to ``Tracer().trace(m)``.
+    This Tracer override the ``is_leaf_module`` function to make symbolic trace
+    right in some cases.
+    """
+    def __init__(self, *args, customed_leaf_module=None, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.customed_leaf_module = customed_leaf_module
+
+    def is_leaf_module(self, m: torch.nn.Module, module_qualified_name : str) -> bool:
+        """
+        A method to specify whether a given ``nn.Module`` is a "leaf" module.
+        Leaf modules are the atomic units that appear in
+        the IR, referenced by ``call_module`` calls. By default,
+        Modules in the PyTorch standard library namespace (torch.nn)
+        are leaf modules. All other modules are traced through and
+        their constituent ops are recorded, unless specified otherwise
+        via this parameter.
+        Args:
+            m (Module): The module being queried about
+            module_qualified_name (str): The path to root of this module. For example,
+                if you have a module hierarchy where submodule ``foo`` contains
+                submodule ``bar``, which contains submodule ``baz``, that module will
+                appear with the qualified name ``foo.bar.baz`` here.
+        """
+        if self.customed_leaf_module and isinstance(m, self.customed_leaf_module):
+            return True
+        return m.__module__.startswith('torch.nn') and not isinstance(m, torch.nn.Sequential)
+
+def duplicate_reused_nodes(graph: torch.fx.Graph, modules: Dict[str, Any] = {}, not_duplicated_prefixes=[]):  # TODO 这是怎么找出哪个call module是重复的？
+    _dup_prefix = '_dup'
+    target_dict = dict()  # TODO node.target指的是实际layer/function的名字
+    dup_modules = dict()  # 就是新增的复制的layer
+    
+    for node in graph.nodes:  # XXX 这里就和ONNX gs一摸一样，遍历节点，但是更细粒度，包括一些参数
+        if node.op == "call_module":  # call_function就是符合torch规范的函数，如torch.add
+            if node.target not in target_dict:  # target_dict其实就是想找出所有的layer
+                target_dict[node.target] = [node]  # XXX node名字是唯一的，重复使用的layer会为node名字加后缀。
+            else:
+                target_dict[node.target].append(node)
+    for key in target_dict:
+        exclude_flag = False# XXX 定义排除机制
+        for exclude_prefix in not_duplicated_prefixes:
+            if exclude_prefix in key:
+                exclude_flag = True
+                break
+        if exclude_flag:
+            continue  # 直接退出？可取吗
+        if len(target_dict[key]) > 1:  # 这里就是看哪个名字重复出现了，那就是说该算子共享了，就需要处理
+            for idx, node in enumerate(target_dict[key]):
+                if idx == 0:  # 第一个的node就是其本身，原汁原味，不变。
+                    continue
+                module = deepcopy(modules[node.target])  # 索引出对应的layer，深度拷贝
+                node.target += _dup_prefix + str(idx)  # 直接改名字
+                dup_modules[node.target] = module
+    graph.lint()
+    return graph, dup_modules
+
+def prepare_constant_dict(graph: torch.fx.Graph, model: torch.nn.Module):
+    def _get_attrs(target, attrs):
+        attrs = attrs.split('.')
+        for att in attrs:
+            target = getattr(target, att)
+        return target
+    constant_dict = dict()
+    for node in graph.nodes:
+        if node.op == 'get_attr':
+            constant_dict[node.target] = _get_attrs(model, node.target)
+    return constant_dict
+
+def eliminate_dead_node(graph: torch.fx.Graph):
+    """
+        Remove all dead code from the graph, based on each node's number of
+        users, and whether the nodes have any side effects. The graph must be
+        topologically sorted before calling.
+    """
+    def is_impure(node):
+        """
+        从高版本抄过来的，懒得升级torch版本了
+        https://pytorch.org/docs/1.9.0/_modules/torch/fx/node.html#Node.is_impure
+        https://pytorch.org/docs/1.9.0/_modules/torch/fx/graph.html#Graph.eliminate_dead_code
+        Returns whether this op is impure, i.e. if its op is a placeholder or
+        output, or if a call_function or call_module which is impure.
+
+        Returns:
+
+            bool: If the op is impure or not.
+        """
+        if node.op in {"placeholder", "output"}:
+            return True
+
+        # Check if an impure function.
+        # if node.op == "call_function":
+        #     return node.target in _side_effectful_functions
+
+        # Check if an impure module.
+        # if node.op == "call_module":
+        #     assert (
+        #         node.graph.owning_module is not None
+        #     ), "self.graph.owning_module not set for purity check"
+        #     target_mod = node.graph.owning_module.get_submodule(node.target)
+        #     assert (
+        #         target_mod is not None
+        #     ), f"Did not find expected submodule target {node.target}"
+        #     return getattr(target_mod, "_is_impure", False)
+
+        return False
+    
+    
+    graph.lint()
+
+    # Reverse iterate so that when we remove a node, any nodes used as an
+    # input to that node have an updated user count that no longer reflects
+    # the removed node.
+    changed = False
+    for node in reversed(graph.nodes):
+        if not is_impure(node) and len(node.users) == 0:
+            graph.erase_node(node)
+            logger.info("Erase node {}. ".format(node.name))  # 确实说明的是，在该node后面加上fakequant用于act
+            changed = True
+
+    return changed
+
+
+def prepare_by_platform(
+        model: torch.nn.Module,
+        deploy_backend: BackendType,
+        structure_detail,
+        prepare_custom_config_dict: Dict[str, Any] = {},
+        custom_tracer: Tracer = None):
+    """
+    Args:
+        model (torch.nn.Module):
+        deploy_backend (BackendType):
+
+    >>> prepare_custom_config_dict : {
+            extra_qconfig_dict : Dict, Find explanations in get_qconfig_by_platform,
+            extra_quantizer_dict: Extra params for quantizer.
+            preserve_attr: Dict, Specify attribute of model which should be preserved
+                after prepare. Since symbolic_trace only store attributes which is
+                in forward. If model.func1 and model.backbone.func2 should be preserved,
+                {"": ["func1"], "backbone": ["func2"] } should work.
+            Attr below is inherited from Pytorch.
+            concrete_args: Specify input for model tracing.
+            extra_fuse_dict: Specify extra fusing patterns and functions.
+        }
+
+    """
+    model_mode = 'Training' if model.training else 'Eval'
+    logger.info("Quantize model Scheme: {} Mode: {}".format(deploy_backend, model_mode))
+
+    # XXX Get Qconfig，该阶段只是在收集和整理信息，没有定义实质性的东西，就是写config
+    extra_qconfig_dict = prepare_custom_config_dict.get('extra_qconfig_dict', {})
+    qconfig = get_qconfig_by_platform(deploy_backend, extra_qconfig_dict)
+
+    _swap_ff_with_fxff(model)  # XXX 替换fx不支持的节点。几乎很少
+    # # Preserve attr.  XXX 
+    # preserve_attr_dict = dict()
+    # if 'preserve_attr' in prepare_custom_config_dict:
+    #     for submodule_name in prepare_custom_config_dict['preserve_attr']:
+    #         cur_module = model
+    #         if submodule_name != "":
+    #             cur_module = getattr(model, submodule_name)
+    #         # preserve_attr_list = prepare_custom_config_dict['preserve_attr'][submodule_name]
+    #         preserve_attr_dict[submodule_name] = cur_module
+    #         # for attr in preserve_attr_list:
+    #         #     preserve_attr_dict[submodule_name][attr] = getattr(cur_module, attr)
+    # Symbolic trace
+    concrete_args = structure_detail.input_concrete_args  # XXX trace 的定制
+    not_duplicated_prefixes = structure_detail.not_duplicated_prefixes
+    customed_leaf_module = prepare_custom_config_dict.get('leaf_module', [])  # XXX trace 的定制 leaf module 就是我们正常定义的层结构，这里是手动说明自定义的层
+    customed_leaf_module.append(mmcv.cnn.bricks.swish.Swish)
+    customed_leaf_module.append(mmcv.cnn.bricks.activation.Clamp)
+    customed_leaf_module.append(mmcv.cnn.bricks.hsigmoid.HSigmoid)
+    customed_leaf_module.append(mmcv.cnn.bricks.scale.Scale)
+    customed_leaf_module.append(mmdet.models.necks.ssd_neck.L2Norm)
+    
+    tracer = CustomedTracer(customed_leaf_module=tuple(customed_leaf_module))
+    if custom_tracer is not None:
+        tracer = custom_tracer
+    
+    if len(concrete_args) == 1 and 'in_num' in concrete_args:
+        model.in_num = concrete_args['in_num']
+    elif len(concrete_args) == 0:
+        pass
+    else:
+        raise NotImplementedError
+    
+    graph = tracer.trace(model)  # XXX graph的node是允许“重复”的，指反复使用一个算子（层）时会反复记录成node。
+    name = model.__class__.__name__ if isinstance(model, torch.nn.Module) else model.__name__
+    modules = dict(model.named_modules())  # XXX 这个操作直接提取出所有的子layer，及其名字，作为name：layer dict
+    # TODO 删除死节点。torch1.9之后才有。此为低版本的滥用
+    eliminate_dead_node(graph)
+    
+    graph, duplicated_modules = duplicate_reused_nodes(graph, modules, not_duplicated_prefixes)  # XXX 意思是，需要把共享的call module复制出来，取消共享。注意，只复制module！在共享head机制下不能复制head！该功能更关注的是串行上的复用问题
+    constant_nodes = prepare_constant_dict(graph, model)  # 确实是用来获取constant。一般出自model的attr
+    # TODO 下面这两步更新到一起？为啥？
+    modules.update(duplicated_modules)  # XXX 确实，必须得复制，因为有的relu是共享的！
+    modules.update(constant_nodes)
+    
+    graph_module = GraphModule(modules, graph, name)  # TODO 这就搞到一起了？？？
+    # Model fusion.
+    extra_fuse_dict = prepare_custom_config_dict.get('extra_fuse_dict', {})  # XXX 指定哪些需要fuse
+    # extra_fuse_dict.update(fuse_custom_config_dict)  # NOTE 加载MQBEnch自定义的fuse，
+    # Prepare
+    import mqbench.custom_quantizer  # noqa: F401
+    extra_quantizer_dict = prepare_custom_config_dict.get('extra_quantizer_dict', {})
+    quantizer = DEFAULT_MODEL_QUANTIZER[deploy_backend](extra_quantizer_dict, extra_fuse_dict)
+    prepared = quantizer.prepare(graph_module, qconfig, structure_detail.further_detail, testing=prepare_custom_config_dict.get('testing', False))  # 返回已经插入quantizer的模型结构
+    # TODO Restore attr.
+    if 'preserve_attr' in structure_detail:
+        for attr_name in structure_detail['preserve_attr']:
+            cur_module = model
+            try:
+                attr = getattr(model, attr_name)
+                
+            except AttributeError:
+                pass
+            else:
+                logger.info("Preserve attr: {}".format(attr_name))
+                _type = type(model)
+                if inspect.ismethod(attr):
+                    attr = types.MethodType(getattr(_type, attr_name), prepared)
+                
+                # preserve_attr_list = prepare_custom_config_dict['preserve_attr'][submodule_name]
+                setattr(prepared, attr_name, attr)
+                
+    # # Restore attr.
+    # if 'preserve_attr' in prepare_custom_config_dict:
+    #     for submodule_name in prepare_custom_config_dict['preserve_attr']:
+    #         cur_module = prepared
+    #         _type = type(model)
+    #         if submodule_name != "":
+    #             cur_module = getattr(prepared, submodule_name)
+    #             _type = type(getattr(model, submodule_name))
+    #         preserve_attr_list = prepare_custom_config_dict['preserve_attr'][submodule_name]
+    #         for attr_name in preserve_attr_list:
+    #             logger.info("Preserve attr: {}.{}".format(submodule_name, attr_name))
+    #             _attr = preserve_attr_dict[submodule_name][attr_name]
+    #             if inspect.ismethod(_attr):
+    #                 _attr = types.MethodType(getattr(_type, attr_name), cur_module)
+    #             setattr(cur_module, attr_name, _attr)
+    return prepared
diff --git a/mqbench/quantization/__init__.py b/mqbench/quantization/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mqbench/quantization/default_bias_fake_quant.py b/mqbench/quantization/default_bias_fake_quant.py
new file mode 100644
index 0000000..bbb47b1
--- /dev/null
+++ b/mqbench/quantization/default_bias_fake_quant.py
@@ -0,0 +1,12 @@
+from mqbench.fake_quantize import TqtFakeQuantize
+from mqbench.observer import MinMaxFloorObserver
+from mqbench.scheme import QuantizeScheme
+
+
+bias_fakeq_param = {}
+bias_qscheme = QuantizeScheme(symmetry=True, per_channel=False, pot_scale=True, bit=8)
+b_fakequantize = TqtFakeQuantize
+b_qconfig = b_fakequantize.with_args(observer=MinMaxFloorObserver, **bias_fakeq_param, **bias_qscheme.to_observer_params())
+bias_fake_quantizer = b_qconfig
+bias_fake_quantizer.p.keywords.pop('sign')
+bias_fake_quantizer.p.keywords.pop('bit')
diff --git a/mqbench/scheme.py b/mqbench/scheme.py
new file mode 100644
index 0000000..d8b526e
--- /dev/null
+++ b/mqbench/scheme.py
@@ -0,0 +1,46 @@
+import torch 
+
+
+class QuantizeScheme(object):  # 结构体类，记录qscheme
+    """Describe quantization scheme.
+    """
+    def __init__(self, symmetry=True, per_channel=False, pot_scale=False, bit=8, **kwargs):
+        self.symmetry = symmetry
+        self.per_channel = per_channel
+        self.pot_scale = pot_scale
+        self.bit = bit
+        if self.per_channel:
+            self.torch_qscheme = torch.per_channel_symmetric if self.symmetry else torch.per_channel_affine  # 原来非对称叫affine
+        else:
+            self.torch_qscheme = torch.per_tensor_symmetric if self.symmetry else torch.per_tensor_affine
+        if 'symmetric_range' in kwargs:
+            self.symmetric_range = kwargs['symmetric_range']
+            del kwargs['symmetric_range']
+        else:
+            self.symmetric_range = False
+            
+        if 'sign' in kwargs:
+            self.sign = kwargs['sign']
+        else:
+            self.sign = True
+            kwargs['sign'] = True
+            
+        self.kwargs = kwargs
+
+    def to_observer_params(self):  # 生成更细致的量化参数
+        naive_para = {
+            'quant_min': (-2 ** (self.bit - 1) + 1 if self.symmetric_range else -2 ** (self.bit - 1)) if (self.symmetry and self.sign) else 0,
+            'quant_max': 2 ** (self.bit - 1) - 1 if (self.symmetry and self.sign) else 2 ** self.bit - 1,
+            'dtype': torch.qint8 if (self.symmetry  and self.sign) else torch.quint8,
+            'pot_scale': self.pot_scale,
+            'qscheme': self.torch_qscheme,
+            'reduce_range': False,
+            'ch_axis': 0 if self.per_channel else -1
+            , 'bit': self.bit
+        }
+        naive_para.update(self.kwargs)
+        return naive_para
+
+    def __str__(self):
+        return "Symmetric: {} / Bitwidth: {} / Per channel: {} / Pot scale: {} / Extra kwargs: {}".format(
+            self.symmetry, self.bit, self.per_channel, self.pot_scale, self.kwargs)
diff --git a/mqbench/tools/__init__.py b/mqbench/tools/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/mqbench/tools/replace_syncbn.py b/mqbench/tools/replace_syncbn.py
new file mode 100644
index 0000000..ac7db69
--- /dev/null
+++ b/mqbench/tools/replace_syncbn.py
@@ -0,0 +1,30 @@
+import torch
+from spring.linklink.nn import SyncBatchNorm2d
+
+
+def replace_bn_to_syncbn(model, custombn=SyncBatchNorm2d):
+    if type(model) in [torch.nn.BatchNorm2d]:
+        return _replace_bn(model, custombn)
+
+    elif type(model) in [torch.nn.intrinsic.qat.ConvBn2d, torch.nn.intrinsic.qat.ConvBnReLU2d]:
+        model.bn = _replace_bn(model.bn, custombn)
+        return model
+
+    elif type(model) in [torch.nn.intrinsic.BNReLU2d]:
+        model[0] = _replace_bn(model[0], custombn)
+        return model
+
+    else:
+        for name, module in model.named_children():
+            setattr(model, name, replace_bn_to_syncbn(module))
+        return model
+
+
+def _replace_bn(bn, custombn):
+    syncbn = custombn(bn.num_features, bn.eps, bn.momentum, bn.affine)
+    if bn.affine:
+        syncbn.weight = bn.weight
+        syncbn.bias = bn.bias
+    syncbn.running_mean = bn.running_mean
+    syncbn.running_var = bn.running_var
+    return syncbn
diff --git a/mqbench/utils/__init__.py b/mqbench/utils/__init__.py
new file mode 100644
index 0000000..90f60fd
--- /dev/null
+++ b/mqbench/utils/__init__.py
@@ -0,0 +1 @@
+from .utils import *
\ No newline at end of file
diff --git a/mqbench/utils/fusion.py b/mqbench/utils/fusion.py
new file mode 100644
index 0000000..beea46e
--- /dev/null
+++ b/mqbench/utils/fusion.py
@@ -0,0 +1,30 @@
+from copy import deepcopy
+
+import torch
+
+
+def fuse_deconv_bn_weights(deconv_w, deconv_b, bn_rm, bn_rv, bn_eps, bn_w, bn_b):
+    if deconv_b is None:
+        deconv_b = torch.zeros_like(bn_rm)
+    if bn_w is None:
+        bn_w = torch.ones_like(bn_rm)
+    if bn_b is None:
+        bn_b = torch.zeros_like(bn_rm)
+    bn_var_rsqrt = torch.rsqrt(bn_rv + bn_eps)
+
+    shape = [1] * len(deconv_w.shape)
+    shape[1] = -1
+    deconv_w = deconv_w * (bn_w * bn_var_rsqrt).reshape(shape)
+    deconv_b = (deconv_b - bn_rm) * bn_var_rsqrt * bn_w + bn_b
+
+    return torch.nn.Parameter(deconv_w), torch.nn.Parameter(deconv_b)
+
+
+def fuse_deconv_bn_eval(deconv, bn):
+    assert (not (deconv.training or bn.training)), 'Fusion only for eval!'
+
+    fused_deconv = deepcopy(deconv)
+    fused_deconv.weight, fused_deconv.bias = fuse_deconv_bn_weights(
+        deconv.weight, deconv.bias, bn.running_mean, bn.running_var, bn.eps,
+        bn.weight, bn.bias)
+    return fused_deconv
\ No newline at end of file
diff --git a/mqbench/utils/hook.py b/mqbench/utils/hook.py
new file mode 100644
index 0000000..f0a79aa
--- /dev/null
+++ b/mqbench/utils/hook.py
@@ -0,0 +1,61 @@
+from functools import partial
+
+import torch
+
+class StopForwardException(Exception):
+    """
+    Used to throw and catch an exception to stop traversing the graph
+    """
+    pass
+
+
+class DataSaverHook:
+    """
+    Forward hook that stores the input and output of a layer/block
+    """
+    def __init__(self, store_input=False, store_output=False, stop_forward=False):
+        self.store_input = store_input
+        self.store_output = store_output
+        self.stop_forward = stop_forward
+
+        self.input_store = None
+        self.output_store = None
+
+    def __call__(self, module, input_batch, output_batch):
+        if self.store_input:
+            self.input_store = input_batch
+        if self.store_output:
+            self.output_store = output_batch
+        if self.stop_forward:
+            raise StopForwardException
+
+
+class PerChannelLoadHook:
+    def __init__(self, module, hook_param=["scale", "zero_point"]):
+        self.hook = module._register_load_state_dict_pre_hook(partial(self.hook_fn, module=module))
+        self.hook_param = hook_param
+
+    def hook_fn(self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs,
+                module):
+        if module.ch_axis == -1:
+            # no per-channel parameters
+            return
+        for module_key, param in module._parameters.items():
+            if module_key not in self.hook_param:
+                continue
+            candidate = prefix + module_key
+            if candidate in state_dict:
+                input_param = state_dict[candidate]
+                if param.shape != input_param.shape:
+                    param.data = torch.ones_like(input_param, dtype=param.dtype, device=param.device)
+        for module_key, param in module._buffers.items():
+            if module_key not in self.hook_param:
+                continue
+            candidate = prefix + module_key
+            if candidate in state_dict:
+                input_param = state_dict[candidate]
+                if param.shape != input_param.shape:
+                    param.data = torch.ones_like(input_param, dtype=param.dtype, device=param.device)
+
+    def close(self):
+        self.hook.remove()
diff --git a/mqbench/utils/logger.py b/mqbench/utils/logger.py
new file mode 100644
index 0000000..5c23b72
--- /dev/null
+++ b/mqbench/utils/logger.py
@@ -0,0 +1,24 @@
+import logging
+import sys
+
+
+MQBENCH_LOGGER_NAME = "MQBENCH"
+logger = logging.getLogger(MQBENCH_LOGGER_NAME)
+logger.propagate = False
+stdout_handler = logging.StreamHandler(sys.stdout)
+fmt = logging.Formatter("[%(name)s] %(levelname)s: %(message)s")
+stdout_handler.setFormatter(fmt)
+stdout_handler.setLevel(logging.DEBUG)
+logger.addHandler(stdout_handler)
+logger.setLevel(logging.INFO)
+logger.parent = None
+
+
+def set_log_level(level):
+    logger.setLevel(level)
+    for handler in logger.handlers:
+        handler.setLevel(level)
+
+
+def disable_logging():
+    logger.handlers = []
diff --git a/mqbench/utils/registry.py b/mqbench/utils/registry.py
new file mode 100644
index 0000000..fb5c863
--- /dev/null
+++ b/mqbench/utils/registry.py
@@ -0,0 +1,43 @@
+from collections import OrderedDict
+
+
+DEFAULT_MODEL_QUANTIZER = OrderedDict()
+
+
+def register_model_quantizer(backend_type):
+    def insert(quantizer_cls):
+        DEFAULT_MODEL_QUANTIZER[backend_type] = quantizer_cls
+        return quantizer_cls
+    return insert
+
+BACKEND_DEPLOY_FUNCTION = OrderedDict()
+
+
+def register_deploy_function(backend_type):
+    def insert(func):
+        if backend_type in BACKEND_DEPLOY_FUNCTION:
+            BACKEND_DEPLOY_FUNCTION[backend_type].append(func)
+        else:
+            BACKEND_DEPLOY_FUNCTION[backend_type] = [func]
+        return func
+    return insert
+
+
+FUSED_MODULE_CONVERT_FUNCTION = OrderedDict()
+
+
+def register_convert_function(module_type):
+    def insert(func):
+        FUSED_MODULE_CONVERT_FUNCTION[module_type] = func
+        return func
+    return insert
+
+
+WEIGHT_EQUALIZATION_FUNCTION = OrderedDict() 
+
+
+def register_weight_equalization_function(layer1, layer2):
+    def insert(func):
+        WEIGHT_EQUALIZATION_FUNCTION[layer1] = {layer2: func}
+        return func
+    return insert
\ No newline at end of file
diff --git a/mqbench/utils/state.py b/mqbench/utils/state.py
new file mode 100644
index 0000000..e5e4c9e
--- /dev/null
+++ b/mqbench/utils/state.py
@@ -0,0 +1,54 @@
+import torch
+
+from mqbench.utils.logger import logger
+
+
+def enable_calibration(model):  # 启动所有的observer，但停用quantizer
+    logger.info('Enable observer and Disable quantize.')
+    for name, submodule in model.named_modules():
+        if isinstance(submodule, torch.quantization.FakeQuantizeBase):
+            logger.debug('Enable observer and Disable quant: {}'.format(name))
+            submodule.enable_observer()
+            submodule.disable_fake_quant()
+
+def enable_calibration_woquantization(model, quantizer_type='fake_quant'):  # 启动对应前缀名字的量化器的observer，关闭量化器。同时停用非名字匹配的quantizer和ob
+    logger.info('Enable observer and Disable quantize for {}'.format(quantizer_type))
+    for name, submodule in model.named_modules():
+        if isinstance(submodule, torch.quantization.FakeQuantizeBase):
+            if quantizer_type not in name:   # TODO 突发！原来weight quantize也是一个独立的个体!但不是layer，但是是怎么访问到的？？
+                submodule.disable_observer()
+                submodule.disable_fake_quant()
+                continue
+            logger.debug('Enable observer and Disable quant: {}'.format(name))
+            submodule.enable_observer()
+            submodule.disable_fake_quant()
+
+def enable_calibration_quantization(model, quantizer_type='fake_quant'):  # 启动对应前缀名字的量化器及ob。同时停用非名字匹配的quantizer和ob
+    logger.info('Enable observer and Enable quantize for {}'.format(quantizer_type))
+    for name, submodule in model.named_modules():
+        if isinstance(submodule, torch.quantization.FakeQuantizeBase):
+            if quantizer_type not in name: 
+                logger.info('Disable observer and Disable quantize for {}'.format(name))
+                submodule.disable_observer()
+                submodule.disable_fake_quant()
+                continue
+            logger.debug('Enable observer and Enable quant: {}'.format(name))
+            submodule.enable_observer()
+            submodule.enable_fake_quant()
+
+def enable_quantization(model):  # 启用所有的quantizer，但停用ob
+    logger.info('Disable observer and Enable quantize.')
+    for name, submodule in model.named_modules():
+        if isinstance(submodule, torch.quantization.FakeQuantizeBase):
+            logger.debug('Disable observer and Enable quant: {}'.format(name))
+            submodule.disable_observer()
+            submodule.enable_fake_quant()
+
+
+def disable_all(model):# 停用所有的quantizer和ob
+    logger.info('Disable observer and Disable quantize.')
+    for name, submodule in model.named_modules():
+        if isinstance(submodule, torch.quantization.FakeQuantizeBase):
+            logger.debug('Disable observer and Disable quantize: {}'.format(name))
+            submodule.disable_observer()
+            submodule.disable_fake_quant()
diff --git a/mqbench/utils/utils.py b/mqbench/utils/utils.py
new file mode 100644
index 0000000..128280a
--- /dev/null
+++ b/mqbench/utils/utils.py
@@ -0,0 +1,179 @@
+import copy
+
+import torch
+import torch.fx
+from torch.fx import GraphModule
+from torch.nn import Module
+
+USE_LINK = False
+USE_DDP = False
+
+try:
+    import spring.linklink as link
+    assert link.is_initialized()
+    USE_LINK = True
+except (ModuleNotFoundError, AssertionError):
+    import torch.distributed as dist
+    if torch.distributed.is_initialized():
+        USE_DDP = True
+
+
+def sync_tensor(tensor):
+    global USE_LINK
+    global USE_DDP
+    if USE_LINK:
+        if tensor.is_cuda is True:
+            tensor.data = tensor.data / link.get_world_size()
+            link.allreduce(tensor.data)
+    elif USE_DDP:
+        tensor.data = tensor.data / dist.get_world_size()
+        dist.all_reduce(tensor.data)
+    return tensor
+
+
+def pot_quantization(tensor: torch.Tensor, mode='round'):
+    log2t = torch.log2(tensor)
+    if mode == 'round':
+        log2t = (torch.round(log2t) - log2t).detach() + log2t
+    else:
+        assert mode == 'floor' 
+        log2t = (torch.floor(log2t) - log2t).detach() + log2t
+    return 2 ** log2t
+
+
+
+def is_symmetric_quant(qscheme: 'torch.qscheme') -> bool:
+    return qscheme in [torch.per_tensor_symmetric, torch.per_channel_symmetric]
+
+
+class no_jit_trace:
+    def __enter__(self):
+        # pylint: disable=protected-access
+        self.state = torch._C._get_tracing_state()
+        torch._C._set_tracing_state(None)
+
+    def __exit__(self, *args):
+        torch._C._set_tracing_state(self.state)
+        self.state = None
+
+
+def is_tracing_state():
+    return torch._C._get_tracing_state()
+
+
+def deepcopy_graphmodule(gm: GraphModule):
+    """Rewrite the deepcopy of GraphModule. (Copy its 'graph'.)
+
+    Args:
+        gm (GraphModule): 
+
+    Returns:
+        GraphModule: A deepcopied gm.
+    """
+    copied_gm = copy.deepcopy(gm)
+    copied_gm.graph = copy.deepcopy(gm.graph)
+    return copied_gm
+
+
+def deepcopy_mixedmodule(mm: Module, module_list: list):
+    """Support for `module_list` which splits modules' nn part and post precess.
+
+    Args:
+        mm (nn.Module)
+        module_list (list): the children of the mm who are a GraphModule.
+
+    Returns:
+        nn.Module
+    """
+    copied_mm = copy.deepcopy(mm)
+    for mname in module_list:
+        mod = getattr(mm, mname)
+        child_graph = copy.deepcopy(mod.graph)
+        copied_child = getattr(copied_mm, mname)
+        setattr(copied_child, 'graph', child_graph)
+    return copied_mm
+
+
+def getitem2node(model: GraphModule) -> dict:
+    def _update_getitem_path(getitem_args_dict):
+        for node in getitem_args_dict:
+            args_list = getitem_args_dict[node]
+            while args_list[0] in getitem_args_dict:
+                args_list = getitem_args_dict[args_list[0]] + args_list[1:]
+            getitem_args_dict[node] = args_list
+        return getitem_args_dict
+
+    def _getitem_from_args(args, original_args_dict):
+        ret = original_args_dict
+        for a in args:
+            try:
+                ret = ret[a]
+            except (IndexError, KeyError):
+                return {}
+        return ret 
+    import operator
+    nodes = list(model.graph.nodes)
+    # the getitem's call graph
+    getitem_args_dict = {}
+    # the dict used in the model 
+    original_key_dict = {}
+    getitem2node = {}
+    for node in nodes:  # TODO 看到这
+        # update the getitems
+        if node.target == operator.getitem:
+            getitem_args_dict[node] = list(node.args)
+            getitem_args_dict = _update_getitem_path(getitem_args_dict)
+            for _node in getitem_args_dict:
+                if _node in getitem2node:
+                    continue
+                val = _getitem_from_args(getitem_args_dict[_node], original_key_dict)
+                if isinstance(val, torch.fx.node.Node):
+                    getitem2node[_node] = val
+        elif node.target == 'update':
+            if node.args[0] not in original_key_dict:
+                original_key_dict[node.args[0]] = {}
+            if isinstance(node.args[1], dict):
+                original_key_dict[node.args[0]].update(node.args[1])
+            elif isinstance(node.args[1], torch.fx.node.Node):
+                original_key_dict[node.args[0]].update(original_key_dict[node.args[1]])
+            else:
+                raise ValueError('Wrong type for update')
+
+
+    return getitem2node
+
+
+def _fix_succ_recursivly(args, target_node, inserted_node):
+    # List / Tuple
+    if isinstance(args, (list, tuple)):
+        _tmp = list(args)
+        for _i, _arg in enumerate(args):
+            if _arg == target_node:
+                _tmp[_i] = inserted_node
+            elif isinstance(_arg, tuple):
+                _tmp[_i] = _fix_succ_recursivly(_arg, target_node, inserted_node)
+            elif isinstance(_arg, list):
+                _tmp[_i] = list(_fix_succ_recursivly(_arg, target_node, inserted_node))
+            elif isinstance(_arg, dict):
+                _tmp[_i] = _fix_succ_recursivly(_arg, target_node, inserted_node)
+        return tuple(_tmp)
+    # Dict
+    elif isinstance(args, dict):
+        _tmp = {}
+        for k, v in args.items():
+            if v == target_node:
+                _tmp[k] = inserted_node
+            elif not isinstance(v, torch.fx.node.Node):
+                _tmp[k] = _fix_succ_recursivly(v, target_node, inserted_node)
+            else:
+                _tmp[k] = v
+        return _tmp
+    else:
+        raise NotImplementedError('{} can not be handled now.'.format(type(args)))
+
+
+def topology_order(model):
+    node2idx = {}
+    for idx, node in enumerate(model.graph.nodes):
+        node2idx[node] = idx 
+    return node2idx
\ No newline at end of file
diff --git a/mqbench/weight_equalization.py b/mqbench/weight_equalization.py
new file mode 100644
index 0000000..2c4ee4a
--- /dev/null
+++ b/mqbench/weight_equalization.py
@@ -0,0 +1,157 @@
+import torch
+from torch.fx.graph_module import GraphModule
+import torch.nn.intrinsic.qat as nniqat
+import torch.nn.qat.modules.conv as qatconv
+import torch.nn as nn
+
+from mqbench.utils.registry import register_weight_equalization_function, WEIGHT_EQUALIZATION_FUNCTION
+from mqbench.fake_quantize.tqt import TqtFakeQuantize
+
+from mqbench.utils.logger import logger
+
+COLLECT_TYPES = [nniqat.ConvBnReLU2d, nniqat.ConvBn2d, qatconv.Conv2d]
+ACT_TYPES = [nn.ReLU]
+POOL_TYPES = [torch.nn.MaxPool2d, torch.nn.AdaptiveAvgPool2d, torch.nn.AvgPool2d]
+MATH_TYPE = [type(len)]
+ALL_OP_TYPE = COLLECT_TYPES + ACT_TYPES + POOL_TYPES + MATH_TYPE
+FAKE_QUANT_TYPE = [TqtFakeQuantize]
+
+
+@register_weight_equalization_function(qatconv.Conv2d, qatconv.Conv2d)
+def weight_equalize_conv_pair(modules, weq_pair):
+    node1, node2 = tuple(weq_pair)
+    weight1 = modules[node1.target].weight.data.clone()
+    if modules[node1.target].bias is None:
+        bias1 = None
+    else:
+        bias1 = modules[node1.target].bias.data.clone() 
+    weight2 = modules[node2.target].weight.data.clone() 
+    weight1, bias1, weight2, s = dfq_weight_equalization(weight1, bias1, weight2) 
+    modules[node1.target].weight.data, modules[node2.target].weight.data = weight1, weight2
+    if bias1 is not None:
+        modules[node1.target].bias.data = bias1 
+    logger.info(f'Weight equalizing {node1.name} and {node2.name}.')
+
+
+def _get_name2node(nodes):
+    name2node = {}
+    for node in nodes:
+        name2node[node.name] = node 
+    return name2node
+
+
+def _get_name2type(nodes, modules):
+    name2type = {}
+    for node in nodes:
+        if node.target in modules:
+            name2type[node.name] = type(modules[node.target])
+        elif type(node.target) in MATH_TYPE:
+            name2type[node.name] = type(node.target)
+        else:
+            name2type[node.name] = None
+    return name2type
+
+
+def _get_name2fanout(nodes, name2type):
+    name2fanout = {} 
+    for node in nodes:
+        if name2type[node.name] in ALL_OP_TYPE:
+            cnt = 0
+            node_users = list(node.users)
+            for u in node_users:
+                if name2type[u.name] in ALL_OP_TYPE: 
+                    cnt += 1 
+                elif name2type[u.name] in FAKE_QUANT_TYPE:
+                    for f in u.users:
+                        if name2type[f.name] in ALL_OP_TYPE:
+                            cnt += 1
+            name2fanout[node.name] = cnt
+        else:
+            name2fanout[node.name] = 0
+    return name2fanout
+
+
+def get_weight_equalization_groups(model: GraphModule, **kwargs):
+    nodes = list(model.graph.nodes)
+    modules = dict(model.named_modules())
+    name2node = _get_name2node(nodes)
+    name2type = _get_name2type(nodes, modules)
+    name2fanout = _get_name2fanout(nodes, name2type)
+    input = [name2node[node_name] for node_name in kwargs['input_shape_dict']]
+    layer_groups = []
+    for node in input:
+        collect_layer_group(node, modules, layer_groups, name2fanout)
+    print([[n.name for n in i] for i in layer_groups])
+    convert_equalization_groups(modules, layer_groups, name2type)
+
+
+def collect_layer_group(node, modules, groups, name2fanout, visited=None, group=None):
+    def _end_collect(grp):
+        if len(grp) > 1 and grp not in groups:
+            groups.append(grp)
+        return []
+
+    visited = [] if not visited else visited
+    group = [] if not group else group 
+
+    if node in visited:
+        return 
+    visited.append(node)
+
+    if node.target not in modules or type(modules[node.target]) in FAKE_QUANT_TYPE:
+        pass 
+    elif type(modules[node.target]) in COLLECT_TYPES:
+        group.append(node)
+        if name2fanout[node.name] > 1:
+            group = _end_collect(group)
+    elif type(modules[node.target]) in POOL_TYPES:
+        if name2fanout[node.name] > 1:
+            group = _end_collect(group)
+    elif type(modules[node.target]) in ACT_TYPES:
+        if name2fanout[node.name] > 1:
+            group = _end_collect(group)
+    else:
+        group = _end_collect(group)
+
+    for child in node.users:
+        collect_layer_group(child, modules, groups, name2fanout, visited, group)
+    _end_collect(group)
+
+
+def convert_equalization_groups(modules, layer_groups, name2type):
+    eq_groups = [] 
+    for grp in layer_groups:
+        assert len(grp) == 2, 'Multi-layers weight equalization not support.'
+        type_list = [name2type[x.name] for x in grp]
+        WEIGHT_EQUALIZATION_FUNCTION[type_list[0]][type_list[1]](modules, grp)
+
+
+def dfq_weight_equalization(weight_1, bias_1, weight_2, s_min=1e-6, s_max=1e6, eps=0):
+    groups = weight_1.shape[0] // weight_2.shape[1] 
+    w1_ch, w2_ch = weight_1.shape[0] // groups, weight_2.shape[1] // groups 
+    scale = torch.zeros([weight_1.shape[0]]) 
+    for grp in range(groups):
+        w1_ch_start, w1_ch_end = w1_ch * grp, w1_ch * (grp + 1)
+        w2_ch_start, w2_ch_end = w2_ch * grp, w2_ch * (grp + 1)
+        w1_ch_part = weight_1[w1_ch_start:w1_ch_end]
+        w2_ch_part = weight_2[w2_ch_start:w2_ch_end]
+
+        w1_dims = (1, 2, 3) if len(weight_1.shape) == 4 else (1)
+        w2_dims = (0, 2, 3) if len(weight_2.shape) == 4 else (0)
+        w1_range = w1_ch_part.abs().amax(dim=w1_dims)
+        w2_range = w2_ch_part.abs().amax(dim=w2_dims)
+        assert w1_range.shape == w2_range.shape, "The equalization pair's weight shape does not match!"
+        s = (w1_range * w2_range + eps).sqrt() / (w2_range + eps) 
+        s = torch.clip(s, s_min, s_max)
+        s = torch.where((w1_range + w2_range) < 0.5, torch.ones_like(s), s)
+        scale[w1_ch_start:w1_ch_end] = s 
+        if bias_1 is not None:
+            bias_1[w1_ch_start:w1_ch_end].mul_(1 / s)
+        w1s_shape = [1] * len(weight_1.shape)
+        w1s_shape[0] = -1
+        weight_1[w1_ch_start:w1_ch_end].mul_(1 / s.reshape(w1s_shape))
+        w2s_shape = [1] * len(weight_2.shape)
+        w2s_shape[1] = -1
+        weight_2[:, w2_ch_start:w2_ch_end].mul_(s.reshape(w2s_shape))
+
+    return weight_1, bias_1, weight_2, scale 
diff --git a/my_equation.py b/my_equation.py
new file mode 100644
index 0000000..54100ee
--- /dev/null
+++ b/my_equation.py
@@ -0,0 +1,389 @@
+import torch
+import torch.nn.functional as F
+import global_placeholder
+import numpy as np
+import math
+# def amplification_function(values):
+#     return values ** 2 + 1
+
+# def my_equation_add_contrast_loss(reg_loss, conf_values, pos_conf_values, ious, pos_mask):
+
+#         conf_greater_iou_flag = pos_conf_values > ious
+#         iou_greater_conf_flag = pos_conf_values < ious
+#         contrast_values = torch.zeros_like(ious)
+#         zeros = torch.zeros_like(ious)
+#         contrast_values[conf_greater_iou_flag] = (pos_conf_values.detach() - ious)[conf_greater_iou_flag]  # 说明要拉iou    # 0.4942  0.7875
+#         contrast_values[iou_greater_conf_flag] = (ious.detach() - pos_conf_values)[iou_greater_conf_flag]  # 说明要拉conf
+        
+        
+#     #!! cls的话，要是有Conf大于IOU的情况，也不会去拉低Conf。 或者说，还是得拉低Conf？？？
+#     # loss = (1 + 0.15 * torch.unsqueeze(torch.square(maximum * delta), dim=1)) * loss  # TODO 还有一个情况就是，数据分布也是imbalance  就应该是更关注高值情况下的差异
+    
+
+#     #!! cls的话，要是有Conf大于IOU的情况，也不会去拉低Conf。 或者说，还是得拉低Conf？？？
+#     # loss = (1 + 0.15 * torch.unsqueeze(torch.square(maximum * delta), dim=1)) * loss  # TODO 还有一个情况就是，数据分布也是imbalance  就应该是更关注高值情况下的差异
+    
+#         maximum = torch.max(pos_conf_values, ious)
+
+#         # contrast_values = torch.abs(pos_conf_values - ious)  # 0.48974 0.78683 拉
+#         margin = 0.2
+#         # reg_loss = reg_loss + torch.max(zeros, contrast_values - margin)  # 泰拉了
+#         # reg_loss = reg_loss + contrast_values  # 0.4907  0.7888
+#         # reg_loss = reg_loss + torch.sin(contrast_values * 3.1415926 / 2) ** 3  # 0.4901  0.7882
+#         # reg_loss = reg_loss + torch.sin(contrast_values * 3.1415926 / 2)  # 0.4942  0.7875  就这个
+#         reg_loss = reg_loss + torch.sin(contrast_values * 3.1415926 / 2) * pos_mask.float()
+#         # reg_loss = reg_loss + info_factor * torch.sin(contrast_values * 3.1415926 / 2) * pos_mask.float()
+#         # reg_loss = reg_loss + info_factor * contrast_values * pos_mask.float()
+        
+#         # reg_loss = reg_loss + maximum * contrast_values # 
+    
+#     return reg_loss
+
+
+def my_equation_add_task_contrast_loss(reg_loss, conf_values, pos_conf_values, ious, pos_mask):
+
+    margin = 0.2
+    zeros = torch.zeros_like(ious)
+    distance = torch.abs(pos_conf_values - ious)
+    # 原文写法
+    # delta = torch.max(zeros, distance - margin)
+    # 原码写法
+    delta = torch.where(distance < 0.2, zeros, distance)
+    information_entropy = - conf_values * conf_values.log() # e为底
+    beta = torch.exp(information_entropy.sum(dim=1))
+
+    reg_loss = reg_loss + (1 / (1 + beta)) * delta * pos_mask.float()
+    
+    return reg_loss
+
+
+
+# def my_equation_similarity_loss(kl_loss, pos_conf_values, ious, pos_mask): # JS散度
+#     batch_distribution_losses = []
+#     for conf, iou, mask in zip(pos_conf_values, ious, pos_mask):  # 这是多batch情况
+        
+#         # softmax_pos_conf_values = F.softmax(pos_conf_values[it, pos_mask[it]], dim=0)  # 化为概率分布 dim 表示batch之间不影响
+#         # softmax_ious = F.softmax(ious[it, pos_mask[it]], dim=0)
+        
+#         conf = conf[mask]
+#         iou = iou[mask]
+#         elem_num = conf.numel()
+#         similarity_loss = 1 - (conf * iou).mean()
+#         # similarity_loss = F.pairwise_distance(conf, iou, dim=-1)
+#         # cosine_loss = 1 - F.cosine_similarity(pos_conf_values[it, pos_mask[it]], ious[it, pos_mask[it]], dim=0)
+        
+        
+#         # means = (softmax_pos_conf_values + softmax_ious) / 2
+#         # distribution_loss = 0.5 * kl_loss(means.log(), softmax_pos_conf_values) + 0.5 * kl_loss(means.log(), softmax_ious) # P、Q 指代target和input
+#         batch_distribution_losses.append(similarity_loss)
+#     # p || q
+#     # target || input
+#     return sum(batch_distribution_losses) / len(batch_distribution_losses)  # NOTE 不平均是 0.4944  0.7929  不应该平均，就是每张图片都有自己的loss！注意后期实现
+    
+# def my_equation_js_loss(kl_loss, pos_conf_values, ious, pos_mask): # JS散度
+
+#         batch_distribution_losses = []
+#         for it in range(pos_conf_values.size()[0]):
+            
+#             softmax_pos_conf_values = F.softmax(pos_conf_values[it, pos_mask[it]], dim=0)  # 化为概率分布 dim 表示batch之间不影响
+#             softmax_ious = F.softmax(ious[it, pos_mask[it]], dim=0)
+            
+#             means = (softmax_pos_conf_values + softmax_ious) / 2
+#             distribution_loss = 0.5 * kl_loss(means.log(), softmax_pos_conf_values) + 0.5 * kl_loss(means.log(), softmax_ious) # P、Q 指代target和input
+#             batch_distribution_losses.append(distribution_loss.sum())
+#         # p || q
+#         # target || input
+#         return sum(batch_distribution_losses)  # NOTE 不平均是 0.4944  0.7929  不应该平均，就是每张图片都有自己的loss！注意后期实现
+#     return 0 
+
+# def my_equation_add_correlation_loss(reg_loss, conf_values, pos_conf_values, ious, pos_mask): # TODO 注意一个问题，新算出来的loss代表的是全集！还得筛选一次正样本
+
+#         # correlation = pos_conf_values * ious + 0.00001
+#         # correlation_loss = - correlation * torch.log10(correlation) - (1 - correlation) * torch.log10((1 - correlation))  # 二项分布信息熵，0.4931  0.7859
+#         # correlation_loss = 0.5 * (- correlation * torch.log10(correlation) - (1 - correlation) * torch.log10((1 - correlation)))  # 二项分布信息熵，
+#         # correlation_loss = (- correlation * torch.log2(correlation) - (1 - correlation) * torch.log2((1 - correlation)))  # 二项分布信息熵，拉
+        
+                
+#         info_entropys = (- conf_values * conf_values.log()).sum(dim=1).exp()
+#         info_factor = 1 / (info_entropys)  # 牛
+#         # info_factor = 1.2 / (info_entropys)  # 这里的常量可以去调一调  拉
+#         # info_factor = 0.8 / (info_entropys)  # 这里的常量可以去调一调   拉
+        
+#         minimum = torch.min(pos_conf_values, ious)
+#         maximum = torch.max(pos_conf_values, ious)
+        
+#         correlation = pos_conf_values * ious
+#         # correlation_loss = 1 - correlation  # 还行
+        
+#         correlation_loss = info_factor * (1 - correlation)  # 还行
+        
+#         # correlation_loss = (1 - maximum)(1 - correlation)  # 
+#         # correlation_loss = torch.cos(correlation * 3.1415926 / 2)
+#         reg_loss = reg_loss + correlation_loss * pos_mask.float()
+#     return reg_loss
+
+def my_equation_add_correlation_loss(reg_loss, cls_branch_factor, reg_branch_factor, conf_values, pos_conf_values, ious, mask): # TODO 注意一个问题，新算出来的loss代表的是全集！还得筛选一次正样本
+
+    # correlation = pos_conf_values * ious + 0.00001
+    # correlation_loss = - correlation * torch.log10(correlation) - (1 - correlation) * torch.log10((1 - correlation))  # 二项分布信息熵，0.4931  0.7859
+    # correlation_loss = 0.5 * (- correlation * torch.log10(correlation) - (1 - correlation) * torch.log10((1 - correlation)))  # 二项分布信息熵，
+    # correlation_loss = (- correlation * torch.log2(correlation) - (1 - correlation) * torch.log2((1 - correlation)))  # 二项分布信息熵，拉
+    
+    # info_factor = 1.2 / (info_entropys)  # 这里的常量可以去调一调  拉
+    # info_factor = 0.8 / (info_entropys)  # 这里的常量可以去调一调   拉
+    
+    # minimum = torch.min(pos_conf_values, ious)
+    # maximum = torch.max(pos_conf_values, ious)
+    
+    # correlation = torch.pow(pos_conf_values, cls_trade_off) * torch.pow(ious, reg_trade_off)
+    eps = 2.220446049250313e-16# -10貌似可以，但是我狠一点
+    # correlation = torch.pow(pos_conf_values+eps, 0.5) * torch.pow(ious+eps, 0.5) - eps
+    correlation = torch.pow(pos_conf_values+eps, ious) * torch.pow(ious+eps, pos_conf_values) - eps
+    # correlation = torch.pow(pos_conf_values+eps, ious.detach()) * torch.pow(ious+eps, pos_conf_values.detach()) - eps  # 这真不行
+    # correlation = torch.pow(pos_conf_values * ious * matched_obj+eps, 1/3) - eps ** 1/3  # 还是不该加这个
+    
+    
+    
+    # correlation = torch.pow(pos_conf_values, 0.5) * torch.pow(ious, 0.5)
+    # correlation = torch.pow(pos_conf_values, 0.5) * ious  # 这个就没事
+    # correlation = pos_conf_values * torch.pow(ious, 0.5)  # 但是还是nan
+    # correlation = (pos_conf_values ** 0.5) * (ious ** 0.5)  一样
+    # correlation = pos_conf_values * ious
+    
+    # correlation = pos_conf_values * ious  #
+    # correlation_loss =(1 - correlation) ** 2  
+    # correlation_loss = torch.exp(-correlation) - torch.exp(-torch.tensor(1.))   # 实在不行，再加一个cls entropy，只对置信度大的进行处理。
+    correlation_loss = (1 + torch.abs(pos_conf_values-ious).detach())*(torch.exp(-correlation) - torch.exp(-torch.tensor(1.)))   # 实在不行，再加一个cls entropy，只对置信度大的进行处理。
+    
+    
+    
+    # correlation_loss = 1.6 * (torch.exp(-correlation) - torch.exp(-torch.tensor(1.)))   # 实在不行，再加一个cls entropy，只对置信度大的进行处理。
+    # correlation_loss = (1 - correlation) * ((1 + correlation) ** 0.8) # 确实这种设计相比于1-iou有用
+    
+    # correlation_loss = info_factor * (1 - correlation)  # 还行
+    
+    # correlation_loss = (1 - maximum)(1 - correlation)  # 
+    # correlation_loss = torch.cos(correlation * 3.1415926 / 2)
+    # information_entropy = - conf_values * conf_values.log() # e为底
+    # beta = torch.exp(information_entropy.sum(dim=1)).detach()   # 所以这个玩意就应该detach
+    # reg_loss = reg_loss + (1 + 1 / (beta)) * correlation_loss * pos_mask.float()  # 所以应不应该有+1呢
+    
+    reg_loss = reg_loss + correlation_loss * mask.float()  
+    return reg_loss
+
+
+def my_equation_add_entropy_iou_loss(reg_loss, conf_values, pos_conf_values, ious, pos_mask): # 轻微提点，但是实际效果就是，TP抬高了，但是TP量反而少了
+
+    beta = 1.2
+    alpha = 1.5
+    gamma = 0.8
+    # iou_loss = alpha * (1 - ious) * ((1 + ious) ** gamma) # 确实这种设计相比于1-iou有用
+    # iou_loss = alpha * (1 - ious)
+    # iou_loss = 1 - ious
+    eps_ious = ious + 0.000001
+    iou_loss = - eps_ious * torch.log2(eps_ious) - (1 - eps_ious) * torch.log2((1 - eps_ious))  # 1# 0.4856  0.7770- NOTE 信息熵这个的底，可以做一个消融实验
+    # iou_loss = - eps_ious * torch.log(eps_ious) - (1 - eps_ious) * torch.log((1 - eps_ious))  # 烂
+    
+
+    
+    reg_loss = reg_loss + iou_loss * pos_mask.float()
+    return reg_loss
+
+# def my_equation_add_gtcls_loss(cls_loss, conf_values, pos_conf_values, ious, pos_mask): # 轻微提点，但是实际效果就是，TP抬高了，但是TP量反而少了
+
+#         # beta = 1.2
+#         # alpha = 1.5
+#         # gamma = 0.8
+#         eps = 1e-10
+#         epsp_pos_conf_values = pos_conf_values + eps
+#         epsm_pos_conf_values = pos_conf_values - eps
+        
+#         gtcls_loss = - epsp_pos_conf_values * torch.log(epsp_pos_conf_values) - (1 - epsm_pos_conf_values) * torch.log((1 - epsm_pos_conf_values))  # cls_3
+#         # gtcls_loss = 1 - pos_conf_values  # cls_1
+        
+#         # gtcls_loss = 0.5 * (1 - pos_conf_values) ** 2 # cls_2
+#         cls_loss = cls_loss + gtcls_loss * pos_mask.float()
+#     return cls_loss
+
+
+def my_equation_add_harmonic_iou_loss(reg_loss, conf_values, pos_conf_values, ious, pos_mask): # 轻微提点，但是实际效果就是，TP抬高了，但是TP量反而少了
+
+    beta = 1.2
+    alpha = 1.5
+    gamma = 0.8
+    iou_loss = alpha * (1 - ious) * ((1 + ious) ** gamma) # 确实这种设计相比于1-iou有用
+
+    reg_loss = reg_loss + iou_loss
+    return reg_loss
+
+
+def my_equation_add_harmonic_conf_loss(cls_loss, conf_values, pos_conf_values, pos_gtconf_values, pos_mask): # 轻微提点，但是实际效果就是，TP抬高了，但是TP量反而少了
+
+    beta = 1.2
+    alpha = 1.5
+    gamma = 0.8
+    conf_loss = alpha * (1 - pos_gtconf_values) * ((1 + pos_gtconf_values) ** gamma)
+
+    cls_loss = cls_loss + conf_loss * pos_mask.float()
+    return cls_loss
+
+def HarDet_loss(pos_reg_loss, pos_cls_loss, conf_values, pos_gtconf_values, pos_conf_values, matched_iou_vals, pos_mask):
+    pos_reg_loss = my_equation_add_harmonic_iou_loss(pos_reg_loss, None, None, matched_iou_vals, pos_mask)
+    pos_reg_loss = (1 + torch.exp( - pos_cls_loss)) * pos_reg_loss * pos_mask.float() # 0.4856  0.7770-
+    pos_cls_loss = (1 + torch.exp( - pos_reg_loss)) * pos_cls_loss * pos_mask.float()
+    pos_reg_loss = my_equation_add_task_contrast_loss(pos_reg_loss, conf_values, pos_gtconf_values, matched_iou_vals, pos_mask)
+    return pos_reg_loss, pos_cls_loss
+
+def HQOD_loss(pos_reg_loss, pos_cls_loss, conf_values, pos_gtconf_values, pos_conf_values, matched_iou_vals, cls_branch_factor, reg_branch_factor, mask):  # 以前那个真不行，就是a/(b+1)
+    # pos_reg_loss = my_equation_add_harmonic_iou_loss(pos_reg_loss, None, None, matched_iou_vals, pos_mask)
+    # pos_reg_loss = (1 + pos_gtconf_values) * pos_reg_loss * pos_mask.float() # 0.4856  0.7770-
+    # pos_cls_loss = (1 + matched_iou_vals) * pos_cls_loss * pos_mask.float()
+    # pos_reg_loss = torch.exp( 1 - matched_iou_vals) * pos_reg_loss * pos_mask.float() # 0.4856  0.7770-
+    # pos_cls_loss = torch.exp( 1 - pos_gtconf_values) * pos_cls_loss * pos_mask.float()
+    # pos_reg_loss = my_equation_add_task_contrast_loss(pos_reg_loss, conf_values, pos_gtconf_values, matched_iou_vals, pos_mask)
+
+    pos_reg_loss = my_equation_add_harmonic_iou_loss(pos_reg_loss, None, None, matched_iou_vals, mask)
+    # pos_cls_loss  = my_equation_add_harmonic_conf_loss(pos_cls_loss, conf_values, pos_conf_values, pos_gtconf_values, pos_mask)
+    pos_reg_loss = my_equation_add_correlation_loss(pos_reg_loss, cls_branch_factor, reg_branch_factor, conf_values, pos_gtconf_values, matched_iou_vals, mask)
+    
+    return pos_reg_loss, pos_cls_loss
+    
+# def HQOD_loss(pos_reg_loss, pos_cls_loss, conf_values, pos_gtconf_values, pos_conf_values, matched_iou_vals, pos_mask):
+#     # pos_reg_loss = (1 + pos_gtconf_values) * (1 + pos_conf_values ** 1.7) * pos_reg_loss * pos_mask.float() # 0.4895  -0.7779?
+#     # pos_cls_loss = (1 + matched_iou_vals ** 1.7) * pos_cls_loss * pos_mask.float() # 
+    
+#     # pos_reg_loss = ((1 + pos_gtconf_values) * (1 + pos_conf_values)).sqrt() * pos_reg_loss * pos_mask.float() # 好 1
+#     # pos_cls_loss = (1 + matched_iou_vals) * pos_cls_loss * pos_mask.float() #     
+#     # pos_reg_loss = my_equation_add_entropy_iou_loss(pos_reg_loss, None, None, matched_iou_vals, pos_mask)
+    
+#     # pos_reg_loss = (1 + pos_conf_values) * pos_reg_loss * pos_mask.float() # 这一个真不行
+#     # pos_reg_loss = my_equation_add_harmonic_iou_loss(pos_reg_loss, None, None, matched_iou_vals, pos_mask)
+        
+#     pos_reg_loss = (1 + pos_gtconf_values) * pos_reg_loss * pos_mask.float() # 好 2
+#     pos_cls_loss = (1 + matched_iou_vals) * pos_cls_loss * pos_mask.float() #  
+#     # pos_reg_loss = (1 + pos_gtconf_values ** 1.7) * pos_reg_loss * pos_mask.float() # 差
+#     # pos_cls_loss = (1 + matched_iou_vals ** 1.7) * pos_cls_loss * pos_mask.float() #  
+#     # pos_reg_loss = (1 + pos_conf_values) * pos_reg_loss * pos_mask.float() # 差
+#     # pos_cls_loss = (1 + matched_iou_vals) * pos_cls_loss * pos_mask.float() #  
+#     # pos_reg_loss = ((1 + pos_gtconf_values) * (1 + pos_conf_values)) * pos_reg_loss * pos_mask.float()  # 差
+#     # pos_cls_loss = (1 + matched_iou_vals) ** 2 * pos_cls_loss * pos_mask.float() # 
+#     # pos_reg_loss = (1 + pos_conf_values * pos_gtconf_values) * pos_reg_loss * pos_mask.float() # 差
+#     # pos_cls_loss = (1 + matched_iou_vals) * pos_cls_loss * pos_mask.float() # 
+#     pos_reg_loss = my_equation_add_entropy_iou_loss(pos_reg_loss, None, None, matched_iou_vals, pos_mask)
+    
+#     # pos_reg_loss = my_equation_add_correlation_loss(pos_reg_loss, pos_conf_values, matched_iou_vals, pos_mask) # 不要
+#     # pos_reg_loss = my_equation_add_harmonic_iou_loss(pos_reg_loss, None, None, matched_iou_vals, pos_mask)
+#     # pos_reg_loss = my_equation_add_iou_loss(pos_reg_loss, None, None, matched_iou_vals, pos_mask)
+#     # pos_reg_loss = my_equation_add_task_contrast_loss(pos_reg_loss, conf_values, pos_gtconf_values, matched_iou_vals, pos_mask)
+    
+#     return pos_reg_loss, pos_cls_loss
+
+# def my_equation_add_correlation_loss(reg_loss, pos_conf_values, ious, pos_mask): # TODO 注意一个问题，新算出来的loss代表的是全集！还得筛选一次正样本
+#     correlation = pos_conf_values * ious + 0.000001
+#     correlation_loss = - correlation * torch.log10(correlation) - (1 - correlation) * torch.log10((1 - correlation))  # 二项分布信息熵，0.4931  0.7859
+#     # correlation_loss = 0.5 * (- correlation * torch.log10(correlation) - (1 - correlation) * torch.log10((1 - correlation)))  # 二项分布信息熵，
+#     # correlation_loss = (- correlation * torch.log2(correlation) - (1 - correlation) * torch.log2((1 - correlation)))  # 二项分布信息熵，拉
+#     # correlation_loss = 1 - correlation  # 拉了
+#     reg_loss = reg_loss + correlation_loss * pos_mask.float()
+#     return reg_loss
+# def my_equation_add_intensive_iou_loss(reg_loss, iou):
+#     reg_loss = reg_loss + (1 - iou)
+#     return reg_loss
+
+# def my_equation_add_kl_between_loss(reg_loss, )
+
+
+
+# def my_equation_for_cls(amp_values, delta, loss): # IOU拉Conf
+#     # loss = (1 + 0.5 * torch.unsqueeze(maximum * delta, dim=1)) * loss  # TODO 还有一个情况就是，数据分布也是imbalance
+#     # loss = (1 + 0.8 * torch.unsqueeze(torch.square(maximum) * delta, dim=1)) * loss  # TODO 还有一个情况就是，数据分布也是imbalance  就应该是更关注高值情况下的差异
+#     # loss = (1 + 0.8 * torch.unsqueeze(maximum * torch.square(delta), dim=1)) * loss  # TODO 还有一个情况就是，数据分布也是imbalance  就应该是更关注高值情况下的差异
+    
+#     #!! cls的话，要是有Conf大于IOU的情况，也不会去拉低Conf。 或者说，还是得拉低Conf？？？
+#     # loss = (1 + 0.15 * torch.unsqueeze(torch.square(maximum * delta), dim=1)) * loss  # TODO 还有一个情况就是，数据分布也是imbalance  就应该是更关注高值情况下的差异
+    
+#     # temp_weight = torch.square(maximum * delta)
+#     # temp_weight = maximum * delta  # 不行
+#     # temp_weight = 0.5 * delta
+
+#         qbit = global_placeholder.quant_bit
+#         if qbit == None: qbit = 8 # 避免fp32时
+#         qbit = float(qbit)
+#         # amp_values = amplification_function(amp_values)
+#         # temp_weight = (qbit / 8)**2 * delta
+        
+#         # temp_weight = (qbit / 8)**2 * torch.sin(delta * 3.1415926 / 2)
+        
+#         # temp_weight = (qbit / 8)**2 * amp_values * torch.sin(delta * 3.1415926 / 2)
+#         temp_weight = delta  # 
+#         # temp_weight = torch.sin(delta * 3.1415926 / 2)  # 目前最优
+#         if temp_weight.shape != loss.shape:
+#             # 如果有问题的话
+#             temp_weight = torch.unsqueeze(temp_weight, dim=1)
+#         loss = (1 + temp_weight) * loss  # TODO 还有一个情况就是，数据分布也是imbalance  就应该是更关注高值情况下的差异
+#         # loss = (loss + 1) ** (1 + temp_weight) - 1
+    
+#     # 回归的话，Conf带不了他
+#     return loss
+
+# def my_equation_for_reg(amp_values, delta, loss):  # Conf拉IOU
+#     # loss = (1 + 0.5 * torch.unsqueeze(maximum * delta, dim=1)) * loss  # TODO 还有一个情况就是，数据分布也是imbalance
+#     # loss = (1 + 0.8 * torch.unsqueeze(torch.square(maximum) * delta, dim=1)) * loss  # TODO 还有一个情况就是，数据分布也是imbalance  就应该是更关注高值情况下的差异
+#     # loss = (1 + 0.8 * torch.unsqueeze(maximum * torch.square(delta), dim=1)) * loss  # TODO 还有一个情况就是，数据分布也是imbalance  就应该是更关注高值情况下的差异
+    
+#     # !! cls的话，要是有Conf大于IOU的情况，也不会去拉低Conf。 或者说，还是得拉低Conf？？？
+#     # loss = (1 + 0.8 * torch.unsqueeze(torch.square(maximum * delta), dim=1)) * loss  # TODO 还有一个情况就是，数据分布也是imbalance  就应该是更关注高值情况下的差异
+    
+#     # temp_weight = torch.square(maximum * delta)
+#     # temp_weight = maximum * delta
+#     # temp_weight = 0.5 * delta
+
+#         qbit = global_placeholder.quant_bit
+#         if qbit == None: qbit = 8 # 避免fp32时
+#         qbit = float(qbit)
+#         # amp_values = amplification_function(amp_values)
+#         # temp_weight = (qbit / 8)**2 * delta
+        
+#         # temp_weight = (qbit / 8)**2 * torch.sin(delta * 3.1415926 / 2) # sin^2?
+        
+#         # temp_weight = (qbit / 8)**2 * amp_values * torch.sin(delta * 3.1415926 / 2)
+#         temp_weight = delta # 
+#         # temp_weight = torch.sin(delta * 3.1415926 / 2) # 目前最优
+#         if temp_weight.shape != loss.shape:
+#             # 如果有问题的话
+#             temp_weight = torch.unsqueeze(temp_weight, dim=1)
+#         loss = (1 + temp_weight) * loss  # TODO 还有一个情况就是，数据分布也是imbalance  就应该是更关注高值情况下的差异
+#         # loss = (loss + 1) ** (1 + temp_weight) - 1
+#     # 回归的话，Conf带不了他
+#     return loss
+
+
+
+# def my_equation_for_cls(amp_values, delta, loss): # IOU拉Conf
+
+#         qbit = global_placeholder.quant_bit
+#         if qbit == None: qbit = 8 # 避免fp32时
+#         qbit = float(qbit)
+#         temp_weight = delta  # 
+#         if temp_weight.shape != loss.shape:
+#             # 如果有问题的话
+#             temp_weight = torch.unsqueeze(temp_weight, dim=1)
+#         loss = (1 + temp_weight) * loss  # TODO 还有一个情况就是，数据分布也是imbalance  就应该是更关注高值情况下的差异
+    
+#     # 回归的话，Conf带不了他
+#     return loss
+
+# def my_equation_for_reg(amp_values, delta, loss):  # Conf拉IOU
+
+#         qbit = global_placeholder.quant_bit
+#         if qbit == None: qbit = 8 # 避免fp32时
+#         qbit = float(qbit)
+#         temp_weight = delta # 
+#         if temp_weight.shape != loss.shape:
+#             # 如果有问题的话
+#             temp_weight = torch.unsqueeze(temp_weight, dim=1)
+#         loss = (1 + temp_weight) * loss  # TODO 还有一个情况就是，数据分布也是imbalance  就应该是更关注高值情况下的差异
+#     return loss
+
+
+# def harmonic_loss(loss)
\ No newline at end of file
diff --git a/plot_curve.py b/plot_curve.py
new file mode 100644
index 0000000..ec772a5
--- /dev/null
+++ b/plot_curve.py
@@ -0,0 +1,914 @@
+import datetime
+import matplotlib.pyplot as plt
+import json
+import os
+import numpy as np
+import scipy.stats as sci
+import pandas as pd
+import matplotlib.image as mpimg
+import mmcv
+
+
+def save_distribution(data, title, pedge=1.1, hlim=0.02):
+    plt.figure()
+    number = data.size
+    edge = np.linspace(-pedge, pedge, 160)
+    bins = [0] * (len(edge) - 1)
+    for i in range(len(edge) - 1):
+        total = np.sum(np.array(edge[i] < data) == np.array(data < edge[i + 1]))
+        bins[i] = total
+
+    bins = np.array(bins) / number  # 做一个归一化
+
+    plt.stem(edge[:-1], bins, markerfmt='C3.')  # 不能直接用hist，因为hist的区间是动态的！（最小刻度会变，很傻吊）
+    plt.title(title)
+    plt.xlim(-1.1 * pedge, 1.1 * pedge)
+    plt.ylim(0, hlim)
+    plt.grid()
+
+    plt.savefig('y_data_fig/{}.jpg'.format(title))
+    plt.close()
+    
+    
+def plot_loss_and_lr(train_loss, learning_rate, output_dir):
+    try:
+        x = list(range(len(train_loss)))
+        fig, ax1 = plt.subplots(1, 1)
+        ax1.plot(x, train_loss, 'r', label='loss')
+        ax1.set_xlabel("step")
+        ax1.set_ylabel("loss")
+        ax1.set_title("Train Loss and lr")
+        plt.legend(loc='best')
+
+        ax2 = ax1.twinx()
+        ax2.plot(x, learning_rate, label='lr')
+        ax2.set_ylabel("learning rate")
+        ax2.set_xlim(0, len(train_loss))  # 设置横坐标整数间隔
+        plt.legend(loc='best')
+
+        handles1, labels1 = ax1.get_legend_handles_labels()
+        handles2, labels2 = ax2.get_legend_handles_labels()
+        plt.legend(handles1 + handles2, labels1 + labels2, loc='upper right')
+
+        fig.subplots_adjust(right=0.8)  # 防止出现保存图片显示不全的情况
+        fig.savefig('{}/loss_and_lr{}.png'.format(output_dir, datetime.datetime.now().strftime("%Y%m%d-%H%M%S")))
+        plt.close()
+        print("successful save loss curve! ")
+    except Exception as e:
+        print(e)
+
+
+def plot_map(mAP, output_dir):
+    try:
+        x = list(range(len(mAP)))
+        plt.plot(x, mAP, label='mAp')
+        plt.xlabel('epoch')
+        plt.ylabel('mAP')
+        plt.title('Eval mAP')
+        plt.xlim(0, len(mAP))
+        plt.legend(loc='best')
+        plt.savefig('{}/mAP.png'.format(output_dir))
+        plt.close()
+        print("successful save mAP curve!")
+    except Exception as e:
+        print(e)
+        
+def generate_axis(rows,cols):
+    return [(row,col) for row in range(rows) for col in range(cols)]
+        
+def plot_ious_and_confi_per_class(tp_special_data, classes_list):
+    det_labels = tp_special_data[:,0].astype(np.int16)
+    num_classes = len(classes_list)
+    label_statistics = [[] for i in range(num_classes)]
+    
+    for it, label in enumerate(det_labels):
+        label_statistics[label].append(tp_special_data[it,1:])  # score IOU
+    
+    plt.figure(figsize=(20,6))
+    plt.subplots_adjust(left=0.05,bottom=None,right=0.95,top=None,wspace=.55,hspace=.35) # 设置子图间距
+
+    for it, statistics in enumerate(label_statistics):
+        statistics = np.array(statistics)
+        statistics[:, 1] = iou_remap(statistics[:, 1])  # IOU数据 重新缩放
+        
+        plt.subplot(2,10,it+1)
+        plt.title(f"{classes_list[it]}")
+        plt.boxplot(statistics)
+        plt.grid()  # 生成网格
+        
+    title_font = {'weight': 'bold', 'size': 18}
+    plt.suptitle('Score and IOU in TP',fontdict=title_font)
+
+    # plt.show()
+    # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/fp32.png")
+    plt.savefig("/workspace/code/Quant/MQBench/test/test_img/w3a3.png")
+    # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/w4a4.png")
+    plt.close()
+
+def loop_plot_ious_and_confi_per_class(loop_data, classes_list, x_script):
+    differ = 0.6  # 用来偏移画图的
+    loop_label_statistics = []
+    num_classes = len(classes_list)
+    for tp_special_data in loop_data:
+        det_labels = tp_special_data[:,0].astype(np.int16)
+        label_statistics = [[] for i in range(num_classes)]
+    
+        for it, label in enumerate(det_labels):
+            label_statistics[label].append(tp_special_data[it,1:])  # score IOU
+        loop_label_statistics.append(label_statistics)
+    
+    plt.figure(figsize=(36,15))
+    plt.subplots_adjust(left=0.04,bottom=0.10,right=0.96,top=0.90,wspace=.15,hspace=.3) # 设置子图间距
+
+    for it in range(num_classes):  # 画20张子图
+        all_position = []
+    
+        plt.subplot(4,5,it+1)
+        plt.title(f"{classes_list[it]}", fontsize=22)
+        for idx, label_statistics in enumerate(loop_label_statistics):  # 遍历文件数据
+            statistics = label_statistics[it]  # 引出对应类下的 socre数据或IOU数据
+            statistics = np.array(statistics)
+            statistics[:, 1] = iou_remap(statistics[:, 1])  # IOU数据 重新缩放
+            # confidence 或 IOU重新排序，只取前K个结果
+            topk = 200
+            confi_data = np.sort(statistics[:, 0])[-topk:]
+            iou_data = np.sort(statistics[:, 1])[-topk:]
+            
+            positions = [1+idx*differ, 5.4+idx*differ]
+            all_position.extend(positions)
+            plt.boxplot([confi_data, iou_data], positions=positions)
+            
+        # 接下来着手处理x坐标信息
+        axis_names = x_script*2
+        all_position.sort()
+        plt.xticks(ticks=all_position,        #设置要显示的x轴刻度，若指定空列表则去掉x轴刻度
+            # , 
+            labels=axis_names,#设置x轴刻度显示的文字，要与ticks对应   
+            fontsize=12,        #设置刻度字体大小
+            rotation=0,        #设置刻度文字旋转角度
+            ha='center', va='center',        #刻度文字对齐方式，当rotation_mode为’anchor'时，对齐方式决定了文字旋转的中心。ha也可以写成horizontalalignment，va也可以写成verticalalignment。
+        )
+        plt.xlabel(f"Score Statistical Results{' '*30}IOU Statistical Results")
+        
+        plt.grid()  # 生成网格
+        
+    plt.suptitle('Score and IOU in TP',fontsize=28)
+
+    # plt.show()
+    plt.savefig("/workspace/code/Quant/MQBench/test/test_img/BOX-fp32-8-6-5-4-3.png")
+    plt.close()
+
+def plot_ious_and_confi(tp_special_data, classes_list, target_dir):
+    det_labels = tp_special_data[:,0].astype(np.int16)  # label score IOU
+    tp_special_data[:, 2] = iou_remap(tp_special_data[:, 2])  # IOU数据 重新缩放
+    
+    plt.figure(figsize=(12,4))
+    plt.boxplot(tp_special_data[:,1:])
+    plt.grid()  # 生成网
+    title_font = {'weight': 'bold', 'size': 14}
+    plt.title('Score and IOU in TP, summarization',fontdict=title_font)
+    # plt.show()
+    # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/fp32_summarization.png")
+    # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/w4a4_summarization.png")
+    plt.savefig(f"/workspace/code/Quant/MQBench/test/test_img/{target_dir}/w3a3_summarization.png")
+    plt.close()
+
+def statistics_by_percentage_bin(loop_data, classes_list, x_script, target_dir):
+    # mode = 'iou' # iou > 0.9 下的讨论  这图也好看
+    mode = 'cls' # cls > 0.9 下的讨论  # 这图好看
+    fig = plt.figure(figsize=(12,8))
+    # plt.subplots_adjust(left=0.08,bottom=0.10,right=0.9,top=0.92,wspace=.2,hspace=.2) # 设置子图间距
+    title_font = {'weight': 'bold', 'size': 22}
+    label_font = {'weight': 'bold', 'size': 16}
+    suptitle_font = {'weight': 'bold', 'size': 40}
+    
+    loop_number = len(loop_data)
+    
+    if mode == 'iou':
+        bin_number = 10
+        edge = np.linspace(0., 1.0, bin_number + 1)
+    elif mode == 'cls':
+        # bin_number = 6
+        # edge = np.array([0., 0.5, 0.6, 0.7, 0.8, 0.9, 1.0])
+        bin_number = 5
+        edge = np.linspace(0.5, 1.0, bin_number + 1)
+    else:
+        raise NotImplementedError
+    
+    edge = np.round(edge, decimals=3)
+    
+    bins = np.zeros(bin_number)
+    bar_width = 0.16
+    mid_position = np.linspace(1, bin_number, bin_number) * 0.75
+    base_position = mid_position - bar_width * (loop_number - 1) / 2
+    x_show = []
+    for i in range(bin_number):
+        x_show.append(f'[{edge[i]}, {edge[i+1]}]')
+    
+    for idx, tp_special_data in enumerate(loop_data):  # 循环绘制每一个数据文件
+        position = base_position + idx * bar_width
+        
+        confi_data = tp_special_data[:, 0]
+        iou_data = tp_special_data[:, 1]
+        if mode == 'iou':
+            percent9_data = iou_data
+            slicing_data = confi_data
+        elif mode == 'cls':
+            percent9_data = confi_data
+            slicing_data = iou_data
+        else:
+            raise NotImplementedError
+            
+        # det_labels = tp_special_data[:,0].astype(np.int16)  # label score IOU
+        percentage = 0.9
+        focused_percent9_data = percent9_data[percent9_data > percentage]
+        focused_slicing_data = slicing_data[percent9_data > percentage]
+        elem_number = focused_slicing_data.size
+        
+        for i in range(bin_number):
+            total = np.sum(np.array(edge[i] <= focused_slicing_data) == np.array(focused_slicing_data < edge[i + 1]))
+            bins[i] = total / elem_number  # 顺便做一个归一化
+            
+        plt.bar(position, bins, width=bar_width, label=x_script[idx])
+    plt.xticks(mid_position, x_show)   # 替换横坐标x的刻度显示内容
+    plt.legend()   # 给出图例
+    if mode == 'iou':
+        plt.xlabel(f"Classification Score Range", fontdict=label_font)
+        plt.ylabel(f"Percentage", fontdict=label_font)
+        plt.ylim(0., 0.9)
+    elif mode == 'cls':
+        plt.xlabel(f"IOU Score Range", fontdict=label_font)
+        plt.ylabel(f"Percentage", fontdict=label_font)
+        # plt.ylim(0.275, 0.725)
+        plt.ylim(0., 0.5)
+        
+        
+    plt.tick_params(labelsize=14)
+    # TODO 把百分比标到头上
+        
+    title_font = {'weight': 'bold', 'size': 14}
+    # plt.title('Score and IOU in TP, summarization',fontdict=title_font)
+    # plt.show()
+    # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/fp32_summarization.png")
+    # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/w4a4_summarization.png")
+    plt.savefig(f"test_img/{target_dir}/statistics_bar_summarization_{mode}0.9.png")
+    plt.close()
+    
+    
+    
+    # plt.figure()
+    # number = data.size
+    # edge = np.linspace(-pedge, pedge, 360)
+    # bins = [0] * (len(edge) - 1)
+    # for i in range(len(edge) - 1):
+    #     total = np.sum(np.array(edge[i] < data) == np.array(data < edge[i + 1]))
+    #     bins[i] = total
+
+    # bins = np.array(bins) / number  # 做一个归一化
+
+    # plt.stem(edge[:-1], bins, markerfmt='C3.')  # 不能直接用hist，因为hist的区间是动态的！（最小刻度会变，很傻吊）
+    # plt.title(title)
+    # plt.xlim(-1.1 * pedge, 1.1 * pedge)
+    # plt.ylim(0, hlim)
+    # plt.grid()
+
+    # plt.savefig('./fig/{}.jpg'.format(title))
+    # plt.close()
+
+def statistics_for_iou_bin(loop_data, classes_list, x_script, target_dir):
+    fig = plt.figure(figsize=(12,8))
+    # plt.subplots_adjust(left=0.08,bottom=0.10,right=0.9,top=0.92,wspace=.2,hspace=.2) # 设置子图间距
+    title_font = {'weight': 'bold', 'size': 22}
+    label_font = {'weight': 'bold', 'size': 16}
+    suptitle_font = {'weight': 'bold', 'size': 40}
+    
+    loop_number = len(loop_data)
+    
+
+    bin_number = 10
+    # edge = np.linspace(0.5, 1.0, bin_number + 1)
+    edge = np.linspace(0., 1.0, bin_number + 1)
+    
+    edge = np.round(edge, decimals=3)
+    
+    bins = np.zeros(bin_number)
+    bar_width = 0.16
+    mid_position = np.linspace(1, bin_number, bin_number) * 0.75
+    base_position = mid_position - bar_width * (loop_number - 1) / 2
+    x_show = []
+    for i in range(bin_number):
+        x_show.append(f'[{edge[i]}, {edge[i+1]}]')
+    
+    for idx, tp_special_data in enumerate(loop_data):  # 循环绘制每一个数据文件
+        position = base_position + idx * bar_width
+        
+        confi_data = tp_special_data[:, 0]
+        iou_data = tp_special_data[:, 1]
+        
+            
+        # det_labels = tp_special_data[:,0].astype(np.int16)  # label score IOU
+        elem_number = iou_data.size
+        
+        for i in range(bin_number):
+            total = np.sum(np.array(edge[i] <= iou_data) == np.array(iou_data < edge[i + 1]))
+            bins[i] = total / elem_number  # 顺便做一个归一化
+            # bins[i] = total  # 
+            
+        plt.bar(position, bins, width=bar_width, label=x_script[idx])
+    plt.xticks(mid_position, x_show)   # 替换横坐标x的刻度显示内容
+    plt.legend()   # 给出图例
+    
+    # Total 
+    plt.xlabel(f"IOU Score Range", fontdict=label_font)
+    plt.ylabel(f"Percentage", fontdict=label_font)
+    # plt.ylim(0., 0.6)
+        
+        
+    plt.tick_params(labelsize=14)
+    # TODO 把百分比标到头上
+        
+    title_font = {'weight': 'bold', 'size': 14}
+    # plt.title('Score and IOU in TP, summarization',fontdict=title_font)
+    # plt.show()
+    # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/fp32_summarization.png")
+    # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/w4a4_summarization.png")
+    plt.savefig(f"test_img/{target_dir}/statistics_bar_summarization_all_iou_variation.png")
+    plt.close()
+    
+    
+    
+    # plt.figure()
+    # number = data.size
+    # edge = np.linspace(-pedge, pedge, 360)
+    # bins = [0] * (len(edge) - 1)
+    # for i in range(len(edge) - 1):
+    #     total = np.sum(np.array(edge[i] < data) == np.array(data < edge[i + 1]))
+    #     bins[i] = total
+
+    # bins = np.array(bins) / number  # 做一个归一化
+
+    # plt.stem(edge[:-1], bins, markerfmt='C3.')  # 不能直接用hist，因为hist的区间是动态的！（最小刻度会变，很傻吊）
+    # plt.title(title)
+    # plt.xlim(-1.1 * pedge, 1.1 * pedge)
+    # plt.ylim(0, hlim)
+    # plt.grid()
+
+    # plt.savefig('./fig/{}.jpg'.format(title))
+    # plt.close()
+
+def loop_box_ious_and_confi(loop_data, classes_list, x_script, target_dir):
+    all_position = []
+    differ = 0.6  # 用来偏移画图的
+    plt.figure(figsize=(12,4))
+    for idx, tp_special_data in enumerate(loop_data):  # 循环绘制每一个数据文件
+    
+        # det_labels = tp_special_data[:,0].astype(np.int16)  # label score IOU
+        # tp_special_data[:, 2] = iou_remap(tp_special_data[:, 2])  # IOU数据 重新缩放
+        # confidence 或 IOU重新排序，只取前K个结果
+        if target_dir =='Retina50':
+            topk = round(0.5 * tp_special_data.shape[0])  # 妙！这才对啊
+        elif target_dir =='SSD300': 
+            topk = round(0.5 * tp_special_data.shape[0])  # 妙！这才对啊
+        sorted_idxs = np.argsort(tp_special_data[:, 2])
+        tp_special_data = tp_special_data[sorted_idxs]  # 进行从小到大排序
+        confi_data = tp_special_data[-topk:][:, 1]
+        iou_data = tp_special_data[-topk:][:, 2]
+        
+        # iou_data = np.sort(tp_special_data[:, 2])
+        
+        positions = [1+idx*differ, 5+idx*differ]
+        all_position.extend(positions)
+        plt.boxplot([confi_data, iou_data], positions=positions, sym='')  # NOTE 注意，隐藏了离群点
+    # 接下来着手处理x坐标信息
+    axis_names = x_script*2
+    all_position.sort()
+    plt.xticks(ticks=all_position,        #设置要显示的x轴刻度，若指定空列表则去掉x轴刻度
+        # , 
+        labels=axis_names,#设置x轴刻度显示的文字，要与ticks对应   
+        fontsize=10,        #设置刻度字体大小
+        rotation=0,        #设置刻度文字旋转角度
+        ha='center', va='center',        #刻度文字对齐方式，当rotation_mode为’anchor'时，对齐方式决定了文字旋转的中心。ha也可以写成horizontalalignment，va也可以写成verticalalignment。
+    )  
+    
+    plt.xlabel(f"Score Statistical Results{' '*70}IOU Statistical Results")
+    
+    plt.ylim([0.1, 1.])
+    
+    plt.grid()  # 生成网
+    
+    title_font = {'weight': 'bold', 'size': 12}
+    plt.title('Score and IOU in TP, summarization',fontdict=title_font)
+    # plt.show()
+    plt.savefig(f"/workspace/code/Quant/MQBench/test_img/{target_dir}/BOX-fp32-8-6-5-4-3-Summarization.png")
+    # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/fp32-8-6-5-4-3-Summarization1.png")
+    plt.close()
+
+def loop_plot_ious_and_confi(loop_data, classes_list, x_script, target_dir):
+    all_position = []
+    differ = 0.6  # 用来偏移画图的
+    plt.figure(figsize=(12,4))
+    count_list = []
+    for idx, tp_special_data in enumerate(loop_data):  # 循环绘制每一个数据文件
+        count_list.append(len(tp_special_data))
+        # # det_labels = tp_special_data[:,0].astype(np.int16)  # label score IOU
+        # tp_special_data[:, 2] = iou_remap(tp_special_data[:, 2])  # IOU数据 重新缩放
+        # # confidence 或 IOU重新排序，只取前K个结果
+        # topk = 10000
+        # confi_data = np.sort(tp_special_data[:, 1])[-topk:]
+        # iou_data = np.sort(tp_special_data[:, 2])[-topk:]
+        
+        positions = [1+idx*differ]
+        all_position.extend(positions)
+    plt.plot(x_script, count_list, )
+    # 接下来着手处理x坐标信息
+    axis_names = x_script
+    all_position.sort()
+    plt.xticks(
+        # ticks=all_position,        #设置要显示的x轴刻度，若指定空列表则去掉x轴刻度
+        # # , 
+        # labels=axis_names,#设置x轴刻度显示的文字，要与ticks对应   
+        fontsize=10,        #设置刻度字体大小
+        rotation=0,        #设置刻度文字旋转角度
+        ha='center', va='center',        #刻度文字对齐方式，当rotation_mode为’anchor'时，对齐方式决定了文字旋转的中心。ha也可以写成horizontalalignment，va也可以写成verticalalignment。
+    )  
+    plt.grid()  # 生成网
+    plt.legend('TP')
+    title_font = {'weight': 'bold', 'size': 12}
+    plt.title('Score and IOU in TP, summarization',fontdict=title_font)
+    # plt.show()
+    plt.savefig(f"/workspace/code/Quant/MQBench/test/test_img/{target_dir}/PLOT-fp32-8-6-5-4-3-Summarization.png")
+    # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/fp32-8-6-5-4-3-Summarization1.png")
+    plt.close()
+
+
+def loop_hexbin_ious_and_confi(loop_data, classes_list, x_script, target_dir):
+    fig = plt.figure(figsize=(20,18))
+    plt.subplots_adjust(left=0.08,bottom=0.10,right=0.9,top=0.88,wspace=0,hspace=.32) # 设置子图间距
+    title_font = {'weight': 'bold', 'size': 22}
+    label_font = {'weight': 'bold', 'size': 20}
+    suptitle_font = {'weight': 'bold', 'size': 40}
+    
+    # 定制一个y=x曲线
+    ideal_dot = np.linspace(.5, 1.0, 20)
+    
+    for idx, tp_special_data in enumerate(loop_data):  # 循环绘制每一个数据文件
+        ax = fig.add_subplot(2,2,idx+1)
+        plt.title(f"{x_script[idx]}", fontdict=title_font)
+        
+        # # 按照类别去绘画
+        # tp_special_data = tp_special_data[tp_special_data[:,0] == 0]
+        
+        # # 按照IOU重新排序，只取前K个结果
+        # topk = 10000
+        # iou_data = tp_special_data[:, 2]
+        # index = np.lexsort((iou_data, ))
+        # tp_special_data = tp_special_data[index][-topk:]
+        # confi_data = tp_special_data[:, 1]
+        # iou_data = tp_special_data[:, 2]
+        
+        confi_data = tp_special_data[:, 0]
+        iou_data = tp_special_data[:, 1]
+        
+        # df = pd.DataFrame(tp_special_data[:, 1:], columns = ['Classification Score','IOU Score'])
+        # sns.jointplot(x=df['Classification Score'], y=df['IOU Score'], # 设置xy轴，显示columns名称
+        #       data = df,  #设置数据
+        #       color = 'b', #设置颜色
+        #     #   s = 50, edgecolor = 'w', linewidth = 1,#设置散点大小、边缘颜色及宽度(只针对scatter)
+        #     #   stat_func=sci.pearsonr,
+        #       kind = 'hex',#设置类型：'scatter','reg','resid','kde','hex'
+        #       #stat_func=<function pearsonr>,
+        #       space = 0.1, #设置散点图和布局图的间距
+        #     #   size = 4, #图表大小(自动调整为正方形))
+        #       ratio = 5, #散点图与布局图高度比，整型
+        #     #   marginal_kws = dict(bins=15, rug =True), #设置柱状图箱数，是否设置rug
+        #       )
+        # df.plot.hexbin(x='Classification Score', y='IOU Score', gridsize=30)
+        # plt.scatter(confi_data, iou_data, s=1)  # 注意，x轴是classification，y轴是iou
+        # hb = plt.hexbin(confi_data, iou_data, gridsize=40, cmap='Blues', vmin=0, vmax=4)  # min max指的是color的上下限
+        hb = plt.hexbin(confi_data, iou_data, gridsize=40, cmap='Blues', vmin=0, vmax=10)  # min max指的是color的上下限
+        plt.plot(ideal_dot, ideal_dot, 'r:', linewidth=5)
+        # cb = plt.colorbar(hb)  
+        # cb.set_label(z)
+        plt.xlabel(f"Classification Score", fontdict=label_font)
+        plt.ylabel(f"IOU Score", fontdict=label_font)
+        plt.tick_params(labelsize=14)
+        # plt.xlim([0.1, 1.0])
+        # plt.ylim([0.65, 1.0])
+        ax.set_aspect(1.0/ax.get_data_ratio(), adjustable='box')
+
+    # # 做成一张主图
+    # fig, axarr = plt.subplots(2, 3, figsize=(25, 16))
+    # subaxis = generate_axis(2,3)
+    # for it, ax_idx in enumerate(subaxis):
+    #     axarr[ax_idx].imshow(mpimg.imread(f"/workspace/code/Quant/MQBench/test/test_img/hex_temp/{it}.png"))
+    
+    # # 去掉 x和 y轴
+    # [ax.set_axis_off() for ax in axarr.ravel()]
+    # plt.suptitle('Score and IOU in TP, summarization', fontsize=22)
+    # plt.tight_layout()
+    # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/HEXSCATTER-fp32-8-6-5-4-3-Summarization.png")
+    position = fig.add_axes([0.92, 0.12, 0.015, .78 ])#位置[左,下,右,上]
+    cb = fig.colorbar(hb, cax=position)
+    # cb.ax.set_yticklabels(['0','5','10','15','20','25','30', '35'], fontsize=18)
+    cb.ax.set_yticklabels(['0', '', '1', '', '2', '', '3', '', '≥4'], fontsize=24)
+    plt.suptitle('Score and IOU in TP+FP, summarization', fontsize=40)
+    # plt.tight_layout()
+    # plt.show()
+    plt.savefig(f"test_img/{target_dir}/HEXBIN-fp32-8-6-5-4-3-Summarization.png")
+    # plt.savefig("test_img/fp32-8-6-5-4-3-Summarization1.png")
+    plt.close()
+
+def loop_dot_ious_and_confi(loop_data, classes_list, x_script, target_dir):
+    fig = plt.figure(figsize=(24,16))
+    plt.subplots_adjust(left=0.08,bottom=0.10,right=0.9,top=0.92,wspace=.2,hspace=.2) # 设置子图间距
+    title_font = {'weight': 'bold', 'size': 22}
+    label_font = {'weight': 'bold', 'size': 20}
+    suptitle_font = {'weight': 'bold', 'size': 40}
+    
+    # 定制一个y=x曲线
+    ideal_dot = np.linspace(.5, 1.0, 20)
+    
+    for idx, tp_special_data in enumerate(loop_data):  # 循环绘制每一个数据文件
+        ax = fig.add_subplot(2,3,idx+1)
+        plt.title(f"{x_script[idx]}", fontdict=title_font)
+        
+        # # 按照类别去绘画
+        # tp_special_data = tp_special_data[tp_special_data[:,0] == 0]
+        
+        # # 按照IOU重新排序，只取前K个结果
+        # topk = 10000
+        # iou_data = tp_special_data[:, 2]
+        # index = np.lexsort((iou_data, ))
+        # tp_special_data = tp_special_data[index][-topk:]
+        # confi_data = tp_special_data[:, 1]
+        # iou_data = tp_special_data[:, 2]
+        
+        confi_data = tp_special_data[:, 0]
+        iou_data = tp_special_data[:, 1]
+        
+        # df = pd.DataFrame(tp_special_data[:, 1:], columns = ['Classification Score','IOU Score'])
+        # sns.jointplot(x=df['Classification Score'], y=df['IOU Score'], # 设置xy轴，显示columns名称
+        #       data = df,  #设置数据
+        #       color = 'b', #设置颜色
+        #     #   s = 50, edgecolor = 'w', linewidth = 1,#设置散点大小、边缘颜色及宽度(只针对scatter)
+        #     #   stat_func=sci.pearsonr,
+        #       kind = 'hex',#设置类型：'scatter','reg','resid','kde','hex'
+        #       #stat_func=<function pearsonr>,
+        #       space = 0.1, #设置散点图和布局图的间距
+        #     #   size = 4, #图表大小(自动调整为正方形))
+        #       ratio = 5, #散点图与布局图高度比，整型
+        #     #   marginal_kws = dict(bins=15, rug =True), #设置柱状图箱数，是否设置rug
+        #       )
+        # df.plot.hexbin(x='Classification Score', y='IOU Score', gridsize=30)
+        # plt.scatter(confi_data, iou_data, s=1)  # 注意，x轴是classification，y轴是iou
+        plt.plot(confi_data, iou_data, 'b.', linewidth=1)  # min max指的是color的上下限
+        plt.plot(ideal_dot, ideal_dot, 'r:', linewidth=4)
+        # cb = plt.colorbar(hb)  
+        # cb.set_label(z)
+        plt.xlabel(f"Classification Score", fontdict=label_font)
+        plt.ylabel(f"IOU Score", fontdict=label_font)
+        plt.tick_params(labelsize=14)
+        # plt.xlim([0.1, 1.0])
+        # plt.ylim([0.65, 1.0])
+        ax.set_aspect(1.0/ax.get_data_ratio(), adjustable='box')
+
+    # # 做成一张主图
+    # fig, axarr = plt.subplots(2, 3, figsize=(25, 16))
+    # subaxis = generate_axis(2,3)
+    # for it, ax_idx in enumerate(subaxis):
+    #     axarr[ax_idx].imshow(mpimg.imread(f"/workspace/code/Quant/MQBench/test/test_img/hex_temp/{it}.png"))
+    
+    # # 去掉 x和 y轴
+    # [ax.set_axis_off() for ax in axarr.ravel()]
+    # plt.suptitle('Score and IOU in TP, summarization', fontsize=22)
+    # plt.tight_layout()
+    # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/HEXSCATTER-fp32-8-6-5-4-3-Summarization.png")
+
+    # cb.ax.set_yticklabels(['0','5','10','15','20','25','30', '35'], fontsize=18)
+    plt.suptitle('Score and IOU in TP, summarization', fontsize=40)
+    # plt.tight_layout()
+    # plt.show()
+    plt.savefig(f"test_img/{target_dir}/dot-fp32-8-6-5-4-3-Summarization.png")
+    # plt.savefig("test_img/fp32-8-6-5-4-3-Summarization1.png")
+    plt.close()
+
+# def loop_violinplot_ious_and_confi(loop_data, classes_list, x_script, target_dir):  # TODO 改成分开绘图吧，合在一起其实不方便？或者着重解决双坐标轴的情况
+#     all_position = []
+#     differ = 0.6  # 用来偏移画图的
+#     plt.figure(figsize=(12,4))
+#     for idx, tp_special_data in enumerate(loop_data):  # 循环绘制每一个数据文件
+    
+        
+#         # IOU数据 重新缩放
+#         # tp_special_data[:, 2] = iou_remap(tp_special_data[:, 2])
+        
+#         # confidence 或 IOU重新排序，只取前K个结果
+#         if target_dir =='Retina50':
+#             topk = round(0.5 * tp_special_data.shape[0])  # 妙！这才对啊
+#         elif target_dir =='SSD300': 
+#             topk = round(0.5 * tp_special_data.shape[0])
+#         sorted_idxs = np.argsort(tp_special_data[:, 2])
+#         tp_special_data = tp_special_data[sorted_idxs]  # 进行从小到大排序
+#         confi_data = tp_special_data[-topk:][:, 1]
+#         iou_data = tp_special_data[-topk:][:, 2]
+        
+#         positions = [1+idx*differ, 5+idx*differ]
+#         all_position.extend(positions)
+#         plt.violinplot([confi_data, iou_data], positions=positions)
+#     # 接下来着手处理x坐标信息
+#     axis_names = x_script*2
+#     all_position.sort()
+#     plt.xticks(ticks=all_position,        #设置要显示的x轴刻度，若指定空列表则去掉x轴刻度
+#         # , 
+#         labels=axis_names,#设置x轴刻度显示的文字，要与ticks对应   
+#         fontsize=10,        #设置刻度字体大小
+#         rotation=0,        #设置刻度文字旋转角度
+#         ha='center', va='center',        #刻度文字对齐方式，当rotation_mode为’anchor'时，对齐方式决定了文字旋转的中心。ha也可以写成horizontalalignment，va也可以写成verticalalignment。
+#     )  
+    
+#     plt.xlabel(f"Classification Statistical Results{' '*64}IOU Statistical Results{' '*8}")
+#     plt.ylim([0.1, 1.])
+#     # 构建右侧坐标
+#     plt.grid()  # 生成网
+    
+#     title_font = {'weight': 'bold', 'size': 12}
+#     plt.title('Score and IOU in TP, summarization',fontdict=title_font)
+#     # plt.show()
+#     plt.savefig(f"/workspace/code/Quant/MQBench/test_img/{target_dir}/VIOLIN-fp32-8-6-5-4-3-Summarization.png")
+#     # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/fp32-8-6-5-4-3-Summarization1.png")
+#     plt.close()
+
+
+def loop_violinplot_ious_and_confi(loop_data, classes_list, x_script, target_dir):  # TODO 改成分开绘图吧，合在一起其实不方便？或者着重解决双坐标轴的情况
+    all_position = []
+    differ = 0.6  # 用来偏移画图的
+    plt.figure(figsize=(12,4))
+    for idx, tp_special_data in enumerate(loop_data):  # 循环绘制每一个数据文件
+    
+        
+        # IOU数据 重新缩放
+        # tp_special_data[:, 2] = iou_remap(tp_special_data[:, 2])
+        
+        # sorted_idxs = np.argsort(tp_special_data[:, 2])
+        # tp_special_data = tp_special_data[sorted_idxs]  # 进行从小到大排序
+        # confi_data = tp_special_data[-topk:][:, 1]
+        # iou_data = tp_special_data[-topk:][:, 2]
+        confi_data = tp_special_data[:, 0]
+        iou_data = tp_special_data[:, 1]
+        
+        positions = [1+idx*differ, 5+idx*differ]
+        all_position.extend(positions)
+        plt.violinplot([confi_data, iou_data], positions=positions)
+    # 接下来着手处理x坐标信息
+    axis_names = x_script*2
+    all_position.sort()
+    plt.xticks(ticks=all_position,        #设置要显示的x轴刻度，若指定空列表则去掉x轴刻度
+        # , 
+        labels=axis_names,#设置x轴刻度显示的文字，要与ticks对应   
+        fontsize=10,        #设置刻度字体大小
+        rotation=0,        #设置刻度文字旋转角度
+        ha='center', va='center',        #刻度文字对齐方式，当rotation_mode为’anchor'时，对齐方式决定了文字旋转的中心。ha也可以写成horizontalalignment，va也可以写成verticalalignment。
+    )  
+    
+    plt.xlabel(f"Classification Statistical Results{' '*64}IOU Statistical Results{' '*8}")
+    plt.ylim([0.1, 1.])
+    # 构建右侧坐标
+    plt.grid()  # 生成网
+    
+    title_font = {'weight': 'bold', 'size': 12}
+    plt.title('Score and IOU in TP, summarization',fontdict=title_font)
+    # plt.show()
+    plt.savefig(f"test_img/{target_dir}/VIOLIN-fp32-8-6-5-4-3-Summarization.png")
+    # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/fp32-8-6-5-4-3-Summarization1.png")
+    plt.close()
+
+
+def loop_bar_fp(loop_data, classes_list, x_script, target_dir):
+    # 绘制FP中background和cls型的占比
+    all_position = []
+    differ = 0.6  # 用来偏移画图的
+    plt.figure(figsize=(12,4))
+    for idx, fp_data in enumerate(loop_data):  # 循环绘制每一个数据文件
+    
+        # det_labels = tp_special_data[:,0].astype(np.int16)  # label score IOU
+        # cls_fp = fp_data[0]
+        # cls_num = len(cls_fp)
+        background_fp1 = fp_data[1]  # cls正确的
+        background1_num = len(background_fp1)
+        background_fp_total = fp_data[2]  # 总的
+        background_total_num = len(background_fp_total)
+        background1_percentage = (background_total_num - background1_num) / background_total_num  # 正确的比例
+        background2_percentage = 1 - background1_percentage  # 纯背景的比例
+        
+        positions = [1+idx*differ]
+        all_position.extend(positions)
+        plt.bar(positions, [background1_percentage], color="b", width=0.4, )
+        plt.bar(positions, [background2_percentage], color="g", bottom=[background1_percentage], width=0.4, )
+    # 接下来着手处理x坐标信息
+    axis_names = x_script
+    all_position.sort()
+    plt.xticks(ticks=all_position,        #设置要显示的x轴刻度，若指定空列表则去掉x轴刻度
+        # , 
+        labels=axis_names,#设置x轴刻度显示的文字，要与ticks对应   
+        fontsize=10,        #设置刻度字体大小
+        rotation=0,        #设置刻度文字旋转角度
+        ha='center', va='center',        #刻度文字对齐方式，当rotation_mode为’anchor'时，对齐方式决定了文字旋转的中心。ha也可以写成horizontalalignment，va也可以写成verticalalignment。
+    )  
+    
+    plt.xlabel(f"Statistical Results in FP")
+    plt.ylabel(f"Number")
+    plt.legend(('cls right','real background'))
+    plt.grid()  # 生成网
+    
+    title_font = {'weight': 'bold', 'size': 12}
+    plt.title('FP, summarization',fontdict=title_font)
+    # plt.show()
+    plt.savefig(f"/workspace/code/Quant/MQBench/test/test_img/{target_dir}/BAR-fp32-8-6-5-4-3-FP-Summarization.png")
+    # plt.savefig("/workspace/code/Quant/MQBench/test/test_img/fp32-8-6-5-4-3-Summarization1.png")
+    plt.close()
+
+def maxminnorm(array):  # 全局归一化
+    max_value=array.max()
+    min_value=array.min()
+    return (array - min_value)/(max_value-min_value)
+
+def iou_remap(array):  # 重新缩放iou数据的区间[0.5,1]->[0,1]
+    return 2*(array - 0.5) 
+
+if __name__ in "__main__":
+    import matplotlib.pyplot as plt
+    import numpy as np
+    import matplotlib
+    
+    # plt.rcParams['font.sans-serif']=['SimHei'] #显示中文标签
+    # # plt.rcParams['font.family'] = 'simhei'
+    # plt.rcParams['axes.unicode_minus'] = False
+    
+    # 加载class json 文件
+    # json_file = '/workspace/code/Quant/MQBench/application/pytorch_object_detection/retinaNet/pascal_voc_classes.json'
+    # assert os.path.exists(json_file), "{} file not exist.".format(json_file)
+    # with open(json_file, 'r') as f:
+    #     class_dict = json.load(f)
+    #     classes_list = list(class_dict.keys())
+    classes_list = None
+
+    # analytic_type = 'FP'  # TP/FP
+    # target = 'SSDLite_voc'  # SSD300
+    target = 'Retina50_voc'  # SSD300
+    # target = 'Retina18_voc'  # SSD300
+    total_fp_path = None
+    total_tp_path =None
+    total_pos_sample_path = None
+    
+    if target == 'Retina50_voc':
+        total_tp_path = [
+                    'work_dirs/retinanet_r50_fpn_1x_voc/tp_special_list.npy',
+                    'work_dirs/retinanet_r50_fpn_voc_w4a4_LSQ/tp_special_list.npy',
+                    'work_dirs/retinanet_r50_fpn_voc_w2a2_LSQ_le/tp_special_list.npy',
+        ]
+        # total_pos_sample_path = [
+        #             'work_dirs/retinanet_r18_fpn_1x_voc/all_pos_sample_data.npy',
+        #             'work_dirs/retinanet_r18_fpn_voc_w4a4_LSQ/all_pos_sample_data.npy',
+        #             'work_dirs/retinanet_r18_fpn_voc_w2a2_LSQ/all_pos_sample_data.npy',
+        # ]
+        # total_fp_path = [
+        #             'work_dirs/retinanet_r18_fpn_1x_voc/all_pos_sample_data.npy',
+        #             'work_dirs/retinanet_r18_fpn_voc_w4a4_LSQ/all_pos_sample_data.npy',
+        #             'work_dirs/retinanet_r18_fpn_voc_w2a2_LSQ/all_pos_sample_data.npy',
+        # ]
+        
+        
+        # x_script = ['fp32']
+        x_script = ['fp32', 'LSQ w4a4', 'LSQ w2a2']    
+    elif target == 'Retina18_voc':
+        total_tp_path = [
+                    'work_dirs/retinanet_r18_fpn_1x_voc/tp_special_list.npy',
+                    'work_dirs/retinanet_r18_fpn_voc_w4a4_LSQ/tp_special_list.npy',
+                    'work_dirs/retinanet_r18_fpn_voc_w2a2_LSQ/tp_special_list.npy',
+        ]
+        # total_pos_sample_path = [
+        #             'work_dirs/retinanet_r18_fpn_1x_voc/all_pos_sample_data.npy',
+        #             'work_dirs/retinanet_r18_fpn_voc_w4a4_LSQ/all_pos_sample_data.npy',
+        #             'work_dirs/retinanet_r18_fpn_voc_w2a2_LSQ/all_pos_sample_data.npy',
+        # ]
+        # total_fp_path = [
+        #             'work_dirs/retinanet_r18_fpn_1x_voc/all_pos_sample_data.npy',
+        #             'work_dirs/retinanet_r18_fpn_voc_w4a4_LSQ/all_pos_sample_data.npy',
+        #             'work_dirs/retinanet_r18_fpn_voc_w2a2_LSQ/all_pos_sample_data.npy',
+        # ]
+        
+        
+        # x_script = ['fp32']
+        x_script = ['fp32', 'LSQ w4a4', 'LSQ w2a2']
+    elif target == 'SSDLite_voc':
+        # SSD300
+        
+        # total_fp_path = [
+        #     'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc/all_pos_sample_data.npy',  # 就是纯eval的时候有问题
+        #     'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4/all_pos_sample_data.npy',  # 就是纯eval的时候有问题
+        #     'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4_mypro_temp/all_pos_sample_data.npy',  # 就是纯eval的时候有问题
+        #     'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4_mypro_temp_qloss/all_pos_sample_data.npy',  # 就是纯eval的时候有问题
+        # ]    
+        # total_tp_path = [
+        #     'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc/tp_special_list.npy',  # 就是纯eval的时候有问题
+        #     'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4/tp_special_list.npy',  # 就是纯eval的时候有问题
+        #     'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4_mypro_temp_qloss/tp_special_list.npy',  # 就是纯eval的时候有问题
+        # ]          
+        # total_fp_path = [
+        #     'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc/cls_wrong_fp_data.npy',  # 就是纯eval的时候有问题
+        #     'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4/cls_wrong_fp_data.npy',  # 就是纯eval的时候有问题
+        #     'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4_mypro_temp_qloss/cls_wrong_fp_data.npy',  # 就是纯eval的时候有问题
+        # ]          
+
+        
+        # 只用在statistica for iou
+        total_tp_path = [
+            'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc/tp_special_list.npy',  # 就是纯eval的时候有问题
+            'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4/tp_special_list.npy',  # 就是纯eval的时候有问题
+            'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4_mypro_temp/tp_special_list.npy',  # 就是纯eval的时候有问题
+            'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4_mypro_temp_qloss/tp_special_list.npy',  # 就是纯eval的时候有问题
+        ]          
+        # total_fp_path = [
+        #     'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc/cls_wrong_fp_data.npy',  # 就是纯eval的时候有问题
+        #     'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4/cls_wrong_fp_data.npy',  # 就是纯eval的时候有问题
+        #     'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4_mypro_temp/cls_wrong_fp_data.npy',  # 就是纯eval的时候有问题
+        #     'work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4_mypro_temp_qloss/cls_wrong_fp_data.npy',  # 就是纯eval的时候有问题
+        # ]        
+        
+        x_script = ['fp32', 'w4a4', 'w4a4_HQOD_noq', 'w4a4_HQOD']
+        # x_script = ['fp32', 'w4a4', 'w4a4_HQOD']
+        # x_script = ['w4a4', 'w4a4_HQOD']
+        # x_script = ['fp32', '4Bit', '4Bit_HarDet', '4Bit_HQOD']
+        
+    else:
+        raise NotImplementedError
+    
+    mmcv.mkdir_or_exist(f'test_img/{target}')
+    
+    data_list = []
+    # for path in total_tp_path:
+    #     data = np.load(path, allow_pickle=True)
+    #     data_list.append(data)
+    if None not in [total_tp_path, total_fp_path]:
+        print('\n HexBin tp+fp Mode!!!!\n ')
+        for tp_path, fp_path in zip(total_tp_path, total_fp_path):
+            tp_data = np.load(tp_path, allow_pickle=True)
+            fp_data = np.load(fp_path, allow_pickle=True)
+            data = np.concatenate([tp_data[:,1:], 
+                                   fp_data, # cls错误型FP
+                                #    np.array(fp_data[1]), # 分类正确但IOU小于阈值型FP
+                                   ])
+            data_list.append(data)
+    elif total_tp_path is not None:
+        print('\n tp Mode!!!!\n ')
+        for path in total_tp_path:
+            data = np.load(path, allow_pickle=True)
+            pccs = np.corrcoef(data[:, 1], data[:, 2])
+            print(f'{pccs[0][1]}\n')
+            data_list.append(data[:, 1:])
+            # data_list.append(data)
+    elif total_fp_path is not None:
+        print('\n fp Mode!!!!\n ')
+        for path in total_fp_path:
+            data = np.load(path, allow_pickle=True)
+            data_list.append(data)
+    elif total_pos_sample_path is not None:
+        print('\n pos sample Mode!!!!\n ')
+        for path in total_pos_sample_path:
+            data = np.load(path, allow_pickle=True)
+            data_list.append(data)
+        
+    # plot_ious_and_confi_per_class(tp_special_data, classes_list)
+    # plot_ious_and_confi(tp_special_data, classes_list)
+    # analytic_type == 'TP':
+    # loop_plot_ious_and_confi_per_class(data_list, classes_list, x_script, target)
+    
+    # loop_plot_ious_and_confi(data_list, classes_list, x_script, target)
+    
+    # loop_box_ious_and_confi(data_list, classes_list, x_script, target)
+    # loop_violinplot_ious_and_confi(data_list, classes_list, x_script, target)  # 这个不行
+    
+    
+    loop_hexbin_ious_and_confi(data_list, classes_list, x_script, target)  # 这个稍微行，可以只展示lsq和HQOD的.
+    # loop_dot_ious_and_confi(data_list, classes_list, x_script, target)  # 这个没必要了
+    # statistics_by_percentage_bin(data_list, classes_list, x_script, target)  # 这个不行
+    # statistics_for_iou_bin(data_list, classes_list, x_script, target)  # 
+    
+        
+        
+    # analytic_type == 'FP':
+        # loop_bar_fp(data_list, classes_list, x_script, target)
+    
+    # -----------log
+    '''
+    1. 其实还有一个点，因为bit是统一w和a的，如果w8a16；w8a32又会怎样呢？
+    2. 讨论的是confi还是classification socre？在RetinaNet中虽然没区别
+    3. hexbin可以理解为热力图的一种.colorbar其实代表了下限，其实实际是超过140的
+    
+    待解决：假BiDet有猫腻，根本不对劲；但是明白他想表达的意思了，即关注nms后存在的问题，就是nms后仍有很多框，而其中的大多数框都是
+    会被判定为FP——因为IOU达不到阈值。我们其实应该关注TP和分类正确的bg型FP   搞清楚FN是怎么来的？！
+    
+    已定制：
+        a. VIOLIN，观察恶化情况；可以看到分类结果先恶化；
+        b. HEXBIN，观察imbalance&disharmonious；可以看到一开始就存在不和谐现象；int8加剧了这个现象；因为mAP的计算机制，所以这影响了mAP的结果。
+    '''
\ No newline at end of file
diff --git a/pytest.ini b/pytest.ini
new file mode 100644
index 0000000..9796e87
--- /dev/null
+++ b/pytest.ini
@@ -0,0 +1,7 @@
+[pytest]
+addopts = --xdoctest --xdoctest-style=auto
+norecursedirs = .git ignore build __pycache__ data docker docs .eggs
+
+filterwarnings= default
+                ignore:.*No cfgstr given in Cacher constructor or call.*:Warning
+                ignore:.*Define the __nice__ method for.*:Warning
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..6981bd7
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,4 @@
+-r requirements/build.txt
+-r requirements/optional.txt
+-r requirements/runtime.txt
+-r requirements/tests.txt
diff --git a/requirements/albu.txt b/requirements/albu.txt
new file mode 100644
index 0000000..f421fbb
--- /dev/null
+++ b/requirements/albu.txt
@@ -0,0 +1 @@
+albumentations>=0.3.2 --no-binary qudida,albumentations
diff --git a/requirements/build.txt b/requirements/build.txt
new file mode 100644
index 0000000..8155829
--- /dev/null
+++ b/requirements/build.txt
@@ -0,0 +1,3 @@
+# These must be installed before building mmdetection
+cython
+numpy
diff --git a/requirements/docs.txt b/requirements/docs.txt
new file mode 100644
index 0000000..b562600
--- /dev/null
+++ b/requirements/docs.txt
@@ -0,0 +1,8 @@
+docutils==0.16.0
+markdown>=3.4.0
+myst-parser
+-e git+https://github.com/open-mmlab/pytorch_sphinx_theme.git#egg=pytorch_sphinx_theme
+sphinx==5.3.0
+sphinx-copybutton
+sphinx_markdown_tables>=0.0.17
+sphinx_rtd_theme
diff --git a/requirements/mminstall.txt b/requirements/mminstall.txt
new file mode 100644
index 0000000..b53dbf4
--- /dev/null
+++ b/requirements/mminstall.txt
@@ -0,0 +1 @@
+mmcv-full>=1.3.17
diff --git a/requirements/optional.txt b/requirements/optional.txt
new file mode 100644
index 0000000..4f0065a
--- /dev/null
+++ b/requirements/optional.txt
@@ -0,0 +1,3 @@
+cityscapesscripts
+imagecorruptions
+scikit-learn
diff --git a/requirements/readthedocs.txt b/requirements/readthedocs.txt
new file mode 100644
index 0000000..e1bf21b
--- /dev/null
+++ b/requirements/readthedocs.txt
@@ -0,0 +1,4 @@
+mmcv
+scipy
+torch
+torchvision
diff --git a/requirements/runtime.txt b/requirements/runtime.txt
new file mode 100644
index 0000000..c815aef
--- /dev/null
+++ b/requirements/runtime.txt
@@ -0,0 +1,6 @@
+matplotlib
+numpy
+pycocotools
+scipy
+six
+terminaltables
diff --git a/requirements/tests.txt b/requirements/tests.txt
new file mode 100644
index 0000000..2ff795a
--- /dev/null
+++ b/requirements/tests.txt
@@ -0,0 +1,15 @@
+asynctest
+codecov
+flake8
+interrogate
+isort==4.3.21
+# Note: used for kwarray.group_items, this may be ported to mmcv in the future.
+kwarray
+-e git+https://github.com/open-mmlab/mmtracking#egg=mmtrack
+onnx==1.7.0
+onnxruntime>=1.8.0
+protobuf<=3.20.1
+pytest
+ubelt
+xdoctest>=0.10.0
+yapf
diff --git a/resources/coco_test_12510.jpg b/resources/coco_test_12510.jpg
new file mode 100644
index 0000000..1271ae1
Binary files /dev/null and b/resources/coco_test_12510.jpg differ
diff --git a/resources/corruptions_sev_3.png b/resources/corruptions_sev_3.png
new file mode 100644
index 0000000..bbbd19a
Binary files /dev/null and b/resources/corruptions_sev_3.png differ
diff --git a/resources/data_pipeline.png b/resources/data_pipeline.png
new file mode 100644
index 0000000..6ac3fee
Binary files /dev/null and b/resources/data_pipeline.png differ
diff --git a/resources/loss_curve.png b/resources/loss_curve.png
new file mode 100644
index 0000000..0242555
Binary files /dev/null and b/resources/loss_curve.png differ
diff --git a/resources/mmdet-logo.png b/resources/mmdet-logo.png
new file mode 100644
index 0000000..a0b6fbd
Binary files /dev/null and b/resources/mmdet-logo.png differ
diff --git a/resources/zhihu_qrcode.jpg b/resources/zhihu_qrcode.jpg
new file mode 100644
index 0000000..c745fb0
Binary files /dev/null and b/resources/zhihu_qrcode.jpg differ
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 0000000..6072221
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,21 @@
+[isort]
+line_length = 79
+multi_line_output = 0
+extra_standard_library = setuptools
+known_first_party = mmdet
+known_third_party = PIL,asynctest,cityscapesscripts,cv2,gather_models,matplotlib,mmcv,numpy,onnx,onnxruntime,pycocotools,pytest,pytorch_sphinx_theme,requests,scipy,seaborn,six,terminaltables,torch,ts,yaml
+no_lines_before = STDLIB,LOCALFOLDER
+default_section = THIRDPARTY
+
+[yapf]
+BASED_ON_STYLE = pep8
+BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true
+SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true
+
+# ignore-words-list needs to be lowercase format. For example, if we want to
+# ignore word "BA", then we need to append "ba" to ignore-words-list rather
+# than "BA"
+[codespell]
+skip = *.ipynb
+quiet-level = 3
+ignore-words-list = patten,nd,ty,mot,hist,formating,winn,gool,datas,wan,confids,TOOD,tood,ba,warmup,nam,dota,DOTA
diff --git a/setup.py b/setup.py
new file mode 100755
index 0000000..535d90e
--- /dev/null
+++ b/setup.py
@@ -0,0 +1,220 @@
+#!/usr/bin/env python
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import os.path as osp
+import platform
+import shutil
+import sys
+import warnings
+from setuptools import find_packages, setup
+
+import torch
+from torch.utils.cpp_extension import (BuildExtension, CppExtension,
+                                       CUDAExtension)
+
+
+def readme():
+    with open('README.md', encoding='utf-8') as f:
+        content = f.read()
+    return content
+
+
+version_file = 'mmdet/version.py'
+
+
+def get_version():
+    with open(version_file, 'r') as f:
+        exec(compile(f.read(), version_file, 'exec'))
+    return locals()['__version__']
+
+
+def make_cuda_ext(name, module, sources, sources_cuda=[]):
+
+    define_macros = []
+    extra_compile_args = {'cxx': []}
+
+    if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1':
+        define_macros += [('WITH_CUDA', None)]
+        extension = CUDAExtension
+        extra_compile_args['nvcc'] = [
+            '-D__CUDA_NO_HALF_OPERATORS__',
+            '-D__CUDA_NO_HALF_CONVERSIONS__',
+            '-D__CUDA_NO_HALF2_OPERATORS__',
+        ]
+        sources += sources_cuda
+    else:
+        print(f'Compiling {name} without CUDA')
+        extension = CppExtension
+
+    return extension(
+        name=f'{module}.{name}',
+        sources=[os.path.join(*module.split('.'), p) for p in sources],
+        define_macros=define_macros,
+        extra_compile_args=extra_compile_args)
+
+
+def parse_requirements(fname='requirements.txt', with_version=True):
+    """Parse the package dependencies listed in a requirements file but strips
+    specific versioning information.
+
+    Args:
+        fname (str): path to requirements file
+        with_version (bool, default=False): if True include version specs
+
+    Returns:
+        List[str]: list of requirements items
+
+    CommandLine:
+        python -c "import setup; print(setup.parse_requirements())"
+    """
+    import re
+    import sys
+    from os.path import exists
+    require_fpath = fname
+
+    def parse_line(line):
+        """Parse information from a line in a requirements text file."""
+        if line.startswith('-r '):
+            # Allow specifying requirements in other files
+            target = line.split(' ')[1]
+            for info in parse_require_file(target):
+                yield info
+        else:
+            info = {'line': line}
+            if line.startswith('-e '):
+                info['package'] = line.split('#egg=')[1]
+            elif '@git+' in line:
+                info['package'] = line
+            else:
+                # Remove versioning from the package
+                pat = '(' + '|'.join(['>=', '==', '>']) + ')'
+                parts = re.split(pat, line, maxsplit=1)
+                parts = [p.strip() for p in parts]
+
+                info['package'] = parts[0]
+                if len(parts) > 1:
+                    op, rest = parts[1:]
+                    if ';' in rest:
+                        # Handle platform specific dependencies
+                        # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies
+                        version, platform_deps = map(str.strip,
+                                                     rest.split(';'))
+                        info['platform_deps'] = platform_deps
+                    else:
+                        version = rest  # NOQA
+                    info['version'] = (op, version)
+            yield info
+
+    def parse_require_file(fpath):
+        with open(fpath, 'r') as f:
+            for line in f.readlines():
+                line = line.strip()
+                if line and not line.startswith('#'):
+                    for info in parse_line(line):
+                        yield info
+
+    def gen_packages_items():
+        if exists(require_fpath):
+            for info in parse_require_file(require_fpath):
+                parts = [info['package']]
+                if with_version and 'version' in info:
+                    parts.extend(info['version'])
+                if not sys.version.startswith('3.4'):
+                    # apparently package_deps are broken in 3.4
+                    platform_deps = info.get('platform_deps')
+                    if platform_deps is not None:
+                        parts.append(';' + platform_deps)
+                item = ''.join(parts)
+                yield item
+
+    packages = list(gen_packages_items())
+    return packages
+
+
+def add_mim_extension():
+    """Add extra files that are required to support MIM into the package.
+
+    These files will be added by creating a symlink to the originals if the
+    package is installed in `editable` mode (e.g. pip install -e .), or by
+    copying from the originals otherwise.
+    """
+
+    # parse installment mode
+    if 'develop' in sys.argv:
+        # installed by `pip install -e .`
+        if platform.system() == 'Windows':
+            # set `copy` mode here since symlink fails on Windows.
+            mode = 'copy'
+        else:
+            mode = 'symlink'
+    elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv:
+        # installed by `pip install .`
+        # or create source distribution by `python setup.py sdist`
+        mode = 'copy'
+    else:
+        return
+
+    filenames = ['tools', 'configs', 'demo', 'model-index.yml']
+    repo_path = osp.dirname(__file__)
+    mim_path = osp.join(repo_path, 'mmdet', '.mim')
+    os.makedirs(mim_path, exist_ok=True)
+
+    for filename in filenames:
+        if osp.exists(filename):
+            src_path = osp.join(repo_path, filename)
+            tar_path = osp.join(mim_path, filename)
+
+            if osp.isfile(tar_path) or osp.islink(tar_path):
+                os.remove(tar_path)
+            elif osp.isdir(tar_path):
+                shutil.rmtree(tar_path)
+
+            if mode == 'symlink':
+                src_relpath = osp.relpath(src_path, osp.dirname(tar_path))
+                os.symlink(src_relpath, tar_path)
+            elif mode == 'copy':
+                if osp.isfile(src_path):
+                    shutil.copyfile(src_path, tar_path)
+                elif osp.isdir(src_path):
+                    shutil.copytree(src_path, tar_path)
+                else:
+                    warnings.warn(f'Cannot copy file {src_path}.')
+            else:
+                raise ValueError(f'Invalid mode {mode}')
+
+
+if __name__ == '__main__':
+    add_mim_extension()
+    setup(
+        name='mmdet',
+        version=get_version(),
+        description='OpenMMLab Detection Toolbox and Benchmark',
+        long_description=readme(),
+        long_description_content_type='text/markdown',
+        author='MMDetection Contributors',
+        author_email='openmmlab@gmail.com',
+        keywords='computer vision, object detection',
+        url='https://github.com/open-mmlab/mmdetection',
+        packages=find_packages(exclude=('configs', 'tools', 'demo')),
+        include_package_data=True,
+        classifiers=[
+            'Development Status :: 5 - Production/Stable',
+            'License :: OSI Approved :: Apache Software License',
+            'Operating System :: OS Independent',
+            'Programming Language :: Python :: 3',
+            'Programming Language :: Python :: 3.7',
+            'Programming Language :: Python :: 3.8',
+            'Programming Language :: Python :: 3.9',
+        ],
+        license='Apache License 2.0',
+        install_requires=parse_requirements('requirements/runtime.txt'),
+        extras_require={
+            'all': parse_requirements('requirements.txt'),
+            'tests': parse_requirements('requirements/tests.txt'),
+            'build': parse_requirements('requirements/build.txt'),
+            'optional': parse_requirements('requirements/optional.txt'),
+            'mim': parse_requirements('requirements/mminstall.txt'),
+        },
+        ext_modules=[],
+        cmdclass={'build_ext': BuildExtension},
+        zip_safe=False)
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diff --git a/tests/test_data/test_datasets/test_coco_dataset.py b/tests/test_data/test_datasets/test_coco_dataset.py
new file mode 100644
index 0000000..77edfdf
--- /dev/null
+++ b/tests/test_data/test_datasets/test_coco_dataset.py
@@ -0,0 +1,58 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import tempfile
+
+import mmcv
+import pytest
+
+from mmdet.datasets import CocoDataset
+
+
+def _create_ids_error_coco_json(json_name):
+    image = {
+        'id': 0,
+        'width': 640,
+        'height': 640,
+        'file_name': 'fake_name.jpg',
+    }
+
+    annotation_1 = {
+        'id': 1,
+        'image_id': 0,
+        'category_id': 0,
+        'area': 400,
+        'bbox': [50, 60, 20, 20],
+        'iscrowd': 0,
+    }
+
+    annotation_2 = {
+        'id': 1,
+        'image_id': 0,
+        'category_id': 0,
+        'area': 900,
+        'bbox': [100, 120, 30, 30],
+        'iscrowd': 0,
+    }
+
+    categories = [{
+        'id': 0,
+        'name': 'car',
+        'supercategory': 'car',
+    }]
+
+    fake_json = {
+        'images': [image],
+        'annotations': [annotation_1, annotation_2],
+        'categories': categories
+    }
+    mmcv.dump(fake_json, json_name)
+
+
+def test_coco_annotation_ids_unique():
+    tmp_dir = tempfile.TemporaryDirectory()
+    fake_json_file = osp.join(tmp_dir.name, 'fake_data.json')
+    _create_ids_error_coco_json(fake_json_file)
+
+    # test annotation ids not unique error
+    with pytest.raises(AssertionError):
+        CocoDataset(ann_file=fake_json_file, classes=('car', ), pipeline=[])
diff --git a/tests/test_data/test_datasets/test_coco_occluded.py b/tests/test_data/test_datasets/test_coco_occluded.py
new file mode 100644
index 0000000..8e612d0
--- /dev/null
+++ b/tests/test_data/test_datasets/test_coco_occluded.py
@@ -0,0 +1,38 @@
+import os.path as osp
+from tempfile import TemporaryDirectory
+
+import mmcv
+import numpy as np
+
+from mmdet.datasets import OccludedSeparatedCocoDataset
+
+
+def test_occluded_separated_coco_dataset():
+    ann = [[
+        'fake1.jpg', 'person', 8, [219.9, 176.12, 11.14, 34.23], {
+            'size': [480, 640],
+            'counts': b'nYW31n>2N2FNbA48Kf=?XBDe=m0OM3M4YOPB8_>L4JXao5'
+        }
+    ]] * 3
+    dummy_mask = np.zeros((10, 10), dtype=np.uint8)
+    dummy_mask[:5, :5] = 1
+    rle = {
+        'size': [480, 640],
+        'counts': b'nYW31n>2N2FNbA48Kf=?XBDe=m0OM3M4YOPB8_>L4JXao5'
+    }
+    res = [([np.array([[50, 60, 70, 80, 0.77]])] * 2, [[rle]] * 2)] * 3
+
+    tempdir = TemporaryDirectory()
+    ann_path = osp.join(tempdir.name, 'coco_occluded.pkl')
+    mmcv.dump(ann, ann_path)
+
+    dataset = OccludedSeparatedCocoDataset(
+        ann_file='tests/data/coco_sample.json',
+        occluded_ann=ann_path,
+        separated_ann=ann_path,
+        pipeline=[],
+        test_mode=True)
+    eval_res = dataset.evaluate(res)
+    assert isinstance(eval_res, dict)
+    assert eval_res['occluded_recall'] == 100
+    assert eval_res['separated_recall'] == 100
diff --git a/tests/test_data/test_datasets/test_common.py b/tests/test_data/test_datasets/test_common.py
new file mode 100644
index 0000000..e3070da
--- /dev/null
+++ b/tests/test_data/test_datasets/test_common.py
@@ -0,0 +1,369 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import logging
+import os.path as osp
+import tempfile
+from unittest.mock import MagicMock, patch
+
+import mmcv
+import numpy as np
+import pytest
+import torch
+import torch.nn as nn
+from mmcv.runner import EpochBasedRunner
+from torch.utils.data import DataLoader
+
+from mmdet.core.evaluation import DistEvalHook, EvalHook
+from mmdet.datasets import DATASETS, CocoDataset, CustomDataset, build_dataset
+
+
+def _create_dummy_coco_json(json_name):
+    image = {
+        'id': 0,
+        'width': 640,
+        'height': 640,
+        'file_name': 'fake_name.jpg',
+    }
+
+    annotation_1 = {
+        'id': 1,
+        'image_id': 0,
+        'category_id': 0,
+        'area': 400,
+        'bbox': [50, 60, 20, 20],
+        'iscrowd': 0,
+    }
+
+    annotation_2 = {
+        'id': 2,
+        'image_id': 0,
+        'category_id': 0,
+        'area': 900,
+        'bbox': [100, 120, 30, 30],
+        'iscrowd': 0,
+    }
+
+    annotation_3 = {
+        'id': 3,
+        'image_id': 0,
+        'category_id': 0,
+        'area': 1600,
+        'bbox': [150, 160, 40, 40],
+        'iscrowd': 0,
+    }
+
+    annotation_4 = {
+        'id': 4,
+        'image_id': 0,
+        'category_id': 0,
+        'area': 10000,
+        'bbox': [250, 260, 100, 100],
+        'iscrowd': 0,
+    }
+
+    categories = [{
+        'id': 0,
+        'name': 'car',
+        'supercategory': 'car',
+    }]
+
+    fake_json = {
+        'images': [image],
+        'annotations':
+        [annotation_1, annotation_2, annotation_3, annotation_4],
+        'categories': categories
+    }
+
+    mmcv.dump(fake_json, json_name)
+
+
+def _create_dummy_custom_pkl(pkl_name):
+    fake_pkl = [{
+        'filename': 'fake_name.jpg',
+        'width': 640,
+        'height': 640,
+        'ann': {
+            'bboxes':
+            np.array([[50, 60, 70, 80], [100, 120, 130, 150],
+                      [150, 160, 190, 200], [250, 260, 350, 360]]),
+            'labels':
+            np.array([0, 0, 0, 0])
+        }
+    }]
+    mmcv.dump(fake_pkl, pkl_name)
+
+
+def _create_dummy_results():
+    boxes = [
+        np.array([[50, 60, 70, 80, 1.0], [100, 120, 130, 150, 0.98],
+                  [150, 160, 190, 200, 0.96], [250, 260, 350, 360, 0.95]])
+    ]
+    return [boxes]
+
+
+@pytest.mark.parametrize('config_path',
+                         ['./configs/_base_/datasets/voc0712.py'])
+def test_dataset_init(config_path, monkeypatch):
+    data_config = mmcv.Config.fromfile(config_path)
+    if 'data' not in data_config:
+        return
+
+    monkeypatch.chdir('./tests/')  # to use ./tests/data
+    stage_names = ['train', 'val', 'test']
+    for stage_name in stage_names:
+        dataset_config = copy.deepcopy(data_config.data.get(stage_name))
+        dataset = build_dataset(dataset_config)
+        dataset[0]
+
+
+def test_dataset_evaluation():
+    tmp_dir = tempfile.TemporaryDirectory()
+    # create dummy data
+    fake_json_file = osp.join(tmp_dir.name, 'fake_data.json')
+    _create_dummy_coco_json(fake_json_file)
+
+    # test single coco dataset evaluation
+    coco_dataset = CocoDataset(
+        ann_file=fake_json_file, classes=('car', ), pipeline=[])
+    fake_results = _create_dummy_results()
+    eval_results = coco_dataset.evaluate(fake_results, classwise=True)
+    assert eval_results['bbox_mAP'] == 1
+    assert eval_results['bbox_mAP_50'] == 1
+    assert eval_results['bbox_mAP_75'] == 1
+
+    # test concat dataset evaluation
+    fake_concat_results = _create_dummy_results() + _create_dummy_results()
+
+    # build concat dataset through two config dict
+    coco_cfg = dict(
+        type='CocoDataset',
+        ann_file=fake_json_file,
+        classes=('car', ),
+        pipeline=[])
+    concat_cfgs = [coco_cfg, coco_cfg]
+    concat_dataset = build_dataset(concat_cfgs)
+    eval_results = concat_dataset.evaluate(fake_concat_results)
+    assert eval_results['0_bbox_mAP'] == 1
+    assert eval_results['0_bbox_mAP_50'] == 1
+    assert eval_results['0_bbox_mAP_75'] == 1
+    assert eval_results['1_bbox_mAP'] == 1
+    assert eval_results['1_bbox_mAP_50'] == 1
+    assert eval_results['1_bbox_mAP_75'] == 1
+
+    # build concat dataset through concatenated ann_file
+    coco_cfg = dict(
+        type='CocoDataset',
+        ann_file=[fake_json_file, fake_json_file],
+        classes=('car', ),
+        pipeline=[])
+    concat_dataset = build_dataset(coco_cfg)
+    eval_results = concat_dataset.evaluate(fake_concat_results)
+    assert eval_results['0_bbox_mAP'] == 1
+    assert eval_results['0_bbox_mAP_50'] == 1
+    assert eval_results['0_bbox_mAP_75'] == 1
+    assert eval_results['1_bbox_mAP'] == 1
+    assert eval_results['1_bbox_mAP_50'] == 1
+    assert eval_results['1_bbox_mAP_75'] == 1
+
+    # create dummy data
+    fake_pkl_file = osp.join(tmp_dir.name, 'fake_data.pkl')
+    _create_dummy_custom_pkl(fake_pkl_file)
+
+    # test single custom dataset evaluation
+    custom_dataset = CustomDataset(
+        ann_file=fake_pkl_file, classes=('car', ), pipeline=[])
+    fake_results = _create_dummy_results()
+    eval_results = custom_dataset.evaluate(fake_results)
+    assert eval_results['mAP'] == 1
+
+    # test concat dataset evaluation
+    fake_concat_results = _create_dummy_results() + _create_dummy_results()
+
+    # build concat dataset through two config dict
+    custom_cfg = dict(
+        type='CustomDataset',
+        ann_file=fake_pkl_file,
+        classes=('car', ),
+        pipeline=[])
+    concat_cfgs = [custom_cfg, custom_cfg]
+    concat_dataset = build_dataset(concat_cfgs)
+    eval_results = concat_dataset.evaluate(fake_concat_results)
+    assert eval_results['0_mAP'] == 1
+    assert eval_results['1_mAP'] == 1
+
+    # build concat dataset through concatenated ann_file
+    concat_cfg = dict(
+        type='CustomDataset',
+        ann_file=[fake_pkl_file, fake_pkl_file],
+        classes=('car', ),
+        pipeline=[])
+    concat_dataset = build_dataset(concat_cfg)
+    eval_results = concat_dataset.evaluate(fake_concat_results)
+    assert eval_results['0_mAP'] == 1
+    assert eval_results['1_mAP'] == 1
+
+    # build concat dataset through explicit type
+    concat_cfg = dict(
+        type='ConcatDataset',
+        datasets=[custom_cfg, custom_cfg],
+        separate_eval=False)
+    concat_dataset = build_dataset(concat_cfg)
+    eval_results = concat_dataset.evaluate(fake_concat_results, metric='mAP')
+    assert eval_results['mAP'] == 1
+    assert len(concat_dataset.datasets[0].data_infos) == \
+        len(concat_dataset.datasets[1].data_infos)
+    assert len(concat_dataset.datasets[0].data_infos) == 1
+    tmp_dir.cleanup()
+
+
+@patch('mmdet.apis.single_gpu_test', MagicMock)
+@patch('mmdet.apis.multi_gpu_test', MagicMock)
+@pytest.mark.parametrize('EvalHookParam', (EvalHook, DistEvalHook))
+def test_evaluation_hook(EvalHookParam):
+    # create dummy data
+    dataloader = DataLoader(torch.ones((5, 2)))
+
+    # 0.1. dataloader is not a DataLoader object
+    with pytest.raises(TypeError):
+        EvalHookParam(dataloader=MagicMock(), interval=-1)
+
+    # 0.2. negative interval
+    with pytest.raises(ValueError):
+        EvalHookParam(dataloader, interval=-1)
+
+    # 1. start=None, interval=1: perform evaluation after each epoch.
+    runner = _build_demo_runner()
+    evalhook = EvalHookParam(dataloader, interval=1)
+    evalhook.evaluate = MagicMock()
+    runner.register_hook(evalhook)
+    runner.run([dataloader], [('train', 1)], 2)
+    assert evalhook.evaluate.call_count == 2  # after epoch 1 & 2
+
+    # 2. start=1, interval=1: perform evaluation after each epoch.
+    runner = _build_demo_runner()
+
+    evalhook = EvalHookParam(dataloader, start=1, interval=1)
+    evalhook.evaluate = MagicMock()
+    runner.register_hook(evalhook)
+    runner.run([dataloader], [('train', 1)], 2)
+    assert evalhook.evaluate.call_count == 2  # after epoch 1 & 2
+
+    # 3. start=None, interval=2: perform evaluation after epoch 2, 4, 6, etc
+    runner = _build_demo_runner()
+    evalhook = EvalHookParam(dataloader, interval=2)
+    evalhook.evaluate = MagicMock()
+    runner.register_hook(evalhook)
+    runner.run([dataloader], [('train', 1)], 2)
+    assert evalhook.evaluate.call_count == 1  # after epoch 2
+
+    # 4. start=1, interval=2: perform evaluation after epoch 1, 3, 5, etc
+    runner = _build_demo_runner()
+    evalhook = EvalHookParam(dataloader, start=1, interval=2)
+    evalhook.evaluate = MagicMock()
+    runner.register_hook(evalhook)
+    runner.run([dataloader], [('train', 1)], 3)
+    assert evalhook.evaluate.call_count == 2  # after epoch 1 & 3
+
+    # 5. start=0/negative, interval=1: perform evaluation after each epoch and
+    #    before epoch 1.
+    runner = _build_demo_runner()
+    evalhook = EvalHookParam(dataloader, start=0)
+    evalhook.evaluate = MagicMock()
+    runner.register_hook(evalhook)
+    runner.run([dataloader], [('train', 1)], 2)
+    assert evalhook.evaluate.call_count == 3  # before epoch1 and after e1 & e2
+
+    # 6. start=0, interval=2, dynamic_intervals=[(3, 1)]: the evaluation
+    # interval is 2 when it is less than 3 epoch, otherwise it is 1.
+    runner = _build_demo_runner()
+    evalhook = EvalHookParam(
+        dataloader, start=0, interval=2, dynamic_intervals=[(3, 1)])
+    evalhook.evaluate = MagicMock()
+    runner.register_hook(evalhook)
+    runner.run([dataloader], [('train', 1)], 4)
+    assert evalhook.evaluate.call_count == 3
+
+    # the evaluation start epoch cannot be less than 0
+    runner = _build_demo_runner()
+    with pytest.raises(ValueError):
+        EvalHookParam(dataloader, start=-2)
+
+    evalhook = EvalHookParam(dataloader, start=0)
+    evalhook.evaluate = MagicMock()
+    runner.register_hook(evalhook)
+    runner.run([dataloader], [('train', 1)], 2)
+    assert evalhook.evaluate.call_count == 3  # before epoch1 and after e1 & e2
+
+    # 6. resuming from epoch i, start = x (x<=i), interval =1: perform
+    #    evaluation after each epoch and before the first epoch.
+    runner = _build_demo_runner()
+    evalhook = EvalHookParam(dataloader, start=1)
+    evalhook.evaluate = MagicMock()
+    runner.register_hook(evalhook)
+    runner._epoch = 2
+    runner.run([dataloader], [('train', 1)], 3)
+    assert evalhook.evaluate.call_count == 2  # before & after epoch 3
+
+    # 7. resuming from epoch i, start = i+1/None, interval =1: perform
+    #    evaluation after each epoch.
+    runner = _build_demo_runner()
+    evalhook = EvalHookParam(dataloader, start=2)
+    evalhook.evaluate = MagicMock()
+    runner.register_hook(evalhook)
+    runner._epoch = 1
+    runner.run([dataloader], [('train', 1)], 3)
+    assert evalhook.evaluate.call_count == 2  # after epoch 2 & 3
+
+
+def _build_demo_runner():
+
+    class Model(nn.Module):
+
+        def __init__(self):
+            super().__init__()
+            self.linear = nn.Linear(2, 1)
+
+        def forward(self, x):
+            return self.linear(x)
+
+        def train_step(self, x, optimizer, **kwargs):
+            return dict(loss=self(x))
+
+        def val_step(self, x, optimizer, **kwargs):
+            return dict(loss=self(x))
+
+    model = Model()
+    tmp_dir = tempfile.mkdtemp()
+
+    runner = EpochBasedRunner(
+        model=model, work_dir=tmp_dir, logger=logging.getLogger())
+    return runner
+
+
+@pytest.mark.parametrize('classes, expected_length', [(['bus'], 2),
+                                                      (['car'], 1),
+                                                      (['bus', 'car'], 2)])
+def test_allow_empty_images(classes, expected_length):
+    dataset_class = DATASETS.get('CocoDataset')
+    # Filter empty images
+    filtered_dataset = dataset_class(
+        ann_file='tests/data/coco_sample.json',
+        img_prefix='tests/data',
+        pipeline=[],
+        classes=classes,
+        filter_empty_gt=True)
+
+    # Get all
+    full_dataset = dataset_class(
+        ann_file='tests/data/coco_sample.json',
+        img_prefix='tests/data',
+        pipeline=[],
+        classes=classes,
+        filter_empty_gt=False)
+
+    assert len(filtered_dataset) == expected_length
+    assert len(filtered_dataset.img_ids) == expected_length
+    assert len(full_dataset) == 3
+    assert len(full_dataset.img_ids) == 3
+    assert filtered_dataset.CLASSES == classes
+    assert full_dataset.CLASSES == classes
diff --git a/tests/test_data/test_datasets/test_custom_dataset.py b/tests/test_data/test_datasets/test_custom_dataset.py
new file mode 100644
index 0000000..4dae464
--- /dev/null
+++ b/tests/test_data/test_datasets/test_custom_dataset.py
@@ -0,0 +1,138 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import unittest
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from mmdet.datasets import DATASETS
+
+
+@patch('mmdet.datasets.CocoDataset.load_annotations', MagicMock())
+@patch('mmdet.datasets.CustomDataset.load_annotations', MagicMock())
+@patch('mmdet.datasets.XMLDataset.load_annotations', MagicMock())
+@patch('mmdet.datasets.CityscapesDataset.load_annotations', MagicMock())
+@patch('mmdet.datasets.CocoDataset._filter_imgs', MagicMock)
+@patch('mmdet.datasets.CustomDataset._filter_imgs', MagicMock)
+@patch('mmdet.datasets.XMLDataset._filter_imgs', MagicMock)
+@patch('mmdet.datasets.CityscapesDataset._filter_imgs', MagicMock)
+@pytest.mark.parametrize('dataset',
+                         ['CocoDataset', 'VOCDataset', 'CityscapesDataset'])
+def test_custom_classes_override_default(dataset):
+    dataset_class = DATASETS.get(dataset)
+    if dataset in ['CocoDataset', 'CityscapesDataset']:
+        dataset_class.coco = MagicMock()
+        dataset_class.cat_ids = MagicMock()
+
+    original_classes = dataset_class.CLASSES
+
+    # Test setting classes as a tuple
+    custom_dataset = dataset_class(
+        ann_file=MagicMock(),
+        pipeline=[],
+        classes=('bus', 'car'),
+        test_mode=True,
+        img_prefix='VOC2007' if dataset == 'VOCDataset' else '')
+
+    assert custom_dataset.CLASSES != original_classes
+    assert custom_dataset.CLASSES == ('bus', 'car')
+    print(custom_dataset)
+
+    # Test setting classes as a list
+    custom_dataset = dataset_class(
+        ann_file=MagicMock(),
+        pipeline=[],
+        classes=['bus', 'car'],
+        test_mode=True,
+        img_prefix='VOC2007' if dataset == 'VOCDataset' else '')
+
+    assert custom_dataset.CLASSES != original_classes
+    assert custom_dataset.CLASSES == ['bus', 'car']
+    print(custom_dataset)
+
+    # Test overriding not a subset
+    custom_dataset = dataset_class(
+        ann_file=MagicMock(),
+        pipeline=[],
+        classes=['foo'],
+        test_mode=True,
+        img_prefix='VOC2007' if dataset == 'VOCDataset' else '')
+
+    assert custom_dataset.CLASSES != original_classes
+    assert custom_dataset.CLASSES == ['foo']
+    print(custom_dataset)
+
+    # Test default behavior
+    custom_dataset = dataset_class(
+        ann_file=MagicMock(),
+        pipeline=[],
+        classes=None,
+        test_mode=True,
+        img_prefix='VOC2007' if dataset == 'VOCDataset' else '')
+
+    assert custom_dataset.CLASSES == original_classes
+    print(custom_dataset)
+
+    # Test sending file path
+    import tempfile
+    with tempfile.TemporaryDirectory() as tmpdir:
+        path = tmpdir + 'classes.txt'
+        with open(path, 'w') as f:
+            f.write('bus\ncar\n')
+    custom_dataset = dataset_class(
+        ann_file=MagicMock(),
+        pipeline=[],
+        classes=path,
+        test_mode=True,
+        img_prefix='VOC2007' if dataset == 'VOCDataset' else '')
+
+    assert custom_dataset.CLASSES != original_classes
+    assert custom_dataset.CLASSES == ['bus', 'car']
+    print(custom_dataset)
+
+
+class CustomDatasetTests(unittest.TestCase):
+
+    def setUp(self):
+        super().setUp()
+        self.data_dir = osp.join(
+            osp.dirname(osp.dirname(osp.dirname(__file__))), 'data')
+        self.dataset_class = DATASETS.get('XMLDataset')
+
+    def test_data_infos__default_db_directories(self):
+        """Test correct data read having a Pacal-VOC directory structure."""
+        test_dataset_root = osp.join(self.data_dir, 'VOCdevkit', 'VOC2007')
+        custom_ds = self.dataset_class(
+            data_root=test_dataset_root,
+            ann_file=osp.join(test_dataset_root, 'ImageSets', 'Main',
+                              'trainval.txt'),
+            pipeline=[],
+            classes=('person', 'dog'),
+            test_mode=True)
+
+        self.assertListEqual([{
+            'id': '000001',
+            'filename': osp.join('JPEGImages', '000001.jpg'),
+            'width': 353,
+            'height': 500
+        }], custom_ds.data_infos)
+
+    def test_data_infos__overridden_db_subdirectories(self):
+        """Test correct data read having a customized directory structure."""
+        test_dataset_root = osp.join(self.data_dir, 'custom_dataset')
+        custom_ds = self.dataset_class(
+            data_root=test_dataset_root,
+            ann_file=osp.join(test_dataset_root, 'trainval.txt'),
+            pipeline=[],
+            classes=('person', 'dog'),
+            test_mode=True,
+            img_prefix='',
+            img_subdir='images',
+            ann_subdir='images')
+
+        self.assertListEqual([{
+            'id': '000001',
+            'filename': osp.join('images', '000001.jpg'),
+            'width': 353,
+            'height': 500
+        }], custom_ds.data_infos)
diff --git a/tests/test_data/test_datasets/test_dataset_wrapper.py b/tests/test_data/test_datasets/test_dataset_wrapper.py
new file mode 100644
index 0000000..ad29678
--- /dev/null
+++ b/tests/test_data/test_datasets/test_dataset_wrapper.py
@@ -0,0 +1,209 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import bisect
+import math
+from collections import defaultdict
+from unittest.mock import MagicMock
+
+import numpy as np
+import pytest
+
+from mmdet.datasets import (ClassBalancedDataset, ConcatDataset, CustomDataset,
+                            MultiImageMixDataset, RepeatDataset)
+
+
+def test_dataset_wrapper():
+    CustomDataset.load_annotations = MagicMock()
+    CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: idx)
+    dataset_a = CustomDataset(
+        ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='')
+    len_a = 10
+    cat_ids_list_a = [
+        np.random.randint(0, 80, num).tolist()
+        for num in np.random.randint(1, 20, len_a)
+    ]
+    ann_info_list_a = []
+    for _ in range(len_a):
+        height = np.random.randint(10, 30)
+        weight = np.random.randint(10, 30)
+        img = np.ones((height, weight, 3))
+        gt_bbox = np.concatenate([
+            np.random.randint(1, 5, (2, 2)),
+            np.random.randint(1, 5, (2, 2)) + 5
+        ],
+                                 axis=1)
+        gt_labels = np.random.randint(0, 80, 2)
+        ann_info_list_a.append(
+            dict(gt_bboxes=gt_bbox, gt_labels=gt_labels, img=img))
+    dataset_a.data_infos = MagicMock()
+    dataset_a.data_infos.__len__.return_value = len_a
+    dataset_a.get_cat_ids = MagicMock(
+        side_effect=lambda idx: cat_ids_list_a[idx])
+    dataset_a.get_ann_info = MagicMock(
+        side_effect=lambda idx: ann_info_list_a[idx])
+    dataset_b = CustomDataset(
+        ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='')
+    len_b = 20
+    cat_ids_list_b = [
+        np.random.randint(0, 80, num).tolist()
+        for num in np.random.randint(1, 20, len_b)
+    ]
+    ann_info_list_b = []
+    for _ in range(len_b):
+        height = np.random.randint(10, 30)
+        weight = np.random.randint(10, 30)
+        img = np.ones((height, weight, 3))
+        gt_bbox = np.concatenate([
+            np.random.randint(1, 5, (2, 2)),
+            np.random.randint(1, 5, (2, 2)) + 5
+        ],
+                                 axis=1)
+        gt_labels = np.random.randint(0, 80, 2)
+        ann_info_list_b.append(
+            dict(gt_bboxes=gt_bbox, gt_labels=gt_labels, img=img))
+    dataset_b.data_infos = MagicMock()
+    dataset_b.data_infos.__len__.return_value = len_b
+    dataset_b.get_cat_ids = MagicMock(
+        side_effect=lambda idx: cat_ids_list_b[idx])
+    dataset_b.get_ann_info = MagicMock(
+        side_effect=lambda idx: ann_info_list_b[idx])
+
+    concat_dataset = ConcatDataset([dataset_a, dataset_b])
+    assert concat_dataset[5] == 5
+    assert concat_dataset[25] == 15
+    assert concat_dataset.get_cat_ids(5) == cat_ids_list_a[5]
+    assert concat_dataset.get_cat_ids(25) == cat_ids_list_b[15]
+    assert concat_dataset.get_ann_info(5) == ann_info_list_a[5]
+    assert concat_dataset.get_ann_info(25) == ann_info_list_b[15]
+    assert len(concat_dataset) == len(dataset_a) + len(dataset_b)
+
+    # Test if ConcatDataset allows dataset classes without the PALETTE
+    # attribute
+    palette_backup = CustomDataset.PALETTE
+    delattr(CustomDataset, 'PALETTE')
+    concat_dataset = ConcatDataset([dataset_a, dataset_b])
+    assert concat_dataset.PALETTE is None
+    CustomDataset.PALETTE = palette_backup
+
+    repeat_dataset = RepeatDataset(dataset_a, 10)
+    assert repeat_dataset[5] == 5
+    assert repeat_dataset[15] == 5
+    assert repeat_dataset[27] == 7
+    assert repeat_dataset.get_cat_ids(5) == cat_ids_list_a[5]
+    assert repeat_dataset.get_cat_ids(15) == cat_ids_list_a[5]
+    assert repeat_dataset.get_cat_ids(27) == cat_ids_list_a[7]
+    assert repeat_dataset.get_ann_info(5) == ann_info_list_a[5]
+    assert repeat_dataset.get_ann_info(15) == ann_info_list_a[5]
+    assert repeat_dataset.get_ann_info(27) == ann_info_list_a[7]
+    assert len(repeat_dataset) == 10 * len(dataset_a)
+
+    # Test if RepeatDataset allows dataset classes without the PALETTE
+    # attribute
+    delattr(CustomDataset, 'PALETTE')
+    repeat_dataset = RepeatDataset(dataset_a, 10)
+    assert repeat_dataset.PALETTE is None
+    CustomDataset.PALETTE = palette_backup
+
+    category_freq = defaultdict(int)
+    for cat_ids in cat_ids_list_a:
+        cat_ids = set(cat_ids)
+        for cat_id in cat_ids:
+            category_freq[cat_id] += 1
+    for k, v in category_freq.items():
+        category_freq[k] = v / len(cat_ids_list_a)
+
+    mean_freq = np.mean(list(category_freq.values()))
+    repeat_thr = mean_freq
+
+    category_repeat = {
+        cat_id: max(1.0, math.sqrt(repeat_thr / cat_freq))
+        for cat_id, cat_freq in category_freq.items()
+    }
+
+    repeat_factors = []
+    for cat_ids in cat_ids_list_a:
+        cat_ids = set(cat_ids)
+        repeat_factor = max({category_repeat[cat_id] for cat_id in cat_ids})
+        repeat_factors.append(math.ceil(repeat_factor))
+    repeat_factors_cumsum = np.cumsum(repeat_factors)
+    repeat_factor_dataset = ClassBalancedDataset(dataset_a, repeat_thr)
+    assert len(repeat_factor_dataset) == repeat_factors_cumsum[-1]
+    for idx in np.random.randint(0, len(repeat_factor_dataset), 3):
+        assert repeat_factor_dataset[idx] == bisect.bisect_right(
+            repeat_factors_cumsum, idx)
+        assert repeat_factor_dataset.get_ann_info(idx) == ann_info_list_a[
+            bisect.bisect_right(repeat_factors_cumsum, idx)]
+    # Test if ClassBalancedDataset allows dataset classes without the PALETTE
+    # attribute
+    delattr(CustomDataset, 'PALETTE')
+    repeat_factor_dataset = ClassBalancedDataset(dataset_a, repeat_thr)
+    assert repeat_factor_dataset.PALETTE is None
+    CustomDataset.PALETTE = palette_backup
+
+    img_scale = (60, 60)
+    pipeline = [
+        dict(type='Mosaic', img_scale=img_scale, pad_val=114.0),
+        dict(
+            type='RandomAffine',
+            scaling_ratio_range=(0.1, 2),
+            border=(-img_scale[0] // 2, -img_scale[1] // 2)),
+        dict(
+            type='MixUp',
+            img_scale=img_scale,
+            ratio_range=(0.8, 1.6),
+            pad_val=114.0),
+        dict(type='RandomFlip', flip_ratio=0.5),
+        dict(type='Resize', img_scale=img_scale, keep_ratio=True),
+        dict(type='Pad', pad_to_square=True, pad_val=114.0),
+    ]
+
+    CustomDataset.load_annotations = MagicMock()
+    results = []
+    for _ in range(2):
+        height = np.random.randint(10, 30)
+        weight = np.random.randint(10, 30)
+        img = np.ones((height, weight, 3))
+        gt_bbox = np.concatenate([
+            np.random.randint(1, 5, (2, 2)),
+            np.random.randint(1, 5, (2, 2)) + 5
+        ],
+                                 axis=1)
+        gt_labels = np.random.randint(0, 80, 2)
+        results.append(dict(gt_bboxes=gt_bbox, gt_labels=gt_labels, img=img))
+
+    CustomDataset.__getitem__ = MagicMock(side_effect=lambda idx: results[idx])
+    dataset_a = CustomDataset(
+        ann_file=MagicMock(), pipeline=[], test_mode=True, img_prefix='')
+    len_a = 2
+    cat_ids_list_a = [
+        np.random.randint(0, 80, num).tolist()
+        for num in np.random.randint(1, 20, len_a)
+    ]
+    dataset_a.data_infos = MagicMock()
+    dataset_a.data_infos.__len__.return_value = len_a
+    dataset_a.get_cat_ids = MagicMock(
+        side_effect=lambda idx: cat_ids_list_a[idx])
+
+    # test dynamic_scale deprecated
+    with pytest.raises(RuntimeError):
+        MultiImageMixDataset(dataset_a, pipeline, (80, 80))
+
+    multi_image_mix_dataset = MultiImageMixDataset(dataset_a, pipeline)
+    for idx in range(len_a):
+        results_ = multi_image_mix_dataset[idx]
+        assert results_['img'].shape == (img_scale[0], img_scale[1], 3)
+
+    # test skip_type_keys
+    multi_image_mix_dataset = MultiImageMixDataset(
+        dataset_a,
+        pipeline,
+        skip_type_keys=('MixUp', 'RandomFlip', 'Resize', 'Pad'))
+    for idx in range(len_a):
+        results_ = multi_image_mix_dataset[idx]
+        assert results_['img'].shape == (img_scale[0], img_scale[1], 3)
+
+    # Test if MultiImageMixDataset allows dataset classes without the PALETTE
+    # attribute
+    delattr(CustomDataset, 'PALETTE')
+    multi_image_mix_dataset = MultiImageMixDataset(dataset_a, pipeline)
+    assert multi_image_mix_dataset.PALETTE is None
+    CustomDataset.PALETTE = palette_backup
diff --git a/tests/test_data/test_datasets/test_objects365.py b/tests/test_data/test_datasets/test_objects365.py
new file mode 100644
index 0000000..7445188
--- /dev/null
+++ b/tests/test_data/test_datasets/test_objects365.py
@@ -0,0 +1,155 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import tempfile
+
+import mmcv
+import pytest
+
+from mmdet.datasets import Objects365V1Dataset, Objects365V2Dataset
+
+
+def _create_objects365_json(json_name):
+    images = [{
+        'file_name': 'fake1.jpg',
+        'height': 800,
+        'width': 800,
+        'id': 0
+    }, {
+        'file_name': 'fake2.jpg',
+        'height': 800,
+        'width': 800,
+        'id': 1
+    }, {
+        'file_name': 'patch16/objects365_v2_00908726.jpg',
+        'height': 800,
+        'width': 800,
+        'id': 2
+    }]
+
+    annotations = [{
+        'bbox': [0, 0, 20, 20],
+        'area': 400.00,
+        'score': 1.0,
+        'category_id': 1,
+        'id': 1,
+        'image_id': 0
+    }, {
+        'bbox': [0, 0, 20, 20],
+        'area': 400.00,
+        'score': 1.0,
+        'category_id': 2,
+        'id': 2,
+        'image_id': 0
+    }, {
+        'bbox': [0, 0, 20, 20],
+        'area': 400.00,
+        'score': 1.0,
+        'category_id': 1,
+        'id': 3,
+        'image_id': 1
+    }, {
+        'bbox': [0, 0, 20, 20],
+        'area': 400.00,
+        'score': 1.0,
+        'category_id': 1,
+        'id': 4,
+        'image_id': 2
+    }]
+
+    categories = [{
+        'id': 1,
+        'name': 'bus',
+        'supercategory': 'none'
+    }, {
+        'id': 2,
+        'name': 'car',
+        'supercategory': 'none'
+    }]
+
+    fake_json = {
+        'images': images,
+        'annotations': annotations,
+        'categories': categories
+    }
+    print(fake_json)
+    mmcv.dump(fake_json, json_name)
+
+
+def _create_ids_error_coco_json(json_name):
+    image = {
+        'id': 0,
+        'width': 640,
+        'height': 640,
+        'file_name': 'fake_name.jpg',
+    }
+
+    annotation_1 = {
+        'id': 1,
+        'image_id': 0,
+        'category_id': 0,
+        'area': 400,
+        'bbox': [50, 60, 20, 20],
+        'iscrowd': 0,
+    }
+
+    annotation_2 = {
+        'id': 1,
+        'image_id': 0,
+        'category_id': 0,
+        'area': 900,
+        'bbox': [100, 120, 30, 30],
+        'iscrowd': 0,
+    }
+
+    categories = [{
+        'id': 0,
+        'name': 'car',
+        'supercategory': 'car',
+    }]
+
+    fake_json = {
+        'images': [image],
+        'annotations': [annotation_1, annotation_2],
+        'categories': categories
+    }
+    mmcv.dump(fake_json, json_name)
+
+
+@pytest.mark.parametrize('datasets',
+                         [Objects365V1Dataset, Objects365V2Dataset])
+def test_annotation_ids_unique(datasets):
+    tmp_dir = tempfile.TemporaryDirectory()
+    fake_json_file = osp.join(tmp_dir.name, 'fake_data.json')
+    _create_ids_error_coco_json(fake_json_file)
+
+    # test annotation ids not unique error
+    with pytest.raises(AssertionError):
+        datasets(ann_file=fake_json_file, classes=('car', ), pipeline=[])
+
+    tmp_dir.cleanup()
+
+
+def test_load_objects365v1_annotations():
+    tmp_dir = tempfile.TemporaryDirectory()
+    fake_json_file = osp.join(tmp_dir.name, 'fake_data.json')
+    _create_objects365_json(fake_json_file)
+
+    dataset = Objects365V1Dataset(
+        ann_file=fake_json_file, classes=('bus', 'car'), pipeline=[])
+
+    # The Objects365V1Dataset do not filter the `objv2_ignore_list`
+    assert len(dataset.data_infos) == 3
+    tmp_dir.cleanup()
+
+
+def test_load_objects365v2_annotations():
+    tmp_dir = tempfile.TemporaryDirectory()
+    fake_json_file = osp.join(tmp_dir.name, 'fake_data.json')
+    _create_objects365_json(fake_json_file)
+
+    dataset = Objects365V2Dataset(
+        ann_file=fake_json_file, classes=('bus', 'car'), pipeline=[])
+
+    # The Objects365V2Dataset need filter the `objv2_ignore_list`
+    assert len(dataset.data_infos) == 2
+    tmp_dir.cleanup()
diff --git a/tests/test_data/test_datasets/test_openimages_dataset.py b/tests/test_data/test_datasets/test_openimages_dataset.py
new file mode 100644
index 0000000..af87e96
--- /dev/null
+++ b/tests/test_data/test_datasets/test_openimages_dataset.py
@@ -0,0 +1,367 @@
+import csv
+import os.path as osp
+import tempfile
+
+import mmcv
+import numpy as np
+import pytest
+
+from mmdet.datasets import OpenImagesChallengeDataset, OpenImagesDataset
+
+
+def _create_ids_error_oid_csv(
+    label_file,
+    fake_csv_file,
+):
+    label_description = ['/m/000002', 'Football']
+    # `newline=''` is used to avoid index error of out of bounds
+    # in Windows system
+    with open(label_file, 'w', newline='') as f:
+        f_csv = csv.writer(f)
+        f_csv.writerow(label_description)
+
+    header = [
+        'ImageID', 'Source', 'LabelName', 'Confidence', 'XMin', 'XMax', 'YMin',
+        'YMax', 'IsOccluded', 'IsTruncated', 'IsGroupOf', 'IsDepiction',
+        'IsInside'
+    ]
+    annotations = [[
+        'color', 'xclick', '/m/000002', '1', '0.022673031', '0.9642005',
+        '0.07103825', '0.80054647', '0', '0', '0', '0', '0'
+    ],
+                   [
+                       '000595fe6fee6369', 'xclick', '/m/000000', '1', '0',
+                       '1', '0', '1', '0', '0', '1', '0', '0'
+                   ]]
+    # `newline=''` is used to avoid index error of out of bounds
+    # in Windows system
+    with open(fake_csv_file, 'w', newline='') as f:
+        f_csv = csv.writer(f)
+        f_csv.writerow(header)
+        f_csv.writerows(annotations)
+
+
+def _create_oid_style_ann(label_file, csv_file, label_level_file):
+    label_description = [['/m/000000', 'Sports equipment'],
+                         ['/m/000001', 'Ball'], ['/m/000002', 'Football'],
+                         ['/m/000004', 'Bicycle']]
+    with open(label_file, 'w', newline='') as f:
+        f_csv = csv.writer(f)
+        f_csv.writerows(label_description)
+
+    header = [
+        'ImageID', 'Source', 'LabelName', 'Confidence', 'XMin', 'XMax', 'YMin',
+        'YMax', 'IsOccluded', 'IsTruncated', 'IsGroupOf', 'IsDepiction',
+        'IsInside'
+    ]
+    annotations = [
+        [
+            'color', 'xclick', '/m/000002', 1, 0.0333333, 0.1, 0.0333333, 0.1,
+            0, 0, 1, 0, 0
+        ],
+        [
+            'color', 'xclick', '/m/000002', 1, 0.1, 0.166667, 0.1, 0.166667, 0,
+            0, 0, 0, 0
+        ],
+    ]
+    # `newline=''` is used to avoid index error of out of bounds
+    # in Windows system
+    with open(csv_file, 'w', newline='') as f:
+        f_csv = csv.writer(f)
+        f_csv.writerow(header)
+        f_csv.writerows(annotations)
+
+    header = ['ImageID', 'Source', 'LabelName', 'Confidence']
+    annotations = [['color', 'xclick', '/m/000002', '1'],
+                   ['color', 'xclick', '/m/000004', '0']]
+    # `newline=''` is used to avoid index error of out of bounds
+    # in Windows system
+    with open(label_level_file, 'w', newline='') as f:
+        f_csv = csv.writer(f)
+        f_csv.writerow(header)
+        f_csv.writerows(annotations)
+
+
+def _create_hierarchy_json(hierarchy_name):
+    fake_hierarchy = \
+        {'LabelName':  '/m/0bl9f',      # entity label
+         'Subcategory': [
+             {
+                 'LabelName': '/m/000000',
+                 'Subcategory':
+                     [
+                         {'LabelName': '/m/000001',
+                          'Subcategory':
+                              [
+                                  {
+                                      'LabelName': '/m/000002'
+                                  }
+                              ]
+                          },
+                         {
+                             'LabelName': '/m/000004'
+                         }
+                     ]
+             }
+         ]
+         }
+
+    mmcv.dump(fake_hierarchy, hierarchy_name)
+
+
+def _create_hierarchy_np(hierarchy_name):
+    fake_hierarchy = np.array([[0, 1, 0, 0, 0], [0, 1, 1, 0,
+                                                 0], [0, 1, 1, 1, 0],
+                               [0, 1, 0, 0, 1], [0, 0, 0, 0, 0]])
+    with open(hierarchy_name, 'wb') as f:
+        np.save(f, fake_hierarchy)
+
+
+def _create_dummy_results():
+    boxes = [
+        np.zeros((0, 5)),
+        np.zeros((0, 5)),
+        np.array([[10, 10, 15, 15, 1.0], [15, 15, 30, 30, 0.98],
+                  [10, 10, 25, 25, 0.98], [28, 28, 35, 35, 0.97],
+                  [30, 30, 51, 51, 0.96], [100, 110, 120, 130, 0.15]]),
+        np.array([[30, 30, 50, 50, 0.51]]),
+    ]
+    return [boxes]
+
+
+def _creat_oid_challenge_style_ann(txt_file, label_file, label_level_file):
+    bboxes = [
+        'validation/color.jpg\n',
+        '4 29\n',
+        '2\n',
+        '1 0.0333333 0.1 0.0333333 0.1 1\n',
+        '1 0.1 0.166667 0.1 0.166667 0\n',
+    ]
+    # `newline=''` is used to avoid index error of out of bounds
+    # in Windows system
+    with open(txt_file, 'w', newline='') as f:
+        f.writelines(bboxes)
+        f.close()
+
+    label_description = [['/m/000000', 'Sports equipment', 1],
+                         ['/m/000001', 'Ball', 2],
+                         ['/m/000002', 'Football', 3],
+                         ['/m/000004', 'Bicycle', 4]]
+    # `newline=''` is used to avoid index error of out of bounds
+    # in Windows system
+    with open(label_file, 'w', newline='') as f:
+        f_csv = csv.writer(f)
+        f_csv.writerows(label_description)
+
+    header = ['ImageID', 'LabelName', 'Confidence']
+    annotations = [['color', '/m/000001', '1'], ['color', '/m/000000', '0']]
+    # `newline=''` is used to avoid index error of out of bounds
+    # in Windows system
+    with open(label_level_file, 'w', newline='') as f:
+        f_csv = csv.writer(f)
+        f_csv.writerow(header)
+        f_csv.writerows(annotations)
+
+
+def _create_metas(meta_file):
+
+    fake_meta = [{
+        'filename': 'data/OpenImages/OpenImages/validation/color.jpg',
+        'ori_shape': (300, 300, 3)
+    }]
+    mmcv.dump(fake_meta, meta_file)
+
+
+def test_oid_annotation_ids_unique():
+    # create fake ann files
+    tmp_dir = tempfile.TemporaryDirectory()
+    fake_label_file = osp.join(tmp_dir.name, 'fake_label.csv')
+    fake_ann_file = osp.join(tmp_dir.name, 'fake_ann.csv')
+    _create_ids_error_oid_csv(fake_label_file, fake_ann_file)
+
+    # test annotation ids not unique error
+    with pytest.raises(AssertionError):
+        OpenImagesDataset(
+            ann_file=fake_ann_file, label_file=fake_label_file, pipeline=[])
+    tmp_dir.cleanup()
+
+
+def test_openimages_dataset():
+    # create fake ann files
+    tmp_dir = tempfile.TemporaryDirectory()
+    label_file = osp.join(tmp_dir.name, 'label_file.csv')
+    ann_file = osp.join(tmp_dir.name, 'ann_file.csv')
+    label_level_file = osp.join(tmp_dir.name, 'label_level_file.csv')
+    _create_oid_style_ann(label_file, ann_file, label_level_file)
+
+    hierarchy_json = osp.join(tmp_dir.name, 'hierarchy.json')
+    _create_hierarchy_json(hierarchy_json)
+
+    # test whether hierarchy_file is not None when set
+    # get_parent_classes is True
+    with pytest.raises(AssertionError):
+        OpenImagesDataset(
+            ann_file=ann_file,
+            label_file=label_file,
+            image_level_ann_file=label_level_file,
+            pipeline=[])
+
+    dataset = OpenImagesDataset(
+        ann_file=ann_file,
+        label_file=label_file,
+        image_level_ann_file=label_level_file,
+        hierarchy_file=hierarchy_json,
+        pipeline=[])
+    ann = dataset.get_ann_info(0)
+    # two legal detection bboxes with `group_of` parameter
+    assert ann['bboxes'].shape[0] == ann['labels'].shape[0] == \
+           ann['gt_is_group_ofs'].shape[0] == 2
+
+    # test load metas from pipeline
+    img_norm_cfg = dict(
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True)
+    test_pipeline = [
+        dict(type='LoadImageFromFile'),
+        dict(
+            type='MultiScaleFlipAug',
+            img_scale=(128, 128),
+            flip=False,
+            transforms=[
+                dict(type='Resize', keep_ratio=True),
+                dict(type='RandomFlip'),
+                dict(type='Normalize', **img_norm_cfg),
+                dict(type='Pad', size_divisor=32),
+                dict(type='ImageToTensor', keys=['img']),
+                dict(type='Collect', keys=['img']),
+            ])
+    ]
+    dataset = OpenImagesDataset(
+        ann_file=ann_file,
+        img_prefix='tests/data',
+        label_file=label_file,
+        image_level_ann_file=label_level_file,
+        load_from_file=False,
+        hierarchy_file=hierarchy_json,
+        pipeline=test_pipeline)
+    dataset.prepare_test_img(0)
+    assert len(dataset.test_img_metas) == 1
+    result = _create_dummy_results()
+    dataset.evaluate(result)
+
+    # test get hierarchy for classes
+    hierarchy_json = osp.join(tmp_dir.name, 'hierarchy.json')
+    _create_hierarchy_json(hierarchy_json)
+
+    # test with hierarchy file wrong suffix
+    with pytest.raises(AssertionError):
+        fake_path = osp.join(tmp_dir.name, 'hierarchy.csv')
+        OpenImagesDataset(
+            ann_file=ann_file,
+            img_prefix='tests/data',
+            label_file=label_file,
+            image_level_ann_file=label_level_file,
+            load_from_file=False,
+            hierarchy_file=fake_path,
+            pipeline=test_pipeline)
+
+    # test load hierarchy file succseefully
+    hierarchy = dataset.get_relation_matrix(hierarchy_json)
+    hierarchy_gt = np.array([[1, 0, 0, 0], [1, 1, 0, 0], [1, 1, 1, 0],
+                             [1, 0, 0, 1]])
+    assert np.equal(hierarchy, hierarchy_gt).all()
+
+    # test evaluation
+    # create fake metas
+    meta_file = osp.join(tmp_dir.name, 'meta.pkl')
+    _create_metas(meta_file)
+
+    dataset = OpenImagesDataset(
+        ann_file=ann_file,
+        label_file=label_file,
+        image_level_ann_file=label_level_file,
+        hierarchy_file=hierarchy_json,
+        meta_file=meta_file,
+        pipeline=[])
+    # test evaluation with using group_of, adding father classes to
+    # GT and annotations, and considering image_level_image,
+    # In the first label (Sports equipment): tp = [0, 1, 0, 0, 1],
+    # fp = [1, 0, 1, 1, 0]
+    # In the second label (Ball), tp = [0, 1, 0, 1], fp = [1, 0, 1, 0].
+    # In the third label (Football), tp = [0, 1, 0, 1], fp = [1, 0, 1, 0].
+    # In the forth label (Bicycle), tp = [0], fp = [1].
+    result = _create_dummy_results()
+    parsed_results = dataset.evaluate(result)
+    assert np.isclose(parsed_results['mAP'], 0.8333, 1e-4)
+
+    dataset = OpenImagesDataset(
+        ann_file=ann_file,
+        label_file=label_file,
+        load_image_level_labels=False,
+        image_level_ann_file=label_level_file,
+        hierarchy_file=hierarchy_json,
+        meta_file=meta_file,
+        pipeline=[])
+
+    # test evaluation with using group_of, adding father classes to
+    # GT and annotations, and not considering image_level_image,
+    # In the first label (Sports equipment): tp = [0, 1, 0, 0, 1],
+    # fp = [1, 0, 1, 1, 0]
+    # In the second label (Ball), tp = [0, 1, 0, 1], fp = [1, 0, 1, 0].
+    # In the third label (Football), tp = [0, 1, 0, 1], fp = [1, 0, 1, 0].
+    # In the forth label (Bicycle), tp = [], fp = [].
+    result = _create_dummy_results()
+    parsed_results = dataset.evaluate(result)
+    assert np.isclose(parsed_results['mAP'], 0.8333, 1e-4)
+    tmp_dir.cleanup()
+
+
+def test_openimages_challenge_dataset():
+    # create fake ann files
+    tmp_dir = tempfile.TemporaryDirectory()
+    ann_file = osp.join(tmp_dir.name, 'ann_file.txt')
+    label_file = osp.join(tmp_dir.name, 'label_file.csv')
+    label_level_file = osp.join(tmp_dir.name, 'label_level_file.csv')
+    _creat_oid_challenge_style_ann(ann_file, label_file, label_level_file)
+
+    dataset = OpenImagesChallengeDataset(
+        ann_file=ann_file,
+        label_file=label_file,
+        load_image_level_labels=False,
+        get_supercategory=False,
+        pipeline=[])
+    ann = dataset.get_ann_info(0)
+
+    # two legal detection bboxes with `group_of` parameter
+    assert ann['bboxes'].shape[0] == ann['labels'].shape[0] == \
+           ann['gt_is_group_ofs'].shape[0] == 2
+
+    dataset.prepare_train_img(0)
+    dataset.prepare_test_img(0)
+
+    meta_file = osp.join(tmp_dir.name, 'meta.pkl')
+    _create_metas(meta_file)
+
+    result = _create_dummy_results()
+    with pytest.raises(AssertionError):
+        fake_json = osp.join(tmp_dir.name, 'hierarchy.json')
+        OpenImagesChallengeDataset(
+            ann_file=ann_file,
+            label_file=label_file,
+            image_level_ann_file=label_level_file,
+            hierarchy_file=fake_json,
+            meta_file=meta_file,
+            pipeline=[])
+
+    hierarchy_file = osp.join(tmp_dir.name, 'hierarchy.np')
+    _create_hierarchy_np(hierarchy_file)
+    dataset = OpenImagesChallengeDataset(
+        ann_file=ann_file,
+        label_file=label_file,
+        image_level_ann_file=label_level_file,
+        hierarchy_file=hierarchy_file,
+        meta_file=meta_file,
+        pipeline=[])
+    dataset.evaluate(result)
+    tmp_dir.cleanup()
diff --git a/tests/test_data/test_datasets/test_panoptic_dataset.py b/tests/test_data/test_datasets/test_panoptic_dataset.py
new file mode 100644
index 0000000..376270d
--- /dev/null
+++ b/tests/test_data/test_datasets/test_panoptic_dataset.py
@@ -0,0 +1,456 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import tempfile
+
+import mmcv
+import numpy as np
+
+from mmdet.core import encode_mask_results
+from mmdet.datasets.api_wrappers import pq_compute_single_core
+from mmdet.datasets.coco_panoptic import INSTANCE_OFFSET, CocoPanopticDataset
+
+try:
+    from panopticapi.utils import id2rgb
+except ImportError:
+    id2rgb = None
+
+
+def _create_panoptic_style_json(json_name):
+    image1 = {
+        'id': 0,
+        'width': 640,
+        'height': 640,
+        'file_name': 'fake_name1.jpg',
+    }
+
+    image2 = {
+        'id': 1,
+        'width': 640,
+        'height': 800,
+        'file_name': 'fake_name2.jpg',
+    }
+
+    images = [image1, image2]
+
+    annotations = [
+        {
+            'segments_info': [{
+                'id': 1,
+                'category_id': 0,
+                'area': 400,
+                'bbox': [50, 60, 20, 20],
+                'iscrowd': 0
+            }, {
+                'id': 2,
+                'category_id': 1,
+                'area': 900,
+                'bbox': [100, 120, 30, 30],
+                'iscrowd': 0
+            }, {
+                'id': 3,
+                'category_id': 2,
+                'iscrowd': 0,
+                'bbox': [1, 189, 612, 285],
+                'area': 70036
+            }],
+            'file_name':
+            'fake_name1.jpg',
+            'image_id':
+            0
+        },
+        {
+            'segments_info': [
+                {
+                    # Different to instance style json, there
+                    # are duplicate ids in panoptic style json
+                    'id': 1,
+                    'category_id': 0,
+                    'area': 400,
+                    'bbox': [50, 60, 20, 20],
+                    'iscrowd': 0
+                },
+                {
+                    'id': 4,
+                    'category_id': 1,
+                    'area': 900,
+                    'bbox': [100, 120, 30, 30],
+                    'iscrowd': 1
+                },
+                {
+                    'id': 5,
+                    'category_id': 2,
+                    'iscrowd': 0,
+                    'bbox': [100, 200, 200, 300],
+                    'area': 66666
+                },
+                {
+                    'id': 6,
+                    'category_id': 0,
+                    'iscrowd': 0,
+                    'bbox': [1, 189, -10, 285],
+                    'area': 70036
+                }
+            ],
+            'file_name':
+            'fake_name2.jpg',
+            'image_id':
+            1
+        }
+    ]
+
+    categories = [{
+        'id': 0,
+        'name': 'car',
+        'supercategory': 'car',
+        'isthing': 1
+    }, {
+        'id': 1,
+        'name': 'person',
+        'supercategory': 'person',
+        'isthing': 1
+    }, {
+        'id': 2,
+        'name': 'wall',
+        'supercategory': 'wall',
+        'isthing': 0
+    }]
+
+    fake_json = {
+        'images': images,
+        'annotations': annotations,
+        'categories': categories
+    }
+    mmcv.dump(fake_json, json_name)
+
+    return fake_json
+
+
+def test_load_panoptic_style_json():
+    tmp_dir = tempfile.TemporaryDirectory()
+    fake_json_file = osp.join(tmp_dir.name, 'fake_data.json')
+    fake_json = _create_panoptic_style_json(fake_json_file)
+
+    dataset = CocoPanopticDataset(
+        ann_file=fake_json_file,
+        classes=[cat['name'] for cat in fake_json['categories']],
+        pipeline=[])
+
+    ann = dataset.get_ann_info(0)
+
+    # two legal instances
+    assert ann['bboxes'].shape[0] == ann['labels'].shape[0] == 2
+    # three masks for both foreground and background
+    assert len(ann['masks']) == 3
+
+    ann = dataset.get_ann_info(1)
+
+    # one legal instance, one illegal instance,
+    # one crowd instance and one background mask
+    assert ann['bboxes'].shape[0] == ann['labels'].shape[0] == 1
+    assert ann['bboxes_ignore'].shape[0] == 1
+    assert len(ann['masks']) == 3
+
+
+def _create_panoptic_gt_annotations(ann_file):
+    categories = [{
+        'id': 0,
+        'name': 'person',
+        'supercategory': 'person',
+        'isthing': 1
+    }, {
+        'id': 1,
+        'name': 'dog',
+        'supercategory': 'dog',
+        'isthing': 1
+    }, {
+        'id': 2,
+        'name': 'wall',
+        'supercategory': 'wall',
+        'isthing': 0
+    }]
+
+    images = [{
+        'id': 0,
+        'width': 80,
+        'height': 60,
+        'file_name': 'fake_name1.jpg',
+    }]
+
+    annotations = [{
+        'segments_info': [{
+            'id': 1,
+            'category_id': 0,
+            'area': 400,
+            'bbox': [10, 10, 10, 40],
+            'iscrowd': 0
+        }, {
+            'id': 2,
+            'category_id': 0,
+            'area': 400,
+            'bbox': [30, 10, 10, 40],
+            'iscrowd': 0
+        }, {
+            'id': 3,
+            'category_id': 1,
+            'iscrowd': 0,
+            'bbox': [50, 10, 10, 5],
+            'area': 50
+        }, {
+            'id': 4,
+            'category_id': 2,
+            'iscrowd': 0,
+            'bbox': [0, 0, 80, 60],
+            'area': 3950
+        }],
+        'file_name':
+        'fake_name1.png',
+        'image_id':
+        0
+    }]
+
+    gt_json = {
+        'images': images,
+        'annotations': annotations,
+        'categories': categories
+    }
+
+    # 4 is the id of the background class annotation.
+    gt = np.zeros((60, 80), dtype=np.int64) + 4
+    gt_bboxes = np.array([[10, 10, 10, 40], [30, 10, 10, 40], [50, 10, 10, 5]],
+                         dtype=np.int64)
+    for i in range(3):
+        x, y, w, h = gt_bboxes[i]
+        gt[y:y + h, x:x + w] = i + 1  # id starts from 1
+
+    gt = id2rgb(gt).astype(np.uint8)
+    img_path = osp.join(osp.dirname(ann_file), 'fake_name1.png')
+    mmcv.imwrite(gt[:, :, ::-1], img_path)
+
+    mmcv.dump(gt_json, ann_file)
+    return gt_json
+
+
+def test_panoptic_evaluation():
+    if id2rgb is None:
+        return
+
+    # TP for background class, IoU=3576/4324=0.827
+    # 2 the category id of the background class
+    pred = np.zeros((60, 80), dtype=np.int64) + 2
+    pred_bboxes = np.array(
+        [
+            [11, 11, 10, 40],  # TP IoU=351/449=0.78
+            [38, 10, 10, 40],  # FP
+            [51, 10, 10, 5]
+        ],  # TP IoU=45/55=0.818
+        dtype=np.int64)
+    pred_labels = np.array([0, 0, 1], dtype=np.int64)
+    for i in range(3):
+        x, y, w, h = pred_bboxes[i]
+        pred[y:y + h, x:x + w] = (i + 1) * INSTANCE_OFFSET + pred_labels[i]
+
+    tmp_dir = tempfile.TemporaryDirectory()
+    ann_file = osp.join(tmp_dir.name, 'panoptic.json')
+    gt_json = _create_panoptic_gt_annotations(ann_file)
+
+    results = [{'pan_results': pred}]
+
+    dataset = CocoPanopticDataset(
+        ann_file=ann_file,
+        seg_prefix=tmp_dir.name,
+        classes=[cat['name'] for cat in gt_json['categories']],
+        pipeline=[])
+
+    # For 'person', sq = 0.78 / 1, rq = 1 / 2( 1 tp + 0.5 * (1 fn + 1 fp))
+    # For 'dog', sq = 0.818, rq = 1 / 1
+    # For 'wall', sq = 0.827, rq = 1 / 1
+    # Here is the results for all classes:
+    # +--------+--------+--------+---------+------------+
+    # |        | PQ     | SQ     | RQ      | categories |
+    # +--------+--------+--------+---------+------------+
+    # | All    | 67.869 | 80.898 | 83.333  |      3     |
+    # | Things | 60.453 | 79.996 | 75.000  |      2     |
+    # | Stuff  | 82.701 | 82.701 | 100.000 |      1     |
+    # +--------+--------+--------+---------+------------+
+    parsed_results = dataset.evaluate(results)
+    assert np.isclose(parsed_results['PQ'], 67.869)
+    assert np.isclose(parsed_results['SQ'], 80.898)
+    assert np.isclose(parsed_results['RQ'], 83.333)
+    assert np.isclose(parsed_results['PQ_th'], 60.453)
+    assert np.isclose(parsed_results['SQ_th'], 79.996)
+    assert np.isclose(parsed_results['RQ_th'], 75.000)
+    assert np.isclose(parsed_results['PQ_st'], 82.701)
+    assert np.isclose(parsed_results['SQ_st'], 82.701)
+    assert np.isclose(parsed_results['RQ_st'], 100.000)
+
+    # test jsonfile_prefix
+    outfile_prefix = osp.join(tmp_dir.name, 'results')
+    parsed_results = dataset.evaluate(results, jsonfile_prefix=outfile_prefix)
+    assert np.isclose(parsed_results['PQ'], 67.869)
+    assert np.isclose(parsed_results['SQ'], 80.898)
+    assert np.isclose(parsed_results['RQ'], 83.333)
+    assert np.isclose(parsed_results['PQ_th'], 60.453)
+    assert np.isclose(parsed_results['SQ_th'], 79.996)
+    assert np.isclose(parsed_results['RQ_th'], 75.000)
+    assert np.isclose(parsed_results['PQ_st'], 82.701)
+    assert np.isclose(parsed_results['SQ_st'], 82.701)
+    assert np.isclose(parsed_results['RQ_st'], 100.000)
+
+    # test classwise
+    parsed_results = dataset.evaluate(results, classwise=True)
+    assert np.isclose(parsed_results['PQ'], 67.869)
+    assert np.isclose(parsed_results['SQ'], 80.898)
+    assert np.isclose(parsed_results['RQ'], 83.333)
+    assert np.isclose(parsed_results['PQ_th'], 60.453)
+    assert np.isclose(parsed_results['SQ_th'], 79.996)
+    assert np.isclose(parsed_results['RQ_th'], 75.000)
+    assert np.isclose(parsed_results['PQ_st'], 82.701)
+    assert np.isclose(parsed_results['SQ_st'], 82.701)
+    assert np.isclose(parsed_results['RQ_st'], 100.000)
+
+    # test the api wrapper of `pq_compute_single_core`
+    # Codes are copied from `coco_panoptic.py` and modified
+    result_files, _ = dataset.format_results(
+        results, jsonfile_prefix=outfile_prefix)
+
+    imgs = dataset.coco.imgs
+    gt_json = dataset.coco.img_ann_map  # image to annotations
+    gt_json = [{
+        'image_id': k,
+        'segments_info': v,
+        'file_name': imgs[k]['segm_file']
+    } for k, v in gt_json.items()]
+    pred_json = mmcv.load(result_files['panoptic'])
+    pred_json = dict((el['image_id'], el) for el in pred_json['annotations'])
+
+    # match the gt_anns and pred_anns in the same image
+    matched_annotations_list = []
+    for gt_ann in gt_json:
+        img_id = gt_ann['image_id']
+        matched_annotations_list.append((gt_ann, pred_json[img_id]))
+    gt_folder = dataset.seg_prefix
+    pred_folder = osp.join(osp.dirname(outfile_prefix), 'panoptic')
+
+    pq_stat = pq_compute_single_core(0, matched_annotations_list, gt_folder,
+                                     pred_folder, dataset.categories)
+    pq_all = pq_stat.pq_average(dataset.categories, isthing=None)[0]
+    assert np.isclose(pq_all['pq'] * 100, 67.869)
+    assert np.isclose(pq_all['sq'] * 100, 80.898)
+    assert np.isclose(pq_all['rq'] * 100, 83.333)
+    assert pq_all['n'] == 3
+
+
+def _create_instance_segmentation_gt_annotations(ann_file):
+    categories = [{
+        'id': 0,
+        'name': 'person',
+        'supercategory': 'person',
+        'isthing': 1
+    }, {
+        'id': 1,
+        'name': 'dog',
+        'supercategory': 'dog',
+        'isthing': 1
+    }, {
+        'id': 2,
+        'name': 'wall',
+        'supercategory': 'wall',
+        'isthing': 0
+    }]
+
+    images = [{
+        'id': 0,
+        'width': 80,
+        'height': 60,
+        'file_name': 'fake_name1.jpg',
+    }]
+
+    person1_polygon = [10, 10, 20, 10, 20, 50, 10, 50, 10, 10]
+    person2_polygon = [30, 10, 40, 10, 40, 50, 30, 50, 30, 10]
+    dog_polygon = [50, 10, 60, 10, 60, 15, 50, 15, 50, 10]
+
+    annotations = [
+        {
+            'id': 0,
+            'image_id': 0,
+            'category_id': 0,
+            'segmentation': [person1_polygon],
+            'area': 400,
+            'bbox': [10, 10, 10, 40],
+            'iscrowd': 0
+        },
+        {
+            'id': 1,
+            'image_id': 0,
+            'category_id': 0,
+            'segmentation': [person2_polygon],
+            'area': 400,
+            'bbox': [30, 10, 10, 40],
+            'iscrowd': 0
+        },
+        {
+            'id': 2,
+            'image_id': 0,
+            'category_id': 1,
+            'segmentation': [dog_polygon],
+            'area': 50,
+            'bbox': [50, 10, 10, 5],
+            'iscrowd': 0
+        },
+    ]
+
+    gt_json = {
+        'images': images,
+        'annotations': annotations,
+        'categories': categories
+    }
+
+    mmcv.dump(gt_json, ann_file)
+
+
+def test_instance_segmentation_evaluation():
+    pred_bbox = [
+        np.array([[11, 10, 20, 50, 0.8], [31, 10, 40, 50, 0.8]]),
+        np.array([[51, 10, 60, 15, 0.7]])
+    ]
+
+    person1_mask = np.zeros((60, 80), dtype=bool)
+    person1_mask[20:50, 11:20] = True
+    person2_mask = np.zeros((60, 80), dtype=bool)
+    person2_mask[20:50, 31:40] = True
+    dog_mask = np.zeros((60, 80), dtype=bool)
+    dog_mask[10:15, 51:60] = True
+
+    pred_mask = [[person1_mask, person2_mask], [
+        dog_mask,
+    ]]
+    results = [{'ins_results': (pred_bbox, encode_mask_results(pred_mask))}]
+
+    tmp_dir = tempfile.TemporaryDirectory()
+    pan_ann_file = osp.join(tmp_dir.name, 'panoptic.json')
+    ins_ann_file = osp.join(tmp_dir.name, 'instance.json')
+    _create_panoptic_gt_annotations(pan_ann_file)
+    _create_instance_segmentation_gt_annotations(ins_ann_file)
+
+    dataset = CocoPanopticDataset(
+        ann_file=pan_ann_file,
+        ins_ann_file=ins_ann_file,
+        seg_prefix=tmp_dir.name,
+        pipeline=[])
+    dataset.THING_CLASSES = ['person', 'dog']
+    dataset.STUFF_CLASSES = ['wall']
+    dataset.CLASSES = dataset.THING_CLASSES + dataset.STUFF_CLASSES
+    parsed_results = dataset.evaluate(results, metric=['segm', 'bbox'])
+
+    # Here is the results for instance segmentation:
+    # {
+    #     'segm_mAP': 0.5005, 'segm_mAP_50': 0.626, 'segm_mAP_75': 0.5,
+    #     'segm_mAP_s': 0.5, 'segm_mAP_m': -1.0, 'segm_mAP_l': -1.0,
+    #     'segm_mAP_copypaste': '0.500 0.626 0.500 0.500 -1.000 -1.000',
+    #     'bbox_mAP': 0.5636, 'bbox_mAP_50': 0.626, 'bbox_mAP_75': 0.626,
+    #     'bbox_mAP_s': 0.564, 'bbox_mAP_m': -1.0, 'bbox_mAP_l': -1.0,
+    #     'bbox_mAP_copypaste': '0.564 0.626 0.626 0.564 -1.000 -1.000'
+    # }
+
+    assert np.isclose(parsed_results['segm_mAP'], 0.5005)
+    assert np.isclose(parsed_results['bbox_mAP'], 0.5636)
diff --git a/tests/test_data/test_datasets/test_xml_dataset.py b/tests/test_data/test_datasets/test_xml_dataset.py
new file mode 100644
index 0000000..f72f13d
--- /dev/null
+++ b/tests/test_data/test_datasets/test_xml_dataset.py
@@ -0,0 +1,23 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+
+from mmdet.datasets import DATASETS
+
+
+def test_xml_dataset():
+    dataconfig = {
+        'ann_file': 'data/VOCdevkit/VOC2007/ImageSets/Main/test.txt',
+        'img_prefix': 'data/VOCdevkit/VOC2007/',
+        'pipeline': [{
+            'type': 'LoadImageFromFile'
+        }]
+    }
+    XMLDataset = DATASETS.get('XMLDataset')
+
+    class XMLDatasetSubClass(XMLDataset):
+        CLASSES = None
+
+    # get_ann_info and _filter_imgs of XMLDataset
+    # would use self.CLASSES, we added CLASSES not NONE
+    with pytest.raises(AssertionError):
+        XMLDatasetSubClass(**dataconfig)
diff --git a/tests/test_data/test_pipelines/test_formatting.py b/tests/test_data/test_pipelines/test_formatting.py
new file mode 100644
index 0000000..2e22898
--- /dev/null
+++ b/tests/test_data/test_pipelines/test_formatting.py
@@ -0,0 +1,24 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+
+from mmcv.utils import build_from_cfg
+
+from mmdet.datasets.builder import PIPELINES
+
+
+def test_default_format_bundle():
+    results = dict(
+        img_prefix=osp.join(osp.dirname(__file__), '../../data'),
+        img_info=dict(filename='color.jpg'))
+    load = dict(type='LoadImageFromFile')
+    load = build_from_cfg(load, PIPELINES)
+    bundle = dict(type='DefaultFormatBundle')
+    bundle = build_from_cfg(bundle, PIPELINES)
+    results = load(results)
+    assert 'pad_shape' not in results
+    assert 'scale_factor' not in results
+    assert 'img_norm_cfg' not in results
+    results = bundle(results)
+    assert 'pad_shape' in results
+    assert 'scale_factor' in results
+    assert 'img_norm_cfg' in results
diff --git a/tests/test_data/test_pipelines/test_loading.py b/tests/test_data/test_pipelines/test_loading.py
new file mode 100644
index 0000000..27ecccf
--- /dev/null
+++ b/tests/test_data/test_pipelines/test_loading.py
@@ -0,0 +1,132 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import os.path as osp
+
+import mmcv
+import numpy as np
+import pytest
+
+from mmdet.core.mask import BitmapMasks, PolygonMasks
+from mmdet.datasets.pipelines import (FilterAnnotations, LoadImageFromFile,
+                                      LoadImageFromWebcam,
+                                      LoadMultiChannelImageFromFiles)
+
+
+class TestLoading:
+
+    @classmethod
+    def setup_class(cls):
+        cls.data_prefix = osp.join(osp.dirname(__file__), '../../data')
+
+    def test_load_img(self):
+        results = dict(
+            img_prefix=self.data_prefix, img_info=dict(filename='color.jpg'))
+        transform = LoadImageFromFile()
+        results = transform(copy.deepcopy(results))
+        assert results['filename'] == osp.join(self.data_prefix, 'color.jpg')
+        assert results['ori_filename'] == 'color.jpg'
+        assert results['img'].shape == (288, 512, 3)
+        assert results['img'].dtype == np.uint8
+        assert results['img_shape'] == (288, 512, 3)
+        assert results['ori_shape'] == (288, 512, 3)
+        assert repr(transform) == transform.__class__.__name__ + \
+            "(to_float32=False, color_type='color', channel_order='bgr', " + \
+            "file_client_args={'backend': 'disk'})"
+
+        # no img_prefix
+        results = dict(
+            img_prefix=None, img_info=dict(filename='tests/data/color.jpg'))
+        transform = LoadImageFromFile()
+        results = transform(copy.deepcopy(results))
+        assert results['filename'] == 'tests/data/color.jpg'
+        assert results['ori_filename'] == 'tests/data/color.jpg'
+        assert results['img'].shape == (288, 512, 3)
+
+        # to_float32
+        transform = LoadImageFromFile(to_float32=True)
+        results = transform(copy.deepcopy(results))
+        assert results['img'].dtype == np.float32
+
+        # gray image
+        results = dict(
+            img_prefix=self.data_prefix, img_info=dict(filename='gray.jpg'))
+        transform = LoadImageFromFile()
+        results = transform(copy.deepcopy(results))
+        assert results['img'].shape == (288, 512, 3)
+        assert results['img'].dtype == np.uint8
+
+        transform = LoadImageFromFile(color_type='unchanged')
+        results = transform(copy.deepcopy(results))
+        assert results['img'].shape == (288, 512)
+        assert results['img'].dtype == np.uint8
+
+    def test_load_multi_channel_img(self):
+        results = dict(
+            img_prefix=self.data_prefix,
+            img_info=dict(filename=['color.jpg', 'color.jpg']))
+        transform = LoadMultiChannelImageFromFiles()
+        results = transform(copy.deepcopy(results))
+        assert results['filename'] == [
+            osp.join(self.data_prefix, 'color.jpg'),
+            osp.join(self.data_prefix, 'color.jpg')
+        ]
+        assert results['ori_filename'] == ['color.jpg', 'color.jpg']
+        assert results['img'].shape == (288, 512, 3, 2)
+        assert results['img'].dtype == np.uint8
+        assert results['img_shape'] == (288, 512, 3, 2)
+        assert results['ori_shape'] == (288, 512, 3, 2)
+        assert results['pad_shape'] == (288, 512, 3, 2)
+        assert results['scale_factor'] == 1.0
+        assert repr(transform) == transform.__class__.__name__ + \
+            "(to_float32=False, color_type='unchanged', " + \
+            "file_client_args={'backend': 'disk'})"
+
+    def test_load_webcam_img(self):
+        img = mmcv.imread(osp.join(self.data_prefix, 'color.jpg'))
+        results = dict(img=img)
+        transform = LoadImageFromWebcam()
+        results = transform(copy.deepcopy(results))
+        assert results['filename'] is None
+        assert results['ori_filename'] is None
+        assert results['img'].shape == (288, 512, 3)
+        assert results['img'].dtype == np.uint8
+        assert results['img_shape'] == (288, 512, 3)
+        assert results['ori_shape'] == (288, 512, 3)
+
+
+def _build_filter_annotations_args():
+    kwargs = (dict(min_gt_bbox_wh=(100, 100)),
+              dict(min_gt_bbox_wh=(100, 100), keep_empty=False),
+              dict(min_gt_bbox_wh=(1, 1)), dict(min_gt_bbox_wh=(.01, .01)),
+              dict(min_gt_bbox_wh=(.01, .01),
+                   by_mask=True), dict(by_mask=True),
+              dict(by_box=False, by_mask=True))
+    targets = (None, 0, 1, 2, 1, 1, 1)
+
+    return list(zip(targets, kwargs))
+
+
+@pytest.mark.parametrize('target, kwargs', _build_filter_annotations_args())
+def test_filter_annotations(target, kwargs):
+    filter_ann = FilterAnnotations(**kwargs)
+    bboxes = np.array([[2., 10., 4., 14.], [2., 10., 2.1, 10.1]])
+    raw_masks = np.zeros((2, 24, 24))
+    raw_masks[0, 10:14, 2:4] = 1
+    bitmap_masks = BitmapMasks(raw_masks, 24, 24)
+    results = dict(gt_bboxes=bboxes, gt_masks=bitmap_masks)
+    results = filter_ann(results)
+    if results is not None:
+        results = results['gt_bboxes'].shape[0]
+    assert results == target
+
+    polygons = [[np.array([2.0, 10.0, 4.0, 10.0, 4.0, 14.0, 2.0, 14.0])],
+                [np.array([2.0, 10.0, 2.1, 10.0, 2.1, 10.1, 2.0, 10.1])]]
+    polygon_masks = PolygonMasks(polygons, 24, 24)
+
+    results = dict(gt_bboxes=bboxes, gt_masks=polygon_masks)
+    results = filter_ann(results)
+
+    if results is not None:
+        results = len(results.get('gt_masks').masks)
+
+    assert results == target
diff --git a/tests/test_data/test_pipelines/test_sampler.py b/tests/test_data/test_pipelines/test_sampler.py
new file mode 100644
index 0000000..8ff9398
--- /dev/null
+++ b/tests/test_data/test_pipelines/test_sampler.py
@@ -0,0 +1,329 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdet.core.bbox.assigners import MaxIoUAssigner
+from mmdet.core.bbox.samplers import (OHEMSampler, RandomSampler,
+                                      ScoreHLRSampler)
+
+
+def test_random_sampler():
+    assigner = MaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+        ignore_iof_thr=0.5,
+        ignore_wrt_candidates=False,
+    )
+    bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 9],
+        [0, 10, 10, 19],
+    ])
+    gt_labels = torch.LongTensor([1, 2])
+    gt_bboxes_ignore = torch.Tensor([
+        [30, 30, 40, 40],
+    ])
+    assign_result = assigner.assign(
+        bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_bboxes_ignore,
+        gt_labels=gt_labels)
+
+    sampler = RandomSampler(
+        num=10, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=True)
+
+    sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)
+
+    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
+    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)
+
+
+def test_random_sampler_empty_gt():
+    assigner = MaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+        ignore_iof_thr=0.5,
+        ignore_wrt_candidates=False,
+    )
+    bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_bboxes = torch.empty(0, 4)
+    gt_labels = torch.empty(0, ).long()
+    assign_result = assigner.assign(bboxes, gt_bboxes, gt_labels=gt_labels)
+
+    sampler = RandomSampler(
+        num=10, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=True)
+
+    sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)
+
+    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
+    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)
+
+
+def test_random_sampler_empty_pred():
+    assigner = MaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+        ignore_iof_thr=0.5,
+        ignore_wrt_candidates=False,
+    )
+    bboxes = torch.empty(0, 4)
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 9],
+        [0, 10, 10, 19],
+    ])
+    gt_labels = torch.LongTensor([1, 2])
+    assign_result = assigner.assign(bboxes, gt_bboxes, gt_labels=gt_labels)
+
+    sampler = RandomSampler(
+        num=10, pos_fraction=0.5, neg_pos_ub=-1, add_gt_as_proposals=True)
+
+    sample_result = sampler.sample(assign_result, bboxes, gt_bboxes, gt_labels)
+
+    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
+    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)
+
+
+def _context_for_ohem():
+    import sys
+    from os.path import dirname
+    sys.path.insert(0, dirname(dirname(dirname(__file__))))
+    from test_models.test_forward import _get_detector_cfg
+
+    model = _get_detector_cfg(
+        'faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py')
+    model['pretrained'] = None
+
+    from mmdet.models import build_detector
+    context = build_detector(model).roi_head
+    return context
+
+
+def test_ohem_sampler():
+
+    assigner = MaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+        ignore_iof_thr=0.5,
+        ignore_wrt_candidates=False,
+    )
+    bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 9],
+        [0, 10, 10, 19],
+    ])
+    gt_labels = torch.LongTensor([1, 2])
+    gt_bboxes_ignore = torch.Tensor([
+        [30, 30, 40, 40],
+    ])
+    assign_result = assigner.assign(
+        bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_bboxes_ignore,
+        gt_labels=gt_labels)
+
+    context = _context_for_ohem()
+
+    sampler = OHEMSampler(
+        num=10,
+        pos_fraction=0.5,
+        context=context,
+        neg_pos_ub=-1,
+        add_gt_as_proposals=True)
+
+    feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]]
+    sample_result = sampler.sample(
+        assign_result, bboxes, gt_bboxes, gt_labels, feats=feats)
+
+    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
+    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)
+
+
+def test_ohem_sampler_empty_gt():
+
+    assigner = MaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+        ignore_iof_thr=0.5,
+        ignore_wrt_candidates=False,
+    )
+    bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_bboxes = torch.empty(0, 4)
+    gt_labels = torch.LongTensor([])
+    gt_bboxes_ignore = torch.Tensor([])
+    assign_result = assigner.assign(
+        bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_bboxes_ignore,
+        gt_labels=gt_labels)
+
+    context = _context_for_ohem()
+
+    sampler = OHEMSampler(
+        num=10,
+        pos_fraction=0.5,
+        context=context,
+        neg_pos_ub=-1,
+        add_gt_as_proposals=True)
+
+    feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]]
+
+    sample_result = sampler.sample(
+        assign_result, bboxes, gt_bboxes, gt_labels, feats=feats)
+
+    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
+    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)
+
+
+def test_ohem_sampler_empty_pred():
+    assigner = MaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+        ignore_iof_thr=0.5,
+        ignore_wrt_candidates=False,
+    )
+    bboxes = torch.empty(0, 4)
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_labels = torch.LongTensor([1, 2, 2, 3])
+    gt_bboxes_ignore = torch.Tensor([])
+    assign_result = assigner.assign(
+        bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_bboxes_ignore,
+        gt_labels=gt_labels)
+
+    context = _context_for_ohem()
+
+    sampler = OHEMSampler(
+        num=10,
+        pos_fraction=0.5,
+        context=context,
+        neg_pos_ub=-1,
+        add_gt_as_proposals=True)
+
+    feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]]
+
+    sample_result = sampler.sample(
+        assign_result, bboxes, gt_bboxes, gt_labels, feats=feats)
+
+    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
+    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)
+
+
+def test_random_sample_result():
+    from mmdet.core.bbox.samplers.sampling_result import SamplingResult
+    SamplingResult.random(num_gts=0, num_preds=0)
+    SamplingResult.random(num_gts=0, num_preds=3)
+    SamplingResult.random(num_gts=3, num_preds=3)
+    SamplingResult.random(num_gts=0, num_preds=3)
+    SamplingResult.random(num_gts=7, num_preds=7)
+    SamplingResult.random(num_gts=7, num_preds=64)
+    SamplingResult.random(num_gts=24, num_preds=3)
+
+    for i in range(3):
+        SamplingResult.random(rng=i)
+
+
+def test_score_hlr_sampler_empty_pred():
+    assigner = MaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+        ignore_iof_thr=0.5,
+        ignore_wrt_candidates=False,
+    )
+    context = _context_for_ohem()
+    sampler = ScoreHLRSampler(
+        num=10,
+        pos_fraction=0.5,
+        context=context,
+        neg_pos_ub=-1,
+        add_gt_as_proposals=True)
+    gt_bboxes_ignore = torch.Tensor([])
+    feats = [torch.rand(1, 256, int(2**i), int(2**i)) for i in [6, 5, 4, 3, 2]]
+
+    # empty bbox
+    bboxes = torch.empty(0, 4)
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_labels = torch.LongTensor([1, 2, 2, 3])
+    assign_result = assigner.assign(
+        bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_bboxes_ignore,
+        gt_labels=gt_labels)
+    sample_result, _ = sampler.sample(
+        assign_result, bboxes, gt_bboxes, gt_labels, feats=feats)
+    assert len(sample_result.neg_inds) == 0
+    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
+    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)
+
+    # empty gt
+    bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_bboxes = torch.empty(0, 4)
+    gt_labels = torch.LongTensor([])
+    assign_result = assigner.assign(
+        bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_bboxes_ignore,
+        gt_labels=gt_labels)
+    sample_result, _ = sampler.sample(
+        assign_result, bboxes, gt_bboxes, gt_labels, feats=feats)
+    assert len(sample_result.pos_inds) == 0
+    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
+    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)
+
+    # non-empty input
+    bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_labels = torch.LongTensor([1, 2, 2, 3])
+    assign_result = assigner.assign(
+        bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_bboxes_ignore,
+        gt_labels=gt_labels)
+    sample_result, _ = sampler.sample(
+        assign_result, bboxes, gt_bboxes, gt_labels, feats=feats)
+    assert len(sample_result.pos_bboxes) == len(sample_result.pos_inds)
+    assert len(sample_result.neg_bboxes) == len(sample_result.neg_inds)
diff --git a/tests/test_data/test_pipelines/test_transform/__init__.py b/tests/test_data/test_pipelines/test_transform/__init__.py
new file mode 100644
index 0000000..d499031
--- /dev/null
+++ b/tests/test_data/test_pipelines/test_transform/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .utils import check_result_same, construct_toy_data, create_random_bboxes
+
+__all__ = ['create_random_bboxes', 'construct_toy_data', 'check_result_same']
diff --git a/tests/test_data/test_pipelines/test_transform/test_img_augment.py b/tests/test_data/test_pipelines/test_transform/test_img_augment.py
new file mode 100644
index 0000000..f28030e
--- /dev/null
+++ b/tests/test_data/test_pipelines/test_transform/test_img_augment.py
@@ -0,0 +1,175 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import mmcv
+import numpy as np
+from mmcv.utils import build_from_cfg
+from numpy.testing import assert_array_equal
+
+from mmdet.datasets.builder import PIPELINES
+from .utils import construct_toy_data
+
+
+def test_adjust_color():
+    results = construct_toy_data()
+    # test wighout aug
+    transform = dict(type='ColorTransform', prob=0, level=10)
+    transform_module = build_from_cfg(transform, PIPELINES)
+    results_transformed = transform_module(copy.deepcopy(results))
+    assert_array_equal(results_transformed['img'], results['img'])
+
+    # test with factor 1
+    img = results['img']
+    transform = dict(type='ColorTransform', prob=1, level=10)
+    transform_module = build_from_cfg(transform, PIPELINES)
+    results_transformed = transform_module(copy.deepcopy(results))
+    assert_array_equal(results_transformed['img'], img)
+
+    # test with factor 0
+    transform_module.factor = 0
+    img_gray = mmcv.bgr2gray(img.copy())
+    img_r = np.stack([img_gray, img_gray, img_gray], axis=-1)
+    results_transformed = transform_module(copy.deepcopy(results))
+    assert_array_equal(results_transformed['img'], img_r)
+
+    # test with factor 0.5
+    transform_module.factor = 0.5
+    results_transformed = transform_module(copy.deepcopy(results))
+    img = results['img']
+    assert_array_equal(
+        results_transformed['img'],
+        np.round(np.clip((img * 0.5 + img_r * 0.5), 0, 255)).astype(img.dtype))
+
+
+def test_imequalize(nb_rand_test=100):
+
+    def _imequalize(img):
+        # equalize the image using PIL.ImageOps.equalize
+        from PIL import Image, ImageOps
+        img = Image.fromarray(img)
+        equalized_img = np.asarray(ImageOps.equalize(img))
+        return equalized_img
+
+    results = construct_toy_data()
+    # test wighout aug
+    transform = dict(type='EqualizeTransform', prob=0)
+    transform_module = build_from_cfg(transform, PIPELINES)
+    results_transformed = transform_module(copy.deepcopy(results))
+    assert_array_equal(results_transformed['img'], results['img'])
+
+    # test equalize with case step=0
+    transform = dict(type='EqualizeTransform', prob=1.)
+    transform_module = build_from_cfg(transform, PIPELINES)
+    img = np.array([[0, 0, 0], [120, 120, 120], [255, 255, 255]],
+                   dtype=np.uint8)
+    img = np.stack([img, img, img], axis=-1)
+    results['img'] = img
+    results_transformed = transform_module(copy.deepcopy(results))
+    assert_array_equal(results_transformed['img'], img)
+
+    # test equalize with randomly sampled image.
+    for _ in range(nb_rand_test):
+        img = np.clip(np.random.uniform(0, 1, (1000, 1200, 3)) * 260, 0,
+                      255).astype(np.uint8)
+        results['img'] = img
+        results_transformed = transform_module(copy.deepcopy(results))
+        assert_array_equal(results_transformed['img'], _imequalize(img))
+
+
+def test_adjust_brightness(nb_rand_test=100):
+
+    def _adjust_brightness(img, factor):
+        # adjust the brightness of image using
+        # PIL.ImageEnhance.Brightness
+        from PIL import Image
+        from PIL.ImageEnhance import Brightness
+        img = Image.fromarray(img)
+        brightened_img = Brightness(img).enhance(factor)
+        return np.asarray(brightened_img)
+
+    results = construct_toy_data()
+    # test wighout aug
+    transform = dict(type='BrightnessTransform', level=10, prob=0)
+    transform_module = build_from_cfg(transform, PIPELINES)
+    results_transformed = transform_module(copy.deepcopy(results))
+    assert_array_equal(results_transformed['img'], results['img'])
+
+    # test case with factor 1.0
+    transform = dict(type='BrightnessTransform', level=10, prob=1.)
+    transform_module = build_from_cfg(transform, PIPELINES)
+    transform_module.factor = 1.0
+    results_transformed = transform_module(copy.deepcopy(results))
+    assert_array_equal(results_transformed['img'], results['img'])
+
+    # test case with factor 0.0
+    transform_module.factor = 0.0
+    results_transformed = transform_module(copy.deepcopy(results))
+    assert_array_equal(results_transformed['img'],
+                       np.zeros_like(results['img']))
+
+    # test with randomly sampled images and factors.
+    for _ in range(nb_rand_test):
+        img = np.clip(np.random.uniform(0, 1, (1000, 1200, 3)) * 260, 0,
+                      255).astype(np.uint8)
+        factor = np.random.uniform()
+        transform_module.factor = factor
+        results['img'] = img
+        np.testing.assert_allclose(
+            transform_module(copy.deepcopy(results))['img'].astype(np.int32),
+            _adjust_brightness(img, factor).astype(np.int32),
+            rtol=0,
+            atol=1)
+
+
+def test_adjust_contrast(nb_rand_test=100):
+
+    def _adjust_contrast(img, factor):
+        from PIL import Image
+        from PIL.ImageEnhance import Contrast
+
+        # Image.fromarray defaultly supports RGB, not BGR.
+        # convert from BGR to RGB
+        img = Image.fromarray(img[..., ::-1], mode='RGB')
+        contrasted_img = Contrast(img).enhance(factor)
+        # convert from RGB to BGR
+        return np.asarray(contrasted_img)[..., ::-1]
+
+    results = construct_toy_data()
+    # test wighout aug
+    transform = dict(type='ContrastTransform', level=10, prob=0)
+    transform_module = build_from_cfg(transform, PIPELINES)
+    results_transformed = transform_module(copy.deepcopy(results))
+    assert_array_equal(results_transformed['img'], results['img'])
+
+    # test case with factor 1.0
+    transform = dict(type='ContrastTransform', level=10, prob=1.)
+    transform_module = build_from_cfg(transform, PIPELINES)
+    transform_module.factor = 1.0
+    results_transformed = transform_module(copy.deepcopy(results))
+    assert_array_equal(results_transformed['img'], results['img'])
+
+    # test case with factor 0.0
+    transform_module.factor = 0.0
+    results_transformed = transform_module(copy.deepcopy(results))
+    np.testing.assert_allclose(
+        results_transformed['img'],
+        _adjust_contrast(results['img'], 0.),
+        rtol=0,
+        atol=1)
+
+    # test adjust_contrast with randomly sampled images and factors.
+    for _ in range(nb_rand_test):
+        img = np.clip(np.random.uniform(0, 1, (1200, 1000, 3)) * 260, 0,
+                      255).astype(np.uint8)
+        factor = np.random.uniform()
+        transform_module.factor = factor
+        results['img'] = img
+        results_transformed = transform_module(copy.deepcopy(results))
+        # Note the gap (less_equal 1) between PIL.ImageEnhance.Contrast
+        # and mmcv.adjust_contrast comes from the gap that converts from
+        # a color image to gray image using mmcv or PIL.
+        np.testing.assert_allclose(
+            transform_module(copy.deepcopy(results))['img'].astype(np.int32),
+            _adjust_contrast(results['img'], factor).astype(np.int32),
+            rtol=0,
+            atol=1)
diff --git a/tests/test_data/test_pipelines/test_transform/test_models_aug_test.py b/tests/test_data/test_pipelines/test_transform/test_models_aug_test.py
new file mode 100644
index 0000000..5eba1ef
--- /dev/null
+++ b/tests/test_data/test_pipelines/test_transform/test_models_aug_test.py
@@ -0,0 +1,131 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+
+import mmcv
+import torch
+from mmcv.parallel import collate
+from mmcv.utils import build_from_cfg
+
+from mmdet.datasets.builder import PIPELINES
+from mmdet.models import build_detector
+
+
+def model_aug_test_template(cfg_file):
+    # get config
+    cfg = mmcv.Config.fromfile(cfg_file)
+    # init model
+    cfg.model.pretrained = None
+    cfg.model.train_cfg = None
+    model = build_detector(cfg.model)
+
+    # init test pipeline and set aug test
+    load_cfg, multi_scale_cfg = cfg.test_pipeline
+    multi_scale_cfg['flip'] = True
+    multi_scale_cfg['flip_direction'] = ['horizontal', 'vertical', 'diagonal']
+    multi_scale_cfg['img_scale'] = [(1333, 800), (800, 600), (640, 480)]
+
+    load = build_from_cfg(load_cfg, PIPELINES)
+    transform = build_from_cfg(multi_scale_cfg, PIPELINES)
+
+    results = dict(
+        img_prefix=osp.join(osp.dirname(__file__), '../../../data'),
+        img_info=dict(filename='color.jpg'))
+    results = transform(load(results))
+    assert len(results['img']) == 12
+    assert len(results['img_metas']) == 12
+
+    results['img'] = [collate([x]) for x in results['img']]
+    results['img_metas'] = [collate([x]).data[0] for x in results['img_metas']]
+    # aug test the model
+    model.eval()
+    with torch.no_grad():
+        aug_result = model(return_loss=False, rescale=True, **results)
+    return aug_result
+
+
+def test_aug_test_size():
+    results = dict(
+        img_prefix=osp.join(osp.dirname(__file__), '../../../data'),
+        img_info=dict(filename='color.jpg'))
+
+    # Define simple pipeline
+    load = dict(type='LoadImageFromFile')
+    load = build_from_cfg(load, PIPELINES)
+
+    # get config
+    transform = dict(
+        type='MultiScaleFlipAug',
+        transforms=[],
+        img_scale=[(1333, 800), (800, 600), (640, 480)],
+        flip=True,
+        flip_direction=['horizontal', 'vertical', 'diagonal'])
+    multi_aug_test_module = build_from_cfg(transform, PIPELINES)
+
+    results = load(results)
+    results = multi_aug_test_module(load(results))
+    # len(["original", "horizontal", "vertical", "diagonal"]) *
+    # len([(1333, 800), (800, 600), (640, 480)])
+    assert len(results['img']) == 12
+
+
+def test_cascade_rcnn_aug_test():
+    aug_result = model_aug_test_template(
+        'configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py')
+    assert len(aug_result[0]) == 80
+
+
+def test_mask_rcnn_aug_test():
+    aug_result = model_aug_test_template(
+        'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py')
+    assert len(aug_result[0]) == 2
+    assert len(aug_result[0][0]) == 80
+    assert len(aug_result[0][1]) == 80
+
+
+def test_htc_aug_test():
+    aug_result = model_aug_test_template('configs/htc/htc_r50_fpn_1x_coco.py')
+    assert len(aug_result[0]) == 2
+    assert len(aug_result[0][0]) == 80
+    assert len(aug_result[0][1]) == 80
+
+
+def test_scnet_aug_test():
+    aug_result = model_aug_test_template(
+        'configs/scnet/scnet_r50_fpn_1x_coco.py')
+    assert len(aug_result[0]) == 2
+    assert len(aug_result[0][0]) == 80
+    assert len(aug_result[0][1]) == 80
+
+
+def test_cornernet_aug_test():
+    # get config
+    cfg = mmcv.Config.fromfile(
+        'configs/cornernet/cornernet_hourglass104_mstest_10x5_210e_coco.py')
+    # init model
+    cfg.model.pretrained = None
+    cfg.model.train_cfg = None
+    model = build_detector(cfg.model)
+
+    # init test pipeline and set aug test
+    load_cfg, multi_scale_cfg = cfg.test_pipeline
+    multi_scale_cfg['flip'] = True
+    multi_scale_cfg['flip_direction'] = ['horizontal', 'vertical', 'diagonal']
+    multi_scale_cfg['scale_factor'] = [0.5, 1.0, 2.0]
+
+    load = build_from_cfg(load_cfg, PIPELINES)
+    transform = build_from_cfg(multi_scale_cfg, PIPELINES)
+
+    results = dict(
+        img_prefix=osp.join(osp.dirname(__file__), '../../../data'),
+        img_info=dict(filename='color.jpg'))
+    results = transform(load(results))
+    assert len(results['img']) == 12
+    assert len(results['img_metas']) == 12
+
+    results['img'] = [collate([x]) for x in results['img']]
+    results['img_metas'] = [collate([x]).data[0] for x in results['img_metas']]
+    # aug test the model
+    model.eval()
+    with torch.no_grad():
+        aug_result = model(return_loss=False, rescale=True, **results)
+    assert len(aug_result[0]) == 80
diff --git a/tests/test_data/test_pipelines/test_transform/test_rotate.py b/tests/test_data/test_pipelines/test_transform/test_rotate.py
new file mode 100644
index 0000000..93f7749
--- /dev/null
+++ b/tests/test_data/test_pipelines/test_transform/test_rotate.py
@@ -0,0 +1,172 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import numpy as np
+import pytest
+from mmcv.utils import build_from_cfg
+
+from mmdet.core.mask import BitmapMasks, PolygonMasks
+from mmdet.datasets.builder import PIPELINES
+from .utils import check_result_same, construct_toy_data
+
+
+def test_rotate():
+    # test assertion for invalid type of max_rotate_angle
+    with pytest.raises(AssertionError):
+        transform = dict(type='Rotate', level=1, max_rotate_angle=(30, ))
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid type of scale
+    with pytest.raises(AssertionError):
+        transform = dict(type='Rotate', level=2, scale=(1.2, ))
+        build_from_cfg(transform, PIPELINES)
+
+    # test ValueError for invalid type of img_fill_val
+    with pytest.raises(ValueError):
+        transform = dict(
+            type='Rotate', level=2, img_fill_val=[
+                128,
+            ])
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid number of elements in center
+    with pytest.raises(AssertionError):
+        transform = dict(type='Rotate', level=2, center=(0.5, ))
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid type of center
+    with pytest.raises(AssertionError):
+        transform = dict(type='Rotate', level=2, center=[0, 0])
+        build_from_cfg(transform, PIPELINES)
+
+    # test case when no rotate aug (level=0)
+    results = construct_toy_data()
+    img_fill_val = (104, 116, 124)
+    seg_ignore_label = 255
+    transform = dict(
+        type='Rotate',
+        level=0,
+        prob=1.,
+        img_fill_val=img_fill_val,
+        seg_ignore_label=seg_ignore_label,
+    )
+    rotate_module = build_from_cfg(transform, PIPELINES)
+    results_wo_rotate = rotate_module(copy.deepcopy(results))
+    check_result_same(results, results_wo_rotate)
+
+    # test case when no rotate aug (prob<=0)
+    transform = dict(
+        type='Rotate', level=10, prob=0., img_fill_val=img_fill_val, scale=0.6)
+    rotate_module = build_from_cfg(transform, PIPELINES)
+    results_wo_rotate = rotate_module(copy.deepcopy(results))
+    check_result_same(results, results_wo_rotate)
+
+    # test clockwise rotation with angle 90
+    results = construct_toy_data()
+    img_fill_val = 128
+    transform = dict(
+        type='Rotate',
+        level=10,
+        max_rotate_angle=90,
+        img_fill_val=img_fill_val,
+        # set random_negative_prob to 0 for clockwise rotation
+        random_negative_prob=0.,
+        prob=1.)
+    rotate_module = build_from_cfg(transform, PIPELINES)
+    results_rotated = rotate_module(copy.deepcopy(results))
+    img_r = np.array([[img_fill_val, 6, 2, img_fill_val],
+                      [img_fill_val, 7, 3, img_fill_val]]).astype(np.uint8)
+    img_r = np.stack([img_r, img_r, img_r], axis=-1)
+    results_gt = copy.deepcopy(results)
+    results_gt['img'] = img_r
+    results_gt['gt_bboxes'] = np.array([[1., 0., 2., 1.]], dtype=np.float32)
+    results_gt['gt_bboxes_ignore'] = np.empty((0, 4), dtype=np.float32)
+    gt_masks = np.array([[0, 1, 1, 0], [0, 0, 1, 0]],
+                        dtype=np.uint8)[None, :, :]
+    results_gt['gt_masks'] = BitmapMasks(gt_masks, 2, 4)
+    results_gt['gt_semantic_seg'] = np.array(
+        [[255, 6, 2, 255], [255, 7, 3,
+                            255]]).astype(results['gt_semantic_seg'].dtype)
+    check_result_same(results_gt, results_rotated)
+
+    # test clockwise rotation with angle 90, PolygonMasks
+    results = construct_toy_data(poly2mask=False)
+    results_rotated = rotate_module(copy.deepcopy(results))
+    gt_masks = [[np.array([2, 0, 2, 1, 1, 1, 1, 0], dtype=np.float)]]
+    results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4)
+    check_result_same(results_gt, results_rotated)
+
+    # test counter-clockwise rotation with angle 90,
+    # and specify the ratation center
+    img_fill_val = (104, 116, 124)
+    transform = dict(
+        type='Rotate',
+        level=10,
+        max_rotate_angle=90,
+        center=(0, 0),
+        img_fill_val=img_fill_val,
+        # set random_negative_prob to 1 for counter-clockwise rotation
+        random_negative_prob=1.,
+        prob=1.)
+    results = construct_toy_data()
+    rotate_module = build_from_cfg(transform, PIPELINES)
+    results_rotated = rotate_module(copy.deepcopy(results))
+    results_gt = copy.deepcopy(results)
+    h, w = results['img'].shape[:2]
+    img_r = np.stack([
+        np.ones((h, w)) * img_fill_val[0],
+        np.ones((h, w)) * img_fill_val[1],
+        np.ones((h, w)) * img_fill_val[2]
+    ],
+                     axis=-1).astype(np.uint8)
+    img_r[0, 0, :] = 1
+    img_r[0, 1, :] = 5
+    results_gt['img'] = img_r
+    results_gt['gt_bboxes'] = np.empty((0, 4), dtype=np.float32)
+    results_gt['gt_bboxes_ignore'] = np.empty((0, 4), dtype=np.float32)
+    results_gt['gt_labels'] = np.empty((0, ), dtype=np.int64)
+    gt_masks = np.empty((0, h, w), dtype=np.uint8)
+    results_gt['gt_masks'] = BitmapMasks(gt_masks, h, w)
+    gt_seg = (np.ones((h, w)) * 255).astype(results['gt_semantic_seg'].dtype)
+    gt_seg[0, 0], gt_seg[0, 1] = 1, 5
+    results_gt['gt_semantic_seg'] = gt_seg
+    check_result_same(results_gt, results_rotated)
+
+    transform = dict(
+        type='Rotate',
+        level=10,
+        max_rotate_angle=90,
+        center=(0),
+        img_fill_val=img_fill_val,
+        random_negative_prob=1.,
+        prob=1.)
+    rotate_module = build_from_cfg(transform, PIPELINES)
+    results_rotated = rotate_module(copy.deepcopy(results))
+    check_result_same(results_gt, results_rotated)
+
+    # test counter-clockwise rotation with angle 90,
+    # and specify the ratation center, PolygonMasks
+    results = construct_toy_data(poly2mask=False)
+    results_rotated = rotate_module(copy.deepcopy(results))
+    gt_masks = [[np.array([0, 0, 0, 0, 1, 0, 1, 0], dtype=np.float)]]
+    results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4)
+    check_result_same(results_gt, results_rotated)
+
+    # test AutoAugment equipped with Rotate
+    policies = [[dict(type='Rotate', level=10, prob=1.)]]
+    autoaug = dict(type='AutoAugment', policies=policies)
+    autoaug_module = build_from_cfg(autoaug, PIPELINES)
+    autoaug_module(copy.deepcopy(results))
+
+    policies = [[
+        dict(type='Rotate', level=10, prob=1.),
+        dict(
+            type='Rotate',
+            level=8,
+            max_rotate_angle=90,
+            center=(0),
+            img_fill_val=img_fill_val)
+    ]]
+    autoaug = dict(type='AutoAugment', policies=policies)
+    autoaug_module = build_from_cfg(autoaug, PIPELINES)
+    autoaug_module(copy.deepcopy(results))
diff --git a/tests/test_data/test_pipelines/test_transform/test_shear.py b/tests/test_data/test_pipelines/test_transform/test_shear.py
new file mode 100644
index 0000000..215d9a3
--- /dev/null
+++ b/tests/test_data/test_pipelines/test_transform/test_shear.py
@@ -0,0 +1,164 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import numpy as np
+import pytest
+from mmcv.utils import build_from_cfg
+
+from mmdet.core.mask import BitmapMasks, PolygonMasks
+from mmdet.datasets.builder import PIPELINES
+from .utils import check_result_same, construct_toy_data
+
+
+def test_shear():
+    # test assertion for invalid type of max_shear_magnitude
+    with pytest.raises(AssertionError):
+        transform = dict(type='Shear', level=1, max_shear_magnitude=(0.5, ))
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid value of max_shear_magnitude
+    with pytest.raises(AssertionError):
+        transform = dict(type='Shear', level=2, max_shear_magnitude=1.2)
+        build_from_cfg(transform, PIPELINES)
+
+    # test ValueError for invalid type of img_fill_val
+    with pytest.raises(ValueError):
+        transform = dict(type='Shear', level=2, img_fill_val=[128])
+        build_from_cfg(transform, PIPELINES)
+
+    results = construct_toy_data()
+    # test case when no shear aug (level=0, direction='horizontal')
+    img_fill_val = (104, 116, 124)
+    seg_ignore_label = 255
+    transform = dict(
+        type='Shear',
+        level=0,
+        prob=1.,
+        img_fill_val=img_fill_val,
+        seg_ignore_label=seg_ignore_label,
+        direction='horizontal')
+    shear_module = build_from_cfg(transform, PIPELINES)
+    results_wo_shear = shear_module(copy.deepcopy(results))
+    check_result_same(results, results_wo_shear)
+
+    # test case when no shear aug (level=0, direction='vertical')
+    transform = dict(
+        type='Shear',
+        level=0,
+        prob=1.,
+        img_fill_val=img_fill_val,
+        seg_ignore_label=seg_ignore_label,
+        direction='vertical')
+    shear_module = build_from_cfg(transform, PIPELINES)
+    results_wo_shear = shear_module(copy.deepcopy(results))
+    check_result_same(results, results_wo_shear)
+
+    # test case when no shear aug (prob<=0)
+    transform = dict(
+        type='Shear',
+        level=10,
+        prob=0.,
+        img_fill_val=img_fill_val,
+        direction='vertical')
+    shear_module = build_from_cfg(transform, PIPELINES)
+    results_wo_shear = shear_module(copy.deepcopy(results))
+    check_result_same(results, results_wo_shear)
+
+    # test shear horizontally, magnitude=1
+    transform = dict(
+        type='Shear',
+        level=10,
+        prob=1.,
+        img_fill_val=img_fill_val,
+        direction='horizontal',
+        max_shear_magnitude=1.,
+        random_negative_prob=0.)
+    shear_module = build_from_cfg(transform, PIPELINES)
+    results_sheared = shear_module(copy.deepcopy(results))
+    results_gt = copy.deepcopy(results)
+    img_s = np.array([[1, 2, 3, 4], [0, 5, 6, 7]], dtype=np.uint8)
+    img_s = np.stack([img_s, img_s, img_s], axis=-1)
+    img_s[1, 0, :] = np.array(img_fill_val)
+    results_gt['img'] = img_s
+    results_gt['gt_bboxes'] = np.array([[0., 0., 3., 1.]], dtype=np.float32)
+    results_gt['gt_bboxes_ignore'] = np.array([[2., 0., 4., 1.]],
+                                              dtype=np.float32)
+    gt_masks = np.array([[0, 1, 1, 0], [0, 0, 1, 0]],
+                        dtype=np.uint8)[None, :, :]
+    results_gt['gt_masks'] = BitmapMasks(gt_masks, 2, 4)
+    results_gt['gt_semantic_seg'] = np.array(
+        [[1, 2, 3, 4], [255, 5, 6, 7]], dtype=results['gt_semantic_seg'].dtype)
+    check_result_same(results_gt, results_sheared)
+
+    # test PolygonMasks with shear horizontally, magnitude=1
+    results = construct_toy_data(poly2mask=False)
+    results_sheared = shear_module(copy.deepcopy(results))
+    print(results_sheared['gt_masks'])
+    gt_masks = [[np.array([0, 0, 2, 0, 3, 1, 1, 1], dtype=np.float)]]
+    results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4)
+    check_result_same(results_gt, results_sheared)
+
+    # test shear vertically, magnitude=-1
+    img_fill_val = 128
+    results = construct_toy_data()
+    transform = dict(
+        type='Shear',
+        level=10,
+        prob=1.,
+        img_fill_val=img_fill_val,
+        direction='vertical',
+        max_shear_magnitude=1.,
+        random_negative_prob=1.)
+    shear_module = build_from_cfg(transform, PIPELINES)
+    results_sheared = shear_module(copy.deepcopy(results))
+    results_gt = copy.deepcopy(results)
+    img_s = np.array([[1, 6, img_fill_val, img_fill_val],
+                      [5, img_fill_val, img_fill_val, img_fill_val]],
+                     dtype=np.uint8)
+    img_s = np.stack([img_s, img_s, img_s], axis=-1)
+    results_gt['img'] = img_s
+    results_gt['gt_bboxes'] = np.empty((0, 4), dtype=np.float32)
+    results_gt['gt_labels'] = np.empty((0, ), dtype=np.int64)
+    results_gt['gt_bboxes_ignore'] = np.empty((0, 4), dtype=np.float32)
+    gt_masks = np.array([[0, 1, 0, 0], [0, 0, 0, 0]],
+                        dtype=np.uint8)[None, :, :]
+    results_gt['gt_masks'] = BitmapMasks(gt_masks, 2, 4)
+    results_gt['gt_semantic_seg'] = np.array(
+        [[1, 6, 255, 255], [5, 255, 255, 255]],
+        dtype=results['gt_semantic_seg'].dtype)
+    check_result_same(results_gt, results_sheared)
+
+    # test PolygonMasks with shear vertically, magnitude=-1
+    results = construct_toy_data(poly2mask=False)
+    results_sheared = shear_module(copy.deepcopy(results))
+    gt_masks = [[np.array([0, 0, 2, 0, 2, 0, 0, 1], dtype=np.float)]]
+    results_gt['gt_masks'] = PolygonMasks(gt_masks, 2, 4)
+    check_result_same(results_gt, results_sheared)
+
+    results = construct_toy_data()
+    # same mask for BitmapMasks and PolygonMasks
+    results['gt_masks'] = BitmapMasks(
+        np.array([[0, 1, 1, 0], [0, 1, 1, 0]], dtype=np.uint8)[None, :, :], 2,
+        4)
+    results['gt_bboxes'] = np.array([[1., 0., 2., 1.]], dtype=np.float32)
+    results_sheared_bitmap = shear_module(copy.deepcopy(results))
+    check_result_same(results_sheared_bitmap, results_sheared)
+
+    # test AutoAugment equipped with Shear
+    policies = [[dict(type='Shear', level=10, prob=1.)]]
+    autoaug = dict(type='AutoAugment', policies=policies)
+    autoaug_module = build_from_cfg(autoaug, PIPELINES)
+    autoaug_module(copy.deepcopy(results))
+
+    policies = [[
+        dict(type='Shear', level=10, prob=1.),
+        dict(
+            type='Shear',
+            level=8,
+            img_fill_val=img_fill_val,
+            direction='vertical',
+            max_shear_magnitude=1.)
+    ]]
+    autoaug = dict(type='AutoAugment', policies=policies)
+    autoaug_module = build_from_cfg(autoaug, PIPELINES)
+    autoaug_module(copy.deepcopy(results))
diff --git a/tests/test_data/test_pipelines/test_transform/test_transform.py b/tests/test_data/test_pipelines/test_transform/test_transform.py
new file mode 100644
index 0000000..1ebc4f3
--- /dev/null
+++ b/tests/test_data/test_pipelines/test_transform/test_transform.py
@@ -0,0 +1,1118 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import os.path as osp
+
+import mmcv
+import numpy as np
+import pytest
+import torch
+from mmcv.utils import build_from_cfg
+
+from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
+from mmdet.datasets.builder import PIPELINES
+from .utils import create_full_masks, create_random_bboxes
+
+
+def test_resize():
+    # test assertion if img_scale is a list
+    with pytest.raises(AssertionError):
+        transform = dict(type='Resize', img_scale=[1333, 800], keep_ratio=True)
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion if len(img_scale) while ratio_range is not None
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='Resize',
+            img_scale=[(1333, 800), (1333, 600)],
+            ratio_range=(0.9, 1.1),
+            keep_ratio=True)
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid multiscale_mode
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='Resize',
+            img_scale=[(1333, 800), (1333, 600)],
+            keep_ratio=True,
+            multiscale_mode='2333')
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion if both scale and scale_factor are set
+    with pytest.raises(AssertionError):
+        results = dict(
+            img_prefix=osp.join(osp.dirname(__file__), '../../../data'),
+            img_info=dict(filename='color.jpg'))
+        load = dict(type='LoadImageFromFile')
+        load = build_from_cfg(load, PIPELINES)
+        transform = dict(type='Resize', img_scale=(1333, 800), keep_ratio=True)
+        transform = build_from_cfg(transform, PIPELINES)
+        results = load(results)
+        results['scale'] = (1333, 800)
+        results['scale_factor'] = 1.0
+        results = transform(results)
+
+    transform = dict(type='Resize', img_scale=(1333, 800), keep_ratio=True)
+    resize_module = build_from_cfg(transform, PIPELINES)
+
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    results['img'] = img
+    results['img2'] = copy.deepcopy(img)
+    results['img_shape'] = img.shape
+    results['ori_shape'] = img.shape
+    # Set initial values for default meta_keys
+    results['pad_shape'] = img.shape
+    results['img_fields'] = ['img', 'img2']
+
+    results = resize_module(results)
+    assert np.equal(results['img'], results['img2']).all()
+
+    results.pop('scale')
+    results.pop('scale_factor')
+    transform = dict(
+        type='Resize',
+        img_scale=(1280, 800),
+        multiscale_mode='value',
+        keep_ratio=False)
+    resize_module = build_from_cfg(transform, PIPELINES)
+    results = resize_module(results)
+    assert np.equal(results['img'], results['img2']).all()
+    assert results['img_shape'] == (800, 1280, 3)
+    assert results['img'].dtype == results['img'].dtype == np.uint8
+
+    results_seg = {
+        'img': img,
+        'img_shape': img.shape,
+        'ori_shape': img.shape,
+        'gt_semantic_seg': copy.deepcopy(img),
+        'gt_seg': copy.deepcopy(img),
+        'seg_fields': ['gt_semantic_seg', 'gt_seg']
+    }
+    transform = dict(
+        type='Resize',
+        img_scale=(640, 400),
+        multiscale_mode='value',
+        keep_ratio=False)
+    resize_module = build_from_cfg(transform, PIPELINES)
+    results_seg = resize_module(results_seg)
+    assert results_seg['gt_semantic_seg'].shape == results_seg['gt_seg'].shape
+    assert results_seg['img_shape'] == (400, 640, 3)
+    assert results_seg['img_shape'] != results_seg['ori_shape']
+    assert results_seg['gt_semantic_seg'].shape == results_seg['img_shape']
+    assert np.equal(results_seg['gt_semantic_seg'],
+                    results_seg['gt_seg']).all()
+
+
+def test_flip():
+    # test assertion for invalid flip_ratio
+    with pytest.raises(AssertionError):
+        transform = dict(type='RandomFlip', flip_ratio=1.5)
+        build_from_cfg(transform, PIPELINES)
+    # test assertion for 0 <= sum(flip_ratio) <= 1
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='RandomFlip',
+            flip_ratio=[0.7, 0.8],
+            direction=['horizontal', 'vertical'])
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for mismatch between number of flip_ratio and direction
+    with pytest.raises(AssertionError):
+        transform = dict(type='RandomFlip', flip_ratio=[0.4, 0.5])
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid direction
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='RandomFlip', flip_ratio=1., direction='horizonta')
+        build_from_cfg(transform, PIPELINES)
+
+    transform = dict(type='RandomFlip', flip_ratio=1.)
+    flip_module = build_from_cfg(transform, PIPELINES)
+
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    original_img = copy.deepcopy(img)
+    results['img'] = img
+    results['img2'] = copy.deepcopy(img)
+    results['img_shape'] = img.shape
+    results['ori_shape'] = img.shape
+    # Set initial values for default meta_keys
+    results['pad_shape'] = img.shape
+    results['scale_factor'] = 1.0
+    results['img_fields'] = ['img', 'img2']
+
+    results = flip_module(results)
+    assert np.equal(results['img'], results['img2']).all()
+
+    flip_module = build_from_cfg(transform, PIPELINES)
+    results = flip_module(results)
+    assert np.equal(results['img'], results['img2']).all()
+    assert np.equal(original_img, results['img']).all()
+
+    # test flip_ratio is float, direction is list
+    transform = dict(
+        type='RandomFlip',
+        flip_ratio=0.9,
+        direction=['horizontal', 'vertical', 'diagonal'])
+    flip_module = build_from_cfg(transform, PIPELINES)
+
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    original_img = copy.deepcopy(img)
+    results['img'] = img
+    results['img_shape'] = img.shape
+    results['ori_shape'] = img.shape
+    # Set initial values for default meta_keys
+    results['pad_shape'] = img.shape
+    results['scale_factor'] = 1.0
+    results['img_fields'] = ['img']
+    results = flip_module(results)
+    if results['flip']:
+        assert np.array_equal(
+            mmcv.imflip(original_img, results['flip_direction']),
+            results['img'])
+    else:
+        assert np.array_equal(original_img, results['img'])
+
+    # test flip_ratio is list, direction is list
+    transform = dict(
+        type='RandomFlip',
+        flip_ratio=[0.3, 0.3, 0.2],
+        direction=['horizontal', 'vertical', 'diagonal'])
+    flip_module = build_from_cfg(transform, PIPELINES)
+
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    original_img = copy.deepcopy(img)
+    results['img'] = img
+    results['img_shape'] = img.shape
+    results['ori_shape'] = img.shape
+    # Set initial values for default meta_keys
+    results['pad_shape'] = img.shape
+    results['scale_factor'] = 1.0
+    results['img_fields'] = ['img']
+    results = flip_module(results)
+    if results['flip']:
+        assert np.array_equal(
+            mmcv.imflip(original_img, results['flip_direction']),
+            results['img'])
+    else:
+        assert np.array_equal(original_img, results['img'])
+
+
+def test_random_crop():
+    # test assertion for invalid random crop
+    with pytest.raises(AssertionError):
+        transform = dict(type='RandomCrop', crop_size=(-1, 0))
+        build_from_cfg(transform, PIPELINES)
+
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    results['img'] = img
+
+    results['img_shape'] = img.shape
+    results['ori_shape'] = img.shape
+    # TODO: add img_fields test
+    results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
+    # Set initial values for default meta_keys
+    results['pad_shape'] = img.shape
+    results['scale_factor'] = 1.0
+
+    h, w, _ = img.shape
+    gt_bboxes = create_random_bboxes(8, w, h)
+    gt_bboxes_ignore = create_random_bboxes(2, w, h)
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
+    results['gt_bboxes'] = gt_bboxes
+    results['gt_bboxes_ignore'] = gt_bboxes_ignore
+    transform = dict(type='RandomCrop', crop_size=(h - 20, w - 20))
+    crop_module = build_from_cfg(transform, PIPELINES)
+    results = crop_module(results)
+    assert results['img'].shape[:2] == (h - 20, w - 20)
+    # All bboxes should be reserved after crop
+    assert results['img_shape'][:2] == (h - 20, w - 20)
+    assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes'].shape[0] == 8
+    assert results['gt_bboxes_ignore'].shape[0] == 2
+
+    def area(bboxes):
+        return np.prod(bboxes[:, 2:4] - bboxes[:, 0:2], axis=1)
+
+    assert (area(results['gt_bboxes']) <= area(gt_bboxes)).all()
+    assert (area(results['gt_bboxes_ignore']) <= area(gt_bboxes_ignore)).all()
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32
+
+    # test assertion for invalid crop_type
+    with pytest.raises(ValueError):
+        transform = dict(
+            type='RandomCrop', crop_size=(1, 1), crop_type='unknown')
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid crop_size
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='RandomCrop', crop_type='relative', crop_size=(0, 0))
+        build_from_cfg(transform, PIPELINES)
+
+    def _construct_toy_data():
+        img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8)
+        img = np.stack([img, img, img], axis=-1)
+        results = dict()
+        # image
+        results['img'] = img
+        results['img_shape'] = img.shape
+        results['img_fields'] = ['img']
+        # bboxes
+        results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
+        results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32)
+        results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]],
+                                               dtype=np.float32)
+        # labels
+        results['gt_labels'] = np.array([1], dtype=np.int64)
+        return results
+
+    # test crop_type "relative_range"
+    results = _construct_toy_data()
+    transform = dict(
+        type='RandomCrop',
+        crop_type='relative_range',
+        crop_size=(0.3, 0.7),
+        allow_negative_crop=True)
+    transform_module = build_from_cfg(transform, PIPELINES)
+    results_transformed = transform_module(copy.deepcopy(results))
+    h, w = results_transformed['img_shape'][:2]
+    assert int(2 * 0.3 + 0.5) <= h <= int(2 * 1 + 0.5)
+    assert int(4 * 0.7 + 0.5) <= w <= int(4 * 1 + 0.5)
+    assert results_transformed['gt_bboxes'].dtype == np.float32
+    assert results_transformed['gt_bboxes_ignore'].dtype == np.float32
+
+    # test crop_type "relative"
+    transform = dict(
+        type='RandomCrop',
+        crop_type='relative',
+        crop_size=(0.3, 0.7),
+        allow_negative_crop=True)
+    transform_module = build_from_cfg(transform, PIPELINES)
+    results_transformed = transform_module(copy.deepcopy(results))
+    h, w = results_transformed['img_shape'][:2]
+    assert h == int(2 * 0.3 + 0.5) and w == int(4 * 0.7 + 0.5)
+    assert results_transformed['gt_bboxes'].dtype == np.float32
+    assert results_transformed['gt_bboxes_ignore'].dtype == np.float32
+
+    # test crop_type "absolute"
+    transform = dict(
+        type='RandomCrop',
+        crop_type='absolute',
+        crop_size=(1, 2),
+        allow_negative_crop=True)
+    transform_module = build_from_cfg(transform, PIPELINES)
+    results_transformed = transform_module(copy.deepcopy(results))
+    h, w = results_transformed['img_shape'][:2]
+    assert h == 1 and w == 2
+    assert results_transformed['gt_bboxes'].dtype == np.float32
+    assert results_transformed['gt_bboxes_ignore'].dtype == np.float32
+
+    # test crop_type "absolute_range"
+    transform = dict(
+        type='RandomCrop',
+        crop_type='absolute_range',
+        crop_size=(1, 20),
+        allow_negative_crop=True)
+    transform_module = build_from_cfg(transform, PIPELINES)
+    results_transformed = transform_module(copy.deepcopy(results))
+    h, w = results_transformed['img_shape'][:2]
+    assert 1 <= h <= 2 and 1 <= w <= 4
+    assert results_transformed['gt_bboxes'].dtype == np.float32
+    assert results_transformed['gt_bboxes_ignore'].dtype == np.float32
+
+
+def test_min_iou_random_crop():
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    results['img'] = img
+
+    results['img_shape'] = img.shape
+    results['ori_shape'] = img.shape
+    results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
+    # Set initial values for default meta_keys
+    results['pad_shape'] = img.shape
+    results['scale_factor'] = 1.0
+    h, w, _ = img.shape
+    gt_bboxes = create_random_bboxes(1, w, h)
+    gt_bboxes_ignore = create_random_bboxes(1, w, h)
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
+    results['gt_bboxes'] = gt_bboxes
+    results['gt_bboxes_ignore'] = gt_bboxes_ignore
+    transform = dict(type='MinIoURandomCrop')
+    crop_module = build_from_cfg(transform, PIPELINES)
+
+    # Test for img_fields
+    results_test = copy.deepcopy(results)
+    results_test['img1'] = results_test['img']
+    results_test['img_fields'] = ['img', 'img1']
+    with pytest.raises(AssertionError):
+        crop_module(results_test)
+    results = crop_module(results)
+    assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32
+
+    patch = np.array([0, 0, results['img_shape'][1], results['img_shape'][0]])
+    ious = bbox_overlaps(patch.reshape(-1, 4),
+                         results['gt_bboxes']).reshape(-1)
+    ious_ignore = bbox_overlaps(
+        patch.reshape(-1, 4), results['gt_bboxes_ignore']).reshape(-1)
+    mode = crop_module.mode
+    if mode == 1:
+        assert np.equal(results['gt_bboxes'], gt_bboxes).all()
+        assert np.equal(results['gt_bboxes_ignore'], gt_bboxes_ignore).all()
+    else:
+        assert (ious >= mode).all()
+        assert (ious_ignore >= mode).all()
+
+
+def test_pad():
+    # test assertion if both size_divisor and size is None
+    with pytest.raises(AssertionError):
+        transform = dict(type='Pad')
+        build_from_cfg(transform, PIPELINES)
+
+    transform = dict(type='Pad', size_divisor=32)
+    transform = build_from_cfg(transform, PIPELINES)
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    original_img = copy.deepcopy(img)
+    results['img'] = img
+    results['img2'] = copy.deepcopy(img)
+    results['img_shape'] = img.shape
+    results['ori_shape'] = img.shape
+    # Set initial values for default meta_keys
+    results['pad_shape'] = img.shape
+    results['scale_factor'] = 1.0
+    results['img_fields'] = ['img', 'img2']
+
+    results = transform(results)
+    assert np.equal(results['img'], results['img2']).all()
+    # original img already divisible by 32
+    assert np.equal(results['img'], original_img).all()
+    img_shape = results['img'].shape
+    assert img_shape[0] % 32 == 0
+    assert img_shape[1] % 32 == 0
+
+    resize_transform = dict(
+        type='Resize', img_scale=(1333, 800), keep_ratio=True)
+    resize_module = build_from_cfg(resize_transform, PIPELINES)
+    results = resize_module(results)
+    results = transform(results)
+    img_shape = results['img'].shape
+    assert np.equal(results['img'], results['img2']).all()
+    assert img_shape[0] % 32 == 0
+    assert img_shape[1] % 32 == 0
+
+    # test the size and size_divisor must be None when pad2square is True
+    with pytest.raises(AssertionError):
+        transform = dict(type='Pad', size_divisor=32, pad_to_square=True)
+        build_from_cfg(transform, PIPELINES)
+
+    transform = dict(type='Pad', pad_to_square=True)
+    transform = build_from_cfg(transform, PIPELINES)
+    results['img'] = img
+    results = transform(results)
+    assert results['img'].shape[0] == results['img'].shape[1]
+
+    # test the pad_val is converted to a dict
+    transform = dict(type='Pad', size_divisor=32, pad_val=0)
+    with pytest.deprecated_call():
+        transform = build_from_cfg(transform, PIPELINES)
+
+    assert isinstance(transform.pad_val, dict)
+    results = transform(results)
+    img_shape = results['img'].shape
+    assert img_shape[0] % 32 == 0
+    assert img_shape[1] % 32 == 0
+
+
+def test_normalize():
+    img_norm_cfg = dict(
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True)
+    transform = dict(type='Normalize', **img_norm_cfg)
+    transform = build_from_cfg(transform, PIPELINES)
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    original_img = copy.deepcopy(img)
+    results['img'] = img
+    results['img2'] = copy.deepcopy(img)
+    results['img_shape'] = img.shape
+    results['ori_shape'] = img.shape
+    # Set initial values for default meta_keys
+    results['pad_shape'] = img.shape
+    results['scale_factor'] = 1.0
+    results['img_fields'] = ['img', 'img2']
+
+    results = transform(results)
+    assert np.equal(results['img'], results['img2']).all()
+
+    mean = np.array(img_norm_cfg['mean'])
+    std = np.array(img_norm_cfg['std'])
+    converted_img = (original_img[..., ::-1] - mean) / std
+    assert np.allclose(results['img'], converted_img)
+
+
+def test_albu_transform():
+    results = dict(
+        img_prefix=osp.join(osp.dirname(__file__), '../../../data'),
+        img_info=dict(filename='color.jpg'))
+
+    # Define simple pipeline
+    load = dict(type='LoadImageFromFile')
+    load = build_from_cfg(load, PIPELINES)
+
+    albu_transform = dict(
+        type='Albu', transforms=[dict(type='ChannelShuffle', p=1)])
+    albu_transform = build_from_cfg(albu_transform, PIPELINES)
+
+    normalize = dict(type='Normalize', mean=[0] * 3, std=[0] * 3, to_rgb=True)
+    normalize = build_from_cfg(normalize, PIPELINES)
+
+    # Execute transforms
+    results = load(results)
+    results = albu_transform(results)
+    results = normalize(results)
+
+    assert results['img'].dtype == np.float32
+
+
+def test_random_center_crop_pad():
+    # test assertion for invalid crop_size while test_mode=False
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='RandomCenterCropPad',
+            crop_size=(-1, 0),
+            test_mode=False,
+            test_pad_mode=None)
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid ratios while test_mode=False
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='RandomCenterCropPad',
+            crop_size=(511, 511),
+            ratios=(1.0),
+            test_mode=False,
+            test_pad_mode=None)
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid mean, std and to_rgb
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='RandomCenterCropPad',
+            crop_size=(511, 511),
+            mean=None,
+            std=None,
+            to_rgb=None,
+            test_mode=False,
+            test_pad_mode=None)
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid crop_size while test_mode=True
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='RandomCenterCropPad',
+            crop_size=(511, 511),
+            ratios=None,
+            border=None,
+            mean=[123.675, 116.28, 103.53],
+            std=[58.395, 57.12, 57.375],
+            to_rgb=True,
+            test_mode=True,
+            test_pad_mode=('logical_or', 127))
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid ratios while test_mode=True
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='RandomCenterCropPad',
+            crop_size=None,
+            ratios=(0.9, 1.0, 1.1),
+            border=None,
+            mean=[123.675, 116.28, 103.53],
+            std=[58.395, 57.12, 57.375],
+            to_rgb=True,
+            test_mode=True,
+            test_pad_mode=('logical_or', 127))
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid border while test_mode=True
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='RandomCenterCropPad',
+            crop_size=None,
+            ratios=None,
+            border=128,
+            mean=[123.675, 116.28, 103.53],
+            std=[58.395, 57.12, 57.375],
+            to_rgb=True,
+            test_mode=True,
+            test_pad_mode=('logical_or', 127))
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid test_pad_mode while test_mode=True
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='RandomCenterCropPad',
+            crop_size=None,
+            ratios=None,
+            border=None,
+            mean=[123.675, 116.28, 103.53],
+            std=[58.395, 57.12, 57.375],
+            to_rgb=True,
+            test_mode=True,
+            test_pad_mode=('do_nothing', 100))
+        build_from_cfg(transform, PIPELINES)
+
+    results = dict(
+        img_prefix=osp.join(osp.dirname(__file__), '../../../data'),
+        img_info=dict(filename='color.jpg'))
+
+    load = dict(type='LoadImageFromFile', to_float32=True)
+    load = build_from_cfg(load, PIPELINES)
+    results = load(results)
+    test_results = copy.deepcopy(results)
+
+    h, w, _ = results['img_shape']
+    gt_bboxes = create_random_bboxes(8, w, h)
+    gt_bboxes_ignore = create_random_bboxes(2, w, h)
+    results['gt_bboxes'] = gt_bboxes
+    results['gt_bboxes_ignore'] = gt_bboxes_ignore
+    train_transform = dict(
+        type='RandomCenterCropPad',
+        crop_size=(h - 20, w - 20),
+        ratios=(1.0, ),
+        border=128,
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True,
+        test_mode=False,
+        test_pad_mode=None)
+    crop_module = build_from_cfg(train_transform, PIPELINES)
+    train_results = crop_module(results)
+    assert train_results['img'].shape[:2] == (h - 20, w - 20)
+    # All bboxes should be reserved after crop
+    assert train_results['pad_shape'][:2] == (h - 20, w - 20)
+    assert train_results['gt_bboxes'].shape[0] == 8
+    assert train_results['gt_bboxes_ignore'].shape[0] == 2
+    assert train_results['gt_bboxes'].dtype == np.float32
+    assert train_results['gt_bboxes_ignore'].dtype == np.float32
+
+    test_transform = dict(
+        type='RandomCenterCropPad',
+        crop_size=None,
+        ratios=None,
+        border=None,
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True,
+        test_mode=True,
+        test_pad_mode=('logical_or', 127))
+    crop_module = build_from_cfg(test_transform, PIPELINES)
+
+    test_results = crop_module(test_results)
+    assert test_results['img'].shape[:2] == (h | 127, w | 127)
+    assert test_results['pad_shape'][:2] == (h | 127, w | 127)
+    assert 'border' in test_results
+
+
+def test_multi_scale_flip_aug():
+    # test assertion if give both scale_factor and img_scale
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='MultiScaleFlipAug',
+            scale_factor=1.0,
+            img_scale=[(1333, 800)],
+            transforms=[dict(type='Resize')])
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion if both scale_factor and img_scale are None
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='MultiScaleFlipAug',
+            scale_factor=None,
+            img_scale=None,
+            transforms=[dict(type='Resize')])
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion if img_scale is not tuple or list of tuple
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='MultiScaleFlipAug',
+            img_scale=[1333, 800],
+            transforms=[dict(type='Resize')])
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion if flip_direction is not str or list of str
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='MultiScaleFlipAug',
+            img_scale=[(1333, 800)],
+            flip_direction=1,
+            transforms=[dict(type='Resize')])
+        build_from_cfg(transform, PIPELINES)
+
+    scale_transform = dict(
+        type='MultiScaleFlipAug',
+        img_scale=[(1333, 800), (1333, 640)],
+        transforms=[dict(type='Resize', keep_ratio=True)])
+    transform = build_from_cfg(scale_transform, PIPELINES)
+
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    results['img'] = img
+    results['img_shape'] = img.shape
+    results['ori_shape'] = img.shape
+    # Set initial values for default meta_keys
+    results['pad_shape'] = img.shape
+    results['img_fields'] = ['img']
+
+    scale_results = transform(copy.deepcopy(results))
+    assert len(scale_results['img']) == 2
+    assert scale_results['img'][0].shape == (750, 1333, 3)
+    assert scale_results['img_shape'][0] == (750, 1333, 3)
+    assert scale_results['img'][1].shape == (640, 1138, 3)
+    assert scale_results['img_shape'][1] == (640, 1138, 3)
+
+    scale_factor_transform = dict(
+        type='MultiScaleFlipAug',
+        scale_factor=[0.8, 1.0, 1.2],
+        transforms=[dict(type='Resize', keep_ratio=False)])
+    transform = build_from_cfg(scale_factor_transform, PIPELINES)
+    scale_factor_results = transform(copy.deepcopy(results))
+    assert len(scale_factor_results['img']) == 3
+    assert scale_factor_results['img'][0].shape == (230, 409, 3)
+    assert scale_factor_results['img_shape'][0] == (230, 409, 3)
+    assert scale_factor_results['img'][1].shape == (288, 512, 3)
+    assert scale_factor_results['img_shape'][1] == (288, 512, 3)
+    assert scale_factor_results['img'][2].shape == (345, 614, 3)
+    assert scale_factor_results['img_shape'][2] == (345, 614, 3)
+
+    # test pipeline of coco_detection
+    results = dict(
+        img_prefix=osp.join(osp.dirname(__file__), '../../../data'),
+        img_info=dict(filename='color.jpg'))
+    load_cfg, multi_scale_cfg = mmcv.Config.fromfile(
+        'configs/_base_/datasets/coco_detection.py').test_pipeline
+    load = build_from_cfg(load_cfg, PIPELINES)
+    transform = build_from_cfg(multi_scale_cfg, PIPELINES)
+    results = transform(load(results))
+    assert len(results['img']) == 1
+    assert len(results['img_metas']) == 1
+    assert isinstance(results['img'][0], torch.Tensor)
+    assert isinstance(results['img_metas'][0], mmcv.parallel.DataContainer)
+    assert results['img_metas'][0].data['ori_shape'] == (288, 512, 3)
+    assert results['img_metas'][0].data['img_shape'] == (750, 1333, 3)
+    assert results['img_metas'][0].data['pad_shape'] == (768, 1344, 3)
+    assert results['img_metas'][0].data['scale_factor'].tolist() == [
+        2.603515625, 2.6041667461395264, 2.603515625, 2.6041667461395264
+    ]
+
+
+def test_cutout():
+    # test n_holes
+    with pytest.raises(AssertionError):
+        transform = dict(type='CutOut', n_holes=(5, 3), cutout_shape=(8, 8))
+        build_from_cfg(transform, PIPELINES)
+    with pytest.raises(AssertionError):
+        transform = dict(type='CutOut', n_holes=(3, 4, 5), cutout_shape=(8, 8))
+        build_from_cfg(transform, PIPELINES)
+    # test cutout_shape and cutout_ratio
+    with pytest.raises(AssertionError):
+        transform = dict(type='CutOut', n_holes=1, cutout_shape=8)
+        build_from_cfg(transform, PIPELINES)
+    with pytest.raises(AssertionError):
+        transform = dict(type='CutOut', n_holes=1, cutout_ratio=0.2)
+        build_from_cfg(transform, PIPELINES)
+    # either of cutout_shape and cutout_ratio should be given
+    with pytest.raises(AssertionError):
+        transform = dict(type='CutOut', n_holes=1)
+        build_from_cfg(transform, PIPELINES)
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='CutOut',
+            n_holes=1,
+            cutout_shape=(2, 2),
+            cutout_ratio=(0.4, 0.4))
+        build_from_cfg(transform, PIPELINES)
+
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+
+    results['img'] = img
+    results['img_shape'] = img.shape
+    results['ori_shape'] = img.shape
+    results['pad_shape'] = img.shape
+    results['img_fields'] = ['img']
+
+    transform = dict(type='CutOut', n_holes=1, cutout_shape=(10, 10))
+    cutout_module = build_from_cfg(transform, PIPELINES)
+    cutout_result = cutout_module(copy.deepcopy(results))
+    assert cutout_result['img'].sum() < img.sum()
+
+    transform = dict(type='CutOut', n_holes=1, cutout_ratio=(0.8, 0.8))
+    cutout_module = build_from_cfg(transform, PIPELINES)
+    cutout_result = cutout_module(copy.deepcopy(results))
+    assert cutout_result['img'].sum() < img.sum()
+
+    transform = dict(
+        type='CutOut',
+        n_holes=(2, 4),
+        cutout_shape=[(10, 10), (15, 15)],
+        fill_in=(255, 255, 255))
+    cutout_module = build_from_cfg(transform, PIPELINES)
+    cutout_result = cutout_module(copy.deepcopy(results))
+    assert cutout_result['img'].sum() > img.sum()
+
+    transform = dict(
+        type='CutOut',
+        n_holes=1,
+        cutout_ratio=(0.8, 0.8),
+        fill_in=(255, 255, 255))
+    cutout_module = build_from_cfg(transform, PIPELINES)
+    cutout_result = cutout_module(copy.deepcopy(results))
+    assert cutout_result['img'].sum() > img.sum()
+
+
+def test_random_shift():
+    # test assertion for invalid shift_ratio
+    with pytest.raises(AssertionError):
+        transform = dict(type='RandomShift', shift_ratio=1.5)
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid max_shift_px
+    with pytest.raises(AssertionError):
+        transform = dict(type='RandomShift', max_shift_px=-1)
+        build_from_cfg(transform, PIPELINES)
+
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    results['img'] = img
+    # TODO: add img_fields test
+    results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
+
+    h, w, _ = img.shape
+    gt_bboxes = create_random_bboxes(8, w, h)
+    gt_bboxes_ignore = create_random_bboxes(2, w, h)
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
+    results['gt_bboxes'] = gt_bboxes
+    results['gt_bboxes_ignore'] = gt_bboxes_ignore
+    transform = dict(type='RandomShift', shift_ratio=1.0)
+    random_shift_module = build_from_cfg(transform, PIPELINES)
+    results = random_shift_module(results)
+
+    assert results['img'].shape[:2] == (h, w)
+    assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32
+
+
+def test_random_affine():
+    # test assertion for invalid translate_ratio
+    with pytest.raises(AssertionError):
+        transform = dict(type='RandomAffine', max_translate_ratio=1.5)
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid scaling_ratio_range
+    with pytest.raises(AssertionError):
+        transform = dict(type='RandomAffine', scaling_ratio_range=(1.5, 0.5))
+        build_from_cfg(transform, PIPELINES)
+
+    with pytest.raises(AssertionError):
+        transform = dict(type='RandomAffine', scaling_ratio_range=(0, 0.5))
+        build_from_cfg(transform, PIPELINES)
+
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    results['img'] = img
+    results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
+
+    h, w, _ = img.shape
+    gt_bboxes = create_random_bboxes(8, w, h)
+    gt_bboxes_ignore = create_random_bboxes(2, w, h)
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
+    results['gt_bboxes'] = gt_bboxes
+    results['gt_bboxes_ignore'] = gt_bboxes_ignore
+    transform = dict(type='RandomAffine')
+    random_affine_module = build_from_cfg(transform, PIPELINES)
+    results = random_affine_module(results)
+
+    assert results['img'].shape[:2] == (h, w)
+    assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32
+
+    # test filter bbox
+    gt_bboxes = np.array([[0, 0, 1, 1], [0, 0, 3, 100]], dtype=np.float32)
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
+    results['gt_bboxes'] = gt_bboxes
+    transform = dict(
+        type='RandomAffine',
+        max_rotate_degree=0.,
+        max_translate_ratio=0.,
+        scaling_ratio_range=(1., 1.),
+        max_shear_degree=0.,
+        border=(0, 0),
+        min_bbox_size=2,
+        max_aspect_ratio=20,
+        skip_filter=False)
+    random_affine_module = build_from_cfg(transform, PIPELINES)
+
+    results = random_affine_module(results)
+
+    assert results['gt_bboxes'].shape[0] == 0
+    assert results['gt_labels'].shape[0] == 0
+    assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32
+
+
+def test_mosaic():
+    # test assertion for invalid img_scale
+    with pytest.raises(AssertionError):
+        transform = dict(type='Mosaic', img_scale=640)
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid probability
+    with pytest.raises(AssertionError):
+        transform = dict(type='Mosaic', prob=1.5)
+        build_from_cfg(transform, PIPELINES)
+
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    results['img'] = img
+    # TODO: add img_fields test
+    results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
+
+    h, w, _ = img.shape
+    gt_bboxes = create_random_bboxes(8, w, h)
+    gt_bboxes_ignore = create_random_bboxes(2, w, h)
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
+    results['gt_bboxes'] = gt_bboxes
+    results['gt_bboxes_ignore'] = gt_bboxes_ignore
+    transform = dict(type='Mosaic', img_scale=(10, 12))
+    mosaic_module = build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid mix_results
+    with pytest.raises(AssertionError):
+        mosaic_module(results)
+
+    results['mix_results'] = [copy.deepcopy(results)] * 3
+    results = mosaic_module(results)
+    assert results['img'].shape[:2] == (20, 24)
+    assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32
+
+
+def test_mixup():
+    # test assertion for invalid img_scale
+    with pytest.raises(AssertionError):
+        transform = dict(type='MixUp', img_scale=640)
+        build_from_cfg(transform, PIPELINES)
+
+    results = dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    results['img'] = img
+    # TODO: add img_fields test
+    results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
+
+    h, w, _ = img.shape
+    gt_bboxes = create_random_bboxes(8, w, h)
+    gt_bboxes_ignore = create_random_bboxes(2, w, h)
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
+    results['gt_bboxes'] = gt_bboxes
+    results['gt_bboxes_ignore'] = gt_bboxes_ignore
+    transform = dict(type='MixUp', img_scale=(10, 12))
+    mixup_module = build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid mix_results
+    with pytest.raises(AssertionError):
+        mixup_module(results)
+
+    with pytest.raises(AssertionError):
+        results['mix_results'] = [copy.deepcopy(results)] * 2
+        mixup_module(results)
+
+    results['mix_results'] = [copy.deepcopy(results)]
+    results = mixup_module(results)
+    assert results['img'].shape[:2] == (288, 512)
+    assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32
+
+    # test filter bbox :
+    # 2 boxes with sides 1 and 3 are filtered as min_bbox_size=5
+    gt_bboxes = np.array([[0, 0, 1, 1], [0, 0, 3, 3]], dtype=np.float32)
+    results['gt_labels'] = np.ones(gt_bboxes.shape[0], dtype=np.int64)
+    results['gt_bboxes'] = gt_bboxes
+    results['gt_bboxes_ignore'] = np.array([], dtype=np.float32)
+    mixresults = results['mix_results'][0]
+    mixresults['gt_labels'] = copy.deepcopy(results['gt_labels'])
+    mixresults['gt_bboxes'] = copy.deepcopy(results['gt_bboxes'])
+    mixresults['gt_bboxes_ignore'] = copy.deepcopy(results['gt_bboxes_ignore'])
+    transform = dict(
+        type='MixUp',
+        img_scale=(10, 12),
+        ratio_range=(1.5, 1.5),
+        min_bbox_size=5,
+        skip_filter=False)
+    mixup_module = build_from_cfg(transform, PIPELINES)
+
+    results = mixup_module(results)
+
+    assert results['gt_bboxes'].shape[0] == 2
+    assert results['gt_labels'].shape[0] == 2
+    assert results['gt_labels'].shape[0] == results['gt_bboxes'].shape[0]
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    assert results['gt_bboxes_ignore'].dtype == np.float32
+
+
+def test_photo_metric_distortion():
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    transform = dict(type='PhotoMetricDistortion')
+    distortion_module = build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid img_fields
+    with pytest.raises(AssertionError):
+        results = dict()
+        results['img'] = img
+        results['img2'] = img
+        results['img_fields'] = ['img', 'img2']
+        distortion_module(results)
+
+    # test uint8 input
+    results = dict()
+    results['img'] = img
+    results = distortion_module(results)
+    assert results['img'].dtype == np.float32
+
+    # test float32 input
+    results = dict()
+    results['img'] = img.astype(np.float32)
+    results = distortion_module(results)
+    assert results['img'].dtype == np.float32
+
+
+def test_copypaste():
+    dst_results, src_results = dict(), dict()
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../../../data/color.jpg'), 'color')
+    dst_results['img'] = img.copy()
+    src_results['img'] = img.copy()
+
+    h, w, _ = img.shape
+
+    dst_bboxes = np.array([[0.2 * w, 0.2 * h, 0.4 * w, 0.4 * h],
+                           [0.5 * w, 0.5 * h, 0.6 * w, 0.6 * h]],
+                          dtype=np.float32)
+    src_bboxes = np.array([[0.1 * w, 0.1 * h, 0.3 * w, 0.5 * h],
+                           [0.4 * w, 0.4 * h, 0.7 * w, 0.7 * h],
+                           [0.8 * w, 0.8 * h, 0.9 * w, 0.9 * h]],
+                          dtype=np.float32)
+    dst_labels = np.ones(dst_bboxes.shape[0], dtype=np.int64)
+    src_labels = np.ones(src_bboxes.shape[0], dtype=np.int64) * 2
+    dst_masks = create_full_masks(dst_bboxes, w, h)
+    src_masks = create_full_masks(src_bboxes, w, h)
+    dst_results['gt_bboxes'] = dst_bboxes.copy()
+    src_results['gt_bboxes'] = src_bboxes.copy()
+    dst_results['gt_labels'] = dst_labels.copy()
+    src_results['gt_labels'] = src_labels.copy()
+    dst_results['gt_masks'] = copy.deepcopy(dst_masks)
+    src_results['gt_masks'] = copy.deepcopy(src_masks)
+
+    results = copy.deepcopy(dst_results)
+
+    transform = dict(type='CopyPaste', selected=False)
+    copypaste_module = build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid mix_results
+    with pytest.raises(AssertionError):
+        copypaste_module(results)
+
+    results['mix_results'] = [copy.deepcopy(src_results)]
+    results = copypaste_module(results)
+    assert results['img'].shape[:2] == (h, w)
+    # one object of destination image is totally occluded
+    assert results['gt_bboxes'].shape[0] == \
+           dst_bboxes.shape[0] + src_bboxes.shape[0] - 1
+    assert results['gt_labels'].shape[0] == \
+           dst_labels.shape[0] + src_labels.shape[0] - 1
+    assert results['gt_masks'].masks.shape[0] == \
+           dst_masks.masks.shape[0] + src_masks.masks.shape[0] - 1
+
+    assert results['gt_labels'].dtype == np.int64
+    assert results['gt_bboxes'].dtype == np.float32
+    # the object of destination image is partially occluded
+    ori_bbox = dst_bboxes[0]
+    occ_bbox = results['gt_bboxes'][0]
+    ori_mask = dst_masks.masks[0]
+    occ_mask = results['gt_masks'].masks[0]
+    assert ori_mask.sum() > occ_mask.sum()
+    assert np.all(np.abs(occ_bbox - ori_bbox) <=
+                  copypaste_module.bbox_occluded_thr) or \
+        occ_mask.sum() > copypaste_module.mask_occluded_thr
+    # test copypaste with selected objects
+    transform = dict(type='CopyPaste')
+    copypaste_module = build_from_cfg(transform, PIPELINES)
+    results = copy.deepcopy(dst_results)
+    results['mix_results'] = [copy.deepcopy(src_results)]
+    copypaste_module(results)
+    # test copypaste with an empty source image
+    results = copy.deepcopy(dst_results)
+    valid_inds = [False] * src_bboxes.shape[0]
+    src_results['gt_bboxes'] = src_bboxes[valid_inds]
+    src_results['gt_labels'] = src_labels[valid_inds]
+    src_results['gt_masks'] = src_masks[valid_inds]
+    results['mix_results'] = [copy.deepcopy(src_results)]
+    copypaste_module(results)
+    # test copy_paste based on bbox
+    dst_results.pop('gt_masks')
+    src_results.pop('gt_masks')
+    dst_bboxes = dst_results['gt_bboxes']
+    src_bboxes = src_results['gt_bboxes']
+    dst_masks = create_full_masks(dst_bboxes, w, h)
+    src_masks = create_full_masks(src_bboxes, w, h)
+    results = copy.deepcopy(dst_results)
+    results['mix_results'] = [copy.deepcopy(src_results)]
+    results = copypaste_module(results)
+    result_masks = create_full_masks(results['gt_bboxes'], w, h)
+    result_masks_np = np.where(result_masks.to_ndarray().sum(0) > 0, 1, 0)
+    masks_np = np.where(
+        (src_masks.to_ndarray().sum(0) + dst_masks.to_ndarray().sum(0)) > 0, 1,
+        0)
+    assert np.all(result_masks_np == masks_np)
+    assert 'gt_masks' not in results
diff --git a/tests/test_data/test_pipelines/test_transform/test_translate.py b/tests/test_data/test_pipelines/test_transform/test_translate.py
new file mode 100644
index 0000000..8a1f9dd
--- /dev/null
+++ b/tests/test_data/test_pipelines/test_transform/test_translate.py
@@ -0,0 +1,516 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+
+import numpy as np
+import pycocotools.mask as maskUtils
+import pytest
+from mmcv.utils import build_from_cfg
+
+from mmdet.core.mask import BitmapMasks, PolygonMasks
+from mmdet.datasets.builder import PIPELINES
+
+
+def _check_keys(results, results_translated):
+    assert len(set(results.keys()).difference(set(
+        results_translated.keys()))) == 0
+    assert len(set(results_translated.keys()).difference(set(
+        results.keys()))) == 0
+
+
+def _pad(h, w, c, pad_val, axis=-1, dtype=np.float32):
+    assert isinstance(pad_val, (int, float, tuple))
+    if isinstance(pad_val, (int, float)):
+        pad_val = tuple([pad_val] * c)
+    assert len(pad_val) == c
+    pad_data = np.stack([np.ones((h, w)) * pad_val[i] for i in range(c)],
+                        axis=axis).astype(dtype)
+    return pad_data
+
+
+def _construct_img(results):
+    h, w = results['img_info']['height'], results['img_info']['width']
+    img = np.random.uniform(0, 1, (h, w, 3)) * 255
+    img = img.astype(np.uint8)
+    results['img'] = img
+    results['img_shape'] = img.shape
+    results['ori_shape'] = img.shape
+    results['img_fields'] = ['img']
+
+
+def _construct_ann_info(h=427, w=640, c=3):
+    bboxes = np.array(
+        [[222.62, 217.82, 241.81, 238.93], [50.5, 329.7, 130.23, 384.96],
+         [175.47, 331.97, 254.8, 389.26]],
+        dtype=np.float32)
+    labels = np.array([9, 2, 2], dtype=np.int64)
+    bboxes_ignore = np.array([[59., 253., 311., 337.]], dtype=np.float32)
+    masks = [
+        [[222.62, 217.82, 222.62, 238.93, 241.81, 238.93, 240.85, 218.78]],
+        [[
+            69.19, 332.17, 82.39, 330.25, 97.24, 329.7, 114.01, 331.35, 116.76,
+            337.39, 119.78, 343.17, 128.03, 344.54, 128.86, 347.84, 124.18,
+            350.59, 129.96, 358.01, 130.23, 366.54, 129.13, 377.81, 125.28,
+            382.48, 119.78, 381.93, 117.31, 377.54, 116.21, 379.46, 114.83,
+            382.21, 107.14, 383.31, 105.49, 378.36, 77.99, 377.54, 75.79,
+            381.11, 69.74, 381.93, 66.72, 378.91, 65.07, 377.81, 63.15, 379.19,
+            62.32, 383.31, 52.7, 384.96, 50.5, 379.46, 51.32, 375.61, 51.6,
+            370.11, 51.6, 364.06, 53.52, 354.99, 56.27, 344.54, 59.57, 336.29,
+            66.45, 332.72
+        ]],
+        [[
+            175.47, 386.86, 175.87, 376.44, 177.08, 351.2, 189.1, 332.77,
+            194.31, 331.97, 236.37, 332.77, 244.79, 342.39, 246.79, 346.79,
+            248.39, 345.99, 251.6, 345.59, 254.8, 348.0, 254.8, 351.6, 250.0,
+            352.0, 250.0, 354.81, 251.6, 358.41, 251.6, 364.42, 251.6, 370.03,
+            252.8, 378.04, 252.8, 384.05, 250.8, 387.26, 246.39, 387.66,
+            245.19, 386.46, 242.38, 388.86, 233.97, 389.26, 232.77, 388.06,
+            232.77, 383.65, 195.91, 381.25, 195.91, 384.86, 191.1, 384.86,
+            187.49, 385.26, 186.69, 382.85, 184.29, 382.45, 183.09, 387.26,
+            178.68, 388.46, 176.28, 387.66
+        ]]
+    ]
+    return dict(
+        bboxes=bboxes, labels=labels, bboxes_ignore=bboxes_ignore, masks=masks)
+
+
+def _load_bboxes(results):
+    ann_info = results['ann_info']
+    results['gt_bboxes'] = ann_info['bboxes'].copy()
+    results['bbox_fields'] = ['gt_bboxes']
+    gt_bboxes_ignore = ann_info.get('bboxes_ignore', None)
+    if gt_bboxes_ignore is not None:
+        results['gt_bboxes_ignore'] = gt_bboxes_ignore.copy()
+        results['bbox_fields'].append('gt_bboxes_ignore')
+
+
+def _load_labels(results):
+    results['gt_labels'] = results['ann_info']['labels'].copy()
+
+
+def _poly2mask(mask_ann, img_h, img_w):
+    if isinstance(mask_ann, list):
+        # polygon -- a single object might consist of multiple parts
+        # we merge all parts into one mask rle code
+        rles = maskUtils.frPyObjects(mask_ann, img_h, img_w)
+        rle = maskUtils.merge(rles)
+    elif isinstance(mask_ann['counts'], list):
+        # uncompressed RLE
+        rle = maskUtils.frPyObjects(mask_ann, img_h, img_w)
+    else:
+        # rle
+        rle = mask_ann
+    mask = maskUtils.decode(rle)
+    return mask
+
+
+def _process_polygons(polygons):
+    polygons = [np.array(p) for p in polygons]
+    valid_polygons = []
+    for polygon in polygons:
+        if len(polygon) % 2 == 0 and len(polygon) >= 6:
+            valid_polygons.append(polygon)
+    return valid_polygons
+
+
+def _load_masks(results, poly2mask=True):
+    h, w = results['img_info']['height'], results['img_info']['width']
+    gt_masks = results['ann_info']['masks']
+    if poly2mask:
+        gt_masks = BitmapMasks([_poly2mask(mask, h, w) for mask in gt_masks],
+                               h, w)
+    else:
+        gt_masks = PolygonMasks(
+            [_process_polygons(polygons) for polygons in gt_masks], h, w)
+    results['gt_masks'] = gt_masks
+    results['mask_fields'] = ['gt_masks']
+
+
+def _construct_semantic_seg(results):
+    h, w = results['img_info']['height'], results['img_info']['width']
+    seg_toy = (np.random.uniform(0, 1, (h, w)) * 255).astype(np.uint8)
+    results['gt_semantic_seg'] = seg_toy
+    results['seg_fields'] = ['gt_semantic_seg']
+
+
+def construct_toy_data(poly2mask=True):
+    img_info = dict(height=427, width=640)
+    ann_info = _construct_ann_info(h=img_info['height'], w=img_info['width'])
+    results = dict(img_info=img_info, ann_info=ann_info)
+    # construct image, similar to 'LoadImageFromFile'
+    _construct_img(results)
+    # 'LoadAnnotations' (bboxes, labels, masks, semantic_seg)
+    _load_bboxes(results)
+    _load_labels(results)
+    _load_masks(results, poly2mask)
+    _construct_semantic_seg(results)
+    return results
+
+
+def test_translate():
+    # test assertion for invalid value of level
+    with pytest.raises(AssertionError):
+        transform = dict(type='Translate', level=-1)
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid type of level
+    with pytest.raises(AssertionError):
+        transform = dict(type='Translate', level=[1])
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid prob
+    with pytest.raises(AssertionError):
+        transform = dict(type='Translate', level=1, prob=-0.5)
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for the num of elements in tuple img_fill_val
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='Translate', level=1, img_fill_val=(128, 128, 128, 128))
+        build_from_cfg(transform, PIPELINES)
+
+    # test ValueError for invalid type of img_fill_val
+    with pytest.raises(ValueError):
+        transform = dict(
+            type='Translate', level=1, img_fill_val=[128, 128, 128])
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid value of img_fill_val
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='Translate', level=1, img_fill_val=(128, -1, 256))
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid value of direction
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='Translate', level=1, img_fill_val=128, direction='diagonal')
+        build_from_cfg(transform, PIPELINES)
+
+    # test assertion for invalid type of max_translate_offset
+    with pytest.raises(AssertionError):
+        transform = dict(
+            type='Translate',
+            level=1,
+            img_fill_val=128,
+            max_translate_offset=(250., ))
+        build_from_cfg(transform, PIPELINES)
+
+    # construct toy data example for unit test
+    results = construct_toy_data()
+
+    def _check_bbox_mask(results,
+                         results_translated,
+                         offset,
+                         direction,
+                         min_size=0.):
+        # The key correspondence from bboxes to labels and masks.
+        bbox2label = {
+            'gt_bboxes': 'gt_labels',
+            'gt_bboxes_ignore': 'gt_labels_ignore'
+        }
+        bbox2mask = {
+            'gt_bboxes': 'gt_masks',
+            'gt_bboxes_ignore': 'gt_masks_ignore'
+        }
+
+        def _translate_bbox(bboxes, offset, direction, max_h, max_w):
+            if direction == 'horizontal':
+                bboxes[:, 0::2] = bboxes[:, 0::2] + offset
+            elif direction == 'vertical':
+                bboxes[:, 1::2] = bboxes[:, 1::2] + offset
+            else:
+                raise ValueError
+            bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, max_w)
+            bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, max_h)
+            return bboxes
+
+        h, w, c = results_translated['img'].shape
+        for key in results_translated.get('bbox_fields', []):
+            label_key, mask_key = bbox2label[key], bbox2mask[key]
+            # check length of key
+            if label_key in results:
+                assert len(results_translated[key]) == len(
+                    results_translated[label_key])
+            if mask_key in results:
+                assert len(results_translated[key]) == len(
+                    results_translated[mask_key])
+            # construct gt_bboxes
+            gt_bboxes = _translate_bbox(
+                copy.deepcopy(results[key]), offset, direction, h, w)
+            valid_inds = (gt_bboxes[:, 2] - gt_bboxes[:, 0] > min_size) & (
+                gt_bboxes[:, 3] - gt_bboxes[:, 1] > min_size)
+            gt_bboxes = gt_bboxes[valid_inds]
+            # check bbox
+            assert np.equal(gt_bboxes, results_translated[key]).all()
+
+            # construct gt_masks
+            if mask_key not in results:
+                # e.g. 'gt_masks_ignore'
+                continue
+            masks, masks_translated = results[mask_key].to_ndarray(
+            ), results_translated[mask_key].to_ndarray()
+            assert masks.dtype == masks_translated.dtype
+            if direction == 'horizontal':
+                masks_pad = _pad(
+                    h,
+                    abs(offset),
+                    masks.shape[0],
+                    0,
+                    axis=0,
+                    dtype=masks.dtype)
+                if offset <= 0:
+                    # left shift
+                    gt_masks = np.concatenate(
+                        (masks[:, :, -offset:], masks_pad), axis=-1)
+                else:
+                    # right shift
+                    gt_masks = np.concatenate(
+                        (masks_pad, masks[:, :, :-offset]), axis=-1)
+            else:
+                masks_pad = _pad(
+                    abs(offset),
+                    w,
+                    masks.shape[0],
+                    0,
+                    axis=0,
+                    dtype=masks.dtype)
+                if offset <= 0:
+                    # top shift
+                    gt_masks = np.concatenate(
+                        (masks[:, -offset:, :], masks_pad), axis=1)
+                else:
+                    # bottom shift
+                    gt_masks = np.concatenate(
+                        (masks_pad, masks[:, :-offset, :]), axis=1)
+            gt_masks = gt_masks[valid_inds]
+            # check masks
+            assert np.equal(gt_masks, masks_translated).all()
+
+    def _check_img_seg(results, results_translated, keys, offset, fill_val,
+                       direction):
+        for key in keys:
+            assert isinstance(results_translated[key], type(results[key]))
+            # assert type(results[key]) == type(results_translated[key])
+            data, data_translated = results[key], results_translated[key]
+            if 'mask' in key:
+                data, data_translated = data.to_ndarray(
+                ), data_translated.to_ndarray()
+            assert data.dtype == data_translated.dtype
+            if 'img' in key:
+                data, data_translated = data.transpose(
+                    (2, 0, 1)), data_translated.transpose((2, 0, 1))
+            elif 'seg' in key:
+                data, data_translated = data[None, :, :], data_translated[
+                    None, :, :]
+            c, h, w = data.shape
+            if direction == 'horizontal':
+                data_pad = _pad(
+                    h, abs(offset), c, fill_val, axis=0, dtype=data.dtype)
+                if offset <= 0:
+                    # left shift
+                    data_gt = np.concatenate((data[:, :, -offset:], data_pad),
+                                             axis=-1)
+                else:
+                    # right shift
+                    data_gt = np.concatenate((data_pad, data[:, :, :-offset]),
+                                             axis=-1)
+            else:
+                data_pad = _pad(
+                    abs(offset), w, c, fill_val, axis=0, dtype=data.dtype)
+                if offset <= 0:
+                    # top shift
+                    data_gt = np.concatenate((data[:, -offset:, :], data_pad),
+                                             axis=1)
+                else:
+                    # bottom shift
+                    data_gt = np.concatenate((data_pad, data[:, :-offset, :]),
+                                             axis=1)
+            if 'mask' in key:
+                # TODO assertion here. ``data_translated`` must be a subset
+                # (or equal) of ``data_gt``
+                pass
+            else:
+                assert np.equal(data_gt, data_translated).all()
+
+    def check_translate(results,
+                        results_translated,
+                        offset,
+                        img_fill_val,
+                        seg_ignore_label,
+                        direction,
+                        min_size=0):
+        # check keys
+        _check_keys(results, results_translated)
+        # check image
+        _check_img_seg(results, results_translated,
+                       results.get('img_fields', ['img']), offset,
+                       img_fill_val, direction)
+        # check segmentation map
+        _check_img_seg(results, results_translated,
+                       results.get('seg_fields', []), offset, seg_ignore_label,
+                       direction)
+        # check masks and bboxes
+        _check_bbox_mask(results, results_translated, offset, direction,
+                         min_size)
+
+    # test case when level=0 (without translate aug)
+    img_fill_val = (104, 116, 124)
+    seg_ignore_label = 255
+    transform = dict(
+        type='Translate',
+        level=0,
+        prob=1.0,
+        img_fill_val=img_fill_val,
+        seg_ignore_label=seg_ignore_label)
+    translate_module = build_from_cfg(transform, PIPELINES)
+    results_wo_translate = translate_module(copy.deepcopy(results))
+    check_translate(
+        copy.deepcopy(results),
+        results_wo_translate,
+        0,
+        img_fill_val,
+        seg_ignore_label,
+        'horizontal',
+    )
+
+    # test case when level>0 and translate horizontally (left shift).
+    transform = dict(
+        type='Translate',
+        level=8,
+        prob=1.0,
+        img_fill_val=img_fill_val,
+        random_negative_prob=1.0,
+        seg_ignore_label=seg_ignore_label)
+    translate_module = build_from_cfg(transform, PIPELINES)
+    offset = translate_module.offset
+    results_translated = translate_module(copy.deepcopy(results))
+    check_translate(
+        copy.deepcopy(results),
+        results_translated,
+        -offset,
+        img_fill_val,
+        seg_ignore_label,
+        'horizontal',
+    )
+
+    # test case when level>0 and translate horizontally (right shift).
+    translate_module.random_negative_prob = 0.0
+    results_translated = translate_module(copy.deepcopy(results))
+    check_translate(
+        copy.deepcopy(results),
+        results_translated,
+        offset,
+        img_fill_val,
+        seg_ignore_label,
+        'horizontal',
+    )
+
+    # test case when level>0 and translate vertically (top shift).
+    transform = dict(
+        type='Translate',
+        level=10,
+        prob=1.0,
+        img_fill_val=img_fill_val,
+        seg_ignore_label=seg_ignore_label,
+        random_negative_prob=1.0,
+        direction='vertical')
+    translate_module = build_from_cfg(transform, PIPELINES)
+    offset = translate_module.offset
+    results_translated = translate_module(copy.deepcopy(results))
+    check_translate(
+        copy.deepcopy(results), results_translated, -offset, img_fill_val,
+        seg_ignore_label, 'vertical')
+
+    # test case when level>0 and translate vertically (bottom shift).
+    translate_module.random_negative_prob = 0.0
+    results_translated = translate_module(copy.deepcopy(results))
+    check_translate(
+        copy.deepcopy(results), results_translated, offset, img_fill_val,
+        seg_ignore_label, 'vertical')
+
+    # test case when no translation is called (prob<=0)
+    transform = dict(
+        type='Translate',
+        level=8,
+        prob=0.0,
+        img_fill_val=img_fill_val,
+        random_negative_prob=0.0,
+        seg_ignore_label=seg_ignore_label)
+    translate_module = build_from_cfg(transform, PIPELINES)
+    results_translated = translate_module(copy.deepcopy(results))
+
+    # test translate vertically with PolygonMasks (top shift)
+    results = construct_toy_data(False)
+    transform = dict(
+        type='Translate',
+        level=10,
+        prob=1.0,
+        img_fill_val=img_fill_val,
+        seg_ignore_label=seg_ignore_label,
+        direction='vertical')
+    translate_module = build_from_cfg(transform, PIPELINES)
+    offset = translate_module.offset
+    translate_module.random_negative_prob = 1.0
+    results_translated = translate_module(copy.deepcopy(results))
+
+    def _translated_gt(masks, direction, offset, out_shape):
+        translated_masks = []
+        for poly_per_obj in masks:
+            translated_poly_per_obj = []
+            for p in poly_per_obj:
+                p = p.copy()
+                if direction == 'horizontal':
+                    p[0::2] = np.clip(p[0::2] + offset, 0, out_shape[1])
+                elif direction == 'vertical':
+                    p[1::2] = np.clip(p[1::2] + offset, 0, out_shape[0])
+                if PolygonMasks([[p]], *out_shape).areas[0] > 0:
+                    # filter invalid (area=0)
+                    translated_poly_per_obj.append(p)
+            if len(translated_poly_per_obj):
+                translated_masks.append(translated_poly_per_obj)
+        translated_masks = PolygonMasks(translated_masks, *out_shape)
+        return translated_masks
+
+    h, w = results['img_shape'][:2]
+    for key in results.get('mask_fields', []):
+        masks = results[key]
+        translated_gt = _translated_gt(masks, 'vertical', -offset, (h, w))
+        assert np.equal(results_translated[key].to_ndarray(),
+                        translated_gt.to_ndarray()).all()
+
+    # test translate horizontally with PolygonMasks (right shift)
+    results = construct_toy_data(False)
+    transform = dict(
+        type='Translate',
+        level=8,
+        prob=1.0,
+        img_fill_val=img_fill_val,
+        random_negative_prob=0.0,
+        seg_ignore_label=seg_ignore_label)
+    translate_module = build_from_cfg(transform, PIPELINES)
+    offset = translate_module.offset
+    results_translated = translate_module(copy.deepcopy(results))
+    h, w = results['img_shape'][:2]
+    for key in results.get('mask_fields', []):
+        masks = results[key]
+        translated_gt = _translated_gt(masks, 'horizontal', offset, (h, w))
+        assert np.equal(results_translated[key].to_ndarray(),
+                        translated_gt.to_ndarray()).all()
+
+    # test AutoAugment equipped with Translate
+    policies = [[dict(type='Translate', level=10, prob=1.)]]
+    autoaug = dict(type='AutoAugment', policies=policies)
+    autoaug_module = build_from_cfg(autoaug, PIPELINES)
+    autoaug_module(copy.deepcopy(results))
+
+    policies = [[
+        dict(type='Translate', level=10, prob=1.),
+        dict(
+            type='Translate',
+            level=8,
+            img_fill_val=img_fill_val,
+            direction='vertical')
+    ]]
+    autoaug = dict(type='AutoAugment', policies=policies)
+    autoaug_module = build_from_cfg(autoaug, PIPELINES)
+    autoaug_module(copy.deepcopy(results))
diff --git a/tests/test_data/test_pipelines/test_transform/utils.py b/tests/test_data/test_pipelines/test_transform/utils.py
new file mode 100644
index 0000000..c3b3920
--- /dev/null
+++ b/tests/test_data/test_pipelines/test_transform/utils.py
@@ -0,0 +1,88 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+
+from mmdet.core.mask import BitmapMasks, PolygonMasks
+
+
+def _check_fields(results, pipeline_results, keys):
+    """Check data in fields from two results are same."""
+    for key in keys:
+        if isinstance(results[key], (BitmapMasks, PolygonMasks)):
+            assert np.equal(results[key].to_ndarray(),
+                            pipeline_results[key].to_ndarray()).all()
+        else:
+            assert np.equal(results[key], pipeline_results[key]).all()
+            assert results[key].dtype == pipeline_results[key].dtype
+
+
+def check_result_same(results, pipeline_results):
+    """Check whether the `pipeline_results` is the same with the predefined
+    `results`.
+
+    Args:
+        results (dict): Predefined results which should be the standard output
+            of the transform pipeline.
+        pipeline_results (dict): Results processed by the transform pipeline.
+    """
+    # check image
+    _check_fields(results, pipeline_results,
+                  results.get('img_fields', ['img']))
+    # check bboxes
+    _check_fields(results, pipeline_results, results.get('bbox_fields', []))
+    # check masks
+    _check_fields(results, pipeline_results, results.get('mask_fields', []))
+    # check segmentations
+    _check_fields(results, pipeline_results, results.get('seg_fields', []))
+    # check gt_labels
+    if 'gt_labels' in results:
+        assert np.equal(results['gt_labels'],
+                        pipeline_results['gt_labels']).all()
+
+
+def construct_toy_data(poly2mask=True):
+    img = np.array([[1, 2, 3, 4], [5, 6, 7, 8]], dtype=np.uint8)
+    img = np.stack([img, img, img], axis=-1)
+    results = dict()
+    # image
+    results['img'] = img
+    results['img_shape'] = img.shape
+    results['img_fields'] = ['img']
+    # bboxes
+    results['bbox_fields'] = ['gt_bboxes', 'gt_bboxes_ignore']
+    results['gt_bboxes'] = np.array([[0., 0., 2., 1.]], dtype=np.float32)
+    results['gt_bboxes_ignore'] = np.array([[2., 0., 3., 1.]],
+                                           dtype=np.float32)
+    # labels
+    results['gt_labels'] = np.array([1], dtype=np.int64)
+    # masks
+    results['mask_fields'] = ['gt_masks']
+    if poly2mask:
+        gt_masks = np.array([[0, 1, 1, 0], [0, 1, 0, 0]],
+                            dtype=np.uint8)[None, :, :]
+        results['gt_masks'] = BitmapMasks(gt_masks, 2, 4)
+    else:
+        raw_masks = [[np.array([0, 0, 2, 0, 2, 1, 0, 1], dtype=np.float)]]
+        results['gt_masks'] = PolygonMasks(raw_masks, 2, 4)
+    # segmentations
+    results['seg_fields'] = ['gt_semantic_seg']
+    results['gt_semantic_seg'] = img[..., 0]
+    return results
+
+
+def create_random_bboxes(num_bboxes, img_w, img_h):
+    bboxes_left_top = np.random.uniform(0, 0.5, size=(num_bboxes, 2))
+    bboxes_right_bottom = np.random.uniform(0.5, 1, size=(num_bboxes, 2))
+    bboxes = np.concatenate((bboxes_left_top, bboxes_right_bottom), 1)
+    bboxes = (bboxes * np.array([img_w, img_h, img_w, img_h])).astype(
+        np.float32)
+    return bboxes
+
+
+def create_full_masks(gt_bboxes, img_w, img_h):
+    xmin, ymin = gt_bboxes[:, 0:1], gt_bboxes[:, 1:2]
+    xmax, ymax = gt_bboxes[:, 2:3], gt_bboxes[:, 3:4]
+    gt_masks = np.zeros((len(gt_bboxes), img_h, img_w), dtype=np.uint8)
+    for i in range(len(gt_bboxes)):
+        gt_masks[i, int(ymin[i]):int(ymax[i]), int(xmin[i]):int(xmax[i])] = 1
+    gt_masks = BitmapMasks(gt_masks, img_h, img_w)
+    return gt_masks
diff --git a/tests/test_data/test_utils.py b/tests/test_data/test_utils.py
new file mode 100644
index 0000000..289df32
--- /dev/null
+++ b/tests/test_data/test_utils.py
@@ -0,0 +1,80 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+
+from mmdet.datasets import get_loading_pipeline, replace_ImageToTensor
+
+
+def test_replace_ImageToTensor():
+    # with MultiScaleFlipAug
+    pipelines = [
+        dict(type='LoadImageFromFile'),
+        dict(
+            type='MultiScaleFlipAug',
+            img_scale=(1333, 800),
+            flip=False,
+            transforms=[
+                dict(type='Resize', keep_ratio=True),
+                dict(type='RandomFlip'),
+                dict(type='Normalize'),
+                dict(type='Pad', size_divisor=32),
+                dict(type='ImageToTensor', keys=['img']),
+                dict(type='Collect', keys=['img']),
+            ])
+    ]
+    expected_pipelines = [
+        dict(type='LoadImageFromFile'),
+        dict(
+            type='MultiScaleFlipAug',
+            img_scale=(1333, 800),
+            flip=False,
+            transforms=[
+                dict(type='Resize', keep_ratio=True),
+                dict(type='RandomFlip'),
+                dict(type='Normalize'),
+                dict(type='Pad', size_divisor=32),
+                dict(type='DefaultFormatBundle'),
+                dict(type='Collect', keys=['img']),
+            ])
+    ]
+    with pytest.warns(UserWarning):
+        assert expected_pipelines == replace_ImageToTensor(pipelines)
+
+    # without MultiScaleFlipAug
+    pipelines = [
+        dict(type='LoadImageFromFile'),
+        dict(type='Resize', keep_ratio=True),
+        dict(type='RandomFlip'),
+        dict(type='Normalize'),
+        dict(type='Pad', size_divisor=32),
+        dict(type='ImageToTensor', keys=['img']),
+        dict(type='Collect', keys=['img']),
+    ]
+    expected_pipelines = [
+        dict(type='LoadImageFromFile'),
+        dict(type='Resize', keep_ratio=True),
+        dict(type='RandomFlip'),
+        dict(type='Normalize'),
+        dict(type='Pad', size_divisor=32),
+        dict(type='DefaultFormatBundle'),
+        dict(type='Collect', keys=['img']),
+    ]
+    with pytest.warns(UserWarning):
+        assert expected_pipelines == replace_ImageToTensor(pipelines)
+
+
+def test_get_loading_pipeline():
+    pipelines = [
+        dict(type='LoadImageFromFile'),
+        dict(type='LoadAnnotations', with_bbox=True),
+        dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
+        dict(type='RandomFlip', flip_ratio=0.5),
+        dict(type='Pad', size_divisor=32),
+        dict(type='DefaultFormatBundle'),
+        dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
+    ]
+    expected_pipelines = [
+        dict(type='LoadImageFromFile'),
+        dict(type='LoadAnnotations', with_bbox=True)
+    ]
+    assert expected_pipelines == \
+           get_loading_pipeline(pipelines)
diff --git a/tests/test_downstream/test_mmtrack.py b/tests/test_downstream/test_mmtrack.py
new file mode 100644
index 0000000..b709d5b
--- /dev/null
+++ b/tests/test_downstream/test_mmtrack.py
@@ -0,0 +1,230 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+from collections import defaultdict
+
+import numpy as np
+import pytest
+import torch
+from mmcv import Config
+
+
+@pytest.mark.parametrize(
+    'cfg_file',
+    ['./tests/data/configs_mmtrack/selsa_faster_rcnn_r101_dc5_1x.py'])
+def test_vid_fgfa_style_forward(cfg_file):
+    config = Config.fromfile(cfg_file)
+    model = copy.deepcopy(config.model)
+    model.pretrains = None
+    model.detector.pretrained = None
+
+    from mmtrack.models import build_model
+    detector = build_model(model)
+
+    # Test forward train with a non-empty truth batch
+    input_shape = (1, 3, 256, 256)
+    mm_inputs = _demo_mm_inputs(input_shape, num_items=[10])
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+    img_metas[0]['is_video_data'] = True
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_labels = mm_inputs['gt_labels']
+    gt_masks = mm_inputs['gt_masks']
+
+    ref_input_shape = (2, 3, 256, 256)
+    ref_mm_inputs = _demo_mm_inputs(ref_input_shape, num_items=[9, 11])
+    ref_img = ref_mm_inputs.pop('imgs')[None]
+    ref_img_metas = ref_mm_inputs.pop('img_metas')
+    ref_img_metas[0]['is_video_data'] = True
+    ref_img_metas[1]['is_video_data'] = True
+    ref_gt_bboxes = ref_mm_inputs['gt_bboxes']
+    ref_gt_labels = ref_mm_inputs['gt_labels']
+    ref_gt_masks = ref_mm_inputs['gt_masks']
+
+    losses = detector.forward(
+        img=imgs,
+        img_metas=img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        ref_img=ref_img,
+        ref_img_metas=[ref_img_metas],
+        ref_gt_bboxes=ref_gt_bboxes,
+        ref_gt_labels=ref_gt_labels,
+        gt_masks=gt_masks,
+        ref_gt_masks=ref_gt_masks,
+        return_loss=True)
+    assert isinstance(losses, dict)
+    loss, _ = detector._parse_losses(losses)
+    loss.requires_grad_(True)
+    assert float(loss.item()) > 0
+    loss.backward()
+
+    # Test forward train with an empty truth batch
+    mm_inputs = _demo_mm_inputs(input_shape, num_items=[0])
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+    img_metas[0]['is_video_data'] = True
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_labels = mm_inputs['gt_labels']
+    gt_masks = mm_inputs['gt_masks']
+
+    ref_mm_inputs = _demo_mm_inputs(ref_input_shape, num_items=[0, 0])
+    ref_imgs = ref_mm_inputs.pop('imgs')[None]
+    ref_img_metas = ref_mm_inputs.pop('img_metas')
+    ref_img_metas[0]['is_video_data'] = True
+    ref_img_metas[1]['is_video_data'] = True
+    ref_gt_bboxes = ref_mm_inputs['gt_bboxes']
+    ref_gt_labels = ref_mm_inputs['gt_labels']
+    ref_gt_masks = ref_mm_inputs['gt_masks']
+
+    losses = detector.forward(
+        img=imgs,
+        img_metas=img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        ref_img=ref_imgs,
+        ref_img_metas=[ref_img_metas],
+        ref_gt_bboxes=ref_gt_bboxes,
+        ref_gt_labels=ref_gt_labels,
+        gt_masks=gt_masks,
+        ref_gt_masks=ref_gt_masks,
+        return_loss=True)
+    assert isinstance(losses, dict)
+    loss, _ = detector._parse_losses(losses)
+    loss.requires_grad_(True)
+    assert float(loss.item()) > 0
+    loss.backward()
+
+    # Test forward test with frame_stride=1 and frame_range=[-1,0]
+    with torch.no_grad():
+        imgs = torch.cat([imgs, imgs.clone()], dim=0)
+        img_list = [g[None, :] for g in imgs]
+        img_metas.extend(copy.deepcopy(img_metas))
+        for i in range(len(img_metas)):
+            img_metas[i]['frame_id'] = i
+            img_metas[i]['num_left_ref_imgs'] = 1
+            img_metas[i]['frame_stride'] = 1
+        ref_imgs = [ref_imgs.clone(), imgs[[0]][None].clone()]
+        ref_img_metas = [
+            copy.deepcopy(ref_img_metas),
+            copy.deepcopy([img_metas[0]])
+        ]
+        results = defaultdict(list)
+        for one_img, one_meta, ref_img, ref_img_meta in zip(
+                img_list, img_metas, ref_imgs, ref_img_metas):
+            result = detector.forward([one_img], [[one_meta]],
+                                      ref_img=[ref_img],
+                                      ref_img_metas=[[ref_img_meta]],
+                                      return_loss=False)
+            for k, v in result.items():
+                results[k].append(v)
+
+
+@pytest.mark.parametrize('cfg_file', [
+    './tests/data/configs_mmtrack/tracktor_faster-rcnn_r50_fpn_4e.py',
+])
+def test_tracktor_forward(cfg_file):
+    config = Config.fromfile(cfg_file)
+    model = copy.deepcopy(config.model)
+    model.pretrains = None
+    model.detector.pretrained = None
+
+    from mmtrack.models import build_model
+    mot = build_model(model)
+    mot.eval()
+
+    input_shape = (1, 3, 256, 256)
+    mm_inputs = _demo_mm_inputs(input_shape, num_items=[10], with_track=True)
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+    with torch.no_grad():
+        imgs = torch.cat([imgs, imgs.clone()], dim=0)
+        img_list = [g[None, :] for g in imgs]
+        img2_metas = copy.deepcopy(img_metas)
+        img2_metas[0]['frame_id'] = 1
+        img_metas.extend(img2_metas)
+        results = defaultdict(list)
+        for one_img, one_meta in zip(img_list, img_metas):
+            result = mot.forward([one_img], [[one_meta]], return_loss=False)
+            for k, v in result.items():
+                results[k].append(v)
+
+
+def _demo_mm_inputs(
+        input_shape=(1, 3, 300, 300),
+        num_items=None,
+        num_classes=10,
+        with_track=False):
+    """Create a superset of inputs needed to run test or train batches.
+
+    Args:
+        input_shape (tuple):
+            input batch dimensions
+
+        num_items (None | List[int]):
+            specifies the number of boxes in each batch item
+
+        num_classes (int):
+            number of different labels a box might have
+    """
+    from mmdet.core import BitmapMasks
+
+    (N, C, H, W) = input_shape
+
+    rng = np.random.RandomState(0)
+
+    imgs = rng.rand(*input_shape)
+
+    img_metas = [{
+        'img_shape': (H, W, C),
+        'ori_shape': (H, W, C),
+        'pad_shape': (H, W, C),
+        'filename': '<demo>.png',
+        'scale_factor': 1.0,
+        'flip': False,
+        'frame_id': 0,
+        'img_norm_cfg': {
+            'mean': (128.0, 128.0, 128.0),
+            'std': (10.0, 10.0, 10.0)
+        }
+    } for i in range(N)]
+
+    gt_bboxes = []
+    gt_labels = []
+    gt_masks = []
+    gt_match_indices = []
+
+    for batch_idx in range(N):
+        if num_items is None:
+            num_boxes = rng.randint(1, 10)
+        else:
+            num_boxes = num_items[batch_idx]
+
+        cx, cy, bw, bh = rng.rand(num_boxes, 4).T
+
+        tl_x = ((cx * W) - (W * bw / 2)).clip(0, W)
+        tl_y = ((cy * H) - (H * bh / 2)).clip(0, H)
+        br_x = ((cx * W) + (W * bw / 2)).clip(0, W)
+        br_y = ((cy * H) + (H * bh / 2)).clip(0, H)
+
+        boxes = np.vstack([tl_x, tl_y, br_x, br_y]).T
+        class_idxs = rng.randint(1, num_classes, size=num_boxes)
+
+        gt_bboxes.append(torch.FloatTensor(boxes))
+        gt_labels.append(torch.LongTensor(class_idxs))
+        if with_track:
+            gt_match_indices.append(torch.arange(boxes.shape[0]))
+
+    mask = np.random.randint(0, 2, (len(boxes), H, W), dtype=np.uint8)
+    gt_masks.append(BitmapMasks(mask, H, W))
+
+    mm_inputs = {
+        'imgs': torch.FloatTensor(imgs).requires_grad_(True),
+        'img_metas': img_metas,
+        'gt_bboxes': gt_bboxes,
+        'gt_labels': gt_labels,
+        'gt_bboxes_ignore': None,
+        'gt_masks': gt_masks,
+    }
+    if with_track:
+        mm_inputs['gt_match_indices'] = gt_match_indices
+    return mm_inputs
diff --git a/tests/test_metrics/test_box_overlap.py b/tests/test_metrics/test_box_overlap.py
new file mode 100644
index 0000000..1d03253
--- /dev/null
+++ b/tests/test_metrics/test_box_overlap.py
@@ -0,0 +1,134 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import pytest
+import torch
+
+from mmdet.core import BboxOverlaps2D, bbox_overlaps
+from mmdet.core.evaluation.bbox_overlaps import \
+    bbox_overlaps as recall_overlaps
+
+
+def test_bbox_overlaps_2d(eps=1e-7):
+
+    def _construct_bbox(num_bbox=None):
+        img_h = int(np.random.randint(3, 1000))
+        img_w = int(np.random.randint(3, 1000))
+        if num_bbox is None:
+            num_bbox = np.random.randint(1, 10)
+        x1y1 = torch.rand((num_bbox, 2))
+        x2y2 = torch.max(torch.rand((num_bbox, 2)), x1y1)
+        bboxes = torch.cat((x1y1, x2y2), -1)
+        bboxes[:, 0::2] *= img_w
+        bboxes[:, 1::2] *= img_h
+        return bboxes, num_bbox
+
+    # is_aligned is True, bboxes.size(-1) == 5 (include score)
+    self = BboxOverlaps2D()
+    bboxes1, num_bbox = _construct_bbox()
+    bboxes2, _ = _construct_bbox(num_bbox)
+    bboxes1 = torch.cat((bboxes1, torch.rand((num_bbox, 1))), 1)
+    bboxes2 = torch.cat((bboxes2, torch.rand((num_bbox, 1))), 1)
+    gious = self(bboxes1, bboxes2, 'giou', True)
+    assert gious.size() == (num_bbox, ), gious.size()
+    assert torch.all(gious >= -1) and torch.all(gious <= 1)
+
+    # is_aligned is True, bboxes1.size(-2) == 0
+    bboxes1 = torch.empty((0, 4))
+    bboxes2 = torch.empty((0, 4))
+    gious = self(bboxes1, bboxes2, 'giou', True)
+    assert gious.size() == (0, ), gious.size()
+    assert torch.all(gious == torch.empty((0, )))
+    assert torch.all(gious >= -1) and torch.all(gious <= 1)
+
+    # is_aligned is True, and bboxes.ndims > 2
+    bboxes1, num_bbox = _construct_bbox()
+    bboxes2, _ = _construct_bbox(num_bbox)
+    bboxes1 = bboxes1.unsqueeze(0).repeat(2, 1, 1)
+    # test assertion when batch dim is not the same
+    with pytest.raises(AssertionError):
+        self(bboxes1, bboxes2.unsqueeze(0).repeat(3, 1, 1), 'giou', True)
+    bboxes2 = bboxes2.unsqueeze(0).repeat(2, 1, 1)
+    gious = self(bboxes1, bboxes2, 'giou', True)
+    assert torch.all(gious >= -1) and torch.all(gious <= 1)
+    assert gious.size() == (2, num_bbox)
+    bboxes1 = bboxes1.unsqueeze(0).repeat(2, 1, 1, 1)
+    bboxes2 = bboxes2.unsqueeze(0).repeat(2, 1, 1, 1)
+    gious = self(bboxes1, bboxes2, 'giou', True)
+    assert torch.all(gious >= -1) and torch.all(gious <= 1)
+    assert gious.size() == (2, 2, num_bbox)
+
+    # is_aligned is False
+    bboxes1, num_bbox1 = _construct_bbox()
+    bboxes2, num_bbox2 = _construct_bbox()
+    gious = self(bboxes1, bboxes2, 'giou')
+    assert torch.all(gious >= -1) and torch.all(gious <= 1)
+    assert gious.size() == (num_bbox1, num_bbox2)
+
+    # is_aligned is False, and bboxes.ndims > 2
+    bboxes1 = bboxes1.unsqueeze(0).repeat(2, 1, 1)
+    bboxes2 = bboxes2.unsqueeze(0).repeat(2, 1, 1)
+    gious = self(bboxes1, bboxes2, 'giou')
+    assert torch.all(gious >= -1) and torch.all(gious <= 1)
+    assert gious.size() == (2, num_bbox1, num_bbox2)
+    bboxes1 = bboxes1.unsqueeze(0)
+    bboxes2 = bboxes2.unsqueeze(0)
+    gious = self(bboxes1, bboxes2, 'giou')
+    assert torch.all(gious >= -1) and torch.all(gious <= 1)
+    assert gious.size() == (1, 2, num_bbox1, num_bbox2)
+
+    # is_aligned is False, bboxes1.size(-2) == 0
+    gious = self(torch.empty(1, 2, 0, 4), bboxes2, 'giou')
+    assert torch.all(gious == torch.empty(1, 2, 0, bboxes2.size(-2)))
+    assert torch.all(gious >= -1) and torch.all(gious <= 1)
+
+    # test allclose between bbox_overlaps and the original official
+    # implementation.
+    bboxes1 = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [32, 32, 38, 42],
+    ])
+    bboxes2 = torch.FloatTensor([
+        [0, 0, 10, 20],
+        [0, 10, 10, 19],
+        [10, 10, 20, 20],
+    ])
+    gious = bbox_overlaps(bboxes1, bboxes2, 'giou', is_aligned=True, eps=eps)
+    gious = gious.numpy().round(4)
+    # the gt is got with four decimal precision.
+    expected_gious = np.array([0.5000, -0.0500, -0.8214])
+    assert np.allclose(gious, expected_gious, rtol=0, atol=eps)
+
+    # test mode 'iof'
+    ious = bbox_overlaps(bboxes1, bboxes2, 'iof', is_aligned=True, eps=eps)
+    assert torch.all(ious >= -1) and torch.all(ious <= 1)
+    assert ious.size() == (bboxes1.size(0), )
+    ious = bbox_overlaps(bboxes1, bboxes2, 'iof', eps=eps)
+    assert torch.all(ious >= -1) and torch.all(ious <= 1)
+    assert ious.size() == (bboxes1.size(0), bboxes2.size(0))
+
+
+def test_voc_recall_overlaps():
+
+    def _construct_bbox(num_bbox=None):
+        img_h = int(np.random.randint(3, 1000))
+        img_w = int(np.random.randint(3, 1000))
+        if num_bbox is None:
+            num_bbox = np.random.randint(1, 10)
+        x1y1 = torch.rand((num_bbox, 2))
+        x2y2 = torch.max(torch.rand((num_bbox, 2)), x1y1)
+        bboxes = torch.cat((x1y1, x2y2), -1)
+        bboxes[:, 0::2] *= img_w
+        bboxes[:, 1::2] *= img_h
+        return bboxes.numpy(), num_bbox
+
+    bboxes1, num_bbox = _construct_bbox()
+    bboxes2, _ = _construct_bbox(num_bbox)
+    ious = recall_overlaps(
+        bboxes1, bboxes2, 'iou', use_legacy_coordinate=False)
+    assert ious.shape == (num_bbox, num_bbox)
+    assert np.all(ious >= -1) and np.all(ious <= 1)
+
+    ious = recall_overlaps(bboxes1, bboxes2, 'iou', use_legacy_coordinate=True)
+    assert ious.shape == (num_bbox, num_bbox)
+    assert np.all(ious >= -1) and np.all(ious <= 1)
diff --git a/tests/test_metrics/test_losses.py b/tests/test_metrics/test_losses.py
new file mode 100644
index 0000000..06fe43d
--- /dev/null
+++ b/tests/test_metrics/test_losses.py
@@ -0,0 +1,241 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+from mmdet.models import Accuracy, build_loss
+
+
+def test_ce_loss():
+    # use_mask and use_sigmoid cannot be true at the same time
+    with pytest.raises(AssertionError):
+        loss_cfg = dict(
+            type='CrossEntropyLoss',
+            use_mask=True,
+            use_sigmoid=True,
+            loss_weight=1.0)
+        build_loss(loss_cfg)
+
+    # test loss with class weights
+    loss_cls_cfg = dict(
+        type='CrossEntropyLoss',
+        use_sigmoid=False,
+        class_weight=[0.8, 0.2],
+        loss_weight=1.0)
+    loss_cls = build_loss(loss_cls_cfg)
+    fake_pred = torch.Tensor([[100, -100]])
+    fake_label = torch.Tensor([1]).long()
+    assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(40.))
+
+    loss_cls_cfg = dict(
+        type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)
+    loss_cls = build_loss(loss_cls_cfg)
+    assert torch.allclose(loss_cls(fake_pred, fake_label), torch.tensor(200.))
+
+
+def test_varifocal_loss():
+    # only sigmoid version of VarifocalLoss is implemented
+    with pytest.raises(AssertionError):
+        loss_cfg = dict(
+            type='VarifocalLoss', use_sigmoid=False, loss_weight=1.0)
+        build_loss(loss_cfg)
+
+    # test that alpha should be greater than 0
+    with pytest.raises(AssertionError):
+        loss_cfg = dict(
+            type='VarifocalLoss',
+            alpha=-0.75,
+            gamma=2.0,
+            use_sigmoid=True,
+            loss_weight=1.0)
+        build_loss(loss_cfg)
+
+    # test that pred and target should be of the same size
+    loss_cls_cfg = dict(
+        type='VarifocalLoss',
+        use_sigmoid=True,
+        alpha=0.75,
+        gamma=2.0,
+        iou_weighted=True,
+        reduction='mean',
+        loss_weight=1.0)
+    loss_cls = build_loss(loss_cls_cfg)
+    with pytest.raises(AssertionError):
+        fake_pred = torch.Tensor([[100.0, -100.0]])
+        fake_target = torch.Tensor([[1.0]])
+        loss_cls(fake_pred, fake_target)
+
+    # test the calculation
+    loss_cls = build_loss(loss_cls_cfg)
+    fake_pred = torch.Tensor([[100.0, -100.0]])
+    fake_target = torch.Tensor([[1.0, 0.0]])
+    assert torch.allclose(loss_cls(fake_pred, fake_target), torch.tensor(0.0))
+
+    # test the loss with weights
+    loss_cls = build_loss(loss_cls_cfg)
+    fake_pred = torch.Tensor([[0.0, 100.0]])
+    fake_target = torch.Tensor([[1.0, 1.0]])
+    fake_weight = torch.Tensor([0.0, 1.0])
+    assert torch.allclose(
+        loss_cls(fake_pred, fake_target, fake_weight), torch.tensor(0.0))
+
+
+def test_kd_loss():
+    # test that temperature should be greater than 1
+    with pytest.raises(AssertionError):
+        loss_cfg = dict(
+            type='KnowledgeDistillationKLDivLoss', loss_weight=1.0, T=0.5)
+        build_loss(loss_cfg)
+
+    # test that pred and target should be of the same size
+    loss_cls_cfg = dict(
+        type='KnowledgeDistillationKLDivLoss', loss_weight=1.0, T=1)
+    loss_cls = build_loss(loss_cls_cfg)
+    with pytest.raises(AssertionError):
+        fake_pred = torch.Tensor([[100, -100]])
+        fake_label = torch.Tensor([1]).long()
+        loss_cls(fake_pred, fake_label)
+
+    # test the calculation
+    loss_cls = build_loss(loss_cls_cfg)
+    fake_pred = torch.Tensor([[100.0, 100.0]])
+    fake_target = torch.Tensor([[1.0, 1.0]])
+    assert torch.allclose(loss_cls(fake_pred, fake_target), torch.tensor(0.0))
+
+    # test the loss with weights
+    loss_cls = build_loss(loss_cls_cfg)
+    fake_pred = torch.Tensor([[100.0, -100.0], [100.0, 100.0]])
+    fake_target = torch.Tensor([[1.0, 0.0], [1.0, 1.0]])
+    fake_weight = torch.Tensor([0.0, 1.0])
+    assert torch.allclose(
+        loss_cls(fake_pred, fake_target, fake_weight), torch.tensor(0.0))
+
+
+def test_seesaw_loss():
+    # only softmax version of Seesaw Loss is implemented
+    with pytest.raises(AssertionError):
+        loss_cfg = dict(type='SeesawLoss', use_sigmoid=True, loss_weight=1.0)
+        build_loss(loss_cfg)
+
+    # test that cls_score.size(-1) == num_classes + 2
+    loss_cls_cfg = dict(
+        type='SeesawLoss', p=0.0, q=0.0, loss_weight=1.0, num_classes=2)
+    loss_cls = build_loss(loss_cls_cfg)
+    # the length of fake_pred should be num_classes + 2 = 4
+    with pytest.raises(AssertionError):
+        fake_pred = torch.Tensor([[-100, 100]])
+        fake_label = torch.Tensor([1]).long()
+        loss_cls(fake_pred, fake_label)
+    # the length of fake_pred should be num_classes + 2 = 4
+    with pytest.raises(AssertionError):
+        fake_pred = torch.Tensor([[-100, 100, -100]])
+        fake_label = torch.Tensor([1]).long()
+        loss_cls(fake_pred, fake_label)
+
+    # test the calculation without p and q
+    loss_cls_cfg = dict(
+        type='SeesawLoss', p=0.0, q=0.0, loss_weight=1.0, num_classes=2)
+    loss_cls = build_loss(loss_cls_cfg)
+    fake_pred = torch.Tensor([[-100, 100, -100, 100]])
+    fake_label = torch.Tensor([1]).long()
+    loss = loss_cls(fake_pred, fake_label)
+    assert torch.allclose(loss['loss_cls_objectness'], torch.tensor(200.))
+    assert torch.allclose(loss['loss_cls_classes'], torch.tensor(0.))
+
+    # test the calculation with p and without q
+    loss_cls_cfg = dict(
+        type='SeesawLoss', p=1.0, q=0.0, loss_weight=1.0, num_classes=2)
+    loss_cls = build_loss(loss_cls_cfg)
+    fake_pred = torch.Tensor([[-100, 100, -100, 100]])
+    fake_label = torch.Tensor([0]).long()
+    loss_cls.cum_samples[0] = torch.exp(torch.Tensor([20]))
+    loss = loss_cls(fake_pred, fake_label)
+    assert torch.allclose(loss['loss_cls_objectness'], torch.tensor(200.))
+    assert torch.allclose(loss['loss_cls_classes'], torch.tensor(180.))
+
+    # test the calculation with q and without p
+    loss_cls_cfg = dict(
+        type='SeesawLoss', p=0.0, q=1.0, loss_weight=1.0, num_classes=2)
+    loss_cls = build_loss(loss_cls_cfg)
+    fake_pred = torch.Tensor([[-100, 100, -100, 100]])
+    fake_label = torch.Tensor([0]).long()
+    loss = loss_cls(fake_pred, fake_label)
+    assert torch.allclose(loss['loss_cls_objectness'], torch.tensor(200.))
+    assert torch.allclose(loss['loss_cls_classes'],
+                          torch.tensor(200.) + torch.tensor(100.).log())
+
+    # test the others
+    loss_cls_cfg = dict(
+        type='SeesawLoss',
+        p=0.0,
+        q=1.0,
+        loss_weight=1.0,
+        num_classes=2,
+        return_dict=False)
+    loss_cls = build_loss(loss_cls_cfg)
+    fake_pred = torch.Tensor([[100, -100, 100, -100]])
+    fake_label = torch.Tensor([0]).long()
+    loss = loss_cls(fake_pred, fake_label)
+    acc = loss_cls.get_accuracy(fake_pred, fake_label)
+    act = loss_cls.get_activation(fake_pred)
+    assert torch.allclose(loss, torch.tensor(0.))
+    assert torch.allclose(acc['acc_objectness'], torch.tensor(100.))
+    assert torch.allclose(acc['acc_classes'], torch.tensor(100.))
+    assert torch.allclose(act, torch.tensor([1., 0., 0.]))
+
+
+def test_accuracy():
+    # test for empty pred
+    pred = torch.empty(0, 4)
+    label = torch.empty(0)
+    accuracy = Accuracy(topk=1)
+    acc = accuracy(pred, label)
+    assert acc.item() == 0
+
+    pred = torch.Tensor([[0.2, 0.3, 0.6, 0.5], [0.1, 0.1, 0.2, 0.6],
+                         [0.9, 0.0, 0.0, 0.1], [0.4, 0.7, 0.1, 0.1],
+                         [0.0, 0.0, 0.99, 0]])
+    # test for top1
+    true_label = torch.Tensor([2, 3, 0, 1, 2]).long()
+    accuracy = Accuracy(topk=1)
+    acc = accuracy(pred, true_label)
+    assert acc.item() == 100
+
+    # test for top1 with score thresh=0.8
+    true_label = torch.Tensor([2, 3, 0, 1, 2]).long()
+    accuracy = Accuracy(topk=1, thresh=0.8)
+    acc = accuracy(pred, true_label)
+    assert acc.item() == 40
+
+    # test for top2
+    accuracy = Accuracy(topk=2)
+    label = torch.Tensor([3, 2, 0, 0, 2]).long()
+    acc = accuracy(pred, label)
+    assert acc.item() == 100
+
+    # test for both top1 and top2
+    accuracy = Accuracy(topk=(1, 2))
+    true_label = torch.Tensor([2, 3, 0, 1, 2]).long()
+    acc = accuracy(pred, true_label)
+    for a in acc:
+        assert a.item() == 100
+
+    # topk is larger than pred class number
+    with pytest.raises(AssertionError):
+        accuracy = Accuracy(topk=5)
+        accuracy(pred, true_label)
+
+    # wrong topk type
+    with pytest.raises(AssertionError):
+        accuracy = Accuracy(topk='wrong type')
+        accuracy(pred, true_label)
+
+    # label size is larger than required
+    with pytest.raises(AssertionError):
+        label = torch.Tensor([2, 3, 0, 1, 2, 0]).long()  # size mismatch
+        accuracy = Accuracy()
+        accuracy(pred, label)
+
+    # wrong pred dimension
+    with pytest.raises(AssertionError):
+        accuracy = Accuracy()
+        accuracy(pred[:, :, None], true_label)
diff --git a/tests/test_metrics/test_mean_ap.py b/tests/test_metrics/test_mean_ap.py
new file mode 100644
index 0000000..5faa7a0
--- /dev/null
+++ b/tests/test_metrics/test_mean_ap.py
@@ -0,0 +1,187 @@
+import numpy as np
+
+from mmdet.core.evaluation.mean_ap import (eval_map, tpfp_default,
+                                           tpfp_imagenet, tpfp_openimages)
+
+det_bboxes = np.array([
+    [0, 0, 10, 10],
+    [10, 10, 20, 20],
+    [32, 32, 38, 42],
+])
+gt_bboxes = np.array([[0, 0, 10, 20], [0, 10, 10, 19], [10, 10, 20, 20]])
+gt_ignore = np.array([[5, 5, 10, 20], [6, 10, 10, 19]])
+
+
+def test_tpfp_imagenet():
+
+    result = tpfp_imagenet(
+        det_bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_ignore,
+        use_legacy_coordinate=True)
+    tp = result[0]
+    fp = result[1]
+    assert tp.shape == (1, 3)
+    assert fp.shape == (1, 3)
+    assert (tp == np.array([[1, 1, 0]])).all()
+    assert (fp == np.array([[0, 0, 1]])).all()
+
+    result = tpfp_imagenet(
+        det_bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_ignore,
+        use_legacy_coordinate=False)
+    tp = result[0]
+    fp = result[1]
+    assert tp.shape == (1, 3)
+    assert fp.shape == (1, 3)
+    assert (tp == np.array([[1, 1, 0]])).all()
+    assert (fp == np.array([[0, 0, 1]])).all()
+
+
+def test_tpfp_default():
+
+    result = tpfp_default(
+        det_bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_ignore,
+        use_legacy_coordinate=True)
+
+    tp = result[0]
+    fp = result[1]
+    assert tp.shape == (1, 3)
+    assert fp.shape == (1, 3)
+    assert (tp == np.array([[1, 1, 0]])).all()
+    assert (fp == np.array([[0, 0, 1]])).all()
+    result = tpfp_default(
+        det_bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_ignore,
+        use_legacy_coordinate=False)
+
+    tp = result[0]
+    fp = result[1]
+    assert tp.shape == (1, 3)
+    assert fp.shape == (1, 3)
+    assert (tp == np.array([[1, 1, 0]])).all()
+    assert (fp == np.array([[0, 0, 1]])).all()
+
+
+def test_eval_map():
+
+    # 2 image and 2 classes
+    det_results = [[det_bboxes, det_bboxes], [det_bboxes, det_bboxes]]
+
+    labels = np.array([0, 1, 1])
+    labels_ignore = np.array([0, 1])
+    gt_info = {
+        'bboxes': gt_bboxes,
+        'bboxes_ignore': gt_ignore,
+        'labels': labels,
+        'labels_ignore': labels_ignore
+    }
+    annotations = [gt_info, gt_info]
+    mean_ap, eval_results = eval_map(
+        det_results, annotations, use_legacy_coordinate=True)
+    assert 0.291 < mean_ap < 0.293
+    mean_ap, eval_results = eval_map(
+        det_results, annotations, use_legacy_coordinate=False)
+    assert 0.291 < mean_ap < 0.293
+
+    # 1 image and 2 classes
+    det_results = [[det_bboxes, det_bboxes]]
+
+    labels = np.array([0, 1, 1])
+    labels_ignore = np.array([0, 1])
+    gt_info = {
+        'bboxes': gt_bboxes,
+        'bboxes_ignore': gt_ignore,
+        'labels': labels,
+        'labels_ignore': labels_ignore
+    }
+    annotations = [gt_info]
+    mean_ap, eval_results = eval_map(
+        det_results, annotations, use_legacy_coordinate=True)
+    assert 0.291 < mean_ap < 0.293
+    mean_ap, eval_results = eval_map(
+        det_results, annotations, use_legacy_coordinate=False)
+    assert 0.291 < mean_ap < 0.293
+
+
+def test_tpfp_openimages():
+
+    det_bboxes = np.array([[10, 10, 15, 15, 1.0], [15, 15, 30, 30, 0.98],
+                           [10, 10, 25, 25, 0.98], [28, 28, 35, 35, 0.97],
+                           [30, 30, 51, 51, 0.96], [100, 110, 120, 130, 0.15]])
+    gt_bboxes = np.array([[10., 10., 30., 30.], [30., 30., 50., 50.]])
+    gt_groups_of = np.array([True, False], dtype=bool)
+    gt_ignore = np.zeros((0, 4))
+
+    # Open Images evaluation using group of.
+    result = tpfp_openimages(
+        det_bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_ignore,
+        gt_bboxes_group_of=gt_groups_of,
+        use_group_of=True,
+        ioa_thr=0.5)
+
+    tp = result[0]
+    fp = result[1]
+    cls_dets = result[2]
+
+    assert tp.shape == (1, 4)
+    assert fp.shape == (1, 4)
+    assert cls_dets.shape == (4, 5)
+
+    assert (tp == np.array([[0, 1, 0, 1]])).all()
+    assert (fp == np.array([[1, 0, 1, 0]])).all()
+    cls_dets_gt = np.array([[28., 28., 35., 35., 0.97],
+                            [30., 30., 51., 51., 0.96],
+                            [100., 110., 120., 130., 0.15],
+                            [10., 10., 15., 15., 1.]])
+    assert (cls_dets == cls_dets_gt).all()
+
+    # Open Images evaluation not using group of.
+    result = tpfp_openimages(
+        det_bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_ignore,
+        gt_bboxes_group_of=gt_groups_of,
+        use_group_of=False,
+        ioa_thr=0.5)
+    tp = result[0]
+    fp = result[1]
+    cls_dets = result[2]
+    assert tp.shape == (1, 6)
+    assert fp.shape == (1, 6)
+    assert cls_dets.shape == (6, 5)
+
+    # Open Images evaluation using group of, and gt is all group of bboxes.
+    gt_groups_of = np.array([True, True], dtype=bool)
+    result = tpfp_openimages(
+        det_bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_ignore,
+        gt_bboxes_group_of=gt_groups_of,
+        use_group_of=True,
+        ioa_thr=0.5)
+    tp = result[0]
+    fp = result[1]
+    cls_dets = result[2]
+    assert tp.shape == (1, 3)
+    assert fp.shape == (1, 3)
+    assert cls_dets.shape == (3, 5)
+
+    # Open Images evaluation with empty gt.
+    gt_bboxes = np.zeros((0, 4))
+    gt_groups_of = np.empty((0))
+    result = tpfp_openimages(
+        det_bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_ignore,
+        gt_bboxes_group_of=gt_groups_of,
+        use_group_of=True,
+        ioa_thr=0.5)
+    fp = result[1]
+    assert (fp == np.array([[1, 1, 1, 1, 1, 1]])).all()
diff --git a/tests/test_metrics/test_recall.py b/tests/test_metrics/test_recall.py
new file mode 100644
index 0000000..f2ca0b1
--- /dev/null
+++ b/tests/test_metrics/test_recall.py
@@ -0,0 +1,46 @@
+import numpy as np
+
+from mmdet.core.evaluation.recall import eval_recalls
+
+det_bboxes = np.array([
+    [0, 0, 10, 10],
+    [10, 10, 20, 20],
+    [32, 32, 38, 42],
+])
+gt_bboxes = np.array([[0, 0, 10, 20], [0, 10, 10, 19], [10, 10, 20, 20]])
+gt_ignore = np.array([[5, 5, 10, 20], [6, 10, 10, 19]])
+
+
+def test_eval_recalls():
+    gts = [gt_bboxes, gt_bboxes, gt_bboxes]
+    proposals = [det_bboxes, det_bboxes, det_bboxes]
+
+    recall = eval_recalls(
+        gts, proposals, proposal_nums=2, use_legacy_coordinate=True)
+    assert recall.shape == (1, 1)
+    assert 0.66 < recall[0][0] < 0.667
+    recall = eval_recalls(
+        gts, proposals, proposal_nums=2, use_legacy_coordinate=False)
+    assert recall.shape == (1, 1)
+    assert 0.66 < recall[0][0] < 0.667
+
+    recall = eval_recalls(
+        gts, proposals, proposal_nums=2, use_legacy_coordinate=True)
+    assert recall.shape == (1, 1)
+    assert 0.66 < recall[0][0] < 0.667
+    recall = eval_recalls(
+        gts,
+        proposals,
+        iou_thrs=[0.1, 0.9],
+        proposal_nums=2,
+        use_legacy_coordinate=False)
+    assert recall.shape == (1, 2)
+    assert recall[0][1] <= recall[0][0]
+    recall = eval_recalls(
+        gts,
+        proposals,
+        iou_thrs=[0.1, 0.9],
+        proposal_nums=2,
+        use_legacy_coordinate=True)
+    assert recall.shape == (1, 2)
+    assert recall[0][1] <= recall[0][0]
diff --git a/tests/test_models/test_backbones/__init__.py b/tests/test_models/test_backbones/__init__.py
new file mode 100644
index 0000000..eb431ba
--- /dev/null
+++ b/tests/test_models/test_backbones/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .utils import check_norm_state, is_block, is_norm
+
+__all__ = ['is_block', 'is_norm', 'check_norm_state']
diff --git a/tests/test_models/test_backbones/test_csp_darknet.py b/tests/test_models/test_backbones/test_csp_darknet.py
new file mode 100644
index 0000000..2a2ad41
--- /dev/null
+++ b/tests/test_models/test_backbones/test_csp_darknet.py
@@ -0,0 +1,116 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from mmdet.models.backbones.csp_darknet import CSPDarknet
+from .utils import check_norm_state, is_norm
+
+
+def test_csp_darknet_backbone():
+    with pytest.raises(ValueError):
+        # frozen_stages must in range(-1, len(arch_setting) + 1)
+        CSPDarknet(frozen_stages=6)
+
+    with pytest.raises(AssertionError):
+        # out_indices in range(len(arch_setting) + 1)
+        CSPDarknet(out_indices=[6])
+
+    # Test CSPDarknet with first stage frozen
+    frozen_stages = 1
+    model = CSPDarknet(frozen_stages=frozen_stages)
+    model.train()
+
+    for mod in model.stem.modules():
+        for param in mod.parameters():
+            assert param.requires_grad is False
+    for i in range(1, frozen_stages + 1):
+        layer = getattr(model, f'stage{i}')
+        for mod in layer.modules():
+            if isinstance(mod, _BatchNorm):
+                assert mod.training is False
+        for param in layer.parameters():
+            assert param.requires_grad is False
+
+    # Test CSPDarknet with norm_eval=True
+    model = CSPDarknet(norm_eval=True)
+    model.train()
+
+    assert check_norm_state(model.modules(), False)
+
+    # Test CSPDarknet-P5 forward with widen_factor=0.5
+    model = CSPDarknet(arch='P5', widen_factor=0.25, out_indices=range(0, 5))
+    model.train()
+
+    imgs = torch.randn(1, 3, 64, 64)
+    feat = model(imgs)
+    assert len(feat) == 5
+    assert feat[0].shape == torch.Size((1, 16, 32, 32))
+    assert feat[1].shape == torch.Size((1, 32, 16, 16))
+    assert feat[2].shape == torch.Size((1, 64, 8, 8))
+    assert feat[3].shape == torch.Size((1, 128, 4, 4))
+    assert feat[4].shape == torch.Size((1, 256, 2, 2))
+
+    # Test CSPDarknet-P6 forward with widen_factor=0.5
+    model = CSPDarknet(
+        arch='P6',
+        widen_factor=0.25,
+        out_indices=range(0, 6),
+        spp_kernal_sizes=(3, 5, 7))
+    model.train()
+
+    imgs = torch.randn(1, 3, 128, 128)
+    feat = model(imgs)
+    assert feat[0].shape == torch.Size((1, 16, 64, 64))
+    assert feat[1].shape == torch.Size((1, 32, 32, 32))
+    assert feat[2].shape == torch.Size((1, 64, 16, 16))
+    assert feat[3].shape == torch.Size((1, 128, 8, 8))
+    assert feat[4].shape == torch.Size((1, 192, 4, 4))
+    assert feat[5].shape == torch.Size((1, 256, 2, 2))
+
+    # Test CSPDarknet forward with dict(type='ReLU')
+    model = CSPDarknet(
+        widen_factor=0.125, act_cfg=dict(type='ReLU'), out_indices=range(0, 5))
+    model.train()
+
+    imgs = torch.randn(1, 3, 64, 64)
+    feat = model(imgs)
+    assert len(feat) == 5
+    assert feat[0].shape == torch.Size((1, 8, 32, 32))
+    assert feat[1].shape == torch.Size((1, 16, 16, 16))
+    assert feat[2].shape == torch.Size((1, 32, 8, 8))
+    assert feat[3].shape == torch.Size((1, 64, 4, 4))
+    assert feat[4].shape == torch.Size((1, 128, 2, 2))
+
+    # Test CSPDarknet with BatchNorm forward
+    model = CSPDarknet(widen_factor=0.125, out_indices=range(0, 5))
+    for m in model.modules():
+        if is_norm(m):
+            assert isinstance(m, _BatchNorm)
+    model.train()
+
+    imgs = torch.randn(1, 3, 64, 64)
+    feat = model(imgs)
+    assert len(feat) == 5
+    assert feat[0].shape == torch.Size((1, 8, 32, 32))
+    assert feat[1].shape == torch.Size((1, 16, 16, 16))
+    assert feat[2].shape == torch.Size((1, 32, 8, 8))
+    assert feat[3].shape == torch.Size((1, 64, 4, 4))
+    assert feat[4].shape == torch.Size((1, 128, 2, 2))
+
+    # Test CSPDarknet with custom arch forward
+    arch_ovewrite = [[32, 56, 3, True, False], [56, 224, 2, True, False],
+                     [224, 512, 1, True, False]]
+    model = CSPDarknet(
+        arch_ovewrite=arch_ovewrite,
+        widen_factor=0.25,
+        out_indices=(0, 1, 2, 3))
+    model.train()
+
+    imgs = torch.randn(1, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size((1, 8, 16, 16))
+    assert feat[1].shape == torch.Size((1, 14, 8, 8))
+    assert feat[2].shape == torch.Size((1, 56, 4, 4))
+    assert feat[3].shape == torch.Size((1, 128, 2, 2))
diff --git a/tests/test_models/test_backbones/test_detectors_resnet.py b/tests/test_models/test_backbones/test_detectors_resnet.py
new file mode 100644
index 0000000..69f462a
--- /dev/null
+++ b/tests/test_models/test_backbones/test_detectors_resnet.py
@@ -0,0 +1,47 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+
+from mmdet.models.backbones import DetectoRS_ResNet
+
+
+def test_detectorrs_resnet_backbone():
+    detectorrs_cfg = dict(
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch',
+        conv_cfg=dict(type='ConvAWS'),
+        sac=dict(type='SAC', use_deform=True),
+        stage_with_sac=(False, True, True, True),
+        output_img=True)
+    """Test init_weights config"""
+    with pytest.raises(AssertionError):
+        # pretrained and init_cfg cannot be specified at the same time
+        DetectoRS_ResNet(
+            **detectorrs_cfg, pretrained='Pretrained', init_cfg='Pretrained')
+
+    with pytest.raises(AssertionError):
+        # init_cfg must be a dict
+        DetectoRS_ResNet(
+            **detectorrs_cfg, pretrained=None, init_cfg=['Pretrained'])
+
+    with pytest.raises(KeyError):
+        # init_cfg must contain the key `type`
+        DetectoRS_ResNet(
+            **detectorrs_cfg,
+            pretrained=None,
+            init_cfg=dict(checkpoint='Pretrained'))
+
+    with pytest.raises(AssertionError):
+        # init_cfg only support initialize pretrained model way
+        DetectoRS_ResNet(
+            **detectorrs_cfg, pretrained=None, init_cfg=dict(type='Trained'))
+
+    with pytest.raises(TypeError):
+        # pretrained mast be a str or None
+        model = DetectoRS_ResNet(
+            **detectorrs_cfg, pretrained=['Pretrained'], init_cfg=None)
+        model.init_weights()
diff --git a/tests/test_models/test_backbones/test_efficientnet.py b/tests/test_models/test_backbones/test_efficientnet.py
new file mode 100644
index 0000000..aa21770
--- /dev/null
+++ b/tests/test_models/test_backbones/test_efficientnet.py
@@ -0,0 +1,25 @@
+import pytest
+import torch
+
+from mmdet.models.backbones import EfficientNet
+
+
+def test_efficientnet_backbone():
+    """Test EfficientNet backbone."""
+    with pytest.raises(AssertionError):
+        # EfficientNet arch should be a key in EfficientNet.arch_settings
+        EfficientNet(arch='c3')
+
+    model = EfficientNet(arch='b0', out_indices=(0, 1, 2, 3, 4, 5, 6))
+    model.train()
+
+    imgs = torch.randn(2, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 7
+    assert feat[0].shape == torch.Size([2, 32, 16, 16])
+    assert feat[1].shape == torch.Size([2, 16, 16, 16])
+    assert feat[2].shape == torch.Size([2, 24, 8, 8])
+    assert feat[3].shape == torch.Size([2, 40, 4, 4])
+    assert feat[4].shape == torch.Size([2, 112, 2, 2])
+    assert feat[5].shape == torch.Size([2, 320, 1, 1])
+    assert feat[6].shape == torch.Size([2, 1280, 1, 1])
diff --git a/tests/test_models/test_backbones/test_hourglass.py b/tests/test_models/test_backbones/test_hourglass.py
new file mode 100644
index 0000000..c26f9c0
--- /dev/null
+++ b/tests/test_models/test_backbones/test_hourglass.py
@@ -0,0 +1,49 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+from mmdet.models.backbones.hourglass import HourglassNet
+
+
+def test_hourglass_backbone():
+    with pytest.raises(AssertionError):
+        # HourglassNet's num_stacks should larger than 0
+        HourglassNet(num_stacks=0)
+
+    with pytest.raises(AssertionError):
+        # len(stage_channels) should equal len(stage_blocks)
+        HourglassNet(
+            stage_channels=[256, 256, 384, 384, 384],
+            stage_blocks=[2, 2, 2, 2, 2, 4])
+
+    with pytest.raises(AssertionError):
+        # len(stage_channels) should lagrer than downsample_times
+        HourglassNet(
+            downsample_times=5,
+            stage_channels=[256, 256, 384, 384, 384],
+            stage_blocks=[2, 2, 2, 2, 2])
+
+    # Test HourglassNet-52
+    model = HourglassNet(
+        num_stacks=1,
+        stage_channels=(64, 64, 96, 96, 96, 128),
+        feat_channel=64)
+    model.train()
+
+    imgs = torch.randn(1, 3, 256, 256)
+    feat = model(imgs)
+    assert len(feat) == 1
+    assert feat[0].shape == torch.Size([1, 64, 64, 64])
+
+    # Test HourglassNet-104
+    model = HourglassNet(
+        num_stacks=2,
+        stage_channels=(64, 64, 96, 96, 96, 128),
+        feat_channel=64)
+    model.train()
+
+    imgs = torch.randn(1, 3, 256, 256)
+    feat = model(imgs)
+    assert len(feat) == 2
+    assert feat[0].shape == torch.Size([1, 64, 64, 64])
+    assert feat[1].shape == torch.Size([1, 64, 64, 64])
diff --git a/tests/test_models/test_backbones/test_hrnet.py b/tests/test_models/test_backbones/test_hrnet.py
new file mode 100644
index 0000000..6ae367b
--- /dev/null
+++ b/tests/test_models/test_backbones/test_hrnet.py
@@ -0,0 +1,111 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+from mmdet.models.backbones.hrnet import HRModule, HRNet
+from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
+
+
+@pytest.mark.parametrize('block', [BasicBlock, Bottleneck])
+def test_hrmodule(block):
+    # Test multiscale forward
+    num_channles = (32, 64)
+    in_channels = [c * block.expansion for c in num_channles]
+    hrmodule = HRModule(
+        num_branches=2,
+        blocks=block,
+        in_channels=in_channels,
+        num_blocks=(4, 4),
+        num_channels=num_channles,
+    )
+
+    feats = [
+        torch.randn(1, in_channels[0], 64, 64),
+        torch.randn(1, in_channels[1], 32, 32)
+    ]
+    feats = hrmodule(feats)
+
+    assert len(feats) == 2
+    assert feats[0].shape == torch.Size([1, in_channels[0], 64, 64])
+    assert feats[1].shape == torch.Size([1, in_channels[1], 32, 32])
+
+    # Test single scale forward
+    num_channles = (32, 64)
+    in_channels = [c * block.expansion for c in num_channles]
+    hrmodule = HRModule(
+        num_branches=2,
+        blocks=block,
+        in_channels=in_channels,
+        num_blocks=(4, 4),
+        num_channels=num_channles,
+        multiscale_output=False,
+    )
+
+    feats = [
+        torch.randn(1, in_channels[0], 64, 64),
+        torch.randn(1, in_channels[1], 32, 32)
+    ]
+    feats = hrmodule(feats)
+
+    assert len(feats) == 1
+    assert feats[0].shape == torch.Size([1, in_channels[0], 64, 64])
+
+
+def test_hrnet_backbone():
+    # only have 3 stages
+    extra = dict(
+        stage1=dict(
+            num_modules=1,
+            num_branches=1,
+            block='BOTTLENECK',
+            num_blocks=(4, ),
+            num_channels=(64, )),
+        stage2=dict(
+            num_modules=1,
+            num_branches=2,
+            block='BASIC',
+            num_blocks=(4, 4),
+            num_channels=(32, 64)),
+        stage3=dict(
+            num_modules=4,
+            num_branches=3,
+            block='BASIC',
+            num_blocks=(4, 4, 4),
+            num_channels=(32, 64, 128)))
+
+    with pytest.raises(AssertionError):
+        # HRNet now only support 4 stages
+        HRNet(extra=extra)
+    extra['stage4'] = dict(
+        num_modules=3,
+        num_branches=3,  # should be 4
+        block='BASIC',
+        num_blocks=(4, 4, 4, 4),
+        num_channels=(32, 64, 128, 256))
+
+    with pytest.raises(AssertionError):
+        # len(num_blocks) should equal num_branches
+        HRNet(extra=extra)
+
+    extra['stage4']['num_branches'] = 4
+
+    # Test hrnetv2p_w32
+    model = HRNet(extra=extra)
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 256, 256)
+    feats = model(imgs)
+    assert len(feats) == 4
+    assert feats[0].shape == torch.Size([1, 32, 64, 64])
+    assert feats[3].shape == torch.Size([1, 256, 8, 8])
+
+    # Test single scale output
+    model = HRNet(extra=extra, multiscale_output=False)
+    model.init_weights()
+    model.train()
+
+    imgs = torch.randn(1, 3, 256, 256)
+    feats = model(imgs)
+    assert len(feats) == 1
+    assert feats[0].shape == torch.Size([1, 32, 64, 64])
diff --git a/tests/test_models/test_backbones/test_mobilenet_v2.py b/tests/test_models/test_backbones/test_mobilenet_v2.py
new file mode 100644
index 0000000..77df7ea
--- /dev/null
+++ b/tests/test_models/test_backbones/test_mobilenet_v2.py
@@ -0,0 +1,173 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+from torch.nn.modules import GroupNorm
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from mmdet.models.backbones.mobilenet_v2 import MobileNetV2
+from .utils import check_norm_state, is_block, is_norm
+
+
+def test_mobilenetv2_backbone():
+    with pytest.raises(ValueError):
+        # frozen_stages must in range(-1, 8)
+        MobileNetV2(frozen_stages=8)
+
+    with pytest.raises(ValueError):
+        # out_indices in range(-1, 8)
+        MobileNetV2(out_indices=[8])
+
+    # Test MobileNetV2 with first stage frozen
+    frozen_stages = 1
+    model = MobileNetV2(frozen_stages=frozen_stages)
+    model.train()
+
+    for mod in model.conv1.modules():
+        for param in mod.parameters():
+            assert param.requires_grad is False
+    for i in range(1, frozen_stages + 1):
+        layer = getattr(model, f'layer{i}')
+        for mod in layer.modules():
+            if isinstance(mod, _BatchNorm):
+                assert mod.training is False
+        for param in layer.parameters():
+            assert param.requires_grad is False
+
+    # Test MobileNetV2 with norm_eval=True
+    model = MobileNetV2(norm_eval=True)
+    model.train()
+
+    assert check_norm_state(model.modules(), False)
+
+    # Test MobileNetV2 forward with widen_factor=1.0
+    model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 8))
+    model.train()
+
+    assert check_norm_state(model.modules(), True)
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 8
+    assert feat[0].shape == torch.Size((1, 16, 112, 112))
+    assert feat[1].shape == torch.Size((1, 24, 56, 56))
+    assert feat[2].shape == torch.Size((1, 32, 28, 28))
+    assert feat[3].shape == torch.Size((1, 64, 14, 14))
+    assert feat[4].shape == torch.Size((1, 96, 14, 14))
+    assert feat[5].shape == torch.Size((1, 160, 7, 7))
+    assert feat[6].shape == torch.Size((1, 320, 7, 7))
+    assert feat[7].shape == torch.Size((1, 1280, 7, 7))
+
+    # Test MobileNetV2 forward with widen_factor=0.5
+    model = MobileNetV2(widen_factor=0.5, out_indices=range(0, 7))
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 7
+    assert feat[0].shape == torch.Size((1, 8, 112, 112))
+    assert feat[1].shape == torch.Size((1, 16, 56, 56))
+    assert feat[2].shape == torch.Size((1, 16, 28, 28))
+    assert feat[3].shape == torch.Size((1, 32, 14, 14))
+    assert feat[4].shape == torch.Size((1, 48, 14, 14))
+    assert feat[5].shape == torch.Size((1, 80, 7, 7))
+    assert feat[6].shape == torch.Size((1, 160, 7, 7))
+
+    # Test MobileNetV2 forward with widen_factor=2.0
+    model = MobileNetV2(widen_factor=2.0, out_indices=range(0, 8))
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert feat[0].shape == torch.Size((1, 32, 112, 112))
+    assert feat[1].shape == torch.Size((1, 48, 56, 56))
+    assert feat[2].shape == torch.Size((1, 64, 28, 28))
+    assert feat[3].shape == torch.Size((1, 128, 14, 14))
+    assert feat[4].shape == torch.Size((1, 192, 14, 14))
+    assert feat[5].shape == torch.Size((1, 320, 7, 7))
+    assert feat[6].shape == torch.Size((1, 640, 7, 7))
+    assert feat[7].shape == torch.Size((1, 2560, 7, 7))
+
+    # Test MobileNetV2 forward with dict(type='ReLU')
+    model = MobileNetV2(
+        widen_factor=1.0, act_cfg=dict(type='ReLU'), out_indices=range(0, 7))
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 7
+    assert feat[0].shape == torch.Size((1, 16, 112, 112))
+    assert feat[1].shape == torch.Size((1, 24, 56, 56))
+    assert feat[2].shape == torch.Size((1, 32, 28, 28))
+    assert feat[3].shape == torch.Size((1, 64, 14, 14))
+    assert feat[4].shape == torch.Size((1, 96, 14, 14))
+    assert feat[5].shape == torch.Size((1, 160, 7, 7))
+    assert feat[6].shape == torch.Size((1, 320, 7, 7))
+
+    # Test MobileNetV2 with BatchNorm forward
+    model = MobileNetV2(widen_factor=1.0, out_indices=range(0, 7))
+    for m in model.modules():
+        if is_norm(m):
+            assert isinstance(m, _BatchNorm)
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 7
+    assert feat[0].shape == torch.Size((1, 16, 112, 112))
+    assert feat[1].shape == torch.Size((1, 24, 56, 56))
+    assert feat[2].shape == torch.Size((1, 32, 28, 28))
+    assert feat[3].shape == torch.Size((1, 64, 14, 14))
+    assert feat[4].shape == torch.Size((1, 96, 14, 14))
+    assert feat[5].shape == torch.Size((1, 160, 7, 7))
+    assert feat[6].shape == torch.Size((1, 320, 7, 7))
+
+    # Test MobileNetV2 with GroupNorm forward
+    model = MobileNetV2(
+        widen_factor=1.0,
+        norm_cfg=dict(type='GN', num_groups=2, requires_grad=True),
+        out_indices=range(0, 7))
+    for m in model.modules():
+        if is_norm(m):
+            assert isinstance(m, GroupNorm)
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 7
+    assert feat[0].shape == torch.Size((1, 16, 112, 112))
+    assert feat[1].shape == torch.Size((1, 24, 56, 56))
+    assert feat[2].shape == torch.Size((1, 32, 28, 28))
+    assert feat[3].shape == torch.Size((1, 64, 14, 14))
+    assert feat[4].shape == torch.Size((1, 96, 14, 14))
+    assert feat[5].shape == torch.Size((1, 160, 7, 7))
+    assert feat[6].shape == torch.Size((1, 320, 7, 7))
+
+    # Test MobileNetV2 with layers 1, 3, 5 out forward
+    model = MobileNetV2(widen_factor=1.0, out_indices=(0, 2, 4))
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 3
+    assert feat[0].shape == torch.Size((1, 16, 112, 112))
+    assert feat[1].shape == torch.Size((1, 32, 28, 28))
+    assert feat[2].shape == torch.Size((1, 96, 14, 14))
+
+    # Test MobileNetV2 with checkpoint forward
+    model = MobileNetV2(
+        widen_factor=1.0, with_cp=True, out_indices=range(0, 7))
+    for m in model.modules():
+        if is_block(m):
+            assert m.with_cp
+    model.train()
+
+    imgs = torch.randn(1, 3, 224, 224)
+    feat = model(imgs)
+    assert len(feat) == 7
+    assert feat[0].shape == torch.Size((1, 16, 112, 112))
+    assert feat[1].shape == torch.Size((1, 24, 56, 56))
+    assert feat[2].shape == torch.Size((1, 32, 28, 28))
+    assert feat[3].shape == torch.Size((1, 64, 14, 14))
+    assert feat[4].shape == torch.Size((1, 96, 14, 14))
+    assert feat[5].shape == torch.Size((1, 160, 7, 7))
+    assert feat[6].shape == torch.Size((1, 320, 7, 7))
diff --git a/tests/test_models/test_backbones/test_pvt.py b/tests/test_models/test_backbones/test_pvt.py
new file mode 100644
index 0000000..029fdb3
--- /dev/null
+++ b/tests/test_models/test_backbones/test_pvt.py
@@ -0,0 +1,103 @@
+import pytest
+import torch
+
+from mmdet.models.backbones.pvt import (PVTEncoderLayer,
+                                        PyramidVisionTransformer,
+                                        PyramidVisionTransformerV2)
+
+
+def test_pvt_block():
+    # test PVT structure and forward
+    block = PVTEncoderLayer(
+        embed_dims=64, num_heads=4, feedforward_channels=256)
+    assert block.ffn.embed_dims == 64
+    assert block.attn.num_heads == 4
+    assert block.ffn.feedforward_channels == 256
+    x = torch.randn(1, 56 * 56, 64)
+    x_out = block(x, (56, 56))
+    assert x_out.shape == torch.Size([1, 56 * 56, 64])
+
+
+def test_pvt():
+    """Test PVT backbone."""
+
+    with pytest.raises(TypeError):
+        # Pretrained arg must be str or None.
+        PyramidVisionTransformer(pretrained=123)
+
+    # test pretrained image size
+    with pytest.raises(AssertionError):
+        PyramidVisionTransformer(pretrain_img_size=(224, 224, 224))
+
+    # Test absolute position embedding
+    temp = torch.randn((1, 3, 224, 224))
+    model = PyramidVisionTransformer(
+        pretrain_img_size=224, use_abs_pos_embed=True)
+    model.init_weights()
+    model(temp)
+
+    # Test normal inference
+    temp = torch.randn((1, 3, 32, 32))
+    model = PyramidVisionTransformer()
+    outs = model(temp)
+    assert outs[0].shape == (1, 64, 8, 8)
+    assert outs[1].shape == (1, 128, 4, 4)
+    assert outs[2].shape == (1, 320, 2, 2)
+    assert outs[3].shape == (1, 512, 1, 1)
+
+    # Test abnormal inference size
+    temp = torch.randn((1, 3, 33, 33))
+    model = PyramidVisionTransformer()
+    outs = model(temp)
+    assert outs[0].shape == (1, 64, 8, 8)
+    assert outs[1].shape == (1, 128, 4, 4)
+    assert outs[2].shape == (1, 320, 2, 2)
+    assert outs[3].shape == (1, 512, 1, 1)
+
+    # Test abnormal inference size
+    temp = torch.randn((1, 3, 112, 137))
+    model = PyramidVisionTransformer()
+    outs = model(temp)
+    assert outs[0].shape == (1, 64, 28, 34)
+    assert outs[1].shape == (1, 128, 14, 17)
+    assert outs[2].shape == (1, 320, 7, 8)
+    assert outs[3].shape == (1, 512, 3, 4)
+
+
+def test_pvtv2():
+    """Test PVTv2 backbone."""
+
+    with pytest.raises(TypeError):
+        # Pretrained arg must be str or None.
+        PyramidVisionTransformerV2(pretrained=123)
+
+    # test pretrained image size
+    with pytest.raises(AssertionError):
+        PyramidVisionTransformerV2(pretrain_img_size=(224, 224, 224))
+
+    # Test normal inference
+    temp = torch.randn((1, 3, 32, 32))
+    model = PyramidVisionTransformerV2()
+    outs = model(temp)
+    assert outs[0].shape == (1, 64, 8, 8)
+    assert outs[1].shape == (1, 128, 4, 4)
+    assert outs[2].shape == (1, 320, 2, 2)
+    assert outs[3].shape == (1, 512, 1, 1)
+
+    # Test abnormal inference size
+    temp = torch.randn((1, 3, 31, 31))
+    model = PyramidVisionTransformerV2()
+    outs = model(temp)
+    assert outs[0].shape == (1, 64, 8, 8)
+    assert outs[1].shape == (1, 128, 4, 4)
+    assert outs[2].shape == (1, 320, 2, 2)
+    assert outs[3].shape == (1, 512, 1, 1)
+
+    # Test abnormal inference size
+    temp = torch.randn((1, 3, 112, 137))
+    model = PyramidVisionTransformerV2()
+    outs = model(temp)
+    assert outs[0].shape == (1, 64, 28, 35)
+    assert outs[1].shape == (1, 128, 14, 18)
+    assert outs[2].shape == (1, 320, 7, 9)
+    assert outs[3].shape == (1, 512, 4, 5)
diff --git a/tests/test_models/test_backbones/test_regnet.py b/tests/test_models/test_backbones/test_regnet.py
new file mode 100644
index 0000000..2f94b11
--- /dev/null
+++ b/tests/test_models/test_backbones/test_regnet.py
@@ -0,0 +1,58 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+from mmdet.models.backbones import RegNet
+
+regnet_test_data = [
+    ('regnetx_400mf',
+     dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22,
+          bot_mul=1.0), [32, 64, 160, 384]),
+    ('regnetx_800mf',
+     dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16,
+          bot_mul=1.0), [64, 128, 288, 672]),
+    ('regnetx_1.6gf',
+     dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18,
+          bot_mul=1.0), [72, 168, 408, 912]),
+    ('regnetx_3.2gf',
+     dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25,
+          bot_mul=1.0), [96, 192, 432, 1008]),
+    ('regnetx_4.0gf',
+     dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23,
+          bot_mul=1.0), [80, 240, 560, 1360]),
+    ('regnetx_6.4gf',
+     dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17,
+          bot_mul=1.0), [168, 392, 784, 1624]),
+    ('regnetx_8.0gf',
+     dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23,
+          bot_mul=1.0), [80, 240, 720, 1920]),
+    ('regnetx_12gf',
+     dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19,
+          bot_mul=1.0), [224, 448, 896, 2240]),
+]
+
+
+@pytest.mark.parametrize('arch_name,arch,out_channels', regnet_test_data)
+def test_regnet_backbone(arch_name, arch, out_channels):
+    with pytest.raises(AssertionError):
+        # ResNeXt depth should be in [50, 101, 152]
+        RegNet(arch_name + '233')
+
+    # Test RegNet with arch_name
+    model = RegNet(arch_name)
+    model.train()
+
+    imgs = torch.randn(1, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, out_channels[0], 8, 8])
+    assert feat[1].shape == torch.Size([1, out_channels[1], 4, 4])
+    assert feat[2].shape == torch.Size([1, out_channels[2], 2, 2])
+    assert feat[3].shape == torch.Size([1, out_channels[3], 1, 1])
+
+    # Test RegNet with arch
+    model = RegNet(arch)
+    assert feat[0].shape == torch.Size([1, out_channels[0], 8, 8])
+    assert feat[1].shape == torch.Size([1, out_channels[1], 4, 4])
+    assert feat[2].shape == torch.Size([1, out_channels[2], 2, 2])
+    assert feat[3].shape == torch.Size([1, out_channels[3], 1, 1])
diff --git a/tests/test_models/test_backbones/test_renext.py b/tests/test_models/test_backbones/test_renext.py
new file mode 100644
index 0000000..4ce2ee6
--- /dev/null
+++ b/tests/test_models/test_backbones/test_renext.py
@@ -0,0 +1,105 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+from mmdet.models.backbones import ResNeXt
+from mmdet.models.backbones.resnext import Bottleneck as BottleneckX
+from .utils import is_block
+
+
+def test_renext_bottleneck():
+    with pytest.raises(AssertionError):
+        # Style must be in ['pytorch', 'caffe']
+        BottleneckX(64, 64, groups=32, base_width=4, style='tensorflow')
+
+    # Test ResNeXt Bottleneck structure
+    block = BottleneckX(
+        64, 64, groups=32, base_width=4, stride=2, style='pytorch')
+    assert block.conv2.stride == (2, 2)
+    assert block.conv2.groups == 32
+    assert block.conv2.out_channels == 128
+
+    # Test ResNeXt Bottleneck with DCN
+    dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
+    with pytest.raises(AssertionError):
+        # conv_cfg must be None if dcn is not None
+        BottleneckX(
+            64,
+            64,
+            groups=32,
+            base_width=4,
+            dcn=dcn,
+            conv_cfg=dict(type='Conv'))
+    BottleneckX(64, 64, dcn=dcn)
+
+    # Test ResNeXt Bottleneck forward
+    block = BottleneckX(64, 16, groups=32, base_width=4)
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+    # Test ResNeXt Bottleneck forward with plugins
+    plugins = [
+        dict(
+            cfg=dict(
+                type='GeneralizedAttention',
+                spatial_range=-1,
+                num_heads=8,
+                attention_type='0010',
+                kv_stride=2),
+            stages=(False, False, True, True),
+            position='after_conv2')
+    ]
+    block = BottleneckX(64, 16, groups=32, base_width=4, plugins=plugins)
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+
+def test_resnext_backbone():
+    with pytest.raises(KeyError):
+        # ResNeXt depth should be in [50, 101, 152]
+        ResNeXt(depth=18)
+
+    # Test ResNeXt with group 32, base_width 4
+    model = ResNeXt(depth=50, groups=32, base_width=4)
+    for m in model.modules():
+        if is_block(m):
+            assert m.conv2.groups == 32
+    model.train()
+
+    imgs = torch.randn(1, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, 256, 8, 8])
+    assert feat[1].shape == torch.Size([1, 512, 4, 4])
+    assert feat[2].shape == torch.Size([1, 1024, 2, 2])
+    assert feat[3].shape == torch.Size([1, 2048, 1, 1])
+
+
+regnet_test_data = [
+    ('regnetx_400mf',
+     dict(w0=24, wa=24.48, wm=2.54, group_w=16, depth=22,
+          bot_mul=1.0), [32, 64, 160, 384]),
+    ('regnetx_800mf',
+     dict(w0=56, wa=35.73, wm=2.28, group_w=16, depth=16,
+          bot_mul=1.0), [64, 128, 288, 672]),
+    ('regnetx_1.6gf',
+     dict(w0=80, wa=34.01, wm=2.25, group_w=24, depth=18,
+          bot_mul=1.0), [72, 168, 408, 912]),
+    ('regnetx_3.2gf',
+     dict(w0=88, wa=26.31, wm=2.25, group_w=48, depth=25,
+          bot_mul=1.0), [96, 192, 432, 1008]),
+    ('regnetx_4.0gf',
+     dict(w0=96, wa=38.65, wm=2.43, group_w=40, depth=23,
+          bot_mul=1.0), [80, 240, 560, 1360]),
+    ('regnetx_6.4gf',
+     dict(w0=184, wa=60.83, wm=2.07, group_w=56, depth=17,
+          bot_mul=1.0), [168, 392, 784, 1624]),
+    ('regnetx_8.0gf',
+     dict(w0=80, wa=49.56, wm=2.88, group_w=120, depth=23,
+          bot_mul=1.0), [80, 240, 720, 1920]),
+    ('regnetx_12gf',
+     dict(w0=168, wa=73.36, wm=2.37, group_w=112, depth=19,
+          bot_mul=1.0), [224, 448, 896, 2240]),
+]
diff --git a/tests/test_models/test_backbones/test_res2net.py b/tests/test_models/test_backbones/test_res2net.py
new file mode 100644
index 0000000..6757869
--- /dev/null
+++ b/tests/test_models/test_backbones/test_res2net.py
@@ -0,0 +1,62 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+from mmdet.models.backbones import Res2Net
+from mmdet.models.backbones.res2net import Bottle2neck
+from .utils import is_block
+
+
+def test_res2net_bottle2neck():
+    with pytest.raises(AssertionError):
+        # Style must be in ['pytorch', 'caffe']
+        Bottle2neck(64, 64, base_width=26, scales=4, style='tensorflow')
+
+    with pytest.raises(AssertionError):
+        # Scale must be larger than 1
+        Bottle2neck(64, 64, base_width=26, scales=1, style='pytorch')
+
+    # Test Res2Net Bottle2neck structure
+    block = Bottle2neck(
+        64, 64, base_width=26, stride=2, scales=4, style='pytorch')
+    assert block.scales == 4
+
+    # Test Res2Net Bottle2neck with DCN
+    dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
+    with pytest.raises(AssertionError):
+        # conv_cfg must be None if dcn is not None
+        Bottle2neck(
+            64,
+            64,
+            base_width=26,
+            scales=4,
+            dcn=dcn,
+            conv_cfg=dict(type='Conv'))
+    Bottle2neck(64, 64, dcn=dcn)
+
+    # Test Res2Net Bottle2neck forward
+    block = Bottle2neck(64, 16, base_width=26, scales=4)
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+
+def test_res2net_backbone():
+    with pytest.raises(KeyError):
+        # Res2Net depth should be in [50, 101, 152]
+        Res2Net(depth=18)
+
+    # Test Res2Net with scales 4, base_width 26
+    model = Res2Net(depth=50, scales=4, base_width=26)
+    for m in model.modules():
+        if is_block(m):
+            assert m.scales == 4
+    model.train()
+
+    imgs = torch.randn(1, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, 256, 8, 8])
+    assert feat[1].shape == torch.Size([1, 512, 4, 4])
+    assert feat[2].shape == torch.Size([1, 1024, 2, 2])
+    assert feat[3].shape == torch.Size([1, 2048, 1, 1])
diff --git a/tests/test_models/test_backbones/test_resnest.py b/tests/test_models/test_backbones/test_resnest.py
new file mode 100644
index 0000000..245fdfd
--- /dev/null
+++ b/tests/test_models/test_backbones/test_resnest.py
@@ -0,0 +1,47 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+from mmdet.models.backbones import ResNeSt
+from mmdet.models.backbones.resnest import Bottleneck as BottleneckS
+
+
+def test_resnest_bottleneck():
+    with pytest.raises(AssertionError):
+        # Style must be in ['pytorch', 'caffe']
+        BottleneckS(64, 64, radix=2, reduction_factor=4, style='tensorflow')
+
+    # Test ResNeSt Bottleneck structure
+    block = BottleneckS(
+        2, 4, radix=2, reduction_factor=4, stride=2, style='pytorch')
+    assert block.avd_layer.stride == 2
+    assert block.conv2.channels == 4
+
+    # Test ResNeSt Bottleneck forward
+    block = BottleneckS(16, 4, radix=2, reduction_factor=4)
+    x = torch.randn(2, 16, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([2, 16, 56, 56])
+
+
+def test_resnest_backbone():
+    with pytest.raises(KeyError):
+        # ResNeSt depth should be in [50, 101, 152, 200]
+        ResNeSt(depth=18)
+
+    # Test ResNeSt with radix 2, reduction_factor 4
+    model = ResNeSt(
+        depth=50,
+        base_channels=4,
+        radix=2,
+        reduction_factor=4,
+        out_indices=(0, 1, 2, 3))
+    model.train()
+
+    imgs = torch.randn(2, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([2, 16, 8, 8])
+    assert feat[1].shape == torch.Size([2, 32, 4, 4])
+    assert feat[2].shape == torch.Size([2, 64, 2, 2])
+    assert feat[3].shape == torch.Size([2, 128, 1, 1])
diff --git a/tests/test_models/test_backbones/test_resnet.py b/tests/test_models/test_backbones/test_resnet.py
new file mode 100644
index 0000000..5448828
--- /dev/null
+++ b/tests/test_models/test_backbones/test_resnet.py
@@ -0,0 +1,632 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+from mmcv import assert_params_all_zeros
+from mmcv.ops import DeformConv2dPack
+from torch.nn.modules import AvgPool2d, GroupNorm
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from mmdet.models.backbones import ResNet, ResNetV1d
+from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
+from mmdet.models.utils import ResLayer, SimplifiedBasicBlock
+from .utils import check_norm_state, is_block, is_norm
+
+
+def test_resnet_basic_block():
+    with pytest.raises(AssertionError):
+        # Not implemented yet.
+        dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
+        BasicBlock(64, 64, dcn=dcn)
+
+    with pytest.raises(AssertionError):
+        # Not implemented yet.
+        plugins = [
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 16),
+                position='after_conv3')
+        ]
+        BasicBlock(64, 64, plugins=plugins)
+
+    with pytest.raises(AssertionError):
+        # Not implemented yet
+        plugins = [
+            dict(
+                cfg=dict(
+                    type='GeneralizedAttention',
+                    spatial_range=-1,
+                    num_heads=8,
+                    attention_type='0010',
+                    kv_stride=2),
+                position='after_conv2')
+        ]
+        BasicBlock(64, 64, plugins=plugins)
+
+    # test BasicBlock structure and forward
+    block = BasicBlock(64, 64)
+    assert block.conv1.in_channels == 64
+    assert block.conv1.out_channels == 64
+    assert block.conv1.kernel_size == (3, 3)
+    assert block.conv2.in_channels == 64
+    assert block.conv2.out_channels == 64
+    assert block.conv2.kernel_size == (3, 3)
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+    # Test BasicBlock with checkpoint forward
+    block = BasicBlock(64, 64, with_cp=True)
+    assert block.with_cp
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+
+def test_resnet_bottleneck():
+    with pytest.raises(AssertionError):
+        # Style must be in ['pytorch', 'caffe']
+        Bottleneck(64, 64, style='tensorflow')
+
+    with pytest.raises(AssertionError):
+        # Allowed positions are 'after_conv1', 'after_conv2', 'after_conv3'
+        plugins = [
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 16),
+                position='after_conv4')
+        ]
+        Bottleneck(64, 16, plugins=plugins)
+
+    with pytest.raises(AssertionError):
+        # Need to specify different postfix to avoid duplicate plugin name
+        plugins = [
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 16),
+                position='after_conv3'),
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 16),
+                position='after_conv3')
+        ]
+        Bottleneck(64, 16, plugins=plugins)
+
+    with pytest.raises(KeyError):
+        # Plugin type is not supported
+        plugins = [dict(cfg=dict(type='WrongPlugin'), position='after_conv3')]
+        Bottleneck(64, 16, plugins=plugins)
+
+    # Test Bottleneck with checkpoint forward
+    block = Bottleneck(64, 16, with_cp=True)
+    assert block.with_cp
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+    # Test Bottleneck style
+    block = Bottleneck(64, 64, stride=2, style='pytorch')
+    assert block.conv1.stride == (1, 1)
+    assert block.conv2.stride == (2, 2)
+    block = Bottleneck(64, 64, stride=2, style='caffe')
+    assert block.conv1.stride == (2, 2)
+    assert block.conv2.stride == (1, 1)
+
+    # Test Bottleneck DCN
+    dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
+    with pytest.raises(AssertionError):
+        Bottleneck(64, 64, dcn=dcn, conv_cfg=dict(type='Conv'))
+    block = Bottleneck(64, 64, dcn=dcn)
+    assert isinstance(block.conv2, DeformConv2dPack)
+
+    # Test Bottleneck forward
+    block = Bottleneck(64, 16)
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+    # Test Bottleneck with 1 ContextBlock after conv3
+    plugins = [
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16),
+            position='after_conv3')
+    ]
+    block = Bottleneck(64, 16, plugins=plugins)
+    assert block.context_block.in_channels == 64
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+    # Test Bottleneck with 1 GeneralizedAttention after conv2
+    plugins = [
+        dict(
+            cfg=dict(
+                type='GeneralizedAttention',
+                spatial_range=-1,
+                num_heads=8,
+                attention_type='0010',
+                kv_stride=2),
+            position='after_conv2')
+    ]
+    block = Bottleneck(64, 16, plugins=plugins)
+    assert block.gen_attention_block.in_channels == 16
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+    # Test Bottleneck with 1 GeneralizedAttention after conv2, 1 NonLocal2D
+    # after conv2, 1 ContextBlock after conv3
+    plugins = [
+        dict(
+            cfg=dict(
+                type='GeneralizedAttention',
+                spatial_range=-1,
+                num_heads=8,
+                attention_type='0010',
+                kv_stride=2),
+            position='after_conv2'),
+        dict(cfg=dict(type='NonLocal2d'), position='after_conv2'),
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16),
+            position='after_conv3')
+    ]
+    block = Bottleneck(64, 16, plugins=plugins)
+    assert block.gen_attention_block.in_channels == 16
+    assert block.nonlocal_block.in_channels == 16
+    assert block.context_block.in_channels == 64
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+    # Test Bottleneck with 1 ContextBlock after conv2, 2 ContextBlock after
+    # conv3
+    plugins = [
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1),
+            position='after_conv2'),
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2),
+            position='after_conv3'),
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=3),
+            position='after_conv3')
+    ]
+    block = Bottleneck(64, 16, plugins=plugins)
+    assert block.context_block1.in_channels == 16
+    assert block.context_block2.in_channels == 64
+    assert block.context_block3.in_channels == 64
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+
+def test_simplied_basic_block():
+    with pytest.raises(AssertionError):
+        # Not implemented yet.
+        dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
+        SimplifiedBasicBlock(64, 64, dcn=dcn)
+
+    with pytest.raises(AssertionError):
+        # Not implemented yet.
+        plugins = [
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 16),
+                position='after_conv3')
+        ]
+        SimplifiedBasicBlock(64, 64, plugins=plugins)
+
+    with pytest.raises(AssertionError):
+        # Not implemented yet
+        plugins = [
+            dict(
+                cfg=dict(
+                    type='GeneralizedAttention',
+                    spatial_range=-1,
+                    num_heads=8,
+                    attention_type='0010',
+                    kv_stride=2),
+                position='after_conv2')
+        ]
+        SimplifiedBasicBlock(64, 64, plugins=plugins)
+
+    with pytest.raises(AssertionError):
+        # Not implemented yet
+        SimplifiedBasicBlock(64, 64, with_cp=True)
+
+    # test SimplifiedBasicBlock structure and forward
+    block = SimplifiedBasicBlock(64, 64)
+    assert block.conv1.in_channels == 64
+    assert block.conv1.out_channels == 64
+    assert block.conv1.kernel_size == (3, 3)
+    assert block.conv2.in_channels == 64
+    assert block.conv2.out_channels == 64
+    assert block.conv2.kernel_size == (3, 3)
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+    # test SimplifiedBasicBlock without norm
+    block = SimplifiedBasicBlock(64, 64, norm_cfg=None)
+    assert block.norm1 is None
+    assert block.norm2 is None
+    x_out = block(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+
+def test_resnet_res_layer():
+    # Test ResLayer of 3 Bottleneck w\o downsample
+    layer = ResLayer(Bottleneck, 64, 16, 3)
+    assert len(layer) == 3
+    assert layer[0].conv1.in_channels == 64
+    assert layer[0].conv1.out_channels == 16
+    for i in range(1, len(layer)):
+        assert layer[i].conv1.in_channels == 64
+        assert layer[i].conv1.out_channels == 16
+    for i in range(len(layer)):
+        assert layer[i].downsample is None
+    x = torch.randn(1, 64, 56, 56)
+    x_out = layer(x)
+    assert x_out.shape == torch.Size([1, 64, 56, 56])
+
+    # Test ResLayer of 3 Bottleneck with downsample
+    layer = ResLayer(Bottleneck, 64, 64, 3)
+    assert layer[0].downsample[0].out_channels == 256
+    for i in range(1, len(layer)):
+        assert layer[i].downsample is None
+    x = torch.randn(1, 64, 56, 56)
+    x_out = layer(x)
+    assert x_out.shape == torch.Size([1, 256, 56, 56])
+
+    # Test ResLayer of 3 Bottleneck with stride=2
+    layer = ResLayer(Bottleneck, 64, 64, 3, stride=2)
+    assert layer[0].downsample[0].out_channels == 256
+    assert layer[0].downsample[0].stride == (2, 2)
+    for i in range(1, len(layer)):
+        assert layer[i].downsample is None
+    x = torch.randn(1, 64, 56, 56)
+    x_out = layer(x)
+    assert x_out.shape == torch.Size([1, 256, 28, 28])
+
+    # Test ResLayer of 3 Bottleneck with stride=2 and average downsample
+    layer = ResLayer(Bottleneck, 64, 64, 3, stride=2, avg_down=True)
+    assert isinstance(layer[0].downsample[0], AvgPool2d)
+    assert layer[0].downsample[1].out_channels == 256
+    assert layer[0].downsample[1].stride == (1, 1)
+    for i in range(1, len(layer)):
+        assert layer[i].downsample is None
+    x = torch.randn(1, 64, 56, 56)
+    x_out = layer(x)
+    assert x_out.shape == torch.Size([1, 256, 28, 28])
+
+    # Test ResLayer of 3 BasicBlock with stride=2 and downsample_first=False
+    layer = ResLayer(BasicBlock, 64, 64, 3, stride=2, downsample_first=False)
+    assert layer[2].downsample[0].out_channels == 64
+    assert layer[2].downsample[0].stride == (2, 2)
+    for i in range(len(layer) - 1):
+        assert layer[i].downsample is None
+    x = torch.randn(1, 64, 56, 56)
+    x_out = layer(x)
+    assert x_out.shape == torch.Size([1, 64, 28, 28])
+
+
+def test_resnest_stem():
+    # Test default stem_channels
+    model = ResNet(50)
+    assert model.stem_channels == 64
+    assert model.conv1.out_channels == 64
+    assert model.norm1.num_features == 64
+
+    # Test default stem_channels, with base_channels=3
+    model = ResNet(50, base_channels=3)
+    assert model.stem_channels == 3
+    assert model.conv1.out_channels == 3
+    assert model.norm1.num_features == 3
+    assert model.layer1[0].conv1.in_channels == 3
+
+    # Test stem_channels=3
+    model = ResNet(50, stem_channels=3)
+    assert model.stem_channels == 3
+    assert model.conv1.out_channels == 3
+    assert model.norm1.num_features == 3
+    assert model.layer1[0].conv1.in_channels == 3
+
+    # Test stem_channels=3, with base_channels=2
+    model = ResNet(50, stem_channels=3, base_channels=2)
+    assert model.stem_channels == 3
+    assert model.conv1.out_channels == 3
+    assert model.norm1.num_features == 3
+    assert model.layer1[0].conv1.in_channels == 3
+
+    # Test V1d stem_channels
+    model = ResNetV1d(depth=50, stem_channels=6)
+    model.train()
+    assert model.stem[0].out_channels == 3
+    assert model.stem[1].num_features == 3
+    assert model.stem[3].out_channels == 3
+    assert model.stem[4].num_features == 3
+    assert model.stem[6].out_channels == 6
+    assert model.stem[7].num_features == 6
+    assert model.layer1[0].conv1.in_channels == 6
+
+
+def test_resnet_backbone():
+    """Test resnet backbone."""
+    with pytest.raises(KeyError):
+        # ResNet depth should be in [18, 34, 50, 101, 152]
+        ResNet(20)
+
+    with pytest.raises(AssertionError):
+        # In ResNet: 1 <= num_stages <= 4
+        ResNet(50, num_stages=0)
+
+    with pytest.raises(AssertionError):
+        # len(stage_with_dcn) == num_stages
+        dcn = dict(type='DCN', deform_groups=1, fallback_on_stride=False)
+        ResNet(50, dcn=dcn, stage_with_dcn=(True, ))
+
+    with pytest.raises(AssertionError):
+        # len(stage_with_plugin) == num_stages
+        plugins = [
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 16),
+                stages=(False, True, True),
+                position='after_conv3')
+        ]
+        ResNet(50, plugins=plugins)
+
+    with pytest.raises(AssertionError):
+        # In ResNet: 1 <= num_stages <= 4
+        ResNet(50, num_stages=5)
+
+    with pytest.raises(AssertionError):
+        # len(strides) == len(dilations) == num_stages
+        ResNet(50, strides=(1, ), dilations=(1, 1), num_stages=3)
+
+    with pytest.raises(TypeError):
+        # pretrained must be a string path
+        model = ResNet(50, pretrained=0)
+
+    with pytest.raises(AssertionError):
+        # Style must be in ['pytorch', 'caffe']
+        ResNet(50, style='tensorflow')
+
+    # Test ResNet50 norm_eval=True
+    model = ResNet(50, norm_eval=True, base_channels=1)
+    model.train()
+    assert check_norm_state(model.modules(), False)
+
+    # Test ResNet50 with torchvision pretrained weight
+    model = ResNet(
+        depth=50, norm_eval=True, pretrained='torchvision://resnet50')
+    model.train()
+    assert check_norm_state(model.modules(), False)
+
+    # Test ResNet50 with first stage frozen
+    frozen_stages = 1
+    model = ResNet(50, frozen_stages=frozen_stages, base_channels=1)
+    model.train()
+    assert model.norm1.training is False
+    for layer in [model.conv1, model.norm1]:
+        for param in layer.parameters():
+            assert param.requires_grad is False
+    for i in range(1, frozen_stages + 1):
+        layer = getattr(model, f'layer{i}')
+        for mod in layer.modules():
+            if isinstance(mod, _BatchNorm):
+                assert mod.training is False
+        for param in layer.parameters():
+            assert param.requires_grad is False
+
+    # Test ResNet50V1d with first stage frozen
+    model = ResNetV1d(depth=50, frozen_stages=frozen_stages, base_channels=2)
+    assert len(model.stem) == 9
+    model.train()
+    assert check_norm_state(model.stem, False)
+    for param in model.stem.parameters():
+        assert param.requires_grad is False
+    for i in range(1, frozen_stages + 1):
+        layer = getattr(model, f'layer{i}')
+        for mod in layer.modules():
+            if isinstance(mod, _BatchNorm):
+                assert mod.training is False
+        for param in layer.parameters():
+            assert param.requires_grad is False
+
+    # Test ResNet18 forward
+    model = ResNet(18)
+    model.train()
+
+    imgs = torch.randn(1, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, 64, 8, 8])
+    assert feat[1].shape == torch.Size([1, 128, 4, 4])
+    assert feat[2].shape == torch.Size([1, 256, 2, 2])
+    assert feat[3].shape == torch.Size([1, 512, 1, 1])
+
+    # Test ResNet18 with checkpoint forward
+    model = ResNet(18, with_cp=True)
+    for m in model.modules():
+        if is_block(m):
+            assert m.with_cp
+
+    # Test ResNet50 with BatchNorm forward
+    model = ResNet(50, base_channels=1)
+    for m in model.modules():
+        if is_norm(m):
+            assert isinstance(m, _BatchNorm)
+    model.train()
+
+    imgs = torch.randn(1, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, 4, 8, 8])
+    assert feat[1].shape == torch.Size([1, 8, 4, 4])
+    assert feat[2].shape == torch.Size([1, 16, 2, 2])
+    assert feat[3].shape == torch.Size([1, 32, 1, 1])
+
+    # Test ResNet50 with layers 1, 2, 3 out forward
+    model = ResNet(50, out_indices=(0, 1, 2), base_channels=1)
+    model.train()
+
+    imgs = torch.randn(1, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 3
+    assert feat[0].shape == torch.Size([1, 4, 8, 8])
+    assert feat[1].shape == torch.Size([1, 8, 4, 4])
+    assert feat[2].shape == torch.Size([1, 16, 2, 2])
+
+    # Test ResNet50 with checkpoint forward
+    model = ResNet(50, with_cp=True, base_channels=1)
+    for m in model.modules():
+        if is_block(m):
+            assert m.with_cp
+    model.train()
+
+    imgs = torch.randn(1, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, 4, 8, 8])
+    assert feat[1].shape == torch.Size([1, 8, 4, 4])
+    assert feat[2].shape == torch.Size([1, 16, 2, 2])
+    assert feat[3].shape == torch.Size([1, 32, 1, 1])
+
+    # Test ResNet50 with GroupNorm forward
+    model = ResNet(
+        50,
+        base_channels=4,
+        norm_cfg=dict(type='GN', num_groups=2, requires_grad=True))
+    for m in model.modules():
+        if is_norm(m):
+            assert isinstance(m, GroupNorm)
+    model.train()
+
+    imgs = torch.randn(1, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, 16, 8, 8])
+    assert feat[1].shape == torch.Size([1, 32, 4, 4])
+    assert feat[2].shape == torch.Size([1, 64, 2, 2])
+    assert feat[3].shape == torch.Size([1, 128, 1, 1])
+
+    # Test ResNet50 with 1 GeneralizedAttention after conv2, 1 NonLocal2D
+    # after conv2, 1 ContextBlock after conv3 in layers 2, 3, 4
+    plugins = [
+        dict(
+            cfg=dict(
+                type='GeneralizedAttention',
+                spatial_range=-1,
+                num_heads=8,
+                attention_type='0010',
+                kv_stride=2),
+            stages=(False, True, True, True),
+            position='after_conv2'),
+        dict(cfg=dict(type='NonLocal2d'), position='after_conv2'),
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16),
+            stages=(False, True, True, False),
+            position='after_conv3')
+    ]
+    model = ResNet(50, plugins=plugins, base_channels=8)
+    for m in model.layer1.modules():
+        if is_block(m):
+            assert not hasattr(m, 'context_block')
+            assert not hasattr(m, 'gen_attention_block')
+            assert m.nonlocal_block.in_channels == 8
+    for m in model.layer2.modules():
+        if is_block(m):
+            assert m.nonlocal_block.in_channels == 16
+            assert m.gen_attention_block.in_channels == 16
+            assert m.context_block.in_channels == 64
+
+    for m in model.layer3.modules():
+        if is_block(m):
+            assert m.nonlocal_block.in_channels == 32
+            assert m.gen_attention_block.in_channels == 32
+            assert m.context_block.in_channels == 128
+
+    for m in model.layer4.modules():
+        if is_block(m):
+            assert m.nonlocal_block.in_channels == 64
+            assert m.gen_attention_block.in_channels == 64
+            assert not hasattr(m, 'context_block')
+    model.train()
+
+    imgs = torch.randn(1, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, 32, 8, 8])
+    assert feat[1].shape == torch.Size([1, 64, 4, 4])
+    assert feat[2].shape == torch.Size([1, 128, 2, 2])
+    assert feat[3].shape == torch.Size([1, 256, 1, 1])
+
+    # Test ResNet50 with 1 ContextBlock after conv2, 1 ContextBlock after
+    # conv3 in layers 2, 3, 4
+    plugins = [
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1),
+            stages=(False, True, True, False),
+            position='after_conv3'),
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2),
+            stages=(False, True, True, False),
+            position='after_conv3')
+    ]
+
+    model = ResNet(50, plugins=plugins, base_channels=8)
+    for m in model.layer1.modules():
+        if is_block(m):
+            assert not hasattr(m, 'context_block')
+            assert not hasattr(m, 'context_block1')
+            assert not hasattr(m, 'context_block2')
+    for m in model.layer2.modules():
+        if is_block(m):
+            assert not hasattr(m, 'context_block')
+            assert m.context_block1.in_channels == 64
+            assert m.context_block2.in_channels == 64
+
+    for m in model.layer3.modules():
+        if is_block(m):
+            assert not hasattr(m, 'context_block')
+            assert m.context_block1.in_channels == 128
+            assert m.context_block2.in_channels == 128
+
+    for m in model.layer4.modules():
+        if is_block(m):
+            assert not hasattr(m, 'context_block')
+            assert not hasattr(m, 'context_block1')
+            assert not hasattr(m, 'context_block2')
+    model.train()
+
+    imgs = torch.randn(1, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, 32, 8, 8])
+    assert feat[1].shape == torch.Size([1, 64, 4, 4])
+    assert feat[2].shape == torch.Size([1, 128, 2, 2])
+    assert feat[3].shape == torch.Size([1, 256, 1, 1])
+
+    # Test ResNet50 zero initialization of residual
+    model = ResNet(50, zero_init_residual=True, base_channels=1)
+    model.init_weights()
+    for m in model.modules():
+        if isinstance(m, Bottleneck):
+            assert assert_params_all_zeros(m.norm3)
+        elif isinstance(m, BasicBlock):
+            assert assert_params_all_zeros(m.norm2)
+    model.train()
+
+    imgs = torch.randn(1, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, 4, 8, 8])
+    assert feat[1].shape == torch.Size([1, 8, 4, 4])
+    assert feat[2].shape == torch.Size([1, 16, 2, 2])
+    assert feat[3].shape == torch.Size([1, 32, 1, 1])
+
+    # Test ResNetV1d forward
+    model = ResNetV1d(depth=50, base_channels=2)
+    model.train()
+
+    imgs = torch.randn(1, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 4
+    assert feat[0].shape == torch.Size([1, 8, 8, 8])
+    assert feat[1].shape == torch.Size([1, 16, 4, 4])
+    assert feat[2].shape == torch.Size([1, 32, 2, 2])
+    assert feat[3].shape == torch.Size([1, 64, 1, 1])
diff --git a/tests/test_models/test_backbones/test_swin.py b/tests/test_models/test_backbones/test_swin.py
new file mode 100644
index 0000000..5369ef2
--- /dev/null
+++ b/tests/test_models/test_backbones/test_swin.py
@@ -0,0 +1,87 @@
+import pytest
+import torch
+
+from mmdet.models.backbones.swin import SwinBlock, SwinTransformer
+
+
+def test_swin_block():
+    # test SwinBlock structure and forward
+    block = SwinBlock(embed_dims=64, num_heads=4, feedforward_channels=256)
+    assert block.ffn.embed_dims == 64
+    assert block.attn.w_msa.num_heads == 4
+    assert block.ffn.feedforward_channels == 256
+    x = torch.randn(1, 56 * 56, 64)
+    x_out = block(x, (56, 56))
+    assert x_out.shape == torch.Size([1, 56 * 56, 64])
+
+    # Test BasicBlock with checkpoint forward
+    block = SwinBlock(
+        embed_dims=64, num_heads=4, feedforward_channels=256, with_cp=True)
+    assert block.with_cp
+    x = torch.randn(1, 56 * 56, 64)
+    x_out = block(x, (56, 56))
+    assert x_out.shape == torch.Size([1, 56 * 56, 64])
+
+
+def test_swin_transformer():
+    """Test Swin Transformer backbone."""
+
+    with pytest.raises(TypeError):
+        # Pretrained arg must be str or None.
+        SwinTransformer(pretrained=123)
+
+    with pytest.raises(AssertionError):
+        # Because swin uses non-overlapping patch embed, so the stride of patch
+        # embed must be equal to patch size.
+        SwinTransformer(strides=(2, 2, 2, 2), patch_size=4)
+
+    # test pretrained image size
+    with pytest.raises(AssertionError):
+        SwinTransformer(pretrain_img_size=(224, 224, 224))
+
+    # Test absolute position embedding
+    temp = torch.randn((1, 3, 224, 224))
+    model = SwinTransformer(pretrain_img_size=224, use_abs_pos_embed=True)
+    model.init_weights()
+    model(temp)
+    # Test different inputs when use absolute position embedding
+    temp = torch.randn((1, 3, 112, 112))
+    model(temp)
+    temp = torch.randn((1, 3, 256, 256))
+    model(temp)
+
+    # Test patch norm
+    model = SwinTransformer(patch_norm=False)
+    model(temp)
+
+    # Test normal inference
+    temp = torch.randn((1, 3, 32, 32))
+    model = SwinTransformer()
+    outs = model(temp)
+    assert outs[0].shape == (1, 96, 8, 8)
+    assert outs[1].shape == (1, 192, 4, 4)
+    assert outs[2].shape == (1, 384, 2, 2)
+    assert outs[3].shape == (1, 768, 1, 1)
+
+    # Test abnormal inference size
+    temp = torch.randn((1, 3, 31, 31))
+    model = SwinTransformer()
+    outs = model(temp)
+    assert outs[0].shape == (1, 96, 8, 8)
+    assert outs[1].shape == (1, 192, 4, 4)
+    assert outs[2].shape == (1, 384, 2, 2)
+    assert outs[3].shape == (1, 768, 1, 1)
+
+    # Test abnormal inference size
+    temp = torch.randn((1, 3, 112, 137))
+    model = SwinTransformer()
+    outs = model(temp)
+    assert outs[0].shape == (1, 96, 28, 35)
+    assert outs[1].shape == (1, 192, 14, 18)
+    assert outs[2].shape == (1, 384, 7, 9)
+    assert outs[3].shape == (1, 768, 4, 5)
+
+    model = SwinTransformer(frozen_stages=4)
+    model.train()
+    for p in model.parameters():
+        assert not p.requires_grad
diff --git a/tests/test_models/test_backbones/test_trident_resnet.py b/tests/test_models/test_backbones/test_trident_resnet.py
new file mode 100644
index 0000000..a79b97e
--- /dev/null
+++ b/tests/test_models/test_backbones/test_trident_resnet.py
@@ -0,0 +1,180 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+from mmdet.models.backbones import TridentResNet
+from mmdet.models.backbones.trident_resnet import TridentBottleneck
+
+
+def test_trident_resnet_bottleneck():
+    trident_dilations = (1, 2, 3)
+    test_branch_idx = 1
+    concat_output = True
+    trident_build_config = (trident_dilations, test_branch_idx, concat_output)
+
+    with pytest.raises(AssertionError):
+        # Style must be in ['pytorch', 'caffe']
+        TridentBottleneck(
+            *trident_build_config, inplanes=64, planes=64, style='tensorflow')
+
+    with pytest.raises(AssertionError):
+        # Allowed positions are 'after_conv1', 'after_conv2', 'after_conv3'
+        plugins = [
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 16),
+                position='after_conv4')
+        ]
+        TridentBottleneck(
+            *trident_build_config, inplanes=64, planes=16, plugins=plugins)
+
+    with pytest.raises(AssertionError):
+        # Need to specify different postfix to avoid duplicate plugin name
+        plugins = [
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 16),
+                position='after_conv3'),
+            dict(
+                cfg=dict(type='ContextBlock', ratio=1. / 16),
+                position='after_conv3')
+        ]
+        TridentBottleneck(
+            *trident_build_config, inplanes=64, planes=16, plugins=plugins)
+
+    with pytest.raises(KeyError):
+        # Plugin type is not supported
+        plugins = [dict(cfg=dict(type='WrongPlugin'), position='after_conv3')]
+        TridentBottleneck(
+            *trident_build_config, inplanes=64, planes=16, plugins=plugins)
+
+    # Test Bottleneck with checkpoint forward
+    block = TridentBottleneck(
+        *trident_build_config, inplanes=64, planes=16, with_cp=True)
+    assert block.with_cp
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56])
+
+    # Test Bottleneck style
+    block = TridentBottleneck(
+        *trident_build_config,
+        inplanes=64,
+        planes=64,
+        stride=2,
+        style='pytorch')
+    assert block.conv1.stride == (1, 1)
+    assert block.conv2.stride == (2, 2)
+    block = TridentBottleneck(
+        *trident_build_config, inplanes=64, planes=64, stride=2, style='caffe')
+    assert block.conv1.stride == (2, 2)
+    assert block.conv2.stride == (1, 1)
+
+    # Test Bottleneck forward
+    block = TridentBottleneck(*trident_build_config, inplanes=64, planes=16)
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56])
+
+    # Test Bottleneck with 1 ContextBlock after conv3
+    plugins = [
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16),
+            position='after_conv3')
+    ]
+    block = TridentBottleneck(
+        *trident_build_config, inplanes=64, planes=16, plugins=plugins)
+    assert block.context_block.in_channels == 64
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56])
+
+    # Test Bottleneck with 1 GeneralizedAttention after conv2
+    plugins = [
+        dict(
+            cfg=dict(
+                type='GeneralizedAttention',
+                spatial_range=-1,
+                num_heads=8,
+                attention_type='0010',
+                kv_stride=2),
+            position='after_conv2')
+    ]
+    block = TridentBottleneck(
+        *trident_build_config, inplanes=64, planes=16, plugins=plugins)
+    assert block.gen_attention_block.in_channels == 16
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56])
+
+    # Test Bottleneck with 1 GeneralizedAttention after conv2, 1 NonLocal2D
+    # after conv2, 1 ContextBlock after conv3
+    plugins = [
+        dict(
+            cfg=dict(
+                type='GeneralizedAttention',
+                spatial_range=-1,
+                num_heads=8,
+                attention_type='0010',
+                kv_stride=2),
+            position='after_conv2'),
+        dict(cfg=dict(type='NonLocal2d'), position='after_conv2'),
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16),
+            position='after_conv3')
+    ]
+    block = TridentBottleneck(
+        *trident_build_config, inplanes=64, planes=16, plugins=plugins)
+    assert block.gen_attention_block.in_channels == 16
+    assert block.nonlocal_block.in_channels == 16
+    assert block.context_block.in_channels == 64
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56])
+
+    # Test Bottleneck with 1 ContextBlock after conv2, 2 ContextBlock after
+    # conv3
+    plugins = [
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=1),
+            position='after_conv2'),
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=2),
+            position='after_conv3'),
+        dict(
+            cfg=dict(type='ContextBlock', ratio=1. / 16, postfix=3),
+            position='after_conv3')
+    ]
+    block = TridentBottleneck(
+        *trident_build_config, inplanes=64, planes=16, plugins=plugins)
+    assert block.context_block1.in_channels == 16
+    assert block.context_block2.in_channels == 64
+    assert block.context_block3.in_channels == 64
+    x = torch.randn(1, 64, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size([block.num_branch, 64, 56, 56])
+
+
+def test_trident_resnet_backbone():
+    tridentresnet_config = dict(
+        num_branch=3,
+        test_branch_idx=1,
+        strides=(1, 2, 2),
+        dilations=(1, 1, 1),
+        trident_dilations=(1, 2, 3),
+        out_indices=(2, ),
+    )
+    """Test tridentresnet backbone."""
+    with pytest.raises(AssertionError):
+        # TridentResNet depth should be in [50, 101, 152]
+        TridentResNet(18, **tridentresnet_config)
+
+    with pytest.raises(AssertionError):
+        # In TridentResNet: num_stages == 3
+        TridentResNet(50, num_stages=4, **tridentresnet_config)
+
+    model = TridentResNet(50, num_stages=3, **tridentresnet_config)
+    model.train()
+
+    imgs = torch.randn(1, 3, 32, 32)
+    feat = model(imgs)
+    assert len(feat) == 1
+    assert feat[0].shape == torch.Size([3, 1024, 2, 2])
diff --git a/tests/test_models/test_backbones/utils.py b/tests/test_models/test_backbones/utils.py
new file mode 100644
index 0000000..9baa994
--- /dev/null
+++ b/tests/test_models/test_backbones/utils.py
@@ -0,0 +1,32 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from torch.nn.modules import GroupNorm
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from mmdet.models.backbones.res2net import Bottle2neck
+from mmdet.models.backbones.resnet import BasicBlock, Bottleneck
+from mmdet.models.backbones.resnext import Bottleneck as BottleneckX
+from mmdet.models.utils import SimplifiedBasicBlock
+
+
+def is_block(modules):
+    """Check if is ResNet building block."""
+    if isinstance(modules, (BasicBlock, Bottleneck, BottleneckX, Bottle2neck,
+                            SimplifiedBasicBlock)):
+        return True
+    return False
+
+
+def is_norm(modules):
+    """Check if is one of the norms."""
+    if isinstance(modules, (GroupNorm, _BatchNorm)):
+        return True
+    return False
+
+
+def check_norm_state(modules, train_state):
+    """Check if norm layer is in correct train state."""
+    for mod in modules:
+        if isinstance(mod, _BatchNorm):
+            if mod.training != train_state:
+                return False
+    return True
diff --git a/tests/test_models/test_dense_heads/test_anchor_head.py b/tests/test_models/test_dense_heads/test_anchor_head.py
new file mode 100644
index 0000000..7414be3
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_anchor_head.py
@@ -0,0 +1,70 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import AnchorHead
+
+
+def test_anchor_head_loss():
+    """Tests anchor head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+
+    cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False))
+    self = AnchorHead(num_classes=4, in_channels=1, train_cfg=cfg)
+
+    # Anchor head expects a multiple levels of features per image
+    feat = [
+        torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2)))
+        for i in range(len(self.anchor_generator.strides))
+    ]
+    cls_scores, bbox_preds = self.forward(feat)
+
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = sum(empty_gt_losses['loss_cls'])
+    empty_box_loss = sum(empty_gt_losses['loss_bbox'])
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                              img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = sum(one_gt_losses['loss_cls'])
+    onegt_box_loss = sum(one_gt_losses['loss_bbox'])
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
diff --git a/tests/test_models/test_dense_heads/test_ascend_head.py b/tests/test_models/test_dense_heads/test_ascend_head.py
new file mode 100644
index 0000000..843a55f
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_ascend_head.py
@@ -0,0 +1,215 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import (AscendAnchorHead, AscendRetinaHead,
+                                      AscendSSDHead)
+
+
+def test_ascend_anchor_head_loss():
+    """Tests AscendAnchorHead loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+
+    cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='AscendMaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.4,
+                min_pos_iou=0,
+                ignore_iof_thr=-1),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False))
+    self = AscendAnchorHead(num_classes=4, in_channels=1, train_cfg=cfg)
+
+    # Anchor head expects a multiple levels of features per image
+    feat = [
+        torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2)))
+        for i in range(len(self.prior_generator.strides))
+    ]
+    cls_scores, bbox_preds = self.forward(feat)
+
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = sum(empty_gt_losses['loss_cls'])
+    empty_box_loss = sum(empty_gt_losses['loss_bbox'])
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                              img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = sum(one_gt_losses['loss_cls'])
+    onegt_box_loss = sum(one_gt_losses['loss_bbox'])
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
+
+
+def test_ascend_retina_head_loss():
+    """Tests AscendRetinaHead loss when truth is empty and non-empty."""
+    img_shape = (800, 1067, 3)
+    pad_shape = (800, 1088, 3)
+    num_classes = 80
+    in_channels = 256
+
+    img_metas = [{
+        'img_shape': img_shape,
+        'scale_factor': 1,
+        'pad_shape': pad_shape
+    }]
+
+    cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='AscendMaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.4,
+                min_pos_iou=0,
+                ignore_iof_thr=-1),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False))
+    self = AscendRetinaHead(
+        num_classes=num_classes, in_channels=in_channels, train_cfg=cfg)
+
+    # Anchor head expects a multiple levels of features per image
+    feat = [
+        torch.rand(1, in_channels, pad_shape[0] // strides[0],
+                   pad_shape[1] // strides[1])
+        for strides in self.prior_generator.strides
+    ]
+    cls_scores, bbox_preds = self.forward(feat)
+
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = sum(empty_gt_losses['loss_cls'])
+    empty_box_loss = sum(empty_gt_losses['loss_bbox'])
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                              img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = sum(one_gt_losses['loss_cls'])
+    onegt_box_loss = sum(one_gt_losses['loss_bbox'])
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
+
+
+def test_ascend_ssd_head_loss():
+    """Tests anchor head loss when truth is empty and non-empty."""
+    img_shape = (320, 320, 3)
+    pad_shape = (320, 320, 3)
+    in_channels = (96, 1280, 512, 256, 256, 128)
+    img_metas = [{
+        'img_shape': img_shape,
+        'scale_factor': 1,
+        'pad_shape': pad_shape
+    }, {
+        'img_shape': img_shape,
+        'scale_factor': 1,
+        'pad_shape': pad_shape
+    }]
+
+    self = AscendSSDHead(
+        in_channels=in_channels,
+        num_classes=80,
+        use_depthwise=True,
+        norm_cfg=dict(type='BN', eps=0.001, momentum=0.03),
+        act_cfg=dict(type='ReLU6'),
+        init_cfg=dict(type='Normal', layer='Conv2d', std=0.001),
+        anchor_generator=dict(
+            type='SSDAnchorGenerator',
+            scale_major=False,
+            strides=[16, 32, 64, 107, 160, 320],
+            ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]],
+            min_sizes=[48, 100, 150, 202, 253, 304],
+            max_sizes=[100, 150, 202, 253, 304, 320]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        train_cfg=mmcv.Config(
+            dict(
+                assigner=dict(
+                    type='AscendMaxIoUAssigner',
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.5,
+                    min_pos_iou=0.,
+                    ignore_iof_thr=-1,
+                    gt_max_assign_all=False),
+                smoothl1_beta=1.,
+                allowed_border=-1,
+                pos_weight=-1,
+                neg_pos_ratio=3,
+                debug=False)))
+
+    # Anchor head expects a multiple levels of features per image
+    feat = [
+        torch.rand(2, in_channels[i],
+                   round(pad_shape[0] / self.prior_generator.strides[i][0]),
+                   round(pad_shape[1] / self.prior_generator.strides[i][1]))
+        for i in range(len(self.prior_generator.strides))
+    ]
+    cls_scores, bbox_preds = self.forward(feat)
+
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4)), torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([]), torch.LongTensor([])]
+
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = sum(empty_gt_losses['loss_cls'])
+    empty_box_loss = sum(empty_gt_losses['loss_bbox'])
+    assert empty_cls_loss.item() >= 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2]), torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                              img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = sum(one_gt_losses['loss_cls'])
+    onegt_box_loss = sum(one_gt_losses['loss_bbox'])
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
diff --git a/tests/test_models/test_dense_heads/test_atss_head.py b/tests/test_models/test_dense_heads/test_atss_head.py
new file mode 100644
index 0000000..18597f4
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_atss_head.py
@@ -0,0 +1,77 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import ATSSHead
+
+
+def test_atss_head_loss():
+    """Tests atss head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    train_cfg = mmcv.Config(
+        dict(
+            assigner=dict(type='ATSSAssigner', topk=9),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False))
+    self = ATSSHead(
+        num_classes=4,
+        in_channels=1,
+        train_cfg=train_cfg,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0))
+    feat = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+    cls_scores, bbox_preds, centernesses = self.forward(feat)
+
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, centernesses,
+                                gt_bboxes, gt_labels, img_metas,
+                                gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = sum(empty_gt_losses['loss_cls'])
+    empty_box_loss = sum(empty_gt_losses['loss_bbox'])
+    empty_centerness_loss = sum(empty_gt_losses['loss_centerness'])
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+    assert empty_centerness_loss.item() == 0, (
+        'there should be no centerness loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, centernesses, gt_bboxes,
+                              gt_labels, img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = sum(one_gt_losses['loss_cls'])
+    onegt_box_loss = sum(one_gt_losses['loss_bbox'])
+    onegt_centerness_loss = sum(one_gt_losses['loss_centerness'])
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
+    assert onegt_centerness_loss.item() > 0, (
+        'centerness loss should be non-zero')
diff --git a/tests/test_models/test_dense_heads/test_autoassign_head.py b/tests/test_models/test_dense_heads/test_autoassign_head.py
new file mode 100644
index 0000000..3c8491f
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_autoassign_head.py
@@ -0,0 +1,91 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads.autoassign_head import AutoAssignHead
+from mmdet.models.dense_heads.paa_head import levels_to_images
+
+
+def test_autoassign_head_loss():
+    """Tests autoassign head loss when truth is empty and non-empty."""
+
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    train_cfg = mmcv.Config(
+        dict(assigner=None, allowed_border=-1, pos_weight=-1, debug=False))
+    self = AutoAssignHead(
+        num_classes=4,
+        in_channels=1,
+        train_cfg=train_cfg,
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3))
+    feat = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+    self.init_weights()
+    cls_scores, bbox_preds, objectnesses = self(feat)
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, objectnesses,
+                                gt_bboxes, gt_labels, img_metas,
+                                gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_pos_loss = empty_gt_losses['loss_pos']
+    empty_neg_loss = empty_gt_losses['loss_neg']
+    empty_center_loss = empty_gt_losses['loss_center']
+    assert empty_neg_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_pos_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+    assert empty_center_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, objectnesses, gt_bboxes,
+                              gt_labels, img_metas, gt_bboxes_ignore)
+    onegt_pos_loss = one_gt_losses['loss_pos']
+    onegt_neg_loss = one_gt_losses['loss_neg']
+    onegt_center_loss = one_gt_losses['loss_center']
+    assert onegt_pos_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_neg_loss.item() > 0, 'box loss should be non-zero'
+    assert onegt_center_loss.item() > 0, 'box loss should be non-zero'
+    n, c, h, w = 10, 4, 20, 20
+    mlvl_tensor = [torch.ones(n, c, h, w) for i in range(5)]
+    results = levels_to_images(mlvl_tensor)
+    assert len(results) == n
+    assert results[0].size() == (h * w * 5, c)
+
+    self = AutoAssignHead(
+        num_classes=4,
+        in_channels=1,
+        train_cfg=train_cfg,
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        strides=(4, ))
+    cls_scores = [torch.ones(2, 4, 5, 5)]
+    bbox_preds = [torch.ones(2, 4, 5, 5)]
+    iou_preds = [torch.ones(2, 1, 5, 5)]
+    cfg = mmcv.Config(
+        dict(
+            nms_pre=1000,
+            min_bbox_size=0,
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.6),
+            max_per_img=100))
+    rescale = False
+    self.get_bboxes(
+        cls_scores, bbox_preds, iou_preds, img_metas, cfg, rescale=rescale)
diff --git a/tests/test_models/test_dense_heads/test_centernet_head.py b/tests/test_models/test_dense_heads/test_centernet_head.py
new file mode 100644
index 0000000..8993a48
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_centernet_head.py
@@ -0,0 +1,107 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+from mmcv import ConfigDict
+
+from mmdet.models.dense_heads import CenterNetHead
+
+
+def test_center_head_loss():
+    """Tests center head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    test_cfg = dict(topK=100, max_per_img=100)
+    self = CenterNetHead(
+        num_classes=4, in_channel=1, feat_channel=4, test_cfg=test_cfg)
+
+    feat = [torch.rand(1, 1, s, s)]
+    center_out, wh_out, offset_out = self.forward(feat)
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(center_out, wh_out, offset_out, gt_bboxes,
+                                gt_labels, img_metas, gt_bboxes_ignore)
+    loss_center = empty_gt_losses['loss_center_heatmap']
+    loss_wh = empty_gt_losses['loss_wh']
+    loss_offset = empty_gt_losses['loss_offset']
+    assert loss_center.item() > 0, 'loss_center should be non-zero'
+    assert loss_wh.item() == 0, (
+        'there should be no loss_wh when there are no true boxes')
+    assert loss_offset.item() == 0, (
+        'there should be no loss_offset when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(center_out, wh_out, offset_out, gt_bboxes,
+                              gt_labels, img_metas, gt_bboxes_ignore)
+    loss_center = one_gt_losses['loss_center_heatmap']
+    loss_wh = one_gt_losses['loss_wh']
+    loss_offset = one_gt_losses['loss_offset']
+    assert loss_center.item() > 0, 'loss_center should be non-zero'
+    assert loss_wh.item() > 0, 'loss_wh should be non-zero'
+    assert loss_offset.item() > 0, 'loss_offset should be non-zero'
+
+
+def test_centernet_head_get_bboxes():
+    """Tests center head generating and decoding the heatmap."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': np.array([1., 1., 1., 1.]),
+        'pad_shape': (s, s, 3),
+        'batch_input_shape': (s, s),
+        'border': (0, 0, 0, 0),
+        'flip': False
+    }]
+    test_cfg = ConfigDict(
+        dict(topk=100, local_maximum_kernel=3, max_per_img=100))
+    gt_bboxes = [
+        torch.Tensor([[10, 20, 200, 240], [40, 50, 100, 200],
+                      [10, 20, 100, 240]])
+    ]
+    gt_labels = [torch.LongTensor([1, 1, 2])]
+
+    self = CenterNetHead(
+        num_classes=4, in_channel=1, feat_channel=4, test_cfg=test_cfg)
+    self.feat_shape = (1, 1, s // 4, s // 4)
+    targets, _ = self.get_targets(gt_bboxes, gt_labels, self.feat_shape,
+                                  img_metas[0]['pad_shape'])
+    center_target = targets['center_heatmap_target']
+    wh_target = targets['wh_target']
+    offset_target = targets['offset_target']
+    # make sure assign target right
+    for i in range(len(gt_bboxes[0])):
+        bbox, label = gt_bboxes[0][i] / 4, gt_labels[0][i]
+        ctx, cty = sum(bbox[0::2]) / 2, sum(bbox[1::2]) / 2
+        int_ctx, int_cty = int(sum(bbox[0::2]) / 2), int(sum(bbox[1::2]) / 2)
+        w, h = bbox[2] - bbox[0], bbox[3] - bbox[1]
+        x_off = ctx - int(ctx)
+        y_off = cty - int(cty)
+        assert center_target[0, label, int_cty, int_ctx] == 1
+        assert wh_target[0, 0, int_cty, int_ctx] == w
+        assert wh_target[0, 1, int_cty, int_ctx] == h
+        assert offset_target[0, 0, int_cty, int_ctx] == x_off
+        assert offset_target[0, 1, int_cty, int_ctx] == y_off
+    # make sure get_bboxes is right
+    detections = self.get_bboxes([center_target], [wh_target], [offset_target],
+                                 img_metas,
+                                 rescale=True,
+                                 with_nms=False)
+    out_bboxes = detections[0][0][:3]
+    out_clses = detections[0][1][:3]
+    for bbox, cls in zip(out_bboxes, out_clses):
+        flag = False
+        for gt_bbox, gt_cls in zip(gt_bboxes[0], gt_labels[0]):
+            if (bbox[:4] == gt_bbox[:4]).all():
+                flag = True
+        assert flag, 'get_bboxes is wrong'
diff --git a/tests/test_models/test_dense_heads/test_corner_head.py b/tests/test_models/test_dense_heads/test_corner_head.py
new file mode 100644
index 0000000..0b549ff
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_corner_head.py
@@ -0,0 +1,167 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
+from mmdet.models.dense_heads import CornerHead
+
+
+def test_corner_head_loss():
+    """Tests corner head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+
+    self = CornerHead(num_classes=4, in_channels=1)
+
+    # Corner head expects a multiple levels of features per image
+    feat = [
+        torch.rand(1, 1, s // 4, s // 4) for _ in range(self.num_feat_levels)
+    ]
+    tl_heats, br_heats, tl_embs, br_embs, tl_offs, br_offs = self.forward(feat)
+
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(tl_heats, br_heats, tl_embs, br_embs, tl_offs,
+                                br_offs, gt_bboxes, gt_labels, img_metas,
+                                gt_bboxes_ignore)
+    empty_det_loss = sum(empty_gt_losses['det_loss'])
+    empty_push_loss = sum(empty_gt_losses['push_loss'])
+    empty_pull_loss = sum(empty_gt_losses['pull_loss'])
+    empty_off_loss = sum(empty_gt_losses['off_loss'])
+    assert empty_det_loss.item() > 0, 'det loss should be non-zero'
+    assert empty_push_loss.item() == 0, (
+        'there should be no push loss when there are no true boxes')
+    assert empty_pull_loss.item() == 0, (
+        'there should be no pull loss when there are no true boxes')
+    assert empty_off_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(tl_heats, br_heats, tl_embs, br_embs, tl_offs,
+                              br_offs, gt_bboxes, gt_labels, img_metas,
+                              gt_bboxes_ignore)
+    onegt_det_loss = sum(one_gt_losses['det_loss'])
+    onegt_push_loss = sum(one_gt_losses['push_loss'])
+    onegt_pull_loss = sum(one_gt_losses['pull_loss'])
+    onegt_off_loss = sum(one_gt_losses['off_loss'])
+    assert onegt_det_loss.item() > 0, 'det loss should be non-zero'
+    assert onegt_push_loss.item() == 0, (
+        'there should be no push loss when there are only one true box')
+    assert onegt_pull_loss.item() > 0, 'pull loss should be non-zero'
+    assert onegt_off_loss.item() > 0, 'off loss should be non-zero'
+
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874],
+                      [123.6667, 123.8757, 138.6326, 251.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2, 3])]
+
+    # equalize the corners' embedding value of different objects to make the
+    # push_loss larger than 0
+    gt_bboxes_ind = (gt_bboxes[0] // 4).int().tolist()
+    for tl_emb_feat, br_emb_feat in zip(tl_embs, br_embs):
+        tl_emb_feat[:, :, gt_bboxes_ind[0][1],
+                    gt_bboxes_ind[0][0]] = tl_emb_feat[:, :,
+                                                       gt_bboxes_ind[1][1],
+                                                       gt_bboxes_ind[1][0]]
+        br_emb_feat[:, :, gt_bboxes_ind[0][3],
+                    gt_bboxes_ind[0][2]] = br_emb_feat[:, :,
+                                                       gt_bboxes_ind[1][3],
+                                                       gt_bboxes_ind[1][2]]
+
+    two_gt_losses = self.loss(tl_heats, br_heats, tl_embs, br_embs, tl_offs,
+                              br_offs, gt_bboxes, gt_labels, img_metas,
+                              gt_bboxes_ignore)
+    twogt_det_loss = sum(two_gt_losses['det_loss'])
+    twogt_push_loss = sum(two_gt_losses['push_loss'])
+    twogt_pull_loss = sum(two_gt_losses['pull_loss'])
+    twogt_off_loss = sum(two_gt_losses['off_loss'])
+    assert twogt_det_loss.item() > 0, 'det loss should be non-zero'
+    assert twogt_push_loss.item() > 0, 'push loss should be non-zero'
+    assert twogt_pull_loss.item() > 0, 'pull loss should be non-zero'
+    assert twogt_off_loss.item() > 0, 'off loss should be non-zero'
+
+
+def test_corner_head_encode_and_decode_heatmap():
+    """Tests corner head generating and decoding the heatmap."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3),
+        'border': (0, 0, 0, 0)
+    }]
+
+    gt_bboxes = [
+        torch.Tensor([[10, 20, 200, 240], [40, 50, 100, 200],
+                      [10, 20, 200, 240]])
+    ]
+    gt_labels = [torch.LongTensor([1, 1, 2])]
+
+    self = CornerHead(num_classes=4, in_channels=1, corner_emb_channels=1)
+
+    feat = [
+        torch.rand(1, 1, s // 4, s // 4) for _ in range(self.num_feat_levels)
+    ]
+
+    targets = self.get_targets(
+        gt_bboxes,
+        gt_labels,
+        feat[0].shape,
+        img_metas[0]['pad_shape'],
+        with_corner_emb=self.with_corner_emb)
+
+    gt_tl_heatmap = targets['topleft_heatmap']
+    gt_br_heatmap = targets['bottomright_heatmap']
+    gt_tl_offset = targets['topleft_offset']
+    gt_br_offset = targets['bottomright_offset']
+    embedding = targets['corner_embedding']
+    [top, left], [bottom, right] = embedding[0][0]
+    gt_tl_embedding_heatmap = torch.zeros([1, 1, s // 4, s // 4])
+    gt_br_embedding_heatmap = torch.zeros([1, 1, s // 4, s // 4])
+    gt_tl_embedding_heatmap[0, 0, top, left] = 1
+    gt_br_embedding_heatmap[0, 0, bottom, right] = 1
+
+    batch_bboxes, batch_scores, batch_clses = self.decode_heatmap(
+        tl_heat=gt_tl_heatmap,
+        br_heat=gt_br_heatmap,
+        tl_off=gt_tl_offset,
+        br_off=gt_br_offset,
+        tl_emb=gt_tl_embedding_heatmap,
+        br_emb=gt_br_embedding_heatmap,
+        img_meta=img_metas[0],
+        k=100,
+        kernel=3,
+        distance_threshold=0.5)
+
+    bboxes = batch_bboxes.view(-1, 4)
+    scores = batch_scores.view(-1, 1)
+    clses = batch_clses.view(-1, 1)
+
+    idx = scores.argsort(dim=0, descending=True)
+    bboxes = bboxes[idx].view(-1, 4)
+    scores = scores[idx].view(-1)
+    clses = clses[idx].view(-1)
+
+    valid_bboxes = bboxes[torch.where(scores > 0.05)]
+    valid_labels = clses[torch.where(scores > 0.05)]
+    max_coordinate = valid_bboxes.max()
+    offsets = valid_labels.to(valid_bboxes) * (max_coordinate + 1)
+    gt_offsets = gt_labels[0].to(gt_bboxes[0]) * (max_coordinate + 1)
+
+    offset_bboxes = valid_bboxes + offsets[:, None]
+    offset_gtbboxes = gt_bboxes[0] + gt_offsets[:, None]
+
+    iou_matrix = bbox_overlaps(offset_bboxes.numpy(), offset_gtbboxes.numpy())
+    assert (iou_matrix == 1).sum() == 3
diff --git a/tests/test_models/test_dense_heads/test_ddod_head.py b/tests/test_models/test_dense_heads/test_ddod_head.py
new file mode 100644
index 0000000..c9e658e
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_ddod_head.py
@@ -0,0 +1,72 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import DDODHead
+
+
+def test_ddod_head_loss():
+    """Tests ddod head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    train_cfg = mmcv.Config(
+        dict(  # ATSSAssigner
+            assigner=dict(type='ATSSAssigner', topk=9, alpha=0.8),
+            reg_assigner=dict(type='ATSSAssigner', topk=9, alpha=0.5),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False))
+    self = DDODHead(
+        num_classes=4,
+        in_channels=1,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        train_cfg=train_cfg,
+        norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
+        loss_iou=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0))
+    feat = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+    cls_scores, bbox_preds, iou_preds = self.forward(feat)
+
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, iou_preds, gt_bboxes,
+                                gt_labels, img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = sum(empty_gt_losses['loss_cls'])
+    empty_box_loss = sum(empty_gt_losses['loss_bbox'])
+    empty_iou_loss = sum(empty_gt_losses['loss_iou'])
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+    assert empty_iou_loss.item() == 0, (
+        'there should be no iou loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, iou_preds, gt_bboxes,
+                              gt_labels, img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = sum(one_gt_losses['loss_cls'])
+    onegt_box_loss = sum(one_gt_losses['loss_bbox'])
+    onegt_iou_loss = sum(one_gt_losses['loss_iou'])
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
+    assert onegt_iou_loss.item() > 0, 'iou loss should be non-zero'
diff --git a/tests/test_models/test_dense_heads/test_dense_heads_attr.py b/tests/test_models/test_dense_heads/test_dense_heads_attr.py
new file mode 100644
index 0000000..d4a57de
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_dense_heads_attr.py
@@ -0,0 +1,44 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import warnings
+
+from terminaltables import AsciiTable
+
+from mmdet.models import dense_heads
+from mmdet.models.dense_heads import *  # noqa: F401,F403
+
+
+def test_dense_heads_test_attr():
+    """Tests inference methods such as simple_test and aug_test."""
+    # make list of dense heads
+    exceptions = ['FeatureAdaption']  # module used in head
+    all_dense_heads = [m for m in dense_heads.__all__ if m not in exceptions]
+
+    # search attributes
+    check_attributes = [
+        'simple_test', 'aug_test', 'simple_test_bboxes', 'simple_test_rpn',
+        'aug_test_rpn'
+    ]
+    table_header = ['head name'] + check_attributes
+    table_data = [table_header]
+    not_found = {k: [] for k in check_attributes}
+    for target_head_name in all_dense_heads:
+        target_head = globals()[target_head_name]
+        target_head_attributes = dir(target_head)
+        check_results = [target_head_name]
+        for check_attribute in check_attributes:
+            found = check_attribute in target_head_attributes
+            check_results.append(found)
+            if not found:
+                not_found[check_attribute].append(target_head_name)
+        table_data.append(check_results)
+    table = AsciiTable(table_data)
+    print()
+    print(table.table)
+
+    # NOTE: this test just checks attributes.
+    # simple_test of RPN heads will not work now.
+    assert len(not_found['simple_test']) == 0, \
+        f'simple_test not found in {not_found["simple_test"]}'
+    if len(not_found['aug_test']) != 0:
+        warnings.warn(f'aug_test not found in {not_found["aug_test"]}. '
+                      'Please implement it or raise NotImplementedError.')
diff --git a/tests/test_models/test_dense_heads/test_detr_head.py b/tests/test_models/test_dense_heads/test_detr_head.py
new file mode 100644
index 0000000..cc2da23
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_detr_head.py
@@ -0,0 +1,104 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from mmcv import ConfigDict
+
+from mmdet.models.dense_heads import DETRHead
+
+
+def test_detr_head_loss():
+    """Tests transformer head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3),
+        'batch_input_shape': (s, s)
+    }]
+    config = ConfigDict(
+        dict(
+            type='DETRHead',
+            num_classes=80,
+            in_channels=200,
+            transformer=dict(
+                type='Transformer',
+                encoder=dict(
+                    type='DetrTransformerEncoder',
+                    num_layers=6,
+                    transformerlayers=dict(
+                        type='BaseTransformerLayer',
+                        attn_cfgs=[
+                            dict(
+                                type='MultiheadAttention',
+                                embed_dims=256,
+                                num_heads=8,
+                                dropout=0.1)
+                        ],
+                        feedforward_channels=2048,
+                        ffn_dropout=0.1,
+                        operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
+                decoder=dict(
+                    type='DetrTransformerDecoder',
+                    return_intermediate=True,
+                    num_layers=6,
+                    transformerlayers=dict(
+                        type='DetrTransformerDecoderLayer',
+                        attn_cfgs=dict(
+                            type='MultiheadAttention',
+                            embed_dims=256,
+                            num_heads=8,
+                            dropout=0.1),
+                        feedforward_channels=2048,
+                        ffn_dropout=0.1,
+                        operation_order=('self_attn', 'norm', 'cross_attn',
+                                         'norm', 'ffn', 'norm')),
+                )),
+            positional_encoding=dict(
+                type='SinePositionalEncoding', num_feats=128, normalize=True),
+            loss_cls=dict(
+                type='CrossEntropyLoss',
+                bg_cls_weight=0.1,
+                use_sigmoid=False,
+                loss_weight=1.0,
+                class_weight=1.0),
+            loss_bbox=dict(type='L1Loss', loss_weight=5.0),
+            loss_iou=dict(type='GIoULoss', loss_weight=2.0)))
+
+    self = DETRHead(**config)
+    self.init_weights()
+    feat = [torch.rand(1, 200, 10, 10)]
+    cls_scores, bbox_preds = self.forward(feat, img_metas)
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    for key, loss in empty_gt_losses.items():
+        if 'cls' in key:
+            assert loss.item() > 0, 'cls loss should be non-zero'
+        elif 'bbox' in key:
+            assert loss.item(
+            ) == 0, 'there should be no box loss when there are no true boxes'
+        elif 'iou' in key:
+            assert loss.item(
+            ) == 0, 'there should be no iou loss when there are no true boxes'
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                              img_metas, gt_bboxes_ignore)
+    for loss in one_gt_losses.values():
+        assert loss.item(
+        ) > 0, 'cls loss, or box loss, or iou loss should be non-zero'
+
+    # test forward_train
+    self.forward_train(feat, img_metas, gt_bboxes, gt_labels)
+
+    # test inference mode
+    self.get_bboxes(cls_scores, bbox_preds, img_metas, rescale=True)
diff --git a/tests/test_models/test_dense_heads/test_fcos_head.py b/tests/test_models/test_dense_heads/test_fcos_head.py
new file mode 100644
index 0000000..5fbe14f
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_fcos_head.py
@@ -0,0 +1,64 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import FCOSHead
+
+
+def test_fcos_head_loss():
+    """Tests fcos head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    train_cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.4,
+                min_pos_iou=0,
+                ignore_iof_thr=-1),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False))
+    # since Focal Loss is not supported on CPU
+    self = FCOSHead(
+        num_classes=4,
+        in_channels=1,
+        train_cfg=train_cfg,
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0))
+    feat = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+    cls_scores, bbox_preds, centerness = self.forward(feat)
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, centerness, gt_bboxes,
+                                gt_labels, img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = empty_gt_losses['loss_cls']
+    empty_box_loss = empty_gt_losses['loss_bbox']
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, centerness, gt_bboxes,
+                              gt_labels, img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = one_gt_losses['loss_cls']
+    onegt_box_loss = one_gt_losses['loss_bbox']
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
diff --git a/tests/test_models/test_dense_heads/test_fsaf_head.py b/tests/test_models/test_dense_heads/test_fsaf_head.py
new file mode 100644
index 0000000..7851055
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_fsaf_head.py
@@ -0,0 +1,82 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import FSAFHead
+
+
+def test_fsaf_head_loss():
+    """Tests anchor head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+
+    cfg = dict(
+        reg_decoded_bbox=True,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=1,
+            scales_per_octave=1,
+            ratios=[1.0],
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(type='TBLRBBoxCoder', normalizer=4.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0,
+            reduction='none'),
+        loss_bbox=dict(
+            type='IoULoss', eps=1e-6, loss_weight=1.0, reduction='none'))
+
+    train_cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='CenterRegionAssigner',
+                pos_scale=0.2,
+                neg_scale=0.2,
+                min_pos_iof=0.01),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False))
+    head = FSAFHead(num_classes=4, in_channels=1, train_cfg=train_cfg, **cfg)
+    if torch.cuda.is_available():
+        head.cuda()
+        # FSAF head expects a multiple levels of features per image
+        feat = [
+            torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2))).cuda()
+            for i in range(len(head.anchor_generator.strides))
+        ]
+        cls_scores, bbox_preds = head.forward(feat)
+        gt_bboxes_ignore = None
+
+        # When truth is non-empty then both cls and box loss should be nonzero
+        #  for random inputs
+        gt_bboxes = [
+            torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]).cuda(),
+        ]
+        gt_labels = [torch.LongTensor([2]).cuda()]
+        one_gt_losses = head.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                  img_metas, gt_bboxes_ignore)
+        onegt_cls_loss = sum(one_gt_losses['loss_cls'])
+        onegt_box_loss = sum(one_gt_losses['loss_bbox'])
+        assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+        assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
+
+        # Test that empty ground truth encourages the network to predict bkg
+        gt_bboxes = [torch.empty((0, 4)).cuda()]
+        gt_labels = [torch.LongTensor([]).cuda()]
+
+        empty_gt_losses = head.loss(cls_scores, bbox_preds, gt_bboxes,
+                                    gt_labels, img_metas, gt_bboxes_ignore)
+        # When there is no truth, the cls loss should be nonzero but there
+        # should be no box loss.
+        empty_cls_loss = sum(empty_gt_losses['loss_cls'])
+        empty_box_loss = sum(empty_gt_losses['loss_bbox'])
+        assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+        assert empty_box_loss.item() == 0, (
+            'there should be no box loss when there are no true boxes')
diff --git a/tests/test_models/test_dense_heads/test_ga_anchor_head.py b/tests/test_models/test_dense_heads/test_ga_anchor_head.py
new file mode 100644
index 0000000..374f71b
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_ga_anchor_head.py
@@ -0,0 +1,91 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import GuidedAnchorHead
+
+
+def test_ga_anchor_head_loss():
+    """Tests anchor head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+
+    cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            ga_assigner=dict(
+                type='ApproxMaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                ignore_iof_thr=-1),
+            ga_sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=-1,
+            center_ratio=0.2,
+            ignore_ratio=0.5,
+            pos_weight=-1,
+            debug=False))
+    head = GuidedAnchorHead(num_classes=4, in_channels=4, train_cfg=cfg)
+
+    # Anchor head expects a multiple levels of features per image
+    if torch.cuda.is_available():
+        head.cuda()
+        feat = [
+            torch.rand(1, 4, s // (2**(i + 2)), s // (2**(i + 2))).cuda()
+            for i in range(len(head.approx_anchor_generator.base_anchors))
+        ]
+        cls_scores, bbox_preds, shape_preds, loc_preds = head.forward(feat)
+
+        # Test that empty ground truth encourages the network to predict
+        # background
+        gt_bboxes = [torch.empty((0, 4)).cuda()]
+        gt_labels = [torch.LongTensor([]).cuda()]
+
+        gt_bboxes_ignore = None
+
+        empty_gt_losses = head.loss(cls_scores, bbox_preds, shape_preds,
+                                    loc_preds, gt_bboxes, gt_labels, img_metas,
+                                    gt_bboxes_ignore)
+
+        # When there is no truth, the cls loss should be nonzero but there
+        # should be no box loss.
+        empty_cls_loss = sum(empty_gt_losses['loss_cls'])
+        empty_box_loss = sum(empty_gt_losses['loss_bbox'])
+        assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+        assert empty_box_loss.item() == 0, (
+            'there should be no box loss when there are no true boxes')
+
+        # When truth is non-empty then both cls and box loss should be nonzero
+        # for random inputs
+        gt_bboxes = [
+            torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]).cuda(),
+        ]
+        gt_labels = [torch.LongTensor([2]).cuda()]
+        one_gt_losses = head.loss(cls_scores, bbox_preds, shape_preds,
+                                  loc_preds, gt_bboxes, gt_labels, img_metas,
+                                  gt_bboxes_ignore)
+        onegt_cls_loss = sum(one_gt_losses['loss_cls'])
+        onegt_box_loss = sum(one_gt_losses['loss_bbox'])
+        assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+        assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
diff --git a/tests/test_models/test_dense_heads/test_gfl_head.py b/tests/test_models/test_dense_heads/test_gfl_head.py
new file mode 100644
index 0000000..6c522fa
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_gfl_head.py
@@ -0,0 +1,74 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import GFLHead
+
+
+def test_gfl_head_loss():
+    """Tests gfl head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    train_cfg = mmcv.Config(
+        dict(
+            assigner=dict(type='ATSSAssigner', topk=9),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False))
+    self = GFLHead(
+        num_classes=4,
+        in_channels=1,
+        train_cfg=train_cfg,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        loss_cls=dict(
+            type='QualityFocalLoss',
+            use_sigmoid=True,
+            beta=2.0,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0))
+    feat = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+    cls_scores, bbox_preds = self.forward(feat)
+
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = sum(empty_gt_losses['loss_cls'])
+    empty_box_loss = sum(empty_gt_losses['loss_bbox'])
+    empty_dfl_loss = sum(empty_gt_losses['loss_dfl'])
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+    assert empty_dfl_loss.item() == 0, (
+        'there should be no dfl loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                              img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = sum(one_gt_losses['loss_cls'])
+    onegt_box_loss = sum(one_gt_losses['loss_bbox'])
+    onegt_dfl_loss = sum(one_gt_losses['loss_dfl'])
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
+    assert onegt_dfl_loss.item() > 0, 'dfl loss should be non-zero'
diff --git a/tests/test_models/test_dense_heads/test_lad_head.py b/tests/test_models/test_dense_heads/test_lad_head.py
new file mode 100644
index 0000000..0ca45f4
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_lad_head.py
@@ -0,0 +1,149 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import numpy as np
+import torch
+
+from mmdet.models.dense_heads import LADHead, lad_head
+from mmdet.models.dense_heads.lad_head import levels_to_images
+
+
+def test_lad_head_loss():
+    """Tests lad head loss when truth is empty and non-empty."""
+
+    class mock_skm:
+
+        def GaussianMixture(self, *args, **kwargs):
+            return self
+
+        def fit(self, loss):
+            pass
+
+        def predict(self, loss):
+            components = np.zeros_like(loss, dtype=np.long)
+            return components.reshape(-1)
+
+        def score_samples(self, loss):
+            scores = np.random.random(len(loss))
+            return scores
+
+    lad_head.skm = mock_skm()
+
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    train_cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.1,
+                neg_iou_thr=0.1,
+                min_pos_iou=0,
+                ignore_iof_thr=-1),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False))
+    # since Focal Loss is not supported on CPU
+    self = LADHead(
+        num_classes=4,
+        in_channels=1,
+        train_cfg=train_cfg,
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5))
+    teacher_model = LADHead(
+        num_classes=4,
+        in_channels=1,
+        train_cfg=train_cfg,
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5))
+    feat = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+    self.init_weights()
+    teacher_model.init_weights()
+
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_bboxes_ignore = None
+
+    outs_teacher = teacher_model(feat)
+    label_assignment_results = teacher_model.get_label_assignment(
+        *outs_teacher, gt_bboxes, gt_labels, img_metas, gt_bboxes_ignore)
+
+    outs = teacher_model(feat)
+    empty_gt_losses = self.loss(*outs, gt_bboxes, gt_labels, img_metas,
+                                gt_bboxes_ignore, label_assignment_results)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = empty_gt_losses['loss_cls']
+    empty_box_loss = empty_gt_losses['loss_bbox']
+    empty_iou_loss = empty_gt_losses['loss_iou']
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+    assert empty_iou_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+
+    label_assignment_results = teacher_model.get_label_assignment(
+        *outs_teacher, gt_bboxes, gt_labels, img_metas, gt_bboxes_ignore)
+
+    one_gt_losses = self.loss(*outs, gt_bboxes, gt_labels, img_metas,
+                              gt_bboxes_ignore, label_assignment_results)
+    onegt_cls_loss = one_gt_losses['loss_cls']
+    onegt_box_loss = one_gt_losses['loss_bbox']
+    onegt_iou_loss = one_gt_losses['loss_iou']
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
+    assert onegt_iou_loss.item() > 0, 'box loss should be non-zero'
+    n, c, h, w = 10, 4, 20, 20
+    mlvl_tensor = [torch.ones(n, c, h, w) for i in range(5)]
+    results = levels_to_images(mlvl_tensor)
+    assert len(results) == n
+    assert results[0].size() == (h * w * 5, c)
+    assert self.with_score_voting
+
+    self = LADHead(
+        num_classes=4,
+        in_channels=1,
+        train_cfg=train_cfg,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5))
+    cls_scores = [torch.ones(2, 4, 5, 5)]
+    bbox_preds = [torch.ones(2, 4, 5, 5)]
+    iou_preds = [torch.ones(2, 1, 5, 5)]
+    cfg = mmcv.Config(
+        dict(
+            nms_pre=1000,
+            min_bbox_size=0,
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.6),
+            max_per_img=100))
+    rescale = False
+    self.get_bboxes(
+        cls_scores, bbox_preds, iou_preds, img_metas, cfg, rescale=rescale)
diff --git a/tests/test_models/test_dense_heads/test_ld_head.py b/tests/test_models/test_dense_heads/test_ld_head.py
new file mode 100644
index 0000000..017135d
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_ld_head.py
@@ -0,0 +1,121 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import GFLHead, LDHead
+
+
+def test_ld_head_loss():
+    """Tests vfnet head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    train_cfg = mmcv.Config(
+        dict(
+            assigner=dict(type='ATSSAssigner', topk=9, ignore_iof_thr=0.1),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False))
+
+    self = LDHead(
+        num_classes=4,
+        in_channels=1,
+        train_cfg=train_cfg,
+        loss_ld=dict(type='KnowledgeDistillationKLDivLoss', loss_weight=1.0),
+        loss_cls=dict(
+            type='QualityFocalLoss',
+            use_sigmoid=True,
+            beta=2.0,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]))
+
+    teacher_model = GFLHead(
+        num_classes=4,
+        in_channels=1,
+        train_cfg=train_cfg,
+        loss_cls=dict(
+            type='QualityFocalLoss',
+            use_sigmoid=True,
+            beta=2.0,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]))
+
+    feat = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+    cls_scores, bbox_preds = self.forward(feat)
+    rand_soft_target = teacher_model.forward(feat)[1]
+
+    # Test that empty ground truth encourages the network to predict
+    # background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_bboxes_ignore = None
+
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                rand_soft_target, img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero, ld loss should
+    # be non-negative but there should be no box loss.
+    empty_cls_loss = sum(empty_gt_losses['loss_cls'])
+    empty_box_loss = sum(empty_gt_losses['loss_bbox'])
+    empty_ld_loss = sum(empty_gt_losses['loss_ld'])
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+    assert empty_ld_loss.item() >= 0, 'ld loss should be non-negative'
+
+    # When truth is non-empty then both cls and box loss should be nonzero
+    # for random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                              rand_soft_target, img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = sum(one_gt_losses['loss_cls'])
+    onegt_box_loss = sum(one_gt_losses['loss_bbox'])
+
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
+
+    gt_bboxes_ignore = gt_bboxes
+
+    # When truth is non-empty but ignored then the cls loss should be nonzero,
+    # but there should be no box loss.
+    ignore_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                 rand_soft_target, img_metas, gt_bboxes_ignore)
+    ignore_cls_loss = sum(ignore_gt_losses['loss_cls'])
+    ignore_box_loss = sum(ignore_gt_losses['loss_bbox'])
+
+    assert ignore_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert ignore_box_loss.item() == 0, 'gt bbox ignored loss should be zero'
+
+    # When truth is non-empty and not ignored then both cls and box loss should
+    # be nonzero for random inputs
+    gt_bboxes_ignore = [torch.randn(1, 4)]
+
+    not_ignore_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes,
+                                     gt_labels, rand_soft_target, img_metas,
+                                     gt_bboxes_ignore)
+    not_ignore_cls_loss = sum(not_ignore_gt_losses['loss_cls'])
+    not_ignore_box_loss = sum(not_ignore_gt_losses['loss_bbox'])
+
+    assert not_ignore_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert not_ignore_box_loss.item(
+    ) > 0, 'gt bbox not ignored loss should be non-zero'
diff --git a/tests/test_models/test_dense_heads/test_mask2former_head.py b/tests/test_models/test_dense_heads/test_mask2former_head.py
new file mode 100644
index 0000000..596a325
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_mask2former_head.py
@@ -0,0 +1,235 @@
+import numpy as np
+import pytest
+import torch
+from mmcv import ConfigDict
+
+from mmdet.core.mask import BitmapMasks
+from mmdet.models.dense_heads import Mask2FormerHead
+
+
+@pytest.mark.parametrize('num_stuff_classes, \
+     label_num', [(53, 100), (0, 80)])
+def test_mask2former_head_loss(num_stuff_classes, label_num):
+    """Tests head loss when truth is empty and non-empty.
+
+    Tests head loss as Panoptic Segmentation and Instance Segmentation. Tests
+    forward_train and simple_test with masks and None as gt_semantic_seg
+    """
+    self = _init_model(num_stuff_classes)
+    img_metas = [{
+        'batch_input_shape': (128, 160),
+        'pad_shape': (128, 160, 3),
+        'img_shape': (126, 160, 3),
+        'ori_shape': (63, 80, 3)
+    }, {
+        'batch_input_shape': (128, 160),
+        'pad_shape': (128, 160, 3),
+        'img_shape': (120, 160, 3),
+        'ori_shape': (60, 80, 3)
+    }]
+    feats = [
+        torch.rand((2, 64 * 2**i, 4 * 2**(3 - i), 5 * 2**(3 - i)))
+        for i in range(4)
+    ]
+    all_cls_scores, all_mask_preds = self.forward(feats, img_metas)
+    # Test that empty ground truth encourages the network to predict background
+    gt_labels_list = [torch.LongTensor([]), torch.LongTensor([])]
+    gt_masks_list = [
+        torch.zeros((0, 128, 160)).long(),
+        torch.zeros((0, 128, 160)).long()
+    ]
+
+    empty_gt_losses = self.loss(all_cls_scores, all_mask_preds, gt_labels_list,
+                                gt_masks_list, img_metas)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no mask loss.
+    for key, loss in empty_gt_losses.items():
+        if 'cls' in key:
+            assert loss.item() > 0, 'cls loss should be non-zero'
+        elif 'mask' in key:
+            assert loss.item(
+            ) == 0, 'there should be no mask loss when there are no true mask'
+        elif 'dice' in key:
+            assert loss.item(
+            ) == 0, 'there should be no dice loss when there are no true mask'
+
+    # when truth is non-empty then both cls, mask, dice loss should be nonzero
+    # random inputs
+    gt_labels_list = [
+        torch.tensor([10, label_num]).long(),
+        torch.tensor([label_num, 10]).long()
+    ]
+    mask1 = torch.zeros((2, 128, 160)).long()
+    mask1[0, :50] = 1
+    mask1[1, 50:] = 1
+    mask2 = torch.zeros((2, 128, 160)).long()
+    mask2[0, :, :50] = 1
+    mask2[1, :, 50:] = 1
+    gt_masks_list = [mask1, mask2]
+    two_gt_losses = self.loss(all_cls_scores, all_mask_preds, gt_labels_list,
+                              gt_masks_list, img_metas)
+    for loss in two_gt_losses.values():
+        assert loss.item() > 0, 'all loss should be non-zero'
+
+    # test forward_train
+    gt_bboxes = None
+    gt_labels = [
+        torch.tensor([10]).long(),
+        torch.tensor([10]).long(),
+    ]
+    thing_mask1 = np.zeros((1, 128, 160), dtype=np.int32)
+    thing_mask1[0, :50] = 1
+    thing_mask2 = np.zeros((1, 128, 160), dtype=np.int32)
+    thing_mask2[0, :, 50:] = 1
+    gt_masks = [
+        BitmapMasks(thing_mask1, 128, 160),
+        BitmapMasks(thing_mask2, 128, 160),
+    ]
+    stuff_mask1 = torch.zeros((1, 128, 160)).long()
+    stuff_mask1[0, :50] = 10
+    stuff_mask1[0, 50:] = 100
+    stuff_mask2 = torch.zeros((1, 128, 160)).long()
+    stuff_mask2[0, :, 50:] = 10
+    stuff_mask2[0, :, :50] = 100
+    gt_semantic_seg = [stuff_mask1, stuff_mask2]
+
+    self.forward_train(feats, img_metas, gt_bboxes, gt_labels, gt_masks,
+                       gt_semantic_seg)
+
+    # test when gt_semantic_seg is None
+    gt_semantic_seg = None
+    self.forward_train(feats, img_metas, gt_bboxes, gt_labels, gt_masks,
+                       gt_semantic_seg)
+
+    # test inference mode
+    self.simple_test(feats, img_metas)
+
+
+def _init_model(num_stuff_classes):
+    base_channels = 64
+    num_things_classes = 80
+    num_classes = num_things_classes + num_stuff_classes
+    config = ConfigDict(
+        dict(
+            type='Mask2FormerHead',
+            in_channels=[base_channels * 2**i for i in range(4)],
+            feat_channels=base_channels,
+            out_channels=base_channels,
+            num_things_classes=num_things_classes,
+            num_stuff_classes=num_stuff_classes,
+            num_queries=100,
+            num_transformer_feat_level=3,
+            pixel_decoder=dict(
+                type='MSDeformAttnPixelDecoder',
+                num_outs=3,
+                norm_cfg=dict(type='GN', num_groups=32),
+                act_cfg=dict(type='ReLU'),
+                encoder=dict(
+                    type='DetrTransformerEncoder',
+                    num_layers=6,
+                    transformerlayers=dict(
+                        type='BaseTransformerLayer',
+                        attn_cfgs=dict(
+                            type='MultiScaleDeformableAttention',
+                            embed_dims=base_channels,
+                            num_heads=8,
+                            num_levels=3,
+                            num_points=4,
+                            im2col_step=64,
+                            dropout=0.0,
+                            batch_first=False,
+                            norm_cfg=None,
+                            init_cfg=None),
+                        ffn_cfgs=dict(
+                            type='FFN',
+                            embed_dims=base_channels,
+                            feedforward_channels=base_channels * 4,
+                            num_fcs=2,
+                            ffn_drop=0.0,
+                            act_cfg=dict(type='ReLU', inplace=True)),
+                        feedforward_channels=base_channels * 4,
+                        ffn_dropout=0.0,
+                        operation_order=('self_attn', 'norm', 'ffn', 'norm')),
+                    init_cfg=None),
+                positional_encoding=dict(
+                    type='SinePositionalEncoding',
+                    num_feats=base_channels // 2,
+                    normalize=True),
+                init_cfg=None),
+            enforce_decoder_input_project=False,
+            positional_encoding=dict(
+                type='SinePositionalEncoding',
+                num_feats=base_channels // 2,
+                normalize=True),
+            transformer_decoder=dict(
+                type='DetrTransformerDecoder',
+                return_intermediate=True,
+                num_layers=9,
+                transformerlayers=dict(
+                    type='DetrTransformerDecoderLayer',
+                    attn_cfgs=dict(
+                        type='MultiheadAttention',
+                        embed_dims=base_channels,
+                        num_heads=8,
+                        attn_drop=0.0,
+                        proj_drop=0.0,
+                        dropout_layer=None,
+                        batch_first=False),
+                    ffn_cfgs=dict(
+                        embed_dims=base_channels,
+                        feedforward_channels=base_channels * 8,
+                        num_fcs=2,
+                        act_cfg=dict(type='ReLU', inplace=True),
+                        ffn_drop=0.0,
+                        dropout_layer=None,
+                        add_identity=True),
+                    # the following parameter was not used,
+                    # just make current api happy
+                    feedforward_channels=base_channels * 8,
+                    operation_order=('cross_attn', 'norm', 'self_attn', 'norm',
+                                     'ffn', 'norm')),
+                init_cfg=None),
+            loss_cls=dict(
+                type='CrossEntropyLoss',
+                use_sigmoid=False,
+                loss_weight=2.0,
+                reduction='mean',
+                class_weight=[1.0] * num_classes + [0.1]),
+            loss_mask=dict(
+                type='CrossEntropyLoss',
+                use_sigmoid=True,
+                reduction='mean',
+                loss_weight=5.0),
+            loss_dice=dict(
+                type='DiceLoss',
+                use_sigmoid=True,
+                activate=True,
+                reduction='mean',
+                naive_dice=True,
+                eps=1.0,
+                loss_weight=5.0),
+            train_cfg=dict(
+                num_points=256,
+                oversample_ratio=3.0,
+                importance_sample_ratio=0.75,
+                assigner=dict(
+                    type='MaskHungarianAssigner',
+                    cls_cost=dict(type='ClassificationCost', weight=2.0),
+                    mask_cost=dict(
+                        type='CrossEntropyLossCost',
+                        weight=5.0,
+                        use_sigmoid=True),
+                    dice_cost=dict(
+                        type='DiceCost', weight=5.0, pred_act=True, eps=1.0)),
+                sampler=dict(type='MaskPseudoSampler')),
+            test_cfg=dict(
+                panoptic_on=True,
+                semantic_on=False,
+                instance_on=True,
+                max_dets_per_image=100,
+                object_mask_thr=0.8,
+                iou_thr=0.8)))
+    self = Mask2FormerHead(**config)
+    self.init_weights()
+
+    return self
diff --git a/tests/test_models/test_dense_heads/test_maskformer_head.py b/tests/test_models/test_dense_heads/test_maskformer_head.py
new file mode 100644
index 0000000..c9bebee
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_maskformer_head.py
@@ -0,0 +1,206 @@
+import numpy as np
+import torch
+from mmcv import ConfigDict
+
+from mmdet.core.mask import BitmapMasks
+from mmdet.models.dense_heads import MaskFormerHead
+
+
+def test_maskformer_head_loss():
+    """Tests head loss when truth is empty and non-empty."""
+    base_channels = 64
+    # batch_input_shape = (128, 160)
+    img_metas = [{
+        'batch_input_shape': (128, 160),
+        'pad_shape': (128, 160, 3),
+        'img_shape': (126, 160, 3),
+        'ori_shape': (63, 80, 3)
+    }, {
+        'batch_input_shape': (128, 160),
+        'pad_shape': (128, 160, 3),
+        'img_shape': (120, 160, 3),
+        'ori_shape': (60, 80, 3)
+    }]
+    feats = [
+        torch.rand((2, 64 * 2**i, 4 * 2**(3 - i), 5 * 2**(3 - i)))
+        for i in range(4)
+    ]
+    num_things_classes = 80
+    num_stuff_classes = 53
+    num_classes = num_things_classes + num_stuff_classes
+    config = ConfigDict(
+        dict(
+            type='MaskFormerHead',
+            in_channels=[base_channels * 2**i for i in range(4)],
+            feat_channels=base_channels,
+            out_channels=base_channels,
+            num_things_classes=num_things_classes,
+            num_stuff_classes=num_stuff_classes,
+            num_queries=100,
+            pixel_decoder=dict(
+                type='TransformerEncoderPixelDecoder',
+                norm_cfg=dict(type='GN', num_groups=32),
+                act_cfg=dict(type='ReLU'),
+                encoder=dict(
+                    type='DetrTransformerEncoder',
+                    num_layers=6,
+                    transformerlayers=dict(
+                        type='BaseTransformerLayer',
+                        attn_cfgs=dict(
+                            type='MultiheadAttention',
+                            embed_dims=base_channels,
+                            num_heads=8,
+                            attn_drop=0.1,
+                            proj_drop=0.1,
+                            dropout_layer=None,
+                            batch_first=False),
+                        ffn_cfgs=dict(
+                            embed_dims=base_channels,
+                            feedforward_channels=base_channels * 8,
+                            num_fcs=2,
+                            act_cfg=dict(type='ReLU', inplace=True),
+                            ffn_drop=0.1,
+                            dropout_layer=None,
+                            add_identity=True),
+                        operation_order=('self_attn', 'norm', 'ffn', 'norm'),
+                        norm_cfg=dict(type='LN'),
+                        init_cfg=None,
+                        batch_first=False),
+                    init_cfg=None),
+                positional_encoding=dict(
+                    type='SinePositionalEncoding',
+                    num_feats=base_channels // 2,
+                    normalize=True)),
+            enforce_decoder_input_project=False,
+            positional_encoding=dict(
+                type='SinePositionalEncoding',
+                num_feats=base_channels // 2,
+                normalize=True),
+            transformer_decoder=dict(
+                type='DetrTransformerDecoder',
+                return_intermediate=True,
+                num_layers=6,
+                transformerlayers=dict(
+                    type='DetrTransformerDecoderLayer',
+                    attn_cfgs=dict(
+                        type='MultiheadAttention',
+                        embed_dims=base_channels,
+                        num_heads=8,
+                        attn_drop=0.1,
+                        proj_drop=0.1,
+                        dropout_layer=None,
+                        batch_first=False),
+                    ffn_cfgs=dict(
+                        embed_dims=base_channels,
+                        feedforward_channels=base_channels * 8,
+                        num_fcs=2,
+                        act_cfg=dict(type='ReLU', inplace=True),
+                        ffn_drop=0.1,
+                        dropout_layer=None,
+                        add_identity=True),
+                    # the following parameter was not used,
+                    # just make current api happy
+                    feedforward_channels=base_channels * 8,
+                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
+                                     'ffn', 'norm')),
+                init_cfg=None),
+            loss_cls=dict(
+                type='CrossEntropyLoss',
+                use_sigmoid=False,
+                loss_weight=1.0,
+                reduction='mean',
+                class_weight=[1.0] * num_classes + [0.1]),
+            loss_mask=dict(
+                type='FocalLoss',
+                use_sigmoid=True,
+                gamma=2.0,
+                alpha=0.25,
+                reduction='mean',
+                loss_weight=20.0),
+            loss_dice=dict(
+                type='DiceLoss',
+                use_sigmoid=True,
+                activate=True,
+                reduction='mean',
+                naive_dice=True,
+                eps=1.0,
+                loss_weight=1.0),
+            train_cfg=dict(
+                assigner=dict(
+                    type='MaskHungarianAssigner',
+                    cls_cost=dict(type='ClassificationCost', weight=1.0),
+                    mask_cost=dict(
+                        type='FocalLossCost', weight=20.0, binary_input=True),
+                    dice_cost=dict(
+                        type='DiceCost', weight=1.0, pred_act=True, eps=1.0)),
+                sampler=dict(type='MaskPseudoSampler')),
+            test_cfg=dict(object_mask_thr=0.8, iou_thr=0.8)))
+    self = MaskFormerHead(**config)
+    self.init_weights()
+    all_cls_scores, all_mask_preds = self.forward(feats, img_metas)
+    # Test that empty ground truth encourages the network to predict background
+    gt_labels_list = [torch.LongTensor([]), torch.LongTensor([])]
+    gt_masks_list = [
+        torch.zeros((0, 128, 160)).long(),
+        torch.zeros((0, 128, 160)).long()
+    ]
+
+    empty_gt_losses = self.loss(all_cls_scores, all_mask_preds, gt_labels_list,
+                                gt_masks_list, img_metas)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no mask loss.
+    for key, loss in empty_gt_losses.items():
+        if 'cls' in key:
+            assert loss.item() > 0, 'cls loss should be non-zero'
+        elif 'mask' in key:
+            assert loss.item(
+            ) == 0, 'there should be no mask loss when there are no true mask'
+        elif 'dice' in key:
+            assert loss.item(
+            ) == 0, 'there should be no dice loss when there are no true mask'
+
+    # when truth is non-empty then both cls, mask, dice loss should be nonzero
+    # random inputs
+    gt_labels_list = [
+        torch.tensor([10, 100]).long(),
+        torch.tensor([100, 10]).long()
+    ]
+    mask1 = torch.zeros((2, 128, 160)).long()
+    mask1[0, :50] = 1
+    mask1[1, 50:] = 1
+    mask2 = torch.zeros((2, 128, 160)).long()
+    mask2[0, :, :50] = 1
+    mask2[1, :, 50:] = 1
+    gt_masks_list = [mask1, mask2]
+    two_gt_losses = self.loss(all_cls_scores, all_mask_preds, gt_labels_list,
+                              gt_masks_list, img_metas)
+    for loss in two_gt_losses.values():
+        assert loss.item() > 0, 'all loss should be non-zero'
+
+    # test forward_train
+    gt_bboxes = None
+    gt_labels = [
+        torch.tensor([10]).long(),
+        torch.tensor([10]).long(),
+    ]
+    thing_mask1 = np.zeros((1, 128, 160), dtype=np.int32)
+    thing_mask1[0, :50] = 1
+    thing_mask2 = np.zeros((1, 128, 160), dtype=np.int32)
+    thing_mask2[0, :, 50:] = 1
+    gt_masks = [
+        BitmapMasks(thing_mask1, 128, 160),
+        BitmapMasks(thing_mask2, 128, 160),
+    ]
+    stuff_mask1 = torch.zeros((1, 128, 160)).long()
+    stuff_mask1[0, :50] = 10
+    stuff_mask1[0, 50:] = 100
+    stuff_mask2 = torch.zeros((1, 128, 160)).long()
+    stuff_mask2[0, :, 50:] = 10
+    stuff_mask2[0, :, :50] = 100
+    gt_semantic_seg = [stuff_mask1, stuff_mask2]
+
+    self.forward_train(feats, img_metas, gt_bboxes, gt_labels, gt_masks,
+                       gt_semantic_seg)
+
+    # test inference mode
+    self.simple_test(feats, img_metas)
diff --git a/tests/test_models/test_dense_heads/test_paa_head.py b/tests/test_models/test_dense_heads/test_paa_head.py
new file mode 100644
index 0000000..1aa7c6a
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_paa_head.py
@@ -0,0 +1,135 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import numpy as np
+import torch
+
+from mmdet.models.dense_heads import PAAHead, paa_head
+from mmdet.models.dense_heads.paa_head import levels_to_images
+
+
+def test_paa_head_loss():
+    """Tests paa head loss when truth is empty and non-empty."""
+
+    class mock_skm:
+
+        def GaussianMixture(self, *args, **kwargs):
+            return self
+
+        def fit(self, loss):
+            pass
+
+        def predict(self, loss):
+            components = np.zeros_like(loss, dtype=np.long)
+            return components.reshape(-1)
+
+        def score_samples(self, loss):
+            scores = np.random.random(len(loss))
+            return scores
+
+    paa_head.skm = mock_skm()
+
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    train_cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.1,
+                neg_iou_thr=0.1,
+                min_pos_iou=0,
+                ignore_iof_thr=-1),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False))
+    # since Focal Loss is not supported on CPU
+    self = PAAHead(
+        num_classes=4,
+        in_channels=1,
+        train_cfg=train_cfg,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5))
+    feat = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+    self.init_weights()
+    cls_scores, bbox_preds, iou_preds = self(feat)
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, iou_preds, gt_bboxes,
+                                gt_labels, img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = empty_gt_losses['loss_cls']
+    empty_box_loss = empty_gt_losses['loss_bbox']
+    empty_iou_loss = empty_gt_losses['loss_iou']
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+    assert empty_iou_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, iou_preds, gt_bboxes,
+                              gt_labels, img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = one_gt_losses['loss_cls']
+    onegt_box_loss = one_gt_losses['loss_bbox']
+    onegt_iou_loss = one_gt_losses['loss_iou']
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
+    assert onegt_iou_loss.item() > 0, 'box loss should be non-zero'
+    n, c, h, w = 10, 4, 20, 20
+    mlvl_tensor = [torch.ones(n, c, h, w) for i in range(5)]
+    results = levels_to_images(mlvl_tensor)
+    assert len(results) == n
+    assert results[0].size() == (h * w * 5, c)
+    assert self.with_score_voting
+
+    self = PAAHead(
+        num_classes=4,
+        in_channels=1,
+        train_cfg=train_cfg,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.3),
+        loss_centerness=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.5))
+    cls_scores = [torch.ones(2, 4, 5, 5)]
+    bbox_preds = [torch.ones(2, 4, 5, 5)]
+    iou_preds = [torch.ones(2, 1, 5, 5)]
+    cfg = mmcv.Config(
+        dict(
+            nms_pre=1000,
+            min_bbox_size=0,
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.6),
+            max_per_img=100))
+    rescale = False
+    self.get_bboxes(
+        cls_scores, bbox_preds, iou_preds, img_metas, cfg, rescale=rescale)
diff --git a/tests/test_models/test_dense_heads/test_pisa_head.py b/tests/test_models/test_dense_heads/test_pisa_head.py
new file mode 100644
index 0000000..996320a
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_pisa_head.py
@@ -0,0 +1,245 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import PISARetinaHead, PISASSDHead
+from mmdet.models.roi_heads import PISARoIHead
+
+
+def test_pisa_retinanet_head_loss():
+    """Tests pisa retinanet head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+
+    cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            isr=dict(k=2., bias=0.),
+            carl=dict(k=1., bias=0.2),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False))
+    self = PISARetinaHead(num_classes=4, in_channels=1, train_cfg=cfg)
+
+    # Anchor head expects a multiple levels of features per image
+    feat = [
+        torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2)))
+        for i in range(len(self.anchor_generator.strides))
+    ]
+    cls_scores, bbox_preds = self.forward(feat)
+
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = empty_gt_losses['loss_cls'].sum()
+    empty_box_loss = empty_gt_losses['loss_bbox'].sum()
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                              img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = one_gt_losses['loss_cls'].sum()
+    onegt_box_loss = one_gt_losses['loss_bbox'].sum()
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
+
+
+def test_pisa_ssd_head_loss():
+    """Tests pisa ssd head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+
+    cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.5,
+                min_pos_iou=0.,
+                ignore_iof_thr=-1,
+                gt_max_assign_all=False),
+            isr=dict(k=2., bias=0.),
+            carl=dict(k=1., bias=0.2),
+            smoothl1_beta=1.,
+            allowed_border=-1,
+            pos_weight=-1,
+            neg_pos_ratio=3,
+            debug=False))
+    ssd_anchor_generator = dict(
+        type='SSDAnchorGenerator',
+        scale_major=False,
+        input_size=300,
+        strides=[1],
+        ratios=([2], ),
+        basesize_ratio_range=(0.15, 0.9))
+    self = PISASSDHead(
+        num_classes=4,
+        in_channels=(1, ),
+        train_cfg=cfg,
+        anchor_generator=ssd_anchor_generator)
+
+    # Anchor head expects a multiple levels of features per image
+    feat = [
+        torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2)))
+        for i in range(len(self.anchor_generator.strides))
+    ]
+    cls_scores, bbox_preds = self.forward(feat)
+
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = sum(empty_gt_losses['loss_cls'])
+    empty_box_loss = sum(empty_gt_losses['loss_bbox'])
+    # SSD is special, #pos:#neg = 1: 3, so empth gt will also lead loss cls = 0
+    assert empty_cls_loss.item() == 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                              img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = sum(one_gt_losses['loss_cls'])
+    onegt_box_loss = sum(one_gt_losses['loss_bbox'])
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
+
+
+def test_pisa_roi_head_loss():
+    """Tests pisa roi head loss when truth is empty and non-empty."""
+    train_cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='ScoreHLRSampler',
+                num=4,
+                pos_fraction=0.25,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=True,
+                k=0.5,
+                bias=0.),
+            isr=dict(k=2., bias=0.),
+            carl=dict(k=1., bias=0.2),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False))
+
+    bbox_roi_extractor = dict(
+        type='SingleRoIExtractor',
+        roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+        out_channels=1,
+        featmap_strides=[1])
+
+    bbox_head = dict(
+        type='Shared2FCBBoxHead',
+        in_channels=1,
+        fc_out_channels=2,
+        roi_feat_size=7,
+        num_classes=4,
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[0., 0., 0., 0.],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        reg_class_agnostic=False,
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+        loss_bbox=dict(type='L1Loss', loss_weight=1.0))
+
+    self = PISARoIHead(bbox_roi_extractor, bbox_head, train_cfg=train_cfg)
+
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+
+    # Anchor head expects a multiple levels of features per image
+    feat = [
+        torch.rand(1, 1, s // (2**(i + 2)), s // (2**(i + 2)))
+        for i in range(1)
+    ]
+
+    proposal_list = [
+        torch.Tensor([[22.6667, 22.8757, 238.6326, 151.8874], [0, 3, 5, 7]])
+    ]
+
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_bboxes_ignore = None
+
+    empty_gt_losses = self.forward_train(feat, img_metas, proposal_list,
+                                         gt_bboxes, gt_labels,
+                                         gt_bboxes_ignore)
+
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = empty_gt_losses['loss_cls'].sum()
+    empty_box_loss = empty_gt_losses['loss_bbox'].sum()
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+
+    one_gt_losses = self.forward_train(feat, img_metas, proposal_list,
+                                       gt_bboxes, gt_labels, gt_bboxes_ignore)
+    onegt_cls_loss = one_gt_losses['loss_cls'].sum()
+    onegt_box_loss = one_gt_losses['loss_bbox'].sum()
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
diff --git a/tests/test_models/test_dense_heads/test_sabl_retina_head.py b/tests/test_models/test_dense_heads/test_sabl_retina_head.py
new file mode 100644
index 0000000..4e89d9a
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_sabl_retina_head.py
@@ -0,0 +1,76 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import SABLRetinaHead
+
+
+def test_sabl_retina_head_loss():
+    """Tests anchor head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+
+    cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='ApproxMaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.4,
+                min_pos_iou=0.0,
+                ignore_iof_thr=-1),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False))
+    head = SABLRetinaHead(
+        num_classes=4,
+        in_channels=3,
+        feat_channels=10,
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        train_cfg=cfg)
+    if torch.cuda.is_available():
+        head.cuda()
+        # Anchor head expects a multiple levels of features per image
+        feat = [
+            torch.rand(1, 3, s // (2**(i + 2)), s // (2**(i + 2))).cuda()
+            for i in range(len(head.approx_anchor_generator.base_anchors))
+        ]
+        cls_scores, bbox_preds = head.forward(feat)
+
+        # Test that empty ground truth encourages the network
+        # to predict background
+        gt_bboxes = [torch.empty((0, 4)).cuda()]
+        gt_labels = [torch.LongTensor([]).cuda()]
+
+        gt_bboxes_ignore = None
+        empty_gt_losses = head.loss(cls_scores, bbox_preds, gt_bboxes,
+                                    gt_labels, img_metas, gt_bboxes_ignore)
+        # When there is no truth, the cls loss should be nonzero but there
+        # should be no box loss.
+        empty_cls_loss = sum(empty_gt_losses['loss_cls'])
+        empty_box_cls_loss = sum(empty_gt_losses['loss_bbox_cls'])
+        empty_box_reg_loss = sum(empty_gt_losses['loss_bbox_reg'])
+        assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+        assert empty_box_cls_loss.item() == 0, (
+            'there should be no box cls loss when there are no true boxes')
+        assert empty_box_reg_loss.item() == 0, (
+            'there should be no box reg loss when there are no true boxes')
+
+        # When truth is non-empty then both cls and box loss should
+        # be nonzero for random inputs
+        gt_bboxes = [
+            torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]).cuda(),
+        ]
+        gt_labels = [torch.LongTensor([2]).cuda()]
+        one_gt_losses = head.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                  img_metas, gt_bboxes_ignore)
+        onegt_cls_loss = sum(one_gt_losses['loss_cls'])
+        onegt_box_cls_loss = sum(one_gt_losses['loss_bbox_cls'])
+        onegt_box_reg_loss = sum(one_gt_losses['loss_bbox_reg'])
+        assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+        assert onegt_box_cls_loss.item() > 0, 'box loss cls should be non-zero'
+        assert onegt_box_reg_loss.item() > 0, 'box loss reg should be non-zero'
diff --git a/tests/test_models/test_dense_heads/test_solo_head.py b/tests/test_models/test_dense_heads/test_solo_head.py
new file mode 100644
index 0000000..16cb4f7
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_solo_head.py
@@ -0,0 +1,284 @@
+import pytest
+import torch
+
+from mmdet.models.dense_heads import (DecoupledSOLOHead,
+                                      DecoupledSOLOLightHead, SOLOHead)
+
+
+def test_solo_head_loss():
+    """Tests solo head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    self = SOLOHead(
+        num_classes=4,
+        in_channels=1,
+        num_grids=[40, 36, 24, 16, 12],
+        loss_mask=dict(type='DiceLoss', use_sigmoid=True, loss_weight=3.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0))
+    feat = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+    mask_preds, cls_preds = self.forward(feat)
+    # Test that empty ground truth encourages the network to
+    # predict background.
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_masks = [torch.empty((0, 550, 550))]
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(
+        mask_preds,
+        cls_preds,
+        gt_labels,
+        gt_masks,
+        img_metas,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_mask_loss = empty_gt_losses['loss_mask']
+    empty_cls_loss = empty_gt_losses['loss_cls']
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_mask_loss.item() == 0, (
+        'there should be no mask loss when there are no true masks')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs.
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    gt_masks = [(torch.rand((1, 256, 256)) > 0.5).float()]
+    one_gt_losses = self.loss(
+        mask_preds,
+        cls_preds,
+        gt_labels,
+        gt_masks,
+        img_metas,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_bboxes_ignore)
+    onegt_mask_loss = one_gt_losses['loss_mask']
+    onegt_cls_loss = one_gt_losses['loss_cls']
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_mask_loss.item() > 0, 'mask loss should be non-zero'
+
+    # When the length of num_grids, scale_ranges, and num_levels are not equal.
+    with pytest.raises(AssertionError):
+        SOLOHead(
+            num_classes=4,
+            in_channels=1,
+            num_grids=[36, 24, 16, 12],
+            loss_mask=dict(type='DiceLoss', use_sigmoid=True, loss_weight=3.0),
+            loss_cls=dict(
+                type='FocalLoss',
+                use_sigmoid=True,
+                gamma=2.0,
+                alpha=0.25,
+                loss_weight=1.0))
+
+    # When input feature length is not equal to num_levels.
+    with pytest.raises(AssertionError):
+        feat = [
+            torch.rand(1, 1, s // feat_size, s // feat_size)
+            for feat_size in [4, 8, 16, 32]
+        ]
+        self.forward(feat)
+
+
+def test_desolo_head_loss():
+    """Tests solo head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    self = DecoupledSOLOHead(
+        num_classes=4,
+        in_channels=1,
+        num_grids=[40, 36, 24, 16, 12],
+        loss_mask=dict(
+            type='DiceLoss', use_sigmoid=True, activate=False,
+            loss_weight=3.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0))
+    feat = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+    mask_preds_x, mask_preds_y, cls_preds = self.forward(feat)
+    # Test that empty ground truth encourages the network to
+    # predict background.
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_masks = [torch.empty((0, 550, 550))]
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(
+        mask_preds_x,
+        mask_preds_y,
+        cls_preds,
+        gt_labels,
+        gt_masks,
+        img_metas,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_mask_loss = empty_gt_losses['loss_mask']
+    empty_cls_loss = empty_gt_losses['loss_cls']
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_mask_loss.item() == 0, (
+        'there should be no mask loss when there are no true masks')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs.
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    gt_masks = [(torch.rand((1, 256, 256)) > 0.5).float()]
+    one_gt_losses = self.loss(
+        mask_preds_x,
+        mask_preds_y,
+        cls_preds,
+        gt_labels,
+        gt_masks,
+        img_metas,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_bboxes_ignore)
+    onegt_mask_loss = one_gt_losses['loss_mask']
+    onegt_cls_loss = one_gt_losses['loss_cls']
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_mask_loss.item() > 0, 'mask loss should be non-zero'
+
+    # When the length of num_grids, scale_ranges, and num_levels are not equal.
+    with pytest.raises(AssertionError):
+        DecoupledSOLOHead(
+            num_classes=4,
+            in_channels=1,
+            num_grids=[36, 24, 16, 12],
+            loss_mask=dict(
+                type='DiceLoss',
+                use_sigmoid=True,
+                activate=False,
+                loss_weight=3.0),
+            loss_cls=dict(
+                type='FocalLoss',
+                use_sigmoid=True,
+                gamma=2.0,
+                alpha=0.25,
+                loss_weight=1.0))
+
+    # When input feature length is not equal to num_levels.
+    with pytest.raises(AssertionError):
+        feat = [
+            torch.rand(1, 1, s // feat_size, s // feat_size)
+            for feat_size in [4, 8, 16, 32]
+        ]
+        self.forward(feat)
+
+
+def test_desolo_light_head_loss():
+    """Tests solo head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    self = DecoupledSOLOLightHead(
+        num_classes=4,
+        in_channels=1,
+        num_grids=[40, 36, 24, 16, 12],
+        loss_mask=dict(
+            type='DiceLoss', use_sigmoid=True, activate=False,
+            loss_weight=3.0),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0))
+    feat = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+    mask_preds_x, mask_preds_y, cls_preds = self.forward(feat)
+    # Test that empty ground truth encourages the network to
+    # predict background.
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_masks = [torch.empty((0, 550, 550))]
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(
+        mask_preds_x,
+        mask_preds_y,
+        cls_preds,
+        gt_labels,
+        gt_masks,
+        img_metas,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_mask_loss = empty_gt_losses['loss_mask']
+    empty_cls_loss = empty_gt_losses['loss_cls']
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_mask_loss.item() == 0, (
+        'there should be no mask loss when there are no true masks')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs.
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    gt_masks = [(torch.rand((1, 256, 256)) > 0.5).float()]
+    one_gt_losses = self.loss(
+        mask_preds_x,
+        mask_preds_y,
+        cls_preds,
+        gt_labels,
+        gt_masks,
+        img_metas,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_bboxes_ignore)
+    onegt_mask_loss = one_gt_losses['loss_mask']
+    onegt_cls_loss = one_gt_losses['loss_cls']
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_mask_loss.item() > 0, 'mask loss should be non-zero'
+
+    # When the length of num_grids, scale_ranges, and num_levels are not equal.
+    with pytest.raises(AssertionError):
+        DecoupledSOLOLightHead(
+            num_classes=4,
+            in_channels=1,
+            num_grids=[36, 24, 16, 12],
+            loss_mask=dict(type='DiceLoss', use_sigmoid=True, loss_weight=3.0),
+            loss_cls=dict(
+                type='FocalLoss',
+                use_sigmoid=True,
+                gamma=2.0,
+                alpha=0.25,
+                loss_weight=1.0))
+
+    # When input feature length is not equal to num_levels.
+    with pytest.raises(AssertionError):
+        feat = [
+            torch.rand(1, 1, s // feat_size, s // feat_size)
+            for feat_size in [4, 8, 16, 32]
+        ]
+        self.forward(feat)
diff --git a/tests/test_models/test_dense_heads/test_tood_head.py b/tests/test_models/test_dense_heads/test_tood_head.py
new file mode 100644
index 0000000..f96364d
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_tood_head.py
@@ -0,0 +1,128 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import TOODHead
+
+
+def test_tood_head_loss():
+    """Tests paa head loss when truth is empty and non-empty."""
+
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    train_cfg = mmcv.Config(
+        dict(
+            initial_epoch=4,
+            initial_assigner=dict(type='ATSSAssigner', topk=9),
+            assigner=dict(type='TaskAlignedAssigner', topk=13),
+            alpha=1,
+            beta=6,
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False))
+    test_cfg = mmcv.Config(
+        dict(
+            nms_pre=1000,
+            min_bbox_size=0,
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.6),
+            max_per_img=100))
+    # since Focal Loss is not supported on CPU
+    self = TOODHead(
+        num_classes=80,
+        in_channels=1,
+        stacked_convs=6,
+        feat_channels=256,
+        anchor_type='anchor_free',
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            octave_base_scale=8,
+            scales_per_octave=1,
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        initial_loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            activated=True,  # use probability instead of logit as input
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_cls=dict(
+            type='QualityFocalLoss',
+            use_sigmoid=True,
+            activated=True,  # use probability instead of logit as input
+            beta=2.0,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=2.0),
+        train_cfg=train_cfg,
+        test_cfg=test_cfg)
+    self.init_weights()
+    feat = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [8, 16, 32, 64, 128]
+    ]
+    cls_scores, bbox_preds = self(feat)
+
+    # test initial assigner and losses
+    self.epoch = 0
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = empty_gt_losses['loss_cls']
+    empty_box_loss = empty_gt_losses['loss_bbox']
+    assert sum(empty_cls_loss).item() > 0, 'cls loss should be non-zero'
+    assert sum(empty_box_loss).item() == 0, (
+        'there should be no box loss when there are no true boxes')
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                              img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = one_gt_losses['loss_cls']
+    onegt_box_loss = one_gt_losses['loss_bbox']
+    assert sum(onegt_cls_loss).item() > 0, 'cls loss should be non-zero'
+    assert sum(onegt_box_loss).item() > 0, 'box loss should be non-zero'
+
+    # test task alignment assigner and losses
+    self.epoch = 10
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = empty_gt_losses['loss_cls']
+    empty_box_loss = empty_gt_losses['loss_bbox']
+    assert sum(empty_cls_loss).item() > 0, 'cls loss should be non-zero'
+    assert sum(empty_box_loss).item() == 0, (
+        'there should be no box loss when there are no true boxes')
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                              img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = one_gt_losses['loss_cls']
+    onegt_box_loss = one_gt_losses['loss_bbox']
+    assert sum(onegt_cls_loss).item() > 0, 'cls loss should be non-zero'
+    assert sum(onegt_box_loss).item() > 0, 'box loss should be non-zero'
diff --git a/tests/test_models/test_dense_heads/test_vfnet_head.py b/tests/test_models/test_dense_heads/test_vfnet_head.py
new file mode 100644
index 0000000..7fec4e5
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_vfnet_head.py
@@ -0,0 +1,63 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import VFNetHead
+
+
+def test_vfnet_head_loss():
+    """Tests vfnet head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    train_cfg = mmcv.Config(
+        dict(
+            assigner=dict(type='ATSSAssigner', topk=9),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False))
+    # since Focal Loss is not supported on CPU
+    self = VFNetHead(
+        num_classes=4,
+        in_channels=1,
+        train_cfg=train_cfg,
+        loss_cls=dict(type='VarifocalLoss', use_sigmoid=True, loss_weight=1.0))
+    if torch.cuda.is_available():
+        self.cuda()
+        feat = [
+            torch.rand(1, 1, s // feat_size, s // feat_size).cuda()
+            for feat_size in [4, 8, 16, 32, 64]
+        ]
+        cls_scores, bbox_preds, bbox_preds_refine = self.forward(feat)
+        # Test that empty ground truth encourages the network to predict
+        # background
+        gt_bboxes = [torch.empty((0, 4)).cuda()]
+        gt_labels = [torch.LongTensor([]).cuda()]
+        gt_bboxes_ignore = None
+        empty_gt_losses = self.loss(cls_scores, bbox_preds, bbox_preds_refine,
+                                    gt_bboxes, gt_labels, img_metas,
+                                    gt_bboxes_ignore)
+        # When there is no truth, the cls loss should be nonzero but there
+        # should be no box loss.
+        empty_cls_loss = empty_gt_losses['loss_cls']
+        empty_box_loss = empty_gt_losses['loss_bbox']
+        assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+        assert empty_box_loss.item() == 0, (
+            'there should be no box loss when there are no true boxes')
+
+        # When truth is non-empty then both cls and box loss should be nonzero
+        # for random inputs
+        gt_bboxes = [
+            torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]).cuda(),
+        ]
+        gt_labels = [torch.LongTensor([2]).cuda()]
+        one_gt_losses = self.loss(cls_scores, bbox_preds, bbox_preds_refine,
+                                  gt_bboxes, gt_labels, img_metas,
+                                  gt_bboxes_ignore)
+        onegt_cls_loss = one_gt_losses['loss_cls']
+        onegt_box_loss = one_gt_losses['loss_bbox']
+        assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+        assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
diff --git a/tests/test_models/test_dense_heads/test_yolact_head.py b/tests/test_models/test_dense_heads/test_yolact_head.py
new file mode 100644
index 0000000..e82e0d7
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_yolact_head.py
@@ -0,0 +1,137 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import YOLACTHead, YOLACTProtonet, YOLACTSegmHead
+
+
+def test_yolact_head_loss():
+    """Tests yolact head losses when truth is empty and non-empty."""
+    s = 550
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    train_cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.5,
+                neg_iou_thr=0.4,
+                min_pos_iou=0.,
+                ignore_iof_thr=-1,
+                gt_max_assign_all=False),
+            smoothl1_beta=1.,
+            allowed_border=-1,
+            pos_weight=-1,
+            neg_pos_ratio=3,
+            debug=False,
+            min_gt_box_wh=[4.0, 4.0]))
+    bbox_head = YOLACTHead(
+        num_classes=80,
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=3,
+            scales_per_octave=1,
+            base_sizes=[8, 16, 32, 64, 128],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[550.0 / x for x in [69, 35, 18, 9, 5]],
+            centers=[(550 * 0.5 / x, 550 * 0.5 / x)
+                     for x in [69, 35, 18, 9, 5]]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]),
+        loss_cls=dict(
+            type='CrossEntropyLoss',
+            use_sigmoid=False,
+            reduction='none',
+            loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.5),
+        num_head_convs=1,
+        num_protos=32,
+        use_ohem=True,
+        train_cfg=train_cfg)
+    segm_head = YOLACTSegmHead(
+        in_channels=256,
+        num_classes=80,
+        loss_segm=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0))
+    mask_head = YOLACTProtonet(
+        num_classes=80,
+        in_channels=256,
+        num_protos=32,
+        max_masks_to_train=100,
+        loss_mask_weight=6.125)
+    feat = [
+        torch.rand(1, 256, feat_size, feat_size)
+        for feat_size in [69, 35, 18, 9, 5]
+    ]
+    cls_score, bbox_pred, coeff_pred = bbox_head.forward(feat)
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_masks = [torch.empty((0, 550, 550))]
+    gt_bboxes_ignore = None
+    empty_gt_losses, sampling_results = bbox_head.loss(
+        cls_score,
+        bbox_pred,
+        gt_bboxes,
+        gt_labels,
+        img_metas,
+        gt_bboxes_ignore=gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = sum(empty_gt_losses['loss_cls'])
+    empty_box_loss = sum(empty_gt_losses['loss_bbox'])
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+
+    # Test segm head and mask head
+    segm_head_outs = segm_head(feat[0])
+    empty_segm_loss = segm_head.loss(segm_head_outs, gt_masks, gt_labels)
+    mask_pred = mask_head(feat[0], coeff_pred, gt_bboxes, img_metas,
+                          sampling_results)
+    empty_mask_loss = mask_head.loss(mask_pred, gt_masks, gt_bboxes, img_metas,
+                                     sampling_results)
+    # When there is no truth, the segm and mask loss should be zero.
+    empty_segm_loss = sum(empty_segm_loss['loss_segm'])
+    empty_mask_loss = sum(empty_mask_loss['loss_mask'])
+    assert empty_segm_loss.item() == 0, (
+        'there should be no segm loss when there are no true boxes')
+    assert empty_mask_loss == 0, (
+        'there should be no mask loss when there are no true boxes')
+
+    # When truth is non-empty then cls, box, mask, segm loss should be
+    # nonzero for random inputs.
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    gt_masks = [(torch.rand((1, 550, 550)) > 0.5).float()]
+
+    one_gt_losses, sampling_results = bbox_head.loss(
+        cls_score,
+        bbox_pred,
+        gt_bboxes,
+        gt_labels,
+        img_metas,
+        gt_bboxes_ignore=gt_bboxes_ignore)
+    one_gt_cls_loss = sum(one_gt_losses['loss_cls'])
+    one_gt_box_loss = sum(one_gt_losses['loss_bbox'])
+    assert one_gt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert one_gt_box_loss.item() > 0, 'box loss should be non-zero'
+
+    one_gt_segm_loss = segm_head.loss(segm_head_outs, gt_masks, gt_labels)
+    mask_pred = mask_head(feat[0], coeff_pred, gt_bboxes, img_metas,
+                          sampling_results)
+    one_gt_mask_loss = mask_head.loss(mask_pred, gt_masks, gt_bboxes,
+                                      img_metas, sampling_results)
+    one_gt_segm_loss = sum(one_gt_segm_loss['loss_segm'])
+    one_gt_mask_loss = sum(one_gt_mask_loss['loss_mask'])
+    assert one_gt_segm_loss.item() > 0, 'segm loss should be non-zero'
+    assert one_gt_mask_loss.item() > 0, 'mask loss should be non-zero'
diff --git a/tests/test_models/test_dense_heads/test_yolof_head.py b/tests/test_models/test_dense_heads/test_yolof_head.py
new file mode 100644
index 0000000..9810374
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_yolof_head.py
@@ -0,0 +1,76 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.dense_heads import YOLOFHead
+
+
+def test_yolof_head_loss():
+    """Tests yolof head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    train_cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='UniformAssigner',
+                pos_ignore_thr=0.15,
+                neg_ignore_thr=0.7),
+            allowed_border=-1,
+            pos_weight=-1,
+            debug=False))
+    self = YOLOFHead(
+        num_classes=4,
+        in_channels=1,
+        reg_decoded_bbox=True,
+        train_cfg=train_cfg,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[1, 2, 4, 8, 16],
+            strides=[32]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1., 1., 1., 1.],
+            add_ctr_clamp=True,
+            ctr_clamp=32),
+        loss_cls=dict(
+            type='FocalLoss',
+            use_sigmoid=True,
+            gamma=2.0,
+            alpha=0.25,
+            loss_weight=1.0),
+        loss_bbox=dict(type='GIoULoss', loss_weight=1.0))
+    feat = [torch.rand(1, 1, s // 32, s // 32)]
+    cls_scores, bbox_preds = self.forward(feat)
+
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    gt_bboxes_ignore = None
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                                img_metas, gt_bboxes_ignore)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = empty_gt_losses['loss_cls']
+    empty_box_loss = empty_gt_losses['loss_bbox']
+    assert empty_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, gt_bboxes, gt_labels,
+                              img_metas, gt_bboxes_ignore)
+    onegt_cls_loss = one_gt_losses['loss_cls']
+    onegt_box_loss = one_gt_losses['loss_bbox']
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
diff --git a/tests/test_models/test_dense_heads/test_yolox_head.py b/tests/test_models/test_dense_heads/test_yolox_head.py
new file mode 100644
index 0000000..f82c8a0
--- /dev/null
+++ b/tests/test_models/test_dense_heads/test_yolox_head.py
@@ -0,0 +1,88 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule
+
+from mmdet.models.dense_heads import YOLOXHead
+
+
+def test_yolox_head_loss():
+    """Tests yolox head loss when truth is empty and non-empty."""
+    s = 256
+    img_metas = [{
+        'img_shape': (s, s, 3),
+        'scale_factor': 1,
+        'pad_shape': (s, s, 3)
+    }]
+    train_cfg = mmcv.Config(
+        dict(
+            assigner=dict(
+                type='SimOTAAssigner',
+                center_radius=2.5,
+                candidate_topk=10,
+                iou_weight=3.0,
+                cls_weight=1.0)))
+    self = YOLOXHead(
+        num_classes=4, in_channels=1, use_depthwise=False, train_cfg=train_cfg)
+    assert not self.use_l1
+    assert isinstance(self.multi_level_cls_convs[0][0], ConvModule)
+
+    feat = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16]
+    ]
+    cls_scores, bbox_preds, objectnesses = self.forward(feat)
+
+    # Test that empty ground truth encourages the network to predict background
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, objectnesses,
+                                gt_bboxes, gt_labels, img_metas)
+    # When there is no truth, the cls loss should be nonzero but there should
+    # be no box loss.
+    empty_cls_loss = empty_gt_losses['loss_cls'].sum()
+    empty_box_loss = empty_gt_losses['loss_bbox'].sum()
+    empty_obj_loss = empty_gt_losses['loss_obj'].sum()
+    assert empty_cls_loss.item() == 0, (
+        'there should be no cls loss when there are no true boxes')
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when there are no true boxes')
+    assert empty_obj_loss.item() > 0, 'objectness loss should be non-zero'
+
+    # When truth is non-empty then both cls and box loss should be nonzero for
+    # random inputs
+    self = YOLOXHead(
+        num_classes=4, in_channels=1, use_depthwise=True, train_cfg=train_cfg)
+    assert isinstance(self.multi_level_cls_convs[0][0],
+                      DepthwiseSeparableConvModule)
+    self.use_l1 = True
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    one_gt_losses = self.loss(cls_scores, bbox_preds, objectnesses, gt_bboxes,
+                              gt_labels, img_metas)
+    onegt_cls_loss = one_gt_losses['loss_cls'].sum()
+    onegt_box_loss = one_gt_losses['loss_bbox'].sum()
+    onegt_obj_loss = one_gt_losses['loss_obj'].sum()
+    onegt_l1_loss = one_gt_losses['loss_l1'].sum()
+    assert onegt_cls_loss.item() > 0, 'cls loss should be non-zero'
+    assert onegt_box_loss.item() > 0, 'box loss should be non-zero'
+    assert onegt_obj_loss.item() > 0, 'obj loss should be non-zero'
+    assert onegt_l1_loss.item() > 0, 'l1 loss should be non-zero'
+
+    # Test groud truth out of bound
+    gt_bboxes = [torch.Tensor([[s * 4, s * 4, s * 4 + 10, s * 4 + 10]])]
+    gt_labels = [torch.LongTensor([2])]
+    empty_gt_losses = self.loss(cls_scores, bbox_preds, objectnesses,
+                                gt_bboxes, gt_labels, img_metas)
+    # When gt_bboxes out of bound, the assign results should be empty,
+    # so the cls and bbox loss should be zero.
+    empty_cls_loss = empty_gt_losses['loss_cls'].sum()
+    empty_box_loss = empty_gt_losses['loss_bbox'].sum()
+    empty_obj_loss = empty_gt_losses['loss_obj'].sum()
+    assert empty_cls_loss.item() == 0, (
+        'there should be no cls loss when gt_bboxes out of bound')
+    assert empty_box_loss.item() == 0, (
+        'there should be no box loss when gt_bboxes out of bound')
+    assert empty_obj_loss.item() > 0, 'objectness loss should be non-zero'
diff --git a/tests/test_models/test_forward.py b/tests/test_models/test_forward.py
new file mode 100644
index 0000000..98f75b8
--- /dev/null
+++ b/tests/test_models/test_forward.py
@@ -0,0 +1,935 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""pytest tests/test_forward.py."""
+import copy
+from os.path import dirname, exists, join
+
+import numpy as np
+import pytest
+import torch
+
+
+def _get_config_directory():
+    """Find the predefined detector config directory."""
+    try:
+        # Assume we are running in the source mmdetection repo
+        repo_dpath = dirname(dirname(dirname(__file__)))
+    except NameError:
+        # For IPython development when this __file__ is not defined
+        import mmdet
+        repo_dpath = dirname(dirname(mmdet.__file__))
+    config_dpath = join(repo_dpath, 'configs')
+    if not exists(config_dpath):
+        raise Exception('Cannot find config path')
+    return config_dpath
+
+
+def _get_config_module(fname):
+    """Load a configuration as a python module."""
+    from mmcv import Config
+    config_dpath = _get_config_directory()
+    config_fpath = join(config_dpath, fname)
+    config_mod = Config.fromfile(config_fpath)
+    return config_mod
+
+
+def _get_detector_cfg(fname):
+    """Grab configs necessary to create a detector.
+
+    These are deep copied to allow for safe modification of parameters without
+    influencing other tests.
+    """
+    config = _get_config_module(fname)
+    model = copy.deepcopy(config.model)
+    return model
+
+
+def _replace_r50_with_r18(model):
+    """Replace ResNet50 with ResNet18 in config."""
+    model = copy.deepcopy(model)
+    if model.backbone.type == 'ResNet':
+        model.backbone.depth = 18
+        model.backbone.base_channels = 2
+        model.neck.in_channels = [2, 4, 8, 16]
+    return model
+
+
+def test_sparse_rcnn_forward():
+    config_path = 'sparse_rcnn/sparse_rcnn_r50_fpn_1x_coco.py'
+    model = _get_detector_cfg(config_path)
+    model = _replace_r50_with_r18(model)
+    model.backbone.init_cfg = None
+    from mmdet.models import build_detector
+    detector = build_detector(model)
+    detector.init_weights()
+    input_shape = (1, 3, 100, 100)
+    mm_inputs = _demo_mm_inputs(input_shape, num_items=[5])
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+    # Test forward train with non-empty truth batch
+    detector.train()
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_bboxes = [item for item in gt_bboxes]
+    gt_labels = mm_inputs['gt_labels']
+    gt_labels = [item for item in gt_labels]
+    losses = detector.forward(
+        imgs,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        return_loss=True)
+    assert isinstance(losses, dict)
+    loss, _ = detector._parse_losses(losses)
+    assert float(loss.item()) > 0
+    detector.forward_dummy(imgs)
+
+    # Test forward train with an empty truth batch
+    mm_inputs = _demo_mm_inputs(input_shape, num_items=[0])
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_bboxes = [item for item in gt_bboxes]
+    gt_labels = mm_inputs['gt_labels']
+    gt_labels = [item for item in gt_labels]
+    losses = detector.forward(
+        imgs,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        return_loss=True)
+    assert isinstance(losses, dict)
+    loss, _ = detector._parse_losses(losses)
+    assert float(loss.item()) > 0
+
+    # Test forward test
+    detector.eval()
+    with torch.no_grad():
+        img_list = [g[None, :] for g in imgs]
+        batch_results = []
+        for one_img, one_meta in zip(img_list, img_metas):
+            result = detector.forward([one_img], [[one_meta]],
+                                      rescale=True,
+                                      return_loss=False)
+            batch_results.append(result)
+
+    # test empty proposal in roi_head
+    with torch.no_grad():
+        # test no proposal in the whole batch
+        detector.roi_head.simple_test([imgs[0][None, :]], torch.empty(
+            (1, 0, 4)), torch.empty((1, 100, 4)), [img_metas[0]],
+                                      torch.ones((1, 4)))
+
+
+def test_rpn_forward():
+    model = _get_detector_cfg('rpn/rpn_r50_fpn_1x_coco.py')
+    model = _replace_r50_with_r18(model)
+    model.backbone.init_cfg = None
+
+    from mmdet.models import build_detector
+    detector = build_detector(model)
+
+    input_shape = (1, 3, 100, 100)
+    mm_inputs = _demo_mm_inputs(input_shape)
+
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+
+    # Test forward train
+    gt_bboxes = mm_inputs['gt_bboxes']
+    losses = detector.forward(
+        imgs, img_metas, gt_bboxes=gt_bboxes, return_loss=True)
+    assert isinstance(losses, dict)
+
+    # Test forward test
+    with torch.no_grad():
+        img_list = [g[None, :] for g in imgs]
+        batch_results = []
+        for one_img, one_meta in zip(img_list, img_metas):
+            result = detector.forward([one_img], [[one_meta]],
+                                      return_loss=False)
+            batch_results.append(result)
+
+
+@pytest.mark.parametrize(
+    'cfg_file',
+    [
+        'reppoints/reppoints_moment_r50_fpn_1x_coco.py',
+        'retinanet/retinanet_r50_fpn_1x_coco.py',
+        'guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py',
+        'ghm/retinanet_ghm_r50_fpn_1x_coco.py',
+        'fcos/fcos_center_r50_caffe_fpn_gn-head_1x_coco.py',
+        'foveabox/fovea_align_r50_fpn_gn-head_4x4_2x_coco.py',
+        # 'free_anchor/retinanet_free_anchor_r50_fpn_1x_coco.py',
+        # 'atss/atss_r50_fpn_1x_coco.py',  # not ready for topk
+        'yolo/yolov3_mobilenetv2_320_300e_coco.py',
+        'yolox/yolox_tiny_8x8_300e_coco.py'
+    ])
+def test_single_stage_forward_gpu(cfg_file):
+    if not torch.cuda.is_available():
+        import pytest
+        pytest.skip('test requires GPU and torch+cuda')
+
+    model = _get_detector_cfg(cfg_file)
+    model = _replace_r50_with_r18(model)
+    model.backbone.init_cfg = None
+
+    from mmdet.models import build_detector
+    detector = build_detector(model)
+
+    input_shape = (2, 3, 128, 128)
+    mm_inputs = _demo_mm_inputs(input_shape)
+
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+
+    detector = detector.cuda()
+    imgs = imgs.cuda()
+    # Test forward train
+    gt_bboxes = [b.cuda() for b in mm_inputs['gt_bboxes']]
+    gt_labels = [g.cuda() for g in mm_inputs['gt_labels']]
+    losses = detector.forward(
+        imgs,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        return_loss=True)
+    assert isinstance(losses, dict)
+
+    # Test forward test
+    detector.eval()
+    with torch.no_grad():
+        img_list = [g[None, :] for g in imgs]
+        batch_results = []
+        for one_img, one_meta in zip(img_list, img_metas):
+            result = detector.forward([one_img], [[one_meta]],
+                                      return_loss=False)
+            batch_results.append(result)
+
+
+def test_faster_rcnn_ohem_forward():
+    model = _get_detector_cfg(
+        'faster_rcnn/faster_rcnn_r50_fpn_ohem_1x_coco.py')
+    model = _replace_r50_with_r18(model)
+    model.backbone.init_cfg = None
+
+    from mmdet.models import build_detector
+    detector = build_detector(model)
+
+    input_shape = (1, 3, 100, 100)
+
+    # Test forward train with a non-empty truth batch
+    mm_inputs = _demo_mm_inputs(input_shape, num_items=[10])
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_labels = mm_inputs['gt_labels']
+    losses = detector.forward(
+        imgs,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        return_loss=True)
+    assert isinstance(losses, dict)
+    loss, _ = detector._parse_losses(losses)
+    assert float(loss.item()) > 0
+
+    # Test forward train with an empty truth batch
+    mm_inputs = _demo_mm_inputs(input_shape, num_items=[0])
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_labels = mm_inputs['gt_labels']
+    losses = detector.forward(
+        imgs,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        return_loss=True)
+    assert isinstance(losses, dict)
+    loss, _ = detector._parse_losses(losses)
+    assert float(loss.item()) > 0
+
+    # Test RoI forward train with an empty proposals
+    feature = detector.extract_feat(imgs[0][None, :])
+    losses = detector.roi_head.forward_train(
+        feature,
+        img_metas, [torch.empty((0, 5))],
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels)
+    assert isinstance(losses, dict)
+
+
+@pytest.mark.parametrize(
+    'cfg_file',
+    [
+        # 'cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py',
+        'mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py',
+        # 'grid_rcnn/grid_rcnn_r50_fpn_gn-head_2x_coco.py',
+        # 'ms_rcnn/ms_rcnn_r50_fpn_1x_coco.py',
+        # 'htc/htc_r50_fpn_1x_coco.py',
+        # 'panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py',
+        # 'scnet/scnet_r50_fpn_20e_coco.py',
+        # 'seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py'  # noqa: E501
+    ])
+def test_two_stage_forward(cfg_file):
+    models_with_semantic = [
+        'htc/htc_r50_fpn_1x_coco.py',
+        'panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py',
+        'scnet/scnet_r50_fpn_20e_coco.py',
+    ]
+    if cfg_file in models_with_semantic:
+        with_semantic = True
+    else:
+        with_semantic = False
+
+    model = _get_detector_cfg(cfg_file)
+    model = _replace_r50_with_r18(model)
+    model.backbone.init_cfg = None
+
+    # Save cost
+    if cfg_file in [
+            'seesaw_loss/mask_rcnn_r50_fpn_random_seesaw_loss_normed_mask_mstrain_2x_lvis_v1.py'  # noqa: E501
+    ]:
+        model.roi_head.bbox_head.num_classes = 80
+        model.roi_head.bbox_head.loss_cls.num_classes = 80
+        model.roi_head.mask_head.num_classes = 80
+        model.test_cfg.rcnn.score_thr = 0.05
+        model.test_cfg.rcnn.max_per_img = 100
+
+    from mmdet.models import build_detector
+    detector = build_detector(model)
+
+    input_shape = (1, 3, 128, 128)
+
+    # Test forward train with a non-empty truth batch
+    mm_inputs = _demo_mm_inputs(
+        input_shape, num_items=[10], with_semantic=with_semantic)
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+    losses = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs)
+    assert isinstance(losses, dict)
+    loss, _ = detector._parse_losses(losses)
+    loss.requires_grad_(True)
+    assert float(loss.item()) > 0
+    loss.backward()
+
+    # Test forward train with an empty truth batch
+    mm_inputs = _demo_mm_inputs(
+        input_shape, num_items=[0], with_semantic=with_semantic)
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+    losses = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs)
+    assert isinstance(losses, dict)
+    loss, _ = detector._parse_losses(losses)
+    loss.requires_grad_(True)
+    assert float(loss.item()) > 0
+    loss.backward()
+
+    # Test RoI forward train with an empty proposals
+    if cfg_file in [
+            'panoptic_fpn/panoptic_fpn_r50_fpn_1x_coco.py'  # noqa: E501
+    ]:
+        mm_inputs.pop('gt_semantic_seg')
+
+    feature = detector.extract_feat(imgs[0][None, :])
+    losses = detector.roi_head.forward_train(feature, img_metas,
+                                             [torch.empty(
+                                                 (0, 5))], **mm_inputs)
+    assert isinstance(losses, dict)
+
+    # Test forward test
+    with torch.no_grad():
+        img_list = [g[None, :] for g in imgs]
+        batch_results = []
+        for one_img, one_meta in zip(img_list, img_metas):
+            result = detector.forward([one_img], [[one_meta]],
+                                      return_loss=False)
+            batch_results.append(result)
+    cascade_models = [
+        'cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py',
+        'htc/htc_r50_fpn_1x_coco.py',
+        'scnet/scnet_r50_fpn_20e_coco.py',
+    ]
+    # test empty proposal in roi_head
+    with torch.no_grad():
+        # test no proposal in the whole batch
+        detector.simple_test(
+            imgs[0][None, :], [img_metas[0]], proposals=[torch.empty((0, 4))])
+
+        # test no proposal of aug
+        features = detector.extract_feats([imgs[0][None, :]] * 2)
+        detector.roi_head.aug_test(features, [torch.empty((0, 4))] * 2,
+                                   [[img_metas[0]]] * 2)
+
+        # test rcnn_test_cfg is None
+        if cfg_file not in cascade_models:
+            feature = detector.extract_feat(imgs[0][None, :])
+            bboxes, scores = detector.roi_head.simple_test_bboxes(
+                feature, [img_metas[0]], [torch.empty((0, 4))], None)
+            assert all([bbox.shape == torch.Size((0, 4)) for bbox in bboxes])
+            assert all([
+                score.shape == torch.Size(
+                    (0, detector.roi_head.bbox_head.fc_cls.out_features))
+                for score in scores
+            ])
+
+        # test no proposal in the some image
+        x1y1 = torch.randint(1, 100, (10, 2)).float()
+        # x2y2 must be greater than x1y1
+        x2y2 = x1y1 + torch.randint(1, 100, (10, 2))
+        detector.simple_test(
+            imgs[0][None, :].repeat(2, 1, 1, 1), [img_metas[0]] * 2,
+            proposals=[torch.empty((0, 4)),
+                       torch.cat([x1y1, x2y2], dim=-1)])
+
+        # test no proposal of aug
+        detector.roi_head.aug_test(
+            features, [torch.cat([x1y1, x2y2], dim=-1),
+                       torch.empty((0, 4))], [[img_metas[0]]] * 2)
+
+        # test rcnn_test_cfg is None
+        if cfg_file not in cascade_models:
+            feature = detector.extract_feat(imgs[0][None, :].repeat(
+                2, 1, 1, 1))
+            bboxes, scores = detector.roi_head.simple_test_bboxes(
+                feature, [img_metas[0]] * 2,
+                [torch.empty((0, 4)),
+                 torch.cat([x1y1, x2y2], dim=-1)], None)
+            assert bboxes[0].shape == torch.Size((0, 4))
+            assert scores[0].shape == torch.Size(
+                (0, detector.roi_head.bbox_head.fc_cls.out_features))
+
+
+@pytest.mark.parametrize(
+    'cfg_file', ['ghm/retinanet_ghm_r50_fpn_1x_coco.py', 'ssd/ssd300_coco.py'])
+def test_single_stage_forward_cpu(cfg_file):
+    model = _get_detector_cfg(cfg_file)
+    model = _replace_r50_with_r18(model)
+    model.backbone.init_cfg = None
+
+    from mmdet.models import build_detector
+    detector = build_detector(model)
+
+    input_shape = (1, 3, 300, 300)
+    mm_inputs = _demo_mm_inputs(input_shape)
+
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+
+    # Test forward train
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_labels = mm_inputs['gt_labels']
+    losses = detector.forward(
+        imgs,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        return_loss=True)
+    assert isinstance(losses, dict)
+
+    # Test forward test
+    detector.eval()
+    with torch.no_grad():
+        img_list = [g[None, :] for g in imgs]
+        batch_results = []
+        for one_img, one_meta in zip(img_list, img_metas):
+            result = detector.forward([one_img], [[one_meta]],
+                                      return_loss=False)
+            batch_results.append(result)
+
+
+def _demo_mm_inputs(input_shape=(1, 3, 300, 300),
+                    num_items=None, num_classes=10,
+                    with_semantic=False):  # yapf: disable
+    """Create a superset of inputs needed to run test or train batches.
+
+    Args:
+        input_shape (tuple):
+            input batch dimensions
+
+        num_items (None | List[int]):
+            specifies the number of boxes in each batch item
+
+        num_classes (int):
+            number of different labels a box might have
+    """
+    from mmdet.core import BitmapMasks
+
+    (N, C, H, W) = input_shape
+
+    rng = np.random.RandomState(0)
+
+    imgs = rng.rand(*input_shape)
+
+    img_metas = [{
+        'img_shape': (H, W, C),
+        'ori_shape': (H, W, C),
+        'pad_shape': (H, W, C),
+        'filename': '<demo>.png',
+        'scale_factor': np.array([1.1, 1.2, 1.1, 1.2]),
+        'flip': False,
+        'flip_direction': None,
+    } for _ in range(N)]
+
+    gt_bboxes = []
+    gt_labels = []
+    gt_masks = []
+
+    for batch_idx in range(N):
+        if num_items is None:
+            num_boxes = rng.randint(1, 10)
+        else:
+            num_boxes = num_items[batch_idx]
+
+        cx, cy, bw, bh = rng.rand(num_boxes, 4).T
+
+        tl_x = ((cx * W) - (W * bw / 2)).clip(0, W)
+        tl_y = ((cy * H) - (H * bh / 2)).clip(0, H)
+        br_x = ((cx * W) + (W * bw / 2)).clip(0, W)
+        br_y = ((cy * H) + (H * bh / 2)).clip(0, H)
+
+        boxes = np.vstack([tl_x, tl_y, br_x, br_y]).T
+        class_idxs = rng.randint(1, num_classes, size=num_boxes)
+
+        gt_bboxes.append(torch.FloatTensor(boxes))
+        gt_labels.append(torch.LongTensor(class_idxs))
+
+    mask = np.random.randint(0, 2, (len(boxes), H, W), dtype=np.uint8)
+    gt_masks.append(BitmapMasks(mask, H, W))
+
+    mm_inputs = {
+        'imgs': torch.FloatTensor(imgs).requires_grad_(True),
+        'img_metas': img_metas,
+        'gt_bboxes': gt_bboxes,
+        'gt_labels': gt_labels,
+        'gt_bboxes_ignore': None,
+        'gt_masks': gt_masks,
+    }
+
+    if with_semantic:
+        # assume gt_semantic_seg using scale 1/8 of the img
+        gt_semantic_seg = np.random.randint(
+            0, num_classes, (1, 1, H // 8, W // 8), dtype=np.uint8)
+        mm_inputs.update(
+            {'gt_semantic_seg': torch.ByteTensor(gt_semantic_seg)})
+
+    return mm_inputs
+
+
+def test_yolact_forward():
+    model = _get_detector_cfg('yolact/yolact_r50_1x8_coco.py')
+    model = _replace_r50_with_r18(model)
+    model.backbone.init_cfg = None
+
+    from mmdet.models import build_detector
+    detector = build_detector(model)
+
+    input_shape = (1, 3, 100, 100)
+    mm_inputs = _demo_mm_inputs(input_shape)
+
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+
+    # Test forward train
+    detector.train()
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_labels = mm_inputs['gt_labels']
+    gt_masks = mm_inputs['gt_masks']
+    losses = detector.forward(
+        imgs,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        gt_masks=gt_masks,
+        return_loss=True)
+    assert isinstance(losses, dict)
+
+    # Test forward dummy for get_flops
+    detector.forward_dummy(imgs)
+
+    # Test forward test
+    detector.eval()
+    with torch.no_grad():
+        img_list = [g[None, :] for g in imgs]
+        batch_results = []
+        for one_img, one_meta in zip(img_list, img_metas):
+            result = detector.forward([one_img], [[one_meta]],
+                                      rescale=True,
+                                      return_loss=False)
+            batch_results.append(result)
+
+
+def test_detr_forward():
+    model = _get_detector_cfg('detr/detr_r50_8x2_150e_coco.py')
+    model.backbone.depth = 18
+    model.bbox_head.in_channels = 512
+    model.backbone.init_cfg = None
+
+    from mmdet.models import build_detector
+    detector = build_detector(model)
+
+    input_shape = (1, 3, 100, 100)
+    mm_inputs = _demo_mm_inputs(input_shape)
+
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+
+    # Test forward train with non-empty truth batch
+    detector.train()
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_labels = mm_inputs['gt_labels']
+    losses = detector.forward(
+        imgs,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        return_loss=True)
+    assert isinstance(losses, dict)
+    loss, _ = detector._parse_losses(losses)
+    assert float(loss.item()) > 0
+
+    # Test forward train with an empty truth batch
+    mm_inputs = _demo_mm_inputs(input_shape, num_items=[0])
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_labels = mm_inputs['gt_labels']
+    losses = detector.forward(
+        imgs,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        return_loss=True)
+    assert isinstance(losses, dict)
+    loss, _ = detector._parse_losses(losses)
+    assert float(loss.item()) > 0
+
+    # Test forward test
+    detector.eval()
+    with torch.no_grad():
+        img_list = [g[None, :] for g in imgs]
+        batch_results = []
+        for one_img, one_meta in zip(img_list, img_metas):
+            result = detector.forward([one_img], [[one_meta]],
+                                      rescale=True,
+                                      return_loss=False)
+            batch_results.append(result)
+
+
+def test_inference_detector():
+    from mmcv import ConfigDict
+
+    from mmdet.apis import inference_detector
+    from mmdet.models import build_detector
+
+    # small RetinaNet
+    num_class = 3
+    model_dict = dict(
+        type='RetinaNet',
+        backbone=dict(
+            type='ResNet',
+            depth=18,
+            num_stages=4,
+            out_indices=(3, ),
+            norm_cfg=dict(type='BN', requires_grad=False),
+            norm_eval=True,
+            style='pytorch'),
+        neck=None,
+        bbox_head=dict(
+            type='RetinaHead',
+            num_classes=num_class,
+            in_channels=512,
+            stacked_convs=1,
+            feat_channels=256,
+            anchor_generator=dict(
+                type='AnchorGenerator',
+                octave_base_scale=4,
+                scales_per_octave=3,
+                ratios=[0.5],
+                strides=[32]),
+            bbox_coder=dict(
+                type='DeltaXYWHBBoxCoder',
+                target_means=[.0, .0, .0, .0],
+                target_stds=[1.0, 1.0, 1.0, 1.0]),
+        ),
+        test_cfg=dict(
+            nms_pre=1000,
+            min_bbox_size=0,
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100))
+
+    rng = np.random.RandomState(0)
+    img1 = rng.rand(100, 100, 3)
+    img2 = rng.rand(100, 100, 3)
+
+    model = build_detector(ConfigDict(model_dict))
+    config = _get_config_module('retinanet/retinanet_r50_fpn_1x_coco.py')
+    model.cfg = config
+    # test single image
+    result = inference_detector(model, img1)
+    assert len(result) == num_class
+    # test multiple image
+    result = inference_detector(model, [img1, img2])
+    assert len(result) == 2 and len(result[0]) == num_class
+
+
+def test_yolox_random_size():
+    from mmdet.models import build_detector
+    model = _get_detector_cfg('yolox/yolox_tiny_8x8_300e_coco.py')
+    model.random_size_range = (2, 2)
+    model.input_size = (64, 96)
+    model.random_size_interval = 1
+
+    detector = build_detector(model)
+    input_shape = (1, 3, 64, 64)
+    mm_inputs = _demo_mm_inputs(input_shape)
+
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+
+    # Test forward train with non-empty truth batch
+    detector.train()
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_labels = mm_inputs['gt_labels']
+    detector.forward(
+        imgs,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        return_loss=True)
+    assert detector._input_size == (64, 96)
+
+
+def test_maskformer_forward():
+    model_cfg = _get_detector_cfg(
+        'maskformer/maskformer_r50_mstrain_16x1_75e_coco.py')
+    base_channels = 32
+    model_cfg.backbone.depth = 18
+    model_cfg.backbone.init_cfg = None
+    model_cfg.backbone.base_channels = base_channels
+    model_cfg.panoptic_head.in_channels = [
+        base_channels * 2**i for i in range(4)
+    ]
+    model_cfg.panoptic_head.feat_channels = base_channels
+    model_cfg.panoptic_head.out_channels = base_channels
+    model_cfg.panoptic_head.pixel_decoder.encoder.\
+        transformerlayers.attn_cfgs.embed_dims = base_channels
+    model_cfg.panoptic_head.pixel_decoder.encoder.\
+        transformerlayers.ffn_cfgs.embed_dims = base_channels
+    model_cfg.panoptic_head.pixel_decoder.encoder.\
+        transformerlayers.ffn_cfgs.feedforward_channels = base_channels * 8
+    model_cfg.panoptic_head.pixel_decoder.\
+        positional_encoding.num_feats = base_channels // 2
+    model_cfg.panoptic_head.positional_encoding.\
+        num_feats = base_channels // 2
+    model_cfg.panoptic_head.transformer_decoder.\
+        transformerlayers.attn_cfgs.embed_dims = base_channels
+    model_cfg.panoptic_head.transformer_decoder.\
+        transformerlayers.ffn_cfgs.embed_dims = base_channels
+    model_cfg.panoptic_head.transformer_decoder.\
+        transformerlayers.ffn_cfgs.feedforward_channels = base_channels * 8
+    model_cfg.panoptic_head.transformer_decoder.\
+        transformerlayers.feedforward_channels = base_channels * 8
+
+    from mmdet.core import BitmapMasks
+    from mmdet.models import build_detector
+    detector = build_detector(model_cfg)
+
+    # Test forward train with non-empty truth batch
+    detector.train()
+    img_metas = [
+        {
+            'batch_input_shape': (128, 160),
+            'img_shape': (126, 160, 3),
+            'ori_shape': (63, 80, 3),
+            'pad_shape': (128, 160, 3)
+        },
+    ]
+    img = torch.rand((1, 3, 128, 160))
+    gt_bboxes = None
+    gt_labels = [
+        torch.tensor([10]).long(),
+    ]
+    thing_mask1 = np.zeros((1, 128, 160), dtype=np.int32)
+    thing_mask1[0, :50] = 1
+    gt_masks = [
+        BitmapMasks(thing_mask1, 128, 160),
+    ]
+    stuff_mask1 = torch.zeros((1, 128, 160)).long()
+    stuff_mask1[0, :50] = 10
+    stuff_mask1[0, 50:] = 100
+    gt_semantic_seg = [
+        stuff_mask1,
+    ]
+    losses = detector.forward(
+        img=img,
+        img_metas=img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        gt_masks=gt_masks,
+        gt_semantic_seg=gt_semantic_seg,
+        return_loss=True)
+    assert isinstance(losses, dict)
+    loss, _ = detector._parse_losses(losses)
+    assert float(loss.item()) > 0
+
+    # Test forward train with an empty truth batch
+    gt_bboxes = [
+        torch.empty((0, 4)).float(),
+    ]
+    gt_labels = [
+        torch.empty((0, )).long(),
+    ]
+    mask = np.zeros((0, 128, 160), dtype=np.uint8)
+    gt_masks = [
+        BitmapMasks(mask, 128, 160),
+    ]
+    gt_semantic_seg = [
+        torch.randint(0, 133, (0, 128, 160)),
+    ]
+    losses = detector.forward(
+        img,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        gt_masks=gt_masks,
+        gt_semantic_seg=gt_semantic_seg,
+        return_loss=True)
+    assert isinstance(losses, dict)
+    loss, _ = detector._parse_losses(losses)
+    assert float(loss.item()) > 0
+
+    # Test forward test
+    detector.eval()
+    with torch.no_grad():
+        img_list = [g[None, :] for g in img]
+        batch_results = []
+        for one_img, one_meta in zip(img_list, img_metas):
+            result = detector.forward([one_img], [[one_meta]],
+                                      rescale=True,
+                                      return_loss=False)
+        batch_results.append(result)
+
+
+@pytest.mark.parametrize('cfg_file', [
+    'mask2former/mask2former_r50_lsj_8x2_50e_coco.py',
+    'mask2former/mask2former_r50_lsj_8x2_50e_coco-panoptic.py'
+])
+def test_mask2former_forward(cfg_file):
+    # Test Panoptic Segmentation and Instance Segmentation
+    model_cfg = _get_detector_cfg(cfg_file)
+    base_channels = 32
+    model_cfg.backbone.depth = 18
+    model_cfg.backbone.init_cfg = None
+    model_cfg.backbone.base_channels = base_channels
+    model_cfg.panoptic_head.in_channels = [
+        base_channels * 2**i for i in range(4)
+    ]
+    model_cfg.panoptic_head.feat_channels = base_channels
+    model_cfg.panoptic_head.out_channels = base_channels
+    model_cfg.panoptic_head.pixel_decoder.encoder.\
+        transformerlayers.attn_cfgs.embed_dims = base_channels
+    model_cfg.panoptic_head.pixel_decoder.encoder.\
+        transformerlayers.ffn_cfgs.embed_dims = base_channels
+    model_cfg.panoptic_head.pixel_decoder.encoder.\
+        transformerlayers.ffn_cfgs.feedforward_channels = base_channels * 4
+    model_cfg.panoptic_head.pixel_decoder.\
+        positional_encoding.num_feats = base_channels // 2
+    model_cfg.panoptic_head.positional_encoding.\
+        num_feats = base_channels // 2
+    model_cfg.panoptic_head.transformer_decoder.\
+        transformerlayers.attn_cfgs.embed_dims = base_channels
+    model_cfg.panoptic_head.transformer_decoder.\
+        transformerlayers.ffn_cfgs.embed_dims = base_channels
+    model_cfg.panoptic_head.transformer_decoder.\
+        transformerlayers.ffn_cfgs.feedforward_channels = base_channels * 8
+    model_cfg.panoptic_head.transformer_decoder.\
+        transformerlayers.feedforward_channels = base_channels * 8
+
+    num_stuff_classes = model_cfg.panoptic_head.num_stuff_classes
+
+    from mmdet.core import BitmapMasks
+    from mmdet.models import build_detector
+    detector = build_detector(model_cfg)
+
+    def _forward_train():
+        losses = detector.forward(
+            img,
+            img_metas,
+            gt_bboxes=gt_bboxes,
+            gt_labels=gt_labels,
+            gt_masks=gt_masks,
+            gt_semantic_seg=gt_semantic_seg,
+            return_loss=True)
+        assert isinstance(losses, dict)
+        loss, _ = detector._parse_losses(losses)
+        assert float(loss.item()) > 0
+
+    # Test forward train with non-empty truth batch
+    detector.train()
+    img_metas = [
+        {
+            'batch_input_shape': (128, 160),
+            'img_shape': (126, 160, 3),
+            'ori_shape': (63, 80, 3),
+            'pad_shape': (128, 160, 3)
+        },
+    ]
+    img = torch.rand((1, 3, 128, 160))
+    gt_bboxes = None
+    gt_labels = [
+        torch.tensor([10]).long(),
+    ]
+    thing_mask1 = np.zeros((1, 128, 160), dtype=np.int32)
+    thing_mask1[0, :50] = 1
+    gt_masks = [
+        BitmapMasks(thing_mask1, 128, 160),
+    ]
+    stuff_mask1 = torch.zeros((1, 128, 160)).long()
+    stuff_mask1[0, :50] = 10
+    stuff_mask1[0, 50:] = 100
+    gt_semantic_seg = [
+        stuff_mask1,
+    ]
+    _forward_train()
+
+    # Test forward train with non-empty truth batch and gt_semantic_seg=None
+    gt_semantic_seg = None
+    _forward_train()
+
+    # Test forward train with an empty truth batch
+    gt_bboxes = [
+        torch.empty((0, 4)).float(),
+    ]
+    gt_labels = [
+        torch.empty((0, )).long(),
+    ]
+    mask = np.zeros((0, 128, 160), dtype=np.uint8)
+    gt_masks = [
+        BitmapMasks(mask, 128, 160),
+    ]
+    gt_semantic_seg = [
+        torch.randint(0, 133, (0, 128, 160)),
+    ]
+    _forward_train()
+
+    # Test forward train with an empty truth batch and gt_semantic_seg=None
+    gt_semantic_seg = None
+    _forward_train()
+
+    # Test forward test
+    detector.eval()
+    with torch.no_grad():
+        img_list = [g[None, :] for g in img]
+        batch_results = []
+        for one_img, one_meta in zip(img_list, img_metas):
+            result = detector.forward([one_img], [[one_meta]],
+                                      rescale=True,
+                                      return_loss=False)
+
+            if num_stuff_classes > 0:
+                assert isinstance(result[0], dict)
+            else:
+                assert isinstance(result[0], tuple)
+
+        batch_results.append(result)
diff --git a/tests/test_models/test_loss.py b/tests/test_models/test_loss.py
new file mode 100644
index 0000000..280f3f6
--- /dev/null
+++ b/tests/test_models/test_loss.py
@@ -0,0 +1,232 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+from mmcv.utils import digit_version
+
+from mmdet.models.losses import (BalancedL1Loss, CrossEntropyLoss, DiceLoss,
+                                 DistributionFocalLoss, FocalLoss,
+                                 GaussianFocalLoss,
+                                 KnowledgeDistillationKLDivLoss, L1Loss,
+                                 MSELoss, QualityFocalLoss, SeesawLoss,
+                                 SmoothL1Loss, VarifocalLoss)
+from mmdet.models.losses.ghm_loss import GHMC, GHMR
+from mmdet.models.losses.iou_loss import (BoundedIoULoss, CIoULoss, DIoULoss,
+                                          GIoULoss, IoULoss)
+
+
+@pytest.mark.parametrize(
+    'loss_class', [IoULoss, BoundedIoULoss, GIoULoss, DIoULoss, CIoULoss])
+def test_iou_type_loss_zeros_weight(loss_class):
+    pred = torch.rand((10, 4))
+    target = torch.rand((10, 4))
+    weight = torch.zeros(10)
+
+    loss = loss_class()(pred, target, weight)
+    assert loss == 0.
+
+
+@pytest.mark.parametrize('loss_class', [
+    BalancedL1Loss, BoundedIoULoss, CIoULoss, CrossEntropyLoss, DIoULoss,
+    FocalLoss, DistributionFocalLoss, MSELoss, SeesawLoss, GaussianFocalLoss,
+    GIoULoss, IoULoss, L1Loss, QualityFocalLoss, VarifocalLoss, GHMR, GHMC,
+    SmoothL1Loss, KnowledgeDistillationKLDivLoss, DiceLoss
+])
+def test_loss_with_reduction_override(loss_class):
+    pred = torch.rand((10, 4))
+    target = torch.rand((10, 4)),
+    weight = None
+
+    with pytest.raises(AssertionError):
+        # only reduction_override from [None, 'none', 'mean', 'sum']
+        # is not allowed
+        reduction_override = True
+        loss_class()(
+            pred, target, weight, reduction_override=reduction_override)
+
+
+@pytest.mark.parametrize('loss_class', [
+    IoULoss, BoundedIoULoss, GIoULoss, DIoULoss, CIoULoss, MSELoss, L1Loss,
+    SmoothL1Loss, BalancedL1Loss
+])
+@pytest.mark.parametrize('input_shape', [(10, 4), (0, 4)])
+def test_regression_losses(loss_class, input_shape):
+    pred = torch.rand(input_shape)
+    target = torch.rand(input_shape)
+    weight = torch.rand(input_shape)
+
+    # Test loss forward
+    loss = loss_class()(pred, target)
+    assert isinstance(loss, torch.Tensor)
+
+    # Test loss forward with weight
+    loss = loss_class()(pred, target, weight)
+    assert isinstance(loss, torch.Tensor)
+
+    # Test loss forward with reduction_override
+    loss = loss_class()(pred, target, reduction_override='mean')
+    assert isinstance(loss, torch.Tensor)
+
+    # Test loss forward with avg_factor
+    loss = loss_class()(pred, target, avg_factor=10)
+    assert isinstance(loss, torch.Tensor)
+
+    with pytest.raises(ValueError):
+        # loss can evaluate with avg_factor only if
+        # reduction is None, 'none' or 'mean'.
+        reduction_override = 'sum'
+        loss_class()(
+            pred, target, avg_factor=10, reduction_override=reduction_override)
+
+    # Test loss forward with avg_factor and reduction
+    for reduction_override in [None, 'none', 'mean']:
+        loss_class()(
+            pred, target, avg_factor=10, reduction_override=reduction_override)
+        assert isinstance(loss, torch.Tensor)
+
+
+@pytest.mark.parametrize('loss_class', [FocalLoss, CrossEntropyLoss])
+@pytest.mark.parametrize('input_shape', [(10, 5), (0, 5)])
+def test_classification_losses(loss_class, input_shape):
+    if input_shape[0] == 0 and digit_version(
+            torch.__version__) < digit_version('1.5.0'):
+        pytest.skip(
+            f'CELoss in PyTorch {torch.__version__} does not support empty'
+            f'tensor.')
+
+    pred = torch.rand(input_shape)
+    target = torch.randint(0, 5, (input_shape[0], ))
+
+    # Test loss forward
+    loss = loss_class()(pred, target)
+    assert isinstance(loss, torch.Tensor)
+
+    # Test loss forward with reduction_override
+    loss = loss_class()(pred, target, reduction_override='mean')
+    assert isinstance(loss, torch.Tensor)
+
+    # Test loss forward with avg_factor
+    loss = loss_class()(pred, target, avg_factor=10)
+    assert isinstance(loss, torch.Tensor)
+
+    with pytest.raises(ValueError):
+        # loss can evaluate with avg_factor only if
+        # reduction is None, 'none' or 'mean'.
+        reduction_override = 'sum'
+        loss_class()(
+            pred, target, avg_factor=10, reduction_override=reduction_override)
+
+    # Test loss forward with avg_factor and reduction
+    for reduction_override in [None, 'none', 'mean']:
+        loss_class()(
+            pred, target, avg_factor=10, reduction_override=reduction_override)
+        assert isinstance(loss, torch.Tensor)
+
+
+@pytest.mark.parametrize('loss_class', [GHMR])
+@pytest.mark.parametrize('input_shape', [(10, 4), (0, 4)])
+def test_GHMR_loss(loss_class, input_shape):
+    pred = torch.rand(input_shape)
+    target = torch.rand(input_shape)
+    weight = torch.rand(input_shape)
+
+    # Test loss forward
+    loss = loss_class()(pred, target, weight)
+    assert isinstance(loss, torch.Tensor)
+
+
+@pytest.mark.parametrize('use_sigmoid', [True, False])
+@pytest.mark.parametrize('reduction', ['sum', 'mean', None])
+@pytest.mark.parametrize('avg_non_ignore', [True, False])
+def test_loss_with_ignore_index(use_sigmoid, reduction, avg_non_ignore):
+    # Test cross_entropy loss
+    loss_class = CrossEntropyLoss(
+        use_sigmoid=use_sigmoid,
+        use_mask=False,
+        ignore_index=255,
+        avg_non_ignore=avg_non_ignore)
+    pred = torch.rand((10, 5))
+    target = torch.randint(0, 5, (10, ))
+
+    ignored_indices = torch.randint(0, 10, (2, ), dtype=torch.long)
+    target[ignored_indices] = 255
+
+    # Test loss forward with default ignore
+    loss_with_ignore = loss_class(pred, target, reduction_override=reduction)
+    assert isinstance(loss_with_ignore, torch.Tensor)
+
+    # Test loss forward with forward ignore
+    target[ignored_indices] = 255
+    loss_with_forward_ignore = loss_class(
+        pred, target, ignore_index=255, reduction_override=reduction)
+    assert isinstance(loss_with_forward_ignore, torch.Tensor)
+
+    # Verify correctness
+    if avg_non_ignore:
+        # manually remove the ignored elements
+        not_ignored_indices = (target != 255)
+        pred = pred[not_ignored_indices]
+        target = target[not_ignored_indices]
+    loss = loss_class(pred, target, reduction_override=reduction)
+
+    assert torch.allclose(loss, loss_with_ignore)
+    assert torch.allclose(loss, loss_with_forward_ignore)
+
+    # test ignore all target
+    pred = torch.rand((10, 5))
+    target = torch.ones((10, ), dtype=torch.long) * 255
+    loss = loss_class(pred, target, reduction_override=reduction)
+    assert loss == 0
+
+
+@pytest.mark.parametrize('naive_dice', [True, False])
+def test_dice_loss(naive_dice):
+    loss_class = DiceLoss
+    pred = torch.rand((10, 4, 4))
+    target = torch.rand((10, 4, 4))
+    weight = torch.rand((10))
+
+    # Test loss forward
+    loss = loss_class(naive_dice=naive_dice)(pred, target)
+    assert isinstance(loss, torch.Tensor)
+
+    # Test loss forward with weight
+    loss = loss_class(naive_dice=naive_dice)(pred, target, weight)
+    assert isinstance(loss, torch.Tensor)
+
+    # Test loss forward with reduction_override
+    loss = loss_class(naive_dice=naive_dice)(
+        pred, target, reduction_override='mean')
+    assert isinstance(loss, torch.Tensor)
+
+    # Test loss forward with avg_factor
+    loss = loss_class(naive_dice=naive_dice)(pred, target, avg_factor=10)
+    assert isinstance(loss, torch.Tensor)
+
+    with pytest.raises(ValueError):
+        # loss can evaluate with avg_factor only if
+        # reduction is None, 'none' or 'mean'.
+        reduction_override = 'sum'
+        loss_class(naive_dice=naive_dice)(
+            pred, target, avg_factor=10, reduction_override=reduction_override)
+
+    # Test loss forward with avg_factor and reduction
+    for reduction_override in [None, 'none', 'mean']:
+        loss_class(naive_dice=naive_dice)(
+            pred, target, avg_factor=10, reduction_override=reduction_override)
+        assert isinstance(loss, torch.Tensor)
+
+    # Test loss forward with has_acted=False and use_sigmoid=False
+    with pytest.raises(NotImplementedError):
+        loss_class(
+            use_sigmoid=False, activate=True, naive_dice=naive_dice)(pred,
+                                                                     target)
+
+    # Test loss forward with weight.ndim != loss.ndim
+    with pytest.raises(AssertionError):
+        weight = torch.rand((2, 8))
+        loss_class(naive_dice=naive_dice)(pred, target, weight)
+
+    # Test loss forward with len(weight) != len(pred)
+    with pytest.raises(AssertionError):
+        weight = torch.rand((8))
+        loss_class(naive_dice=naive_dice)(pred, target, weight)
diff --git a/tests/test_models/test_loss_compatibility.py b/tests/test_models/test_loss_compatibility.py
new file mode 100644
index 0000000..97759b8
--- /dev/null
+++ b/tests/test_models/test_loss_compatibility.py
@@ -0,0 +1,201 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""pytest tests/test_loss_compatibility.py."""
+import copy
+from os.path import dirname, exists, join
+
+import numpy as np
+import pytest
+import torch
+
+
+def _get_config_directory():
+    """Find the predefined detector config directory."""
+    try:
+        # Assume we are running in the source mmdetection repo
+        repo_dpath = dirname(dirname(dirname(__file__)))
+    except NameError:
+        # For IPython development when this __file__ is not defined
+        import mmdet
+        repo_dpath = dirname(dirname(mmdet.__file__))
+    config_dpath = join(repo_dpath, 'configs')
+    if not exists(config_dpath):
+        raise Exception('Cannot find config path')
+    return config_dpath
+
+
+def _get_config_module(fname):
+    """Load a configuration as a python module."""
+    from mmcv import Config
+    config_dpath = _get_config_directory()
+    config_fpath = join(config_dpath, fname)
+    config_mod = Config.fromfile(config_fpath)
+    return config_mod
+
+
+def _get_detector_cfg(fname):
+    """Grab configs necessary to create a detector.
+
+    These are deep copied to allow for safe modification of parameters without
+    influencing other tests.
+    """
+    config = _get_config_module(fname)
+    model = copy.deepcopy(config.model)
+    return model
+
+
+@pytest.mark.parametrize('loss_bbox', [
+    dict(type='L1Loss', loss_weight=1.0),
+    dict(type='GHMR', mu=0.02, bins=10, momentum=0.7, loss_weight=10.0),
+    dict(type='IoULoss', loss_weight=1.0),
+    dict(type='BoundedIoULoss', loss_weight=1.0),
+    dict(type='GIoULoss', loss_weight=1.0),
+    dict(type='DIoULoss', loss_weight=1.0),
+    dict(type='CIoULoss', loss_weight=1.0),
+    dict(type='MSELoss', loss_weight=1.0),
+    dict(type='SmoothL1Loss', loss_weight=1.0),
+    dict(type='BalancedL1Loss', loss_weight=1.0)
+])
+def test_bbox_loss_compatibility(loss_bbox):
+    """Test loss_bbox compatibility.
+
+    Using Faster R-CNN as a sample, modifying the loss function in the config
+    file to verify the compatibility of Loss APIS
+    """
+    # Faster R-CNN config dict
+    config_path = '_base_/models/faster_rcnn_r50_fpn.py'
+    cfg_model = _get_detector_cfg(config_path)
+
+    input_shape = (1, 3, 256, 256)
+    mm_inputs = _demo_mm_inputs(input_shape, num_items=[10])
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+
+    if 'IoULoss' in loss_bbox['type']:
+        cfg_model.roi_head.bbox_head.reg_decoded_bbox = True
+
+    cfg_model.roi_head.bbox_head.loss_bbox = loss_bbox
+
+    from mmdet.models import build_detector
+    detector = build_detector(cfg_model)
+
+    loss = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs)
+    assert isinstance(loss, dict)
+    loss, _ = detector._parse_losses(loss)
+    assert float(loss.item()) > 0
+
+
+@pytest.mark.parametrize('loss_cls', [
+    dict(type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
+    dict(
+        type='FocalLoss',
+        use_sigmoid=True,
+        gamma=2.0,
+        alpha=0.25,
+        loss_weight=1.0),
+    dict(
+        type='GHMC', bins=30, momentum=0.75, use_sigmoid=True, loss_weight=1.0)
+])
+def test_cls_loss_compatibility(loss_cls):
+    """Test loss_cls compatibility.
+
+    Using Faster R-CNN as a sample, modifying the loss function in the config
+    file to verify the compatibility of Loss APIS
+    """
+    # Faster R-CNN config dict
+    config_path = '_base_/models/faster_rcnn_r50_fpn.py'
+    cfg_model = _get_detector_cfg(config_path)
+
+    input_shape = (1, 3, 256, 256)
+    mm_inputs = _demo_mm_inputs(input_shape, num_items=[10])
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+
+    # verify class loss function compatibility
+    # for loss_cls in loss_clses:
+    cfg_model.roi_head.bbox_head.loss_cls = loss_cls
+
+    from mmdet.models import build_detector
+    detector = build_detector(cfg_model)
+
+    loss = detector.forward(imgs, img_metas, return_loss=True, **mm_inputs)
+    assert isinstance(loss, dict)
+    loss, _ = detector._parse_losses(loss)
+    assert float(loss.item()) > 0
+
+
+def _demo_mm_inputs(input_shape=(1, 3, 300, 300),
+                    num_items=None, num_classes=10,
+                    with_semantic=False):  # yapf: disable
+    """Create a superset of inputs needed to run test or train batches.
+
+    Args:
+        input_shape (tuple):
+            input batch dimensions
+
+        num_items (None | List[int]):
+            specifies the number of boxes in each batch item
+
+        num_classes (int):
+            number of different labels a box might have
+    """
+    from mmdet.core import BitmapMasks
+
+    (N, C, H, W) = input_shape
+
+    rng = np.random.RandomState(0)
+
+    imgs = rng.rand(*input_shape)
+
+    img_metas = [{
+        'img_shape': (H, W, C),
+        'ori_shape': (H, W, C),
+        'pad_shape': (H, W, C),
+        'filename': '<demo>.png',
+        'scale_factor': np.array([1.1, 1.2, 1.1, 1.2]),
+        'flip': False,
+        'flip_direction': None,
+    } for _ in range(N)]
+
+    gt_bboxes = []
+    gt_labels = []
+    gt_masks = []
+
+    for batch_idx in range(N):
+        if num_items is None:
+            num_boxes = rng.randint(1, 10)
+        else:
+            num_boxes = num_items[batch_idx]
+
+        cx, cy, bw, bh = rng.rand(num_boxes, 4).T
+
+        tl_x = ((cx * W) - (W * bw / 2)).clip(0, W)
+        tl_y = ((cy * H) - (H * bh / 2)).clip(0, H)
+        br_x = ((cx * W) + (W * bw / 2)).clip(0, W)
+        br_y = ((cy * H) + (H * bh / 2)).clip(0, H)
+
+        boxes = np.vstack([tl_x, tl_y, br_x, br_y]).T
+        class_idxs = rng.randint(1, num_classes, size=num_boxes)
+
+        gt_bboxes.append(torch.FloatTensor(boxes))
+        gt_labels.append(torch.LongTensor(class_idxs))
+
+    mask = np.random.randint(0, 2, (len(boxes), H, W), dtype=np.uint8)
+    gt_masks.append(BitmapMasks(mask, H, W))
+
+    mm_inputs = {
+        'imgs': torch.FloatTensor(imgs).requires_grad_(True),
+        'img_metas': img_metas,
+        'gt_bboxes': gt_bboxes,
+        'gt_labels': gt_labels,
+        'gt_bboxes_ignore': None,
+        'gt_masks': gt_masks,
+    }
+
+    if with_semantic:
+        # assume gt_semantic_seg using scale 1/8 of the img
+        gt_semantic_seg = np.random.randint(
+            0, num_classes, (1, 1, H // 8, W // 8), dtype=np.uint8)
+        mm_inputs.update(
+            {'gt_semantic_seg': torch.ByteTensor(gt_semantic_seg)})
+
+    return mm_inputs
diff --git a/tests/test_models/test_necks.py b/tests/test_models/test_necks.py
new file mode 100644
index 0000000..ff8c78d
--- /dev/null
+++ b/tests/test_models/test_necks.py
@@ -0,0 +1,673 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+from torch.nn.modules.batchnorm import _BatchNorm
+
+from mmdet.models.necks import (FPG, FPN, FPN_CARAFE, NASFCOS_FPN, NASFPN,
+                                YOLOXPAFPN, ChannelMapper, CTResNetNeck,
+                                DilatedEncoder, DyHead, SSDNeck, YOLOV3Neck)
+
+
+def test_fpn():
+    """Tests fpn."""
+    s = 64
+    in_channels = [8, 16, 32, 64]
+    feat_sizes = [s // 2**i for i in range(4)]  # [64, 32, 16, 8]
+    out_channels = 8
+
+    # end_level=-1 is equal to end_level=3
+    FPN(in_channels=in_channels,
+        out_channels=out_channels,
+        start_level=0,
+        end_level=-1,
+        num_outs=5)
+    FPN(in_channels=in_channels,
+        out_channels=out_channels,
+        start_level=0,
+        end_level=3,
+        num_outs=5)
+
+    # `num_outs` is not equal to end_level - start_level + 1
+    with pytest.raises(AssertionError):
+        FPN(in_channels=in_channels,
+            out_channels=out_channels,
+            start_level=1,
+            end_level=2,
+            num_outs=3)
+
+    # `num_outs` is not equal to len(in_channels) - start_level
+    with pytest.raises(AssertionError):
+        FPN(in_channels=in_channels,
+            out_channels=out_channels,
+            start_level=1,
+            num_outs=2)
+
+    # `end_level` is larger than len(in_channels) - 1
+    with pytest.raises(AssertionError):
+        FPN(in_channels=in_channels,
+            out_channels=out_channels,
+            start_level=1,
+            end_level=4,
+            num_outs=2)
+
+    # `num_outs` is not equal to end_level - start_level
+    with pytest.raises(AssertionError):
+        FPN(in_channels=in_channels,
+            out_channels=out_channels,
+            start_level=1,
+            end_level=3,
+            num_outs=1)
+
+    # Invalid `add_extra_convs` option
+    with pytest.raises(AssertionError):
+        FPN(in_channels=in_channels,
+            out_channels=out_channels,
+            start_level=1,
+            add_extra_convs='on_xxx',
+            num_outs=5)
+
+    fpn_model = FPN(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        start_level=1,
+        add_extra_convs=True,
+        num_outs=5)
+
+    # FPN expects a multiple levels of features per image
+    feats = [
+        torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i])
+        for i in range(len(in_channels))
+    ]
+    outs = fpn_model(feats)
+    assert fpn_model.add_extra_convs == 'on_input'
+    assert len(outs) == fpn_model.num_outs
+    for i in range(fpn_model.num_outs):
+        outs[i].shape[1] == out_channels
+        outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
+
+    # Tests for fpn with no extra convs (pooling is used instead)
+    fpn_model = FPN(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        start_level=1,
+        add_extra_convs=False,
+        num_outs=5)
+    outs = fpn_model(feats)
+    assert len(outs) == fpn_model.num_outs
+    assert not fpn_model.add_extra_convs
+    for i in range(fpn_model.num_outs):
+        outs[i].shape[1] == out_channels
+        outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
+
+    # Tests for fpn with lateral bns
+    fpn_model = FPN(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        start_level=1,
+        add_extra_convs=True,
+        no_norm_on_lateral=False,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        num_outs=5)
+    outs = fpn_model(feats)
+    assert len(outs) == fpn_model.num_outs
+    assert fpn_model.add_extra_convs == 'on_input'
+    for i in range(fpn_model.num_outs):
+        outs[i].shape[1] == out_channels
+        outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
+    bn_exist = False
+    for m in fpn_model.modules():
+        if isinstance(m, _BatchNorm):
+            bn_exist = True
+    assert bn_exist
+
+    # Bilinear upsample
+    fpn_model = FPN(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        start_level=1,
+        add_extra_convs=True,
+        upsample_cfg=dict(mode='bilinear', align_corners=True),
+        num_outs=5)
+    fpn_model(feats)
+    outs = fpn_model(feats)
+    assert len(outs) == fpn_model.num_outs
+    assert fpn_model.add_extra_convs == 'on_input'
+    for i in range(fpn_model.num_outs):
+        outs[i].shape[1] == out_channels
+        outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
+
+    # Scale factor instead of fixed upsample size upsample
+    fpn_model = FPN(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        start_level=1,
+        add_extra_convs=True,
+        upsample_cfg=dict(scale_factor=2),
+        num_outs=5)
+    outs = fpn_model(feats)
+    assert len(outs) == fpn_model.num_outs
+    for i in range(fpn_model.num_outs):
+        outs[i].shape[1] == out_channels
+        outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
+
+    # Extra convs source is 'inputs'
+    fpn_model = FPN(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        add_extra_convs='on_input',
+        start_level=1,
+        num_outs=5)
+    assert fpn_model.add_extra_convs == 'on_input'
+    outs = fpn_model(feats)
+    assert len(outs) == fpn_model.num_outs
+    for i in range(fpn_model.num_outs):
+        outs[i].shape[1] == out_channels
+        outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
+
+    # Extra convs source is 'laterals'
+    fpn_model = FPN(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        add_extra_convs='on_lateral',
+        start_level=1,
+        num_outs=5)
+    assert fpn_model.add_extra_convs == 'on_lateral'
+    outs = fpn_model(feats)
+    assert len(outs) == fpn_model.num_outs
+    for i in range(fpn_model.num_outs):
+        outs[i].shape[1] == out_channels
+        outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
+
+    # Extra convs source is 'outputs'
+    fpn_model = FPN(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        add_extra_convs='on_output',
+        start_level=1,
+        num_outs=5)
+    assert fpn_model.add_extra_convs == 'on_output'
+    outs = fpn_model(feats)
+    assert len(outs) == fpn_model.num_outs
+    for i in range(fpn_model.num_outs):
+        outs[i].shape[1] == out_channels
+        outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
+
+
+def test_channel_mapper():
+    """Tests ChannelMapper."""
+    s = 64
+    in_channels = [8, 16, 32, 64]
+    feat_sizes = [s // 2**i for i in range(4)]  # [64, 32, 16, 8]
+    out_channels = 8
+    kernel_size = 3
+    feats = [
+        torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i])
+        for i in range(len(in_channels))
+    ]
+
+    # in_channels must be a list
+    with pytest.raises(AssertionError):
+        channel_mapper = ChannelMapper(
+            in_channels=10, out_channels=out_channels, kernel_size=kernel_size)
+    # the length of channel_mapper's inputs must be equal to the length of
+    # in_channels
+    with pytest.raises(AssertionError):
+        channel_mapper = ChannelMapper(
+            in_channels=in_channels[:-1],
+            out_channels=out_channels,
+            kernel_size=kernel_size)
+        channel_mapper(feats)
+
+    channel_mapper = ChannelMapper(
+        in_channels=in_channels,
+        out_channels=out_channels,
+        kernel_size=kernel_size)
+
+    outs = channel_mapper(feats)
+    assert len(outs) == len(feats)
+    for i in range(len(feats)):
+        outs[i].shape[1] == out_channels
+        outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
+
+
+def test_dilated_encoder():
+    in_channels = 16
+    out_channels = 32
+    out_shape = 34
+    dilated_encoder = DilatedEncoder(in_channels, out_channels, 16, 2,
+                                     [2, 4, 6, 8])
+    feat = [torch.rand(1, in_channels, 34, 34)]
+    out_feat = dilated_encoder(feat)[0]
+    assert out_feat.shape == (1, out_channels, out_shape, out_shape)
+
+
+def test_ct_resnet_neck():
+    # num_filters/num_kernels must be a list
+    with pytest.raises(TypeError):
+        CTResNetNeck(
+            in_channel=10, num_deconv_filters=10, num_deconv_kernels=4)
+
+    # num_filters/num_kernels must be same length
+    with pytest.raises(AssertionError):
+        CTResNetNeck(
+            in_channel=10,
+            num_deconv_filters=(10, 10),
+            num_deconv_kernels=(4, ))
+
+    in_channels = 16
+    num_filters = (8, 8)
+    num_kernels = (4, 4)
+    feat = torch.rand(1, 16, 4, 4)
+    ct_resnet_neck = CTResNetNeck(
+        in_channel=in_channels,
+        num_deconv_filters=num_filters,
+        num_deconv_kernels=num_kernels,
+        use_dcn=False)
+
+    # feat must be list or tuple
+    with pytest.raises(AssertionError):
+        ct_resnet_neck(feat)
+
+    out_feat = ct_resnet_neck([feat])[0]
+    assert out_feat.shape == (1, num_filters[-1], 16, 16)
+
+    if torch.cuda.is_available():
+        # test dcn
+        ct_resnet_neck = CTResNetNeck(
+            in_channel=in_channels,
+            num_deconv_filters=num_filters,
+            num_deconv_kernels=num_kernels)
+        ct_resnet_neck = ct_resnet_neck.cuda()
+        feat = feat.cuda()
+        out_feat = ct_resnet_neck([feat])[0]
+        assert out_feat.shape == (1, num_filters[-1], 16, 16)
+
+
+def test_yolov3_neck():
+    # num_scales, in_channels, out_channels must be same length
+    with pytest.raises(AssertionError):
+        YOLOV3Neck(num_scales=3, in_channels=[16, 8, 4], out_channels=[8, 4])
+
+    # len(feats) must equal to num_scales
+    with pytest.raises(AssertionError):
+        neck = YOLOV3Neck(
+            num_scales=3, in_channels=[16, 8, 4], out_channels=[8, 4, 2])
+        feats = (torch.rand(1, 4, 16, 16), torch.rand(1, 8, 16, 16))
+        neck(feats)
+
+    # test normal channels
+    s = 32
+    in_channels = [16, 8, 4]
+    out_channels = [8, 4, 2]
+    feat_sizes = [s // 2**i for i in range(len(in_channels) - 1, -1, -1)]
+    feats = [
+        torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i])
+        for i in range(len(in_channels) - 1, -1, -1)
+    ]
+    neck = YOLOV3Neck(
+        num_scales=3, in_channels=in_channels, out_channels=out_channels)
+    outs = neck(feats)
+
+    assert len(outs) == len(feats)
+    for i in range(len(outs)):
+        assert outs[i].shape == \
+               (1, out_channels[i], feat_sizes[i], feat_sizes[i])
+
+    # test more flexible setting
+    s = 32
+    in_channels = [32, 8, 16]
+    out_channels = [19, 21, 5]
+    feat_sizes = [s // 2**i for i in range(len(in_channels) - 1, -1, -1)]
+    feats = [
+        torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i])
+        for i in range(len(in_channels) - 1, -1, -1)
+    ]
+    neck = YOLOV3Neck(
+        num_scales=3, in_channels=in_channels, out_channels=out_channels)
+    outs = neck(feats)
+
+    assert len(outs) == len(feats)
+    for i in range(len(outs)):
+        assert outs[i].shape == \
+               (1, out_channels[i], feat_sizes[i], feat_sizes[i])
+
+
+def test_ssd_neck():
+    # level_strides/level_paddings must be same length
+    with pytest.raises(AssertionError):
+        SSDNeck(
+            in_channels=[8, 16],
+            out_channels=[8, 16, 32],
+            level_strides=[2],
+            level_paddings=[2, 1])
+
+    # length of out_channels must larger than in_channels
+    with pytest.raises(AssertionError):
+        SSDNeck(
+            in_channels=[8, 16],
+            out_channels=[8],
+            level_strides=[2],
+            level_paddings=[2])
+
+    # len(out_channels) - len(in_channels) must equal to len(level_strides)
+    with pytest.raises(AssertionError):
+        SSDNeck(
+            in_channels=[8, 16],
+            out_channels=[4, 16, 64],
+            level_strides=[2, 2],
+            level_paddings=[2, 2])
+
+    # in_channels must be same with out_channels[:len(in_channels)]
+    with pytest.raises(AssertionError):
+        SSDNeck(
+            in_channels=[8, 16],
+            out_channels=[4, 16, 64],
+            level_strides=[2],
+            level_paddings=[2])
+
+    ssd_neck = SSDNeck(
+        in_channels=[4],
+        out_channels=[4, 8, 16],
+        level_strides=[2, 1],
+        level_paddings=[1, 0])
+    feats = (torch.rand(1, 4, 16, 16), )
+    outs = ssd_neck(feats)
+    assert outs[0].shape == (1, 4, 16, 16)
+    assert outs[1].shape == (1, 8, 8, 8)
+    assert outs[2].shape == (1, 16, 6, 6)
+
+    # test SSD-Lite Neck
+    ssd_neck = SSDNeck(
+        in_channels=[4, 8],
+        out_channels=[4, 8, 16],
+        level_strides=[1],
+        level_paddings=[1],
+        l2_norm_scale=None,
+        use_depthwise=True,
+        norm_cfg=dict(type='BN'),
+        act_cfg=dict(type='ReLU6'))
+    assert not hasattr(ssd_neck, 'l2_norm')
+
+    from mmcv.cnn.bricks import DepthwiseSeparableConvModule
+    assert isinstance(ssd_neck.extra_layers[0][-1],
+                      DepthwiseSeparableConvModule)
+
+    feats = (torch.rand(1, 4, 8, 8), torch.rand(1, 8, 8, 8))
+    outs = ssd_neck(feats)
+    assert outs[0].shape == (1, 4, 8, 8)
+    assert outs[1].shape == (1, 8, 8, 8)
+    assert outs[2].shape == (1, 16, 8, 8)
+
+
+def test_yolox_pafpn():
+    s = 64
+    in_channels = [8, 16, 32, 64]
+    feat_sizes = [s // 2**i for i in range(4)]  # [64, 32, 16, 8]
+    out_channels = 24
+    feats = [
+        torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i])
+        for i in range(len(in_channels))
+    ]
+    neck = YOLOXPAFPN(in_channels=in_channels, out_channels=out_channels)
+    outs = neck(feats)
+    assert len(outs) == len(feats)
+    for i in range(len(feats)):
+        assert outs[i].shape[1] == out_channels
+        assert outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
+
+    # test depth-wise
+    neck = YOLOXPAFPN(
+        in_channels=in_channels, out_channels=out_channels, use_depthwise=True)
+
+    from mmcv.cnn.bricks import DepthwiseSeparableConvModule
+    assert isinstance(neck.downsamples[0], DepthwiseSeparableConvModule)
+
+    outs = neck(feats)
+    assert len(outs) == len(feats)
+    for i in range(len(feats)):
+        assert outs[i].shape[1] == out_channels
+        assert outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
+
+
+def test_dyhead():
+    s = 64
+    in_channels = 8
+    out_channels = 16
+    feat_sizes = [s // 2**i for i in range(4)]  # [64, 32, 16, 8]
+    feats = [
+        torch.rand(1, in_channels, feat_sizes[i], feat_sizes[i])
+        for i in range(len(feat_sizes))
+    ]
+    neck = DyHead(
+        in_channels=in_channels, out_channels=out_channels, num_blocks=3)
+    outs = neck(feats)
+    assert len(outs) == len(feats)
+    for i in range(len(outs)):
+        assert outs[i].shape[1] == out_channels
+        assert outs[i].shape[2] == outs[i].shape[3] == s // (2**i)
+
+    feat = torch.rand(1, 8, 4, 4)
+    # input feat must be tuple or list
+    with pytest.raises(AssertionError):
+        neck(feat)
+
+
+def test_fpg():
+    # end_level=-1 is equal to end_level=3
+    norm_cfg = dict(type='BN', requires_grad=True)
+    FPG(in_channels=[8, 16, 32, 64],
+        out_channels=8,
+        inter_channels=8,
+        num_outs=5,
+        add_extra_convs=True,
+        start_level=1,
+        end_level=-1,
+        stack_times=9,
+        paths=['bu'] * 9,
+        same_down_trans=None,
+        same_up_trans=dict(
+            type='conv',
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        across_lateral_trans=dict(
+            type='conv',
+            kernel_size=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        across_down_trans=dict(
+            type='interpolation_conv',
+            mode='nearest',
+            kernel_size=3,
+            norm_cfg=norm_cfg,
+            order=('act', 'conv', 'norm'),
+            inplace=False),
+        across_up_trans=None,
+        across_skip_trans=dict(
+            type='conv',
+            kernel_size=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        output_trans=dict(
+            type='last_conv',
+            kernel_size=3,
+            order=('act', 'conv', 'norm'),
+            inplace=False),
+        norm_cfg=norm_cfg,
+        skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])
+    FPG(in_channels=[8, 16, 32, 64],
+        out_channels=8,
+        inter_channels=8,
+        num_outs=5,
+        add_extra_convs=True,
+        start_level=1,
+        end_level=3,
+        stack_times=9,
+        paths=['bu'] * 9,
+        same_down_trans=None,
+        same_up_trans=dict(
+            type='conv',
+            kernel_size=3,
+            stride=2,
+            padding=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        across_lateral_trans=dict(
+            type='conv',
+            kernel_size=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        across_down_trans=dict(
+            type='interpolation_conv',
+            mode='nearest',
+            kernel_size=3,
+            norm_cfg=norm_cfg,
+            order=('act', 'conv', 'norm'),
+            inplace=False),
+        across_up_trans=None,
+        across_skip_trans=dict(
+            type='conv',
+            kernel_size=1,
+            norm_cfg=norm_cfg,
+            inplace=False,
+            order=('act', 'conv', 'norm')),
+        output_trans=dict(
+            type='last_conv',
+            kernel_size=3,
+            order=('act', 'conv', 'norm'),
+            inplace=False),
+        norm_cfg=norm_cfg,
+        skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])
+
+    # `end_level` is larger than len(in_channels) - 1
+    with pytest.raises(AssertionError):
+        FPG(in_channels=[8, 16, 32, 64],
+            out_channels=8,
+            stack_times=9,
+            paths=['bu'] * 9,
+            start_level=1,
+            end_level=4,
+            num_outs=2,
+            skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])
+
+    # `num_outs` is not equal to end_level - start_level + 1
+    with pytest.raises(AssertionError):
+        FPG(in_channels=[8, 16, 32, 64],
+            out_channels=8,
+            stack_times=9,
+            paths=['bu'] * 9,
+            start_level=1,
+            end_level=2,
+            num_outs=3,
+            skip_inds=[(0, 1, 2, 3), (0, 1, 2), (0, 1), (0, ), ()])
+
+
+def test_fpn_carafe():
+    # end_level=-1 is equal to end_level=3
+    FPN_CARAFE(
+        in_channels=[8, 16, 32, 64],
+        out_channels=8,
+        start_level=0,
+        end_level=3,
+        num_outs=4)
+    FPN_CARAFE(
+        in_channels=[8, 16, 32, 64],
+        out_channels=8,
+        start_level=0,
+        end_level=-1,
+        num_outs=4)
+    # `end_level` is larger than len(in_channels) - 1
+    with pytest.raises(AssertionError):
+        FPN_CARAFE(
+            in_channels=[8, 16, 32, 64],
+            out_channels=8,
+            start_level=1,
+            end_level=4,
+            num_outs=2)
+
+    # `num_outs` is not equal to end_level - start_level + 1
+    with pytest.raises(AssertionError):
+        FPN_CARAFE(
+            in_channels=[8, 16, 32, 64],
+            out_channels=8,
+            start_level=1,
+            end_level=2,
+            num_outs=3)
+
+
+def test_nas_fpn():
+    # end_level=-1 is equal to end_level=3
+    NASFPN(
+        in_channels=[8, 16, 32, 64],
+        out_channels=8,
+        stack_times=9,
+        start_level=0,
+        end_level=3,
+        num_outs=4)
+    NASFPN(
+        in_channels=[8, 16, 32, 64],
+        out_channels=8,
+        stack_times=9,
+        start_level=0,
+        end_level=-1,
+        num_outs=4)
+    # `end_level` is larger than len(in_channels) - 1
+    with pytest.raises(AssertionError):
+        NASFPN(
+            in_channels=[8, 16, 32, 64],
+            out_channels=8,
+            stack_times=9,
+            start_level=1,
+            end_level=4,
+            num_outs=2)
+
+    # `num_outs` is not equal to end_level - start_level + 1
+    with pytest.raises(AssertionError):
+        NASFPN(
+            in_channels=[8, 16, 32, 64],
+            out_channels=8,
+            stack_times=9,
+            start_level=1,
+            end_level=2,
+            num_outs=3)
+
+
+def test_nasfcos_fpn():
+    # end_level=-1 is equal to end_level=3
+    NASFCOS_FPN(
+        in_channels=[8, 16, 32, 64],
+        out_channels=8,
+        start_level=0,
+        end_level=3,
+        num_outs=4)
+    NASFCOS_FPN(
+        in_channels=[8, 16, 32, 64],
+        out_channels=8,
+        start_level=0,
+        end_level=-1,
+        num_outs=4)
+
+    # `end_level` is larger than len(in_channels) - 1
+    with pytest.raises(AssertionError):
+        NASFCOS_FPN(
+            in_channels=[8, 16, 32, 64],
+            out_channels=8,
+            start_level=1,
+            end_level=4,
+            num_outs=2)
+
+    # `num_outs` is not equal to end_level - start_level + 1
+    with pytest.raises(AssertionError):
+        NASFCOS_FPN(
+            in_channels=[8, 16, 32, 64],
+            out_channels=8,
+            start_level=1,
+            end_level=2,
+            num_outs=3)
diff --git a/tests/test_models/test_plugins.py b/tests/test_models/test_plugins.py
new file mode 100644
index 0000000..8afd1f9
--- /dev/null
+++ b/tests/test_models/test_plugins.py
@@ -0,0 +1,167 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+from mmcv import ConfigDict
+from mmcv.cnn import build_plugin_layer
+
+from mmdet.models.plugins import DropBlock
+
+
+def test_dropblock():
+    feat = torch.rand(1, 1, 11, 11)
+    drop_prob = 1.0
+    dropblock = DropBlock(drop_prob, block_size=11, warmup_iters=0)
+    out_feat = dropblock(feat)
+    assert (out_feat == 0).all() and out_feat.shape == feat.shape
+    drop_prob = 0.5
+    dropblock = DropBlock(drop_prob, block_size=5, warmup_iters=0)
+    out_feat = dropblock(feat)
+    assert out_feat.shape == feat.shape
+
+    # drop_prob must be (0,1]
+    with pytest.raises(AssertionError):
+        DropBlock(1.5, 3)
+
+    # block_size cannot be an even number
+    with pytest.raises(AssertionError):
+        DropBlock(0.5, 2)
+
+    # warmup_iters cannot be less than 0
+    with pytest.raises(AssertionError):
+        DropBlock(0.5, 3, -1)
+
+
+def test_pixel_decoder():
+    base_channels = 64
+    pixel_decoder_cfg = ConfigDict(
+        dict(
+            type='PixelDecoder',
+            in_channels=[base_channels * 2**i for i in range(4)],
+            feat_channels=base_channels,
+            out_channels=base_channels,
+            norm_cfg=dict(type='GN', num_groups=32),
+            act_cfg=dict(type='ReLU')))
+    self = build_plugin_layer(pixel_decoder_cfg)[1]
+    img_metas = [{}, {}]
+    feats = [
+        torch.rand((2, base_channels * 2**i, 4 * 2**(3 - i), 5 * 2**(3 - i)))
+        for i in range(4)
+    ]
+    mask_feature, memory = self(feats, img_metas)
+
+    assert (memory == feats[-1]).all()
+    assert mask_feature.shape == feats[0].shape
+
+
+def test_transformer_encoder_pixel_decoder():
+    base_channels = 64
+    pixel_decoder_cfg = ConfigDict(
+        dict(
+            type='TransformerEncoderPixelDecoder',
+            in_channels=[base_channels * 2**i for i in range(4)],
+            feat_channels=base_channels,
+            out_channels=base_channels,
+            norm_cfg=dict(type='GN', num_groups=32),
+            act_cfg=dict(type='ReLU'),
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=dict(
+                        type='MultiheadAttention',
+                        embed_dims=base_channels,
+                        num_heads=8,
+                        attn_drop=0.1,
+                        proj_drop=0.1,
+                        dropout_layer=None,
+                        batch_first=False),
+                    ffn_cfgs=dict(
+                        embed_dims=base_channels,
+                        feedforward_channels=base_channels * 8,
+                        num_fcs=2,
+                        act_cfg=dict(type='ReLU', inplace=True),
+                        ffn_drop=0.1,
+                        dropout_layer=None,
+                        add_identity=True),
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm'),
+                    norm_cfg=dict(type='LN'),
+                    init_cfg=None,
+                    batch_first=False),
+                init_cfg=None),
+            positional_encoding=dict(
+                type='SinePositionalEncoding',
+                num_feats=base_channels // 2,
+                normalize=True)))
+    self = build_plugin_layer(pixel_decoder_cfg)[1]
+    img_metas = [{
+        'batch_input_shape': (128, 160),
+        'img_shape': (120, 160, 3),
+    }, {
+        'batch_input_shape': (128, 160),
+        'img_shape': (125, 160, 3),
+    }]
+    feats = [
+        torch.rand((2, base_channels * 2**i, 4 * 2**(3 - i), 5 * 2**(3 - i)))
+        for i in range(4)
+    ]
+    mask_feature, memory = self(feats, img_metas)
+
+    assert memory.shape[-2:] == feats[-1].shape[-2:]
+    assert mask_feature.shape == feats[0].shape
+
+
+def test_msdeformattn_pixel_decoder():
+    base_channels = 64
+    pixel_decoder_cfg = ConfigDict(
+        dict(
+            type='MSDeformAttnPixelDecoder',
+            in_channels=[base_channels * 2**i for i in range(4)],
+            strides=[4, 8, 16, 32],
+            feat_channels=base_channels,
+            out_channels=base_channels,
+            num_outs=3,
+            norm_cfg=dict(type='GN', num_groups=32),
+            act_cfg=dict(type='ReLU'),
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=dict(
+                        type='MultiScaleDeformableAttention',
+                        embed_dims=base_channels,
+                        num_heads=8,
+                        num_levels=3,
+                        num_points=4,
+                        im2col_step=64,
+                        dropout=0.0,
+                        batch_first=False,
+                        norm_cfg=None,
+                        init_cfg=None),
+                    ffn_cfgs=dict(
+                        type='FFN',
+                        embed_dims=base_channels,
+                        feedforward_channels=base_channels * 4,
+                        num_fcs=2,
+                        ffn_drop=0.0,
+                        act_cfg=dict(type='ReLU', inplace=True)),
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm')),
+                init_cfg=None),
+            positional_encoding=dict(
+                type='SinePositionalEncoding',
+                num_feats=base_channels // 2,
+                normalize=True),
+            init_cfg=None), )
+    self = build_plugin_layer(pixel_decoder_cfg)[1]
+    feats = [
+        torch.rand((2, base_channels * 2**i, 4 * 2**(3 - i), 5 * 2**(3 - i)))
+        for i in range(4)
+    ]
+    mask_feature, multi_scale_features = self(feats)
+
+    assert mask_feature.shape == feats[0].shape
+    assert len(multi_scale_features) == 3
+    multi_scale_features = multi_scale_features[::-1]
+    for i in range(3):
+        assert multi_scale_features[i].shape[-2:] == feats[i + 1].shape[-2:]
diff --git a/tests/test_models/test_roi_heads/__init__.py b/tests/test_models/test_roi_heads/__init__.py
new file mode 100644
index 0000000..83cfd58
--- /dev/null
+++ b/tests/test_models/test_roi_heads/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .utils import _dummy_bbox_sampling
+
+__all__ = ['_dummy_bbox_sampling']
diff --git a/tests/test_models/test_roi_heads/test_bbox_head.py b/tests/test_models/test_roi_heads/test_bbox_head.py
new file mode 100644
index 0000000..e839d06
--- /dev/null
+++ b/tests/test_models/test_roi_heads/test_bbox_head.py
@@ -0,0 +1,251 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import numpy as np
+import pytest
+import torch
+
+from mmdet.core import bbox2roi
+from mmdet.models.roi_heads.bbox_heads import BBoxHead
+from .utils import _dummy_bbox_sampling
+
+
+def test_bbox_head_loss():
+    """Tests bbox head loss when truth is empty and non-empty."""
+    self = BBoxHead(in_channels=8, roi_feat_size=3)
+
+    # Dummy proposals
+    proposal_list = [
+        torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874]]),
+    ]
+
+    target_cfg = mmcv.Config(dict(pos_weight=1))
+
+    # Test bbox loss when truth is empty
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+
+    sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes,
+                                            gt_labels)
+
+    bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels,
+                                    target_cfg)
+    labels, label_weights, bbox_targets, bbox_weights = bbox_targets
+
+    # Create dummy features "extracted" for each sampled bbox
+    num_sampled = sum(len(res.bboxes) for res in sampling_results)
+    rois = bbox2roi([res.bboxes for res in sampling_results])
+    dummy_feats = torch.rand(num_sampled, 8 * 3 * 3)
+    cls_scores, bbox_preds = self.forward(dummy_feats)
+
+    losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights,
+                       bbox_targets, bbox_weights)
+    assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero'
+    assert losses.get('loss_bbox', 0) == 0, 'empty gt loss should be zero'
+
+    # Test bbox loss when truth is non-empty
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+
+    sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes,
+                                            gt_labels)
+    rois = bbox2roi([res.bboxes for res in sampling_results])
+
+    bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels,
+                                    target_cfg)
+    labels, label_weights, bbox_targets, bbox_weights = bbox_targets
+
+    # Create dummy features "extracted" for each sampled bbox
+    num_sampled = sum(len(res.bboxes) for res in sampling_results)
+    dummy_feats = torch.rand(num_sampled, 8 * 3 * 3)
+    cls_scores, bbox_preds = self.forward(dummy_feats)
+
+    losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights,
+                       bbox_targets, bbox_weights)
+    assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero'
+    assert losses.get('loss_bbox', 0) > 0, 'box-loss should be non-zero'
+
+
+@pytest.mark.parametrize('num_sample', [0, 1, 2])
+def test_bbox_head_get_bboxes(num_sample):
+    self = BBoxHead(reg_class_agnostic=True)
+
+    num_class = 6
+    rois = torch.rand((num_sample, 5))
+    cls_score = torch.rand((num_sample, num_class))
+    bbox_pred = torch.rand((num_sample, 4))
+
+    scale_factor = np.array([2.0, 2.0, 2.0, 2.0])
+    det_bboxes, det_labels = self.get_bboxes(
+        rois, cls_score, bbox_pred, None, scale_factor, rescale=True)
+    if num_sample == 0:
+        assert len(det_bboxes) == 0 and len(det_labels) == 0
+    else:
+        assert det_bboxes.shape == bbox_pred.shape
+        assert det_labels.shape == cls_score.shape
+
+
+def test_refine_boxes():
+    """Mirrors the doctest in
+    ``mmdet.models.bbox_heads.bbox_head.BBoxHead.refine_boxes`` but checks for
+    multiple values of n_roi / n_img."""
+    self = BBoxHead(reg_class_agnostic=True)
+
+    test_settings = [
+
+        # Corner case: less rois than images
+        {
+            'n_roi': 2,
+            'n_img': 4,
+            'rng': 34285940
+        },
+
+        # Corner case: no images
+        {
+            'n_roi': 0,
+            'n_img': 0,
+            'rng': 52925222
+        },
+
+        # Corner cases: few images / rois
+        {
+            'n_roi': 1,
+            'n_img': 1,
+            'rng': 1200281
+        },
+        {
+            'n_roi': 2,
+            'n_img': 1,
+            'rng': 1200282
+        },
+        {
+            'n_roi': 2,
+            'n_img': 2,
+            'rng': 1200283
+        },
+        {
+            'n_roi': 1,
+            'n_img': 2,
+            'rng': 1200284
+        },
+
+        # Corner case: no rois few images
+        {
+            'n_roi': 0,
+            'n_img': 1,
+            'rng': 23955860
+        },
+        {
+            'n_roi': 0,
+            'n_img': 2,
+            'rng': 25830516
+        },
+
+        # Corner case: no rois many images
+        {
+            'n_roi': 0,
+            'n_img': 10,
+            'rng': 671346
+        },
+        {
+            'n_roi': 0,
+            'n_img': 20,
+            'rng': 699807
+        },
+
+        # Corner case: cal_similarity num rois and images
+        {
+            'n_roi': 20,
+            'n_img': 20,
+            'rng': 1200238
+        },
+        {
+            'n_roi': 10,
+            'n_img': 20,
+            'rng': 1200238
+        },
+        {
+            'n_roi': 5,
+            'n_img': 5,
+            'rng': 1200238
+        },
+
+        # ----------------------------------
+        # Common case: more rois than images
+        {
+            'n_roi': 100,
+            'n_img': 1,
+            'rng': 337156
+        },
+        {
+            'n_roi': 150,
+            'n_img': 2,
+            'rng': 275898
+        },
+        {
+            'n_roi': 500,
+            'n_img': 5,
+            'rng': 4903221
+        },
+    ]
+
+    for demokw in test_settings:
+        try:
+            n_roi = demokw['n_roi']
+            n_img = demokw['n_img']
+            rng = demokw['rng']
+
+            print(f'Test refine_boxes case: {demokw!r}')
+            tup = _demodata_refine_boxes(n_roi, n_img, rng=rng)
+            rois, labels, bbox_preds, pos_is_gts, img_metas = tup
+            bboxes_list = self.refine_bboxes(rois, labels, bbox_preds,
+                                             pos_is_gts, img_metas)
+            assert len(bboxes_list) == n_img
+            assert sum(map(len, bboxes_list)) <= n_roi
+            assert all(b.shape[1] == 4 for b in bboxes_list)
+        except Exception:
+            print(f'Test failed with demokw={demokw!r}')
+            raise
+
+
+def _demodata_refine_boxes(n_roi, n_img, rng=0):
+    """Create random test data for the
+    ``mmdet.models.bbox_heads.bbox_head.BBoxHead.refine_boxes`` method."""
+    import numpy as np
+
+    from mmdet.core.bbox.demodata import ensure_rng, random_boxes
+    try:
+        import kwarray
+    except ImportError:
+        import pytest
+        pytest.skip('kwarray is required for this test')
+    scale = 512
+    rng = ensure_rng(rng)
+    img_metas = [{'img_shape': (scale, scale)} for _ in range(n_img)]
+    # Create rois in the expected format
+    roi_boxes = random_boxes(n_roi, scale=scale, rng=rng)
+    if n_img == 0:
+        assert n_roi == 0, 'cannot have any rois if there are no images'
+        img_ids = torch.empty((0, ), dtype=torch.long)
+        roi_boxes = torch.empty((0, 4), dtype=torch.float32)
+    else:
+        img_ids = rng.randint(0, n_img, (n_roi, ))
+        img_ids = torch.from_numpy(img_ids)
+    rois = torch.cat([img_ids[:, None].float(), roi_boxes], dim=1)
+    # Create other args
+    labels = rng.randint(0, 2, (n_roi, ))
+    labels = torch.from_numpy(labels).long()
+    bbox_preds = random_boxes(n_roi, scale=scale, rng=rng)
+    # For each image, pretend random positive boxes are gts
+    is_label_pos = (labels.numpy() > 0).astype(np.int)
+    lbl_per_img = kwarray.group_items(is_label_pos, img_ids.numpy())
+    pos_per_img = [sum(lbl_per_img.get(gid, [])) for gid in range(n_img)]
+    # randomly generate with numpy then sort with torch
+    _pos_is_gts = [
+        rng.randint(0, 2, (npos, )).astype(np.uint8) for npos in pos_per_img
+    ]
+    pos_is_gts = [
+        torch.from_numpy(p).sort(descending=True)[0] for p in _pos_is_gts
+    ]
+    return rois, labels, bbox_preds, pos_is_gts, img_metas
diff --git a/tests/test_models/test_roi_heads/test_mask_head.py b/tests/test_models/test_roi_heads/test_mask_head.py
new file mode 100644
index 0000000..89a476d
--- /dev/null
+++ b/tests/test_models/test_roi_heads/test_mask_head.py
@@ -0,0 +1,97 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.models.roi_heads.mask_heads import (DynamicMaskHead, FCNMaskHead,
+                                               MaskIoUHead)
+from .utils import _dummy_bbox_sampling
+
+
+def test_mask_head_loss():
+    """Test mask head loss when mask target is empty."""
+    self = FCNMaskHead(
+        num_convs=1,
+        roi_feat_size=6,
+        in_channels=8,
+        conv_out_channels=8,
+        num_classes=8)
+
+    # Dummy proposals
+    proposal_list = [
+        torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874]]),
+    ]
+
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+    sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes,
+                                            gt_labels)
+
+    # create dummy mask
+    import numpy as np
+
+    from mmdet.core import BitmapMasks
+    dummy_mask = np.random.randint(0, 2, (1, 160, 240), dtype=np.uint8)
+    gt_masks = [BitmapMasks(dummy_mask, 160, 240)]
+
+    # create dummy train_cfg
+    train_cfg = mmcv.Config(dict(mask_size=12, mask_thr_binary=0.5))
+
+    # Create dummy features "extracted" for each sampled bbox
+    num_sampled = sum(len(res.bboxes) for res in sampling_results)
+    dummy_feats = torch.rand(num_sampled, 8, 6, 6)
+
+    mask_pred = self.forward(dummy_feats)
+    mask_targets = self.get_targets(sampling_results, gt_masks, train_cfg)
+    pos_labels = torch.cat([res.pos_gt_labels for res in sampling_results])
+    loss_mask = self.loss(mask_pred, mask_targets, pos_labels)
+
+    onegt_mask_loss = sum(loss_mask['loss_mask'])
+    assert onegt_mask_loss.item() > 0, 'mask loss should be non-zero'
+
+    # test mask_iou_head
+    mask_iou_head = MaskIoUHead(
+        num_convs=1,
+        num_fcs=1,
+        roi_feat_size=6,
+        in_channels=8,
+        conv_out_channels=8,
+        fc_out_channels=8,
+        num_classes=8)
+
+    pos_mask_pred = mask_pred[range(mask_pred.size(0)), pos_labels]
+    mask_iou_pred = mask_iou_head(dummy_feats, pos_mask_pred)
+    pos_mask_iou_pred = mask_iou_pred[range(mask_iou_pred.size(0)), pos_labels]
+
+    mask_iou_targets = mask_iou_head.get_targets(sampling_results, gt_masks,
+                                                 pos_mask_pred, mask_targets,
+                                                 train_cfg)
+    loss_mask_iou = mask_iou_head.loss(pos_mask_iou_pred, mask_iou_targets)
+    onegt_mask_iou_loss = loss_mask_iou['loss_mask_iou'].sum()
+    assert onegt_mask_iou_loss.item() >= 0
+
+    # test dynamic_mask_head
+    dummy_proposal_feats = torch.rand(num_sampled, 8)
+    dynamic_mask_head = DynamicMaskHead(
+        dynamic_conv_cfg=dict(
+            type='DynamicConv',
+            in_channels=8,
+            feat_channels=8,
+            out_channels=8,
+            input_feat_shape=6,
+            with_proj=False,
+            act_cfg=dict(type='ReLU', inplace=True),
+            norm_cfg=dict(type='LN')),
+        num_convs=1,
+        num_classes=8,
+        in_channels=8,
+        roi_feat_size=6)
+
+    mask_pred = dynamic_mask_head(dummy_feats, dummy_proposal_feats)
+
+    mask_target = dynamic_mask_head.get_targets(sampling_results, gt_masks,
+                                                train_cfg)
+    loss_mask = dynamic_mask_head.loss(mask_pred, mask_target, pos_labels)
+    loss_mask = loss_mask['loss_mask'].sum()
+    assert loss_mask.item() >= 0
diff --git a/tests/test_models/test_roi_heads/test_roi_extractor.py b/tests/test_models/test_roi_heads/test_roi_extractor.py
new file mode 100644
index 0000000..b79dff9
--- /dev/null
+++ b/tests/test_models/test_roi_heads/test_roi_extractor.py
@@ -0,0 +1,114 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+from mmdet.models.roi_heads.roi_extractors import GenericRoIExtractor
+
+
+def test_groie():
+    # test with pre/post
+    cfg = dict(
+        roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+        out_channels=256,
+        featmap_strides=[4, 8, 16, 32],
+        pre_cfg=dict(
+            type='ConvModule',
+            in_channels=256,
+            out_channels=256,
+            kernel_size=5,
+            padding=2,
+            inplace=False,
+        ),
+        post_cfg=dict(
+            type='ConvModule',
+            in_channels=256,
+            out_channels=256,
+            kernel_size=5,
+            padding=2,
+            inplace=False))
+
+    groie = GenericRoIExtractor(**cfg)
+
+    feats = (
+        torch.rand((1, 256, 200, 336)),
+        torch.rand((1, 256, 100, 168)),
+        torch.rand((1, 256, 50, 84)),
+        torch.rand((1, 256, 25, 42)),
+    )
+
+    rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]])
+
+    res = groie(feats, rois)
+    assert res.shape == torch.Size([1, 256, 7, 7])
+
+    # test w.o. pre/post
+    cfg = dict(
+        roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+        out_channels=256,
+        featmap_strides=[4, 8, 16, 32])
+
+    groie = GenericRoIExtractor(**cfg)
+
+    feats = (
+        torch.rand((1, 256, 200, 336)),
+        torch.rand((1, 256, 100, 168)),
+        torch.rand((1, 256, 50, 84)),
+        torch.rand((1, 256, 25, 42)),
+    )
+
+    rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]])
+
+    res = groie(feats, rois)
+    assert res.shape == torch.Size([1, 256, 7, 7])
+
+    # test w.o. pre/post concat
+    cfg = dict(
+        aggregation='concat',
+        roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+        out_channels=256 * 4,
+        featmap_strides=[4, 8, 16, 32])
+
+    groie = GenericRoIExtractor(**cfg)
+
+    feats = (
+        torch.rand((1, 256, 200, 336)),
+        torch.rand((1, 256, 100, 168)),
+        torch.rand((1, 256, 50, 84)),
+        torch.rand((1, 256, 25, 42)),
+    )
+
+    rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]])
+
+    res = groie(feats, rois)
+    assert res.shape == torch.Size([1, 1024, 7, 7])
+
+    # test not supported aggregate method
+    with pytest.raises(AssertionError):
+        cfg = dict(
+            aggregation='not support',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+            out_channels=1024,
+            featmap_strides=[4, 8, 16, 32])
+        _ = GenericRoIExtractor(**cfg)
+
+    # test concat channels number
+    cfg = dict(
+        aggregation='concat',
+        roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=2),
+        out_channels=256 * 5,  # 256*5 != 256*4
+        featmap_strides=[4, 8, 16, 32])
+
+    groie = GenericRoIExtractor(**cfg)
+
+    feats = (
+        torch.rand((1, 256, 200, 336)),
+        torch.rand((1, 256, 100, 168)),
+        torch.rand((1, 256, 50, 84)),
+        torch.rand((1, 256, 25, 42)),
+    )
+
+    rois = torch.tensor([[0.0000, 587.8285, 52.1405, 886.2484, 341.5644]])
+
+    # out_channels does not sum of feat channels
+    with pytest.raises(AssertionError):
+        _ = groie(feats, rois)
diff --git a/tests/test_models/test_roi_heads/test_sabl_bbox_head.py b/tests/test_models/test_roi_heads/test_sabl_bbox_head.py
new file mode 100644
index 0000000..d412e3a
--- /dev/null
+++ b/tests/test_models/test_roi_heads/test_sabl_bbox_head.py
@@ -0,0 +1,77 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import mmcv
+import torch
+
+from mmdet.core import bbox2roi
+from mmdet.models.roi_heads.bbox_heads import SABLHead
+from .utils import _dummy_bbox_sampling
+
+
+def test_sabl_bbox_head_loss():
+    """Tests bbox head loss when truth is empty and non-empty."""
+    self = SABLHead(
+        num_classes=4,
+        cls_in_channels=3,
+        reg_in_channels=3,
+        cls_out_channels=3,
+        reg_offset_out_channels=3,
+        reg_cls_out_channels=3,
+        roi_feat_size=7)
+
+    # Dummy proposals
+    proposal_list = [
+        torch.Tensor([[23.6667, 23.8757, 228.6326, 153.8874]]),
+    ]
+
+    target_cfg = mmcv.Config(dict(pos_weight=1))
+
+    # Test bbox loss when truth is empty
+    gt_bboxes = [torch.empty((0, 4))]
+    gt_labels = [torch.LongTensor([])]
+
+    sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes,
+                                            gt_labels)
+
+    bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels,
+                                    target_cfg)
+    labels, label_weights, bbox_targets, bbox_weights = bbox_targets
+
+    # Create dummy features "extracted" for each sampled bbox
+    num_sampled = sum(len(res.bboxes) for res in sampling_results)
+    rois = bbox2roi([res.bboxes for res in sampling_results])
+    dummy_feats = torch.rand(num_sampled, 3, 7, 7)
+    cls_scores, bbox_preds = self.forward(dummy_feats)
+
+    losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights,
+                       bbox_targets, bbox_weights)
+    assert losses.get('loss_cls', 0) > 0, 'cls-loss should be non-zero'
+    assert losses.get('loss_bbox_cls',
+                      0) == 0, 'empty gt bbox-cls-loss should be zero'
+    assert losses.get('loss_bbox_reg',
+                      0) == 0, 'empty gt bbox-reg-loss should be zero'
+
+    # Test bbox loss when truth is non-empty
+    gt_bboxes = [
+        torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]]),
+    ]
+    gt_labels = [torch.LongTensor([2])]
+
+    sampling_results = _dummy_bbox_sampling(proposal_list, gt_bboxes,
+                                            gt_labels)
+    rois = bbox2roi([res.bboxes for res in sampling_results])
+
+    bbox_targets = self.get_targets(sampling_results, gt_bboxes, gt_labels,
+                                    target_cfg)
+    labels, label_weights, bbox_targets, bbox_weights = bbox_targets
+
+    # Create dummy features "extracted" for each sampled bbox
+    num_sampled = sum(len(res.bboxes) for res in sampling_results)
+    dummy_feats = torch.rand(num_sampled, 3, 7, 7)
+    cls_scores, bbox_preds = self.forward(dummy_feats)
+
+    losses = self.loss(cls_scores, bbox_preds, rois, labels, label_weights,
+                       bbox_targets, bbox_weights)
+    assert losses.get('loss_bbox_cls',
+                      0) > 0, 'empty gt bbox-cls-loss should be zero'
+    assert losses.get('loss_bbox_reg',
+                      0) > 0, 'empty gt bbox-reg-loss should be zero'
diff --git a/tests/test_models/test_roi_heads/utils.py b/tests/test_models/test_roi_heads/utils.py
new file mode 100644
index 0000000..748cb0e
--- /dev/null
+++ b/tests/test_models/test_roi_heads/utils.py
@@ -0,0 +1,38 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+
+from mmdet.core import build_assigner, build_sampler
+
+
+def _dummy_bbox_sampling(proposal_list, gt_bboxes, gt_labels):
+    """Create sample results that can be passed to BBoxHead.get_targets."""
+    num_imgs = 1
+    feat = torch.rand(1, 1, 3, 3)
+    assign_config = dict(
+        type='MaxIoUAssigner',
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+        min_pos_iou=0.5,
+        ignore_iof_thr=-1)
+    sampler_config = dict(
+        type='RandomSampler',
+        num=512,
+        pos_fraction=0.25,
+        neg_pos_ub=-1,
+        add_gt_as_proposals=True)
+    bbox_assigner = build_assigner(assign_config)
+    bbox_sampler = build_sampler(sampler_config)
+    gt_bboxes_ignore = [None for _ in range(num_imgs)]
+    sampling_results = []
+    for i in range(num_imgs):
+        assign_result = bbox_assigner.assign(proposal_list[i], gt_bboxes[i],
+                                             gt_bboxes_ignore[i], gt_labels[i])
+        sampling_result = bbox_sampler.sample(
+            assign_result,
+            proposal_list[i],
+            gt_bboxes[i],
+            gt_labels[i],
+            feats=feat)
+        sampling_results.append(sampling_result)
+
+    return sampling_results
diff --git a/tests/test_models/test_seg_heads/test_maskformer_fusion_head.py b/tests/test_models/test_seg_heads/test_maskformer_fusion_head.py
new file mode 100644
index 0000000..8d5131f
--- /dev/null
+++ b/tests/test_models/test_seg_heads/test_maskformer_fusion_head.py
@@ -0,0 +1,53 @@
+import pytest
+import torch
+from mmcv import ConfigDict
+
+from mmdet.models.seg_heads.panoptic_fusion_heads import MaskFormerFusionHead
+
+
+def test_maskformer_fusion_head():
+    img_metas = [
+        {
+            'batch_input_shape': (128, 160),
+            'img_shape': (126, 160, 3),
+            'ori_shape': (63, 80, 3),
+            'pad_shape': (128, 160, 3)
+        },
+    ]
+    num_things_classes = 80
+    num_stuff_classes = 53
+    num_classes = num_things_classes + num_stuff_classes
+    config = ConfigDict(
+        type='MaskFormerFusionHead',
+        num_things_classes=num_things_classes,
+        num_stuff_classes=num_stuff_classes,
+        loss_panoptic=None,
+        test_cfg=dict(
+            panoptic_on=True,
+            semantic_on=False,
+            instance_on=True,
+            max_per_image=100,
+            object_mask_thr=0.8,
+            iou_thr=0.8,
+            filter_low_score=False),
+        init_cfg=None)
+
+    self = MaskFormerFusionHead(**config)
+
+    # test forward_train
+    assert self.forward_train() == dict()
+
+    mask_cls_results = torch.rand((1, 100, num_classes + 1))
+    mask_pred_results = torch.rand((1, 100, 128, 160))
+
+    # test panoptic_postprocess and instance_postprocess
+    results = self.simple_test(mask_cls_results, mask_pred_results, img_metas)
+    assert 'ins_results' in results[0] and 'pan_results' in results[0]
+
+    # test semantic_postprocess
+    config.test_cfg.semantic_on = True
+    with pytest.raises(AssertionError):
+        self.simple_test(mask_cls_results, mask_pred_results, img_metas)
+
+    with pytest.raises(NotImplementedError):
+        self.semantic_postprocess(mask_cls_results, mask_pred_results)
diff --git a/tests/test_models/test_utils/test_brick_wrappers.py b/tests/test_models/test_utils/test_brick_wrappers.py
new file mode 100644
index 0000000..9aa5bd0
--- /dev/null
+++ b/tests/test_models/test_utils/test_brick_wrappers.py
@@ -0,0 +1,93 @@
+from unittest.mock import patch
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from mmdet.models.utils import AdaptiveAvgPool2d, adaptive_avg_pool2d
+
+if torch.__version__ != 'parrots':
+    torch_version = '1.7'
+else:
+    torch_version = 'parrots'
+
+
+@patch('torch.__version__', torch_version)
+def test_adaptive_avg_pool2d():
+    # Test the empty batch dimension
+    # Test the two input conditions
+    x_empty = torch.randn(0, 3, 4, 5)
+    # 1. tuple[int, int]
+    wrapper_out = adaptive_avg_pool2d(x_empty, (2, 2))
+    assert wrapper_out.shape == (0, 3, 2, 2)
+    # 2. int
+    wrapper_out = adaptive_avg_pool2d(x_empty, 2)
+    assert wrapper_out.shape == (0, 3, 2, 2)
+
+    # wrapper op with 3-dim input
+    x_normal = torch.randn(3, 3, 4, 5)
+    wrapper_out = adaptive_avg_pool2d(x_normal, (2, 2))
+    ref_out = F.adaptive_avg_pool2d(x_normal, (2, 2))
+    assert wrapper_out.shape == (3, 3, 2, 2)
+    assert torch.equal(wrapper_out, ref_out)
+
+    wrapper_out = adaptive_avg_pool2d(x_normal, 2)
+    ref_out = F.adaptive_avg_pool2d(x_normal, 2)
+    assert wrapper_out.shape == (3, 3, 2, 2)
+    assert torch.equal(wrapper_out, ref_out)
+
+
+@patch('torch.__version__', torch_version)
+def test_AdaptiveAvgPool2d():
+    # Test the empty batch dimension
+    x_empty = torch.randn(0, 3, 4, 5)
+    # Test the four input conditions
+    # 1. tuple[int, int]
+    wrapper = AdaptiveAvgPool2d((2, 2))
+    wrapper_out = wrapper(x_empty)
+    assert wrapper_out.shape == (0, 3, 2, 2)
+
+    # 2. int
+    wrapper = AdaptiveAvgPool2d(2)
+    wrapper_out = wrapper(x_empty)
+    assert wrapper_out.shape == (0, 3, 2, 2)
+
+    # 3. tuple[None, int]
+    wrapper = AdaptiveAvgPool2d((None, 2))
+    wrapper_out = wrapper(x_empty)
+    assert wrapper_out.shape == (0, 3, 4, 2)
+
+    # 3. tuple[int, None]
+    wrapper = AdaptiveAvgPool2d((2, None))
+    wrapper_out = wrapper(x_empty)
+    assert wrapper_out.shape == (0, 3, 2, 5)
+
+    # Test the normal batch dimension
+    x_normal = torch.randn(3, 3, 4, 5)
+    wrapper = AdaptiveAvgPool2d((2, 2))
+    ref = nn.AdaptiveAvgPool2d((2, 2))
+    wrapper_out = wrapper(x_normal)
+    ref_out = ref(x_normal)
+    assert wrapper_out.shape == (3, 3, 2, 2)
+    assert torch.equal(wrapper_out, ref_out)
+
+    wrapper = AdaptiveAvgPool2d(2)
+    ref = nn.AdaptiveAvgPool2d(2)
+    wrapper_out = wrapper(x_normal)
+    ref_out = ref(x_normal)
+    assert wrapper_out.shape == (3, 3, 2, 2)
+    assert torch.equal(wrapper_out, ref_out)
+
+    wrapper = AdaptiveAvgPool2d((None, 2))
+    ref = nn.AdaptiveAvgPool2d((None, 2))
+    wrapper_out = wrapper(x_normal)
+    ref_out = ref(x_normal)
+    assert wrapper_out.shape == (3, 3, 4, 2)
+    assert torch.equal(wrapper_out, ref_out)
+
+    wrapper = AdaptiveAvgPool2d((2, None))
+    ref = nn.AdaptiveAvgPool2d((2, None))
+    wrapper_out = wrapper(x_normal)
+    ref_out = ref(x_normal)
+    assert wrapper_out.shape == (3, 3, 2, 5)
+    assert torch.equal(wrapper_out, ref_out)
diff --git a/tests/test_models/test_utils/test_conv_upsample.py b/tests/test_models/test_utils/test_conv_upsample.py
new file mode 100644
index 0000000..95a0ccc
--- /dev/null
+++ b/tests/test_models/test_utils/test_conv_upsample.py
@@ -0,0 +1,24 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+from mmdet.models.utils import ConvUpsample
+
+
+@pytest.mark.parametrize('num_layers', [0, 1, 2])
+def test_conv_upsample(num_layers):
+    num_upsample = num_layers if num_layers > 0 else 0
+    num_layers = num_layers if num_layers > 0 else 1
+    layer = ConvUpsample(
+        10,
+        5,
+        num_layers=num_layers,
+        num_upsample=num_upsample,
+        conv_cfg=None,
+        norm_cfg=None)
+
+    size = 5
+    x = torch.randn((1, 10, size, size))
+    size = size * pow(2, num_upsample)
+    x = layer(x)
+    assert x.shape[-2:] == (size, size)
diff --git a/tests/test_models/test_utils/test_inverted_residual.py b/tests/test_models/test_utils/test_inverted_residual.py
new file mode 100644
index 0000000..14a331a
--- /dev/null
+++ b/tests/test_models/test_utils/test_inverted_residual.py
@@ -0,0 +1,76 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+from mmcv.cnn import is_norm
+from torch.nn.modules import GroupNorm
+
+from mmdet.models.utils import InvertedResidual, SELayer
+
+
+def test_inverted_residual():
+
+    with pytest.raises(AssertionError):
+        # stride must be in [1, 2]
+        InvertedResidual(16, 16, 32, stride=3)
+
+    with pytest.raises(AssertionError):
+        # se_cfg must be None or dict
+        InvertedResidual(16, 16, 32, se_cfg=list())
+
+    with pytest.raises(AssertionError):
+        # in_channeld and mid_channels must be the same if
+        # with_expand_conv is False
+        InvertedResidual(16, 16, 32, with_expand_conv=False)
+
+    # Test InvertedResidual forward, stride=1
+    block = InvertedResidual(16, 16, 32, stride=1)
+    x = torch.randn(1, 16, 56, 56)
+    x_out = block(x)
+    assert getattr(block, 'se', None) is None
+    assert block.with_res_shortcut
+    assert x_out.shape == torch.Size((1, 16, 56, 56))
+
+    # Test InvertedResidual forward, stride=2
+    block = InvertedResidual(16, 16, 32, stride=2)
+    x = torch.randn(1, 16, 56, 56)
+    x_out = block(x)
+    assert not block.with_res_shortcut
+    assert x_out.shape == torch.Size((1, 16, 28, 28))
+
+    # Test InvertedResidual forward with se layer
+    se_cfg = dict(channels=32)
+    block = InvertedResidual(16, 16, 32, stride=1, se_cfg=se_cfg)
+    x = torch.randn(1, 16, 56, 56)
+    x_out = block(x)
+    assert isinstance(block.se, SELayer)
+    assert x_out.shape == torch.Size((1, 16, 56, 56))
+
+    # Test InvertedResidual forward, with_expand_conv=False
+    block = InvertedResidual(32, 16, 32, with_expand_conv=False)
+    x = torch.randn(1, 32, 56, 56)
+    x_out = block(x)
+    assert getattr(block, 'expand_conv', None) is None
+    assert x_out.shape == torch.Size((1, 16, 56, 56))
+
+    # Test InvertedResidual forward with GroupNorm
+    block = InvertedResidual(
+        16, 16, 32, norm_cfg=dict(type='GN', num_groups=2))
+    x = torch.randn(1, 16, 56, 56)
+    x_out = block(x)
+    for m in block.modules():
+        if is_norm(m):
+            assert isinstance(m, GroupNorm)
+    assert x_out.shape == torch.Size((1, 16, 56, 56))
+
+    # Test InvertedResidual forward with HSigmoid
+    block = InvertedResidual(16, 16, 32, act_cfg=dict(type='HSigmoid'))
+    x = torch.randn(1, 16, 56, 56)
+    x_out = block(x)
+    assert x_out.shape == torch.Size((1, 16, 56, 56))
+
+    # Test InvertedResidual forward with checkpoint
+    block = InvertedResidual(16, 16, 32, with_cp=True)
+    x = torch.randn(1, 16, 56, 56)
+    x_out = block(x)
+    assert block.with_cp
+    assert x_out.shape == torch.Size((1, 16, 56, 56))
diff --git a/tests/test_models/test_utils/test_model_misc.py b/tests/test_models/test_utils/test_model_misc.py
new file mode 100644
index 0000000..93de336
--- /dev/null
+++ b/tests/test_models/test_utils/test_model_misc.py
@@ -0,0 +1,36 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import torch
+from torch.autograd import gradcheck
+
+from mmdet.models.utils import interpolate_as, sigmoid_geometric_mean
+
+
+def test_interpolate_as():
+    source = torch.rand((1, 5, 4, 4))
+    target = torch.rand((1, 1, 16, 16))
+
+    # Test 4D source and target
+    result = interpolate_as(source, target)
+    assert result.shape == torch.Size((1, 5, 16, 16))
+
+    # Test 3D target
+    result = interpolate_as(source, target.squeeze(0))
+    assert result.shape == torch.Size((1, 5, 16, 16))
+
+    # Test 3D source
+    result = interpolate_as(source.squeeze(0), target)
+    assert result.shape == torch.Size((5, 16, 16))
+
+    # Test type(target) == np.ndarray
+    target = np.random.rand(16, 16)
+    result = interpolate_as(source.squeeze(0), target)
+    assert result.shape == torch.Size((5, 16, 16))
+
+
+def test_sigmoid_geometric_mean():
+    x = torch.randn(20, 20, dtype=torch.double, requires_grad=True)
+    y = torch.randn(20, 20, dtype=torch.double, requires_grad=True)
+    inputs = (x, y)
+    test = gradcheck(sigmoid_geometric_mean, inputs, eps=1e-6, atol=1e-4)
+    assert test
diff --git a/tests/test_models/test_utils/test_position_encoding.py b/tests/test_models/test_utils/test_position_encoding.py
new file mode 100644
index 0000000..1119410
--- /dev/null
+++ b/tests/test_models/test_utils/test_position_encoding.py
@@ -0,0 +1,39 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+from mmdet.models.utils import (LearnedPositionalEncoding,
+                                SinePositionalEncoding)
+
+
+def test_sine_positional_encoding(num_feats=16, batch_size=2):
+    # test invalid type of scale
+    with pytest.raises(AssertionError):
+        module = SinePositionalEncoding(
+            num_feats, scale=(3., ), normalize=True)
+
+    module = SinePositionalEncoding(num_feats)
+    h, w = 10, 6
+    mask = (torch.rand(batch_size, h, w) > 0.5).to(torch.int)
+    assert not module.normalize
+    out = module(mask)
+    assert out.shape == (batch_size, num_feats * 2, h, w)
+
+    # set normalize
+    module = SinePositionalEncoding(num_feats, normalize=True)
+    assert module.normalize
+    out = module(mask)
+    assert out.shape == (batch_size, num_feats * 2, h, w)
+
+
+def test_learned_positional_encoding(num_feats=16,
+                                     row_num_embed=10,
+                                     col_num_embed=10,
+                                     batch_size=2):
+    module = LearnedPositionalEncoding(num_feats, row_num_embed, col_num_embed)
+    assert module.row_embed.weight.shape == (row_num_embed, num_feats)
+    assert module.col_embed.weight.shape == (col_num_embed, num_feats)
+    h, w = 10, 6
+    mask = torch.rand(batch_size, h, w) > 0.5
+    out = module(mask)
+    assert out.shape == (batch_size, num_feats * 2, h, w)
diff --git a/tests/test_models/test_utils/test_se_layer.py b/tests/test_models/test_utils/test_se_layer.py
new file mode 100644
index 0000000..b525b91
--- /dev/null
+++ b/tests/test_models/test_utils/test_se_layer.py
@@ -0,0 +1,54 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+import torch.nn.functional as F
+from mmcv.cnn import constant_init
+
+from mmdet.models.utils import DyReLU, SELayer
+
+
+def test_se_layer():
+    with pytest.raises(AssertionError):
+        # act_cfg sequence length must equal to 2
+        SELayer(channels=32, act_cfg=(dict(type='ReLU'), ))
+
+    with pytest.raises(AssertionError):
+        # act_cfg sequence must be a tuple of dict
+        SELayer(channels=32, act_cfg=[dict(type='ReLU'), dict(type='ReLU')])
+
+    # Test SELayer forward
+    layer = SELayer(channels=32)
+    layer.init_weights()
+    layer.train()
+
+    x = torch.randn((1, 32, 10, 10))
+    x_out = layer(x)
+    assert x_out.shape == torch.Size((1, 32, 10, 10))
+
+
+def test_dyrelu():
+    with pytest.raises(AssertionError):
+        # act_cfg sequence length must equal to 2
+        DyReLU(channels=32, act_cfg=(dict(type='ReLU'), ))
+
+    with pytest.raises(AssertionError):
+        # act_cfg sequence must be a tuple of dict
+        DyReLU(channels=32, act_cfg=[dict(type='ReLU'), dict(type='ReLU')])
+
+    # Test DyReLU forward
+    layer = DyReLU(channels=32)
+    layer.init_weights()
+    layer.train()
+    x = torch.randn((1, 32, 10, 10))
+    x_out = layer(x)
+    assert x_out.shape == torch.Size((1, 32, 10, 10))
+
+    # DyReLU should act as standard (static) ReLU
+    # when eliminating the effect of SE-like module
+    layer = DyReLU(channels=32)
+    constant_init(layer.conv2.conv, 0)
+    layer.train()
+    x = torch.randn((1, 32, 10, 10))
+    x_out = layer(x)
+    relu_out = F.relu(x)
+    assert torch.equal(x_out, relu_out)
diff --git a/tests/test_models/test_utils/test_transformer.py b/tests/test_models/test_utils/test_transformer.py
new file mode 100644
index 0000000..9c6efb4
--- /dev/null
+++ b/tests/test_models/test_utils/test_transformer.py
@@ -0,0 +1,569 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+from mmcv.utils import ConfigDict
+
+from mmdet.models.utils.transformer import (AdaptivePadding,
+                                            DetrTransformerDecoder,
+                                            DetrTransformerEncoder, PatchEmbed,
+                                            PatchMerging, Transformer)
+
+
+def test_adaptive_padding():
+
+    for padding in ('same', 'corner'):
+        kernel_size = 16
+        stride = 16
+        dilation = 1
+        input = torch.rand(1, 1, 15, 17)
+        pool = AdaptivePadding(
+            kernel_size=kernel_size,
+            stride=stride,
+            dilation=dilation,
+            padding=padding)
+        out = pool(input)
+        # padding to divisible by 16
+        assert (out.shape[2], out.shape[3]) == (16, 32)
+        input = torch.rand(1, 1, 16, 17)
+        out = pool(input)
+        # padding to divisible by 16
+        assert (out.shape[2], out.shape[3]) == (16, 32)
+
+        kernel_size = (2, 2)
+        stride = (2, 2)
+        dilation = (1, 1)
+
+        adap_pad = AdaptivePadding(
+            kernel_size=kernel_size,
+            stride=stride,
+            dilation=dilation,
+            padding=padding)
+        input = torch.rand(1, 1, 11, 13)
+        out = adap_pad(input)
+        # padding to divisible by 2
+        assert (out.shape[2], out.shape[3]) == (12, 14)
+
+        kernel_size = (2, 2)
+        stride = (10, 10)
+        dilation = (1, 1)
+
+        adap_pad = AdaptivePadding(
+            kernel_size=kernel_size,
+            stride=stride,
+            dilation=dilation,
+            padding=padding)
+        input = torch.rand(1, 1, 10, 13)
+        out = adap_pad(input)
+        #  no padding
+        assert (out.shape[2], out.shape[3]) == (10, 13)
+
+        kernel_size = (11, 11)
+        adap_pad = AdaptivePadding(
+            kernel_size=kernel_size,
+            stride=stride,
+            dilation=dilation,
+            padding=padding)
+        input = torch.rand(1, 1, 11, 13)
+        out = adap_pad(input)
+        #  all padding
+        assert (out.shape[2], out.shape[3]) == (21, 21)
+
+        # test padding as kernel is (7,9)
+        input = torch.rand(1, 1, 11, 13)
+        stride = (3, 4)
+        kernel_size = (4, 5)
+        dilation = (2, 2)
+        # actually (7, 9)
+        adap_pad = AdaptivePadding(
+            kernel_size=kernel_size,
+            stride=stride,
+            dilation=dilation,
+            padding=padding)
+        dilation_out = adap_pad(input)
+        assert (dilation_out.shape[2], dilation_out.shape[3]) == (16, 21)
+        kernel_size = (7, 9)
+        dilation = (1, 1)
+        adap_pad = AdaptivePadding(
+            kernel_size=kernel_size,
+            stride=stride,
+            dilation=dilation,
+            padding=padding)
+        kernel79_out = adap_pad(input)
+        assert (kernel79_out.shape[2], kernel79_out.shape[3]) == (16, 21)
+        assert kernel79_out.shape == dilation_out.shape
+
+    # assert only support "same" "corner"
+    with pytest.raises(AssertionError):
+        AdaptivePadding(
+            kernel_size=kernel_size,
+            stride=stride,
+            dilation=dilation,
+            padding=1)
+
+
+def test_patch_embed():
+    B = 2
+    H = 3
+    W = 4
+    C = 3
+    embed_dims = 10
+    kernel_size = 3
+    stride = 1
+    dummy_input = torch.rand(B, C, H, W)
+    patch_merge_1 = PatchEmbed(
+        in_channels=C,
+        embed_dims=embed_dims,
+        kernel_size=kernel_size,
+        stride=stride,
+        padding=0,
+        dilation=1,
+        norm_cfg=None)
+
+    x1, shape = patch_merge_1(dummy_input)
+    # test out shape
+    assert x1.shape == (2, 2, 10)
+    # test outsize is correct
+    assert shape == (1, 2)
+    # test L = out_h * out_w
+    assert shape[0] * shape[1] == x1.shape[1]
+
+    B = 2
+    H = 10
+    W = 10
+    C = 3
+    embed_dims = 10
+    kernel_size = 5
+    stride = 2
+    dummy_input = torch.rand(B, C, H, W)
+    # test dilation
+    patch_merge_2 = PatchEmbed(
+        in_channels=C,
+        embed_dims=embed_dims,
+        kernel_size=kernel_size,
+        stride=stride,
+        padding=0,
+        dilation=2,
+        norm_cfg=None,
+    )
+
+    x2, shape = patch_merge_2(dummy_input)
+    # test out shape
+    assert x2.shape == (2, 1, 10)
+    # test outsize is correct
+    assert shape == (1, 1)
+    # test L = out_h * out_w
+    assert shape[0] * shape[1] == x2.shape[1]
+
+    stride = 2
+    input_size = (10, 10)
+
+    dummy_input = torch.rand(B, C, H, W)
+    # test stride and norm
+    patch_merge_3 = PatchEmbed(
+        in_channels=C,
+        embed_dims=embed_dims,
+        kernel_size=kernel_size,
+        stride=stride,
+        padding=0,
+        dilation=2,
+        norm_cfg=dict(type='LN'),
+        input_size=input_size)
+
+    x3, shape = patch_merge_3(dummy_input)
+    # test out shape
+    assert x3.shape == (2, 1, 10)
+    # test outsize is correct
+    assert shape == (1, 1)
+    # test L = out_h * out_w
+    assert shape[0] * shape[1] == x3.shape[1]
+
+    # test the init_out_size with nn.Unfold
+    assert patch_merge_3.init_out_size[1] == (input_size[0] - 2 * 4 -
+                                              1) // 2 + 1
+    assert patch_merge_3.init_out_size[0] == (input_size[0] - 2 * 4 -
+                                              1) // 2 + 1
+    H = 11
+    W = 12
+    input_size = (H, W)
+    dummy_input = torch.rand(B, C, H, W)
+    # test stride and norm
+    patch_merge_3 = PatchEmbed(
+        in_channels=C,
+        embed_dims=embed_dims,
+        kernel_size=kernel_size,
+        stride=stride,
+        padding=0,
+        dilation=2,
+        norm_cfg=dict(type='LN'),
+        input_size=input_size)
+
+    _, shape = patch_merge_3(dummy_input)
+    # when input_size equal to real input
+    # the out_size should be equal to `init_out_size`
+    assert shape == patch_merge_3.init_out_size
+
+    input_size = (H, W)
+    dummy_input = torch.rand(B, C, H, W)
+    # test stride and norm
+    patch_merge_3 = PatchEmbed(
+        in_channels=C,
+        embed_dims=embed_dims,
+        kernel_size=kernel_size,
+        stride=stride,
+        padding=0,
+        dilation=2,
+        norm_cfg=dict(type='LN'),
+        input_size=input_size)
+
+    _, shape = patch_merge_3(dummy_input)
+    # when input_size equal to real input
+    # the out_size should be equal to `init_out_size`
+    assert shape == patch_merge_3.init_out_size
+
+    # test adap padding
+    for padding in ('same', 'corner'):
+        in_c = 2
+        embed_dims = 3
+        B = 2
+
+        # test stride is 1
+        input_size = (5, 5)
+        kernel_size = (5, 5)
+        stride = (1, 1)
+        dilation = 1
+        bias = False
+
+        x = torch.rand(B, in_c, *input_size)
+        patch_embed = PatchEmbed(
+            in_channels=in_c,
+            embed_dims=embed_dims,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias)
+
+        x_out, out_size = patch_embed(x)
+        assert x_out.size() == (B, 25, 3)
+        assert out_size == (5, 5)
+        assert x_out.size(1) == out_size[0] * out_size[1]
+
+        # test kernel_size == stride
+        input_size = (5, 5)
+        kernel_size = (5, 5)
+        stride = (5, 5)
+        dilation = 1
+        bias = False
+
+        x = torch.rand(B, in_c, *input_size)
+        patch_embed = PatchEmbed(
+            in_channels=in_c,
+            embed_dims=embed_dims,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias)
+
+        x_out, out_size = patch_embed(x)
+        assert x_out.size() == (B, 1, 3)
+        assert out_size == (1, 1)
+        assert x_out.size(1) == out_size[0] * out_size[1]
+
+        # test kernel_size == stride
+        input_size = (6, 5)
+        kernel_size = (5, 5)
+        stride = (5, 5)
+        dilation = 1
+        bias = False
+
+        x = torch.rand(B, in_c, *input_size)
+        patch_embed = PatchEmbed(
+            in_channels=in_c,
+            embed_dims=embed_dims,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias)
+
+        x_out, out_size = patch_embed(x)
+        assert x_out.size() == (B, 2, 3)
+        assert out_size == (2, 1)
+        assert x_out.size(1) == out_size[0] * out_size[1]
+
+        # test different kernel_size with different stride
+        input_size = (6, 5)
+        kernel_size = (6, 2)
+        stride = (6, 2)
+        dilation = 1
+        bias = False
+
+        x = torch.rand(B, in_c, *input_size)
+        patch_embed = PatchEmbed(
+            in_channels=in_c,
+            embed_dims=embed_dims,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias)
+
+        x_out, out_size = patch_embed(x)
+        assert x_out.size() == (B, 3, 3)
+        assert out_size == (1, 3)
+        assert x_out.size(1) == out_size[0] * out_size[1]
+
+
+def test_patch_merging():
+
+    # Test the model with int padding
+    in_c = 3
+    out_c = 4
+    kernel_size = 3
+    stride = 3
+    padding = 1
+    dilation = 1
+    bias = False
+    # test the case `pad_to_stride` is False
+    patch_merge = PatchMerging(
+        in_channels=in_c,
+        out_channels=out_c,
+        kernel_size=kernel_size,
+        stride=stride,
+        padding=padding,
+        dilation=dilation,
+        bias=bias)
+    B, L, C = 1, 100, 3
+    input_size = (10, 10)
+    x = torch.rand(B, L, C)
+    x_out, out_size = patch_merge(x, input_size)
+    assert x_out.size() == (1, 16, 4)
+    assert out_size == (4, 4)
+    # assert out size is consistent with real output
+    assert x_out.size(1) == out_size[0] * out_size[1]
+    in_c = 4
+    out_c = 5
+    kernel_size = 6
+    stride = 3
+    padding = 2
+    dilation = 2
+    bias = False
+    patch_merge = PatchMerging(
+        in_channels=in_c,
+        out_channels=out_c,
+        kernel_size=kernel_size,
+        stride=stride,
+        padding=padding,
+        dilation=dilation,
+        bias=bias)
+    B, L, C = 1, 100, 4
+    input_size = (10, 10)
+    x = torch.rand(B, L, C)
+    x_out, out_size = patch_merge(x, input_size)
+    assert x_out.size() == (1, 4, 5)
+    assert out_size == (2, 2)
+    # assert out size is consistent with real output
+    assert x_out.size(1) == out_size[0] * out_size[1]
+
+    # Test with adaptive padding
+    for padding in ('same', 'corner'):
+        in_c = 2
+        out_c = 3
+        B = 2
+
+        # test stride is 1
+        input_size = (5, 5)
+        kernel_size = (5, 5)
+        stride = (1, 1)
+        dilation = 1
+        bias = False
+        L = input_size[0] * input_size[1]
+
+        x = torch.rand(B, L, in_c)
+        patch_merge = PatchMerging(
+            in_channels=in_c,
+            out_channels=out_c,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias)
+
+        x_out, out_size = patch_merge(x, input_size)
+        assert x_out.size() == (B, 25, 3)
+        assert out_size == (5, 5)
+        assert x_out.size(1) == out_size[0] * out_size[1]
+
+        # test kernel_size == stride
+        input_size = (5, 5)
+        kernel_size = (5, 5)
+        stride = (5, 5)
+        dilation = 1
+        bias = False
+        L = input_size[0] * input_size[1]
+
+        x = torch.rand(B, L, in_c)
+        patch_merge = PatchMerging(
+            in_channels=in_c,
+            out_channels=out_c,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias)
+
+        x_out, out_size = patch_merge(x, input_size)
+        assert x_out.size() == (B, 1, 3)
+        assert out_size == (1, 1)
+        assert x_out.size(1) == out_size[0] * out_size[1]
+
+        # test kernel_size == stride
+        input_size = (6, 5)
+        kernel_size = (5, 5)
+        stride = (5, 5)
+        dilation = 1
+        bias = False
+        L = input_size[0] * input_size[1]
+
+        x = torch.rand(B, L, in_c)
+        patch_merge = PatchMerging(
+            in_channels=in_c,
+            out_channels=out_c,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias)
+
+        x_out, out_size = patch_merge(x, input_size)
+        assert x_out.size() == (B, 2, 3)
+        assert out_size == (2, 1)
+        assert x_out.size(1) == out_size[0] * out_size[1]
+
+        # test different kernel_size with different stride
+        input_size = (6, 5)
+        kernel_size = (6, 2)
+        stride = (6, 2)
+        dilation = 1
+        bias = False
+        L = input_size[0] * input_size[1]
+
+        x = torch.rand(B, L, in_c)
+        patch_merge = PatchMerging(
+            in_channels=in_c,
+            out_channels=out_c,
+            kernel_size=kernel_size,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            bias=bias)
+
+        x_out, out_size = patch_merge(x, input_size)
+        assert x_out.size() == (B, 3, 3)
+        assert out_size == (1, 3)
+        assert x_out.size(1) == out_size[0] * out_size[1]
+
+
+def test_detr_transformer_dencoder_encoder_layer():
+    config = ConfigDict(
+        dict(
+            return_intermediate=True,
+            num_layers=6,
+            transformerlayers=dict(
+                type='DetrTransformerDecoderLayer',
+                attn_cfgs=dict(
+                    type='MultiheadAttention',
+                    embed_dims=256,
+                    num_heads=8,
+                    dropout=0.1),
+                feedforward_channels=2048,
+                ffn_dropout=0.1,
+                operation_order=(
+                    'norm',
+                    'self_attn',
+                    'norm',
+                    'cross_attn',
+                    'norm',
+                    'ffn',
+                ))))
+    assert DetrTransformerDecoder(**config).layers[0].pre_norm
+    assert len(DetrTransformerDecoder(**config).layers) == 6
+
+    DetrTransformerDecoder(**config)
+    with pytest.raises(AssertionError):
+        config = ConfigDict(
+            dict(
+                return_intermediate=True,
+                num_layers=6,
+                transformerlayers=[
+                    dict(
+                        type='DetrTransformerDecoderLayer',
+                        attn_cfgs=dict(
+                            type='MultiheadAttention',
+                            embed_dims=256,
+                            num_heads=8,
+                            dropout=0.1),
+                        feedforward_channels=2048,
+                        ffn_dropout=0.1,
+                        operation_order=('self_attn', 'norm', 'cross_attn',
+                                         'norm', 'ffn', 'norm'))
+                ] * 5))
+        DetrTransformerDecoder(**config)
+
+    config = ConfigDict(
+        dict(
+            num_layers=6,
+            transformerlayers=dict(
+                type='DetrTransformerDecoderLayer',
+                attn_cfgs=dict(
+                    type='MultiheadAttention',
+                    embed_dims=256,
+                    num_heads=8,
+                    dropout=0.1),
+                feedforward_channels=2048,
+                ffn_dropout=0.1,
+                operation_order=('norm', 'self_attn', 'norm', 'cross_attn',
+                                 'norm', 'ffn', 'norm'))))
+
+    with pytest.raises(AssertionError):
+        # len(operation_order) == 6
+        DetrTransformerEncoder(**config)
+
+
+def test_transformer():
+    config = ConfigDict(
+        dict(
+            encoder=dict(
+                type='DetrTransformerEncoder',
+                num_layers=6,
+                transformerlayers=dict(
+                    type='BaseTransformerLayer',
+                    attn_cfgs=[
+                        dict(
+                            type='MultiheadAttention',
+                            embed_dims=256,
+                            num_heads=8,
+                            dropout=0.1)
+                    ],
+                    feedforward_channels=2048,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
+            decoder=dict(
+                type='DetrTransformerDecoder',
+                return_intermediate=True,
+                num_layers=6,
+                transformerlayers=dict(
+                    type='DetrTransformerDecoderLayer',
+                    attn_cfgs=dict(
+                        type='MultiheadAttention',
+                        embed_dims=256,
+                        num_heads=8,
+                        dropout=0.1),
+                    feedforward_channels=2048,
+                    ffn_dropout=0.1,
+                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
+                                     'ffn', 'norm')),
+            )))
+    transformer = Transformer(**config)
+    transformer.init_weights()
diff --git a/tests/test_onnx/__init__.py b/tests/test_onnx/__init__.py
new file mode 100644
index 0000000..76d466f
--- /dev/null
+++ b/tests/test_onnx/__init__.py
@@ -0,0 +1,4 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from .utils import ort_validate
+
+__all__ = ['ort_validate']
diff --git a/tests/test_onnx/test_head.py b/tests/test_onnx/test_head.py
new file mode 100644
index 0000000..978c46a
--- /dev/null
+++ b/tests/test_onnx/test_head.py
@@ -0,0 +1,453 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+from functools import partial
+
+import mmcv
+import numpy as np
+import pytest
+import torch
+from mmcv.cnn import Scale
+
+from mmdet import digit_version
+from mmdet.models import build_detector
+from mmdet.models.dense_heads import (FCOSHead, FSAFHead, RetinaHead, SSDHead,
+                                      YOLOV3Head)
+from .utils import ort_validate
+
+data_path = osp.join(osp.dirname(__file__), 'data')
+
+if digit_version(torch.__version__) <= digit_version('1.5.0'):
+    pytest.skip(
+        'ort backend does not support version below 1.5.0',
+        allow_module_level=True)
+
+
+def test_cascade_onnx_export():
+
+    config_path = './configs/cascade_rcnn/cascade_rcnn_r50_fpn_1x_coco.py'
+    cfg = mmcv.Config.fromfile(config_path)
+    model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
+    with torch.no_grad():
+        model.forward = partial(model.forward, img_metas=[[dict()]])
+
+        dynamic_axes = {
+            'input_img': {
+                0: 'batch',
+                2: 'width',
+                3: 'height'
+            },
+            'dets': {
+                0: 'batch',
+                1: 'num_dets',
+            },
+            'labels': {
+                0: 'batch',
+                1: 'num_dets',
+            },
+        }
+        torch.onnx.export(
+            model, [torch.rand(1, 3, 400, 500)],
+            'tmp.onnx',
+            output_names=['dets', 'labels'],
+            input_names=['input_img'],
+            keep_initializers_as_inputs=True,
+            do_constant_folding=True,
+            verbose=False,
+            opset_version=11,
+            dynamic_axes=dynamic_axes)
+
+
+def test_faster_onnx_export():
+
+    config_path = './configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py'
+    cfg = mmcv.Config.fromfile(config_path)
+    model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
+    with torch.no_grad():
+        model.forward = partial(model.forward, img_metas=[[dict()]])
+
+        dynamic_axes = {
+            'input_img': {
+                0: 'batch',
+                2: 'width',
+                3: 'height'
+            },
+            'dets': {
+                0: 'batch',
+                1: 'num_dets',
+            },
+            'labels': {
+                0: 'batch',
+                1: 'num_dets',
+            },
+        }
+        torch.onnx.export(
+            model, [torch.rand(1, 3, 400, 500)],
+            'tmp.onnx',
+            output_names=['dets', 'labels'],
+            input_names=['input_img'],
+            keep_initializers_as_inputs=True,
+            do_constant_folding=True,
+            verbose=False,
+            opset_version=11,
+            dynamic_axes=dynamic_axes)
+
+
+def retinanet_config():
+    """RetinanNet Head Config."""
+    head_cfg = dict(
+        stacked_convs=6,
+        feat_channels=2,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]))
+
+    test_cfg = mmcv.Config(
+        dict(
+            deploy_nms_pre=0,
+            min_bbox_size=0,
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100))
+
+    model = RetinaHead(
+        num_classes=4, in_channels=1, test_cfg=test_cfg, **head_cfg)
+    model.requires_grad_(False)
+
+    return model
+
+
+def test_retina_head_forward_single():
+    """Test RetinaNet Head single forward in torch and onnxruntime env."""
+    retina_model = retinanet_config()
+
+    feat = torch.rand(1, retina_model.in_channels, 32, 32)
+    # validate the result between the torch and ort
+    ort_validate(retina_model.forward_single, feat)
+
+
+def test_retina_head_forward():
+    """Test RetinaNet Head forward in torch and onnxruntime env."""
+    retina_model = retinanet_config()
+    s = 128
+    # RetinaNet head expects a multiple levels of features per image
+    feats = [
+        torch.rand(1, retina_model.in_channels, s // (2**(i + 2)),
+                   s // (2**(i + 2)))  # [32, 16, 8, 4, 2]
+        for i in range(len(retina_model.prior_generator.strides))
+    ]
+    ort_validate(retina_model.forward, feats)
+
+
+def test_retinanet_head_onnx_export():
+    """Test RetinaNet Head _get_bboxes() in torch and onnxruntime env."""
+    retina_model = retinanet_config()
+    s = 128
+    img_metas = [{
+        'img_shape_for_onnx': torch.Tensor([s, s]),
+        'scale_factor': np.ones(4),
+        'pad_shape': (s, s, 3),
+        'img_shape': (s, s, 2)
+    }]
+
+    # The data of retina_head_get_bboxes.pkl contains two parts:
+    # cls_score(list(Tensor)) and bboxes(list(Tensor)),
+    # where each torch.Tensor is generated by torch.rand().
+    # the cls_score's size: (1, 36, 32, 32), (1, 36, 16, 16),
+    # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2).
+    # the bboxes's size: (1, 36, 32, 32), (1, 36, 16, 16),
+    # (1, 36, 8, 8), (1, 36, 4, 4), (1, 36, 2, 2)
+    retina_head_data = 'retina_head_get_bboxes.pkl'
+    feats = mmcv.load(osp.join(data_path, retina_head_data))
+    cls_score = feats[:5]
+    bboxes = feats[5:]
+
+    retina_model.onnx_export = partial(
+        retina_model.onnx_export, img_metas=img_metas, with_nms=False)
+    ort_validate(retina_model.onnx_export, (cls_score, bboxes))
+
+
+def yolo_config():
+    """YoloV3 Head Config."""
+    head_cfg = dict(
+        anchor_generator=dict(
+            type='YOLOAnchorGenerator',
+            base_sizes=[[(116, 90), (156, 198), (373, 326)],
+                        [(30, 61), (62, 45), (59, 119)],
+                        [(10, 13), (16, 30), (33, 23)]],
+            strides=[32, 16, 8]),
+        bbox_coder=dict(type='YOLOBBoxCoder'))
+
+    test_cfg = mmcv.Config(
+        dict(
+            deploy_nms_pre=0,
+            min_bbox_size=0,
+            score_thr=0.05,
+            conf_thr=0.005,
+            nms=dict(type='nms', iou_threshold=0.45),
+            max_per_img=100))
+
+    model = YOLOV3Head(
+        num_classes=4,
+        in_channels=[1, 1, 1],
+        out_channels=[16, 8, 4],
+        test_cfg=test_cfg,
+        **head_cfg)
+    model.requires_grad_(False)
+    # yolov3 need eval()
+    model.cpu().eval()
+    return model
+
+
+def test_yolov3_head_forward():
+    """Test Yolov3 head forward() in torch and ort env."""
+    yolo_model = yolo_config()
+
+    # Yolov3 head expects a multiple levels of features per image
+    feats = [
+        torch.rand(1, 1, 64 // (2**(i + 2)), 64 // (2**(i + 2)))
+        for i in range(len(yolo_model.in_channels))
+    ]
+    ort_validate(yolo_model.forward, feats)
+
+
+def test_yolov3_head_onnx_export():
+    """Test yolov3 head get_bboxes() in torch and ort env."""
+    yolo_model = yolo_config()
+    s = 128
+    img_metas = [{
+        'img_shape_for_onnx': torch.Tensor([s, s]),
+        'img_shape': (s, s, 3),
+        'scale_factor': np.ones(4),
+        'pad_shape': (s, s, 3)
+    }]
+
+    # The data of yolov3_head_get_bboxes.pkl contains
+    # a list of torch.Tensor, where each torch.Tensor
+    # is generated by torch.rand and each tensor size is:
+    # (1, 27, 32, 32), (1, 27, 16, 16), (1, 27, 8, 8).
+    yolo_head_data = 'yolov3_head_get_bboxes.pkl'
+    pred_maps = mmcv.load(osp.join(data_path, yolo_head_data))
+
+    yolo_model.onnx_export = partial(
+        yolo_model.onnx_export, img_metas=img_metas, with_nms=False)
+    ort_validate(yolo_model.onnx_export, pred_maps)
+
+
+def fcos_config():
+    """FCOS Head Config."""
+    test_cfg = mmcv.Config(
+        dict(
+            deploy_nms_pre=0,
+            min_bbox_size=0,
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100))
+
+    model = FCOSHead(num_classes=4, in_channels=1, test_cfg=test_cfg)
+
+    model.requires_grad_(False)
+    return model
+
+
+def test_fcos_head_forward_single():
+    """Test fcos forward single in torch and ort env."""
+    fcos_model = fcos_config()
+
+    feat = torch.rand(1, fcos_model.in_channels, 32, 32)
+    fcos_model.forward_single = partial(
+        fcos_model.forward_single,
+        scale=Scale(1.0).requires_grad_(False),
+        stride=(4, ))
+    ort_validate(fcos_model.forward_single, feat)
+
+
+def test_fcos_head_forward():
+    """Test fcos forward in mutil-level feature map."""
+    fcos_model = fcos_config()
+    s = 128
+    feats = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+    ort_validate(fcos_model.forward, feats)
+
+
+def test_fcos_head_onnx_export():
+    """Test fcos head get_bboxes() in ort."""
+    fcos_model = fcos_config()
+    s = 128
+    img_metas = [{
+        'img_shape_for_onnx': torch.Tensor([s, s]),
+        'img_shape': (s, s, 3),
+        'scale_factor': np.ones(4),
+        'pad_shape': (s, s, 3)
+    }]
+
+    cls_scores = [
+        torch.rand(1, fcos_model.num_classes, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+    bboxes = [
+        torch.rand(1, 4, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+    centerness = [
+        torch.rand(1, 1, s // feat_size, s // feat_size)
+        for feat_size in [4, 8, 16, 32, 64]
+    ]
+
+    fcos_model.onnx_export = partial(
+        fcos_model.onnx_export, img_metas=img_metas, with_nms=False)
+    ort_validate(fcos_model.onnx_export, (cls_scores, bboxes, centerness))
+
+
+def fsaf_config():
+    """FSAF Head Config."""
+    cfg = dict(
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=1,
+            scales_per_octave=1,
+            ratios=[1.0],
+            strides=[8, 16, 32, 64, 128]))
+
+    test_cfg = mmcv.Config(
+        dict(
+            deploy_nms_pre=0,
+            min_bbox_size=0,
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100))
+
+    model = FSAFHead(num_classes=4, in_channels=1, test_cfg=test_cfg, **cfg)
+    model.requires_grad_(False)
+    return model
+
+
+def test_fsaf_head_forward_single():
+    """Test RetinaNet Head forward_single() in torch and onnxruntime env."""
+    fsaf_model = fsaf_config()
+
+    feat = torch.rand(1, fsaf_model.in_channels, 32, 32)
+    ort_validate(fsaf_model.forward_single, feat)
+
+
+def test_fsaf_head_forward():
+    """Test RetinaNet Head forward in torch and onnxruntime env."""
+    fsaf_model = fsaf_config()
+    s = 128
+    feats = [
+        torch.rand(1, fsaf_model.in_channels, s // (2**(i + 2)),
+                   s // (2**(i + 2)))
+        for i in range(len(fsaf_model.anchor_generator.strides))
+    ]
+    ort_validate(fsaf_model.forward, feats)
+
+
+def test_fsaf_head_onnx_export():
+    """Test RetinaNet Head get_bboxes in torch and onnxruntime env."""
+    fsaf_model = fsaf_config()
+    s = 256
+    img_metas = [{
+        'img_shape_for_onnx': torch.Tensor([s, s]),
+        'scale_factor': np.ones(4),
+        'pad_shape': (s, s, 3),
+        'img_shape': (s, s, 2)
+    }]
+
+    # The data of fsaf_head_get_bboxes.pkl contains two parts:
+    # cls_score(list(Tensor)) and bboxes(list(Tensor)),
+    # where each torch.Tensor is generated by torch.rand().
+    # the cls_score's size: (1, 4, 64, 64), (1, 4, 32, 32),
+    # (1, 4, 16, 16), (1, 4, 8, 8), (1, 4, 4, 4).
+    # the bboxes's size: (1, 4, 64, 64), (1, 4, 32, 32),
+    # (1, 4, 16, 16), (1, 4, 8, 8), (1, 4, 4, 4).
+    fsaf_head_data = 'fsaf_head_get_bboxes.pkl'
+    feats = mmcv.load(osp.join(data_path, fsaf_head_data))
+    cls_score = feats[:5]
+    bboxes = feats[5:]
+
+    fsaf_model.onnx_export = partial(
+        fsaf_model.onnx_export, img_metas=img_metas, with_nms=False)
+    ort_validate(fsaf_model.onnx_export, (cls_score, bboxes))
+
+
+def ssd_config():
+    """SSD Head Config."""
+    cfg = dict(
+        anchor_generator=dict(
+            type='SSDAnchorGenerator',
+            scale_major=False,
+            input_size=300,
+            basesize_ratio_range=(0.15, 0.9),
+            strides=[8, 16, 32, 64, 100, 300],
+            ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[0.1, 0.1, 0.2, 0.2]))
+
+    test_cfg = mmcv.Config(
+        dict(
+            deploy_nms_pre=0,
+            nms=dict(type='nms', iou_threshold=0.45),
+            min_bbox_size=0,
+            score_thr=0.02,
+            max_per_img=200))
+
+    model = SSDHead(
+        num_classes=4,
+        in_channels=(4, 8, 4, 2, 2, 2),
+        test_cfg=test_cfg,
+        **cfg)
+
+    model.requires_grad_(False)
+    return model
+
+
+def test_ssd_head_forward():
+    """Test SSD Head forward in torch and onnxruntime env."""
+    ssd_model = ssd_config()
+
+    featmap_size = [38, 19, 10, 6, 5, 3, 1]
+
+    feats = [
+        torch.rand(1, ssd_model.in_channels[i], featmap_size[i],
+                   featmap_size[i]) for i in range(len(ssd_model.in_channels))
+    ]
+    ort_validate(ssd_model.forward, feats)
+
+
+def test_ssd_head_onnx_export():
+    """Test SSD Head get_bboxes in torch and onnxruntime env."""
+    ssd_model = ssd_config()
+    s = 300
+    img_metas = [{
+        'img_shape_for_onnx': torch.Tensor([s, s]),
+        'scale_factor': np.ones(4),
+        'pad_shape': (s, s, 3),
+        'img_shape': (s, s, 2)
+    }]
+
+    # The data of ssd_head_get_bboxes.pkl contains two parts:
+    # cls_score(list(Tensor)) and bboxes(list(Tensor)),
+    # where each torch.Tensor is generated by torch.rand().
+    # the cls_score's size: (1, 20, 38, 38), (1, 30, 19, 19),
+    # (1, 30, 10, 10), (1, 30, 5, 5), (1, 20, 3, 3), (1, 20, 1, 1).
+    # the bboxes's size: (1, 16, 38, 38), (1, 24, 19, 19),
+    # (1, 24, 10, 10), (1, 24, 5, 5), (1, 16, 3, 3), (1, 16, 1, 1).
+    ssd_head_data = 'ssd_head_get_bboxes.pkl'
+    feats = mmcv.load(osp.join(data_path, ssd_head_data))
+    cls_score = feats[:6]
+    bboxes = feats[6:]
+
+    ssd_model.onnx_export = partial(
+        ssd_model.onnx_export, img_metas=img_metas, with_nms=False)
+    ort_validate(ssd_model.onnx_export, (cls_score, bboxes))
diff --git a/tests/test_onnx/test_neck.py b/tests/test_onnx/test_neck.py
new file mode 100644
index 0000000..a1a5cc8
--- /dev/null
+++ b/tests/test_onnx/test_neck.py
@@ -0,0 +1,163 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+
+import mmcv
+import pytest
+import torch
+
+from mmdet import digit_version
+from mmdet.models.necks import FPN, YOLOV3Neck
+from .utils import ort_validate
+
+if digit_version(torch.__version__) <= digit_version('1.5.0'):
+    pytest.skip(
+        'ort backend does not support version below 1.5.0',
+        allow_module_level=True)
+
+# Control the returned model of fpn_neck_config()
+fpn_test_step_names = {
+    'fpn_normal': 0,
+    'fpn_wo_extra_convs': 1,
+    'fpn_lateral_bns': 2,
+    'fpn_bilinear_upsample': 3,
+    'fpn_scale_factor': 4,
+    'fpn_extra_convs_inputs': 5,
+    'fpn_extra_convs_laterals': 6,
+    'fpn_extra_convs_outputs': 7,
+}
+
+# Control the returned model of yolo_neck_config()
+yolo_test_step_names = {'yolo_normal': 0}
+
+data_path = osp.join(osp.dirname(__file__), 'data')
+
+
+def fpn_neck_config(test_step_name):
+    """Return the class containing the corresponding attributes according to
+    the fpn_test_step_names."""
+    s = 64
+    in_channels = [8, 16, 32, 64]
+    feat_sizes = [s // 2**i for i in range(4)]  # [64, 32, 16, 8]
+    out_channels = 8
+
+    feats = [
+        torch.rand(1, in_channels[i], feat_sizes[i], feat_sizes[i])
+        for i in range(len(in_channels))
+    ]
+
+    if (fpn_test_step_names[test_step_name] == 0):
+        fpn_model = FPN(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            add_extra_convs=True,
+            num_outs=5)
+    elif (fpn_test_step_names[test_step_name] == 1):
+        fpn_model = FPN(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            add_extra_convs=False,
+            num_outs=5)
+    elif (fpn_test_step_names[test_step_name] == 2):
+        fpn_model = FPN(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            add_extra_convs=True,
+            no_norm_on_lateral=False,
+            norm_cfg=dict(type='BN', requires_grad=True),
+            num_outs=5)
+    elif (fpn_test_step_names[test_step_name] == 3):
+        fpn_model = FPN(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            add_extra_convs=True,
+            upsample_cfg=dict(mode='bilinear', align_corners=True),
+            num_outs=5)
+    elif (fpn_test_step_names[test_step_name] == 4):
+        fpn_model = FPN(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            add_extra_convs=True,
+            upsample_cfg=dict(scale_factor=2),
+            num_outs=5)
+    elif (fpn_test_step_names[test_step_name] == 5):
+        fpn_model = FPN(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            add_extra_convs='on_input',
+            num_outs=5)
+    elif (fpn_test_step_names[test_step_name] == 6):
+        fpn_model = FPN(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            add_extra_convs='on_lateral',
+            num_outs=5)
+    elif (fpn_test_step_names[test_step_name] == 7):
+        fpn_model = FPN(
+            in_channels=in_channels,
+            out_channels=out_channels,
+            add_extra_convs='on_output',
+            num_outs=5)
+    return fpn_model, feats
+
+
+def yolo_neck_config(test_step_name):
+    """Config yolov3 Neck."""
+    in_channels = [16, 8, 4]
+    out_channels = [8, 4, 2]
+
+    # The data of yolov3_neck.pkl contains a list of
+    # torch.Tensor, where each torch.Tensor is generated by
+    # torch.rand and each tensor size is:
+    # (1, 4, 64, 64), (1, 8, 32, 32), (1, 16, 16, 16).
+    yolov3_neck_data = 'yolov3_neck.pkl'
+    feats = mmcv.load(osp.join(data_path, yolov3_neck_data))
+
+    if (yolo_test_step_names[test_step_name] == 0):
+        yolo_model = YOLOV3Neck(
+            in_channels=in_channels, out_channels=out_channels, num_scales=3)
+    return yolo_model, feats
+
+
+def test_fpn_normal():
+    outs = fpn_neck_config('fpn_normal')
+    ort_validate(*outs)
+
+
+def test_fpn_wo_extra_convs():
+    outs = fpn_neck_config('fpn_wo_extra_convs')
+    ort_validate(*outs)
+
+
+def test_fpn_lateral_bns():
+    outs = fpn_neck_config('fpn_lateral_bns')
+    ort_validate(*outs)
+
+
+def test_fpn_bilinear_upsample():
+    outs = fpn_neck_config('fpn_bilinear_upsample')
+    ort_validate(*outs)
+
+
+def test_fpn_scale_factor():
+    outs = fpn_neck_config('fpn_scale_factor')
+    ort_validate(*outs)
+
+
+def test_fpn_extra_convs_inputs():
+    outs = fpn_neck_config('fpn_extra_convs_inputs')
+    ort_validate(*outs)
+
+
+def test_fpn_extra_convs_laterals():
+    outs = fpn_neck_config('fpn_extra_convs_laterals')
+    ort_validate(*outs)
+
+
+def test_fpn_extra_convs_outputs():
+    outs = fpn_neck_config('fpn_extra_convs_outputs')
+    ort_validate(*outs)
+
+
+def test_yolo_normal():
+    outs = yolo_neck_config('yolo_normal')
+    ort_validate(*outs)
diff --git a/tests/test_onnx/utils.py b/tests/test_onnx/utils.py
new file mode 100644
index 0000000..ad95e9e
--- /dev/null
+++ b/tests/test_onnx/utils.py
@@ -0,0 +1,137 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import os.path as osp
+import warnings
+
+import numpy as np
+import onnx
+import onnxruntime as ort
+import torch
+import torch.nn as nn
+
+ort_custom_op_path = ''
+try:
+    from mmcv.ops import get_onnxruntime_op_path
+    ort_custom_op_path = get_onnxruntime_op_path()
+except (ImportError, ModuleNotFoundError):
+    warnings.warn('If input model has custom op from mmcv, \
+        you may have to build mmcv with ONNXRuntime from source.')
+
+
+class WrapFunction(nn.Module):
+    """Wrap the function to be tested for torch.onnx.export tracking."""
+
+    def __init__(self, wrapped_function):
+        super(WrapFunction, self).__init__()
+        self.wrapped_function = wrapped_function
+
+    def forward(self, *args, **kwargs):
+        return self.wrapped_function(*args, **kwargs)
+
+
+def ort_validate(model, feats, onnx_io='tmp.onnx'):
+    """Validate the output of the onnxruntime backend is the same as the output
+    generated by torch.
+
+    Args:
+        model (nn.Module | function): the function of model or model
+            to be verified.
+        feats (tuple(list(torch.Tensor)) | list(torch.Tensor) | torch.Tensor):
+            the input of model.
+        onnx_io (str): the name of onnx output file.
+    """
+    # if model is not an instance of nn.Module, then it is a normal
+    # function and it should be wrapped.
+    if isinstance(model, nn.Module):
+        wrap_model = model
+    else:
+        wrap_model = WrapFunction(model)
+    wrap_model.cpu().eval()
+    with torch.no_grad():
+        torch.onnx.export(
+            wrap_model,
+            feats,
+            onnx_io,
+            export_params=True,
+            keep_initializers_as_inputs=True,
+            do_constant_folding=True,
+            verbose=False,
+            opset_version=11)
+
+    if isinstance(feats, tuple):
+        ort_feats = []
+        for feat in feats:
+            ort_feats += feat
+    else:
+        ort_feats = feats
+    # default model name: tmp.onnx
+    onnx_outputs = get_ort_model_output(ort_feats)
+
+    # remove temp file
+    if osp.exists(onnx_io):
+        os.remove(onnx_io)
+
+    if isinstance(feats, tuple):
+        torch_outputs = convert_result_list(wrap_model.forward(*feats))
+    else:
+        torch_outputs = convert_result_list(wrap_model.forward(feats))
+    torch_outputs = [
+        torch_output.detach().numpy() for torch_output in torch_outputs
+    ]
+
+    # match torch_outputs and onnx_outputs
+    for i in range(len(onnx_outputs)):
+        np.testing.assert_allclose(
+            torch_outputs[i], onnx_outputs[i], rtol=1e-03, atol=1e-05)
+
+
+def get_ort_model_output(feat, onnx_io='tmp.onnx'):
+    """Run the model in onnxruntime env.
+
+    Args:
+        feat (list[Tensor]): A list of tensors from torch.rand,
+            each is a 4D-tensor.
+
+    Returns:
+        list[np.array]: onnxruntime infer result, each is a np.array
+    """
+
+    onnx_model = onnx.load(onnx_io)
+    onnx.checker.check_model(onnx_model)
+
+    session_options = ort.SessionOptions()
+    # register custom op for onnxruntime
+    if osp.exists(ort_custom_op_path):
+        session_options.register_custom_ops_library(ort_custom_op_path)
+    sess = ort.InferenceSession(onnx_io, session_options)
+    if isinstance(feat, torch.Tensor):
+        onnx_outputs = sess.run(None,
+                                {sess.get_inputs()[0].name: feat.numpy()})
+    else:
+        onnx_outputs = sess.run(None, {
+            sess.get_inputs()[i].name: feat[i].numpy()
+            for i in range(len(feat))
+        })
+    return onnx_outputs
+
+
+def convert_result_list(outputs):
+    """Convert the torch forward outputs containing tuple or list to a list
+    only containing torch.Tensor.
+
+    Args:
+        output (list(Tensor) | tuple(list(Tensor) | ...): the outputs
+        in torch env, maybe containing nested structures such as list
+        or tuple.
+
+    Returns:
+        list(Tensor): a list only containing torch.Tensor
+    """
+    # recursive end condition
+    if isinstance(outputs, torch.Tensor):
+        return [outputs]
+
+    ret = []
+    for sub in outputs:
+        ret += convert_result_list(sub)
+    return ret
diff --git a/tests/test_runtime/async_benchmark.py b/tests/test_runtime/async_benchmark.py
new file mode 100644
index 0000000..aa692c4
--- /dev/null
+++ b/tests/test_runtime/async_benchmark.py
@@ -0,0 +1,102 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import asyncio
+import os
+import shutil
+import urllib
+
+import mmcv
+import torch
+
+from mmdet.apis import (async_inference_detector, inference_detector,
+                        init_detector)
+from mmdet.utils.contextmanagers import concurrent
+from mmdet.utils.profiling import profile_time
+
+
+async def main():
+    """Benchmark between async and synchronous inference interfaces.
+
+    Sample runs for 20 demo images on K80 GPU, model - mask_rcnn_r50_fpn_1x:
+
+    async       sync
+
+    7981.79 ms  9660.82 ms
+    8074.52 ms  9660.94 ms
+    7976.44 ms  9406.83 ms
+
+    Async variant takes about 0.83-0.85 of the time of the synchronous
+    interface.
+    """
+    project_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
+    project_dir = os.path.join(project_dir, '..')
+
+    config_file = os.path.join(
+        project_dir, 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py')
+    checkpoint_file = os.path.join(
+        project_dir,
+        'checkpoints/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth')
+
+    if not os.path.exists(checkpoint_file):
+        url = ('https://download.openmmlab.com/mmdetection/v2.0'
+               '/mask_rcnn/mask_rcnn_r50_fpn_1x_coco'
+               '/mask_rcnn_r50_fpn_1x_coco_20200205-d4b0c5d6.pth')
+        print(f'Downloading {url} ...')
+        local_filename, _ = urllib.request.urlretrieve(url)
+        os.makedirs(os.path.dirname(checkpoint_file), exist_ok=True)
+        shutil.move(local_filename, checkpoint_file)
+        print(f'Saved as {checkpoint_file}')
+    else:
+        print(f'Using existing checkpoint {checkpoint_file}')
+
+    device = 'cuda:0'
+    model = init_detector(
+        config_file, checkpoint=checkpoint_file, device=device)
+
+    # queue is used for concurrent inference of multiple images
+    streamqueue = asyncio.Queue()
+    # queue size defines concurrency level
+    streamqueue_size = 4
+
+    for _ in range(streamqueue_size):
+        streamqueue.put_nowait(torch.cuda.Stream(device=device))
+
+    # test a single image and show the results
+    img = mmcv.imread(os.path.join(project_dir, 'demo/demo.jpg'))
+
+    # warmup
+    await async_inference_detector(model, img)
+
+    async def detect(img):
+        async with concurrent(streamqueue):
+            return await async_inference_detector(model, img)
+
+    num_of_images = 20
+    with profile_time('benchmark', 'async'):
+        tasks = [
+            asyncio.create_task(detect(img)) for _ in range(num_of_images)
+        ]
+        async_results = await asyncio.gather(*tasks)
+
+    with torch.cuda.stream(torch.cuda.default_stream()):
+        with profile_time('benchmark', 'sync'):
+            sync_results = [
+                inference_detector(model, img) for _ in range(num_of_images)
+            ]
+
+    result_dir = os.path.join(project_dir, 'demo')
+    model.show_result(
+        img,
+        async_results[0],
+        score_thr=0.5,
+        show=False,
+        out_file=os.path.join(result_dir, 'result_async.jpg'))
+    model.show_result(
+        img,
+        sync_results[0],
+        score_thr=0.5,
+        show=False,
+        out_file=os.path.join(result_dir, 'result_sync.jpg'))
+
+
+if __name__ == '__main__':
+    asyncio.run(main())
diff --git a/tests/test_runtime/test_apis.py b/tests/test_runtime/test_apis.py
new file mode 100644
index 0000000..2394d12
--- /dev/null
+++ b/tests/test_runtime/test_apis.py
@@ -0,0 +1,32 @@
+import os
+from pathlib import Path
+
+import pytest
+
+from mmdet.apis import init_detector
+
+
+def test_init_detector():
+    project_dir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))
+    project_dir = os.path.join(project_dir, '..')
+
+    config_file = os.path.join(
+        project_dir, 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py')
+
+    # test init_detector with config_file: str and cfg_options
+    cfg_options = dict(
+        model=dict(
+            backbone=dict(
+                depth=18,
+                init_cfg=dict(
+                    type='Pretrained', checkpoint='torchvision://resnet18'))))
+    model = init_detector(config_file, device='cpu', cfg_options=cfg_options)
+
+    # test init_detector with :obj:`Path`
+    config_path_object = Path(config_file)
+    model = init_detector(config_path_object, device='cpu')
+
+    # test init_detector with undesirable type
+    with pytest.raises(TypeError):
+        config_list = [config_file]
+        model = init_detector(config_list)  # noqa: F841
diff --git a/tests/test_runtime/test_async.py b/tests/test_runtime/test_async.py
new file mode 100644
index 0000000..1af1501
--- /dev/null
+++ b/tests/test_runtime/test_async.py
@@ -0,0 +1,83 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Tests for async interface."""
+
+import asyncio
+import os
+import sys
+
+import asynctest
+import mmcv
+import torch
+
+from mmdet.apis import async_inference_detector, init_detector
+
+if sys.version_info >= (3, 7):
+    from mmdet.utils.contextmanagers import concurrent
+
+
+class AsyncTestCase(asynctest.TestCase):
+    use_default_loop = False
+    forbid_get_event_loop = True
+
+    TEST_TIMEOUT = int(os.getenv('ASYNCIO_TEST_TIMEOUT', '30'))
+
+    def _run_test_method(self, method):
+        result = method()
+        if asyncio.iscoroutine(result):
+            self.loop.run_until_complete(
+                asyncio.wait_for(result, timeout=self.TEST_TIMEOUT))
+
+
+class MaskRCNNDetector:
+
+    def __init__(self,
+                 model_config,
+                 checkpoint=None,
+                 streamqueue_size=3,
+                 device='cuda:0'):
+
+        self.streamqueue_size = streamqueue_size
+        self.device = device
+        # build the model and load checkpoint
+        self.model = init_detector(
+            model_config, checkpoint=None, device=self.device)
+        self.streamqueue = None
+
+    async def init(self):
+        self.streamqueue = asyncio.Queue()
+        for _ in range(self.streamqueue_size):
+            stream = torch.cuda.Stream(device=self.device)
+            self.streamqueue.put_nowait(stream)
+
+    if sys.version_info >= (3, 7):
+
+        async def apredict(self, img):
+            if isinstance(img, str):
+                img = mmcv.imread(img)
+            async with concurrent(self.streamqueue):
+                result = await async_inference_detector(self.model, img)
+            return result
+
+
+class AsyncInferenceTestCase(AsyncTestCase):
+
+    if sys.version_info >= (3, 7):
+
+        async def test_simple_inference(self):
+            if not torch.cuda.is_available():
+                import pytest
+
+                pytest.skip('test requires GPU and torch+cuda')
+
+            ori_grad_enabled = torch.is_grad_enabled()
+            root_dir = os.path.dirname(os.path.dirname(__name__))
+            model_config = os.path.join(
+                root_dir, 'configs/mask_rcnn/mask_rcnn_r50_fpn_1x_coco.py')
+            detector = MaskRCNNDetector(model_config)
+            await detector.init()
+            img_path = os.path.join(root_dir, 'demo/demo.jpg')
+            bboxes, _ = await detector.apredict(img_path)
+            self.assertTrue(bboxes)
+            # asy inference detector will hack grad_enabled,
+            # so restore here to avoid it to influence other tests
+            torch.set_grad_enabled(ori_grad_enabled)
diff --git a/tests/test_runtime/test_config.py b/tests/test_runtime/test_config.py
new file mode 100644
index 0000000..dce88f4
--- /dev/null
+++ b/tests/test_runtime/test_config.py
@@ -0,0 +1,373 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from os.path import dirname, exists, join
+from unittest.mock import Mock
+
+import pytest
+
+from mmdet.core import BitmapMasks, PolygonMasks
+from mmdet.datasets.builder import DATASETS
+from mmdet.datasets.utils import NumClassCheckHook
+
+
+def _get_config_directory():
+    """Find the predefined detector config directory."""
+    try:
+        # Assume we are running in the source mmdetection repo
+        repo_dpath = dirname(dirname(__file__))
+        repo_dpath = join(repo_dpath, '..')
+    except NameError:
+        # For IPython development when this __file__ is not defined
+        import mmdet
+        repo_dpath = dirname(dirname(mmdet.__file__))
+    config_dpath = join(repo_dpath, 'configs')
+    if not exists(config_dpath):
+        raise Exception('Cannot find config path')
+    return config_dpath
+
+
+def _check_numclasscheckhook(detector, config_mod):
+    dummy_runner = Mock()
+    dummy_runner.model = detector
+
+    def get_dataset_name_classes(dataset):
+        # deal with `RepeatDataset`,`ConcatDataset`,`ClassBalancedDataset`..
+        if isinstance(dataset, (list, tuple)):
+            dataset = dataset[0]
+        while ('dataset' in dataset):
+            dataset = dataset['dataset']
+            # ConcatDataset
+            if isinstance(dataset, (list, tuple)):
+                dataset = dataset[0]
+        return dataset['type'], dataset.get('classes', None)
+
+    compatible_check = NumClassCheckHook()
+    dataset_name, CLASSES = get_dataset_name_classes(
+        config_mod['data']['train'])
+    if CLASSES is None:
+        CLASSES = DATASETS.get(dataset_name).CLASSES
+    dummy_runner.data_loader.dataset.CLASSES = CLASSES
+    compatible_check.before_train_epoch(dummy_runner)
+
+    dummy_runner.data_loader.dataset.CLASSES = None
+    compatible_check.before_train_epoch(dummy_runner)
+
+    dataset_name, CLASSES = get_dataset_name_classes(config_mod['data']['val'])
+    if CLASSES is None:
+        CLASSES = DATASETS.get(dataset_name).CLASSES
+    dummy_runner.data_loader.dataset.CLASSES = CLASSES
+    compatible_check.before_val_epoch(dummy_runner)
+    dummy_runner.data_loader.dataset.CLASSES = None
+    compatible_check.before_val_epoch(dummy_runner)
+
+
+def _check_roi_head(config, head):
+    # check consistency between head_config and roi_head
+    assert config['type'] == head.__class__.__name__
+
+    # check roi_align
+    bbox_roi_cfg = config.bbox_roi_extractor
+    bbox_roi_extractor = head.bbox_roi_extractor
+    _check_roi_extractor(bbox_roi_cfg, bbox_roi_extractor)
+
+    # check bbox head infos
+    bbox_cfg = config.bbox_head
+    bbox_head = head.bbox_head
+    _check_bbox_head(bbox_cfg, bbox_head)
+
+    if head.with_mask:
+        # check roi_align
+        if config.mask_roi_extractor:
+            mask_roi_cfg = config.mask_roi_extractor
+            mask_roi_extractor = head.mask_roi_extractor
+            _check_roi_extractor(mask_roi_cfg, mask_roi_extractor,
+                                 bbox_roi_extractor)
+
+        # check mask head infos
+        mask_head = head.mask_head
+        mask_cfg = config.mask_head
+        _check_mask_head(mask_cfg, mask_head)
+
+    # check arch specific settings, e.g., cascade/htc
+    if config['type'] in ['CascadeRoIHead', 'HybridTaskCascadeRoIHead']:
+        assert config.num_stages == len(head.bbox_head)
+        assert config.num_stages == len(head.bbox_roi_extractor)
+
+        if head.with_mask:
+            assert config.num_stages == len(head.mask_head)
+            assert config.num_stages == len(head.mask_roi_extractor)
+
+    elif config['type'] in ['MaskScoringRoIHead']:
+        assert (hasattr(head, 'mask_iou_head')
+                and head.mask_iou_head is not None)
+        mask_iou_cfg = config.mask_iou_head
+        mask_iou_head = head.mask_iou_head
+        assert (mask_iou_cfg.fc_out_channels ==
+                mask_iou_head.fc_mask_iou.in_features)
+
+    elif config['type'] in ['GridRoIHead']:
+        grid_roi_cfg = config.grid_roi_extractor
+        grid_roi_extractor = head.grid_roi_extractor
+        _check_roi_extractor(grid_roi_cfg, grid_roi_extractor,
+                             bbox_roi_extractor)
+
+        config.grid_head.grid_points = head.grid_head.grid_points
+
+
+def _check_roi_extractor(config, roi_extractor, prev_roi_extractor=None):
+    import torch.nn as nn
+
+    # Separate roi_extractor and prev_roi_extractor checks for flexibility
+    if isinstance(roi_extractor, nn.ModuleList):
+        roi_extractor = roi_extractor[0]
+    if prev_roi_extractor and isinstance(prev_roi_extractor, nn.ModuleList):
+        prev_roi_extractor = prev_roi_extractor[0]
+
+    assert (len(config.featmap_strides) == len(roi_extractor.roi_layers))
+    assert (config.out_channels == roi_extractor.out_channels)
+    from torch.nn.modules.utils import _pair
+    assert (_pair(config.roi_layer.output_size) ==
+            roi_extractor.roi_layers[0].output_size)
+
+    if 'use_torchvision' in config.roi_layer:
+        assert (config.roi_layer.use_torchvision ==
+                roi_extractor.roi_layers[0].use_torchvision)
+    elif 'aligned' in config.roi_layer:
+        assert (
+            config.roi_layer.aligned == roi_extractor.roi_layers[0].aligned)
+
+    if prev_roi_extractor:
+        assert (roi_extractor.roi_layers[0].aligned ==
+                prev_roi_extractor.roi_layers[0].aligned)
+        assert (roi_extractor.roi_layers[0].use_torchvision ==
+                prev_roi_extractor.roi_layers[0].use_torchvision)
+
+
+def _check_mask_head(mask_cfg, mask_head):
+    import torch.nn as nn
+    if isinstance(mask_cfg, list):
+        for single_mask_cfg, single_mask_head in zip(mask_cfg, mask_head):
+            _check_mask_head(single_mask_cfg, single_mask_head)
+    elif isinstance(mask_head, nn.ModuleList):
+        for single_mask_head in mask_head:
+            _check_mask_head(mask_cfg, single_mask_head)
+    else:
+        assert mask_cfg['type'] == mask_head.__class__.__name__
+        assert mask_cfg.in_channels == mask_head.in_channels
+        class_agnostic = mask_cfg.get('class_agnostic', False)
+        out_dim = (1 if class_agnostic else mask_cfg.num_classes)
+        if hasattr(mask_head, 'conv_logits'):
+            assert (mask_cfg.conv_out_channels ==
+                    mask_head.conv_logits.in_channels)
+            assert mask_head.conv_logits.out_channels == out_dim
+        else:
+            assert mask_cfg.fc_out_channels == mask_head.fc_logits.in_features
+            assert (mask_head.fc_logits.out_features == out_dim *
+                    mask_head.output_area)
+
+
+def _check_bbox_head(bbox_cfg, bbox_head):
+    import torch.nn as nn
+    if isinstance(bbox_cfg, list):
+        for single_bbox_cfg, single_bbox_head in zip(bbox_cfg, bbox_head):
+            _check_bbox_head(single_bbox_cfg, single_bbox_head)
+    elif isinstance(bbox_head, nn.ModuleList):
+        for single_bbox_head in bbox_head:
+            _check_bbox_head(bbox_cfg, single_bbox_head)
+    else:
+        assert bbox_cfg['type'] == bbox_head.__class__.__name__
+        if bbox_cfg['type'] == 'SABLHead':
+            assert bbox_cfg.cls_in_channels == bbox_head.cls_in_channels
+            assert bbox_cfg.reg_in_channels == bbox_head.reg_in_channels
+
+            cls_out_channels = bbox_cfg.get('cls_out_channels', 1024)
+            assert (cls_out_channels == bbox_head.fc_cls.in_features)
+            assert (bbox_cfg.num_classes + 1 == bbox_head.fc_cls.out_features)
+
+        elif bbox_cfg['type'] == 'DIIHead':
+            assert bbox_cfg['num_ffn_fcs'] == bbox_head.ffn.num_fcs
+            # 3 means FC and LN and Relu
+            assert bbox_cfg['num_cls_fcs'] == len(bbox_head.cls_fcs) // 3
+            assert bbox_cfg['num_reg_fcs'] == len(bbox_head.reg_fcs) // 3
+            assert bbox_cfg['in_channels'] == bbox_head.in_channels
+            assert bbox_cfg['in_channels'] == bbox_head.fc_cls.in_features
+            assert bbox_cfg['in_channels'] == bbox_head.fc_reg.in_features
+            assert bbox_cfg['in_channels'] == bbox_head.attention.embed_dims
+            assert bbox_cfg[
+                'feedforward_channels'] == bbox_head.ffn.feedforward_channels
+
+        else:
+            assert bbox_cfg.in_channels == bbox_head.in_channels
+            with_cls = bbox_cfg.get('with_cls', True)
+
+            if with_cls:
+                fc_out_channels = bbox_cfg.get('fc_out_channels', 2048)
+                assert (fc_out_channels == bbox_head.fc_cls.in_features)
+                if bbox_head.custom_cls_channels:
+                    assert (bbox_head.loss_cls.get_cls_channels(
+                        bbox_head.num_classes) == bbox_head.fc_cls.out_features
+                            )
+                else:
+                    assert (bbox_cfg.num_classes +
+                            1 == bbox_head.fc_cls.out_features)
+            with_reg = bbox_cfg.get('with_reg', True)
+            if with_reg:
+                out_dim = (4 if bbox_cfg.reg_class_agnostic else 4 *
+                           bbox_cfg.num_classes)
+                assert bbox_head.fc_reg.out_features == out_dim
+
+
+def _check_anchorhead(config, head):
+    # check consistency between head_config and roi_head
+    assert config['type'] == head.__class__.__name__
+    assert config.in_channels == head.in_channels
+
+    num_classes = (
+        config.num_classes -
+        1 if config.loss_cls.get('use_sigmoid', False) else config.num_classes)
+    if config['type'] == 'ATSSHead':
+        assert (config.feat_channels == head.atss_cls.in_channels)
+        assert (config.feat_channels == head.atss_reg.in_channels)
+        assert (config.feat_channels == head.atss_centerness.in_channels)
+    elif config['type'] == 'SABLRetinaHead':
+        assert (config.feat_channels == head.retina_cls.in_channels)
+        assert (config.feat_channels == head.retina_bbox_reg.in_channels)
+        assert (config.feat_channels == head.retina_bbox_cls.in_channels)
+    else:
+        assert (config.in_channels == head.conv_cls.in_channels)
+        assert (config.in_channels == head.conv_reg.in_channels)
+        assert (head.conv_cls.out_channels == num_classes * head.num_anchors)
+        assert head.fc_reg.out_channels == 4 * head.num_anchors
+
+
+# Only tests a representative subset of configurations
+# TODO: test pipelines using Albu, current Albu throw None given empty GT
+@pytest.mark.parametrize(
+    'config_rpath',
+    [
+        'wider_face/ssd300_wider_face.py',
+        'pascal_voc/ssd300_voc0712.py',
+        'pascal_voc/ssd512_voc0712.py',
+        # 'albu_example/mask_rcnn_r50_fpn_1x.py',
+        'foveabox/fovea_align_r50_fpn_gn-head_mstrain_640-800_4x4_2x_coco.py',
+        'mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py',
+        'mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain_1x_coco.py',
+        'mask_rcnn/mask_rcnn_r50_fpn_fp16_1x_coco.py'
+    ])
+def test_config_data_pipeline(config_rpath):
+    """Test whether the data pipeline is valid and can process corner cases.
+
+    CommandLine:
+        xdoctest -m tests/test_runtime/
+            test_config.py test_config_build_data_pipeline
+    """
+    import numpy as np
+    from mmcv import Config
+
+    from mmdet.datasets.pipelines import Compose
+
+    config_dpath = _get_config_directory()
+    print(f'Found config_dpath = {config_dpath}')
+
+    def dummy_masks(h, w, num_obj=3, mode='bitmap'):
+        assert mode in ('polygon', 'bitmap')
+        if mode == 'bitmap':
+            masks = np.random.randint(0, 2, (num_obj, h, w), dtype=np.uint8)
+            masks = BitmapMasks(masks, h, w)
+        else:
+            masks = []
+            for i in range(num_obj):
+                masks.append([])
+                masks[-1].append(
+                    np.random.uniform(0, min(h - 1, w - 1), (8 + 4 * i, )))
+                masks[-1].append(
+                    np.random.uniform(0, min(h - 1, w - 1), (10 + 4 * i, )))
+            masks = PolygonMasks(masks, h, w)
+        return masks
+
+    config_fpath = join(config_dpath, config_rpath)
+    cfg = Config.fromfile(config_fpath)
+
+    # remove loading pipeline
+    loading_pipeline = cfg.train_pipeline.pop(0)
+    loading_ann_pipeline = cfg.train_pipeline.pop(0)
+    cfg.test_pipeline.pop(0)
+
+    train_pipeline = Compose(cfg.train_pipeline)
+    test_pipeline = Compose(cfg.test_pipeline)
+
+    print(f'Building data pipeline, config_fpath = {config_fpath}')
+
+    print(f'Test training data pipeline: \n{train_pipeline!r}')
+    img = np.random.randint(0, 255, size=(888, 666, 3), dtype=np.uint8)
+    if loading_pipeline.get('to_float32', False):
+        img = img.astype(np.float32)
+    mode = 'bitmap' if loading_ann_pipeline.get('poly2mask',
+                                                True) else 'polygon'
+    results = dict(
+        filename='test_img.png',
+        ori_filename='test_img.png',
+        img=img,
+        img_shape=img.shape,
+        ori_shape=img.shape,
+        gt_bboxes=np.array([[35.2, 11.7, 39.7, 15.7]], dtype=np.float32),
+        gt_labels=np.array([1], dtype=np.int64),
+        gt_masks=dummy_masks(img.shape[0], img.shape[1], mode=mode),
+    )
+    results['img_fields'] = ['img']
+    results['bbox_fields'] = ['gt_bboxes']
+    results['mask_fields'] = ['gt_masks']
+    output_results = train_pipeline(results)
+    assert output_results is not None
+
+    print(f'Test testing data pipeline: \n{test_pipeline!r}')
+    results = dict(
+        filename='test_img.png',
+        ori_filename='test_img.png',
+        img=img,
+        img_shape=img.shape,
+        ori_shape=img.shape,
+        gt_bboxes=np.array([[35.2, 11.7, 39.7, 15.7]], dtype=np.float32),
+        gt_labels=np.array([1], dtype=np.int64),
+        gt_masks=dummy_masks(img.shape[0], img.shape[1], mode=mode),
+    )
+    results['img_fields'] = ['img']
+    results['bbox_fields'] = ['gt_bboxes']
+    results['mask_fields'] = ['gt_masks']
+    output_results = test_pipeline(results)
+    assert output_results is not None
+
+    # test empty GT
+    print('Test empty GT with training data pipeline: '
+          f'\n{train_pipeline!r}')
+    results = dict(
+        filename='test_img.png',
+        ori_filename='test_img.png',
+        img=img,
+        img_shape=img.shape,
+        ori_shape=img.shape,
+        gt_bboxes=np.zeros((0, 4), dtype=np.float32),
+        gt_labels=np.array([], dtype=np.int64),
+        gt_masks=dummy_masks(img.shape[0], img.shape[1], num_obj=0, mode=mode),
+    )
+    results['img_fields'] = ['img']
+    results['bbox_fields'] = ['gt_bboxes']
+    results['mask_fields'] = ['gt_masks']
+    output_results = train_pipeline(results)
+    assert output_results is not None
+
+    print(f'Test empty GT with testing data pipeline: \n{test_pipeline!r}')
+    results = dict(
+        filename='test_img.png',
+        ori_filename='test_img.png',
+        img=img,
+        img_shape=img.shape,
+        ori_shape=img.shape,
+        gt_bboxes=np.zeros((0, 4), dtype=np.float32),
+        gt_labels=np.array([], dtype=np.int64),
+        gt_masks=dummy_masks(img.shape[0], img.shape[1], num_obj=0, mode=mode),
+    )
+    results['img_fields'] = ['img']
+    results['bbox_fields'] = ['gt_bboxes']
+    results['mask_fields'] = ['gt_masks']
+    output_results = test_pipeline(results)
+    assert output_results is not None
diff --git a/tests/test_runtime/test_eval_hook.py b/tests/test_runtime/test_eval_hook.py
new file mode 100644
index 0000000..ac0f5e9
--- /dev/null
+++ b/tests/test_runtime/test_eval_hook.py
@@ -0,0 +1,252 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import tempfile
+import unittest.mock as mock
+from collections import OrderedDict
+from unittest.mock import MagicMock, patch
+
+import pytest
+import torch
+import torch.nn as nn
+from mmcv.runner import EpochBasedRunner, build_optimizer
+from mmcv.utils import get_logger
+from torch.utils.data import DataLoader, Dataset
+
+from mmdet.core import DistEvalHook, EvalHook
+
+
+class ExampleDataset(Dataset):
+
+    def __init__(self):
+        self.index = 0
+        self.eval_result = [0.1, 0.4, 0.3, 0.7, 0.2, 0.05, 0.4, 0.6]
+
+    def __getitem__(self, idx):
+        results = dict(imgs=torch.tensor([1]))
+        return results
+
+    def __len__(self):
+        return 1
+
+    @mock.create_autospec
+    def evaluate(self, results, logger=None):
+        pass
+
+
+class EvalDataset(ExampleDataset):
+
+    def evaluate(self, results, logger=None):
+        mean_ap = self.eval_result[self.index]
+        output = OrderedDict(mAP=mean_ap, index=self.index, score=mean_ap)
+        self.index += 1
+        return output
+
+
+class ExampleModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.conv = nn.Linear(1, 1)
+        self.test_cfg = None
+
+    def forward(self, imgs, rescale=False, return_loss=False):
+        return imgs
+
+    def train_step(self, data_batch, optimizer, **kwargs):
+        outputs = {
+            'loss': 0.5,
+            'log_vars': {
+                'accuracy': 0.98
+            },
+            'num_samples': 1
+        }
+        return outputs
+
+
+@pytest.mark.skipif(
+    not torch.cuda.is_available(), reason='requires CUDA support')
+@patch('mmdet.apis.single_gpu_test', MagicMock)
+@patch('mmdet.apis.multi_gpu_test', MagicMock)
+@pytest.mark.parametrize('EvalHookCls', (EvalHook, DistEvalHook))
+def test_eval_hook(EvalHookCls):
+    with pytest.raises(TypeError):
+        # dataloader must be a pytorch DataLoader
+        test_dataset = ExampleDataset()
+        data_loader = [
+            DataLoader(
+                test_dataset,
+                batch_size=1,
+                sampler=None,
+                num_worker=0,
+                shuffle=False)
+        ]
+        EvalHookCls(data_loader)
+
+    with pytest.raises(KeyError):
+        # rule must be in keys of rule_map
+        test_dataset = ExampleDataset()
+        data_loader = DataLoader(
+            test_dataset,
+            batch_size=1,
+            sampler=None,
+            num_workers=0,
+            shuffle=False)
+        EvalHookCls(data_loader, save_best='auto', rule='unsupport')
+
+    with pytest.raises(ValueError):
+        # key_indicator must be valid when rule_map is None
+        test_dataset = ExampleDataset()
+        data_loader = DataLoader(
+            test_dataset,
+            batch_size=1,
+            sampler=None,
+            num_workers=0,
+            shuffle=False)
+        EvalHookCls(data_loader, save_best='unsupport')
+
+    optimizer_cfg = dict(
+        type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001)
+
+    test_dataset = ExampleDataset()
+    loader = DataLoader(test_dataset, batch_size=1)
+    model = ExampleModel()
+    optimizer = build_optimizer(model, optimizer_cfg)
+
+    data_loader = DataLoader(test_dataset, batch_size=1)
+    eval_hook = EvalHookCls(data_loader, save_best=None)
+    with tempfile.TemporaryDirectory() as tmpdir:
+        logger = get_logger('test_eval')
+        runner = EpochBasedRunner(
+            model=model,
+            batch_processor=None,
+            optimizer=optimizer,
+            work_dir=tmpdir,
+            logger=logger)
+        runner.register_hook(eval_hook)
+        runner.run([loader], [('train', 1)], 1)
+        assert runner.meta is None or 'best_score' not in runner.meta[
+            'hook_msgs']
+        assert runner.meta is None or 'best_ckpt' not in runner.meta[
+            'hook_msgs']
+
+    # when `save_best` is set to 'auto', first metric will be used.
+    loader = DataLoader(EvalDataset(), batch_size=1)
+    model = ExampleModel()
+    data_loader = DataLoader(EvalDataset(), batch_size=1)
+    eval_hook = EvalHookCls(data_loader, interval=1, save_best='auto')
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        logger = get_logger('test_eval')
+        runner = EpochBasedRunner(
+            model=model,
+            batch_processor=None,
+            optimizer=optimizer,
+            work_dir=tmpdir,
+            logger=logger)
+        runner.register_checkpoint_hook(dict(interval=1))
+        runner.register_hook(eval_hook)
+        runner.run([loader], [('train', 1)], 8)
+
+        real_path = osp.join(tmpdir, 'best_mAP_epoch_4.pth')
+
+        assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path)
+        assert runner.meta['hook_msgs']['best_score'] == 0.7
+
+    loader = DataLoader(EvalDataset(), batch_size=1)
+    model = ExampleModel()
+    data_loader = DataLoader(EvalDataset(), batch_size=1)
+    eval_hook = EvalHookCls(data_loader, interval=1, save_best='mAP')
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        logger = get_logger('test_eval')
+        runner = EpochBasedRunner(
+            model=model,
+            batch_processor=None,
+            optimizer=optimizer,
+            work_dir=tmpdir,
+            logger=logger)
+        runner.register_checkpoint_hook(dict(interval=1))
+        runner.register_hook(eval_hook)
+        runner.run([loader], [('train', 1)], 8)
+
+        real_path = osp.join(tmpdir, 'best_mAP_epoch_4.pth')
+
+        assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path)
+        assert runner.meta['hook_msgs']['best_score'] == 0.7
+
+    data_loader = DataLoader(EvalDataset(), batch_size=1)
+    eval_hook = EvalHookCls(
+        data_loader, interval=1, save_best='score', rule='greater')
+    with tempfile.TemporaryDirectory() as tmpdir:
+        logger = get_logger('test_eval')
+        runner = EpochBasedRunner(
+            model=model,
+            batch_processor=None,
+            optimizer=optimizer,
+            work_dir=tmpdir,
+            logger=logger)
+        runner.register_checkpoint_hook(dict(interval=1))
+        runner.register_hook(eval_hook)
+        runner.run([loader], [('train', 1)], 8)
+
+        real_path = osp.join(tmpdir, 'best_score_epoch_4.pth')
+
+        assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path)
+        assert runner.meta['hook_msgs']['best_score'] == 0.7
+
+    data_loader = DataLoader(EvalDataset(), batch_size=1)
+    eval_hook = EvalHookCls(data_loader, save_best='mAP', rule='less')
+    with tempfile.TemporaryDirectory() as tmpdir:
+        logger = get_logger('test_eval')
+        runner = EpochBasedRunner(
+            model=model,
+            batch_processor=None,
+            optimizer=optimizer,
+            work_dir=tmpdir,
+            logger=logger)
+        runner.register_checkpoint_hook(dict(interval=1))
+        runner.register_hook(eval_hook)
+        runner.run([loader], [('train', 1)], 8)
+
+        real_path = osp.join(tmpdir, 'best_mAP_epoch_6.pth')
+
+        assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path)
+        assert runner.meta['hook_msgs']['best_score'] == 0.05
+
+    data_loader = DataLoader(EvalDataset(), batch_size=1)
+    eval_hook = EvalHookCls(data_loader, save_best='mAP')
+    with tempfile.TemporaryDirectory() as tmpdir:
+        logger = get_logger('test_eval')
+        runner = EpochBasedRunner(
+            model=model,
+            batch_processor=None,
+            optimizer=optimizer,
+            work_dir=tmpdir,
+            logger=logger)
+        runner.register_checkpoint_hook(dict(interval=1))
+        runner.register_hook(eval_hook)
+        runner.run([loader], [('train', 1)], 2)
+
+        real_path = osp.join(tmpdir, 'best_mAP_epoch_2.pth')
+
+        assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path)
+        assert runner.meta['hook_msgs']['best_score'] == 0.4
+
+        resume_from = osp.join(tmpdir, 'latest.pth')
+        loader = DataLoader(ExampleDataset(), batch_size=1)
+        eval_hook = EvalHookCls(data_loader, save_best='mAP')
+        runner = EpochBasedRunner(
+            model=model,
+            batch_processor=None,
+            optimizer=optimizer,
+            work_dir=tmpdir,
+            logger=logger)
+        runner.register_checkpoint_hook(dict(interval=1))
+        runner.register_hook(eval_hook)
+        runner.resume(resume_from)
+        runner.run([loader], [('train', 1)], 8)
+
+        real_path = osp.join(tmpdir, 'best_mAP_epoch_4.pth')
+
+        assert runner.meta['hook_msgs']['best_ckpt'] == osp.realpath(real_path)
+        assert runner.meta['hook_msgs']['best_score'] == 0.7
diff --git a/tests/test_runtime/test_fp16.py b/tests/test_runtime/test_fp16.py
new file mode 100644
index 0000000..e3dd432
--- /dev/null
+++ b/tests/test_runtime/test_fp16.py
@@ -0,0 +1,301 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import pytest
+import torch
+import torch.nn as nn
+from mmcv.runner import auto_fp16, force_fp32
+from mmcv.runner.fp16_utils import cast_tensor_type
+
+
+def test_cast_tensor_type():
+    inputs = torch.FloatTensor([5.])
+    src_type = torch.float32
+    dst_type = torch.int32
+    outputs = cast_tensor_type(inputs, src_type, dst_type)
+    assert isinstance(outputs, torch.Tensor)
+    assert outputs.dtype == dst_type
+
+    inputs = 'tensor'
+    src_type = str
+    dst_type = str
+    outputs = cast_tensor_type(inputs, src_type, dst_type)
+    assert isinstance(outputs, str)
+
+    inputs = np.array([5.])
+    src_type = np.ndarray
+    dst_type = np.ndarray
+    outputs = cast_tensor_type(inputs, src_type, dst_type)
+    assert isinstance(outputs, np.ndarray)
+
+    inputs = dict(
+        tensor_a=torch.FloatTensor([1.]), tensor_b=torch.FloatTensor([2.]))
+    src_type = torch.float32
+    dst_type = torch.int32
+    outputs = cast_tensor_type(inputs, src_type, dst_type)
+    assert isinstance(outputs, dict)
+    assert outputs['tensor_a'].dtype == dst_type
+    assert outputs['tensor_b'].dtype == dst_type
+
+    inputs = [torch.FloatTensor([1.]), torch.FloatTensor([2.])]
+    src_type = torch.float32
+    dst_type = torch.int32
+    outputs = cast_tensor_type(inputs, src_type, dst_type)
+    assert isinstance(outputs, list)
+    assert outputs[0].dtype == dst_type
+    assert outputs[1].dtype == dst_type
+
+    inputs = 5
+    outputs = cast_tensor_type(inputs, None, None)
+    assert isinstance(outputs, int)
+
+
+def test_auto_fp16():
+
+    with pytest.raises(TypeError):
+        # ExampleObject is not a subclass of nn.Module
+
+        class ExampleObject:
+
+            @auto_fp16()
+            def __call__(self, x):
+                return x
+
+        model = ExampleObject()
+        input_x = torch.ones(1, dtype=torch.float32)
+        model(input_x)
+
+    # apply to all input args
+    class ExampleModule(nn.Module):
+
+        @auto_fp16()
+        def forward(self, x, y):
+            return x, y
+
+    model = ExampleModule()
+    input_x = torch.ones(1, dtype=torch.float32)
+    input_y = torch.ones(1, dtype=torch.float32)
+    output_x, output_y = model(input_x, input_y)
+    assert output_x.dtype == torch.float32
+    assert output_y.dtype == torch.float32
+
+    model.fp16_enabled = True
+    output_x, output_y = model(input_x, input_y)
+    assert output_x.dtype == torch.half
+    assert output_y.dtype == torch.half
+
+    if torch.cuda.is_available():
+        model.cuda()
+        output_x, output_y = model(input_x.cuda(), input_y.cuda())
+        assert output_x.dtype == torch.half
+        assert output_y.dtype == torch.half
+
+    # apply to specified input args
+    class ExampleModule(nn.Module):
+
+        @auto_fp16(apply_to=('x', ))
+        def forward(self, x, y):
+            return x, y
+
+    model = ExampleModule()
+    input_x = torch.ones(1, dtype=torch.float32)
+    input_y = torch.ones(1, dtype=torch.float32)
+    output_x, output_y = model(input_x, input_y)
+    assert output_x.dtype == torch.float32
+    assert output_y.dtype == torch.float32
+
+    model.fp16_enabled = True
+    output_x, output_y = model(input_x, input_y)
+    assert output_x.dtype == torch.half
+    assert output_y.dtype == torch.float32
+
+    if torch.cuda.is_available():
+        model.cuda()
+        output_x, output_y = model(input_x.cuda(), input_y.cuda())
+        assert output_x.dtype == torch.half
+        assert output_y.dtype == torch.float32
+
+    # apply to optional input args
+    class ExampleModule(nn.Module):
+
+        @auto_fp16(apply_to=('x', 'y'))
+        def forward(self, x, y=None, z=None):
+            return x, y, z
+
+    model = ExampleModule()
+    input_x = torch.ones(1, dtype=torch.float32)
+    input_y = torch.ones(1, dtype=torch.float32)
+    input_z = torch.ones(1, dtype=torch.float32)
+    output_x, output_y, output_z = model(input_x, y=input_y, z=input_z)
+    assert output_x.dtype == torch.float32
+    assert output_y.dtype == torch.float32
+    assert output_z.dtype == torch.float32
+
+    model.fp16_enabled = True
+    output_x, output_y, output_z = model(input_x, y=input_y, z=input_z)
+    assert output_x.dtype == torch.half
+    assert output_y.dtype == torch.half
+    assert output_z.dtype == torch.float32
+
+    if torch.cuda.is_available():
+        model.cuda()
+        output_x, output_y, output_z = model(
+            input_x.cuda(), y=input_y.cuda(), z=input_z.cuda())
+        assert output_x.dtype == torch.half
+        assert output_y.dtype == torch.half
+        assert output_z.dtype == torch.float32
+
+    # out_fp32=True
+    class ExampleModule(nn.Module):
+
+        @auto_fp16(apply_to=('x', 'y'), out_fp32=True)
+        def forward(self, x, y=None, z=None):
+            return x, y, z
+
+    model = ExampleModule()
+    input_x = torch.ones(1, dtype=torch.half)
+    input_y = torch.ones(1, dtype=torch.float32)
+    input_z = torch.ones(1, dtype=torch.float32)
+    output_x, output_y, output_z = model(input_x, y=input_y, z=input_z)
+    assert output_x.dtype == torch.half
+    assert output_y.dtype == torch.float32
+    assert output_z.dtype == torch.float32
+
+    model.fp16_enabled = True
+    output_x, output_y, output_z = model(input_x, y=input_y, z=input_z)
+    assert output_x.dtype == torch.float32
+    assert output_y.dtype == torch.float32
+    assert output_z.dtype == torch.float32
+
+    if torch.cuda.is_available():
+        model.cuda()
+        output_x, output_y, output_z = model(
+            input_x.cuda(), y=input_y.cuda(), z=input_z.cuda())
+        assert output_x.dtype == torch.float32
+        assert output_y.dtype == torch.float32
+        assert output_z.dtype == torch.float32
+
+
+def test_force_fp32():
+
+    with pytest.raises(TypeError):
+        # ExampleObject is not a subclass of nn.Module
+
+        class ExampleObject:
+
+            @force_fp32()
+            def __call__(self, x):
+                return x
+
+        model = ExampleObject()
+        input_x = torch.ones(1, dtype=torch.float32)
+        model(input_x)
+
+    # apply to all input args
+    class ExampleModule(nn.Module):
+
+        @force_fp32()
+        def forward(self, x, y):
+            return x, y
+
+    model = ExampleModule()
+    input_x = torch.ones(1, dtype=torch.half)
+    input_y = torch.ones(1, dtype=torch.half)
+    output_x, output_y = model(input_x, input_y)
+    assert output_x.dtype == torch.half
+    assert output_y.dtype == torch.half
+
+    model.fp16_enabled = True
+    output_x, output_y = model(input_x, input_y)
+    assert output_x.dtype == torch.float32
+    assert output_y.dtype == torch.float32
+
+    if torch.cuda.is_available():
+        model.cuda()
+        output_x, output_y = model(input_x.cuda(), input_y.cuda())
+        assert output_x.dtype == torch.float32
+        assert output_y.dtype == torch.float32
+
+    # apply to specified input args
+    class ExampleModule(nn.Module):
+
+        @force_fp32(apply_to=('x', ))
+        def forward(self, x, y):
+            return x, y
+
+    model = ExampleModule()
+    input_x = torch.ones(1, dtype=torch.half)
+    input_y = torch.ones(1, dtype=torch.half)
+    output_x, output_y = model(input_x, input_y)
+    assert output_x.dtype == torch.half
+    assert output_y.dtype == torch.half
+
+    model.fp16_enabled = True
+    output_x, output_y = model(input_x, input_y)
+    assert output_x.dtype == torch.float32
+    assert output_y.dtype == torch.half
+
+    if torch.cuda.is_available():
+        model.cuda()
+        output_x, output_y = model(input_x.cuda(), input_y.cuda())
+        assert output_x.dtype == torch.float32
+        assert output_y.dtype == torch.half
+
+    # apply to optional input args
+    class ExampleModule(nn.Module):
+
+        @force_fp32(apply_to=('x', 'y'))
+        def forward(self, x, y=None, z=None):
+            return x, y, z
+
+    model = ExampleModule()
+    input_x = torch.ones(1, dtype=torch.half)
+    input_y = torch.ones(1, dtype=torch.half)
+    input_z = torch.ones(1, dtype=torch.half)
+    output_x, output_y, output_z = model(input_x, y=input_y, z=input_z)
+    assert output_x.dtype == torch.half
+    assert output_y.dtype == torch.half
+    assert output_z.dtype == torch.half
+
+    model.fp16_enabled = True
+    output_x, output_y, output_z = model(input_x, y=input_y, z=input_z)
+    assert output_x.dtype == torch.float32
+    assert output_y.dtype == torch.float32
+    assert output_z.dtype == torch.half
+
+    if torch.cuda.is_available():
+        model.cuda()
+        output_x, output_y, output_z = model(
+            input_x.cuda(), y=input_y.cuda(), z=input_z.cuda())
+        assert output_x.dtype == torch.float32
+        assert output_y.dtype == torch.float32
+        assert output_z.dtype == torch.half
+
+    # out_fp16=True
+    class ExampleModule(nn.Module):
+
+        @force_fp32(apply_to=('x', 'y'), out_fp16=True)
+        def forward(self, x, y=None, z=None):
+            return x, y, z
+
+    model = ExampleModule()
+    input_x = torch.ones(1, dtype=torch.float32)
+    input_y = torch.ones(1, dtype=torch.half)
+    input_z = torch.ones(1, dtype=torch.half)
+    output_x, output_y, output_z = model(input_x, y=input_y, z=input_z)
+    assert output_x.dtype == torch.float32
+    assert output_y.dtype == torch.half
+    assert output_z.dtype == torch.half
+
+    model.fp16_enabled = True
+    output_x, output_y, output_z = model(input_x, y=input_y, z=input_z)
+    assert output_x.dtype == torch.half
+    assert output_y.dtype == torch.half
+    assert output_z.dtype == torch.half
+
+    if torch.cuda.is_available():
+        model.cuda()
+        output_x, output_y, output_z = model(
+            input_x.cuda(), y=input_y.cuda(), z=input_z.cuda())
+        assert output_x.dtype == torch.half
+        assert output_y.dtype == torch.half
+        assert output_z.dtype == torch.half
diff --git a/tests/test_utils/test_anchor.py b/tests/test_utils/test_anchor.py
new file mode 100644
index 0000000..a9aef72
--- /dev/null
+++ b/tests/test_utils/test_anchor.py
@@ -0,0 +1,769 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""
+CommandLine:
+    pytest tests/test_utils/test_anchor.py
+    xdoctest tests/test_utils/test_anchor.py zero
+
+"""
+import pytest
+import torch
+
+
+def test_standard_points_generator():
+    from mmdet.core.anchor import build_prior_generator
+
+    # teat init
+    anchor_generator_cfg = dict(
+        type='MlvlPointGenerator', strides=[4, 8], offset=0)
+    anchor_generator = build_prior_generator(anchor_generator_cfg)
+    assert anchor_generator is not None
+    assert anchor_generator.num_base_priors == [1, 1]
+    # test_stride
+    from mmdet.core.anchor import MlvlPointGenerator
+
+    # Square strides
+    mlvl_points = MlvlPointGenerator(strides=[4, 10], offset=0)
+    mlvl_points_half_stride_generator = MlvlPointGenerator(
+        strides=[4, 10], offset=0.5)
+    assert mlvl_points.num_levels == 2
+
+    # assert self.num_levels == len(featmap_sizes)
+    with pytest.raises(AssertionError):
+        mlvl_points.grid_priors(featmap_sizes=[(2, 2)], device='cpu')
+    priors = mlvl_points.grid_priors(
+        featmap_sizes=[(2, 2), (4, 8)], device='cpu')
+    priors_with_stride = mlvl_points.grid_priors(
+        featmap_sizes=[(2, 2), (4, 8)], with_stride=True, device='cpu')
+    assert len(priors) == 2
+
+    # assert last dimension is (coord_x, coord_y, stride_w, stride_h).
+    assert priors_with_stride[0].size(1) == 4
+    assert priors_with_stride[0][0][2] == 4
+    assert priors_with_stride[0][0][3] == 4
+    assert priors_with_stride[1][0][2] == 10
+    assert priors_with_stride[1][0][3] == 10
+
+    stride_4_feat_2_2 = priors[0]
+    assert (stride_4_feat_2_2[1] - stride_4_feat_2_2[0]).sum() == 4
+    assert stride_4_feat_2_2.size(0) == 4
+    assert stride_4_feat_2_2.size(1) == 2
+
+    stride_10_feat_4_8 = priors[1]
+    assert (stride_10_feat_4_8[1] - stride_10_feat_4_8[0]).sum() == 10
+    assert stride_10_feat_4_8.size(0) == 4 * 8
+    assert stride_10_feat_4_8.size(1) == 2
+
+    # assert the offset of 0.5 * stride
+    priors_half_offset = mlvl_points_half_stride_generator.grid_priors(
+        featmap_sizes=[(2, 2), (4, 8)], device='cpu')
+
+    assert (priors_half_offset[0][0] - priors[0][0]).sum() == 4 * 0.5 * 2
+    assert (priors_half_offset[1][0] - priors[1][0]).sum() == 10 * 0.5 * 2
+    if torch.cuda.is_available():
+        anchor_generator_cfg = dict(
+            type='MlvlPointGenerator', strides=[4, 8], offset=0)
+        anchor_generator = build_prior_generator(anchor_generator_cfg)
+        assert anchor_generator is not None
+        # Square strides
+        mlvl_points = MlvlPointGenerator(strides=[4, 10], offset=0)
+        mlvl_points_half_stride_generator = MlvlPointGenerator(
+            strides=[4, 10], offset=0.5)
+        assert mlvl_points.num_levels == 2
+
+        # assert self.num_levels == len(featmap_sizes)
+        with pytest.raises(AssertionError):
+            mlvl_points.grid_priors(featmap_sizes=[(2, 2)], device='cuda')
+        priors = mlvl_points.grid_priors(
+            featmap_sizes=[(2, 2), (4, 8)], device='cuda')
+        priors_with_stride = mlvl_points.grid_priors(
+            featmap_sizes=[(2, 2), (4, 8)], with_stride=True, device='cuda')
+        assert len(priors) == 2
+
+        # assert last dimension is (coord_x, coord_y, stride_w, stride_h).
+        assert priors_with_stride[0].size(1) == 4
+        assert priors_with_stride[0][0][2] == 4
+        assert priors_with_stride[0][0][3] == 4
+        assert priors_with_stride[1][0][2] == 10
+        assert priors_with_stride[1][0][3] == 10
+
+        stride_4_feat_2_2 = priors[0]
+        assert (stride_4_feat_2_2[1] - stride_4_feat_2_2[0]).sum() == 4
+        assert stride_4_feat_2_2.size(0) == 4
+        assert stride_4_feat_2_2.size(1) == 2
+
+        stride_10_feat_4_8 = priors[1]
+        assert (stride_10_feat_4_8[1] - stride_10_feat_4_8[0]).sum() == 10
+        assert stride_10_feat_4_8.size(0) == 4 * 8
+        assert stride_10_feat_4_8.size(1) == 2
+
+        # assert the offset of 0.5 * stride
+        priors_half_offset = mlvl_points_half_stride_generator.grid_priors(
+            featmap_sizes=[(2, 2), (4, 8)], device='cuda')
+
+        assert (priors_half_offset[0][0] - priors[0][0]).sum() == 4 * 0.5 * 2
+        assert (priors_half_offset[1][0] - priors[1][0]).sum() == 10 * 0.5 * 2
+
+
+def test_sparse_prior():
+    from mmdet.core.anchor import MlvlPointGenerator
+    mlvl_points = MlvlPointGenerator(strides=[4, 10], offset=0)
+    prior_indexs = torch.Tensor([0, 2, 4, 5, 6, 9]).long()
+
+    featmap_sizes = [(3, 5), (6, 4)]
+    grid_anchors = mlvl_points.grid_priors(
+        featmap_sizes=featmap_sizes, with_stride=False, device='cpu')
+    sparse_prior = mlvl_points.sparse_priors(
+        prior_idxs=prior_indexs,
+        featmap_size=featmap_sizes[0],
+        level_idx=0,
+        device='cpu')
+
+    assert not sparse_prior.is_cuda
+    assert (sparse_prior == grid_anchors[0][prior_indexs]).all()
+    sparse_prior = mlvl_points.sparse_priors(
+        prior_idxs=prior_indexs,
+        featmap_size=featmap_sizes[1],
+        level_idx=1,
+        device='cpu')
+    assert (sparse_prior == grid_anchors[1][prior_indexs]).all()
+
+    from mmdet.core.anchor import AnchorGenerator
+    mlvl_anchors = AnchorGenerator(
+        strides=[16, 32], ratios=[1.], scales=[1.], base_sizes=[4, 8])
+    prior_indexs = torch.Tensor([0, 2, 4, 5, 6, 9]).long()
+
+    featmap_sizes = [(3, 5), (6, 4)]
+    grid_anchors = mlvl_anchors.grid_priors(
+        featmap_sizes=featmap_sizes, device='cpu')
+    sparse_prior = mlvl_anchors.sparse_priors(
+        prior_idxs=prior_indexs,
+        featmap_size=featmap_sizes[0],
+        level_idx=0,
+        device='cpu')
+    assert (sparse_prior == grid_anchors[0][prior_indexs]).all()
+    sparse_prior = mlvl_anchors.sparse_priors(
+        prior_idxs=prior_indexs,
+        featmap_size=featmap_sizes[1],
+        level_idx=1,
+        device='cpu')
+    assert (sparse_prior == grid_anchors[1][prior_indexs]).all()
+
+    # for ssd
+    from mmdet.core.anchor.anchor_generator import SSDAnchorGenerator
+    featmap_sizes = [(38, 38), (19, 19), (10, 10)]
+    anchor_generator = SSDAnchorGenerator(
+        scale_major=False,
+        input_size=300,
+        basesize_ratio_range=(0.15, 0.9),
+        strides=[8, 16, 32],
+        ratios=[[2], [2, 3], [2, 3]])
+    ssd_anchors = anchor_generator.grid_anchors(featmap_sizes, device='cpu')
+    for i in range(len(featmap_sizes)):
+        sparse_ssd_anchors = anchor_generator.sparse_priors(
+            prior_idxs=prior_indexs,
+            level_idx=i,
+            featmap_size=featmap_sizes[i],
+            device='cpu')
+        assert (sparse_ssd_anchors == ssd_anchors[i][prior_indexs]).all()
+
+    # for yolo
+    from mmdet.core.anchor.anchor_generator import YOLOAnchorGenerator
+    featmap_sizes = [(38, 38), (19, 19), (10, 10)]
+    anchor_generator = YOLOAnchorGenerator(
+        strides=[32, 16, 8],
+        base_sizes=[
+            [(116, 90), (156, 198), (373, 326)],
+            [(30, 61), (62, 45), (59, 119)],
+            [(10, 13), (16, 30), (33, 23)],
+        ])
+    yolo_anchors = anchor_generator.grid_anchors(featmap_sizes, device='cpu')
+    for i in range(len(featmap_sizes)):
+        sparse_yolo_anchors = anchor_generator.sparse_priors(
+            prior_idxs=prior_indexs,
+            level_idx=i,
+            featmap_size=featmap_sizes[i],
+            device='cpu')
+        assert (sparse_yolo_anchors == yolo_anchors[i][prior_indexs]).all()
+
+    if torch.cuda.is_available():
+        mlvl_points = MlvlPointGenerator(strides=[4, 10], offset=0)
+        prior_indexs = torch.Tensor([0, 3, 4, 5, 6, 7, 1, 2, 4, 5, 6,
+                                     9]).long().cuda()
+
+        featmap_sizes = [(6, 8), (6, 4)]
+        grid_anchors = mlvl_points.grid_priors(
+            featmap_sizes=featmap_sizes, with_stride=False, device='cuda')
+        sparse_prior = mlvl_points.sparse_priors(
+            prior_idxs=prior_indexs,
+            featmap_size=featmap_sizes[0],
+            level_idx=0,
+            device='cuda')
+        assert (sparse_prior == grid_anchors[0][prior_indexs]).all()
+        sparse_prior = mlvl_points.sparse_priors(
+            prior_idxs=prior_indexs,
+            featmap_size=featmap_sizes[1],
+            level_idx=1,
+            device='cuda')
+        assert (sparse_prior == grid_anchors[1][prior_indexs]).all()
+        assert sparse_prior.is_cuda
+        mlvl_anchors = AnchorGenerator(
+            strides=[16, 32],
+            ratios=[1., 2.5],
+            scales=[1., 5.],
+            base_sizes=[4, 8])
+        prior_indexs = torch.Tensor([4, 5, 6, 7, 0, 2, 50, 4, 5, 6,
+                                     9]).long().cuda()
+
+        featmap_sizes = [(13, 5), (16, 4)]
+        grid_anchors = mlvl_anchors.grid_priors(
+            featmap_sizes=featmap_sizes, device='cuda')
+        sparse_prior = mlvl_anchors.sparse_priors(
+            prior_idxs=prior_indexs,
+            featmap_size=featmap_sizes[0],
+            level_idx=0,
+            device='cuda')
+        assert (sparse_prior == grid_anchors[0][prior_indexs]).all()
+        sparse_prior = mlvl_anchors.sparse_priors(
+            prior_idxs=prior_indexs,
+            featmap_size=featmap_sizes[1],
+            level_idx=1,
+            device='cuda')
+        assert (sparse_prior == grid_anchors[1][prior_indexs]).all()
+
+        # for ssd
+        from mmdet.core.anchor.anchor_generator import SSDAnchorGenerator
+        featmap_sizes = [(38, 38), (19, 19), (10, 10)]
+        anchor_generator = SSDAnchorGenerator(
+            scale_major=False,
+            input_size=300,
+            basesize_ratio_range=(0.15, 0.9),
+            strides=[8, 16, 32],
+            ratios=[[2], [2, 3], [2, 3]])
+        ssd_anchors = anchor_generator.grid_anchors(
+            featmap_sizes, device='cuda')
+        for i in range(len(featmap_sizes)):
+            sparse_ssd_anchors = anchor_generator.sparse_priors(
+                prior_idxs=prior_indexs,
+                level_idx=i,
+                featmap_size=featmap_sizes[i],
+                device='cuda')
+            assert (sparse_ssd_anchors == ssd_anchors[i][prior_indexs]).all()
+
+        # for yolo
+        from mmdet.core.anchor.anchor_generator import YOLOAnchorGenerator
+        featmap_sizes = [(38, 38), (19, 19), (10, 10)]
+        anchor_generator = YOLOAnchorGenerator(
+            strides=[32, 16, 8],
+            base_sizes=[
+                [(116, 90), (156, 198), (373, 326)],
+                [(30, 61), (62, 45), (59, 119)],
+                [(10, 13), (16, 30), (33, 23)],
+            ])
+        yolo_anchors = anchor_generator.grid_anchors(
+            featmap_sizes, device='cuda')
+        for i in range(len(featmap_sizes)):
+            sparse_yolo_anchors = anchor_generator.sparse_priors(
+                prior_idxs=prior_indexs,
+                level_idx=i,
+                featmap_size=featmap_sizes[i],
+                device='cuda')
+            assert (sparse_yolo_anchors == yolo_anchors[i][prior_indexs]).all()
+
+
+def test_standard_anchor_generator():
+    from mmdet.core.anchor import build_anchor_generator
+    anchor_generator_cfg = dict(
+        type='AnchorGenerator',
+        scales=[8],
+        ratios=[0.5, 1.0, 2.0],
+        strides=[4, 8])
+
+    anchor_generator = build_anchor_generator(anchor_generator_cfg)
+    assert anchor_generator.num_base_priors == \
+           anchor_generator.num_base_anchors
+    assert anchor_generator.num_base_priors == [3, 3]
+    assert anchor_generator is not None
+
+
+def test_strides():
+    from mmdet.core import AnchorGenerator
+
+    # Square strides
+    self = AnchorGenerator([10], [1.], [1.], [10])
+    anchors = self.grid_anchors([(2, 2)], device='cpu')
+
+    expected_anchors = torch.tensor([[-5., -5., 5., 5.], [5., -5., 15., 5.],
+                                     [-5., 5., 5., 15.], [5., 5., 15., 15.]])
+
+    assert torch.equal(anchors[0], expected_anchors)
+
+    # Different strides in x and y direction
+    self = AnchorGenerator([(10, 20)], [1.], [1.], [10])
+    anchors = self.grid_anchors([(2, 2)], device='cpu')
+
+    expected_anchors = torch.tensor([[-5., -5., 5., 5.], [5., -5., 15., 5.],
+                                     [-5., 15., 5., 25.], [5., 15., 15., 25.]])
+
+    assert torch.equal(anchors[0], expected_anchors)
+
+
+def test_ssd_anchor_generator():
+    from mmdet.core.anchor import build_anchor_generator
+    if torch.cuda.is_available():
+        device = 'cuda'
+    else:
+        device = 'cpu'
+
+    # min_sizes max_sizes must set at the same time
+    with pytest.raises(AssertionError):
+        anchor_generator_cfg = dict(
+            type='SSDAnchorGenerator',
+            scale_major=False,
+            min_sizes=[48, 100, 150, 202, 253, 300],
+            max_sizes=None,
+            strides=[8, 16, 32, 64, 100, 300],
+            ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]])
+        build_anchor_generator(anchor_generator_cfg)
+
+    # length of min_sizes max_sizes must be the same
+    with pytest.raises(AssertionError):
+        anchor_generator_cfg = dict(
+            type='SSDAnchorGenerator',
+            scale_major=False,
+            min_sizes=[48, 100, 150, 202, 253, 300],
+            max_sizes=[100, 150, 202, 253],
+            strides=[8, 16, 32, 64, 100, 300],
+            ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]])
+        build_anchor_generator(anchor_generator_cfg)
+
+    # test setting anchor size manually
+    anchor_generator_cfg = dict(
+        type='SSDAnchorGenerator',
+        scale_major=False,
+        min_sizes=[48, 100, 150, 202, 253, 304],
+        max_sizes=[100, 150, 202, 253, 304, 320],
+        strides=[16, 32, 64, 107, 160, 320],
+        ratios=[[2, 3], [2, 3], [2, 3], [2, 3], [2, 3], [2, 3]])
+
+    featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)]
+    anchor_generator = build_anchor_generator(anchor_generator_cfg)
+
+    expected_base_anchors = [
+        torch.Tensor([[-16.0000, -16.0000, 32.0000, 32.0000],
+                      [-26.6410, -26.6410, 42.6410, 42.6410],
+                      [-25.9411, -8.9706, 41.9411, 24.9706],
+                      [-8.9706, -25.9411, 24.9706, 41.9411],
+                      [-33.5692, -5.8564, 49.5692, 21.8564],
+                      [-5.8564, -33.5692, 21.8564, 49.5692]]),
+        torch.Tensor([[-34.0000, -34.0000, 66.0000, 66.0000],
+                      [-45.2372, -45.2372, 77.2372, 77.2372],
+                      [-54.7107, -19.3553, 86.7107, 51.3553],
+                      [-19.3553, -54.7107, 51.3553, 86.7107],
+                      [-70.6025, -12.8675, 102.6025, 44.8675],
+                      [-12.8675, -70.6025, 44.8675, 102.6025]]),
+        torch.Tensor([[-43.0000, -43.0000, 107.0000, 107.0000],
+                      [-55.0345, -55.0345, 119.0345, 119.0345],
+                      [-74.0660, -21.0330, 138.0660, 85.0330],
+                      [-21.0330, -74.0660, 85.0330, 138.0660],
+                      [-97.9038, -11.3013, 161.9038, 75.3013],
+                      [-11.3013, -97.9038, 75.3013, 161.9038]]),
+        torch.Tensor([[-47.5000, -47.5000, 154.5000, 154.5000],
+                      [-59.5332, -59.5332, 166.5332, 166.5332],
+                      [-89.3356, -17.9178, 196.3356, 124.9178],
+                      [-17.9178, -89.3356, 124.9178, 196.3356],
+                      [-121.4371, -4.8124, 228.4371, 111.8124],
+                      [-4.8124, -121.4371, 111.8124, 228.4371]]),
+        torch.Tensor([[-46.5000, -46.5000, 206.5000, 206.5000],
+                      [-58.6651, -58.6651, 218.6651, 218.6651],
+                      [-98.8980, -9.4490, 258.8980, 169.4490],
+                      [-9.4490, -98.8980, 169.4490, 258.8980],
+                      [-139.1044, 6.9652, 299.1044, 153.0348],
+                      [6.9652, -139.1044, 153.0348, 299.1044]]),
+        torch.Tensor([[8.0000, 8.0000, 312.0000, 312.0000],
+                      [4.0513, 4.0513, 315.9487, 315.9487],
+                      [-54.9605, 52.5198, 374.9604, 267.4802],
+                      [52.5198, -54.9605, 267.4802, 374.9604],
+                      [-103.2717, 72.2428, 423.2717, 247.7572],
+                      [72.2428, -103.2717, 247.7572, 423.2717]])
+    ]
+
+    base_anchors = anchor_generator.base_anchors
+    for i, base_anchor in enumerate(base_anchors):
+        assert base_anchor.allclose(expected_base_anchors[i])
+
+    # check valid flags
+    expected_valid_pixels = [2400, 600, 150, 54, 24, 6]
+    multi_level_valid_flags = anchor_generator.valid_flags(
+        featmap_sizes, (320, 320), device)
+    for i, single_level_valid_flag in enumerate(multi_level_valid_flags):
+        assert single_level_valid_flag.sum() == expected_valid_pixels[i]
+
+    # check number of base anchors for each level
+    assert anchor_generator.num_base_anchors == [6, 6, 6, 6, 6, 6]
+
+    # check anchor generation
+    anchors = anchor_generator.grid_anchors(featmap_sizes, device)
+    assert len(anchors) == 6
+
+    # test vgg ssd anchor setting
+    anchor_generator_cfg = dict(
+        type='SSDAnchorGenerator',
+        scale_major=False,
+        input_size=300,
+        basesize_ratio_range=(0.15, 0.9),
+        strides=[8, 16, 32, 64, 100, 300],
+        ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]])
+
+    featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)]
+    anchor_generator = build_anchor_generator(anchor_generator_cfg)
+
+    # check base anchors
+    expected_base_anchors = [
+        torch.Tensor([[-6.5000, -6.5000, 14.5000, 14.5000],
+                      [-11.3704, -11.3704, 19.3704, 19.3704],
+                      [-10.8492, -3.4246, 18.8492, 11.4246],
+                      [-3.4246, -10.8492, 11.4246, 18.8492]]),
+        torch.Tensor([[-14.5000, -14.5000, 30.5000, 30.5000],
+                      [-25.3729, -25.3729, 41.3729, 41.3729],
+                      [-23.8198, -7.9099, 39.8198, 23.9099],
+                      [-7.9099, -23.8198, 23.9099, 39.8198],
+                      [-30.9711, -4.9904, 46.9711, 20.9904],
+                      [-4.9904, -30.9711, 20.9904, 46.9711]]),
+        torch.Tensor([[-33.5000, -33.5000, 65.5000, 65.5000],
+                      [-45.5366, -45.5366, 77.5366, 77.5366],
+                      [-54.0036, -19.0018, 86.0036, 51.0018],
+                      [-19.0018, -54.0036, 51.0018, 86.0036],
+                      [-69.7365, -12.5788, 101.7365, 44.5788],
+                      [-12.5788, -69.7365, 44.5788, 101.7365]]),
+        torch.Tensor([[-44.5000, -44.5000, 108.5000, 108.5000],
+                      [-56.9817, -56.9817, 120.9817, 120.9817],
+                      [-76.1873, -22.0937, 140.1873, 86.0937],
+                      [-22.0937, -76.1873, 86.0937, 140.1873],
+                      [-100.5019, -12.1673, 164.5019, 76.1673],
+                      [-12.1673, -100.5019, 76.1673, 164.5019]]),
+        torch.Tensor([[-53.5000, -53.5000, 153.5000, 153.5000],
+                      [-66.2185, -66.2185, 166.2185, 166.2185],
+                      [-96.3711, -23.1855, 196.3711, 123.1855],
+                      [-23.1855, -96.3711, 123.1855, 196.3711]]),
+        torch.Tensor([[19.5000, 19.5000, 280.5000, 280.5000],
+                      [6.6342, 6.6342, 293.3658, 293.3658],
+                      [-34.5549, 57.7226, 334.5549, 242.2774],
+                      [57.7226, -34.5549, 242.2774, 334.5549]]),
+    ]
+    base_anchors = anchor_generator.base_anchors
+    for i, base_anchor in enumerate(base_anchors):
+        assert base_anchor.allclose(expected_base_anchors[i])
+
+    # check valid flags
+    expected_valid_pixels = [5776, 2166, 600, 150, 36, 4]
+    multi_level_valid_flags = anchor_generator.valid_flags(
+        featmap_sizes, (300, 300), device)
+    for i, single_level_valid_flag in enumerate(multi_level_valid_flags):
+        assert single_level_valid_flag.sum() == expected_valid_pixels[i]
+
+    # check number of base anchors for each level
+    assert anchor_generator.num_base_anchors == [4, 6, 6, 6, 4, 4]
+
+    # check anchor generation
+    anchors = anchor_generator.grid_anchors(featmap_sizes, device)
+    assert len(anchors) == 6
+
+
+def test_anchor_generator_with_tuples():
+    from mmdet.core.anchor import build_anchor_generator
+    if torch.cuda.is_available():
+        device = 'cuda'
+    else:
+        device = 'cpu'
+
+    anchor_generator_cfg = dict(
+        type='SSDAnchorGenerator',
+        scale_major=False,
+        input_size=300,
+        basesize_ratio_range=(0.15, 0.9),
+        strides=[8, 16, 32, 64, 100, 300],
+        ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]])
+
+    featmap_sizes = [(38, 38), (19, 19), (10, 10), (5, 5), (3, 3), (1, 1)]
+    anchor_generator = build_anchor_generator(anchor_generator_cfg)
+    anchors = anchor_generator.grid_anchors(featmap_sizes, device)
+
+    anchor_generator_cfg_tuples = dict(
+        type='SSDAnchorGenerator',
+        scale_major=False,
+        input_size=300,
+        basesize_ratio_range=(0.15, 0.9),
+        strides=[(8, 8), (16, 16), (32, 32), (64, 64), (100, 100), (300, 300)],
+        ratios=[[2], [2, 3], [2, 3], [2, 3], [2], [2]])
+
+    anchor_generator_tuples = build_anchor_generator(
+        anchor_generator_cfg_tuples)
+    anchors_tuples = anchor_generator_tuples.grid_anchors(
+        featmap_sizes, device)
+    for anchor, anchor_tuples in zip(anchors, anchors_tuples):
+        assert torch.equal(anchor, anchor_tuples)
+
+
+def test_yolo_anchor_generator():
+    from mmdet.core.anchor import build_anchor_generator
+    if torch.cuda.is_available():
+        device = 'cuda'
+    else:
+        device = 'cpu'
+
+    anchor_generator_cfg = dict(
+        type='YOLOAnchorGenerator',
+        strides=[32, 16, 8],
+        base_sizes=[
+            [(116, 90), (156, 198), (373, 326)],
+            [(30, 61), (62, 45), (59, 119)],
+            [(10, 13), (16, 30), (33, 23)],
+        ])
+
+    featmap_sizes = [(14, 18), (28, 36), (56, 72)]
+    anchor_generator = build_anchor_generator(anchor_generator_cfg)
+
+    # check base anchors
+    expected_base_anchors = [
+        torch.Tensor([[-42.0000, -29.0000, 74.0000, 61.0000],
+                      [-62.0000, -83.0000, 94.0000, 115.0000],
+                      [-170.5000, -147.0000, 202.5000, 179.0000]]),
+        torch.Tensor([[-7.0000, -22.5000, 23.0000, 38.5000],
+                      [-23.0000, -14.5000, 39.0000, 30.5000],
+                      [-21.5000, -51.5000, 37.5000, 67.5000]]),
+        torch.Tensor([[-1.0000, -2.5000, 9.0000, 10.5000],
+                      [-4.0000, -11.0000, 12.0000, 19.0000],
+                      [-12.5000, -7.5000, 20.5000, 15.5000]])
+    ]
+    base_anchors = anchor_generator.base_anchors
+    for i, base_anchor in enumerate(base_anchors):
+        assert base_anchor.allclose(expected_base_anchors[i])
+
+    # check number of base anchors for each level
+    assert anchor_generator.num_base_anchors == [3, 3, 3]
+
+    # check anchor generation
+    anchors = anchor_generator.grid_anchors(featmap_sizes, device)
+    assert len(anchors) == 3
+
+
+def test_retina_anchor():
+    from mmdet.models import build_head
+    if torch.cuda.is_available():
+        device = 'cuda'
+    else:
+        device = 'cpu'
+
+    # head configs modified from
+    # configs/nas_fpn/retinanet_r50_fpn_crop640_50e.py
+    bbox_head = dict(
+        type='RetinaSepBNHead',
+        num_classes=4,
+        num_ins=5,
+        in_channels=4,
+        stacked_convs=1,
+        feat_channels=4,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]))
+
+    retina_head = build_head(bbox_head)
+    assert retina_head.anchor_generator is not None
+
+    # use the featmap sizes in NASFPN setting to test retina head
+    featmap_sizes = [(80, 80), (40, 40), (20, 20), (10, 10), (5, 5)]
+    # check base anchors
+    expected_base_anchors = [
+        torch.Tensor([[-22.6274, -11.3137, 22.6274, 11.3137],
+                      [-28.5088, -14.2544, 28.5088, 14.2544],
+                      [-35.9188, -17.9594, 35.9188, 17.9594],
+                      [-16.0000, -16.0000, 16.0000, 16.0000],
+                      [-20.1587, -20.1587, 20.1587, 20.1587],
+                      [-25.3984, -25.3984, 25.3984, 25.3984],
+                      [-11.3137, -22.6274, 11.3137, 22.6274],
+                      [-14.2544, -28.5088, 14.2544, 28.5088],
+                      [-17.9594, -35.9188, 17.9594, 35.9188]]),
+        torch.Tensor([[-45.2548, -22.6274, 45.2548, 22.6274],
+                      [-57.0175, -28.5088, 57.0175, 28.5088],
+                      [-71.8376, -35.9188, 71.8376, 35.9188],
+                      [-32.0000, -32.0000, 32.0000, 32.0000],
+                      [-40.3175, -40.3175, 40.3175, 40.3175],
+                      [-50.7968, -50.7968, 50.7968, 50.7968],
+                      [-22.6274, -45.2548, 22.6274, 45.2548],
+                      [-28.5088, -57.0175, 28.5088, 57.0175],
+                      [-35.9188, -71.8376, 35.9188, 71.8376]]),
+        torch.Tensor([[-90.5097, -45.2548, 90.5097, 45.2548],
+                      [-114.0350, -57.0175, 114.0350, 57.0175],
+                      [-143.6751, -71.8376, 143.6751, 71.8376],
+                      [-64.0000, -64.0000, 64.0000, 64.0000],
+                      [-80.6349, -80.6349, 80.6349, 80.6349],
+                      [-101.5937, -101.5937, 101.5937, 101.5937],
+                      [-45.2548, -90.5097, 45.2548, 90.5097],
+                      [-57.0175, -114.0350, 57.0175, 114.0350],
+                      [-71.8376, -143.6751, 71.8376, 143.6751]]),
+        torch.Tensor([[-181.0193, -90.5097, 181.0193, 90.5097],
+                      [-228.0701, -114.0350, 228.0701, 114.0350],
+                      [-287.3503, -143.6751, 287.3503, 143.6751],
+                      [-128.0000, -128.0000, 128.0000, 128.0000],
+                      [-161.2699, -161.2699, 161.2699, 161.2699],
+                      [-203.1873, -203.1873, 203.1873, 203.1873],
+                      [-90.5097, -181.0193, 90.5097, 181.0193],
+                      [-114.0350, -228.0701, 114.0350, 228.0701],
+                      [-143.6751, -287.3503, 143.6751, 287.3503]]),
+        torch.Tensor([[-362.0387, -181.0193, 362.0387, 181.0193],
+                      [-456.1401, -228.0701, 456.1401, 228.0701],
+                      [-574.7006, -287.3503, 574.7006, 287.3503],
+                      [-256.0000, -256.0000, 256.0000, 256.0000],
+                      [-322.5398, -322.5398, 322.5398, 322.5398],
+                      [-406.3747, -406.3747, 406.3747, 406.3747],
+                      [-181.0193, -362.0387, 181.0193, 362.0387],
+                      [-228.0701, -456.1401, 228.0701, 456.1401],
+                      [-287.3503, -574.7006, 287.3503, 574.7006]])
+    ]
+    base_anchors = retina_head.anchor_generator.base_anchors
+    for i, base_anchor in enumerate(base_anchors):
+        assert base_anchor.allclose(expected_base_anchors[i])
+
+    # check valid flags
+    expected_valid_pixels = [57600, 14400, 3600, 900, 225]
+    multi_level_valid_flags = retina_head.anchor_generator.valid_flags(
+        featmap_sizes, (640, 640), device)
+    for i, single_level_valid_flag in enumerate(multi_level_valid_flags):
+        assert single_level_valid_flag.sum() == expected_valid_pixels[i]
+
+    # check number of base anchors for each level
+    assert retina_head.anchor_generator.num_base_anchors == [9, 9, 9, 9, 9]
+
+    # check anchor generation
+    anchors = retina_head.anchor_generator.grid_anchors(featmap_sizes, device)
+    assert len(anchors) == 5
+
+
+def test_guided_anchor():
+    from mmdet.models import build_head
+    if torch.cuda.is_available():
+        device = 'cuda'
+    else:
+        device = 'cpu'
+    # head configs modified from
+    # configs/guided_anchoring/ga_retinanet_r50_fpn_1x_coco.py
+    bbox_head = dict(
+        type='GARetinaHead',
+        num_classes=8,
+        in_channels=4,
+        stacked_convs=1,
+        feat_channels=4,
+        approx_anchor_generator=dict(
+            type='AnchorGenerator',
+            octave_base_scale=4,
+            scales_per_octave=3,
+            ratios=[0.5, 1.0, 2.0],
+            strides=[8, 16, 32, 64, 128]),
+        square_anchor_generator=dict(
+            type='AnchorGenerator',
+            ratios=[1.0],
+            scales=[4],
+            strides=[8, 16, 32, 64, 128]))
+
+    ga_retina_head = build_head(bbox_head)
+    assert ga_retina_head.approx_anchor_generator is not None
+
+    # use the featmap sizes in NASFPN setting to test ga_retina_head
+    featmap_sizes = [(100, 152), (50, 76), (25, 38), (13, 19), (7, 10)]
+    # check base anchors
+    expected_approxs = [
+        torch.Tensor([[-22.6274, -11.3137, 22.6274, 11.3137],
+                      [-28.5088, -14.2544, 28.5088, 14.2544],
+                      [-35.9188, -17.9594, 35.9188, 17.9594],
+                      [-16.0000, -16.0000, 16.0000, 16.0000],
+                      [-20.1587, -20.1587, 20.1587, 20.1587],
+                      [-25.3984, -25.3984, 25.3984, 25.3984],
+                      [-11.3137, -22.6274, 11.3137, 22.6274],
+                      [-14.2544, -28.5088, 14.2544, 28.5088],
+                      [-17.9594, -35.9188, 17.9594, 35.9188]]),
+        torch.Tensor([[-45.2548, -22.6274, 45.2548, 22.6274],
+                      [-57.0175, -28.5088, 57.0175, 28.5088],
+                      [-71.8376, -35.9188, 71.8376, 35.9188],
+                      [-32.0000, -32.0000, 32.0000, 32.0000],
+                      [-40.3175, -40.3175, 40.3175, 40.3175],
+                      [-50.7968, -50.7968, 50.7968, 50.7968],
+                      [-22.6274, -45.2548, 22.6274, 45.2548],
+                      [-28.5088, -57.0175, 28.5088, 57.0175],
+                      [-35.9188, -71.8376, 35.9188, 71.8376]]),
+        torch.Tensor([[-90.5097, -45.2548, 90.5097, 45.2548],
+                      [-114.0350, -57.0175, 114.0350, 57.0175],
+                      [-143.6751, -71.8376, 143.6751, 71.8376],
+                      [-64.0000, -64.0000, 64.0000, 64.0000],
+                      [-80.6349, -80.6349, 80.6349, 80.6349],
+                      [-101.5937, -101.5937, 101.5937, 101.5937],
+                      [-45.2548, -90.5097, 45.2548, 90.5097],
+                      [-57.0175, -114.0350, 57.0175, 114.0350],
+                      [-71.8376, -143.6751, 71.8376, 143.6751]]),
+        torch.Tensor([[-181.0193, -90.5097, 181.0193, 90.5097],
+                      [-228.0701, -114.0350, 228.0701, 114.0350],
+                      [-287.3503, -143.6751, 287.3503, 143.6751],
+                      [-128.0000, -128.0000, 128.0000, 128.0000],
+                      [-161.2699, -161.2699, 161.2699, 161.2699],
+                      [-203.1873, -203.1873, 203.1873, 203.1873],
+                      [-90.5097, -181.0193, 90.5097, 181.0193],
+                      [-114.0350, -228.0701, 114.0350, 228.0701],
+                      [-143.6751, -287.3503, 143.6751, 287.3503]]),
+        torch.Tensor([[-362.0387, -181.0193, 362.0387, 181.0193],
+                      [-456.1401, -228.0701, 456.1401, 228.0701],
+                      [-574.7006, -287.3503, 574.7006, 287.3503],
+                      [-256.0000, -256.0000, 256.0000, 256.0000],
+                      [-322.5398, -322.5398, 322.5398, 322.5398],
+                      [-406.3747, -406.3747, 406.3747, 406.3747],
+                      [-181.0193, -362.0387, 181.0193, 362.0387],
+                      [-228.0701, -456.1401, 228.0701, 456.1401],
+                      [-287.3503, -574.7006, 287.3503, 574.7006]])
+    ]
+    approxs = ga_retina_head.approx_anchor_generator.base_anchors
+    for i, base_anchor in enumerate(approxs):
+        assert base_anchor.allclose(expected_approxs[i])
+
+    # check valid flags
+    expected_valid_pixels = [136800, 34200, 8550, 2223, 630]
+    multi_level_valid_flags = ga_retina_head.approx_anchor_generator \
+        .valid_flags(featmap_sizes, (800, 1216), device)
+    for i, single_level_valid_flag in enumerate(multi_level_valid_flags):
+        assert single_level_valid_flag.sum() == expected_valid_pixels[i]
+
+    # check number of base anchors for each level
+    assert ga_retina_head.approx_anchor_generator.num_base_anchors == [
+        9, 9, 9, 9, 9
+    ]
+
+    # check approx generation
+    squares = ga_retina_head.square_anchor_generator.grid_anchors(
+        featmap_sizes, device)
+    assert len(squares) == 5
+
+    expected_squares = [
+        torch.Tensor([[-16., -16., 16., 16.]]),
+        torch.Tensor([[-32., -32., 32., 32]]),
+        torch.Tensor([[-64., -64., 64., 64.]]),
+        torch.Tensor([[-128., -128., 128., 128.]]),
+        torch.Tensor([[-256., -256., 256., 256.]])
+    ]
+    squares = ga_retina_head.square_anchor_generator.base_anchors
+    for i, base_anchor in enumerate(squares):
+        assert base_anchor.allclose(expected_squares[i])
+
+    # square_anchor_generator does not check valid flags
+    # check number of base anchors for each level
+    assert (ga_retina_head.square_anchor_generator.num_base_anchors == [
+        1, 1, 1, 1, 1
+    ])
+
+    # check square generation
+    anchors = ga_retina_head.square_anchor_generator.grid_anchors(
+        featmap_sizes, device)
+    assert len(anchors) == 5
diff --git a/tests/test_utils/test_assigner.py b/tests/test_utils/test_assigner.py
new file mode 100644
index 0000000..7cdb08b
--- /dev/null
+++ b/tests/test_utils/test_assigner.py
@@ -0,0 +1,700 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Tests the Assigner objects.
+
+CommandLine:
+    pytest tests/test_utils/test_assigner.py
+    xdoctest tests/test_utils/test_assigner.py zero
+"""
+import pytest
+import torch
+
+from mmdet.core.bbox.assigners import (ApproxMaxIoUAssigner,
+                                       AscendMaxIoUAssigner,
+                                       CenterRegionAssigner, HungarianAssigner,
+                                       MaskHungarianAssigner, MaxIoUAssigner,
+                                       PointAssigner, SimOTAAssigner,
+                                       TaskAlignedAssigner, UniformAssigner)
+
+
+def test_max_iou_assigner():
+    self = MaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+    )
+    bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 9],
+        [0, 10, 10, 19],
+    ])
+    gt_labels = torch.LongTensor([2, 3])
+    assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels)
+    assert len(assign_result.gt_inds) == 4
+    assert len(assign_result.labels) == 4
+
+    expected_gt_inds = torch.LongTensor([1, 0, 2, 0])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+
+
+def test_max_iou_assigner_with_ignore():
+    self = MaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+        ignore_iof_thr=0.5,
+        ignore_wrt_candidates=False,
+    )
+    bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [30, 32, 40, 42],
+    ])
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 9],
+        [0, 10, 10, 19],
+    ])
+    gt_bboxes_ignore = torch.Tensor([
+        [30, 30, 40, 40],
+    ])
+    assign_result = self.assign(
+        bboxes, gt_bboxes, gt_bboxes_ignore=gt_bboxes_ignore)
+
+    expected_gt_inds = torch.LongTensor([1, 0, 2, -1])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+
+
+def test_max_iou_assigner_with_empty_gt():
+    """Test corner case where an image might have no true detections."""
+    self = MaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+    )
+    bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_bboxes = torch.empty(0, 4)
+    assign_result = self.assign(bboxes, gt_bboxes)
+
+    expected_gt_inds = torch.LongTensor([0, 0, 0, 0])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+
+
+def test_max_iou_assigner_with_empty_boxes():
+    """Test corner case where a network might predict no boxes."""
+    self = MaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+    )
+    bboxes = torch.empty((0, 4))
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 9],
+        [0, 10, 10, 19],
+    ])
+    gt_labels = torch.LongTensor([2, 3])
+
+    # Test with gt_labels
+    assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels)
+    assert len(assign_result.gt_inds) == 0
+    assert tuple(assign_result.labels.shape) == (0, )
+
+    # Test without gt_labels
+    assign_result = self.assign(bboxes, gt_bboxes, gt_labels=None)
+    assert len(assign_result.gt_inds) == 0
+    assert assign_result.labels is None
+
+
+def test_max_iou_assigner_with_empty_boxes_and_ignore():
+    """Test corner case where a network might predict no boxes and
+    ignore_iof_thr is on."""
+    self = MaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+        ignore_iof_thr=0.5,
+    )
+    bboxes = torch.empty((0, 4))
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 9],
+        [0, 10, 10, 19],
+    ])
+    gt_bboxes_ignore = torch.Tensor([
+        [30, 30, 40, 40],
+    ])
+    gt_labels = torch.LongTensor([2, 3])
+
+    # Test with gt_labels
+    assign_result = self.assign(
+        bboxes,
+        gt_bboxes,
+        gt_labels=gt_labels,
+        gt_bboxes_ignore=gt_bboxes_ignore)
+    assert len(assign_result.gt_inds) == 0
+    assert tuple(assign_result.labels.shape) == (0, )
+
+    # Test without gt_labels
+    assign_result = self.assign(
+        bboxes, gt_bboxes, gt_labels=None, gt_bboxes_ignore=gt_bboxes_ignore)
+    assert len(assign_result.gt_inds) == 0
+    assert assign_result.labels is None
+
+
+def test_max_iou_assigner_with_empty_boxes_and_gt():
+    """Test corner case where a network might predict no boxes and no gt."""
+    self = MaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+    )
+    bboxes = torch.empty((0, 4))
+    gt_bboxes = torch.empty((0, 4))
+    assign_result = self.assign(bboxes, gt_bboxes)
+    assert len(assign_result.gt_inds) == 0
+
+
+def test_point_assigner():
+    self = PointAssigner()
+    points = torch.FloatTensor([  # [x, y, stride]
+        [0, 0, 1],
+        [10, 10, 1],
+        [5, 5, 1],
+        [32, 32, 1],
+    ])
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 9],
+        [0, 10, 10, 19],
+    ])
+    assign_result = self.assign(points, gt_bboxes)
+    expected_gt_inds = torch.LongTensor([1, 2, 1, 0])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+
+
+def test_point_assigner_with_empty_gt():
+    """Test corner case where an image might have no true detections."""
+    self = PointAssigner()
+    points = torch.FloatTensor([  # [x, y, stride]
+        [0, 0, 1],
+        [10, 10, 1],
+        [5, 5, 1],
+        [32, 32, 1],
+    ])
+    gt_bboxes = torch.FloatTensor([])
+    assign_result = self.assign(points, gt_bboxes)
+
+    expected_gt_inds = torch.LongTensor([0, 0, 0, 0])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+
+
+def test_point_assigner_with_empty_boxes_and_gt():
+    """Test corner case where an image might predict no points and no gt."""
+    self = PointAssigner()
+    points = torch.FloatTensor([])
+    gt_bboxes = torch.FloatTensor([])
+    assign_result = self.assign(points, gt_bboxes)
+    assert len(assign_result.gt_inds) == 0
+
+
+def test_approx_iou_assigner():
+    self = ApproxMaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+    )
+    bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 9],
+        [0, 10, 10, 19],
+    ])
+    approxs_per_octave = 1
+    approxs = bboxes
+    squares = bboxes
+    assign_result = self.assign(approxs, squares, approxs_per_octave,
+                                gt_bboxes)
+
+    expected_gt_inds = torch.LongTensor([1, 0, 2, 0])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+
+
+def test_approx_iou_assigner_with_empty_gt():
+    """Test corner case where an image might have no true detections."""
+    self = ApproxMaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+    )
+    bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_bboxes = torch.FloatTensor([])
+    approxs_per_octave = 1
+    approxs = bboxes
+    squares = bboxes
+    assign_result = self.assign(approxs, squares, approxs_per_octave,
+                                gt_bboxes)
+
+    expected_gt_inds = torch.LongTensor([0, 0, 0, 0])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+
+
+def test_approx_iou_assigner_with_empty_boxes():
+    """Test corner case where an network might predict no boxes."""
+    self = ApproxMaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+    )
+    bboxes = torch.empty((0, 4))
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 9],
+        [0, 10, 10, 19],
+    ])
+    approxs_per_octave = 1
+    approxs = bboxes
+    squares = bboxes
+    assign_result = self.assign(approxs, squares, approxs_per_octave,
+                                gt_bboxes)
+    assert len(assign_result.gt_inds) == 0
+
+
+def test_approx_iou_assigner_with_empty_boxes_and_gt():
+    """Test corner case where an network might predict no boxes and no gt."""
+    self = ApproxMaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+    )
+    bboxes = torch.empty((0, 4))
+    gt_bboxes = torch.empty((0, 4))
+    approxs_per_octave = 1
+    approxs = bboxes
+    squares = bboxes
+    assign_result = self.assign(approxs, squares, approxs_per_octave,
+                                gt_bboxes)
+    assert len(assign_result.gt_inds) == 0
+
+
+def test_random_assign_result():
+    """Test random instantiation of assign result to catch corner cases."""
+    from mmdet.core.bbox.assigners.assign_result import AssignResult
+    AssignResult.random()
+
+    AssignResult.random(num_gts=0, num_preds=0)
+    AssignResult.random(num_gts=0, num_preds=3)
+    AssignResult.random(num_gts=3, num_preds=3)
+    AssignResult.random(num_gts=0, num_preds=3)
+    AssignResult.random(num_gts=7, num_preds=7)
+    AssignResult.random(num_gts=7, num_preds=64)
+    AssignResult.random(num_gts=24, num_preds=3)
+
+
+def test_center_region_assigner():
+    self = CenterRegionAssigner(pos_scale=0.3, neg_scale=1)
+    bboxes = torch.FloatTensor([[0, 0, 10, 10], [10, 10, 20, 20], [8, 8, 9,
+                                                                   9]])
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 11, 11],  # match bboxes[0]
+        [10, 10, 20, 20],  # match bboxes[1]
+        [4.5, 4.5, 5.5, 5.5],  # match bboxes[0] but area is too small
+        [0, 0, 10, 10],  # match bboxes[1] and has a smaller area than gt[0]
+    ])
+    gt_labels = torch.LongTensor([2, 3, 4, 5])
+    assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels)
+    assert len(assign_result.gt_inds) == 3
+    assert len(assign_result.labels) == 3
+    expected_gt_inds = torch.LongTensor([4, 2, 0])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+    shadowed_labels = assign_result.get_extra_property('shadowed_labels')
+    # [8, 8, 9, 9] in the shadowed region of [0, 0, 11, 11] (label: 2)
+    assert torch.any(shadowed_labels == torch.LongTensor([[2, 2]]))
+    # [8, 8, 9, 9] in the shadowed region of [0, 0, 10, 10] (label: 5)
+    assert torch.any(shadowed_labels == torch.LongTensor([[2, 5]]))
+    # [0, 0, 10, 10] is already assigned to [4.5, 4.5, 5.5, 5.5].
+    #   Therefore, [0, 0, 11, 11] (label: 2) is shadowed
+    assert torch.any(shadowed_labels == torch.LongTensor([[0, 2]]))
+
+
+def test_center_region_assigner_with_ignore():
+    self = CenterRegionAssigner(
+        pos_scale=0.5,
+        neg_scale=1,
+    )
+    bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+    ])
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],  # match bboxes[0]
+        [10, 10, 20, 20],  # match bboxes[1]
+    ])
+    gt_bboxes_ignore = torch.FloatTensor([
+        [0, 0, 10, 10],  # match bboxes[0]
+    ])
+    gt_labels = torch.LongTensor([1, 2])
+    assign_result = self.assign(
+        bboxes,
+        gt_bboxes,
+        gt_bboxes_ignore=gt_bboxes_ignore,
+        gt_labels=gt_labels)
+    assert len(assign_result.gt_inds) == 2
+    assert len(assign_result.labels) == 2
+
+    expected_gt_inds = torch.LongTensor([-1, 2])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+
+
+def test_center_region_assigner_with_empty_bboxes():
+    self = CenterRegionAssigner(
+        pos_scale=0.5,
+        neg_scale=1,
+    )
+    bboxes = torch.empty((0, 4)).float()
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],  # match bboxes[0]
+        [10, 10, 20, 20],  # match bboxes[1]
+    ])
+    gt_labels = torch.LongTensor([1, 2])
+    assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels)
+    assert assign_result.gt_inds is None or assign_result.gt_inds.numel() == 0
+    assert assign_result.labels is None or assign_result.labels.numel() == 0
+
+
+def test_center_region_assigner_with_empty_gts():
+    self = CenterRegionAssigner(
+        pos_scale=0.5,
+        neg_scale=1,
+    )
+    bboxes = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+    ])
+    gt_bboxes = torch.empty((0, 4)).float()
+    gt_labels = torch.empty((0, )).long()
+    assign_result = self.assign(bboxes, gt_bboxes, gt_labels=gt_labels)
+    assert len(assign_result.gt_inds) == 2
+    expected_gt_inds = torch.LongTensor([0, 0])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+
+
+def test_hungarian_match_assigner():
+    self = HungarianAssigner()
+    assert self.iou_cost.iou_mode == 'giou'
+
+    # test no gt bboxes
+    bbox_pred = torch.rand((10, 4))
+    cls_pred = torch.rand((10, 81))
+    gt_bboxes = torch.empty((0, 4)).float()
+    gt_labels = torch.empty((0, )).long()
+    img_meta = dict(img_shape=(10, 8, 3))
+    assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels,
+                                img_meta)
+    assert torch.all(assign_result.gt_inds == 0)
+    assert torch.all(assign_result.labels == -1)
+
+    # test with gt bboxes
+    gt_bboxes = torch.FloatTensor([[0, 0, 5, 7], [3, 5, 7, 8]])
+    gt_labels = torch.LongTensor([1, 20])
+    assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels,
+                                img_meta)
+
+    assert torch.all(assign_result.gt_inds > -1)
+    assert (assign_result.gt_inds > 0).sum() == gt_bboxes.size(0)
+    assert (assign_result.labels > -1).sum() == gt_bboxes.size(0)
+
+    # test iou mode
+    self = HungarianAssigner(
+        iou_cost=dict(type='IoUCost', iou_mode='iou', weight=1.0))
+    assert self.iou_cost.iou_mode == 'iou'
+    assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels,
+                                img_meta)
+    assert torch.all(assign_result.gt_inds > -1)
+    assert (assign_result.gt_inds > 0).sum() == gt_bboxes.size(0)
+    assert (assign_result.labels > -1).sum() == gt_bboxes.size(0)
+
+    # test focal loss mode
+    self = HungarianAssigner(
+        iou_cost=dict(type='IoUCost', iou_mode='giou', weight=1.0),
+        cls_cost=dict(type='FocalLossCost', weight=1.))
+    assert self.iou_cost.iou_mode == 'giou'
+    assign_result = self.assign(bbox_pred, cls_pred, gt_bboxes, gt_labels,
+                                img_meta)
+    assert torch.all(assign_result.gt_inds > -1)
+    assert (assign_result.gt_inds > 0).sum() == gt_bboxes.size(0)
+    assert (assign_result.labels > -1).sum() == gt_bboxes.size(0)
+
+
+def test_uniform_assigner():
+    self = UniformAssigner(0.15, 0.7, 1)
+    pred_bbox = torch.FloatTensor([
+        [1, 1, 12, 8],
+        [4, 4, 20, 20],
+        [1, 5, 15, 15],
+        [30, 5, 32, 42],
+    ])
+    anchor = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 9],
+        [0, 10, 10, 19],
+    ])
+    gt_labels = torch.LongTensor([2, 3])
+    assign_result = self.assign(
+        pred_bbox, anchor, gt_bboxes, gt_labels=gt_labels)
+    assert len(assign_result.gt_inds) == 4
+    assert len(assign_result.labels) == 4
+
+    expected_gt_inds = torch.LongTensor([-1, 0, 2, 0])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+
+
+def test_uniform_assigner_with_empty_gt():
+    """Test corner case where an image might have no true detections."""
+    self = UniformAssigner(0.15, 0.7, 1)
+    pred_bbox = torch.FloatTensor([
+        [1, 1, 12, 8],
+        [4, 4, 20, 20],
+        [1, 5, 15, 15],
+        [30, 5, 32, 42],
+    ])
+    anchor = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_bboxes = torch.empty(0, 4)
+    assign_result = self.assign(pred_bbox, anchor, gt_bboxes)
+
+    expected_gt_inds = torch.LongTensor([0, 0, 0, 0])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+
+
+def test_uniform_assigner_with_empty_boxes():
+    """Test corner case where a network might predict no boxes."""
+    self = UniformAssigner(0.15, 0.7, 1)
+    pred_bbox = torch.empty((0, 4))
+    anchor = torch.empty((0, 4))
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 9],
+        [0, 10, 10, 19],
+    ])
+    gt_labels = torch.LongTensor([2, 3])
+
+    # Test with gt_labels
+    assign_result = self.assign(
+        pred_bbox, anchor, gt_bboxes, gt_labels=gt_labels)
+    assert len(assign_result.gt_inds) == 0
+    assert tuple(assign_result.labels.shape) == (0, )
+
+    # Test without gt_labels
+    assign_result = self.assign(pred_bbox, anchor, gt_bboxes, gt_labels=None)
+    assert len(assign_result.gt_inds) == 0
+
+
+def test_sim_ota_assigner():
+    self = SimOTAAssigner(
+        center_radius=2.5, candidate_topk=1, iou_weight=3.0, cls_weight=1.0)
+    pred_scores = torch.FloatTensor([[0.2], [0.8]])
+    priors = torch.Tensor([[0, 12, 23, 34], [4, 5, 6, 7]])
+    decoded_bboxes = torch.Tensor([[[30, 40, 50, 60]], [[4, 5, 6, 7]]])
+    gt_bboxes = torch.Tensor([[23.6667, 23.8757, 238.6326, 151.8874]])
+    gt_labels = torch.LongTensor([2])
+    assign_result = self.assign(pred_scores, priors, decoded_bboxes, gt_bboxes,
+                                gt_labels)
+
+    expected_gt_inds = torch.LongTensor([0, 0])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+
+
+def test_task_aligned_assigner():
+    with pytest.raises(AssertionError):
+        TaskAlignedAssigner(topk=0)
+
+    self = TaskAlignedAssigner(topk=13)
+    pred_score = torch.FloatTensor([[0.1, 0.2], [0.2, 0.3], [0.3, 0.4],
+                                    [0.4, 0.5]])
+    pred_bbox = torch.FloatTensor([
+        [1, 1, 12, 8],
+        [4, 4, 20, 20],
+        [1, 5, 15, 15],
+        [30, 5, 32, 42],
+    ])
+    anchor = torch.FloatTensor([
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ])
+    gt_bboxes = torch.FloatTensor([
+        [0, 0, 10, 9],
+        [0, 10, 10, 19],
+    ])
+    gt_labels = torch.LongTensor([0, 1])
+    assign_result = self.assign(
+        pred_score,
+        pred_bbox,
+        anchor,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels)
+    assert len(assign_result.gt_inds) == 4
+    assert len(assign_result.labels) == 4
+
+    # test empty gt
+    gt_bboxes = torch.empty(0, 4)
+    gt_labels = torch.empty(0, 2)
+    assign_result = self.assign(
+        pred_score, pred_bbox, anchor, gt_bboxes=gt_bboxes)
+    expected_gt_inds = torch.LongTensor([0, 0, 0, 0])
+    assert torch.all(assign_result.gt_inds == expected_gt_inds)
+
+
+def test_mask_hungarian_match_assigner():
+    # test no gt masks
+    assigner_cfg = dict(
+        cls_cost=dict(type='ClassificationCost', weight=1.0),
+        mask_cost=dict(type='FocalLossCost', weight=20.0, binary_input=True),
+        dice_cost=dict(type='DiceCost', weight=1.0, pred_act=True, eps=1.0))
+    self = MaskHungarianAssigner(**assigner_cfg)
+    cls_pred = torch.rand((10, 133))
+    mask_pred = torch.rand((10, 50, 50))
+
+    gt_labels = torch.empty((0, )).long()
+    gt_masks = torch.empty((0, 50, 50)).float()
+    img_meta = None
+    assign_result = self.assign(cls_pred, mask_pred, gt_labels, gt_masks,
+                                img_meta)
+    assert torch.all(assign_result.gt_inds == 0)
+    assert torch.all(assign_result.labels == -1)
+
+    # test with gt masks of naive_dice is True
+    gt_labels = torch.LongTensor([10, 100])
+    gt_masks = torch.zeros((2, 50, 50)).long()
+    gt_masks[0, :25] = 1
+    gt_masks[0, 25:] = 1
+    assign_result = self.assign(cls_pred, mask_pred, gt_labels, gt_masks,
+                                img_meta)
+    assert torch.all(assign_result.gt_inds > -1)
+    assert (assign_result.gt_inds > 0).sum() == gt_labels.size(0)
+    assert (assign_result.labels > -1).sum() == gt_labels.size(0)
+
+    # test with cls mode
+    assigner_cfg = dict(
+        cls_cost=dict(type='ClassificationCost', weight=1.0),
+        mask_cost=dict(type='FocalLossCost', weight=0.0, binary_input=True),
+        dice_cost=dict(type='DiceCost', weight=0.0, pred_act=True, eps=1.0))
+    self = MaskHungarianAssigner(**assigner_cfg)
+    assign_result = self.assign(cls_pred, mask_pred, gt_labels, gt_masks,
+                                img_meta)
+    assert torch.all(assign_result.gt_inds > -1)
+    assert (assign_result.gt_inds > 0).sum() == gt_labels.size(0)
+    assert (assign_result.labels > -1).sum() == gt_labels.size(0)
+
+    # test with mask focal mode
+    assigner_cfg = dict(
+        cls_cost=dict(type='ClassificationCost', weight=0.0),
+        mask_cost=dict(type='FocalLossCost', weight=1.0, binary_input=True),
+        dice_cost=dict(type='DiceCost', weight=0.0, pred_act=True, eps=1.0))
+    self = MaskHungarianAssigner(**assigner_cfg)
+    assign_result = self.assign(cls_pred, mask_pred, gt_labels, gt_masks,
+                                img_meta)
+    assert torch.all(assign_result.gt_inds > -1)
+    assert (assign_result.gt_inds > 0).sum() == gt_labels.size(0)
+    assert (assign_result.labels > -1).sum() == gt_labels.size(0)
+
+    # test with mask dice mode
+    assigner_cfg = dict(
+        cls_cost=dict(type='ClassificationCost', weight=0.0),
+        mask_cost=dict(type='FocalLossCost', weight=0.0, binary_input=True),
+        dice_cost=dict(type='DiceCost', weight=1.0, pred_act=True, eps=1.0))
+    self = MaskHungarianAssigner(**assigner_cfg)
+    assign_result = self.assign(cls_pred, mask_pred, gt_labels, gt_masks,
+                                img_meta)
+    assert torch.all(assign_result.gt_inds > -1)
+    assert (assign_result.gt_inds > 0).sum() == gt_labels.size(0)
+    assert (assign_result.labels > -1).sum() == gt_labels.size(0)
+
+    # test with mask dice mode that naive_dice is False
+    assigner_cfg = dict(
+        cls_cost=dict(type='ClassificationCost', weight=0.0),
+        mask_cost=dict(type='FocalLossCost', weight=0.0, binary_input=True),
+        dice_cost=dict(
+            type='DiceCost',
+            weight=1.0,
+            pred_act=True,
+            eps=1.0,
+            naive_dice=False))
+    self = MaskHungarianAssigner(**assigner_cfg)
+    assign_result = self.assign(cls_pred, mask_pred, gt_labels, gt_masks,
+                                img_meta)
+    assert torch.all(assign_result.gt_inds > -1)
+    assert (assign_result.gt_inds > 0).sum() == gt_labels.size(0)
+    assert (assign_result.labels > -1).sum() == gt_labels.size(0)
+
+    # test with mask bce mode
+    assigner_cfg = dict(
+        cls_cost=dict(type='ClassificationCost', weight=0.0),
+        mask_cost=dict(
+            type='CrossEntropyLossCost', weight=1.0, use_sigmoid=True),
+        dice_cost=dict(type='DiceCost', weight=0.0, pred_act=True, eps=1.0))
+    self = MaskHungarianAssigner(**assigner_cfg)
+    assign_result = self.assign(cls_pred, mask_pred, gt_labels, gt_masks,
+                                img_meta)
+    assert torch.all(assign_result.gt_inds > -1)
+    assert (assign_result.gt_inds > 0).sum() == gt_labels.size(0)
+    assert (assign_result.labels > -1).sum() == gt_labels.size(0)
+
+    # test with ce mode of CrossEntropyLossCost which is not supported yet
+    assigner_cfg = dict(
+        cls_cost=dict(type='ClassificationCost', weight=0.0),
+        mask_cost=dict(
+            type='CrossEntropyLossCost', weight=1.0, use_sigmoid=False),
+        dice_cost=dict(type='DiceCost', weight=0.0, pred_act=True, eps=1.0))
+    with pytest.raises(AssertionError):
+        self = MaskHungarianAssigner(**assigner_cfg)
+
+
+def test_ascend_max_iou_assigner():
+    self = AscendMaxIoUAssigner(
+        pos_iou_thr=0.5,
+        neg_iou_thr=0.5,
+    )
+    batch_bboxes = torch.FloatTensor([[
+        [0, 0, 10, 10],
+        [10, 10, 20, 20],
+        [5, 5, 15, 15],
+        [32, 32, 38, 42],
+    ]])
+    batch_gt_bboxes = torch.FloatTensor([[
+        [0, 0, 10, 9],
+        [0, 10, 10, 19],
+    ]])
+    batch_gt_labels = torch.LongTensor([[2, 3]])
+    batch_bboxes_ignore_mask = torch.IntTensor([[1, 1, 1, 1]])
+    assign_result = self.assign(
+        batch_bboxes,
+        batch_gt_bboxes,
+        batch_gt_labels=batch_gt_labels,
+        batch_bboxes_ignore_mask=batch_bboxes_ignore_mask)
+
+    expected_batch_pos_mask = torch.IntTensor([1, 0, 1, 0])
+    expected_batch_anchor_gt_indes = torch.IntTensor([0, 0, 1, 0])
+    expected_batch_anchor_gt_labels = torch.IntTensor([2, 0, 3, 0])
+
+    assert torch.all(assign_result.batch_pos_mask == expected_batch_pos_mask)
+    assert torch.all(
+        assign_result.batch_anchor_gt_indes *
+        assign_result.batch_pos_mask == expected_batch_anchor_gt_indes)
+    assert torch.all(
+        assign_result.batch_anchor_gt_labels *
+        assign_result.batch_pos_mask == expected_batch_anchor_gt_labels)
diff --git a/tests/test_utils/test_coder.py b/tests/test_utils/test_coder.py
new file mode 100644
index 0000000..f23649d
--- /dev/null
+++ b/tests/test_utils/test_coder.py
@@ -0,0 +1,127 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+import torch
+
+from mmdet.core.bbox.coder import (DeltaXYWHBBoxCoder, DistancePointBBoxCoder,
+                                   TBLRBBoxCoder, YOLOBBoxCoder)
+
+
+def test_yolo_bbox_coder():
+    coder = YOLOBBoxCoder()
+    bboxes = torch.Tensor([[-42., -29., 74., 61.], [-10., -29., 106., 61.],
+                           [22., -29., 138., 61.], [54., -29., 170., 61.]])
+    pred_bboxes = torch.Tensor([[0.4709, 0.6152, 0.1690, -0.4056],
+                                [0.5399, 0.6653, 0.1162, -0.4162],
+                                [0.4654, 0.6618, 0.1548, -0.4301],
+                                [0.4786, 0.6197, 0.1896, -0.4479]])
+    grid_size = 32
+    expected_decode_bboxes = torch.Tensor(
+        [[-53.6102, -10.3096, 83.7478, 49.6824],
+         [-15.8700, -8.3901, 114.4236, 50.9693],
+         [11.1822, -8.0924, 146.6034, 50.4476],
+         [41.2068, -8.9232, 181.4236, 48.5840]])
+    assert expected_decode_bboxes.allclose(
+        coder.decode(bboxes, pred_bboxes, grid_size))
+
+
+def test_delta_bbox_coder():
+    coder = DeltaXYWHBBoxCoder()
+
+    rois = torch.Tensor([[0., 0., 1., 1.], [0., 0., 1., 1.], [0., 0., 1., 1.],
+                         [5., 5., 5., 5.]])
+    deltas = torch.Tensor([[0., 0., 0., 0.], [1., 1., 1., 1.],
+                           [0., 0., 2., -1.], [0.7, -1.9, -0.5, 0.3]])
+    expected_decode_bboxes = torch.Tensor([[0.0000, 0.0000, 1.0000, 1.0000],
+                                           [0.1409, 0.1409, 2.8591, 2.8591],
+                                           [0.0000, 0.3161, 4.1945, 0.6839],
+                                           [5.0000, 5.0000, 5.0000, 5.0000]])
+
+    out = coder.decode(rois, deltas, max_shape=(32, 32))
+    assert expected_decode_bboxes.allclose(out, atol=1e-04)
+    out = coder.decode(rois, deltas, max_shape=torch.Tensor((32, 32)))
+    assert expected_decode_bboxes.allclose(out, atol=1e-04)
+
+    batch_rois = rois.unsqueeze(0).repeat(2, 1, 1)
+    batch_deltas = deltas.unsqueeze(0).repeat(2, 1, 1)
+    batch_out = coder.decode(batch_rois, batch_deltas, max_shape=(32, 32))[0]
+    assert out.allclose(batch_out)
+    batch_out = coder.decode(
+        batch_rois, batch_deltas, max_shape=[(32, 32), (32, 32)])[0]
+    assert out.allclose(batch_out)
+
+    # test max_shape is not equal to batch
+    with pytest.raises(AssertionError):
+        coder.decode(
+            batch_rois, batch_deltas, max_shape=[(32, 32), (32, 32), (32, 32)])
+
+    rois = torch.zeros((0, 4))
+    deltas = torch.zeros((0, 4))
+    out = coder.decode(rois, deltas, max_shape=(32, 32))
+    assert rois.shape == out.shape
+
+    # test add_ctr_clamp
+    coder = DeltaXYWHBBoxCoder(add_ctr_clamp=True, ctr_clamp=2)
+
+    rois = torch.Tensor([[0., 0., 6., 6.], [0., 0., 1., 1.], [0., 0., 1., 1.],
+                         [5., 5., 5., 5.]])
+    deltas = torch.Tensor([[1., 1., 2., 2.], [1., 1., 1., 1.],
+                           [0., 0., 2., -1.], [0.7, -1.9, -0.5, 0.3]])
+    expected_decode_bboxes = torch.Tensor([[0.0000, 0.0000, 27.1672, 27.1672],
+                                           [0.1409, 0.1409, 2.8591, 2.8591],
+                                           [0.0000, 0.3161, 4.1945, 0.6839],
+                                           [5.0000, 5.0000, 5.0000, 5.0000]])
+
+    out = coder.decode(rois, deltas, max_shape=(32, 32))
+    assert expected_decode_bboxes.allclose(out, atol=1e-04)
+
+
+def test_tblr_bbox_coder():
+    coder = TBLRBBoxCoder(normalizer=15.)
+
+    rois = torch.Tensor([[0., 0., 1., 1.], [0., 0., 1., 1.], [0., 0., 1., 1.],
+                         [5., 5., 5., 5.]])
+    deltas = torch.Tensor([[0., 0., 0., 0.], [1., 1., 1., 1.],
+                           [0., 0., 2., -1.], [0.7, -1.9, -0.5, 0.3]])
+    expected_decode_bboxes = torch.Tensor([[0.5000, 0.5000, 0.5000, 0.5000],
+                                           [0.0000, 0.0000, 12.0000, 13.0000],
+                                           [0.0000, 0.5000, 0.0000, 0.5000],
+                                           [5.0000, 5.0000, 5.0000, 5.0000]])
+
+    out = coder.decode(rois, deltas, max_shape=(13, 12))
+    assert expected_decode_bboxes.allclose(out)
+    out = coder.decode(rois, deltas, max_shape=torch.Tensor((13, 12)))
+    assert expected_decode_bboxes.allclose(out)
+
+    batch_rois = rois.unsqueeze(0).repeat(2, 1, 1)
+    batch_deltas = deltas.unsqueeze(0).repeat(2, 1, 1)
+    batch_out = coder.decode(batch_rois, batch_deltas, max_shape=(13, 12))[0]
+    assert out.allclose(batch_out)
+    batch_out = coder.decode(
+        batch_rois, batch_deltas, max_shape=[(13, 12), (13, 12)])[0]
+    assert out.allclose(batch_out)
+
+    # test max_shape is not equal to batch
+    with pytest.raises(AssertionError):
+        coder.decode(batch_rois, batch_deltas, max_shape=[(13, 12)])
+
+    rois = torch.zeros((0, 4))
+    deltas = torch.zeros((0, 4))
+    out = coder.decode(rois, deltas, max_shape=(32, 32))
+    assert rois.shape == out.shape
+
+
+def test_distance_point_bbox_coder():
+    coder = DistancePointBBoxCoder()
+
+    points = torch.Tensor([[74., 61.], [-29., 106.], [138., 61.], [29., 170.]])
+    gt_bboxes = torch.Tensor([[74., 61., 75., 62.], [0., 104., 0., 112.],
+                              [100., 90., 100., 120.], [0., 120., 100., 120.]])
+    expected_distance = torch.Tensor([[0., 0., 1., 1.], [0., 2., 29., 6.],
+                                      [38., 0., 0., 50.], [29., 50., 50., 0.]])
+    out_distance = coder.encode(points, gt_bboxes, max_dis=50, eps=0)
+    assert expected_distance.allclose(out_distance)
+
+    distance = torch.Tensor([[0., 0, 1., 1.], [1., 2., 10., 6.],
+                             [22., -29., 138., 61.], [54., -29., 170., 61.]])
+    out_bbox = coder.decode(points, distance, max_shape=(120, 100))
+    assert gt_bboxes.allclose(out_bbox)
diff --git a/tests/test_utils/test_compat_config.py b/tests/test_utils/test_compat_config.py
new file mode 100644
index 0000000..5f8178a
--- /dev/null
+++ b/tests/test_utils/test_compat_config.py
@@ -0,0 +1,115 @@
+import pytest
+from mmcv import ConfigDict
+
+from mmdet.utils.compat_config import (compat_imgs_per_gpu, compat_loader_args,
+                                       compat_runner_args)
+
+
+def test_compat_runner_args():
+    cfg = ConfigDict(dict(total_epochs=12))
+    with pytest.warns(None) as record:
+        cfg = compat_runner_args(cfg)
+    assert len(record) == 1
+    assert 'runner' in record.list[0].message.args[0]
+    assert 'runner' in cfg
+    assert cfg.runner.type == 'EpochBasedRunner'
+    assert cfg.runner.max_epochs == cfg.total_epochs
+
+
+def test_compat_loader_args():
+    cfg = ConfigDict(dict(data=dict(val=dict(), test=dict(), train=dict())))
+    cfg = compat_loader_args(cfg)
+    # auto fill loader args
+    assert 'val_dataloader' in cfg.data
+    assert 'train_dataloader' in cfg.data
+    assert 'test_dataloader' in cfg.data
+    cfg = ConfigDict(
+        dict(
+            data=dict(
+                samples_per_gpu=1,
+                persistent_workers=True,
+                workers_per_gpu=1,
+                val=dict(samples_per_gpu=3),
+                test=dict(samples_per_gpu=2),
+                train=dict())))
+
+    cfg = compat_loader_args(cfg)
+
+    assert cfg.data.train_dataloader.workers_per_gpu == 1
+    assert cfg.data.train_dataloader.samples_per_gpu == 1
+    assert cfg.data.train_dataloader.persistent_workers
+    assert cfg.data.val_dataloader.workers_per_gpu == 1
+    assert cfg.data.val_dataloader.samples_per_gpu == 3
+    assert cfg.data.test_dataloader.workers_per_gpu == 1
+    assert cfg.data.test_dataloader.samples_per_gpu == 2
+
+    # test test is a list
+    cfg = ConfigDict(
+        dict(
+            data=dict(
+                samples_per_gpu=1,
+                persistent_workers=True,
+                workers_per_gpu=1,
+                val=dict(samples_per_gpu=3),
+                test=[dict(samples_per_gpu=2),
+                      dict(samples_per_gpu=3)],
+                train=dict())))
+
+    cfg = compat_loader_args(cfg)
+    assert cfg.data.test_dataloader.samples_per_gpu == 3
+
+    # assert can not set args at the same time
+    cfg = ConfigDict(
+        dict(
+            data=dict(
+                samples_per_gpu=1,
+                persistent_workers=True,
+                workers_per_gpu=1,
+                val=dict(samples_per_gpu=3),
+                test=dict(samples_per_gpu=2),
+                train=dict(),
+                train_dataloader=dict(samples_per_gpu=2))))
+    # samples_per_gpu can not be set in `train_dataloader`
+    # and data field at the same time
+    with pytest.raises(AssertionError):
+        compat_loader_args(cfg)
+    cfg = ConfigDict(
+        dict(
+            data=dict(
+                samples_per_gpu=1,
+                persistent_workers=True,
+                workers_per_gpu=1,
+                val=dict(samples_per_gpu=3),
+                test=dict(samples_per_gpu=2),
+                train=dict(),
+                val_dataloader=dict(samples_per_gpu=2))))
+    # samples_per_gpu can not be set in `val_dataloader`
+    # and data field at the same time
+    with pytest.raises(AssertionError):
+        compat_loader_args(cfg)
+    cfg = ConfigDict(
+        dict(
+            data=dict(
+                samples_per_gpu=1,
+                persistent_workers=True,
+                workers_per_gpu=1,
+                val=dict(samples_per_gpu=3),
+                test=dict(samples_per_gpu=2),
+                test_dataloader=dict(samples_per_gpu=2))))
+    # samples_per_gpu can not be set in `test_dataloader`
+    # and data field at the same time
+    with pytest.raises(AssertionError):
+        compat_loader_args(cfg)
+
+
+def test_compat_imgs_per_gpu():
+    cfg = ConfigDict(
+        dict(
+            data=dict(
+                imgs_per_gpu=1,
+                samples_per_gpu=2,
+                val=dict(),
+                test=dict(),
+                train=dict())))
+    cfg = compat_imgs_per_gpu(cfg)
+    assert cfg.data.samples_per_gpu == cfg.data.imgs_per_gpu
diff --git a/tests/test_utils/test_general_data.py b/tests/test_utils/test_general_data.py
new file mode 100644
index 0000000..c5525fd
--- /dev/null
+++ b/tests/test_utils/test_general_data.py
@@ -0,0 +1,591 @@
+import copy
+
+import numpy as np
+import pytest
+import torch
+
+from mmdet.core import GeneralData, InstanceData
+
+
+def _equal(a, b):
+    if isinstance(a, (torch.Tensor, np.ndarray)):
+        return (a == b).all()
+    else:
+        return a == b
+
+
+def test_general_data():
+
+    # test init
+    meta_info = dict(
+        img_size=[256, 256],
+        path='dadfaff',
+        scale_factor=np.array([1.5, 1.5]),
+        img_shape=torch.rand(4))
+
+    data = dict(
+        bboxes=torch.rand(4, 4),
+        labels=torch.rand(4),
+        masks=np.random.rand(4, 2, 2))
+
+    instance_data = GeneralData(meta_info=meta_info)
+    assert 'img_size' in instance_data
+    assert instance_data.img_size == [256, 256]
+    assert instance_data['img_size'] == [256, 256]
+    assert 'path' in instance_data
+    assert instance_data.path == 'dadfaff'
+
+    # test nice_repr
+    repr_instance_data = instance_data.new(data=data)
+    nice_repr = str(repr_instance_data)
+    for line in nice_repr.split('\n'):
+        if 'masks' in line:
+            assert 'shape' in line
+            assert '(4, 2, 2)' in line
+        if 'bboxes' in line:
+            assert 'shape' in line
+            assert 'torch.Size([4, 4])' in line
+        if 'path' in line:
+            assert 'dadfaff' in line
+        if 'scale_factor' in line:
+            assert '[1.5 1.5]' in line
+
+    instance_data = GeneralData(
+        meta_info=meta_info, data=dict(bboxes=torch.rand(5)))
+    assert 'bboxes' in instance_data
+    assert len(instance_data.bboxes) == 5
+
+    # data should be a dict
+    with pytest.raises(AssertionError):
+        GeneralData(data=1)
+
+    # test set data
+    instance_data = GeneralData()
+    instance_data.set_data(data)
+    assert 'bboxes' in instance_data
+    assert len(instance_data.bboxes) == 4
+    assert 'masks' in instance_data
+    assert len(instance_data.masks) == 4
+    # data should be a dict
+    with pytest.raises(AssertionError):
+        instance_data.set_data(data=1)
+
+    # test set_meta
+    instance_data = GeneralData()
+    instance_data.set_meta_info(meta_info)
+    assert 'img_size' in instance_data
+    assert instance_data.img_size == [256, 256]
+    assert instance_data['img_size'] == [256, 256]
+    assert 'path' in instance_data
+    assert instance_data.path == 'dadfaff'
+    # can skip same value when overwrite
+    instance_data.set_meta_info(meta_info)
+
+    # meta should be a dict
+    with pytest.raises(AssertionError):
+        instance_data.set_meta_info(meta_info='fjhka')
+
+    # attribute in `_meta_info_field` is immutable once initialized
+    instance_data.set_meta_info(meta_info)
+    # meta should be immutable
+    with pytest.raises(KeyError):
+        instance_data.set_meta_info(dict(img_size=[254, 251]))
+    with pytest.raises(KeyError):
+        duplicate_meta_info = copy.deepcopy(meta_info)
+        duplicate_meta_info['path'] = 'dada'
+        instance_data.set_meta_info(duplicate_meta_info)
+    with pytest.raises(KeyError):
+        duplicate_meta_info = copy.deepcopy(meta_info)
+        duplicate_meta_info['scale_factor'] = np.array([1.5, 1.6])
+        instance_data.set_meta_info(duplicate_meta_info)
+
+    # test new_instance_data
+    instance_data = GeneralData(meta_info)
+    new_instance_data = instance_data.new()
+    for k, v in instance_data.meta_info_items():
+        assert k in new_instance_data
+        _equal(v, new_instance_data[k])
+
+    instance_data = GeneralData(meta_info, data=data)
+    temp_meta = copy.deepcopy(meta_info)
+    temp_data = copy.deepcopy(data)
+    temp_data['time'] = '12212'
+    temp_meta['img_norm'] = np.random.random(3)
+
+    new_instance_data = instance_data.new(meta_info=temp_meta, data=temp_data)
+    for k, v in new_instance_data.meta_info_items():
+        if k in instance_data:
+            _equal(v, instance_data[k])
+        else:
+            assert _equal(v, temp_meta[k])
+            assert k == 'img_norm'
+
+    for k, v in new_instance_data.items():
+        if k in instance_data:
+            _equal(v, instance_data[k])
+        else:
+            assert k == 'time'
+            assert _equal(v, temp_data[k])
+
+    # test keys
+    instance_data = GeneralData(meta_info, data=dict(bboxes=10))
+    assert 'bboxes' in instance_data.keys()
+    instance_data.b = 10
+    assert 'b' in instance_data
+
+    # test meta keys
+    instance_data = GeneralData(meta_info, data=dict(bboxes=10))
+    assert 'path' in instance_data.meta_info_keys()
+    assert len(instance_data.meta_info_keys()) == len(meta_info)
+    instance_data.set_meta_info(dict(workdir='fafaf'))
+    assert 'workdir' in instance_data
+    assert len(instance_data.meta_info_keys()) == len(meta_info) + 1
+
+    # test values
+    instance_data = GeneralData(meta_info, data=dict(bboxes=10))
+    assert 10 in instance_data.values()
+    assert len(instance_data.values()) == 1
+
+    # test meta values
+    instance_data = GeneralData(meta_info, data=dict(bboxes=10))
+    # torch 1.3 eq() can not compare str and tensor
+    from mmdet import digit_version
+    if digit_version(torch.__version__) >= [1, 4]:
+        assert 'dadfaff' in instance_data.meta_info_values()
+    assert len(instance_data.meta_info_values()) == len(meta_info)
+
+    # test items
+    instance_data = GeneralData(data=data)
+    for k, v in instance_data.items():
+        assert k in data
+        assert _equal(v, data[k])
+
+    # test meta_info_items
+    instance_data = GeneralData(meta_info=meta_info)
+    for k, v in instance_data.meta_info_items():
+        assert k in meta_info
+        assert _equal(v, meta_info[k])
+
+    # test __setattr__
+    new_instance_data = GeneralData(data=data)
+    new_instance_data.mask = torch.rand(3, 4, 5)
+    new_instance_data.bboxes = torch.rand(2, 4)
+    assert 'mask' in new_instance_data
+    assert len(new_instance_data.mask) == 3
+    assert len(new_instance_data.bboxes) == 2
+
+    # test instance_data_field has been updated
+    assert 'mask' in new_instance_data._data_fields
+    assert 'bboxes' in new_instance_data._data_fields
+
+    for k in data:
+        assert k in new_instance_data._data_fields
+
+    # '_meta_info_field', '_data_fields' is immutable.
+    with pytest.raises(AttributeError):
+        new_instance_data._data_fields = None
+    with pytest.raises(AttributeError):
+        new_instance_data._meta_info_fields = None
+    with pytest.raises(AttributeError):
+        del new_instance_data._data_fields
+    with pytest.raises(AttributeError):
+        del new_instance_data._meta_info_fields
+
+    # key in _meta_info_field is immutable
+    new_instance_data.set_meta_info(meta_info)
+    with pytest.raises(KeyError):
+        del new_instance_data.img_size
+    with pytest.raises(KeyError):
+        del new_instance_data.scale_factor
+    for k in new_instance_data.meta_info_keys():
+        with pytest.raises(AttributeError):
+            new_instance_data[k] = None
+
+    # test __delattr__
+    # test key can be removed in instance_data_field
+    assert 'mask' in new_instance_data._data_fields
+    assert 'mask' in new_instance_data.keys()
+    assert 'mask' in new_instance_data
+    assert hasattr(new_instance_data, 'mask')
+    del new_instance_data.mask
+    assert 'mask' not in new_instance_data.keys()
+    assert 'mask' not in new_instance_data
+    assert 'mask' not in new_instance_data._data_fields
+    assert not hasattr(new_instance_data, 'mask')
+
+    # tset __delitem__
+    new_instance_data.mask = torch.rand(1, 2, 3)
+    assert 'mask' in new_instance_data._data_fields
+    assert 'mask' in new_instance_data
+    assert hasattr(new_instance_data, 'mask')
+    del new_instance_data['mask']
+    assert 'mask' not in new_instance_data
+    assert 'mask' not in new_instance_data._data_fields
+    assert 'mask' not in new_instance_data
+    assert not hasattr(new_instance_data, 'mask')
+
+    # test __setitem__
+    new_instance_data['mask'] = torch.rand(1, 2, 3)
+    assert 'mask' in new_instance_data._data_fields
+    assert 'mask' in new_instance_data.keys()
+    assert hasattr(new_instance_data, 'mask')
+
+    # test data_fields has been updated
+    assert 'mask' in new_instance_data.keys()
+    assert 'mask' in new_instance_data._data_fields
+
+    # '_meta_info_field', '_data_fields' is immutable.
+    with pytest.raises(AttributeError):
+        del new_instance_data['_data_fields']
+    with pytest.raises(AttributeError):
+        del new_instance_data['_meta_info_field']
+
+    #  test __getitem__
+    new_instance_data.mask is new_instance_data['mask']
+
+    # test get
+    assert new_instance_data.get('mask') is new_instance_data.mask
+    assert new_instance_data.get('none_attribute', None) is None
+    assert new_instance_data.get('none_attribute', 1) == 1
+
+    # test pop
+    mask = new_instance_data.mask
+    assert new_instance_data.pop('mask') is mask
+    assert new_instance_data.pop('mask', None) is None
+    assert new_instance_data.pop('mask', 1) == 1
+
+    # '_meta_info_field', '_data_fields' is immutable.
+    with pytest.raises(KeyError):
+        new_instance_data.pop('_data_fields')
+    with pytest.raises(KeyError):
+        new_instance_data.pop('_meta_info_field')
+    # attribute in `_meta_info_field` is immutable
+    with pytest.raises(KeyError):
+        new_instance_data.pop('img_size')
+    # test pop attribute in instance_data_filed
+    new_instance_data['mask'] = torch.rand(1, 2, 3)
+    new_instance_data.pop('mask')
+    # test data_field has been updated
+    assert 'mask' not in new_instance_data
+    assert 'mask' not in new_instance_data._data_fields
+    assert 'mask' not in new_instance_data
+
+    # test_keys
+    new_instance_data.mask = torch.ones(1, 2, 3)
+    'mask' in new_instance_data.keys()
+    has_flag = False
+    for key in new_instance_data.keys():
+        if key == 'mask':
+            has_flag = True
+    assert has_flag
+
+    # test values
+    assert len(list(new_instance_data.keys())) == len(
+        list(new_instance_data.values()))
+    mask = new_instance_data.mask
+    has_flag = False
+    for value in new_instance_data.values():
+        if value is mask:
+            has_flag = True
+    assert has_flag
+
+    # test items
+    assert len(list(new_instance_data.keys())) == len(
+        list(new_instance_data.items()))
+    mask = new_instance_data.mask
+    has_flag = False
+    for key, value in new_instance_data.items():
+        if value is mask:
+            assert key == 'mask'
+            has_flag = True
+    assert has_flag
+
+    # test device
+    new_instance_data = GeneralData()
+    if torch.cuda.is_available():
+        newnew_instance_data = new_instance_data.new()
+        devices = ('cpu', 'cuda')
+        for i in range(10):
+            device = devices[i % 2]
+            newnew_instance_data[f'{i}'] = torch.rand(1, 2, 3, device=device)
+        newnew_instance_data = newnew_instance_data.cpu()
+        for value in newnew_instance_data.values():
+            assert not value.is_cuda
+        newnew_instance_data = new_instance_data.new()
+        devices = ('cuda', 'cpu')
+        for i in range(10):
+            device = devices[i % 2]
+            newnew_instance_data[f'{i}'] = torch.rand(1, 2, 3, device=device)
+        newnew_instance_data = newnew_instance_data.cuda()
+        for value in newnew_instance_data.values():
+            assert value.is_cuda
+    # test to
+    double_instance_data = instance_data.new()
+    double_instance_data.long = torch.LongTensor(1, 2, 3, 4)
+    double_instance_data.bool = torch.BoolTensor(1, 2, 3, 4)
+    double_instance_data = instance_data.to(torch.double)
+    for k, v in double_instance_data.items():
+        if isinstance(v, torch.Tensor):
+            assert v.dtype is torch.double
+
+    # test .cpu() .cuda()
+    if torch.cuda.is_available():
+        cpu_instance_data = double_instance_data.new()
+        cpu_instance_data.mask = torch.rand(1)
+        cuda_tensor = torch.rand(1, 2, 3).cuda()
+        cuda_instance_data = cpu_instance_data.to(cuda_tensor.device)
+        for value in cuda_instance_data.values():
+            assert value.is_cuda
+        cpu_instance_data = cuda_instance_data.cpu()
+        for value in cpu_instance_data.values():
+            assert not value.is_cuda
+        cuda_instance_data = cpu_instance_data.cuda()
+        for value in cuda_instance_data.values():
+            assert value.is_cuda
+
+    # test detach
+    grad_instance_data = double_instance_data.new()
+    grad_instance_data.mask = torch.rand(2, requires_grad=True)
+    grad_instance_data.mask_1 = torch.rand(2, requires_grad=True)
+    detach_instance_data = grad_instance_data.detach()
+    for value in detach_instance_data.values():
+        assert not value.requires_grad
+
+    # test numpy
+    tensor_instance_data = double_instance_data.new()
+    tensor_instance_data.mask = torch.rand(2, requires_grad=True)
+    tensor_instance_data.mask_1 = torch.rand(2, requires_grad=True)
+    numpy_instance_data = tensor_instance_data.numpy()
+    for value in numpy_instance_data.values():
+        assert isinstance(value, np.ndarray)
+    if torch.cuda.is_available():
+        tensor_instance_data = double_instance_data.new()
+        tensor_instance_data.mask = torch.rand(2)
+        tensor_instance_data.mask_1 = torch.rand(2)
+        tensor_instance_data = tensor_instance_data.cuda()
+        numpy_instance_data = tensor_instance_data.numpy()
+        for value in numpy_instance_data.values():
+            assert isinstance(value, np.ndarray)
+
+    instance_data['_c'] = 10000
+    instance_data.get('dad', None) is None
+    assert hasattr(instance_data, '_c')
+    del instance_data['_c']
+    assert not hasattr(instance_data, '_c')
+    instance_data.a = 1000
+    instance_data['a'] = 2000
+    assert instance_data['a'] == 2000
+    assert instance_data.a == 2000
+    assert instance_data.get('a') == instance_data['a'] == instance_data.a
+    instance_data._meta = 1000
+    assert '_meta' in instance_data.keys()
+    if torch.cuda.is_available():
+        instance_data.bbox = torch.ones(2, 3, 4, 5).cuda()
+        instance_data.score = torch.ones(2, 3, 4, 4)
+    else:
+        instance_data.bbox = torch.ones(2, 3, 4, 5)
+
+    assert len(instance_data.new().keys()) == 0
+    with pytest.raises(AttributeError):
+        instance_data.img_size = 100
+
+    for k, v in instance_data.items():
+        if k == 'bbox':
+            assert isinstance(v, torch.Tensor)
+    assert 'a' in instance_data
+    instance_data.pop('a')
+    assert 'a' not in instance_data
+
+    cpu_instance_data = instance_data.cpu()
+    for k, v in cpu_instance_data.items():
+        if isinstance(v, torch.Tensor):
+            assert not v.is_cuda
+
+    assert isinstance(cpu_instance_data.numpy().bbox, np.ndarray)
+
+    if torch.cuda.is_available():
+        cuda_resutls = instance_data.cuda()
+        for k, v in cuda_resutls.items():
+            if isinstance(v, torch.Tensor):
+                assert v.is_cuda
+
+
+def test_instance_data():
+    meta_info = dict(
+        img_size=(256, 256),
+        path='dadfaff',
+        scale_factor=np.array([1.5, 1.5, 1, 1]))
+
+    data = dict(
+        bboxes=torch.rand(4, 4),
+        masks=torch.rand(4, 2, 2),
+        labels=np.random.rand(4),
+        size=[(i, i) for i in range(4)])
+
+    # test init
+    instance_data = InstanceData(meta_info)
+    assert 'path' in instance_data
+    instance_data = InstanceData(meta_info, data=data)
+    assert len(instance_data) == 4
+    instance_data.set_data(data)
+    assert len(instance_data) == 4
+
+    meta_info = copy.deepcopy(meta_info)
+    meta_info['img_name'] = 'flag'
+
+    # test newinstance_data
+    new_instance_data = instance_data.new(meta_info=meta_info)
+    for k, v in new_instance_data.meta_info_items():
+        if k in instance_data:
+            _equal(v, instance_data[k])
+        else:
+            assert _equal(v, meta_info[k])
+            assert k == 'img_name'
+    # meta info is immutable
+    with pytest.raises(KeyError):
+        meta_info = copy.deepcopy(meta_info)
+        meta_info['path'] = 'fdasfdsd'
+        instance_data.new(meta_info=meta_info)
+
+    # data fields should have same length
+    with pytest.raises(AssertionError):
+        temp_data = copy.deepcopy(data)
+        temp_data['bboxes'] = torch.rand(5, 4)
+        instance_data.new(data=temp_data)
+
+    temp_data = copy.deepcopy(data)
+    temp_data['scores'] = torch.rand(4)
+    new_instance_data = instance_data.new(data=temp_data)
+    for k, v in new_instance_data.items():
+        if k in instance_data:
+            _equal(v, instance_data[k])
+        else:
+            assert k == 'scores'
+            assert _equal(v, temp_data[k])
+
+    instance_data = instance_data.new()
+
+    # test __setattr__
+    # '_meta_info_field', '_data_fields' is immutable.
+    with pytest.raises(AttributeError):
+        instance_data._data_fields = dict()
+    with pytest.raises(AttributeError):
+        instance_data._data_fields = dict()
+
+    # all attribute in instance_data_field should be
+    # (torch.Tensor, np.ndarray, list))
+    with pytest.raises(AssertionError):
+        instance_data.a = 1000
+
+    # instance_data field should has same length
+    new_instance_data = instance_data.new()
+    new_instance_data.det_bbox = torch.rand(100, 4)
+    new_instance_data.det_label = torch.arange(100)
+    with pytest.raises(AssertionError):
+        new_instance_data.scores = torch.rand(101, 1)
+    new_instance_data.none = [None] * 100
+    with pytest.raises(AssertionError):
+        new_instance_data.scores = [None] * 101
+    new_instance_data.numpy_det = np.random.random([100, 1])
+    with pytest.raises(AssertionError):
+        new_instance_data.scores = np.random.random([101, 1])
+
+    # isinstance(str, slice, int, torch.LongTensor, torch.BoolTensor)
+    item = torch.Tensor([1, 2, 3, 4])
+    with pytest.raises(AssertionError):
+        new_instance_data[item]
+    len(new_instance_data[item.long()]) == 1
+
+    # when input is a bool tensor, The shape of
+    # the input at index 0 should equal to
+    # the value length in instance_data_field
+    with pytest.raises(AssertionError):
+        new_instance_data[item.bool()]
+
+    for i in range(len(new_instance_data)):
+        assert new_instance_data[i].det_label == i
+        assert len(new_instance_data[i]) == 1
+
+    # assert the index should in 0 ~ len(instance_data) -1
+    with pytest.raises(IndexError):
+        new_instance_data[101]
+
+    # assert the index should not be an empty tensor
+    new_new_instance_data = new_instance_data.new()
+    with pytest.raises(AssertionError):
+        new_new_instance_data[0]
+
+    # test str
+    with pytest.raises(AssertionError):
+        instance_data.img_size_dummmy = meta_info['img_size']
+
+    # test slice
+    ten_ressults = new_instance_data[:10]
+    len(ten_ressults) == 10
+    for v in ten_ressults.values():
+        assert len(v) == 10
+
+    # test Longtensor
+    long_tensor = torch.randint(100, (50, ))
+    long_index_instance_data = new_instance_data[long_tensor]
+    assert len(long_index_instance_data) == len(long_tensor)
+    for key, value in long_index_instance_data.items():
+        if not isinstance(value, list):
+            assert (long_index_instance_data[key] == new_instance_data[key]
+                    [long_tensor]).all()
+        else:
+            len(long_tensor) == len(value)
+
+    # test bool tensor
+    bool_tensor = torch.rand(100) > 0.5
+    bool_index_instance_data = new_instance_data[bool_tensor]
+    assert len(bool_index_instance_data) == bool_tensor.sum()
+    for key, value in bool_index_instance_data.items():
+        if not isinstance(value, list):
+            assert (bool_index_instance_data[key] == new_instance_data[key]
+                    [bool_tensor]).all()
+        else:
+            assert len(value) == bool_tensor.sum()
+
+    num_instance = 1000
+    instance_data_list = []
+
+    # assert len(instance_lists) > 0
+    with pytest.raises(AssertionError):
+        instance_data.cat(instance_data_list)
+
+    for _ in range(2):
+        instance_data['bbox'] = torch.rand(num_instance, 4)
+        instance_data['label'] = torch.rand(num_instance, 1)
+        instance_data['mask'] = torch.rand(num_instance, 224, 224)
+        instance_data['instances_infos'] = [1] * num_instance
+        instance_data['cpu_bbox'] = np.random.random((num_instance, 4))
+        if torch.cuda.is_available():
+            instance_data.cuda_tensor = torch.rand(num_instance).cuda()
+            assert instance_data.cuda_tensor.is_cuda
+            cuda_instance_data = instance_data.cuda()
+            assert cuda_instance_data.cuda_tensor.is_cuda
+
+        assert len(instance_data[0]) == 1
+        with pytest.raises(IndexError):
+            return instance_data[num_instance + 1]
+        with pytest.raises(AssertionError):
+            instance_data.centerness = torch.rand(num_instance + 1, 1)
+
+        mask_tensor = torch.rand(num_instance) > 0.5
+        length = mask_tensor.sum()
+        assert len(instance_data[mask_tensor]) == length
+
+        index_tensor = torch.LongTensor([1, 5, 8, 110, 399])
+        length = len(index_tensor)
+
+        assert len(instance_data[index_tensor]) == length
+
+        instance_data_list.append(instance_data)
+
+    cat_resutls = InstanceData.cat(instance_data_list)
+    assert len(cat_resutls) == num_instance * 2
+
+    instances = InstanceData(data=dict(bboxes=torch.rand(4, 4)))
+    # cat only single instance
+    assert len(InstanceData.cat([instances])) == 4
diff --git a/tests/test_utils/test_hook.py b/tests/test_utils/test_hook.py
new file mode 100644
index 0000000..49cd5ca
--- /dev/null
+++ b/tests/test_utils/test_hook.py
@@ -0,0 +1,415 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import logging
+import shutil
+import sys
+import tempfile
+from unittest.mock import MagicMock, Mock, call, patch
+
+import numpy as np
+import pytest
+import torch
+import torch.nn as nn
+from mmcv.runner import (CheckpointHook, IterTimerHook, PaviLoggerHook,
+                         build_runner)
+from torch.nn.init import constant_
+from torch.utils.data import DataLoader, Dataset
+
+from mmdet.core.hook import ExpMomentumEMAHook, YOLOXLrUpdaterHook
+from mmdet.core.hook.sync_norm_hook import SyncNormHook
+from mmdet.core.hook.sync_random_size_hook import SyncRandomSizeHook
+
+
+def _build_demo_runner_without_hook(runner_type='EpochBasedRunner',
+                                    max_epochs=1,
+                                    max_iters=None,
+                                    multi_optimziers=False):
+
+    class Model(nn.Module):
+
+        def __init__(self):
+            super().__init__()
+            self.linear = nn.Linear(2, 1)
+            self.conv = nn.Conv2d(3, 3, 3)
+
+        def forward(self, x):
+            return self.linear(x)
+
+        def train_step(self, x, optimizer, **kwargs):
+            return dict(loss=self(x))
+
+        def val_step(self, x, optimizer, **kwargs):
+            return dict(loss=self(x))
+
+    model = Model()
+
+    if multi_optimziers:
+        optimizer = {
+            'model1':
+            torch.optim.SGD(model.linear.parameters(), lr=0.02, momentum=0.95),
+            'model2':
+            torch.optim.SGD(model.conv.parameters(), lr=0.01, momentum=0.9),
+        }
+    else:
+        optimizer = torch.optim.SGD(model.parameters(), lr=0.02, momentum=0.95)
+
+    tmp_dir = tempfile.mkdtemp()
+    runner = build_runner(
+        dict(type=runner_type),
+        default_args=dict(
+            model=model,
+            work_dir=tmp_dir,
+            optimizer=optimizer,
+            logger=logging.getLogger(),
+            max_epochs=max_epochs,
+            max_iters=max_iters))
+    return runner
+
+
+def _build_demo_runner(runner_type='EpochBasedRunner',
+                       max_epochs=1,
+                       max_iters=None,
+                       multi_optimziers=False):
+    log_config = dict(
+        interval=1, hooks=[
+            dict(type='TextLoggerHook'),
+        ])
+
+    runner = _build_demo_runner_without_hook(runner_type, max_epochs,
+                                             max_iters, multi_optimziers)
+
+    runner.register_checkpoint_hook(dict(interval=1))
+    runner.register_logger_hooks(log_config)
+    return runner
+
+
+@pytest.mark.parametrize('multi_optimziers', (True, False))
+def test_yolox_lrupdater_hook(multi_optimziers):
+    """xdoctest -m tests/test_hooks.py test_cosine_runner_hook."""
+    # Only used to prevent program errors
+    YOLOXLrUpdaterHook(0, min_lr_ratio=0.05)
+
+    sys.modules['pavi'] = MagicMock()
+    loader = DataLoader(torch.ones((10, 2)))
+    runner = _build_demo_runner(multi_optimziers=multi_optimziers)
+
+    hook_cfg = dict(
+        type='YOLOXLrUpdaterHook',
+        warmup='exp',
+        by_epoch=False,
+        warmup_by_epoch=True,
+        warmup_ratio=1,
+        warmup_iters=5,  # 5 epoch
+        num_last_epochs=15,
+        min_lr_ratio=0.05)
+    runner.register_hook_from_cfg(hook_cfg)
+    runner.register_hook_from_cfg(dict(type='IterTimerHook'))
+    runner.register_hook(IterTimerHook())
+
+    # add pavi hook
+    hook = PaviLoggerHook(interval=1, add_graph=False, add_last_ckpt=True)
+    runner.register_hook(hook)
+    runner.run([loader], [('train', 1)])
+    shutil.rmtree(runner.work_dir)
+
+    # TODO: use a more elegant way to check values
+    assert hasattr(hook, 'writer')
+    if multi_optimziers:
+        calls = [
+            call(
+                'train', {
+                    'learning_rate/model1': 8.000000000000001e-06,
+                    'learning_rate/model2': 4.000000000000001e-06,
+                    'momentum/model1': 0.95,
+                    'momentum/model2': 0.9
+                }, 1),
+            call(
+                'train', {
+                    'learning_rate/model1': 0.00039200000000000004,
+                    'learning_rate/model2': 0.00019600000000000002,
+                    'momentum/model1': 0.95,
+                    'momentum/model2': 0.9
+                }, 7),
+            call(
+                'train', {
+                    'learning_rate/model1': 0.0008000000000000001,
+                    'learning_rate/model2': 0.0004000000000000001,
+                    'momentum/model1': 0.95,
+                    'momentum/model2': 0.9
+                }, 10)
+        ]
+    else:
+        calls = [
+            call('train', {
+                'learning_rate': 8.000000000000001e-06,
+                'momentum': 0.95
+            }, 1),
+            call('train', {
+                'learning_rate': 0.00039200000000000004,
+                'momentum': 0.95
+            }, 7),
+            call('train', {
+                'learning_rate': 0.0008000000000000001,
+                'momentum': 0.95
+            }, 10)
+        ]
+    hook.writer.add_scalars.assert_has_calls(calls, any_order=True)
+
+
+def test_ema_hook():
+    """xdoctest -m tests/test_hooks.py test_ema_hook."""
+
+    class DemoModel(nn.Module):
+
+        def __init__(self):
+            super().__init__()
+            self.conv = nn.Conv2d(
+                in_channels=1,
+                out_channels=2,
+                kernel_size=1,
+                padding=1,
+                bias=True)
+            self.bn = nn.BatchNorm2d(2)
+
+            self._init_weight()
+
+        def _init_weight(self):
+            constant_(self.conv.weight, 0)
+            constant_(self.conv.bias, 0)
+            constant_(self.bn.weight, 0)
+            constant_(self.bn.bias, 0)
+
+        def forward(self, x):
+            return self.bn(self.conv(x)).sum()
+
+        def train_step(self, x, optimizer, **kwargs):
+            return dict(loss=self(x))
+
+        def val_step(self, x, optimizer, **kwargs):
+            return dict(loss=self(x))
+
+    loader = DataLoader(torch.ones((1, 1, 1, 1)))
+    runner = _build_demo_runner()
+    demo_model = DemoModel()
+    runner.model = demo_model
+    ema_hook = ExpMomentumEMAHook(
+        momentum=0.0002,
+        total_iter=1,
+        skip_buffers=True,
+        interval=2,
+        resume_from=None)
+    checkpointhook = CheckpointHook(interval=1, by_epoch=True)
+    runner.register_hook(ema_hook, priority='HIGHEST')
+    runner.register_hook(checkpointhook)
+    runner.run([loader, loader], [('train', 1), ('val', 1)])
+    checkpoint = torch.load(f'{runner.work_dir}/epoch_1.pth')
+    num_eam_params = 0
+    for name, value in checkpoint['state_dict'].items():
+        if 'ema' in name:
+            num_eam_params += 1
+            value.fill_(1)
+    assert num_eam_params == 4
+    torch.save(checkpoint, f'{runner.work_dir}/epoch_1.pth')
+
+    work_dir = runner.work_dir
+    resume_ema_hook = ExpMomentumEMAHook(
+        momentum=0.5,
+        total_iter=10,
+        skip_buffers=True,
+        interval=1,
+        resume_from=f'{work_dir}/epoch_1.pth')
+    runner = _build_demo_runner(max_epochs=2)
+    runner.model = demo_model
+    runner.register_hook(resume_ema_hook, priority='HIGHEST')
+    checkpointhook = CheckpointHook(interval=1, by_epoch=True)
+    runner.register_hook(checkpointhook)
+    runner.run([loader, loader], [('train', 1), ('val', 1)])
+    checkpoint = torch.load(f'{runner.work_dir}/epoch_2.pth')
+    num_eam_params = 0
+    desired_output = [0.9094, 0.9094]
+    for name, value in checkpoint['state_dict'].items():
+        if 'ema' in name:
+            num_eam_params += 1
+            assert value.sum() == 2
+        else:
+            if ('weight' in name) or ('bias' in name):
+                np.allclose(value.data.cpu().numpy().reshape(-1),
+                            desired_output, 1e-4)
+    assert num_eam_params == 4
+    shutil.rmtree(runner.work_dir)
+    shutil.rmtree(work_dir)
+
+
+def test_sync_norm_hook():
+    # Only used to prevent program errors
+    SyncNormHook()
+
+    loader = DataLoader(torch.ones((5, 2)))
+    runner = _build_demo_runner()
+    runner.register_hook_from_cfg(dict(type='SyncNormHook'))
+    runner.run([loader, loader], [('train', 1), ('val', 1)])
+    shutil.rmtree(runner.work_dir)
+
+
+def test_sync_random_size_hook():
+    # Only used to prevent program errors
+    SyncRandomSizeHook()
+
+    class DemoDataset(Dataset):
+
+        def __getitem__(self, item):
+            return torch.ones(2)
+
+        def __len__(self):
+            return 5
+
+        def update_dynamic_scale(self, dynamic_scale):
+            pass
+
+    loader = DataLoader(DemoDataset())
+    runner = _build_demo_runner()
+    runner.register_hook_from_cfg(
+        dict(type='SyncRandomSizeHook', device='cpu'))
+    runner.run([loader, loader], [('train', 1), ('val', 1)])
+    shutil.rmtree(runner.work_dir)
+
+    if torch.cuda.is_available():
+        runner = _build_demo_runner()
+        runner.register_hook_from_cfg(
+            dict(type='SyncRandomSizeHook', device='cuda'))
+        runner.run([loader, loader], [('train', 1), ('val', 1)])
+        shutil.rmtree(runner.work_dir)
+
+
+@pytest.mark.parametrize('set_loss', [
+    dict(set_loss_nan=False, set_loss_inf=False),
+    dict(set_loss_nan=True, set_loss_inf=False),
+    dict(set_loss_nan=False, set_loss_inf=True)
+])
+def test_check_invalid_loss_hook(set_loss):
+    # Check whether loss is valid during training.
+
+    class DemoModel(nn.Module):
+
+        def __init__(self, set_loss_nan=False, set_loss_inf=False):
+            super().__init__()
+            self.set_loss_nan = set_loss_nan
+            self.set_loss_inf = set_loss_inf
+            self.linear = nn.Linear(2, 1)
+
+        def forward(self, x):
+            return self.linear(x)
+
+        def train_step(self, x, optimizer, **kwargs):
+            if self.set_loss_nan:
+                return dict(loss=torch.tensor(float('nan')))
+            elif self.set_loss_inf:
+                return dict(loss=torch.tensor(float('inf')))
+            else:
+                return dict(loss=self(x))
+
+    loader = DataLoader(torch.ones((5, 2)))
+    runner = _build_demo_runner()
+
+    demo_model = DemoModel(**set_loss)
+    runner.model = demo_model
+    runner.register_hook_from_cfg(
+        dict(type='CheckInvalidLossHook', interval=1))
+    if not set_loss['set_loss_nan'] \
+            and not set_loss['set_loss_inf']:
+        # check loss is valid
+        runner.run([loader], [('train', 1)])
+    else:
+        # check loss is nan or inf
+        with pytest.raises(AssertionError):
+            runner.run([loader], [('train', 1)])
+    shutil.rmtree(runner.work_dir)
+
+
+def test_set_epoch_info_hook():
+    """Test SetEpochInfoHook."""
+
+    class DemoModel(nn.Module):
+
+        def __init__(self):
+            super().__init__()
+            self.epoch = 0
+            self.linear = nn.Linear(2, 1)
+
+        def forward(self, x):
+            return self.linear(x)
+
+        def train_step(self, x, optimizer, **kwargs):
+            return dict(loss=self(x))
+
+        def set_epoch(self, epoch):
+            self.epoch = epoch
+
+    loader = DataLoader(torch.ones((5, 2)))
+    runner = _build_demo_runner(max_epochs=3)
+
+    demo_model = DemoModel()
+    runner.model = demo_model
+    runner.register_hook_from_cfg(dict(type='SetEpochInfoHook'))
+    runner.run([loader], [('train', 1)])
+    assert demo_model.epoch == 2
+
+
+def test_memory_profiler_hook():
+    from collections import namedtuple
+
+    # test ImportError without psutil and memory_profiler
+    with pytest.raises(ImportError):
+        from mmdet.core.hook import MemoryProfilerHook
+        MemoryProfilerHook(1)
+
+    # test ImportError without memory_profiler
+    sys.modules['psutil'] = MagicMock()
+    with pytest.raises(ImportError):
+        from mmdet.core.hook import MemoryProfilerHook
+        MemoryProfilerHook(1)
+
+    sys.modules['memory_profiler'] = MagicMock()
+
+    def _mock_virtual_memory():
+        virtual_memory_type = namedtuple(
+            'virtual_memory', ['total', 'available', 'percent', 'used'])
+        return virtual_memory_type(
+            total=270109085696,
+            available=250416816128,
+            percent=7.3,
+            used=17840881664)
+
+    def _mock_swap_memory():
+        swap_memory_type = namedtuple('swap_memory', [
+            'total',
+            'used',
+            'percent',
+        ])
+        return swap_memory_type(total=8589930496, used=0, percent=0.0)
+
+    def _mock_memory_usage():
+        return [40.22265625]
+
+    mock_virtual_memory = Mock(return_value=_mock_virtual_memory())
+    mock_swap_memory = Mock(return_value=_mock_swap_memory())
+    mock_memory_usage = Mock(return_value=_mock_memory_usage())
+
+    @patch('psutil.swap_memory', mock_swap_memory)
+    @patch('psutil.virtual_memory', mock_virtual_memory)
+    @patch('memory_profiler.memory_usage', mock_memory_usage)
+    def _test_memory_profiler_hook():
+        from mmdet.core.hook import MemoryProfilerHook
+        hook = MemoryProfilerHook(1)
+        runner = _build_demo_runner()
+
+        assert not mock_memory_usage.called
+        assert not mock_swap_memory.called
+        assert not mock_memory_usage.called
+
+        hook.after_iter(runner)
+
+        assert mock_memory_usage.called
+        assert mock_swap_memory.called
+        assert mock_memory_usage.called
+
+    _test_memory_profiler_hook()
diff --git a/tests/test_utils/test_layer_decay_optimizer_constructor.py b/tests/test_utils/test_layer_decay_optimizer_constructor.py
new file mode 100644
index 0000000..674f665
--- /dev/null
+++ b/tests/test_utils/test_layer_decay_optimizer_constructor.py
@@ -0,0 +1,164 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+import torch.nn as nn
+from mmcv.cnn import ConvModule
+
+from mmdet.core.optimizers import LearningRateDecayOptimizerConstructor
+
+base_lr = 1
+decay_rate = 2
+base_wd = 0.05
+weight_decay = 0.05
+
+expected_stage_wise_lr_wd_convnext = [{
+    'weight_decay': 0.0,
+    'lr_scale': 128
+}, {
+    'weight_decay': 0.0,
+    'lr_scale': 1
+}, {
+    'weight_decay': 0.05,
+    'lr_scale': 64
+}, {
+    'weight_decay': 0.0,
+    'lr_scale': 64
+}, {
+    'weight_decay': 0.05,
+    'lr_scale': 32
+}, {
+    'weight_decay': 0.0,
+    'lr_scale': 32
+}, {
+    'weight_decay': 0.05,
+    'lr_scale': 16
+}, {
+    'weight_decay': 0.0,
+    'lr_scale': 16
+}, {
+    'weight_decay': 0.05,
+    'lr_scale': 8
+}, {
+    'weight_decay': 0.0,
+    'lr_scale': 8
+}, {
+    'weight_decay': 0.05,
+    'lr_scale': 128
+}, {
+    'weight_decay': 0.05,
+    'lr_scale': 1
+}]
+
+expected_layer_wise_lr_wd_convnext = [{
+    'weight_decay': 0.0,
+    'lr_scale': 128
+}, {
+    'weight_decay': 0.0,
+    'lr_scale': 1
+}, {
+    'weight_decay': 0.05,
+    'lr_scale': 64
+}, {
+    'weight_decay': 0.0,
+    'lr_scale': 64
+}, {
+    'weight_decay': 0.05,
+    'lr_scale': 32
+}, {
+    'weight_decay': 0.0,
+    'lr_scale': 32
+}, {
+    'weight_decay': 0.05,
+    'lr_scale': 16
+}, {
+    'weight_decay': 0.0,
+    'lr_scale': 16
+}, {
+    'weight_decay': 0.05,
+    'lr_scale': 2
+}, {
+    'weight_decay': 0.0,
+    'lr_scale': 2
+}, {
+    'weight_decay': 0.05,
+    'lr_scale': 128
+}, {
+    'weight_decay': 0.05,
+    'lr_scale': 1
+}]
+
+
+class ToyConvNeXt(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.stages = nn.ModuleList()
+        for i in range(4):
+            stage = nn.Sequential(ConvModule(3, 4, kernel_size=1, bias=True))
+            self.stages.append(stage)
+        self.norm0 = nn.BatchNorm2d(2)
+
+        # add some variables to meet unit test coverate rate
+        self.cls_token = nn.Parameter(torch.ones(1))
+        self.mask_token = nn.Parameter(torch.ones(1))
+        self.pos_embed = nn.Parameter(torch.ones(1))
+        self.stem_norm = nn.Parameter(torch.ones(1))
+        self.downsample_norm0 = nn.BatchNorm2d(2)
+        self.downsample_norm1 = nn.BatchNorm2d(2)
+        self.downsample_norm2 = nn.BatchNorm2d(2)
+        self.lin = nn.Parameter(torch.ones(1))
+        self.lin.requires_grad = False
+        self.downsample_layers = nn.ModuleList()
+        for _ in range(4):
+            stage = nn.Sequential(nn.Conv2d(3, 4, kernel_size=1, bias=True))
+            self.downsample_layers.append(stage)
+
+
+class ToyDetector(nn.Module):
+
+    def __init__(self, backbone):
+        super().__init__()
+        self.backbone = backbone
+        self.head = nn.Conv2d(2, 2, kernel_size=1, groups=2)
+
+
+class PseudoDataParallel(nn.Module):
+
+    def __init__(self, model):
+        super().__init__()
+        self.module = model
+
+
+def check_optimizer_lr_wd(optimizer, gt_lr_wd):
+    assert isinstance(optimizer, torch.optim.AdamW)
+    assert optimizer.defaults['lr'] == base_lr
+    assert optimizer.defaults['weight_decay'] == base_wd
+    param_groups = optimizer.param_groups
+    print(param_groups)
+    assert len(param_groups) == len(gt_lr_wd)
+    for i, param_dict in enumerate(param_groups):
+        assert param_dict['weight_decay'] == gt_lr_wd[i]['weight_decay']
+        assert param_dict['lr_scale'] == gt_lr_wd[i]['lr_scale']
+        assert param_dict['lr_scale'] == param_dict['lr']
+
+
+def test_learning_rate_decay_optimizer_constructor():
+
+    # Test lr wd for ConvNeXT
+    backbone = ToyConvNeXt()
+    model = PseudoDataParallel(ToyDetector(backbone))
+    optimizer_cfg = dict(
+        type='AdamW', lr=base_lr, betas=(0.9, 0.999), weight_decay=0.05)
+    # stagewise decay
+    stagewise_paramwise_cfg = dict(
+        decay_rate=decay_rate, decay_type='stage_wise', num_layers=6)
+    optim_constructor = LearningRateDecayOptimizerConstructor(
+        optimizer_cfg, stagewise_paramwise_cfg)
+    optimizer = optim_constructor(model)
+    check_optimizer_lr_wd(optimizer, expected_stage_wise_lr_wd_convnext)
+    # layerwise decay
+    layerwise_paramwise_cfg = dict(
+        decay_rate=decay_rate, decay_type='layer_wise', num_layers=6)
+    optim_constructor = LearningRateDecayOptimizerConstructor(
+        optimizer_cfg, layerwise_paramwise_cfg)
+    optimizer = optim_constructor(model)
+    check_optimizer_lr_wd(optimizer, expected_layer_wise_lr_wd_convnext)
diff --git a/tests/test_utils/test_logger.py b/tests/test_utils/test_logger.py
new file mode 100644
index 0000000..900d6b6
--- /dev/null
+++ b/tests/test_utils/test_logger.py
@@ -0,0 +1,47 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import pytest
+
+from mmdet.utils import get_caller_name, log_img_scale
+
+
+def callee_func():
+    caller_name = get_caller_name()
+    return caller_name
+
+
+class CallerClassForTest:
+
+    def __init__(self):
+        self.caller_name = callee_func()
+
+
+def test_get_caller_name():
+    # test the case that caller is a function
+    caller_name = callee_func()
+    assert caller_name == 'test_get_caller_name'
+
+    # test the case that caller is a method in a class
+    caller_class = CallerClassForTest()
+    assert caller_class.caller_name == 'CallerClassForTest.__init__'
+
+
+def test_log_img_scale():
+    img_scale = (800, 1333)
+    done_logging = log_img_scale(img_scale)
+    assert done_logging
+
+    img_scale = (1333, 800)
+    done_logging = log_img_scale(img_scale, shape_order='wh')
+    assert done_logging
+
+    with pytest.raises(ValueError):
+        img_scale = (1333, 800)
+        done_logging = log_img_scale(img_scale, shape_order='xywh')
+
+    img_scale = (640, 640)
+    done_logging = log_img_scale(img_scale, skip_square=False)
+    assert done_logging
+
+    img_scale = (640, 640)
+    done_logging = log_img_scale(img_scale, skip_square=True)
+    assert not done_logging
diff --git a/tests/test_utils/test_masks.py b/tests/test_utils/test_masks.py
new file mode 100644
index 0000000..226ca61
--- /dev/null
+++ b/tests/test_utils/test_masks.py
@@ -0,0 +1,713 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import numpy as np
+import pytest
+import torch
+
+from mmdet.core import BitmapMasks, PolygonMasks, mask2bbox
+
+
+def dummy_raw_bitmap_masks(size):
+    """
+    Args:
+        size (tuple): expected shape of dummy masks, (H, W) or (N, H, W)
+
+    Return:
+        ndarray: dummy mask
+    """
+    return np.random.randint(0, 2, size, dtype=np.uint8)
+
+
+def dummy_raw_polygon_masks(size):
+    """
+    Args:
+        size (tuple): expected shape of dummy masks, (N, H, W)
+
+    Return:
+        list[list[ndarray]]: dummy mask
+    """
+    num_obj, height, width = size
+    polygons = []
+    for _ in range(num_obj):
+        num_points = np.random.randint(5) * 2 + 6
+        polygons.append([np.random.uniform(0, min(height, width), num_points)])
+    return polygons
+
+
+def dummy_bboxes(num, max_height, max_width):
+    x1y1 = np.random.randint(0, min(max_height // 2, max_width // 2), (num, 2))
+    wh = np.random.randint(0, min(max_height // 2, max_width // 2), (num, 2))
+    x2y2 = x1y1 + wh
+    return np.concatenate([x1y1, x2y2], axis=1).squeeze().astype(np.float32)
+
+
+def test_bitmap_mask_init():
+    # init with empty ndarray masks
+    raw_masks = np.empty((0, 28, 28), dtype=np.uint8)
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    assert len(bitmap_masks) == 0
+    assert bitmap_masks.height == 28
+    assert bitmap_masks.width == 28
+
+    # init with empty list masks
+    raw_masks = []
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    assert len(bitmap_masks) == 0
+    assert bitmap_masks.height == 28
+    assert bitmap_masks.width == 28
+
+    # init with ndarray masks contain 3 instances
+    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    assert len(bitmap_masks) == 3
+    assert bitmap_masks.height == 28
+    assert bitmap_masks.width == 28
+
+    # init with list masks contain 3 instances
+    raw_masks = [dummy_raw_bitmap_masks((28, 28)) for _ in range(3)]
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    assert len(bitmap_masks) == 3
+    assert bitmap_masks.height == 28
+    assert bitmap_masks.width == 28
+
+    # init with raw masks of unsupported type
+    with pytest.raises(AssertionError):
+        raw_masks = [[dummy_raw_bitmap_masks((28, 28))]]
+        BitmapMasks(raw_masks, 28, 28)
+
+
+def test_bitmap_mask_rescale():
+    # rescale with empty bitmap masks
+    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    rescaled_masks = bitmap_masks.rescale((56, 72))
+    assert len(rescaled_masks) == 0
+    assert rescaled_masks.height == 56
+    assert rescaled_masks.width == 56
+
+    # rescale with bitmap masks contain 1 instances
+    raw_masks = np.array([[[1, 0, 0, 0], [0, 1, 0, 1]]])
+    bitmap_masks = BitmapMasks(raw_masks, 2, 4)
+    rescaled_masks = bitmap_masks.rescale((8, 8))
+    assert len(rescaled_masks) == 1
+    assert rescaled_masks.height == 4
+    assert rescaled_masks.width == 8
+    truth = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0],
+                       [0, 0, 1, 1, 0, 0, 1, 1], [0, 0, 1, 1, 0, 0, 1, 1]]])
+    assert (rescaled_masks.masks == truth).all()
+
+
+def test_bitmap_mask_resize():
+    # resize with empty bitmap masks
+    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    resized_masks = bitmap_masks.resize((56, 72))
+    assert len(resized_masks) == 0
+    assert resized_masks.height == 56
+    assert resized_masks.width == 72
+
+    # resize with bitmap masks contain 1 instances
+    raw_masks = np.diag(np.ones(4, dtype=np.uint8))[np.newaxis, ...]
+    bitmap_masks = BitmapMasks(raw_masks, 4, 4)
+    resized_masks = bitmap_masks.resize((8, 8))
+    assert len(resized_masks) == 1
+    assert resized_masks.height == 8
+    assert resized_masks.width == 8
+    truth = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [1, 1, 0, 0, 0, 0, 0, 0],
+                       [0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0],
+                       [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 1, 1, 0, 0],
+                       [0, 0, 0, 0, 0, 0, 1, 1], [0, 0, 0, 0, 0, 0, 1, 1]]])
+    assert (resized_masks.masks == truth).all()
+
+    # resize to non-square
+    raw_masks = np.diag(np.ones(4, dtype=np.uint8))[np.newaxis, ...]
+    bitmap_masks = BitmapMasks(raw_masks, 4, 4)
+    resized_masks = bitmap_masks.resize((4, 8))
+    assert len(resized_masks) == 1
+    assert resized_masks.height == 4
+    assert resized_masks.width == 8
+    truth = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0],
+                       [0, 0, 0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0, 1, 1]]])
+    assert (resized_masks.masks == truth).all()
+
+
+def test_bitmap_mask_get_bboxes():
+    # resize with empty bitmap masks
+    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    bboxes = bitmap_masks.get_bboxes()
+    assert len(bboxes) == 0
+
+    # resize with bitmap masks contain 1 instances
+    raw_masks = np.array([[[0, 0, 0, 0, 0, 0, 0, 0], [0, 1, 1, 1, 0, 0, 0, 0],
+                           [0, 0, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 0, 0, 0],
+                           [0, 0, 1, 1, 1, 1, 0, 0], [0, 0, 1, 0, 0, 0, 0, 0],
+                           [0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0,
+                                                      0]]])
+    bitmap_masks = BitmapMasks(raw_masks, 8, 8)
+    bboxes = bitmap_masks.get_bboxes()
+    assert len(bboxes) == 1
+    truth = np.array([[1, 1, 6, 6]])
+    assert (bboxes == truth).all()
+
+    # resize to non-square
+    raw_masks = np.array([[[1, 1, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0, 0, 0],
+                           [0, 0, 0, 0, 0, 1, 0, 0], [0, 0, 0, 0, 0, 0, 0,
+                                                      0]]])
+    bitmap_masks = BitmapMasks(raw_masks, 4, 8)
+    bboxes = bitmap_masks.get_bboxes()
+    truth = np.array([[0, 0, 6, 3]])
+    assert (bboxes == truth).all()
+
+
+def test_bitmap_mask_flip():
+    # flip with empty bitmap masks
+    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    flipped_masks = bitmap_masks.flip(flip_direction='horizontal')
+    assert len(flipped_masks) == 0
+    assert flipped_masks.height == 28
+    assert flipped_masks.width == 28
+
+    # horizontally flip with bitmap masks contain 3 instances
+    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    flipped_masks = bitmap_masks.flip(flip_direction='horizontal')
+    flipped_flipped_masks = flipped_masks.flip(flip_direction='horizontal')
+    assert flipped_masks.masks.shape == (3, 28, 28)
+    assert (bitmap_masks.masks == flipped_flipped_masks.masks).all()
+    assert (flipped_masks.masks == raw_masks[:, :, ::-1]).all()
+
+    # vertically flip with bitmap masks contain 3 instances
+    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    flipped_masks = bitmap_masks.flip(flip_direction='vertical')
+    flipped_flipped_masks = flipped_masks.flip(flip_direction='vertical')
+    assert len(flipped_masks) == 3
+    assert flipped_masks.height == 28
+    assert flipped_masks.width == 28
+    assert (bitmap_masks.masks == flipped_flipped_masks.masks).all()
+    assert (flipped_masks.masks == raw_masks[:, ::-1, :]).all()
+
+    # diagonal flip with bitmap masks contain 3 instances
+    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    flipped_masks = bitmap_masks.flip(flip_direction='diagonal')
+    flipped_flipped_masks = flipped_masks.flip(flip_direction='diagonal')
+    assert len(flipped_masks) == 3
+    assert flipped_masks.height == 28
+    assert flipped_masks.width == 28
+    assert (bitmap_masks.masks == flipped_flipped_masks.masks).all()
+    assert (flipped_masks.masks == raw_masks[:, ::-1, ::-1]).all()
+
+
+def test_bitmap_mask_pad():
+    # pad with empty bitmap masks
+    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    padded_masks = bitmap_masks.pad((56, 56))
+    assert len(padded_masks) == 0
+    assert padded_masks.height == 56
+    assert padded_masks.width == 56
+
+    # pad with bitmap masks contain 3 instances
+    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    padded_masks = bitmap_masks.pad((56, 56))
+    assert len(padded_masks) == 3
+    assert padded_masks.height == 56
+    assert padded_masks.width == 56
+    assert (padded_masks.masks[:, 28:, 28:] == 0).all()
+
+
+def test_bitmap_mask_crop():
+    # crop with empty bitmap masks
+    dummy_bbox = np.array([0, 10, 10, 27], dtype=np.int)
+    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    cropped_masks = bitmap_masks.crop(dummy_bbox)
+    assert len(cropped_masks) == 0
+    assert cropped_masks.height == 17
+    assert cropped_masks.width == 10
+
+    # crop with bitmap masks contain 3 instances
+    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    cropped_masks = bitmap_masks.crop(dummy_bbox)
+    assert len(cropped_masks) == 3
+    assert cropped_masks.height == 17
+    assert cropped_masks.width == 10
+    x1, y1, x2, y2 = dummy_bbox
+    assert (cropped_masks.masks == raw_masks[:, y1:y2, x1:x2]).all()
+
+    # crop with invalid bbox
+    with pytest.raises(AssertionError):
+        dummy_bbox = dummy_bboxes(2, 28, 28)
+        bitmap_masks.crop(dummy_bbox)
+
+
+def test_bitmap_mask_crop_and_resize():
+    dummy_bbox = dummy_bboxes(5, 28, 28)
+    inds = np.random.randint(0, 3, (5, ))
+
+    # crop and resize with empty bitmap masks
+    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    cropped_resized_masks = bitmap_masks.crop_and_resize(
+        dummy_bbox, (56, 56), inds)
+    assert len(cropped_resized_masks) == 0
+    assert cropped_resized_masks.height == 56
+    assert cropped_resized_masks.width == 56
+
+    # crop and resize with bitmap masks contain 3 instances
+    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    cropped_resized_masks = bitmap_masks.crop_and_resize(
+        dummy_bbox, (56, 56), inds)
+    assert len(cropped_resized_masks) == 5
+    assert cropped_resized_masks.height == 56
+    assert cropped_resized_masks.width == 56
+
+
+def test_bitmap_mask_expand():
+    # expand with empty bitmap masks
+    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    expanded_masks = bitmap_masks.expand(56, 56, 12, 14)
+    assert len(expanded_masks) == 0
+    assert expanded_masks.height == 56
+    assert expanded_masks.width == 56
+
+    # expand with bitmap masks contain 3 instances
+    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    expanded_masks = bitmap_masks.expand(56, 56, 12, 14)
+    assert len(expanded_masks) == 3
+    assert expanded_masks.height == 56
+    assert expanded_masks.width == 56
+    assert (expanded_masks.masks[:, :12, :14] == 0).all()
+    assert (expanded_masks.masks[:, 12 + 28:, 14 + 28:] == 0).all()
+
+
+def test_bitmap_mask_area():
+    # area of empty bitmap mask
+    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    assert bitmap_masks.areas.sum() == 0
+
+    # area of bitmap masks contain 3 instances
+    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    areas = bitmap_masks.areas
+    assert len(areas) == 3
+    assert (areas == raw_masks.sum((1, 2))).all()
+
+
+def test_bitmap_mask_to_ndarray():
+    # empty bitmap masks to ndarray
+    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    ndarray_masks = bitmap_masks.to_ndarray()
+    assert isinstance(ndarray_masks, np.ndarray)
+    assert ndarray_masks.shape == (0, 28, 28)
+
+    # bitmap masks contain 3 instances to ndarray
+    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    ndarray_masks = bitmap_masks.to_ndarray()
+    assert isinstance(ndarray_masks, np.ndarray)
+    assert ndarray_masks.shape == (3, 28, 28)
+    assert (ndarray_masks == raw_masks).all()
+
+
+def test_bitmap_mask_to_tensor():
+    # empty bitmap masks to tensor
+    raw_masks = dummy_raw_bitmap_masks((0, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    tensor_masks = bitmap_masks.to_tensor(dtype=torch.uint8, device='cpu')
+    assert isinstance(tensor_masks, torch.Tensor)
+    assert tensor_masks.shape == (0, 28, 28)
+
+    # bitmap masks contain 3 instances to tensor
+    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    tensor_masks = bitmap_masks.to_tensor(dtype=torch.uint8, device='cpu')
+    assert isinstance(tensor_masks, torch.Tensor)
+    assert tensor_masks.shape == (3, 28, 28)
+    assert (tensor_masks.numpy() == raw_masks).all()
+
+
+def test_bitmap_mask_index():
+    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    assert (bitmap_masks[0].masks == raw_masks[0]).all()
+    assert (bitmap_masks[range(2)].masks == raw_masks[range(2)]).all()
+
+
+def test_bitmap_mask_iter():
+    raw_masks = dummy_raw_bitmap_masks((3, 28, 28))
+    bitmap_masks = BitmapMasks(raw_masks, 28, 28)
+    for i, bitmap_mask in enumerate(bitmap_masks):
+        assert bitmap_mask.shape == (28, 28)
+        assert (bitmap_mask == raw_masks[i]).all()
+
+
+def test_polygon_mask_init():
+    # init with empty masks
+    raw_masks = []
+    polygon_masks = BitmapMasks(raw_masks, 28, 28)
+    assert len(polygon_masks) == 0
+    assert polygon_masks.height == 28
+    assert polygon_masks.width == 28
+
+    # init with masks contain 3 instances
+    raw_masks = dummy_raw_polygon_masks((3, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    assert isinstance(polygon_masks.masks, list)
+    assert isinstance(polygon_masks.masks[0], list)
+    assert isinstance(polygon_masks.masks[0][0], np.ndarray)
+    assert len(polygon_masks) == 3
+    assert polygon_masks.height == 28
+    assert polygon_masks.width == 28
+    assert polygon_masks.to_ndarray().shape == (3, 28, 28)
+
+    # init with raw masks of unsupported type
+    with pytest.raises(AssertionError):
+        raw_masks = [[[]]]
+        PolygonMasks(raw_masks, 28, 28)
+
+        raw_masks = [dummy_raw_polygon_masks((3, 28, 28))]
+        PolygonMasks(raw_masks, 28, 28)
+
+
+def test_polygon_mask_rescale():
+    # rescale with empty polygon masks
+    raw_masks = dummy_raw_polygon_masks((0, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    rescaled_masks = polygon_masks.rescale((56, 72))
+    assert len(rescaled_masks) == 0
+    assert rescaled_masks.height == 56
+    assert rescaled_masks.width == 56
+    assert rescaled_masks.to_ndarray().shape == (0, 56, 56)
+
+    # rescale with polygon masks contain 3 instances
+    raw_masks = [[np.array([1, 1, 3, 1, 4, 3, 2, 4, 1, 3], dtype=np.float)]]
+    polygon_masks = PolygonMasks(raw_masks, 5, 5)
+    rescaled_masks = polygon_masks.rescale((12, 10))
+    assert len(rescaled_masks) == 1
+    assert rescaled_masks.height == 10
+    assert rescaled_masks.width == 10
+    assert rescaled_masks.to_ndarray().shape == (1, 10, 10)
+    truth = np.array(
+        [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+         [0, 0, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+         [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+         [0, 0, 0, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
+        np.uint8)
+    assert (rescaled_masks.to_ndarray() == truth).all()
+
+
+def test_polygon_mask_resize():
+    # resize with empty polygon masks
+    raw_masks = dummy_raw_polygon_masks((0, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    resized_masks = polygon_masks.resize((56, 72))
+    assert len(resized_masks) == 0
+    assert resized_masks.height == 56
+    assert resized_masks.width == 72
+    assert resized_masks.to_ndarray().shape == (0, 56, 72)
+    assert len(resized_masks.get_bboxes()) == 0
+
+    # resize with polygon masks contain 1 instance 1 part
+    raw_masks1 = [[np.array([1, 1, 3, 1, 4, 3, 2, 4, 1, 3], dtype=np.float)]]
+    polygon_masks1 = PolygonMasks(raw_masks1, 5, 5)
+    resized_masks1 = polygon_masks1.resize((10, 10))
+    assert len(resized_masks1) == 1
+    assert resized_masks1.height == 10
+    assert resized_masks1.width == 10
+    assert resized_masks1.to_ndarray().shape == (1, 10, 10)
+    truth1 = np.array(
+        [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
+         [0, 0, 1, 1, 1, 1, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+         [0, 0, 1, 1, 1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 1, 0, 0],
+         [0, 0, 0, 1, 1, 1, 1, 0, 0, 0], [0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]],
+        np.uint8)
+    assert (resized_masks1.to_ndarray() == truth1).all()
+    bboxes = resized_masks1.get_bboxes()
+    bbox_truth = np.array([[2, 2, 8, 8]])
+    assert (bboxes == bbox_truth).all()
+
+    # resize with polygon masks contain 1 instance 2 part
+    raw_masks2 = [[
+        np.array([0., 0., 1., 0., 1., 1.]),
+        np.array([1., 1., 2., 1., 2., 2., 1., 2.])
+    ]]
+    polygon_masks2 = PolygonMasks(raw_masks2, 3, 3)
+    resized_masks2 = polygon_masks2.resize((6, 6))
+    assert len(resized_masks2) == 1
+    assert resized_masks2.height == 6
+    assert resized_masks2.width == 6
+    assert resized_masks2.to_ndarray().shape == (1, 6, 6)
+    truth2 = np.array(
+        [[0, 1, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 0, 0],
+         [0, 0, 1, 1, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0]], np.uint8)
+    assert (resized_masks2.to_ndarray() == truth2).all()
+
+    # resize with polygon masks contain 2 instances
+    raw_masks3 = [raw_masks1[0], raw_masks2[0]]
+    polygon_masks3 = PolygonMasks(raw_masks3, 5, 5)
+    resized_masks3 = polygon_masks3.resize((10, 10))
+    assert len(resized_masks3) == 2
+    assert resized_masks3.height == 10
+    assert resized_masks3.width == 10
+    assert resized_masks3.to_ndarray().shape == (2, 10, 10)
+    truth3 = np.stack([truth1, np.pad(truth2, ((0, 4), (0, 4)), 'constant')])
+    assert (resized_masks3.to_ndarray() == truth3).all()
+
+    # resize to non-square
+    raw_masks4 = [[np.array([1, 1, 3, 1, 4, 3, 2, 4, 1, 3], dtype=np.float)]]
+    polygon_masks4 = PolygonMasks(raw_masks4, 5, 5)
+    resized_masks4 = polygon_masks4.resize((5, 10))
+    assert len(resized_masks4) == 1
+    assert resized_masks4.height == 5
+    assert resized_masks4.width == 10
+    assert resized_masks4.to_ndarray().shape == (1, 5, 10)
+    truth4 = np.array(
+        [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 1, 1, 1, 1, 1, 0, 0, 0],
+         [0, 0, 1, 1, 1, 1, 1, 1, 0, 0], [0, 0, 0, 1, 1, 1, 0, 0, 0, 0],
+         [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], np.uint8)
+    assert (resized_masks4.to_ndarray() == truth4).all()
+
+
+def test_polygon_mask_flip():
+    # flip with empty polygon masks
+    raw_masks = dummy_raw_polygon_masks((0, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    flipped_masks = polygon_masks.flip(flip_direction='horizontal')
+    assert len(flipped_masks) == 0
+    assert flipped_masks.height == 28
+    assert flipped_masks.width == 28
+    assert flipped_masks.to_ndarray().shape == (0, 28, 28)
+
+    # TODO: fixed flip correctness checking after v2.0_coord is merged
+    # horizontally flip with polygon masks contain 3 instances
+    raw_masks = dummy_raw_polygon_masks((3, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    flipped_masks = polygon_masks.flip(flip_direction='horizontal')
+    flipped_flipped_masks = flipped_masks.flip(flip_direction='horizontal')
+    assert len(flipped_masks) == 3
+    assert flipped_masks.height == 28
+    assert flipped_masks.width == 28
+    assert flipped_masks.to_ndarray().shape == (3, 28, 28)
+    assert (polygon_masks.to_ndarray() == flipped_flipped_masks.to_ndarray()
+            ).all()
+
+    # vertically flip with polygon masks contain 3 instances
+    raw_masks = dummy_raw_polygon_masks((3, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    flipped_masks = polygon_masks.flip(flip_direction='vertical')
+    flipped_flipped_masks = flipped_masks.flip(flip_direction='vertical')
+    assert len(flipped_masks) == 3
+    assert flipped_masks.height == 28
+    assert flipped_masks.width == 28
+    assert flipped_masks.to_ndarray().shape == (3, 28, 28)
+    assert (polygon_masks.to_ndarray() == flipped_flipped_masks.to_ndarray()
+            ).all()
+
+    # diagonal flip with polygon masks contain 3 instances
+    raw_masks = dummy_raw_polygon_masks((3, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    flipped_masks = polygon_masks.flip(flip_direction='diagonal')
+    flipped_flipped_masks = flipped_masks.flip(flip_direction='diagonal')
+    assert len(flipped_masks) == 3
+    assert flipped_masks.height == 28
+    assert flipped_masks.width == 28
+    assert flipped_masks.to_ndarray().shape == (3, 28, 28)
+    assert (polygon_masks.to_ndarray() == flipped_flipped_masks.to_ndarray()
+            ).all()
+
+
+def test_polygon_mask_crop():
+    dummy_bbox = np.array([0, 10, 10, 27], dtype=np.int)
+    # crop with empty polygon masks
+    raw_masks = dummy_raw_polygon_masks((0, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    cropped_masks = polygon_masks.crop(dummy_bbox)
+    assert len(cropped_masks) == 0
+    assert cropped_masks.height == 17
+    assert cropped_masks.width == 10
+    assert cropped_masks.to_ndarray().shape == (0, 17, 10)
+
+    # crop with polygon masks contain 1 instances
+    raw_masks = [[np.array([1., 3., 5., 1., 5., 6., 1, 6])]]
+    polygon_masks = PolygonMasks(raw_masks, 7, 7)
+    bbox = np.array([0, 0, 3, 4])
+    cropped_masks = polygon_masks.crop(bbox)
+    assert len(cropped_masks) == 1
+    assert cropped_masks.height == 4
+    assert cropped_masks.width == 3
+    assert cropped_masks.to_ndarray().shape == (1, 4, 3)
+    truth = np.array([[0, 0, 0], [0, 0, 0], [0, 0, 1], [0, 1, 1]])
+    assert (cropped_masks.to_ndarray() == truth).all()
+
+    # crop with invalid bbox
+    with pytest.raises(AssertionError):
+        dummy_bbox = dummy_bboxes(2, 28, 28)
+        polygon_masks.crop(dummy_bbox)
+
+
+def test_polygon_mask_pad():
+    # pad with empty polygon masks
+    raw_masks = dummy_raw_polygon_masks((0, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    padded_masks = polygon_masks.pad((56, 56))
+    assert len(padded_masks) == 0
+    assert padded_masks.height == 56
+    assert padded_masks.width == 56
+    assert padded_masks.to_ndarray().shape == (0, 56, 56)
+
+    # pad with polygon masks contain 3 instances
+    raw_masks = dummy_raw_polygon_masks((3, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    padded_masks = polygon_masks.pad((56, 56))
+    assert len(padded_masks) == 3
+    assert padded_masks.height == 56
+    assert padded_masks.width == 56
+    assert padded_masks.to_ndarray().shape == (3, 56, 56)
+    assert (padded_masks.to_ndarray()[:, 28:, 28:] == 0).all()
+
+
+def test_polygon_mask_expand():
+    with pytest.raises(NotImplementedError):
+        raw_masks = dummy_raw_polygon_masks((0, 28, 28))
+        polygon_masks = PolygonMasks(raw_masks, 28, 28)
+        polygon_masks.expand(56, 56, 10, 17)
+
+
+def test_polygon_mask_crop_and_resize():
+    dummy_bbox = dummy_bboxes(5, 28, 28)
+    inds = np.random.randint(0, 3, (5, ))
+
+    # crop and resize with empty polygon masks
+    raw_masks = dummy_raw_polygon_masks((0, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    cropped_resized_masks = polygon_masks.crop_and_resize(
+        dummy_bbox, (56, 56), inds)
+    assert len(cropped_resized_masks) == 0
+    assert cropped_resized_masks.height == 56
+    assert cropped_resized_masks.width == 56
+    assert cropped_resized_masks.to_ndarray().shape == (0, 56, 56)
+
+    # crop and resize with polygon masks contain 3 instances
+    raw_masks = dummy_raw_polygon_masks((3, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    cropped_resized_masks = polygon_masks.crop_and_resize(
+        dummy_bbox, (56, 56), inds)
+    assert len(cropped_resized_masks) == 5
+    assert cropped_resized_masks.height == 56
+    assert cropped_resized_masks.width == 56
+    assert cropped_resized_masks.to_ndarray().shape == (5, 56, 56)
+
+
+def test_polygon_mask_area():
+    # area of empty polygon masks
+    raw_masks = dummy_raw_polygon_masks((0, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    assert polygon_masks.areas.sum() == 0
+
+    # area of polygon masks contain 1 instance
+    # here we hack a case that the gap between the area of bitmap and polygon
+    # is minor
+    raw_masks = [[np.array([1, 1, 5, 1, 3, 4])]]
+    polygon_masks = PolygonMasks(raw_masks, 6, 6)
+    polygon_area = polygon_masks.areas
+    bitmap_area = polygon_masks.to_bitmap().areas
+    assert len(polygon_area) == 1
+    assert np.isclose(polygon_area, bitmap_area).all()
+
+
+def test_polygon_mask_to_bitmap():
+    # polygon masks contain 3 instances to bitmap
+    raw_masks = dummy_raw_polygon_masks((3, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    bitmap_masks = polygon_masks.to_bitmap()
+    assert (polygon_masks.to_ndarray() == bitmap_masks.to_ndarray()).all()
+
+
+def test_polygon_mask_to_ndarray():
+    # empty polygon masks to ndarray
+    raw_masks = dummy_raw_polygon_masks((0, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    ndarray_masks = polygon_masks.to_ndarray()
+    assert isinstance(ndarray_masks, np.ndarray)
+    assert ndarray_masks.shape == (0, 28, 28)
+
+    # polygon masks contain 3 instances to ndarray
+    raw_masks = dummy_raw_polygon_masks((3, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    ndarray_masks = polygon_masks.to_ndarray()
+    assert isinstance(ndarray_masks, np.ndarray)
+    assert ndarray_masks.shape == (3, 28, 28)
+
+
+def test_polygon_to_tensor():
+    # empty polygon masks to tensor
+    raw_masks = dummy_raw_polygon_masks((0, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    tensor_masks = polygon_masks.to_tensor(dtype=torch.uint8, device='cpu')
+    assert isinstance(tensor_masks, torch.Tensor)
+    assert tensor_masks.shape == (0, 28, 28)
+
+    # polygon masks contain 3 instances to tensor
+    raw_masks = dummy_raw_polygon_masks((3, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    tensor_masks = polygon_masks.to_tensor(dtype=torch.uint8, device='cpu')
+    assert isinstance(tensor_masks, torch.Tensor)
+    assert tensor_masks.shape == (3, 28, 28)
+    assert (tensor_masks.numpy() == polygon_masks.to_ndarray()).all()
+
+
+def test_polygon_mask_index():
+    raw_masks = dummy_raw_polygon_masks((3, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    # index by integer
+    polygon_masks[0]
+    # index by list
+    polygon_masks[[0, 1]]
+    # index by ndarray
+    polygon_masks[np.asarray([0, 1])]
+    with pytest.raises(ValueError):
+        # invalid index
+        polygon_masks[torch.Tensor([1, 2])]
+
+
+def test_polygon_mask_iter():
+    raw_masks = dummy_raw_polygon_masks((3, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    for i, polygon_mask in enumerate(polygon_masks):
+        assert np.equal(polygon_mask, raw_masks[i]).all()
+
+
+def test_mask2bbox():
+    # no instance
+    masks = torch.zeros((1, 20, 15), dtype=torch.bool)
+    bboxes_empty_gt = torch.tensor([[0, 0, 0, 0]]).float()
+    bboxes = mask2bbox(masks)
+    assert torch.allclose(bboxes_empty_gt.float(), bboxes)
+
+    # the entire mask is an instance
+    bboxes_full_gt = torch.tensor([[0, 0, 15, 20]]).float()
+    masks = torch.ones((1, 20, 15), dtype=torch.bool)
+    bboxes = mask2bbox(masks)
+    assert torch.allclose(bboxes_full_gt, bboxes)
+
+    # a pentagon-shaped instance
+    bboxes_gt = torch.tensor([[2, 2, 7, 6]]).float()
+    masks = torch.zeros((1, 20, 15), dtype=torch.bool)
+    masks[0, 2, 4] = True
+    masks[0, 3, 3:6] = True
+    masks[0, 4, 2:7] = True
+    masks[0, 5, 2:7] = True
+    bboxes = mask2bbox(masks)
+    assert torch.allclose(bboxes_gt, bboxes)
diff --git a/tests/test_utils/test_memory.py b/tests/test_utils/test_memory.py
new file mode 100644
index 0000000..840601c
--- /dev/null
+++ b/tests/test_utils/test_memory.py
@@ -0,0 +1,98 @@
+import numpy as np
+import pytest
+import torch
+
+from mmdet.utils import AvoidOOM
+from mmdet.utils.memory import cast_tensor_type
+
+
+def test_avoidoom():
+    tensor = torch.from_numpy(np.random.random((20, 20)))
+    if torch.cuda.is_available():
+        tensor = tensor.cuda()
+        # get default result
+        default_result = torch.mm(tensor, tensor.transpose(1, 0))
+
+        # when not occurred OOM error
+        AvoidCudaOOM = AvoidOOM()
+        result = AvoidCudaOOM.retry_if_cuda_oom(torch.mm)(tensor,
+                                                          tensor.transpose(
+                                                              1, 0))
+        assert default_result.device == result.device and \
+               default_result.dtype == result.dtype and \
+               torch.equal(default_result, result)
+
+        # calculate with fp16 and convert back to source type
+        AvoidCudaOOM = AvoidOOM(test=True)
+        result = AvoidCudaOOM.retry_if_cuda_oom(torch.mm)(tensor,
+                                                          tensor.transpose(
+                                                              1, 0))
+        assert default_result.device == result.device and \
+               default_result.dtype == result.dtype and \
+               torch.allclose(default_result, result, 1e-3)
+
+        # calculate on cpu and convert back to source device
+        AvoidCudaOOM = AvoidOOM(test=True)
+        result = AvoidCudaOOM.retry_if_cuda_oom(torch.mm)(tensor,
+                                                          tensor.transpose(
+                                                              1, 0))
+        assert result.dtype == default_result.dtype and \
+               result.device == default_result.device and \
+               torch.allclose(default_result, result)
+
+        # do not calculate on cpu and the outputs will be same as input
+        AvoidCudaOOM = AvoidOOM(test=True, to_cpu=False)
+        result = AvoidCudaOOM.retry_if_cuda_oom(torch.mm)(tensor,
+                                                          tensor.transpose(
+                                                              1, 0))
+        assert result.dtype == default_result.dtype and \
+               result.device == default_result.device
+
+    else:
+        default_result = torch.mm(tensor, tensor.transpose(1, 0))
+        AvoidCudaOOM = AvoidOOM()
+        result = AvoidCudaOOM.retry_if_cuda_oom(torch.mm)(tensor,
+                                                          tensor.transpose(
+                                                              1, 0))
+        assert default_result.device == result.device and \
+               default_result.dtype == result.dtype and \
+               torch.equal(default_result, result)
+
+
+def test_cast_tensor_type():
+    inputs = torch.rand(10)
+    if torch.cuda.is_available():
+        inputs = inputs.cuda()
+    with pytest.raises(AssertionError):
+        cast_tensor_type(inputs, src_type=None, dst_type=None)
+    # input is a float
+    out = cast_tensor_type(10., dst_type=torch.half)
+    assert out == 10. and isinstance(out, float)
+    # convert Tensor to fp16 and re-convert to fp32
+    fp16_out = cast_tensor_type(inputs, dst_type=torch.half)
+    assert fp16_out.dtype == torch.half
+    fp32_out = cast_tensor_type(fp16_out, dst_type=torch.float32)
+    assert fp32_out.dtype == torch.float32
+
+    # input is a list
+    list_input = [inputs, inputs]
+    list_outs = cast_tensor_type(list_input, dst_type=torch.half)
+    assert len(list_outs) == len(list_input) and \
+           isinstance(list_outs, list)
+    for out in list_outs:
+        assert out.dtype == torch.half
+    # input is a dict
+    dict_input = {'test1': inputs, 'test2': inputs}
+    dict_outs = cast_tensor_type(dict_input, dst_type=torch.half)
+    assert len(dict_outs) == len(dict_input) and \
+           isinstance(dict_outs, dict)
+
+    # convert the input tensor to CPU and re-convert to GPU
+    if torch.cuda.is_available():
+        cpu_device = torch.empty(0).device
+        gpu_device = inputs.device
+        cpu_out = cast_tensor_type(inputs, dst_type=cpu_device)
+        assert cpu_out.device == cpu_device
+
+        gpu_out = cast_tensor_type(inputs, dst_type=gpu_device)
+        assert gpu_out.device == gpu_device
diff --git a/tests/test_utils/test_misc.py b/tests/test_utils/test_misc.py
new file mode 100644
index 0000000..80d9114
--- /dev/null
+++ b/tests/test_utils/test_misc.py
@@ -0,0 +1,204 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import tempfile
+
+import numpy as np
+import pytest
+import torch
+
+from mmdet.core.bbox import distance2bbox
+from mmdet.core.mask.structures import BitmapMasks, PolygonMasks
+from mmdet.core.utils import (center_of_mass, filter_scores_and_topk,
+                              flip_tensor, mask2ndarray, select_single_mlvl)
+from mmdet.utils import find_latest_checkpoint
+
+
+def dummy_raw_polygon_masks(size):
+    """
+    Args:
+        size (tuple): expected shape of dummy masks, (N, H, W)
+
+    Return:
+        list[list[ndarray]]: dummy mask
+    """
+    num_obj, height, width = size
+    polygons = []
+    for _ in range(num_obj):
+        num_points = np.random.randint(5) * 2 + 6
+        polygons.append([np.random.uniform(0, min(height, width), num_points)])
+    return polygons
+
+
+def test_mask2ndarray():
+    raw_masks = np.ones((3, 28, 28))
+    bitmap_mask = BitmapMasks(raw_masks, 28, 28)
+    output_mask = mask2ndarray(bitmap_mask)
+    assert np.allclose(raw_masks, output_mask)
+
+    raw_masks = dummy_raw_polygon_masks((3, 28, 28))
+    polygon_masks = PolygonMasks(raw_masks, 28, 28)
+    output_mask = mask2ndarray(polygon_masks)
+    assert output_mask.shape == (3, 28, 28)
+
+    raw_masks = np.ones((3, 28, 28))
+    output_mask = mask2ndarray(raw_masks)
+    assert np.allclose(raw_masks, output_mask)
+
+    raw_masks = torch.ones((3, 28, 28))
+    output_mask = mask2ndarray(raw_masks)
+    assert np.allclose(raw_masks, output_mask)
+
+    # test unsupported type
+    raw_masks = []
+    with pytest.raises(TypeError):
+        output_mask = mask2ndarray(raw_masks)
+
+
+def test_distance2bbox():
+    point = torch.Tensor([[74., 61.], [-29., 106.], [138., 61.], [29., 170.]])
+
+    distance = torch.Tensor([[0., 0, 1., 1.], [1., 2., 10., 6.],
+                             [22., -29., 138., 61.], [54., -29., 170., 61.]])
+    expected_decode_bboxes = torch.Tensor([[74., 61., 75., 62.],
+                                           [0., 104., 0., 112.],
+                                           [100., 90., 100., 120.],
+                                           [0., 120., 100., 120.]])
+    out_bbox = distance2bbox(point, distance, max_shape=(120, 100))
+    assert expected_decode_bboxes.allclose(out_bbox)
+    out = distance2bbox(point, distance, max_shape=torch.Tensor((120, 100)))
+    assert expected_decode_bboxes.allclose(out)
+
+    batch_point = point.unsqueeze(0).repeat(2, 1, 1)
+    batch_distance = distance.unsqueeze(0).repeat(2, 1, 1)
+    batch_out = distance2bbox(
+        batch_point, batch_distance, max_shape=(120, 100))[0]
+    assert out.allclose(batch_out)
+    batch_out = distance2bbox(
+        batch_point, batch_distance, max_shape=[(120, 100), (120, 100)])[0]
+    assert out.allclose(batch_out)
+
+    batch_out = distance2bbox(point, batch_distance, max_shape=(120, 100))[0]
+    assert out.allclose(batch_out)
+
+    # test max_shape is not equal to batch
+    with pytest.raises(AssertionError):
+        distance2bbox(
+            batch_point,
+            batch_distance,
+            max_shape=[(120, 100), (120, 100), (32, 32)])
+
+    rois = torch.zeros((0, 4))
+    deltas = torch.zeros((0, 4))
+    out = distance2bbox(rois, deltas, max_shape=(120, 100))
+    assert rois.shape == out.shape
+
+    rois = torch.zeros((2, 0, 4))
+    deltas = torch.zeros((2, 0, 4))
+    out = distance2bbox(rois, deltas, max_shape=(120, 100))
+    assert rois.shape == out.shape
+
+
+@pytest.mark.parametrize('mask', [
+    torch.ones((28, 28)),
+    torch.zeros((28, 28)),
+    torch.rand(28, 28) > 0.5,
+    torch.tensor([[0, 0, 0, 0], [0, 1, 1, 0], [0, 1, 1, 0], [0, 0, 0, 0]])
+])
+def test_center_of_mass(mask):
+    center_h, center_w = center_of_mass(mask)
+    if mask.shape[0] == 4:
+        assert center_h == 1.5
+        assert center_w == 1.5
+    assert isinstance(center_h, torch.Tensor) \
+           and isinstance(center_w, torch.Tensor)
+    assert 0 <= center_h <= 28 \
+           and 0 <= center_w <= 28
+
+
+def test_flip_tensor():
+    img = np.random.random((1, 3, 10, 10))
+    src_tensor = torch.from_numpy(img)
+
+    # test flip_direction parameter error
+    with pytest.raises(AssertionError):
+        flip_tensor(src_tensor, 'flip')
+
+    # test tensor dimension
+    with pytest.raises(AssertionError):
+        flip_tensor(src_tensor[0], 'vertical')
+
+    hfilp_tensor = flip_tensor(src_tensor, 'horizontal')
+    expected_hflip_tensor = torch.from_numpy(img[..., ::-1, :].copy())
+    expected_hflip_tensor.allclose(hfilp_tensor)
+
+    vfilp_tensor = flip_tensor(src_tensor, 'vertical')
+    expected_vflip_tensor = torch.from_numpy(img[..., ::-1].copy())
+    expected_vflip_tensor.allclose(vfilp_tensor)
+
+    diag_filp_tensor = flip_tensor(src_tensor, 'diagonal')
+    expected_diag_filp_tensor = torch.from_numpy(img[..., ::-1, ::-1].copy())
+    expected_diag_filp_tensor.allclose(diag_filp_tensor)
+
+
+def test_select_single_mlvl():
+    mlvl_tensors = [torch.rand(2, 1, 10, 10)] * 5
+    mlvl_tensor_list = select_single_mlvl(mlvl_tensors, 1)
+    assert len(mlvl_tensor_list) == 5 and mlvl_tensor_list[0].ndim == 3
+
+
+def test_filter_scores_and_topk():
+    score = torch.tensor([[0.1, 0.3, 0.2], [0.12, 0.7, 0.9], [0.02, 0.8, 0.08],
+                          [0.4, 0.1, 0.08]])
+    bbox_pred = torch.tensor([[0.2, 0.3], [0.4, 0.7], [0.1, 0.1], [0.5, 0.1]])
+    score_thr = 0.15
+    nms_pre = 4
+    # test results type error
+    with pytest.raises(NotImplementedError):
+        filter_scores_and_topk(score, score_thr, nms_pre, (score, ))
+
+    filtered_results = filter_scores_and_topk(
+        score, score_thr, nms_pre, results=dict(bbox_pred=bbox_pred))
+    filtered_score, labels, keep_idxs, results = filtered_results
+    assert filtered_score.allclose(torch.tensor([0.9, 0.8, 0.7, 0.4]))
+    assert labels.allclose(torch.tensor([2, 1, 1, 0]))
+    assert keep_idxs.allclose(torch.tensor([1, 2, 1, 3]))
+    assert results['bbox_pred'].allclose(
+        torch.tensor([[0.4, 0.7], [0.1, 0.1], [0.4, 0.7], [0.5, 0.1]]))
+
+
+def test_find_latest_checkpoint():
+    with tempfile.TemporaryDirectory() as tmpdir:
+        path = tmpdir
+        latest = find_latest_checkpoint(path)
+        # There are no checkpoints in the path.
+        assert latest is None
+
+        path = osp.join(tmpdir, 'none')
+        latest = find_latest_checkpoint(path)
+        # The path does not exist.
+        assert latest is None
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        with open(osp.join(tmpdir, 'latest.pth'), 'w') as f:
+            f.write('latest')
+        path = tmpdir
+        latest = find_latest_checkpoint(path)
+        assert latest == osp.join(tmpdir, 'latest.pth')
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        with open(osp.join(tmpdir, 'iter_4000.pth'), 'w') as f:
+            f.write('iter_4000')
+        with open(osp.join(tmpdir, 'iter_8000.pth'), 'w') as f:
+            f.write('iter_8000')
+        path = tmpdir
+        latest = find_latest_checkpoint(path)
+        assert latest == osp.join(tmpdir, 'iter_8000.pth')
+
+    with tempfile.TemporaryDirectory() as tmpdir:
+        with open(osp.join(tmpdir, 'epoch_1.pth'), 'w') as f:
+            f.write('epoch_1')
+        with open(osp.join(tmpdir, 'epoch_2.pth'), 'w') as f:
+            f.write('epoch_2')
+        path = tmpdir
+        latest = find_latest_checkpoint(path)
+        assert latest == osp.join(tmpdir, 'epoch_2.pth')
diff --git a/tests/test_utils/test_nms.py b/tests/test_utils/test_nms.py
new file mode 100644
index 0000000..5fa92dc
--- /dev/null
+++ b/tests/test_utils/test_nms.py
@@ -0,0 +1,75 @@
+import pytest
+import torch
+
+from mmdet.core.post_processing import mask_matrix_nms
+
+
+def _create_mask(N, h, w):
+    masks = torch.rand((N, h, w)) > 0.5
+    labels = torch.rand(N)
+    scores = torch.rand(N)
+    return masks, labels, scores
+
+
+def test_nms_input_errors():
+    with pytest.raises(AssertionError):
+        mask_matrix_nms(
+            torch.rand((10, 28, 28)), torch.rand(11), torch.rand(11))
+    with pytest.raises(AssertionError):
+        masks = torch.rand((10, 28, 28))
+        mask_matrix_nms(
+            masks,
+            torch.rand(11),
+            torch.rand(11),
+            mask_area=masks.sum((1, 2)).float()[:8])
+    with pytest.raises(NotImplementedError):
+        mask_matrix_nms(
+            torch.rand((10, 28, 28)),
+            torch.rand(10),
+            torch.rand(10),
+            kernel='None')
+    # test an empty results
+    masks, labels, scores = _create_mask(0, 28, 28)
+    score, label, mask, keep_ind = \
+        mask_matrix_nms(masks, labels, scores)
+    assert len(score) == len(label) == \
+           len(mask) == len(keep_ind) == 0
+
+    # do not use update_thr, nms_pre and max_num
+    masks, labels, scores = _create_mask(1000, 28, 28)
+    score, label, mask, keep_ind = \
+        mask_matrix_nms(masks, labels, scores)
+    assert len(score) == len(label) == \
+           len(mask) == len(keep_ind) == 1000
+    # only use nms_pre
+    score, label, mask, keep_ind = \
+        mask_matrix_nms(masks, labels, scores, nms_pre=500)
+    assert len(score) == len(label) == \
+           len(mask) == len(keep_ind) == 500
+    # use max_num
+    score, label, mask, keep_ind = \
+        mask_matrix_nms(masks, labels, scores,
+                        nms_pre=500, max_num=100)
+    assert len(score) == len(label) == \
+           len(mask) == len(keep_ind) == 100
+
+    masks, labels, _ = _create_mask(1, 28, 28)
+    scores = torch.Tensor([1.0])
+    masks = masks.expand(1000, 28, 28)
+    labels = labels.expand(1000)
+    scores = scores.expand(1000)
+
+    # assert scores is decayed and update_thr is worked
+    # if with the same mask, label, and all scores = 1
+    # the first score will set to 1, others will decay.
+    score, label, mask, keep_ind = \
+        mask_matrix_nms(masks,
+                        labels,
+                        scores,
+                        nms_pre=500,
+                        max_num=100,
+                        kernel='gaussian',
+                        sigma=2.0,
+                        filter_thr=0.5)
+    assert len(score) == 1
+    assert score[0] == 1
diff --git a/tests/test_utils/test_replace_cfg_vals.py b/tests/test_utils/test_replace_cfg_vals.py
new file mode 100644
index 0000000..85d9d0e
--- /dev/null
+++ b/tests/test_utils/test_replace_cfg_vals.py
@@ -0,0 +1,83 @@
+import os.path as osp
+import tempfile
+from copy import deepcopy
+
+import pytest
+from mmcv.utils import Config
+
+from mmdet.utils import replace_cfg_vals
+
+
+def test_replace_cfg_vals():
+    temp_file = tempfile.NamedTemporaryFile()
+    cfg_path = f'{temp_file.name}.py'
+    with open(cfg_path, 'w') as f:
+        f.write('configs')
+
+    ori_cfg_dict = dict()
+    ori_cfg_dict['cfg_name'] = osp.basename(temp_file.name)
+    ori_cfg_dict['work_dir'] = 'work_dirs/${cfg_name}/${percent}/${fold}'
+    ori_cfg_dict['percent'] = 5
+    ori_cfg_dict['fold'] = 1
+    ori_cfg_dict['model_wrapper'] = dict(
+        type='SoftTeacher', detector='${model}')
+    ori_cfg_dict['model'] = dict(
+        type='FasterRCNN',
+        backbone=dict(type='ResNet'),
+        neck=dict(type='FPN'),
+        rpn_head=dict(type='RPNHead'),
+        roi_head=dict(type='StandardRoIHead'),
+        train_cfg=dict(
+            rpn=dict(
+                assigner=dict(type='MaxIoUAssigner'),
+                sampler=dict(type='RandomSampler'),
+            ),
+            rpn_proposal=dict(nms=dict(type='nms', iou_threshold=0.7)),
+            rcnn=dict(
+                assigner=dict(type='MaxIoUAssigner'),
+                sampler=dict(type='RandomSampler'),
+            ),
+        ),
+        test_cfg=dict(
+            rpn=dict(nms=dict(type='nms', iou_threshold=0.7)),
+            rcnn=dict(nms=dict(type='nms', iou_threshold=0.5)),
+        ),
+    )
+    ori_cfg_dict['iou_threshold'] = dict(
+        rpn_proposal_nms='${model.train_cfg.rpn_proposal.nms.iou_threshold}',
+        test_rpn_nms='${model.test_cfg.rpn.nms.iou_threshold}',
+        test_rcnn_nms='${model.test_cfg.rcnn.nms.iou_threshold}',
+    )
+
+    ori_cfg_dict['str'] = 'Hello, world!'
+    ori_cfg_dict['dict'] = {'Hello': 'world!'}
+    ori_cfg_dict['list'] = [
+        'Hello, world!',
+    ]
+    ori_cfg_dict['tuple'] = ('Hello, world!', )
+    ori_cfg_dict['test_str'] = 'xxx${str}xxx'
+
+    ori_cfg = Config(ori_cfg_dict, filename=cfg_path)
+    updated_cfg = replace_cfg_vals(deepcopy(ori_cfg))
+
+    assert updated_cfg.work_dir \
+        == f'work_dirs/{osp.basename(temp_file.name)}/5/1'
+    assert updated_cfg.model.detector == ori_cfg.model
+    assert updated_cfg.iou_threshold.rpn_proposal_nms \
+        == ori_cfg.model.train_cfg.rpn_proposal.nms.iou_threshold
+    assert updated_cfg.test_str == 'xxxHello, world!xxx'
+    ori_cfg_dict['test_dict'] = 'xxx${dict}xxx'
+    ori_cfg_dict['test_list'] = 'xxx${list}xxx'
+    ori_cfg_dict['test_tuple'] = 'xxx${tuple}xxx'
+    with pytest.raises(AssertionError):
+        cfg = deepcopy(ori_cfg)
+        cfg['test_dict'] = 'xxx${dict}xxx'
+        updated_cfg = replace_cfg_vals(cfg)
+    with pytest.raises(AssertionError):
+        cfg = deepcopy(ori_cfg)
+        cfg['test_list'] = 'xxx${list}xxx'
+        updated_cfg = replace_cfg_vals(cfg)
+    with pytest.raises(AssertionError):
+        cfg = deepcopy(ori_cfg)
+        cfg['test_tuple'] = 'xxx${tuple}xxx'
+        updated_cfg = replace_cfg_vals(cfg)
diff --git a/tests/test_utils/test_setup_env.py b/tests/test_utils/test_setup_env.py
new file mode 100644
index 0000000..70f01b8
--- /dev/null
+++ b/tests/test_utils/test_setup_env.py
@@ -0,0 +1,68 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import multiprocessing as mp
+import os
+import platform
+
+import cv2
+from mmcv import Config
+
+from mmdet.utils import setup_multi_processes
+
+
+def test_setup_multi_processes():
+    # temp save system setting
+    sys_start_mehod = mp.get_start_method(allow_none=True)
+    sys_cv_threads = cv2.getNumThreads()
+    # pop and temp save system env vars
+    sys_omp_threads = os.environ.pop('OMP_NUM_THREADS', default=None)
+    sys_mkl_threads = os.environ.pop('MKL_NUM_THREADS', default=None)
+
+    # test config without setting env
+    config = dict(data=dict(workers_per_gpu=2))
+    cfg = Config(config)
+    setup_multi_processes(cfg)
+    assert os.getenv('OMP_NUM_THREADS') == '1'
+    assert os.getenv('MKL_NUM_THREADS') == '1'
+    # when set to 0, the num threads will be 1
+    assert cv2.getNumThreads() == 1
+    if platform.system() != 'Windows':
+        assert mp.get_start_method() == 'fork'
+
+    # test num workers <= 1
+    os.environ.pop('OMP_NUM_THREADS')
+    os.environ.pop('MKL_NUM_THREADS')
+    config = dict(data=dict(workers_per_gpu=0))
+    cfg = Config(config)
+    setup_multi_processes(cfg)
+    assert 'OMP_NUM_THREADS' not in os.environ
+    assert 'MKL_NUM_THREADS' not in os.environ
+
+    # test manually set env var
+    os.environ['OMP_NUM_THREADS'] = '4'
+    config = dict(data=dict(workers_per_gpu=2))
+    cfg = Config(config)
+    setup_multi_processes(cfg)
+    assert os.getenv('OMP_NUM_THREADS') == '4'
+
+    # test manually set opencv threads and mp start method
+    config = dict(
+        data=dict(workers_per_gpu=2),
+        opencv_num_threads=4,
+        mp_start_method='spawn')
+    cfg = Config(config)
+    setup_multi_processes(cfg)
+    assert cv2.getNumThreads() == 4
+    assert mp.get_start_method() == 'spawn'
+
+    # revert setting to avoid affecting other programs
+    if sys_start_mehod:
+        mp.set_start_method(sys_start_mehod, force=True)
+    cv2.setNumThreads(sys_cv_threads)
+    if sys_omp_threads:
+        os.environ['OMP_NUM_THREADS'] = sys_omp_threads
+    else:
+        os.environ.pop('OMP_NUM_THREADS')
+    if sys_mkl_threads:
+        os.environ['MKL_NUM_THREADS'] = sys_mkl_threads
+    else:
+        os.environ.pop('MKL_NUM_THREADS')
diff --git a/tests/test_utils/test_split_batch.py b/tests/test_utils/test_split_batch.py
new file mode 100644
index 0000000..d770f9f
--- /dev/null
+++ b/tests/test_utils/test_split_batch.py
@@ -0,0 +1,95 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+from copy import deepcopy
+
+import mmcv
+import numpy as np
+import torch
+
+from mmdet.utils import split_batch
+
+
+def test_split_batch():
+    img_root = osp.join(osp.dirname(__file__), '../data/color.jpg')
+    img = mmcv.imread(img_root, 'color')
+    h, w, _ = img.shape
+    gt_bboxes = np.array([[0.2 * w, 0.2 * h, 0.4 * w, 0.4 * h],
+                          [0.6 * w, 0.6 * h, 0.8 * w, 0.8 * h]],
+                         dtype=np.float32)
+    gt_lables = np.ones(gt_bboxes.shape[0], dtype=np.int64)
+
+    img = torch.tensor(img).permute(2, 0, 1)
+    meta = dict()
+    meta['filename'] = img_root
+    meta['ori_shape'] = img.shape
+    meta['img_shape'] = img.shape
+    meta['img_norm_cfg'] = {
+        'mean': np.array([103.53, 116.28, 123.675], dtype=np.float32),
+        'std': np.array([1., 1., 1.], dtype=np.float32),
+        'to_rgb': False
+    }
+    meta['pad_shape'] = img.shape
+    # For example, tag include sup, unsup_teacher and unsup_student,
+    # in order to distinguish the difference between the three groups of data,
+    # the scale_factor of sup is [0.5, 0.5, 0.5, 0.5]
+    # the scale_factor of unsup_teacher is [1.0, 1.0, 1.0, 1.0]
+    # the scale_factor of unsup_student is [2.0, 2.0, 2.0, 2.0]
+    imgs = img.unsqueeze(0).repeat(9, 1, 1, 1)
+    img_metas = []
+    tags = [
+        'sup', 'unsup_teacher', 'unsup_student', 'unsup_teacher',
+        'unsup_student', 'unsup_teacher', 'unsup_student', 'unsup_teacher',
+        'unsup_student'
+    ]
+    for tag in tags:
+        img_meta = deepcopy(meta)
+        if tag == 'sup':
+            img_meta['scale_factor'] = [0.5, 0.5, 0.5, 0.5]
+            img_meta['tag'] = 'sup'
+        elif tag == 'unsup_teacher':
+            img_meta['scale_factor'] = [1.0, 1.0, 1.0, 1.0]
+            img_meta['tag'] = 'unsup_teacher'
+        elif tag == 'unsup_student':
+            img_meta['scale_factor'] = [2.0, 2.0, 2.0, 2.0]
+            img_meta['tag'] = 'unsup_student'
+        else:
+            continue
+        img_metas.append(img_meta)
+    kwargs = dict()
+    kwargs['gt_bboxes'] = [torch.tensor(gt_bboxes)] + [torch.zeros(0, 4)] * 8
+    kwargs['gt_lables'] = [torch.tensor(gt_lables)] + [torch.zeros(0, )] * 8
+    data_groups = split_batch(imgs, img_metas, kwargs)
+    assert set(data_groups.keys()) == set(tags)
+    assert data_groups['sup']['img'].shape == (1, 3, h, w)
+    assert data_groups['unsup_teacher']['img'].shape == (4, 3, h, w)
+    assert data_groups['unsup_student']['img'].shape == (4, 3, h, w)
+    # the scale_factor of sup is [0.5, 0.5, 0.5, 0.5]
+    assert data_groups['sup']['img_metas'][0]['scale_factor'] == [
+        0.5, 0.5, 0.5, 0.5
+    ]
+    # the scale_factor of unsup_teacher is [1.0, 1.0, 1.0, 1.0]
+    assert data_groups['unsup_teacher']['img_metas'][0]['scale_factor'] == [
+        1.0, 1.0, 1.0, 1.0
+    ]
+    assert data_groups['unsup_teacher']['img_metas'][1]['scale_factor'] == [
+        1.0, 1.0, 1.0, 1.0
+    ]
+    assert data_groups['unsup_teacher']['img_metas'][2]['scale_factor'] == [
+        1.0, 1.0, 1.0, 1.0
+    ]
+    assert data_groups['unsup_teacher']['img_metas'][3]['scale_factor'] == [
+        1.0, 1.0, 1.0, 1.0
+    ]
+    # the scale_factor of unsup_student is [2.0, 2.0, 2.0, 2.0]
+    assert data_groups['unsup_student']['img_metas'][0]['scale_factor'] == [
+        2.0, 2.0, 2.0, 2.0
+    ]
+    assert data_groups['unsup_student']['img_metas'][1]['scale_factor'] == [
+        2.0, 2.0, 2.0, 2.0
+    ]
+    assert data_groups['unsup_student']['img_metas'][2]['scale_factor'] == [
+        2.0, 2.0, 2.0, 2.0
+    ]
+    assert data_groups['unsup_student']['img_metas'][3]['scale_factor'] == [
+        2.0, 2.0, 2.0, 2.0
+    ]
diff --git a/tests/test_utils/test_version.py b/tests/test_utils/test_version.py
new file mode 100644
index 0000000..87d2fab
--- /dev/null
+++ b/tests/test_utils/test_version.py
@@ -0,0 +1,16 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from mmdet import digit_version
+
+
+def test_version_check():
+    assert digit_version('1.0.5') > digit_version('1.0.5rc0')
+    assert digit_version('1.0.5') > digit_version('1.0.4rc0')
+    assert digit_version('1.0.5') > digit_version('1.0rc0')
+    assert digit_version('1.0.0') > digit_version('0.6.2')
+    assert digit_version('1.0.0') > digit_version('0.2.16')
+    assert digit_version('1.0.5rc0') > digit_version('1.0.0rc0')
+    assert digit_version('1.0.0rc1') > digit_version('1.0.0rc0')
+    assert digit_version('1.0.0rc2') > digit_version('1.0.0rc0')
+    assert digit_version('1.0.0rc2') > digit_version('1.0.0rc1')
+    assert digit_version('1.0.1rc1') > digit_version('1.0.0rc1')
+    assert digit_version('1.0.0') > digit_version('1.0.0rc1')
diff --git a/tests/test_utils/test_visualization.py b/tests/test_utils/test_visualization.py
new file mode 100644
index 0000000..1dbdb2b
--- /dev/null
+++ b/tests/test_utils/test_visualization.py
@@ -0,0 +1,173 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import os.path as osp
+import tempfile
+
+import mmcv
+import numpy as np
+import pytest
+import torch
+
+from mmdet.core import visualization as vis
+from mmdet.datasets import (CityscapesDataset, CocoDataset,
+                            CocoPanopticDataset, VOCDataset)
+
+
+def test_color():
+    assert vis.color_val_matplotlib(mmcv.Color.blue) == (0., 0., 1.)
+    assert vis.color_val_matplotlib('green') == (0., 1., 0.)
+    assert vis.color_val_matplotlib((1, 2, 3)) == (3 / 255, 2 / 255, 1 / 255)
+    assert vis.color_val_matplotlib(100) == (100 / 255, 100 / 255, 100 / 255)
+    assert vis.color_val_matplotlib(np.zeros(3, dtype=np.int)) == (0., 0., 0.)
+    # forbid white color
+    with pytest.raises(TypeError):
+        vis.color_val_matplotlib([255, 255, 255])
+    # forbid float
+    with pytest.raises(TypeError):
+        vis.color_val_matplotlib(1.0)
+    # overflowed
+    with pytest.raises(AssertionError):
+        vis.color_val_matplotlib((0, 0, 500))
+
+
+def test_imshow_det_bboxes():
+    tmp_filename = osp.join(tempfile.gettempdir(), 'det_bboxes_image',
+                            'image.jpg')
+    image = np.ones((10, 10, 3), np.uint8)
+    bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]])
+    label = np.array([0, 1])
+    out_image = vis.imshow_det_bboxes(
+        image, bbox, label, out_file=tmp_filename, show=False)
+    assert osp.isfile(tmp_filename)
+    assert image.shape == out_image.shape
+    assert not np.allclose(image, out_image)
+    os.remove(tmp_filename)
+
+    # test grayscale images
+    image = np.ones((10, 10), np.uint8)
+    bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]])
+    label = np.array([0, 1])
+    out_image = vis.imshow_det_bboxes(
+        image, bbox, label, out_file=tmp_filename, show=False)
+    assert osp.isfile(tmp_filename)
+    assert image.shape == out_image.shape[:2]
+    os.remove(tmp_filename)
+
+    # test shaped (0,)
+    image = np.ones((10, 10, 3), np.uint8)
+    bbox = np.ones((0, 4))
+    label = np.ones((0, ))
+    vis.imshow_det_bboxes(
+        image, bbox, label, out_file=tmp_filename, show=False)
+    assert osp.isfile(tmp_filename)
+    os.remove(tmp_filename)
+
+    # test mask
+    image = np.ones((10, 10, 3), np.uint8)
+    bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]])
+    label = np.array([0, 1])
+    segms = np.random.random((2, 10, 10)) > 0.5
+    segms = np.array(segms, np.int32)
+    vis.imshow_det_bboxes(
+        image, bbox, label, segms, out_file=tmp_filename, show=False)
+    assert osp.isfile(tmp_filename)
+    os.remove(tmp_filename)
+
+    # test tensor mask type error
+    with pytest.raises(AttributeError):
+        segms = torch.tensor(segms)
+        vis.imshow_det_bboxes(image, bbox, label, segms, show=False)
+
+
+def test_imshow_gt_det_bboxes():
+    tmp_filename = osp.join(tempfile.gettempdir(), 'det_bboxes_image',
+                            'image.jpg')
+    image = np.ones((10, 10, 3), np.uint8)
+    bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]])
+    label = np.array([0, 1])
+    annotation = dict(gt_bboxes=bbox, gt_labels=label)
+    det_result = np.array([[2, 1, 3, 3, 0], [3, 4, 6, 6, 1]])
+    result = [det_result]
+    out_image = vis.imshow_gt_det_bboxes(
+        image, annotation, result, out_file=tmp_filename, show=False)
+    assert osp.isfile(tmp_filename)
+    assert image.shape == out_image.shape
+    assert not np.allclose(image, out_image)
+    os.remove(tmp_filename)
+
+    # test grayscale images
+    image = np.ones((10, 10), np.uint8)
+    bbox = np.array([[2, 1, 3, 3], [3, 4, 6, 6]])
+    label = np.array([0, 1])
+    annotation = dict(gt_bboxes=bbox, gt_labels=label)
+    det_result = np.array([[2, 1, 3, 3, 0], [3, 4, 6, 6, 1]])
+    result = [det_result]
+    vis.imshow_gt_det_bboxes(
+        image, annotation, result, out_file=tmp_filename, show=False)
+    assert osp.isfile(tmp_filename)
+    os.remove(tmp_filename)
+
+    # test numpy mask
+    gt_mask = np.ones((2, 10, 10))
+    annotation['gt_masks'] = gt_mask
+    vis.imshow_gt_det_bboxes(
+        image, annotation, result, out_file=tmp_filename, show=False)
+    assert osp.isfile(tmp_filename)
+    os.remove(tmp_filename)
+
+    # test tensor mask
+    gt_mask = torch.ones((2, 10, 10))
+    annotation['gt_masks'] = gt_mask
+    vis.imshow_gt_det_bboxes(
+        image, annotation, result, out_file=tmp_filename, show=False)
+    assert osp.isfile(tmp_filename)
+    os.remove(tmp_filename)
+
+    # test unsupported type
+    annotation['gt_masks'] = []
+    with pytest.raises(TypeError):
+        vis.imshow_gt_det_bboxes(image, annotation, result, show=False)
+
+
+def test_palette():
+    assert vis.palette_val([(1, 2, 3)])[0] == (1 / 255, 2 / 255, 3 / 255)
+
+    # test list
+    palette = [(1, 0, 0), (0, 1, 0), (0, 0, 1)]
+    palette_ = vis.get_palette(palette, 3)
+    for color, color_ in zip(palette, palette_):
+        assert color == color_
+
+    # test tuple
+    palette = vis.get_palette((1, 2, 3), 3)
+    assert len(palette) == 3
+    for color in palette:
+        assert color == (1, 2, 3)
+
+    # test color str
+    palette = vis.get_palette('red', 3)
+    assert len(palette) == 3
+    for color in palette:
+        assert color == (255, 0, 0)
+
+    # test dataset str
+    palette = vis.get_palette('coco', len(CocoDataset.CLASSES))
+    assert len(palette) == len(CocoDataset.CLASSES)
+    assert palette[0] == (220, 20, 60)
+    palette = vis.get_palette('coco', len(CocoPanopticDataset.CLASSES))
+    assert len(palette) == len(CocoPanopticDataset.CLASSES)
+    assert palette[-1] == (250, 141, 255)
+    palette = vis.get_palette('voc', len(VOCDataset.CLASSES))
+    assert len(palette) == len(VOCDataset.CLASSES)
+    assert palette[0] == (106, 0, 228)
+    palette = vis.get_palette('citys', len(CityscapesDataset.CLASSES))
+    assert len(palette) == len(CityscapesDataset.CLASSES)
+    assert palette[0] == (220, 20, 60)
+
+    # test random
+    palette1 = vis.get_palette('random', 3)
+    palette2 = vis.get_palette(None, 3)
+    for color1, color2 in zip(palette1, palette2):
+        assert isinstance(color1, tuple)
+        assert isinstance(color2, tuple)
+        assert color1 == color2
diff --git a/tmp/temp_00 b/tmp/temp_00
new file mode 100644
index 0000000..3024cbb
--- /dev/null
+++ b/tmp/temp_00
@@ -0,0 +1 @@
+tools/starter_scripts/retinanet_18/dist_train_retinanet_18_coco_AQD.sh
diff --git a/tmp/temp_01 b/tmp/temp_01
new file mode 100644
index 0000000..8b13789
--- /dev/null
+++ b/tmp/temp_01
@@ -0,0 +1 @@
+
diff --git a/tools/analysis_tools/analyze_logs.py b/tools/analysis_tools/analyze_logs.py
new file mode 100755
index 0000000..ca13ea8
--- /dev/null
+++ b/tools/analysis_tools/analyze_logs.py
@@ -0,0 +1,204 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import json
+from collections import defaultdict
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+
+
+def cal_train_time(log_dicts, args):
+    for i, log_dict in enumerate(log_dicts):
+        print(f'{"-" * 5}Analyze train time of {args.json_logs[i]}{"-" * 5}')
+        all_times = []
+        for epoch in log_dict.keys():
+            if args.include_outliers:
+                all_times.append(log_dict[epoch]['time'])
+            else:
+                all_times.append(log_dict[epoch]['time'][1:])
+        if not all_times:
+            raise KeyError(
+                'Please reduce the log interval in the config so that'
+                'interval is less than iterations of one epoch.')
+        all_times = np.array(all_times)
+        epoch_ave_time = all_times.mean(-1)
+        slowest_epoch = epoch_ave_time.argmax()
+        fastest_epoch = epoch_ave_time.argmin()
+        std_over_epoch = epoch_ave_time.std()
+        print(f'slowest epoch {slowest_epoch + 1}, '
+              f'average time is {epoch_ave_time[slowest_epoch]:.4f}')
+        print(f'fastest epoch {fastest_epoch + 1}, '
+              f'average time is {epoch_ave_time[fastest_epoch]:.4f}')
+        print(f'time std over epochs is {std_over_epoch:.4f}')
+        print(f'average iter time: {np.mean(all_times):.4f} s/iter')
+        print()
+
+
+def plot_curve(log_dicts, args):
+    if args.backend is not None:
+        plt.switch_backend(args.backend)
+    sns.set_style(args.style)
+    # if legend is None, use {filename}_{key} as legend
+    legend = args.legend
+    if legend is None:
+        legend = []
+        for json_log in args.json_logs:
+            for metric in args.keys:
+                legend.append(f'{json_log}_{metric}')
+    assert len(legend) == (len(args.json_logs) * len(args.keys))
+    metrics = args.keys
+
+    num_metrics = len(metrics)
+    for i, log_dict in enumerate(log_dicts):
+        epochs = list(log_dict.keys())
+        for j, metric in enumerate(metrics):
+            print(f'plot curve of {args.json_logs[i]}, metric is {metric}')
+            if metric not in log_dict[epochs[int(args.eval_interval) - 1]]:
+                if 'mAP' in metric:
+                    raise KeyError(
+                        f'{args.json_logs[i]} does not contain metric '
+                        f'{metric}. Please check if "--no-validate" is '
+                        'specified when you trained the model.')
+                raise KeyError(
+                    f'{args.json_logs[i]} does not contain metric {metric}. '
+                    'Please reduce the log interval in the config so that '
+                    'interval is less than iterations of one epoch.')
+
+            if 'mAP' in metric:
+                xs = []
+                ys = []
+                for epoch in epochs:
+                    ys += log_dict[epoch][metric]
+                    if 'val' in log_dict[epoch]['mode']:
+                        xs.append(epoch)
+                plt.xlabel('epoch')
+                plt.plot(xs, ys, label=legend[i * num_metrics + j], marker='o')
+            else:
+                xs = []
+                ys = []
+                num_iters_per_epoch = log_dict[epochs[0]]['iter'][-2]
+                for epoch in epochs:
+                    iters = log_dict[epoch]['iter']
+                    if log_dict[epoch]['mode'][-1] == 'val':
+                        iters = iters[:-1]
+                    xs.append(
+                        np.array(iters) + (epoch - 1) * num_iters_per_epoch)
+                    ys.append(np.array(log_dict[epoch][metric][:len(iters)]))
+                xs = np.concatenate(xs)
+                ys = np.concatenate(ys)
+                plt.xlabel('iter')
+                plt.plot(
+                    xs, ys, label=legend[i * num_metrics + j], linewidth=0.5)
+            plt.legend()
+        if args.title is not None:
+            plt.title(args.title)
+    if args.out is None:
+        plt.show()
+    else:
+        print(f'save curve to: {args.out}')
+        plt.savefig(args.out)
+        plt.cla()
+
+
+def add_plot_parser(subparsers):
+    parser_plt = subparsers.add_parser(
+        'plot_curve', help='parser for plotting curves')
+    parser_plt.add_argument(
+        'json_logs',
+        type=str,
+        nargs='+',
+        help='path of train log in json format')
+    parser_plt.add_argument(
+        '--keys',
+        type=str,
+        nargs='+',
+        default=['bbox_mAP'],
+        help='the metric that you want to plot')
+    parser_plt.add_argument(
+        '--start-epoch',
+        type=str,
+        default='1',
+        help='the epoch that you want to start')
+    parser_plt.add_argument(
+        '--eval-interval',
+        type=str,
+        default='1',
+        help='the eval interval when training')
+    parser_plt.add_argument('--title', type=str, help='title of figure')
+    parser_plt.add_argument(
+        '--legend',
+        type=str,
+        nargs='+',
+        default=None,
+        help='legend of each plot')
+    parser_plt.add_argument(
+        '--backend', type=str, default=None, help='backend of plt')
+    parser_plt.add_argument(
+        '--style', type=str, default='dark', help='style of plt')
+    parser_plt.add_argument('--out', type=str, default=None)
+
+
+def add_time_parser(subparsers):
+    parser_time = subparsers.add_parser(
+        'cal_train_time',
+        help='parser for computing the average time per training iteration')
+    parser_time.add_argument(
+        'json_logs',
+        type=str,
+        nargs='+',
+        help='path of train log in json format')
+    parser_time.add_argument(
+        '--include-outliers',
+        action='store_true',
+        help='include the first value of every epoch when computing '
+        'the average time')
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Analyze Json Log')
+    # currently only support plot curve and calculate average train time
+    subparsers = parser.add_subparsers(dest='task', help='task parser')
+    add_plot_parser(subparsers)
+    add_time_parser(subparsers)
+    args = parser.parse_args()
+    return args
+
+
+def load_json_logs(json_logs):
+    # load and convert json_logs to log_dict, key is epoch, value is a sub dict
+    # keys of sub dict is different metrics, e.g. memory, bbox_mAP
+    # value of sub dict is a list of corresponding values of all iterations
+    log_dicts = [dict() for _ in json_logs]
+    for json_log, log_dict in zip(json_logs, log_dicts):
+        with open(json_log, 'r') as log_file:
+            for i, line in enumerate(log_file):
+                log = json.loads(line.strip())
+                # skip the first training info line
+                if i == 0:
+                    continue
+                # skip lines without `epoch` field
+                if 'epoch' not in log:
+                    continue
+                epoch = log.pop('epoch')
+                if epoch not in log_dict:
+                    log_dict[epoch] = defaultdict(list)
+                for k, v in log.items():
+                    log_dict[epoch][k].append(v)
+    return log_dicts
+
+
+def main():
+    args = parse_args()
+
+    json_logs = args.json_logs
+    for json_log in json_logs:
+        assert json_log.endswith('.json')
+
+    log_dicts = load_json_logs(json_logs)
+
+    eval(args.task)(log_dicts, args)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/analysis_tools/analyze_results.py b/tools/analysis_tools/analyze_results.py
new file mode 100644
index 0000000..4d8b60c
--- /dev/null
+++ b/tools/analysis_tools/analyze_results.py
@@ -0,0 +1,369 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os.path as osp
+from multiprocessing import Pool
+
+import mmcv
+import numpy as np
+from mmcv import Config, DictAction
+
+from mmdet.core.evaluation import eval_map
+from mmdet.core.visualization import imshow_gt_det_bboxes
+from mmdet.datasets import build_dataset, get_loading_pipeline
+from mmdet.datasets.api_wrappers import pq_compute_single_core
+from mmdet.utils import replace_cfg_vals, update_data_root
+
+
+def bbox_map_eval(det_result, annotation, nproc=4):
+    """Evaluate mAP of single image det result.
+
+    Args:
+        det_result (list[list]): [[cls1_det, cls2_det, ...], ...].
+            The outer list indicates images, and the inner list indicates
+            per-class detected bboxes.
+        annotation (dict): Ground truth annotations where keys of
+             annotations are:
+
+            - bboxes: numpy array of shape (n, 4)
+            - labels: numpy array of shape (n, )
+            - bboxes_ignore (optional): numpy array of shape (k, 4)
+            - labels_ignore (optional): numpy array of shape (k, )
+
+        nproc (int): Processes used for computing mAP.
+            Default: 4.
+
+    Returns:
+        float: mAP
+    """
+
+    # use only bbox det result
+    if isinstance(det_result, tuple):
+        bbox_det_result = [det_result[0]]
+    else:
+        bbox_det_result = [det_result]
+    # mAP
+    iou_thrs = np.linspace(
+        .5, 0.95, int(np.round((0.95 - .5) / .05)) + 1, endpoint=True)
+
+    processes = []
+    workers = Pool(processes=nproc)
+    for thr in iou_thrs:
+        p = workers.apply_async(eval_map, (bbox_det_result, [annotation]), {
+            'iou_thr': thr,
+            'logger': 'silent',
+            'nproc': 1
+        })
+        processes.append(p)
+
+    workers.close()
+    workers.join()
+
+    mean_aps = []
+    for p in processes:
+        mean_aps.append(p.get()[0])
+
+    return sum(mean_aps) / len(mean_aps)
+
+
+class ResultVisualizer:
+    """Display and save evaluation results.
+
+    Args:
+        show (bool): Whether to show the image. Default: True.
+        wait_time (float): Value of waitKey param. Default: 0.
+        score_thr (float): Minimum score of bboxes to be shown.
+           Default: 0.
+        overlay_gt_pred (bool): Whether to plot gts and predictions on the
+            same image. If False, predictions and gts will be plotted on two
+            same image which will be concatenated in vertical direction.
+            The image above is drawn with gt, and the image below is drawn
+            with the prediction result. Default: False.
+    """
+
+    def __init__(self,
+                 show=False,
+                 wait_time=0,
+                 score_thr=0,
+                 overlay_gt_pred=False):
+        self.show = show
+        self.wait_time = wait_time
+        self.score_thr = score_thr
+        self.overlay_gt_pred = overlay_gt_pred
+
+    def _save_image_gts_results(self,
+                                dataset,
+                                results,
+                                performances,
+                                out_dir=None):
+        """Display or save image with groung truths and predictions from a
+        model.
+
+        Args:
+            dataset (Dataset): A PyTorch dataset.
+            results (list): Object detection or panoptic segmentation
+                results from test results pkl file.
+            performances (dict): A dict contains samples's indices
+                in dataset and model's performance on them.
+            out_dir (str, optional): The filename to write the image.
+                Defaults: None.
+        """
+        mmcv.mkdir_or_exist(out_dir)
+
+        for performance_info in performances:
+            index, performance = performance_info
+            data_info = dataset.prepare_train_img(index)
+
+            # calc save file path
+            filename = data_info['filename']
+            if data_info['img_prefix'] is not None:
+                filename = osp.join(data_info['img_prefix'], filename)
+            else:
+                filename = data_info['filename']
+            fname, name = osp.splitext(osp.basename(filename))
+            save_filename = fname + '_' + str(round(performance, 3)) + name
+            out_file = osp.join(out_dir, save_filename)
+            imshow_gt_det_bboxes(
+                data_info['img'],
+                data_info,
+                results[index],
+                dataset.CLASSES,
+                gt_bbox_color=dataset.PALETTE,
+                gt_text_color=(200, 200, 200),
+                gt_mask_color=dataset.PALETTE,
+                det_bbox_color=dataset.PALETTE,
+                det_text_color=(200, 200, 200),
+                det_mask_color=dataset.PALETTE,
+                show=self.show,
+                score_thr=self.score_thr,
+                wait_time=self.wait_time,
+                out_file=out_file,
+                overlay_gt_pred=self.overlay_gt_pred)
+
+    def evaluate_and_show(self,
+                          dataset,
+                          results,
+                          topk=20,
+                          show_dir='work_dir'):
+        """Evaluate and show results.
+
+        Args:
+            dataset (Dataset): A PyTorch dataset.
+            results (list): Object detection or panoptic segmentation
+                results from test results pkl file.
+            topk (int): Number of the highest topk and
+                lowest topk after evaluation index sorting. Default: 20.
+            show_dir (str, optional): The filename to write the image.
+                Default: 'work_dir'
+            eval_fn (callable, optional): Eval function, Default: None.
+        """
+
+        assert topk > 0
+        if (topk * 2) > len(dataset):
+            topk = len(dataset) // 2
+
+        if isinstance(results[0], dict):
+            good_samples, bad_samples = self.panoptic_evaluate(
+                dataset, results, topk=topk)
+        elif isinstance(results[0], list):
+            good_samples, bad_samples = self.detection_evaluate(
+                dataset, results, topk=topk)
+        elif isinstance(results[0], tuple):
+            results_ = [result[0] for result in results]
+            good_samples, bad_samples = self.detection_evaluate(
+                dataset, results_, topk=topk)
+        else:
+            raise 'The format of result is not supported yet. ' \
+                'Current dict for panoptic segmentation and list ' \
+                'or tuple for object detection are supported.'
+
+        good_dir = osp.abspath(osp.join(show_dir, 'good'))
+        bad_dir = osp.abspath(osp.join(show_dir, 'bad'))
+        self._save_image_gts_results(dataset, results, good_samples, good_dir)
+        self._save_image_gts_results(dataset, results, bad_samples, bad_dir)
+
+    def detection_evaluate(self, dataset, results, topk=20, eval_fn=None):
+        """Evaluation for object detection.
+
+        Args:
+            dataset (Dataset): A PyTorch dataset.
+            results (list): Object detection results from test
+                results pkl file.
+            topk (int): Number of the highest topk and
+                lowest topk after evaluation index sorting. Default: 20.
+            eval_fn (callable, optional): Eval function, Default: None.
+
+        Returns:
+            tuple: A tuple contains good samples and bad samples.
+                good_mAPs (dict[int, float]): A dict contains good
+                    samples's indices in dataset and model's
+                    performance on them.
+                bad_mAPs (dict[int, float]): A dict contains bad
+                    samples's indices in dataset and model's
+                    performance on them.
+        """
+        if eval_fn is None:
+            eval_fn = bbox_map_eval
+        else:
+            assert callable(eval_fn)
+
+        prog_bar = mmcv.ProgressBar(len(results))
+        _mAPs = {}
+        for i, (result, ) in enumerate(zip(results)):
+            # self.dataset[i] should not call directly
+            # because there is a risk of mismatch
+            data_info = dataset.prepare_train_img(i)
+            mAP = eval_fn(result, data_info['ann_info'])
+            _mAPs[i] = mAP
+            prog_bar.update()
+        # descending select topk image
+        _mAPs = list(sorted(_mAPs.items(), key=lambda kv: kv[1]))
+        good_mAPs = _mAPs[-topk:]
+        bad_mAPs = _mAPs[:topk]
+
+        return good_mAPs, bad_mAPs
+
+    def panoptic_evaluate(self, dataset, results, topk=20):
+        """Evaluation for panoptic segmentation.
+
+        Args:
+            dataset (Dataset): A PyTorch dataset.
+            results (list): Panoptic segmentation results from test
+                results pkl file.
+            topk (int): Number of the highest topk and
+                lowest topk after evaluation index sorting. Default: 20.
+
+        Returns:
+            tuple: A tuple contains good samples and bad samples.
+                good_pqs (dict[int, float]): A dict contains good
+                    samples's indices in dataset and model's
+                    performance on them.
+                bad_pqs (dict[int, float]): A dict contains bad
+                    samples's indices in dataset and model's
+                    performance on them.
+        """
+        # image to annotations
+        gt_json = dataset.coco.img_ann_map
+
+        result_files, tmp_dir = dataset.format_results(results)
+        pred_json = mmcv.load(result_files['panoptic'])['annotations']
+        pred_folder = osp.join(tmp_dir.name, 'panoptic')
+        gt_folder = dataset.seg_prefix
+
+        pqs = {}
+        prog_bar = mmcv.ProgressBar(len(results))
+        for i in range(len(results)):
+            data_info = dataset.prepare_train_img(i)
+            image_id = data_info['img_info']['id']
+            gt_ann = {
+                'image_id': image_id,
+                'segments_info': gt_json[image_id],
+                'file_name': data_info['img_info']['segm_file']
+            }
+            pred_ann = pred_json[i]
+            pq_stat = pq_compute_single_core(
+                i, [(gt_ann, pred_ann)],
+                gt_folder,
+                pred_folder,
+                dataset.categories,
+                dataset.file_client,
+                print_log=False)
+            pq_results, classwise_results = pq_stat.pq_average(
+                dataset.categories, isthing=None)
+            pqs[i] = pq_results['pq']
+            prog_bar.update()
+
+        if tmp_dir is not None:
+            tmp_dir.cleanup()
+
+        # descending select topk image
+        pqs = list(sorted(pqs.items(), key=lambda kv: kv[1]))
+        good_pqs = pqs[-topk:]
+        bad_pqs = pqs[:topk]
+
+        return good_pqs, bad_pqs
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='MMDet eval image prediction result for each')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument(
+        'prediction_path', help='prediction path where test pkl result')
+    parser.add_argument(
+        'show_dir', help='directory where painted images will be saved')
+    parser.add_argument('--show', action='store_true', help='show results')
+    parser.add_argument(
+        '--wait-time',
+        type=float,
+        default=0,
+        help='the interval of show (s), 0 is block')
+    parser.add_argument(
+        '--topk',
+        default=20,
+        type=int,
+        help='saved Number of the highest topk '
+        'and lowest topk after index sorting')
+    parser.add_argument(
+        '--show-score-thr',
+        type=float,
+        default=0,
+        help='score threshold (default: 0.)')
+    parser.add_argument(
+        '--overlay-gt-pred',
+        action='store_true',
+        help='whether to plot gts and predictions on the same image.'
+        'If False, predictions and gts will be plotted on two same'
+        'image which will be concatenated in vertical direction.'
+        'The image above is drawn with gt, and the image below is'
+        'drawn with the prediction result.')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    mmcv.check_file_exist(args.prediction_path)
+
+    cfg = Config.fromfile(args.config)
+
+    # replace the ${key} with the value of cfg.key
+    cfg = replace_cfg_vals(cfg)
+
+    # update data root according to MMDET_DATASETS
+    update_data_root(cfg)
+
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+    cfg.data.test.test_mode = True
+
+    cfg.data.test.pop('samples_per_gpu', 0)
+    if cfg.data.train.type in ('MultiImageMixDataset', 'ClassBalancedDataset',
+                               'RepeatDataset', 'ConcatDataset'):
+        cfg.data.test.pipeline = get_loading_pipeline(
+            cfg.data.train.dataset.pipeline)
+    else:
+        cfg.data.test.pipeline = get_loading_pipeline(cfg.data.train.pipeline)
+
+    dataset = build_dataset(cfg.data.test)
+    outputs = mmcv.load(args.prediction_path)
+
+    result_visualizer = ResultVisualizer(args.show, args.wait_time,
+                                         args.show_score_thr,
+                                         args.overlay_gt_pred)
+    result_visualizer.evaluate_and_show(
+        dataset, outputs, topk=args.topk, show_dir=args.show_dir)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/analysis_tools/benchmark.py b/tools/analysis_tools/benchmark.py
new file mode 100644
index 0000000..c956968
--- /dev/null
+++ b/tools/analysis_tools/benchmark.py
@@ -0,0 +1,195 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import copy
+import os
+import time
+
+import torch
+from mmcv import Config, DictAction
+from mmcv.cnn import fuse_conv_bn
+from mmcv.parallel import MMDistributedDataParallel
+from mmcv.runner import init_dist, load_checkpoint, wrap_fp16_model
+
+from mmdet.datasets import (build_dataloader, build_dataset,
+                            replace_ImageToTensor)
+from mmdet.models import build_detector
+from mmdet.utils import replace_cfg_vals, update_data_root
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='MMDet benchmark a model')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint', help='checkpoint file')
+    parser.add_argument(
+        '--repeat-num',
+        type=int,
+        default=1,
+        help='number of repeat times of measurement for averaging the results')
+    parser.add_argument(
+        '--max-iter', type=int, default=2000, help='num of max iter')
+    parser.add_argument(
+        '--log-interval', type=int, default=50, help='interval of logging')
+    parser.add_argument(
+        '--fuse-conv-bn',
+        action='store_true',
+        help='Whether to fuse conv and bn, this will slightly increase'
+        'the inference speed')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--launcher',
+        choices=['none', 'pytorch', 'slurm', 'mpi'],
+        default='none',
+        help='job launcher')
+    parser.add_argument('--local_rank', type=int, default=0)
+    args = parser.parse_args()
+    if 'LOCAL_RANK' not in os.environ:
+        os.environ['LOCAL_RANK'] = str(args.local_rank)
+    return args
+
+
+def measure_inference_speed(cfg, checkpoint, max_iter, log_interval,
+                            is_fuse_conv_bn):
+    # set cudnn_benchmark
+    if cfg.get('cudnn_benchmark', False):
+        torch.backends.cudnn.benchmark = True
+    cfg.model.pretrained = None
+    cfg.data.test.test_mode = True
+
+    # build the dataloader
+    samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)
+    if samples_per_gpu > 1:
+        # Replace 'ImageToTensor' to 'DefaultFormatBundle'
+        cfg.data.test.pipeline = replace_ImageToTensor(cfg.data.test.pipeline)
+    dataset = build_dataset(cfg.data.test)
+    data_loader = build_dataloader(
+        dataset,
+        samples_per_gpu=1,
+        # Because multiple processes will occupy additional CPU resources,
+        # FPS statistics will be more unstable when workers_per_gpu is not 0.
+        # It is reasonable to set workers_per_gpu to 0.
+        workers_per_gpu=0,
+        dist=True,
+        shuffle=False)
+
+    # build the model and load checkpoint
+    cfg.model.train_cfg = None
+    model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
+    fp16_cfg = cfg.get('fp16', None)
+    if fp16_cfg is not None:
+        wrap_fp16_model(model)
+    load_checkpoint(model, checkpoint, map_location='cpu')
+    if is_fuse_conv_bn:
+        model = fuse_conv_bn(model)
+
+    model = MMDistributedDataParallel(
+        model.cuda(),
+        device_ids=[torch.cuda.current_device()],
+        broadcast_buffers=False)
+    model.eval()
+
+    # the first several iterations may be very slow so skip them
+    num_warmup = 5
+    pure_inf_time = 0
+    fps = 0
+
+    # benchmark with 2000 image and take the average
+    for i, data in enumerate(data_loader):
+
+        torch.cuda.synchronize()
+        start_time = time.perf_counter()
+
+        with torch.no_grad():
+            model(return_loss=False, rescale=True, **data)
+
+        torch.cuda.synchronize()
+        elapsed = time.perf_counter() - start_time
+
+        if i >= num_warmup:
+            pure_inf_time += elapsed
+            if (i + 1) % log_interval == 0:
+                fps = (i + 1 - num_warmup) / pure_inf_time
+                print(
+                    f'Done image [{i + 1:<3}/ {max_iter}], '
+                    f'fps: {fps:.1f} img / s, '
+                    f'times per image: {1000 / fps:.1f} ms / img',
+                    flush=True)
+
+        if (i + 1) == max_iter:
+            fps = (i + 1 - num_warmup) / pure_inf_time
+            print(
+                f'Overall fps: {fps:.1f} img / s, '
+                f'times per image: {1000 / fps:.1f} ms / img',
+                flush=True)
+            break
+    return fps
+
+
+def repeat_measure_inference_speed(cfg,
+                                   checkpoint,
+                                   max_iter,
+                                   log_interval,
+                                   is_fuse_conv_bn,
+                                   repeat_num=1):
+    assert repeat_num >= 1
+
+    fps_list = []
+
+    for _ in range(repeat_num):
+        #
+        cp_cfg = copy.deepcopy(cfg)
+
+        fps_list.append(
+            measure_inference_speed(cp_cfg, checkpoint, max_iter, log_interval,
+                                    is_fuse_conv_bn))
+
+    if repeat_num > 1:
+        fps_list_ = [round(fps, 1) for fps in fps_list]
+        times_pre_image_list_ = [round(1000 / fps, 1) for fps in fps_list]
+        mean_fps_ = sum(fps_list_) / len(fps_list_)
+        mean_times_pre_image_ = sum(times_pre_image_list_) / len(
+            times_pre_image_list_)
+        print(
+            f'Overall fps: {fps_list_}[{mean_fps_:.1f}] img / s, '
+            f'times per image: '
+            f'{times_pre_image_list_}[{mean_times_pre_image_:.1f}] ms / img',
+            flush=True)
+        return fps_list
+
+    return fps_list[0]
+
+
+def main():
+    args = parse_args()
+
+    cfg = Config.fromfile(args.config)
+
+    # replace the ${key} with the value of cfg.key
+    cfg = replace_cfg_vals(cfg)
+
+    # update data root according to MMDET_DATASETS
+    update_data_root(cfg)
+
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+
+    if args.launcher == 'none':
+        raise NotImplementedError('Only supports distributed mode')
+    else:
+        init_dist(args.launcher, **cfg.dist_params)
+
+    repeat_measure_inference_speed(cfg, args.checkpoint, args.max_iter,
+                                   args.log_interval, args.fuse_conv_bn,
+                                   args.repeat_num)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/analysis_tools/coco_error_analysis.py b/tools/analysis_tools/coco_error_analysis.py
new file mode 100644
index 0000000..102ea4e
--- /dev/null
+++ b/tools/analysis_tools/coco_error_analysis.py
@@ -0,0 +1,339 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import copy
+import os
+from argparse import ArgumentParser
+from multiprocessing import Pool
+
+import matplotlib.pyplot as plt
+import numpy as np
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+
+
+def makeplot(rs, ps, outDir, class_name, iou_type):
+    cs = np.vstack([
+        np.ones((2, 3)),
+        np.array([0.31, 0.51, 0.74]),
+        np.array([0.75, 0.31, 0.30]),
+        np.array([0.36, 0.90, 0.38]),
+        np.array([0.50, 0.39, 0.64]),
+        np.array([1, 0.6, 0]),
+    ])
+    areaNames = ['allarea', 'small', 'medium', 'large']
+    types = ['C75', 'C50', 'Loc', 'Sim', 'Oth', 'BG', 'FN']
+    for i in range(len(areaNames)):
+        area_ps = ps[..., i, 0]
+        figure_title = iou_type + '-' + class_name + '-' + areaNames[i]
+        aps = [ps_.mean() for ps_ in area_ps]
+        ps_curve = [
+            ps_.mean(axis=1) if ps_.ndim > 1 else ps_ for ps_ in area_ps
+        ]
+        ps_curve.insert(0, np.zeros(ps_curve[0].shape))
+        fig = plt.figure()
+        ax = plt.subplot(111)
+        for k in range(len(types)):
+            ax.plot(rs, ps_curve[k + 1], color=[0, 0, 0], linewidth=0.5)
+            ax.fill_between(
+                rs,
+                ps_curve[k],
+                ps_curve[k + 1],
+                color=cs[k],
+                label=str(f'[{aps[k]:.3f}]' + types[k]),
+            )
+        plt.xlabel('recall')
+        plt.ylabel('precision')
+        plt.xlim(0, 1.0)
+        plt.ylim(0, 1.0)
+        plt.title(figure_title)
+        plt.legend()
+        # plt.show()
+        fig.savefig(outDir + f'/{figure_title}.png')
+        plt.close(fig)
+
+
+def autolabel(ax, rects):
+    """Attach a text label above each bar in *rects*, displaying its height."""
+    for rect in rects:
+        height = rect.get_height()
+        if height > 0 and height <= 1:  # for percent values
+            text_label = '{:2.0f}'.format(height * 100)
+        else:
+            text_label = '{:2.0f}'.format(height)
+        ax.annotate(
+            text_label,
+            xy=(rect.get_x() + rect.get_width() / 2, height),
+            xytext=(0, 3),  # 3 points vertical offset
+            textcoords='offset points',
+            ha='center',
+            va='bottom',
+            fontsize='x-small',
+        )
+
+
+def makebarplot(rs, ps, outDir, class_name, iou_type):
+    areaNames = ['allarea', 'small', 'medium', 'large']
+    types = ['C75', 'C50', 'Loc', 'Sim', 'Oth', 'BG', 'FN']
+    fig, ax = plt.subplots()
+    x = np.arange(len(areaNames))  # the areaNames locations
+    width = 0.60  # the width of the bars
+    rects_list = []
+    figure_title = iou_type + '-' + class_name + '-' + 'ap bar plot'
+    for i in range(len(types) - 1):
+        type_ps = ps[i, ..., 0]
+        aps = [ps_.mean() for ps_ in type_ps.T]
+        rects_list.append(
+            ax.bar(
+                x - width / 2 + (i + 1) * width / len(types),
+                aps,
+                width / len(types),
+                label=types[i],
+            ))
+
+    # Add some text for labels, title and custom x-axis tick labels, etc.
+    ax.set_ylabel('Mean Average Precision (mAP)')
+    ax.set_title(figure_title)
+    ax.set_xticks(x)
+    ax.set_xticklabels(areaNames)
+    ax.legend()
+
+    # Add score texts over bars
+    for rects in rects_list:
+        autolabel(ax, rects)
+
+    # Save plot
+    fig.savefig(outDir + f'/{figure_title}.png')
+    plt.close(fig)
+
+
+def get_gt_area_group_numbers(cocoEval):
+    areaRng = cocoEval.params.areaRng
+    areaRngStr = [str(aRng) for aRng in areaRng]
+    areaRngLbl = cocoEval.params.areaRngLbl
+    areaRngStr2areaRngLbl = dict(zip(areaRngStr, areaRngLbl))
+    areaRngLbl2Number = dict.fromkeys(areaRngLbl, 0)
+    for evalImg in cocoEval.evalImgs:
+        if evalImg:
+            for gtIgnore in evalImg['gtIgnore']:
+                if not gtIgnore:
+                    aRngLbl = areaRngStr2areaRngLbl[str(evalImg['aRng'])]
+                    areaRngLbl2Number[aRngLbl] += 1
+    return areaRngLbl2Number
+
+
+def make_gt_area_group_numbers_plot(cocoEval, outDir, verbose=True):
+    areaRngLbl2Number = get_gt_area_group_numbers(cocoEval)
+    areaRngLbl = areaRngLbl2Number.keys()
+    if verbose:
+        print('number of annotations per area group:', areaRngLbl2Number)
+
+    # Init figure
+    fig, ax = plt.subplots()
+    x = np.arange(len(areaRngLbl))  # the areaNames locations
+    width = 0.60  # the width of the bars
+    figure_title = 'number of annotations per area group'
+
+    rects = ax.bar(x, areaRngLbl2Number.values(), width)
+
+    # Add some text for labels, title and custom x-axis tick labels, etc.
+    ax.set_ylabel('Number of annotations')
+    ax.set_title(figure_title)
+    ax.set_xticks(x)
+    ax.set_xticklabels(areaRngLbl)
+
+    # Add score texts over bars
+    autolabel(ax, rects)
+
+    # Save plot
+    fig.tight_layout()
+    fig.savefig(outDir + f'/{figure_title}.png')
+    plt.close(fig)
+
+
+def make_gt_area_histogram_plot(cocoEval, outDir):
+    n_bins = 100
+    areas = [ann['area'] for ann in cocoEval.cocoGt.anns.values()]
+
+    # init figure
+    figure_title = 'gt annotation areas histogram plot'
+    fig, ax = plt.subplots()
+
+    # Set the number of bins
+    ax.hist(np.sqrt(areas), bins=n_bins)
+
+    # Add some text for labels, title and custom x-axis tick labels, etc.
+    ax.set_xlabel('Squareroot Area')
+    ax.set_ylabel('Number of annotations')
+    ax.set_title(figure_title)
+
+    # Save plot
+    fig.tight_layout()
+    fig.savefig(outDir + f'/{figure_title}.png')
+    plt.close(fig)
+
+
+def analyze_individual_category(k,
+                                cocoDt,
+                                cocoGt,
+                                catId,
+                                iou_type,
+                                areas=None):
+    nm = cocoGt.loadCats(catId)[0]
+    print(f'--------------analyzing {k + 1}-{nm["name"]}---------------')
+    ps_ = {}
+    dt = copy.deepcopy(cocoDt)
+    nm = cocoGt.loadCats(catId)[0]
+    imgIds = cocoGt.getImgIds()
+    dt_anns = dt.dataset['annotations']
+    select_dt_anns = []
+    for ann in dt_anns:
+        if ann['category_id'] == catId:
+            select_dt_anns.append(ann)
+    dt.dataset['annotations'] = select_dt_anns
+    dt.createIndex()
+    # compute precision but ignore superclass confusion
+    gt = copy.deepcopy(cocoGt)
+    child_catIds = gt.getCatIds(supNms=[nm['supercategory']])
+    for idx, ann in enumerate(gt.dataset['annotations']):
+        if ann['category_id'] in child_catIds and ann['category_id'] != catId:
+            gt.dataset['annotations'][idx]['ignore'] = 1
+            gt.dataset['annotations'][idx]['iscrowd'] = 1
+            gt.dataset['annotations'][idx]['category_id'] = catId
+    cocoEval = COCOeval(gt, copy.deepcopy(dt), iou_type)
+    cocoEval.params.imgIds = imgIds
+    cocoEval.params.maxDets = [100]
+    cocoEval.params.iouThrs = [0.1]
+    cocoEval.params.useCats = 1
+    if areas:
+        cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]],
+                                   [areas[0], areas[1]], [areas[1], areas[2]]]
+    cocoEval.evaluate()
+    cocoEval.accumulate()
+    ps_supercategory = cocoEval.eval['precision'][0, :, k, :, :]
+    ps_['ps_supercategory'] = ps_supercategory
+    # compute precision but ignore any class confusion
+    gt = copy.deepcopy(cocoGt)
+    for idx, ann in enumerate(gt.dataset['annotations']):
+        if ann['category_id'] != catId:
+            gt.dataset['annotations'][idx]['ignore'] = 1
+            gt.dataset['annotations'][idx]['iscrowd'] = 1
+            gt.dataset['annotations'][idx]['category_id'] = catId
+    cocoEval = COCOeval(gt, copy.deepcopy(dt), iou_type)
+    cocoEval.params.imgIds = imgIds
+    cocoEval.params.maxDets = [100]
+    cocoEval.params.iouThrs = [0.1]
+    cocoEval.params.useCats = 1
+    if areas:
+        cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]],
+                                   [areas[0], areas[1]], [areas[1], areas[2]]]
+    cocoEval.evaluate()
+    cocoEval.accumulate()
+    ps_allcategory = cocoEval.eval['precision'][0, :, k, :, :]
+    ps_['ps_allcategory'] = ps_allcategory
+    return k, ps_
+
+
+def analyze_results(res_file,
+                    ann_file,
+                    res_types,
+                    out_dir,
+                    extraplots=None,
+                    areas=None):
+    for res_type in res_types:
+        assert res_type in ['bbox', 'segm']
+    if areas:
+        assert len(areas) == 3, '3 integers should be specified as areas, \
+            representing 3 area regions'
+
+    directory = os.path.dirname(out_dir + '/')
+    if not os.path.exists(directory):
+        print(f'-------------create {out_dir}-----------------')
+        os.makedirs(directory)
+
+    cocoGt = COCO(ann_file)
+    cocoDt = cocoGt.loadRes(res_file)
+    imgIds = cocoGt.getImgIds()
+    for res_type in res_types:
+        res_out_dir = out_dir + '/' + res_type + '/'
+        res_directory = os.path.dirname(res_out_dir)
+        if not os.path.exists(res_directory):
+            print(f'-------------create {res_out_dir}-----------------')
+            os.makedirs(res_directory)
+        iou_type = res_type
+        cocoEval = COCOeval(
+            copy.deepcopy(cocoGt), copy.deepcopy(cocoDt), iou_type)
+        cocoEval.params.imgIds = imgIds
+        cocoEval.params.iouThrs = [0.75, 0.5, 0.1]
+        cocoEval.params.maxDets = [100]
+        if areas:
+            cocoEval.params.areaRng = [[0**2, areas[2]], [0**2, areas[0]],
+                                       [areas[0], areas[1]],
+                                       [areas[1], areas[2]]]
+        cocoEval.evaluate()
+        cocoEval.accumulate()
+        ps = cocoEval.eval['precision']
+        ps = np.vstack([ps, np.zeros((4, *ps.shape[1:]))])
+        catIds = cocoGt.getCatIds()
+        recThrs = cocoEval.params.recThrs
+        with Pool(processes=48) as pool:
+            args = [(k, cocoDt, cocoGt, catId, iou_type, areas)
+                    for k, catId in enumerate(catIds)]
+            analyze_results = pool.starmap(analyze_individual_category, args)
+        for k, catId in enumerate(catIds):
+            nm = cocoGt.loadCats(catId)[0]
+            print(f'--------------saving {k + 1}-{nm["name"]}---------------')
+            analyze_result = analyze_results[k]
+            assert k == analyze_result[0]
+            ps_supercategory = analyze_result[1]['ps_supercategory']
+            ps_allcategory = analyze_result[1]['ps_allcategory']
+            # compute precision but ignore superclass confusion
+            ps[3, :, k, :, :] = ps_supercategory
+            # compute precision but ignore any class confusion
+            ps[4, :, k, :, :] = ps_allcategory
+            # fill in background and false negative errors and plot
+            ps[ps == -1] = 0
+            ps[5, :, k, :, :] = ps[4, :, k, :, :] > 0
+            ps[6, :, k, :, :] = 1.0
+            makeplot(recThrs, ps[:, :, k], res_out_dir, nm['name'], iou_type)
+            if extraplots:
+                makebarplot(recThrs, ps[:, :, k], res_out_dir, nm['name'],
+                            iou_type)
+        makeplot(recThrs, ps, res_out_dir, 'allclass', iou_type)
+        if extraplots:
+            makebarplot(recThrs, ps, res_out_dir, 'allclass', iou_type)
+            make_gt_area_group_numbers_plot(
+                cocoEval=cocoEval, outDir=res_out_dir, verbose=True)
+            make_gt_area_histogram_plot(cocoEval=cocoEval, outDir=res_out_dir)
+
+
+def main():
+    parser = ArgumentParser(description='COCO Error Analysis Tool')
+    parser.add_argument('result', help='result file (json format) path')
+    parser.add_argument('out_dir', help='dir to save analyze result images')
+    parser.add_argument(
+        '--ann',
+        default='data/coco/annotations/instances_val2017.json',
+        help='annotation file path')
+    parser.add_argument(
+        '--types', type=str, nargs='+', default=['bbox'], help='result types')
+    parser.add_argument(
+        '--extraplots',
+        action='store_true',
+        help='export extra bar/stat plots')
+    parser.add_argument(
+        '--areas',
+        type=int,
+        nargs='+',
+        default=[1024, 9216, 10000000000],
+        help='area regions')
+    args = parser.parse_args()
+    analyze_results(
+        args.result,
+        args.ann,
+        args.types,
+        out_dir=args.out_dir,
+        extraplots=args.extraplots,
+        areas=args.areas)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/analysis_tools/coco_occluded_separated_recall.py b/tools/analysis_tools/coco_occluded_separated_recall.py
new file mode 100644
index 0000000..cbc0ee2
--- /dev/null
+++ b/tools/analysis_tools/coco_occluded_separated_recall.py
@@ -0,0 +1,44 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from argparse import ArgumentParser
+
+import mmcv
+from mmcv.utils import print_log
+
+from mmdet.datasets import OccludedSeparatedCocoDataset
+
+
+def main():
+    parser = ArgumentParser(
+        description='Compute recall of COCO occluded and separated masks '
+        'presented in paper https://arxiv.org/abs/2210.10046.')
+    parser.add_argument('result', help='result file (pkl format) path')
+    parser.add_argument('--out', help='file path to save evaluation results')
+    parser.add_argument(
+        '--score-thr',
+        type=float,
+        default=0.3,
+        help='Score threshold for the recall calculation. Defaults to 0.3')
+    parser.add_argument(
+        '--iou-thr',
+        type=float,
+        default=0.75,
+        help='IoU threshold for the recall calculation. Defaults to 0.75.')
+    parser.add_argument(
+        '--ann',
+        default='data/coco/annotations/instances_val2017.json',
+        help='coco annotation file path')
+    args = parser.parse_args()
+
+    results = mmcv.load(args.result)
+    assert isinstance(results[0], tuple), \
+        'The results must be predicted by instance segmentation model.'
+    dataset = OccludedSeparatedCocoDataset(
+        ann_file=args.ann, pipeline=[], test_mode=True)
+    metric_res = dataset.evaluate(results)
+    if args.out is not None:
+        mmcv.dump(metric_res, args.out)
+        print_log(f'Evaluation results have been saved to {args.out}.')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/analysis_tools/confusion_matrix copy.py b/tools/analysis_tools/confusion_matrix copy.py
new file mode 100644
index 0000000..5b52ea4
--- /dev/null
+++ b/tools/analysis_tools/confusion_matrix copy.py	
@@ -0,0 +1,273 @@
+import argparse
+import os
+
+import matplotlib.pyplot as plt
+import mmcv
+import numpy as np
+from matplotlib.ticker import MultipleLocator
+from mmcv import Config, DictAction
+from mmcv.ops import nms
+
+from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
+from mmdet.datasets import build_dataset
+from mmdet.utils import replace_cfg_vals, update_data_root
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Generate confusion matrix from detection results')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument(
+        'prediction_path', help='prediction path where test .pkl result')
+    parser.add_argument(
+        'save_dir', help='directory where confusion matrix will be saved')
+    parser.add_argument(
+        '--show', action='store_true', help='show confusion matrix')
+    parser.add_argument(
+        '--color-theme',
+        default='plasma',
+        help='theme of the matrix color map')
+    parser.add_argument(
+        '--score-thr',
+        type=float,
+        default=0.3,
+        help='score threshold to filter detection bboxes')
+    parser.add_argument(
+        '--tp-iou-thr',
+        type=float,
+        default=0.5,
+        help='IoU threshold to be considered as matched')
+    parser.add_argument(
+        '--nms-iou-thr',
+        type=float,
+        default=None,
+        help='nms IoU threshold, only applied when users want to change the'
+        'nms IoU threshold.')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    args = parser.parse_args()
+    return args
+
+
+def calculate_confusion_matrix(dataset,
+                               results,
+                               score_thr=0,
+                               nms_iou_thr=None,
+                               tp_iou_thr=0.5):
+    """Calculate the confusion matrix.
+
+    Args:
+        dataset (Dataset): Test or val dataset.
+        results (list[ndarray]): A list of detection results in each image.
+        score_thr (float|optional): Score threshold to filter bboxes.
+            Default: 0.
+        nms_iou_thr (float|optional): nms IoU threshold, the detection results
+            have done nms in the detector, only applied when users want to
+            change the nms IoU threshold. Default: None.
+        tp_iou_thr (float|optional): IoU threshold to be considered as matched.
+            Default: 0.5.
+    """
+    num_classes = len(dataset.CLASSES)
+    confusion_matrix = np.zeros(shape=[num_classes + 1, num_classes + 1])
+    assert len(dataset) == len(results)
+    prog_bar = mmcv.ProgressBar(len(results))
+    for idx, per_img_res in enumerate(results):
+        if isinstance(per_img_res, tuple):
+            res_bboxes, _ = per_img_res
+        else:
+            res_bboxes = per_img_res
+        ann = dataset.get_ann_info(idx)
+        gt_bboxes = ann['bboxes']
+        labels = ann['labels']
+        analyze_per_img_dets(confusion_matrix, gt_bboxes, labels, res_bboxes,
+                             score_thr, tp_iou_thr, nms_iou_thr)
+        prog_bar.update()
+    return confusion_matrix
+
+
+def analyze_per_img_dets(confusion_matrix,
+                         gt_bboxes,
+                         gt_labels,
+                         result,
+                         score_thr=0,
+                         tp_iou_thr=0.5,
+                         nms_iou_thr=None):
+    """Analyze detection results on each image.
+
+    Args:
+        confusion_matrix (ndarray): The confusion matrix,
+            has shape (num_classes + 1, num_classes + 1).
+        gt_bboxes (ndarray): Ground truth bboxes, has shape (num_gt, 4).
+        gt_labels (ndarray): Ground truth labels, has shape (num_gt).
+        result (ndarray): Detection results, has shape
+            (num_classes, num_bboxes, 5).
+        score_thr (float): Score threshold to filter bboxes.
+            Default: 0.
+        tp_iou_thr (float): IoU threshold to be considered as matched.
+            Default: 0.5.
+        nms_iou_thr (float|optional): nms IoU threshold, the detection results
+            have done nms in the detector, only applied when users want to
+            change the nms IoU threshold. Default: None.
+    """
+    true_positives = np.zeros_like(gt_labels)
+    for det_label, det_bboxes in enumerate(result):
+        if nms_iou_thr:
+            det_bboxes, _ = nms(
+                det_bboxes[:, :4],
+                det_bboxes[:, -1],
+                nms_iou_thr,
+                score_threshold=score_thr)
+        ious = bbox_overlaps(det_bboxes[:, :4], gt_bboxes)
+        for i, det_bbox in enumerate(det_bboxes):
+            score = det_bbox[4]
+            det_match = 0
+            if score >= score_thr:
+                for j, gt_label in enumerate(gt_labels):
+                    if ious[i, j] >= tp_iou_thr:
+                        det_match += 1
+                        if gt_label == det_label:
+                            true_positives[j] += 1  # TP
+                        confusion_matrix[gt_label, det_label] += 1
+                if det_match == 0:  # BG FP
+                    confusion_matrix[-1, det_label] += 1
+    for num_tp, gt_label in zip(true_positives, gt_labels):
+        if num_tp == 0:  # FN
+            confusion_matrix[gt_label, -1] += 1
+
+
+def plot_confusion_matrix(confusion_matrix,
+                          labels,
+                          save_dir=None,
+                          show=True,
+                          title='Normalized Confusion Matrix',
+                          color_theme='plasma'):
+    """Draw confusion matrix with matplotlib.
+
+    Args:
+        confusion_matrix (ndarray): The confusion matrix.
+        labels (list[str]): List of class names.
+        save_dir (str|optional): If set, save the confusion matrix plot to the
+            given path. Default: None.
+        show (bool): Whether to show the plot. Default: True.
+        title (str): Title of the plot. Default: `Normalized Confusion Matrix`.
+        color_theme (str): Theme of the matrix color map. Default: `plasma`.
+    """
+    # normalize the confusion matrix
+    per_label_sums = confusion_matrix.sum(axis=1)[:, np.newaxis]
+    confusion_matrix = \
+        confusion_matrix.astype(np.float32) / per_label_sums * 100
+
+    num_classes = len(labels)
+    fig, ax = plt.subplots(
+        figsize=(0.5 * num_classes, 0.5 * num_classes * 0.8), dpi=180)
+    cmap = plt.get_cmap(color_theme)
+    im = ax.imshow(confusion_matrix, cmap=cmap)
+    plt.colorbar(mappable=im, ax=ax)
+
+    title_font = {'weight': 'bold', 'size': 12}
+    ax.set_title(title, fontdict=title_font)
+    label_font = {'size': 10}
+    plt.ylabel('Ground Truth Label', fontdict=label_font)
+    plt.xlabel('Prediction Label', fontdict=label_font)
+
+    # draw locator
+    xmajor_locator = MultipleLocator(1)
+    xminor_locator = MultipleLocator(0.5)
+    ax.xaxis.set_major_locator(xmajor_locator)
+    ax.xaxis.set_minor_locator(xminor_locator)
+    ymajor_locator = MultipleLocator(1)
+    yminor_locator = MultipleLocator(0.5)
+    ax.yaxis.set_major_locator(ymajor_locator)
+    ax.yaxis.set_minor_locator(yminor_locator)
+
+    # draw grid
+    ax.grid(True, which='minor', linestyle='-')
+
+    # draw label
+    ax.set_xticks(np.arange(num_classes))
+    ax.set_yticks(np.arange(num_classes))
+    ax.set_xticklabels(labels)
+    ax.set_yticklabels(labels)
+
+    ax.tick_params(
+        axis='x', bottom=False, top=True, labelbottom=False, labeltop=True)
+    plt.setp(
+        ax.get_xticklabels(), rotation=45, ha='left', rotation_mode='anchor')
+
+    # draw confution matrix value
+    for i in range(num_classes):
+        for j in range(num_classes):
+            ax.text(
+                j,
+                i,
+                '{}%'.format(
+                    int(confusion_matrix[
+                        i,
+                        j]) if not np.isnan(confusion_matrix[i, j]) else -1),
+                ha='center',
+                va='center',
+                color='w',
+                size=7)
+
+    ax.set_ylim(len(confusion_matrix) - 0.5, -0.5)  # matplotlib>3.1.1
+
+    fig.tight_layout()
+    if save_dir is not None:
+        plt.savefig(
+            os.path.join(save_dir, 'confusion_matrix.png'), format='png')
+    if show:
+        plt.show()
+
+
+def main():
+    args = parse_args()
+
+    cfg = Config.fromfile(args.config)
+
+    # replace the ${key} with the value of cfg.key
+    cfg = replace_cfg_vals(cfg)
+
+    # update data root according to MMDET_DATASETS
+    update_data_root(cfg)
+
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+
+    results = mmcv.load(args.prediction_path)
+    assert isinstance(results, list)
+    if isinstance(results[0], list):
+        pass
+    elif isinstance(results[0], tuple):
+        results = [result[0] for result in results]
+    else:
+        raise TypeError('invalid type of prediction results')
+
+    if isinstance(cfg.data.test, dict):
+        cfg.data.test.test_mode = True
+    elif isinstance(cfg.data.test, list):
+        for ds_cfg in cfg.data.test:
+            ds_cfg.test_mode = True
+    dataset = build_dataset(cfg.data.test)
+
+    confusion_matrix = calculate_confusion_matrix(dataset, results,
+                                                  args.score_thr,
+                                                  args.nms_iou_thr,
+                                                  args.tp_iou_thr)
+    plot_confusion_matrix(
+        confusion_matrix,
+        dataset.CLASSES + ('background', ),
+        save_dir=args.save_dir,
+        show=args.show,
+        color_theme=args.color_theme)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/analysis_tools/confusion_matrix.py b/tools/analysis_tools/confusion_matrix.py
new file mode 100644
index 0000000..2b5dd5f
--- /dev/null
+++ b/tools/analysis_tools/confusion_matrix.py
@@ -0,0 +1,313 @@
+import argparse
+import os
+
+import matplotlib.pyplot as plt
+import mmcv
+import numpy as np
+from matplotlib.ticker import MultipleLocator
+from mmcv import Config, DictAction
+from mmcv.ops import nms
+
+from mmdet.core.evaluation.bbox_overlaps import bbox_overlaps
+from mmdet.datasets import build_dataset
+from mmdet.utils import replace_cfg_vals, update_data_root
+
+# 一下数据都是经过nms后的
+global_tp_special_list = []
+global_cls_wrong_fp_data = []  # cls型FP
+global_loc_wrong_fp_data = []  # iou型FP
+global_all_pos_sample_data = []  # 记录所有positive sample的IOU(max)情况。iou>0
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Generate confusion matrix from detection results')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument(
+        'prediction_path', help='prediction path where test .pkl result')
+    parser.add_argument(
+        'save_dir', help='directory where confusion matrix will be saved')
+    parser.add_argument(
+        '--show', action='store_true', help='show confusion matrix')
+    parser.add_argument(
+        '--color-theme',
+        default='plasma',
+        help='theme of the matrix color map')
+    parser.add_argument(
+        '--score-thr',
+        type=float,
+        default=0.05,
+        help='score threshold to filter detection bboxes')
+    parser.add_argument(
+        '--tp-iou-thr',
+        type=float,
+        default=0.5,
+        help='IoU threshold to be considered as matched')
+    parser.add_argument(
+        '--nms-iou-thr',
+        type=float,
+        default=None,
+        help='nms IoU threshold, only applied when users want to change the'
+        'nms IoU threshold.')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    args = parser.parse_args()
+    return args
+
+
+def calculate_confusion_matrix(dataset,
+                               results,
+                               score_thr=0,
+                               nms_iou_thr=None,
+                               tp_iou_thr=0.5):
+    """Calculate the confusion matrix.
+
+    Args:
+        dataset (Dataset): Test or val dataset.
+        results (list[ndarray]): A list of detection results in each image.
+        score_thr (float|optional): Score threshold to filter bboxes.
+            Default: 0.
+        nms_iou_thr (float|optional): nms IoU threshold, the detection results
+            have done nms in the detector, only applied when users want to
+            change the nms IoU threshold. Default: None.
+        tp_iou_thr (float|optional): IoU threshold to be considered as matched.
+            Default: 0.5.
+    """
+    num_classes = len(dataset.CLASSES)
+    confusion_matrix = np.zeros(shape=[num_classes + 1, num_classes + 1])
+    assert len(dataset) == len(results)
+    prog_bar = mmcv.ProgressBar(len(results))
+    for idx, per_img_res in enumerate(results):
+        if isinstance(per_img_res, tuple):
+            res_bboxes, _ = per_img_res
+        else:
+            res_bboxes = per_img_res
+        ann = dataset.get_ann_info(idx)
+        gt_bboxes = ann['bboxes']
+        labels = ann['labels']
+        analyze_per_img_dets(confusion_matrix, gt_bboxes, labels, res_bboxes,
+                             score_thr, tp_iou_thr, nms_iou_thr)
+        prog_bar.update()
+    return confusion_matrix
+
+# here
+def analyze_per_img_dets(confusion_matrix,
+                         gt_bboxes,
+                         gt_labels,
+                         result,
+                         score_thr=0,
+                         tp_iou_thr=0.5,
+                         nms_iou_thr=None):
+    """Analyze detection results on each image.
+
+    Args:
+        confusion_matrix (ndarray): The confusion matrix,
+            has shape (num_classes + 1, num_classes + 1).
+        gt_bboxes (ndarray): Ground truth bboxes, has shape (num_gt, 4).
+        gt_labels (ndarray): Ground truth labels, has shape (num_gt).
+        result (ndarray): Detection results, has shape
+            (num_classes, num_bboxes, 5).
+        score_thr (float): Score threshold to filter bboxes.
+            Default: 0.
+        tp_iou_thr (float): IoU threshold to be considered as matched.
+            Default: 0.5.
+        nms_iou_thr (float|optional): nms IoU threshold, the detection results
+            have done nms in the detector, only applied when users want to
+            change the nms IoU threshold. Default: None.
+    """
+    global global_tp_special_list
+    global global_cls_wrong_fp_data
+    global global_loc_wrong_fp_data
+    global global_all_pos_sample_data
+    
+    true_positives = np.zeros_like(gt_labels)
+    for det_label, det_bboxes in enumerate(result):  # TODO 看一下这个逻辑是啥， results是啥
+        if nms_iou_thr:
+            det_bboxes, _ = nms(
+                det_bboxes[:, :4],
+                det_bboxes[:, -1],
+                nms_iou_thr,
+                score_threshold=score_thr)
+        ious = bbox_overlaps(det_bboxes[:, :4], gt_bboxes)
+        for i, det_bbox in enumerate(det_bboxes):
+            score = det_bbox[4]
+            
+            max_iou = ious[i].max()
+            if max_iou>0.00001:
+                # 如果真的有交集,那么就可以统计
+                global_all_pos_sample_data.append([score, max_iou])
+            det_match = 0
+            if score >= score_thr:
+                for j, gt_label in enumerate(gt_labels):
+                    if ious[i, j] >= tp_iou_thr:
+                        det_match += 1
+                        if gt_label == det_label:
+                            true_positives[j] += 1  # TP
+                            # XXX 收集一下每个TP的label、confidence、IOU情况？？
+                            global_tp_special_list.append([det_label, score, ious[i, j]])
+                        else:
+                            # 说明是cls 错误型FP
+                            global_cls_wrong_fp_data.append([score, ious[i, j]])
+                        
+                        confusion_matrix[gt_label, det_label] += 1
+                    elif ious[i, j]>0 and gt_label == det_label:
+                        # 说明是IOU不够的 错误型FP
+                        global_loc_wrong_fp_data.append([score, ious[i, j]])
+                if det_match == 0:  # BG FP
+                    confusion_matrix[-1, det_label] += 1
+    for num_tp, gt_label in zip(true_positives, gt_labels):
+        if num_tp == 0:  # FN
+            confusion_matrix[gt_label, -1] += 1
+
+
+def plot_confusion_matrix(confusion_matrix,
+                          labels,
+                          save_dir=None,
+                          show=True,
+                          title='Normalized Confusion Matrix',
+                          color_theme='plasma'):
+    """Draw confusion matrix with matplotlib.
+
+    Args:
+        confusion_matrix (ndarray): The confusion matrix.
+        labels (list[str]): List of class names.
+        save_dir (str|optional): If set, save the confusion matrix plot to the
+            given path. Default: None.
+        show (bool): Whether to show the plot. Default: True.
+        title (str): Title of the plot. Default: `Normalized Confusion Matrix`.
+        color_theme (str): Theme of the matrix color map. Default: `plasma`.
+    """
+    # normalize the confusion matrix
+    per_label_sums = confusion_matrix.sum(axis=1)[:, np.newaxis]
+    confusion_matrix = \
+        confusion_matrix.astype(np.float32) / per_label_sums * 100
+
+    num_classes = len(labels)
+    fig, ax = plt.subplots(
+        figsize=(0.5 * num_classes, 0.5 * num_classes * 0.8), dpi=180)
+    cmap = plt.get_cmap(color_theme)
+    im = ax.imshow(confusion_matrix, cmap=cmap)
+    plt.colorbar(mappable=im, ax=ax)
+
+    title_font = {'weight': 'bold', 'size': 12}
+    ax.set_title(title, fontdict=title_font)
+    label_font = {'size': 10}
+    plt.ylabel('Ground Truth Label', fontdict=label_font)
+    plt.xlabel('Prediction Label', fontdict=label_font)
+
+    # draw locator
+    xmajor_locator = MultipleLocator(1)
+    xminor_locator = MultipleLocator(0.5)
+    ax.xaxis.set_major_locator(xmajor_locator)
+    ax.xaxis.set_minor_locator(xminor_locator)
+    ymajor_locator = MultipleLocator(1)
+    yminor_locator = MultipleLocator(0.5)
+    ax.yaxis.set_major_locator(ymajor_locator)
+    ax.yaxis.set_minor_locator(yminor_locator)
+
+    # draw grid
+    ax.grid(True, which='minor', linestyle='-')
+
+    # draw label
+    ax.set_xticks(np.arange(num_classes))
+    ax.set_yticks(np.arange(num_classes))
+    ax.set_xticklabels(labels)
+    ax.set_yticklabels(labels)
+
+    ax.tick_params(
+        axis='x', bottom=False, top=True, labelbottom=False, labeltop=True)
+    plt.setp(
+        ax.get_xticklabels(), rotation=45, ha='left', rotation_mode='anchor')
+
+    # draw confution matrix value
+    for i in range(num_classes):
+        for j in range(num_classes):
+            ax.text(
+                j,
+                i,
+                '{}%'.format(
+                    int(confusion_matrix[
+                        i,
+                        j]) if not np.isnan(confusion_matrix[i, j]) else -1),
+                ha='center',
+                va='center',
+                color='w',
+                size=7)
+
+    ax.set_ylim(len(confusion_matrix) - 0.5, -0.5)  # matplotlib>3.1.1
+
+    fig.tight_layout()
+    if save_dir is not None:
+        plt.savefig(
+            os.path.join(save_dir, 'confusion_matrix.png'), format='png')
+        
+        global global_tp_special_list
+        global global_cls_wrong_fp_data
+        global global_loc_wrong_fp_data
+        global global_all_pos_sample_data
+        
+        global_tp_special_list = np.array(global_tp_special_list)
+        np.save(os.path.join(save_dir, 'tp_special_list.npy'), global_tp_special_list)
+        global_cls_wrong_fp_data = np.array(global_cls_wrong_fp_data)
+        np.save(os.path.join(save_dir, 'cls_wrong_fp_data.npy'), global_cls_wrong_fp_data)
+        global_loc_wrong_fp_data = np.array(global_loc_wrong_fp_data)
+        np.save(os.path.join(save_dir, 'loc_wrong_fp_data.npy'), global_loc_wrong_fp_data)
+        global_all_pos_sample_data = np.array(global_all_pos_sample_data)
+        np.save(os.path.join(save_dir, 'all_pos_sample_data.npy'), global_all_pos_sample_data)
+        
+        
+    if show:
+        plt.show()
+
+
+def main():
+    args = parse_args()
+
+    cfg = Config.fromfile(args.config)
+
+    # replace the ${key} with the value of cfg.key
+    cfg = replace_cfg_vals(cfg)
+
+    # update data root according to MMDET_DATASETS
+    update_data_root(cfg)
+
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+
+    results = mmcv.load(args.prediction_path)
+    assert isinstance(results, list)
+    if isinstance(results[0], list):
+        pass
+    elif isinstance(results[0], tuple):
+        results = [result[0] for result in results]
+    else:
+        raise TypeError('invalid type of prediction results')
+
+    if isinstance(cfg.data.test, dict):
+        cfg.data.test.test_mode = True
+    elif isinstance(cfg.data.test, list):
+        for ds_cfg in cfg.data.test:
+            ds_cfg.test_mode = True
+    dataset = build_dataset(cfg.data.test)
+
+    confusion_matrix = calculate_confusion_matrix(dataset, results,
+                                                  args.score_thr,
+                                                  args.nms_iou_thr,
+                                                  args.tp_iou_thr)
+    plot_confusion_matrix(
+        confusion_matrix,
+        dataset.CLASSES + ('background', ),
+        save_dir=args.save_dir,
+        show=args.show,
+        color_theme=args.color_theme)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/analysis_tools/eval_metric.py b/tools/analysis_tools/eval_metric.py
new file mode 100644
index 0000000..7caafe9
--- /dev/null
+++ b/tools/analysis_tools/eval_metric.py
@@ -0,0 +1,88 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+
+import mmcv
+from mmcv import Config, DictAction
+
+from mmdet.datasets import build_dataset
+from mmdet.utils import replace_cfg_vals, update_data_root
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Evaluate metric of the '
+                                     'results saved in pkl format')
+    parser.add_argument('config', help='Config of the model')
+    parser.add_argument('pkl_results', help='Results in pickle format')
+    parser.add_argument(
+        '--format-only',
+        action='store_true',
+        help='Format the output results without perform evaluation. It is'
+        'useful when you want to format the result to a specific format and '
+        'submit it to the test server')
+    parser.add_argument(
+        '--eval',
+        type=str,
+        nargs='+',
+        help='Evaluation metrics, which depends on the dataset, e.g., "bbox",'
+        ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--eval-options',
+        nargs='+',
+        action=DictAction,
+        help='custom options for evaluation, the key-value pair in xxx=yyy '
+        'format will be kwargs for dataset.evaluate() function')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    cfg = Config.fromfile(args.config)
+
+    # replace the ${key} with the value of cfg.key
+    cfg = replace_cfg_vals(cfg)
+
+    # update data root according to MMDET_DATASETS
+    update_data_root(cfg)
+
+    assert args.eval or args.format_only, (
+        'Please specify at least one operation (eval/format the results) with '
+        'the argument "--eval", "--format-only"')
+    if args.eval and args.format_only:
+        raise ValueError('--eval and --format_only cannot be both specified')
+
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+    cfg.data.test.test_mode = True
+
+    dataset = build_dataset(cfg.data.test)
+    outputs = mmcv.load(args.pkl_results)
+
+    kwargs = {} if args.eval_options is None else args.eval_options
+    if args.format_only:
+        dataset.format_results(outputs, **kwargs)
+    if args.eval:
+        eval_kwargs = cfg.get('evaluation', {}).copy()
+        # hard-code way to remove EvalHook args
+        for key in [
+                'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',
+                'rule'
+        ]:
+            eval_kwargs.pop(key, None)
+        eval_kwargs.update(dict(metric=args.eval, **kwargs))
+        print(dataset.evaluate(outputs, **eval_kwargs))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/analysis_tools/get_flops.py b/tools/analysis_tools/get_flops.py
new file mode 100644
index 0000000..4df8732
--- /dev/null
+++ b/tools/analysis_tools/get_flops.py
@@ -0,0 +1,97 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+
+import numpy as np
+import torch
+from mmcv import Config, DictAction
+
+from mmdet.models import build_detector
+
+try:
+    from mmcv.cnn import get_model_complexity_info
+except ImportError:
+    raise ImportError('Please upgrade mmcv to >0.6.2')
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Train a detector')
+    parser.add_argument('config', help='train config file path')
+    parser.add_argument(
+        '--shape',
+        type=int,
+        nargs='+',
+        default=[1280, 800],
+        help='input image size')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--size-divisor',
+        type=int,
+        default=32,
+        help='Pad the input image, the minimum size that is divisible '
+        'by size_divisor, -1 means do not pad the image.')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+
+    args = parse_args()
+
+    if len(args.shape) == 1:
+        h = w = args.shape[0]
+    elif len(args.shape) == 2:
+        h, w = args.shape
+    else:
+        raise ValueError('invalid input shape')
+    ori_shape = (3, h, w)
+    divisor = args.size_divisor
+    if divisor > 0:
+        h = int(np.ceil(h / divisor)) * divisor
+        w = int(np.ceil(w / divisor)) * divisor
+
+    input_shape = (3, h, w)
+
+    cfg = Config.fromfile(args.config)
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+
+    model = build_detector(
+        cfg.model,
+        train_cfg=cfg.get('train_cfg'),
+        test_cfg=cfg.get('test_cfg'))
+    if torch.cuda.is_available():
+        model.cuda()
+    model.eval()
+
+    if hasattr(model, 'forward_dummy'):
+        model.forward = model.forward_dummy
+    else:
+        raise NotImplementedError(
+            'FLOPs counter is currently not currently supported with {}'.
+            format(model.__class__.__name__))
+
+    flops, params = get_model_complexity_info(model, input_shape)
+    split_line = '=' * 30
+
+    if divisor > 0 and \
+            input_shape != ori_shape:
+        print(f'{split_line}\nUse size divisor set input shape '
+              f'from {ori_shape} to {input_shape}\n')
+    print(f'{split_line}\nInput shape: {input_shape}\n'
+          f'Flops: {flops}\nParams: {params}\n{split_line}')
+    print('!!!Please be cautious if you use the results in papers. '
+          'You may need to check if all ops are supported and verify that the '
+          'flops computation is correct.')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/analysis_tools/optimize_anchors.py b/tools/analysis_tools/optimize_anchors.py
new file mode 100644
index 0000000..421998f
--- /dev/null
+++ b/tools/analysis_tools/optimize_anchors.py
@@ -0,0 +1,376 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Optimize anchor settings on a specific dataset.
+
+This script provides two method to optimize YOLO anchors including k-means
+anchor cluster and differential evolution. You can use ``--algorithm k-means``
+and ``--algorithm differential_evolution`` to switch two method.
+
+Example:
+    Use k-means anchor cluster::
+
+        python tools/analysis_tools/optimize_anchors.py ${CONFIG} \
+        --algorithm k-means --input-shape ${INPUT_SHAPE [WIDTH HEIGHT]} \
+        --output-dir ${OUTPUT_DIR}
+    Use differential evolution to optimize anchors::
+
+        python tools/analysis_tools/optimize_anchors.py ${CONFIG} \
+        --algorithm differential_evolution \
+        --input-shape ${INPUT_SHAPE [WIDTH HEIGHT]} \
+        --output-dir ${OUTPUT_DIR}
+"""
+import argparse
+import os.path as osp
+
+import mmcv
+import numpy as np
+import torch
+from mmcv import Config
+from scipy.optimize import differential_evolution
+
+from mmdet.core import bbox_cxcywh_to_xyxy, bbox_overlaps, bbox_xyxy_to_cxcywh
+from mmdet.datasets import build_dataset
+from mmdet.utils import get_root_logger, replace_cfg_vals, update_data_root
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Optimize anchor parameters.')
+    parser.add_argument('config', help='Train config file path.')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for calculating.')
+    parser.add_argument(
+        '--input-shape',
+        type=int,
+        nargs='+',
+        default=[608, 608],
+        help='input image size')
+    parser.add_argument(
+        '--algorithm',
+        default='differential_evolution',
+        help='Algorithm used for anchor optimizing.'
+        'Support k-means and differential_evolution for YOLO.')
+    parser.add_argument(
+        '--iters',
+        default=1000,
+        type=int,
+        help='Maximum iterations for optimizer.')
+    parser.add_argument(
+        '--output-dir',
+        default=None,
+        type=str,
+        help='Path to save anchor optimize result.')
+
+    args = parser.parse_args()
+    return args
+
+
+class BaseAnchorOptimizer:
+    """Base class for anchor optimizer.
+
+    Args:
+        dataset (obj:`Dataset`): Dataset object.
+        input_shape (list[int]): Input image shape of the model.
+            Format in [width, height].
+        logger (obj:`logging.Logger`): The logger for logging.
+        device (str, optional): Device used for calculating.
+            Default: 'cuda:0'
+        out_dir (str, optional): Path to save anchor optimize result.
+            Default: None
+    """
+
+    def __init__(self,
+                 dataset,
+                 input_shape,
+                 logger,
+                 device='cuda:0',
+                 out_dir=None):
+        self.dataset = dataset
+        self.input_shape = input_shape
+        self.logger = logger
+        self.device = device
+        self.out_dir = out_dir
+        bbox_whs, img_shapes = self.get_whs_and_shapes()
+        ratios = img_shapes.max(1, keepdims=True) / np.array([input_shape])
+
+        # resize to input shape
+        self.bbox_whs = bbox_whs / ratios
+
+    def get_whs_and_shapes(self):
+        """Get widths and heights of bboxes and shapes of images.
+
+        Returns:
+            tuple[np.ndarray]: Array of bbox shapes and array of image
+            shapes with shape (num_bboxes, 2) in [width, height] format.
+        """
+        self.logger.info('Collecting bboxes from annotation...')
+        bbox_whs = []
+        img_shapes = []
+        prog_bar = mmcv.ProgressBar(len(self.dataset))
+        for idx in range(len(self.dataset)):
+            ann = self.dataset.get_ann_info(idx)
+            data_info = self.dataset.data_infos[idx]
+            img_shape = np.array([data_info['width'], data_info['height']])
+            gt_bboxes = ann['bboxes']
+            for bbox in gt_bboxes:
+                wh = bbox[2:4] - bbox[0:2]
+                img_shapes.append(img_shape)
+                bbox_whs.append(wh)
+            prog_bar.update()
+        print('\n')
+        bbox_whs = np.array(bbox_whs)
+        img_shapes = np.array(img_shapes)
+        self.logger.info(f'Collected {bbox_whs.shape[0]} bboxes.')
+        return bbox_whs, img_shapes
+
+    def get_zero_center_bbox_tensor(self):
+        """Get a tensor of bboxes centered at (0, 0).
+
+        Returns:
+            Tensor: Tensor of bboxes with shape (num_bboxes, 4)
+            in [xmin, ymin, xmax, ymax] format.
+        """
+        whs = torch.from_numpy(self.bbox_whs).to(
+            self.device, dtype=torch.float32)
+        bboxes = bbox_cxcywh_to_xyxy(
+            torch.cat([torch.zeros_like(whs), whs], dim=1))
+        return bboxes
+
+    def optimize(self):
+        raise NotImplementedError
+
+    def save_result(self, anchors, path=None):
+        anchor_results = []
+        for w, h in anchors:
+            anchor_results.append([round(w), round(h)])
+        self.logger.info(f'Anchor optimize result:{anchor_results}')
+        if path:
+            json_path = osp.join(path, 'anchor_optimize_result.json')
+            mmcv.dump(anchor_results, json_path)
+            self.logger.info(f'Result saved in {json_path}')
+
+
+class YOLOKMeansAnchorOptimizer(BaseAnchorOptimizer):
+    r"""YOLO anchor optimizer using k-means. Code refer to `AlexeyAB/darknet.
+    <https://github.com/AlexeyAB/darknet/blob/master/src/detector.c>`_.
+
+    Args:
+        num_anchors (int) : Number of anchors.
+        iters (int): Maximum iterations for k-means.
+    """
+
+    def __init__(self, num_anchors, iters, **kwargs):
+
+        super(YOLOKMeansAnchorOptimizer, self).__init__(**kwargs)
+        self.num_anchors = num_anchors
+        self.iters = iters
+
+    def optimize(self):
+        anchors = self.kmeans_anchors()
+        self.save_result(anchors, self.out_dir)
+
+    def kmeans_anchors(self):
+        self.logger.info(
+            f'Start cluster {self.num_anchors} YOLO anchors with K-means...')
+        bboxes = self.get_zero_center_bbox_tensor()
+        cluster_center_idx = torch.randint(
+            0, bboxes.shape[0], (self.num_anchors, )).to(self.device)
+
+        assignments = torch.zeros((bboxes.shape[0], )).to(self.device)
+        cluster_centers = bboxes[cluster_center_idx]
+        if self.num_anchors == 1:
+            cluster_centers = self.kmeans_maximization(bboxes, assignments,
+                                                       cluster_centers)
+            anchors = bbox_xyxy_to_cxcywh(cluster_centers)[:, 2:].cpu().numpy()
+            anchors = sorted(anchors, key=lambda x: x[0] * x[1])
+            return anchors
+
+        prog_bar = mmcv.ProgressBar(self.iters)
+        for i in range(self.iters):
+            converged, assignments = self.kmeans_expectation(
+                bboxes, assignments, cluster_centers)
+            if converged:
+                self.logger.info(f'K-means process has converged at iter {i}.')
+                break
+            cluster_centers = self.kmeans_maximization(bboxes, assignments,
+                                                       cluster_centers)
+            prog_bar.update()
+        print('\n')
+        avg_iou = bbox_overlaps(bboxes,
+                                cluster_centers).max(1)[0].mean().item()
+
+        anchors = bbox_xyxy_to_cxcywh(cluster_centers)[:, 2:].cpu().numpy()
+        anchors = sorted(anchors, key=lambda x: x[0] * x[1])
+        self.logger.info(f'Anchor cluster finish. Average IOU: {avg_iou}')
+
+        return anchors
+
+    def kmeans_maximization(self, bboxes, assignments, centers):
+        """Maximization part of EM algorithm(Expectation-Maximization)"""
+        new_centers = torch.zeros_like(centers)
+        for i in range(centers.shape[0]):
+            mask = (assignments == i)
+            if mask.sum():
+                new_centers[i, :] = bboxes[mask].mean(0)
+        return new_centers
+
+    def kmeans_expectation(self, bboxes, assignments, centers):
+        """Expectation part of EM algorithm(Expectation-Maximization)"""
+        ious = bbox_overlaps(bboxes, centers)
+        closest = ious.argmax(1)
+        converged = (closest == assignments).all()
+        return converged, closest
+
+
+class YOLODEAnchorOptimizer(BaseAnchorOptimizer):
+    """YOLO anchor optimizer using differential evolution algorithm.
+
+    Args:
+        num_anchors (int) : Number of anchors.
+        iters (int): Maximum iterations for k-means.
+        strategy (str): The differential evolution strategy to use.
+            Should be one of:
+
+                - 'best1bin'
+                - 'best1exp'
+                - 'rand1exp'
+                - 'randtobest1exp'
+                - 'currenttobest1exp'
+                - 'best2exp'
+                - 'rand2exp'
+                - 'randtobest1bin'
+                - 'currenttobest1bin'
+                - 'best2bin'
+                - 'rand2bin'
+                - 'rand1bin'
+
+            Default: 'best1bin'.
+        population_size (int): Total population size of evolution algorithm.
+            Default: 15.
+        convergence_thr (float): Tolerance for convergence, the
+            optimizing stops when ``np.std(pop) <= abs(convergence_thr)
+            + convergence_thr * np.abs(np.mean(population_energies))``,
+            respectively. Default: 0.0001.
+        mutation (tuple[float]): Range of dithering randomly changes the
+            mutation constant. Default: (0.5, 1).
+        recombination (float): Recombination constant of crossover probability.
+            Default: 0.7.
+    """
+
+    def __init__(self,
+                 num_anchors,
+                 iters,
+                 strategy='best1bin',
+                 population_size=15,
+                 convergence_thr=0.0001,
+                 mutation=(0.5, 1),
+                 recombination=0.7,
+                 **kwargs):
+
+        super(YOLODEAnchorOptimizer, self).__init__(**kwargs)
+
+        self.num_anchors = num_anchors
+        self.iters = iters
+        self.strategy = strategy
+        self.population_size = population_size
+        self.convergence_thr = convergence_thr
+        self.mutation = mutation
+        self.recombination = recombination
+
+    def optimize(self):
+        anchors = self.differential_evolution()
+        self.save_result(anchors, self.out_dir)
+
+    def differential_evolution(self):
+        bboxes = self.get_zero_center_bbox_tensor()
+
+        bounds = []
+        for i in range(self.num_anchors):
+            bounds.extend([(0, self.input_shape[0]), (0, self.input_shape[1])])
+
+        result = differential_evolution(
+            func=self.avg_iou_cost,
+            bounds=bounds,
+            args=(bboxes, ),
+            strategy=self.strategy,
+            maxiter=self.iters,
+            popsize=self.population_size,
+            tol=self.convergence_thr,
+            mutation=self.mutation,
+            recombination=self.recombination,
+            updating='immediate',
+            disp=True)
+        self.logger.info(
+            f'Anchor evolution finish. Average IOU: {1 - result.fun}')
+        anchors = [(w, h) for w, h in zip(result.x[::2], result.x[1::2])]
+        anchors = sorted(anchors, key=lambda x: x[0] * x[1])
+        return anchors
+
+    @staticmethod
+    def avg_iou_cost(anchor_params, bboxes):
+        assert len(anchor_params) % 2 == 0
+        anchor_whs = torch.tensor(
+            [[w, h]
+             for w, h in zip(anchor_params[::2], anchor_params[1::2])]).to(
+                 bboxes.device, dtype=bboxes.dtype)
+        anchor_boxes = bbox_cxcywh_to_xyxy(
+            torch.cat([torch.zeros_like(anchor_whs), anchor_whs], dim=1))
+        ious = bbox_overlaps(bboxes, anchor_boxes)
+        max_ious, _ = ious.max(1)
+        cost = 1 - max_ious.mean().item()
+        return cost
+
+
+def main():
+    logger = get_root_logger()
+    args = parse_args()
+    cfg = args.config
+    cfg = Config.fromfile(cfg)
+
+    # replace the ${key} with the value of cfg.key
+    cfg = replace_cfg_vals(cfg)
+
+    # update data root according to MMDET_DATASETS
+    update_data_root(cfg)
+
+    input_shape = args.input_shape
+    assert len(input_shape) == 2
+
+    anchor_type = cfg.model.bbox_head.anchor_generator.type
+    assert anchor_type == 'YOLOAnchorGenerator', \
+        f'Only support optimize YOLOAnchor, but get {anchor_type}.'
+
+    base_sizes = cfg.model.bbox_head.anchor_generator.base_sizes
+    num_anchors = sum([len(sizes) for sizes in base_sizes])
+
+    train_data_cfg = cfg.data.train
+    while 'dataset' in train_data_cfg:
+        train_data_cfg = train_data_cfg['dataset']
+    dataset = build_dataset(train_data_cfg)
+
+    if args.algorithm == 'k-means':
+        optimizer = YOLOKMeansAnchorOptimizer(
+            dataset=dataset,
+            input_shape=input_shape,
+            device=args.device,
+            num_anchors=num_anchors,
+            iters=args.iters,
+            logger=logger,
+            out_dir=args.output_dir)
+    elif args.algorithm == 'differential_evolution':
+        optimizer = YOLODEAnchorOptimizer(
+            dataset=dataset,
+            input_shape=input_shape,
+            device=args.device,
+            num_anchors=num_anchors,
+            iters=args.iters,
+            logger=logger,
+            out_dir=args.output_dir)
+    else:
+        raise NotImplementedError(
+            f'Only support k-means and differential_evolution, '
+            f'but get {args.algorithm}')
+
+    optimizer.optimize()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/analysis_tools/plot_pr_curves.py b/tools/analysis_tools/plot_pr_curves.py
new file mode 100644
index 0000000..ecc9be2
--- /dev/null
+++ b/tools/analysis_tools/plot_pr_curves.py
@@ -0,0 +1,102 @@
+import os
+import sys
+import mmcv
+import numpy as np
+import argparse
+import matplotlib.pyplot as plt
+
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+
+from mmcv import Config
+from mmdet.datasets import build_dataset
+
+
+def plot_pr_curve(config_file, result_file, out_pic, metric="bbox"):
+    """plot precison-recall curve based on testing results of pkl file.
+
+        Args:
+            config_file (list[list | tuple]): config file path.
+            result_file (str): pkl file of testing results path.
+            metric (str): Metrics to be evaluated. Options are
+                'bbox', 'segm'.
+    """
+    
+    cfg = Config.fromfile(config_file)
+    # turn on test mode of dataset
+    if isinstance(cfg.data.test, dict):
+        cfg.data.test.test_mode = True
+    elif isinstance(cfg.data.test, list):
+        for ds_cfg in cfg.data.test:
+            ds_cfg.test_mode = True
+
+    # build dataset
+    dataset = build_dataset(cfg.data.test)
+    # load result file in pkl format
+    pkl_results = mmcv.load(result_file)
+    # convert pkl file (list[list | tuple | ndarray]) to json
+    json_results, _ = dataset.format_results(pkl_results)
+    # initialize COCO instance
+    coco = COCO(annotation_file=cfg.data.test.ann_file)
+    coco_gt = coco
+    coco_dt = coco_gt.loadRes(json_results[metric]) 
+    # initialize COCOeval instance
+    coco_eval = COCOeval(coco_gt, coco_dt, metric)
+    coco_eval.evaluate()
+    coco_eval.accumulate()
+    coco_eval.summarize()
+    # extract eval data
+    precisions = coco_eval.eval["precision"]
+    '''
+    precisions[T, R, K, A, M]
+    T: iou thresholds [0.5 : 0.05 : 0.95], idx from 0 to 9
+    R: recall thresholds [0 : 0.01 : 1], idx from 0 to 100
+    K: category, idx from 0 to ...
+    A: area range, (all, small, medium, large), idx from 0 to 3
+    M: max dets, (1, 10, 100), idx from 0 to 2
+    '''
+    pr_array1 = precisions[0, :, 0, 0, 2] 
+    pr_array2 = precisions[1, :, 0, 0, 2] 
+    pr_array3 = precisions[2, :, 0, 0, 2] 
+    pr_array4 = precisions[3, :, 0, 0, 2] 
+    pr_array5 = precisions[4, :, 0, 0, 2] 
+    pr_array6 = precisions[5, :, 0, 0, 2] 
+    pr_array7 = precisions[6, :, 0, 0, 2] 
+    pr_array8 = precisions[7, :, 0, 0, 2] 
+    pr_array9 = precisions[8, :, 0, 0, 2] 
+    pr_array10 = precisions[9, :, 0, 0, 2] 
+
+    x = np.arange(0.0, 1.01, 0.01)
+    # plot PR curve
+    plt.plot(x, pr_array1, label="iou=0.5")
+    plt.plot(x, pr_array2, label="iou=0.55")
+    plt.plot(x, pr_array3, label="iou=0.6")
+    plt.plot(x, pr_array4, label="iou=0.65")
+    plt.plot(x, pr_array5, label="iou=0.7")
+    plt.plot(x, pr_array6, label="iou=0.75")
+    plt.plot(x, pr_array7, label="iou=0.8")
+    plt.plot(x, pr_array8, label="iou=0.85")
+    plt.plot(x, pr_array9, label="iou=0.9")
+    plt.plot(x, pr_array10, label="iou=0.95")
+
+    plt.xlabel("recall")
+    plt.ylabel("precison")
+    plt.xlim(0, 1.0)
+    plt.ylim(0, 1.01)
+    plt.grid(True)
+    legend_entity = plt.legend(loc="lower right")
+    for handle in legend_entity.legendHandles:
+        # handle.set_sizes([30])
+        handle.set_alpha(0.5)
+    plt.savefig(out_pic)
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('config', help='config file path')
+    parser.add_argument('pkl_result_file', help='pkl result file path')
+    parser.add_argument('--out', default='pr_curve.png')
+    parser.add_argument('--eval', default='bbox')
+    cfg = parser.parse_args()
+
+    plot_pr_curve(config_file=cfg.config, result_file=cfg.pkl_result_file, out_pic=cfg.out, metric=cfg.eval)
+
diff --git a/tools/analysis_tools/robustness_eval.py b/tools/analysis_tools/robustness_eval.py
new file mode 100644
index 0000000..da5ec28
--- /dev/null
+++ b/tools/analysis_tools/robustness_eval.py
@@ -0,0 +1,251 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+from argparse import ArgumentParser
+
+import mmcv
+import numpy as np
+
+
+def print_coco_results(results):
+
+    def _print(result, ap=1, iouThr=None, areaRng='all', maxDets=100):
+        titleStr = 'Average Precision' if ap == 1 else 'Average Recall'
+        typeStr = '(AP)' if ap == 1 else '(AR)'
+        iouStr = '0.50:0.95' \
+            if iouThr is None else f'{iouThr:0.2f}'
+        iStr = f' {titleStr:<18} {typeStr} @[ IoU={iouStr:<9} | '
+        iStr += f'area={areaRng:>6s} | maxDets={maxDets:>3d} ] = {result:0.3f}'
+        print(iStr)
+
+    stats = np.zeros((12, ))
+    stats[0] = _print(results[0], 1)
+    stats[1] = _print(results[1], 1, iouThr=.5)
+    stats[2] = _print(results[2], 1, iouThr=.75)
+    stats[3] = _print(results[3], 1, areaRng='small')
+    stats[4] = _print(results[4], 1, areaRng='medium')
+    stats[5] = _print(results[5], 1, areaRng='large')
+    stats[6] = _print(results[6], 0, maxDets=1)
+    stats[7] = _print(results[7], 0, maxDets=10)
+    stats[8] = _print(results[8], 0)
+    stats[9] = _print(results[9], 0, areaRng='small')
+    stats[10] = _print(results[10], 0, areaRng='medium')
+    stats[11] = _print(results[11], 0, areaRng='large')
+
+
+def get_coco_style_results(filename,
+                           task='bbox',
+                           metric=None,
+                           prints='mPC',
+                           aggregate='benchmark'):
+
+    assert aggregate in ['benchmark', 'all']
+
+    if prints == 'all':
+        prints = ['P', 'mPC', 'rPC']
+    elif isinstance(prints, str):
+        prints = [prints]
+    for p in prints:
+        assert p in ['P', 'mPC', 'rPC']
+
+    if metric is None:
+        metrics = [
+            'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10', 'AR100',
+            'ARs', 'ARm', 'ARl'
+        ]
+    elif isinstance(metric, list):
+        metrics = metric
+    else:
+        metrics = [metric]
+
+    for metric_name in metrics:
+        assert metric_name in [
+            'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10', 'AR100',
+            'ARs', 'ARm', 'ARl'
+        ]
+
+    eval_output = mmcv.load(filename)
+
+    num_distortions = len(list(eval_output.keys()))
+    results = np.zeros((num_distortions, 6, len(metrics)), dtype='float32')
+
+    for corr_i, distortion in enumerate(eval_output):
+        for severity in eval_output[distortion]:
+            for metric_j, metric_name in enumerate(metrics):
+                mAP = eval_output[distortion][severity][task][metric_name]
+                results[corr_i, severity, metric_j] = mAP
+
+    P = results[0, 0, :]
+    if aggregate == 'benchmark':
+        mPC = np.mean(results[:15, 1:, :], axis=(0, 1))
+    else:
+        mPC = np.mean(results[:, 1:, :], axis=(0, 1))
+    rPC = mPC / P
+
+    print(f'\nmodel: {osp.basename(filename)}')
+    if metric is None:
+        if 'P' in prints:
+            print(f'Performance on Clean Data [P] ({task})')
+            print_coco_results(P)
+        if 'mPC' in prints:
+            print(f'Mean Performance under Corruption [mPC] ({task})')
+            print_coco_results(mPC)
+        if 'rPC' in prints:
+            print(f'Relative Performance under Corruption [rPC] ({task})')
+            print_coco_results(rPC)
+    else:
+        if 'P' in prints:
+            print(f'Performance on Clean Data [P] ({task})')
+            for metric_i, metric_name in enumerate(metrics):
+                print(f'{metric_name:5} =  {P[metric_i]:0.3f}')
+        if 'mPC' in prints:
+            print(f'Mean Performance under Corruption [mPC] ({task})')
+            for metric_i, metric_name in enumerate(metrics):
+                print(f'{metric_name:5} =  {mPC[metric_i]:0.3f}')
+        if 'rPC' in prints:
+            print(f'Relative Performance under Corruption [rPC] ({task})')
+            for metric_i, metric_name in enumerate(metrics):
+                print(f'{metric_name:5} => {rPC[metric_i] * 100:0.1f} %')
+
+    return results
+
+
+def get_voc_style_results(filename, prints='mPC', aggregate='benchmark'):
+
+    assert aggregate in ['benchmark', 'all']
+
+    if prints == 'all':
+        prints = ['P', 'mPC', 'rPC']
+    elif isinstance(prints, str):
+        prints = [prints]
+    for p in prints:
+        assert p in ['P', 'mPC', 'rPC']
+
+    eval_output = mmcv.load(filename)
+
+    num_distortions = len(list(eval_output.keys()))
+    results = np.zeros((num_distortions, 6, 20), dtype='float32')
+
+    for i, distortion in enumerate(eval_output):
+        for severity in eval_output[distortion]:
+            mAP = [
+                eval_output[distortion][severity][j]['ap']
+                for j in range(len(eval_output[distortion][severity]))
+            ]
+            results[i, severity, :] = mAP
+
+    P = results[0, 0, :]
+    if aggregate == 'benchmark':
+        mPC = np.mean(results[:15, 1:, :], axis=(0, 1))
+    else:
+        mPC = np.mean(results[:, 1:, :], axis=(0, 1))
+    rPC = mPC / P
+
+    print(f'\nmodel: {osp.basename(filename)}')
+    if 'P' in prints:
+        print(f'Performance on Clean Data [P] in AP50 = {np.mean(P):0.3f}')
+    if 'mPC' in prints:
+        print('Mean Performance under Corruption [mPC] in AP50 = '
+              f'{np.mean(mPC):0.3f}')
+    if 'rPC' in prints:
+        print('Relative Performance under Corruption [rPC] in % = '
+              f'{np.mean(rPC) * 100:0.1f}')
+
+    return np.mean(results, axis=2, keepdims=True)
+
+
+def get_results(filename,
+                dataset='coco',
+                task='bbox',
+                metric=None,
+                prints='mPC',
+                aggregate='benchmark'):
+    assert dataset in ['coco', 'voc', 'cityscapes']
+
+    if dataset in ['coco', 'cityscapes']:
+        results = get_coco_style_results(
+            filename,
+            task=task,
+            metric=metric,
+            prints=prints,
+            aggregate=aggregate)
+    elif dataset == 'voc':
+        if task != 'bbox':
+            print('Only bbox analysis is supported for Pascal VOC')
+            print('Will report bbox results\n')
+        if metric not in [None, ['AP'], ['AP50']]:
+            print('Only the AP50 metric is supported for Pascal VOC')
+            print('Will report AP50 metric\n')
+        results = get_voc_style_results(
+            filename, prints=prints, aggregate=aggregate)
+
+    return results
+
+
+def get_distortions_from_file(filename):
+
+    eval_output = mmcv.load(filename)
+
+    return get_distortions_from_results(eval_output)
+
+
+def get_distortions_from_results(eval_output):
+    distortions = []
+    for i, distortion in enumerate(eval_output):
+        distortions.append(distortion.replace('_', ' '))
+    return distortions
+
+
+def main():
+    parser = ArgumentParser(description='Corruption Result Analysis')
+    parser.add_argument('filename', help='result file path')
+    parser.add_argument(
+        '--dataset',
+        type=str,
+        choices=['coco', 'voc', 'cityscapes'],
+        default='coco',
+        help='dataset type')
+    parser.add_argument(
+        '--task',
+        type=str,
+        nargs='+',
+        choices=['bbox', 'segm'],
+        default=['bbox'],
+        help='task to report')
+    parser.add_argument(
+        '--metric',
+        nargs='+',
+        choices=[
+            None, 'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10',
+            'AR100', 'ARs', 'ARm', 'ARl'
+        ],
+        default=None,
+        help='metric to report')
+    parser.add_argument(
+        '--prints',
+        type=str,
+        nargs='+',
+        choices=['P', 'mPC', 'rPC'],
+        default='mPC',
+        help='corruption benchmark metric to print')
+    parser.add_argument(
+        '--aggregate',
+        type=str,
+        choices=['all', 'benchmark'],
+        default='benchmark',
+        help='aggregate all results or only those \
+        for benchmark corruptions')
+
+    args = parser.parse_args()
+
+    for task in args.task:
+        get_results(
+            args.filename,
+            dataset=args.dataset,
+            task=task,
+            metric=args.metric,
+            prints=args.prints,
+            aggregate=args.aggregate)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/analysis_tools/test_robustness.py b/tools/analysis_tools/test_robustness.py
new file mode 100644
index 0000000..0c1ddbe
--- /dev/null
+++ b/tools/analysis_tools/test_robustness.py
@@ -0,0 +1,387 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import copy
+import os
+import os.path as osp
+
+import mmcv
+import torch
+from mmcv import DictAction
+from mmcv.parallel import MMDataParallel, MMDistributedDataParallel
+from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
+                         wrap_fp16_model)
+from pycocotools.coco import COCO
+from pycocotools.cocoeval import COCOeval
+
+from mmdet import datasets
+from mmdet.apis import multi_gpu_test, set_random_seed, single_gpu_test
+from mmdet.core import eval_map
+from mmdet.datasets import build_dataloader, build_dataset
+from mmdet.models import build_detector
+from tools.analysis_tools.robustness_eval import get_results
+
+
+def coco_eval_with_return(result_files,
+                          result_types,
+                          coco,
+                          max_dets=(100, 300, 1000)):
+    for res_type in result_types:
+        assert res_type in ['proposal', 'bbox', 'segm', 'keypoints']
+
+    if mmcv.is_str(coco):
+        coco = COCO(coco)
+    assert isinstance(coco, COCO)
+
+    eval_results = {}
+    for res_type in result_types:
+        result_file = result_files[res_type]
+        assert result_file.endswith('.json')
+
+        coco_dets = coco.loadRes(result_file)
+        img_ids = coco.getImgIds()
+        iou_type = 'bbox' if res_type == 'proposal' else res_type
+        cocoEval = COCOeval(coco, coco_dets, iou_type)
+        cocoEval.params.imgIds = img_ids
+        if res_type == 'proposal':
+            cocoEval.params.useCats = 0
+            cocoEval.params.maxDets = list(max_dets)
+        cocoEval.evaluate()
+        cocoEval.accumulate()
+        cocoEval.summarize()
+        if res_type == 'segm' or res_type == 'bbox':
+            metric_names = [
+                'AP', 'AP50', 'AP75', 'APs', 'APm', 'APl', 'AR1', 'AR10',
+                'AR100', 'ARs', 'ARm', 'ARl'
+            ]
+            eval_results[res_type] = {
+                metric_names[i]: cocoEval.stats[i]
+                for i in range(len(metric_names))
+            }
+        else:
+            eval_results[res_type] = cocoEval.stats
+
+    return eval_results
+
+
+def voc_eval_with_return(result_file,
+                         dataset,
+                         iou_thr=0.5,
+                         logger='print',
+                         only_ap=True):
+    det_results = mmcv.load(result_file)
+    annotations = [dataset.get_ann_info(i) for i in range(len(dataset))]
+    if hasattr(dataset, 'year') and dataset.year == 2007:
+        dataset_name = 'voc07'
+    else:
+        dataset_name = dataset.CLASSES
+    mean_ap, eval_results = eval_map(
+        det_results,
+        annotations,
+        scale_ranges=None,
+        iou_thr=iou_thr,
+        dataset=dataset_name,
+        logger=logger)
+
+    if only_ap:
+        eval_results = [{
+            'ap': eval_results[i]['ap']
+        } for i in range(len(eval_results))]
+
+    return mean_ap, eval_results
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='MMDet test detector')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint', help='checkpoint file')
+    parser.add_argument('--out', help='output result file')
+    parser.add_argument(
+        '--corruptions',
+        type=str,
+        nargs='+',
+        default='benchmark',
+        choices=[
+            'all', 'benchmark', 'noise', 'blur', 'weather', 'digital',
+            'holdout', 'None', 'gaussian_noise', 'shot_noise', 'impulse_noise',
+            'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur', 'snow',
+            'frost', 'fog', 'brightness', 'contrast', 'elastic_transform',
+            'pixelate', 'jpeg_compression', 'speckle_noise', 'gaussian_blur',
+            'spatter', 'saturate'
+        ],
+        help='corruptions')
+    parser.add_argument(
+        '--severities',
+        type=int,
+        nargs='+',
+        default=[0, 1, 2, 3, 4, 5],
+        help='corruption severity levels')
+    parser.add_argument(
+        '--eval',
+        type=str,
+        nargs='+',
+        choices=['proposal', 'proposal_fast', 'bbox', 'segm', 'keypoints'],
+        help='eval types')
+    parser.add_argument(
+        '--iou-thr',
+        type=float,
+        default=0.5,
+        help='IoU threshold for pascal voc evaluation')
+    parser.add_argument(
+        '--summaries',
+        type=bool,
+        default=False,
+        help='Print summaries for every corruption and severity')
+    parser.add_argument(
+        '--workers', type=int, default=32, help='workers per gpu')
+    parser.add_argument('--show', action='store_true', help='show results')
+    parser.add_argument(
+        '--show-dir', help='directory where painted images will be saved')
+    parser.add_argument(
+        '--show-score-thr',
+        type=float,
+        default=0.3,
+        help='score threshold (default: 0.3)')
+    parser.add_argument('--tmpdir', help='tmp dir for writing some results')
+    parser.add_argument('--seed', type=int, default=None, help='random seed')
+    parser.add_argument(
+        '--launcher',
+        choices=['none', 'pytorch', 'slurm', 'mpi'],
+        default='none',
+        help='job launcher')
+    parser.add_argument('--local_rank', type=int, default=0)
+    parser.add_argument(
+        '--final-prints',
+        type=str,
+        nargs='+',
+        choices=['P', 'mPC', 'rPC'],
+        default='mPC',
+        help='corruption benchmark metric to print at the end')
+    parser.add_argument(
+        '--final-prints-aggregate',
+        type=str,
+        choices=['all', 'benchmark'],
+        default='benchmark',
+        help='aggregate all results or only those for benchmark corruptions')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    args = parser.parse_args()
+    if 'LOCAL_RANK' not in os.environ:
+        os.environ['LOCAL_RANK'] = str(args.local_rank)
+    return args
+
+
+def main():
+    args = parse_args()
+
+    assert args.out or args.show or args.show_dir, \
+        ('Please specify at least one operation (save or show the results) '
+         'with the argument "--out", "--show" or "show-dir"')
+
+    if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
+        raise ValueError('The output file must be a pkl file.')
+
+    cfg = mmcv.Config.fromfile(args.config)
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+    # set cudnn_benchmark
+    if cfg.get('cudnn_benchmark', False):
+        torch.backends.cudnn.benchmark = True
+    cfg.model.pretrained = None
+    cfg.data.test.test_mode = True
+    if args.workers == 0:
+        args.workers = cfg.data.workers_per_gpu
+
+    # init distributed env first, since logger depends on the dist info.
+    if args.launcher == 'none':
+        distributed = False
+    else:
+        distributed = True
+        init_dist(args.launcher, **cfg.dist_params)
+
+    # set random seeds
+    if args.seed is not None:
+        set_random_seed(args.seed)
+
+    if 'all' in args.corruptions:
+        corruptions = [
+            'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur',
+            'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog',
+            'brightness', 'contrast', 'elastic_transform', 'pixelate',
+            'jpeg_compression', 'speckle_noise', 'gaussian_blur', 'spatter',
+            'saturate'
+        ]
+    elif 'benchmark' in args.corruptions:
+        corruptions = [
+            'gaussian_noise', 'shot_noise', 'impulse_noise', 'defocus_blur',
+            'glass_blur', 'motion_blur', 'zoom_blur', 'snow', 'frost', 'fog',
+            'brightness', 'contrast', 'elastic_transform', 'pixelate',
+            'jpeg_compression'
+        ]
+    elif 'noise' in args.corruptions:
+        corruptions = ['gaussian_noise', 'shot_noise', 'impulse_noise']
+    elif 'blur' in args.corruptions:
+        corruptions = [
+            'defocus_blur', 'glass_blur', 'motion_blur', 'zoom_blur'
+        ]
+    elif 'weather' in args.corruptions:
+        corruptions = ['snow', 'frost', 'fog', 'brightness']
+    elif 'digital' in args.corruptions:
+        corruptions = [
+            'contrast', 'elastic_transform', 'pixelate', 'jpeg_compression'
+        ]
+    elif 'holdout' in args.corruptions:
+        corruptions = ['speckle_noise', 'gaussian_blur', 'spatter', 'saturate']
+    elif 'None' in args.corruptions:
+        corruptions = ['None']
+        args.severities = [0]
+    else:
+        corruptions = args.corruptions
+
+    rank, _ = get_dist_info()
+    aggregated_results = {}
+    for corr_i, corruption in enumerate(corruptions):
+        aggregated_results[corruption] = {}
+        for sev_i, corruption_severity in enumerate(args.severities):
+            # evaluate severity 0 (= no corruption) only once
+            if corr_i > 0 and corruption_severity == 0:
+                aggregated_results[corruption][0] = \
+                    aggregated_results[corruptions[0]][0]
+                continue
+
+            test_data_cfg = copy.deepcopy(cfg.data.test)
+            # assign corruption and severity
+            if corruption_severity > 0:
+                corruption_trans = dict(
+                    type='Corrupt',
+                    corruption=corruption,
+                    severity=corruption_severity)
+                # TODO: hard coded "1", we assume that the first step is
+                # loading images, which needs to be fixed in the future
+                test_data_cfg['pipeline'].insert(1, corruption_trans)
+
+            # print info
+            print(f'\nTesting {corruption} at severity {corruption_severity}')
+
+            # build the dataloader
+            # TODO: support multiple images per gpu
+            #       (only minor changes are needed)
+            dataset = build_dataset(test_data_cfg)
+            data_loader = build_dataloader(
+                dataset,
+                samples_per_gpu=1,
+                workers_per_gpu=args.workers,
+                dist=distributed,
+                shuffle=False)
+
+            # build the model and load checkpoint
+            cfg.model.train_cfg = None
+            model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
+            fp16_cfg = cfg.get('fp16', None)
+            if fp16_cfg is not None:
+                wrap_fp16_model(model)
+            checkpoint = load_checkpoint(
+                model, args.checkpoint, map_location='cpu')
+            # old versions did not save class info in checkpoints,
+            # this walkaround is for backward compatibility
+            if 'CLASSES' in checkpoint.get('meta', {}):
+                model.CLASSES = checkpoint['meta']['CLASSES']
+            else:
+                model.CLASSES = dataset.CLASSES
+
+            if not distributed:
+                model = MMDataParallel(model, device_ids=[0])
+                show_dir = args.show_dir
+                if show_dir is not None:
+                    show_dir = osp.join(show_dir, corruption)
+                    show_dir = osp.join(show_dir, str(corruption_severity))
+                    if not osp.exists(show_dir):
+                        osp.makedirs(show_dir)
+                outputs = single_gpu_test(model, data_loader, args.show,
+                                          show_dir, args.show_score_thr)
+            else:
+                model = MMDistributedDataParallel(
+                    model.cuda(),
+                    device_ids=[torch.cuda.current_device()],
+                    broadcast_buffers=False)
+                outputs = multi_gpu_test(model, data_loader, args.tmpdir)
+
+            if args.out and rank == 0:
+                eval_results_filename = (
+                    osp.splitext(args.out)[0] + '_results' +
+                    osp.splitext(args.out)[1])
+                mmcv.dump(outputs, args.out)
+                eval_types = args.eval
+                if cfg.dataset_type == 'VOCDataset':
+                    if eval_types:
+                        for eval_type in eval_types:
+                            if eval_type == 'bbox':
+                                test_dataset = mmcv.runner.obj_from_dict(
+                                    cfg.data.test, datasets)
+                                logger = 'print' if args.summaries else None
+                                mean_ap, eval_results = \
+                                    voc_eval_with_return(
+                                        args.out, test_dataset,
+                                        args.iou_thr, logger)
+                                aggregated_results[corruption][
+                                    corruption_severity] = eval_results
+                            else:
+                                print('\nOnly "bbox" evaluation \
+                                is supported for pascal voc')
+                else:
+                    if eval_types:
+                        print(f'Starting evaluate {" and ".join(eval_types)}')
+                        if eval_types == ['proposal_fast']:
+                            result_file = args.out
+                        else:
+                            if not isinstance(outputs[0], dict):
+                                result_files = dataset.results2json(
+                                    outputs, args.out)
+                            else:
+                                for name in outputs[0]:
+                                    print(f'\nEvaluating {name}')
+                                    outputs_ = [out[name] for out in outputs]
+                                    result_file = args.out
+                                    + f'.{name}'
+                                    result_files = dataset.results2json(
+                                        outputs_, result_file)
+                        eval_results = coco_eval_with_return(
+                            result_files, eval_types, dataset.coco)
+                        aggregated_results[corruption][
+                            corruption_severity] = eval_results
+                    else:
+                        print('\nNo task was selected for evaluation;'
+                              '\nUse --eval to select a task')
+
+                # save results after each evaluation
+                mmcv.dump(aggregated_results, eval_results_filename)
+
+    if rank == 0:
+        # print final results
+        print('\nAggregated results:')
+        prints = args.final_prints
+        aggregate = args.final_prints_aggregate
+
+        if cfg.dataset_type == 'VOCDataset':
+            get_results(
+                eval_results_filename,
+                dataset='voc',
+                prints=prints,
+                aggregate=aggregate)
+        else:
+            get_results(
+                eval_results_filename,
+                dataset='coco',
+                prints=prints,
+                aggregate=aggregate)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/confusion_matrix.sh b/tools/confusion_matrix.sh
new file mode 100644
index 0000000..38b8fe9
--- /dev/null
+++ b/tools/confusion_matrix.sh
@@ -0,0 +1,37 @@
+# python tools/analysis_tools/confusion_matrix.py \
+#     configs/ssd/ssdlite_mobilenetv2_scratch_600e_voc.py \
+#     work_dirs/ssdlite_mobilenetv2_scratch_600e_voc/results.pkl \
+#     work_dirs/ssdlite_mobilenetv2_scratch_600e_voc \
+
+# python tools/analysis_tools/confusion_matrix.py \
+#     work_dirs/retinanet_r18_fpn_1x_voc/retinanet_r18_fpn_1x_voc.py \
+#     work_dirs/retinanet_r18_fpn_1x_voc/results.pkl \
+#     work_dirs/retinanet_r18_fpn_1x_voc \
+
+# python tools/analysis_tools/confusion_matrix.py \
+#     work_dirs/retinanet_r18_fpn_voc_w4a4_LSQ/retinanet_r18_fpn_1x_voc_quant_w4a4.py \
+#     work_dirs/retinanet_r18_fpn_voc_w4a4_LSQ/best_bbox_mAP_epoch_16.pkl \
+#     work_dirs/retinanet_r18_fpn_voc_w4a4_LSQ \
+
+
+# python tools/analysis_tools/confusion_matrix.py \
+#     work_dirs/retinanet_r18_fpn_voc_w2a2_LSQ/retinanet_r18_fpn_1x_voc_quant_w2a2.py \
+#     work_dirs/retinanet_r18_fpn_voc_w2a2_LSQ/best_bbox_mAP_epoch_15.pkl \
+#     work_dirs/retinanet_r18_fpn_voc_w2a2_LSQ \
+
+# python tools/analysis_tools/confusion_matrix.py \
+#     work_dirs/retinanet_r50_fpn_1x_voc/retinanet_r50_fpn_1x_voc.py \
+#     work_dirs/retinanet_r50_fpn_1x_voc/results.pkl \
+#     work_dirs/retinanet_r50_fpn_1x_voc \
+
+# python tools/analysis_tools/confusion_matrix.py \
+#     work_dirs/retinanet_r50_fpn_voc_w4a4_LSQ/retinanet_r50_fpn_1x_voc_quant_w4a4.py \
+#     work_dirs/retinanet_r50_fpn_voc_w4a4_LSQ/best_bbox_mAP_epoch_15.pkl \
+#     work_dirs/retinanet_r50_fpn_voc_w4a4_LSQ \
+
+python tools/analysis_tools/confusion_matrix.py \
+    work_dirs/retinanet_r50_fpn_voc_w2a2_LSQ_le/retinanet_r50_fpn_1x_voc_quant_w2a2.py \
+    work_dirs/retinanet_r50_fpn_voc_w2a2_LSQ_le/best_bbox_mAP_epoch_17.pkl \
+    work_dirs/retinanet_r50_fpn_voc_w2a2_LSQ_le \
+
+
diff --git a/tools/confusion_matrix1.sh b/tools/confusion_matrix1.sh
new file mode 100644
index 0000000..1748550
--- /dev/null
+++ b/tools/confusion_matrix1.sh
@@ -0,0 +1,10 @@
+python tools/analysis_tools/confusion_matrix.py \
+    # split
+    configs/ssd/ssdlite_mobilenetv2_scratch_600e_voc.py \
+    work_dirs/ssdlite_mobilenetv2_scratch_600e_voc/results.pkl \
+    work_dirs/ssdlite_mobilenetv2_scratch_600e_voc \
+    # split
+    # configs/ssd/ssdlite_mobilenetv2_scratch_600e_voc.py \
+    # work_dirs/ssdlite_mobilenetv2_scratch_600e_voc/results.pkl \
+    # work_dirs/ssdlite_mobilenetv2_scratch_600e_voc \
+
diff --git a/tools/dataset_converters/cityscapes.py b/tools/dataset_converters/cityscapes.py
new file mode 100644
index 0000000..c8e44b9
--- /dev/null
+++ b/tools/dataset_converters/cityscapes.py
@@ -0,0 +1,152 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import glob
+import os.path as osp
+
+import cityscapesscripts.helpers.labels as CSLabels
+import mmcv
+import numpy as np
+import pycocotools.mask as maskUtils
+
+
+def collect_files(img_dir, gt_dir):
+    suffix = 'leftImg8bit.png'
+    files = []
+    for img_file in glob.glob(osp.join(img_dir, '**/*.png')):
+        assert img_file.endswith(suffix), img_file
+        inst_file = gt_dir + img_file[
+            len(img_dir):-len(suffix)] + 'gtFine_instanceIds.png'
+        # Note that labelIds are not converted to trainId for seg map
+        segm_file = gt_dir + img_file[
+            len(img_dir):-len(suffix)] + 'gtFine_labelIds.png'
+        files.append((img_file, inst_file, segm_file))
+    assert len(files), f'No images found in {img_dir}'
+    print(f'Loaded {len(files)} images from {img_dir}')
+
+    return files
+
+
+def collect_annotations(files, nproc=1):
+    print('Loading annotation images')
+    if nproc > 1:
+        images = mmcv.track_parallel_progress(
+            load_img_info, files, nproc=nproc)
+    else:
+        images = mmcv.track_progress(load_img_info, files)
+
+    return images
+
+
+def load_img_info(files):
+    img_file, inst_file, segm_file = files
+    inst_img = mmcv.imread(inst_file, 'unchanged')
+    # ids < 24 are stuff labels (filtering them first is about 5% faster)
+    unique_inst_ids = np.unique(inst_img[inst_img >= 24])
+    anno_info = []
+    for inst_id in unique_inst_ids:
+        # For non-crowd annotations, inst_id // 1000 is the label_id
+        # Crowd annotations have <1000 instance ids
+        label_id = inst_id // 1000 if inst_id >= 1000 else inst_id
+        label = CSLabels.id2label[label_id]
+        if not label.hasInstances or label.ignoreInEval:
+            continue
+
+        category_id = label.id
+        iscrowd = int(inst_id < 1000)
+        mask = np.asarray(inst_img == inst_id, dtype=np.uint8, order='F')
+        mask_rle = maskUtils.encode(mask[:, :, None])[0]
+
+        area = maskUtils.area(mask_rle)
+        # convert to COCO style XYWH format
+        bbox = maskUtils.toBbox(mask_rle)
+
+        # for json encoding
+        mask_rle['counts'] = mask_rle['counts'].decode()
+
+        anno = dict(
+            iscrowd=iscrowd,
+            category_id=category_id,
+            bbox=bbox.tolist(),
+            area=area.tolist(),
+            segmentation=mask_rle)
+        anno_info.append(anno)
+    video_name = osp.basename(osp.dirname(img_file))
+    img_info = dict(
+        # remove img_prefix for filename
+        file_name=osp.join(video_name, osp.basename(img_file)),
+        height=inst_img.shape[0],
+        width=inst_img.shape[1],
+        anno_info=anno_info,
+        segm_file=osp.join(video_name, osp.basename(segm_file)))
+
+    return img_info
+
+
+def cvt_annotations(image_infos, out_json_name):
+    out_json = dict()
+    img_id = 0
+    ann_id = 0
+    out_json['images'] = []
+    out_json['categories'] = []
+    out_json['annotations'] = []
+    for image_info in image_infos:
+        image_info['id'] = img_id
+        anno_infos = image_info.pop('anno_info')
+        out_json['images'].append(image_info)
+        for anno_info in anno_infos:
+            anno_info['image_id'] = img_id
+            anno_info['id'] = ann_id
+            out_json['annotations'].append(anno_info)
+            ann_id += 1
+        img_id += 1
+    for label in CSLabels.labels:
+        if label.hasInstances and not label.ignoreInEval:
+            cat = dict(id=label.id, name=label.name)
+            out_json['categories'].append(cat)
+
+    if len(out_json['annotations']) == 0:
+        out_json.pop('annotations')
+
+    mmcv.dump(out_json, out_json_name)
+    return out_json
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert Cityscapes annotations to COCO format')
+    parser.add_argument('cityscapes_path', help='cityscapes data path')
+    parser.add_argument('--img-dir', default='leftImg8bit', type=str)
+    parser.add_argument('--gt-dir', default='gtFine', type=str)
+    parser.add_argument('-o', '--out-dir', help='output path')
+    parser.add_argument(
+        '--nproc', default=1, type=int, help='number of process')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    cityscapes_path = args.cityscapes_path
+    out_dir = args.out_dir if args.out_dir else cityscapes_path
+    mmcv.mkdir_or_exist(out_dir)
+
+    img_dir = osp.join(cityscapes_path, args.img_dir)
+    gt_dir = osp.join(cityscapes_path, args.gt_dir)
+
+    set_name = dict(
+        train='instancesonly_filtered_gtFine_train.json',
+        val='instancesonly_filtered_gtFine_val.json',
+        test='instancesonly_filtered_gtFine_test.json')
+
+    for split, json_name in set_name.items():
+        print(f'Converting {split} into {json_name}')
+        with mmcv.Timer(
+                print_tmpl='It took {}s to convert Cityscapes annotation'):
+            files = collect_files(
+                osp.join(img_dir, split), osp.join(gt_dir, split))
+            image_infos = collect_annotations(files, nproc=args.nproc)
+            cvt_annotations(image_infos, osp.join(out_dir, json_name))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/dataset_converters/images2coco.py b/tools/dataset_converters/images2coco.py
new file mode 100644
index 0000000..1c4e2f1
--- /dev/null
+++ b/tools/dataset_converters/images2coco.py
@@ -0,0 +1,101 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+
+import mmcv
+from PIL import Image
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert images to coco format without annotations')
+    parser.add_argument('img_path', help='The root path of images')
+    parser.add_argument(
+        'classes', type=str, help='The text file name of storage class list')
+    parser.add_argument(
+        'out',
+        type=str,
+        help='The output annotation json file name, The save dir is in the '
+        'same directory as img_path')
+    parser.add_argument(
+        '-e',
+        '--exclude-extensions',
+        type=str,
+        nargs='+',
+        help='The suffix of images to be excluded, such as "png" and "bmp"')
+    args = parser.parse_args()
+    return args
+
+
+def collect_image_infos(path, exclude_extensions=None):
+    img_infos = []
+
+    images_generator = mmcv.scandir(path, recursive=True)
+    for image_path in mmcv.track_iter_progress(list(images_generator)):
+        if exclude_extensions is None or (
+                exclude_extensions is not None
+                and not image_path.lower().endswith(exclude_extensions)):
+            image_path = os.path.join(path, image_path)
+            img_pillow = Image.open(image_path)
+            img_info = {
+                'filename': image_path,
+                'width': img_pillow.width,
+                'height': img_pillow.height,
+            }
+            img_infos.append(img_info)
+    return img_infos
+
+
+def cvt_to_coco_json(img_infos, classes):
+    image_id = 0
+    coco = dict()
+    coco['images'] = []
+    coco['type'] = 'instance'
+    coco['categories'] = []
+    coco['annotations'] = []
+    image_set = set()
+
+    for category_id, name in enumerate(classes):
+        category_item = dict()
+        category_item['supercategory'] = str('none')
+        category_item['id'] = int(category_id)
+        category_item['name'] = str(name)
+        coco['categories'].append(category_item)
+
+    for img_dict in img_infos:
+        file_name = img_dict['filename']
+        assert file_name not in image_set
+        image_item = dict()
+        image_item['id'] = int(image_id)
+        image_item['file_name'] = str(file_name)
+        image_item['height'] = int(img_dict['height'])
+        image_item['width'] = int(img_dict['width'])
+        coco['images'].append(image_item)
+        image_set.add(file_name)
+
+        image_id += 1
+    return coco
+
+
+def main():
+    args = parse_args()
+    assert args.out.endswith(
+        'json'), 'The output file name must be json suffix'
+
+    # 1 load image list info
+    img_infos = collect_image_infos(args.img_path, args.exclude_extensions)
+
+    # 2 convert to coco format data
+    classes = mmcv.list_from_file(args.classes)
+    coco_info = cvt_to_coco_json(img_infos, classes)
+
+    # 3 dump
+    save_dir = os.path.join(args.img_path, '..', 'annotations')
+    mmcv.mkdir_or_exist(save_dir)
+    save_path = os.path.join(save_dir, args.out)
+    mmcv.dump(coco_info, save_path)
+    print(f'save json file: {save_path}')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/dataset_converters/pascal_voc.py b/tools/dataset_converters/pascal_voc.py
new file mode 100644
index 0000000..20f8801
--- /dev/null
+++ b/tools/dataset_converters/pascal_voc.py
@@ -0,0 +1,237 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os.path as osp
+import xml.etree.ElementTree as ET
+
+import mmcv
+import numpy as np
+
+from mmdet.core import voc_classes
+
+label_ids = {name: i for i, name in enumerate(voc_classes())}
+
+
+def parse_xml(args):
+    xml_path, img_path = args
+    tree = ET.parse(xml_path)
+    root = tree.getroot()
+    size = root.find('size')
+    w = int(size.find('width').text)
+    h = int(size.find('height').text)
+    bboxes = []
+    labels = []
+    bboxes_ignore = []
+    labels_ignore = []
+    for obj in root.findall('object'):
+        name = obj.find('name').text
+        label = label_ids[name]
+        difficult = int(obj.find('difficult').text)
+        bnd_box = obj.find('bndbox')
+        bbox = [
+            int(bnd_box.find('xmin').text),
+            int(bnd_box.find('ymin').text),
+            int(bnd_box.find('xmax').text),
+            int(bnd_box.find('ymax').text)
+        ]
+        if difficult:
+            bboxes_ignore.append(bbox)
+            labels_ignore.append(label)
+        else:
+            bboxes.append(bbox)
+            labels.append(label)
+    if not bboxes:
+        bboxes = np.zeros((0, 4))
+        labels = np.zeros((0, ))
+    else:
+        bboxes = np.array(bboxes, ndmin=2) - 1
+        labels = np.array(labels)
+    if not bboxes_ignore:
+        bboxes_ignore = np.zeros((0, 4))
+        labels_ignore = np.zeros((0, ))
+    else:
+        bboxes_ignore = np.array(bboxes_ignore, ndmin=2) - 1
+        labels_ignore = np.array(labels_ignore)
+    annotation = {
+        'filename': img_path,
+        'width': w,
+        'height': h,
+        'ann': {
+            'bboxes': bboxes.astype(np.float32),
+            'labels': labels.astype(np.int64),
+            'bboxes_ignore': bboxes_ignore.astype(np.float32),
+            'labels_ignore': labels_ignore.astype(np.int64)
+        }
+    }
+    return annotation
+
+
+def cvt_annotations(devkit_path, years, split, out_file):
+    if not isinstance(years, list):
+        years = [years]
+    annotations = []
+    for year in years:
+        filelist = osp.join(devkit_path,
+                            f'VOC{year}/ImageSets/Main/{split}.txt')
+        if not osp.isfile(filelist):
+            print(f'filelist does not exist: {filelist}, '
+                  f'skip voc{year} {split}')
+            return
+        img_names = mmcv.list_from_file(filelist)
+        xml_paths = [
+            osp.join(devkit_path, f'VOC{year}/Annotations/{img_name}.xml')
+            for img_name in img_names
+        ]
+        img_paths = [
+            f'VOC{year}/JPEGImages/{img_name}.jpg' for img_name in img_names
+        ]
+        part_annotations = mmcv.track_progress(parse_xml,
+                                               list(zip(xml_paths, img_paths)))
+        annotations.extend(part_annotations)
+    if out_file.endswith('json'):
+        annotations = cvt_to_coco_json(annotations)
+    mmcv.dump(annotations, out_file)
+    return annotations
+
+
+def cvt_to_coco_json(annotations):
+    image_id = 0
+    annotation_id = 0
+    coco = dict()
+    coco['images'] = []
+    coco['type'] = 'instance'
+    coco['categories'] = []
+    coco['annotations'] = []
+    image_set = set()
+
+    def addAnnItem(annotation_id, image_id, category_id, bbox, difficult_flag):
+        annotation_item = dict()
+        annotation_item['segmentation'] = []
+
+        seg = []
+        # bbox[] is x1,y1,x2,y2
+        # left_top
+        seg.append(int(bbox[0]))
+        seg.append(int(bbox[1]))
+        # left_bottom
+        seg.append(int(bbox[0]))
+        seg.append(int(bbox[3]))
+        # right_bottom
+        seg.append(int(bbox[2]))
+        seg.append(int(bbox[3]))
+        # right_top
+        seg.append(int(bbox[2]))
+        seg.append(int(bbox[1]))
+
+        annotation_item['segmentation'].append(seg)
+
+        xywh = np.array(
+            [bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]])
+        annotation_item['area'] = int(xywh[2] * xywh[3])
+        if difficult_flag == 1:
+            annotation_item['ignore'] = 0
+            annotation_item['iscrowd'] = 1
+        else:
+            annotation_item['ignore'] = 0
+            annotation_item['iscrowd'] = 0
+        annotation_item['image_id'] = int(image_id)
+        annotation_item['bbox'] = xywh.astype(int).tolist()
+        annotation_item['category_id'] = int(category_id)
+        annotation_item['id'] = int(annotation_id)
+        coco['annotations'].append(annotation_item)
+        return annotation_id + 1
+
+    for category_id, name in enumerate(voc_classes()):
+        category_item = dict()
+        category_item['supercategory'] = str('none')
+        category_item['id'] = int(category_id)
+        category_item['name'] = str(name)
+        coco['categories'].append(category_item)
+
+    for ann_dict in annotations:
+        file_name = ann_dict['filename']
+        ann = ann_dict['ann']
+        assert file_name not in image_set
+        image_item = dict()
+        image_item['id'] = int(image_id)
+        image_item['file_name'] = str(file_name)
+        image_item['height'] = int(ann_dict['height'])
+        image_item['width'] = int(ann_dict['width'])
+        coco['images'].append(image_item)
+        image_set.add(file_name)
+
+        bboxes = ann['bboxes'][:, :4]
+        labels = ann['labels']
+        for bbox_id in range(len(bboxes)):
+            bbox = bboxes[bbox_id]
+            label = labels[bbox_id]
+            annotation_id = addAnnItem(
+                annotation_id, image_id, label, bbox, difficult_flag=0)
+
+        bboxes_ignore = ann['bboxes_ignore'][:, :4]
+        labels_ignore = ann['labels_ignore']
+        for bbox_id in range(len(bboxes_ignore)):
+            bbox = bboxes_ignore[bbox_id]
+            label = labels_ignore[bbox_id]
+            annotation_id = addAnnItem(
+                annotation_id, image_id, label, bbox, difficult_flag=1)
+
+        image_id += 1
+
+    return coco
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert PASCAL VOC annotations to mmdetection format')
+    parser.add_argument('devkit_path', help='pascal voc devkit path')
+    parser.add_argument('-o', '--out-dir', help='output path')
+    parser.add_argument(
+        '--out-format',
+        default='pkl',
+        choices=('pkl', 'coco'),
+        help='output format, "coco" indicates coco annotation format')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    devkit_path = args.devkit_path
+    out_dir = args.out_dir if args.out_dir else devkit_path
+    mmcv.mkdir_or_exist(out_dir)
+
+    years = []
+    if osp.isdir(osp.join(devkit_path, 'VOC2007')):
+        years.append('2007')
+    if osp.isdir(osp.join(devkit_path, 'VOC2012')):
+        years.append('2012')
+    if '2007' in years and '2012' in years:
+        years.append(['2007', '2012'])
+    if not years:
+        raise IOError(f'The devkit path {devkit_path} contains neither '
+                      '"VOC2007" nor "VOC2012" subfolder')
+    out_fmt = f'.{args.out_format}'
+    if args.out_format == 'coco':
+        out_fmt = '.json'
+    for year in years:
+        if year == '2007':
+            prefix = 'voc07'
+        elif year == '2012':
+            prefix = 'voc12'
+        elif year == ['2007', '2012']:
+            prefix = 'voc0712'
+        for split in ['train', 'val', 'trainval']:
+            dataset_name = prefix + '_' + split
+            print(f'processing {dataset_name} ...')
+            cvt_annotations(devkit_path, year, split,
+                            osp.join(out_dir, dataset_name + out_fmt))
+        if not isinstance(year, list):
+            dataset_name = prefix + '_test'
+            print(f'processing {dataset_name} ...')
+            cvt_annotations(devkit_path, year, 'test',
+                            osp.join(out_dir, dataset_name + out_fmt))
+    print('Done!')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/deployment/mmdet2torchserve.py b/tools/deployment/mmdet2torchserve.py
new file mode 100644
index 0000000..70a081a
--- /dev/null
+++ b/tools/deployment/mmdet2torchserve.py
@@ -0,0 +1,110 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+from argparse import ArgumentParser, Namespace
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+import mmcv
+
+try:
+    from model_archiver.model_packaging import package_model
+    from model_archiver.model_packaging_utils import ModelExportUtils
+except ImportError:
+    package_model = None
+
+
+def mmdet2torchserve(
+    config_file: str,
+    checkpoint_file: str,
+    output_folder: str,
+    model_name: str,
+    model_version: str = '1.0',
+    force: bool = False,
+):
+    """Converts MMDetection model (config + checkpoint) to TorchServe `.mar`.
+
+    Args:
+        config_file:
+            In MMDetection config format.
+            The contents vary for each task repository.
+        checkpoint_file:
+            In MMDetection checkpoint format.
+            The contents vary for each task repository.
+        output_folder:
+            Folder where `{model_name}.mar` will be created.
+            The file created will be in TorchServe archive format.
+        model_name:
+            If not None, used for naming the `{model_name}.mar` file
+            that will be created under `output_folder`.
+            If None, `{Path(checkpoint_file).stem}` will be used.
+        model_version:
+            Model's version.
+        force:
+            If True, if there is an existing `{model_name}.mar`
+            file under `output_folder` it will be overwritten.
+    """
+    mmcv.mkdir_or_exist(output_folder)
+
+    config = mmcv.Config.fromfile(config_file)
+
+    with TemporaryDirectory() as tmpdir:
+        config.dump(f'{tmpdir}/config.py')
+
+        args = Namespace(
+            **{
+                'model_file': f'{tmpdir}/config.py',
+                'serialized_file': checkpoint_file,
+                'handler': f'{Path(__file__).parent}/mmdet_handler.py',
+                'model_name': model_name or Path(checkpoint_file).stem,
+                'version': model_version,
+                'export_path': output_folder,
+                'force': force,
+                'requirements_file': None,
+                'extra_files': None,
+                'runtime': 'python',
+                'archive_format': 'default'
+            })
+        manifest = ModelExportUtils.generate_manifest_json(args)
+        package_model(args, manifest)
+
+
+def parse_args():
+    parser = ArgumentParser(
+        description='Convert MMDetection models to TorchServe `.mar` format.')
+    parser.add_argument('config', type=str, help='config file path')
+    parser.add_argument('checkpoint', type=str, help='checkpoint file path')
+    parser.add_argument(
+        '--output-folder',
+        type=str,
+        required=True,
+        help='Folder where `{model_name}.mar` will be created.')
+    parser.add_argument(
+        '--model-name',
+        type=str,
+        default=None,
+        help='If not None, used for naming the `{model_name}.mar`'
+        'file that will be created under `output_folder`.'
+        'If None, `{Path(checkpoint_file).stem}` will be used.')
+    parser.add_argument(
+        '--model-version',
+        type=str,
+        default='1.0',
+        help='Number used for versioning.')
+    parser.add_argument(
+        '-f',
+        '--force',
+        action='store_true',
+        help='overwrite the existing `{model_name}.mar`')
+    args = parser.parse_args()
+
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    if package_model is None:
+        raise ImportError('`torch-model-archiver` is required.'
+                          'Try: pip install torch-model-archiver')
+
+    mmdet2torchserve(args.config, args.checkpoint, args.output_folder,
+                     args.model_name, args.model_version, args.force)
diff --git a/tools/deployment/mmdet_handler.py b/tools/deployment/mmdet_handler.py
new file mode 100644
index 0000000..18fc230
--- /dev/null
+++ b/tools/deployment/mmdet_handler.py
@@ -0,0 +1,71 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import base64
+import os
+
+import mmcv
+import torch
+from ts.torch_handler.base_handler import BaseHandler
+
+from mmdet.apis import inference_detector, init_detector
+
+
+class MMdetHandler(BaseHandler):
+    threshold = 0.5
+
+    def initialize(self, context):
+        properties = context.system_properties
+        self.map_location = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.device = torch.device(self.map_location + ':' +
+                                   str(properties.get('gpu_id')) if torch.cuda.
+                                   is_available() else self.map_location)
+        self.manifest = context.manifest
+
+        model_dir = properties.get('model_dir')
+        serialized_file = self.manifest['model']['serializedFile']
+        checkpoint = os.path.join(model_dir, serialized_file)
+        self.config_file = os.path.join(model_dir, 'config.py')
+
+        self.model = init_detector(self.config_file, checkpoint, self.device)
+        self.initialized = True
+
+    def preprocess(self, data):
+        images = []
+
+        for row in data:
+            image = row.get('data') or row.get('body')
+            if isinstance(image, str):
+                image = base64.b64decode(image)
+            image = mmcv.imfrombytes(image)
+            images.append(image)
+
+        return images
+
+    def inference(self, data, *args, **kwargs):
+        results = inference_detector(self.model, data)
+        return results
+
+    def postprocess(self, data):
+        # Format output following the example ObjectDetectionHandler format
+        output = []
+        for image_index, image_result in enumerate(data):
+            output.append([])
+            if isinstance(image_result, tuple):
+                bbox_result, segm_result = image_result
+                if isinstance(segm_result, tuple):
+                    segm_result = segm_result[0]  # ms rcnn
+            else:
+                bbox_result, segm_result = image_result, None
+
+            for class_index, class_result in enumerate(bbox_result):
+                class_name = self.model.CLASSES[class_index]
+                for bbox in class_result:
+                    bbox_coords = bbox[:-1].tolist()
+                    score = float(bbox[-1])
+                    if score >= self.threshold:
+                        output[image_index].append({
+                            'class_name': class_name,
+                            'bbox': bbox_coords,
+                            'score': score
+                        })
+
+        return output
diff --git a/tools/deployment/onnx2tensorrt.py b/tools/deployment/onnx2tensorrt.py
new file mode 100644
index 0000000..b59e52a
--- /dev/null
+++ b/tools/deployment/onnx2tensorrt.py
@@ -0,0 +1,266 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+import warnings
+
+import numpy as np
+import onnx
+import torch
+from mmcv import Config
+from mmcv.tensorrt import is_tensorrt_plugin_loaded, onnx2trt, save_trt_engine
+
+from mmdet.core.export import preprocess_example_input
+from mmdet.core.export.model_wrappers import (ONNXRuntimeDetector,
+                                              TensorRTDetector)
+from mmdet.datasets import DATASETS
+
+
+def get_GiB(x: int):
+    """return x GiB."""
+    return x * (1 << 30)
+
+
+def onnx2tensorrt(onnx_file,
+                  trt_file,
+                  input_config,
+                  verify=False,
+                  show=False,
+                  workspace_size=1,
+                  verbose=False):
+    import tensorrt as trt
+    onnx_model = onnx.load(onnx_file)
+    max_shape = input_config['max_shape']
+    min_shape = input_config['min_shape']
+    opt_shape = input_config['opt_shape']
+    fp16_mode = False
+    # create trt engine and wrapper
+    opt_shape_dict = {'input': [min_shape, opt_shape, max_shape]}
+    max_workspace_size = get_GiB(workspace_size)
+    trt_engine = onnx2trt(
+        onnx_model,
+        opt_shape_dict,
+        log_level=trt.Logger.VERBOSE if verbose else trt.Logger.ERROR,
+        fp16_mode=fp16_mode,
+        max_workspace_size=max_workspace_size)
+    save_dir, _ = osp.split(trt_file)
+    if save_dir:
+        os.makedirs(save_dir, exist_ok=True)
+    save_trt_engine(trt_engine, trt_file)
+    print(f'Successfully created TensorRT engine: {trt_file}')
+
+    if verify:
+        # prepare input
+        one_img, one_meta = preprocess_example_input(input_config)
+        img_list, img_meta_list = [one_img], [[one_meta]]
+        img_list = [_.cuda().contiguous() for _ in img_list]
+
+        # wrap ONNX and TensorRT model
+        onnx_model = ONNXRuntimeDetector(onnx_file, CLASSES, device_id=0)
+        trt_model = TensorRTDetector(trt_file, CLASSES, device_id=0)
+
+        # inference with wrapped model
+        with torch.no_grad():
+            onnx_results = onnx_model(
+                img_list, img_metas=img_meta_list, return_loss=False)[0]
+            trt_results = trt_model(
+                img_list, img_metas=img_meta_list, return_loss=False)[0]
+
+        if show:
+            out_file_ort, out_file_trt = None, None
+        else:
+            out_file_ort, out_file_trt = 'show-ort.png', 'show-trt.png'
+        show_img = one_meta['show_img']
+        score_thr = 0.3
+        onnx_model.show_result(
+            show_img,
+            onnx_results,
+            score_thr=score_thr,
+            show=True,
+            win_name='ONNXRuntime',
+            out_file=out_file_ort)
+        trt_model.show_result(
+            show_img,
+            trt_results,
+            score_thr=score_thr,
+            show=True,
+            win_name='TensorRT',
+            out_file=out_file_trt)
+        with_mask = trt_model.with_masks
+        # compare a part of result
+        if with_mask:
+            compare_pairs = list(zip(onnx_results, trt_results))
+        else:
+            compare_pairs = [(onnx_results, trt_results)]
+        err_msg = 'The numerical values are different between Pytorch' + \
+                  ' and ONNX, but it does not necessarily mean the' + \
+                  ' exported ONNX model is problematic.'
+        # check the numerical value
+        for onnx_res, pytorch_res in compare_pairs:
+            for o_res, p_res in zip(onnx_res, pytorch_res):
+                np.testing.assert_allclose(
+                    o_res, p_res, rtol=1e-03, atol=1e-05, err_msg=err_msg)
+        print('The numerical values are the same between Pytorch and ONNX')
+
+
+def parse_normalize_cfg(test_pipeline):
+    transforms = None
+    for pipeline in test_pipeline:
+        if 'transforms' in pipeline:
+            transforms = pipeline['transforms']
+            break
+    assert transforms is not None, 'Failed to find `transforms`'
+    norm_config_li = [_ for _ in transforms if _['type'] == 'Normalize']
+    assert len(norm_config_li) == 1, '`norm_config` should only have one'
+    norm_config = norm_config_li[0]
+    return norm_config
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert MMDetection models from ONNX to TensorRT')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('model', help='Filename of input ONNX model')
+    parser.add_argument(
+        '--trt-file',
+        type=str,
+        default='tmp.trt',
+        help='Filename of output TensorRT engine')
+    parser.add_argument(
+        '--input-img', type=str, default='', help='Image for test')
+    parser.add_argument(
+        '--show', action='store_true', help='Whether to show output results')
+    parser.add_argument(
+        '--dataset',
+        type=str,
+        default='coco',
+        help='Dataset name. This argument is deprecated and will be \
+        removed in future releases.')
+    parser.add_argument(
+        '--verify',
+        action='store_true',
+        help='Verify the outputs of ONNXRuntime and TensorRT')
+    parser.add_argument(
+        '--verbose',
+        action='store_true',
+        help='Whether to verbose logging messages while creating \
+                TensorRT engine. Defaults to False.')
+    parser.add_argument(
+        '--to-rgb',
+        action='store_false',
+        help='Feed model with RGB or BGR image. Default is RGB. This \
+        argument is deprecated and will be removed in future releases.')
+    parser.add_argument(
+        '--shape',
+        type=int,
+        nargs='+',
+        default=[400, 600],
+        help='Input size of the model')
+    parser.add_argument(
+        '--mean',
+        type=float,
+        nargs='+',
+        default=[123.675, 116.28, 103.53],
+        help='Mean value used for preprocess input data. This argument \
+        is deprecated and will be removed in future releases.')
+    parser.add_argument(
+        '--std',
+        type=float,
+        nargs='+',
+        default=[58.395, 57.12, 57.375],
+        help='Variance value used for preprocess input data. \
+        This argument is deprecated and will be removed in future releases.')
+    parser.add_argument(
+        '--min-shape',
+        type=int,
+        nargs='+',
+        default=None,
+        help='Minimum input size of the model in TensorRT')
+    parser.add_argument(
+        '--max-shape',
+        type=int,
+        nargs='+',
+        default=None,
+        help='Maximum input size of the model in TensorRT')
+    parser.add_argument(
+        '--workspace-size',
+        type=int,
+        default=1,
+        help='Max workspace size in GiB')
+
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+
+    assert is_tensorrt_plugin_loaded(), 'TensorRT plugin should be compiled.'
+    args = parse_args()
+    warnings.warn(
+        'Arguments like `--to-rgb`, `--mean`, `--std`, `--dataset` would be \
+        parsed directly from config file and are deprecated and will be \
+        removed in future releases.')
+    if not args.input_img:
+        args.input_img = osp.join(osp.dirname(__file__), '../../demo/demo.jpg')
+
+    cfg = Config.fromfile(args.config)
+
+    def parse_shape(shape):
+        if len(shape) == 1:
+            shape = (1, 3, shape[0], shape[0])
+        elif len(args.shape) == 2:
+            shape = (1, 3) + tuple(shape)
+        else:
+            raise ValueError('invalid input shape')
+        return shape
+
+    if args.shape:
+        input_shape = parse_shape(args.shape)
+    else:
+        img_scale = cfg.test_pipeline[1]['img_scale']
+        input_shape = (1, 3, img_scale[1], img_scale[0])
+
+    if not args.max_shape:
+        max_shape = input_shape
+    else:
+        max_shape = parse_shape(args.max_shape)
+
+    if not args.min_shape:
+        min_shape = input_shape
+    else:
+        min_shape = parse_shape(args.min_shape)
+
+    dataset = DATASETS.get(cfg.data.test['type'])
+    assert (dataset is not None)
+    CLASSES = dataset.CLASSES
+    normalize_cfg = parse_normalize_cfg(cfg.test_pipeline)
+
+    input_config = {
+        'min_shape': min_shape,
+        'opt_shape': input_shape,
+        'max_shape': max_shape,
+        'input_shape': input_shape,
+        'input_path': args.input_img,
+        'normalize_cfg': normalize_cfg
+    }
+    # Create TensorRT engine
+    onnx2tensorrt(
+        args.model,
+        args.trt_file,
+        input_config,
+        verify=args.verify,
+        show=args.show,
+        workspace_size=args.workspace_size,
+        verbose=args.verbose)
+
+    # Following strings of text style are from colorama package
+    bright_style, reset_style = '\x1b[1m', '\x1b[0m'
+    red_text, blue_text = '\x1b[31m', '\x1b[34m'
+    white_background = '\x1b[107m'
+
+    msg = white_background + bright_style + red_text
+    msg += 'DeprecationWarning: This tool will be deprecated in future. '
+    msg += blue_text + 'Welcome to use the unified model deployment toolbox '
+    msg += 'MMDeploy: https://github.com/open-mmlab/mmdeploy'
+    msg += reset_style
+    warnings.warn(msg)
diff --git a/tools/deployment/pytorch2onnx.py b/tools/deployment/pytorch2onnx.py
new file mode 100644
index 0000000..ee856cc
--- /dev/null
+++ b/tools/deployment/pytorch2onnx.py
@@ -0,0 +1,343 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os.path as osp
+import warnings
+from functools import partial
+
+import numpy as np
+import onnx
+import torch
+from mmcv import Config, DictAction
+
+from mmdet.core.export import build_model_from_cfg, preprocess_example_input
+from mmdet.core.export.model_wrappers import ONNXRuntimeDetector
+
+
+def pytorch2onnx(model,
+                 input_img,
+                 input_shape,
+                 normalize_cfg,
+                 opset_version=11,
+                 show=False,
+                 output_file='tmp.onnx',
+                 verify=False,
+                 test_img=None,
+                 do_simplify=False,
+                 dynamic_export=None,
+                 skip_postprocess=False):
+
+    input_config = {
+        'input_shape': input_shape,
+        'input_path': input_img,
+        'normalize_cfg': normalize_cfg
+    }
+    # prepare input
+    one_img, one_meta = preprocess_example_input(input_config)
+    img_list, img_meta_list = [one_img], [[one_meta]]
+
+    if skip_postprocess:
+        warnings.warn('Not all models support export onnx without post '
+                      'process, especially two stage detectors!')
+        model.forward = model.forward_dummy
+        torch.onnx.export(
+            model,
+            one_img,
+            output_file,
+            input_names=['input'],
+            export_params=True,
+            keep_initializers_as_inputs=True,
+            do_constant_folding=True,
+            verbose=show,
+            opset_version=opset_version)
+
+        print(f'Successfully exported ONNX model without '
+              f'post process: {output_file}')
+        return
+
+    # replace original forward function
+    origin_forward = model.forward
+    model.forward = partial(
+        model.forward,
+        img_metas=img_meta_list,
+        return_loss=False,
+        rescale=False)
+
+    output_names = ['dets', 'labels']
+    if model.with_mask:
+        output_names.append('masks')
+    input_name = 'input'
+    dynamic_axes = None
+    if dynamic_export:
+        dynamic_axes = {
+            input_name: {
+                0: 'batch',
+                2: 'height',
+                3: 'width'
+            },
+            'dets': {
+                0: 'batch',
+                1: 'num_dets',
+            },
+            'labels': {
+                0: 'batch',
+                1: 'num_dets',
+            },
+        }
+        if model.with_mask:
+            dynamic_axes['masks'] = {0: 'batch', 1: 'num_dets'}
+
+    torch.onnx.export(
+        model,
+        img_list,
+        output_file,
+        input_names=[input_name],
+        output_names=output_names,
+        export_params=True,
+        keep_initializers_as_inputs=True,
+        do_constant_folding=True,
+        verbose=show,
+        opset_version=opset_version,
+        dynamic_axes=dynamic_axes)
+
+    model.forward = origin_forward
+
+    if do_simplify:
+        import onnxsim
+
+        from mmdet import digit_version
+
+        min_required_version = '0.4.0'
+        assert digit_version(onnxsim.__version__) >= digit_version(
+            min_required_version
+        ), f'Requires to install onnxsim>={min_required_version}'
+
+        model_opt, check_ok = onnxsim.simplify(output_file)
+        if check_ok:
+            onnx.save(model_opt, output_file)
+            print(f'Successfully simplified ONNX model: {output_file}')
+        else:
+            warnings.warn('Failed to simplify ONNX model.')
+    print(f'Successfully exported ONNX model: {output_file}')
+
+    if verify:
+        # check by onnx
+        onnx_model = onnx.load(output_file)
+        onnx.checker.check_model(onnx_model)
+
+        # wrap onnx model
+        onnx_model = ONNXRuntimeDetector(output_file, model.CLASSES, 0)
+        if dynamic_export:
+            # scale up to test dynamic shape
+            h, w = [int((_ * 1.5) // 32 * 32) for _ in input_shape[2:]]
+            h, w = min(1344, h), min(1344, w)
+            input_config['input_shape'] = (1, 3, h, w)
+
+        if test_img is None:
+            input_config['input_path'] = input_img
+
+        # prepare input once again
+        one_img, one_meta = preprocess_example_input(input_config)
+        img_list, img_meta_list = [one_img], [[one_meta]]
+
+        # get pytorch output
+        with torch.no_grad():
+            pytorch_results = model(
+                img_list,
+                img_metas=img_meta_list,
+                return_loss=False,
+                rescale=True)[0]
+
+        img_list = [_.cuda().contiguous() for _ in img_list]
+        if dynamic_export:
+            img_list = img_list + [_.flip(-1).contiguous() for _ in img_list]
+            img_meta_list = img_meta_list * 2
+        # get onnx output
+        onnx_results = onnx_model(
+            img_list, img_metas=img_meta_list, return_loss=False)[0]
+        # visualize predictions
+        score_thr = 0.3
+        if show:
+            out_file_ort, out_file_pt = None, None
+        else:
+            out_file_ort, out_file_pt = 'show-ort.png', 'show-pt.png'
+
+        show_img = one_meta['show_img']
+        model.show_result(
+            show_img,
+            pytorch_results,
+            score_thr=score_thr,
+            show=True,
+            win_name='PyTorch',
+            out_file=out_file_pt)
+        onnx_model.show_result(
+            show_img,
+            onnx_results,
+            score_thr=score_thr,
+            show=True,
+            win_name='ONNXRuntime',
+            out_file=out_file_ort)
+
+        # compare a part of result
+        if model.with_mask:
+            compare_pairs = list(zip(onnx_results, pytorch_results))
+        else:
+            compare_pairs = [(onnx_results, pytorch_results)]
+        err_msg = 'The numerical values are different between Pytorch' + \
+                  ' and ONNX, but it does not necessarily mean the' + \
+                  ' exported ONNX model is problematic.'
+        # check the numerical value
+        for onnx_res, pytorch_res in compare_pairs:
+            for o_res, p_res in zip(onnx_res, pytorch_res):
+                np.testing.assert_allclose(
+                    o_res, p_res, rtol=1e-03, atol=1e-05, err_msg=err_msg)
+        print('The numerical values are the same between Pytorch and ONNX')
+
+
+def parse_normalize_cfg(test_pipeline):
+    transforms = None
+    for pipeline in test_pipeline:
+        if 'transforms' in pipeline:
+            transforms = pipeline['transforms']
+            break
+    assert transforms is not None, 'Failed to find `transforms`'
+    norm_config_li = [_ for _ in transforms if _['type'] == 'Normalize']
+    assert len(norm_config_li) == 1, '`norm_config` should only have one'
+    norm_config = norm_config_li[0]
+    return norm_config
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert MMDetection models to ONNX')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint', help='checkpoint file')
+    parser.add_argument('--input-img', type=str, help='Images for input')
+    parser.add_argument(
+        '--show',
+        action='store_true',
+        help='Show onnx graph and detection outputs')
+    parser.add_argument('--output-file', type=str, default='tmp.onnx')
+    parser.add_argument('--opset-version', type=int, default=11)
+    parser.add_argument(
+        '--test-img', type=str, default=None, help='Images for test')
+    parser.add_argument(
+        '--dataset',
+        type=str,
+        default='coco',
+        help='Dataset name. This argument is deprecated and will be removed \
+        in future releases.')
+    parser.add_argument(
+        '--verify',
+        action='store_true',
+        help='verify the onnx model output against pytorch output')
+    parser.add_argument(
+        '--simplify',
+        action='store_true',
+        help='Whether to simplify onnx model.')
+    parser.add_argument(
+        '--shape',
+        type=int,
+        nargs='+',
+        default=[800, 1216],
+        help='input image size')
+    parser.add_argument(
+        '--mean',
+        type=float,
+        nargs='+',
+        default=[123.675, 116.28, 103.53],
+        help='mean value used for preprocess input data.This argument \
+        is deprecated and will be removed in future releases.')
+    parser.add_argument(
+        '--std',
+        type=float,
+        nargs='+',
+        default=[58.395, 57.12, 57.375],
+        help='variance value used for preprocess input data. '
+        'This argument is deprecated and will be removed in future releases.')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='Override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--dynamic-export',
+        action='store_true',
+        help='Whether to export onnx with dynamic axis.')
+    parser.add_argument(
+        '--skip-postprocess',
+        action='store_true',
+        help='Whether to export model without post process. Experimental '
+        'option. We do not guarantee the correctness of the exported '
+        'model.')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    warnings.warn('Arguments like `--mean`, `--std`, `--dataset` would be \
+        parsed directly from config file and are deprecated and \
+        will be removed in future releases.')
+
+    assert args.opset_version == 11, 'MMDet only support opset 11 now'
+
+    try:
+        from mmcv.onnx.symbolic import register_extra_symbolics
+    except ModuleNotFoundError:
+        raise NotImplementedError('please update mmcv to version>=v1.0.4')
+    register_extra_symbolics(args.opset_version)
+
+    cfg = Config.fromfile(args.config)
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+
+    if args.shape is None:
+        img_scale = cfg.test_pipeline[1]['img_scale']
+        input_shape = (1, 3, img_scale[1], img_scale[0])
+    elif len(args.shape) == 1:
+        input_shape = (1, 3, args.shape[0], args.shape[0])
+    elif len(args.shape) == 2:
+        input_shape = (1, 3) + tuple(args.shape)
+    else:
+        raise ValueError('invalid input shape')
+
+    # build the model and load checkpoint
+    model = build_model_from_cfg(args.config, args.checkpoint,
+                                 args.cfg_options)
+
+    if not args.input_img:
+        args.input_img = osp.join(osp.dirname(__file__), '../../demo/demo.jpg')
+
+    normalize_cfg = parse_normalize_cfg(cfg.test_pipeline)
+
+    # convert model to onnx file
+    pytorch2onnx(
+        model,
+        args.input_img,
+        input_shape,
+        normalize_cfg,
+        opset_version=args.opset_version,
+        show=args.show,
+        output_file=args.output_file,
+        verify=args.verify,
+        test_img=args.test_img,
+        do_simplify=args.simplify,
+        dynamic_export=args.dynamic_export,
+        skip_postprocess=args.skip_postprocess)
+
+    # Following strings of text style are from colorama package
+    bright_style, reset_style = '\x1b[1m', '\x1b[0m'
+    red_text, blue_text = '\x1b[31m', '\x1b[34m'
+    white_background = '\x1b[107m'
+
+    msg = white_background + bright_style + red_text
+    msg += 'DeprecationWarning: This tool will be deprecated in future. '
+    msg += blue_text + 'Welcome to use the unified model deployment toolbox '
+    msg += 'MMDeploy: https://github.com/open-mmlab/mmdeploy'
+    msg += reset_style
+    warnings.warn(msg)
diff --git a/tools/deployment/test.py b/tools/deployment/test.py
new file mode 100644
index 0000000..db8d696
--- /dev/null
+++ b/tools/deployment/test.py
@@ -0,0 +1,157 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import warnings
+
+import mmcv
+from mmcv import Config, DictAction
+from mmcv.parallel import MMDataParallel
+
+from mmdet.apis import single_gpu_test
+from mmdet.datasets import (build_dataloader, build_dataset,
+                            replace_ImageToTensor)
+from mmdet.utils import compat_cfg
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='MMDet test (and eval) an ONNX model using ONNXRuntime')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('model', help='Input model file')
+    parser.add_argument('--out', help='output result file in pickle format')
+    parser.add_argument(
+        '--format-only',
+        action='store_true',
+        help='Format the output results without perform evaluation. It is'
+        'useful when you want to format the result to a specific format and '
+        'submit it to the test server')
+    parser.add_argument(
+        '--backend',
+        required=True,
+        choices=['onnxruntime', 'tensorrt'],
+        help='Backend for input model to run. ')
+    parser.add_argument(
+        '--eval',
+        type=str,
+        nargs='+',
+        help='evaluation metrics, which depends on the dataset, e.g., "bbox",'
+        ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
+    parser.add_argument('--show', action='store_true', help='show results')
+    parser.add_argument(
+        '--show-dir', help='directory where painted images will be saved')
+    parser.add_argument(
+        '--show-score-thr',
+        type=float,
+        default=0.3,
+        help='score threshold (default: 0.3)')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--eval-options',
+        nargs='+',
+        action=DictAction,
+        help='custom options for evaluation, the key-value pair in xxx=yyy '
+        'format will be kwargs for dataset.evaluate() function')
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    assert args.out or args.eval or args.format_only or args.show \
+        or args.show_dir, \
+        ('Please specify at least one operation (save/eval/format/show the '
+         'results / save the results) with the argument "--out", "--eval"'
+         ', "--format-only", "--show" or "--show-dir"')
+
+    if args.eval and args.format_only:
+        raise ValueError('--eval and --format_only cannot be both specified')
+
+    if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
+        raise ValueError('The output file must be a pkl file.')
+
+    cfg = Config.fromfile(args.config)
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+    cfg = compat_cfg(cfg)
+    # in case the test dataset is concatenated
+    samples_per_gpu = 1
+    if isinstance(cfg.data.test, dict):
+        cfg.data.test.test_mode = True
+        samples_per_gpu = cfg.data.test.pop('samples_per_gpu', 1)
+        if samples_per_gpu > 1:
+            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
+            cfg.data.test.pipeline = replace_ImageToTensor(
+                cfg.data.test.pipeline)
+    elif isinstance(cfg.data.test, list):
+        for ds_cfg in cfg.data.test:
+            ds_cfg.test_mode = True
+        samples_per_gpu = max(
+            [ds_cfg.pop('samples_per_gpu', 1) for ds_cfg in cfg.data.test])
+        if samples_per_gpu > 1:
+            for ds_cfg in cfg.data.test:
+                ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline)
+
+    # build the dataloader
+    dataset = build_dataset(cfg.data.test)
+    data_loader = build_dataloader(
+        dataset,
+        samples_per_gpu=samples_per_gpu,
+        workers_per_gpu=cfg.data.workers_per_gpu,
+        dist=False,
+        shuffle=False)
+
+    if args.backend == 'onnxruntime':
+        from mmdet.core.export.model_wrappers import ONNXRuntimeDetector
+        model = ONNXRuntimeDetector(
+            args.model, class_names=dataset.CLASSES, device_id=0)
+    elif args.backend == 'tensorrt':
+        from mmdet.core.export.model_wrappers import TensorRTDetector
+        model = TensorRTDetector(
+            args.model, class_names=dataset.CLASSES, device_id=0)
+
+    model = MMDataParallel(model, device_ids=[0])
+    outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
+                              args.show_score_thr)
+
+    if args.out:
+        print(f'\nwriting results to {args.out}')
+        mmcv.dump(outputs, args.out)
+    kwargs = {} if args.eval_options is None else args.eval_options
+    if args.format_only:
+        dataset.format_results(outputs, **kwargs)
+    if args.eval:
+        eval_kwargs = cfg.get('evaluation', {}).copy()
+        # hard-code way to remove EvalHook args
+        for key in [
+                'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',
+                'rule'
+        ]:
+            eval_kwargs.pop(key, None)
+        eval_kwargs.update(dict(metric=args.eval, **kwargs))
+        print(dataset.evaluate(outputs, **eval_kwargs))
+
+
+if __name__ == '__main__':
+    main()
+
+    # Following strings of text style are from colorama package
+    bright_style, reset_style = '\x1b[1m', '\x1b[0m'
+    red_text, blue_text = '\x1b[31m', '\x1b[34m'
+    white_background = '\x1b[107m'
+
+    msg = white_background + bright_style + red_text
+    msg += 'DeprecationWarning: This tool will be deprecated in future. '
+    msg += blue_text + 'Welcome to use the unified model deployment toolbox '
+    msg += 'MMDeploy: https://github.com/open-mmlab/mmdeploy'
+    msg += reset_style
+    warnings.warn(msg)
diff --git a/tools/deployment/test_torchserver.py b/tools/deployment/test_torchserver.py
new file mode 100644
index 0000000..dd45234
--- /dev/null
+++ b/tools/deployment/test_torchserver.py
@@ -0,0 +1,74 @@
+from argparse import ArgumentParser
+
+import numpy as np
+import requests
+
+from mmdet.apis import inference_detector, init_detector, show_result_pyplot
+from mmdet.core import bbox2result
+
+
+def parse_args():
+    parser = ArgumentParser()
+    parser.add_argument('img', help='Image file')
+    parser.add_argument('config', help='Config file')
+    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument('model_name', help='The model name in the server')
+    parser.add_argument(
+        '--inference-addr',
+        default='127.0.0.1:8080',
+        help='Address and port of the inference server')
+    parser.add_argument(
+        '--device', default='cuda:0', help='Device used for inference')
+    parser.add_argument(
+        '--score-thr', type=float, default=0.5, help='bbox score threshold')
+    args = parser.parse_args()
+    return args
+
+
+def parse_result(input, model_class):
+    bbox = []
+    label = []
+    score = []
+    for anchor in input:
+        bbox.append(anchor['bbox'])
+        label.append(model_class.index(anchor['class_name']))
+        score.append([anchor['score']])
+    bboxes = np.append(bbox, score, axis=1)
+    labels = np.array(label)
+    result = bbox2result(bboxes, labels, len(model_class))
+    return result
+
+
+def main(args):
+    # build the model from a config file and a checkpoint file
+    model = init_detector(args.config, args.checkpoint, device=args.device)
+    # test a single image
+    model_result = inference_detector(model, args.img)
+    for i, anchor_set in enumerate(model_result):
+        anchor_set = anchor_set[anchor_set[:, 4] >= 0.5]
+        model_result[i] = anchor_set
+    # show the results
+    show_result_pyplot(
+        model,
+        args.img,
+        model_result,
+        score_thr=args.score_thr,
+        title='pytorch_result')
+    url = 'http://' + args.inference_addr + '/predictions/' + args.model_name
+    with open(args.img, 'rb') as image:
+        response = requests.post(url, image)
+    server_result = parse_result(response.json(), model.CLASSES)
+    show_result_pyplot(
+        model,
+        args.img,
+        server_result,
+        score_thr=args.score_thr,
+        title='server_result')
+
+    for i in range(len(model.CLASSES)):
+        assert np.allclose(model_result[i], server_result[i])
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)
diff --git a/tools/dist_test.sh b/tools/dist_test.sh
new file mode 100755
index 0000000..dea131b
--- /dev/null
+++ b/tools/dist_test.sh
@@ -0,0 +1,22 @@
+#!/usr/bin/env bash
+
+CONFIG=$1
+CHECKPOINT=$2
+GPUS=$3
+NNODES=${NNODES:-1}
+NODE_RANK=${NODE_RANK:-0}
+PORT=${PORT:-29500}
+MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
+
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+python -m torch.distributed.launch \
+    --nnodes=$NNODES \
+    --node_rank=$NODE_RANK \
+    --master_addr=$MASTER_ADDR \
+    --nproc_per_node=$GPUS \
+    --master_port=$PORT \
+    $(dirname "$0")/test.py \
+    $CONFIG \
+    $CHECKPOINT \
+    --launcher pytorch \
+    ${@:4}
diff --git a/tools/dist_train.sh b/tools/dist_train.sh
new file mode 100755
index 0000000..aa71bf4
--- /dev/null
+++ b/tools/dist_train.sh
@@ -0,0 +1,20 @@
+#!/usr/bin/env bash
+
+CONFIG=$1
+GPUS=$2
+NNODES=${NNODES:-1}
+NODE_RANK=${NODE_RANK:-0}
+PORT=${PORT:-29500}
+MASTER_ADDR=${MASTER_ADDR:-"127.0.0.1"}
+
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+python -m torch.distributed.launch \
+    --nnodes=$NNODES \
+    --node_rank=$NODE_RANK \
+    --master_addr=$MASTER_ADDR \
+    --nproc_per_node=$GPUS \
+    --master_port=$PORT \
+    $(dirname "$0")/train.py \
+    $CONFIG \
+    --seed 0 \
+    --launcher pytorch ${@:3}
diff --git a/tools/fast_test.py b/tools/fast_test.py
new file mode 100644
index 0000000..318c7f3
--- /dev/null
+++ b/tools/fast_test.py
@@ -0,0 +1,541 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+import time
+import warnings
+
+import mmcv
+import torch
+from mmcv import Config, DictAction
+from mmcv.cnn import fuse_conv_bn
+from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
+                         wrap_fp16_model)
+
+from mmdet.apis import multi_gpu_test, single_gpu_test
+from mmdet.datasets import (build_dataloader, build_dataset,
+                            replace_ImageToTensor)
+from mmdet.models import build_detector
+from mmdet.utils import (build_ddp, build_dp, fast_compat_cfg, get_device,
+                         replace_cfg_vals, rfnext_init_model,
+                         setup_multi_processes, update_data_root)
+from mqb_general_process import make_qmodel_for_mmd, prepocess
+from mqbench.utils.state import *
+import global_placeholder
+from mqb_general_process import *
+from mmcv.image import tensor2imgs
+import numpy as np
+from mmdet.core.visualization import imshow_det_bboxes
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='MMDet test (and eval) a model')
+    parser.add_argument('config', help='test config file path')
+    # parser.add_argument('checkpoint', help='checkpoint file')
+    parser.add_argument('pkl_result_path', help='pkl result file')
+    # parser.add_argument('quant_config', default=None, help='quant config file path')
+    # parser.add_argument('--aqd-mode', type=int, default=0, help='when bigger than 0 , it means switch on aqd, and equals the neck output level num')
+    # parser.add_argument('--quantize', 
+        # action='store_true', help='quant flag')
+    parser.add_argument('--seed', type=int, default=None, help='random seed')
+    
+    parser.add_argument(
+        '--work-dir',
+        help='the directory to save the file containing evaluation metrics')
+    parser.add_argument('--out', help='output result file in pickle format')
+    parser.add_argument(
+        '--fuse-conv-bn',
+        action='store_true',
+        help='Whether to fuse conv and bn, this will slightly increase'
+        'the inference speed')
+    parser.add_argument(
+        '--gpu-ids',
+        type=int,
+        nargs='+',
+        help='(Deprecated, please use --gpu-id) ids of gpus to use '
+        '(only applicable to non-distributed training)')
+    parser.add_argument(
+        '--gpu-id',
+        type=int,
+        default=0,
+        help='id of gpu to use '
+        '(only applicable to non-distributed testing)')
+    parser.add_argument(
+        '--format-only',
+        action='store_true',
+        help='Format the output results without perform evaluation. It is'
+        'useful when you want to format the result to a specific format and '
+        'submit it to the test server')
+    parser.add_argument(
+        '--eval',
+        type=str,
+        nargs='+',
+        help='evaluation metrics, which depends on the dataset, e.g., "bbox",'
+        ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
+    parser.add_argument('--show', action='store_true', help='show results')
+    parser.add_argument(
+        '--show-dir', help='directory where painted images will be saved')
+    parser.add_argument(
+        '--show-score-thr',
+        type=float,
+        default=0.3,
+        help='score threshold (default: 0.3)')
+    parser.add_argument(
+        '--gpu-collect',
+        action='store_true',
+        help='whether to use gpu to collect results.')
+    parser.add_argument(
+        '--tmpdir',
+        help='tmp directory used for collecting results from multiple '
+        'workers, available when gpu-collect is not specified')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--options',
+        nargs='+',
+        action=DictAction,
+        help='custom options for evaluation, the key-value pair in xxx=yyy '
+        'format will be kwargs for dataset.evaluate() function (deprecate), '
+        'change to --eval-options instead.')
+    parser.add_argument(
+        '--eval-options',
+        nargs='+',
+        action=DictAction,
+        help='custom options for evaluation, the key-value pair in xxx=yyy '
+        'format will be kwargs for dataset.evaluate() function')
+    parser.add_argument(
+        '--launcher',
+        choices=['none', 'pytorch', 'slurm', 'mpi'],
+        default='none',
+        help='job launcher')
+    parser.add_argument('--local_rank', type=int, default=0)
+    args = parser.parse_args()
+    if 'LOCAL_RANK' not in os.environ:
+        os.environ['LOCAL_RANK'] = str(args.local_rank)
+
+    if args.options and args.eval_options:
+        raise ValueError(
+            '--options and --eval-options cannot be both '
+            'specified, --options is deprecated in favor of --eval-options')
+    if args.options:
+        warnings.warn('--options is deprecated in favor of --eval-options')
+        args.eval_options = args.options
+    return args
+
+
+def main():
+    args = parse_args()
+
+    assert args.out or args.eval or args.format_only or args.show \
+        or args.show_dir, \
+        ('Please specify at least one operation (save/eval/format/show the '
+         'results / save the results) with the argument "--out", "--eval"'
+         ', "--format-only", "--show" or "--show-dir"')
+
+    if args.eval and args.format_only:
+        raise ValueError('--eval and --format_only cannot be both specified')
+
+    if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
+        raise ValueError('The output file must be a pkl file.')
+
+    cfg = Config.fromfile(args.config)
+
+    # replace the ${key} with the value of cfg.key
+    cfg = replace_cfg_vals(cfg)
+
+    # update data root according to MMDET_DATASETS
+    update_data_root(cfg)
+
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+
+    cfg = fast_compat_cfg(cfg)
+
+    # set multi-process settings
+    setup_multi_processes(cfg)
+    set_random_seed(args.seed)
+    # set cudnn_benchmark
+    if cfg.get('cudnn_benchmark', False):
+        torch.backends.cudnn.benchmark = True
+
+    # if 'pretrained' in cfg.model:
+    #     cfg.model.pretrained = None
+    # elif 'init_cfg' in cfg.model.backbone:
+    #     cfg.model.backbone.init_cfg = None
+
+    # if cfg.model.get('neck'):
+    #     if isinstance(cfg.model.neck, list):
+    #         for neck_cfg in cfg.model.neck:
+    #             if neck_cfg.get('rfp_backbone'):
+    #                 if neck_cfg.rfp_backbone.get('pretrained'):
+    #                     neck_cfg.rfp_backbone.pretrained = None
+    #     elif cfg.model.neck.get('rfp_backbone'):
+    #         if cfg.model.neck.rfp_backbone.get('pretrained'):
+    #             cfg.model.neck.rfp_backbone.pretrained = None
+
+    if args.gpu_ids is not None:
+        cfg.gpu_ids = args.gpu_ids[0:1]
+        warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
+                      'Because we only support single GPU mode in '
+                      'non-distributed testing. Use the first GPU '
+                      'in `gpu_ids` now.')
+    else:
+        cfg.gpu_ids = [args.gpu_id]
+    cfg.device = get_device()
+    # init distributed env first, since logger depends on the dist info.
+    if args.launcher == 'none':
+        distributed = False
+    else:
+        distributed = True
+        # init_dist(args.launcher, **cfg.dist_params)
+
+    test_dataloader_default_args = dict(
+        samples_per_gpu=1, workers_per_gpu=2, dist=distributed, shuffle=False)
+
+    # in case the test dataset is concatenated
+    if isinstance(cfg.data.test, dict):
+        cfg.data.test.test_mode = True
+        if cfg.data.test_dataloader.get('samples_per_gpu', 1) > 1:
+            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
+            cfg.data.test.pipeline = replace_ImageToTensor(
+                cfg.data.test.pipeline)
+    elif isinstance(cfg.data.test, list):
+        for ds_cfg in cfg.data.test:
+            ds_cfg.test_mode = True
+        if cfg.data.test_dataloader.get('samples_per_gpu', 1) > 1:
+            for ds_cfg in cfg.data.test:
+                ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline)
+
+    test_loader_cfg = {
+        **test_dataloader_default_args,
+        **cfg.data.get('test_dataloader', {})
+    }
+
+    rank, _ = get_dist_info()
+    # allows not to create
+    if args.work_dir is not None and rank == 0:
+        mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
+        timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+        json_file = osp.join(args.work_dir, f'eval_{timestamp}.json')
+
+    # build the dataloader
+    dataset = build_dataset(cfg.data.test)
+    data_loader = build_dataloader(dataset, **test_loader_cfg)
+
+    # build the model and load checkpoint
+    # cfg.model.train_cfg = None
+    # model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
+    # global setting
+    # if args.aqd_mode != 0:
+    #     global_placeholder.modify_AQD_mode(args.aqd_mode)
+    
+    # 如果是QAT模型，那么就需要提前定义好量化模型结构
+    # if args.quantize:
+    #     quant_config = prepocess(args.quant_config)
+    #     # copy_config_file(args.quant_config, cfg.work_dir)
+    #     logger.info(quant_config)
+    #     global_placeholder.modify_quant_bit(quant_config.extra_prepare_dict.extra_qconfig_dict.w_qscheme.bit)
+    #     global_placeholder.modify_quant_algorithm(quant_config.quantize.quant_algorithm)
+    #     global_placeholder.modify_buff_flag(quant_config.training.my_buff_flag)
+    #     global_placeholder.modify_qloss_flag(quant_config.training.qloss_flag)
+    #     global_placeholder.modify_fold_bn_flag(quant_config.training.fold_bn_flag)
+        
+    #     model.train()
+    #     model = make_qmodel_for_mmd(model, quant_config, cfg.trace_config)
+        
+    # else:
+    #     if 'HQOD' in cfg.work_dir:
+    #         logger.info("插播!!!!直接启用harmony！！")
+    #         logger.info("插播!!!!直接启用harmony！！")
+    #         logger.info("插播!!!!直接启用harmony！！\n")
+    #         global_placeholder.modify_buff_flag(1) # 为mypro
+    #     elif 'HarDet' in cfg.work_dir:
+    #         logger.info("插播!!!!直接启用harmony！！")
+    #         logger.info("插播!!!!直接启用harmony！！")
+    #         logger.info("插播!!!!直接启用harmony！！\n")
+    #         global_placeholder.modify_buff_flag(2) # 为hardet
+            
+    # # 检查 num levels 一致性
+    # if global_placeholder.aqd_mode != 0 and model.neck.num_outs != global_placeholder.aqd_mode:
+    #     # 说明 num levels给的不对
+    #     raise  ValueError(f'num levels给的不对! aqd_mode={global_placeholder.aqd_mode} 而 neck.num_outs={model.neck.num_outs}')
+    
+    # init rfnext if 'RFSearchHook' is defined in cfg
+    # rfnext_init_model(model, cfg=cfg)
+    # fp16_cfg = cfg.get('fp16', None)
+    # if fp16_cfg is None and cfg.get('device', None) == 'npu':
+    #     fp16_cfg = dict(loss_scale='dynamic')
+    # if fp16_cfg is not None:
+    #     wrap_fp16_model(model)
+    # checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
+    # if args.fuse_conv_bn:
+    #     model = fuse_conv_bn(model)
+    # # old versions did not save class info in checkpoints, this walkaround is
+    # # for backward compatibility
+    # if 'CLASSES' in checkpoint.get('meta', {}):
+    #     model.CLASSES = checkpoint['meta']['CLASSES']
+    # else:
+    #     model.CLASSES = dataset.CLASSES
+
+    
+    # if args.quantize:
+    #     enable_quantization(model.backbone)
+    #     enable_quantization(model.neck)
+    #     model_general_architecture = cfg.trace_config.get('model_general_architecture', None)
+    #     if model_general_architecture == 'FasterRCNN':
+    #         enable_quantization(model.rpn_head)
+    #         enable_quantization(model.roi_head.bbox_head)
+    #     else:
+    #         enable_quantization(model.bbox_head)
+
+    # model.eval()
+    # if not distributed:
+    #     model = build_dp(model, cfg.device, device_ids=cfg.gpu_ids)
+    #     outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
+    #                               args.show_score_thr)
+    # else:
+    #     model = build_ddp(
+    #         model,
+    #         cfg.device,
+    #         device_ids=[int(os.environ['LOCAL_RANK'])],
+    #         broadcast_buffers=False)
+
+    #     # In multi_gpu_test, if tmpdir is None, some tesnors
+    #     # will init on cuda by default, and no device choice supported.
+    #     # Init a tmpdir to avoid error on npu here.
+    #     if cfg.device == 'npu' and args.tmpdir is None:
+    #         args.tmpdir = './npu_tmpdir'
+
+    #     outputs = multi_gpu_test(
+    #         model, data_loader, args.tmpdir, args.gpu_collect
+    #         or cfg.evaluation.get('gpu_collect', False))
+
+    # TODO 加载pkl 文件
+    outputs = mmcv.load(args.pkl_result_path)
+    print(f'Loaded pkl result : {args.pkl_result_path}')
+    
+    # # create work_dir
+    # mmcv.mkdir_or_exist(osp.abspath(os.path.join(args.show_dir, 'imgs')))
+    single_gpu_draw(outputs, data_loader, out_dir=args.show_dir)
+    rank, _ = get_dist_info()
+    if rank == 0:
+        # if args.out:
+        #     print(f'\nwriting results to {args.out}')
+        #     mmcv.dump(outputs, args.out)
+        kwargs = {} if args.eval_options is None else args.eval_options
+        if args.format_only:
+            dataset.format_results(outputs, **kwargs)
+        if args.eval:
+            eval_kwargs = cfg.get('evaluation', {}).copy()
+            # hard-code way to remove EvalHook args
+            for key in [
+                    'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',
+                    'rule', 'dynamic_intervals'
+            ]:
+                eval_kwargs.pop(key, None)
+            eval_kwargs.update(dict(metric=args.eval, **kwargs))
+            metric = dataset.evaluate(outputs, **eval_kwargs)
+            print(metric)
+            metric_dict = dict(config=args.config, metric=metric)
+            if args.work_dir is not None and rank == 0:
+                mmcv.dump(metric_dict, json_file)
+
+
+def set_random_seed(seed, deterministic=True):
+    """Set random seed.
+
+    Args:
+        seed (int): Seed to be used.
+        deterministic (bool): Whether to set the deterministic option for
+            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
+            to True and `torch.backends.cudnn.benchmark` to False.
+            Default: False.
+    """
+    import random
+
+    import numpy as np
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    if deterministic:
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.benchmark = False
+
+
+
+
+
+def single_gpu_draw(results,
+                    data_loader,
+                    show=False,
+                    out_dir=None,
+                    show_score_thr=0.3):
+    # model.eval()
+    # results = []
+    dataset = data_loader.dataset
+    PALETTE = getattr(dataset, 'PALETTE', None)
+    prog_bar = mmcv.ProgressBar(len(dataset))
+    for i, data in enumerate(data_loader):
+        # with torch.no_grad():
+        #     result = model(return_loss=False, rescale=True, **data)
+        
+        batch_size = data_loader.batch_size
+        
+        result = results[i*batch_size:(i+1)*batch_size]
+        
+
+        if show or out_dir:
+            if batch_size == 1 and isinstance(data['img'][0], torch.Tensor):
+                img_tensor = data['img'][0]
+            else:
+                img_tensor = data['img'][0].data[0]
+            img_metas = data['img_metas'][0].data[0]
+            imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
+            assert len(imgs) == len(img_metas)
+            
+            selected_imgs = None
+            selected_imgs = ['000062.jpg', '000069.jpg', '000074.jpg', '000108.jpg', '000852.jpg'
+                             ,'001285.jpg','001745.jpg','006193.jpg']
+
+            for i, (img, img_meta) in enumerate(zip(imgs, img_metas)):
+                if selected_imgs is not None:
+                    # 说明是筛选模式
+                    matched_flag = has_matched_img(img_meta, selected_imgs)
+                    if not matched_flag:
+                        # 跳过循环
+                        continue
+                h, w, _ = img_meta['img_shape']
+                img_show = img[:h, :w, :]
+
+                ori_h, ori_w = img_meta['ori_shape'][:-1]
+                img_show = mmcv.imresize(img_show, (ori_w, ori_h))
+
+                if out_dir:
+                    out_file = osp.join(out_dir, img_meta['ori_filename'])
+                else:
+                    out_file = None
+
+                show_result(
+                    dataset,
+                    img_show,
+                    result[i],
+                    bbox_color=PALETTE,
+                    text_color=PALETTE,
+                    mask_color=PALETTE,
+                    show=show,
+                    out_file=out_file,
+                    score_thr=show_score_thr)
+
+
+        for _ in range(batch_size):
+            prog_bar.update()
+    return 
+
+
+def has_matched_img(img_meta, selected_imgs):
+    for img_key in selected_imgs:
+        if img_key in img_meta['filename']:
+            return True
+    return False
+    
+    
+def show_result(dataset,
+                img,
+                result,
+                score_thr=0.3,
+                bbox_color=(72, 101, 241),
+                text_color=(72, 101, 241),
+                mask_color=None,
+                thickness=2,
+                font_size=13,
+                win_name='',
+                show=False,
+                wait_time=0,
+                out_file=None):
+    """Draw `result` over `img`.
+
+    Args:
+        img (str or Tensor): The image to be displayed.
+        result (Tensor or tuple): The results to draw over `img`
+            bbox_result or (bbox_result, segm_result).
+        score_thr (float, optional): Minimum score of bboxes to be shown.
+            Default: 0.3.
+        bbox_color (str or tuple(int) or :obj:`Color`):Color of bbox lines.
+            The tuple of color should be in BGR order. Default: 'green'
+        text_color (str or tuple(int) or :obj:`Color`):Color of texts.
+            The tuple of color should be in BGR order. Default: 'green'
+        mask_color (None or str or tuple(int) or :obj:`Color`):
+            Color of masks. The tuple of color should be in BGR order.
+            Default: None
+        thickness (int): Thickness of lines. Default: 2
+        font_size (int): Font size of texts. Default: 13
+        win_name (str): The window name. Default: ''
+        wait_time (float): Value of waitKey param.
+            Default: 0.
+        show (bool): Whether to show the image.
+            Default: False.
+        out_file (str or None): The filename to write the image.
+            Default: None.
+
+    Returns:
+        img (Tensor): Only if not `show` or `out_file`
+    """
+    img = mmcv.imread(img)
+    img = img.copy()
+    if isinstance(result, tuple):
+        bbox_result, segm_result = result
+        if isinstance(segm_result, tuple):
+            segm_result = segm_result[0]  # ms rcnn
+    else:
+        bbox_result, segm_result = result, None
+    bboxes = np.vstack(bbox_result)
+    labels = [
+        np.full(bbox.shape[0], i, dtype=np.int32)
+        for i, bbox in enumerate(bbox_result)
+    ]
+    labels = np.concatenate(labels)
+    # draw segmentation masks
+    segms = None
+    if segm_result is not None and len(labels) > 0:  # non empty
+        segms = mmcv.concat_list(segm_result)
+        if isinstance(segms[0], torch.Tensor):
+            segms = torch.stack(segms, dim=0).detach().cpu().numpy()
+        else:
+            segms = np.stack(segms, axis=0)
+    # if out_file specified, do not show image in window
+    if out_file is not None:
+        show = False
+    # draw bounding boxes
+    img = imshow_det_bboxes(
+        img,
+        bboxes,
+        labels,
+        segms,
+        class_names=dataset.CLASSES,
+        score_thr=score_thr,
+        bbox_color=bbox_color,
+        text_color=text_color,
+        mask_color=mask_color,
+        thickness=thickness,
+        font_size=font_size,
+        win_name=win_name,
+        show=show,
+        wait_time=wait_time,
+        out_file=out_file)
+
+    if not (show or out_file):
+        return img
+
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/misc/browse_dataset.py b/tools/misc/browse_dataset.py
new file mode 100644
index 0000000..d9fb285
--- /dev/null
+++ b/tools/misc/browse_dataset.py
@@ -0,0 +1,137 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+from collections import Sequence
+from pathlib import Path
+
+import mmcv
+import numpy as np
+from mmcv import Config, DictAction
+
+from mmdet.core.utils import mask2ndarray
+from mmdet.core.visualization import imshow_det_bboxes
+from mmdet.datasets.builder import build_dataset
+from mmdet.utils import replace_cfg_vals, update_data_root
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Browse a dataset')
+    parser.add_argument('config', help='train config file path')
+    parser.add_argument(
+        '--skip-type',
+        type=str,
+        nargs='+',
+        default=['DefaultFormatBundle', 'Normalize', 'Collect'],
+        help='skip some useless pipeline')
+    parser.add_argument(
+        '--output-dir',
+        default=None,
+        type=str,
+        help='If there is no display interface, you can save it')
+    parser.add_argument('--not-show', default=False, action='store_true')
+    parser.add_argument(
+        '--show-interval',
+        type=float,
+        default=2,
+        help='the interval of show (s)')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    args = parser.parse_args()
+    return args
+
+
+def retrieve_data_cfg(config_path, skip_type, cfg_options):
+
+    def skip_pipeline_steps(config):
+        config['pipeline'] = [
+            x for x in config.pipeline if x['type'] not in skip_type
+        ]
+
+    cfg = Config.fromfile(config_path)
+
+    # replace the ${key} with the value of cfg.key
+    cfg = replace_cfg_vals(cfg)
+
+    # update data root according to MMDET_DATASETS
+    update_data_root(cfg)
+
+    if cfg_options is not None:
+        cfg.merge_from_dict(cfg_options)
+    train_data_cfg = cfg.data.train
+    while 'dataset' in train_data_cfg and train_data_cfg[
+            'type'] != 'MultiImageMixDataset':
+        train_data_cfg = train_data_cfg['dataset']
+
+    if isinstance(train_data_cfg, Sequence):
+        [skip_pipeline_steps(c) for c in train_data_cfg]
+    else:
+        skip_pipeline_steps(train_data_cfg)
+
+    return cfg
+
+
+def main():
+    args = parse_args()
+    cfg = retrieve_data_cfg(args.config, args.skip_type, args.cfg_options)
+
+    if 'gt_semantic_seg' in cfg.train_pipeline[-1]['keys']:
+        cfg.data.train.pipeline = [
+            p for p in cfg.data.train.pipeline if p['type'] != 'SegRescale'
+        ]
+    dataset = build_dataset(cfg.data.train)
+
+    progress_bar = mmcv.ProgressBar(len(dataset))
+
+    for item in dataset:
+        filename = os.path.join(args.output_dir,
+                                Path(item['filename']).name
+                                ) if args.output_dir is not None else None
+
+        gt_bboxes = item['gt_bboxes']
+        gt_labels = item['gt_labels']
+        gt_masks = item.get('gt_masks', None)
+        if gt_masks is not None:
+            gt_masks = mask2ndarray(gt_masks)
+
+        gt_seg = item.get('gt_semantic_seg', None)
+        if gt_seg is not None:
+            pad_value = 255  # the padding value of gt_seg
+            sem_labels = np.unique(gt_seg)
+            all_labels = np.concatenate((gt_labels, sem_labels), axis=0)
+            all_labels, counts = np.unique(all_labels, return_counts=True)
+            stuff_labels = all_labels[np.logical_and(counts < 2,
+                                                     all_labels != pad_value)]
+            stuff_masks = gt_seg[None] == stuff_labels[:, None, None]
+            gt_labels = np.concatenate((gt_labels, stuff_labels), axis=0)
+            gt_masks = np.concatenate((gt_masks, stuff_masks.astype(np.uint8)),
+                                      axis=0)
+            # If you need to show the bounding boxes,
+            # please comment the following line
+            gt_bboxes = None
+
+        imshow_det_bboxes(
+            item['img'],
+            gt_bboxes,
+            gt_labels,
+            gt_masks,
+            class_names=dataset.CLASSES,
+            show=not args.not_show,
+            wait_time=args.show_interval,
+            out_file=filename,
+            bbox_color=dataset.PALETTE,
+            text_color=(200, 200, 200),
+            mask_color=dataset.PALETTE)
+
+        progress_bar.update()
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/misc/download_dataset.py b/tools/misc/download_dataset.py
new file mode 100644
index 0000000..ac37cd2
--- /dev/null
+++ b/tools/misc/download_dataset.py
@@ -0,0 +1,190 @@
+import argparse
+import tarfile
+from itertools import repeat
+from multiprocessing.pool import ThreadPool
+from pathlib import Path
+from tarfile import TarFile
+from zipfile import ZipFile
+
+import torch
+from mmcv.utils.path import mkdir_or_exist
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Download datasets for training')
+    parser.add_argument(
+        '--dataset-name', type=str, help='dataset name', default='coco2017')
+    parser.add_argument(
+        '--save-dir',
+        type=str,
+        help='the dir to save dataset',
+        default='data/coco')
+    parser.add_argument(
+        '--unzip',
+        action='store_true',
+        help='whether unzip dataset or not, zipped files will be saved')
+    parser.add_argument(
+        '--delete',
+        action='store_true',
+        help='delete the download zipped files')
+    parser.add_argument(
+        '--threads', type=int, help='number of threading', default=4)
+    args = parser.parse_args()
+    return args
+
+
+def download(url, dir, unzip=True, delete=False, threads=1):
+
+    def download_one(url, dir):
+        f = dir / Path(url).name
+        if Path(url).is_file():
+            Path(url).rename(f)
+        elif not f.exists():
+            print(f'Downloading {url} to {f}')
+            torch.hub.download_url_to_file(url, f, progress=True)
+        if unzip and f.suffix in ('.zip', '.tar'):
+            print(f'Unzipping {f.name}')
+            if f.suffix == '.zip':
+                ZipFile(f).extractall(path=dir)
+            elif f.suffix == '.tar':
+                TarFile(f).extractall(path=dir)
+            if delete:
+                f.unlink()
+                print(f'Delete {f}')
+
+    dir = Path(dir)
+    if threads > 1:
+        pool = ThreadPool(threads)
+        pool.imap(lambda x: download_one(*x), zip(url, repeat(dir)))
+        pool.close()
+        pool.join()
+    else:
+        for u in [url] if isinstance(url, (str, Path)) else url:
+            download_one(u, dir)
+
+
+def download_objects365v2(url, dir, unzip=True, delete=False, threads=1):
+
+    def download_single(url, dir):
+
+        if 'train' in url:
+            saving_dir = dir / Path('train_zip')
+            mkdir_or_exist(saving_dir)
+            f = saving_dir / Path(url).name
+
+            unzip_dir = dir / Path('train')
+            mkdir_or_exist(unzip_dir)
+        elif 'val' in url:
+            saving_dir = dir / Path('val')
+            mkdir_or_exist(saving_dir)
+            f = saving_dir / Path(url).name
+
+            unzip_dir = dir / Path('val')
+            mkdir_or_exist(unzip_dir)
+        else:
+            raise NotImplementedError
+
+        if Path(url).is_file():
+            Path(url).rename(f)
+        elif not f.exists():
+            print(f'Downloading {url} to {f}')
+            torch.hub.download_url_to_file(url, f, progress=True)
+
+        if unzip and str(f).endswith('.tar.gz'):
+            print(f'Unzipping {f.name}')
+            tar = tarfile.open(f)
+            tar.extractall(path=unzip_dir)
+            if delete:
+                f.unlink()
+                print(f'Delete {f}')
+
+    # process annotations
+    full_url = []
+    for _url in url:
+        if 'zhiyuan_objv2_train.tar.gz' in _url or \
+                'zhiyuan_objv2_val.json' in _url:
+            full_url.append(_url)
+        elif 'train' in _url:
+            for i in range(51):
+                full_url.append(f'{_url}patch{i}.tar.gz')
+        elif 'val/images/v1' in _url:
+            for i in range(16):
+                full_url.append(f'{_url}patch{i}.tar.gz')
+        elif 'val/images/v2' in _url:
+            for i in range(16, 44):
+                full_url.append(f'{_url}patch{i}.tar.gz')
+        else:
+            raise NotImplementedError
+
+    dir = Path(dir)
+    if threads > 1:
+        pool = ThreadPool(threads)
+        pool.imap(lambda x: download_single(*x), zip(full_url, repeat(dir)))
+        pool.close()
+        pool.join()
+    else:
+        for u in full_url:
+            download_single(u, dir)
+
+
+def main():
+    args = parse_args()
+    path = Path(args.save_dir)
+    if not path.exists():
+        path.mkdir(parents=True, exist_ok=True)
+    data2url = dict(
+        # TODO: Support for downloading Panoptic Segmentation of COCO
+        coco2017=[
+            'http://images.cocodataset.org/zips/train2017.zip',
+            'http://images.cocodataset.org/zips/val2017.zip',
+            'http://images.cocodataset.org/zips/test2017.zip',
+            'http://images.cocodataset.org/annotations/' +
+            'annotations_trainval2017.zip'
+        ],
+        lvis=[
+            'https://s3-us-west-2.amazonaws.com/dl.fbaipublicfiles.com/LVIS/lvis_v1_train.json.zip',  # noqa
+            'https://s3-us-west-2.amazonaws.com/dl.fbaipublicfiles.com/LVIS/lvis_v1_train.json.zip',  # noqa
+        ],
+        voc2007=[
+            'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar',  # noqa
+            'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar',  # noqa
+            'http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCdevkit_08-Jun-2007.tar',  # noqa
+        ],
+        # Note: There is no download link for Objects365-V1 right now. If you
+        # would like to download Objects365-V1, please visit
+        # http://www.objects365.org/ to concat the author.
+        objects365v2=[
+            # training annotations
+            'https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/train/zhiyuan_objv2_train.tar.gz',  # noqa
+            # validation annotations
+            'https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/val/zhiyuan_objv2_val.json',  # noqa
+            # training url root
+            'https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/train/',  # noqa
+            # validation url root_1
+            'https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/val/images/v1/',  # noqa
+            # validation url root_2
+            'https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/val/images/v2/'  # noqa
+        ])
+    url = data2url.get(args.dataset_name, None)
+    if url is None:
+        print('Only support COCO, VOC, LVIS, and Objects365v2 now!')
+        return
+    if args.dataset_name == 'objects365v2':
+        download_objects365v2(
+            url,
+            dir=path,
+            unzip=args.unzip,
+            delete=args.delete,
+            threads=args.threads)
+    else:
+        download(
+            url,
+            dir=path,
+            unzip=args.unzip,
+            delete=args.delete,
+            threads=args.threads)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/misc/gen_coco_panoptic_test_info.py b/tools/misc/gen_coco_panoptic_test_info.py
new file mode 100644
index 0000000..5ad315d
--- /dev/null
+++ b/tools/misc/gen_coco_panoptic_test_info.py
@@ -0,0 +1,34 @@
+import argparse
+import os.path as osp
+
+import mmcv
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Generate COCO test image information '
+        'for COCO panoptic segmentation.')
+    parser.add_argument('data_root', help='Path to COCO annotation directory.')
+    args = parser.parse_args()
+
+    return args
+
+
+def main():
+    args = parse_args()
+    data_root = args.data_root
+    val_info = mmcv.load(osp.join(data_root, 'panoptic_val2017.json'))
+    test_old_info = mmcv.load(
+        osp.join(data_root, 'image_info_test-dev2017.json'))
+
+    # replace categories from image_info_test-dev2017.json
+    # with categories from panoptic_val2017.json which
+    # has attribute `isthing`.
+    test_info = test_old_info
+    test_info.update({'categories': val_info['categories']})
+    mmcv.dump(test_info,
+              osp.join(data_root, 'panoptic_image_info_test-dev2017.json'))
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/misc/get_image_metas.py b/tools/misc/get_image_metas.py
new file mode 100644
index 0000000..a9957d9
--- /dev/null
+++ b/tools/misc/get_image_metas.py
@@ -0,0 +1,116 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Get test image metas on a specific dataset.
+
+Here is an example to run this script.
+
+Example:
+    python tools/misc/get_image_metas.py ${CONFIG} \
+    --out ${OUTPUT FILE NAME}
+"""
+import argparse
+import csv
+import os.path as osp
+from multiprocessing import Pool
+
+import mmcv
+from mmcv import Config
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Collect image metas')
+    parser.add_argument('config', help='Config file path')
+    parser.add_argument(
+        '--out',
+        default='validation-image-metas.pkl',
+        help='The output image metas file name. The save dir is in the '
+        'same directory as `dataset.ann_file` path')
+    parser.add_argument(
+        '--nproc',
+        default=4,
+        type=int,
+        help='Processes used for get image metas')
+    args = parser.parse_args()
+    return args
+
+
+def get_metas_from_csv_style_ann_file(ann_file):
+    data_infos = []
+    cp_filename = None
+    with open(ann_file, 'r') as f:
+        reader = csv.reader(f)
+        for i, line in enumerate(reader):
+            if i == 0:
+                continue
+            img_id = line[0]
+            filename = f'{img_id}.jpg'
+            if filename != cp_filename:
+                data_infos.append(dict(filename=filename))
+                cp_filename = filename
+    return data_infos
+
+
+def get_metas_from_txt_style_ann_file(ann_file):
+    with open(ann_file) as f:
+        lines = f.readlines()
+    i = 0
+    data_infos = []
+    while i < len(lines):
+        filename = lines[i].rstrip()
+        data_infos.append(dict(filename=filename))
+        skip_lines = int(lines[i + 2]) + 3
+        i += skip_lines
+    return data_infos
+
+
+def get_image_metas(data_info, img_prefix):
+    file_client = mmcv.FileClient(backend='disk')
+    filename = data_info.get('filename', None)
+    if filename is not None:
+        if img_prefix is not None:
+            filename = osp.join(img_prefix, filename)
+        img_bytes = file_client.get(filename)
+        img = mmcv.imfrombytes(img_bytes, flag='color')
+        meta = dict(filename=filename, ori_shape=img.shape)
+    else:
+        raise NotImplementedError('Missing `filename` in data_info')
+    return meta
+
+
+def main():
+    args = parse_args()
+    assert args.out.endswith('pkl'), 'The output file name must be pkl suffix'
+
+    # load config files
+    cfg = Config.fromfile(args.config)
+    ann_file = cfg.data.test.ann_file
+    img_prefix = cfg.data.test.img_prefix
+
+    print(f'{"-" * 5} Start Processing {"-" * 5}')
+    if ann_file.endswith('csv'):
+        data_infos = get_metas_from_csv_style_ann_file(ann_file)
+    elif ann_file.endswith('txt'):
+        data_infos = get_metas_from_txt_style_ann_file(ann_file)
+    else:
+        shuffix = ann_file.split('.')[-1]
+        raise NotImplementedError('File name must be csv or txt suffix but '
+                                  f'get {shuffix}')
+
+    print(f'Successfully load annotation file from {ann_file}')
+    print(f'Processing {len(data_infos)} images...')
+    pool = Pool(args.nproc)
+    # get image metas with multiple processes
+    image_metas = pool.starmap(
+        get_image_metas,
+        zip(data_infos, [img_prefix for _ in range(len(data_infos))]),
+    )
+    pool.close()
+
+    # save image metas
+    root_path = cfg.data.test.ann_file.rsplit('/', 1)[0]
+    save_path = osp.join(root_path, args.out)
+    mmcv.dump(image_metas, save_path)
+    print(f'Image meta file save to: {save_path}')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/misc/print_config.py b/tools/misc/print_config.py
new file mode 100644
index 0000000..f10f538
--- /dev/null
+++ b/tools/misc/print_config.py
@@ -0,0 +1,60 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import warnings
+
+from mmcv import Config, DictAction
+
+from mmdet.utils import replace_cfg_vals, update_data_root
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Print the whole config')
+    parser.add_argument('config', help='config file path')
+    parser.add_argument(
+        '--options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file (deprecate), '
+        'change to --cfg-options instead.')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    args = parser.parse_args()
+
+    if args.options and args.cfg_options:
+        raise ValueError(
+            '--options and --cfg-options cannot be both '
+            'specified, --options is deprecated in favor of --cfg-options')
+    if args.options:
+        warnings.warn('--options is deprecated in favor of --cfg-options')
+        args.cfg_options = args.options
+
+    return args
+
+
+def main():
+    args = parse_args()
+
+    cfg = Config.fromfile(args.config)
+
+    # replace the ${key} with the value of cfg.key
+    cfg = replace_cfg_vals(cfg)
+
+    # update data root according to MMDET_DATASETS
+    update_data_root(cfg)
+
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+    print(f'Config:\n{cfg.pretty_text}')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/misc/split_coco.py b/tools/misc/split_coco.py
new file mode 100644
index 0000000..78cc655
--- /dev/null
+++ b/tools/misc/split_coco.py
@@ -0,0 +1,109 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os.path as osp
+
+import mmcv
+import numpy as np
+
+prog_description = '''K-Fold coco split.
+
+To split coco data for semi-supervised object detection:
+    python tools/misc/split_coco.py
+'''
+
+
+def parse_args():
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        '--data-root',
+        type=str,
+        help='The data root of coco dataset.',
+        default='./data/coco/')
+    parser.add_argument(
+        '--out-dir',
+        type=str,
+        help='The output directory of coco semi-supervised annotations.',
+        default='./data/coco_semi_annos/')
+    parser.add_argument(
+        '--labeled-percent',
+        type=float,
+        nargs='+',
+        help='The percentage of labeled data in the training set.',
+        default=[1, 2, 5, 10])
+    parser.add_argument(
+        '--fold',
+        type=int,
+        help='K-fold cross validation for semi-supervised object detection.',
+        default=5)
+    args = parser.parse_args()
+    return args
+
+
+def split_coco(data_root, out_dir, percent, fold):
+    """Split COCO data for Semi-supervised object detection.
+
+    Args:
+        data_root (str): The data root of coco dataset.
+        out_dir (str): The output directory of coco semi-supervised
+            annotations.
+        percent (float): The percentage of labeled data in the training set.
+        fold (int): The fold of dataset and set as random seed for data split.
+    """
+
+    def save_anns(name, images, annotations):
+        sub_anns = dict()
+        sub_anns['images'] = images
+        sub_anns['annotations'] = annotations
+        sub_anns['licenses'] = anns['licenses']
+        sub_anns['categories'] = anns['categories']
+        sub_anns['info'] = anns['info']
+
+        mmcv.mkdir_or_exist(out_dir)
+        mmcv.dump(sub_anns, f'{out_dir}/{name}.json')
+
+    # set random seed with the fold
+    np.random.seed(fold)
+    ann_file = osp.join(data_root, 'annotations/instances_train2017.json')
+    anns = mmcv.load(ann_file)
+
+    image_list = anns['images']
+    labeled_total = int(percent / 100. * len(image_list))
+    labeled_inds = set(
+        np.random.choice(range(len(image_list)), size=labeled_total))
+    labeled_ids, labeled_images, unlabeled_images = [], [], []
+
+    for i in range(len(image_list)):
+        if i in labeled_inds:
+            labeled_images.append(image_list[i])
+            labeled_ids.append(image_list[i]['id'])
+        else:
+            unlabeled_images.append(image_list[i])
+
+    # get all annotations of labeled images
+    labeled_ids = set(labeled_ids)
+    labeled_annotations, unlabeled_annotations = [], []
+
+    for ann in anns['annotations']:
+        if ann['image_id'] in labeled_ids:
+            labeled_annotations.append(ann)
+        else:
+            unlabeled_annotations.append(ann)
+
+    # save labeled and unlabeled
+    labeled_name = f'instances_train2017.{fold}@{percent}'
+    unlabeled_name = f'instances_train2017.{fold}@{percent}-unlabeled'
+
+    save_anns(labeled_name, labeled_images, labeled_annotations)
+    save_anns(unlabeled_name, unlabeled_images, unlabeled_annotations)
+
+
+def multi_wrapper(args):
+    return split_coco(*args)
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    arguments_list = [(args.data_root, args.out_dir, p, f)
+                      for f in range(1, args.fold + 1)
+                      for p in args.labeled_percent]
+    mmcv.track_parallel_progress(multi_wrapper, arguments_list, args.fold)
diff --git a/tools/model_converters/detectron2pytorch.py b/tools/model_converters/detectron2pytorch.py
new file mode 100644
index 0000000..b7264d5
--- /dev/null
+++ b/tools/model_converters/detectron2pytorch.py
@@ -0,0 +1,83 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+from collections import OrderedDict
+
+import mmcv
+import torch
+
+arch_settings = {50: (3, 4, 6, 3), 101: (3, 4, 23, 3)}
+
+
+def convert_bn(blobs, state_dict, caffe_name, torch_name, converted_names):
+    # detectron replace bn with affine channel layer
+    state_dict[torch_name + '.bias'] = torch.from_numpy(blobs[caffe_name +
+                                                              '_b'])
+    state_dict[torch_name + '.weight'] = torch.from_numpy(blobs[caffe_name +
+                                                                '_s'])
+    bn_size = state_dict[torch_name + '.weight'].size()
+    state_dict[torch_name + '.running_mean'] = torch.zeros(bn_size)
+    state_dict[torch_name + '.running_var'] = torch.ones(bn_size)
+    converted_names.add(caffe_name + '_b')
+    converted_names.add(caffe_name + '_s')
+
+
+def convert_conv_fc(blobs, state_dict, caffe_name, torch_name,
+                    converted_names):
+    state_dict[torch_name + '.weight'] = torch.from_numpy(blobs[caffe_name +
+                                                                '_w'])
+    converted_names.add(caffe_name + '_w')
+    if caffe_name + '_b' in blobs:
+        state_dict[torch_name + '.bias'] = torch.from_numpy(blobs[caffe_name +
+                                                                  '_b'])
+        converted_names.add(caffe_name + '_b')
+
+
+def convert(src, dst, depth):
+    """Convert keys in detectron pretrained ResNet models to pytorch style."""
+    # load arch_settings
+    if depth not in arch_settings:
+        raise ValueError('Only support ResNet-50 and ResNet-101 currently')
+    block_nums = arch_settings[depth]
+    # load caffe model
+    caffe_model = mmcv.load(src, encoding='latin1')
+    blobs = caffe_model['blobs'] if 'blobs' in caffe_model else caffe_model
+    # convert to pytorch style
+    state_dict = OrderedDict()
+    converted_names = set()
+    convert_conv_fc(blobs, state_dict, 'conv1', 'conv1', converted_names)
+    convert_bn(blobs, state_dict, 'res_conv1_bn', 'bn1', converted_names)
+    for i in range(1, len(block_nums) + 1):
+        for j in range(block_nums[i - 1]):
+            if j == 0:
+                convert_conv_fc(blobs, state_dict, f'res{i + 1}_{j}_branch1',
+                                f'layer{i}.{j}.downsample.0', converted_names)
+                convert_bn(blobs, state_dict, f'res{i + 1}_{j}_branch1_bn',
+                           f'layer{i}.{j}.downsample.1', converted_names)
+            for k, letter in enumerate(['a', 'b', 'c']):
+                convert_conv_fc(blobs, state_dict,
+                                f'res{i + 1}_{j}_branch2{letter}',
+                                f'layer{i}.{j}.conv{k+1}', converted_names)
+                convert_bn(blobs, state_dict,
+                           f'res{i + 1}_{j}_branch2{letter}_bn',
+                           f'layer{i}.{j}.bn{k + 1}', converted_names)
+    # check if all layers are converted
+    for key in blobs:
+        if key not in converted_names:
+            print(f'Not Convert: {key}')
+    # save checkpoint
+    checkpoint = dict()
+    checkpoint['state_dict'] = state_dict
+    torch.save(checkpoint, dst)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Convert model keys')
+    parser.add_argument('src', help='src detectron model path')
+    parser.add_argument('dst', help='save path')
+    parser.add_argument('depth', type=int, help='ResNet model depth')
+    args = parser.parse_args()
+    convert(args.src, args.dst, args.depth)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/model_converters/publish_model.py b/tools/model_converters/publish_model.py
new file mode 100644
index 0000000..219fcdf
--- /dev/null
+++ b/tools/model_converters/publish_model.py
@@ -0,0 +1,43 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import subprocess
+
+import torch
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Process a checkpoint to be published')
+    parser.add_argument('in_file', help='input checkpoint filename')
+    parser.add_argument('out_file', help='output checkpoint filename')
+    args = parser.parse_args()
+    return args
+
+
+def process_checkpoint(in_file, out_file):
+    checkpoint = torch.load(in_file, map_location='cpu')
+    # remove optimizer for smaller file size
+    if 'optimizer' in checkpoint:
+        del checkpoint['optimizer']
+    # if it is necessary to remove some sensitive data in checkpoint['meta'],
+    # add the code here.
+    if torch.__version__ >= '1.6':
+        torch.save(checkpoint, out_file, _use_new_zipfile_serialization=False)
+    else:
+        torch.save(checkpoint, out_file)
+    sha = subprocess.check_output(['sha256sum', out_file]).decode()
+    if out_file.endswith('.pth'):
+        out_file_name = out_file[:-4]
+    else:
+        out_file_name = out_file
+    final_file = out_file_name + f'-{sha[:8]}.pth'
+    subprocess.Popen(['mv', out_file, final_file])
+
+
+def main():
+    args = parse_args()
+    process_checkpoint(args.in_file, args.out_file)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/model_converters/regnet2mmdet.py b/tools/model_converters/regnet2mmdet.py
new file mode 100644
index 0000000..fbf8c8f
--- /dev/null
+++ b/tools/model_converters/regnet2mmdet.py
@@ -0,0 +1,90 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+from collections import OrderedDict
+
+import torch
+
+
+def convert_stem(model_key, model_weight, state_dict, converted_names):
+    new_key = model_key.replace('stem.conv', 'conv1')
+    new_key = new_key.replace('stem.bn', 'bn1')
+    state_dict[new_key] = model_weight
+    converted_names.add(model_key)
+    print(f'Convert {model_key} to {new_key}')
+
+
+def convert_head(model_key, model_weight, state_dict, converted_names):
+    new_key = model_key.replace('head.fc', 'fc')
+    state_dict[new_key] = model_weight
+    converted_names.add(model_key)
+    print(f'Convert {model_key} to {new_key}')
+
+
+def convert_reslayer(model_key, model_weight, state_dict, converted_names):
+    split_keys = model_key.split('.')
+    layer, block, module = split_keys[:3]
+    block_id = int(block[1:])
+    layer_name = f'layer{int(layer[1:])}'
+    block_name = f'{block_id - 1}'
+
+    if block_id == 1 and module == 'bn':
+        new_key = f'{layer_name}.{block_name}.downsample.1.{split_keys[-1]}'
+    elif block_id == 1 and module == 'proj':
+        new_key = f'{layer_name}.{block_name}.downsample.0.{split_keys[-1]}'
+    elif module == 'f':
+        if split_keys[3] == 'a_bn':
+            module_name = 'bn1'
+        elif split_keys[3] == 'b_bn':
+            module_name = 'bn2'
+        elif split_keys[3] == 'c_bn':
+            module_name = 'bn3'
+        elif split_keys[3] == 'a':
+            module_name = 'conv1'
+        elif split_keys[3] == 'b':
+            module_name = 'conv2'
+        elif split_keys[3] == 'c':
+            module_name = 'conv3'
+        new_key = f'{layer_name}.{block_name}.{module_name}.{split_keys[-1]}'
+    else:
+        raise ValueError(f'Unsupported conversion of key {model_key}')
+    print(f'Convert {model_key} to {new_key}')
+    state_dict[new_key] = model_weight
+    converted_names.add(model_key)
+
+
+def convert(src, dst):
+    """Convert keys in pycls pretrained RegNet models to mmdet style."""
+    # load caffe model
+    regnet_model = torch.load(src)
+    blobs = regnet_model['model_state']
+    # convert to pytorch style
+    state_dict = OrderedDict()
+    converted_names = set()
+    for key, weight in blobs.items():
+        if 'stem' in key:
+            convert_stem(key, weight, state_dict, converted_names)
+        elif 'head' in key:
+            convert_head(key, weight, state_dict, converted_names)
+        elif key.startswith('s'):
+            convert_reslayer(key, weight, state_dict, converted_names)
+
+    # check if all layers are converted
+    for key in blobs:
+        if key not in converted_names:
+            print(f'not converted: {key}')
+    # save checkpoint
+    checkpoint = dict()
+    checkpoint['state_dict'] = state_dict
+    torch.save(checkpoint, dst)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Convert model keys')
+    parser.add_argument('src', help='src detectron model path')
+    parser.add_argument('dst', help='save path')
+    args = parser.parse_args()
+    convert(args.src, args.dst)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/model_converters/selfsup2mmdet.py b/tools/model_converters/selfsup2mmdet.py
new file mode 100644
index 0000000..bc8cce1
--- /dev/null
+++ b/tools/model_converters/selfsup2mmdet.py
@@ -0,0 +1,42 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+from collections import OrderedDict
+
+import torch
+
+
+def moco_convert(src, dst):
+    """Convert keys in pycls pretrained moco models to mmdet style."""
+    # load caffe model
+    moco_model = torch.load(src)
+    blobs = moco_model['state_dict']
+    # convert to pytorch style
+    state_dict = OrderedDict()
+    for k, v in blobs.items():
+        if not k.startswith('module.encoder_q.'):
+            continue
+        old_k = k
+        k = k.replace('module.encoder_q.', '')
+        state_dict[k] = v
+        print(old_k, '->', k)
+    # save checkpoint
+    checkpoint = dict()
+    checkpoint['state_dict'] = state_dict
+    torch.save(checkpoint, dst)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Convert model keys')
+    parser.add_argument('src', help='src detectron model path')
+    parser.add_argument('dst', help='save path')
+    parser.add_argument(
+        '--selfsup', type=str, choices=['moco', 'swav'], help='save path')
+    args = parser.parse_args()
+    if args.selfsup == 'moco':
+        moco_convert(args.src, args.dst)
+    elif args.selfsup == 'swav':
+        print('SWAV does not need to convert the keys')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/model_converters/upgrade_model_version.py b/tools/model_converters/upgrade_model_version.py
new file mode 100644
index 0000000..36ee607
--- /dev/null
+++ b/tools/model_converters/upgrade_model_version.py
@@ -0,0 +1,210 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import re
+import tempfile
+from collections import OrderedDict
+
+import torch
+from mmcv import Config
+
+
+def is_head(key):
+    valid_head_list = [
+        'bbox_head', 'mask_head', 'semantic_head', 'grid_head', 'mask_iou_head'
+    ]
+
+    return any(key.startswith(h) for h in valid_head_list)
+
+
+def parse_config(config_strings):
+    temp_file = tempfile.NamedTemporaryFile()
+    config_path = f'{temp_file.name}.py'
+    with open(config_path, 'w') as f:
+        f.write(config_strings)
+
+    config = Config.fromfile(config_path)
+    is_two_stage = True
+    is_ssd = False
+    is_retina = False
+    reg_cls_agnostic = False
+    if 'rpn_head' not in config.model:
+        is_two_stage = False
+        # check whether it is SSD
+        if config.model.bbox_head.type == 'SSDHead':
+            is_ssd = True
+        elif config.model.bbox_head.type == 'RetinaHead':
+            is_retina = True
+    elif isinstance(config.model['bbox_head'], list):
+        reg_cls_agnostic = True
+    elif 'reg_class_agnostic' in config.model.bbox_head:
+        reg_cls_agnostic = config.model.bbox_head \
+            .reg_class_agnostic
+    temp_file.close()
+    return is_two_stage, is_ssd, is_retina, reg_cls_agnostic
+
+
+def reorder_cls_channel(val, num_classes=81):
+    # bias
+    if val.dim() == 1:
+        new_val = torch.cat((val[1:], val[:1]), dim=0)
+    # weight
+    else:
+        out_channels, in_channels = val.shape[:2]
+        # conv_cls for softmax output
+        if out_channels != num_classes and out_channels % num_classes == 0:
+            new_val = val.reshape(-1, num_classes, in_channels, *val.shape[2:])
+            new_val = torch.cat((new_val[:, 1:], new_val[:, :1]), dim=1)
+            new_val = new_val.reshape(val.size())
+        # fc_cls
+        elif out_channels == num_classes:
+            new_val = torch.cat((val[1:], val[:1]), dim=0)
+        # agnostic | retina_cls | rpn_cls
+        else:
+            new_val = val
+
+    return new_val
+
+
+def truncate_cls_channel(val, num_classes=81):
+
+    # bias
+    if val.dim() == 1:
+        if val.size(0) % num_classes == 0:
+            new_val = val[:num_classes - 1]
+        else:
+            new_val = val
+    # weight
+    else:
+        out_channels, in_channels = val.shape[:2]
+        # conv_logits
+        if out_channels % num_classes == 0:
+            new_val = val.reshape(num_classes, in_channels, *val.shape[2:])[1:]
+            new_val = new_val.reshape(-1, *val.shape[1:])
+        # agnostic
+        else:
+            new_val = val
+
+    return new_val
+
+
+def truncate_reg_channel(val, num_classes=81):
+    # bias
+    if val.dim() == 1:
+        # fc_reg | rpn_reg
+        if val.size(0) % num_classes == 0:
+            new_val = val.reshape(num_classes, -1)[:num_classes - 1]
+            new_val = new_val.reshape(-1)
+        # agnostic
+        else:
+            new_val = val
+    # weight
+    else:
+        out_channels, in_channels = val.shape[:2]
+        # fc_reg | rpn_reg
+        if out_channels % num_classes == 0:
+            new_val = val.reshape(num_classes, -1, in_channels,
+                                  *val.shape[2:])[1:]
+            new_val = new_val.reshape(-1, *val.shape[1:])
+        # agnostic
+        else:
+            new_val = val
+
+    return new_val
+
+
+def convert(in_file, out_file, num_classes):
+    """Convert keys in checkpoints.
+
+    There can be some breaking changes during the development of mmdetection,
+    and this tool is used for upgrading checkpoints trained with old versions
+    to the latest one.
+    """
+    checkpoint = torch.load(in_file)
+    in_state_dict = checkpoint.pop('state_dict')
+    out_state_dict = OrderedDict()
+    meta_info = checkpoint['meta']
+    is_two_stage, is_ssd, is_retina, reg_cls_agnostic = parse_config(
+        '#' + meta_info['config'])
+    if meta_info['mmdet_version'] <= '0.5.3' and is_retina:
+        upgrade_retina = True
+    else:
+        upgrade_retina = False
+
+    # MMDetection v2.5.0 unifies the class order in RPN
+    # if the model is trained in version<v2.5.0
+    # The RPN model should be upgraded to be used in version>=2.5.0
+    if meta_info['mmdet_version'] < '2.5.0':
+        upgrade_rpn = True
+    else:
+        upgrade_rpn = False
+
+    for key, val in in_state_dict.items():
+        new_key = key
+        new_val = val
+        if is_two_stage and is_head(key):
+            new_key = 'roi_head.{}'.format(key)
+
+        # classification
+        if upgrade_rpn:
+            m = re.search(
+                r'(conv_cls|retina_cls|rpn_cls|fc_cls|fcos_cls|'
+                r'fovea_cls).(weight|bias)', new_key)
+        else:
+            m = re.search(
+                r'(conv_cls|retina_cls|fc_cls|fcos_cls|'
+                r'fovea_cls).(weight|bias)', new_key)
+        if m is not None:
+            print(f'reorder cls channels of {new_key}')
+            new_val = reorder_cls_channel(val, num_classes)
+
+        # regression
+        if upgrade_rpn:
+            m = re.search(r'(fc_reg).(weight|bias)', new_key)
+        else:
+            m = re.search(r'(fc_reg|rpn_reg).(weight|bias)', new_key)
+        if m is not None and not reg_cls_agnostic:
+            print(f'truncate regression channels of {new_key}')
+            new_val = truncate_reg_channel(val, num_classes)
+
+        # mask head
+        m = re.search(r'(conv_logits).(weight|bias)', new_key)
+        if m is not None:
+            print(f'truncate mask prediction channels of {new_key}')
+            new_val = truncate_cls_channel(val, num_classes)
+
+        m = re.search(r'(cls_convs|reg_convs).\d.(weight|bias)', key)
+        # Legacy issues in RetinaNet since V1.x
+        # Use ConvModule instead of nn.Conv2d in RetinaNet
+        # cls_convs.0.weight -> cls_convs.0.conv.weight
+        if m is not None and upgrade_retina:
+            param = m.groups()[1]
+            new_key = key.replace(param, f'conv.{param}')
+            out_state_dict[new_key] = val
+            print(f'rename the name of {key} to {new_key}')
+            continue
+
+        m = re.search(r'(cls_convs).\d.(weight|bias)', key)
+        if m is not None and is_ssd:
+            print(f'reorder cls channels of {new_key}')
+            new_val = reorder_cls_channel(val, num_classes)
+
+        out_state_dict[new_key] = new_val
+    checkpoint['state_dict'] = out_state_dict
+    torch.save(checkpoint, out_file)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Upgrade model version')
+    parser.add_argument('in_file', help='input checkpoint file')
+    parser.add_argument('out_file', help='output checkpoint file')
+    parser.add_argument(
+        '--num-classes',
+        type=int,
+        default=81,
+        help='number of classes of the original model')
+    args = parser.parse_args()
+    convert(args.in_file, args.out_file, args.num_classes)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/model_converters/upgrade_ssd_version.py b/tools/model_converters/upgrade_ssd_version.py
new file mode 100644
index 0000000..befff45
--- /dev/null
+++ b/tools/model_converters/upgrade_ssd_version.py
@@ -0,0 +1,58 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import tempfile
+from collections import OrderedDict
+
+import torch
+from mmcv import Config
+
+
+def parse_config(config_strings):
+    temp_file = tempfile.NamedTemporaryFile()
+    config_path = f'{temp_file.name}.py'
+    with open(config_path, 'w') as f:
+        f.write(config_strings)
+
+    config = Config.fromfile(config_path)
+    # check whether it is SSD
+    if config.model.bbox_head.type != 'SSDHead':
+        raise AssertionError('This is not a SSD model.')
+
+
+def convert(in_file, out_file):
+    checkpoint = torch.load(in_file)
+    in_state_dict = checkpoint.pop('state_dict')
+    out_state_dict = OrderedDict()
+    meta_info = checkpoint['meta']
+    parse_config('#' + meta_info['config'])
+    for key, value in in_state_dict.items():
+        if 'extra' in key:
+            layer_idx = int(key.split('.')[2])
+            new_key = 'neck.extra_layers.{}.{}.conv.'.format(
+                layer_idx // 2, layer_idx % 2) + key.split('.')[-1]
+        elif 'l2_norm' in key:
+            new_key = 'neck.l2_norm.weight'
+        elif 'bbox_head' in key:
+            new_key = key[:21] + '.0' + key[21:]
+        else:
+            new_key = key
+        out_state_dict[new_key] = value
+    checkpoint['state_dict'] = out_state_dict
+
+    if torch.__version__ >= '1.6':
+        torch.save(checkpoint, out_file, _use_new_zipfile_serialization=False)
+    else:
+        torch.save(checkpoint, out_file)
+
+
+def main():
+    parser = argparse.ArgumentParser(description='Upgrade SSD version')
+    parser.add_argument('in_file', help='input checkpoint file')
+    parser.add_argument('out_file', help='output checkpoint file')
+
+    args = parser.parse_args()
+    convert(args.in_file, args.out_file)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/pr_curve.sh b/tools/pr_curve.sh
new file mode 100644
index 0000000..ed34934
--- /dev/null
+++ b/tools/pr_curve.sh
@@ -0,0 +1,22 @@
+# python tools/analysis_tools/plot_pr_curves.py \
+#     configs/ssd/ssdlite_mobilenetv2_scratch_600e_voc.py \
+#     work_dirs/ssdlite_mobilenetv2_scratch_600e_voc/results.pkl \
+#     --out work_dirs/ssdlite_mobilenetv2_scratch_600e_voc/pr_curve \
+
+
+# python tools/analysis_tools/plot_pr_curves.py \
+#     configs/ssd/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4.py \
+#     work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4/results.pkl \
+#     --out work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4/pr_curve \
+
+
+# python tools/analysis_tools/plot_pr_curves.py \
+#     configs/ssd/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4.py \
+#     work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4_mypro_temp_qloss/results.pkl \
+#     --out work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4_mypro_temp_qloss/pr_curve \
+
+python tools/analysis_tools/plot_pr_curves.py \
+    configs/ssd/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4.py \
+    work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4_mypro_temp/results.pkl \
+    --out work_dirs/ssdlite_mobilenetv2_scratch_600e_voc_quant_w4a4_mypro_temp/pr_curve \
+
diff --git a/tools/slurm_test.sh b/tools/slurm_test.sh
new file mode 100755
index 0000000..6dd67e5
--- /dev/null
+++ b/tools/slurm_test.sh
@@ -0,0 +1,24 @@
+#!/usr/bin/env bash
+
+set -x
+
+PARTITION=$1
+JOB_NAME=$2
+CONFIG=$3
+CHECKPOINT=$4
+GPUS=${GPUS:-8}
+GPUS_PER_NODE=${GPUS_PER_NODE:-8}
+CPUS_PER_TASK=${CPUS_PER_TASK:-5}
+PY_ARGS=${@:5}
+SRUN_ARGS=${SRUN_ARGS:-""}
+
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+srun -p ${PARTITION} \
+    --job-name=${JOB_NAME} \
+    --gres=gpu:${GPUS_PER_NODE} \
+    --ntasks=${GPUS} \
+    --ntasks-per-node=${GPUS_PER_NODE} \
+    --cpus-per-task=${CPUS_PER_TASK} \
+    --kill-on-bad-exit=1 \
+    ${SRUN_ARGS} \
+    python -u tools/test.py ${CONFIG} ${CHECKPOINT} --launcher="slurm" ${PY_ARGS}
diff --git a/tools/slurm_train.sh b/tools/slurm_train.sh
new file mode 100755
index 0000000..b3feb3d
--- /dev/null
+++ b/tools/slurm_train.sh
@@ -0,0 +1,24 @@
+#!/usr/bin/env bash
+
+set -x
+
+PARTITION=$1
+JOB_NAME=$2
+CONFIG=$3
+WORK_DIR=$4
+GPUS=${GPUS:-8}
+GPUS_PER_NODE=${GPUS_PER_NODE:-8}
+CPUS_PER_TASK=${CPUS_PER_TASK:-5}
+SRUN_ARGS=${SRUN_ARGS:-""}
+PY_ARGS=${@:5}
+
+PYTHONPATH="$(dirname $0)/..":$PYTHONPATH \
+srun -p ${PARTITION} \
+    --job-name=${JOB_NAME} \
+    --gres=gpu:${GPUS_PER_NODE} \
+    --ntasks=${GPUS} \
+    --ntasks-per-node=${GPUS_PER_NODE} \
+    --cpus-per-task=${CPUS_PER_TASK} \
+    --kill-on-bad-exit=1 \
+    ${SRUN_ARGS} \
+    python -u tools/train.py ${CONFIG} --work-dir=${WORK_DIR} --launcher="slurm" ${PY_ARGS}
diff --git a/tools/starter_scripts/atss_50/qat/coco/dist_lsq_HQOD_atss_50_coco_w2a2.sh b/tools/starter_scripts/atss_50/qat/coco/dist_lsq_HQOD_atss_50_coco_w2a2.sh
new file mode 100755
index 0000000..c34c6c2
--- /dev/null
+++ b/tools/starter_scripts/atss_50/qat/coco/dist_lsq_HQOD_atss_50_coco_w2a2.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=6,7 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12327 \
+    --nproc_per_node=2 \
+    tools/train.py \
+    configs/atss/atss_r50_fpn_1x_coco_quant_w2a2.py \
+    mqbconfig/lsq/quant_config_mypro_w2a2_weight_loose.yaml \
+    --work-dir /work_dirs/atss_r50_fpn_1x_coco_w2a2_LSQ_HQOD \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/atss_50/qat/coco/dist_lsq_HQOD_atss_50_coco_w4a4.sh b/tools/starter_scripts/atss_50/qat/coco/dist_lsq_HQOD_atss_50_coco_w4a4.sh
new file mode 100755
index 0000000..0d19d86
--- /dev/null
+++ b/tools/starter_scripts/atss_50/qat/coco/dist_lsq_HQOD_atss_50_coco_w4a4.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=5 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12326 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/atss/atss_r50_fpn_1x_coco_quant_w4a4.py \
+    mqbconfig/lsq/quant_config_mypro_w4a4.yaml \
+    --work-dir /work_dirs/atss_r50_fpn_1x_coco_w4a4_LSQ_HQOD \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/atss_50/qat/coco/dist_lsq_atss_50_coco_w2a2.sh b/tools/starter_scripts/atss_50/qat/coco/dist_lsq_atss_50_coco_w2a2.sh
new file mode 100755
index 0000000..89a7015
--- /dev/null
+++ b/tools/starter_scripts/atss_50/qat/coco/dist_lsq_atss_50_coco_w2a2.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=4,5 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12325 \
+    --nproc_per_node=2 \
+    tools/train.py \
+    configs/atss/atss_r50_fpn_1x_coco_quant_w2a2.py \
+    mqbconfig/lsq/quant_config_w2a2_weight_loose.yaml \
+    --work-dir /work_dirs/atss_r50_fpn_1x_coco_w2a2_LSQ \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/atss_50/qat/coco/dist_lsq_atss_50_coco_w4a4.sh b/tools/starter_scripts/atss_50/qat/coco/dist_lsq_atss_50_coco_w4a4.sh
new file mode 100755
index 0000000..91758b5
--- /dev/null
+++ b/tools/starter_scripts/atss_50/qat/coco/dist_lsq_atss_50_coco_w4a4.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=3 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12324 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/atss/atss_r50_fpn_1x_coco_quant_w4a4.py \
+    mqbconfig/lsq/quant_config_w4a4.yaml \
+    --work-dir /work_dirs/atss_r50_fpn_1x_coco_w4a4_LSQ \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/atss_50/qat/coco/dist_tqt_HQOD_atss_50_coco_w2a2.sh b/tools/starter_scripts/atss_50/qat/coco/dist_tqt_HQOD_atss_50_coco_w2a2.sh
new file mode 100755
index 0000000..0400f32
--- /dev/null
+++ b/tools/starter_scripts/atss_50/qat/coco/dist_tqt_HQOD_atss_50_coco_w2a2.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12323 \
+    --nproc_per_node=8 \
+    tools/train.py \
+    configs/atss/atss_r50_fpn_1x_coco_quant_w2a2.py \
+    mqbconfig/tqt/quant_config_mypro_w2a2.yaml \
+    --work-dir /work_dirs/atss_r50_fpn_1x_coco_w2a2_TQT_HQOD \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/atss_50/qat/coco/dist_tqt_HQOD_atss_50_coco_w4a4.sh b/tools/starter_scripts/atss_50/qat/coco/dist_tqt_HQOD_atss_50_coco_w4a4.sh
new file mode 100755
index 0000000..56eb947
--- /dev/null
+++ b/tools/starter_scripts/atss_50/qat/coco/dist_tqt_HQOD_atss_50_coco_w4a4.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=7 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12322 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/atss/atss_r50_fpn_1x_coco_quant_w4a4.py \
+    mqbconfig/tqt/quant_config_mypro_w4a4.yaml \
+    --work-dir /work_dirs/atss_r50_fpn_1x_coco_w4a4_TQT_HQOD \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/atss_50/qat/coco/dist_tqt_atss_50_coco_w2a2.sh b/tools/starter_scripts/atss_50/qat/coco/dist_tqt_atss_50_coco_w2a2.sh
new file mode 100755
index 0000000..3292a54
--- /dev/null
+++ b/tools/starter_scripts/atss_50/qat/coco/dist_tqt_atss_50_coco_w2a2.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12321 \
+    --nproc_per_node=8 \
+    tools/train.py \
+    configs/atss/atss_r50_fpn_1x_coco_quant_w2a2.py \
+    mqbconfig/tqt/quant_config_w2a2.yaml \
+    --work-dir /work_dirs/atss_r50_fpn_1x_coco_w2a2_TQT \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/atss_50/qat/coco/dist_tqt_atss_50_coco_w4a4.sh b/tools/starter_scripts/atss_50/qat/coco/dist_tqt_atss_50_coco_w4a4.sh
new file mode 100755
index 0000000..1538d02
--- /dev/null
+++ b/tools/starter_scripts/atss_50/qat/coco/dist_tqt_atss_50_coco_w4a4.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=6 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12320 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/atss/atss_r50_fpn_1x_coco_quant_w4a4.py \
+    mqbconfig/tqt/quant_config_w4a4.yaml \
+    --work-dir /work_dirs/atss_r50_fpn_1x_coco_w4a4_TQT \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/experiment1.txt b/tools/starter_scripts/experiment1.txt
new file mode 100644
index 0000000..d7b83d4
--- /dev/null
+++ b/tools/starter_scripts/experiment1.txt
@@ -0,0 +1,8 @@
+tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w4a4.sh
+tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w2a2.sh
+tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HQOD_retinanet_18_coco_w4a4.sh
+tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HQOD_retinanet_18_coco_w2a2.sh
+tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_retinanet_50_coco_w4a4.sh
+tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_retinanet_50_coco_w2a2.sh
+tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_HQOD_retinanet_50_coco_w4a4.sh
+tools/starter_scripts/retinanet_18/dist_train_retinanet_18_coco_AQD.sh
\ No newline at end of file
diff --git a/tools/starter_scripts/main.sh b/tools/starter_scripts/main.sh
new file mode 100644
index 0000000..3cd3518
--- /dev/null
+++ b/tools/starter_scripts/main.sh
@@ -0,0 +1,41 @@
+# 删除临时文件 clean
+rm -f ./temp_* && rm -f ./tmp/*
+
+# 把上一次的tmux窗口删除
+tmux kill-session -t $tmux_s_name
+tmux kill-session -t fake
+# 得先制造一个无用的session，否则无法通过conf文件新建session。不太清楚为什么，玄学。
+tmux new-session -d -s fake
+
+# 创建一个新的tmux会话，并命名为my_session
+tmux new-session -d -s $tmux_s_name
+# 加载配置文件定制当前tmux会话
+tmux source-file tools/starter_scripts/main.tmux.conf
+echo 'Create Tmux!!'
+
+echo 'sleep 3s, wait zsh init.Maybe nothing.'
+sleep 3
+
+# # 将命令分别发送到每个窗格并在前台执行
+split -l 1 -d $commands_file temp_
+mv -f ./temp_* ./tmp
+
+# sendkey_w1
+for i in 0 1 2 3; do
+    # 这一步是避免zsh的更新提示
+    tmux send-keys -t $tmux_s_name:w1.$i "n" Enter
+    # 先切到对应的conda环境
+    tmux send-keys -t $tmux_s_name:w1.$i "conda activate LLML" Enter
+    sleep 1
+    tmux send-keys -t $tmux_s_name:w1.$i "cat ./tmp/temp_0$i | xargs -I {} sh -c 'echo {};{}'" Enter
+done
+
+# sendkey_w2
+for i in 4 5 6 7; do
+    # 这一步是避免zsh的更新提示
+    tmux send-keys -t $tmux_s_name:w2.$(($i - 4)) "n" Enter
+    tmux send-keys -t $tmux_s_name:w2.$(($i - 4)) "conda activate LLML" Enter
+    sleep 1
+    tmux send-keys -t $tmux_s_name:w2.$(($i - 4)) "cat ./tmp/temp_0$i | xargs -I {} sh -c 'echo {};{}'" Enter
+done
+
diff --git a/tools/starter_scripts/main.tmux.conf b/tools/starter_scripts/main.tmux.conf
new file mode 100644
index 0000000..a40564e
--- /dev/null
+++ b/tools/starter_scripts/main.tmux.conf
@@ -0,0 +1,23 @@
+
+
+# 启用鼠标支持
+set -g mouse on
+
+# 创建第一个窗口
+rename-window -t 0 "w1"
+
+# 将窗口设置为2x2布局，并创建4个面板
+split-window -v
+split-window -h
+select-pane -t 0
+split-window -h
+
+
+# 创建第二个窗口
+new-window -n "w2"
+
+# 将窗口设置为2x2布局，并创建4个面板
+split-window -v
+split-window -h
+select-pane -t 0
+split-window -h
diff --git a/tools/starter_scripts/retinanet_18/dist_train_retinanet_18_coco_AQD.sh b/tools/starter_scripts/retinanet_18/dist_train_retinanet_18_coco_AQD.sh
new file mode 100755
index 0000000..00a7769
--- /dev/null
+++ b/tools/starter_scripts/retinanet_18/dist_train_retinanet_18_coco_AQD.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12312 \
+    --nproc_per_node=8 \
+    tools/train.py \
+    configs/retinanet/retinanet_r18_fpn_1x_coco.py \
+    shit \
+    --work-dir /work_dirs/retinanet_r18_fpn_1x_coco_AQD \
+    --aqd-mode 5 \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HQOD_retinanet_18_coco_w2a2.sh b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HQOD_retinanet_18_coco_w2a2.sh
new file mode 100755
index 0000000..2a60ef1
--- /dev/null
+++ b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HQOD_retinanet_18_coco_w2a2.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=5 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12303 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w2a2.py \
+    mqbconfig/lsq/quant_config_mypro_w2a2.yaml \
+    --work-dir /work_dirs/retinanet_r18_fpn_coco_w2a2_LSQ_HQOD \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HQOD_retinanet_18_coco_w2a2_abla_HIoU.sh b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HQOD_retinanet_18_coco_w2a2_abla_HIoU.sh
new file mode 100755
index 0000000..aa396b3
--- /dev/null
+++ b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HQOD_retinanet_18_coco_w2a2_abla_HIoU.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=1 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12303 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w2a2.py \
+    mqbconfig/lsq/quant_config_mypro_w2a2.yaml \
+    --work-dir /work_dirs/retinanet_r18_fpn_coco_w2a2_LSQ_HQOD_Abla_HIoU \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HQOD_retinanet_18_coco_w4a4.sh b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HQOD_retinanet_18_coco_w4a4.sh
new file mode 100755
index 0000000..fc0b6a9
--- /dev/null
+++ b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HQOD_retinanet_18_coco_w4a4.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=0 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12302 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w4a4.py \
+    mqbconfig/lsq/quant_config_mypro_w4a4.yaml \
+    --work-dir work_dirs/retinanet_r18_fpn_coco_w4a4_LSQ_HQOD \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HarDet_retinanet_18_coco_w2a2.sh b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HarDet_retinanet_18_coco_w2a2.sh
new file mode 100755
index 0000000..12bcf49
--- /dev/null
+++ b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_HarDet_retinanet_18_coco_w2a2.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=0 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12394 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w2a2.py \
+    mqbconfig/lsq/quant_config_hardet_w2a2.yaml \
+    --work-dir /work_dirs/retinanet_r18_fpn_coco_w2a2_LSQ_HarDet \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w2a2.sh b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w2a2.sh
new file mode 100755
index 0000000..966318a
--- /dev/null
+++ b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w2a2.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=1 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12301 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w2a2.py \
+    mqbconfig/lsq/quant_config_w2a2.yaml \
+    --work-dir /work_dirs/retinanet_r18_fpn_coco_w2a2_LSQ \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w2a2_AQD.sh b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w2a2_AQD.sh
new file mode 100755
index 0000000..4297635
--- /dev/null
+++ b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w2a2_AQD.sh
@@ -0,0 +1,14 @@
+CUDA_VISIBLE_DEVICES=6,7 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12301 \
+    --nproc_per_node=2 \
+    tools/train.py \
+    configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w2a2_aqd.py \
+    mqbconfig/lsq/quant_config_w2a2.yaml \
+    --work-dir /work_dirs/retinanet_r18_fpn_coco_w2a2_LSQ_AQD \
+    --aqd-mode 5 \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w2a2_HQOD_AQD.sh b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w2a2_HQOD_AQD.sh
new file mode 100755
index 0000000..dbc3404
--- /dev/null
+++ b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w2a2_HQOD_AQD.sh
@@ -0,0 +1,14 @@
+CUDA_VISIBLE_DEVICES=4,5 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12313 \
+    --nproc_per_node=2 \
+    tools/train.py \
+    configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w2a2_aqd.py \
+    mqbconfig/lsq/quant_config_mypro_w2a2.yaml \
+    --work-dir /work_dirs/retinanet_r18_fpn_coco_w2a2_LSQ_HQOD_AQD \
+    --aqd-mode 5 \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w4a4.sh b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w4a4.sh
new file mode 100755
index 0000000..a706b01
--- /dev/null
+++ b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w4a4.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=0 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12300 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w4a4.py \
+    mqbconfig/lsq/quant_config_w4a4.yaml \
+    --work-dir /work_dirs/retinanet_r18_fpn_coco_w4a4_LSQ \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w4a4_AQD.sh b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w4a4_AQD.sh
new file mode 100755
index 0000000..f2ba216
--- /dev/null
+++ b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w4a4_AQD.sh
@@ -0,0 +1,14 @@
+CUDA_VISIBLE_DEVICES=0,1 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12214 \
+    --nproc_per_node=2 \
+    tools/train.py \
+    configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w4a4_aqd.py \
+    mqbconfig/lsq/quant_config_w4a4.yaml \
+    --work-dir /work_dirs/retinanet_r18_fpn_coco_w4a4_LSQ_AQD \
+    --aqd-mode 5 \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w4a4_HQOD_AQD.sh b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w4a4_HQOD_AQD.sh
new file mode 100755
index 0000000..0fc947b
--- /dev/null
+++ b/tools/starter_scripts/retinanet_18/qat/coco/dist_lsq_retinanet_18_coco_w4a4_HQOD_AQD.sh
@@ -0,0 +1,14 @@
+CUDA_VISIBLE_DEVICES=2,3 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12314 \
+    --nproc_per_node=2 \
+    tools/train.py \
+    configs/retinanet/retinanet_r18_fpn_1x_coco_quant_w4a4_aqd.py \
+    mqbconfig/lsq/quant_config_mypro_w4a4.yaml \
+    --work-dir /work_dirs/retinanet_r18_fpn_coco_w4a4_LSQ_HQOD_AQD \
+    --aqd-mode 5 \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_HQOD_retinanet_50_coco_w2a2.sh b/tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_HQOD_retinanet_50_coco_w2a2.sh
new file mode 100755
index 0000000..1a6fe6a
--- /dev/null
+++ b/tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_HQOD_retinanet_50_coco_w2a2.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=4,5,6,7 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12307 \
+    --nproc_per_node=4 \
+    tools/train.py \
+    configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w2a2.py \
+    mqbconfig/lsq/quant_config_mypro_w2a2_weight_loose.yaml \
+    --work-dir /work_dirs/retinanet_r50_fpn_coco_w2a2_LSQ_HQOD \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_HQOD_retinanet_50_coco_w4a4.sh b/tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_HQOD_retinanet_50_coco_w4a4.sh
new file mode 100755
index 0000000..a04596b
--- /dev/null
+++ b/tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_HQOD_retinanet_50_coco_w4a4.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=1 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12306 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w4a4.py \
+    mqbconfig/lsq/quant_config_mypro_w4a4.yaml \
+    --work-dir /work_dirs/retinanet_r50_fpn_coco_w4a4_LSQ_HQOD \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_retinanet_50_coco_w2a2.sh b/tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_retinanet_50_coco_w2a2.sh
new file mode 100755
index 0000000..32f9173
--- /dev/null
+++ b/tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_retinanet_50_coco_w2a2.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=0,1,2,3 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12305 \
+    --nproc_per_node=4 \
+    tools/train.py \
+    configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w2a2.py \
+    mqbconfig/lsq/quant_config_w2a2_weight_loose.yaml \
+    --work-dir /work_dirs/retinanet_r50_fpn_coco_w2a2_LSQ \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_retinanet_50_coco_w4a4.sh b/tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_retinanet_50_coco_w4a4.sh
new file mode 100755
index 0000000..0d6ab0f
--- /dev/null
+++ b/tools/starter_scripts/retinanet_50/qat/coco/dist_lsq_retinanet_50_coco_w4a4.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=4 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12304 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/retinanet/retinanet_r50_fpn_1x_coco_quant_w4a4.py \
+    mqbconfig/lsq/quant_config_w4a4.yaml \
+    --work-dir /work_dirs/retinanet_r50_fpn_coco_w4a4_LSQ \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/yolox_s/qat/coco/dist_lsq_HQOD_yolox_s_coco_w2a2.sh b/tools/starter_scripts/yolox_s/qat/coco/dist_lsq_HQOD_yolox_s_coco_w2a2.sh
new file mode 100755
index 0000000..317f194
--- /dev/null
+++ b/tools/starter_scripts/yolox_s/qat/coco/dist_lsq_HQOD_yolox_s_coco_w2a2.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=3 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12311 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/yolox/yolox_s_8x8_300e_coco_quant_w2a2.py \
+    mqbconfig/lsq/quant_config_mypro_w2a2_weight_loose.yaml \
+    --work-dir /work_dirs/yolox_s_coco_w2a2_LSQ_HQOD \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/yolox_s/qat/coco/dist_lsq_HQOD_yolox_s_coco_w4a4.sh b/tools/starter_scripts/yolox_s/qat/coco/dist_lsq_HQOD_yolox_s_coco_w4a4.sh
new file mode 100755
index 0000000..ccdb37d
--- /dev/null
+++ b/tools/starter_scripts/yolox_s/qat/coco/dist_lsq_HQOD_yolox_s_coco_w4a4.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=4,5 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12310 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/yolox/yolox_s_8x8_300e_coco_quant_w4a4.py \
+    mqbconfig/lsq/quant_config_mypro_w4a4.yaml \
+    --work-dir /work_dirs/yolox_s_coco_w4a4_LSQ_HQOD \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/yolox_s/qat/coco/dist_lsq_yolox_s_coco_w2a2.sh b/tools/starter_scripts/yolox_s/qat/coco/dist_lsq_yolox_s_coco_w2a2.sh
new file mode 100755
index 0000000..d005ba9
--- /dev/null
+++ b/tools/starter_scripts/yolox_s/qat/coco/dist_lsq_yolox_s_coco_w2a2.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=1 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12309 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/yolox/yolox_s_8x8_300e_coco_quant_w2a2.py \
+    mqbconfig/lsq/quant_config_w2a2_weight_loose.yaml \
+    --work-dir /work_dirs/yolox_s_coco_w2a2_LSQ \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/starter_scripts/yolox_s/qat/coco/dist_lsq_yolox_s_coco_w4a4.sh b/tools/starter_scripts/yolox_s/qat/coco/dist_lsq_yolox_s_coco_w4a4.sh
new file mode 100755
index 0000000..871fd56
--- /dev/null
+++ b/tools/starter_scripts/yolox_s/qat/coco/dist_lsq_yolox_s_coco_w4a4.sh
@@ -0,0 +1,13 @@
+CUDA_VISIBLE_DEVICES=2,3 \
+python -m torch.distributed.launch \
+    --use_env \
+    --master_port=12308 \
+    --nproc_per_node=1 \
+    tools/train.py \
+    configs/yolox/yolox_s_8x8_300e_coco_quant_w4a4.py \
+    mqbconfig/lsq/quant_config_w4a4.yaml \
+    --work-dir /work_dirs/yolox_s_coco_w4a4_LSQ \
+    --quantize \
+    --seed 1005 \
+    --deterministic \
+    --launcher pytorch 
diff --git a/tools/test.py b/tools/test.py
new file mode 100644
index 0000000..9084cdd
--- /dev/null
+++ b/tools/test.py
@@ -0,0 +1,372 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+import time
+import warnings
+
+import mmcv
+import torch
+from mmcv import Config, DictAction
+from mmcv.cnn import fuse_conv_bn
+from mmcv.runner import (get_dist_info, init_dist, load_checkpoint,
+                         wrap_fp16_model)
+
+from mmdet.apis import multi_gpu_test, single_gpu_test
+from mmdet.datasets import (build_dataloader, build_dataset,
+                            replace_ImageToTensor)
+from mmdet.models import build_detector
+from mmdet.utils import (build_ddp, build_dp, compat_cfg, get_device,
+                         replace_cfg_vals, rfnext_init_model,
+                         setup_multi_processes, update_data_root)
+from mqb_general_process import make_qmodel_for_mmd, prepocess
+from mqbench.utils.state import *
+import global_placeholder
+from mqb_general_process import *
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='MMDet test (and eval) a model')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint', help='checkpoint file')
+    parser.add_argument('quant_config', default=None, help='quant config file path')
+    parser.add_argument('--aqd-mode', type=int, default=0, help='when bigger than 0 , it means switch on aqd, and equals the neck output level num')
+    parser.add_argument('--quantize', 
+        action='store_true', help='quant flag')
+    parser.add_argument('--seed', type=int, default=None, help='random seed')
+    
+    parser.add_argument(
+        '--work-dir',
+        help='the directory to save the file containing evaluation metrics')
+    parser.add_argument('--out', help='output result file in pickle format')
+    parser.add_argument(
+        '--fuse-conv-bn',
+        action='store_true',
+        help='Whether to fuse conv and bn, this will slightly increase'
+        'the inference speed')
+    parser.add_argument(
+        '--gpu-ids',
+        type=int,
+        nargs='+',
+        help='(Deprecated, please use --gpu-id) ids of gpus to use '
+        '(only applicable to non-distributed training)')
+    parser.add_argument(
+        '--gpu-id',
+        type=int,
+        default=0,
+        help='id of gpu to use '
+        '(only applicable to non-distributed testing)')
+    parser.add_argument(
+        '--format-only',
+        action='store_true',
+        help='Format the output results without perform evaluation. It is'
+        'useful when you want to format the result to a specific format and '
+        'submit it to the test server')
+    parser.add_argument(
+        '--eval',
+        type=str,
+        nargs='+',
+        help='evaluation metrics, which depends on the dataset, e.g., "bbox",'
+        ' "segm", "proposal" for COCO, and "mAP", "recall" for PASCAL VOC')
+    parser.add_argument('--show', action='store_true', help='show results')
+    parser.add_argument(
+        '--show-dir', help='directory where painted images will be saved')
+    parser.add_argument(
+        '--show-score-thr',
+        type=float,
+        default=0.3,
+        help='score threshold (default: 0.3)')
+    parser.add_argument(
+        '--gpu-collect',
+        action='store_true',
+        help='whether to use gpu to collect results.')
+    parser.add_argument(
+        '--tmpdir',
+        help='tmp directory used for collecting results from multiple '
+        'workers, available when gpu-collect is not specified')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--options',
+        nargs='+',
+        action=DictAction,
+        help='custom options for evaluation, the key-value pair in xxx=yyy '
+        'format will be kwargs for dataset.evaluate() function (deprecate), '
+        'change to --eval-options instead.')
+    parser.add_argument(
+        '--eval-options',
+        nargs='+',
+        action=DictAction,
+        help='custom options for evaluation, the key-value pair in xxx=yyy '
+        'format will be kwargs for dataset.evaluate() function')
+    parser.add_argument(
+        '--launcher',
+        choices=['none', 'pytorch', 'slurm', 'mpi'],
+        default='none',
+        help='job launcher')
+    parser.add_argument('--local_rank', type=int, default=0)
+    args = parser.parse_args()
+    if 'LOCAL_RANK' not in os.environ:
+        os.environ['LOCAL_RANK'] = str(args.local_rank)
+
+    if args.options and args.eval_options:
+        raise ValueError(
+            '--options and --eval-options cannot be both '
+            'specified, --options is deprecated in favor of --eval-options')
+    if args.options:
+        warnings.warn('--options is deprecated in favor of --eval-options')
+        args.eval_options = args.options
+    return args
+
+
+def main():
+    args = parse_args()
+
+    assert args.out or args.eval or args.format_only or args.show \
+        or args.show_dir, \
+        ('Please specify at least one operation (save/eval/format/show the '
+         'results / save the results) with the argument "--out", "--eval"'
+         ', "--format-only", "--show" or "--show-dir"')
+
+    if args.eval and args.format_only:
+        raise ValueError('--eval and --format_only cannot be both specified')
+
+    if args.out is not None and not args.out.endswith(('.pkl', '.pickle')):
+        raise ValueError('The output file must be a pkl file.')
+
+    cfg = Config.fromfile(args.config)
+
+    # replace the ${key} with the value of cfg.key
+    cfg = replace_cfg_vals(cfg)
+
+    # update data root according to MMDET_DATASETS
+    update_data_root(cfg)
+
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+
+    cfg = compat_cfg(cfg)
+
+    # set multi-process settings
+    setup_multi_processes(cfg)
+    set_random_seed(args.seed)
+    # set cudnn_benchmark
+    if cfg.get('cudnn_benchmark', False):
+        torch.backends.cudnn.benchmark = True
+
+    if 'pretrained' in cfg.model:
+        cfg.model.pretrained = None
+    elif 'init_cfg' in cfg.model.backbone:
+        cfg.model.backbone.init_cfg = None
+
+    if cfg.model.get('neck'):
+        if isinstance(cfg.model.neck, list):
+            for neck_cfg in cfg.model.neck:
+                if neck_cfg.get('rfp_backbone'):
+                    if neck_cfg.rfp_backbone.get('pretrained'):
+                        neck_cfg.rfp_backbone.pretrained = None
+        elif cfg.model.neck.get('rfp_backbone'):
+            if cfg.model.neck.rfp_backbone.get('pretrained'):
+                cfg.model.neck.rfp_backbone.pretrained = None
+
+    if args.gpu_ids is not None:
+        cfg.gpu_ids = args.gpu_ids[0:1]
+        warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
+                      'Because we only support single GPU mode in '
+                      'non-distributed testing. Use the first GPU '
+                      'in `gpu_ids` now.')
+    else:
+        cfg.gpu_ids = [args.gpu_id]
+    cfg.device = get_device()
+    # init distributed env first, since logger depends on the dist info.
+    if args.launcher == 'none':
+        distributed = False
+    else:
+        distributed = True
+        init_dist(args.launcher, **cfg.dist_params)
+
+    test_dataloader_default_args = dict(
+        samples_per_gpu=1, workers_per_gpu=2, dist=distributed, shuffle=False)
+
+    # in case the test dataset is concatenated
+    if isinstance(cfg.data.test, dict):
+        cfg.data.test.test_mode = True
+        if cfg.data.test_dataloader.get('samples_per_gpu', 1) > 1:
+            # Replace 'ImageToTensor' to 'DefaultFormatBundle'
+            cfg.data.test.pipeline = replace_ImageToTensor(
+                cfg.data.test.pipeline)
+    elif isinstance(cfg.data.test, list):
+        for ds_cfg in cfg.data.test:
+            ds_cfg.test_mode = True
+        if cfg.data.test_dataloader.get('samples_per_gpu', 1) > 1:
+            for ds_cfg in cfg.data.test:
+                ds_cfg.pipeline = replace_ImageToTensor(ds_cfg.pipeline)
+
+    test_loader_cfg = {
+        **test_dataloader_default_args,
+        **cfg.data.get('test_dataloader', {})
+    }
+
+    rank, _ = get_dist_info()
+    # allows not to create
+    if args.work_dir is not None and rank == 0:
+        mmcv.mkdir_or_exist(osp.abspath(args.work_dir))
+        timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+        json_file = osp.join(args.work_dir, f'eval_{timestamp}.json')
+
+    # build the dataloader
+    dataset = build_dataset(cfg.data.test)
+    data_loader = build_dataloader(dataset, **test_loader_cfg)
+
+    # build the model and load checkpoint
+    cfg.model.train_cfg = None
+    model = build_detector(cfg.model, test_cfg=cfg.get('test_cfg'))
+    # global setting
+    if args.aqd_mode != 0:
+        global_placeholder.modify_AQD_mode(args.aqd_mode)
+    
+    # 如果是QAT模型，那么就需要提前定义好量化模型结构
+    if args.quantize:
+        quant_config = prepocess(args.quant_config)
+        # copy_config_file(args.quant_config, cfg.work_dir)
+        logger.info(quant_config)
+        global_placeholder.modify_quant_bit(quant_config.extra_prepare_dict.extra_qconfig_dict.w_qscheme.bit)
+        global_placeholder.modify_quant_algorithm(quant_config.quantize.quant_algorithm)
+        global_placeholder.modify_buff_flag(quant_config.training.my_buff_flag)
+        global_placeholder.modify_qloss_flag(quant_config.training.qloss_flag)
+        global_placeholder.modify_fold_bn_flag(quant_config.training.fold_bn_flag)
+        
+        model.train()
+        model = make_qmodel_for_mmd(model, quant_config, cfg.trace_config)
+        
+    # else:
+    #     if 'HQOD' in cfg.work_dir:
+    #         logger.info("插播!!!!直接启用harmony！！")
+    #         logger.info("插播!!!!直接启用harmony！！")
+    #         logger.info("插播!!!!直接启用harmony！！\n")
+    #         global_placeholder.modify_buff_flag(1) # 为mypro
+    #     elif 'HarDet' in cfg.work_dir:
+    #         logger.info("插播!!!!直接启用harmony！！")
+    #         logger.info("插播!!!!直接启用harmony！！")
+    #         logger.info("插播!!!!直接启用harmony！！\n")
+    #         global_placeholder.modify_buff_flag(2) # 为hardet
+            
+    # 检查 num levels 一致性
+    if global_placeholder.aqd_mode != 0 and model.neck.num_outs != global_placeholder.aqd_mode:
+        # 说明 num levels给的不对
+        raise  ValueError(f'num levels给的不对! aqd_mode={global_placeholder.aqd_mode} 而 neck.num_outs={model.neck.num_outs}')
+    
+    # init rfnext if 'RFSearchHook' is defined in cfg
+    rfnext_init_model(model, cfg=cfg)
+    fp16_cfg = cfg.get('fp16', None)
+    if fp16_cfg is None and cfg.get('device', None) == 'npu':
+        fp16_cfg = dict(loss_scale='dynamic')
+    if fp16_cfg is not None:
+        wrap_fp16_model(model)
+    checkpoint = load_checkpoint(model, args.checkpoint, map_location='cpu')
+    head_list = []
+    head_name_list = []
+    head_data_list = []
+    for key in checkpoint['state_dict'].keys():
+        if 'bbox_head' in key and 'scale' in key:
+            head_list.append([key, checkpoint['state_dict'][key].data.item()])
+            head_name_list.append(key)
+            head_data_list.append(checkpoint['state_dict'][key].data.item())
+    if args.fuse_conv_bn:
+        model = fuse_conv_bn(model)
+    # old versions did not save class info in checkpoints, this walkaround is
+    # for backward compatibility
+    if 'CLASSES' in checkpoint.get('meta', {}):
+        model.CLASSES = checkpoint['meta']['CLASSES']
+    else:
+        model.CLASSES = dataset.CLASSES
+
+    
+    if args.quantize:
+        enable_quantization(model.backbone)
+        enable_quantization(model.neck)
+        model_general_architecture = cfg.trace_config.get('model_general_architecture', None)
+        if model_general_architecture == 'FasterRCNN':
+            enable_quantization(model.rpn_head)
+            enable_quantization(model.roi_head.bbox_head)
+        else:
+            enable_quantization(model.bbox_head)
+
+    model.eval()
+    if not distributed:
+        model = build_dp(model, cfg.device, device_ids=cfg.gpu_ids)
+        outputs = single_gpu_test(model, data_loader, args.show, args.show_dir,
+                                  args.show_score_thr)
+    else:
+        model = build_ddp(
+            model,
+            cfg.device,
+            device_ids=[int(os.environ['LOCAL_RANK'])],
+            broadcast_buffers=False)
+
+        # In multi_gpu_test, if tmpdir is None, some tesnors
+        # will init on cuda by default, and no device choice supported.
+        # Init a tmpdir to avoid error on npu here.
+        if cfg.device == 'npu' and args.tmpdir is None:
+            args.tmpdir = './npu_tmpdir'
+
+        outputs = multi_gpu_test(
+            model, data_loader, args.tmpdir, args.gpu_collect
+            or cfg.evaluation.get('gpu_collect', False))
+
+    rank, _ = get_dist_info()
+    if rank == 0:
+        if args.out:
+            print(f'\nwriting results to {args.out}')
+            mmcv.dump(outputs, args.out)
+        kwargs = {} if args.eval_options is None else args.eval_options
+        if args.format_only:
+            dataset.format_results(outputs, **kwargs)
+        if args.eval:
+            eval_kwargs = cfg.get('evaluation', {}).copy()
+            # hard-code way to remove EvalHook args
+            for key in [
+                    'interval', 'tmpdir', 'start', 'gpu_collect', 'save_best',
+                    'rule', 'dynamic_intervals'
+            ]:
+                eval_kwargs.pop(key, None)
+            eval_kwargs.update(dict(metric=args.eval, **kwargs))
+            metric = dataset.evaluate(outputs, **eval_kwargs)
+            print(metric)
+            metric_dict = dict(config=args.config, metric=metric)
+            if args.work_dir is not None and rank == 0:
+                mmcv.dump(metric_dict, json_file)
+
+
+def set_random_seed(seed, deterministic=True):
+    """Set random seed.
+
+    Args:
+        seed (int): Seed to be used.
+        deterministic (bool): Whether to set the deterministic option for
+            CUDNN backend, i.e., set `torch.backends.cudnn.deterministic`
+            to True and `torch.backends.cudnn.benchmark` to False.
+            Default: False.
+    """
+    import random
+
+    import numpy as np
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    torch.cuda.manual_seed_all(seed)
+    if deterministic:
+        torch.backends.cudnn.deterministic = True
+        torch.backends.cudnn.benchmark = False
+
+
+if __name__ == '__main__':
+    main()
diff --git a/tools/train.py b/tools/train.py
new file mode 100644
index 0000000..ff61dc0
--- /dev/null
+++ b/tools/train.py
@@ -0,0 +1,303 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import copy
+import os
+import os.path as osp
+import time
+import warnings
+
+import mmcv
+import torch
+import torch.distributed as dist
+from mmcv import Config, DictAction
+from mmcv.runner import get_dist_info, init_dist
+from mmcv.utils import get_git_hash
+
+from mmdet import __version__
+from mmdet.apis import init_random_seed, set_random_seed, train_detector, qat_detector
+from mmdet.datasets import build_dataset
+from mmdet.models import build_detector
+from mmdet.utils import (collect_env, get_device, get_root_logger,
+                         replace_cfg_vals, rfnext_init_model,
+                         setup_multi_processes, update_data_root)
+
+import sys
+sys.path.append(".")  # 这玩意挺香的
+import global_placeholder
+from mqb_general_process import *
+from mqbench.utils.state import *
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Train a detector')
+    parser.add_argument('config', help='train config file path')
+    parser.add_argument('quant_config', default=None, help='quant config file path')
+    parser.add_argument('--aqd-mode', type=int, default=0, help='when bigger than 0 , it means switch on aqd, and equals the neck output level num')
+    parser.add_argument('--quantize', 
+        action='store_true', help='quant flag')
+    parser.add_argument('--work-dir', help='the dir to save logs and models')
+    parser.add_argument(
+        '--resume-from', help='the checkpoint file to resume from')
+    parser.add_argument(
+        '--auto-resume',
+        action='store_true',
+        help='resume from the latest checkpoint automatically')
+    parser.add_argument(
+        '--no-validate',
+        action='store_true',
+        help='whether not to evaluate the checkpoint during training')
+    group_gpus = parser.add_mutually_exclusive_group()
+    group_gpus.add_argument(
+        '--gpus',
+        type=int,
+        help='(Deprecated, please use --gpu-id) number of gpus to use '
+        '(only applicable to non-distributed training)')
+    group_gpus.add_argument(
+        '--gpu-ids',
+        type=int,
+        nargs='+',
+        help='(Deprecated, please use --gpu-id) ids of gpus to use '
+        '(only applicable to non-distributed training)')
+    group_gpus.add_argument(
+        '--gpu-id',
+        type=int,
+        default=0,
+        help='id of gpu to use '
+        '(only applicable to non-distributed training)')
+    parser.add_argument('--seed', type=int, default=None, help='random seed')
+    parser.add_argument(
+        '--diff-seed',
+        action='store_true',
+        help='Whether or not set different seeds for different ranks')
+    parser.add_argument(
+        '--deterministic',
+        action='store_true',
+        help='whether to set deterministic options for CUDNN backend.')
+    parser.add_argument(
+        '--options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file (deprecate), '
+        'change to --cfg-options instead.')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--launcher',
+        choices=['none', 'pytorch', 'slurm', 'mpi'],
+        default='none',
+        help='job launcher')
+    parser.add_argument('--local_rank', type=int, default=0)
+    parser.add_argument(
+        '--auto-scale-lr',
+        action='store_true',
+        help='enable automatically scaling LR.')
+    args = parser.parse_args()
+    if 'LOCAL_RANK' not in os.environ:
+        os.environ['LOCAL_RANK'] = str(args.local_rank)
+
+    if args.options and args.cfg_options:
+        raise ValueError(
+            '--options and --cfg-options cannot be both '
+            'specified, --options is deprecated in favor of --cfg-options')
+    if args.options:
+        warnings.warn('--options is deprecated in favor of --cfg-options')
+        args.cfg_options = args.options
+
+    return args
+
+
+def main():
+    args = parse_args()
+
+    cfg = Config.fromfile(args.config)
+
+    # replace the ${key} with the value of cfg.key
+    cfg = replace_cfg_vals(cfg)
+
+    # update data root according to MMDET_DATASETS
+    update_data_root(cfg)
+
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+
+    if args.auto_scale_lr:
+        if 'auto_scale_lr' in cfg and \
+                'enable' in cfg.auto_scale_lr and \
+                'base_batch_size' in cfg.auto_scale_lr:
+            cfg.auto_scale_lr.enable = True
+        else:
+            warnings.warn('Can not find "auto_scale_lr" or '
+                          '"auto_scale_lr.enable" or '
+                          '"auto_scale_lr.base_batch_size" in your'
+                          ' configuration file. Please update all the '
+                          'configuration files to mmdet >= 2.24.1.')
+
+    # set multi-process settings
+    setup_multi_processes(cfg)
+
+    # set cudnn_benchmark
+    if cfg.get('cudnn_benchmark', False):
+        torch.backends.cudnn.benchmark = True
+
+    # work_dir is determined in this priority: CLI > segment in file > filename
+    if args.work_dir is not None:
+        # update configs according to CLI args if args.work_dir is not None
+        cfg.work_dir = args.work_dir
+    elif cfg.get('work_dir', None) is None:
+        # use config filename as default work_dir if cfg.work_dir is None
+        cfg.work_dir = osp.join('./work_dirs',
+                                osp.splitext(osp.basename(args.config))[0])
+
+    if args.resume_from is not None:
+        cfg.resume_from = args.resume_from
+    cfg.auto_resume = args.auto_resume
+    if args.gpus is not None:
+        cfg.gpu_ids = range(1)
+        warnings.warn('`--gpus` is deprecated because we only support '
+                      'single GPU mode in non-distributed training. '
+                      'Use `gpus=1` now.')
+    if args.gpu_ids is not None:
+        cfg.gpu_ids = args.gpu_ids[0:1]
+        warnings.warn('`--gpu-ids` is deprecated, please use `--gpu-id`. '
+                      'Because we only support single GPU mode in '
+                      'non-distributed training. Use the first GPU '
+                      'in `gpu_ids` now.')
+    if args.gpus is None and args.gpu_ids is None:
+        cfg.gpu_ids = [args.gpu_id]
+
+    # init distributed env first, since logger depends on the dist info.
+    if args.launcher == 'none':
+        distributed = False
+    else:
+        distributed = True
+        init_dist(args.launcher, **cfg.dist_params)
+        # re-set gpu_ids with distributed training mode
+        _, world_size = get_dist_info()
+        cfg.gpu_ids = range(world_size)
+
+
+
+    # create work_dir
+    mmcv.mkdir_or_exist(osp.abspath(cfg.work_dir))
+    # dump config
+    cfg.dump(osp.join(cfg.work_dir, osp.basename(args.config)))
+    # init the logger before other steps
+    timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
+    log_file = osp.join(cfg.work_dir, f'{timestamp}.log')
+    logger = get_root_logger(log_file=log_file, log_level=cfg.log_level)
+
+    # TODO 改叫全局设置吧，确实可以引进，舒服一点  后期可以优化一下这里
+    if args.aqd_mode != 0:
+        global_placeholder.modify_AQD_mode(args.aqd_mode)
+    if args.quantize:
+    # 加载qat config 文件
+        quant_config = prepocess(args.quant_config)
+        copy_config_file(args.quant_config, cfg.work_dir)
+        logger.info(quant_config)
+        global_placeholder.modify_quant_bit(quant_config.extra_prepare_dict.extra_qconfig_dict.w_qscheme.bit)
+        global_placeholder.modify_quant_algorithm(quant_config.quantize.quant_algorithm)
+        global_placeholder.modify_buff_flag(quant_config.training.my_buff_flag)
+        global_placeholder.modify_qloss_flag(quant_config.training.qloss_flag)
+        global_placeholder.modify_fold_bn_flag(quant_config.training.fold_bn_flag)
+    else:
+        if 'HQOD' in cfg.work_dir:
+            logger.info("插播!!!!直接启用harmony！！")
+            logger.info("插播!!!!直接启用harmony！！")
+            logger.info("插播!!!!直接启用harmony！！\n")
+            global_placeholder.modify_buff_flag(1) # 为mypro
+        elif 'HarDet' in cfg.work_dir:
+            logger.info("插播!!!!直接启用harmony！！")
+            logger.info("插播!!!!直接启用harmony！！")
+            logger.info("插播!!!!直接启用harmony！！\n")
+            global_placeholder.modify_buff_flag(2) # 为hardet
+        # else:
+        #     raise NotImplementedError
+
+    # init the meta dict to record some important information such as
+    # environment info and seed, which will be logged
+    meta = dict()
+    # log env info
+    env_info_dict = collect_env()
+    env_info = '\n'.join([(f'{k}: {v}') for k, v in env_info_dict.items()])
+    dash_line = '-' * 60 + '\n'
+    logger.info('Environment info:\n' + dash_line + env_info + '\n' +
+                dash_line)
+    meta['env_info'] = env_info
+    meta['config'] = cfg.pretty_text
+    # log some basic info
+    logger.info(f'Distributed training: {distributed}')
+    logger.info(f'Config:\n{cfg.pretty_text}')
+
+    cfg.device = get_device()
+    # set random seeds
+    seed = init_random_seed(args.seed, device=cfg.device)
+    seed = seed + dist.get_rank() if args.diff_seed else seed
+    logger.info(f'Set random seed to {seed}, '
+                f'deterministic: {args.deterministic}')
+    set_random_seed(seed, deterministic=args.deterministic)
+    cfg.seed = seed
+    meta['seed'] = seed
+    meta['exp_name'] = osp.basename(args.config)
+
+    model = build_detector(  # 这里应该是加载了预训练模型勒
+        cfg.model,
+        train_cfg=cfg.get('train_cfg'),
+        test_cfg=cfg.get('test_cfg'))
+    model.init_weights()
+
+    # 检查 num levels 一致性
+    if global_placeholder.aqd_mode != 0 and model.neck.num_outs != global_placeholder.aqd_mode:
+        # 说明 num levels给的不对
+        raise  ValueError(f'num levels给的不对! aqd_mode={global_placeholder.aqd_mode} 而 neck.num_outs={model.neck.num_outs}')
+    
+    # init rfnext if 'RFSearchHook' is defined in cfg
+    rfnext_init_model(model, cfg=cfg)
+
+    datasets = [build_dataset(cfg.data.train)]
+    if len(cfg.workflow) == 2:
+        assert 'val' in [mode for (mode, _) in cfg.workflow]
+        val_dataset = copy.deepcopy(cfg.data.val)
+        val_dataset.pipeline = cfg.data.train.get(
+            'pipeline', cfg.data.train.dataset.get('pipeline'))
+        datasets.append(build_dataset(val_dataset))
+    if cfg.checkpoint_config is not None:
+        # save mmdet version, config file content and class names in
+        # checkpoints as meta data
+        cfg.checkpoint_config.meta = dict(
+            mmdet_version=__version__ + get_git_hash()[:7],
+            CLASSES=datasets[0].CLASSES)
+    # add an attribute for visualization convenience
+    model.CLASSES = datasets[0].CLASSES
+    if args.quantize:
+        logger.info('QAT Mode On!')
+        qat_detector(
+            model,
+            datasets,
+            cfg,
+            quant_config,
+            distributed=distributed,
+            validate=(not args.no_validate),
+            timestamp=timestamp,
+            meta=meta)
+    else:
+        logger.info('Simple Train!!')
+        train_detector(
+            model,
+            datasets,
+            cfg,
+            distributed=distributed,
+            validate=(not args.no_validate),
+            timestamp=timestamp,
+            meta=meta)
+
+
+if __name__ == '__main__':
+    main()